From 60ef906f019c78777dd4531fdd8e28dd5463f937 Mon Sep 17 00:00:00 2001
From: Simran Shaikh <141727239+SimranShaikh20@users.noreply.github.com>
Date: Sun, 10 Nov 2024 18:47:15 +0530
Subject: [PATCH 1/7] Create Train.csv

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 create mode 100644 Research Topic Prediction using Deep Learning/Dataset/Train.csv

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+ID,TITLE,ABSTRACT,Computer Science,Physics,Mathematics,Statistics,Quantitative Biology,Quantitative Finance
+1,Reconstructing Subject-Specific Effect Maps,"  Predictive models allow subject-specific inference when analyzing disease
+related alterations in neuroimaging data. Given a subject's data, inference can
+be made at two levels: global, i.e. identifiying condition presence for the
+subject, and local, i.e. detecting condition effect on each individual
+measurement extracted from the subject's data. While global inference is widely
+used, local inference, which can be used to form subject-specific effect maps,
+is rarely used because existing models often yield noisy detections composed of
+dispersed isolated islands. In this article, we propose a reconstruction
+method, named RSM, to improve subject-specific detections of predictive
+modeling approaches and in particular, binary classifiers. RSM specifically
+aims to reduce noise due to sampling error associated with using a finite
+sample of examples to train classifiers. The proposed method is a wrapper-type
+algorithm that can be used with different binary classifiers in a diagnostic
+manner, i.e. without information on condition presence. Reconstruction is posed
+as a Maximum-A-Posteriori problem with a prior model whose parameters are
+estimated from training data in a classifier-specific fashion. Experimental
+evaluation is performed on synthetically generated data and data from the
+Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results on
+synthetic data demonstrate that using RSM yields higher detection accuracy
+compared to using models directly or with bootstrap averaging. Analyses on the
+ADNI dataset show that RSM can also improve correlation between
+subject-specific detections in cortical thickness data and non-imaging markers
+of Alzheimer's Disease (AD), such as the Mini Mental State Examination Score
+and Cerebrospinal Fluid amyloid-$\beta$ levels. Further reliability studies on
+the longitudinal ADNI dataset show improvement on detection reliability when
+RSM is used.
+",1,0,0,0,0,0
+2,Rotation Invariance Neural Network,"  Rotation invariance and translation invariance have great values in image
+recognition tasks. In this paper, we bring a new architecture in convolutional
+neural network (CNN) named cyclic convolutional layer to achieve rotation
+invariance in 2-D symbol recognition. We can also get the position and
+orientation of the 2-D symbol by the network to achieve detection purpose for
+multiple non-overlap target. Last but not least, this architecture can achieve
+one-shot learning in some cases using those invariance.
+",1,0,0,0,0,0
+3,Spherical polyharmonics and Poisson kernels for polyharmonic functions,"  We introduce and develop the notion of spherical polyharmonics, which are a
+natural generalisation of spherical harmonics. In particular we study the
+theory of zonal polyharmonics, which allows us, analogously to zonal harmonics,
+to construct Poisson kernels for polyharmonic functions on the union of rotated
+balls. We find the representation of Poisson kernels and zonal polyharmonics in
+terms of the Gegenbauer polynomials. We show the connection between the
+classical Poisson kernel for harmonic functions on the ball, Poisson kernels
+for polyharmonic functions on the union of rotated balls, and the Cauchy-Hua
+kernel for holomorphic functions on the Lie ball.
+",0,0,1,0,0,0
+4,A finite element approximation for the stochastic Maxwell--Landau--Lifshitz--Gilbert system,"  The stochastic Landau--Lifshitz--Gilbert (LLG) equation coupled with the
+Maxwell equations (the so called stochastic MLLG system) describes the creation
+of domain walls and vortices (fundamental objects for the novel nanostructured
+magnetic memories). We first reformulate the stochastic LLG equation into an
+equation with time-differentiable solutions. We then propose a convergent
+$\theta$-linear scheme to approximate the solutions of the reformulated system.
+As a consequence, we prove convergence of the approximate solutions, with no or
+minor conditions on time and space steps (depending on the value of $\theta$).
+Hence, we prove the existence of weak martingale solutions of the stochastic
+MLLG system. Numerical results are presented to show applicability of the
+method.
+",0,0,1,0,0,0
+5,Comparative study of Discrete Wavelet Transforms and Wavelet Tensor Train decomposition to feature extraction of FTIR data of medicinal plants,"  Fourier-transform infra-red (FTIR) spectra of samples from 7 plant species
+were used to explore the influence of preprocessing and feature extraction on
+efficiency of machine learning algorithms. Wavelet Tensor Train (WTT) and
+Discrete Wavelet Transforms (DWT) were compared as feature extraction
+techniques for FTIR data of medicinal plants. Various combinations of signal
+processing steps showed different behavior when applied to classification and
+clustering tasks. Best results for WTT and DWT found through grid search were
+similar, significantly improving quality of clustering as well as
+classification accuracy for tuned logistic regression in comparison to original
+spectra. Unlike DWT, WTT has only one parameter to be tuned (rank), making it a
+more versatile and easier to use as a data processing tool in various signal
+processing applications.
+",1,0,0,1,0,0
+6,On maximizing the fundamental frequency of the complement of an obstacle,"  Let $\Omega \subset \mathbb{R}^n$ be a bounded domain satisfying a
+Hayman-type asymmetry condition, and let $ D $ be an arbitrary bounded domain
+referred to as ""obstacle"". We are interested in the behaviour of the first
+Dirichlet eigenvalue $ \lambda_1(\Omega \setminus (x+D)) $. First, we prove an
+upper bound on $ \lambda_1(\Omega \setminus (x+D)) $ in terms of the distance
+of the set $ x+D $ to the set of maximum points $ x_0 $ of the first Dirichlet
+ground state $ \phi_{\lambda_1} > 0 $ of $ \Omega $. In short, a direct
+corollary is that if \begin{equation} \mu_\Omega := \max_{x}\lambda_1(\Omega
+\setminus (x+D)) \end{equation} is large enough in terms of $ \lambda_1(\Omega)
+$, then all maximizer sets $ x+D $ of $ \mu_\Omega $ are close to each maximum
+point $ x_0 $ of $ \phi_{\lambda_1} $.
+Second, we discuss the distribution of $ \phi_{\lambda_1(\Omega)} $ and the
+possibility to inscribe wavelength balls at a given point in $ \Omega $.
+Finally, we specify our observations to convex obstacles $ D $ and show that
+if $ \mu_\Omega $ is sufficiently large with respect to $ \lambda_1(\Omega) $,
+then all maximizers $ x+D $ of $ \mu_\Omega $ contain all maximum points $ x_0
+$ of $ \phi_{\lambda_1(\Omega)} $.
+",0,0,1,0,0,0
+7,On the rotation period and shape of the hyperbolic asteroid 1I/`Oumuamua (2017) U1 from its lightcurve,"  We observed the newly discovered hyperbolic minor planet 1I/`Oumuamua (2017
+U1) on 2017 October 30 with Lowell Observatory's 4.3-m Discovery Channel
+Telescope. From these observations, we derived a partial lightcurve with
+peak-to-trough amplitude of at least 1.2 mag. This lightcurve segment rules out
+rotation periods less than 3 hr and suggests that the period is at least 5 hr.
+On the assumption that the variability is due to a changing cross section, the
+axial ratio is at least 3:1. We saw no evidence for a coma or tail in either
+individual images or in a stacked image having an equivalent exposure time of
+9000 s.
+",0,1,0,0,0,0
+8,Adverse effects of polymer coating on heat transport at solid-liquid interface,"  The ability of metallic nanoparticles to supply heat to a liquid environment
+under exposure to an external optical field has attracted growing interest for
+biomedical applications. Controlling the thermal transport properties at a
+solid-liquid interface then appears to be particularly relevant. In this work,
+we address the thermal transport between water and a gold surface coated by a
+polymer layer. Using molecular dynamics simulations, we demonstrate that
+increasing the polymer density displaces the domain resisting to the heat flow,
+while it doesn't affect the final amount of thermal energy released in the
+liquid. This unexpected behavior results from a trade-off established by the
+increasing polymer density which couples more efficiently with the solid but
+initiates a counterbalancing resistance with the liquid.
+",0,1,0,0,0,0
+9,"SPH calculations of Mars-scale collisions: the role of the Equation of State, material rheologies, and numerical effects","  We model large-scale ($\approx$2000km) impacts on a Mars-like planet using a
+Smoothed Particle Hydrodynamics code. The effects of material strength and of
+using different Equations of State on the post-impact material and temperature
+distributions are investigated. The properties of the ejected material in terms
+of escaping and disc mass are analysed as well. We also study potential
+numerical effects in the context of density discontinuities and rigid body
+rotation. We find that in the large-scale collision regime considered here
+(with impact velocities of 4km/s), the effect of material strength is
+substantial for the post-impact distribution of the temperature and the
+impactor material, while the influence of the Equation of State is more subtle
+and present only at very high temperatures.
+",0,1,0,0,0,0
+10,$\mathcal{R}_{0}$ fails to predict the outbreak potential in the presence of natural-boosting immunity,"  Time varying susceptibility of host at individual level due to waning and
+boosting immunity is known to induce rich long-term behavior of disease
+transmission dynamics. Meanwhile, the impact of the time varying heterogeneity
+of host susceptibility on the shot-term behavior of epidemics is not
+well-studied, even though the large amount of the available epidemiological
+data are the short-term epidemics. Here we constructed a parsimonious
+mathematical model describing the short-term transmission dynamics taking into
+account natural-boosting immunity by reinfection, and obtained the explicit
+solution for our model. We found that our system show ""the delayed epidemic"",
+the epidemic takes off after negative slope of the epidemic curve at the
+initial phase of epidemic, in addition to the common classification in the
+standard SIR model, i.e., ""no epidemic"" as $\mathcal{R}_{0}\leq1$ or normal
+epidemic as $\mathcal{R}_{0}>1$. Employing the explicit solution we derived the
+condition for each classification.
+",0,0,0,0,1,0
+11,A global sensitivity analysis and reduced order models for hydraulically-fractured horizontal wells,"  We present a systematic global sensitivity analysis using the Sobol method
+which can be utilized to rank the variables that affect two quantity of
+interests -- pore pressure depletion and stress change -- around a
+hydraulically-fractured horizontal well based on their degree of importance.
+These variables include rock properties and stimulation design variables. A
+fully-coupled poroelastic hydraulic fracture model is used to account for pore
+pressure and stress changes due to production. To ease the computational cost
+of a simulator, we also provide reduced order models (ROMs), which can be used
+to replace the complex numerical model with a rather simple analytical model,
+for calculating the pore pressure and stresses at different locations around
+hydraulic fractures. The main findings of this research are: (i) mobility,
+production pressure, and fracture half-length are the main contributors to the
+changes in the quantities of interest. The percentage of the contribution of
+each parameter depends on the location with respect to pre-existing hydraulic
+fractures and the quantity of interest. (ii) As the time progresses, the effect
+of mobility decreases and the effect of production pressure increases. (iii)
+These two variables are also dominant for horizontal stresses at large
+distances from hydraulic fractures. (iv) At zones close to hydraulic fracture
+tips or inside the spacing area, other parameters such as fracture spacing and
+half-length are the dominant factors that affect the minimum horizontal stress.
+The results of this study will provide useful guidelines for the stimulation
+design of legacy wells and secondary operations such as refracturing and infill
+drilling.
+",1,0,0,0,0,0
+12,Role-separating ordering in social dilemmas controlled by topological frustration,"  ""Three is a crowd"" is an old proverb that applies as much to social
+interactions, as it does to frustrated configurations in statistical physics
+models. Accordingly, social relations within a triangle deserve special
+attention. With this motivation, we explore the impact of topological
+frustration on the evolutionary dynamics of the snowdrift game on a triangular
+lattice. This topology provides an irreconcilable frustration, which prevents
+anti-coordination of competing strategies that would be needed for an optimal
+outcome of the game. By using different strategy updating protocols, we observe
+complex spatial patterns in dependence on payoff values that are reminiscent to
+a honeycomb-like organization, which helps to minimize the negative consequence
+of the topological frustration. We relate the emergence of these patterns to
+the microscopic dynamics of the evolutionary process, both by means of
+mean-field approximations and Monte Carlo simulations. For comparison, we also
+consider the same evolutionary dynamics on the square lattice, where of course
+the topological frustration is absent. However, with the deletion of diagonal
+links of the triangular lattice, we can gradually bridge the gap to the square
+lattice. Interestingly, in this case the level of cooperation in the system is
+a direct indicator of the level of topological frustration, thus providing a
+method to determine frustration levels in an arbitrary interaction network.
+",0,1,0,0,0,0
+13,Dynamics of exciton magnetic polarons in CdMnSe/CdMgSe quantum wells: the effect of self-localization,"  We study the exciton magnetic polaron (EMP) formation in (Cd,Mn)Se/(Cd,Mg)Se
+diluted-magnetic-semiconductor quantum wells using time-resolved
+photoluminescence (PL). The magnetic field and temperature dependencies of this
+dynamics allow us to separate the non-magnetic and magnetic contributions to
+the exciton localization. We deduce the EMP energy of 14 meV, which is in
+agreement with time-integrated measurements based on selective excitation and
+the magnetic field dependence of the PL circular polarization degree. The
+polaron formation time of 500 ps is significantly longer than the corresponding
+values reported earlier. We propose that this behavior is related to strong
+self-localization of the EMP, accompanied with a squeezing of the heavy-hole
+envelope wavefunction. This conclusion is also supported by the decrease of the
+exciton lifetime from 600 ps to 200 - 400 ps with increasing magnetic field and
+temperature.
+",0,1,0,0,0,0
+14,On Varieties of Ordered Automata,"  The classical Eilenberg correspondence, based on the concept of the syntactic
+monoid, relates varieties of regular languages with pseudovarieties of finite
+monoids. Various modifications of this correspondence appeared, with more
+general classes of regular languages on one hand and classes of more complex
+algebraic structures on the other hand. For example, classes of languages need
+not be closed under complementation or all preimages under homomorphisms, while
+monoids can be equipped with a compatible order or they can have a
+distinguished set of generators. Such generalized varieties and pseudovarieties
+also have natural counterparts formed by classes of finite (ordered) automata.
+In this paper the previous approaches are combined. The notion of positive
+$\mathcal C$-varieties of ordered semiautomata (i.e. no initial and final
+states are specified) is introduced and their correspondence with positive
+$\mathcal C$-varieties of languages is proved.
+",1,0,0,0,0,0
+15,Direct Evidence of Spontaneous Abrikosov Vortex State in Ferromagnetic Superconductor EuFe$_2$(As$_{1-x}$P$_x$)$_2$ with $x=0.21$,"  Using low-temperature Magnetic Force Microscopy (MFM) we provide direct
+experimental evidence for spontaneous vortex phase (SVP) formation in
+EuFe$_2$(As$_{0.79}$P$_{0.21}$)$_2$ single crystal with the superconducting
+$T^{\rm 0}_{\rm SC}=23.6$~K and ferromagnetic $T_{\rm FM}\sim17.7$~K transition
+temperatures. Spontaneous vortex-antivortex (V-AV) pairs are imaged in the
+vicinity of $T_{\rm FM}$. Also, upon cooling cycle near $T_{\rm FM}$ we observe
+the first-order transition from the short period domain structure, which
+appears in the Meissner state, into the long period domain structure with
+spontaneous vortices. It is the first experimental observation of this scenario
+in the ferromagnetic superconductors. Low-temperature phase is characterized by
+much larger domains in V-AV state and peculiar branched striped structures at
+the surface, which are typical for uniaxial ferromagnets with perpendicular
+magnetic anisotropy (PMA). The domain wall parameters at various temperatures
+are estimated.
+",0,1,0,0,0,0
+16,A rank 18 Waring decomposition of $sM_{\langle 3\rangle}$ with 432 symmetries,"  The recent discovery that the exponent of matrix multiplication is determined
+by the rank of the symmetrized matrix multiplication tensor has invigorated
+interest in better understanding symmetrized matrix multiplication. I present
+an explicit rank 18 Waring decomposition of $sM_{\langle 3\rangle}$ and
+describe its symmetry group.
+",0,0,1,0,0,0
+17,The PdBI Arcsecond Whirlpool Survey (PAWS). The Role of Spiral Arms in Cloud and Star Formation,"  The process that leads to the formation of the bright star forming sites
+observed along prominent spiral arms remains elusive. We present results of a
+multi-wavelength study of a spiral arm segment in the nearby grand-design
+spiral galaxy M51 that belongs to a spiral density wave and exhibits nine gas
+spurs. The combined observations of the(ionized, atomic, molecular, dusty)
+interstellar medium (ISM) with star formation tracers (HII regions, young
+<10Myr stellar clusters) suggest (1) no variation in giant molecular cloud
+(GMC) properties between arm and gas spurs, (2) gas spurs and extinction
+feathers arising from the same structure with a close spatial relation between
+gas spurs and ongoing/recent star formation (despite higher gas surface
+densities in the spiral arm), (3) no trend in star formation age either along
+the arm or along a spur, (4) evidence for strong star formation feedback in gas
+spurs: (5) tentative evidence for star formation triggered by stellar feedback
+for one spur, and (6) GMC associations (GMAs) being no special entities but the
+result of blending of gas arm/spur cross-sections in lower resolution
+observations. We conclude that there is no evidence for a coherent star
+formation onset mechanism that can be solely associated to the presence of the
+spiral density wave. This suggests that other (more localized) mechanisms are
+important to delay star formation such that it occurs in spurs. The evidence of
+star formation proceeding over several million years within individual spurs
+implies that the mechanism that leads to star formation acts or is sustained
+over a longer time-scale.
+",0,1,0,0,0,0
+18,Higher structure in the unstable Adams spectral sequence,"  We describe a variant construction of the unstable Adams spectral the
+sequence for a space $Y$, associated to any free simplicial resolution of
+$H^*(Y;R)$ for $R=\mathbb{F}_p$ or $\mathbb{Q}$. We use this construction to
+describe the differentials and filtration in the spectral sequence in terms of
+appropriate systems of higher cohomology operations.
+",0,0,1,0,0,0
+19,Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference using Five Empirical Applications,"  When investigators seek to estimate causal effects, they often assume that
+selection into treatment is based only on observed covariates. Under this
+identification strategy, analysts must adjust for observed confounders. While
+basic regression models have long been the dominant method of statistical
+adjustment, more robust methods based on matching or weighting have become more
+common. Of late, even more flexible methods based on machine learning methods
+have been developed for statistical adjustment. These machine learning methods
+are designed to be black box methods with little input from the researcher.
+Recent research used a data competition to evaluate various methods of
+statistical adjustment and found that black box methods out performed all other
+methods of statistical adjustment. Matching methods with covariate
+prioritization are designed for direct input from substantive investigators in
+direct contrast to black methods. In this article, we use a different research
+design to compare matching with covariate prioritization to black box methods.
+We use black box methods to replicate results from five studies where matching
+with covariate prioritization was used to customize the statistical adjustment
+in direct response to substantive expertise. We find little difference across
+the methods. We conclude with advice for investigators.
+",0,0,0,1,0,0
+20,Acoustic Impedance Calculation via Numerical Solution of the Inverse Helmholtz Problem,"  Assigning homogeneous boundary conditions, such as acoustic impedance, to the
+thermoviscous wave equations (TWE) derived by transforming the linearized
+Navier-Stokes equations (LNSE) to the frequency domain yields a so-called
+Helmholtz solver, whose output is a discrete set of complex eigenfunction and
+eigenvalue pairs. The proposed method -- the inverse Helmholtz solver (iHS) --
+reverses such procedure by returning the value of acoustic impedance at one or
+more unknown impedance boundaries (IBs) of a given domain via spatial
+integration of the TWE for a given real-valued frequency with assigned
+conditions on other boundaries. The iHS procedure is applied to a second-order
+spatial discretization of the TWEs derived on an unstructured grid with
+staggered grid arrangement. The momentum equation only is extended to the
+center of each IB face where pressure and velocity components are co-located
+and treated as unknowns. One closure condition considered for the iHS is the
+assignment of the surface gradient of pressure phase over the IBs,
+corresponding to assigning the shape of the acoustic waveform at the IB. The
+iHS procedure is carried out independently for each frequency in order to
+return the complete broadband complex impedance distribution at the IBs in any
+desired frequency range. The iHS approach is first validated against Rott's
+theory for both inviscid and viscous, rectangular and circular ducts. The
+impedance of a geometrically complex toy cavity is then reconstructed and
+verified against companion full compressible unstructured Navier-Stokes
+simulations resolving the cavity geometry and one-dimensional impedance test
+tube calculations based on time-domain impedance boundary conditions (TDIBC).
+The iHS methodology is also shown to capture thermoacoustic effects, with
+reconstructed impedance values quantitatively in agreement with thermoacoustic
+growth rates.
+",0,1,0,0,0,0
+21,Deciphering noise amplification and reduction in open chemical reaction networks,"  The impact of random fluctuations on the dynamical behavior a complex
+biological systems is a longstanding issue, whose understanding would shed
+light on the evolutionary pressure that nature imposes on the intrinsic noise
+levels and would allow rationally designing synthetic networks with controlled
+noise. Using the Itō stochastic differential equation formalism, we performed
+both analytic and numerical analyses of several model systems containing
+different molecular species in contact with the environment and interacting
+with each other through mass-action kinetics. These systems represent for
+example biomolecular oligomerization processes, complex-breakage reactions,
+signaling cascades or metabolic networks. For chemical reaction networks with
+zero deficiency values, which admit a detailed- or complex-balanced steady
+state, all molecular species are uncorrelated. The number of molecules of each
+species follow a Poisson distribution and their Fano factors, which measure the
+intrinsic noise, are equal to one. Systems with deficiency one have an
+unbalanced non-equilibrium steady state and a non-zero S-flux, defined as the
+flux flowing between the complexes multiplied by an adequate stoichiometric
+coefficient. In this case, the noise on each species is reduced if the flux
+flows from the species of lowest to highest complexity, and is amplified is the
+flux goes in the opposite direction. These results are generalized to systems
+of deficiency two, which possess two independent non-vanishing S-fluxes, and we
+conjecture that a similar relation holds for higher deficiency systems.
+",0,0,0,0,1,0
+22,Many-Body Localization: Stability and Instability,"  Rare regions with weak disorder (Griffiths regions) have the potential to
+spoil localization. We describe a non-perturbative construction of local
+integrals of motion (LIOMs) for a weakly interacting spin chain in one
+dimension, under a physically reasonable assumption on the statistics of
+eigenvalues. We discuss ideas about the situation in higher dimensions, where
+one can no longer ensure that interactions involving the Griffiths regions are
+much smaller than the typical energy-level spacing for such regions. We argue
+that ergodicity is restored in dimension d > 1, although equilibration should
+be extremely slow, similar to the dynamics of glasses.
+",0,1,1,0,0,0
+23,Fault Detection and Isolation Tools (FDITOOLS) User's Guide,"  The Fault Detection and Isolation Tools (FDITOOLS) is a collection of MATLAB
+functions for the analysis and solution of fault detection and model detection
+problems. The implemented functions are based on the computational procedures
+described in the Chapters 5, 6 and 7 of the book: ""A. Varga, Solving Fault
+Diagnosis Problems - Linear Synthesis Techniques, Springer, 2017"". This
+document is the User's Guide for the version V1.0 of FDITOOLS. First, we
+present the mathematical background for solving several basic exact and
+approximate synthesis problems of fault detection filters and model detection
+filters. Then, we give in-depth information on the command syntax of the main
+analysis and synthesis functions. Several examples illustrate the use of the
+main functions of FDITOOLS.
+",1,0,0,0,0,0
+24,Complexity of Deciding Detectability in Discrete Event Systems,"  Detectability of discrete event systems (DESs) is a question whether the
+current and subsequent states can be determined based on observations. Shu and
+Lin designed a polynomial-time algorithm to check strong (periodic)
+detectability and an exponential-time (polynomial-space) algorithm to check
+weak (periodic) detectability. Zhang showed that checking weak (periodic)
+detectability is PSpace-complete. This intractable complexity opens a question
+whether there are structurally simpler DESs for which the problem is tractable.
+In this paper, we show that it is not the case by considering DESs represented
+as deterministic finite automata without non-trivial cycles, which are
+structurally the simplest deadlock-free DESs. We show that even for such very
+simple DESs, checking weak (periodic) detectability remains intractable. On the
+contrary, we show that strong (periodic) detectability of DESs can be
+efficiently verified on a parallel computer.
+",1,0,0,0,0,0
+25,The Knaster-Tarski theorem versus monotone nonexpansive mappings,"  Let $X$ be a partially ordered set with the property that each family of
+order intervals of the form $[a,b],[a,\rightarrow )$ with the finite
+intersection property has a nonempty intersection. We show that every directed
+subset of $X$ has a supremum. Then we apply the above result to prove that if
+$X$ is a topological space with a partial order $\preceq $ for which the order
+intervals are compact, $\mathcal{F}$ a nonempty commutative family of monotone
+maps from $X$ into $X$ and there exists $c\in X$ such that $c\preceq Tc$ for
+every $T\in \mathcal{F}$, then the set of common fixed points of $\mathcal{F}$
+is nonempty and has a maximal element. The result, specialized to the case of
+Banach spaces gives a general fixed point theorem that drops almost all
+assumptions from the recent results in this area. An application to the theory
+of integral equations of Urysohn's type is also given.
+",0,0,1,0,0,0
+26,Efficient methods for computing integrals in electronic structure calculations,"  Efficient methods are proposed, for computing integrals appeaing in
+electronic structure calculations. The methods consist of two parts: the first
+part is to represent the integrals as contour integrals and the second one is
+to evaluate the contour integrals by the Clenshaw-Curtis quadrature. The
+efficiency of the proposed methods is demonstrated through numerical
+experiments.
+",0,1,0,0,0,0
+27,Diffraction-Aware Sound Localization for a Non-Line-of-Sight Source,"  We present a novel sound localization algorithm for a non-line-of-sight
+(NLOS) sound source in indoor environments. Our approach exploits the
+diffraction properties of sound waves as they bend around a barrier or an
+obstacle in the scene. We combine a ray tracing based sound propagation
+algorithm with a Uniform Theory of Diffraction (UTD) model, which simulate
+bending effects by placing a virtual sound source on a wedge in the
+environment. We precompute the wedges of a reconstructed mesh of an indoor
+scene and use them to generate diffraction acoustic rays to localize the 3D
+position of the source. Our method identifies the convergence region of those
+generated acoustic rays as the estimated source position based on a particle
+filter. We have evaluated our algorithm in multiple scenarios consisting of a
+static and dynamic NLOS sound source. In our tested cases, our approach can
+localize a source position with an average accuracy error, 0.7m, measured by
+the L2 distance between estimated and actual source locations in a 7m*7m*3m
+room. Furthermore, we observe 37% to 130% improvement in accuracy over a
+state-of-the-art localization method that does not model diffraction effects,
+especially when a sound source is not visible to the robot.
+",1,0,0,0,0,0
+28,"Jacob's ladders, crossbreeding in the set of $ζ$-factorization formulas and selection of families of $ζ$-kindred real continuous functions","  In this paper we introduce the notion of $\zeta$-crossbreeding in a set of
+$\zeta$-factorization formulas and also the notion of complete hybrid formula
+as the final result of that crossbreeding. The last formula is used as a
+criterion for selection of families of $\zeta$-kindred elements in class of
+real continuous functions.
+Dedicated to recalling of Gregory Mendel's pea-crossbreeding.
+",0,0,1,0,0,0
+29,Minimax Estimation of the $L_1$ Distance,"  We consider the problem of estimating the $L_1$ distance between two discrete
+probability measures $P$ and $Q$ from empirical data in a nonasymptotic and
+large alphabet setting. When $Q$ is known and one obtains $n$ samples from $P$,
+we show that for every $Q$, the minimax rate-optimal estimator with $n$ samples
+achieves performance comparable to that of the maximum likelihood estimator
+(MLE) with $n\ln n$ samples. When both $P$ and $Q$ are unknown, we construct
+minimax rate-optimal estimators whose worst case performance is essentially
+that of the known $Q$ case with $Q$ being uniform, implying that $Q$ being
+uniform is essentially the most difficult case. The \emph{effective sample size
+enlargement} phenomenon, identified in Jiao \emph{et al.} (2015), holds both in
+the known $Q$ case for every $Q$ and the $Q$ unknown case. However, the
+construction of optimal estimators for $\|P-Q\|_1$ requires new techniques and
+insights beyond the approximation-based method of functional estimation in Jiao
+\emph{et al.} (2015).
+",0,0,1,1,0,0
+30,Density large deviations for multidimensional stochastic hyperbolic conservation laws,"  We investigate the density large deviation function for a multidimensional
+conservation law in the vanishing viscosity limit, when the probability
+concentrates on weak solutions of a hyperbolic conservation law conservation
+law. When the conductivity and dif-fusivity matrices are proportional, i.e. an
+Einstein-like relation is satisfied, the problem has been solved in [4]. When
+this proportionality does not hold, we compute explicitly the large deviation
+function for a step-like density profile, and we show that the associated
+optimal current has a non trivial structure. We also derive a lower bound for
+the large deviation function, valid for a general weak solution, and leave the
+general large deviation function upper bound as a conjecture.
+",0,1,1,0,0,0
+31,mixup: Beyond Empirical Risk Minimization,"  Large deep neural networks are powerful, but exhibit undesirable behaviors
+such as memorization and sensitivity to adversarial examples. In this work, we
+propose mixup, a simple learning principle to alleviate these issues. In
+essence, mixup trains a neural network on convex combinations of pairs of
+examples and their labels. By doing so, mixup regularizes the neural network to
+favor simple linear behavior in-between training examples. Our experiments on
+the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show
+that mixup improves the generalization of state-of-the-art neural network
+architectures. We also find that mixup reduces the memorization of corrupt
+labels, increases the robustness to adversarial examples, and stabilizes the
+training of generative adversarial networks.
+",1,0,0,1,0,0
+32,Equality of the usual definitions of Brakke flow,"  In 1978 Brakke introduced the mean curvature flow in the setting of geometric
+measure theory. There exist multiple variants of the original definition. Here
+we prove that most of them are indeed equal. One central point is to correct
+the proof of Brakke's §3.5, where he develops an estimate for the evolution
+of the measure of time-dependent test functions.
+",0,0,1,0,0,0
+33,Dynamic Base Station Repositioning to Improve Spectral Efficiency of Drone Small Cells,"  With recent advancements in drone technology, researchers are now considering
+the possibility of deploying small cells served by base stations mounted on
+flying drones. A major advantage of such drone small cells is that the
+operators can quickly provide cellular services in areas of urgent demand
+without having to pre-install any infrastructure. Since the base station is
+attached to the drone, technically it is feasible for the base station to
+dynamic reposition itself in response to the changing locations of users for
+reducing the communication distance, decreasing the probability of signal
+blocking, and ultimately increasing the spectral efficiency. In this paper, we
+first propose distributed algorithms for autonomous control of drone movements,
+and then model and analyse the spectral efficiency performance of a drone small
+cell to shed new light on the fundamental benefits of dynamic repositioning. We
+show that, with dynamic repositioning, the spectral efficiency of drone small
+cells can be increased by nearly 100\% for realistic drone speed, height, and
+user traffic model and without incurring any major increase in drone energy
+consumption.
+",1,0,0,0,0,0
+34,An Unsupervised Homogenization Pipeline for Clustering Similar Patients using Electronic Health Record Data,"  Electronic health records (EHR) contain a large variety of information on the
+clinical history of patients such as vital signs, demographics, diagnostic
+codes and imaging data. The enormous potential for discovery in this rich
+dataset is hampered by its complexity and heterogeneity.
+We present the first study to assess unsupervised homogenization pipelines
+designed for EHR clustering. To identify the optimal pipeline, we tested
+accuracy on simulated data with varying amounts of redundancy, heterogeneity,
+and missingness. We identified two optimal pipelines: 1) Multiple Imputation by
+Chained Equations (MICE) combined with Local Linear Embedding; and 2) MICE,
+Z-scoring, and Deep Autoencoders.
+",0,0,0,0,1,0
+35,Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics,"  Artificial Neural Network computation relies on intensive vector-matrix
+multiplications. Recently, the emerging nonvolatile memory (NVM) crossbar array
+showed a feasibility of implementing such operations with high energy
+efficiency, thus there are many works on efficiently utilizing emerging NVM
+crossbar array as analog vector-matrix multiplier. However, its nonlinear I-V
+characteristics restrain critical design parameters, such as the read voltage
+and weight range, resulting in substantial accuracy loss. In this paper,
+instead of optimizing hardware parameters to a given neural network, we propose
+a methodology of reconstructing a neural network itself optimized to resistive
+memory crossbar arrays. To verify the validity of the proposed method, we
+simulated various neural network with MNIST and CIFAR-10 dataset using two
+different specific Resistive Random Access Memory (RRAM) model. Simulation
+results show that our proposed neural network produces significantly higher
+inference accuracies than conventional neural network when the synapse devices
+have nonlinear I-V characteristics.
+",1,0,0,0,0,0
+36,Rate-Distortion Region of a Gray-Wyner Model with Side Information,"  In this work, we establish a full single-letter characterization of the
+rate-distortion region of an instance of the Gray-Wyner model with side
+information at the decoders. Specifically, in this model an encoder observes a
+pair of memoryless, arbitrarily correlated, sources $(S^n_1,S^n_2)$ and
+communicates with two receivers over an error-free rate-limited link of
+capacity $R_0$, as well as error-free rate-limited individual links of
+capacities $R_1$ to the first receiver and $R_2$ to the second receiver. Both
+receivers reproduce the source component $S^n_2$ losslessly; and Receiver $1$
+also reproduces the source component $S^n_1$ lossily, to within some prescribed
+fidelity level $D_1$. Also, Receiver $1$ and Receiver $2$ are equipped
+respectively with memoryless side information sequences $Y^n_1$ and $Y^n_2$.
+Important in this setup, the side information sequences are arbitrarily
+correlated among them, and with the source pair $(S^n_1,S^n_2)$; and are not
+assumed to exhibit any particular ordering. Furthermore, by specializing the
+main result to two Heegard-Berger models with successive refinement and
+scalable coding, we shed light on the roles of the common and private
+descriptions that the encoder should produce and what they should carry
+optimally. We develop intuitions by analyzing the developed single-letter
+optimal rate-distortion regions of these models, and discuss some insightful
+binary examples.
+",1,0,1,0,0,0
+37,Fourier-based numerical approximation of the Weertman equation for moving dislocations,"  This work discusses the numerical approximation of a nonlinear
+reaction-advection-diffusion equation, which is a dimensionless form of the
+Weertman equation. This equation models steadily-moving dislocations in
+materials science. It reduces to the celebrated Peierls-Nabarro equation when
+its advection term is set to zero. The approach rests on considering a
+time-dependent formulation, which admits the equation under study as its
+long-time limit. Introducing a Preconditioned Collocation Scheme based on
+Fourier transforms, the iterative numerical method presented solves the
+time-dependent problem, delivering at convergence the desired numerical
+solution to the Weertman equation. Although it rests on an explicit
+time-evolution scheme, the method allows for large time steps, and captures the
+solution in a robust manner. Numerical results illustrate the efficiency of the
+approach for several types of nonlinearities.
+",0,1,0,0,0,0
+38,Design Decisions for Weave: A Real-Time Web-based Collaborative Visualization Framework,"  There are many web-based visualization systems available to date, each having
+its strengths and limitations. The goals these systems set out to accomplish
+influence design decisions and determine how reusable and scalable they are.
+Weave is a new web-based visualization platform with the broad goal of enabling
+visualization of any available data by anyone for any purpose. Our open source
+framework supports highly interactive linked visualizations for users of
+varying skill levels. What sets Weave apart from other systems is its
+consideration for real-time remote collaboration with session history. We
+provide a detailed account of the various framework designs we considered with
+comparisons to existing state-of-the-art systems.
+",1,0,0,0,0,0
+39,Suzaku Analysis of the Supernova Remnant G306.3-0.9 and the Gamma-ray View of Its Neighborhood,"  We present an investigation of the supernova remnant (SNR) G306.3$-$0.9 using
+archival multi-wavelength data. The Suzaku spectra are well described by
+two-component thermal plasma models: The soft component is in ionization
+equilibrium and has a temperature $\sim$0.59 keV, while the hard component has
+temperature $\sim$3.2 keV and ionization time-scale $\sim$$2.6\times10^{10}$
+cm$^{-3}$ s. We clearly detected Fe K-shell line at energy of $\sim$6.5 keV
+from this remnant. The overabundances of Si, S, Ar, Ca, and Fe confirm that the
+X-ray emission has an ejecta origin. The centroid energy of the Fe-K line
+supports that G306.3$-$0.9 is a remnant of a Type Ia supernova (SN) rather than
+a core-collapse SN. The GeV gamma-ray emission from G306.3$-$0.9 and its
+surrounding were analyzed using about 6 years of Fermi data. We report about
+the non-detection of G306.3$-$0.9 and the detection of a new extended gamma-ray
+source in the south-west of G306.3$-$0.9 with a significance of
+$\sim$13$\sigma$. We discuss several scenarios for these results with the help
+of data from other wavebands to understand the SNR and its neighborhood.
+",0,1,0,0,0,0
+40,Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention,"  Previous approaches to training syntax-based sentiment classification models
+required phrase-level annotated corpora, which are not readily available in
+many languages other than English. Thus, we propose the use of tree-structured
+Long Short-Term Memory with an attention mechanism that pays attention to each
+subtree of the parse tree. Experimental results indicate that our model
+achieves the state-of-the-art performance in a Japanese sentiment
+classification task.
+",1,0,0,0,0,0
+41,"Covariances, Robustness, and Variational Bayes","  Mean-field Variational Bayes (MFVB) is an approximate Bayesian posterior
+inference technique that is increasingly popular due to its fast runtimes on
+large-scale datasets. However, even when MFVB provides accurate posterior means
+for certain parameters, it often mis-estimates variances and covariances.
+Furthermore, prior robustness measures have remained undeveloped for MFVB. By
+deriving a simple formula for the effect of infinitesimal model perturbations
+on MFVB posterior means, we provide both improved covariance estimates and
+local robustness measures for MFVB, thus greatly expanding the practical
+usefulness of MFVB posterior approximations. The estimates for MFVB posterior
+covariances rely on a result from the classical Bayesian robustness literature
+relating derivatives of posterior expectations to posterior covariances and
+include the Laplace approximation as a special case. Our key condition is that
+the MFVB approximation provides good estimates of a select subset of posterior
+means---an assumption that has been shown to hold in many practical settings.
+In our experiments, we demonstrate that our methods are simple, general, and
+fast, providing accurate posterior uncertainty estimates and robustness
+measures with runtimes that can be an order of magnitude faster than MCMC.
+",0,0,0,1,0,0
+42,Are multi-factor Gaussian term structure models still useful? An empirical analysis on Italian BTPs,"  In this paper, we empirically study models for pricing Italian sovereign
+bonds under a reduced form framework, by assuming different dynamics for the
+short-rate process. We analyze classical Cox-Ingersoll-Ross and Vasicek
+multi-factor models, with a focus on optimization algorithms applied in the
+calibration exercise. The Kalman filter algorithm together with a maximum
+likelihood estimation method are considered to fit the Italian term-structure
+over a 12-year horizon, including the global financial crisis and the euro area
+sovereign debt crisis. Analytic formulas for the gradient vector and the
+Hessian matrix of the likelihood function are provided.
+",0,0,0,0,0,1
+43,Probing valley filtering effect by Andreev reflection in zigzag graphene nanoribbon,"  Ballistic point contact (BPC) with zigzag edges in graphene is a main
+candidate of a valley filter, in which the polarization of the valley degree of
+freedom can be selected by using a local gate voltage. Here, we propose to
+detect the valley filtering effect by Andreev reflection. Because electrons in
+the lowest conduction band and the highest valence band of the BPC possess
+opposite chirality, the inter-band Andreev reflection is strongly suppressed,
+after multiple scattering and interference. We draw this conclusion by both the
+scattering matrix analysis and the numerical simulation. The Andreev reflection
+as a function of the incident energy of electrons and the local gate voltage at
+the BPC is obtained, by which the parameter region for a perfect valley filter
+and the direction of valley polarization can be determined. The Andreev
+reflection exhibits an oscillatory decay with the length of the BPC, indicating
+a negative correlation to valley polarization.
+",0,1,0,0,0,0
+44,Generalized Approximate Message-Passing Decoder for Universal Sparse Superposition Codes,"  Sparse superposition (SS) codes were originally proposed as a
+capacity-achieving communication scheme over the additive white Gaussian noise
+channel (AWGNC) [1]. Very recently, it was discovered that these codes are
+universal, in the sense that they achieve capacity over any memoryless channel
+under generalized approximate message-passing (GAMP) decoding [2], although
+this decoder has never been stated for SS codes. In this contribution we
+introduce the GAMP decoder for SS codes, we confirm empirically the
+universality of this communication scheme through its study on various channels
+and we provide the main analysis tools: state evolution and potential. We also
+compare the performance of GAMP with the Bayes-optimal MMSE decoder. We
+empirically illustrate that despite the presence of a phase transition
+preventing GAMP to reach the optimal performance, spatial coupling allows to
+boost the performance that eventually tends to capacity in a proper limit. We
+also prove that, in contrast with the AWGNC case, SS codes for binary input
+channels have a vanishing error floor in the limit of large codewords.
+Moreover, the performance of Hadamard-based encoders is assessed for practical
+implementations.
+",1,0,1,0,0,0
+45,LAAIR: A Layered Architecture for Autonomous Interactive Robots,"  When developing general purpose robots, the overarching software architecture
+can greatly affect the ease of accomplishing various tasks. Initial efforts to
+create unified robot systems in the 1990s led to hybrid architectures,
+emphasizing a hierarchy in which deliberative plans direct the use of reactive
+skills. However, since that time there has been significant progress in the
+low-level skills available to robots, including manipulation and perception,
+making it newly feasible to accomplish many more tasks in real-world domains.
+There is thus renewed optimism that robots will be able to perform a wide array
+of tasks while maintaining responsiveness to human operators. However, the top
+layer in traditional hybrid architectures, designed to achieve long-term goals,
+can make it difficult to react quickly to human interactions during goal-driven
+execution. To mitigate this difficulty, we propose a novel architecture that
+supports such transitions by adding a top-level reactive module which has
+flexible access to both reactive skills and a deliberative control module. To
+validate this architecture, we present a case study of its application on a
+domestic service robot platform.
+",1,0,0,0,0,0
+46,3D Human Pose Estimation in RGBD Images for Robotic Task Learning,"  We propose an approach to estimate 3D human pose in real world units from a
+single RGBD image and show that it exceeds performance of monocular 3D pose
+estimation approaches from color as well as pose estimation exclusively from
+depth. Our approach builds on robust human keypoint detectors for color images
+and incorporates depth for lifting into 3D. We combine the system with our
+learning from demonstration framework to instruct a service robot without the
+need of markers. Experiments in real world settings demonstrate that our
+approach enables a PR2 robot to imitate manipulation actions observed from a
+human teacher.
+",1,0,0,0,0,0
+47,Simultaneous non-vanishing for Dirichlet L-functions,"  We extend the work of Fouvry, Kowalski and Michel on correlation between
+Hecke eigenvalues of modular forms and algebraic trace functions in order to
+establish an asymptotic formula for a generalized cubic moment of modular
+L-functions at the central point s = 1/2 and for prime moduli q. As an
+application, we exploit our recent result on the mollification of the fourth
+moment of Dirichlet L-functions to derive that for any pair
+$(\omega_1,\omega_2)$ of multiplicative characters modulo q, there is a
+positive proportion of $\chi$ (mod q) such that $L(\chi, 1/2 ), L(\chi\omega_1,
+1/2 )$ and $L(\chi\omega_2, 1/2)$ are simultaneously not too small.
+",0,0,1,0,0,0
+48,Wehrl Entropy Based Quantification of Nonclassicality for Single Mode Quantum Optical States,"  Nonclassical states of a quantized light are described in terms of
+Glauber-Sudarshan P distribution which is not a genuine classical probability
+distribution. Despite several attempts, defining a uniform measure of
+nonclassicality (NC) for the single mode quantum states of light is yet an open
+task. In our previous work [Phys. Rev. A 95, 012330 (2017)] we have shown that
+the existing well-known measures fail to quantify the NC of single mode states
+that are generated under multiple NC-inducing operations. Recently, Ivan et.
+al. [Quantum. Inf. Process. 11, 853 (2012)] have defined a measure of
+non-Gaussian character of quantum optical states in terms of Wehrl entropy.
+Here, we adopt this concept in the context of single mode NC. In this paper, we
+propose a new quantification of NC for the single mode quantum states of light
+as the difference between the total Wehrl entropy of the state and the maximum
+Wehrl entropy arising due to its classical characteristics. This we achieve by
+subtracting from its Wehrl entropy, the maximum Wehrl entropy attainable by any
+classical state that has same randomness as measured in terms of von-Neumann
+entropy. We obtain analytic expressions of NC for most of the states, in
+particular, all pure states and Gaussian mixed states. However, the evaluation
+of NC for the non-Gaussian mixed states is subject to extensive numerical
+computation that lies beyond the scope of the current work. We show that, along
+with the states generated under single NC-inducing operations, also for the
+broader class of states that are generated under multiple NC-inducing
+operations, our quantification enumerates the NC consistently.
+",1,1,0,0,0,0
+49,Attention-based Natural Language Person Retrieval,"  Following the recent progress in image classification and captioning using
+deep learning, we develop a novel natural language person retrieval system
+based on an attention mechanism. More specifically, given the description of a
+person, the goal is to localize the person in an image. To this end, we first
+construct a benchmark dataset for natural language person retrieval. To do so,
+we generate bounding boxes for persons in a public image dataset from the
+segmentation masks, which are then annotated with descriptions and attributes
+using the Amazon Mechanical Turk. We then adopt a region proposal network in
+Faster R-CNN as a candidate region generator. The cropped images based on the
+region proposals as well as the whole images with attention weights are fed
+into Convolutional Neural Networks for visual feature extraction, while the
+natural language expression and attributes are input to Bidirectional Long
+Short- Term Memory (BLSTM) models for text feature extraction. The visual and
+text features are integrated to score region proposals, and the one with the
+highest score is retrieved as the output of our system. The experimental
+results show significant improvement over the state-of-the-art method for
+generic object retrieval and this line of research promises to benefit search
+in surveillance video footage.
+",1,0,0,0,0,0
+50,Large Scale Automated Forecasting for Monitoring Network Safety and Security,"  Real time large scale streaming data pose major challenges to forecasting, in
+particular defying the presence of human experts to perform the corresponding
+analysis. We present here a class of models and methods used to develop an
+automated, scalable and versatile system for large scale forecasting oriented
+towards safety and security monitoring. Our system provides short and long term
+forecasts and uses them to detect safety and security issues in relation with
+multiple internet connected devices well in advance they might take place.
+",0,0,0,1,0,0
+51,Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific Data,"  Machine learning algorithms such as linear regression, SVM and neural network
+have played an increasingly important role in the process of scientific
+discovery. However, none of them is both interpretable and accurate on
+nonlinear datasets. Here we present contextual regression, a method that joins
+these two desirable properties together using a hybrid architecture of neural
+network embedding and dot product layer. We demonstrate its high prediction
+accuracy and sensitivity through the task of predictive feature selection on a
+simulated dataset and the application of predicting open chromatin sites in the
+human genome. On the simulated data, our method achieved high fidelity recovery
+of feature contributions under random noise levels up to 200%. On the open
+chromatin dataset, the application of our method not only outperformed the
+state of the art method in terms of accuracy, but also unveiled two previously
+unfound open chromatin related histone marks. Our method can fill the blank of
+accurate and interpretable nonlinear modeling in scientific data mining tasks.
+",1,0,0,1,0,0
+52,Multi-time correlators in continuous measurement of qubit observables,"  We consider multi-time correlators for output signals from linear detectors,
+continuously measuring several qubit observables at the same time. Using the
+quantum Bayesian formalism, we show that for unital (symmetric) evolution in
+the absence of phase backaction, an $N$-time correlator can be expressed as a
+product of two-time correlators when $N$ is even. For odd $N$, there is a
+similar factorization, which also includes a single-time average. Theoretical
+predictions agree well with experimental results for two detectors, which
+simultaneously measure non-commuting qubit observables.
+",0,1,0,0,0,0
+53,"Parallelism, Concurrency and Distribution in Constraint Handling Rules: A Survey","  Constraint Handling Rules is an effective concurrent declarative programming
+language and a versatile computational logic formalism. CHR programs consist of
+guarded reactive rules that transform multisets of constraints. One of the main
+features of CHR is its inherent concurrency. Intuitively, rules can be applied
+to parts of a multiset in parallel. In this comprehensive survey, we give an
+overview of concurrent and parallel as well as distributed CHR semantics,
+standard and more exotic, that have been proposed over the years at various
+levels of refinement. These semantics range from the abstract to the concrete.
+They are related by formal soundness results. Their correctness is established
+as correspondence between parallel and sequential computations. We present
+common concise sample CHR programs that have been widely used in experiments
+and benchmarks. We review parallel CHR implementations in software and
+hardware. The experimental results obtained show a consistent parallel speedup.
+Most implementations are available online. The CHR formalism can also be used
+to implement and reason with models for concurrency. To this end, the Software
+Transaction Model, the Actor Model, Colored Petri Nets and the Join-Calculus
+have been faithfully encoded in CHR. Under consideration in Theory and Practice
+of Logic Programming (TPLP).
+",1,0,0,0,0,0
+54,Robustness against the channel effect in pathological voice detection,"  Many people are suffering from voice disorders, which can adversely affect
+the quality of their lives. In response, some researchers have proposed
+algorithms for automatic assessment of these disorders, based on voice signals.
+However, these signals can be sensitive to the recording devices. Indeed, the
+channel effect is a pervasive problem in machine learning for healthcare. In
+this study, we propose a detection system for pathological voice, which is
+robust against the channel effect. This system is based on a bidirectional LSTM
+network. To increase the performance robustness against channel mismatch, we
+integrate domain adversarial training (DAT) to eliminate the differences
+between the devices. When we train on data recorded on a high-quality
+microphone and evaluate on smartphone data without labels, our robust detection
+system increases the PR-AUC from 0.8448 to 0.9455 (and 0.9522 with target
+sample labels). To the best of our knowledge, this is the first study applying
+unsupervised domain adaptation to pathological voice detection. Notably, our
+system does not need target device sample labels, which allows for
+generalization to many new devices.
+",1,0,0,0,0,0
+55,An Effective Framework for Constructing Exponent Lattice Basis of Nonzero Algebraic Numbers,"  Computing a basis for the exponent lattice of algebraic numbers is a basic
+problem in the field of computational number theory with applications to many
+other areas. The main cost of a well-known algorithm
+\cite{ge1993algorithms,kauers2005algorithms} solving the problem is on
+computing the primitive element of the extended field generated by the given
+algebraic numbers. When the extended field is of large degree, the problem
+seems intractable by the tool implementing the algorithm. In this paper, a
+special kind of exponent lattice basis is introduced. An important feature of
+the basis is that it can be inductively constructed, which allows us to deal
+with the given algebraic numbers one by one when computing the basis. Based on
+this, an effective framework for constructing exponent lattice basis is
+proposed. Through computing a so-called pre-basis first and then solving some
+linear Diophantine equations, the basis can be efficiently constructed. A new
+certificate for multiplicative independence and some techniques for decreasing
+degrees of algebraic numbers are provided to speed up the computation. The new
+algorithm has been implemented with Mathematica and its effectiveness is
+verified by testing various examples. Moreover, the algorithm is applied to
+program verification for finding invariants of linear loops.
+",1,0,0,0,0,0
+56,Competing evolutionary paths in growing populations with applications to multidrug resistance,"  Investigating the emergence of a particular cell type is a recurring theme in
+models of growing cellular populations. The evolution of resistance to therapy
+is a classic example. Common questions are: when does the cell type first
+occur, and via which sequence of steps is it most likely to emerge? For growing
+populations, these questions can be formulated in a general framework of
+branching processes spreading through a graph from a root to a target vertex.
+Cells have a particular fitness value on each vertex and can transition along
+edges at specific rates. Vertices represents cell states, say \mic{genotypes
+}or physical locations, while possible transitions are acquiring a mutation or
+cell migration. We focus on the setting where cells at the root vertex have the
+highest fitness and transition rates are small. Simple formulas are derived for
+the time to reach the target vertex and for the probability that it is reached
+along a given path in the graph. We demonstrate our results on \mic{several
+scenarios relevant to the emergence of drug resistance}, including: the
+orderings of resistance-conferring mutations in bacteria and the impact of
+imperfect drug penetration in cancer.
+",0,0,0,0,1,0
+57,Transient flows in active porous media,"  Stimuli-responsive materials that modify their shape in response to changes
+in environmental conditions -- such as solute concentration, temperature, pH,
+and stress -- are widespread in nature and technology. Applications include
+micro- and nanoporous materials used in filtration and flow control. The
+physiochemical mechanisms that induce internal volume modifications have been
+widely studies. The coupling between induced volume changes and solute
+transport through porous materials, however, is not well understood. Here, we
+consider advective and diffusive transport through a small channel linking two
+large reservoirs. A section of stimulus-responsive material regulates the
+channel permeability, which is a function of the local solute concentration. We
+derive an exact solution to the coupled transport problem and demonstrate the
+existence of a flow regime in which the steady state is reached via a damped
+oscillation around the equilibrium concentration value. Finally, the
+feasibility of an experimental observation of the phenomena is discussed.
+Please note that this version of the paper has not been formally peer reviewed,
+revised or accepted by a journal.
+",0,1,0,0,0,0
+58,An information model for modular robots: the Hardware Robot Information Model (HRIM),"  Today's landscape of robotics is dominated by vertical integration where
+single vendors develop the final product leading to slow progress, expensive
+products and customer lock-in. Opposite to this, an horizontal integration
+would result in a rapid development of cost-effective mass-market products with
+an additional consumer empowerment. The transition of an industry from vertical
+integration to horizontal integration is typically catalysed by de facto
+industry standards that enable a simplified and seamless integration of
+products. However, in robotics there is currently no leading candidate for a
+global plug-and-play standard.
+This paper tackles the problem of incompatibility between robot components
+that hinder the reconfigurability and flexibility demanded by the robotics
+industry. Particularly, it presents a model to create plug-and-play robot
+hardware components. Rather than iteratively evolving previous ontologies, our
+proposed model answers the needs identified by the industry while facilitating
+interoperability, measurability and comparability of robotics technology. Our
+approach differs significantly with the ones presented before as it is
+hardware-oriented and establishes a clear set of actions towards the
+integration of this model in real environments and with real manufacturers.
+",1,0,0,0,0,0
+59,Detecting Adversarial Samples Using Density Ratio Estimates,"  Machine learning models, especially based on deep architectures are used in
+everyday applications ranging from self driving cars to medical diagnostics. It
+has been shown that such models are dangerously susceptible to adversarial
+samples, indistinguishable from real samples to human eye, adversarial samples
+lead to incorrect classifications with high confidence. Impact of adversarial
+samples is far-reaching and their efficient detection remains an open problem.
+We propose to use direct density ratio estimation as an efficient model
+agnostic measure to detect adversarial samples. Our proposed method works
+equally well with single and multi-channel samples, and with different
+adversarial sample generation methods. We also propose a method to use density
+ratio estimates for generating adversarial samples with an added constraint of
+preserving density ratio.
+",1,0,0,1,0,0
+60,The Query Complexity of Cake Cutting,"  We study the query complexity of cake cutting and give lower and upper bounds
+for computing approximately envy-free, perfect, and equitable allocations with
+the minimum number of cuts. The lower bounds are tight for computing connected
+envy-free allocations among n=3 players and for computing perfect and equitable
+allocations with minimum number of cuts between n=2 players.
+We also formalize moving knife procedures and show that a large subclass of
+this family, which captures all the known moving knife procedures, can be
+simulated efficiently with arbitrarily small error in the Robertson-Webb query
+model.
+",1,0,0,0,0,0
+61,Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition,"  This paper studies the emotion recognition from musical tracks in the
+2-dimensional valence-arousal (V-A) emotional space. We propose a method based
+on convolutional (CNN) and recurrent neural networks (RNN), having
+significantly fewer parameters compared with the state-of-the-art method for
+the same task. We utilize one CNN layer followed by two branches of RNNs
+trained separately for arousal and valence. The method was evaluated using the
+'MediaEval2015 emotion in music' dataset. We achieved an RMSE of 0.202 for
+arousal and 0.268 for valence, which is the best result reported on this
+dataset.
+",1,0,0,0,0,0
+62,Timed Automata with Polynomial Delay and their Expressiveness,"  We consider previous models of Timed, Probabilistic and Stochastic Timed
+Automata, we introduce our model of Timed Automata with Polynomial Delay and we
+characterize the expressiveness of these models relative to each other.
+",1,0,0,0,0,0
+63,Superconducting properties of Cu intercalated Bi$_2$Se$_3$ studied by Muon Spin Spectroscopy,"  We present muon spin rotation measurements on superconducting Cu intercalated
+Bi$_2$Se$_3$, which was suggested as a realization of a topological
+superconductor. We observe a clear evidence of the superconducting transition
+below 4 K, where the width of magnetic field distribution increases as the
+temperature is decreased. The measured broadening at mK temperatures suggests a
+large London penetration depth in the $ab$ plane ($\lambda_{\mathrm{eff}}\sim
+1.6$ $\mathrm{\mu}$m). We show that the temperature dependence of this
+broadening follows the BCS prediction, but could be consistent with several gap
+symmetries.
+",0,1,0,0,0,0
+64,Time-domain THz spectroscopy reveals coupled protein-hydration dielectric response in solutions of native and fibrils of human lyso-zyme,"  Here we reveal details of the interaction between human lysozyme proteins,
+both native and fibrils, and their water environment by intense terahertz time
+domain spectroscopy. With the aid of a rigorous dielectric model, we determine
+the amplitude and phase of the oscillating dipole induced by the THz field in
+the volume containing the protein and its hydration water. At low
+concentrations, the amplitude of this induced dipolar response decreases with
+increasing concentration. Beyond a certain threshold, marking the onset of the
+interactions between the extended hydration shells, the amplitude remains fixed
+but the phase of the induced dipolar response, which is initially in phase with
+the applied THz field, begins to change. The changes observed in the THz
+response reveal protein-protein interactions me-diated by extended hydration
+layers, which may control fibril formation and may have an important role in
+chemical recognition phenomena.
+",0,1,0,0,0,0
+65,Inversion of Qubit Energy Levels in Qubit-Oscillator Circuits in the Deep-Strong-Coupling Regime,"  We report on experimentally measured light shifts of superconducting flux
+qubits deep-strongly coupled to LC oscillators, where the coupling constants
+are comparable to the qubit and oscillator resonance frequencies. By using
+two-tone spectroscopy, the energies of the six lowest levels of each circuit
+are determined. We find huge Lamb shifts that exceed 90% of the bare qubit
+frequencies and inversions of the qubits' ground and excited states when there
+are a finite number of photons in the oscillator. Our experimental results
+agree with theoretical predictions based on the quantum Rabi model.
+",0,1,0,0,0,0
+66,Deep Multiple Instance Feature Learning via Variational Autoencoder,"  We describe a novel weakly supervised deep learning framework that combines
+both the discriminative and generative models to learn meaningful
+representation in the multiple instance learning (MIL) setting. MIL is a weakly
+supervised learning problem where labels are associated with groups of
+instances (referred as bags) instead of individual instances. To address the
+essential challenge in MIL problems raised from the uncertainty of positive
+instances label, we use a discriminative model regularized by variational
+autoencoders (VAEs) to maximize the differences between latent representations
+of all instances and negative instances. As a result, the hidden layer of the
+variational autoencoder learns meaningful representation. This representation
+can effectively be used for MIL problems as illustrated by better performance
+on the standard benchmark datasets comparing to the state-of-the-art
+approaches. More importantly, unlike most related studies, the proposed
+framework can be easily scaled to large dataset problems, as illustrated by the
+audio event detection and segmentation task. Visualization also confirms the
+effectiveness of the latent representation in discriminating positive and
+negative classes.
+",0,0,0,1,0,0
+67,Regularity of envelopes in Kähler classes,"  We establish the C^{1,1} regularity of quasi-psh envelopes in a Kahler class,
+confirming a conjecture of Berman.
+",0,0,1,0,0,0
+68,$S^1$-equivariant Index theorems and Morse inequalities on complex manifolds with boundary,"  Let $M$ be a complex manifold of dimension $n$ with smooth connected boundary
+$X$. Assume that $\overline M$ admits a holomorphic $S^1$-action preserving the
+boundary $X$ and the $S^1$-action is transversal and CR on $X$. We show that
+the $\overline\partial$-Neumann Laplacian on $M$ is transversally elliptic and
+as a consequence, the $m$-th Fourier component of the $q$-th Dolbeault
+cohomology group $H^q_m(\overline M)$ is finite dimensional, for every
+$m\in\mathbb Z$ and every $q=0,1,\ldots,n$. This enables us to define
+$\sum^{n}_{j=0}(-1)^j{\rm dim\,}H^q_m(\overline M)$ the $m$-th Fourier
+component of the Euler characteristic on $M$ and to study large $m$-behavior of
+$H^q_m(\overline M)$. In this paper, we establish an index formula for
+$\sum^{n}_{j=0}(-1)^j{\rm dim\,}H^q_m(\overline M)$ and Morse inequalities for
+$H^q_m(\overline M)$.
+",0,0,1,0,0,0
+69,Internal Model from Observations for Reward Shaping,"  Reinforcement learning methods require careful design involving a reward
+function to obtain the desired action policy for a given task. In the absence
+of hand-crafted reward functions, prior work on the topic has proposed several
+methods for reward estimation by using expert state trajectories and action
+pairs. However, there are cases where complete or good action information
+cannot be obtained from expert demonstrations. We propose a novel reinforcement
+learning method in which the agent learns an internal model of observation on
+the basis of expert-demonstrated state trajectories to estimate rewards without
+completely learning the dynamics of the external environment from state-action
+pairs. The internal model is obtained in the form of a predictive model for the
+given expert state distribution. During reinforcement learning, the agent
+predicts the reward as a function of the difference between the actual state
+and the state predicted by the internal model. We conducted multiple
+experiments in environments of varying complexity, including the Super Mario
+Bros and Flappy Bird games. We show our method successfully trains good
+policies directly from expert game-play videos.
+",1,0,0,1,0,0
+70,Characterizations of quasitrivial symmetric nondecreasing associative operations,"  In this paper we are interested in the class of n-ary operations on an
+arbitrary chain that are quasitrivial, symmetric, nondecreasing, and
+associative. We first provide a description of these operations. We then prove
+that associativity can be replaced with bisymmetry in the definition of this
+class. Finally we investigate the special situation where the chain is finite.
+",0,0,1,0,0,0
+71,Multivariate Dependency Measure based on Copula and Gaussian Kernel,"  We propose a new multivariate dependency measure. It is obtained by
+considering a Gaussian kernel based distance between the copula transform of
+the given d-dimensional distribution and the uniform copula and then
+appropriately normalizing it. The resulting measure is shown to satisfy a
+number of desirable properties. A nonparametric estimate is proposed for this
+dependency measure and its properties (finite sample as well as asymptotic) are
+derived. Some comparative studies of the proposed dependency measure estimate
+with some widely used dependency measure estimates on artificial datasets are
+included. A non-parametric test of independence between two or more random
+variables based on this measure is proposed. A comparison of the proposed test
+with some existing nonparametric multivariate test for independence is
+presented.
+",0,0,1,1,0,0
+72,The nature of the tensor order in Cd2Re2O7,"  The pyrochlore metal Cd2Re2O7 has been recently investigated by
+second-harmonic generation (SHG) reflectivity. In this paper, we develop a
+general formalism that allows for the identification of the relevant tensor
+components of the SHG from azimuthal scans. We demonstrate that the secondary
+order parameter identified by SHG at the structural phase transition is the
+x2-y2 component of the axial toroidal quadrupole. This differs from the 3z2-r2
+symmetry of the atomic displacements associated with the I-4m2 crystal
+structure that was previously thought to be its origin. Within the same
+formalism, we suggest that the primary order parameter detected in the SHG
+experiment is the 3z2-r2 component of the magnetic quadrupole. We discuss the
+general mechanism driving the phase transition in our proposed framework, and
+suggest experiments, particularly resonant X-ray scattering ones, that could
+clarify this issue.
+",0,1,0,0,0,0
+73,Efficient and consistent inference of ancestral sequences in an evolutionary model with insertions and deletions under dense taxon sampling,"  In evolutionary biology, the speciation history of living organisms is
+represented graphically by a phylogeny, that is, a rooted tree whose leaves
+correspond to current species and branchings indicate past speciation events.
+Phylogenies are commonly estimated from molecular sequences, such as DNA
+sequences, collected from the species of interest. At a high level, the idea
+behind this inference is simple: the further apart in the Tree of Life are two
+species, the greater is the number of mutations to have accumulated in their
+genomes since their most recent common ancestor. In order to obtain accurate
+estimates in phylogenetic analyses, it is standard practice to employ
+statistical approaches based on stochastic models of sequence evolution on a
+tree. For tractability, such models necessarily make simplifying assumptions
+about the evolutionary mechanisms involved. In particular, commonly omitted are
+insertions and deletions of nucleotides -- also known as indels.
+Properly accounting for indels in statistical phylogenetic analyses remains a
+major challenge in computational evolutionary biology. Here we consider the
+problem of reconstructing ancestral sequences on a known phylogeny in a model
+of sequence evolution incorporating nucleotide substitutions, insertions and
+deletions, specifically the classical TKF91 process. We focus on the case of
+dense phylogenies of bounded height, which we refer to as the taxon-rich
+setting, where statistical consistency is achievable. We give the first
+polynomial-time ancestral reconstruction algorithm with provable guarantees
+under constant rates of mutation. Our algorithm succeeds when the phylogeny
+satisfies the ""big bang"" condition, a necessary and sufficient condition for
+statistical consistency in this context.
+",1,0,1,1,0,0
+74,Flow Characteristics and Cores of Complex Network and Multiplex Type Systems,"  Subject of research is complex networks and network systems. The network
+system is defined as a complex network in which flows are moved. Classification
+of flows in the network is carried out on the basis of ordering and continuity.
+It is shown that complex networks with different types of flows generate
+various network systems. Flow analogues of the basic concepts of the theory of
+complex networks are introduced and the main problems of this theory in terms
+of flow characteristics are formulated. Local and global flow characteristics
+of networks bring closer the theory of complex networks to the systems theory
+and systems analysis. Concept of flow core of network system is introduced and
+defined how it simplifies the process of its investigation. Concepts of kernel
+and flow core of multiplex are determined. Features of operation of multiplex
+type systems are analyzed.
+",1,1,0,0,0,0
+75,Pattern-forming fronts in a Swift-Hohenberg equation with directional quenching - parallel and oblique stripes,"  We study the effect of domain growth on the orientation of striped phases in
+a Swift-Hohenberg equation. Domain growth is encoded in a step-like parameter
+dependence that allows stripe formation in a half plane, and suppresses
+patterns in the complement, while the boundary of the pattern-forming region is
+propagating with fixed normal velocity. We construct front solutions that leave
+behind stripes in the pattern-forming region that are parallel to or at a small
+oblique angle to the boundary.
+Technically, the construction of stripe formation parallel to the boundary
+relies on ill-posed, infinite-dimensional spatial dynamics. Stripes forming at
+a small oblique angle are constructed using a functional-analytic, perturbative
+approach. Here, the main difficulties are the presence of continuous spectrum
+and the fact that small oblique angles appear as a singular perturbation in a
+traveling-wave problem. We resolve the former difficulty using a farfield-core
+decomposition and Fredholm theory in weighted spaces. The singular perturbation
+problem is resolved using preconditioners and boot-strapping.
+",0,1,0,0,0,0
+76,Generalized Minimum Distance Estimators in Linear Regression with Dependent Errors,"  This paper discusses minimum distance estimation method in the linear
+regression model with dependent errors which are strongly mixing. The
+regression parameters are estimated through the minimum distance estimation
+method, and asymptotic distributional properties of the estimators are
+discussed. A simulation study compares the performance of the minimum distance
+estimator with other well celebrated estimator. This simulation study shows the
+superiority of the minimum distance estimator over another estimator. KoulMde
+(R package) which was used for the simulation study is available online. See
+section 4 for the detail.
+",0,0,1,1,0,0
+77,Live Service Migration in Mobile Edge Clouds,"  Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user,
+by installing small cloud infrastructures at the network edge. This enables a
+new breed of real-time applications, such as instantaneous object recognition
+and safety assistance in intelligent transportation systems, that require very
+low latency. One key issue that comes with proximity is how to ensure that
+users always receive good performance as they move across different locations.
+Migrating services between MECs is seen as the means to achieve this. This
+article presents a layered framework for migrating active service applications
+that are encapsulated either in virtual machines (VMs) or containers. This
+layering approach allows a substantial reduction in service downtime. The
+framework is easy to implement using readily available technologies, and one of
+its key advantages is that it supports containers, which is a promising
+emerging technology that offers tangible benefits over VMs. The migration
+performance of various real applications is evaluated by experiments under the
+presented framework. Insights drawn from the experimentation results are
+discussed.
+",1,0,0,0,0,0
+78,Induced density correlations in a sonic black hole condensate,"  Analog black/white hole pairs, consisting of a region of supersonic flow,
+have been achieved in a recent experiment by J. Steinhauer using an elongated
+Bose-Einstein condensate. A growing standing density wave, and a checkerboard
+feature in the density-density correlation function, were observed in the
+supersonic region. We model the density-density correlation function, taking
+into account both quantum fluctuations and the shot-to-shot variation of atom
+number normally present in ultracold-atom experiments. We find that quantum
+fluctuations alone produce some, but not all, of the features of the
+correlation function, whereas atom-number fluctuation alone can produce all the
+observed features, and agreement is best when both are included. In both cases,
+the density-density correlation is not intrinsic to the fluctuations, but
+rather is induced by modulation of the standing wave caused by the
+fluctuations.
+",0,1,0,0,0,0
+79,Genus growth in $\mathbb{Z}_p$-towers of function fields,"  Let $K$ be a function field over a finite field $k$ of characteristic $p$ and
+let $K_{\infty}/K$ be a geometric extension with Galois group $\mathbb{Z}_p$.
+Let $K_n$ be the corresponding subextension with Galois group
+$\mathbb{Z}/p^n\mathbb{Z}$ and genus $g_n$. In this paper, we give a simple
+explicit formula $g_n$ in terms of an explicit Witt vector construction of the
+$\mathbb{Z}_p$-tower. This formula leads to a tight lower bound on $g_n$ which
+is quadratic in $p^n$. Furthermore, we determine all $\mathbb{Z}_p$-towers for
+which the genus sequence is stable, in the sense that there are $a,b,c \in
+\mathbb{Q}$ such that $g_n=a p^{2n}+b p^n +c$ for $n$ large enough. Such genus
+stable towers are expected to have strong stable arithmetic properties for
+their zeta functions. A key technical contribution of this work is a new
+simplified formula for the Schmid-Witt symbol coming from local class field
+theory.
+",0,0,1,0,0,0
+80,Topological Phases emerging from Spin-Orbital Physics,"  We study the evolution of spin-orbital correlations in an inhomogeneous
+quantum system with an impurity replacing a doublon by a holon orbital degree
+of freedom. Spin-orbital entanglement is large when spin correlations are
+antiferromagnetic, while for a ferromagnetic host we obtain a pure orbital
+description. In this regime the orbital model can be mapped on spinless
+fermions and we uncover topological phases with zero energy modes at the edge
+or at the domain between magnetically inequivalent regions.
+",0,1,0,0,0,0
+81,"Accurate and Diverse Sampling of Sequences based on a ""Best of Many"" Sample Objective","  For autonomous agents to successfully operate in the real world, anticipation
+of future events and states of their environment is a key competence. This
+problem has been formalized as a sequence extrapolation problem, where a number
+of observations are used to predict the sequence into the future. Real-world
+scenarios demand a model of uncertainty of such predictions, as predictions
+become increasingly uncertain -- in particular on long time horizons. While
+impressive results have been shown on point estimates, scenarios that induce
+multi-modal distributions over future sequences remain challenging. Our work
+addresses these challenges in a Gaussian Latent Variable model for sequence
+prediction. Our core contribution is a ""Best of Many"" sample objective that
+leads to more accurate and more diverse predictions that better capture the
+true variations in real-world sequence data. Beyond our analysis of improved
+model fit, our models also empirically outperform prior work on three diverse
+tasks ranging from traffic scenes to weather data.
+",0,0,0,1,0,0
+82,Exploring RNN-Transducer for Chinese Speech Recognition,"  End-to-end approaches have drawn much attention recently for significantly
+simplifying the construction of an automatic speech recognition (ASR) system.
+RNN transducer (RNN-T) is one of the popular end-to-end methods. Previous
+studies have shown that RNN-T is difficult to train and a very complex training
+process is needed for a reasonable performance. In this paper, we explore RNN-T
+for a Chinese large vocabulary continuous speech recognition (LVCSR) task and
+aim to simplify the training process while maintaining performance. First, a
+new strategy of learning rate decay is proposed to accelerate the model
+convergence. Second, we find that adding convolutional layers at the beginning
+of the network and using ordered data can discard the pre-training process of
+the encoder without loss of performance. Besides, we design experiments to find
+a balance among the usage of GPU memory, training circle and model performance.
+Finally, we achieve 16.9% character error rate (CER) on our test set which is
+2% absolute improvement from a strong BLSTM CE system with language model
+trained on the same text corpus.
+",1,0,0,0,0,0
+83,A Debt-Aware Learning Approach for Resource Adaptations in Cloud Elasticity Management,"  Elasticity is a cloud property that enables applications and its execution
+systems to dynamically acquire and release shared computational resources on
+demand. Moreover, it unfolds the advantage of economies of scale in the cloud
+through a drop in the average costs of these shared resources. However, it is
+still an open challenge to achieve a perfect match between resource demand and
+provision in autonomous elasticity management. Resource adaptation decisions
+essentially involve a trade-off between economics and performance, which
+produces a gap between the ideal and actual resource provisioning. This gap, if
+not properly managed, can negatively impact the aggregate utility of a cloud
+customer in the long run. To address this limitation, we propose a technical
+debt-aware learning approach for autonomous elasticity management based on a
+reinforcement learning of elasticity debts in resource provisioning; the
+adaptation pursues strategic decisions that trades off economics against
+performance. We extend CloudSim and Burlap to evaluate our approach. The
+evaluation shows that a reinforcement learning of technical debts in elasticity
+obtains a higher utility for a cloud customer, while conforming expected levels
+of performance.
+",1,0,0,0,0,0
+84,Semi-simplicial spaces,"  This is an exposition of homotopical results on the geometric realization of
+semi-simplicial spaces. We then use these to derive basic foundational results
+about classifying spaces of topological categories, possibly without units. The
+topics considered include: fibrancy conditions on topological categories; the
+effect on classifying spaces of freely adjoining units; approximate notions of
+units; Quillen's Theorems A and B for non-unital topological categories; the
+effect on classifying spaces of changing the topology on the space of objects;
+the Group-Completion Theorem.
+",0,0,1,0,0,0
+85,"Constraints, Lazy Constraints, or Propagators in ASP Solving: An Empirical Analysis","  Answer Set Programming (ASP) is a well-established declarative paradigm. One
+of the successes of ASP is the availability of efficient systems.
+State-of-the-art systems are based on the ground+solve approach. In some
+applications this approach is infeasible because the grounding of one or few
+constraints is expensive. In this paper, we systematically compare alternative
+strategies to avoid the instantiation of problematic constraints, that are
+based on custom extensions of the solver. Results on real and synthetic
+benchmarks highlight some strengths and weaknesses of the different strategies.
+(Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.)
+",1,0,0,0,0,0
+86,A Unified Approach to Nonlinear Transformation Materials,"  The advances in geometric approaches to optical devices due to transformation
+optics has led to the development of cloaks, concentrators, and other devices.
+It has also been shown that transformation optics can be used to gravitational
+fields from general relativity. However, the technique is currently constrained
+to linear devices, as a consistent approach to nonlinearity (including both the
+case of a nonlinear background medium and a nonlinear transformation) remains
+an open question. Here we show that nonlinearity can be incorporated into
+transformation optics in a consistent way. We use this to illustrate a number
+of novel effects, including cloaking an optical soliton, modeling nonlinear
+solutions to Einstein's field equations, controlling transport in a Debye
+solid, and developing a set of constitutive to relations for relativistic
+cloaks in arbitrary nonlinear backgrounds.
+",0,1,0,0,0,0
+87,Stationary crack propagation in a two-dimensional visco-elastic network model,"  We investigate crack propagation in a simple two-dimensional visco-elastic
+model and find a scaling regime in the relation between the propagation
+velocity and energy release rate or fracture energy, together with lower and
+upper bounds of the scaling regime. On the basis of our result, the existence
+of the lower and upper bounds is expected to be universal or model-independent:
+the present simple simulation model provides generic insight into the physics
+of crack propagation, and the model will be a first step towards the
+development of a more refined coarse-grained model. Relatively abrupt changes
+of velocity are predicted near the lower and upper bounds for the scaling
+regime and the positions of the bounds could be good markers for the
+development of tough polymers, for which we provide simple views that could be
+useful as guiding principles for toughening polymer-based materials.
+",0,1,0,0,0,0
+88,A note on the fundamental group of Kodaira fibrations,"  The fundamental group $\pi$ of a Kodaira fibration is, by definition, the
+extension of a surface group $\Pi_b$ by another surface group $\Pi_g$, i.e. \[
+1 \rightarrow \Pi_g \rightarrow \pi \rightarrow \Pi_b \rightarrow 1. \]
+Conversely, we can inquire about what conditions need to be satisfied by a
+group of that sort in order to be the fundamental group of a Kodaira fibration.
+In this short note we collect some restriction on the image of the classifying
+map $m \colon \Pi_b \to \Gamma_g$ in terms of the coinvariant homology of
+$\Pi_g$. In particular, we observe that if $\pi$ is the fundamental group of a
+Kodaira fibration with relative irregularity $g-s$, then $g \leq 1+ 6s$, and we
+show that this effectively constrains the possible choices for $\pi$, namely
+that there are group extensions as above that fail to satisfy this bound, hence
+cannot be the fundamental group of a Kodaira fibration. In particular this
+provides examples of symplectic $4$--manifolds that fail to admit a Kähler
+structure for reasons that eschew the usual obstructions.
+",0,0,1,0,0,0
+89,Photo-Chemically Directed Self-Assembly of Carbon Nanotubes on Surfaces,"  Transistors incorporating single-wall carbon nanotubes (CNTs) as the channel
+material are used in a variety of electronics applications. However, a
+competitive CNT-based technology requires the precise placement of CNTs at
+predefined locations of a substrate. One promising placement approach is to use
+chemical recognition to bind CNTs from solution at the desired locations on a
+surface. Producing the chemical pattern on the substrate is challenging. Here
+we describe a one-step patterning approach based on a highly photosensitive
+surface monolayer. The monolayer contains chromophopric group as light
+sensitive body with heteroatoms as high quantum yield photolysis center. As
+deposited, the layer will bind CNTs from solution. However, when exposed to
+ultraviolet (UV) light with a low dose (60 mJ/cm2) similar to that used for
+conventional photoresists, the monolayer cleaves and no longer binds CNTs.
+These features allow standard, wafer-scale UV lithography processes to be used
+to form a patterned chemical monolayer without the need for complex substrate
+patterning or monolayer stamping.
+",0,1,0,0,0,0
+90,Split-and-augmented Gibbs sampler - Application to large-scale inference problems,"  This paper derives two new optimization-driven Monte Carlo algorithms
+inspired from variable splitting and data augmentation. In particular, the
+formulation of one of the proposed approaches is closely related to the
+alternating direction method of multipliers (ADMM) main steps. The proposed
+framework enables to derive faster and more efficient sampling schemes than the
+current state-of-the-art methods and can embed the latter. By sampling
+efficiently the parameter to infer as well as the hyperparameters of the
+problem, the generated samples can be used to approximate Bayesian estimators
+of the parameters to infer. Additionally, the proposed approach brings
+confidence intervals at a low cost contrary to optimization methods.
+Simulations on two often-studied signal processing problems illustrate the
+performance of the two proposed samplers. All results are compared to those
+obtained by recent state-of-the-art optimization and MCMC algorithms used to
+solve these problems.
+",0,0,0,1,0,0
+91,Does a generalized Chaplygin gas correctly describe the cosmological dark sector?,"  Yes, but only for a parameter value that makes it almost coincide with the
+standard model. We reconsider the cosmological dynamics of a generalized
+Chaplygin gas (gCg) which is split into a cold dark matter (CDM) part and a
+dark energy (DE) component with constant equation of state. This model, which
+implies a specific interaction between CDM and DE, has a $\Lambda$CDM limit and
+provides the basis for studying deviations from the latter. Including matter
+and radiation, we use the (modified) CLASS code \cite{class} to construct the
+CMB and matter power spectra in order to search for a gCg-based concordance
+model that is in agreement with the SNIa data from the JLA sample and with
+recent Planck data. The results reveal that the gCg parameter $\alpha$ is
+restricted to $|\alpha|\lesssim 0.05$, i.e., to values very close to the
+$\Lambda$CDM limit $\alpha =0$. This excludes, in particular, models in which
+DE decays linearly with the Hubble rate.
+",0,1,0,0,0,0
+92,The effects of subdiffusion on the NTA size measurements of extracellular vesicles in biological samples,"  The interest in the extracellular vesicles (EVs) is rapidly growing as they
+became reliable biomarkers for many diseases. For this reason, fast and
+accurate techniques of EVs size characterization are the matter of utmost
+importance. One increasingly popular technique is the Nanoparticle Tracking
+Analysis (NTA), in which the diameters of EVs are calculated from their
+diffusion constants. The crucial assumption here is that the diffusion in NTA
+follows the Stokes-Einstein relation, i.e. that the Mean Square Displacement
+(MSD) of a particle grows linearly in time (MSD $\propto t$). However, we show
+that NTA violates this assumption in both artificial and biological samples,
+i.e. a large population of particles show a strongly sub-diffusive behaviour
+(MSD $\propto t^\alpha$, $0<\alpha<1$). To support this observation we present
+a range of experimental results for both polystyrene beads and EVs. This is
+also related to another problem: for the same samples there exists a huge
+discrepancy (by the factor of 2-4) between the sizes measured with NTA and with
+the direct imaging methods, such as AFM. This can be remedied by e.g. the
+Finite Track Length Adjustment (FTLA) method in NTA, but its applicability is
+limited in the biological and poly-disperse samples. On the other hand, the
+models of sub-diffusion rarely provide the direct relation between the size of
+a particle and the generalized diffusion constant. However, we solve this last
+problem by introducing the logarithmic model of sub-diffusion, aimed at
+retrieving the size data. In result, we propose a novel protocol of NTA data
+analysis. The accuracy of our method is on par with FTLA for small
+($\simeq$200nm) particles. We apply our method to study the EVs samples and
+corroborate the results with AFM.
+",0,1,0,0,0,0
+93,Empirical regression quantile process with possible application to risk analysis,"  The processes of the averaged regression quantiles and of their modifications
+provide useful tools in the regression models when the covariates are not fully
+under our control. As an application we mention the probabilistic risk
+assessment in the situation when the return depends on some exogenous
+variables. The processes enable to evaluate the expected $\alpha$-shortfall
+($0\leq\alpha\leq 1$) and other measures of the risk, recently generally
+accepted in the financial literature, but also help to measure the risk in
+environment analysis and elsewhere.
+",0,0,1,1,0,0
+94,Primordial perturbations from inflation with a hyperbolic field-space,"  We study primordial perturbations from hyperinflation, proposed recently and
+based on a hyperbolic field-space. In the previous work, it was shown that the
+field-space angular momentum supported by the negative curvature modifies the
+background dynamics and enhances fluctuations of the scalar fields
+qualitatively, assuming that the inflationary background is almost de Sitter.
+In this work, we confirm and extend the analysis based on the standard approach
+of cosmological perturbation in multi-field inflation. At the background level,
+to quantify the deviation from de Sitter, we introduce the slow-varying
+parameters and show that steep potentials, which usually can not drive
+inflation, can drive inflation. At the linear perturbation level, we obtain the
+power spectrum of primordial curvature perturbation and express the spectral
+tilt and running in terms of the slow-varying parameters. We show that
+hyperinflation with power-law type potentials has already been excluded by the
+recent Planck observations, while exponential-type potential with the exponent
+of order unity can be made consistent with observations as far as the power
+spectrum is concerned. We also argue that, in the context of a simple $D$-brane
+inflation, the hyperinflation requires exponentially large hyperbolic extra
+dimensions but that masses of Kaluza-Klein gravitons can be kept relatively
+heavy.
+",0,1,0,0,0,0
+95,Role of Vanadyl Oxygen in Understanding Metallic Behavior of V2O5(001) Nanorods,"  Vanadium pentoxide (V2O5), the most stable member of vanadium oxide family,
+exhibits interesting semiconductor to metal transition in the temperature range
+of 530-560 K. The metallic behavior originates because of the reduction of V2O5
+through oxygen vacancies. In the present report, V2O5 nanorods in the
+orthorhombic phase with crystal orientation of (001) are grown using vapor
+transport process. Among three nonequivalent oxygen atoms in a VO5 pyramidal
+formula unit in V2O5 structure, the role of terminal vanadyl oxygen (OI) in the
+formation of metallic phase above the transition temperature is established
+from the temperature-dependent Raman spectroscopic studies. The origin of the
+metallic behavior of V2O5 is also understood due to the breakdown of pdpi bond
+between OI and nearest V atom instigated by the formation of vanadyl OI
+vacancy, confirmed from the downward shift of the bottom most split-off
+conduction bands in the material with increasing temperature.
+",0,1,0,0,0,0
+96,Graph Convolution: A High-Order and Adaptive Approach,"  In this paper, we presented a novel convolutional neural network framework
+for graph modeling, with the introduction of two new modules specially designed
+for graph-structured data: the $k$-th order convolution operator and the
+adaptive filtering module. Importantly, our framework of High-order and
+Adaptive Graph Convolutional Network (HA-GCN) is a general-purposed
+architecture that fits various applications on both node and graph centrics, as
+well as graph generative models. We conducted extensive experiments on
+demonstrating the advantages of our framework. Particularly, our HA-GCN
+outperforms the state-of-the-art models on node classification and molecule
+property prediction tasks. It also generates 32% more real molecules on the
+molecule generation task, both of which will significantly benefit real-world
+applications such as material design and drug screening.
+",1,0,0,1,0,0
+97,Learning Sparse Representations in Reinforcement Learning with Sparse Coding,"  A variety of representation learning approaches have been investigated for
+reinforcement learning; much less attention, however, has been given to
+investigating the utility of sparse coding. Outside of reinforcement learning,
+sparse coding representations have been widely used, with non-convex objectives
+that result in discriminative representations. In this work, we develop a
+supervised sparse coding objective for policy evaluation. Despite the
+non-convexity of this objective, we prove that all local minima are global
+minima, making the approach amenable to simple optimization strategies. We
+empirically show that it is key to use a supervised objective, rather than the
+more straightforward unsupervised sparse coding approach. We compare the
+learned representations to a canonical fixed sparse representation, called
+tile-coding, demonstrating that the sparse coding representation outperforms a
+wide variety of tilecoding representations.
+",1,0,0,1,0,0
+98,Almost euclidean Isoperimetric Inequalities in spaces satisfying local Ricci curvature lower bounds,"  Motivated by Perelman's Pseudo Locality Theorem for the Ricci flow, we prove
+that if a Riemannian manifold has Ricci curvature bounded below in a metric
+ball which moreover has almost maximal volume, then in a smaller ball (in a
+quantified sense) it holds an almost-euclidean isoperimetric inequality. The
+result is actually established in the more general framework of non-smooth
+spaces satisfying local Ricci curvature lower bounds in a synthetic sense via
+optimal transportation.
+",0,0,1,0,0,0
+99,Exponential Sums and Riesz energies,"  We bound an exponential sum that appears in the study of irregularities of
+distribution (the low-frequency Fourier energy of the sum of several Dirac
+measures) by geometric quantities: a special case is that for all $\left\{ x_1,
+\dots, x_N\right\} \subset \mathbb{T}^2$, $X \geq 1$ and a universal $c>0$ $$
+\sum_{i,j=1}^{N}{ \frac{X^2}{1 + X^4 \|x_i -x_j\|^4}} \lesssim \sum_{k \in
+\mathbb{Z}^2 \atop \|k\| \leq X}{ \left| \sum_{n=1}^{N}{ e^{2 \pi i
+\left\langle k, x_n \right\rangle}}\right|^2} \lesssim \sum_{i,j=1}^{N}{ X^2
+e^{-c X^2\|x_i -x_j\|^2}}.$$ Since this exponential sum is intimately tied to
+rather subtle distribution properties of the points, we obtain nonlocal
+structural statements for near-minimizers of the Riesz-type energy. In the
+regime $X \gtrsim N^{1/2}$ both upper and lower bound match for
+maximally-separated point sets satisfying $\|x_i -x_j\| \gtrsim N^{-1/2}$.
+",0,0,1,0,0,0
+100,One dimensionalization in the spin-1 Heisenberg model on the anisotropic triangular lattice,"  We investigate the effect of dimensional crossover in the ground state of the
+antiferromagnetic spin-$1$ Heisenberg model on the anisotropic triangular
+lattice that interpolates between the regime of weakly coupled Haldane chains
+($J^{\prime}\! \!\ll\!\! J$) and the isotropic triangular lattice
+($J^{\prime}\!\!=\!\!J$). We use the density-matrix renormalization group
+(DMRG) and Schwinger boson theory performed at the Gaussian correction level
+above the saddle-point solution. Our DMRG results show an abrupt transition
+between decoupled spin chains and the spirally ordered regime at
+$(J^{\prime}/J)_c\sim 0.42$, signaled by the sudden closing of the spin gap.
+Coming from the magnetically ordered side, the computation of the spin
+stiffness within Schwinger boson theory predicts the instability of the spiral
+magnetic order toward a magnetically disordered phase with one-dimensional
+features at $(J^{\prime}/J)_c \sim 0.43$. The agreement of these complementary
+methods, along with the strong difference found between the intra- and the
+interchain DMRG short spin-spin correlations; for sufficiently large values of
+the interchain coupling, suggests that the interplay between the quantum
+fluctuations and the dimensional crossover effects gives rise to the
+one-dimensionalization phenomenon in this frustrated spin-$1$ Hamiltonian.
+",0,1,0,0,0,0
+101,Memory Aware Synapses: Learning what (not) to forget,"  Humans can learn in a continuous manner. Old rarely utilized knowledge can be
+overwritten by new incoming information while important, frequently used
+knowledge is prevented from being erased. In artificial learning systems,
+lifelong learning so far has focused mainly on accumulating knowledge over
+tasks and overcoming catastrophic forgetting. In this paper, we argue that,
+given the limited model capacity and the unlimited new information to be
+learned, knowledge has to be preserved or erased selectively. Inspired by
+neuroplasticity, we propose a novel approach for lifelong learning, coined
+Memory Aware Synapses (MAS). It computes the importance of the parameters of a
+neural network in an unsupervised and online manner. Given a new sample which
+is fed to the network, MAS accumulates an importance measure for each parameter
+of the network, based on how sensitive the predicted output function is to a
+change in this parameter. When learning a new task, changes to important
+parameters can then be penalized, effectively preventing important knowledge
+related to previous tasks from being overwritten. Further, we show an
+interesting connection between a local version of our method and Hebb's
+rule,which is a model for the learning process in the brain. We test our method
+on a sequence of object recognition tasks and on the challenging problem of
+learning an embedding for predicting $<$subject, predicate, object$>$ triplets.
+We show state-of-the-art performance and, for the first time, the ability to
+adapt the importance of the parameters based on unlabeled data towards what the
+network needs (not) to forget, which may vary depending on test conditions.
+",1,0,0,1,0,0
+102,Uniform Spectral Convergence of the Stochastic Galerkin Method for the Linear Semiconductor Boltzmann Equation with Random Inputs and Diffusive Scalings,"  In this paper, we study the generalized polynomial chaos (gPC) based
+stochastic Galerkin method for the linear semiconductor Boltzmann equation
+under diffusive scaling and with random inputs from an anisotropic collision
+kernel and the random initial condition. While the numerical scheme and the
+proof of uniform-in-Knudsen-number regularity of the distribution function in
+the random space has been introduced in [Jin-Liu-16'], the main goal of this
+paper is to first obtain a sharper estimate on the regularity of the
+solution-an exponential decay towards its local equilibrium, which then lead to
+the uniform spectral convergence of the stochastic Galerkin method for the
+problem under study.
+",0,0,1,0,0,0
+103,On Improving the Capacity of Solving Large-scale Wireless Network Design Problems by Genetic Algorithms,"  Over the last decade, wireless networks have experienced an impressive growth
+and now play a main role in many telecommunications systems. As a consequence,
+scarce radio resources, such as frequencies, became congested and the need for
+effective and efficient assignment methods arose. In this work, we present a
+Genetic Algorithm for solving large instances of the Power, Frequency and
+Modulation Assignment Problem, arising in the design of wireless networks. To
+our best knowledge, this is the first Genetic Algorithm that is proposed for
+such problem. Compared to previous works, our approach allows a wider
+exploration of the set of power solutions, while eliminating sources of
+numerical problems. The performance of the algorithm is assessed by tests over
+a set of large realistic instances of a Fixed WiMAX Network.
+",1,0,1,0,0,0
+104,Quasi two-dimensional Fermi surface topography of the delafossite PdRhO$_2$,"  We report on a combined study of the de Haas-van Alphen effect and angle
+resolved photoemission spectroscopy on single crystals of the metallic
+delafossite PdRhO$_2$ rounded off by \textit{ab initio} band structure
+calculations. A high sensitivity torque magnetometry setup with SQUID readout
+and synchrotron-based photoemission with a light spot size of
+$~50\,\mu\mathrm{m}$ enabled high resolution data to be obtained from samples
+as small as $150\times100\times20\,(\mu\mathrm{m})^3$. The Fermi surface shape
+is nearly cylindrical with a rounded hexagonal cross section enclosing a
+Luttinger volume of 1.00(1) electrons per formula unit.
+",0,1,0,0,0,0
+105,A Variational Characterization of Rényi Divergences,"  Atar, Chowdhary and Dupuis have recently exhibited a variational formula for
+exponential integrals of bounded measurable functions in terms of Rényi
+divergences. We develop a variational characterization of the Rényi
+divergences between two probability distributions on a measurable sace in terms
+of relative entropies. When combined with the elementary variational formula
+for exponential integrals of bounded measurable functions in terms of relative
+entropy, this yields the variational formula of Atar, Chowdhary and Dupuis as a
+corollary. We also develop an analogous variational characterization of the
+Rényi divergence rates between two stationary finite state Markov chains in
+terms of relative entropy rates. When combined with Varadhan's variational
+characterization of the spectral radius of square matrices with nonnegative
+entries in terms of relative entropy, this yields an analog of the variational
+formula of Atar, Chowdary and Dupuis in the framework of finite state Markov
+chains.
+",1,0,1,1,0,0
+106,Interlayer coupling and gate-tunable excitons in transition metal dichalcogenide heterostructures,"  Bilayer van der Waals (vdW) heterostructures such as MoS2/WS2 and MoSe2/WSe2
+have attracted much attention recently, particularly because of their type II
+band alignments and the formation of interlayer exciton as the lowest-energy
+excitonic state. In this work, we calculate the electronic and optical
+properties of such heterostructures with the first-principles GW+Bethe-Salpeter
+Equation (BSE) method and reveal the important role of interlayer coupling in
+deciding the excited-state properties, including the band alignment and
+excitonic properties. Our calculation shows that due to the interlayer
+coupling, the low energy excitons can be widely tunable by a vertical gate
+field. In particular, the dipole oscillator strength and radiative lifetime of
+the lowest energy exciton in these bilayer heterostructures is varied by over
+an order of magnitude within a practical external gate field. We also build a
+simple model that captures the essential physics behind this tunability and
+allows the extension of the ab initio results to a large range of electric
+fields. Our work clarifies the physical picture of interlayer excitons in
+bilayer vdW heterostructures and predicts a wide range of gate-tunable
+excited-state properties of 2D optoelectronic devices.
+",0,1,0,0,0,0
+107,Enumeration of singular varieties with tangency conditions,"  We construct the algebraic cobordism theory of bundles and divisors on
+varieties. It has a simple basis (over Q) from projective spaces and its rank
+is equal to the number of Chern numbers. An application of this algebraic
+cobordism theory is the enumeration of singular subvarieties with give tangent
+conditions with a fixed smooth divisor, where the subvariety is the zero locus
+of a section of a vector bundle. We prove that the generating series of numbers
+of such subvarieties gives a homomorphism from the algebraic cobordism group to
+the power series ring. This implies that the enumeration of singular
+subvarieties with tangency conditions is governed by universal polynomials of
+Chern numbers, when the vector bundle is sufficiently ample. This result
+combines and generalizes the Caporaso-Harris recursive formula, Gottsche's
+conjecture, classical De Jonquiere's Formula and node polynomials from tropical
+geometry.
+",0,0,1,0,0,0
+108,In-home and remote use of robotic body surrogates by people with profound motor deficits,"  People with profound motor deficits could perform useful physical tasks for
+themselves by controlling robots that are comparable to the human body. Whether
+this is possible without invasive interfaces has been unclear, due to the
+robot's complexity and the person's limitations. We developed a novel,
+augmented reality interface and conducted two studies to evaluate the extent to
+which it enabled people with profound motor deficits to control robotic body
+surrogates. 15 novice users achieved meaningful improvements on a clinical
+manipulation assessment when controlling the robot in Atlanta from locations
+across the United States. Also, one expert user performed 59 distinct tasks in
+his own home over seven days, including self-care tasks such as feeding. Our
+results demonstrate that people with profound motor deficits can effectively
+control robotic body surrogates without invasive interfaces.
+",1,0,0,0,0,0
+109,ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information,"  Object detection in wide area motion imagery (WAMI) has drawn the attention
+of the computer vision research community for a number of years. WAMI proposes
+a number of unique challenges including extremely small object sizes, both
+sparse and densely-packed objects, and extremely large search spaces (large
+video frames). Nearly all state-of-the-art methods in WAMI object detection
+report that appearance-based classifiers fail in this challenging data and
+instead rely almost entirely on motion information in the form of background
+subtraction or frame-differencing. In this work, we experimentally verify the
+failure of appearance-based classifiers in WAMI, such as Faster R-CNN and a
+heatmap-based fully convolutional neural network (CNN), and propose a novel
+two-stage spatio-temporal CNN which effectively and efficiently combines both
+appearance and motion information to significantly surpass the state-of-the-art
+in WAMI object detection. To reduce the large search space, the first stage
+(ClusterNet) takes in a set of extremely large video frames, combines the
+motion and appearance information within the convolutional architecture, and
+proposes regions of objects of interest (ROOBI). These ROOBI can contain from
+one to clusters of several hundred objects due to the large video frame size
+and varying object density in WAMI. The second stage (FoveaNet) then estimates
+the centroid location of all objects in that given ROOBI simultaneously via
+heatmap estimation. The proposed method exceeds state-of-the-art results on the
+WPAFB 2009 dataset by 5-16% for moving objects and nearly 50% for stopped
+objects, as well as being the first proposed method in wide area motion imagery
+to detect completely stationary objects.
+",1,0,0,0,0,0
+110,Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds,"  Monte Carlo Tree Search (MCTS), most famously used in game-play artificial
+intelligence (e.g., the game of Go), is a well-known strategy for constructing
+approximate solutions to sequential decision problems. Its primary innovation
+is the use of a heuristic, known as a default policy, to obtain Monte Carlo
+estimates of downstream values for states in a decision tree. This information
+is used to iteratively expand the tree towards regions of states and actions
+that an optimal policy might visit. However, to guarantee convergence to the
+optimal action, MCTS requires the entire tree to be expanded asymptotically. In
+this paper, we propose a new technique called Primal-Dual MCTS that utilizes
+sampled information relaxation upper bounds on potential actions, creating the
+possibility of ""ignoring"" parts of the tree that stem from highly suboptimal
+choices. This allows us to prove that despite converging to a partial decision
+tree in the limit, the recommended action from Primal-Dual MCTS is optimal. The
+new approach shows significant promise when used to optimize the behavior of a
+single driver navigating a graph while operating on a ride-sharing platform.
+Numerical experiments on a real dataset of 7,000 trips in New Jersey suggest
+that Primal-Dual MCTS improves upon standard MCTS by producing deeper decision
+trees and exhibits a reduced sensitivity to the size of the action space.
+",1,0,1,0,0,0
+111,Fermi-edge singularity and the functional renormalization group,"  We study the Fermi-edge singularity, describing the response of a degenerate
+electron system to optical excitation, in the framework of the functional
+renormalization group (fRG). Results for the (interband) particle-hole
+susceptibility from various implementations of fRG (one- and two-
+particle-irreducible, multi-channel Hubbard-Stratonovich, flowing
+susceptibility) are compared to the summation of all leading logarithmic (log)
+diagrams, achieved by a (first-order) solution of the parquet equations. For
+the (zero-dimensional) special case of the X-ray-edge singularity, we show that
+the leading log formula can be analytically reproduced in a consistent way from
+a truncated, one-loop fRG flow. However, reviewing the underlying diagrammatic
+structure, we show that this derivation relies on fortuitous partial
+cancellations special to the form of and accuracy applied to the X-ray-edge
+singularity and does not generalize.
+",0,1,0,0,0,0
+112,"Towards ""AlphaChem"": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies","  Retrosynthesis is a technique to plan the chemical synthesis of organic
+molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a
+search tree is built by analysing molecules recursively and dissecting them
+into simpler molecular building blocks until one obtains a set of known
+building blocks. The search space is intractably large, and it is difficult to
+determine the value of retrosynthetic positions. Here, we propose to model
+retrosynthesis as a Markov Decision Process. In combination with a Deep Neural
+Network policy learned from essentially the complete published knowledge of
+chemistry, Monte Carlo Tree Search (MCTS) can be used to evaluate positions. In
+exploratory studies, we demonstrate that MCTS with neural network policies
+outperforms the traditionally used best-first search with hand-coded
+heuristics.
+",1,1,0,0,0,0
+113,The quasi-Assouad dimension for stochastically self-similar sets,"  The class of stochastically self-similar sets contains many famous examples
+of random sets, e.g. Mandelbrot percolation and general fractal percolation.
+Under the assumption of the uniform open set condition and some mild
+assumptions on the iterated function systems used, we show that the
+quasi-Assouad dimension of self-similar random recursive sets is almost surely
+equal to the almost sure Hausdorff dimension of the set. We further comment on
+random homogeneous and $V$-variable sets and the removal of overlap conditions.
+",0,0,1,0,0,0
+114,Influence of Spin Orbit Coupling in the Iron-Based Superconductors,"  We report on the influence of spin-orbit coupling (SOC) in the Fe-based
+superconductors (FeSCs) via application of circularly-polarized spin and
+angle-resolved photoemission spectroscopy. We combine this technique in
+representative members of both the Fe-pnictides and Fe-chalcogenides with ab
+initio density functional theory and tight-binding calculations to establish an
+ubiquitous modification of the electronic structure in these materials imbued
+by SOC. The influence of SOC is found to be concentrated on the hole pockets
+where the superconducting gap is generally found to be largest. This result
+contests descriptions of superconductivity in these materials in terms of pure
+spin-singlet eigenstates, raising questions regarding the possible pairing
+mechanisms and role of SOC therein.
+",0,1,0,0,0,0
+115,Effect of Meltdown and Spectre Patches on the Performance of HPC Applications,"  In this work we examine how the updates addressing Meltdown and Spectre
+vulnerabilities impact the performance of HPC applications. To study this we
+use the application kernel module of XDMoD to test the performance before and
+after the application of the vulnerability patches. We tested the performance
+difference for multiple application and benchmarks including: NWChem, NAMD,
+HPCC, IOR, MDTest and IMB. The results show that although some specific
+functions can have performance decreased by as much as 74%, the majority of
+individual metrics indicates little to no decrease in performance. The
+real-world applications show a 2-3% decrease in performance for single node
+jobs and a 5-11% decrease for parallel multi node jobs.
+",1,0,0,0,0,0
+116,Gene regulatory network inference: an introductory survey,"  Gene regulatory networks are powerful abstractions of biological systems.
+Since the advent of high-throughput measurement technologies in biology in the
+late 90s, reconstructing the structure of such networks has been a central
+computational problem in systems biology. While the problem is certainly not
+solved in its entirety, considerable progress has been made in the last two
+decades, with mature tools now available. This chapter aims to provide an
+introduction to the basic concepts underpinning network inference tools,
+attempting a categorisation which highlights commonalities and relative
+strengths. While the chapter is meant to be self-contained, the material
+presented should provide a useful background to the later, more specialised
+chapters of this book.
+",0,0,0,0,1,0
+117,Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network,"  Glaucoma is the second leading cause of blindness all over the world, with
+approximately 60 million cases reported worldwide in 2010. If undiagnosed in
+time, glaucoma causes irreversible damage to the optic nerve leading to
+blindness. The optic nerve head examination, which involves measurement of
+cup-to-disc ratio, is considered one of the most valuable methods of structural
+diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation
+of optic disc and optic cup on eye fundus images and can be performed by modern
+computer vision algorithms. This work presents universal approach for automatic
+optic disc and cup segmentation, which is based on deep learning, namely,
+modification of U-Net convolutional neural network. Our experiments include
+comparison with the best known methods on publicly available databases
+DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation,
+our method achieves quality comparable to current state-of-the-art methods,
+outperforming them in terms of the prediction time.
+",1,0,0,1,0,0
+118,"Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks","  The life of the modern world essentially depends on the work of the large
+artificial homogeneous networks, such as wired and wireless communication
+systems, networks of roads and pipelines. The support of their effective
+continuous functioning requires automatic screening and permanent optimization
+with processing of the huge amount of data by high-performance distributed
+systems. We propose new meta-algorithm of large homogeneous network analysis,
+its decomposition into alternative sets of loosely connected subnets, and
+parallel optimization of the most independent elements. This algorithm is based
+on a network-specific correlation function, Simulated Annealing technique, and
+is adapted to work in the computer cluster. On the example of large wireless
+network, we show that proposed algorithm essentially increases speed of
+parallel optimization. The elaborated general approach can be used for analysis
+and optimization of the wide range of networks, including such specific types
+as artificial neural networks or organized in networks physiological systems of
+living organisms.
+",1,0,1,0,0,0
+119,Robust Contextual Bandit via the Capped-$\ell_{2}$ norm,"  This paper considers the actor-critic contextual bandit for the mobile health
+(mHealth) intervention. The state-of-the-art decision-making methods in mHealth
+generally assume that the noise in the dynamic system follows the Gaussian
+distribution. Those methods use the least-square-based algorithm to estimate
+the expected reward, which is prone to the existence of outliers. To deal with
+the issue of outliers, we propose a novel robust actor-critic contextual bandit
+method for the mHealth intervention. In the critic updating, the
+capped-$\ell_{2}$ norm is used to measure the approximation error, which
+prevents outliers from dominating our objective. A set of weights could be
+achieved from the critic updating. Considering them gives a weighted objective
+for the actor updating. It provides the badly noised sample in the critic
+updating with zero weights for the actor updating. As a result, the robustness
+of both actor-critic updating is enhanced. There is a key parameter in the
+capped-$\ell_{2}$ norm. We provide a reliable method to properly set it by
+making use of one of the most fundamental definitions of outliers in
+statistics. Extensive experiment results demonstrate that our method can
+achieve almost identical results compared with the state-of-the-art methods on
+the dataset without outliers and dramatically outperform them on the datasets
+noised by outliers.
+",1,0,0,1,0,0
+120,Improper posteriors are not improper,"  In 1933 Kolmogorov constructed a general theory that defines the modern
+concept of conditional expectation. In 1955 Renyi fomulated a new axiomatic
+theory for probability motivated by the need to include unbounded measures. We
+introduce a general concept of conditional expectation in Renyi spaces. In this
+theory improper priors are allowed, and the resulting posterior can also be
+improper.
+In 1965 Lindley published his classic text on Bayesian statistics using the
+theory of Renyi, but retracted this idea in 1973 due to the appearance of
+marginalization paradoxes presented by Dawid, Stone, and Zidek. The paradoxes
+are investigated, and the seemingly conflicting results are explained. The
+theory of Renyi can hence be used as an axiomatic basis for statistics that
+allows use of unbounded priors.
+Keywords: Haldane's prior; Poisson intensity; Marginalization paradox;
+Measure theory; conditional probability space; axioms for statistics;
+conditioning on a sigma field; improper prior
+",0,0,1,1,0,0
+121,Fault Tolerant Consensus Agreement Algorithm,"  Recently a new fault tolerant and simple mechanism was designed for solving
+commit consensus problem. It is based on replicated validation of messages sent
+between transaction participants and a special dispatcher validator manager
+node. This paper presents a correctness, safety proofs and performance analysis
+of this algorithm.
+",1,0,0,0,0,0
+122,Congestion Barcodes: Exploring the Topology of Urban Congestion Using Persistent Homology,"  This work presents a new method to quantify connectivity in transportation
+networks. Inspired by the field of topological data analysis, we propose a
+novel approach to explore the robustness of road network connectivity in the
+presence of congestion on the roadway. The robustness of the pattern is
+summarized in a congestion barcode, which can be constructed directly from
+traffic datasets commonly used for navigation. As an initial demonstration, we
+illustrate the main technique on a publicly available traffic dataset in a
+neighborhood in New York City.
+",1,0,1,0,0,0
+123,Once in a blue moon: detection of 'bluing' during debris transits in the white dwarf WD1145+017,"  The first transiting planetesimal orbiting a white dwarf was recently
+detected in K2 data of WD1145+017 and has been followed up intensively. The
+multiple, long, and variable transits suggest the transiting objects are dust
+clouds, probably produced by a disintegrating asteroid. In addition, the system
+contains circumstellar gas, evident by broad absorption lines, mostly in the
+u'-band, and a dust disc, indicated by an infrared excess. Here we present the
+first detection of a change in colour of WD1145+017 during transits, using
+simultaneous multi-band fast-photometry ULTRACAM measurements over the
+u'g'r'i'-bands. The observations reveal what appears to be 'bluing' during
+transits; transits are deeper in the redder bands, with a u'-r' colour
+difference of up to ~-0.05 mag. We explore various possible explanations for
+the bluing. 'Spectral' photometry obtained by integrating over bandpasses in
+the spectroscopic data in- and out-of-transit, compared to the photometric
+data, shows that the observed colour difference is most likely the result of
+reduced circumstellar absorption in the spectrum during transits. This
+indicates that the transiting objects and the gas share the same line-of-sight,
+and that the gas covers the white dwarf only partially, as would be expected if
+the gas, the transiting debris, and the dust emitting the infrared excess, are
+part of the same general disc structure (although possibly at different radii).
+In addition, we present the results of a week-long monitoring campaign of the
+system.
+",0,1,0,0,0,0
+124,"Viscous dynamics of drops and bubbles in Hele-Shaw cells: drainage, drag friction, coalescence, and bursting","  In this review article, we discuss recent studies on drops and bubbles in
+Hele-Shaw cells, focusing on how scaling laws exhibit crossovers from the
+three-dimensional counterparts and focusing on topics in which viscosity plays
+an important role. By virtue of progresses in analytical theory and high-speed
+imaging, dynamics of drops and bubbles have actively been studied with the aid
+of scaling arguments. However, compared with three dimensional problems,
+studies on the corresponding problems in Hele-Shaw cells are still limited.
+This review demonstrates that the effect of confinement in the Hele-Shaw cell
+introduces new physics allowing different scaling regimes to appear. For this
+purpose, we discuss various examples that are potentially important for
+industrial applications handling drops and bubbles in confined spaces by
+showing agreement between experiments and scaling theories. As a result, this
+review provides a collection of problems in hydrodynamics that may be
+analytically solved or that may be worth studying numerically in the near
+future.
+",0,1,0,0,0,0
+125,Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features,"  Stacking-based deep neural network (S-DNN), in general, denotes a deep neural
+network (DNN) resemblance in terms of its very deep, feedforward network
+architecture. The typical S-DNN aggregates a variable number of individually
+learnable modules in series to assemble a DNN-alike alternative to the targeted
+object recognition tasks. This work likewise devises an S-DNN instantiation,
+dubbed deep analytic network (DAN), on top of the spectral histogram (SH)
+features. The DAN learning principle relies on ridge regression, and some key
+DNN constituents, specifically, rectified linear unit, fine-tuning, and
+normalization. The DAN aptitude is scrutinized on three repositories of varying
+domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10
+(natural objects). The empirical results unveil that DAN escalates the SH
+baseline performance over a sufficiently deep layer.
+",1,0,0,0,0,0
+126,Superconductivity and Frozen Electronic States at the (111) LaAlO$_3$/SrTiO$_3$ Interface,"  In spite of Anderson's theorem, disorder is known to affect superconductivity
+in conventional s-wave superconductors. In most superconductors, the degree of
+disorder is fixed during sample preparation. Here we report measurements of the
+superconducting properties of the two-dimensional gas that forms at the
+interface between LaAlO$_3$ (LAO) and SrTiO$_3$ (STO) in the (111) crystal
+orientation, a system that permits \emph{in situ} tuning of carrier density and
+disorder by means of a back gate voltage $V_g$. Like the (001) oriented LAO/STO
+interface, superconductivity at the (111) LAO/STO interface can be tuned by
+$V_g$. In contrast to the (001) interface, superconductivity in these (111)
+samples is anisotropic, being different along different interface crystal
+directions, consistent with the strong anisotropy already observed other
+transport properties at the (111) LAO/STO interface. In addition, we find that
+the (111) interface samples ""remember"" the backgate voltage $V_F$ at which they
+are cooled at temperatures near the superconducting transition temperature
+$T_c$, even if $V_g$ is subsequently changed at lower temperatures. The low
+energy scale and other characteristics of this memory effect ($<1$ K)
+distinguish it from charge-trapping effects previously observed in (001)
+interface samples.
+",0,1,0,0,0,0
+127,Emittance preservation of an electron beam in a loaded quasi-linear plasma wakefield,"  We investigate beam loading and emittance preservation for a high-charge
+electron beam being accelerated in quasi-linear plasma wakefields driven by a
+short proton beam. The structure of the studied wakefields are similar to those
+of a long, modulated proton beam, such as the AWAKE proton driver. We show that
+by properly choosing the electron beam parameters and exploiting two well known
+effects, beam loading of the wakefield and full blow out of plasma electrons by
+the accelerated beam, the electron beam can gain large amounts of energy with a
+narrow final energy spread (%-level) and without significant emittance growth.
+",0,1,0,0,0,0
+128,Detection of Nonlinearly Distorted OFDM Signals via Generalized Approximate Message Passing,"  In this paper, we propose a practical receiver for multicarrier signals
+subjected to a strong memoryless nonlinearity. The receiver design is based on
+a generalized approximate message passing (GAMP) framework, and this allows
+real-time algorithm implementation in software or hardware with moderate
+complexity. We demonstrate that the proposed receiver can provide more than a
+2dB gain compared with an ideal uncoded linear OFDM transmission at a BER range
+$10^{-4}\div10^{-6}$ in the AWGN channel, when the OFDM signal is subjected to
+clipping nonlinearity and the crest-factor of the clipped waveform is only
+1.9dB. Simulation results also demonstrate that the proposed receiver provides
+significant performance gain in frequency-selective multipath channels
+",1,0,0,0,0,0
+129,Nonlinear fractal meaning of the Hubble constant,"  According to astrophysical observations value of recession velocity in a
+certain point is proportional to a distance to this point. The proportionality
+coefficient is the Hubble constant measured with 5% accuracy. It is used in
+many cosmological theories describing dark energy, dark matter, baryons, and
+their relation with the cosmological constant introduced by Einstein.
+In the present work we have determined a limit value of the global Hubble
+constant (in a big distance from a point of observations) theoretically without
+using any empirical constants on the base of our own fractal model used for the
+description a relation between distance to an observed galaxy and coordinate of
+its center. The distance has been defined as a nonlinear fractal measure with
+scale of measurement corresponding to a deviation of the measure from its fixed
+value (zero-gravity radius). We have suggested a model of specific anisotropic
+fractal for simulation a radial Universe expansion. Our theoretical results
+have shown existence of an inverse proportionality between accuracy of
+determination the Hubble constant and accuracy of calculation a coordinates of
+galaxies leading to ambiguity results obtained at cosmological observations.
+",0,1,0,0,0,0
+130,SEA: String Executability Analysis by Abstract Interpretation,"  Dynamic languages often employ reflection primitives to turn dynamically
+generated text into executable code at run-time. These features make standard
+static analysis extremely hard if not impossible because its essential data
+structures, i.e., the control-flow graph and the system of recursive equations
+associated with the program to analyse, are themselves dynamically mutating
+objects. We introduce SEA, an abstract interpreter for automatic sound string
+executability analysis of dynamic languages employing bounded (i.e, finitely
+nested) reflection and dynamic code generation. Strings are statically
+approximated in an abstract domain of finite state automata with basic
+operations implemented as symbolic transducers. SEA combines standard program
+analysis together with string executability analysis. The analysis of a call to
+reflection determines a call to the same abstract interpreter over a code which
+is synthesised directly from the result of the static string executability
+analysis at that program point. The use of regular languages for approximating
+dynamically generated code structures allows SEA to soundly approximate safety
+properties of self modifying programs yet maintaining efficiency. Soundness
+here means that the semantics of the code synthesised by the analyser to
+resolve reflection over-approximates the semantics of the code dynamically
+built at run-rime by the program at that point.
+",1,0,0,0,0,0
+131,On the trade-off between labels and weights in quantitative bisimulation,"  Reductions for transition systems have been recently introduced as a uniform
+and principled method for comparing the expressiveness of system models with
+respect to a range of properties, especially bisimulations. In this paper we
+study the expressiveness (w.r.t. bisimulations) of models for quantitative
+computations such as weighted labelled transition systems (WLTSs), uniform
+labelled transition systems (ULTraSs), and state-to-function transition systems
+(FuTSs). We prove that there is a trade-off between labels and weights: at one
+extreme lays the class of (unlabelled) weighted transition systems where
+information is presented using weights only; at the other lays the class of
+labelled transition systems (LTSs) where information is shifted on labels.
+These categories of systems cannot be further reduced in any significant way
+and subsume all the aforementioned models.
+",1,0,0,0,0,0
+132,Poynting's theorem in magnetic turbulence,"  Poynting's theorem is used to obtain an expression for the turbulent
+power-spectral density as function of frequency and wavenumber in low-frequency
+magnetic turbulence. No reference is made to Elsasser variables as is usually
+done in magnetohydrodynamic turbulence mixing mechanical and electromagnetic
+turbulence. We rather stay with an implicit form of the mechanical part of
+turbulence as suggested by electromagnetic theory in arbitrary media. All of
+mechanics and flows is included into a turbulent response function which by
+appropriate observations can be determined from knowledge of the turbulent
+fluctuation spectra. This approach is not guided by the wish of developing a
+complete theory of turbulence. It aims on the identification of the response
+function from observations as input into a theory which afterwards attempts its
+interpretation. Combination of both the magnetic and electric power spectral
+densities leads to a representation of the turbulent response function, i.e.
+the turbulent conductivity spectrum $\sigma_{\omega k}$ as function of
+frequency $\omega$ and wavenumber $k$. {It is given as the ratio of magnetic to
+electric power spectral densities in frequency space. This knowledge allows for
+formally writing down a turbulent dispersion relation. Power law inertial range
+spectra result in a power law turbulent conductivity spectrum. These can be
+compared with observations in the solar wind. Keywords: MHD turbulence,
+turbulent dispersion relation, turbulent response function, solar wind
+turbulence
+",0,1,0,0,0,0
+133,Polar factorization of conformal and projective maps of the sphere in the sense of optimal mass transport,"  Let M be a compact Riemannian manifold and let $\mu$,d be the associated
+measure and distance on M. Robert McCann obtained, generalizing results for the
+Euclidean case by Yann Brenier, the polar factorization of Borel maps S : M ->
+M pushing forward $\mu$ to a measure $\nu$: each S factors uniquely a.e. into
+the composition S = T \circ U, where U : M -> M is volume preserving and T : M
+-> M is the optimal map transporting $\mu$ to $\nu$ with respect to the cost
+function d^2/2.
+In this article we study the polar factorization of conformal and projective
+maps of the sphere S^n. For conformal maps, which may be identified with
+elements of the identity component of O(1,n+1), we prove that the polar
+factorization in the sense of optimal mass transport coincides with the
+algebraic polar factorization (Cartan decomposition) of this Lie group. For the
+projective case, where the group GL_+(n+1) is involved, we find necessary and
+sufficient conditions for these two factorizations to agree.
+",0,0,1,0,0,0
+134,Representing numbers as the sum of squares and powers in the ring $\mathbb{Z}_n$,"  We examine the representation of numbers as the sum of two squares in
+$\mathbb{Z}_n$ for a general positive integer $n$. Using this information we
+make some comments about the density of positive integers which can be
+represented as the sum of two squares and powers of $2$ in $\mathbb{N}$.
+",0,0,1,0,0,0
+135,Spatial Regression and the Bayesian Filter,"  Regression for spatially dependent outcomes poses many challenges, for
+inference and for computation. Non-spatial models and traditional spatial
+mixed-effects models each have their advantages and disadvantages, making it
+difficult for practitioners to determine how to carry out a spatial regression
+analysis. We discuss the data-generating mechanisms implicitly assumed by
+various popular spatial regression models, and discuss the implications of
+these assumptions. We propose Bayesian spatial filtering as an approximate
+middle way between non-spatial models and traditional spatial mixed models. We
+show by simulation that our Bayesian spatial filtering model has several
+desirable properties and hence may be a useful addition to a spatial
+statistician's toolkit.
+",0,0,0,1,0,0
+136,Behaviour of electron content in the ionospheric D-region during solar X-ray flares,"  One of the most important parameters in ionospheric plasma research also
+having a wide practical application in wireless satellite telecommunications is
+the total electron content (TEC) representing the columnal electron number
+density. The F region with high electron density provides the biggest
+contribution to TEC while the relatively weakly ionized plasma of the D region
+(60 km - 90 km above Earths surface) is often considered as a negligible cause
+of satellite signal disturbances. However, sudden intensive ionization
+processes like those induced by solar X ray flares can cause relative increases
+of electron density that are significantly larger in the D-region than in
+regions at higher altitudes. Therefore, one cannot exclude a priori the D
+region from investigations of ionospheric influences on propagation of
+electromagnetic signals emitted by satellites. We discuss here this problem
+which has not been sufficiently treated in literature so far. The obtained
+results are based on data collected from the D region monitoring by very low
+frequency radio waves and on vertical TEC calculations from the Global
+Navigation Satellite System (GNSS) signal analyses, and they show noticeable
+variations in the D region electron content (TECD) during activity of a solar X
+ray flare (it rises by a factor of 136 in the considered case) when TECD
+contribution to TEC can reach several percent and which cannot be neglected in
+practical applications like global positioning procedures by satellites.
+",0,1,0,0,0,0
+137,Fractional compound Poisson processes with multiple internal states,"  For the particles undergoing the anomalous diffusion with different waiting
+time distributions for different internal states, we derive the Fokker-Planck
+and Feymann-Kac equations, respectively, describing positions of the particles
+and functional distributions of the trajectories of particles; in particular,
+the equations governing the functional distribution of internal states are also
+obtained. The dynamics of the stochastic processes are analyzed and the
+applications, calculating the distribution of the first passage time and the
+distribution of the fraction of the occupation time, of the equations are
+given.
+",0,0,1,1,0,0
+138,Zero-point spin-fluctuations of single adatoms,"  Stabilizing the magnetic signal of single adatoms is a crucial step towards
+their successful usage in widespread technological applications such as
+high-density magnetic data storage devices. The quantum mechanical nature of
+these tiny objects, however, introduces intrinsic zero-point spin-fluctuations
+that tend to destabilize the local magnetic moment of interest by dwindling the
+magnetic anisotropy potential barrier even at absolute zero temperature. Here,
+we elucidate the origins and quantify the effect of the fundamental ingredients
+determining the magnitude of the fluctuations, namely the ($i$) local magnetic
+moment, ($ii$) spin-orbit coupling and ($iii$) electron-hole Stoner
+excitations. Based on a systematic first-principles study of 3d and 4d adatoms,
+we demonstrate that the transverse contribution of the fluctuations is
+comparable in size to the magnetic moment itself, leading to a remarkable
+$\gtrsim$50$\%$ reduction of the magnetic anisotropy energy. Our analysis gives
+rise to a comprehensible diagram relating the fluctuation magnitude to
+characteristic features of adatoms, providing practical guidelines for
+designing magnetically stable nanomagnets with minimal quantum fluctuations.
+",0,1,0,0,0,0
+139,Exploration-exploitation tradeoffs dictate the optimal distributions of phenotypes for populations subject to fitness fluctuations,"  We study a minimal model for the growth of a phenotypically heterogeneous
+population of cells subject to a fluctuating environment in which they can
+replicate (by exploiting available resources) and modify their phenotype within
+a given landscape (thereby exploring novel configurations). The model displays
+an exploration-exploitation trade-off whose specifics depend on the statistics
+of the environment. Most notably, the phenotypic distribution corresponding to
+maximum population fitness (i.e. growth rate) requires a non-zero exploration
+rate when the magnitude of environmental fluctuations changes randomly over
+time, while a purely exploitative strategy turns out to be optimal in two-state
+environments, independently of the statistics of switching times. We obtain
+analytical insight into the limiting cases of very fast and very slow
+exploration rates by directly linking population growth to the features of the
+environment.
+",0,0,0,0,1,0
+140,Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms,"  Electronic Health Records (EHR) are data generated during routine clinical
+care. EHR offer researchers unprecedented phenotypic breadth and depth and have
+the potential to accelerate the pace of precision medicine at scale. A main EHR
+use-case is creating phenotyping algorithms to define disease status, onset and
+severity. Currently, no common machine-readable standard exists for defining
+phenotyping algorithms which often are stored in human-readable formats. As a
+result, the translation of algorithms to implementation code is challenging and
+sharing across the scientific community is problematic. In this paper, we
+evaluate openEHR, a formal EHR data specification, for computable
+representations of EHR phenotyping algorithms.
+",1,0,0,0,0,0
+141,Optimizing Mission Critical Data Dissemination in Massive IoT Networks,"  Mission critical data dissemination in massive Internet of things (IoT)
+networks imposes constraints on the message transfer delay between devices. Due
+to low power and communication range of IoT devices, data is foreseen to be
+relayed over multiple device-to-device (D2D) links before reaching the
+destination. The coexistence of a massive number of IoT devices poses a
+challenge in maximizing the successful transmission capacity of the overall
+network alongside reducing the multi-hop transmission delay in order to support
+mission critical applications. There is a delicate interplay between the
+carrier sensing threshold of the contention based medium access protocol and
+the choice of packet forwarding strategy selected at each hop by the devices.
+The fundamental problem in optimizing the performance of such networks is to
+balance the tradeoff between conflicting performance objectives such as the
+spatial frequency reuse, transmission quality, and packet progress towards the
+destination. In this paper, we use a stochastic geometry approach to quantify
+the performance of multi-hop massive IoT networks in terms of the spatial
+frequency reuse and the transmission quality under different packet forwarding
+schemes. We also develop a comprehensive performance metric that can be used to
+optimize the system to achieve the best performance. The results can be used to
+select the best forwarding scheme and tune the carrier sensing threshold to
+optimize the performance of the network according to the delay constraints and
+transmission quality requirements.
+",1,0,0,0,0,0
+142,Interference of two co-directional exclusion processes in the presence of a static bottleneck: a biologically motivated model,"  We develope a two-species exclusion process with a distinct pair of entry and
+exit sites for each species of rigid rods. The relatively slower forward
+stepping of the rods in an extended bottleneck region, located in between the
+two entry sites, controls the extent of interference of the co-directional flow
+of the two species of rods. The relative positions of the sites of entry of the
+two species of rods with respect to the location of the bottleneck are
+motivated by a biological phenomenon. However, the primary focus of the study
+here is to explore the effects of the interference of the flow of the two
+species of rods on their spatio-temporal organization and the regulations of
+this interference by the extended bottleneck. By a combination of mean-field
+theory and computer simulation we calculate the flux of both species of rods
+and their density profiles as well as the composite phase diagrams of the
+system. If the bottleneck is sufficiently stringent some of the phases become
+practically unrealizable although not ruled out on the basis of any fundamental
+physical principle. Moreover the extent of suppression of flow of the
+downstream entrants by the flow of the upstream entrants can also be regulated
+by the strength of the bottleneck. We speculate on the possible implications of
+the results in the context of the biological phenomenon that motivated the
+formulation of the theoretical model.
+",0,1,0,0,0,0
+143,Gaussian fluctuations of Jack-deformed random Young diagrams,"  We introduce a large class of random Young diagrams which can be regarded as
+a natural one-parameter deformation of some classical Young diagram ensembles;
+a deformation which is related to Jack polynomials and Jack characters. We show
+that each such a random Young diagram converges asymptotically to some limit
+shape and that the fluctuations around the limit are asymptotically Gaussian.
+",0,0,1,0,0,0
+144,Revisiting (logarithmic) scaling relations using renormalization group,"  We explicitly compute the critical exponents associated with logarithmic
+corrections (the so-called hatted exponents) starting from the renormalization
+group equations and the mean field behavior for a wide class of models at the
+upper critical behavior (for short and long range $\phi^n$-theories) and below
+it. This allows us to check the scaling relations among these critical
+exponents obtained by analysing the complex singularities (Lee-Yang and Fisher
+zeroes) of these models. Moreover, we have obtained an explicit method to
+compute the $\hat{\coppa}$ exponent [defined by $\xi\sim L (\log
+L)^{\hat{\coppa}}$] and, finally, we have found a new derivation of the scaling
+law associated with it.
+",0,1,0,0,0,0
+145,Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods,"  We obtain a Bernstein-type inequality for sums of Banach-valued random
+variables satisfying a weak dependence assumption of general type and under
+certain smoothness assumptions of the underlying Banach norm. We use this
+inequality in order to investigate in the asymptotical regime the error upper
+bounds for the broad family of spectral regularization methods for reproducing
+kernel decision rules, when trained on a sample coming from a $\tau-$mixing
+process.
+",0,0,1,1,0,0
+146,Evolution of the Kondo lattice electronic structure above the transport coherence temperature,"  The temperature-dependent evolution of the Kondo lattice is a long-standing
+topic of theoretical and experimental investigation and yet it lacks a truly
+microscopic description of the relation of the basic $f$-$d$ hybridization
+processes to the fundamental temperature scales of Kondo screening and
+Fermi-liquid lattice coherence. Here, the temperature-dependence of $f$-$d$
+hybridized band dispersions and Fermi-energy $f$ spectral weight in the Kondo
+lattice system CeCoIn$_5$ is investigated using $f$-resonant angle-resolved
+photoemission (ARPES) with sufficient detail to allow direct comparison to
+first principles dynamical mean field theory (DMFT) calculations containing
+full realism of crystalline electric field states. The ARPES results, for two
+orthogonal (001) and (100) cleaved surfaces and three different $f$-$d$
+hybridization scenarios, with additional microscopic insight provided by DMFT,
+reveal $f$ participation in the Fermi surface at temperatures much higher than
+the lattice coherence temperature, $T^*\approx$ 45 K, commonly believed to be
+the onset for such behavior. The identification of a $T$-dependent crystalline
+electric field degeneracy crossover in the DMFT theory $below$ $T^*$ is
+specifically highlighted.
+",0,1,0,0,0,0
+147,On A Conjecture Regarding Permutations Which Destroy Arithmetic Progressions,"  Hegarty conjectured for $n\neq 2, 3, 5, 7$ that $\mathbb{Z}/n\mathbb{Z}$ has
+a permutation which destroys all arithmetic progressions mod $n$. For $n\ge
+n_0$, Hegarty and Martinsson demonstrated that $\mathbb{Z}/n\mathbb{Z}$ has an
+arithmetic-progression destroying permutation. However $n_0\approx 1.4\times
+10^{14}$ and thus resolving the conjecture in full remained out of reach of any
+computational techniques. However, this paper using constructions modeled after
+those used by Elkies and Swaminathan for the case of $\mathbb{Z}/p\mathbb{Z}$
+with $p$ being prime, establish the conjecture in full. Furthermore our results
+do not rely on the fact that it suffices to study when $n<n_0$ and thus our
+results completely independent of the proof given by Hegarty and Martinsson.
+",0,0,1,0,0,0
+148,Inverse monoids and immersions of cell complexes,"  An immersion $f : {\mathcal D} \rightarrow \mathcal C$ between cell complexes
+is a local homeomorphism onto its image that commutes with the characteristic
+maps of the cell complexes. We study immersions between finite-dimensional
+connected $\Delta$-complexes by replacing the fundamental group of the base
+space by an appropriate inverse monoid. We show how conjugacy classes of the
+closed inverse submonoids of this inverse monoid may be used to classify
+connected immersions into the complex. This extends earlier results of Margolis
+and Meakin for immersions between graphs and of Meakin and Szakács on
+immersions into $2$-dimensional $CW$-complexes.
+",0,0,1,0,0,0
+149,Not even wrong: The spurious link between biodiversity and ecosystem functioning,"  Resolving the relationship between biodiversity and ecosystem functioning has
+been one of the central goals of modern ecology. Early debates about the
+relationship were finally resolved with the advent of a statistical
+partitioning scheme that decomposed the biodiversity effect into a ""selection""
+effect and a ""complementarity"" effect. We prove that both the biodiversity
+effect and its statistical decomposition into selection and complementarity are
+fundamentally flawed because these methods use a naïve null expectation based
+on neutrality, likely leading to an overestimate of the net biodiversity
+effect, and they fail to account for the nonlinear abundance-ecosystem
+functioning relationships observed in nature. Furthermore, under such
+nonlinearity no statistical scheme can be devised to partition the biodiversity
+effects. We also present an alternative metric providing a more reasonable
+estimate of biodiversity effect. Our results suggest that all studies conducted
+since the early 1990s likely overestimated the positive effects of biodiversity
+on ecosystem functioning.
+",0,0,0,0,1,0
+150,Evidence of Fraud in Brazil's Electoral Campaigns Via the Benford's Law,"  The principle of democracy is that the people govern through elected
+representatives. Therefore, a democracy is healthy as long as the elected
+politicians do represent the people. We have analyzed data from the Brazilian
+electoral court (Tribunal Superior Eleitoral, TSE) concerning money donations
+for the electoral campaigns and the election results. Our work points to two
+disturbing conclusions: money is a determining factor on whether a candidate is
+elected or not (as opposed to representativeness); secondly, the use of
+Benford's Law to analyze the declared donations received by the parties and
+electoral campaigns shows evidence of fraud in the declarations. A better term
+to define Brazil's government system is what we define as chrimatocracy (govern
+by money).
+",1,0,0,1,0,0
+151,A Berkeley View of Systems Challenges for AI,"  With the increasing commoditization of computer vision, speech recognition
+and machine translation systems and the widespread deployment of learning-based
+back-end technologies such as digital advertising and intelligent
+infrastructures, AI (Artificial Intelligence) has moved from research labs to
+production. These changes have been made possible by unprecedented levels of
+data and computation, by methodological advances in machine learning, by
+innovations in systems software and architectures, and by the broad
+accessibility of these technologies.
+The next generation of AI systems promises to accelerate these developments
+and increasingly impact our lives via frequent interactions and making (often
+mission-critical) decisions on our behalf, often in highly personalized
+contexts. Realizing this promise, however, raises daunting challenges. In
+particular, we need AI systems that make timely and safe decisions in
+unpredictable environments, that are robust against sophisticated adversaries,
+and that can process ever increasing amounts of data across organizations and
+individuals without compromising confidentiality. These challenges will be
+exacerbated by the end of the Moore's Law, which will constrain the amount of
+data these technologies can store and process. In this paper, we propose
+several open research directions in systems, architectures, and security that
+can address these challenges and help unlock AI's potential to improve lives
+and society.
+",1,0,0,0,0,0
+152,"Equivariant infinite loop space theory, I. The space level story","  We rework and generalize equivariant infinite loop space theory, which shows
+how to construct G-spectra from G-spaces with suitable structure. There is a
+naive version which gives naive G-spectra for any topological group G, but our
+focus is on the construction of genuine G-spectra when G is finite.
+We give new information about the Segal and operadic equivariant infinite
+loop space machines, supplying many details that are missing from the
+literature, and we prove by direct comparison that the two machines give
+equivalent output when fed equivalent input. The proof of the corresponding
+nonequivariant uniqueness theorem, due to May and Thomason, works for naive
+G-spectra for general G but fails hopelessly for genuine G-spectra when G is
+finite. Even in the nonequivariant case, our comparison theorem is considerably
+more precise, giving a direct point-set level comparison.
+We have taken the opportunity to update this general area, equivariant and
+nonequivariant, giving many new proofs, filling in some gaps, and giving some
+corrections to results in the literature.
+",0,0,1,0,0,0
+153,Arithmetic purity of strong approximation for homogeneous spaces,"  We prove that any open subset $U$ of a semi-simple simply connected
+quasi-split linear algebraic group $G$ with ${codim} (G\setminus U, G)\geq 2$
+over a number field satisfies strong approximation by establishing a fibration
+of $G$ over a toric variety. We also prove a similar result of strong
+approximation with Brauer-Manin obstruction for a partial equivariant smooth
+compactification of a homogeneous space where all invertible functions are
+constant and the semi-simple part of the linear algebraic group is quasi-split.
+Some semi-abelian varieties of any given dimension where the complements of a
+rational point do not satisfy strong approximation with Brauer-Manin
+obstruction are given.
+",0,0,1,0,0,0
+154,Flatness results for nonlocal minimal cones and subgraphs,"  We show that nonlocal minimal cones which are non-singular subgraphs outside
+the origin are necessarily halfspaces.
+The proof is based on classical ideas of~\cite{DG1} and on the computation of
+the linearized nonlocal mean curvature operator, which is proved to satisfy a
+suitable maximum principle.
+With this, we obtain new, and somehow simpler, proofs of the Bernstein-type
+results for nonlocal minimal surfaces which have been recently established
+in~\cite{FV}. In addition, we establish a new nonlocal Bernstein-Moser-type
+result which classifies Lipschitz nonlocal minimal subgraphs outside a ball.
+",0,0,1,0,0,0
+155,Effective Asymptotic Formulae for Multilinear Averages of Multiplicative Functions,"  Let $f_1,\ldots,f_k : \mathbb{N} \rightarrow \mathbb{C}$ be multiplicative
+functions taking values in the closed unit disc. Using an analytic approach in
+the spirit of Halász' mean value theorem, we compute multidimensional
+averages of the shape $$x^{-l} \sum_{\mathbf{n} \in [x]^l} \prod_{1 \leq j \leq
+k} f_j(L_j(\mathbf{n}))$$ as $x \rightarrow \infty$, where $[x] := [1,x]$ and
+$L_1,\ldots, L_k$ are affine linear forms that satisfy some natural conditions.
+Our approach gives a new proof of a result of Frantzikinakis and Host that is
+distinct from theirs, with \emph{explicit} main and error terms. \\ As an
+application of our formulae, we establish a \emph{local-to-global} principle
+for Gowers norms of multiplicative functions. We also compute the asymptotic
+densities of the sets of integers $n$ such that a given multiplicative function
+$f: \mathbb{N} \rightarrow \{-1, 1\}$ yields a fixed sign pattern of length 3
+or 4 on almost all 3- and 4-term arithmetic progressions, respectively, with
+first term $n$.
+",0,0,1,0,0,0
+156,On the apparent permeability of porous media in rarefied gas flows,"  The apparent gas permeability of the porous medium is an important parameter
+in the prediction of unconventional gas production, which was first
+investigated systematically by Klinkenberg in 1941 and found to increase with
+the reciprocal mean gas pressure (or equivalently, the Knudsen number).
+Although the underlying rarefaction effects are well-known, the reason that the
+correction factor in Klinkenberg's famous equation decreases when the Knudsen
+number increases has not been fully understood. Most of the studies idealize
+the porous medium as a bundle of straight cylindrical tubes, however, according
+to the gas kinetic theory, this only results in an increase of the correction
+factor with the Knudsen number, which clearly contradicts Klinkenberg's
+experimental observations. Here, by solving the Bhatnagar-Gross-Krook equation
+in simplified (but not simple) porous media, we identify, for the first time,
+two key factors that can explain Klinkenberg's experimental results: the
+tortuous flow path and the non-unitary tangential momentum accommodation
+coefficient for the gas-surface interaction. Moreover, we find that
+Klinkenberg's results can only be observed when the ratio between the apparent
+and intrinsic permeabilities is $\lesssim30$; at large ratios (or Knudsen
+numbers) the correction factor increases with the Knudsen number. Our numerical
+results could also serve as benchmarking cases to assess the accuracy of
+macroscopic models and/or numerical schemes for the modeling/simulation of
+rarefied gas flows in complex geometries over a wide range of gas rarefaction.
+",0,1,0,0,0,0
+157,Small subgraphs and their extensions in a random distance graph,"  In previous papers, threshold probabilities for the properties of a random
+distance graph to contain strictly balanced graphs were found. We extend this
+result to arbitrary graphs and prove that the number of copies of a strictly
+balanced graph has asymptotically Poisson distribution at the threshold.
+",0,0,1,0,0,0
+158,Increasing the Reusability of Enforcers with Lifecycle Events,"  Runtime enforcement can be effectively used to improve the reliability of
+software applications. However, it often requires the definition of ad hoc
+policies and enforcement strategies, which might be expensive to identify and
+implement. This paper discusses how to exploit lifecycle events to obtain
+useful enforcement strategies that can be easily reused across applications,
+thus reducing the cost of adoption of the runtime enforcement technology. The
+paper finally sketches how this idea can be used to define libraries that can
+automatically overcome problems related to applications misusing them.
+",1,0,0,0,0,0
+159,A Fast Interior Point Method for Atomic Norm Soft Thresholding,"  The atomic norm provides a generalization of the $\ell_1$-norm to continuous
+parameter spaces. When applied as a sparse regularizer for line spectral
+estimation the solution can be obtained by solving a convex optimization
+problem. This problem is known as atomic norm soft thresholding (AST). It can
+be cast as a semidefinite program and solved by standard methods. In the
+semidefinite formulation there are $O(N^2)$ dual variables and a standard
+primal-dual interior point method requires at least $O(N^6)$ flops per
+iteration. That has lead researcher to consider alternating direction method of
+multipliers (ADMM) for the solution of AST, but this method is still somewhat
+slow for large problem sizes. To obtain a faster algorithm we reformulate AST
+as a non-symmetric conic program. That has two properties of key importance to
+its numerical solution: the conic formulation has only $O(N)$ dual variables
+and the Toeplitz structure inherent to AST is preserved. Based on it we derive
+FastAST which is a primal-dual interior point method for solving AST. Two
+variants are considered with the fastest one requiring only $O(N^2)$ flops per
+iteration. Extensive numerical experiments demonstrate that FastAST solves AST
+significantly faster than a state-of-the-art solver based on ADMM.
+",1,0,0,0,0,0
+160,Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments,"  We study the problem of causal structure learning over a set of random
+variables when the experimenter is allowed to perform at most $M$ experiments
+in a non-adaptive manner. We consider the optimal learning strategy in terms of
+minimizing the portions of the structure that remains unknown given the limited
+number of experiments in both Bayesian and minimax setting. We characterize the
+theoretical optimal solution and propose an algorithm, which designs the
+experiments efficiently in terms of time complexity. We show that for bounded
+degree graphs, in the minimax case and in the Bayesian case with uniform
+priors, our proposed algorithm is a $\rho$-approximation algorithm, where
+$\rho$ is independent of the order of the underlying graph. Simulations on both
+synthetic and real data show that the performance of our algorithm is very
+close to the optimal solution.
+",1,0,0,1,0,0
+161,Economically Efficient Combined Plant and Controller Design Using Batch Bayesian Optimization: Mathematical Framework and Airborne Wind Energy Case Study,"  We present a novel data-driven nested optimization framework that addresses
+the problem of coupling between plant and controller optimization. This
+optimization strategy is tailored towards instances where a closed-form
+expression for the system dynamic response is unobtainable and simulations or
+experiments are necessary. Specifically, Bayesian Optimization, which is a
+data-driven technique for finding the optimum of an unknown and
+expensive-to-evaluate objective function, is employed to solve a nested
+optimization problem. The underlying objective function is modeled by a
+Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive
+uncertainty information from the GP to determine the best subsequent control or
+plant parameters. The proposed framework differs from the majority of co-design
+literature where there exists a closed-form model of the system dynamics.
+Furthermore, we utilize the idea of Batch Bayesian Optimization at the plant
+optimization level to generate a set of plant designs at each iteration of the
+overall optimization process, recognizing that there will exist economies of
+scale in running multiple experiments in each iteration of the plant design
+process. We validate the proposed framework for a Buoyant Airborne Turbine
+(BAT). We choose the horizontal stabilizer area, longitudinal center of mass
+relative to center of buoyancy (plant parameters), and the pitch angle
+set-point (controller parameter) as our decision variables. Our results
+demonstrate that these plant and control parameters converge to their
+respective optimal values within only a few iterations.
+",1,0,0,0,0,0
+162,The 10 phases of spin chains with two Ising symmetries,"  We explore the topological properties of quantum spin-1/2 chains with two
+Ising symmetries. This class of models does not possess any of the symmetries
+that are required to protect the Haldane phase. Nevertheless, we show that
+there are 4 symmetry-protected topological phases, in addition to 6 phases that
+spontaneously break one or both Ising symmetries. By mapping the model to
+one-dimensional interacting fermions with particle-hole and time-reversal
+symmetry, we obtain integrable parent Hamiltonians for the conventional and
+topological phases of the spin model. We use these Hamiltonians to characterize
+the physical properties of all 10 phases, identify their local and nonlocal
+order parameters, and understand the effects of weak perturbations that respect
+the Ising symmetries. Our study provides the first explicit example of a class
+of spin chains with several topologically non-trivial phases, and binds
+together the topological classifications of interacting bosons and fermions.
+",0,1,0,0,0,0
+163,Generalized subspace subcodes with application in cryptology,"  Most of the codes that have an algebraic decoding algorithm are derived from
+the Reed Solomon codes. They are obtained by taking equivalent codes, for
+example the generalized Reed Solomon codes, or by using the so-called subfield
+subcode method, which leads to Alternant codes and Goppa codes over the
+underlying prime field, or over some intermediate subfield. The main advantages
+of these constructions is to preserve both the minimum distance and the
+decoding algorithm of the underlying Reed Solomon code. In this paper, we
+propose a generalization of the subfield subcode construction by introducing
+the notion of subspace subcodes and a generalization of the equivalence of
+codes which leads to the notion of generalized subspace subcodes. When the
+dimension of the selected subspaces is equal to one, we show that our approach
+gives exactly the family of the codes obtained by equivalence and subfield
+subcode technique. However, our approach highlights the links between the
+subfield subcode of a code defined over an extension field and the operation of
+puncturing the $q$-ary image of this code. When the dimension of the subspaces
+is greater than one, we obtain codes whose alphabet is no longer a finite
+field, but a set of r-uples. We explain why these codes are practically as
+efficient for applications as the codes defined on an extension of degree r. In
+addition, they make it possible to obtain decodable codes over a large alphabet
+having parameters previously inaccessible. As an application, we give some
+examples that can be used in public key cryptosystems such as McEliece.
+",1,0,0,0,0,0
+164,Lagrangian fibers of Gelfand-Cetlin systems,"  Motivated by the study of Nishinou-Nohara-Ueda on the Floer thoery of
+Gelfand-Cetlin systems over complex partial flag manifolds, we provide a
+complete description of the topology of Gelfand-Cetlin fibers. We prove that
+all fibers are \emph{smooth} isotropic submanifolds and give a complete
+description of the fiber to be Lagrangian in terms of combinatorics of
+Gelfand-Cetlin polytope. Then we study (non-)displaceability of Lagrangian
+fibers. After a few combinatorial and numercal tests for the displaceability,
+using the bulk-deformation of Floer cohomology by Schubert cycles, we prove
+that every full flag manifold $\mathcal{F}(n)$ ($n \geq 3$) with a monotone
+Kirillov-Kostant-Souriau symplectic form carries a continuum of
+non-displaceable Lagrangian tori which degenerates to a non-torus fiber in the
+Hausdorff limit. In particular, the Lagrangian $S^3$-fiber in $\mathcal{F}(3)$
+is non-displaceable the question of which was raised by Nohara-Ueda who
+computed its Floer cohomology to be vanishing.
+",0,0,1,0,0,0
+165,A local ensemble transform Kalman particle filter for convective scale data assimilation,"  Ensemble data assimilation methods such as the Ensemble Kalman Filter (EnKF)
+are a key component of probabilistic weather forecasting. They represent the
+uncertainty in the initial conditions by an ensemble which incorporates
+information coming from the physical model with the latest observations.
+High-resolution numerical weather prediction models ran at operational centers
+are able to resolve non-linear and non-Gaussian physical phenomena such as
+convection. There is therefore a growing need to develop ensemble assimilation
+algorithms able to deal with non-Gaussianity while staying computationally
+feasible. In the present paper we address some of these needs by proposing a
+new hybrid algorithm based on the Ensemble Kalman Particle Filter. It is fully
+formulated in ensemble space and uses a deterministic scheme such that it has
+the ensemble transform Kalman filter (ETKF) instead of the stochastic EnKF as a
+limiting case. A new criterion for choosing the proportion of particle filter
+and ETKF update is also proposed. The new algorithm is implemented in the COSMO
+framework and numerical experiments in a quasi-operational convective-scale
+setup are conducted. The results show the feasibility of the new algorithm in
+practice and indicate a strong potential for such local hybrid methods, in
+particular for forecasting non-Gaussian variables such as wind and hourly
+precipitation.
+",0,1,0,1,0,0
+166,Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm,"  In this paper, we consider the Tensor Robust Principal Component Analysis
+(TRPCA) problem, which aims to exactly recover the low-rank and sparse
+components from their sum. Our model is based on the recently proposed
+tensor-tensor product (or t-product) [13]. Induced by the t-product, we first
+rigorously deduce the tensor spectral norm, tensor nuclear norm, and tensor
+average rank, and show that the tensor nuclear norm is the convex envelope of
+the tensor average rank within the unit ball of the tensor spectral norm. These
+definitions, their relationships and properties are consistent with matrix
+cases. Equipped with the new tensor nuclear norm, we then solve the TRPCA
+problem by solving a convex program and provide the theoretical guarantee for
+the exact recovery. Our TRPCA model and recovery guarantee include matrix RPCA
+as a special case. Numerical experiments verify our results, and the
+applications to image recovery and background modeling problems demonstrate the
+effectiveness of our method.
+",0,0,0,1,0,0
+167,Resolving the age bimodality of galaxy stellar populations on kpc scales,"  Galaxies in the local Universe are known to follow bimodal distributions in
+the global stellar populations properties. We analyze the distribution of the
+local average stellar-population ages of 654,053 sub-galactic regions resolved
+on ~1-kpc scales in a volume-corrected sample of 394 galaxies, drawn from the
+CALIFA-DR3 integral-field-spectroscopy survey and complemented by SDSS imaging.
+We find a bimodal local-age distribution, with an old and a young peak
+primarily due to regions in early-type galaxies and star-forming regions of
+spirals, respectively. Within spiral galaxies, the older ages of bulges and
+inter-arm regions relative to spiral arms support an internal age bimodality.
+Although regions of higher stellar-mass surface-density, mu*, are typically
+older, mu* alone does not determine the stellar population age and a bimodal
+distribution is found at any fixed mu*. We identify an ""old ridge"" of regions
+of age ~9 Gyr, independent of mu*, and a ""young sequence"" of regions with age
+increasing with mu* from 1-1.5 Gyr to 4-5 Gyr. We interpret the former as
+regions containing only old stars, and the latter as regions where the relative
+contamination of old stellar populations by young stars decreases as mu*
+increases. The reason why this bimodal age distribution is not inconsistent
+with the unimodal shape of the cosmic-averaged star-formation history is that
+i) the dominating contribution by young stars biases the age low with respect
+to the average epoch of star formation, and ii) the use of a single average age
+per region is unable to represent the full time-extent of the star-formation
+history of ""young-sequence"" regions.
+",0,1,0,0,0,0
+168,Hidden long evolutionary memory in a model biochemical network,"  We introduce a minimal model for the evolution of functional
+protein-interaction networks using a sequence-based mutational algorithm, and
+apply the model to study neutral drift in networks that yield oscillatory
+dynamics. Starting with a functional core module, random evolutionary drift
+increases network complexity even in the absence of specific selective
+pressures. Surprisingly, we uncover a hidden order in sequence space that gives
+rise to long-term evolutionary memory, implying strong constraints on network
+evolution due to the topology of accessible sequence space.
+",0,1,0,0,0,0
+169,On Study of the Reliable Fully Convolutional Networks with Tree Arranged Outputs (TAO-FCN) for Handwritten String Recognition,"  The handwritten string recognition is still a challengeable task, though the
+powerful deep learning tools were introduced. In this paper, based on TAO-FCN,
+we proposed an end-to-end system for handwritten string recognition. Compared
+with the conventional methods, there is no preprocess nor manually designed
+rules employed. With enough labelled data, it is easy to apply the proposed
+method to different applications. Although the performance of the proposed
+method may not be comparable with the state-of-the-art approaches, it's
+usability and robustness are more meaningful for practical applications.
+",1,0,0,0,0,0
+170,Marcel Riesz on Nörlund Means,"  We note that the necessary and sufficient conditions established by Marcel
+Riesz for the inclusion of regular Nörlund summation methods are in fact
+applicable quite generally.
+",0,0,1,0,0,0
+171,Mathematics of Isogeny Based Cryptography,"  These lectures notes were written for a summer school on Mathematics for
+post-quantum cryptography in Thiès, Senegal. They try to provide a guide for
+Masters' students to get through the vast literature on elliptic curves,
+without getting lost on their way to learning isogeny based cryptography. They
+are by no means a reference text on the theory of elliptic curves, nor on
+cryptography; students are encouraged to complement these notes with some of
+the books recommended in the bibliography.
+The presentation is divided in three parts, roughly corresponding to the
+three lectures given. In an effort to keep the reader interested, each part
+alternates between the fundamental theory of elliptic curves, and applications
+in cryptography. We often prefer to have the main ideas flow smoothly, rather
+than having a rigorous presentation as one would have in a more classical book.
+The reader will excuse us for the inaccuracies and the omissions.
+",1,0,0,0,0,0
+172,Modeling of drug diffusion in a solid tumor leading to tumor cell death,"  It has been shown recently that changing the fluidic properties of a drug can
+improve its efficacy in ablating solid tumors. We develop a modeling framework
+for tumor ablation, and present the simplest possible model for drug diffusion
+in a spherical tumor with leaky boundaries and assuming cell death eventually
+leads to ablation of that cell effectively making the two quantities
+numerically equivalent. The death of a cell after a given exposure time depends
+on both the concentration of the drug and the amount of oxygen available to the
+cell. Higher oxygen availability leads to cell death at lower drug
+concentrations. It can be assumed that a minimum concentration is required for
+a cell to die, effectively connecting diffusion with efficacy. The
+concentration threshold decreases as exposure time increases, which allows us
+to compute dose-response curves. Furthermore, these curves can be plotted at
+much finer time intervals compared to that of experiments, which is used to
+produce a dose-threshold-response surface giving an observer a complete picture
+of the drug's efficacy for an individual. In addition, since the diffusion,
+leak coefficients, and the availability of oxygen is different for different
+individuals and tumors, we produce artificial replication data through
+bootstrapping to simulate error. While the usual data-driven model with
+Sigmoidal curves use 12 free parameters, our mechanistic model only has two
+free parameters, allowing it to be open to scrutiny rather than forcing
+agreement with data. Even so, the simplest model in our framework, derived
+here, shows close agreement with the bootstrapped curves, and reproduces well
+established relations, such as Haber's rule.
+",0,0,0,0,1,0
+173,Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap,"  To identify the estimand in missing data problems and observational studies,
+it is common to base the statistical estimation on the ""missing at random"" and
+""no unmeasured confounder"" assumptions. However, these assumptions are
+unverifiable using empirical data and pose serious threats to the validity of
+the qualitative conclusions of the statistical inference. A sensitivity
+analysis asks how the conclusions may change if the unverifiable assumptions
+are violated to a certain degree. In this paper we consider a marginal
+sensitivity model which is a natural extension of Rosenbaum's sensitivity model
+that is widely used for matched observational studies. We aim to construct
+confidence intervals based on inverse probability weighting estimators, such
+that asymptotically the intervals have at least nominal coverage of the
+estimand whenever the data generating distribution is in the collection of
+marginal sensitivity models. We use a percentile bootstrap and a generalized
+minimax/maximin inequality to transform this intractable problem to a linear
+fractional programming problem, which can be solved very efficiently. We
+illustrate our method using a real dataset to estimate the causal effect of
+fish consumption on blood mercury level.
+",0,0,1,1,0,0
+174,From 4G to 5G: Self-organized Network Management meets Machine Learning,"  In this paper, we provide an analysis of self-organized network management,
+with an end-to-end perspective of the network. Self-organization as applied to
+cellular networks is usually referred to Self-organizing Networks (SONs), and
+it is a key driver for improving Operations, Administration, and Maintenance
+(OAM) activities. SON aims at reducing the cost of installation and management
+of 4G and future 5G networks, by simplifying operational tasks through the
+capability to configure, optimize and heal itself. To satisfy 5G network
+management requirements, this autonomous management vision has to be extended
+to the end to end network. In literature and also in some instances of products
+available in the market, Machine Learning (ML) has been identified as the key
+tool to implement autonomous adaptability and take advantage of experience when
+making decisions. In this paper, we survey how network management can
+significantly benefit from ML solutions. We review and provide the basic
+concepts and taxonomy for SON, network management and ML. We analyse the
+available state of the art in the literature, standardization, and in the
+market. We pay special attention to 3rd Generation Partnership Project (3GPP)
+evolution in the area of network management and to the data that can be
+extracted from 3GPP networks, in order to gain knowledge and experience in how
+the network is working, and improve network performance in a proactive way.
+Finally, we go through the main challenges associated with this line of
+research, in both 4G and in what 5G is getting designed, while identifying new
+directions for research.
+",1,0,0,0,0,0
+175,Cyber Risk Analysis of Combined Data Attacks Against Power System State Estimation,"  Understanding smart grid cyber attacks is key for developing appropriate
+protection and recovery measures. Advanced attacks pursue maximized impact at
+minimized costs and detectability. This paper conducts risk analysis of
+combined data integrity and availability attacks against the power system state
+estimation. We compare the combined attacks with pure integrity attacks - false
+data injection (FDI) attacks. A security index for vulnerability assessment to
+these two kinds of attacks is proposed and formulated as a mixed integer linear
+programming problem. We show that such combined attacks can succeed with fewer
+resources than FDI attacks. The combined attacks with limited knowledge of the
+system model also expose advantages in keeping stealth against the bad data
+detection. Finally, the risk of combined attacks to reliable system operation
+is evaluated using the results from vulnerability assessment and attack impact
+analysis. The findings in this paper are validated and supported by a detailed
+case study.
+",1,0,0,0,0,0
+176,A New Family of Near-metrics for Universal Similarity,"  We propose a family of near-metrics based on local graph diffusion to capture
+similarity for a wide class of data sets. These quasi-metametrics, as their
+names suggest, dispense with one or two standard axioms of metric spaces,
+specifically distinguishability and symmetry, so that similarity between data
+points of arbitrary type and form could be measured broadly and effectively.
+The proposed near-metric family includes the forward k-step diffusion and its
+reverse, typically on the graph consisting of data objects and their features.
+By construction, this family of near-metrics is particularly appropriate for
+categorical data, continuous data, and vector representations of images and
+text extracted via deep learning approaches. We conduct extensive experiments
+to evaluate the performance of this family of similarity measures and compare
+and contrast with traditional measures of similarity used for each specific
+application and with the ground truth when available. We show that for
+structured data including categorical and continuous data, the near-metrics
+corresponding to normalized forward k-step diffusion (k small) work as one of
+the best performing similarity measures; for vector representations of text and
+images including those extracted from deep learning, the near-metrics derived
+from normalized and reverse k-step graph diffusion (k very small) exhibit
+outstanding ability to distinguish data points from different classes.
+",1,0,0,1,0,0
+177,Poisoning Attacks to Graph-Based Recommender Systems,"  Recommender system is an important component of many web services to help
+users locate items that match their interests. Several studies showed that
+recommender systems are vulnerable to poisoning attacks, in which an attacker
+injects fake data to a given system such that the system makes recommendations
+as the attacker desires. However, these poisoning attacks are either agnostic
+to recommendation algorithms or optimized to recommender systems that are not
+graph-based. Like association-rule-based and matrix-factorization-based
+recommender systems, graph-based recommender system is also deployed in
+practice, e.g., eBay, Huawei App Store. However, how to design optimized
+poisoning attacks for graph-based recommender systems is still an open problem.
+In this work, we perform a systematic study on poisoning attacks to graph-based
+recommender systems. Due to limited resources and to avoid detection, we assume
+the number of fake users that can be injected into the system is bounded. The
+key challenge is how to assign rating scores to the fake users such that the
+target item is recommended to as many normal users as possible. To address the
+challenge, we formulate the poisoning attacks as an optimization problem,
+solving which determines the rating scores for the fake users. We also propose
+techniques to solve the optimization problem. We evaluate our attacks and
+compare them with existing attacks under white-box (recommendation algorithm
+and its parameters are known), gray-box (recommendation algorithm is known but
+its parameters are unknown), and black-box (recommendation algorithm is
+unknown) settings using two real-world datasets. Our results show that our
+attack is effective and outperforms existing attacks for graph-based
+recommender systems. For instance, when 1% fake users are injected, our attack
+can make a target item recommended to 580 times more normal users in certain
+scenarios.
+",0,0,0,1,0,0
+178,SU-RUG at the CoNLL-SIGMORPHON 2017 shared task: Morphological Inflection with Attentional Sequence-to-Sequence Models,"  This paper describes the Stockholm University/University of Groningen
+(SU-RUG) system for the SIGMORPHON 2017 shared task on morphological
+inflection. Our system is based on an attentional sequence-to-sequence neural
+network model using Long Short-Term Memory (LSTM) cells, with joint training of
+morphological inflection and the inverse transformation, i.e. lemmatization and
+morphological analysis. Our system outperforms the baseline with a large
+margin, and our submission ranks as the 4th best team for the track we
+participate in (task 1, high-resource).
+",1,0,0,0,0,0
+179,"Neural system identification for large populations separating ""what"" and ""where""","  Neuroscientists classify neurons into different types that perform similar
+computations at different locations in the visual field. Traditional methods
+for neural system identification do not capitalize on this separation of 'what'
+and 'where'. Learning deep convolutional feature spaces that are shared among
+many neurons provides an exciting path forward, but the architectural design
+needs to account for data limitations: While new experimental techniques enable
+recordings from thousands of neurons, experimental time is limited so that one
+can sample only a small fraction of each neuron's response space. Here, we show
+that a major bottleneck for fitting convolutional neural networks (CNNs) to
+neural data is the estimation of the individual receptive field locations, a
+problem that has been scratched only at the surface thus far. We propose a CNN
+architecture with a sparse readout layer factorizing the spatial (where) and
+feature (what) dimensions. Our network scales well to thousands of neurons and
+short recordings and can be trained end-to-end. We evaluate this architecture
+on ground-truth data to explore the challenges and limitations of CNN-based
+system identification. Moreover, we show that our network model outperforms
+current state-of-the art system identification models of mouse primary visual
+cortex.
+",1,0,0,1,0,0
+180,On the Deployment of Distributed Antennas for Wireless Power Transfer with Safety Electromagnetic Radiation Level Requirement,"  The extremely low efficiency is regarded as the bottleneck of Wireless Power
+Transfer (WPT) technology. To tackle this problem, either enlarging the
+transfer power or changing the infrastructure of WPT system could be an
+intuitively proposed way. However, the drastically important issue on the user
+exposure of electromagnetic radiation is rarely considered while we try to
+improve the efficiency of WPT. In this paper, a Distributed Antenna Power
+Beacon (DA-PB) based WPT system where these antennas are uniformly distributed
+on a circle is analyzed and optimized with the safety electromagnetic radiation
+level (SERL) requirement. In this model, three key questions are intended to be
+answered: 1) With the SERL, what is the performance of the harvested power at
+the users ? 2) How do we configure the parameters to maximize the efficiency of
+WPT? 3) Under the same constraints, does the DA-PB still have performance gain
+than the Co-located Antenna PB (CA-PB)? First, the minimum antenna height of
+DA-PB is derived to make the radio frequency (RF) electromagnetic radiation
+power density at any location of the charging cell lower than the SERL
+published by the Federal Communications Commission (FCC). Second, the
+closed-form expressions of average harvested Direct Current (DC) power per user
+in the charging cell for pass-loss exponent 2 and 4 are also provided. In order
+to maximize the average efficiency of WPT, the optimal radii for distributed
+antennas elements (DAEs) are derived when the pass-loss exponent takes the
+typical value $2$ and $4$. For comparison, the CA-PB is also analyzed as a
+benchmark. Simulation results verify our derived theoretical results. And it is
+shown that the proposed DA-PB indeed achieves larger average harvested DC power
+than CA-PB and can improve the efficiency of WPT.
+",1,0,0,0,0,0
+181,A simulation technique for slurries interacting with moving parts and deformable solids with applications,"  A numerical method for particle-laden fluids interacting with a deformable
+solid domain and mobile rigid parts is proposed and implemented in a full
+engineering system. The fluid domain is modeled with a lattice Boltzmann
+representation, the particles and rigid parts are modeled with a discrete
+element representation, and the deformable solid domain is modeled using a
+Lagrangian mesh. The main issue of this work, since separately each of these
+methods is a mature tool, is to develop coupling and model-reduction approaches
+in order to efficiently simulate coupled problems of this nature, as occur in
+various geological and engineering applications. The lattice Boltzmann method
+incorporates a large-eddy simulation technique using the Smagorinsky turbulence
+model. The discrete element method incorporates spherical and polyhedral
+particles for stiff contact interactions. A neo-Hookean hyperelastic model is
+used for the deformable solid. We provide a detailed description of how to
+couple the three solvers within a unified algorithm. The technique we propose
+for rubber modeling/coupling exploits a simplification that prevents having to
+solve a finite-element problem each time step. We also develop a technique to
+reduce the domain size of the full system by replacing certain zones with
+quasi-analytic solutions, which act as effective boundary conditions for the
+lattice Boltzmann method. The major ingredients of the routine are are
+separately validated. To demonstrate the coupled method in full, we simulate
+slurry flows in two kinds of piston-valve geometries. The dynamics of the valve
+and slurry are studied and reported over a large range of input parameters.
+",1,0,0,0,0,0
+182,Dissipative hydrodynamics in superspace,"  We construct a Schwinger-Keldysh effective field theory for relativistic
+hydrodynamics for charged matter in a thermal background using a superspace
+formalism. Superspace allows us to efficiently impose the symmetries of the
+problem and to obtain a simple expression for the effective action. We show
+that the theory we obtain is compatible with the Kubo-Martin-Schwinger
+condition, which in turn implies that Green's functions obey the
+fluctuation-dissipation theorem. Our approach complements and extends existing
+formulations found in the literature.
+",0,1,0,0,0,0
+183,The Two-fold Role of Observables in Classical and Quantum Kinematics,"  Observables have a dual nature in both classical and quantum kinematics: they
+are at the same time \emph{quantities}, allowing to separate states by means of
+their numerical values, and \emph{generators of transformations}, establishing
+relations between different states. In this work, we show how this two-fold
+role of observables constitutes a key feature in the conceptual analysis of
+classical and quantum kinematics, shedding a new light on the distinguishing
+feature of the quantum at the kinematical level. We first take a look at the
+algebraic description of both classical and quantum observables in terms of
+Jordan-Lie algebras and show how the two algebraic structures are the precise
+mathematical manifestation of the two-fold role of observables. Then, we turn
+to the geometric reformulation of quantum kinematics in terms of Kähler
+manifolds. A key achievement of this reformulation is to show that the two-fold
+role of observables is the constitutive ingredient defining what an observable
+is. Moreover, it points to the fact that, from the restricted point of view of
+the transformational role of observables, classical and quantum kinematics
+behave in exactly the same way. Finally, we present Landsman's general
+framework of Poisson spaces with transition probability, which highlights with
+unmatched clarity that the crucial difference between the two kinematics lies
+in the way the two roles of observables are related to each other.
+",0,1,0,0,0,0
+184,On the isoperimetric quotient over scalar-flat conformal classes,"  Let $(M,g)$ be a smooth compact Riemannian manifold of dimension $n$ with
+smooth boundary $\partial M$. Suppose that $(M,g)$ admits a scalar-flat
+conformal metric. We prove that the supremum of the isoperimetric quotient over
+the scalar-flat conformal class is strictly larger than the best constant of
+the isoperimetric inequality in the Euclidean space, and consequently is
+achieved, if either (i) $n\ge 12$ and $\partial M$ has a nonumbilic point; or
+(ii) $n\ge 10$, $\partial M$ is umbilic and the Weyl tensor does not vanish at
+some boundary point.
+",0,0,1,0,0,0
+185,On the Spectrum of Random Features Maps of High Dimensional Data,"  Random feature maps are ubiquitous in modern statistical machine learning,
+where they generalize random projections by means of powerful, yet often
+difficult to analyze nonlinear operators. In this paper, we leverage the
+""concentration"" phenomenon induced by random matrix theory to perform a
+spectral analysis on the Gram matrix of these random feature maps, here for
+Gaussian mixture models of simultaneously large dimension and size. Our results
+are instrumental to a deeper understanding on the interplay of the nonlinearity
+and the statistics of the data, thereby allowing for a better tuning of random
+feature-based techniques.
+",0,0,0,1,0,0
+186,Minimum energy path calculations with Gaussian process regression,"  The calculation of minimum energy paths for transitions such as atomic and/or
+spin re-arrangements is an important task in many contexts and can often be
+used to determine the mechanism and rate of transitions. An important challenge
+is to reduce the computational effort in such calculations, especially when ab
+initio or electron density functional calculations are used to evaluate the
+energy since they can require large computational effort. Gaussian process
+regression is used here to reduce significantly the number of energy
+evaluations needed to find minimum energy paths of atomic rearrangements. By
+using results of previous calculations to construct an approximate energy
+surface and then converge to the minimum energy path on that surface in each
+Gaussian process iteration, the number of energy evaluations is reduced
+significantly as compared with regular nudged elastic band calculations. For a
+test problem involving rearrangements of a heptamer island on a crystal
+surface, the number of energy evaluations is reduced to less than a fifth. The
+scaling of the computational effort with the number of degrees of freedom as
+well as various possible further improvements to this approach are discussed.
+",0,1,0,1,0,0
+187,Evaluating Roles of Central Users in Online Communication Networks: A Case Study of #PanamaLeaks,"  Social media has changed the ways of communication, where everyone is
+equipped with the power to express their opinions to others in online
+discussion platforms. Previously, a number of stud- ies have been presented to
+identify opinion leaders in online discussion networks. Feng (""Are you
+connected? Evaluating information cascade in online discussion about the
+#RaceTogether campaign"", Computers in Human Behavior, 2016) identified five
+types of central users and their communication patterns in an online
+communication network of a limited time span. However, to trace the change in
+communication pattern, a long-term analysis is required. In this study, we
+critically analyzed framework presented by Feng based on five types of central
+users in online communication network and their communication pattern in a
+long-term manner. We take another case study presented by Udnor et al.
+(""Determining social media impact on the politics of developing countries using
+social network analytics"", Program, 2016) to further understand the dynamics as
+well as to perform validation . Results indicate that there may not exist all
+of these central users in an online communication network in a long-term
+manner. Furthermore, we discuss the changing positions of opinion leaders and
+their power to keep isolates interested in an online discussion network.
+",1,1,0,1,0,0
+188,Best polynomial approximation on the triangle,"  Let $E_n(f)_{\alpha,\beta,\gamma}$ denote the error of best approximation by
+polynomials of degree at most $n$ in the space
+$L^2(\varpi_{\alpha,\beta,\gamma})$ on the triangle $\{(x,y): x, y \ge 0, x+y
+\le 1\}$, where $\varpi_{\alpha,\beta,\gamma}(x,y) := x^\alpha y ^\beta
+(1-x-y)^\gamma$ for $\alpha,\beta,\gamma > -1$. Our main result gives a sharp
+estimate of $E_n(f)_{\alpha,\beta,\gamma}$ in terms of the error of best
+approximation for higher order derivatives of $f$ in appropriate Sobolev
+spaces. The result also leads to a characterization of
+$E_n(f)_{\alpha,\beta,\gamma}$ by a weighted $K$-functional.
+",0,0,1,0,0,0
+189,SecureTime: Secure Multicast Time Synchronization,"  Due to the increasing dependency of critical infrastructure on synchronized
+clocks, network time synchronization protocols have become an attractive target
+for attackers. We identify data origin authentication as the key security
+objective and suggest to employ recently proposed high-performance digital
+signature schemes (Ed25519 and MQQ-SIG)) as foundation of a novel set of
+security measures to secure multicast time synchronization. We conduct
+experiments to verify the computational and communication efficiency for using
+these signatures in the standard time synchronization protocols NTP and PTP. We
+propose additional security measures to prevent replay attacks and to mitigate
+delay attacks. Our proposed solutions cover 1-step mode for NTP and PTP and we
+extend our security measures specifically to 2-step mode (PTP) and show that
+they have no impact on time synchronization's precision.
+",1,0,0,0,0,0
+190,Solving the multi-site and multi-orbital Dynamical Mean Field Theory using Density Matrix Renormalization,"  We implement an efficient numerical method to calculate response functions of
+complex impurities based on the Density Matrix Renormalization Group (DMRG) and
+use it as the impurity-solver of the Dynamical Mean Field Theory (DMFT). This
+method uses the correction vector to obtain precise Green's functions on the
+real frequency axis at zero temperature. By using a self-consistent bath
+configuration with very low entanglement, we take full advantage of the DMRG to
+calculate dynamical response functions paving the way to treat large effective
+impurities such as those corresponding to multi-orbital interacting models and
+multi-site or multi-momenta clusters. This method leads to reliable
+calculations of non-local self energies at arbitrary dopings and interactions
+and at any energy scale.
+",0,1,0,0,0,0
+191,Topologically Invariant Double Dirac States in Bismuth based Perovskites: Consequence of Ambivalent Charge States and Covalent Bonding,"  Bulk and surface electronic structures, calculated using density functional
+theory and a tight-binding model Hamiltonian, reveal the existence of two
+topologically invariant (TI) surface states in the family of cubic Bi
+perovskites (ABiO$_3$; A = Na, K, Rb, Cs, Mg, Ca, Sr and Ba). The two TI
+states, one lying in the valence band (TI-V) and other lying in the conduction
+band (TI-C) are formed out of bonding and antibonding states of the
+Bi-$\{$s,p$\}$ - O-$\{$p$\}$ coordinated covalent interaction. Below a certain
+critical thickness of the film, which varies with A, TI states of top and
+bottom surfaces couple to destroy the Dirac type linear dispersion and
+consequently to open surface energy gaps. The origin of s-p band inversion,
+necessary to form a TI state, classifies the family of ABiO$_3$ into two. For
+class-I (A = Na, K, Rb, Cs and Mg) the band inversion, leading to TI-C state,
+is induced by spin-orbit coupling of the Bi-p states and for class-II (A = Ca,
+Sr and Ba) the band inversion is induced through weak but sensitive second
+neighbor Bi-Bi interactions.
+",0,1,0,0,0,0
+192,Identitas: A Better Way To Be Meaningless,"  It is often recommended that identifiers for ontology terms should be
+semantics-free or meaningless. In practice, ontology developers tend to use
+numeric identifiers, starting at 1 and working upwards. In this paper we
+present a critique of current ontology semantics-free identifiers;
+monotonically increasing numbers have a number of significant usability flaws
+which make them unsuitable as a default option, and we present a series of
+alternatives. We have provide an implementation of these alternatives which can
+be freely combined.
+",1,0,0,0,0,0
+193,Learning from Between-class Examples for Deep Sound Recognition,"  Deep learning methods have achieved high performance in sound recognition
+tasks. Deciding how to feed the training data is important for further
+performance improvement. We propose a novel learning method for deep sound
+recognition: Between-Class learning (BC learning). Our strategy is to learn a
+discriminative feature space by recognizing the between-class sounds as
+between-class sounds. We generate between-class sounds by mixing two sounds
+belonging to different classes with a random ratio. We then input the mixed
+sound to the model and train the model to output the mixing ratio. The
+advantages of BC learning are not limited only to the increase in variation of
+the training data; BC learning leads to an enlargement of Fisher's criterion in
+the feature space and a regularization of the positional relationship among the
+feature distributions of the classes. The experimental results show that BC
+learning improves the performance on various sound recognition networks,
+datasets, and data augmentation schemes, in which BC learning proves to be
+always beneficial. Furthermore, we construct a new deep sound recognition
+network (EnvNet-v2) and train it with BC learning. As a result, we achieved a
+performance surpasses the human level.
+",1,0,0,1,0,0
+194,DAGGER: A sequential algorithm for FDR control on DAGs,"  We propose a linear-time, single-pass, top-down algorithm for multiple
+testing on directed acyclic graphs (DAGs), where nodes represent hypotheses and
+edges specify a partial ordering in which hypotheses must be tested. The
+procedure is guaranteed to reject a sub-DAG with bounded false discovery rate
+(FDR) while satisfying the logical constraint that a rejected node's parents
+must also be rejected. It is designed for sequential testing settings, when the
+DAG structure is known a priori, but the $p$-values are obtained selectively
+(such as in a sequence of experiments), but the algorithm is also applicable in
+non-sequential settings when all $p$-values can be calculated in advance (such
+as variable/model selection). Our DAGGER algorithm, shorthand for Greedily
+Evolving Rejections on DAGs, provably controls the false discovery rate under
+independence, positive dependence or arbitrary dependence of the $p$-values.
+The DAGGER procedure specializes to known algorithms in the special cases of
+trees and line graphs, and simplifies to the classical Benjamini-Hochberg
+procedure when the DAG has no edges. We explore the empirical performance of
+DAGGER using simulations, as well as a real dataset corresponding to a gene
+ontology, showing favorable performance in terms of time and power.
+",0,0,1,1,0,0
+195,On nonlinear profile decompositions and scattering for a NLS-ODE model,"  In this paper, we consider a Hamiltonian system combining a nonlinear Schr\""
+odinger equation (NLS) and an ordinary differential equation (ODE). This system
+is a simplified model of the NLS around soliton solutions. Following Nakanishi
+\cite{NakanishiJMSJ}, we show scattering of $L^2$ small $H^1$ radial solutions.
+The proof is based on Nakanishi's framework and Fermi Golden Rule estimates on
+$L^4$ in time norms.
+",0,0,1,0,0,0
+196,Blockchain and human episodic memory,"  We relate the concepts used in decentralized ledger technology to studies of
+episodic memory in the mammalian brain. Specifically, we introduce the standard
+concepts of linked list, hash functions, and sharding, from computer science.
+We argue that these concepts may be more relevant to studies of the neural
+mechanisms of memory than has been previously appreciated. In turn, we also
+highlight that certain phenomena studied in the brain, namely metacognition,
+reality monitoring, and how perceptual conscious experiences come about, may
+inspire development in blockchain technology too, specifically regarding
+probabilistic consensus protocols.
+",0,0,0,0,1,0
+197,Epidemic Spreading and Aging in Temporal Networks with Memory,"  Time-varying network topologies can deeply influence dynamical processes
+mediated by them. Memory effects in the pattern of interactions among
+individuals are also known to affect how diffusive and spreading phenomena take
+place. In this paper we analyze the combined effect of these two ingredients on
+epidemic dynamics on networks. We study the susceptible-infected-susceptible
+(SIS) and the susceptible-infected-removed (SIR) models on the recently
+introduced activity-driven networks with memory. By means of an activity-based
+mean-field approach we derive, in the long time limit, analytical predictions
+for the epidemic threshold as a function of the parameters describing the
+distribution of activities and the strength of the memory effects. Our results
+show that memory reduces the threshold, which is the same for SIS and SIR
+dynamics, therefore favouring epidemic spreading. The theoretical approach
+perfectly agrees with numerical simulations in the long time asymptotic regime.
+Strong aging effects are present in the preasymptotic regime and the epidemic
+threshold is deeply affected by the starting time of the epidemics. We discuss
+in detail the origin of the model-dependent preasymptotic corrections, whose
+understanding could potentially allow for epidemic control on correlated
+temporal networks.
+",1,0,0,0,1,0
+198,"The Shattered Gradients Problem: If resnets are the answer, then what is the question?","  A long-standing obstacle to progress in deep learning is the problem of
+vanishing and exploding gradients. Although, the problem has largely been
+overcome via carefully constructed initializations and batch normalization,
+architectures incorporating skip-connections such as highway and resnets
+perform much better than standard feedforward architectures despite well-chosen
+initialization and batch normalization. In this paper, we identify the
+shattered gradients problem. Specifically, we show that the correlation between
+gradients in standard feedforward networks decays exponentially with depth
+resulting in gradients that resemble white noise whereas, in contrast, the
+gradients in architectures with skip-connections are far more resistant to
+shattering, decaying sublinearly. Detailed empirical evidence is presented in
+support of the analysis, on both fully-connected networks and convnets.
+Finally, we present a new ""looks linear"" (LL) initialization that prevents
+shattering, with preliminary experiments showing the new initialization allows
+to train very deep networks without the addition of skip-connections.
+",1,0,0,1,0,0
+199,Pr$_2$Ir$_2$O$_7$: when Luttinger semimetal meets Melko-Hertog-Gingras spin ice state,"  We study the band structure topology and engineering from the interplay
+between local moments and itinerant electrons in the context of pyrochlore
+iridates. For the metallic iridate Pr$_2$Ir$_2$O$_7$, the Ir $5d$ conduction
+electrons interact with the Pr $4f$ local moments via the $f$-$d$ exchange.
+While the Ir electrons form a Luttinger semimetal, the Pr moments can be tuned
+into an ordered spin ice with a finite ordering wavevector, dubbed
+""Melko-Hertog-Gingras"" state, by varying Ir and O contents. We point out that
+the ordered spin ice of the Pr local moments generates an internal magnetic
+field that reconstructs the band structure of the Luttinger semimetal. Besides
+the broad existence of Weyl nodes, we predict that the magnetic translation of
+the ""Melko-Hertog-Gingras"" state for the Pr moments protects the Dirac band
+touching at certain time reversal invariant momenta for the Ir conduction
+electrons. We propose the magnetic fields to control the Pr magnetic structure
+and thereby indirectly influence the topological and other properties of the Ir
+electrons. Our prediction may be immediately tested in the ordered
+Pr$_2$Ir$_2$O$_7$ samples. We expect our work to stimulate a detailed
+examination of the band structure, magneto-transport, and other properties of
+Pr$_2$Ir$_2$O$_7$.
+",0,1,0,0,0,0
+200,A 2-edge partial inverse problem for the Sturm-Liouville operators with singular potentials on a star-shaped graph,"  Boundary value problems for Sturm-Liouville operators with potentials from
+the class $W_2^{-1}$ on a star-shaped graph are considered. We assume that the
+potentials are known on all the edges of the graph except two, and show that
+the potentials on the remaining edges can be constructed by fractional parts of
+two spectra. A uniqueness theorem is proved, and an algorithm for the
+constructive solution of the partial inverse problem is provided. The main
+ingredient of the proofs is the Riesz-basis property of specially constructed
+systems of functions.
+",0,0,1,0,0,0
+201,Jastrow form of the Ground State Wave Functions for Fractional Quantum Hall States,"  The topological morphology--order of zeros at the positions of electrons with
+respect to a specific electron--of Laughlin state at filling fractions $1/m$
+($m$ odd) is homogeneous as every electron feels zeros of order $m$ at the
+positions of other electrons. Although fairly accurate ground state wave
+functions for most of the other quantum Hall states in the lowest Landau level
+are quite well-known, it had been an open problem in expressing the ground
+state wave functions in terms of flux-attachment to particles, {\em a la}, this
+morphology of Laughlin state. With a very general consideration of
+flux-particle relations only, in spherical geometry, we here report a novel
+method for determining morphologies of these states. Based on these, we
+construct almost exact ground state wave-functions for the Coulomb interaction.
+Although the form of interaction may change the ground state wave-function, the
+same morphology constructs the latter irrespective of the nature of the
+interaction between electrons.
+",0,1,0,0,0,0
+202,On a common refinement of Stark units and Gross-Stark units,"  The purpose of this paper is to formulate and study a common refinement of a
+version of Stark's conjecture and its $p$-adic analogue, in terms of Fontaine's
+$p$-adic period ring and $p$-adic Hodge theory. We construct period-ring-valued
+functions under a generalization of Yoshida's conjecture on the transcendental
+parts of CM-periods. Then we conjecture a reciprocity law on their special
+values concerning the absolute Frobenius action. We show that our conjecture
+implies a part of Stark's conjecture when the base field is an arbitrary real
+field and the splitting place is its real place. It also implies a refinement
+of the Gross-Stark conjecture under a certain assumption. When the base field
+is the rational number field, our conjecture follows from Coleman's formula on
+Fermat curves. We also prove some partial results in other cases.
+",0,0,1,0,0,0
+203,An Integrated Decision and Control Theoretic Solution to Multi-Agent Co-Operative Search Problems,"  This paper considers the problem of autonomous multi-agent cooperative target
+search in an unknown environment using a decentralized framework under a
+no-communication scenario. The targets are considered as static targets and the
+agents are considered to be homogeneous. The no-communication scenario
+translates as the agents do not exchange either the information about the
+environment or their actions among themselves. We propose an integrated
+decision and control theoretic solution for a search problem which generates
+feasible agent trajectories. In particular, a perception based algorithm is
+proposed which allows an agent to estimate the probable strategies of other
+agents' and to choose a decision based on such estimation. The algorithm shows
+robustness with respect to the estimation accuracy to a certain degree. The
+performance of the algorithm is compared with random strategies and numerical
+simulation shows considerable advantages.
+",1,0,0,0,0,0
+204,Chain effects of clean water: The Mills-Reincke phenomenon in early twentieth-century Japan,"  This study explores the validity of chain effects of clean water, which are
+known as the ""Mills-Reincke phenomenon,"" in early twentieth-century Japan.
+Recent studies have reported that water purifications systems are responsible
+for huge contributions to human capital. Although a few studies have
+investigated the short-term effects of water-supply systems in pre-war Japan,
+little is known about the benefits associated with these systems. By analyzing
+city-level cause-specific mortality data from the years 1922-1940, we found
+that eliminating typhoid fever infections decreased the risk of deaths due to
+non-waterborne diseases. Our estimates show that for one additional typhoid
+death, there were approximately one to three deaths due to other causes, such
+as tuberculosis and pneumonia. This suggests that the observed Mills-Reincke
+phenomenon could have resulted from the prevention typhoid fever in a
+previously-developing Asian country.
+",0,0,0,1,1,0
+205,Learning Transferable Architectures for Scalable Image Recognition,"  Developing neural network image classification models often requires
+significant architecture engineering. In this paper, we study a method to learn
+the model architectures directly on the dataset of interest. As this approach
+is expensive when the dataset is large, we propose to search for an
+architectural building block on a small dataset and then transfer the block to
+a larger dataset. The key contribution of this work is the design of a new
+search space (the ""NASNet search space"") which enables transferability. In our
+experiments, we search for the best convolutional layer (or ""cell"") on the
+CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking
+together more copies of this cell, each with their own parameters to design a
+convolutional architecture, named ""NASNet architecture"". We also introduce a
+new regularization technique called ScheduledDropPath that significantly
+improves generalization in the NASNet models. On CIFAR-10 itself, NASNet
+achieves 2.4% error rate, which is state-of-the-art. On ImageNet, NASNet
+achieves, among the published works, state-of-the-art accuracy of 82.7% top-1
+and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than
+the best human-invented architectures while having 9 billion fewer FLOPS - a
+reduction of 28% in computational demand from the previous state-of-the-art
+model. When evaluated at different levels of computational cost, accuracies of
+NASNets exceed those of the state-of-the-art human-designed models. For
+instance, a small version of NASNet also achieves 74% top-1 accuracy, which is
+3.1% better than equivalently-sized, state-of-the-art models for mobile
+platforms. Finally, the learned features by NASNet used with the Faster-RCNN
+framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO
+dataset.
+",1,0,0,0,0,0
+206,Fast Multi-frame Stereo Scene Flow with Motion Segmentation,"  We propose a new multi-frame method for efficiently computing scene flow
+(dense depth and optical flow) and camera ego-motion for a dynamic scene
+observed from a moving stereo camera rig. Our technique also segments out
+moving objects from the rigid scene. In our method, we first estimate the
+disparity map and the 6-DOF camera motion using stereo matching and visual
+odometry. We then identify regions inconsistent with the estimated camera
+motion and compute per-pixel optical flow only at these regions. This flow
+proposal is fused with the camera motion-based flow proposal using fusion moves
+to obtain the final optical flow and motion segmentation. This unified
+framework benefits all four tasks - stereo, optical flow, visual odometry and
+motion segmentation leading to overall higher accuracy and efficiency. Our
+method is currently ranked third on the KITTI 2015 scene flow benchmark.
+Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3
+orders of magnitude faster than the top six methods. We also report a thorough
+evaluation on challenging Sintel sequences with fast camera and object motion,
+where our method consistently outperforms OSF [Menze and Geiger, 2015], which
+is currently ranked second on the KITTI benchmark.
+",1,0,0,0,0,0
+207,Pointed $p^2q$-dimensional Hopf algebras in positive characteristic,"  Let $\K$ be an algebraically closed field of positive characteristic $p$. We
+mainly classify pointed Hopf algebras over $\K$ of dimension $p^2q$, $pq^2$ and
+$pqr$ where $p,q,r$ are distinct prime numbers. We obtain a complete
+classification of such Hopf algebras except two subcases when they are not
+generated by the first terms of coradical filtration. In particular, we obtain
+many new examples of non-commutative and non-cocommutative finite-dimensional
+Hopf algebras.
+",0,0,1,0,0,0
+208,Weak Form of Stokes-Dirac Structures and Geometric Discretization of Port-Hamiltonian Systems,"  We present the mixed Galerkin discretization of distributed parameter
+port-Hamiltonian systems. On the prototypical example of hyperbolic systems of
+two conservation laws in arbitrary spatial dimension, we derive the main
+contributions: (i) A weak formulation of the underlying geometric
+(Stokes-Dirac) structure with a segmented boundary according to the causality
+of the boundary ports. (ii) The geometric approximation of the Stokes-Dirac
+structure by a finite-dimensional Dirac structure is realized using a mixed
+Galerkin approach and power-preserving linear maps, which define minimal
+discrete power variables. (iii) With a consistent approximation of the
+Hamiltonian, we obtain finite-dimensional port-Hamiltonian state space models.
+By the degrees of freedom in the power-preserving maps, the resulting family of
+structure-preserving schemes allows for trade-offs between centered
+approximations and upwinding. We illustrate the method on the example of
+Whitney finite elements on a 2D simplicial triangulation and compare the
+eigenvalue approximation in 1D with a related approach.
+",1,0,0,0,0,0
+209,Clamped seismic metamaterials: Ultra-low broad frequency stop-bands,"  The regularity of earthquakes, their destructive power, and the nuisance of
+ground vibration in urban environments, all motivate designs of defence
+structures to lessen the impact of seismic and ground vibration waves on
+buildings. Low frequency waves, in the range $1$ to $10$ Hz for earthquakes and
+up to a few tens of Hz for vibrations generated by human activities, cause a
+large amount of damage, or inconvenience, depending on the geological
+conditions they can travel considerable distances and may match the resonant
+fundamental frequency of buildings. The ultimate aim of any seismic
+metamaterial, or any other seismic shield, is to protect over this entire range
+of frequencies, the long wavelengths involved, and low frequency, have meant
+this has been unachievable to date.
+Elastic flexural waves, applicable in the mechanical vibrations of thin
+elastic plates, can be designed to have a broad zero-frequency stop-band using
+a periodic array of very small clamped circles. Inspired by this experimental
+and theoretical observation, all be it in a situation far removed from seismic
+waves, we demonstrate that it is possible to achieve elastic surface (Rayleigh)
+and body (pressure P and shear S) wave reflectors at very large wavelengths in
+structured soils modelled as a fully elastic layer periodically clamped to
+bedrock.
+We identify zero frequency stop-bands that only exist in the limit of columns
+of concrete clamped at their base to the bedrock. In a realistic configuration
+of a sedimentary basin 15 meters deep we observe a zero frequency stop-band
+covering a broad frequency range of $0$ to $30$ Hz.
+",0,1,0,0,0,0
+210,Difference analogue of second main theorems for meromorphic mapping into algebraic variety,"  In this paper, we prove some difference analogue of second main theorems of
+meromorphic mapping from Cm into an algebraic variety V intersecting a finite
+set of fixed hypersurfaces in subgeneral position. As an application, we prove
+a result on algebraically degenerate of holomorphic curves intersecting
+hypersurfaces and difference analogue of Picard's theorem on holomorphic
+curves. Furthermore, we obtain a second main theorem of meromorphic mappings
+intersecting hypersurfaces in N-subgeneral position for Veronese embedding in
+Pn(C) and a uniqueness theorem sharing hypersurfaces.
+",0,0,1,0,0,0
+211,An Effective Way to Improve YouTube-8M Classification Accuracy in Google Cloud Platform,"  Large-scale datasets have played a significant role in progress of neural
+network and deep learning areas. YouTube-8M is such a benchmark dataset for
+general multi-label video classification. It was created from over 7 million
+YouTube videos (450,000 hours of video) and includes video labels from a
+vocabulary of 4716 classes (3.4 labels/video on average). It also comes with
+pre-extracted audio & visual features from every second of video (3.2 billion
+feature vectors in total). Google cloud recently released the datasets and
+organized 'Google Cloud & YouTube-8M Video Understanding Challenge' on Kaggle.
+Competitors are challenged to develop classification algorithms that assign
+video-level labels using the new and improved Youtube-8M V2 dataset. Inspired
+by the competition, we started exploration of audio understanding and
+classification using deep learning algorithms and ensemble methods. We built
+several baseline predictions according to the benchmark paper and public github
+tensorflow code. Furthermore, we improved global prediction accuracy (GAP) from
+base level 77% to 80.7% through approaches of ensemble.
+",1,0,0,1,0,0
+212,Experimental Design of a Prescribed Burn Instrumentation,"  Observational data collected during experiments, such as the planned Fire and
+Smoke Model Evaluation Experiment (FASMEE), are critical for progressing and
+transitioning coupled fire-atmosphere models like WRF-SFIRE and WRF-SFIRE-CHEM
+into operational use. Historical meteorological data, representing typical
+weather conditions for the anticipated burn locations and times, have been
+processed to initialize and run a set of simulations representing the planned
+experimental burns. Based on an analysis of these numerical simulations, this
+paper provides recommendations on the experimental setup that include the
+ignition procedures, size and duration of the burns, and optimal sensor
+placement. New techniques are developed to initialize coupled fire-atmosphere
+simulations with weather conditions typical of the planned burn locations and
+time of the year. Analysis of variation and sensitivity analysis of simulation
+design to model parameters by repeated Latin Hypercube Sampling are used to
+assess the locations of the sensors. The simulations provide the locations of
+the measurements that maximize the expected variation of the sensor outputs
+with the model parameters.
+",0,0,0,1,0,0
+213,Seifert surgery on knots via Reidemeister torsion and Casson-Walker-Lescop invariant III,"  For a knot $K$ in a homology $3$-sphere $\Sigma$, let $M$ be the result of
+$2/q$-surgery on $K$, and let $X$ be the universal abelian covering of $M$. Our
+first theorem is that if the first homology of $X$ is finite cyclic and $M$ is
+a Seifert fibered space with $N\ge 3$ singular fibers, then $N\ge 4$ if and
+only if the first homology of the universal abelian covering of $X$ is
+infinite. Our second theorem is that under an appropriate assumption on the
+Alexander polynomial of $K$, if $M$ is a Seifert fibered space, then $q=\pm 1$
+(i.e.\ integral surgery).
+",0,0,1,0,0,0
+214,Sparse mean localization by information theory,"  Sparse feature selection is necessary when we fit statistical models, we have
+access to a large group of features, don't know which are relevant, but assume
+that most are not. Alternatively, when the number of features is larger than
+the available data the model becomes over parametrized and the sparse feature
+selection task involves selecting the most informative variables for the model.
+When the model is a simple location model and the number of relevant features
+does not grow with the total number of features, sparse feature selection
+corresponds to sparse mean estimation. We deal with a simplified mean
+estimation problem consisting of an additive model with gaussian noise and mean
+that is in a restricted, finite hypothesis space. This restriction simplifies
+the mean estimation problem into a selection problem of combinatorial nature.
+Although the hypothesis space is finite, its size is exponential in the
+dimension of the mean. In limited data settings and when the size of the
+hypothesis space depends on the amount of data or on the dimension of the data,
+choosing an approximation set of hypotheses is a desirable approach. Choosing a
+set of hypotheses instead of a single one implies replacing the bias-variance
+trade off with a resolution-stability trade off. Generalization capacity
+provides a resolution selection criterion based on allowing the learning
+algorithm to communicate the largest amount of information in the data to the
+learner without error. In this work the theory of approximation set coding and
+generalization capacity is explored in order to understand this approach. We
+then apply the generalization capacity criterion to the simplified sparse mean
+estimation problem and detail an importance sampling algorithm which at once
+solves the difficulty posed by large hypothesis spaces and the slow convergence
+of uniform sampling algorithms.
+",0,0,0,1,0,0
+215,Joint Power and Admission Control based on Channel Distribution Information: A Novel Two-Timescale Approach,"  In this letter, we consider the joint power and admission control (JPAC)
+problem by assuming that only the channel distribution information (CDI) is
+available. Under this assumption, we formulate a new chance (probabilistic)
+constrained JPAC problem, where the signal to interference plus noise ratio
+(SINR) outage probability of the supported links is enforced to be not greater
+than a prespecified tolerance. To efficiently deal with the chance SINR
+constraint, we employ the sample approximation method to convert them into
+finitely many linear constraints. Then, we propose a convex approximation based
+deflation algorithm for solving the sample approximation JPAC problem. Compared
+to the existing works, this letter proposes a novel two-timescale JPAC
+approach, where admission control is performed by the proposed deflation
+algorithm based on the CDI in a large timescale and transmission power is
+adapted instantly with fast fadings in a small timescale. The effectiveness of
+the proposed algorithm is illustrated by simulations.
+",1,0,1,0,0,0
+216,A Closer Look at the Alpha Persei Coronal Conundrum,"  A ROSAT survey of the Alpha Per open cluster in 1993 detected its brightest
+star, mid-F supergiant Alpha Persei: the X-ray luminosity and spectral hardness
+were similar to coronally active late-type dwarf members. Later, in 2010, a
+Hubble Cosmic Origins Spectrograph SNAPshot of Alpha Persei found
+far-ultraviolet coronal proxy SiIV unexpectedly weak. This, and a suspicious
+offset of the ROSAT source, suggested that a late-type companion might be
+responsible for the X-rays. Recently, a multi-faceted program tested that
+premise. Groundbased optical coronography, and near-UV imaging with HST Wide
+Field Camera 3, searched for any close-in faint candidate coronal objects, but
+without success. Then, a Chandra pointing found the X-ray source single and
+coincident with the bright star. Significantly, the SiIV emissions of Alpha
+Persei, in a deeper FUV spectrum collected by HST COS as part of the joint
+program, aligned well with chromospheric atomic oxygen (which must be intrinsic
+to the luminous star), within the context of cooler late-F and early-G
+supergiants, including Cepheid variables. This pointed to the X-rays as the
+fundamental anomaly. The over-luminous X-rays still support the case for a
+hyperactive dwarf secondary, albeit now spatially unresolved. However, an
+alternative is that Alpha Persei represents a novel class of coronal source.
+Resolving the first possibility now has become more difficult, because the easy
+solution -- a well separated companion -- has been eliminated. Testing the
+other possibility will require a broader high-energy census of the early-F
+supergiants.
+",0,1,0,0,0,0
+217,The challenge of realistic music generation: modelling raw audio at scale,"  Realistic music generation is a challenging task. When building generative
+models of music that are learnt from data, typically high-level representations
+such as scores or MIDI are used that abstract away the idiosyncrasies of a
+particular performance. But these nuances are very important for our perception
+of musicality and realism, so in this work we embark on modelling music in the
+raw audio domain. It has been shown that autoregressive models excel at
+generating raw audio waveforms of speech, but when applied to music, we find
+them biased towards capturing local signal structure at the expense of
+modelling long-range correlations. This is problematic because music exhibits
+structure at many different timescales. In this work, we explore autoregressive
+discrete autoencoders (ADAs) as a means to enable autoregressive models to
+capture long-range correlations in waveforms. We find that they allow us to
+unconditionally generate piano music directly in the raw audio domain, which
+shows stylistic consistency across tens of seconds.
+",0,0,0,1,0,0
+218,Interpretations of family size distributions: The Datura example,"  Young asteroid families are unique sources of information about fragmentation
+physics and the structure of their parent bodies, since their physical
+properties have not changed much since their birth. Families have different
+properties such as age, size, taxonomy, collision severity and others, and
+understanding the effect of those properties on our observations of the
+size-frequency distribution (SFD) of family fragments can give us important
+insights into the hypervelocity collision processes at scales we cannot achieve
+in our laboratories. Here we take as an example the very young Datura family,
+with a small 8-km parent body, and compare its size distribution to other
+families, with both large and small parent bodies, and created by both
+catastrophic and cratering formation events. We conclude that most likely
+explanation for the shallower size distribution compared to larger families is
+a more pronounced observational bias because of its small size. Its size
+distribution is perfectly normal when its parent body size is taken into
+account. We also discuss some other possibilities. In addition, we study
+another common feature: an offset or ""bump"" in the distribution occurring for a
+few of the larger elements. We hypothesize that it can be explained by a newly
+described regime of cratering, ""spall cratering"", which controls the majority
+of impact craters on the surface of small asteroids like Datura.
+",0,1,0,0,0,0
+219,"Intersections of $ω$ classes in $\overline{\mathcal{M}}_{g,n}$","  We provide a graph formula which describes an arbitrary monomial in {\omega}
+classes (also referred to as stable {\psi} classes) in terms of a simple family
+of dual graphs (pinwheel graphs) with edges decorated by rational functions in
+{\psi} classes. We deduce some numerical consequences and in particular a
+combinatorial formula expressing top intersections of \k{appa} classes on Mg in
+terms of top intersections of {\psi} classes.
+",0,0,1,0,0,0
+220,GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging,"  Tomography has made a radical impact on diverse fields ranging from the study
+of 3D atomic arrangements in matter to the study of human health in medicine.
+Despite its very diverse applications, the core of tomography remains the same,
+that is, a mathematical method must be implemented to reconstruct the 3D
+structure of an object from a number of 2D projections. In many scientific
+applications, however, the number of projections that can be measured is
+limited due to geometric constraints, tolerable radiation dose and/or
+acquisition speed. Thus it becomes an important problem to obtain the
+best-possible reconstruction from a limited number of projections. Here, we
+present the mathematical implementation of a tomographic algorithm, termed
+GENeralized Fourier Iterative REconstruction (GENFIRE). By iterating between
+real and reciprocal space, GENFIRE searches for a global solution that is
+concurrently consistent with the measured data and general physical
+constraints. The algorithm requires minimal human intervention and also
+incorporates angular refinement to reduce the tilt angle error. We demonstrate
+that GENFIRE can produce superior results relative to several other popular
+tomographic reconstruction techniques by numerical simulations, and by
+experimentally by reconstructing the 3D structure of a porous material and a
+frozen-hydrated marine cyanobacterium. Equipped with a graphical user
+interface, GENFIRE is freely available from our website and is expected to find
+broad applications across different disciplines.
+",0,1,0,0,0,0
+221,GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium,"  Generative Adversarial Networks (GANs) excel at creating realistic images
+with complex models for which maximum likelihood is infeasible. However, the
+convergence of GAN training has still not been proved. We propose a two
+time-scale update rule (TTUR) for training GANs with stochastic gradient
+descent on arbitrary GAN loss functions. TTUR has an individual learning rate
+for both the discriminator and the generator. Using the theory of stochastic
+approximation, we prove that the TTUR converges under mild assumptions to a
+stationary local Nash equilibrium. The convergence carries over to the popular
+Adam optimization, for which we prove that it follows the dynamics of a heavy
+ball with friction and thus prefers flat minima in the objective landscape. For
+the evaluation of the performance of GANs at image generation, we introduce the
+""Fréchet Inception Distance"" (FID) which captures the similarity of generated
+images to real ones better than the Inception Score. In experiments, TTUR
+improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP)
+outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN
+Bedrooms, and the One Billion Word Benchmark.
+",1,0,0,1,0,0
+222,"SPIRou Input Catalog: Activity, Rotation and Magnetic Field of Cool Dwarfs","  Based on optical high-resolution spectra obtained with CFHT/ESPaDOnS, we
+present new measurements of activity and magnetic field proxies of 442 low-mass
+K5-M7 dwarfs. The objects were analysed as potential targets to search for
+planetary-mass companions with the new spectropolarimeter and high-precision
+velocimeter, SPIRou. We have analysed their high-resolution spectra in an
+homogeneous way: circular polarisation, chromospheric features, and Zeeman
+broadening of the FeH infrared line. The complex relationship between these
+activity indicators is analysed: while no strong connection is found between
+the large-scale and small-scale magnetic fields, the latter relates with the
+non-thermal flux originating in the chromosphere.
+We then examine the relationship between various activity diagnostics and the
+optical radial-velocity jitter available in the literature, especially for
+planet host stars. We use this to derive for all stars an activity merit
+function (higher for quieter stars) with the goal of identifying the most
+favorable stars where the radial-velocity jitter is low enough for planet
+searches. We find that the main contributors to the RV jitter are the
+large-scale magnetic field and the chromospheric non-thermal emission.
+In addition, three stars (GJ 1289, GJ 793, and GJ 251) have been followed
+along their rotation using the spectropolarimetric mode, and we derive their
+magnetic topology. These very slow rotators are good representatives of future
+SPIRou targets. They are compared to other stars where the magnetic topology is
+also known. The poloidal component of the magnetic field is predominent in all
+three stars.
+",0,1,0,0,0,0
+223,Objective Procedure for Reconstructing Couplings in Complex Systems,"  Inferring directional connectivity from point process data of multiple
+elements is desired in various scientific fields such as neuroscience,
+geography, economics, etc. Here, we propose an inference procedure for this
+goal based on the kinetic Ising model. The procedure is composed of two steps:
+(1) determination of the time-bin size for transforming the point-process data
+to discrete time binary data and (2) screening of relevant couplings from the
+estimated networks. For these, we develop simple methods based on information
+theory and computational statistics. Applications to data from artificial and
+\textit{in vitro} neuronal networks show that the proposed procedure performs
+fairly well when identifying relevant couplings, including the discrimination
+of their signs, with low computational cost. These results highlight the
+potential utility of the kinetic Ising model to analyze real interacting
+systems with event occurrences.
+",0,0,0,0,1,0
+224,Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach,"  Support vector machines (SVMs) are an important tool in modern data analysis.
+Traditionally, support vector machines have been fitted via quadratic
+programming, either using purpose-built or off-the-shelf algorithms. We present
+an alternative approach to SVM fitting via the majorization--minimization (MM)
+paradigm. Algorithms that are derived via MM algorithm constructions can be
+shown to monotonically decrease their objectives at each iteration, as well as
+be globally convergent to stationary points. We demonstrate the construction of
+iteratively-reweighted least-squares (IRLS) algorithms, via the MM paradigm,
+for SVM risk minimization problems involving the hinge, least-square,
+squared-hinge, and logistic losses, and 1-norm, 2-norm, and elastic net
+penalizations. Successful implementations of our algorithms are presented via
+some numerical examples.
+",1,0,0,1,0,0
+225,Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries,"  Estimating vaccination uptake is an integral part of ensuring public health.
+It was recently shown that vaccination uptake can be estimated automatically
+from web data, instead of slowly collected clinical records or population
+surveys. All prior work in this area assumes that features of vaccination
+uptake collected from the web are temporally regular. We present the first ever
+method to remove this assumption from vaccination uptake estimation: our method
+dynamically adapts to temporal fluctuations in time series web data used to
+estimate vaccination uptake. We show our method to outperform the state of the
+art compared to competitive baselines that use not only web data but also
+curated clinical data. This performance improvement is more pronounced for
+vaccines whose uptake has been irregular due to negative media attention (HPV-1
+and HPV-2), problems in vaccine supply (DiTeKiPol), and targeted at children of
+12 years old (whose vaccination is more irregular compared to younger
+children).
+",1,0,0,1,0,0
+226,Over Recurrence for Mixing Transformations,"  We show that every invertible strong mixing transformation on a Lebesgue
+space has strictly over-recurrent sets. Also, we give an explicit procedure for
+constructing strong mixing transformations with no under-recurrent sets. This
+answers both parts of a question of V. Bergelson.
+We define $\epsilon$-over-recurrence and show that given $\epsilon > 0$, any
+ergodic measure preserving invertible transformation (including discrete
+spectrum) has $\epsilon$-over-recurrent sets of arbitrarily small measure.
+Discrete spectrum transformations and rotations do not have over-recurrent
+sets, but we construct a weak mixing rigid transformation with strictly
+over-recurrent sets.
+",0,0,1,0,0,0
+227,Joint Atlas-Mapping of Multiple Histological Series combined with Multimodal MRI of Whole Marmoset Brains,"  Development of a mesoscale neural circuitry map of the common marmoset is an
+essential task due to the ideal characteristics of the marmoset as a model
+organism for neuroscience research. To facilitate this development there is a
+need for new computational tools to cross-register multi-modal data sets
+containing MRI volumes as well as multiple histological series, and to register
+the combined data set to a common reference atlas. We present a fully automatic
+pipeline for same-subject-MRI guided reconstruction of image volumes from a
+series of histological sections of different modalities, followed by
+diffeomorphic mapping to a reference atlas. We show registration results for
+Nissl, myelin, CTB, and fluorescent tracer images using a same-subject ex-vivo
+MRI as our reference and show that our method achieves accurate registration
+and eliminates artifactual warping that may be result from the absence of a
+reference MRI data set. Examination of the determinant of the local metric
+tensor of the diffeomorphic mapping between each subject's ex-vivo MRI and
+resultant Nissl reconstruction allows an unprecedented local quantification of
+geometrical distortions resulting from the histological processing, showing a
+slight shrinkage, a median linear scale change of ~-1% in going from the
+ex-vivo MRI to the tape-transfer generated histological image data.
+",0,0,0,0,1,0
+228,A Practical Approach for Successive Omniscience,"  The system that we study in this paper contains a set of users that observe a
+discrete memoryless multiple source and communicate via noise-free channels
+with the aim of attaining omniscience, the state that all users recover the
+entire multiple source. We adopt the concept of successive omniscience (SO),
+i.e., letting the local omniscience in some user subset be attained before the
+global omniscience in the entire system, and consider the problem of how to
+efficiently attain omniscience in a successive manner. Based on the existing
+results on SO, we propose a CompSetSO algorithm for determining a complimentary
+set, a user subset in which the local omniscience can be attained first without
+increasing the sum-rate, the total number of communications, for the global
+omniscience. We also derive a sufficient condition for a user subset to be
+complimentary so that running the CompSetSO algorithm only requires a lower
+bound, instead of the exact value, of the minimum sum-rate for attaining global
+omniscience. The CompSetSO algorithm returns a complimentary user subset in
+polynomial time. We show by example how to recursively apply the CompSetSO
+algorithm so that the global omniscience can be attained by multi-stages of SO.
+",1,0,0,0,0,0
+229,Scholars on Twitter: who and how many are they?,"  In this paper we present a novel methodology for identifying scholars with a
+Twitter account. By combining bibliometric data from Web of Science and Twitter
+users identified by Altmetric.com we have obtained the largest set of
+individual scholars matched with Twitter users made so far. Our methodology
+consists of a combination of matching algorithms, considering different
+linguistic elements of both author names and Twitter names; followed by a
+rule-based scoring system that weights the common occurrence of several
+elements related with the names, individual elements and activities of both
+Twitter users and scholars matched. Our results indicate that about 2% of the
+overall population of scholars in the Web of Science is active on Twitter. By
+domain we find a strong presence of researchers from the Social Sciences and
+the Humanities. Natural Sciences is the domain with the lowest level of
+scholars on Twitter. Researchers on Twitter also tend to be younger than those
+that are not on Twitter. As this is a bibliometric-based approach, it is
+important to highlight the reliance of the method on the number of publications
+produced and tweeted by the scholars, thus the share of scholars on Twitter
+ranges between 1% and 5% depending on their level of productivity. Further
+research is suggested in order to improve and expand the methodology.
+",1,0,0,0,0,0
+230,General notions of regression depth function,"  As a measure for the centrality of a point in a set of multivariate data,
+statistical depth functions play important roles in multivariate analysis,
+because one may conveniently construct descriptive as well as inferential
+procedures relying on them. Many depth notions have been proposed in the
+literature to fit to different applications. However, most of them are mainly
+developed for the location setting. In this paper, we discuss the possibility
+of extending some of them into the regression setting. A general concept of
+regression depth function is also provided.
+",0,0,0,1,0,0
+231,Photonic topological pumping through the edges of a dynamical four-dimensional quantum Hall system,"  When a two-dimensional electron gas is exposed to a perpendicular magnetic
+field and an in-plane electric field, its conductance becomes quantized in the
+transverse in-plane direction: this is known as the quantum Hall (QH) effect.
+This effect is a result of the nontrivial topology of the system's electronic
+band structure, where an integer topological invariant known as the first Chern
+number leads to the quantization of the Hall conductance. Interestingly, it was
+shown that the QH effect can be generalized mathematically to four spatial
+dimensions (4D), but this effect has never been realized for the obvious reason
+that experimental systems are bound to three spatial dimensions. In this work,
+we harness the high tunability and control offered by photonic waveguide arrays
+to experimentally realize a dynamically-generated 4D QH system using a 2D array
+of coupled optical waveguides. The inter-waveguide separation is constructed
+such that the propagation of light along the device samples over
+higher-dimensional momenta in the directions orthogonal to the two physical
+dimensions, thus realizing a 2D topological pump. As a result, the device's
+band structure is associated with 4D topological invariants known as second
+Chern numbers which support a quantized bulk Hall response with a 4D symmetry.
+In a finite-sized system, the 4D topological bulk response is carried by
+localized edges modes that cross the sample as a function of of the modulated
+auxiliary momenta. We directly observe this crossing through photon pumping
+from edge-to-edge and corner-to-corner of our system. These are equivalent to
+the pumping of charge across a 4D system from one 3D hypersurface to the
+opposite one and from one 2D hyperedge to another, and serve as first
+experimental realization of higher-dimensional topological physics.
+",0,1,0,0,0,0
+232,On Scalable Inference with Stochastic Gradient Descent,"  In many applications involving large dataset or online updating, stochastic
+gradient descent (SGD) provides a scalable way to compute parameter estimates
+and has gained increasing popularity due to its numerical convenience and
+memory efficiency. While the asymptotic properties of SGD-based estimators have
+been established decades ago, statistical inference such as interval estimation
+remains much unexplored. The traditional resampling method such as the
+bootstrap is not computationally feasible since it requires to repeatedly draw
+independent samples from the entire dataset. The plug-in method is not
+applicable when there are no explicit formulas for the covariance matrix of the
+estimator. In this paper, we propose a scalable inferential procedure for
+stochastic gradient descent, which, upon the arrival of each observation,
+updates the SGD estimate as well as a large number of randomly perturbed SGD
+estimates. The proposed method is easy to implement in practice. We establish
+its theoretical properties for a general class of models that includes
+generalized linear models and quantile regression models as special cases. The
+finite-sample performance and numerical utility is evaluated by simulation
+studies and two real data applications.
+",1,0,0,1,0,0
+233,The g-Good-Neighbor Conditional Diagnosability of Locally Twisted Cubes,"  In the work of Peng et al. in 2012, a new measure was proposed for fault
+diagnosis of systems: namely, g-good-neighbor conditional diagnosability, which
+requires that any fault-free vertex has at least g fault-free neighbors in the
+system. In this paper, we establish the g-good-neighbor conditional
+diagnosability of locally twisted cubes under the PMC model and the MM^* model.
+",1,0,1,0,0,0
+234,Coherence for lenses and open games,"  Categories of polymorphic lenses in computer science, and of open games in
+compositional game theory, have a curious structure that is reminiscent of
+compact closed categories, but differs in some crucial ways. Specifically they
+have a family of morphisms that behave like the counits of a compact closed
+category, but have no corresponding units; and they have a `partial' duality
+that behaves like transposition in a compact closed category when it is
+defined. We axiomatise this structure, which we refer to as a `teleological
+category'. We precisely define a diagrammatic language suitable for these
+categories, and prove a coherence theorem for them. This underpins the use of
+diagrammatic reasoning in compositional game theory, which has previously been
+used only informally.
+",1,0,0,0,0,0
+235,Streaming Algorithm for Euler Characteristic Curves of Multidimensional Images,"  We present an efficient algorithm to compute Euler characteristic curves of
+gray scale images of arbitrary dimension. In various applications the Euler
+characteristic curve is used as a descriptor of an image.
+Our algorithm is the first streaming algorithm for Euler characteristic
+curves. The usage of streaming removes the necessity to store the entire image
+in RAM. Experiments show that our implementation handles terabyte scale images
+on commodity hardware. Due to lock-free parallelism, it scales well with the
+number of processor cores. Our software---CHUNKYEuler---is available as open
+source on Bitbucket.
+Additionally, we put the concept of the Euler characteristic curve in the
+wider context of computational topology. In particular, we explain the
+connection with persistence diagrams.
+",1,0,1,0,0,0
+236,An automata group of intermediate growth and exponential activity,"  We give a new example of an automata group of intermediate growth. It is
+generated by an automaton with 4 states on an alphabet with 8 letters. This
+automata group has exponential activity and its limit space is not simply
+connected.
+",0,0,1,0,0,0
+237,Tuning across the BCS-BEC crossover in the multiband superconductor Fe$_{1+y}$Se$_x$Te$_{1-x}$ : An angle-resolved photoemission study,"  The crossover from Bardeen-Cooper-Schrieffer (BCS) superconductivity to
+Bose-Einstein condensation (BEC) is difficult to realize in quantum materials
+because, unlike in ultracold atoms, one cannot tune the pairing interaction. We
+realize the BCS-BEC crossover in a nearly compensated semimetal
+Fe$_{1+y}$Se$_x$Te$_{1-x}$ by tuning the Fermi energy, $\epsilon_F$, via
+chemical doping, which permits us to systematically change $\Delta /
+\epsilon_F$ from 0.16 to 0.5 were $\Delta$ is the superconducting (SC) gap. We
+use angle-resolved photoemission spectroscopy to measure the Fermi energy, the
+SC gap and characteristic changes in the SC state electronic dispersion as the
+system evolves from a BCS to a BEC regime. Our results raise important
+questions about the crossover in multiband superconductors which go beyond
+those addressed in the context of cold atoms.
+",0,1,0,0,0,0
+238,GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking,"  Model compression is essential for serving large deep neural nets on devices
+with limited resources or applications that require real-time responses. As a
+case study, a state-of-the-art neural language model usually consists of one or
+more recurrent layers sandwiched between an embedding layer used for
+representing input tokens and a softmax layer for generating output tokens. For
+problems with a very large vocabulary size, the embedding and the softmax
+matrices can account for more than half of the model size. For instance, the
+bigLSTM model achieves state-of- the-art performance on the One-Billion-Word
+(OBW) dataset with around 800k vocabulary, and its word embedding and softmax
+matrices use more than 6GBytes space, and are responsible for over 90% of the
+model parameters. In this paper, we propose GroupReduce, a novel compression
+method for neural language models, based on vocabulary-partition (block) based
+low-rank matrix approximation and the inherent frequency distribution of tokens
+(the power-law distribution of words). The experimental results show our method
+can significantly outperform traditional compression methods such as low-rank
+approximation and pruning. On the OBW dataset, our method achieved 6.6 times
+compression rate for the embedding and softmax matrices, and when combined with
+quantization, our method can achieve 26 times compression rate, which
+translates to a factor of 12.8 times compression for the entire model with very
+little degradation in perplexity.
+",0,0,0,1,0,0
+239,Morphological characterization of Ge ion implanted SiO2 matrix using multifractal technique,"  200 nm thick SiO2 layers grown on Si substrates and Ge ions of 150 keV energy
+were implanted into SiO2 matrix with Different fluences. The implanted samples
+were annealed at 950 C for 30 minutes in Ar ambience. Topographical studies of
+implanted as well as annealed samples were captured by the atomic force
+microscopy (AFM). Two dimension (2D) multifractal detrended fluctuation
+analysis (MFDFA) based on the partition function approach has been used to
+study the surfaces of ion implanted and annealed samples. The partition
+function is used to calculate generalized Hurst exponent with the segment size.
+Moreover, it is seen that the generalized Hurst exponents vary nonlinearly with
+the moment, thereby exhibiting the multifractal nature. The multifractality of
+surface is pronounced after annealing for the surface implanted with fluence
+7.5X1016 ions/cm^2.
+",0,1,0,0,0,0
+240,Preliminary corrosion studies of IN-RAFM steel with stagnant Lead Lithium at 550 C,"  Corrosion of Indian RAFMS (reduced activation ferritic martensitic steel)
+material with liquid metal, Lead Lithium ( Pb-Li) has been studied under static
+condition, maintaining Pb-Li at 550 C for different time durations, 2500, 5000
+and 9000 hours. Corrosion rate was calculated from weight loss measurements.
+Microstructure analysis was carried out using SEM and chemical composition by
+SEM-EDX measurements. Micro Vickers hardness and tensile testing were also
+carried out. Chromium was found leaching from the near surface regions and
+surface hardness was found to decrease in all the three cases. Grain boundaries
+were affected. Some grains got detached from the surface giving rise to pebble
+like structures in the surface micrographs. There was no significant reduction
+in the tensile strength, after exposure to liquid metal. This paper discusses
+the experimental details and the results obtained.
+",0,1,0,0,0,0
+241,Magnetocapillary self-assemblies: locomotion and micromanipulation along a liquid interface,"  This paper presents an overview and discussion of magnetocapillary
+self-assemblies. New results are presented, in particular concerning the
+possible development of future applications. These self-organizing structures
+possess the notable ability to move along an interface when powered by an
+oscillatory, uniform magnetic field. The system is constructed as follows. Soft
+magnetic particles are placed on a liquid interface, and submitted to a
+magnetic induction field. An attractive force due to the curvature of the
+interface around the particles competes with an interaction between magnetic
+dipoles. Ordered structures can spontaneously emerge from these conditions.
+Furthermore, time-dependent magnetic fields can produce a wide range of dynamic
+behaviours, including non-time-reversible deformation sequences that produce
+translational motion at low Reynolds number. In other words, due to a
+spontaneous breaking of time-reversal symmetry, the assembly can turn into a
+surface microswimmer. Trajectories have been shown to be precisely
+controllable. As a consequence, this system offers a way to produce microrobots
+able to perform different tasks. This is illustrated in this paper by the
+capture, transport and release of a floating cargo, and the controlled mixing
+of fluids at low Reynolds number.
+",0,1,0,0,0,0
+242,On asymptotically minimax nonparametric detection of signal in Gaussian white noise,"  For the problem of nonparametric detection of signal in Gaussian white noise
+we point out strong asymptotically minimax tests. The sets of alternatives are
+a ball in Besov space $B^r_{2\infty}$ with ""small"" balls in $L_2$ removed.
+",0,0,1,1,0,0
+243,Bayesian Metabolic Flux Analysis reveals intracellular flux couplings,"  Metabolic flux balance analyses are a standard tool in analysing metabolic
+reaction rates compatible with measurements, steady-state and the metabolic
+reaction network stoichiometry. Flux analysis methods commonly place
+unrealistic assumptions on fluxes due to the convenience of formulating the
+problem as a linear programming model, and most methods ignore the notable
+uncertainty in flux estimates. We introduce a novel paradigm of Bayesian
+metabolic flux analysis that models the reactions of the whole genome-scale
+cellular system in probabilistic terms, and can infer the full flux vector
+distribution of genome-scale metabolic systems based on exchange and
+intracellular (e.g. 13C) flux measurements, steady-state assumptions, and
+target function assumptions. The Bayesian model couples all fluxes jointly
+together in a simple truncated multivariate posterior distribution, which
+reveals informative flux couplings. Our model is a plug-in replacement to
+conventional metabolic balance methods, such as flux balance analysis (FBA).
+Our experiments indicate that we can characterise the genome-scale flux
+covariances, reveal flux couplings, and determine more intracellular unobserved
+fluxes in C. acetobutylicum from 13C data than flux variability analysis. The
+COBRA compatible software is available at github.com/markusheinonen/bamfa
+",0,0,0,1,0,0
+244,Robust Estimation of Change-Point Location,"  We introduce a robust estimator of the location parameter for the
+change-point in the mean based on the Wilcoxon statistic and establish its
+consistency for $L_1$ near epoch dependent processes. It is shown that the
+consistency rate depends on the magnitude of change. A simulation study is
+performed to evaluate finite sample properties of the Wilcoxon-type estimator
+in standard cases, as well as under heavy-tailed distributions and disturbances
+by outliers, and to compare it with a CUSUM-type estimator. It shows that the
+Wilcoxon-type estimator is equivalent to the CUSUM-type estimator in standard
+cases, but outperforms the CUSUM-type estimator in presence of heavy tails or
+outliers in the data.
+",0,0,1,1,0,0
+245,Growing length scale accompanying the vitrification: A perspective based on non-singular density fluctuations,"  In glass forming liquids close to the glass transition point, even a very
+slight increase in the macroscopic density results in a dramatic slowing down
+of the macroscopic relaxation. Concomitantly, the local density itself
+fluctuates in space. Therefore, one can imagine that even very small local
+density variations control the local glassy nature. Based on this perspective,
+a model for describing growing length scale accompanying the vitrification is
+introduced, in which we assume that in a subsystem whose density is above a
+certain threshold value, $\rho_{\rm c}$, owing to steric constraints, particle
+rearrangements are highly suppressed for a sufficiently long time period
+($\sim$ structural relaxation time). We regard such a subsystem as a glassy
+cluster. Then, based on the statistics of the subsystem-density, we predict
+that with compression (increasing average density $\rho$) at a fixed
+temperature $T$ in supercooled states, the characteristic length of the
+clusters, $\xi$, diverges as $\xi\sim(\rho_{\rm c}-\rho)^{-2/d}$, where $d$ is
+the spatial dimensionality. This $\xi$ measures the average persistence length
+of the steric constraints in blocking the rearrangement motions and is
+determined by the subsystem density. Additionally, with decreasing $T$ at a
+fixed $\rho$, the length scale diverges in the same manner as $\xi\sim(T-T_{\rm
+c})^{-2/d}$, for which $\rho$ is identical to $\rho_{\rm c}$ at $T=T_{\rm c}$.
+The exponent describing the diverging length scale is the same as the one
+predicted by some theoretical models and indeed has been observed in some
+simulations and experiments. However, the basic mechanism for this divergence
+is different; that is, we do not invoke thermodynamic anomalies associated with
+the thermodynamic phase transition as the origin of the growing length scale.
+We further present arguements for the cooperative properties based on the
+clusters.
+",0,1,0,0,0,0
+246,Many-Objective Pareto Local Search,"  We propose a new Pareto Local Search Algorithm for the many-objective
+combinatorial optimization. Pareto Local Search proved to be a very effective
+tool in the case of the bi-objective combinatorial optimization and it was used
+in a number of the state-of-the-art algorithms for problems of this kind. On
+the other hand, the standard Pareto Local Search algorithm becomes very
+inefficient for problems with more than two objectives. We build an effective
+Many-Objective Pareto Local Search algorithm using three new mechanisms: the
+efficient update of large Pareto archives with ND-Tree data structure, a new
+mechanism for the selection of the promising solutions for the neighborhood
+exploration, and a partial exploration of the neighborhoods. We apply the
+proposed algorithm to the instances of two different problems, i.e. the
+traveling salesperson problem and the traveling salesperson problem with
+profits with up to 5 objectives showing high effectiveness of the proposed
+algorithm.
+",1,0,0,0,0,0
+247,From Natural to Artificial Camouflage: Components and Systems,"  We identify the components of bio-inspired artificial camouflage systems
+including actuation, sensing, and distributed computation. After summarizing
+recent results in understanding the physiology and system-level performance of
+a variety of biological systems, we describe computational algorithms that can
+generate similar patterns and have the potential for distributed
+implementation. We find that the existing body of work predominately treats
+component technology in an isolated manner that precludes a material-like
+implementation that is scale-free and robust. We conclude with open research
+challenges towards the realization of integrated camouflage solutions.
+",1,0,0,0,1,0
+248,Bayesian nonparametric inference for the M/G/1 queueing systems based on the marked departure process,"  In the present work we study Bayesian nonparametric inference for the
+continuous-time M/G/1 queueing system. In the focus of the study is the
+unobservable service time distribution. We assume that the only available data
+of the system are the marked departure process of customers with the marks
+being the queue lengths just after departure instants. These marks constitute
+an embedded Markov chain whose distribution may be parametrized by stochastic
+matrices of a special delta form. We develop the theory in order to obtain
+integral mixtures of Markov measures with respect to suitable prior
+distributions. We have found a sufficient statistic with a distribution of a
+so-called S-structure sheding some new light on the inner statistical structure
+of the M/G/1 queue. Moreover, it allows to update suitable prior distributions
+to the posterior. Our inference methods are validated by large sample results
+as posterior consistency and posterior normality.
+",0,0,1,1,0,0
+249,On some polynomials and series of Bloch-Polya Type,"  We will show that $(1-q)(1-q^2)\dots (1-q^m)$ is a polynomial in $q$ with
+coefficients from $\{-1,0,1\}$ iff $m=1,\ 2,\ 3,$ or $5$ and explore some
+interesting consequences of this result. We find explicit formulas for the
+$q$-series coefficients of $(1-q^2)(1-q^3)(1-q^4)(1-q^5)\dots$ and
+$(1-q^3)(1-q^4)(1-q^5)(1-q^6)\dots$. In doing so, we extend certain
+observations made by Sudler in 1964. We also discuss the classification of the
+products $(1-q)(1-q^2)\dots (1-q^m)$ and some related series with respect to
+their absolute largest coefficients.
+",0,0,1,0,0,0
+250,"Improvement in the UAV position estimation with low-cost GPS, INS and vision-based system: Application to a quadrotor UAV","  In this paper, we develop a position estimation system for Unmanned Aerial
+Vehicles formed by hardware and software. It is based on low-cost devices: GPS,
+commercial autopilot sensors and dense optical flow algorithm implemented in an
+onboard microcomputer. Comparative tests were conducted using our approach and
+the conventional one, where only fusion of GPS and inertial sensors are used.
+Experiments were conducted using a quadrotor in two flying modes: hovering and
+trajectory tracking in outdoor environments. Results demonstrate the
+effectiveness of the proposed approach in comparison with the conventional
+approaches presented in the vast majority of commercial drones.
+",1,0,0,0,0,0
+251,Structured low rank decomposition of multivariate Hankel matrices,"  We study the decomposition of a multivariate Hankel matrix H\_$\sigma$ as a
+sum of Hankel matrices of small rank in correlation with the decomposition of
+its symbol $\sigma$ as a sum of polynomial-exponential series. We present a new
+algorithm to compute the low rank decomposition of the Hankel operator and the
+decomposition of its symbol exploiting the properties of the associated
+Artinian Gorenstein quotient algebra A\_$\sigma$. A basis of A\_$\sigma$ is
+computed from the Singular Value Decomposition of a sub-matrix of the Hankel
+matrix H\_$\sigma$. The frequencies and the weights are deduced from the
+generalized eigenvectors of pencils of shifted sub-matrices of H $\sigma$.
+Explicit formula for the weights in terms of the eigenvectors avoid us to solve
+a Vandermonde system. This new method is a multivariate generalization of the
+so-called Pencil method for solving Prony-type decomposition problems. We
+analyse its numerical behaviour in the presence of noisy input moments, and
+describe a rescaling technique which improves the numerical quality of the
+reconstruction for frequencies of high amplitudes. We also present a new Newton
+iteration, which converges locally to the closest multivariate Hankel matrix of
+low rank and show its impact for correcting errors on input moments.
+",0,0,1,0,0,0
+252,Linear time-periodic dynamical systems: An H2 analysis and a model reduction framework,"  Linear time-periodic (LTP) dynamical systems frequently appear in the
+modeling of phenomena related to fluid dynamics, electronic circuits, and
+structural mechanics via linearization centered around known periodic orbits of
+nonlinear models. Such LTP systems can reach orders that make repeated
+simulation or other necessary analysis prohibitive, motivating the need for
+model reduction.
+We develop here an algorithmic framework for constructing reduced models that
+retains the linear time-periodic structure of the original LTP system. Our
+approach generalizes optimal approaches that have been established previously
+for linear time-invariant (LTI) model reduction problems. We employ an
+extension of the usual H2 Hardy space defined for the LTI setting to
+time-periodic systems and within this broader framework develop an a posteriori
+error bound expressible in terms of related LTI systems. Optimization of this
+bound motivates our algorithm. We illustrate the success of our method on two
+numerical examples.
+",1,0,0,0,0,0
+253,Software metadata: How much is enough?,"  Broad efforts are underway to capture metadata about research software and
+retain it across services; notable in this regard is the CodeMeta project. What
+metadata are important to have about (research) software? What metadata are
+useful for searching for codes? What would you like to learn about astronomy
+software? This BoF sought to gather information on metadata most desired by
+researchers and users of astro software and others interested in registering,
+indexing, capturing, and doing research on this software. Information from this
+BoF could conceivably result in changes to the Astrophysics Source Code Library
+(ASCL) or other resources for the benefit of the community or provide input
+into other projects concerned with software metadata.
+",1,1,0,0,0,0
+254,A Categorical Approach for Recognizing Emotional Effects of Music,"  Recently, digital music libraries have been developed and can be plainly
+accessed. Latest research showed that current organization and retrieval of
+music tracks based on album information are inefficient. Moreover, they
+demonstrated that people use emotion tags for music tracks in order to search
+and retrieve them. In this paper, we discuss separability of a set of emotional
+labels, proposed in the categorical emotion expression, using Fisher's
+separation theorem. We determine a set of adjectives to tag music parts: happy,
+sad, relaxing, exciting, epic and thriller. Temporal, frequency and energy
+features have been extracted from the music parts. It could be seen that the
+maximum separability within the extracted features occurs between relaxing and
+epic music parts. Finally, we have trained a classifier using Support Vector
+Machines to automatically recognize and generate emotional labels for a music
+part. Accuracy for recognizing each label has been calculated; where the
+results show that epic music can be recognized more accurately (77.4%),
+comparing to the other types of music.
+",1,0,0,1,0,0
+255,Utilizing artificial neural networks to predict demand for weather-sensitive products at retail stores,"  One key requirement for effective supply chain management is the quality of
+its inventory management. Various inventory management methods are typically
+employed for different types of products based on their demand patterns,
+product attributes, and supply network. In this paper, our goal is to develop
+robust demand prediction methods for weather sensitive products at retail
+stores. We employ historical datasets from Walmart, whose customers and markets
+are often exposed to extreme weather events which can have a huge impact on
+sales regarding the affected stores and products. We want to accurately predict
+the sales of 111 potentially weather-sensitive products around the time of
+major weather events at 45 of Walmart retails locations in the U.S.
+Intuitively, we may expect an uptick in the sales of umbrellas before a big
+thunderstorm, but it is difficult for replenishment managers to predict the
+level of inventory needed to avoid being out-of-stock or overstock during and
+after that storm. While they rely on a variety of vendor tools to predict sales
+around extreme weather events, they mostly employ a time-consuming process that
+lacks a systematic measure of effectiveness. We employ all the methods critical
+to any analytics project and start with data exploration. Critical features are
+extracted from the raw historical dataset for demand forecasting accuracy and
+robustness. In particular, we employ Artificial Neural Network for forecasting
+demand for each product sold around the time of major weather events. Finally,
+we evaluate our model to evaluate their accuracy and robustness.
+",1,0,0,1,0,0
+256,Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry,"  We propose a deformable generator model to disentangle the appearance and
+geometric information from images into two independent latent vectors. The
+appearance generator produces the appearance information, including color,
+illumination, identity or category, of an image. The geometric generator
+produces displacement of the coordinates of each pixel and performs geometric
+warping, such as stretching and rotation, on the appearance generator to obtain
+the final synthesized image. The proposed model can learn both representations
+from image data in an unsupervised manner. The learned geometric generator can
+be conveniently transferred to the other image datasets to facilitate
+downstream AI tasks.
+",0,0,0,1,0,0
+257,Gaussian Kernel in Quantum Paradigm,"  The Gaussian kernel is a very popular kernel function used in many
+machine-learning algorithms, especially in support vector machines (SVM). For
+nonlinear training instances in machine learning, it often outperforms
+polynomial kernels in model accuracy. We use Gaussian kernel profoundly in
+formulating nonlinear classical SVM. In the recent research, P. Rebentrost
+et.al. discuss a very elegant quantum version of least square support vector
+machine using the quantum version of polynomial kernel, which is exponentially
+faster than the classical counterparts. In this paper, we have demonstrated a
+quantum version of the Gaussian kernel and analyzed its complexity in the
+context of quantum SVM. Our analysis shows that the computational complexity of
+the quantum Gaussian kernel is O(\epsilon^(-1)logN) with N-dimensional
+instances and \epsilon with a Taylor remainder error term |R_m (\epsilon^(-1)
+logN)|.
+",1,0,0,0,0,0
+258,Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems,"  Security, privacy, and fairness have become critical in the era of data
+science and machine learning. More and more we see that achieving universally
+secure, private, and fair systems is practically impossible. We have seen for
+example how generative adversarial networks can be used to learn about the
+expected private training data; how the exploitation of additional data can
+reveal private information in the original one; and how what looks like
+unrelated features can teach us about each other. Confronted with this
+challenge, in this paper we open a new line of research, where the security,
+privacy, and fairness is learned and used in a closed environment. The goal is
+to ensure that a given entity (e.g., the company or the government), trusted to
+infer certain information with our data, is blocked from inferring protected
+information from it. For example, a hospital might be allowed to produce
+diagnosis on the patient (the positive task), without being able to infer the
+gender of the subject (negative task). Similarly, a company can guarantee that
+internally it is not using the provided data for any undesired task, an
+important goal that is not contradicting the virtually impossible challenge of
+blocking everybody from the undesired task. We design a system that learns to
+succeed on the positive task while simultaneously fail at the negative one, and
+illustrate this with challenging cases where the positive task is actually
+harder than the negative one being blocked. Fairness, to the information in the
+negative task, is often automatically obtained as a result of this proposed
+approach. The particular framework and examples open the door to security,
+privacy, and fairness in very important closed scenarios, ranging from private
+data accumulation companies like social networks to law-enforcement and
+hospitals.
+",1,0,0,1,0,0
+259,Performance of Energy Harvesting Receivers with Power Optimization,"  The difficulty of modeling energy consumption in communication systems leads
+to challenges in energy harvesting (EH) systems, in which nodes scavenge energy
+from their environment. An EH receiver must harvest enough energy for
+demodulating and decoding. The energy required depends upon factors, like code
+rate and signal-to-noise ratio, which can be adjusted dynamically. We consider
+a receiver which harvests energy from ambient sources and the transmitter,
+meaning the received signal is used for both EH and information decoding.
+Assuming a generalized function for energy consumption, we maximize the total
+number of information bits decoded, under both average and peak power
+constraints at the transmitter, by carefully optimizing the power used for EH,
+power used for information transmission, fraction of time for EH, and code
+rate. For transmission over a single block, we find there exist problem
+parameters for which either maximizing power for information transmission or
+maximizing power for EH is optimal. In the general case, the optimal solution
+is a tradeoff of the two. For transmission over multiple blocks, we give an
+upper bound on performance and give sufficient and necessary conditions to
+achieve this bound. Finally, we give some numerical results to illustrate our
+results and analysis.
+",1,0,0,0,0,0
+260,On Convergence Rate of a Continuous-Time Distributed Self-Appraisal Model with Time-Varying Relative Interaction Matrices,"  This paper studies a recently proposed continuous-time distributed
+self-appraisal model with time-varying interactions among a network of $n$
+individuals which are characterized by a sequence of time-varying relative
+interaction matrices. The model describes the evolution of the
+social-confidence levels of the individuals via a reflected appraisal mechanism
+in real time. We first show by example that when the relative interaction
+matrices are stochastic (not doubly stochastic), the social-confidence levels
+of the individuals may not converge to a steady state. We then show that when
+the relative interaction matrices are doubly stochastic, the $n$ individuals'
+self-confidence levels will all converge to $1/n$, which indicates a democratic
+state, exponentially fast under appropriate assumptions, and provide an
+explicit expression of the convergence rate.
+",0,0,1,0,0,0
+261,Closing the loop on multisensory interactions: A neural architecture for multisensory causal inference and recalibration,"  When the brain receives input from multiple sensory systems, it is faced with
+the question of whether it is appropriate to process the inputs in combination,
+as if they originated from the same event, or separately, as if they originated
+from distinct events. Furthermore, it must also have a mechanism through which
+it can keep sensory inputs calibrated to maintain the accuracy of its internal
+representations. We have developed a neural network architecture capable of i)
+approximating optimal multisensory spatial integration, based on Bayesian
+causal inference, and ii) recalibrating the spatial encoding of sensory
+systems. The architecture is based on features of the dorsal processing
+hierarchy, including the spatial tuning properties of unisensory neurons and
+the convergence of different sensory inputs onto multisensory neurons.
+Furthermore, we propose that these unisensory and multisensory neurons play
+dual roles in i) encoding spatial location as separate or integrated estimates
+and ii) accumulating evidence for the independence or relatedness of
+multisensory stimuli. We further propose that top-down feedback connections
+spanning the dorsal pathway play key a role in recalibrating spatial encoding
+at the level of early unisensory cortices. Our proposed architecture provides
+possible explanations for a number of human electrophysiological and
+neuroimaging results and generates testable predictions linking neurophysiology
+with behaviour.
+",0,0,0,0,1,0
+262,Block CUR: Decomposing Matrices using Groups of Columns,"  A common problem in large-scale data analysis is to approximate a matrix
+using a combination of specifically sampled rows and columns, known as CUR
+decomposition. Unfortunately, in many real-world environments, the ability to
+sample specific individual rows or columns of the matrix is limited by either
+system constraints or cost. In this paper, we consider matrix approximation by
+sampling predefined \emph{blocks} of columns (or rows) from the matrix. We
+present an algorithm for sampling useful column blocks and provide novel
+guarantees for the quality of the approximation. This algorithm has application
+in problems as diverse as biometric data analysis to distributed computing. We
+demonstrate the effectiveness of the proposed algorithms for computing the
+Block CUR decomposition of large matrices in a distributed setting with
+multiple nodes in a compute cluster, where such blocks correspond to columns
+(or rows) of the matrix stored on the same node, which can be retrieved with
+much less overhead than retrieving individual columns stored across different
+nodes. In the biometric setting, the rows correspond to different users and
+columns correspond to users' biometric reaction to external stimuli, {\em
+e.g.,}~watching video content, at a particular time instant. There is
+significant cost in acquiring each user's reaction to lengthy content so we
+sample a few important scenes to approximate the biometric response. An
+individual time sample in this use case cannot be queried in isolation due to
+the lack of context that caused that biometric reaction. Instead, collections
+of time segments ({\em i.e.,} blocks) must be presented to the user. The
+practical application of these algorithms is shown via experimental results
+using real-world user biometric data from a content testing environment.
+",1,0,0,1,0,0
+263,Synchronous Observation on the Spontaneous Transformation of Liquid Metal under Free Falling Microgravity Situation,"  The unusually high surface tension of room temperature liquid metal is
+molding it as unique material for diverse newly emerging areas. However, unlike
+its practices on earth, such metal fluid would display very different behaviors
+when working in space where gravity disappears and surface property dominates
+the major physics. So far, few direct evidences are available to understand
+such effect which would impede further exploration of liquid metal use for
+space. Here to preliminarily probe into this intriguing issue, a low cost
+experimental strategy to simulate microgravity environment on earth was
+proposed through adopting bridges with high enough free falling distance as the
+test platform. Then using digital cameras amounted along x, y, z directions on
+outside wall of the transparent container with liquid metal and allied solution
+inside, synchronous observations on the transient flow and transformational
+activities of liquid metal were performed. Meanwhile, an unmanned aerial
+vehicle was adopted to record the whole free falling dynamics of the test
+capsule from the far end which can help justify subsequent experimental
+procedures. A series of typical fundamental phenomena were thus observed as:
+(a) A relatively large liquid metal object would spontaneously transform from
+its original planar pool state into a sphere and float in the container if
+initiating the free falling; (b) The liquid metal changes its three-dimensional
+shape due to dynamic microgravity strength due to free falling and rebound of
+the test capsule; and (c) A quick spatial transformation of liquid metal
+immersed in the solution can easily be induced via external electrical fields.
+The mechanisms of the surface tension driven liquid metal actuation in space
+were interpreted. All these findings indicated that microgravity effect should
+be fully treated in developing future generation liquid metal space
+technologies.
+",0,1,0,0,0,0
+264,Continuously tempered Hamiltonian Monte Carlo,"  Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MCMC)
+method for performing approximate inference in complex probabilistic models of
+continuous variables. In common with many MCMC methods, however, the standard
+HMC approach performs poorly in distributions with multiple isolated modes. We
+present a method for augmenting the Hamiltonian system with an extra continuous
+temperature control variable which allows the dynamic to bridge between
+sampling a complex target distribution and a simpler unimodal base
+distribution. This augmentation both helps improve mixing in multimodal targets
+and allows the normalisation constant of the target distribution to be
+estimated. The method is simple to implement within existing HMC code,
+requiring only a standard leapfrog integrator. We demonstrate experimentally
+that the method is competitive with annealed importance sampling and simulating
+tempering methods at sampling from challenging multimodal distributions and
+estimating their normalising constants.
+",0,0,0,1,0,0
+265,Automated Synthesis of Safe Digital Controllers for Sampled-Data Stochastic Nonlinear Systems,"  We present a new method for the automated synthesis of digital controllers
+with formal safety guarantees for systems with nonlinear dynamics, noisy output
+measurements, and stochastic disturbances. Our method derives digital
+controllers such that the corresponding closed-loop system, modeled as a
+sampled-data stochastic control system, satisfies a safety specification with
+probability above a given threshold. The proposed synthesis method alternates
+between two steps: generation of a candidate controller pc, and verification of
+the candidate. pc is found by maximizing a Monte Carlo estimate of the safety
+probability, and by using a non-validated ODE solver for simulating the system.
+Such a candidate is therefore sub-optimal but can be generated very rapidly. To
+rule out unstable candidate controllers, we prove and utilize Lyapunov's
+indirect method for instability of sampled-data nonlinear systems. In the
+subsequent verification step, we use a validated solver based on SMT
+(Satisfiability Modulo Theories) to compute a numerically and statistically
+valid confidence interval for the safety probability of pc. If the probability
+so obtained is not above the threshold, we expand the search space for
+candidates by increasing the controller degree. We evaluate our technique on
+three case studies: an artificial pancreas model, a powertrain control model,
+and a quadruple-tank process.
+",1,0,0,0,0,0
+266,Magnus integrators on multicore CPUs and GPUs,"  In the present paper we consider numerical methods to solve the discrete
+Schrödinger equation with a time dependent Hamiltonian (motivated by problems
+encountered in the study of spin systems). We will consider both short-range
+interactions, which lead to evolution equations involving sparse matrices, and
+long-range interactions, which lead to dense matrices. Both of these settings
+show very different computational characteristics. We use Magnus integrators
+for time integration and employ a framework based on Leja interpolation to
+compute the resulting action of the matrix exponential. We consider both
+traditional Magnus integrators (which are extensively used for these types of
+problems in the literature) as well as the recently developed commutator-free
+Magnus integrators and implement them on modern CPU and GPU (graphics
+processing unit) based systems.
+We find that GPUs can yield a significant speed-up (up to a factor of $10$ in
+the dense case) for these types of problems. In the sparse case GPUs are only
+advantageous for large problem sizes and the achieved speed-ups are more
+modest. In most cases the commutator-free variant is superior but especially on
+the GPU this advantage is rather small. In fact, none of the advantage of
+commutator-free methods on GPUs (and on multi-core CPUs) is due to the
+elimination of commutators. This has important consequences for the design of
+more efficient numerical methods.
+",1,1,0,0,0,0
+267,High Dimensional Estimation and Multi-Factor Models,"  This paper re-investigates the estimation of multiple factor models relaxing
+the convention that the number of factors is small and using a new approach for
+identifying factors. We first obtain the collection of all possible factors and
+then provide a simultaneous test, security by security, of which factors are
+significant. Since the collection of risk factors is large and highly
+correlated, high-dimension methods (including the LASSO and prototype
+clustering) have to be used. The multi-factor model is shown to have a
+significantly better fit than the Fama-French 5-factor model. Robustness tests
+are also provided.
+",0,0,0,1,0,1
+268,Scaling Law for Three-body Collisions in Identical Fermions with $p$-wave Interactions,"  We experimentally confirmed the threshold behavior and scattering length
+scaling law of the three-body loss coefficients in an ultracold spin-polarized
+gas of $^6$Li atoms near a $p$-wave Feshbach resonance. We measured the
+three-body loss coefficients as functions of temperature and scattering volume,
+and found that the threshold law and the scattering length scaling law hold in
+limited temperature and magnetic field regions. We also found that the
+breakdown of the scaling laws is due to the emergence of the effective-range
+term. This work is an important first step toward full understanding of the
+loss of identical fermions with $p$-wave interactions.
+",0,1,0,0,0,0
+269,An Expanded Local Variance Gamma model,"  The paper proposes an expanded version of the Local Variance Gamma model of
+Carr and Nadtochiy by adding drift to the governing underlying process. Still
+in this new model it is possible to derive an ordinary differential equation
+for the option price which plays a role of Dupire's equation for the standard
+local volatility model. It is shown how calibration of multiple smiles (the
+whole local volatility surface) can be done in such a case. Further, assuming
+the local variance to be a piecewise linear function of strike and piecewise
+constant function of time this ODE is solved in closed form in terms of
+Confluent hypergeometric functions. Calibration of the model to market smiles
+does not require solving any optimization problem and, in contrast, can be done
+term-by-term by solving a system of non-linear algebraic equations for each
+maturity, which is fast.
+",0,0,0,0,0,1
+270,An attentive neural architecture for joint segmentation and parsing and its application to real estate ads,"  In processing human produced text using natural language processing (NLP)
+techniques, two fundamental subtasks that arise are (i) segmentation of the
+plain text into meaningful subunits (e.g., entities), and (ii) dependency
+parsing, to establish relations between subunits. In this paper, we develop a
+relatively simple and effective neural joint model that performs both
+segmentation and dependency parsing together, instead of one after the other as
+in most state-of-the-art works. We will focus in particular on the real estate
+ad setting, aiming to convert an ad to a structured description, which we name
+property tree, comprising the tasks of (1) identifying important entities of a
+property (e.g., rooms) from classifieds and (2) structuring them into a tree
+format. In this work, we propose a new joint model that is able to tackle the
+two tasks simultaneously and construct the property tree by (i) avoiding the
+error propagation that would arise from the subtasks one after the other in a
+pipelined fashion, and (ii) exploiting the interactions between the subtasks.
+For this purpose, we perform an extensive comparative study of the pipeline
+methods and the new proposed joint model, reporting an improvement of over
+three percentage points in the overall edge F1 score of the property tree.
+Also, we propose attention methods, to encourage our model to focus on salient
+tokens during the construction of the property tree. Thus we experimentally
+demonstrate the usefulness of attentive neural architectures for the proposed
+joint model, showcasing a further improvement of two percentage points in edge
+F1 score for our application.
+",1,0,0,0,0,0
+271,Multilevel maximum likelihood estimation with application to covariance matrices,"  The asymptotic variance of the maximum likelihood estimate is proved to
+decrease when the maximization is restricted to a subspace that contains the
+true parameter value. Maximum likelihood estimation allows a systematic fitting
+of covariance models to the sample, which is important in data assimilation.
+The hierarchical maximum likelihood approach is applied to the spectral
+diagonal covariance model with different parameterizations of eigenvalue decay,
+and to the sparse inverse covariance model with specified parameter values on
+different sets of nonzero entries. It is shown computationally that using
+smaller sets of parameters can decrease the sampling noise in high dimension
+substantially.
+",0,0,1,1,0,0
+272,Auto-Meta: Automated Gradient Based Meta Learner Search,"  Fully automating machine learning pipelines is one of the key challenges of
+current artificial intelligence research, since practical machine learning
+often requires costly and time-consuming human-powered processes such as model
+design, algorithm development, and hyperparameter tuning. In this paper, we
+verify that automated architecture search synergizes with the effect of
+gradient-based meta learning. We adopt the progressive neural architecture
+search \cite{liu:pnas_google:DBLP:journals/corr/abs-1712-00559} to find optimal
+architectures for meta-learners. The gradient based meta-learner whose
+architecture was automatically found achieved state-of-the-art results on the
+5-shot 5-way Mini-ImageNet classification problem with $74.65\%$ accuracy,
+which is $11.54\%$ improvement over the result obtained by the first
+gradient-based meta-learner called MAML
+\cite{finn:maml:DBLP:conf/icml/FinnAL17}. To our best knowledge, this work is
+the first successful neural architecture search implementation in the context
+of meta learning.
+",0,0,0,1,0,0
+273,Convergence of the Forward-Backward Algorithm: Beyond the Worst Case with the Help of Geometry,"  We provide a comprehensive study of the convergence of forward-backward
+algorithm under suitable geometric conditions leading to fast rates. We present
+several new results and collect in a unified view a variety of results
+scattered in the literature, often providing simplified proofs. Novel
+contributions include the analysis of infinite dimensional convex minimization
+problems, allowing the case where minimizers might not exist. Further, we
+analyze the relation between different geometric conditions, and discuss novel
+connections with a priori conditions in linear inverse problems, including
+source conditions, restricted isometry properties and partial smoothness.
+",0,0,1,1,0,0
+274,Calibration-Free Relaxation-Based Multi-Color Magnetic Particle Imaging,"  Magnetic Particle Imaging (MPI) is a novel imaging modality with important
+applications such as angiography, stem cell tracking, and cancer imaging.
+Recently, there have been efforts to increase the functionality of MPI via
+multi-color imaging methods that can distinguish the responses of different
+nanoparticles, or nanoparticles in different environmental conditions. The
+proposed techniques typically rely on extensive calibrations that capture the
+differences in the harmonic responses of the nanoparticles. In this work, we
+propose a method to directly estimate the relaxation time constant of the
+nanoparticles from the MPI signal, which is then used to generate a multi-color
+relaxation map. The technique is based on the underlying mirror symmetry of the
+adiabatic MPI signal when the same region is scanned back and forth. We
+validate the proposed method via extensive simulations, and via experiments on
+our in-house Magnetic Particle Spectrometer (MPS) setup at 550 Hz and our
+in-house MPI scanner at 9.7 kHz. Our results show that nanoparticles can be
+successfully distinguished with the proposed technique, without any calibration
+or prior knowledge about the nanoparticles.
+",0,1,0,0,0,0
+275,Neural Machine Translation,"  Draft of textbook chapter on neural machine translation. a comprehensive
+treatment of the topic, ranging from introduction to neural networks,
+computation graphs, description of the currently dominant attentional
+sequence-to-sequence model, recent refinements, alternative architectures and
+challenges. Written as chapter for the textbook Statistical Machine
+Translation. Used in the JHU Fall 2017 class on machine translation.
+",1,0,0,0,0,0
+276,On algebraically integrable domains in Euclidean spaces,"  Let $D$ be a bounded domain $D$ in $\mathbb R^n $ with infinitely smooth
+boundary and $n$ is odd. We prove that if the volume cut off from the domain by
+a hyperplane is an algebraic function of the hyperplane, free of real singular
+points, then the domain is an ellipsoid. This partially answers a question of
+V.I. Arnold: whether odd-dimensional ellipsoids are the only algebraically
+integrable domains?
+",0,0,1,0,0,0
+277,Vocabulary-informed Extreme Value Learning,"  The novel unseen classes can be formulated as the extreme values of known
+classes. This inspired the recent works on open-set recognition
+\cite{Scheirer_2013_TPAMI,Scheirer_2014_TPAMIb,EVM}, which however can have no
+way of naming the novel unseen classes. To solve this problem, we propose the
+Extreme Value Learning (EVL) formulation to learn the mapping from visual
+feature to semantic space. To model the margin and coverage distributions of
+each class, the Vocabulary-informed Learning (ViL) is adopted by using vast
+open vocabulary in the semantic space. Essentially, by incorporating the EVL
+and ViL, we for the first time propose a novel semantic embedding paradigm --
+Vocabulary-informed Extreme Value Learning (ViEVL), which embeds the visual
+features into semantic space in a probabilistic way. The learned embedding can
+be directly used to solve supervised learning, zero-shot and open set
+recognition simultaneously. Experiments on two benchmark datasets demonstrate
+the effectiveness of proposed frameworks.
+",1,0,1,1,0,0
+278,Cross-layer optimized routing with low duty cycle TDMA across multiple wireless body area networks,"  In this paper, we study the performance of two cross-layer optimized dynamic
+routing techniques for radio interference mitigation across multiple coexisting
+wireless body area networks (BANs), based on real-life measurements. At the
+network layer, the best route is selected according to channel state
+information from the physical layer, associated with low duty cycle TDMA at the
+MAC layer. The routing techniques (i.e., shortest path routing (SPR), and novel
+cooperative multi-path routing (CMR) incorporating 3-branch selection
+combining) perform real-time and reliable data transfer across BANs operating
+near the 2.4 GHz ISM band. An open-access experimental data set of 'everyday'
+mixed-activities is used for analyzing the proposed cross-layer optimization.
+We show that CMR gains up to 14 dB improvement with 8.3% TDMA duty cycle, and
+even 10 dB improvement with 0.2% TDMA duty cycle over SPR, at 10% outage
+probability at a realistic signal-to-interference-plus-noise ratio (SINR).
+Acceptable packet delivery ratios (PDR) and spectral efficiencies are obtained
+from SPR and CMR with reasonably sensitive receivers across a range of TDMA low
+duty cycles, with up to 9 dB improvement of CMR over SPR at 90% PDR. The
+distribution fits for received SINR through routing are also derived and
+validated with theoretical analysis.
+",1,0,0,0,0,0
+279,A Team-Formation Algorithm for Faultline Minimization,"  In recent years, the proliferation of online resumes and the need to evaluate
+large populations of candidates for on-site and virtual teams have led to a
+growing interest in automated team-formation. Given a large pool of candidates,
+the general problem requires the selection of a team of experts to complete a
+given task. Surprisingly, while ongoing research has studied numerous
+variations with different constraints, it has overlooked a factor with a
+well-documented impact on team cohesion and performance: team faultlines.
+Addressing this gap is challenging, as the available measures for faultlines in
+existing teams cannot be efficiently applied to faultline optimization. In this
+work, we meet this challenge with a new measure that can be efficiently used
+for both faultline measurement and minimization. We then use the measure to
+solve the problem of automatically partitioning a large population into
+low-faultline teams. By introducing faultlines to the team-formation
+literature, our work creates exciting opportunities for algorithmic work on
+faultline optimization, as well as on work that combines and studies the
+connection of faultlines with other influential team characteristics.
+",1,0,0,0,0,0
+280,A Survey of Model Compression and Acceleration for Deep Neural Networks,"  Deep convolutional neural networks (CNNs) have recently achieved great
+success in many visual recognition tasks. However, existing deep neural network
+models are computationally expensive and memory intensive, hindering their
+deployment in devices with low memory resources or in applications with strict
+latency requirements. Therefore, a natural thought is to perform model
+compression and acceleration in deep networks without significantly decreasing
+the model performance. During the past few years, tremendous progress has been
+made in this area. In this paper, we survey the recent advanced techniques for
+compacting and accelerating CNNs model developed. These techniques are roughly
+categorized into four schemes: parameter pruning and sharing, low-rank
+factorization, transferred/compact convolutional filters, and knowledge
+distillation. Methods of parameter pruning and sharing will be described at the
+beginning, after that the other techniques will be introduced. For each scheme,
+we provide insightful analysis regarding the performance, related applications,
+advantages, and drawbacks etc. Then we will go through a few very recent
+additional successful methods, for example, dynamic capacity networks and
+stochastic depths networks. After that, we survey the evaluation matrix, the
+main datasets used for evaluating the model performance and recent benchmarking
+efforts. Finally, we conclude this paper, discuss remaining challenges and
+possible directions on this topic.
+",1,0,0,0,0,0
+281,Non-Parametric Calibration of Probabilistic Regression,"  The task of calibration is to retrospectively adjust the outputs from a
+machine learning model to provide better probability estimates on the target
+variable. While calibration has been investigated thoroughly in classification,
+it has not yet been well-established for regression tasks. This paper considers
+the problem of calibrating a probabilistic regression model to improve the
+estimated probability densities over the real-valued targets. We propose to
+calibrate a regression model through the cumulative probability density, which
+can be derived from calibrating a multi-class classifier. We provide three
+non-parametric approaches to solve the problem, two of which provide empirical
+estimates and the third providing smooth density estimates. The proposed
+approaches are experimentally evaluated to show their ability to improve the
+performance of regression models on the predictive likelihood.
+",0,0,0,1,0,0
+282,Cyclotron resonant scattering feature simulations. II. Description of the CRSF simulation process,"  Cyclotron resonant scattering features (CRSFs) are formed by scattering of
+X-ray photons off quantized plasma electrons in the strong magnetic field (of
+the order 10^12 G) close to the surface of an accreting X-ray pulsar. The line
+profiles of CRSFs cannot be described by an analytic expression. Numerical
+methods such as Monte Carlo (MC) simulations of the scattering processes are
+required in order to predict precise line shapes for a given physical setup,
+which can be compared to observations to gain information about the underlying
+physics in these systems.
+A versatile simulation code is needed for the generation of synthetic
+cyclotron lines. Sophisticated geometries should be investigatable by making
+their simulation possible for the first time.
+The simulation utilizes the mean free path tables described in the first
+paper of this series for the fast interpolation of propagation lengths. The
+code is parallelized to make the very time consuming simulations possible on
+convenient time scales. Furthermore, it can generate responses to
+mono-energetic photon injections, producing Green's functions, which can be
+used later to generate spectra for arbitrary continua.
+We develop a new simulation code to generate synthetic cyclotron lines for
+complex scenarios, allowing for unprecedented physical interpretation of the
+observed data. An associated XSPEC model implementation is used to fit
+synthetic line profiles to NuSTAR data of Cep X-4. The code has been developed
+with the main goal of overcoming previous geometrical constraints in MC
+simulations of CRSFs. By applying this code also to more simple, classic
+geometries used in previous works, we furthermore address issues of code
+verification and cross-comparison of various models. The XSPEC model and the
+Green's function tables are available online at
+this http URL .
+",0,1,0,0,0,0
+283,New quantum mds constacylıc codes,"  This paper is devoted to the study of the construction of new quantum MDS
+codes. Based on constacyclic codes over Fq2 , we derive four new families of
+quantum MDS codes, one of which is an explicit generalization of the
+construction given in Theorem 7 in [22]. We also extend the result of Theorem
+3:3 given in [17].
+",1,0,0,0,0,0
+284,Infinitary first-order categorical logic,"  We present a unified categorical treatment of completeness theorems for
+several classical and intuitionistic infinitary logics with a proposed
+axiomatization. This provides new completeness theorems and subsumes previous
+ones by Gödel, Kripke, Beth, Karp, Joyal, Makkai and Fourman/Grayson. As an
+application we prove, using large cardinals assumptions, the disjunction and
+existence properties for infinitary intuitionistic first-order logics.
+",0,0,1,0,0,0
+285,Stochastic Gradient Monomial Gamma Sampler,"  Recent advances in stochastic gradient techniques have made it possible to
+estimate posterior distributions from large datasets via Markov Chain Monte
+Carlo (MCMC). However, when the target posterior is multimodal, mixing
+performance is often poor. This results in inadequate exploration of the
+posterior distribution. A framework is proposed to improve the sampling
+efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo. A
+generalized kinetic function is leveraged, delivering superior stationary
+mixing, especially for multimodal distributions. Techniques are also discussed
+to overcome the practical issues introduced by this generalization. It is shown
+that the proposed approach is better at exploring complex multimodal posterior
+distributions, as demonstrated on multiple applications and in comparison with
+other stochastic gradient MCMC methods.
+",1,0,0,1,0,0
+286,Gini estimation under infinite variance,"  We study the problems related to the estimation of the Gini index in presence
+of a fat-tailed data generating process, i.e. one in the stable distribution
+class with finite mean but infinite variance (i.e. with tail index
+$\alpha\in(1,2)$). We show that, in such a case, the Gini coefficient cannot be
+reliably estimated using conventional nonparametric methods, because of a
+downward bias that emerges under fat tails. This has important implications for
+the ongoing discussion about economic inequality.
+We start by discussing how the nonparametric estimator of the Gini index
+undergoes a phase transition in the symmetry structure of its asymptotic
+distribution, as the data distribution shifts from the domain of attraction of
+a light-tailed distribution to that of a fat-tailed one, especially in the case
+of infinite variance. We also show how the nonparametric Gini bias increases
+with lower values of $\alpha$. We then prove that maximum likelihood estimation
+outperforms nonparametric methods, requiring a much smaller sample size to
+reach efficiency.
+Finally, for fat-tailed data, we provide a simple correction mechanism to the
+small sample bias of the nonparametric estimator based on the distance between
+the mode and the mean of its asymptotic distribution.
+",0,0,0,1,0,0
+287,Training Neural Networks Using Features Replay,"  Training a neural network using backpropagation algorithm requires passing
+error gradients sequentially through the network. The backward locking prevents
+us from updating network layers in parallel and fully leveraging the computing
+resources. Recently, there are several works trying to decouple and parallelize
+the backpropagation algorithm. However, all of them suffer from severe accuracy
+loss or memory explosion when the neural network is deep. To address these
+challenging issues, we propose a novel parallel-objective formulation for the
+objective function of the neural network. After that, we introduce features
+replay algorithm and prove that it is guaranteed to converge to critical points
+for the non-convex problem under certain conditions. Finally, we apply our
+method to training deep convolutional neural networks, and the experimental
+results show that the proposed method achieves {faster} convergence, {lower}
+memory consumption, and {better} generalization error than compared methods.
+",0,0,0,1,0,0
+288,The anti-spherical category,"  We study a diagrammatic categorification (the ""anti-spherical category"") of
+the anti-spherical module for any Coxeter group. We deduce that Deodhar's
+(sign) parabolic Kazhdan-Lusztig polynomials have non-negative coefficients,
+and that a monotonicity conjecture of Brenti's holds. The main technical
+observation is a localisation procedure for the anti-spherical category, from
+which we construct a ""light leaves"" basis of morphisms. Our techniques may be
+used to calculate many new elements of the $p$-canonical basis in the
+anti-spherical module. The results use generators and relations for Soergel
+bimodules (""Soergel calculus"") in a crucial way.
+",0,0,1,0,0,0
+289,Trajectories and orbital angular momentum of necklace beams in nonlinear colloidal suspensions,"  Recently, we have predicted that the modulation instability of optical vortex
+solitons propagating in nonlinear colloidal suspensions with exponential
+saturable nonlinearity leads to formation of necklace beams (NBs)
+[S.~Z.~Silahli, W.~Walasik and N.~M.~Litchinitser, Opt.~Lett., \textbf{40},
+5714 (2015)]. Here, we investigate the dynamics of NB formation and
+propagation, and show that the distance at which the NB is formed depends on
+the input power of the vortex beam. Moreover, we show that the NB trajectories
+are not necessarily tangent to the initial vortex ring, and that their
+velocities have components stemming both from the beam diffraction and from the
+beam orbital angular momentum. We also demonstrate the generation of twisted
+solitons and analyze the influence of losses on their propagation. Finally, we
+investigate the conservation of the orbital angular momentum in necklace and
+twisted beams. Our studies, performed in ideal lossless media and in realistic
+colloidal suspensions with losses, provide a detailed description of NB
+dynamics and may be useful in studies of light propagation in highly scattering
+colloids and biological samples.
+",0,1,0,0,0,0
+290,Unified Treatment of Spin Torques using a Coupled Magnetisation Dynamics and Three-Dimensional Spin Current Solver,"  A three-dimensional spin current solver based on a generalised spin
+drift-diffusion description, including the spin Hall effect, is integrated with
+a magnetisation dynamics solver. The resulting model is shown to simultaneously
+reproduce the spin-orbit torques generated using the spin Hall effect, spin
+pumping torques generated by magnetisation dynamics in multilayers, as well as
+the spin transfer torques acting on magnetisation regions with spatial
+gradients, whilst field-like and spin-like torques are reproduced in a spin
+valve geometry. Two approaches to modelling interfaces are analysed, one based
+on the spin mixing conductance and the other based on continuity of spin
+currents where the spin dephasing length governs the absorption of transverse
+spin components. In both cases analytical formulas are derived for the
+spin-orbit torques in a heavy metal / ferromagnet bilayer geometry, showing in
+general both field-like and damping-like torques are generated. The limitations
+of the analytical approach are discussed, showing that even in a simple bilayer
+geometry, due to the non-uniformity of the spin currents, a full
+three-dimensional treatment is required. Finally the model is applied to the
+quantitative analysis of the spin Hall angle in Pt by reproducing published
+experimental data on the ferromagnetic resonance linewidth in the bilayer
+geometry.
+",0,1,0,0,0,0
+291,A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization,"  This paper aims to explore models based on the extreme gradient boosting
+(XGBoost) approach for business risk classification. Feature selection (FS)
+algorithms and hyper-parameter optimizations are simultaneously considered
+during model training. The five most commonly used FS methods including weight
+by Gini, weight by Chi-square, hierarchical variable clustering, weight by
+correlation, and weight by information are applied to alleviate the effect of
+redundant features. Two hyper-parameter optimization approaches, random search
+(RS) and Bayesian tree-structured Parzen Estimator (TPE), are applied in
+XGBoost. The effect of different FS and hyper-parameter optimization methods on
+the model performance are investigated by the Wilcoxon Signed Rank Test. The
+performance of XGBoost is compared to the traditionally utilized logistic
+regression (LR) model in terms of classification accuracy, area under the curve
+(AUC), recall, and F1 score obtained from the 10-fold cross validation. Results
+show that hierarchical clustering is the optimal FS method for LR while weight
+by Chi-square achieves the best performance in XG-Boost. Both TPE and RS
+optimization in XGBoost outperform LR significantly. TPE optimization shows a
+superiority over RS since it results in a significantly higher accuracy and a
+marginally higher AUC, recall and F1 score. Furthermore, XGBoost with TPE
+tuning shows a lower variability than the RS method. Finally, the ranking of
+feature importance based on XGBoost enhances the model interpretation.
+Therefore, XGBoost with Bayesian TPE hyper-parameter optimization serves as an
+operative while powerful approach for business risk modeling.
+",1,0,0,1,0,0
+292,Rheology of High-Capillary Number Flow in Porous Media,"  Immiscible fluids flowing at high capillary numbers in porous media may be
+characterized by an effective viscosity. We demonstrate that the effective
+viscosity is well described by the Lichtenecker-Rother equation. The exponent
+$\alpha$ in this equation takes either the value 1 or 0.6 in two- and 0.5 in
+three-dimensional systems depending on the pore geometry. Our arguments are
+based on analytical and numerical methods.
+",0,1,0,0,0,0
+293,Quantum Charge Pumps with Topological Phases in Creutz Ladder,"  Quantum charge pumping phenomenon connects band topology through the dynamics
+of a one-dimensional quantum system. In terms of a microscopic model, the
+Su-Schrieffer-Heeger/Rice-Mele quantum pump continues to serve as a fruitful
+starting point for many considerations of topological physics. Here we present
+a generalized Creutz scheme as a distinct two-band quantum pump model. By
+noting that it undergoes two kinds of topological band transitions accompanying
+with a Zak-phase-difference of $\pi$ and $2\pi$, respectively, various charge
+pumping schemes are studied by applying an elaborate Peierl's phase
+substitution. Translating into real space, the transportation of quantized
+charges is a result of cooperative quantum interference effect. In particular,
+an all-flux quantum pump emerges which operates with time-varying fluxes only
+and transports two charge units. This puts cold atoms with artificial gauge
+fields as an unique system where this kind of phenomena can be realized.
+",0,1,0,0,0,0
+294,On Deep Neural Networks for Detecting Heart Disease,"  Heart disease is the leading cause of death, and experts estimate that
+approximately half of all heart attacks and strokes occur in people who have
+not been flagged as ""at risk."" Thus, there is an urgent need to improve the
+accuracy of heart disease diagnosis. To this end, we investigate the potential
+of using data analysis, and in particular the design and use of deep neural
+networks (DNNs) for detecting heart disease based on routine clinical data. Our
+main contribution is the design, evaluation, and optimization of DNN
+architectures of increasing depth for heart disease diagnosis. This work led to
+the discovery of a novel five layer DNN architecture - named Heart Evaluation
+for Algorithmic Risk-reduction and Optimization Five (HEARO-5) -- that yields
+best prediction accuracy. HEARO-5's design employs regularization optimization
+and automatically deals with missing data and/or data outliers. To evaluate and
+tune the architectures we use k-way cross-validation as well as Matthews
+correlation coefficient (MCC) to measure the quality of our classifications.
+The study is performed on the publicly available Cleveland dataset of medical
+information, and we are making our developments open source, to further
+facilitate openness and research on the use of DNNs in medicine. The HEARO-5
+architecture, yielding 99% accuracy and 0.98 MCC, significantly outperforms
+currently published research in the area.
+",1,0,0,1,0,0
+295,Exponential Stability Analysis via Integral Quadratic Constraints,"  The theory of integral quadratic constraints (IQCs) allows verification of
+stability and gain-bound properties of systems containing nonlinear or
+uncertain elements. Gain bounds often imply exponential stability, but it can
+be challenging to compute useful numerical bounds on the exponential decay
+rate. This work presents a generalization of the classical IQC results of
+Megretski and Rantzer that leads to a tractable computational procedure for
+finding exponential rate certificates that are far less conservative than ones
+computed from $L_2$ gain bounds alone. An expanded library of IQCs for
+certifying exponential stability is also provided and the effectiveness of the
+technique is demonstrated via numerical examples.
+",1,0,1,0,0,0
+296,Fermi acceleration of electrons inside foreshock transient cores,"  Foreshock transients upstream of Earth's bow shock have been recently
+observed to accelerate electrons to many times their thermal energy. How such
+acceleration occurs is unknown, however. Using THEMIS case studies, we examine
+a subset of acceleration events (31 of 247 events) in foreshock transients with
+cores that exhibit gradual electron energy increases accompanied by low
+background magnetic field strength and large-amplitude magnetic fluctuations.
+Using the evolution of electron distributions and the energy increase rates at
+multiple spacecraft, we suggest that Fermi acceleration between a converging
+foreshock transient's compressional boundary and the bow shock is responsible
+for the observed electron acceleration. We then show that a one-dimensional
+test particle simulation of an ideal Fermi acceleration model in fluctuating
+fields prescribed by the observations can reproduce the observed evolution of
+electron distributions, energy increase rate, and pitch-angle isotropy,
+providing further support for our hypothesis. Thus, Fermi acceleration is
+likely the principal electron acceleration mechanism in at least this subset of
+foreshock transient cores.
+",0,1,0,0,0,0
+297,Quantum Speed Limit is Not Quantum,"  The quantum speed limit (QSL), or the energy-time uncertainty relation,
+describes the fundamental maximum rate for quantum time evolution and has been
+regarded as being unique in quantum mechanics. In this study, we obtain a
+classical speed limit corresponding to the QSL using the Hilbert space for the
+classical Liouville equation. Thus, classical mechanics has a fundamental speed
+limit, and QSL is not a purely quantum phenomenon but a universal dynamical
+property of the Hilbert space. Furthermore, we obtain similar speed limits for
+the imaginary-time Schroedinger equations such as the master equation.
+",0,1,0,0,0,0
+298,Adaptive Diffusion Processes of Time-Varying Local Information on Networks,"  This paper mainly discusses the diffusion on complex networks with
+time-varying couplings. We propose a model to describe the adaptive diffusion
+process of local topological and dynamical information, and find that the
+Barabasi-Albert scale-free network (BA network) is beneficial to the diffusion
+and leads nodes to arrive at a larger state value than other networks do. The
+ability of diffusion for a node is related to its own degree. Specifically,
+nodes with smaller degrees are more likely to change their states and reach
+larger values, while those with larger degrees tend to stick to their original
+states. We introduce state entropy to analyze the thermodynamic mechanism of
+the diffusion process, and interestingly find that this kind of diffusion
+process is a minimization process of state entropy. We use the inequality
+constrained optimization method to reveal the restriction function of the
+minimization and find that it has the same form as the Gibbs free energy. The
+thermodynamical concept allows us to understand dynamical processes on complex
+networks from a brand-new perspective. The result provides a convenient means
+of optimizing relevant dynamical processes on practical circuits as well as
+related complex systems.
+",1,0,0,0,0,0
+299,ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder,"  This paper proposes a non-parallel many-to-many voice conversion (VC) method
+using a variant of the conditional variational autoencoder (VAE) called an
+auxiliary classifier VAE (ACVAE). The proposed method has three key features.
+First, it adopts fully convolutional architectures to construct the encoder and
+decoder networks so that the networks can learn conversion rules that capture
+time dependencies in the acoustic feature sequences of source and target
+speech. Second, it uses an information-theoretic regularization for the model
+training to ensure that the information in the attribute class label will not
+be lost in the conversion process. With regular CVAEs, the encoder and decoder
+are free to ignore the attribute class label input. This can be problematic
+since in such a situation, the attribute class label will have little effect on
+controlling the voice characteristics of input speech at test time. Such
+situations can be avoided by introducing an auxiliary classifier and training
+the encoder and decoder so that the attribute classes of the decoder outputs
+are correctly predicted by the classifier. Third, it avoids producing
+buzzy-sounding speech at test time by simply transplanting the spectral details
+of the input speech into its converted version. Subjective evaluation
+experiments revealed that this simple method worked reasonably well in a
+non-parallel many-to-many speaker identity conversion task.
+",1,0,0,1,0,0
+300,Spectral analysis of jet turbulence,"  Informed by LES data and resolvent analysis of the mean flow, we examine the
+structure of turbulence in jets in the subsonic, transonic, and supersonic
+regimes. Spectral (frequency-space) proper orthogonal decomposition is used to
+extract energy spectra and decompose the flow into energy-ranked coherent
+structures. The educed structures are generally well predicted by the resolvent
+analysis. Over a range of low frequencies and the first few azimuthal mode
+numbers, these jets exhibit a low-rank response characterized by
+Kelvin-Helmholtz (KH) type wavepackets associated with the annular shear layer
+up to the end of the potential core and that are excited by forcing in the
+very-near-nozzle shear layer. These modes too the have been experimentally
+observed before and predicted by quasi-parallel stability theory and other
+approximations--they comprise a considerable portion of the total turbulent
+energy. At still lower frequencies, particularly for the axisymmetric mode, and
+again at high frequencies for all azimuthal wavenumbers, the response is not
+low rank, but consists of a family of similarly amplified modes. These modes,
+which are primarily active downstream of the potential core, are associated
+with the Orr mechanism. They occur also as sub-dominant modes in the range of
+frequencies dominated by the KH response. Our global analysis helps tie
+together previous observations based on local spatial stability theory, and
+explains why quasi-parallel predictions were successful at some frequencies and
+azimuthal wavenumbers, but failed at others.
+",0,1,0,0,0,0
+301,A dual framework for low-rank tensor completion,"  One of the popular approaches for low-rank tensor completion is to use the
+latent trace norm regularization. However, most existing works in this
+direction learn a sparse combination of tensors. In this work, we fill this gap
+by proposing a variant of the latent trace norm that helps in learning a
+non-sparse combination of tensors. We develop a dual framework for solving the
+low-rank tensor completion problem. We first show a novel characterization of
+the dual solution space with an interesting factorization of the optimal
+solution. Overall, the optimal solution is shown to lie on a Cartesian product
+of Riemannian manifolds. Furthermore, we exploit the versatile Riemannian
+optimization framework for proposing computationally efficient trust region
+algorithm. The experiments illustrate the efficacy of the proposed algorithm on
+several real-world datasets across applications.
+",1,0,0,1,0,0
+302,La notion d'involution dans le Brouillon Project de Girard Desargues,"  Nous tentons dans cet article de proposer une thèse cohérente concernant
+la formation de la notion d'involution dans le Brouillon Project de Desargues.
+Pour cela, nous donnons une analyse détaillée des dix premières pages
+dudit Brouillon, comprenant les développements de cas particuliers qui aident
+à comprendre l'intention de Desargues. Nous mettons cette analyse en regard
+de la lecture qu'en fait Jean de Beaugrand et que l'on trouve dans les Advis
+Charitables.
+The purpose of this article is to propose a coherent thesis on how Girard
+Desargues arrived at the notion of involution in his Brouillon Project of 1639.
+To this purpose we give a detailed analysis of the ten first pages of the
+Brouillon, including developments of particular cases which help to understand
+the goal of Desargues, as well as to clarify the links between the notion of
+involution and that of harmonic division. We compare the conclusions of this
+analysis with the very critical reading Jean de Beaugrand made of the Brouillon
+Project in the Advis Charitables of 1640.
+",0,0,1,0,0,0
+303,Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT,"  X-ray computed tomography (CT) using sparse projection views is a recent
+approach to reduce the radiation dose. However, due to the insufficient
+projection views, an analytic reconstruction approach using the filtered back
+projection (FBP) produces severe streaking artifacts. Recently, deep learning
+approaches using large receptive field neural networks such as U-Net have
+demonstrated impressive performance for sparse- view CT reconstruction.
+However, theoretical justification is still lacking. Inspired by the recent
+theory of deep convolutional framelets, the main goal of this paper is,
+therefore, to reveal the limitation of U-Net and propose new multi-resolution
+deep learning schemes. In particular, we show that the alternative U- Net
+variants such as dual frame and the tight frame U-Nets satisfy the so-called
+frame condition which make them better for effective recovery of high frequency
+edges in sparse view- CT. Using extensive experiments with real patient data
+set, we demonstrate that the new network architectures provide better
+reconstruction performance.
+",1,0,0,1,0,0
+304,Lie $\infty$-algebroids and singular foliations,"  A singular (or Hermann) foliation on a smooth manifold $M$ can be seen as a
+subsheaf of the sheaf $\mathfrak{X}$ of vector fields on $M$. We show that if
+this singular foliation admits a resolution (in the sense of sheaves)
+consisting of sections of a graded vector bundle of finite type, then one can
+lift the Lie bracket of vector fields to a Lie $\infty$-algebroid structure on
+this resolution, that we call a universal Lie $\infty$-algebroid associated to
+the foliation. The name is justified because it is isomorphic (up to homotopy)
+to any other Lie $\infty$-algebroid structure built on any other resolution of
+the given singular foliation.
+",0,0,1,0,0,0
+305,Distinct evolutions of Weyl fermion quasiparticles and Fermi arcs with bulk band topology in Weyl semimetals,"  The Weyl semimetal phase is a recently discovered topological quantum state
+of matter characterized by the presence of topologically protected degeneracies
+near the Fermi level. These degeneracies are the source of exotic phenomena,
+including the realization of chiral Weyl fermions as quasiparticles in the bulk
+and the formation of Fermi arc states on the surfaces. Here, we demonstrate
+that these two key signatures show distinct evolutions with the bulk band
+topology by performing angle-resolved photoemission spectroscopy, supported by
+first-principle calculations, on transition-metal monophosphides. While Weyl
+fermion quasiparticles exist only when the chemical potential is located
+between two saddle points of the Weyl cone features, the Fermi arc states
+extend in a larger energy scale and are robust across the bulk Lifshitz
+transitions associated with the recombination of two non-trivial Fermi surfaces
+enclosing one Weyl point into a single trivial Fermi surface enclosing two Weyl
+points of opposite chirality. Therefore, in some systems (e.g. NbP),
+topological Fermi arc states are preserved even if Weyl fermion quasiparticles
+are absent in the bulk. Our findings not only provide insight into the
+relationship between the exotic physical phenomena and the intrinsic bulk band
+topology in Weyl semimetals, but also resolve the apparent puzzle of the
+different magneto-transport properties observed in TaAs, TaP and NbP, where the
+Fermi arc states are similar.
+",0,1,0,0,0,0
+306,Assessing inter-modal and inter-regional dependencies in prodromal Alzheimer's disease using multimodal MRI/PET and Gaussian graphical models,"  A sequence of pathological changes takes place in Alzheimer's disease, which
+can be assessed in vivo using various brain imaging methods. Currently, there
+is no appropriate statistical model available that can easily integrate
+multiple imaging modalities, being able to utilize the additional information
+provided from the combined data. We applied Gaussian graphical models (GGMs)
+for analyzing the conditional dependency networks of multimodal neuroimaging
+data and assessed alterations of the network structure in mild cognitive
+impairment (MCI) and Alzheimer's dementia (AD) compared to cognitively healthy
+controls.
+Data from N=667 subjects were obtained from the Alzheimer's Disease
+Neuroimaging Initiative. Mean amyloid load (AV45-PET), glucose metabolism
+(FDG-PET), and gray matter volume (MRI) was calculated for each brain region.
+Separate GGMs were estimated using a Bayesian framework for the combined
+multimodal data for each diagnostic category. Graph-theoretical statistics were
+calculated to determine network alterations associated with disease severity.
+Network measures clustering coefficient, path length and small-world
+coefficient were significantly altered across diagnostic groups, with a
+biphasic u-shape trajectory, i.e. increased small-world coefficient in early
+MCI, intermediate values in late MCI, and decreased values in AD patients
+compared to controls. In contrast, no group differences were found for
+clustering coefficient and small-world coefficient when estimating conditional
+dependency networks on single imaging modalities.
+GGMs provide a useful methodology to analyze the conditional dependency
+networks of multimodal neuroimaging data.
+",0,0,0,1,1,0
+307,On the economics of knowledge creation and sharing,"  This work bridges the technical concepts underlying distributed computing and
+blockchain technologies with their profound socioeconomic and sociopolitical
+implications, particularly on academic research and the healthcare industry.
+Several examples from academia, industry, and healthcare are explored
+throughout this paper. The limiting factor in contemporary life sciences
+research is often funding: for example, to purchase expensive laboratory
+equipment and materials, to hire skilled researchers and technicians, and to
+acquire and disseminate data through established academic channels. In the case
+of the U.S. healthcare system, hospitals generate massive amounts of data, only
+a small minority of which is utilized to inform current and future medical
+practice. Similarly, corporations too expend large amounts of money to collect,
+secure and transmit data from one centralized source to another. In all three
+scenarios, data moves under the traditional paradigm of centralization, in
+which data is hosted and curated by individuals and organizations and of
+benefit to only a small subset of people.
+",1,0,0,0,0,0
+308,Seismic fragility curves for structures using non-parametric representations,"  Fragility curves are commonly used in civil engineering to assess the
+vulnerability of structures to earthquakes. The probability of failure
+associated with a prescribed criterion (e.g. the maximal inter-storey drift of
+a building exceeding a certain threshold) is represented as a function of the
+intensity of the earthquake ground motion (e.g. peak ground acceleration or
+spectral acceleration). The classical approach relies on assuming a lognormal
+shape of the fragility curves; it is thus parametric. In this paper, we
+introduce two non-parametric approaches to establish the fragility curves
+without employing the above assumption, namely binned Monte Carlo simulation
+and kernel density estimation. As an illustration, we compute the fragility
+curves for a three-storey steel frame using a large number of synthetic ground
+motions. The curves obtained with the non-parametric approaches are compared
+with respective curves based on the lognormal assumption. A similar comparison
+is presented for a case when a limited number of recorded ground motions is
+available. It is found that the accuracy of the lognormal curves depends on the
+ground motion intensity measure, the failure criterion and most importantly, on
+the employed method for estimating the parameters of the lognormal shape.
+",0,0,0,1,0,0
+309,Metastable Markov chains: from the convergence of the trace to the convergence of the finite-dimensional distributions,"  We consider continuous-time Markov chains which display a family of wells at
+the same depth. We provide sufficient conditions which entail the convergence
+of the finite-dimensional distributions of the order parameter to the ones of a
+finite state Markov chain. We also show that the state of the process can be
+represented as a time-dependent convex combination of metastable states, each
+of which is supported on one well.
+",0,0,1,0,0,0
+310,Construction of embedded periodic surfaces in $\mathbb{R}^n$,"  We construct embedded minimal surfaces which are $n$-periodic in
+$\mathbb{R}^n$. They are new for codimension $n-2\ge 2$. We start with a Jordan
+curve of edges of the $n$-dimensional cube. It bounds a Plateau minimal disk
+which Schwarz reflection extends to a complete minimal surface. Studying the
+group of Schwarz reflections, we can characterize those Jordan curves for which
+the complete surface is embedded. For example, for $n=4$ exactly five such
+Jordan curves generate embedded surfaces. Our results apply to surface classes
+other than minimal as well, for instance polygonal surfaces.
+",0,0,1,0,0,0
+311,A System of Three Super Earths Transiting the Late K-Dwarf GJ 9827 at Thirty Parsecs,"  We report the discovery of three small transiting planets orbiting GJ 9827, a
+bright (K = 7.2) nearby late K-type dwarf star. GJ 9827 hosts a $1.62\pm0.11$
+$R_{\rm \oplus}$ super Earth on a 1.2 day period, a $1.269^{+0.087}_{-0.089}$
+$R_{\rm \oplus}$ super Earth on a 3.6 day period, and a $2.07\pm0.14$ $R_{\rm
+\oplus}$ super Earth on a 6.2 day period. The radii of the planets transiting
+GJ 9827 span the transition between predominantly rocky and gaseous planets,
+and GJ 9827 b and c fall in or close to the known gap in the radius
+distribution of small planets between these populations. At a distance of 30
+parsecs, GJ 9827 is the closest exoplanet host discovered by K2 to date, making
+these planets well-suited for atmospheric studies with the upcoming James Webb
+Space Telescope. The GJ 9827 system provides a valuable opportunity to
+characterize interior structure and atmospheric properties of coeval planets
+spanning the rocky to gaseous transition.
+",0,1,0,0,0,0
+312,State Sum Invariants of Three Manifolds from Spherical Multi-fusion Categories,"  We define a family of quantum invariants of closed oriented $3$-manifolds
+using spherical multi-fusion categories. The state sum nature of this invariant
+leads directly to $(2+1)$-dimensional topological quantum field theories
+($\text{TQFT}$s), which generalize the Turaev-Viro-Barrett-Westbury
+($\text{TVBW}$) $\text{TQFT}$s from spherical fusion categories. The invariant
+is given as a state sum over labeled triangulations, which is mostly parallel
+to, but richer than the $\text{TVBW}$ approach in that here the labels live not
+only on $1$-simplices but also on $0$-simplices. It is shown that a
+multi-fusion category in general cannot be a spherical fusion category in the
+usual sense. Thus we introduce the concept of a spherical multi-fusion category
+by imposing a weakened version of sphericity. Besides containing the
+$\text{TVBW}$ theory, our construction also includes the recent higher gauge
+theory $(2+1)$-$\text{TQFT}$s given by Kapustin and Thorngren, which was not
+known to have a categorical origin before.
+",0,1,1,0,0,0
+313,Sparse Neural Networks Topologies,"  We propose Sparse Neural Network architectures that are based on random or
+structured bipartite graph topologies. Sparse architectures provide compression
+of the models learned and speed-ups of computations, they can also surpass
+their unstructured or fully connected counterparts. As we show, even more
+compact topologies of the so-called SNN (Sparse Neural Network) can be achieved
+with the use of structured graphs of connections between consecutive layers of
+neurons. In this paper, we investigate how the accuracy and training speed of
+the models depend on the topology and sparsity of the neural network. Previous
+approaches using sparcity are all based on fully connected neural network
+models and create sparcity during training phase, instead we explicitly define
+a sparse architectures of connections before the training. Building compact
+neural network models is coherent with empirical observations showing that
+there is much redundancy in learned neural network models. We show
+experimentally that the accuracy of the models learned with neural networks
+depends on expander-like properties of the underlying topologies such as the
+spectral gap and algebraic connectivity rather than the density of the graphs
+of connections.
+",1,0,0,1,0,0
+314,Human perception in computer vision,"  Computer vision has made remarkable progress in recent years. Deep neural
+network (DNN) models optimized to identify objects in images exhibit
+unprecedented task-trained accuracy and, remarkably, some generalization
+ability: new visual problems can now be solved more easily based on previous
+learning. Biological vision (learned in life and through evolution) is also
+accurate and general-purpose. Is it possible that these different learning
+regimes converge to similar problem-dependent optimal computations? We
+therefore asked whether the human system-level computation of visual perception
+has DNN correlates and considered several anecdotal test cases. We found that
+perceptual sensitivity to image changes has DNN mid-computation correlates,
+while sensitivity to segmentation, crowding and shape has DNN end-computation
+correlates. Our results quantify the applicability of using DNN computation to
+estimate perceptual loss, and are consistent with the fascinating theoretical
+view that properties of human perception are a consequence of
+architecture-independent visual learning.
+",1,0,0,0,0,0
+315,Analyses and estimation of certain design parameters of micro-grooved heat pipes,"  A numerical analysis of heat conduction through the cover plate of a heat
+pipe is carried out to determine the temperature of the working substance,
+average temperature of heating and cooling surfaces, heat spread in the
+transmitter, and the heat bypass through the cover plate. Analysis has been
+extended for the estimation of heat transfer requirements at the outer surface
+of the con- denser under different heat load conditions using Genetic
+Algorithm. This paper also presents the estimation of an average heat transfer
+coefficient for the boiling and condensation of the working substance inside
+the microgrooves corresponding to a known temperature of the heat source. The
+equation of motion of the working fluid in the meniscus of an equilateral
+triangular groove has been presented from which a new term called the minimum
+surface tension required for avoiding the dry out condition is defined.
+Quantitative results showing the effect of thickness of cover plate, heat load,
+angle of inclination and viscosity of the working fluid on the different
+aspects of the heat transfer, minimum surface tension required to avoid dry
+out, velocity distribution of the liquid, and radius of liquid meniscus inside
+the micro-grooves have been presented and discussed.
+",0,1,0,0,0,0
+316,"Merge decompositions, two-sided Krohn-Rhodes, and aperiodic pointlikes","  This paper provides short proofs of two fundamental theorems of finite
+semigroup theory whose previous proofs were significantly longer, namely the
+two-sided Krohn-Rhodes decomposition theorem and Henckell's aperiodic pointlike
+theorem, using a new algebraic technique that we call the merge decomposition.
+A prototypical application of this technique decomposes a semigroup $T$ into a
+two-sided semidirect product whose components are built from two subsemigroups
+$T_1,T_2$, which together generate $T$, and the subsemigroup generated by their
+setwise product $T_1T_2$. In this sense we decompose $T$ by merging the
+subsemigroups $T_1$ and $T_2$. More generally, our technique merges semigroup
+homomorphisms from free semigroups.
+",1,0,1,0,0,0
+317,Deep Multimodal Image-Repurposing Detection,"  Nefarious actors on social media and other platforms often spread rumors and
+falsehoods through images whose metadata (e.g., captions) have been modified to
+provide visual substantiation of the rumor/falsehood. This type of modification
+is referred to as image repurposing, in which often an unmanipulated image is
+published along with incorrect or manipulated metadata to serve the actor's
+ulterior motives. We present the Multimodal Entity Image Repurposing (MEIR)
+dataset, a substantially challenging dataset over that which has been
+previously available to support research into image repurposing detection. The
+new dataset includes location, person, and organization manipulations on
+real-world data sourced from Flickr. We also present a novel, end-to-end, deep
+multimodal learning model for assessing the integrity of an image by combining
+information extracted from the image with related information from a knowledge
+base. The proposed method is compared against state-of-the-art techniques on
+existing datasets as well as MEIR, where it outperforms existing methods across
+the board, with AUC improvement up to 0.23.
+",1,0,0,0,0,0
+318,DeepFense: Online Accelerated Defense Against Adversarial Deep Learning,"  Recent advances in adversarial Deep Learning (DL) have opened up a largely
+unexplored surface for malicious attacks jeopardizing the integrity of
+autonomous DL systems. With the wide-spread usage of DL in critical and
+time-sensitive applications, including unmanned vehicles, drones, and video
+surveillance systems, online detection of malicious inputs is of utmost
+importance. We propose DeepFense, the first end-to-end automated framework that
+simultaneously enables efficient and safe execution of DL models. DeepFense
+formalizes the goal of thwarting adversarial attacks as an optimization problem
+that minimizes the rarely observed regions in the latent feature space spanned
+by a DL network. To solve the aforementioned minimization problem, a set of
+complementary but disjoint modular redundancies are trained to validate the
+legitimacy of the input samples in parallel with the victim DL model. DeepFense
+leverages hardware/software/algorithm co-design and customized acceleration to
+achieve just-in-time performance in resource-constrained settings. The proposed
+countermeasure is unsupervised, meaning that no adversarial sample is leveraged
+to train modular redundancies. We further provide an accompanying API to reduce
+the non-recurring engineering cost and ensure automated adaptation to various
+platforms. Extensive evaluations on FPGAs and GPUs demonstrate up to two orders
+of magnitude performance improvement while enabling online adversarial sample
+detection.
+",1,0,0,1,0,0
+319,Retrospective Higher-Order Markov Processes for User Trails,"  Users form information trails as they browse the web, checkin with a
+geolocation, rate items, or consume media. A common problem is to predict what
+a user might do next for the purposes of guidance, recommendation, or
+prefetching. First-order and higher-order Markov chains have been widely used
+methods to study such sequences of data. First-order Markov chains are easy to
+estimate, but lack accuracy when history matters. Higher-order Markov chains,
+in contrast, have too many parameters and suffer from overfitting the training
+data. Fitting these parameters with regularization and smoothing only offers
+mild improvements. In this paper we propose the retrospective higher-order
+Markov process (RHOMP) as a low-parameter model for such sequences. This model
+is a special case of a higher-order Markov chain where the transitions depend
+retrospectively on a single history state instead of an arbitrary combination
+of history states. There are two immediate computational advantages: the number
+of parameters is linear in the order of the Markov chain and the model can be
+fit to large state spaces. Furthermore, by providing a specific structure to
+the higher-order chain, RHOMPs improve the model accuracy by efficiently
+utilizing history states without risks of overfitting the data. We demonstrate
+how to estimate a RHOMP from data and we demonstrate the effectiveness of our
+method on various real application datasets spanning geolocation data, review
+sequences, and business locations. The RHOMP model uniformly outperforms
+higher-order Markov chains, Kneser-Ney regularization, and tensor
+factorizations in terms of prediction accuracy.
+",1,0,0,1,0,0
+320,Frank-Wolfe with Subsampling Oracle,"  We analyze two novel randomized variants of the Frank-Wolfe (FW) or
+conditional gradient algorithm. While classical FW algorithms require solving a
+linear minimization problem over the domain at each iteration, the proposed
+method only requires to solve a linear minimization problem over a small
+\emph{subset} of the original domain. The first algorithm that we propose is a
+randomized variant of the original FW algorithm and achieves a
+$\mathcal{O}(1/t)$ sublinear convergence rate as in the deterministic
+counterpart. The second algorithm is a randomized variant of the Away-step FW
+algorithm, and again as its deterministic counterpart, reaches linear (i.e.,
+exponential) convergence rate making it the first provably convergent
+randomized variant of Away-step FW. In both cases, while subsampling reduces
+the convergence rate by a constant factor, the linear minimization step can be
+a fraction of the cost of that of the deterministic versions, especially when
+the data is streamed. We illustrate computational gains of the algorithms on
+regression problems, involving both $\ell_1$ and latent group lasso penalties.
+",0,0,0,1,0,0
+321,A mean value formula and a Liouville theorem for the complex Monge-Ampère equation,"  In this paper, we prove a mean value formula for bounded subharmonic
+Hermitian matrix valued function on a complete Riemannian manifold with
+nonnegative Ricci curvature. As its application, we obtain a Liouville type
+theorem for the complex Monge-Ampère equation on product manifolds.
+",0,0,1,0,0,0
+322,Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution,"  In this work, we investigate the value of uncertainty modeling in 3D
+super-resolution with convolutional neural networks (CNNs). Deep learning has
+shown success in a plethora of medical image transformation problems, such as
+super-resolution (SR) and image synthesis. However, the highly ill-posed nature
+of such problems results in inevitable ambiguity in the learning of networks.
+We propose to account for intrinsic uncertainty through a per-patch
+heteroscedastic noise model and for parameter uncertainty through approximate
+Bayesian inference in the form of variational dropout. We show that the
+combined benefits of both lead to the state-of-the-art performance SR of
+diffusion MR brain images in terms of errors compared to ground truth. We
+further show that the reduced error scores produce tangible benefits in
+downstream tractography. In addition, the probabilistic nature of the methods
+naturally confers a mechanism to quantify uncertainty over the super-resolved
+output. We demonstrate through experiments on both healthy and pathological
+brains the potential utility of such an uncertainty measure in the risk
+assessment of the super-resolved images for subsequent clinical use.
+",1,0,0,0,0,0
+323,Yu-Shiba-Rusinov bands in superconductors in contact with a magnetic insulator,"  Superconductor-Ferromagnet (SF) heterostructures are of interest due to
+numerous phenomena related to the spin-dependent interaction of Cooper pairs
+with the magnetization. Here we address the effects of a magnetic insulator on
+the density of states of a superconductor based on a recently developed
+boundary condition for strongly spin-dependent interfaces. We show that the
+boundary to a magnetic insulator has a similar effect like the presence of
+magnetic impurities. In particular we find that the impurity effects of
+strongly scattering localized spins leading to the formation of Shiba bands can
+be mapped onto the boundary problem.
+",0,1,0,0,0,0
+324,Bayesian Optimization for Probabilistic Programs,"  We present the first general purpose framework for marginal maximum a
+posteriori estimation of probabilistic program variables. By using a series of
+code transformations, the evidence of any probabilistic program, and therefore
+of any graphical model, can be optimized with respect to an arbitrary subset of
+its sampled variables. To carry out this optimization, we develop the first
+Bayesian optimization package to directly exploit the source code of its
+target, leading to innovations in problem-independent hyperpriors, unbounded
+optimization, and implicit constraint satisfaction; delivering significant
+performance improvements over prominent existing packages. We present
+applications of our method to a number of tasks including engineering design
+and parameter optimization.
+",1,0,0,1,0,0
+325,Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model,"  Long-term load forecasting plays a vital role for utilities and planners in
+terms of grid development and expansion planning. An overestimate of long-term
+electricity load will result in substantial wasted investment in the
+construction of excess power facilities, while an underestimate of future load
+will result in insufficient generation and unmet demand. This paper presents
+first-of-its-kind approach to use multiplicative error model (MEM) in
+forecasting load for long-term horizon. MEM originates from the structure of
+autoregressive conditional heteroscedasticity (ARCH) model where conditional
+variance is dynamically parameterized and it multiplicatively interacts with an
+innovation term of time-series. Historical load data, accessed from a U.S.
+regional transmission operator, and recession data for years 1993-2016 is used
+in this study. The superiority of considering volatility is proven by
+out-of-sample forecast results as well as directional accuracy during the great
+economic recession of 2008. To incorporate future volatility, backtesting of
+MEM model is performed. Two performance indicators used to assess the proposed
+model are mean absolute percentage error (for both in-sample model fit and
+out-of-sample forecasts) and directional accuracy.
+",0,0,0,1,0,0
+326,Geometrically stopped Markovian random growth processes and Pareto tails,"  Many empirical studies document power law behavior in size distributions of
+economic interest such as cities, firms, income, and wealth. One mechanism for
+generating such behavior combines independent and identically distributed
+Gaussian additive shocks to log-size with a geometric age distribution. We
+generalize this mechanism by allowing the shocks to be non-Gaussian (but
+light-tailed) and dependent upon a Markov state variable. Our main results
+provide sharp bounds on tail probabilities and simple formulas for Pareto
+exponents. We present two applications: (i) we show that the tails of the
+wealth distribution in a heterogeneous-agent dynamic general equilibrium model
+with idiosyncratic endowment risk decay exponentially, unlike models with
+investment risk where the tails may be Paretian, and (ii) we show that a random
+growth model for the population dynamics of Japanese prefectures is consistent
+with the observed Pareto exponent but only after allowing for Markovian
+dynamics.
+",0,0,1,0,0,0
+327,Mathematics of Topological Quantum Computing,"  In topological quantum computing, information is encoded in ""knotted"" quantum
+states of topological phases of matter, thus being locked into topology to
+prevent decay. Topological precision has been confirmed in quantum Hall liquids
+by experiments to an accuracy of $10^{-10}$, and harnessed to stabilize quantum
+memory. In this survey, we discuss the conceptual development of this
+interdisciplinary field at the juncture of mathematics, physics and computer
+science. Our focus is on computing and physical motivations, basic mathematical
+notions and results, open problems and future directions related to and/or
+inspired by topological quantum computing.
+",0,1,1,0,0,0
+328,Graph Clustering using Effective Resistance,"  $ \def\vecc#1{\boldsymbol{#1}} $We design a polynomial time algorithm that
+for any weighted undirected graph $G = (V, E,\vecc w)$ and sufficiently large
+$\delta > 1$, partitions $V$ into subsets $V_1, \ldots, V_h$ for some $h\geq
+1$, such that
+$\bullet$ at most $\delta^{-1}$ fraction of the weights are between clusters,
+i.e. \[ w(E - \cup_{i = 1}^h E(V_i)) \lesssim \frac{w(E)}{\delta};\]
+$\bullet$ the effective resistance diameter of each of the induced subgraphs
+$G[V_i]$ is at most $\delta^3$ times the average weighted degree, i.e. \[
+\max_{u, v \in V_i} \mathsf{Reff}_{G[V_i]}(u, v) \lesssim \delta^3 \cdot
+\frac{|V|}{w(E)} \quad \text{ for all } i=1, \ldots, h.\]
+In particular, it is possible to remove one percent of weight of edges of any
+given graph such that each of the resulting connected components has effective
+resistance diameter at most the inverse of the average weighted degree.
+Our proof is based on a new connection between effective resistance and low
+conductance sets. We show that if the effective resistance between two vertices
+$u$ and $v$ is large, then there must be a low conductance cut separating $u$
+from $v$. This implies that very mildly expanding graphs have constant
+effective resistance diameter. We believe that this connection could be of
+independent interest in algorithm design.
+",1,0,0,0,0,0
+329,Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?,"  Self-supervised learning (SSL) is a reliable learning mechanism in which a
+robot enhances its perceptual capabilities. Typically, in SSL a trusted,
+primary sensor cue provides supervised training data to a secondary sensor cue.
+In this article, a theoretical analysis is performed on the fusion of the
+primary and secondary cue in a minimal model of SSL. A proof is provided that
+determines the specific conditions under which it is favorable to perform
+fusion. In short, it is favorable when (i) the prior on the target value is
+strong or (ii) the secondary cue is sufficiently accurate. The theoretical
+findings are validated with computational experiments. Subsequently, a
+real-world case study is performed to investigate if fusion in SSL is also
+beneficial when assumptions of the minimal model are not met. In particular, a
+flying robot learns to map pressure measurements to sonar height measurements
+and then fuses the two, resulting in better height estimation. Fusion is also
+beneficial in the opposite case, when pressure is the primary cue. The analysis
+and results are encouraging to study SSL fusion also for other robots and
+sensors.
+",1,0,0,0,0,0
+330,Playing Atari with Six Neurons,"  Deep reinforcement learning on Atari games maps pixel directly to actions;
+internally, the deep neural network bears the responsibility of both extracting
+useful information and making decisions based on it. Aiming at devoting entire
+deep networks to decision making alone, we propose a new method for learning
+policies and compact state representations separately but simultaneously for
+policy approximation in reinforcement learning. State representations are
+generated by a novel algorithm based on Vector Quantization and Sparse Coding,
+trained online along with the network, and capable of growing its dictionary
+size over time. We also introduce new techniques allowing both the neural
+network and the evolution strategy to cope with varying dimensions. This
+enables networks of only 6 to 18 neurons to learn to play a selection of Atari
+games with performance comparable---and occasionally superior---to
+state-of-the-art techniques using evolution strategies on deep networks two
+orders of magnitude larger.
+",0,0,0,1,0,0
+331,Contraction and uniform convergence of isotonic regression,"  We consider the problem of isotonic regression, where the underlying signal
+$x$ is assumed to satisfy a monotonicity constraint, that is, $x$ lies in the
+cone $\{ x\in\mathbb{R}^n : x_1 \leq \dots \leq x_n\}$. We study the isotonic
+projection operator (projection to this cone), and find a necessary and
+sufficient condition characterizing all norms with respect to which this
+projection is contractive. This enables a simple and non-asymptotic analysis of
+the convergence properties of isotonic regression, yielding uniform confidence
+bands that adapt to the local Lipschitz properties of the signal.
+",0,0,1,1,0,0
+332,A Systematic Approach for Exploring Tradeoffs in Predictive HVAC Control Systems for Buildings,"  Heating, Ventilation, and Cooling (HVAC) systems are often the most
+significant contributor to the energy usage, and the operational cost, of large
+office buildings. Therefore, to understand the various factors affecting the
+energy usage, and to optimize the operational efficiency of building HVAC
+systems, energy analysts and architects often create simulations (e.g.,
+EnergyPlus or DOE-2), of buildings prior to construction or renovation to
+determine energy savings and quantify the Return-on-Investment (ROI). While
+useful, these simulations usually use static HVAC control strategies such as
+lowering room temperature at night, or reactive control based on simulated room
+occupancy. Recently, advances have been made in HVAC control algorithms that
+predict room occupancy. However, these algorithms depend on costly sensor
+installations and the tradeoffs between predictive accuracy, energy savings,
+comfort and expenses are not well understood. Current simulation frameworks do
+not support easy analysis of these tradeoffs. Our contribution is a simulation
+framework that can be used to explore this design space by generating objective
+estimates of the energy savings and occupant comfort for different levels of
+HVAC prediction and control performance. We validate our framework on a
+real-world occupancy dataset spanning 6 months for 235 rooms in a large
+university office building. Using the gold standard of energy use modeling and
+simulation (Revit and Energy Plus), we compare the energy consumption and
+occupant comfort in 29 independent simulations that explore our parameter
+space. Our results highlight a number of potentially useful tradeoffs with
+respect to energy savings, comfort, and algorithmic performance among
+predictive, reactive, and static schedules, for a stakeholder of our building.
+",1,0,0,0,0,0
+333,On types of degenerate critical points of real polynomial functions,"  In this paper, we consider the problem of identifying the type (local
+minimizer, maximizer or saddle point) of a given isolated real critical point
+$c$, which is degenerate, of a multivariate polynomial function $f$. To this
+end, we introduce the definition of faithful radius of $c$ by means of the
+curve of tangency of $f$. We show that the type of $c$ can be determined by the
+global extrema of $f$ over the Euclidean ball centered at $c$ with a faithful
+radius.We propose algorithms to compute a faithful radius of $c$ and determine
+its type.
+",0,0,1,0,0,0
+334,Contribution of Data Categories to Readmission Prediction Accuracy,"  Identification of patients at high risk for readmission could help reduce
+morbidity and mortality as well as healthcare costs. Most of the existing
+studies on readmission prediction did not compare the contribution of data
+categories. In this study we analyzed relative contribution of 90,101 variables
+across 398,884 admission records corresponding to 163,468 patients, including
+patient demographics, historical hospitalization information, discharge
+disposition, diagnoses, procedures, medications and laboratory test results. We
+established an interpretable readmission prediction model based on Logistic
+Regression in scikit-learn, and added the available variables to the model one
+by one in order to analyze the influences of individual data categories on
+readmission prediction accuracy. Diagnosis related groups (c-statistic
+increment of 0.0933) and discharge disposition (c-statistic increment of
+0.0269) were the strongest contributors to model accuracy. Additionally, we
+also identified the top ten contributing variables in every data category.
+",0,0,0,0,1,0
+335,Tropical recurrent sequences,"  Tropical recurrent sequences are introduced satisfying a given vector (being
+a tropical counterpart of classical linear recurrent sequences). We consider
+the case when Newton polygon of the vector has a single (bounded) edge. In this
+case there are periodic tropical recurrent sequences which are similar to
+classical linear recurrent sequences. A question is studied when there exists a
+non-periodic tropical recurrent sequence satisfying a given vector, and partial
+answers are provided to this question. Also an algorithm is designed which
+tests existence of non-periodic tropical recurrent sequences satisfying a given
+vector with integer coordinates. Finally, we introduce a tropical entropy of a
+vector and provide some bounds on it.
+",1,0,0,0,0,0
+336,Invariance of Weight Distributions in Rectified MLPs,"  An interesting approach to analyzing neural networks that has received
+renewed attention is to examine the equivalent kernel of the neural network.
+This is based on the fact that a fully connected feedforward network with one
+hidden layer, a certain weight distribution, an activation function, and an
+infinite number of neurons can be viewed as a mapping into a Hilbert space. We
+derive the equivalent kernels of MLPs with ReLU or Leaky ReLU activations for
+all rotationally-invariant weight distributions, generalizing a previous result
+that required Gaussian weight distributions. Additionally, the Central Limit
+Theorem is used to show that for certain activation functions, kernels
+corresponding to layers with weight distributions having $0$ mean and finite
+absolute third moment are asymptotically universal, and are well approximated
+by the kernel corresponding to layers with spherical Gaussian weights. In deep
+networks, as depth increases the equivalent kernel approaches a pathological
+fixed point, which can be used to argue why training randomly initialized
+networks can be difficult. Our results also have implications for weight
+initialization.
+",1,0,0,1,0,0
+337,Learning to Adapt by Minimizing Discrepancy,"  We explore whether useful temporal neural generative models can be learned
+from sequential data without back-propagation through time. We investigate the
+viability of a more neurocognitively-grounded approach in the context of
+unsupervised generative modeling of sequences. Specifically, we build on the
+concept of predictive coding, which has gained influence in cognitive science,
+in a neural framework. To do so we develop a novel architecture, the Temporal
+Neural Coding Network, and its learning algorithm, Discrepancy Reduction. The
+underlying directed generative model is fully recurrent, meaning that it
+employs structural feedback connections and temporal feedback connections,
+yielding information propagation cycles that create local learning signals.
+This facilitates a unified bottom-up and top-down approach for information
+transfer inside the architecture. Our proposed algorithm shows promise on the
+bouncing balls generative modeling problem. Further experiments could be
+conducted to explore the strengths and weaknesses of our approach.
+",1,0,0,1,0,0
+338,Incarnation of Majorana Fermions in Kitaev Quantum Spin Lattice,"  Kitaev quantum spin liquid is a topological magnetic quantum state
+characterized by Majorana fermions of fractionalized spin excitations, which
+are identical to their own antiparticles. Here, we demonstrate emergence of
+Majorana fermions thermally fractionalized in the Kitaev honeycomb spin lattice
+{\alpha}-RuCl3. The specific heat data unveil the characteristic two-stage
+release of magnetic entropy involving localized and itinerant Majorana
+fermions. The inelastic neutron scattering results further corroborate these
+two distinct fermions by exhibiting quasielastic excitations at low energies
+around the Brillouin zone center and Y-shaped magnetic continuum at high
+energies, which are evident for the ferromagnetic Kitaev model. Our results
+provide an opportunity to build a unified conceptual framework of
+fractionalized excitations, applicable also for the quantum Hall states,
+superconductors, and frustrated magnets.
+",0,1,0,0,0,0
+339,Haro 11: Where is the Lyman continuum source?,"  Identifying the mechanism by which high energy Lyman continuum (LyC) photons
+escaped from early galaxies is one of the most pressing questions in cosmic
+evolution. Haro 11 is the best known local LyC leaking galaxy, providing an
+important opportunity to test our understanding of LyC escape. The observed LyC
+emission in this galaxy presumably originates from one of the three bright,
+photoionizing knots known as A, B, and C. It is known that Knot C has strong
+Ly$\alpha$ emission, and Knot B hosts an unusually bright ultraluminous X-ray
+source, which may be a low-luminosity AGN. To clarify the LyC source, we carry
+out ionization-parameter mapping (IPM) by obtaining narrow-band imaging from
+the Hubble Space Telescope WFC3 and ACS cameras to construct spatially resolved
+ratio maps of [OIII]/[OII] emission from the galaxy. IPM traces the ionization
+structure of the interstellar medium and allows us to identify optically thin
+regions. To optimize the continuum subtraction, we introduce a new method for
+determining the best continuum scale factor derived from the mode of the
+continuum-subtracted, image flux distribution. We find no conclusive evidence
+of LyC escape from Knots B or C, but instead, we identify a high-ionization
+region extending over at least 1 kpc from Knot A. Knot A shows evidence of an
+extremely young age ($\lesssim 1$ Myr), perhaps containing very massive stars
+($>100$ M$_\odot$). It is weak in Ly$\alpha$, so if it is confirmed as the LyC
+source, our results imply that LyC emission may be independent of Ly$\alpha$
+emission.
+",0,1,0,0,0,0
+340,Typed Closure Conversion for the Calculus of Constructions,"  Dependently typed languages such as Coq are used to specify and verify the
+full functional correctness of source programs. Type-preserving compilation can
+be used to preserve these specifications and proofs of correctness through
+compilation into the generated target-language programs. Unfortunately,
+type-preserving compilation of dependent types is hard. In essence, the problem
+is that dependent type systems are designed around high-level compositional
+abstractions to decide type checking, but compilation interferes with the
+type-system rules for reasoning about run-time terms.
+We develop a type-preserving closure-conversion translation from the Calculus
+of Constructions (CC) with strong dependent pairs ($\Sigma$ types)---a subset
+of the core language of Coq---to a type-safe, dependently typed compiler
+intermediate language named CC-CC. The central challenge in this work is how to
+translate the source type-system rules for reasoning about functions into
+target type-system rules for reasoning about closures. To justify these rules,
+we prove soundness of CC-CC by giving a model in CC. In addition to type
+preservation, we prove correctness of separate compilation.
+",1,0,0,0,0,0
+341,Untangling Planar Curves,"  Any generic closed curve in the plane can be transformed into a simple closed
+curve by a finite sequence of local transformations called homotopy moves. We
+prove that simplifying a planar closed curve with $n$ self-crossings requires
+$\Theta(n^{3/2})$ homotopy moves in the worst case. Our algorithm improves the
+best previous upper bound $O(n^2)$, which is already implicit in the classical
+work of Steinitz; the matching lower bound follows from the construction of
+closed curves with large defect, a topological invariant of generic closed
+curves introduced by Aicardi and Arnold. Our lower bound also implies that
+$\Omega(n^{3/2})$ facial electrical transformations are required to reduce any
+plane graph with treewidth $\Omega(\sqrt{n})$ to a single vertex, matching
+known upper bounds for rectangular and cylindrical grid graphs. More generally,
+we prove that transforming one immersion of $k$ circles with at most $n$
+self-crossings into another requires $\Theta(n^{3/2} + nk + k^2)$ homotopy
+moves in the worst case. Finally, we prove that transforming one
+noncontractible closed curve to another on any orientable surface requires
+$\Omega(n^2)$ homotopy moves in the worst case; this lower bound is tight if
+the curve is homotopic to a simple closed curve.
+",1,0,1,0,0,0
+342,Greedy-Merge Degrading has Optimal Power-Law,"  Consider a channel with a given input distribution. Our aim is to degrade it
+to a channel with at most L output letters. One such degradation method is the
+so called ""greedy-merge"" algorithm. We derive an upper bound on the reduction
+in mutual information between input and output. For fixed input alphabet size
+and variable L, the upper bound is within a constant factor of an
+algorithm-independent lower bound. Thus, we establish that greedy-merge is
+optimal in the power-law sense.
+",1,0,1,0,0,0
+343,Radially distributed values and normal families,"  Let $L_0$ and $L_1$ be two distinct rays emanating from the origin and let
+${\mathcal F}$ be the family of all functions holomorphic in the unit disk
+${\mathbb D}$ for which all zeros lie on $L_0$ while all $1$-points lie on
+$L_1$. It is shown that ${\mathcal F}$ is normal in ${\mathbb
+D}\backslash\{0\}$. The case where $L_0$ is the positive real axis and $L_1$ is
+the negative real axis is studied in more detail.
+",0,0,1,0,0,0
+344,Characterizing Exoplanet Habitability,"  A habitable exoplanet is a world that can maintain stable liquid water on its
+surface. Techniques and approaches to characterizing such worlds are essential,
+as performing a census of Earth-like planets that may or may not have life will
+inform our understanding of how frequently life originates and is sustained on
+worlds other than our own. Observational techniques like high contrast imaging
+and transit spectroscopy can reveal key indicators of habitability for
+exoplanets. Both polarization measurements and specular reflectance from oceans
+(also known as ""glint"") can provide direct evidence for surface liquid water,
+while constraining surface pressure and temperature (from moderate resolution
+spectra) can indicate liquid water stability. Indirect evidence for
+habitability can come from a variety of sources, including observations of
+variability due to weather, surface mapping studies, and/or measurements of
+water vapor or cloud profiles that indicate condensation near a surface.
+Approaches to making the types of measurements that indicate habitability are
+diverse, and have different considerations for the required wavelength range,
+spectral resolution, maximum noise levels, stellar host temperature, and
+observing geometry.
+",0,1,0,0,0,0
+345,Deep Learning for Classification Tasks on Geospatial Vector Polygons,"  In this paper, we evaluate the accuracy of deep learning approaches on
+geospatial vector geometry classification tasks. The purpose of this evaluation
+is to investigate the ability of deep learning models to learn from geometry
+coordinates directly. Previous machine learning research applied to geospatial
+polygon data did not use geometries directly, but derived properties thereof.
+These are produced by way of extracting geometry properties such as Fourier
+descriptors. Instead, our introduced deep neural net architectures are able to
+learn on sequences of coordinates mapped directly from polygons. In three
+classification tasks we show that the deep learning architectures are
+competitive with common learning algorithms that require extracted features.
+",0,0,0,1,0,0
+346,The Distance Standard Deviation,"  The distance standard deviation, which arises in distance correlation
+analysis of multivariate data, is studied as a measure of spread. New
+representations for the distance standard deviation are obtained in terms of
+Gini's mean difference and in terms of the moments of spacings of order
+statistics. Inequalities for the distance variance are derived, proving that
+the distance standard deviation is bounded above by the classical standard
+deviation and by Gini's mean difference. Further, it is shown that the distance
+standard deviation satisfies the axiomatic properties of a measure of spread.
+Explicit closed-form expressions for the distance variance are obtained for a
+broad class of parametric distributions. The asymptotic distribution of the
+sample distance variance is derived.
+",0,0,1,1,0,0
+347,Combining learned and analytical models for predicting action effects,"  One of the most basic skills a robot should possess is predicting the effect
+of physical interactions with objects in the environment. This enables optimal
+action selection to reach a certain goal state. Traditionally, dynamics are
+approximated by physics-based analytical models. These models rely on specific
+state representations that may be hard to obtain from raw sensory data,
+especially if no knowledge of the object shape is assumed. More recently, we
+have seen learning approaches that can predict the effect of complex physical
+interactions directly from sensory input. It is however an open question how
+far these models generalize beyond their training data. In this work, we
+investigate the advantages and limitations of neural network based learning
+approaches for predicting the effects of actions based on sensory input and
+show how analytical and learned models can be combined to leverage the best of
+both worlds. As physical interaction task, we use planar pushing, for which
+there exists a well-known analytical model and a large real-world dataset. We
+propose to use a convolutional neural network to convert raw depth images or
+organized point clouds into a suitable representation for the analytical model
+and compare this approach to using neural networks for both, perception and
+prediction. A systematic evaluation of the proposed approach on a very large
+real-world dataset shows two main advantages of the hybrid architecture.
+Compared to a pure neural network, it significantly (i) reduces required
+training data and (ii) improves generalization to novel physical interaction.
+",1,0,0,0,0,0
+348,Leavitt path algebras: Graded direct-finiteness and graded $Σ$-injective simple modules,"  In this paper, we give a complete characterization of Leavitt path algebras
+which are graded $\Sigma $-$V$ rings, that is, rings over which a direct sum of
+arbitrary copies of any graded simple module is graded injective. Specifically,
+we show that a Leavitt path algebra $L$ over an arbitrary graph $E$ is a graded
+$\Sigma $-$V$ ring if and only if it is a subdirect product of matrix rings of
+arbitrary size but with finitely many non-zero entries over $K$ or
+$K[x,x^{-1}]$ with appropriate matrix gradings. We also obtain a graphical
+characterization of such a graded $\Sigma $-$V$ ring $L$% . When the graph $E$
+is finite, we show that $L$ is a graded $\Sigma $-$V$ ring $\Longleftrightarrow
+L$ is graded directly-finite $\Longleftrightarrow L $ has bounded index of
+nilpotence $\Longleftrightarrow $ $L$ is graded semi-simple. Examples show that
+the equivalence of these properties in the preceding statement no longer holds
+when the graph $E$ is infinite. Following this, we also characterize Leavitt
+path algebras $L$ which are non-graded $\Sigma $-$V$ rings. Graded rings which
+are graded directly-finite are explored and it is shown that if a Leavitt path
+algebra $L$ is a graded $\Sigma$-$V$ ring, then $L$ is always graded
+directly-finite. Examples show the subtle differences between graded and
+non-graded directly-finite rings. Leavitt path algebras which are graded
+directly-finite are shown to be directed unions of graded semisimple rings.
+Using this, we give an alternative proof of a theorem of Vaš \cite{V} on
+directly-finite Leavitt path algebras.
+",0,0,1,0,0,0
+349,Ensemble Estimation of Mutual Information,"  We derive the mean squared error convergence rates of kernel density-based
+plug-in estimators of mutual information measures between two multidimensional
+random variables $\mathbf{X}$ and $\mathbf{Y}$ for two cases: 1) $\mathbf{X}$
+and $\mathbf{Y}$ are both continuous; 2) $\mathbf{X}$ is continuous and
+$\mathbf{Y}$ is discrete. Using the derived rates, we propose an ensemble
+estimator of these information measures for the second case by taking a
+weighted sum of the plug-in estimators with varied bandwidths. The resulting
+ensemble estimator achieves the $1/N$ parametric convergence rate when the
+conditional densities of the continuous variables are sufficiently smooth. To
+the best of our knowledge, this is the first nonparametric mutual information
+estimator known to achieve the parametric convergence rate for this case, which
+frequently arises in applications (e.g. variable selection in classification).
+The estimator is simple to implement as it uses the solution to an offline
+convex optimization problem and simple plug-in estimators. A central limit
+theorem is also derived for the ensemble estimator. Ensemble estimators that
+achieve the parametric rate are also derived for the first case ($\mathbf{X}$
+and $\mathbf{Y}$ are both continuous) and another case 3) $\mathbf{X}$ and
+$\mathbf{Y}$ may have any mixture of discrete and continuous components.
+",1,0,1,1,0,0
+350,Social media mining for identification and exploration of health-related information from pregnant women,"  Widespread use of social media has led to the generation of substantial
+amounts of information about individuals, including health-related information.
+Social media provides the opportunity to study health-related information about
+selected population groups who may be of interest for a particular study. In
+this paper, we explore the possibility of utilizing social media to perform
+targeted data collection and analysis from a particular population group --
+pregnant women. We hypothesize that we can use social media to identify cohorts
+of pregnant women and follow them over time to analyze crucial health-related
+information. To identify potentially pregnant women, we employ simple
+rule-based searches that attempt to detect pregnancy announcements with
+moderate precision. To further filter out false positives and noise, we employ
+a supervised classifier using a small number of hand-annotated data. We then
+collect their posts over time to create longitudinal health timelines and
+attempt to divide the timelines into different pregnancy trimesters. Finally,
+we assess the usefulness of the timelines by performing a preliminary analysis
+to estimate drug intake patterns of our cohort at different trimesters. Our
+rule-based cohort identification technique collected 53,820 users over thirty
+months from Twitter. Our pregnancy announcement classification technique
+achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user
+timelines. Analysis of the timelines revealed that pertinent health-related
+information, such as drug-intake and adverse reactions can be mined from the
+data. Our approach to using user timelines in this fashion has produced very
+encouraging results and can be employed for other important tasks where
+cohorts, for which health-related information may not be available from other
+sources, are required to be followed over time to derive population-based
+estimates.
+",1,0,0,0,0,0
+351,The vortex method for 2D ideal flows in the exterior of a disk,"  The vortex method is a common numerical and theoretical approach used to
+implement the motion of an ideal flow, in which the vorticity is approximated
+by a sum of point vortices, so that the Euler equations read as a system of
+ordinary differential equations. Such a method is well justified in the full
+plane, thanks to the explicit representation formulas of Biot and Savart. In an
+exterior domain, we also replace the impermeable boundary by a collection of
+point vortices generating the circulation around the obstacle. The density of
+these point vortices is chosen in order that the flow remains tangent at
+midpoints between adjacent vortices. In this work, we provide a rigorous
+justification for this method in exterior domains. One of the main mathematical
+difficulties being that the Biot-Savart kernel defines a singular integral
+operator when restricted to a curve. For simplicity and clarity, we only treat
+the case of the unit disk in the plane approximated by a uniformly distributed
+mesh of point vortices. The complete and general version of our work is
+available in [arXiv:1707.01458].
+",0,0,1,0,0,0
+352,Neeman's characterization of K(R-Proj) via Bousfield localization,"  Let $R$ be an associative ring with unit and denote by $K({\rm R
+\mbox{-}Proj})$ the homotopy category of complexes of projective left
+$R$-modules. Neeman proved the theorem that $K({\rm R \mbox{-}Proj})$ is
+$\aleph_1$-compactly generated, with the category $K^+ ({\rm R \mbox{-}proj})$
+of left bounded complexes of finitely generated projective $R$-modules
+providing an essentially small class of such generators. Another proof of
+Neeman's theorem is explained, using recent ideas of Christensen and Holm, and
+Emmanouil. The strategy of the proof is to show that every complex in $K({\rm R
+\mbox{-}Proj})$ vanishes in the Bousfield localization $K({\rm R
+\mbox{-}Flat})/\langle K^+ ({\rm R \mbox{-}proj}) \rangle.$
+",0,0,1,0,0,0
+353,Qualification Conditions in Semi-algebraic Programming,"  For an arbitrary finite family of semi-algebraic/definable functions, we
+consider the corresponding inequality constraint set and we study qualification
+conditions for perturbations of this set. In particular we prove that all
+positive diagonal perturbations, save perhaps a finite number of them, ensure
+that any point within the feasible set satisfies Mangasarian-Fromovitz
+constraint qualification. Using the Milnor-Thom theorem, we provide a bound for
+the number of singular perturbations when the constraints are polynomial
+functions. Examples show that the order of magnitude of our exponential bound
+is relevant. Our perturbation approach provides a simple protocol to build
+sequences of ""regular"" problems approximating an arbitrary
+semi-algebraic/definable problem. Applications to sequential quadratic
+programming methods and sum of squares relaxation are provided.
+",0,0,1,0,0,0
+354,Curvature in Hamiltonian Mechanics And The Einstein-Maxwell-Dilaton Action,"  Riemannian geometry is a particular case of Hamiltonian mechanics: the orbits
+of the hamiltonian $H=\frac{1}{2}g^{ij}p_{i}p_{j}$ are the geodesics. Given a
+symplectic manifold (\Gamma,\omega), a hamiltonian $H:\Gamma\to\mathbb{R}$ and
+a Lagrangian sub-manifold $M\subset\Gamma$ we find a generalization of the
+notion of curvature. The particular case
+$H=\frac{1}{2}g^{ij}\left[p_{i}-A_{i}\right]\left[p_{j}-A_{j}\right]+\phi $ of
+a particle moving in a gravitational, electromagnetic and scalar fields is
+studied in more detail. The integral of the generalized Ricci tensor w.r.t. the
+Boltzmann weight reduces to the action principle
+$\int\left[R+\frac{1}{4}F_{ik}F_{jl}g^{kl}g^{ij}-g^{ij}\partial_{i}\phi\partial_{j}\phi\right]e^{-\phi}\sqrt{g}d^{n}q$
+for the scalar, vector and tensor fields.
+",0,0,1,0,0,0
+355,End-to-End Navigation in Unknown Environments using Neural Networks,"  We investigate how a neural network can learn perception actions loops for
+navigation in unknown environments. Specifically, we consider how to learn to
+navigate in environments populated with cul-de-sacs that represent convex local
+minima that the robot could fall into instead of finding a set of feasible
+actions that take it to the goal. Traditional methods rely on maintaining a
+global map to solve the problem of over coming a long cul-de-sac. However, due
+to errors induced from local and global drift, it is highly challenging to
+maintain such a map for long periods of time. One way to mitigate this problem
+is by using learning techniques that do not rely on hand engineered map
+representations and instead output appropriate control policies directly from
+their sensory input. We first demonstrate that such a problem cannot be solved
+directly by deep reinforcement learning due to the sparse reward structure of
+the environment. Further, we demonstrate that deep supervised learning also
+cannot be used directly to solve this problem. We then investigate network
+models that offer a combination of reinforcement learning and supervised
+learning and highlight the significance of adding fully differentiable memory
+units to such networks. We evaluate our networks on their ability to generalize
+to new environments and show that adding memory to such networks offers huge
+jumps in performance
+",1,0,0,0,0,0
+356,A Combinatorial Approach to the Opposite Bi-Free Partial $S$-Transform,"  In this paper, we present a combinatorial approach to the opposite 2-variable
+bi-free partial $S$-transforms where the opposite multiplication is used on the
+right. In addition, extensions of this partial $S$-transforms to the
+conditional bi-free and operator-valued bi-free settings are discussed.
+",0,0,1,0,0,0
+357,Roche-lobe overflow in eccentric planet-star systems,"  Many giant exoplanets are found near their Roche limit and in mildly
+eccentric orbits. In this study we examine the fate of such planets through
+Roche-lobe overflow as a function of the physical properties of the binary
+components, including the eccentricity and the asynchronicity of the rotating
+planet. We use a direct three-body integrator to compute the trajectories of
+the lost mass in the ballistic limit and investigate the possible outcomes. We
+find three different outcomes for the mass transferred through the Lagrangian
+point $L_{1}$: (i) self-accretion by the planet, (ii) direct impact on the
+stellar surface, (iii) disk formation around the star. We explore the parameter
+space of the three different regimes and find that at low eccentricities,
+$e\lesssim 0.2$, mass overflow leads to disk formation for most systems, while
+for higher eccentricities or retrograde orbits self-accretion is the only
+possible outcome. We conclude that the assumption often made in previous work
+that when a planet overflows its Roche lobe it is quickly disrupted and
+accreted by the star is not always valid.
+",0,1,0,0,0,0
+358,A short-orbit spectrometer for low-energy pion detection in electroproduction experiments at MAMI,"  A new Short-Orbit Spectrometer (SOS) has been constructed and installed
+within the experimental facility of the A1 collaboration at Mainz Microtron
+(MAMI), with the goal to detect low-energy pions. It is equipped with a
+Browne-Buechner magnet and a detector system consisting of two helium-ethane
+based drift chambers and a scintillator telescope made of five layers. The
+detector system allows detection of pions in the momentum range of 50 - 147
+MeV/c, which corresponds to 8.7 - 63 MeV kinetic energy. The spectrometer can
+be placed at a distance range of 54 - 66 cm from the target center. Two
+collimators are available for the measurements, one having 1.8 msr aperture and
+the other having 7 msr aperture. The Short-Orbit Spectrometer has been
+successfully calibrated and used in coincidence measurements together with the
+standard magnetic spectrometers of the A1 collaboration.
+",0,1,0,0,0,0
+359,A one-dimensional model for water desalination by flow-through electrode capacitive deionization,"  Capacitive deionization (CDI) is a fast-emerging water desalination
+technology in which a small cell voltage of ~1 V across porous carbon
+electrodes removes salt from feedwaters via electrosorption. In flow-through
+electrode (FTE) CDI cell architecture, feedwater is pumped through macropores
+or laser perforated channels in porous electrodes, enabling highly compact
+cells with parallel flow and electric field, as well as rapid salt removal. We
+here present a one-dimensional model describing water desalination by FTE CDI,
+and a comparison to data from a custom-built experimental cell. The model
+employs simple cell boundary conditions derived via scaling arguments. We show
+good model-to-data fits with reasonable values for fitting parameters such as
+the Stern layer capacitance, micropore volume, and attraction energy. Thus, we
+demonstrate that from an engineering modeling perspective, an FTE CDI cell may
+be described with simpler one-dimensional models, unlike more typical
+flow-between electrodes architecture where 2D models are required.
+",1,1,0,0,0,0
+360,Deep Learning Scooping Motion using Bilateral Teleoperations,"  We present bilateral teleoperation system for task learning and robot motion
+generation. Our system includes a bilateral teleoperation platform and a deep
+learning software. The deep learning software refers to human demonstration
+using the bilateral teleoperation platform to collect visual images and robotic
+encoder values. It leverages the datasets of images and robotic encoder
+information to learn about the inter-modal correspondence between visual images
+and robot motion. In detail, the deep learning software uses a combination of
+Deep Convolutional Auto-Encoders (DCAE) over image regions, and Recurrent
+Neural Network with Long Short-Term Memory units (LSTM-RNN) over robot motor
+angles, to learn motion taught be human teleoperation. The learnt models are
+used to predict new motion trajectories for similar tasks. Experimental results
+show that our system has the adaptivity to generate motion for similar scooping
+tasks. Detailed analysis is performed based on failure cases of the
+experimental results. Some insights about the cans and cannots of the system
+are summarized.
+",1,0,0,0,0,0
+361,XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification,"  We propose two multimodal deep learning architectures that allow for
+cross-modal dataflow (XFlow) between the feature extractors, thereby extracting
+more interpretable features and obtaining a better representation than through
+unimodal learning, for the same amount of training data. These models can
+usefully exploit correlations between audio and visual data, which have a
+different dimensionality and are therefore nontrivially exchangeable. Our work
+improves on existing multimodal deep learning metholodogies in two essential
+ways: (1) it presents a novel method for performing cross-modality (before
+features are learned from individual modalities) and (2) extends the previously
+proposed cross-connections, which only transfer information between streams
+that process compatible data. Both cross-modal architectures outperformed their
+baselines (by up to 7.5%) when evaluated on the AVletters dataset.
+",1,0,0,1,0,0
+362,Making Sense of Physics through Stories: High School Students Narratives about Electric Charges and Interactions,"  Educational research has shown that narratives are useful tools that can help
+young students make sense of scientific phenomena. Based on previous research,
+I argue that narratives can also become tools for high school students to make
+sense of concepts such as the electric field. In this paper I examine high
+school students visual and oral narratives in which they describe the
+interaction among electric charges as if they were characters of a cartoon
+series. The study investigates: given the prompt to produce narratives for
+electrostatic phenomena during a classroom activity prior to receiving formal
+instruction, (1) what ideas of electrostatics do students attend to in their
+narratives?; (2) what role do students narratives play in their understanding
+of electrostatics? The participants were a group of high school students
+engaged in an open-ended classroom activity prior to receiving formal
+instruction about electrostatics. During the activity, the group was asked to
+draw comic strips for electric charges. In addition to individual work,
+students shared their work within small groups as well as with the whole group.
+Post activity, six students from a small group were interviewed individually
+about their work. In this paper I present two cases in which students produced
+narratives to express their ideas about electrostatics in different ways. In
+each case, I present student work for the comic strip activity (visual
+narratives), their oral descriptions of their work (oral narratives) during the
+interview and/or to their peers during class, and the their ideas of the
+electric interactions expressed through their narratives.
+",0,1,0,0,0,0
+363,Preventing Hospital Acquired Infections Through a Workflow-Based Cyber-Physical System,"  Hospital acquired infections (HAI) are infections acquired within the
+hospital from healthcare workers, patients or from the environment, but which
+have no connection to the initial reason for the patient's hospital admission.
+HAI are a serious world-wide problem, leading to an increase in mortality
+rates, duration of hospitalisation as well as significant economic burden on
+hospitals. Although clear preventive guidelines exist, studies show that
+compliance to them is frequently poor. This paper details the software
+perspective for an innovative, business process software based cyber-physical
+system that will be implemented as part of a European Union-funded research
+project. The system is composed of a network of sensors mounted in different
+sites around the hospital, a series of wearables used by the healthcare workers
+and a server side workflow engine. For better understanding, we describe the
+system through the lens of a single, simple clinical workflow that is
+responsible for a significant portion of all hospital infections. The goal is
+that when completed, the system will be configurable in the sense of
+facilitating the creation and automated monitoring of those clinical workflows
+that when combined, account for over 90\% of hospital infections.
+",1,0,0,0,0,0
+364,OpenML Benchmarking Suites and the OpenML100,"  We advocate the use of curated, comprehensive benchmark suites of machine
+learning datasets, backed by standardized OpenML-based interfaces and
+complementary software toolkits written in Python, Java and R. Major
+distinguishing features of OpenML benchmark suites are (a) ease of use through
+standardized data formats, APIs, and existing client libraries; (b)
+machine-readable meta-information regarding the contents of the suite; and (c)
+online sharing of results, enabling large scale comparisons. As a first such
+suite, we propose the OpenML100, a machine learning benchmark suite of
+100~classification datasets carefully curated from the thousands of datasets
+available on OpenML.org.
+",1,0,0,1,0,0
+365,On orbifold constructions associated with the Leech lattice vertex operator algebra,"  In this article, we study orbifold constructions associated with the Leech
+lattice vertex operator algebra. As an application, we prove that the structure
+of a strongly regular holomorphic vertex operator algebra of central charge
+$24$ is uniquely determined by its weight one Lie algebra if the Lie algebra
+has the type $A_{3,4}^3A_{1,2}$, $A_{4,5}^2$, $D_{4,12}A_{2,6}$, $A_{6,7}$,
+$A_{7,4}A_{1,1}^3$, $D_{5,8}A_{1,2}$ or $D_{6,5}A_{1,1}^2$ by using the reverse
+orbifold construction. Our result also provides alternative constructions of
+these vertex operator algebras (except for the case $A_{6,7}$) from the Leech
+lattice vertex operator algebra.
+",0,0,1,0,0,0
+366,From mindless mathematics to thinking meat?,"  Deconstruction of the theme of the 2017 FQXi essay contest is already an
+interesting exercise in its own right: Teleology is rarely useful in physics
+--- the only known mainstream physics example (black hole event horizons) has a
+very mixed score-card --- so the ""goals"" and ""aims and intentions"" alluded to
+in the theme of the 2017 FQXi essay contest are already somewhat pushing the
+limits. Furthermore, ""aims and intentions"" certainly carries the implication of
+consciousness, and opens up a whole can of worms related to the mind-body
+problem. As for ""mindless mathematical laws"", that allusion is certainly in
+tension with at least some versions of the ""mathematical universe hypothesis"".
+Finally ""wandering towards a goal"" again carries the implication of
+consciousness, with all its attendant problems.
+In this essay I will argue, simply because we do not yet have any really good
+mathematical or physical theory of consciousness, that the theme of this essay
+contest is premature, and unlikely to lead to any resolution that would be
+widely accepted in the mathematics or physics communities.
+",0,1,0,0,0,0
+367,Phase correction for ALMA - Investigating water vapour radiometer scaling:The long-baseline science verification data case study,"  The Atacama Large millimetre/submillimetre Array (ALMA) makes use of water
+vapour radiometers (WVR), which monitor the atmospheric water vapour line at
+183 GHz along the line of sight above each antenna to correct for phase delays
+introduced by the wet component of the troposphere. The application of WVR
+derived phase corrections improve the image quality and facilitate successful
+observations in weather conditions that were classically marginal or poor. We
+present work to indicate that a scaling factor applied to the WVR solutions can
+act to further improve the phase stability and image quality of ALMA data. We
+find reduced phase noise statistics for 62 out of 75 datasets from the
+long-baseline science verification campaign after a WVR scaling factor is
+applied. The improvement of phase noise translates to an expected coherence
+improvement in 39 datasets. When imaging the bandpass source, we find 33 of the
+39 datasets show an improvement in the signal-to-noise ratio (S/N) between a
+few to ~30 percent. There are 23 datasets where the S/N of the science image is
+improved: 6 by <1%, 11 between 1 and 5%, and 6 above 5%. The higher frequencies
+studied (band 6 and band 7) are those most improved, specifically datasets with
+low precipitable water vapour (PWV), <1mm, where the dominance of the wet
+component is reduced. Although these improvements are not profound, phase
+stability improvements via the WVR scaling factor come into play for the higher
+frequency (>450 GHz) and long-baseline (>5km) observations. These inherently
+have poorer phase stability and are taken in low PWV (<1mm) conditions for
+which we find the scaling to be most effective. A promising explanation for the
+scaling factor is the mixing of dry and wet air components, although other
+origins are discussed. We have produced a python code to allow ALMA users to
+undertake WVR scaling tests and make improvements to their data.
+",0,1,0,0,0,0
+368,On the $μ$-ordinary locus of a Shimura variety,"  In this paper, we study the $\mu$-ordinary locus of a Shimura variety with
+parahoric level structure. Under the axioms in \cite{HR}, we show that
+$\mu$-ordinary locus is a union of some maximal Ekedahl-Kottwitz-Oort-Rapoport
+strata introduced in \cite{HR} and we give criteria on the density of the
+$\mu$-ordinary locus.
+",0,0,1,0,0,0
+369,Scatteract: Automated extraction of data from scatter plots,"  Charts are an excellent way to convey patterns and trends in data, but they
+do not facilitate further modeling of the data or close inspection of
+individual data points. We present a fully automated system for extracting the
+numerical values of data points from images of scatter plots. We use deep
+learning techniques to identify the key components of the chart, and optical
+character recognition together with robust regression to map from pixels to the
+coordinate system of the chart. We focus on scatter plots with linear scales,
+which already have several interesting challenges. Previous work has done fully
+automatic extraction for other types of charts, but to our knowledge this is
+the first approach that is fully automatic for scatter plots. Our method
+performs well, achieving successful data extraction on 89% of the plots in our
+test set.
+",1,0,0,1,0,0
+370,When the Universe Expands Too Fast: Relentless Dark Matter,"  We consider a modification to the standard cosmological history consisting of
+introducing a new species $\phi$ whose energy density red-shifts with the scale
+factor $a$ like $\rho_\phi \propto a^{-(4+n)}$. For $n>0$, such a red-shift is
+faster than radiation, hence the new species dominates the energy budget of the
+universe at early times while it is completely negligible at late times. If
+equality with the radiation energy density is achieved at low enough
+temperatures, dark matter can be produced as a thermal relic during the new
+cosmological phase. Dark matter freeze-out then occurs at higher temperatures
+compared to the standard case, implying that reproducing the observed abundance
+requires significantly larger annihilation rates. Here, we point out a
+completely new phenomenon, which we refer to as $\textit{relentless}$ dark
+matter: for large enough $n$, unlike the standard case where annihilation ends
+shortly after the departure from thermal equilibrium, dark matter particles
+keep annihilating long after leaving chemical equilibrium, with a significant
+depletion of the final relic abundance. Relentless annihilation occurs for $n
+\geq 2$ and $n \geq 4$ for s-wave and p-wave annihilation, respectively, and it
+thus occurs in well motivated scenarios such as a quintessence with a kination
+phase. We discuss a few microscopic realizations for the new cosmological
+component and highlight the phenomenological consequences of our calculations
+for dark matter searches.
+",0,1,0,0,0,0
+371,Polygons with prescribed edge slopes: configuration space and extremal points of perimeter,"  We describe the configuration space $\mathbf{S}$ of polygons with prescribed
+edge slopes, and study the perimeter $\mathcal{P}$ as a Morse function on
+$\mathbf{S}$. We characterize critical points of $\mathcal{P}$ (these are
+\textit{tangential} polygons) and compute their Morse indices. This setup is
+motivated by a number of results about critical points and Morse indices of the
+oriented area function defined on the configuration space of polygons with
+prescribed edge lengths (flexible polygons). As a by-product, we present an
+independent computation of the Morse index of the area function (obtained
+earlier by G. Panina and A. Zhukova).
+",0,0,1,0,0,0
+372,An Asymptotically Efficient Metropolis-Hastings Sampler for Bayesian Inference in Large-Scale Educational Measuremen,"  This paper discusses a Metropolis-Hastings algorithm developed by
+\citeA{MarsmanIsing}. The algorithm is derived from first principles, and it is
+proven that the algorithm becomes more efficient with more data and meets the
+growing demands of large scale educational measurement.
+",0,0,0,1,0,0
+373,When Streams of Optofluidics Meet the Sea of Life,"  Luke P. Lee is a Tan Chin Tuan Centennial Professor at the National
+University of Singapore. In this contribution he describes the power of
+optofluidics as a research tool and reviews new insights within the areas of
+single cell analysis, microphysiological analysis, and integrated systems.
+",0,0,0,0,1,0
+374,"L lines, C points and Chern numbers: understanding band structure topology using polarization fields","  Topology has appeared in different physical contexts. The most prominent
+application is topologically protected edge transport in condensed matter
+physics. The Chern number, the topological invariant of gapped Bloch
+Hamiltonians, is an important quantity in this field. Another example of
+topology, in polarization physics, are polarization singularities, called L
+lines and C points. By establishing a connection between these two theories, we
+develop a novel technique to visualize and potentially measure the Chern
+number: it can be expressed either as the winding of the polarization azimuth
+along L lines in reciprocal space, or in terms of the handedness and the index
+of the C points. For mechanical systems, this is directly connected to the
+visible motion patterns.
+",0,1,0,0,0,0
+375,Willis Theory via Graphs,"  We study the scale and tidy subgroups of an endomorphism of a totally
+disconnected locally compact group using a geometric framework. This leads to
+new interpretations of tidy subgroups and the scale function. Foremost, we
+obtain a geometric tidying procedure which applies to endomorphisms as well as
+a geometric proof of the fact that tidiness is equivalent to being minimizing
+for a given endomorphism. Our framework also yields an endomorphism version of
+the Baumgartner-Willis tree representation theorem. We conclude with a
+construction of new endomorphisms of totally disconnected locally compact
+groups from old via HNN-extensions.
+",0,0,1,0,0,0
+376,The Effect of Site-Specific Spectral Densities on the High-Dimensional Exciton-Vibrational Dynamics in the FMO Complex,"  The coupled exciton-vibrational dynamics of a three-site model of the FMO
+complex is investigated using the Multi-layer Multi-configuration
+Time-dependent Hartree (ML-MCTDH) approach. Emphasis is put on the effect of
+the spectral density on the exciton state populations as well as on the
+vibrational and vibronic non-equilibrium excitations. Models which use either a
+single or site-specific spectral densities are contrasted to a spectral density
+adapted from experiment. For the transfer efficiency, the total integrated
+Huang-Rhys factor is found to be more important than details of the spectral
+distributions. However, the latter are relevant for the obtained
+non-equilibrium vibrational and vibronic distributions and thus influence the
+actual pattern of population relaxation.
+",0,1,0,0,0,0
+377,Space dependent adhesion forces mediated by transient elastic linkages : new convergence and global existence results,"  In the first part of this work we show the convergence with respect to an
+asymptotic parameter {\epsilon} of a delayed heat equation. It represents a
+mathematical extension of works considered previously by the authors [Milisic
+et al. 2011, Milisic et al. 2016]. Namely, this is the first result involving
+delay operators approximating protein linkages coupled with a spatial elliptic
+second order operator. For the sake of simplicity we choose the Laplace
+operator, although more general results could be derived. The main arguments
+are (i) new energy estimates and (ii) a stability result extended from the
+previous work to this more involved context. They allow to prove convergence of
+the delay operator to a friction term together with the Laplace operator in the
+same asymptotic regime considered without the space dependence in [Milisic et
+al, 2011]. In a second part we extend fixed-point results for the fully
+non-linear model introduced in [Milisic et al, 2016] and prove global existence
+in time. This shows that the blow-up scenario observed previously does not
+occur. Since the latter result was interpreted as a rupture of adhesion forces,
+we discuss the possibility of bond breaking both from the analytic and
+numerical point of view.
+",0,0,1,0,0,0
+378,On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising,"  We study the Nonparametric Maximum Likelihood Estimator (NPMLE) for
+estimating Gaussian location mixture densities in $d$-dimensions from
+independent observations. Unlike usual likelihood-based methods for fitting
+mixtures, NPMLEs are based on convex optimization. We prove finite sample
+results on the Hellinger accuracy of every NPMLE. Our results imply, in
+particular, that every NPMLE achieves near parametric risk (up to logarithmic
+multiplicative factors) when the true density is a discrete Gaussian mixture
+without any prior information on the number of mixture components. NPMLEs can
+naturally be used to yield empirical Bayes estimates of the Oracle Bayes
+estimator in the Gaussian denoising problem. We prove bounds for the accuracy
+of the empirical Bayes estimate as an approximation to the Oracle Bayes
+estimator. Here our results imply that the empirical Bayes estimator performs
+at nearly the optimal level (up to logarithmic multiplicative factors) for
+denoising in clustering situations without any prior knowledge of the number of
+clusters.
+",0,0,1,1,0,0
+379,Gravitational wave production from preheating: parameter dependence,"  Parametric resonance is among the most efficient phenomena generating
+gravitational waves (GWs) in the early Universe. The dynamics of parametric
+resonance, and hence of the GWs, depend exclusively on the resonance parameter
+$q$. The latter is determined by the properties of each scenario: the initial
+amplitude and potential curvature of the oscillating field, and its coupling to
+other species. Previous works have only studied the GW production for fixed
+value(s) of $q$. We present an analytical derivation of the GW amplitude
+dependence on $q$, valid for any scenario, which we confront against numerical
+results. By running lattice simulations in an expanding grid, we study for a
+wide range of $q$ values, the production of GWs in post-inflationary preheating
+scenarios driven by parametric resonance. We present simple fits for the final
+amplitude and position of the local maxima in the GW spectrum. Our
+parametrization allows to predict the location and amplitude of the GW
+background today, for an arbitrary $q$. The GW signal can be rather large, as
+$h^2\Omega_{\rm GW}(f_p) \lesssim 10^{-11}$, but it is always peaked at high
+frequencies $f_p \gtrsim 10^{7}$ Hz. We also discuss the case of
+spectator-field scenarios, where the oscillatory field can be e.g.~a curvaton,
+or the Standard Model Higgs.
+",0,1,0,0,0,0
+380,IP determination and 1+1 REMPI spectrum of SiO at 210-220 nm with implications for SiO$^{+}$ ion trap loading,"  The 1+1 REMPI spectrum of SiO in the 210-220 nm range is recorded. Observed
+bands are assigned to the $A-X$ vibrational bands $(v``=0-3, v`=5-10)$ and a
+tentative assignment is given to the 2-photon transition from $X$ to the
+n=12-13 $[X^{2}{\Sigma}^{+},v^{+}=1]$ Rydberg states at 216-217 nm. We estimate
+the IP of SiO to be 11.59(1) eV. The SiO$^{+}$ cation has previously been
+identified as a molecular candidate amenable to laser control. Our work allows
+us to identify an efficient method for loading cold SiO$^{+}$ from an ablated
+sample of SiO into an ion trap via the $(5,0)$ $A-X$ band at 213.977 nm.
+",0,1,0,0,0,0
+381,Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions,"  Robots and automated systems are increasingly being introduced to unknown and
+dynamic environments where they are required to handle disturbances, unmodeled
+dynamics, and parametric uncertainties. Robust and adaptive control strategies
+are required to achieve high performance in these dynamic environments. In this
+paper, we propose a novel adaptive model predictive controller that combines
+model predictive control (MPC) with an underlying $\mathcal{L}_1$ adaptive
+controller to improve trajectory tracking of a system subject to unknown and
+changing disturbances. The $\mathcal{L}_1$ adaptive controller forces the
+system to behave in a predefined way, as specified by a reference model. A
+higher-level model predictive controller then uses this reference model to
+calculate the optimal reference input based on a cost function, while taking
+into account input and state constraints. We focus on the experimental
+validation of the proposed approach and demonstrate its effectiveness in
+experiments on a quadrotor. We show that the proposed approach has a lower
+trajectory tracking error compared to non-predictive, adaptive approaches and a
+predictive, non-adaptive approach, even when external wind disturbances are
+applied.
+",1,0,0,0,0,0
+382,An enhanced method to compute the similarity between concepts of ontology,"  With the use of ontologies in several domains such as semantic web,
+information retrieval, artificial intelligence, the concept of similarity
+measuring has become a very important domain of research. Therefore, in the
+current paper, we propose our method of similarity measuring which uses the
+Dijkstra algorithm to define and compute the shortest path. Then, we use this
+one to compute the semantic distance between two concepts defined in the same
+hierarchy of ontology. Afterward, we base on this result to compute the
+semantic similarity. Finally, we present an experimental comparison between our
+method and other methods of similarity measuring.
+",1,0,0,0,0,0
+383,Eigenvalues of symmetric tridiagonal interval matrices revisited,"  In this short note, we present a novel method for computing exact lower and
+upper bounds of eigenvalues of a symmetric tridiagonal interval matrix.
+Compared to the known methods, our approach is fast, simple to present and to
+implement, and avoids any assumptions. Our construction explicitly yields those
+matrices for which particular lower and upper bounds are attained.
+",1,0,0,0,0,0
+384,PRE-render Content Using Tiles (PRECUT). 1. Large-Scale Compound-Target Relationship Analyses,"  Visualizing a complex network is computationally intensive process and
+depends heavily on the number of components in the network. One way to solve
+this problem is not to render the network in real time. PRE-render Content
+Using Tiles (PRECUT) is a process to convert any complex network into a
+pre-rendered network. Tiles are generated from pre-rendered images at different
+zoom levels, and navigating the network simply becomes delivering relevant
+tiles. PRECUT is exemplified by performing large-scale compound-target
+relationship analyses. Matched molecular pair (MMP) networks were created using
+compounds and the target class description found in the ChEMBL database. To
+visualize MMP networks, the MMP network viewer has been implemented in COMBINE
+and as a web application, hosted at this http URL.
+",1,0,0,0,0,0
+385,The list chromatic number of graphs with small clique number,"  We prove that every triangle-free graph with maximum degree $\Delta$ has list
+chromatic number at most $(1+o(1))\frac{\Delta}{\ln \Delta}$. This matches the
+best-known bound for graphs of girth at least 5. We also provide a new proof
+that for any $r\geq 4$ every $K_r$-free graph has list-chromatic number at most
+$200r\frac{\Delta\ln\ln\Delta}{\ln\Delta}$.
+",0,0,1,0,0,0
+386,Topology of Large-Scale Structures of Galaxies in Two Dimensions - Systematic Effects,"  We study the two-dimensional topology of the galactic distribution when
+projected onto two-dimensional spherical shells. Using the latest Horizon Run 4
+simulation data, we construct the genus of the two-dimensional field and
+consider how this statistic is affected by late-time nonlinear effects --
+principally gravitational collapse and redshift space distortion (RSD). We also
+consider systematic and numerical artifacts such as shot noise, galaxy bias,
+and finite pixel effects. We model the systematics using a Hermite polynomial
+expansion and perform a comprehensive analysis of known effects on the
+two-dimensional genus, with a view toward using the statistic for cosmological
+parameter estimation. We find that the finite pixel effect is dominated by an
+amplitude drop and can be made less than $1\%$ by adopting pixels smaller than
+$1/3$ of the angular smoothing length. Nonlinear gravitational evolution
+introduces time-dependent coefficients of the zeroth, first, and second Hermite
+polynomials, but the genus amplitude changes by less than $1\%$ between $z=1$
+and $z=0$ for smoothing scales $R_{\rm G} > 9 {\rm Mpc/h}$. Non-zero terms are
+measured up to third order in the Hermite polynomial expansion when studying
+RSD. Differences in shapes of the genus curves in real and redshift space are
+small when we adopt thick redshift shells, but the amplitude change remains a
+significant $\sim {\cal O}(10\%)$ effect. The combined effects of galaxy
+biasing and shot noise produce systematic effects up to the second Hermite
+polynomial. It is shown that, when sampling, the use of galaxy mass cuts
+significantly reduces the effect of shot noise relative to random sampling.
+",0,1,0,0,0,0
+387,Wiki-index of authors popularity,"  The new index of the author's popularity estimation is represented in the
+paper. The index is calculated on the basis of Wikipedia encyclopedia analysis
+(Wikipedia Index - WI). Unlike the conventional existed citation indices, the
+suggested mark allows to evaluate not only the popularity of the author, as it
+can be done by means of calculating the general citation number or by the
+Hirsch index, which is often used to measure the author's research rate. The
+index gives an opportunity to estimate the author's popularity, his/her
+influence within the sought-after area ""knowledge area"" in the Internet - in
+the Wikipedia. The suggested index is supposed to be calculated in frames of
+the subject domain, and it, on the one hand, avoids the mistaken computation of
+the homonyms, and on the other hand - provides the entirety of the subject
+area. There are proposed algorithms and the technique of the Wikipedia Index
+calculation through the network encyclopedia sounding, the exemplified
+calculations of the index for the prominent researchers, and also the methods
+of the information networks formation - models of the subject domains by the
+automatic monitoring and networks information reference resources analysis. The
+considered in the paper notion network corresponds the terms-heads of the
+Wikipedia articles.
+",1,0,0,0,0,0
+388,Belyi map for the sporadic group J1,"  We compute the genus 0 Belyi map for the sporadic Janko group J1 of degree
+266 and describe the applied method. This yields explicit polynomials having J1
+as a Galois group over K(t), [K:Q] = 7.
+",0,0,1,0,0,0
+389,"Convolution Semigroups of Probability Measures on Gelfand Pairs, Revisited","  Our goal is to find classes of convolution semigroups on Lie groups $G$ that
+give rise to interesting processes in symmetric spaces $G/K$. The
+$K$-bi-invariant convolution semigroups are a well-studied example. An
+appealing direction for the next step is to generalise to right $K$-invariant
+convolution semigroups, but recent work of Liao has shown that these are in
+one-to-one correspondence with $K$-bi-invariant convolution semigroups. We
+investigate a weaker notion of right $K$-invariance, but show that this is, in
+fact, the same as the usual notion. Another possible approach is to use
+generalised notions of negative definite functions, but this also leads to
+nothing new. We finally find an interesting class of convolution semigroups
+that are obtained by making use of the Cartan decomposition of a semisimple Lie
+group, and the solution of certain stochastic differential equations. Examples
+suggest that these are well-suited for generating random motion along geodesics
+in symmetric spaces.
+",0,0,1,0,0,0
+390,Toward Optimal Coupon Allocation in Social Networks: An Approximate Submodular Optimization Approach,"  CMO Council reports that 71\% of internet users in the U.S. were influenced
+by coupons and discounts when making their purchase decisions. It has also been
+shown that offering coupons to a small fraction of users (called seed users)
+may affect the purchase decisions of many other users in a social network. This
+motivates us to study the optimal coupon allocation problem, and our objective
+is to allocate coupons to a set of users so as to maximize the expected
+cascade. Different from existing studies on influence maximizaton (IM), our
+framework allows a general utility function and a more complex set of
+constraints. In particular, we formulate our problem as an approximate
+submodular maximization problem subject to matroid and knapsack constraints.
+Existing techniques relying on the submodularity of the utility function, such
+as greedy algorithm, can not work directly on a non-submodular function. We use
+$\epsilon$ to measure the difference between our function and its closest
+submodular function and propose a novel approximate algorithm with
+approximation ratio $\beta(\epsilon)$ with $\lim_{\epsilon\rightarrow
+0}\beta(\epsilon)=1-1/e$. This is the best approximation guarantee for
+approximate submodular maximization subject to a partition matroid and knapsack
+constraints, our results apply to a broad range of optimization problems that
+can be formulated as an approximate submodular maximization problem.
+",1,0,0,0,0,0
+391,Lefschetz duality for intersection (co)homology,"  We prove the Lefschetz duality for intersection (co)homology in the framework
+of $\partial$-pesudomanifolds. We work with general perversities and without
+restriction on the coefficient ring.
+",0,0,1,0,0,0
+392,Empirical determination of the optimum attack for fragmentation of modular networks,"  All possible removals of $n=5$ nodes from networks of size $N=100$ are
+performed in order to find the optimal set of nodes which fragments the
+original network into the smallest largest connected component. The resulting
+attacks are ordered according to the size of the largest connected component
+and compared with the state of the art methods of network attacks. We chose
+attacks of size $5$ on relative small networks of size $100$ because the number
+of all possible attacks, ${100}\choose{5}$ $\approx 10^8$, is at the verge of
+the possible to compute with the available standard computers. Besides, we
+applied the procedure in a series of networks with controlled and varied
+modularity, comparing the resulting statistics with the effect of removing the
+same amount of vertices, according to the known most efficient disruption
+strategies, i.e., High Betweenness Adaptive attack (HBA), Collective Index
+attack (CI), and Modular Based Attack (MBA). Results show that modularity has
+an inverse relation with robustness, with $Q_c \approx 0.7$ being the critical
+value. For modularities lower than $Q_c$, all heuristic method gives mostly the
+same results than with random attacks, while for bigger $Q$, networks are less
+robust and highly vulnerable to malicious attacks.
+",1,0,0,0,0,0
+393,Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence,"  In this paper, we study stochastic non-convex optimization with non-convex
+random functions. Recent studies on non-convex optimization revolve around
+establishing second-order convergence, i.e., converging to a nearly
+second-order optimal stationary points. However, existing results on stochastic
+non-convex optimization are limited, especially with a high probability
+second-order convergence. We propose a novel updating step (named NCG-S) by
+leveraging a stochastic gradient and a noisy negative curvature of a stochastic
+Hessian, where the stochastic gradient and Hessian are based on a proper
+mini-batch of random functions. Building on this step, we develop two
+algorithms and establish their high probability second-order convergence. To
+the best of our knowledge, the proposed stochastic algorithms are the first
+with a second-order convergence in {\it high probability} and a time complexity
+that is {\it almost linear} in the problem's dimensionality.
+",1,0,0,1,0,0
+394,On the optimality and sharpness of Laguerre's lower bound on the smallest eigenvalue of a symmetric positive definite matrix,"  Lower bounds on the smallest eigenvalue of a symmetric positive definite
+matrices $A\in\mathbb{R}^{m\times m}$ play an important role in condition
+number estimation and in iterative methods for singular value computation. In
+particular, the bounds based on ${\rm Tr}(A^{-1})$ and ${\rm Tr}(A^{-2})$
+attract attention recently because they can be computed in $O(m)$ work when $A$
+is tridiagonal. In this paper, we focus on these bounds and investigate their
+properties in detail. First, we consider the problem of finding the optimal
+bound that can be computed solely from ${\rm Tr}(A^{-1})$ and ${\rm
+Tr}(A^{-2})$ and show that so called Laguerre's lower bound is the optimal one
+in terms of sharpness. Next, we study the gap between the Laguerre bound and
+the smallest eigenvalue. We characterize the situation in which the gap becomes
+largest in terms of the eigenvalue distribution of $A$ and show that the gap
+becomes smallest when ${\rm Tr}(A^{-2})/\{{\rm Tr}(A^{-1})\}^2$ approaches 1 or
+$\frac{1}{m}$. These results will be useful, for example, in designing
+efficient shift strategies for singular value computation algorithms.
+",1,0,1,0,0,0
+395,Attitude Control of a 2U Cubesat by Magnetic and Air Drag Torques,"  This paper describes the development of a magnetic attitude control subsystem
+for a 2U cubesat. Due to the presence of gravity gradient torques, the
+satellite dynamics are open-loop unstable near the desired pointing
+configuration. Nevertheless the linearized time-varying system is completely
+controllable, under easily verifiable conditions, and the system's disturbance
+rejection capabilities can be enhanced by adding air drag panels exemplifying a
+beneficial interplay between hardware design and control. In the paper,
+conditions for the complete controllability for the case of a magnetically
+controlled satellite with passive air drag panels are developed, and simulation
+case studies with the LQR and MPC control designs applied in combination with a
+nonlinear time-varying input transformation are presented to demonstrate the
+ability of the closed-loop system to satisfy mission objectives despite
+disturbance torques.
+",0,0,1,0,0,0
+396,"Random Forests, Decision Trees, and Categorical Predictors: The ""Absent Levels"" Problem","  One advantage of decision tree based methods like random forests is their
+ability to natively handle categorical predictors without having to first
+transform them (e.g., by using feature engineering techniques). However, in
+this paper, we show how this capability can lead to an inherent ""absent levels""
+problem for decision tree based methods that has never been thoroughly
+discussed, and whose consequences have never been carefully explored. This
+problem occurs whenever there is an indeterminacy over how to handle an
+observation that has reached a categorical split which was determined when the
+observation in question's level was absent during training. Although these
+incidents may appear to be innocuous, by using Leo Breiman and Adele Cutler's
+random forests FORTRAN code and the randomForest R package (Liaw and Wiener,
+2002) as motivating case studies, we examine how overlooking the absent levels
+problem can systematically bias a model. Furthermore, by using three real data
+examples, we illustrate how absent levels can dramatically alter a model's
+performance in practice, and we empirically demonstrate how some simple
+heuristics can be used to help mitigate the effects of the absent levels
+problem until a more robust theoretical solution is found.
+",1,0,0,1,0,0
+397,Temporal correlation detection using computational phase-change memory,"  For decades, conventional computers based on the von Neumann architecture
+have performed computation by repeatedly transferring data between their
+processing and their memory units, which are physically separated. As
+computation becomes increasingly data-centric and as the scalability limits in
+terms of performance and power are being reached, alternative computing
+paradigms are searched for in which computation and storage are collocated. A
+fascinating new approach is that of computational memory where the physics of
+nanoscale memory devices are used to perform certain computational tasks within
+the memory unit in a non-von Neumann manner. Here we present a large-scale
+experimental demonstration using one million phase-change memory devices
+organized to perform a high-level computational primitive by exploiting the
+crystallization dynamics. Also presented is an application of such a
+computational memory to process real-world data-sets. The results show that
+this co-existence of computation and storage at the nanometer scale could be
+the enabler for new, ultra-dense, low power, and massively parallel computing
+systems.
+",1,0,0,0,0,0
+398,Complexity and capacity bounds for quantum channels,"  We generalise some well-known graph parameters to operator systems by
+considering their underlying quantum channels. In particular, we introduce the
+quantum complexity as the dimension of the smallest co-domain Hilbert space a
+quantum channel requires to realise a given operator system as its
+non-commutative confusability graph. We describe quantum complexity as a
+generalised minimum semidefinite rank and, in the case of a graph operator
+system, as a quantum intersection number. The quantum complexity and a closely
+related quantum version of orthogonal rank turn out to be upper bounds for the
+Shannon zero-error capacity of a quantum channel, and we construct examples for
+which these bounds beat the best previously known general upper bound for the
+capacity of quantum channels, given by the quantum Lovász theta number.
+",0,0,1,0,0,0
+399,Quantum Interference of Glory Rescattering in Strong-Field Atomic Ionization,"  During the ionization of atoms irradiated by linearly polarized intense laser
+fields, we find for the first time that the transverse momentum distribution of
+photoelectrons can be well fitted by a squared zeroth-order Bessel function
+because of the quantum interference effect of Glory rescattering. The
+characteristic of the Bessel function is determined by the common angular
+momentum of a bunch of semiclassical paths termed as Glory trajectories, which
+are launched with different nonzero initial transverse momenta distributed on a
+specific circle in the momentum plane and finally deflected to the same
+asymptotic momentum, which is along the polarization direction, through
+post-tunneling rescattering. Glory rescattering theory (GRT) based on the
+semiclassical path-integral formalism is developed to address this effect
+quantitatively. Our theory can resolve the long-standing discrepancies between
+existing theories and experiments on the fringe location, predict the sudden
+transition of the fringe structure in holographic patterns, and shed light on
+the quantum interference aspects of low-energy structures in strong-field
+atomic ionization.
+",0,1,0,0,0,0
+400,On vector measures and extensions of transfunctions,"  We are interested in extending operators defined on positive measures, called
+here transfunctions, to signed measures and vector measures. Our methods use a
+somewhat nonstandard approach to measures and vector measures. The necessary
+background, including proofs of some auxiliary results, is included.
+",0,0,1,0,0,0
+401,Deep Within-Class Covariance Analysis for Robust Audio Representation Learning,"  Convolutional Neural Networks (CNNs) can learn effective features, though
+have been shown to suffer from a performance drop when the distribution of the
+data changes from training to test data. In this paper we analyze the internal
+representations of CNNs and observe that the representations of unseen data in
+each class, spread more (with higher variance) in the embedding space of the
+CNN compared to representations of the training data. More importantly, this
+difference is more extreme if the unseen data comes from a shifted
+distribution. Based on this observation, we objectively evaluate the degree of
+representation's variance in each class via eigenvalue decomposition on the
+within-class covariance of the internal representations of CNNs and observe the
+same behaviour. This can be problematic as larger variances might lead to
+mis-classification if the sample crosses the decision boundary of its class. We
+apply nearest neighbor classification on the representations and empirically
+show that the embeddings with the high variance actually have significantly
+worse KNN classification performances, although this could not be foreseen from
+their end-to-end classification results. To tackle this problem, we propose
+Deep Within-Class Covariance Analysis (DWCCA), a deep neural network layer that
+significantly reduces the within-class covariance of a DNN's representation,
+improving performance on unseen test data from a shifted distribution. We
+empirically evaluate DWCCA on two datasets for Acoustic Scene Classification
+(DCASE2016 and DCASE2017). We demonstrate that not only does DWCCA
+significantly improve the network's internal representation, it also increases
+the end-to-end classification accuracy, especially when the test set exhibits a
+distribution shift. By adding DWCCA to a VGG network, we achieve around 6
+percentage points improvement in the case of a distribution mismatch.
+",1,0,0,0,0,0
+402,Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret,"  We present an efficient second-order algorithm with
+$\tilde{O}(\frac{1}{\eta}\sqrt{T})$ regret for the bandit online multiclass
+problem. The regret bound holds simultaneously with respect to a family of loss
+functions parameterized by $\eta$, for a range of $\eta$ restricted by the norm
+of the competitor. The family of loss functions ranges from hinge loss
+($\eta=0$) to squared hinge loss ($\eta=1$). This provides a solution to the
+open problem of (J. Abernethy and A. Rakhlin. An efficient bandit algorithm for
+$\sqrt{T}$-regret in online multiclass prediction? In COLT, 2009). We test our
+algorithm experimentally, showing that it also performs favorably against
+earlier algorithms.
+",0,0,0,1,0,0
+403,Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning,"  Swarm systems constitute a challenging problem for reinforcement learning
+(RL) as the algorithm needs to learn decentralized control policies that can
+cope with limited local sensing and communication abilities of the agents.
+While it is often difficult to directly define the behavior of the agents,
+simple communication protocols can be defined more easily using prior knowledge
+about the given task. In this paper, we propose a number of simple
+communication protocols that can be exploited by deep reinforcement learning to
+find decentralized control policies in a multi-robot swarm environment. The
+protocols are based on histograms that encode the local neighborhood relations
+of the agents and can also transmit task-specific information, such as the
+shortest distance and direction to a desired target. In our framework, we use
+an adaptation of Trust Region Policy Optimization to learn complex
+collaborative tasks, such as formation building and building a communication
+link. We evaluate our findings in a simulated 2D-physics environment, and
+compare the implications of different communication protocols.
+",1,0,0,1,0,0
+404,Towards exascale real-time RFI mitigation,"  We describe the design and implementation of an extremely scalable real-time
+RFI mitigation method, based on the offline AOFlagger. All algorithms scale
+linearly in the number of samples. We describe how we implemented the flagger
+in the LOFAR real-time pipeline, on both CPUs and GPUs. Additionally, we
+introduce a novel simple history-based flagger that helps reduce the impact of
+our small window on the data.
+By examining an observation of a known pulsar, we demonstrate that our
+flagger can achieve much higher quality than a simple thresholder, even when
+running in real time, on a distributed system. The flagger works on visibility
+data, but also on raw voltages, and beam formed data. The algorithms are
+scale-invariant, and work on microsecond to second time scales. We are
+currently implementing a prototype for the time domain pipeline of the SKA
+central signal processor.
+",0,1,0,0,0,0
+405,Learning body-affordances to simplify action spaces,"  Controlling embodied agents with many actuated degrees of freedom is a
+challenging task. We propose a method that can discover and interpolate between
+context dependent high-level actions or body-affordances. These provide an
+abstract, low-dimensional interface indexing high-dimensional and time-
+extended action policies. Our method is related to recent ap- proaches in the
+machine learning literature but is conceptually simpler and easier to
+implement. More specifically our method requires the choice of a n-dimensional
+target sensor space that is endowed with a distance metric. The method then
+learns an also n-dimensional embedding of possibly reactive body-affordances
+that spread as far as possible throughout the target sensor space.
+",1,0,0,0,0,0
+406,Cayley properties of the line graphs induced by of consecutive layers of the hypercube,"  Let $n >3$ and $ 0< k < \frac{n}{2} $ be integers. In this paper, we
+investigate some algebraic properties of the line graph of the graph $
+{Q_n}(k,k+1) $ where $ {Q_n}(k,k+1) $ is the subgraph of the hypercube $Q_n$
+which is induced by the set of vertices of weights $k$ and $k+1$. In the first
+step, we determine the automorphism groups of these graphs for all values of
+$k$. In the second step, we study Cayley properties of the line graph of these
+graphs. In particular, we show that for $ k>2, $ if $ 2k+1 \neq n$, then the
+line graph of the graph $ {Q_n}(k,k+1) $ is a vertex-transitive non Cayley
+graph. Also, we show that the line graph of the graph $ {Q_n}(1,2) $ is a
+Cayley graph if and only if $ n$ is a power of a prime $p$.
+",0,0,1,0,0,0
+407,Beyond the technical challenges for deploying Machine Learning solutions in a software company,"  Recently software development companies started to embrace Machine Learning
+(ML) techniques for introducing a series of advanced functionality in their
+products such as personalisation of the user experience, improved search,
+content recommendation and automation. The technical challenges for tackling
+these problems are heavily researched in literature. A less studied area is a
+pragmatic approach to the role of humans in a complex modern industrial
+environment where ML based systems are developed. Key stakeholders affect the
+system from inception and up to operation and maintenance. Product managers
+want to embed ""smart"" experiences for their users and drive the decisions on
+what should be built next; software engineers are challenged to build or
+utilise ML software tools that require skills that are well outside of their
+comfort zone; legal and risk departments may influence design choices and data
+access; operations teams are requested to maintain ML systems which are
+non-stationary in their nature and change behaviour over time; and finally ML
+practitioners should communicate with all these stakeholders to successfully
+build a reliable system. This paper discusses some of the challenges we faced
+in Atlassian as we started investing more in the ML space.
+",1,0,0,1,0,0
+408,Class-Splitting Generative Adversarial Networks,"  Generative Adversarial Networks (GANs) produce systematically better quality
+samples when class label information is provided., i.e. in the conditional GAN
+setup. This is still observed for the recently proposed Wasserstein GAN
+formulation which stabilized adversarial training and allows considering high
+capacity network architectures such as ResNet. In this work we show how to
+boost conditional GAN by augmenting available class labels. The new classes
+come from clustering in the representation space learned by the same GAN model.
+The proposed strategy is also feasible when no class information is available,
+i.e. in the unsupervised setup. Our generated samples reach state-of-the-art
+Inception scores for CIFAR-10 and STL-10 datasets in both supervised and
+unsupervised setup.
+",0,0,0,1,0,0
+409,Dynamical system analysis of dark energy models in scalar coupled metric-torsion theories,"  We study the phase space dynamics of cosmological models in the theoretical
+formulations of non-minimal metric-torsion couplings with a scalar field, and
+investigate in particular the critical points which yield stable solutions
+exhibiting cosmic acceleration driven by the {\em dark energy}. The latter is
+defined in a way that it effectively has no direct interaction with the
+cosmological fluid, although in an equivalent scalar-tensor cosmological setup
+the scalar field interacts with the fluid (which we consider to be the
+pressureless dust). Determining the conditions for the existence of the stable
+critical points we check their physical viability, in both Einstein and Jordan
+frames. We also verify that in either of these frames, the evolution of the
+universe at the corresponding stable points matches with that given by the
+respective exact solutions we have found in an earlier work (arXiv: 1611.00654
+[gr-qc]). We not only examine the regions of physical relevance for the
+trajectories in the phase space when the coupling parameter is varied, but also
+demonstrate the evolution profiles of the cosmological parameters of interest
+along fiducial trajectories in the effectively non-interacting scenarios, in
+both Einstein and Jordan frames.
+",0,1,0,0,0,0
+410,J-MOD$^{2}$: Joint Monocular Obstacle Detection and Depth Estimation,"  In this work, we propose an end-to-end deep architecture that jointly learns
+to detect obstacles and estimate their depth for MAV flight applications. Most
+of the existing approaches either rely on Visual SLAM systems or on depth
+estimation models to build 3D maps and detect obstacles. However, for the task
+of avoiding obstacles this level of complexity is not required. Recent works
+have proposed multi task architectures to both perform scene understanding and
+depth estimation. We follow their track and propose a specific architecture to
+jointly estimate depth and obstacles, without the need to compute a global map,
+but maintaining compatibility with a global SLAM system if needed. The network
+architecture is devised to exploit the joint information of the obstacle
+detection task, that produces more reliable bounding boxes, with the depth
+estimation one, increasing the robustness of both to scenario changes. We call
+this architecture J-MOD$^{2}$. We test the effectiveness of our approach with
+experiments on sequences with different appearance and focal lengths and
+compare it to SotA multi task methods that jointly perform semantic
+segmentation and depth estimation. In addition, we show the integration in a
+full system using a set of simulated navigation experiments where a MAV
+explores an unknown scenario and plans safe trajectories by using our detection
+model.
+",1,0,0,0,0,0
+411,The Calabi flow with rough initial data,"  In this paper, we prove that there exists a dimensional constant $\delta > 0$
+such that given any background Kähler metric $\omega$, the Calabi flow with
+initial data $u_0$ satisfying \begin{equation*} \partial \bar \partial u_0 \in
+L^\infty (M) \text{ and } (1- \delta )\omega < \omega_{u_0} < (1+\delta
+)\omega, \end{equation*} admits a unique short time solution and it becomes
+smooth immediately, where $\omega_{u_0} : = \omega +\sqrt{-1}\partial
+\bar\partial u_0$. The existence time depends on initial data $u_0$ and the
+metric $\omega$. As a corollary, we get that Calabi flow has short time
+existence for any initial data satisfying \begin{equation*} \partial \bar
+\partial u_0 \in C^0(M) \text{ and } \omega_{u_0} > 0, \end{equation*} which
+should be interpreted as a ""continuous Kähler metric"". A main technical
+ingredient is Schauder-type estimates for biharmonic heat equation on
+Riemannian manifolds with time weighted Hölder norms.
+",0,0,1,0,0,0
+412,Star Formation Activity in the molecular cloud G35.20$-$0.74: onset of cloud-cloud collision,"  To probe the star-formation (SF) processes, we present results of an analysis
+of the molecular cloud G35.20$-$0.74 (hereafter MCG35.2) using multi-frequency
+observations. The MCG35.2 is depicted in a velocity range of 30-40 km s$^{-1}$.
+An almost horseshoe-like structure embedded within the MCG35.2 is evident in
+the infrared and millimeter images and harbors the previously known sites,
+ultra-compact/hyper-compact G35.20$-$0.74N H\,{\sc ii} region, Ap2-1, and
+Mercer 14 at its base. The site, Ap2-1 is found to be excited by a radio
+spectral type of B0.5V star where the distribution of 20 cm and H$\alpha$
+emission is surrounded by the extended molecular hydrogen emission. Using the
+{\it Herschel} 160-500 $\mu$m and photometric 1-24 $\mu$m data analysis,
+several embedded clumps and clusters of young stellar objects (YSOs) are
+investigated within the MCG35.2, revealing the SF activities. Majority of the
+YSOs clusters and massive clumps (500-4250 M$_{\odot}$) are seen toward the
+horseshoe-like structure. The position-velocity analysis of $^{13}$CO emission
+shows a blue-shifted peak (at 33 km s$^{-1}$) and a red-shifted peak (at 37 km
+s$^{-1}$) interconnected by lower intensity intermediated velocity emission,
+tracing a broad bridge feature. The presence of such broad bridge feature
+suggests the onset of a collision between molecular components in the MCG35.2.
+A noticeable change in the H-band starlight mean polarization angles has also
+been observed in the MCG35.2, probably tracing the interaction between
+molecular components. Taken together, it seems that the cloud-cloud collision
+process has influenced the birth of massive stars and YSOs clusters in the
+MCG35.2.
+",0,1,0,0,0,0
+413,Oblivious Routing via Random Walks,"  We present novel oblivious routing algorithms for both splittable and
+unsplittable multicommodity flow. Our algorithm for minimizing congestion for
+\emph{unsplittable} multicommodity flow is the first oblivious routing
+algorithm for this setting. As an intermediate step towards this algorithm, we
+present a novel generalization of Valiant's classical load balancing scheme for
+packet-switched networks to arbitrary graphs, which is of independent interest.
+Our algorithm for minimizing congestion for \emph{splittable} multicommodity
+flow improves upon the state-of-the-art, in terms of both running time and
+performance, for graphs that exhibit good expansion guarantees. Our algorithms
+rely on diffusing traffic via iterative applications of the random walk
+operator. Consequently, the performance guarantees of our algorithms are
+derived from the convergence of the random walk operator to the stationary
+distribution and are expressed in terms of the spectral gap of the graph (which
+dominates the mixing time).
+",1,0,0,0,0,0
+414,On Functional Graphs of Quadratic Polynomials,"  We study functional graphs generated by quadratic polynomials over prime
+fields. We introduce efficient algorithms for methodical computations and
+provide the values of various direct and cumulative statistical parameters of
+interest. These include: the number of connected functional graphs, the number
+of graphs having a maximal cycle, the number of cycles of fixed size, the
+number of components of fixed size, as well as the shape of trees extracted
+from functional graphs. We particularly focus on connected functional graphs,
+that is, the graphs which contain only one component (and thus only one cycle).
+Based on the results of our computations, we formulate several conjectures
+highlighting the similarities and differences between these functional graphs
+and random mappings.
+",0,0,1,0,0,0
+415,Helmholtz decomposition theorem and Blumenthal's extension by regularization,"  Helmholtz decomposition theorem for vector fields is usually presented with
+too strong restrictions on the fields and only for time independent fields.
+Blumenthal showed in 1905 that decomposition is possible for any asymptotically
+weakly decreasing vector field. He used a regularization method in his proof
+which can be extended to prove the theorem even for vector fields
+asymptotically increasing sublinearly. Blumenthal's result is then applied to
+the time-dependent fields of the dipole radiation and an artificial sublinearly
+increasing field.
+",0,1,0,0,0,0
+416,A homotopy decomposition of the fibre of the squaring map on $Ω^3S^{17}$,"  We use Richter's $2$-primary proof of Gray's conjecture to give a homotopy
+decomposition of the fibre $\Omega^3S^{17}\{2\}$ of the $H$-space squaring map
+on the triple loop space of the $17$-sphere. This induces a splitting of the
+mod-$2$ homotopy groups $\pi_\ast(S^{17}; \mathbb{Z}/2\mathbb{Z})$ in terms of
+the integral homotopy groups of the fibre of the double suspension
+$E^2:S^{2n-1} \to \Omega^2S^{2n+1}$ and refines a result of Cohen and Selick,
+who gave similar decompositions for $S^5$ and $S^9$. We relate these
+decompositions to various Whitehead products in the homotopy groups of mod-$2$
+Moore spaces and Stiefel manifolds to show that the Whitehead square $[i_{2n},
+i_{2n}]$ of the inclusion of the bottom cell of the Moore space $P^{2n+1}(2)$
+is divisible by $2$ if and only if $2n=2, 4, 8$ or $16$.
+",0,0,1,0,0,0
+417,Spaces of orders of some one-relator groups,"  We show that certain orderable groups admit no isolated left orders. The
+groups we consider are cyclic amalgamations of a free group with a general
+orderable group, the HNN extensions of free groups over cyclic subgroups, and a
+particular class of one-relator groups. In order to prove the results about
+orders, we develop perturbation techniques for actions of these groups on the
+line.
+",0,0,1,0,0,0
+418,Adversarial Attacks on Neural Network Policies,"  Machine learning classifiers are known to be vulnerable to inputs maliciously
+constructed by adversaries to force misclassification. Such adversarial
+examples have been extensively studied in the context of computer vision
+applications. In this work, we show adversarial attacks are also effective when
+targeting neural network policies in reinforcement learning. Specifically, we
+show existing adversarial example crafting techniques can be used to
+significantly degrade test-time performance of trained policies. Our threat
+model considers adversaries capable of introducing small perturbations to the
+raw input of the policy. We characterize the degree of vulnerability across
+tasks and training algorithms, for a subclass of adversarial-example attacks in
+white-box and black-box settings. Regardless of the learned task or training
+algorithm, we observe a significant drop in performance, even with small
+adversarial perturbations that do not interfere with human perception. Videos
+are available at this http URL.
+",1,0,0,1,0,0
+419,Stellar streams as gravitational experiments I. The case of Sagittarius,"  Tidal streams of disrupting dwarf galaxies orbiting around their host galaxy
+offer a unique way to constrain the shape of galactic gravitational potentials.
+Such streams can be used as leaning tower gravitational experiments on galactic
+scales. The most well motivated modification of gravity proposed as an
+alternative to dark matter on galactic scales is Milgromian dynamics (MOND),
+and we present here the first ever N-body simulations of the dynamical
+evolution of the disrupting Sagittarius dwarf galaxy in this framework. Using a
+realistic baryonic mass model for the Milky Way, we attempt to reproduce the
+present-day spatial and kinematic structure of the Sagittarius dwarf and its
+immense tidal stream that wraps around the Milky Way. With very little freedom
+on the original structure of the progenitor, constrained by the total
+luminosity of the Sagittarius structure and by the observed stellar mass-size
+relation for isolated dwarf galaxies, we find reasonable agreement between our
+simulations and observations of this system. The observed stellar velocities in
+the leading arm can be reproduced if we include a massive hot gas corona around
+the Milky Way that is flattened in the direction of the principal plane of its
+satellites. This is the first time that tidal dissolution in MOND has been
+tested rigorously at these mass and acceleration scales.
+",0,1,0,0,0,0
+420,Tuning quantum non-local effects in graphene plasmonics,"  The response of an electron system to electromagnetic fields with sharp
+spatial variations is strongly dependent on quantum electronic properties, even
+in ambient conditions, but difficult to access experimentally. We use
+propagating graphene plasmons, together with an engineered dielectric-metallic
+environment, to probe the graphene electron liquid and unveil its detailed
+electronic response at short wavelengths.The near-field imaging experiments
+reveal a parameter-free match with the full theoretical quantum description of
+the massless Dirac electron gas, in which we identify three types of quantum
+effects as keys to understanding the experimental response of graphene to
+short-ranged terahertz electric fields. The first type is of single-particle
+nature and is related to shape deformations of the Fermi surface during a
+plasmon oscillations. The second and third types are a many-body effect
+controlled by the inertia and compressibility of the interacting electron
+liquid in graphene. We demonstrate how, in principle, our experimental approach
+can determine the full spatiotemporal response of an electron system.
+",0,1,0,0,0,0
+421,Flows along arch filaments observed in the GRIS 'very fast spectroscopic mode',"  A new generation of solar instruments provides improved spectral, spatial,
+and temporal resolution, thus facilitating a better understanding of dynamic
+processes on the Sun. High-resolution observations often reveal
+multiple-component spectral line profiles, e.g., in the near-infrared He I
+10830 \AA\ triplet, which provides information about the chromospheric velocity
+and magnetic fine structure. We observed an emerging flux region, including two
+small pores and an arch filament system, on 2015 April 17 with the 'very fast
+spectroscopic mode' of the GREGOR Infrared Spectrograph (GRIS) situated at the
+1.5-meter GREGOR solar telescope at Observatorio del Teide, Tenerife, Spain. We
+discuss this method of obtaining fast (one per minute) spectral scans of the
+solar surface and its potential to follow dynamic processes on the Sun. We
+demonstrate the performance of the 'very fast spectroscopic mode' by tracking
+chromospheric high-velocity features in the arch filament system.
+",0,1,0,0,0,0
+422,Rethinking Information Sharing for Actionable Threat Intelligence,"  In the past decade, the information security and threat landscape has grown
+significantly making it difficult for a single defender to defend against all
+attacks at the same time. This called for introduc- ing information sharing, a
+paradigm in which threat indicators are shared in a community of trust to
+facilitate defenses. Standards for representation, exchange, and consumption of
+indicators are pro- posed in the literature, although various issues are
+undermined. In this paper, we rethink information sharing for actionable
+intelli- gence, by highlighting various issues that deserve further explo-
+ration. We argue that information sharing can benefit from well- defined use
+models, threat models, well-understood risk by mea- surement and robust
+scoring, well-understood and preserved pri- vacy and quality of indicators and
+robust mechanism to avoid free riding behavior of selfish agent. We call for
+using the differential nature of data and community structures for optimizing
+sharing.
+",1,0,0,0,0,0
+423,More new classes of permutation trinomials over $\mathbb{F}_{2^n}$,"  Permutation polynomials over finite fields have wide applications in many
+areas of science and engineering. In this paper, we present six new classes of
+permutation trinomials over $\mathbb{F}_{2^n}$ which have explicit forms by
+determining the solutions of some equations.
+",0,0,1,0,0,0
+424,Distributive Aronszajn trees,"  Ben-David and Shelah proved that if $\lambda$ is a singular strong-limit
+cardinal and $2^\lambda=\lambda^+$, then $\square^*_\lambda$ entails the
+existence of a normal $\lambda$-distributive $\lambda^+$-Aronszajn tree. Here,
+it is proved that the same conclusion remains valid after replacing the
+hypothesis $\square^*_\lambda$ by $\square(\lambda^+,{<}\lambda)$.
+As $\square(\lambda^+,{<}\lambda)$ does not impose a bound on the order-type
+of the witnessing clubs, our construction is necessarily different from that of
+Ben-David and Shelah, and instead uses walks on ordinals augmented with club
+guessing.
+A major component of this work is the study of postprocessing functions and
+their effect on square sequences. A byproduct of this study is the finding that
+for $\kappa$ regular uncountable, $\square(\kappa)$ entails the existence of a
+partition of $\kappa$ into $\kappa$ many fat sets. When contrasted with a
+classic model of Magidor, this shows that it is equiconsistent with the
+existence of a weakly compact cardinal that $\omega_2$ cannot be split into two
+fat sets.
+",0,0,1,0,0,0
+425,Analytical solutions for the radial Scarf II potential,"  The real Scarf II potential is discussed as a radial problem. This potential
+has been studied extensively as a one-dimensional problem, and now these
+results are used to construct its bound and resonance solutions for $l=0$ by
+setting the origin at some arbitrary value of the coordinate. The solutions
+with appropriate boundary conditions are composed as the linear combination of
+the two independent solutions of the Schrödinger equation. The asymptotic
+expression of these solutions is used to construct the $S_0(k)$ s-wave
+$S$-matrix, the poles of which supply the $k$ values corresponding to the
+bound, resonance and anti-bound solutions. The location of the discrete energy
+eigenvalues is analyzed, and the relation of the solutions of the radial and
+one-dimensional Scarf II potentials is discussed. It is shown that the
+generalized Woods--Saxon potential can be generated from the Rosen--Morse II
+potential in the same way as the radial Scarf II potential is obtained from its
+one-dimensional correspondent. Based on this analogy, possible applications are
+also pointed out.
+",0,1,0,0,0,0
+426,Gated Multimodal Units for Information Fusion,"  This paper presents a novel model for multimodal learning based on gated
+neural networks. The Gated Multimodal Unit (GMU) model is intended to be used
+as an internal unit in a neural network architecture whose purpose is to find
+an intermediate representation based on a combination of data from different
+modalities. The GMU learns to decide how modalities influence the activation of
+the unit using multiplicative gates. It was evaluated on a multilabel scenario
+for genre classification of movies using the plot and the poster. The GMU
+improved the macro f-score performance of single-modality approaches and
+outperformed other fusion strategies, including mixture of experts models.
+Along with this work, the MM-IMDb dataset is released which, to the best of our
+knowledge, is the largest publicly available multimodal dataset for genre
+prediction on movies.
+",0,0,0,1,0,0
+427,Why Condorcet Consistency is Essential,"  In a single winner election with several candidates and ranked choice or
+rating scale ballots, a Condorcet winner is one who wins all their two way
+races by majority rule or MR. A voting system has Condorcet consistency or CC
+if it names any Condorcet winner the winner. Many voting systems lack CC, but a
+three step line of reasoning is used here to show why it is necessary. In step
+1 we show that we can dismiss all the electoral criteria which conflict with
+CC. In step 2 we point out that CC follows almost automatically if we can agree
+that MR is the only acceptable system for elections with two candidates. In
+step 3 we make that argument for MR. This argument itself has three parts.
+First, in races with two candidates, the only well known alternatives to MR can
+sometimes name as winner a candidate who is preferred over their opponent by
+only one voter, with all others preferring the opponent. That is unacceptable.
+Second, those same systems are also extremely susceptible to strategic
+insincere voting. Third, in simulation studies using spatial models with two
+candidates, the best known alternative to MR picks the best or most centrist
+candidate significantly less often than MR does.
+",0,0,0,1,0,0
+428,Birefringence induced by pp-wave modes in an electromagnetically active dynamic aether,"  In the framework of the Einstein-Maxwell-aether theory we study the
+birefringence effect, which can occur in the pp-wave symmetric dynamic aether.
+The dynamic aether is considered to be latently birefringent quasi-medium,
+which displays this hidden property if and only if the aether motion is
+non-uniform, i.e., when the aether flow is characterized by the non-vanishing
+expansion, shear, vorticity or acceleration. In accordance with the
+dynamo-optical scheme of description of the interaction between electromagnetic
+waves and the dynamic aether, we shall model the susceptibility tensors by the
+terms linear in the covariant derivative of the aether velocity four-vector.
+When the pp-wave modes appear in the dynamic aether, we deal with a
+gravitationally induced degeneracy removal with respect to hidden
+susceptibility parameters. As a consequence, the phase velocities of
+electromagnetic waves possessing orthogonal polarizations do not coincide, thus
+displaying the birefringence effect. Two electromagnetic field configurations
+are studied in detail: longitudinal and transversal with respect to the aether
+pp-wave front. For both cases the solutions are found, which reveal anomalies
+in the electromagnetic response on the action of the pp-wave aether mode.
+",0,1,0,0,0,0
+429,On generalizations of $p$-sets and their applications,"  The $p$-set, which is in a simple analytic form, is well distributed in unit
+cubes. The well-known Weil's exponential sum theorem presents an upper bound of
+the exponential sum over the $p$-set. Based on the result, one shows that the
+$p$-set performs well in numerical integration, in compressed sensing as well
+as in UQ. However, $p$-set is somewhat rigid since the cardinality of the
+$p$-set is a prime $p$ and the set only depends on the prime number $p$. The
+purpose of this paper is to present generalizations of $p$-sets, say
+$\mathcal{P}_{d,p}^{{\mathbf a},\epsilon}$, which is more flexible.
+Particularly, when a prime number $p$ is given, we have many different choices
+of the new $p$-sets. Under the assumption that Goldbach conjecture holds, for
+any even number $m$, we present a point set, say ${\mathcal L}_{p,q}$, with
+cardinality $m-1$ by combining two different new $p$-sets, which overcomes a
+major bottleneck of the $p$-set. We also present the upper bounds of the
+exponential sums over $\mathcal{P}_{d,p}^{{\mathbf a},\epsilon}$ and ${\mathcal
+L}_{p,q}$, which imply these sets have many potential applications.
+",1,0,1,0,0,0
+430,Robot human interface for housekepeer with wireless capabilities,"  This paper presents the design and implementation of a Human Interface for a
+housekeeper robot. It bases on the idea of making the robot understand the
+human needs without making the human go through the details of robots work, for
+example, the way that the robot implements the work or the method that the
+robot uses to plan the path in order to reach the work area. The interface
+commands based on idioms of the natural human language and designed in a manner
+that the user gives the robot several commands with their execution date/time.
+",1,0,0,0,0,0
+431,Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers,"  This work proposes a new algorithm for training a re-weighted L2 Support
+Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Candès
+et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In
+particular, the margin required for each training vector is set independently,
+defining a new weighted SVM model. These weights are selected to be binary, and
+they are automatically adapted during the training of the model, resulting in a
+variation of the Frank-Wolfe optimization algorithm with essentially the same
+computational complexity as the original algorithm. As shown experimentally,
+this algorithm is computationally cheaper to apply since it requires less
+iterations to converge, and it produces models with a sparser representation in
+terms of support vectors and which are more stable with respect to the
+selection of the regularization hyper-parameter.
+",1,0,0,1,0,0
+432,Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation,"  Learning-based approaches to robotic manipulation are limited by the
+scalability of data collection and accessibility of labels. In this paper, we
+present a multi-task domain adaptation framework for instance grasping in
+cluttered scenes by utilizing simulated robot experiments. Our neural network
+takes monocular RGB images and the instance segmentation mask of a specified
+target object as inputs, and predicts the probability of successfully grasping
+the specified object for each candidate motor command. The proposed transfer
+learning framework trains a model for instance grasping in simulation and uses
+a domain-adversarial loss to transfer the trained model to real robots using
+indiscriminate grasping data, which is available both in simulation and the
+real world. We evaluate our model in real-world robot experiments, comparing it
+with alternative model architectures as well as an indiscriminate grasping
+baseline.
+",1,0,0,0,0,0
+433,Bounded Depth Ascending HNN Extensions and $π_1$-Semistability at $\infty$,"  A 1-ended finitely presented group has semistable fundamental group at
+$\infty$ if it acts geometrically on some (equivalently any) simply connected
+and locally finite complex $X$ with the property that any two proper rays in
+$X$ are properly homotopic. If $G$ has semistable fundamental group at $\infty$
+then one can unambiguously define the fundamental group at $\infty$ for $G$.
+The problem, asking if all finitely presented groups have semistable
+fundamental group at $\infty$ has been studied for over 40 years. If $G$ is an
+ascending HNN extension of a finitely presented group then indeed, $G$ has
+semistable fundamental group at $\infty$, but since the early 1980's it has
+been suggested that the finitely presented groups that are ascending HNN
+extensions of {\it finitely generated} groups may include a group with
+non-semistable fundamental group at $\infty$. Ascending HNN extensions
+naturally break into two classes, those with bounded depth and those with
+unbounded depth. Our main theorem shows that bounded depth finitely presented
+ascending HNN extensions of finitely generated groups have semistable
+fundamental group at $\infty$. Semistability is equivalent to two weaker
+asymptotic conditions on the group holding simultaneously. We show one of these
+conditions holds for all ascending HNN extensions, regardless of depth. We give
+a technique for constructing ascending HNN extensions with unbounded depth.
+This work focuses attention on a class of groups that may contain a group with
+non-semistable fundamental group at $\infty$.
+",0,0,1,0,0,0
+434,AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles,"  Developing and testing algorithms for autonomous vehicles in real world is an
+expensive and time consuming process. Also, in order to utilize recent advances
+in machine intelligence and deep learning we need to collect a large amount of
+annotated training data in a variety of conditions and environments. We present
+a new simulator built on Unreal Engine that offers physically and visually
+realistic simulations for both of these goals. Our simulator includes a physics
+engine that can operate at a high frequency for real-time hardware-in-the-loop
+(HITL) simulations with support for popular protocols (e.g. MavLink). The
+simulator is designed from the ground up to be extensible to accommodate new
+types of vehicles, hardware platforms and software protocols. In addition, the
+modular design enables various components to be easily usable independently in
+other projects. We demonstrate the simulator by first implementing a quadrotor
+as an autonomous vehicle and then experimentally comparing the software
+components with real-world flights.
+",1,0,0,0,0,0
+435,Hausdorff dimensions in $p$-adic analytic groups,"  Let $G$ be a finitely generated pro-$p$ group, equipped with the $p$-power
+series. The associated metric and Hausdorff dimension function give rise to the
+Hausdorff spectrum, which consists of the Hausdorff dimensions of closed
+subgroups of $G$. In the case where $G$ is $p$-adic analytic, the Hausdorff
+dimension function is well understood; in particular, the Hausdorff spectrum
+consists of finitely many rational numbers closely linked to the analytic
+dimensions of subgroups of $G$.
+Conversely, it is a long-standing open question whether the finiteness of the
+Hausdorff spectrum implies that $G$ is $p$-adic analytic. We prove that the
+answer is yes, in a strong sense, under the extra condition that $G$ is
+soluble.
+Furthermore, we explore the problem and related questions also for other
+filtration series, such as the lower $p$-series, the Frattini series, the
+modular dimension subgroup series and quite general filtration series. For
+instance, we prove, for odd primes $p$, that every countably based pro-$p$
+group $G$ with an open subgroup mapping onto 2 copies of the $p$-adic integers
+admits a filtration series such that the corresponding Hausdorff spectrum
+contains an infinite real interval.
+",0,0,1,0,0,0
+436,Real-time brain machine interaction via social robot gesture control,"  Brain-Machine Interaction (BMI) system motivates interesting and promising
+results in forward/feedback control consistent with human intention. It holds
+great promise for advancements in patient care and applications to
+neurorehabilitation. Here, we propose a novel neurofeedback-based BCI robotic
+platform using a personalized social robot in order to assist patients having
+cognitive deficits through bilateral rehabilitation and mental training. For
+initial testing of the platform, electroencephalography (EEG) brainwaves of a
+human user were collected in real time during tasks of imaginary movements.
+First, the brainwaves associated with imagined body kinematics parameters were
+decoded to control a cursor on a computer screen in training protocol. Then,
+the experienced subject was able to interact with a social robot via our
+real-time BMI robotic platform. Corresponding to subject's imagery performance,
+he/she received specific gesture movements and eye color changes as
+neural-based feedback from the robot. This hands-free neurofeedback interaction
+not only can be used for mind control of a social robot's movements, but also
+sets the stage for application to enhancing and recovering mental abilities
+such as attention via training in humans by providing real-time neurofeedback
+from a social robot.
+",1,0,0,0,0,0
+437,City-Scale Road Audit System using Deep Learning,"  Road networks in cities are massive and is a critical component of mobility.
+Fast response to defects, that can occur not only due to regular wear and tear
+but also because of extreme events like storms, is essential. Hence there is a
+need for an automated system that is quick, scalable and cost-effective for
+gathering information about defects. We propose a system for city-scale road
+audit, using some of the most recent developments in deep learning and semantic
+segmentation. For building and benchmarking the system, we curated a dataset
+which has annotations required for road defects. However, many of the labels
+required for road audit have high ambiguity which we overcome by proposing a
+label hierarchy. We also propose a multi-step deep learning model that segments
+the road, subdivide the road further into defects, tags the frame for each
+defect and finally localizes the defects on a map gathered using GPS. We
+analyze and evaluate the models on image tagging as well as segmentation at
+different levels of the label hierarchy.
+",1,0,0,0,0,0
+438,Mass and moment of inertia govern the transition in the dynamics and wakes of freely rising and falling cylinders,"  In this Letter, we study the motion and wake-patterns of freely rising and
+falling cylinders in quiescent fluid. We show that the amplitude of oscillation
+and the overall system-dynamics are intricately linked to two parameters: the
+particle's mass-density relative to the fluid $m^* \equiv \rho_p/\rho_f$ and
+its relative moment-of-inertia $I^* \equiv {I}_p/{I}_f$. This supersedes the
+current understanding that a critical mass density ($m^*\approx$ 0.54) alone
+triggers the sudden onset of vigorous vibrations. Using over 144 combinations
+of ${m}^*$ and $I^*$, we comprehensively map out the parameter space covering
+very heavy ($m^* > 10$) to very buoyant ($m^* < 0.1$) particles. The entire
+data collapses into two scaling regimes demarcated by a transitional Strouhal
+number, $St_t \approx 0.17$. $St_t$ separates a mass-dominated regime from a
+regime dominated by the particle's moment of inertia. A shift from one regime
+to the other also marks a gradual transition in the wake-shedding pattern: from
+the classical $2S$~(2-Single) vortex mode to a $2P$~(2-Pairs) vortex mode.
+Thus, auto-rotation can have a significant influence on the trajectories and
+wakes of freely rising isotropic bodies.
+",0,1,0,0,0,0
+439,It Takes Two to Tango: Towards Theory of AI's Mind,"  Theory of Mind is the ability to attribute mental states (beliefs, intents,
+knowledge, perspectives, etc.) to others and recognize that these mental states
+may differ from one's own. Theory of Mind is critical to effective
+communication and to teams demonstrating higher collective performance. To
+effectively leverage the progress in Artificial Intelligence (AI) to make our
+lives more productive, it is important for humans and AI to work well together
+in a team. Traditionally, there has been much emphasis on research to make AI
+more accurate, and (to a lesser extent) on having it better understand human
+intentions, tendencies, beliefs, and contexts. The latter involves making AI
+more human-like and having it develop a theory of our minds. In this work, we
+argue that for human-AI teams to be effective, humans must also develop a
+theory of AI's mind (ToAIM) - get to know its strengths, weaknesses, beliefs,
+and quirks. We instantiate these ideas within the domain of Visual Question
+Answering (VQA). We find that using just a few examples (50), lay people can be
+trained to better predict responses and oncoming failures of a complex VQA
+model. We further evaluate the role existing explanation (or interpretability)
+modalities play in helping humans build ToAIM. Explainable AI has received
+considerable scientific and popular attention in recent times. Surprisingly, we
+find that having access to the model's internal states - its confidence in its
+top-k predictions, explicit or implicit attention maps which highlight regions
+in the image (and words in the question) the model is looking at (and listening
+to) while answering a question about an image - do not help people better
+predict its behavior.
+",1,0,0,0,0,0
+440,"On variation of dynamical canonical heights, and Intersection numbers","  We study families of varieties endowed with polarized canonical eigensystems
+of several maps, inducing canonical heights on the dominating variety as well
+as on the ""good"" fibers of the family. We show explicitely the dependence on
+the parameter for global and local canonical heights defined by Kawaguchi when
+the fibers change, extending previous works of J. Silverman and others.
+Finally, fixing an absolute value $v \in K$ and a variety $V/K$, we descript
+the Kawaguchi`s canonical local height $\hat{\lambda}_{V,E,\mathcal{Q},}(.,v)$
+as an intersection number, provided that the polarized system $(V,\mathcal{Q})$
+has a certain weak Néron model over Spec$(\mathcal{O}_v)$ to be defined and
+under some conditions depending on the special fiber. With this we extend
+Néron's work strengthening Silverman's results, which were for systems
+having only one map.
+",0,0,1,0,0,0
+441,Enhancing the Spectral Hardening of Cosmic TeV Photons by Mixing with Axionlike Particles in the Magnetized Cosmic Web,"  Large-scale extragalactic magnetic fields may induce conversions between
+very-high-energy photons and axionlike particles (ALPs), thereby shielding the
+photons from absorption on the extragalactic background light. However, in
+simplified ""cell"" models, used so far to represent extragalactic magnetic
+fields, this mechanism would be strongly suppressed by current astrophysical
+bounds. Here we consider a recent model of extragalactic magnetic fields
+obtained from large-scale cosmological simulations. Such simulated magnetic
+fields would have large enhancement in the filaments of matter. As a result,
+photon-ALP conversions would produce a significant spectral hardening for
+cosmic TeV photons. This effect would be probed with the upcoming Cherenkov
+Telescope Array detector. This possible detection would give a unique chance to
+perform a tomography of the magnetized cosmic web with ALPs.
+",0,1,0,0,0,0
+442,Forecasting in the light of Big Data,"  Predicting the future state of a system has always been a natural motivation
+for science and practical applications. Such a topic, beyond its obvious
+technical and societal relevance, is also interesting from a conceptual point
+of view. This owes to the fact that forecasting lends itself to two equally
+radical, yet opposite methodologies. A reductionist one, based on the first
+principles, and the naive inductivist one, based only on data. This latter view
+has recently gained some attention in response to the availability of
+unprecedented amounts of data and increasingly sophisticated algorithmic
+analytic techniques. The purpose of this note is to assess critically the role
+of big data in reshaping the key aspects of forecasting and in particular the
+claim that bigger data leads to better predictions. Drawing on the
+representative example of weather forecasts we argue that this is not generally
+the case. We conclude by suggesting that a clever and context-dependent
+compromise between modelling and quantitative analysis stands out as the best
+forecasting strategy, as anticipated nearly a century ago by Richardson and von
+Neumann.
+",0,1,0,0,0,0
+443,Adelic point groups of elliptic curves,"  We show that for an elliptic curve E defined over a number field K, the group
+E(A) of points of E over the adele ring A of K is a topological group that can
+be analyzed in terms of the Galois representation associated to the torsion
+points of E. An explicit description of E(A) is given, and we prove that for K
+of degree n, almost all elliptic curves over K have an adelic point group
+topologically isomorphic to a universal group depending on n. We also show that
+there exist infinitely many elliptic curves over K having a different adelic
+point group.
+",0,0,1,0,0,0
+444,Position Aided Beam Alignment for Millimeter Wave Backhaul Systems with Large Phased Arrays,"  Wireless backhaul communication has been recently realized with large
+antennas operating in the millimeter wave (mmWave) frequency band and
+implementing highly directional beamforming. In this paper, we focus on the
+alignment problem of narrow beams between fixed position network nodes in
+mmWave backhaul systems that are subject to small displacements due to wind
+flow or ground vibration. We consider nodes equipped with antenna arrays that
+are capable of performing only analog processing and communicate through
+wireless channels including a line-of-sight component. Aiming at minimizing the
+time needed to achieve beam alignment, we present an efficient method that
+capitalizes on the exchange of position information between the nodes to design
+their beamforming and combining vectors. Some numerical results on the outage
+probability with the proposed beam alignment method offer useful preliminary
+insights on the impact of some system and operation parameters.
+",1,0,1,0,0,0
+445,Deep & Cross Network for Ad Click Predictions,"  Feature engineering has been the key to the success of many prediction
+models. However, the process is non-trivial and often requires manual feature
+engineering or exhaustive searching. DNNs are able to automatically learn
+feature interactions; however, they generate all the interactions implicitly,
+and are not necessarily efficient in learning all types of cross features. In
+this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits
+of a DNN model, and beyond that, it introduces a novel cross network that is
+more efficient in learning certain bounded-degree feature interactions. In
+particular, DCN explicitly applies feature crossing at each layer, requires no
+manual feature engineering, and adds negligible extra complexity to the DNN
+model. Our experimental results have demonstrated its superiority over the
+state-of-art algorithms on the CTR prediction dataset and dense classification
+dataset, in terms of both model accuracy and memory usage.
+",1,0,0,1,0,0
+446,Fan-type spin structure in uni-axial chiral magnets,"  We investigate the spin structure of a uni-axial chiral magnet near the
+transition temperatures in low fields perpendicular to the helical axis. We
+find a fan-type modulation structure where the clockwise and counterclockwise
+windings appear alternatively along the propagation direction of the modulation
+structure. This structure is often realized in a Yoshimori-type (non-chiral)
+helimagnet but it is rarely realized in a chiral helimagnet. To discuss
+underlying physics of this structure, we reconsider the phase diagram (phase
+boundary and crossover lines) through the free energy and asymptotic behaviors
+of isolated solitons. The fan structure appears slightly below the phase
+boundary of the continuous transition of instability-type. In this region,
+there are no solutions containing any types of isolated solitons to the mean
+field equations.
+",0,1,0,0,0,0
+447,Rotation of a synchronous viscoelastic shell,"  Several natural satellites of the giant planets have shown evidence of a
+global internal ocean, coated by a thin, icy crust. This crust is probably
+viscoelastic, which would alter its rotational response. This response would
+translate into several rotational quantities, i.e. the obliquity, and the
+librations at different frequencies, for which the crustal elasticity reacts
+differently. This study aims at modelling the global response of the
+viscoelastic crust. For that, I derive the time-dependency of the tensor of
+inertia, which I combine with the time evolution of the rotational quantities,
+thanks to an iterative algorithm. This algorithm combines numerical simulations
+of the rotation with a digital filtering of the resulting tensor of inertia.
+The algorithm works very well in the elastic case, provided the problem is not
+resonant. However, considering tidal dissipation adds different phase lags to
+the oscillating contributions, which challenge the convergence of the
+algorithm.
+",0,1,0,0,0,0
+448,Direct estimation of density functionals using a polynomial basis,"  A number of fundamental quantities in statistical signal processing and
+information theory can be expressed as integral functions of two probability
+density functions. Such quantities are called density functionals as they map
+density functions onto the real line. For example, information divergence
+functions measure the dissimilarity between two probability density functions
+and are useful in a number of applications. Typically, estimating these
+quantities requires complete knowledge of the underlying distribution followed
+by multi-dimensional integration. Existing methods make parametric assumptions
+about the data distribution or use non-parametric density estimation followed
+by high-dimensional integration. In this paper, we propose a new alternative.
+We introduce the concept of ""data-driven basis functions"" - functions of
+distributions whose value we can estimate given only samples from the
+underlying distributions without requiring distribution fitting or direct
+integration. We derive a new data-driven complete basis that is similar to the
+deterministic Bernstein polynomial basis and develop two methods for performing
+basis expansions of functionals of two distributions. We also show that the new
+basis set allows us to approximate functions of distributions as closely as
+desired. Finally, we evaluate the methodology by developing data driven
+estimators for the Kullback-Leibler divergences and the Hellinger distance and
+by constructing empirical estimates of tight bounds on the Bayes error rate.
+",1,0,0,1,0,0
+449,Experimental Evidence on a Refined Conjecture of the BSD type,"  Let $E/\mathbb{Q}$ be an elliptic curve of level $N$ and rank equal to $1$.
+Let $p$ be a prime of ordinary reduction. We experimentally study conjecture
+$4$ of B. Mazur and J. Tate in his article ""Refined Conjectures of the Birch
+and Swinnerton-Dyer Type"". We report the computational evidence.
+",0,0,1,0,0,0
+450,The Tu--Deng Conjecture holds almost surely,"  The Tu--Deng Conjecture is concerned with the sum of digits $w(n)$ of $n$ in
+base~$2$ (the Hamming weight of the binary expansion of $n$) and states the
+following: assume that $k$ is a positive integer and $1\leq t<2^k-1$. Then
+\[\Bigl \lvert\Bigl\{(a,b)\in\bigl\{0,\ldots,2^k-2\bigr\}^2:a+b\equiv t\bmod
+2^k-1, w(a)+w(b)<k\Bigr\}\Bigr \rvert\leq 2^{k-1}.\]
+We prove that the Tu--Deng Conjecture holds almost surely in the following
+sense: the proportion of $t\in[1,2^k-2]$ such that the above inequality holds
+approaches $1$ as $k\rightarrow\infty$.
+Moreover, we prove that the Tu--Deng Conjecture implies a conjecture due to
+T.~W.~Cusick concerning the sum of digits of $n$ and $n+t$.
+",1,0,1,0,0,0
+451,Convergence Analysis of the Dynamics of a Special Kind of Two-Layered Neural Networks with $\ell_1$ and $\ell_2$ Regularization,"  In this paper, we made an extension to the convergence analysis of the
+dynamics of two-layered bias-free networks with one $ReLU$ output. We took into
+consideration two popular regularization terms: the $\ell_1$ and $\ell_2$ norm
+of the parameter vector $w$, and added it to the square loss function with
+coefficient $\lambda/2$. We proved that when $\lambda$ is small, the weight
+vector $w$ converges to the optimal solution $\hat{w}$ (with respect to the new
+loss function) with probability $\geq (1-\varepsilon)(1-A_d)/2$ under random
+initiations in a sphere centered at the origin, where $\varepsilon$ is a small
+value and $A_d$ is a constant. Numerical experiments including phase diagrams
+and repeated simulations verified our theory.
+",1,0,0,1,0,0
+452,"From bare interactions, low--energy constants and unitary gas to nuclear density functionals without free parameters: application to neutron matter","  We further progress along the line of Ref. [Phys. Rev. {\bf A 94}, 043614
+(2016)] where a functional for Fermi systems with anomalously large $s$-wave
+scattering length $a_s$ was proposed that has no free parameters. The
+functional is designed to correctly reproduce the unitary limit in Fermi gases
+together with the leading-order contributions in the s- and p-wave channels at
+low density. The functional is shown to be predictive up to densities
+$\sim0.01$ fm$^{-3}$ that is much higher densities compared to the Lee-Yang
+functional, valid for $\rho < 10^{-6}$ fm$^{-3}$. The form of the functional
+retained in this work is further motivated. It is shown that the new functional
+corresponds to an expansion of the energy in $(a_s k_F)$ and $(r_e k_F)$ to all
+orders, where $r_e$ is the effective range and $k_F$ is the Fermi momentum. One
+conclusion from the present work is that, except in the extremely low--density
+regime, nuclear systems can be treated perturbatively in $-(a_s k_F)^{-1}$ with
+respect to the unitary limit. Starting from the functional, we introduce
+density--dependent scales and show that scales associated to the bare
+interaction are strongly renormalized by medium effects. As a consequence, some
+of the scales at play around saturation are dominated by the unitary gas
+properties and not directly to low-energy constants. For instance, we show that
+the scale in the s-wave channel around saturation is proportional to the
+so-called Bertsch parameter $\xi_0$ and becomes independent of $a_s$. We also
+point out that these scales are of the same order of magnitude than those
+empirically obtained in the Skyrme energy density functional. We finally
+propose a slight modification of the functional such that it becomes accurate
+up to the saturation density $\rho\simeq 0.16$ fm$^{-3}$.
+",0,1,0,0,0,0
+453,EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract),"  Marker-based and marker-less optical skeletal motion-capture methods use an
+outside-in arrangement of cameras placed around a scene, with viewpoints
+converging on the center. They often create discomfort by possibly needed
+marker suits, and their recording volume is severely restricted and often
+constrained to indoor scenes with controlled backgrounds. We therefore propose
+a new method for real-time, marker-less and egocentric motion capture which
+estimates the full-body skeleton pose from a lightweight stereo pair of fisheye
+cameras that are attached to a helmet or virtual-reality headset. It combines
+the strength of a new generative pose estimation framework for fisheye views
+with a ConvNet-based body-part detector trained on a new automatically
+annotated and augmented dataset. Our inside-in method captures full-body motion
+in general indoor and outdoor scenes, and also crowded scenes.
+",1,0,0,0,0,0
+454,Diffusion Maps meet Nyström,"  Diffusion maps are an emerging data-driven technique for non-linear
+dimensionality reduction, which are especially useful for the analysis of
+coherent structures and nonlinear embeddings of dynamical systems. However, the
+computational complexity of the diffusion maps algorithm scales with the number
+of observations. Thus, long time-series data presents a significant challenge
+for fast and efficient embedding. We propose integrating the Nyström method
+with diffusion maps in order to ease the computational demand. We achieve a
+speedup of roughly two to four times when approximating the dominant diffusion
+map components.
+",0,0,0,1,0,0
+455,Multiphase Flows of N Immiscible Incompressible Fluids: An Outflow/Open Boundary Condition and Algorithm,"  We present a set of effective outflow/open boundary conditions and an
+associated algorithm for simulating the dynamics of multiphase flows consisting
+of $N$ ($N\geqslant 2$) immiscible incompressible fluids in domains involving
+outflows or open boundaries. These boundary conditions are devised based on the
+properties of energy stability and reduction consistency. The energy stability
+property ensures that the contributions of these boundary conditions to the
+energy balance will not cause the total energy of the N-phase system to
+increase over time. Therefore, these open/outflow boundary conditions are very
+effective in overcoming the backflow instability in multiphase systems. The
+reduction consistency property ensures that if some fluid components are absent
+from the N-phase system then these N-phase boundary conditions will reduce to
+those corresponding boundary conditions for the equivalent smaller system. Our
+numerical algorithm for the proposed boundary conditions together with the
+N-phase governing equations involves only the solution of a set of de-coupled
+individual Helmholtz-type equations within each time step, and the resultant
+linear algebraic systems after discretization involve only constant and
+time-independent coefficient matrices which can be pre-computed. Therefore, the
+algorithm is computationally very efficient and attractive. We present
+extensive numerical experiments for flow problems involving multiple fluid
+components and inflow/outflow boundaries to test the proposed method. In
+particular, we compare in detail the simulation results of a three-phase
+capillary wave problem with Prosperetti's exact physical solution and
+demonstrate that the method developed herein produces physically accurate
+results.
+",0,1,0,0,0,0
+456,Deadly dark matter cusps vs faint and extended star clusters: Eridanus II and Andromeda XXV,"  The recent detection of two faint and extended star clusters in the central
+regions of two Local Group dwarf galaxies, Eridanus II and Andromeda XXV,
+raises the question of whether clusters with such low densities can survive the
+tidal field of cold dark matter haloes with central density cusps. Using both
+analytic arguments and a suite of collisionless N-body simulations, I show that
+these clusters are extremely fragile and quickly disrupted in the presence of
+central cusps $\rho\sim r^{-\alpha}$ with $\alpha\gtrsim 0.2$. Furthermore, the
+scenario in which the clusters where originally more massive and sank to the
+center of the halo requires extreme fine tuning and does not naturally
+reproduce the observed systems. In turn, these clusters are long lived in cored
+haloes, whose central regions are safe shelters for $\alpha\lesssim 0.2$. The
+only viable scenario for hosts that have preserved their primoridal cusp to the
+present time is that the clusters formed at rest at the bottom of the
+potential, which is easily tested by measurement of the clusters proper
+velocity within the host. This offers means to readily probe the central
+density profile of two dwarf galaxies as faint as $L_V\sim5\times 10^5 L_\odot$
+and $L_V\sim6\times10^4 L_\odot$, in which stellar feedback is unlikely to be
+effective.
+",0,1,0,0,0,0
+457,"Mutual Information, Relative Entropy and Estimation Error in Semi-martingale Channels","  Fundamental relations between information and estimation have been
+established in the literature for the continuous-time Gaussian and Poisson
+channels, in a long line of work starting from the classical representation
+theorems by Duncan and Kabanov respectively. In this work, we demonstrate that
+such relations hold for a much larger family of continuous-time channels. We
+introduce the family of semi-martingale channels where the channel output is a
+semi-martingale stochastic process, and the channel input modulates the
+characteristics of the semi-martingale. For these channels, which includes as a
+special case the continuous time Gaussian and Poisson models, we establish new
+representations relating the mutual information between the channel input and
+output to an optimal causal filtering loss, thereby unifying and considerably
+extending results from the Gaussian and Poisson settings. Extensions to the
+setting of mismatched estimation are also presented where the relative entropy
+between the laws governing the output of the channel under two different input
+distributions is equal to the cumulative difference between the estimation loss
+incurred by using the mismatched and optimal causal filters respectively. The
+main tool underlying these results is the Doob--Meyer decomposition of a class
+of likelihood ratio sub-martingales. The results in this work can be viewed as
+the continuous-time analogues of recent generalizations for relations between
+information and estimation for discrete-time Lévy channels.
+",1,0,0,0,0,0
+458,Testing redMaPPer centring probabilities using galaxy clustering and galaxy-galaxy lensing,"  Galaxy cluster centring is a key issue for precision cosmology studies using
+galaxy surveys. Mis-identification of central galaxies causes systematics in
+various studies such as cluster lensing, satellite kinematics, and galaxy
+clustering. The red-sequence Matched-filter Probabilistic Percolation
+(redMaPPer) estimates the probability that each member galaxy is central from
+photometric information rather than specifying one central galaxy. The
+redMaPPer estimates can be used for calibrating the off-centring effect,
+however, the centring algorithm has not previously been well-tested. We test
+the centring probabilities of redMaPPer cluster catalog using the projected
+cross correlation between redMaPPer clusters with photometric red galaxies and
+galaxy-galaxy lensing. We focus on the subsample of redMaPPer clusters in which
+the redMaPPer central galaxies (RMCGs) are not the brightest member galaxies
+(BMEM) and both of them have spectroscopic redshift. This subsample represents
+nearly 10% of the whole cluster sample. We find a clear difference in the
+cross-correlation measurements between RMCGs and BMEMs, and the estimated
+centring probability is 74$\pm$10% for RMCGs and 13$\pm$4% for BMEMs in the
+Gaussian offset model and 78$\pm$9% for RMCGs and 5$\pm$5% for BMEMs in the NFW
+offset model. These values are in agreement with the centring probability
+values reported by redMaPPer (75% for RMCG and 10% for BMEMs) within 1$\sigma$.
+Our analysis provides a strong consistency test of the redMaPPer centring
+probabilities. Our results suggest that redMaPPer centring probabilities are
+reliably estimated. We confirm that the brightest galaxy in the cluster is not
+always the central galaxy as has been shown in previous works.
+",0,1,0,0,0,0
+459,Criterion of positivity for semilinear problems with applications in biology,"  The goal of this article is to provide an useful criterion of positivity and
+well-posedness for a wide range of infinite dimensional semilinear abstract
+Cauchy problems. This criterion is based on some weak assumptions on the
+non-linear part of the semilinear problem and on the existence of a strongly
+continuous semigroup generated by the differential operator. To illustrate a
+large variety of applications, we exhibit the feasibility of this criterion
+through three examples in mathematical biology: epidemiology, predator-prey
+interactions and oncology.
+",0,0,1,0,0,0
+460,Axiomatic quantum mechanics: Necessity and benefits for the physics studies,"  The ongoing progress in quantum theory emphasizes the crucial role of the
+very basic principles of quantum theory. However, this is not properly followed
+in teaching quantum mechanics on the graduate and undergraduate levels of
+physics studies. The existing textbooks typically avoid the axiomatic
+presentation of the theory. We emphasize usefulness of the systematic,
+axiomatic approach to the basics of quantum theory as well as its importance in
+the light of the modern scientific-research context.
+",0,1,0,0,0,0
+461,Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs,"  Non-conding RNAs play a key role in the post-transcriptional regulation of
+mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact
+with their target RNAs through protein-mediated, sequence-specific binding,
+giving rise to extended and highly heterogeneous miRNA-RNA interaction
+networks. Within such networks, competition to bind miRNAs can generate an
+effective positive coupling between their targets. Competing endogenous RNAs
+(ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk.
+Albeit potentially weak, ceRNA interactions can occur both dynamically,
+affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA
+networks as a whole can be implicated in the composition of the cell's
+proteome. Many features of ceRNA interactions, including the conditions under
+which they become significant, can be unraveled by mathematical and in silico
+models. We review the understanding of the ceRNA effect obtained within such
+frameworks, focusing on the methods employed to quantify it, its role in the
+processing of gene expression noise, and how network topology can determine its
+reach.
+",0,0,0,0,1,0
+462,Shortening binary complexes and commutativity of $K$-theory with infinite products,"  We show that in Grayson's model of higher algebraic $K$-theory using binary
+acyclic complexes, the complexes of length two suffice to generate the whole
+group. Moreover, we prove that the comparison map from Nenashev's model for
+$K_1$ to Grayson's model for $K_1$ is an isomorphism. It follows that algebraic
+$K$-theory of exact categories commutes with infinite products.
+",0,0,1,0,0,0
+463,Cost-Effective Seed Selection in Online Social Networks,"  We study the min-cost seed selection problem in online social networks, where
+the goal is to select a set of seed nodes with the minimum total cost such that
+the expected number of influenced nodes in the network exceeds a predefined
+threshold. We propose several algorithms that outperform the previous studies
+both on the theoretical approximation ratios and on the experimental
+performance. Under the case where the nodes have heterogeneous costs, our
+algorithms are the first bi- criteria approximation algorithms with polynomial
+running time and provable logarithmic performance bounds using a general
+contagion model. Under the case where the users have uniform costs, our
+algorithms achieve logarithmic approximation ratio and provable time complexity
+which is smaller than that of existing algorithms in orders of magnitude. We
+conduct extensive experiments using real social networks. The experimental
+results show that, our algorithms significantly outperform the existing
+algorithms both on the total cost and on the running time, and also scale well
+to billion-scale networks.
+",1,0,0,0,0,0
+464,Fast Meta-Learning for Adaptive Hierarchical Classifier Design,"  We propose a new splitting criterion for a meta-learning approach to
+multiclass classifier design that adaptively merges the classes into a
+tree-structured hierarchy of increasingly difficult binary classification
+problems. The classification tree is constructed from empirical estimates of
+the Henze-Penrose bounds on the pairwise Bayes misclassification rates that
+rank the binary subproblems in terms of difficulty of classification. The
+proposed empirical estimates of the Bayes error rate are computed from the
+minimal spanning tree (MST) of the samples from each pair of classes. Moreover,
+a meta-learning technique is presented for quantifying the one-vs-rest Bayes
+error rate for each individual class from a single MST on the entire dataset.
+Extensive simulations on benchmark datasets show that the proposed hierarchical
+method can often be learned much faster than competing methods, while achieving
+competitive accuracy.
+",1,0,0,1,0,0
+465,Vibrational Density Matrix Renormalization Group,"  Variational approaches for the calculation of vibrational wave functions and
+energies are a natural route to obtain highly accurate results with
+controllable errors. However, the unfavorable scaling and the resulting high
+computational cost of standard variational approaches limit their application
+to small molecules with only few vibrational modes. Here, we demonstrate how
+the density matrix renormalization group (DMRG) can be exploited to optimize
+vibrational wave functions (vDMRG) expressed as matrix product states. We study
+the convergence of these calculations with respect to the size of the local
+basis of each mode, the number of renormalized block states, and the number of
+DMRG sweeps required. We demonstrate the high accuracy achieved by vDMRG for
+small molecules that were intensively studied in the literature. We then
+proceed to show that the complete fingerprint region of the sarcosyn-glycin
+dipeptide can be calculated with vDMRG.
+",0,1,0,0,0,0
+466,Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering,"  In this work, we present a methodology that enables an agent to make
+efficient use of its exploratory actions by autonomously identifying possible
+objectives in its environment and learning them in parallel. The identification
+of objectives is achieved using an online and unsupervised adaptive clustering
+algorithm. The identified objectives are learned (at least partially) in
+parallel using Q-learning. Using a simulated agent and environment, it is shown
+that the converged or partially converged value function weights resulting from
+off-policy learning can be used to accumulate knowledge about multiple
+objectives without any additional exploration. We claim that the proposed
+approach could be useful in scenarios where the objectives are initially
+unknown or in real world scenarios where exploration is typically a time and
+energy intensive process. The implications and possible extensions of this work
+are also briefly discussed.
+",1,0,0,0,0,0
+467,Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization,"  Recent studies on diffusion-based sampling methods have shown that Langevin
+Monte Carlo (LMC) algorithms can be beneficial for non-convex optimization, and
+rigorous theoretical guarantees have been proven for both asymptotic and
+finite-time regimes. Algorithmically, LMC-based algorithms resemble the
+well-known gradient descent (GD) algorithm, where the GD recursion is perturbed
+by an additive Gaussian noise whose variance has a particular form. Fractional
+Langevin Monte Carlo (FLMC) is a recently proposed extension of LMC, where the
+Gaussian noise is replaced by a heavy-tailed {\alpha}-stable noise. As opposed
+to its Gaussian counterpart, these heavy-tailed perturbations can incur large
+jumps and it has been empirically demonstrated that the choice of
+{\alpha}-stable noise can provide several advantages in modern machine learning
+problems, both in optimization and sampling contexts. However, as opposed to
+LMC, only asymptotic convergence properties of FLMC have been yet established.
+In this study, we analyze the non-asymptotic behavior of FLMC for non-convex
+optimization and prove finite-time bounds for its expected suboptimality. Our
+results show that the weak-error of FLMC increases faster than LMC, which
+suggests using smaller step-sizes in FLMC. We finally extend our results to the
+case where the exact gradients are replaced by stochastic gradients and show
+that similar results hold in this setting as well.
+",1,0,0,1,0,0
+468,Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the Art,"  We use automatic speech recognition to assess spoken English learner
+pronunciation based on the authentic intelligibility of the learners' spoken
+responses determined from support vector machine (SVM) classifier or deep
+learning neural network model predictions of transcription correctness. Using
+numeric features produced by PocketSphinx alignment mode and many recognition
+passes searching for the substitution and deletion of each expected phoneme and
+insertion of unexpected phonemes in sequence, the SVM models achieve 82 percent
+agreement with the accuracy of Amazon Mechanical Turk crowdworker
+transcriptions, up from 75 percent reported by multiple independent
+researchers. Using such features with SVM classifier probability prediction
+models can help computer-aided pronunciation teaching (CAPT) systems provide
+intelligibility remediation.
+",1,0,0,1,0,0
+469,Second-Order Analysis and Numerical Approximation for Bang-Bang Bilinear Control Problems,"  We consider bilinear optimal control problems, whose objective functionals do
+not depend on the controls. Hence, bang-bang solutions will appear. We
+investigate sufficient second-order conditions for bang-bang controls, which
+guarantee local quadratic growth of the objective functional in $L^1$. In
+addition, we prove that for controls that are not bang-bang, no such growth can
+be expected. Finally, we study the finite-element discretization, and prove
+error estimates of bang-bang controls in $L^1$-norms.
+",0,0,1,0,0,0
+470,On the letter frequencies and entropy of written Marathi,"  We carry out a comprehensive analysis of letter frequencies in contemporary
+written Marathi. We determine sets of letters which statistically predominate
+any large generic Marathi text, and use these sets to estimate the entropy of
+Marathi.
+",1,0,0,0,0,0
+471,Robust Orchestration of Concurrent Application Workflows in Mobile Device Clouds,"  A hybrid mobile/fixed device cloud that harnesses sensing, computing,
+communication, and storage capabilities of mobile and fixed devices in the
+field as well as those of computing and storage servers in remote datacenters
+is envisioned. Mobile device clouds can be harnessed to enable innovative
+pervasive applications that rely on real-time, in-situ processing of sensor
+data collected in the field. To support concurrent mobile applications on the
+device cloud, a robust and secure distributed computing framework, called
+Maestro, is proposed. The key components of Maestro are (i) a task scheduling
+mechanism that employs controlled task replication in addition to task
+reallocation for robustness and (ii) Dedup for task deduplication among
+concurrent pervasive workflows. An architecture-based solution that relies on
+task categorization and authorized access to the categories of tasks is
+proposed for different levels of protection. Experimental evaluation through
+prototype testbed of Android- and Linux-based mobile devices as well as
+simulations is performed to demonstrate Maestro's capabilities.
+",1,0,0,0,0,0
+472,Anisotropy and multiband superconductivity in Sr2RuO4,"  Despite numerous studies the exact nature of the order parameter in
+superconducting Sr2RuO4 remains unresolved. We have extended previous
+small-angle neutron scattering studies of the vortex lattice in this material
+to a wider field range, higher temperatures, and with the field applied close
+to both the <100> and <110> basal plane directions. Measurements at high field
+were made possible by the use of both spin polarization and analysis to improve
+the signal-to-noise ratio. Rotating the field towards the basal plane causes a
+distortion of the square vortex lattice observed for H // <001>, and also a
+symmetry change to a distorted triangular symmetry for fields close to <100>.
+The vortex lattice distortion allows us to determine the intrinsic
+superconducting anisotropy between the c-axis and the Ru-O basal plane,
+yielding a value of ~60 at low temperature and low to intermediate fields. This
+greatly exceeds the upper critical field anisotropy of ~20 at low temperature,
+reminiscent of Pauli limiting. Indirect evidence for Pauli paramagnetic effects
+on the unpaired quasiparticles in the vortex cores are observed, but a direct
+detection lies below the measurement sensitivity. The superconducting
+anisotropy is found to be independent of temperature but increases for fields >
+1 T, indicating multiband superconductvity in Sr2RuO4. Finally, the temperature
+dependence of the scattered intensity provides further support for gap nodes or
+deep minima in the superconducting gap.
+",0,1,0,0,0,0
+473,"Time-Reversal Breaking in QCD$_4$, Walls, and Dualities in 2+1 Dimensions","  We study $SU(N)$ Quantum Chromodynamics (QCD) in 3+1 dimensions with $N_f$
+degenerate fundamental quarks with mass $m$ and a $\theta$-parameter. For
+generic $m$ and $\theta$ the theory has a single gapped vacuum. However, as
+$\theta$ is varied through $\theta=\pi$ for large $m$ there is a first order
+transition. For $N_f=1$ the first order transition line ends at a point with a
+massless $\eta'$ particle (for all $N$) and for $N_f>1$ the first order
+transition ends at $m=0$, where, depending on the value of $N_f$, the IR theory
+has free Nambu-Goldstone bosons, an interacting conformal field theory, or a
+free gauge theory. Even when the $4d$ bulk is smooth, domain walls and
+interfaces can have interesting phase transitions separating different $3d$
+phases. These turn out to be the phases of the recently studied $3d$
+Chern-Simons matter theories, thus relating the dynamics of QCD$_4$ and
+QCD$_3$, and, in particular, making contact with the recently discussed
+dualities in 2+1 dimensions. For example, when the massless $4d$ theory has an
+$SU(N_f)$ sigma model, the domain wall theory at low (nonzero) mass supports a
+$3d$ massless $CP^{N_f-1}$ nonlinear $\sigma$-model with a Wess-Zumino term, in
+agreement with the conjectured dynamics in 2+1 dimensions.
+",0,1,0,0,0,0
+474,Comparative Investigation of the High Pressure Autoignition of the Butanol Isomers,"  Investigation of the autoignition delay of the butanol isomers has been
+performed at elevated pressures of 15 bar and 30 bar and low to intermediate
+temperatures of 680-860 K. The reactivity of the stoichiometric isomers of
+butanol, in terms of inverse ignition delay, was ranked as n-butanol >
+sec-butanol ~ iso-butanol > tert-butanol at a compressed pressure of 15 bar but
+changed to n-butanol > tert-butanol > sec-butanol > iso-butanol at 30 bar. For
+the temperature and pressure conditions in this study, no NTC or two-stage
+ignition behavior were observed. However, for both of the compressed pressures
+studied in this work, tert-butanol exhibited unique pre-ignition heat release
+characteristics. As such, tert-butanol was further studied at two additional
+equivalence ratios ($\phi$ = 0.5 and 2.0) to help determine the cause of the
+heat release.
+",0,1,0,0,0,0
+475,Selecting optimal minimum spanning trees that share a topological correspondence with phylogenetic trees,"  Choi et. al (2011) introduced a minimum spanning tree (MST)-based method
+called CLGrouping, for constructing tree-structured probabilistic graphical
+models, a statistical framework that is commonly used for inferring
+phylogenetic trees. While CLGrouping works correctly if there is a unique MST,
+we observe an indeterminacy in the method in the case that there are multiple
+MSTs. In this work we remove this indeterminacy by introducing so-called
+vertex-ranked MSTs. We note that the effectiveness of CLGrouping is inversely
+related to the number of leaves in the MST. This motivates the problem of
+finding a vertex-ranked MST with the minimum number of leaves (MLVRMST). We
+provide a polynomial time algorithm for the MLVRMST problem, and prove its
+correctness for graphs whose edges are weighted with tree-additive distances.
+",1,0,1,0,0,0
+476,Noisy Natural Gradient as Variational Inference,"  Variational Bayesian neural nets combine the flexibility of deep learning
+with Bayesian uncertainty estimation. Unfortunately, there is a tradeoff
+between cheap but simple variational families (e.g.~fully factorized) or
+expensive and complicated inference procedures. We show that natural gradient
+ascent with adaptive weight noise implicitly fits a variational posterior to
+maximize the evidence lower bound (ELBO). This insight allows us to train
+full-covariance, fully factorized, or matrix-variate Gaussian variational
+posteriors using noisy versions of natural gradient, Adam, and K-FAC,
+respectively, making it possible to scale up to modern-size ConvNets. On
+standard regression benchmarks, our noisy K-FAC algorithm makes better
+predictions and matches Hamiltonian Monte Carlo's predictive variances better
+than existing methods. Its improved uncertainty estimates lead to more
+efficient exploration in active learning, and intrinsic motivation for
+reinforcement learning.
+",1,0,0,1,0,0
+477,A Game of Life on Penrose tilings,"  We define rules for cellular automata played on quasiperiodic tilings of the
+plane arising from the multigrid method in such a way that these cellular
+automata are isomorphic to Conway's Game of Life. Although these tilings are
+nonperiodic, determining the next state of each tile is a local computation,
+requiring only knowledge of the local structure of the tiling and the states of
+finitely many nearby tiles. As an example, we show a version of a ""glider""
+moving through a region of a Penrose tiling. This constitutes a potential
+theoretical framework for a method of executing computations in
+non-periodically structured substrates such as quasicrystals.
+",0,1,1,0,0,0
+478,"Single and Multiple Vortex Rings in Three-Dimensional Bose-Einstein Condensates: Existence, Stability and Dynamics","  In the present work, we explore the existence, stability and dynamics of
+single and multiple vortex ring states that can arise in Bose-Einstein
+condensates. Earlier works have illustrated the bifurcation of such states, in
+the vicinity of the linear limit, for isotropic or anisotropic
+three-dimensional harmonic traps. Here, we extend these states to the regime of
+large chemical potentials, the so-called Thomas-Fermi limit, and explore their
+properties such as equilibrium radii and inter-ring distance, for multi-ring
+states, as well as their vibrational spectra and possible instabilities. In
+this limit, both the existence and stability characteristics can be partially
+traced to a particle picture that considers the rings as individual particles
+oscillating within the trap and interacting pairwise with one another. Finally,
+we examine some representative instability scenarios of the multi-ring dynamics
+including breakup and reconnections, as well as the transient formation of
+vortex lines.
+",0,1,0,0,0,0
+479,Dimension-free Wasserstein contraction of nonlinear filters,"  For a class of partially observed diffusions, sufficient conditions are given
+for the map from initial condition of the signal to filtering distribution to
+be contractive with respect to Wasserstein distances, with rate which has no
+dependence on the dimension of the state-space and is stable under tensor
+products of the model. The main assumptions are that the signal has affine
+drift and constant diffusion coefficient, and that the likelihood functions are
+log-concave. Contraction estimates are obtained from an $h$-process
+representation of the transition probabilities of the signal reweighted so as
+to condition on the observations.
+",0,0,1,1,0,0
+480,Vortex Nucleation Limited Mobility of Free Electron Bubbles in the Gross-Pitaevskii Model of a Superfluid,"  We study the motion of an electron bubble in the zero temperature limit where
+neither phonons nor rotons provide a significant contribution to the drag
+exerted on an ion moving within the superfluid. By using the Gross-Clark model,
+in which a Gross-Pitaevskii equation for the superfluid wavefunction is coupled
+to a Schrödinger equation for the electron wavefunction, we study how
+vortex nucleation affects the measured drift velocity of the ion. We use
+parameters that give realistic values of the ratio of the radius of the bubble
+with respect to the healing length in superfluid $^4$He at a pressure of one
+bar. By performing fully 3D spatio-temporal simulations of the superfluid
+coupled to an electron, that is modelled within an adiabatic approximation and
+moving under the influence of an applied electric field, we are able to recover
+the key dynamics of the ion-vortex interactions that arise and the subsequent
+ion-vortex complexes that can form. Using the numerically computed drift
+velocity of the ion as a function of the applied electric field, we determine
+the vortex-nucleation limited mobility of the ion to recover values in
+reasonable agreement with measured data.
+",0,1,0,0,0,0
+481,Radio variability and non-thermal components in stars evolving toward planetary nebulae,"  We present new JVLA multi-frequency measurements of a set of stars in
+transition from the post-AGB to the Planetary Nebula phase monitored in the
+radio range over several years. Clear variability is found for five sources.
+Their light curves show increasing and decreasing patterns. New radio
+observations at high angular resolution are also presented for two sources.
+Among these is IRAS 18062+2410, whose radio structure is compared to
+near-infrared images available in the literature. With these new maps, we can
+estimate inner and outer radii of 0.03$""$ and 0.08$""$ for the ionised shell, an
+ionised mass of $3.2\times10^{-4}$ M$_\odot$, and a density at the inner radius
+of $7.7\times 10^{-5}$ cm$^{-3}$, obtained by modelling the radio shell with
+the new morphological constraints. The combination of multi-frequency data and,
+where available, spectral-index maps leads to the detection of spectral indices
+not due to thermal emission, contrary to what one would expect in planetary
+nebulae. Our results allow us to hypothesise the existence of a link between
+radio variability and non-thermal emission mechanisms in the nebulae. This link
+seems to hold for IRAS 22568+6141 and may generally hold for those nebulae
+where the radio flux decreases over time.
+",0,1,0,0,0,0
+482,Sequential testing for structural stability in approximate factor models,"  We develop an on-line monitoring procedure to detect a change in a large
+approximate factor model. Our statistics are based on a well-known property of
+the $% \left( r+1\right) $-th eigenvalue of the sample covariance matrix of the
+data (having defined $r$ as the number of common factors): whilst under the
+null the $\left( r+1\right) $-th eigenvalue is bounded, under the alternative
+of a change (either in the loadings, or in the number of factors itself) it
+becomes spiked. Given that the sample eigenvalue cannot be estimated
+consistently under the null, we regularise the problem by randomising the test
+statistic in conjunction with sample conditioning, obtaining a sequence of
+\textit{i.i.d.}, asymptotically chi-square statistics which are then employed
+to build the monitoring scheme. Numerical evidence shows that our procedure
+works very well in finite samples, with a very small probability of false
+detections and tight detection times in presence of a genuine change-point.
+",0,0,0,1,0,0
+483,Susceptibility Propagation by Using Diagonal Consistency,"  A susceptibility propagation that is constructed by combining a belief
+propagation and a linear response method is used for approximate computation
+for Markov random fields. Herein, we formulate a new, improved susceptibility
+propagation by using the concept of a diagonal matching method that is based on
+mean-field approaches to inverse Ising problems. The proposed susceptibility
+propagation is robust for various network structures, and it is reduced to the
+ordinary susceptibility propagation and to the adaptive
+Thouless-Anderson-Palmer equation in special cases.
+",0,0,1,1,0,0
+484,Performance Analysis of Ultra-Dense Networks with Elevated Base Stations,"  This paper analyzes the downlink performance of ultra-dense networks with
+elevated base stations (BSs). We consider a general dual-slope pathloss model
+with distance-dependent probability of line-of-sight (LOS) transmission between
+BSs and receivers. Specifically, we consider the scenario where each link may
+be obstructed by randomly placed buildings. Using tools from stochastic
+geometry, we show that both coverage probability and area spectral efficiency
+decay to zero as the BS density grows large. Interestingly, we show that the BS
+height alone has a detrimental effect on the system performance even when the
+standard single-slope pathloss model is adopted.
+",1,0,0,0,0,0
+485,Learning to Drive in a Day,"  We demonstrate the first application of deep reinforcement learning to
+autonomous driving. From randomly initialised parameters, our model is able to
+learn a policy for lane following in a handful of training episodes using a
+single monocular image as input. We provide a general and easy to obtain
+reward: the distance travelled by the vehicle without the safety driver taking
+control. We use a continuous, model-free deep reinforcement learning algorithm,
+with all exploration and optimisation performed on-vehicle. This demonstrates a
+new framework for autonomous driving which moves away from reliance on defined
+logical rules, mapping, and direct supervision. We discuss the challenges and
+opportunities to scale this approach to a broader range of autonomous driving
+tasks.
+",1,0,0,1,0,0
+486,Strong-coupling of WSe2 in ultra-compact plasmonic nanocavities at room temperature,"  Strong-coupling of monolayer metal dichalcogenide semiconductors with light
+offers encouraging prospects for realistic exciton devices at room temperature.
+However, the nature of this coupling depends extremely sensitively on the
+optical confinement and the orientation of electronic dipoles and fields. Here,
+we show how plasmon strong coupling can be achieved in compact robust
+easily-assembled gold nano-gap resonators at room temperature. We prove that
+strong coupling is impossible with monolayers due to the large exciton
+coherence size, but resolve clear anti-crossings for 8 layer devices with Rabi
+splittings exceeding 135 meV. We show that such structures improve on prospects
+for nonlinear exciton functionalities by at least 10^4, while retaining quantum
+efficiencies above 50%.
+",0,1,0,0,0,0
+487,Stigmergy-based modeling to discover urban activity patterns from positioning data,"  Positioning data offer a remarkable source of information to analyze crowds
+urban dynamics. However, discovering urban activity patterns from the emergent
+behavior of crowds involves complex system modeling. An alternative approach is
+to adopt computational techniques belonging to the emergent paradigm, which
+enables self-organization of data and allows adaptive analysis. Specifically,
+our approach is based on stigmergy. By using stigmergy each sample position is
+associated with a digital pheromone deposit, which progressively evaporates and
+aggregates with other deposits according to their spatiotemporal proximity.
+Based on this principle, we exploit positioning data to identify high density
+areas (hotspots) and characterize their activity over time. This
+characterization allows the comparison of dynamics occurring in different days,
+providing a similarity measure exploitable by clustering techniques. Thus, we
+cluster days according to their activity behavior, discovering unexpected urban
+activity patterns. As a case study, we analyze taxi traces in New York City
+during 2015.
+",1,1,0,0,0,0
+488,BiHom-Lie colour algebras structures,"  BiHom-Lie Colour algebra is a generalized Hom-Lie Colour algebra endowed with
+two commuting multiplicative linear maps. The main purpose of this paper is to
+define representations and a cohomology of BiHom-Lie colour algebras and to
+study some key constructions and properties.
+Moreover, we discuss $\alpha^{k}\beta^l$-generalized derivations,
+$\alpha^{k}\beta^l$-quasi-derivations and $\alpha^{k}\beta^l$-quasi-centroid.
+We provide some properties and their relationships with BiHom-Jordan colour
+algebra.
+",0,0,1,0,0,0
+489,Clustering of Gamma-Ray bursts through kernel principal component analysis,"  We consider the problem related to clustering of gamma-ray bursts (from
+""BATSE"" catalogue) through kernel principal component analysis in which our
+proposed kernel outperforms results of other competent kernels in terms of
+clustering accuracy and we obtain three physically interpretable groups of
+gamma-ray bursts. The effectivity of the suggested kernel in combination with
+kernel principal component analysis in revealing natural clusters in noisy and
+nonlinear data while reducing the dimension of the data is also explored in two
+simulated data sets.
+",0,1,0,1,0,0
+490,Bounded gaps between primes in short intervals,"  Baker, Harman, and Pintz showed that a weak form of the Prime Number Theorem
+holds in intervals of the form $[x-x^{0.525},x]$ for large $x$. In this paper,
+we extend a result of Maynard and Tao concerning small gaps between primes to
+intervals of this length. More precisely, we prove that for any $\delta\in
+[0.525,1]$ there exist positive integers $k,d$ such that for sufficiently large
+$x$, the interval $[x-x^\delta,x]$ contains $\gg_{k} \frac{x^\delta}{(\log
+x)^k}$ pairs of consecutive primes differing by at most $d$. This confirms a
+speculation of Maynard that results on small gaps between primes can be refined
+to the setting of short intervals of this length.
+",0,0,1,0,0,0
+491,Handover analysis of the Improved Phantom Cells,"  Improved Phantom cell is a new scenario which has been introduced recently to
+enhance the capacity of Heterogeneous Networks (HetNets). The main trait of
+this scenario is that, besides maximizing the total network capacity in both
+indoor and outdoor environments, it claims to reduce the handover number
+compared to the conventional scenarios. In this paper, by a comprehensive
+review of the Improved Phantom cells structure, an appropriate algorithm will
+be introduced for the handover procedure of this scenario. To reduce the number
+of handover in the proposed algorithm, various parameters such as the received
+Signal to Interference plus Noise Ratio (SINR) at the user equipment (UE),
+users access conditions to the phantom cells, and users staying time in the
+target cell based on its velocity, has been considered. Theoretical analyses
+and simulation results show that applying the suggested algorithm the improved
+phantom cell structure has a much better performance than conventional HetNets
+in terms of the number of handover.
+",1,0,0,0,0,0
+492,Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation,"  We address the problem of localisation of objects as bounding boxes in images
+with weak labels. This weakly supervised object localisation problem has been
+tackled in the past using discriminative models where each object class is
+localised independently from other classes. We propose a novel framework based
+on Bayesian joint topic modelling. Our framework has three distinctive
+advantages over previous works: (1) All object classes and image backgrounds
+are modelled jointly together in a single generative model so that ""explaining
+away"" inference can resolve ambiguity and lead to better learning and
+localisation. (2) The Bayesian formulation of the model enables easy
+integration of prior knowledge about object appearance to compensate for
+limited supervision. (3) Our model can be learned with a mixture of weakly
+labelled and unlabelled data, allowing the large volume of unlabelled images on
+the Internet to be exploited for learning. Extensive experiments on the
+challenging VOC dataset demonstrate that our approach outperforms the
+state-of-the-art competitors.
+",1,0,0,0,0,0
+493,Psychological model of the investor and manager behavior in risk,"  All people have to make risky decisions in everyday life. And we do not know
+how true they are. But is it possible to mathematically assess the correctness
+of our choice? This article discusses the model of decision making under risk
+on the example of project management. This is a game with two players, one of
+which is Investor, and the other is the Project Manager. Each player makes a
+risky decision for himself, based on his past experience. With the help of a
+mathematical model, the players form a level of confidence, depending on who
+the player accepts the strategy or does not accept. The project manager
+assesses the costs and compares them with the level of confidence. An investor
+evaluates past results. Also visit the case where the strategy of the player
+accepts the part.
+",0,0,0,0,0,1
+494,Constraints on Super-Earths Interiors from Stellar Abundances,"  Modeling the interior of exoplanets is essential to go further than the
+conclusions provided by mean density measurements. In addition to the still
+limited precision on the planets' fundamental parameters, models are limited by
+the existence of degeneracies on their compositions. Here we present a model of
+internal structure dedicated to the study of solid planets up to ~10 Earth
+masses, i.e. Super-Earths. When the measurement is available, the assumption
+that the bulk Fe/Si ratio of a planet is similar to that of its host star
+allows us to significantly reduce the existing degeneracy and more precisely
+constrain the planet's composition. Based on our model, we provide an update of
+the mass-radius relationships used to provide a first estimate of a planet's
+composition from density measurements. Our model is also applied to the cases
+of two well-known exoplanets, CoRoT-7b and Kepler-10b, using their recently
+updated parameters. The core mass fractions of CoRoT-7b and Kepler-10b are
+found to lie within the 10-37% and 10-33% ranges, respectively, allowing both
+planets to be compatible with an Earth-like composition. We also extend the
+recent study of Proxima Centauri b, and show that its radius may reach 1.94
+Earth radii in the case of a 5 Earth masses planet, as there is a 96.7%
+probability that the real mass of Proxima Centauri b is below this value.
+",0,1,0,0,0,0
+495,Software correlator for Radioastron mission,"  In this paper we discuss the characteristics and operation of Astro Space
+Center (ASC) software FX correlator that is an important component of
+space-ground interferometer for Radioastron project. This project performs
+joint observations of compact radio sources using 10 meter space radio
+telescope (SRT) together with ground radio telescopes at 92, 18, 6 and 1.3 cm
+wavelengths. In this paper we describe the main features of space-ground VLBI
+data processing of Radioastron project using ASC correlator. Quality of
+implemented fringe search procedure provides positive results without
+significant losses in correlated amplitude. ASC Correlator has a computational
+power close to real time operation. The correlator has a number of processing
+modes: ""Continuum"", ""Spectral Line"", ""Pulsars"", ""Giant Pulses"",""Coherent"".
+Special attention is paid to peculiarities of Radioastron space-ground VLBI
+data processing. The algorithms of time delay and delay rate calculation are
+also discussed, which is a matter of principle for data correlation of
+space-ground interferometers. During 5 years of Radioastron space radio
+telescope (SRT) successful operation, ASC correlator showed high potential of
+satisfying steady growing needs of current and future ground and space VLBI
+science. Results of ASC software correlator operation are demonstrated.
+",0,1,0,0,0,0
+496,Isogenies for point counting on genus two hyperelliptic curves with maximal real multiplication,"  Schoof's classic algorithm allows point-counting for elliptic curves over
+finite fields in polynomial time. This algorithm was subsequently improved by
+Atkin, using factorizations of modular polynomials, and by Elkies, using a
+theory of explicit isogenies. Moving to Jacobians of genus-2 curves, the
+current state of the art for point counting is a generalization of Schoof's
+algorithm. While we are currently missing the tools we need to generalize
+Elkies' methods to genus 2, recently Martindale and Milio have computed
+analogues of modular polynomials for genus-2 curves whose Jacobians have real
+multiplication by maximal orders of small discriminant. In this article, we
+prove Atkin-style results for genus-2 Jacobians with real multiplication by
+maximal orders, with a view to using these new modular polynomials to improve
+the practicality of point-counting algorithms for these curves.
+",1,0,1,0,0,0
+497,On the self-duality of rings of integers in tame and abelian extensions,"  Let $L/K$ be a tame and Galois extension of number fields with group $G$. It
+is well-known that any ambiguous ideal in $L$ is locally free over
+$\mathcal{O}_KG$ (of rank one), and so it defines a class in the locally free
+class group of $\mathcal{O}_KG$, where $\mathcal{O}_K$ denotes the ring of
+integers of $K$. In this paper, we shall study the relationship among the
+classes arising from the ring of integers $\mathcal{O}_L$ of $L$, the inverse
+different $\mathfrak{D}_{L/K}^{-1}$ of $L/K$, and the square root of the
+inverse different $A_{L/K}$ of $L/K$ (if it exists), in the case that $G$ is
+abelian. They are naturally related because $A_{L/K}^2 =
+\mathfrak{D}_{L/K}^{-1} = \mathcal{O}_L^*$, and $A_{L/K}$ is special because
+$A_{L/K} = A_{L/K}^*$, where $*$ denotes dual with respect to the trace of
+$L/K$.
+",0,0,1,0,0,0
+498,Forecasting Transformative AI: An Expert Survey,"  Transformative AI technologies have the potential to reshape critical aspects
+of society in the near future. However, in order to properly prepare policy
+initiatives for the arrival of such technologies accurate forecasts and
+timelines are necessary. A survey was administered to attendees of three AI
+conferences during the summer of 2018 (ICML, IJCAI and the HLAI conference).
+The survey included questions for estimating AI capabilities over the next
+decade, questions for forecasting five scenarios of transformative AI and
+questions concerning the impact of computational resources in AI research.
+Respondents indicated a median of 21.5% of human tasks (i.e., all tasks that
+humans are currently paid to do) can be feasibly automated now, and that this
+figure would rise to 40% in 5 years and 60% in 10 years. Median forecasts
+indicated a 50% probability of AI systems being capable of automating 90% of
+current human tasks in 25 years and 99% of current human tasks in 50 years. The
+conference of attendance was found to have a statistically significant impact
+on all forecasts, with attendees of HLAI providing more optimistic timelines
+with less uncertainty. These findings suggest that AI experts expect major
+advances in AI technology to continue over the next decade to a degree that
+will likely have profound transformative impacts on society.
+",1,0,0,0,0,0
+499,"Change of grading, injective dimension and dualizing complexes","  Let $G,H$ be groups, $\phi: G \rightarrow H$ a group morphism, and $A$ a
+$G$-graded algebra. The morphism $\phi$ induces an $H$-grading on $A$, and on
+any $G$-graded $A$-module, which thus becomes an $H$-graded $A$-module. Given
+an injective $G$-graded $A$-module, we give bounds for its injective dimension
+when seen as $H$-graded $A$-module. Following ideas by Van den Bergh, we give
+an application of our results to the stability of dualizing complexes through
+change of grading.
+",0,0,1,0,0,0
+500,Approximately certifying the restricted isometry property is hard,"  A matrix is said to possess the Restricted Isometry Property (RIP) if it acts
+as an approximate isometry when restricted to sparse vectors. Previous work has
+shown it to be NP-hard to determine whether a matrix possess this property, but
+only in a narrow range of parameters. In this work, we show that it is NP-hard
+to make this determination for any accuracy parameter, even when we restrict
+ourselves to instances which are either RIP or far from being RIP. This result
+implies that it is NP-hard to approximate the range of parameters for which a
+matrix possesses the Restricted Isometry Property with accuracy better than
+some constant. Ours is the first work to prove such a claim without any
+additional assumptions.
+",1,0,0,0,0,0
+501,A compact design for velocity-map imaging energetic electrons and ions,"  We present a compact design for a velocity-map imaging spectrometer for
+energetic electrons and ions. The standard geometry by Eppink and Parker [A. T.
+J. B. Eppink and D. H. Parker, Rev. Sci. Instrum. 68, 3477 (1997)] is augmented
+by just two extended electrodes so as to realize an additional einzel lens. In
+this way, for a maximum electrode voltage of 7 kV we experimentally demonstrate
+imaging of electrons with energies up to 65 eV. Simulations show that energy
+acceptances of <270 and <1,200 eV with an energy resolution of dE / E <5% are
+achievable for electrode voltages of <20 kV when using diameters of the
+position-sensitive detector of 42 and 78 mm, respectively.
+",0,1,0,0,0,0
+502,Multiset Combinatorial Batch Codes,"  Batch codes, first introduced by Ishai, Kushilevitz, Ostrovsky, and Sahai,
+mimic a distributed storage of a set of $n$ data items on $m$ servers, in such
+a way that any batch of $k$ data items can be retrieved by reading at most some
+$t$ symbols from each server. Combinatorial batch codes, are replication-based
+batch codes in which each server stores a subset of the data items.
+In this paper, we propose a generalization of combinatorial batch codes,
+called multiset combinatorial batch codes (MCBC), in which $n$ data items are
+stored in $m$ servers, such that any multiset request of $k$ items, where any
+item is requested at most $r$ times, can be retrieved by reading at most $t$
+items from each server. The setup of this new family of codes is motivated by
+recent work on codes which enable high availability and parallel reads in
+distributed storage systems. The main problem under this paradigm is to
+minimize the number of items stored in the servers, given the values of
+$n,m,k,r,t$, which is denoted by $N(n,k,m,t;r)$. We first give a necessary and
+sufficient condition for the existence of MCBCs. Then, we present several
+bounds on $N(n,k,m,t;r)$ and constructions of MCBCs. In particular, we
+determine the value of $N(n,k,m,1;r)$ for any $n\geq
+\left\lfloor\frac{k-1}{r}\right\rfloor{m\choose k-1}-(m-k+1)A(m,4,k-2)$, where
+$A(m,4,k-2)$ is the maximum size of a binary constant weight code of length
+$m$, distance four and weight $k-2$. We also determine the exact value of
+$N(n,k,m,1;r)$ when $r\in\{k,k-1\}$ or $k=m$.
+",1,0,1,0,0,0
+503,Thermoelectric Cooperative Effect in Three-Terminal Elastic Transport through a Quantum Dot,"  The energy efficiency and power of a three-terminal thermoelectric nanodevice
+are studied by considering elastic tunneling through a single quantum dot.
+Facilitated by the three-terminal geometry, the nanodevice is able to generate
+simultaneously two electrical powers by utilizing only one temperature bias.
+These two electrical powers can add up constructively or destructively,
+depending on their signs. It is demonstrated that the constructive addition
+leads to the enhancement of both energy efficiency and output power for various
+system parameters. In fact, such enhancement, dubbed as thermoelectric
+cooperative effect, can lead to maximum efficiency and power no less than when
+only one of the electrical power is harvested.
+",0,1,0,0,0,0
+504,DeepSaucer: Unified Environment for Verifying Deep Neural Networks,"  In recent years, a number of methods for verifying DNNs have been developed.
+Because the approaches of the methods differ and have their own limitations, we
+think that a number of verification methods should be applied to a developed
+DNN. To apply a number of methods to the DNN, it is necessary to translate
+either the implementation of the DNN or the verification method so that one
+runs in the same environment as the other. Since those translations are
+time-consuming, a utility tool, named DeepSaucer, which helps to retain and
+reuse implementations of DNNs, verification methods, and their environments, is
+proposed. In DeepSaucer, code snippets of loading DNNs, running verification
+methods, and creating their environments are retained and reused as software
+assets in order to reduce cost of verifying DNNs. The feasibility of DeepSaucer
+is confirmed by implementing it on the basis of Anaconda, which provides
+virtual environment for loading a DNN and running a verification method. In
+addition, the effectiveness of DeepSaucer is demonstrated by usecase examples.
+",1,0,0,0,0,0
+505,Checklists to Support Test Charter Design in Exploratory Testing,"  During exploratory testing sessions the tester simultaneously learns, designs
+and executes tests. The activity is iterative and utilizes the skills of the
+tester and provides flexibility and creativity.Test charters are used as a
+vehicle to support the testers during the testing. The aim of this study is to
+support practitioners in the design of test charters through checklists. We
+aimed to identify factors allowing practitioners to critically reflect on their
+designs and contents of test charters to support practitioners in making
+informed decisions of what to include in test charters. The factors and
+contents have been elicited through interviews. Overall, 30 factors and 35
+content elements have been elicited.
+",1,0,0,0,0,0
+506,Fast non-destructive parallel readout of neutral atom registers in optical potentials,"  We demonstrate the parallel and non-destructive readout of the hyperfine
+state for optically trapped $^{87}$Rb atoms. The scheme is based on
+state-selective fluorescence imaging and achieves detection fidelities $>$98%
+within 10$\,$ms, while keeping 99% of the atoms trapped. For the read-out of
+dense arrays of neutral atoms in optical lattices, where the fluorescence
+images of neighboring atoms overlap, we apply a novel image analysis technique
+using Bayesian inference to determine the internal state of multiple atoms. Our
+method is scalable to large neutral atom registers relevant for future quantum
+information processing tasks requiring fast and non-destructive readout and can
+also be used for the simultaneous read-out of quantum information stored in
+internal qubit states and in the atoms' positions.
+",0,1,0,0,0,0
+507,Binaural Source Localization based on Modulation-Domain Features and Decision Pooling,"  In this work we apply Amplitude Modulation Spectrum (AMS) features to the
+source localization problem. Our approach computes 36 bilateral features for 2s
+long signal segments and estimates the azimuthal directions of a sound source
+through a binaurally trained classifier. This directional information of a
+sound source could be e.g. used to steer the beamformer in a hearing aid to the
+source of interest in order to increase the SNR. We evaluated our approach on
+the development set of the IEEE-AASP Challenge on sound source localization and
+tracking (LOCATA) and achieved a 4.25° smaller MAE than the baseline
+approach. Additionally, our approach is computationally less complex.
+",1,0,0,0,0,0
+508,Dynamic Shrinkage Processes,"  We propose a novel class of dynamic shrinkage processes for Bayesian time
+series and regression analysis. Building upon a global-local framework of prior
+construction, in which continuous scale mixtures of Gaussian distributions are
+employed for both desirable shrinkage properties and computational
+tractability, we model dependence among the local scale parameters. The
+resulting processes inherit the desirable shrinkage behavior of popular
+global-local priors, such as the horseshoe prior, but provide additional
+localized adaptivity, which is important for modeling time series data or
+regression functions with local features. We construct a computationally
+efficient Gibbs sampling algorithm based on a Pólya-Gamma scale mixture
+representation of the proposed process. Using dynamic shrinkage processes, we
+develop a Bayesian trend filtering model that produces more accurate estimates
+and tighter posterior credible intervals than competing methods, and apply the
+model for irregular curve-fitting of minute-by-minute Twitter CPU usage data.
+In addition, we develop an adaptive time-varying parameter regression model to
+assess the efficacy of the Fama-French five-factor asset pricing model with
+momentum added as a sixth factor. Our dynamic analysis of manufacturing and
+healthcare industry data shows that with the exception of the market risk, no
+other risk factors are significant except for brief periods.
+",0,0,0,1,0,0
+509,A Multiple Source Framework for the Identification of Activities of Daily Living Based on Mobile Device Data,"  The monitoring of the lifestyles may be performed based on a system for the
+recognition of Activities of Daily Living (ADL) and their environments,
+combining the results obtained with the user agenda. The system may be
+developed with the use of the off-the-shelf mobile devices commonly used,
+because they have several types of sensors available, including motion,
+magnetic, acoustic, and location sensors. Data acquisition, data processing,
+data fusion, and artificial intelligence methods are applied in different
+stages of the system developed, which recognizes the ADL with pattern
+recognition methods. The motion and magnetic sensors allow the recognition of
+activities with movement, but the acoustic sensors allow the recognition of the
+environments. The fusion of the motion, magnetic and acoustic sensors allows
+the differentiation of other ADL. On the other hand, the location sensors
+allows the recognition of ADL with large movement, and the combination of these
+sensors with the other sensors increases the number of ADL recognized by the
+system. This study consists on the comparison of different types of ANN for
+choosing the best methods for the recognition of several ADL, which they are
+implemented in a system for the recognition of ADL that combines the sensors
+data with the users agenda for the monitoring of the lifestyles. Conclusions
+point to the use of Deep Neural Networks (DNN) with normalized data for the
+identification of ADL with 85.89% of accuracy, the use of Feedforward neural
+networks with non-normalized data for the identification of the environments
+with 86.50% of accuracy, and the use of DNN with normalized data for the
+identification of standing activities with 100% of accuracy, proving the
+reliability of the framework presented in this study.
+",1,0,0,0,0,0
+510,Maximum likelihood estimators based on the block maxima method,"  The extreme value index is a fundamental parameter in univariate Extreme
+Value Theory (EVT). It captures the tail behavior of a distribution and is
+central in the extrapolation beyond observed data. Among other semi-parametric
+methods (such as the popular Hill's estimator), the Block Maxima (BM) and
+Peaks-Over-Threshold (POT) methods are widely used for assessing the extreme
+value index and related normalizing constants. We provide asymptotic theory for
+the maximum likelihood estimators (MLE) based on the BM method. Our main result
+is the asymptotic normality of the MLE with a non-trivial bias depending on the
+extreme value index and on the so-called second order parameter. Our approach
+combines asymptotic expansions of the likelihood process and of the empirical
+quantile process of block maxima. The results permit to complete the comparison
+of most common semi-parametric estimators in EVT (MLE and probability weighted
+moment estimators based on the POT or BM methods) through their asymptotic
+variances, biases and optimal mean square errors.
+",0,0,1,1,0,0
+511,Hipsters on Networks: How a Small Group of Individuals Can Lead to an Anti-Establishment Majority,"  The spread of opinions, memes, diseases, and ""alternative facts"" in a
+population depends both on the details of the spreading process and on the
+structure of the social and communication networks on which they spread. In
+this paper, we explore how \textit{anti-establishment} nodes (e.g.,
+\textit{hipsters}) influence the spreading dynamics of two competing products.
+We consider a model in which spreading follows a deterministic rule for
+updating node states (which describe which product has been adopted) in which
+an adjustable fraction $p_{\rm Hip}$ of the nodes in a network are hipsters,
+who choose to adopt the product that they believe is the less popular of the
+two. The remaining nodes are conformists, who choose which product to adopt by
+considering which products their immediate neighbors have adopted. We simulate
+our model on both synthetic and real networks, and we show that the hipsters
+have a major effect on the final fraction of people who adopt each product:
+even when only one of the two products exists at the beginning of the
+simulations, a very small fraction of hipsters in a network can still cause the
+other product to eventually become the more popular one. To account for this
+behavior, we construct an approximation for the steady-state adoption fraction
+on $k$-regular trees in the limit of few hipsters. Additionally, our
+simulations demonstrate that a time delay $\tau$ in the knowledge of the
+product distribution in a population, as compared to immediate knowledge of
+product adoption among nearest neighbors, can have a large effect on the final
+distribution of product adoptions. Our simple model and analysis may help shed
+light on the road to success for anti-establishment choices in elections, as
+such success can arise rather generically in our model from a small number of
+anti-establishment individuals and ordinary processes of social influence on
+normal individuals.
+",1,1,0,0,0,0
+512,Optimal Identity Testing with High Probability,"  We study the problem of testing identity against a given distribution with a
+focus on the high confidence regime. More precisely, given samples from an
+unknown distribution $p$ over $n$ elements, an explicitly given distribution
+$q$, and parameters $0< \epsilon, \delta < 1$, we wish to distinguish, {\em
+with probability at least $1-\delta$}, whether the distributions are identical
+versus $\varepsilon$-far in total variation distance. Most prior work focused
+on the case that $\delta = \Omega(1)$, for which the sample complexity of
+identity testing is known to be $\Theta(\sqrt{n}/\epsilon^2)$. Given such an
+algorithm, one can achieve arbitrarily small values of $\delta$ via black-box
+amplification, which multiplies the required number of samples by
+$\Theta(\log(1/\delta))$.
+We show that black-box amplification is suboptimal for any $\delta = o(1)$,
+and give a new identity tester that achieves the optimal sample complexity. Our
+new upper and lower bounds show that the optimal sample complexity of identity
+testing is \[
+\Theta\left( \frac{1}{\epsilon^2}\left(\sqrt{n \log(1/\delta)} +
+\log(1/\delta) \right)\right) \] for any $n, \varepsilon$, and $\delta$. For
+the special case of uniformity testing, where the given distribution is the
+uniform distribution $U_n$ over the domain, our new tester is surprisingly
+simple: to test whether $p = U_n$ versus $d_{\mathrm TV}(p, U_n) \geq
+\varepsilon$, we simply threshold $d_{\mathrm TV}(\widehat{p}, U_n)$, where
+$\widehat{p}$ is the empirical probability distribution. The fact that this
+simple ""plug-in"" estimator is sample-optimal is surprising, even in the
+constant $\delta$ case. Indeed, it was believed that such a tester would not
+attain sublinear sample complexity even for constant values of $\varepsilon$
+and $\delta$.
+",1,0,1,1,0,0
+513,Handling state space explosion in verification of component-based systems: A review,"  Component-based design is a different way of constructing systems which
+offers numerous benefits, in particular, decreasing the complexity of system
+design. However, deploying components into a system is a challenging and
+error-prone task. Model checking is one of the reliable methods that
+automatically and systematically analyse the correctness of a given system. Its
+brute-force check of the state space significantly expands the level of
+confidence in the system. Nevertheless, model checking is limited by a critical
+problem so-called State Space Explosion (SSE). To benefit from model checking,
+appropriate methods to reduce SSE, is required. In two last decades, a great
+number of methods to mitigate the state space explosion have been proposed
+which have many similarities, dissimilarities, and unclear concepts in some
+cases. This research, firstly, aims at present a review and brief discussion of
+the methods of handling SSE problem and classify them based on their
+similarities, principle and characteristics. Second, it investigates the
+methods for handling SSE problem in verifying Component-based system (CBS) and
+provides insight into CBS verification limitations that have not been addressed
+yet. The analysis in this research has revealed the patterns, specific
+features, and gaps in the state-of-the-art methods. In addition, we identified
+and discussed suitable methods to soften SSE problem in CBS and underlined the
+key challenges for future research efforts.
+",1,0,1,0,0,0
+514,A Framework for Relating the Structures and Recovery Statistics in Pressure Time-Series Surveys for Dust Devils,"  Dust devils are likely the dominant source of dust for the martian
+atmosphere, but the amount and frequency of dust-lifting depend on the
+statistical distribution of dust devil parameters. Dust devils exhibit pressure
+perturbations and, if they pass near a barometric sensor, they may register as
+a discernible dip in a pressure time-series. Leveraging this fact, several
+surveys using barometric sensors on landed spacecraft have revealed dust devil
+structures and occurrence rates. However powerful they are, though, such
+surveys suffer from non-trivial biases that skew the inferred dust devil
+properties. For example, such surveys are most sensitive to dust devils with
+the widest and deepest pressure profiles, but the recovered profiles will be
+distorted, broader and shallow than the actual profiles. In addition, such
+surveys often do not provide wind speed measurements alongside the pressure
+time series, and so the durations of the dust devil signals in the time series
+cannot be directly converted to profile widths. Fortunately, simple statistical
+and geometric considerations can de-bias these surveys, allowing conversion of
+the duration of dust devil signals into physical widths, given only a
+distribution of likely translation velocities, and the recovery of the
+underlying distributions of physical parameters. In this study, we develop a
+scheme for de-biasing such surveys. Applying our model to an in-situ survey
+using data from the Phoenix lander suggests a larger dust flux and a dust devil
+occurrence rate about ten times larger than previously inferred. Comparing our
+results to dust devil track surveys suggests only about one in five
+low-pressure cells lifts sufficient dust to leave a visible track.
+",0,1,0,0,0,0
+515,"Investigation on different physical aspects such as structural, elastic, mechanical, optical properties and Debye temperature of Fe2ScM (M = P and As) semiconductors: a DFT based first principles study","  With the help of first principles calculation method based on the density
+functional theory we have investigated the structural, elastic, mechanical
+properties and Debye temperature of Fe2ScM (M = P and As) compounds under
+pressure up to 60 GPa. The optical properties have been investigated under zero
+pressure. Our calculated optimized structural parameters of both the compounds
+are in good agreement with the other theoretical results. The calculated
+elastic constants show that Fe2ScM (M = P and As) compounds are mechanically
+stable up to 60 GPa.
+",0,1,0,0,0,0
+516,Optimal Envelope Approximation in Fourier Basis with Applications in TV White Space,"  Lowpass envelope approximation of smooth continuous-variable signals are
+introduced in this work. Envelope approximations are necessary when a given
+signal has to be approximated always to a larger value (such as in TV white
+space protection regions). In this work, a near-optimal approximate algorithm
+for finding a signal's envelope, while minimizing a mean-squared cost function,
+is detailed. The sparse (lowpass) signal approximation is obtained in the
+linear Fourier series basis. This approximate algorithm works by discretizing
+the envelope property from an infinite number of points to a large (but finite)
+number of points. It is shown that this approximate algorithm is near-optimal
+and can be solved by using efficient convex optimization programs available in
+the literature. Simulation results are provided towards the end to gain more
+insights into the analytical results presented.
+",1,0,0,0,0,0
+517,The curl operator on odd-dimensional manifolds,"  We study the spectral properties of curl, a linear differential operator of
+first order acting on differential forms of appropriate degree on an
+odd-dimensional closed oriented Riemannian manifold. In three dimensions its
+eigenvalues are the electromagnetic oscillation frequencies in vacuum without
+external sources. In general, the spectrum consists of the eigenvalue 0 with
+infinite multiplicity and further real discrete eigenvalues of finite
+multiplicity. We compute the Weyl asymptotics and study the zeta-function. We
+give a sharp lower eigenvalue bound for positively curved manifolds and analyze
+the equality case. Finally, we compute the spectrum for flat tori, round
+spheres and 3-dimensional spherical space forms.
+",0,0,1,0,0,0
+518,Topology optimization for transient response of structures subjected to dynamic loads,"  This paper presents a topology optimization framework for structural problems
+subjected to transient loading. The mechanical model assumes a linear elastic
+isotropic material, infinitesimal strains, and a dynamic response. The
+optimization problem is solved using the gradient-based optimizer Method of
+Moving Asymptotes (MMA) with time-dependent sensitivities provided via the
+adjoint method. The stiffness of materials is interpolated using the Solid
+Isotropic Material with Penalization (SIMP) method and the Heaviside Projection
+Method (HPM) is used to stabilize the problem numerically and improve the
+manufacturability of the topology-optimized designs. Both static and dynamic
+optimization examples are considered here. The resulting optimized designs
+demonstrate the ability of topology optimization to tailor the transient
+response of structures.
+",0,1,1,0,0,0
+519,Generalized Lambert series and arithmetic nature of odd zeta values,"  It is pointed out that the generalized Lambert series
+$\displaystyle\sum_{n=1}^{\infty}\frac{n^{N-2h}}{e^{n^{N}x}-1}$ studied by
+Kanemitsu, Tanigawa and Yoshimoto can be found on page $332$ of Ramanujan's
+Lost Notebook in a slightly more general form. We extend an important
+transformation of this series obtained by Kanemitsu, Tanigawa and Yoshimoto by
+removing restrictions on the parameters $N$ and $h$ that they impose. From our
+extension we deduce a beautiful new generalization of Ramanujan's famous
+formula for odd zeta values which, for $N$ odd and $m>0$, gives a relation
+between $\zeta(2m+1)$ and $\zeta(2Nm+1)$. A result complementary to the
+aforementioned generalization is obtained for any even $N$ and
+$m\in\mathbb{Z}$. It generalizes a transformation of Wigert and can be regarded
+as a formula for $\zeta\left(2m+1-\frac{1}{N}\right)$. Applications of these
+transformations include a generalization of the transformation for the
+logarithm of Dedekind eta-function $\eta(z)$, Zudilin- and Rivoal-type results
+on transcendence of certain values, and a transcendence criterion for Euler's
+constant $\gamma$.
+",0,0,1,0,0,0
+520,Perfect phylogenies via branchings in acyclic digraphs and a generalization of Dilworth's theorem,"  Motivated by applications in cancer genomics and following the work of
+Hajirasouliha and Raphael (WABI 2014), Hujdurović et al. (IEEE TCBB, to
+appear) introduced the minimum conflict-free row split (MCRS) problem: split
+each row of a given binary matrix into a bitwise OR of a set of rows so that
+the resulting matrix corresponds to a perfect phylogeny and has the minimum
+possible number of rows among all matrices with this property. Hajirasouliha
+and Raphael also proposed the study of a similar problem, in which the task is
+to minimize the number of distinct rows of the resulting matrix. Hujdurović
+et al. proved that both problems are NP-hard, gave a related characterization
+of transitively orientable graphs, and proposed a polynomial-time heuristic
+algorithm for the MCRS problem based on coloring cocomparability graphs.
+We give new, more transparent formulations of the two problems, showing that
+the problems are equivalent to two optimization problems on branchings in a
+derived directed acyclic graph. Building on these formulations, we obtain new
+results on the two problems, including: (i) a strengthening of the heuristic by
+Hujdurović et al. via a new min-max result in digraphs generalizing
+Dilworth's theorem, which may be of independent interest, (ii) APX-hardness
+results for both problems, (iii) approximation algorithms, and (iv)
+exponential-time algorithms solving the two problems to optimality faster than
+the naïve brute-force approach. Our work relates to several well studied
+notions in combinatorial optimization: chain partitions in partially ordered
+sets, laminar hypergraphs, and (classical and weighted) colorings of graphs.
+",1,0,1,0,0,0
+521,Towards a Service-oriented Platform for Intelligent Apps in Intermediate Cities,"  Smart cities are a growing trend in many cities in Argentina. In particular,
+the so-called intermediate cities present a context and requirements different
+from those of large cities with respect to smart cities. One aspect of
+relevance is to encourage the development of applications (generally for mobile
+devices) that enable citizens to take advantage of data and services normally
+associated with the city, for example, in the urban mobility domain. In this
+work, a platform is proposed for intermediate cities that provide ""high level""
+services and that allow the construction of software applications that consume
+those services. Our platform-centric strategy focused aims to integrate systems
+and heterogeneous data sources, and provide ""intelligent"" services to different
+applications. Examples of these services include: construction of user
+profiles, recommending local events, and collaborative sensing based on data
+mining techniques, among others. In this work, the design of this platform
+(currently in progress) is described, and experiences of applications for urban
+mobility are discussed, which are being migrated in the form of reusable
+services provided by the platform
+",1,0,0,0,0,0
+522,Fully Bayesian Estimation Under Informative Sampling,"  Bayesian estimation is increasingly popular for performing model based
+inference to support policymaking. These data are often collected from surveys
+under informative sampling designs where subject inclusion probabilities are
+designed to be correlated with the response variable of interest. Sampling
+weights constructed from marginal inclusion probabilities are typically used to
+form an exponentiated pseudo likelihood that adjusts the population likelihood
+for estimation on the sample due to ease-of-estimation. We propose an
+alternative adjustment based on a Bayes rule construction that simultaneously
+performs weight smoothing and estimates the population model parameters in a
+fully Bayesian construction. We formulate conditions on known marginal and
+pairwise inclusion probabilities that define a class of sampling designs where
+$L_{1}$ consistency of the joint posterior is guaranteed. We compare
+performances between the two approaches on synthetic data, which reveals that
+our fully Bayesian approach better estimates posterior uncertainty without a
+requirement to calibrate the normalization of the sampling weights. We
+demonstrate our method on an application concerning the National Health and
+Nutrition Examination Survey exploring the relationship between caffeine
+consumption and systolic blood pressure.
+",0,0,1,1,0,0
+523,Bistable reaction equations with doubly nonlinear diffusion,"  Reaction-diffusion equations appear in biology and chemistry, and combine
+linear diffusion with different kind of reaction terms. Some of them are
+remarkable from the mathematical point of view, since they admit families of
+travelling waves that describe the asymptotic behaviour of a larger class of
+solutions $0\leq u(x,t)\leq 1$ of the problem posed in the real line. We
+investigate here the existence of waves with constant propagation speed, when
+the linear diffusion is replaced by the ""slow"" doubly nonlinear diffusion. In
+the present setting we consider bistable reaction terms, which present
+interesting differences w.r.t. the Fisher-KPP framework recently studied in
+\cite{AA-JLV:art}. We find different families of travelling waves that are
+employed to describe the wave propagation of more general solutions and to
+study the stability/instability of the steady states, even when we extend the
+study to several space dimensions. A similar study is performed in the critical
+case that we call ""pseudo-linear"", i.e., when the operator is still nonlinear
+but has homogeneity one. With respect to the classical model and the
+""pseudo-linear"" case, the travelling waves of the ""slow"" diffusion setting
+exhibit free boundaries. \\ Finally, as a complement of \cite{AA-JLV:art}, we
+study the asymptotic behaviour of more general solutions in the presence of a
+""heterozygote superior"" reaction function and doubly nonlinear diffusion
+(""slow"" and ""pseudo-linear"").
+",0,0,1,0,0,0
+524,Static Free Space Detection with Laser Scanner using Occupancy Grid Maps,"  Drivable free space information is vital for autonomous vehicles that have to
+plan evasive maneuvers in real-time. In this paper, we present a new efficient
+method for environmental free space detection with laser scanner based on 2D
+occupancy grid maps (OGM) to be used for Advanced Driving Assistance Systems
+(ADAS) and Collision Avoidance Systems (CAS). Firstly, we introduce an enhanced
+inverse sensor model tailored for high-resolution laser scanners for building
+OGM. It compensates the unreflected beams and deals with the ray casting to
+grid cells accuracy and computational effort problems. Secondly, we introduce
+the 'vehicle on a circle for grid maps' map alignment algorithm that allows
+building more accurate local maps by avoiding the computationally expensive
+inaccurate operations of image sub-pixel shifting and rotation. The resulted
+grid map is more convenient for ADAS features than existing methods, as it
+allows using less memory sizes, and hence, results into a better real-time
+performance. Thirdly, we present an algorithm to detect what we call the
+'in-sight edges'. These edges guarantee modeling the free space area with a
+single polygon of a fixed number of vertices regardless the driving situation
+and map complexity. The results from real world experiments show the
+effectiveness of our approach.
+",1,0,0,0,0,0
+525,Proactive Edge Computing in Latency-Constrained Fog Networks,"  In this paper, the fundamental problem of distribution and proactive caching
+of computing tasks in fog networks is studied under latency and reliability
+constraints. In the proposed scenario, computing can be executed either locally
+at the user device or offloaded to an edge cloudlet. Moreover, cloudlets
+exploit both their computing and storage capabilities by proactively caching
+popular task computation results to minimize computing latency. To this end, a
+clustering method to group spatially proximate user devices with mutual task
+popularity interests and their serving cloudlets is proposed. Then, cloudlets
+can proactively cache the popular tasks' computations of their cluster members
+to minimize computing latency. Additionally, the problem of distributing tasks
+to cloudlets is formulated as a matching game in which a cost function of
+computing delay is minimized under latency and reliability constraints.
+Simulation results show that the proposed scheme guarantees reliable
+computations with bounded latency and achieves up to 91% decrease in computing
+latency as compared to baseline schemes.
+",1,0,0,0,0,0
+526,Deuterium fractionation and H2D+ evolution in turbulent and magnetized cloud cores,"  High-mass stars are expected to form from dense prestellar cores. Their
+precise formation conditions are widely discussed, including their virial
+condition, which results in slow collapse for super-virial cores with strong
+support by turbulence or magnetic fields, or fast collapse for sub-virial
+sources. To disentangle their formation processes, measurements of the
+deuterium fractions are frequently employed to approximately estimate the ages
+of these cores and to obtain constraints on their dynamical evolution. We here
+present 3D magneto-hydrodynamical simulations including for the first time an
+accurate non-equilibrium chemical network with 21 gas-phase species plus dust
+grains and 213 reactions. With this network we model the deuteration process in
+fully depleted prestellar cores in great detail and determine its response to
+variations in the initial conditions. We explore the dependence on the initial
+gas column density, the turbulent Mach number, the mass-to-magnetic flux ratio
+and the distribution of the magnetic field, as well as the initial
+ortho-to-para ratio of H2. We find excellent agreement with recent observations
+of deuterium fractions in quiescent sources. Our results show that deuteration
+is rather efficient, even when assuming a conservative ortho-to-para ratio of 3
+and highly sub-virial initial conditions, leading to large deuterium fractions
+already within roughly a free-fall time. We discuss the implications of our
+results and give an outlook to relevant future investigations.
+",0,1,0,0,0,0
+527,Experimental data over quantum mechanics simulations for inferring the repulsive exponent of the Lennard-Jones potential in Molecular Dynamics,"  The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD)
+simulations and among the most widely used computational kernels in science.
+The potential models atomistic attraction and repulsion with century old
+prescribed parameters ($q=6, \; p=12$, respectively), originally related by a
+factor of two for simplicity of calculations. We re-examine the value of the
+repulsion exponent through data driven uncertainty quantification. We perform
+Hierarchical Bayesian inference on MD simulations of argon using experimental
+data of the radial distribution function (RDF) for a range of thermodynamic
+conditions, as well as dimer interaction energies from quantum mechanics
+simulations. The experimental data suggest a repulsion exponent ($p \approx
+6.5$), in contrast to the quantum simulations data that support values closer
+to the original ($p=12$) exponent. Most notably, we find that predictions of
+RDF, diffusion coefficient and density of argon are more accurate and robust in
+producing the correct argon phase around its triple point, when using the
+values inferred from experimental data over those from quantum mechanics
+simulations. The present results suggest the need for data driven recalibration
+of the LJ potential across MD simulations.
+",0,1,0,1,0,0
+528,Ensemble Sampling,"  Thompson sampling has emerged as an effective heuristic for a broad range of
+online decision problems. In its basic form, the algorithm requires computing
+and sampling from a posterior distribution over models, which is tractable only
+for simple special cases. This paper develops ensemble sampling, which aims to
+approximate Thompson sampling while maintaining tractability even in the face
+of complex models such as neural networks. Ensemble sampling dramatically
+expands on the range of applications for which Thompson sampling is viable. We
+establish a theoretical basis that supports the approach and present
+computational results that offer further insight.
+",1,0,0,1,0,0
+529,Failures of Gradient-Based Deep Learning,"  In recent years, Deep Learning has become the go-to solution for a broad
+range of applications, often outperforming state-of-the-art. However, it is
+important, for both theoreticians and practitioners, to gain a deeper
+understanding of the difficulties and limitations associated with common
+approaches and algorithms. We describe four types of simple problems, for which
+the gradient-based algorithms commonly used in deep learning either fail or
+suffer from significant difficulties. We illustrate the failures through
+practical experiments, and provide theoretical insights explaining their
+source, and how they might be remedied.
+",1,0,0,1,0,0
+530,Searching for the Transit of the Earth--mass exoplanet Proxima~Centauri~b in Antarctica: Preliminary Result,"  Proxima Centauri is known as the closest star from the Sun. Recently, radial
+velocity observations revealed the existence of an Earth-mass planet around it.
+With an orbital period of ~11 days, the surface of Proxima Centauri b is
+temperate and might be habitable. We took a photometric monitoring campaign to
+search for its transit, using the Bright Star Survey Telescope at the Zhongshan
+Station in Antarctica. A transit-like signal appearing on 2016 September 8th,
+is identified tentatively. Its midtime, $T_{C}=2,457,640.1990\pm0.0017$ HJD, is
+consistent with the predicted ephemeris based on RV orbit in a 1$\sigma$
+confidence interval. Time-correlated noise is pronounced in the light curve of
+Proxima Centauri, affecting detection of transits. We develop a technique, in a
+Gaussian process framework, to gauge the statistical significance of potential
+transit detection. The tentative transit signal reported here, has a confidence
+level of $2.5\sigma$. Further detection of its periodic signals is necessary to
+confirm the planetary transit of Proxima Centauri b. We plan to monitor Proxima
+Centauri in next Polar night at Dome A in Antarctica, taking the advantage of
+continuous darkness. \citet{Kipping17} reported two tentative transit-like
+signals of Proxima Centauri b, observed by the Microvariability and Oscillation
+of Stars space Telescope in 2014 and 2015, respectively. The midtransit time of
+our detection is 138 minutes later than that predicted by their transit
+ephemeris. If all the signals are real transits, the misalignment of the epochs
+plausibly suggests transit timing variations of Proxima Centauri b induced by
+an outer planet in this system.
+",0,1,0,0,0,0
+531,A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks,"  In this paper, we propose an optimization-based sparse learning approach to
+identify the set of most influential reactions in a chemical reaction network.
+This reduced set of reactions is then employed to construct a reduced chemical
+reaction mechanism, which is relevant to chemical interaction network modeling.
+The problem of identifying influential reactions is first formulated as a
+mixed-integer quadratic program, and then a relaxation method is leveraged to
+reduce the computational complexity of our approach. Qualitative and
+quantitative validation of the sparse encoding approach demonstrates that the
+model captures important network structural properties with moderate
+computational load.
+",1,0,0,0,0,0
+532,Parity-Forbidden Transitions and Their Impacts on the Optical Absorption Properties of Lead-Free Metal Halide Perovskites and Double Perovskites,"  Using density-functional theory calculations, we analyze the optical
+absorption properties of lead (Pb)-free metal halide perovskites
+(AB$^{2+}$X$_3$) and double perovskites (AB$^+$B$^{3+}$X$_6$) (A = Cs or
+monovalent organic ion, B$^{2+}$ = non-Pb divalent metal, B$^+$ = monovalent
+metal, B$^{3+}$ = trivalent metal, X = halogen). We show that, if B$^{2+}$ is
+not Sn or Ge, Pb-free metal halide perovskites exhibit poor optical absorptions
+because of their indirect bandgap nature. Among the nine possible types of
+Pb-free metal halide double perovskites, six have direct bandgaps. Of these six
+types, four show inversion symmetry-induced parity-forbidden or weak
+transitions between band edges, making them not ideal for thin-film solar cell
+application. Only one type of Pb-free double perovskite shows optical
+absorption and electronic properties suitable for solar cell applications,
+namely those with B$^+$ = In, Tl and B$^{3+}$ = Sb, Bi. Our results provide
+important insights for designing new metal halide perovskites and double
+perovskites for optoelectronic applications.
+",0,1,0,0,0,0
+533,Identification of microRNA clusters cooperatively acting on Epithelial to Mesenchymal Transition in Triple Negative Breast Cancer,"  MicroRNAs play important roles in many biological processes. Their aberrant
+expression can have oncogenic or tumor suppressor function directly
+participating to carcinogenesis, malignant transformation, invasiveness and
+metastasis. Indeed, miRNA profiles can distinguish not only between normal and
+cancerous tissue but they can also successfully classify different subtypes of
+a particular cancer. Here, we focus on a particular class of transcripts
+encoding polycistronic miRNA genes that yields multiple miRNA components. We
+describe clustered MiRNA Master Regulator Analysis (ClustMMRA), a fully
+redesigned release of the MMRA computational pipeline (MiRNA Master Regulator
+Analysis), developed to search for clustered miRNAs potentially driving cancer
+molecular subtyping. Genomically clustered miRNAs are frequently co-expressed
+to target different components of pro-tumorigenic signalling pathways. By
+applying ClustMMRA to breast cancer patient data, we identified key miRNA
+clusters driving the phenotype of different tumor subgroups. The pipeline was
+applied to two independent breast cancer datasets, providing statistically
+concordant results between the two analysis. We validated in cell lines the
+miR-199/miR-214 as a novel cluster of miRNAs promoting the triple negative
+subtype phenotype through its control of proliferation and EMT.
+",0,0,0,0,1,0
+534,A Belief Propagation Algorithm for Multipath-Based SLAM,"  We present a simultaneous localization and mapping (SLAM) algorithm that is
+based on radio signals and the association of specular multipath components
+(MPCs) with geometric features. Especially in indoor scenarios, robust
+localization from radio signals is challenging due to diffuse multipath
+propagation, unknown MPC-feature association, and limited visibility of
+features. In our approach, specular reflections at flat surfaces are described
+in terms of virtual anchors (VAs) that are mirror images of the physical
+anchors (PAs). The positions of these VAs and possibly also of the PAs are
+unknown. We develop a Bayesian model of the SLAM problem including the unknown
+MPC-VA/PA association. We represent this model by a factor graph, which enables
+the use of the belief propagation (BP) scheme for efficient marginalization of
+the joint posterior distribution. The resulting BP-based SLAM algorithm detects
+the VAs associated with the PAs and estimates jointly the time-varying position
+of the mobile agent and the positions of the VAs and possibly also of the PAs,
+thereby leveraging the MPCs in the radio signal for improved accuracy and
+robustness of agent localization. A core aspect of the algorithm is BP-based
+probabilistic MPC-VA/PA association. Moreover, for improved initialization of
+new VA positions, the states of unobserved potential VAs are modeled as a
+random finite set and propagated in time by means of a ""zero-measurement""
+probability hypothesis density filter. The proposed BP-based SLAM algorithm has
+a low computational complexity and scales well in all relevant system
+parameters. Experimental results using both synthetically generated
+measurements and real ultra-wideband radio signals demonstrate the excellent
+performance of the algorithm in challenging indoor environments.
+",1,0,0,0,0,0
+535,Local methods for blocks of finite simple groups,"  This survey is about old and new results about the modular representation
+theory of finite reductive groups with a strong emphasis on local methods. This
+includes subpairs, Brauer's Main Theorems, fusion, Rickard equivalences. In the
+defining characteristic we describe the relation between $p$-local subgroups
+and parabolic subgroups, then give classical consequences on simple modules and
+blocks, including the Alperin weight conjecture in that case. In the
+non-defining characteristics, we sketch a picture of the local methods
+pioneered by Fong-Srinivasan in the determination of blocks and their ordinary
+characters. This includes the relationship with Lusztig's twisted induction and
+the determination of defect groups. We conclude with a survey of the results
+and methods by Bonnafé-Dat-Rouquier giving Morita equivalences between blocks
+that preserve defect groups and the local structures.
+The text grew out of the course and talks given by the author in July and
+September 2016 during the program ""Local representation theory and simple
+groups"" at CIB Lausanne. Written Oct 2017, to appear in a proceedings volume
+published by EMS.
+",0,0,1,0,0,0
+536,Motion Planning for a Humanoid Mobile Manipulator System,"  A high redundant non-holonomic humanoid mobile dual-arm manipulator system is
+presented in this paper where the motion planning to realize ""human-like""
+autonomous navigation and manipulation tasks is studied. Firstly, an improved
+MaxiMin NSGA-II algorithm, which optimizes five objective functions to solve
+the problems of singularity, redundancy, and coupling between mobile base and
+manipulator simultaneously, is proposed to design the optimal pose to
+manipulate the target object. Then, in order to link the initial pose and that
+optimal pose, an off-line motion planning algorithm is designed. In detail, an
+efficient direct-connect bidirectional RRT and gradient descent algorithm is
+proposed to reduce the sampled nodes largely, and a geometric optimization
+method is proposed for path pruning. Besides, head forward behaviors are
+realized by calculating the reasonable orientations and assigning them to the
+mobile base to improve the quality of human-robot interaction. Thirdly, the
+extension to on-line planning is done by introducing real-time sensing,
+collision-test and control cycles to update robotic motion in dynamic
+environments. Fourthly, an EEs' via-point-based multi-objective genetic
+algorithm is proposed to design the ""human-like"" via-poses by optimizing four
+objective functions. Finally, numerous simulations are presented to validate
+the effectiveness of proposed algorithms.
+",1,0,0,0,0,0
+537,End-to-end Planning of Fixed Millimeter-Wave Networks,"  This article discusses a framework to support the design and end-to-end
+planning of fixed millimeter-wave networks. Compared to traditional techniques,
+the framework allows an organization to quickly plan a deployment in a
+cost-effective way. We start by using LiDAR data---basically, a 3D point cloud
+captured from a city---to estimate potential sites to deploy antennas and
+whether there is line-of-sight between them. With that data on hand, we use
+combinatorial optimization techniques to determine the optimal set of locations
+and how they should communicate with each other, to satisfy engineering (e.g.,
+latency, polarity), design (e.g., reliability) and financial (e.g., total cost
+of operation) constraints. The primary goal is to connect as many people as
+possible to the network. Our methodology can be used for strategic planning
+when an organization is in the process of deciding whether to adopt a
+millimeter-wave technology or choosing between locations, or for operational
+planning when conducting a detailed design of the actual network to be deployed
+in a selected location.
+",1,0,1,0,0,0
+538,A giant planet undergoing extreme ultraviolet irradiation by its hot massive-star host,"  The amount of ultraviolet irradiation and ablation experienced by a planet
+depends strongly on the temperature of its host star. Of the thousands of
+extra-solar planets now known, only four giant planets have been found that
+transit hot, A-type stars (temperatures of 7300-10,000K), and none are known to
+transit even hotter B-type stars. WASP-33 is an A-type star with a temperature
+of ~7430K, which hosts the hottest known transiting planet; the planet is
+itself as hot as a red dwarf star of type M. The planet displays a large heat
+differential between its day-side and night-side, and is highly inflated,
+traits that have been linked to high insolation. However, even at the
+temperature of WASP-33b's day-side, its atmosphere likely resembles the
+molecule-dominated atmospheres of other planets, and at the level of
+ultraviolet irradiation it experiences, its atmosphere is unlikely to be
+significantly ablated over the lifetime of its star. Here we report
+observations of the bright star HD 195689, which reveal a close-in (orbital
+period ~1.48 days) transiting giant planet, KELT-9b. At ~10,170K, the host star
+is at the dividing line between stars of type A and B, and we measure the
+KELT-9b's day-side temperature to be ~4600K. This is as hot as stars of stellar
+type K4. The molecules in K stars are entirely dissociated, and thus the
+primary sources of opacity in the day-side atmosphere of KELT-9b are likely
+atomic metals. Furthermore, KELT-9b receives ~700 times more extreme
+ultraviolet radiation (wavelengths shorter than 91.2 nanometers) than WASP-33b,
+leading to a predicted range of mass-loss rates that could leave the planet
+largely stripped of its envelope during the main-sequence lifetime of the host
+star.
+",0,1,0,0,0,0
+539,"Characterizing videos, audience and advertising in Youtube channels for kids","  Online video services, messaging systems, games and social media services are
+tremendously popular among young people and children in many countries. Most of
+the digital services offered on the internet are advertising funded, which
+makes advertising ubiquitous in children's everyday life. To understand the
+impact of advertising-based digital services on children, we study the
+collective behavior of users of YouTube for kids channels and present the
+demographics of a large number of users. We collected data from 12,848 videos
+from 17 channels in US and UK and 24 channels in Brazil. The channels in
+English have been viewed more than 37 billion times. We also collected more
+than 14 million comments made by users. Based on a combination of text-analysis
+and face recognition tools, we show the presence of racial and gender biases in
+our large sample of users. We also identify children actively using YouTube,
+although the minimum age for using the service is 13 years in most countries.
+We provide comparisons of user behavior among the three countries, which
+represent large user populations in the global North and the global South.
+",1,0,0,0,0,0
+540,Vanishing theorems for the negative K-theory of stacks,"  We prove that the homotopy algebraic K-theory of tame quasi-DM stacks
+satisfies cdh-descent. We apply this descent result to prove that if X is a
+Noetherian tame quasi-DM stack and i < -dim(X), then K_i(X)[1/n] = 0 (resp.
+K_i(X, Z/n) = 0) provided that n is nilpotent on X (resp. is invertible on X).
+Our descent and vanishing results apply more generally to certain Artin stacks
+whose stabilizers are extensions of finite group schemes by group schemes of
+multiplicative type.
+",0,0,1,0,0,0
+541,Gravitational radiation from compact binary systems in screened modified gravity,"  Screened modified gravity (SMG) is a kind of scalar-tensor theory with
+screening mechanisms, which can suppress the fifth force in dense regions and
+allow theories to evade the solar system and laboratory tests. In this paper,
+we investigate how the screening mechanisms in SMG affect the gravitational
+radiation damping effects, calculate in detail the rate of the energy loss due
+to the emission of tensor and scalar gravitational radiations, and derive their
+contributions to the change in the orbital period of the binary system. We find
+that the scalar radiation depends on the screened parameters and the
+propagation speed of scalar waves, and the scalar dipole radiation dominates
+the orbital decay of the binary system. For strongly self-gravitating bodies,
+all effects of scalar sector are strongly suppressed by the screening
+mechanisms in SMG. By comparing our results to observations of binary system
+PSR J1738+0333, we place the stringent constraints on the screening mechanisms
+in SMG. As an application of these results, we focus on three specific models
+of SMG (chameleon, symmetron, and dilaton), and derive the constraints on the
+model parameters, respectively.
+",0,1,0,0,0,0
+542,weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming,"  Selective weed treatment is a critical step in autonomous crop management as
+related to crop health and yield. However, a key challenge is reliable, and
+accurate weed detection to minimize damage to surrounding plants. In this
+paper, we present an approach for dense semantic weed classification with
+multispectral images collected by a micro aerial vehicle (MAV). We use the
+recently developed encoder-decoder cascaded Convolutional Neural Network (CNN),
+Segnet, that infers dense semantic classes while allowing any number of input
+image channels and class balancing with our sugar beet and weed datasets. To
+obtain training datasets, we established an experimental field with varying
+herbicide levels resulting in field plots containing only either crop or weed,
+enabling us to use the Normalized Difference Vegetation Index (NDVI) as a
+distinguishable feature for automatic ground truth generation. We train 6
+models with different numbers of input channels and condition (fine-tune) it to
+achieve about 0.8 F1-score and 0.78 Area Under the Curve (AUC) classification
+metrics. For model deployment, an embedded GPU system (Jetson TX2) is tested
+for MAV integration. Dataset used in this paper is released to support the
+community and future work.
+",1,0,0,0,0,0
+543,An explicit determination of the $K$-theoretic structure constants of the affine Grassmannian associated to $SL_2$,"  Let $G:=\widehat{SL_2}$ denote the affine Kac-Moody group associated to
+$SL_2$ and $\bar{\mathcal{X}}$ the associated affine Grassmannian. We determine
+an inductive formula for the Schubert basis structure constants in the
+torus-equivariant Grothendieck group of $\bar{\mathcal{X}}$. In the case of
+ordinary (non-equivariant) $K$-theory we find an explicit closed form for the
+structure constants. We also determine an inductive formula for the structure
+constants in the torus-equivariant cohomology ring, and use this formula to
+find closed forms for some of the structure constants.
+",0,0,1,0,0,0
+544,The Correct Application of Variance Concept in Measurement Theory,"  The existing measurement theory interprets the variance as the dispersion of
+measured value, which is actually contrary to a general mathematical knowledge
+that the variance of a constant is 0. This paper will fully demonstrate that
+the variance in measurement theory is actually the evaluation of probability
+interval of an error instead of the dispersion of a measured value, point out
+the key point of mistake in the existing interpretation, and fully interpret a
+series of changes in conceptual logic and processing method brought about by
+this new concept.
+",0,0,1,1,0,0
+545,A Generalized Framework for the Estimation of Causal Moderation Effects with Randomized Treatments and Non-Randomized Moderators,"  Researchers are often interested in analyzing conditional treatment effects.
+One variant of this is ""causal moderation,"" which implies that intervention
+upon a third (moderator) variable would alter the treatment effect. This study
+presents a generalized, non-parametric framework for estimating causal
+moderation effects given randomized treatments and non-randomized moderators
+that achieves a number of goals. First, it highlights how conventional
+approaches do not constitute unbiased or consistent estimators of causal
+moderation effects. Second, it offers researchers a simple, transparent
+approach for estimating causal moderation effects and lays out the assumptions
+under which this can be performed consistently and/or without bias. Third, as
+part of the estimation process, it allows researchers to implement their
+preferred method of covariate adjustment, including parametric and
+non-parametric methods, or alternative identification strategies of their
+choosing. Fourth, it provides a set-up whereby sensitivity analysis designed
+for the average-treatment-effect context can be extended to the moderation
+context. An original application is also presented.
+",0,0,0,1,0,0
+546,Spherical Functions on Riemannian Symmetric Spaces,"  This paper deals with some simple results about spherical functions of type
+$\delta$, namely new integral formulas, new results about behavior at infinity
+and some facts about the related $C_\sigma$ functions.
+",0,0,1,0,0,0
+547,Modular curves with infinitely many cubic points,"  In this study, we determine all modular curves $X_0(N)$ that admit infinitely
+many cubic points.
+",0,0,1,0,0,0
+548,Semi-Analytical Perturbative Approaches to Third Body Resonant Trajectories,"  In the framework of multi-body dynamics, successive encounters with a third
+body, even if well outside of its sphere of influence, can noticeably alter the
+trajectory of a spacecraft. Examples of these effects have already been
+exploited by past missions such as SMART-1, as well as are proposed to benefit
+future missions to Jupiter, Saturn or Neptune, and disposal strategies from
+Earth's High Eccentric or Libration Point Orbits. This paper revises three
+totally different descriptions of the effects of the third body gravitational
+perturbation. These are the averaged dynamics of the classical third body
+perturbing function, the Opik's close encounter theory and the Keplerian map
+approach. The first two techniques have respectively been applied to the cases
+of a spacecraft either always remaining very far or occasionally experiencing
+extremely close approaches to the third body. However, the paper also seeks
+solutions for trajectories that undergo one or more close approaches at
+distances in the order of the sphere of influence of the third body. The paper
+attempts to gain insight into the accuracy of these different perturbative
+techniques into each of these scenarios, as compared with the motion in the
+Circular Restricted Three Body Problem.
+",0,1,0,0,0,0
+549, Robust and Imperceptible Adversarial Attacks on Capsule Networks,"  Capsule Networks envision an innovative point of view about the
+representation of the objects in the brain and preserve the hierarchical
+spatial relationships between them. This type of networks exhibits a huge
+potential for several Machine Learning tasks like image classification, while
+outperforming Convolutional Neural Networks (CNNs). A large body of work has
+explored adversarial examples for CNNs, but their efficacy to Capsule Networks
+is not well explored. In our work, we study the vulnerabilities in Capsule
+Networks to adversarial attacks. These perturbations, added to the test inputs,
+are small and imperceptible to humans, but fool the network to mis-predict. We
+propose a greedy algorithm to automatically generate targeted imperceptible
+adversarial examples in a black-box attack scenario. We show that this kind of
+attacks, when applied to the German Traffic Sign Recognition Benchmark (GTSRB),
+mislead Capsule Networks. Moreover, we apply the same kind of adversarial
+attacks to a 9-layer CNN and analyze the outcome, compared to the Capsule
+Networks to study their differences / commonalities.
+",1,0,0,1,0,0
+550,Thermophoretic MHD Flow and Non-linear Radiative Heat Transfer with Convective Boundary Conditions over a Non-linearly Stretching Sheet,"  The effects of MHD boundary layer flow of non-linear thermal radiation with
+convective heat transfer and non-uniform heat source/sink in presence of
+thermophortic velocity and chemical reaction investigated in this study.
+Suitable similarity transformation are used to solve the partial ordinary
+differential equation of considered governing flow. Runge-Kutta fourth fifth
+order Fehlberg method with shooting techniques are used to solved
+non-dimensional governing equations. The variation of different parameters such
+as thermophoretic parameter, chemical reaction parameter, non- uniform heat
+source/sink parameters are studied on velocity, temperature and concentration
+profiles, and are described by suitable graphs and tables. The obtained results
+are in very well agreement with previous results.
+",0,1,0,0,0,0
+551,Resting-state ASL : Toward an optimal sequence duration,"  Resting-state functional Arterial Spin Labeling (rs-fASL) in clinical daily
+practice and academic research stay discreet compared to resting-state BOLD.
+However, by giving direct access to cerebral blood flow maps, rs-fASL leads to
+significant clinical subject scaled application as CBF can be considered as a
+biomarker in common neuropathology. Our work here focuses on the link between
+overall quality of rs-fASL and duration of acquisition. To this end, we
+consider subject self-Default Mode Network (DMN), and assess DMN quality
+depletion compared to a gold standard DMN depending on the duration of
+acquisition.
+",0,0,0,0,1,0
+552,Learning Neural Models for End-to-End Clustering,"  We propose a novel end-to-end neural network architecture that, once trained,
+directly outputs a probabilistic clustering of a batch of input examples in one
+pass. It estimates a distribution over the number of clusters $k$, and for each
+$1 \leq k \leq k_\mathrm{max}$, a distribution over the individual cluster
+assignment for each data point. The network is trained in advance in a
+supervised fashion on separate data to learn grouping by any perceptual
+similarity criterion based on pairwise labels (same/different group). It can
+then be applied to different data containing different groups. We demonstrate
+promising performance on high-dimensional data like images (COIL-100) and
+speech (TIMIT). We call this ``learning to cluster'' and show its conceptual
+difference to deep metric learning, semi-supervise clustering and other related
+approaches while having the advantage of performing learnable clustering fully
+end-to-end.
+",0,0,0,1,0,0
+553,Anisotropic exchange and spin-wave damping in pure and electron-doped Sr$_2$IrO$_4$,"  The collective magnetic excitations in the spin-orbit Mott insulator
+(Sr$_{1-x}$La$_x$)$_2$IrO$_4$ ($x=0,\,0.01,\,0.04,\, 0.1$) were investigated by
+means of resonant inelastic x-ray scattering. We report significant magnon
+energy gaps at both the crystallographic and antiferromagnetic zone centers at
+all doping levels, along with a remarkably pronounced momentum-dependent
+lifetime broadening. The spin-wave gap is accounted for by a significant
+anisotropy in the interactions between $J_\text{eff}=1/2$ isospins, thus
+marking the departure of Sr$_2$IrO$_4$ from the essentially isotropic
+Heisenberg model appropriate for the superconducting cuprates.
+",0,1,0,0,0,0
+554,Anomalous transport properties in Nb/Bi1.95Sb0.05Se3 hybrid structure,"  We report the proximity induced anomalous transport behavior in a Nb
+Bi1.95Sb0.05Se3 heterostructure. Mechanically Exfoliated single crystal of
+Bi1.95Sb0.05Se3 topological insulator (TI) is partially covered with a 100 nm
+thick Niobium superconductor using DC magnetron sputtering by shadow masking
+technique. The magnetotransport (MR) measurements have been performed
+simultaneously on the TI sample with and without Nb top layer in the
+temperature,T, range of 3 to 8 K, and a magnetic field B up to 15 T. MR on TI
+region shows Subnikov de Haas oscillation at fields greater than 5 T. Anomalous
+linear change in resistance is observed in the field range of negative 4T to
+positive 4T at which Nb is superconducting. At 0 T field, the temperature
+dependence of resistance on the Nb covered region revealed a superconducting
+transition (TC) at 8.2 K, whereas TI area showed similar TC with the absence of
+zero resistance states due to the additional resistance from superconductor
+(SC) TI interface. Interestingly below the TC the R vs T measured on TI showed
+an enhancement in resistance for positive field and prominent fall in
+resistance for negative field direction. This indicates the directional
+dependent scattering of the Cooper pairs on the surface of the TI due to the
+superposition of spin singlet and triplet states in the superconductor and TI
+respectively.
+",0,1,0,0,0,0
+555,State-dependent Priority Scheduling for Networked Control Systems,"  Networked control systems (NCS) have attracted considerable attention in
+recent years. While the stabilizability and optimal control of NCS for a given
+communication system has already been studied extensively, the design of the
+communication system for NCS has recently seen an increase in more thorough
+investigation. In this paper, we address an optimal scheduling problem for a
+set of NCS sharing a dedicated communication channel, providing performance
+bounds and asymptotic stability. We derive a suboptimal scheduling policy with
+dynamic state-based priorities calculated at the sensors, which are then used
+for stateless priority queuing in the network, making it both scalable and
+efficient to implement on routers or multi-layer switches. These properties are
+beneficial towards leveraging existing IP networks for control, which will be a
+crucial factor for the proliferation of wide-area NCS applications. By allowing
+for an arbitrary number of concurrent transmissions, we are able to investigate
+the relationship between available bandwidth, transmission rate, and delay. To
+demonstrate the feasibility of our approach, we provide a proof-of-concept
+implementation of the priority scheduler using real networking hardware.
+",1,0,0,0,0,0
+556,Deformation conditions for pseudorepresentations,"  Given a property of representations satisfying a basic stability condition,
+Ramakrishna developed a variant of Mazur's Galois deformation theory for
+representations with that property. We introduce an axiomatic definition of
+pseudorepresentations with such a property. Among other things, we show that
+pseudorepresentations with a property enjoy a good deformation theory,
+generalizing Ramakrishna's theory to pseudorepresentations.
+",0,0,1,0,0,0
+557,Adaptive local surface refinement based on LR NURBS and its application to contact,"  A novel adaptive local surface refinement technique based on Locally Refined
+Non-Uniform Rational B-Splines (LR NURBS) is presented. LR NURBS can model
+complex geometries exactly and are the rational extension of LR B-splines. The
+local representation of the parameter space overcomes the drawback of
+non-existent local refinement in standard NURBS-based isogeometric analysis.
+For a convenient embedding into general finite element code, the Bézier
+extraction operator for LR NURBS is formulated. An automatic remeshing
+technique is presented that allows adaptive local refinement and coarsening of
+LR NURBS. In this work, LR NURBS are applied to contact computations of 3D
+solids and membranes. For solids, LR NURBS-enriched finite elements are used to
+discretize the contact surfaces with LR NURBS finite elements, while the rest
+of the body is discretized by linear Lagrange finite elements. For membranes,
+the entire surface is discretized by LR NURBS. Various numerical examples are
+shown, and they demonstrate the benefit of using LR NURBS: Compared to uniform
+refinement, LR NURBS can achieve high accuracy at lower computational cost.
+",1,0,0,0,0,0
+558,"Approximate Program Smoothing Using Mean-Variance Statistics, with Application to Procedural Shader Bandlimiting","  This paper introduces a general method to approximate the convolution of an
+arbitrary program with a Gaussian kernel. This process has the effect of
+smoothing out a program. Our compiler framework models intermediate values in
+the program as random variables, by using mean and variance statistics. Our
+approach breaks the input program into parts and relates the statistics of the
+different parts, under the smoothing process. We give several approximations
+that can be used for the different parts of the program. These include the
+approximation of Dorn et al., a novel adaptive Gaussian approximation, Monte
+Carlo sampling, and compactly supported kernels. Our adaptive Gaussian
+approximation is accurate up to the second order in the standard deviation of
+the smoothing kernel, and mathematically smooth. We show how to construct a
+compiler that applies chosen approximations to given parts of the input
+program. Because each expression can have multiple approximation choices, we
+use a genetic search to automatically select the best approximations. We apply
+this framework to the problem of automatically bandlimiting procedural shader
+programs. We evaluate our method on a variety of complex shaders, including
+shaders with parallax mapping, animation, and spatially varying statistics. The
+resulting smoothed shader programs outperform previous approaches both
+numerically, and aesthetically, due to the smoothing properties of our
+approximations.
+",1,0,0,0,0,0
+559,Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach,"  Large-scale computational experiments, often running over weeks and over
+large datasets, are used extensively in fields such as epidemiology,
+meteorology, computational biology, and healthcare to understand phenomena, and
+design high-stakes policies affecting everyday health and economy. For
+instance, the OpenMalaria framework is a computationally-intensive simulation
+used by various non-governmental and governmental agencies to understand
+malarial disease spread and effectiveness of intervention strategies, and
+subsequently design healthcare policies. Given that such shared results form
+the basis of inferences drawn, technological solutions designed, and day-to-day
+policies drafted, it is essential that the computations are validated and
+trusted. In particular, in a multi-agent environment involving several
+independent computing agents, a notion of trust in results generated by peers
+is critical in facilitating transparency, accountability, and collaboration.
+Using a novel combination of distributed validation of atomic computation
+blocks and a blockchain-based immutable audits mechanism, this work proposes a
+universal framework for distributed trust in computations. In particular we
+address the scalaibility problem by reducing the storage and communication
+costs using a lossy compression scheme. This framework guarantees not only
+verifiability of final results, but also the validity of local computations,
+and its cost-benefit tradeoffs are studied using a synthetic example of
+training a neural network.
+",1,0,0,0,0,0
+560,Empirical analysis of non-linear activation functions for Deep Neural Networks in classification tasks,"  We provide an overview of several non-linear activation functions in a neural
+network architecture that have proven successful in many machine learning
+applications. We conduct an empirical analysis on the effectiveness of using
+these function on the MNIST classification task, with the aim of clarifying
+which functions produce the best results overall. Based on this first set of
+results, we examine the effects of building deeper architectures with an
+increasing number of hidden layers. We also survey the impact of using, on the
+same task, different initialisation schemes for the weights of our neural
+network. Using these sets of experiments as a base, we conclude by providing a
+optimal neural network architecture that yields impressive results in accuracy
+on the MNIST classification task.
+",1,0,0,1,0,0
+561,A Survey of Deep Learning Techniques for Mobile Robot Applications,"  Advancements in deep learning over the years have attracted research into how
+deep artificial neural networks can be used in robotic systems. This research
+survey will present a summarization of the current research with a specific
+focus on the gains and obstacles for deep learning to be applied to mobile
+robotics.
+",1,0,0,0,0,0
+562,Design of the Artificial: lessons from the biological roots of general intelligence,"  Our desire and fascination with intelligent machines dates back to the
+antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought
+(syllogism) and Heron of Alexandria's mechanical machines and automata.
+However, the quest for Artificial General Intelligence (AGI) is troubled with
+repeated failures of strategies and approaches throughout the history. This
+decade has seen a shift in interest towards bio-inspired software and hardware,
+with the assumption that such mimicry entails intelligence. Though these steps
+are fruitful in certain directions and have advanced automation, their singular
+design focus renders them highly inefficient in achieving AGI. Which set of
+requirements have to be met in the design of AGI? What are the limits in the
+design of the artificial? Here, a careful examination of computation in
+biological systems hints that evolutionary tinkering of contextual processing
+of information enabled by a hierarchical architecture is the key to build AGI.
+",1,1,0,0,0,0
+563,Continuum of quantum fluctuations in a three-dimensional $S\!=\!1$ Heisenberg magnet,"  Conventional crystalline magnets are characterized by symmetry breaking and
+normal modes of excitation called magnons with quantized angular momentum
+$\hbar$. Neutron scattering correspondingly features extra magnetic Bragg
+diffraction at low temperatures and dispersive inelastic scattering associated
+with single magnon creation and annihilation. Exceptions are anticipated in
+so-called quantum spin liquids as exemplified by the one-dimensional spin-1/2
+chain which has no magnetic order and where magnons accordingly fractionalize
+into spinons with angular momentum $\hbar/2$. This is spectacularly revealed by
+a continuum of inelastic neutron scattering associated with two-spinon
+processes and the absence of magnetic Bragg diffraction. Here, we report
+evidence for these same key features of a quantum spin liquid in the
+three-dimensional Heisenberg antiferromagnet NaCaNi$_2$F$_7$. Through specific
+heat and neutron scattering measurements, Monte Carlo simulations, and analytic
+approximations to the equal time correlations, we show that NaCaNi$_2$F$_7$ is
+an almost ideal realization of the spin-1 antiferromagnetic Heisenberg model on
+a pyrochlore lattice with weak connectivity and frustrated interactions.
+Magnetic Bragg diffraction is absent and 90\% of the spectral weight forms a
+continuum of magnetic scattering not dissimilar to that of the spin-1/2 chain
+but with low energy pinch points indicating NaCaNi$_2$F$_7$ is in a Coulomb
+phase. The residual entropy and diffuse elastic scattering points to an exotic
+state of matter driven by frustration, quantum fluctuations and weak exchange
+disorder.
+",0,1,0,0,0,0
+564,SimProp v2r4: Monte Carlo simulation code for UHECR propagation,"  We introduce the new version of SimProp, a Monte Carlo code for simulating
+the propagation of ultra-high energy cosmic rays in intergalactic space. This
+version, SimProp v2r4, together with an overall improvement of the code
+capabilities with a substantial reduction in the computation time, also
+computes secondary cosmogenic particles such as electron-positron pairs and
+gamma rays produced during the propagation of ultra-high energy cosmic rays. As
+recently pointed out by several authors, the flux of this secondary radiation
+and its products, within reach of the current observatories, provides useful
+information about models of ultra-high energy cosmic ray sources which would be
+hard to discriminate otherwise.
+",0,1,0,0,0,0
+565,Non-negative Matrix Factorization via Archetypal Analysis,"  Given a collection of data points, non-negative matrix factorization (NMF)
+suggests to express them as convex combinations of a small set of `archetypes'
+with non-negative entries. This decomposition is unique only if the true
+archetypes are non-negative and sufficiently sparse (or the weights are
+sufficiently sparse), a regime that is captured by the separability condition
+and its generalizations.
+In this paper, we study an approach to NMF that can be traced back to the
+work of Cutler and Breiman (1994) and does not require the data to be
+separable, while providing a generally unique decomposition. We optimize the
+trade-off between two objectives: we minimize the distance of the data points
+from the convex envelope of the archetypes (which can be interpreted as an
+empirical risk), while minimizing the distance of the archetypes from the
+convex envelope of the data (which can be interpreted as a data-dependent
+regularization). The archetypal analysis method of (Cutler, Breiman, 1994) is
+recovered as the limiting case in which the last term is given infinite weight.
+We introduce a `uniqueness condition' on the data which is necessary for
+exactly recovering the archetypes from noiseless data. We prove that, under
+uniqueness (plus additional regularity conditions on the geometry of the
+archetypes), our estimator is robust. While our approach requires solving a
+non-convex optimization problem, we find that standard optimization methods
+succeed in finding good solutions both for real and synthetic data.
+",1,0,0,0,0,0
+566,Muon Reconstruction in the Daya Bay Water Pools,"  Muon reconstruction in the Daya Bay water pools would serve to verify the
+simulated muon fluxes and offer the possibility of studying cosmic muons in
+general. This reconstruction is, however, complicated by many optical obstacles
+and the small coverage of photomultiplier tubes (PMTs) as compared to other
+large water Cherenkov detectors. The PMTs' timing information is useful only in
+the case of direct, unreflected Cherenkov light. This requires PMTs to be added
+and removed as an hypothesized muon trajectory is iteratively improved, to
+account for the changing effects of obstacles and direction of light.
+Therefore, muon reconstruction in the Daya Bay water pools does not lend itself
+to a general fitting procedure employing smoothly varying functions with
+continuous derivatives. Here, an algorithm is described which overcomes these
+complications. It employs the method of Least Mean Squares to determine an
+hypothesized trajectory from the PMTs' charge-weighted positions. This
+initially hypothesized trajectory is then iteratively refined using the PMTs'
+timing information. Reconstructions with simulated data reproduce the simulated
+trajectory to within about 5 degrees in direction and about 45 cm in position
+at the pool surface, with a bias that tends to pull tracks away from the
+vertical by about 3 degrees.
+",0,1,0,0,0,0
+567,Improving Foot-Mounted Inertial Navigation Through Real-Time Motion Classification,"  We present a method to improve the accuracy of a foot-mounted,
+zero-velocity-aided inertial navigation system (INS) by varying estimator
+parameters based on a real-time classification of motion type. We train a
+support vector machine (SVM) classifier using inertial data recorded by a
+single foot-mounted sensor to differentiate between six motion types (walking,
+jogging, running, sprinting, crouch-walking, and ladder-climbing) and report
+mean test classification accuracy of over 90% on a dataset with five different
+subjects. From these motion types, we select two of the most common (walking
+and running), and describe a method to compute optimal zero-velocity detection
+parameters tailored to both a specific user and motion type by maximizing the
+detector F-score. By combining the motion classifier with a set of optimal
+detection parameters, we show how we can reduce INS position error during mixed
+walking and running motion. We evaluate our adaptive system on a total of 5.9
+km of indoor pedestrian navigation performed by five different subjects moving
+along a 130 m path with surveyed ground truth markers.
+",1,0,0,0,0,0
+568,An Extended Low Fat Allocator API and Applications,"  The primary function of memory allocators is to allocate and deallocate
+chunks of memory primarily through the malloc API. Many memory allocators also
+implement other API extensions, such as deriving the size of an allocated
+object from the object's pointer, or calculating the base address of an
+allocation from an interior pointer. In this paper, we propose a general
+purpose extended allocator API built around these common extensions. We argue
+that such extended APIs have many applications and demonstrate several use
+cases, such as (manual) memory error detection, meta data storage, typed
+pointers and compact data-structures. Because most existing allocators were not
+designed for the extended API, traditional implementations are expensive or not
+possible.
+Recently, the LowFat allocator for heap and stack objects has been developed.
+The LowFat allocator is an implementation of the idea of low-fat pointers,
+where object bounds information (size and base) are encoded into the native
+machine pointer representation itself. The ""killer app"" for low-fat pointers is
+automated bounds check instrumentation for program hardening and bug detection.
+However, the LowFat allocator can also be used to implement highly optimized
+version of the extended allocator API, which makes the new applications (listed
+above) possible. In this paper, we implement and evaluate several applications
+based efficient memory allocator API extensions using low-fat pointers. We also
+extend the LowFat allocator to cover global objects for the first time.
+",1,0,0,0,0,0
+569,Exoplanet Atmosphere Retrieval using Multifractal Analysis of Secondary Eclipse Spectra,"  We extend a data-based model-free multifractal method of exoplanet detection
+to probe exoplanetary atmospheres. Whereas the transmission spectrum is studied
+during the primary eclipse, we analyze the emission spectrum during the
+secondary eclipse, thereby probing the atmospheric limb. In addition to the
+spectral structure of exoplanet atmospheres, the approach provides information
+to study phenomena such as atmospheric flows, tidal-locking behavior, and the
+dayside-nightside redistribution of energy. The approach is demonstrated using
+Spitzer data for exoplanet HD189733b. The central advantage of the method is
+the lack of model assumptions in the detection and observational schemes.
+",0,1,0,1,0,0
+570,Permutation Tests for Infection Graphs,"  We formulate and analyze a novel hypothesis testing problem for inferring the
+edge structure of an infection graph. In our model, a disease spreads over a
+network via contagion or random infection, where the random variables governing
+the rates of contracting the disease from neighbors or random infection are
+independent exponential random variables with unknown rate parameters. A subset
+of nodes is also censored uniformly at random. Given the statuses of nodes in
+the network, the goal is to determine the underlying graph. We present a
+procedure based on permutation testing, and we derive sufficient conditions for
+the validity of our test in terms of automorphism groups of the graphs
+corresponding to the null and alternative hypotheses. Further, the test is
+valid more generally for infection processes satisfying a basic symmetry
+condition. Our test is easy to compute and does not involve estimating unknown
+parameters governing the process. We also derive risk bounds for our
+permutation test in a variety of settings, and motivate our test statistic in
+terms of approximate equivalence to likelihood ratio testing and maximin tests.
+We conclude with an application to real data from an HIV infection network.
+",1,0,1,1,0,0
+571,A novel distribution-free hybrid regression model for manufacturing process efficiency improvement,"  This work is motivated by a particular problem of a modern paper
+manufacturing industry, in which maximum efficiency of the fiber-filler
+recovery process is desired. A lot of unwanted materials along with valuable
+fibers and fillers come out as a by-product of the paper manufacturing process
+and mostly goes as waste. The job of an efficient Krofta supracell is to
+separate the unwanted materials from the valuable ones so that fibers and
+fillers can be collected from the waste materials and reused in the
+manufacturing process. The efficiency of Krofta depends on several crucial
+process parameters and monitoring them is a difficult proposition. To solve
+this problem, we propose a novel hybridization of regression trees (RT) and
+artificial neural networks (ANN), hybrid RT-ANN model, to solve the problem of
+low recovery percentage of the supracell. This model is used to achieve the
+goal of improving supracell efficiency, viz., gain in percentage recovery. In
+addition, theoretical results for the universal consistency of the proposed
+model are given with the optimal value of a vital model parameter. Experimental
+findings show that the proposed hybrid RT-ANN model achieves higher accuracy in
+predicting Krofta recovery percentage than other conventional regression models
+for solving the Krofta efficiency problem. This work will help the paper
+manufacturing company to become environmentally friendly with minimal
+ecological damage and improved waste recovery.
+",0,0,0,1,0,0
+572,Exploring deep learning as an event classification method for the Cherenkov Telescope Array,"  Telescopes based on the imaging atmospheric Cherenkov technique (IACTs)
+detect images of the atmospheric showers generated by gamma rays and cosmic
+rays as they are absorbed by the atmosphere. The much more frequent cosmic-ray
+events form the main background when looking for gamma-ray sources, and
+therefore IACT sensitivity is significantly driven by the capability to
+distinguish between these two types of events. Supervised learning algorithms,
+like random forests and boosted decision trees, have been shown to effectively
+classify IACT events. In this contribution we present results from exploratory
+work using deep learning as an event classification method for the Cherenkov
+Telescope Array (CTA). CTA, conceived as an array of tens of IACTs, is an
+international project for a next-generation ground-based gamma-ray observatory,
+aiming to improve on the sensitivity of current-generation experiments by an
+order of magnitude and provide energy coverage from 20 GeV to more than 300
+TeV.
+",0,1,0,0,0,0
+573,Photo-Induced Bandgap Renormalization Governs the Ultrafast Response of Single-Layer MoS2,"  Transition metal dichalcogenides (TMDs) are emerging as promising
+two-dimensional (2d) semiconductors for optoelectronic and flexible devices.
+However, a microscopic explanation of their photophysics -- of pivotal
+importance for the understanding and optimization of device operation -- is
+still lacking. Here we use femtosecond transient absorption spectroscopy, with
+pump pulse tunability and broadband probing, to monitor the relaxation dynamics
+of single-layer MoS2 over the entire visible range, upon photoexcitation of
+different excitonic transitions. We find that, irrespective of excitation
+photon energy, the transient absorption spectrum shows the simultaneous
+bleaching of all excitonic transitions and corresponding red-shifted
+photoinduced absorption bands. First-principle modeling of the ultrafast
+optical response reveals that a transient bandgap renormalization, caused by
+the presence of photo-excited carriers, is primarily responsible for the
+observed features. Our results demonstrate the strong impact of many-body
+effects in the transient optical response of TMDs even in the
+low-excitation-density regime.
+",0,1,0,0,0,0
+574,Improved Quantile Regression Estimators when the Errors are Independently and Non-identically Distributed,"  In a classical regression model, it is usually assumed that the explanatory
+variables are independent of each other and error terms are normally
+distributed. But when these assumptions are not met, situations like the error
+terms are not independent or they are not identically distributed or both of
+these, LSE will not be robust. Hence, quantile regression has been used to
+complement this deficiency of classical regression analysis and to improve the
+least square estimation (LSE). In this study, we consider preliminary test and
+shrinkage estimation strategies for quantile regression models with
+independently and non-identically distributed (i.ni.d.) errors. A Monte Carlo
+simulation study is conducted to assess the relative performance of the
+estimators. Also, we numerically compare their performance with Ridge, Lasso,
+Elastic Net penalty estimation strategies. A real data example is presented to
+illustrate the usefulness of the suggested methods. Finally, we obtain the
+asymptotic results of suggested estimators
+",0,0,1,1,0,0
+575,Language Modeling by Clustering with Word Embeddings for Text Readability Assessment,"  We present a clustering-based language model using word embeddings for text
+readability prediction. Presumably, an Euclidean semantic space hypothesis
+holds true for word embeddings whose training is done by observing word
+co-occurrences. We argue that clustering with word embeddings in the metric
+space should yield feature representations in a higher semantic space
+appropriate for text regression. Also, by representing features in terms of
+histograms, our approach can naturally address documents of varying lengths. An
+empirical evaluation using the Common Core Standards corpus reveals that the
+features formed on our clustering-based language model significantly improve
+the previously known results for the same corpus in readability prediction. We
+also evaluate the task of sentence matching based on semantic relatedness using
+the Wiki-SimpleWiki corpus and find that our features lead to superior matching
+performance.
+",1,0,0,0,0,0
+576,Discriminant circle bundles over local models of Strebel graphs and Boutroux curves,"  We study special circle bundles over two elementary moduli spaces of
+meromorphic quadratic differentials with real periods denoted by $\mathcal
+Q_0^{\mathbb R}(-7)$ and $\mathcal Q^{\mathbb R}_0([-3]^2)$. The space
+$\mathcal Q_0^{\mathbb R}(-7)$ is the moduli space of meromorphic quadratic
+differentials on the Riemann sphere with one pole of order 7 with real periods;
+it appears naturally in the study of a neighbourhood of the Witten's cycle
+$W_1$ in the combinatorial model based on Jenkins-Strebel quadratic
+differentials of $\mathcal M_{g,n}$. The space $\mathcal Q^{\mathbb
+R}_0([-3]^2)$ is the moduli space of meromorphic quadratic differentials on the
+Riemann sphere with two poles of order at most 3 with real periods; it appears
+in description of a neighbourhood of Kontsevich's boundary $W_{-1,-1}$ of the
+combinatorial model. The application of the formalism of the Bergman
+tau-function to the combinatorial model (with the goal of computing
+analytically Poincare dual cycles to certain combinations of tautological
+classes) requires the study of special sections of circle bundles over
+$\mathcal Q_0^{\mathbb R}(-7)$ and $\mathcal Q^{\mathbb R}_0([-3]^2)$; in the
+case of the space $\mathcal Q_0^{\mathbb R}(-7)$ a section of this circle
+bundle is given by the argument of the modular discriminant. We study the
+spaces $\mathcal Q_0^{\mathbb R}(-7)$ and $\mathcal Q^{\mathbb R}_0([-3]^2)$,
+also called the spaces of Boutroux curves, in detail, together with
+corresponding circle bundles.
+",0,1,1,0,0,0
+577,Far-field theory for trajectories of magnetic ellipsoids in rectangular and circular channels,"  We report a method to control the positions of ellipsoidal magnets in flowing
+channels of rectangular or circular cross section at low Reynolds number.A
+static uniform magnetic field is used to pin the particle orientation, and the
+particles move with translational drift velocities resulting from hydrodynamic
+interactions with the channel walls which can be described using Blake's image
+tensor.Building on his insights, we are able to present a far-field theory
+predicting the particle motion in rectangular channels, and validate the
+accuracy of the theory by comparing to numerical solutions using the boundary
+element method.We find that, by changing the direction of the applied magnetic
+field, the motion can be controlled so that particles move either to a curved
+focusing region or to the channel walls.We also use simulations to show that
+the particles are focused to a single line in a circular channel.Our results
+suggest ways to focus and segregate magnetic particles in lab-on-a-chip
+devices.
+",0,1,0,0,0,0
+578,On M-functions associated with modular forms,"  Let $f$ be a primitive cusp form of weight $k$ and level $N,$ let $\chi$ be a
+Dirichlet character of conductor coprime with $N,$ and let
+$\mathfrak{L}(f\otimes \chi, s)$ denote either $\log L(f\otimes \chi, s)$ or
+$(L'/L)(f\otimes \chi, s).$ In this article we study the distribution of the
+values of $\mathfrak{L}$ when either $\chi$ or $f$ vary. First, for a
+quasi-character $\psi\colon \mathbb{C} \to \mathbb{C}^\times$ we find the limit
+for the average $\mathrm{Avg}\_\chi \psi(L(f\otimes\chi, s)),$ when $f$ is
+fixed and $\chi$ varies through the set of characters with prime conductor that
+tends to infinity. Second, we prove an equidistribution result for the values
+of $\mathfrak{L}(f\otimes \chi,s)$ by establishing analytic properties of the
+above limit function. Third, we study the limit of the harmonic average
+$\mathrm{Avg}^h\_f \psi(L(f, s)),$ when $f$ runs through the set of primitive
+cusp forms of given weight $k$ and level $N\to \infty.$ Most of the results are
+obtained conditionally on the Generalized Riemann Hypothesis for
+$L(f\otimes\chi, s).$
+",0,0,1,0,0,0
+579,Learning Graph Representations by Dendrograms,"  Hierarchical graph clustering is a common technique to reveal the multi-scale
+structure of complex networks. We propose a novel metric for assessing the
+quality of a hierarchical clustering. This metric reflects the ability to
+reconstruct the graph from the dendrogram, which encodes the hierarchy. The
+optimal representation of the graph defines a class of reducible linkages
+leading to regular dendrograms by greedy agglomerative clustering.
+",1,0,0,1,0,0
+580,Slow and Long-ranged Dynamical Heterogeneities in Dissipative Fluids,"  A two-dimensional bidisperse granular fluid is shown to exhibit pronounced
+long-ranged dynamical heterogeneities as dynamical arrest is approached. Here
+we focus on the most direct approach to study these heterogeneities: we
+identify clusters of slow particles and determine their size, $N_c$, and their
+radius of gyration, $R_G$. We show that $N_c\propto R_G^{d_f}$, providing
+direct evidence that the most immobile particles arrange in fractal objects
+with a fractal dimension, $d_f$, that is observed to increase with packing
+fraction $\phi$. The cluster size distribution obeys scaling, approaching an
+algebraic decay in the limit of structural arrest, i.e., $\phi\to\phi_c$.
+Alternatively, dynamical heterogeneities are analyzed via the four-point
+structure factor $S_4(q,t)$ and the dynamical susceptibility $\chi_4(t)$.
+$S_4(q,t)$ is shown to obey scaling in the full range of packing fractions,
+$0.6\leq\phi\leq 0.805$, and to become increasingly long-ranged as
+$\phi\to\phi_c$. Finite size scaling of $\chi_4(t)$ provides a consistency
+check for the previously analyzed divergences of $\chi_4(t)\propto
+(\phi-\phi_c)^{-\gamma_{\chi}}$ and the correlation length $\xi\propto
+(\phi-\phi_c)^{-\gamma_{\xi}}$. We check the robustness of our results with
+respect to our definition of mobility. The divergences and the scaling for
+$\phi\to\phi_c$ suggest a non-equilibrium glass transition which seems
+qualitatively independent of the coefficient of restitution.
+",0,1,0,0,0,0
+581,A Globally Linearly Convergent Method for Pointwise Quadratically Supportable Convex-Concave Saddle Point Problems,"  We study the \emph{Proximal Alternating Predictor-Corrector} (PAPC) algorithm
+introduced recently by Drori, Sabach and Teboulle to solve nonsmooth structured
+convex-concave saddle point problems consisting of the sum of a smooth convex
+function, a finite collection of nonsmooth convex functions and bilinear terms.
+We introduce the notion of pointwise quadratic supportability, which is a
+relaxation of a standard strong convexity assumption and allows us to show that
+the primal sequence is R-linearly convergent to an optimal solution and the
+primal-dual sequence is globally Q-linearly convergent. We illustrate the
+proposed method on total variation denoising problems and on locally adaptive
+estimation in signal/image deconvolution and denoising with multiresolution
+statistical constraints.
+",0,0,1,0,0,0
+582,Stability of casein micelles cross-linked with genipin: a physicochemical study as a function of pH,"  Chemical or enzymatic cross-linking of casein micelles (CMs) increases their
+stability against dissociating agents. In this paper, a comparative study of
+stability between native CMs and CMs cross-linked with genipin (CMs-GP) as a
+function of pH is described. Stability to temperature and ethanol were
+investigated in the pH range 2.0-7.0. The size and the charge
+($\zeta$-potential) of the particles were determined by dynamic light
+scattering. Native CMs precipitated below pH 5.5, CMs-GP precipitated from pH
+3.5 to 4.5, whereas no precipitation was observed at pH 2.0-3.0 or pH 4.5-7.0.
+The isoelectric point of CMs-GP was determined to be pH 3.7. Highest stability
+against heat and ethanol was observed for CMs-GP at pH 2, where visible
+coagulation was determined only after 800 s at 140 $^\circ$C or 87.5% (v/v) of
+ethanol. These results confirmed the hypothesis that cross-linking by GP
+increased the stability of CMs.
+",0,1,0,0,0,0
+583,Kites and Residuated Lattices,"  We investigate a construction of an integral residuated lattice starting from
+an integral residuated lattice and two sets with an injective mapping from one
+set into the second one. The resulting algebra has a shape of a Chinese cascade
+kite, therefore, we call this algebra simply a kite. We describe subdirectly
+irreducible kites and we classify them. We show that the variety of integral
+residuated lattices generated by kites is generated by all finite-dimensional
+kites. In particular, we describe some homomorphisms among kites.
+",0,0,1,0,0,0
+584,TED Talk Recommender Using Speech Transcripts,"  Nowadays, online video platforms mostly recommend related videos by analyzing
+user-driven data such as viewing patterns, rather than the content of the
+videos. However, content is more important than any other element when videos
+aim to deliver knowledge. Therefore, we have developed a web application which
+recommends related TED lecture videos to the users, considering the content of
+the videos from the transcripts. TED Talk Recommender constructs a network for
+recommending videos that are similar content-wise and providing a user
+interface.
+",1,0,0,0,0,0
+585,Attitude Control of the Asteroid Origins Satellite 1 (AOSAT 1),"  Exploration of asteroids and small-bodies can provide valuable insight into
+the origins of the solar system, into the origins of Earth and the origins of
+the building blocks of life. However, the low-gravity and unknown surface
+conditions of asteroids presents a daunting challenge for surface exploration,
+manipulation and for resource processing. This has resulted in the loss of
+several landers or shortened missions. Fundamental studies are required to
+obtain better readings of the material surface properties and physical models
+of these small bodies. The Asteroid Origins Satellite 1 (AOSAT 1) is a CubeSat
+centrifuge laboratory that spins at up to 4 rpm to simulate the milligravity
+conditions of sub 1 km asteroids. Such a laboratory will help to de-risk
+development and testing of landing and resource processing technology for
+asteroids. Inside the laboratory are crushed meteorites, the remains of
+asteroids. The laboratory is equipped with cameras and actuators to perform a
+series of science experiments to better understand material properties and
+asteroid surface physics. These results will help to improve our physics models
+of asteroids. The CubeSat has been designed to be low-cost and contains 3-axis
+magnetorquers and a single reaction-wheel to induce spin. In our work, we first
+analyze how the attitude control system will de-tumble the spacecraft after
+deployment. Further analysis has been conducted to analyze the impact and
+stability of the attitude control system to shifting mass (crushed meteorites)
+inside the spacecraft as its spinning in its centrifuge mode. AOSAT 1 will be
+the first in a series of low-cost CubeSat centrifuges that will be launched
+setting the stage for a larger, permanent, on-orbit centrifuge laboratory for
+experiments in planetary science, life sciences and manufacturing.
+",1,1,0,0,0,0
+586,Triplet Network with Attention for Speaker Diarization,"  In automatic speech processing systems, speaker diarization is a crucial
+front-end component to separate segments from different speakers. Inspired by
+the recent success of deep neural networks (DNNs) in semantic inferencing,
+triplet loss-based architectures have been successfully used for this problem.
+However, existing work utilizes conventional i-vectors as the input
+representation and builds simple fully connected networks for metric learning,
+thus not fully leveraging the modeling power of DNN architectures. This paper
+investigates the importance of learning effective representations from the
+sequences directly in metric learning pipelines for speaker diarization. More
+specifically, we propose to employ attention models to learn embeddings and the
+metric jointly in an end-to-end fashion. Experiments are conducted on the
+CALLHOME conversational speech corpus. The diarization results demonstrate
+that, besides providing a unified model, the proposed approach achieves
+improved performance when compared against existing approaches.
+",0,0,0,1,0,0
+587,Dynamics of cracks in disordered materials,"  Predicting when rupture occurs or cracks progress is a major challenge in
+numerous elds of industrial, societal and geophysical importance. It remains
+largely unsolved: Stress enhancement at cracks and defects, indeed, makes the
+macroscale dynamics extremely sensitive to the microscale material disorder.
+This results in giant statistical uctuations and non-trivial behaviors upon
+upscaling dicult to assess via the continuum approaches of engineering. These
+issues are examined here. We will see: How linear elastic fracture mechanics
+sidetracks the diculty by reducing the problem to that of the propagation of a
+single crack in an eective material free of defects, How slow cracks sometimes
+display jerky dynamics, with sudden violent events incompatible with the
+previous approach, and how some paradigms of statistical physics can explain
+it, How abnormally fast cracks sometimes emerge due to the formation of
+microcracks at very small scales.
+",0,1,0,0,0,0
+588,Optimum Decoder for Multiplicative Spread Spectrum Image Watermarking with Laplacian Modeling,"  This paper investigates the multiplicative spread spectrum watermarking
+method for the image. The information bit is spreaded into middle-frequency
+Discrete Cosine Transform (DCT) coefficients of each block of an image using a
+generated pseudo-random sequence. Unlike the conventional signal modeling, we
+suppose that both signal and noise are distributed with Laplacian distribution
+because the sample loss of digital media can be better modeled with this
+distribution than the Gaussian one. We derive the optimum decoder for the
+proposed embedding method thanks to the maximum likelihood decoding scheme. We
+also analyze our watermarking system in the presence of noise and provide
+analytical evaluations and several simulations. The results show that it has
+the suitable performance and transparency required for watermarking
+applications.
+",1,0,0,0,0,0
+589,Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems,"  We present a representation learning algorithm that learns a low-dimensional
+latent dynamical system from high-dimensional \textit{sequential} raw data,
+e.g., video. The framework builds upon recent advances in amortized inference
+methods that use both an inference network and a refinement procedure to output
+samples from a variational distribution given an observation sequence, and
+takes advantage of the duality between control and inference to approximately
+solve the intractable inference problem using the path integral control
+approach. The learned dynamical model can be used to predict and plan the
+future states; we also present the efficient planning method that exploits the
+learned low-dimensional latent dynamics. Numerical experiments show that the
+proposed path-integral control based variational inference method leads to
+tighter lower bounds in statistical model learning of sequential data. The
+supplementary video: this https URL
+",1,0,0,1,0,0
+590,Semiblind subgraph reconstruction in Gaussian graphical models,"  Consider a social network where only a few nodes (agents) have meaningful
+interactions in the sense that the conditional dependency graph over node
+attribute variables (behaviors) is sparse. A company that can only observe the
+interactions between its own customers will generally not be able to accurately
+estimate its customers' dependency subgraph: it is blinded to any external
+interactions of its customers and this blindness creates false edges in its
+subgraph. In this paper we address the semiblind scenario where the company has
+access to a noisy summary of the complementary subgraph connecting external
+agents, e.g., provided by a consolidator. The proposed framework applies to
+other applications as well, including field estimation from a network of awake
+and sleeping sensors and privacy-constrained information sharing over social
+subnetworks. We propose a penalized likelihood approach in the context of a
+graph signal obeying a Gaussian graphical models (GGM). We use a convex-concave
+iterative optimization algorithm to maximize the penalized likelihood.
+",1,0,0,1,0,0
+591,Hybrid Collaborative Recommendation via Semi-AutoEncoder,"  In this paper, we present a novel structure, Semi-AutoEncoder, based on
+AutoEncoder. We generalize it into a hybrid collaborative filtering model for
+rating prediction as well as personalized top-n recommendations. Experimental
+results on two real-world datasets demonstrate its state-of-the-art
+performances.
+",1,0,0,0,0,0
+592,The Linear Point: A cleaner cosmological standard ruler,"  We show how a characteristic length scale imprinted in the galaxy two-point
+correlation function, dubbed the ""linear point"", can serve as a comoving
+cosmological standard ruler. In contrast to the Baryon Acoustic Oscillation
+peak location, this scale is constant in redshift and is unaffected by
+non-linear effects to within $0.5$ percent precision. We measure the location
+of the linear point in the galaxy correlation function of the LOWZ and CMASS
+samples from the Twelfth Data Release (DR12) of the Baryon Oscillation
+Spectroscopic Survey (BOSS) collaboration. We combine our linear-point
+measurement with cosmic-microwave-background constraints from the Planck
+satellite to estimate the isotropic-volume distance $D_{V}(z)$, without relying
+on a model-template or reconstruction method. We find $D_V(0.32)=1264\pm 28$
+Mpc and $D_V(0.57)=2056\pm 22$ Mpc respectively, consistent with the quoted
+values from the BOSS collaboration. This remarkable result suggests that all
+the distance information contained in the baryon acoustic oscillations can be
+conveniently compressed into the single length associated with the linear
+point.
+",0,1,0,0,0,0
+593,Incompressible limit of the Navier-Stokes model with a growth term,"  Starting from isentropic compressible Navier-Stokes equations with growth
+term in the continuity equation, we rigorously justify that performing an
+incompressible limit one arrives to the two-phase free boundary fluid system.
+",0,0,1,0,0,0
+594,Stochastic Primal-Dual Method on Riemannian Manifolds with Bounded Sectional Curvature,"  We study a stochastic primal-dual method for constrained optimization over
+Riemannian manifolds with bounded sectional curvature. We prove non-asymptotic
+convergence to the optimal objective value. More precisely, for the class of
+hyperbolic manifolds, we establish a convergence rate that is related to the
+sectional curvature lower bound. To prove a convergence rate in terms of
+sectional curvature for the elliptic manifolds, we leverage Toponogov's
+comparison theorem. In addition, we provide convergence analysis for the
+asymptotically elliptic manifolds, where the sectional curvature at each given
+point on manifold is locally bounded from below by the distance function. We
+demonstrate the performance of the primal-dual algorithm on the sphere for the
+non-negative principle component analysis (PCA). In particular, under the
+non-negativity constraint on the principle component and for the symmetric
+spiked covariance model, we empirically show that the primal-dual approach
+outperforms the spectral method. We also examine the performance of the
+primal-dual method for the anchored synchronization from partial noisy
+measurements of relative rotations on the Lie group SO(3). Lastly, we show that
+the primal-dual algorithm can be applied to the weighted MAX-CUT problem under
+constraints on the admissible cut. Specifically, we propose different
+approximation algorithms for the weighted MAX-CUT problem based on optimizing a
+function on the manifold of direct products of the unit spheres as well as the
+manifold of direct products of the rotation groups.
+",0,0,1,0,0,0
+595,Nudging the particle filter,"  We investigate a new sampling scheme aimed at improving the performance of
+particle filters whenever (a) there is a significant mismatch between the
+assumed model dynamics and the actual system, or (b) the posterior probability
+tends to concentrate in relatively small regions of the state space. The
+proposed scheme pushes some particles towards specific regions where the
+likelihood is expected to be high, an operation known as nudging in the
+geophysics literature. We re-interpret nudging in a form applicable to any
+particle filtering scheme, as it does not involve any changes in the rest of
+the algorithm. Since the particles are modified, but the importance weights do
+not account for this modification, the use of nudging leads to additional bias
+in the resulting estimators. However, we prove analytically that nudged
+particle filters can still attain asymptotic convergence with the same error
+rates as conventional particle methods. Simple analysis also yields an
+alternative interpretation of the nudging operation that explains its
+robustness to model errors. Finally, we show numerical results that illustrate
+the improvements that can be attained using the proposed scheme. In particular,
+we present nonlinear tracking examples with synthetic data and a model
+inference example using real-world financial data.
+",0,0,0,1,0,0
+596,Quantum oscillations and a non-trivial Berry phase in the noncentrosymmetric superconductor BiPd,"  We report the measurements of de Haas-van Alphen (dHvA) oscillations in the
+noncentrosymmetric superconductor BiPd. Several pieces of a complex multi-sheet
+Fermi surface are identified, including a small pocket (frequency 40 T) which
+is three dimensional and anisotropic. From the temperature dependence of the
+amplitude of the oscillations, the cyclotron effective mass is ($0.18$ $\pm$
+0.1) $m_e$. Further analysis showed a non-trivial $\pi$-Berry phase is
+associated with the 40 T pocket, which strongly supports the presence of
+topological states in bulk BiPd and may result in topological superconductivity
+due to the proximity coupling to other bands.
+",0,1,0,0,0,0
+597,ExSIS: Extended Sure Independence Screening for Ultrahigh-dimensional Linear Models,"  Statistical inference can be computationally prohibitive in
+ultrahigh-dimensional linear models. Correlation-based variable screening, in
+which one leverages marginal correlations for removal of irrelevant variables
+from the model prior to statistical inference, can be used to overcome this
+challenge. Prior works on correlation-based variable screening either impose
+strong statistical priors on the linear model or assume specific post-screening
+inference methods. This paper first extends the analysis of correlation-based
+variable screening to arbitrary linear models and post-screening inference
+techniques. In particular, ($i$) it shows that a condition---termed the
+screening condition---is sufficient for successful correlation-based screening
+of linear models, and ($ii$) it provides insights into the dependence of
+marginal correlation-based screening on different problem parameters. Numerical
+experiments confirm that these insights are not mere artifacts of analysis;
+rather, they are reflective of the challenges associated with marginal
+correlation-based variable screening. Second, the paper explicitly derives the
+screening condition for two families of linear models, namely, sub-Gaussian
+linear models and arbitrary (random or deterministic) linear models. In the
+process, it establishes that---under appropriate conditions---it is possible to
+reduce the dimension of an ultrahigh-dimensional, arbitrary linear model to
+almost the sample size even when the number of active variables scales almost
+linearly with the sample size.
+",0,0,1,1,0,0
+598,Unveiling ADP-binding sites and channels in respiratory complexes: Validation of Murburn concept as a holistic explanation for oxidative phosphorylation,"  Mitochondrial oxidative phosphorylation (mOxPhos) makes ATP, the energy
+currency of life. Chemiosmosis, a proton centric mechanism, advocates that
+Complex V harnesses a transmembrane potential (TMP) for ATP synthesis. This
+perception of cellular respiration requires oxygen to stay tethered at Complex
+IV (an association inhibited by cyanide) and diffusible reactive oxygen species
+(DROS) are considered wasteful and toxic products. With new mechanistic
+insights on heme and flavin enzymes, an oxygen or DROS centric explanation
+(called murburn concept) was recently proposed for mOxPhos. In the new
+mechanism, TMP is not directly harnessed, protons are a rate limiting reactant
+and DROS within matrix serve as the chemical coupling agents that directly link
+NADH oxidation with ATP synthesis. Herein, we report multiple ADP binding sites
+and solvent accessible DROS channels in respiratory proteins, which validate
+the oxygen or DROS centric power generation (ATP synthesis) system in mOxPhos.
+Since cyanide's heme binding Kd is high (mM), low doses (uM) of cyanide is
+lethal because cyanide disrupts DROS dynamics in mOxPhos. The critical study
+also provides comprehensive arguments against Mitchell's and Boyer's
+explanations and extensive support for murburn concept based holistic
+perspectives for mOxPhos.
+",0,0,0,0,1,0
+599,Banach synaptic algebras,"  Using a representation theorem of Erik Alfsen, Frederic Schultz, and Erling
+Stormer for special JB-algebras, we prove that a synaptic algebra is norm
+complete (i.e., Banach) if and only if it is isomorphic to the self-adjoint
+part of a Rickart C*-algebra. Also, we give conditions on a Banach synaptic
+algebra that are equivalent to the condition that it is isomorphic to the
+self-adjoint part of an AW*-algebra. Moreover, we study some relationships
+between synaptic algebras and so-called generalized Hermitian algebras.
+",0,0,1,0,0,0
+600,"Pressure tuning of structure, superconductivity and novel magnetic order in the Ce-underdoped electron-doped cuprate T'-Pr_1.3-xLa_0.7Ce_xCuO_4 (x = 0.1)","  High-pressure neutron powder diffraction, muon-spin rotation and
+magnetization studies of the structural, magnetic and the superconducting
+properties of the Ce-underdoped superconducting (SC) electron-doped cuprate
+system T'-Pr_1.3-xLa_0.7Ce_xCuO_4 with x = 0.1 are reported. A strong reduction
+of the lattice constants a and c is observed under pressure. However, no
+indication of any pressure induced phase transition from T' to T structure is
+observed up to the maximum applied pressure of p = 11 GPa. Large and non-linear
+increase of the short-range magnetic order temperature T_so in
+T'-Pr_1.3-xLa_0.7Ce_xCuO_4 (x = 0.1) was observed under pressure.
+Simultaneously pressure causes a non-linear decrease of the SC transition
+temperature T_c. All these experiments establish the short-range magnetic order
+as an intrinsic and a new competing phase in SC T'-Pr_1.2La_0.7Ce_0.1CuO_4. The
+observed pressure effects may be interpreted in terms of the improved nesting
+conditions through the reduction of the in-plane and out-of-plane lattice
+constants upon hydrostatic pressure.
+",0,1,0,0,0,0
+601,Regrasping by Fixtureless Fixturing,"  This paper presents a fixturing strategy for regrasping that does not require
+a physical fixture. To regrasp an object in a gripper, a robot pushes the
+object against external contact/s in the environment such that the external
+contact keeps the object stationary while the fingers slide over the object. We
+call this manipulation technique fixtureless fixturing. Exploiting the
+mechanics of pushing, we characterize a convex polyhedral set of pushes that
+results in fixtureless fixturing. These pushes are robust against uncertainty
+in the object inertia, grasping force, and the friction at the contacts. We
+propose a sampling-based planner that uses the sets of robust pushes to rapidly
+build a tree of reachable grasps. A path in this tree is a pushing strategy,
+possibly involving pushes from different sides, to regrasp the object. We
+demonstrate the experimental validity and robustness of the proposed
+manipulation technique with different regrasp examples on a manipulation
+platform. Such a fast and flexible regrasp planner facilitates versatile and
+flexible automation solutions.
+",1,0,0,0,0,0
+602,Subset Labeled LDA for Large-Scale Multi-Label Classification,"  Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard
+unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address
+multi-label learning tasks. Previous work has shown it to perform in par with
+other state-of-the-art multi-label methods. Nonetheless, with increasing label
+sets sizes LLDA encounters scalability issues. In this work, we introduce
+Subset LLDA, a simple variant of the standard LLDA algorithm, that not only can
+effectively scale up to problems with hundreds of thousands of labels but also
+improves over the LLDA state-of-the-art. We conduct extensive experiments on
+eight data sets, with label sets sizes ranging from hundreds to hundreds of
+thousands, comparing our proposed algorithm with the previously proposed LLDA
+algorithms (Prior--LDA, Dep--LDA), as well as the state of the art in extreme
+multi-label classification. The results show a steady advantage of our method
+over the other LLDA algorithms and competitive results compared to the extreme
+multi-label classification algorithms.
+",0,0,0,1,0,0
+603,A Hybrid Approach to Video Source Identification,"  Multimedia Forensics allows to determine whether videos or images have been
+captured with the same device, and thus, eventually, by the same person.
+Currently, the most promising technology to achieve this task, exploits the
+unique traces left by the camera sensor into the visual content. Anyway, image
+and video source identification are still treated separately from one another.
+This approach is limited and anachronistic if we consider that most of the
+visual media are today acquired using smartphones, that capture both images and
+videos. In this paper we overcome this limitation by exploring a new approach
+that allows to synergistically exploit images and videos to study the device
+from which they both come. Indeed, we prove it is possible to identify the
+source of a digital video by exploiting a reference sensor pattern noise
+generated from still images taken by the same device of the query video. The
+proposed method provides comparable or even better performance, when compared
+to the current video identification strategies, where a reference pattern is
+estimated from video frames. We also show how this strategy can be effective
+even in case of in-camera digitally stabilized videos, where a non-stabilized
+reference is not available, by solving some state-of-the-art limitations. We
+explore a possible direct application of this result, that is social media
+profile linking, i.e. discovering relationships between two or more social
+media profiles by comparing the visual contents - images or videos - shared
+therein.
+",1,0,0,0,0,0
+604,Asymptotics of ABC,"  We present an informal review of recent work on the asymptotics of
+Approximate Bayesian Computation (ABC). In particular we focus on how does the
+ABC posterior, or point estimates obtained by ABC, behave in the limit as we
+have more data? The results we review show that ABC can perform well in terms
+of point estimation, but standard implementations will over-estimate the
+uncertainty about the parameters. If we use the regression correction of
+Beaumont et al. then ABC can also accurately quantify this uncertainty. The
+theoretical results also have practical implications for how to implement ABC.
+",0,0,1,1,0,0
+605,Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity,"  We consider the problem of learning sparse polymatrix games from observations
+of strategic interactions. We show that a polynomial time method based on
+$\ell_{1,2}$-group regularized logistic regression recovers a game, whose Nash
+equilibria are the $\epsilon$-Nash equilibria of the game from which the data
+was generated (true game), in $\mathcal{O}(m^4 d^4 \log (pd))$ samples of
+strategy profiles --- where $m$ is the maximum number of pure strategies of a
+player, $p$ is the number of players, and $d$ is the maximum degree of the game
+graph. Under slightly more stringent separability conditions on the payoff
+matrices of the true game, we show that our method learns a game with the exact
+same Nash equilibria as the true game. We also show that $\Omega(d \log (pm))$
+samples are necessary for any method to consistently recover a game, with the
+same Nash-equilibria as the true game, from observations of strategic
+interactions. We verify our theoretical results through simulation experiments.
+",1,0,0,0,0,0
+606,Superposition solutions to the extended KdV equation for water surface waves,"  The KdV equation can be derived in the shallow water limit of the Euler
+equations. Over the last few decades, this equation has been extended to
+include higher order effects. Although this equation has only one conservation
+law, exact periodic and solitonic solutions exist. Khare and Saxena
+\cite{KhSa,KhSa14,KhSa15} demonstrated the possibility of generating new exact
+solutions by combining known ones for several fundamental equations (e.g.,
+Korteweg - de Vries, Nonlinear Schrödinger). Here we find that this
+construction can be repeated for higher order, non-integrable extensions of
+these equations. Contrary to many statements in the literature, there seems to
+be no correlation between integrability and the number of nonlinear one
+variable wave solutions.
+",0,1,0,0,0,0
+607,Boosting the Actor with Dual Critic,"  This paper proposes a new actor-critic-style algorithm called Dual
+Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian
+dual form of the Bellman optimality equation, which can be viewed as a
+two-player game between the actor and a critic-like function, which is named as
+dual critic. Compared to its actor-critic relatives, Dual-AC has the desired
+property that the actor and dual critic are updated cooperatively to optimize
+the same objective function, providing a more transparent way for learning the
+critic that is directly related to the objective function of the actor. We then
+provide a concrete algorithm that can effectively solve the minimax
+optimization problem, using techniques of multi-step bootstrapping, path
+regularization, and stochastic dual ascent algorithm. We demonstrate that the
+proposed algorithm achieves the state-of-the-art performances across several
+benchmarks.
+",1,0,0,0,0,0
+608,Counting Dominating Sets of Graphs,"  Counting dominating sets in a graph $G$ is closely related to the
+neighborhood complex of $G$. We exploit this relation to prove that the number
+of dominating sets $d(G)$ of a graph is determined by the number of complete
+bipartite subgraphs of its complement. More precisely, we state the following.
+Let $G$ be a simple graph of order $n$ such that its complement has exactly
+$a(G)$ subgraphs isomorphic to $K_{2p,2q}$ and exactly $b(G)$ subgraphs
+isomorphic to $K_{2p+1,2q+1}$. Then $d(G) = 2^n -1 + 2[a(G)-b(G)]$. We also
+show some new relations between the domination polynomial and the neighborhood
+polynomial of a graph.
+",0,0,1,0,0,0
+609,High SNR Consistent Compressive Sensing,"  High signal to noise ratio (SNR) consistency of model selection criteria in
+linear regression models has attracted a lot of attention recently. However,
+most of the existing literature on high SNR consistency deals with model order
+selection. Further, the limited literature available on the high SNR
+consistency of subset selection procedures (SSPs) is applicable to linear
+regression with full rank measurement matrices only. Hence, the performance of
+SSPs used in underdetermined linear models (a.k.a compressive sensing (CS)
+algorithms) at high SNR is largely unknown. This paper fills this gap by
+deriving necessary and sufficient conditions for the high SNR consistency of
+popular CS algorithms like $l_0$-minimization, basis pursuit de-noising or
+LASSO, orthogonal matching pursuit and Dantzig selector. Necessary conditions
+analytically establish the high SNR inconsistency of CS algorithms when used
+with the tuning parameters discussed in literature. Novel tuning parameters
+with SNR adaptations are developed using the sufficient conditions and the
+choice of SNR adaptations are discussed analytically using convergence rate
+analysis. CS algorithms with the proposed tuning parameters are numerically
+shown to be high SNR consistent and outperform existing tuning parameters in
+the moderate to high SNR regime.
+",1,0,0,1,0,0
+610,Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions,"  Reduction of communication and efficient partitioning are key issues for
+achieving scalability in hierarchical $N$-Body algorithms like FMM. In the
+present work, we propose four independent strategies to improve partitioning
+and reduce communication. First of all, we show that the conventional wisdom of
+using space-filling curve partitioning may not work well for boundary integral
+problems, which constitute about 50% of FMM's application user base. We propose
+an alternative method which modifies orthogonal recursive bisection to solve
+the cell-partition misalignment that has kept it from scaling previously.
+Secondly, we optimize the granularity of communication to find the optimal
+balance between a bulk-synchronous collective communication of the local
+essential tree and an RDMA per task per cell. Finally, we take the dynamic
+sparse data exchange proposed by Hoefler et al. and extend it to a hierarchical
+sparse data exchange, which is demonstrated at scale to be faster than the MPI
+library's MPI_Alltoallv that is commonly used.
+",1,0,0,0,0,0
+611,Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement,"  In this paper, we focus on fully automatic traffic surveillance camera
+calibration, which we use for speed measurement of passing vehicles. We improve
+over a recent state-of-the-art camera calibration method for traffic
+surveillance based on two detected vanishing points. More importantly, we
+propose a novel automatic scene scale inference method. The method is based on
+matching bounding boxes of rendered 3D models of vehicles with detected
+bounding boxes in the image. The proposed method can be used from arbitrary
+viewpoints, since it has no constraints on camera placement. We evaluate our
+method on the recent comprehensive dataset for speed measurement BrnoCompSpeed.
+Experiments show that our automatic camera calibration method by detection of
+two vanishing points reduces error by 50% (mean distance ratio error reduced
+from 0.18 to 0.09) compared to the previous state-of-the-art method. We also
+show that our scene scale inference method is more precise, outperforming both
+state-of-the-art automatic calibration method for speed measurement (error
+reduction by 86% -- 7.98km/h to 1.10km/h) and manual calibration (error
+reduction by 19% -- 1.35km/h to 1.10km/h). We also present qualitative results
+of the proposed automatic camera calibration method on video sequences obtained
+from real surveillance cameras in various places, and under different lighting
+conditions (night, dawn, day).
+",1,0,0,0,0,0
+612,Technical Report for Real-Time Certified Probabilistic Pedestrian Forecasting,"  The success of autonomous systems will depend upon their ability to safely
+navigate human-centric environments. This motivates the need for a real-time,
+probabilistic forecasting algorithm for pedestrians, cyclists, and other agents
+since these predictions will form a necessary step in assessing the risk of any
+action. This paper presents a novel approach to probabilistic forecasting for
+pedestrians based on weighted sums of ordinary differential equations that are
+learned from historical trajectory information within a fixed scene. The
+resulting algorithm is embarrassingly parallel and is able to work at real-time
+speeds using a naive Python implementation. The quality of predicted locations
+of agents generated by the proposed algorithm is validated on a variety of
+examples and considerably higher than existing state of the art approaches over
+long time horizons.
+",1,0,1,0,0,0
+613,Distance Measure Machines,"  This paper presents a distance-based discriminative framework for learning
+with probability distributions. Instead of using kernel mean embeddings or
+generalized radial basis kernels, we introduce embeddings based on
+dissimilarity of distributions to some reference distributions denoted as
+templates. Our framework extends the theory of similarity of Balcan et al.
+(2008) to the population distribution case and we show that, for some learning
+problems, some dissimilarity on distribution achieves low-error linear decision
+functions with high probability. Our key result is to prove that the theory
+also holds for empirical distributions. Algorithmically, the proposed approach
+consists in computing a mapping based on pairwise dissimilarity where learning
+a linear decision function is amenable. Our experimental results show that the
+Wasserstein distance embedding performs better than kernel mean embeddings and
+computing Wasserstein distance is far more tractable than estimating pairwise
+Kullback-Leibler divergence of empirical distributions.
+",0,0,0,1,0,0
+614,DSBGK Method to Incorporate the CLL Reflection Model and to Simulate Gas Mixtures,"  Molecular reflections on usual wall surfaces can be statistically described
+by the Maxwell diffuse reflection model, which has been successfully applied in
+the DSBGK simulations. We develop the DSBGK algorithm to implement the
+Cercignani-Lampis-Lord (CLL) reflection model, which is widely applied to
+polished surfaces and used particularly in modeling space shuttles to predict
+the heat and force loads exerted by the high-speed flows around the surfaces.
+We also extend the DSBGK method to simulate gas mixtures and high contrast of
+number densities of different components can be handled at a cost of memory
+usage much lower than that needed by the DSMC simulations because the average
+numbers of simulated molecules of different components per cell can be equal in
+the DSBGK simulations.
+",0,1,0,0,0,0
+615,"Tropical formulae for summation over a part of SL(2, Z)","  Let $f(a,b,c,d)=\sqrt{a^2+b^2}+\sqrt{c^2+d^2}-\sqrt{(a+c)^2+(b+d)^2}$, let
+$(a,b,c,d)$ stand for $a,b,c,d\in\mathbb Z_{\geq 0}$ such that $ad-bc=1$.
+Define \begin{equation} \label{eq_main} F(s) = \sum_{(a,b,c,d)} f(a,b,c,d)^s.
+\end{equation} In other words, we consider the sum of the powers of the
+triangle inequality defects for the lattice parallelograms (in the first
+quadrant) of area one.
+We prove that $F(s)$ converges when $s>1/2$ and diverges at $s=1/2$. We also
+prove $$\sum\limits_{\substack{(a,b,c,d),\\ 1\leq a\leq b, 1\leq c\leq d}}
+\frac{1}{(a+b)^2(c+d)^2(a+b+c+d)^2} = 1/24,$$ and show a general method to
+obtain such formulae. The method comes from the consideration of the tropical
+analogue of the caustic curves, whose moduli give a complete set of continuous
+invariants on the space of convex domains.
+",0,0,1,0,0,0
+616,Efficient sampling of conditioned Markov jump processes,"  We consider the task of generating draws from a Markov jump process (MJP)
+between two time points at which the process is known. Resulting draws are
+typically termed bridges and the generation of such bridges plays a key role in
+simulation-based inference algorithms for MJPs. The problem is challenging due
+to the intractability of the conditioned process, necessitating the use of
+computationally intensive methods such as weighted resampling or Markov chain
+Monte Carlo. An efficient implementation of such schemes requires an
+approximation of the intractable conditioned hazard/propensity function that is
+both cheap and accurate. In this paper, we review some existing approaches to
+this problem before outlining our novel contribution. Essentially, we leverage
+the tractability of a Gaussian approximation of the MJP and suggest a
+computationally efficient implementation of the resulting conditioned hazard
+approximation. We compare and contrast our approach with existing methods using
+three examples.
+",0,0,0,1,0,0
+617,Holography and thermalization in optical pump-probe spectroscopy,"  Using holography, we model experiments in which a 2+1D strange metal is
+pumped by a laser pulse into a highly excited state, after which the time
+evolution of the optical conductivity is probed. We consider a finite-density
+state with mildly broken translation invariance and excite it by oscillating
+electric field pulses. At zero density, the optical conductivity would assume
+its thermalized value immediately after the pumping has ended. At finite
+density, pulses with significant DC components give rise to slow exponential
+relaxation, governed by a vector quasinormal mode. In contrast, for
+high-frequency pulses the amplitude of the quasinormal mode is strongly
+suppressed, so that the optical conductivity assumes its thermalized value
+effectively instantaneously. This surprising prediction may provide a stimulus
+for taking up the challenge to realize these experiments in the laboratory.
+Such experiments would test a crucial open question faced by applied
+holography: Are its predictions artefacts of the large $N$ limit or do they
+enjoy sufficient UV independence to hold at least qualitatively in real-world
+systems?
+",0,1,0,0,0,0
+618,On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis,"  In this paper, we study random subsampling of Gaussian process regression,
+one of the simplest approximation baselines, from a theoretical perspective.
+Although subsampling discards a large part of training data, we show provable
+guarantees on the accuracy of the predictive mean/variance and its
+generalization ability. For analysis, we consider embedding kernel matrices
+into graphons, which encapsulate the difference of the sample size and enables
+us to evaluate the approximation and generalization errors in a unified manner.
+The experimental results show that the subsampling approximation achieves a
+better trade-off regarding accuracy and runtime than the Nyström and random
+Fourier expansion methods.
+",1,0,0,1,0,0
+619,Representing Hybrid Automata by Action Language Modulo Theories,"  Both hybrid automata and action languages are formalisms for describing the
+evolution of dynamic systems. This paper establishes a formal relationship
+between them. We show how to succinctly represent hybrid automata in an action
+language which in turn is defined as a high-level notation for answer set
+programming modulo theories (ASPMT) --- an extension of answer set programs to
+the first-order level similar to the way satisfiability modulo theories (SMT)
+extends propositional satisfiability (SAT). We first show how to represent
+linear hybrid automata with convex invariants by an action language modulo
+theories. A further translation into SMT allows for computing them using SMT
+solvers that support arithmetic over reals. Next, we extend the representation
+to the general class of non-linear hybrid automata allowing even non-convex
+invariants. We represent them by an action language modulo ODE (Ordinary
+Differential Equations), which can be compiled into satisfiability modulo ODE.
+We developed a prototype system cplus2aspmt based on these translations, which
+allows for a succinct representation of hybrid transition systems that can be
+computed effectively by the state-of-the-art SMT solver dReal.
+",1,0,0,0,0,0
+620,An enthalpy-based multiple-relaxation-time lattice Boltzmann method for solid-liquid phase change heat transfer in metal foams,"  In this paper, an enthalpy-based multiple-relaxation-time (MRT) lattice
+Boltzmann (LB) method is developed for solid-liquid phase change heat transfer
+in metal foams under local thermal non-equilibrium (LTNE) condition. The
+enthalpy-based MRT-LB method consists of three different MRT-LB models: one for
+flow field based on the generalized non-Darcy model, and the other two for
+phase change material (PCM) and metal foam temperature fields described by the
+LTNE model. The moving solid-liquid phase interface is implicitly tracked
+through the liquid fraction, which is simultaneously obtained when the energy
+equations of PCM and metal foam are solved. The present method has several
+distinctive features. First, as compared with previous studies, the present
+method avoids the iteration procedure, thus it retains the inherent merits of
+the standard LB method and is superior over the iteration method in terms of
+accuracy and computational efficiency. Second, a volumetric LB scheme instead
+of the bounce-back scheme is employed to realize the no-slip velocity condition
+in the interface and solid phase regions, which is consistent with the actual
+situation. Last but not least, the MRT collision model is employed, and with
+additional degrees of freedom, it has the ability to reduce the numerical
+diffusion across phase interface induced by solid-liquid phase change.
+Numerical tests demonstrate that the present method can be served as an
+accurate and efficient numerical tool for studying metal foam enhanced
+solid-liquid phase change heat transfer in latent heat storage. Finally,
+comparisons and discussions are made to offer useful information for practical
+applications of the present method.
+",0,1,0,0,0,0
+621,Birth of a subaqueous barchan dune,"  Barchan dunes are crescentic shape dunes with horns pointing downstream. The
+present paper reports the formation of subaqueous barchan dunes from initially
+conical heaps in a rectangular channel. Because the most unique feature of a
+barchan dune is its horns, we associate the timescale for the appearance of
+horns to the formation of a barchan dune. A granular heap initially conical was
+placed on the bottom wall of a closed conduit and it was entrained by a water
+flow in turbulent regime. After a certain time, horns appear and grow, until an
+equilibrium length is reached. Our results show the existence of the timescales
+$0.5t_c$ and $2.5t_c$ for the appearance and equilibrium of horns,
+respectively, where $t_c$ is a characteristic time that scales with the grains
+diameter, gravity acceleration, densities of the fluid and grains, and shear
+and threshold velocities.
+",0,1,0,0,0,0
+622,Uncoupled isotonic regression via minimum Wasserstein deconvolution,"  Isotonic regression is a standard problem in shape-constrained estimation
+where the goal is to estimate an unknown nondecreasing regression function $f$
+from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots
+n$. While this problem is well understood both statistically and
+computationally, much less is known about its uncoupled counterpart where one
+is given only the unordered sets $\{x_1, \ldots, x_n\}$ and $\{y_1, \ldots,
+y_n\}$. In this work, we leverage tools from optimal transport theory to derive
+minimax rates under weak moments conditions on $y_i$ and to give an efficient
+algorithm achieving optimal rates. Both upper and lower bounds employ
+moment-matching arguments that are also pertinent to learning mixtures of
+distributions and deconvolution.
+",0,0,0,1,0,0
+623,Failure of Smooth Pasting Principle and Nonexistence of Equilibrium Stopping Rules under Time-Inconsistency,"  This paper considers a time-inconsistent stopping problem in which the
+inconsistency arises from non-constant time preference rates. We show that the
+smooth pasting principle, the main approach that has been used to construct
+explicit solutions for conventional time-consistent optimal stopping problems,
+may fail under time-inconsistency. Specifically, we prove that the smooth
+pasting principle solves a time-inconsistent problem within the intra-personal
+game theoretic framework if and only if a certain inequality on the model
+primitives is satisfied. We show that the violation of this inequality can
+happen even for very simple non-exponential discount functions. Moreover, we
+demonstrate that the stopping problem does not admit any intra-personal
+equilibrium whenever the smooth pasting principle fails. The ""negative"" results
+in this paper caution blindly extending the classical approaches for
+time-consistent stopping problems to their time-inconsistent counterparts.
+",0,0,0,0,0,1
+624,Perils of Zero-Interaction Security in the Internet of Things,"  The Internet of Things (IoT) demands authentication systems which can provide
+both security and usability. Recent research utilizes the rich sensing
+capabilities of smart devices to build security schemes operating without human
+interaction, such as zero-interaction pairing (ZIP) and zero-interaction
+authentication (ZIA). Prior work proposed a number of ZIP and ZIA schemes and
+reported promising results. However, those schemes were often evaluated under
+conditions which do not reflect realistic IoT scenarios. In addition, drawing
+any comparison among the existing schemes is impossible due to the lack of a
+common public dataset and unavailability of scheme implementations.
+In this paper, we address these challenges by conducting the first
+large-scale comparative study of ZIP and ZIA schemes, carried out under
+realistic conditions. We collect and release the most comprehensive dataset in
+the domain to date, containing over 4250 hours of audio recordings and 1
+billion sensor readings from three different scenarios, and evaluate five
+state-of-the-art schemes based on these data. Our study reveals that the
+effectiveness of the existing proposals is highly dependent on the scenario
+they are used in. In particular, we show that these schemes are subject to
+error rates between 0.6% and 52.8%.
+",1,0,0,0,0,0
+625,"Coarse-grained simulation of auxetic, two-dimensional crystal dynamics","  The increasing number of protein-based metamaterials demands reliable and
+efficient methods to study the physicochemical properties they may display. In
+this regard, we develop a simulation strategy based on Molecular Dynamics (MD)
+that addresses the geometric degrees of freedom of an auxetic two-dimensional
+protein crystal. This model consists of a network of impenetrable rigid squares
+linked through massless rigid rods, thus featuring a large number of both
+holonomic and nonholonomic constraints. Our MD methodology is optimized to
+study highly constrained systems and allows for the simulation of long-time
+dynamics with reasonably large timesteps. The data extracted from the
+simulations shows a persistent motional interdependence among the protein
+subunits in the crystal. We characterize the dynamical correlations featured by
+these subunits and identify two regimes characterized by their locality or
+nonlocality, depending on the geometric parameters of the crystal. From the
+same data, we also calculate the Poisson\rq{}s (longitudinal to axial strain)
+ratio of the crystal, and learn that, due to holonomic constraints (rigidness
+of the rod links), the crystal remains auxetic even after significant changes
+in the original geometry. The nonholonomic ones (collisions between subunits)
+increase the number of inhomogeneous deformations of the crystal, thus driving
+it away from an isotropic response. Our work provides the first simulation of
+the dynamics of protein crystals and offers insights into promising mechanical
+properties afforded by these materials.
+",0,1,0,0,0,0
+626,Core2Vec: A core-preserving feature learning framework for networks,"  Recent advances in the field of network representation learning are mostly
+attributed to the application of the skip-gram model in the context of graphs.
+State-of-the-art analogues of skip-gram model in graphs define a notion of
+neighbourhood and aim to find the vector representation for a node, which
+maximizes the likelihood of preserving this neighborhood.
+In this paper, we take a drastic departure from the existing notion of
+neighbourhood of a node by utilizing the idea of coreness. More specifically,
+we utilize the well-established idea that nodes with similar core numbers play
+equivalent roles in the network and hence induce a novel and an organic notion
+of neighbourhood. Based on this idea, we propose core2vec, a new algorithmic
+framework for learning low dimensional continuous feature mapping for a node.
+Consequently, the nodes having similar core numbers are relatively closer in
+the vector space that we learn.
+We further demonstrate the effectiveness of core2vec by comparing word
+similarity scores obtained by our method where the node representations are
+drawn from standard word association graphs against scores computed by other
+state-of-the-art network representation techniques like node2vec, DeepWalk and
+LINE. Our results always outperform these existing methods
+",1,0,0,0,0,0
+627,Predicting wind pressures around circular cylinders using machine learning techniques,"  Numerous studies have been carried out to measure wind pressures around
+circular cylinders since the early 20th century due to its engineering
+significance. Consequently, a large amount of wind pressure data sets have
+accumulated, which presents an excellent opportunity for using machine learning
+(ML) techniques to train models to predict wind pressures around circular
+cylinders. Wind pressures around smooth circular cylinders are a function of
+mainly the Reynolds number (Re), turbulence intensity (Ti) of the incident
+wind, and circumferential angle of the cylinder. Considering these three
+parameters as the inputs, this study trained two ML models to predict mean and
+fluctuating pressures respectively. Three machine learning algorithms including
+decision tree regressor, random forest, and gradient boosting regression trees
+(GBRT) were tested. The GBRT models exhibited the best performance for
+predicting both mean and fluctuating pressures, and they are capable of making
+accurate predictions for Re ranging from 10^4 to 10^6 and Ti ranging from 0% to
+15%. It is believed that the GBRT models provide very efficient and economical
+alternative to traditional wind tunnel tests and computational fluid dynamic
+simulations for determining wind pressures around smooth circular cylinders
+within the studied Re and Ti range.
+",1,0,0,1,0,0
+628,Randomly coloring simple hypergraphs with fewer colors,"  We study the problem of constructing a (near) uniform random proper
+$q$-coloring of a simple $k$-uniform hypergraph with $n$ vertices and maximum
+degree $\Delta$. (Proper in that no edge is mono-colored and simple in that two
+edges have maximum intersection of size one). We show that if $q\geq
+\max\{C_k\log n,500k^3\Delta^{1/(k-1)}\}$ then the Glauber Dynamics will become
+close to uniform in $O(n\log n)$ time, given a random (improper) start. This
+improves on the results in Frieze and Melsted [5].
+",1,0,0,0,0,0
+629,"Contributed Discussion to Uncertainty Quantification for the Horseshoe by Stéphanie van der Pas, Botond Szabó and Aad van der Vaart","  We begin by introducing the main ideas of the paper under discussion. We
+discuss some interesting issues regarding adaptive component-wise credible
+intervals. We then briefly touch upon the concepts of self-similarity and
+excessive bias restriction. This is then followed by some comments on the
+extensive simulation study carried out in the paper.
+",0,0,1,1,0,0
+630,Channel masking for multivariate time series shapelets,"  Time series shapelets are discriminative sub-sequences and their similarity
+to time series can be used for time series classification. Initial shapelet
+extraction algorithms searched shapelets by complete enumeration of all
+possible data sub-sequences. Research on shapelets for univariate time series
+proposed a mechanism called shapelet learning which parameterizes the shapelets
+and learns them jointly with a prediction model in an optimization procedure.
+Trivial extension of this method to multivariate time series does not yield
+very good results due to the presence of noisy channels which lead to
+overfitting. In this paper we propose a shapelet learning scheme for
+multivariate time series in which we introduce channel masks to discount noisy
+channels and serve as an implicit regularization.
+",1,0,0,0,0,0
+631,Spin mediated enhanced negative magnetoresistance in Ni80Fe20 and p-silicon bilayer,"  In this work, we present an experimental study of spin mediated enhanced
+negative magnetoresistance in Ni80Fe20 (50 nm)/p-Si (350 nm) bilayer. The
+resistance measurement shows a reduction of ~2.5% for the bilayer specimen as
+compared to 1.3% for Ni80Fe20 (50 nm) on oxide specimen for an out-of-plane
+applied magnetic field of 3T. In the Ni80Fe20-only film, the negative
+magnetoresistance behavior is attributed to anisotropic magnetoresistance. We
+propose that spin polarization due to spin-Hall effect is the underlying cause
+of the enhanced negative magnetoresistance observed in the bilayer. Silicon has
+weak spin orbit coupling so spin Hall magnetoresistance measurement is not
+feasible. We use V2{\omega} and V3{\omega} measurement as a function of
+magnetic field and angular rotation of magnetic field in direction normal to
+electric current to elucidate the spin-Hall effect. The angular rotation of
+magnetic field shows a sinusoidal behavior for both V2{\omega} and V3{\omega},
+which is attributed to the spin phonon interactions resulting from the
+spin-Hall effect mediated spin polarization. We propose that the spin
+polarization leads to a decrease in hole-phonon scattering resulting in
+enhanced negative magnetoresistance.
+",0,1,0,0,0,0
+632,On the Limitations of Representing Functions on Sets,"  Recent work on the representation of functions on sets has considered the use
+of summation in a latent space to enforce permutation invariance. In
+particular, it has been conjectured that the dimension of this latent space may
+remain fixed as the cardinality of the sets under consideration increases.
+However, we demonstrate that the analysis leading to this conjecture requires
+mappings which are highly discontinuous and argue that this is only of limited
+practical use. Motivated by this observation, we prove that an implementation
+of this model via continuous mappings (as provided by e.g. neural networks or
+Gaussian processes) actually imposes a constraint on the dimensionality of the
+latent space. Practical universal function representation for set inputs can
+only be achieved with a latent dimension at least the size of the maximum
+number of input elements.
+",1,0,0,1,0,0
+633,Learning Models from Data with Measurement Error: Tackling Underreporting,"  Measurement error in observational datasets can lead to systematic bias in
+inferences based on these datasets. As studies based on observational data are
+increasingly used to inform decisions with real-world impact, it is critical
+that we develop a robust set of techniques for analyzing and adjusting for
+these biases. In this paper we present a method for estimating the distribution
+of an outcome given a binary exposure that is subject to underreporting. Our
+method is based on a missing data view of the measurement error problem, where
+the true exposure is treated as a latent variable that is marginalized out of a
+joint model. We prove three different conditions under which the outcome
+distribution can still be identified from data containing only error-prone
+observations of the exposure. We demonstrate this method on synthetic data and
+analyze its sensitivity to near violations of the identifiability conditions.
+Finally, we use this method to estimate the effects of maternal smoking and
+opioid use during pregnancy on childhood obesity, two import problems from
+public health. Using the proposed method, we estimate these effects using only
+subject-reported drug use data and substantially refine the range of estimates
+generated by a sensitivity analysis-based approach. Further, the estimates
+produced by our method are consistent with existing literature on both the
+effects of maternal smoking and the rate at which subjects underreport smoking.
+",1,0,0,1,0,0
+634,A simple introduction to Karmarkar's Algorithm for Linear Programming,"  An extremely simple, description of Karmarkar's algorithm with very few
+technical terms is given.
+",1,0,0,0,0,0
+635,Magneto-inductive Passive Relaying in Arbitrarily Arranged Networks,"  We consider a wireless sensor network that uses inductive near-field coupling
+for wireless powering or communication, or for both. The severely limited range
+of an inductively coupled source-destination pair can be improved using
+resonant relay devices, which are purely passive in nature. Utilization of such
+magneto-inductive relays has only been studied for regular network topologies,
+allowing simplified assumptions on the mutual antenna couplings. In this work
+we present an analysis of magneto-inductive passive relaying in arbitrarily
+arranged networks. We find that the resulting channel has characteristics
+similar to multipath fading: the channel power gain is governed by a
+non-coherent sum of phasors, resulting in increased frequency selectivity. We
+propose and study two strategies to increase the channel power gain of random
+relay networks: i) deactivation of individual relays by open-circuit switching
+and ii) frequency tuning. The presented results show that both methods improve
+the utilization of available passive relays, leading to reliable and
+significant performance gains.
+",1,0,0,0,0,0
+636,Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces,"  Transfer operators such as the Perron--Frobenius or Koopman operator play an
+important role in the global analysis of complex dynamical systems. The
+eigenfunctions of these operators can be used to detect metastable sets, to
+project the dynamics onto the dominant slow processes, or to separate
+superimposed signals. We extend transfer operator theory to reproducing kernel
+Hilbert spaces and show that these operators are related to Hilbert space
+representations of conditional distributions, known as conditional mean
+embeddings in the machine learning community. Moreover, numerical methods to
+compute empirical estimates of these embeddings are akin to data-driven methods
+for the approximation of transfer operators such as extended dynamic mode
+decomposition and its variants. One main benefit of the presented kernel-based
+approaches is that these methods can be applied to any domain where a
+similarity measure given by a kernel is available. We illustrate the results
+with the aid of guiding examples and highlight potential applications in
+molecular dynamics as well as video and text data analysis.
+",1,0,0,1,0,0
+637,Asymptotics of the bound state induced by $δ$-interaction supported on a weakly deformed plane,"  In this paper we consider the three-dimensional Schrödinger operator with
+a $\delta$-interaction of strength $\alpha > 0$ supported on an unbounded
+surface parametrized by the mapping $\mathbb{R}^2\ni x\mapsto (x,\beta f(x))$,
+where $\beta \in [0,\infty)$ and $f\colon \mathbb{R}^2\rightarrow\mathbb{R}$,
+$f\not\equiv 0$, is a $C^2$-smooth, compactly supported function. The surface
+supporting the interaction can be viewed as a local deformation of the plane.
+It is known that the essential spectrum of this Schrödinger operator
+coincides with $[-\frac14\alpha^2,+\infty)$. We prove that for all sufficiently
+small $\beta > 0$ its discrete spectrum is non-empty and consists of a unique
+simple eigenvalue. Moreover, we obtain an asymptotic expansion of this
+eigenvalue in the limit $\beta \rightarrow 0+$. In particular, this eigenvalue
+tends to $-\frac14\alpha^2$ exponentially fast as $\beta\rightarrow 0+$.
+",0,0,1,0,0,0
+638,Análise comparativa de pesquisas de origens e destinos: uma abordagem baseada em Redes Complexas,"  In this paper, a comparative study was conducted between complex networks
+representing origin and destination survey data. Similarities were found
+between the characteristics of the networks of Brazilian cities with networks
+of foreign cities. Power laws were found in the distributions of edge weights
+and this scale - free behavior can occur due to the economic characteristics of
+the cities.
+",1,0,0,0,0,0
+639,Inverse Kinematics for Control of Tensegrity Soft Robots: Existence and Optimality of Solutions,"  Tension-network (`tensegrity') robots encounter many control challenges as
+articulated soft robots, due to the structures' high-dimensional nonlinear
+dynamics. Control approaches have been developed which use the inverse
+kinematics of tensegrity structures, either for open-loop control or as
+equilibrium inputs for closed-loop controllers. However, current formulations
+of the tensegrity inverse kinematics problem are limited in robotics
+applications: first, they can lead to higher than needed cable tensions, and
+second, may lack solutions when applied to robots with high node-to-cable
+ratios. This work provides progress in both directions. To address the first
+limitation, the objective function for the inverse kinematics optimization
+problem is modified to produce cable tensions as low or lower than before, thus
+reducing the load on the robots' motors. For the second, a reformulation of the
+static equilibrium constraint is proposed, which produces solutions independent
+of the number of nodes within each rigid body. Simulation results using the
+second reformulation on a specific tensegrity spine robot show reasonable
+open-loop control results, whereas the previous formulation could not produce
+any solution.
+",1,0,0,0,0,0
+640,Smallest eigenvalue density for regular or fixed-trace complex Wishart-Laguerre ensemble and entanglement in coupled kicked tops,"  The statistical behaviour of the smallest eigenvalue has important
+implications for systems which can be modeled using a Wishart-Laguerre
+ensemble, the regular one or the fixed trace one. For example, the density of
+the smallest eigenvalue of the Wishart-Laguerre ensemble plays a crucial role
+in characterizing multiple channel telecommunication systems. Similarly, in the
+quantum entanglement problem, the smallest eigenvalue of the fixed trace
+ensemble carries information regarding the nature of entanglement.
+For real Wishart-Laguerre matrices, there exists an elegant recurrence scheme
+suggested by Edelman to directly obtain the exact expression for the smallest
+eigenvalue density. In the case of complex Wishart-Laguerre matrices, for
+finding exact and explicit expressions for the smallest eigenvalue density,
+existing results based on determinants become impractical when the determinants
+involve large-size matrices. In this work, we derive a recurrence scheme for
+the complex case which is analogous to that of Edelman's for the real case.
+This is used to obtain exact results for the smallest eigenvalue density for
+both the regular, and the fixed trace complex Wishart-Laguerre ensembles. We
+validate our analytical results using Monte Carlo simulations. We also study
+scaled Wishart-Laguerre ensemble and investigate its efficacy in approximating
+the fixed-trace ensemble. Eventually, we apply our result for the fixed-trace
+ensemble to investigate the behaviour of the smallest eigenvalue in the
+paradigmatic system of coupled kicked tops.
+",0,1,1,1,0,0
+641,Development of probabilistic dam breach model using Bayesian inference,"  Dam breach models are commonly used to predict outflow hydrographs of
+potentially failing dams and are key ingredients for evaluating flood risk. In
+this paper a new dam breach modeling framework is introduced that shall improve
+the reliability of hydrograph predictions of homogeneous earthen embankment
+dams. Striving for a small number of parameters, the simplified physics-based
+model describes the processes of failing embankment dams by breach enlargement,
+driven by progressive surface erosion. Therein the erosion rate of dam material
+is modeled by empirical sediment transport formulations. Embedding the model
+into a Bayesian multilevel framework allows for quantitative analysis of
+different categories of uncertainties. To this end, data available in
+literature of observed peak discharge and final breach width of historical dam
+failures was used to perform model inversion by applying Markov Chain Monte
+Carlo simulation. Prior knowledge is mainly based on non-informative
+distribution functions. The resulting posterior distribution shows that the
+main source of uncertainty is a correlated subset of parameters, consisting of
+the residual error term and the epistemic term quantifying the breach erosion
+rate. The prediction intervals of peak discharge and final breach width are
+congruent with values known from literature. To finally predict the outflow
+hydrograph for real case applications, an alternative residual model was
+formulated that assumes perfect data and a perfect model. The fully
+probabilistic fashion of hydrograph prediction has the potential to improve the
+adequate risk management of downstream flooding.
+",0,0,0,1,0,0
+642,Large Magellanic Cloud Near-Infrared Synoptic Survey. V. Period-Luminosity Relations of Miras,"  We study the near-infrared properties of 690 Mira candidates in the central
+region of the Large Magellanic Cloud, based on time-series observations at
+JHKs. We use densely-sampled I-band observations from the OGLE project to
+generate template light curves in the near infrared and derive robust mean
+magnitudes at those wavelengths. We obtain near-infrared Period-Luminosity
+relations for Oxygen-rich Miras with a scatter as low as 0.12 mag at Ks. We
+study the Period-Luminosity-Color relations and the color excesses of
+Carbon-rich Miras, which show evidence for a substantially different reddening
+law.
+",0,0,0,1,0,0
+643,One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network,"  There is an inherent need for autonomous cars, drones, and other robots to
+have a notion of how their environment behaves and to anticipate changes in the
+near future. In this work, we focus on anticipating future appearance given the
+current frame of a video. Existing work focuses on either predicting the future
+appearance as the next frame of a video, or predicting future motion as optical
+flow or motion trajectories starting from a single video frame. This work
+stretches the ability of CNNs (Convolutional Neural Networks) to predict an
+anticipation of appearance at an arbitrarily given future time, not necessarily
+the next video frame. We condition our predicted future appearance on a
+continuous time variable that allows us to anticipate future frames at a given
+temporal distance, directly from the input video frame. We show that CNNs can
+learn an intrinsic representation of typical appearance changes over time and
+successfully generate realistic predictions at a deliberate time difference in
+the near future.
+",1,0,0,0,0,0
+644,Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access,"  We consider the problem of dynamic spectrum access for network utility
+maximization in multichannel wireless networks. The shared bandwidth is divided
+into K orthogonal channels. In the beginning of each time slot, each user
+selects a channel and transmits a packet with a certain transmission
+probability. After each time slot, each user that has transmitted a packet
+receives a local observation indicating whether its packet was successfully
+delivered or not (i.e., ACK signal). The objective is a multi-user strategy for
+accessing the spectrum that maximizes a certain network utility in a
+distributed manner without online coordination or message exchanges between
+users. Obtaining an optimal solution for the spectrum access problem is
+computationally expensive in general due to the large state space and partial
+observability of the states. To tackle this problem, we develop a novel
+distributed dynamic spectrum access algorithm based on deep multi-user
+reinforcement leaning. Specifically, at each time slot, each user maps its
+current state to spectrum access actions based on a trained deep-Q network used
+to maximize the objective function. Game theoretic analysis of the system
+dynamics is developed for establishing design principles for the implementation
+of the algorithm. Experimental results demonstrate strong performance of the
+algorithm.
+",1,0,0,0,0,0
+645,Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming,"  We design a new myopic strategy for a wide class of sequential design of
+experiment (DOE) problems, where the goal is to collect data in order to to
+fulfil a certain problem specific goal. Our approach, Myopic Posterior Sampling
+(MPS), is inspired by the classical posterior (Thompson) sampling algorithm for
+multi-armed bandits and leverages the flexibility of probabilistic programming
+and approximate Bayesian inference to address a broad set of problems.
+Empirically, this general-purpose strategy is competitive with more specialised
+methods in a wide array of DOE tasks, and more importantly, enables addressing
+complex DOE goals where no existing method seems applicable. On the theoretical
+side, we leverage ideas from adaptive submodularity and reinforcement learning
+to derive conditions under which MPS achieves sublinear regret against natural
+benchmark policies.
+",0,0,0,1,0,0
+646,Data-Driven Sparse Structure Selection for Deep Neural Networks,"  Deep convolutional neural networks have liberated its extraordinary power on
+various tasks. However, it is still very challenging to deploy state-of-the-art
+models into real-world applications due to their high computational complexity.
+How can we design a compact and effective network without massive experiments
+and expert knowledge? In this paper, we propose a simple and effective
+framework to learn and prune deep models in an end-to-end manner. In our
+framework, a new type of parameter -- scaling factor is first introduced to
+scale the outputs of specific structures, such as neurons, groups or residual
+blocks. Then we add sparsity regularizations on these factors, and solve this
+optimization problem by a modified stochastic Accelerated Proximal Gradient
+(APG) method. By forcing some of the factors to zero, we can safely remove the
+corresponding structures, thus prune the unimportant parts of a CNN. Comparing
+with other structure selection methods that may need thousands of trials or
+iterative fine-tuning, our method is trained fully end-to-end in one training
+pass without bells and whistles. We evaluate our method, Sparse Structure
+Selection with several state-of-the-art CNNs, and demonstrate very promising
+results with adaptive depth and width selection.
+",1,0,0,0,0,0
+647,Scaling laws and bounds for the turbulent G.O. Roberts dynamo,"  Numerical simulations of the G.O. Roberts dynamo are presented. Dynamos both
+with and without a significant mean field are obtained. Exact bounds are
+derived for the total energy which conform with the Kolmogorov phenomenology of
+turbulence. Best fits to numerical data show the same functional dependences as
+the inequalities obtained from optimum theory.
+",0,1,0,0,0,0
+648,Control Strategies for the Fokker-Planck Equation,"  Using a projection-based decoupling of the Fokker-Planck equation, control
+strategies that allow to speed up the convergence to the stationary
+distribution are investigated. By means of an operator theoretic framework for
+a bilinear control system, two different feedback control laws are proposed.
+Projected Riccati and Lyapunov equations are derived and properties of the
+associated solutions are given. The well-posedness of the closed loop systems
+is shown and local and global stabilization results, respectively, are
+obtained. An essential tool in the construction of the controls is the choice
+of appropriate control shape functions. Results for a two dimensional double
+well potential illustrate the theoretical findings in a numerical setup.
+",0,0,1,0,0,0
+649,On Popov's formula involving the Von Mangoldt function,"  We offer a generalization of a formula of Popov involving the Von Mangoldt
+function. Some commentary on its relation to other results in analytic number
+theory is mentioned as well as an analogue involving the m$\ddot{o}$bius
+function.
+",0,0,1,0,0,0
+650,On fibering compact manifold over the circle,"  In this paper, we show that any compact manifold that carries a
+SL(n;R)-foliation is fibered on the circle S^1.
+",0,0,1,0,0,0
+651,Phonon-Induced Topological Transition to a Type-II Weyl Semimetal,"  Given the importance of crystal symmetry for the emergence of topological
+quantum states, we have studied, as exemplified in NbNiTe2, the interplay of
+crystal symmetry, atomic displacements (lattice vibration), band degeneracy,
+and band topology. For NbNiTe2 structure in space group 53 (Pmna) - having an
+inversion center arising from two glide planes and one mirror plane with a
+2-fold rotation and screw axis - a full gap opening exists between two band
+manifolds near the Fermi energy. Upon atomic displacements by optical phonons,
+the symmetry lowers to space group 28 (Pma2), eliminating one glide plane along
+c, the associated rotation and screw axis, and the inversion center. As a
+result, twenty Weyl points emerge, including four type-II Weyl points in the
+G-X direction at the boundary between a pair of adjacent electron and hole
+bands. Thus, optical phonons may offer control of the transition to a Weyl
+fermion state.
+",0,1,0,0,0,0
+652,Multi-hop assortativities for networks classification,"  Several social, medical, engineering and biological challenges rely on
+discovering the functionality of networks from their structure and node
+metadata, when it is available. For example, in chemoinformatics one might want
+to detect whether a molecule is toxic based on structure and atomic types, or
+discover the research field of a scientific collaboration network. Existing
+techniques rely on counting or measuring structural patterns that are known to
+show large variations from network to network, such as the number of triangles,
+or the assortativity of node metadata. We introduce the concept of multi-hop
+assortativity, that captures the similarity of the nodes situated at the
+extremities of a randomly selected path of a given length. We show that
+multi-hop assortativity unifies various existing concepts and offers a
+versatile family of 'fingerprints' to characterize networks. These fingerprints
+allow in turn to recover the functionalities of a network, with the help of the
+machine learning toolbox. Our method is evaluated empirically on established
+social and chemoinformatic network benchmarks. Results reveal that our
+assortativity based features are competitive providing highly accurate results
+often outperforming state of the art methods for the network classification
+task.
+",1,0,0,1,0,0
+653,A Bayesian Nonparametrics based Robust Particle Filter Algorithm,"  This paper is concerned with the online estimation of a nonlinear dynamic
+system from a series of noisy measurements. The focus is on cases wherein
+outliers are present in-between normal noises. We assume that the outliers
+follow an unknown generating mechanism which deviates from that of normal
+noises, and then model the outliers using a Bayesian nonparametric model called
+Dirichlet process mixture (DPM). A sequential particle-based algorithm is
+derived for posterior inference for the outlier model as well as the state of
+the system to be estimated. The resulting algorithm is termed DPM based robust
+PF (DPM-RPF). The nonparametric feature makes this algorithm allow the data to
+""speak for itself"" to determine the complexity and structure of the outlier
+model. Simulation results show that it performs remarkably better than two
+state-of-the-art methods especially when outliers appear frequently along time.
+",0,0,0,1,0,0
+654,Highly accurate model for prediction of lung nodule malignancy with CT scans,"  Computed tomography (CT) examinations are commonly used to predict lung
+nodule malignancy in patients, which are shown to improve noninvasive early
+diagnosis of lung cancer. It remains challenging for computational approaches
+to achieve performance comparable to experienced radiologists. Here we present
+NoduleX, a systematic approach to predict lung nodule malignancy from CT data,
+based on deep learning convolutional neural networks (CNN). For training and
+validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort.
+All nodules were identified and classified by four experienced thoracic
+radiologists who participated in the LIDC project. NoduleX achieves high
+accuracy for nodule malignancy classification, with an AUC of ~0.99. This is
+commensurate with the analysis of the dataset by experienced radiologists. Our
+approach, NoduleX, provides an effective framework for highly accurate nodule
+malignancy prediction with the model trained on a large patient population. Our
+results are replicable with software available at
+this http URL.
+",0,0,0,1,1,0
+655,Rotating Rayleigh-Taylor turbulence,"  The turbulent Rayleigh--Taylor system in a rotating reference frame is
+investigated by direct numerical simulations within the Oberbeck-Boussinesq
+approximation. On the basis of theoretical arguments, supported by our
+simulations, we show that the Rossby number decreases in time, and therefore
+the Coriolis force becomes more important as the system evolves and produces
+many effects on Rayleigh--Taylor turbulence. We find that rotation reduces the
+intensity of turbulent velocity fluctuations and therefore the growth rate of
+the temperature mixing layer. Moreover, in presence of rotation the conversion
+of potential energy into turbulent kinetic energy is found to be less effective
+and the efficiency of the heat transfer is reduced. Finally, during the
+evolution of the mixing layer we observe the development of a
+cyclone-anticyclone asymmetry.
+",0,1,0,0,0,0
+656,Evolutionary dynamics of N-person Hawk-Dove games,"  In the animal world, the competition between individuals belonging to
+different species for a resource often requires the cooperation of several
+individuals in groups. This paper proposes a generalization of the Hawk-Dove
+Game for an arbitrary number of agents: the N-person Hawk-Dove Game. In this
+model, doves exemplify the cooperative behavior without intraspecies conflict,
+while hawks represent the aggressive behavior. In the absence of hawks, doves
+share the resource equally and avoid conflict, but having hawks around lead to
+doves escaping without fighting. Conversely, hawks fight for the resource at
+the cost of getting injured. Nevertheless, if doves are present in sufficient
+number to expel the hawks, they can aggregate to protect the resource, and thus
+avoid being plundered by hawks. We derive and numerically solve an exact
+equation for the evolution of the system in both finite and infinite well-mixed
+populations, finding the conditions for stable coexistence between both
+species. Furthermore, by varying the different parameters, we found a scenario
+of bifurcations that leads the system from dominating hawks and coexistence to
+bi-stability, multiple interior equilibria and dominating doves.
+",0,1,0,0,0,0
+657,A global model for predicting the arrival of imported dengue infections,"  With approximately half of the world's population at risk of contracting
+dengue, this mosquito-borne disease is of global concern. International
+travellers significantly contribute to dengue's rapid and large-scale spread by
+importing the disease from endemic into non-endemic countries. To prevent
+future outbreaks and dengue from establishing in non-endemic countries,
+knowledge about the arrival time and location of infected travellers is
+crucial. We propose a network model that predicts the monthly number of dengue
+infected air passengers arriving at any given airport. We consider
+international air travel volumes, monthly dengue incidence rates and temporal
+infection dynamics. Our findings shed light onto dengue importation routes and
+reveal country-specific reporting rates that have been until now largely
+unknown.
+",1,0,0,0,1,0
+658,Contextually Customized Video Summaries via Natural Language,"  The best summary of a long video differs among different people due to its
+highly subjective nature. Even for the same person, the best summary may change
+with time or mood. In this paper, we introduce the task of generating
+customized video summaries through simple text. First, we train a deep
+architecture to effectively learn semantic embeddings of video frames by
+leveraging the abundance of image-caption data via a progressive and residual
+manner. Given a user-specific text description, our algorithm is able to select
+semantically relevant video segments and produce a temporally aligned video
+summary. In order to evaluate our textually customized video summaries, we
+conduct experimental comparison with baseline methods that utilize ground-truth
+information. Despite the challenging baselines, our method still manages to
+show comparable or even exceeding performance. We also show that our method is
+able to generate semantically diverse video summaries by only utilizing the
+learned visual embeddings.
+",1,0,0,0,0,0
+659,Observation of surface plasmon polaritons in 2D electron gas of surface electron accumulation in InN nanostructures,"  Recently, heavily doped semiconductors are emerging as an alternate for low
+loss plasmonic materials. InN, belonging to the group III nitrides, possesses
+the unique property of surface electron accumulation (SEA) which provides two
+dimensional electron gas (2DEG) system. In this report, we demonstrated the
+surface plasmon properties of InN nanoparticles originating from SEA using the
+real space mapping of the surface plasmon fields for the first time. The SEA is
+confirmed by Raman studies which are further corroborated by photoluminescence
+and photoemission spectroscopic studies. The frequency of 2DEG corresponding to
+SEA is found to be in the THz region. The periodic fringes are observed in the
+near-field scanning optical microscopic images of InN nanostructures. The
+observed fringes are attributed to the interference of propagated and back
+reflected surface plasmon polaritons (SPPs). The observation of SPPs is solely
+attributed to the 2DEG corresponding to the SEA of InN. In addition, resonance
+kind of behavior with the enhancement of the near-field intensity is observed
+in the near-field images of InN nanostructures. Observation of SPPs indicates
+that InN with SEA can be a promising THz plasmonic material for the light
+confinement.
+",0,1,0,0,0,0
+660,Asymmetric Mach-Zehnder atom interferometers,"  It is shown that using beam splitters with non-equal wave vectors results in
+a new recoil diagram which is qualitatively different from the well-known
+diagram associated with the Mach-Zehnder atom interferometer. We predict a new
+asymmetric Mach-Zehnder atom interferometer (AMZAI) and study it when one uses
+a Raman beam splitter. The main feature is that the phase of AMZAI contains a
+quantum part proportional to the recoil frequency. A response sensitive only to
+the quantum phase was found. A new technique to measure the recoil frequency
+and fine structure constant is proposed and studied outside of the Raman-Nath
+approximation.
+",0,1,0,0,0,0
+661,Partial Information Stochastic Differential Games for Backward Stochastic Systems Driven By Lévy Processes,"  In this paper, we consider a partial information two-person zero-sum
+stochastic differential game problem where the system is governed by a backward
+stochastic differential equation driven by Teugels martingales associated with
+a Lévy process and an independent Brownian motion. One sufficient (a
+verification theorem) and one necessary conditions for the existence of optimal
+controls are proved. To illustrate the general results, a linear quadratic
+stochastic differential game problem is discussed.
+",0,0,1,0,0,0
+662,Inter-Session Modeling for Session-Based Recommendation,"  In recent years, research has been done on applying Recurrent Neural Networks
+(RNNs) as recommender systems. Results have been promising, especially in the
+session-based setting where RNNs have been shown to outperform state-of-the-art
+models. In many of these experiments, the RNN could potentially improve the
+recommendations by utilizing information about the user's past sessions, in
+addition to its own interactions in the current session. A problem for
+session-based recommendation, is how to produce accurate recommendations at the
+start of a session, before the system has learned much about the user's current
+interests. We propose a novel approach that extends a RNN recommender to be
+able to process the user's recent sessions, in order to improve
+recommendations. This is done by using a second RNN to learn from recent
+sessions, and predict the user's interest in the current session. By feeding
+this information to the original RNN, it is able to improve its
+recommendations. Our experiments on two different datasets show that the
+proposed approach can significantly improve recommendations throughout the
+sessions, compared to a single RNN working only on the current session. The
+proposed model especially improves recommendations at the start of sessions,
+and is therefore able to deal with the cold start problem within sessions.
+",1,0,0,0,0,0
+663,Multiscale Modeling of Shock Wave Localization in Porous Energetic Material,"  Shock wave interactions with defects, such as pores, are known to play a key
+role in the chemical initiation of energetic materials. The shock response of
+hexanitrostilbene is studied through a combination of large scale reactive
+molecular dynamics and mesoscale hydrodynamic simulations. In order to extend
+our simulation capability at the mesoscale to include weak shock conditions (<
+6 GPa), atomistic simulations of pore collapse are used to define a strain rate
+dependent strength model. Comparing these simulation methods allows us to
+impose physically-reasonable constraints on the mesoscale model parameters. In
+doing so, we have been able to study shock waves interacting with pores as a
+function of this viscoplastic material response. We find that the pore collapse
+behavior of weak shocks is characteristically different to that of strong
+shocks.
+",0,1,0,0,0,0
+664,Cryptoasset Factor Models,"  We propose factor models for the cross-section of daily cryptoasset returns
+and provide source code for data downloads, computing risk factors and
+backtesting them out-of-sample. In ""cryptoassets"" we include all
+cryptocurrencies and a host of various other digital assets (coins and tokens)
+for which exchange market data is available. Based on our empirical analysis,
+we identify the leading factor that appears to strongly contribute into daily
+cryptoasset returns. Our results suggest that cross-sectional statistical
+arbitrage trading may be possible for cryptoassets subject to efficient
+executions and shorting.
+",0,0,0,0,0,1
+665,Multi-dimensional Graph Fourier Transform,"  Many signals on Cartesian product graphs appear in the real world, such as
+digital images, sensor observation time series, and movie ratings on Netflix.
+These signals are ""multi-dimensional"" and have directional characteristics
+along each factor graph. However, the existing graph Fourier transform does not
+distinguish these directions, and assigns 1-D spectra to signals on product
+graphs. Further, these spectra are often multi-valued at some frequencies. Our
+main result is a multi-dimensional graph Fourier transform that solves such
+problems associated with the conventional GFT. Using algebraic properties of
+Cartesian products, the proposed transform rearranges 1-D spectra obtained by
+the conventional GFT into the multi-dimensional frequency domain, of which each
+dimension represents a directional frequency along each factor graph. Thus, the
+multi-dimensional graph Fourier transform enables directional frequency
+analysis, in addition to frequency analysis with the conventional GFT.
+Moreover, this rearrangement resolves the multi-valuedness of spectra in some
+cases. The multi-dimensional graph Fourier transform is a foundation of novel
+filterings and stationarities that utilize dimensional information of graph
+signals, which are also discussed in this study. The proposed methods are
+applicable to a wide variety of data that can be regarded as signals on
+Cartesian product graphs. This study also notes that multivariate graph signals
+can be regarded as 2-D univariate graph signals. This correspondence provides
+natural definitions of the multivariate graph Fourier transform and the
+multivariate stationarity based on their 2-D univariate versions.
+",1,0,0,1,0,0
+666,Criteria for the Application of Double Exponential Transformation,"  The double exponential formula was introduced for calculating definite
+integrals with singular point oscillation functions and Fourier-integrals. The
+double exponential transformation is not only useful for numerical computations
+but it is also used in different methods of Sinc theory. In this paper we use
+double exponential transformation for calculating particular improper
+integrals. By improving integral estimates having singular final points. By
+comparison between double exponential transformations and single exponential
+transformations it is proved that the error margin of double exponential
+transformations is smaller. Finally Fourier-integral and double exponential
+transformations are discussed.
+",0,0,1,0,0,0
+667,Ultra-high strain in epitaxial silicon carbide nanostructures utilizing residual stress amplification,"  Strain engineering has attracted great attention, particularly for epitaxial
+films grown on a different substrate. Residual strains of SiC have been widely
+employed to form ultra-high frequency and high Q factor resonators. However, to
+date the highest residual strain of SiC was reported to be limited to
+approximately 0.6%. Large strains induced into SiC could lead to several
+interesting physical phenomena, as well as significant improvement of resonant
+frequencies. We report an unprecedented nano strain-amplifier structure with an
+ultra-high residual strain up to 8% utilizing the natural residual stress
+between epitaxial 3C SiC and Si. In addition, the applied strain can be tuned
+by changing the dimensions of the amplifier structure. The possibility of
+introducing such a controllable and ultra-high strain will open the door to
+investigating the physics of SiC in large strain regimes, and the development
+of ultra sensitive mechanical sensors.
+",0,1,0,0,0,0
+668,The Diverse Club: The Integrative Core of Complex Networks,"  A complex system can be represented and analyzed as a network, where nodes
+represent the units of the network and edges represent connections between
+those units. For example, a brain network represents neurons as nodes and axons
+between neurons as edges. In many networks, some nodes have a
+disproportionately high number of edges. These nodes also have many edges
+between each other, and are referred to as the rich club. In many different
+networks, the nodes of this club are assumed to support global network
+integration. However, another set of nodes potentially exhibits a connectivity
+structure that is more advantageous to global network integration. Here, in a
+myriad of different biological and man-made networks, we discover the diverse
+club--a set of nodes that have edges diversely distributed across the network.
+The diverse club exhibits, to a greater extent than the rich club, properties
+consistent with an integrative network function--these nodes are more highly
+interconnected and their edges are more critical for efficient global
+integration. Moreover, we present a generative evolutionary network model that
+produces networks with a diverse club but not a rich club, thus demonstrating
+that these two clubs potentially evolved via distinct selection pressures.
+Given the variety of different networks that we analyzed--the c. elegans, the
+macaque brain, the human brain, the United States power grid, and global air
+traffic--the diverse club appears to be ubiquitous in complex networks. These
+results warrant the distinction and analysis of two critical clubs of nodes in
+all complex systems.
+",0,1,0,0,0,0
+669,Bayesian Semisupervised Learning with Deep Generative Models,"  Neural network based generative models with discriminative components are a
+powerful approach for semi-supervised learning. However, these techniques a)
+cannot account for model uncertainty in the estimation of the model's
+discriminative component and b) lack flexibility to capture complex stochastic
+patterns in the label generation process. To avoid these problems, we first
+propose to use a discriminative component with stochastic inputs for increased
+noise flexibility. We show how an efficient Gibbs sampling procedure can
+marginalize the stochastic inputs when inferring missing labels in this model.
+Following this, we extend the discriminative component to be fully Bayesian and
+produce estimates of uncertainty in its parameter values. This opens the door
+for semi-supervised Bayesian active learning.
+",0,0,0,1,0,0
+670,Robust Detection of Covariate-Treatment Interactions in Clinical Trials,"  Detection of interactions between treatment effects and patient descriptors
+in clinical trials is critical for optimizing the drug development process. The
+increasing volume of data accumulated in clinical trials provides a unique
+opportunity to discover new biomarkers and further the goal of personalized
+medicine, but it also requires innovative robust biomarker detection methods
+capable of detecting non-linear, and sometimes weak, signals. We propose a set
+of novel univariate statistical tests, based on the theory of random walks,
+which are able to capture non-linear and non-monotonic covariate-treatment
+interactions. We also propose a novel combined test, which leverages the power
+of all of our proposed univariate tests into a single general-case tool. We
+present results for both synthetic trials as well as real-world clinical
+trials, where we compare our method with state-of-the-art techniques and
+demonstrate the utility and robustness of our approach.
+",0,0,0,1,0,0
+671,The Future of RICH Detectors through the Light of the LHCb RICH,"  The limitations in performance of the present RICH system in the LHCb
+experiment are given by the natural chromatic dispersion of the gaseous
+Cherenkov radiator, the aberrations of the optical system and the pixel size of
+the photon detectors. Moreover, the overall PID performance can be affected by
+high detector occupancy as the pattern recognition becomes more difficult with
+high particle multiplicities. This paper shows a way to improve performance by
+systematically addressing each of the previously mentioned limitations. These
+ideas are applied in the present and future upgrade phases of the LHCb
+experiment. Although applied to specific circumstances, they are used as a
+paradigm on what is achievable in the development and realisation of high
+precision RICH detectors.
+",0,1,0,0,0,0
+672,Stability of Valuations: Higher Rational Rank,"  Given a klt singularity $x\in (X, D)$, we show that a quasi-monomial
+valuation $v$ with a finitely generated associated graded ring is the minimizer
+of the normalized volume function $\widehat{\rm vol}_{(X,D),x}$, if and only if
+$v$ induces a degeneration to a K-semistable log Fano cone singularity.
+Moreover, such a minimizer is unique among all quasi-monomial valuations up to
+rescaling. As a consequence, we prove that for a klt singularity $x\in X$ on
+the Gromov-Hausdorff limit of Kähler-Einstein Fano manifolds, the
+intermediate K-semistable cone associated to its metric tangent cone is
+uniquely determined by the algebraic structure of $x\in X$, hence confirming a
+conjecture by Donaldson-Sun.
+",0,0,1,0,0,0
+673,Higgs mode and its decay in a two dimensional antiferromagnet,"  Condensed-matter analogs of the Higgs boson in particle physics allow
+insights into its behavior in different symmetries and dimensionalities.
+Evidence for the Higgs mode has been reported in a number of different
+settings, including ultracold atomic gases, disordered superconductors, and
+dimerized quantum magnets. However, decay processes of the Higgs mode (which
+are eminently important in particle physics) have not yet been studied in
+condensed matter due to the lack of a suitable material system coupled to a
+direct experimental probe. A quantitative understanding of these processes is
+particularly important for low-dimensional systems where the Higgs mode decays
+rapidly and has remained elusive to most experimental probes. Here, we discover
+and study the Higgs mode in a two-dimensional antiferromagnet using
+spin-polarized inelastic neutron scattering. Our spin-wave spectra of
+Ca$_2$RuO$_4$ directly reveal a well-defined, dispersive Higgs mode, which
+quickly decays into transverse Goldstone modes at the antiferromagnetic
+ordering wavevector. Through a complete mapping of the transverse modes in the
+reciprocal space, we uniquely specify the minimal model Hamiltonian and
+describe the decay process. We thus establish a novel condensed matter platform
+for research on the dynamics of the Higgs mode.
+",0,1,0,0,0,0
+674,Robust and Efficient Boosting Method using the Conditional Risk,"  Well-known for its simplicity and effectiveness in classification, AdaBoost,
+however, suffers from overfitting when class-conditional distributions have
+significant overlap. Moreover, it is very sensitive to noise that appears in
+the labels. This article tackles the above limitations simultaneously via
+optimizing a modified loss function (i.e., the conditional risk). The proposed
+approach has the following two advantages. (1) It is able to directly take into
+account label uncertainty with an associated label confidence. (2) It
+introduces a ""trustworthiness"" measure on training samples via the Bayesian
+risk rule, and hence the resulting classifier tends to have finite sample
+performance that is superior to that of the original AdaBoost when there is a
+large overlap between class conditional distributions. Theoretical properties
+of the proposed method are investigated. Extensive experimental results using
+synthetic data and real-world data sets from UCI machine learning repository
+are provided. The empirical study shows the high competitiveness of the
+proposed method in predication accuracy and robustness when compared with the
+original AdaBoost and several existing robust AdaBoost algorithms.
+",0,0,0,1,0,0
+675,High Dimensional Robust Estimation of Sparse Models via Trimmed Hard Thresholding,"  We study the problem of sparsity constrained $M$-estimation with arbitrary
+corruptions to both {\em explanatory and response} variables in the
+high-dimensional regime, where the number of variables $d$ is larger than the
+sample size $n$. Our main contribution is a highly efficient gradient-based
+optimization algorithm that we call Trimmed Hard Thresholding -- a robust
+variant of Iterative Hard Thresholding (IHT) by using trimmed mean in gradient
+computations. Our algorithm can deal with a wide class of sparsity constrained
+$M$-estimation problems, and we can tolerate a nearly dimension independent
+fraction of arbitrarily corrupted samples. More specifically, when the
+corrupted fraction satisfies $\epsilon \lesssim {1} /\left({\sqrt{k} \log
+(nd)}\right)$, where $k$ is the sparsity of the parameter, we obtain accurate
+estimation and model selection guarantees with optimal sample complexity.
+Furthermore, we extend our algorithm to sparse Gaussian graphical model
+(precision matrix) estimation via a neighborhood selection approach. We
+demonstrate the effectiveness of robust estimation in sparse linear, logistic
+regression, and sparse precision matrix estimation on synthetic and real-world
+US equities data.
+",1,0,1,1,0,0
+676,Stop talking to me -- a communication-avoiding ADER-DG realisation,"  We present a communication- and data-sensitive formulation of ADER-DG for
+hyperbolic differential equation systems. Sensitive here has multiple flavours:
+First, the formulation reduces the persistent memory footprint. This reduces
+pressure on the memory subsystem. Second, the formulation realises the
+underlying predictor-corrector scheme with single-touch semantics, i.e., each
+degree of freedom is read on average only once per time step from the main
+memory. This reduces communication through the memory controllers. Third, the
+formulation breaks up the tight coupling of the explicit time stepping's
+algorithmic steps to mesh traversals. This averages out data access peaks.
+Different operations and algorithmic steps are ran on different grid entities.
+Finally, the formulation hides distributed memory data transfer behind the
+computation aligned with the mesh traversal. This reduces pressure on the
+machine interconnects. All techniques applied by our formulation are elaborated
+by means of a rigorous task formalism. They break up ADER-DG's tight causal
+coupling of compute steps and can be generalised to other predictor-corrector
+schemes.
+",1,0,0,0,0,0
+677,The effect of prior probabilities on quantification and propagation of imprecise probabilities resulting from small datasets,"  This paper outlines a methodology for Bayesian multimodel uncertainty
+quantification (UQ) and propagation and presents an investigation into the
+effect of prior probabilities on the resulting uncertainties. The UQ
+methodology is adapted from the information-theoretic method previously
+presented by the authors (Zhang and Shields, 2018) to a fully Bayesian
+construction that enables greater flexibility in quantifying uncertainty in
+probability model form. Being Bayesian in nature and rooted in UQ from small
+datasets, prior probabilities in both probability model form and model
+parameters are shown to have a significant impact on quantified uncertainties
+and, consequently, on the uncertainties propagated through a physics-based
+model. These effects are specifically investigated for a simplified plate
+buckling problem with uncertainties in material properties derived from a small
+number of experiments using noninformative priors and priors derived from past
+studies of varying appropriateness. It is illustrated that prior probabilities
+can have a significant impact on multimodel UQ for small datasets and
+inappropriate (but seemingly reasonable) priors may even have lingering effects
+that bias probabilities even for large datasets. When applied to uncertainty
+propagation, this may result in probability bounds on response quantities that
+do not include the true probabilities.
+",0,0,0,1,0,0
+678,Hierarchical loss for classification,"  Failing to distinguish between a sheepdog and a skyscraper should be worse
+and penalized more than failing to distinguish between a sheepdog and a poodle;
+after all, sheepdogs and poodles are both breeds of dogs. However, existing
+metrics of failure (so-called ""loss"" or ""win"") used in textual or visual
+classification/recognition via neural networks seldom view a sheepdog as more
+similar to a poodle than to a skyscraper. We define a metric that, inter alia,
+can penalize failure to distinguish between a sheepdog and a skyscraper more
+than failure to distinguish between a sheepdog and a poodle. Unlike previously
+employed possibilities, this metric is based on an ultrametric tree associated
+with any given tree organization into a semantically meaningful hierarchy of a
+classifier's classes.
+",1,0,0,1,0,0
+679,"An efficient data structure for counting all linear extensions of a poset, calculating its jump number, and the likes","  Achieving the goals in the title (and others) relies on a cardinality-wise
+scanning of the ideals of the poset. Specifically, the relevant numbers
+attached to the k+1 element ideals are inferred from the corresponding numbers
+of the k-element (order) ideals. Crucial in all of this is a compressed
+representation (using wildcards) of the ideal lattice. The whole scheme invites
+distributed computation.
+",1,0,0,0,0,0
+680,Perception-in-the-Loop Adversarial Examples,"  We present a scalable, black box, perception-in-the-loop technique to find
+adversarial examples for deep neural network classifiers. Black box means that
+our procedure only has input-output access to the classifier, and not to the
+internal structure, parameters, or intermediate confidence values.
+Perception-in-the-loop means that the notion of proximity between inputs can be
+directly queried from human participants rather than an arbitrarily chosen
+metric. Our technique is based on covariance matrix adaptation evolution
+strategy (CMA-ES), a black box optimization approach. CMA-ES explores the
+search space iteratively in a black box manner, by generating populations of
+candidates according to a distribution, choosing the best candidates according
+to a cost function, and updating the posterior distribution to favor the best
+candidates. We run CMA-ES using human participants to provide the fitness
+function, using the insight that the choice of best candidates in CMA-ES can be
+naturally modeled as a perception task: pick the top $k$ inputs perceptually
+closest to a fixed input. We empirically demonstrate that finding adversarial
+examples is feasible using small populations and few iterations. We compare the
+performance of CMA-ES on the MNIST benchmark with other black-box approaches
+using $L_p$ norms as a cost function, and show that it performs favorably both
+in terms of success in finding adversarial examples and in minimizing the
+distance between the original and the adversarial input. In experiments on the
+MNIST, CIFAR10, and GTSRB benchmarks, we demonstrate that CMA-ES can find
+perceptually similar adversarial inputs with a small number of iterations and
+small population sizes when using perception-in-the-loop. Finally, we show that
+networks trained specifically to be robust against $L_\infty$ norm can still be
+susceptible to perceptually similar adversarial examples.
+",1,0,0,1,0,0
+681,Deep Fluids: A Generative Network for Parameterized Fluid Simulations,"  This paper presents a novel generative model to synthesize fluid simulations
+from a set of reduced parameters. A convolutional neural network is trained on
+a collection of discrete, parameterizable fluid simulation velocity fields. Due
+to the capability of deep learning architectures to learn representative
+features of the data, our generative model is able to accurately approximate
+the training data set, while providing plausible interpolated in-betweens. The
+proposed generative model is optimized for fluids by a novel loss function that
+guarantees divergence-free velocity fields at all times. In addition, we
+demonstrate that we can handle complex parameterizations in reduced spaces, and
+advance simulations in time by integrating in the latent space with a second
+network. Our method models a wide variety of fluid behaviors, thus enabling
+applications such as fast construction of simulations, interpolation of fluids
+with different parameters, time re-sampling, latent space simulations, and
+compression of fluid simulation data. Reconstructed velocity fields are
+generated up to 700x faster than traditional CPU solvers, while achieving
+compression rates of over 1300x.
+",0,0,0,1,0,0
+682,Bias Reduction in Instrumental Variable Estimation through First-Stage Shrinkage,"  The two-stage least-squares (2SLS) estimator is known to be biased when its
+first-stage fit is poor. I show that better first-stage prediction can
+alleviate this bias. In a two-stage linear regression model with Normal noise,
+I consider shrinkage in the estimation of the first-stage instrumental variable
+coefficients. For at least four instrumental variables and a single endogenous
+regressor, I establish that the standard 2SLS estimator is dominated with
+respect to bias. The dominating IV estimator applies James-Stein type shrinkage
+in a first-stage high-dimensional Normal-means problem followed by a
+control-function approach in the second stage. It preserves invariances of the
+structural instrumental variable equations.
+",0,0,1,1,0,0
+683,An Unsupervised Learning Classifier with Competitive Error Performance,"  An unsupervised learning classification model is described. It achieves
+classification error probability competitive with that of popular supervised
+learning classifiers such as SVM or kNN. The model is based on the incremental
+execution of small step shift and rotation operations upon selected
+discriminative hyperplanes at the arrival of input samples. When applied, in
+conjunction with a selected feature extractor, to a subset of the ImageNet
+dataset benchmark, it yields 6.2 % Top 3 probability of error; this exceeds by
+merely about 2 % the result achieved by (supervised) k-Nearest Neighbor, both
+using same feature extractor. This result may also be contrasted with popular
+unsupervised learning schemes such as k-Means which is shown to be practically
+useless on same dataset.
+",0,0,0,1,0,0
+684,Exploring the predictability of range-based volatility estimators using RNNs,"  We investigate the predictability of several range-based stock volatility
+estimators, and compare them to the standard close-to-close estimator which is
+most commonly acknowledged as the volatility. The patterns of volatility
+changes are analyzed using LSTM recurrent neural networks, which are a state of
+the art method of sequence learning. We implement the analysis on all current
+constituents of the Dow Jones Industrial Average index, and report averaged
+evaluation results. We find that changes in the values of range-based
+estimators are more predictable than that of the estimator using daily closing
+values only.
+",0,0,0,1,0,1
+685,Mean squared displacement and sinuosity of three-dimensional random search movements,"  Correlated random walks (CRW) have been used for a long time as a null model
+for animal's random search movement in two dimensions (2D). An increasing
+number of studies focus on animals' movement in three dimensions (3D), but the
+key properties of CRW, such as the way the mean squared displacement is related
+to the path length, are well known only in 1D and 2D. In this paper I derive
+such properties for 3D CRW, in a consistent way with the expression of these
+properties in 2D. This should allow 3D CRW to act as a null model when
+analyzing actual 3D movements similarly to what is done in 2D
+",0,0,0,0,1,0
+686,Context-Aware Pedestrian Motion Prediction In Urban Intersections,"  This paper presents a novel context-based approach for pedestrian motion
+prediction in crowded, urban intersections, with the additional flexibility of
+prediction in similar, but new, environments. Previously, Chen et. al. combined
+Markovian-based and clustering-based approaches to learn motion primitives in a
+grid-based world and subsequently predict pedestrian trajectories by modeling
+the transition between learned primitives as a Gaussian Process (GP). This work
+extends that prior approach by incorporating semantic features from the
+environment (relative distance to curbside and status of pedestrian traffic
+lights) in the GP formulation for more accurate predictions of pedestrian
+trajectories over the same timescale. We evaluate the new approach on
+real-world data collected using one of the vehicles in the MIT Mobility On
+Demand fleet. The results show 12.5% improvement in prediction accuracy and a
+2.65 times reduction in Area Under the Curve (AUC), which is used as a metric
+to quantify the span of predicted set of trajectories, such that a lower AUC
+corresponds to a higher level of confidence in the future direction of
+pedestrian motion.
+",1,0,0,1,0,0
+687,EnergyNet: Energy-based Adaptive Structural Learning of Artificial Neural Network Architectures,"  We present E NERGY N ET , a new framework for analyzing and building
+artificial neural network architectures. Our approach adaptively learns the
+structure of the networks in an unsupervised manner. The methodology is based
+upon the theoretical guarantees of the energy function of restricted Boltzmann
+machines (RBM) of infinite number of nodes. We present experimental results to
+show that the final network adapts to the complexity of a given problem.
+",1,0,0,0,0,0
+688,Local Algorithms for Hierarchical Dense Subgraph Discovery,"  Finding the dense regions of a graph and relations among them is a
+fundamental problem in network analysis. Core and truss decompositions reveal
+dense subgraphs with hierarchical relations. The incremental nature of
+algorithms for computing these decompositions and the need for global
+information at each step of the algorithm hinders scalable parallelization and
+approximations since the densest regions are not revealed until the end. In a
+previous work, Lu et al. proposed to iteratively compute the $h$-indices of
+neighbor vertex degrees to obtain the core numbers and prove that the
+convergence is obtained after a finite number of iterations. This work
+generalizes the iterative $h$-index computation for truss decomposition as well
+as nucleus decomposition which leverages higher-order structures to generalize
+core and truss decompositions. In addition, we prove convergence bounds on the
+number of iterations. We present a framework of local algorithms to obtain the
+core, truss, and nucleus decompositions. Our algorithms are local, parallel,
+offer high scalability, and enable approximations to explore time and quality
+trade-offs. Our shared-memory implementation verifies the efficiency,
+scalability, and effectiveness of our local algorithms on real-world networks.
+",1,0,0,0,0,0
+689,Robust Gesture-Based Communication for Underwater Human-Robot Interaction in the context of Search and Rescue Diver Missions,"  We propose a robust gesture-based communication pipeline for divers to
+instruct an Autonomous Underwater Vehicle (AUV) to assist them in performing
+high-risk tasks and helping in case of emergency. A gesture communication
+language (CADDIAN) is developed, based on consolidated and standardized diver
+gestures, including an alphabet, syntax and semantics, ensuring a logical
+consistency. A hierarchical classification approach is introduced for hand
+gesture recognition based on stereo imagery and multi-descriptor aggregation to
+specifically cope with underwater image artifacts, e.g. light backscatter or
+color attenuation. Once the classification task is finished, a syntax check is
+performed to filter out invalid command sequences sent by the diver or
+generated by errors in the classifier. Throughout this process, the diver
+receives constant feedback from an underwater tablet to acknowledge or abort
+the mission at any time. The objective is to prevent the AUV from executing
+unnecessary, infeasible or potentially harmful motions. Experimental results
+under different environmental conditions in archaeological exploration and
+bridge inspection applications show that the system performs well in the field.
+",1,0,0,0,0,0
+690,High-$T_\textrm {C}$ superconductivity in Cs$_3$C$_{60}$ compounds governed by local Cs-C$_{60}$ Coulomb interactions,"  Unique among alkali-doped $\textit {A}$$_3$C$_{60}$ fullerene compounds, the
+A15 and fcc forms of Cs$_3$C$_{60}$ exhibit superconducting states varying
+under hydrostatic pressure with highest transition temperatures at $T_\textrm
+{C}$$^\textrm {meas}$ = 38.3 and 35.2 K, respectively. Herein it is argued that
+these two compounds under pressure represent the optimal materials of the
+$\textit {A}$$_3$C$_{60}$ family, and that the C$_{60}$-associated
+superconductivity is mediated through Coulombic interactions with charges on
+the alkalis. A derivation of the interlayer Coulombic pairing model of
+high-$T_\textrm {C}$ superconductivity employing non-planar geometry is
+introduced, generalizing the picture of two interacting layers to an
+interaction between charge reservoirs located on the C$_{60}$ and alkali ions.
+The optimal transition temperature follows the algebraic expression, $T_\textrm
+{C0}$ = (12.474 nm$^2$ K)/$\ell$${\zeta}$, where $\ell$ relates to the mean
+spacing between interacting surface charges on the C$_{60}$ and ${\zeta}$ is
+the average radial distance between the C$_{60}$ surface and the neighboring Cs
+ions. Values of $T_\textrm {C0}$ for the measured cation stoichiometries of
+Cs$_{3-\textrm{x}}$C$_{60}$ with x $\approx$ 0 are found to be 38.19 and 36.88
+K for the A15 and fcc forms, respectively, with the dichotomy in transition
+temperature reflecting the larger ${\zeta}$ and structural disorder in the fcc
+form. In the A15 form, modeled interacting charges and Coulomb potential
+e$^2$/${\zeta}$ are shown to agree quantitatively with findings from
+nuclear-spin relaxation and mid-infrared optical conductivity. In the fcc form,
+suppression of $T_\textrm {C}$$^\textrm {meas}$ below $T_\textrm {C0}$ is
+ascribed to native structural disorder. Phononic effects in conjunction with
+Coulombic pairing are discussed.
+",0,1,0,0,0,0
+691,Analysis and mitigation of interface losses in trenched superconducting coplanar waveguide resonators,"  Improving the performance of superconducting qubits and resonators generally
+results from a combination of materials and fabrication process improvements
+and design modifications that reduce device sensitivity to residual losses. One
+instance of this approach is to use trenching into the device substrate in
+combination with superconductors and dielectrics with low intrinsic losses to
+improve quality factors and coherence times. Here we demonstrate titanium
+nitride coplanar waveguide resonators with mean quality factors exceeding two
+million and controlled trenching reaching 2.2 $\mu$m into the silicon
+substrate. Additionally, we measure sets of resonators with a range of sizes
+and trench depths and compare these results with finite-element simulations to
+demonstrate quantitative agreement with a model of interface dielectric loss.
+We then apply this analysis to determine the extent to which trenching can
+improve resonator performance.
+",0,1,0,0,0,0
+692,Recent Operation of the FNAL Magnetron $H^{-}$ Ion Source,"  This paper will detail changes in the operational paradigm of the Fermi
+National Accelerator Laboratory (FNAL) magnetron $H^{-}$ ion source due to
+upgrades in the accelerator system. Prior to November of 2012 the $H^{-}$ ions
+for High Energy Physics (HEP) experiments were extracted at ~18 keV vertically
+downward into a 90 degree bending magnet and accelerated through a
+Cockcroft-Walton accelerating column to 750 keV. Following the upgrade in the
+fall of 2012 the $H^{-}$ ions are now directly extracted from a magnetron at 35
+keV and accelerated to 750 keV by a Radio Frequency Quadrupole (RFQ). This
+change in extraction energy as well as the orientation of the ion source
+required not only a redesign of the ion source, but an updated understanding of
+its operation at these new values. Discussed in detail are the changes to the
+ion source timing, arc discharge current, hydrogen gas pressure, and cesium
+delivery system that were needed to maintain consistent operation at >99%
+uptime for HEP, with an increased ion source lifetime of over 9 months.
+",0,1,0,0,0,0
+693,A Ball Breaking Away from a Fluid,"  We consider the withdrawal of a ball from a fluid reservoir to understand the
+longevity of the connection between that ball and the fluid it breaks away
+from, at intermediate Reynolds numbers. Scaling arguments based on the
+processes observed as the ball interacts with the fluid surface were applied to
+the `pinch-off time', when the ball breaks its connection with the fluid from
+which it has been withdrawn, measured experimentally. At the lowest Reynolds
+numbers tested, pinch-off occurs in a `surface seal' close to the reservoir
+surface, where at larger Reynolds numbers pinch-off occurs in an `ejecta seal'
+close to the ball. Our scaling analysis shows that the connection between ball
+and fluid is controlled by the fluid film draining from the ball as it
+continues to be winched away from the fluid reservoir. The draining flow itself
+depends on the amount of fluid coating the ball on exit from the reservoir. We
+consider the possibilities that this coating was created through: a surface
+tension driven Landau Levitch Derjaguin wetting of the surface; a
+visco-inertial quick coating; or alternatively through the inertia of the fluid
+moving with the ball through the reservoir. We show that although the pinch-off
+mechanism is controlled by viscosity, the coating mechanism is governed by a
+different length and timescale, dictated by the inertial added mass of the ball
+when submersed.
+",0,1,0,0,0,0
+694,Unveiling the internal entanglement structure of the Kondo singlet,"  We disentangle all the individual degrees of freedom in the quantum impurity
+problem to deconstruct the Kondo singlet, both in real and energy space, by
+studying the contribution of each individual free electron eigenstate. This is
+a problem of two spins coupled to a bath, where the bath is formed by the
+remaining conduction electrons. Being a mixed state, we resort to the
+""concurrence"" to quantify entanglement. We identify ""projected natural
+orbitals"" that allow us to individualize a single-particle electronic wave
+function that is responsible of more than $90\%$ of the impurity screening. In
+the weak coupling regime, the impurity is entangled to an electron at the Fermi
+level, while in the strong coupling regime, the impurity counterintuitively
+entangles mostly with the high energy electrons and disentangles completely
+from the low-energy states carving a ""hole"" around the Fermi level. This
+enables one to use concurrence as a pseudo order parameter to compute the
+characteristic ""size"" of the Kondo cloud, beyond which electrons are are weakly
+correlated to the impurity and are dominated by the physics of the boundary.
+",0,1,0,0,0,0
+695,A parallel orbital-updating based plane-wave basis method for electronic structure calculations,"  Motivated by the recently proposed parallel orbital-updating approach in real
+space method, we propose a parallel orbital-updating based plane-wave basis
+method for electronic structure calculations, for solving the corresponding
+eigenvalue problems. In addition, we propose two new modified parallel
+orbital-updating methods. Compared to the traditional plane-wave methods, our
+methods allow for two-level parallelization, which is particularly interesting
+for large scale parallelization. Numerical experiments show that these new
+methods are more reliable and efficient for large scale calculations on modern
+supercomputers
+",0,1,1,0,0,0
+696,Dynamics of the multi-soliton waves in the sine-Gordon model with two identical point impurities,"  The particular type of four-kink multi-solitons (or quadrons) adiabatic
+dynamics of the sine-Gordon equation in a model with two identical point
+attracting impurities has been studied. This model can be used for describing
+magnetization localized waves in multilayer ferromagnet. The quadrons structure
+and properties has been numerically investigated. The cases of both large and
+small distances between impurities has been viewed. The dependence of the
+localized in impurity region nonlinear high-amplitude waves frequencies on the
+distance between the impurities has been found. For an analytical description
+of two bound localized on impurities nonlinear waves dynamics, using
+perturbation theory, the system of differential equations for harmonic
+oscillators with elastic link has been found. The analytical model
+qualitatively describes the results of the sine-Gordon equation numerical
+simulation.
+",0,1,0,0,0,0
+697,Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder,"  Estimates of the Hubble constant, $H_0$, from the distance ladder and the
+cosmic microwave background (CMB) differ at the $\sim$3-$\sigma$ level,
+indicating a potential issue with the standard $\Lambda$CDM cosmology.
+Interpreting this tension correctly requires a model comparison calculation
+depending on not only the traditional `$n$-$\sigma$' mismatch but also the
+tails of the likelihoods. Determining the form of the tails of the local $H_0$
+likelihood is impossible with the standard Gaussian least-squares
+approximation, as it requires using non-Gaussian distributions to faithfully
+represent anchor likelihoods and model outliers in the Cepheid and supernova
+(SN) populations, and simultaneous fitting of the full distance-ladder dataset
+to correctly propagate uncertainties. We have developed a Bayesian hierarchical
+model that describes the full distance ladder, from nearby geometric anchors
+through Cepheids to Hubble-Flow SNe. This model does not rely on any
+distributions being Gaussian, allowing outliers to be modeled and obviating the
+need for arbitrary data cuts. Sampling from the $\sim$3000-parameter joint
+posterior using Hamiltonian Monte Carlo, we find $H_0$ = (72.72 $\pm$ 1.67)
+${\rm km\,s^{-1}\,Mpc^{-1}}$ when applied to the outlier-cleaned Riess et al.
+(2016) data, and ($73.15 \pm 1.78$) ${\rm km\,s^{-1}\,Mpc^{-1}}$ with SN
+outliers reintroduced. Our high-fidelity sampling of the low-$H_0$ tail of the
+distance-ladder likelihood allows us to apply Bayesian model comparison to
+assess the evidence for deviation from $\Lambda$CDM. We set up this comparison
+to yield a lower limit on the odds of the underlying model being $\Lambda$CDM
+given the distance-ladder and Planck XIII (2016) CMB data. The odds against
+$\Lambda$CDM are at worst 10:1 or 7:1, depending on whether the SNe outliers
+are cut or modeled, or 60:1 if an approximation to the Planck Int. XLVI (2016)
+likelihood is used.
+",0,1,0,0,0,0
+698,Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access,"  A multi-user multi-armed bandit (MAB) framework is used to develop algorithms
+for uncoordinated spectrum access. The number of users is assumed to be unknown
+to each user. A stochastic setting is first considered, where the rewards on a
+channel are the same for each user. In contrast to prior work, it is assumed
+that the number of users can possibly exceed the number of channels, and that
+rewards can be non-zero even under collisions. The proposed algorithm consists
+of an estimation phase and an allocation phase. It is shown that if every user
+adopts the algorithm, the system wide regret is constant with time with high
+probability. The regret guarantees hold for any number of users and channels,
+in particular, even when the number of users is less than the number of
+channels. Next, an adversarial multi-user MAB framework is considered, where
+the rewards on the channels are user-dependent. It is assumed that the number
+of users is less than the number of channels, and that the users receive zero
+reward on collision. The proposed algorithm combines the Exp3.P algorithm
+developed in prior work for single user adversarial bandits with a collision
+resolution mechanism to achieve sub-linear regret. It is shown that if every
+user employs the proposed algorithm, the system wide regret is of the order
+$O(T^\frac{3}{4})$ over a horizon of time $T$. The algorithms in both
+stochastic and adversarial scenarios are extended to the dynamic case where the
+number of users in the system evolves over time and are shown to lead to
+sub-linear regret.
+",0,0,0,1,0,0
+699,A Comparative Analysis of Contact Models in Trajectory Optimization for Manipulation,"  In this paper, we analyze the effects of contact models on contact-implicit
+trajectory optimization for manipulation. We consider three different
+approaches: (1) a contact model that is based on complementarity constraints,
+(2) a smooth contact model, and our proposed method (3) a variable smooth
+contact model. We compare these models in simulation in terms of physical
+accuracy, quality of motions, and computation time. In each case, the
+optimization process is initialized by setting all torque variables to zero,
+namely, without a meaningful initial guess. For simulations, we consider a
+pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our
+results demonstrate that the optimization based on the proposed variable smooth
+contact model provides a good trade-off between the physical fidelity and
+quality of motions at the cost of increased computation time.
+",1,0,0,0,0,0
+700,Computationally Efficient Measures of Internal Neuron Importance,"  The challenge of assigning importance to individual neurons in a network is
+of interest when interpreting deep learning models. In recent work, Dhamdhere
+et al. proposed Total Conductance, a ""natural refinement of Integrated
+Gradients"" for attributing importance to internal neurons. Unfortunately, the
+authors found that calculating conductance in tensorflow required the addition
+of several custom gradient operators and did not scale well. In this work, we
+show that the formula for Total Conductance is mathematically equivalent to
+Path Integrated Gradients computed on a hidden layer in the network. We provide
+a scalable implementation of Total Conductance using standard tensorflow
+gradient operators that we call Neuron Integrated Gradients. We compare Neuron
+Integrated Gradients to DeepLIFT, a pre-existing computationally efficient
+approach that is applicable to calculating internal neuron importance. We find
+that DeepLIFT produces strong empirical results and is faster to compute, but
+because it lacks the theoretical properties of Neuron Integrated Gradients, it
+may not always be preferred in practice. Colab notebook reproducing results:
+this http URL
+",0,0,0,1,0,0
+701,Mutual Interpretability of Robinson Arithmetic and Adjunctive Set Theory with Extensionality,"  An elementary rheory of concatenation is introduced and used to establish
+mutual interpretability of Robinson arithmetic, Minimal Predicative Set Theory,
+the quantifier-free part of Kirby's finitary set theory, and Adjunctive Set
+Theory, with or without extensionality.
+",0,0,1,0,0,0
+702,Coordination of Dynamic Software Components with JavaBIP,"  JavaBIP allows the coordination of software components by clearly separating
+the functional and coordination aspects of the system behavior. JavaBIP
+implements the principles of the BIP component framework rooted in rigorous
+operational semantics. Recent work both on BIP and JavaBIP allows the
+coordination of static components defined prior to system deployment, i.e., the
+architecture of the coordinated system is fixed in terms of its component
+instances. Nevertheless, modern systems, often make use of components that can
+register and deregister dynamically during system execution. In this paper, we
+present an extension of JavaBIP that can handle this type of dynamicity. We use
+first-order interaction logic to define synchronization constraints based on
+component types. Additionally, we use directed graphs with edge coloring to
+model dependencies among components that determine the validity of an online
+system. We present the software architecture of our implementation, provide and
+discuss performance evaluation results.
+",1,0,0,0,0,0
+703,Is It Safe to Uplift This Patch? An Empirical Study on Mozilla Firefox,"  In rapid release development processes, patches that fix critical issues, or
+implement high-value features are often promoted directly from the development
+channel to a stabilization channel, potentially skipping one or more
+stabilization channels. This practice is called patch uplift. Patch uplift is
+risky, because patches that are rushed through the stabilization phase can end
+up introducing regressions in the code. This paper examines patch uplift
+operations at Mozilla, with the aim to identify the characteristics of uplifted
+patches that introduce regressions. Through statistical and manual analyses, we
+quantitatively and qualitatively investigate the reasons behind patch uplift
+decisions and the characteristics of uplifted patches that introduced
+regressions. Additionally, we interviewed three Mozilla release managers to
+understand organizational factors that affect patch uplift decisions and
+outcomes. Results show that most patches are uplifted because of a wrong
+functionality or a crash. Uplifted patches that lead to faults tend to have
+larger patch size, and most of the faults are due to semantic or memory errors
+in the patches. Also, release managers are more inclined to accept patch uplift
+requests that concern certain specific components, and-or that are submitted by
+certain specific developers.
+",1,0,0,0,0,0
+704,Gamorithm,"  Examining games from a fresh perspective we present the idea of game-inspired
+and game-based algorithms, dubbed ""gamorithms"".
+",1,0,0,0,0,0
+705,Common change point estimation in panel data from the least squares and maximum likelihood viewpoints,"  We establish the convergence rates and asymptotic distributions of the common
+break change-point estimators, obtained by least squares and maximum likelihood
+in panel data models and compare their asymptotic variances. Our model
+assumptions accommodate a variety of commonly encountered probability
+distributions and, in particular, models of particular interest in econometrics
+beyond the commonly analyzed Gaussian model, including the zero-inflated
+Poisson model for count data, and the probit and tobit models. We also provide
+novel results for time dependent data in the signal-plus-noise model, with
+emphasis on a wide array of noise processes, including Gaussian process,
+MA$(\infty)$ and $m$-dependent processes. The obtained results show that
+maximum likelihood estimation requires a stronger signal-to-noise model
+identifiability condition compared to its least squares counterpart. Finally,
+since there are three different asymptotic regimes that depend on the behavior
+of the norm difference of the model parameters before and after the change
+point, which cannot be realistically assumed to be known, we develop a novel
+data driven adaptive procedure that provides valid confidence intervals for the
+common break, without requiring a priori knowledge of the asymptotic regime the
+problem falls in.
+",0,0,1,1,0,0
+706,Outage analysis in two-way communication with RF energy harvesting relay and co-channel interference,"  The study of relays with the scope of energy-harvesting (EH) looks
+interesting as a means of enabling sustainable, wireless communication without
+the need to recharge or replace the battery driving the relays. However,
+reliability of such communication systems becomes an important design challenge
+when such relays scavenge energy from the information bearing RF signals
+received from the source, using the technique of simultaneous wireless
+information and power transfer (SWIPT). To this aim, this work studies
+bidirectional communication in a decode-and-forward (DF) relay assisted
+cooperative wireless network in presence of co-channel interference (CCI). In
+order to quantify the reliability of the bidirectional communication systems, a
+closed form expression for the outage probability of the system is derived for
+both power splitting (PS) and time switching (TS) mode of operation of the
+relay. Simulation results are used to validate the accuracy of our analytical
+results and illustrate the dependence of the outage probability on various
+system parameters, like PS factor, TS factor, and distance of the relay from
+both the users. Results of performance comparison between PS relaying (PSR) and
+TS relaying (TSR) schemes are also presented. Besides, simulation results are
+also used to illustrate the spectral-efficiency and the energy-efficiency of
+the proposed system. The results show that, both in terms of spectral
+efficiency and the energy-efficiency, the two-way communication system in
+presence of moderate CCI power, performs better than the similar system without
+CCI. Additionally, it is also found that PSR is superior to TSR protocol in
+terms of peak energy-efficiency.
+",1,0,0,0,0,0
+707,KGAN: How to Break The Minimax Game in GAN,"  Generative Adversarial Networks (GANs) were intuitively and attractively
+explained under the perspective of game theory, wherein two involving parties
+are a discriminator and a generator. In this game, the task of the
+discriminator is to discriminate the real and generated (i.e., fake) data,
+whilst the task of the generator is to generate the fake data that maximally
+confuses the discriminator. In this paper, we propose a new viewpoint for GANs,
+which is termed as the minimizing general loss viewpoint. This viewpoint shows
+a connection between the general loss of a classification problem regarding a
+convex loss function and a f-divergence between the true and fake data
+distributions. Mathematically, we proposed a setting for the classification
+problem of the true and fake data, wherein we can prove that the general loss
+of this classification problem is exactly the negative f-divergence for a
+certain convex function f. This allows us to interpret the problem of learning
+the generator for dismissing the f-divergence between the true and fake data
+distributions as that of maximizing the general loss which is equivalent to the
+min-max problem in GAN if the Logistic loss is used in the classification
+problem. However, this viewpoint strengthens GANs in two ways. First, it allows
+us to employ any convex loss function for the discriminator. Second, it
+suggests that rather than limiting ourselves in NN-based discriminators, we can
+alternatively utilize other powerful families. Bearing this viewpoint, we then
+propose using the kernel-based family for discriminators. This family has two
+appealing features: i) a powerful capacity in classifying non-linear nature
+data and ii) being convex in the feature space. Using the convexity of this
+family, we can further develop Fenchel duality to equivalently transform the
+max-min problem to the max-max dual problem.
+",1,0,0,1,0,0
+708,Computing representation matrices for the action of Frobenius to cohomology groups,"  This paper is concerned with the computation of representation matrices for
+the action of Frobenius to the cohomology groups of algebraic varieties.
+Specifically we shall give an algorithm to compute the matrices for arbitrary
+algebraic varieties with defining equations over perfect fields of positive
+characteristic, and estimate its complexity. Moreover, we propose a specific
+efficient method, which works for complete intersections.
+",1,0,1,0,0,0
+709,The solitary g-mode frequencies in early B-type stars,"  We present possible explanations of pulsations in early B-type main sequence
+stars which arise purely from the excitation of gravity modes. There are three
+stars with this type of oscillations detected from the BRITE light curves:
+$\kappa$ Cen, a Car, $\kappa$ Vel. We show that by changing metallicity or the
+opacity profile it is possible in some models to dump pressure modes keeping
+gravity modes unstable. Other possible scenario involves pulsations of a lower
+mass companion.
+",0,1,0,0,0,0
+710,Composition of Credal Sets via Polyhedral Geometry,"  Recently introduced composition operator for credal sets is an analogy of
+such operators in probability, possibility, evidence and valuation-based
+systems theories. It was designed to construct multidimensional models (in the
+framework of credal sets) from a system of low- dimensional credal sets. In
+this paper we study its potential from the computational point of view
+utilizing methods of polyhedral geometry.
+",1,0,0,0,0,0
+711,The microarchitecture of a multi-threaded RISC-V compliant processing core family for IoT end-nodes,"  Internet-of-Things end-nodes demand low power processing platforms
+characterized by heterogeneous dedicated units, controlled by a processor core
+running concurrent control threads. Such architecture scheme fits one of the
+main target application domain of the RISC-V instruction set. We present an
+open-source processing core compliant with RISC-V on the software side and with
+the popular Pulpino processor platform on the hardware side, while supporting
+interleaved multi-threading for IoT applications. The latter feature is a novel
+contribution in this application domain. We report details about the
+microarchitecture design along with performance data.
+",1,0,0,0,0,0
+712,A Recent Survey on the Applications of Genetic Programming in Image Processing,"  During the last two decades, Genetic Programming (GP) has been largely used
+to tackle optimization, classification, and automatic features selection
+related tasks. The widespread use of GP is mainly due to its flexible and
+comprehensible tree-type structure. Similarly, research is also gaining
+momentum in the field of Image Processing (IP) because of its promising results
+over wide areas of applications ranging from medical IP to multispectral
+imaging. IP is mainly involved in applications such as computer vision, pattern
+recognition, image compression, storage and transmission, and medical
+diagnostics. This prevailing nature of images and their associated algorithm
+i.e complexities gave an impetus to the exploration of GP. GP has thus been
+used in different ways for IP since its inception. Many interesting GP
+techniques have been developed and employed in the field of IP. To give the
+research community an extensive view of these techniques, this paper presents
+the diverse applications of GP in IP and provides useful resources for further
+research. Also, comparison of different parameters used in ten different
+applications of IP are summarized in tabular form. Moreover, analysis of
+different parameters used in IP related tasks is carried-out to save the time
+needed in future for evaluating the parameters of GP. As more advancement is
+made in GP methodologies, its success in solving complex tasks not only related
+to IP but also in other fields will increase. Additionally, guidelines are
+provided for applying GP in IP related tasks, pros and cons of GP techniques
+are discussed, and some future directions are also set.
+",1,0,0,0,0,0
+713,Optimal Input Placement in Lattice Graphs,"  The control of dynamical, networked systems continues to receive much
+attention across the engineering and scientific research fields. Of particular
+interest is the proper way to determine which nodes of the network should
+receive external control inputs in order to effectively and efficiently control
+portions of the network. Published methods to accomplish this task either find
+a minimal set of driver nodes to guarantee controllability or a larger set of
+driver nodes which optimizes some control metric. Here, we investigate the
+control of lattice systems which provides analytical insight into the
+relationship between network structure and controllability. First we derive a
+closed form expression for the individual elements of the controllability
+Gramian of infinite lattice systems. Second, we focus on nearest neighbor
+lattices for which the distance between nodes appears in the expression for the
+controllability Gramian. We show that common control energy metrics scale
+exponentially with respect to the maximum distance between a driver node and a
+target node.
+",1,0,0,0,0,0
+714,Construction of constant mean curvature n-noids using the DPW method,"  We construct constant mean curvature surfaces in euclidean space with genus
+zero and n ends asymptotic to Delaunay surfaces using the DPW method.
+",0,0,1,0,0,0
+715,"Universality in numerical computation with random data. Case studies, analytic results and some speculations","  We discuss various universality aspects of numerical computations using
+standard algorithms. These aspects include empirical observations and rigorous
+results. We also make various speculations about computation in a broader
+sense.
+",0,1,1,0,0,0
+716,The X-ray reflection spectrum of the radio-loud quasar 4C 74.26,"  The relativistic jets created by some active galactic nuclei are important
+agents of AGN feedback. In spite of this, our understanding of what produces
+these jets is still incomplete. X-ray observations, which can probe the
+processes operating in the central regions in immediate vicinity of the
+supermassive black hole, the presumed jet launching point, are potentially
+particularly valuable in illuminating the jet formation process. Here, we
+present the hard X-ray NuSTAR observations of the radio-loud quasar 4C 74.26 in
+a joint analysis with quasi-simultaneous, soft X-ray Swift observations. Our
+spectral analysis reveals a high-energy cut-off of 183$_{-35}^{+51}$ keV and
+confirms the presence of ionized reflection in the source. From the average
+spectrum we detect that the accretion disk is mildly recessed with an inner
+radius of $R_\mathrm{in}=4-180\,R_\mathrm{g}$. However, no significant
+evolution of the inner radius is seen during the three months covered by our
+NuSTAR campaign. This lack of variation could mean that the jet formation in
+this radio-loud quasar differs from what is observed in broad-line radio
+galaxies.
+",0,1,0,0,0,0
+717,Tree Structured Synthesis of Gaussian Trees,"  A new synthesis scheme is proposed to effectively generate a random vector
+with prescribed joint density that induces a (latent) Gaussian tree structure.
+The quality of synthesis is measured by total variation distance between the
+synthesized and desired statistics. The proposed layered and successive
+encoding scheme relies on the learned structure of tree to use minimal number
+of common random variables to synthesize the desired density. We characterize
+the achievable rate region for the rate tuples of multi-layer latent Gaussian
+tree, through which the number of bits needed to simulate such Gaussian joint
+density are determined. The random sources used in our algorithm are the latent
+variables at the top layer of tree, the additive independent Gaussian noises,
+and the Bernoulli sign inputs that capture the ambiguity of correlation signs
+between the variables.
+",1,0,0,0,0,0
+718,Information Retrieval and Recommendation System for Astronomical Observatories,"  We present a machine learning based information retrieval system for
+astronomical observatories that tries to address user defined queries related
+to an instrument. In the modern instrumentation scenario where heterogeneous
+systems and talents are simultaneously at work, the ability to supply with the
+right information helps speeding up the detector maintenance operations.
+Enhancing the detector uptime leads to increased coincidence observation and
+improves the likelihood for the detection of astrophysical signals. Besides,
+such efforts will efficiently disseminate technical knowledge to a wider
+audience and will help the ongoing efforts to build upcoming detectors like the
+LIGO-India etc even at the design phase to foresee possible challenges. The
+proposed method analyses existing documented efforts at the site to
+intelligently group together related information to a query and to present it
+on-line to the user. The user in response can further go into interesting links
+and find already developed solutions or probable ways to address the present
+situation optimally. A web application that incorporates the above idea has
+been implemented and tested for LIGO Livingston, LIGO Hanford and Virgo
+observatories.
+",0,1,0,0,0,0
+719,Database Learning: Toward a Database that Becomes Smarter Every Time,"  In today's databases, previous query answers rarely benefit answering future
+queries. For the first time, to the best of our knowledge, we change this
+paradigm in an approximate query processing (AQP) context. We make the
+following observation: the answer to each query reveals some degree of
+knowledge about the answer to another query because their answers stem from the
+same underlying distribution that has produced the entire dataset. Exploiting
+and refining this knowledge should allow us to answer queries more
+analytically, rather than by reading enormous amounts of raw data. Also,
+processing more queries should continuously enhance our knowledge of the
+underlying distribution, and hence lead to increasingly faster response times
+for future queries.
+We call this novel idea---learning from past query answers---Database
+Learning. We exploit the principle of maximum entropy to produce answers, which
+are in expectation guaranteed to be more accurate than existing sample-based
+approximations. Empowered by this idea, we build a query engine on top of Spark
+SQL, called Verdict. We conduct extensive experiments on real-world query
+traces from a large customer of a major database vendor. Our results
+demonstrate that Verdict supports 73.7% of these queries, speeding them up by
+up to 23.0x for the same accuracy level compared to existing AQP systems.
+",1,0,0,0,0,0
+720,A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models,"  Beam search is a desirable choice of test-time decoding algorithm for neural
+sequence models because it potentially avoids search errors made by simpler
+greedy methods. However, typical cross entropy training procedures for these
+models do not directly consider the behaviour of the final decoding method. As
+a result, for cross-entropy trained models, beam decoding can sometimes yield
+reduced test performance when compared with greedy decoding. In order to train
+models that can more effectively make use of beam search, we propose a new
+training procedure that focuses on the final loss metric (e.g. Hamming loss)
+evaluated on the output of beam search. While well-defined, this ""direct loss""
+objective is itself discontinuous and thus difficult to optimize. Hence, in our
+approach, we form a sub-differentiable surrogate objective by introducing a
+novel continuous approximation of the beam search decoding procedure. In
+experiments, we show that optimizing this new training objective yields
+substantially better results on two sequence tasks (Named Entity Recognition
+and CCG Supertagging) when compared with both cross entropy trained greedy
+decoding and cross entropy trained beam decoding baselines.
+",1,0,0,0,0,0
+721,Measurably entire functions and their growth,"  In 1997 B. Weiss introduced the notion of measurably entire functions and
+proved that they exist on every arbitrary free C- action defined on standard
+probability space. In the same paper he asked about the minimal possible growth
+of measurably entire functions. In this work we show that for every arbitrary
+free C- action defined on a standard probability space there exists a
+measurably entire function whose growth does not exceed exp (exp[log^p |z|])
+for any p > 3. This complements a recent result by Buhovski, Glücksam,
+Logunov, and Sodin (arXiv:1703.08101) who showed that such functions cannot
+grow slower than exp (exp[log^p |z|]) for any p < 2.
+",0,0,1,0,0,0
+722,"Graph Theoretical Models of Closed n-Dimensional Manifolds: Digital Models of a Moebius Strip, a Torus, a Projective Plane a Klein Bottle and n-Dimensional Spheres","  In this paper, we show how to construct graph theoretical models of
+n-dimensional continuous objects and manifolds. These models retain topological
+properties of their continuous counterparts. An LCL collection of n-cells in
+Euclidean space is introduced and investigated. If an LCL collection of n-cells
+is a cover of a continuous n-dimensional manifold then the intersection graph
+of this cover is a digital closed n-dimensional manifold with the same topology
+as its continuous counterpart. As an example, we prove that the digital model
+of a continuous n-dimensional sphere is a digital n-sphere with at least 2n+2
+points, the digital model of a continuous projective plane is a digital
+projective plane with at least eleven points, the digital model of a continuous
+Klein bottle is the digital Klein bottle with at least sixteen points, the
+digital model of a continuous torus is the digital torus with at least sixteen
+points and the digital model of a continuous Moebius band is the digital
+Moebius band with at least twelve points.
+",1,0,1,0,0,0
+723,Completely $p$-primitive binary quadratic forms,"  Let $f(x,y)=ax^2+bxy+cy^2$ be a binary quadratic form with integer
+coefficients. For a prime $p$ not dividing the discriminant of $f$, we say $f$
+is completely $p$-primitive if for any non-zero integer $N$, the diophantine
+equation $f(x,y)=N$ has always an integer solution $(x,y)=(m,n)$ with
+$(m,n,p)=1$ whenever it has an integer solution. In this article, we study
+various properties of completely $p$-primitive binary quadratic forms. In
+particular, we give a necessary and sufficient condition for a definite binary
+quadratic form $f$ to be completely $p$-primitive.
+",0,0,1,0,0,0
+724,Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM,"  The numerical availability of statistical inference methods for a modern and
+robust analysis of longitudinal- and multivariate data in factorial experiments
+is an essential element in research and education. While existing approaches
+that rely on specific distributional assumptions of the data (multivariate
+normality and/or characteristic covariance matrices) are implemented in
+statistical software packages, there is a need for user-friendly software that
+can be used for the analysis of data that do not fulfill the aforementioned
+assumptions and provide accurate p-value and confidence interval estimates.
+Therefore, newly developed statistical methods for the analysis of repeated
+measures designs and multivariate data that neither assume multivariate
+normality nor specific covariance matrices have been implemented in the freely
+available R-package MANOVA.RM. The package is equipped with a graphical user
+interface for plausible applications in academia and other educational purpose.
+Several motivating examples illustrate the application of the methods.
+",0,0,0,1,0,0
+725,Multiple Illumination Phaseless Super-Resolution (MIPS) with Applications To Phaseless DOA Estimation and Diffraction Imaging,"  Phaseless super-resolution is the problem of recovering an unknown signal
+from measurements of the magnitudes of the low frequency Fourier transform of
+the signal. This problem arises in applications where measuring the phase, and
+making high-frequency measurements, are either too costly or altogether
+infeasible. The problem is especially challenging because it combines the
+difficult problems of phase retrieval and classical super-resolution
+",1,0,1,0,0,0
+726,Local Marchenko-Pastur Law for Random Bipartite Graphs,"  This paper is the first chapter of three of the author's undergraduate
+thesis. We study the random matrix ensemble of covariance matrices arising from
+random $(d_b, d_w)$-regular bipartite graphs on a set of $M$ black vertices and
+$N$ white vertices, for $d_b \gg \log^4 N$. We simultaneously prove that the
+Green's functions of these covariance matrices and the adjacency matrices of
+the underlying graphs agree with the corresponding limiting law (e.g.
+Marchenko-Pastur law for covariance matrices) down to the optimal scale. This
+is an improvement from the previously known mesoscopic results. We obtain
+eigenvector delocalization for the covariance matrix ensemble as consequence,
+as well as a weak rigidity estimate.
+",0,0,1,1,0,0
+727,Photoelectron Yields of Scintillation Counters with Embedded Wavelength-Shifting Fibers Read Out With Silicon Photomultipliers,"  Photoelectron yields of extruded scintillation counters with titanium dioxide
+coating and embedded wavelength shifting fibers read out by silicon
+photomultipliers have been measured at the Fermilab Test Beam Facility using
+120\,GeV protons. The yields were measured as a function of transverse,
+longitudinal, and angular positions for a variety of scintillator compositions
+and reflective coating mixtures, fiber diameters, and photosensor sizes. Timing
+performance was also studied. These studies were carried out by the Cosmic Ray
+Veto Group of the Mu2e collaboration as part of their R\&D program.
+",0,1,0,0,0,0
+728,"Precise measurement of hyperfine structure in the $ \rm {3\,S_{1/2}} $ state of $ \rm{^7Li} $","  We report a precise measurement of hyperfine structure in the $ \rm
+{3\,S_{1/2}} $ state of the odd isotope of Li, namely $ \rm {^7Li} $. The state
+is excited from the ground $ \rm {2\,S_{1/2}} $ state (which has the same
+parity) using two single-photon transitions via the intermediate $ \rm
+{2\,P_{3/2}} $ state. The value of the hyperfine constant we measure is $ A =
+93.095(52)$ MHz, which resolves two discrepant values reported in the
+literature measured using other techniques. Our value is also consistent with
+theoretical calculations.
+",0,1,0,0,0,0
+729,Reconstructing global fields from dynamics in the abelianized Galois group,"  We study a dynamical system induced by the Artin reciprocity map for a global
+field. We translate the conjugacy of such dynamical systems into various
+arithmetical properties that are equivalent to field isomorphism, relating it
+to anabelian geometry.
+",0,0,1,0,0,0
+730,Algebraic models of the Euclidean plane,"  We introduce a new invariant, the real (logarithmic)-Kodaira dimension, that
+allows to distinguish smooth real algebraic surfaces up to birational
+diffeomorphism. As an application, we construct infinite families of smooth
+rational real algebraic surfaces with trivial homology groups, whose real loci
+are diffeomorphic to $\mathbb{R}^2$, but which are pairwise not birationally
+diffeomorphic. There are thus infinitely many non-trivial models of the
+euclidean plane, contrary to the compact case.
+",0,0,1,0,0,0
+731,Decentralization of Multiagent Policies by Learning What to Communicate,"  Effective communication is required for teams of robots to solve
+sophisticated collaborative tasks. In practice it is typical for both the
+encoding and semantics of communication to be manually defined by an expert;
+this is true regardless of whether the behaviors themselves are bespoke,
+optimization based, or learned. We present an agent architecture and training
+methodology using neural networks to learn task-oriented communication
+semantics based on the example of a communication-unaware expert policy. A
+perimeter defense game illustrates the system's ability to handle dynamically
+changing numbers of agents and its graceful degradation in performance as
+communication constraints are tightened or the expert's observability
+assumptions are broken.
+",1,0,0,0,0,0
+732,The tumbling rotational state of 1I/`Oumuamua,"  The discovery of 1I/2017 U1 ('Oumuamua) has provided the first glimpse of a
+planetesimal born in another planetary system. This interloper exhibits a
+variable colour within a range that is broadly consistent with local small
+bodies such as the P/D type asteroids, Jupiter Trojans, and dynamically excited
+Kuiper Belt Objects. 1I/'Oumuamua appears unusually elongated in shape, with an
+axial ratio exceeding 5:1. Rotation period estimates are inconsistent and
+varied, with reported values between 6.9 and 8.3 hours. Here we analyse all
+available optical photometry reported to date. No single rotation period can
+explain the exhibited brightness variations. Rather, 1I/'Oumuamua appears to be
+in an excited rotational state undergoing Non-Principal Axis (NPA) rotation, or
+tumbling. A satisfactory solution has apparent lightcurve frequencies of 0.135
+and 0.126 hr-1 and implies a longest-to-shortest axis ratio of 5:1, though the
+available data are insufficient to uniquely constrain the true frequencies and
+shape. Assuming a body that responds to NPA rotation in a similar manner to
+Solar System asteroids and comets, the timescale to damp 1I/'Oumuamua's
+tumbling is at least a billion years. 1I/'Oumuamua was likely set tumbling
+within its parent planetary system, and will remain tumbling well after it has
+left ours.
+",0,1,0,0,0,0
+733,Homology theory formulas for generalized Riemann-Hurwitz and generalized monoidal transformations,"  In the context of orientable circuits and subcomplexes of these as
+representing certain singular spaces, we consider characteristic class formulas
+generalizing those classical results as seen for the Riemann-Hurwitz formula
+for regulating the topology of branched covering maps and that for monoidal
+transformations which include the standard blowing-up process. Here the results
+are presented as cap product pairings, which will be elements of a suitable
+homology theory, rather than characteristic numbers as would be the case when
+taking Kronecker products once Poincaré duality is defined. We further
+consider possible applications and examples including branched covering maps,
+singular varieties involving virtual tangent bundles, the
+Chern-Schwartz-MacPherson class, the homology L-class, generalized signature,
+and the cohomology signature class.
+",0,0,1,0,0,0
+734,Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment,"  Purpose: Basic surgical skills of suturing and knot tying are an essential
+part of medical training. Having an automated system for surgical skills
+assessment could help save experts time and improve training efficiency. There
+have been some recent attempts at automated surgical skills assessment using
+either video analysis or acceleration data. In this paper, we present a novel
+approach for automated assessment of OSATS based surgical skills and provide an
+analysis of different features on multi-modal data (video and accelerometer
+data). Methods: We conduct the largest study, to the best of our knowledge, for
+basic surgical skills assessment on a dataset that contained video and
+accelerometer data for suturing and knot-tying tasks. We introduce ""entropy
+based"" features - Approximate Entropy (ApEn) and Cross-Approximate Entropy
+(XApEn), which quantify the amount of predictability and regularity of
+fluctuations in time-series data. The proposed features are compared to
+existing methods of Sequential Motion Texture (SMT), Discrete Cosine Transform
+(DCT) and Discrete Fourier Transform (DFT), for surgical skills assessment.
+Results: We report average performance of different features across all
+applicable OSATS criteria for suturing and knot tying tasks. Our analysis shows
+that the proposed entropy based features out-perform previous state-of-the-art
+methods using video data. For accelerometer data, our method performs better
+for suturing only. We also show that fusion of video and acceleration features
+can improve overall performance with the proposed entropy features achieving
+highest accuracy. Conclusions: Automated surgical skills assessment can be
+achieved with high accuracy using the proposed entropy features. Such a system
+can significantly improve the efficiency of surgical training in medical
+schools and teaching hospitals.
+",1,0,0,0,0,0
+735,A Theory of Complex Stochastic Systems with Two Types of Counteracting Entities,"  Many complex systems share two characteristics: 1) they are stochastic in
+nature, and 2) they are characterized by a large number of factors. At the same
+time, various natural complex systems appear to have two types of intertwined
+constituents that exhibit counteracting effects on their equilibrium. In this
+study, we employ these few characteristics to lay the groundwork for analyzing
+such complex systems. The equilibrium point of these systems is generally
+studied either through the kinetic notion of equilibrium or its energetic
+notion, but not both. We postulate that these systems attempt to regulate the
+state vector of their constituents such that both the kinetic and the energetic
+notions of equilibrium are met. Based on this postulate, we prove: 1) the
+existence of a point such that the kinetic notion of equilibrium is met for the
+less abundant constituents and, at the same time, the state vector of more
+abundant entities is regulated to minimize the energetic notion of equilibrium;
+2) the effect of unboundedly increasing less (more) abundant constituents
+stabilizes (destabilizes) the system; and 3) the (unrestricted) equilibrium of
+the system is the point at which the number of stabilizing and destabilizing
+entities increase unboundedly with the same rate.
+",0,1,0,0,0,0
+736,Mixing of odd- and even-frequency pairings in strongly correlated electron systems under magnetic field,"  Even- and odd-frequency superconductivity coexist due to broken time-reversal
+symmetry under magnetic field. In order to describe this mixing, we extend the
+linearized Eliashberg equation for the spin and charge fluctuation mechanism in
+strongly correlated electron systems. We apply this extended Eliashberg
+equation to the odd-frequency superconductivity on a quasi-one-dimensional
+isosceles triangular lattice under in-plane magnetic field and examine the
+effect of the even-frequency component.
+",0,1,0,0,0,0
+737,Stability of the Poincaré bundle,"  Let X be an irreducible smooth projective curve, of genus at least two, over
+an algebraically closed field k. Let $\mathcal{M}^d_G$ denote the moduli stack
+of principal G-bundles over X of fixed topological type $d \in \pi_1(G)$, where
+G is any almost simple affine algebraic group over k. We prove that the
+universal bundle over $X \times \mathcal{M}^d_G$ is stable with respect to any
+polarization on $X \times \mathcal{M}^d_G$. A similar result is proved for the
+Poincaré adjoint bundle over $X \times M_G^{d, rs}$, where $M_G^{d, rs}$ is
+the coarse moduli space of regularly stable principal G-bundles over X of fixed
+topological type d.
+",0,0,1,0,0,0
+738,Existence results for primitive elements in cubic and quartic extensions of a finite field,"  With $\Fq$ the finite field of $q$ elements, we investigate the following
+question. If $\gamma$ generates $\Fqn$ over $\Fq$ and $\beta$ is a non-zero
+element of $\Fqn$, is there always an $a \in \Fq$ such that $\beta(\gamma + a)$
+is a primitive element? We resolve this case when $n=3$, thereby proving a
+conjecture by Cohen. We also improve substantially on what is known when $n=4$.
+",0,0,1,0,0,0
+739,Morse geodesics in torsion groups,"  In this paper we exhibit Morse geodesics, often called ""hyperbolic
+directions"", in infinite unbounded torsion groups. The groups studied are
+lacunary hyperbolic groups and constructed using graded small cancellation
+conditions. In all previously known examples, Morse geodesics were found in
+groups which also contained Morse elements, infinite order elements whose
+cyclic subgroup gives a Morse quasi-geodesic. Our result presents the first
+example of a group which contains Morse geodesics but no Morse elements. In
+fact, we show that there is an isometrically embedded $7$-regular tree inside
+such groups where every infinite, simple path is a Morse geodesic.
+",0,0,1,0,0,0
+740,Global and local thermometry schemes in coupled quantum systems,"  We study the ultimate bounds on the estimation of temperature for an
+interacting quantum system. We consider two coupled bosonic modes that are
+assumed to be thermal and using quantum estimation theory establish the role
+the Hamiltonian parameters play in thermometry. We show that in the case of a
+conserved particle number the interaction between the modes leads to a decrease
+in the overall sensitivity to temperature, while interestingly, if particle
+exchange is allowed with the thermal bath the converse is true. We explain this
+dichotomy by examining the energy spectra. Finally, we devise experimentally
+implementable thermometry schemes that rely only on locally accessible
+information from the total system, showing that almost Heisenberg limited
+precision can still be achieved, and we address the (im)possibility for
+multiparameter estimation in the system.
+",0,1,0,0,0,0
+741,The statistical significance filter leads to overconfident expectations of replicability,"  We show that publishing results using the statistical significance
+filter---publishing only when the p-value is less than 0.05---leads to a
+vicious cycle of overoptimistic expectation of the replicability of results.
+First, we show analytically that when true statistical power is relatively low,
+computing power based on statistically significant results will lead to
+overestimates of power. Then, we present a case study using 10 experimental
+comparisons drawn from a recently published meta-analysis in psycholinguistics
+(Jäger et al., 2017). We show that the statistically significant results
+yield an illusion of replicability. This illusion holds even if the researcher
+doesn't conduct any formal power analysis but just uses statistical
+significance to informally assess robustness (i.e., replicability) of results.
+",0,0,1,1,0,0
+742,A levitated nanoparticle as a classical two-level atom,"  The center-of-mass motion of a single optically levitated nanoparticle
+resembles three uncoupled harmonic oscillators. We show how a suitable
+modulation of the optical trapping potential can give rise to a coupling
+between two of these oscillators, such that their dynamics are governed by a
+classical equation of motion that resembles the Schrödinger equation for a
+two-level system. Based on experimental data, we illustrate the dynamics of
+this parametrically coupled system both in the frequency and in the time
+domain. We discuss the limitations and differences of the mechanical analogue
+in comparison to a true quantum mechanical system.
+",0,1,0,0,0,0
+743,Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences,"  We introduce new techniques to the analysis of neural spatiotemporal dynamics
+via applying $\epsilon$-machine reconstruction to electroencephalography (EEG)
+microstate sequences. Microstates are short duration quasi-stable states of the
+dynamically changing electrical field topographies recorded via an array of
+electrodes from the human scalp, and cluster into four canonical classes. The
+sequence of microstates observed under particular conditions can be considered
+an information source with unknown underlying structure. $\epsilon$-machines
+are discrete dynamical system automata with state-dependent probabilities on
+different future observations (in this case the next measured EEG microstate).
+They artificially reproduce underlying structure in an optimally predictive
+manner as generative models exhibiting dynamics emulating the behaviour of the
+source. Here we present experiments using both simulations and empirical data
+supporting the value of associating these discrete dynamical systems with
+mental states (e.g. mind-wandering, focused attention, etc.) and with clinical
+populations. The neurodynamics of mental states and clinical populations can
+then be further characterized by properties of these dynamical systems,
+including: i) statistical complexity (determined by the number of states of the
+corresponding $\epsilon$-automaton); ii) entropy rate; iii) characteristic
+sequence patterning (syntax, probabilistic grammars); iv) duration, persistence
+and stability of dynamical patterns; and v) algebraic measures such as
+Krohn-Rhodes complexity or holonomy length of the decompositions of these. The
+potential applications include the characterization of mental states in
+neurodynamic terms for mental health diagnostics, well-being interventions,
+human-machine interface, and others on both subject-specific and
+group/population-level.
+",1,1,0,0,0,0
+744,The LCES HIRES/Keck Precision Radial Velocity Exoplanet Survey,"  We describe a 20-year survey carried out by the Lick-Carnegie Exoplanet
+Survey Team (LCES), using precision radial velocities from HIRES on the Keck-I
+telescope to find and characterize extrasolar planetary systems orbiting nearby
+F, G, K, and M dwarf stars. We provide here 60,949 precision radial velocities
+for 1,624 stars contained in that survey. We tabulate a list of 357 significant
+periodic signals that are of constant period and phase, and not coincident in
+period and/or phase with stellar activity indices. These signals are thus
+strongly suggestive of barycentric reflex motion of the star induced by one or
+more candidate exoplanets in Keplerian motion about the host star. Of these
+signals, 225 have already been published as planet claims, 60 are classified as
+significant unpublished planet candidates that await photometric follow-up to
+rule out activity-related causes, and 54 are also unpublished, but are
+classified as ""significant"" signals that require confirmation by additional
+data before rising to classification as planet candidates. Of particular
+interest is our detection of a candidate planet with a minimum mass of 3.9
+Earth masses and an orbital period of 9.9 days orbiting Lalande 21185, the
+fourth-closest main sequence star to the Sun. For each of our exoplanetary
+candidate signals, we provide the period and semi-amplitude of the Keplerian
+orbital fit, and a likelihood ratio estimate of its statistical significance.
+We also tabulate 18 Keplerian-like signals that we classify as likely arising
+from stellar activity.
+",0,1,0,0,0,0
+745,Artificial Intelligence Based Malware Analysis,"  Artificial intelligence methods have often been applied to perform specific
+functions or tasks in the cyber-defense realm. However, as adversary methods
+become more complex and difficult to divine, piecemeal efforts to understand
+cyber-attacks, and malware-based attacks in particular, are not providing
+sufficient means for malware analysts to understand the past, present and
+future characteristics of malware.
+In this paper, we present the Malware Analysis and Attributed using Genetic
+Information (MAAGI) system. The underlying idea behind the MAAGI system is that
+there are strong similarities between malware behavior and biological organism
+behavior, and applying biologically inspired methods to corpora of malware can
+help analysts better understand the ecosystem of malware attacks. Due to the
+sophistication of the malware and the analysis, the MAAGI system relies heavily
+on artificial intelligence techniques to provide this capability. It has
+already yielded promising results over its development life, and will hopefully
+inspire more integration between the artificial intelligence and cyber--defense
+communities.
+",1,0,0,0,0,0
+746,Bounded height in families of dynamical systems,"  Let a and b be algebraic numbers such that exactly one of a and b is an
+algebraic integer, and let f_t(z):=z^2+t be a family of polynomials
+parametrized by t. We prove that the set of all algebraic numbers t for which
+there exist positive integers m and n such that f_t^m(a)=f_t^n(b) has bounded
+Weil height. This is a special case of a more general result supporting a new
+bounded height conjecture in dynamics. Our results fit into the general setting
+of the principle of unlikely intersections in arithmetic dynamics.
+",0,0,1,0,0,0
+747,Input-to-State Stability of a Clamped-Free Damped String in the Presence of Distributed and Boundary Disturbances,"  This note establishes the input-to-state stability (ISS) property for a
+clamped-free damped string with respect to distributed and boundary
+disturbances. While efficient methods for establishing ISS properties for
+distributed parameter systems with respect to distributed disturbances have
+been developed during the last decades, establishing ISS properties with
+respect to boundary disturbances remains challenging. One of the well-known
+methods for well-posedness analysis of systems with boundary inputs is to use
+an adequate lifting operator, which transfers the boundary disturbance to a
+distributed one. However, the resulting distributed disturbance involves time
+derivatives of the boundary perturbation. Thus, the subsequent ISS estimate
+depends on its amplitude, and may not be expressed in the strict form of ISS
+properties. To solve this problem, we show for a clamped-free damped string
+equation that the projection of the original system trajectories in an adequate
+Riesz basis can be used to establish the desired ISS property.
+",1,0,0,0,0,0
+748,A New Achievable Rate Region for Multiple-Access Channel with States,"  The problem of reliable communication over the multiple-access channel (MAC)
+with states is investigated. We propose a new coding scheme for this problem
+which uses quasi-group codes (QGC). We derive a new computable single-letter
+characterization of the achievable rate region. As an example, we investigate
+the problem of doubly-dirty MAC with modulo-$4$ addition. It is shown that the
+sum-rate $R_1+R_2=1$ bits per channel use is achievable using the new scheme.
+Whereas, the natural extension of the Gel'fand-Pinsker scheme, sum-rates
+greater than $0.32$ are not achievable.
+",1,0,0,0,0,0
+749,Reassessing Graphene Absorption and Emission Spectroscopy,"  We present a new paradigm for understanding optical absorption and hot
+electron dynamics experiments in graphene. Our analysis pivots on assigning
+proper importance to phonon assisted indirect processes and bleaching of direct
+processes. We show indirect processes figure in the excess absorption in the UV
+region. Experiments which were thought to indicate ultrafast relaxation of
+electrons and holes, reaching a thermal distribution from an extremely
+non-thermal one in under 5-10 fs, instead are explained by the nascent electron
+and hole distributions produced by indirect transitions. These need no
+relaxation or ad-hoc energy removal to agree with the observed emission spectra
+and fast pulsed absorption spectra. The fast emission following pulsed
+absorption is dominated by phonon assisted processes, which vastly outnumber
+direct ones and are always available, connecting any electron with any hole any
+time. Calculations are given, including explicitly calculating the magnitude of
+indirect processes, supporting these views.
+",0,1,0,0,0,0
+750,A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes,"  We propose a probabilistic model for interpreting gene expression levels that
+are observed through single-cell RNA sequencing. In the model, each cell has a
+low-dimensional latent representation. Additional latent variables account for
+technical effects that may erroneously set some observations of gene expression
+levels to zero. Conditional distributions are specified by neural networks,
+giving the proposed model enough flexibility to fit the data well. We use
+variational inference and stochastic optimization to approximate the posterior
+distribution. The inference procedure scales to over one million cells, whereas
+competing algorithms do not. Even for smaller datasets, for several tasks, the
+proposed procedure outperforms state-of-the-art methods like ZIFA and
+ZINB-WaVE. We also extend our framework to take into account batch effects and
+other confounding factors and propose a natural Bayesian hypothesis framework
+for differential expression that outperforms tradition DESeq2.
+",1,0,0,1,0,0
+751,Analytic heating rate of neutron star merger ejecta derived from Fermi's theory of beta decay,"  Macronovae (kilonovae) that arise in binary neutron star mergers are powered
+by radioactive beta decay of hundreds of $r$-process nuclides. We derive, using
+Fermi's theory of beta decay, an analytic estimate of the nuclear heating rate.
+We show that the heating rate evolves as a power law ranging between $t^{-6/5}$
+to $t^{-4/3}$. The overall magnitude of the heating rate is determined by the
+mean values of nuclear quantities, e.g., the nuclear matrix elements of beta
+decay. These values are specified by using nuclear experimental data. We
+discuss the role of higher order beta transitions and the robustness of the
+power law. The robust and simple form of the heating rate suggests that
+observations of the late-time bolometric light curve $\propto t^{-\frac{4}{3}}$
+would be a direct evidence of a $r$-process driven macronova. Such observations
+could also enable us to estimate the total amount of $r$-process nuclei
+produced in the merger.
+",0,1,0,0,0,0
+752,Molecular Simulation of Caloric Properties of Fluids Modelled by Force Fields with Intramolecular Contributions: Application to Heat Capacities,"  The calculation of caloric properties such as heat capacity, Joule-Thomson
+coefficients and the speed of sound by classical force-field-based molecular
+simulation methodology has received scant attention in the literature,
+particularly for systems composed of complex molecules whose force fields (FFs)
+are characterized by a combination of intramolecular and intermolecular terms
+(referred to herein as ""flexible FFs""). The calculation of a thermodynamic
+property for a system whose molecules are described by such a FF involves the
+calculation of the residual property prior to its addition to the corresponding
+ideal-gas (IG) property, the latter of which is separately calculated, either
+using thermochemical compilations or nowadays accurate quantum mechanical
+calculations. Although the simulation of a volumetric residual property
+proceeds by simply replacing the intermolecular FF in the rigid molecule case
+by the total (intramolecular plus intermolecular) FF, this is not the case for
+a caloric property. We discuss the methodology required in performing such
+calculations, and focus on the example of the molar heat capacity at constant
+pressure, $c_P$, one of the most important caloric properties. We also consider
+three approximations for the calculation procedure, and illustrate their
+consequences for the examples of the relatively simple molecule 2-propanol,
+${\rm CH_3CH(OH)CH_3}$, and for monoethanolamine, ${\rm HO(CH_2)_2NH_2}$, an
+important fluid used in carbon capture.
+",0,1,0,0,0,0
+753,On well-posedness of a velocity-vorticity formulation of the Navier-Stokes equations with no-slip boundary conditions,"  We study well-posedness of a velocity-vorticity formulation of the
+Navier--Stokes equations, supplemented with no-slip velocity boundary
+conditions, a no-penetration vorticity boundary condition, along with a natural
+vorticity boundary condition depending on a pressure functional. In the
+stationary case we prove existence and uniqueness of a suitable weak solution
+to the system under a small data condition. The topic of the paper is driven by
+recent developments of vorticity based numerical methods for the Navier--Stokes
+equations.
+",0,0,1,0,0,0
+754,Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories,"  Generative Adversarial Networks (GANs) represent a promising class of
+generative networks that combine neural networks with game theory. From
+generating realistic images and videos to assisting musical creation, GANs are
+transforming many fields of arts and sciences. However, their application to
+healthcare has not been fully realized, more specifically in generating
+electronic health records (EHR) data. In this paper, we propose a framework for
+exploring the value of GANs in the context of continuous laboratory time series
+data. We devise an unsupervised evaluation method that measures the predictive
+power of synthetic laboratory test time series. Further, we show that when it
+comes to predicting the impact of drug exposure on laboratory test data,
+incorporating representation learning of the training cohorts prior to training
+GAN models is beneficial.
+",1,0,0,1,0,0
+755,Properties of Kinetic Transition Networks for Atomic Clusters and Glassy Solids,"  A database of minima and transition states corresponds to a network where the
+minima represent nodes and the transition states correspond to edges between
+the pairs of minima they connect via steepest-descent paths. Here we construct
+networks for small clusters bound by the Morse potential for a selection of
+physically relevant parameters, in two and three dimensions. The properties of
+these unweighted and undirected networks are analysed to examine two features:
+whether they are small-world, where the shortest path between nodes involves
+only a small number or edges; and whether they are scale-free, having a degree
+distribution that follows a power law. Small-world character is present, but
+statistical tests show that a power law is not a good fit, so the networks are
+not scale-free. These results for clusters are compared with the corresponding
+properties for the molecular and atomic structural glass formers
+ortho-terphenyl and binary Lennard-Jones. These glassy systems do not show
+small-world properties, suggesting that such behaviour is linked to the
+structure-seeking landscapes of the Morse clusters.
+",0,1,0,1,0,0
+756,Asynchronous Byzantine Machine Learning (the case of SGD),"  Asynchronous distributed machine learning solutions have proven very
+effective so far, but always assuming perfectly functioning workers. In
+practice, some of the workers can however exhibit Byzantine behavior, caused by
+hardware failures, software bugs, corrupt data, or even malicious attacks. We
+introduce \emph{Kardam}, the first distributed asynchronous stochastic gradient
+descent (SGD) algorithm that copes with Byzantine workers. Kardam consists of
+two complementary components: a filtering and a dampening component. The first
+is scalar-based and ensures resilience against $\frac{1}{3}$ Byzantine workers.
+Essentially, this filter leverages the Lipschitzness of cost functions and acts
+as a self-stabilizer against Byzantine workers that would attempt to corrupt
+the progress of SGD. The dampening component bounds the convergence rate by
+adjusting to stale information through a generic gradient weighting scheme. We
+prove that Kardam guarantees almost sure convergence in the presence of
+asynchrony and Byzantine behavior, and we derive its convergence rate. We
+evaluate Kardam on the CIFAR-100 and EMNIST datasets and measure its overhead
+with respect to non Byzantine-resilient solutions. We empirically show that
+Kardam does not introduce additional noise to the learning procedure but does
+induce a slowdown (the cost of Byzantine resilience) that we both theoretically
+and empirically show to be less than $f/n$, where $f$ is the number of
+Byzantine failures tolerated and $n$ the total number of workers.
+Interestingly, we also empirically observe that the dampening component is
+interesting in its own right for it enables to build an SGD algorithm that
+outperforms alternative staleness-aware asynchronous competitors in
+environments with honest workers.
+",0,0,0,1,0,0
+757,A Recorded Debating Dataset,"  This paper describes an English audio and textual dataset of debating
+speeches, a unique resource for the growing research field of computational
+argumentation and debating technologies. We detail the process of speech
+recording by professional debaters, the transcription of the speeches with an
+Automatic Speech Recognition (ASR) system, their consequent automatic
+processing to produce a text that is more ""NLP-friendly"", and in parallel --
+the manual transcription of the speeches in order to produce gold-standard
+""reference"" transcripts. We release 60 speeches on various controversial
+topics, each in five formats corresponding to the different stages in the
+production of the data. The intention is to allow utilizing this resource for
+multiple research purposes, be it the addition of in-domain training data for a
+debate-specific ASR system, or applying argumentation mining on either noisy or
+clean debate transcripts. We intend to make further releases of this data in
+the future.
+",1,0,0,0,0,0
+758,Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos,"  Deep learning has been demonstrated to achieve excellent results for image
+classification and object detection. However, the impact of deep learning on
+video analysis (e.g. action detection and recognition) has been limited due to
+complexity of video data and lack of annotations. Previous convolutional neural
+networks (CNN) based video action detection approaches usually consist of two
+major steps: frame-level action proposal detection and association of proposals
+across frames. Also, these methods employ two-stream CNN framework to handle
+spatial and temporal feature separately. In this paper, we propose an
+end-to-end deep network called Tube Convolutional Neural Network (T-CNN) for
+action detection in videos. The proposed architecture is a unified network that
+is able to recognize and localize action based on 3D convolution features. A
+video is first divided into equal length clips and for each clip a set of tube
+proposals are generated next based on 3D Convolutional Network (ConvNet)
+features. Finally, the tube proposals of different clips are linked together
+employing network flow and spatio-temporal action detection is performed using
+these linked video proposals. Extensive experiments on several video datasets
+demonstrate the superior performance of T-CNN for classifying and localizing
+actions in both trimmed and untrimmed videos compared to state-of-the-arts.
+",1,0,0,0,0,0
+759,Distances and Isomorphism between Networks and the Stability of Network Invariants,"  We develop the theoretical foundations of a network distance that has
+recently been applied to various subfields of topological data analysis, namely
+persistent homology and hierarchical clustering. While this network distance
+has previously appeared in the context of finite networks, we extend the
+setting to that of compact networks. The main challenge in this new setting is
+the lack of an easy notion of sampling from compact networks; we solve this
+problem in the process of obtaining our results. The generality of our setting
+means that we automatically establish results for exotic objects such as
+directed metric spaces and Finsler manifolds. We identify readily computable
+network invariants and establish their quantitative stability under this
+network distance. We also discuss the computational complexity involved in
+precisely computing this distance, and develop easily-computable lower bounds
+by using the identified invariants. By constructing a wide range of explicit
+examples, we show that these lower bounds are effective in distinguishing
+between networks. Finally, we provide a simple algorithm that computes a lower
+bound on the distance between two networks in polynomial time and illustrate
+our metric and invariant constructions on a database of random networks and a
+database of simulated hippocampal networks.
+",1,0,1,0,0,0
+760,"Expropriations, Property Confiscations and New Offshore Entities: Evidence from the Panama Papers","  Using the Panama Papers, we show that the beginning of media reporting on
+expropriations and property confiscations in a country increases the
+probability that offshore entities are incorporated by agents from the same
+country in the same month. This result is robust to the use of country-year
+fixed effects and the exclusion of tax havens. Further analysis shows that the
+effect is driven by countries with non-corrupt and effective governments, which
+supports the notion that offshore entities are incorporated when reasonably
+well-intended and well-functioning governments become more serious about
+fighting organized crime by confiscating proceeds of crime.
+",0,0,0,0,0,1
+761,Revealing Hidden Potentials of the q-Space Signal in Breast Cancer,"  Mammography screening for early detection of breast lesions currently suffers
+from high amounts of false positive findings, which result in unnecessary
+invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many
+of these false-positive findings prior to biopsy. Current approaches estimate
+tissue properties by means of quantitative parameters taken from generative,
+biophysical models fit to the q-space encoded signal under certain assumptions
+regarding noise and spatial homogeneity. This process is prone to fitting
+instability and partial information loss due to model simplicity. We reveal
+unexplored potentials of the signal by integrating all data processing
+components into a convolutional neural network (CNN) architecture that is
+designed to propagate clinical target information down to the raw input images.
+This approach enables simultaneous and target-specific optimization of image
+normalization, signal exploitation, global representation learning and
+classification. Using a multicentric data set of 222 patients, we demonstrate
+that our approach significantly improves clinical decision making with respect
+to the current state of the art.
+",1,0,0,0,0,0
+762,Concave Flow on Small Depth Directed Networks,"  Small depth networks arise in a variety of network related applications,
+often in the form of maximum flow and maximum weighted matching. Recent works
+have generalized such methods to include costs arising from concave functions.
+In this paper we give an algorithm that takes a depth $D$ network and strictly
+increasing concave weight functions of flows on the edges and computes a $(1 -
+\epsilon)$-approximation to the maximum weight flow in time $mD \epsilon^{-1}$
+times an overhead that is logarithmic in the various numerical parameters
+related to the magnitudes of gradients and capacities.
+Our approach is based on extending the scaling algorithm for approximate
+maximum weighted matchings by [Duan-Pettie JACM`14] to the setting of small
+depth networks, and then generalizing it to concave functions. In this more
+restricted setting of linear weights in the range $[w_{\min}, w_{\max}]$, it
+produces a $(1 - \epsilon)$-approximation in time $O(mD \epsilon^{-1} \log(
+w_{\max} /w_{\min}))$. The algorithm combines a variety of tools and provides a
+unified approach towards several problems involving small depth networks.
+",1,0,0,0,0,0
+763,Knowledge Management Strategies and Processes in Agile Software Development: A Systematic Literature Review,"  Knowledge-intensive companies that adopt Agile Software Development (ASD)
+relay on efficient implementation of Knowledge Management (KM) strategies to
+promotes different Knowledge Processes (KPs) to gain competitive advantage.
+This study aims to explore how companies that adopt ASD implement KM strategies
+utilizing practices that promote the KPs in the different organizational
+layers. Through a systematic literature review, we analyzed 32 primary studies,
+selected by automated search and snowballing in the extant literature. To
+analyze the data, we applied narrative synthesis. Most of the identified KM
+practices implement personalization strategies (81 %), supported by
+codification (19 %). Our review shows that the primary studies do not report KM
+practices in the strategic layer and two of them in the product portfolio
+layer; on the other hand, in the project layer, the studies report 33 practices
+that implement personalization strategy, and seven practices that implement
+codification. KM strategies in ASD promote mainly the knowledge transfer
+process with practices that stimulate social interaction to share tacit
+knowledge in the project layer. As a result of using informal communication, a
+significant amount of knowledge can be lost or not properly transferred to
+other individuals and, instead of propagating the knowledge, it remains inside
+a few individuals minds.
+",1,0,0,0,0,0
+764,A supernova at 50 pc: Effects on the Earth's atmosphere and biota,"  Recent 60Fe results have suggested that the estimated distances of supernovae
+in the last few million years should be reduced from 100 pc to 50 pc. Two
+events or series of events are suggested, one about 2.7 million years to 1.7
+million years ago, and another may at 6.5 to 8.7 million years ago. We ask what
+effects such supernovae are expected to have on the terrestrial atmosphere and
+biota. Assuming that the Local Bubble was formed before the event being
+considered, and that the supernova and the Earth were both inside a weak,
+disordered magnetic field at that time, TeV-PeV cosmic rays at Earth will
+increase by a factor of a few hundred. Tropospheric ionization will increase
+proportionately, and the overall muon radiation load on terrestrial organisms
+will increase by a factor of 150. All return to pre-burst levels within 10kyr.
+In the case of an ordered magnetic field, effects depend strongly on the field
+orientation. The upper bound in this case is with a largely coherent field
+aligned along the line of sight to the supernova, in which case TeV-PeV cosmic
+ray flux increases are 10^4; in the case of a transverse field they are below
+current levels. We suggest a substantial increase in the extended effects of
+supernovae on Earth and in the lethal distance estimate; more work is
+needed.This paper is an explicit followup to Thomas et al. (2016). We also here
+provide more detail on the computational procedures used in both works.
+",0,1,0,0,0,0
+765,Surface tension of flowing soap films,"  The surface tension of flowing soap films is measured with respect to the
+film thickness and the concentration of soap solution. We perform this
+measurement by measuring the curvature of the nylon wires that bound the soap
+film channel and use the measured curvature to parametrize the relation between
+the surface tension and the tension of the wire. We find the surface tension of
+our soap films increases when the film is relatively thin or made of soap
+solution of low concentration, otherwise it approaches an asymptotic value 30
+mN/m. A simple adsorption model with only two parameters describes our
+observations reasonably well. With our measurements, we are also able to
+measure Gibbs elasticity for our soap film.
+",0,1,0,0,0,0
+766,Plugo: a VLC Systematic Perspective of Large-scale Indoor Localization,"  Indoor localization based on Visible Light Communication (VLC) has been in
+favor with both the academia and industry for years. In this paper, we present
+a prototyping photodiode-based VLC system towards large-scale localization.
+Specially, we give in-depth analysis of the design constraints and
+considerations for large-scale indoor localization research. After that we
+identify the key enablers for such systems: 1) distributed architecture, 2)
+one-way communication, and 3) random multiple access. Accordingly, we propose
+Plugo -- a photodiode-based VLC system conforming to the aforementioned
+criteria. We present a compact design of the VLC-compatible LED bulbs featuring
+plug-and-go use-cases. The basic framed slotted Additive Links On-line Hawaii
+Area (ALOHA) is exploited to achieve random multiple access over the shared
+optical medium. We show its effectiveness in beacon broadcasting by
+experiments, and further demonstrate its scalability to large-scale scenarios
+through simulations. Finally, preliminary localization experiments are
+conducted using fingerprinting-based methods in a customized testbed, achieving
+an average accuracy of 0.14 m along with a 90-percentile accuracy of 0.33 m.
+",1,0,0,0,0,0
+767,Deep Convolutional Networks as shallow Gaussian Processes,"  We show that the output of a (residual) convolutional neural network (CNN)
+with an appropriate prior over the weights and biases is a Gaussian process
+(GP) in the limit of infinitely many convolutional filters, extending similar
+results for dense networks. For a CNN, the equivalent kernel can be computed
+exactly and, unlike ""deep kernels"", has very few parameters: only the
+hyperparameters of the original CNN. Further, we show that this kernel has two
+properties that allow it to be computed efficiently; the cost of evaluating the
+kernel for a pair of images is similar to a single forward pass through the
+original CNN with only one filter per layer. The kernel equivalent to a
+32-layer ResNet obtains 0.84% classification error on MNIST, a new record for
+GPs with a comparable number of parameters.
+",0,0,0,1,0,0
+768,Dynamic Watermarking for General LTI Systems,"  Detecting attacks in control systems is an important aspect of designing
+secure and resilient control systems. Recently, a dynamic watermarking approach
+was proposed for detecting malicious sensor attacks for SISO LTI systems with
+partial state observations and MIMO LTI systems with a full rank input matrix
+and full state observations; however, these previous approaches cannot be
+applied to general LTI systems that are MIMO and have partial state
+observations. This paper designs a dynamic watermarking approach for detecting
+malicious sensor attacks for general LTI systems, and we provide a new set of
+asymptotic and statistical tests. We prove these tests can detect attacks that
+follow a specified attack model (more general than replay attacks), and we also
+show that these tests simplify to existing tests when the system is SISO or has
+full rank input matrix and full state observations. The benefit of our approach
+is demonstrated with a simulation analysis of detecting sensor attacks in
+autonomous vehicles. Our approach can distinguish between sensor attacks and
+wind disturbance (through an internal model principle framework), whereas
+improperly designed tests cannot distinguish between sensor attacks and wind
+disturbance.
+",1,0,1,0,0,0
+769,FluxMarker: Enhancing Tactile Graphics with Dynamic Tactile Markers,"  For people with visual impairments, tactile graphics are an important means
+to learn and explore information. However, raised line tactile graphics created
+with traditional materials such as embossing are static. While available
+refreshable displays can dynamically change the content, they are still too
+expensive for many users, and are limited in size. These factors limit
+wide-spread adoption and the representation of large graphics or data sets. In
+this paper, we present FluxMaker, an inexpensive scalable system that renders
+dynamic information on top of static tactile graphics with movable tactile
+markers. These dynamic tactile markers can be easily reconfigured and used to
+annotate static raised line tactile graphics, including maps, graphs, and
+diagrams. We developed a hardware prototype that actuates magnetic tactile
+markers driven by low-cost and scalable electromagnetic coil arrays, which can
+be fabricated with standard printed circuit board manufacturing. We evaluate
+our prototype with six participants with visual impairments and found positive
+results across four application areas: location finding or navigating on
+tactile maps, data analysis, and physicalization, feature identification for
+tactile graphics, and drawing support. The user study confirms advantages in
+application domains such as education and data exploration.
+",1,0,0,0,0,0
+770,Regularising Non-linear Models Using Feature Side-information,"  Very often features come with their own vectorial descriptions which provide
+detailed information about their properties. We refer to these vectorial
+descriptions as feature side-information. In the standard learning scenario,
+input is represented as a vector of features and the feature side-information
+is most often ignored or used only for feature selection prior to model
+fitting. We believe that feature side-information which carries information
+about features intrinsic property will help improve model prediction if used in
+a proper way during learning process. In this paper, we propose a framework
+that allows for the incorporation of the feature side-information during the
+learning of very general model families to improve the prediction performance.
+We control the structures of the learned models so that they reflect features
+similarities as these are defined on the basis of the side-information. We
+perform experiments on a number of benchmark datasets which show significant
+predictive performance gains, over a number of baselines, as a result of the
+exploitation of the side-information.
+",1,0,0,1,0,0
+771,On Diamond's $L^1$ criterion for asymptotic density of Beurling generalized integers,"  We give a short proof of the $L^{1}$ criterion for Beurling generalized
+integers to have a positive asymptotic density. We actually prove the existence
+of density under a weaker hypothesis. We also discuss related sufficient
+conditions for the estimate $m(x)=\sum_{n_{k}\leq x} \mu(n_k)/n_k=o(1)$, with
+$\mu$ the Beurling analog of the Moebius function.
+",0,0,1,0,0,0
+772,"""i have a feeling trump will win.................."": Forecasting Winners and Losers from User Predictions on Twitter","  Social media users often make explicit predictions about upcoming events.
+Such statements vary in the degree of certainty the author expresses toward the
+outcome:""Leonardo DiCaprio will win Best Actor"" vs. ""Leonardo DiCaprio may win""
+or ""No way Leonardo wins!"". Can popular beliefs on social media predict who
+will win? To answer this question, we build a corpus of tweets annotated for
+veridicality on which we train a log-linear classifier that detects positive
+veridicality with high precision. We then forecast uncertain outcomes using the
+wisdom of crowds, by aggregating users' explicit predictions. Our method for
+forecasting winners is fully automated, relying only on a set of contenders as
+input. It requires no training data of past outcomes and outperforms sentiment
+and tweet volume baselines on a broad range of contest prediction tasks. We
+further demonstrate how our approach can be used to measure the reliability of
+individual accounts' predictions and retrospectively identify surprise
+outcomes.
+",1,0,0,0,0,0
+773,SLAM-Assisted Coverage Path Planning for Indoor LiDAR Mapping Systems,"  Applications involving autonomous navigation and planning of mobile agents
+can benefit greatly by employing online Simultaneous Localization and Mapping
+(SLAM) techniques, however, their proper implementation still warrants an
+efficient amalgamation with any offline path planning method that may be used
+for the particular application. In this paper, such a case of amalgamation is
+considered for a LiDAR-based indoor mapping system which presents itself as a
+2D coverage path planning problem implemented along with online SLAM. This
+paper shows how classic offline Coverage Path Planning (CPP) can be altered for
+use with online SLAM by proposing two modifications: (i) performing convex
+decomposition of the polygonal coverage area to allow for an arbitrary choice
+of an initial point while still tracing the shortest coverage path and (ii)
+using a new approach to stitch together the different cells within the
+polygonal area to form a continuous coverage path. Furthermore, an alteration
+to the SLAM operation to suit the coverage path planning strategy is also made
+that evaluates navigation errors in terms of an area coverage cost function.
+The implementation results show how the combination of the two modified offline
+and online planning strategies allow for an improvement in the total area
+coverage by the mapping system - the modification thus presents an approach for
+modifying offline and online navigation strategies for robust operation.
+",1,0,0,0,0,0
+774,Low energy bands and transport properties of chromium arsenide,"  We apply a method that combines the tight-binding approximation and the
+Lowdin down-folding procedure to evaluate the electronic band structure of the
+newly discovered pressure-induced superconductor CrAs. By integrating out all
+low-lying arsenic degrees of freedom, we derive an effective Hamiltonian model
+describing the Cr d bands near the Fermi level. We calculate and make
+predictions for the energy spectra, the Fermi surface, the density of states
+and transport and magnetic properties of this compound. Our results are
+consistent with local-density approximation calculations as well as they show
+good agreement with available experimental data for resistivity and Cr magnetic
+moment.
+",0,1,0,0,0,0
+775,High Dynamic Range Imaging Technology,"  In this lecture note, we describe high dynamic range (HDR) imaging systems;
+such systems are able to represent luminances of much larger brightness and,
+typically, also a larger range of colors than conventional standard dynamic
+range (SDR) imaging systems. The larger luminance range greatly improve the
+overall quality of visual content, making it appears much more realistic and
+appealing to observers. HDR is one of the key technologies of the future
+imaging pipeline, which will change the way the digital visual content is
+represented and manipulated today.
+",1,0,0,0,0,0
+776,Classification of pro-$p$ PD$^2$ pairs and the pro-$p$ curve complex,"  We classify pro-$p$ Poincaré duality pairs in dimension two. We then use
+this classification to build a pro-$p$ analogue of the curve complex and
+establish its basic properties. We conclude with some statements concerning
+separability properties of the mapping class group.
+",0,0,1,0,0,0
+777,A Survey on Content-Aware Video Analysis for Sports,"  Sports data analysis is becoming increasingly large-scale, diversified, and
+shared, but difficulty persists in rapidly accessing the most crucial
+information. Previous surveys have focused on the methodologies of sports video
+analysis from the spatiotemporal viewpoint instead of a content-based
+viewpoint, and few of these studies have considered semantics. This study
+develops a deeper interpretation of content-aware sports video analysis by
+examining the insight offered by research into the structure of content under
+different scenarios. On the basis of this insight, we provide an overview of
+the themes particularly relevant to the research on content-aware systems for
+broadcast sports. Specifically, we focus on the video content analysis
+techniques applied in sportscasts over the past decade from the perspectives of
+fundamentals and general review, a content hierarchical model, and trends and
+challenges. Content-aware analysis methods are discussed with respect to
+object-, event-, and context-oriented groups. In each group, the gap between
+sensation and content excitement must be bridged using proper strategies. In
+this regard, a content-aware approach is required to determine user demands.
+Finally, the paper summarizes the future trends and challenges for sports video
+analysis. We believe that our findings can advance the field of research on
+content-aware video analysis for broadcast sports.
+",1,0,0,0,0,0
+778,Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure,"  In kernel methods, temporal information on the data is commonly included by
+using time-delayed embeddings as inputs. Recently, an alternative formulation
+was proposed by defining a gamma-filter explicitly in a reproducing kernel
+Hilbert space, giving rise to a complex model where multiple kernels operate on
+different temporal combinations of the input signal. In the original
+formulation, the kernels are then simply combined to obtain a single kernel
+matrix (for instance by averaging), which provides computational benefits but
+discards important information on the temporal structure of the signal.
+Inspired by works on multiple kernel learning, we overcome this drawback by
+considering the different kernels separately. We propose an efficient strategy
+to adaptively combine and select these kernels during the training phase. The
+resulting batch and online algorithms automatically learn to process highly
+nonlinear temporal information extracted from the input signal, which is
+implicitly encoded in the kernel values. We evaluate our proposal on several
+artificial and real tasks, showing that it can outperform classical approaches
+both in batch and online settings.
+",1,0,0,1,0,0
+779,Gaussian-Dirichlet Posterior Dominance in Sequential Learning,"  We consider the problem of sequential learning from categorical observations
+bounded in [0,1]. We establish an ordering between the Dirichlet posterior over
+categorical outcomes and a Gaussian posterior under observations with N(0,1)
+noise. We establish that, conditioned upon identical data with at least two
+observations, the posterior mean of the categorical distribution will always
+second-order stochastically dominate the posterior mean of the Gaussian
+distribution. These results provide a useful tool for the analysis of
+sequential learning under categorical outcomes.
+",0,0,1,1,0,0
+780,Learning Combinations of Sigmoids Through Gradient Estimation,"  We develop a new approach to learn the parameters of regression models with
+hidden variables. In a nutshell, we estimate the gradient of the regression
+function at a set of random points, and cluster the estimated gradients. The
+centers of the clusters are used as estimates for the parameters of hidden
+units. We justify this approach by studying a toy model, whereby the regression
+function is a linear combination of sigmoids. We prove that indeed the
+estimated gradients concentrate around the parameter vectors of the hidden
+units, and provide non-asymptotic bounds on the number of required samples. To
+the best of our knowledge, no comparable guarantees have been proven for linear
+combinations of sigmoids.
+",1,0,0,1,0,0
+781,Fine Structure and Lifetime of Dark Excitons in Transition Metal Dichalcogenide Monolayers,"  The intricate interplay between optically dark and bright excitons governs
+the light-matter interaction in transition metal dichalcogenide monolayers. We
+have performed a detailed investigation of the ""spin-forbidden"" dark excitons
+in WSe2 monolayers by optical spectroscopy in an out-of-plane magnetic field
+Bz. In agreement with the theoretical predictions deduced from group theory
+analysis, magneto-photoluminescence experiments reveal a zero field splitting
+$\delta=0.6 \pm 0.1$ meV between two dark exciton states. The low energy state
+being strictly dipole forbidden (perfectly dark) at Bz=0 while the upper state
+is partially coupled to light with z polarization (""grey"" exciton). The first
+determination of the dark neutral exciton lifetime $\tau_D$ in a transition
+metal dichalcogenide monolayer is obtained by time-resolved photoluminescence.
+We measure $\tau_D \sim 110 \pm 10$ ps for the grey exciton state, i.e. two
+orders of magnitude longer than the radiative lifetime of the bright neutral
+exciton at T=12 K.
+",0,1,0,0,0,0
+782,On bifibrations of model categories,"  In this article, we develop a notion of Quillen bifibration which combines
+the two notions of Grothendieck bifibration and of Quillen model structure. In
+particular, given a bifibration $p:\mathcal E\to\mathcal B$, we describe when a
+family of model structures on the fibers $\mathcal E_A$ and on the basis
+category $\mathcal B$ combines into a model structure on the total category
+$\mathcal E$, such that the functor $p$ preserves cofibrations, fibrations and
+weak equivalences. Using this Grothendieck construction for model structures,
+we revisit the traditional definition of Reedy model structures, and possible
+generalizations, and exhibit their bifibrational nature.
+",1,0,1,0,0,0
+783,Canonical sine and cosine Transforms For Integrable Boehmians,"  In this paper we define canonical sine and cosine transform, convolution
+operations, prove convolution theorems in space of integrable functions on real
+space. Further, obtain some results require to construct the spaces of
+integrable Boehmians then extend this canonical sine and canonical cosine
+transforms to space of integrable Boehmians and obtain their properties.
+",0,0,1,0,0,0
+784,Deep Learning Sparse Ternary Projections for Compressed Sensing of Images,"  Compressed sensing (CS) is a sampling theory that allows reconstruction of
+sparse (or compressible) signals from an incomplete number of measurements,
+using of a sensing mechanism implemented by an appropriate projection matrix.
+The CS theory is based on random Gaussian projection matrices, which satisfy
+recovery guarantees with high probability; however, sparse ternary {0, -1, +1}
+projections are more suitable for hardware implementation. In this paper, we
+present a deep learning approach to obtain very sparse ternary projections for
+compressed sensing. Our deep learning architecture jointly learns a pair of a
+projection matrix and a reconstruction operator in an end-to-end fashion. The
+experimental results on real images demonstrate the effectiveness of the
+proposed approach compared to state-of-the-art methods, with significant
+advantage in terms of complexity.
+",1,0,0,1,0,0
+785,Learning a Predictive Model for Music Using PULSE,"  Predictive models for music are studied by researchers of algorithmic
+composition, the cognitive sciences and machine learning. They serve as base
+models for composition, can simulate human prediction and provide a
+multidisciplinary application domain for learning algorithms. A particularly
+well established and constantly advanced subtask is the prediction of
+monophonic melodies. As melodies typically involve non-Markovian dependencies
+their prediction requires a capable learning algorithm. In this thesis, I apply
+the recent feature discovery and learning method PULSE to the realm of symbolic
+music modeling. PULSE is comprised of a feature generating operation and
+L1-regularized optimization. These are used to iteratively expand and cull the
+feature set, effectively exploring feature spaces that are too large for common
+feature selection approaches. I design a general Python framework for PULSE,
+propose task-optimized feature generating operations and various
+music-theoretically motivated features that are evaluated on a standard corpus
+of monophonic folk and chorale melodies. The proposed method significantly
+outperforms comparable state-of-the-art models. I further discuss the free
+parameters of the learning algorithm and analyze the feature composition of the
+learned models. The models learned by PULSE afford an easy inspection and are
+musicologically interpreted for the first time.
+",1,0,0,0,0,0
+786,Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM,"  Many real-world data sets, especially in biology, are produced by highly
+multivariate and nonlinear complex dynamical systems. In this paper, we focus
+on brain imaging data, including both calcium imaging and functional MRI data.
+Standard vector-autoregressive models are limited by their linearity
+assumptions, while nonlinear general-purpose, large-scale temporal models, such
+as LSTM networks, typically require large amounts of training data, not always
+readily available in biological applications; furthermore, such models have
+limited interpretability. We introduce here a novel approach for learning a
+nonlinear differential equation model aimed at capturing brain dynamics.
+Specifically, we propose a variable-projection optimization approach to
+estimate the parameters of the multivariate (coupled) van der Pol oscillator,
+and demonstrate that such a model can accurately represent nonlinear dynamics
+of the brain data. Furthermore, in order to improve the predictive accuracy
+when forecasting future brain-activity time series, we use this analytical
+model as an unlimited source of simulated data for pretraining LSTM; such
+model-specific data augmentation approach consistently improves LSTM
+performance on both calcium and fMRI imaging data.
+",0,0,0,1,1,0
+787,Neural networks for topology optimization,"  In this research, we propose a deep learning based approach for speeding up
+the topology optimization methods. The problem we seek to solve is the layout
+problem. The main novelty of this work is to state the problem as an image
+segmentation task. We leverage the power of deep learning methods as the
+efficient pixel-wise image labeling technique to perform the topology
+optimization. We introduce convolutional encoder-decoder architecture and the
+overall approach of solving the above-described problem with high performance.
+The conducted experiments demonstrate the significant acceleration of the
+optimization process. The proposed approach has excellent generalization
+properties. We demonstrate the ability of the application of the proposed model
+to other problems. The successful results, as well as the drawbacks of the
+current method, are discussed.
+",1,0,0,0,0,0
+788,Abstract Interpretation with Unfoldings,"  We present and evaluate a technique for computing path-sensitive interference
+conditions during abstract interpretation of concurrent programs. In lieu of
+fixed point computation, we use prime event structures to compactly represent
+causal dependence and interference between sequences of transformers. Our main
+contribution is an unfolding algorithm that uses a new notion of independence
+to avoid redundant transformer application, thread-local fixed points to reduce
+the size of the unfolding, and a novel cutoff criterion based on subsumption to
+guarantee termination of the analysis. Our experiments show that the abstract
+unfolding produces an order of magnitude fewer false alarms than a mature
+abstract interpreter, while being several orders of magnitude faster than
+solver-based tools that have the same precision.
+",1,0,0,0,0,0
+789,Perturbation theory for cosmologies with non-linear structure,"  The next generation of cosmological surveys will operate over unprecedented
+scales, and will therefore provide exciting new opportunities for testing
+general relativity. The standard method for modelling the structures that these
+surveys will observe is to use cosmological perturbation theory for linear
+structures on horizon-sized scales, and Newtonian gravity for non-linear
+structures on much smaller scales. We propose a two-parameter formalism that
+generalizes this approach, thereby allowing interactions between large and
+small scales to be studied in a self-consistent and well-defined way. This uses
+both post-Newtonian gravity and cosmological perturbation theory, and can be
+used to model realistic cosmological scenarios including matter, radiation and
+a cosmological constant. We find that the resulting field equations can be
+written as a hierarchical set of perturbation equations. At leading-order,
+these equations allow us to recover a standard set of Friedmann equations, as
+well as a Newton-Poisson equation for the inhomogeneous part of the Newtonian
+energy density in an expanding background. For the perturbations in the
+large-scale cosmology, however, we find that the field equations are sourced by
+both non-linear and mode-mixing terms, due to the existence of small-scale
+structures. These extra terms should be expected to give rise to new
+gravitational effects, through the mixing of gravitational modes on small and
+large scales - effects that are beyond the scope of standard linear
+cosmological perturbation theory. We expect our formalism to be useful for
+accurately modelling gravitational physics in universes that contain non-linear
+structures, and for investigating the effects of non-linear gravity in the era
+of ultra-large-scale surveys.
+",0,1,0,0,0,0
+790,"Heads or tails in zero gravity: an example of a classical contextual ""measurement""","  Playing the game of heads or tails in zero gravity demonstrates that there
+exists a contextual ""measurement"" in classical mechanics. When the coin is
+flipped, its orientation is a continuous variable. However, the ""measurement""
+that occurs when the coin is caught by clapping two hands together gives a
+discrete value (heads or tails) that depends on the context (orientation of the
+hands). It is then shown that there is a strong analogy with the spin
+measurement of the Stern-Gerlach experiment, and in particular with Stern and
+Gerlach's sequential measurements. Finally, we clarify the analogy by recalling
+how the de Broglie-Bohm interpretation simply explains the spin ""measurement"".
+",0,1,0,0,0,0
+791,Amortized Inference Regularization,"  The variational autoencoder (VAE) is a popular model for density estimation
+and representation learning. Canonically, the variational principle suggests to
+prefer an expressive inference model so that the variational approximation is
+accurate. However, it is often overlooked that an overly-expressive inference
+model can be detrimental to the test set performance of both the amortized
+posterior approximator and, more importantly, the generative density estimator.
+In this paper, we leverage the fact that VAEs rely on amortized inference and
+propose techniques for amortized inference regularization (AIR) that control
+the smoothness of the inference model. We demonstrate that, by applying AIR, it
+is possible to improve VAE generalization on both inference and generative
+performance. Our paper challenges the belief that amortized inference is simply
+a mechanism for approximating maximum likelihood training and illustrates that
+regularization of the amortization family provides a new direction for
+understanding and improving generalization in VAEs.
+",0,0,0,1,0,0
+792,Phase Synchronization on Spacially Embeded Duplex Networks with Total Cost Constraint,"  Synchronization on multiplex networks have attracted increasing attention in
+the past few years. We investigate collective behaviors of Kuramoto oscillators
+on single layer and duplex spacial networks with total cost restriction, which
+was introduced by Li et. al [Li G., Reis S. D., Moreira A. A., Havlin S.,
+Stanley H. E. and Jr A. J., {\it Phys. Rev. Lett.} 104, 018701 (2010)] and
+termed as the Li network afterwards. In the Li network model, with the increase
+of its spacial exponent, the network's structure will vary from the random type
+to the small-world one, and finally to the regular lattice.We first explore how
+the spacial exponent influences the synchronizability of Kuramoto oscillators
+on single layer Li networks and find that the closer the Li network is to a
+regular lattice, the more difficult for it to evolve into synchronization. Then
+we investigate synchronizability of duplex Li networks and find that the
+existence of inter-layer interaction can greatly enhance inter-layer and global
+synchronizability. When the inter-layer coupling strength is larger than a
+certain critical value, whatever the intra-layer coupling strength is, the
+inter-layer synchronization will always occur. Furthermore, on single layer Li
+networks, nodes with larger degrees more easily reach global synchronization,
+while on duplex Li networks, this phenomenon becomes much less obvious.
+Finally, we study the impact of inter-link density on global synchronization
+and obtain that sparse inter-links can lead to the emergence of global
+synchronization for duplex Li networks just as dense inter-links do. In a word,
+inter-layer interaction plays a vital role in determining synchronizability for
+duplex spacial networks with total cost constraint.
+",0,1,0,0,0,0
+793,Linear complexity of Legendre-polynomial quotients,"  We continue to investigate binary sequence $(f_u)$ over $\{0,1\}$ defined by
+$(-1)^{f_u}=\left(\frac{(u^w-u^{wp})/p}{p}\right)$ for integers $u\ge 0$, where
+$\left(\frac{\cdot}{p}\right)$ is the Legendre symbol and we restrict
+$\left(\frac{0}{p}\right)=1$. In an earlier work, the linear complexity of
+$(f_u)$ was determined for $w=p-1$ under the assumption of $2^{p-1}\not\equiv 1
+\pmod {p^2}$. In this work, we give possible values on the linear complexity of
+$(f_u)$ for all $1\le w<p-1$ under the same conditions. We also state that the
+case of larger $w(\geq p)$ can be reduced to that of $0\leq w\leq p-1$.
+",1,0,1,0,0,0
+794,The Time Dimension of Science: Connecting the Past to the Future,"  A central question in science of science concerns how time affects citations.
+Despite the long-standing interests and its broad impact, we lack systematic
+answers to this simple yet fundamental question. By reviewing and classifying
+prior studies for the past 50 years, we find a significant lack of consensus in
+the literature, primarily due to the coexistence of retrospective and
+prospective approaches to measuring citation age distributions. These two
+approaches have been pursued in parallel, lacking any known connections between
+the two. Here we developed a new theoretical framework that not only allows us
+to connect the two approaches through precise mathematical relationships, it
+also helps us reconcile the interplay between temporal decay of citations and
+the growth of science, helping us uncover new functional forms characterizing
+citation age distributions. We find retrospective distribution follows a
+lognormal distribution with exponential cutoff, while prospective distribution
+is governed by the interplay between a lognormal distribution and the growth in
+the number of references. Most interestingly, the two approaches can be
+connected once rescaled by the growth of publications and citations. We further
+validate our framework using both large-scale citation datasets and analytical
+models capturing citation dynamics. Together this paper presents a
+comprehensive analysis of the time dimension of science, representing a new
+empirical and theoretical basis for all future studies in this area.
+",1,1,0,0,0,0
+795,Excited states of defect lines in silicon: A first-principles study based on hydrogen cluster analogues,"  Excited states of a single donor in bulk silicon have previously been studied
+extensively based on effective mass theory. However, a proper theoretical
+description of the excited states of a donor cluster is still scarce. Here we
+study the excitations of lines of defects within a single-valley spherical band
+approximation, thus mapping the problem to a scaled hydrogen atom array. A
+series of detailed full configuration-interaction and time-dependent hybrid
+density-functional theory calculations have been performed to understand linear
+clusters of up to 10 donors. Our studies illustrate the generic features of
+their excited states, addressing the competition between formation of
+inter-donor ionic states and intra-donor atomic excited states. At short
+inter-donor distances, excited states of donor molecules are dominant, at
+intermediate distances ionic states play an important role, and at long
+distances the intra-donor excitations are predominant as expected. The
+calculations presented here emphasise the importance of correlations between
+donor electrons, and are thus complementary to other recent approaches that
+include effective mass anisotropy and multi-valley effects. The exchange
+splittings between relevant excited states have also been estimated for a donor
+pair and for a three-donor arrays; the splittings are much larger than those in
+the ground state in the range of donor separations between 10 and 20 nm. This
+establishes a solid theoretical basis for the use of excited-state exchange
+interactions for controllable quantum gate operations in silicon.
+",0,1,0,0,0,0
+796,Neutral Carbon Emission in luminous infrared galaxies The \CI\ Lines as Total Molecular Gas Tracers,"  We present a statistical study on the [C I]($^{3} \rm P_{1} \rightarrow {\rm
+^3 P}_{0}$), [C I] ($^{3} \rm P_{2} \rightarrow {\rm ^3 P}_{1}$) lines
+(hereafter [C I] (1$-$0) and [C I] (2$-$1), respectively) and the CO (1$-$0)
+line for a sample of (ultra)luminous infrared galaxies [(U)LIRGs]. We explore
+the correlations between the luminosities of CO (1$-$0) and [C I] lines, and
+find that $L'_\mathrm{CO(1-0)}$ correlates almost linearly with both $L'_
+\mathrm{[CI](1-0)}$ and $L'_\mathrm{[CI](2-1)}$, suggesting that [C I] lines
+can trace total molecular gas mass at least for (U)LIRGs. We also investigate
+the dependence of $L'_\mathrm{[CI](1-0)}$/$L'_\mathrm{CO(1-0)}$,
+$L'_\mathrm{[CI](2-1)}$/$L'_\mathrm{CO(1-0)}$ and
+$L'_\mathrm{[CI](2-1)}$/$L'_\mathrm{[CI](1-0)}$ on the far-infrared color of
+60-to-100 $\mu$m, and find non-correlation, a weak correlation and a modest
+correlation, respectively. Under the assumption that these two carbon
+transitions are optically thin, we further calculate the [C I] line excitation
+temperatures, atomic carbon masses, and the mean [C I] line flux-to-H$_2$ mass
+conversion factors for our sample. The resulting $\mathrm{H_2}$ masses using
+these [C I]-based conversion factors roughly agree with those derived from
+$L'_\mathrm{CO(1-0)}$ and CO-to-H$_2$ conversion factor.
+",0,1,0,0,0,0
+797,Kernel Approximation Methods for Speech Recognition,"  We study large-scale kernel methods for acoustic modeling in speech
+recognition and compare their performance to deep neural networks (DNNs). We
+perform experiments on four speech recognition datasets, including the TIMIT
+and Broadcast News benchmark tasks, and compare these two types of models on
+frame-level performance metrics (accuracy, cross-entropy), as well as on
+recognition metrics (word/character error rate). In order to scale kernel
+methods to these large datasets, we use the random Fourier feature method of
+Rahimi and Recht (2007). We propose two novel techniques for improving the
+performance of kernel acoustic models. First, in order to reduce the number of
+random features required by kernel models, we propose a simple but effective
+method for feature selection. The method is able to explore a large number of
+non-linear features while maintaining a compact model more efficiently than
+existing approaches. Second, we present a number of frame-level metrics which
+correlate very strongly with recognition performance when computed on the
+heldout set; we take advantage of these correlations by monitoring these
+metrics during training in order to decide when to stop learning. This
+technique can noticeably improve the recognition performance of both DNN and
+kernel models, while narrowing the gap between them. Additionally, we show that
+the linear bottleneck method of Sainath et al. (2013) improves the performance
+of our kernel models significantly, in addition to speeding up training and
+making the models more compact. Together, these three methods dramatically
+improve the performance of kernel acoustic models, making their performance
+comparable to DNNs on the tasks we explored.
+",1,0,0,1,0,0
+798,Composition Factors of Tensor Products of Symmetric Powers,"  We determine the composition factors of the tensor product $S(E)\otimes S(E)$
+of two copies of the symmetric algebra of the natural module $E$ of a general
+linear group over an algebraically closed field of positive characteristic. Our
+main result may be regarded as a substantial generalisation of the tensor
+product theorem of Krop and Sullivan, on composition factors of $S(E)$. We
+earlier answered the question of which polynomially injective modules are
+infinitesimally injective in terms of the ""divisibility index"". We are now able
+to give an explicit description of the divisibility index for polynomial
+modules for general linear groups of degree at most $3$.
+",0,0,1,0,0,0
+799,Stochastic Ratcheting on a Funneled Energy Landscape is Necessary for Highly Efficient Contractility of Actomyosin Force Dipoles,"  Current understanding of how contractility emerges in disordered actomyosin
+networks of non-muscle cells is still largely based on the intuition derived
+from earlier works on muscle contractility. This view, however, largely
+overlooks the free energy gain following passive cross-linker binding, which,
+even in the absence of active fluctuations, provides a thermodynamic drive
+towards highly overlapping filamentous states. In this work, we shed light on
+this phenomenon, showing that passive cross-linkers, when considered in the
+context of two anti-parallel filaments, generate noticeable contractile forces.
+However, as binding free energy of cross-linkers is increased, a sharp onset of
+kinetic arrest follows, greatly diminishing effectiveness of this contractility
+mechanism, allowing the network to contract only with weakly resisting tensions
+at its boundary. We have carried out stochastic simulations elucidating this
+mechanism, followed by a mean-field treatment that predicts how contractile
+forces asymptotically scale at small and large binding energies, respectively.
+Furthermore, when considering an active contractile filament pair, based on
+non-muscle myosin II, we found that the non-processive nature of these motors
+leads to highly inefficient force generation, due to recoil slippage of the
+overlap during periods when the motor is dissociated. However, we discovered
+that passive cross-linkers can serve as a structural ratchet during these
+unbound motor time spans, resulting in vast force amplification. Our results
+shed light on the non-equilibrium effects of transiently binding proteins in
+biological active matter, as observed in the non-muscle actin cytoskeleton,
+showing that highly efficient contractile force dipoles result from synergy of
+passive cross-linker and active motor dynamics, via a ratcheting mechanism on a
+funneled energy landscape.
+",0,1,0,0,0,0
+800,Mitigation of Phase Noise in Massive MIMO Systems: A Rate-Splitting Approach,"  This work encompasses Rate-Splitting (RS), providing significant benefits in
+multi-user settings in the context of huge degrees of freedom promised by
+massive Multiple-Input Multiple-Output (MIMO). However, the requirement of
+massive MIMO for cost-efficient implementation makes them more prone to
+hardware imperfections such as phase noise (PN). As a result, we focus on a
+realistic broadcast channel with a large number of antennas and hampered by the
+unavoidable PN. Moreover, we employ the RS transmission strategy, and we show
+its robustness against PN, since the sum-rate does not saturate at high
+signal-to-noise ratio (SNR). Although, the analytical results are obtained by
+means of the deterministic equivalent analysis, they coincide with simulation
+results even for finite system dimensions.
+",1,0,0,0,0,0
+801,Tangle-tree duality in abstract separation systems,"  We prove a general width duality theorem for combinatorial structures with
+well-defined notions of cohesion and separation. These might be graphs and
+matroids, but can be much more general or quite different. The theorem asserts
+a duality between the existence of high cohesiveness somewhere local and a
+global overall tree structure.
+We describe cohesive substructures in a unified way in the format of tangles:
+as orientations of low-order separations satisfying certain consistency axioms.
+These axioms can be expressed without reference to the underlying structure,
+such as a graph or matroid, but just in terms of the poset of the separations
+themselves. This makes it possible to identify tangles, and apply our
+tangle-tree duality theorem, in very diverse settings.
+Our result implies all the classical duality theorems for width parameters in
+graph minor theory, such as path-width, tree-width, branch-width or rank-width.
+It yields new, tangle-type, duality theorems for tree-width and path-width. It
+implies the existence of width parameters dual to cohesive substructures such
+as $k$-blocks, edge-tangles, or given subsets of tangles, for which no width
+duality theorems were previously known.
+Abstract separation systems can be found also in structures quite unlike
+graphs and matroids. For example, our theorem can be applied to image analysis
+by capturing the regions of an image as tangles of separations defined as
+natural partitions of its set of pixels. It can be applied in big data contexts
+by capturing clusters as tangles. It can be applied in the social sciences,
+e.g. by capturing as tangles the few typical mindsets of individuals found by a
+survey. It could also be applied in pure mathematics, e.g. to separations of
+compact manifolds.
+",0,0,1,0,0,0
+802,Attention-Based Guided Structured Sparsity of Deep Neural Networks,"  Network pruning is aimed at imposing sparsity in a neural network
+architecture by increasing the portion of zero-valued weights for reducing its
+size regarding energy-efficiency consideration and increasing evaluation speed.
+In most of the conducted research efforts, the sparsity is enforced for network
+pruning without any attention to the internal network characteristics such as
+unbalanced outputs of the neurons or more specifically the distribution of the
+weights and outputs of the neurons. That may cause severe accuracy drop due to
+uncontrolled sparsity. In this work, we propose an attention mechanism that
+simultaneously controls the sparsity intensity and supervised network pruning
+by keeping important information bottlenecks of the network to be active. On
+CIFAR-10, the proposed method outperforms the best baseline method by 6% and
+reduced the accuracy drop by 2.6x at the same level of sparsity.
+",0,0,0,1,0,0
+803,A Graph Model with Indirect Co-location Links,"  Graph models are widely used to analyse diffusion processes embedded in
+social contacts and to develop applications. A range of graph models are
+available to replicate the underlying social structures and dynamics
+realistically. However, most of the current graph models can only consider
+concurrent interactions among individuals in the co-located interaction
+networks. However, they do not account for indirect interactions that can
+transmit spreading items to individuals who visit the same locations at
+different times but within a certain time limit. The diffusion phenomena
+occurring through direct and indirect interactions is called same place
+different time (SPDT) diffusion. This paper introduces a model to synthesize
+co-located interaction graphs capturing both direct interactions, where
+individuals meet at a location, and indirect interactions, where individuals
+visit the same location at different times within a set timeframe. We analyze
+60 million location updates made by 2 million users from a social networking
+application to characterize the graph properties, including the space-time
+correlations and its time evolving characteristics, such as bursty or ongoing
+behaviors. The generated synthetic graph reproduces diffusion dynamics of a
+realistic contact graph, and reduces the prediction error by up to 82% when
+compare to other contact graph models demonstrating its potential for
+forecasting epidemic spread.
+",1,0,0,0,0,0
+804,Essentially No Barriers in Neural Network Energy Landscape,"  Training neural networks involves finding minima of a high-dimensional
+non-convex loss function. Knowledge of the structure of this energy landscape
+is sparse. Relaxing from linear interpolations, we construct continuous paths
+between minima of recent neural network architectures on CIFAR10 and CIFAR100.
+Surprisingly, the paths are essentially flat in both the training and test
+landscapes. This implies that neural networks have enough capacity for
+structural changes, or that these changes are small between minima. Also, each
+minimum has at least one vanishing Hessian eigenvalue in addition to those
+resulting from trivial invariance.
+",0,0,0,1,0,0
+805,Homogenization of nonlinear elliptic systems in nonreflexive Musielak-Orlicz spaces,"  We study the homogenization process for families of strongly nonlinear
+elliptic systems with the homogeneous Dirichlet boundary conditions. The growth
+and the coercivity of the elliptic operator is assumed to be indicated by a
+general inhomogeneous anisotropic $N-$function, which may be possibly also
+dependent on the spatial variable, i.e., the homogenization process will change
+the characteristic function spaces at each step. Such a problem is well known
+and there exists many positive results for the function satisfying $\Delta_2$
+and $\nabla_2$ conditions an being in addition Hölder continuous with
+respect to the spatial variable. We shall show that cases these conditions can
+be neglected and will deal with a rather general problem in general function
+space setting.
+",0,0,1,0,0,0
+806,A Generalization of Permanent Inequalities and Applications in Counting and Optimization,"  A polynomial $p\in\mathbb{R}[z_1,\dots,z_n]$ is real stable if it has no
+roots in the upper-half complex plane. Gurvits's permanent inequality gives a
+lower bound on the coefficient of the $z_1z_2\dots z_n$ monomial of a real
+stable polynomial $p$ with nonnegative coefficients. This fundamental
+inequality has been used to attack several counting and optimization problems.
+Here, we study a more general question: Given a stable multilinear polynomial
+$p$ with nonnegative coefficients and a set of monomials $S$, we show that if
+the polynomial obtained by summing up all monomials in $S$ is real stable, then
+we can lowerbound the sum of coefficients of monomials of $p$ that are in $S$.
+We also prove generalizations of this theorem to (real stable) polynomials that
+are not multilinear. We use our theorem to give a new proof of Schrijver's
+inequality on the number of perfect matchings of a regular bipartite graph,
+generalize a recent result of Nikolov and Singh, and give deterministic
+polynomial time approximation algorithms for several counting problems.
+",1,0,1,0,0,0
+807,Proceedings Fifth International Workshop on Verification and Program Transformation,"  This volume contains the proceedings of the Fifth International Workshop on
+Verification and Program Transformation (VPT 2017). The workshop took place in
+Uppsala, Sweden, on April 29th, 2017, affiliated with the European Joint
+Conferences on Theory and Practice of Software (ETAPS). The aim of the VPT
+workshop series is to provide a forum where people from the areas of program
+transformation and program verification can fruitfully exchange ideas and gain
+a deeper understanding of the interactions between those two fields. Seven
+papers were presented at the workshop. Additionally, three invited talks were
+given by Javier Esparza (Technische Universität München, Germany), Manuel
+Hermenegildo (IMDEA Software Institute, Madrid, Spain), and Alexey Khoroshilov
+(Linux Verification Center, ISPRAS, Moscow, Russia).
+",1,0,0,0,0,0
+808,H-infinity Filtering for Cloud-Aided Semi-active Suspension with Delayed Information,"  This chapter presents an H-infinity filtering framework for cloud-aided
+semiactive suspension system with time-varying delays. In this system, road
+profile information is downloaded from a cloud database to facilitate onboard
+estimation of suspension states. Time-varying data transmission delays are
+considered and assumed to be bounded. A quarter-car linear suspension model is
+used and an H-infinity filter is designed with both onboard sensor measurements
+and delayed road profile information from the cloud. The filter design
+procedure is designed based on linear matrix inequalities (LMIs). Numerical
+simulation results are reported that illustrates the fusion of cloud-based and
+on-board information that can be achieved in Vehicleto- Cloud-to-Vehicle
+(V2C2V) implementation.
+",1,0,0,0,0,0
+809,Deep Spatio-Temporal Random Fields for Efficient Video Segmentation,"  In this work we introduce a time- and memory-efficient method for structured
+prediction that couples neuron decisions across both space at time. We show
+that we are able to perform exact and efficient inference on a densely
+connected spatio-temporal graph by capitalizing on recent advances on deep
+Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a)
+efficient, (b) has a unique global minimum, and (c) can be trained end-to-end
+alongside contemporary deep networks for video understanding. We experiment
+with multiple connectivity patterns in the temporal domain, and present
+empirical improvements over strong baselines on the tasks of both semantic and
+instance segmentation of videos.
+",0,0,0,1,0,0
+810,A Coherent vorticity preserving eddy viscosity correction for Large-Eddy Simulation,"  This paper introduces a new approach to Large-Eddy Simulation (LES) where
+subgrid-scale (SGS) dissipation is applied proportionally to the degree of
+local spectral broadening, hence mitigated or deactivated in regions dominated
+by large-scale and/or laminar vortical motion. The proposed Coherent vorticity
+preserving (CvP) LES methodology is based on the evaluation of the ratio of the
+test-filtered to resolved (or grid-filtered) enstrophy $\sigma$. Values of
+$\sigma$ close to 1 indicate low sub-test-filter turbulent activity, justifying
+local deactivation of the SGS dissipation. The intensity of the SGS dissipation
+is progressively increased for $\sigma < 1$ which corresponds to a small-scale
+spectral broadening. The SGS dissipation is then fully activated in developed
+turbulence characterized by $\sigma \le \sigma_{eq}$, where the value
+$\sigma_{eq}$ is derived assuming a Kolmogorov spectrum. The proposed approach
+can be applied to any eddy-viscosity model, is algorithmically simple and
+computationally inexpensive. LES of Taylor-Green vortex breakdown demonstrates
+that the CvP methodology improves the performance of traditional, non-dynamic
+dissipative SGS models, capturing the peak of total turbulent kinetic energy
+dissipation during transition. Similar accuracy is obtained by adopting
+Germano's dynamic procedure albeit at more than twice the computational
+overhead. A CvP-LES of a pair of unstable periodic helical vortices is shown to
+predict accurately the experimentally observed growth rate using coarse
+resolutions. The ability of the CvP methodology to dynamically sort the
+coherent, large-scale motion from the smaller, broadband scales during
+transition is demonstrated via flow visualizations. LES of compressible channel
+are carried out and show a good match with a reference DNS.
+",1,1,0,0,0,0
+811,A Universal Marginalizer for Amortized Inference in Generative Models,"  We consider the problem of inference in a causal generative model where the
+set of available observations differs between data instances. We show how
+combining samples drawn from the graphical model with an appropriate masking
+function makes it possible to train a single neural network to approximate all
+the corresponding conditional marginal distributions and thus amortize the cost
+of inference. We further demonstrate that the efficiency of importance sampling
+may be improved by basing proposals on the output of the neural network. We
+also outline how the same network can be used to generate samples from an
+approximate joint posterior via a chain decomposition of the graph.
+",1,0,0,1,0,0
+812,Approximating the Backbone in the Weighted Maximum Satisfiability Problem,"  The weighted Maximum Satisfiability problem (weighted MAX-SAT) is a NP-hard
+problem with numerous applications arising in artificial intelligence. As an
+efficient tool for heuristic design, the backbone has been applied to
+heuristics design for many NP-hard problems. In this paper, we investigated the
+computational complexity for retrieving the backbone in weighted MAX-SAT and
+developed a new algorithm for solving this problem. We showed that it is
+intractable to retrieve the full backbone under the assumption that . Moreover,
+it is intractable to retrieve a fixed fraction of the backbone as well. And
+then we presented a backbone guided local search (BGLS) with Walksat operator
+for weighted MAX-SAT. BGLS consists of two phases: the first phase samples the
+backbone information from local optima and the backbone phase conducts local
+search under the guideline of backbone. Extensive experimental results on the
+benchmark showed that BGLS outperforms the existing heuristics in both solution
+quality and runtime.
+",1,0,0,0,0,0
+813,Fulde-Ferrell-Larkin-Ovchinnikov state in spin-orbit-coupled superconductors,"  We show that in the presence of magnetic field, two superconducting phases
+with the center-of-mass momentum of Cooper pair parallel to the magnetic field
+are induced in spin-orbit-coupled superconductor Li$_2$Pd$_3$B. Specifically,
+at small magnetic field, the center-of-mass momentum is induced due to the
+energy-spectrum distortion and no unpairing region with vanishing singlet
+correlation appears. We refer to this superconducting state as the drift-BCS
+state. By further increasing the magnetic field, the superconducting state
+falls into the Fulde-Ferrell-Larkin-Ovchinnikov state with the emergence of the
+unpairing regions. The observed abrupt enhancement of the center-of-mass
+momenta and suppression on the order parameters during the crossover indicate
+the first-order phase transition. Enhanced Pauli limit and hence enlarged
+magnetic-field regime of the Fulde-Ferrell-Larkin-Ovchinnikov state, due to the
+spin-flip terms of the spin-orbit coupling, are revealed. We also address the
+triplet correlations induced by the spin-orbit coupling, and show that the
+Cooper-pair spin polarizations, generated by the magnetic field and
+center-of-mass momentum with the triplet correlations, exhibit totally
+different magnetic-field dependences between the drift-BCS and
+Fulde-Ferrell-Larkin-Ovchinnikov states.
+",0,1,0,0,0,0
+814,New type integral inequalities for convex functions with applications II,"  We have recently established some integral inequalities for convex functions
+via the Hermite-Hadamard's inequalities. In continuation here, we also
+establish some interesting new integral inequalities for convex functions via
+the Hermite--Hadamard's inequalities and Jensen's integral inequality. Useful
+applications involving special means are also included.
+",0,0,1,0,0,0
+815,Geometric Insights into Support Vector Machine Behavior using the KKT Conditions,"  The support vector machine (SVM) is a powerful and widely used classification
+algorithm. This paper uses the Karush-Kuhn-Tucker conditions to provide
+rigorous mathematical proof for new insights into the behavior of SVM. These
+insights provide perhaps unexpected relationships between SVM and two other
+linear classifiers: the mean difference and the maximal data piling direction.
+For example, we show that in many cases SVM can be viewed as a cropped version
+of these classifiers. By carefully exploring these connections we show how SVM
+tuning behavior is affected by characteristics including: balanced vs.
+unbalanced classes, low vs. high dimension, separable vs. non-separable data.
+These results provide further insights into tuning SVM via cross-validation by
+explaining observed pathological behavior and motivating improved
+cross-validation methodology. Finally, we also provide new results on the
+geometry of complete data piling directions in high dimensional space.
+",0,0,0,1,0,0
+816,Smart Grids Data Analysis: A Systematic Mapping Study,"  Data analytics and data science play a significant role in nowadays society.
+In the context of Smart Grids (SG), the collection of vast amounts of data has
+seen the emergence of a plethora of data analysis approaches. In this paper, we
+conduct a Systematic Mapping Study (SMS) aimed at getting insights about
+different facets of SG data analysis: application sub-domains (e.g., power load
+control), aspects covered (e.g., forecasting), used techniques (e.g.,
+clustering), tool-support, research methods (e.g., experiments/simulations),
+replicability/reproducibility of research. The final goal is to provide a view
+of the current status of research. Overall, we found that each sub-domain has
+its peculiarities in terms of techniques, approaches and research methodologies
+applied. Simulations and experiments play a crucial role in many areas. The
+replicability of studies is limited concerning the provided implemented
+algorithms, and to a lower extent due to the usage of private datasets.
+",1,0,0,0,0,0
+817,"Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints","  In this paper, we present a new task that investigates how people interact
+with and make judgments about towers of blocks. In Experiment~1, participants
+in the lab solved a series of problems in which they had to re-configure three
+blocks from an initial to a final configuration. We recorded whether they used
+one hand or two hands to do so. In Experiment~2, we asked participants online
+to judge whether they think the person in the lab used one or two hands. The
+results revealed a close correspondence between participants' actions in the
+lab, and the mental simulations of participants online. To explain
+participants' actions and mental simulations, we develop a model that plans
+over a symbolic representation of the situation, executes the plan using a
+geometric solver, and checks the plan's feasibility by taking into account the
+physical constraints of the scene. Our model explains participants' actions and
+judgments to a high degree of quantitative accuracy.
+",1,0,0,1,0,0
+818,Complex Networks: from Classical to Quantum,"  Recent progress in applying complex network theory to problems faced in
+quantum information and computation has resulted in a beneficial crossover
+between two fields. Complex network methods have successfully been used to
+characterize quantum walk and transport models, entangled communication
+networks, graph-theoretic models of emergent space-time and in detecting
+mesoscale structure in quantum systems. Information physics is setting the
+stage for a theory of complex and networked systems with quantum
+information-inspired methods appearing in complex network science, including
+information-theoretic distance and correlation measures for network
+characterization. Novel quantum induced effects have been predicted in random
+graphs---where edges represent entangled links---and quantum computer
+algorithms have recently been proposed to offer super-polynomial enhancement
+for several network and graph theoretic problems. Here we review the results at
+the cutting edge, pinpointing the similarities and reconciling the differences
+found in the series of results at the intersection of these two fields.
+",1,1,0,0,0,0
+819,On the K-theory of C*-algebras for substitution tilings (a pedestrian version),"  Under suitable conditions, a substitution tiling gives rise to a Smale space,
+from which three equivalence relations can be constructed, namely the stable,
+unstable, and asymptotic equivalence relations. We denote with $S$, $U$, and
+$A$ their corresponding $C^*$-algebras in the sense of Renault. In this article
+we show that the $K$-theories of $S$ and $U$ can be computed from the
+cohomology and homology of a single cochain complex with connecting maps for
+tilings of the line and of the plane. Moreover, we provide formulas to compute
+the $K$-theory for these three $C^*$-algebras. Furthermore, we show that the
+$K$-theory groups for tilings of dimension 1 are always torsion free. For
+tilings of dimension 2, only $K_0(U)$ and $K_1(S)$ can contain torsion.
+",0,0,1,0,0,0
+820,The Tutte embedding of the mated-CRT map converges to Liouville quantum gravity,"  We prove that the Tutte embeddings (a.k.a. harmonic/embeddings) of certain
+random planar maps converge to $\gamma$-Liouville quantum gravity
+($\gamma$-LQG). Specifically, we treat mated-CRT maps, which are discretized
+matings of correlated continuum random trees, and $\gamma$ ranges from $0$ to
+$2$ as one varies the correlation parameter. We also show that the associated
+space-filling path on the embedded map converges to space-filling
+SLE$_{\kappa}$ for $\kappa =16/\gamma^2$ (in the annealed sense) and that
+simple random walk on the embedded map converges to Brownian motion (in the
+quenched sense). Our arguments also yield analogous statements for the Smith
+(square tiling) embedding of the mated-CRT map.
+This work constitutes the first proof that a discrete conformal embedding of
+a random planar map converges to LQG. Many more such statements have been
+conjectured. Since the mated-CRT map can be viewed as a coarse-grained
+approximation to other random planar maps (the UIPT, tree-weighted maps,
+bipolar-oriented maps, etc.), our results indicate a potential approach for
+proving that embeddings of these maps converge to LQG as well.
+To prove the main result, we establish several (independently interesting)
+theorems about LQG surfaces decorated by space-filling SLE. There is a natural
+way to use the SLE curve to divide the plane into `cells' corresponding to
+vertices of the mated-CRT map. We study the law of the shape of the
+origin-containing cell, in particular proving moments for the ratio of its
+squared diameter to its area. We also give bounds on the degree of the
+origin-containing cell and establish a form of ergodicity for the entire
+configuration. Ultimately, we use these properties to show (using a general
+theorem proved in a separate paper) that random walk on these cells converges
+to a time change of Brownian motion, which in turn leads to the Tutte embedding
+result.
+",0,0,1,0,0,0
+821,On the Computation of the Shannon Capacity of a Discrete Channel with Noise,"  Muroga [M52] showed how to express the Shannon channel capacity of a discrete
+channel with noise [S49] as an explicit function of the transition
+probabilities. His method accommodates channels with any finite number of input
+symbols, any finite number of output symbols and any transition probability
+matrix. Silverman [S55] carried out Muroga's method in the special case of a
+binary channel (and went on to analyse ""cascades"" of several such binary
+channels).
+This article is a note on the resulting formula for the capacity C(a, c) of a
+single binary channel. We aim to clarify some of the arguments and correct a
+small error. In service of this aim, we first formulate several of Shannon's
+definitions and proofs in terms of discrete measure-theoretic probability
+theory. We provide an alternate proof to Silverman's, of the feasibility of the
+optimal input distribution for a binary channel. For convenience, we also
+express C(a, c) in a single expression explicitly dependent on a and c only,
+which Silverman stopped short of doing.
+",1,0,0,0,0,0
+822,Gorenstein homological properties of tensor rings,"  Let $R$ be a two-sided noetherian ring and $M$ be a nilpotent $R$-bimodule,
+which is finitely generated on both sides. We study Gorenstein homological
+properties of the tensor ring $T_R(M)$. Under certain conditions, the ring $R$
+is Gorenstein if and only if so is $T_R(M)$. We characterize Gorenstein
+projective $T_R(M)$-modules in terms of $R$-modules.
+",0,0,1,0,0,0
+823,Compositional Human Pose Regression,"  Regression based methods are not performing as well as detection based
+methods for human pose estimation. A central problem is that the structural
+information in the pose is not well exploited in the previous regression
+methods. In this work, we propose a structure-aware regression approach. It
+adopts a reparameterized pose representation using bones instead of joints. It
+exploits the joint connection structure to define a compositional loss function
+that encodes the long range interactions in the pose. It is simple, effective,
+and general for both 2D and 3D pose estimation in a unified setting.
+Comprehensive evaluation validates the effectiveness of our approach. It
+significantly advances the state-of-the-art on Human3.6M and is competitive
+with state-of-the-art results on MPII.
+",1,0,0,0,0,0
+824,Stochastic functional differential equations and sensitivity to their initial path,"  We consider systems with memory represented by stochastic functional
+differential equations. Substantially, these are stochastic differential
+equations with coefficients depending on the past history of the process
+itself. Such coefficients are hence defined on a functional space. Models with
+memory appear in many applications ranging from biology to finance. Here we
+consider the results of some evaluations based on these models (e.g. the prices
+of some financial products) and the risks connected to the choice of these
+models. In particular we focus on the impact of the initial condition on the
+evaluations. This problem is known as the analysis of sensitivity to the
+initial condition and, in the terminology of finance, it is referred to as the
+Delta. In this work the initial condition is represented by the relevant past
+history of the stochastic functional differential equation. This naturally
+leads to the redesign of the definition of Delta. We suggest to define it as a
+functional directional derivative, this is a natural choice. For this we study
+a representation formula which allows for its computation without requiring
+that the evaluation functional is differentiable. This feature is particularly
+relevant for applications. Our formula is achieved by studying an appropriate
+relationship between Malliavin derivative and functional directional
+derivative. For this we introduce the technique of {\it randomisation of the
+initial condition}.
+",0,0,1,0,0,0
+825,Remarks on Inner Functions and Optimal Approximants,"  We discuss the concept of inner function in reproducing kernel Hilbert spaces
+with an orthogonal basis of monomials and examine connections between inner
+functions and optimal polynomial approximants to $1/f$, where $f$ is a function
+in the space. We revisit some classical examples from this perspective, and
+show how a construction of Shapiro and Shields can be modified to produce inner
+functions.
+",0,0,1,0,0,0
+826,A Stochastic Model for File Lifetime and Security in Data Center Networks,"  Data center networks are an important infrastructure in various applications
+of modern information technologies. Note that each data center always has a
+finite lifetime, thus once a data center fails, then it will lose all its
+storage files and useful information. For this, it is necessary to replicate
+and copy each important file into other data centers such that this file can
+increase its lifetime of staying in a data center network. In this paper, we
+describe a large-scale data center network with a file d-threshold policy,
+which is to replicate each important file into at most d-1 other data centers
+such that this file can maintain in the data center network under a given level
+of data security in the long-term. To this end, we develop three relevant
+Markov processes to propose two effective methods for assessing the file
+lifetime and data security. By using the RG-factorizations, we show that the
+two methods are used to be able to more effectively evaluate the file lifetime
+of large-scale data center networks. We hope the methodology and results given
+in this paper are applicable in the file lifetime study of more general data
+center networks with replication mechanism.
+",1,0,0,0,0,0
+827,Asymptotic Confidence Regions for High-dimensional Structured Sparsity,"  In the setting of high-dimensional linear regression models, we propose two
+frameworks for constructing pointwise and group confidence sets for penalized
+estimators which incorporate prior knowledge about the organization of the
+non-zero coefficients. This is done by desparsifying the estimator as in van de
+Geer et al. [18] and van de Geer and Stucky [17], then using an appropriate
+estimator for the precision matrix $\Theta$. In order to estimate the precision
+matrix a corresponding structured matrix norm penalty has to be introduced.
+After normalization the result is an asymptotic pivot.
+The asymptotic behavior is studied and simulations are added to study the
+differences between the two schemes.
+",0,0,1,1,0,0
+828,Building a Structured Query Engine,"  Finding patterns in data and being able to retrieve information from those
+patterns is an important task in Information retrieval. Complex search
+requirements which are not fulfilled by simple string matching and require
+exploring certain patterns in data demand a better query engine that can
+support searching via structured queries. In this article, we built a
+structured query engine which supports searching data through structured
+queries on the lines of ElasticSearch. We will show how we achieved real time
+indexing and retrieving of data through a RESTful API and how complex queries
+can be created and processed using efficient data structures we created for
+storing the data in structured way. Finally, we will conclude with an example
+of movie recommendation system built on top of this query engine.
+",1,0,0,0,0,0
+829,A Theoretical Perspective of Solving Phaseless Compressed Sensing via Its Nonconvex Relaxation,"  As a natural extension of compressive sensing and the requirement of some
+practical problems, Phaseless Compressed Sensing (PCS) has been introduced and
+studied recently. Many theoretical results have been obtained for PCS with the
+aid of its convex relaxation. Motivated by successful applications of nonconvex
+relaxed methods for solving compressive sensing, in this paper, we try to
+investigate PCS via its nonconvex relaxation. Specifically, we relax PCS in the
+real context by the corresponding $\ell_p$-minimization with $p\in (0,1)$. We
+show that there exists a constant $p^\ast\in (0,1]$ such that for any fixed
+$p\in(0, p^\ast)$, every optimal solution to the $\ell_p$-minimization also
+solves the concerned problem; and derive an expression of such a constant
+$p^\ast$ by making use of the known data and the sparsity level of the
+concerned problem. These provide a theoretical basis for solving this class of
+problems via the corresponding $\ell_p$-minimization.
+",0,0,1,0,0,0
+830,A Comprehensive Study of Ly$α$ Emission in the High-redshift Galaxy Population,"  We present an exhaustive census of Lyman alpha (Ly$\alpha$) emission in the
+general galaxy population at $3<z<4.6$. We use the Michigan/Magellan Fiber
+System (M2FS) spectrograph to study a stellar mass (M$_*$) selected sample of
+625 galaxies homogeneously distributed in the range
+$7.6<\log{\mbox{M$_*$/M$_{\odot}$}}<10.6$. Our sample is selected from the
+3D-HST/CANDELS survey, which provides the complementary data to estimate
+Ly$\alpha$ equivalent widths ($W_{Ly\alpha}$) and escape fractions ($f_{esc}$)
+for our galaxies. We find both quantities to anti-correlate with M$_*$,
+star-formation rate (SFR), UV luminosity, and UV slope ($\beta$). We then model
+the $W_{Ly\alpha}$ distribution as a function of M$_{UV}$ and $\beta$ using a
+Bayesian approach. Based on our model and matching the properties of typical
+Lyman break galaxy (LBG) selections, we conclude that the $W_{Ly\alpha}$
+distribution in such samples is heavily dependent on the limiting M$_{UV}$ of
+the survey. Regarding narrowband surveys, we find their $W_{Ly\alpha}$
+selections to bias samples toward low M$_*$, while their line-flux limitations
+preferentially leave out low-SFR galaxies. We can also use our model to predict
+the fraction of Ly$\alpha$-emitting LBGs at $4\leqslant z\leqslant 7$. We show
+that reported drops in the Ly$\alpha$ fraction at $z\geqslant6$, usually
+attributed to the rapidly increasing neutral gas fraction of the universe, can
+also be explained by survey M$_{UV}$ incompleteness. This result does not
+dismiss reionization occurring at $z\sim7$, but highlights that current data is
+not inconsistent with this process taking place at $z>7$.
+",0,1,0,0,0,0
+831,Optimal Timing of Decisions: A General Theory Based on Continuation Values,"  Building on insights of Jovanovic (1982) and subsequent authors, we develop a
+comprehensive theory of optimal timing of decisions based around continuation
+value functions and operators that act on them. Optimality results are provided
+under general settings, with bounded or unbounded reward functions. This
+approach has several intrinsic advantages that we exploit in developing the
+theory. One is that continuation value functions are smoother than value
+functions, allowing for sharper analysis of optimal policies and more efficient
+computation. Another is that, for a range of problems, the continuation value
+function exists in a lower dimensional space than the value function,
+mitigating the curse of dimensionality. In one typical experiment, this reduces
+the computation time from over a week to less than three minutes.
+",0,0,1,0,0,0
+832,OSIRIS-REx Contamination Control Strategy and Implementation,"  OSIRIS-REx will return pristine samples of carbonaceous asteroid Bennu. This
+article describes how pristine was defined based on expectations of Bennu and
+on a realistic understanding of what is achievable with a constrained schedule
+and budget, and how that definition flowed to requirements and implementation.
+To return a pristine sample, the OSIRIS- REx spacecraft sampling hardware was
+maintained at level 100 A/2 and <180 ng/cm2 of amino acids and hydrazine on the
+sampler head through precision cleaning, control of materials, and vigilance.
+Contamination is further characterized via witness material exposed to the
+spacecraft assembly and testing environment as well as in space. This
+characterization provided knowledge of the expected background and will be used
+in conjunction with archived spacecraft components for comparison with the
+samples when they are delivered to Earth for analysis. Most of all, the
+cleanliness of the OSIRIS-REx spacecraft was achieved through communication
+among scientists, engineers, managers, and technicians.
+",0,1,0,0,0,0
+833,Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging,"  We consider $d$-dimensional linear stochastic approximation algorithms (LSAs)
+with a constant step-size and the so called Polyak-Ruppert (PR) averaging of
+iterates. LSAs are widely applied in machine learning and reinforcement
+learning (RL), where the aim is to compute an appropriate $\theta_{*} \in
+\mathbb{R}^d$ (that is an optimum or a fixed point) using noisy data and $O(d)$
+updates per iteration. In this paper, we are motivated by the problem (in RL)
+of policy evaluation from experience replay using the \emph{temporal
+difference} (TD) class of learning algorithms that are also LSAs. For LSAs with
+a constant step-size, and PR averaging, we provide bounds for the mean squared
+error (MSE) after $t$ iterations. We assume that data is \iid with finite
+variance (underlying distribution being $P$) and that the expected dynamics is
+Hurwitz. For a given LSA with PR averaging, and data distribution $P$
+satisfying the said assumptions, we show that there exists a range of constant
+step-sizes such that its MSE decays as $O(\frac{1}{t})$.
+We examine the conditions under which a constant step-size can be chosen
+uniformly for a class of data distributions $\mathcal{P}$, and show that not
+all data distributions `admit' such a uniform constant step-size. We also
+suggest a heuristic step-size tuning algorithm to choose a constant step-size
+of a given LSA for a given data distribution $P$. We compare our results with
+related work and also discuss the implication of our results in the context of
+TD algorithms that are LSAs.
+",1,0,0,1,0,0
+834,Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval,"  We study the fundamental tradeoffs between statistical accuracy and
+computational tractability in the analysis of high dimensional heterogeneous
+data. As examples, we study sparse Gaussian mixture model, mixture of sparse
+linear regressions, and sparse phase retrieval model. For these models, we
+exploit an oracle-based computational model to establish conjecture-free
+computationally feasible minimax lower bounds, which quantify the minimum
+signal strength required for the existence of any algorithm that is both
+computationally tractable and statistically accurate. Our analysis shows that
+there exist significant gaps between computationally feasible minimax risks and
+classical ones. These gaps quantify the statistical price we must pay to
+achieve computational tractability in the presence of data heterogeneity. Our
+results cover the problems of detection, estimation, support recovery, and
+clustering, and moreover, resolve several conjectures of Azizyan et al. (2013,
+2015); Verzelen and Arias-Castro (2017); Cai et al. (2016). Interestingly, our
+results reveal a new but counter-intuitive phenomenon in heterogeneous data
+analysis that more data might lead to less computation complexity.
+",0,0,0,1,0,0
+835,Modality Attention for End-to-End Audio-visual Speech Recognition,"  Audio-visual speech recognition (AVSR) system is thought to be one of the
+most promising solutions for robust speech recognition, especially in noisy
+environment. In this paper, we propose a novel multimodal attention based
+method for audio-visual speech recognition which could automatically learn the
+fused representation from both modalities based on their importance. Our method
+is realized using state-of-the-art sequence-to-sequence (Seq2seq)
+architectures. Experimental results show that relative improvements from 2% up
+to 36% over the auditory modality alone are obtained depending on the different
+signal-to-noise-ratio (SNR). Compared to the traditional feature concatenation
+methods, our proposed approach can achieve better recognition performance under
+both clean and noisy conditions. We believe modality attention based end-to-end
+method can be easily generalized to other multimodal tasks with correlated
+information.
+",1,0,0,0,0,0
+836,Affine Rough Models,"  The goal of this survey article is to explain and elucidate the affine
+structure of recent models appearing in the rough volatility literature, and
+show how it leads to exponential-affine transform formulas.
+",0,0,0,0,0,1
+837,When flux standards go wild: white dwarfs in the age of Kepler,"  White dwarf stars have been used as flux standards for decades, thanks to
+their staid simplicity. We have empirically tested their photometric stability
+by analyzing the light curves of 398 high-probability candidates and
+spectroscopically confirmed white dwarfs observed during the original Kepler
+mission and later with K2 Campaigns 0-8. We find that the vast majority (>97
+per cent) of non-pulsating and apparently isolated white dwarfs are stable to
+better than 1 per cent in the Kepler bandpass on 1-hr to 10-d timescales,
+confirming that these stellar remnants are useful flux standards. From the
+cases that do exhibit significant variability, we caution that binarity,
+magnetism, and pulsations are three important attributes to rule out when
+establishing white dwarfs as flux standards, especially those hotter than
+30,000 K.
+",0,1,0,0,0,0
+838,Multi-view Low-rank Sparse Subspace Clustering,"  Most existing approaches address multi-view subspace clustering problem by
+constructing the affinity matrix on each view separately and afterwards propose
+how to extend spectral clustering algorithm to handle multi-view data. This
+paper presents an approach to multi-view subspace clustering that learns a
+joint subspace representation by constructing affinity matrix shared among all
+views. Relying on the importance of both low-rank and sparsity constraints in
+the construction of the affinity matrix, we introduce the objective that
+balances between the agreement across different views, while at the same time
+encourages sparsity and low-rankness of the solution. Related low-rank and
+sparsity constrained optimization problem is for each view solved using the
+alternating direction method of multipliers. Furthermore, we extend our
+approach to cluster data drawn from nonlinear subspaces by solving the
+corresponding problem in a reproducing kernel Hilbert space. The proposed
+algorithm outperforms state-of-the-art multi-view subspace clustering
+algorithms on one synthetic and four real-world datasets.
+",1,0,0,1,0,0
+839,"Single-trial P300 Classification using PCA with LDA, QDA and Neural Networks","  The P300 event-related potential (ERP), evoked in scalp-recorded
+electroencephalography (EEG) by external stimuli, has proven to be a reliable
+response for controlling a BCI. The P300 component of an event related
+potential is thus widely used in brain-computer interfaces to translate the
+subjects' intent by mere thoughts into commands to control artificial devices.
+The main challenge in the classification of P300 trials in
+electroencephalographic (EEG) data is the low signal-to-noise ratio (SNR) of
+the P300 response. To overcome the low SNR of individual trials, it is common
+practice to average together many consecutive trials, which effectively
+diminishes the random noise. Unfortunately, when more repeated trials are
+required for applications such as the P300 speller, the communication rate is
+greatly reduced. This has resulted in a need for better methods to improve
+single-trial classification accuracy of P300 response. In this work, we use
+Principal Component Analysis (PCA) as a preprocessing method and use Linear
+Discriminant Analysis (LDA)and neural networks for classification. The results
+show that a combination of PCA with these methods provided as high as 13\%
+accuracy gain for single-trial classification while using only 3 to 4 principal
+components.
+",1,0,0,1,0,0
+840,Stable representations of posets,"  The purpose of this paper is to study stable representations of partially
+ordered sets (posets) and compare it to the well known theory for quivers. In
+particular, we prove that every indecomposable representation of a poset of
+finite type is stable with respect to some weight and construct that weight
+explicitly in terms of the dimension vector. We show that if a poset is
+primitive then Coxeter transformations preserve stable representations. When
+the base field is the field of complex numbers we establish the connection
+between the polystable representations and the unitary $\chi$-representations
+of posets. This connection explains the similarity of the results obtained in
+the series of papers.
+",0,0,1,0,0,0
+841,Automatic segmentation of trees in dynamic outdoor environments,"  Segmentation in dynamic outdoor environments can be difficult when the
+illumination levels and other aspects of the scene cannot be controlled.
+Specifically in orchard and vineyard automation contexts, a background material
+is often used to shield a camera's field of view from other rows of crops. In
+this paper, we describe a method that uses superpixels to determine low texture
+regions of the image that correspond to the background material, and then show
+how this information can be integrated with the color distribution of the image
+to compute optimal segmentation parameters to segment objects of interest.
+Quantitative and qualitative experiments demonstrate the suitability of this
+approach for dynamic outdoor environments, specifically for tree reconstruction
+and apple flower detection applications.
+",1,0,0,0,0,0
+842,A Practical Bandit Method with Advantages in Neural Network Tuning,"  Stochastic bandit algorithms can be used for challenging non-convex
+optimization problems. Hyperparameter tuning of neural networks is particularly
+challenging, necessitating new approaches. To this end, we present a method
+that adaptively partitions the combined space of hyperparameters, context, and
+training resources (e.g., total number of training iterations). By adaptively
+partitioning the space, the algorithm is able to focus on the portions of the
+hyperparameter search space that are most relevant in a practical way. By
+including the resources in the combined space, the method tends to use fewer
+training resources overall. Our experiments show that this method can surpass
+state-of-the-art methods in tuning neural networks on benchmark datasets. In
+some cases, our implementations can achieve the same levels of accuracy on
+benchmark datasets as existing state-of-the-art approaches while saving over
+50% of our computational resources (e.g. time, training iterations).
+",1,0,0,1,0,0
+843,Dynamic Security Analysis of Power Systems by a Sampling-Based Algorithm,"  Dynamic security analysis is an important problem of power systems on
+ensuring safe operation and stable power supply even when certain faults occur.
+No matter such faults are caused by vulnerabilities of system components,
+physical attacks, or cyber-attacks that are more related to cyber-security,
+they eventually affect the physical stability of a power system. Examples of
+the loss of physical stability include the Northeast blackout of 2003 in North
+America and the 2015 system-wide blackout in Ukraine. The nonlinear hybrid
+nature, that is, nonlinear continuous dynamics integrated with discrete
+switching, and the high degree of freedom property of power system dynamics
+make it challenging to conduct the dynamic security analysis. In this paper, we
+use the hybrid automaton model to describe the dynamics of a power system and
+mainly deal with the index-1 differential-algebraic equation models regarding
+the continuous dynamics in different discrete states. The analysis problem is
+formulated as a reachability problem of the associated hybrid model. A
+sampling-based algorithm is then proposed by integrating modeling and
+randomized simulation of the hybrid dynamics to search for a feasible execution
+connecting an initial state of the post-fault system and a target set in the
+desired operation mode. The proposed method enables the use of existing power
+system simulators for the synthesis of discrete switching and control
+strategies through randomized simulation. The effectiveness and performance of
+the proposed approach are demonstrated with an application to the dynamic
+security analysis of the New England 39-bus benchmark power system exhibiting
+hybrid dynamics. In addition to evaluating the dynamic security, the proposed
+method searches for a feasible strategy to ensure the dynamic security of the
+system in face of disruptions.
+",1,0,0,0,0,0
+844,Stochastic and Chance-Constrained Conic Distribution System Expansion Planning Using Bilinear Benders Decomposition,"  Second order conic programming (SOCP) has been used to model various
+applications in power systems, such as operation and expansion planning. In
+this paper, we present a two-stage stochastic mixed integer SOCP (MISOCP) model
+for the distribution system expansion planning problem that considers
+uncertainty and also captures the nonlinear AC power flow. To avoid costly
+investment plans due to some extreme scenarios, we further present a
+chance-constrained variant that could lead to cost-effective solutions. To
+address the computational challenge, we extend the basic Benders decomposition
+method and develop a bilinear variant to compute stochastic and
+chance-constrained MISOCP formulations. A set of numerical experiments is
+performed to illustrate the performance of our models and computational
+methods. In particular, results show that our Benders decomposition algorithms
+drastically outperform a professional MISOCP solver in handling stochastic
+scenarios by orders of magnitude.
+",0,0,1,0,0,0
+845,Multi-Hop Extensions of Energy-Efficient Wireless Sensor Network Time Synchronization,"  We present the multi-hop extensions of the recently proposed energy-efficient
+time synchronization scheme for wireless sensor networks, which is based on the
+asynchronous source clock frequency recovery and reversed two-way message
+exchanges. We consider two hierarchical extensions based on packet relaying and
+time-translating gateways, respectively, and analyze their performance with
+respect to the number of layers and the delay variations through simulations.
+The simulation results demonstrate that the time synchronization performance of
+the packet relaying, which has lower complexity, is close to that of
+time-translating gateways.
+",1,0,0,0,0,0
+846,Value added or misattributed? A multi-institution study on the educational benefit of labs for reinforcing physics content,"  Instructional labs are widely seen as a unique, albeit expensive, way to
+teach scientific content. We measured the effectiveness of introductory lab
+courses at achieving this educational goal across nine different lab courses at
+three very different institutions. These institutions and courses encompassed a
+broad range of student populations and instructional styles. The nine courses
+studied had two key things in common: the labs aimed to reinforce the content
+presented in lectures, and the labs were optional. By comparing the performance
+of students who did and did not take the labs (with careful normalization for
+selection effects), we found universally and precisely no added value to
+learning from taking the labs as measured by course exam performance. This work
+should motivate institutions and departments to reexamine the goals and conduct
+of their lab courses, given their resource-intensive nature. We show why these
+results make sense when looking at the comparative mental processes of students
+involved in research and instructional labs, and offer alternative goals and
+instructional approaches that would make lab courses more educationally
+valuable.
+",0,1,0,0,0,0
+847,Smoothed Noise and Mexican Hat Coupling Produce Pattern in a Stochastic Neural Field,"  The formation of pattern in biological systems may be modeled by a set of
+reaction-diffusion equations. A diffusion-type coupling operator biologically
+significant in neuroscience is a difference of Gaussian functions (Mexican Hat
+operator) used as a spatial-convolution kernel. We are interested in the
+difference among behaviors of \emph{stochastic} neural field equations, namely
+space-time stochastic differential-integral equations, and similar
+deterministic ones. We explore, quantitatively, how the parameters of our model
+that measure the shape of the coupling kernel, coupling strength, and aspects
+of the spatially-smoothed space-time noise, control the pattern in the
+resulting evolving random field. We find that a spatial pattern that is damped
+in time in a deterministic system may be sustained and amplified by
+stochasticity, most strikingly at an optimal spatio-temporal noise level. In
+addition, we find that spatially-smoothed noise alone causes pattern formation
+even without spatial coupling.
+",0,0,0,0,1,0
+848,NeuroNER: an easy-to-use program for named-entity recognition based on neural networks,"  Named-entity recognition (NER) aims at identifying entities of interest in a
+text. Artificial neural networks (ANNs) have recently been shown to outperform
+existing NER systems. However, ANNs remain challenging to use for non-expert
+users. In this paper, we present NeuroNER, an easy-to-use named-entity
+recognition tool based on ANNs. Users can annotate entities using a graphical
+web-based user interface (BRAT): the annotations are then used to train an ANN,
+which in turn predict entities' locations and categories in new texts. NeuroNER
+makes this annotation-training-prediction flow smooth and accessible to anyone.
+",1,0,0,1,0,0
+849,Macro diversity in Cellular Networks with Random Blockages,"  Blocking objects (blockages) between a transmitter and receiver cause
+wireless communication links to transition from line-of-sight (LOS) to
+non-line-of-sight (NLOS) propagation, which can greatly reduce the received
+power, particularly at higher frequencies such as millimeter wave (mmWave). We
+consider a cellular network in which a mobile user attempts to connect to two
+or more base stations (BSs) simultaneously, to increase the probability of at
+least one LOS link, which is a form of macrodiversity. We develop a framework
+for determining the LOS probability as a function of the number of BSs, when
+taking into account the correlation between blockages: for example, a single
+blockage close to the device -- including the user's own body -- could block
+multiple BSs. We consider the impact of the size of blocking objects on the
+system reliability probability and show that macrodiversity gains are higher
+when the blocking objects are small. We also show that the BS density must
+scale as the square of the blockage density to maintain a given level of
+reliability.
+",1,0,1,0,0,0
+850,Opinion dynamics model based on cognitive biases,"  We present an introduction to a novel model of an individual and group
+opinion dynamics, taking into account different ways in which different sources
+of information are filtered due to cognitive biases. The agent based model,
+using Bayesian updating of the individual belief distribution, is based on the
+recent psychology work by Dan Kahan. Open nature of the model allows to study
+the effects of both static and time-dependent biases and information processing
+filters. In particular, the paper compares the effects of two important
+psychological mechanisms: the confirmation bias and the politically motivated
+reasoning. Depending on the effectiveness of the information filtering (agent
+bias), the agents confronted with an objective information source may either
+reach a consensus based on the truth, or remain divided despite the evidence.
+In general, the model might provide an understanding into the increasingly
+polarized modern societies, especially as it allows mixing of different types
+of filters: psychological, social, and algorithmic.
+",1,1,0,0,0,0
+851,ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness,"  Convolutional Neural Networks (CNNs) are commonly thought to recognise
+objects by learning increasingly complex representations of object shapes. Some
+recent studies suggest a more important role of image textures. We here put
+these conflicting hypotheses to a quantitative test by evaluating CNNs and
+human observers on images with a texture-shape cue conflict. We show that
+ImageNet-trained CNNs are strongly biased towards recognising textures rather
+than shapes, which is in stark contrast to human behavioural evidence and
+reveals fundamentally different classification strategies. We then demonstrate
+that the same standard architecture (ResNet-50) that learns a texture-based
+representation on ImageNet is able to learn a shape-based representation
+instead when trained on ""Stylized-ImageNet"", a stylized version of ImageNet.
+This provides a much better fit for human behavioural performance in our
+well-controlled psychophysical lab setting (nine experiments totalling 48,560
+psychophysical trials across 97 observers) and comes with a number of
+unexpected emergent benefits such as improved object detection performance and
+previously unseen robustness towards a wide range of image distortions,
+highlighting advantages of a shape-based representation.
+",0,0,0,0,1,0
+852,Explicit evaluation of harmonic sums,"  In this paper, we obtain some formulae for harmonic sums, alternating
+harmonic sums and Stirling number sums by using the method of integral
+representations of series. As applications of these formulae, we give explicit
+formula of several quadratic and cubic Euler sums through zeta values and
+linear sums. Furthermore, some relationships between harmonic numbers and
+Stirling numbers of the first kind are established.
+",0,0,1,0,0,0
+853,Complete Cyclic Proof Systems for Inductive Entailments,"  In this paper we develop cyclic proof systems for the problem of inclusion
+between the least sets of models of mutually recursive predicates, when the
+ground constraints in the inductive definitions belong to the quantifier-free
+fragments of (i) First Order Logic with the canonical Herbrand interpretation
+and (ii) Separation Logic, respectively. Inspired by classical
+automata-theoretic techniques of proving language inclusion between tree
+automata, we give a small set of inference rules, that are proved to be sound
+and complete, under certain semantic restrictions, involving the set of
+constraints in the inductive system. Moreover, we investigate the decidability
+and computational complexity of these restrictions for all the logical
+fragments considered and provide a proof search semi-algorithm that becomes a
+decision procedure for the entailment problem, for those systems that fulfill
+the restrictions.
+",1,0,0,0,0,0
+854,HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning,"  In this paper, we introduce a new model for leveraging unlabeled data to
+improve generalization performances of image classifiers: a two-branch
+encoder-decoder architecture called HybridNet. The first branch receives
+supervision signal and is dedicated to the extraction of invariant
+class-related representations. The second branch is fully unsupervised and
+dedicated to model information discarded by the first branch to reconstruct
+input data. To further support the expected behavior of our model, we propose
+an original training objective. It favors stability in the discriminative
+branch and complementarity between the learned representations in the two
+branches. HybridNet is able to outperform state-of-the-art results on CIFAR-10,
+SVHN and STL-10 in various semi-supervised settings. In addition,
+visualizations and ablation studies validate our contributions and the behavior
+of the model on both CIFAR-10 and STL-10 datasets.
+",0,0,0,1,0,0
+855,Learning Distributed Representations of Texts and Entities from Knowledge Base,"  We describe a neural network model that jointly learns distributed
+representations of texts and knowledge base (KB) entities. Given a text in the
+KB, we train our proposed model to predict entities that are relevant to the
+text. Our model is designed to be generic with the ability to address various
+NLP tasks with ease. We train the model using a large corpus of texts and their
+entity annotations extracted from Wikipedia. We evaluated the model on three
+important NLP tasks (i.e., sentence textual similarity, entity linking, and
+factoid question answering) involving both unsupervised and supervised
+settings. As a result, we achieved state-of-the-art results on all three of
+these tasks. Our code and trained models are publicly available for further
+academic research.
+",1,0,0,0,0,0
+856,Moduli Spaces of Unordered $n\ge5$ Points on the Riemann Sphere and Their Singularities,"  For $n\ge5$, it is well known that the moduli space $\mathfrak{M_{0,\:n}}$ of
+unordered $n$ points on the Riemann sphere is a quotient space of the Zariski
+open set $K_n$ of $\mathbb C^{n-3}$ by an $S_n$ action. The stabilizers of this
+$S_n$ action at certain points of this Zariski open set $K_n$ correspond to the
+groups fixing the sets of $n$ points on the Riemann sphere. Let $\alpha$ be a
+subset of $n$ distinct points on the Riemann sphere. We call the group of all
+linear fractional transformations leaving $\alpha$ invariant the stabilizer of
+$\alpha$, which is finite by observation. For each non-trivial finite subgroup
+$G$ of the group ${\rm PSL}(2,{\Bbb C})$ of linear fractional transformations,
+we give the necessary and sufficient condition for finite subsets of the
+Riemann sphere under which the stabilizers of them are conjugate to $G$. We
+also prove that there does exist some finite subset of the Riemann sphere whose
+stabilizer coincides with $G$. Next we obtain the irreducible decompositions of
+the representations of the stabilizers on the tangent spaces at the
+singularities of $\mathfrak{M_{0,\:n}}$. At last, on $\mathfrak{M_{0,\:5}}$ and
+$\mathfrak{M_{0,\:6}}$, we work out explicitly the singularities and the
+representations of their stabilizers on the tangent spaces at them.
+",0,0,1,0,0,0
+857,Multipath Error Correction in Radio Interferometric Positioning Systems,"  The radio interferometric positioning system (RIPS) is an accurate node
+localization method featuring a novel phase-based ranging process. Multipath is
+the limiting error source for RIPS in ground-deployed scenarios or indoor
+applications. There are four distinct channels involved in the ranging process
+for RIPS. Multipath reflections affect both the phase and amplitude of the
+ranging signal for each channel. By exploiting untapped amplitude information,
+we put forward a scheme to estimate each channel's multipath profile, which is
+then subsequently used to correct corresponding errors in phase measurements.
+Simulations show that such a scheme is very effective in reducing multipath
+phase errors, which are essentially brought down to the level of receiver noise
+under moderate multipath conditions. It is further demonstrated that ranging
+errors in RIPS are also greatly reduced via the proposed scheme.
+",1,0,0,0,0,0
+858,An evaluation homomorphism for quantum toroidal gl(n) algebras,"  We present an affine analog of the evaluation map for quantum groups. Namely
+we introduce a surjective homomorphism from the quantum toroidal gl(n) algebra
+to the quantum affine gl(n) algebra completed with respect to the homogeneous
+grading. We give a brief discussion of evaluation modules.
+",0,0,1,0,0,0
+859,"A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms","  In this paper we present a framework for risk-sensitive model predictive
+control (MPC) of linear systems affected by stochastic multiplicative
+uncertainty. Our key innovation is to consider a time-consistent, dynamic risk
+evaluation of the cumulative cost as the objective function to be minimized.
+This framework is axiomatically justified in terms of time-consistency of risk
+assessments, is amenable to dynamic optimization, and is unifying in the sense
+that it captures a full range of risk preferences from risk-neutral (i.e.,
+expectation) to worst case. Within this framework, we propose and analyze an
+online risk-sensitive MPC algorithm that is provably stabilizing. Furthermore,
+by exploiting the dual representation of time-consistent, dynamic risk
+measures, we cast the computation of the MPC control law as a convex
+optimization problem amenable to real-time implementation. Simulation results
+are presented and discussed.
+",1,0,1,0,0,0
+860,Monitoring Telluric Absorption with CAMAL,"  Ground-based astronomical observations may be limited by telluric water vapor
+absorption, which is highly variable in time and significantly complicates both
+spectroscopy and photometry in the near-infrared (NIR). To achieve the
+sensitivity required to detect Earth-sized exoplanets in the NIR, simultaneous
+monitoring of precipitable water vapor (PWV) becomes necessary to mitigate the
+impact of variable telluric lines on radial velocity measurements and transit
+light curves. To address this issue, we present the Camera for the Automatic
+Monitoring of Atmospheric Lines (CAMAL), a stand-alone, inexpensive six-inch
+aperture telescope dedicated to measuring PWV at the Fred Lawrence Whipple
+Observatory on Mount Hopkins. CAMAL utilizes three narrowband NIR filters to
+trace the amount of atmospheric water vapor affecting simultaneous observations
+with the MINiature Exoplanet Radial Velocity Array (MINERVA) and MINERVA-Red
+telescopes. Here we present the current design of CAMAL, discuss our data
+analysis methods, and show results from 11 nights of PWV measurements taken
+with CAMAL. For seven nights of data, we have independent PWV measurements
+extracted from high-resolution stellar spectra taken with the Tillinghast
+Reflector Echelle Spectrometer (TRES) also located on Mount Hopkins. We use the
+TRES spectra to calibrate the CAMAL absolute PWV scale. Comparisons between
+CAMAL and TRES PWV estimates show excellent agreement, matching to within 1 mm
+over a 10 mm range in PWV. Analysis of CAMAL's photometric precision propagates
+to PWV measurements precise to better than 0.5 mm in dry (PWV < 4 mm)
+conditions. We also find that CAMAL-derived PWVs are highly correlated with
+those from a GPS-based water vapor monitor located approximately 90 km away at
+Kitt Peak National Observatory, with a root mean square PWV difference of 0.8
+mm.
+",0,1,0,0,0,0
+861,Common Knowledge in a Logic of Gossips,"  Gossip protocols aim at arriving, by means of point-to-point or group
+communications, at a situation in which all the agents know each other secrets.
+Recently a number of authors studied distributed epistemic gossip protocols.
+These protocols use as guards formulas from a simple epistemic logic, which
+makes their analysis and verification substantially easier.
+We study here common knowledge in the context of such a logic. First, we
+analyze when it can be reduced to iterated knowledge. Then we show that the
+semantics and truth for formulas without nested common knowledge operator are
+decidable. This implies that implementability, partial correctness and
+termination of distributed epistemic gossip protocols that use non-nested
+common knowledge operator is decidable, as well. Given that common knowledge is
+equivalent to an infinite conjunction of nested knowledge, these results are
+non-trivial generalizations of the corresponding decidability results for the
+original epistemic logic, established in (Apt & Wojtczak, 2016).
+K. R. Apt & D. Wojtczak (2016): On Decidability of a Logic of Gossips. In
+Proc. of JELIA 2016, pp. 18-33, doi:10.1007/ 978-3-319-48758-8_2.
+",1,0,0,0,0,0
+862,Network-theoretic approach to sparsified discrete vortex dynamics,"  We examine discrete vortex dynamics in two-dimensional flow through a
+network-theoretic approach. The interaction of the vortices is represented with
+a graph, which allows the use of network-theoretic approaches to identify key
+vortex-to-vortex interactions. We employ sparsification techniques on these
+graph representations based on spectral theory for constructing sparsified
+models and evaluating the dynamics of vortices in the sparsified setup.
+Identification of vortex structures based on graph sparsification and sparse
+vortex dynamics are illustrated through an example of point-vortex clusters
+interacting amongst themselves. We also evaluate the performance of
+sparsification with increasing number of point vortices. The
+sparsified-dynamics model developed with spectral graph theory requires reduced
+number of vortex-to-vortex interactions but agrees well with the full nonlinear
+dynamics. Furthermore, the sparsified model derived from the sparse graphs
+conserves the invariants of discrete vortex dynamics. We highlight the
+similarities and differences between the present sparsified-dynamics model and
+the reduced-order models.
+",0,1,0,0,0,0
+863,Bäcklund Transformation and Quasi-Integrable Deformation of Mixed Fermi-Pasta-Ulam and Frenkel-Kontorova Models,"  In this paper we study a non-linear partial differential equation (PDE),
+proposed by N. Kudryashov [arXiv:1611.06813v1[nlin.SI]], using continuum limit
+approximation of mixed Fermi-Pasta-Ulam and Frenkel-Kontorova Models. This
+generalized semi-discrete equation can be considered as a model for the
+description of non-linear dislocation waves in crystal lattice and the
+corresponding continuous system can be called mixed generalized potential KdV
+and sine-Gordon equation. We obtain the Bäcklund transformation of this
+equation in Riccati form in inverse method. We further study the
+quasi-integrable deformation of this model.
+",0,1,1,0,0,0
+864,Data-Mining Textual Responses to Uncover Misconception Patterns,"  An important, yet largely unstudied, problem in student data analysis is to
+detect misconceptions from students' responses to open-response questions.
+Misconception detection enables instructors to deliver more targeted feedback
+on the misconceptions exhibited by many students in their class, thus improving
+the quality of instruction. In this paper, we propose a new natural language
+processing-based framework to detect the common misconceptions among students'
+textual responses to short-answer questions. We propose a probabilistic model
+for students' textual responses involving misconceptions and experimentally
+validate it on a real-world student-response dataset. Experimental results show
+that our proposed framework excels at classifying whether a response exhibits
+one or more misconceptions. More importantly, it can also automatically detect
+the common misconceptions exhibited across responses from multiple students to
+multiple questions; this property is especially important at large scale, since
+instructors will no longer need to manually specify all possible misconceptions
+that students might exhibit.
+",1,0,0,1,0,0
+865,Symmetries of handlebodies and their fixed points: Dihedral extended Schottky groups,"  A Schottky structure on a handlebody $M$ of genus $g$ is provided by a
+Schottky group of rank $g$. A symmetry (an orientation-reversing involution) of
+$M$ is known to have at most $(g+1)$ connected components of fixed points. Each
+of these components is either a point or a compact bordered surface (either
+orientable or not) whose boundary is contained in the border of $M$. In this
+paper, we derive sharp upper bounds for the total number of connected
+components of the sets of fixed points of given two or three symmetries of $M$.
+In order to obtain such an upper bound, we obtain a geometrical structure
+description of those extended Kleinian groups $K$ containing a Schottky group
+$\Gamma$ as finite index normal subgroup so that $K/\Gamma$ is a dihedral group
+(called dihedral Schottky groups). Our upper bounds turn out to be different to
+the corresponding ones at the level of closed Riemann surfaces. In contrast to
+the case of Riemann surfaces, we observe that $M$ cannot have two different
+maximal symmetries.
+",0,0,1,0,0,0
+866,Speaker Diarization using Deep Recurrent Convolutional Neural Networks for Speaker Embeddings,"  In this paper we propose a new method of speaker diarization that employs a
+deep learning architecture to learn speaker embeddings. In contrast to the
+traditional approaches that build their speaker embeddings using manually
+hand-crafted spectral features, we propose to train for this purpose a
+recurrent convolutional neural network applied directly on magnitude
+spectrograms. To compare our approach with the state of the art, we collect and
+release for the public an additional dataset of over 6 hours of fully annotated
+broadcast material. The results of our evaluation on the new dataset and three
+other benchmark datasets show that our proposed method significantly
+outperforms the competitors and reduces diarization error rate by a large
+margin of over 30% with respect to the baseline.
+",1,0,0,0,0,0
+867,A Robust Multi-Batch L-BFGS Method for Machine Learning,"  This paper describes an implementation of the L-BFGS method designed to deal
+with two adversarial situations. The first occurs in distributed computing
+environments where some of the computational nodes devoted to the evaluation of
+the function and gradient are unable to return results on time. A similar
+challenge occurs in a multi-batch approach in which the data points used to
+compute function and gradients are purposely changed at each iteration to
+accelerate the learning process. Difficulties arise because L-BFGS employs
+gradient differences to update the Hessian approximations, and when these
+gradients are computed using different data points the updating process can be
+unstable. This paper shows how to perform stable quasi-Newton updating in the
+multi-batch setting, studies the convergence properties for both convex and
+nonconvex functions, and illustrates the behavior of the algorithm in a
+distributed computing platform on binary classification logistic regression and
+neural network training problems that arise in machine learning.
+",1,0,1,1,0,0
+868,Network of vertically c-oriented prism shaped InN nanowalls grown on c-GaN/sapphire template by chemical vapor deposition technique,"  Networks of vertically c-oriented prism shaped InN nanowalls, are grown on
+c-GaN/sapphire templates using a CVD technique, where pure indium and ammonia
+are used as metal and nitrogen precursors. A systematic study of the growth,
+structural and electronic properties of these samples shows a preferential
+growth of the islands along [11-20] and [0001] directions leading to the
+formation of such a network structure, where the vertically [0001] oriented
+tapered walls are laterally align along one of the three [11-20] directions.
+Inclined facets of these walls are identified as r-planes [(1-102)-planes] of
+wurtzite InN. Onset of absorption for these samples is observed to be higher
+than the band gap of InN suggesting a high background carrier concentration in
+this material. Study of the valence band edge through XPS indicates the
+formation of positive depletion regions below the r-plane side facets of the
+walls. This is in contrast with the observation for c-plane InN epilayers,
+where electron accumulation is often reported below the top surface.
+",0,1,0,0,0,0
+869,Enhanced version of AdaBoostM1 with J48 Tree learning method,"  Machine Learning focuses on the construction and study of systems that can
+learn from data. This is connected with the classification problem, which
+usually is what Machine Learning algorithms are designed to solve. When a
+machine learning method is used by people with no special expertise in machine
+learning, it is important that the method be robust in classification, in the
+sense that reasonable performance is obtained with minimal tuning of the
+problem at hand. Algorithms are evaluated based on how robust they can classify
+the given data. In this paper, we propose a quantifiable measure of robustness,
+and describe a particular learning method that is robust according to this
+measure in the context of classification problem. We proposed Adaptive Boosting
+(AdaBoostM1) with J48(C4.5 tree) as a base learner with tuning weight threshold
+(P) and number of iterations (I) for boosting algorithm. To benchmark the
+performance, we used the baseline classifier, AdaBoostM1 with Decision Stump as
+base learner without tuning parameters. By tuning parameters and using J48 as
+base learner, we are able to reduce the overall average error rate ratio
+(errorC/errorNB) from 2.4 to 0.9 for development sets of data and 2.1 to 1.2
+for evaluation sets of data.
+",0,0,0,1,0,0
+870,A Game of Tax Evasion: evidences from an agent-based model,"  This paper presents a simple agent-based model of an economic system,
+populated by agents playing different games according to their different view
+about social cohesion and tax payment. After a first set of simulations,
+correctly replicating results of existing literature, a wider analysis is
+presented in order to study the effects of a dynamic-adaptation rule, in which
+citizens may possibly decide to modify their individual tax compliance
+according to individual criteria, such as, the strength of their ethical
+commitment, the satisfaction gained by consumption of the public good and the
+perceived opinion of neighbors. Results show the presence of thresholds levels
+in the composition of society - between taxpayers and evaders - which explain
+the extent of damages deriving from tax evasion.
+",0,0,0,0,0,1
+871,Variability response functions for statically determinate beams with arbitrary nonlinear constitutive laws,"  The variability response function (VRF) is generalized to statically
+determinate Euler Bernoulli beams with arbitrary stress-strain laws following
+Cauchy elastic behavior. The VRF is a Green's function that maps the spectral
+density function (SDF) of a statistically homogeneous random field describing
+the correlation structure of input uncertainty to the variance of a response
+quantity. The appeal of such Green's functions is that the variance can be
+determined for any correlation structure by a trivial computation of a
+convolution integral. The method introduced in this work derives VRFs in closed
+form for arbitrary nonlinear Cauchy-elastic constitutive laws and is
+demonstrated through three examples. It is shown why and how higher order
+spectra of the random field affect the response variance for nonlinear
+constitutive laws. In the general sense, the VRF for a statically determinate
+beam is found to be a matrix kernel whose inner product by a matrix of higher
+order SDFs and statistical moments is integrated to give the response variance.
+The resulting VRF matrix is unique regardless of the random field's marginal
+probability density function (PDF) and SDFs.
+",0,1,0,0,0,0
+872,Bayesian Uncertainty Estimation for Batch Normalized Deep Networks,"  We show that training a deep network using batch normalization is equivalent
+to approximate inference in Bayesian models. We further demonstrate that this
+finding allows us to make meaningful estimates of the model uncertainty using
+conventional architectures, without modifications to the network or the
+training procedure. Our approach is thoroughly validated by measuring the
+quality of uncertainty in a series of empirical experiments on different tasks.
+It outperforms baselines with strong statistical significance, and displays
+competitive performance with recent Bayesian approaches.
+",0,0,0,1,0,0
+873,Multi-Antenna Coded Caching,"  In this paper we consider a single-cell downlink scenario where a
+multiple-antenna base station delivers contents to multiple cache-enabled user
+terminals. Based on the multicasting opportunities provided by the so-called
+Coded Caching technique, we investigate three delivery approaches. Our baseline
+scheme employs the coded caching technique on top of max-min fair multicasting.
+The second one consists of a joint design of Zero-Forcing (ZF) and coded
+caching, where the coded chunks are formed in the signal domain (complex
+field). The third scheme is similar to the second one with the difference that
+the coded chunks are formed in the data domain (finite field). We derive
+closed-form rate expressions where our results suggest that the latter two
+schemes surpass the first one in terms of Degrees of Freedom (DoF). However, at
+the intermediate SNR regime forming coded chunks in the signal domain results
+in power loss, and will deteriorate throughput of the second scheme. The main
+message of our paper is that the schemes performing well in terms of DoF may
+not be directly appropriate for intermediate SNR regimes, and modified schemes
+should be employed.
+",1,0,1,0,0,0
+874,Convolved subsampling estimation with applications to block bootstrap,"  The block bootstrap approximates sampling distributions from dependent data
+by resampling data blocks. A fundamental problem is establishing its
+consistency for the distribution of a sample mean, as a prototypical statistic.
+We use a structural relationship with subsampling to characterize the bootstrap
+in a new and general manner. While subsampling and block bootstrap differ, the
+block bootstrap distribution of a sample mean equals that of a $k$-fold
+self-convolution of a subsampling distribution. Motivated by this, we provide
+simple necessary and sufficient conditions for a convolved subsampling
+estimator to produce a normal limit that matches the target of bootstrap
+estimation. These conditions may be linked to consistency properties of an
+original subsampling distribution, which are often obtainable under minimal
+assumptions. Through several examples, the results are shown to validate the
+block bootstrap for means under significantly weakened assumptions in many
+existing (and some new) dependence settings, which also addresses a standing
+conjecture of Politis, Romano and Wolf(1999). Beyond sample means, the
+convolved subsampling estimator may not match the block bootstrap, but instead
+provides a hybrid-resampling estimator of interest in its own right. For
+general statistics with normal limits, results also establish the consistency
+of convolved subsampling under minimal dependence conditions, including
+non-stationarity.
+",0,0,1,1,0,0
+875,Computing Simple Multiple Zeros of Polynomial Systems,"  Given a polynomial system f associated with a simple multiple zero x of
+multiplicity {\mu}, we give a computable lower bound on the minimal distance
+between the simple multiple zero x and other zeros of f. If x is only given
+with limited accuracy, we propose a numerical criterion that f is certified to
+have {\mu} zeros (counting multiplicities) in a small ball around x.
+Furthermore, for simple double zeros and simple triple zeros whose Jacobian is
+of normalized form, we define modified Newton iterations and prove the
+quantified quadratic convergence when the starting point is close to the exact
+simple multiple zero. For simple multiple zeros of arbitrary multiplicity whose
+Jacobian matrix may not have a normalized form, we perform unitary
+transformations and modified Newton iterations, and prove its non-quantified
+quadratic convergence and its quantified convergence for simple triple zeros.
+",0,0,1,0,0,0
+876,Latent Geometry and Memorization in Generative Models,"  It can be difficult to tell whether a trained generative model has learned to
+generate novel examples or has simply memorized a specific set of outputs. In
+published work, it is common to attempt to address this visually, for example
+by displaying a generated example and its nearest neighbor(s) in the training
+set (in, for example, the L2 metric). As any generative model induces a
+probability density on its output domain, we propose studying this density
+directly. We first study the geometry of the latent representation and
+generator, relate this to the output density, and then develop techniques to
+compute and inspect the output density. As an application, we demonstrate that
+""memorization"" tends to a density made of delta functions concentrated on the
+memorized examples. We note that without first understanding the geometry, the
+measurement would be essentially impossible to make.
+",1,0,0,1,0,0
+877,Refracting Metasurfaces without Spurious Diffraction,"  Refraction represents one of the most fundamental operations that may be
+performed by a metasurface. However, simple phasegradient metasurface designs
+suffer from restricted angular deflection due to spurious diffraction orders.
+It has been recently shown, using a circuit-based approach, that refraction
+without spurious diffraction, or diffraction-free, can fortunately be achieved
+by a transverse metasurface exhibiting either loss-gain or bianisotropy. Here,
+we rederive these conditions using a medium-based - and hence more insightfull
+- approach based on Generalized Sheet Transition Conditions (GSTCs) and surface
+susceptibility tensors, and experimentally demonstrate two diffraction-free
+refractive metasurfaces that are essentially lossless, passive, bianisotropic
+and reciprocal.
+",0,1,0,0,0,0
+878,Accurate Motion Estimation through Random Sample Aggregated Consensus,"  We reconsider the classic problem of estimating accurately a 2D
+transformation from point matches between images containing outliers. RANSAC
+discriminates outliers by randomly generating minimalistic sampled hypotheses
+and verifying their consensus over the input data. Its response is based on the
+single hypothesis that obtained the largest inlier support. In this article we
+show that the resulting accuracy can be improved by aggregating all generated
+hypotheses. This yields RANSAAC, a framework that improves systematically over
+RANSAC and its state-of-the-art variants by statistically aggregating
+hypotheses. To this end, we introduce a simple strategy that allows to rapidly
+average 2D transformations, leading to an almost negligible extra computational
+cost. We give practical applications on projective transforms and
+homography+distortion models and demonstrate a significant performance gain in
+both cases.
+",1,0,0,0,0,0
+879,Landau Collision Integral Solver with Adaptive Mesh Refinement on Emerging Architectures,"  The Landau collision integral is an accurate model for the small-angle
+dominated Coulomb collisions in fusion plasmas. We investigate a high order
+accurate, fully conservative, finite element discretization of the nonlinear
+multi-species Landau integral with adaptive mesh refinement using the PETSc
+library (www.mcs.anl.gov/petsc). We develop algorithms and techniques to
+efficiently utilize emerging architectures with an approach that minimizes
+memory usage and movement and is suitable for vector processing. The Landau
+collision integral is vectorized with Intel AVX-512 intrinsics and the solver
+sustains as much as 22% of the theoretical peak flop rate of the Second
+Generation Intel Xeon Phi, Knights Landing, processor.
+",1,0,0,0,0,0
+880,A Topologist's View of Kinematic Maps and Manipulation Complexity,"  In this paper we combine a survey of the most important topological
+properties of kinematic maps that appear in robotics, with the exposition of
+some basic results regarding the topological complexity of a map. In
+particular, we discuss mechanical devices that consist of rigid parts connected
+by joints and show how the geometry of the joints determines the forward
+kinematic map that relates the configuration of joints with the pose of the
+end-effector of the device. We explain how to compute the dimension of the
+joint space and describe topological obstructions for a kinematic map to be a
+fibration or to admit a continuous section. In the second part of the paper we
+define the complexity of a continuous map and show how the concept can be
+viewed as a measure of the difficulty to find a robust manipulation plan for a
+given mechanical device. We also derive some basic estimates for the complexity
+and relate it to the degree of instability of a manipulation plan.
+",1,0,1,0,0,0
+881,Faster Tensor Canonicalization,"  The Butler-Portugal algorithm for obtaining the canonical form of a tensor
+expression with respect to slot symmetries and dummy-index renaming suffers, in
+certain cases with a high degree of symmetry, from $O(n!)$ explosion in both
+computation time and memory. We present a modified algorithm which alleviates
+this problem in the most common cases---tensor expressions with subsets of
+indices which are totally symmetric or totally antisymmetric---in polynomial
+time. We also present an implementation of the label-renaming mechanism which
+improves upon that of the original Butler-Portugal algorithm, thus providing a
+significant speed increase for the average case as well as the highly-symmetric
+special case. The worst-case behavior remains $O(n!)$, although it occurs in
+more limited situations unlikely to appear in actual computations. We comment
+on possible strategies to take if the nature of a computation should make these
+situations more likely.
+",1,0,0,0,0,0
+882,Mass Preconditioning for the Exact One-Flavor Action in Lattice QCD with Domain-Wall Fermion,"  The mass-preconditioning (MP) technique has become a standard tool to enhance
+the efficiency of the hybrid Monte-Carlo simulation (HMC) of lattice QCD with
+dynamical quarks, for 2-flavors QCD with degenerate quark masses, as well as
+its extension to the case of one-flavor by taking the square-root of the
+fermion determinant of 2-flavors with degenerate masses. However, for lattice
+QCD with domain-wall fermion, the fermion determinant of any single fermion
+flavor can be expressed as a functional integral with an exact pseudofermion
+action $ \phi^\dagger H^{-1} \phi $, where $ H^{-1} $ is a positive-definite
+Hermitian operator without taking square-root, and with the chiral structure
+\cite{Chen:2014hyy}. Consequently, the mass-preconditioning for the exact
+one-flavor action (EOFA) does not necessarily follow the conventional (old) MP
+pattern. In this paper, we present a new mass-preconditioning for the EOFA,
+which is more efficient than the old MP which we have used in Refs.
+\cite{Chen:2014hyy,Chen:2014bbc}. We perform numerical tests in lattice QCD
+with $ N_f = 1 $ and $ N_f = 1+1+1+1 $ optimal domain-wall quarks, with one
+mass-preconditioner applied to one of the exact one-flavor actions, and we find
+that the efficiency of the new MP is more than 20\% higher than that of the old
+MP.
+",0,1,0,0,0,0
+883,Finite-time scaling at the Anderson transition for vibrations in solids,"  A model in which a three-dimensional elastic medium is represented by a
+network of identical masses connected by springs of random strengths and
+allowed to vibrate only along a selected axis of the reference frame, exhibits
+an Anderson localization transition. To study this transition, we assume that
+the dynamical matrix of the network is given by a product of a sparse random
+matrix with real, independent, Gaussian-distributed non-zero entries and its
+transpose. A finite-time scaling analysis of system's response to an initial
+excitation allows us to estimate the critical parameters of the localization
+transition. The critical exponent is found to be $\nu = 1.57 \pm 0.02$ in
+agreement with previous studies of Anderson transition belonging to the
+three-dimensional orthogonal universality class.
+",0,1,0,0,0,0
+884,Universal and shape dependent features of surface superconductivity,"  We analyze the response of a type II superconducting wire to an external
+magnetic field parallel to it in the framework of Ginzburg-Landau theory. We
+focus on the surface superconductivity regime of applied field between the
+second and third critical values, where the superconducting state survives only
+close to the sample's boundary. Our first finding is that, in first
+approximation, the shape of the boundary plays no role in determining the
+density of superconducting electrons. A second order term is however isolated,
+directly proportional to the mean curvature of the boundary. This demonstrates
+that points of higher boundary curvature (counted inwards) attract
+superconducting electrons.
+",0,1,1,0,0,0
+885,Weighted Low Rank Approximation for Background Estimation Problems,"  Classical principal component analysis (PCA) is not robust to the presence of
+sparse outliers in the data. The use of the $\ell_1$ norm in the Robust PCA
+(RPCA) method successfully eliminates the weakness of PCA in separating the
+sparse outliers. In this paper, by sticking a simple weight to the Frobenius
+norm, we propose a weighted low rank (WLR) method to avoid the often
+computationally expensive algorithms relying on the $\ell_1$ norm. As a proof
+of concept, a background estimation model has been presented and compared with
+two $\ell_1$ norm minimization algorithms. We illustrate that as long as a
+simple weight matrix is inferred from the data, one can use the weighted
+Frobenius norm and achieve the same or better performance.
+",0,0,1,0,0,0
+886,Controlling Chiral Domain Walls in Antiferromagnets Using Spin-Wave Helicity,"  In antiferromagnets, the Dzyaloshinskii-Moriya interaction lifts the
+degeneracy of left- and right-circularly polarized spin waves. This
+relativistic coupling increases the efficiency of spin-wave-induced domain wall
+motion and leads to higher drift velocities. We show that in biaxial
+antiferromagnets, the spin-wave helicity controls both the direction and
+magnitude of the magnonic force on chiral domain walls. By contrast, in
+uniaxial antiferromagnets, the magnonic force is propulsive with a helicity
+dependent strength.
+",0,1,0,0,0,0
+887,The Spatial Shape of Avalanches,"  In disordered elastic systems, driven by displacing a parabolic confining
+potential adiabatically slowly, all advance of the system is in bursts, termed
+avalanches. Avalanches have a finite extension in time, which is much smaller
+than the waiting-time between them. Avalanches also have a finite extension
+$\ell$ in space, i.e. only a part of the interface of size $\ell$ moves during
+an avalanche. Here we study their spatial shape $\left< S(x)\right>_{\ell}$
+given $\ell$, as well as its fluctuations encoded in the second cumulant
+$\left< S^{2}(x)\right>_{\ell}^{\rm c}$. We establish scaling relations
+governing the behavior close to the boundary. We then give analytic results for
+the Brownian force model, in which the microscopic disorder for each degree of
+freedom is a random walk. Finally, we confirm these results with numerical
+simulations. To do this properly we elucidate the influence of discretization
+effects, which also confirms the assumptions entering into the scaling ansatz.
+This allows us to reach the scaling limit already for avalanches of moderate
+size. We find excellent agreement for the universal shape, its fluctuations,
+including all amplitudes.
+",0,1,0,0,0,0
+888,Multiple Topological Electronic Phases in Superconductor MoC,"  The search for a superconductor with non-s-wave pairing is important not only
+for understanding unconventional mechanisms of superconductivity but also for
+finding new types of quasiparticles such as Majorana bound states. Materials
+with both topological band structure and superconductivity are promising
+candidates as $p+ip$ superconducting states can be generated through pairing
+the spin-polarized topological surface states. In this work, the electronic and
+phonon properties of the superconductor molybdenum carbide (MoC) are studied
+with first-principles methods. Our calculations show that nontrivial band
+topology and superconductivity coexist in both structural phases of MoC,
+namely, the cubic $\alpha$ and hexagonal $\gamma$ phases. The $\alpha$ phase is
+a strong topological insulator and the $\gamma$ phase is a topological nodal
+line semimetal with drumhead surface states. In addition, hole doping can
+stabilize the crystal structure of the $\alpha$ phase and elevate the
+transition temperature in the $\gamma$ phase. Therefore, MoC in different
+structural forms can be a practical material platform for studying topological
+superconductivity and elusive Majorana fermions.
+",0,1,0,0,0,0
+889,Extension complexity of stable set polytopes of bipartite graphs,"  The extension complexity $\mathsf{xc}(P)$ of a polytope $P$ is the minimum
+number of facets of a polytope that affinely projects to $P$. Let $G$ be a
+bipartite graph with $n$ vertices, $m$ edges, and no isolated vertices. Let
+$\mathsf{STAB}(G)$ be the convex hull of the stable sets of $G$. It is easy to
+see that $n \leqslant \mathsf{xc} (\mathsf{STAB}(G)) \leqslant n+m$. We improve
+both of these bounds. For the upper bound, we show that $\mathsf{xc}
+(\mathsf{STAB}(G))$ is $O(\frac{n^2}{\log n})$, which is an improvement when
+$G$ has quadratically many edges. For the lower bound, we prove that
+$\mathsf{xc} (\mathsf{STAB}(G))$ is $\Omega(n \log n)$ when $G$ is the
+incidence graph of a finite projective plane. We also provide examples of
+$3$-regular bipartite graphs $G$ such that the edge vs stable set matrix of $G$
+has a fooling set of size $|E(G)|$.
+",1,0,0,0,0,0
+890,Performance Scaling Law for Multi-Cell Multi-User Massive MIMO,"  This work provides a comprehensive scaling law based performance analysis for
+multi-cell multi-user massive multiple-input-multiple-output (MIMO) downlink
+systems. Imperfect channel state information (CSI), pilot contamination, and
+channel spatial correlation are all considered. First, a sum- rate lower bound
+is derived by exploiting the asymptotically deterministic property of the
+received signal power, while keeping the random nature of other components in
+the signal-to-interference-plus-noise-ratio (SINR) intact. Via a general
+scaling model on important network parameters, including the number of users,
+the channel training energy and the data transmission power, with respect to
+the number of base station antennas, the asymptotic scaling law of the
+effective SINR is obtained, which reveals quantitatively the tradeoff of the
+network parameters. More importantly, pilot contamination and pilot
+contamination elimination (PCE) are considered in the analytical framework. In
+addition, the applicability of the derived asymptotic scaling law in practical
+systems with large but finite antenna numbers are discussed. Finally,
+sufficient conditions on the parameter scalings for the SINR to be
+asymptotically deterministic in the sense of mean square convergence are
+provided, which covers existing results on such analysis as special cases and
+shows the effect of PCE explicitly.
+",1,0,0,0,0,0
+891,On the Status of the Measurement Problem: Recalling the Relativistic Transactional Interpretation,"  In view of a resurgence of concern about the measurement problem, it is
+pointed out that the Relativistic Transactional Interpretation (RTI) remedies
+issues previously considered as drawbacks or refutations of the original TI.
+Specifically, once one takes into account relativistic processes that are not
+representable at the non-relativistic level (such as particle creation and
+annihilation, and virtual propagation), absorption is quantitatively defined in
+unambiguous physical terms. In addition, specifics of the relativistic
+transactional model demonstrate that the Maudlin `contingent absorber'
+challenge to the original TI cannot even be mounted: basic features of
+established relativistic field theories (in particular, the asymmetry between
+field sources and the bosonic fields, and the fact that slow-moving bound
+states, such as atoms, are not offer waves) dictate that the `slow-moving offer
+wave' required for the challenge scenario cannot exist. It is concluded that
+issues previously considered obstacles for TI are no longer legitimately viewed
+as such, and that reconsideration of the transactional picture is warranted in
+connection with solving the measurement problem.
+",0,1,0,0,0,0
+892,Pressure-tuning of bond-directional exchange interactions and magnetic frustration in hyperhoneycomb iridate $β$-$\mathrm{Li_2IrO_3}$,"  We explore the response of Ir $5d$ orbitals to pressure in
+$\beta$-$\mathrm{Li_2IrO_3}$, a hyperhoneycomb iridate in proximity to a Kitaev
+quantum spin liquid (QSL) ground state. X-ray absorption spectroscopy reveals a
+reconstruction of the electronic ground state below 2 GPa, the same pressure
+range where x-ray magnetic circular dichroism shows an apparent collapse of
+magnetic order. The electronic reconstruction, which manifests a reduction in
+the effective spin-orbit (SO) interaction in $5d$ orbitals, pushes
+$\beta$-$\mathrm{Li_2IrO_3}$ further away from the pure $J_{\rm eff}=1/2$
+limit. Although lattice symmetry is preserved across the electronic transition,
+x-ray diffraction shows a highly anisotropic compression of the hyperhoneycomb
+lattice which affects the balance of bond-directional Ir-Ir exchange
+interactions driven by spin-orbit coupling at Ir sites. An enhancement of
+symmetric anisotropic exchange over Kitaev and Heisenberg exchange interactions
+seen in theoretical calculations that use precisely this anisotropic Ir-Ir bond
+compression provides one possible route to realization of a QSL state in this
+hyperhoneycomb iridate at high pressures.
+",0,1,0,0,0,0
+893,On perpetuities with gamma-like tails,"  An infinite convergent sum of independent and identically distributed random
+variables discounted by a multiplicative random walk is called perpetuity,
+because of a possible actuarial application. We give three disjoint groups of
+sufficient conditions which ensure that the distribution right tail of a
+perpetuity $\mathbb{P}\{X>x\}$ is asymptotic to $ax^ce^{-bx}$ as $x\to\infty$
+for some $a,b>0$ and $c\in\mathbb{R}$. Our results complement those of Denisov
+and Zwart [J. Appl. Probab. 44 (2007), 1031--1046]. As an auxiliary tool we
+provide criteria for the finiteness of the one-sided exponential moments of
+perpetuities. Several examples are given in which the distributions of
+perpetuities are explicitly identified.
+",0,0,1,0,0,0
+894,Computationally Efficient Estimation of the Spectral Gap of a Markov Chain,"  We consider the problem of estimating from sample paths the absolute spectral
+gap $\gamma_*$ of a reversible, irreducible and aperiodic Markov chain
+$(X_t)_{t \in \mathbb{N}}$ over a finite state $\Omega$. We propose the ${\tt
+UCPI}$ (Upper Confidence Power Iteration) algorithm for this problem, a
+low-complexity algorithm which estimates the spectral gap in time ${\cal O}(n)$
+and memory space ${\cal O}((\ln n)^2)$ given $n$ samples. This is in stark
+contrast with most known methods which require at least memory space ${\cal
+O}(|\Omega|)$, so that they cannot be applied to large state spaces.
+Furthermore, ${\tt UCPI}$ is amenable to parallel implementation.
+",0,0,0,1,0,0
+895,Mixed Precision Training of Convolutional Neural Networks using Integer Operations,"  The state-of-the-art (SOTA) for mixed precision training is dominated by
+variants of low precision floating point operations, and in particular, FP16
+accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a
+lot of research has also happened in the domain of low and mixed-precision
+Integer training, these works either present results for non-SOTA networks (for
+instance only AlexNet for ImageNet-1K), or relatively small datasets (like
+CIFAR-10). In this work, we train state-of-the-art visual understanding neural
+networks on the ImageNet-1K dataset, with Integer operations on General Purpose
+(GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate
+(FMA) operations which take two pairs of INT16 operands and accumulate results
+into an INT32 output.We propose a shared exponent representation of tensors and
+develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network
+operations. The nuances of developing an efficient integer convolution kernel
+is examined, including methods to handle overflow of the INT32 accumulator. We
+implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and
+these networks achieve or exceed SOTA accuracy within the same number of
+iterations as their FP32 counterparts without any change in hyper-parameters
+and with a 1.8X improvement in end-to-end training throughput. To the best of
+our knowledge these results represent the first INT16 training results on GP
+hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported
+accuracy using half-precision
+",1,0,0,0,0,0
+896,Performance of a small size telescope (SST-1M) camera for gamma-ray astronomy with the Cherenkov Telescope Array,"  The foreseen implementations of the Small Size Telescopes (SST) in CTA will
+provide unique insights into the highest energy gamma rays offering fundamental
+means to discover and under- stand the sources populating the Galaxy and our
+local neighborhood. Aiming at such a goal, the SST-1M is one of the three
+different implementations that are being prototyped and tested for CTA. SST-1M
+is a Davies-Cotton single mirror telescope equipped with a unique camera
+technology based on SiPMs with demonstrated advantages over classical
+photomultipliers in terms of duty-cycle. In this contribution, we describe the
+telescope components, the camera, and the trigger and readout system. The
+results of the commissioning of the camera using a dedicated test setup are
+then presented. The performances of the camera first prototype in terms of
+expected trigger rates and trigger efficiencies for different night-sky
+background conditions are presented, and the camera response is compared to
+end-to-end simulations.
+",0,1,0,0,0,0
+897,Bounded solutions for a class of Hamiltonian systems,"  We obtain bounded for all $t$ solutions of ordinary differential equations as
+limits of the solutions of the corresponding Dirichlet problems on $(-L,L)$,
+with $L \rightarrow \infty$. We derive a priori estimates for the Dirichlet
+problems, allowing passage to the limit, via a diagonal sequence. This approach
+carries over to the PDE case.
+",0,0,1,0,0,0
+898,Cosmological perturbation effects on gravitational-wave luminosity distance estimates,"  Waveforms of gravitational waves provide information about a variety of
+parameters for the binary system merging. However, standard calculations have
+been performed assuming a FLRW universe with no perturbations. In reality this
+assumption should be dropped: we show that the inclusion of cosmological
+perturbations translates into corrections to the estimate of astrophysical
+parameters derived for the merging binary systems. We compute corrections to
+the estimate of the luminosity distance due to velocity, volume, lensing and
+gravitational potential effects. Our results show that the amplitude of the
+corrections will be negligible for current instruments, mildly important for
+experiments like the planned DECIGO, and very important for future ones such as
+the Big Bang Observer.
+",0,1,0,0,0,0
+899,Biocompatible Writing of Data into DNA,"  A simple DNA-based data storage scheme is demonstrated in which information
+is written using ""addressing"" oligonucleotides. In contrast to other methods
+that allow arbitrary code to be stored, the resulting DNA is suitable for
+downstream enzymatic and biological processing. This capability is crucial for
+DNA computers, and may allow for a diverse array of computational operations to
+be carried out using this DNA. Although here we use gel-based methods for
+information readout, we also propose more advanced methods involving
+protein/DNA complexes and atomic force microscopy/nano-pore schemes for data
+readout.
+",1,1,0,0,0,0
+900,Faster Clustering via Non-Backtracking Random Walks,"  This paper presents VEC-NBT, a variation on the unsupervised graph clustering
+technique VEC, which improves upon the performance of the original algorithm
+significantly for sparse graphs. VEC employs a novel application of the
+state-of-the-art word2vec model to embed a graph in Euclidean space via random
+walks on the nodes of the graph. In VEC-NBT, we modify the original algorithm
+to use a non-backtracking random walk instead of the normal backtracking random
+walk used in VEC. We introduce a modification to a non-backtracking random
+walk, which we call a begrudgingly-backtracking random walk, and show
+empirically that using this model of random walks for VEC-NBT requires shorter
+walks on the graph to obtain results with comparable or greater accuracy than
+VEC, especially for sparser graphs.
+",1,0,0,1,0,0
+901,Nanostructured complex oxides as a route towards thermal behavior in artificial spin ice systems,"  We have used soft x-ray photoemission electron microscopy to image the
+magnetization of single domain La$_{0.7}$Sr$_{0.3}$MnO$_{3}$ nano-islands
+arranged in geometrically frustrated configurations such as square ice and
+kagome ice geometries. Upon thermal randomization, ensembles of nano-islands
+with strong inter-island magnetic coupling relax towards low-energy
+configurations. Statistical analysis shows that the likelihood of ensembles
+falling into low-energy configurations depends strongly on the annealing
+temperature. Annealing to just below the Curie temperature of the ferromagnetic
+film (T$_{C}$ = 338 K) allows for a much greater probability of achieving low
+energy configurations as compared to annealing above the Curie temperature. At
+this thermally active temperature of 325 K, the ensemble of ferromagnetic
+nano-islands explore their energy landscape over time and eventually transition
+to lower energy states as compared to the frozen-in configurations obtained
+upon cooling from above the Curie temperature. Thus, this materials system
+allows for a facile method to systematically study thermal evolution of
+artificial spin ice arrays of nano-islands at temperatures modestly above room
+temperature.
+",0,1,0,0,0,0
+902,Finding Submodularity Hidden in Symmetric Difference,"  A set function $f$ on a finite set $V$ is submodular if $f(X) + f(Y) \geq f(X
+\cup Y) + f(X \cap Y)$ for any pair $X, Y \subseteq V$. The symmetric
+difference transformation (SD-transformation) of $f$ by a canonical set $S
+\subseteq V$ is a set function $g$ given by $g(X) = f(X \vartriangle S)$ for $X
+\subseteq V$,where $X \vartriangle S = (X \setminus S) \cup (S \setminus X)$
+denotes the symmetric difference between $X$ and $S$. Submodularity and
+SD-transformations are regarded as the counterparts of convexity and affine
+transformations in a discrete space, respectively. However, submodularity is
+not preserved under SD-transformations, in contrast to the fact that convexity
+is invariant under affine transformations. This paper presents a
+characterization of SD-stransformations preserving submodularity. Then, we are
+concerned with the problem of discovering a canonical set $S$, given the
+SD-transformation $g$ of a submodular function $f$ by $S$, provided that $g(X)$
+is given by a function value oracle. A submodular function $f$ on $V$ is said
+to be strict if $f(X) + f(Y) > f(X \cup Y) + f(X \cap Y)$ holds whenever both
+$X \setminus Y$ and $Y \setminus X$ are nonempty. We show that the problem is
+solved by using ${\rm O}(|V|)$ oracle calls when $f$ is strictly submodular,
+although it requires exponentially many oracle calls in general.
+",1,0,0,0,0,0
+903,On the Consistency of Quick Shift,"  Quick Shift is a popular mode-seeking and clustering algorithm. We present
+finite sample statistical consistency guarantees for Quick Shift on mode and
+cluster recovery under mild distributional assumptions. We then apply our
+results to construct a consistent modal regression algorithm.
+",1,0,0,1,0,0
+904,Adaptive Quantization for Deep Neural Network,"  In recent years Deep Neural Networks (DNNs) have been rapidly developed in
+various applications, together with increasingly complex architectures. The
+performance gain of these DNNs generally comes with high computational costs
+and large memory consumption, which may not be affordable for mobile platforms.
+Deep model quantization can be used for reducing the computation and memory
+costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we
+propose an optimization framework for deep model quantization. First, we
+propose a measurement to estimate the effect of parameter quantization errors
+in individual layers on the overall model prediction accuracy. Then, we propose
+an optimization process based on this measurement for finding optimal
+quantization bit-width for each layer. This is the first work that
+theoretically analyse the relationship between parameter quantization errors of
+individual layers and model accuracy. Our new quantization algorithm
+outperforms previous quantization optimization methods, and achieves 20-40%
+higher compression rate compared to equal bit-width quantization at the same
+model prediction accuracy.
+",1,0,0,1,0,0
+905,A Simple Convolutional Generative Network for Next Item Recommendation,"  Convolutional Neural Networks (CNNs) have been recently introduced in the
+domain of session-based next item recommendation. An ordered collection of past
+items the user has interacted with in a session (or sequence) are embedded into
+a 2-dimensional latent matrix, and treated as an image. The convolution and
+pooling operations are then applied to the mapped item embeddings. In this
+paper, we first examine the typical session-based CNN recommender and show that
+both the generative model and network architecture are suboptimal when modeling
+long-range dependencies in the item sequence. To address the issues, we
+introduce a simple, but very effective generative model that is capable of
+learning high-level representation from both short- and long-range item
+dependencies. The network architecture of the proposed model is formed of a
+stack of \emph{holed} convolutional layers, which can efficiently increase the
+receptive fields without relying on the pooling operation. Another contribution
+is the effective use of residual block structure in recommender systems, which
+can ease the optimization for much deeper networks. The proposed generative
+model attains state-of-the-art accuracy with less training time in the next
+item recommendation task. It accordingly can be used as a powerful
+recommendation baseline to beat in future, especially when there are long
+sequences of user feedback.
+",0,0,0,1,0,0
+906,High-temperature terahertz optical diode effect without magnetic order in polar FeZnMo$_3$O$_8$,"  We present a terahertz spectroscopic study of polar ferrimagnet
+FeZnMo$_3$O$_8$. Our main finding is a giant high-temperature optical diode
+effect, or nonreciprocal directional dichroism, where the transmitted light
+intensity in one direction is over 100 times lower than intensity transmitted
+in the opposite direction. The effect takes place in the paramagnetic phase
+with no long-range magnetic order in the crystal, which contrasts sharply with
+all existing reports of the terahertz optical diode effect in other
+magnetoelectric materials, where the long-range magnetic ordering is a
+necessary prerequisite. In \fzmo, the effect occurs resonantly with a strong
+magnetic dipole active transition centered at 1.27 THz and assigned as electron
+spin resonance between the eigenstates of the single-ion anisotropy
+Hamiltonian. We propose that the optical diode effect in paramagnetic
+FeZnMo$_3$O$_8$ is driven by signle-ion terms in magnetoelectric free energy.
+",0,1,0,0,0,0
+907,Character Networks and Book Genre Classification,"  We compare the social character networks of biographical, legendary and
+fictional texts, in search for marks of genre differentiation. We examine the
+degree distribution of character appearance and find a power law that does not
+depend on the literary genre or historical content. We also analyze local and
+global complex networks measures, in particular, correlation plots between the
+recently introduced Lobby (or Hirsh $H(1)$) index and Degree, Betweenness and
+Closeness centralities. Assortativity plots, which previous literature claims
+to separate fictional from real social networks, were also studied. We've found
+no relevant differences in the books for these network measures and we give a
+plausible explanation why the previous assortativity result is not correct.
+",1,1,0,0,0,0
+908,Phase diagrams of Bose-Hubbard model and antiferromagnetic spin-1/2 models on a honeycomb lattice,"  Motivated by the recent experimental realization of the Haldane model by
+ultracold fermions in an optical lattice, we investigate phase diagrams of the
+hard-core Bose-Hubbard model on a honeycomb lattice. This model is closely
+related with a spin-1/2 antiferromagnetic (AF) quantum spin model.
+Nearest-neighbor (NN) hopping amplitude is positive and it prefers an AF
+configurations of phases of Bose-Einstein condensates. On the other hand, an
+amplitude of the next-NN hopping depends on an angle variable as in the Haldane
+model. Phase diagrams are obtained by means of an extended path-integral
+Monte-Carlo simulations. Besides the AF state, a 120$^o$-order state, there
+appear other phases including a Bose metal in which no long-range orders exist.
+",0,1,0,0,0,0
+909,Forecasting the Impact of Stellar Activity on Transiting Exoplanet Spectra,"  Exoplanet host star activity, in the form of unocculted star spots or
+faculae, alters the observed transmission and emission spectra of the
+exoplanet. This effect can be exacerbated when combining data from different
+epochs if the stellar photosphere varies between observations due to activity.
+redHere we present a method to characterize and correct for relative changes
+due to stellar activity by exploiting multi-epoch ($\ge$2 visits/transits)
+observations to place them in a consistent reference frame. Using measurements
+from portions of the planet's orbit where negligible planet transmission or
+emission can be assumed, we determine changes to the stellar spectral
+amplitude. With the analytical methods described here, we predict the impact of
+stellar variability on transit observations. Supplementing these forecasts with
+Kepler-measured stellar variabilities for F-, G-, K-, and M-dwarfs, and
+predicted transit precisions by JWST's NIRISS, NIRCam, and MIRI, we conclude
+that stellar activity does not impact infrared transiting exoplanet
+observations of most presently-known or predicted TESS targets by current or
+near-future platforms, such as JWST.
+",0,1,0,0,0,0
+910,A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms,"  Developers of Molecular Dynamics (MD) codes face significant challenges when
+adapting existing simulation packages to new hardware. In a continuously
+diversifying hardware landscape it becomes increasingly difficult for
+scientists to be experts both in their own domain (physics/chemistry/biology)
+and specialists in the low level parallelisation and optimisation of their
+codes. To address this challenge, we describe a ""Separation of Concerns""
+approach for the development of parallel and optimised MD codes: the science
+specialist writes code at a high abstraction level in a domain specific
+language (DSL), which is then translated into efficient computer code by a
+scientific programmer. In a related context, an abstraction for the solution of
+partial differential equations with grid based methods has recently been
+implemented in the (Py)OP2 library. Inspired by this approach, we develop a
+Python code generation system for molecular dynamics simulations on different
+parallel architectures, including massively parallel distributed memory systems
+and GPUs. We demonstrate the efficiency of the auto-generated code by studying
+its performance and scalability on different hardware and compare it to other
+state-of-the-art simulation packages. With growing data volumes the extraction
+of physically meaningful information from the simulation becomes increasingly
+challenging and requires equally efficient implementations. A particular
+advantage of our approach is the easy expression of such analysis algorithms.
+We consider two popular methods for deducing the crystalline structure of a
+material from the local environment of each atom, show how they can be
+expressed in our abstraction and implement them in the code generation
+framework.
+",1,1,0,0,0,0
+911,CMB anisotropies at all orders: the non-linear Sachs-Wolfe formula,"  We obtain the non-linear generalization of the Sachs-Wolfe + integrated
+Sachs-Wolfe (ISW) formula describing the CMB temperature anisotropies. Our
+formula is valid at all orders in perturbation theory, is also valid in all
+gauges and includes scalar, vector and tensor modes. A direct consequence of
+our results is that the maps of the logarithmic temperature anisotropies are
+much cleaner than the usual CMB maps, because they automatically remove many
+secondary anisotropies. This can for instance, facilitate the search for
+primordial non-Gaussianity in future works. It also disentangles the non-linear
+ISW from other effects. Finally, we provide a method which can iteratively be
+used to obtain the lensing solution at the desired order.
+",0,1,0,0,0,0
+912,On a binary system of Prendiville: The cubic case,"  We prove sharp decoupling inequalities for a class of two dimensional
+non-degenerate surfaces in R^5, introduced by Prendiville. As a consequence, we
+obtain sharp bounds on the number of integer solutions of the Diophantine
+systems associated with these surfaces.
+",0,0,1,0,0,0
+913,Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation,"  In this paper, we introduce a simple, yet powerful pipeline for medical image
+segmentation that combines Fully Convolutional Networks (FCNs) with Fully
+Convolutional Residual Networks (FC-ResNets). We propose and examine a design
+that takes particular advantage of recent advances in the understanding of both
+Convolutional Neural Networks as well as ResNets. Our approach focuses upon the
+importance of a trainable pre-processing when using FC-ResNets and we show that
+a low-capacity FCN model can serve as a pre-processor to normalize medical
+input data. In our image segmentation pipeline, we use FCNs to obtain
+normalized images, which are then iteratively refined by means of a FC-ResNet
+to generate a segmentation prediction. As in other fully convolutional
+approaches, our pipeline can be used off-the-shelf on different image
+modalities. We show that using this pipeline, we exhibit state-of-the-art
+performance on the challenging Electron Microscopy benchmark, when compared to
+other 2D methods. We improve segmentation results on CT images of liver
+lesions, when contrasting with standard FCN methods. Moreover, when applying
+our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we
+reach results that are competitive even when compared to 3D methods. The
+obtained results illustrate the strong potential and versatility of the
+pipeline by achieving highly accurate results on multi-modality images from
+different anatomical regions and organs.
+",1,0,0,0,0,0
+914,Strong isomorphism in Marinatto-Weber type quantum games,"  Our purpose is to focus attention on a new criterion for quantum schemes by
+bringing together the notions of quantum game and game isomorphism. A quantum
+game scheme is required to generate the classical game as a special case. Now,
+given a quantum game scheme and two isomorphic classical games, we additionally
+require the resulting quantum games to be isomorphic as well. We show how this
+isomorphism condition influences the players' strategy sets. We are concerned
+with the Marinatto-Weber type quantum game scheme and the strong isomorphism
+between games in strategic form.
+",1,0,0,0,0,0
+915,Riemannian stochastic variance reduced gradient,"  Stochastic variance reduction algorithms have recently become popular for
+minimizing the average of a large but finite number of loss functions. In this
+paper, we propose a novel Riemannian extension of the Euclidean stochastic
+variance reduced gradient algorithm (R-SVRG) to a manifold search space. The
+key challenges of averaging, adding, and subtracting multiple gradients are
+addressed with retraction and vector transport. We present a global convergence
+analysis of the proposed algorithm with a decay step size and a local
+convergence rate analysis under a fixed step size under some natural
+assumptions. The proposed algorithm is applied to problems on the Grassmann
+manifold, such as principal component analysis, low-rank matrix completion, and
+computation of the Karcher mean of subspaces, and outperforms the standard
+Riemannian stochastic gradient descent algorithm in each case.
+",1,0,1,1,0,0
+916,Causal Inference by Stochastic Complexity,"  The algorithmic Markov condition states that the most likely causal direction
+between two random variables X and Y can be identified as that direction with
+the lowest Kolmogorov complexity. Due to the halting problem, however, this
+notion is not computable.
+We hence propose to do causal inference by stochastic complexity. That is, we
+propose to approximate Kolmogorov complexity via the Minimum Description Length
+(MDL) principle, using a score that is mini-max optimal with regard to the
+model class under consideration. This means that even in an adversarial
+setting, such as when the true distribution is not in this class, we still
+obtain the optimal encoding for the data relative to the class.
+We instantiate this framework, which we call CISC, for pairs of univariate
+discrete variables, using the class of multinomial distributions. Experiments
+show that CISC is highly accurate on synthetic, benchmark, as well as
+real-world data, outperforming the state of the art by a margin, and scales
+extremely well with regard to sample and domain sizes.
+",1,0,0,0,0,0
+917,Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,"  The recently proposed Temporal Ensembling has achieved state-of-the-art
+results in several semi-supervised learning benchmarks. It maintains an
+exponential moving average of label predictions on each training example, and
+penalizes predictions that are inconsistent with this target. However, because
+the targets change only once per epoch, Temporal Ensembling becomes unwieldy
+when learning large datasets. To overcome this problem, we propose Mean
+Teacher, a method that averages model weights instead of label predictions. As
+an additional benefit, Mean Teacher improves test accuracy and enables training
+with fewer labels than Temporal Ensembling. Without changing the network
+architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250
+labels, outperforming Temporal Ensembling trained with 1000 labels. We also
+show that a good network architecture is crucial to performance. Combining Mean
+Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with
+4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels
+from 35.24% to 9.11%.
+",1,0,0,1,0,0
+918,WOMBAT: A Scalable and High Performance Astrophysical MHD Code,"  We present a new code for astrophysical magneto-hydrodynamics specifically
+designed and optimized for high performance and scaling on modern and future
+supercomputers. We describe a novel hybrid OpenMP/MPI programming model that
+emerged from a collaboration between Cray, Inc. and the University of
+Minnesota. This design utilizes MPI-RMA optimized for thread scaling, which
+allows the code to run extremely efficiently at very high thread counts ideal
+for the latest generation of the multi-core and many-core architectures. Such
+performance characteristics are needed in the era of ""exascale"" computing. We
+describe and demonstrate our high-performance design in detail with the intent
+that it may be used as a model for other, future astrophysical codes intended
+for applications demanding exceptional performance.
+",0,1,0,0,0,0
+919,Securing Virtual Network Function Placement with High Availability Guarantees,"  Virtual Network Functions as a Service (VNFaaS) is currently under attentive
+study by telecommunications and cloud stakeholders as a promising business and
+technical direction consisting of providing network functions as a service on a
+cloud (NFV Infrastructure), instead of delivering standalone network
+appliances, in order to provide higher scalability and reduce maintenance
+costs. However, the functioning of such NFVI hosting the VNFs is fundamental
+for all the services and applications running on top of it, forcing to
+guarantee a high availability level. Indeed the availability of an VNFaaS
+relies on the failure rate of its single components, namely the servers, the
+virtualization software, and the communication network. The proper assignment
+of the virtual machines implementing network functions to NFVI servers and
+their protection is essential to guarantee high availability. We model the High
+Availability Virtual Network Function Placement (HA-VNFP) as the problem of
+finding the best assignment of virtual machines to servers guaranteeing
+protection by replication. We propose a probabilistic approach to measure the
+real availability of a system and design both efficient and effective
+algorithms that can be used by stakeholders for both online and offline
+planning.
+",1,0,0,0,0,0
+920,The Case for Pyriproxyfen as a Potential Cause for Microcephaly; From Biology to Epidemiology,"  The Zika virus has been found in individual cases but has not been confirmed
+as the cause of in the large number of cases of microcephaly in Brazil in
+2015-6. Indeed, disparities between the incidence of Zika and microcephaly
+across geographic locations has led to questions about the virus's role. Here
+we consider whether the insecticide pyriproxyfen used in Brazilian drinking
+water might be the primary cause or a cofactor. Pyriproxifen is a juvenile
+hormone analog which has been shown to correspond in mammals to a number of fat
+soluble regulatory molecules including retinoic acid, a metabolite of vitamin
+A, with which it has cross-reactivity and whose application during development
+has been shown to cause microcephaly. Methoprene, another juvenile hormone
+analog approved as an insecticide in the 1970s has been shown to cause
+developmental disorders in mammals. Isotretinoin is another retinoid causing
+microcephaly via activation of the retinoid X receptor in developing fetuses.
+We review tests of pyriproxyfen by the manufacturer Sumitomo, which actually
+found some evidence for this effect, including low brain mass and
+arhinencephaly in exposed rat pups. Pyriproxyfen use in Brazil is
+unprecedented, never having been applied to a water supply on a large scale.
+Claims that its geographical pattern of use rule it out as a cause have not
+been documented or confirmed. On the other hand, the very few microcephaly
+cases reported in Colombia and the wide discrepancies of incidence in different
+states across Brazil despite large numbers of Zika cases undermine the claim
+that Zika is the cause. Given this combination of potential molecular
+mechanism, toxicological and epidemiological evidence we strongly recommend
+that the use of pyriproxyfen in Brazil be suspended until the potential causal
+link to microcephaly is investigated further.
+",0,1,0,0,0,0
+921,A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates,"  This paper considers the problem of decentralized optimization with a
+composite objective containing smooth and non-smooth terms. To solve the
+problem, a proximal-gradient scheme is studied. Specifically, the smooth and
+nonsmooth terms are dealt with by gradient update and proximal update,
+respectively. The studied algorithm is closely related to a previous
+decentralized optimization algorithm, PG-EXTRA [37], but has a few advantages.
+First of all, in our new scheme, agents use uncoordinated step-sizes and the
+stable upper bounds on step-sizes are independent from network topology. The
+step-sizes depend on local objective functions, and they can be as large as
+that of the gradient descent. Secondly, for the special case without non-smooth
+terms, linear convergence can be achieved under the strong convexity
+assumption. The dependence of the convergence rate on the objective functions
+and the network are separated, and the convergence rate of our new scheme is as
+good as one of the two convergence rates that match the typical rates for the
+general gradient descent and the consensus averaging. We also provide some
+numerical experiments to demonstrate the efficacy of the introduced algorithms
+and validate our theoretical discoveries.
+",0,0,1,1,0,0
+922,CODA: Enabling Co-location of Computation and Data for Near-Data Processing,"  Recent studies have demonstrated that near-data processing (NDP) is an
+effective technique for improving performance and energy efficiency of
+data-intensive workloads. However, leveraging NDP in realistic systems with
+multiple memory modules introduces a new challenge. In today's systems, where
+no computation occurs in memory modules, the physical address space is
+interleaved at a fine granularity among all memory modules to help improve the
+utilization of processor-memory interfaces by distributing the memory traffic.
+However, this is at odds with efficient use of NDP, which requires careful
+placement of data in memory modules such that near-data computations and their
+exclusively used data can be localized in individual memory modules, while
+distributing shared data among memory modules to reduce hotspots. In order to
+address this new challenge, we propose a set of techniques that (1) enable
+collections of OS pages to either be fine-grain interleaved among memory
+modules (as is done today) or to be placed contiguously on individual memory
+modules (as is desirable for NDP private data), and (2) decide whether to
+localize or distribute each memory object based on its anticipated access
+pattern and steer computations to the memory where the data they access is
+located. Our evaluations across a wide range of workloads show that the
+proposed mechanism improves performance by 31% and reduces 38% remote data
+accesses over a baseline system that cannot exploit computate-data affinity
+characteristics.
+",1,0,0,0,0,0
+923,Generative Models for Spear Phishing Posts on Social Media,"  Historically, machine learning in computer security has prioritized defense:
+think intrusion detection systems, malware classification, and botnet traffic
+identification. Offense can benefit from data just as well. Social networks,
+with their access to extensive personal data, bot-friendly APIs, colloquial
+syntax, and prevalence of shortened links, are the perfect venues for spreading
+machine-generated malicious content. We aim to discover what capabilities an
+adversary might utilize in such a domain. We present a long short-term memory
+(LSTM) neural network that learns to socially engineer specific users into
+clicking on deceptive URLs. The model is trained with word vector
+representations of social media posts, and in order to make a click-through
+more likely, it is dynamically seeded with topics extracted from the target's
+timeline. We augment the model with clustering to triage high value targets
+based on their level of social engagement, and measure success of the LSTM's
+phishing expedition using click-rates of IP-tracked links. We achieve state of
+the art success rates, tripling those of historic email attack campaigns, and
+outperform humans manually performing the same task.
+",0,0,0,1,0,0
+924,SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring,"  Deep learning has demonstrated tremendous potential for Automatic Text
+Scoring (ATS) tasks. In this paper, we describe a new neural architecture that
+enhances vanilla neural network models with auxiliary neural coherence
+features. Our new method proposes a new \textsc{SkipFlow} mechanism that models
+relationships between snapshots of the hidden representations of a long
+short-term memory (LSTM) network as it reads. Subsequently, the semantic
+relationships between multiple snapshots are used as auxiliary features for
+prediction. This has two main benefits. Firstly, essays are typically long
+sequences and therefore the memorization capability of the LSTM network may be
+insufficient. Implicit access to multiple snapshots can alleviate this problem
+by acting as a protection against vanishing gradients. The parameters of the
+\textsc{SkipFlow} mechanism also acts as an auxiliary memory. Secondly,
+modeling relationships between multiple positions allows our model to learn
+features that represent and approximate textual coherence. In our model, we
+call this \textit{neural coherence} features. Overall, we present a unified
+deep learning architecture that generates neural coherence features as it reads
+in an end-to-end fashion. Our approach demonstrates state-of-the-art
+performance on the benchmark ASAP dataset, outperforming not only feature
+engineering baselines but also other deep learning models.
+",1,0,0,0,0,0
+925,Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning,"  This paper introduces a method, based on deep reinforcement learning, for
+automatically generating a general purpose decision making function. A Deep
+Q-Network agent was trained in a simulated environment to handle speed and lane
+change decisions for a truck-trailer combination. In a highway driving case, it
+is shown that the method produced an agent that matched or surpassed the
+performance of a commonly used reference model. To demonstrate the generality
+of the method, the exact same algorithm was also tested by training it for an
+overtaking case on a road with oncoming traffic. Furthermore, a novel way of
+applying a convolutional neural network to high level input that represents
+interchangeable objects is also introduced.
+",1,0,0,0,0,0
+926,Reduction of topological $\mathbb{Z}$ classification in cold atomic systems,"  One of the most challenging problems in correlated topological systems is a
+realization of the reduction of topological classification, but very few
+experimental platforms have been proposed so far. We here demonstrate that
+ultracold dipolar fermions (e.g., $^{167}$Er, $^{161}$Dy, and $^{53}$Cr) loaded
+in an optical lattice of two-leg ladder geometry can be the first promising
+testbed for the reduction $\mathbb{Z}\to\mathbb{Z}_4$, where solid evidence for
+the reduction is available thanks to their high controllability. We further
+give a detailed account of how to experimentally access this phenomenon; around
+the edges, the destruction of one-particle gapless excitations can be observed
+by the local radio frequency spectroscopy, while that of gapless spin
+excitations can be observed by a time-dependent spin expectation value of a
+superposed state of the ground state and the first excited state. We clarify
+that even when the reduction occurs, a gapless edge mode is recovered around a
+dislocation, which can be another piece of evidence for the reduction.
+",0,1,0,0,0,0
+927,Reducing biases on $H_0$ measurements using strong lensing and galaxy dynamics: results from the EAGLE simulation,"  Cosmological parameter constraints from observations of time-delay lenses are
+becoming increasingly precise. However, there may be significant bias and
+scatter in these measurements due to, among other things, the so-called
+mass-sheet degeneracy. To estimate these uncertainties, we analyze strong
+lenses from the largest EAGLE hydrodynamical simulation. We apply a mass-sheet
+transformation to the radial density profiles of lenses, and by selecting
+lenses near isothermality, we find that the bias on H0 can be reduced to 5%
+with an intrinsic scatter of 10%, confirming previous results performed on a
+different simulation data set. We further investigate whether combining lensing
+observables with kinematic constraints helps to minimize this bias. We do not
+detect any significant dependence of the bias on lens model parameters or
+observational properties of the galaxy, but depending on the source--lens
+configuration, a bias may still exist. Cross lenses provide an accurate
+estimate of the Hubble constant, while fold (double) lenses tend to be biased
+low (high). With kinematic constraints, double lenses show bias and intrinsic
+scatter of 6% and 10%, respectively, while quad lenses show bias and intrinsic
+scatter of 0.5% and 10%, respectively. For lenses with a reduced $\chi^2 > 1$,
+a power-law dependence of the $\chi^2$ on the lens environment (number of
+nearby galaxies) is seen. Lastly, we model, in greater detail, the cases of two
+double lenses that are significantly biased. We are able to remove the bias,
+suggesting that the remaining biases could also be reduced by carefully taking
+into account additional sources of systematic uncertainty.
+",0,1,0,0,0,0
+928,Characterizing the ionospheric current pattern response to southward and northward IMF turnings with dynamical SuperMAG correlation networks,"  We characterize the response of the quiet time (no substorms or storms)
+large-scale ionospheric transient equivalent currents to north-south and
+south-north IMF turnings by using a dynamical network of ground-based
+magnetometers. Canonical correlation between all pairs of SuperMAG magnetometer
+stations in the Northern Hemisphere (magnetic latitude (MLAT) 50-82$^{\circ}$)
+is used to establish the extent of near-simultaneous magnetic response between
+regions of magnetic local time-MLAT. Parameters and maps that describe
+spatial-temporal correlation are used to characterize the system and its
+response to the turnings aggregated over several hundred events. We find that
+regions that experience large increases in correlation post turning coincide
+with typical locations of a two-cell convection system and are influenced by
+the interplanetary magnetic field $\mathit{B}_{y}$. The time between the
+turnings reaching the magnetopause and a network response is found to be
+$\sim$8-10 min and correlation in the dayside occurs 2-8 min before that in the
+nightside.
+",0,1,0,0,0,0
+929,On a question of Buchweitz about ranks of syzygies of modules of finite length,"  Let R be a local ring of dimension d. Buchweitz asks if the rank of the d-th
+syzygy of a module of finite lengh is greater than or equal to the rank of the
+d-th syzygy of the residue field, unless the module has finite projective
+dimension. Assuming that R is Gorenstein, we prove that if the question is
+affrmative, then R is a hypersurface. If moreover R has dimension two, then we
+show that the converse also holds true.
+",0,0,1,0,0,0
+930,Learning convex bounds for linear quadratic control policy synthesis,"  Learning to make decisions from observed data in dynamic environments remains
+a problem of fundamental importance in a number of fields, from artificial
+intelligence and robotics, to medicine and finance. This paper concerns the
+problem of learning control policies for unknown linear dynamical systems so as
+to maximize a quadratic reward function. We present a method to optimize the
+expected value of the reward over the posterior distribution of the unknown
+system parameters, given data. The algorithm involves sequential convex
+programing, and enjoys reliable local convergence and robust stability
+guarantees. Numerical simulations and stabilization of a real-world inverted
+pendulum are used to demonstrate the approach, with strong performance and
+robustness properties observed in both.
+",0,0,0,1,0,0
+931,General mixed multi-soliton solution to the multi-component Maccari system,"  Based on the KP hierarchy reduction method, the general bright-dark mixed
+multi-soliton solution of the multi-component Maccari system is constructed.
+The multi-component Maccari system considered comprised of multiple (say $M$)
+short-wave components and one long-wave component with all possible
+combinations of nonlinearities including all-focusing, all-defocusing and mixed
+types. We firstly derive the two-bright-one-dark (2-b-1-d) and
+one-bright-two-dark (1-b-2-d) mixed multi-soliton solutions to the
+three-component Maccari system in detail. For the interaction between two
+solitons, the asymptotic analysis shows that inelastic collision can take place
+in a $M$-component Maccari system with $M \geq 3$ only if the bright parts of
+the mixed solitons appear at least in two short-wave components. The
+energy-exchanging inelastic collision characterized by an intensity
+redistribution among the bright parts of the mixed solitons. While the dark
+parts of the mixed solitons and the solitons in the long-wave component always
+undergo elastic collision which just accompanied by a position shift. In the
+end, we extend the corresponding analysis to the $M$-component Maccari system
+to obtain its mixed multi-soliton solution. The formula obtained unifies the
+all-bright, all-dark and mixed multi-soliton solutions.
+",0,1,0,0,0,0
+932,Resolution and Relevance Trade-offs in Deep Learning,"  Deep learning has been successfully applied to various tasks, but its
+underlying mechanism remains unclear. Neural networks associate similar inputs
+in the visible layer to the same state of hidden variables in deep layers. The
+fraction of inputs that are associated to the same state is a natural measure
+of similarity and is simply related to the cost in bits required to represent
+these inputs. The degeneracy of states with the same information cost provides
+instead a natural measure of noise and is simply related the entropy of the
+frequency of states, that we call relevance. Representations with minimal
+noise, at a given level of similarity (resolution), are those that maximise the
+relevance. A signature of such efficient representations is that frequency
+distributions follow power laws. We show, in extensive numerical experiments,
+that deep neural networks extract a hierarchy of efficient representations from
+data, because they i) achieve low levels of noise (i.e. high relevance) and ii)
+exhibit power law distributions. We also find that the layer that is most
+efficient to reliably generate patterns of training data is the one for which
+relevance and resolution are traded at the same price, which implies that
+frequency distribution follows Zipf's law.
+",1,0,0,0,0,0
+933,Heisenberg Modules over Quantum 2-tori are metrized quantum vector bundles,"  The modular Gromov-Hausdorff propinquity is a distance on classes of modules
+endowed with quantum metric information, in the form of a metric form of a
+connection and a left Hilbert module structure. This paper proves that the
+family of Heisenberg modules over quantum two tori, when endowed with their
+canonical connections, form a family of metrized quantum vector bundles, as a
+first step in proving that Heisenberg modules form a continuous family for the
+modular Gromov-Hausdorff propinquity.
+",0,0,1,0,0,0
+934,GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs,"  We present a prototype of a software tool for exploration of multiple
+combinatorial optimisation problems in large real-world and synthetic complex
+networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial
+Explorer), provides a unified framework for scalable computation and
+presentation of high-quality suboptimal solutions and bounds for a number of
+widely studied combinatorial optimisation problems. Efficient representation
+and applicability to large-scale graphs and complex networks are particularly
+considered in its design. The problems currently supported include maximum
+clique, graph colouring, maximum independent set, minimum vertex clique
+covering, minimum dominating set, as well as the longest simple cycle problem.
+Suboptimal solutions and intervals for optimal objective values are estimated
+using scalable heuristics. The tool is designed with extensibility in mind,
+with the view of further problems and both new fast and high-performance
+heuristics to be added in the future. GraphCombEx has already been successfully
+used as a support tool in a number of recent research studies using
+combinatorial optimisation to analyse complex networks, indicating its promise
+as a research software tool.
+",1,0,0,0,0,0
+935,Approches d'analyse distributionnelle pour améliorer la désambiguïsation sémantique,"  Word sense disambiguation (WSD) improves many Natural Language Processing
+(NLP) applications such as Information Retrieval, Machine Translation or
+Lexical Simplification. WSD is the ability of determining a word sense among
+different ones within a polysemic lexical unit taking into account the context.
+The most straightforward approach uses a semantic proximity measure between the
+word sense candidates of the target word and those of its context. Such a
+method very easily entails a combinatorial explosion. In this paper, we propose
+two methods based on distributional analysis which enable to reduce the
+exponential complexity without losing the coherence. We present a comparison
+between the selection of distributional neighbors and the linearly nearest
+neighbors. The figures obtained show that selecting distributional neighbors
+leads to better results.
+",1,0,0,0,0,0
+936,Non-degenerate parametric resonance in tunable superconducting cavity,"  We develop a theory for non-degenerate parametric resonance in a tunable
+superconducting cavity. We focus on nonlinear effects that are caused by
+nonlinear Josephson elements connected to the cavity. We analyze parametric
+amplification in a strong nonlinear regime at the parametric instability
+threshold, and calculate maximum gain values. Above the threshold, in the
+parametric oscillator regime the linear cavity response diverges at the
+oscillator frequency at all pump strengths. We show that this divergence is
+related to the continuous degeneracy of the free oscillator state with respect
+to the phase. Applying on-resonance input lifts the degeneracy and removes the
+divergence. We also investigate the quantum noise squeezing. It is shown that
+in the strong amplification regime the noise undergoes four-mode squeezing, and
+that in this regime the output signal to noise ratio can significantly exceed
+the input value. We also analyze the intermode frequency conversion and
+identify parameters at which full conversion is achieved.
+",0,1,0,0,0,0
+937,Toward Controlled Generation of Text,"  Generic generation and manipulation of text is challenging and has limited
+success compared to recent deep generative modeling in visual domain. This
+paper aims at generating plausible natural language sentences, whose attributes
+are dynamically controlled by learning disentangled latent representations with
+designated semantics. We propose a new neural generative model which combines
+variational auto-encoders and holistic attribute discriminators for effective
+imposition of semantic structures. With differentiable approximation to
+discrete text samples, explicit constraints on independent attribute controls,
+and efficient collaborative learning of generator and discriminators, our model
+learns highly interpretable representations from even only word annotations,
+and produces realistic sentences with desired attributes. Quantitative
+evaluation validates the accuracy of sentence and attribute generation.
+",1,0,0,1,0,0
+938,Human peripheral blur is optimal for object recognition,"  Our eyes sample a disproportionately large amount of information at the
+centre of gaze with increasingly sparse sampling into the periphery. This
+sampling scheme is widely believed to be a wiring constraint whereby high
+resolution at the centre is achieved by sacrificing spatial acuity in the
+periphery. Here we propose that this sampling scheme may be optimal for object
+recognition because the relevant spatial content is dense near an object and
+sparse in the surrounding vicinity. We tested this hypothesis by training deep
+convolutional neural networks on full-resolution and foveated images. Our main
+finding is that networks trained on images with foveated sampling show better
+object classification compared to networks trained on full resolution images.
+Importantly, blurring images according to the human blur function yielded the
+best performance compared to images with shallower or steeper blurring. Taken
+together our results suggest that, peripheral blurring in our eyes may have
+evolved for optimal object recognition, rather than merely to satisfy wiring
+constraints.
+",0,0,0,0,1,0
+939,Acceleration of Mean Square Distance Calculations with Floating Close Structure in Metadynamics Simulations,"  Molecular dynamics simulates the~movements of atoms. Due to its high cost,
+many methods have been developed to ""push the~simulation forward"". One of them,
+metadynamics, can hasten the~molecular dynamics with the~help of variables
+describing the~simulated process. However, the~evaluation of these variables
+can include numerous mean square distance calculations that introduce
+substantial computational demands, thus jeopardize the~benefit of the~approach.
+Recently, we proposed an~approximative method that significantly reduces
+the~number of these distance calculations. Here we evaluate the~performance and
+the~scalability on two molecular systems. We assess the~maximal theoretical
+speed-up based on the reduction of distance computations and Ahmdal's law and
+compare it to the~practical speed-up achieved with our implementation.
+",1,0,0,0,0,0
+940,Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding,"  Developers increasingly rely on text matching tools to analyze the relation
+between natural language words and APIs. However, semantic gaps, namely textual
+mismatches between words and APIs, negatively affect these tools. Previous
+studies have transformed words or APIs into low-dimensional vectors for
+matching; however, inaccurate results were obtained due to the failure of
+modeling words and APIs simultaneously. To resolve this problem, two main
+challenges are to be addressed: the acquisition of massive words and APIs for
+mining and the alignment of words and APIs for modeling. Therefore, this study
+proposes Word2API to effectively estimate relatedness of words and APIs.
+Word2API collects millions of commonly used words and APIs from code
+repositories to address the acquisition challenge. Then, a shuffling strategy
+is used to transform related words and APIs into tuples to address the
+alignment challenge. Using these tuples, Word2API models words and APIs
+simultaneously. Word2API outperforms baselines by 10%-49.6% of relatedness
+estimation in terms of precision and NDCG. Word2API is also effective on
+solving typical software tasks, e.g., query expansion and API documents
+linking. A simple system with Word2API-expanded queries recommends up to 21.4%
+more related APIs for developers. Meanwhile, Word2API improves comparison
+algorithms by 7.9%-17.4% in linking questions in Question&Answer communities to
+API documents.
+",1,0,0,0,0,0
+941,Feedback optimal controllers for the Heston model,"  We prove the existence of an optimal feedback controller for a stochastic
+optimization problem constituted by a variation of the Heston model, where a
+stochastic input process is added in order to minimize a given performance
+criterion. The stochastic feedback controller is searched by solving a
+nonlinear backward parabolic equation for which one proves the existence of a
+martingale solution.
+",0,0,1,0,0,0
+942,User Experience of the CoSTAR System for Instruction of Collaborative Robots,"  How can we enable novice users to create effective task plans for
+collaborative robots? Must there be a tradeoff between generalizability and
+ease of use? To answer these questions, we conducted a user study with the
+CoSTAR system, which integrates perception and reasoning into a Behavior
+Tree-based task plan editor. In our study, we ask novice users to perform
+simple pick-and-place assembly tasks under varying perception and planning
+capabilities. Our study shows that users found Behavior Trees to be an
+effective way of specifying task plans. Furthermore, users were also able to
+more quickly, effectively, and generally author task plans with the addition of
+CoSTAR's planning, perception, and reasoning capabilities. Despite these
+improvements, concepts associated with these capabilities were rated by users
+as less usable, and our results suggest a direction for further refinement.
+",1,0,0,0,0,0
+943,Graph complexity and Mahler measure,"  The (torsion) complexity of a finite edge-weighted graph is defined to be the
+order of the torsion subgroup of the abelian group presented by its Laplacian
+matrix. When G is d-periodic (i.e., G has a free action of the rank-d free
+abelian group by graph automorphisms, with finite quotient) the Mahler measure
+of its Laplacian determinant polynomial is the growth rate of the complexity of
+finite quotients of G. Lehmer's question, an open question about the roots of
+monic integral polynomials, is equivalent to a question about the complexity
+growth of edge-weighted 1-periodic graphs.
+",0,0,1,0,0,0
+944,Neutronic Analysis on Potential Accident Tolerant Fuel-Cladding Combination U$_3$Si$_2$-FeCrAl,"  Neutronic performance is investigated for a potential accident tolerant fuel
+(ATF),which consists of U$_3$Si$_2$ fuel and FeCrAl cladding. In comparison
+with current UO$_2$-Zr system, FeCrAl has a better oxidation resistance but a
+larger thermal neutron absorption cross section. U$_3$Si$_2$ has a higher
+thermal conductivity and a higher uranium density, which can compensate the
+reactivity suppressed by FeCrAl. Based on neutronic investigations, a possible
+U$_3$Si$_2$-FeCrAl fuel-cladding systemis taken into consideration. Fundamental
+properties of the suggested fuel-cladding combination are investigated in a
+fuel assembly.These properties include moderator and fuel temperature
+coefficients, control rods worth, radial power distribution (in a fuel rod),
+and different void reactivity coefficients. The present work proves that the
+new combination has less reactivity variation during its service lifetime.
+Although, compared with the current system, it has a little larger deviation on
+power distribution and a little less negative temperature coefficient and void
+reactivity coefficient and its control rods worth is less important, variations
+of these parameters are less important during the service lifetime of fuel.
+Hence, U$_3$Si$_2$-FeCrAl system is a potential ATF candidate from a neutronic
+view.
+",0,1,0,0,0,0
+945,Total-positivity preservers,"  We prove that the only entrywise transforms of rectangular matrices which
+preserve total positivity or total non-negativity are either constant or
+linear. This follows from an extended classification of preservers of these two
+properties for matrices of fixed dimension. We also prove that the same
+assertions hold upon working only with symmetric matrices; for total-positivity
+preservers our proofs proceed through solving two totally positive completion
+problems.
+",0,0,1,0,0,0
+946,A revision of the subtract-with-borrow random number generators,"  The most popular and widely used subtract-with-borrow generator, also known
+as RANLUX, is reimplemented as a linear congruential generator using large
+integer arithmetic with the modulus size of 576 bits. Modern computers, as well
+as the specific structure of the modulus inferred from RANLUX, allow for the
+development of a fast modular multiplication -- the core of the procedure. This
+was previously believed to be slow and have too high cost in terms of computing
+resources. Our tests show a significant gain in generation speed which is
+comparable with other fast, high quality random number generators. An
+additional feature is the fast skipping of generator states leading to a
+seeding scheme which guarantees the uniqueness of random number sequences.
+",1,1,0,0,0,0
+947,A Macdonald refined topological vertex,"  We consider the refined topological vertex of Iqbal et al, as a function of
+two parameters (x, y), and deform it by introducing Macdonald parameters (q,
+t), as in the work of Vuletic on plane partitions, to obtain 'a Macdonald
+refined topological vertex'. In the limit q -> t, we recover the refined
+topological vertex of Iqbal et al. In the limit x -> y, we obtain a
+qt-deformation of the topological vertex of Aganagic et al. Copies of the
+vertex can be glued to obtain qt-deformed 5D instanton partition functions that
+have well-defined 4D limits and, for generic values of (q, t), contain
+infinite-towers of poles for every pole in the limit q -> t.
+",0,0,1,0,0,0
+948,Bias voltage effects on tunneling magnetoresistance in Fe/MgAl${}_2$O${}_4$/Fe(001) junctions: Comparative study with Fe/MgO/Fe(001) junctions,"  We investigate bias voltage effects on the spin-dependent transport
+properties of Fe/MgAl${}_2$O${}_4$/Fe(001) magnetic tunneling junctions (MTJs)
+by comparing them with those of Fe/MgO/Fe(001) MTJs. By means of the
+nonequilibrium Green's function method and the density functional theory, we
+calculate bias voltage dependences of magnetoresistance (MR) ratios in both the
+MTJs. We find that in both the MTJs, the MR ratio decreases as the bias voltage
+increases and finally vanishes at a critical bias voltage $V_{\rm c}$. We also
+find that the critical bias voltage $V_{\rm c}$ of the MgAl${}_2$O${}_4$-based
+MTJ is clearly larger than that of the MgO-based MTJ. Since the in-plane
+lattice constant of the Fe/MgAl${}_2$O${}_4$/Fe(001) supercell is twice that of
+the Fe/MgO/Fe(001) one, the Fe electrodes in the MgAl${}_2$O${}_4$-based MTJs
+have an identical band structure to that obtained by folding the Fe band
+structure of the MgO-based MTJs in the Brillouin zone of the in-plane wave
+vector. We show that such a difference in the Fe band structure is the origin
+of the difference in the critical bias voltage $V_{\rm c}$ between the
+MgAl${}_2$O${}_4$- and MgO-based MTJs.
+",0,1,0,0,0,0
+949,Listen to Your Face: Inferring Facial Action Units from Audio Channel,"  Extensive efforts have been devoted to recognizing facial action units (AUs).
+However, it is still challenging to recognize AUs from spontaneous facial
+displays especially when they are accompanied with speech. Different from all
+prior work that utilized visual observations for facial AU recognition, this
+paper presents a novel approach that recognizes speech-related AUs exclusively
+from audio signals based on the fact that facial activities are highly
+correlated with voice during speech. Specifically, dynamic and physiological
+relationships between AUs and phonemes are modeled through a continuous time
+Bayesian network (CTBN); then AU recognition is performed by probabilistic
+inference via the CTBN model.
+A pilot audiovisual AU-coded database has been constructed to evaluate the
+proposed audio-based AU recognition framework. The database consists of a
+""clean"" subset with frontal and neutral faces and a challenging subset
+collected with large head movements and occlusions. Experimental results on
+this database show that the proposed CTBN model achieves promising recognition
+performance for 7 speech-related AUs and outperforms the state-of-the-art
+visual-based methods especially for those AUs that are activated at low
+intensities or ""hardly visible"" in the visual channel. Furthermore, the CTBN
+model yields more impressive recognition performance on the challenging subset,
+where the visual-based approaches suffer significantly.
+",1,0,0,0,0,0
+950,Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learning,"  We found an easy and quick post-learning method named ""Icing on the Cake"" to
+enhance a classification performance in deep learning. The method is that we
+train only the final classifier again after an ordinary training is done.
+",0,0,0,1,0,0
+951,Minimal Effort Back Propagation for Convolutional Neural Networks,"  As traditional neural network consumes a significant amount of computing
+resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet
+effective technique to alleviate this problem. In this technique, only a small
+subset of the full gradients are computed to update the model parameters. In
+this paper we extend this technique into the Convolutional Neural Network(CNN)
+to reduce calculation in back propagation, and the surprising results verify
+its validity in CNN: only 5\% of the gradients are passed back but the model
+still achieves the same effect as the traditional CNN, or even better. We also
+show that the top-$k$ selection of gradients leads to a sparse calculation in
+back propagation, which may bring significant computational benefits for high
+computational complexity of convolution operation in CNN.
+",1,0,0,1,0,0
+952,Estimating Achievable Range of Ground Robots Operating on Single Battery Discharge for Operational Efficacy Amelioration,"  Mobile robots are increasingly being used to assist with active pursuit and
+law enforcement. One major limitation for such missions is the resource
+(battery) allocated to the robot. Factors like nature and agility of evader,
+terrain over which pursuit is being carried out, plausible traversal velocity
+and the amount of necessary data to be collected all influence how long the
+robot can last in the field and how far it can travel. In this paper, we
+develop an analytical model that analyzes the energy utilization for a variety
+of components mounted on a robot to estimate the maximum operational range
+achievable by the robot operating on a single battery discharge. We categorize
+the major consumers of energy as: 1.) ancillary robotic functions such as
+computation, communication, sensing etc., and 2.) maneuvering which involves
+propulsion, steering etc. Both these consumers draw power from the common power
+source but the achievable range is largely affected by the proportion of power
+available for maneuvering. For this case study, we performed experiments with
+real robots on planar and graded surfaces and evaluated the estimation error
+for each case.
+",1,0,0,0,0,0
+953,Likelihood ratio test for variance components in nonlinear mixed effects models,"  Mixed effects models are widely used to describe heterogeneity in a
+population. A crucial issue when adjusting such a model to data consists in
+identifying fixed and random effects. From a statistical point of view, it
+remains to test the nullity of the variances of a given subset of random
+effects. Some authors have proposed to use the likelihood ratio test and have
+established its asymptotic distribution in some particular cases. Nevertheless,
+to the best of our knowledge, no general variance components testing procedure
+has been fully investigated yet. In this paper, we study the likelihood ratio
+test properties to test that the variances of a general subset of the random
+effects are equal to zero in both linear and nonlinear mixed effects model,
+extending the existing results. We prove that the asymptotic distribution of
+the test is a chi-bar-square distribution, that is to say a mixture of
+chi-square distributions, and we identify the corresponding weights. We
+highlight in particular that the limiting distribution depends on the presence
+of correlations between the random effects but not on the linear or nonlinear
+structure of the mixed effects model. We illustrate the finite sample size
+properties of the test procedure through simulation studies and apply the test
+procedure to two real datasets of dental growth and of coucal growth.
+",0,0,0,1,0,0
+954,Hölder continuous solutions of the Monge-Ampère equation on compact Hermitian manifolds,"  We show that a positive Borel measure of positive finite total mass, on
+compact Hermitian manifolds, admits a Holder continuous quasi-plurisubharmonic
+solution to the Monge-Ampere equation if and only if it is dominated locally by
+Monge-Ampere measures of Holder continuous plurisubharmonic functions.
+",0,0,1,0,0,0
+955,The beamformer and correlator for the Large European Array for Pulsars,"  The Large European Array for Pulsars combines Europe's largest radio
+telescopes to form a tied-array telescope that provides high signal-to-noise
+observations of millisecond pulsars (MSPs) with the objective to increase the
+sensitivity of detecting low-frequency gravitational waves. As part of this
+endeavor we have developed a software correlator and beamformer which enables
+the formation of a tied-array beam from the raw voltages from each of
+telescopes. We explain the concepts and techniques involved in the process of
+adding the raw voltages coherently. We further present the software processing
+pipeline that is specifically designed to deal with data from widely spaced,
+inhomogeneous radio telescopes and describe the steps involved in preparing,
+correlating and creating the tied-array beam. This includes polarization
+calibration, bandpass correction, frequency dependent phase correction,
+interference mitigation and pulsar gating. A link is provided where the
+software can be obtained.
+",0,1,0,0,0,0
+956,Solving $\ell^p\!$-norm regularization with tensor kernels,"  In this paper, we discuss how a suitable family of tensor kernels can be used
+to efficiently solve nonparametric extensions of $\ell^p$ regularized learning
+methods. Our main contribution is proposing a fast dual algorithm, and showing
+that it allows to solve the problem efficiently. Our results contrast recent
+findings suggesting kernel methods cannot be extended beyond Hilbert setting.
+Numerical experiments confirm the effectiveness of the method.
+",0,0,1,1,0,0
+957,Adversarial Phenomenon in the Eyes of Bayesian Deep Learning,"  Deep Learning models are vulnerable to adversarial examples, i.e.\ images
+obtained via deliberate imperceptible perturbations, such that the model
+misclassifies them with high confidence. However, class confidence by itself is
+an incomplete picture of uncertainty. We therefore use principled Bayesian
+methods to capture model uncertainty in prediction for observing adversarial
+misclassification. We provide an extensive study with different Bayesian neural
+networks attacked in both white-box and black-box setups. The behaviour of the
+networks for noise, attacks and clean test data is compared. We observe that
+Bayesian neural networks are uncertain in their predictions for adversarial
+perturbations, a behaviour similar to the one observed for random Gaussian
+perturbations. Thus, we conclude that Bayesian neural networks can be
+considered for detecting adversarial examples.
+",1,0,0,1,0,0
+958,Comparative Climates of TRAPPIST-1 planetary system: results from a simple climate-vegetation model,"  The recent discovery of the planetary system hosted by the ultracool dwarf
+star TRAPPIST-1 could open new perspectives into the investigation of planetary
+climates of Earth-sized exoplanets, their atmospheres and their possible
+habitability. In this paper, we use a simple climate-vegetation energy-balance
+model to study the climate of the seven TRAPPIST-1 planets and the climate
+dependence on the global albedo, on the fraction of vegetation that could cover
+their surfaces and on the different greenhouse conditions. The model allows us
+to investigate whether liquid water could be maintained on the planetary
+surfaces (i.e., by defining a ""surface water zone"") in different planetary
+conditions, with or without the presence of greenhouse effect.
+It is shown that planet TRAPPIST-1d seems to be the most stable from an
+Earth-like perspective, since it resides in the surface water zone for a wide
+range of reasonable values of the model parameters. Moreover, according to the
+model outer planets (f, g and h) cannot host liquid water on their surfaces,
+even for Earth-like conditions, entering a snowball state. Although very
+simple, the model allows to extract the main features of the TRAPPIST-1
+planetary climates.
+",0,1,0,0,0,0
+959,Boundedness of $\mathbb{Q}$-Fano varieties with degrees and alpha-invariants bounded from below,"  We show that $\mathbb{Q}$-Fano varieties of fixed dimension with
+anti-canonical degrees and alpha-invariants bounded from below form a bounded
+family. As a corollary, K-semistable $\mathbb{Q}$-Fano varieties of fixed
+dimension with anti-canonical degrees bounded from below form a bounded family.
+",0,0,1,0,0,0
+960,Width Hierarchies for Quantum and Classical Ordered Binary Decision Diagrams with Repeated Test,"  We consider quantum, nondterministic and probabilistic versions of known
+computational model Ordered Read-$k$-times Branching Programs or Ordered Binary
+Decision Diagrams with repeated test ($k$-QOBDD, $k$-NOBDD and $k$-POBDD). We
+show width hierarchy for complexity classes of Boolean function computed by
+these models and discuss relation between different variants of $k$-OBDD.
+",1,0,0,0,0,0
+961,Complementary views on electron spectra: From Fluctuation Diagnostics to real space correlations,"  We study the relation between the microscopic properties of a many-body
+system and the electron spectra, experimentally accessible by photoemission. In
+a recent paper [Phys. Rev. Lett. 114, 236402 (2015)], we introduced the
+""fluctuation diagnostics"" approach, to extract the dominant wave vector
+dependent bosonic fluctuations from the electronic self-energy. Here, we first
+reformulate the theory in terms of fermionic modes, to render its connection
+with resonance valence bond (RVB) fluctuations more transparent. Secondly, by
+using a large-U expansion, where U is the Coulomb interaction, we relate the
+fluctuations to real space correlations. Therefore, it becomes possible to
+study how electron spectra are related to charge, spin, superconductivity and
+RVB-like real space correlations, broadening the analysis of an earlier work
+[Phys. Rev. B 89, 245130 (2014)]. This formalism is applied to the pseudogap
+physics of the two-dimensional Hubbard model, studied in the dynamical cluster
+approximation. We perform calculations for embedded clusters with up to 32
+sites, having three inequivalent K-points at the Fermi surface. We find that as
+U is increased, correlation functions gradually attain values consistent with
+an RVB state. This first happens for correlation functions involving the
+antinodal point and gradually spreads to the nodal point along the Fermi
+surface. Simultaneously a pseudogap opens up along the Fermi surface. We relate
+this to a crossover from a Kondo-like state to an RVB-like localized cluster
+state and to the presence of RVB and spin fluctuations. These changes are
+caused by a strong momentum dependence in the cluster bath-couplings along the
+Fermi surface. We also show, from a more algorithmic perspective, how the
+time-consuming calculations in fluctuation diagnostics can be drastically
+simplified.
+",0,1,0,0,0,0
+962,Stall force of a cargo driven by N interacting motor proteins,"  We study a generic one-dimensional model for an intracellular cargo driven by
+N motor proteins against an external applied force. The model includes
+motor-cargo and motor-motor interactions. The cargo motion is described by an
+over-damped Langevin equation, while motor dynamics is specified by hopping
+rates which follow a local detailed balance condition with respect to change in
+energy per hopping event. Based on this model, we show that the stall force,
+the mean external force corresponding to zero mean cargo velocity, is
+completely independent of the details of the interactions and is, therefore,
+always equal to the sum of the stall forces of the individual motors. This
+exact result is arrived on the basis of a simple assumption: the (macroscopic)
+state of stall of the cargo is analogous to a state of thermodynamic
+equilibrium, and is characterized by vanishing net probability current between
+any two microstates, with the latter specified by motor positions relative to
+the cargo. The corresponding probability distribution of the microstates under
+stall is also determined. These predictions are in complete agreement with
+numerical simulations, carried out using specific forms of interaction
+potentials.
+",0,1,0,0,0,0
+963,Dynamics of a Camphoric Acid boat at the air-water interface,"  We report experiments on an agarose gel tablet loaded with camphoric acid
+(c-boat) set into self-motion by interfacial tension gradients at the air-water
+interface. We observe three distinct modes of c-boat motion: harmonic mode
+where the c-boat speed oscillates sinusoidally in time, a steady mode where the
+c-boat maintains constant speed, and a relaxation oscillation mode where the
+c-boat maintains near-zero speed between sudden jumps in speed and position at
+regular time intervals. Whereas all three modes have been separately reported
+before in different systems, we show they belong to a common description.
+Through control of the air-water surface tension with Sodium Dodecyl Sulfate
+(SDS), we experimentally deduce the three self-propulsive modes result from
+surface tension difference between Camphoric Acid (CA) and the ambient
+surroundings.
+",0,1,0,0,0,0
+964,Separation of time scales and direct computation of weights in deep neural networks,"  Artificial intelligence is revolutionizing our lives at an ever increasing
+pace. At the heart of this revolution is the recent advancements in deep neural
+networks (DNN), learning to perform sophisticated, high-level tasks. However,
+training DNNs requires massive amounts of data and is very computationally
+intensive. Gaining analytical understanding of the solutions found by DNNs can
+help us devise more efficient training algorithms, replacing the commonly used
+mthod of stochastic gradient descent (SGD). We analyze the dynamics of SGD and
+show that, indeed, direct computation of the solutions is possible in many
+cases. We show that a high performing setup used in DNNs introduces a
+separation of time-scales in the training dynamics, allowing SGD to train
+layers from the lowest (closest to input) to the highest. We then show that for
+each layer, the distribution of solutions found by SGD can be estimated using a
+class-based principal component analysis (PCA) of the layer's input. This
+finding allows us to forgo SGD entirely and directly derive the DNN parameters
+using this class-based PCA, which can be well estimated using significantly
+less data than SGD. We implement these results on image datasets MNIST, CIFAR10
+and CIFAR100 and find that, in fact, layers derived using our class-based PCA
+perform comparable or superior to neural networks of the same size and
+architecture trained using SGD. We also confirm that the class-based PCA often
+converges using a fraction of the data required for SGD. Thus, using our method
+training time can be reduced both by requiring less training data than SGD, and
+by eliminating layers in the costly backpropagation step of the training.
+",1,0,0,1,0,0
+965,$q$-deformed quadrature operator and optical tomogram,"  In this letter, we define the homodyne $q$-deformed quadrature operator.
+Analytic expression for the wavefunctions of $q$-deformed oscillator in the
+quadrature basis are found. Furthermore, we compute the explicit analytical
+expression for the tomogram of the $q$-deformed coherent states by finding the
+eigenstates of the $q$-deformed quadrature operator.
+",0,1,1,0,0,0
+966,Efficient Graph Edit Distance Computation and Verification via Anchor-aware Lower Bound Estimation,"  Graph edit distance (GED) is an important similarity measure adopted in a
+similarity-based analysis between two graphs, and computing GED is a primitive
+operator in graph database analysis. Partially due to the NP-hardness, the
+existing techniques for computing GED are only able to process very small
+graphs with less than 30 vertices. Motivated by this, in this paper we
+systematically study the problems of both GED computation, and GED verification
+(i.e., verify whether the GED between two graphs is no larger than a user-given
+threshold). Firstly, we develop a unified framework that can be instantiated
+into either a best-first search approach AStar+ or a depth-first search
+approach DFS+. Secondly, we design anchor-aware lower bound estimation
+techniques to compute tighter lower bounds for intermediate search states,
+which significantly reduce the search spaces of both AStar+ and DFS+. We also
+propose efficient techniques to compute the lower bounds. Thirdly, based on our
+unified framework, we contrast AStar+ with DFS+ regarding their time and space
+complexities, and recommend that AStar+ is better than DFS+ by having a much
+smaller search space. Extensive empirical studies validate that AStar+ performs
+better than DFS+, and show that our AStar+-BMa approach outperforms the
+state-of-the-art technique by more than four orders of magnitude.
+",1,0,0,0,0,0
+967,Asymptotic measures and links in simplicial complexes,"  We introduce canonical measures on a locally finite simplicial complex $K$
+and study their asymptotic behavior under infinitely many barycentric
+subdivisions. We also compute the face polynomial of the asymptotic link and
+dual block of a simplex in the $d^{th}$ barycentric subdivision $Sd^d(K)$ of
+$K$, $d\gg0$. It is almost everywhere constant. When $K$ is finite, we study
+the limit face polynomial of $Sd^d(K)$ after F.Brenti-V.Welker and
+E.Delucchi-A.Pixton-L.Sabalka.
+",0,0,1,0,0,0
+968,Usability of Humanly Computable Passwords,"  Reusing passwords across multiple websites is a common practice that
+compromises security. Recently, Blum and Vempala have proposed password
+strategies to help people calculate, in their heads, passwords for different
+sites without dependence on third-party tools or external devices. Thus far,
+the security and efficiency of these ""mental algorithms"" has been analyzed only
+theoretically. But are such methods usable? We present the first usability
+study of humanly computable password strategies, involving a learning phase (to
+learn a password strategy), then a rehearsal phase (to login to a few
+websites), and multiple follow-up tests. In our user study, with training,
+participants were able to calculate a deterministic eight-character password
+for an arbitrary new website in under 20 seconds.
+",1,0,0,0,0,0
+969,Shared urbanism: Big data on accommodation sharing in urban Australia,"  As affordability pressures and tight rental markets in global cities mount,
+online shared accommodation sites proliferate. Home sharing arrangements
+present dilemmas for planning that aims to improve health and safety standards,
+while supporting positives such as the usage of dormant stock and the relieving
+of rental pressures on middle/lower income earners. Currently, no formal data
+exists on this internationally growing trend. Here, we present a first
+quantitative glance on shared accommodation practices across all major urban
+centers of Australia enabled via collection and analysis of thousands of online
+listings. We examine, countrywide, the spatial and short time scale temporal
+characteristics of this market, along with preliminary analysis on rents,
+dwelling types and other characteristics. Findings have implications for
+housing policy makers and planning practitioners seeking to monitor and respond
+to housing policy and affordability pressures in formal and informal housing
+markets.
+",1,1,0,0,0,0
+970,Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis,"  Interbank markets are often characterised in terms of a core-periphery
+network structure, with a highly interconnected core of banks holding the
+market together, and a periphery of banks connected mostly to the core but not
+internally. This paradigm has recently been challenged for short time scales,
+where interbank markets seem better characterised by a bipartite structure with
+more core-periphery connections than inside the core. Using a novel
+core-periphery detection method on the eMID interbank market, we enrich this
+picture by showing that the network is actually characterised by multiple
+core-periphery pairs. Moreover, a transition from core-periphery to bipartite
+structures occurs by shortening the temporal scale of data aggregation. We
+further show how the global financial crisis transformed the market, in terms
+of composition, multiplicity and internal organisation of core-periphery pairs.
+By unveiling such a fine-grained organisation and transformation of the
+interbank market, our method can find important applications in the
+understanding of how distress can propagate over financial networks.
+",0,0,0,0,0,1
+971,Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples,"  We present a novel algorithm that uses exact learning and abstraction to
+extract a deterministic finite automaton describing the state dynamics of a
+given trained RNN. We do this using Angluin's L* algorithm as a learner and the
+trained RNN as an oracle. Our technique efficiently extracts accurate automata
+from trained RNNs, even when the state vectors are large and require fine
+differentiation.
+",1,0,0,0,0,0
+972,Performance analysis of smart digital signage system based on software-defined IoT and invisible image sensor communication,"  Everything in the world is being connected, and things are becoming
+interactive. The future of the interactive world depends on the future Internet
+of Things (IoT). Software-defined networking (SDN) technology, a new paradigm
+in the networking area, can be useful in creating an IoT because it can handle
+interactivity by controlling physical devices, transmission of data among them,
+and data acquisition. However, digital signage can be one of the promising
+technologies in this era of technology that is progressing toward the
+interactive world, connecting users to the IoT network through device-to-device
+communication technology. This article illustrates a novel prototype that is
+mainly focused on a smart digital signage system comprised of software-defined
+IoT (SD-IoT) and invisible image sensor communication technology. We have
+proposed an SDN scheme with a view to initiating its flexibility and
+compatibility for an IoT network-based smart digital signage system. The idea
+of invisible communication can make the users of the technology trendier to it,
+and the usage of unused resources such as images and videos can be ensured. In
+addition, this communication has paved the way for interactivity between the
+user and digital signage, where the digital signage and the camera of a
+smartphone can be operated as a transmitter and a receiver, respectively. The
+proposed scheme might be applicable to real-world applications because SDN has
+the flexibility to adapt with the alteration of network status without any
+hardware modifications while displays and smartphones are available everywhere.
+A performance analysis of this system showed the advantages of an SD-IoT
+network over an Internet protocol-based IoT network considering a queuing
+analysis for a dynamic link allocation process in the case of user access to
+the IoT network.
+",1,0,0,0,0,0
+973,What pebbles are made of: Interpretation of the V883 Ori disk,"  Recently, an Atacama Large Millimeter/submillimeter Array (ALMA) observation
+of the water snow line in the protoplanetary disk around the FU Orionis star
+V883 Ori was reported. The radial variation of the spectral index at
+mm-wavelengths around the snow line was interpreted as being due to a pileup of
+particles interior to the snow line. However, radial transport of solids in the
+outer disk operates on timescales much longer than the typical timescale of an
+FU Ori outburst ($10^{1}$--$10^{2}$ yr). Consequently, a steady-state pileup is
+unlikely. We argue that it is only necessary to consider water evaporation and
+re-coagulation of silicates to explain the recent ALMA observation of V883 Ori
+because these processes are short enough to have had their impact since the
+outburst. Our model requires the inner disk to have already been optically
+thick before the outburst, and our results suggest that the carbon content of
+pebbles is low.
+",0,1,0,0,0,0
+974,Existence and uniqueness of steady weak solutions to the Navier-Stokes equations in $\mathbb{R}^2$,"  The existence of weak solutions to the stationary Navier-Stokes equations in
+the whole plane $\mathbb{R}^2$ is proven. This particular geometry was the only
+case left open since the work of Leray in 1933. The reason is that due to the
+absence of boundaries the local behavior of the solutions cannot be controlled
+by the enstrophy in two dimensions. We overcome this difficulty by constructing
+approximate weak solutions having a prescribed mean velocity on some given
+bounded set. As a corollary, we obtain infinitely many weak solutions in
+$\mathbb{R}^2$ parameterized by this mean velocity, which is reminiscent of the
+expected convergence of the velocity field at large distances to any prescribed
+constant vector field. This explicit parameterization of the weak solutions
+allows us to prove a weak-strong uniqueness theorem for small data. The
+question of the asymptotic behavior of the weak solutions remains however open,
+when the uniqueness theorem doesn't apply.
+",0,0,1,0,0,0
+975,More on products of Baire spaces,"  New results on the Baire product problem are presented. It is shown that an
+arbitrary product of almost locally ccc Baire spaces is Baire; moreover, the
+product of a Baire space and a 1st countable space which is $\beta$-unfavorable
+in the strong Choquet game is Baire.
+",0,0,1,0,0,0
+976,Social versus Moral preferences in the Ultimatum Game: A theoretical model and an experiment,"  In the Ultimatum Game (UG) one player, named ""proposer"", has to decide how to
+allocate a certain amount of money between herself and a ""responder"". If the
+offer is greater than or equal to the responder's minimum acceptable offer
+(MAO), then the money is split as proposed, otherwise, neither the proposer nor
+the responder get anything. The UG has intrigued generations of behavioral
+scientists because people in experiments blatantly violate the equilibrium
+predictions that self-interested proposers offer the minimum available non-zero
+amount, and self-interested responders accept. Why are these predictions
+violated? Previous research has mainly focused on the role of social
+preferences. Little is known about the role of general moral preferences for
+doing the right thing, preferences that have been shown to play a major role in
+other social interactions (e.g., Dictator Game and Prisoner's Dilemma). Here I
+develop a theoretical model and an experiment designed to pit social
+preferences against moral preferences. I find that, although people recognize
+that offering half and rejecting low offers are the morally right things to do,
+moral preferences have no causal impact on UG behavior. The experimental data
+are indeed well fit by a model according to which: (i) high UG offers are
+motivated by inequity aversion and, to a lesser extent, self-interest; (ii)
+high MAOs are motivated by inequity aversion.
+",0,0,0,0,1,0
+977,Moderate Deviation Analysis for Classical-Quantum Channels and Quantum Hypothesis Testing,"  In this work, we study the tradeoffs between the error probabilities of
+classical-quantum channels and the blocklength $n$ when the transmission rates
+approach the channel capacity at a rate slower than $1/\sqrt{n}$, a research
+topic known as moderate deviation analysis. We show that the optimal error
+probability vanishes under this rate convergence. Our main technical
+contributions are a tight quantum sphere-packing bound, obtained via Chaganty
+and Sethuraman's concentration inequality in strong large deviation theory, and
+asymptotic expansions of error-exponent functions. Moderate deviation analysis
+for quantum hypothesis testing is also established. The converse directly
+follows from our channel coding result, while the achievability relies on a
+martingale inequality.
+",1,0,0,0,0,0
+978,A simple recipe for making accurate parametric inference in finite sample,"  Constructing tests or confidence regions that control over the error rates in
+the long-run is probably one of the most important problem in statistics. Yet,
+the theoretical justification for most methods in statistics is asymptotic. The
+bootstrap for example, despite its simplicity and its widespread usage, is an
+asymptotic method. There are in general no claim about the exactness of
+inferential procedures in finite sample. In this paper, we propose an
+alternative to the parametric bootstrap. We setup general conditions to
+demonstrate theoretically that accurate inference can be claimed in finite
+sample.
+",0,0,1,1,0,0
+979,GBDT of discrete skew-selfadjoint Dirac systems and explicit solutions of the corresponding non-stationary problems,"  Generalized Bäcklund-Darboux transformations (GBDTs) of discrete
+skew-selfadjoint Dirac systems have been successfully used for explicit solving
+of direct and inverse problems of Weyl-Titchmarsh theory. During explicit
+solving of the direct and inverse problems, we considered GBDTs of the trivial
+initial systems. However, GBDTs of arbitrary discrete skew-selfadjoint Dirac
+systems are important as well and we introduce these transformations in the
+present paper. The obtained results are applied to the construction of explicit
+solutions of the interesting related non-stationary systems.
+",0,0,1,0,0,0
+980,Panel collapse and its applications,"  We describe a procedure called panel collapse for replacing a CAT(0) cube
+complex $\Psi$ by a ""lower complexity"" CAT(0) cube complex $\Psi_\bullet$
+whenever $\Psi$ contains a codimension-$2$ hyperplane that is extremal in one
+of the codimension-$1$ hyperplanes containing it. Although $\Psi_\bullet$ is
+not in general a subcomplex of $\Psi$, it is a subspace consisting of a
+subcomplex together with some cubes that sit inside $\Psi$ ""diagonally"". The
+hyperplanes of $\Psi_\bullet$ extend to hyperplanes of $\Psi$. Applying this
+procedure, we prove: if a group $G$ acts cocompactly on a CAT(0) cube complex
+$\Psi$, then there is a CAT(0) cube complex $\Omega$ so that $G$ acts
+cocompactly on $\Omega$ and for each hyperplane $H$ of $\Omega$, the stabiliser
+in $G$ of $H$ acts on $H$ essentially.
+Using panel collapse, we obtain a new proof of Stallings's theorem on groups
+with more than one end. As another illustrative example, we show that panel
+collapse applies to the exotic cubulations of free groups constructed by Wise.
+Next, we show that the CAT(0) cube complexes constructed by Cashen-Macura can
+be collapsed to trees while preserving all of the necessary group actions. (It
+also illustrates that our result applies to actions of some non-discrete
+groups.) We also discuss possible applications to quasi-isometric rigidity for
+certain classes of graphs of free groups with cyclic edge groups. Panel
+collapse is also used in forthcoming work of the first-named author and Wilton
+to study fixed-point sets of finite subgroups of $\mathrm{Out}(F_n)$ on the
+free splitting complex.
+",0,0,1,0,0,0
+981,Smooth Neighbors on Teacher Graphs for Semi-supervised Learning,"  The recently proposed self-ensembling methods have achieved promising results
+in deep semi-supervised learning, which penalize inconsistent predictions of
+unlabeled data under different perturbations. However, they only consider
+adding perturbations to each single data point, while ignoring the connections
+between data samples. In this paper, we propose a novel method, called Smooth
+Neighbors on Teacher Graphs (SNTG). In SNTG, a graph is constructed based on
+the predictions of the teacher model, i.e., the implicit self-ensemble of
+models. Then the graph serves as a similarity measure with respect to which the
+representations of ""similar"" neighboring points are learned to be smooth on the
+low-dimensional manifold. We achieve state-of-the-art results on
+semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for
+CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular,
+the improvements are significant when the labels are fewer. For the
+non-augmented MNIST with only 20 labels, the error rate is reduced from
+previous 4.81% to 1.36%. Our method also shows robustness to noisy labels.
+",1,0,0,1,0,0
+982,Detecting Heavy Flows in the SDN Match and Action Model,"  Efficient algorithms and techniques to detect and identify large flows in a
+high throughput traffic stream in the SDN match-and-action model are presented.
+This is in contrast to previous work that either deviated from the match and
+action model by requiring additional switch level capabilities or did not
+exploit the SDN data plane. Our construction has two parts; (a) how to sample
+in an SDN match and action model, (b) how to detect large flows efficiently and
+in a scalable way, in the SDN model.
+Our large flow detection methods provide high accuracy and present a good and
+practical tradeoff between switch - controller traffic, and the number of
+entries required in the switch flow table. Based on different parameters, we
+differentiate between heavy flows, elephant flows and bulky flows and present
+efficient algorithms to detect flows of the different types.
+Additionally, as part of our heavy flow detection scheme, we present sampling
+methods to sample packets with arbitrary probability $p$ per packet or per byte
+that traverses an SDN switch.
+Finally, we show how our algorithms can be adapted to a distributed
+monitoring SDN setting with multiple switches, and easily scale with the number
+of monitoring switches.
+",1,0,0,0,0,0
+983,Optimal Resource Allocation with Node and Link Capacity Constraints in Complex Networks,"  With the tremendous increase of the Internet traffic, achieving the best
+performance with limited resources is becoming an extremely urgent problem. In
+order to address this concern, in this paper, we build an optimization problem
+which aims to maximize the total utility of traffic flows with the capacity
+constraint of nodes and links in the network. Based on Duality Theory, we
+propose an iterative algorithm which adjusts the rates of traffic flows and
+capacity of nodes and links simultaneously to maximize the total utility.
+Simulation results show that our algorithm performs better than the NUP
+algorithm on BA and ER network models, which has shown to get the best
+performance so far. Since our research combines the topology information with
+capacity constraint, it may give some insights for resource allocation in real
+communication networks.
+",1,1,0,0,0,0
+984,On the complexity of solving a decision problem with flow-depending costs: the case of the IJsselmeer dikes,"  We consider a fundamental integer programming (IP) model for cost-benefit
+analysis flood protection through dike building in the Netherlands, due to
+Verweij and Zwaneveld.
+Experimental analysis with data for the Ijsselmeer lead to integral optimal
+solution of the linear programming relaxation of the IP model.
+This naturally led to the question of integrality of the polytope associated
+with the IP model.
+In this paper we first give a negative answer to this question by
+establishing non-integrality of the polytope.
+Second, we establish natural conditions that guarantee the linear programming
+relaxation of the IP model to be integral.
+We then test the most recent data on flood probabilities, damage and
+investment costs of the IJsselmeer for these conditions.
+Third, we show that the IP model can be solved in polynomial time when the
+number of dike segments, or the number of feasible barrier heights, are
+constant.
+",0,0,0,0,0,1
+985,Achieving Spectrum Efficient Communication Under Cross-Technology Interference,"  In wireless communication, heterogeneous technologies such as WiFi, ZigBee
+and BlueTooth operate in the same ISM band.With the exponential growth in the
+number of wireless devices, the ISM band becomes more and more crowded. These
+heterogeneous devices have to compete with each other to access spectrum
+resources, generating cross-technology interference (CTI). Since CTI may
+destroy wireless communication, this field is facing an urgent and challenging
+need to investigate spectrum efficiency under CTI. In this paper, we introduce
+a novel framework to address this problem from two aspects. On the one hand,
+from the perspective of each communication technology itself, we propose novel
+channel/link models to capture the channel/link status under CTI. On the other
+hand, we investigate spectrum efficiency from the perspective by taking all
+heterogeneous technologies as a whole and building crosstechnology
+communication among them. The capability of direct communication among
+heterogeneous devices brings great opportunities to harmoniously sharing the
+spectrum with collaboration rather than competition.
+",1,0,0,0,0,0
+986,A Galactic Cosmic Ray Electron Intensity Increase of a factor of up to 100 At Energies between 3 and 50 MeV in the Heliosheath between the Termination Shock and the Heliopause Due to Solar Modulation As Measured by Voyager 1,"  We have derived background corrected intensities of 3-50 MeV galactic
+electrons observed by Voyager 1 as it passes through the heliosheath from 95 to
+122 AU. The overall intensity change of the background corrected data from the
+inner to the outer boundary of the heliosheath is a maximum of a factor ~100 at
+15 MeV. At lower energies this fractional change becomes less and the corrected
+electron spectra in the heliosheath becomes progressively steeper, reaching
+values ~ -2.5 for the spectral index just outside of the termination shock. At
+higher energies the spectra of electrons has an exponent changing from the
+negative LIS spectral index of -1.3 to values approaching zero in the
+heliosheath as a result of the solar modulation of the galactic electron
+component. The large modulation effects observed below ~100 MV are possible
+evidence for enhanced diffusion as part of the modulation process for electrons
+in the heliosheath.
+",0,1,0,0,0,0
+987,Statistical foundations for assessing the difference between the classical and weighted-Gini betas,"  The `beta' is one of the key quantities in the capital asset pricing model
+(CAPM). In statistical language, the beta can be viewed as the slope of the
+regression line fitted to financial returns on the market against the returns
+on the asset under consideration. The insurance counterpart of CAPM, called the
+weighted insurance pricing model (WIPM), gives rise to the so-called
+weighted-Gini beta. The aforementioned two betas may or may not coincide,
+depending on the form of the underlying regression function, and this has
+profound implications when designing portfolios and allocating risk capital. To
+facilitate these tasks, in this paper we develop large-sample statistical
+inference results that, in a straightforward fashion, imply confidence
+intervals for, and hypothesis tests about, the equality of the two betas.
+",0,0,1,1,0,0
+988,Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization,"  In partially observed environments, it can be useful for a human to provide
+the robot with declarative information that represents probabilistic relational
+constraints on properties of objects in the world, augmenting the robot's
+sensory observations. For instance, a robot tasked with a search-and-rescue
+mission may be informed by the human that two victims are probably in the same
+room. An important question arises: how should we represent the robot's
+internal knowledge so that this information is correctly processed and combined
+with raw sensory information? In this paper, we provide an efficient belief
+state representation that dynamically selects an appropriate factoring,
+combining aspects of the belief when they are correlated through information
+and separating them when they are not. This strategy works in open domains, in
+which the set of possible objects is not known in advance, and provides
+significant improvements in inference time over a static factoring, leading to
+more efficient planning for complex partially observed tasks. We validate our
+approach experimentally in two open-domain planning problems: a 2D discrete
+gridworld task and a 3D continuous cooking task. A supplementary video can be
+found at this http URL.
+",1,0,0,0,0,0
+989,Characterizing complex networks using Entropy-degree diagrams: unveiling changes in functional brain connectivity induced by Ayahuasca,"  Open problems abound in the theory of complex networks, which has found
+successful application to diverse fields of science. With the aim of further
+advancing the understanding of the brain's functional connectivity, we propose
+to evaluate a network metric which we term the geodesic entropy. This entropy,
+in a way that can be made precise, quantifies the Shannon entropy of the
+distance distribution to a specific node from all other nodes. Measurements of
+geodesic entropy allow for the characterization of the structural information
+of a network that takes into account the distinct role of each node into the
+network topology. The measurement and characterization of this structural
+information has the potential to greatly improve our understanding of sustained
+activity and other emergent behaviors in networks, such as self-organized
+criticality sometimes seen in such contexts. We apply these concepts and
+methods to study the effects of how the psychedelic Ayahuasca affects the
+functional connectivity of the human brain. We show that the geodesic entropy
+is able to differentiate the functional networks of the human brain in two
+different states of consciousness in the resting state: (i) the ordinary waking
+state and (ii) a state altered by ingestion of the Ayahuasca. The entropy of
+the nodes of brain networks from subjects under the influence of Ayahuasca
+diverge significantly from those of the ordinary waking state. The functional
+brain networks from subjects in the altered state have, on average, a larger
+geodesic entropy compared to the ordinary state. We conclude that geodesic
+entropy is a useful tool for analyzing complex networks and discuss how and why
+it may bring even further valuable insights into the study of the human brain
+and other empirical networks.
+",0,0,0,0,1,0
+990,On Integral Upper Limits Assuming Power Law Spectra and the Sensitivity in High-Energy Astronomy,"  The high-energy non-thermal universe is dominated by power law-like spectra.
+Therefore results in high-energy astronomy are often reported as parameters of
+power law fits, or, in the case of a non-detection, as an upper limit assuming
+the underlying unseen spectrum behaves as a power law. In this paper I
+demonstrate a simple and powerful one-to-one relation of the integral upper
+limit in the two dimensional power law parameter space into the spectrum
+parameter space and use this method to unravel the so far convoluted question
+of the sensitivity of astroparticle telescopes.
+",0,1,0,0,0,0
+991,Estimates for solutions of Dirac equations and an application to a geometric elliptic-parabolic problem,"  We develop estimates for the solutions and derive existence and uniqueness
+results of various local boundary value problems for Dirac equations that
+improve all relevant results known in the literature. With these estimates at
+hand, we derive a general existence, uniqueness and regularity theorem for
+solutions of Dirac equations with such boundary conditions. We also apply these
+estimates to a new nonlinear elliptic-parabolic problem, the Dirac-harmonic
+heat flow on Riemannian spin manifolds. This problem is motivated by the
+supersymmetric nonlinear $\sigma$-model and combines a harmonic heat flow type
+equation with a Dirac equation that depends nonlinearly on the flow.
+",0,0,1,0,0,0
+992,Analysis of Political Party Twitter Accounts' Retweeters During Japan's 2017 Election,"  In modern election campaigns, political parties utilize social media to
+advertise their policies and candidates and to communicate to the electorate.
+In Japan's latest general election in 2017, the 48th general election for the
+Lower House, social media, especially Twitter, was actively used. In this
+paper, we analyze the users who retweeted tweets of political parties on
+Twitter during the election. Our aim is to clarify what kinds of users are
+diffusing (retweeting) tweets of political parties. The results indicate that
+the characteristics of retweeters of the largest ruling party (Liberal
+Democratic Party of Japan) and the largest opposition party (The Constitutional
+Democratic Party of Japan) were similar, even though the retweeters did not
+overlap each other. We also found that a particular opposition party (Japanese
+Communist Party) had quite different characteristics from other political
+parties.
+",1,0,0,0,0,0
+993,When a triangle is isosceles?,"  In 1840 Jacob Steiner on Christian Rudolf's request proved that a triangle
+with two equal bisectors is isosceles. But what about changing the bisectors to
+cevians? Cevian is any line segment in a triangle with one endpoint on a vertex
+of the triangle and other endpoint on the opposite side. Not for any pairs of
+equal cevians the triangle is isosceles. Theorem. If for a triangle ABC there
+are equal cevians issuing from A and B, which intersect on the bisector or on
+the median of the angle C, then AC=BC (so the triangle ABC is isosceles).
+Proposition. Let ABC be an isosceles triangle. Define circle C to be the circle
+symmetric relative to AB to the circumscribed circle of the triangle ABC. Then
+the locus of intersection points of pairs of equal cevians is the union of the
+base AB, the triangle's axis of symmetry, and the circle C.
+",0,0,1,0,0,0
+994,Anomaly detecting and ranking of the cloud computing platform by multi-view learning,"  Anomaly detecting as an important technical in cloud computing is applied to
+support smooth running of the cloud platform. Traditional detecting methods
+based on statistic, analysis, etc. lead to the high false-alarm rate due to
+non-adaptive and sensitive parameters setting. We presented an online model for
+anomaly detecting using machine learning theory. However, most existing methods
+based on machine learning linked all features from difference sub-systems into
+a long feature vector directly, which is difficult to both exploit the
+complement information between sub-systems and ignore multi-view features
+enhancing the classification performance. Aiming to this problem, the proposed
+method automatic fuses multi-view features and optimize the discriminative
+model to enhance the accuracy. This model takes advantage of extreme learning
+machine (ELM) to improve detection efficiency. ELM is the single hidden layer
+neural network, which is transforming iterative solution the output weights to
+solution of linear equations and avoiding the local optimal solution. Moreover,
+we rank anomies according to the relationship between samples and the
+classification boundary, and then assigning weights for ranked anomalies,
+retraining the classification model finally. Our method exploits the complement
+information between sub-systems sufficiently, and avoids the influence from
+imbalance dataset, therefore, deal with various challenges from the cloud
+computing platform. We deploy the privately cloud platform by Openstack,
+verifying the proposed model and comparing results to the state-of-the-art
+methods with better efficiency and simplicity.
+",1,0,0,1,0,0
+995,On self-affine sets,"  We survey the dimension theory of self-affine sets for general mathematical
+audience. The article is in Finnish.
+",0,0,1,0,0,0
+996,The effect of phase change on stability of convective flow in a layer of volatile liquid driven by a horizontal temperature gradient,"  Buoyancy-thermocapillary convection in a layer of volatile liquid driven by a
+horizontal temperature gradient arises in a variety of situations. Recent
+studies have shown that the composition of the gas phase, which is typically a
+mixture of vapour and air, has a noticeable effect on the critical Marangoni
+number describing the onset of convection as well as on the observed convection
+pattern. Specifically, as the total pressure or, equivalently, the average
+concentration of air is decreased, the threshold of the instability leading to
+the emergence of convective rolls is found to increase rather significantly. We
+present a linear stability analysis of the problem which shows that this trend
+can be readily understood by considering the transport of heat and vapour
+through the gas phase. In particular, we show that transport in the gas phase
+has a noticeable effect even at atmospheric conditions, when phase change is
+greatly suppressed.
+",0,1,0,0,0,0
+997,A graph model of message passing processes,"  In the paper we consider a graph model of message passing processes and
+present a method verification of message passing processes. The method is
+illustrated by an example of a verification of sliding window protocol.
+",1,0,0,0,0,0
+998,Learning the Sparse and Low Rank PARAFAC Decomposition via the Elastic Net,"  In this article, we derive a Bayesian model to learning the sparse and low
+rank PARAFAC decomposition for the observed tensor with missing values via the
+elastic net, with property to find the true rank and sparse factor matrix which
+is robust to the noise. We formulate efficient block coordinate descent
+algorithm and admax stochastic block coordinate descent algorithm to solve it,
+which can be used to solve the large scale problem. To choose the appropriate
+rank and sparsity in PARAFAC decomposition, we will give a solution path by
+gradually increasing the regularization to increase the sparsity and decrease
+the rank. When we find the sparse structure of the factor matrix, we can fixed
+the sparse structure, using a small to regularization to decreasing the
+recovery error, and one can choose the proper decomposition from the solution
+path with sufficient sparse factor matrix with low recovery error. We test the
+power of our algorithm on the simulation data and real data, which show it is
+powerful.
+",0,0,1,1,0,0
+999,"Holomorphic differentials, thermostats and Anosov flows","  We introduce a new family of thermostat flows on the unit tangent bundle of
+an oriented Riemannian $2$-manifold. Suitably reparametrised, these flows
+include the geodesic flow of metrics of negative Gauss curvature and the
+geodesic flow induced by the Hilbert metric on the quotient surface of
+divisible convex sets. We show that the family of flows can be parametrised in
+terms of certain weighted holomorphic differentials and investigate their
+properties. In particular, we prove that they admit a dominated splitting and
+we identify special cases in which the flows are Anosov. In the latter case, we
+study when they admit an invariant measure in the Lebesgue class and the
+regularity of the weak foliations.
+",0,0,1,0,0,0
+1000,"Multiplicative slices, relativistic Toda and shifted quantum affine algebras","  We introduce the shifted quantum affine algebras. They map homomorphically
+into the quantized $K$-theoretic Coulomb branches of $3d\ {\mathcal N}=4$ SUSY
+quiver gauge theories. In type $A$, they are endowed with a coproduct, and they
+act on the equivariant $K$-theory of parabolic Laumon spaces. In type $A_1$,
+they are closely related to the open relativistic quantum Toda lattice of type
+$A$.
+",0,0,1,0,0,0
+1001,Statistical Latent Space Approach for Mixed Data Modelling and Applications,"  The analysis of mixed data has been raising challenges in statistics and
+machine learning. One of two most prominent challenges is to develop new
+statistical techniques and methodologies to effectively handle mixed data by
+making the data less heterogeneous with minimum loss of information. The other
+challenge is that such methods must be able to apply in large-scale tasks when
+dealing with huge amount of mixed data. To tackle these challenges, we
+introduce parameter sharing and balancing extensions to our recent model, the
+mixed-variate restricted Boltzmann machine (MV.RBM) which can transform
+heterogeneous data into homogeneous representation. We also integrate
+structured sparsity and distance metric learning into RBM-based models. Our
+proposed methods are applied in various applications including latent patient
+profile modelling in medical data analysis and representation learning for
+image retrieval. The experimental results demonstrate the models perform better
+than baseline methods in medical data and outperform state-of-the-art rivals in
+image dataset.
+",1,0,0,1,0,0
+1002,Near-field coupling of gold plasmonic antennas for sub-100 nm magneto-thermal microscopy,"  The development of spintronic technology with increasingly dense, high-speed,
+and complex devices will be accelerated by accessible microscopy techniques
+capable of probing magnetic phenomena on picosecond time scales and at deeply
+sub-micron length scales. A recently developed time-resolved magneto-thermal
+microscope provides a path towards this goal if it is augmented with a
+picosecond, nanoscale heat source. We theoretically study adiabatic
+nanofocusing and near-field heat induction using conical gold plasmonic
+antennas to generate sub-100 nm thermal gradients for time-resolved
+magneto-thermal imaging. Finite element calculations of antenna-sample
+interactions reveal focused electromagnetic loss profiles that are either
+peaked directly under the antenna or are annular, depending on the sample's
+conductivity, the antenna's apex radius, and the tip-sample separation. We find
+that the thermal gradient is confined to 40 nm to 60 nm full width at half
+maximum for realistic ranges of sample conductivity and apex radius. To
+mitigate this variation, which is undesirable for microscopy, we investigate
+the use of a platinum capping layer on top of the sample as a thermal
+transduction layer to produce heat uniformly across different sample materials.
+After determining the optimal capping layer thickness, we simulate the
+evolution of the thermal gradient in the underlying sample layer, and find that
+the temporal width is below 10 ps. These results lay a theoretical foundation
+for nanoscale, time-resolved magneto-thermal imaging.
+",0,1,0,0,0,0
+1003,A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets,"  Motivation: P values derived from the null hypothesis significance testing
+framework are strongly affected by sample size, and are known to be
+irreproducible in underpowered studies, yet no suitable replacement has been
+proposed. Results: Here we present implementations of non-parametric
+standardized median effect size estimates, dNEF, for high-throughput sequencing
+datasets. Case studies are shown for transcriptome and tag-sequencing datasets.
+The dNEF measure is shown to be more repro- ducible and robust than P values
+and requires sample sizes as small as 3 to reproducibly identify differentially
+abundant features. Availability: Source code and binaries freely available at:
+this https URL, omicplotR, and
+this https URL.
+",0,0,0,0,1,0
+1004,High temperature thermodynamics of the honeycomb-lattice Kitaev-Heisenberg model: A high temperature series expansion study,"  We develop high temperature series expansions for the thermodynamic
+properties of the honeycomb-lattice Kitaev-Heisenberg model. Numerical results
+for uniform susceptibility, heat capacity and entropy as a function of
+temperature for different values of the Kitaev coupling $K$ and Heisenberg
+exachange coupling $J$ (with $|J|\le |K|$) are presented. These expansions show
+good convergence down to a temperature of a fraction of $K$ and in some cases
+down to $T=K/10$. In the Kitaev exchange dominated regime, the inverse
+susceptibility has a nearly linear temperature dependence over a wide
+temperature range. However, we show that already at temperatures $10$-times the
+Curie-Weiss temperature, the effective Curie-Weiss constant estimated from the
+data can be off by a factor of 2. We find that the magnitude of the heat
+capacity maximum at the short-range order peak, is substantially smaller for
+small $J/K$ than for $J$ of order or larger than $K$. We suggest that this
+itself represents a simple marker for the relative importance of the Kitaev
+terms in these systems. Somewhat surprisingly, both heat capacity and
+susceptibility data on Na$_2$IrO$_3$ are consistent with a dominant {\it
+antiferromagnetic} Kitaev exchange constant of about $300-400$ $K$.
+",0,1,0,0,0,0
+1005,"Laplace Beltrami operator in the Baran metric and pluripotential equilibrium measure: the ball, the simplex and the sphere","  The Baran metric $\delta_E$ is a Finsler metric on the interior of $E\subset
+\R^n$ arising from Pluripotential Theory. We consider the few instances, namely
+$E$ being the ball, the simplex, or the sphere, where $\delta_E$ is known to be
+Riemaniann and we prove that the eigenfunctions of the associated Laplace
+Beltrami operator (with no boundary conditions) are the orthogonal polynomials
+with respect to the pluripotential equilibrium measure $\mu_E$ of $E.$ We
+conjecture that this may hold in a wider generality.
+The considered differential operators have been already introduced in the
+framework of orthogonal polynomials and studied in connection with certain
+symmetry groups. In this work instead we highlight the relationships between
+orthogonal polynomials with respect to $\mu_E$ and the Riemaniann structure
+naturally arising from Pluripotential Theory
+",0,0,1,0,0,0
+1006,Magnetic polarons in a nonequilibrium polariton condensate,"  We consider a condensate of exciton-polaritons in a diluted magnetic
+semiconductor microcavity. Such system may exhibit magnetic self-trapping in
+the case of sufficiently strong coupling between polaritons and magnetic ions
+embedded in the semiconductor. We investigate the effect of the nonequilibrium
+nature of exciton-polaritons on the physics of the resulting self-trapped
+magnetic polarons. We find that multiple polarons can exist at the same time,
+and derive a critical condition for self-trapping which is different to the one
+predicted previously in the equilibrium case. Using the Bogoliubov-de Gennes
+approximation, we calculate the excitation spectrum and provide a physical
+explanation in terms of the effective magnetic attraction between polaritons,
+mediated by the ion subsystem.
+",0,1,0,0,0,0
+1007,Inference in Sparse Graphs with Pairwise Measurements and Side Information,"  We consider the statistical problem of recovering a hidden ""ground truth""
+binary labeling for the vertices of a graph up to low Hamming error from noisy
+edge and vertex measurements. We present new algorithms and a sharp
+finite-sample analysis for this problem on trees and sparse graphs with poor
+expansion properties such as hypergrids and ring lattices. Our method
+generalizes and improves over that of Globerson et al. (2015), who introduced
+the problem for two-dimensional grid lattices.
+For trees we provide a simple, efficient, algorithm that infers the ground
+truth with optimal Hamming error has optimal sample complexity and implies
+recovery results for all connected graphs. Here, the presence of side
+information is critical to obtain a non-trivial recovery rate. We then show how
+to adapt this algorithm to tree decompositions of edge-subgraphs of certain
+graph families such as lattices, resulting in optimal recovery error rates that
+can be obtained efficiently
+The thrust of our analysis is to 1) use the tree decomposition along with
+edge measurements to produce a small class of viable vertex labelings and 2)
+apply an analysis influenced by statistical learning theory to show that we can
+infer the ground truth from this class using vertex measurements. We show the
+power of our method in several examples including hypergrids, ring lattices,
+and the Newman-Watts model for small world graphs. For two-dimensional grids,
+our results improve over Globerson et al. (2015) by obtaining optimal recovery
+in the constant-height regime.
+",1,0,0,0,0,0
+1008,Oracle Importance Sampling for Stochastic Simulation Models,"  We consider the problem of estimating an expected outcome from a stochastic
+simulation model using importance sampling. We propose a two-stage procedure
+that involves a regression stage and a sampling stage to construct our
+estimator. We introduce a parametric and a nonparametric regression estimator
+in the first stage and study how the allocation between the two stages affects
+the performance of final estimator. We derive the oracle property for both
+approaches. We analyze the empirical performances of our approaches using two
+simulated data and a case study on wind turbine reliability evaluation.
+",0,0,0,1,0,0
+1009,The Generalized Cross Validation Filter,"  Generalized cross validation (GCV) is one of the most important approaches
+used to estimate parameters in the context of inverse problems and
+regularization techniques. A notable example is the determination of the
+smoothness parameter in splines. When the data are generated by a state space
+model, like in the spline case, efficient algorithms are available to evaluate
+the GCV score with complexity that scales linearly in the data set size.
+However, these methods are not amenable to on-line applications since they rely
+on forward and backward recursions. Hence, if the objective has been evaluated
+at time $t-1$ and new data arrive at time t, then O(t) operations are needed to
+update the GCV score. In this paper we instead show that the update cost is
+$O(1)$, thus paving the way to the on-line use of GCV. This result is obtained
+by deriving the novel GCV filter which extends the classical Kalman filter
+equations to efficiently propagate the GCV score over time. We also illustrate
+applications of the new filter in the context of state estimation and on-line
+regularized linear system identification.
+",1,0,0,1,0,0
+1010,Coherence of Biochemical Oscillations is Bounded by Driving Force and Network Topology,"  Biochemical oscillations are prevalent in living organisms. Systems with a
+small number of constituents cannot sustain coherent oscillations for an
+indefinite time because of fluctuations in the period of oscillation. We show
+that the number of coherent oscillations that quantifies the precision of the
+oscillator is universally bounded by the thermodynamic force that drives the
+system out of equilibrium and by the topology of the underlying biochemical
+network of states. Our results are valid for arbitrary Markov processes, which
+are commonly used to model biochemical reactions. We apply our results to a
+model for a single KaiC protein and to an activator-inhibitor model that
+consists of several molecules. From a mathematical perspective, based on strong
+numerical evidence, we conjecture a universal constraint relating the imaginary
+and real parts of the first non-trivial eigenvalue of a stochastic matrix.
+",0,1,0,0,0,0
+1011,How Do Classifiers Induce Agents To Invest Effort Strategically?,"  Algorithms are often used to produce decision-making rules that classify or
+evaluate individuals. When these individuals have incentives to be classified a
+certain way, they may behave strategically to influence their outcomes. We
+develop a model for how strategic agents can invest effort in order to change
+the outcomes they receive, and we give a tight characterization of when such
+agents can be incentivized to invest specified forms of effort into improving
+their outcomes as opposed to ""gaming"" the classifier. We show that whenever any
+""reasonable"" mechanism can do so, a simple linear mechanism suffices.
+",0,0,0,1,0,0
+1012,Guiding Reinforcement Learning Exploration Using Natural Language,"  In this work we present a technique to use natural language to help
+reinforcement learning generalize to unseen environments. This technique uses
+neural machine translation, specifically the use of encoder-decoder networks,
+to learn associations between natural language behavior descriptions and
+state-action information. We then use this learned model to guide agent
+exploration using a modified version of policy shaping to make it more
+effective at learning in unseen environments. We evaluate this technique using
+the popular arcade game, Frogger, under ideal and non-ideal conditions. This
+evaluation shows that our modified policy shaping algorithm improves over a
+Q-learning agent as well as a baseline version of policy shaping.
+",1,0,0,1,0,0
+1013,Of the People: Voting Is More Effective with Representative Candidates,"  In light of the classic impossibility results of Arrow and Gibbard and
+Satterthwaite regarding voting with ordinal rules, there has been recent
+interest in characterizing how well common voting rules approximate the social
+optimum. In order to quantify the quality of approximation, it is natural to
+consider the candidates and voters as embedded within a common metric space,
+and to ask how much further the chosen candidate is from the population as
+compared to the socially optimal one. We use this metric preference model to
+explore a fundamental and timely question: does the social welfare of a
+population improve when candidates are representative of the population? If so,
+then by how much, and how does the answer depend on the complexity of the
+metric space?
+We restrict attention to the most fundamental and common social choice
+setting: a population of voters, two independently drawn candidates, and a
+majority rule election. When candidates are not representative of the
+population, it is known that the candidate selected by the majority rule can be
+thrice as far from the population as the socially optimal one. We examine how
+this ratio improves when candidates are drawn independently from the population
+of voters. Our results are two-fold: When the metric is a line, the ratio
+improves from $3$ to $4-2\sqrt{2}$, roughly $1.1716$; this bound is tight. When
+the metric is arbitrary, we show a lower bound of $1.5$ and a constant upper
+bound strictly better than $2$ on the approximation ratio of the majority rule.
+The positive result depends in part on the assumption that candidates are
+independent and identically distributed. However, we show that independence
+alone is not enough to achieve the upper bound: even when candidates are drawn
+independently, if the population of candidates can be different from the
+voters, then an upper bound of $2$ on the approximation is tight.
+",1,0,0,0,0,0
+1014,Cell Coverage Extension with Orthogonal Random Precoding for Massive MIMO Systems,"  In this paper, we investigate a coverage extension scheme based on orthogonal
+random precoding (ORP) for the downlink of massive multiple-input
+multiple-output (MIMO) systems. In this scheme, a precoding matrix consisting
+of orthogonal vectors is employed at the transmitter to enhance the maximum
+signal-to-interference-plus-noise ratio (SINR) of the user. To analyze and
+optimize the ORP scheme in terms of cell coverage, we derive the analytical
+expressions of the downlink coverage probability for two receiver structures,
+namely, the single-antenna (SA) receiver and multiple-antenna receiver with
+antenna selection (AS). The simulation results show that the analytical
+expressions accurately capture the coverage behaviors of the systems employing
+the ORP scheme. It is also shown that the optimal coverage performance is
+achieved when a single precoding vector is used under the condition that the
+threshold of the signal-to-noise ratio of the coverage is greater than one. The
+performance of the ORP scheme is further analyzed when different random
+precoder groups are utilized over multiple time slots to exploit precoding
+diversity. The numerical results show that the proposed ORP scheme over
+multiple time slots provides a substantial coverage gain over the space-time
+coding scheme despite its low feedback overhead.
+",1,0,1,0,0,0
+1015,Hidden Community Detection in Social Networks,"  We introduce a new paradigm that is important for community detection in the
+realm of network analysis. Networks contain a set of strong, dominant
+communities, which interfere with the detection of weak, natural community
+structure. When most of the members of the weak communities also belong to
+stronger communities, they are extremely hard to be uncovered. We call the weak
+communities the hidden community structure.
+We present a novel approach called HICODE (HIdden COmmunity DEtection) that
+identifies the hidden community structure as well as the dominant community
+structure. By weakening the strength of the dominant structure, one can uncover
+the hidden structure beneath. Likewise, by reducing the strength of the hidden
+structure, one can more accurately identify the dominant structure. In this
+way, HICODE tackles both tasks simultaneously.
+Extensive experiments on real-world networks demonstrate that HICODE
+outperforms several state-of-the-art community detection methods in uncovering
+both the dominant and the hidden structure. In the Facebook university social
+networks, we find multiple non-redundant sets of communities that are strongly
+associated with residential hall, year of registration or career position of
+the faculties or students, while the state-of-the-art algorithms mainly locate
+the dominant ground truth category. In the Due to the difficulty of labeling
+all ground truth communities in real-world datasets, HICODE provides a
+promising approach to pinpoint the existing latent communities and uncover
+communities for which there is no ground truth. Finding this unknown structure
+is an extremely important community detection problem.
+",1,1,0,1,0,0
+1016,Two-photon exchange correction to the hyperfine splitting in muonic hydrogen,"  We reevaluate the Zemach, recoil and polarizability corrections to the
+hyperfine splitting in muonic hydrogen expressing them through the low-energy
+proton structure constants and obtain the precise values of the Zemach radius
+and two-photon exchange (TPE) contribution. The uncertainty of TPE correction
+to S energy levels in muonic hydrogen of 105 ppm exceeds the ppm accuracy level
+of the forthcoming 1S hyperfine splitting measurements at PSI, J-PARC and
+RIKEN-RAL.
+",0,1,0,0,0,0
+1017,Ising Models with Latent Conditional Gaussian Variables,"  Ising models describe the joint probability distribution of a vector of
+binary feature variables. Typically, not all the variables interact with each
+other and one is interested in learning the presumably sparse network structure
+of the interacting variables. However, in the presence of latent variables, the
+conventional method of learning a sparse model might fail. This is because the
+latent variables induce indirect interactions of the observed variables. In the
+case of only a few latent conditional Gaussian variables these spurious
+interactions contribute an additional low-rank component to the interaction
+parameters of the observed Ising model. Therefore, we propose to learn a sparse
++ low-rank decomposition of the parameters of an Ising model using a convex
+regularized likelihood problem. We show that the same problem can be obtained
+as the dual of a maximum-entropy problem with a new type of relaxation, where
+the sample means collectively need to match the expected values only up to a
+given tolerance. The solution to the convex optimization problem has
+consistency properties in the high-dimensional setting, where the number of
+observed binary variables and the number of latent conditional Gaussian
+variables are allowed to grow with the number of training samples.
+",1,0,0,1,0,0
+1018,Quasiparticles and charge transfer at the two surfaces of the honeycomb iridate Na$_2$IrO$_3$,"  Direct experimental investigations of the low-energy electronic structure of
+the Na$_2$IrO$_3$ iridate insulator are sparse and draw two conflicting
+pictures. One relies on flat bands and a clear gap, the other involves
+dispersive states approaching the Fermi level, pointing to surface metallicity.
+Here, by a combination of angle-resolved photoemission, photoemission electron
+microscopy, and x-ray absorption, we show that the correct picture is more
+complex and involves an anomalous band, arising from charge transfer from Na
+atoms to Ir-derived states. Bulk quasiparticles do exist, but in one of the two
+possible surface terminations the charge transfer is smaller and they remain
+elusive.
+",0,1,0,0,0,0
+1019,Breaking Bivariate Records,"  We establish a fundamental property of bivariate Pareto records for
+independent observations uniformly distributed in the unit square. We prove
+that the asymptotic conditional distribution of the number of records broken by
+an observation given that the observation sets a record is Geometric with
+parameter 1/2.
+",0,0,1,1,0,0
+1020,A Bag-of-Paths Node Criticality Measure,"  This work compares several node (and network) criticality measures
+quantifying to which extend each node is critical with respect to the
+communication flow between nodes of the network, and introduces a new measure
+based on the Bag-of-Paths (BoP) framework. Network disconnection simulation
+experiments show that the new BoP measure outperforms all the other measures on
+a sample of Erdos-Renyi and Albert-Barabasi graphs. Furthermore, a faster
+(still O(n^3)), approximate, BoP criticality relying on the Sherman-Morrison
+rank-one update of a matrix is introduced for tackling larger networks. This
+approximate measure shows similar performances as the original, exact, one.
+",1,1,0,0,0,0
+1021,Generation and analysis of lamplighter programs,"  We consider a programming language based on the lamplighter group that uses
+only composition and iteration as control structures. We derive generating
+functions and counting formulas for this language and special subsets of it,
+establishing lower and upper bounds on the growth rate of semantically distinct
+programs. Finally, we show how to sample random programs and analyze the
+distribution of runtimes induced by such sampling.
+",1,0,1,0,0,0
+1022,A Projection-Based Reformulation and Decomposition Algorithm for Global Optimization of a Class of Mixed Integer Bilevel Linear Programs,"  We propose an extended variant of the reformulation and decomposition
+algorithm for solving a special class of mixed-integer bilevel linear programs
+(MIBLPs) where continuous and integer variables are involved in both upper- and
+lower-level problems. In particular, we consider MIBLPs with upper-level
+constraints that involve lower-level variables. We assume that the inducible
+region is nonempty and all variables are bounded. By using the reformulation
+and decomposition scheme, an MIBLP is first converted into its equivalent
+single-level formulation, then computed by a column-and-constraint generation
+based decomposition algorithm. The solution procedure is enhanced by a
+projection strategy that does not require the relatively complete response
+property. To ensure its performance, we prove that our new method converges to
+the global optimal solution in a finite number of iterations. A large-scale
+computational study on random instances and instances of hierarchical supply
+chain planning are presented to demonstrate the effectiveness of the algorithm.
+",0,0,1,0,0,0
+1023,Preduals for spaces of operators involving Hilbert spaces and trace-class operators,"  Continuing the study of preduals of spaces $\mathcal{L}(H,Y)$ of bounded,
+linear maps, we consider the situation that $H$ is a Hilbert space. We
+establish a natural correspondence between isometric preduals of
+$\mathcal{L}(H,Y)$ and isometric preduals of $Y$.
+The main ingredient is a Tomiyama-type result which shows that every
+contractive projection that complements $\mathcal{L}(H,Y)$ in its bidual is
+automatically a right $\mathcal{L}(H)$-module map.
+As an application, we show that isometric preduals of
+$\mathcal{L}(\mathcal{S}_1)$, the algebra of operators on the space of
+trace-class operators, correspond to isometric preduals of $\mathcal{S}_1$
+itself (and there is an abundance of them). On the other hand, the compact
+operators are the unique predual of $\mathcal{S}_1$ making its multiplication
+separately weak* continuous.
+",0,0,1,0,0,0
+1024,Computing maximum cliques in $B_2$-EPG graphs,"  EPG graphs, introduced by Golumbic et al. in 2009, are edge-intersection
+graphs of paths on an orthogonal grid. The class $B_k$-EPG is the subclass of
+EPG graphs where the path on the grid associated to each vertex has at most $k$
+bends. Epstein et al. showed in 2013 that computing a maximum clique in
+$B_1$-EPG graphs is polynomial. As remarked in [Heldt et al., 2014], when the
+number of bends is at least $4$, the class contains $2$-interval graphs for
+which computing a maximum clique is an NP-hard problem. The complexity status
+of the Maximum Clique problem remains open for $B_2$ and $B_3$-EPG graphs. In
+this paper, we show that we can compute a maximum clique in polynomial time in
+$B_2$-EPG graphs given a representation of the graph.
+Moreover, we show that a simple counting argument provides a
+${2(k+1)}$-approximation for the coloring problem on $B_k$-EPG graphs without
+knowing the representation of the graph. It generalizes a result of [Epstein et
+al, 2013] on $B_1$-EPG graphs (where the representation was needed).
+",1,0,0,0,0,0
+1025,Interactions between Health Searchers and Search Engines,"  The Web is an important resource for understanding and diagnosing medical
+conditions. Based on exposure to online content, people may develop undue
+health concerns, believing that common and benign symptoms are explained by
+serious illnesses. In this paper, we investigate potential strategies to mine
+queries and searcher histories for clues that could help search engines choose
+the most appropriate information to present in response to exploratory medical
+queries. To do this, we performed a longitudinal study of health search
+behavior using the logs of a popular search engine. We found that query
+variations which might appear innocuous (e.g. ""bad headache"" vs ""severe
+headache"") may hold valuable information about the searcher which could be used
+by search engines to improve performance. Furthermore, we investigated how
+medically concerned users respond differently to search engine result pages
+(SERPs) and find that their disposition for clicking on concerning pages is
+pronounced, potentially leading to a self-reinforcement of concern. Finally, we
+studied to which degree variations in the SERP impact future search and
+real-world health-seeking behavior and obtained some surprising results (e.g.,
+viewing concerning pages may lead to a short-term reduction of real-world
+health seeking).
+",1,0,0,0,0,0
+1026,Effect algebras as presheaves on finite Boolean algebras,"  For an effect algebra $A$, we examine the category of all morphisms from
+finite Boolean algebras into $A$. This category can be described as a category
+of elements of a presheaf $R(A)$ on the category of finite Boolean algebras. We
+prove that some properties (being an orthoalgebra, the Riesz decomposition
+property, being a Boolean algebra) of an effect algebra $A$ can be
+characterized by properties of the category of elements of the presheaf $R(A)$.
+We prove that the tensor product of of effect algebras arises as a left Kan
+extension of the free product of finite Boolean algebras along the inclusion
+functor. As a consequence, the tensor product of effect algebras can be
+expressed by means of the Day convolution of presheaves on finite Boolean
+algebras.
+",0,0,1,0,0,0
+1027,Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices,"  In a previous work we have detailed the requirements to obtain a maximal
+performance benefit by implementing fully connected deep neural networks (DNN)
+in form of arrays of resistive devices for deep learning. This concept of
+Resistive Processing Unit (RPU) devices we extend here towards convolutional
+neural networks (CNNs). We show how to map the convolutional layers to RPU
+arrays such that the parallelism of the hardware can be fully utilized in all
+three cycles of the backpropagation algorithm. We find that the noise and bound
+limitations imposed due to analog nature of the computations performed on the
+arrays effect the training accuracy of the CNNs. Noise and bound management
+techniques are presented that mitigate these problems without introducing any
+additional complexity in the analog circuits and can be addressed by the
+digital circuits. In addition, we discuss digitally programmable update
+management and device variability reduction techniques that can be used
+selectively for some of the layers in a CNN. We show that combination of all
+those techniques enables a successful application of the RPU concept for
+training CNNs. The techniques discussed here are more general and can be
+applied beyond CNN architectures and therefore enables applicability of RPU
+approach for large class of neural network architectures.
+",1,0,0,1,0,0
+1028,Absolute versus convective helical magnetorotational instabilities in Taylor-Couette flows,"  We study magnetic Taylor-Couette flow in a system having nondimensional radii
+$r_i=1$ and $r_o=2$, and periodic in the axial direction with wavelengths
+$h\ge100$. The rotation ratio of the inner and outer cylinders is adjusted to
+be slightly in the Rayleigh-stable regime, where magnetic fields are required
+to destabilize the flow, in this case triggering the axisymmetric helical
+magnetorotational instability (HMRI). Two choices of imposed magnetic field are
+considered, both having the same azimuthal component $B_\phi=r^{-1}$, but
+differing axial components. The first choice has $B_z=0.1$, and yields the
+familiar HMRI, consisting of unidirectionally traveling waves. The second
+choice has $B_z\approx0.1\sin(2\pi z/h)$, and yields HMRI waves that travel in
+opposite directions depending on the sign of $B_z$. The first configuration
+corresponds to a convective instability, the second to an absolute instability.
+The two variants behave very similarly regarding both linear onset as well as
+nonlinear equilibration.
+",0,1,0,0,0,0
+1029,Symmetries and multipeakon solutions for the modified two-component Camassa-Holm system,"  Compared with the two-component Camassa-Holm system, the modified
+two-component Camassa-Holm system introduces a regularized density which makes
+possible the existence of solutions of lower regularity, and in particular of
+multipeakon solutions. In this paper, we derive a new pointwise invariant for
+the modified two-component Camassa-Holm system. The derivation of the invariant
+uses directly the symmetry of the system, following the classical argument of
+Noether's theorem. The existence of the multipeakon solutions can be directly
+inferred from this pointwise invariant. This derivation shows the strong
+connection between symmetries and the existence of special solutions. The
+observation also holds for the scalar Camassa-Holm equation and, for
+comparison, we have also included the corresponding derivation. Finally, we
+compute explicitly the solutions obtained for the peakon-antipeakon case. We
+observe the existence of a periodic solution which has not been reported in the
+literature previously. This case shows the attractive effect that the
+introduction of an elastic potential can have on the solutions.
+",0,0,1,0,0,0
+1030,Selection of quasi-stationary states in the Navier-Stokes equation on the torus,"  The two dimensional incompressible Navier-Stokes equation on $D_\delta := [0,
+2\pi\delta] \times [0, 2\pi]$ with $\delta \approx 1$, periodic boundary
+conditions, and viscosity $0 < \nu \ll 1$ is considered. Bars and dipoles, two
+explicitly given quasi-stationary states of the system, evolve on the time
+scale $\mathcal{O}(e^{-\nu t})$ and have been shown to play a key role in its
+long-time evolution. Of particular interest is the role that $\delta$ plays in
+selecting which of these two states is observed. Recent numerical studies
+suggest that, after a transient period of rapid decay of the high Fourier
+modes, the bar state will be selected if $\delta \neq 1$, while the dipole will
+be selected if $\delta = 1$. Our results support this claim and seek to
+mathematically formalize it. We consider the system in Fourier space, project
+it onto a center manifold consisting of the lowest eight Fourier modes, and use
+this as a model to study the selection of bars and dipoles. It is shown for
+this ODE model that the value of $\delta$ controls the behavior of the
+asymptotic ratio of the low modes, thus determining the likelihood of observing
+a bar state or dipole after an initial transient period. Moreover, in our
+model, for all $\delta \approx 1$, there is an initial time period in which the
+high modes decay at the rapid rate $\mathcal{O}(e^{-t/\nu})$, while the low
+modes evolve at the slower $\mathcal{O}(e^{-\nu t})$ rate. The results for the
+ODE model are proven using energy estimates and invariant manifolds and further
+supported by formal asymptotic expansions and numerics.
+",0,0,1,0,0,0
+1031,Geometric Enclosing Networks,"  Training model to generate data has increasingly attracted research attention
+and become important in modern world applications. We propose in this paper a
+new geometry-based optimization approach to address this problem. Orthogonal to
+current state-of-the-art density-based approaches, most notably VAE and GAN, we
+present a fresh new idea that borrows the principle of minimal enclosing ball
+to train a generator G\left(\bz\right) in such a way that both training and
+generated data, after being mapped to the feature space, are enclosed in the
+same sphere. We develop theory to guarantee that the mapping is bijective so
+that its inverse from feature space to data space results in expressive
+nonlinear contours to describe the data manifold, hence ensuring data generated
+are also lying on the data manifold learned from training data. Our model
+enjoys a nice geometric interpretation, hence termed Geometric Enclosing
+Networks (GEN), and possesses some key advantages over its rivals, namely
+simple and easy-to-control optimization formulation, avoidance of mode
+collapsing and efficiently learn data manifold representation in a completely
+unsupervised manner. We conducted extensive experiments on synthesis and
+real-world datasets to illustrate the behaviors, strength and weakness of our
+proposed GEN, in particular its ability to handle multi-modal data and quality
+of generated data.
+",1,0,0,1,0,0
+1032,A pliable lasso for the Cox model,"  We introduce a pliable lasso method for estimation of interaction effects in
+the Cox proportional hazards model framework. The pliable lasso is a linear
+model that includes interactions between covariates X and a set of modifying
+variables Z and assumes sparsity of the main effects and interaction effects.
+The hierarchical penalty excludes interaction effects when the corresponding
+main effects are zero: this avoids overfitting and an explosion of model
+complexity. We extend this method to the Cox model for survival data,
+incorporating modifiers that are either fixed or varying in time into the
+partial likelihood. For example, this allows modeling of survival times that
+differ based on interactions of genes with age, gender, or other demographic
+information. The optimization is done by blockwise coordinate descent on a
+second order approximation of the objective.
+",0,0,0,1,0,0
+1033,Localized magnetic moments with tunable spin exchange in a gas of ultracold fermions,"  We report on the experimental realization of a state-dependent lattice for a
+two-orbital fermionic quantum gas with strong interorbital spin exchange. In
+our state-dependent lattice, the ground and metastable excited electronic
+states of $^{173}$Yb take the roles of itinerant and localized magnetic
+moments, respectively. Repulsive on-site interactions in conjunction with the
+tunnel mobility lead to spin exchange between mobile and localized particles,
+modeling the coupling term in the well-known Kondo Hamiltonian. In addition, we
+find that this exchange process can be tuned resonantly by varying the on-site
+confinement. We attribute this to a resonant coupling to center-of-mass excited
+bound states of one interorbital scattering channel.
+",0,1,0,0,0,0
+1034,Khintchine's Theorem with random fractions,"  We prove versions of Khintchine's Theorem (1924) for approximations by
+rational numbers whose numerators lie in randomly chosen sets of integers, and
+we explore the extent to which the monotonicity assumption can be removed.
+Roughly speaking, we show that if the number of available fractions for each
+denominator grows too fast, then the monotonicity assumption cannot be removed.
+There are questions in this random setting which may be seen as cognates of the
+Duffin-Schaeffer Conjecture (1941), and are likely to be more accessible. We
+point out that the direct random analogue of the Duffin-Schaeffer Conjecture,
+like the Duffin-Schaeffer Conjecture itself, implies Catlin's Conjecture
+(1976). It is not obvious whether the Duffin-Schaeffer Conjecture and its
+random version imply one another, and it is not known whether Catlin's
+Conjecture implies either of them. The question of whether Catlin implies
+Duffin-Schaeffer has been unsettled for decades.
+",0,0,1,0,0,0
+1035,A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes,"  Neural networks with random hidden nodes have gained increasing interest from
+researchers and practical applications. This is due to their unique features
+such as very fast training and universal approximation property. In these
+networks the weights and biases of hidden nodes determining the nonlinear
+feature mapping are set randomly and are not learned. Appropriate selection of
+the intervals from which weights and biases are selected is extremely
+important. This topic has not yet been sufficiently explored in the literature.
+In this work a method of generating random weights and biases is proposed. This
+method generates the parameters of the hidden nodes in such a way that
+nonlinear fragments of the activation functions are located in the input space
+regions with data and can be used to construct the surface approximating a
+nonlinear target function. The weights and biases are dependent on the input
+data range and activation function type. The proposed methods allows us to
+control the generalization degree of the model. These all lead to improvement
+in approximation performance of the network. Several experiments show very
+promising results.
+",1,0,0,1,0,0
+1036,The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification,"  Artificial neural networks have been successfully applied to a variety of
+machine learning tasks, including image recognition, semantic segmentation, and
+machine translation. However, few studies fully investigated ensembles of
+artificial neural networks. In this work, we investigated multiple widely used
+ensemble methods, including unweighted averaging, majority voting, the Bayes
+Optimal Classifier, and the (discrete) Super Learner, for image recognition
+tasks, with deep neural networks as candidate algorithms. We designed several
+experiments, with the candidate algorithms being the same network structure
+with different model checkpoints within a single training process, networks
+with same structure but trained multiple times stochastically, and networks
+with different structure. In addition, we further studied the over-confidence
+phenomenon of the neural networks, as well as its impact on the ensemble
+methods. Across all of our experiments, the Super Learner achieved best
+performance among all the ensemble methods in this study.
+",1,0,0,1,0,0
+1037,Representation of big data by dimension reduction,"  Suppose the data consist of a set $S$ of points $x_j, 1 \leq j \leq J$,
+distributed in a bounded domain $D \subset R^N$, where $N$ and $J$ are large
+numbers. In this paper an algorithm is proposed for checking whether there
+exists a manifold $\mathbb{M}$ of low dimension near which many of the points
+of $S$ lie and finding such $\mathbb{M}$ if it exists. There are many dimension
+reduction algorithms, both linear and non-linear. Our algorithm is simple to
+implement and has some advantages compared with the known algorithms. If there
+is a manifold of low dimension near which most of the data points lie, the
+proposed algorithm will find it. Some numerical results are presented
+illustrating the algorithm and analyzing its performance compared to the
+classical PCA (principal component analysis) and Isomap.
+",1,0,0,1,0,0
+1038,Out-degree reducing partitions of digraphs,"  Let $k$ be a fixed integer. We determine the complexity of finding a
+$p$-partition $(V_1, \dots, V_p)$ of the vertex set of a given digraph such
+that the maximum out-degree of each of the digraphs induced by $V_i$, ($1\leq
+i\leq p$) is at least $k$ smaller than the maximum out-degree of $D$. We show
+that this problem is polynomial-time solvable when $p\geq 2k$ and ${\cal
+NP}$-complete otherwise. The result for $k=1$ and $p=2$ answers a question
+posed in \cite{bangTCS636}. We also determine, for all fixed non-negative
+integers $k_1,k_2,p$, the complexity of deciding whether a given digraph of
+maximum out-degree $p$ has a $2$-partition $(V_1,V_2)$ such that the digraph
+induced by $V_i$ has maximum out-degree at most $k_i$ for $i\in [2]$. It
+follows from this characterization that the problem of deciding whether a
+digraph has a 2-partition $(V_1,V_2)$ such that each vertex $v\in V_i$ has at
+least as many neighbours in the set $V_{3-i}$ as in $V_i$, for $i=1,2$ is
+${\cal NP}$-complete. This solves a problem from \cite{kreutzerEJC24} on
+majority colourings.
+",1,0,0,0,0,0
+1039,Introduction to Plasma Physics,"  These notes are intended to provide a brief primer in plasma physics,
+introducing common definitions, basic properties, and typical processes found
+in plasmas. These concepts are inherent in contemporary plasma-based
+accelerator schemes, and thus provide a foundation for the more advanced
+expositions that follow in this volume. No prior knowledge of plasma physics is
+required, but the reader is assumed to be familiar with basic electrodynamics
+and fluid mechanics.
+",0,1,0,0,0,0
+1040,Presymplectic convexity and (ir)rational polytopes,"  In this paper, we extend the Atiyah--Guillemin--Sternberg convexity theorem
+and Delzant's classification of symplectic toric manifolds to presymplectic
+manifolds. We also define and study the Morita equivalence of presymplectic
+toric manifolds and of their corresponding framed momentum polytopes, which may
+be rational or non-rational. Toric orbifolds, quasifolds and non-commutative
+toric varieties may be viewed as the quotient of our presymplectic toric
+manifolds by the kernel isotropy foliation of the presymplectic form.
+",0,0,1,0,0,0
+1041,Unsupervised Learning of Mixture Regression Models for Longitudinal Data,"  This paper is concerned with learning of mixture regression models for
+individuals that are measured repeatedly. The adjective ""unsupervised"" implies
+that the number of mixing components is unknown and has to be determined,
+ideally by data driven tools. For this purpose, a novel penalized method is
+proposed to simultaneously select the number of mixing components and to
+estimate the mixing proportions and unknown parameters in the models. The
+proposed method is capable of handling both continuous and discrete responses
+by only requiring the first two moment conditions of the model distribution. It
+is shown to be consistent in both selecting the number of components and
+estimating the mixing proportions and unknown regression parameters. Further, a
+modified EM algorithm is developed to seamlessly integrate model selection and
+estimation. Simulation studies are conducted to evaluate the finite sample
+performance of the proposed procedure. And it is further illustrated via an
+analysis of a primary biliary cirrhosis data set.
+",0,0,0,1,0,0
+1042,Anomalous electron states,"  By the certain macroscopic perturbations in condensed matter anomalous
+electron wells can be formed due to a local reduction of electromagnetic zero
+point energy. These wells are narrow, of the width $\sim 10^{-11}cm$, and with
+the depth $\sim 1MeV$. Such anomalous states, from the formal standpoint of
+quantum mechanics, correspond to a singular solution of a wave equation
+produced by the non-physical $\delta(\vec R)$ source. The resolution, on the
+level of the Standard Model, of the tiny region around the formal singularity
+shows that the state is physical. The creation of those states in an atomic
+system is of the formal probability $\exp(-1000)$. The probability becomes not
+small under a perturbation which rapidly varies in space, on the scale
+$10^{-11}cm$. In condensed matter such perturbation may relate to acoustic
+shock waves. In this process the short scale is the length of the standing de
+Broglie wave of a reflected lattice atom. Under electron transitions in the
+anomalous well (anomalous atom) $keV$ X-rays are expected to be emitted. A
+macroscopic amount of anomalous atoms, of the size $10^{-11}cm$ each, can be
+formed in a solid resulting in ${\it collapsed}$ ${\it matter}$ with $10^9$
+times enhanced density.
+",0,1,0,0,0,0
+1043,Theoretical calculation of the fine-structure constant and the permittivity of the vacuum,"  Light traveling through the vacuum interacts with virtual particles similarly
+to the way that light traveling through a dielectric interacts with ordinary
+matter. And just as the permittivity of a dielectric can be calculated, the
+permittivity $\epsilon_0$ of the vacuum can be calculated, yielding an equation
+for the fine-structure constant $\alpha$. The most important contributions to
+the value of $\alpha$ arise from interactions in the vacuum of photons with
+virtual, bound states of charged lepton-antilepton pairs. Considering only
+these contributions, the fully screened $\alpha \cong 1/(8^2\sqrt{3\pi/2})
+\cong 1/139$.
+",0,1,0,0,0,0
+1044,LEADER: fast estimates of asteroid shape elongation and spin latitude distributions from scarce photometry,"  Many asteroid databases with lightcurve brightness measurements (e.g. WISE,
+Pan-STARRS1) contain enormous amounts of data for asteroid shape and spin
+modelling. While lightcurve inversion is not plausible for individual targets
+with scarce data, it is possible for large populations with thousands of
+asteroids, where the distributions of the shape and spin characteristics of the
+populations are obtainable.
+We aim to introduce a software implementation of a method that computes the
+joint shape elongation p and spin latitude beta distributions for a population,
+with the brightness observations given in an asteroid database. Other main
+goals are to include a method for performing validity checks of the algorithm,
+and a tool for a statistical comparison of populations.
+The LEADER software package read the brightness measurement data for a
+user-defined subpopulation from a given database. The observations were used to
+compute estimates of the brightness variations of the population members. A
+cumulative distribution function (CDF) was constructed of these estimates. A
+superposition of known analytical basis functions yielded this CDF as a
+function of the (shape, spin) distribution. The joint distribution can be
+reconstructed by solving a linear constrained inverse problem. To test the
+validity of the method, the algorithm can be run with synthetic asteroid
+models, where the shape and spin characteristics are known, and by using the
+geometries taken from the examined database.
+LEADER is a fast and robust software package for solving shape and spin
+distributions for large populations. There are major differences in the quality
+and coverage of measurements depending on the database used, so synthetic
+simulations are always necessary before a database can be reliably used. We
+show examples of differences in the results when switching to another database.
+",0,1,0,0,0,0
+1045,Calibrated Projection in MATLAB: Users' Manual,"  We present the calibrated-projection MATLAB package implementing the method
+to construct confidence intervals proposed by Kaido, Molinari and Stoye (2017).
+This manual provides details on how to use the package for inference on
+projections of partially identified parameters. It also explains how to use the
+MATLAB functions we developed to compute confidence intervals on solutions of
+nonlinear optimization problems with estimated constraints.
+",0,0,0,1,0,0
+1046,Atomic Clock Measurements of Quantum Scattering Phase Shifts Spanning Feshbach Resonances at Ultralow Fields,"  We use an atomic fountain clock to measure quantum scattering phase shifts
+precisely through a series of narrow, low-field Feshbach resonances at average
+collision energies below $1\,\mu$K. Our low spread in collision energy yields
+phase variations of order $\pm \pi/2$ for target atoms in several $F,m_F$
+states. We compare them to a theoretical model and establish the accuracy of
+the measurements and the theoretical uncertainties from the fitted potential.
+We find overall excellent agreement, with small statistically significant
+differences that remain unexplained.
+",0,1,0,0,0,0
+1047,Temporal processing and context dependency in C. elegans mechanosensation,"  A quantitative understanding of how sensory signals are transformed into
+motor outputs places useful constraints on brain function and helps reveal the
+brain's underlying computations. We investigate how the nematode C. elegans
+responds to time-varying mechanosensory signals using a high-throughput
+optogenetic assay and automated behavior quantification. In the prevailing
+picture of the touch circuit, the animal's behavior is determined by which
+neurons are stimulated and by the stimulus amplitude. In contrast, we find that
+the behavioral response is tuned to temporal properties of mechanosensory
+signals, like its integral and derivative, that extend over many seconds.
+Mechanosensory signals, even in the same neurons, can be tailored to elicit
+different behavioral responses. Moreover, we find that the animal's response
+also depends on its behavioral context. Most dramatically, the animal ignores
+all tested mechanosensory stimuli during turns. Finally, we present a
+linear-nonlinear model that predicts the animal's behavioral response to
+stimulus.
+",0,0,0,0,1,0
+1048,On the putative essential discreteness of q-generalized entropies,"  It has been argued in [EPL {\bf 90} (2010) 50004], entitled {\it Essential
+discreteness in generalized thermostatistics with non-logarithmic entropy},
+that ""continuous Hamiltonian systems with long-range interactions and the
+so-called q-Gaussian momentum distributions are seen to be outside the scope of
+non-extensive statistical mechanics"". The arguments are clever and appealing.
+We show here that, however, some mathematical subtleties render them
+unconvincing
+",0,1,0,0,0,0
+1049,Spin Distribution of Primordial Black Holes,"  We estimate the spin distribution of primordial black holes based on the
+recent study of the critical phenomena in the gravitational collapse of a
+rotating radiation fluid. We find that primordial black holes are mostly slowly
+rotating.
+",0,1,0,0,0,0
+1050,Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA,"  Deep convolutional neural networks (CNN) based solutions are the current
+state- of-the-art for computer vision tasks. Due to the large size of these
+models, they are typically run on clusters of CPUs or GPUs. However, power
+requirements and cost budgets can be a major hindrance in adoption of CNN for
+IoT applications. Recent research highlights that CNN contain significant
+redundancy in their structure and can be quantized to lower bit-width
+parameters and activations, while maintaining acceptable accuracy. Low
+bit-width and especially single bit-width (binary) CNN are particularly
+suitable for mobile applications based on FPGA implementation, due to the
+bitwise logic operations involved in binarized CNN. Moreover, the transition to
+lower bit-widths opens new avenues for performance optimizations and model
+improvement. In this paper, we present an automatic flow from trained
+TensorFlow models to FPGA system on chip implementation of binarized CNN. This
+flow involves quantization of model parameters and activations, generation of
+network and model in embedded-C, followed by automatic generation of the FPGA
+accelerator for binary convolutions. The automated flow is demonstrated through
+implementation of binarized ""YOLOV2"" on the low cost, low power Cyclone- V FPGA
+device. Experiments on object detection using binarized YOLOV2 demonstrate
+significant performance benefit in terms of model size and inference speed on
+FPGA as compared to CPU and mobile CPU platforms. Furthermore, the entire
+automated flow from trained models to FPGA synthesis can be completed within
+one hour.
+",1,0,0,0,0,0
+1051,Pulse rate estimation using imaging photoplethysmography: generic framework and comparison of methods on a publicly available dataset,"  Objective: to establish an algorithmic framework and a benchmark dataset for
+comparing methods of pulse rate estimation using imaging photoplethysmography
+(iPPG). Approach: first we reveal essential steps of pulse rate estimation from
+facial video and review methods applied at each of the steps. Then we
+investigate performance of these methods for DEAP dataset
+www.eecs.qmul.ac.uk/mmv/datasets/deap/ containing facial videos and reference
+contact photoplethysmograms. Main results: best assessment precision is
+achieved when pulse rate is estimated using continuous wavelet transform from
+iPPG extracted by the POS method (overall mean absolute error below 2 heart
+beats per minute). Significance: we provide a generic framework for theoretical
+comparison of methods for pulse rate estimation from iPPG and report results
+for the most popular methods on a publicly available dataset that can be used
+as a benchmark.
+",0,1,0,0,0,0
+1052,Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution,"  Convolutional neural networks have recently demonstrated high-quality
+reconstruction for single-image super-resolution. In this paper, we propose the
+Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively
+reconstruct the sub-band residuals of high-resolution images. At each pyramid
+level, our model takes coarse-resolution feature maps as input, predicts the
+high-frequency residuals, and uses transposed convolutions for upsampling to
+the finer level. Our method does not require the bicubic interpolation as the
+pre-processing step and thus dramatically reduces the computational complexity.
+We train the proposed LapSRN with deep supervision using a robust Charbonnier
+loss function and achieve high-quality reconstruction. Furthermore, our network
+generates multi-scale predictions in one feed-forward pass through the
+progressive reconstruction, thereby facilitates resource-aware applications.
+Extensive quantitative and qualitative evaluations on benchmark datasets show
+that the proposed algorithm performs favorably against the state-of-the-art
+methods in terms of speed and accuracy.
+",1,0,0,0,0,0
+1053,Foundation for a series of efficient simulation algorithms,"  Compute the coarsest simulation preorder included in an initial preorder is
+used to reduce the resources needed to analyze a given transition system. This
+technique is applied on many models like Kripke structures, labeled graphs,
+labeled transition systems or even word and tree automata. Let (Q,
+$\rightarrow$) be a given transition system and Rinit be an initial preorder
+over Q. Until now, algorithms to compute Rsim , the coarsest simulation
+included in Rinit , are either memory efficient or time efficient but not both.
+In this paper we propose the foundation for a series of efficient simulation
+algorithms with the introduction of the notion of maximal transitions and the
+notion of stability of a preorder with respect to a coarser one. As an
+illustration we solve an open problem by providing the first algorithm with the
+best published time complexity, O(|Psim |.|$\rightarrow$|), and a bit space
+complexity in O(|Psim |^2. log(|Psim |) + |Q|. log(|Q|)), with Psim the
+partition induced by Rsim.
+",1,0,0,0,0,0
+1054,A Review of Macroscopic Motion in Thermodynamic Equilibrium,"  A principle on the macroscopic motion of systems in thermodynamic
+equilibrium, rarely discussed in texts, is reviewed: Very small but still
+macroscopic parts of a fully isolated system in thermal equilibrium move as if
+points of a rigid body, macroscopic energy being dissipated to increase
+internal energy, and increase entropy along. It appears particularly important
+in Space physics, when dissipation involves long-range fields at
+Electromagnetism and Gravitation, rather than short-range contact forces. It is
+shown how new physics, Special Relativity as regards Electromagnetism, first
+Newtonian theory then General Relativity as regards Gravitation, determine
+different dissipative processes involved in the approach to that equilibrium.
+",0,1,0,0,0,0
+1055,Emergent electronic structure of CaFe2As2,"  CaFe2As2 exhibits collapsed tetragonal (cT) structure and varied exotic
+behavior under pressure at low temperatures that led to debate on linking the
+structural changes to its exceptional electronic properties like
+superconductivity, magnetism, etc. Here, we investigate the electronic
+structure of CaFe2As2 forming in different structures employing density
+functional theory. The results indicate better stability of the cT phase with
+enhancement in hybridization induced effects and shift of the energy bands
+towards lower energies. The Fermi surface centered around $\Gamma$ point
+gradually vanishes with the increase in pressure. Consequently, the nesting
+between the hole and electron Fermi surfaces associated to the spin density
+wave state disappears indicating a pathway to achieve the proximity to quantum
+fluctuations. The magnetic moment at the Fe sites diminishes in the cT phase
+consistent with the magnetic susceptibility results. Notably, the hybridization
+of Ca 4s states (Ca-layer may be treated as a charge reservoir layer akin to
+those in cuprate superconductors) is significantly enhanced in the cT phase
+revealing its relevance in its interesting electronic properties.
+",0,1,0,0,0,0
+1056,Lord Kelvin's method of images approach to the Rotenberg model and its asymptotics,"  We study a mathematical model of cell populations dynamics proposed by M.
+Rotenberg and investigated by M. Boulanouar. Here, a cell is characterized by
+her maturity and speed of maturation. The growth of cell populations is
+described by a partial differential equation with a boundary condition. In the
+first part of the paper we exploit semigroup theory approach and apply Lord
+Kelvin's method of images in order to give a new proof that the model is well
+posed. Next, we use a semi-explicit formula for the semigroup related to the
+model obtained by the method of images in order to give growth estimates for
+the semigroup. The main part of the paper is devoted to the asymptotic
+behaviour of the semigroup. We formulate conditions for the asymptotic
+stability of the semigroup in the case in which the average number of viable
+daughters per mitosis equals one. To this end we use methods developed by K.
+Pichór and R. Rudnicki.
+",0,0,1,0,0,0
+1057,Study of the Magnetizing Relationship of the Kickers for CSNS,"  The extraction system of CSNS mainly consists of two kinds of magnets: eight
+kickers and one lambertson magnet. In this paper, firstly, the magnetic test
+results of the eight kickers were introduced and then the filed uniformity and
+magnetizing relationship of the kickers were given. Secondly, during the beam
+commissioning in the future, in order to obtain more accurate magnetizing
+relationship, a new method to measure the magnetizing coefficients of the
+kickers by the real extraction beam was given and the data analysis would also
+be processed.
+",0,1,0,0,0,0
+1058,"Smart ""Predict, then Optimize""","  Many real-world analytics problems involve two significant challenges:
+prediction and optimization. Due to the typically complex nature of each
+challenge, the standard paradigm is to predict, then optimize. By and large,
+machine learning tools are intended to minimize prediction error and do not
+account for how the predictions will be used in a downstream optimization
+problem. In contrast, we propose a new and very general framework, called Smart
+""Predict, then Optimize"" (SPO), which directly leverages the optimization
+problem structure, i.e., its objective and constraints, for designing
+successful analytics tools. A key component of our framework is the SPO loss
+function, which measures the quality of a prediction by comparing the objective
+values of the solutions generated using the predicted and observed parameters,
+respectively. Training a model with respect to the SPO loss is computationally
+challenging, and therefore we also develop a surrogate loss function, called
+the SPO+ loss, which upper bounds the SPO loss, has desirable convexity
+properties, and is statistically consistent under mild conditions. We also
+propose a stochastic gradient descent algorithm which allows for situations in
+which the number of training samples is large, model regularization is desired,
+and/or the optimization problem of interest is nonlinear or integer. Finally,
+we perform computational experiments to empirically verify the success of our
+SPO framework in comparison to the standard predict-then-optimize approach.
+",1,0,0,1,0,0
+1059,U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling,"  Novel data acquisition schemes have been an emerging need for scanning
+microscopy based imaging techniques to reduce the time in data acquisition and
+to minimize probing radiation in sample exposure. Varies sparse sampling
+schemes have been studied and are ideally suited for such applications where
+the images can be reconstructed from a sparse set of measurements. Dynamic
+sparse sampling methods, particularly supervised learning based iterative
+sampling algorithms, have shown promising results for sampling pixel locations
+on the edges or boundaries during imaging. However, dynamic sampling for
+imaging skeleton-like objects such as metal dendrites remains difficult. Here,
+we address a new unsupervised learning approach using Hierarchical Gaussian
+Mixture Mod- els (HGMM) to dynamically sample metal dendrites. This technique
+is very useful if the users are interested in fast imaging the primary and
+secondary arms of metal dendrites in solidification process in materials
+science.
+",0,0,0,1,0,0
+1060,On a registration-based approach to sensor network localization,"  We consider a registration-based approach for localizing sensor networks from
+range measurements. This is based on the assumption that one can find
+overlapping cliques spanning the network. That is, for each sensor, one can
+identify geometric neighbors for which all inter-sensor ranges are known. Such
+cliques can be efficiently localized using multidimensional scaling. However,
+since each clique is localized in some local coordinate system, we are required
+to register them in a global coordinate system. In other words, our approach is
+based on transforming the localization problem into a problem of registration.
+In this context, the main contributions are as follows. First, we describe an
+efficient method for partitioning the network into overlapping cliques. Second,
+we study the problem of registering the localized cliques, and formulate a
+necessary rigidity condition for uniquely recovering the global sensor
+coordinates. In particular, we present a method for efficiently testing
+rigidity, and a proposal for augmenting the partitioned network to enforce
+rigidity. A recently proposed semidefinite relaxation of global registration is
+used for registering the cliques. We present simulation results on random and
+structured sensor networks to demonstrate that the proposed method compares
+favourably with state-of-the-art methods in terms of run-time, accuracy, and
+scalability.
+",1,0,1,0,0,0
+1061,Density estimation on small datasets,"  How might a smooth probability distribution be estimated, with accurately
+quantified uncertainty, from a limited amount of sampled data? Here we describe
+a field-theoretic approach that addresses this problem remarkably well in one
+dimension, providing an exact nonparametric Bayesian posterior without relying
+on tunable parameters or large-data approximations. Strong non-Gaussian
+constraints, which require a non-perturbative treatment, are found to play a
+major role in reducing distribution uncertainty. A software implementation of
+this method is provided.
+",1,0,0,0,1,0
+1062,"Generalized Euler classes, differential forms and commutative DGAs","  In the context of commutative differential graded algebras over $\mathbb Q$,
+we show that an iteration of ""odd spherical fibration"" creates a ""total space""
+commutative differential graded algebra with only odd degree cohomology. Then
+we show for such a commutative differential graded algebra that, for any of its
+""fibrations"" with ""fiber"" of finite cohomological dimension, the induced map on
+cohomology is injective.
+",0,0,1,0,0,0
+1063,Episodic memory for continual model learning,"  Both the human brain and artificial learning agents operating in real-world
+or comparably complex environments are faced with the challenge of online model
+selection. In principle this challenge can be overcome: hierarchical Bayesian
+inference provides a principled method for model selection and it converges on
+the same posterior for both off-line (i.e. batch) and online learning. However,
+maintaining a parameter posterior for each model in parallel has in general an
+even higher memory cost than storing the entire data set and is consequently
+clearly unfeasible. Alternatively, maintaining only a limited set of models in
+memory could limit memory requirements. However, sufficient statistics for one
+model will usually be insufficient for fitting a different kind of model,
+meaning that the agent loses information with each model change. We propose
+that episodic memory can circumvent the challenge of limited memory-capacity
+online model selection by retaining a selected subset of data points. We design
+a method to compute the quantities necessary for model selection even when the
+data is discarded and only statistics of one (or few) learnt models are
+available. We demonstrate on a simple model that a limited-sized episodic
+memory buffer, when the content is optimised to retain data with statistics not
+matching the current representation, can resolve the fundamental challenge of
+online model selection.
+",1,0,0,1,0,0
+1064,Security Trust Zone in 5G Networks,"  Fifth Generation (5G) telecommunication system is going to deliver a flexible
+radio access network (RAN). Security functions such as authorization,
+authentication and accounting (AAA) are expected to be distributed from central
+clouds to edge clouds. We propose a novel architectural security solution that
+applies to 5G networks. It is called Trust Zone (TZ) that is designed as an
+enhancement of the 5G AAA in the edge cloud. TZ also provides an autonomous and
+decentralized security policy for different tenants under variable network
+conditions. TZ also initiates an ability of disaster cognition and extends the
+security functionalities to a set of flexible and highly available emergency
+services in the edge cloud.
+",1,0,0,0,0,0
+1065,Upper-Bounding the Regularization Constant for Convex Sparse Signal Reconstruction,"  Consider reconstructing a signal $x$ by minimizing a weighted sum of a convex
+differentiable negative log-likelihood (NLL) (data-fidelity) term and a convex
+regularization term that imposes a convex-set constraint on $x$ and enforces
+its sparsity using $\ell_1$-norm analysis regularization. We compute upper
+bounds on the regularization tuning constant beyond which the regularization
+term overwhelmingly dominates the NLL term so that the set of minimum points of
+the objective function does not change. Necessary and sufficient conditions for
+irrelevance of sparse signal regularization and a condition for the existence
+of finite upper bounds are established. We formulate an optimization problem
+for finding these bounds when the regularization term can be globally minimized
+by a feasible $x$ and also develop an alternating direction method of
+multipliers (ADMM) type method for their computation. Simulation examples show
+that the derived and empirical bounds match.
+",0,0,1,1,0,0
+1066,On the Privacy of the Opal Data Release: A Response,"  This document is a response to a report from the University of Melbourne on
+the privacy of the Opal dataset release. The Opal dataset was released by
+Data61 (CSIRO) in conjunction with the Transport for New South Wales (TfNSW).
+The data consists of two separate weeks of ""tap-on/tap-off"" data of individuals
+who used any of the four different modes of public transport from TfNSW: buses,
+light rail, train and ferries. These taps are recorded through the smart
+ticketing system, known as Opal, available in the state of New South Wales,
+Australia.
+",1,0,0,0,0,0
+1067,Long time behavior of Gross-Pitaevskii equation at positive temperature,"  The stochastic Gross-Pitaevskii equation is used as a model to describe
+Bose-Einstein condensation at positive temperature. The equation is a complex
+Ginzburg Landau equation with a trapping potential and an additive space-time
+white noise. Two important questions for this system are the global existence
+of solutions in the support of the Gibbs measure, and the convergence of those
+solutions to the equilibrium for large time. In this paper, we give a proof of
+these two results in one space dimension. In order to prove the convergence to
+equilibrium, we use the associated purely dissipative equation as an auxiliary
+equation, for which the convergence may be obtained using standard techniques.
+Global existence is obtained for all initial data, and not almost surely with
+respect to the invariant measure.
+",0,0,1,0,0,0
+1068,Isomorphism and Morita equivalence classes for crossed products of irrational rotation algebras by cyclic subgroups of $SL_2(\mathbb{Z})$,"  Let $\theta, \theta'$ be irrational numbers and $A, B$ be matrices in
+$SL_2(\mathbb{Z})$ of infinite order. We compute the $K$-theory of the crossed
+product $\mathcal{A}_{\theta}\rtimes_A \mathbb{Z}$ and show that
+$\mathcal{A}_{\theta} \rtimes_A\mathbb{Z}$ and $\mathcal{A}_{\theta'} \rtimes_B
+\mathbb{Z}$ are $*$-isomorphic if and only if $\theta = \pm\theta'
+\pmod{\mathbb{Z}}$ and $I-A^{-1}$ is matrix equivalent to $I-B^{-1}$. Combining
+this result and an explicit construction of equivariant bimodules, we show that
+$\mathcal{A}_{\theta} \rtimes_A\mathbb{Z}$ and $\mathcal{A}_{\theta'} \rtimes_B
+\mathbb{Z}$ are Morita equivalent if and only if $\theta$ and $\theta'$ are in
+the same $GL_2(\mathbb{Z})$ orbit and $I-A^{-1}$ is matrix equivalent to
+$I-B^{-1}$. Finally, we determine the Morita equivalence class of
+$\mathcal{A}_{\theta} \rtimes F$ for any finite subgroup $F$ of
+$SL_2(\mathbb{Z})$.
+",0,0,1,0,0,0
+1069,Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems,"  This paper proposes a new convex model predictive control strategy for
+dynamic optimal power flow between battery energy storage systems distributed
+in an AC microgrid. The proposed control strategy uses a new problem
+formulation, based on a linear d-q reference frame voltage-current model and
+linearised power flow approximations. This allows the optimal power flows to be
+solved as a convex optimisation problem, for which fast and robust solvers
+exist. The proposed method does not assume real and reactive power flows are
+decoupled, allowing line losses, voltage constraints and converter current
+constraints to be addressed. In addition, non-linear variations in the charge
+and discharge efficiencies of lithium ion batteries are analysed and included
+in the control strategy. Real-time digital simulations were carried out for an
+islanded microgrid based on the IEEE 13 bus prototypical feeder, with
+distributed battery energy storage systems and intermittent photovoltaic
+generation. It is shown that the proposed control strategy approaches the
+performance of a strategy based on non-convex optimisation, while reducing the
+required computation time by a factor of 1000, making it suitable for a
+real-time model predictive control implementation.
+",1,0,0,0,0,0
+1070,On noncommutative geometry of the Standard Model: fermion multiplet as internal forms,"  We unveil the geometric nature of the multiplet of fundamental fermions in
+the Standard Model of fundamental particles as a noncommutative analogue of de
+Rham forms on the internal finite quantum space.
+",0,0,1,0,0,0
+1071,A Review of Dynamic Network Models with Latent Variables,"  We present a selective review of statistical modeling of dynamic networks. We
+focus on models with latent variables, specifically, the latent space models
+and the latent class models (or stochastic blockmodels), which investigate both
+the observed features and the unobserved structure of networks. We begin with
+an overview of the static models, and then we introduce the dynamic extensions.
+For each dynamic model, we also discuss its applications that have been studied
+in the literature, with the data source listed in Appendix. Based on the
+review, we summarize a list of open problems and challenges in dynamic network
+modeling with latent variables.
+",0,0,0,1,0,0
+1072,LevelHeaded: Making Worst-Case Optimal Joins Work in the Common Case,"  Pipelines combining SQL-style business intelligence (BI) queries and linear
+algebra (LA) are becoming increasingly common in industry. As a result, there
+is a growing need to unify these workloads in a single framework.
+Unfortunately, existing solutions either sacrifice the inherent benefits of
+exclusively using a relational database (e.g. logical and physical
+independence) or incur orders of magnitude performance gaps compared to
+specialized engines (or both). In this work we study applying a new type of
+query processing architecture to standard BI and LA benchmarks. To do this we
+present a new in-memory query processing engine called LevelHeaded. LevelHeaded
+uses worst-case optimal joins as its core execution mechanism for both BI and
+LA queries. With LevelHeaded, we show how crucial optimizations for BI and LA
+queries can be captured in a worst-case optimal query architecture. Using these
+optimizations, LevelHeaded outperforms other relational database engines
+(LogicBlox, MonetDB, and HyPer) by orders of magnitude on standard LA
+benchmarks, while performing on average within 31% of the best-of-breed BI
+(HyPer) and LA (Intel MKL) solutions on their own benchmarks. Our results show
+that such a single query processing architecture is capable of delivering
+competitive performance on both BI and LA queries.
+",1,0,0,0,0,0
+1073,Few-shot learning of neural networks from scratch by pseudo example optimization,"  In this paper, we propose a simple but effective method for training neural
+networks with a limited amount of training data. Our approach inherits the idea
+of knowledge distillation that transfers knowledge from a deep or wide
+reference model to a shallow or narrow target model. The proposed method
+employs this idea to mimic predictions of reference estimators that are more
+robust against overfitting than the network we want to train. Different from
+almost all the previous work for knowledge distillation that requires a large
+amount of labeled training data, the proposed method requires only a small
+amount of training data. Instead, we introduce pseudo training examples that
+are optimized as a part of model parameters. Experimental results for several
+benchmark datasets demonstrate that the proposed method outperformed all the
+other baselines, such as naive training of the target model and standard
+knowledge distillation.
+",0,0,0,1,0,0
+1074,Identities and congruences involving the Fubini polynomials,"  In this paper, we investigate the umbral representation of the Fubini
+polynomials $F_{x}^{n}:=F_{n}(x)$ to derive some properties involving these
+polynomials. For any prime number $p$ and any polynomial $f$ with integer
+coefficients, we show $(f(F_{x}))^{p}\equiv f(F_{x})$ and we give other curious
+congruences.
+",0,0,1,0,0,0
+1075,Introduction to Delay Models and Their Wave Solutions,"  In this paper, a brief review of delay population models and their
+applications in ecology is provided. The inclusion of diffusion and nonlocality
+terms in delay models has given more capabilities to these models enabling them
+to capture several ecological phenomena such as the Allee effect, waves of
+invasive species and spatio-temporal competitions of interacting species.
+Moreover, recent advances in the studies of traveling and stationary wave
+solutions of delay models are outlined. In particular, the existence of
+stationary and traveling wave solutions of delay models, stability of wave
+solutions, formation of wavefronts in the special domain, and possible outcomes
+of delay models are discussed.
+",0,0,1,0,0,0
+1076,On Dummett's Pragmatist Justification Procedure,"  I show that propositional intuitionistic logic is complete with respect to an
+adaptation of Dummett's pragmatist justification procedure. In particular,
+given a pragmatist justification of an argument, I show how to obtain a natural
+deduction derivation of the conclusion of the argument from, at most, the same
+assumptions.
+",0,0,1,0,0,0
+1077,Evidence for a radiatively driven disc-wind in PDS 456?,"  We present a newly discovered correlation between the wind outflow velocity
+and the X-ray luminosity in the luminous ($L_{\rm bol}\sim10^{47}\,\rm
+erg\,s^{-1}$) nearby ($z=0.184$) quasar PDS\,456. All the contemporary
+XMM-Newton, NuSTAR and Suzaku observations from 2001--2014 were revisited and
+we find that the centroid energy of the blueshifted Fe\,K absorption profile
+increases with luminosity. This translates into a correlation between the wind
+outflow velocity and the hard X-ray luminosity (between 7--30\,keV) where we
+find that $v_{\rm w}/c \propto L_{7-30}^{\gamma}$ where $\gamma=0.22\pm0.04$.
+We also show that this is consistent with a wind that is predominately
+radiatively driven, possibly resulting from the high Eddington ratio of
+PDS\,456.
+",0,1,0,0,0,0
+1078,From a normal insulator to a topological insulator in plumbene,"  Plumbene, similar to silicene, has a buckled honeycomb structure with a large
+band gap ($\sim 400$ meV). All previous studies have shown that it is a normal
+insulator. Here, we perform first-principles calculations and employ a
+sixteen-band tight-binding model with nearest-neighbor and
+next-nearest-neighbor hopping terms to investigate electronic structures and
+topological properties of the plumbene monolayer. We find that it can become a
+topological insulator with a large bulk gap ($\sim 200$ meV) through electron
+doping, and the nontrivial state is very robust with respect to external
+strain. Plumbene can be an ideal candidate for realizing the quantum spin Hall
+effect at room temperature. By investigating effects of external electric and
+magnetic fields on electronic structures and transport properties of plumbene,
+we present two rich phase diagrams with and without electron doping, and
+propose a theoretical design for a four-state spin-valley filter.
+",0,1,0,0,0,0
+1079,High-sensitivity Kinetic Inductance Detectors for CALDER,"  Providing a background discrimination tool is crucial for enhancing the
+sensitivity of next-generation experiments searching for neutrinoless double-
+beta decay. The development of high-sensitivity (< 20 eV RMS) cryogenic light
+detectors allows simultaneous read-out of the light and heat signals and
+enables background suppression through particle identification. The Cryogenic
+wide- Area Light Detector with Excellent Resolution (CALDER) R&D already proved
+the potential of this technique using the phonon-mediated Kinetic Inductance
+Detectors (KIDs) approach. The first array prototype with 4 Aluminum KIDs on a
+2 $\times$ 2 cm2 Silicon substrate showed a baseline resolution of 154 $\pm$ 7
+eV RMS. Improving the design and the readout of the resonator, the next CALDER
+prototype featured an energy resolution of 82 $\pm$ 4 eV, by sampling the same
+substrate with a single Aluminum KID.
+",0,1,0,0,0,0
+1080,Bounding the composition length of primitive permutation groups and completely reducible linear groups,"  We obtain upper bounds on the composition length of a finite permutation
+group in terms of the degree and the number of orbits, and analogous bounds for
+primitive, quasiprimitive and semiprimitive groups. Similarly, we obtain upper
+bounds on the composition length of a finite completely reducible linear group
+in terms of some of its parameters. In almost all cases we show that the bounds
+are sharp, and describe the extremal examples.
+",0,0,1,0,0,0
+1081,A Bernstein Inequality For Spatial Lattice Processes,"  In this article we present a Bernstein inequality for sums of random
+variables which are defined on a spatial lattice structure. The inequality can
+be used to derive concentration inequalities. It can be useful to obtain
+consistency properties for nonparametric estimators of conditional expectation
+functions.
+",0,0,1,1,0,0
+1082,An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures,"  We explore different approaches to integrating a simple convolutional neural
+network (CNN) with the Lucene search engine in a multi-stage ranking
+architecture. Our models are trained using the PyTorch deep learning toolkit,
+which is implemented in C/C++ with a Python frontend. One obvious integration
+strategy is to expose the neural network directly as a service. For this, we
+use Apache Thrift, a software framework for building scalable cross-language
+services. In exploring alternative architectures, we observe that once trained,
+the feedforward evaluation of neural networks is quite straightforward.
+Therefore, we can extract the parameters of a trained CNN from PyTorch and
+import the model into Java, taking advantage of the Java Deeplearning4J library
+for feedforward evaluation. This has the advantage that the entire end-to-end
+system can be implemented in Java. As a third approach, we can extract the
+neural network from PyTorch and ""compile"" it into a C++ program that exposes a
+Thrift service. We evaluate these alternatives in terms of performance (latency
+and throughput) as well as ease of integration. Experiments show that
+feedforward evaluation of the convolutional neural network is significantly
+slower in Java, while the performance of the compiled C++ network does not
+consistently beat the PyTorch implementation.
+",1,0,0,0,0,0
+1083,Dispersive Regimes of the Dicke Model,"  We study two dispersive regimes in the dynamics of $N$ two-level atoms
+interacting with a bosonic mode for long interaction times. Firstly, we analyze
+the dispersive multiqubit quantum Rabi model for the regime in which the qubit
+frequencies are equal and smaller than the mode frequency, and for values of
+the coupling strength similar or larger than the mode frequency, namely, the
+deep strong coupling regime. Secondly, we address an interaction that is
+dependent on the photon number, where the coupling strength is comparable to
+the geometric mean of the qubit and mode frequencies. We show that the
+associated dynamics is analytically tractable and provide useful frameworks
+with which to analyze the system behavior. In the deep strong coupling regime,
+we unveil the structure of unexpected resonances for specific values of the
+coupling, present for $N\ge2$, and in the photon-number-dependent regime we
+demonstrate that all the nontrivial dynamical behavior occurs in the atomic
+degrees of freedom for a given Fock state. We verify these assertions with
+numerical simulations of the qubit population and photon-statistic dynamics.
+",0,1,0,0,0,0
+1084,"ZebraLancer: Crowdsource Knowledge atop Open Blockchain, Privately and Anonymously","  We design and implement the first private and anonymous decentralized
+crowdsourcing system ZebraLancer. It realizes the fair exchange (i.e. security
+against malicious workers and dishonest requesters) without using any
+third-party arbiter. More importantly, it overcomes two fundamental challenges
+of decentralization, i.e. data leakage and identity breach.
+First, our outsource-then-prove methodology resolves the critical tension
+between blockchain transparency and data confidentiality without sacrificing
+the fairness of exchange. ZebraLancer ensures: a requester will not pay more
+than what data deserve, according to a policy announced when her task is
+published through the blockchain; each worker indeed gets a payment based on
+the policy, if submits data to the blockchain; the above properties are
+realized not only without a central arbiter, but also without leaking the data
+to blockchain network.
+Furthermore, the blockchain transparency might allow one to infer private
+information of workers/requesters through their participation history.
+ZebraLancer solves the problem by allowing anonymous participations without
+surrendering user accountability. Specifically, workers cannot misuse anonymity
+to submit multiple times to reap rewards, and an anonymous requester cannot
+maliciously submit colluded answers to herself to repudiate payments. The idea
+behind is a subtle linkability: if one authenticates twice in a task, everybody
+can tell, or else staying anonymous. To realize such delicate linkability, we
+put forth a novel cryptographic notion, the common-prefix-linkable anonymous
+authentication.
+Finally, we implement our protocol for a common image annotation task and
+deploy it in a test net of Ethereum. The experiment results show the
+applicability of our protocol and highlight subtleties of tailoring the
+protocol to be compatible with the existing real-world open blockchain.
+",1,0,0,0,0,0
+1085,"Fast, Better Training Trick -- Random Gradient","  In this paper, we will show an unprecedented method to accelerate training
+and improve performance, which called random gradient (RG). This method can be
+easier to the training of any model without extra calculation cost, we use
+Image classification, Semantic segmentation, and GANs to confirm this method
+can improve speed which is training model in computer vision. The central idea
+is using the loss multiplied by a random number to random reduce the
+back-propagation gradient. We can use this method to produce a better result in
+Pascal VOC, Cifar, Cityscapes datasets.
+",0,0,0,1,0,0
+1086,Expressions of Sentiments During Code Reviews: Male vs. Female,"  Background: As most of the software development organizations are
+male-dominated, female developers encountering various negative workplace
+experiences reported feeling like they ""do not belong"". Exposures to
+discriminatory expletives or negative critiques from their male colleagues may
+further exacerbate those feelings. Aims: The primary goal of this study is to
+identify the differences in expressions of sentiments between male and female
+developers during various software engineering tasks. Method: On this goal, we
+mined the code review repositories of six popular open source projects. We used
+a semi-automated approach leveraging the name as well as multiple social
+networks to identify the gender of a developer. Using SentiSE, a customized and
+state-of-the-art sentiment analysis tool for the software engineering domain,
+we classify each communication as negative, positive, or neutral. We also
+compute the frequencies of sentiment words, emoticons, and expletives used by
+each developer. Results: Our results suggest that the likelihood of using
+sentiment words, emoticons, and expletives during code reviews varies based on
+the gender of a developer, as females are significantly less likely to express
+sentiments than males. Although female developers were more neutral to their
+male colleagues than to another female, male developers from three out of the
+six projects were not only writing more frequent negative comments but also
+withholding positive encouragements from their female counterparts. Conclusion:
+Our results provide empirical evidence of another factor behind the negative
+work place experiences encountered by the female developers that may be
+contributing to the diminishing number of females in the SE industry.
+",1,0,0,0,0,0
+1087,Monotonicity patterns and functional inequalities for classical and generalized Wright functions,"  In this paper our aim is to present the completely monotonicity and convexity
+properties for the Wright function. As consequences of these results, we
+present some functional inequalities. Moreover, we derive the monotonicity and
+log-convexity results for the generalized Wright functions. As applications, we
+present several new inequalities (like Turán type inequalities) and we prove
+some geometric properties for four--parametric Mittag--Leffler functions.
+",0,0,1,0,0,0
+1088,Multiple VLAD encoding of CNNs for image classification,"  Despite the effectiveness of convolutional neural networks (CNNs) especially
+in image classification tasks, the effect of convolution features on learned
+representations is still limited. It mostly focuses on the salient object of
+the images, but ignores the variation information on clutter and local. In this
+paper, we propose a special framework, which is the multiple VLAD encoding
+method with the CNNs features for image classification. Furthermore, in order
+to improve the performance of the VLAD coding method, we explore the
+multiplicity of VLAD encoding with the extension of three kinds of encoding
+algorithms, which are the VLAD-SA method, the VLAD-LSA and the VLAD-LLC method.
+Finally, we equip the spatial pyramid patch (SPM) on VLAD encoding to add the
+spatial information of CNNs feature. In particular, the power of SPM leads our
+framework to yield better performance compared to the existing method.
+",1,0,0,0,0,0
+1089,Index of Dirac operators and classification of topological insulators,"  Real and complex Clifford bundles and Dirac operators defined on them are
+considered. By using the index theorems of Dirac operators, table of
+topological invariants is constructed from the Clifford chessboard. Through the
+relations between K-theory groups, Grothendieck groups and symmetric spaces,
+the periodic table of topological insulators and superconductors is obtained.
+This gives the result that the periodic table of real and complex topological
+phases is originated from the Clifford chessboard and index theorems.
+",0,1,0,0,0,0
+1090,Centroid vetting of transiting planet candidates from the Next Generation Transit Survey,"  The Next Generation Transit Survey (NGTS), operating in Paranal since 2016,
+is a wide-field survey to detect Neptunes and super-Earths transiting bright
+stars, which are suitable for precise radial velocity follow-up and
+characterisation. Thereby, its sub-mmag photometric precision and ability to
+identify false positives are crucial. Particularly, variable background objects
+blended in the photometric aperture frequently mimic Neptune-sized transits and
+are costly in follow-up time. These objects can best be identified with the
+centroiding technique: if the photometric flux is lost off-centre during an
+eclipse, the flux centroid shifts towards the centre of the target star.
+Although this method has successfully been employed by the Kepler mission, it
+has previously not been implemented from the ground. We present a
+fully-automated centroid vetting algorithm developed for NGTS, enabled by our
+high-precision auto-guiding. Our method allows detecting centroid shifts with
+an average precision of 0.75 milli-pixel, and down to 0.25 milli-pixel for
+specific targets, for a pixel size of 4.97 arcsec. The algorithm is now part of
+the NGTS candidate vetting pipeline and automatically employed for all detected
+signals. Further, we develop a joint Bayesian fitting model for all photometric
+and centroid data, allowing to disentangle which object (target or background)
+is causing the signal, and what its astrophysical parameters are. We
+demonstrate our method on two NGTS objects of interest. These achievements make
+NGTS the first ground-based wide-field transit survey ever to successfully
+apply the centroiding technique for automated candidate vetting, enabling the
+production of a robust candidate list before follow-up.
+",0,1,0,0,0,0
+1091,Galaxy And Mass Assembly: the evolution of the cosmic spectral energy distribution from z = 1 to z = 0,"  We present the evolution of the Cosmic Spectral Energy Distribution (CSED)
+from $z = 1 - 0$. Our CSEDs originate from stacking individual spectral energy
+distribution fits based on panchromatic photometry from the Galaxy and Mass
+Assembly (GAMA) and COSMOS datasets in ten redshift intervals with completeness
+corrections applied. Below $z = 0.45$, we have credible SED fits from 100 nm to
+1 mm. Due to the relatively low sensitivity of the far-infrared data, our
+far-infrared CSEDs contain a mix of predicted and measured fluxes above $z =
+0.45$. Our results include appropriate errors to highlight the impact of these
+corrections. We show that the bolometric energy output of the Universe has
+declined by a factor of roughly four -- from $5.1 \pm 1.0$ at $z \sim 1$ to
+$1.3 \pm 0.3 \times 10^{35}~h_{70}$~W~Mpc$^{-3}$ at the current epoch. We show
+that this decrease is robust to cosmic variance, SED modelling and other
+various types of error. Our CSEDs are also consistent with an increase in the
+mean age of stellar populations. We also show that dust attenuation has
+decreased over the same period, with the photon escape fraction at 150~nm
+increasing from $16 \pm 3$ at $z \sim 1$ to $24 \pm 5$ per cent at the current
+epoch, equivalent to a decrease in $A_\mathrm{FUV}$ of 0.4~mag. Our CSEDs
+account for $68 \pm 12$ and $61 \pm 13$ per cent of the cosmic optical and
+infrared backgrounds respectively as defined from integrated galaxy counts and
+are consistent with previous estimates of the cosmic infrared background with
+redshift.
+",0,1,0,0,0,0
+1092,Large sums of Hecke eigenvalues of holomorphic cusp forms,"  Let $f$ be a Hecke cusp form of weight $k$ for the full modular group, and
+let $\{\lambda_f(n)\}_{n\geq 1}$ be the sequence of its normalized Fourier
+coefficients. Motivated by the problem of the first sign change of
+$\lambda_f(n)$, we investigate the range of $x$ (in terms of $k$) for which
+there are cancellations in the sum $S_f(x)=\sum_{n\leq x} \lambda_f(n)$. We
+first show that $S_f(x)=o(x\log x)$ implies that $\lambda_f(n)<0$ for some
+$n\leq x$. We also prove that $S_f(x)=o(x\log x)$ in the range $\log x/\log\log
+k\to \infty$ assuming the Riemann hypothesis for $L(s, f)$, and furthermore
+that this range is best possible unconditionally. More precisely, we establish
+the existence of many Hecke cusp forms $f$ of large weight $k$, for which
+$S_f(x)\gg_A x\log x$, when $x=(\log k)^A.$ Our results are $GL_2$ analogues of
+work of Granville and Soundararajan for character sums, and could also be
+generalized to other families of automorphic forms.
+",0,0,1,0,0,0
+1093,EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,"  Recent studies have highlighted the vulnerability of deep neural networks
+(DNNs) to adversarial examples - a visually indistinguishable adversarial image
+can easily be crafted to cause a well-trained model to misclassify. Existing
+methods for crafting adversarial examples are based on $L_2$ and $L_\infty$
+distortion metrics. However, despite the fact that $L_1$ distortion accounts
+for the total variation and encourages sparsity in the perturbation, little has
+been developed for crafting $L_1$-based adversarial examples. In this paper, we
+formulate the process of attacking DNNs via adversarial examples as an
+elastic-net regularized optimization problem. Our elastic-net attacks to DNNs
+(EAD) feature $L_1$-oriented adversarial examples and include the
+state-of-the-art $L_2$ attack as a special case. Experimental results on MNIST,
+CIFAR10 and ImageNet show that EAD can yield a distinct set of adversarial
+examples with small $L_1$ distortion and attains similar attack performance to
+the state-of-the-art methods in different attack scenarios. More importantly,
+EAD leads to improved attack transferability and complements adversarial
+training for DNNs, suggesting novel insights on leveraging $L_1$ distortion in
+adversarial machine learning and security implications of DNNs.
+",1,0,0,1,0,0
+1094,Playtime Measurement with Survival Analysis,"  Maximizing product use is a central goal of many businesses, which makes
+retention and monetization two central analytics metrics in games. Player
+retention may refer to various duration variables quantifying product use:
+total playtime or session playtime are popular research targets, and active
+playtime is well-suited for subscription games. Such research often has the
+goal of increasing player retention or conversely decreasing player churn.
+Survival analysis is a framework of powerful tools well suited for retention
+type data. This paper contributes new methods to game analytics on how playtime
+can be analyzed using survival analysis without covariates. Survival and hazard
+estimates provide both a visual and an analytic interpretation of the playtime
+phenomena as a funnel type nonparametric estimate. Metrics based on the
+survival curve can be used to aggregate this playtime information into a single
+statistic. Comparison of survival curves between cohorts provides a scientific
+AB-test. All these methods work on censored data and enable computation of
+confidence intervals. This is especially important in time and sample limited
+data which occurs during game development. Throughout this paper, we illustrate
+the application of these methods to real world game development problems on the
+Hipster Sheep mobile game.
+",1,0,0,1,0,0
+1095,Asymptotic formula of the number of Newton polygons,"  In this paper, we enumerate Newton polygons asymptotically. The number of
+Newton polygons is computable by a simple recurrence equation, but unexpectedly
+the asymptotic formula of its logarithm contains growing oscillatory terms. As
+the terms come from non-trivial zeros of the Riemann zeta function, an
+estimation of the amplitude of the oscillating part is equivalent to the
+Riemann hypothesis.
+",0,0,1,0,0,0
+1096,Invariant-based inverse engineering of crane control parameters,"  By applying invariant-based inverse engineering in the small-oscillations
+regime, we design the time dependence of the control parameters of an overhead
+crane (trolley displacement and rope length), to transport a load between two
+positions at different heights with minimal final energy excitation for a
+microcanonical ensemble of initial conditions. The analogies between ion
+transport in multisegmented traps or neutral atom transport in moving optical
+lattices and load manipulation by cranes opens a route for a useful transfer of
+techniques among very different fields.
+",0,1,0,0,0,0
+1097,Leaf Space Isometries of Singular Riemannian Foliations and Their Spectral Properties,"  In this paper, the authors consider leaf spaces of singular Riemannian
+foliations $\mathcal{F}$ on compact manifolds $M$ and the associated
+$\mathcal{F}$-basic spectrum on $M$, $spec_B(M, \mathcal{F}),$ counted with
+multiplicities. Recently, a notion of smooth isometry $\varphi:
+M_1/\mathcal{F}_1\rightarrow M_2/\mathcal{F}_2$ between the leaf spaces of such
+singular Riemannian foliations $(M_1,\mathcal{F}_1)$ and $(M_2,\mathcal{F}_2)$
+has appeared in the literature. In this paper, the authors provide an example
+to show that the existence a smooth isometry of leaf spaces as above is not
+sufficient to guarantee the equality of $spec_B(M_1,\mathcal{F}_1)$ and
+$spec_B(M_2,\mathcal{F}_2).$ The authors then prove that if some additional
+conditions involving the geometry of the leaves are satisfied, then the
+equality of $spec_B(M_1,\mathcal{F}_1)$ and $spec_B(M_2,\mathcal{F}_2)$ is
+guaranteed. Consequences and applications to orbifold spectral theory,
+isometric group actions, and their reductions are also explored.
+",0,0,1,0,0,0
+1098,Backward Monte-Carlo applied to muon transport,"  We discuss a backward Monte-Carlo technique for muon transport problem, with
+emphasis on its application in muography. Backward Monte-Carlo allows exclusive
+sampling of a final state by reversing the simulation flow. In practice it can
+be made analogous to an adjoint Monte-Carlo, though it is more versatile for
+muon transport. A backward Monte-Carlo was implemented as a dedicated muon
+transport library: PUMAS. It is shown for case studies relevant for muography
+imaging that the implementations of forward and backward Monte-Carlo schemes
+agree to better than 1%.
+",0,1,0,0,0,0
+1099,Functional importance of noise in neuronal information processing,"  Noise is an inherent part of neuronal dynamics, and thus of the brain. It can
+be observed in neuronal activity at different spatiotemporal scales, including
+in neuronal membrane potentials, local field potentials,
+electroencephalography, and magnetoencephalography. A central research topic in
+contemporary neuroscience is to elucidate the functional role of noise in
+neuronal information processing. Experimental studies have shown that a
+suitable level of noise may enhance the detection of weak neuronal signals by
+means of stochastic resonance. In response, theoretical research, based on the
+theory of stochastic processes, nonlinear dynamics, and statistical physics,
+has made great strides in elucidating the mechanism and the many benefits of
+stochastic resonance in neuronal systems. In this perspective, we review recent
+research dedicated to neuronal stochastic resonance in biophysical mathematical
+models. We also explore the regulation of neuronal stochastic resonance, and we
+outline important open questions and directions for future research. A deeper
+understanding of neuronal stochastic resonance may afford us new insights into
+the highly impressive information processing in the brain.
+",0,0,0,0,1,0
+1100,Stochastic Variance Reduction Methods for Policy Evaluation,"  Policy evaluation is a crucial step in many reinforcement-learning
+procedures, which estimates a value function that predicts states' long-term
+value under a given policy. In this paper, we focus on policy evaluation with
+linear function approximation over a fixed dataset. We first transform the
+empirical policy evaluation problem into a (quadratic) convex-concave saddle
+point problem, and then present a primal-dual batch gradient method, as well as
+two stochastic variance reduction methods for solving the problem. These
+algorithms scale linearly in both sample size and feature dimension. Moreover,
+they achieve linear convergence even when the saddle-point problem has only
+strong concavity in the dual variables but no strong convexity in the primal
+variables. Numerical experiments on benchmark problems demonstrate the
+effectiveness of our methods.
+",1,0,1,1,0,0
+1101,Self-consistent dynamical model of the Broad Line Region,"  We develope a self-consistent description of the Broad Line Region based on
+the concept of the failed wind powered by the radiation pressure acting on
+dusty accretion disk atmosphere in Keplerian motion. The material raised high
+above the disk is illuminated, dust evaportes, and the matter falls back
+towards the disk. This material is the source of emission lines. The model
+predicts the inner and outer radius of the region, the cloud dynamics under the
+dust radiation pressure and, subsequently, just the gravitational field of the
+central black hole, which results in assymetry between the rise and fall.
+Knowledge of the dynamics allows to predict the shapes of the emission lines as
+functions of the basic parameters of an active nucleus: black hole mass,
+accretion rate, black hole spin (or accretion efficiency) and the viewing angle
+with respect to the symmetry axis. Here we show preliminary results based on
+analytical approximations to the cloud motion.
+",0,1,0,0,0,0
+1102,Measuring the polarization of electromagnetic fields using Rabi-rate measurements with spatial resolution: experiment and theory,"  When internal states of atoms are manipulated using coherent optical or
+radio-frequency (RF) radiation, it is essential to know the polarization of the
+radiation with respect to the quantization axis of the atom. We first present a
+measurement of the two-dimensional spatial distribution of the electric-field
+amplitude of a linearly-polarized pulsed RF electric field at $\sim 25.6\,$GHz
+and its angle with respect to a static electric field. The measurements exploit
+coherent population transfer between the $35$s and $35$p Rydberg states of
+helium atoms in a pulsed supersonic beam. Based on this experimental result, we
+develop a general framework in the form of a set of equations relating the five
+independent polarization parameters of a coherently oscillating field in a
+fixed laboratory frame to Rabi rates of transitions between a ground and three
+excited states of an atom with arbitrary quantization axis. We then explain how
+these equations can be used to fully characterize the polarization in a minimum
+of five Rabi rate measurements by rotation of an external bias-field, or,
+knowing the polarization of the driving field, to determine the orientation of
+the static field using two measurements. The presented technique is not limited
+to Rydberg atoms and RF fields but can also be applied to characterize optical
+fields. The technique has potential for sensing the spatiotemporal properties
+of electromagnetic fields, e.g., in metrology devices or in hybrid experiments
+involving atoms close to surfaces.
+",0,1,0,0,0,0
+1103,Software-based Microarchitectural Attacks,"  Modern processors are highly optimized systems where every single cycle of
+computation time matters. Many optimizations depend on the data that is being
+processed. Software-based microarchitectural attacks exploit effects of these
+optimizations. Microarchitectural side-channel attacks leak secrets from
+cryptographic computations, from general purpose computations, or from the
+kernel. This leakage even persists across all common isolation boundaries, such
+as processes, containers, and virtual machines. Microarchitectural fault
+attacks exploit the physical imperfections of modern computer systems.
+Shrinking process technology introduces effects between isolated hardware
+elements that can be exploited by attackers to take control of the entire
+system. These attacks are especially interesting in scenarios where the
+attacker is unprivileged or even sandboxed.
+In this thesis, we focus on microarchitectural attacks and defenses on
+commodity systems. We investigate known and new side channels and show that
+microarchitectural attacks can be fully automated. Furthermore, we show that
+these attacks can be mounted in highly restricted environments such as
+sandboxed JavaScript code in websites. We show that microarchitectural attacks
+exist on any modern computer system, including mobile devices (e.g.,
+smartphones), personal computers, and commercial cloud systems. This thesis
+consists of two parts. In the first part, we provide background on modern
+processor architectures and discuss state-of-the-art attacks and defenses in
+the area of microarchitectural side-channel attacks and microarchitectural
+fault attacks. In the second part, a selection of our papers are provided
+without modification from their original publications. I have co-authored these
+papers, which have subsequently been anonymously peer-reviewed, accepted, and
+presented at renowned international conferences.
+",1,0,0,0,0,0
+1104,Pixelwise Instance Segmentation with a Dynamically Instantiated Network,"  Semantic segmentation and object detection research have recently achieved
+rapid progress. However, the former task has no notion of different instances
+of the same object, and the latter operates at a coarse, bounding-box level. We
+propose an Instance Segmentation system that produces a segmentation map where
+each pixel is assigned an object class and instance identity label. Most
+approaches adapt object detectors to produce segments instead of boxes. In
+contrast, our method is based on an initial semantic segmentation module, which
+feeds into an instance subnetwork. This subnetwork uses the initial
+category-level segmentation, along with cues from the output of an object
+detector, within an end-to-end CRF to predict instances. This part of our model
+is dynamically instantiated to produce a variable number of instances per
+image. Our end-to-end approach requires no post-processing and considers the
+image holistically, instead of processing independent proposals. Therefore,
+unlike some related work, a pixel cannot belong to multiple instances.
+Furthermore, far more precise segmentations are achieved, as shown by our
+state-of-the-art results (particularly at high IoU thresholds) on the Pascal
+VOC and Cityscapes datasets.
+",1,0,0,0,0,0
+1105,Binary Matrix Factorization via Dictionary Learning,"  Matrix factorization is a key tool in data analysis; its applications include
+recommender systems, correlation analysis, signal processing, among others.
+Binary matrices are a particular case which has received significant attention
+for over thirty years, especially within the field of data mining. Dictionary
+learning refers to a family of methods for learning overcomplete basis (also
+called frames) in order to efficiently encode samples of a given type; this
+area, now also about twenty years old, was mostly developed within the signal
+processing field. In this work we propose two binary matrix factorization
+methods based on a binary adaptation of the dictionary learning paradigm to
+binary matrices. The proposed algorithms focus on speed and scalability; they
+work with binary factors combined with bit-wise operations and a few auxiliary
+integer ones. Furthermore, the methods are readily applicable to online binary
+matrix factorization. Another important issue in matrix factorization is the
+choice of rank for the factors; we address this model selection problem with an
+efficient method based on the Minimum Description Length principle. Our
+preliminary results show that the proposed methods are effective at producing
+interpretable factorizations of various data types of different nature.
+",0,0,0,1,0,0
+1106,Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts,"  Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose.
+Linguistic features, mainly from parsers, have been used to detect MCI, but
+this is not suitable for large-scale assessments. MCI disfluencies produce
+non-grammatical speech that requires manual or high precision automatic
+correction of transcripts. In this paper, we modeled transcripts into complex
+networks and enriched them with word embedding (CNE) to better represent short
+texts produced in neuropsychological assessments. The network measurements were
+applied with well-known classifiers to automatically identify MCI in
+transcripts, in a binary classification task. A comparison was made with the
+performance of traditional approaches using Bag of Words (BoW) and linguistic
+features for three datasets: DementiaBank in English, and Cinderella and
+Arizona-Battery in Portuguese. Overall, CNE provided higher accuracy than using
+only complex networks, while Support Vector Machine was superior to other
+classifiers. CNE provided the highest accuracies for DementiaBank and
+Cinderella, but BoW was more efficient for the Arizona-Battery dataset probably
+owing to its short narratives. The approach using linguistic features yielded
+higher accuracy if the transcriptions of the Cinderella dataset were manually
+revised. Taken together, the results indicate that complex networks enriched
+with embedding is promising for detecting MCI in large-scale assessments
+",1,0,0,0,0,0
+1107,The many faces of degeneracy in conic optimization,"  Slater's condition -- existence of a ""strictly feasible solution"" -- is a
+common assumption in conic optimization. Without strict feasibility,
+first-order optimality conditions may be meaningless, the dual problem may
+yield little information about the primal, and small changes in the data may
+render the problem infeasible. Hence, failure of strict feasibility can
+negatively impact off-the-shelf numerical methods, such as primal-dual interior
+point methods, in particular. New optimization modelling techniques and convex
+relaxations for hard nonconvex problems have shown that the loss of strict
+feasibility is a more pronounced phenomenon than has previously been realized.
+In this text, we describe various reasons for the loss of strict feasibility,
+whether due to poor modelling choices or (more interestingly) rich underlying
+structure, and discuss ways to cope with it and, in many pronounced cases, how
+to use it as an advantage. In large part, we emphasize the facial reduction
+preprocessing technique due to its mathematical elegance, geometric
+transparency, and computational potential.
+",0,0,1,0,0,0
+1108,Strong homotopy types of acyclic categories and $Δ$-complexes,"  We extend the homotopy theories based on point reduction for finite spaces
+and simplicial complexes to finite acyclic categories and $\Delta$-complexes,
+respectively. The functors of classifying spaces and face posets are compatible
+with these homotopy theories. In contrast with the classical settings of finite
+spaces and simplicial complexes, the universality of morphisms and simplices
+plays a central role in this paper.
+",0,0,1,0,0,0
+1109,Boundary problems for the fractional and tempered fractional operators,"  For characterizing the Brownian motion in a bounded domain: $\Omega$, it is
+well-known that the boundary conditions of the classical diffusion equation
+just rely on the given information of the solution along the boundary of a
+domain; on the contrary, for the Lévy flights or tempered Lévy flights in a
+bounded domain, it involves the information of a solution in the complementary
+set of $\Omega$, i.e., $\mathbb{R}^n\backslash \Omega$, with the potential
+reason that paths of the corresponding stochastic process are discontinuous.
+Guided by probability intuitions and the stochastic perspectives of anomalous
+diffusion, we show the reasonable ways, ensuring the clear physical meaning and
+well-posedness of the partial differential equations (PDEs), of specifying
+`boundary' conditions for space fractional PDEs modeling the anomalous
+diffusion. Some properties of the operators are discussed, and the
+well-posednesses of the PDEs with generalized boundary conditions are proved.
+",0,0,1,0,0,0
+1110,Bohm's approach to quantum mechanics: Alternative theory or practical picture?,"  Since its inception Bohmian mechanics has been generally regarded as a
+hidden-variable theory aimed at providing an objective description of quantum
+phenomena. To date, this rather narrow conception of Bohm's proposal has caused
+it more rejection than acceptance. Now, after 65 years of Bohmian mechanics,
+should still be such an interpretational aspect the prevailing appraisal? Why
+not favoring a more pragmatic view, as a legitimate picture of quantum
+mechanics, on equal footing in all respects with any other more conventional
+quantum picture? These questions are used here to introduce a discussion on an
+alternative way to deal with Bohmian mechanics at present, enhancing its aspect
+as an efficient and useful picture or formulation to tackle, explore, describe
+and explain quantum phenomena where phase and correlation (entanglement) are
+key elements. This discussion is presented through two complementary blocks.
+The first block is aimed at briefly revisiting the historical context that gave
+rise to the appearance of Bohmian mechanics, and how this approach or analogous
+ones have been used in different physical contexts. This discussion is used to
+emphasize a more pragmatic view to the detriment of the more conventional
+hidden-variable (ontological) approach that has been a leitmotif within the
+quantum foundations. The second block focuses on some particular formal aspects
+of Bohmian mechanics supporting the view presented here, with special emphasis
+on the physical meaning of the local phase field and the associated velocity
+field encoded within the wave function. As an illustration, a simple model of
+Young's two-slit experiment is considered. The simplicity of this model allows
+to understand in an easy manner how the information conveyed by the Bohmian
+formulation relates to other more conventional concepts in quantum mechanics.
+This sort of pedagogical application is also aimed at ...
+",0,1,0,0,0,0
+1111,Continuous-wave virtual-state lasing from cold ytterbium atoms,"  While conventional lasers are based on gain media with three or four real
+levels, unconventional lasers including virtual levels and two-photon processes
+offer new opportunities. We study lasing that involves a two-photon process
+through a virtual lower level, which we realize in a cloud of cold ytterbium
+atoms that are magneto-optically trapped inside a cavity. We pump the atoms on
+the narrow $^1$S$_0$ $\to$ $^3$P$_1$ line and generate laser emission on the
+same transition. Lasing is verified by a threshold behavior of output power
+vs.\ pump power and atom number, a flat $g^{(2)}$ correlation function above
+threshold, and the polarization properties of the output. In the proposed
+lasing mechanism the MOT beams create the virtual lower level of the lasing
+transition. The laser process runs continuously, needs no further repumping,
+and might be adapted to other atoms or transitions such as the ultra narrow
+$^1$S$_0$ $\to$ $^3$P$_0$ clock transition in ytterbium.
+",0,1,0,0,0,0
+1112,Assessment of Future Changes in Intensity-Duration-Frequency Curves for Southern Ontario using North American (NA)-CORDEX Models with Nonstationary Methods,"  The evaluation of possible climate change consequence on extreme rainfall has
+significant implications for the design of engineering structure and
+socioeconomic resources development. While many studies have assessed the
+impact of climate change on design rainfall using global and regional climate
+model (RCM) predictions, to date, there has been no comprehensive comparison or
+evaluation of intensity-duration-frequency (IDF) statistics at regional scale,
+considering both stationary versus nonstationary models for the future climate.
+To understand how extreme precipitation may respond to future IDF curves, we
+used an ensemble of three RCMs participating in the North-American (NA)-CORDEX
+domain over eight rainfall stations across Southern Ontario, one of the most
+densely populated and major economic region in Canada. The IDF relationships
+are derived from multi-model RCM simulations and compared with the
+station-based observations. We modeled precipitation extremes, at different
+durations using extreme value distributions considering parameters that are
+either stationary or nonstationary, as a linear function of time. Our results
+showed that extreme precipitation intensity driven by future climate forcing
+shows a significant increase in intensity for 10-year events in 2050s
+(2030-2070) relative to 1970-2010 baseline period across most of the locations.
+However, for longer return periods, an opposite trend is noted. Surprisingly,
+in term of design storms, no significant differences were found when comparing
+stationary and nonstationary IDF estimation methods for the future (2050s) for
+the larger return periods. The findings, which are specific to regional
+precipitation extremes, suggest no immediate reason for alarm, but the need for
+progressive updating of the design standards in light of global warming.
+",0,1,0,1,0,0
+1113,Improved Algorithms for Computing the Cycle of Minimum Cost-to-Time Ratio in Directed Graphs,"  We study the problem of finding the cycle of minimum cost-to-time ratio in a
+directed graph with $ n $ nodes and $ m $ edges. This problem has a long
+history in combinatorial optimization and has recently seen interesting
+applications in the context of quantitative verification. We focus on strongly
+polynomial algorithms to cover the use-case where the weights are relatively
+large compared to the size of the graph. Our main result is an algorithm with
+running time $ \tilde O (m^{3/4} n^{3/2}) $, which gives the first improvement
+over Megiddo's $ \tilde O (n^3) $ algorithm [JACM'83] for sparse graphs. We
+further demonstrate how to obtain both an algorithm with running time $ n^3 /
+2^{\Omega{(\sqrt{\log n})}} $ on general graphs and an algorithm with running
+time $ \tilde O (n) $ on constant treewidth graphs. To obtain our main result,
+we develop a parallel algorithm for negative cycle detection and single-source
+shortest paths that might be of independent interest.
+",1,0,0,0,0,0
+1114,"Interplay of dust alignment, grain growth and magnetic fields in polarization: lessons from the emission-to-extinction ratio","  Polarized extinction and emission from dust in the interstellar medium (ISM)
+are hard to interpret, as they have a complex dependence on dust optical
+properties, grain alignment and magnetic field orientation. This is
+particularly true in molecular clouds. The data available today are not yet
+used to their full potential.
+The combination of emission and extinction, in particular, provides
+information not available from either of them alone. We combine data from the
+scientific literature on polarized dust extinction with Planck data on
+polarized emission and we use them to constrain the possible variations in dust
+and environmental conditions inside molecular clouds, and especially
+translucent lines of sight, taking into account magnetic field orientation.
+We focus on the dependence between \lambda_max -- the wavelength of maximum
+polarization in extinction -- and other observables such as the extinction
+polarization, the emission polarization and the ratio of the two. We set out to
+reproduce these correlations using Monte-Carlo simulations where the relevant
+quantities in a dust model -- grain alignment, size distribution and magnetic
+field orientation -- vary to mimic the diverse conditions expected inside
+molecular clouds.
+None of the quantities chosen can explain the observational data on its own:
+the best results are obtained when all quantities vary significantly across and
+within clouds. However, some of the data -- most notably the stars with low
+emission-to-extinction polarization ratio -- are not reproduced by our
+simulation. Our results suggest not only that dust evolution is necessary to
+explain polarization in molecular clouds, but that a simple change in size
+distribution is not sufficient to explain the data, and point the way for
+future and more sophisticated models.
+",0,1,0,0,0,0
+1115,Asynchronous Distributed Variational Gaussian Processes for Regression,"  Gaussian processes (GPs) are powerful non-parametric function estimators.
+However, their applications are largely limited by the expensive computational
+cost of the inference procedures. Existing stochastic or distributed
+synchronous variational inferences, although have alleviated this issue by
+scaling up GPs to millions of samples, are still far from satisfactory for
+real-world large applications, where the data sizes are often orders of
+magnitudes larger, say, billions. To solve this problem, we propose ADVGP, the
+first Asynchronous Distributed Variational Gaussian Process inference for
+regression, on the recent large-scale machine learning platform,
+PARAMETERSERVER. ADVGP uses a novel, flexible variational framework based on a
+weight space augmentation, and implements the highly efficient, asynchronous
+proximal gradient optimization. While maintaining comparable or better
+predictive performance, ADVGP greatly improves upon the efficiency of the
+existing variational methods. With ADVGP, we effortlessly scale up GP
+regression to a real-world application with billions of samples and demonstrate
+an excellent, superior prediction accuracy to the popular linear models.
+",0,0,0,1,0,0
+1116,Bayesian Unification of Gradient and Bandit-based Learning for Accelerated Global Optimisation,"  Bandit based optimisation has a remarkable advantage over gradient based
+approaches due to their global perspective, which eliminates the danger of
+getting stuck at local optima. However, for continuous optimisation problems or
+problems with a large number of actions, bandit based approaches can be
+hindered by slow learning. Gradient based approaches, on the other hand,
+navigate quickly in high-dimensional continuous spaces through local
+optimisation, following the gradient in fine grained steps. Yet, apart from
+being susceptible to local optima, these schemes are less suited for online
+learning due to their reliance on extensive trial-and-error before the optimum
+can be identified. In this paper, we propose a Bayesian approach that unifies
+the above two paradigms in one single framework, with the aim of combining
+their advantages. At the heart of our approach we find a stochastic linear
+approximation of the function to be optimised, where both the gradient and
+values of the function are explicitly captured. This allows us to learn from
+both noisy function and gradient observations, and predict these properties
+across the action space to support optimisation. We further propose an
+accompanying bandit driven exploration scheme that uses Bayesian credible
+bounds to trade off exploration against exploitation. Our empirical results
+demonstrate that by unifying bandit and gradient based learning, one obtains
+consistently improved performance across a wide spectrum of problem
+environments. Furthermore, even when gradient feedback is unavailable, the
+flexibility of our model, including gradient prediction, still allows us
+outperform competing approaches, although with a smaller margin. Due to the
+pervasiveness of bandit based optimisation, our scheme opens up for improved
+performance both in meta-optimisation and in applications where gradient
+related information is readily available.
+",1,0,0,0,0,0
+1117,Stacco: Differentially Analyzing Side-Channel Traces for Detecting SSL/TLS Vulnerabilities in Secure Enclaves,"  Intel Software Guard Extension (SGX) offers software applications enclave to
+protect their confidentiality and integrity from malicious operating systems.
+The SSL/TLS protocol, which is the de facto standard for protecting
+transport-layer network communications, has been broadly deployed for a secure
+communication channel. However, in this paper, we show that the marriage
+between SGX and SSL may not be smooth sailing.
+Particularly, we consider a category of side-channel attacks against SSL/TLS
+implementations in secure enclaves, which we call the control-flow inference
+attacks. In these attacks, the malicious operating system kernel may perform a
+powerful man-in-the-kernel attack to collect execution traces of the enclave
+programs at page, cacheline, or branch level, while positioning itself in the
+middle of the two communicating parties. At the center of our work is a
+differential analysis framework, dubbed Stacco, to dynamically analyze the
+SSL/TLS implementations and detect vulnerabilities that can be exploited as
+decryption oracles. Surprisingly, we found exploitable vulnerabilities in the
+latest versions of all the SSL/TLS libraries we have examined.
+To validate the detected vulnerabilities, we developed a man-in-the-kernel
+adversary to demonstrate Bleichenbacher attacks against the latest OpenSSL
+library running in the SGX enclave (with the help of Graphene) and completely
+broke the PreMasterSecret encrypted by a 4096-bit RSA public key with only
+57286 queries. We also conducted CBC padding oracle attacks against the latest
+GnuTLS running in Graphene-SGX and an open-source SGX-implementation of mbedTLS
+(i.e., mbedTLS-SGX) that runs directly inside the enclave, and showed that it
+only needs 48388 and 25717 queries, respectively, to break one block of AES
+ciphertext. Empirical evaluation suggests these man-in-the-kernel attacks can
+be completed within 1 or 2 hours.
+",1,0,0,0,0,0
+1118,Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups,"  Sensor setups consisting of a combination of 3D range scanner lasers and
+stereo vision systems are becoming a popular choice for on-board perception
+systems in vehicles; however, the combined use of both sources of information
+implies a tedious calibration process. We present a method for extrinsic
+calibration of lidar-stereo camera pairs without user intervention. Our
+calibration approach is aimed to cope with the constraints commonly found in
+automotive setups, such as low-resolution and specific sensor poses. To
+demonstrate the performance of our method, we also introduce a novel approach
+for the quantitative assessment of the calibration results, based on a
+simulation environment. Tests using real devices have been conducted as well,
+proving the usability of the system and the improvement over the existing
+approaches. Code is available at this http URL
+",1,0,0,0,0,0
+1119,"Representations of Super $W(2,2)$ algebra $\mathfrak{L}$","  In paper, we study the representation theory of super $W(2,2)$ algebra
+${\mathfrak{L}}$. We prove that ${\mathfrak{L}}$ has no mixed irreducible
+modules and give the classification of irreducible modules of intermediate
+series. We determinate the conjugate-linear anti-involution of ${\mathfrak{L}}$
+and give the unitary modules of intermediate series.
+",0,0,1,0,0,0
+1120,Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics,"  Software developers frequently issue generic natural language queries for
+code search while using code search engines (e.g., GitHub native search,
+Krugle). Such queries often do not lead to any relevant results due to
+vocabulary mismatch problems. In this paper, we propose a novel technique that
+automatically identifies relevant and specific API classes from Stack Overflow
+Q & A site for a programming task written as a natural language query, and then
+reformulates the query for improved code search. We first collect candidate API
+classes from Stack Overflow using pseudo-relevance feedback and two term
+weighting algorithms, and then rank the candidates using Borda count and
+semantic proximity between query keywords and the API classes. The semantic
+proximity has been determined by an analysis of 1.3 million questions and
+answers of Stack Overflow. Experiments using 310 code search queries report
+that our technique suggests relevant API classes with 48% precision and 58%
+recall which are 32% and 48% higher respectively than those of the
+state-of-the-art. Comparisons with two state-of-the-art studies and three
+popular search engines (e.g., Google, Stack Overflow, and GitHub native search)
+report that our reformulated queries (1) outperform the queries of the
+state-of-the-art, and (2) significantly improve the code search results
+provided by these contemporary search engines.
+",1,0,0,0,0,0
+1121,Detecting Bot Activity in the Ethereum Blockchain Network,"  The Ethereum blockchain network is a decentralized platform enabling smart
+contract execution and transactions of Ether (ETH) [1], its designated
+cryptocurrency. Ethereum is the second most popular cryptocurrency with a
+market cap of more than 100 billion USD, with hundreds of thousands of
+transactions executed daily by hundreds of thousands of unique wallets. Tens of
+thousands of those wallets are newly generated each day. The Ethereum platform
+enables anyone to freely open multiple new wallets [2] free of charge
+(resulting in a large number of wallets that are controlled by the same
+entities). This attribute makes the Ethereum network a breeding space for
+activity by software robots (bots). The existence of bots is widespread in
+different digital technologies and there are various approaches to detect their
+activity such as rule-base, clustering, machine learning and more [3,4]. In
+this work we demonstrate how bot detection can be implemented using a network
+theory approach.
+",1,0,0,0,0,0
+1122,Near-infrared laser thermal conjunctivoplasty,"  Conjunctivochalasis is a common cause of tear dysfunction due to the
+conjunctiva becoming loose and wrinkly with age. The current solutions to this
+disease include either surgical excision in the operating room, or
+thermoreduction of the loose tissue with hot wire in the clinic. We developed a
+near-infrared (NIR) laser thermal conjunctivoplasty (LTC) system, which gently
+shrinks the redundant tissue. The NIR light is mainly absorbed by water, so the
+heating is even and there is no bleeding. The system utilizes a 1460-nm
+programmable laser diode system as a light source. A miniaturized handheld
+probe delivers the laser light and focuses the laser into a 10x1 mm2 line. A
+foot pedal is used to deliver a preset number of calibrated laser pulses. A
+fold of loose conjunctiva is grasped by a pair of forceps. The infrared laser
+light is delivered through an optical fiber and a laser line is focused exactly
+on the conjunctival fold by a cylindrical lens. Ex vivo experiments using
+porcine eye were performed with the optimal laser parameters. It was found that
+up to 50% of conjunctiva shrinkage could be achieved.
+",0,1,0,0,0,0
+1123,Superconductivity at 7.3 K in the 133-type Cr-based RbCr3As3 single crystals,"  Here we report the preparation and superconductivity of the 133-type Cr-based
+quasi-one-dimensional (Q1D) RbCr3As3 single crystals. The samples were prepared
+by the deintercalation of Rb+ ions from the 233-type Rb2Cr3As3 crystals which
+were grown from a high-temperature solution growth method. The RbCr3As3
+compound crystallizes in a centrosymmetric structure with the space group of
+P63/m (No. 176) different with its non-centrosymmetric Rb2Cr3As3
+superconducting precursor, and the refined lattice parameters are a = 9.373(3)
+{\AA} and c = 4.203(7) {\AA}. Electrical resistivity and magnetic
+susceptibility characterizations reveal the occurrence of superconductivity
+with an interestingly higher onset Tc of 7.3 K than other Cr-based
+superconductors, and a high upper critical field Hc2(0) near 70 T in this
+133-type RbCr3As3 crystals.
+",0,1,0,0,0,0
+1124,Solution of parabolic free boundary problems using transmuted heat polynomials,"  A numerical method for free boundary problems for the equation \[
+u_{xx}-q(x)u=u_t \] is proposed. The method is based on recent results from
+transmutation operators theory allowing one to construct efficiently a complete
+system of solutions for this equation generalizing the system of heat
+polynomials. The corresponding implementation algorithm is presented.
+",0,0,1,0,0,0
+1125,"Neural-Network Quantum States, String-Bond States, and Chiral Topological States","  Neural-Network Quantum States have been recently introduced as an Ansatz for
+describing the wave function of quantum many-body systems. We show that there
+are strong connections between Neural-Network Quantum States in the form of
+Restricted Boltzmann Machines and some classes of Tensor-Network states in
+arbitrary dimensions. In particular we demonstrate that short-range Restricted
+Boltzmann Machines are Entangled Plaquette States, while fully connected
+Restricted Boltzmann Machines are String-Bond States with a nonlocal geometry
+and low bond dimension. These results shed light on the underlying architecture
+of Restricted Boltzmann Machines and their efficiency at representing many-body
+quantum states. String-Bond States also provide a generic way of enhancing the
+power of Neural-Network Quantum States and a natural generalization to systems
+with larger local Hilbert space. We compare the advantages and drawbacks of
+these different classes of states and present a method to combine them
+together. This allows us to benefit from both the entanglement structure of
+Tensor Networks and the efficiency of Neural-Network Quantum States into a
+single Ansatz capable of targeting the wave function of strongly correlated
+systems. While it remains a challenge to describe states with chiral
+topological order using traditional Tensor Networks, we show that
+Neural-Network Quantum States and their String-Bond States extension can
+describe a lattice Fractional Quantum Hall state exactly. In addition, we
+provide numerical evidence that Neural-Network Quantum States can approximate a
+chiral spin liquid with better accuracy than Entangled Plaquette States and
+local String-Bond States. Our results demonstrate the efficiency of neural
+networks to describe complex quantum wave functions and pave the way towards
+the use of String-Bond States as a tool in more traditional machine-learning
+applications.
+",0,1,0,1,0,0
+1126,Nondestructive testing of grating imperfections using grating-based X-ray phase-contrast imaging,"  We reported the usage of grating-based X-ray phase-contrast imaging in
+nondestructive testing of grating imperfections. It was found that
+electroplating flaws could be easily detected by conventional absorption
+signal, and in particular, we observed that the grating defects resulting from
+uneven ultraviolet exposure could be clearly discriminated with phase-contrast
+signal. The experimental results demonstrate that grating-based X-ray
+phase-contrast imaging, with a conventional low-brilliance X-ray source, a
+large field of view and a reasonable compact setup, which simultaneously yields
+phase- and attenuation-contrast signal of the sample, can be ready-to-use in
+fast nondestructive testing of various imperfections in gratings and other
+similar photoetching products.
+",0,1,0,0,0,0
+1127,End-to-End Information Extraction without Token-Level Supervision,"  Most state-of-the-art information extraction approaches rely on token-level
+labels to find the areas of interest in text. Unfortunately, these labels are
+time-consuming and costly to create, and consequently, not available for many
+real-life IE tasks. To make matters worse, token-level labels are usually not
+the desired output, but just an intermediary step. End-to-end (E2E) models,
+which take raw text as input and produce the desired output directly, need not
+depend on token-level labels. We propose an E2E model based on pointer
+networks, which can be trained directly on pairs of raw input and output text.
+We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT
+movie corpus and compare to neural baselines that do use token-level labels. We
+achieve competitive results, within a few percentage points of the baselines,
+showing the feasibility of E2E information extraction without the need for
+token-level labels. This opens up new possibilities, as for many tasks
+currently addressed by human extractors, raw input and output data are
+available, but not token-level labels.
+",1,0,0,0,0,0
+1128,Lipschitz regularity of solutions to two-phase free boundary problems,"  We prove Lipschitz continuity of viscosity solutions to a class of two-phase
+free boundary problems governed by fully nonlinear operators.
+",0,0,1,0,0,0
+1129,VTA: An Open Hardware-Software Stack for Deep Learning,"  Hardware acceleration is an enabler for ubiquitous and efficient deep
+learning. With hardware accelerators being introduced in datacenter and edge
+devices, it is time to acknowledge that hardware specialization is central to
+the deep learning system stack.
+This technical report presents the Versatile Tensor Accelerator (VTA), an
+open, generic, and customizable deep learning accelerator design. VTA is a
+programmable accelerator that exposes a RISC-like programming abstraction to
+describe operations at the tensor level. We designed VTA to expose the most
+salient and common characteristics of mainstream deep learning accelerators,
+such as tensor operations, DMA load/stores, and explicit compute/memory
+arbitration.
+VTA is more than a standalone accelerator design: it's an end-to-end solution
+that includes drivers, a JIT runtime, and an optimizing compiler stack based on
+TVM. The current release of VTA includes a behavioral hardware simulator, as
+well as the infrastructure to deploy VTA on low-cost FPGA development boards
+for fast prototyping.
+By extending the TVM stack with a customizable, and open source deep learning
+hardware accelerator design, we are exposing a transparent end-to-end deep
+learning stack from the high-level deep learning framework, down to the actual
+hardware design and implementation. This forms a truly end-to-end, from
+software-to-hardware open source stack for deep learning systems.
+",0,0,0,1,0,0
+1130,Efficient Localized Inference for Large Graphical Models,"  We propose a new localized inference algorithm for answering marginalization
+queries in large graphical models with the correlation decay property. Given a
+query variable and a large graphical model, we define a much smaller model in a
+local region around the query variable in the target model so that the marginal
+distribution of the query variable can be accurately approximated. We introduce
+two approximation error bounds based on the Dobrushin's comparison theorem and
+apply our bounds to derive a greedy expansion algorithm that efficiently guides
+the selection of neighbor nodes for localized inference. We verify our
+theoretical bounds on various datasets and demonstrate that our localized
+inference algorithm can provide fast and accurate approximation for large
+graphical models.
+",1,0,0,1,0,0
+1131,Multi-kink collisions in the $ϕ^6$ model,"  We study simultaneous collisions of two, three, and four kinks and antikinks
+of the $\phi^6$ model at the same spatial point. Unlike the $\phi^4$ kinks, the
+$\phi^6$ kinks are asymmetric and this enriches the variety of the collision
+scenarios. In our numerical simulations we observe both reflection and bound
+state formation depending on the number of kinks and on their spatial ordering
+in the initial configuration. We also analyze the extreme values of the energy
+densities and the field gradient observed during the collisions. Our results
+suggest that very high energy densities can be produced in multi-kink
+collisions in a controllable manner. Appearance of high energy density spots in
+multi-kink collisions can be important in various physical applications of the
+Klein-Gordon model.
+",0,1,0,0,0,0
+1132,InverseFaceNet: Deep Monocular Inverse Face Rendering,"  We introduce InverseFaceNet, a deep convolutional inverse rendering framework
+for faces that jointly estimates facial pose, shape, expression, reflectance
+and illumination from a single input image. By estimating all parameters from
+just a single image, advanced editing possibilities on a single face image,
+such as appearance editing and relighting, become feasible in real time. Most
+previous learning-based face reconstruction approaches do not jointly recover
+all dimensions, or are severely limited in terms of visual quality. In
+contrast, we propose to recover high-quality facial pose, shape, expression,
+reflectance and illumination using a deep neural network that is trained using
+a large, synthetically created training corpus. Our approach builds on a novel
+loss function that measures model-space similarity directly in parameter space
+and significantly improves reconstruction accuracy. We further propose a
+self-supervised bootstrapping process in the network training loop, which
+iteratively updates the synthetic training corpus to better reflect the
+distribution of real-world imagery. We demonstrate that this strategy
+outperforms completely synthetically trained networks. Finally, we show
+high-quality reconstructions and compare our approach to several
+state-of-the-art approaches.
+",1,0,0,0,0,0
+1133,Exact partial information decompositions for Gaussian systems based on dependency constraints,"  The Partial Information Decomposition (PID) [arXiv:1004.2515] provides a
+theoretical framework to characterize and quantify the structure of
+multivariate information sharing. A new method (Idep) has recently been
+proposed for computing a two-predictor PID over discrete spaces.
+[arXiv:1709.06653] A lattice of maximum entropy probability models is
+constructed based on marginal dependency constraints, and the unique
+information that a particular predictor has about the target is defined as the
+minimum increase in joint predictor-target mutual information when that
+particular predictor-target marginal dependency is constrained. Here, we apply
+the Idep approach to Gaussian systems, for which the marginally constrained
+maximum entropy models are Gaussian graphical models. Closed form solutions for
+the Idep PID are derived for both univariate and multivariate Gaussian systems.
+Numerical and graphical illustrations are provided, together with practical and
+theoretical comparisons of the Idep PID with the minimum mutual information PID
+(Immi). [arXiv:1411.2832] In particular, it is proved that the Immi method
+generally produces larger estimates of redundancy and synergy than does the
+Idep method. In discussion of the practical examples, the PIDs are complemented
+by the use of deviance tests for the comparison of Gaussian graphical models.
+",0,0,0,1,1,0
+1134,Deep Multimodal Subspace Clustering Networks,"  We present convolutional neural network (CNN) based approaches for
+unsupervised multimodal subspace clustering. The proposed framework consists of
+three main stages - multimodal encoder, self-expressive layer, and multimodal
+decoder. The encoder takes multimodal data as input and fuses them to a latent
+space representation. The self-expressive layer is responsible for enforcing
+the self-expressiveness property and acquiring an affinity matrix corresponding
+to the data points. The decoder reconstructs the original input data. The
+network uses the distance between the decoder's reconstruction and the original
+input in its training. We investigate early, late and intermediate fusion
+techniques and propose three different encoders corresponding to them for
+spatial fusion. The self-expressive layers and multimodal decoders are
+essentially the same for different spatial fusion-based approaches. In addition
+to various spatial fusion-based methods, an affinity fusion-based network is
+also proposed in which the self-expressive layer corresponding to different
+modalities is enforced to be the same. Extensive experiments on three datasets
+show that the proposed methods significantly outperform the state-of-the-art
+multimodal subspace clustering methods.
+",0,0,0,1,0,0
+1135,Finite-time generalization of the thermodynamic uncertainty relation,"  For fluctuating currents in non-equilibrium steady states, the recently
+discovered thermodynamic uncertainty relation expresses a fundamental relation
+between their variance and the overall entropic cost associated with the
+driving. We show that this relation holds not only for the long-time limit of
+fluctuations, as described by large deviation theory, but also for fluctuations
+on arbitrary finite time scales. This generalization facilitates applying the
+thermodynamic uncertainty relation to single molecule experiments, for which
+infinite timescales are not accessible. Importantly, often this finite-time
+variant of the relation allows inferring a bound on the entropy production that
+is even stronger than the one obtained from the long-time limit. We illustrate
+the relation for the fluctuating work that is performed by a stochastically
+switching laser tweezer on a trapped colloidal particle.
+",0,1,0,0,0,0
+1136,Composition Properties of Inferential Privacy for Time-Series Data,"  With the proliferation of mobile devices and the internet of things,
+developing principled solutions for privacy in time series applications has
+become increasingly important. While differential privacy is the gold standard
+for database privacy, many time series applications require a different kind of
+guarantee, and a number of recent works have used some form of inferential
+privacy to address these situations.
+However, a major barrier to using inferential privacy in practice is its lack
+of graceful composition -- even if the same or related sensitive data is used
+in multiple releases that are safe individually, the combined release may have
+poor privacy properties. In this paper, we study composition properties of a
+form of inferential privacy called Pufferfish when applied to time-series data.
+We show that while general Pufferfish mechanisms may not compose gracefully, a
+specific Pufferfish mechanism, called the Markov Quilt Mechanism, which was
+recently introduced, has strong composition properties comparable to that of
+pure differential privacy when applied to time series data.
+",1,0,0,1,0,0
+1137,Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization,"  Understanding the origin, nature, and functional significance of complex
+patterns of neural activity, as recorded by diverse electrophysiological and
+neuroimaging techniques, is a central challenge in neuroscience. Such patterns
+include collective oscillations emerging out of neural synchronization as well
+as highly heterogeneous outbursts of activity interspersed by periods of
+quiescence, called ""neuronal avalanches."" Much debate has been generated about
+the possible scale invariance or criticality of such avalanches and its
+relevance for brain function. Aimed at shedding light onto this, here we
+analyze the large-scale collective properties of the cortex by using a
+mesoscopic approach following the principle of parsimony of Landau-Ginzburg.
+Our model is similar to that of Wilson-Cowan for neural dynamics but crucially,
+includes stochasticity and space; synaptic plasticity and inhibition are
+considered as possible regulatory mechanisms. Detailed analyses uncover a phase
+diagram including down-state, synchronous, asynchronous, and up-state phases
+and reveal that empirical findings for neuronal avalanches are consistently
+reproduced by tuning our model to the edge of synchronization. This reveals
+that the putative criticality of cortical dynamics does not correspond to a
+quiescent-to-active phase transition as usually assumed in theoretical
+approaches but to a synchronization phase transition, at which incipient
+oscillations and scale-free avalanches coexist. Furthermore, our model also
+accounts for up and down states as they occur (e.g., during deep sleep). This
+approach constitutes a framework to rationalize the possible collective phases
+and phase transitions of cortical networks in simple terms, thus helping to
+shed light on basic aspects of brain functioning from a very broad perspective.
+",0,0,0,0,1,0
+1138,Recurrent Autoregressive Networks for Online Multi-Object Tracking,"  The main challenge of online multi-object tracking is to reliably associate
+object trajectories with detections in each video frame based on their tracking
+history. In this work, we propose the Recurrent Autoregressive Network (RAN), a
+temporal generative modeling framework to characterize the appearance and
+motion dynamics of multiple objects over time. The RAN couples an external
+memory and an internal memory. The external memory explicitly stores previous
+inputs of each trajectory in a time window, while the internal memory learns to
+summarize long-term tracking history and associate detections by processing the
+external memory. We conduct experiments on the MOT 2015 and 2016 datasets to
+demonstrate the robustness of our tracking method in highly crowded and
+occluded scenes. Our method achieves top-ranked results on the two benchmarks.
+",1,0,0,0,0,0
+1139,The occurrence of transverse and longitudinal electric currents in the classical plasma under the action of N transverse electromagnetic waves,"  Classical plasma with arbitrary degree of degeneration of electronic gas is
+considered. In plasma N (N>2) collinear electromagnatic waves are propagated.
+It is required to find the response of plasma to these waves. Distribution
+function in square-law approximation on quantities of two small parameters from
+Vlasov equation is received. The formula for electric current calculation is
+deduced. It is demonstrated that the nonlinearity account leads to occurrence
+of the longitudinal electric current directed along a wave vector. This
+longitudinal current is orthogonal to the known transversal current received at
+the linear analysis. The case of small values of wave number is considered.
+",0,1,0,0,0,0
+1140,Well-posedness of a Model for the Growth of Tree Stems and Vines,"  The paper studies a PDE model for the growth of a tree stem or a vine, having
+the form of a differential inclusion with state constraints. The equations
+describe the elongation due to cell growth, and the response to gravity and to
+external obstacles.
+The main theorem shows that the evolution problem is well posed, until a
+specific ""breakdown configuration"" is reached. A formula is proved,
+characterizing the reaction produced by unilateral constraints. At a.e. time t,
+this is determined by the minimization of an elastic energy functional under
+suitable constraints.
+",0,0,1,0,0,0
+1141,Yonsei evolutionary population synthesis (YEPS). II. Spectro-photometric evolution of helium-enhanced stellar populations,"  The discovery of multiple stellar populations in Milky Way globular clusters
+(GCs) has stimulated various follow-up studies on helium-enhanced stellar
+populations. Here we present the evolutionary population synthesis models for
+the spectro-photometric evolution of simple stellar populations (SSPs) with
+varying initial helium abundance ($Y_{\rm ini}$). We show that $Y_{\rm ini}$
+brings about {dramatic} changes in spectro-photometric properties of SSPs. Like
+the normal-helium SSPs, the integrated spectro-photometric evolution of
+helium-enhanced SSPs is also dependent on metallicity and age for a given
+$Y_{\rm ini}$. {We discuss the implications and prospects for the
+helium-enhanced populations in relation to the second-generation populations
+found in the Milky Way GCs.} All of the models are available at
+\url{this http URL}.
+",0,1,0,0,0,0
+1142,Large deviation theorem for random covariance matrices,"  We establish a large deviation theorem for the empirical spectral
+distribution of random covariance matrices whose entries are independent random
+variables with mean 0, variance 1 and having controlled forth moments. Some new
+properties of Laguerre polynomials are also given.
+",0,0,1,0,0,0
+1143,Hindsight policy gradients,"  A reinforcement learning agent that needs to pursue different goals across
+episodes requires a goal-conditional policy. In addition to their potential to
+generalize desirable behavior to unseen goals, such policies may also enable
+higher-level planning based on subgoals. In sparse-reward environments, the
+capacity to exploit information about the degree to which an arbitrary goal has
+been achieved while another goal was intended appears crucial to enable sample
+efficient learning. However, reinforcement learning agents have only recently
+been endowed with such capacity for hindsight. In this paper, we demonstrate
+how hindsight can be introduced to policy gradient methods, generalizing this
+idea to a broad class of successful algorithms. Our experiments on a diverse
+selection of sparse-reward environments show that hindsight leads to a
+remarkable increase in sample efficiency.
+",1,0,0,0,0,0
+1144,Abundances in photoionized nebulae of the Local Group and nucleosynthesis of intermediate mass stars,"  Photoionized nebulae, comprising HII regions and planetary nebulae, are
+excellent laboratories to investigate the nucleosynthesis and chemical
+evolution of several elements in the Galaxy and other galaxies of the Local
+Group. Our purpose in this investigation is threefold: (i) compare the
+abundances of HII regions and planetary nebulae in each system in order to
+investigate the differences derived from the age and origin of these objects,
+(ii) compare the chemical evolution in different systems, such as the Milky
+Way, the Magellanic Clouds, and other galaxies of the Local Group, and (iii)
+investigate to what extent the nucleosynthesis contributions from the
+progenitor stars affect the observed abundances in planetary nebulae, which
+constrains the nucleosynthesis of intermediate mass stars. We show that all
+objects in the samples present similar trends concerning distance-independent
+correlations, and some constraints can be defined on the production of He and N
+by the PN progenitor stars.
+",0,1,0,0,0,0
+1145,Are Thousands of Samples Really Needed to Generate Robust Gene-List for Prediction of Cancer Outcome?,"  The prediction of cancer prognosis and metastatic potential immediately after
+the initial diagnoses is a major challenge in current clinical research. The
+relevance of such a signature is clear, as it will free many patients from the
+agony and toxic side-effects associated with the adjuvant chemotherapy
+automatically and sometimes carelessly subscribed to them. Motivated by this
+issue, Ein-Dor (2006) and Zuk (2007) presented a Bayesian model which leads to
+the following conclusion: Thousands of samples are needed to generate a robust
+gene list for predicting outcome. This conclusion is based on existence of some
+statistical assumptions. The current work raises doubts over this determination
+by showing that: (1) These assumptions are not consistent with additional
+assumptions such as sparsity and Gaussianity. (2) The empirical Bayes
+methodology which was suggested in order to test the relevant assumptions
+doesn't detect severe violations of the model assumptions and consequently an
+overestimation of the required sample size might be incurred.
+",0,0,0,1,0,0
+1146,Multi-scale analysis of lead-lag relationships in high-frequency financial markets,"  We propose a novel estimation procedure for scale-by-scale lead-lag
+relationships of financial assets observed at a high-frequency in a
+non-synchronous manner. The proposed estimation procedure does not require any
+interpolation processing of the original data and is applicable to quite fine
+resolution data. The validity of the proposed estimators is shown under the
+continuous-time framework developed in our previous work Hayashi and Koike
+(2016). An empirical application shows promising results of the proposed
+approach.
+",0,0,0,1,0,0
+1147,Approximations of the allelic frequency spectrum in general supercritical branching populations,"  We consider a general branching population where the lifetimes of individuals
+are i.i.d.\ with arbitrary distribution and where each individual gives birth
+to new individuals at Poisson times independently from each other. In addition,
+we suppose that individuals experience mutations at Poissonian rate $\theta$
+under the infinitely many alleles assumption assuming that types are
+transmitted from parents to offspring. This mechanism leads to a partition of
+the population by type, called the allelic partition. The main object of this
+work is the frequency spectrum $A(k,t)$ which counts the number of families of
+size $k$ in the population at time $t$. The process $(A(k,t),\
+t\in\mathbb{R}_+)$ is an example of non-Markovian branching process belonging
+to the class of general branching processes counted by random characteristics.
+In this work, we propose methods of approximation to replace the frequency
+spectrum by simpler quantities. Our main goal is study the asymptotic error
+made during these approximations through central limit theorems. In a last
+section, we perform several numerical analysis using this model, in particular
+to analyze the behavior of one of these approximations with respect to Sabeti's
+Extended Haplotype Homozygosity [18].
+",0,0,1,0,0,0
+1148,Anomaly Detection Using Optimally-Placed Micro-PMU Sensors in Distribution Grids,"  As the distribution grid moves toward a tightly-monitored network, it is
+important to automate the analysis of the enormous amount of data produced by
+the sensors to increase the operators situational awareness about the system.
+In this paper, focusing on Micro-Phasor Measurement Unit ($\mu$PMU) data, we
+propose a hierarchical architecture for monitoring the grid and establish a set
+of analytics and sensor fusion primitives for the detection of abnormal
+behavior in the control perimeter. Due to the key role of the $\mu$PMU devices
+in our architecture, a source-constrained optimal $\mu$PMU placement is also
+described that finds the best location of the devices with respect to our
+rules. The effectiveness of the proposed methods are tested through the
+synthetic and real $\mu$PMU data.
+",1,0,0,0,0,0
+1149,Electron-Phonon Interaction in Ternary Rare-Earth Copper Antimonides LaCuSb2 and La(Cu0.8Ag0.2)Sb2 probed by Yanson Point-Contact Spectroscopy,"  Investigation of the electron-phonon interaction (EPI) in LaCuSb2 and
+La(Cu0.8Ag0.2)Sb2 compounds by Yanson point-contact spectroscopy (PCS) has been
+carried out. Point-contact spectra display a pronounced broad maximum in the
+range of 10÷20 mV caused by EPI. Variation of the position of this maximum
+is likely connected with anisotropic phonon spectrum in these layered
+compounds. The absence of phonon features after the main maximum allows the
+assessment of the Debye energy of about 40 meV. The EPI constant for the
+LaCuSb2 compound was estimated to be {\lambda}=0.2+/-0.03. A zero-bias minimum
+in differential resistance for the latter compound is observed for some point
+contacts, which vanishes at about 6 K, pointing to the formation of
+superconducting phase under point contact, while superconducting critical
+temperature of the bulk sample is only 1K.
+",0,1,0,0,0,0
+1150,Ultrahigh Magnetic Field Phases in Frustrated Triangular-lattice Magnet CuCrO$_2$,"  The magnetic phases of a triangular-lattice antiferromagnet, CuCrO$_2$, were
+investigated in magnetic fields along to the $c$ axis, $H$ // [001], up to 120
+T. Faraday rotation and magneto-absorption spectroscopy were used to unveil the
+rich physics of magnetic phases. An up-up-down (UUD) magnetic structure phase
+was observed around 90--105 T at temperatures around 10 K. Additional distinct
+anomalies adjacent to the UUD phase were uncovered and the Y-shaped and the
+V-shaped phases are proposed to be viable candidates. These ordered phases are
+emerged as a result of the interplay of geometrical spin frustration, single
+ion anisotropy and thermal fluctuations in an environment of extremely high
+magnetic fields.
+",0,1,0,0,0,0
+1151,Preserving Differential Privacy in Convolutional Deep Belief Networks,"  The remarkable development of deep learning in medicine and healthcare domain
+presents obvious privacy issues, when deep neural networks are built on users'
+personal and highly sensitive data, e.g., clinical records, user profiles,
+biomedical images, etc. However, only a few scientific studies on preserving
+privacy in deep learning have been conducted. In this paper, we focus on
+developing a private convolutional deep belief network (pCDBN), which
+essentially is a convolutional deep belief network (CDBN) under differential
+privacy. Our main idea of enforcing epsilon-differential privacy is to leverage
+the functional mechanism to perturb the energy-based objective functions of
+traditional CDBNs, rather than their results. One key contribution of this work
+is that we propose the use of Chebyshev expansion to derive the approximate
+polynomial representation of objective functions. Our theoretical analysis
+shows that we can further derive the sensitivity and error bounds of the
+approximate polynomial representation. As a result, preserving differential
+privacy in CDBNs is feasible. We applied our model in a health social network,
+i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for
+human behavior prediction, human behavior classification, and handwriting digit
+recognition tasks. Theoretical analysis and rigorous experimental evaluations
+show that the pCDBN is highly effective. It significantly outperforms existing
+solutions.
+",1,0,0,1,0,0
+1152,Injectivity almost everywhere and mappings with finite distortion in nonlinear elasticity,"  We show that a sufficient condition for the weak limit of a sequence of
+$W^1_q$-homeomorphisms with finite distortion to be almost everywhere injective
+for $q \geq n-1$, can be stated by means of composition operators. Applying
+this result, we study nonlinear elasticity problems with respect to these new
+classes of mappings. Furthermore, we impose loose growth conditions on the
+stored-energy function for the class of $W^1_n$-homeomorphisms with finite
+distortion and integrable inner as well as outer distortion coefficients.
+",0,0,1,0,0,0
+1153,A probabilistic approach to the leader problem in random graphs,"  Consider the classical Erdos-Renyi random graph process wherein one starts
+with an empty graph on $n$ vertices at time $t=0$. At each stage, an edge is
+chosen uniformly at random and placed in the graph. After the original
+fundamental work in [19], Erdős suggested that one should view the original
+random graph process as a ""race of components"". This suggested understanding
+functionals such as the time for fixation of the identity of the maximal
+component, sometimes referred to as the ""leader problem"". Using refined
+combinatorial techniques, {\L}uczak [25] provided a complete analysis of this
+question including the close relationship to the critical scaling window of the
+Erdos-Renyi process. In this paper, we abstract this problem to the context of
+the multiplicative coalescent which by the work of Aldous in [3] describes the
+evolution of the Erdos-Renyi random graph in the critical regime. Further,
+different entrance boundaries of this process have arisen in the study of heavy
+tailed network models in the critical regime with degree exponent $\tau \in
+(3,4)$. The leader problem in the context of the Erdos-Renyi random graph also
+played an important role in the study of the scaling limit of the minimal
+spanning tree on the complete graph [2]. In this paper we provide a
+probabilistic analysis of the leader problem for the multiplicative coalescent
+in the context of entrance boundaries of relevance to critical random graphs.
+As a special case we recover {\L}uczak's result in [25] for the Erdos-Renyi
+random graph.
+",0,0,1,0,0,0
+1154,An improvement on LSB+ method,"  The Least Significant Bit (LSB) substitution is an old and simple data hiding
+method that could almost effortlessly be implemented in spatial or transform
+domain over any digital media. This method can be attacked by several
+steganalysis methods, because it detectably changes statistical and perceptual
+characteristics of the cover signal. A typical method for steganalysis of the
+LSB substitution is the histogram attack that attempts to diagnose anomalies in
+the cover image's histogram. A well-known method to stand the histogram attack
+is the LSB+ steganography that intentionally embeds some extra bits to make the
+histogram look natural. However, the LSB+ method still affects the perceptual
+and statistical characteristics of the cover signal. In this paper, we propose
+a new method for image steganography, called LSB++, which improves over the
+LSB+ image steganography by decreasing the amount of changes made to the
+perceptual and statistical attributes of the cover image. We identify some
+sensitive pixels affecting the signal characteristics, and then lock and keep
+them from the extra bit embedding process of the LSB+ method, by introducing a
+new embedding key. Evaluation results show that, without reducing the embedding
+capacity, our method can decrease potentially detectable changes caused by the
+embedding process.
+",1,0,0,0,0,0
+1155,Oxygen reduction mechanisms in nanostructured La0.8Sr0.2MnO3 cathodes for Solid Oxide Fuel Cells,"  In this work we outline the mechanisms contributing to the oxygen reduction
+reaction in nanostructured cathodes of La0.8Sr0.2MnO3 (LSM) for Solid Oxide
+Fuel Cells (SOFC). These cathodes, developed from LSM nanostructured tubes, can
+be used at lower temperatures compared to microstructured ones, and this is a
+crucial fact to avoid the degradation of the fuel cell components. This
+reduction of the operating temperatures stems mainly from two factors: i) the
+appearance of significant oxide ion diffusion through the cathode material in
+which the nanostructure plays a key role and ii) an optimized gas phase
+diffusion of oxygen through the porous structure of the cathode, which becomes
+negligible. A detailed analysis of our Electrochemical Impedance Spectroscopy
+supported by first principles calculations point towards an improved overall
+cathodic performance driven by a fast transport of oxide ions through the
+cathode surface.
+",0,1,0,0,0,0
+1156,Learning Interpretable Models with Causal Guarantees,"  Machine learning has shown much promise in helping improve the quality of
+medical, legal, and economic decision-making. In these applications, machine
+learning models must satisfy two important criteria: (i) they must be causal,
+since the goal is typically to predict individual treatment effects, and (ii)
+they must be interpretable, so that human decision makers can validate and
+trust the model predictions. There has recently been much progress along each
+direction independently, yet the state-of-the-art approaches are fundamentally
+incompatible. We propose a framework for learning causal interpretable
+models---from observational data---that can be used to predict individual
+treatment effects. Our framework can be used with any algorithm for learning
+interpretable models. Furthermore, we prove an error bound on the treatment
+effects predicted by our model. Finally, in an experiment on real-world data,
+we show that the models trained using our framework significantly outperform a
+number of baselines.
+",1,0,0,1,0,0
+1157,Achromatic super-oscillatory lenses with sub-wavelength focusing,"  Lenses are crucial to light-enabled technologies. Conventional lenses have
+been perfected to achieve near-diffraction-limited resolution and minimal
+chromatic aberrations. However, such lenses are bulky and cannot focus light
+into a hotspot smaller than half wavelength of light. Pupil filters, initially
+suggested by Toraldo di Francia, can overcome the resolution constraints of
+conventional lenses, but are not intrinsically chromatically corrected. Here we
+report single-element planar lenses that not only deliver sub-wavelength
+focusing (beating the diffraction limit of conventional refractive lenses) but
+also focus light of different colors into the same hotspot. Using the principle
+of super-oscillations we designed and fabricated a range of binary dielectric
+and metallic lenses for visible and infrared parts of the spectrum that are
+manufactured on silicon wafers, silica substrates and optical fiber tips. Such
+low cost, compact lenses could be useful in mobile devices, data storage,
+surveillance, robotics, space applications, imaging, manufacturing with light,
+and spatially resolved nonlinear microscopies.
+",0,1,0,0,0,0
+1158,Time-dependent linear-response variational Monte Carlo,"  We present the extension of variational Monte Carlo (VMC) to the calculation
+of electronic excitation energies and oscillator strengths using time-dependent
+linear-response theory. By exploiting the analogy existing between the linear
+method for wave-function optimisation and the generalised eigenvalue equation
+of linear-response theory, we formulate the equations of linear-response VMC
+(LR-VMC). This LR-VMC approach involves the first-and second-order derivatives
+of the wave function with respect to the parameters. We perform first tests of
+the LR-VMC method within the Tamm-Dancoff approximation using
+single-determinant Jastrow-Slater wave functions with different Slater basis
+sets on some singlet and triplet excitations of the beryllium atom. Comparison
+with reference experimental data and with configuration-interaction-singles
+(CIS) results shows that LR-VMC generally outperforms CIS for excitation
+energies and is thus a promising approach for calculating electronic
+excited-state properties of atoms and molecules.
+",0,1,0,0,0,0
+1159,Wireless Power Transfer for Distributed Estimation in Sensor Networks,"  This paper studies power allocation for distributed estimation of an unknown
+scalar random source in sensor networks with a multiple-antenna fusion center
+(FC), where wireless sensors are equipped with radio-frequency based energy
+harvesting technology. The sensors' observation is locally processed by using
+an uncoded amplify-and-forward scheme. The processed signals are then sent to
+the FC, and are coherently combined at the FC, at which the best linear
+unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve
+the following two power allocation problems: 1) minimizing distortion under
+various power constraints; and 2) minimizing total transmit power under
+distortion constraints, where the distortion is measured in terms of
+mean-squared error of the BLUE. Two iterative algorithms are developed to solve
+the non-convex problems, which converge at least to a local optimum. In
+particular, the above algorithms are designed to jointly optimize the
+amplification coefficients, energy beamforming, and receive filtering. For each
+problem, a suboptimal design, a single-antenna FC scenario, and a common
+harvester deployment for colocated sensors, are also studied. Using the
+powerful semidefinite relaxation framework, our result is shown to be valid for
+any number of sensors, each with different noise power, and for an arbitrarily
+number of antennas at the FC.
+",1,0,1,0,0,0
+1160,About a non-standard interpolation problem,"  Using algebraic methods, and motivated by the one variable case, we study a
+multipoint interpolation problem in the setting of several complex variables.
+The duality realized by the residue generator associated with an underlying
+Gorenstein algebra, using the Lagrange interpolation polynomial, plays a key
+role in the arguments.
+",0,0,1,0,0,0
+1161,Quantum spin liquid signatures in Kitaev-like frustrated magnets,"  Motivated by recent experiments on $\alpha$-RuCl$_3$, we investigate a
+possible quantum spin liquid ground state of the honeycomb-lattice spin model
+with bond-dependent interactions. We consider the $K-\Gamma$ model, where $K$
+and $\Gamma$ represent the Kitaev and symmetric-anisotropic interactions
+between spin-1/2 moments on the honeycomb lattice. Using the infinite density
+matrix renormalization group (iDMRG), we provide compelling evidence for the
+existence of quantum spin liquid phases in an extended region of the phase
+diagram. In particular, we use transfer matrix spectra to show the evolution of
+two-particle excitations with well-defined two-dimensional dispersion, which is
+a strong signature of quantum spin liquid. These results are compared with
+predictions from Majorana mean-field theory and used to infer the quasiparticle
+excitation spectra. Further, we compute the dynamical structure factor using
+finite size cluster computations and show that the results resemble the
+scattering continuum seen in neutron scattering experiments on
+$\alpha$-RuCl$_3$. We discuss these results in light of recent and future
+experiments.
+",0,1,0,0,0,0
+1162,Equivalent electric circuit of magnetosphere-ionosphere-atmosphere interaction,"  The aim of this study is to investigate the magnetospheric disturbances
+effects on complicated nonlinear system of atmospheric processes. During
+substorms and storms, the ionosphere was subjected to rather a significant
+Joule heating, and the power of precipitating energetic particles was also
+great. Nevertheless, there were no abnormal variations of meteoparameters in
+the lower atmosphere. If there is a mechanism for the powerful magnetospheric
+disturbance effect on meteorological processes in the atmosphere, it supposes a
+more complicated series of many intermediates, and is not associated directly
+with the energy that arrives into the ionosphere during storms. I discuss the
+problem of the effect of the solar wind electric field sharp increase via the
+global electric circuit during magnetospheric disturbances on the cloud layer
+formation.
+",0,1,0,0,0,0
+1163,Charge polarization effects on the optical response of blue-emitting superlattices,"  In the new approach to study the optical response of periodic structures,
+successfully applied to study the optical properties of blue-emitting InGaN/GaN
+superlattices, the spontaneous charge polarization was neglected. To search the
+effect of this quantum confined Stark phenomenon we study the optical response,
+assuming parabolic band edge modulations in the conduction and valence bands.
+We discuss the consequences on the eigenfunction symmetries and the ensuing
+optical transition selection rules. Using the new approach in the WKB
+approximation of the finite periodic systems theory, we determine the energy
+eigenvalues, their corresponding eigenfunctions and the subband structures in
+the conduction and valence bands. We calculate the photoluminescence as a
+function of the charge localization strength, and compare with the experimental
+result. We show that for subbands close to the barrier edge the optical
+response and the surface states are sensitive to charge polarization strength.
+",0,1,0,0,0,0
+1164,Replica analysis of overfitting in regression models for time-to-event data,"  Overfitting, which happens when the number of parameters in a model is too
+large compared to the number of data points available for determining these
+parameters, is a serious and growing problem in survival analysis. While modern
+medicine presents us with data of unprecedented dimensionality, these data
+cannot yet be used effectively for clinical outcome prediction. Standard error
+measures in maximum likelihood regression, such as p-values and z-scores, are
+blind to overfitting, and even for Cox's proportional hazards model (the main
+tool of medical statisticians), one finds in literature only rules of thumb on
+the number of samples required to avoid overfitting. In this paper we present a
+mathematical theory of overfitting in regression models for time-to-event data,
+which aims to increase our quantitative understanding of the problem and
+provide practical tools with which to correct regression outcomes for the
+impact of overfitting. It is based on the replica method, a statistical
+mechanical technique for the analysis of heterogeneous many-variable systems
+that has been used successfully for several decades in physics, biology, and
+computer science, but not yet in medical statistics. We develop the theory
+initially for arbitrary regression models for time-to-event data, and verify
+its predictions in detail for the popular Cox model.
+",0,1,0,1,0,0
+1165,System Description: Russell - A Logical Framework for Deductive Systems,"  Russell is a logical framework for the specification and implementation of
+deductive systems. It is a high-level language with respect to Metamath
+language, so inherently it uses a Metamath foundations, i.e. it doesn't rely on
+any particular formal calculus, but rather is a pure logical framework. The
+main difference with Metamath is in the proof language and approach to syntax:
+the proofs have a declarative form, i.e. consist of actual expressions, which
+are used in proofs, while syntactic grammar rules are separated from the
+meaningful rules of inference.
+Russell is implemented in c++14 and is distributed under GPL v3 license. The
+repository contains translators from Metamath to Russell and back. Original
+Metamath theorem base (almost 30 000 theorems) can be translated to Russell,
+verified, translated back to Metamath and verified with the original Metamath
+verifier. Russell can be downloaded from the repository
+this https URL
+",1,0,1,0,0,0
+1166,Spinor analysis,"  ""Let us call the novel quantities which, in addition to the vectors and
+tensors, have appeared in the quantum mechanics of the spinning electron, and
+which in the case of the Lorentz group are quite differently transformed from
+tensors, as spinors for short. Is there no spinor analysis that every physicist
+can learn, such as tensor analysis, and with the aid of which all the possible
+spinors can be formed, and secondly, all the invariant equations in which
+spinors occur?"" So Mr Ehrenfest asked me and the answer will be given below.
+",0,1,0,0,0,0
+1167,Identifiability of phylogenetic parameters from k-mer data under the coalescent,"  Distances between sequences based on their $k$-mer frequency counts can be
+used to reconstruct phylogenies without first computing a sequence alignment.
+Past work has shown that effective use of k-mer methods depends on 1)
+model-based corrections to distances based on $k$-mers and 2) breaking long
+sequences into blocks to obtain repeated trials from the sequence-generating
+process. Good performance of such methods is based on having many high-quality
+blocks with many homologous sites, which can be problematic to guarantee a
+priori.
+Nature provides natural blocks of sequences into homologous regions---namely,
+the genes. However, directly using past work in this setting is problematic
+because of possible discordance between different gene trees and the underlying
+species tree. Using the multispecies coalescent model as a basis, we derive
+model-based moment formulas that involve the divergence times and the
+coalescent parameters. From this setting, we prove identifiability results for
+the tree and branch length parameters under the Jukes-Cantor model of sequence
+mutations.
+",0,0,1,0,0,0
+1168,Short-Time Nonlinear Effects in the Exciton-Polariton System,"  In the exciton-polariton system, a linear dispersive photon field is coupled
+to a nonlinear exciton field. Short-time analysis of the lossless system shows
+that, when the photon field is excited, the time required for that field to
+exhibit nonlinear effects is longer than the time required for the nonlinear
+Schrödinger equation, in which the photon field itself is nonlinear. When the
+initial condition is scaled by $\epsilon^\alpha$, it is found that the relative
+error committed by omitting the nonlinear term in the exciton-polariton system
+remains within $\epsilon$ for all times up to $t=C\epsilon^\beta$, where
+$\beta=(1-\alpha(p-1))/(p+2)$. This is in contrast to $\beta=1-\alpha(p-1)$ for
+the nonlinear Schrödinger equation.
+",0,0,1,0,0,0
+1169,GTC Observations of an Overdense Region of LAEs at z=6.5,"  We present the results of our search for the faint galaxies near the end of
+the Reionisation Epoch. This has been done using very deep OSIRIS images
+obtained at the Gran Telescopio Canarias (GTC). Our observations focus around
+two close, massive Lyman Alpha Emitters (LAEs) at redshift 6.5, discovered in
+the SXDS field within a large-scale overdense region (Ouchi et al. 2010). The
+total GTC observing time in three medium band filters (F883w35, F913w25 and
+F941w33) is over 34 hours covering $7.0\times8.5$ arcmin$^2$ (or $\sim30,000$
+Mpc$^3$ at $z=6.5$). In addition to the two spectroscopically confirmed LAEs in
+the field, we have identified 45 other LAE candidates. The preliminary
+luminosity function derived from our observations, assuming a spectroscopic
+confirmation success rate of $\frac{2}{3}$ as in previous surveys, suggests
+this area is about 2 times denser than the general field galaxy population at
+$z=6.5$. If confirmed spectroscopically, our results will imply the discovery
+of one of the earliest protoclusters in the universe, which will evolve to
+resemble the most massive galaxy clusters today.
+",0,1,0,0,0,0
+1170,"Comment on Photothermal radiometry parametric identifiability theory for reliable and unique nondestructive coating thickness and thermophysical measurements, J. Appl. Phys. 121(9), 095101 (2017)","  A recent paper [X. Guo, A. Mandelis, J. Tolev and K. Tang, J. Appl. Phys.,
+121, 095101 (2017)] intends to demonstrate that from the photothermal
+radiometry signal obtained on a coated opaque sample in 1D transfer, one should
+be able to identify separately the following three parameters of the coating:
+thermal diffusivity, thermal conductivity and thickness. In this comment, it is
+shown that the three parameters are correlated in the considered experimental
+arrangement, the identifiability criterion is in error and the thickness
+inferred therefrom is not trustable.
+",0,1,0,0,0,0
+1171,Computing the projected reachable set of switched affine systems: an application to systems biology,"  A fundamental question in systems biology is what combinations of mean and
+variance of the species present in a stochastic biochemical reaction network
+are attainable by perturbing the system with an external signal. To address
+this question, we show that the moments evolution in any generic network can be
+either approximated or, under suitable assumptions, computed exactly as the
+solution of a switched affine system. Motivated by this application, we propose
+a new method to approximate the reachable set of switched affine systems. A
+remarkable feature of our approach is that it allows one to easily compute
+projections of the reachable set for pairs of moments of interest, without
+requiring the computation of the full reachable set, which can be prohibitive
+for large networks. As a second contribution, we also show how to select the
+external signal in order to maximize the probability of reaching a target set.
+To illustrate the method we study a renown model of controlled gene expression
+and we derive estimates of the reachable set, for the protein mean and
+variance, that are more accurate than those available in the literature and
+consistent with experimental data.
+",1,0,1,0,0,0
+1172,Temporal Action Localization by Structured Maximal Sums,"  We address the problem of temporal action localization in videos. We pose
+action localization as a structured prediction over arbitrary-length temporal
+windows, where each window is scored as the sum of frame-wise classification
+scores. Additionally, our model classifies the start, middle, and end of each
+action as separate components, allowing our system to explicitly model each
+action's temporal evolution and take advantage of informative temporal
+dependencies present in this structure. In this framework, we localize actions
+by searching for the structured maximal sum, a problem for which we develop a
+novel, provably-efficient algorithmic solution. The frame-wise classification
+scores are computed using features from a deep Convolutional Neural Network
+(CNN), which are trained end-to-end to directly optimize for a novel structured
+objective. We evaluate our system on the THUMOS 14 action detection benchmark
+and achieve competitive performance.
+",1,0,0,0,0,0
+1173,Using Transfer Learning for Image-Based Cassava Disease Detection,"  Cassava is the third largest source of carbohydrates for human food in the
+world but is vulnerable to virus diseases, which threaten to destabilize food
+security in sub-Saharan Africa. Novel methods of cassava disease detection are
+needed to support improved control which will prevent this crisis. Image
+recognition offers both a cost effective and scalable technology for disease
+detection. New transfer learning methods offer an avenue for this technology to
+be easily deployed on mobile devices. Using a dataset of cassava disease images
+taken in the field in Tanzania, we applied transfer learning to train a deep
+convolutional neural network to identify three diseases and two types of pest
+damage (or lack thereof). The best trained model accuracies were 98% for brown
+leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage
+(GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic
+disease (CMD). The best model achieved an overall accuracy of 93% for data not
+used in the training process. Our results show that the transfer learning
+approach for image recognition of field images offers a fast, affordable, and
+easily deployable strategy for digital plant disease detection.
+",1,0,0,0,0,0
+1174,Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments,"  Ability to continuously learn and adapt from limited experience in
+nonstationary environments is an important milestone on the path towards
+general intelligence. In this paper, we cast the problem of continuous
+adaptation into the learning-to-learn framework. We develop a simple
+gradient-based meta-learning algorithm suitable for adaptation in dynamically
+changing and adversarial scenarios. Additionally, we design a new multi-agent
+competitive environment, RoboSumo, and define iterated adaptation games for
+testing various aspects of continuous adaptation strategies. We demonstrate
+that meta-learning enables significantly more efficient adaptation than
+reactive baselines in the few-shot regime. Our experiments with a population of
+agents that learn and compete suggest that meta-learners are the fittest.
+",1,0,0,0,0,0
+1175,Alpha-Divergences in Variational Dropout,"  We investigate the use of alternative divergences to Kullback-Leibler (KL) in
+variational inference(VI), based on the Variational Dropout \cite{kingma2015}.
+Stochastic gradient variational Bayes (SGVB) \cite{aevb} is a general framework
+for estimating the evidence lower bound (ELBO) in Variational Bayes. In this
+work, we extend the SGVB estimator with using Alpha-Divergences, which are
+alternative to divergences to VI' KL objective. The Gaussian dropout can be
+seen as a local reparametrization trick of the SGVB objective. We extend the
+Variational Dropout to use alpha divergences for variational inference. Our
+results compare $\alpha$-divergence variational dropout with standard
+variational dropout with correlated and uncorrelated weight noise. We show that
+the $\alpha$-divergence with $\alpha \rightarrow 1$ (or KL divergence) is still
+a good measure for use in variational inference, in spite of the efficient use
+of Alpha-divergences for Dropout VI \cite{Li17}. $\alpha \rightarrow 1$ can
+yield the lowest training error, and optimizes a good lower bound for the
+evidence lower bound (ELBO) among all values of the parameter $\alpha \in
+[0,\infty)$.
+",1,0,0,1,0,0
+1176,Curvature properties of Robinson-Trautman metric,"  The curvature properties of Robinson-Trautman metric have been investigated.
+It is shown that Robinson-Trautman metric admits several kinds of
+pseudosymmetric type structures such as Weyl pseudosymmetric, Ricci
+pseudosymmetric, pseudosymmetric Weyl conformal curvature tensor etc. Also it
+is shown that the difference $R\cdot R - Q(S,R)$ is linearly dependent with
+$Q(g,C)$ but the metric is not Ricci generalized pseudosymmetric. Moreover, it
+is proved that this metric is Roter type, 2-quasi-Einstein, Ricci tensor is
+Riemann compatible and its Weyl conformal curvature 2-forms are recurrent. It
+is also shown that the energy momentum tensor of the metric is pseudosymmetric
+and the conditions under which such tensor is of Codazzi type and cyclic
+parallel have been investigated. Finally, we have made a comparison between the
+curvature properties of Robinson-Trautman metric and Som-Raychaudhuri metric.
+",0,0,1,0,0,0
+1177,Dehn invariant of flexible polyhedra,"  We prove that the Dehn invariant of any flexible polyhedron in Euclidean
+space of dimension greater than or equal to 3 is constant during the flexion.
+In dimensions 3 and 4 this implies that any flexible polyhedron remains
+scissors congruent to itself during the flexion. This proves the Strong Bellows
+Conjecture posed by Connelly in 1979. It was believed that this conjecture was
+disproved by Alexandrov and Connelly in 2009. However, we find an error in
+their counterexample. Further, we show that the Dehn invariant of a flexible
+polyhedron in either sphere or Lobachevsky space of dimension greater than or
+equal to 3 is constant during the flexion if and only if this polyhedron
+satisfies the usual Bellows Conjecture, i.e., its volume is constant during
+every flexion of it. Using previous results due to the first listed author, we
+deduce that the Dehn invariant is constant during the flexion for every bounded
+flexible polyhedron in odd-dimensional Lobachevsky space and for every flexible
+polyhedron with sufficiently small edge lengths in any space of constant
+curvature of dimension greater than or equal to 3.
+",0,0,1,0,0,0
+1178,On Evaluation of Embodied Navigation Agents,"  Skillful mobile operation in three-dimensional environments is a primary
+topic of study in Artificial Intelligence. The past two years have seen a surge
+of creative work on navigation. This creative output has produced a plethora of
+sometimes incompatible task definitions and evaluation protocols. To coordinate
+ongoing and future research in this area, we have convened a working group to
+study empirical methodology in navigation research. The present document
+summarizes the consensus recommendations of this working group. We discuss
+different problem statements and the role of generalization, present evaluation
+measures, and provide standard scenarios that can be used for benchmarking.
+",1,0,0,0,0,0
+1179,Single Magnetic Impurity in Tilted Dirac Surface States,"  We utilize variational method to investigate the Kondo screening of a
+spin-1/2 magnetic impurity in tilted Dirac surface states with the Dirac cone
+tilted along the $k_y$-axis. We mainly study about the effect of the tilting
+term on the binding energy and the spin-spin correlation between magnetic
+impurity and conduction electrons, and compare the results with the
+counterparts in a two dimensional helical metal. The binding energy has a
+critical value while the Dirac cone is slightly tilted. However, as the tilting
+term increases, the density of states around the Fermi surface becomes
+significant, such that the impurity and the host material always favor a bound
+state. The diagonal and the off-diagonal terms of the spin-spin correlation
+between the magnetic impurity and conduction electrons are also studied. Due to
+the spin-orbit coupling and the tilting of the spectra, various components of
+spin-spin correlation show very strong anisotropy in coordinate space, and are
+of power-law decay with respect to the spatial displacements.
+",0,1,0,0,0,0
+1180,Leveraging the Path Signature for Skeleton-based Human Action Recognition,"  Human action recognition in videos is one of the most challenging tasks in
+computer vision. One important issue is how to design discriminative features
+for representing spatial context and temporal dynamics. Here, we introduce a
+path signature feature to encode information from intra-frame and inter-frame
+contexts. A key step towards leveraging this feature is to construct the proper
+trajectories (paths) for the data steam. In each frame, the correlated
+constraints of human joints are treated as small paths, then the spatial path
+signature features are extracted from them. In video data, the evolution of
+these spatial features over time can also be regarded as paths from which the
+temporal path signature features are extracted. Eventually, all these features
+are concatenated to constitute the input vector of a fully connected neural
+network for action classification. Experimental results on four standard
+benchmark action datasets, J-HMDB, SBU Dataset, Berkeley MHAD, and NTURGB+D
+demonstrate that the proposed approach achieves state-of-the-art accuracy even
+in comparison with recent deep learning based models.
+",1,0,0,0,0,0
+1181,How Many Subpopulations is Too Many? Exponential Lower Bounds for Inferring Population Histories,"  Reconstruction of population histories is a central problem in population
+genetics. Existing coalescent-based methods, like the seminal work of Li and
+Durbin (Nature, 2011), attempt to solve this problem using sequence data but
+have no rigorous guarantees. Determining the amount of data needed to correctly
+reconstruct population histories is a major challenge. Using a variety of tools
+from information theory, the theory of extremal polynomials, and approximation
+theory, we prove new sharp information-theoretic lower bounds on the problem of
+reconstructing population structure -- the history of multiple subpopulations
+that merge, split and change sizes over time. Our lower bounds are exponential
+in the number of subpopulations, even when reconstructing recent histories. We
+demonstrate the sharpness of our lower bounds by providing algorithms for
+distinguishing and learning population histories with matching dependence on
+the number of subpopulations.
+",0,0,0,0,1,0
+1182,Source localization in an ocean waveguide using supervised machine learning,"  Source localization in ocean acoustics is posed as a machine learning problem
+in which data-driven methods learn source ranges directly from observed
+acoustic data. The pressure received by a vertical linear array is preprocessed
+by constructing a normalized sample covariance matrix (SCM) and used as the
+input. Three machine learning methods (feed-forward neural networks (FNN),
+support vector machines (SVM) and random forests (RF)) are investigated in this
+paper, with focus on the FNN. The range estimation problem is solved both as a
+classification problem and as a regression problem by these three machine
+learning algorithms. The results of range estimation for the Noise09 experiment
+are compared for FNN, SVM, RF and conventional matched-field processing and
+demonstrate the potential of machine learning for underwater source
+localization..
+",1,1,0,0,0,0
+1183,Mining Illegal Insider Trading of Stocks: A Proactive Approach,"  Illegal insider trading of stocks is based on releasing non-public
+information (e.g., new product launch, quarterly financial report, acquisition
+or merger plan) before the information is made public. Detecting illegal
+insider trading is difficult due to the complex, nonlinear, and non-stationary
+nature of the stock market. In this work, we present an approach that detects
+and predicts illegal insider trading proactively from large heterogeneous
+sources of structured and unstructured data using a deep-learning based
+approach combined with discrete signal processing on the time series data. In
+addition, we use a tree-based approach that visualizes events and actions to
+aid analysts in their understanding of large amounts of unstructured data.
+Using existing data, we have discovered that our approach has a good success
+rate in detecting illegal insider trading patterns.
+",0,0,0,1,0,1
+1184,Discovery of Extreme [OIII]+H$β$ Emitting Galaxies Tracing an Overdensity at z~3.5 in CDF-South,"  Using deep multi-wavelength photometry of galaxies from ZFOURGE, we group
+galaxies at $2.5<z<4.0$ by the shape of their spectral energy distributions
+(SEDs). We identify a population of galaxies with excess emission in the
+$K_s$-band, which corresponds to [OIII]+H$\beta$ emission at $2.95<z<3.65$.
+This population includes 78% of the bluest galaxies with UV slopes steeper than
+$\beta = -2$. We de-redshift and scale this photometry to build two composite
+SEDs, enabling us to measure equivalent widths of these Extreme [OIII]+H$\beta$
+Emission Line Galaxies (EELGs) at $z\sim3.5$. We identify 60 galaxies that
+comprise a composite SED with [OIII]+H$\beta$ rest-frame equivalent width of
+$803\pm228$\AA\ and another 218 galaxies in a composite SED with equivalent
+width of $230\pm90$\AA. These EELGs are analogous to the `green peas' found in
+the SDSS, and are thought to be undergoing their first burst of star formation
+due to their blue colors ($\beta < -1.6$), young ages
+($\log(\rm{age}/yr)\sim7.2$), and low dust attenuation values. Their strong
+nebular emission lines and compact sizes (typically $\sim1.4$ kpc) are
+consistent with the properties of the star-forming galaxies possibly
+responsible for reionizing the universe at $z>6$. Many of the EELGs also
+exhibit Lyman-$\alpha$ emission. Additionally, we find that many of these
+sources are clustered in an overdensity in the Chandra Deep Field South, with
+five spectroscopically confirmed members at $z=3.474 \pm 0.004$. The spatial
+distribution and photometric redshifts of the ZFOURGE population further
+confirm the overdensity highlighted by the EELGs.
+",0,1,0,0,0,0
+1185,Predictive Simulations for Tuning Electronic and Optical Properties of SubPc Derivatives,"  Boron subphthalocyanine chloride is an electron donor material used in small
+molecule organic photovoltaics with an unusually large molecular dipole moment.
+Using first-principles calculations, we investigate enhancing the electronic
+and optical properties of boron subphthalocyanine chloride, by substituting the
+boron and chlorine atoms with other trivalent and halogen atoms in order to
+modify the molecular dipole moment. Gas phase molecular structures and
+properties are predicted with hybrid functionals. Using positions and
+orientations of the known compounds as the starting coordinates for these
+molecules, stable crystalline structures are derived following a procedure that
+involves perturbation and accurate total energy minimization. Electronic
+structure and photonic properties of the predicted crystals are computed using
+the GW method and the Bethe-Salpeter equation, respectively. Finally, a simple
+transport model is use to demonstrate the importance of molecular dipole
+moments on device performance.
+",0,1,0,0,0,0
+1186,Learning to attend in a brain-inspired deep neural network,"  Recent machine learning models have shown that including attention as a
+component results in improved model accuracy and interpretability, despite the
+concept of attention in these approaches only loosely approximating the brain's
+attention mechanism. Here we extend this work by building a more brain-inspired
+deep network model of the primate ATTention Network (ATTNet) that learns to
+shift its attention so as to maximize the reward. Using deep reinforcement
+learning, ATTNet learned to shift its attention to the visual features of a
+target category in the context of a search task. ATTNet's dorsal layers also
+learned to prioritize these shifts of attention so as to maximize success of
+the ventral pathway classification and receive greater reward. Model behavior
+was tested against the fixations made by subjects searching images for the same
+cued category. Both subjects and ATTNet showed evidence for attention being
+preferentially directed to target goals, behaviorally measured as oculomotor
+guidance to targets. More fundamentally, ATTNet learned to shift its attention
+to target like objects and spatially route its visual inputs to accomplish the
+task. This work makes a step toward a better understanding of the role of
+attention in the brain and other computational systems.
+",0,0,0,0,1,0
+1187,Anisotropic functional Laplace deconvolution,"  In the present paper we consider the problem of estimating a
+three-dimensional function $f$ based on observations from its noisy Laplace
+convolution. Our study is motivated by the analysis of Dynamic Contrast
+Enhanced (DCE) imaging data. We construct an adaptive wavelet-Laguerre
+estimator of $f$, derive minimax lower bounds for the $L^2$-risk when $f$
+belongs to a three-dimensional Laguerre-Sobolev ball and demonstrate that the
+wavelet-Laguerre estimator is adaptive and asymptotically near-optimal in a
+wide range of Laguerre-Sobolev spaces. We carry out a limited simulations study
+and show that the estimator performs well in a finite sample setting. Finally,
+we use the technique for the solution of the Laplace deconvolution problem on
+the basis of DCE Computerized Tomography data.
+",0,0,0,1,0,0
+1188,Prediction of many-electron wavefunctions using atomic potentials,"  For a given many-electron molecule, it is possible to define a corresponding
+one-electron Schrödinger equation, using potentials derived from simple
+atomic densities, whose solution predicts fairly accurate molecular orbitals
+for single- and multi-determinant wavefunctions for the molecule. The energy is
+not predicted and must be evaluated by calculating Coulomb and exchange
+interactions over the predicted orbitals. Potentials are found by minimizing
+the energy of predicted wavefunctions. There exist slightly less accurate
+average potentials for first-row atoms that can be used without modification in
+different molecules. For a test set of molecules representing different bonding
+environments, these average potentials give wavefunctions with energies that
+deviate from exact self-consistent field or configuration interaction energies
+by less than 0.08 eV and 0.03 eV per bond or valence electron pair,
+respectively.
+",0,1,0,0,0,0
+1189,Free energy of formation of a crystal nucleus in incongruent solidification: Implication for modeling the crystallization of aqueous nitric acid droplets in type 1 polar stratospheric clouds,"  Using the formalism of the classical nucleation theory, we derive an
+expression for the reversible work $W_*$ of formation of a binary crystal
+nucleus in a liquid binary solution of non-stoichiometric composition
+(incongruent crystallization). Applied to the crystallization of aqueous nitric
+acid (NA) droplets, the new expression more adequately takes account of the
+effect of nitric acid vapor compared to the conventional expression of
+MacKenzie, Kulmala, Laaksonen, and Vesala (MKLV) [J.Geophys.Res. 102, 19729
+(1997)]. The predictions of both MKLV and modified expressions for the average
+liquid-solid interfacial tension $\sigma^{ls}$ of nitric acid dihydrate (NAD)
+crystals are compared by using existing experimental data on the incongruent
+crystallization of aqueous NA droplets of composition relevant to polar
+stratospheric clouds (PSCs). The predictions based on the MKLV expression are
+higher by about 5% compared to predictions based on our modified expression.
+This results in similar differences between the predictions of both expressions
+for the solid-vapor interfacial tension $\sigma^{sv}$ of NAD crystal nuclei.
+The latter can be obtained by analyzing of experimental data on crystal
+nucleation rates in aqueous NA droplets and exploiting the dominance of the
+surface-stimulated mode of crystal nucleation in small droplets and its
+negligibility in large ones. Applying that method, our expression for $W_*$
+provides an estimate for $\sigma^{sv}$ of NAD in the range from 92 dyn/cm to
+100 dyn/cm, while the MKLV expression predicts it in the range from 95 dyn/cm
+to 105 dyn/cm. The predictions of both expressions for $W_*$ become identical
+in the case of congruent crystallization; this was also demonstrated by
+applying our method to the nucleation of nitric acid trihydrate (NAT) crystals
+in PSC droplets of stoichiometric composition.
+",0,1,0,0,0,0
+1190,Ensemble learning with Conformal Predictors: Targeting credible predictions of conversion from Mild Cognitive Impairment to Alzheimer's Disease,"  Most machine learning classifiers give predictions for new examples
+accurately, yet without indicating how trustworthy predictions are. In the
+medical domain, this hampers their integration in decision support systems,
+which could be useful in the clinical practice. We use a supervised learning
+approach that combines Ensemble learning with Conformal Predictors to predict
+conversion from Mild Cognitive Impairment to Alzheimer's Disease. Our goal is
+to enhance the classification performance (Ensemble learning) and complement
+each prediction with a measure of credibility (Conformal Predictors). Our
+results showed the superiority of the proposed approach over a similar ensemble
+framework with standard classifiers.
+",0,0,0,1,0,0
+1191,Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning,"  Deep reinforcement learning for multi-agent cooperation and competition has
+been a hot topic recently. This paper focuses on cooperative multi-agent
+problem based on actor-critic methods under local observations settings. Multi
+agent deep deterministic policy gradient obtained state of art results for some
+multi-agent games, whereas, it cannot scale well with growing amount of agents.
+In order to boost scalability, we propose a parameter sharing deterministic
+policy gradient method with three variants based on neural networks, including
+actor-critic sharing, actor sharing and actor sharing with partially shared
+critic. Benchmarks from rllab show that the proposed method has advantages in
+learning speed and memory efficiency, well scales with growing amount of
+agents, and moreover, it can make full use of reward sharing and
+exchangeability if possible.
+",1,0,0,0,0,0
+1192,Repair Strategies for Storage on Mobile Clouds,"  We study the data reliability problem for a community of devices forming a
+mobile cloud storage system. We consider the application of regenerating codes
+for file maintenance within a geographically-limited area. Such codes require
+lower bandwidth to regenerate lost data fragments compared to file replication
+or reconstruction. We investigate threshold-based repair strategies where data
+repair is initiated after a threshold number of data fragments have been lost
+due to node mobility. We show that at a low departure-to-repair rate regime, a
+lazy repair strategy in which repairs are initiated after several nodes have
+left the system outperforms eager repair in which repairs are initiated after a
+single departure. This optimality is reversed when nodes are highly mobile. We
+further compare distributed and centralized repair strategies and derive the
+optimal repair threshold for minimizing the average repair cost per unit of
+time, as a function of underlying code parameters. In addition, we examine
+cooperative repair strategies and show performance improvements compared to
+non-cooperative codes. We investigate several models for the time needed for
+node repair including a simple fixed time model that allows for the computation
+of closed-form expressions and a more realistic model that takes into account
+the number of repaired nodes. We derive the conditions under which the former
+model approximates the latter. Finally, an extended model where additional
+failures are allowed during the repair process is investigated. Overall, our
+results establish the joint effect of code design and repair algorithms on the
+maintenance cost of distributed storage systems.
+",1,0,0,0,0,0
+1193,Mean-variance portfolio selection under partial information with drift uncertainty,"  This paper studies a mean-variance portfolio selection problem under partial
+information with drift uncertainty. It is proved that all the contingent claims
+in this model are attainable in the sense of Xiong and Zhou. Further, we
+propose a numerical scheme to approximate the optimal portfolio. Malliavin
+calculus and the strong law of large numbers play important roles in this
+scheme.
+",0,0,0,0,0,1
+1194,Learning from MOM's principles: Le Cam's approach,"  We obtain estimation error rates for estimators obtained by aggregation of
+regularized median-of-means tests, following a construction of Le Cam. The
+results hold with exponentially large probability -- as in the gaussian
+framework with independent noise- under only weak moments assumptions on data
+and without assuming independence between noise and design. Any norm may be
+used for regularization. When it has some sparsity inducing power we recover
+sparse rates of convergence.
+The procedure is robust since a large part of data may be corrupted, these
+outliers have nothing to do with the oracle we want to reconstruct. Our general
+risk bound is of order \begin{equation*} \max\left(\mbox{minimax rate in the
+i.i.d. setup}, \frac{\text{number of outliers}}{\text{number of
+observations}}\right) \enspace. \end{equation*}In particular, the number of
+outliers may be as large as (number of data) $\times$(minimax rate) without
+affecting this rate. The other data do not have to be identically distributed
+but should only have equivalent $L^1$ and $L^2$ moments.
+For example, the minimax rate $s \log(ed/s)/N$ of recovery of a $s$-sparse
+vector in $\mathbb{R}^d$ is achieved with exponentially large probability by a
+median-of-means version of the LASSO when the noise has $q_0$ moments for some
+$q_0>2$, the entries of the design matrix should have $C_0\log(ed)$ moments and
+the dataset can be corrupted up to $C_1 s \log(ed/s)$ outliers.
+",0,0,1,1,0,0
+1195,On a Neumann-type series for modified Bessel functions of the first kind,"  In this paper, we are interested in a Neumann-type series for modified Bessel
+functions of the first kind which arises in the study of Dunkl operators
+associated with dihedral groups and as an instance of the Laguerre semigroup
+constructed by Ben Said-Kobayashi-Orsted. We first revisit the particular case
+corresponding to the group of square-preserving symmetries for which we give
+two new and different proofs other than the existing ones. The first proof uses
+the expansion of powers in a Neumann series of Bessel functions while the
+second one is based on a quadratic transformation for the Gauss hypergeometric
+function and opens the way to derive further expressions when the orders of the
+underlying dihedral groups are powers of two. More generally, we give another
+proof of De Bie \& al formula expressing this series as a $\Phi_2$-Horn
+confluent hypergeometric function. In the course of proving, we shed the light
+on the occurrence of multiple angles in their formula through elementary
+symmetric functions, and get a new representation of Gegenbauer polynomials.
+",0,0,1,0,0,0
+1196,Generalized Log-sine integrals and Bell polynomials,"  In this paper, we investigate the integral of $x^n\log^m(\sin(x))$ for
+natural numbers $m$ and $n$. In doing so, we recover some well-known results
+and remark on some relations to the log-sine integral
+$\operatorname{Ls}_{n+m+1}^{(n)}(\theta)$. Later, we use properties of Bell
+polynomials to find a closed expression for the derivative of the central
+binomial and shifted central binomial coefficients in terms of polygamma
+functions and harmonic numbers.
+",0,0,1,0,0,0
+1197,A Modern Search for Wolf-Rayet Stars in the Magellanic Clouds. III. A Third Year of Discoveries,"  For the past three years we have been conducting a survey for WR stars in the
+Large and Small Magellanic Clouds (LMC, SMC). Our previous work has resulted in
+the discovery of a new type of WR star in the LMC, which we are calling WN3/O3.
+These stars have the emission-line properties of a WN3 star (strong N V but no
+N IV), plus the absorption-line properties of an O3 star (Balmer hydrogen plus
+Pickering He II but no He I). Yet these stars are 15x fainter than an O3 V star
+would be by itself, ruling out these being WN3+O3 binaries. Here we report the
+discovery of two more members of this class, bringing the total number of these
+objects to 10, 6.5% of the LMC's total WR population. The optical spectra of
+nine of these WN3/O3s are virtually indistinguishable from each other, but one
+of the newly found stars is significantly different, showing a lower excitation
+emission and absorption spectrum (WN4/O4-ish). In addition, we have newly
+classified three unusual Of-type stars, including one with a strong C III 4650
+line, and two rapidly rotating ""Oef"" stars. We also ""rediscovered"" a low mass
+x-ray binary, RX J0513.9-6951, and demonstrate its spectral variability.
+Finally, we discuss the spectra of ten low priority WR candidates that turned
+out not to have He II emission. These include both a Be star and a B[e] star.
+",0,1,0,0,0,0
+1198,Mathematical modeling of Zika disease in pregnant women and newborns with microcephaly in Brazil,"  We propose a new mathematical model for the spread of Zika virus. Special
+attention is paid to the transmission of microcephaly. Numerical simulations
+show the accuracy of the model with respect to the Zika outbreak occurred in
+Brazil.
+",0,0,1,0,0,0
+1199,A Noninformative Prior on a Space of Distribution Functions,"  In a given problem, the Bayesian statistical paradigm requires the
+specification of a prior distribution that quantifies relevant information
+about the unknowns of main interest external to the data. In cases where little
+such information is available, the problem under study may possess an
+invariance under a transformation group that encodes a lack of information,
+leading to a unique prior---this idea was explored at length by E.T. Jaynes.
+Previous successful examples have included location-scale invariance under
+linear transformation, multiplicative invariance of the rate at which events in
+a counting process are observed, and the derivation of the Haldane prior for a
+Bernoulli success probability. In this paper we show that this method can be
+extended, by generalizing Jaynes, in two ways: (1) to yield families of
+approximately invariant priors, and (2) to the infinite-dimensional setting,
+yielding families of priors on spaces of distribution functions. Our results
+can be used to describe conditions under which a particular Dirichlet Process
+posterior arises from an optimal Bayesian analysis, in the sense that
+invariances in the prior and likelihood lead to one and only one posterior
+distribution.
+",0,0,1,1,0,0
+1200,Towards a realistic NNLIF model: Analysis and numerical solver for excitatory-inhibitory networks with delay and refractory periods,"  The Network of Noisy Leaky Integrate and Fire (NNLIF) model describes the
+behavior of a neural network at mesoscopic level. It is one of the simplest
+self-contained mean-field models considered for that purpose. Even so, to study
+the mathematical properties of the model some simplifications were necessary
+Cáceres-Carrillo-Perthame(2011), Cáceres-Perthame(2014),
+Cáceres-Schneider(2017), which disregard crucial phenomena. In this work we
+deal with the general NNLIF model without simplifications. It involves a
+network with two populations (excitatory and inhibitory), with transmission
+delays between the neurons and where the neurons remain in a refractory state
+for a certain time. We have studied the number of steady states in terms of the
+model parameters, the long time behaviour via the entropy method and
+Poincaré's inequality, blow-up phenomena, and the importance of transmission
+delays between excitatory neurons to prevent blow-up and to give rise to
+synchronous solutions. Besides analytical results, we have presented a
+numerical resolutor for this model, based on high order flux-splitting WENO
+schemes and an explicit third order TVD Runge-Kutta method, in order to
+describe the wide range of phenomena exhibited by the network: blow-up,
+asynchronous/synchronous solutions and instability/stability of the steady
+states; the solver also allows us to observe the time evolution of the firing
+rates, refractory states and the probability distributions of the excitatory
+and inhibitory populations.
+",0,0,1,0,0,0
+1201,An upper bound on the distinguishing index of graphs with minimum degree at least two,"  The distinguishing index of a simple graph $G$, denoted by $D'(G)$, is the
+least number of labels in an edge labeling of $G$ not preserved by any
+non-trivial automorphism. It was conjectured by Pilśniak (2015) that for any
+2-connected graph $D'(G) \leq \lceil \sqrt{\Delta (G)}\rceil +1$. We prove a
+more general result for the distinguishing index of graphs with minimum degree
+at least two from which the conjecture follows. Also we present graphs $G$ for
+which $D'(G)\leq \lceil \sqrt{\Delta }\rceil$.
+",0,0,1,0,0,0
+1202,Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization,"  Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy'
+image from a pathological one, could be helpful in tasks such as anomaly
+detection, understanding changes induced by pathology and disease or even as
+data augmentation. We treat this task as a factor decomposition problem: we aim
+to separate what appears to be healthy and where disease is (as a map). The two
+factors are then recombined (by a network) to reconstruct the input disease
+image. We train our models in an adversarial way using either paired or
+unpaired settings, where we pair disease images and maps (as segmentation
+masks) when available. We quantitatively evaluate the quality of pseudo healthy
+images. We show in a series of experiments, performed in ISLES and BraTS
+datasets, that our method is better than conditional GAN and CycleGAN,
+highlighting challenges in using adversarial methods in the image translation
+task of pseudo healthy image generation.
+",1,0,0,1,0,0
+1203,"Closure operators, frames, and neatest representations","  Given a poset $P$ and a standard closure operator $\Gamma:\wp(P)\to\wp(P)$ we
+give a necessary and sufficient condition for the lattice of $\Gamma$-closed
+sets of $\wp(P)$ to be a frame in terms of the recursive construction of the
+$\Gamma$-closure of sets. We use this condition to show that given a set
+$\mathcal{U}$ of distinguished joins from $P$, the lattice of
+$\mathcal{U}$-ideals of $P$ fails to be a frame if and only if it fails to be
+$\sigma$-distributive, with $\sigma$ depending on the cardinalities of sets in
+$\mathcal{U}$. From this we deduce that if a poset has the property that
+whenever $a\wedge(b\vee c)$ is defined for $a,b,c\in P$ it is necessarily equal
+to $(a\wedge b)\vee (a\wedge c)$, then it has an $(\omega,3)$-representation.
+This answers a question from the literature.
+",0,0,1,0,0,0
+1204,The structure of rationally factorized Lax type flows and their analytical integrability,"  The work is devoted to constructing a wide class of differential-functional
+dynamical systems, whose rich algebraic structure makes their integrability
+analytically effective. In particular, there is analyzed in detail the operator
+Lax type equations for factorized seed elements, there is proved an important
+theorem about their operator factorization and the related analytical solution
+scheme to the corresponding nonlinear differential-functional dynamical
+systems.
+",0,1,0,0,0,0
+1205,Learning Policy Representations in Multiagent Systems,"  Modeling agent behavior is central to understanding the emergence of complex
+phenomena in multiagent systems. Prior work in agent modeling has largely been
+task-specific and driven by hand-engineering domain-specific prior knowledge.
+We propose a general learning framework for modeling agent behavior in any
+multiagent system using only a handful of interaction data. Our framework casts
+agent modeling as a representation learning problem. Consequently, we construct
+a novel objective inspired by imitation learning and agent identification and
+design an algorithm for unsupervised learning of representations of agent
+policies. We demonstrate empirically the utility of the proposed framework in
+(i) a challenging high-dimensional competitive environment for continuous
+control and (ii) a cooperative environment for communication, on supervised
+predictive tasks, unsupervised clustering, and policy optimization using deep
+reinforcement learning.
+",0,0,0,1,0,0
+1206,Jamming-Resistant Receivers for the Massive MIMO Uplink,"  We design a jamming-resistant receiver scheme to enhance the robustness of a
+massive MIMO uplink system against jamming. We assume that a jammer attacks the
+system both in the pilot and data transmission phases. The key feature of the
+proposed scheme is that, in the pilot phase, we estimate not only the
+legitimate channel, but also the jamming channel by exploiting a purposely
+unused pilot sequence. The jamming channel estimate is used to constructed
+linear receive filters that reject the impact of the jamming signal. The
+performance of the proposed scheme is analytically evaluated using asymptotic
+properties of massive MIMO. The optimal regularized zero-forcing receiver and
+the optimal power allocation are also studied. Numerical results are provided
+to verify our analysis and show that the proposed scheme greatly improves the
+achievable rates, as compared to conventional receivers. Interestingly, the
+proposed scheme works particularly well under strong jamming attacks, since the
+improved estimate of the jamming channel outweighs the extra jamming power.
+",1,0,0,0,0,0
+1207,Multiplex core-periphery organization of the human connectome,"  The behavior of many complex systems is determined by a core of densely
+interconnected units. While many methods are available to identify the core of
+a network when connections between nodes are all of the same type, a principled
+approach to define the core when multiple types of connectivity are allowed is
+still lacking. Here we introduce a general framework to define and extract the
+core-periphery structure of multi-layer networks by explicitly taking into
+account the connectivity of the nodes at each layer. We show how our method
+works on synthetic networks with different size, density, and overlap between
+the cores at the different layers. We then apply the method to multiplex brain
+networks whose layers encode information both on the anatomical and the
+functional connectivity among regions of the human cortex. Results confirm the
+presence of the main known hubs, but also suggest the existence of novel brain
+core regions that have been discarded by previous analysis which focused
+exclusively on the structural layer. Our work is a step forward in the
+identification of the core of the human connectome, and contributes to shed
+light to a fundamental question in modern neuroscience.
+",1,0,0,0,1,0
+1208,Towards Learned Clauses Database Reduction Strategies Based on Dominance Relationship,"  Clause Learning is one of the most important components of a conflict driven
+clause learning (CDCL) SAT solver that is effective on industrial instances.
+Since the number of learned clauses is proved to be exponential in the worse
+case, it is necessary to identify the most relevant clauses to maintain and
+delete the irrelevant ones. As reported in the literature, several learned
+clauses deletion strategies have been proposed. However the diversity in both
+the number of clauses to be removed at each step of reduction and the results
+obtained with each strategy creates confusion to determine which criterion is
+better. Thus, the problem to select which learned clauses are to be removed
+during the search step remains very challenging. In this paper, we propose a
+novel approach to identify the most relevant learned clauses without favoring
+or excluding any of the proposed measures, but by adopting the notion of
+dominance relationship among those measures. Our approach bypasses the problem
+of the diversity of results and reaches a compromise between the assessments of
+these measures. Furthermore, the proposed approach also avoids another
+non-trivial problem which is the amount of clauses to be deleted at each
+reduction of the learned clause database.
+",1,0,0,0,0,0
+1209,Integrable structure of products of finite complex Ginibre random matrices,"  We consider the squared singular values of the product of $M$ standard
+complex Gaussian matrices. Since the squared singular values form a
+determinantal point process with a particular Meijer G-function kernel, the gap
+probabilities are given by a Fredholm determinant based on this kernel. It was
+shown by Strahov \cite{St14} that a hard edge scaling limit of the gap
+probabilities is described by Hamiltonian differential equations which can be
+formulated as an isomonodromic deformation system similar to the theory of the
+Kyoto school. We generalize this result to the case of finite matrices by first
+finding a representation of the finite kernel in integrable form. As a result
+we obtain the Hamiltonian structure for a finite size matrices and formulate it
+in terms of a $(M+1) \times (M+1)$ matrix Schlesinger system. The case $M=1$
+reproduces the Tracy and Widom theory which results in the Painlevé V
+equation for the $(0,s)$ gap probability. Some integrals of motion for $M = 2$
+are identified, and a coupled system of differential equations in two unknowns
+is presented which uniquely determines the corresponding $(0,s)$ gap
+probability.
+",0,1,1,0,0,0
+1210,Global well-posedness of the 3D primitive equations with horizontal viscosity and vertical diffusivity,"  In this paper, we consider the 3D primitive equations of oceanic and
+atmospheric dynamics with only horizontal eddy viscosities in the horizontal
+momentum equations and only vertical diffusivity in the temperature equation.
+Global well-posedness of strong solutions is established for any initial data
+such that the initial horizontal velocity $v_0\in H^2(\Omega)$ and the initial
+temperature $T_0\in H^1(\Omega)\cap L^\infty(\Omega)$ with $\nabla_HT_0\in
+L^q(\Omega)$, for some $q\in(2,\infty)$. Moreover, the strong solutions enjoy
+correspondingly more regularities if the initial temperature belongs to
+$H^2(\Omega)$. The main difficulties are the absence of the vertical viscosity
+and the lack of the horizontal diffusivity, which, interact with each other,
+thus causing the ""\,mismatching\,"" of regularities between the horizontal
+momentum and temperature equations. To handle this ""mismatching"" of
+regularities, we introduce several auxiliary functions, i.e., $\eta, \theta,
+\varphi,$ and $\psi$ in the paper, which are the horizontal curls or some
+appropriate combinations of the temperature with the horizontal divergences of
+the horizontal velocity $v$ or its vertical derivative $\partial_zv$. To
+overcome the difficulties caused by the absence of the horizontal diffusivity,
+which leads to the requirement of some $L^1_t(W^{1,\infty}_\textbf{x})$-type a
+priori estimates on $v$, we decompose the velocity into the
+""temperature-independent"" and temperature-dependent parts and deal with them in
+different ways, by using the logarithmic Sobolev inequalities of the
+Brézis-Gallouet-Wainger and Beale-Kato-Majda types, respectively.
+Specifically, a logarithmic Sobolev inequality of the limiting type, introduced
+in our previous work [12], is used, and a new logarithmic type Gronwall
+inequality is exploited.
+",0,1,1,0,0,0
+1211,Superintegrable systems on 3-dimensional curved spaces: Eisenhart formalism and separability,"  The Eisenhart geometric formalism, which transforms an Euclidean natural
+Hamiltonian $H=T+V$ into a geodesic Hamiltonian ${\cal T}$ with one additional
+degree of freedom, is applied to the four families of quadratically
+superintegrable systems with multiple separability in the Euclidean plane.
+Firstly, the separability and superintegrability of such four geodesic
+Hamiltonians ${\cal T}_r$ ($r=a,b,c,d$) in a three-dimensional curved space are
+studied and then these four systems are modified with the addition of a
+potential ${\cal U}_r$ leading to ${\cal H}_r={\cal T}_r +{\cal U}_r$.
+Secondly, we study the superintegrability of the four Hamiltonians
+$\widetilde{\cal H}_r= {\cal H}_r/ \mu_r$, where $\mu_r$ is a certain
+position-dependent mass, that enjoys the same separability as the original
+system ${\cal H}_r$. All the Hamiltonians here studied describe superintegrable
+systems on non-Euclidean three-dimensional manifolds with a broken spherically
+symmetry.
+",0,1,1,0,0,0
+1212,An Incentive-Based Online Optimization Framework for Distribution Grids,"  This paper formulates a time-varying social-welfare maximization problem for
+distribution grids with distributed energy resources (DERs) and develops online
+distributed algorithms to identify (and track) its solutions. In the considered
+setting, network operator and DER-owners pursue given operational and economic
+objectives, while concurrently ensuring that voltages are within prescribed
+limits. The proposed algorithm affords an online implementation to enable
+tracking of the solutions in the presence of time-varying operational
+conditions and changing optimization objectives. It involves a strategy where
+the network operator collects voltage measurements throughout the feeder to
+build incentive signals for the DER-owners in real time; DERs then adjust the
+generated/consumed powers in order to avoid the violation of the voltage
+constraints while maximizing given objectives. The stability of the proposed
+schemes is analytically established and numerically corroborated.
+",1,0,1,0,0,0
+1213,Ricci solitons on Ricci pseudosymmetric $(LCS)_n$-manifolds,"  The object of the present paper is to study some types of Ricci
+pseudosymmetric $(LCS)_n$-manifolds whose metric is Ricci soliton. We found the
+conditions when Ricci soliton on concircular Ricci pseudosymmetric, projective
+Ricci pseudosymmetric, $W_{3}$-Ricci pseudosymmetric, conharmonic Ricci
+pseudosymmetric, conformal Ricci pseudosymmetric $(LCS)_n$-manifolds to be
+shrinking, steady and expanding. We also construct an example of concircular
+Ricci pseudosymmetric $(LCS)_3$-manifold whose metric is Ricci soliton.
+",0,0,1,0,0,0
+1214,Optimal Gossip Algorithms for Exact and Approximate Quantile Computations,"  This paper gives drastically faster gossip algorithms to compute exact and
+approximate quantiles.
+Gossip algorithms, which allow each node to contact a uniformly random other
+node in each round, have been intensely studied and been adopted in many
+applications due to their fast convergence and their robustness to failures.
+Kempe et al. [FOCS'03] gave gossip algorithms to compute important aggregate
+statistics if every node is given a value. In particular, they gave a beautiful
+$O(\log n + \log \frac{1}{\epsilon})$ round algorithm to $\epsilon$-approximate
+the sum of all values and an $O(\log^2 n)$ round algorithm to compute the exact
+$\phi$-quantile, i.e., the the $\lceil \phi n \rceil$ smallest value.
+We give an quadratically faster and in fact optimal gossip algorithm for the
+exact $\phi$-quantile problem which runs in $O(\log n)$ rounds. We furthermore
+show that one can achieve an exponential speedup if one allows for an
+$\epsilon$-approximation. We give an $O(\log \log n + \log \frac{1}{\epsilon})$
+round gossip algorithm which computes a value of rank between $\phi n$ and
+$(\phi+\epsilon)n$ at every node.% for any $0 \leq \phi \leq 1$ and $0 <
+\epsilon < 1$. Our algorithms are extremely simple and very robust - they can
+be operated with the same running times even if every transmission fails with
+a, potentially different, constant probability. We also give a matching
+$\Omega(\log \log n + \log \frac{1}{\epsilon})$ lower bound which shows that
+our algorithm is optimal for all values of $\epsilon$.
+",1,0,0,0,0,0
+1215,Influence of Heat Treatment on the Corrosion Behavior of Purified Magnesium and AZ31 Alloy,"  Magnesium and its alloys are ideal for biodegradable implants due to their
+biocompatibility and their low-stress shielding. However, they can corrode too
+rapidly in the biological environment. The objective of this research was to
+develop heat treatments to slow the corrosion of high purified magnesium and
+AZ31 alloy in simulated body fluid at 37°C. Heat treatments were performed
+at different temperatures and times. Hydrogen evolution, weight loss, PDP, and
+EIS methods were used to measure the corrosion rates. Results show that heat
+treating can increase the corrosion resistance of HP-Mg by 2x and AZ31 by 10x.
+",0,1,0,0,0,0
+1216,Towards a scientific blockchain framework for reproducible data analysis,"  Publishing reproducible analyses is a long-standing and widespread challenge
+for the scientific community, funding bodies and publishers. Although a
+definitive solution is still elusive, the problem is recognized to affect all
+disciplines and lead to a critical system inefficiency. Here, we propose a
+blockchain-based approach to enhance scientific reproducibility, with a focus
+on life science studies and precision medicine. While the interest of encoding
+permanently into an immutable ledger all the study key information-including
+endpoints, data and metadata, protocols, analytical methods and all
+findings-has been already highlighted, here we apply the blockchain approach to
+solve the issue of rewarding time and expertise of scientists that commit to
+verify reproducibility. Our mechanism builds a trustless ecosystem of
+researchers, funding bodies and publishers cooperating to guarantee digital and
+permanent access to information and reproducible results. As a natural
+byproduct, a procedure to quantify scientists' and institutions' reputation for
+ranking purposes is obtained.
+",1,0,0,0,0,0
+1217,Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data,"  Google uses continuous streams of data from industry partners in order to
+deliver accurate results to users. Unexpected drops in traffic can be an
+indication of an underlying issue and may be an early warning that remedial
+action may be necessary. Detecting such drops is non-trivial because streams
+are variable and noisy, with roughly regular spikes (in many different shapes)
+in traffic data. We investigated the question of whether or not we can predict
+anomalies in these data streams. Our goal is to utilize Machine Learning and
+statistical approaches to classify anomalous drops in periodic, but noisy,
+traffic patterns. Since we do not have a large body of labeled examples to
+directly apply supervised learning for anomaly classification, we approached
+the problem in two parts. First we used TensorFlow to train our various models
+including DNNs, RNNs, and LSTMs to perform regression and predict the expected
+value in the time series. Secondly we created anomaly detection rules that
+compared the actual values to predicted values. Since the problem requires
+finding sustained anomalies, rather than just short delays or momentary
+inactivity in the data, our two detection methods focused on continuous
+sections of activity rather than just single points. We tried multiple
+combinations of our models and rules and found that using the intersection of
+our two anomaly detection methods proved to be an effective method of detecting
+anomalies on almost all of our models. In the process we also found that not
+all data fell within our experimental assumptions, as one data stream had no
+periodicity, and therefore no time based model could predict it.
+",1,0,0,1,0,0
+1218,A parallel approach to bi-objective integer programming,"  To obtain a better understanding of the trade-offs between various
+objectives, Bi-Objective Integer Programming (BOIP) algorithms calculate the
+set of all non-dominated vectors and present these as the solution to a BOIP
+problem. Historically, these algorithms have been compared in terms of the
+number of single-objective IPs solved and total CPU time taken to produce the
+solution to a problem. This is equitable, as researchers can often have access
+to widely differing amounts of computing power. However, the real world has
+recently seen a large uptake of multi-core processors in computers, laptops,
+tablets and even mobile phones. With this in mind, we look at how to best
+utilise parallel processing to improve the elapsed time of optimisation
+algorithms. We present two methods of parallelising the recursive algorithm
+presented by Ozlen, Burton and MacRae. Both new methods utilise two threads and
+improve running times. One of the new methods, the Meeting algorithm, halves
+running time to achieve near-perfect parallelisation. The results are compared
+with the efficiency of parallelisation within the commercial IP solver IBM ILOG
+CPLEX, and the new methods are both shown to perform better.
+",1,0,1,0,0,0
+1219,The adaptive zero-error capacity for a class of channels with noisy feedback,"  The adaptive zero-error capacity of discrete memoryless channels (DMC) with
+noiseless feedback has been shown to be positive whenever there exists at least
+one channel output ""disprover"", i.e. a channel output that cannot be reached
+from at least one of the inputs. Furthermore, whenever there exists a
+disprover, the adaptive zero-error capacity attains the Shannon (small-error)
+capacity. Here, we study the zero-error capacity of a DMC when the channel
+feedback is noisy rather than perfect. We show that the adaptive zero-error
+capacity with noisy feedback is lower bounded by the forward channel's
+zero-undetected error capacity, and show that under certain conditions this is
+tight.
+",1,0,0,0,0,0
+1220,Comparison of Self-Aware and Organic Computing Systems,"  With increasing complexity and heterogeneity of computing devices, it has
+become crucial for system to be autonomous, adaptive to dynamic environment,
+robust, flexible, and having so called self-*properties. These autonomous
+systems are called organic computing(OC) systems. OC system was proposed as a
+solution to tackle complex systems. Design time decisions have been shifted to
+run time in highly complex and interconnected systems as it is very hard to
+consider all scenarios and their appropriate actions in advance. Consequently,
+Self-awareness becomes crucial for these adaptive autonomous systems. To cope
+with evolving environment and changing user needs, system need to have
+knowledge about itself and its surroundings. Literature review shows that for
+autonomous and intelligent systems, researchers are concerned about knowledge
+acquisition, representation and learning which is necessary for a system to
+adapt. This paper is written to compare self-awareness and organic computing by
+discussing their definitions, properties and architecture.
+",1,0,0,0,0,0
+1221,First Order Methods beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems,"  We focus on nonconvex and nonsmooth minimization problems with a composite
+objective, where the differentiable part of the objective is freed from the
+usual and restrictive global Lipschitz gradient continuity assumption. This
+longstanding smoothness restriction is pervasive in first order methods (FOM),
+and was recently circumvent for convex composite optimization by Bauschke,
+Bolte and Teboulle, through a simple and elegant framework which captures, all
+at once, the geometry of the function and of the feasible set. Building on this
+work, we tackle genuine nonconvex problems. We first complement and extend
+their approach to derive a full extended descent lemma by introducing the
+notion of smooth adaptable functions. We then consider a Bregman-based proximal
+gradient methods for the nonconvex composite model with smooth adaptable
+functions, which is proven to globally converge to a critical point under
+natural assumptions on the problem's data. To illustrate the power and
+potential of our general framework and results, we consider a broad class of
+quadratic inverse problems with sparsity constraints which arises in many
+fundamental applications, and we apply our approach to derive new globally
+convergent schemes for this class.
+",1,0,1,0,0,0
+1222,An Army of Me: Sockpuppets in Online Discussion Communities,"  In online discussion communities, users can interact and share information
+and opinions on a wide variety of topics. However, some users may create
+multiple identities, or sockpuppets, and engage in undesired behavior by
+deceiving others or manipulating discussions. In this work, we study
+sockpuppetry across nine discussion communities, and show that sockpuppets
+differ from ordinary users in terms of their posting behavior, linguistic
+traits, as well as social network structure. Sockpuppets tend to start fewer
+discussions, write shorter posts, use more personal pronouns such as ""I"", and
+have more clustered ego-networks. Further, pairs of sockpuppets controlled by
+the same individual are more likely to interact on the same discussion at the
+same time than pairs of ordinary users. Our analysis suggests a taxonomy of
+deceptive behavior in discussion communities. Pairs of sockpuppets can vary in
+their deceptiveness, i.e., whether they pretend to be different users, or their
+supportiveness, i.e., if they support arguments of other sockpuppets controlled
+by the same user. We apply these findings to a series of prediction tasks,
+notably, to identify whether a pair of accounts belongs to the same underlying
+user or not. Altogether, this work presents a data-driven view of deception in
+online discussion communities and paves the way towards the automatic detection
+of sockpuppets.
+",1,1,0,1,0,0
+1223,Robust Bayesian Optimization with Student-t Likelihood,"  Bayesian optimization has recently attracted the attention of the automatic
+machine learning community for its excellent results in hyperparameter tuning.
+BO is characterized by the sample efficiency with which it can optimize
+expensive black-box functions. The efficiency is achieved in a similar fashion
+to the learning to learn methods: surrogate models (typically in the form of
+Gaussian processes) learn the target function and perform intelligent sampling.
+This surrogate model can be applied even in the presence of noise; however, as
+with most regression methods, it is very sensitive to outlier data. This can
+result in erroneous predictions and, in the case of BO, biased and inefficient
+exploration. In this work, we present a GP model that is robust to outliers
+which uses a Student-t likelihood to segregate outliers and robustly conduct
+Bayesian optimization. We present numerical results evaluating the proposed
+method in both artificial functions and real problems.
+",1,0,0,1,0,0
+1224,Vehicle Localization and Control on Roads with Prior Grade Map,"  We propose a map-aided vehicle localization method for GPS-denied
+environments. This approach exploits prior knowledge of the road grade map and
+vehicle on-board sensor measurements to accurately estimate the longitudinal
+position of the vehicle. Real-time localization is crucial to systems that
+utilize position-dependent information for planning and control. We validate
+the effectiveness of the localization method on a hierarchical control system.
+The higher level planner optimizes the vehicle velocity to minimize the energy
+consumption for a given route by employing traffic condition and road grade
+data. The lower level is a cruise control system that tracks the
+position-dependent optimal reference velocity. Performance of the proposed
+localization algorithm is evaluated using both simulations and experiments.
+",1,0,0,0,0,0
+1225,Estimating Tactile Data for Adaptive Grasping of Novel Objects,"  We present an adaptive grasping method that finds stable grasps on novel
+objects. The main contributions of this paper is in the computation of the
+probability of success of grasps in the vicinity of an already applied grasp.
+Our method performs grasp adaptions by simulating tactile data for grasps in
+the vicinity of the current grasp. The simulated data is used to evaluate
+hypothetical grasps and thereby guide us toward better grasps. We demonstrate
+the applicability of our method by constructing a system that can plan, apply
+and adapt grasps on novel objects. Experiments are conducted on objects from
+the YCB object set and the success rate of our method is 88%. Our experiments
+show that the application of our grasp adaption method improves grasp stability
+significantly.
+",1,0,0,0,0,0
+1226,Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints,"  Algorithm-dependent generalization error bounds are central to statistical
+learning theory. A learning algorithm may use a large hypothesis space, but the
+limited number of iterations controls its model capacity and generalization
+error. The impacts of stochastic gradient methods on generalization error for
+non-convex learning problems not only have important theoretical consequences,
+but are also critical to generalization errors of deep learning.
+In this paper, we study the generalization errors of Stochastic Gradient
+Langevin Dynamics (SGLD) with non-convex objectives. Two theories are proposed
+with non-asymptotic discrete-time analysis, using Stability and PAC-Bayesian
+results respectively. The stability-based theory obtains a bound of
+$O\left(\frac{1}{n}L\sqrt{\beta T_k}\right)$, where $L$ is uniform Lipschitz
+parameter, $\beta$ is inverse temperature, and $T_k$ is aggregated step sizes.
+For PAC-Bayesian theory, though the bound has a slower $O(1/\sqrt{n})$ rate,
+the contribution of each step is shown with an exponentially decaying factor by
+imposing $\ell^2$ regularization, and the uniform Lipschitz constant is also
+replaced by actual norms of gradients along trajectory. Our bounds have no
+implicit dependence on dimensions, norms or other capacity measures of
+parameter, which elegantly characterizes the phenomenon of ""Fast Training
+Guarantees Generalization"" in non-convex settings. This is the first
+algorithm-dependent result with reasonable dependence on aggregated step sizes
+for non-convex learning, and has important implications to statistical learning
+aspects of stochastic gradient methods in complicated models such as deep
+learning.
+",1,0,1,1,0,0
+1227,Near-UV OH Prompt Emission in the Innermost Coma of 103P/Hartley 2,"  The Deep Impact spacecraft fly-by of comet 103P/Hartley 2 occurred on 2010
+November 4, one week after perihelion with a closest approach (CA) distance of
+about 700 km. We used narrowband images obtained by the Medium Resolution
+Imager (MRI) onboard the spacecraft to study the gas and dust in the innermost
+coma. We derived an overall dust reddening of 15\%/100 nm between 345 and 749
+nm and identified a blue enhancement in the dust coma in the sunward direction
+within 5 km from the nucleus, which we interpret as a localized enrichment in
+water ice. OH column density maps show an anti-sunward enhancement throughout
+the encounter except for the highest resolution images, acquired at CA, where a
+radial jet becomes visible in the innermost coma, extending up to 12 km from
+the nucleus. The OH distribution in the inner coma is very different from that
+expected for a fragment species. Instead, it correlates well with the water
+vapor map derived by the HRI-IR instrument onboard Deep Impact
+\citep{AHearn2011}. Radial profiles of the OH column density and derived water
+production rates show an excess of OH emission during CA that cannot be
+explained with pure fluorescence. We attribute this excess to a prompt emission
+process where photodissociation of H$_2$O directly produces excited
+OH*($A^2\it{\Sigma}^+$) radicals. Our observations provide the first direct
+imaging of Near-UV prompt emission of OH. We therefore suggest the use of a
+dedicated filter centered at 318.8 nm to directly trace the water in the coma
+of comets.
+",0,1,0,0,0,0
+1228,Effective gravity and effective quantum equations in a system inspired by walking droplets experiments,"  In this paper we suggest a macroscopic toy system in which a potential-like
+energy is generated by a non-uniform pulsation of the medium (i.e. pulsation of
+transverse standing oscillations that the elastic medium of the system tends to
+support at each point). This system is inspired by walking droplets experiments
+with submerged barriers. We first show that a Poincaré-Lorentz covariant
+formalization of the system causes inconsistency and contradiction. The
+contradiction is solved by using a general covariant formulation and by
+assuming a relation between the metric associated with the elastic medium and
+the pulsation of the medium. (Calculations are performed in a Newtonian-like
+metric, constant in time). We find ($i$) an effective Schrödinger equation
+with external potential, ($ii$) an effective de Broglie-Bohm guidance formula
+and ($iii$) an energy of the `particle' which has a direct counterpart in
+general relativity as well as in quantum mechanics. We analyze the wave and the
+`particle' in an effective free fall and with a harmonic potential. This
+potential-like energy is an effective gravitational potential, rooted in the
+pulsation of the medium at each point. The latter, also conceivable as a
+natural clock, makes easy to understand why proper time varies from place to
+place.
+",0,1,0,0,0,0
+1229,A gradient estimate for nonlocal minimal graphs,"  We consider the class of measurable functions defined in all of
+$\mathbb{R}^n$ that give rise to a nonlocal minimal graph over a ball of
+$\mathbb{R}^n$. We establish that the gradient of any such function is bounded
+in the interior of the ball by a power of its oscillation. This estimate,
+together with previously known results, leads to the $C^\infty$ regularity of
+the function in the ball. While the smoothness of nonlocal minimal graphs was
+known for $n = 1, 2$ (but without a quantitative bound), in higher dimensions
+only their continuity had been established.
+To prove the gradient bound, we show that the normal to a nonlocal minimal
+graph is a supersolution of a truncated fractional Jacobi operator, for which
+we prove a weak Harnack inequality. To this end, we establish a new universal
+fractional Sobolev inequality on nonlocal minimal surfaces.
+Our estimate provides an extension to the fractional setting of the
+celebrated gradient bounds of Finn and of Bombieri, De Giorgi & Miranda for
+solutions of the classical mean curvature equation.
+",0,0,1,0,0,0
+1230,The GAPS Programme with HARPS-N@TNG XIV. Investigating giant planet migration history via improved eccentricity and mass determination for 231 transiting planets,"  We carried out a Bayesian homogeneous determination of the orbital parameters
+of 231 transiting giant planets (TGPs) that are alone or have distant
+companions; we employed DE-MCMC methods to analyse radial-velocity (RV) data
+from the literature and 782 new high-accuracy RVs obtained with the HARPS-N
+spectrograph for 45 systems over 3 years. Our work yields the largest sample of
+systems with a transiting giant exoplanet and coherently determined orbital,
+planetary, and stellar parameters. We found that the orbital parameters of TGPs
+in non-compact planetary systems are clearly shaped by tides raised by their
+host stars. Indeed, the most eccentric planets have relatively large orbital
+separations and/or high mass ratios, as expected from the equilibrium tide
+theory. This feature would be the outcome of high-eccentricity migration (HEM).
+The distribution of $\alpha=a/a_R$, where $a$ and $a_R$ are the semi-major axis
+and the Roche limit, for well-determined circular orbits peaks at 2.5; this
+also agrees with expectations from the HEM. The few planets of our sample with
+circular orbits and $\alpha >5$ values may have migrated through disc-planet
+interactions instead of HEM. By comparing circularisation times with stellar
+ages, we found that hot Jupiters with $a < 0.05$ au have modified tidal quality
+factors $10^{5} < Q'_p < 10^{9}$, and that stellar $Q'_s > 10^{6}-10^{7}$ are
+required to explain the presence of eccentric planets at the same orbital
+distance. As a by-product of our analysis, we detected a non-zero eccentricity
+for HAT-P-29; we determined that five planets that were previously regarded to
+have hints of non-zero eccentricity have circular orbits or undetermined
+eccentricities; we unveiled curvatures caused by distant companions in the RV
+time series of HAT-P-2, HAT-P-22, and HAT-P-29; and we revised the planetary
+parameters of CoRoT-1b.
+",0,1,0,0,0,0
+1231,Injective stabilization of additive functors. I. Preliminaries,"  This paper is the first one in a series of three dealing with the concept of
+injective stabilization of the tensor product and its applications. Its primary
+goal is to collect known facts and establish a basic operational calculus that
+will be used in the subsequent parts. This is done in greater generality than
+is necessary for the stated goal. Several results of independent interest are
+also established. They include, among other things, connections with
+satellites, an explicit construction of the stabilization of a finitely
+presented functor, various exactness properties of the injectively stable
+functors, a construction, from a functor and a short exact sequence, of a
+doubly-infinite exact sequence by splicing the injective stabilization of the
+functor and its derived functors. When specialized to the tensor product with a
+finitely presented module, the injective stabilization with coefficients in the
+ring is isomorphic to the 1-torsion functor. The Auslander-Reiten formula is
+extended to a more general formula, which holds for arbitrary (i.e., not
+necessarily finite) modules over arbitrary associative rings with identity.
+Weakening of the assumptions in the theorems of Eilenberg and Watts leads to
+characterizations of the requisite zeroth derived functors.
+The subsequent papers, provide applications of the developed techniques.
+Part~II deals with new notions of torsion module and cotorsion module of a
+module. This is done for arbitrary modules over arbitrary rings. Part~III
+introduces a new concept, called the asymptotic stabilization of the tensor
+product. The result is closely related to different variants of stable homology
+(these are generalizations of Tate homology to arbitrary rings). A comparison
+transformation from Vogel homology to the asymptotic stabilization of the
+tensor product is constructed and shown to be epic.
+",0,0,1,0,0,0
+1232,Three hypergraph eigenvector centralities,"  Eigenvector centrality is a standard network analysis tool for determining
+the importance of (or ranking of) entities in a connected system that is
+represented by a graph. However, many complex systems and datasets have natural
+multi-way interactions that are more faithfully modeled by a hypergraph. Here
+we extend the notion of graph eigenvector centrality to uniform hypergraphs.
+Traditional graph eigenvector centralities are given by a positive eigenvector
+of the adjacency matrix, which is guaranteed to exist by the Perron-Frobenius
+theorem under some mild conditions. The natural representation of a hypergraph
+is a hypermatrix (colloquially, a tensor). Using recently established
+Perron-Frobenius theory for tensors, we develop three tensor eigenvectors
+centralities for hypergraphs, each with different interpretations. We show that
+these centralities can reveal different information on real-world data by
+analyzing hypergraphs constructed from n-gram frequencies, co-tagging on stack
+exchange, and drug combinations observed in patient emergency room visits.
+",1,0,0,0,0,0
+1233,Reinforcement Learning using Augmented Neural Networks,"  Neural networks allow Q-learning reinforcement learning agents such as deep
+Q-networks (DQN) to approximate complex mappings from state spaces to value
+functions. However, this also brings drawbacks when compared to other function
+approximators such as tile coding or their generalisations, radial basis
+functions (RBF) because they introduce instability due to the side effect of
+globalised updates present in neural networks. This instability does not even
+vanish in neural networks that do not have any hidden layers. In this paper, we
+show that simple modifications to the structure of the neural network can
+improve stability of DQN learning when a multi-layer perceptron is used for
+function approximation.
+",0,0,0,1,0,0
+1234,Instantons and Fluctuations in a Lagrangian Model of Turbulence,"  We perform a detailed analytical study of the Recent Fluid Deformation (RFD)
+model for the onset of Lagrangian intermittency, within the context of the
+Martin-Siggia-Rose-Janssen-de Dominicis (MSRJD) path integral formalism. The
+model is based, as a key point, upon local closures for the pressure Hessian
+and the viscous dissipation terms in the stochastic dynamical equations for the
+velocity gradient tensor. We carry out a power counting hierarchical
+classification of the several perturbative contributions associated to
+fluctuations around the instanton-evaluated MSRJD action, along the lines of
+the cumulant expansion. The most relevant Feynman diagrams are then integrated
+out into the renormalized effective action, for the computation of velocity
+gradient probability distribution functions (vgPDFs). While the subleading
+perturbative corrections do not affect the global shape of the vgPDFs in an
+appreciable qualitative way, it turns out that they have a significant role in
+the accurate description of their non-Gaussian cores.
+",0,1,0,0,0,0
+1235,The heavy path approach to Galton-Watson trees with an application to Apollonian networks,"  We study the heavy path decomposition of conditional Galton-Watson trees. In
+a standard Galton-Watson tree conditional on its size $n$, we order all
+children by their subtree sizes, from large (heavy) to small. A node is marked
+if it is among the $k$ heaviest nodes among its siblings. Unmarked nodes and
+their subtrees are removed, leaving only a tree of marked nodes, which we call
+the $k$-heavy tree. We study various properties of these trees, including their
+size and the maximal distance from any original node to the $k$-heavy tree. In
+particular, under some moment condition, the $2$-heavy tree is with high
+probability larger than $cn$ for some constant $c > 0$, and the maximal
+distance from the $k$-heavy tree is $O(n^{1/(k+1)})$ in probability. As a
+consequence, for uniformly random Apollonian networks of size $n$, the expected
+size of the longest simple path is $\Omega(n)$.
+",1,0,1,0,0,0
+1236,Perishability of Data: Dynamic Pricing under Varying-Coefficient Models,"  We consider a firm that sells a large number of products to its customers in
+an online fashion. Each product is described by a high dimensional feature
+vector, and the market value of a product is assumed to be linear in the values
+of its features. Parameters of the valuation model are unknown and can change
+over time. The firm sequentially observes a product's features and can use the
+historical sales data (binary sale/no sale feedbacks) to set the price of
+current product, with the objective of maximizing the collected revenue. We
+measure the performance of a dynamic pricing policy via regret, which is the
+expected revenue loss compared to a clairvoyant that knows the sequence of
+model parameters in advance.
+We propose a pricing policy based on projected stochastic gradient descent
+(PSGD) and characterize its regret in terms of time $T$, features dimension
+$d$, and the temporal variability in the model parameters, $\delta_t$. We
+consider two settings. In the first one, feature vectors are chosen
+antagonistically by nature and we prove that the regret of PSGD pricing policy
+is of order $O(\sqrt{T} + \sum_{t=1}^T \sqrt{t}\delta_t)$. In the second
+setting (referred to as stochastic features model), the feature vectors are
+drawn independently from an unknown distribution. We show that in this case,
+the regret of PSGD pricing policy is of order $O(d^2 \log T + \sum_{t=1}^T
+t\delta_t/d)$.
+",1,0,0,1,0,0
+1237,A fast algorithm for maximal propensity score matching,"  We present a new algorithm which detects the maximal possible number of
+matched disjoint pairs satisfying a given caliper when a bipartite matching is
+done with respect to a scalar index (e.g., propensity score), and constructs a
+corresponding matching. Variable width calipers are compatible with the
+technique, provided that the width of the caliper is a Lipschitz function of
+the index. If the observations are ordered with respect to the index then the
+matching needs $O(N)$ operations, where $N$ is the total number of subjects to
+be matched. The case of 1-to-$n$ matching is also considered.
+We offer also a new fast algorithm for optimal complete one-to-one matching
+on a scalar index when the treatment and control groups are of the same size.
+This allows us to improve greedy nearest neighbor matching on a scalar index.
+Keywords: propensity score matching, nearest neighbor matching, matching with
+caliper, variable width caliper.
+",1,0,0,1,0,0
+1238,"Fractional quantum Hall systems near nematicity: bimetric theory, composite fermions, and Dirac brackets","  We perform a detailed comparison of the Dirac composite fermion and the
+recently proposed bimetric theory for a quantum Hall Jain states near half
+filling. By tuning the composite Fermi liquid to the vicinity of a nematic
+phase transition, we find that the two theories are equivalent to each other.
+We verify that the single mode approximation for the response functions and the
+static structure factor becomes reliable near the phase transition. We show
+that the dispersion relation of the nematic mode near the phase transition can
+be obtained from the Dirac brackets between the components of the nematic order
+parameter. The dispersion is quadratic at low momenta and has a magnetoroton
+minimum at a finite momentum, which is not related to any nearby inhomogeneous
+phase.
+",0,1,0,0,0,0
+1239,Recognizing Objects In-the-wild: Where Do We Stand?,"  The ability to recognize objects is an essential skill for a robotic system
+acting in human-populated environments. Despite decades of effort from the
+robotic and vision research communities, robots are still missing good visual
+perceptual systems, preventing the use of autonomous agents for real-world
+applications. The progress is slowed down by the lack of a testbed able to
+accurately represent the world perceived by the robot in-the-wild. In order to
+fill this gap, we introduce a large-scale, multi-view object dataset collected
+with an RGB-D camera mounted on a mobile robot. The dataset embeds the
+challenges faced by a robot in a real-life application and provides a useful
+tool for validating object recognition algorithms. Besides describing the
+characteristics of the dataset, the paper evaluates the performance of a
+collection of well-established deep convolutional networks on the new dataset
+and analyzes the transferability of deep representations from Web images to
+robotic data. Despite the promising results obtained with such representations,
+the experiments demonstrate that object classification with real-life robotic
+data is far from being solved. Finally, we provide a comparative study to
+analyze and highlight the open challenges in robot vision, explaining the
+discrepancies in the performance.
+",1,0,0,0,0,0
+1240,Generalizing Geometric Brownian Motion,"  To convert standard Brownian motion $Z$ into a positive process, Geometric
+Brownian motion (GBM) $e^{\beta Z_t}, \beta >0$ is widely used. We generalize
+this positive process by introducing an asymmetry parameter $ \alpha \geq 0$
+which describes the instantaneous volatility whenever the process reaches a new
+low. For our new process, $\beta$ is the instantaneous volatility as prices
+become arbitrarily high. Our generalization preserves the positivity, constant
+proportional drift, and tractability of GBM, while expressing the instantaneous
+volatility as a randomly weighted $L^2$ mean of $\alpha$ and $\beta$. The
+running minimum and relative drawup of this process are also analytically
+tractable. Letting $\alpha = \beta$, our positive process reduces to Geometric
+Brownian motion. By adding a jump to default to the new process, we introduce a
+non-negative martingale with the same tractabilities. Assuming a security's
+dynamics are driven by these processes in risk neutral measure, we price
+several derivatives including vanilla, barrier and lookback options.
+",0,0,0,0,0,1
+1241,Challenges testing the no-hair theorem with gravitational waves,"  General relativity's no-hair theorem states that isolated astrophysical black
+holes are described by only two numbers: mass and spin. As a consequence, there
+are strict relationships between the frequency and damping time of the
+different modes of a perturbed Kerr black hole. Testing the no-hair theorem has
+been a longstanding goal of gravitational-wave astronomy. The recent detection
+of gravitational waves from black hole mergers would seem to make such tests
+imminent. We investigate how constraints on black hole ringdown parameters
+scale with the loudness of the ringdown signal---subject to the constraint that
+the post-merger remnant must be allowed to settle into a perturbative,
+Kerr-like state. In particular, we require that---for a given detector---the
+gravitational waveform predicted by numerical relativity is indistinguishable
+from an exponentially damped sine after time $t^\text{cut}$. By requiring the
+post-merger remnant to settle into such a perturbative state, we find that
+confidence intervals for ringdown parameters do not necessarily shrink with
+louder signals. In at least some cases, more sensitive measurements probe later
+times without necessarily providing tighter constraints on ringdown frequencies
+and damping times. Preliminary investigations are unable to explain this result
+in terms of a numerical relativity artifact.
+",0,1,0,0,0,0
+1242,Speculation On a Source of Dark Matter,"  By drawing an analogy with superfluid 4He vortices we suggest that dark
+matter may consist of irreducibly small remnants of cosmic strings.
+",0,1,0,0,0,0
+1243,Analyzing Cloud Optical Properties Using Sky Cameras,"  Clouds play a significant role in the fluctuation of solar radiation received
+by the earth's surface. It is important to study the various cloud properties,
+as it impacts the total solar irradiance falling on the earth's surface. One of
+such important optical properties of the cloud is the Cloud Optical Thickness
+(COT). It is defined with the amount of light that can pass through the clouds.
+The COT values are generally obtained from satellite images. However, satellite
+images have a low temporal- and spatial- resolutions; and are not suitable for
+study in applications as solar energy generation and forecasting. Therefore,
+ground-based sky cameras are now getting popular in such fields. In this paper,
+we analyze the cloud optical thickness value, from the ground-based sky
+cameras, and provide future research directions.
+",0,1,0,0,0,0
+1244,Response Formulae for $n$-point Correlations in Statistical Mechanical Systems and Application to a Problem of Coarse Graining,"  Predicting the response of a system to perturbations is a key challenge in
+mathematical and natural sciences. Under suitable conditions on the nature of
+the system, of the perturbation, and of the observables of interest, response
+theories allow to construct operators describing the smooth change of the
+invariant measure of the system of interest as a function of the small
+parameter controlling the intensity of the perturbation. In particular,
+response theories can be developed both for stochastic and chaotic
+deterministic dynamical systems, where in the latter case stricter conditions
+imposing some degree of structural stability are required. In this paper we
+extend previous findings and derive general response formulae describing how
+n-point correlations are affected by perturbations to the vector flow. We also
+show how to compute the response of the spectral properties of the system to
+perturbations. We then apply our results to the seemingly unrelated problem of
+coarse graining in multiscale systems: we find explicit formulae describing the
+change in the terms describing parameterisation of the neglected degrees of
+freedom resulting from applying perturbations to the full system. All the terms
+envisioned by the Mori-Zwanzig theory - the deterministic, stochastic, and
+non-Markovian terms - are affected at 1st order in the perturbation. The
+obtained results provide a more comprehesive understanding of the response of
+statistical mechanical systems to perturbations and contribute to the goal of
+constructing accurate and robust parameterisations and are of potential
+relevance for fields like molecular dynamics, condensed matter, and geophysical
+fluid dynamics. We envision possible applications of our general results to the
+study of the response of climate variability to anthropogenic and natural
+forcing and to the study of the equivalence of thermostatted statistical
+mechanical systems.
+",0,1,1,0,0,0
+1245,The Australian PCEHR system: Ensuring Privacy and Security through an Improved Access Control Mechanism,"  An Electronic Health Record (EHR) is designed to store diverse data
+accurately from a range of health care providers and to capture the status of a
+patient by a range of health care providers across time. Realising the numerous
+benefits of the system, EHR adoption is growing globally and many countries
+invest heavily in electronic health systems. In Australia, the Government
+invested $467 million to build key components of the Personally Controlled
+Electronic Health Record (PCEHR) system in July 2012. However, in the last
+three years, the uptake from individuals and health care providers has not been
+satisfactory. Unauthorised access of the PCEHR was one of the major barriers.
+We propose an improved access control model for the PCEHR system to resolve the
+unauthorised access issue. We discuss the unauthorised access issue with real
+examples and present a potential solution to overcome the issue to make the
+PCEHR system a success in Australia.
+",1,0,0,0,0,0
+1246,Randomized Kernel Methods for Least-Squares Support Vector Machines,"  The least-squares support vector machine is a frequently used kernel method
+for non-linear regression and classification tasks. Here we discuss several
+approximation algorithms for the least-squares support vector machine
+classifier. The proposed methods are based on randomized block kernel matrices,
+and we show that they provide good accuracy and reliable scaling for
+multi-class classification problems with relatively large data sets. Also, we
+present several numerical experiments that illustrate the practical
+applicability of the proposed methods.
+",1,0,0,1,0,0
+1247,Optimal Transport: Fast Probabilistic Approximation with Exact Solvers,"  We propose a simple subsampling scheme for fast randomized approximate
+computation of optimal transport distances. This scheme operates on a random
+subset of the full data and can use any exact algorithm as a black-box
+back-end, including state-of-the-art solvers and entropically penalized
+versions. It is based on averaging the exact distances between empirical
+measures generated from independent samples from the original measures and can
+easily be tuned towards higher accuracy or shorter computation times. To this
+end, we give non-asymptotic deviation bounds for its accuracy in the case of
+discrete optimal transport problems. In particular, we show that in many
+important instances, including images (2D-histograms), the approximation error
+is independent of the size of the full problem. We present numerical
+experiments that demonstrate that a very good approximation in typical
+applications can be obtained in a computation time that is several orders of
+magnitude smaller than what is required for exact computation of the full
+problem.
+",0,0,0,1,0,0
+1248,Reliable Clustering of Bernoulli Mixture Models,"  A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors
+with independent Bernoulli dimensions. The problem of clustering BMM data
+arises in a variety of real-world applications, ranging from population
+genetics to activity analysis in social networks. In this paper, we have
+analyzed the information-theoretic PAC-learnability of BMMs, when the number of
+clusters is unknown. In particular, we stipulate certain conditions on both
+sample complexity and the dimension of the model in order to guarantee the
+Probably Approximately Correct (PAC)-clusterability of a given dataset. To the
+best of our knowledge, these findings are the first non-asymptotic (PAC) bounds
+on the sample complexity of learning BMMs.
+",1,0,0,1,0,0
+1249,"Short-time behavior of the heat kernel and Weyl's law on $RCD^*(K, N)$-spaces","  In this paper, we prove pointwise convergence of heat kernels for
+mGH-convergent sequences of $RCD^*(K,N)$-spaces. We obtain as a corollary
+results on the short-time behavior of the heat kernel in $RCD^*(K,N)$-spaces.
+We use then these results to initiate the study of Weyl's law in the $RCD$
+setting
+",0,0,1,0,0,0
+1250,Football and Beer - a Social Media Analysis on Twitter in Context of the FIFA Football World Cup 2018,"  In many societies alcohol is a legal and common recreational substance and
+socially accepted. Alcohol consumption often comes along with social events as
+it helps people to increase their sociability and to overcome their
+inhibitions. On the other hand we know that increased alcohol consumption can
+lead to serious health issues, such as cancer, cardiovascular diseases and
+diseases of the digestive system, to mention a few. This work examines alcohol
+consumption during the FIFA Football World Cup 2018, particularly the usage of
+alcohol related information on Twitter. For this we analyse the tweeting
+behaviour and show that the tournament strongly increases the interest in beer.
+Furthermore we show that countries who had to leave the tournament at early
+stage might have done something good to their fans as the interest in beer
+decreased again.
+",1,0,0,0,0,0
+1251,Cross-stream migration of a surfactant-laden deformable droplet in a Poiseuille flow,"  The motion of a viscous deformable droplet suspended in an unbounded
+Poiseuille flow in the presence of bulk-insoluble surfactants is studied
+analytically. Assuming the convective transport of fluid and heat to be
+negligible, we perform a small-deformation perturbation analysis to obtain the
+droplet migration velocity. The droplet dynamics strongly depends on the
+distribution of surfactants along the droplet interface, which is governed by
+the relative strength of convective transport of surfactants as compared with
+the diffusive transport of surfactants. The present study is focused on the
+following two limits: (i) when the surfactant transport is dominated by surface
+diffusion, and (ii) when the surfactant transport is dominated by surface
+convection. In the first limiting case, it is seen that the axial velocity of
+the droplet decreases with increase in the advection of the surfactants along
+the surface. The variation of cross-stream migration velocity, on the other
+hand, is analyzed over three different regimes based on the ratio of the
+viscosity of the droplet phase to that of the carrier phase. In the first
+regime the migration velocity decreases with increase in surface advection of
+the surfactants although there is no change in direction of droplet migration.
+For the second regime, the direction of the cross-stream migration of the
+droplet changes depending on different parameters. In the third regime, the
+migration velocity is merely affected by any change in the surfactant
+distribution. For the other limit of higher surface advection in comparison to
+surface diffusion of the surfactants, the axial velocity of the droplet is
+found to be independent of the surfactant distribution. However, the
+cross-stream velocity is found to decrease with increase in non-uniformity in
+surfactant distribution.
+",0,1,0,0,0,0
+1252,PCA in Data-Dependent Noise (Correlated-PCA): Nearly Optimal Finite Sample Guarantees,"  We study Principal Component Analysis (PCA) in a setting where a part of the
+corrupting noise is data-dependent and, as a result, the noise and the true
+data are correlated. Under a bounded-ness assumption on the true data and the
+noise, and a simple assumption on data-noise correlation, we obtain a nearly
+optimal sample complexity bound for the most commonly used PCA solution,
+singular value decomposition (SVD). This bound is a significant improvement
+over the bound obtained by Vaswani and Guo in recent work (NIPS 2016) where
+this ""correlated-PCA"" problem was first studied; and it holds under a
+significantly weaker data-noise correlation assumption than the one used for
+this earlier result.
+",1,0,0,1,0,0
+1253,Using a Predator-Prey Model to Explain Variations of Cloud Spot Price,"  The spot pricing scheme has been considered to be resource-efficient for
+providers and cost-effective for consumers in the Cloud market. Nevertheless,
+unlike the static and straightforward strategies of trading on-demand and
+reserved Cloud services, the market-driven mechanism for trading spot service
+would be complicated for both implementation and understanding. The largely
+invisible market activities and their complex interactions could especially
+make Cloud consumers hesitate to enter the spot market. To reduce the
+complexity in understanding the Cloud spot market, we decided to reveal the
+backend information behind spot price variations. Inspired by the methodology
+of reverse engineering, we developed a Predator-Prey model that can simulate
+the interactions between demand and resource based on the visible spot price
+traces. The simulation results have shown some basic regular patterns of market
+activities with respect to Amazon's spot instance type m3.large. Although the
+findings of this study need further validation by using practical data, our
+work essentially suggests a promising approach (i.e.~using a Predator-Prey
+model) to investigate spot market activities.
+",1,0,0,0,0,0
+1254,Separatrix crossing in rotation of a body with changing geometry of masses,"  We consider free rotation of a body whose parts move slowly with respect to
+each other under the action of internal forces. This problem can be considered
+as a perturbation of the Euler-Poinsot problem. The dynamics has an approximate
+conservation law - an adiabatic invariant. This allows to describe the
+evolution of rotation in the adiabatic approximation. The evolution leads to an
+overturn in the rotation of the body: the vector of angular velocity crosses
+the separatrix of the Euler-Poinsot problem. This crossing leads to a
+quasi-random scattering in body's dynamics. We obtain formulas for
+probabilities of capture into different domains in the phase space at
+separatrix crossings.
+",0,1,0,0,0,0
+1255,SING: Symbol-to-Instrument Neural Generator,"  Recent progress in deep learning for audio synthesis opens the way to models
+that directly produce the waveform, shifting away from the traditional paradigm
+of relying on vocoders or MIDI synthesizers for speech or music generation.
+Despite their successes, current state-of-the-art neural audio synthesizers
+such as WaveNet and SampleRNN suffer from prohibitive training and inference
+times because they are based on autoregressive models that generate audio
+samples one at a time at a rate of 16kHz. In this work, we study the more
+computationally efficient alternative of generating the waveform frame-by-frame
+with large strides. We present SING, a lightweight neural audio synthesizer for
+the original task of generating musical notes given desired instrument, pitch
+and velocity. Our model is trained end-to-end to generate notes from nearly
+1000 instruments with a single decoder, thanks to a new loss function that
+minimizes the distances between the log spectrograms of the generated and
+target waveforms. On the generalization task of synthesizing notes for pairs of
+pitch and instrument not seen during training, SING produces audio with
+significantly improved perceptual quality compared to a state-of-the-art
+autoencoder based on WaveNet as measured by a Mean Opinion Score (MOS), and is
+about 32 times faster for training and 2, 500 times faster for inference.
+",1,0,0,0,0,0
+1256,Path-by-path regularization by noise for scalar conservation laws,"  We prove a path-by-path regularization by noise result for scalar
+conservation laws. In particular, this proves regularizing properties for
+scalar conservation laws driven by fractional Brownian motion and generalizes
+the respective results obtained in [Gess, Souganidis; Comm. Pure Appl. Math.
+(2017)]. In addition, we introduce a new path-by-path scaling property which is
+shown to be sufficient to imply regularizing effects.
+",0,0,1,0,0,0
+1257,An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog,"  We present a novel end-to-end trainable neural network model for
+task-oriented dialog systems. The model is able to track dialog state, issue
+API calls to knowledge base (KB), and incorporate structured KB query results
+into system responses to successfully complete task-oriented dialogs. The
+proposed model produces well-structured system responses by jointly learning
+belief tracking and KB result processing conditioning on the dialog history. We
+evaluate the model in a restaurant search domain using a dataset that is
+converted from the second Dialog State Tracking Challenge (DSTC2) corpus.
+Experiment results show that the proposed model can robustly track dialog state
+given the dialog history. Moreover, our model demonstrates promising results in
+producing appropriate system responses, outperforming prior end-to-end
+trainable neural network models using per-response accuracy evaluation metrics.
+",1,0,0,0,0,0
+1258,Neural IR Meets Graph Embedding: A Ranking Model for Product Search,"  Recently, neural models for information retrieval are becoming increasingly
+popular. They provide effective approaches for product search due to their
+competitive advantages in semantic matching. However, it is challenging to use
+graph-based features, though proved very useful in IR literature, in these
+neural approaches. In this paper, we leverage the recent advances in graph
+embedding techniques to enable neural retrieval models to exploit
+graph-structured data for automatic feature extraction. The proposed approach
+can not only help to overcome the long-tail problem of click-through data, but
+also incorporate external heterogeneous information to improve search results.
+Extensive experiments on a real-world e-commerce dataset demonstrate
+significant improvement achieved by our proposed approach over multiple strong
+baselines both as an individual retrieval model and as a feature used in
+learning-to-rank frameworks.
+",1,0,0,0,0,0
+1259,"Scaling up the software development process, a case study highlighting the complexities of large team software development","  Diamond Light Source is the UK's National Synchrotron Facility and as such
+provides access to world class experimental services for UK and international
+researchers. As a user facility, that is one that focuses on providing a good
+user experience to our varied visitors, Diamond invests heavily in software
+infrastructure and staff. Over 100 members of the 600 strong workforce consider
+software development as a significant tool to help them achieve their primary
+role. These staff work on a diverse number of different software packages,
+providing support for installation and configuration, maintenance and bug
+fixing, as well as additional research and development of software when
+required.
+This talk focuses on one of the software projects undertaken to unify and
+improve the user experience of several experiments. The ""mapping project"" is a
+large 2 year, multi group project targeting the collection and processing
+experiments which involve scanning an X-ray beam over a sample and building up
+an image of that sample, similar to the way that google maps bring together
+small pieces of information to produce a full map of the world. The project
+itself is divided into several work packages, ranging from teams of one to 5 or
+6 in size, with varying levels of time commitment to the project. This paper
+aims to explore one of these work packages as a case study, highlighting the
+experiences of the project team, the methodologies employed, their outcomes,
+and the lessons learnt from the experience.
+",1,0,0,0,0,0
+1260,Lyapunov exponents for products of matrices,"  Let ${\bf M}=(M_1,\ldots, M_k)$ be a tuple of real $d\times d$ matrices.
+Under certain irreducibility assumptions, we give checkable criteria for
+deciding whether ${\bf M}$ possesses the following property: there exist two
+constants $\lambda\in {\Bbb R}$ and $C>0$ such that for any $n\in {\Bbb N}$ and
+any $i_1, \ldots, i_n \in \{1,\ldots, k\}$, either $M_{i_1} \cdots M_{i_n}={\bf
+0}$ or $C^{-1} e^{\lambda n} \leq \| M_{i_1} \cdots M_{i_n} \| \leq C
+e^{\lambda n}$, where $\|\cdot\|$ is a matrix norm. The proof is based on
+symbolic dynamics and the thermodynamic formalism for matrix products. As
+applications, we are able to check the absolute continuity of a class of
+overlapping self-similar measures on ${\Bbb R}$, the absolute continuity of
+certain self-affine measures in ${\Bbb R}^d$ and the dimensional regularity of
+a class of sofic affine-invariant sets in the plane.
+",0,0,1,0,0,0
+1261,A definitive improvement of a game-theoretic bound and the long tightness game,"  The main goal of the paper is the full proof of a cardinal inequality for a
+space with points $G_\delta $, obtained with the help of a long version of the
+Menger game. This result, which improves a similar one of Scheepers and Tall,
+was already established by the authors under the Continuum Hypothesis. The
+paper is completed by few remarks on a long version of the tightness game.
+",0,0,1,0,0,0
+1262,Group-Server Queues,"  By analyzing energy-efficient management of data centers, this paper proposes
+and develops a class of interesting {\it Group-Server Queues}, and establishes
+two representative group-server queues through loss networks and impatient
+customers, respectively. Furthermore, such two group-server queues are given
+model descriptions and necessary interpretation. Also, simple mathematical
+discussion is provided, and simulations are made to study the expected queue
+lengths, the expected sojourn times and the expected virtual service times. In
+addition, this paper also shows that this class of group-server queues are
+often encountered in many other practical areas including communication
+networks, manufacturing systems, transportation networks, financial networks
+and healthcare systems. Note that the group-server queues are always used to
+design effectively dynamic control mechanisms through regrouping and
+recombining such many servers in a large-scale service system by means of, for
+example, bilateral threshold control, and customers transfer to the buffer or
+server groups. This leads to the large-scale service system that is divided
+into several adaptive and self-organizing subsystems through scheduling of
+batch customers and regrouping of service resources, which make the middle
+layer of this service system more effectively managed and strengthened under a
+dynamic, real-time and even reward optimal framework. Based on this,
+performance of such a large-scale service system may be improved greatly in
+terms of introducing and analyzing such group-server queues. Therefore, not
+only analysis of group-server queues is regarded as a new interesting research
+direction, but there also exists many theoretical challenges, basic
+difficulties and open problems in the area of queueing networks.
+",1,0,0,0,0,0
+1263,MC$^2$: Multi-wavelength and dynamical analysis of the merging galaxy cluster ZwCl 0008.8+5215: An older and less massive Bullet Cluster,"  We analyze a rich dataset including Subaru/SuprimeCam, HST/ACS and WFC3,
+Keck/DEIMOS, Chandra/ACIS-I, and JVLA/C and D array for the merging galaxy
+cluster ZwCl 0008.8+5215. With a joint Subaru/HST weak gravitational lensing
+analysis, we identify two dominant subclusters and estimate the masses to be
+M$_{200}=\text{5.7}^{+\text{2.8}}_{-\text{1.8}}\times\text{10}^{\text{14}}\,\text{M}_{\odot}$
+and 1.2$^{+\text{1.4}}_{-\text{0.6}}\times10^{14}$ M$_{\odot}$. We estimate the
+projected separation between the two subclusters to be
+924$^{+\text{243}}_{-\text{206}}$ kpc. We perform a clustering analysis on
+confirmed cluster member galaxies and estimate the line of sight velocity
+difference between the two subclusters to be 92$\pm$164 km s$^{-\text{1}}$. We
+further motivate, discuss, and analyze the merger scenario through an analysis
+of the 42 ks of Chandra/ACIS-I and JVLA/C and D polarization data. The X-ray
+surface brightness profile reveals a remnant core reminiscent of the Bullet
+Cluster. The X-ray luminosity in the 0.5-7.0 keV band is
+1.7$\pm$0.1$\times$10$^{\text{44}}$ erg s$^{-\text{1}}$ and the X-ray
+temperature is 4.90$\pm$0.13 keV. The radio relics are polarized up to 40$\%$.
+We implement a Monte Carlo dynamical analysis and estimate the merger velocity
+at pericenter to be 1800$^{+\text{400}}_{-\text{300}}$ km s$^{-\text{1}}$. ZwCl
+0008.8+5215 is a low-mass version of the Bullet Cluster and therefore may prove
+useful in testing alternative models of dark matter. We do not find significant
+offsets between dark matter and galaxies, as the uncertainties are large with
+the current lensing data. Furthermore, in the east, the BCG is offset from
+other luminous cluster galaxies, which poses a puzzle for defining dark matter
+-- galaxy offsets.
+",0,1,0,0,0,0
+1264,Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints,"  Adaptive designs for multi-armed clinical trials have become increasingly
+popular recently in many areas of medical research because of their potential
+to shorten development times and to increase patient response. However,
+developing response-adaptive trial designs that offer patient benefit while
+ensuring the resulting trial avoids bias and provides a statistically rigorous
+comparison of the different treatments included is highly challenging. In this
+paper, the theory of Multi-Armed Bandit Problems is used to define a family of
+near optimal adaptive designs in the context of a clinical trial with a
+normally distributed endpoint with known variance. Through simulation studies
+based on an ongoing trial as a motivation we report the operating
+characteristics (type I error, power, bias) and patient benefit of these
+approaches and compare them to traditional and existing alternative designs.
+These results are then compared to those recently published in the context of
+Bernoulli endpoints. Many limitations and advantages are similar in both cases
+but there are also important differences, specially with respect to type I
+error control. This paper proposes a simulation-based testing procedure to
+correct for the observed type I error inflation that bandit-based and adaptive
+rules can induce. Results presented extend recent work by considering a
+normally distributed endpoint, a very common case in clinical practice yet
+mostly ignored in the response-adaptive theoretical literature, and illustrate
+the potential advantages of using these methods in a rare disease context. We
+also recommend a suitable modified implementation of the bandit-based adaptive
+designs for the case of common diseases.
+",0,0,0,1,0,0
+1265,Convexification of Queueing Formulas by Mixed-Integer Second-Order Cone Programming: An Application to a Discrete Location Problem with Congestion,"  Mixed-Integer Second-Order Cone Programs (MISOCPs) form a nice class of
+mixed-inter convex programs, which can be solved very efficiently due to the
+recent advances in optimization solvers. Our paper bridges the gap between
+modeling a class of optimization problems and using MISOCP solvers. It is shown
+how various performance metrics of M/G/1 queues can be molded by different
+MISOCPs. To motivate our method practically, it is first applied to a
+challenging stochastic location problem with congestion, which is broadly used
+to design socially optimal service networks. Four different MISOCPs are
+developed and compared on sets of benchmark test problems. The new formulations
+efficiently solve large-size test problems, which cannot be solved by the best
+existing method. Then, the general applicability of our method is shown for
+similar optimization problems that use queue-theoretic performance measures to
+address customer satisfaction and service quality.
+",1,0,0,0,0,0
+1266,Discriminative Metric Learning with Deep Forest,"  A Discriminative Deep Forest (DisDF) as a metric learning algorithm is
+proposed in the paper. It is based on the Deep Forest or gcForest proposed by
+Zhou and Feng and can be viewed as a gcForest modification. The case of the
+fully supervised learning is studied when the class labels of individual
+training examples are known. The main idea underlying the algorithm is to
+assign weights to decision trees in random forest in order to reduce distances
+between objects from the same class and to increase them between objects from
+different classes. The weights are training parameters. A specific objective
+function which combines Euclidean and Manhattan distances and simplifies the
+optimization problem for training the DisDF is proposed. The numerical
+experiments illustrate the proposed distance metric algorithm.
+",1,0,0,1,0,0
+1267,An accurate and robust genuinely multidimensional Riemann solver for Euler equations based on TV flux splitting,"  A simple robust genuinely multidimensional convective pressure split (CPS) ,
+contact preserving, shock stable Riemann solver (GM-K-CUSP-X) for Euler
+equations of gas dynamics is developed. The convective and pressure components
+of the Euler system are separated following the Toro-Vazquez type PDE flux
+splitting [Toro et al, 2012]. Upwind discretization of these components are
+achieved using the framework of Mandal et al [Mandal et al, 2015]. The
+robustness of the scheme is studied on a few two dimensional test problems. The
+results demonstrate the efficacy of the scheme over the corresponding
+conventional two state version of the solver. Results from two classic strong
+shock test cases associated with the infamous Carbuncle phenomenon, indicate
+that the present solver is completely free of any such numerical instabilities
+albeit possessing contact resolution abilities.Such a finding emphasizes the
+pre-existing notion about the positive effects that multidimensional flow
+modelling may have towards curing of shock instabilities.
+",0,1,1,0,0,0
+1268,Strong Convergence Rate of Splitting Schemes for Stochastic Nonlinear Schrödinger Equations,"  We prove the optimal strong convergence rate of a fully discrete scheme,
+based on a splitting approach, for a stochastic nonlinear Schrödinger (NLS)
+equation. The main novelty of our method lies on the uniform a priori estimate
+and exponential integrability of a sequence of splitting processes which are
+used to approximate the solution of the stochastic NLS equation. We show that
+the splitting processes converge to the solution with strong order $1/2$. Then
+we use the Crank--Nicolson scheme to temporally discretize the splitting
+process and get the temporal splitting scheme which also possesses strong order
+$1/2$. To obtain a full discretization, we apply this splitting Crank--Nicolson
+scheme to the spatially discrete equation which is achieved through the
+spectral Galerkin approximation. Furthermore, we establish the convergence of
+this fully discrete scheme with optimal strong convergence rate
+$\mathcal{O}(N^{-2}+\tau^\frac12)$, where $N$ denotes the dimension of the
+approximate space and $\tau$ denotes the time step size. To the best of our
+knowledge, this is the first result about strong convergence rates of
+temporally numerical approximations and fully discrete schemes for stochastic
+NLS equations, or even for stochastic partial differential equations (SPDEs)
+with non-monotone coefficients. Numerical experiments verify our theoretical
+result.
+",0,0,1,0,0,0
+1269,Controllability of Conjunctive Boolean Networks with Application to Gene Regulation,"  A Boolean network is a finite state discrete time dynamical system. At each
+step, each variable takes a value from a binary set. The value update rule for
+each variable is a local function which depends only on a selected subset of
+variables. Boolean networks have been used in modeling gene regulatory
+networks. We focus in this paper on a special class of Boolean networks, namely
+the conjunctive Boolean networks (CBNs), whose value update rule is comprised
+of only logic AND operations. It is known that any trajectory of a Boolean
+network will enter a periodic orbit. Periodic orbits of a CBN have been
+completely understood. In this paper, we investigate the orbit-controllability
+and state-controllability of a CBN: We ask the question of how one can steer a
+CBN to enter any periodic orbit or to reach any final state, from any initial
+state. We establish necessary and sufficient conditions for a CBN to be
+orbit-controllable and state-controllable. Furthermore, explicit control laws
+are presented along the analysis.
+",0,0,1,0,0,0
+1270,Fast-neutron and gamma-ray imaging with a capillary liquid xenon converter coupled to a gaseous photomultiplier,"  Gamma-ray and fast-neutron imaging was performed with a novel liquid xenon
+(LXe) scintillation detector read out by a Gaseous Photomultiplier (GPM). The
+100 mm diameter detector prototype comprised a capillary-filled LXe
+converter/scintillator, coupled to a triple-THGEM imaging-GPM, with its first
+electrode coated by a CsI UV-photocathode, operated in Ne/5%CH4 cryogenic
+temperatures. Radiation localization in 2D was derived from
+scintillation-induced photoelectron avalanches, measured on the GPM's segmented
+anode. The localization properties of Co-60 gamma-rays and a mixed
+fast-neutron/gamma-ray field from an AmBe neutron source were derived from
+irradiation of a Pb edge absorber. Spatial resolutions of 12+/-2 mm and 10+/-2
+mm (FWHM) were reached with Co-60 and AmBe sources, respectively. The
+experimental results are in good agreement with GEANT4 simulations. The
+calculated ultimate expected resolutions for our application-relevant 4.4 and
+15.1 MeV gamma-rays and 1-15 MeV neutrons are 2-4 mm and ~2 mm (FWHM),
+respectively. These results indicate the potential applicability of the new
+detector concept to Fast-Neutron Resonance Radiography (FNRR) and
+Dual-Discrete-Energy Gamma Radiography (DDEGR) of large objects.
+",0,1,0,0,0,0
+1271,Is Task Board Customization Beneficial? - An Eye Tracking Study,"  The task board is an essential artifact in many agile development approaches.
+It provides a good overview of the project status. Teams often customize their
+task boards according to the team members' needs. They modify the structure of
+boards, define colored codings for different purposes, and introduce different
+card sizes. Although the customizations are intended to improve the task
+board's usability and effectiveness, they may also complicate its comprehension
+and use. The increased effort impedes the work of both the team and team
+externals. Hence, task board customization is in conflict with the agile
+practice of fast and easy overview for everyone. In an eye tracking study with
+30 participants, we compared an original task board design with three
+customized ones to investigate which design shortened the required time to
+identify a particular story card. Our findings yield that only the customized
+task board design with modified structures reduces the required time. The
+original task board design is more beneficial than individual colored codings
+and changed card sizes. According to our findings, agile teams should rethink
+their current task board design. They may be better served by focusing on the
+original task board design and by applying only carefully selected adjustments.
+In case of customization, a task board's structure should be adjusted since
+this is the only beneficial kind of customization, that additionally complies
+more precisely with the concept of fast and easy project overview.
+",1,0,0,0,0,0
+1272,Characterizing the impact of model error in hydrogeologic time series recovery inverse problems,"  Hydrogeologic models are commonly over-smoothed relative to reality, owing to
+the difficulty of obtaining accurate high-resolution information about the
+subsurface. When used in an inversion context, such models may introduce
+systematic biases which cannot be encapsulated by an unbiased ""observation
+noise"" term of the type assumed by standard regularization theory and typical
+Bayesian formulations. Despite its importance, model error is difficult to
+encapsulate systematically and is often neglected. Here, model error is
+considered for a hydrogeologically important class of inverse problems that
+includes interpretation of hydraulic transients and contaminant source history
+inference: reconstruction of a time series that has been convolved against a
+transfer function (i.e., impulse response) that is only approximately known.
+Using established harmonic theory along with two results established here
+regarding triangular Toeplitz matrices, upper and lower error bounds are
+derived for the effect of systematic model error on time series recovery for
+both well-determined and over-determined inverse problems. A Monte Carlo study
+of a realistic hydraulic reconstruction problem is presented, and the lower
+error bound is seen informative about expected behavior. A possible diagnostic
+criterion for blind transfer function characterization is also uncovered.
+",0,0,1,0,0,0
+1273,Suszko's Problem: Mixed Consequence and Compositionality,"  Suszko's problem is the problem of finding the minimal number of truth values
+needed to semantically characterize a syntactic consequence relation. Suszko
+proved that every Tarskian consequence relation can be characterized using only
+two truth values. Malinowski showed that this number can equal three if some of
+Tarski's structural constraints are relaxed. By so doing, Malinowski introduced
+a case of so-called mixed consequence, allowing the notion of a designated
+value to vary between the premises and the conclusions of an argument. In this
+paper we give a more systematic perspective on Suszko's problem and on mixed
+consequence. First, we prove general representation theorems relating
+structural properties of a consequence relation to their semantic
+interpretation, uncovering the semantic counterpart of substitution-invariance,
+and establishing that (intersective) mixed consequence is fundamentally the
+semantic counterpart of the structural property of monotonicity. We use those
+to derive maximum-rank results proved recently in a different setting by French
+and Ripley, as well as by Blasio, Marcos and Wansing, for logics with various
+structural properties (reflexivity, transitivity, none, or both). We strengthen
+these results into exact rank results for non-permeable logics (roughly, those
+which distinguish the role of premises and conclusions). We discuss the
+underlying notion of rank, and the associated reduction proposed independently
+by Scott and Suszko. As emphasized by Suszko, that reduction fails to preserve
+compositionality in general, meaning that the resulting semantics is no longer
+truth-functional. We propose a modification of that notion of reduction,
+allowing us to prove that over compact logics with what we call regular
+connectives, rank results are maintained even if we request the preservation of
+truth-functionality and additional semantic properties.
+",1,0,1,0,0,0
+1274,Optimization of distributions differences for classification,"  In this paper we introduce a new classification algorithm called Optimization
+of Distributions Differences (ODD). The algorithm aims to find a transformation
+from the feature space to a new space where the instances in the same class are
+as close as possible to one another while the gravity centers of these classes
+are as far as possible from one another. This aim is formulated as a
+multiobjective optimization problem that is solved by a hybrid of an
+evolutionary strategy and the Quasi-Newton method. The choice of the
+transformation function is flexible and could be any continuous space function.
+We experiment with a linear and a non-linear transformation in this paper. We
+show that the algorithm can outperform 6 other state-of-the-art classification
+methods, namely naive Bayes, support vector machines, linear discriminant
+analysis, multi-layer perceptrons, decision trees, and k-nearest neighbors, in
+12 standard classification datasets. Our results show that the method is less
+sensitive to the imbalanced number of instances comparing to these methods. We
+also show that ODD maintains its performance better than other classification
+methods in these datasets, hence, offers a better generalization ability.
+",1,0,0,1,0,0
+1275,Galois descent of semi-affinoid spaces,"  We study the Galois descent of semi-affinoid non-archimedean analytic spaces.
+These are the non-archimedean analytic spaces which admit an affine special
+formal scheme as model over a complete discrete valuation ring, such as for
+example open or closed polydiscs or polyannuli. Using Weil restrictions and
+Galois fixed loci for semi-affinoid spaces and their formal models, we describe
+a formal model of a $K$-analytic space $X$, provided that $X\otimes_KL$ is
+semi-affinoid for some finite tamely ramified extension $L$ of $K$. As an
+application, we study the forms of analytic annuli that are trivialized by a
+wide class of Galois extensions that includes totally tamely ramified
+extensions. In order to do so, we first establish a Weierstrass preparation
+result for analytic functions on annuli, and use it to linearize finite order
+automorphisms of annuli. Finally, we explain how from these results one can
+deduce a non-archimedean analytic proof of the existence of resolutions of
+singularities of surfaces in characteristic zero.
+",0,0,1,0,0,0
+1276,"Synthesis, Crystal Structure, and Physical Properties of New Layered Oxychalcogenide La2O2Bi3AgS6","  We have synthesized a new layered oxychalcogenide La2O2Bi3AgS6. From
+synchrotron X-ray diffraction and Rietveld refinement, the crystal structure of
+La2O2Bi3AgS6 was refined using a model of the P4/nmm space group with a =
+4.0644(1) {\AA} and c = 19.412(1) {\AA}, which is similar to the related
+compound LaOBiPbS3, while the interlayer bonds (M2-S1 bonds) are apparently
+shorter in La2O2Bi3AgS6. The tunneling electron microscopy (TEM) image
+confirmed the lattice constant derived from Rietveld refinement (c ~ 20 {\AA}).
+The electrical resistivity and Seebeck coefficient suggested that the
+electronic states of La2O2Bi3AgS6 are more metallic than those of LaOBiS2 and
+LaOBiPbS3. The insertion of a rock-salt-type chalcogenide into the van der
+Waals gap of BiS2-based layered compounds, such as LaOBiS2, will be a useful
+strategy for designing new layered functional materials in the layered
+chalcogenide family.
+",0,1,0,0,0,0
+1277,Planar Graph Perfect Matching is in NC,"  Is perfect matching in NC? That is, is there a deterministic fast parallel
+algorithm for it? This has been an outstanding open question in theoretical
+computer science for over three decades, ever since the discovery of RNC
+matching algorithms. Within this question, the case of planar graphs has
+remained an enigma: On the one hand, counting the number of perfect matchings
+is far harder than finding one (the former is #P-complete and the latter is in
+P), and on the other, for planar graphs, counting has long been known to be in
+NC whereas finding one has resisted a solution.
+In this paper, we give an NC algorithm for finding a perfect matching in a
+planar graph. Our algorithm uses the above-stated fact about counting matchings
+in a crucial way. Our main new idea is an NC algorithm for finding a face of
+the perfect matching polytope at which $\Omega(n)$ new conditions, involving
+constraints of the polytope, are simultaneously satisfied. Several other ideas
+are also needed, such as finding a point in the interior of the minimum weight
+face of this polytope and finding a balanced tight odd set in NC.
+",1,0,0,0,0,0
+1278,Recommendation under Capacity Constraints,"  In this paper, we investigate the common scenario where every candidate item
+for recommendation is characterized by a maximum capacity, i.e., number of
+seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the
+prevalence of the task of recommending items under capacity constraints in a
+variety of settings, to the best of our knowledge, none of the known
+recommender methods is designed to respect capacity constraints. To close this
+gap, we extend three state-of-the art latent factor recommendation approaches:
+probabilistic matrix factorization (PMF), geographical matrix factorization
+(GeoMF), and bayesian personalized ranking (BPR), to optimize for both
+recommendation accuracy and expected item usage that respects the capacity
+constraints. We introduce the useful concepts of user propensity to listen and
+item capacity. Our experimental results in real-world datasets, both for the
+domain of item recommendation and POI recommendation, highlight the benefit of
+our method for the setting of recommendation under capacity constraints.
+",1,0,0,1,0,0
+1279,Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles,"  Using deep reinforcement learning, we train control policies for autonomous
+vehicles leading a platoon of vehicles onto a roundabout. Using Flow, a library
+for deep reinforcement learning in micro-simulators, we train two policies, one
+policy with noise injected into the state and action space and one without any
+injected noise. In simulation, the autonomous vehicle learns an emergent
+metering behavior for both policies in which it slows to allow for smoother
+merging. We then directly transfer this policy without any tuning to the
+University of Delaware Scaled Smart City (UDSSC), a 1:25 scale testbed for
+connected and automated vehicles. We characterize the performance of both
+policies on the scaled city. We show that the noise-free policy winds up
+crashing and only occasionally metering. However, the noise-injected policy
+consistently performs the metering behavior and remains collision-free,
+suggesting that the noise helps with the zero-shot policy transfer.
+Additionally, the transferred, noise-injected policy leads to a 5% reduction of
+average travel time and a reduction of 22% in maximum travel time in the UDSSC.
+Videos of the controllers can be found at
+this https URL.
+",1,0,0,0,0,0
+1280,Relative Singularity Categories,"  We study the following generalization of singularity categories. Let X be a
+quasi-projective Gorenstein scheme with isolated singularities and A a
+non-commutative resolution of singularities of X in the sense of Van den Bergh.
+We introduce the relative singularity category as the Verdier quotient of the
+bounded derived category of coherent sheaves on A modulo the category of
+perfect complexes on X. We view it as a measure for the difference between X
+and A. The main results of this thesis are the following.
+(i) We prove an analogue of Orlov's localization result in our setup. If X
+has isolated singularities, then this reduces the study of the relative
+singularity categories to the affine case.
+(ii) We prove Hom-finiteness and idempotent completeness of the relative
+singularity categories in the complete local situation and determine its
+Grothendieck group.
+(iii) We give a complete and explicit description of the relative singularity
+categories when X has only nodal singularities and the resolution is given by a
+sheaf of Auslander algebras.
+(iv) We study relations between relative singularity categories and classical
+singularity categories. For a simple hypersurface singularity and its Auslander
+resolution, we show that these categories determine each other.
+(v) The developed technique leads to the following `purely commutative'
+application: a description of Iyama & Wemyss triangulated category for rational
+surface singularities in terms of the singularity category of the rational
+double point resolution.
+(vi) We give a description of singularity categories of gentle algebras.
+",0,0,1,0,0,0
+1281,Challenges to Keeping the Computer Industry Centered in the US,"  It is undeniable that the worldwide computer industry's center is the US,
+specifically in Silicon Valley. Much of the reason for the success of Silicon
+Valley had to do with Moore's Law: the observation by Intel co-founder Gordon
+Moore that the number of transistors on a microchip doubled at a rate of
+approximately every two years. According to the International Technology
+Roadmap for Semiconductors, Moore's Law will end in 2021. How can we rethink
+computing technology to restart the historic explosive performance growth?
+Since 2012, the IEEE Rebooting Computing Initiative (IEEE RCI) has been working
+with industry and the US government to find new computing approaches to answer
+this question. In parallel, the CCC has held a number of workshops addressing
+similar questions. This whitepaper summarizes some of the IEEE RCI and CCC
+findings. The challenge for the US is to lead this new era of computing. Our
+international competitors are not sitting still: China has invested
+significantly in a variety of approaches such as neuromorphic computing, chip
+fabrication facilities, computer architecture, and high-performance simulation
+and data analytics computing, for example. We must act now, otherwise, the
+center of the computer industry will move from Silicon Valley and likely move
+off shore entirely.
+",1,0,0,0,0,0
+1282,"Contiguous Relations, Laplace's Methods and Continued Fractions for 3F2(1)","  Using contiguous relations we construct an infinite number of continued
+fraction expansions for ratios of generalized hypergeometric series 3F2(1). We
+establish exact error term estimates for their approximants and prove their
+rapid convergences. To do so we develop a discrete version of Laplace's method
+for hypergeometric series in addition to the use of ordinary (continuous)
+Laplace's method for Euler's hypergeometric integrals.
+",0,0,1,0,0,0
+1283,Arimoto-Rényi Conditional Entropy and Bayesian $M$-ary Hypothesis Testing,"  This paper gives upper and lower bounds on the minimum error probability of
+Bayesian $M$-ary hypothesis testing in terms of the Arimoto-Rényi conditional
+entropy of an arbitrary order $\alpha$. The improved tightness of these bounds
+over their specialized versions with the Shannon conditional entropy
+($\alpha=1$) is demonstrated. In particular, in the case where $M$ is finite,
+we show how to generalize Fano's inequality under both the conventional and
+list-decision settings. As a counterpart to the generalized Fano's inequality,
+allowing $M$ to be infinite, a lower bound on the Arimoto-Rényi conditional
+entropy is derived as a function of the minimum error probability. Explicit
+upper and lower bounds on the minimum error probability are obtained as a
+function of the Arimoto-Rényi conditional entropy for both positive and
+negative $\alpha$. Furthermore, we give upper bounds on the minimum error
+probability as functions of the Rényi divergence. In the setup of discrete
+memoryless channels, we analyze the exponentially vanishing decay of the
+Arimoto-Rényi conditional entropy of the transmitted codeword given the
+channel output when averaged over a random coding ensemble.
+",1,0,1,1,0,0
+1284,A note on the violation of Bell's inequality,"  With Bell's inequalities one has a formal expression to show how essentially
+all local theories of natural phenomena that are formulated within the
+framework of realism may be tested using a simple experimental arrangement. For
+the case of entangled pairs of spin-1/2 particles we propose an alternative
+measurement setup which is consistent to the necessary assumptions
+corresponding to the derivation of the Bell inequalities. We find that the Bell
+inequalities are never violated with respect to our suggested measurement
+process.
+",0,1,0,0,0,0
+1285,Real-Time Model Predictive Control for Energy Management in Autonomous Underwater Vehicle,"  Improving endurance is crucial for extending the spatial and temporal
+operation range of autonomous underwater vehicles (AUVs). Considering the
+hardware constraints and the performance requirements, an intelligent energy
+management system is required to extend the operation range of AUVs. This paper
+presents a novel model predictive control (MPC) framework for energy-optimal
+point-to-point motion control of an AUV. In this scheme, the energy management
+problem of an AUV is reformulated as a surge motion optimization problem in two
+stages. First, a system-level energy minimization problem is solved by managing
+the trade-off between the energies required for overcoming the positive
+buoyancy and surge drag force in static optimization. Next, an MPC with a
+special cost function formulation is proposed to deal with transients and
+system dynamics. A switching logic for handling the transition between the
+static and dynamic stages is incorporated to reduce the computational efforts.
+Simulation results show that the proposed method is able to achieve
+near-optimal energy consumption with considerable lower computational
+complexity.
+",1,0,0,0,0,0
+1286,Surjective H-Colouring over Reflexive Digraphs,"  The Surjective H-Colouring problem is to test if a given graph allows a
+vertex-surjective homomorphism to a fixed graph H. The complexity of this
+problem has been well studied for undirected (partially) reflexive graphs. We
+introduce endo-triviality, the property of a structure that all of its
+endomorphisms that do not have range of size 1 are automorphisms, as a means to
+obtain complexity-theoretic classifications of Surjective H-Colouring in the
+case of reflexive digraphs.
+Chen [2014] proved, in the setting of constraint satisfaction problems, that
+Surjective H-Colouring is NP-complete if H has the property that all of its
+polymorphisms are essentially unary. We give the first concrete application of
+his result by showing that every endo-trivial reflexive digraph H has this
+property. We then use the concept of endo-triviality to prove, as our main
+result, a dichotomy for Surjective H-Colouring when H is a reflexive
+tournament: if H is transitive, then Surjective H-Colouring is in NL, otherwise
+it is NP-complete.
+By combining this result with some known and new results we obtain a
+complexity classification for Surjective H-Colouring when H is a partially
+reflexive digraph of size at most 3.
+",1,0,0,0,0,0
+1287,A modal typing system for self-referential programs and specifications,"  This paper proposes a modal typing system that enables us to handle
+self-referential formulae, including ones with negative self-references, which
+on one hand, would introduce a logical contradiction, namely Russell's paradox,
+in the conventional setting, while on the other hand, are necessary to capture
+a certain class of programs such as fixed-point combinators and objects with
+so-called binary methods in object-oriented programming. The proposed system
+provides a basis for axiomatic semantics of such a wider range of programs and
+a new framework for natural construction of recursive programs in the
+proofs-as-programs paradigm.
+",1,0,0,0,0,0
+1288,Scalable Realistic Recommendation Datasets through Fractal Expansions,"  Recommender System research suffers currently from a disconnect between the
+size of academic data sets and the scale of industrial production systems. In
+order to bridge that gap we propose to generate more massive user/item
+interaction data sets by expanding pre-existing public data sets. User/item
+incidence matrices record interactions between users and items on a given
+platform as a large sparse matrix whose rows correspond to users and whose
+columns correspond to items. Our technique expands such matrices to larger
+numbers of rows (users), columns (items) and non zero values (interactions)
+while preserving key higher order statistical properties. We adapt the
+Kronecker Graph Theory to user/item incidence matrices and show that the
+corresponding fractal expansions preserve the fat-tailed distributions of user
+engagements, item popularity and singular value spectra of user/item
+interaction matrices. Preserving such properties is key to building large
+realistic synthetic data sets which in turn can be employed reliably to
+benchmark Recommender Systems and the systems employed to train them. We
+provide algorithms to produce such expansions and apply them to the MovieLens
+20 million data set comprising 20 million ratings of 27K movies by 138K users.
+The resulting expanded data set has 10 billion ratings, 2 million items and
+864K users in its smaller version and can be scaled up or down. A larger
+version features 655 billion ratings, 7 million items and 17 million users.
+",1,0,0,1,0,0
+1289,Implementation of a Distributed Coherent Quantum Observer,"  This paper considers the problem of implementing a previously proposed
+distributed direct coupling quantum observer for a closed linear quantum
+system. By modifying the form of the previously proposed observer, the paper
+proposes a possible experimental implementation of the observer plant system
+using a non-degenerate parametric amplifier and a chain of optical cavities
+which are coupled together via optical interconnections. It is shown that the
+distributed observer converges to a consensus in a time averaged sense in which
+an output of each element of the observer estimates the specified output of the
+quantum plant.
+",1,0,1,0,0,0
+1290,Stacking and stability,"  Stacking is a general approach for combining multiple models toward greater
+predictive accuracy. It has found various application across different domains,
+ensuing from its meta-learning nature. Our understanding, nevertheless, on how
+and why stacking works remains intuitive and lacking in theoretical insight. In
+this paper, we use the stability of learning algorithms as an elemental
+analysis framework suitable for addressing the issue. To this end, we analyze
+the hypothesis stability of stacking, bag-stacking, and dag-stacking and
+establish a connection between bag-stacking and weighted bagging. We show that
+the hypothesis stability of stacking is a product of the hypothesis stability
+of each of the base models and the combiner. Moreover, in bag-stacking and
+dag-stacking, the hypothesis stability depends on the sampling strategy used to
+generate the training set replicates. Our findings suggest that 1) subsampling
+and bootstrap sampling improve the stability of stacking, and 2) stacking
+improves the stability of both subbagging and bagging.
+",1,0,0,1,0,0
+1291,Accelerating Discrete Wavelet Transforms on GPUs,"  The two-dimensional discrete wavelet transform has a huge number of
+applications in image-processing techniques. Until now, several papers compared
+the performance of such transform on graphics processing units (GPUs). However,
+all of them only dealt with lifting and convolution computation schemes. In
+this paper, we show that corresponding horizontal and vertical lifting parts of
+the lifting scheme can be merged into non-separable lifting units, which halves
+the number of steps. We also discuss an optimization strategy leading to a
+reduction in the number of arithmetic operations. The schemes were assessed
+using the OpenCL and pixel shaders. The proposed non-separable lifting scheme
+outperforms the existing schemes in many cases, irrespective of its higher
+complexity.
+",1,0,0,0,0,0
+1292,Accelerated Consensus via Min-Sum Splitting,"  We apply the Min-Sum message-passing protocol to solve the consensus problem
+in distributed optimization. We show that while the ordinary Min-Sum algorithm
+does not converge, a modified version of it known as Splitting yields
+convergence to the problem solution. We prove that a proper choice of the
+tuning parameters allows Min-Sum Splitting to yield subdiffusive accelerated
+convergence rates, matching the rates obtained by shift-register methods. The
+acceleration scheme embodied by Min-Sum Splitting for the consensus problem
+bears similarities with lifted Markov chains techniques and with multi-step
+first order methods in convex optimization.
+",0,0,1,0,0,0
+1293,Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models,"  In the last few years, we have seen the transformative impact of deep
+learning in many applications, particularly in speech recognition and computer
+vision. Inspired by Google's Inception-ResNet deep convolutional neural network
+(CNN) for image classification, we have developed ""Chemception"", a deep CNN for
+the prediction of chemical properties, using just the images of 2D drawings of
+molecules. We develop Chemception without providing any additional explicit
+chemistry knowledge, such as basic concepts like periodicity, or advanced
+features like molecular descriptors and fingerprints. We then show how
+Chemception can serve as a general-purpose neural network architecture for
+predicting toxicity, activity, and solvation properties when trained on a
+modest database of 600 to 40,000 compounds. When compared to multi-layer
+perceptron (MLP) deep neural networks trained with ECFP fingerprints,
+Chemception slightly outperforms in activity and solvation prediction and
+slightly underperforms in toxicity prediction. Having matched the performance
+of expert-developed QSAR/QSPR deep learning models, our work demonstrates the
+plausibility of using deep neural networks to assist in computational chemistry
+research, where the feature engineering process is performed primarily by a
+deep learning algorithm.
+",1,0,0,1,0,0
+1294,Constraining the Milky Way assembly history with Galactic Archaeology. Ludwig Biermann Award Lecture 2015,"  The aim of Galactic Archaeology is to recover the evolutionary history of the
+Milky Way from its present day kinematical and chemical state. Because stars
+move away from their birth sites, the current dynamical information alone is
+not sufficient for this task. The chemical composition of stellar atmospheres,
+on the other hand, is largely preserved over the stellar lifetime and, together
+with accurate ages, can be used to recover the birthplaces of stars currently
+found at the same Galactic radius. In addition to the availability of large
+stellar samples with accurate 6D kinematics and chemical abundance
+measurements, this requires detailed modeling with both dynamical and chemical
+evolution taken into account. An important first step is to understand the
+variety of dynamical processes that can take place in the Milky Way, including
+the perturbative effects of both internal (bar and spiral structure) and
+external (infalling satellites) agents. We discuss here (1) how to constrain
+the Galactic bar, spiral structure, and merging satellites by their effect on
+the local and global disc phase-space, (2) the effect of multiple patterns on
+the disc dynamics, and (3) the importance of radial migration and merger
+perturbations for the formation of the Galactic thick disc. Finally, we discuss
+the construction of Milky Way chemo-dynamical models and relate to
+observations.
+",0,1,0,0,0,0
+1295,A unified view of entropy-regularized Markov decision processes,"  We propose a general framework for entropy-regularized average-reward
+reinforcement learning in Markov decision processes (MDPs). Our approach is
+based on extending the linear-programming formulation of policy optimization in
+MDPs to accommodate convex regularization functions. Our key result is showing
+that using the conditional entropy of the joint state-action distributions as
+regularization yields a dual optimization problem closely resembling the
+Bellman optimality equations. This result enables us to formalize a number of
+state-of-the-art entropy-regularized reinforcement learning algorithms as
+approximate variants of Mirror Descent or Dual Averaging, and thus to argue
+about the convergence properties of these methods. In particular, we show that
+the exact version of the TRPO algorithm of Schulman et al. (2015) actually
+converges to the optimal policy, while the entropy-regularized policy gradient
+methods of Mnih et al. (2016) may fail to converge to a fixed point. Finally,
+we illustrate empirically the effects of using various regularization
+techniques on learning performance in a simple reinforcement learning setup.
+",1,0,0,1,0,0
+1296,Arithmetic Circuits for Multilevel Qudits Based on Quantum Fourier Transform,"  We present some basic integer arithmetic quantum circuits, such as adders and
+multipliers-accumulators of various forms, as well as diagonal operators, which
+operate on multilevel qudits. The integers to be processed are represented in
+an alternative basis after they have been Fourier transformed. Several
+arithmetic circuits operating on Fourier transformed integers have appeared in
+the literature for two level qubits. Here we extend these techniques on
+multilevel qudits, as they may offer some advantages relative to qubits
+implementations. The arithmetic circuits presented can be used as basic
+building blocks for higher level algorithms such as quantum phase estimation,
+quantum simulation, quantum optimization etc., but they can also be used in the
+implementation of a quantum fractional Fourier transform as it is shown in a
+companion work presented separately.
+",1,0,0,0,0,0
+1297,Quantifying the distribution of editorial power and manuscript decision bias at the mega-journal PLOS ONE,"  We analyzed the longitudinal activity of nearly 7,000 editors at the
+mega-journal PLOS ONE over the 10-year period 2006-2015. Using the
+article-editor associations, we develop editor-specific measures of power,
+activity, article acceptance time, citation impact, and editorial renumeration
+(an analogue to self-citation). We observe remarkably high levels of power
+inequality among the PLOS ONE editors, with the top-10 editors responsible for
+3,366 articles -- corresponding to 2.4% of the 141,986 articles we analyzed.
+Such high inequality levels suggest the presence of unintended incentives,
+which may reinforce unethical behavior in the form of decision-level biases at
+the editorial level. Our results indicate that editors may become apathetic in
+judging the quality of articles and susceptible to modes of power-driven
+misconduct. We used the longitudinal dimension of editor activity to develop
+two panel regression models which test and verify the presence of editor-level
+bias. In the first model we analyzed the citation impact of articles, and in
+the second model we modeled the decision time between an article being
+submitted and ultimately accepted by the editor. We focused on two variables
+that represent social factors that capture potential conflicts-of-interest: (i)
+we accounted for the social ties between editors and authors by developing a
+measure of repeat authorship among an editor's article set, and (ii) we
+accounted for the rate of citations directed towards the editor's own
+publications in the reference list of each article he/she oversaw. Our results
+indicate that these two factors play a significant role in the editorial
+decision process. Moreover, these two effects appear to increase with editor
+age, which is consistent with behavioral studies concerning the evolution of
+misbehavior and response to temptation in power-driven environments.
+",1,1,0,0,0,0
+1298,Threshold Constraints with Guarantees for Parity Objectives in Markov Decision Processes,"  The beyond worst-case synthesis problem was introduced recently by Bruyère
+et al. [BFRR14]: it aims at building system controllers that provide strict
+worst-case performance guarantees against an antagonistic environment while
+ensuring higher expected performance against a stochastic model of the
+environment. Our work extends the framework of [BFRR14] and follow-up papers,
+which focused on quantitative objectives, by addressing the case of
+$\omega$-regular conditions encoded as parity objectives, a natural way to
+represent functional requirements of systems.
+We build strategies that satisfy a main parity objective on all plays, while
+ensuring a secondary one with sufficient probability. This setting raises new
+challenges in comparison to quantitative objectives, as one cannot easily mix
+different strategies without endangering the functional properties of the
+system. We establish that, for all variants of this problem, deciding the
+existence of a strategy lies in ${\sf NP} \cap {\sf coNP}$, the same complexity
+class as classical parity games. Hence, our framework provides additional
+modeling power while staying in the same complexity class.
+[BFRR14] Véronique Bruyère, Emmanuel Filiot, Mickael Randour, and
+Jean-François Raskin. Meet your expectations with guarantees: Beyond
+worst-case synthesis in quantitative games. In Ernst W. Mayr and Natacha
+Portier, editors, 31st International Symposium on Theoretical Aspects of
+Computer Science, STACS 2014, March 5-8, 2014, Lyon, France, volume 25 of
+LIPIcs, pages 199-213. Schloss Dagstuhl - Leibniz - Zentrum fuer Informatik,
+2014.
+",1,0,1,0,0,0
+1299,GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures,"  VAEs (Variational AutoEncoders) have proved to be powerful in the context of
+density modeling and have been used in a variety of contexts for creative
+purposes. In many settings, the data we model possesses continuous attributes
+that we would like to take into account at generation time. We propose in this
+paper GLSR-VAE, a Geodesic Latent Space Regularization for the Variational
+AutoEncoder architecture and its generalizations which allows a fine control on
+the embedding of the data into the latent space. When augmenting the VAE loss
+with this regularization, changes in the learned latent space reflects changes
+of the attributes of the data. This deeper understanding of the VAE latent
+space structure offers the possibility to modulate the attributes of the
+generated data in a continuous way. We demonstrate its efficiency on a
+monophonic music generation task where we manage to generate variations of
+discrete sequences in an intended and playful way.
+",1,0,0,1,0,0
+1300,Timing Solution and Single-pulse Properties for Eight Rotating Radio Transients,"  Rotating radio transients (RRATs), loosely defined as objects that are
+discovered through only their single pulses, are sporadic pulsars that have a
+wide range of emission properties. For many of them, we must measure their
+periods and determine timing solutions relying on the timing of their
+individual pulses, while some of the less sporadic RRATs can be timed by using
+folding techniques as we do for other pulsars. Here, based on Parkes and Green
+Bank Telescope (GBT) observations, we introduce our results on eight RRATs
+including their timing-derived rotation parameters, positions, and dispersion
+measures (DMs), along with a comparison of the spin-down properties of RRATs
+and normal pulsars. Using data for 24 RRATs, we find that their period
+derivatives are generally larger than those of normal pulsars, independent of
+any intrinsic correlation with period, indicating that RRATs' highly sporadic
+emission may be associated with intrinsically larger magnetic fields. We carry
+out Lomb$-$Scargle tests to search for periodicities in RRATs' pulse detection
+times with long timescales. Periodicities are detected for all targets, with
+significant candidates of roughly 3.4 hr for PSR J1623$-$0841 and 0.7 hr for
+PSR J1839$-$0141. We also analyze their single-pulse amplitude distributions,
+finding that log-normal distributions provide the best fits, as is the case for
+most pulsars. However, several RRATs exhibit power-law tails, as seen for
+pulsars emitting giant pulses. This, along with consideration of the selection
+effects against the detection of weak pulses, imply that RRAT pulses generally
+represent the tail of a normal intensity distribution.
+",0,1,0,0,0,0
+1301,Strong and broadly tunable plasmon resonances in thick films of aligned carbon nanotubes,"  Low-dimensional plasmonic materials can function as high quality terahertz
+and infrared antennas at deep subwavelength scales. Despite these antennas'
+strong coupling to electromagnetic fields, there is a pressing need to further
+strengthen their absorption. We address this problem by fabricating thick films
+of aligned, uniformly sized carbon nanotubes and showing that their plasmon
+resonances are strong, narrow, and broadly tunable. With thicknesses ranging
+from 25 to 250 nm, our films exhibit peak attenuation reaching 70%, quality
+factors reaching 9, and electrostatically tunable peak frequencies by a factor
+of 2.3x. Excellent nanotube alignment leads to the attenuation being 99%
+linearly polarized along the nanotube axis. Increasing the film thickness
+blueshifts the plasmon resonators down to peak wavelengths as low as 1.4
+micrometers, promoting them to a new near-infrared regime in which they can
+both overlap the S11 nanotube exciton energy and access the technologically
+important infrared telecom band.
+",0,1,0,0,0,0
+1302,The coordination of centralised and distributed generation,"  In this paper, we analyse the interaction between centralised carbon emissive
+technologies and distributed intermittent non-emissive technologies. A
+representative consumer can satisfy his electricity demand by investing in
+distributed generation (solar panels) and by buying power from a centralised
+firm at a price the firm sets. Distributed generation is intermittent and
+induces an externality cost to the consumer. The firm provides non-random
+electricity generation subject to a carbon tax and to transmission costs. The
+objective of the consumer is to satisfy her demand while minimising investment
+costs, payments to the firm, and intermittency costs. The objective of the firm
+is to satisfy the consumer's residual demand while minimising investment costs,
+demand deviation costs, and maximising the payments from the consumer. We
+formulate the investment decisions as McKean-Vlasov control problems with
+stochastic coefficients. We provide explicit, price model-free solutions to the
+optimal decision problems faced by each player, the solution of the Pareto
+optimum, and the laissez-faire market situation represented by a Stackelberg
+equilibrium where the firm is the leader. We find that, from the social
+planner's point of view, the high adjustment cost of centralised technology
+damages the development of distributed generation. The Stackelberg equilibrium
+leads to significant deviation from the socially desirable ratio of centralised
+versus distributed generation. In a situation where a power system is to be
+built from zero, the optimal strategy of the firm is high price/low
+market-share, but is low price/large market share for existing power systems.
+Further, from a regulation policy, we find that a carbon tax or a subsidy to
+distributed technology has the same efficiency in achieving a given level of
+distributed generation.
+",0,0,1,0,0,0
+1303,Computation of annular capacity by Hamiltonian Floer theory of non-contractible periodic trajectories,"  The first author introduced a relative symplectic capacity $C$ for a
+symplectic manifold $(N,\omega_N)$ and its subset $X$ which measures the
+existence of non-contractible periodic trajectories of Hamiltonian isotopies on
+the product of $N$ with the annulus $A_R=(R,R)\times\mathbb{R}/\mathbb{Z}$. In
+the present paper, we give an exact computation of the capacity $C$ of the
+$2n$-torus $\mathbb{T}^{2n}$ relative to a Lagrangian submanifold
+$\mathbb{T}^n$ which implies the existence of non-contractible Hamiltonian
+periodic trajectories on $A_R\times\mathbb{T}^{2n}$. Moreover, we give a lower
+bound on the number of such trajectories.
+",0,0,1,0,0,0
+1304,A New Torsion Pendulum for Gravitational Reference Sensor Technology Development,"  We report on the design and sensitivity of a new torsion pendulum for
+measuring the performance of ultra-precise inertial sensors and for the
+development of associated technologies for space-based gravitational wave
+observatories and geodesy missions. The apparatus comprises a 1 m-long, 50
+um-diameter, tungsten fiber that supports an inertial member inside a vacuum
+system. The inertial member is an aluminum crossbar with four hollow cubic test
+masses at each end. This structure converts the rotation of the torsion
+pendulum into translation of the test masses. Two test masses are enclosed in
+capacitive sensors which provide readout and actuation. These test masses are
+electrically insulated from the rest of the cross-bar and their electrical
+charge is controlled by photoemission using fiber-coupled ultraviolet light
+emitting diodes. The capacitive readout measures the test mass displacement
+with a broadband sensitivity of 30 nm / sqrt(Hz), and is complemented by a
+laser interferometer with a sensitivity of about 0.5 nm / sqrt(Hz). The
+performance of the pendulum, as determined by the measured residual torque
+noise and expressed in terms of equivalent force acting on a single test mass,
+is roughly 200 fN / sqrt(Hz) around 2 mHz, which is about a factor of 20 above
+the thermal noise limit of the fiber.
+",0,1,0,0,0,0
+1305,Optical Angular Momentum in Classical Electrodynamics,"  Invoking Maxwell's classical equations in conjunction with expressions for
+the electromagnetic (EM) energy, momentum, force, and torque, we use a few
+simple examples to demonstrate the nature of the EM angular momentum. The
+energy and the angular momentum of an EM field will be shown to have an
+intimate relationship; a source radiating EM angular momentum will, of
+necessity, pick up an equal but opposite amount of mechanical angular momentum;
+and the spin and orbital angular momenta of the EM field, when absorbed by a
+small particle, will be seen to elicit different responses from the particle.
+",0,1,0,0,0,0
+1306,Efficient variational Bayesian neural network ensembles for outlier detection,"  In this work we perform outlier detection using ensembles of neural networks
+obtained by variational approximation of the posterior in a Bayesian neural
+network setting. The variational parameters are obtained by sampling from the
+true posterior by gradient descent. We show our outlier detection results are
+comparable to those obtained using other efficient ensembling methods.
+",1,0,0,1,0,0
+1307,Emergent high-spin state above 7 GPa in superconducting FeSe,"  The local electronic and magnetic properties of superconducting FeSe have
+been investigated by K$\beta$ x-ray emission (XES) and simultaneous x-ray
+absorption spectroscopy (XAS) at the Fe K-edge at high pressure and low
+temperature. Our results indicate a sluggish decrease of the local Fe spin
+moment under pressure up to 7~GPa, in line with previous reports, followed by a
+sudden increase at higher pressure which has been hitherto unobserved. The
+magnetic surge is preceded by an abrupt change of the Fe local structure as
+observed by the decrease of the XAS pre-edge region intensity and corroborated
+by ab-initio simulations. This pressure corresponds to a structural transition,
+previously detected by x-ray diffraction, from the $Cmma$ form to the denser
+$Pbnm$ form with octahedral coordination of iron. Finally, the near-edge region
+of the XAS spectra shows a change before this transition at 5~GPa,
+corresponding well with the onset pressure of the previously observed
+enhancement of $T_c$. Our results emphasize the delicate interplay between
+structural, magnetic, and superconducting properties in FeSe under pressure.
+",0,1,0,0,0,0
+1308,Verification in Staged Tile Self-Assembly,"  We prove the unique assembly and unique shape verification problems,
+benchmark measures of self-assembly model power, are
+$\mathrm{coNP}^{\mathrm{NP}}$-hard and contained in $\mathrm{PSPACE}$ (and in
+$\mathrm{\Pi}^\mathrm{P}_{2s}$ for staged systems with $s$ stages). En route,
+we prove that unique shape verification problem in the 2HAM is
+$\mathrm{coNP}^{\mathrm{NP}}$-complete.
+",1,0,0,0,0,0
+1309,Combinets: Creativity via Recombination of Neural Networks,"  One of the defining characteristics of human creativity is the ability to
+make conceptual leaps, creating something surprising from typical knowledge. In
+comparison, deep neural networks often struggle to handle cases outside of
+their training data, which is especially problematic for problems with limited
+training data. Approaches exist to transfer knowledge from problems with
+sufficient data to those with insufficient data, but they tend to require
+additional training or a domain-specific method of transfer. We present a new
+approach, conceptual expansion, that serves as a general representation for
+reusing existing trained models to derive new models without backpropagation.
+We evaluate our approach on few-shot variations of two tasks: image
+classification and image generation, and outperform standard transfer learning
+approaches.
+",0,0,0,1,0,0
+1310,Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval,"  This paper addresses the problem of large scale image retrieval, with the aim
+of accurately ranking the similarity of a large number of images to a given
+query image. To achieve this, we propose a novel Siamese network. This network
+consists of two computational strands, each comprising of a CNN component
+followed by a Fisher vector component. The CNN component produces dense, deep
+convolutional descriptors that are then aggregated by the Fisher Vector method.
+Crucially, we propose to simultaneously learn both the CNN filter weights and
+Fisher Vector model parameters. This allows us to account for the evolving
+distribution of deep descriptors over the course of the learning process. We
+show that the proposed approach gives significant improvements over the
+state-of-the-art methods on the Oxford and Paris image retrieval datasets.
+Additionally, we provide a baseline performance measure for both these datasets
+with the inclusion of 1 million distractors.
+",1,0,0,0,0,0
+1311,"Gender Disparities in Science? Dropout, Productivity, Collaborations and Success of Male and Female Computer Scientists","  Scientific collaborations shape ideas as well as innovations and are both the
+substrate for, and the outcome of, academic careers. Recent studies show that
+gender inequality is still present in many scientific practices ranging from
+hiring to peer-review processes and grant applications. In this work, we
+investigate gender-specific differences in collaboration patterns of more than
+one million computer scientists over the course of 47 years. We explore how
+these patterns change over years and career ages and how they impact scientific
+success. Our results highlight that successful male and female scientists
+reveal the same collaboration patterns: compared to scientists in the same
+career age, they tend to collaborate with more colleagues than other
+scientists, seek innovations as brokers and establish longer-lasting and more
+repetitive collaborations. However, women are on average less likely to adapt
+the collaboration patterns that are related with success, more likely to embed
+into ego networks devoid of structural holes, and they exhibit stronger gender
+homophily as well as a consistently higher dropout rate than men in all career
+ages.
+",1,1,0,0,0,0
+1312,Estimating a network from multiple noisy realizations,"  Complex interactions between entities are often represented as edges in a
+network. In practice, the network is often constructed from noisy measurements
+and inevitably contains some errors. In this paper we consider the problem of
+estimating a network from multiple noisy observations where edges of the
+original network are recorded with both false positives and false negatives.
+This problem is motivated by neuroimaging applications where brain networks of
+a group of patients with a particular brain condition could be viewed as noisy
+versions of an unobserved true network corresponding to the disease. The key to
+optimally leveraging these multiple observations is to take advantage of
+network structure, and here we focus on the case where the true network
+contains communities. Communities are common in real networks in general and in
+particular are believed to be presented in brain networks. Under a community
+structure assumption on the truth, we derive an efficient method to estimate
+the noise levels and the original network, with theoretical guarantees on the
+convergence of our estimates. We show on synthetic networks that the
+performance of our method is close to an oracle method using the true parameter
+values, and apply our method to fMRI brain data, demonstrating that it
+constructs stable and plausible estimates of the population network.
+",0,0,1,1,0,0
+1313,Autonomous drone race: A computationally efficient vision-based navigation and control strategy,"  Drone racing is becoming a popular sport where human pilots have to control
+their drones to fly at high speed through complex environments and pass a
+number of gates in a pre-defined sequence. In this paper, we develop an
+autonomous system for drones to race fully autonomously using only onboard
+resources. Instead of commonly used visual navigation methods, such as
+simultaneous localization and mapping and visual inertial odometry, which are
+computationally expensive for micro aerial vehicles (MAVs), we developed the
+highly efficient snake gate detection algorithm for visual navigation, which
+can detect the gate at 20HZ on a Parrot Bebop drone. Then, with the gate
+detection result, we developed a robust pose estimation algorithm which has
+better tolerance to detection noise than a state-of-the-art perspective-n-point
+method. During the race, sometimes the gates are not in the drone's field of
+view. For this case, a state prediction-based feed-forward control strategy is
+developed to steer the drone to fly to the next gate. Experiments show that the
+drone can fly a half-circle with 1.5m radius within 2 seconds with only 30cm
+error at the end of the circle without any position feedback. Finally, the
+whole system is tested in a complex environment (a showroom in the faculty of
+Aerospace Engineering, TU Delft). The result shows that the drone can complete
+the track of 15 gates with a speed of 1.5m/s which is faster than the speeds
+exhibited at the 2016 and 2017 IROS autonomous drone races.
+",1,0,0,0,0,0
+1314,"Experimental investigations on nucleation, bubble growth, and micro-explosion characteristics during the combustion of ethanol/Jet A-1 fuel droplets","  The combustion characteristics of ethanol/Jet A-1 fuel droplets having three
+different proportions of ethanol (10%, 30%, and 50% by vol.) are investigated
+in the present study. The large volatility differential between ethanol and Jet
+A-1 and the nominal immiscibility of the fuels seem to result in combustion
+characteristics that are rather different from our previous work on butanol/Jet
+A-1 droplets (miscible blends). Abrupt explosion was facilitated in fuel
+droplets comprising lower proportions of ethanol (10%), possibly due to
+insufficient nucleation sites inside the droplet and the partially unmixed fuel
+mixture. For the fuel droplets containing higher proportions of ethanol (30%
+and 50%), micro-explosion occurred through homogeneous nucleation, leading to
+the ejection of secondary droplets and subsequent significant reduction in the
+overall droplet lifetime. The rate of bubble growth is nearly similar in all
+the blends of ethanol; however, the evolution of ethanol vapor bubble is
+significantly faster than that of a vapor bubble in the blends of butanol. The
+probability of disruptive behavior is considerably higher in ethanol/Jet A-1
+blends than that of butanol/Jet A-1 blends. The Sauter mean diameter of the
+secondary droplets produced from micro-explosion is larger for blends with a
+higher proportion of ethanol. Both abrupt explosion and micro-explosion create
+a large-scale distortion of the flame, which surrounds the parent droplet. The
+secondary droplets generated from abrupt explosion undergo rapid evaporation
+whereas the secondary droplets from micro-explosion carry their individual
+flame and evaporate slowly. The growth of vapor bubble was also witnessed in
+the secondary droplets, which leads to the further breakup of the droplet
+(puffing/micro-explosion).
+",0,1,0,0,0,0
+1315,Hypergraph $p$-Laplacian: A Differential Geometry View,"  The graph Laplacian plays key roles in information processing of relational
+data, and has analogies with the Laplacian in differential geometry. In this
+paper, we generalize the analogy between graph Laplacian and differential
+geometry to the hypergraph setting, and propose a novel hypergraph
+$p$-Laplacian. Unlike the existing two-node graph Laplacians, this
+generalization makes it possible to analyze hypergraphs, where the edges are
+allowed to connect any number of nodes. Moreover, we propose a semi-supervised
+learning method based on the proposed hypergraph $p$-Laplacian, and formalize
+them as the analogue to the Dirichlet problem, which often appears in physics.
+We further explore theoretical connections to normalized hypergraph cut on a
+hypergraph, and propose normalized cut corresponding to hypergraph
+$p$-Laplacian. The proposed $p$-Laplacian is shown to outperform standard
+hypergraph Laplacians in the experiment on a hypergraph semi-supervised
+learning and normalized cut setting.
+",1,0,0,1,0,0
+1316,Controlling motile disclinations in a thick nematogenic material with an electric field,"  Manipulating topological disclination networks that arise in a
+symmetry-breaking phase transfor- mation in widely varied systems including
+anisotropic materials can potentially lead to the design of novel materials
+like conductive microwires, self-assembled resonators, and active anisotropic
+matter. However, progress in this direction is hindered by a lack of control of
+the kinetics and microstructure due to inherent complexity arising from
+competing energy and topology. We have studied thermal and electrokinetic
+effects on disclinations in a three-dimensional nonabsorbing nematic material
+with a positive and negative sign of the dielectric anisotropy. The electric
+flux lines are highly non-uniform in uniaxial media after an electric field
+below the Fréedericksz threshold is switched on, and the kinetics of the
+disclination lines is slowed down. In biaxial media, depending on the sign of
+the dielectric anisotropy, apart from the slowing down of the disclination
+kinetics, a non-uniform electric field filters out disclinations of different
+topology by inducing a kinetic asymmetry. These results enhance the current
+understanding of forced disclination networks and establish the pre- sented
+method, which we call fluctuating electronematics, as a potentially useful tool
+for designing materials with novel properties in silico.
+",0,1,0,0,0,0
+1317,How Generative Adversarial Networks and Their Variants Work: An Overview,"  Generative Adversarial Networks (GAN) have received wide attention in the
+machine learning field for their potential to learn high-dimensional, complex
+real data distribution. Specifically, they do not rely on any assumptions about
+the distribution and can generate real-like samples from latent space in a
+simple manner. This powerful property leads GAN to be applied to various
+applications such as image synthesis, image attribute editing, image
+translation, domain adaptation and other academic fields. In this paper, we aim
+to discuss the details of GAN for those readers who are familiar with, but do
+not comprehend GAN deeply or who wish to view GAN from various perspectives. In
+addition, we explain how GAN operates and the fundamental meaning of various
+objective functions that have been suggested recently. We then focus on how the
+GAN can be combined with an autoencoder framework. Finally, we enumerate the
+GAN variants that are applied to various tasks and other fields for those who
+are interested in exploiting GAN for their research.
+",1,0,0,0,0,0
+1318,Principal Boundary on Riemannian Manifolds,"  We revisit the classification problem and focus on nonlinear methods for
+classification on manifolds. For multivariate datasets lying on an embedded
+nonlinear Riemannian manifold within the higher-dimensional space, our aim is
+to acquire a classification boundary between the classes with labels. Motivated
+by the principal flow [Panaretos, Pham and Yao, 2014], a curve that moves along
+a path of the maximum variation of the data, we introduce the principal
+boundary. From the classification perspective, the principal boundary is
+defined as an optimal curve that moves in between the principal flows traced
+out from two classes of the data, and at any point on the boundary, it
+maximizes the margin between the two classes. We estimate the boundary in
+quality with its direction supervised by the two principal flows. We show that
+the principal boundary yields the usual decision boundary found by the support
+vector machine, in the sense that locally, the two boundaries coincide. By
+means of examples, we illustrate how to find, use and interpret the principal
+boundary.
+",1,0,0,1,0,0
+1319,Exploiting network topology for large-scale inference of nonlinear reaction models,"  The development of chemical reaction models aids understanding and prediction
+in areas ranging from biology to electrochemistry and combustion. A systematic
+approach to building reaction network models uses observational data not only
+to estimate unknown parameters, but also to learn model structure. Bayesian
+inference provides a natural approach to this data-driven construction of
+models. Yet traditional Bayesian model inference methodologies that numerically
+evaluate the evidence for each model are often infeasible for nonlinear
+reaction network inference, as the number of plausible models can be
+combinatorially large. Alternative approaches based on model-space sampling can
+enable large-scale network inference, but their realization presents many
+challenges. In this paper, we present new computational methods that make
+large-scale nonlinear network inference tractable. First, we exploit the
+topology of networks describing potential interactions among chemical species
+to design improved ""between-model"" proposals for reversible-jump Markov chain
+Monte Carlo. Second, we introduce a sensitivity-based determination of move
+types which, when combined with network-aware proposals, yields significant
+additional gains in sampling performance. These algorithms are demonstrated on
+inference problems drawn from systems biology, with nonlinear differential
+equation models of species interactions.
+",1,0,0,1,0,0
+1320,Numerical investigations of non-uniqueness for the Navier-Stokes initial value problem in borderline spaces,"  We consider the Cauchy problem for the incompressible Navier-Stokes equations
+in $\mathbb{R}^3$ for a one-parameter family of explicit scale-invariant
+axi-symmetric initial data, which is smooth away from the origin and invariant
+under the reflection with respect to the $xy$-plane. Working in the class of
+axi-symmetric fields, we calculate numerically scale-invariant solutions of the
+Cauchy problem in terms of their profile functions, which are smooth. The
+solutions are necessarily unique for small data, but for large data we observe
+a breaking of the reflection symmetry of the initial data through a
+pitchfork-type bifurcation. By a variation of previous results by Jia &
+Šverák (2013) it is known rigorously that if the behavior seen here
+numerically can be proved, optimal non-uniqueness examples for the Cauchy
+problem can be established, and two different solutions can exists for the same
+initial datum which is divergence-free, smooth away from the origin, compactly
+supported, and locally $(-1)$-homogeneous near the origin. In particular,
+assuming our (finite-dimensional) numerics represents faithfully the behavior
+of the full (infinite-dimensional) system, the problem of uniqueness of the
+Leray-Hopf solutions (with non-smooth initial data) has a negative answer and,
+in addition, the perturbative arguments such those by Kato (1984) and Koch &
+Tataru (2001), or the weak-strong uniqueness results by Leray, Prodi, Serrin,
+Ladyzhenskaya and others, already give essentially optimal results. There are
+no singularities involved in the numerics, as we work only with smooth profile
+functions. It is conceivable that our calculations could be upgraded to a
+computer-assisted proof, although this would involve a substantial amount of
+additional work and calculations, including a much more detailed analysis of
+the asymptotic expansions of the solutions at large distances.
+",0,1,1,0,0,0
+1321,Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics,"  Inspired by the success of deep learning techniques in the physical and
+chemical sciences, we apply a modification of an autoencoder type deep neural
+network to the task of dimension reduction of molecular dynamics data. We can
+show that our time-lagged autoencoder reliably finds low-dimensional embeddings
+for high-dimensional feature spaces which capture the slow dynamics of the
+underlying stochastic processes - beyond the capabilities of linear dimension
+reduction techniques.
+",1,1,0,1,0,0
+1322,Some integrable maps and their Hirota bilinear forms,"  We introduce a two-parameter family of birational maps, which reduces to a
+family previously found by Demskoi, Tran, van der Kamp and Quispel (DTKQ) when
+one of the parameters is set to zero. The study of the singularity confinement
+pattern for these maps leads to the introduction of a tau function satisfying a
+homogeneous recurrence which has the Laurent property, and the tropical (or
+ultradiscrete) analogue of this homogeneous recurrence confirms the quadratic
+degree growth found empirically by Demskoi et al. We prove that the tau
+function also satisfies two different bilinear equations, each of which is a
+reduction of the Hirota-Miwa equation (also known as the discrete KP equation,
+or the octahedron recurrence). Furthermore, these bilinear equations are
+related to reductions of particular two-dimensional integrable lattice
+equations, of discrete KdV or discrete Toda type. These connections, as well as
+the cluster algebra structure of the bilinear equations, allow a direct
+construction of Poisson brackets, Lax pairs and first integrals for the
+birational maps. As a consequence of the latter results, we show how each
+member of the family can be lifted to a system that is integrable in the
+Liouville sense, clarifying observations made previously in the original DTKQ
+case.
+",0,1,0,0,0,0
+1323,Dynamics of higher-order rational solitons for the nonlocal nonlinear Schrodinger equation with the self-induced parity-time-symmetric potential,"  The integrable nonlocal nonlinear Schrodinger (NNLS) equation with the
+self-induced parity-time-symmetric potential [Phys. Rev. Lett. 110 (2013)
+064105] is investigated, which is an integrable extension of the standard NLS
+equation. Its novel higher-order rational solitons are found using the nonlocal
+version of the generalized perturbation (1, N-1)-fold Darboux transformation.
+These rational solitons illustrate abundant wave structures for the distinct
+choices of parameters (e.g., the strong and weak interactions of bright and
+dark rational solitons). Moreover, we also explore the dynamical behaviors of
+these higher-order rational solitons with some small noises on the basis of
+numerical simulations.
+",0,1,1,0,0,0
+1324,N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning,"  While bigger and deeper neural network architectures continue to advance the
+state-of-the-art for many computer vision tasks, real-world adoption of these
+networks is impeded by hardware and speed constraints. Conventional model
+compression methods attempt to address this problem by modifying the
+architecture manually or using pre-defined heuristics. Since the space of all
+reduced architectures is very large, modifying the architecture of a deep
+neural network in this way is a difficult task. In this paper, we tackle this
+issue by introducing a principled method for learning reduced network
+architectures in a data-driven way using reinforcement learning. Our approach
+takes a larger `teacher' network as input and outputs a compressed `student'
+network derived from the `teacher' network. In the first stage of our method, a
+recurrent policy network aggressively removes layers from the large `teacher'
+model. In the second stage, another recurrent policy network carefully reduces
+the size of each remaining layer. The resulting network is then evaluated to
+obtain a reward -- a score based on the accuracy and compression of the
+network. Our approach uses this reward signal with policy gradients to train
+the policies to find a locally optimal student network. Our experiments show
+that we can achieve compression rates of more than 10x for models such as
+ResNet-34 while maintaining similar performance to the input `teacher' network.
+We also present a valuable transfer learning result which shows that policies
+which are pre-trained on smaller `teacher' networks can be used to rapidly
+speed up training on larger `teacher' networks.
+",1,0,0,1,0,0
+1325,Spectral analysis of stationary random bivariate signals,"  A novel approach towards the spectral analysis of stationary random bivariate
+signals is proposed. Using the Quaternion Fourier Transform, we introduce a
+quaternion-valued spectral representation of random bivariate signals seen as
+complex-valued sequences. This makes possible the definition of a scalar
+quaternion-valued spectral density for bivariate signals. This spectral density
+can be meaningfully interpreted in terms of frequency-dependent polarization
+attributes. A natural decomposition of any random bivariate signal in terms of
+unpolarized and polarized components is introduced. Nonparametric spectral
+density estimation is investigated, and we introduce the polarization
+periodogram of a random bivariate signal. Numerical experiments support our
+theoretical analysis, illustrating the relevance of the approach on synthetic
+data.
+",0,0,0,1,0,0
+1326,Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition,"  We propose a method (TT-GP) for approximate inference in Gaussian Process
+(GP) models. We build on previous scalable GP research including stochastic
+variational inference based on inducing inputs, kernel interpolation, and
+structure exploiting algebra. The key idea of our method is to use Tensor Train
+decomposition for variational parameters, which allows us to train GPs with
+billions of inducing inputs and achieve state-of-the-art results on several
+benchmarks. Further, our approach allows for training kernels based on deep
+neural networks without any modifications to the underlying GP model. A neural
+network learns a multidimensional embedding for the data, which is used by the
+GP to make the final prediction. We train GP and neural network parameters
+end-to-end without pretraining, through maximization of GP marginal likelihood.
+We show the efficiency of the proposed approach on several regression and
+classification benchmark datasets including MNIST, CIFAR-10, and Airline.
+",1,0,0,1,0,0
+1327,Some preliminary results on the set of principal congruences of a finite lattice,"  In the second edition of the congruence lattice book, Problem 22.1 asks for a
+characterization of subsets $Q$ of a finite distributive lattice $D$ such that
+there is a finite lattice $L$ whose congruence lattice is isomorphic to $D$ and
+under this isomorphism $Q$ corresponds the the principal congruences of $L$. In
+this note, we prove some preliminary results.
+",0,0,1,0,0,0
+1328,Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks,"  Vasculature is known to be of key biological significance, especially in the
+study of cancer. As such, considerable effort has been focused on the automated
+measurement and analysis of vasculature in medical and pre-clinical images. In
+tumors in particular, the vascular networks may be extremely irregular and the
+appearance of the individual vessels may not conform to classical descriptions
+of vascular appearance. Typically, vessels are extracted by either a
+segmentation and thinning pipeline, or by direct tracking. Neither of these
+methods are well suited to microscopy images of tumor vasculature. In order to
+address this we propose a method to directly extract a medial representation of
+the vessels using Convolutional Neural Networks. We then show that these
+two-dimensional centerlines can be meaningfully extended into 3D in anisotropic
+and complex microscopy images using the recently popularized Convolutional Long
+Short-Term Memory units (ConvLSTM). We demonstrate the effectiveness of this
+hybrid convolutional-recurrent architecture over both 2D and 3D convolutional
+comparators.
+",1,0,0,0,0,0
+1329,Search for Interstellar LiH in the Milky Way,"  We report the results of a sensitive search for the 443.952902 GHz $J=1-0$
+transition of the LiH molecule toward two interstellar clouds in the Milky Way,
+W49N and Sgr B2 (Main), that has been carried out using the Atacama Pathfinder
+Experiment (APEX) telescope. The results obtained toward W49N place an upper
+limit of $1.9 \times 10^{-11}\, (3\sigma)$ on the LiH abundance, $N({\rm
+LiH})/N({\rm H}_2)$, in a foreground, diffuse molecular cloud along the
+sight-line to W49N, corresponding to 0.5% of the solar system lithium
+abundance. Those obtained toward Sgr B2 (Main) place an abundance limit $N({\rm
+LiH})/N({\rm H}_2) < 3.6 \times 10^{-13} \,(3\sigma)$ in the dense gas within
+the Sgr B2 cloud itself. These limits are considerably smaller that those
+implied by the tentative detection of LiH reported previously for the $z=0.685$
+absorber toward B0218+357.
+",0,1,0,0,0,0
+1330,Neural Probabilistic Model for Non-projective MST Parsing,"  In this paper, we propose a probabilistic parsing model, which defines a
+proper conditional probability distribution over non-projective dependency
+trees for a given sentence, using neural representations as inputs. The neural
+network architecture is based on bi-directional LSTM-CNNs which benefits from
+both word- and character-level representations automatically, by using
+combination of bidirectional LSTM and CNN. On top of the neural network, we
+introduce a probabilistic structured layer, defining a conditional log-linear
+model over non-projective trees. We evaluate our model on 17 different
+datasets, across 14 different languages. By exploiting Kirchhoff's Matrix-Tree
+Theorem (Tutte, 1984), the partition functions and marginals can be computed
+efficiently, leading to a straight-forward end-to-end model training procedure
+via back-propagation. Our parser achieves state-of-the-art parsing performance
+on nine datasets.
+",1,0,0,1,0,0
+1331,Modularity of complex networks models,"  Modularity is designed to measure the strength of division of a network into
+clusters (known also as communities). Networks with high modularity have dense
+connections between the vertices within clusters but sparse connections between
+vertices of different clusters. As a result, modularity is often used in
+optimization methods for detecting community structure in networks, and so it
+is an important graph parameter from a practical point of view. Unfortunately,
+many existing non-spatial models of complex networks do not generate graphs
+with high modularity; on the other hand, spatial models naturally create
+clusters. We investigate this phenomenon by considering a few examples from
+both sub-classes. We prove precise theoretical results for the classical model
+of random d-regular graphs as well as the preferential attachment model, and
+contrast these results with the ones for the spatial preferential attachment
+(SPA) model that is a model for complex networks in which vertices are embedded
+in a metric space, and each vertex has a sphere of influence whose size
+increases if the vertex gains an in-link, and otherwise decreases with time.
+The results obtained in this paper can be used for developing statistical tests
+for models selection and to measure statistical significance of clusters
+observed in complex networks.
+",0,0,1,0,0,0
+1332,BOLD5000: A public fMRI dataset of 5000 images,"  Vision science, particularly machine vision, has been revolutionized by
+introducing large-scale image datasets and statistical learning approaches.
+Yet, human neuroimaging studies of visual perception still rely on small
+numbers of images (around 100) due to time-constrained experimental procedures.
+To apply statistical learning approaches that integrate neuroscience, the
+number of images used in neuroimaging must be significantly increased. We
+present BOLD5000, a human functional MRI (fMRI) study that includes almost
+5,000 distinct images depicting real-world scenes. Beyond dramatically
+increasing image dataset size relative to prior fMRI studies, BOLD5000 also
+accounts for image diversity, overlapping with standard computer vision
+datasets by incorporating images from the Scene UNderstanding (SUN), Common
+Objects in Context (COCO), and ImageNet datasets. The scale and diversity of
+these image datasets, combined with a slow event-related fMRI design, enable
+fine-grained exploration into the neural representation of a wide range of
+visual features, categories, and semantics. Concurrently, BOLD5000 brings us
+closer to realizing Marr's dream of a singular vision science - the intertwined
+study of biological and computer vision.
+",0,0,0,0,1,0
+1333,Prospects for gravitational wave astronomy with next generation large-scale pulsar timing arrays,"  Next generation radio telescopes, namely the Five-hundred-meter Aperture
+Spherical Telescope (FAST) and the Square Kilometer Array (SKA), will
+revolutionize the pulsar timing arrays (PTAs) based gravitational wave (GW)
+searches. We review some of the characteristics of FAST and SKA, and the
+resulting PTAs, that are pertinent to the detection of gravitational wave
+signals from individual supermassive black hole binaries.
+",0,1,0,0,0,0
+1334,On Identifiability of Nonnegative Matrix Factorization,"  In this letter, we propose a new identification criterion that guarantees the
+recovery of the low-rank latent factors in the nonnegative matrix factorization
+(NMF) model, under mild conditions. Specifically, using the proposed criterion,
+it suffices to identify the latent factors if the rows of one factor are
+\emph{sufficiently scattered} over the nonnegative orthant, while no structural
+assumption is imposed on the other factor except being full-rank. This is by
+far the mildest condition under which the latent factors are provably
+identifiable from the NMF model.
+",1,0,0,1,0,0
+1335,Optimal Non-blocking Decentralized Supervisory Control Using G-Control Consistency,"  Supervisory control synthesis encounters with computational complexity. This
+can be reduced by decentralized supervisory control approach. In this paper, we
+define intrinsic control consistency for a pair of states of the plant.
+G-control consistency (GCC) is another concept which is defined for a natural
+projection w.r.t. the plant. We prove that, if a natural projection is output
+control consistent for the closed language of the plant, and is a natural
+observer for the marked language of the plant, then it is G-control consistent.
+Namely, we relax the conditions for synthesis the optimal non-blocking
+decentralized supervisory control by substituting GCC property for L-OCC and
+Lm-observer properties of a natural projection. We propose a method to
+synthesize the optimal non-blocking decentralized supervisory control based on
+GCC property for a natural projection. In fact, we change the approach from
+language-based properties of a natural projection to DES-based property by
+defining GCC property.
+",1,0,0,0,0,0
+1336,Fair mixing: the case of dichotomous preferences,"  Agents vote to choose a fair mixture of public outcomes; each agent likes or
+dislikes each outcome. We discuss three outstanding voting rules. The
+Conditional Utilitarian rule, a variant of the random dictator, is
+Strategyproof and guarantees to any group of like-minded agents an influence
+proportional to its size. It is easier to compute and more efficient than the
+familiar Random Priority rule. Its worst case (resp. average) inefficiency is
+provably (resp. in numerical experiments) low if the number of agents is low.
+The efficient Egalitarian rule protects similarly individual agents but not
+coalitions. It is Excludable Strategyproof: I do not want to lie if I cannot
+consume outcomes I claim to dislike. The efficient Nash Max Product rule offers
+the strongest welfare guarantees to coalitions, who can force any outcome with
+a probability proportional to their size. But it fails even the excludable form
+of Strategyproofness.
+",1,0,0,0,0,0
+1337,Inverse system characterizations of the (hereditarily) just infinite property in profinite groups,"  We give criteria on an inverse system of finite groups that ensure the limit
+is just infinite or hereditarily just infinite. More significantly, these
+criteria are 'universal' in that all (hereditarily) just infinite profinite
+groups arise as limits of the specified form.
+This is a corrected and revised version of the article: 'Inverse system
+characterizations of the (hereditarily) just infinite property in profinite
+groups', Bull. LMS vol 44, 3 (2012) 413--425.
+",0,0,1,0,0,0
+1338,"p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning","  Recent advances in learning Deep Neural Network (DNN) architectures have
+received a great deal of attention due to their ability to outperform
+state-of-the-art classifiers across a wide range of applications, with little
+or no feature engineering. In this paper, we broadly study the applicability of
+deep learning to website fingerprinting. We show that unsupervised DNNs can be
+used to extract low-dimensional feature vectors that improve the performance of
+state-of-the-art website fingerprinting attacks. When used as classifiers, we
+show that they can match or exceed performance of existing attacks across a
+range of application scenarios, including fingerprinting Tor website traces,
+fingerprinting search engine queries over Tor, defeating fingerprinting
+defenses, and fingerprinting TLS-encrypted websites. Finally, we show that DNNs
+can be used to predict the fingerprintability of a website based on its
+contents, achieving 99% accuracy on a data set of 4500 website downloads.
+",1,0,0,1,0,0
+1339,Equal confidence weighted expectation value estimates,"  In this article the issues are discussed with the Bayesian approach,
+least-square fits, and most-likely fits. Trying to counter these issues, a
+method, based on weighted confidence, is proposed for estimating probabilities
+and other observables. This method sums over different model parameter
+combinations but does not require the need for making assumptions on priors or
+underlying probability functions. Moreover, by construction the results are
+invariant under reparametrization of the model parameters. In one case the
+result appears similar as in Bayesian statistics but in general there is no
+agreement. The binomial distribution is also studied which turns out to be
+useful for making predictions on production processes without the need to make
+further assumptions. In the last part, the case of a simple linear fit (a
+multi-variate example) is studied using the standard approaches and the
+confidence weighted approach.
+",0,0,1,1,0,0
+1340,Protein Folding and Machine Learning: Fundamentals,"  In spite of decades of research, much remains to be discovered about folding:
+the detailed structure of the initial (unfolded) state, vestigial folding
+instructions remaining only in the unfolded state, the interaction of the
+molecule with the solvent, instantaneous power at each point within the
+molecule during folding, the fact that the process is stable in spite of myriad
+possible disturbances, potential stabilization of trajectory by chaos, and, of
+course, the exact physical mechanism (code or instructions) by which the
+folding process is specified in the amino acid sequence. Simulations based upon
+microscopic physics have had some spectacular successes and continue to
+improve, particularly as super-computer capabilities increase. The simulations,
+exciting as they are, are still too slow and expensive to deal with the
+enormous number of molecules of interest. In this paper, we introduce an
+approximate model based upon physics, empirics, and information science which
+is proposed for use in machine learning applications in which very large
+numbers of sub-simulations must be made. In particular, we focus upon machine
+learning applications in the learning phase and argue that our model is
+sufficiently close to the physics that, in spite of its approximate nature, can
+facilitate stepping through machine learning solutions to explore the mechanics
+of folding mentioned above. We particularly emphasize the exploration of energy
+flow (power) within the molecule during folding, the possibility of energy
+scale invariance (above a threshold), vestigial information in the unfolded
+state as attractive targets for such machine language analysis, and statistical
+analysis of an ensemble of folding micro-steps.
+",0,0,0,0,1,0
+1341,Discrete configuration spaces of squares and hexagons,"  We consider generalizations of the familiar fifteen-piece sliding puzzle on
+the 4 by 4 square grid. On larger grids with more pieces and more holes,
+asymptotically how fast can we move the puzzle into the solved state? We also
+give a variation with sliding hexagons. The square puzzles and the hexagon
+puzzles are both discrete versions of configuration spaces of disks, which are
+of interest in statistical mechanics and topological robotics. The
+combinatorial theorems and proofs in this paper suggest followup questions in
+both combinatorics and topology, and may turn out to be useful for proving
+topological statements about configuration spaces.
+",1,0,1,0,0,0
+1342,On the non commutative Iwasawa main conjecture for abelian varieties over function fields,"  We establish the Iwasawa main conjecture for semi-stable abelian varieties
+over a function field of characteristic $p$ under certain restrictive
+assumptions. Namely we consider $p$-torsion free $p$-adic Lie extensions of the
+base field which contain the constant $\mathbb Z_p$-extension and are
+everywhere unramified. Under the classical $\mu=0$ hypothesis we give a proof
+which mainly relies on the interpretation of the Selmer complex in terms of
+$p$-adic cohomology [TV] together with the trace formulas of [EL1].
+",0,0,1,0,0,0
+1343,Absorption and Emission Probabilities of Electrons in Electric and Magnetic Fields for FEL,"  We consider induced emission of ultrarelativistic electrons in strong
+electric (magnetic) fields that are uniform along the direction of the electron
+motion and are not uniform in the transverse direction. The stimulated
+absorption and emission probabilities are found in such system.
+",0,1,0,0,0,0
+1344,"Design, Development and Evaluation of a UAV to Study Air Quality in Qatar","  Measuring gases for air quality monitoring is a challenging task that claims
+a lot of time of observation and large numbers of sensors. The aim of this
+project is to develop a partially autonomous unmanned aerial vehicle (UAV)
+equipped with sensors, in order to monitor and collect air quality real time
+data in designated areas and send it to the ground base. This project is
+designed and implemented by a multidisciplinary team from electrical and
+computer engineering departments. The electrical engineering team responsible
+for implementing air quality sensors for detecting real time data and transmit
+it from the plane to the ground. On the other hand, the computer engineering
+team is in charge of Interface sensors and provide platform to view and
+visualize air quality data and live video streaming. The proposed project
+contains several sensors to measure Temperature, Humidity, Dust, CO, CO2 and
+O3. The collected data is transmitted to a server over a wireless internet
+connection and the server will store, and supply these data to any party who
+has permission to access it through android phone or website in semi-real time.
+The developed UAV has carried several field tests in Al Shamal airport in
+Qatar, with interesting results and proof of concept outcomes.
+",1,0,0,0,0,0
+1345,Gaussian Process bandits with adaptive discretization,"  In this paper, the problem of maximizing a black-box function $f:\mathcal{X}
+\to \mathbb{R}$ is studied in the Bayesian framework with a Gaussian Process
+(GP) prior. In particular, a new algorithm for this problem is proposed, and
+high probability bounds on its simple and cumulative regret are established.
+The query point selection rule in most existing methods involves an exhaustive
+search over an increasingly fine sequence of uniform discretizations of
+$\mathcal{X}$. The proposed algorithm, in contrast, adaptively refines
+$\mathcal{X}$ which leads to a lower computational complexity, particularly
+when $\mathcal{X}$ is a subset of a high dimensional Euclidean space. In
+addition to the computational gains, sufficient conditions are identified under
+which the regret bounds of the new algorithm improve upon the known results.
+Finally an extension of the algorithm to the case of contextual bandits is
+proposed, and high probability bounds on the contextual regret are presented.
+",1,0,0,1,0,0
+1346,Conditional Time Series Forecasting with Convolutional Neural Networks,"  We present a method for conditional time series forecasting based on an
+adaptation of the recent deep convolutional WaveNet architecture. The proposed
+network contains stacks of dilated convolutions that allow it to access a broad
+range of history when forecasting, a ReLU activation function and conditioning
+is performed by applying multiple convolutional filters in parallel to separate
+time series which allows for the fast processing of data and the exploitation
+of the correlation structure between the multivariate time series. We test and
+analyze the performance of the convolutional network both unconditionally as
+well as conditionally for financial time series forecasting using the S&P500,
+the volatility index, the CBOE interest rate and several exchange rates and
+extensively compare it to the performance of the well-known autoregressive
+model and a long-short term memory network. We show that a convolutional
+network is well-suited for regression-type problems and is able to effectively
+learn dependencies in and between the series without the need for long
+historical time series, is a time-efficient and easy to implement alternative
+to recurrent-type networks and tends to outperform linear and recurrent models.
+",0,0,0,1,0,0
+1347,Automated Assistants to Identify and Prompt Action on Visual News Bias,"  Bias is a common problem in today's media, appearing frequently in text and
+in visual imagery. Users on social media websites such as Twitter need better
+methods for identifying bias. Additionally, activists --those who are motivated
+to effect change related to some topic, need better methods to identify and
+counteract bias that is contrary to their mission. With both of these use cases
+in mind, in this paper we propose a novel tool called UnbiasedCrowd that
+supports identification of, and action on bias in visual news media. In
+particular, it addresses the following key challenges (1) identification of
+bias; (2) aggregation and presentation of evidence to users; (3) enabling
+activists to inform the public of bias and take action by engaging people in
+conversation with bots. We describe a preliminary study on the Twitter platform
+that explores the impressions that activists had of our tool, and how people
+reacted and engaged with online bots that exposed visual bias. We conclude by
+discussing design and implication of our findings for creating future systems
+to identify and counteract the effects of news bias.
+",1,0,0,0,0,0
+1348,A Matched Filter Technique For Slow Radio Transient Detection And First Demonstration With The Murchison Widefield Array,"  Many astronomical sources produce transient phenomena at radio frequencies,
+but the transient sky at low frequencies (<300 MHz) remains relatively
+unexplored. Blind surveys with new widefield radio instruments are setting
+increasingly stringent limits on the transient surface density on various
+timescales. Although many of these instruments are limited by classical
+confusion noise from an ensemble of faint, unresolved sources, one can in
+principle detect transients below the classical confusion limit to the extent
+that the classical confusion noise is independent of time. We develop a
+technique for detecting radio transients that is based on temporal matched
+filters applied directly to time series of images rather than relying on
+source-finding algorithms applied to individual images. This technique has
+well-defined statistical properties and is applicable to variable and transient
+searches for both confusion-limited and non-confusion-limited instruments.
+Using the Murchison Widefield Array as an example, we demonstrate that the
+technique works well on real data despite the presence of classical confusion
+noise, sidelobe confusion noise, and other systematic errors. We searched for
+transients lasting between 2 minutes and 3 months. We found no transients and
+set improved upper limits on the transient surface density at 182 MHz for flux
+densities between ~20--200 mJy, providing the best limits to date for hour- and
+month-long transients.
+",0,1,0,0,0,0
+1349,Shape and Energy Consistent Pseudopotentials for Correlated Electron systems,"  A method is developed for generating pseudopotentials for use in
+correlated-electron calculations. The paradigms of shape and energy consistency
+are combined and defined in terms of correlated-electron wave-functions. The
+resulting energy consistent correlated electron pseudopotentials (eCEPPs) are
+constructed for H, Li--F, Sc--Fe, and Cu. Their accuracy is quantified by
+comparing the relaxed molecular geometries and dissociation energies they
+provide with all electron results, with all quantities evaluated using coupled
+cluster singles doubles and triples calculations. Errors inherent in the
+pseudopotentials are also compared with those arising from a number of
+approximations commonly used with pseudopotentials. The eCEPPs provide a
+significant improvement in optimised geometries and dissociation energies for
+small molecules, with errors for the latter being an order-of-magnitude smaller
+than for Hartree-Fock-based pseudopotentials available in the literature.
+Gaussian basis sets are optimised for use with these pseudopotentials.
+",0,1,0,0,0,0
+1350,Bayes model selection,"  We offer a general Bayes theoretic framework to tackle the model selection
+problem under a two-step prior design: the first-step prior serves to assess
+the model selection uncertainty, and the second-step prior quantifies the prior
+belief on the strength of the signals within the model chosen from the first
+step.
+We establish non-asymptotic oracle posterior contraction rates under (i) a
+new Bernstein-inequality condition on the log likelihood ratio of the
+statistical experiment, (ii) a local entropy condition on the dimensionality of
+the models, and (iii) a sufficient mass condition on the second-step prior near
+the best approximating signal for each model. The first-step prior can be
+designed generically. The resulting posterior mean also satisfies an oracle
+inequality, thus automatically serving as an adaptive point estimator in a
+frequentist sense. Model mis-specification is allowed in these oracle rates.
+The new Bernstein-inequality condition not only eliminates the convention of
+constructing explicit tests with exponentially small type I and II errors, but
+also suggests the intrinsic metric to use in a given statistical experiment,
+both as a loss function and as an entropy measurement. This gives a unified
+reduction scheme for many experiments considered in Ghoshal & van der
+Vaart(2007) and beyond. As an illustration for the scope of our general results
+in concrete applications, we consider (i) trace regression, (ii)
+shape-restricted isotonic/convex regression, (iii) high-dimensional partially
+linear regression and (iv) covariance matrix estimation in the sparse factor
+model. These new results serve either as theoretical justification of practical
+prior proposals in the literature, or as an illustration of the generic
+construction scheme of a (nearly) minimax adaptive estimator for a
+multi-structured experiment.
+",0,0,1,1,0,0
+1351,Phase Congruency Parameter Optimization for Enhanced Detection of Image Features for both Natural and Medical Applications,"  Following the presentation and proof of the hypothesis that image features
+are particularly perceived at points where the Fourier components are maximally
+in phase, the concept of phase congruency (PC) is introduced. Subsequently, a
+two-dimensional multi-scale phase congruency (2D-MSPC) is developed, which has
+been an important tool for detecting and evaluation of image features. However,
+the 2D-MSPC requires many parameters to be appropriately tuned for optimal
+image features detection. In this paper, we defined a criterion for parameter
+optimization of the 2D-MSPC, which is a function of its maximum and minimum
+moments. We formulated the problem in various optimal and suboptimal
+frameworks, and discussed the conditions and features of the suboptimal
+solutions. The effectiveness of the proposed method was verified through
+several examples, ranging from natural objects to medical images from patients
+with a neurological disease, multiple sclerosis.
+",1,0,1,0,0,0
+1352,Community structure detection and evaluation during the pre- and post-ictal hippocampal depth recordings,"  Detecting and evaluating regions of brain under various circumstances is one
+of the most interesting topics in computational neuroscience. However, the
+majority of the studies on detecting communities of a functional connectivity
+network of the brain is done on networks obtained from coherency attributes,
+and not from correlation. This lack of studies, in part, is due to the fact
+that many common methods for clustering graphs require the nodes of the network
+to be `positively' linked together, a property that is guaranteed by a
+coherency matrix, by definition. However, correlation matrices reveal more
+information regarding how each pair of nodes are linked together. In this
+study, for the first time we simultaneously examine four inherently different
+network clustering methods (spectral, heuristic, and optimization methods)
+applied to the functional connectivity networks of the CA1 region of the
+hippocampus of an anaesthetized rat during pre-ictal and post-ictal states. The
+networks are obtained from correlation matrices, and its results are compared
+with the ones obtained by applying the same methods to coherency matrices. The
+correlation matrices show a much finer community structure compared to the
+coherency matrices. Furthermore, we examine the potential smoothing effect of
+choosing various window sizes for computing the correlation/coherency matrices.
+",1,0,0,0,1,0
+1353,"Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications","  The global sensitivity analysis of a numerical model aims to quantify, by
+means of sensitivity indices estimate, the contributions of each uncertain
+input variable to the model output uncertainty. The so-called Sobol' indices,
+which are based on the functional variance analysis, present a difficult
+interpretation in the presence of statistical dependence between inputs. The
+Shapley effect was recently introduced to overcome this problem as they
+allocate the mutual contribution (due to correlation and interaction) of a
+group of inputs to each individual input within the group.In this paper, using
+several new analytical results, we study the effects of linear correlation
+between some Gaussian input variables on Shapley effects, and compare these
+effects to classical first-order and total Sobol' indices.This illustrates the
+interest, in terms of sensitivity analysis setting and interpretation, of the
+Shapley effects in the case of dependent inputs. We also investigate the
+numerical convergence of the estimated Shapley effects. For the practical issue
+of computationally demanding computer models, we show that the substitution of
+the original model by a metamodel (here, kriging) makes it possible to estimate
+these indices with precision at a reasonable computational cost.
+",0,0,1,1,0,0
+1354,Ridesourcing Car Detection by Transfer Learning,"  Ridesourcing platforms like Uber and Didi are getting more and more popular
+around the world. However, unauthorized ridesourcing activities taking
+advantages of the sharing economy can greatly impair the healthy development of
+this emerging industry. As the first step to regulate on-demand ride services
+and eliminate black market, we design a method to detect ridesourcing cars from
+a pool of cars based on their trajectories. Since licensed ridesourcing car
+traces are not openly available and may be completely missing in some cities
+due to legal issues, we turn to transferring knowledge from public transport
+open data, i.e, taxis and buses, to ridesourcing detection among ordinary
+vehicles. We propose a two-stage transfer learning framework. In Stage 1, we
+take taxi and bus data as input to learn a random forest (RF) classifier using
+trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we
+use the RF to label all the candidate cars. In Stage 2, leveraging the subset
+of high confident labels from the previous stage as input, we further learn a
+convolutional neural network (CNN) classifier for ridesourcing detection, and
+iteratively refine RF and CNN, as well as the feature set, via a co-training
+process. Finally, we use the resulting ensemble of RF and CNN to identify the
+ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus
+traces show that our transfer learning framework, with no need of a pre-labeled
+ridesourcing dataset, can achieve similar accuracy as the supervised learning
+methods.
+",1,0,0,1,0,0
+1355,A Semantic Cross-Species Derived Data Management Application,"  Managing dynamic information in large multi-site, multi-species, and
+multi-discipline consortia is a challenging task for data management
+applications. Often in academic research studies the goals for informatics
+teams are to build applications that provide extract-transform-load (ETL)
+functionality to archive and catalog source data that has been collected by the
+research teams. In consortia that cross species and methodological or
+scientific domains, building interfaces that supply data in a usable fashion
+and make intuitive sense to scientists from dramatically different backgrounds
+increases the complexity for developers. Further, reusing source data from
+outside one's scientific domain is fraught with ambiguities in understanding
+the data types, analysis methodologies, and how to combine the data with those
+from other research teams. We report on the design, implementation, and
+performance of a semantic data management application to support the NIMH
+funded Conte Center at the University of California, Irvine. The Center is
+testing a theory of the consequences of ""fragmented"" (unpredictable, high
+entropy) early-life experiences on adolescent cognitive and emotional outcomes
+in both humans and rodents. It employs cross-species neuroimaging, epigenomic,
+molecular, and neuroanatomical approaches in humans and rodents to assess the
+potential consequences of fragmented unpredictable experience on brain
+structure and circuitry. To address this multi-technology, multi-species
+approach, the system uses semantic web techniques based on the Neuroimaging
+Data Model (NIDM) to facilitate data ETL functionality. We find this approach
+enables a low-cost, easy to maintain, and semantically meaningful information
+management system, enabling the diverse research teams to access and use the
+data.
+",1,0,0,0,0,0
+1356,Retrosynthetic reaction prediction using neural sequence-to-sequence models,"  We describe a fully data driven model that learns to perform a retrosynthetic
+reaction prediction task, which is treated as a sequence-to-sequence mapping
+problem. The end-to-end trained model has an encoder-decoder architecture that
+consists of two recurrent neural networks, which has previously shown great
+success in solving other sequence-to-sequence prediction tasks such as machine
+translation. The model is trained on 50,000 experimental reaction examples from
+the United States patent literature, which span 10 broad reaction types that
+are commonly used by medicinal chemists. We find that our model performs
+comparably with a rule-based expert system baseline model, and also overcomes
+certain limitations associated with rule-based expert systems and with any
+machine learning approach that contains a rule-based expert system component.
+Our model provides an important first step towards solving the challenging
+problem of computational retrosynthetic analysis.
+",1,0,0,1,0,0
+1357,"Redshift, metallicity and size of two extended dwarf Irregular galaxies. A link between dwarf Irregulars and Ultra Diffuse Galaxies?","  We present the results of the spectroscopic and photometric follow-up of two
+field galaxies that were selected as possible stellar counterparts of local
+high velocity clouds. Our analysis shows that the two systems are distant (D>20
+Mpc) dwarf irregular galaxies unrelated to the local HI clouds. However, the
+newly derived distance and structural parameters reveal that the two galaxies
+have luminosities and effective radii very similar to the recently identified
+Ultra Diffuse Galaxies (UDGs). At odds with classical UDGs, they are remarkably
+isolated, having no known giant galaxy within ~2.0 Mpc. Moreover, one of them
+has a very high gas content compared to galaxies of similar stellar mass, with
+a HI to stellar mass ratio M_HI/M_* ~90, typical of almost-dark dwarfs.
+Expanding on this finding, we show that extended dwarf irregulars overlap the
+distribution of UDGs in the M_V vs. log(r_e) plane and that the sequence
+including dwarf spheroidals, dwarf irregulars and UDGs appears as continuously
+populated in this plane.
+",0,1,0,0,0,0
+1358,Collapsing hyperkähler manifolds,"  Given a projective hyperkahler manifold with a holomorphic Lagrangian
+fibration, we prove that hyperkahler metrics with volume of the torus fibers
+shrinking to zero collapse in the Gromov-Hausdorff sense (and smoothly away
+from the singular fibers) to a compact metric space which is a half-dimensional
+special Kahler manifold outside a singular set of real Hausdorff codimension 2
+and is homeomorphic to the base projective space.
+",0,0,1,0,0,0
+1359,Fast amortized inference of neural activity from calcium imaging data with variational autoencoders,"  Calcium imaging permits optical measurement of neural activity. Since
+intracellular calcium concentration is an indirect measurement of neural
+activity, computational tools are necessary to infer the true underlying
+spiking activity from fluorescence measurements. Bayesian model inversion can
+be used to solve this problem, but typically requires either computationally
+expensive MCMC sampling, or faster but approximate maximum-a-posteriori
+optimization. Here, we introduce a flexible algorithmic framework for fast,
+efficient and accurate extraction of neural spikes from imaging data. Using the
+framework of variational autoencoders, we propose to amortize inference by
+training a deep neural network to perform model inversion efficiently. The
+recognition network is trained to produce samples from the posterior
+distribution over spike trains. Once trained, performing inference amounts to a
+fast single forward pass through the network, without the need for iterative
+optimization or sampling. We show that amortization can be applied flexibly to
+a wide range of nonlinear generative models and significantly improves upon the
+state of the art in computation time, while achieving competitive accuracy. Our
+framework is also able to represent posterior distributions over spike-trains.
+We demonstrate the generality of our method by proposing the first
+probabilistic approach for separating backpropagating action potentials from
+putative synaptic inputs in calcium imaging of dendritic spines.
+",1,0,0,1,0,0
+1360,Hidden multiparticle excitation in weakly interacting Bose-Einstein Condensate,"  We investigate multiparticle excitation effect on a collective density
+excitation as well as a single-particle excitation in a weakly interacting
+Bose--Einstein condensate (BEC). We find that although the weakly interacting
+BEC offers weak multiparticle excitation spectrum at low temperatures, this
+multiparticle excitation effect may not remain hidden, but emerges as
+bimodality in the density response function through the single-particle
+excitation. Identification of spectra in the BEC between the single-particle
+excitation and the density excitation is also assessed at nonzero temperatures,
+which has been known to be unique nature in the BEC at absolute zero
+temperature.
+",0,1,0,0,0,0
+1361,Hausdorff Measure: Lost in Translation,"  In the present article we describe how one can define Hausdorff measure
+allowing empty elements in coverings, and using infinite countable coverings
+only. In addition, we discuss how the use of different nonequivalent
+interpretations of the notion ""countable set"", that is typical for classical
+and modern mathematics, may lead to contradictions.
+",0,0,1,0,0,0
+1362,Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use,"  The popular Alternating Least Squares (ALS) algorithm for tensor
+decomposition is efficient and easy to implement, but often converges to poor
+local optima---particularly when the weights of the factors are non-uniform. We
+propose a modification of the ALS approach that is as efficient as standard
+ALS, but provably recovers the true factors with random initialization under
+standard incoherence assumptions on the factors of the tensor. We demonstrate
+the significant practical superiority of our approach over traditional ALS for
+a variety of tasks on synthetic data---including tensor factorization on exact,
+noisy and over-complete tensors, as well as tensor completion---and for
+computing word embeddings from a third-order word tri-occurrence tensor.
+",1,0,0,1,0,0
+1363,Domain Generalization by Marginal Transfer Learning,"  Domain generalization is the problem of assigning class labels to an
+unlabeled test data set, given several labeled training data sets drawn from
+similar distributions. This problem arises in several applications where data
+distributions fluctuate because of biological, technical, or other sources of
+variation. We develop a distribution-free, kernel-based approach that predicts
+a classifier from the marginal distribution of features, by leveraging the
+trends present in related classification tasks. This approach involves
+identifying an appropriate reproducing kernel Hilbert space and optimizing a
+regularized empirical risk over the space. We present generalization error
+analysis, describe universal kernels, and establish universal consistency of
+the proposed methodology. Experimental results on synthetic data and three real
+data applications demonstrate the superiority of the method with respect to a
+pooling strategy.
+",0,0,0,1,0,0
+1364,(Non-)formality of the extended Swiss Cheese operads,"  We study two colored operads of configurations of little $n$-disks in a unit
+$n$-disk, with the centers of the small disks of one color restricted to an
+$m$-plane, $m<n$. We compute the rational homotopy type of these \emph{extended
+Swiss Cheese operads} and show how they are connected to the rational homotopy
+types of the inclusion maps from the little $m$-disks to the little $n$-disks
+operad.
+",0,0,1,0,0,0
+1365,Pricing options and computing implied volatilities using neural networks,"  This paper proposes a data-driven approach, by means of an Artificial Neural
+Network (ANN), to value financial options and to calculate implied volatilities
+with the aim of accelerating the corresponding numerical methods. With ANNs
+being universal function approximators, this method trains an optimized ANN on
+a data set generated by a sophisticated financial model, and runs the trained
+ANN as an agent of the original solver in a fast and efficient way. We test
+this approach on three different types of solvers, including the analytic
+solution for the Black-Scholes equation, the COS method for the Heston
+stochastic volatility model and Brent's iterative root-finding method for the
+calculation of implied volatilities. The numerical results show that the ANN
+solver can reduce the computing time significantly.
+",1,0,0,0,0,1
+1366,Effect of magnetization on the tunneling anomaly in compressible quantum Hall states,"  Tunneling of electrons into a two-dimensional electron system is known to
+exhibit an anomaly at low bias, in which the tunneling conductance vanishes due
+to a many-body interaction effect. Recent experiments have measured this
+anomaly between two copies of the half-filled Landau level as a function of
+in-plane magnetic field, and they suggest that increasing spin polarization
+drives a deeper suppression of tunneling. Here we present a theory of the
+tunneling anomaly between two copies of the partially spin-polarized
+Halperin-Lee-Read state, and we show that the conventional description of the
+tunneling anomaly, based on the Coulomb self-energy of the injected charge
+packet, is inconsistent with the experimental observation. We propose that the
+experiment is operating in a different regime, not previously considered, in
+which the charge-spreading action is determined by the compressibility of the
+composite fermions.
+",0,1,0,0,0,0
+1367,Learning to Acquire Information,"  We consider the problem of diagnosis where a set of simple observations are
+used to infer a potentially complex hidden hypothesis. Finding the optimal
+subset of observations is intractable in general, thus we focus on the problem
+of active diagnosis, where the agent selects the next most-informative
+observation based on the results of previous observations. We show that under
+the assumption of uniform observation entropy, one can build an implication
+model which directly predicts the outcome of the potential next observation
+conditioned on the results of past observations, and selects the observation
+with the maximum entropy. This approach enjoys reduced computation complexity
+by bypassing the complicated hypothesis space, and can be trained on
+observation data alone, learning how to query without knowledge of the hidden
+hypothesis.
+",1,0,0,1,0,0
+1368,How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV,"  This work explores the feasibility of steering a drone with a (recurrent)
+neural network, based on input from a forward looking camera, in the context of
+a high-level navigation task. We set up a generic framework for training a
+network to perform navigation tasks based on imitation learning. It can be
+applied to both aerial and land vehicles. As a proof of concept we apply it to
+a UAV (Unmanned Aerial Vehicle) in a simulated environment, learning to cross a
+room containing a number of obstacles. So far only feedforward neural networks
+(FNNs) have been used to train UAV control. To cope with more complex tasks, we
+propose the use of recurrent neural networks (RNN) instead and successfully
+train an LSTM (Long-Short Term Memory) network for controlling UAVs. Vision
+based control is a sequential prediction problem, known for its highly
+correlated input data. The correlation makes training a network hard,
+especially an RNN. To overcome this issue, we investigate an alternative
+sampling method during training, namely window-wise truncated backpropagation
+through time (WW-TBPTT). Further, end-to-end training requires a lot of data
+which often is not available. Therefore, we compare the performance of
+retraining only the Fully Connected (FC) and LSTM control layers with networks
+which are trained end-to-end. Performing the relatively simple task of crossing
+a room already reveals important guidelines and good practices for training
+neural control networks. Different visualizations help to explain the behavior
+learned.
+",1,0,0,0,0,0
+1369,Range-efficient consistent sampling and locality-sensitive hashing for polygons,"  Locality-sensitive hashing (LSH) is a fundamental technique for similarity
+search and similarity estimation in high-dimensional spaces. The basic idea is
+that similar objects should produce hash collisions with probability
+significantly larger than objects with low similarity. We consider LSH for
+objects that can be represented as point sets in either one or two dimensions.
+To make the point sets finite size we consider the subset of points on a grid.
+Directly applying LSH (e.g. min-wise hashing) to these point sets would require
+time proportional to the number of points. We seek to achieve time that is much
+lower than direct approaches.
+Technically, we introduce new primitives for range-efficient consistent
+sampling (of independent interest), and show how to turn such samples into LSH
+values. Another application of our technique is a data structure for quickly
+estimating the size of the intersection or union of a set of preprocessed
+polygons. Curiously, our consistent sampling method uses transformation to a
+geometric problem.
+",1,0,0,0,0,0
+1370,Decoupled Greedy Learning of CNNs,"  A commonly cited inefficiency of neural network training by back-propagation
+is the update locking problem: each layer must wait for the signal to propagate
+through the network before updating. We consider and analyze a training
+procedure, Decoupled Greedy Learning (DGL), that addresses this problem more
+effectively and at scales beyond those of previous solutions. It is based on a
+greedy relaxation of the joint training objective, recently shown to be
+effective in the context of Convolutional Neural Networks (CNNs) on large-scale
+image classification. We consider an optimization of this objective that
+permits us to decouple the layer training, allowing for layers or modules in
+networks to be trained with a potentially linear parallelization in layers. We
+show theoretically and empirically that this approach converges. In addition,
+we empirically find that it can lead to better generalization than sequential
+greedy optimization and even standard end-to-end back-propagation. We show that
+an extension of this approach to asynchronous settings, where modules can
+operate with large communication delays, is possible with the use of a replay
+buffer. We demonstrate the effectiveness of DGL on the CIFAR-10 datasets
+against alternatives and on the large-scale ImageNet dataset, where we are able
+to effectively train VGG and ResNet-152 models.
+",1,0,0,1,0,0
+1371,Discrete time Pontryagin maximum principle for optimal control problems under state-action-frequency constraints,"  We establish a Pontryagin maximum principle for discrete time optimal control
+problems under the following three types of constraints: a) constraints on the
+states pointwise in time, b) constraints on the control actions pointwise in
+time, and c) constraints on the frequency spectrum of the optimal control
+trajectories. While the first two types of constraints are already included in
+the existing versions of the Pontryagin maximum principle, it turns out that
+the third type of constraints cannot be recast in any of the standard forms of
+the existing results for the original control system. We provide two different
+proofs of our Pontryagin maximum principle in this article, and include several
+special cases fine-tuned to control-affine nonlinear and linear system models.
+In particular, for minimization of quadratic cost functions and linear time
+invariant control systems, we provide tight conditions under which the optimal
+controls under frequency constraints are either normal or abnormal.
+",1,0,1,0,0,0
+1372,Quantitative evaluation of an active Chemotaxis model in Discrete time,"  A system of $N$ particles in a chemical medium in $\mathbb{R}^{d}$ is studied
+in a discrete time setting. Underlying interacting particle system in
+continuous time can be expressed as \begin{eqnarray} dX_{i}(t)
+&=&[-(I-A)X_{i}(t) + \bigtriangledown h(t,X_{i}(t))]dt + dW_{i}(t), \,\,
+X_{i}(0)=x_{i}\in \mathbb{R}^{d}\,\,\forall i=1,\ldots,N\nonumber\\
+\frac{\partial}{\partial t} h(t,x)&=&-\alpha h(t,x) + D\bigtriangleup h(t,x)
++\frac{\beta}{n} \sum_{i=1}^{N} g(X_{i}(t),x),\quad h(0,\cdot) =
+h(\cdot).\label{main} \end{eqnarray} where $X_{i}(t)$ is the location of the
+$i$th particle at time $t$ and $h(t,x)$ is the function measuring the
+concentration of the medium at location $x$ with $h(0,x) = h(x)$. In this
+article we describe a general discrete time non-linear formulation of the
+aforementioned model and a strongly coupled particle system approximating it.
+Similar models have been studied before (Budhiraja et al.(2011)) under a
+restrictive compactness assumption on the domain of particles. In current work
+the particles take values in $\R^{d}$ and consequently the stability analysis
+is particularly challenging. We provide sufficient conditions for the existence
+of a unique fixed point for the dynamical system governing the large $N$
+asymptotics of the particle empirical measure. We also provide uniform in time
+convergence rates for the particle empirical measure to the corresponding limit
+measure under suitable conditions on the model.
+",0,0,1,0,0,0
+1373,Deep Learning the Ising Model Near Criticality,"  It is well established that neural networks with deep architectures perform
+better than shallow networks for many tasks in machine learning. In statistical
+physics, while there has been recent interest in representing physical data
+with generative modelling, the focus has been on shallow neural networks. A
+natural question to ask is whether deep neural networks hold any advantage over
+shallow networks in representing such data. We investigate this question by
+using unsupervised, generative graphical models to learn the probability
+distribution of a two-dimensional Ising system. Deep Boltzmann machines, deep
+belief networks, and deep restricted Boltzmann networks are trained on thermal
+spin configurations from this system, and compared to the shallow architecture
+of the restricted Boltzmann machine. We benchmark the models, focussing on the
+accuracy of generating energetic observables near the phase transition, where
+these quantities are most difficult to approximate. Interestingly, after
+training the generative networks, we observe that the accuracy essentially
+depends only on the number of neurons in the first hidden layer of the network,
+and not on other model details such as network depth or model type. This is
+evidence that shallow networks are more efficient than deep networks at
+representing physical probability distributions associated with Ising systems
+near criticality.
+",1,1,0,1,0,0
+1374,A supervised approach to time scale detection in dynamic networks,"  For any stream of time-stamped edges that form a dynamic network, an
+important choice is the aggregation granularity that an analyst uses to bin the
+data. Picking such a windowing of the data is often done by hand, or left up to
+the technology that is collecting the data. However, the choice can make a big
+difference in the properties of the dynamic network. This is the time scale
+detection problem. In previous work, this problem is often solved with a
+heuristic as an unsupervised task. As an unsupervised problem, it is difficult
+to measure how well a given algorithm performs. In addition, we show that the
+quality of the windowing is dependent on which task an analyst wants to perform
+on the network after windowing. Therefore the time scale detection problem
+should not be handled independently from the rest of the analysis of the
+network.
+We introduce a framework that tackles both of these issues: By measuring the
+performance of the time scale detection algorithm based on how well a given
+task is accomplished on the resulting network, we are for the first time able
+to directly compare different time scale detection algorithms to each other.
+Using this framework, we introduce time scale detection algorithms that take a
+supervised approach: they leverage ground truth on training data to find a good
+windowing of the test data. We compare the supervised approach to previous
+approaches and several baselines on real data.
+",1,0,0,0,0,0
+1375,Binarized octree generation for Cartesian adaptive mesh refinement around immersed geometries,"  We revisit the generation of balanced octrees for adaptive mesh refinement
+(AMR) of Cartesian domains with immersed complex geometries. In a recent short
+note [Hasbestan and Senocak, J. Comput. Phys. vol. 351:473-477 (2017)], we
+showed that the data-locality of the Z-order curve in hashed linear octree
+generation methods may not be perfect because of potential collisions in the
+hash table. Building on that observation, we propose a binarized octree
+generation method that complies with the Z-order curve exactly. Similar to a
+hashed linear octree generation method, we use Morton encoding to index the
+nodes of an octree, but use a red-black tree in place of the hash table.
+Red-black tree is a special kind of a binary tree, which we use for insertion
+and deletion of elements during mesh adaptation. By strictly working with the
+bitwise representation of the octree, we remove computer hardware limitations
+on the depth of adaptation on a single processor. Additionally, we introduce a
+geometry encoding technique for rapidly tagging the solid geometry for
+refinement. Our results for several geometries with different levels of
+adaptations show that the binarized octree generation outperforms the linear
+octree generation in terms of runtime performance at the expense of only a
+slight increase in memory usage. We provide the current AMR capability as
+open-source software.
+",1,1,0,0,0,0
+1376,Adversarial Examples: Opportunities and Challenges,"  With the advent of the era of artificial intelligence(AI), deep neural
+networks (DNNs) have shown huge superiority over human in image recognition,
+speech processing, autonomous vehicles and medical diagnosis. However, recent
+studies indicate that DNNs are vulnerable to adversarial examples (AEs) which
+are designed by attackers to fool deep learning models. Different from real
+examples, AEs can hardly be distinguished from human eyes, but mislead the
+model to predict incorrect outputs and therefore threaten security critical
+deep-learning applications. In recent years, the generation and defense of AEs
+have become a research hotspot in the field of AI security. This article
+reviews the latest research progress of AEs. First, we introduce the concept,
+cause, characteristic and evaluation metrics of AEs, then give a survey on the
+state-of-the-art AE generation methods with the discussion of advantages and
+disadvantages. After that we review the existing defenses and discuss their
+limitations. Finally, the future research opportunities and challenges of AEs
+are prospected.
+",0,0,0,1,0,0
+1377,Decoupling Learning Rules from Representations,"  In the artificial intelligence field, learning often corresponds to changing
+the parameters of a parameterized function. A learning rule is an algorithm or
+mathematical expression that specifies precisely how the parameters should be
+changed. When creating an artificial intelligence system, we must make two
+decisions: what representation should be used (i.e., what parameterized
+function should be used) and what learning rule should be used to search
+through the resulting set of representable functions. Using most learning
+rules, these two decisions are coupled in a subtle (and often unintentional)
+way. That is, using the same learning rule with two different representations
+that can represent the same sets of functions can result in two different
+outcomes. After arguing that this coupling is undesirable, particularly when
+using artificial neural networks, we present a method for partially decoupling
+these two decisions for a broad class of learning rules that span unsupervised
+learning, reinforcement learning, and supervised learning.
+",1,0,0,1,0,0
+1378,Schur P-positivity and involution Stanley symmetric functions,"  The involution Stanley symmetric functions $\hat{F}_y$ are the stable limits
+of the analogues of Schubert polynomials for the orbits of the orthogonal group
+in the flag variety. These symmetric functions are also generating functions
+for involution words, and are indexed by the involutions in the symmetric
+group. By construction each $\hat{F}_y$ is a sum of Stanley symmetric functions
+and therefore Schur positive. We prove the stronger fact that these power
+series are Schur $P$-positive. We give an algorithm to efficiently compute the
+decomposition of $\hat{F}_y$ into Schur $P$-summands, and prove that this
+decomposition is triangular with respect to the dominance order on partitions.
+As an application, we derive pattern avoidance conditions which characterize
+the involution Stanley symmetric functions which are equal to Schur
+$P$-functions. We deduce as a corollary that the involution Stanley symmetric
+function of the reverse permutation is a Schur $P$-function indexed by a
+shifted staircase shape. These results lead to alternate proofs of theorems of
+Ardila-Serrano and DeWitt on skew Schur functions which are Schur
+$P$-functions. We also prove new Pfaffian formulas for certain related
+involution Schubert polynomials.
+",0,0,1,0,0,0
+1379,A Riemannian gossip approach to subspace learning on Grassmann manifold,"  In this paper, we focus on subspace learning problems on the Grassmann
+manifold. Interesting applications in this setting include low-rank matrix
+completion and low-dimensional multivariate regression, among others. Motivated
+by privacy concerns, we aim to solve such problems in a decentralized setting
+where multiple agents have access to (and solve) only a part of the whole
+optimization problem. The agents communicate with each other to arrive at a
+consensus, i.e., agree on a common quantity, via the gossip protocol.
+We propose a novel cost function for subspace learning on the Grassmann
+manifold, which is a weighted sum of several sub-problems (each solved by an
+agent) and the communication cost among the agents. The cost function has a
+finite sum structure. In the proposed modeling approach, different agents learn
+individual local subspace but they achieve asymptotic consensus on the global
+learned subspace. The approach is scalable and parallelizable. Numerical
+experiments show the efficacy of the proposed decentralized algorithms on
+various matrix completion and multivariate regression benchmarks.
+",1,0,1,0,0,0
+1380,"Space-Valued Diagrams, Type-Theoretically (Extended Abstract)","  Topologists are sometimes interested in space-valued diagrams over a given
+index category, but it is tricky to say what such a diagram even is if we look
+for a notion that is stable under equivalence. The same happens in (homotopy)
+type theory, where it is known only for special cases how one can define a type
+of type-valued diagrams over a given index category. We offer several
+constructions. We first show how to define homotopy coherent diagrams which
+come with all higher coherence laws explicitly, with two variants that come
+with assumption on the index category or on the type theory. Further, we
+present a construction of diagrams over certain Reedy categories. As an
+application, we add the degeneracies to the well-known construction of
+semisimplicial types, yielding a construction of simplicial types up to any
+given finite level. The current paper is only an extended abstract, and a full
+version is to follow. In the full paper, we will show that the different
+notions of diagrams are equivalent to each other and to the known notion of
+Reedy fibrant diagrams whenever the statement makes sense. In the current
+paper, we only sketch some core ideas of the proofs.
+",1,0,1,0,0,0
+1381,Properties of cyanobacterial UV-absorbing pigments suggest their evolution was driven by optimizing photon dissipation rather than photoprotection,"  An ancient repertoire of UV absorbing pigments which survive today in the
+phylogenetically oldest extant photosynthetic organisms the cyanobacteria point
+to a direction in evolutionary adaptation of the pigments and their associated
+biota from largely UVC absorbing pigments in the Archean to pigments covering
+ever more of the longer wavelength UV and visible in the Phanerozoic.Such a
+scenario implies selection of photon dissipation rather than photoprotection
+over the evolutionary history of life.This is consistent with the thermodynamic
+dissipation theory of the origin and evolution of life which suggests that the
+most important hallmark of biological evolution has been the covering of Earths
+surface with organic pigment molecules and water to absorb and dissipate ever
+more completely the prevailing surface solar spectrum.In this article we
+compare a set of photophysical photochemical biosynthetic and other germane
+properties of the two dominant classes of cyanobacterial UV absorbing pigments
+the mycosporine like amino acids MAAs and scytonemins.Pigment wavelengths of
+maximum absorption correspond with the time dependence of the prevailing Earth
+surface solar spectrum and we proffer this as evidence for the selection of
+photon dissipation rather than photoprotection over the history of life on
+Earth.
+",0,1,0,0,0,0
+1382,Output Impedance Diffusion into Lossy Power Lines,"  Output impedances are inherent elements of power sources in the electrical
+grids. In this paper, we give an answer to the following question: What is the
+effect of output impedances on the inductivity of the power network? To address
+this question, we propose a measure to evaluate the inductivity of a power
+grid, and we compute this measure for various types of output impedances.
+Following this computation, it turns out that network inductivity highly
+depends on the algebraic connectivity of the network. By exploiting the derived
+expressions of the proposed measure, one can tune the output impedances in
+order to enforce a desired level of inductivity on the power system.
+Furthermore, the results show that the more ""connected"" the network is, the
+more the output impedances diffuse into the network. Finally, using Kron
+reduction, we provide examples that demonstrate the utility and validity of the
+method.
+",1,0,0,0,0,0
+1383,Enhancing the significance of gravitational wave bursts through signal classification,"  The quest to observe gravitational waves challenges our ability to
+discriminate signals from detector noise. This issue is especially relevant for
+transient gravitational waves searches with a robust eyes wide open approach,
+the so called all- sky burst searches. Here we show how signal classification
+methods inspired by broad astrophysical characteristics can be implemented in
+all-sky burst searches preserving their generality. In our case study, we apply
+a multivariate analyses based on artificial neural networks to classify waves
+emitted in compact binary coalescences. We enhance by orders of magnitude the
+significance of signals belonging to this broad astrophysical class against the
+noise background. Alternatively, at a given level of mis-classification of
+noise events, we can detect about 1/4 more of the total signal population. We
+also show that a more general strategy of signal classification can actually be
+performed, by testing the ability of artificial neural networks in
+discriminating different signal classes. The possible impact on future
+observations by the LIGO-Virgo network of detectors is discussed by analysing
+recoloured noise from previous LIGO-Virgo data with coherent WaveBurst, one of
+the flagship pipelines dedicated to all-sky searches for transient
+gravitational waves.
+",0,1,0,0,0,0
+1384,Model-based Clustering with Sparse Covariance Matrices,"  Finite Gaussian mixture models are widely used for model-based clustering of
+continuous data. Nevertheless, since the number of model parameters scales
+quadratically with the number of variables, these models can be easily
+over-parameterized. For this reason, parsimonious models have been developed
+via covariance matrix decompositions or assuming local independence. However,
+these remedies do not allow for direct estimation of sparse covariance matrices
+nor do they take into account that the structure of association among the
+variables can vary from one cluster to the other. To this end, we introduce
+mixtures of Gaussian covariance graph models for model-based clustering with
+sparse covariance matrices. A penalized likelihood approach is employed for
+estimation and a general penalty term on the graph configurations can be used
+to induce different levels of sparsity and incorporate prior knowledge. Model
+estimation is carried out using a structural-EM algorithm for parameters and
+graph structure estimation, where two alternative strategies based on a genetic
+algorithm and an efficient stepwise search are proposed for inference. With
+this approach, sparse component covariance matrices are directly obtained. The
+framework results in a parsimonious model-based clustering of the data via a
+flexible model for the within-group joint distribution of the variables.
+Extensive simulated data experiments and application to illustrative datasets
+show that the method attains good classification performance and model quality.
+",0,0,0,1,0,0
+1385,An Assessment of Data Transfer Performance for Large-Scale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6,"  We document the data transfer workflow, data transfer performance, and other
+aspects of staging approximately 56 terabytes of climate model output data from
+the distributed Coupled Model Intercomparison Project (CMIP5) archive to the
+National Energy Research Supercomputing Center (NERSC) at the Lawrence Berkeley
+National Laboratory required for tracking and characterizing extratropical
+storms, a phenomena of importance in the mid-latitudes. We present this
+analysis to illustrate the current challenges in assembling multi-model data
+sets at major computing facilities for large-scale studies of CMIP5 data.
+Because of the larger archive size of the upcoming CMIP6 phase of model
+intercomparison, we expect such data transfers to become of increasing
+importance, and perhaps of routine necessity. We find that data transfer rates
+using the ESGF are often slower than what is typically available to US
+residences and that there is significant room for improvement in the data
+transfer capabilities of the ESGF portal and data centers both in terms of
+workflow mechanics and in data transfer performance. We believe performance
+improvements of at least an order of magnitude are within technical reach using
+current best practices, as illustrated by the performance we achieved in
+transferring the complete raw data set between two high performance computing
+facilities. To achieve these performance improvements, we recommend: that
+current best practices (such as the Science DMZ model) be applied to the data
+servers and networks at ESGF data centers; that sufficient financial and human
+resources be devoted at the ESGF data centers for systems and network
+engineering tasks to support high performance data movement; and that
+performance metrics for data transfer between ESGF data centers and major
+computing facilities used for climate data analysis be established, regularly
+tested, and published.
+",1,1,0,0,0,0
+1386,Generalization for Adaptively-chosen Estimators via Stable Median,"  Datasets are often reused to perform multiple statistical analyses in an
+adaptive way, in which each analysis may depend on the outcomes of previous
+analyses on the same dataset. Standard statistical guarantees do not account
+for these dependencies and little is known about how to provably avoid
+overfitting and false discovery in the adaptive setting. We consider a natural
+formalization of this problem in which the goal is to design an algorithm that,
+given a limited number of i.i.d.~samples from an unknown distribution, can
+answer adaptively-chosen queries about that distribution.
+We present an algorithm that estimates the expectations of $k$ arbitrary
+adaptively-chosen real-valued estimators using a number of samples that scales
+as $\sqrt{k}$. The answers given by our algorithm are essentially as accurate
+as if fresh samples were used to evaluate each estimator. In contrast, prior
+work yields error guarantees that scale with the worst-case sensitivity of each
+estimator. We also give a version of our algorithm that can be used to verify
+answers to such queries where the sample complexity depends logarithmically on
+the number of queries $k$ (as in the reusable holdout technique).
+Our algorithm is based on a simple approximate median algorithm that
+satisfies the strong stability guarantees of differential privacy. Our
+techniques provide a new approach for analyzing the generalization guarantees
+of differentially private algorithms.
+",1,0,0,1,0,0
+1387,Automated Problem Identification: Regression vs Classification via Evolutionary Deep Networks,"  Regression or classification? This is perhaps the most basic question faced
+when tackling a new supervised learning problem. We present an Evolutionary
+Deep Learning (EDL) algorithm that automatically solves this by identifying the
+question type with high accuracy, along with a proposed deep architecture.
+Typically, a significant amount of human insight and preparation is required
+prior to executing machine learning algorithms. For example, when creating deep
+neural networks, the number of parameters must be selected in advance and
+furthermore, a lot of these choices are made based upon pre-existing knowledge
+of the data such as the use of a categorical cross entropy loss function.
+Humans are able to study a dataset and decide whether it represents a
+classification or a regression problem, and consequently make decisions which
+will be applied to the execution of the neural network. We propose the
+Automated Problem Identification (API) algorithm, which uses an evolutionary
+algorithm interface to TensorFlow to manipulate a deep neural network to decide
+if a dataset represents a classification or a regression problem. We test API
+on 16 different classification, regression and sentiment analysis datasets with
+up to 10,000 features and up to 17,000 unique target values. API achieves an
+average accuracy of $96.3\%$ in identifying the problem type without hardcoding
+any insights about the general characteristics of regression or classification
+problems. For example, API successfully identifies classification problems even
+with 1000 target values. Furthermore, the algorithm recommends which loss
+function to use and also recommends a neural network architecture. Our work is
+therefore a step towards fully automated machine learning.
+",1,0,0,1,0,0
+1388,Attribution of extreme rainfall in Southeast China during May 2015,"  Anthropogenic climate change increased the probability that a short-duration,
+intense rainfall event would occur in parts of southeast China. This type of
+event occurred in May 2015, causing serious flooding.
+",0,1,0,0,0,0
+1389,The Kellogg property and boundary regularity for p-harmonic functions with respect to the Mazurkiewicz boundary and other compactifications,"  In this paper boundary regularity for p-harmonic functions is studied with
+respect to the Mazurkiewicz boundary and other compactifications. In
+particular, the Kellogg property (which says that the set of irregular boundary
+points has capacity zero) is obtained for a large class of compactifications,
+but also two examples when it fails are given. This study is done for complete
+metric spaces equipped with doubling measures supporting a p-Poincaré
+inequality, but the results are new also in unweighted Euclidean spaces.
+",0,0,1,0,0,0
+1390,Nonparametric Inference via Bootstrapping the Debiased Estimator,"  In this paper, we propose to construct confidence bands by bootstrapping the
+debiased kernel density estimator (for density estimation) and the debiased
+local polynomial regression estimator (for regression analysis). The idea of
+using a debiased estimator was first introduced in Calonico et al. (2015),
+where they construct a confidence interval of the density function (and
+regression function) at a given point by explicitly estimating stochastic
+variations. We extend their ideas and propose a bootstrap approach for
+constructing confidence bands that is uniform for every point in the support.
+We prove that the resulting bootstrap confidence band is asymptotically valid
+and is compatible with most tuning parameter selection approaches, such as the
+rule of thumb and cross-validation. We further generalize our method to
+confidence sets of density level sets and inverse regression problems.
+Simulation studies confirm the validity of the proposed confidence bands/sets.
+",0,0,1,1,0,0
+1391,Solving constraint-satisfaction problems with distributed neocortical-like neuronal networks,"  Finding actions that satisfy the constraints imposed by both external inputs
+and internal representations is central to decision making. We demonstrate that
+some important classes of constraint satisfaction problems (CSPs) can be solved
+by networks composed of homogeneous cooperative-competitive modules that have
+connectivity similar to motifs observed in the superficial layers of neocortex.
+The winner-take-all modules are sparsely coupled by programming neurons that
+embed the constraints onto the otherwise homogeneous modular computational
+substrate. We show rules that embed any instance of the CSPs planar four-color
+graph coloring, maximum independent set, and Sudoku on this substrate, and
+provide mathematical proofs that guarantee these graph coloring problems will
+convergence to a solution. The network is composed of non-saturating linear
+threshold neurons. Their lack of right saturation allows the overall network to
+explore the problem space driven through the unstable dynamics generated by
+recurrent excitation. The direction of exploration is steered by the constraint
+neurons. While many problems can be solved using only linear inhibitory
+constraints, network performance on hard problems benefits significantly when
+these negative constraints are implemented by non-linear multiplicative
+inhibition. Overall, our results demonstrate the importance of instability
+rather than stability in network computation, and also offer insight into the
+computational role of dual inhibitory mechanisms in neural circuits.
+",0,0,0,0,1,0
+1392,Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks,"  A major challenge in brain tumor treatment planning and quantitative
+evaluation is determination of the tumor extent. The noninvasive magnetic
+resonance imaging (MRI) technique has emerged as a front-line diagnostic tool
+for brain tumors without ionizing radiation. Manual segmentation of brain tumor
+extent from 3D MRI volumes is a very time-consuming task and the performance is
+highly relied on operator's experience. In this context, a reliable fully
+automatic segmentation method for the brain tumor segmentation is necessary for
+an efficient measurement of the tumor extent. In this study, we propose a fully
+automatic method for brain tumor segmentation, which is developed using U-Net
+based deep convolutional networks. Our method was evaluated on Multimodal Brain
+Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade
+brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our
+method can obtain promising segmentation efficiently.
+",1,0,0,0,0,0
+1393,Asymptotic Blind-spot Analysis of Localization Networks under Correlated Blocking using a Poisson Line Process,"  In a localization network, the line-of-sight between anchors (transceivers)
+and targets may be blocked due to the presence of obstacles in the environment.
+Due to the non-zero size of the obstacles, the blocking is typically correlated
+across both anchor and target locations, with the extent of correlation
+increasing with obstacle size. If a target does not have line-of-sight to a
+minimum number of anchors, then its position cannot be estimated unambiguously
+and is, therefore, said to be in a blind-spot. However, the analysis of the
+blind-spot probability of a given target is challenging due to the inherent
+randomness in the obstacle locations and sizes. In this letter, we develop a
+new framework to analyze the worst-case impact of correlated blocking on the
+blind-spot probability of a typical target; in particular, we model the
+obstacles by a Poisson line process and the anchor locations by a Poisson point
+process. For this setup, we define the notion of the asymptotic blind-spot
+probability of the typical target and derive a closed-form expression for it as
+a function of the area distribution of a typical Poisson-Voronoi cell. As an
+upper bound for the more realistic case when obstacles have finite dimensions,
+the asymptotic blind-spot probability is useful as a design tool to ensure that
+the blind-spot probability of a typical target does not exceed a desired
+threshold, $\epsilon$.
+",1,0,0,0,0,0
+1394,The relation between galaxy morphology and colour in the EAGLE simulation,"  We investigate the relation between kinematic morphology, intrinsic colour
+and stellar mass of galaxies in the EAGLE cosmological hydrodynamical
+simulation. We calculate the intrinsic u-r colours and measure the fraction of
+kinetic energy invested in ordered corotation of 3562 galaxies at z=0 with
+stellar masses larger than $10^{10}M_{\odot}$. We perform a visual inspection
+of gri-composite images and find that our kinematic morphology correlates
+strongly with visual morphology. EAGLE produces a galaxy population for which
+morphology is tightly correlated with the location in the colour- mass diagram,
+with the red sequence mostly populated by elliptical galaxies and the blue
+cloud by disc galaxies. Satellite galaxies are more likely to be on the red
+sequence than centrals, and for satellites the red sequence is morphologically
+more diverse. These results show that the connection between mass, intrinsic
+colour and morphology arises from galaxy formation models that reproduce the
+observed galaxy mass function and sizes.
+",0,1,0,0,0,0
+1395,An alternative to continuous univariate distributions supported on a bounded interval: The BMT distribution,"  In this paper, we introduce the BMT distribution as an unimodal alternative
+to continuous univariate distributions supported on a bounded interval. The
+ideas behind the mathematical formulation of this new distribution come from
+computer aid geometric design, specifically from Bezier curves. First, we
+review general properties of a distribution given by parametric equations and
+extend the definition of a Bezier distribution. Then, after proposing the BMT
+cumulative distribution function, we derive its probability density function
+and a closed-form expression for quantile function, median, interquartile
+range, mode, and moments. The domain change from [0,1] to [c,d] is mentioned.
+Estimation of parameters is approached by the methods of maximum likelihood and
+maximum product of spacing. We test the numerical estimation procedures using
+some simulated data. Usefulness and flexibility of the new distribution are
+illustrated in three real data sets. The BMT distribution has a significant
+potential to estimate domain parameters and to model data outside the scope of
+the beta or similar distributions.
+",0,0,1,1,0,0
+1396,Deep Object Centric Policies for Autonomous Driving,"  While learning visuomotor skills in an end-to-end manner is appealing, deep
+neural networks are often uninterpretable and fail in surprising ways. For
+robotics tasks, such as autonomous driving, models that explicitly represent
+objects may be more robust to new scenes and provide intuitive visualizations.
+We describe a taxonomy of object-centric models which leverage both object
+instances and end-to-end learning. In the Grand Theft Auto V simulator, we show
+that object centric models outperform object-agnostic methods in scenes with
+other vehicles and pedestrians, even with an imperfect detector. We also
+demonstrate that our architectures perform well on real world environments by
+evaluating on the Berkeley DeepDrive Video dataset.
+",1,0,0,0,0,0
+1397,A Search for Laser Emission with Megawatt Thresholds from 5600 FGKM Stars,"  We searched high resolution spectra of 5600 nearby stars for emission lines
+that are both inconsistent with a natural origin and unresolved spatially, as
+would be expected from extraterrestrial optical lasers. The spectra were
+obtained with the Keck 10-meter telescope, including light coming from within
+0.5 arcsec of the star, corresponding typically to within a few to tens of au
+of the star, and covering nearly the entire visible wavelength range from 3640
+to 7890 angstroms. We establish detection thresholds by injecting synthetic
+laser emission lines into our spectra and blindly analyzing them for
+detections. We compute flux density detection thresholds for all wavelengths
+and spectral types sampled. Our detection thresholds for the power of the
+lasers themselves range from 3 kW to 13 MW, independent of distance to the star
+but dependent on the competing ""glare"" of the spectral energy distribution of
+the star and on the wavelength of the laser light, launched from a benchmark,
+diffraction-limited 10-meter class telescope. We found no such laser emission
+coming from the planetary region around any of the 5600 stars. As they contain
+roughly 2000 lukewarm, Earth-size planets, we rule out models of the Milky Way
+in which over 0.1 percent of warm, Earth-size planets harbor technological
+civilizations that, intentionally or not, are beaming optical lasers toward us.
+A next generation spectroscopic laser search will be done by the Breakthrough
+Listen initiative, targeting more stars, especially stellar types overlooked
+here including spectral types O, B, A, early F, late M, and brown dwarfs, and
+astrophysical exotica.
+",0,1,0,0,0,0
+1398,Learning Large Scale Ordinary Differential Equation Systems,"  Learning large scale nonlinear ordinary differential equation (ODE) systems
+from data is known to be computationally and statistically challenging. We
+present a framework together with the adaptive integral matching (AIM)
+algorithm for learning polynomial or rational ODE systems with a sparse network
+structure. The framework allows for time course data sampled from multiple
+environments representing e.g. different interventions or perturbations of the
+system. The algorithm AIM combines an initial penalised integral matching step
+with an adapted least squares step based on solving the ODE numerically. The R
+package episode implements AIM together with several other algorithms and is
+available from CRAN. It is shown that AIM achieves state-of-the-art network
+recovery for the in silico phosphoprotein abundance data from the eighth DREAM
+challenge with an AUROC of 0.74, and it is demonstrated via a range of
+numerical examples that AIM has good statistical properties while being
+computationally feasible even for large systems.
+",0,0,1,1,0,0
+1399,Linear Time Clustering for High Dimensional Mixtures of Gaussian Clouds,"  Clustering mixtures of Gaussian distributions is a fundamental and
+challenging problem that is ubiquitous in various high-dimensional data
+processing tasks. While state-of-the-art work on learning Gaussian mixture
+models has focused primarily on improving separation bounds and their
+generalization to arbitrary classes of mixture models, less emphasis has been
+paid to practical computational efficiency of the proposed solutions. In this
+paper, we propose a novel and highly efficient clustering algorithm for $n$
+points drawn from a mixture of two arbitrary Gaussian distributions in
+$\mathbb{R}^p$. The algorithm involves performing random 1-dimensional
+projections until a direction is found that yields a user-specified clustering
+error $e$. For a 1-dimensional separation parameter $\gamma$ satisfying
+$\gamma=Q^{-1}(e)$, the expected number of such projections is shown to be
+bounded by $o(\ln p)$, when $\gamma$ satisfies $\gamma\leq
+c\sqrt{\ln{\ln{p}}}$, with $c$ as the separability parameter of the two
+Gaussians in $\mathbb{R}^p$. Consequently, the expected overall running time of
+the algorithm is linear in $n$ and quasi-linear in $p$ at $o(\ln{p})O(np)$, and
+the sample complexity is independent of $p$. This result stands in contrast to
+prior works which provide polynomial, with at-best quadratic, running time in
+$p$ and $n$. We show that our bound on the expected number of 1-dimensional
+projections extends to the case of three or more Gaussian components, and we
+present a generalization of our results to mixture distributions beyond the
+Gaussian model.
+",1,0,0,0,0,0
+1400,Estimation of Relationship between Stimulation Current and Force Exerted during Isometric Contraction,"  In this study, we developed a method to estimate the relationship between
+stimulation current and volatility during isometric contraction. In functional
+electrical stimulation (FES), joints are driven by applying voltage to muscles.
+This technology has been used for a long time in the field of rehabilitation,
+and recently application oriented research has been reported. However,
+estimation of the relationship between stimulus value and exercise capacity has
+not been discussed to a great extent. Therefore, in this study, a human muscle
+model was estimated using the transfer function estimation method with fast
+Fourier transform. It was found that the relationship between stimulation
+current and force exerted could be expressed by a first-order lag system. In
+verification of the force estimate, the ability of the proposed model to
+estimate the exerted force under steady state response was found to be good.
+",0,0,0,0,1,0
+1401,Facial Keypoints Detection,"  Detect facial keypoints is a critical element in face recognition. However,
+there is difficulty to catch keypoints on the face due to complex influences
+from original images, and there is no guidance to suitable algorithms. In this
+paper, we study different algorithms that can be applied to locate keyponits.
+Specifically: our framework (1)prepare the data for further investigation
+(2)Using PCA and LBP to process the data (3) Apply different algorithms to
+analysis data, including linear regression models, tree based model, neural
+network and convolutional neural network, etc. Finally we will give our
+conclusion and further research topic. A comprehensive set of experiments on
+dataset demonstrates the effectiveness of our framework.
+",1,0,0,1,0,0
+1402,Transitions from a Kondo-like diamagnetic insulator into a modulated ferromagnetic metal in $\bm{\mathrm{FeGa}_{3-y}\mathrm{Ge}_y}$,"  One initial and essential question of magnetism is whether the magnetic
+properties of a material are governed by localized moments or itinerant
+electrons. Here we expose the case for the weakly ferromagnetic system
+FeGa$_{3-y}$Ge$_y$ wherein these two opposite models are reconciled, such that
+the magnetic susceptibility is quantitatively explained by taking into account
+the effects of spin-spin correlation. With the electron doping introduced by Ge
+substitution, the diamagnetic insulating parent compound FeGa$_3$ becomes a
+paramagnetic metal as early as at $ y=0.01 $, and turns into a weakly
+ferromagnetic metal around the quantum critical point $ y=0.15 $. Within the
+ferromagnetic regime of FeGa$_{3-y}$Ge$_y$, the magnetic properties are of a
+weakly itinerant ferromagnetic nature, located in the intermediate regime
+between the localized and the itinerant dominance. Our analysis implies a
+potential universality for all itinerant-electron ferromagnets.
+",0,1,0,0,0,0
+1403,"Sample, computation vs storage tradeoffs for classification using tensor subspace models","  In this paper, we exhibit the tradeoffs between the (training) sample,
+computation and storage complexity for the problem of supervised classification
+using signal subspace estimation. Our main tool is the use of tensor subspaces,
+i.e. subspaces with a Kronecker structure, for embedding the data into lower
+dimensions. Among the subspaces with a Kronecker structure, we show that using
+subspaces with a hierarchical structure for representing data leads to improved
+tradeoffs. One of the main reasons for the improvement is that embedding data
+into these hierarchical Kronecker structured subspaces prevents overfitting at
+higher latent dimensions.
+",1,0,0,1,0,0
+1404,One-step Estimation of Networked Population Size with Anonymity Using Respondent-Driven Capture-Recapture and Hashing,"  Estimates of population size for hidden and hard-to-reach individuals are of
+particular interest to health officials when health problems are concentrated
+in such populations. Efforts to derive these estimates are often frustrated by
+a range of factors including social stigma or an association with illegal
+activities that ordinarily preclude conventional survey strategies. This paper
+builds on and extends prior work that proposed a method to meet these
+challenges. Here we describe a rigorous formalization of a one-step,
+network-based population estimation procedure that can be employed under
+conditions of anonymity. The estimation procedure is designed to be implemented
+alongside currently accepted strategies for research with hidden populations.
+Simulation experiments are described that test the efficacy of the method
+across a range of implementation conditions and hidden population sizes. The
+results of these experiments show that reliable population estimates can be
+derived for hidden, networked population as large as 12,500 and perhaps larger
+for one family of random graphs. As such, the method shows potential for
+cost-effective implementation health and disease surveillance officials
+concerned with hidden populations. Limitations and future work are discussed in
+the concluding section.
+",1,0,0,1,0,0
+1405,Real single ion solvation free energies with quantum mechanical simulation,"  Single ion solvation free energies are one of the most important properties
+of electrolyte solutions and yet there is ongoing debate about what these
+values are. Only the values for neutral ion pairs are known. Here, we use DFT
+interaction potentials with molecular dynamics simulation (DFT-MD) combined
+with a modified version of the quasi-chemical theory (QCT) to calculate these
+energies for the lithium and fluoride ions. A method to correct for the error
+in the DFT functional is developed and very good agreement with the
+experimental value for the lithium fluoride pair is obtained. Moreover, this
+method partitions the energies into physically intuitive terms such as surface
+potential, cavity and charging energies which are amenable to descriptions with
+reduced models. Our research suggests that lithium's solvation free energy is
+dominated by the free energetics of a charged hard sphere, whereas fluoride
+exhibits significant quantum mechanical behavior that cannot be simply
+described with a reduced model.
+",0,1,0,0,0,0
+1406,Crowdsourcing with Sparsely Interacting Workers,"  We consider estimation of worker skills from worker-task interaction data
+(with unknown labels) for the single-coin crowd-sourcing binary classification
+model in symmetric noise. We define the (worker) interaction graph whose nodes
+are workers and an edge between two nodes indicates whether or not the two
+workers participated in a common task. We show that skills are asymptotically
+identifiable if and only if an appropriate limiting version of the interaction
+graph is irreducible and has odd-cycles. We then formulate a weighted rank-one
+optimization problem to estimate skills based on observations on an
+irreducible, aperiodic interaction graph. We propose a gradient descent scheme
+and show that for such interaction graphs estimates converge asymptotically to
+the global minimum. We characterize noise robustness of the gradient scheme in
+terms of spectral properties of signless Laplacians of the interaction graph.
+We then demonstrate that a plug-in estimator based on the estimated skills
+achieves state-of-art performance on a number of real-world datasets. Our
+results have implications for rank-one matrix completion problem in that
+gradient descent can provably recover $W \times W$ rank-one matrices based on
+$W+1$ off-diagonal observations of a connected graph with a single odd-cycle.
+",1,0,0,0,0,0
+1407,Training deep learning based denoisers without ground truth data,"  Recent deep learning based denoisers often outperform state-of-the-art
+conventional denoisers such as BM3D. They are typically trained to minimize the
+mean squared error (MSE) between the output of a deep neural network and the
+ground truth image. In deep learning based denoisers, it is important to use
+high quality noiseless ground truth for high performance, but it is often
+challenging or even infeasible to obtain such a clean image in application
+areas such as hyperspectral remote sensing and medical imaging. We propose a
+Stein's Unbiased Risk Estimator (SURE) based method for training deep neural
+network denoisers only with noisy images. We demonstrated that our SURE based
+method without ground truth was able to train deep neural network denoisers to
+yield performance close to deep learning denoisers trained with ground truth
+and to outperform state-of-the-art BM3D. Further improvements were achieved by
+including noisy test images for training denoiser networks using our proposed
+SURE based method.
+",0,0,0,1,0,0
+1408,Language Design and Renormalization,"  Here we consider some well-known facts in syntax from a physics perspective,
+which allows us to establish some remarkable equivalences. Specifically, we
+observe that the operation MERGE put forward by N. Chomsky in 1995 can be
+interpreted as a physical information coarse-graining. Thus, MERGE in
+linguistics entails information renormalization in physics, according to
+different time scales. We make this point mathematically formal in terms of
+language models, i.e., probability distributions over word sequences, widely
+used in natural language processing as well as other ambits. In this setting,
+MERGE corresponds to a 3-index probability tensor implementing a
+coarse-graining, akin to a probabilistic context-free grammar. The probability
+vectors of meaningful sentences are naturally given by stochastic tensor
+networks (TN) that are mostly loop-free, such as Tree Tensor Networks and
+Matrix Product States. These structures have short-ranged correlations in the
+syntactic distance by construction and, because of the peculiarities of human
+language, they are extremely efficient to manipulate computationally. We also
+propose how to obtain such language models from probability distributions of
+certain TN quantum states, which we show to be efficiently preparable by a
+quantum computer. Moreover, using tools from entanglement theory, we use these
+quantum states to prove classical lower bounds on the perplexity of the
+probability distribution for a set of words in a sentence. Implications of
+these results are discussed in the ambits of theoretical and computational
+linguistics, artificial intelligence, programming languages, RNA and protein
+sequencing, quantum many-body systems, and beyond. Our work shows how many of
+the key linguistic ideas from the last century, including developments in
+computational linguistics, fit perfectly with known physical concepts linked to
+renormalization.
+",1,1,0,0,0,0
+1409,On the geometry of the moduli space of sheaves supported on curves of genus two in a quadric surface,"  We study the moduli space of stable sheaves of Euler characteristic 2,
+supported on curves of arithmetic genus 2 contained in a smooth quadric
+surface. We show that this moduli space is rational. We compute its Betti
+numbers and we give a classification of the stable sheaves involving locally
+free resolutions.
+",0,0,1,0,0,0
+1410,"Attention Solves Your TSP, Approximately","  The development of efficient (heuristic) algorithms for practical
+combinatorial optimization problems is costly, so we want to automatically
+learn them instead. We show the feasibility of this approach on the important
+Travelling Salesman Problem (TSP). We learn a heuristic algorithm that uses a
+Neural Network policy to construct a tour. As an alternative to the Pointer
+Network, our model is based entirely on (graph) attention layers and is
+invariant to the input order of the nodes. We train the model efficiently using
+REINFORCE with a simple and robust baseline based on a deterministic (greedy)
+rollout of the best policy so far. We significantly improve over results from
+previous works that consider learned heuristics for the TSP, reducing the
+optimality gap for a single tour construction from 1.51% to 0.32% for instances
+with 20 nodes, from 4.59% to 1.71% for 50 nodes and from 6.89% to 4.43% for 100
+nodes. Additionally, we improve over a recent Reinforcement Learning framework
+for two variants of the Vehicle Routing Problem (VRP).
+",0,0,0,1,0,0
+1411,A Distributed Online Pricing Strategy for Demand Response Programs,"  We study a demand response problem from utility (also referred to as
+operator)'s perspective with realistic settings, in which the utility faces
+uncertainty and limited communication. Specifically, the utility does not know
+the cost function of consumers and cannot have multiple rounds of information
+exchange with consumers. We formulate an optimization problem for the utility
+to minimize its operational cost considering time-varying demand response
+targets and responses of consumers. We develop a joint online learning and
+pricing algorithm. In each time slot, the utility sends out a price signal to
+all consumers and estimates the cost functions of consumers based on their
+noisy responses. We measure the performance of our algorithm using regret
+analysis and show that our online algorithm achieves logarithmic regret with
+respect to the operating horizon. In addition, our algorithm employs linear
+regression to estimate the aggregate response of consumers, making it easy to
+implement in practice. Simulation experiments validate the theoretic results
+and show that the performance gap between our algorithm and the offline
+optimality decays quickly.
+",1,0,1,0,0,0
+1412,Highly Nonlinear and Low Confinement Loss Photonic Crystal Fiber Using GaP Slot Core,"  This paper presents a triangular lattice photonic crystal fiber with very
+high nonlinear coefficient. Finite element method (FEM) is used to scrutinize
+different optical properties of proposed highly nonlinear photonic crystal
+fiber (HNL-PCF). The HNL-PCF exhibits a high nonlinearity up to $10\times10^{4}
+W^{-1}km^{-1}$ over the wavelength of 1500 nm to 1700 nm. Moreover, proposed
+HNL-PCF shows a very low confinement loss of $10^{-3} dB/km$ at 1550 nm
+wavelength. Furthermore, chromatic dispersion, dispersion slope, effective area
+etc. are also analyzed thoroughly. The proposed fiber will be a suitable
+candidate for broadband dispersion compensation, sensor devices and
+supercontinuum generation.
+",0,1,0,0,0,0
+1413,Is Epicurus the father of Reinforcement Learning?,"  The Epicurean Philosophy is commonly thought as simplistic and hedonistic.
+Here I discuss how this is a misconception and explore its link to
+Reinforcement Learning. Based on the letters of Epicurus, I construct an
+objective function for hedonism which turns out to be equivalent of the
+Reinforcement Learning objective function when omitting the discount factor. I
+then discuss how Plato and Aristotle 's views that can be also loosely linked
+to Reinforcement Learning, as well as their weaknesses in relationship to it.
+Finally, I emphasise the close affinity of the Epicurean views and the Bellman
+equation.
+",1,0,0,1,0,0
+1414,Low-Precision Floating-Point Schemes for Neural Network Training,"  The use of low-precision fixed-point arithmetic along with stochastic
+rounding has been proposed as a promising alternative to the commonly used
+32-bit floating point arithmetic to enhance training neural networks training
+in terms of performance and energy efficiency. In the first part of this paper,
+the behaviour of the 12-bit fixed-point arithmetic when training a
+convolutional neural network with the CIFAR-10 dataset is analysed, showing
+that such arithmetic is not the most appropriate for the training phase. After
+that, the paper presents and evaluates, under the same conditions, alternative
+low-precision arithmetics, starting with the 12-bit floating-point arithmetic.
+These two representations are then leveraged using local scaling in order to
+increase accuracy and get closer to the baseline 32-bit floating-point
+arithmetic. Finally, the paper introduces a simplified model in which both the
+outputs and the gradients of the neural networks are constrained to
+power-of-two values, just using 7 bits for their representation. The evaluation
+demonstrates a minimal loss in accuracy for the proposed Power-of-Two neural
+network, avoiding the use of multiplications and divisions and thereby,
+significantly reducing the training time as well as the energy consumption and
+memory requirements during the training and inference phases.
+",0,0,0,1,0,0
+1415,Deep Person Re-Identification with Improved Embedding and Efficient Training,"  Person re-identification task has been greatly boosted by deep convolutional
+neural networks (CNNs) in recent years. The core of which is to enlarge the
+inter-class distinction as well as reduce the intra-class variance. However, to
+achieve this, existing deep models prefer to adopt image pairs or triplets to
+form verification loss, which is inefficient and unstable since the number of
+training pairs or triplets grows rapidly as the number of training data grows.
+Moreover, their performance is limited since they ignore the fact that
+different dimension of embedding may play different importance. In this paper,
+we propose to employ identification loss with center loss to train a deep model
+for person re-identification. The training process is efficient since it does
+not require image pairs or triplets for training while the inter-class
+distinction and intra-class variance are well handled. To boost the
+performance, a new feature reweighting (FRW) layer is designed to explicitly
+emphasize the importance of each embedding dimension, thus leading to an
+improved embedding. Experiments on several benchmark datasets have shown the
+superiority of our method over the state-of-the-art alternatives on both
+accuracy and speed.
+",1,0,0,0,0,0
+1416,Unsupervised speech representation learning using WaveNet autoencoders,"  We consider the task of unsupervised extraction of meaningful latent
+representations of speech by applying autoencoding neural networks to speech
+waveforms. The goal is to learn a representation able to capture high level
+semantic content from the signal, e.g. phoneme identities, while being
+invariant to confounding low level details in the signal such as the underlying
+pitch contour or background noise. The behavior of autoencoder models depends
+on the kind of constraint that is applied to the latent representation. We
+compare three variants: a simple dimensionality reduction bottleneck, a
+Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE
+(VQ-VAE). We analyze the quality of learned representations in terms of speaker
+independence, the ability to predict phonetic content, and the ability to
+accurately reconstruct individual spectrogram frames. Moreover, for discrete
+encodings extracted using the VQ-VAE, we measure the ease of mapping them to
+phonemes. We introduce a regularization scheme that forces the representations
+to focus on the phonetic content of the utterance and report performance
+comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic
+unit discovery task.
+",1,0,0,1,0,0
+1417,Many-body localization caused by temporal disorder,"  The many-body localization (MBL) is commonly related to a strong spatial
+disorder. We show that MBL may alternatively be generated by adding a temporal
+disorder to periodically driven many-body systems. We reach this conclusion by
+mapping the evolution of such systems on the dynamics of the time-independent,
+disordered, Hubbard-like models. Our result opens the way to experimental
+studies of MBL in systems that reveal crystalline structures in the time
+domain. In particular, we discuss two relevant setups which can be implemented
+in experiments on ultra-cold atomic gases.
+",0,1,0,0,0,0
+1418,"Second-generation p-values: improved rigor, reproducibility, & transparency in statistical analyses","  Verifying that a statistically significant result is scientifically
+meaningful is not only good scientific practice, it is a natural way to control
+the Type I error rate. Here we introduce a novel extension of the p-value - a
+second-generation p-value - that formally accounts for scientific relevance and
+leverages this natural Type I Error control. The approach relies on a
+pre-specified interval null hypothesis that represents the collection of effect
+sizes that are scientifically uninteresting or are practically null. The
+second-generation p-value is the proportion of data-supported hypotheses that
+are also null hypotheses. As such, second-generation p-values indicate when the
+data are compatible with null hypotheses, or with alternative hypotheses, or
+when the data are inconclusive. Moreover, second-generation p-values provide a
+proper scientific adjustment for multiple comparisons and reduce false
+discovery rates. This is an advance for environments rich in data, where
+traditional p-value adjustments are needlessly punitive. Second-generation
+p-values promote transparency, rigor and reproducibility of scientific results
+by a priori specifying which candidate hypotheses are practically meaningful
+and by providing a more reliable statistical summary of when the data are
+compatible with alternative or null hypotheses.
+",0,0,0,1,0,0
+1419,Latency Optimal Broadcasting in Noisy Wireless Mesh Networks,"  In this paper, we adopt a new noisy wireless network model introduced very
+recently by Censor-Hillel et al. in [ACM PODC 2017, CHHZ17]. More specifically,
+for a given noise parameter $p\in [0,1],$ any sender has a probability of $p$
+of transmitting noise or any receiver of a single transmission in its
+neighborhood has a probability $p$ of receiving noise.
+In this paper, we first propose a new asymptotically latency-optimal
+approximation algorithm (under faultless model) that can complete
+single-message broadcasting task in $D+O(\log^2 n)$ time units/rounds in any
+WMN of size $n,$ and diameter $D$. We then show this diameter-linear
+broadcasting algorithm remains robust under the noisy wireless network model
+and also improves the currently best known result in CHHZ17 by a
+$\Theta(\log\log n)$ factor.
+In this paper, we also further extend our robust single-message broadcasting
+algorithm to $k$ multi-message broadcasting scenario and show it can broadcast
+$k$ messages in $O(D+k\log n+\log^2 n)$ time rounds. This new robust
+multi-message broadcasting scheme is not only asymptotically optimal but also
+answers affirmatively the problem left open in CHHZ17 on the existence of an
+algorithm that is robust to sender and receiver faults and can broadcast $k$
+messages in $O(D+k\log n + polylog(n))$ time rounds.
+",1,0,0,0,0,0
+1420,Construction of Directed 2K Graphs,"  We study the problem of constructing synthetic graphs that resemble
+real-world directed graphs in terms of their degree correlations. We define the
+problem of directed 2K construction (D2K) that takes as input the directed
+degree sequence (DDS) and a joint degree and attribute matrix (JDAM) so as to
+capture degree correlation specifically in directed graphs. We provide
+necessary and sufficient conditions to decide whether a target D2K is
+realizable, and we design an efficient algorithm that creates realizations with
+that target D2K. We evaluate our algorithm in creating synthetic graphs that
+target real-world directed graphs (such as Twitter) and we show that it brings
+significant benefits compared to state-of-the-art approaches.
+",1,0,0,0,0,0
+1421,Pattern Generation Strategies for Improving Recognition of Handwritten Mathematical Expressions,"  Recognition of Handwritten Mathematical Expressions (HMEs) is a challenging
+problem because of the ambiguity and complexity of two-dimensional handwriting.
+Moreover, the lack of large training data is a serious issue, especially for
+academic recognition systems. In this paper, we propose pattern generation
+strategies that generate shape and structural variations to improve the
+performance of recognition systems based on a small training set. For data
+generation, we employ the public databases: CROHME 2014 and 2016 of online
+HMEs. The first strategy employs local and global distortions to generate shape
+variations. The second strategy decomposes an online HME into sub-online HMEs
+to get more structural variations. The hybrid strategy combines both these
+strategies to maximize shape and structural variations. The generated online
+HMEs are converted to images for offline HME recognition. We tested our
+strategies in an end-to-end recognition system constructed from a recent deep
+learning model: Convolutional Neural Network and attention-based
+encoder-decoder. The results of experiments on the CROHME 2014 and 2016
+databases demonstrate the superiority and effectiveness of our strategies: our
+hybrid strategy achieved classification rates of 48.78% and 45.60%,
+respectively, on these databases. These results are competitive compared to
+others reported in recent literature. Our generated datasets are openly
+available for research community and constitute a useful resource for the HME
+recognition research in future.
+",1,0,0,0,0,0
+1422,Actions of automorphism groups of Lie groups,"  This is an expository article on properties of actions on Lie groups by
+subgroups of their automorphism groups. After recalling various results on the
+structure of the automorphism groups, we discuss actions with dense orbits,
+invariant and quasi-invariant measures, the induced actions on the spaces of
+probability measures on the groups, and results concerning various issues in
+ergodic theory, topological dynamics, smooth dynamical systems, and probability
+theory on Lie groups.
+",0,0,1,0,0,0
+1423,Interplay between relativistic energy corrections and resonant excitations in x-ray multiphoton ionization dynamics of Xe atoms,"  In this paper, we theoretically study x-ray multiphoton ionization dynamics
+of heavy atoms taking into account relativistic and resonance effects. When an
+atom is exposed to an intense x-ray pulse generated by an x-ray free-electron
+laser (XFEL), it is ionized to a highly charged ion via a sequence of
+single-photon ionization and accompanying relaxation processes, and its final
+charge state is limited by the last ionic state that can be ionized by a
+single-photon ionization. If x-ray multiphoton ionization involves deep
+inner-shell electrons in heavy atoms, energy shifts by relativistic effects
+play an important role in ionization dynamics, as pointed out in [Phys.\ Rev.\
+Lett.\ \textbf{110}, 173005 (2013)]. On the other hand, if the x-ray beam has a
+broad energy bandwidth, the high-intensity x-ray pulse can drive resonant
+photo-excitations for a broad range of ionic states and ionize even beyond the
+direct one-photon ionization limit, as first proposed in [Nature\ Photon.\
+\textbf{6}, 858 (2012)]. To investigate both relativistic and resonance
+effects, we extend the \textsc{xatom} toolkit to incorporate relativistic
+energy corrections and resonant excitations in x-ray multiphoton ionization
+dynamics calculations. Charge-state distributions are calculated for Xe atoms
+interacting with intense XFEL pulses at a photon energy of 1.5~keV and 5.5~keV,
+respectively. For both photon energies, we demonstrate that the role of
+resonant excitations in ionization dynamics is altered due to significant
+shifts of orbital energy levels by relativistic effects. Therefore it is
+necessary to take into account both effects to accurately simulate multiphoton
+multiple ionization dynamics at high x-ray intensity.
+",0,1,0,0,0,0
+1424,Collective spin excitations of helices and magnetic skyrmions: review and perspectives of magnonics in non-centrosymmetric magnets,"  Magnetic materials hosting correlated electrons play an important role for
+information technology and signal processing. The currently used ferro-, ferri-
+and antiferromagnetic materials provide microscopic moments (spins) that are
+mainly collinear. Recently more complex spin structures such as spin helices
+and cycloids have regained a lot of interest. The interest has been initiated
+by the discovery of the skyrmion lattice phase in non-centrosymmetric helical
+magnets. In this review we address how spin helices and skyrmion lattices
+enrich the microwave characteristics of magnetic materials. When discussing
+perspectives for microwave electronics and magnonics we focus particularly on
+insulating materials as they avoid eddy current losses, offer low spin-wave
+damping, and might allow for electric field control of collective spin
+excitations. Thereby, they further fuel the vision of magnonics operated at low
+energy consumption.
+",0,1,0,0,0,0
+1425,On the Relation between Color Image Denoising and Classification,"  Large amount of image denoising literature focuses on single channel images
+and often experimentally validates the proposed methods on tens of images at
+most. In this paper, we investigate the interaction between denoising and
+classification on large scale dataset. Inspired by classification models, we
+propose a novel deep learning architecture for color (multichannel) image
+denoising and report on thousands of images from ImageNet dataset as well as
+commonly used imagery. We study the importance of (sufficient) training data,
+how semantic class information can be traded for improved denoising results. As
+a result, our method greatly improves PSNR performance by 0.34 - 0.51 dB on
+average over state-of-the art methods on large scale dataset. We conclude that
+it is beneficial to incorporate in classification models. On the other hand, we
+also study how noise affect classification performance. In the end, we come to
+a number of interesting conclusions, some being counter-intuitive.
+",1,0,0,0,0,0
+1426,A simplicial decomposition framework for large scale convex quadratic programming,"  In this paper, we analyze in depth a simplicial decomposition like
+algorithmic framework for large scale convex quadratic programming. In
+particular, we first propose two tailored strategies for handling the master
+problem. Then, we describe a few techniques for speeding up the solution of the
+pricing problem. We report extensive numerical experiments on both real
+portfolio optimization and general quadratic programming problems, showing the
+efficiency and robustness of the method when compared to Cplex.
+",0,0,1,0,0,0
+1427,A Logic of Blockchain Updates,"  Blockchains are distributed data structures that are used to achieve
+consensus in systems for cryptocurrencies (like Bitcoin) or smart contracts
+(like Ethereum). Although blockchains gained a lot of popularity recently,
+there is no logic-based model for blockchains available. We introduce BCL, a
+dynamic logic to reason about blockchain updates, and show that BCL is sound
+and complete with respect to a simple blockchain model.
+",1,0,0,0,0,0
+1428,A proof of the Flaherty-Keller formula on the effective property of densely packed elastic composites,"  We prove in a mathematically rigorous way the asymptotic formula of Flaherty
+and Keller on the effective property of densely packed periodic elastic
+composites with hard inclusions. The proof is based on the primal-dual
+variational principle, where the upper bound is derived by using the
+Keller-type test functions and the lower bound by singular functions made of
+nuclei of strain. Singular functions are solutions of the Lamé system and
+capture precisely singular behavior of the stress in the narrow region between
+two adjacent hard inclusions.
+",0,0,1,0,0,0
+1429,Regret Bounds for Reinforcement Learning via Markov Chain Concentration,"  We give a simple optimistic algorithm for which it is easy to derive regret
+bounds of $\tilde{O}(\sqrt{t_{\rm mix} SAT})$ after $T$ steps in uniformly
+ergodic Markov decision processes with $S$ states, $A$ actions, and mixing time
+parameter $t_{\rm mix}$. These bounds are the first regret bounds in the
+general, non-episodic setting with an optimal dependence on all given
+parameters. They could only be improved by using an alternative mixing time
+parameter.
+",0,0,0,1,0,0
+1430,Superradiance with local phase-breaking effects,"  We study the superradiant evolution of a set of $N$ two-level systems
+spontaneously radiating under the effect of phase-breaking mechanisms. We
+investigate the dynamics generated by non-radiative losses and pure dephasing,
+and their interplay with spontaneous emission. Our results show that in the
+parameter region relevant to many solid-state cavity quantum electrodynamics
+experiments, even with a dephasing rate much faster than the radiative lifetime
+of a single two-level system, a sub-optimal collective superfluorescent burst
+is still observable. We also apply our theory to the dilute excitation regime,
+often used to describe optical excitations in solid-state systems. In this
+regime, excitations can be described in terms of bright and dark bosonic
+quasiparticles. We show how the effect of dephasing and losses in this regime
+translates into inter-mode scattering rates and quasiparticle lifetimes.
+",0,1,0,0,0,0
+1431,Kinetic model of selectivity and conductivity of the KcsA filter,"  We introduce a self-consistent multi-species kinetic theory based on the
+structure of the narrow voltage-gated potassium channel. Transition rates
+depend on a complete energy spectrum with contributions including the
+dehydration amongst species, interaction with the dipolar charge of the filter
+and, bulk solution properties. It displays high selectivity between species
+coexisting with fast conductivity, and Coulomb blockade phenomena, and it fits
+well to data.
+",0,1,0,0,0,0
+1432,The affine approach to homogeneous geodesics in homogeneous Finsler spaces,"  In a recent paper, it was claimed that any homogeneous Finsler space of odd
+dimension admits a homogeneous geodesic through any point. For the proof, the
+algebraic method dealing with the reductive decomposition of the Lie algebra of
+the isometry group was used. However, the proof contains a serious gap. In the
+present paper, homogeneous geodesics in Finsler homogeneous spaces are studied
+using the affine method, which was developed in earlier papers by the author.
+The mentioned statement is proved correctly and it is further proved that any
+homogeneous Berwald space or homogeneous reversible Finsler space admits a
+homogeneous geodesic through any point.
+",0,0,1,0,0,0
+1433,About Synchronized Globular Cluster Formation over Supra-galactic Scales,"  Observational and theoretical arguments support the idea that violent events
+connected with $AGN$ activity and/or intense star forming episodes have played
+a significant role in the early phases of galaxy formation at high red shifts.
+Being old stellar systems, globular clusters seem adequate candidates to search
+for the eventual signatures that might have been left by those energetic
+phenomena. The analysis of the colour distributions of several thousands of
+globular clusters in the Virgo and Fornax galaxy clusters reveals the existence
+of some interesting and previously undetected features. A simple pattern
+recognition technique, indicates the presence of ""colour modulations"",
+distinctive for each galaxy cluster. The results suggest that the globular
+cluster formation process has not been completely stochastic but, rather,
+included a significant fraction of globulars that formed in a synchronized way
+and over supra-galactic spatial scales.
+",0,1,0,0,0,0
+1434,Geometric counting on wavefront real spherical spaces,"  We provide $L^p$-versus $L^\infty$-bounds for eigenfunctions on a real
+spherical space $Z$ of wavefront type. It is shown that these bounds imply a
+non-trivial error term estimate for lattice counting on $Z$. The paper also
+serves as an introduction to geometric counting on spaces of the mentioned
+type. Section 7 on higher rank is new and extends the result from v1 to higher
+rank. Final version. To appear in Acta Math. Sinica.
+",0,0,1,0,0,0
+1435,Fate of the spin-\frac{1}{2} Kondo effect in the presence of temperature gradients,"  We consider a strongly interacting quantum dot connected to two leads held at
+quite different temperatures. Our aim is to study the behavior of the Kondo
+effect in the presence of large thermal biases. We use three different
+approaches, namely, a perturbation formalism based on the Kondo Hamiltonian, a
+slave-boson mean-field theory for the Anderson model at large charging energies
+and a truncated equation-of-motion approach beyond the Hartree-Fock
+approximation. The two former formalisms yield a suppression of the Kondo peak
+for thermal gradients above the Kondo temperature, showing a remarkably good
+agreement despite their different ranges of validity. The third technique
+allows us to analyze the full density of states within a wide range of
+energies. Additionally, we have investigated the quantum transport properties
+(electric current and thermocurrent) beyond linear response. In the
+voltage-driven case, we reproduce the split differential conductance due to the
+presence of different electrochemical potentials. In the temperature-driven
+case, we observe a strongly nonlinear thermocurrent as a function of the
+applied thermal gradient. Depending on the parameters, we can find nontrivial
+zeros in the electric current for finite values of the temperature bias.
+Importantly, these thermocurrent zeros yield direct access to the system's
+characteristic energy scales (Kondo temperature and charging energy).
+",0,1,0,0,0,0
+1436,Extragalactic source population studies at very high energies in the Cherenkov Telescope Array era,"  The Cherenkov Telescope Array (CTA) is the next generation ground-based
+$\gamma$-ray observatory. It will provide an order of magnitude better
+sensitivity and an extended energy coverage, 20 GeV - 300 TeV, relative to
+current Imaging Atmospheric Cherenkov Telescopes (IACTs). IACTs, despite
+featuring an excellent sensitivity, are characterized by a limited field of
+view that makes the blind search of new sources very time inefficient.
+Fortunately, the $\textit{Fermi}$-LAT collaboration recently released a new
+catalog of 1,556 sources detected in the 10 GeV - 2 TeV range by the Large Area
+Telescope (LAT) in the first 7 years of its operation (the 3FHL catalog). This
+catalog is currently the most appropriate description of the sky that will be
+energetically accessible to CTA. Here, we discuss a detailed analysis of the
+extragalactic source population (mostly blazars) that will be studied in the
+near future by CTA. This analysis is based on simulations built from the
+expected array configurations and information reported in the 3FHL catalog.
+These results show the improvements that CTA will provide on the extragalactic
+TeV source population studies, which will be carried out by Key Science
+Projects as well as dedicated proposals.
+",0,1,0,0,0,0
+1437,Modeling the SBC Tanzania Production-Distribution Logistics Network,"  The increase in customer expectation in terms of cost and services rendered,
+coupled with competitive business environment and uncertainty in cost of raw
+materials have posed challenges on effective supply chain engineering making it
+essential to do cost-benefit analysis before making final decisions on
+production distribution logistics. This paper provides a conceptual model that
+provide guidance in supply chain decision making for business expansion. It
+presents a mathematical model for production-distribution of an integrated
+supply chain derived from current operations of SBC Tanzania Ltd which is a
+major supply chain that manages products' distribution in whole of Tanzania. In
+addition to finding the optimal cost, we also carried out a sensitivity
+analysis on the model so as to find ways in which the company can expand at
+optimal cost, while meeting customers' demands. Genetic algorithms is used to
+run the simulation for their efficient in solving combinatorial problems.
+",0,0,1,0,0,0
+1438,Dark matter in dwarf galaxies,"  Although the cusp-core controversy for dwarf galaxies is seen as a problem, I
+argue that the cored central profiles can be explained by flattened cusps
+because they suffer from conflicting measurements and poor statistics and
+because there is a large number of conventional processes that could have
+flattened them since their creation, none of which requires new physics. Other
+problems, such as ""too big to fail"", are not discussed.
+",0,1,0,0,0,0
+1439,A Survey of Parallel A*,"  A* is a best-first search algorithm for finding optimal-cost paths in graphs.
+A* benefits significantly from parallelism because in many applications, A* is
+limited by memory usage, so distributed memory implementations of A* that use
+all of the aggregate memory on the cluster enable problems that can not be
+solved by serial, single-machine implementations to be solved. We survey
+approaches to parallel A*, focusing on decentralized approaches to A* which
+partition the state space among processors. We also survey approaches to
+parallel, limited-memory variants of A* such as parallel IDA*.
+",1,0,0,0,0,0
+1440,Large second harmonic generation enhancement in SiN waveguides by all-optically induced quasi phase matching,"  Integrated waveguides exhibiting efficient second-order nonlinearities are
+crucial to obtain compact and low power optical signal processing devices.
+Silicon nitride (SiN) has shown second harmonic generation (SHG) capabilities
+in resonant structures and single-pass devices leveraging intermodal phase
+matching, which is defined by waveguide design. Lithium niobate allows
+compensating for the phase mismatch using periodically poled waveguides,
+however the latter are not reconfigurable and remain difficult to integrate
+with SiN or silicon (Si) circuits. Here we show the all-optical enhancement of
+SHG in SiN waveguides by more than 30 dB. We demonstrate that a Watt-level
+laser causes a periodic modification of the waveguide second-order
+susceptibility. The resulting second order nonlinear grating has a periodicity
+allowing for quasi phase matching (QPM) between the pump and SH mode. Moreover,
+changing the pump wavelength or polarization updates the period, relaxing phase
+matching constraints imposed by the waveguide geometry. We show that the
+grating is long term inscribed in the waveguides, and we estimate a second
+order nonlinearity of the order of 0.3 pm/V, while a maximum conversion
+efficiency (CE) of 1.8x10-6 W-1 cm-2 is reached.
+",0,1,0,0,0,0
+1441,Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning,"  In this paper, we revisit the large-scale constrained linear regression
+problem and propose faster methods based on some recent developments in
+sketching and optimization. Our algorithms combine (accelerated) mini-batch SGD
+with a new method called two-step preconditioning to achieve an approximate
+solution with a time complexity lower than that of the state-of-the-art
+techniques for the low precision case. Our idea can also be extended to the
+high precision case, which gives an alternative implementation to the Iterative
+Hessian Sketch (IHS) method with significantly improved time complexity.
+Experiments on benchmark and synthetic datasets suggest that our methods indeed
+outperform existing ones considerably in both the low and high precision cases.
+",0,0,0,1,0,0
+1442,Geometrical dependence of domain wall propagation and nucleation fields in magnetic domain wall sensor devices,"  We study the key domain wall properties in segmented nanowires loop-based
+structures used in domain wall based sensors. The two reasons for device
+failure, namely the distribution of domain wall propagation field (depinning)
+and the nucleation field are determined with Magneto-Optical Kerr Effect (MOKE)
+and Giant Magnetoresistance (GMR) measurements for thousands of elements to
+obtain significant statistics. Single layers of Ni$_{81}$Fe$_{19}$, a complete
+GMR stack with Co$_{90}$Fe$_{10}$/Ni$_{81}$Fe$_{19}$ as a free layer and a
+single layer of Co$_{90}$Fe$_{10}$ are deposited and industrially patterned to
+determine the influence of the shape anisotropy, the magnetocrystalline
+anisotropy and the fabrication processes. We show that the propagation field is
+little influenced by the geometry but significantly by material parameters. The
+domain wall nucleation fields can be described by a typical Stoner-Wohlfarth
+model related to the measured geometrical parameters of the wires and fitted by
+considering the process parameters. The GMR effect is subsequently measured in
+a substantial number of devices (3000), in order to accurately gauge the
+variation between devices. This reveals a corrected upper limit to the
+nucleation fields of the sensors that can be exploited for fast
+characterization of working elements.
+",0,1,0,0,0,0
+1443,Faster Rates for Policy Learning,"  This article improves the existing proven rates of regret decay in optimal
+policy estimation. We give a margin-free result showing that the regret decay
+for estimating a within-class optimal policy is second-order for empirical risk
+minimizers over Donsker classes, with regret decaying at a faster rate than the
+standard error of an efficient estimator of the value of an optimal policy. We
+also give a result from the classification literature that shows that faster
+regret decay is possible via plug-in estimation provided a margin condition
+holds. Four examples are considered. In these examples, the regret is expressed
+in terms of either the mean value or the median value; the number of possible
+actions is either two or finitely many; and the sampling scheme is either
+independent and identically distributed or sequential, where the latter
+represents a contextual bandit sampling scheme.
+",0,0,1,1,0,0
+1444,Anisotropic Exchange in ${\bf LiCu_2O_2}$,"  We investigate the magnetic properties of the multiferroic quantum-spin
+system LiCu$_2$O$_2$ by electron spin resonance (ESR) measurements at $X$- and
+$Q$-band frequencies in a wide temperature range $(T_{\rm N1} \leq T \leq
+300$\,K). The observed anisotropies of the $g$ tensor and the ESR linewidth in
+untwinned single crystals result from the crystal-electric field and from local
+exchange geometries acting on the magnetic Cu$^{2+}$ ions in the zigzag-ladder
+like structure of LiCu$_2$O$_2$. Supported by a microscopic analysis of the
+exchange paths involved, we show that both the symmetric anisotropic exchange
+interaction and the antisymmetric Dzyaloshinskii-Moriya interaction provide the
+dominant spin-spin relaxation channels in this material.
+",0,1,0,0,0,0
+1445,Which friends are more popular than you? Contact strength and the friendship paradox in social networks,"  The friendship paradox states that in a social network, egos tend to have
+lower degree than their alters, or, ""your friends have more friends than you
+do"". Most research has focused on the friendship paradox and its implications
+for information transmission, but treating the network as static and
+unweighted. Yet, people can dedicate only a finite fraction of their attention
+budget to each social interaction: a high-degree individual may have less time
+to dedicate to individual social links, forcing them to modulate the quantities
+of contact made to their different social ties. Here we study the friendship
+paradox in the context of differing contact volumes between egos and alters,
+finding a connection between contact volume and the strength of the friendship
+paradox. The most frequently contacted alters exhibit a less pronounced
+friendship paradox compared with the ego, whereas less-frequently contacted
+alters are more likely to be high degree and give rise to the paradox. We argue
+therefore for a more nuanced version of the friendship paradox: ""your closest
+friends have slightly more friends than you do"", and in certain networks even:
+""your best friend has no more friends than you do"". We demonstrate that this
+relationship is robust, holding in both a social media and a mobile phone
+dataset. These results have implications for information transfer and influence
+in social networks, which we explore using a simple dynamical model.
+",1,1,0,0,0,0
+1446,Stochastic Optimization with Bandit Sampling,"  Many stochastic optimization algorithms work by estimating the gradient of
+the cost function on the fly by sampling datapoints uniformly at random from a
+training set. However, the estimator might have a large variance, which
+inadvertently slows down the convergence rate of the algorithms. One way to
+reduce this variance is to sample the datapoints from a carefully selected
+non-uniform distribution. In this work, we propose a novel non-uniform sampling
+approach that uses the multi-armed bandit framework. Theoretically, we show
+that our algorithm asymptotically approximates the optimal variance within a
+factor of 3. Empirically, we show that using this datapoint-selection technique
+results in a significant reduction in the convergence time and variance of
+several stochastic optimization algorithms such as SGD, SVRG and SAGA. This
+approach for sampling datapoints is general, and can be used in conjunction
+with any algorithm that uses an unbiased gradient estimation -- we expect it to
+have broad applicability beyond the specific examples explored in this work.
+",1,0,0,1,0,0
+1447,Learning Robust Options,"  Robust reinforcement learning aims to produce policies that have strong
+guarantees even in the face of environments/transition models whose parameters
+have strong uncertainty. Existing work uses value-based methods and the usual
+primitive action setting. In this paper, we propose robust methods for learning
+temporally abstract actions, in the framework of options. We present a Robust
+Options Policy Iteration (ROPI) algorithm with convergence guarantees, which
+learns options that are robust to model uncertainty. We utilize ROPI to learn
+robust options with the Robust Options Deep Q Network (RO-DQN) that solves
+multiple tasks and mitigates model misspecification due to model uncertainty.
+We present experimental results which suggest that policy iteration with linear
+features may have an inherent form of robustness when using coarse feature
+representations. In addition, we present experimental results which demonstrate
+that robustness helps policy iteration implemented on top of deep neural
+networks to generalize over a much broader range of dynamics than non-robust
+policy iteration.
+",0,0,0,1,0,0
+1448,Levitation of non-magnetizable droplet inside ferrofluid,"  The central theme of this work is that a stable levitation of a denser
+non-magnetizable liquid droplet, against gravity, inside a relatively lighter
+ferrofluid -- a system barely considered in ferrohydrodynamics -- is possible,
+and exhibits unique interfacial features; the stability of the levitation
+trajectory, however, is subject to an appropriate magnetic field modulation. We
+explore the shapes and the temporal dynamics of a plane non-magnetizable
+droplet levitating inside ferrofluid against gravity due to a spatially
+complex, but systematically generated, magnetic field in two dimensions. The
+effect of the viscosity ratio, the stability of the levitation path and the
+possibility of existence of multiple-stable equilibrium states is investigated.
+We find, for certain conditions on the viscosity ratio, that there can be
+developments of cusps and singularities at the droplet surface; this phenomenon
+we also observe experimentally and compared with the simulations. Our
+simulations closely replicate the singular projection on the surface of the
+levitating droplet. Finally, we present an dynamical model for the vertical
+trajectory of the droplet. This model reveals a condition for the onset of
+levitation and the relation for the equilibrium levitation height. The
+linearization of the model around the steady state captures that the nature of
+the equilibrium point goes under a transition from being a spiral to a node
+depending upon the control parameters, which essentially means that the
+temporal route to the equilibrium can be either monotonic or undulating. The
+analytical model for the droplet trajectory is in close agreement with the
+detailed simulations. (See draft for full abstract).
+",0,1,0,0,0,0
+1449,Simultaneous Detection of H and D NMR Signals in a micro-Tesla Field,"  We present NMR spectra of remote-magnetized deuterated water, detected in an
+unshielded environment by means of a differential atomic magnetometer. The
+measurements are performed in a $\mu$T field, while pulsed techniques are
+applied -following the sample displacement- in a 100~$\mu$T field, to tip both
+D and H nuclei by controllable amounts. The broadband nature of the detection
+system enables simultaneous detection of the two signals and accurate
+evaluation of their decay times. The outcomes of the experiment demonstrate the
+potential of ultra-low-field NMR spectroscopy in important applications where
+the correlation between proton and deuteron spin-spin relaxation rates as a
+function of external parameters contains significant information.
+",0,1,0,0,0,0
+1450,Learning Deep Networks from Noisy Labels with Dropout Regularization,"  Large datasets often have unreliable labels-such as those obtained from
+Amazon's Mechanical Turk or social media platforms-and classifiers trained on
+mislabeled datasets often exhibit poor performance. We present a simple,
+effective technique for accounting for label noise when training deep neural
+networks. We augment a standard deep network with a softmax layer that models
+the label noise statistics. Then, we train the deep network and noise model
+jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled)
+dataset. The augmented model is overdetermined, so in order to encourage the
+learning of a non-trivial noise model, we apply dropout regularization to the
+weights of the noise model during training. Numerical experiments on noisy
+versions of the CIFAR-10 and MNIST datasets show that the proposed dropout
+technique outperforms state-of-the-art methods.
+",1,0,0,1,0,0
+1451,On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs,"  Modern networks are of huge sizes as well as high dynamics, which challenges
+the efficiency of community detection algorithms. In this paper, we study the
+problem of overlapping community detection on distributed and dynamic graphs.
+Given a distributed, undirected and unweighted graph, the goal is to detect
+overlapping communities incrementally as the graph is dynamically changing. We
+propose an efficient algorithm, called \textit{randomized Speaker-Listener
+Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener
+Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of
+label propagation. Besides detecting high-quality communities, rSLPA can
+incrementally update the detected communities after a batch of edge insertion
+and deletion operations. To the best of our knowledge, rSLPA is the first
+algorithm that can incrementally capture the same communities as those obtained
+by applying the detection algorithm from the scratch on the updated graph.
+Extensive experiments are conducted on both synthetic and real-world datasets,
+and the results show that our algorithm can achieve high accuracy and
+efficiency at the same time.
+",1,0,0,0,0,0
+1452,Structured Black Box Variational Inference for Latent Time Series Models,"  Continuous latent time series models are prevalent in Bayesian modeling;
+examples include the Kalman filter, dynamic collaborative filtering, or dynamic
+topic models. These models often benefit from structured, non mean field
+variational approximations that capture correlations between time steps. Black
+box variational inference with reparameterization gradients (BBVI) allows us to
+explore a rich new class of Bayesian non-conjugate latent time series models;
+however, a naive application of BBVI to such structured variational models
+would scale quadratically in the number of time steps. We describe a BBVI
+algorithm analogous to the forward-backward algorithm which instead scales
+linearly in time. It allows us to efficiently sample from the variational
+distribution and estimate the gradients of the ELBO. Finally, we show results
+on the recently proposed dynamic word embedding model, which was trained using
+our method.
+",1,0,0,1,0,0
+1453,$L^p$ Norms of Eigenfunctions on Regular Graphs and on the Sphere,"  We prove upper bounds on the $L^p$ norms of eigenfunctions of the discrete
+Laplacian on regular graphs. We then apply these ideas to study the $L^p$ norms
+of joint eigenfunctions of the Laplacian and an averaging operator over a
+finite collection of algebraic rotations of the $2$-sphere. Under mild
+conditions, such joint eigenfunctions are shown to satisfy for large $p$ the
+same bounds as those known for Laplace eigenfunctions on a surface of
+non-positive curvature.
+",0,0,1,0,0,0
+1454,Spatially distributed multipartite entanglement enables Einstein-Podolsky-Rosen steering of atomic clouds,"  A key resource for distributed quantum-enhanced protocols is entanglement
+between spatially separated modes. Yet, the robust generation and detection of
+nonlocal entanglement between spatially separated regions of an ultracold
+atomic system remains a challenge. Here, we use spin mixing in a tightly
+confined Bose-Einstein condensate to generate an entangled state of
+indistinguishable particles in a single spatial mode. We show experimentally
+that this local entanglement can be spatially distributed by self-similar
+expansion of the atomic cloud. Spatially resolved spin read-out is used to
+reveal a particularly strong form of quantum correlations known as
+Einstein-Podolsky-Rosen steering between distinct parts of the expanded cloud.
+Based on the strength of Einstein-Podolsky-Rosen steering we construct a
+witness, which testifies up to genuine five-partite entanglement.
+",0,1,0,0,0,0
+1455,"Multipath IP Routing on End Devices: Motivation, Design, and Performance","  Most end devices are now equipped with multiple network interfaces.
+Applications can exploit all available interfaces and benefit from multipath
+transmission. Recently Multipath TCP (MPTCP) was proposed to implement
+multipath transmission at the transport layer and has attracted lots of
+attention from academia and industry. However, MPTCP only supports TCP-based
+applications and its multipath routing flexibility is limited. In this paper,
+we investigate the possibility of orchestrating multipath transmission from the
+network layer of end devices, and develop a Multipath IP (MPIP) design
+consisting of signaling, session and path management, multipath routing, and
+NAT traversal. We implement MPIP in Linux and Android kernels. Through
+controlled lab experiments and Internet experiments, we demonstrate that MPIP
+can effectively achieve multipath gains at the network layer. It not only
+supports the legacy TCP and UDP protocols, but also works seamlessly with
+MPTCP. By facilitating user-defined customized routing, MPIP can route traffic
+from competing applications in a coordinated fashion to maximize the aggregate
+user Quality-of-Experience.
+",1,0,0,0,0,0
+1456,Defense semantics of argumentation: encoding reasons for accepting arguments,"  In this paper we show how the defense relation among abstract arguments can
+be used to encode the reasons for accepting arguments. After introducing a
+novel notion of defenses and defense graphs, we propose a defense semantics
+together with a new notion of defense equivalence of argument graphs, and
+compare defense equivalence with standard equivalence and strong equivalence,
+respectively. Then, based on defense semantics, we define two kinds of reasons
+for accepting arguments, i.e., direct reasons and root reasons, and a notion of
+root equivalence of argument graphs. Finally, we show how the notion of root
+equivalence can be used in argumentation summarization.
+",1,0,0,0,0,0
+1457,Fast Global Convergence via Landscape of Empirical Loss,"  While optimizing convex objective (loss) functions has been a powerhouse for
+machine learning for at least two decades, non-convex loss functions have
+attracted fast growing interests recently, due to many desirable properties
+such as superior robustness and classification accuracy, compared with their
+convex counterparts. The main obstacle for non-convex estimators is that it is
+in general intractable to find the optimal solution. In this paper, we study
+the computational issues for some non-convex M-estimators. In particular, we
+show that the stochastic variance reduction methods converge to the global
+optimal with linear rate, by exploiting the statistical property of the
+population loss. En route, we improve the convergence analysis for the batch
+gradient method in \cite{mei2016landscape}.
+",0,0,0,1,0,0
+1458,Photodetector figures of merit in terms of POVMs,"  A photodetector may be characterized by various figures of merit such as
+response time, bandwidth, dark count rate, efficiency, wavelength resolution,
+and photon-number resolution. On the other hand, quantum theory says that any
+measurement device is fully described by its POVM, which stands for
+Positive-Operator-Valued Measure, and which generalizes the textbook notion of
+the eigenstates of the appropriate hermitian operator (the ""observable"") as
+measurement outcomes. Here we show how to define a multitude of photodetector
+figures of merit in terms of a given POVM. We distinguish classical and quantum
+figures of merit and issue a conjecture regarding trade-off relations between
+them. We discuss the relationship between POVM elements and photodetector
+clicks, and how models of photodetectors may be tested by measuring either POVM
+elements or figures of merit. Finally, the POVM is advertised as a
+platform-independent way of comparing different types of photodetectors, since
+any such POVM refers to the Hilbert space of the incoming light, and not to any
+Hilbert space internal to the detector.
+",0,1,0,0,0,0
+1459,Kinetics of Protein-DNA Interactions: First-Passage Analysis,"  All living systems can function only far away from equilibrium, and for this
+reason chemical kinetic methods are critically important for uncovering the
+mechanisms of biological processes. Here we present a new theoretical method of
+investigating dynamics of protein-DNA interactions, which govern all major
+biological processes. It is based on a first-passage analysis of biochemical
+and biophysical transitions, and it provides a fully analytic description of
+the processes. Our approach is explained for the case of a single protein
+searching for a specific binding site on DNA. In addition, the application of
+the method to investigations of the effect of DNA sequence heterogeneity, and
+the role multiple targets and traps in the protein search dynamics are
+discussed.
+",0,0,0,0,1,0
+1460,Jamming transitions induced by an attraction in pedestrian flow,"  We numerically study jamming transitions in pedestrian flow interacting with
+an attraction, mostly based on the social force model for pedestrians who can
+join the attraction. We formulate the joining probability as a function of
+social influence from others, reflecting that individual choice behavior is
+likely influenced by others. By controlling pedestrian influx and the social
+influence parameter, we identify various pedestrian flow patterns. For the
+bidirectional flow scenario, we observe a transition from the free flow phase
+to the freezing phase, in which oppositely walking pedestrians reach a complete
+stop and block each other. On the other hand, a different transition behavior
+appears in the unidirectional flow scenario, i.e., from the free flow phase to
+the localized jam phase and then to the extended jam phase. It is also observed
+that the extended jam phase can end up in freezing phenomena with a certain
+probability when pedestrian flux is high with strong social influence. This
+study highlights that attractive interactions between pedestrians and an
+attraction can trigger jamming transitions by increasing the number of
+conflicts among pedestrians near the attraction. In order to avoid excessive
+pedestrian jams, we suggest suppressing the number of conflicts under a certain
+level by moderating pedestrian influx especially when the social influence is
+strong.
+",0,1,0,0,0,0
+1461,A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer,"  Survival analysis has been developed and applied in the number of areas
+including manufacturing, finance, economics and healthcare. In healthcare
+domain, usually clinical data are high-dimensional, sparse and complex and
+sometimes there exists few amount of time-to-event (labeled) instances.
+Therefore building an accurate survival model from electronic health records is
+challenging. With this motivation, we address this issue and provide a new
+survival analysis framework using deep learning and active learning with a
+novel sampling strategy. First, our approach provides better representation
+with lower dimensions from clinical features using labeled (time-to-event) and
+unlabeled (censored) instances and then actively trains the survival model by
+labeling the censored data using an oracle. As a clinical assistive tool, we
+introduce a simple effective treatment recommendation approach based on our
+survival model. In the experimental study, we apply our approach on
+SEER-Medicare data related to prostate cancer among African-Americans and white
+patients. The results indicate that our approach outperforms significantly than
+baseline models.
+",0,0,0,1,0,0
+1462,Detecting Topological Changes in Dynamic Community Networks,"  The study of time-varying (dynamic) networks (graphs) is of fundamental
+importance for computer network analytics. Several methods have been proposed
+to detect the effect of significant structural changes in a time series of
+graphs. The main contribution of this work is a detailed analysis of a dynamic
+community graph model. This model is formed by adding new vertices, and
+randomly attaching them to the existing nodes. It is a dynamic extension of the
+well-known stochastic blockmodel. The goal of the work is to detect the time at
+which the graph dynamics switches from a normal evolution -- where balanced
+communities grow at the same rate -- to an abnormal behavior -- where
+communities start merging. In order to circumvent the problem of decomposing
+each graph into communities, we use a metric to quantify changes in the graph
+topology as a function of time. The detection of anomalies becomes one of
+testing the hypothesis that the graph is undergoing a significant structural
+change. In addition the the theoretical analysis of the test statistic, we
+perform Monte Carlo simulations of our dynamic graph model to demonstrate that
+our test can detect changes in graph topology.
+",1,0,0,0,0,0
+1463,Online Boosting Algorithms for Multi-label Ranking,"  We consider the multi-label ranking approach to multi-label learning.
+Boosting is a natural method for multi-label ranking as it aggregates weak
+predictions through majority votes, which can be directly used as scores to
+produce a ranking of the labels. We design online boosting algorithms with
+provable loss bounds for multi-label ranking. We show that our first algorithm
+is optimal in terms of the number of learners required to attain a desired
+accuracy, but it requires knowledge of the edge of the weak learners. We also
+design an adaptive algorithm that does not require this knowledge and is hence
+more practical. Experimental results on real data sets demonstrate that our
+algorithms are at least as good as existing batch boosting algorithms.
+",1,0,0,1,0,0
+1464,Semisuper Efimov effect of two-dimensional bosons at a three-body resonance,"  Wave-particle duality in quantum mechanics allows for a halo bound state
+whose spatial extension far exceeds a range of the interaction potential. What
+is even more striking is that such quantum halos can be arbitrarily large on
+special occasions. The two examples known so far are the Efimov effect and the
+super Efimov effect, which predict that spatial extensions of higher excited
+states grow exponentially and double exponentially, respectively. Here, we
+establish yet another new class of arbitrarily large quantum halos formed by
+spinless bosons with short-range interactions in two dimensions. When the
+two-body interaction is absent but the three-body interaction is resonant, four
+bosons exhibit an infinite tower of bound states whose spatial extensions scale
+as $R_n\sim e^{(\pi n)^2/27}$ for a large $n$. The emergent scaling law is
+universal and is termed a semisuper Efimov effect, which together with the
+Efimov and super Efimov effects constitutes a trio of few-body universality
+classes allowing for arbitrarily large quantum halos.
+",0,1,0,0,0,0
+1465,Free quantitative fourth moment theorems on Wigner space,"  We prove a quantitative Fourth Moment Theorem for Wigner integrals of any
+order with symmetric kernels, generalizing an earlier result from Kemp et al.
+(2012). The proof relies on free stochastic analysis and uses a new biproduct
+formula for bi-integrals. A consequence of our main result is a
+Nualart-Ortiz-Latorre type characterization of convergence in law to the
+semicircular distribution for Wigner integrals. As an application, we provide
+Berry-Esseen type bounds in the context of the free Breuer-Major theorem for
+the free fractional Brownian motion.
+",0,0,1,0,0,0
+1466,Optimizing the Latent Space of Generative Networks,"  Generative Adversarial Networks (GANs) have been shown to be able to sample
+impressively realistic images. GAN training consists of a saddle point
+optimization problem that can be thought of as an adversarial game between a
+generator which produces the images, and a discriminator, which judges if the
+images are real. Both the generator and the discriminator are commonly
+parametrized as deep convolutional neural networks. The goal of this paper is
+to disentangle the contribution of the optimization procedure and the network
+parametrization to the success of GANs. To this end we introduce and study
+Generative Latent Optimization (GLO), a framework to train a generator without
+the need to learn a discriminator, thus avoiding challenging adversarial
+optimization problems. We show experimentally that GLO enjoys many of the
+desirable properties of GANs: learning from large data, synthesizing
+visually-appealing samples, interpolating meaningfully between samples, and
+performing linear arithmetic with noise vectors.
+",1,0,0,1,0,0
+1467,Conservativity of realizations on motives of abelian type over finite fields,"  We show that the l-adic realization functor is conservative when restricted
+to the Chow motives of abelian type over a finite field. A weak version of this
+conservativity result extends to mixed motives of abelian type.
+",0,0,1,0,0,0
+1468,Towards understanding startup product development as effectual entrepreneurial behaviors,"  Software startups face with multiple technical and business challenges, which
+could make the startup journey longer, or even become a failure. Little is
+known about entrepreneurial decision making as a direct force to startup
+development outcome. In this study, we attempted to apply a behaviour theory of
+entrepreneurial firms to understand the root-cause of some software startup s
+challenges. Six common challenges related to prototyping and product
+development in twenty software startups were identified. We found the behaviour
+theory as a useful theoretical lens to explain the technical challenges.
+Software startups search for local optimal solutions, emphasise on short-run
+feedback rather than long-run strategies, which results in vague prototype
+planning, paradox of demonstration and evolving throw-away prototypes. The
+finding implies that effectual entrepreneurial processes might require a more
+suitable product development approach than the current state-of-practice.
+",1,0,0,0,0,0
+1469,Generalized Dirac structure beyond the linear regime in graphene,"  We show that a generalized Dirac structure survives beyond the linear regime
+of the low-energy dispersion relations of graphene. A generalized uncertainty
+principle of the kind compatible with specific quantum gravity scenarios with a
+fundamental minimal length (here graphene lattice spacing) and Lorentz
+violation (here the particle/hole asymmetry, the trigonal warping, etc.) is
+naturally obtained. We then show that the corresponding emergent field theory
+is a table-top realization of such scenarios, by explicitly computing the third
+order Hamiltonian, and giving the general recipe for any order. Remarkably, our
+results imply that going beyond the low-energy approximation does not spoil the
+well-known correspondence with analogue massless quantum electrodynamics
+phenomena (as usually believed), but rather it is a way to obtain experimental
+signatures of quantum-gravity-like corrections to such phenomena.
+",0,1,0,0,0,0
+1470,Generative Mixture of Networks,"  A generative model based on training deep architectures is proposed. The
+model consists of K networks that are trained together to learn the underlying
+distribution of a given data set. The process starts with dividing the input
+data into K clusters and feeding each of them into a separate network. After
+few iterations of training networks separately, we use an EM-like algorithm to
+train the networks together and update the clusters of the data. We call this
+model Mixture of Networks. The provided model is a platform that can be used
+for any deep structure and be trained by any conventional objective function
+for distribution modeling. As the components of the model are neural networks,
+it has high capability in characterizing complicated data distributions as well
+as clustering data. We apply the algorithm on MNIST hand-written digits and
+Yale face datasets. We also demonstrate the clustering ability of the model
+using some real-world and toy examples.
+",1,0,0,1,0,0
+1471,Shape-dependence of the barrier for skyrmions on a two-lane racetrack,"  Single magnetic skyrmions are localized whirls in the magnetization with an
+integer winding number. They have been observed on nano-meter scales up to room
+temperature in multilayer structures. Due to their small size, topological
+winding number, and their ability to be manipulated by extremely tiny forces,
+they are often called interesting candidates for future memory devices. The
+two-lane racetrack has to exhibit two lanes that are separated by an energy
+barrier. The information is then encoded in the position of a skyrmion which is
+located in one of these close-by lanes. The artificial barrier between the
+lanes can be created by an additional nanostrip on top of the track. Here we
+study the dependence of the potential barrier on the shape of the additional
+nanostrip, calculating the potentials for a rectangular, triangular, and
+parabolic cross section, as well as interpolations between the first two. We
+find that a narrow barrier is always repulsive and that the height of the
+potential strongly depends on the shape of the nanostrip, whereas the shape of
+the potential is more universal. We finally show that the shape-dependence is
+redundant for possible applications.
+",0,1,0,0,0,0
+1472,"Further Results on Size and Power of Heteroskedasticity and Autocorrelation Robust Tests, with an Application to Trend Testing","  We complement the theory developed in Preinerstorfer and Pötscher (2016)
+with further finite sample results on size and power of heteroskedasticity and
+autocorrelation robust tests. These allows us, in particular, to show that the
+sufficient conditions for the existence of size-controlling critical values
+recently obtained in Pötscher and Preinerstorfer (2016) are often also
+necessary. We furthermore apply the results obtained to tests for hypotheses on
+deterministic trends in stationary time series regressions, and find that many
+tests currently used are strongly size-distorted.
+",0,0,1,1,0,0
+1473,A powerful approach to the study of moderate effect modification in observational studies,"  Effect modification means the magnitude or stability of a treatment effect
+varies as a function of an observed covariate. Generally, larger and more
+stable treatment effects are insensitive to larger biases from unmeasured
+covariates, so a causal conclusion may be considerably firmer if this pattern
+is noted if it occurs. We propose a new strategy, called the submax-method,
+that combines exploratory and confirmatory efforts to determine whether there
+is stronger evidence of causality - that is, greater insensitivity to
+unmeasured confounding - in some subgroups of individuals. It uses the joint
+distribution of test statistics that split the data in various ways based on
+certain observed covariates. For $L$ binary covariates, the method splits the
+population $L$ times into two subpopulations, perhaps first men and women,
+perhaps then smokers and nonsmokers, computing a test statistic from each
+subpopulation, and appends the test statistic for the whole population, making
+$2L+1$ test statistics in total. Although $L$ binary covariates define $2^{L}$
+interaction groups, only $2L+1$ tests are performed, and at least $L+1$ of
+these tests use at least half of the data. The submax-method achieves the
+highest design sensitivity and the highest Bahadur efficiency of its component
+tests. Moreover, the form of the test is sufficiently tractable that its large
+sample power may be studied analytically. The simulation suggests that the
+submax method exhibits superior performance, in comparison with an approach
+using CART, when there is effect modification of moderate size. Using data from
+the NHANES I Epidemiologic Follow-Up Survey, an observational study of the
+effects of physical activity on survival is used to illustrate the method. The
+method is implemented in the $\texttt{R}$ package $\texttt{submax}$ which
+contains the NHANES example.
+",0,0,0,1,0,0
+1474,"Ad-blocking: A Study on Performance, Privacy and Counter-measures","  Many internet ventures rely on advertising for their revenue. However, users
+feel discontent by the presence of ads on the websites they visit, as the
+data-size of ads is often comparable to that of the actual content. This has an
+impact not only on the loading time of webpages, but also on the internet bill
+of the user in some cases. In absence of a mutually-agreed procedure for opting
+out of advertisements, many users resort to ad-blocking browser-extensions. In
+this work, we study the performance of popular ad-blockers on a large set of
+news websites. Moreover, we investigate the benefits of ad-blockers on user
+privacy as well as the mechanisms used by websites to counter them. Finally, we
+explore the traffic overhead due to the ad-blockers themselves.
+",1,0,0,0,0,0
+1475,On the quantum differentiation of smooth real-valued functions,"  Calculating the value of $C^{k\in\{1,\infty\}}$ class of smoothness
+real-valued function's derivative in point of $\mathbb{R}^+$ in radius of
+convergence of its Taylor polynomial (or series), applying an analog of
+Newton's binomial theorem and $q$-difference operator. $(P,q)$-power difference
+introduced in section 5. Additionally, by means of Newton's interpolation
+formula, the discrete analog of Taylor series, interpolation using
+$q$-difference and $p,q$-power difference is shown.
+",0,0,1,0,0,0
+1476,On recognizing shapes of polytopes from their shadows,"  Let $P$ and $Q$ be two convex polytopes both contained in the interior of an
+Euclidean ball $r\textbf{B}^{d}$. We prove that $P=Q$ provided that their sight
+cones from any point on the sphere $rS^{d-1}$ are congruent. We also prove an
+analogous result for spherical projections.
+",0,0,1,0,0,0
+1477,Variational methods for steady-state Darcy/Fick flow in swollen and poroelastic solids,"  Existence of steady states in elastic media at small strains with diffusion
+of a solvent or fluid due to Fick's or Darcy's laws is proved by combining
+usage of variational methods inspired from static situations with Schauder's
+fixed-point arguments. In the plain variant, the problem consists in the force
+equilibrium coupled with the continuity equation, and the underlying operator
+is non-potential and non-pseudomonotone so that conventional methods are not
+applicable. In advanced variants, electrically-charged multi-component flows
+through an electrically charged elastic solid are treated, employing critical
+points of the saddle-point type. Eventually, anisothermal variants involving
+heat-transfer equation are treated, too.
+",0,0,1,0,0,0
+1478,Case Studies on Plasma Wakefield Accelerator Design,"  The field of plasma-based particle accelerators has seen tremendous progress
+over the past decade and experienced significant growth in the number of
+activities. During this process, the involved scientific community has expanded
+from traditional university-based research and is now encompassing many large
+research laboratories worldwide, such as BNL, CERN, DESY, KEK, LBNL and SLAC.
+As a consequence, there is a strong demand for a consolidated effort in
+education at the intersection of accelerator, laser and plasma physics. The
+CERN Accelerator School on Plasma Wake Acceleration has been organized as a
+result of this development. In this paper, we describe the interactive
+component of this one-week school, which consisted of three case studies to be
+solved in 11 working groups by the participants of the CERN Accelerator School.
+",0,1,0,0,0,0
+1479,GANs for Biological Image Synthesis,"  In this paper, we propose a novel application of Generative Adversarial
+Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy.
+Compared to natural images, cells tend to have a simpler and more geometric
+global structure that facilitates image generation. However, the correlation
+between the spatial pattern of different fluorescent proteins reflects
+important biological functions, and synthesized images have to capture these
+relationships to be relevant for biological applications. We adapt GANs to the
+task at hand and propose new models with casual dependencies between image
+channels that can generate multi-channel images, which would be impossible to
+obtain experimentally. We evaluate our approach using two independent
+techniques and compare it against sensible baselines. Finally, we demonstrate
+that by interpolating across the latent space we can mimic the known changes in
+protein localization that occur through time during the cell cycle, allowing us
+to predict temporal evolution from static images.
+",1,0,0,1,0,0
+1480,An objective classification of Saturn cloud features from Cassini ISS images,"  A clustering algorithm is applied to Cassini Imaging Science Subsystem
+continuum and methane band images of Saturns northern hemisphere to objectively
+classify regional albedo features and aid in their dynamical interpretation.
+The procedure is based on a technique applied previously to visible-infrared
+images of Earth. It provides a new perspective on giant planet cloud morphology
+and its relationship to the dynamics and a meteorological context for the
+analysis of other types of simultaneous Saturn observations. The method
+identifies six clusters that exhibit distinct morphology, vertical structure,
+and preferred latitudes of occurrence. These correspond to areas dominated by
+deep convective cells; low contrast areas, some including thinner and thicker
+clouds possibly associated with baroclinic instability; regions with possible
+isolated thin cirrus clouds; darker areas due to thinner low level clouds or
+clearer skies due to downwelling, or due to absorbing particles; and fields of
+relatively shallow cumulus clouds. The spatial associations among these cloud
+types suggest that dynamically, there are three distinct types of latitude
+bands on Saturn: deep convectively disturbed latitudes in cyclonic shear
+regions poleward of the eastward jets; convectively suppressed regions near and
+surrounding the westward jets; and baroclinically unstable latitudes near
+eastward jet cores and in the anti-cyclonic regions equatorward of them. These
+are roughly analogous to some of the features of Earths tropics, subtropics,
+and midlatitudes, respectively. Temporal variations of feature contrast and
+cluster occurrence suggest that the upper tropospheric haze in the northern
+hemisphere may have thickened by 2014.
+",0,1,0,0,0,0
+1481,The Peridynamic Stress Tensors and the Non-local to Local Passage,"  We re-examine the notion of stress in peridynamics. Based on the idea of
+traction we define two new peridynamic stress tensors $\mathbf{P}^{\mathbf{y}}$
+and $\mathbf{P}$ which stand, respectively, for analogues of the Cauchy and 1st
+Piola-Kirchhoff stress tensors from classical elasticity. We show that the
+tensor $\mathbf{P}$ differs from the earlier defined peridynamic stress tensor
+$\nu$; though their divergence is equal. We address the question of symmetry of
+the tensor $\mathbf{P}^{\mathbf{y}}$ which proves to be symmetric in case of
+bond-based peridynamics; as opposed to the inverse Piola transform of $\nu$
+(corresponding to the analogue of Cauchy stress tensor) which fails to be
+symmetric in general. We also derive a general formula of the force-flux in
+peridynamics and compute the limit of $\mathbf{P}$ for vanishing non-locality,
+denoted by $\mathbf{P}_0$. We show that this tensor $\mathbf{P}_0$ surprisingly
+coincides with the collapsed tensor $\nu_0$, a limit of the original tensor
+$\nu$. At the end, using this flux-formula, we suggest an explanation why the
+collapsed tensor $\mathbf{P}_0$ (and hence $\nu_0$) can be indeed identified
+with the 1st Piola-Kirchhoff stress tensor.
+",0,1,0,0,0,0
+1482,Identification of Unmodeled Objects from Symbolic Descriptions,"  Successful human-robot cooperation hinges on each agent's ability to process
+and exchange information about the shared environment and the task at hand.
+Human communication is primarily based on symbolic abstractions of object
+properties, rather than precise quantitative measures. A comprehensive robotic
+framework thus requires an integrated communication module which is able to
+establish a link and convert between perceptual and abstract information.
+The ability to interpret composite symbolic descriptions enables an
+autonomous agent to a) operate in unstructured and cluttered environments, in
+tasks which involve unmodeled or never seen before objects; and b) exploit the
+aggregation of multiple symbolic properties as an instance of ensemble
+learning, to improve identification performance even when the individual
+predicates encode generic information or are imprecisely grounded.
+We propose a discriminative probabilistic model which interprets symbolic
+descriptions to identify the referent object contextually w.r.t.\ the structure
+of the environment and other objects. The model is trained using a collected
+dataset of identifications, and its performance is evaluated by quantitative
+measures and a live demo developed on the PR2 robot platform, which integrates
+elements of perception, object extraction, object identification and grasping.
+",1,0,0,1,0,0
+1483,Balanced News Using Constrained Bandit-based Personalization,"  We present a prototype for a news search engine that presents balanced
+viewpoints across liberal and conservative articles with the goal of
+de-polarizing content and allowing users to escape their filter bubble. The
+balancing is done according to flexible user-defined constraints, and leverages
+recent advances in constrained bandit optimization. We showcase our balanced
+news feed by displaying it side-by-side with the news feed produced by a
+traditional (polarized) feed.
+",1,0,0,0,0,0
+1484,Intuitionistic Layered Graph Logic: Semantics and Proof Theory,"  Models of complex systems are widely used in the physical and social
+sciences, and the concept of layering, typically building upon graph-theoretic
+structure, is a common feature. We describe an intuitionistic substructural
+logic called ILGL that gives an account of layering. The logic is a bunched
+system, combining the usual intuitionistic connectives, together with a
+non-commutative, non-associative conjunction (used to capture layering) and its
+associated implications. We give soundness and completeness theorems for a
+labelled tableaux system with respect to a Kripke semantics on graphs. We then
+give an equivalent relational semantics, itself proven equivalent to an
+algebraic semantics via a representation theorem. We utilise this result in two
+ways. First, we prove decidability of the logic by showing the finite
+embeddability property holds for the algebraic semantics. Second, we prove a
+Stone-type duality theorem for the logic. By introducing the notions of ILGL
+hyperdoctrine and indexed layered frame we are able to extend this result to a
+predicate version of the logic and prove soundness and completeness theorems
+for an extension of the layered graph semantics . We indicate the utility of
+predicate ILGL with a resource-labelled bigraph model.
+",1,0,0,0,0,0
+1485,Learning Efficient Image Representation for Person Re-Identification,"  Color names based image representation is successfully used in person
+re-identification, due to the advantages of being compact, intuitively
+understandable as well as being robust to photometric variance. However, there
+exists the diversity between underlying distribution of color names' RGB values
+and that of image pixels' RGB values, which may lead to inaccuracy when
+directly comparing them in Euclidean space. In this paper, we propose a new
+method named soft Gaussian mapping (SGM) to address this problem. We model the
+discrepancies between color names and pixels using a Gaussian and utilize the
+inverse of covariance matrix to bridge the gap between them. Based on SGM, an
+image could be converted to several soft Gaussian maps. In each soft Gaussian
+map, we further seek to establish stable and robust descriptors within a local
+region through a max pooling operation. Then, a robust image representation
+based on color names is obtained by concatenating the statistical descriptors
+in each stripe. When labeled data are available, one discriminative subspace
+projection matrix is learned to build efficient representations of an image via
+cross-view coupling learning. Experiments on the public datasets - VIPeR,
+PRID450S and CUHK03, demonstrate the effectiveness of our method.
+",1,0,0,0,0,0
+1486,Exciting Nucleons in Compton Scattering and Hydrogen-Like Atoms,"  This PhD thesis is devoted to the low-energy structure of the nucleon (proton
+and neutron) as seen through electromagnetic probes, e.g., electron and Compton
+scattering. The research presented here is based primarily on dispersion theory
+and chiral effective-field theory. The main motivation is the recent proton
+radius puzzle, which is the discrepancy between the classic proton charge
+radius determinations (based on electron-proton scattering and normal hydrogen
+spectroscopy) and the highly precise extraction based on first muonic-hydrogen
+experiments by the CREMA Collaboration. The precision of muonic-hydrogen
+experiments is presently limited by the knowledge of proton structure effects
+beyond the charge radius. A major part of this thesis is devoted to calculating
+these effects using everything we know about the nucleon electromagnetic
+structure from both theory and experiment.
+The thesis consists of eight chapters. The first and last are, respectively,
+the introduction and conclusion. The remainder of this thesis can roughly be
+divided into the following three topics: finite-size effects in hydrogen-like
+atoms, real and virtual Compton scattering, and two-photon-exchange effects.
+",0,1,0,0,0,0
+1487,Multiple universalities in order-disorder magnetic phase transitions,"  Phase transitions in isotropic quantum antiferromagnets are associated with
+the condensation of bosonic triplet excitations. In three dimensional quantum
+antiferromagnets, such as TlCuCl$_3$, condensation can be either pressure or
+magnetic field induced. The corresponding magnetic order obeys universal
+scaling with thermal critical exponent $\phi$. Employing a relativistic quantum
+field theory, the present work predicts the emergence of multiple (three)
+universalities under combined pressure and field tuning. Changes of
+universality are signalled by changes of the critical exponent $\phi$.
+Explicitly, we predict the existence of two new exponents $\phi=1$ and $1/2$ as
+well as recovering the known exponent $\phi=3/2$. We also predict logarithmic
+corrections to the power law scaling.
+",0,1,0,0,0,0
+1488,Exact Inference of Causal Relations in Dynamical Systems,"  From philosophers of ancient times to modern economists, biologists and other
+researchers are engaged in revealing causal relations. The most challenging
+problem is inferring the type of the causal relationship: whether it is uni- or
+bi-directional or only apparent - implied by a hidden common cause only. Modern
+technology provides us tools to record data from complex systems such as the
+ecosystem of our planet or the human brain, but understanding their functioning
+needs detection and distinction of causal relationships of the system
+components without interventions. Here we present a new method, which
+distinguishes and assigns probabilities to the presence of all the possible
+causal relations between two or more time series from dynamical systems. The
+new method is validated on synthetic datasets and applied to EEG
+(electroencephalographic) data recorded in epileptic patients. Given the
+universality of our method, it may find application in many fields of science.
+",0,0,0,0,1,0
+1489,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,"  Deep neural networks are increasingly being used in a variety of machine
+learning applications applied to rich user data on the cloud. However, this
+approach introduces a number of privacy and efficiency challenges, as the cloud
+operator can perform secondary inferences on the available data. Recently,
+advances in edge processing have paved the way for more efficient, and private,
+data processing at the source for simple tasks and lighter models, though they
+remain a challenge for larger, and more complicated models. In this paper, we
+present a hybrid approach for breaking down large, complex deep models for
+cooperative, privacy-preserving analytics. We do this by breaking down the
+popular deep architectures and fine-tune them in a particular way. We then
+evaluate the privacy benefits of this approach based on the information exposed
+to the cloud service. We also asses the local inference cost of different
+layers on a modern handset for mobile applications. Our evaluations show that
+by using certain kind of fine-tuning and embedding techniques and at a small
+processing costs, we can greatly reduce the level of information available to
+unintended tasks applied to the data feature on the cloud, and hence achieving
+the desired tradeoff between privacy and performance.
+",1,0,0,0,0,0
+1490,Gradient Method With Inexact Oracle for Composite Non-Convex Optimization,"  In this paper, we develop new first-order method for composite non-convex
+minimization problems with simple constraints and inexact oracle. The objective
+function is given as a sum of ""`hard""', possibly non-convex part, and
+""`simple""' convex part. Informally speaking, oracle inexactness means that, for
+the ""`hard""' part, at any point we can approximately calculate the value of the
+function and construct a quadratic function, which approximately bounds this
+function from above. We give several examples of such inexactness: smooth
+non-convex functions with inexact Hölder-continuous gradient, functions given
+by auxiliary uniformly concave maximization problem, which can be solved only
+approximately. For the introduced class of problems, we propose a gradient-type
+method, which allows to use different proximal setup to adapt to geometry of
+the feasible set, adaptively chooses controlled oracle error, allows for
+inexact proximal mapping. We provide convergence rate for our method in terms
+of the norm of generalized gradient mapping and show that, in the case of
+inexact Hölder-continuous gradient, our method is universal with respect to
+Hölder parameters of the problem. Finally, in a particular case, we show that
+small value of the norm of generalized gradient mapping at a point means that a
+necessary condition of local minimum approximately holds at that point.
+",0,0,1,0,0,0
+1491,Kernel Implicit Variational Inference,"  Recent progress in variational inference has paid much attention to the
+flexibility of variational posteriors. One promising direction is to use
+implicit distributions, i.e., distributions without tractable densities as the
+variational posterior. However, existing methods on implicit posteriors still
+face challenges of noisy estimation and computational infeasibility when
+applied to models with high-dimensional latent variables. In this paper, we
+present a new approach named Kernel Implicit Variational Inference that
+addresses these challenges. As far as we know, for the first time implicit
+variational inference is successfully applied to Bayesian neural networks,
+which shows promising results on both regression and classification tasks.
+",1,0,0,1,0,0
+1492,The Ringel dual of the Auslander-Dlab-Ringel algebra,"  The ADR algebra $R_A$ of a finite-dimensional algebra $A$ is a
+quasihereditary algebra. In this paper we study the Ringel dual
+$\mathcal{R}(R_A)$ of $R_A$. We prove that $\mathcal{R}(R_A)$ can be identified
+with $(R_{A^{op}})^{op}$, under certain 'minimal' regularity conditions for
+$A$. We also give necessary and sufficient conditions for the ADR algebra to be
+Ringel selfdual.
+",0,0,1,0,0,0
+1493,"The socle filtrations of principal series representations of $SL(3,\mathbb{R})$ and $Sp(2,\mathbb{R})$","  We study the structure of the $(\mathfrak{g},K)$-modules of the principal
+series representations of $SL(3,\mathbb{R})$ and $Sp(2,\mathbb{R})$ induced
+from minimal parabolic subgroups, in the case when the infinitesimal character
+is nonsingular. The composition factors of these modules are known by
+Kazhdan-Lusztig-Vogan conjecture. In this paper, we give complete descriptions
+of the socle filtrations of these modules.
+",0,0,1,0,0,0
+1494,Improving the phase response of an atom interferometer by means of temporal pulse shaping,"  We study theoretically and experimentally the influence of temporally shaping
+the light pulses in an atom interferometer, with a focus on the phase response
+of the interferometer. We show that smooth light pulse shapes allow rejecting
+high frequency phase fluctuations (above the Rabi frequency) and thus relax the
+requirements on the phase noise or frequency noise of the interrogation lasers
+driving the interferometer. The light pulse shape is also shown to modify the
+scale factor of the interferometer, which has to be taken into account in the
+evaluation of its accuracy budget. We discuss the trade-offs to operate when
+choosing a particular pulse shape, by taking into account phase noise
+rejection, velocity selectivity, and applicability to large momentum transfer
+atom interferometry.
+",0,1,0,0,0,0
+1495,Helium-like and Lithium-like ions: Ground state energy,"  It is shown that the non-relativistic ground state energy of helium-like and
+lithium-like ions with static nuclei can be interpolated in full physics range
+of nuclear charges $Z$ with accuracy of not less than 6 decimal digits (d.d.)
+or 7-8 significant digits (s.d.) using a meromorphic function in appropriate
+variable with a few free parameters. It is demonstrated that finite nuclear
+mass effects do not change 4-5 s.d. for $Z \in [1,50]$ for 2-,3-electron
+systems and the leading relativistic and QED corrections leave unchanged 3-4
+s.d. for $Z \in [1,12]$ in the ground state energy for 2-electron system, thus,
+the interpolation reproduces definitely those figures. A meaning of proposed
+interpolation is in a construction of unified, {\it two-point} Pade approximant
+(for both small and large $Z$ expansions) with fitting some parameters at
+intermediate $Z$.
+",0,1,0,0,0,0
+1496,Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method,"  Traditional data cleaning identifies dirty data by classifying original data
+sequences, which is a class$-$imbalanced problem since the proportion of
+incorrect data is much less than the proportion of correct ones for most
+diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using
+machine learning algorithms to classify diagnostic data based on
+class$-$imbalanced training set, most classifiers are biased towards the major
+class and show very poor classification rates on the minor class. By
+transforming the direct classification problem about original data sequences
+into a classification problem about the physical similarity between data
+sequences, the class$-$balanced effect of Time$-$Domain Global Similarity
+(TDGS) method on training set structure is investigated in this paper.
+Meanwhile, the impact of improved training set structure on data cleaning
+performance of TDGS method is demonstrated with an application example in EAST
+POlarimetry$-$INTerferometry (POINT) system.
+",1,0,0,0,0,0
+1497,Eigenvalue Solvers for Modeling Nuclear Reactors on Leadership Class Machines,"  Three complementary methods have been implemented in the code Denovo that
+accelerate neutral particle transport calculations with methods that use
+leadership-class computers fully and effectively: a multigroup block (MG)
+Krylov solver, a Rayleigh Quotient Iteration (RQI) eigenvalue solver, and a
+multigrid in energy (MGE) preconditioner. The MG Krylov solver converges more
+quickly than Gauss Seidel and enables energy decomposition such that Denovo can
+scale to hundreds of thousands of cores. RQI should converge in fewer
+iterations than power iteration (PI) for large and challenging problems. RQI
+creates shifted systems that would not be tractable without the MG Krylov
+solver. It also creates ill-conditioned matrices. The MGE preconditioner
+reduces iteration count significantly when used with RQI and takes advantage of
+the new energy decomposition such that it can scale efficiently. Each
+individual method has been described before, but this is the first time they
+have been demonstrated to work together effectively.
+The combination of solvers enables the RQI eigenvalue solver to work better
+than the other available solvers for large reactors problems on leadership
+class machines. Using these methods together, RQI converged in fewer iterations
+and in less time than PI for a full pressurized water reactor core. These
+solvers also performed better than an Arnoldi eigenvalue solver for a reactor
+benchmark problem when energy decomposition is needed. The MG Krylov, MGE
+preconditioner, and RQI solver combination also scales well in energy. This
+solver set is a strong choice for very large and challenging problems.
+",1,1,0,0,0,0
+1498,"Thermoregulation in mice, rats and humans: An insight into the evolution of human hairlessness","  The thermoregulation system in animals removes body heat in hot temperatures
+and retains body heat in cold temperatures. The better the animal removes heat,
+the worse the animal retains heat and visa versa. It is the balance between
+these two conflicting goals that determines the mammal's size, heart rate and
+amount of hair. The rat's loss of tail hair and human's loss of its body hair
+are responses to these conflicting thermoregulation needs as these animals
+evolved to larger size over time.
+",0,0,0,0,1,0
+1499,Koszul A-infinity algebras and free loop space homology,"  We introduce a notion of Koszul A-infinity algebra that generalizes Priddy's
+notion of a Koszul algebra and we use it to construct small A-infinity algebra
+models for Hochschild cochains. As an application, this yields new techniques
+for computing free loop space homology algebras of manifolds that are either
+formal or coformal (over a field or over the integers). We illustrate these
+techniques in two examples.
+",0,0,1,0,0,0
+1500,Learning RBM with a DC programming Approach,"  By exploiting the property that the RBM log-likelihood function is the
+difference of convex functions, we formulate a stochastic variant of the
+difference of convex functions (DC) programming to minimize the negative
+log-likelihood. Interestingly, the traditional contrastive divergence algorithm
+is a special case of the above formulation and the hyperparameters of the two
+algorithms can be chosen such that the amount of computation per mini-batch is
+identical. We show that for a given computational budget the proposed algorithm
+almost always reaches a higher log-likelihood more rapidly, compared to the
+standard contrastive divergence algorithm. Further, we modify this algorithm to
+use the centered gradients and show that it is more efficient and effective
+compared to the standard centered gradient algorithm on benchmark datasets.
+",1,0,0,1,0,0
+1501,Duality and Universal Transport in a Mixed-Dimension Electrodynamics,"  We consider a theory of a two-component Dirac fermion localized on a (2+1)
+dimensional brane coupled to a (3+1) dimensional bulk. Using the fermionic
+particle-vortex duality, we show that the theory has a strong-weak duality that
+maps the coupling $e$ to $\tilde e=(8\pi)/e$. We explore the theory at
+$e^2=8\pi$ where it is self-dual. The electrical conductivity of the theory is
+a constant independent of frequency. When the system is at finite density and
+magnetic field at filling factor $\nu=\frac12$, the longitudinal and Hall
+conductivity satisfies a semicircle law, and the ratio of the longitudinal and
+Hall thermal electric coefficients is completely determined by the Hall angle.
+The thermal Hall conductivity is directly related to the thermal electric
+coefficients.
+",0,1,0,0,0,0
+1502,Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm,"  Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise
+sequence alignment, a quintessential problem in bioinformatics. PHMMs include
+three types of hidden states: match, insertion and deletion. Most previous
+studies have used one or two hidden states for each PHMM state type. However,
+few studies have examined the number of states suitable for representing
+sequence data or improving alignment accuracy.We developed a novel method to
+select superior models (including the number of hidden states) for PHMM. Our
+method selects models with the highest posterior probability using Factorized
+Information Criteria (FIC), which is widely utilised in model selection for
+probabilistic models with hidden variables. Our simulations indicated this
+method has excellent model selection capabilities with slightly improved
+alignment accuracy. We applied our method to DNA datasets from 5 and 28
+species, ultimately selecting more complex models than those used in previous
+studies.
+",0,0,0,1,0,0
+1503,Experimental evidence for Glycolaldehyde and Ethylene Glycol formation by surface hydrogenation of CO molecules under dense molecular cloud conditions,"  This study focuses on the formation of two molecules of astrobiological
+importance - glycolaldehyde (HC(O)CH2OH) and ethylene glycol (H2C(OH)CH2OH) -
+by surface hydrogenation of CO molecules. Our experiments aim at simulating the
+CO freeze-out stage in interstellar dark cloud regions, well before thermal and
+energetic processing become dominant. It is shown that along with the formation
+of H2CO and CH3OH - two well established products of CO hydrogenation - also
+molecules with more than one carbon atom form. The key step in this process is
+believed to be the recombination of two HCO radicals followed by the formation
+of a C-C bond. The experimentally established reaction pathways are implemented
+into a continuous-time random-walk Monte Carlo model, previously used to model
+the formation of CH3OH on astrochemical time-scales, to study their impact on
+the solid-state abundances in dense interstellar clouds of glycolaldehyde and
+ethylene glycol.
+",0,1,0,0,0,0
+1504,New Methods of Enhancing Prediction Accuracy in Linear Models with Missing Data,"  In this paper, prediction for linear systems with missing information is
+investigated. New methods are introduced to improve the Mean Squared Error
+(MSE) on the test set in comparison to state-of-the-art methods, through
+appropriate tuning of Bias-Variance trade-off. First, the use of proposed Soft
+Weighted Prediction (SWP) algorithm and its efficacy are depicted and compared
+to previous works for non-missing scenarios. The algorithm is then modified and
+optimized for missing scenarios. It is shown that controlled over-fitting by
+suggested algorithms will improve prediction accuracy in various cases.
+Simulation results approve our heuristics in enhancing the prediction accuracy.
+",1,0,0,1,0,0
+1505,Revisiting Imidazolium Based Ionic Liquids: Effect of the Conformation Bias of the [NTf$_{2}$] Anion Studied By Molecular Dynamics Simulations,"  We study ionic liquids composed 1-alkyl-3-methylimidazolium cations and
+bis(trifluoromethyl-sulfonyl)imide anions ([C$_n$MIm][NTf$_2$]) with varying
+chain-length $n\!=\!2, 4, 6, 8$ by using molecular dynamics simulations. We
+show that a reparametrization of the dihedral potentials as well as charges of
+the [NTf$_2$] anion leads to an improvment of the force field model introduced
+by Köddermann {\em et al.} [ChemPhysChem, \textbf{8}, 2464 (2007)] (KPL-force
+field). A crucial advantage of the new parameter set is that the minimum energy
+conformations of the anion ({\em trans} and {\em gauche}), as deduced from {\em
+ab initio} calculations and {\sc Raman} experiments, are now both well
+represented by our model. In addition, the results for [C$_n$MIm][NTf$_2$] show
+that this modification leads to an even better agreement between experiment and
+molecular dynamics simulation as demonstrated for densities, diffusion
+coefficients, vaporization enthalpies, reorientational correlation times, and
+viscosities. Even though we focused on a better representation of the anion
+conformation, also the alkyl chain-length dependence of the cation behaves
+closer to the experiment. We strongly encourage to use the new NGKPL force
+field for the [NTf$_2$] anion instead of the earlier KPL parameter set for
+computer simulations aiming to describe the thermodynamics, dynamics and also
+structure of imidazolium based ionic liquids.
+",0,1,0,0,0,0
+1506,"Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling","  Tick is a statistical learning library for Python~3, with a particular
+emphasis on time-dependent models, such as point processes, and tools for
+generalized linear models and survival analysis. The core of the library is an
+optimization module providing model computational classes, solvers and proximal
+operators for regularization. tick relies on a C++ implementation and
+state-of-the-art optimization algorithms to provide very fast computations in a
+single node multi-core setting. Source code and documentation can be downloaded
+from this https URL
+",0,0,0,1,0,0
+1507,An energy method for rough partial differential equations,"  We present a well-posedness and stability result for a class of nondegenerate
+linear parabolic equations driven by rough paths. More precisely, we introduce
+a notion of weak solution that satisfies an intrinsic formulation of the
+equation in a suitable Sobolev space of negative order. Weak solutions are then
+shown to satisfy the corresponding en- ergy estimates which are deduced
+directly from the equation. Existence is obtained by showing compactness of a
+suitable sequence of approximate solutions whereas unique- ness relies on a
+doubling of variables argument and a careful analysis of the passage to the
+diagonal. Our result is optimal in the sense that the assumptions on the
+deterministic part of the equation as well as the initial condition are the
+same as in the classical PDEs theory.
+",0,0,1,0,0,0
+1508,Sparse Inverse Covariance Estimation for Chordal Structures,"  In this paper, we consider the Graphical Lasso (GL), a popular optimization
+problem for learning the sparse representations of high-dimensional datasets,
+which is well-known to be computationally expensive for large-scale problems.
+Recently, we have shown that the sparsity pattern of the optimal solution of GL
+is equivalent to the one obtained from simply thresholding the sample
+covariance matrix, for sparse graphs under different conditions. We have also
+derived a closed-form solution that is optimal when the thresholded sample
+covariance matrix has an acyclic structure. As a major generalization of the
+previous result, in this paper we derive a closed-form solution for the GL for
+graphs with chordal structures. We show that the GL and thresholding
+equivalence conditions can significantly be simplified and are expected to hold
+for high-dimensional problems if the thresholded sample covariance matrix has a
+chordal structure. We then show that the GL and thresholding equivalence is
+enough to reduce the GL to a maximum determinant matrix completion problem and
+drive a recursive closed-form solution for the GL when the thresholded sample
+covariance matrix has a chordal structure. For large-scale problems with up to
+450 million variables, the proposed method can solve the GL problem in less
+than 2 minutes, while the state-of-the-art methods converge in more than 2
+hours.
+",0,0,0,1,0,0
+1509,Orthogonal free quantum group factors are strongly 1-bounded,"  We prove that the orthogonal free quantum group factors
+$\mathcal{L}(\mathbb{F}O_N)$ are strongly $1$-bounded in the sense of Jung. In
+particular, they are not isomorphic to free group factors. This result is
+obtained by establishing a spectral regularity result for the edge reversing
+operator on the quantum Cayley tree associated to $\mathbb{F}O_N$, and
+combining this result with a recent free entropy dimension rank theorem of Jung
+and Shlyakhtenko.
+",0,0,1,0,0,0
+1510,Enhanced spin ordering temperature in ultrathin FeTe films grown on a topological insulator,"  We studied the temperature dependence of the diagonal double-stripe spin
+order in one and two unit cell thick layers of FeTe grown on the topological
+insulator Bi_2Te_3 via spin-polarized scanning tunneling microscopy. The spin
+order persists up to temperatures which are higher than the transition
+temperature reported for bulk Fe_1+yTe with lowest possible excess Fe content
+y. The enhanced spin order stability is assigned to a strongly decreased y with
+respect to the lowest values achievable in bulk crystal growth, and effects due
+to the interface between the FeTe and the topological insulator. The result is
+relevant for understanding the recent observation of a coexistence of
+superconducting correlations and spin order in this system.
+",0,1,0,0,0,0
+1511,High Order Hierarchical Divergence-free Constrained Transport $H(div)$ Finite Element Method for Magnetic Induction Equation,"  In this paper, we will use the interior functions of an hierarchical basis
+for high order $BDM_p$ elements to enforce the divergence-free condition of a
+magnetic field $B$ approximated by the H(div) $BDM_p$ basis. The resulting
+constrained finite element method can be used to solve magnetic induction
+equation in MHD equations. The proposed procedure is based on the fact that the
+scalar $(p-1)$-th order polynomial space on each element can be decomposed as
+an orthogonal sum of the subspace defined by the divergence of the interior
+functions of the $p$-th order $BDM_p$ basis and the constant function.
+Therefore, the interior functions can be used to remove element-wise all higher
+order terms except the constant in the divergence error of the finite element
+solution of $B$-field. The constant terms from each element can be then easily
+corrected using a first order H(div) basis globally. Numerical results for a
+3-D magnetic induction equation show the effectiveness of the proposed method
+in enforcing divergence-free condition of the magnetic field.
+",0,0,1,0,0,0
+1512,REMOTEGATE: Incentive-Compatible Remote Configuration of Security Gateways,"  Imagine that a malicious hacker is trying to attack a server over the
+Internet and the server wants to block the attack packets as close to their
+point of origin as possible. However, the security gateway ahead of the source
+of attack is untrusted. How can the server block the attack packets through
+this gateway? In this paper, we introduce REMOTEGATE, a trustworthy mechanism
+for allowing any party (server) on the Internet to configure a security gateway
+owned by a second party, at a certain agreed upon reward that the former pays
+to the latter for its service. We take an interactive incentive-compatible
+approach, for the case when both the server and the gateway are rational, to
+devise a protocol that will allow the server to help the security gateway
+generate and deploy a policy rule that filters the attack packets before they
+reach the server. The server will reward the gateway only when the latter can
+successfully verify that it has generated and deployed the correct rule for the
+issue. This mechanism will enable an Internet-scale approach to improving
+security and privacy, backed by digital payment incentives.
+",1,0,0,0,0,0
+1513,Distributed Event-Triggered Control for Global Consensus of Multi-Agent Systems with Input Saturation,"  We consider the global consensus problem for multi-agent systems with input
+saturation over digraphs. Under a mild connectivity condition that the
+underlying digraph has a directed spanning tree, we use Lyapunov methods to
+show that the widely used distributed consensus protocol, which solves the
+consensus problem for the case without input saturation constraints, also
+solves the global consensus problem for the case with input saturation
+constraints. In order to reduce the overall need of communication and system
+updates, we then propose a distributed event-triggered control law. Global
+consensus is still realized and Zeno behavior is excluded. Numerical
+simulations are provided to illustrate the effectiveness of the theoretical
+results.
+",0,0,1,0,0,0
+1514,Autocommuting probability of a finite group relative to its subgroups,"  Let $H \subseteq K$ be two subgroups of a finite group $G$ and Aut$(K)$ the
+automorphism group of $K$. The autocommuting probability of $G$ relative to its
+subgroups $H$ and $K$, denoted by ${\rm Pr}(H, {\rm Aut}(K))$, is the
+probability that the autocommutator of a randomly chosen pair of elements, one
+from $H$ and the other from Aut$(K)$, is equal to the identity element of $G$.
+In this paper, we study ${\rm Pr}(H, {\rm Aut}(K))$ through a generalization.
+",0,0,1,0,0,0
+1515,Total variation regularization with variable Lebesgue prior,"  This work proposes the variable exponent Lebesgue modular as a replacement
+for the 1-norm in total variation (TV) regularization. It allows the exponent
+to vary with spatial location and thus enables users to locally select whether
+to preserve edges or smooth intensity variations. In contrast to earlier work
+using TV-like methods with variable exponents, the exponent function is here
+computed offline as a fixed parameter of the final optimization problem,
+resulting in a convex goal functional. The obtained formulas for the convex
+conjugate and the proximal operators are simple in structure and can be
+evaluated very efficiently, an important property for practical usability.
+Numerical results with variable $L^p$ TV prior in denoising and tomography
+problems on synthetic data compare favorably to total generalized variation
+(TGV) and TV.
+",0,0,1,0,0,0
+1516,Radio observations confirm young stellar populations in local analogues to $z\sim5$ Lyman break galaxies,"  We present radio observations at 1.5 GHz of 32 local objects selected to
+reproduce the physical properties of $z\sim5$ star-forming galaxies. We also
+report non-detections of five such sources in the sub-millimetre. We find a
+radio-derived star formation rate which is typically half that derived from
+H$\alpha$ emission for the same objects. These observations support previous
+indications that we are observing galaxies with a young dominant stellar
+population, which has not yet established a strong supernova-driven synchrotron
+continuum. We stress caution when applying star formation rate calibrations to
+stellar populations younger than 100 Myr. We calibrate the conversions for
+younger galaxies, which are dominated by a thermal radio emission component. We
+improve the size constraints for these sources, compared to previous unresolved
+ground-based optical observations. Their physical size limits indicate very
+high star formation rate surface densities, several orders of magnitude higher
+than the local galaxy population. In typical nearby galaxies, this would imply
+the presence of galaxy-wide winds. Given the young stellar populations, it is
+unclear whether a mechanism exists in our sources that can deposit sufficient
+kinetic energy into the interstellar medium to drive such outflows.
+",0,1,0,0,0,0
+1517,Sparse Deep Neural Network Exact Solutions,"  Deep neural networks (DNNs) have emerged as key enablers of machine learning.
+Applying larger DNNs to more diverse applications is an important challenge.
+The computations performed during DNN training and inference are dominated by
+operations on the weight matrices describing the DNN. As DNNs incorporate more
+layers and more neurons per layers, these weight matrices may be required to be
+sparse because of memory limitations. Sparse DNNs are one possible approach,
+but the underlying theory is in the early stages of development and presents a
+number of challenges, including determining the accuracy of inference and
+selecting nonzero weights for training. Associative array algebra has been
+developed by the big data community to combine and extend database, matrix, and
+graph/network concepts for use in large, sparse data problems. Applying this
+mathematics to DNNs simplifies the formulation of DNN mathematics and reveals
+that DNNs are linear over oscillating semirings. This work uses associative
+array DNNs to construct exact solutions and corresponding perturbation models
+to the rectified linear unit (ReLU) DNN equations that can be used to construct
+test vectors for sparse DNN implementations over various precisions. These
+solutions can be used for DNN verification, theoretical explorations of DNN
+properties, and a starting point for the challenge of sparse training.
+",0,0,0,1,0,0
+1518,Variation formulas for an extended Gompf invariant,"  In 1998, R. Gompf defined a homotopy invariant $\theta_G$ of oriented 2-plane
+fields in 3-manifolds. This invariant is defined for oriented 2-plane fields
+$\xi$ in a closed oriented 3-manifold $M$ when the first Chern class $c_1(\xi)$
+is a torsion element of $H^2(M;\mathbb{Z})$. In this article, we define an
+extension of the Gompf invariant for all compact oriented 3-manifolds with
+boundary and we study its iterated variations under Lagrangian-preserving
+surgeries. It follows that the extended Gompf invariant is a degree two
+invariant with respect to a suitable finite type invariant theory.
+",0,0,1,0,0,0
+1519,A SAT+CAS Approach to Finding Good Matrices: New Examples and Counterexamples,"  We enumerate all circulant good matrices with odd orders divisible by 3 up to
+order 70. As a consequence of this we find a previously overlooked set of good
+matrices of order 27 and a new set of good matrices of order 57. We also find
+that circulant good matrices do not exist in the orders 51, 63, and 69, thereby
+finding three new counterexamples to the conjecture that such matrices exist in
+all odd orders. Additionally, we prove a new relationship between the entries
+of good matrices and exploit this relationship in our enumeration algorithm.
+Our method applies the SAT+CAS paradigm of combining computer algebra
+functionality with modern SAT solvers to efficiently search large spaces which
+are specified by both algebraic and logical constraints.
+",1,0,0,0,0,0
+1520,An Exploration of Mimic Architectures for Residual Network Based Spectral Mapping,"  Spectral mapping uses a deep neural network (DNN) to map directly from noisy
+speech to clean speech. Our previous study found that the performance of
+spectral mapping improves greatly when using helpful cues from an acoustic
+model trained on clean speech. The mapper network learns to mimic the input
+favored by the spectral classifier and cleans the features accordingly. In this
+study, we explore two new innovations: we replace a DNN-based spectral mapper
+with a residual network that is more attuned to the goal of predicting clean
+speech. We also examine how integrating long term context in the mimic
+criterion (via wide-residual biLSTM networks) affects the performance of
+spectral mapping compared to DNNs. Our goal is to derive a model that can be
+used as a preprocessor for any recognition system; the features derived from
+our model are passed through the standard Kaldi ASR pipeline and achieve a WER
+of 9.3%, which is the lowest recorded word error rate for CHiME-2 dataset using
+only feature adaptation.
+",1,0,0,0,0,0
+1521,Deep Neural Networks to Enable Real-time Multimessenger Astrophysics,"  Gravitational wave astronomy has set in motion a scientific revolution. To
+further enhance the science reach of this emergent field, there is a pressing
+need to increase the depth and speed of the gravitational wave algorithms that
+have enabled these groundbreaking discoveries. To contribute to this effort, we
+introduce Deep Filtering, a new highly scalable method for end-to-end
+time-series signal processing, based on a system of two deep convolutional
+neural networks, which we designed for classification and regression to rapidly
+detect and estimate parameters of signals in highly noisy time-series data
+streams. We demonstrate a novel training scheme with gradually increasing noise
+levels, and a transfer learning procedure between the two networks. We showcase
+the application of this method for the detection and parameter estimation of
+gravitational waves from binary black hole mergers. Our results indicate that
+Deep Filtering significantly outperforms conventional machine learning
+techniques, achieves similar performance compared to matched-filtering while
+being several orders of magnitude faster thus allowing real-time processing of
+raw big data with minimal resources. More importantly, Deep Filtering extends
+the range of gravitational wave signals that can be detected with ground-based
+gravitational wave detectors. This framework leverages recent advances in
+artificial intelligence algorithms and emerging hardware architectures, such as
+deep-learning-optimized GPUs, to facilitate real-time searches of gravitational
+wave sources and their electromagnetic and astro-particle counterparts.
+",1,1,0,0,0,0
+1522,Contribution of cellular automata to the understanding of corrosion phenomena,"  We present a stochastic CA modelling approach of corrosion based on spatially
+separated electrochemical half-reactions, diffusion, acido-basic neutralization
+in solution and passive properties of the oxide layers. Starting from different
+initial conditions, a single framework allows one to describe generalised
+corrosion, localised corrosion, reactive and passive surfaces, including
+occluded corrosion phenomena as well. Spontaneous spatial separation of anodic
+and cathodic zones is associated with bare metal and passivated metal on the
+surface. This separation is also related to local acidification of the
+solution. This spontaneous change is associated with a much faster corrosion
+rate. Material morphology is closely related to corrosion kinetics, which can
+be used for technological applications.
+",0,1,0,0,0,0
+1523,Involutive bordered Floer homology,"  We give a bordered extension of involutive HF-hat and use it to give an
+algorithm to compute involutive HF-hat for general 3-manifolds. We also explain
+how the mapping class group action on HF-hat can be computed using bordered
+Floer homology. As applications, we prove that involutive HF-hat satisfies a
+surgery exact triangle and compute HFI-hat of the branched double covers of all
+10-crossing knots.
+",0,0,1,0,0,0
+1524,"Orbital Evolution, Activity, and Mass Loss of Comet C/1995 O1 (Hale-Bopp). I. Close Encounter with Jupiter in Third Millennium BCE and Effects of Outgassing on the Comet's Motion and Physical Properties","  This comprehensive study of comet C/1995 O1 focuses first on investigating
+its orbital motion over a period of 17.6 yr (1993-2010). The comet is suggested
+to have approached Jupiter to 0.005 AU on -2251 November 7, in general
+conformity with Marsden's (1999) proposal of a Jovian encounter nearly 4300 yr
+ago. The variations of sizable nongravitational effects with heliocentric
+distance correlate with the evolution of outgassing, asymmetric relative to
+perihelion. The future orbital period will shorten to ~1000 yr because of
+orbital-cascade resonance effects. We find that the sublimation curves of
+parent molecules are fitted with the type of a law used for the
+nongravitational acceleration, determine their orbit-integrated mass loss, and
+conclude that the share of water ice was at most 57%, and possibly less than
+50%, of the total outgassed mass. Even though organic parent molecules (many
+still unidentified) had very low abundances relative to water individually,
+their high molar mass and sheer number made them, summarily, important
+potential mass contributors to the total production of gas. The mass loss of
+dust per orbit exceeded that of water ice by a factor of ~12, a dust loading
+high enough to imply a major role for heavy organic molecules of low volatility
+in accelerating the minuscule dust particles in the expanding halos to terminal
+velocities as high as 0.7 km s^{-1}. In Part II, the comet's nucleus will be
+modeled as a compact cluster of massive fragments to conform to the integrated
+nongravitational effect.
+",0,1,0,0,0,0
+1525,A Note on Property Testing Sum of Squares and Multivariate Polynomial Interpolation,"  In this paper, we investigate property testing whether or not a degree d
+multivariate poly- nomial is a sum of squares or is far from a sum of squares.
+We show that if we require that the property tester always accepts YES
+instances and uses random samples, $n^{\Omega(d)}$ samples are required, which
+is not much fewer than it would take to completely determine the polynomial. To
+prove this lower bound, we show that with high probability, multivariate
+polynomial in- terpolation matches arbitrary values on random points and the
+resulting polynomial has small norm. We then consider a particular polynomial
+which is non-negative yet not a sum of squares and use pseudo-expectation
+values to prove it is far from being a sum of squares.
+",1,0,0,0,0,0
+1526,Closed-form mathematical expressions for the exponentiated Cauchy-Rayleigh distribution,"  The Cauchy-Rayleigh (CR) distribution has been successfully used to describe
+asymmetric and heavy-tail events from radar imagery. Employing such model to
+describe lifetime data may then seem attractive, but some drawbacks arise: its
+probability density function does not cover non-modal behavior as well as the
+CR hazard rate function (hrf) assumes only one form. To outperform this
+difficulty, we introduce an extended CR model, called exponentiated
+Cauchy-Rayleigh (ECR) distribution. This model has two parameters and hrf with
+decreasing, decreasing-increasing-decreasing and upside-down bathtub forms. In
+this paper, several closed-form mathematical expressions for the ECR model are
+proposed: median, mode, probability weighted, log-, incomplete and order
+statistic moments and Fisher information matrix. We propose three estimation
+procedures for the ECR parameters: maximum likelihood (ML), bias corrected ML
+and percentile-based methods. A simulation study is done to assess the
+performance of estimators. An application to survival time of heart problem
+patients illustrates the usefulness of the ECR model. Results point out that
+the ECR distribution may outperform classical lifetime models, such as the
+gamma, Birnbaun-Saunders, Weibull and log-normal laws, before heavy-tail data.
+",0,0,1,1,0,0
+1527,"HTEM data improve 3D modelling of aquifers in Paris Basin, France","  In Paris Basin, we evaluate how HTEM data complement the usual borehole,
+geological and deep seismic data used for modelling aquifer geometries. With
+these traditional data, depths between ca. 50 to 300m are often relatively
+ill-constrained, as most boreholes lie within the first tens of meters of the
+underground and petroleum seismic is blind shallower than ca. 300m. We have
+fully reprocessed and re-inverted 540km of flight lines of a SkyTEM survey of
+2009, acquired on a 40x12km zone with 400m line spacing. The resistivity model
+is first ""calibrated"" with respect to ca. 50 boreholes available on the study
+area. Overall, the correlation between EM resistivity models and the
+hydrogeological horizons clearly shows that the geological units in which the
+aquifers are developed almost systematically correspond to relative increase of
+resistivity, whatever the ""background"" resistivity environment and the
+lithology of the aquifer. In 3D Geomodeller software, this allows interpreting
+11 aquifer/aquitar layers along the flight lines and then jointly interpolating
+them in 3D along with the borehole data. The resulting model displays 3D
+aquifer geometries consistent with the SIGES ""reference"" regional
+hydrogeological model and improves it in between the boreholes and on the
+50-300m depth range.
+",0,1,0,0,0,0
+1528,"Implementing GraphQL as a Query Language for Deductive Databases in SWI-Prolog Using DCGs, Quasi Quotations, and Dicts","  The methods to access large relational databases in a distributed system are
+well established: the relational query language SQL often serves as a language
+for data access and manipulation, and in addition public interfaces are exposed
+using communication protocols like REST. Similarly to REST, GraphQL is the
+query protocol of an application layer developed by Facebook. It provides a
+unified interface between the client and the server for data fetching and
+manipulation. Using GraphQL's type system, it is possible to specify data
+handling of various sources and to combine, e.g., relational with NoSQL
+databases. In contrast to REST, GraphQL provides a single API endpoint and
+supports flexible queries over linked data.
+GraphQL can also be used as an interface for deductive databases. In this
+paper, we give an introduction of GraphQL and a comparison to REST. Using
+language features recently added to SWI-Prolog 7, we have developed the Prolog
+library GraphQL.pl, which implements the GraphQL type system and query syntax
+as a domain-specific language with the help of definite clause grammars (DCG),
+quasi quotations, and dicts. Using our library, the type system created for a
+deductive database can be validated, while the query system provides a unified
+interface for data access and introspection.
+",1,0,0,0,0,0
+1529,Social Network based Short-Term Stock Trading System,"  This paper proposes a novel adaptive algorithm for the automated short-term
+trading of financial instrument. The algorithm adopts a semantic sentiment
+analysis technique to inspect the Twitter posts and to use them to predict the
+behaviour of the stock market. Indeed, the algorithm is specifically developed
+to take advantage of both the sentiment and the past values of a certain
+financial instrument in order to choose the best investment decision. This
+allows the algorithm to ensure the maximization of the obtainable profits by
+trading on the stock market. We have conducted an investment simulation and
+compared the performance of our proposed with a well-known benchmark (DJTATO
+index) and the optimal results, in which an investor knows in advance the
+future price of a product. The result shows that our approach outperforms the
+benchmark and achieves the performance score close to the optimal result.
+",1,0,0,0,0,1
+1530,New Determinant Expressions of the Multi-indexed Orthogonal Polynomials in Discrete Quantum Mechanics,"  The multi-indexed orthogonal polynomials (the Meixner, little $q$-Jacobi
+(Laguerre), ($q$-)Racah, Wilson, Askey-Wilson types) satisfying second order
+difference equations were constructed in discrete quantum mechanics. They are
+polynomials in the sinusoidal coordinates $\eta(x)$ ($x$ is the coordinate of
+quantum system) and expressed in terms of the Casorati determinants whose
+matrix elements are functions of $x$ at various points. By using shape
+invariance properties, we derive various equivalent determinant expressions,
+especially those whose matrix elements are functions of the same point $x$.
+Except for the ($q$-)Racah case, they can be expressed in terms of $\eta$ only,
+without explicit $x$-dependence.
+",0,1,1,0,0,0
+1531,Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior,"  We describe an approach to understand the peculiar and counterintuitive
+generalization properties of deep neural networks. The approach involves going
+beyond worst-case theoretical capacity control frameworks that have been
+popular in machine learning in recent years to revisit old ideas in the
+statistical mechanics of neural networks. Within this approach, we present a
+prototypical Very Simple Deep Learning (VSDL) model, whose behavior is
+controlled by two control parameters, one describing an effective amount of
+data, or load, on the network (that decreases when noise is added to the
+input), and one with an effective temperature interpretation (that increases
+when algorithms are early stopped). Using this model, we describe how a very
+simple application of ideas from the statistical mechanics theory of
+generalization provides a strong qualitative description of recently-observed
+empirical results regarding the inability of deep neural networks not to
+overfit training data, discontinuous learning and sharp transitions in the
+generalization properties of learning algorithms, etc.
+",1,0,0,1,0,0
+1532,Towards an Understanding of the Effects of Augmented Reality Games on Disaster Management,"  Location-based augmented reality games have entered the mainstream with the
+nearly overnight success of Niantic's Pokémon Go. Unlike traditional video
+games, the fact that players of such games carry out actions in the external,
+physical world to accomplish in-game objectives means that the large-scale
+adoption of such games motivate people, en masse, to do things and go places
+they would not have otherwise done in unprecedented ways. The social
+implications of such mass-mobilisation of individual players are, in general,
+difficult to anticipate or characterise, even for the short-term. In this work,
+we focus on disaster relief, and the short- and long-term implications that a
+proliferation of AR games like Pokémon Go, may have in disaster-prone regions
+of the world. We take a distributed cognition approach and focus on one natural
+disaster-prone region of New Zealand, the city of Wellington.
+",1,0,0,0,0,0
+1533,Integrating a Global Induction Mechanism into a Sequent Calculus,"  Most interesting proofs in mathematics contain an inductive argument which
+requires an extension of the LK-calculus to formalize. The most commonly used
+calculi for induction contain a separate rule or axiom which reduces the valid
+proof theoretic properties of the calculus. To the best of our knowledge, there
+are no such calculi which allow cut-elimination to a normal form with the
+subformula property, i.e. every formula occurring in the proof is a subformula
+of the end sequent. Proof schemata are a variant of LK-proofs able to simulate
+induction by linking proofs together. There exists a schematic normal form
+which has comparable proof theoretic behaviour to normal forms with the
+subformula property. However, a calculus for the construction of proof schemata
+does not exist. In this paper, we introduce a calculus for proof schemata and
+prove soundness and completeness with respect to a fragment of the inductive
+arguments formalizable in Peano arithmetic.
+",1,0,0,0,0,0
+1534,An analytic resolution to the competition between Lyman-Werner radiation and metal winds in direct collapse black hole hosts,"  A near pristine atomic cooling halo close to a star forming galaxy offers a
+natural pathway for forming massive direct collapse black hole (DCBH) seeds
+which could be the progenitors of the $z>6$ redshift quasars. The close
+proximity of the haloes enables a sufficient Lyman-Werner flux to effectively
+dissociate H$_2$ in the core of the atomic cooling halo. A mild background may
+also be required to delay star formation in the atomic cooling halo, often
+attributed to distant background galaxies. In this letter we investigate the
+impact of metal enrichment from both the background galaxies and the close star
+forming galaxy under extremely unfavourable conditions such as instantaneous
+metal mixing. We find that within the time window of DCBH formation, the level
+of enrichment never exceeds the critical threshold (Z$_{cr} \sim 1 \times
+10^{-5} \ \rm Z_{\odot})$, and attains a maximum metallicity of Z $\sim 2
+\times 10^{-6} \ \rm Z_{\odot}$. As the system evolves, the metallicity
+eventually exceeds the critical threshold, long after the DCBH has formed.
+",0,1,0,0,0,0
+1535,Driving Interactive Graph Exploration Using 0-Dimensional Persistent Homology Features,"  Graphs are commonly used to encode relationships among entities, yet, their
+abstractness makes them incredibly difficult to analyze. Node-link diagrams are
+a popular method for drawing graphs. Classical techniques for the node-link
+diagrams include various layout methods that rely on derived information to
+position points, which often lack interactive exploration functionalities; and
+force-directed layouts, which ignore global structures of the graph. This paper
+addresses the graph drawing challenge by leveraging topological features of a
+graph as derived information for interactive graph drawing. We first discuss
+extracting topological features from a graph using persistent homology. We then
+introduce an interactive persistence barcodes to study the substructures of a
+force-directed graph layout; in particular, we add contracting and repulsing
+forces guided by the 0-dimensional persistent homology features. Finally, we
+demonstrate the utility of our approach across three datasets.
+",1,0,0,0,0,0
+1536,Identification of Conduit Countries and Community Structures in the Withholding Tax Networks,"  Due to economic globalization, each country's economic law, including tax
+laws and tax treaties, has been forced to work as a single network. However,
+each jurisdiction (country or region) has not made its economic law under the
+assumption that its law functions as an element of one network, so it has
+brought unexpected results. We thought that the results are exactly
+international tax avoidance. To contribute to the solution of international tax
+avoidance, we tried to investigate which part of the network is vulnerable.
+Specifically, focusing on treaty shopping, which is one of international tax
+avoidance methods, we attempt to identified which jurisdiction are likely to be
+used for treaty shopping from tax liabilities and the relationship between
+jurisdictions which are likely to be used for treaty shopping and others. For
+that purpose, based on withholding tax rates imposed on dividends, interest,
+and royalties by jurisdictions, we produced weighted multiple directed graphs,
+computed the centralities and detected the communities. As a result, we
+clarified the jurisdictions that are likely to be used for treaty shopping and
+pointed out that there are community structures. The results of this study
+suggested that fewer jurisdictions need to introduce more regulations for
+prevention of treaty abuse worldwide.
+",0,0,0,0,0,1
+1537,A comprehensive study of batch construction strategies for recurrent neural networks in MXNet,"  In this work we compare different batch construction methods for mini-batch
+training of recurrent neural networks. While popular implementations like
+TensorFlow and MXNet suggest a bucketing approach to improve the
+parallelization capabilities of the recurrent training process, we propose a
+simple ordering strategy that arranges the training sequences in a stochastic
+alternatingly sorted way. We compare our method to sequence bucketing as well
+as various other batch construction strategies on the CHiME-4 noisy speech
+recognition corpus. The experiments show that our alternated sorting approach
+is able to compete both in training time and recognition performance while
+being conceptually simpler to implement.
+",1,0,0,1,0,0
+1538,On a class of shift-invariant subspaces of the Drury-Arveson space,"  In the Drury-Arveson space, we consider the subspace of functions whose
+Taylor coefficients are supported in the complement of a set
+$Y\subset\mathbb{N}^d$ with the property that $Y+e_j\subset Y$ for all
+$j=1,\dots,d$. This is an easy example of shift-invariant subspace, which can
+be considered as a RKHS in is own right, with a kernel that can be explicitely
+calculated. Moreover, every such a space can be seen as an intersection of
+kernels of Hankel operators, whose symbols can be explicity calcuated as well.
+Finally, this is the right space on which Drury's inequality can be optimally
+adapted to a sub-family of the commuting and contractive operators originally
+considered by Drury.
+",0,0,1,0,0,0
+1539,Airborne gamma-ray spectroscopy for modeling cosmic radiation and effective dose in the lower atmosphere,"  In this paper we present the results of a $\sim$5 hour airborne gamma-ray
+survey carried out over the Tyrrhenian sea in which the height range (77-3066)
+m has been investigated. Gamma-ray spectroscopy measurements have been
+performed by using the AGRS_16L detector, a module of four 4L NaI(Tl) crystals.
+The experimental setup was mounted on the Radgyro, a prototype aircraft
+designed for multisensorial acquisitions in the field of proximal remote
+sensing. By acquiring high-statistics spectra over the sea (i.e. in the absence
+of signals having geological origin) and by spanning a wide spectrum of
+altitudes it has been possible to split the measured count rate into a constant
+aircraft component and a cosmic component exponentially increasing with
+increasing height. The monitoring of the count rate having pure cosmic origin
+in the >3 MeV energy region allowed to infer the background count rates in the
+$^{40}$K, $^{214}$Bi and $^{208}$Tl photopeaks, which need to be subtracted in
+processing airborne gamma-ray data in order to estimate the potassium, uranium
+and thorium abundances in the ground. Moreover, a calibration procedure has
+been carried out by implementing the CARI-6P and EXPACS dosimetry tools,
+according to which the annual cosmic effective dose to human population has
+been linearly related to the measured cosmic count rates.
+",0,1,0,0,0,0
+1540,Search for axions in streaming dark matter,"  A new search strategy for the detection of the elusive dark matter (DM) axion
+is proposed. The idea is based on streaming DM axions, whose flux might get
+temporally enormously enhanced due to gravitational lensing. This can happen if
+the Sun or some planet (including the Moon) is found along the direction of a
+DM stream propagating towards the Earth location. The experimental requirements
+to the axion haloscope are a wide-band performance combined with a fast axion
+rest mass scanning mode, which are feasible. Once both conditions have been
+implemented in a haloscope, the axion search can continue parasitically almost
+as before. Interestingly, some new DM axion detectors are operating wide-band
+by default. In order not to miss the actually unpredictable timing of a
+potential short duration signal, a network of co-ordinated axion antennae is
+required, preferentially distributed world-wide. The reasoning presented here
+for the axions applies to some degree also to any other DM candidates like the
+WIMPs.
+",0,1,0,0,0,0
+1541,Faster integer and polynomial multiplication using cyclotomic coefficient rings,"  We present an algorithm that computes the product of two n-bit integers in
+O(n log n (4\sqrt 2)^{log^* n}) bit operations. Previously, the best known
+bound was O(n log n 6^{log^* n}). We also prove that for a fixed prime p,
+polynomials in F_p[X] of degree n may be multiplied in O(n log n 4^{log^* n})
+bit operations; the previous best bound was O(n log n 8^{log^* n}).
+",1,0,0,0,0,0
+1542,Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks,"  We study the stochastic multi-armed bandit (MAB) problem in the presence of
+side-observations across actions that occur as a result of an underlying
+network structure. In our model, a bipartite graph captures the relationship
+between actions and a common set of unknowns such that choosing an action
+reveals observations for the unknowns that it is connected to. This models a
+common scenario in online social networks where users respond to their friends'
+activity, thus providing side information about each other's preferences. Our
+contributions are as follows: 1) We derive an asymptotic lower bound (with
+respect to time) as a function of the bi-partite network structure on the
+regret of any uniformly good policy that achieves the maximum long-term average
+reward. 2) We propose two policies - a randomized policy; and a policy based on
+the well-known upper confidence bound (UCB) policies - both of which explore
+each action at a rate that is a function of its network position. We show,
+under mild assumptions, that these policies achieve the asymptotic lower bound
+on the regret up to a multiplicative factor, independent of the network
+structure. Finally, we use numerical examples on a real-world social network
+and a routing example network to demonstrate the benefits obtained by our
+policies over other existing policies.
+",1,0,0,1,0,0
+1543,Verifying Safety of Functional Programs with Rosette/Unbound,"  The goal of unbounded program verification is to discover an inductive
+invariant that safely over-approximates all possible program behaviors.
+Functional languages featuring higher order and recursive functions become more
+popular due to the domain-specific needs of big data analytics, web, and
+security. We present Rosette/Unbound, the first program verifier for Racket
+exploiting the automated constrained Horn solver on its backend. One of the key
+features of Rosette/Unbound is the ability to synchronize recursive
+computations over the same inputs allowing to verify programs that iterate over
+unbounded data streams multiple times. Rosette/Unbound is successfully
+evaluated on a set of non-trivial recursive and higher order functional
+programs.
+",1,0,0,0,0,0
+1544,Extended Formulations for Polytopes of Regular Matroids,"  We present a simple proof of the fact that the base (and independence)
+polytope of a rank $n$ regular matroid over $m$ elements has an extension
+complexity $O(mn)$.
+",1,0,1,0,0,0
+1545,Multiscale Change-point Segmentation: Beyond Step Functions,"  Modern multiscale type segmentation methods are known to detect multiple
+change-points with high statistical accuracy, while allowing for fast
+computation. Underpinning theory has been developed mainly for models that
+assume the signal as a piecewise constant function. In this paper this will be
+extended to certain function classes beyond such step functions in a
+nonparametric regression setting, revealing certain multiscale segmentation
+methods as robust to deviation from such piecewise constant functions. Our main
+finding is the adaptation over such function classes for a universal
+thresholding, which includes bounded variation functions, and (piecewise)
+Hölder functions of smoothness order $ 0 < \alpha \le1$ as special cases.
+From this we derive statistical guarantees on feature detection in terms of
+jumps and modes. Another key finding is that these multiscale segmentation
+methods perform nearly (up to a log-factor) as well as the oracle piecewise
+constant segmentation estimator (with known jump locations), and the best
+piecewise constant approximants of the (unknown) true signal. Theoretical
+findings are examined by various numerical simulations.
+",0,0,1,1,0,0
+1546,Data Motif-based Proxy Benchmarks for Big Data and AI Workloads,"  For the architecture community, reasonable simulation time is a strong
+requirement in addition to performance data accuracy. However, emerging big
+data and AI workloads are too huge at binary size level and prohibitively
+expensive to run on cycle-accurate simulators. The concept of data motif, which
+is identified as a class of units of computation performed on initial or
+intermediate data, is the first step towards building proxy benchmark to mimic
+the real-world big data and AI workloads. However, there is no practical way to
+construct a proxy benchmark based on the data motifs to help simulation-based
+research. In this paper, we embark on a study to bridge the gap between data
+motif and a practical proxy benchmark. We propose a data motif-based proxy
+benchmark generating methodology by means of machine learning method, which
+combine data motifs with different weights to mimic the big data and AI
+workloads. Furthermore, we implement various data motifs using light-weight
+stacks and apply the methodology to five real-world workloads to construct a
+suite of proxy benchmarks, considering the data types, patterns, and
+distributions. The evaluation results show that our proxy benchmarks shorten
+the execution time by 100s times on real systems while maintaining the average
+system and micro-architecture performance data accuracy above 90%, even
+changing the input data sets or cluster configurations. Moreover, the generated
+proxy benchmarks reflect consistent performance trends across different
+architectures. To facilitate the community, we will release the proxy
+benchmarks on the project homepage this http URL.
+",1,0,0,0,0,0
+1547,The neighborhood lattice for encoding partial correlations in a Hilbert space,"  Neighborhood regression has been a successful approach in graphical and
+structural equation modeling, with applications to learning undirected and
+directed graphical models. We extend these ideas by defining and studying an
+algebraic structure called the neighborhood lattice based on a generalized
+notion of neighborhood regression. We show that this algebraic structure has
+the potential to provide an economic encoding of all conditional independence
+statements in a Gaussian distribution (or conditional uncorrelatedness in
+general), even in the cases where no graphical model exists that could
+""perfectly"" encode all such statements. We study the computational complexity
+of computing these structures and show that under a sparsity assumption, they
+can be computed in polynomial time, even in the absence of the assumption of
+perfectness to a graph. On the other hand, assuming perfectness, we show how
+these neighborhood lattices may be ""graphically"" computed using the separation
+properties of the so-called partial correlation graph. We also draw connections
+with directed acyclic graphical models and Bayesian networks. We derive these
+results using an abstract generalization of partial uncorrelatedness, called
+partial orthogonality, which allows us to use algebraic properties of
+projection operators on Hilbert spaces to significantly simplify and extend
+existing ideas and arguments. Consequently, our results apply to a wide range
+of random objects and data structures, such as random vectors, data matrices,
+and functions.
+",1,0,1,1,0,0
+1548,The 2-adic complexity of a class of binary sequences with almost optimal autocorrelation,"  Pseudo-random sequences with good statistical property, such as low
+autocorrelation, high linear complexity and large 2-adic complexity, have been
+applied in stream cipher. In general, it is difficult to give both the linear
+complexity and 2-adic complexity of a periodic binary sequence. Cai and Ding
+\cite{Cai Ying} gave a class of sequences with almost optimal autocorrelation
+by constructing almost difference sets. Wang \cite{Wang Qi} proved that one
+type of those sequences by Cai and Ding has large linear complexity. Sun et al.
+\cite{Sun Yuhua} showed that another type of sequences by Cai and Ding has also
+large linear complexity. Additionally, Sun et al. also generalized the
+construction by Cai and Ding using $d$-form function with difference-balanced
+property. In this paper, we first give the detailed autocorrelation
+distribution of the sequences was generalized from Cai and Ding \cite{Cai Ying}
+by Sun et al. \cite{Sun Yuhua}. Then, inspired by the method of Hu \cite{Hu
+Honggang}, we analyse their 2-adic complexity and give a lower bound on the
+2-adic complexity of these sequences. Our result show that the 2-adic
+complexity of these sequences is at least $N-\mathrm{log}_2\sqrt{N+1}$ and that
+it reach $N-1$ in many cases, which are large enough to resist the rational
+approximation algorithm (RAA) for feedback with carry shift registers (FCSRs).
+",1,0,1,0,0,0
+1549,Nesterov's Smoothing Technique and Minimizing Differences of Convex Functions for Hierarchical Clustering,"  A bilevel hierarchical clustering model is commonly used in designing optimal
+multicast networks. In this paper, we consider two different formulations of
+the bilevel hierarchical clustering problem, a discrete optimization problem
+which can be shown to be NP-hard. Our approach is to reformulate the problem as
+a continuous optimization problem by making some relaxations on the
+discreteness conditions. Then Nesterov's smoothing technique and a numerical
+algorithm for minimizing differences of convex functions called the DCA are
+applied to cope with the nonsmoothness and nonconvexity of the problem.
+Numerical examples are provided to illustrate our method.
+",0,0,1,0,0,0
+1550,Minimal solutions to generalized Lambda-semiflows and gradient flows in metric spaces,"  Generalized Lambda-semiflows are an abstraction of semiflows with
+non-periodic solutions, for which there may be more than one solution
+corresponding to given initial data. A select class of solutions to generalized
+Lambda-semiflows is introduced. It is proved that such minimal solutions are
+unique corresponding to given ranges and generate all other solutions by time
+reparametrization. Special qualities of minimal solutions are shown. The
+concept of minimal solutions is applied to gradient flows in metric spaces and
+generalized semiflows. Generalized semiflows have been introduced by Ball.
+",0,0,1,0,0,0
+1551,Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information,"  We consider variants of trust-region and cubic regularization methods for
+non-convex optimization, in which the Hessian matrix is approximated. Under
+mild conditions on the inexact Hessian, and using approximate solution of the
+corresponding sub-problems, we provide iteration complexity to achieve $
+\epsilon $-approximate second-order optimality which have shown to be tight.
+Our Hessian approximation conditions constitute a major relaxation over the
+existing ones in the literature. Consequently, we are able to show that such
+mild conditions allow for the construction of the approximate Hessian through
+various random sampling methods. In this light, we consider the canonical
+problem of finite-sum minimization, provide appropriate uniform and non-uniform
+sub-sampling strategies to construct such Hessian approximations, and obtain
+optimal iteration complexity for the corresponding sub-sampled trust-region and
+cubic regularization methods.
+",1,0,0,1,0,0
+1552,$G 1$-smooth splines on quad meshes with 4-split macro-patch elements,"  We analyze the space of differentiable functions on a quad-mesh $\cM$, which
+are composed of 4-split spline macro-patch elements on each quadrangular face.
+We describe explicit transition maps across shared edges, that satisfy
+conditions which ensure that the space of differentiable functions is ample on
+a quad-mesh of arbitrary topology. These transition maps define a finite
+dimensional vector space of $G^{1}$ spline functions of bi-degree $\le (k,k)$
+on each quadrangular face of $\cM$. We determine the dimension of this space of
+$G^{1}$ spline functions for $k$ big enough and provide explicit constructions
+of basis functions attached respectively to vertices, edges and faces. This
+construction requires the analysis of the module of syzygies of univariate
+b-spline functions with b-spline function coefficients. New results on their
+generators and dimensions are provided. Examples of bases of $G^{1}$ splines of
+small degree for simple topological surfaces are detailed and illustrated by
+parametric surface constructions.
+",0,0,1,0,0,0
+1553,BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset,"  Bangla handwriting recognition is becoming a very important issue nowadays.
+It is potentially a very important task specially for Bangla speaking
+population of Bangladesh and West Bengal. By keeping that in our mind we are
+introducing a comprehensive Bangla handwritten character dataset named
+BanglaLekha-Isolated. This dataset contains Bangla handwritten numerals, basic
+characters and compound characters. This dataset was collected from multiple
+geographical location within Bangladesh and includes sample collected from a
+variety of aged groups. This dataset can also be used for other classification
+problems i.e: gender, age, district. This is the largest dataset on Bangla
+handwritten characters yet.
+",1,0,0,0,0,0
+1554,Estimates for maximal functions associated to hypersurfaces in $\Bbb R^3$ with height $h<2:$ Part I,"  In this article, we continue the study of the problem of $L^p$-boundedness of
+the maximal operator $M$ associated to averages along isotropic dilates of a
+given, smooth hypersurface $S$ of finite type in 3-dimensional Euclidean space.
+An essentially complete answer to this problem had been given about seven years
+ago by the last named two authors in joint work with M. Kempe for the case
+where the height h of the given surface is at least two. In the present
+article, we turn to the case $h<2.$ More precisely, in this Part I, we study
+the case where $h<2,$ assuming that $S$ is contained in a sufficiently small
+neighborhood of a given point $x^0\in S$ at which both principal curvatures of
+$S$ vanish. Under these assumptions and a natural transversality assumption, we
+show that, as in the case where $h\ge 2,$ the critical Lebesgue exponent for
+the boundedness of $M$ remains to be $p_c=h,$ even though the proof of this
+result turns out to require new methods, some of which are inspired by the more
+recent work by the last named two authors on Fourier restriction to S. Results
+on the case where $h<2$ and exactly one principal curvature of $S$ does not
+vanish at $x^0$ will appear elsewhere.
+",0,0,1,0,0,0
+1555,Using Inertial Sensors for Position and Orientation Estimation,"  In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes)
+have become widely available due to their small size and low cost. Inertial
+sensor measurements are obtained at high sampling rates and can be integrated
+to obtain position and orientation information. These estimates are accurate on
+a short time scale, but suffer from integration drift over longer time scales.
+To overcome this issue, inertial sensors are typically combined with additional
+sensors and models. In this tutorial we focus on the signal processing aspects
+of position and orientation estimation using inertial sensors. We discuss
+different modeling choices and a selected number of important algorithms. The
+algorithms include optimization-based smoothing and filtering as well as
+computationally cheaper extended Kalman filter and complementary filter
+implementations. The quality of their estimates is illustrated using both
+experimental and simulated data.
+",1,0,0,0,0,0
+1556,Heisenberg equation for a nonrelativistic particle on a hypersurface: from the centripetal force to a curvature induced force,"  In classical mechanics, a nonrelativistic particle constrained on an $N-1$
+curved hypersurface embedded in $N$ flat space experiences the centripetal
+force only. In quantum mechanics, the situation is totally different for the
+presence of the geometric potential. We demonstrate that the motion of the
+quantum particle is ""driven"" by not only the the centripetal force, but also a
+curvature induced force proportional to the Laplacian of the mean curvature,
+which is fundamental in the interface physics, causing curvature driven
+interface evolution.
+",0,1,0,0,0,0
+1557,On constraining projections of future climate using observations and simulations from multiple climate models,"  A new Bayesian framework is presented that can constrain projections of
+future climate using historical observations by exploiting robust estimates of
+emergent relationships between multiple climate models. We argue that emergent
+relationships can be interpreted as constraints on model inadequacy, but that
+projections may be biased if we do not account for internal variability in
+climate model projections. We extend the previously proposed coexchangeable
+framework to account for natural variability in the Earth system and internal
+variability simulated by climate models. A detailed theoretical comparison with
+previous multi-model projection frameworks is provided.
+The proposed framework is applied to projecting surface temperature in the
+Arctic at the end of the 21st century. A subset of available climate models are
+selected in order to satisfy the assumptions of the framework. All available
+initial condition runs from each model are utilized in order maximize the
+utility of the data. Projected temperatures in some regions are more than 2C
+lower when constrained by historical observations. The uncertainty about the
+climate response is reduced by up to 30% where strong constraints exist.
+",0,0,0,1,0,0
+1558,Higher order molecular organisation as a source of biological function,"  Molecular interactions have widely been modelled as networks. The local
+wiring patterns around molecules in molecular networks are linked with their
+biological functions. However, networks model only pairwise interactions
+between molecules and cannot explicitly and directly capture the higher order
+molecular organisation, such as protein complexes and pathways. Hence, we ask
+if hypergraphs (hypernetworks), that directly capture entire complexes and
+pathways along with protein-protein interactions (PPIs), carry additional
+functional information beyond what can be uncovered from networks of pairwise
+molecular interactions. The mathematical formalism of a hypergraph has long
+been known, but not often used in studying molecular networks due to the lack
+of sophisticated algorithms for mining the underlying biological information
+hidden in the wiring patterns of molecular systems modelled as hypernetworks.
+We propose a new, multi-scale, protein interaction hypernetwork model that
+utilizes hypergraphs to capture different scales of protein organization,
+including PPIs, protein complexes and pathways. In analogy to graphlets, we
+introduce hypergraphlets, small, connected, non-isomorphic, induced
+sub-hypergraphs of a hypergraph, to quantify the local wiring patterns of these
+multi-scale molecular hypergraphs and to mine them for new biological
+information. We apply them to model the multi-scale protein networks of baker
+yeast and human and show that the higher order molecular organisation captured
+by these hypergraphs is strongly related to the underlying biology.
+Importantly, we demonstrate that our new models and data mining tools reveal
+different, but complementary biological information compared to classical PPI
+networks. We apply our hypergraphlets to successfully predict biological
+functions of uncharacterised proteins.
+",0,0,0,0,1,0
+1559,The Massive CO White Dwarf in the Symbiotic Recurrent Nova RS Ophiuchi,"  If accreting white dwarfs (WD) in binary systems are to produce type Ia
+supernovae (SNIa), they must grow to nearly the Chandrasekhar mass and ignite
+carbon burning. Proving conclusively that a WD has grown substantially since
+its birth is a challenging task. Slow accretion of hydrogen inevitably leads to
+the erosion, rather than the growth of WDs. Rapid hydrogen accretion does lead
+to growth of a helium layer, due to both decreased degeneracy and the
+inhibition of mixing of the accreted hydrogen with the underlying WD. However,
+until recently, simulations of helium-accreting WDs all claimed to show the
+explosive ejection of a helium envelope once it exceeded $\sim 10^{-1}\, \rm
+M_{\odot}$. Because CO WDs cannot be born with masses in excess of $\sim 1.1\,
+\rm M_{\odot}$, any such object, in excess of $\sim 1.2\, \rm M_{\odot}$, must
+have grown substantially. We demonstrate that the WD in the symbiotic nova RS
+Oph is in the mass range 1.2-1.4\,M$_{\odot}$. We compare UV spectra of RS Oph
+with those of novae with ONe WDs, and with novae erupting on CO WDs. The RS Oph
+WD is clearly made of CO, demonstrating that it has grown substantially since
+birth. It is a prime candidate to eventually produce an SNIa.
+",0,1,0,0,0,0
+1560,Semi-equivelar maps on the torus are Archimedean,"  If the face-cycles at all the vertices in a map on a surface are of same type
+then the map is called semi-equivelar. There are eleven types of Archimedean
+tilings on the plane. All the Archimedean tilings are semi-equivelar maps. If a
+map $X$ on the torus is a quotient of an Archimedean tiling on the plane then
+the map $X$ is semi-equivelar. We show that each semi-equivelar map on the
+torus is a quotient of an Archimedean tiling on the plane.
+Vertex-transitive maps are semi-equivelar maps. We know that four types of
+semi-equivelar maps on the torus are always vertex-transitive and there are
+examples of other seven types of semi-equivelar maps which are not
+vertex-transitive. We show that the number of ${\rm Aut}(Y)$-orbits of vertices
+for any semi-equivelar map $Y$ on the torus is at most six. In fact, the number
+of orbits is at most three except one type of semi-equivelar maps. Our bounds
+on the number of orbits are sharp.
+",0,0,1,0,0,0
+1561,Dynamics of Porous Dust Aggregates and Gravitational Instability of Their Disk,"  We consider the dynamics of porous icy dust aggregates in a turbulent gas
+disk and investigate the stability of the disk. We evaluate the random velocity
+of porous dust aggregates by considering their self-gravity, collisions,
+aerodynamic drag, turbulent stirring and scattering due to gas. We extend our
+previous work by introducing the anisotropic velocity dispersion and the
+relaxation time of the random velocity. We find the minimum mass solar nebular
+model to be gravitationally unstable if the turbulent viscosity parameter
+$\alpha$ is less than about $4 \times 10^{-3}$. The upper limit of $\alpha$ for
+the onset of gravitational instability is derived as a function of the disk
+parameters. We discuss the implications of the gravitational instability for
+planetesimal formation.
+",0,1,0,0,0,0
+1562,Localization landscape theory of disorder in semiconductors II: Urbach tails of disordered quantum well layers,"  Urbach tails in semiconductors are often associated to effects of
+compositional disorder. The Urbach tail observed in InGaN alloy quantum wells
+of solar cells and LEDs by biased photocurrent spectroscopy is shown to be
+characteristic of the ternary alloy disorder. The broadening of the absorption
+edge observed for quantum wells emitting from violet to green (indium content
+ranging from 0 to 28\%) corresponds to a typical Urbach energy of 20~meV. A 3D
+absorption model is developed based on a recent theory of disorder-induced
+localization which provides the effective potential seen by the localized
+carriers without having to resort to the solution of the Schrödinger equation
+in a disordered potential. This model incorporating compositional disorder
+accounts well for the experimental broadening of the Urbach tail of the
+absorption edge. For energies below the Urbach tail of the InGaN quantum wells,
+type-II well-to-barrier transitions are observed and modeled. This contribution
+to the below bandgap absorption is particularly efficient in near-UV emitting
+quantum wells. When reverse biasing the device, the well-to-barrier below
+bandgap absorption exhibits a red shift, while the Urbach tail corresponding to
+the absorption within the quantum wells is blue shifted, due to the partial
+compensation of the internal piezoelectric fields by the external bias. The
+good agreement between the measured Urbach tail and its modeling by the new
+localization theory demonstrates the applicability of the latter to
+compositional disorder effects in nitride semiconductors.
+",0,1,0,0,0,0
+1563,Global teleconnectivity structures of the El Niño-Southern Oscillation and large volcanic eruptions -- An evolving network perspective,"  Recent work has provided ample evidence that global climate dynamics at
+time-scales between multiple weeks and several years can be severely affected
+by the episodic occurrence of both, internal (climatic) and external
+(non-climatic) perturbations. Here, we aim to improve our understanding on how
+regional to local disruptions of the ""normal"" state of the global surface air
+temperature field affect the corresponding global teleconnectivity structure.
+Specifically, we present an approach to quantify teleconnectivity based on
+different characteristics of functional climate network analysis. Subsequently,
+we apply this framework to study the impacts of different phases of the El
+Niño-Southern Oscillation (ENSO) as well as the three largest volcanic
+eruptions since the mid 20th century on the dominating spatiotemporal
+co-variability patterns of daily surface air temperatures. Our results confirm
+the existence of global effects of ENSO which result in episodic breakdowns of
+the hierarchical organization of the global temperature field. This is
+associated with the emergence of strong teleconnections. At more regional
+scales, similar effects are found after major volcanic eruptions. Taken
+together, the resulting time-dependent patterns of network connectivity allow a
+tracing of the spatial extents of the dominating effects of both types of
+climate disruptions. We discuss possible links between these observations and
+general aspects of atmospheric circulation.
+",0,1,0,0,0,0
+1564,Unbiased Shrinkage Estimation,"  Shrinkage estimation usually reduces variance at the cost of bias. But when
+we care only about some parameters of a model, I show that we can reduce
+variance without incurring bias if we have additional information about the
+distribution of covariates. In a linear regression model with homoscedastic
+Normal noise, I consider shrinkage estimation of the nuisance parameters
+associated with control variables. For at least three control variables and
+exogenous treatment, I establish that the standard least-squares estimator is
+dominated with respect to squared-error loss in the treatment effect even among
+unbiased estimators and even when the target parameter is low-dimensional. I
+construct the dominating estimator by a variant of James-Stein shrinkage in a
+high-dimensional Normal-means problem. It can be interpreted as an invariant
+generalized Bayes estimator with an uninformative (improper) Jeffreys prior in
+the target parameter.
+",0,0,1,1,0,0
+1565,Characterizing The Influence of Continuous Integration. Empirical Results from 250+ Open Source and Proprietary Projects,"  Continuous integration (CI) tools integrate code changes by automatically
+compiling, building, and executing test cases upon submission of code changes.
+Use of CI tools is getting increasingly popular, yet how proprietary projects
+reap the benefits of CI remains unknown. To investigate the influence of CI on
+software development, we analyze 150 open source software (OSS) projects, and
+123 proprietary projects. For OSS projects, we observe the expected benefits
+after CI adoption, e.g., improvements in bug and issue resolution. However, for
+the proprietary projects, we cannot make similar observations. Our findings
+indicate that only adoption of CI might not be enough to the improve software
+development process. CI can be effective for software development if
+practitioners use CI's feedback mechanism efficiently, by applying the practice
+of making frequent commits. For our set of proprietary projects we observe
+practitioners commit less frequently, and hence not use CI effectively for
+obtaining feedback on the submitted code changes. Based on our findings we
+recommend industry practitioners to adopt the best practices of CI to reap the
+benefits of CI tools for example, making frequent commits.
+",1,0,0,0,0,0
+1566,Why Abeta42 Is Much More Toxic Than Abeta40,"  Amyloid precursor with 770 amino acids dimerizes and aggregates, as do its c
+terminal 99 amino acids and amyloid 40,42 amino acids fragments. The titled
+question has been discussed extensively, and here it is addressed further using
+thermodynamic scaling theory to analyze mutational trends in structural factors
+and kinetics. Special attention is given to Family Alzheimer's Disease
+mutations outside amyloid 42. The scaling analysis is connected to extensive
+docking simulations which included membranes, thereby confirming their results
+and extending them to Amyloid precursor.
+",0,0,0,0,1,0
+1567,A Polynomial Time Algorithm for Spatio-Temporal Security Games,"  An ever-important issue is protecting infrastructure and other valuable
+targets from a range of threats from vandalism to theft to piracy to terrorism.
+The ""defender"" can rarely afford the needed resources for a 100% protection.
+Thus, the key question is, how to provide the best protection using the limited
+available resources. We study a practically important class of security games
+that is played out in space and time, with targets and ""patrols"" moving on a
+real line. A central open question here is whether the Nash equilibrium (i.e.,
+the minimax strategy of the defender) can be computed in polynomial time. We
+resolve this question in the affirmative. Our algorithm runs in time polynomial
+in the input size, and only polylogarithmic in the number of possible patrol
+locations (M). Further, we provide a continuous extension in which patrol
+locations can take arbitrary real values. Prior work obtained polynomial-time
+algorithms only under a substantial assumption, e.g., a constant number of
+rounds. Further, all these algorithms have running times polynomial in M, which
+can be very large.
+",1,0,0,0,0,0
+1568,TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent,"  In this paper, we introduce a method for adapting the step-sizes of temporal
+difference (TD) learning. The performance of TD methods often depends on well
+chosen step-sizes, yet few algorithms have been developed for setting the
+step-size automatically for TD learning. An important limitation of current
+methods is that they adapt a single step-size shared by all the weights of the
+learning system. A vector step-size enables greater optimization by specifying
+parameters on a per-feature basis. Furthermore, adapting parameters at
+different rates has the added benefit of being a simple form of representation
+learning. We generalize Incremental Delta Bar Delta (IDBD)---a vectorized
+adaptive step-size method for supervised learning---to TD learning, which we
+name TIDBD. We demonstrate that TIDBD is able to find appropriate step-sizes in
+both stationary and non-stationary prediction tasks, outperforming ordinary TD
+methods and TD methods with scalar step-size adaptation; we demonstrate that it
+can differentiate between features which are relevant and irrelevant for a
+given task, performing representation learning; and we show on a real-world
+robot prediction task that TIDBD is able to outperform ordinary TD methods and
+TD methods augmented with AlphaBound and RMSprop.
+",0,0,0,1,0,0
+1569,Enhanced clustering tendency of Cu-impurities with a number of oxygen vacancies in heavy carbon-loaded TiO2 - the bulk and surface morphologies,"  The over threshold carbon-loadings (~50 at.%) of initial TiO2-hosts and
+posterior Cu-sensitization (~7 at.%) was made using pulsed ion-implantation
+technique in sequential mode with 1 hour vacuum-idle cycle between sequential
+stages of embedding. The final Cx-TiO2:Cu samples were qualified using XPS
+wide-scan elemental analysis, core-levels and valence band mappings. The
+results obtained were discussed on the theoretic background employing
+DFT-calculations. The combined XPS and DFT analysis allows to establish and
+prove the final formula of the synthesized samples as Cx-TiO2:[Cu+][Cu2+] for
+the bulk and Cx-TiO2:[Cu+][Cu0] for thin-films. It was demonstrated the in the
+mode of heavy carbon-loadings the remaining majority of neutral C-C bonds
+(sp3-type) is dominating and only a lack of embedded carbon is fabricating the
+O-C=O clusters. No valence base-band width altering was established after
+sequential carbon-copper modification of the atomic structure of initial
+TiO2-hosts except the dominating majority of Cu 3s states after
+Cu-sensitization. The crucial role of neutral carbon low-dimensional impurities
+as the precursors for the new phases growth was shown for Cu-sensitized Cx-TiO2
+intermediate-state hosts.
+",0,1,0,0,0,0
+1570,On Controllable Abundance Of Saturated-input Linear Discrete Systems,"  Several theorems on the volume computing of the polyhedron spanned by a
+n-dimensional vector set with the finite-interval parameters are presented and
+proved firstly, and then are used in the analysis of the controllable regions
+of the linear discrete time-invariant systems with saturated inputs. A new
+concept and continuous measure on the control ability, control efficiency of
+the input variables, and the diversity of the control laws, named as the
+controllable abundance, is proposed based on the volume computing of the
+regions and is applied to the actuator placing and configuring problems, the
+optimizing problems of dynamics and kinematics of the controlled plants, etc..
+The numerical experiments show the effectiveness of the new concept and methods
+for investigating and optimizing the control ability and efficiency.
+",1,0,1,0,0,0
+1571,Localization and dynamics of sulfur-oxidizing microbes in natural sediment,"  Organic material in anoxic sediment represents a globally significant carbon
+reservoir that acts to stabilize Earth's atmospheric composition. The dynamics
+by which microbes organize to consume this material remain poorly understood.
+Here we observe the collective dynamics of a microbial community, collected
+from a salt marsh, as it comes to steady state in a two-dimensional ecosystem,
+covered by flowing water and under constant illumination. Microbes form a very
+thin front at the oxic-anoxic interface that moves towards the surface with
+constant velocity and comes to rest at a fixed depth. Fronts are stable to all
+perturbations while in the sediment, but develop bioconvective plumes in water.
+We observe the transient formation of parallel fronts. We model these dynamics
+to understand how they arise from the coupling between metabolism, aerotaxis,
+and diffusion. These results identify the typical timescale for the oxygen flux
+and penetration depth to reach steady state.
+",0,1,0,0,0,0
+1572,Probabilistic Surfel Fusion for Dense LiDAR Mapping,"  With the recent development of high-end LiDARs, more and more systems are
+able to continuously map the environment while moving and producing spatially
+redundant information. However, none of the previous approaches were able to
+effectively exploit this redundancy in a dense LiDAR mapping problem. In this
+paper, we present a new approach for dense LiDAR mapping using probabilistic
+surfel fusion. The proposed system is capable of reconstructing a high-quality
+dense surface element (surfel) map from spatially redundant multiple views.
+This is achieved by a proposed probabilistic surfel fusion along with a
+geometry considered data association. The proposed surfel data association
+method considers surface resolution as well as high measurement uncertainty
+along its beam direction which enables the mapping system to be able to control
+surface resolution without introducing spatial digitization. The proposed
+fusion method successfully suppresses the map noise level by considering
+measurement noise caused by laser beam incident angle and depth distance in a
+Bayesian filtering framework. Experimental results with simulated and real data
+for the dense surfel mapping prove the ability of the proposed method to
+accurately find the canonical form of the environment without further
+post-processing.
+",1,0,0,0,0,0
+1573,Quantum Paramagnet and Frustrated Quantum Criticality in a Spin-One Diamond Lattice Antiferromagnet,"  Motivated by the proposal of topological quantum paramagnet in the diamond
+lattice antiferromagnet NiRh$_2$O$_4$, we propose a minimal model to describe
+the magnetic interaction and properties of the diamond material with the
+spin-one local moments. Our model includes the first and second neighbor
+Heisenberg interactions as well as a local single-ion spin anisotropy that is
+allowed by the spin-one nature of the local moment and the tetragonal symmetry
+of the system. We point out that there exists a quantum phase transition from a
+trivial quantum paramagnet when the single-ion spin anisotropy is dominant to
+the magnetic ordered states when the exchange is dominant. Due to the
+frustrated spin interaction, the magnetic excitation in the quantum
+paramagnetic state supports extensively degenerate band minima in the spectra.
+As the system approaches the transition, extensively degenerate bosonic modes
+become critical at the criticality, giving rise to unusual magnetic properties.
+Our phase diagram and experimental predictions for different phases provide a
+guildeline for the identification of the ground state for NiRh$_2$O$_4$.
+Although our results are fundamentally different from the proposal of
+topological quantum paramagnet, it represents interesting possibilities for
+spin-one diamond lattice antiferromagnets.
+",0,1,0,0,0,0
+1574,Characterizations of minimal dominating sets and the well-dominated property in lexicographic product graphs,"  A graph is said to be well-dominated if all its minimal dominating sets are
+of the same size. The class of well-dominated graphs forms a subclass of the
+well studied class of well-covered graphs. While the recognition problem for
+the class of well-covered graphs is known to be co-NP-complete, the recognition
+complexity of well-dominated graphs is open.
+In this paper we introduce the notion of an irreducible dominating set, a
+variant of dominating set generalizing both minimal dominating sets and minimal
+total dominating sets. Based on this notion, we characterize the family of
+minimal dominating sets in a lexicographic product of two graphs and derive a
+characterization of the well-dominated lexicographic product graphs. As a side
+result motivated by this study, we give a polynomially testable
+characterization of well-dominated graphs with domination number two, and show,
+more generally, that well-dominated graphs can be recognized in polynomial time
+in any class of graphs with bounded domination number. Our results include a
+characterization of dominating sets in lexicographic product graphs, which
+generalizes the expression for the domination number of such graphs following
+from works of Zhang et al. (2011) and of Šumenjak et al. (2012).
+",1,0,1,0,0,0
+1575,To the Acceleration of Charged Particles with Travelling Laser Focus,"  We describe here the latest results of calculations with FlexPDE code of
+wake-fields induced by the bunch in micro-structures. These structures,
+illuminated by swept laser bust, serve for acceleration of charged particles.
+The basis of the scheme is a fast sweeping device for the laser bunch. After
+sweeping, the laser bunch has a slope ~45o with respect to the direction of
+propagation. So the every cell of the microstructure becomes excited locally
+only for the moment when the particles are there. Self-consistent parameters of
+collider based on this idea allow consideration this type of collider as a
+candidate for the near-future accelerator era.
+",0,1,0,0,0,0
+1576,Affiliation networks with an increasing degree sequence,"  Affiliation network is one kind of two-mode social network with two different
+sets of nodes (namely, a set of actors and a set of social events) and edges
+representing the affiliation of the actors with the social events. Although a
+number of statistical models are proposed to analyze affiliation networks, the
+asymptotic behaviors of the estimator are still unknown or have not been
+properly explored. In this paper, we study an affiliation model with the degree
+sequence as the exclusively natural sufficient statistic in the exponential
+family distributions. We establish the uniform consistency and asymptotic
+normality of the maximum likelihood estimator when the numbers of actors and
+events both go to infinity. Simulation studies and a real data example
+demonstrate our theoretical results.
+",0,0,1,1,0,0
+1577,Coarse Grained Parallel Selection,"  We analyze the running time of the Saukas-Song algorithm for selection on a
+coarse grained multicomputer without expressing the running time in terms of
+communication rounds. This shows that while in the best case the Saukas-Song
+algorithm runs in asymptotically optimal time, in general it does not. We
+propose other algorithms for coarse grained selection that have optimal
+expected running time.
+",1,0,0,0,0,0
+1578,An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning,"  Agent-based Internet of Things (IoT) applications have recently emerged as
+applications that can involve sensors, wireless devices, machines and software
+that can exchange data and be accessed remotely. Such applications have been
+proposed in several domains including health care, smart cities and
+agriculture. However, despite their increased adoption, deploying these
+applications in specific settings has been very challenging because of the
+complex static and dynamic variability of the physical devices such as sensors
+and actuators, the software application behavior and the environment in which
+the application is embedded. In this paper, we propose a modeling approach for
+IoT analytics based on learning embodied agents (i.e. situated agents). The
+approach involves: (i) a variability model of IoT embodied agents; (ii)
+feedback evaluative machine learning; and (iii) reconfiguration of a group of
+agents in accordance with environmental context. The proposed approach advances
+the state of the art in that it facilitates the development of Agent-based IoT
+applications by explicitly capturing their complex and dynamic variabilities
+and supporting their self-configuration based on an context-aware and machine
+learning-based approach.
+",1,0,0,0,0,0
+1579,Aggregation of Classifiers: A Justifiable Information Granularity Approach,"  In this study, we introduce a new approach to combine multi-classifiers in an
+ensemble system. Instead of using numeric membership values encountered in
+fixed combining rules, we construct interval membership values associated with
+each class prediction at the level of meta-data of observation by using
+concepts of information granules. In the proposed method, uncertainty
+(diversity) of findings produced by the base classifiers is quantified by
+interval-based information granules. The discriminative decision model is
+generated by considering both the bounds and the length of the obtained
+intervals. We select ten and then fifteen learning algorithms to build a
+heterogeneous ensemble system and then conducted the experiment on a number of
+UCI datasets. The experimental results demonstrate that the proposed approach
+performs better than the benchmark algorithms including six fixed combining
+methods, one trainable combining method, AdaBoost, Bagging, and Random
+Subspace.
+",1,0,0,1,0,0
+1580,FRET-based nanocommunication with luciferase and channelrhodopsin molecules for in-body medical systems,"  The paper is concerned with an in-body system gathering data for medical
+purposes. It is focused on communication between the following two components
+of the system: liposomes gathering the data inside human veins and a detector
+collecting the data from liposomes. Foerster Resonance Energy Transfer (FRET)
+is considered as a mechanism for communication between the system components.
+The usage of bioluminescent molecules as an energy source for generating FRET
+signals is suggested and the performance evaluation of this approach is given.
+FRET transmission may be initiated without an aid of an external laser, which
+is crucial in case of communication taking place inside of human body. It is
+also shown how to solve the problem of FRET signals recording. The usage of
+channelrhodopsin molecules, able to receive FRET signals and convert them into
+voltage, is proposed. The communication system is modelled with molecular
+structures and spectral characteristics of the proposed molecules and further
+validated by using Monte Carlo computer simulations, calculating the data
+throughput and the bit error rate.
+",0,0,0,0,1,0
+1581,FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search,"  We present FLASH (\textbf{F}ast \textbf{L}SH \textbf{A}lgorithm for
+\textbf{S}imilarity search accelerated with \textbf{H}PC), a similarity search
+system for ultra-high dimensional datasets on a single machine, that does not
+require similarity computations and is tailored for high-performance computing
+platforms. By leveraging a LSH style randomized indexing procedure and
+combining it with several principled techniques, such as reservoir sampling,
+recent advances in one-pass minwise hashing, and count based estimations, we
+reduce the computational and parallelization costs of similarity search, while
+retaining sound theoretical guarantees.
+We evaluate FLASH on several real, high-dimensional datasets from different
+domains, including text, malicious URL, click-through prediction, social
+networks, etc. Our experiments shed new light on the difficulties associated
+with datasets having several million dimensions. Current state-of-the-art
+implementations either fail on the presented scale or are orders of magnitude
+slower than FLASH. FLASH is capable of computing an approximate k-NN graph,
+from scratch, over the full webspam dataset (1.3 billion nonzeros) in less than
+10 seconds. Computing a full k-NN graph in less than 10 seconds on the webspam
+dataset, using brute-force ($n^2D$), will require at least 20 teraflops. We
+provide CPU and GPU implementations of FLASH for replicability of our results.
+",1,0,0,0,0,0
+1582,Neural Sequence Model Training via $α$-divergence Minimization,"  We propose a new neural sequence model training method in which the objective
+function is defined by $\alpha$-divergence. We demonstrate that the objective
+function generalizes the maximum-likelihood (ML)-based and reinforcement
+learning (RL)-based objective functions as special cases (i.e., ML corresponds
+to $\alpha \to 0$ and RL to $\alpha \to1$). We also show that the gradient of
+the objective function can be considered a mixture of ML- and RL-based
+objective gradients. The experimental results of a machine translation task
+show that minimizing the objective function with $\alpha > 0$ outperforms
+$\alpha \to 0$, which corresponds to ML-based methods.
+",1,0,0,1,0,0
+1583,Output Range Analysis for Deep Neural Networks,"  Deep neural networks (NN) are extensively used for machine learning tasks
+such as image classification, perception and control of autonomous systems.
+Increasingly, these deep NNs are also been deployed in high-assurance
+applications. Thus, there is a pressing need for developing techniques to
+verify neural networks to check whether certain user-expected properties are
+satisfied. In this paper, we study a specific verification problem of computing
+a guaranteed range for the output of a deep neural network given a set of
+inputs represented as a convex polyhedron. Range estimation is a key primitive
+for verifying deep NNs. We present an efficient range estimation algorithm that
+uses a combination of local search and linear programming problems to
+efficiently find the maximum and minimum values taken by the outputs of the NN
+over the given input set. In contrast to recently proposed ""monolithic""
+optimization approaches, we use local gradient descent to repeatedly find and
+eliminate local minima of the function. The final global optimum is certified
+using a mixed integer programming instance. We implement our approach and
+compare it with Reluplex, a recently proposed solver for deep neural networks.
+We demonstrate the effectiveness of the proposed approach for verification of
+NNs used in automated control as well as those used in classification.
+",1,0,0,1,0,0
+1584,Projection Theorems of Divergences and Likelihood Maximization Methods,"  Projection theorems of divergences enable us to find reverse projection of a
+divergence on a specific statistical model as a forward projection of the
+divergence on a different but rather ""simpler"" statistical model, which, in
+turn, results in solving a system of linear equations. Reverse projection of
+divergences are closely related to various estimation methods such as the
+maximum likelihood estimation or its variants in robust statistics. We consider
+projection theorems of three parametric families of divergences that are widely
+used in robust statistics, namely the Rényi divergences (or the Cressie-Reed
+power divergences), density power divergences, and the relative
+$\alpha$-entropy (or the logarithmic density power divergences). We explore
+these projection theorems from the usual likelihood maximization approach and
+from the principle of sufficiency. In particular, we show the equivalence of
+solving the estimation problems by the projection theorems of the respective
+divergences and by directly solving the corresponding estimating equations. We
+also derive the projection theorem for the density power divergences.
+",0,0,1,1,0,0
+1585,Optimal Timing in Dynamic and Robust Attacker Engagement During Advanced Persistent Threats,"  Advanced persistent threats (APTs) are stealthy attacks which make use of
+social engineering and deception to give adversaries insider access to
+networked systems. Against APTs, active defense technologies aim to create and
+exploit information asymmetry for defenders. In this paper, we study a scenario
+in which a powerful defender uses honeynets for active defense in order to
+observe an attacker who has penetrated the network. Rather than immediately
+eject the attacker, the defender may elect to gather information. We introduce
+an undiscounted, infinite-horizon Markov decision process on a continuous state
+space in order to model the defender's problem. We find a threshold of
+information that the defender should gather about the attacker before ejecting
+him. Then we study the robustness of this policy using a Stackelberg game.
+Finally, we simulate the policy for a conceptual network. Our results provide a
+quantitative foundation for studying optimal timing for attacker engagement in
+network defense.
+",1,0,0,0,0,0
+1586,The Mismeasure of Mergers: Revised Limits on Self-interacting Dark Matter in Merging Galaxy Clusters,"  In an influential recent paper, Harvey et al (2015) derive an upper limit to
+the self-interaction cross section of dark matter ($\sigma_{\rm DM} < 0.47$
+cm$^2$/g at 95\% confidence) by averaging the dark matter-galaxy offsets in a
+sample of merging galaxy clusters. Using much more comprehensive data on the
+same clusters, we identify several substantial errors in their offset
+measurements. Correcting these errors relaxes the upper limit on $\sigma_{\rm
+DM}$ to $\lesssim 2$ cm$^2$/g, following the Harvey et al prescription for
+relating offsets to cross sections in a simple solid body scattering model.
+Furthermore, many clusters in the sample violate the assumptions behind this
+prescription, so even this revised upper limit should be used with caution.
+Although this particular sample does not tightly constrain self-interacting
+dark matter models when analyzed this way, we discuss how merger ensembles may
+be used more effectively in the future. We conclude that errors inherent in
+using single-band imaging to identify mass and light peaks do not necessarily
+average out in a sample of this size, particularly when a handful of
+substructures constitute a majority of the weight in the ensemble.
+",0,1,0,0,0,0
+1587,International crop trade networks: The impact of shocks and cascades,"  Analyzing available FAO data from 176 countries over 21 years, we observe an
+increase of complexity in the international trade of maize, rice, soy, and
+wheat. A larger number of countries play a role as producers or intermediaries,
+either for trade or food processing. In consequence, we find that the trade
+networks become more prone to failure cascades caused by exogenous shocks. In
+our model, countries compensate for demand deficits by imposing export
+restrictions. To capture these, we construct higher-order trade dependency
+networks for the different crops and years. These networks reveal hidden
+dependencies between countries and allow to discuss policy implications.
+",0,0,0,0,0,1
+1588,Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes,"  Debate and deliberation play essential roles in politics and government, but
+most models presume that debates are won mainly via superior style or agenda
+control. Ideally, however, debates would be won on the merits, as a function of
+which side has the stronger arguments. We propose a predictive model of debate
+that estimates the effects of linguistic features and the latent persuasive
+strengths of different topics, as well as the interactions between the two.
+Using a dataset of 118 Oxford-style debates, our model's combination of content
+(as latent topics) and style (as linguistic features) allows us to predict
+audience-adjudicated winners with 74% accuracy, significantly outperforming
+linguistic features alone (66%). Our model finds that winning sides employ
+stronger arguments, and allows us to identify the linguistic features
+associated with strong or weak arguments.
+",1,0,0,0,0,0
+1589,Gene regulatory networks: a primer in biological processes and statistical modelling,"  Modelling gene regulatory networks not only requires a thorough understanding
+of the biological system depicted but also the ability to accurately represent
+this system from a mathematical perspective. Throughout this chapter, we aim to
+familiarise the reader with the biological processes and molecular factors at
+play in the process of gene expression regulation.We first describe the
+different interactions controlling each step of the expression process, from
+transcription to mRNA and protein decay. In the second section, we provide
+statistical tools to accurately represent this biological complexity in the
+form of mathematical models. Amongst other considerations, we discuss the
+topological properties of biological networks, the application of deterministic
+and stochastic frameworks and the quantitative modelling of regulation. We
+particularly focus on the use of such models for the simulation of expression
+data that can serve as a benchmark for the testing of network inference
+algorithms.
+",0,0,0,1,1,0
+1590,Mathematical Knowledge and the Role of an Observer: Ontological and epistemological aspects,"  As David Berlinski writes (1997), the existence and nature of mathematics is
+a more compelling and far deeper problem than any of the problems raised by
+mathematics itself. Here we analyze the essence of mathematics making the main
+emphasis on mathematics as an advanced system of knowledge. This knowledge
+consists of structures and represents structures, existence of which depends on
+observers in a nonstandard way. Structural nature of mathematics explains its
+reasonable effectiveness.
+",0,0,1,0,0,0
+1591,Persuasive Technology For Human Development: Review and Case Study,"  Technology is an extremely potent tool that can be leveraged for human
+development and social good. Owing to the great importance of environment and
+human psychology in driving human behavior, and the ubiquity of technology in
+modern life, there is a need to leverage the insights and capabilities of both
+fields together for nudging people towards a behavior that is optimal in some
+sense (personal or social). In this regard, the field of persuasive technology,
+which proposes to infuse technology with appropriate design and incentives
+using insights from psychology, behavioral economics, and human-computer
+interaction holds a lot of promise. Whilst persuasive technology is already
+being developed and is at play in many commercial applications, it can have the
+great social impact in the field of Information and Communication Technology
+for Development (ICTD) which uses Information and Communication Technology
+(ICT) for human developmental ends such as education and health. In this paper
+we will explore what persuasive technology is and how it can be used for the
+ends of human development. To develop the ideas in a concrete setting, we
+present a case study outlining how persuasive technology can be used for human
+development in Pakistan, a developing South Asian country, that suffers from
+many of the problems that plague typical developing country.
+",1,0,0,0,0,0
+1592,Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study,"  The central aim in this paper is to address variable selection questions in
+nonlinear and nonparametric regression. Motivated by statistical genetics,
+where nonlinear interactions are of particular interest, we introduce a novel
+and interpretable way to summarize the relative importance of predictor
+variables. Methodologically, we develop the ""RelATive cEntrality"" (RATE)
+measure to prioritize candidate genetic variants that are not just marginally
+important, but whose associations also stem from significant covarying
+relationships with other variants in the data. We illustrate RATE through
+Bayesian Gaussian process regression, but the methodological innovations apply
+to other ""black box"" methods. It is known that nonlinear models often exhibit
+greater predictive accuracy than linear models, particularly for phenotypes
+generated by complex genetic architectures. With detailed simulations and two
+real data association mapping studies, we show that applying RATE enables an
+explanation for this improved performance.
+",0,0,0,1,1,0
+1593,Activit{é} motrice des truies en groupes dans les diff{é}rents syst{è}mes de logement,"  Assessment of the motor activity of group-housed sows in commercial farms.
+The objective of this study was to specify the level of motor activity of
+pregnant sows housed in groups in different housing systems. Eleven commercial
+farms were selected for this study. Four housing systems were represented:
+small groups of five to seven sows (SG), free access stalls (FS) with exercise
+area, electronic sow feeder with a stable group (ESFsta) or a dynamic group
+(ESFdyn). Ten sows in mid-gestation were observed in each farm. The
+observations of motor activity were made for 6 hours at the first meal or at
+the start of the feeding sequence, two consecutive days and at regular
+intervals of 4 minutes. The results show that the motor activity of
+group-housed sows depends on the housing system. The activity is higher with
+the ESFdyn system (standing: 55.7%), sows are less active in the SG system
+(standing: 26.5%), and FS system is intermediate. The distance traveled by sows
+in ESF system is linked to a larger area available. Thus, sows travel an
+average of 362 m $\pm$ 167 m in the ESFdyn system with an average available
+surface of 446 m${}^2$ whereas sows in small groups travel 50 m $\pm$ 15 m for
+15 m${}^2$ available.
+",0,0,0,0,1,0
+1594,Linking High-Energy Cosmic Particles by Black-Hole Jets Embedded in Large-Scale Structures,"  The origin of ultrahigh-energy cosmic rays (UHECRs) is a half-century old
+enigma (Linsley 1963). The mystery has been deepened by an intriguing
+coincidence: over ten orders of magnitude in energy, the energy generation
+rates of UHECRs, PeV neutrinos, and isotropic sub-TeV gamma rays are
+comparable, which hints at a grand-unified picture (Murase and Waxman 2016).
+Here we report that powerful black hole jets in aggregates of galaxies can
+supply the common origin of all of these phenomena. Once accelerated by a jet,
+low-energy cosmic rays confined in the radio lobe are adiabatically cooled;
+higher-energy cosmic rays leaving the source interact with the magnetized
+cluster environment and produce neutrinos and gamma rays; the highest-energy
+particles escape from the host cluster and contribute to the observed cosmic
+rays above 100 PeV. The model is consistent with the spectrum, composition, and
+isotropy of the observed UHECRs, and also explains the IceCube neutrinos and
+the non-blazar component of the Fermi gamma-ray background, assuming a
+reasonable energy output from black hole jets in clusters.
+",0,1,0,0,0,0
+1595,Quantifying and suppressing ranking bias in a large citation network,"  It is widely recognized that citation counts for papers from different fields
+cannot be directly compared because different scientific fields adopt different
+citation practices. Citation counts are also strongly biased by paper age since
+older papers had more time to attract citations. Various procedures aim at
+suppressing these biases and give rise to new normalized indicators, such as
+the relative citation count. We use a large citation dataset from Microsoft
+Academic Graph and a new statistical framework based on the Mahalanobis
+distance to show that the rankings by well known indicators, including the
+relative citation count and Google's PageRank score, are significantly biased
+by paper field and age. We propose a general normalization procedure motivated
+by the $z$-score which produces much less biased rankings when applied to
+citation count and PageRank score.
+",1,1,0,1,0,0
+1596,Posterior Concentration for Bayesian Regression Trees and Forests,"  Since their inception in the 1980's, regression trees have been one of the
+more widely used non-parametric prediction methods. Tree-structured methods
+yield a histogram reconstruction of the regression surface, where the bins
+correspond to terminal nodes of recursive partitioning. Trees are powerful, yet
+susceptible to over-fitting. Strategies against overfitting have traditionally
+relied on pruning greedily grown trees. The Bayesian framework offers an
+alternative remedy against overfitting through priors. Roughly speaking, a good
+prior charges smaller trees where overfitting does not occur. While the
+consistency of random histograms, trees and their ensembles has been studied
+quite extensively, the theoretical understanding of the Bayesian counterparts
+has been missing. In this paper, we take a step towards understanding why/when
+do Bayesian trees and their ensembles not overfit. To address this question, we
+study the speed at which the posterior concentrates around the true smooth
+regression function. We propose a spike-and-tree variant of the popular
+Bayesian CART prior and establish new theoretical results showing that
+regression trees (and their ensembles) (a) are capable of recovering smooth
+regression surfaces, achieving optimal rates up to a log factor, (b) can adapt
+to the unknown level of smoothness and (c) can perform effective dimension
+reduction when p>n. These results provide a piece of missing theoretical
+evidence explaining why Bayesian trees (and additive variants thereof) have
+worked so well in practice.
+",0,0,1,1,0,0
+1597,Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques,"  Oral Disintegrating Tablets (ODTs) is a novel dosage form that can be
+dissolved on the tongue within 3min or less especially for geriatric and
+pediatric patients. Current ODT formulation studies usually rely on the
+personal experience of pharmaceutical experts and trial-and-error in the
+laboratory, which is inefficient and time-consuming. The aim of current
+research was to establish the prediction model of ODT formulations with direct
+compression process by Artificial Neural Network (ANN) and Deep Neural Network
+(DNN) techniques. 145 formulation data were extracted from Web of Science. All
+data sets were divided into three parts: training set (105 data), validation
+set (20) and testing set (20). ANN and DNN were compared for the prediction of
+the disintegrating time. The accuracy of the ANN model has reached 85.60%,
+80.00% and 75.00% on the training set, validation set and testing set
+respectively, whereas that of the DNN model was 85.60%, 85.00% and 80.00%,
+respectively. Compared with the ANN, DNN showed the better prediction for ODT
+formulations. It is the first time that deep neural network with the improved
+dataset selection algorithm is applied to formulation prediction on small data.
+The proposed predictive approach could evaluate the critical parameters about
+quality control of formulation, and guide research and process development. The
+implementation of this prediction model could effectively reduce drug product
+development timeline and material usage, and proactively facilitate the
+development of a robust drug product.
+",0,0,0,1,0,0
+1598,HNCcorr: A Novel Combinatorial Approach for Cell Identification in Calcium-Imaging Movies,"  Calcium imaging has emerged as a workhorse method in neuroscience to
+investigate patterns of neuronal activity. Instrumentation to acquire calcium
+imaging movies has rapidly progressed and has become standard across labs.
+Still, algorithms to automatically detect and extract activity signals from
+calcium imaging movies are highly variable from~lab~to~lab and more advanced
+algorithms are continuously being developed. Here we present HNCcorr, a novel
+algorithm for cell identification in calcium imaging movies based on
+combinatorial optimization. The algorithm identifies cells by finding distinct
+groups of highly similar pixels in correlation space, where a pixel is
+represented by the vector of correlations to a set of other pixels. The HNCcorr
+algorithm achieves the best known results for the cell identification benchmark
+of Neurofinder, and guarantees an optimal solution to the underlying
+deterministic optimization model resulting in a transparent mapping from input
+data to outcome.
+",0,0,1,0,0,0
+1599,Intense cross-tail field-aligned currents in the plasma sheet at lunar distances,"  Field-aligned currents in the Earth's magnetotail are traditionally
+associated with transient plasma flows and strong plasma pressure gradients in
+the near-Earth side. In this paper we demonstrate a new field-aligned current
+system present at the lunar orbit tail. Using magnetotail current sheet
+observations by two ARTEMIS probes at $\sim60 R_E$, we analyze statistically
+the current sheet structure and current density distribution closest to the
+neutral sheet. For about half of our 130 current sheet crossings, the
+equatorial magnetic field component across-the tail (along the main, cross-tail
+current) contributes significantly to the vertical pressure balance. This
+magnetic field component peaks at the equator, near the cross-tail current
+maximum. For those cases, a significant part of the tail current, having an
+intensity in the range 1-10nA/m$^2$, flows along the magnetic field lines (it
+is both field-aligned and cross-tail). We suggest that this current system
+develops in order to compensate the thermal pressure by particles that on its
+own is insufficient to fend off the lobe magnetic pressure.
+",0,1,0,0,0,0
+1600,First non-icosahedral boron allotrope synthesized at high pressure and high temperature,"  Theoretical predictions of pressure-induced phase transformations often
+become long-standing enigmas because of limitations of contemporary available
+experimental possibilities. Hitherto the existence of a non-icosahedral boron
+allotrope has been one of them. Here we report on the first non-icosahedral
+boron allotrope, which we denoted as {\zeta}-B, with the orthorhombic
+{\alpha}-Ga-type structure (space group Cmce) synthesized in a diamond anvil
+cell at extreme high-pressure high-temperature conditions (115 GPa and 2100 K).
+The structure of {\zeta}-B was solved using single-crystal synchrotron X-ray
+diffraction and its compressional behavior was studied in the range of very
+high pressures (115 GPa to 135 GPa). Experimental validation of theoretical
+predictions reveals the degree of our up-to-date comprehension of condensed
+matter and promotes further development of the solid state physics and
+chemistry.
+",0,1,0,0,0,0
+1601,A Result of Uniqueness of Solutions of the Shigesada-Kawasaki-Teramoto Equations,"  We derive the uniqueness of weak solutions to the Shigesada-Kawasaki-Teramoto
+(SKT) systems using the adjoint problem argument. Combining with [PT17] we then
+derive the well-posedness for the SKT systems in space dimension $d\le 4$
+",0,0,1,0,0,0
+1602,Mind the Gap: A Well Log Data Analysis,"  The main task in oil and gas exploration is to gain an understanding of the
+distribution and nature of rocks and fluids in the subsurface. Well logs are
+records of petro-physical data acquired along a borehole, providing direct
+information about what is in the subsurface. The data collected by logging
+wells can have significant economic consequences, due to the costs inherent to
+drilling wells, and the potential return of oil deposits. In this paper, we
+describe preliminary work aimed at building a general framework for well log
+prediction.
+First, we perform a descriptive and exploratory analysis of the gaps in the
+neutron porosity logs of more than a thousand wells in the North Sea. Then, we
+generate artificial gaps in the neutron logs that reflect the statistics
+collected before. Finally, we compare Artificial Neural Networks, Random
+Forests, and three algorithms of Linear Regression in the prediction of missing
+gaps on a well-by-well basis.
+",1,0,0,1,0,0
+1603,Inconsistency of Template Estimation with the Fr{é}chet mean in Quotient Space,"  We tackle the problem of template estimation when data have been randomly
+transformed under an isometric group action in the presence of noise. In order
+to estimate the template, one often minimizes the variance when the influence
+of the transformations have been removed (computation of the Fr{é}chet mean
+in quotient space). The consistency bias is defined as the distance (possibly
+zero) between the orbit of the template and the orbit of one element which
+minimizes the variance. In this article we establish an asymptotic behavior of
+the consistency bias with respect to the noise level. This behavior is linear
+with respect to the noise level. As a result the inconsistency is unavoidable
+as soon as the noise is large enough. In practice, the template estimation with
+a finite sample is often done with an algorithm called max-max. We show the
+convergence of this algorithm to an empirical Karcher mean. Finally, our
+numerical experiments show that the bias observed in practice cannot be
+attributed to the small sample size or to a convergence problem but is indeed
+due to the previously studied inconsistency.
+",0,0,1,1,0,0
+1604,All-optical switching and unidirectional plasmon launching with electron-hole plasma driven silicon nanoantennas,"  High-index dielectric nanoparticles have become a powerful platform for
+modern light science, enabling various fascinating applications, especially in
+nonlinear nanophotonics for which they enable special types of optical
+nonlinearity, such as electron-hole plasma photoexcitation, which are not
+inherent to plasmonic nanostructures. Here, we propose a novel geometry for
+highly tunable all-dielectric nanoantennas, consisting of a chain of silicon
+nanoparticles excited by an electric dipole source, which allows tuning their
+radiation properties via electron-hole plasma photoexcitation. We show that the
+slowly guided modes determining the Van Hove singularity of the nanoantenna are
+very sensitive to the nanoparticle permittivity, opening up the ability to
+utilize this effect for efficient all-optical modulation. We show that by
+pumping several boundary nanoparticles with relatively low intensities may
+cause dramatic variations in the nanoantenna radiation power patterns and
+Purcell factor. We also demonstrate that ultrafast pumping of the designed
+nanoantenna allows unidirectional launching of surface plasmon-polaritons, with
+interesting implications for modern nonlinear nanophotonics.
+",0,1,0,0,0,0
+1605,Group chasing tactics: how to catch a faster prey?,"  We propose a bio-inspired, agent-based approach to describe the natural
+phenomenon of group chasing in both two and three dimensions. Using a set of
+local interaction rules we created a continuous-space and discrete-time model
+with time delay, external noise and limited acceleration. We implemented a
+unique collective chasing strategy, optimized its parameters and studied its
+properties when chasing a much faster, erratic escaper. We show that collective
+chasing strategies can significantly enhance the chasers' success rate. Our
+realistic approach handles group chasing within closed, soft boundaries -
+contrasting most of those published in the literature with periodic ones -- and
+resembles several properties of pursuits observed in nature, such as the
+emergent encircling or the escaper's zigzag motion.
+",0,1,0,1,0,0
+1606,On solving a restricted linear congruence using generalized Ramanujan sums,"  Consider the linear congruence equation $x_1+\ldots+x_k \equiv b\,(\text{mod
+} n)$ for $b,n\in\mathbb{Z}$. By $(a,b)_s$, we mean the largest
+$l^s\in\mathbb{N}$ which divides $a$ and $b$ simultaneously. For each $d_j|n$,
+define $\mathcal{C}_{j,s} = \{1\leq x\leq n^s | (x,n^s)_s = d^s_j\}$. Bibak et
+al. gave a formula using Ramanujan sums for the number of solutions of the
+above congruence equation with some gcd restrictions on $x_i$. We generalize
+their result with generalized gcd restrictions on $x_i$ by proving that for the
+above linear congruence, the number of solutions is
+$$\frac{1}{n^s}\sum\limits_{d|n}c_{d,s}(b)\prod\limits_{j=1}^{\tau(n)}\left(c_{\frac{n}{d_j},s}(\frac{n^s}{d^s})\right)^{g_j}$$
+where $g_j = |\{x_1,\ldots, x_k\}\cap \mathcal{C}_{j,s}|$ for $j=1,\ldots
+\tau(n)$ and $c_{d,s}$ denote the generalized ramanujan sum defined by E.
+Cohen.
+",0,0,1,0,0,0
+1607,Magnetization spin dynamics in a (LuBi)3Fe5O12 (BLIG) epitaxial film,"  Bismuth substituted lutetium iron garnet (BLIG) films exhibit larger Faraday
+rotation, and have a higher Curie temperature than yttrium iron garnet. We have
+observed magnetic stripe domains and measured domain widths of 1.4 {\mu}{\mu}m
+using Fourier domain polarization microscopy, Faraday rotation experiments
+yield a coercive field of 5 Oe. These characterizations form the basis of
+micromagnetic simulations that allow us to estimate and compare spin wave
+excitations in BLIG films. We observed that these films support thermal magnons
+with a precessional frequency of 7 GHz with a line width of 400 MHz. Further,
+we studied the dependence of precessional frequency on the externally applied
+magnetic field. Brillouin light scattering experiments and precession
+frequencies predicted by simulations show similar trend with increasing field.
+",0,1,0,0,0,0
+1608,Probing for sparse and fast variable selection with model-based boosting,"  We present a new variable selection method based on model-based gradient
+boosting and randomly permuted variables. Model-based boosting is a tool to fit
+a statistical model while performing variable selection at the same time. A
+drawback of the fitting lies in the need of multiple model fits on slightly
+altered data (e.g. cross-validation or bootstrap) to find the optimal number of
+boosting iterations and prevent overfitting. In our proposed approach, we
+augment the data set with randomly permuted versions of the true variables, so
+called shadow variables, and stop the step-wise fitting as soon as such a
+variable would be added to the model. This allows variable selection in a
+single fit of the model without requiring further parameter tuning. We show
+that our probing approach can compete with state-of-the-art selection methods
+like stability selection in a high-dimensional classification benchmark and
+apply it on gene expression data for the estimation of riboflavin production of
+Bacillus subtilis.
+",0,0,0,1,0,0
+1609,A surface-hopping method for semiclassical calculations of cross sections for radiative association with electronic transitions,"  A semicalssical method based on surface-hopping techniques is developed to
+model the dynamics of radiative association with electronic transitions in
+arbitrary polyatomic systems. It can be proven that our method is an extension
+of the established semiclassical formula used in the characterization of
+diatomic molecule- formation. Our model is tested for diatomic molecules. It
+gives the same cross sections as the former semiclassical formula, but contrary
+to the former method it allows us to follow the fate of the trajectories after
+the emission of a photon. This means that we can characterize the rovibrational
+states of the stabilized molecules: using semiclassial quantization we can
+obtain quantum state resolved cross sections or emission spectra for the
+radiative association process. The calculated semiclassical state resolved
+spectra show good agreement with the result of quantum mechanical perturbation
+theory. Furthermore our surface-hopping model is not only applicable for the
+description of radiative association but it can be use for semiclassical
+characterization of any molecular process where spontaneous emission occurs.
+",0,1,0,0,0,0
+1610,Holographic coherent states from random tensor networks,"  Random tensor networks provide useful models that incorporate various
+important features of holographic duality. A tensor network is usually defined
+for a fixed graph geometry specified by the connection of tensors. In this
+paper, we generalize the random tensor network approach to allow quantum
+superposition of different spatial geometries. We set up a framework in which
+all possible bulk spatial geometries, characterized by weighted adjacent
+matrices of all possible graphs, are mapped to the boundary Hilbert space and
+form an overcomplete basis of the boundary. We name such an overcomplete basis
+as holographic coherent states. A generic boundary state can be expanded on
+this basis, which describes the state as a superposition of different spatial
+geometries in the bulk. We discuss how to define distinct classical geometries
+and small fluctuations around them. We show that small fluctuations around
+classical geometries define ""code subspaces"" which are mapped to the boundary
+Hilbert space isometrically with quantum error correction properties. In
+addition, we also show that the overlap between different geometries is
+suppressed exponentially as a function of the geometrical difference between
+the two geometries. The geometrical difference is measured in an area law
+fashion, which is a manifestation of the holographic nature of the states
+considered.
+",0,1,0,0,0,0
+1611,Persistent Spread Measurement for Big Network Data Based on Register Intersection,"  Persistent spread measurement is to count the number of distinct elements
+that persist in each network flow for predefined time periods. It has many
+practical applications, including detecting long-term stealthy network
+activities in the background of normal-user activities, such as stealthy DDoS
+attack, stealthy network scan, or faked network trend, which cannot be detected
+by traditional flow cardinality measurement. With big network data, one
+challenge is to measure the persistent spreads of a massive number of flows
+without incurring too much memory overhead as such measurement may be performed
+at the line speed by network processors with fast but small on-chip memory. We
+propose a highly compact Virtual Intersection HyperLogLog (VI-HLL) architecture
+for this purpose. It achieves far better memory efficiency than the best prior
+work of V-Bitmap, and in the meantime drastically extends the measurement
+range. Theoretical analysis and extensive experiments demonstrate that VI-HLL
+provides good measurement accuracy even in very tight memory space of less than
+1 bit per flow.
+",1,0,0,0,0,0
+1612,High-dimensional Linear Regression for Dependent Observations with Application to Nowcasting,"  In the last few years, an extensive literature has been focused on the
+$\ell_1$ penalized least squares (Lasso) estimators of high dimensional linear
+regression when the number of covariates $p$ is considerably larger than the
+sample size $n$. However, there is limited attention paid to the properties of
+the estimators when the errors or/and the covariates are serially dependent. In
+this study, we investigate the theoretical properties of the Lasso estimators
+for linear regression with random design under serially dependent and/or
+non-sub-Gaussian errors and covariates. In contrast to the traditional case in
+which the errors are i.i.d and have finite exponential moments, we show that
+$p$ can at most be a power of $n$ if the errors have only polynomial moments.
+In addition, the rate of convergence becomes slower due to the serial
+dependencies in errors and the covariates. We also consider sign consistency
+for model selection via Lasso when there are serial correlations in the errors
+or the covariates or both. Adopting the framework of functional dependence
+measure, we provide a detailed description on how the rates of convergence and
+the selection consistencies of the estimators depend on the dependence measures
+and moment conditions of the errors and the covariates. Simulation results show
+that Lasso regression can be substantially more powerful than the mixed
+frequency data sampling regression (MIDAS) in the presence of irrelevant
+variables. We apply the results obtained for the Lasso method to nowcasting
+mixing frequency data in which serially correlated errors and a large number of
+covariates are common. In real examples, the Lasso procedure outperforms the
+MIDAS in both forecasting and nowcasting.
+",0,0,1,1,0,0
+1613,Dynamic control of the optical emission from GaN/InGaN nanowire quantum dots by surface acoustic waves,"  The optical emission of InGaN quantum dots embedded in GaN nanowires is
+dynamically controlled by a surface acoustic wave (SAW). The emission energy of
+both the exciton and biexciton lines is modulated over a 1.5 meV range at ~330
+MHz. A small but systematic difference in the exciton and biexciton spectral
+modulation reveals a linear change of the biexciton binding energy with the SAW
+amplitude. The present results are relevant for the dynamic control of
+individual single photon emitters based on nitride semiconductors.
+",0,1,0,0,0,0
+1614,Robust Tracking and Behavioral Modeling of Movements of Biological Collectives from Ordinary Video Recordings,"  We propose a novel computational method to extract information about
+interactions among individuals with different behavioral states in a biological
+collective from ordinary video recordings. Assuming that individuals are acting
+as finite state machines, our method first detects discrete behavioral states
+of those individuals and then constructs a model of their state transitions,
+taking into account the positions and states of other individuals in the
+vicinity. We have tested the proposed method through applications to two
+real-world biological collectives: termites in an experimental setting and
+human pedestrians in a university campus. For each application, a robust
+tracking system was developed in-house, utilizing interactive human
+intervention (for termite tracking) or online agent-based simulation (for
+pedestrian tracking). In both cases, significant interactions were detected
+between nearby individuals with different states, demonstrating the
+effectiveness of the proposed method.
+",1,1,0,0,0,0
+1615,Understanding Membership Inferences on Well-Generalized Learning Models,"  Membership Inference Attack (MIA) determines the presence of a record in a
+machine learning model's training data by querying the model. Prior work has
+shown that the attack is feasible when the model is overfitted to its training
+data or when the adversary controls the training algorithm. However, when the
+model is not overfitted and the adversary does not control the training
+algorithm, the threat is not well understood. In this paper, we report a study
+that discovers overfitting to be a sufficient but not a necessary condition for
+an MIA to succeed. More specifically, we demonstrate that even a
+well-generalized model contains vulnerable instances subject to a new
+generalized MIA (GMIA). In GMIA, we use novel techniques for selecting
+vulnerable instances and detecting their subtle influences ignored by
+overfitting metrics. Specifically, we successfully identify individual records
+with high precision in real-world datasets by querying black-box machine
+learning models. Further we show that a vulnerable record can even be
+indirectly attacked by querying other related records and existing
+generalization techniques are found to be less effective in protecting the
+vulnerable instances. Our findings sharpen the understanding of the fundamental
+cause of the problem: the unique influences the training instance may have on
+the model.
+",0,0,0,1,0,0
+1616,Identification of Near-Infrared [Se III] and [Kr VI] Emission Lines in Planetary Nebulae,"  We identify [Se III] 1.0994 micron in the planetary nebula (PN) NGC 5315 and
+[Kr VI] 1.2330 micron in three PNe, from spectra obtained with the FIRE
+spectrometer on the 6.5-m Baade Telescope. Se and Kr are the two most
+widely-detected neutron-capture elements in astrophysical nebulae, and can be
+enriched by s-process nucleosynthesis in PN progenitor stars. The detection of
+[Se III] 1.0994 micron is particularly valuable when paired with observations
+of [Se IV] 2.2858 micron, as it can be used to improve the accuracy of nebular
+Se abundance determinations, and allows Se ionization correction factor (ICF)
+schemes to be empirically tested for the first time. We present new effective
+collision strength calculations for Se^{2+} and Kr^{5+}, which we use to
+compute ionic abundances. In NGC 5315, we find that the Se abundance computed
+from Se^{3+}/H^+ is lower than that determined with ICFs that incorporate
+Se^{2+}/H^+. We compute new Kr ICFs that take Kr^{5+}/H^+ into account, by
+fitting correlations found in grids of Cloudy models between Kr ionic fractions
+and those of more abundant elements, and use these to derive Kr abundances in
+four PNe. Observations of [Se III] and [Kr VI] in a larger sample of PNe, with
+a range of excitation levels, are needed to rigorously test the ICF
+prescriptions for Se and our new Kr ICFs.
+",0,1,0,0,0,0
+1617,On reduction of differential inclusions and Lyapunov stability,"  In this paper, locally Lipschitz regular functions are utilized to identify
+and remove infeasible directions from differential inclusions. The resulting
+reduced differential inclusion is point-wise smaller (in the sense of set
+containment) than the original differential inclusion. The reduced inclusion is
+utilized to develop a generalized notion of a derivative in the direction(s) of
+a set-valued map for locally Lipschitz candidate Lyapunov functions. The
+developed generalized derivative yields less conservative statements of
+Lyapunov stability results, invariance-like results, and Matrosov results for
+differential inclusions. Illustrative examples are included to demonstrate the
+utility of the developed stability theorems.
+",1,0,0,0,0,0
+1618,Deep Generative Networks For Sequence Prediction,"  This thesis investigates unsupervised time series representation learning for
+sequence prediction problems, i.e. generating nice-looking input samples given
+a previous history, for high dimensional input sequences by decoupling the
+static input representation from the recurrent sequence representation. We
+introduce three models based on Generative Stochastic Networks (GSN) for
+unsupervised sequence learning and prediction. Experimental results for these
+three models are presented on pixels of sequential handwritten digit (MNIST)
+data, videos of low-resolution bouncing balls, and motion capture data. The
+main contribution of this thesis is to provide evidence that GSNs are a viable
+framework to learn useful representations of complex sequential input data, and
+to suggest a new framework for deep generative models to learn complex
+sequences by decoupling static input representations from dynamic time
+dependency representations.
+",0,0,0,1,0,0
+1619,Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks,"  In this work, we aim at building a bridge from poor behavioral data to an
+effective, quick-response, and robust behavior model for online identity theft
+detection. We concentrate on this issue in online social networks (OSNs) where
+users usually have composite behavioral records, consisting of
+multi-dimensional low-quality data, e.g., offline check-ins and online user
+generated content (UGC). As an insightful result, we find that there is a
+complementary effect among different dimensions of records for modeling users'
+behavioral patterns. To deeply exploit such a complementary effect, we propose
+a joint model to capture both online and offline features of a user's composite
+behavior. We evaluate the proposed joint model by comparing with some typical
+models on two real-world datasets: Foursquare and Yelp. In the widely-used
+setting of theft simulation (simulating thefts via behavioral replacement), the
+experimental results show that our model outperforms the existing ones, with
+the AUC values $0.956$ in Foursquare and $0.947$ in Yelp, respectively.
+Particularly, the recall (True Positive Rate) can reach up to $65.3\%$ in
+Foursquare and $72.2\%$ in Yelp with the corresponding disturbance rate (False
+Positive Rate) below $1\%$. It is worth mentioning that these performances can
+be achieved by examining only one composite behavior (visiting a place and
+posting a tip online simultaneously) per authentication, which guarantees the
+low response latency of our method. This study would give the cybersecurity
+community new insights into whether and how a real-time online identity
+authentication can be improved via modeling users' composite behavioral
+patterns.
+",1,0,0,0,0,0
+1620,Asymptotic properties of the set of systoles of arithmetic Riemann surfaces,"  The purpose this article is to try to understand the mysterious coincidence
+between the asymptotic behavior of the volumes of the Moduli Space of closed
+hyperbolic surfaces of genus $g$ with respect to the Weil-Petersson metric and
+the asymptotic behavior of the number of arithmetic closed hyperbolic surfaces
+of genus $g$. If the set of arithmetic surfaces is well distributed then its
+image for any interesting function should be well distributed too. We
+investigate the distribution of the function systole. We give several results
+indicating that the systoles of arithmetic surfaces can not be concentrated,
+consequently the same holds for the set of arithmetic surfaces. The proofs are
+based in different techniques: combinatorics (obtaining regular graphs with any
+girth from results of B. Bollobas and constructions with cages and Ramanujan
+graphs), group theory (constructing finite index subgroups of surface groups
+from finite index subgroups of free groups using results of G. Baumslag) and
+geometric group theory (linking the geometry of graphs with the geometry of
+coverings of a surface).
+",0,0,1,0,0,0
+1621,Nonlinear elliptic equations on Carnot groups,"  This article concerns a class of elliptic equations on Carnot groups
+depending on one real positive parameter and involving a subcritical
+nonlinearity (for the critical case we refer to G. Molica Bisci and D.
+Repovš, Yamabe-type equations on Carnot groups, Potential Anal. 46:2
+(2017), 369-383; arXiv:1705.10100 [math.AP]). As a special case of our results
+we prove the existence of at least one nontrivial solution for a subelliptic
+equation defined on a smooth and bounded domain $D$ of the Heisenberg group
+$\mathbb{H}^n=\mathbb{C}^n\times \mathbb{R}$. The main approach is based on
+variational methods.
+",0,0,1,0,0,0
+1622,Raptor Codes for Higher-Order Modulation Using a Multi-Edge Framework,"  In this paper, we represent Raptor codes as multi-edge type low-density
+parity-check (MET-LDPC) codes, which gives a general framework to design them
+for higher-order modulation using MET density evolution. We then propose an
+efficient Raptor code design method for higher-order modulation, where we
+design distinct degree distributions for distinct bit levels. We consider a
+joint decoding scheme based on belief propagation for Raptor codes and also
+derive an exact expression for the stability condition. In several examples, we
+demonstrate that the higher-order modulated Raptor codes designed using the
+multi-edge framework outperform previously reported higher-order modulation
+codes in literature.
+",1,0,1,0,0,0
+1623,A Tidy Data Model for Natural Language Processing using cleanNLP,"  The package cleanNLP provides a set of fast tools for converting a textual
+corpus into a set of normalized tables. The underlying natural language
+processing pipeline utilizes Stanford's CoreNLP library, exposing a number of
+annotation tasks for text written in English, French, German, and Spanish.
+Annotators include tokenization, part of speech tagging, named entity
+recognition, entity linking, sentiment analysis, dependency parsing,
+coreference resolution, and information extraction.
+",1,0,0,1,0,0
+1624,Relaxation of the EM Algorithm via Quantum Annealing for Gaussian Mixture Models,"  We propose a modified expectation-maximization algorithm by introducing the
+concept of quantum annealing, which we call the deterministic quantum annealing
+expectation-maximization (DQAEM) algorithm. The expectation-maximization (EM)
+algorithm is an established algorithm to compute maximum likelihood estimates
+and applied to many practical applications. However, it is known that EM
+heavily depends on initial values and its estimates are sometimes trapped by
+local optima. To solve such a problem, quantum annealing (QA) was proposed as a
+novel optimization approach motivated by quantum mechanics. By employing QA, we
+then formulate DQAEM and present a theorem that supports its stability.
+Finally, we demonstrate numerical simulations to confirm its efficiency.
+",0,1,0,1,0,0
+1625,Multiband Superconductivity in the time reversal symmetry broken superconductor Re6Zr,"  We report point contact Andreev Reflection (PCAR) measurements on a
+high-quality single crystal of the non-centrosymmetric superconductor Re6Zr. We
+observe that the PCAR spectra can be fitted by taking two isotropic
+superconducting gaps with Delta_1 ~ 0.79 meV and Delta_2 ~ 0.22 meV
+respectively, suggesting that there are at least two bands which contribute to
+superconductivity. Combined with the observation of time reversal symmetry
+breaking at the superconducting transition from muon spin relaxation
+measurements (Phys. Rev. Lett. 112, 107002 (2014)), our results imply an
+unconventional superconducting order in this compound: A multiband singlet
+state that breaks time reversal symmetry or a triplet state dominated by
+interband pairing.
+",0,1,0,0,0,0
+1626,Influence of broken-pair excitations on the exact pair wavefunction,"  Doubly occupied configuration interaction (DOCI), the exact diagonalization
+of the Hamiltonian in the paired (seniority zero) sector of the Hilbert space,
+is a combinatorial cost wave function that can be very efficiently approximated
+by pair coupled cluster doubles (pCCD) at mean-field computational cost. As
+such, it is a very interesting candidate as a starting point for building the
+full configuration interaction (FCI) ground state eigenfunction belonging to
+all (not just paired) seniority sectors. The true seniority zero sector of FCI
+(referred to here as FCI${}_0$) includes the effect of coupling between all
+seniority sectors rather than just seniority zero, and is, in principle,
+different from DOCI. We here study the accuracy with which DOCI approximates
+FCI${}_0$. Using a set of small Hubbard lattices, where FCI is possible, we
+show that DOCI $\sim$ FCI${}_0$ under weak correlation. However, in the strong
+correlation regime, the nature of the FCI${}_0$ wavefunction can change
+significantly, rendering DOCI and pCCD a less than ideal starting point for
+approximating FCI.
+",0,1,0,0,0,0
+1627,Dynamical regularities of US equities opening and closing auctions,"  We first investigate the evolution of opening and closing auctions volumes of
+US equities along the years. We then report dynamical properties of pre-auction
+periods: the indicative match price is strongly mean-reverting because the
+imbalance is; the final auction price reacts to a single auction order
+placement or cancellation in markedly different ways in the opening and closing
+auctions when computed conditionally on imbalance improving or worsening
+events; the indicative price reverts towards the mid price of the regular limit
+order book but is not especially bound to the spread.
+",0,0,0,0,0,1
+1628,"Comment on ""Laser cooling of $^{173}$Yb for isotope separation and precision hyperfine spectroscopy""","  We present measurements of the hyperfine splitting in the Yb-173
+$6s6p~^1P_1^{\rm o} (F^{\prime}=3/2,7/2)$ states that disagree significantly
+with those measured previously by Das and Natarajan [Phys. Rev. A 76, 062505
+(2007)]. We point out inconsistencies in their measurements and suggest that
+their error is due to optical pumping and improper determination of the atomic
+line center. Our measurements are made using an optical frequency comb. We use
+an optical pumping scheme to improve the signal-to-background ratio for the
+$F^{\prime}=3/2$ component.
+",0,1,0,0,0,0
+1629,Training GANs with Optimism,"  We address the issue of limit cycling behavior in training Generative
+Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for
+training Wasserstein GANs. Recent theoretical results have shown that
+optimistic mirror decent (OMD) can enjoy faster regret rates in the context of
+zero-sum games. WGANs is exactly a context of solving a zero-sum game with
+simultaneous no-regret dynamics. Moreover, we show that optimistic mirror
+decent addresses the limit cycling problem in training WGANs. We formally show
+that in the case of bi-linear zero-sum games the last iterate of OMD dynamics
+converges to an equilibrium, in contrast to GD dynamics which are bound to
+cycle. We also portray the huge qualitative difference between GD and OMD
+dynamics with toy examples, even when GD is modified with many adaptations
+proposed in the recent literature, such as gradient penalty or momentum. We
+apply OMD WGAN training to a bioinformatics problem of generating DNA
+sequences. We observe that models trained with OMD achieve consistently smaller
+KL divergence with respect to the true underlying distribution, than models
+trained with GD variants. Finally, we introduce a new algorithm, Optimistic
+Adam, which is an optimistic variant of Adam. We apply it to WGAN training on
+CIFAR10 and observe improved performance in terms of inception score as
+compared to Adam.
+",1,0,0,1,0,0
+1630,New concepts of inertial measurements with multi-species atom interferometry,"  In the field of cold atom inertial sensors, we present and analyze innovative
+configurations for improving their measurement range and sensitivity,
+especially attracting for onboard applications. These configurations rely on
+multi-species atom interferometry, involving the simultaneous manipulation of
+different atomic species in a unique instrument to deduce inertial
+measurements. Using a dual-species atom accelerometer manipulating
+simultaneously both isotopes of rubidium, we report a preliminary experimental
+realization of original concepts involving the implementation of two atom
+interferometers first with different interrogation times and secondly in phase
+quadrature. These results open the door to a new generation of atomic sensors
+relying on high performance multi-species atom interferometric measurements.
+",0,1,0,0,0,0
+1631,LAMN in a class of parametric models for null recurrent diffusion,"  We study statistical models for one-dimensional diffusions which are
+recurrent null. A first parameter in the drift is the principal one, and
+determines regular varying rates of convergence for the score and the
+information process. A finite number of other parameters, of secondary
+importance, introduces additional flexibility for the modelization of the
+drift, and does not perturb the null recurrent behaviour. Under time-continuous
+observation we obtain local asymptotic mixed normality (LAMN), state a local
+asymptotic minimax bound, and specify asymptotically optimal estimators.
+",0,0,1,1,0,0
+1632,A recipe for topological observables of density matrices,"  Meaningful topological invariants for mixed quantum states are challenging to
+identify as there is no unique way to define them, and most choices do not
+directly relate to physical observables. Here, we propose a simple pragmatic
+approach to construct topological invariants of mixed states while preserving a
+connection to physical observables, by continuously deforming known topological
+invariants for pure (ground) states. Our approach relies on expectation values
+of many-body operators, with no reference to single-particle (e.g., Bloch)
+wavefunctions. To illustrate it, we examine extensions to mixed states of
+$U(1)$ geometric (Berry) phases and their corresponding topological invariant
+(winding or Chern number). We discuss measurement schemes, and provide a
+detailed construction of invariants for thermal or more general mixed states of
+quantum systems with (at least) $U(1)$ charge-conservation symmetry, such as
+quantum Hall insulators.
+",0,1,0,0,0,0
+1633,Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task Learning,"  Deep learning (DL) advances state-of-the-art reinforcement learning (RL), by
+incorporating deep neural networks in learning representations from the input
+to RL. However, the conventional deep neural network architecture is limited in
+learning representations for multi-task RL (MT-RL), as multiple tasks can refer
+to different kinds of representations. In this paper, we thus propose a novel
+deep neural network architecture, namely generalization tower network (GTN),
+which can achieve MT-RL within a single learned model. Specifically, the
+architecture of GTN is composed of both horizontal and vertical streams. In our
+GTN architecture, horizontal streams are used to learn representation shared in
+similar tasks. In contrast, the vertical streams are introduced to be more
+suitable for handling diverse tasks, which encodes hierarchical shared
+knowledge of these tasks. The effectiveness of the introduced vertical stream
+is validated by experimental results. Experimental results further verify that
+our GTN architecture is able to advance the state-of-the-art MT-RL, via being
+tested on 51 Atari games.
+",1,0,0,1,0,0
+1634,On a class of infinitely differentiable functions in ${\mathbb R}^n$ admitting holomorphic extension in ${\mathbb C}^n$,"  A space $G(M, \varPhi)$ of infinitely differentiable functions in ${\mathbb
+R}^n$ constructed with a help of a family
+$\varPhi=\{\varphi_m\}_{m=1}^{\infty}$ of real-valued functions $\varphi_m
+\in~C({\mathbb R}^n)$ and a logarithmically convex sequence $M$ of positive
+numbers is considered in the article. In view of conditions on $M$ each
+function of $G(M, \varPhi)$ can be extended to an entire function in ${\mathbb
+C}^n$. Imposed conditions on $M$ and $\varPhi$ allow to describe the space of
+such extensions.
+",0,0,1,0,0,0
+1635,"Spatially resolved, energy-filtered imaging of core level and valence band photoemission of highly p and n doped silicon patterns","  An accurate description of spatial variations in the energy levels of
+patterned semiconductor substrates on the micron and sub-micron scale as a
+function of local doping is an important technological challenge for the
+microelectronics industry. Spatially resolved surface analysis by photoelectron
+spectromicroscopy can provide an invaluable contribution thanks to the
+relatively non-destructive, quantitative analysis. We present results on highly
+doped n and p type patterns on, respectively, p and n type silicon substrates.
+Using synchrotron radiation and spherical aberration-corrected energy
+filtering, we have obtained a spectroscopic image series at the Si 2p core
+level and across the valence band. Local band alignments are extracted,
+accounting for doping, band bending and surface photovoltage.
+",0,1,0,0,0,0
+1636,Effects of Degree Correlations in Interdependent Security: Good or Bad?,"  We study the influence of degree correlations or network mixing in
+interdependent security. We model the interdependence in security among agents
+using a dependence graph and employ a population game model to capture the
+interaction among many agents when they are strategic and have various security
+measures they can choose to defend themselves. The overall network security is
+measured by what we call the average risk exposure (ARE) from neighbors, which
+is proportional to the total (expected) number of attacks in the network.
+We first show that there exists a unique pure-strategy Nash equilibrium of a
+population game. Then, we prove that as the agents with larger degrees in the
+dependence graph see higher risks than those with smaller degrees, the overall
+network security deteriorates in that the ARE experienced by agents increases
+and there are more attacks in the network. Finally, using this finding, we
+demonstrate that the effects of network mixing on ARE depend on the (cost)
+effectiveness of security measures available to agents; if the security
+measures are not effective, increasing assortativity of dependence graph
+results in higher ARE. On the other hand, if the security measures are
+effective at fending off the damages and losses from attacks, increasing
+assortativity reduces the ARE experienced by agents.
+",1,1,0,0,0,0
+1637,Towards more Reliable Transfer Learning,"  Multi-source transfer learning has been proven effective when within-target
+labeled data is scarce. Previous work focuses primarily on exploiting domain
+similarities and assumes that source domains are richly or at least comparably
+labeled. While this strong assumption is never true in practice, this paper
+relaxes it and addresses challenges related to sources with diverse labeling
+volume and diverse reliability. The first challenge is combining domain
+similarity and source reliability by proposing a new transfer learning method
+that utilizes both source-target similarities and inter-source relationships.
+The second challenge involves pool-based active learning where the oracle is
+only available in source domains, resulting in an integrated active transfer
+learning framework that incorporates distribution matching and uncertainty
+sampling. Extensive experiments on synthetic and two real-world datasets
+clearly demonstrate the superiority of our proposed methods over several
+baselines including state-of-the-art transfer learning methods.
+",0,0,0,1,0,0
+1638,Forming short-period Wolf-Rayet X-ray binaries and double black holes through stable mass transfer,"  We show that black-hole High-Mass X-ray Binaries (HMXBs) with O- or B-type
+donor stars and relatively short orbital periods, of order one week to several
+months may survive spiral in, to then form Wolf-Rayet (WR) X-ray binaries with
+orbital periods of order a day to a few days; while in systems where the
+compact star is a neutron star, HMXBs with these orbital periods never survive
+spiral-in. We therefore predict that WR X-ray binaries can only harbor black
+holes. The reason why black-hole HMXBs with these orbital periods may survive
+spiral in is: the combination of a radiative envelope of the donor star, and a
+high mass of the compact star. In this case, when the donor begins to overflow
+its Roche lobe, the systems are able to spiral in slowly with stable Roche-lobe
+overflow, as is shown by the system SS433. In this case the transferred mass is
+ejected from the vicinity of the compact star (so-called ""isotropic
+re-emission"" mass loss mode, or ""SS433-like mass loss""), leading to gradual
+spiral-in. If the mass ratio of donor and black hole is $>3.5$, these systems
+will go into CE evolution and are less likely to survive. If they survive, they
+produce WR X-ray binaries with orbital periods of a few hours to one day.
+Several of the well-known WR+O binaries in our Galaxy and the Magellanic
+Clouds, with orbital periods in the range between a week and several months,
+are expected to evolve into close WR-Black-Hole binaries,which may later
+produce close double black holes. The galactic formation rate of double black
+holes resulting from such systems is still uncertain, as it depends on several
+poorly known factors in this evolutionary picture. It might possibly be as high
+as $\sim 10^{-5}$ per year.
+",0,1,0,0,0,0
+1639,Syzygies of Cohen-Macaulay modules over one dimensional Cohen-Macaulay local rings,"  We study syzygies of (maximal) Cohen-Macaulay modules over one dimensional
+Cohen-Macaulay local rings. We compare these modules to Cohen-Macaulay modules
+over the endomorphism ring of the maximal ideal. After this comparison, we give
+several characterizations of almost Gorenstein rings in terms of syzygies of
+Cohen-Macaulay modules.
+",0,0,1,0,0,0
+1640,Chirality provides a direct fitness advantage and facilitates intermixing in cellular aggregates,"  Chirality in shape and motility can evolve rapidly in microbes and cancer
+cells. To determine how chirality affects cell fitness, we developed a model of
+chiral growth in compact aggregates such as microbial colonies and solid
+tumors. Our model recapitulates previous experimental findings and shows that
+mutant cells can invade by increasing their chirality or switching their
+handedness. The invasion results either in a takeover or stable coexistence
+between the mutant and the ancestor depending on their relative chirality. For
+large chiralities, the coexistence is accompanied by strong intermixing between
+the cells, while spatial segregation occurs otherwise. We show that the
+competition within the aggregate is mediated by bulges in regions where the
+cells with different chiralities meet. The two-way coupling between aggregate
+shape and natural selection is described by the chiral Kardar-Parisi-Zhang
+equation coupled to the Burgers' equation with multiplicative noise. We solve
+for the key features of this theory to explain the origin of selection on
+chirality. Overall, our work suggests that chirality could be an important
+ecological trait that mediates competition, invasion, and spatial structure in
+cellular populations.
+",0,1,0,0,0,0
+1641,Learning to Draw Samples with Amortized Stein Variational Gradient Descent,"  We propose a simple algorithm to train stochastic neural networks to draw
+samples from given target distributions for probabilistic inference. Our method
+is based on iteratively adjusting the neural network parameters so that the
+output changes along a Stein variational gradient direction (Liu & Wang, 2016)
+that maximally decreases the KL divergence with the target distribution. Our
+method works for any target distribution specified by their unnormalized
+density function, and can train any black-box architectures that are
+differentiable in terms of the parameters we want to adapt. We demonstrate our
+method with a number of applications, including variational autoencoder (VAE)
+with expressive encoders to model complex latent space structures, and
+hyper-parameter learning of MCMC samplers that allows Bayesian inference to
+adaptively improve itself when seeing more data.
+",0,0,0,1,0,0
+1642,Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition,"  This note proposes a simple and general framework of dynamic mode
+decomposition (DMD) and a mode selection for large datasets. The proposed
+framework explicitly introduces a preconditioning step using an incremental
+proper orthogonal decomposition to DMD and mode selection algorithms. By
+performing the preconditioning step, the DMD and the mode selection can be
+performed with low memory consumption and small computational complexity and
+can be applied to large datasets. In addition, a simple mode selection
+algorithm based on a greedy method is proposed. The proposed framework is
+applied to the analysis of a three-dimensional flows around a circular
+cylinder.
+",0,1,0,0,0,0
+1643,A functional perspective on emergent supersymmetry,"  We investigate the emergence of ${\cal N}=1$ supersymmetry in the long-range
+behavior of three-dimensional parity-symmetric Yukawa systems. We discuss a
+renormalization approach that manifestly preserves supersymmetry whenever such
+symmetry is realized, and use it to prove that supersymmetry-breaking operators
+are irrelevant, thus proving that such operators are suppressed in the
+infrared. All our findings are illustrated with the aid of the
+$\epsilon$-expansion and a functional variant of perturbation theory, but we
+provide numerical estimates of critical exponents that are based on the
+non-perturbative functional renormalization group.
+",0,1,0,0,0,0
+1644,Variants of RMSProp and Adagrad with Logarithmic Regret Bounds,"  Adaptive gradient methods have become recently very popular, in particular as
+they have been shown to be useful in the training of deep neural networks. In
+this paper we have analyzed RMSProp, originally proposed for the training of
+deep neural networks, in the context of online convex optimization and show
+$\sqrt{T}$-type regret bounds. Moreover, we propose two variants SC-Adagrad and
+SC-RMSProp for which we show logarithmic regret bounds for strongly convex
+functions. Finally, we demonstrate in the experiments that these new variants
+outperform other adaptive gradient techniques or stochastic gradient descent in
+the optimization of strongly convex functions as well as in training of deep
+neural networks.
+",1,0,0,1,0,0
+1645,"Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions","  We study connections between Dykstra's algorithm for projecting onto an
+intersection of convex sets, the augmented Lagrangian method of multipliers or
+ADMM, and block coordinate descent. We prove that coordinate descent for a
+regularized regression problem, in which the (separable) penalty functions are
+seminorms, is exactly equivalent to Dykstra's algorithm applied to the dual
+problem. ADMM on the dual problem is also seen to be equivalent, in the special
+case of two sets, with one being a linear subspace. These connections, aside
+from being interesting in their own right, suggest new ways of analyzing and
+extending coordinate descent. For example, from existing convergence theory on
+Dykstra's algorithm over polyhedra, we discern that coordinate descent for the
+lasso problem converges at an (asymptotically) linear rate. We also develop two
+parallel versions of coordinate descent, based on the Dykstra and ADMM
+connections.
+",0,0,1,1,0,0
+1646,A Topological Perspective on Interacting Algebraic Theories,"  Techniques from higher categories and higher-dimensional rewriting are
+becoming increasingly important for understanding the finer, computational
+properties of higher algebraic theories that arise, among other fields, in
+quantum computation. These theories have often the property of containing
+simpler sub-theories, whose interaction is regulated in a limited number of
+ways, which reveals a topological substrate when pictured by string diagrams.
+By exploring the double nature of computads as presentations of higher
+algebraic theories, and combinatorial descriptions of ""directed spaces"", we
+develop a basic language of directed topology for the compositional study of
+algebraic theories. We present constructions of computads, all with clear
+analogues in standard topology, that capture in great generality such notions
+as homomorphisms and actions, and the interactions of monoids and comonoids
+that lead to the theory of Frobenius algebras and of bialgebras. After a number
+of examples, we describe how a fragment of the ZX calculus can be reconstructed
+in this framework.
+",1,0,1,0,0,0
+1647,Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation,"  We introduce dynamic nested sampling: a generalisation of the nested sampling
+algorithm in which the number of ""live points"" varies to allocate samples more
+efficiently. In empirical tests the new method significantly improves
+calculation accuracy compared to standard nested sampling with the same number
+of samples; this increase in accuracy is equivalent to speeding up the
+computation by factors of up to ~72 for parameter estimation and ~7 for
+evidence calculations. We also show that the accuracy of both parameter
+estimation and evidence calculations can be improved simultaneously. In
+addition, unlike in standard nested sampling, more accurate results can be
+obtained by continuing the calculation for longer. Popular standard nested
+sampling implementations can be easily adapted to perform dynamic nested
+sampling, and several dynamic nested sampling software packages are now
+publicly available.
+",0,1,0,1,0,0
+1648,Multistage Adaptive Testing of Sparse Signals,"  Multistage design has been used in a wide range of scientific fields. By
+allocating sensing resources adaptively, one can effectively eliminate null
+locations and localize signals with a smaller study budget. We formulate a
+decision-theoretic framework for simultaneous multi- stage adaptive testing and
+study how to minimize the total number of measurements while meeting
+pre-specified constraints on both the false positive rate (FPR) and missed
+discovery rate (MDR). The new procedure, which effectively pools information
+across individual tests using a simultaneous multistage adaptive ranking and
+thresholding (SMART) approach, can achieve precise error rates control and lead
+to great savings in total study costs. Numerical studies confirm the
+effectiveness of SMART for FPR and MDR control and show that it achieves
+substantial power gain over existing methods. The SMART procedure is
+demonstrated through the analysis of high-throughput screening data and spatial
+imaging data.
+",0,0,0,1,0,0
+1649,On the commutativity of the powerspace constructions,"  We investigate powerspace constructions on topological spaces, with a
+particular focus on the category of quasi-Polish spaces. We show that the upper
+and lower powerspaces commute on all quasi-Polish spaces, and show more
+generally that this commutativity is equivalent to the topological property of
+consonance. We then investigate powerspace constructions on the open set
+lattices of quasi-Polish spaces, and provide a complete characterization of how
+the upper and lower powerspaces distribute over the open set lattice
+construction.
+",1,0,1,0,0,0
+1650,Bounds on poloidal kinetic energy in plane layer convection,"  A numerical method is presented which conveniently computes upper bounds on
+heat transport and poloidal energy in plane layer convection for infinite and
+finite Prandtl numbers. The bounds obtained for the heat transport coincide
+with earlier results. These bounds imply upper bounds for the poloidal energy
+which follow directly from the definitions of dissipation and energy. The same
+constraints used for computing upper bounds on the heat transport lead to
+improved bounds for the poloidal energy.
+",0,1,0,0,0,0
+1651,Concentration of Multilinear Functions of the Ising Model with Applications to Network Data,"  We prove near-tight concentration of measure for polynomial functions of the
+Ising model under high temperature. For any degree $d$, we show that a
+degree-$d$ polynomial of a $n$-spin Ising model exhibits exponential tails that
+scale as $\exp(-r^{2/d})$ at radius $r=\tilde{\Omega}_d(n^{d/2})$. Our
+concentration radius is optimal up to logarithmic factors for constant $d$,
+improving known results by polynomial factors in the number of spins. We
+demonstrate the efficacy of polynomial functions as statistics for testing the
+strength of interactions in social networks in both synthetic and real world
+data.
+",1,0,1,1,0,0
+1652,Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning,"  This paper proposes an exploration method for deep reinforcement learning
+based on parameter space noise. Recent studies have experimentally shown that
+parameter space noise results in better exploration than the commonly used
+action space noise. Previous methods devised a way to update the diagonal
+covariance matrix of a noise distribution and did not consider the direction of
+the noise vector and its correlation. In addition, fast updates of the noise
+distribution are required to facilitate policy learning. We propose a method
+that deforms the noise distribution according to the accumulated returns and
+the noises that have led to the returns. Moreover, this method switches
+isotropic exploration and directional exploration in parameter space with
+regard to obtained rewards. We validate our exploration strategy in the OpenAI
+Gym continuous environments and modified environments with sparse rewards. The
+proposed method achieves results that are competitive with a previous method at
+baseline tasks. Moreover, our approach exhibits better performance in sparse
+reward environments by exploration with the switching strategy.
+",0,0,0,1,0,0
+1653,Fast and high-accuracy measuring technique for transmittance spectrum in VIS-NIR,"  In this paper, based on the framework of traditional spectrophotometry, we
+put forward a novel fast and high-accuracy technique for measuring
+transmittance spectrum in VIS-NIR wave range, its key feature is that during
+the measurement procedure, the output wavelength of the grating monochromator
+is kept increasing continuously and at the same time, the photoelectric
+detectors execute a concurrently continuous data acquisition routine. Initial
+experiment result shows that the newly proposed technique could shorten the
+time consumed for measuring the transmittance spectrum down to 50% that of the
+conventional spectrophotometric method, a relative error of 0.070% and a
+repeatability error of 0.042% are generated. Compared with the current mostly
+used techniques (spectrophotometry, methods based on multi-channel spectrometer
+and strategy using Fourier transform spectrometer) for obtaining transmittance
+spectrum in VIS-NIR, the new strategy has at all once the following advantages,
+firstly the measuring speed could be greatly quicken, fast measurement of
+transmittance spectrum in VIS-NIR is therefore promising, which would find wide
+application in dynamic environment, secondly high measuring accuracy
+(0.1%-0.3%) is available, and finally the measuring system has high mechanical
+stability because the motor of the grating monochromator is rotating
+continuously during the measurement.
+",0,1,0,0,0,0
+1654,The solution to the initial value problem for the ultradiscrete Somos-4 and 5 equations,"  We propose a method to solve the initial value problem for the ultradiscrete
+Somos-4 and Somos-5 equations by expressing terms in the equations as convex
+polygons and regarding max-plus algebras as those on polygons.
+",0,1,0,0,0,0
+1655,Width-tuned magnetic order oscillation on zigzag edges of honeycomb nanoribbons,"  Quantum confinement and interference often generate exotic properties in
+nanostructures. One recent highlight is the experimental indication of a
+magnetic phase transition in zigzag-edged graphene nanoribbons at the critical
+ribbon width of about 7 nm [G. Z. Magda et al., Nature \textbf{514}, 608
+(2014)]. Here we show theoretically that with further increase in the ribbon
+width, the magnetic correlation of the two edges can exhibit an intriguing
+oscillatory behavior between antiferromagnetic and ferromagnetic, driven by
+acquiring the positive coherence between the two edges to lower the free
+energy. The oscillation effect is readily tunable in applied magnetic fields.
+These novel properties suggest new experimental manifestation of the edge
+magnetic orders in graphene nanoribbons, and enhance the hopes of graphene-like
+spintronic nanodevices functioning at room temperature.
+",0,1,0,0,0,0
+1656,Fast and accurate approximation of the full conditional for gamma shape parameters,"  The gamma distribution arises frequently in Bayesian models, but there is not
+an easy-to-use conjugate prior for the shape parameter of a gamma. This
+inconvenience is usually dealt with by using either Metropolis-Hastings moves,
+rejection sampling methods, or numerical integration. However, in models with a
+large number of shape parameters, these existing methods are slower or more
+complicated than one would like, making them burdensome in practice. It turns
+out that the full conditional distribution of the gamma shape parameter is well
+approximated by a gamma distribution, even for small sample sizes, when the
+prior on the shape parameter is also a gamma distribution. This article
+introduces a quick and easy algorithm for finding a gamma distribution that
+approximates the full conditional distribution of the shape parameter. We
+empirically demonstrate the speed and accuracy of the approximation across a
+wide range of conditions. If exactness is required, the approximation can be
+used as a proposal distribution for Metropolis-Hastings.
+",0,0,0,1,0,0
+1657,Two variants of the Froiduire-Pin Algorithm for finite semigroups,"  In this paper, we present two algorithms based on the Froidure-Pin Algorithm
+for computing the structure of a finite semigroup from a generating set. As was
+the case with the original algorithm of Froidure and Pin, the algorithms
+presented here produce the left and right Cayley graphs, a confluent
+terminating rewriting system, and a reduced word of the rewriting system for
+every element of the semigroup.
+If $U$ is any semigroup, and $A$ is a subset of $U$, then we denote by
+$\langle A\rangle$ the least subsemigroup of $U$ containing $A$. If $B$ is any
+other subset of $U$, then, roughly speaking, the first algorithm we present
+describes how to use any information about $\langle A\rangle$, that has been
+found using the Froidure-Pin Algorithm, to compute the semigroup $\langle A\cup
+B\rangle$. More precisely, we describe the data structure for a finite
+semigroup $S$ given by Froidure and Pin, and how to obtain such a data
+structure for $\langle A\cup B\rangle$ from that for $\langle A\rangle$. The
+second algorithm is a lock-free concurrent version of the Froidure-Pin
+Algorithm.
+",1,0,1,0,0,0
+1658,Holon Wigner Crystal in a Lightly Doped Kagome Quantum Spin Liquid,"  We address the problem of a lightly doped spin-liquid through a large-scale
+density-matrix renormalization group (DMRG) study of the $t$-$J$ model on a
+Kagome lattice with a small but non-zero concentration, $\delta$, of doped
+holes. It is now widely accepted that the undoped ($\delta=0$) spin 1/2
+Heisenberg antiferromagnet has a spin-liquid groundstate. Theoretical arguments
+have been presented that light doping of such a spin-liquid could give rise to
+a high temperature superconductor or an exotic topological Fermi liquid metal
+(FL$^\ast$). Instead, we infer that the doped holes form an insulating
+charge-density wave state with one doped-hole per unit cell - i.e. a Wigner
+crystal (WC). Spin correlations remain short-ranged, as in the spin-liquid
+parent state, from which we infer that the state is a crystal of spinless
+holons (WC$^\ast$), rather than of holes. Our results may be relevant to Kagome
+lattice Herbertsmithite $\rm ZnCu_3(OH)_6Cl_2$ upon doping.
+",0,1,0,0,0,0
+1659,Accelerated Block Coordinate Proximal Gradients with Applications in High Dimensional Statistics,"  Nonconvex optimization problems arise in different research fields and arouse
+lots of attention in signal processing, statistics and machine learning. In
+this work, we explore the accelerated proximal gradient method and some of its
+variants which have been shown to converge under nonconvex context recently. We
+show that a novel variant proposed here, which exploits adaptive momentum and
+block coordinate update with specific update rules, further improves the
+performance of a broad class of nonconvex problems. In applications to sparse
+linear regression with regularizations like Lasso, grouped Lasso, capped
+$\ell_1$ and SCAP, the proposed scheme enjoys provable local linear
+convergence, with experimental justification.
+",1,0,0,1,0,0
+1660,Phase retrieval using alternating minimization in a batch setting,"  This paper considers the problem of phase retrieval, where the goal is to
+recover a signal $z\in C^n$ from the observations $y_i=|a_i^* z|$,
+$i=1,2,\cdots,m$. While many algorithms have been proposed, the alternating
+minimization algorithm has been one of the most commonly used methods, and it
+is very simple to implement. Current work has proved that when the observation
+vectors $\{a_i\}_{i=1}^m$ are sampled from a complex Gaussian distribution
+$N(0, I)$, it recovers the underlying signal with a good initialization when
+$m=O(n)$, or with random initialization when $m=O(n^2)$, and it conjectured
+that random initialization succeeds with $m=O(n)$. This work proposes a
+modified alternating minimization method in a batch setting, and proves that
+when $m=O(n\log^{3}n)$, the proposed algorithm with random initialization
+recovers the underlying signal with high probability. The proof is based on the
+observation that after each iteration of alternating minimization, with high
+probability, the angle between the estimated signal and the underlying signal
+is reduced.
+",0,0,1,1,0,0
+1661,Inverse statistical problems: from the inverse Ising problem to data science,"  Inverse problems in statistical physics are motivated by the challenges of
+`big data' in different fields, in particular high-throughput experiments in
+biology. In inverse problems, the usual procedure of statistical physics needs
+to be reversed: Instead of calculating observables on the basis of model
+parameters, we seek to infer parameters of a model based on observations. In
+this review, we focus on the inverse Ising problem and closely related
+problems, namely how to infer the coupling strengths between spins given
+observed spin correlations, magnetisations, or other data. We review
+applications of the inverse Ising problem, including the reconstruction of
+neural connections, protein structure determination, and the inference of gene
+regulatory networks. For the inverse Ising problem in equilibrium, a number of
+controlled and uncontrolled approximate solutions have been developed in the
+statistical mechanics community. A particularly strong method,
+pseudolikelihood, stems from statistics. We also review the inverse Ising
+problem in the non-equilibrium case, where the model parameters must be
+reconstructed based on non-equilibrium statistics.
+",0,1,0,0,0,0
+1662,Static structure of chameleon dark Matter as an explanation of dwarf spheroidal galactic core,"  We propose a novel mechanism which explains cored dark matter density profile
+in recently observed dark matter rich dwarf spheroidal galaxies. In our
+scenario, dark matter particle mass decreases gradually as function of distance
+towards the center of a dwarf galaxy due to its interaction with a chameleon
+scalar. At closer distance towards galactic center the strength of attractive
+scalar fifth force becomes much stronger than gravity and is balanced by the
+Fermi pressure of dark matter cloud, thus an equilibrium static configuration
+of dark matter halo is obtained. Like the case of soliton star or fermion
+Q-star, the stability of the dark matter halo is obtained as the scalar
+achieves a static profile and reaches an asymptotic value away from the
+galactic center. For simple scalar-dark matter interaction and quadratic scalar
+self interaction potential, we show that dark matter behaves exactly like cold
+dark matter (CDM) beyond few $\rm{kpc}$ away from galactic center but at closer
+distance it becomes lighter and fermi pressure cannot be ignored anymore. Using
+Thomas-Fermi approximation, we numerically solve the radial static profile of
+the scalar field, fermion mass and dark matter energy density as a function of
+distance. We find that for fifth force mediated by an ultra light scalar, it is
+possible to obtain a flattened dark matter density profile towards galactic
+center. In our scenario, the fifth force can be neglected at distance $ r \geq
+1\, \rm{kpc}$ from galactic center and dark matter can be simply treated as
+heavy non-relativistic particles beyond this distance, thus reproducing the
+success of CDM at large scales.
+",0,1,0,0,0,0
+1663,Multi-Stakeholder Recommendation: Applications and Challenges,"  Recommender systems have been successfully applied to assist decision making
+by producing a list of item recommendations tailored to user preferences.
+Traditional recommender systems only focus on optimizing the utility of the end
+users who are the receiver of the recommendations. By contrast,
+multi-stakeholder recommendation attempts to generate recommendations that
+satisfy the needs of both the end users and other parties or stakeholders. This
+paper provides an overview and discussion about the multi-stakeholder
+recommendations from the perspective of practical applications, available data
+sets, corresponding research challenges and potential solutions.
+",1,0,0,0,0,0
+1664,Unbalancing Sets and an Almost Quadratic Lower Bound for Syntactically Multilinear Arithmetic Circuits,"  We prove a lower bound of $\Omega(n^2/\log^2 n)$ on the size of any
+syntactically multilinear arithmetic circuit computing some explicit
+multilinear polynomial $f(x_1, \ldots, x_n)$. Our approach expands and improves
+upon a result of Raz, Shpilka and Yehudayoff ([RSY08]), who proved a lower
+bound of $\Omega(n^{4/3}/\log^2 n)$ for the same polynomial. Our improvement
+follows from an asymptotically optimal lower bound for a generalized version of
+Galvin's problem in extremal set theory.
+",1,0,0,0,0,0
+1665,Saturated absorption competition microscopy,"  We introduce the concept of saturated absorption competition (SAC) microscopy
+as a means of providing sub-diffraction spatial resolution in fluorescence
+imaging. Unlike the post-competition process between stimulated and spontaneous
+emission that is used in stimulated emission depletion (STED) microscopy, SAC
+microscopy breaks the diffraction limit by emphasizing a pre-competition
+process that occurs in the fluorescence absorption stage in a manner that
+shares similarities with ground-state depletion (GSD) microscopy. Moreover,
+unlike both STED and GSD microscopy, SAC microscopy offers a reduction in
+complexity and cost by utilizing only a single continuous-wave laser diode and
+an illumination intensity that is ~ 20x smaller than that used in STED. Our
+approach can be physically implemented in a confocal microscope by dividing the
+input laser source into a time-modulated primary excitation beam and a
+doughnut-shaped saturation beam, and subsequently employing a homodyne
+detection scheme to select the modulated fluorescence signal. Herein, we
+provide both a physico-chemical model of SAC and experimentally demonstrate by
+way of a proof-of-concept experiment a transverse spatial resolution of
+~lambda/6.
+",0,1,0,0,0,0
+1666,Topological and non inertial effects on the interbank light absorption,"  In this work, we investigate the combined influence of the nontrivial
+topology introduced by a disclination and non inertial effects due to rotation,
+in the energy levels and the wave functions of a noninteracting electron gas
+confined to a two-dimensional pseudoharmonic quantum dot, under the influence
+of an external uniform magnetic field. The exact solutions for energy
+eigenvalues and wave functions are computed as functions of the applied
+magnetic field strength, the disclination topological charge, magnetic quantum
+number and the rotation speed of the sample. We investigate the modifications
+on the light interband absorption coefficient and absorption threshold
+frequency. We observe novel features in the system, including a range of
+magnetic field without corresponding absorption phenomena, which is due to a
+tripartite term of the Hamiltonian, involving magnetic field, the topological
+charge of the defect and the rotation frequency.
+",0,1,0,0,0,0
+1667,Evolution of antiferromagnetic domains in the all-in-all-out ordered pyrochlore Nd$_2$Zr$_2$O$_7$,"  We report the observation of magnetic domains in the exotic,
+antiferromagnetically ordered all-in-all-out state of Nd$_2$Zr$_2$O$_7$,
+induced by spin canting. The all-in-all-out state can be realized by Ising-like
+spins on a pyrochlore lattice and is established in Nd$_2$Zr$_2$O$_7$ below
+0.31 K for external magnetic fields up to 0.14 T. Two different spin
+arrangements can fulfill this configuration which leads to the possibility of
+magnetic domains. The all-in-all-out domain structure can be controlled by an
+external magnetic field applied parallel to the [111] direction. This is a
+result of different spin canting mechanism for the two all-in-all-out
+configurations for such a direction of the magnetic field. The change of the
+domain structure is observed through a hysteresis in the magnetic
+susceptibility. No hysteresis occurs, however, in case the external magnetic
+field is applied along [100].
+",0,1,0,0,0,0
+1668,Phylogenetic networks that are their own fold-ups,"  Phylogenetic networks are becoming of increasing interest to evolutionary
+biologists due to their ability to capture complex non-treelike evolutionary
+processes. From a combinatorial point of view, such networks are certain types
+of rooted directed acyclic graphs whose leaves are labelled by, for example,
+species. A number of mathematically interesting classes of phylogenetic
+networks are known. These include the biologically relevant class of stable
+phylogenetic networks whose members are defined via certain fold-up and un-fold
+operations that link them with concepts arising within the theory of, for
+example, graph fibrations. Despite this exciting link, the structural
+complexity of stable phylogenetic networks is still relatively poorly
+understood. Employing the popular tree-based, reticulation-visible, and
+tree-child properties which allow one to gauge this complexity in one way or
+another, we provide novel characterizations for when a stable phylogenetic
+network satisfies either one of these three properties.
+",0,0,0,0,1,0
+1669,Hyperbolic inverse mean curvature flow,"  In this paper, we prove the short-time existence of hyperbolic inverse (mean)
+curvature flow (with or without the specified forcing term) under the
+assumption that the initial compact smooth hypersurface of $\mathbb{R}^{n+1}$
+($n\geqslant2$) is mean convex and star-shaped. Several interesting examples
+and some hyperbolic evolution equations for geometric quantities of the
+evolving hypersurfaces have been shown. Besides, under different assumptions
+for the initial velocity, we can get the expansion and the convergence results
+of a hyperbolic inverse mean curvature flow in the plane $\mathbb{R}^2$, whose
+evolving curves move normally.
+",0,0,1,0,0,0
+1670,Theoretically Principled Trade-off between Robustness and Accuracy,"  We identify a trade-off between robustness and accuracy that serves as a
+guiding principle in the design of defenses against adversarial examples.
+Although the problem has been widely studied empirically, much remains unknown
+concerning the theory underlying this trade-off. In this work, we quantify the
+trade-off in terms of the gap between the risk for adversarial examples and the
+risk for non-adversarial examples. The challenge is to provide tight bounds on
+this quantity in terms of a surrogate loss. We give an optimal upper bound on
+this quantity in terms of classification-calibrated loss, which matches the
+lower bound in the worst case. Inspired by our theoretical analysis, we also
+design a new defense method, TRADES, to trade adversarial robustness off
+against accuracy. Our proposed algorithm performs well experimentally in
+real-world datasets. The methodology is the foundation of our entry to the
+NeurIPS 2018 Adversarial Vision Challenge in which we won the 1st place out of
+1,995 submissions in the robust model track, surpassing the runner-up approach
+by $11.41\%$ in terms of mean $\ell_2$ perturbation distance.
+",1,0,0,1,0,0
+1671,A new charge reconstruction algorithm for the DAMPE silicon microstrip detector,"  The DArk Matter Particle Explorer (DAMPE) is one of the four satellites
+within the Strategic Pioneer Research Program in Space Science of the Chinese
+Academy of Science (CAS). The Silicon-Tungsten Tracker (STK), which is composed
+of 768 singled-sided silicon microstrip detectors, is one of the four
+subdetectors in DAMPE, providing track reconstruction and charge identification
+for relativistic charged particles. The charge response of DAMPE silicon
+microstrip detectors is complicated, depending on the incident angle and impact
+position. A new charge reconstruction algorithm for the DAMPE silicon
+microstrip detector is introduced in this paper. This algorithm can correct the
+complicated charge response, and was proved applicable by the ion test beam.
+",0,1,0,0,0,0
+1672,The Structure Transfer Machine Theory and Applications,"  Representation learning is a fundamental but challenging problem, especially
+when the distribution of data is unknown. We propose a new representation
+learning method, termed Structure Transfer Machine (STM), which enables feature
+learning process to converge at the representation expectation in a
+probabilistic way. We theoretically show that such an expected value of the
+representation (mean) is achievable if the manifold structure can be
+transferred from the data space to the feature space. The resulting structure
+regularization term, named manifold loss, is incorporated into the loss
+function of the typical deep learning pipeline. The STM architecture is
+constructed to enforce the learned deep representation to satisfy the intrinsic
+manifold structure from the data, which results in robust features that suit
+various application scenarios, such as digit recognition, image classification
+and object tracking. Compared to state-of-the-art CNN architectures, we achieve
+the better results on several commonly used benchmarks\footnote{The source code
+is available. this https URL }.
+",0,0,0,1,0,0
+1673,Inverse Fractional Knapsack Problem with Profits and Costs Modification,"  We address in this paper the problem of modifying both profits and costs of a
+fractional knapsack problem optimally such that a prespecified solution becomes
+an optimal solution with prespect to new parameters. This problem is called the
+inverse fractional knapsack problem. Concerning the $l_1$-norm, we first prove
+that the problem is NP-hard. The problem can be however solved in quadratic
+time if we only modify profit parameters. Additionally, we develop a
+quadratic-time algorithm that solves the inverse fractional knapsack problem
+under $l_\infty$-norm.
+",1,0,1,0,0,0
+1674,Large dimensional analysis of general margin based classification methods,"  Margin-based classifiers have been popular in both machine learning and
+statistics for classification problems. Since a large number of classifiers are
+available, one natural question is which type of classifiers should be used
+given a particular classification task. We aim to answering this question by
+investigating the asymptotic performance of a family of large-margin
+classifiers in situations where the data dimension $p$ and the sample $n$ are
+both large. This family covers a broad range of classifiers including support
+vector machine, distance weighted discrimination, penalized logistic
+regression, and large-margin unified machine as special cases. The asymptotic
+results are described by a set of nonlinear equations and we observe a close
+match of them with Monte Carlo simulation on finite data samples. Our
+analytical studies shed new light on how to select the best classifier among
+various classification methods as well as on how to choose the optimal tuning
+parameters for a given method.
+",1,0,0,1,0,0
+1675,"Complete reducibility, Kulshammer's question, conjugacy classes: a D_4 example","  Let $k$ be a nonperfect separably closed field. Let $G$ be a connected
+reductive algebraic group defined over $k$. We study rationality problems for
+Serre's notion of complete reducibility of subgroups of $G$. In particular, we
+present a new example of subgroup $H$ of $G$ of type $D_4$ in characteristic
+$2$ such that $H$ is $G$-completely reducible but not $G$-completely reducible
+over $k$ (or vice versa). This is new: all known such examples are for $G$ of
+exceptional type. We also find a new counterexample for Külshammer's question
+on representations of finite groups for $G$ of type $D_4$. A problem concerning
+the number of conjugacy classes is also considered. The notion of nonseparable
+subgroups plays a crucial role in all our constructions.
+",0,0,1,0,0,0
+1676,Covariance structure associated with an equality between two general ridge estimators,"  In a general linear model, this paper derives a necessary and sufficient
+condition under which two general ridge estimators coincide with each other.
+The condition is given as a structure of the dispersion matrix of the error
+term. Since the class of estimators considered here contains linear unbiased
+estimators such as the ordinary least squares estimator and the best linear
+unbiased estimator, our result can be viewed as a generalization of the
+well-known theorems on the equality between these two estimators, which have
+been fully studied in the literature. Two related problems are also considered:
+equality between two residual sums of squares, and classification of dispersion
+matrices by a perturbation approach.
+",0,0,1,1,0,0
+1677,Three natural subgroups of the Brauer-Picard group of a Hopf algebra with applications,"  In this article we construct three explicit natural subgroups of the
+Brauer-Picard group of the category of representations of a finite-dimensional
+Hopf algebra. In examples the Brauer Picard group decomposes into an ordered
+product of these subgroups, somewhat similar to a Bruhat decomposition.
+Our construction returns for any Hopf algebra three types of braided
+autoequivalences and correspondingly three families of invertible bimodule
+categories. This gives examples of so-called (2-)Morita equivalences and
+defects in topological field theories. We have a closer look at the case of
+quantum groups and Nichols algebras and give interesting applications. Finally,
+we briefly discuss the three families of group-theoretic extensions.
+",0,0,1,0,0,0
+1678,UAV Visual Teach and Repeat Using Only Semantic Object Features,"  We demonstrate the use of semantic object detections as robust features for
+Visual Teach and Repeat (VTR). Recent CNN-based object detectors are able to
+reliably detect objects of tens or hundreds of categories in a video at frame
+rates. We show that such detections are repeatable enough to use as landmarks
+for VTR, without any low-level image features. Since object detections are
+highly invariant to lighting and surface appearance changes, our VTR can cope
+with global lighting changes and local movements of the landmark objects. In
+the teaching phase, we build a series of compact scene descriptors: a list of
+detected object labels and their image-plane locations. In the repeating phase,
+we use Seq-SLAM-like relocalization to identify the most similar learned scene,
+then use a motion control algorithm based on the funnel lane theory to navigate
+the robot along the previously piloted trajectory. We evaluate the method on a
+commodity UAV, examining the robustness of the algorithm to new viewpoints,
+lighting conditions, and movements of landmark objects. The results suggest
+that semantic object features could be useful due to their invariance to
+superficial appearance changes compared to low-level image features.
+",1,0,0,0,0,0
+1679,Towards a Deep Reinforcement Learning Approach for Tower Line Wars,"  There have been numerous breakthroughs with reinforcement learning in the
+recent years, perhaps most notably on Deep Reinforcement Learning successfully
+playing and winning relatively advanced computer games. There is undoubtedly an
+anticipation that Deep Reinforcement Learning will play a major role when the
+first AI masters the complicated game plays needed to beat a professional
+Real-Time Strategy game player. For this to be possible, there needs to be a
+game environment that targets and fosters AI research, and specifically Deep
+Reinforcement Learning. Some game environments already exist, however, these
+are either overly simplistic such as Atari 2600 or complex such as Starcraft II
+from Blizzard Entertainment. We propose a game environment in between Atari
+2600 and Starcraft II, particularly targeting Deep Reinforcement Learning
+algorithm research. The environment is a variant of Tower Line Wars from
+Warcraft III, Blizzard Entertainment. Further, as a proof of concept that the
+environment can harbor Deep Reinforcement algorithms, we propose and apply a
+Deep Q-Reinforcement architecture. The architecture simplifies the state space
+so that it is applicable to Q-learning, and in turn improves performance
+compared to current state-of-the-art methods. Our experiments show that the
+proposed architecture can learn to play the environment well, and score 33%
+better than standard Deep Q-learning which in turn proves the usefulness of the
+game environment.
+",1,0,0,0,0,0
+1680,A Case for an Atmosphere on Super-Earth 55 Cancri e,"  One of the primary questions when characterizing Earth-sized and
+super-Earth-sized exoplanets is whether they have a substantial atmosphere like
+Earth and Venus or a bare-rock surface like Mercury. Phase curves of the
+planets in thermal emission provide clues to this question, because a
+substantial atmosphere would transport heat more efficiently than a bare-rock
+surface. Analyzing phase curve photometric data around secondary eclipse has
+previously been used to study energy transport in the atmospheres of hot
+Jupiters. Here we use phase curve, Spitzer time-series photometry to study the
+thermal emission properties of the super-Earth exoplanet 55 Cancri e. We
+utilize a semi-analytical framework to fit a physical model to the infrared
+photometric data at 4.5 micron. The model uses parameters of planetary
+properties including Bond albedo, heat redistribution efficiency (i.e., ratio
+between radiative timescale and advective timescale of the atmosphere), and
+atmospheric greenhouse factor. The phase curve of 55 Cancri e is dominated by
+thermal emission with an eastward-shifted hot spot. We determine the heat
+redistribution efficiency to be ~1.47, which implies that the advective
+timescale is on the same order as the radiative timescale. This requirement
+cannot be met by the bare-rock planet scenario because heat transport by
+currents of molten lava would be too slow. The phase curve thus favors the
+scenario with a substantial atmosphere. Our constraints on the heat
+redistribution efficiency translate to an atmospheric pressure of ~1.4 bar. The
+Spitzer 4.5-micron band is thus a window into the deep atmosphere of the planet
+55 Cancri e.
+",0,1,0,0,0,0
+1681,From LiDAR to Underground Maps via 5G - Business Models Enabling a System-of-Systems Approach to Mapping the Kankberg Mine,"  With ever-increasing productivity targets in mining operations, there is a
+growing interest in mining automation. The PIMM project addresses the
+fundamental challenge of network communication by constructing a pilot 5G
+network in the underground mine Kankberg. In this report, we discuss how such a
+5G network could constitute the essential infrastructure to organize existing
+systems in Kankberg into a system-of-systems (SoS). In this report, we analyze
+a scenario in which LiDAR equipped vehicles operating in the mine are connected
+to existing mine mapping and positioning solutions. The approach is motivated
+by the approaching era of remote controlled, or even autonomous, vehicles in
+mining operations. The proposed SoS could ensure continuously updated maps of
+Kankberg, rendered in unprecedented detail, supporting both productivity and
+safety in the underground mine. We present four different SoS solutions from an
+organizational point of view, discussing how development and operations of the
+constituent systems could be distributed among Boliden and external
+stakeholders, e.g., the vehicle suppliers, the hauling company, and the
+developers of the mapping software. The four scenarios are compared from both
+technical and business perspectives, and based on trade-off discussions and
+SWOT analyses. We conclude our report by recommending continued research along
+two future paths, namely a closer cooperation with the vehicle suppliers, and
+further feasibility studies regarding establishing a Kankberg software
+ecosystem.
+",1,0,0,0,0,0
+1682,The number of realizations of a Laman graph,"  Laman graphs model planar frameworks that are rigid for a general choice of
+distances between the vertices. There are finitely many ways, up to isometries,
+to realize a Laman graph in the plane. Such realizations can be seen as
+solutions of systems of quadratic equations prescribing the distances between
+pairs of points. Using ideas from algebraic and tropical geometry, we provide a
+recursive formula for the number of complex solutions of such systems.
+",1,0,1,0,0,0
+1683,UV Detector based on InAlN/GaN-on-Si HEMT Stack with Photo-to-Dark Current Ratio > 107,"  We demonstrate an InAlN/GaN-on-Si HEMT based UV detector with photo to dark
+current ratio > 107. Ti/Al/Ni/Au metal stack was evaporated and rapid thermal
+annealed for Ohmic contacts to the 2D electron gas (2DEG) at the InAlN/GaN
+interface while the channel + barrier was recess etched to a depth of 20 nm to
+pinch-off the 2DEG between Source-Drain pads. Spectral responsivity (SR) of 34
+A/W at 367 nm was measured at 5 V in conjunction with very high photo to dark
+current ratio of > 10^7. The photo to dark current ratio at a fixed bias was
+found to be decreasing with increase in recess length of the PD. The fabricated
+devices were found to exhibit a UV-to-visible rejection ratio of >103 with a
+low dark current < 32 pA at 5 V. Transient measurements showed rise and fall
+times in the range of 3-4 ms. The gain mechanism was investigated and carrier
+lifetimes were estimated which matched well with those reported elsewhere.
+",0,1,0,0,0,0
+1684,Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings,"  This paper is the first attempt to learn the policy of an inquiry dialog
+system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks
+represent dialog states and dialog acts with logical formulae. In order to make
+learning inquiry dialog policies more effective, we introduce a logical formula
+embedding framework based on a recursive neural network. The results of
+experiments to evaluate the effect of 1) the DRL and 2) the logical formula
+embedding framework show that the combination of the two are as effective or
+even better than existing rule-based methods for inquiry dialog policies.
+",1,0,0,0,0,0
+1685,Proof of Time's Arrow with Perfectly Chaotic Superdiffusion,"  The problem of Time's Arrow is rigorously solved in a certain microscopic
+system associated with a Hamiltonian using only information about the
+microscopic system. This microscopic system obeys an equation with time
+reversal symmetry. In detail, we prove that a symplectic map with time reversal
+symmetry is an Anosov diffeomorphism. This result guarantees that any initial
+density function defined except for a zero volume set converges to the unique
+invariant density (uniform distribution) in the sense of mixing. In addition,
+we discover that there is a mathematical structure which connects Time's Arrow
+(Anosov diffeomorphism) with superdiffusion in our system. In particular, the
+mechanism of this superdiffusion in our system is different from those
+previously found.
+",0,1,0,0,0,0
+1686,Using Phone Sensors and an Artificial Neural Network to Detect Gait Changes During Drinking Episodes in the Natural Environment,"  Phone sensors could be useful in assessing changes in gait that occur with
+alcohol consumption. This study determined (1) feasibility of collecting
+gait-related data during drinking occasions in the natural environment, and (2)
+how gait-related features measured by phone sensors relate to estimated blood
+alcohol concentration (eBAC). Ten young adult heavy drinkers were prompted to
+complete a 5-step gait task every hour from 8pm to 12am over four consecutive
+weekends. We collected 3-xis accelerometer, gyroscope, and magnetometer data
+from phone sensors, and computed 24 gait-related features using a sliding
+window technique. eBAC levels were calculated at each time point based on
+Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial
+neural network model to analyze associations between sensor features and eBACs
+in training (70% of the data) and validation and test (30% of the data)
+datasets. We analyzed 128 data points where both eBAC and gait-related sensor
+data was captured, either when not drinking (n=60), while eBAC was ascending
+(n=55) or eBAC was descending (n=13). 21 data points were captured at times
+when the eBAC was greater than the legal limit (0.08 mg/dl). Using a Bayesian
+regularized neural network, gait-related phone sensor features showed a high
+correlation with eBAC (Pearson's r > 0.9), and >95% of estimated eBAC would
+fall between -0.012 and +0.012 of actual eBAC. It is feasible to collect
+gait-related data from smartphone sensors during drinking occasions in the
+natural environment. Sensor-based features can be used to infer gait changes
+associated with elevated blood alcohol content.
+",1,0,0,1,0,0
+1687,A Lattice Model of Charge-Pattern-Dependent Polyampholyte Phase Separation,"  In view of recent intense experimental and theoretical interests in the
+biophysics of liquid-liquid phase separation (LLPS) of intrinsically disordered
+proteins (IDPs), heteropolymer models with chain molecules configured as
+self-avoiding walks on the simple cubic lattice are constructed to study how
+phase behaviors depend on the sequence of monomers along the chains. To address
+pertinent general principles, we focus primarily on two fully charged
+50-monomer sequences with significantly different charge patterns. Each monomer
+in our models occupies a single lattice site and all monomers interact via a
+screened pairwise Coulomb potential. Phase diagrams are obtained by extensive
+Monte Carlo sampling performed at multiple temperatures on ensembles of 300
+chains in boxes of sizes ranging from $52\times 52\times 52$ to $246\times
+246\times 246$ to simulate a large number of different systems with the overall
+polymer volume fraction $\phi$ in each system varying from $0.001$ to $0.1$.
+Phase separation in the model systems is characterized by the emergence of a
+large cluster connected by inter-monomer nearest-neighbor lattice contacts and
+by large fluctuations in local polymer density. The simulated critical
+temperatures, $T_{\rm cr}$, of phase separation for the two sequences differ
+significantly, whereby the sequence with a more ""blocky"" charge pattern
+exhibits a substantially higher propensity to phase separate. The trend is
+consistent with our sequence-specific random-phase-approximation (RPA) polymer
+theory, but the variation of the simulated $T_{\rm cr}$ with a previously
+proposed ""sequence charge decoration"" pattern parameter is milder than that
+predicted by RPA. Ramifications of our findings for the development of
+analytical theory and simulation protocols of IDP LLPS are discussed.
+",0,0,0,0,1,0
+1688,"""Noiseless"" thermal noise measurement of atomic force microscopy cantilevers","  When measuring quadratic values representative of random fluctuations, such
+as the thermal noise of Atomic Force Microscopy (AFM) cantilevers, the
+background measurement noise cannot be averaged to zero. We present a signal
+processing method that allows to get rid of this limitation using the
+ubiquitous optical beam deflection sensor of standard AFMs. We demonstrate a
+two orders of magnitude enhancement of the signal to noise ratio in our
+experiment, allowing the calibration of stiff cantilevers or easy
+identification of higher order modes from thermal noise measurements.
+",0,1,0,0,0,0
+1689,A Forward Model at Purkinje Cell Synapses Facilitates Cerebellar Anticipatory Control,"  How does our motor system solve the problem of anticipatory control in spite
+of a wide spectrum of response dynamics from different musculo-skeletal
+systems, transport delays as well as response latencies throughout the central
+nervous system? To a great extent, our highly-skilled motor responses are a
+result of a reactive feedback system, originating in the brain-stem and spinal
+cord, combined with a feed-forward anticipatory system, that is adaptively
+fine-tuned by sensory experience and originates in the cerebellum. Based on
+that interaction we design the counterfactual predictive control (CFPC)
+architecture, an anticipatory adaptive motor control scheme in which a
+feed-forward module, based on the cerebellum, steers an error feedback
+controller with counterfactual error signals. Those are signals that trigger
+reactions as actual errors would, but that do not code for any current or
+forthcoming errors. In order to determine the optimal learning strategy, we
+derive a novel learning rule for the feed-forward module that involves an
+eligibility trace and operates at the synaptic level. In particular, our
+eligibility trace provides a mechanism beyond co-incidence detection in that it
+convolves a history of prior synaptic inputs with error signals. In the context
+of cerebellar physiology, this solution implies that Purkinje cell synapses
+should generate eligibility traces using a forward model of the system being
+controlled. From an engineering perspective, CFPC provides a general-purpose
+anticipatory control architecture equipped with a learning rule that exploits
+the full dynamics of the closed-loop system.
+",1,0,1,0,0,0
+1690,De Rham and twisted cohomology of Oeljeklaus-Toma manifolds,"  Oeljeklaus-Toma (OT) manifolds are complex non-Kähler manifolds whose
+construction arises from specific number fields. In this note, we compute their
+de Rham cohomology in terms of invariants associated to the background number
+field. This is done by two distinct approaches, one using invariant cohomology
+and the other one using the Leray-Serre spectral sequence. In addition, we
+compute also their Morse-Novikov cohomology. As an application, we show that
+the low degree Chern classes of any complex vector bundle on an OT manifold
+vanish in the real cohomology. Other applications concern the OT manifolds
+admitting locally conformally Kähler (LCK) metrics: we show that there is
+only one possible Lee class of an LCK metric, and we determine all the possible
+Morse-Novikov classes of an LCK metric, which implies the nondegeneracy of
+certain Lefschetz maps in cohomology.
+",0,0,1,0,0,0
+1691,Episode-Based Active Learning with Bayesian Neural Networks,"  We investigate different strategies for active learning with Bayesian deep
+neural networks. We focus our analysis on scenarios where new, unlabeled data
+is obtained episodically, such as commonly encountered in mobile robotics
+applications. An evaluation of different strategies for acquisition, updating,
+and final training on the CIFAR-10 dataset shows that incremental network
+updates with final training on the accumulated acquisition set are essential
+for best performance, while limiting the amount of required human labeling
+labor.
+",1,0,0,1,0,0
+1692,Origin of X-ray and gamma-ray emission from the Galactic central region,"  We study a possible connection between different non-thermal emissions from
+the inner few parsecs of the Galaxy. We analyze the origin of the gamma-ray
+source 2FGL J1745.6-2858 (or 3FGL J1745.6-2859c) in the Galactic Center and the
+diffuse hard X-ray component recently found by NuSTAR, as well as the radio
+emission and processes of hydrogen ionization from this area. We assume that a
+source in the GC injected energetic particles with power-law spectrum into the
+surrounding medium in the past or continues to inject until now. The energetic
+particles may be protons, electrons or a combination of both. These particles
+diffuse to the surrounding medium and interact with gas, magnetic field and
+background photons to produce non-thermal emissions. We study the spectral and
+spatial features of the hard X-ray emission and gamma-ray emission by the
+particles from the central source. Our goal is to examine whether the hard
+X-ray and gamma-ray emissions have a common origin. Our estimations show that
+in the case of pure hadronic models the expected flux of hard X-ray emission is
+too low. Despite protons can produce a non-zero contribution in gamma-ray
+emission, it is unlikely that they and their secondary electrons can make a
+significant contribution in hard X-ray flux. In the case of pure leptonic
+models it is possible to reproduce both X-ray and gamma-ray emissions for both
+transient and continuous supply models. However, in the case of continuous
+supply model the ionization rate of molecular hydrogen may significantly exceed
+the observed value.
+",0,1,0,0,0,0
+1693,Freeness and The Partial Transposes of Wishart Random Matrices,"  We show that the partial transposes of complex Wishart random matrices are
+asymptotically free. We also investigate regimes where the number of blocks is
+fixed but the size of the blocks increases. This gives a example where the
+partial transpose produces freeness at the operator level. Finally we
+investigate the case of real Wishart matrices.
+",0,0,1,0,0,0
+1694,Coset space construction for the conformal group. I. Unbroken phase,"  The technique for constructing conformally invariant theories within the
+coset space construction is developed. It reproduces all consequences of the
+conformal invariance and Lagrangians of widely-known conformal field theories.
+The method of induced representations, which plays the key role in the
+construction, allows to reveal a special role of the ""Nambu-Goldstone fields""
+for special conformal transformations. Namely, their dependence on the
+coordinates turns out to be fixed by the symmetries. This results in the
+appearance of the constraints on possible forms of Lagrangians, which ensure
+that discrete symmetries are indeed symmetries of the theory.
+",0,0,1,0,0,0
+1695,Fixed points of polarity type operators,"  A well-known result says that the Euclidean unit ball is the unique fixed
+point of the polarity operator. This result implies that if, in $\mathbb{R}^n$,
+the unit ball of some norm is equal to the unit ball of the dual norm, then the
+norm must be Euclidean. Motivated by these results and by relatively recent
+results in convex analysis and convex geometry regarding various properties of
+order reversing operators, we consider, in a real Hilbert space setting, a more
+general fixed point equation in which the polarity operator is composed with a
+continuous invertible linear operator. We show that if the linear operator is
+positive definite, then the considered equation is uniquely solvable by an
+ellipsoid. Otherwise, the equation can have several (possibly infinitely many)
+solutions or no solution at all. Our analysis yields a few by-products of
+possible independent interest, among them results related to coercive bilinear
+forms (essentially a quantitative convex analytic converse to the celebrated
+Lax-Milgram theorem from partial differential equations) and a characterization
+of real Hilbertian spaces.
+",0,0,1,0,0,0
+1696,Multiple Improvements of Multiple Imputation Likelihood Ratio Tests,"  Multiple imputation (MI) inference handles missing data by first properly
+imputing the missing values $m$ times, and then combining the $m$ analysis
+results from applying a complete-data procedure to each of the completed
+datasets. However, the existing method for combining likelihood ratio tests has
+multiple defects: (i) the combined test statistic can be negative in practice
+when the reference null distribution is a standard $F$ distribution; (ii) it is
+not invariant to re-parametrization; (iii) it fails to ensure monotonic power
+due to its use of an inconsistent estimator of the fraction of missing
+information (FMI) under the alternative hypothesis; and (iv) it requires
+non-trivial access to the likelihood ratio test statistic as a function of
+estimated parameters instead of datasets. This paper shows, via both
+theoretical derivations and empirical investigations, that essentially all of
+these problems can be straightforwardly addressed if we are willing to perform
+an additional likelihood ratio test by stacking the $m$ completed datasets as
+one big completed dataset. A particularly intriguing finding is that the FMI
+itself can be estimated consistently by a likelihood ratio statistic for
+testing whether the $m$ completed datasets produced by MI can be regarded
+effectively as samples coming from a common model. Practical guidelines are
+provided based on an extensive comparison of existing MI tests.
+",0,0,1,1,0,0
+1697,Estimating the unseen from multiple populations,"  Given samples from a distribution, how many new elements should we expect to
+find if we continue sampling this distribution? This is an important and
+actively studied problem, with many applications ranging from unseen species
+estimation to genomics. We generalize this extrapolation and related unseen
+estimation problems to the multiple population setting, where population $j$
+has an unknown distribution $D_j$ from which we observe $n_j$ samples. We
+derive an optimal estimator for the total number of elements we expect to find
+among new samples across the populations. Surprisingly, we prove that our
+estimator's accuracy is independent of the number of populations. We also
+develop an efficient optimization algorithm to solve the more general problem
+of estimating multi-population frequency distributions. We validate our methods
+and theory through extensive experiments. Finally, on a real dataset of human
+genomes across multiple ancestries, we demonstrate how our approach for unseen
+estimation can enable cohort designs that can discover interesting mutations
+with greater efficiency.
+",1,0,0,1,0,0
+1698,Continued Fractions and $q$-Series Generating Functions for the Generalized Sum-of-Divisors Functions,"  We construct new continued fraction expansions of Jacobi-type J-fractions in
+$z$ whose power series expansions generate the ratio of the $q$-Pochhamer
+symbols, $(a; q)_n / (b; q)_n$, for all integers $n \geq 0$ and where $a,b,q
+\in \mathbb{C}$ are non-zero and defined such that $|q| < 1$ and $|b/a| < |z| <
+1$. If we set the parameters $(a, b) := (q, q^2)$ in these generalized series
+expansions, then we have a corresponding J-fraction enumerating the sequence of
+terms $(1-q) / (1-q^{n+1})$ over all integers $n \geq 0$. Thus we are able to
+define new $q$-series expansions which correspond to the Lambert series
+generating the divisor function, $d(n)$, when we set $z \mapsto q$ in our new
+J-fraction expansions. By repeated differentiation with respect to $z$, we also
+use these generating functions to formulate new $q$-series expansions of the
+generating functions for the sums-of-divisors functions, $\sigma_{\alpha}(n)$,
+when $\alpha \in \mathbb{Z}^{+}$. To expand the new $q$-series generating
+functions for these special arithmetic functions we define a generalized
+classes of so-termed Stirling-number-like ""$q$-coefficients"", or Stirling
+$q$-coefficients, whose properties, relations to elementary symmetric
+polynomials, and relations to the convergents to our infinite J-fractions are
+also explored within the results proved in the article.
+",0,0,1,0,0,0
+1699,Implications of a wavelength dependent PSF for weak lensing measurements,"  The convolution of galaxy images by the point-spread function (PSF) is the
+dominant source of bias for weak gravitational lensing studies, and an accurate
+estimate of the PSF is required to obtain unbiased shape measurements. The PSF
+estimate for a galaxy depends on its spectral energy distribution (SED),
+because the instrumental PSF is generally a function of the wavelength. In this
+paper we explore various approaches to determine the resulting `effective' PSF
+using broad-band data. Considering the Euclid mission as a reference, we find
+that standard SED template fitting methods result in biases that depend on
+source redshift, although this may be remedied if the algorithms can be
+optimised for this purpose. Using a machine-learning algorithm we show that, at
+least in principle, the required accuracy can be achieved with the current
+survey parameters. It is also possible to account for the correlations between
+photometric redshift and PSF estimates that arise from the use of the same
+photometry. We explore the impact of errors in photometric calibration, errors
+in the assumed wavelength dependence of the PSF model and limitations of the
+adopted template libraries. Our results indicate that the required accuracy for
+Euclid can be achieved using the data that are planned to determine photometric
+redshifts.
+",0,1,0,0,0,0
+1700,Improving TSP tours using dynamic programming over tree decomposition,"  Given a traveling salesman problem (TSP) tour $H$ in graph $G$ a $k$-move is
+an operation which removes $k$ edges from $H$, and adds $k$ edges of $G$ so
+that a new tour $H'$ is formed. The popular $k$-OPT heuristics for TSP finds a
+local optimum by starting from an arbitrary tour $H$ and then improving it by a
+sequence of $k$-moves.
+Until 2016, the only known algorithm to find an improving $k$-move for a
+given tour was the naive solution in time $O(n^k)$. At ICALP'16 de Berg,
+Buchin, Jansen and Woeginger showed an $O(n^{\lfloor 2/3k \rfloor+1})$-time
+algorithm.
+We show an algorithm which runs in $O(n^{(1/4+\epsilon_k)k})$ time, where
+$\lim \epsilon_k = 0$. We are able to show that it improves over the state of
+the art for every $k=5,\ldots,10$. For the most practically relevant case $k=5$
+we provide a slightly refined algorithm running in $O(n^{3.4})$ time. We also
+show that for the $k=4$ case, improving over the $O(n^3)$-time algorithm of de
+Berg et al. would be a major breakthrough: an $O(n^{3-\epsilon})$-time
+algorithm for any $\epsilon>0$ would imply an $O(n^{3-\delta})$-time algorithm
+for the ALL PAIRS SHORTEST PATHS problem, for some $\delta>0$.
+",1,0,0,0,0,0
+1701,Semi-Supervised Approaches to Efficient Evaluation of Model Prediction Performance,"  In many modern machine learning applications, the outcome is expensive or
+time-consuming to collect while the predictor information is easy to obtain.
+Semi-supervised learning (SSL) aims at utilizing large amounts of `unlabeled'
+data along with small amounts of `labeled' data to improve the efficiency of a
+classical supervised approach. Though numerous SSL classification and
+prediction procedures have been proposed in recent years, no methods currently
+exist to evaluate the prediction performance of a working regression model. In
+the context of developing phenotyping algorithms derived from electronic
+medical records (EMR), we present an efficient two-step estimation procedure
+for evaluating a binary classifier based on various prediction performance
+measures in the semi-supervised (SS) setting. In step I, the labeled data is
+used to obtain a non-parametrically calibrated estimate of the conditional risk
+function. In step II, SS estimates of the prediction accuracy parameters are
+constructed based on the estimated conditional risk function and the unlabeled
+data. We demonstrate that under mild regularity conditions the proposed
+estimators are consistent and asymptotically normal. Importantly, the
+asymptotic variance of the SS estimators is always smaller than that of the
+supervised counterparts under correct model specification. We also correct for
+potential overfitting bias in the SS estimators in finite sample with
+cross-validation and develop a perturbation resampling procedure to approximate
+their distributions. Our proposals are evaluated through extensive simulation
+studies and illustrated with two real EMR studies aiming to develop phenotyping
+algorithms for rheumatoid arthritis and multiple sclerosis.
+",0,0,0,1,0,0
+1702,Linearization of the box-ball system: an elementary approach,"  Kuniba, Okado, Takagi and Yamada have found that the time-evolution of the
+Takahashi-Satsuma box-ball system can be linearized by considering rigged
+configurations associated with states of the box-ball system. We introduce a
+simple way to understand the rigged configuration of $\mathfrak{sl}_2$-type,
+and give an elementary proof of the linearization property. Our approach can be
+applied to a box-ball system with finite carrier, which is related to a
+discrete modified KdV equation, and also to the combinatorial $R$-matrix of
+$A_1^{(1)}$-type. We also discuss combinatorial statistics and related
+fermionic formulas associated with the states of the box-ball systems. A
+fermionic-type formula we obtain for the finite carrier case seems to be new.
+",0,1,0,0,0,0
+1703,Controlling Sources of Inaccuracy in Stochastic Kriging,"  Scientists and engineers commonly use simulation models to study real systems
+for which actual experimentation is costly, difficult, or impossible. Many
+simulations are stochastic in the sense that repeated runs with the same input
+configuration will result in different outputs. For expensive or time-consuming
+simulations, stochastic kriging \citep{ankenman} is commonly used to generate
+predictions for simulation model outputs subject to uncertainty due to both
+function approximation and stochastic variation. Here, we develop and justify a
+few guidelines for experimental design, which ensure accuracy of stochastic
+kriging emulators. We decompose error in stochastic kriging predictions into
+nominal, numeric, parameter estimation and parameter estimation numeric
+components and provide means to control each in terms of properties of the
+underlying experimental design. The design properties implied for each source
+of error are weakly conflicting and broad principles are proposed. In brief,
+space-filling properties ""small fill distance"" and ""large separation distance""
+should balance with replication at distinct input configurations, with number
+of replications depending on the relative magnitudes of stochastic and process
+variability. Non-stationarity implies higher input density in more active
+regions, while regression functions imply a balance with traditional design
+properties. A few examples are presented to illustrate the results.
+",0,0,1,1,0,0
+1704,Implications of Decentralized Q-learning Resource Allocation in Wireless Networks,"  Reinforcement Learning is gaining attention by the wireless networking
+community due to its potential to learn good-performing configurations only
+from the observed results. In this work we propose a stateless variation of
+Q-learning, which we apply to exploit spatial reuse in a wireless network. In
+particular, we allow networks to modify both their transmission power and the
+channel used solely based on the experienced throughput. We concentrate in a
+completely decentralized scenario in which no information about neighbouring
+nodes is available to the learners. Our results show that although the
+algorithm is able to find the best-performing actions to enhance aggregate
+throughput, there is high variability in the throughput experienced by the
+individual networks. We identify the cause of this variability as the
+adversarial setting of our setup, in which the most played actions provide
+intermittent good/poor performance depending on the neighbouring decisions. We
+also evaluate the effect of the intrinsic learning parameters of the algorithm
+on this variability.
+",1,0,0,0,0,0
+1705,Exponential Ergodicity of the Bouncy Particle Sampler,"  Non-reversible Markov chain Monte Carlo schemes based on piecewise
+deterministic Markov processes have been recently introduced in applied
+probability, automatic control, physics and statistics. Although these
+algorithms demonstrate experimentally good performance and are accordingly
+increasingly used in a wide range of applications, geometric ergodicity results
+for such schemes have only been established so far under very restrictive
+assumptions. We give here verifiable conditions on the target distribution
+under which the Bouncy Particle Sampler algorithm introduced in \cite{P_dW_12}
+is geometrically ergodic. This holds whenever the target satisfies a curvature
+condition and has tails decaying at least as fast as an exponential and at most
+as fast as a Gaussian distribution. This allows us to provide a central limit
+theorem for the associated ergodic averages. When the target has tails thinner
+than a Gaussian distribution, we propose an original modification of this
+scheme that is geometrically ergodic. For thick-tailed target distributions,
+such as $t$-distributions, we extend the idea pioneered in \cite{J_G_12} in a
+random walk Metropolis context. We apply a change of variable to obtain a
+transformed target satisfying the tail conditions for geometric ergodicity. By
+sampling the transformed target using the Bouncy Particle Sampler and mapping
+back the Markov process to the original parameterization, we obtain a
+geometrically ergodic algorithm.
+",0,0,0,1,0,0
+1706,Analysis and X-ray tomography,"  These are lecture notes for the course ""MATS4300 Analysis and X-ray
+tomography"" given at the University of Jyväskylä in Fall 2017. The course
+is a broad overview of various tools in analysis that can be used to study
+X-ray tomography. The focus is on tools and ideas, not so much on technical
+details and minimal assumptions. Only very basic functional analysis is assumed
+as background. Exercise problems are included.
+",0,0,1,0,0,0
+1707,Spatially Transformed Adversarial Examples,"  Recent studies show that widely used deep neural networks (DNNs) are
+vulnerable to carefully crafted adversarial examples. Many advanced algorithms
+have been proposed to generate adversarial examples by leveraging the
+$\mathcal{L}_p$ distance for penalizing perturbations. Researchers have
+explored different defense methods to defend against such adversarial attacks.
+While the effectiveness of $\mathcal{L}_p$ distance as a metric of perceptual
+quality remains an active research area, in this paper we will instead focus on
+a different type of perturbation, namely spatial transformation, as opposed to
+manipulating the pixel values directly as in prior works. Perturbations
+generated through spatial transformation could result in large $\mathcal{L}_p$
+distance measures, but our extensive experiments show that such spatially
+transformed adversarial examples are perceptually realistic and more difficult
+to defend against with existing defense systems. This potentially provides a
+new direction in adversarial example generation and the design of corresponding
+defenses. We visualize the spatial transformation based perturbation for
+different examples and show that our technique can produce realistic
+adversarial examples with smooth image deformation. Finally, we visualize the
+attention of deep networks with different types of adversarial examples to
+better understand how these examples are interpreted.
+",0,0,0,1,0,0
+1708,Arrow Categories of Monoidal Model Categories,"  We prove that the arrow category of a monoidal model category, equipped with
+the pushout product monoidal structure and the projective model structure, is a
+monoidal model category. This answers a question posed by Mark Hovey, and has
+the important consequence that it allows for the consideration of a monoidal
+product in cubical homotopy theory. As illustrations we include numerous
+examples of non-cofibrantly generated monoidal model categories, including
+chain complexes, small categories, topological spaces, and pro-categories.
+",0,0,1,0,0,0
+1709,Differential-operator representations of Weyl group and singular vectors,"  Given a suitable ordering of the positive root system associated with a
+semisimple Lie algebra, there exists a natural correspondence between Verma
+modules and related polynomial algebras. With this, the Lie algebra action on a
+Verma module can be interpreted as a differential operator action on
+polynomials, and thus on the corresponding truncated formal power series. We
+prove that the space of truncated formal power series is a
+differential-operator representation of the Weyl group $W$. We also introduce a
+system of partial differential equations to investigate singular vectors in the
+Verma module. It is shown that the solution space of the system in the space of
+truncated formal power series is the span of $\{w(1)\ |\ w\in W\}$. Those
+$w(1)$ that are polynomials correspond to singular vectors in the Verma module.
+This elementary approach by partial differential equations also gives a new
+proof of the well-known BGG-Verma Theorem.
+",0,0,1,0,0,0
+1710,Faithful Semitoric Systems,"  This paper consists of two parts. The first provides a review of the basic
+properties of integrable and almost-toric systems, with a particular emphasis
+on the integral affine structure associated to an integrable system. The second
+part introduces faithful semitoric systems, a generalization of semitoric
+systems (introduced by Vu Ngoc and classified by Pelayo and Vu Ngoc) that
+provides the language to develop surgeries on almost-toric systems in dimension
+4. We prove that faithful semitoric systems are natural building blocks of
+almost-toric systems. Moreover, we show that they enjoy many of the properties
+that their (proper) semitoric counterparts do.
+",0,1,1,0,0,0
+1711,HoloScope: Topology-and-Spike Aware Fraud Detection,"  As online fraudsters invest more resources, including purchasing large pools
+of fake user accounts and dedicated IPs, fraudulent attacks become less obvious
+and their detection becomes increasingly challenging. Existing approaches such
+as average degree maximization suffer from the bias of including more nodes
+than necessary, resulting in lower accuracy and increased need for manual
+verification. Hence, we propose HoloScope, which uses information from graph
+topology and temporal spikes to more accurately detect groups of fraudulent
+users. In terms of graph topology, we introduce ""contrast suspiciousness,"" a
+dynamic weighting approach, which allows us to more accurately detect
+fraudulent blocks, particularly low-density blocks. In terms of temporal
+spikes, HoloScope takes into account the sudden bursts and drops of fraudsters'
+attacking patterns. In addition, we provide theoretical bounds for how much
+this increases the time cost needed for fraudsters to conduct adversarial
+attacks. Additionally, from the perspective of ratings, HoloScope incorporates
+the deviation of rating scores in order to catch fraudsters more accurately.
+Moreover, HoloScope has a concise framework and sub-quadratic time complexity,
+making the algorithm reproducible and scalable. Extensive experiments showed
+that HoloScope achieved significant accuracy improvements on synthetic and real
+data, compared with state-of-the-art fraud detection methods.
+",1,0,0,0,0,0
+1712,On Approximation Guarantees for Greedy Low Rank Optimization,"  We provide new approximation guarantees for greedy low rank matrix estimation
+under standard assumptions of restricted strong convexity and smoothness. Our
+novel analysis also uncovers previously unknown connections between the low
+rank estimation and combinatorial optimization, so much so that our bounds are
+reminiscent of corresponding approximation bounds in submodular maximization.
+Additionally, we also provide statistical recovery guarantees. Finally, we
+present empirical comparison of greedy estimation with established baselines on
+two important real-world problems.
+",1,0,0,1,0,0
+1713,Topology Estimation in Bulk Power Grids: Guarantees on Exact Recovery,"  The topology of a power grid affects its dynamic operation and settlement in
+the electricity market. Real-time topology identification can enable faster
+control action following an emergency scenario like failure of a line. This
+article discusses a graphical model framework for topology estimation in bulk
+power grids (both loopy transmission and radial distribution) using
+measurements of voltage collected from the grid nodes. The graphical model for
+the probability distribution of nodal voltages in linear power flow models is
+shown to include additional edges along with the operational edges in the true
+grid. Our proposed estimation algorithms first learn the graphical model and
+subsequently extract the operational edges using either thresholding or a
+neighborhood counting scheme. For grid topologies containing no three-node
+cycles (two buses do not share a common neighbor), we prove that an exact
+extraction of the operational topology is theoretically guaranteed. This
+includes a majority of distribution grids that have radial topologies. For
+grids that include cycles of length three, we provide sufficient conditions
+that ensure existence of algorithms for exact reconstruction. In particular,
+for grids with constant impedance per unit length and uniform injection
+covariances, this observation leads to conditions on geographical placement of
+the buses. The performance of algorithms is demonstrated in test case
+simulations.
+",1,0,1,1,0,0
+1714,Wasserstein Introspective Neural Networks,"  We present Wasserstein introspective neural networks (WINN) that are both a
+generator and a discriminator within a single model. WINN provides a
+significant improvement over the recent introspective neural networks (INN)
+method by enhancing INN's generative modeling capability. WINN has three
+interesting properties: (1) A mathematical connection between the formulation
+of the INN algorithm and that of Wasserstein generative adversarial networks
+(WGAN) is made. (2) The explicit adoption of the Wasserstein distance into INN
+results in a large enhancement to INN, achieving compelling results even with a
+single classifier --- e.g., providing nearly a 20 times reduction in model size
+over INN for unsupervised generative modeling. (3) When applied to supervised
+classification, WINN also gives rise to improved robustness against adversarial
+examples in terms of the error reduction. In the experiments, we report
+encouraging results on unsupervised learning problems including texture, face,
+and object modeling, as well as a supervised classification task against
+adversarial attacks.
+",1,0,0,0,0,0
+1715,Convexity of level lines of Martin functions and applications,"  Let $\Omega$ be an unbounded domain in $\mathbb{R}\times\mathbb{R}^{d}.$ A
+positive harmonic function $u$ on $\Omega$ that vanishes on the boundary of
+$\Omega$ is called a Martin function. In this note, we show that, when $\Omega$
+is convex, the superlevel sets of a Martin function are also convex. As a
+consequence we obtain that if in addition $\Omega$ is symmetric, then the
+maximum of any Martin function along a slice $\Omega\cap
+(\{t\}\times\mathbb{R}^d)$ is attained at $(t,0).$
+",0,0,1,0,0,0
+1716,New skein invariants of links,"  We introduce new skein invariants of links based on a procedure where we
+first apply the skein relation only to crossings of distinct components, so as
+to produce collections of unlinked knots. We then evaluate the resulting knots
+using a given invariant. A skein invariant can be computed on each link solely
+by the use of skein relations and a set of initial conditions. The new
+procedure, remarkably, leads to generalizations of the known skein invariants.
+We make skein invariants of classical links, $H[R]$, $K[Q]$ and $D[T]$, based
+on the invariants of knots, $R$, $Q$ and $T$, denoting the regular isotopy
+version of the Homflypt polynomial, the Kauffman polynomial and the Dubrovnik
+polynomial. We provide skein theoretic proofs of the well-definedness of these
+invariants. These invariants are also reformulated into summations of the
+generating invariants ($R$, $Q$, $T$) on sublinks of a given link $L$, obtained
+by partitioning $L$ into collections of sublinks.
+",0,0,1,0,0,0
+1717,HSTREAM: A directive-based language extension for heterogeneous stream computing,"  Big data streaming applications require utilization of heterogeneous parallel
+computing systems, which may comprise multiple multi-core CPUs and many-core
+accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such
+systems require advanced knowledge of several hardware architectures and
+device-specific programming models, including OpenMP and CUDA. In this paper,
+we present HSTREAM, a compiler directive-based language extension to support
+programming stream computing applications for heterogeneous parallel computing
+systems. HSTREAM source-to-source compiler aims to increase the programming
+productivity by enabling programmers to annotate the parallel regions for
+heterogeneous execution and generate target specific code. The HSTREAM runtime
+automatically distributes the workload across CPUs and accelerating devices. We
+demonstrate the usefulness of HSTREAM language extension with various
+applications from the STREAM benchmark. Experimental evaluation results show
+that HSTREAM can keep the same programming simplicity as OpenMP, and the
+generated code can deliver performance beyond what CPUs-only and GPUs-only
+executions can deliver.
+",1,0,0,0,0,0
+1718,Faddeev-Jackiw approach of the noncommutative spacetime Podolsky electromagnetic theory,"  The interest in higher derivatives field theories has its origin mainly in
+their influence concerning the renormalization properties of physical models
+and to remove ultraviolet divergences. The noncommutative Podolsky theory is a
+constrained system that cannot by directly quantized by the canonical way. In
+this work we have used the Faddeev-Jackiw method in order to obtain the Dirac
+brackets of the NC Podolsky theory.
+",0,1,0,0,0,0
+1719,Spin Transport and Accumulation in 2D Weyl Fermion System,"  In this work, we study the spin Hall effect and Rashba-Edelstein effect of a
+2D Weyl fermion system in the clean limit using the Kubo formalism. Spin
+transport is solely due to the spin-torque current in this strongly spin-orbit
+coupled (SOC) system, and chiral spin-flip scattering off non-SOC scalar
+impurities, with potential strength $V$ and size $a$, gives rise to a
+skew-scattering mechanism for the spin Hall effect. The key result is that the
+resultant spin-Hall angle has a fixed sign, with $\theta^{SH} \sim O
+\left(\tfrac{V^2}{v_F^2/a^2} (k_F a)^4 \right)$ being a strongly-dependent
+function of $k_F a$, with $k_F$ and $v_F$ being the Fermi wave-vector and Fermi
+velocity respectively. This, therefore, allows for the possibility of tuning
+the SHE by adjusting the Fermi energy or impurity size.
+",0,1,0,0,0,0
+1720,Model-Based Control Using Koopman Operators,"  This paper explores the application of Koopman operator theory to the control
+of robotic systems. The operator is introduced as a method to generate
+data-driven models that have utility for model-based control methods. We then
+motivate the use of the Koopman operator towards augmenting model-based
+control. Specifically, we illustrate how the operator can be used to obtain a
+linearizable data-driven model for an unknown dynamical process that is useful
+for model-based control synthesis. Simulated results show that with increasing
+complexity in the choice of the basis functions, a closed-loop controller is
+able to invert and stabilize a cart- and VTOL-pendulum systems. Furthermore,
+the specification of the basis function are shown to be of importance when
+generating a Koopman operator for specific robotic systems. Experimental
+results with the Sphero SPRK robot explore the utility of the Koopman operator
+in a reduced state representation setting where increased complexity in the
+basis function improve open- and closed-loop controller performance in various
+terrains, including sand.
+",1,0,0,0,0,0
+1721,Turbulence Hierarchy in a Random Fibre Laser,"  Turbulence is a challenging feature common to a wide range of complex
+phenomena. Random fibre lasers are a special class of lasers in which the
+feedback arises from multiple scattering in a one-dimensional disordered
+cavity-less medium. Here, we report on statistical signatures of turbulence in
+the distribution of intensity fluctuations in a continuous-wave-pumped
+erbium-based random fibre laser, with random Bragg grating scatterers. The
+distribution of intensity fluctuations in an extensive data set exhibits three
+qualitatively distinct behaviours: a Gaussian regime below threshold, a mixture
+of two distributions with exponentially decaying tails near the threshold, and
+a mixture of distributions with stretched-exponential tails above threshold.
+All distributions are well described by a hierarchical stochastic model that
+incorporates Kolmogorov's theory of turbulence, which includes energy cascade
+and the intermittence phenomenon. Our findings have implications for explaining
+the remarkably challenging turbulent behaviour in photonics, using a random
+fibre laser as the experimental platform.
+",0,1,0,0,0,0
+1722,Optimal Rates for Learning with Nyström Stochastic Gradient Methods,"  In the setting of nonparametric regression, we propose and study a
+combination of stochastic gradient methods with Nyström subsampling, allowing
+multiple passes over the data and mini-batches. Generalization error bounds for
+the studied algorithm are provided. Particularly, optimal learning rates are
+derived considering different possible choices of the step-size, the mini-batch
+size, the number of iterations/passes, and the subsampling level. In comparison
+with state-of-the-art algorithms such as the classic stochastic gradient
+methods and kernel ridge regression with Nyström, the studied algorithm has
+advantages on the computational complexity, while achieving the same optimal
+learning rates. Moreover, our results indicate that using mini-batches can
+reduce the total computational cost while achieving the same optimal
+statistical results.
+",1,0,1,1,0,0
+1723,"Run Procrustes, Run! On the convergence of accelerated Procrustes Flow","  In this work, we present theoretical results on the convergence of non-convex
+accelerated gradient descent in matrix factorization models. The technique is
+applied to matrix sensing problems with squared loss, for the estimation of a
+rank $r$ optimal solution $X^\star \in \mathbb{R}^{n \times n}$. We show that
+the acceleration leads to linear convergence rate, even under non-convex
+settings where the variable $X$ is represented as $U U^\top$ for $U \in
+\mathbb{R}^{n \times r}$. Our result has the same dependence on the condition
+number of the objective --and the optimal solution-- as that of the recent
+results on non-accelerated algorithms. However, acceleration is observed in
+practice, both in synthetic examples and in two real applications: neuronal
+multi-unit activities recovery from single electrode recordings, and quantum
+state tomography on quantum computing simulators.
+",0,0,0,1,0,0
+1724,A note on the bijectivity of antipode of a Hopf algebra and its applications,"  Certain sufficient homological and ring-theoretical conditions are given for
+a Hopf algebra to have bijective antipode with applications to noetherian Hopf
+algebras regarding their homological behaviors.
+",0,0,1,0,0,0
+1725,Perfect Edge Domination: Hard and Solvable Cases,"  Let $G$ be an undirected graph. An edge of $G$ dominates itself and all edges
+adjacent to it. A subset $E'$ of edges of $G$ is an edge dominating set of $G$,
+if every edge of the graph is dominated by some edge of $E'$. We say that $E'$
+is a perfect edge dominating set of $G$, if every edge not in $E'$ is dominated
+by exactly one edge of $E'$. The perfect edge dominating problem is to
+determine a least cardinality perfect edge dominating set of $G$. For this
+problem, we describe two NP-completeness proofs, for the classes of claw-free
+graphs of degree at most 3, and for bounded degree graphs, of maximum degree at
+most $d \geq 3$ and large girth. In contrast, we prove that the problem admits
+an $O(n)$ time solution, for cubic claw-free graphs. In addition, we prove a
+complexity dichotomy theorem for the perfect edge domination problem, based on
+the results described in the paper. Finally, we describe a linear time
+algorithm for finding a minimum weight perfect edge dominating set of a
+$P_5$-free graph. The algorithm is robust, in the sense that, given an
+arbitrary graph $G$, either it computes a minimum weight perfect edge
+dominating set of $G$, or it exhibits an induced subgraph of $G$, isomorphic to
+a $P_5$.
+",1,0,0,0,0,0
+1726,On the presentation of Hecke-Hopf algebras for non-simply-laced type,"  Hecke-Hopf algebras were defined by A. Berenstein and D. Kazhdan. We give an
+explicit presentation of an Hecke-Hopf algebra when the parameter $m_{ij},$
+associated to any two distinct vertices $i$ and $j$ in the presentation of a
+Coxeter group, equals $4,$ $5$ or $6$. As an application, we give a proof of a
+conjecture of Berenstein and Kazhdan when the Coxeter group is crystallographic
+and non-simply-laced. As another application, we show that another conjecture
+of Berenstein and Kazhdan holds when $m_{ij},$ associated to any two distinct
+vertices $i$ and $j,$ equals $4$ and that the conjecture does not hold when
+some $m_{ij}$ equals $6$ by giving a counterexample to it.
+",0,0,1,0,0,0
+1727,ILP-based Alleviation of Dense Meander Segments with Prioritized Shifting and Progressive Fixing in PCB Routing,"  Length-matching is an important technique to bal- ance delays of bus signals
+in high-performance PCB routing. Existing routers, however, may generate very
+dense meander segments. Signals propagating along these meander segments
+exhibit a speedup effect due to crosstalk between the segments of the same
+wire, thus leading to mismatch of arrival times even under the same physical
+wire length. In this paper, we present a post-processing method to enlarge the
+width and the distance of meander segments and hence distribute them more
+evenly on the board so that crosstalk can be reduced. In the proposed
+framework, we model the sharing of available routing areas after removing dense
+meander segments from the initial routing, as well as the generation of relaxed
+meander segments and their groups for wire length compensation. This model is
+transformed into an ILP problem and solved for a balanced distribution of wire
+patterns. In addition, we adjust the locations of long wire segments according
+to wire priorities to swap free spaces toward critical wires that need much
+length compensation. To reduce the problem space of the ILP model, we also
+introduce a progressive fixing technique so that wire patterns are grown
+gradually from the edge of the routing toward the center area. Experimental
+results show that the proposed method can expand meander segments significantly
+even under very tight area constraints, so that the speedup effect can be
+alleviated effectively in high- performance PCB designs.
+",1,0,0,0,0,0
+1728,Membrane Trafficking in the Yeast Saccharomyces cerevisiae Model,"  The yeast Saccharomyces cerevisiae is one of the best characterized
+eukaryotic models. The secretory pathway was the first trafficking pathway
+clearly understood mainly thanks to the work done in the laboratory of Randy
+Schekman in the 1980s. They have isolated yeast sec mutants unable to secrete
+an extracellular enzyme and these SEC genes were identified as encoding key
+effectors of the secretory machinery. For this work, the 2013 Nobel Prize in
+Physiology and Medicine has been awarded to Randy Schekman; the prize is shared
+with James Rothman and Thomas S{ü}dhof. Here, we present the different
+trafficking pathways of yeast S. cerevisiae. At the Golgi apparatus newly
+synthesized proteins are sorted between those transported to the plasma
+membrane (PM), or the external medium, via the exocytosis or secretory pathway
+(SEC), and those targeted to the vacuole either through endosomes (vacuolar
+protein sorting or VPS pathway) or directly (alkaline phosphatase or ALP
+pathway). Plasma membrane proteins can be internalized by endocytosis (END) and
+transported to endosomes where they are sorted between those targeted for
+vacuolar degradation and those redirected to the Golgi (recycling or RCY
+pathway). Studies in yeast S. cerevisiae allowed the identification of most of
+the known effectors, protein complexes, and trafficking pathways in eukaryotic
+cells, and most of them are conserved among eukaryotes.
+",0,0,0,0,1,0
+1729,Synthesizing Programs for Images using Reinforced Adversarial Learning,"  Advances in deep generative networks have led to impressive results in recent
+years. Nevertheless, such models can often waste their capacity on the minutiae
+of datasets, presumably due to weak inductive biases in their decoders. This is
+where graphics engines may come in handy since they abstract away low-level
+details and represent images as high-level programs. Current methods that
+combine deep learning and renderers are limited by hand-crafted likelihood or
+distance functions, a need for large amounts of supervision, or difficulties in
+scaling their inference algorithms to richer datasets. To mitigate these
+issues, we present SPIRAL, an adversarially trained agent that generates a
+program which is executed by a graphics engine to interpret and sample images.
+The goal of this agent is to fool a discriminator network that distinguishes
+between real and rendered data, trained with a distributed reinforcement
+learning setup without any supervision. A surprising finding is that using the
+discriminator's output as a reward signal is the key to allow the agent to make
+meaningful progress at matching the desired output rendering. To the best of
+our knowledge, this is the first demonstration of an end-to-end, unsupervised
+and adversarial inverse graphics agent on challenging real world (MNIST,
+Omniglot, CelebA) and synthetic 3D datasets.
+",0,0,0,1,0,0
+1730,A Correspondence Between Random Neural Networks and Statistical Field Theory,"  A number of recent papers have provided evidence that practical design
+questions about neural networks may be tackled theoretically by studying the
+behavior of random networks. However, until now the tools available for
+analyzing random neural networks have been relatively ad-hoc. In this work, we
+show that the distribution of pre-activations in random neural networks can be
+exactly mapped onto lattice models in statistical physics. We argue that
+several previous investigations of stochastic networks actually studied a
+particular factorial approximation to the full lattice model. For random linear
+networks and random rectified linear networks we show that the corresponding
+lattice models in the wide network limit may be systematically approximated by
+a Gaussian distribution with covariance between the layers of the network. In
+each case, the approximate distribution can be diagonalized by Fourier
+transformation. We show that this approximation accurately describes the
+results of numerical simulations of wide random neural networks. Finally, we
+demonstrate that in each case the large scale behavior of the random networks
+can be approximated by an effective field theory.
+",1,1,0,1,0,0
+1731,The nature of the progenitor of the M31 North-western stream: globular clusters as milestones of its orbit,"  We examine the nature, possible orbits and physical properties of the
+progenitor of the North-western stellar stream (NWS) in the halo of the
+Andromeda galaxy (M31). The progenitor is assumed to be an accreting dwarf
+galaxy with globular clusters (GCs). It is, in general, difficult to determine
+the progenitor's orbit precisely because of many necessary parameters.
+Recently, Veljanoski et al. 2014 reported five GCs whose positions and radial
+velocities suggest an association with the stream. We use this data to
+constrain the orbital motions of the progenitor using test-particle
+simulations. Our simulations split the orbit solutions into two branches
+according to whether the stream ends up in the foreground or in the background
+of M31. Upcoming observations that will determine the distance to the NWS will
+be able to reject one of the two branches. In either case, the solutions
+require that the pericentric radius of any possible orbit be over 2 kpc. We
+estimate the efficiency of the tidal disruption and confirm the consistency
+with the assumption for the progenitor being a dwarf galaxy. The progenitor
+requires the mass $\ga 2\times10^6 M_{\sun}$ and half-light radius $\ga 30$ pc.
+In addition, $N$-body simulations successfully reproduce the basic observed
+features of the NWS and the GCs' line-of-sight velocities.
+",0,1,0,0,0,0
+1732,On codimension two flats in Fermat-type arrangements,"  In the present note we study certain arrangements of codimension $2$ flats in
+projective spaces, we call them ""Fermat arrangements"". We describe algebraic
+properties of their defining ideals. In particular, we show that they provide
+counterexamples to an expected containment relation between ordinary and
+symbolic powers of homogeneous ideals.
+",0,0,1,0,0,0
+1733,Invariant Causal Prediction for Sequential Data,"  We investigate the problem of inferring the causal predictors of a response
+$Y$ from a set of $d$ explanatory variables $(X^1,\dots,X^d)$. Classical
+ordinary least squares regression includes all predictors that reduce the
+variance of $Y$. Using only the causal predictors instead leads to models that
+have the advantage of remaining invariant under interventions, loosely speaking
+they lead to invariance across different ""environments"" or ""heterogeneity
+patterns"". More precisely, the conditional distribution of $Y$ given its causal
+predictors remains invariant for all observations. Recent work exploits such a
+stability to infer causal relations from data with different but known
+environments. We show that even without having knowledge of the environments or
+heterogeneity pattern, inferring causal relations is possible for time-ordered
+(or any other type of sequentially ordered) data. In particular, this allows
+detecting instantaneous causal relations in multivariate linear time series
+which is usually not the case for Granger causality. Besides novel methodology,
+we provide statistical confidence bounds and asymptotic detection results for
+inferring causal predictors, and present an application to monetary policy in
+macroeconomics.
+",0,0,1,1,0,0
+1734,Smoothing with Couplings of Conditional Particle Filters,"  In state space models, smoothing refers to the task of estimating a latent
+stochastic process given noisy measurements related to the process. We propose
+an unbiased estimator of smoothing expectations. The lack-of-bias property has
+methodological benefits: independent estimators can be generated in parallel,
+and confidence intervals can be constructed from the central limit theorem to
+quantify the approximation error. To design unbiased estimators, we combine a
+generic debiasing technique for Markov chains with a Markov chain Monte Carlo
+algorithm for smoothing. The resulting procedure is widely applicable and we
+show in numerical experiments that the removal of the bias comes at a
+manageable increase in variance. We establish the validity of the proposed
+estimators under mild assumptions. Numerical experiments are provided on toy
+models, including a setting of highly-informative observations, and a realistic
+Lotka-Volterra model with an intractable transition density.
+",0,0,0,1,0,0
+1735,Formation of High Pressure Gradients at the Free Surface of a Liquid Dielectric in a Tangential Electric Field,"  Nonlinear dynamics of the free surface of an ideal incompressible
+non-conducting fluid with high dielectric constant subjected by strong
+horizontal electric field is simulated on the base of the method of conformal
+transformations. It is demonstrated that interaction of counter-propagating
+waves leads to formation of regions with steep wave front at the fluid surface;
+angles of the boundary inclination tend to {\pi}/2, and the curvature of
+surface extremely increases. A significant concentration of the energy of the
+system occurs at these points. From the physical point of view, the appearance
+of these singularities corresponds to formation of regions at the fluid surface
+where pressure exerted by electric field undergoes a discontinuity and
+dynamical pressure increases almost an order of magnitude.
+",0,1,0,0,0,0
+1736,Subsampling for Ridge Regression via Regularized Volume Sampling,"  Given $n$ vectors $\mathbf{x}_i\in \mathbb{R}^d$, we want to fit a linear
+regression model for noisy labels $y_i\in\mathbb{R}$. The ridge estimator is a
+classical solution to this problem. However, when labels are expensive, we are
+forced to select only a small subset of vectors $\mathbf{x}_i$ for which we
+obtain the labels $y_i$. We propose a new procedure for selecting the subset of
+vectors, such that the ridge estimator obtained from that subset offers strong
+statistical guarantees in terms of the mean squared prediction error over the
+entire dataset of $n$ labeled vectors. The number of labels needed is
+proportional to the statistical dimension of the problem which is often much
+smaller than $d$. Our method is an extension of a joint subsampling procedure
+called volume sampling. A second major contribution is that we speed up volume
+sampling so that it is essentially as efficient as leverage scores, which is
+the main i.i.d. subsampling procedure for this task. Finally, we show
+theoretically and experimentally that volume sampling has a clear advantage
+over any i.i.d. sampling when labels are expensive.
+",1,0,0,0,0,0
+1737,Ab initio calculations of the concentration dependent band gap reduction in dilute nitrides,"  While being of persistent interest for the integration of lattice-matched
+laser devices with silicon circuits, the electronic structure of dilute nitride
+III/V-semiconductors has presented a challenge to ab initio computational
+approaches. The root of this lies in the strong distortion N atoms exert on
+most host materials. Here, we resolve these issues by combining density
+functional theory calculations based on the meta-GGA functional presented by
+Tran and Blaha (TB09) with a supercell approach for the dilute nitride Ga(NAs).
+Exploring the requirements posed to supercells, we show that the distortion
+field of a single N atom must be allowed to decrease so far, that it does not
+overlap with its periodic images. This also prevents spurious electronic
+interactions between translational symmetric atoms, allowing to compute band
+gaps in very good agreement with experimentally derived reference values. These
+results open up the field of dilute nitride compound semiconductors to
+predictive ab initio calculations.
+",0,1,0,0,0,0
+1738,Outliers and related problems,"  We define outliers as a set of observations which contradicts the proposed
+mathematical (statistical) model and we discuss the frequently observed types
+of the outliers. Further we explore what changes in the model have to be made
+in order to avoid the occurance of the outliers. We observe that some variants
+of the outliers lead to classical results in probability, such as the law of
+large numbers and the concept of heavy tailed distributions.
+Key words: outlier; the law of large numbers; heavy tailed distributions;
+model rejection.
+",0,0,1,1,0,0
+1739,On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions,"  We propose a DC proximal Newton algorithm for solving nonconvex regularized
+sparse learning problems in high dimensions. Our proposed algorithm integrates
+the proximal Newton algorithm with multi-stage convex relaxation based on the
+difference of convex (DC) programming, and enjoys both strong computational and
+statistical guarantees. Specifically, by leveraging a sophisticated
+characterization of sparse modeling structures/assumptions (i.e., local
+restricted strong convexity and Hessian smoothness), we prove that within each
+stage of convex relaxation, our proposed algorithm achieves (local) quadratic
+convergence, and eventually obtains a sparse approximate local optimum with
+optimal statistical properties after only a few convex relaxations. Numerical
+experiments are provided to support our theory.
+",1,0,1,1,0,0
+1740,Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning,"  In reinforcement learning, agents learn by performing actions and observing
+their outcomes. Sometimes, it is desirable for a human operator to
+\textit{interrupt} an agent in order to prevent dangerous situations from
+happening. Yet, as part of their learning process, agents may link these
+interruptions, that impact their reward, to specific states and deliberately
+avoid them. The situation is particularly challenging in a multi-agent context
+because agents might not only learn from their own past interruptions, but also
+from those of other agents. Orseau and Armstrong defined \emph{safe
+interruptibility} for one learner, but their work does not naturally extend to
+multi-agent systems. This paper introduces \textit{dynamic safe
+interruptibility}, an alternative definition more suited to decentralized
+learning problems, and studies this notion in two learning frameworks:
+\textit{joint action learners} and \textit{independent learners}. We give
+realistic sufficient conditions on the learning algorithm to enable dynamic
+safe interruptibility in the case of joint action learners, yet show that these
+conditions are not sufficient for independent learners. We show however that if
+agents can detect interruptions, it is possible to prune the observations to
+ensure dynamic safe interruptibility even for independent learners.
+",1,0,0,1,0,0
+1741,Heuristic Optimization for Automated Distribution System Planning in Network Integration Studies,"  Network integration studies try to assess the impact of future developments,
+such as the increase of Renewable Energy Sources or the introduction of Smart
+Grid Technologies, on large-scale network areas. Goals can be to support
+strategic alignment in the regulatory framework or to adapt the network
+planning principles of Distribution System Operators. This study outlines an
+approach for the automated distribution system planning that can calculate
+network reconfiguration, reinforcement and extension plans in a fully automated
+fashion. This allows the estimation of the expected cost in massive
+probabilistic simulations of large numbers of real networks and constitutes a
+core component of a framework for large-scale network integration studies.
+Exemplary case study results are presented that were performed in cooperation
+with different major distribution system operators. The case studies cover the
+estimation of expected network reinforcement costs, technical and economical
+assessment of smart grid technologies and structural network optimisation.
+",1,0,0,0,0,0
+1742,The Sizes and Depletions of the Dust and Gas Cavities in the Transitional Disk J160421.7-213028,"  We report ALMA Cycle 2 observations of 230 GHz (1.3 mm) dust continuum
+emission, and $^{12}$CO, $^{13}$CO, and C$^{18}$O J = 2-1 line emission, from
+the Upper Scorpius transitional disk [PZ99] J160421.7-213028, with an angular
+resolution of ~0"".25 (35 AU). Armed with these data and existing H-band
+scattered light observations, we measure the size and depth of the disk's
+central cavity, and the sharpness of its outer edge, in three components:
+sub-$\mu$m-sized ""small"" dust traced by scattered light, millimeter-sized ""big""
+dust traced by the millimeter continuum, and gas traced by line emission. Both
+dust populations feature a cavity of radius $\sim$70 AU that is depleted by
+factors of at least 1000 relative to the dust density just outside. The
+millimeter continuum data are well explained by a cavity with a sharp edge.
+Scattered light observations can be fitted with a cavity in small dust that has
+either a sharp edge at 60 AU, or an edge that transitions smoothly over an
+annular width of 10 AU near 60 AU. In gas, the data are consistent with a
+cavity that is smaller, about 15 AU in radius, and whose surface density at 15
+AU is $10^{3\pm1}$ times smaller than the surface density at 70 AU; the gas
+density grades smoothly between these two radii. The CO isotopologue
+observations rule out a sharp drop in gas surface density at 30 AU or a
+double-drop model as found by previous modeling. Future observations are needed
+to assess the nature of these gas and dust cavities, e.g., whether they are
+opened by multiple as-yet-unseen planets or photoevaporation.
+",0,1,0,0,0,0
+1743,Demystifying AlphaGo Zero as AlphaGo GAN,"  The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a
+worldwide discussion of the future of our human society with a mixed mood of
+hope, anxiousness, excitement and fear. We try to dymystify AlphaGo Zero by a
+qualitative analysis to indicate that AlphaGo Zero can be understood as a
+specially structured GAN system which is expected to possess an inherent good
+convergence property. Thus we deduct the success of AlphaGo Zero may not be a
+sign of a new generation of AI.
+",1,0,0,1,0,0
+1744,Effects of pressure and magnetic field on the re-entrant superconductor Eu(Fe$_{0.93}$Rh$_{0.07}$)$_2$As$_2$,"  Electron-doped Eu(Fe$_{0.93}$Rh$_{0.07}$)$_2$As$_2$ has been systematically
+studied by high pressure investigations of the magnetic and electrical
+transport properties, in order to unravel the complex interplay of
+superconductivity and magnetism. The compound reveals an exceedingly broad
+re-entrant transition to the superconducting state between $T_{\rm{c,on}} =
+19.8$ K and $T_{\rm{c,0}} = 5.2$ K due to a canted A-type antiferromagnetic
+ordering of the Eu$^{2+}$ moments at $T_{\rm{N}} = 16.6$ K and a re-entrant
+spin glass transition at $T_{\rm{SG}} = 14.1$ K. At ambient pressure evidences
+for the coexistence of superconductivity and ferromagnetism could be observed,
+as well as a magnetic-field-induced enhancement of the zero-resistance
+temperature $T_{\rm{c,0}}$ up to $7.2$ K with small magnetic fields applied
+parallel to the \textit{ab}-plane of the crystal. We attribute the
+field-induced-enhancement of superconductivity to the suppression of the
+ferromagnetic component of the Eu$^{2+}$ moments along the \textit{c}-axis,
+which leads to a reduction of the orbital pair breaking effect. Application of
+hydrostatic pressure suppresses the superconducting state around $14$ kbar
+along with a linear temperature dependence of the resistivity, implying that a
+non-Fermi liquid region is located at the boundary of the superconducting
+phase. At intermediate pressure, an additional feature in the resistivity
+curves is identified, which can be suppressed by external magnetic fields and
+competes with the superconducting phase. We suggest that the effect of negative
+pressure by the chemical Rh substitution in
+Eu(Fe$_{0.93}$Rh$_{0.07}$)$_2$As$_2$ is partially reversed, leading to a
+re-activation of the spin density wave.
+",0,1,0,0,0,0
+1745,Commissioning and Operation,"  Chapter 16 in High-Luminosity Large Hadron Collider (HL-LHC) : Preliminary
+Design Report. The Large Hadron Collider (LHC) is one of the largest scientific
+instruments ever built. Since opening up a new energy frontier for exploration
+in 2010, it has gathered a global user community of about 7,000 scientists
+working in fundamental particle physics and the physics of hadronic matter at
+extreme temperature and density. To sustain and extend its discovery potential,
+the LHC will need a major upgrade in the 2020s. This will increase its
+luminosity (rate of collisions) by a factor of five beyond the original design
+value and the integrated luminosity (total collisions created) by a factor ten.
+The LHC is already a highly complex and exquisitely optimised machine so this
+upgrade must be carefully conceived and will require about ten years to
+implement. The new configuration, known as High Luminosity LHC (HL-LHC), will
+rely on a number of key innovations that push accelerator technology beyond its
+present limits. Among these are cutting-edge 11-12 tesla superconducting
+magnets, compact superconducting cavities for beam rotation with ultra-precise
+phase control, new technology and physical processes for beam collimation and
+300 metre-long high-power superconducting links with negligible energy
+dissipation. The present document describes the technologies and components
+that will be used to realise the project and is intended to serve as the basis
+for the detailed engineering design of HL-LHC.
+",0,1,0,0,0,0
+1746,Only in the standard representation the Dirac theory is a quantum theory of a single fermion,"  It is shown that the relativistic quantum mechanics of a single fermion can
+be developed only on the basis of the standard representation of the Dirac
+bispinor. As in the nonrelativistic quantum mechanics, the arbitrariness in
+defining the bispinor, as a four-component wave function, is restricted by its
+multiplication by an arbitrary phase factor. We reveal the role of the large
+and small components of the bispinor, establish their link in the
+nonrelativistic limit with the Pauli spinor, as well as explain the role of
+states with negative energies. The Klein tunneling is treated here as a
+physical phenomenon analogous to the propagation of the electromagnetic wave in
+a medium with negative dielectric permittivity and permeability. For the case
+of localized stationary states we define the effective one-particle operators
+which act in the space of the large component but contain the contributions of
+both components. The effective operator of energy is presented in a compact
+analytical form.
+",0,1,0,0,0,0
+1747,Stable absorbing boundary conditions for molecular dynamics in general domains,"  A new type of absorbing boundary conditions for molecular dynamics
+simulations are presented. The exact boundary conditions for crystalline solids
+with harmonic approximation are expressed as a dynamic Dirichlet- to-Neumann
+(DtN) map. It connects the displacement of the atoms at the boundary to the
+traction on these atoms. The DtN map is valid for a domain with general
+geometry. To avoid evaluating the time convo- lution of the dynamic DtN map, we
+approximate the associated kernel function by rational functions in the Laplace
+domain. The parameters in the approximations are determined by interpolations.
+The explicit forms of the zeroth, first, and second order approximations will
+be presented. The stability of the molecular dynamics model, supplemented with
+these absorbing boundary conditions is established. Two numerical simulations
+are performed to demonstrate the effectiveness of the methods.
+",0,1,0,0,0,0
+1748,Algebraic operads up to homotopy,"  This paper deals with the homotopy theory of differential graded operads. We
+endow the Koszul dual category of curved conilpotent cooperads, where the
+notion of quasi-isomorphism barely makes sense, with a model category structure
+Quillen equivalent to that of operads. This allows us to describe the homotopy
+properties of differential graded operads in a simpler and richer way, using
+obstruction methods.
+",0,0,1,0,0,0
+1749,Nonlinear Kalman Filtering with Divergence Minimization,"  We consider the nonlinear Kalman filtering problem using Kullback-Leibler
+(KL) and $\alpha$-divergence measures as optimization criteria. Unlike linear
+Kalman filters, nonlinear Kalman filters do not have closed form Gaussian
+posteriors because of a lack of conjugacy due to the nonlinearity in the
+likelihood. In this paper we propose novel algorithms to optimize the forward
+and reverse forms of the KL divergence, as well as the alpha-divergence which
+contains these two as limiting cases. Unlike previous approaches, our
+algorithms do not make approximations to the divergences being optimized, but
+use Monte Carlo integration techniques to derive unbiased algorithms for direct
+optimization. We assess performance on radar and sensor tracking, and options
+pricing problems, showing general improvement over the UKF and EKF, as well as
+competitive performance with particle filtering.
+",0,0,1,1,0,0
+1750,On Chern number inequality in dimension 3,"  We prove that if $X---> X^+$ is a threefold terminal flip, then
+$c_1(X).c_2(X)\leq c_1(X^+).c_2(X^+)$ where $c_1(X)$ and $c_2(X)$ denote the
+Chern classes. This gives the affirmative answer to a Question by Xie
+\cite{Xie2}. We obtain the similar but weaker result in the case of divisorial
+contraction to curves.
+",0,0,1,0,0,0
+1751,Enhancing SDO/HMI images using deep learning,"  The Helioseismic and Magnetic Imager (HMI) provides continuum images and
+magnetograms with a cadence better than one per minute. It has been
+continuously observing the Sun 24 hours a day for the past 7 years. The obvious
+trade-off between full disk observations and spatial resolution makes HMI not
+enough to analyze the smallest-scale events in the solar atmosphere. Our aim is
+to develop a new method to enhance HMI data, simultaneously deconvolving and
+super-resolving images and magnetograms. The resulting images will mimic
+observations with a diffraction-limited telescope twice the diameter of HMI.
+Our method, which we call Enhance, is based on two deep fully convolutional
+neural networks that input patches of HMI observations and output deconvolved
+and super-resolved data. The neural networks are trained on synthetic data
+obtained from simulations of the emergence of solar active regions. We have
+obtained deconvolved and supper-resolved HMI images. To solve this ill-defined
+problem with infinite solutions we have used a neural network approach to add
+prior information from the simulations. We test Enhance against Hinode data
+that has been degraded to a 28 cm diameter telescope showing very good
+consistency. The code is open source.
+",1,1,0,0,0,0
+1752,Suppression of the superconductivity in ultrathin amorphous Mo$_{78}$Ge$_{22}$ thin films observed by STM,"  In contact with a superconductor, a normal metal modifies its properties due
+to Andreev reflection. In the current work, the local density of states (LDOS)
+of superconductor - normal metal Mo$_{78}$Ge$_{22}$ - Au bilayers are studied
+by means of STM applied from the Au side. Three bilayers have been prepared on
+silicate glass substrate consisting of 100, 10 and 5 nm MoGe thin films covered
+always by 5 nm Au layer. The tunneling spectra were measured at temperatures
+from 0.5 K to 7 K. The two-dimensional cross-correlation between topography and
+normalized zero-bias conductance (ZBC) indicates a proximity effect between 100
+and 10 nm MoGe thin films and Au layer where a superconducting gap slightly
+smaller than that of bulk MoGe is observed. The effect of the thinnest 5 nm
+MoGe layer on Au leads to much smaller gap moreover the LDOS reveals almost
+completely suppressed coherence peaks. This is attributed to a strong
+pair-breaking effect of spin-flip processes at the interface between MoGe films
+and the substrate.
+",0,1,0,0,0,0
+1753,Functional data analysis in the Banach space of continuous functions,"  Functional data analysis is typically conducted within the $L^2$-Hilbert
+space framework. There is by now a fully developed statistical toolbox allowing
+for the principled application of the functional data machinery to real-world
+problems, often based on dimension reduction techniques such as functional
+principal component analysis. At the same time, there have recently been a
+number of publications that sidestep dimension reduction steps and focus on a
+fully functional $L^2$-methodology. This paper goes one step further and
+develops data analysis methodology for functional time series in the space of
+all continuous functions. The work is motivated by the fact that objects with
+rather different shapes may still have a small $L^2$-distance and are therefore
+identified as similar when using an $L^2$-metric. However, in applications it
+is often desirable to use metrics reflecting the visualization of the curves in
+the statistical analysis. The methodological contributions are focused on
+developing two-sample and change-point tests as well as confidence bands, as
+these procedures appear do be conducive to the proposed setting. Particular
+interest is put on relevant differences; that is, on not trying to test for
+exact equality, but rather for pre-specified deviations under the null
+hypothesis.
+The procedures are justified through large-sample theory. To ensure
+practicability, non-standard bootstrap procedures are developed and
+investigated addressing particular features that arise in the problem of
+testing relevant hypotheses. The finite sample properties are explored through
+a simulation study and an application to annual temperature profiles.
+",0,0,1,1,0,0
+1754,Bayesian Recurrent Neural Networks,"  In this work we explore a straightforward variational Bayes scheme for
+Recurrent Neural Networks. Firstly, we show that a simple adaptation of
+truncated backpropagation through time can yield good quality uncertainty
+estimates and superior regularisation at only a small extra computational cost
+during training, also reducing the amount of parameters by 80\%. Secondly, we
+demonstrate how a novel kind of posterior approximation yields further
+improvements to the performance of Bayesian RNNs. We incorporate local gradient
+information into the approximate posterior to sharpen it around the current
+batch statistics. We show how this technique is not exclusive to recurrent
+neural networks and can be applied more widely to train Bayesian neural
+networks. We also empirically demonstrate how Bayesian RNNs are superior to
+traditional RNNs on a language modelling benchmark and an image captioning
+task, as well as showing how each of these methods improve our model over a
+variety of other schemes for training them. We also introduce a new benchmark
+for studying uncertainty for language models so future methods can be easily
+compared.
+",1,0,0,1,0,0
+1755,Cross-Correlation Redshift Calibration Without Spectroscopic Calibration Samples in DES Science Verification Data,"  Galaxy cross-correlations with high-fidelity redshift samples hold the
+potential to precisely calibrate systematic photometric redshift uncertainties
+arising from the unavailability of complete and representative training and
+validation samples of galaxies. However, application of this technique in the
+Dark Energy Survey (DES) is hampered by the relatively low number density,
+small area, and modest redshift overlap between photometric and spectroscopic
+samples. We propose instead using photometric catalogs with reliable
+photometric redshifts for photo-z calibration via cross-correlations. We verify
+the viability of our proposal using redMaPPer clusters from the Sloan Digital
+Sky Survey (SDSS) to successfully recover the redshift distribution of SDSS
+spectroscopic galaxies. We demonstrate how to combine photo-z with
+cross-correlation data to calibrate photometric redshift biases while
+marginalizing over possible clustering bias evolution in either the calibration
+or unknown photometric samples. We apply our method to DES Science Verification
+(DES SV) data in order to constrain the photometric redshift distribution of a
+galaxy sample selected for weak lensing studies, constraining the mean of the
+tomographic redshift distributions to a statistical uncertainty of $\Delta z
+\sim \pm 0.01$. We forecast that our proposal can in principle control
+photometric redshift uncertainties in DES weak lensing experiments at a level
+near the intrinsic statistical noise of the experiment over the range of
+redshifts where redMaPPer clusters are available. Our results provide strong
+motivation to launch a program to fully characterize the systematic errors from
+bias evolution and photo-z shapes in our calibration procedure.
+",0,1,0,0,0,0
+1756,Completely bounded bimodule maps and spectral synthesis,"  We initiate the study of the completely bounded multipliers of the Haagerup
+tensor product $A(G)\otimes_{\rm h} A(G)$ of two copies of the Fourier algebra
+$A(G)$ of a locally compact group $G$. If $E$ is a closed subset of $G$ we let
+$E^{\sharp} = \{(s,t) : st\in E\}$ and show that if $E^{\sharp}$ is a set of
+spectral synthesis for $A(G)\otimes_{\rm h} A(G)$ then $E$ is a set of local
+spectral synthesis for $A(G)$. Conversely, we prove that if $E$ is a set of
+spectral synthesis for $A(G)$ and $G$ is a Moore group then $E^{\sharp}$ is a
+set of spectral synthesis for $A(G)\otimes_{\rm h} A(G)$. Using the natural
+identification of the space of all completely bounded weak* continuous
+$VN(G)'$-bimodule maps with the dual of $A(G)\otimes_{\rm h} A(G)$, we show
+that, in the case $G$ is weakly amenable, such a map leaves the multiplication
+algebra of $L^{\infty}(G)$ invariant if and only if its support is contained in
+the antidiagonal of $G$.
+",0,0,1,0,0,0
+1757,Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation,"  Black-box risk scoring models permeate our lives, yet are typically
+proprietary or opaque. We propose Distill-and-Compare, a model distillation and
+comparison approach to audit such models. To gain insight into black-box
+models, we treat them as teachers, training transparent student models to mimic
+the risk scores assigned by black-box models. We compare the student model
+trained with distillation to a second un-distilled transparent model trained on
+ground-truth outcomes, and use differences between the two models to gain
+insight into the black-box model. Our approach can be applied in a realistic
+setting, without probing the black-box model API. We demonstrate the approach
+on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending
+Club. We also propose a statistical test to determine if a data set is missing
+key features used to train the black-box model. Our test finds that the
+ProPublica data is likely missing key feature(s) used in COMPAS.
+",1,0,0,1,0,0
+1758,An influence-based fast preceding questionnaire model for elderly assessments,"  To improve the efficiency of elderly assessments, an influence-based fast
+preceding questionnaire model (FPQM) is proposed. Compared with traditional
+assessments, the FPQM optimizes questionnaires by reordering their attributes.
+The values of low-ranking attributes can be predicted by the values of the
+high-ranking attributes. Therefore, the number of attributes can be reduced
+without redesigning the questionnaires. A new function for calculating the
+influence of the attributes is proposed based on probability theory. Reordering
+and reducing algorithms are given based on the attributes' influences. The
+model is verified through a practical application. The practice in an
+elderly-care company shows that the FPQM can reduce the number of attributes by
+90.56% with a prediction accuracy of 98.39%. Compared with other methods, such
+as the Expert Knowledge, Rough Set and C4.5 methods, the FPQM achieves the best
+performance. In addition, the FPQM can also be applied to other questionnaires.
+",1,0,0,0,0,0
+1759,A GPU Accelerated Discontinuous Galerkin Incompressible Flow Solver,"  We present a GPU-accelerated version of a high-order discontinuous Galerkin
+discretization of the unsteady incompressible Navier-Stokes equations. The
+equations are discretized in time using a semi-implicit scheme with explicit
+treatment of the nonlinear term and implicit treatment of the split Stokes
+operators. The pressure system is solved with a conjugate gradient method
+together with a fully GPU-accelerated multigrid preconditioner which is
+designed to minimize memory requirements and to increase overall performance. A
+semi-Lagrangian subcycling advection algorithm is used to shift the
+computational load per timestep away from the pressure Poisson solve by
+allowing larger timestep sizes in exchange for an increased number of advection
+steps. Numerical results confirm we achieve the design order accuracy in time
+and space. We optimize the performance of the most time-consuming kernels by
+tuning the fine-grain parallelism, memory utilization, and maximizing
+bandwidth. To assess overall performance we present an empirically calibrated
+roofline performance model for a target GPU to explain the achieved efficiency.
+We demonstrate that, in the most cases, the kernels used in the solver are
+close to their empirically predicted roofline performance.
+",1,0,0,0,0,0
+1760,The Auger Engineering Radio Array and multi-hybrid cosmic ray detection (TAUP 2015),"  The Auger Engineering Radio Array (AERA) aims at the detection of air showers
+induced by high-energy cosmic rays. As an extension of the Pierre Auger
+Observatory, it measures complementary information to the particle detectors,
+fluorescence telescopes and to the muon scintillators of the Auger Muons and
+Infill for the Ground Array (AMIGA). AERA is sensitive to all fundamental
+parameters of an extensive air shower such as the arrival direction, energy and
+depth of shower maximum. Since the radio emission is induced purely by the
+electromagnetic component of the shower, in combination with the AMIGA muon
+counters, AERA is perfect for separate measurements of the electrons and muons
+in the shower, if combined with a muon counting detector like AMIGA. In
+addition to the depth of the shower maximum, the ratio of the electron and muon
+number serves as a measure of the primary particle mass.
+",0,1,0,0,0,0
+1761,Historic Emergence of Diversity in Painting: Heterogeneity in Chromatic Distance in Images and Characterization of Massive Painting Data Set,"  Painting is an art form that has long functioned as a major channel for the
+creative expression and communication of humans, its evolution taking place
+under an interplay with the science, technology, and social environments of the
+times. Therefore, understanding the process based on comprehensive data could
+shed light on how humans acted and manifested creatively under changing
+conditions. Yet, there exist few systematic frameworks that characterize the
+process for painting, which would require robust statistical methods for
+defining painting characteristics and identifying human's creative
+developments, and data of high quality and sufficient quantity. Here we propose
+that the color contrast of a painting image signifying the heterogeneity in
+inter-pixel chromatic distance can be a useful representation of its style,
+integrating both the color and geometry. From the color contrasts of paintings
+from a large-scale, comprehensive archive of 179,853 high-quality images
+spanning several centuries we characterize the temporal evolutionary patterns
+of paintings, and present a deep study of an extraordinary expansion in
+creative diversity and individuality that came to define the modern era.
+",1,1,0,0,0,0
+1762,Cage Size and Jump Precursors in Glass-Forming Liquids: Experiment and Simulations,"  Glassy dynamics is intermittent, as particles suddenly jump out of the cage
+formed by their neighbours, and heterogeneous, as these jumps are not uniformly
+distributed across the system. Relating these features of the dynamics to the
+diverse local environments explored by the particles is essential to
+rationalize the relaxation process. Here we investigate this issue
+characterizing the local environment of a particle with the amplitude of its
+short time vibrational motion, as determined by segmenting in cages and jumps
+the particle trajectories. Both simulations of supercooled liquids and
+experiments on colloidal suspensions show that particles in large cages are
+likely to jump after a small time-lag, and that, on average, the cage enlarges
+shortly before the particle jumps. At large time-lags, the cage has essentially
+a constant value, which is smaller for longer-lasting cages. Finally, we
+clarify how this coupling between cage size and duration controls the average
+behaviour and opens the way to a better understanding of the relaxation process
+in glass--forming liquids.
+",0,1,0,0,0,0
+1763,A Comprehensive Survey on Bengali Phoneme Recognition,"  Hidden Markov model based various phoneme recognition methods for Bengali
+language is reviewed. Automatic phoneme recognition for Bengali language using
+multilayer neural network is reviewed. Usefulness of multilayer neural network
+over single layer neural network is discussed. Bangla phonetic feature table
+construction and enhancement for Bengali speech recognition is also discussed.
+Comparison among these methods is discussed.
+",1,0,0,0,0,0
+1764,Factorization of arithmetic automorphic periods,"  In this paper, we prove that the arithmetic automorphic periods for $GL_{n}$
+over a CM field factorize through the infinite places. This generalizes a
+conjecture of Shimura in 1983, and is predicted by the Langlands correspondence
+between automorphic representations and motives.
+",0,0,1,0,0,0
+1765,Multielectronic processes in particle and antiparticle collisions with rare gases,"  In this chapter we analyze the multiple ionization by impact of |Z|=1
+projectiles: electrons, positrons, protons and antiprotons. Differences and
+similarities among the cross sections by these four projectiles allows us to
+have an insight on the physics involved. Mass and charge effects, energy
+thresholds, and relative importance of collisional and post-collisional
+processes are discussed. For this purpose, we performed a detailed
+theoretical-experimental comparison for single up to quintuple ionization of
+Ne, Ar, Kr and Xe by particles and antiparticles. We include an extensive
+compilation of the available data for the sixteen collisional systems, and the
+theoretical cross sections by means of the continuum distorted wave eikonal
+initial state approximation. We underline here that post-collisional ionization
+is decisive to describe multiple ionization by light projectiles, covering
+almost the whole energy range, from threshold to high energies. The
+normalization of positron and antiproton measurements to electron impact ones,
+the lack of data in certain cases, and the future prospects are presented and
+discussed.
+",0,1,0,0,0,0
+1766,A Floating Cylinder on An Unbounded Bath,"  In this paper, we reconsider a circular cylinder horizontally floating on an
+unbounded reservoir in a gravitational field directed downwards, which was
+studied by Bhatnargar and Finn in 2006. We follow their approach but with some
+modifications. We establish the relation between the total energy relative to
+the undisturbed state and the total force. There is a monotone relation between
+the height of the centre and the wetting angle. We study the number of
+equilibria, the floating configurations and their stability for all parameter
+values. We find that the system admits at most two equilibrium points for
+arbitrary contact angle, the one with smaller wetting angle is stable and the
+one with larger wetting angle is unstable. The initial model has a limitation
+that the fluid interfaces may intersect. We show that the stable equilibrium
+point never lies in the intersection region, while the unstable equilibrium
+point may lie in the intersection region.
+",0,1,1,0,0,0
+1767,Switching between Limit Cycles in a Model of Running Using Exponentially Stabilizing Discrete Control Lyapunov Function,"  This paper considers the problem of switching between two periodic motions,
+also known as limit cycles, to create agile running motions. For each limit
+cycle, we use a control Lyapunov function to estimate the region of attraction
+at the apex of the flight phase. We switch controllers at the apex, only if the
+current state of the robot is within the region of attraction of the subsequent
+limit cycle. If the intersection between two limit cycles is the null set, then
+we construct additional limit cycles till we are able to achieve sufficient
+overlap of the region of attraction between sequential limit cycles.
+Additionally, we impose an exponential convergence condition on the control
+Lyapunov function that allows us to rapidly transition between limit cycles.
+Using the approach we demonstrate switching between 5 limit cycles in about 5
+steps with the speed changing from 2 m/s to 5 m/s.
+",1,0,0,0,0,0
+1768,Locally Private Bayesian Inference for Count Models,"  As more aspects of social interaction are digitally recorded, there is a
+growing need to develop privacy-preserving data analysis methods. Social
+scientists will be more likely to adopt these methods if doing so entails
+minimal change to their current methodology. Toward that end, we present a
+general and modular method for privatizing Bayesian inference for Poisson
+factorization, a broad class of models that contains some of the most widely
+used models in the social sciences. Our method satisfies local differential
+privacy, which ensures that no single centralized server need ever store the
+non-privatized data. To formulate our local-privacy guarantees, we introduce
+and focus on limited-precision local privacy---the local privacy analog of
+limited-precision differential privacy (Flood et al., 2013). We present two
+case studies, one involving social networks and one involving text corpora,
+that test our method's ability to form the posterior distribution over latent
+variables under different levels of noise, and demonstrate our method's utility
+over a naïve approach, wherein inference proceeds as usual, treating the
+privatized data as if it were not privatized.
+",1,0,0,1,0,0
+1769,Carrier Diffusion in Thin-Film CH3NH3PbI3 Perovskite Measured using Four-Wave Mixing,"  We report the application of femtosecond four-wave mixing (FWM) to the study
+of carrier transport in solution-processed CH3NH3PbI3. The diffusion
+coefficient was extracted through direct detection of the lateral diffusion of
+carriers utilizing the transient grating technique, coupled with simultaneous
+measurement of decay kinetics exploiting the versatility of the boxcar
+excitation beam geometry. The observation of exponential decay of the transient
+grating versus interpulse delay indicates diffusive transport with negligible
+trapping within the first nanosecond following excitation. The in-plane
+transport geometry in our experiments enabled the diffusion length to be
+compared directly with the grain size, indicating that carriers move across
+multiple grain boundaries prior to recombination. Our experiments illustrate
+the broad utility of FWM spectroscopy for rapid characterization of macroscopic
+film transport properties.
+",0,1,0,0,0,0
+1770,On effective Birkhoff's ergodic theorem for computable actions of amenable groups,"  We introduce computable actions of computable groups and prove the following
+versions of effective Birkhoff's ergodic theorem. Let $\Gamma$ be a computable
+amenable group, then there always exists a canonically computable tempered
+two-sided F{\o}lner sequence $(F_n)_{n \geq
+1}$ in $\Gamma$. For a computable, measure-preserving, ergodic action of
+$\Gamma$ on a Cantor space $\{0,1\}^{\mathbb N}$ endowed with a computable
+probability measure $\mu$, it is shown that for every bounded lower
+semicomputable function $f$ on $\{0,1\}^{\mathbb N}$ and for every Martin-Löf
+random $\omega \in \{0,1\}^{\mathbb N}$ the equality \[ \lim\limits_{n \to
+\infty} \frac{1}{|F_n|} \sum\limits_{g \in F_n} f(g \cdot \omega) = \int\limits
+f d \mu \] holds, where the averages are taken with respect to a canonically
+computable tempered two-sided F{\o}lner sequence $(F_n)_{n \geq
+1}$. We also prove the same identity for all lower semicomputable $f$'s in
+the special case when $\Gamma$ is a computable group of polynomial growth and
+$F_n:=\mathrm{B}(n)$ is the F{\o}lner sequence of balls around the neutral
+element of $\Gamma$.
+",0,0,1,0,0,0
+1771,Gender Differences in Participation and Reward on Stack Overflow,"  Programming is a valuable skill in the labor market, making the
+underrepresentation of women in computing an increasingly important issue.
+Online question and answer platforms serve a dual purpose in this field: they
+form a body of knowledge useful as a reference and learning tool, and they
+provide opportunities for individuals to demonstrate credible, verifiable
+expertise. Issues, such as male-oriented site design or overrepresentation of
+men among the site's elite may therefore compound the issue of women's
+underrepresentation in IT. In this paper we audit the differences in behavior
+and outcomes between men and women on Stack Overflow, the most popular of these
+Q&A sites. We observe significant differences in how men and women participate
+in the platform and how successful they are. For example, the average woman has
+roughly half of the reputation points, the primary measure of success on the
+site, of the average man. Using an Oaxaca-Blinder decomposition, an econometric
+technique commonly applied to analyze differences in wages between groups, we
+find that most of the gap in success between men and women can be explained by
+differences in their activity on the site and differences in how these
+activities are rewarded. Specifically, 1) men give more answers than women and
+2) are rewarded more for their answers on average, even when controlling for
+possible confounders such as tenure or buy-in to the site. Women ask more
+questions and gain more reward per question. We conclude with a hypothetical
+redesign of the site's scoring system based on these behavioral differences,
+cutting the reputation gap in half.
+",1,0,0,0,0,0
+1772,Invariant surface area functionals and singular Yamabe problem in 3-dimensional CR geometry,"  We express two CR invariant surface area elements in terms of quantities in
+pseudohermitian geometry. We deduce the Euler-Lagrange equations of the
+associated energy functionals. Many solutions are given and discussed. In
+relation to the singular CR Yamabe problem, we show that one of the energy
+functionals appears as the coefficient (up to a constant multiple) of the log
+term in the associated volume renormalization.
+",0,0,1,0,0,0
+1773,"Dynamic dipole polarizabilities of heteronuclear alkali dimers: optical response, trapping and control of ultracold molecules","  In this article we address the general approach for calculating dynamical
+dipole polarizabilities of small quantum systems, based on a sum-over-states
+formula involving in principle the entire energy spectrum of the system. We
+complement this method by a few-parameter model involving a limited number of
+effective transitions, allowing for a compact and accurate representation of
+both the isotropic and anisotropic components of the polarizability. We apply
+the method to the series of ten heteronuclear molecules composed of two of
+($^7$Li,$^{23}$Na,$^{39}$K,$^{87}$Rb,$^{133}$Cs) alkali-metal atoms. We rely on
+both up-to-date spectroscopically-determined potential energy curves for the
+lowest electronic states, and on our systematic studies of these systems
+performed during the last decade for higher excited states and for permanent
+and transition dipole moments. Such a compilation is timely for the
+continuously growing researches on ultracold polar molecules. Indeed the
+knowledge of the dynamic dipole polarizabilities is crucial to model the
+optical response of molecules when trapped in optical lattices, and to
+determine optimal lattice frequencies ensuring optimal transfer to the absolute
+ground state of initially weakly-bound molecules. When they exist, we determine
+the so-called ""magic frequencies"" where the ac-Stark shift and thus the viewed
+trap depth, is the same for both weakly-bound and ground-state molecules.
+",0,1,0,0,0,0
+1774,Minor-free graphs have light spanners,"  We show that every $H$-minor-free graph has a light $(1+\epsilon)$-spanner,
+resolving an open problem of Grigni and Sissokho and proving a conjecture of
+Grigni and Hung. Our lightness bound is
+\[O\left(\frac{\sigma_H}{\epsilon^3}\log \frac{1}{\epsilon}\right)\] where
+$\sigma_H = |V(H)|\sqrt{\log |V(H)|}$ is the sparsity coefficient of
+$H$-minor-free graphs. That is, it has a practical dependency on the size of
+the minor $H$. Our result also implies that the polynomial time approximation
+scheme (PTAS) for the Travelling Salesperson Problem (TSP) in $H$-minor-free
+graphs by Demaine, Hajiaghayi and Kawarabayashi is an efficient PTAS whose
+running time is $2^{O_H\left(\frac{1}{\epsilon^4}\log
+\frac{1}{\epsilon}\right)}n^{O(1)}$ where $O_H$ ignores dependencies on the
+size of $H$. Our techniques significantly deviate from existing lines of
+research on spanners for $H$-minor-free graphs, but build upon the work of
+Chechik and Wulff-Nilsen for spanners of general graphs.
+",1,0,0,0,0,0
+1775,Adversarial Generation of Natural Language,"  Generative Adversarial Networks (GANs) have gathered a lot of attention from
+the computer vision community, yielding impressive results for image
+generation. Advances in the adversarial generation of natural language from
+noise however are not commensurate with the progress made in generating images,
+and still lag far behind likelihood based methods. In this paper, we take a
+step towards generating natural language with a GAN objective alone. We
+introduce a simple baseline that addresses the discrete output space problem
+without relying on gradient estimators and show that it is able to achieve
+state-of-the-art results on a Chinese poem generation dataset. We present
+quantitative results on generating sentences from context-free and
+probabilistic context-free grammars, and qualitative language modeling results.
+A conditional version is also described that can generate sequences conditioned
+on sentence characteristics.
+",1,0,0,1,0,0
+1776,Likely Transiting Exocomets Detected by Kepler,"  We present the first good evidence for exocomet transits of a host star in
+continuum light in data from the Kepler mission. The Kepler star in question,
+KIC 3542116, is of spectral type F2V and is quite bright at K_p = 10. The
+transits have a distinct asymmetric shape with a steeper ingress and slower
+egress that can be ascribed to objects with a trailing dust tail passing over
+the stellar disk. There are three deeper transits with depths of ~0.1% that
+last for about a day, and three that are several times more shallow and of
+shorter duration. The transits were found via an exhaustive visual search of
+the entire Kepler photometric data set, which we describe in some detail. We
+review the methods we use to validate the Kepler data showing the comet
+transits, and rule out instrumental artefacts as sources of the signals. We fit
+the transits with a simple dust-tail model, and find that a transverse comet
+speed of ~35-50 km/s and a minimum amount of dust present in the tail of ~10^16
+g are required to explain the larger transits. For a dust replenishment time of
+~10 days, and a comet lifetime of only ~300 days, this implies a total cometary
+mass of > 3 x 10^17 g, or about the mass of Halley's comet. We also discuss the
+number of comets and orbital geometry that would be necessary to explain the
+six transits detected over the four years of Kepler prime-field observations.
+Finally, we also report the discovery of a single comet-shaped transit in KIC
+11084727 with very similar transit and host-star properties.
+",0,1,0,0,0,0
+1777,Origins of bond and spin order in rare-earth nickelate bulk and heterostructures,"  We analyze the charge- and spin response functions of rare-earth nickelates
+RNiO3 and their heterostructures using random-phase approximation in a two-band
+Hubbard model. The inter-orbital charge fluctuation is found to be the driving
+mechanism for the rock-salt type bond order in bulk RNiO3, and good agreement
+of the ordering temperature with experimental values is achieved for all RNiO3
+using realistic crystal structures and interaction parameters. We further show
+that magnetic ordering in bulk is not driven by the spin fluctuation and should
+be instead explained as ordering of localized moments. This picture changes for
+low-dimensional heterostructures, where the charge fluctuation is suppressed
+and overtaken by the enhanced spin instability, which results in a
+spin-density-wave ground state observed in recent experiments. Predictions for
+spectroscopy allow for further experimental testing of our claims.
+",0,1,0,0,0,0
+1778,Canonical Truth,"  We introduce and study a notion of canonical set theoretical truth, which
+means truth in a `canonical model', i.e. a transitive class model that is
+uniquely characterized by some $\in$-formula. We show that this notion of truth
+is `informative', i.e. there are statements that hold in all canonical models
+but do not follow from ZFC, such as Reitz' ground model axiom or the
+nonexistence of measurable cardinals. We also show that ZF+$V=L[\mathbb{R}]$+AD
+has no canonical models. On the other hand, we show that there are canonical
+models for `every real has sharp'. Moreover, we consider `theory-canonical'
+statements that only fix a transitive class model of ZFC up to elementary
+equivalence and show that it is consistent relative to large cardinals that
+there are theory-canonical models with measurable cardinals and that
+theory-canonicity is still informative in the sense explained above.
+",0,0,1,0,0,0
+1779,AACT: Application-Aware Cooperative Time Allocation for Internet of Things,"  As the number of Internet of Things (IoT) devices keeps increasing, data is
+required to be communicated and processed by these devices at unprecedented
+rates. Cooperation among wireless devices by exploiting Device-to-Device (D2D)
+connections is promising, where aggregated resources in a cooperative setup can
+be utilized by all devices, which would increase the total utility of the
+setup. In this paper, we focus on the resource allocation problem for
+cooperating IoT devices with multiple heterogeneous applications. In
+particular, we develop Application-Aware Cooperative Time allocation (AACT)
+framework, which optimizes the time that each application utilizes the
+aggregated system resources by taking into account heterogeneous device
+constraints and application requirements. AACT is grounded on the concept of
+Rolling Horizon Control (RHC) where decisions are made by iteratively solving a
+convex optimization problem over a moving control window of estimated system
+parameters. The simulation results demonstrate significant performance gains.
+",1,0,0,0,0,0
+1780,COPA: Constrained PARAFAC2 for Sparse & Large Datasets,"  PARAFAC2 has demonstrated success in modeling irregular tensors, where the
+tensor dimensions vary across one of the modes. An example scenario is modeling
+treatments across a set of patients with the varying number of medical
+encounters over time. Despite recent improvements on unconstrained PARAFAC2,
+its model factors are usually dense and sensitive to noise which limits their
+interpretability. As a result, the following open challenges remain: a) various
+modeling constraints, such as temporal smoothness, sparsity and non-negativity,
+are needed to be imposed for interpretable temporal modeling and b) a scalable
+approach is required to support those constraints efficiently for large
+datasets. To tackle these challenges, we propose a {\it CO}nstrained {\it
+PA}RAFAC2 (COPA) method, which carefully incorporates optimization constraints
+such as temporal smoothness, sparsity, and non-negativity in the resulting
+factors. To efficiently support all those constraints, COPA adopts a hybrid
+optimization framework using alternating optimization and alternating direction
+method of multiplier (AO-ADMM). As evaluated on large electronic health record
+(EHR) datasets with hundreds of thousands of patients, COPA achieves
+significant speedups (up to 36 times faster) over prior PARAFAC2 approaches
+that only attempt to handle a subset of the constraints that COPA enables.
+Overall, our method outperforms all the baselines attempting to handle a subset
+of the constraints in terms of speed, while achieving the same level of
+accuracy. Through a case study on temporal phenotyping of medically complex
+children, we demonstrate how the constraints imposed by COPA reveal concise
+phenotypes and meaningful temporal profiles of patients. The clinical
+interpretation of both the phenotypes and the temporal profiles was confirmed
+by a medical expert.
+",0,0,0,1,0,0
+1781,Effect of Composition Gradient on Magnetothermal Instability Modified by Shear and Rotation,"  We model the intracluster medium as a weakly collisional plasma that is a
+binary mixture of the hydrogen and the helium ions, along with free electrons.
+When, owing to the helium sedimentation, the gradient of the mean molecular
+weight (or equivalently, composition or helium ions' concentration) of the
+plasma is not negligible, it can have appreciable influence on the stability
+criteria of the thermal convective instabilities, e.g., the heat-flux-buoyancy
+instability and the magnetothermal instability (MTI). These instabilities are
+consequences of the anisotropic heat conduction occurring preferentially along
+the magnetic field lines. In this paper, without ignoring the magnetic tension,
+we first present the mathematical criterion for the onset of composition
+gradient modified MTI. Subsequently, we relax the commonly adopted equilibrium
+state in which the plasma is at rest, and assume that the plasma is in a
+sheared state which may be due to differential rotation. We discuss how the
+concentration gradient affects the coupling between the Kelvin--Helmholtz
+instability and the MTI in rendering the plasma unstable or stable. We derive
+exact stability criterion by working with the sharp boundary case in which the
+physical variables---temperature, mean molecular weight, density, and magnetic
+field---change discontinuously from one constant value to another on crossing
+the boundary. Finally, we perform the linear stability analysis for the case of
+the differentially rotating plasma that is thermally and compositionally
+stratified as well. By assuming axisymmetric perturbations, we find the
+corresponding dispersion relation and the explicit mathematical expression
+determining the onset of the modified MTI.
+",0,1,0,0,0,0
+1782,A Verified Algorithm Enumerating Event Structures,"  An event structure is a mathematical abstraction modeling concepts as
+causality, conflict and concurrency between events. While many other
+mathematical structures, including groups, topological spaces, rings, abound
+with algorithms and formulas to generate, enumerate and count particular sets
+of their members, no algorithm or formulas are known to generate or count all
+the possible event structures over a finite set of events. We present an
+algorithm to generate such a family, along with a functional implementation
+verified using Isabelle/HOL. As byproducts, we obtain a verified enumeration of
+all possible preorders and partial orders. While the integer sequences counting
+preorders and partial orders are already listed on OEIS (On-line Encyclopedia
+of Integer Sequences), the one counting event structures is not. We therefore
+used our algorithm to submit a formally verified addition, which has been
+successfully reviewed and is now part of the OEIS.
+",1,0,0,0,0,0
+1783,Missing Data as Part of the Social Behavior in Real-World Financial Complex Systems,"  Many real-world networks are known to exhibit facts that counter our
+knowledge prescribed by the theories on network creation and communication
+patterns. A common prerequisite in network analysis is that information on
+nodes and links will be complete because network topologies are extremely
+sensitive to missing information of this kind. Therefore, many real-world
+networks that fail to meet this criterion under random sampling may be
+discarded.
+In this paper we offer a framework for interpreting the missing observations
+in network data under the hypothesis that these observations are not missing at
+random. We demonstrate the methodology with a case study of a financial trade
+network, where the awareness of agents to the data collection procedure by a
+self-interested observer may result in strategic revealing or withholding of
+information. The non-random missingness has been overlooked despite the
+possibility of this being an important feature of the processes by which the
+network is generated. The analysis demonstrates that strategic information
+withholding may be a valid general phenomenon in complex systems. The evidence
+is sufficient to support the existence of an influential observer and to offer
+a compelling dynamic mechanism for the creation of the network.
+",0,0,0,1,0,0
+1784,Quantum Monte Carlo simulation of a two-dimensional Majorana lattice model,"  We study interacting Majorana fermions in two dimensions as a low-energy
+effective model of a vortex lattice in two-dimensional time-reversal-invariant
+topological superconductors. For that purpose, we implement ab-initio quantum
+Monte Carlo simulation to the Majorana fermion system in which the
+path-integral measure is given by a semi-positive Pfaffian. We discuss
+spontaneous breaking of time-reversal symmetry at finite temperature.
+",0,1,0,0,0,0
+1785,Geometric Rescaling Algorithms for Submodular Function Minimization,"  We present a new class of polynomial-time algorithms for submodular function
+minimization (SFM), as well as a unified framework to obtain strongly
+polynomial SFM algorithms. Our new algorithms are based on simple iterative
+methods for the minimum-norm problem, such as the conditional gradient and the
+Fujishige-Wolfe algorithms. We exhibit two techniques to turn simple iterative
+methods into polynomial-time algorithms.
+Firstly, we use the geometric rescaling technique, which has recently gained
+attention in linear programming. We adapt this technique to SFM and obtain a
+weakly polynomial bound $O((n^4\cdot EO + n^5)\log (n L))$.
+Secondly, we exhibit a general combinatorial black-box approach to turn any
+strongly polynomial $\varepsilon L$-approximate SFM oracle into a strongly
+polynomial exact SFM algorithm. This framework can be applied to a wide range
+of combinatorial and continuous algorithms, including pseudo-polynomial ones.
+In particular, we can obtain strongly polynomial algorithms by a repeated
+application of the conditional gradient or of the Fujishige-Wolfe algorithm.
+Combined with the geometric rescaling technique, the black-box approach
+provides a $O((n^5\cdot EO + n^6)\log^2 n)$ algorithm. Finally, we show that
+one of the techniques we develop in the paper can also be combined with the
+cutting-plane method of Lee, Sidford, and Wong, yielding a simplified variant
+of their $O(n^3 \log^2 n \cdot EO + n^4\log^{O(1)} n)$ algorithm.
+",1,0,1,0,0,0
+1786,Statistical PT-symmetric lasing in an optical fiber network,"  PT-symmetry in optics is a condition whereby the real and imaginary parts of
+the refractive index across a photonic structure are deliberately balanced.
+This balance can lead to a host of novel optical phenomena, such as
+unidirectional invisibility, loss-induced lasing, single-mode lasing from
+multimode resonators, and non-reciprocal effects in conjunction with
+nonlinearities. Because PT-symmetry has been thought of as fragile,
+experimental realizations to date have been usually restricted to on-chip
+micro-devices. Here, we demonstrate that certain features of PT-symmetry are
+sufficiently robust to survive the statistical fluctuations associated with a
+macroscopic optical cavity. We construct optical-fiber-based coupled-cavities
+in excess of a kilometer in length (the free spectral range is less than 0.8
+fm) with balanced gain and loss in two sub-cavities and examine the lasing
+dynamics. In such a macroscopic system, fluctuations can lead to a
+cavity-detuning exceeding the free spectral range. Nevertheless, by varying the
+gain-loss contrast, we observe that both the lasing threshold and the growth of
+the laser power follow the predicted behavior of a stable PT-symmetric
+structure. Furthermore, a statistical symmetry-breaking point is observed upon
+varying the cavity loss. These findings indicate that PT-symmetry is a more
+robust optical phenomenon than previously expected, and points to potential
+applications in optical fiber networks and fiber lasers.
+",0,1,0,0,0,0
+1787,Transforming Single Domain Magnetic CoFe2O4 Nanoparticles from Hydrophobic to Hydrophilic By Novel Mechanochemical Ligand Exchange,"  Single phase, uniform size (~9 nm) Cobalt Ferrite (CFO) nanoparticles have
+been synthesized by hydrothermal synthesis using oleic acid as a surfactant.
+The as synthesized oleic acid coated CFO (OA-CFO) nanoparticles were well
+dispersible in nonpolar solvents but not dispersible in water. The OA-CFO
+nanoparticles have been successfully transformed to highly water dispersible
+citric acid coated CFO (CA-CFO) nanoparticles using a novel single step ligand
+exchange process by mechanochemical milling, in which small chain citric acid
+molecules replace the original large chain oleic acid molecules available on
+CFO nanoparticles. The contact angle measurement shows that OA-CFO
+nanoparticles are hydrophobic whereas CA-CFO nanoparticles are superhydrophilic
+in nature. The potentiality of as synthesized OA-CFO and mechanochemically
+transformed CA-CFO nanoparticles for the demulsification of highly stabilized
+water-in-oil and oil-in-water emulsions has been demonstrated.
+",0,1,0,0,0,0
+1788,Probing the dusty stellar populations of the Local Volume Galaxies with JWST/MIRI,"  The Mid-Infrared Instrument (MIRI) for the {\em James Webb Space Telescope}
+(JWST) will revolutionize our understanding of infrared stellar populations in
+the Local Volume. Using the rich {\em Spitzer}-IRS spectroscopic data-set and
+spectral classifications from the Surveying the Agents of Galaxy Evolution
+(SAGE)-Spectroscopic survey of over a thousand objects in the Magellanic
+Clouds, the Grid of Red supergiant and Asymptotic giant branch star ModelS
+({\sc grams}), and the grid of YSO models by Robitaille et al. (2006), we
+calculate the expected flux-densities and colors in the MIRI broadband filters
+for prominent infrared stellar populations. We use these fluxes to explore the
+{\em JWST}/MIRI colours and magnitudes for composite stellar population studies
+of Local Volume galaxies. MIRI colour classification schemes are presented;
+these diagrams provide a powerful means of identifying young stellar objects,
+evolved stars and extragalactic background galaxies in Local Volume galaxies
+with a high degree of confidence. Finally, we examine which filter combinations
+are best for selecting populations of sources based on their JWST colours.
+",0,1,0,0,0,0
+1789,PythonRobotics: a Python code collection of robotics algorithms,"  This paper describes an Open Source Software (OSS) project: PythonRobotics.
+This is a collection of robotics algorithms implemented in the Python
+programming language. The focus of the project is on autonomous navigation, and
+the goal is for beginners in robotics to understand the basic ideas behind each
+algorithm. In this project, the algorithms which are practical and widely used
+in both academia and industry are selected. Each sample code is written in
+Python3 and only depends on some standard modules for readability and ease of
+use. It includes intuitive animations to understand the behavior of the
+simulation.
+",1,0,0,0,0,0
+1790,A Liouville theorem for the Euler equations in the plane,"  This paper is concerned with qualitative properties of bounded steady flows
+of an ideal incompressible fluid with no stagnation point in the
+two-dimensional plane R^2. We show that any such flow is a shear flow, that is,
+it is parallel to some constant vector. The proof of this Liouville-type result
+is firstly based on the study of the geometric properties of the level curves
+of the stream function and secondly on the derivation of some estimates on the
+at most logarithmic growth of the argument of the flow. These estimates lead to
+the conclusion that the streamlines of the flow are all parallel lines.
+",0,0,1,0,0,0
+1791,First On-Site True Gamma-Ray Imaging-Spectroscopy of Contamination near Fukushima Plant,"  We have developed an Electron Tracking Compton Camera (ETCC), which provides
+a well-defined Point Spread Function (PSF) by reconstructing a direction of
+each gamma as a point and realizes simultaneous measurement of brightness and
+spectrum of MeV gamma-rays for the first time. Here, we present the results of
+our on-site pilot gamma-imaging-spectroscopy with ETCC at three contaminated
+locations in the vicinity of the Fukushima Daiichi Nuclear Power Plants in
+Japan in 2014. The obtained distribution of brightness (or emissivity) with
+remote-sensing observations is unambiguously converted into the dose
+distribution. We confirm that the dose distribution is consistent with the one
+taken by conventional mapping measurements with a dosimeter physically placed
+at each grid point. Furthermore, its imaging spectroscopy, boosted by
+Compton-edge-free spectra, reveals complex radioactive features in a
+quantitative manner around each individual target point in the
+background-dominated environment. Notably, we successfully identify a ""micro
+hot spot"" of residual caesium contamination even in an already decontaminated
+area. These results show that the ETCC performs exactly as the geometrical
+optics predicts, demonstrates its versatility in the field radiation
+measurement, and reveals potentials for application in many fields, including
+the nuclear industry, medical field, and astronomy.
+",0,1,0,0,0,0
+1792,Weak Versus Strong Disorder Superfluid-Bose Glass Transition in One Dimension,"  Using large-scale simulations based on matrix product state and quantum Monte
+Carlo techniques, we study the superfluid to Bose glass-transition for
+one-dimensional attractive hard-core bosons at zero temperature, across the
+full regime from weak to strong disorder. As a function of interaction and
+disorder strength, we identify a Berezinskii-Kosterlitz-Thouless critical line
+with two different regimes. At small attraction where critical disorder is weak
+compared to the bandwidth, the critical Luttinger parameter $K_c$ takes its
+universal Giamarchi-Schulz value $K_{c}=3/2$. Conversely, a non-universal
+$K_c>3/2$ emerges for stronger attraction where weak-link physics is relevant.
+In this strong disorder regime, the transition is characterized by self-similar
+power-law distributed weak links with a continuously varying characteristic
+exponent $\alpha$.
+",0,1,0,0,0,0
+1793,Quantum Structures in Human Decision-making: Towards Quantum Expected Utility,"  {\it Ellsberg thought experiments} and empirical confirmation of Ellsberg
+preferences pose serious challenges to {\it subjective expected utility theory}
+(SEUT). We have recently elaborated a quantum-theoretic framework for human
+decisions under uncertainty which satisfactorily copes with the Ellsberg
+paradox and other puzzles of SEUT. We apply here the quantum-theoretic
+framework to the {\it Ellsberg two-urn example}, showing that the paradox can
+be explained by assuming a state change of the conceptual entity that is the
+object of the decision ({\it decision-making}, or {\it DM}, {\it entity}) and
+representing subjective probabilities by quantum probabilities. We also model
+the empirical data we collected in a DM test on human participants within the
+theoretic framework above. The obtained results are relevant, as they provide a
+line to model real life, e.g., financial and medical, decisions that show the
+same empirical patterns as the two-urn experiment.
+",0,0,0,0,1,1
+1794,Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF,"  Event learning is one of the most important problems in AI. However,
+notwithstanding significant research efforts, it is still a very complex task,
+especially when the events involve the interaction of humans or agents with
+other objects, as it requires modeling human kinematics and object movements.
+This study proposes a methodology for learning complex human-object interaction
+(HOI) events, involving the recording, annotation and classification of event
+interactions. For annotation, we allow multiple interpretations of a motion
+capture by slicing over its temporal span, for classification, we use
+Long-Short Term Memory (LSTM) sequential models with Conditional Randon Field
+(CRF) for constraints of outputs. Using a setup involving captures of
+human-object interaction as three dimensional inputs, we argue that this
+approach could be used for event types involving complex spatio-temporal
+dynamics.
+",1,0,0,0,0,0
+1795,Book Review Interferometry and Synthesis in Radio Astronomy - 3rd Ed,"  Review of the third edition of ""Interferometry and Synthesis in Radio
+Astronomy"" by Thompson, Moran and Swenson
+",0,1,0,0,0,0
+1796,A Kernel Theory of Modern Data Augmentation,"  Data augmentation, a technique in which a training set is expanded with
+class-preserving transformations, is ubiquitous in modern machine learning
+pipelines. In this paper, we seek to establish a theoretical framework for
+understanding modern data augmentation techniques. We start by showing that for
+kernel classifiers, data augmentation can be approximated by first-order
+feature averaging and second-order variance regularization components. We
+connect this general approximation framework to prior work in invariant
+kernels, tangent propagation, and robust optimization. Next, we explicitly
+tackle the compositional aspect of modern data augmentation techniques,
+proposing a novel model of data augmentation as a Markov process. Under this
+model, we show that performing $k$-nearest neighbors with data augmentation is
+asymptotically equivalent to a kernel classifier. Finally, we illustrate ways
+in which our theoretical framework can be leveraged to accelerate machine
+learning workflows in practice, including reducing the amount of computation
+needed to train on augmented data, and predicting the utility of a
+transformation prior to training.
+",0,0,0,1,0,0
+1797,Carrier driven coupling in ferromagnetic oxide heterostructures,"  Transition metal oxides are well known for their complex magnetic and
+electrical properties. When brought together in heterostructure geometries,
+they show particular promise for spintronics and colossal magnetoresistance
+applications. In this letter, we propose a new mechanism for the coupling
+between layers of itinerant ferromagnetic materials in heterostructures. The
+coupling is mediated by charge carriers that strive to maximally delocalize
+through the heterostructure to gain kinetic energy. In doing so, they force a
+ferromagnetic or antiferromagnetic coupling between the constituent layers. To
+illustrate this, we focus on heterostructures composed of SrRuO$_3$ and
+La$_{1-x}$A$_{x}$MnO$_3$ (A=Ca/Sr). Our mechanism is consistent with
+antiferromagnetic alignment that is known to occur in multilayers of
+SrRuO$_3$-La$_{1-x}$A$_{x}$MnO$_3$. To support our assertion, we present a
+minimal Kondo-lattice model which reproduces the known magnetization properties
+of such multilayers. In addition, we discuss a quantum well model for
+heterostructures and argue that the spin-dependent density of states determines
+the nature of the coupling. As a smoking gun signature, we propose that
+bilayers with the same constituents will oscillate between ferromagnetic and
+antiferromagnetic coupling upon tuning the relative thicknesses of the layers.
+",0,1,0,0,0,0
+1798,Data Dropout in Arbitrary Basis for Deep Network Regularization,"  An important problem in training deep networks with high capacity is to
+ensure that the trained network works well when presented with new inputs
+outside the training dataset. Dropout is an effective regularization technique
+to boost the network generalization in which a random subset of the elements of
+the given data and the extracted features are set to zero during the training
+process. In this paper, a new randomized regularization technique in which we
+withhold a random part of the data without necessarily turning off the
+neurons/data-elements is proposed. In the proposed method, of which the
+conventional dropout is shown to be a special case, random data dropout is
+performed in an arbitrary basis, hence the designation Generalized Dropout. We
+also present a framework whereby the proposed technique can be applied
+efficiently to convolutional neural networks. The presented numerical
+experiments demonstrate that the proposed technique yields notable performance
+gain. Generalized Dropout provides new insight into the idea of dropout, shows
+that we can achieve different performance gains by using different bases
+matrices, and opens up a new research question as of how to choose optimal
+bases matrices that achieve maximal performance gain.
+",1,0,0,1,0,0
+1799,Efficient algorithms to discover alterations with complementary functional association in cancer,"  Recent large cancer studies have measured somatic alterations in an
+unprecedented number of tumours. These large datasets allow the identification
+of cancer-related sets of genetic alterations by identifying relevant
+combinatorial patterns. Among such patterns, mutual exclusivity has been
+employed by several recent methods that have shown its effectivenes in
+characterizing gene sets associated to cancer. Mutual exclusivity arises
+because of the complementarity, at the functional level, of alterations in
+genes which are part of a group (e.g., a pathway) performing a given function.
+The availability of quantitative target profiles, from genetic perturbations or
+from clinical phenotypes, provides additional information that can be leveraged
+to improve the identification of cancer related gene sets by discovering groups
+with complementary functional associations with such targets.
+In this work we study the problem of finding groups of mutually exclusive
+alterations associated with a quantitative (functional) target. We propose a
+combinatorial formulation for the problem, and prove that the associated
+computation problem is computationally hard. We design two algorithms to solve
+the problem and implement them in our tool UNCOVER. We provide analytic
+evidence of the effectiveness of UNCOVER in finding high-quality solutions and
+show experimentally that UNCOVER finds sets of alterations significantly
+associated with functional targets in a variety of scenarios. In addition, our
+algorithms are much faster than the state-of-the-art, allowing the analysis of
+large datasets of thousands of target profiles from cancer cell lines. We show
+that on one such dataset from project Achilles our methods identify several
+significant gene sets with complementary functional associations with targets.
+",0,0,0,0,1,0
+1800,Laplace operators on holomorphic Lie algebroids,"  The paper introduces Laplace-type operators for functions defined on the
+tangent space of a Finsler Lie algebroid, using a volume form on the
+prolongation of the algebroid. It also presents the construction of a
+horizontal Laplace operator for forms defined on the prolongation of the
+algebroid. All of the Laplace operators considered in the paper are also
+locally expressed using the Chern-Finsler connection of the algebroid.
+",0,0,1,0,0,0
+1801,Composing Differential Privacy and Secure Computation: A case study on scaling private record linkage,"  Private record linkage (PRL) is the problem of identifying pairs of records
+that are similar as per an input matching rule from databases held by two
+parties that do not trust one another. We identify three key desiderata that a
+PRL solution must ensure: 1) perfect precision and high recall of matching
+pairs, 2) a proof of end-to-end privacy, and 3) communication and computational
+costs that scale subquadratically in the number of input records. We show that
+all of the existing solutions for PRL - including secure 2-party computation
+(S2PC), and their variants that use non-private or differentially private (DP)
+blocking to ensure subquadratic cost - violate at least one of the three
+desiderata. In particular, S2PC techniques guarantee end-to-end privacy but
+have either low recall or quadratic cost. In contrast, no end-to-end privacy
+guarantee has been formalized for solutions that achieve subquadratic cost.
+This is true even for solutions that compose DP and S2PC: DP does not permit
+the release of any exact information about the databases, while S2PC algorithms
+for PRL allow the release of matching records.
+In light of this deficiency, we propose a novel privacy model, called output
+constrained differential privacy, that shares the strong privacy protection of
+DP, but allows for the truthful release of the output of a certain function
+applied to the data. We apply this to PRL, and show that protocols satisfying
+this privacy model permit the disclosure of the true matching records, but
+their execution is insensitive to the presence or absence of a single
+non-matching record. We find that prior work that combine DP and S2PC
+techniques even fail to satisfy this end-to-end privacy model. Hence, we
+develop novel protocols that provably achieve this end-to-end privacy
+guarantee, together with the other two desiderata of PRL.
+",1,0,0,0,0,0
+1802,Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction,"  Despite the recent popularity of deep generative state space models, few
+comparisons have been made between network architectures and the inference
+steps of the Bayesian filtering framework -- with most models simultaneously
+approximating both state transition and update steps with a single recurrent
+neural network (RNN). In this paper, we introduce the Recurrent Neural Filter
+(RNF), a novel recurrent variational autoencoder architecture that learns
+distinct representations for each Bayesian filtering step, captured by a series
+of encoders and decoders. Testing this on three real-world time series
+datasets, we demonstrate that decoupling representations not only improves the
+accuracy of one-step-ahead forecasts while providing realistic uncertainty
+estimates, but also facilitates multistep prediction through the separation of
+encoder stages.
+",1,0,0,1,0,0
+1803,A Submodularity-Based Approach for Multi-Agent Optimal Coverage Problems,"  We consider the optimal coverage problem where a multi-agent network is
+deployed in an environment with obstacles to maximize a joint event detection
+probability. The objective function of this problem is non-convex and no global
+optimum is guaranteed by gradient-based algorithms developed to date. We first
+show that the objective function is monotone submodular, a class of functions
+for which a simple greedy algorithm is known to be within 0.63 of the optimal
+solution. We then derive two tighter lower bounds by exploiting the curvature
+information (total curvature and elemental curvature) of the objective
+function. We further show that the tightness of these lower bounds is
+complementary with respect to the sensing capabilities of the agents. The
+greedy algorithm solution can be subsequently used as an initial point for a
+gradient-based algorithm to obtain solutions even closer to the global optimum.
+Simulation results show that this approach leads to significantly better
+performance relative to previously used algorithms.
+",1,0,1,0,0,0
+1804,A GPU-based Multi-level Algorithm for Boundary Value Problems,"  A novel and scalable geometric multi-level algorithm is presented for the
+numerical solution of elliptic partial differential equations, specially
+designed to run with high occupancy of streaming processors inside Graphics
+Processing Units(GPUs). The algorithm consists of iterative, superposed
+operations on a single grid, and it is composed of two simple full-grid
+routines: a restriction and a coarsened interpolation-relaxation. The
+restriction is used to collect sources using recursive coarsened averages, and
+the interpolation-relaxation simultaneously applies coarsened finite-difference
+operators and interpolations. The routines are scheduled in a saw-like refining
+cycle. Convergence to machine precision is achieved repeating the full cycle
+using accumulated residuals and successively collecting the solution. Its total
+number of operations scale linearly with the number of nodes. It provides an
+attractive fast solver for Boundary Value Problems (BVPs), specially for
+simulations running entirely in the GPU. Applications shown in this work
+include the deformation of two-dimensional grids, the computation of
+three-dimensional streamlines for a singular trifoil-knot vortex and the
+calculation of three-dimensional electric potentials in heterogeneous
+dielectric media.
+",1,1,0,0,0,0
+1805,Counterexample-Guided k-Induction Verification for Fast Bug Detection,"  Recently, the k-induction algorithm has proven to be a successful approach
+for both finding bugs and proving correctness. However, since the algorithm is
+an incremental approach, it might waste resources trying to prove incorrect
+programs. In this paper, we propose to extend the k-induction algorithm in
+order to shorten the number of steps required to find a property violation. We
+convert the algorithm into a meet-in-the-middle bidirectional search algorithm,
+using the counterexample produced from over-approximating the program. The
+preliminary results show that the number of steps required to find a property
+violation is reduced to $\lfloor\frac{k}{2} + 1\rfloor$ and the verification
+time for programs with large state space is reduced considerably.
+",1,0,0,0,0,0
+1806,Cautious Model Predictive Control using Gaussian Process Regression,"  Gaussian process (GP) regression has been widely used in supervised machine
+learning due to its flexibility and inherent ability to describe uncertainty in
+function estimation. In the context of control, it is seeing increasing use for
+modeling of nonlinear dynamical systems from data, as it allows the direct
+assessment of residual model uncertainty. We present a model predictive control
+(MPC) approach that integrates a nominal system with an additive nonlinear part
+of the dynamics modeled as a GP. Approximation techniques for propagating the
+state distribution are reviewed and we describe a principled way of formulating
+the chance constrained MPC problem, which takes into account residual
+uncertainties provided by the GP model to enable cautious control. Using
+additional approximations for efficient computation, we finally demonstrate the
+approach in a simulation example, as well as in a hardware implementation for
+autonomous racing of remote controlled race cars, highlighting improvements
+with regard to both performance and safety over a nominal controller.
+",1,0,1,0,0,0
+1807,Probabilistic Trajectory Segmentation by Means of Hierarchical Dirichlet Process Switching Linear Dynamical Systems,"  Using movement primitive libraries is an effective means to enable robots to
+solve more complex tasks. In order to build these movement libraries, current
+algorithms require a prior segmentation of the demonstration trajectories. A
+promising approach is to model the trajectory as being generated by a set of
+Switching Linear Dynamical Systems and inferring a meaningful segmentation by
+inspecting the transition points characterized by the switching dynamics. With
+respect to the learning, a nonparametric Bayesian approach is employed
+utilizing a Gibbs sampler.
+",1,0,0,1,0,0
+1808,Radio Frequency Interference Mitigation,"  Radio astronomy observational facilities are under constant upgradation and
+development to achieve better capabilities including increasing the time and
+frequency resolutions of the recorded data, and increasing the receiving and
+recording bandwidth. As only a limited spectrum resource has been allocated to
+radio astronomy by the International Telecommunication Union, this results in
+the radio observational instrumentation being inevitably exposed to undesirable
+radio frequency interference (RFI) signals which originate mainly from
+terrestrial human activity and are becoming stronger with time. RFIs degrade
+the quality of astronomical data and even lead to data loss. The impact of RFIs
+on scientific outcome is becoming progressively difficult to manage. In this
+article, we motivate the requirement for RFI mitigation, and review the RFI
+characteristics, mitigation techniques and strategies. Mitigation strategies
+adopted at some representative observatories, telescopes and arrays are also
+introduced. We also discuss and present advantages and shortcomings of the four
+classes of RFI mitigation strategies, applicable at the connected causal
+stages: preventive, pre-detection, pre-correlation and post-correlation. The
+proper identification and flagging of RFI is key to the reduction of data loss
+and improvement in data quality, and is also the ultimate goal of developing
+RFI mitigation techniques. This can be achieved through a strategy involving a
+combination of the discussed techniques in stages. Recent advances in high
+speed digital signal processing and high performance computing allow for
+performing RFI excision of large data volumes generated from large telescopes
+or arrays in both real time and offline modes, aiding the proposed strategy.
+",0,1,0,0,0,0
+1809,Online Calibration of Phasor Measurement Unit Using Density-Based Spatial Clustering,"  Data quality of Phasor Measurement Unit (PMU) is receiving increasing
+attention as it has been identified as one of the limiting factors that affect
+many wide-area measurement system (WAMS) based applications. In general,
+existing PMU calibration methods include offline testing and model based
+approaches. However, in practice, the effectiveness of both is limited due to
+the very strong assumptions employed. This paper presents a novel framework for
+online bias error detection and calibration of PMU measurement using
+density-based spatial clustering of applications with noise (DBSCAN) based on
+much relaxed assumptions. With a new problem formulation, the proposed data
+mining based methodology is applicable across a wide spectrum of practical
+conditions and one side-product of it is more accurate transmission line
+parameters for EMS database and protective relay settings. Case studies
+demonstrate the effectiveness of the proposed approach.
+",1,0,0,0,0,0
+1810,Some basic properties of bounded solutions of parabolic equations with p-Laplacian diffusion,"  We provide a detailed (and fully rigorous) derivation of several fundamental
+properties of bounded weak solutions to initial-value problems for general
+conservative 2nd-order parabolic equations with p-Laplacian diffusion and
+(arbitrary) bounded and integrable initial data.
+",0,0,1,0,0,0
+1811,Andreev Reflection without Fermi surface alignment in High T$_{c}$-Topological heterostructures,"  We address the controversy over the proximity effect between topological
+materials and high T$_{c}$ superconductors. Junctions are produced between
+Bi$_{2}$Sr$_{2}$CaCu$_{2}$O$_{8+\delta}$ and materials with different Fermi
+surfaces (Bi$_{2}$Te$_{3}$ \& graphite). Both cases reveal tunneling spectra
+consistent with Andreev reflection. This is confirmed by magnetic field that
+shifts features via the Doppler effect. This is modeled with a single parameter
+that accounts for tunneling into a screening supercurrent. Thus the tunneling
+involves Cooper pairs crossing the heterostructure, showing the Fermi surface
+mis-match does not hinder the ability to form transparent interfaces, which is
+accounted for by the extended Brillouin zone and different lattice symmetries.
+",0,1,0,0,0,0
+1812,Structural Data Recognition with Graph Model Boosting,"  This paper presents a novel method for structural data recognition using a
+large number of graph models. In general, prevalent methods for structural data
+recognition have two shortcomings: 1) Only a single model is used to capture
+structural variation. 2) Naive recognition methods are used, such as the
+nearest neighbor method. In this paper, we propose strengthening the
+recognition performance of these models as well as their ability to capture
+structural variation. The proposed method constructs a large number of graph
+models and trains decision trees using the models. This paper makes two main
+contributions. The first is a novel graph model that can quickly perform
+calculations, which allows us to construct several models in a feasible amount
+of time. The second contribution is a novel approach to structural data
+recognition: graph model boosting. Comprehensive structural variations can be
+captured with a large number of graph models constructed in a boosting
+framework, and a sophisticated classifier can be formed by aggregating the
+decision trees. Consequently, we can carry out structural data recognition with
+powerful recognition capability in the face of comprehensive structural
+variation. The experiments shows that the proposed method achieves impressive
+results and outperforms existing methods on datasets of IAM graph database
+repository.
+",1,0,0,1,0,0
+1813,Exceptional points in two simple textbook examples,"  We propose to introduce the concept of exceptional points in intermediate
+courses on mathematics and classical mechanics by means of simple textbook
+examples. The first one is an ordinary second-order differential equation with
+constant coefficients. The second one is the well known damped harmonic
+oscillator. They enable one to connect the occurrence of linearly dependent
+exponential solutions with a defective matrix that cannot be diagonalized but
+can be transformed into a Jordan canonical form.
+",0,1,0,0,0,0
+1814,Bootstrap of residual processes in regression: to smooth or not to smooth ?,"  In this paper we consider a location model of the form $Y = m(X) +
+\varepsilon$, where $m(\cdot)$ is the unknown regression function, the error
+$\varepsilon$ is independent of the $p$-dimensional covariate $X$ and
+$E(\varepsilon)=0$. Given i.i.d. data $(X_1,Y_1),\ldots,(X_n,Y_n)$ and given an
+estimator $\hat m(\cdot)$ of the function $m(\cdot)$ (which can be parametric
+or nonparametric of nature), we estimate the distribution of the error term
+$\varepsilon$ by the empirical distribution of the residuals $Y_i-\hat m(X_i)$,
+$i=1,\ldots,n$. To approximate the distribution of this estimator, Koul and
+Lahiri (1994) and Neumeyer (2008, 2009) proposed bootstrap procedures, based on
+smoothing the residuals either before or after drawing bootstrap samples. So
+far it has been an open question whether a classical non-smooth residual
+bootstrap is asymptotically valid in this context. In this paper we solve this
+open problem, and show that the non-smooth residual bootstrap is consistent. We
+illustrate this theoretical result by means of simulations, that show the
+accuracy of this bootstrap procedure for various models, testing procedures and
+sample sizes.
+",0,0,1,1,0,0
+1815,Polynomiality for the Poisson centre of truncated maximal parabolic subalgebras,"  We show that the Poisson centre of truncated maximal parabolic subalgebras of
+a simple Lie algebra of type B, D and E_6 is a polynomial algebra.
+In roughly half of the cases the polynomiality of the Poisson centre was
+already known by a completely different method.
+For the rest of the cases, our approach is to construct an algebraic slice in
+the sense of Kostant given by an adapted pair and the computation of an
+improved upper bound for the Poisson centre.
+",0,0,1,0,0,0
+1816,Row-Centric Lossless Compression of Markov Images,"  Motivated by the question of whether the recently introduced Reduced Cutset
+Coding (RCC) offers rate-complexity performance benefits over conventional
+context-based conditional coding for sources with two-dimensional Markov
+structure, this paper compares several row-centric coding strategies that vary
+in the amount of conditioning as well as whether a model or an empirical table
+is used in the encoding of blocks of rows. The conclusion is that, at least for
+sources exhibiting low-order correlations, 1-sided model-based conditional
+coding is superior to the method of RCC for a given constraint on complexity,
+and conventional context-based conditional coding is nearly as good as the
+1-sided model-based coding.
+",1,0,0,0,0,0
+1817,Planetesimal formation by the streaming instability in a photoevaporating disk,"  Recent years have seen growing interest in the streaming instability as a
+candidate mechanism to produce planetesimals. However, these investigations
+have been limited to small-scale simulations. We now present the results of a
+global protoplanetary disk evolution model that incorporates planetesimal
+formation by the streaming instability, along with viscous accretion,
+photoevaporation by EUV, FUV, and X-ray photons, dust evolution, the water ice
+line, and stratified turbulence. Our simulations produce massive (60-130
+$M_\oplus$) planetesimal belts beyond 100 au and up to $\sim 20 M_\oplus$ of
+planetesimals in the middle regions (3-100 au). Our most comprehensive model
+forms 8 $M_\oplus$ of planetesimals inside 3 au, where they can give rise to
+terrestrial planets. The planetesimal mass formed in the inner disk depends
+critically on the timing of the formation of an inner cavity in the disk by
+high-energy photons. Our results show that the combination of photoevaporation
+and the streaming instability are efficient at converting the solid component
+of protoplanetary disks into planetesimals. Our model, however, does not form
+enough early planetesimals in the inner and middle regions of the disk to give
+rise to giant planets and super-Earths with gaseous envelopes. Additional
+processes such as particle pileups and mass loss driven by MHD winds may be
+needed to drive the formation of early planetesimal generations in the planet
+forming regions of protoplanetary disks.
+",0,1,0,0,0,0
+1818,Fault Tolerant Thermal Control of Steam Turbine Shell Deflections,"  The metal-to-metal clearances of a steam turbine during full or part load
+operation are among the main drivers of efficiency. The requirement to add
+clearances is driven by a number of factors including the relative movements of
+the steam turbine shell and rotor during transient conditions such as startup
+and shutdown. This paper includes a description of a control algorithm to
+manage external heating blankets for the thermal control of the shell
+deflections during turbine shutdown. The proposed method is tolerant of changes
+in the heat loss characteristics of the system as well as simultaneous
+component failures.
+",1,0,0,0,0,0
+1819,Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data,"  Summary statistics of genome-wide association studies (GWAS) teach causal
+relationship between millions of genetic markers and tens and thousands of
+phenotypes. However, underlying biological mechanisms are yet to be elucidated.
+We can achieve necessary interpretation of GWAS in a causal mediation
+framework, looking to establish a sparse set of mediators between genetic and
+downstream variables, but there are several challenges. Unlike existing methods
+rely on strong and unrealistic assumptions, we tackle practical challenges
+within a principled summary-based causal inference framework. We analyzed the
+proposed methods in extensive simulations generated from real-world genetic
+data. We demonstrated only our approach can accurately redeem causal genes,
+even without knowing actual individual-level data, despite the presence of
+competing non-causal trails.
+",1,0,0,1,1,0
+1820,Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks,"  Biological networks are a very convenient modelling and visualisation tool to
+discover knowledge from modern high-throughput genomics and postgenomics data
+sets. Indeed, biological entities are not isolated, but are components of
+complex multi-level systems. We go one step further and advocate for the
+consideration of causal representations of the interactions in living
+systems.We present the causal formalism and bring it out in the context of
+biological networks, when the data is observational. We also discuss its
+ability to decipher the causal information flow as observed in gene expression.
+We also illustrate our exploration by experiments on small simulated networks
+as well as on a real biological data set.
+",0,0,0,1,1,0
+1821,Hierarchical Bloom Filter Trees for Approximate Matching,"  Bytewise approximate matching algorithms have in recent years shown
+significant promise in de- tecting files that are similar at the byte level.
+This is very useful for digital forensic investigators, who are regularly faced
+with the problem of searching through a seized device for pertinent data. A
+common scenario is where an investigator is in possession of a collection of
+""known-illegal"" files (e.g. a collection of child abuse material) and wishes to
+find whether copies of these are stored on the seized device. Approximate
+matching addresses shortcomings in traditional hashing, which can only find
+identical files, by also being able to deal with cases of merged files,
+embedded files, partial files, or if a file has been changed in any way.
+Most approximate matching algorithms work by comparing pairs of files, which
+is not a scalable approach when faced with large corpora. This paper
+demonstrates the effectiveness of using a ""Hierarchical Bloom Filter Tree""
+(HBFT) data structure to reduce the running time of
+collection-against-collection matching, with a specific focus on the MRSH-v2
+algorithm. Three experiments are discussed, which explore the effects of
+different configurations of HBFTs. The proposed approach dramatically reduces
+the number of pairwise comparisons required, and demonstrates substantial speed
+gains, while maintaining effectiveness.
+",1,0,0,0,0,0
+1822,GANDALF - Graphical Astrophysics code for N-body Dynamics And Lagrangian Fluids,"  GANDALF is a new hydrodynamics and N-body dynamics code designed for
+investigating planet formation, star formation and star cluster problems.
+GANDALF is written in C++, parallelised with both OpenMP and MPI and contains a
+python library for analysis and visualisation. The code has been written with a
+fully object-oriented approach to easily allow user-defined implementations of
+physics modules or other algorithms. The code currently contains
+implementations of Smoothed Particle Hydrodynamics, Meshless Finite-Volume and
+collisional N-body schemes, but can easily be adapted to include additional
+particle schemes. We present in this paper the details of its implementation,
+results from the test suite, serial and parallel performance results and
+discuss the planned future development. The code is freely available as an open
+source project on the code-hosting website github at
+this https URL and is available under the GPLv2
+license.
+",0,1,0,0,0,0
+1823,Pre-freezing transition in Boltzmann-Gibbs measures associated with log-correlated fields,"  We consider Boltzmann-Gibbs measures associated with log-correlated Gaussian
+fields as potentials and study their multifractal properties which exhibit
+phase transitions. In particular, the pre-freezing and freezing phenomena of
+the annealed exponent, predicted by Fyodorov using a modified
+replica-symmetry-breaking ansatz, are generalised to arbitrary dimension and
+verified using results from Gaussian multiplicative chaos theory.
+",0,1,0,0,0,0
+1824,Learning Combinatorial Optimization Algorithms over Graphs,"  The design of good heuristics or approximation algorithms for NP-hard
+combinatorial optimization problems often requires significant specialized
+knowledge and trial-and-error. Can we automate this challenging, tedious
+process, and learn the algorithms instead? In many real-world applications, it
+is typically the case that the same optimization problem is solved again and
+again on a regular basis, maintaining the same problem structure but differing
+in the data. This provides an opportunity for learning heuristic algorithms
+that exploit the structure of such recurring problems. In this paper, we
+propose a unique combination of reinforcement learning and graph embedding to
+address this challenge. The learned greedy policy behaves like a meta-algorithm
+that incrementally constructs a solution, and the action is determined by the
+output of a graph embedding network capturing the current state of the
+solution. We show that our framework can be applied to a diverse range of
+optimization problems over graphs, and learns effective algorithms for the
+Minimum Vertex Cover, Maximum Cut and Traveling Salesman problems.
+",1,0,0,1,0,0
+1825,Optimal Oil Production and Taxation in Presence of Global Disruptions,"  This paper studies the optimal extraction policy of an oil field as well as
+the efficient taxation of the revenues generated. Taking into account the fact
+that the oil price in worldwide commodity markets fluctuates randomly following
+global and seasonal macroeconomic parameters, we model the evolution of the oil
+price as a mean reverting regime-switching jump diffusion process. Given that
+oil producing countries rely on oil sale revenues as well as taxes levied on
+oil companies for a good portion of the revenue side of their budgets, we
+formulate this problem as a differential game where the two players are the
+mining company whose aim is to maximize the revenues generated from its
+extracting activities and the government agency in charge of regulating and
+taxing natural resources. We prove the existence of a Nash equilibrium and the
+convergence of an approximating scheme for the value functions. Furthermore,
+optimal extraction and fiscal policies that should be applied when the
+equilibrium is reached are derived.A numerical example is presented to
+illustrate these results.
+",0,0,1,0,0,0
+1826,Critical well-posedness and scattering results for fractional Hartree-type equations,"  Scattering for the mass-critical fractional Schrödinger equation with a
+cubic Hartree-type nonlinearity for initial data in a small ball in the
+scale-invariant space of three-dimensional radial and square-integrable initial
+data is established. For this, we prove a bilinear estimate for free solutions
+and extend it to perturbations of bounded quadratic variation. This result is
+shown to be sharp by proving the unboundedness of a third order derivative of
+the flow map in the super-critical range.
+",0,0,1,0,0,0
+1827,Lightweight Multilingual Software Analysis,"  Developer preferences, language capabilities and the persistence of older
+languages contribute to the trend that large software codebases are often
+multilingual, that is, written in more than one computer language. While
+developers can leverage monolingual software development tools to build
+software components, companies are faced with the problem of managing the
+resultant large, multilingual codebases to address issues with security,
+efficiency, and quality metrics. The key challenge is to address the opaque
+nature of the language interoperability interface: one language calling
+procedures in a second (which may call a third, or even back to the first),
+resulting in a potentially tangled, inefficient and insecure codebase. An
+architecture is proposed for lightweight static analysis of large multilingual
+codebases: the MLSA architecture. Its modular and table-oriented structure
+addresses the open-ended nature of multiple languages and language
+interoperability APIs. We focus here as an application on the construction of
+call-graphs that capture both inter-language and intra-language calls. The
+algorithms for extracting multilingual call-graphs from codebases are
+presented, and several examples of multilingual software engineering analysis
+are discussed. The state of the implementation and testing of MLSA is
+presented, and the implications for future work are discussed.
+",1,0,0,0,0,0
+1828,Room-temperature 1.54 $μ$m photoluminescence of Er:O$_x$ centers at extremely low concentration in silicon,"  The demand for single photon sources at $\lambda~=~1.54~\mu$m, which follows
+from the consistent development of quantum networks based on commercial optical
+fibers, makes Er:O$_x$ centers in Si still a viable resource thanks to the
+optical transition of $Er^{3+}~:~^4I_{13/2}~\rightarrow~^4I_{15/2}$. Yet, to
+date, the implementation of such system remains hindered by its extremely low
+emission rate. In this Letter, we explore the room-temperature
+photoluminescence (PL) at the telecomm wavelength of very low implantation
+doses of $Er:O_x$ in $Si$. The emitted photons, excited by a $\lambda~=~792~nm$
+laser in both large areas and confined dots of diameter down to $5~\mu$m, are
+collected by an inverted confocal microscope. The lower-bound number of
+detectable emission centers within our diffraction-limited illumination spot is
+estimated to be down to about 10$^4$, corresponding to an emission rate per
+individual ion of about $4~\times~10^{3}$ photons/s.
+",0,1,0,0,0,0
+1829,Sparse Algorithm for Robust LSSVM in Primal Space,"  As enjoying the closed form solution, least squares support vector machine
+(LSSVM) has been widely used for classification and regression problems having
+the comparable performance with other types of SVMs. However, LSSVM has two
+drawbacks: sensitive to outliers and lacking sparseness. Robust LSSVM (R-LSSVM)
+overcomes the first partly via nonconvex truncated loss function, but the
+current algorithms for R-LSSVM with the dense solution are faced with the
+second drawback and are inefficient for training large-scale problems. In this
+paper, we interpret the robustness of R-LSSVM from a re-weighted viewpoint and
+give a primal R-LSSVM by the representer theorem. The new model may have sparse
+solution if the corresponding kernel matrix has low rank. Then approximating
+the kernel matrix by a low-rank matrix and smoothing the loss function by
+entropy penalty function, we propose a convergent sparse R-LSSVM (SR-LSSVM)
+algorithm to achieve the sparse solution of primal R-LSSVM, which overcomes two
+drawbacks of LSSVM simultaneously. The proposed algorithm has lower complexity
+than the existing algorithms and is very efficient for training large-scale
+problems. Many experimental results illustrate that SR-LSSVM can achieve better
+or comparable performance with less training time than related algorithms,
+especially for training large scale problems.
+",1,0,0,1,0,0
+1830,Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning,"  We consider the networked multi-agent reinforcement learning (MARL) problem
+in a fully decentralized setting, where agents learn to coordinate to achieve
+the joint success. This problem is widely encountered in many areas including
+traffic control, distributed control, and smart grids. We assume that the
+reward function for each agent can be different and observed only locally by
+the agent itself. Furthermore, each agent is located at a node of a
+communication network and can exchanges information only with its neighbors.
+Using softmax temporal consistency and a decentralized optimization method, we
+obtain a principled and data-efficient iterative algorithm. In the first step
+of each iteration, an agent computes its local policy and value gradients and
+then updates only policy parameters. In the second step, the agent propagates
+to its neighbors the messages based on its value function and then updates its
+own value function. Hence we name the algorithm value propagation. We prove a
+non-asymptotic convergence rate 1/T with the nonlinear function approximation.
+To the best of our knowledge, it is the first MARL algorithm with convergence
+guarantee in the control, off-policy and non-linear function approximation
+setting. We empirically demonstrate the effectiveness of our approach in
+experiments.
+",1,0,0,1,0,0
+1831,"Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible","  Non-interactive Local Differential Privacy (LDP) requires data analysts to
+collect data from users through noisy channel at once. In this paper, we extend
+the frontiers of Non-interactive LDP learning and estimation from several
+aspects. For learning with smooth generalized linear losses, we propose an
+approximate stochastic gradient oracle estimated from non-interactive LDP
+channel, using Chebyshev expansion. Combined with inexact gradient methods, we
+obtain an efficient algorithm with quasi-polynomial sample complexity bound.
+For the high-dimensional world, we discover that under $\ell_2$-norm assumption
+on data points, high-dimensional sparse linear regression and mean estimation
+can be achieved with logarithmic dependence on dimension, using random
+projection and approximate recovery. We also extend our methods to Kernel Ridge
+Regression. Our work is the first one that makes learning and estimation
+possible for a broad range of learning tasks under non-interactive LDP model.
+",1,0,0,0,0,0
+1832,Learning Independent Causal Mechanisms,"  Statistical learning relies upon data sampled from a distribution, and we
+usually do not care what actually generated it in the first place. From the
+point of view of causal modeling, the structure of each distribution is induced
+by physical mechanisms that give rise to dependences between observables.
+Mechanisms, however, can be meaningful autonomous modules of generative models
+that make sense beyond a particular entailed data distribution, lending
+themselves to transfer between problems. We develop an algorithm to recover a
+set of independent (inverse) mechanisms from a set of transformed data points.
+The approach is unsupervised and based on a set of experts that compete for
+data generated by the mechanisms, driving specialization. We analyze the
+proposed method in a series of experiments on image data. Each expert learns to
+map a subset of the transformed data back to a reference distribution. The
+learned mechanisms generalize to novel domains. We discuss implications for
+transfer learning and links to recent trends in generative modeling.
+",1,0,0,1,0,0
+1833,"A Bayesian Model for False Information Belief Impact, Optimal Design, and Fake News Containment","  This work is a technical approach to modeling false information nature,
+design, belief impact and containment in multi-agent networks. We present a
+Bayesian mathematical model for source information and viewer's belief, and how
+the former impacts the latter in a media (network) of broadcasters and viewers.
+Given the proposed model, we study how a particular information (true or false)
+can be optimally designed into a report, so that on average it conveys the most
+amount of the original intended information to the viewers of the network.
+Consequently, the model allows us to study susceptibility of a particular group
+of viewers to false information, as a function of statistical metrics of the
+their prior beliefs (e.g. bias, hesitation, open-mindedness, credibility
+assessment etc.). In addition, based on the same model we can study false
+information ""containment"" strategies imposed by network administrators.
+Specifically, we study a credibility assessment strategy, where every
+disseminated report must be within a certain distance of the truth. We study
+the trade-off between false and true information-belief convergence using this
+scheme which leads to ways for optimally deciding how truth sensitive an
+information dissemination network should operate.
+",1,0,0,0,0,0
+1834,Topological dynamics of gyroscopic and Floquet lattices from Newton's laws,"  Despite intense interest in realizing topological phases across a variety of
+electronic, photonic and mechanical platforms, the detailed microscopic origin
+of topological behavior often remains elusive. To bridge this conceptual gap,
+we show how hallmarks of topological modes - boundary localization and
+chirality - emerge from Newton's laws in mechanical topological systems. We
+first construct a gyroscopic lattice with analytically solvable edge modes, and
+show how the Lorentz and spring restoring forces conspire to support very
+robust ""dangling bond"" boundary modes. The chirality and locality of these
+modes intuitively emerges from microscopic balancing of restoring forces and
+cyclotron tendencies. Next, we introduce the highlight of this work, a very
+experimentally realistic mechanical non-equilibrium (Floquet) Chern lattice
+driven by AC electromagnets. Through appropriate synchronization of the AC
+driving protocol, the Floquet lattice is ""pushed around"" by a rotating
+potential analogous to an object washed ashore by water waves. Besides hosting
+""dangling bond"" chiral modes analogous to the gyroscopic boundary modes, our
+Floquet Chern lattice also supports peculiar half-period chiral modes with no
+static analog. With key parameters controlled electronically, our setup has the
+advantage of being dynamically tunable for applications involving arbitrary
+Floquet modulations. The physical intuition gleaned from our two prototypical
+topological systems are applicable not just to arbitrarily complicated
+mechanical systems, but also photonic and electrical topological setups.
+",0,1,1,0,0,0
+1835,Stability of axisymmetric chiral skyrmions,"  We examine topological solitons in a minimal variational model for a chiral
+magnet, so-called chiral skyrmions. In the regime of large background fields,
+we prove linear stability of axisymmetric chiral skyrmions under arbitrary
+perturbations in the energy space, a long-standing open question in physics
+literature. Moreover, we show strict local minimality of axisymmetric chiral
+skyrmions and nearby existence of moving soliton solution for the
+Landau-Lifshitz-Gilbert equation driven by a small spin transfer torque.
+",0,0,1,0,0,0
+1836,Efficiency versus instability in plasma accelerators,"  Plasma wake-field acceleration is one of the main technologies being
+developed for future high-energy colliders. Potentially, it can create a
+cost-effective path to the highest possible energies for e+e- or
+{\gamma}-{\gamma} colliders and produce a profound effect on the developments
+for high-energy physics. Acceleration in a blowout regime, where all plasma
+electrons are swept away from the axis, is presently considered to be the
+primary choice for beam acceleration. In this paper, we derive a universal
+efficiency-instability relation, between the power efficiency and the key
+instability parameter of the trailing bunch for beam acceleration in the
+blowout regime. We also show that the suppression of instability in the
+trailing bunch can be achieved through BNS damping by the introduction of a
+beam energy variation along the bunch. Unfortunately, in the high efficiency
+regime, the required energy variation is quite high, and is not presently
+compatible with collider-quality beams. We would like to stress that the
+development of the instability imposes a fundamental limitation on the
+acceleration efficiency, and it is unclear how it could be overcome for
+high-luminosity linear colliders. With minor modifications, the considered
+limitation on the power efficiency is applicable to other types of
+acceleration.
+",0,1,0,0,0,0
+1837,Resistivity bound for hydrodynamic bad metals,"  We obtain a rigorous upper bound on the resistivity $\rho$ of an electron
+fluid whose electronic mean free path is short compared to the scale of spatial
+inhomogeneities. When such a hydrodynamic electron fluid supports a non-thermal
+diffusion process -- such as an imbalance mode between different bands -- we
+show that the resistivity bound becomes $\rho \lesssim A \, \Gamma$. The
+coefficient $A$ is independent of temperature and inhomogeneity lengthscale,
+and $\Gamma$ is a microscopic momentum-preserving scattering rate. In this way
+we obtain a unified and novel mechanism -- without umklapp -- for $\rho \sim
+T^2$ in a Fermi liquid and the crossover to $\rho \sim T$ in quantum critical
+regimes. This behavior is widely observed in transition metal oxides, organic
+metals, pnictides and heavy fermion compounds and has presented a longstanding
+challenge to transport theory. Our hydrodynamic bound allows phonon
+contributions to diffusion constants, including thermal diffusion, to directly
+affect the electrical resistivity.
+",0,1,0,0,0,0
+1838,Minimal Exploration in Structured Stochastic Bandits,"  This paper introduces and addresses a wide class of stochastic bandit
+problems where the function mapping the arm to the corresponding reward
+exhibits some known structural properties. Most existing structures (e.g.
+linear, Lipschitz, unimodal, combinatorial, dueling, ...) are covered by our
+framework. We derive an asymptotic instance-specific regret lower bound for
+these problems, and develop OSSB, an algorithm whose regret matches this
+fundamental limit. OSSB is not based on the classical principle of ""optimism in
+the face of uncertainty"" or on Thompson sampling, and rather aims at matching
+the minimal exploration rates of sub-optimal arms as characterized in the
+derivation of the regret lower bound. We illustrate the efficiency of OSSB
+using numerical experiments in the case of the linear bandit problem and show
+that OSSB outperforms existing algorithms, including Thompson sampling.
+",1,0,0,1,0,0
+1839,Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains,"  While modern day web applications aim to create impact at the civilization
+level, they have become vulnerable to adversarial activity, where the next
+cyber-attack can take any shape and can originate from anywhere. The increasing
+scale and sophistication of attacks, has prompted the need for a data driven
+solution, with machine learning forming the core of many cybersecurity systems.
+Machine learning was not designed with security in mind, and the essential
+assumption of stationarity, requiring that the training and testing data follow
+similar distributions, is violated in an adversarial domain. In this paper, an
+adversary's view point of a classification based system, is presented. Based on
+a formal adversarial model, the Seed-Explore-Exploit framework is presented,
+for simulating the generation of data driven and reverse engineering attacks on
+classifiers. Experimental evaluation, on 10 real world datasets and using the
+Google Cloud Prediction Platform, demonstrates the innate vulnerability of
+classifiers and the ease with which evasion can be carried out, without any
+explicit information about the classifier type, the training data or the
+application domain. The proposed framework, algorithms and empirical
+evaluation, serve as a white hat analysis of the vulnerabilities, and aim to
+foster the development of secure machine learning frameworks.
+",1,0,0,1,0,0
+1840,On Optimistic versus Randomized Exploration in Reinforcement Learning,"  We discuss the relative merits of optimistic and randomized approaches to
+exploration in reinforcement learning. Optimistic approaches presented in the
+literature apply an optimistic boost to the value estimate at each state-action
+pair and select actions that are greedy with respect to the resulting
+optimistic value function. Randomized approaches sample from among
+statistically plausible value functions and select actions that are greedy with
+respect to the random sample. Prior computational experience suggests that
+randomized approaches can lead to far more statistically efficient learning. We
+present two simple analytic examples that elucidate why this is the case. In
+principle, there should be optimistic approaches that fare well relative to
+randomized approaches, but that would require intractable computation.
+Optimistic approaches that have been proposed in the literature sacrifice
+statistical efficiency for the sake of computational efficiency. Randomized
+approaches, on the other hand, may enable simultaneous statistical and
+computational efficiency.
+",1,0,0,1,0,0
+1841,Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations,"  Size, weight, and power constrained platforms impose constraints on
+computational resources that introduce unique challenges in implementing
+localization algorithms. We present a framework to perform fast localization on
+such platforms enabled by the compressive capabilities of Gaussian Mixture
+Model representations of point cloud data. Given raw structural data from a
+depth sensor and pitch and roll estimates from an on-board attitude reference
+system, a multi-hypothesis particle filter localizes the vehicle by exploiting
+the likelihood of the data originating from the mixture model. We demonstrate
+analysis of this likelihood in the vicinity of the ground truth pose and detail
+its utilization in a particle filter-based vehicle localization strategy, and
+later present results of real-time implementations on a desktop system and an
+off-the-shelf embedded platform that outperform localization results from
+running a state-of-the-art algorithm on the same environment.
+",1,0,0,0,0,0
+1842,Generalized two-field $α$-attractor models from geometrically finite hyperbolic surfaces,"  We consider four-dimensional gravity coupled to a non-linear sigma model
+whose scalar manifold is a non-compact geometrically finite surface $\Sigma$
+endowed with a Riemannian metric of constant negative curvature. When the
+space-time is an FLRW universe, such theories produce a very wide
+generalization of two-field $\alpha$-attractor models, being parameterized by a
+positive constant $\alpha$, by the choice of a finitely-generated surface group
+$\Gamma\subset \mathrm{PSL}(2,\mathbb{R})$ (which is isomorphic with the
+fundamental group of $\Sigma$) and by the choice of a scalar potential defined
+on $\Sigma$. The traditional two-field $\alpha$-attractor models arise when
+$\Gamma$ is the trivial group, in which case $\Sigma$ is the Poincaré disk.
+We give a general prescription for the study of such models through
+uniformization in the so-called ""non-elementary"" case and discuss some of their
+qualitative features in the gradient flow approximation, which we relate to
+Morse theory. We also discuss some aspects of the SRST approximation in these
+models, showing that it is generally not well-suited for studying dynamics near
+cusp ends. When $\Sigma$ is non-compact and the scalar potential is
+""well-behaved"" at the ends, we show that, in the {\em naive} local one-field
+truncation, our generalized models have the same universal behavior as ordinary
+one-field $\alpha$-attractors if inflation happens near any of the ends of
+$\Sigma$ where the extended potential has a local maximum, for trajectories
+which are well approximated by non-canonically parameterized geodesics near the
+ends, we also discuss spiral trajectories near the ends.
+",0,1,1,0,0,0
+1843,The Geodetic Hull Number is Hard for Chordal Graphs,"  We show the hardness of the geodetic hull number for chordal graphs.
+",1,0,0,0,0,0
+1844,"$\overline{M}_{1,n}$ is usually not uniruled in characteristic $p$","  Using etale cohomology, we define a birational invariant for varieties in
+characteristic $p$ that serves as an obstruction to uniruledness - a variant on
+an obstruction to unirationality due to Ekedahl. We apply this to
+$\overline{M}_{1,n}$ and show that $\overline{M}_{1,n}$ is not uniruled in
+characteristic $p$ as long as $n \geq p \geq 11$. To do this, we use Deligne's
+description of the etale cohomology of $\overline{M}_{1,n}$ and apply the
+theory of congruences between modular forms.
+",0,0,1,0,0,0
+1845,Active Community Detection: A Maximum Likelihood Approach,"  We propose novel semi-supervised and active learning algorithms for the
+problem of community detection on networks. The algorithms are based on
+optimizing the likelihood function of the community assignments given a graph
+and an estimate of the statistical model that generated it. The optimization
+framework is inspired by prior work on the unsupervised community detection
+problem in Stochastic Block Models (SBM) using Semi-Definite Programming (SDP).
+In this paper we provide the next steps in the evolution of learning
+communities in this context which involves a constrained semi-definite
+programming algorithm, and a newly presented active learning algorithm. The
+active learner intelligently queries nodes that are expected to maximize the
+change in the model likelihood. Experimental results show that this active
+learning algorithm outperforms the random-selection semi-supervised version of
+the same algorithm as well as other state-of-the-art active learning
+algorithms. Our algorithms significantly improved performance is demonstrated
+on both real-world and SBM-generated networks even when the SBM has a signal to
+noise ratio (SNR) below the known unsupervised detectability threshold.
+",1,0,0,1,0,0
+1846,Continuum Limit of Posteriors in Graph Bayesian Inverse Problems,"  We consider the problem of recovering a function input of a differential
+equation formulated on an unknown domain $M$. We assume to have access to a
+discrete domain $M_n=\{x_1, \dots, x_n\} \subset M$, and to noisy measurements
+of the output solution at $p\le n$ of those points. We introduce a graph-based
+Bayesian inverse problem, and show that the graph-posterior measures over
+functions in $M_n$ converge, in the large $n$ limit, to a posterior over
+functions in $M$ that solves a Bayesian inverse problem with known domain.
+The proofs rely on the variational formulation of the Bayesian update, and on
+a new topology for the study of convergence of measures over functions on point
+clouds to a measure over functions on the continuum. Our framework, techniques,
+and results may serve to lay the foundations of robust uncertainty
+quantification of graph-based tasks in machine learning. The ideas are
+presented in the concrete setting of recovering the initial condition of the
+heat equation on an unknown manifold.
+",0,0,1,1,0,0
+1847,Automatic Conflict Detection in Police Body-Worn Audio,"  Automatic conflict detection has grown in relevance with the advent of
+body-worn technology, but existing metrics such as turn-taking and overlap are
+poor indicators of conflict in police-public interactions. Moreover, standard
+techniques to compute them fall short when applied to such diversified and
+noisy contexts. We develop a pipeline catered to this task combining adaptive
+noise removal, non-speech filtering and new measures of conflict based on the
+repetition and intensity of phrases in speech. We demonstrate the effectiveness
+of our approach on body-worn audio data collected by the Los Angeles Police
+Department.
+",1,0,0,1,0,0
+1848,The cobordism hypothesis,"  Assuming a conjecture about factorization homology with adjoints, we prove
+the cobordism hypothesis, after Baez-Dolan, Costello, Hopkins-Lurie, and Lurie.
+",0,0,1,0,0,0
+1849,LAMOST telescope reveals that Neptunian cousins of hot Jupiters are mostly single offspring of stars that are rich in heavy elements,"  We discover a population of short-period, Neptune-size planets sharing key
+similarities with hot Jupiters: both populations are preferentially hosted by
+metal-rich stars, and both are preferentially found in Kepler systems with
+single transiting planets. We use accurate LAMOST DR4 stellar parameters for
+main-sequence stars to study the distributions of short-period 1d < P < 10d
+Kepler planets as a function of host star metallicity. The radius distribution
+of planets around metal-rich stars is more ""puffed up"" as compared to that
+around metal-poor hosts. In two period-radius regimes, planets preferentially
+reside around metal-rich stars, while there are hardly any planets around
+metal-poor stars. One is the well-known hot Jupiters, and the other is a
+population of Neptune-size planets (2 R_Earth <~ R_p <~ 6 R_Earth), dubbed as
+""Hoptunes"". Also like hot Jupiters, Hoptunes occur more frequently in systems
+with single transiting planets though the fraction of Hoptunes occurring in
+multiples is larger than that of hot Jupiters. About 1% of solar-type stars
+host ""Hoptunes"", and the frequencies of Hoptunes and hot Jupiters increase with
+consistent trends as a function of [Fe/H]. In the planet radius distribution,
+hot Jupiters and Hoptunes are separated by a ""valley"" at approximately Saturn
+size (in the range of 6 R_Earth <~ R_p <~ 10 R_Earth), and this ""hot-Saturn
+valley"" represents approximately an order-of-magnitude decrease in planet
+frequency compared to hot Jupiters and Hoptunes. The empirical ""kinship""
+between Hoptunes and hot Jupiters suggests likely common processes (migration
+and/or formation) responsible for their existence.
+",0,1,0,0,0,0
+1850,A Latent Variable Model for Two-Dimensional Canonical Correlation Analysis and its Variational Inference,"  Describing the dimension reduction (DR) techniques by means of probabilistic
+models has recently been given special attention. Probabilistic models, in
+addition to a better interpretability of the DR methods, provide a framework
+for further extensions of such algorithms. One of the new approaches to the
+probabilistic DR methods is to preserving the internal structure of data. It is
+meant that it is not necessary that the data first be converted from the matrix
+or tensor format to the vector format in the process of dimensionality
+reduction. In this paper, a latent variable model for matrix-variate data for
+canonical correlation analysis (CCA) is proposed. Since in general there is not
+any analytical maximum likelihood solution for this model, we present two
+approaches for learning the parameters. The proposed methods are evaluated
+using the synthetic data in terms of convergence and quality of mappings. Also,
+real data set is employed for assessing the proposed methods with several
+probabilistic and none-probabilistic CCA based approaches. The results confirm
+the superiority of the proposed methods with respect to the competing
+algorithms. Moreover, this model can be considered as a framework for further
+extensions.
+",1,0,0,1,0,0
+1851,Model enumeration in propositional circumscription via unsatisfiable core analysis,"  Many practical problems are characterized by a preference relation over
+admissible solutions, where preferred solutions are minimal in some sense. For
+example, a preferred diagnosis usually comprises a minimal set of reasons that
+is sufficient to cause the observed anomaly. Alternatively, a minimal
+correction subset comprises a minimal set of reasons whose deletion is
+sufficient to eliminate the observed anomaly. Circumscription formalizes such
+preference relations by associating propositional theories with minimal models.
+The resulting enumeration problem is addressed here by means of a new algorithm
+taking advantage of unsatisfiable core analysis. Empirical evidence of the
+efficiency of the algorithm is given by comparing the performance of the
+resulting solver, CIRCUMSCRIPTINO, with HCLASP, CAMUS MCS, LBX and MCSLS on the
+enumeration of minimal models for problems originating from practical
+applications.
+This paper is under consideration for acceptance in TPLP.
+",1,0,0,0,0,0
+1852,Structured Neural Summarization,"  Summarization of long sequences into a concise statement is a core problem in
+natural language processing, requiring non-trivial understanding of the input.
+Based on the promising results of graph neural networks on highly structured
+data, we develop a framework to extend existing sequence encoders with a graph
+component that can reason about long-distance relationships in weakly
+structured data such as text. In an extensive evaluation, we show that the
+resulting hybrid sequence-graph models outperform both pure sequence models as
+well as pure graph models on a range of summarization tasks.
+",1,0,0,0,0,0
+1853,Variations on a Visserian Theme,"  A first order theory T is said to be ""tight"" if for any two deductively
+closed extensions U and V of T (both of which are formulated in the language of
+T), U and V are bi-interpretable iff U = V. By a theorem of Visser, PA (Peano
+Arithmetic) is tight. Here we show that Z_2 (second order arithmetic), ZF
+(Zermelo-Fraenkel set theory), and KM (Kelley-Morse theory of classes) are also
+tight theories.
+",0,0,1,0,0,0
+1854,"Galerkin Least-Squares Stabilization in Ice Sheet Modeling - Accuracy, Robustness, and Comparison to other Techniques","  We investigate the accuracy and robustness of one of the most common methods
+used in glaciology for the discretization of the $\mathfrak{p}$-Stokes
+equations: equal order finite elements with Galerkin Least-Squares (GLS)
+stabilization. Furthermore we compare the results to other stabilized methods.
+We find that the vertical velocity component is more sensitive to the choice of
+GLS stabilization parameter than horizontal velocity. Additionally, the
+accuracy of the vertical velocity component is especially important since
+errors in this component can cause ice surface instabilities and propagate into
+future ice volume predictions. If the element cell size is set to the minimum
+edge length and the stabilization parameter is allowed to vary non-linearly
+with viscosity, the GLS stabilization parameter found in literature is a good
+choice on simple domains. However, near ice margins the standard parameter
+choice may result in significant oscillations in the vertical component of the
+surface velocity. For these cases, other stabilization techniques, such as the
+interior penalty method, result in better accuracy and are less sensitive to
+the choice of the stabilization parameter. During this work we also discovered
+that the manufactured solutions often used to evaluate errors in glaciology are
+not reliable due to high artificial surface forces at singularities. We perform
+our numerical experiments in both FEniCS and Elmer/Ice.
+",0,1,0,0,0,0
+1855,Improved Query Reformulation for Concept Location using CodeRank and Document Structures,"  During software maintenance, developers usually deal with a significant
+number of software change requests. As a part of this, they often formulate an
+initial query from the request texts, and then attempt to map the concepts
+discussed in the request to relevant source code locations in the software
+system (a.k.a., concept location). Unfortunately, studies suggest that they
+often perform poorly in choosing the right search terms for a change task. In
+this paper, we propose a novel technique --ACER-- that takes an initial query,
+identifies appropriate search terms from the source code using a novel term
+weight --CodeRank, and then suggests effective reformulation to the initial
+query by exploiting the source document structures, query quality analysis and
+machine learning. Experiments with 1,675 baseline queries from eight subject
+systems report that our technique can improve 71% of the baseline queries which
+is highly promising. Comparison with five closely related existing techniques
+in query reformulation not only validates our empirical findings but also
+demonstrates the superiority of our technique.
+",1,0,0,0,0,0
+1856,High-performance parallel computing in the classroom using the public goods game as an example,"  The use of computers in statistical physics is common because the sheer
+number of equations that describe the behavior of an entire system particle by
+particle often makes it impossible to solve them exactly. Monte Carlo methods
+form a particularly important class of numerical methods for solving problems
+in statistical physics. Although these methods are simple in principle, their
+proper use requires a good command of statistical mechanics, as well as
+considerable computational resources. The aim of this paper is to demonstrate
+how the usage of widely accessible graphics cards on personal computers can
+elevate the computing power in Monte Carlo simulations by orders of magnitude,
+thus allowing live classroom demonstration of phenomena that would otherwise be
+out of reach. As an example, we use the public goods game on a square lattice
+where two strategies compete for common resources in a social dilemma
+situation. We show that the second-order phase transition to an absorbing phase
+in the system belongs to the directed percolation universality class, and we
+compare the time needed to arrive at this result by means of the main processor
+and by means of a suitable graphics card. Parallel computing on graphics
+processing units has been developed actively during the last decade, to the
+point where today the learning curve for entry is anything but steep for those
+familiar with programming. The subject is thus ripe for inclusion in graduate
+and advanced undergraduate curricula, and we hope that this paper will
+facilitate this process in the realm of physics education. To that end, we
+provide a documented source code for an easy reproduction of presented results
+and for further development of Monte Carlo simulations of similar systems.
+",0,1,0,0,0,0
+1857,Coupled spin-charge dynamics in helical Fermi liquids beyond the random phase approximation,"  We consider a helical system of fermions with a generic spin (or pseudospin)
+orbit coupling. Using the equation of motion approach for the single-particle
+distribution functions, and a mean-field decoupling of the higher order
+distribution functions, we find a closed form for the charge and spin density
+fluctuations in terms of the charge and spin density linear response functions.
+Approximating the nonlocal exchange term with a Hubbard-like local-field
+factor, we obtain coupled spin and charge density response matrix beyond the
+random phase approximation, whose poles give the dispersion of four collective
+spin-charge modes. We apply our generic technique to the well-explored
+two-dimensional system with Rashba spin-orbit coupling and illustrate how it
+gives results for the collective modes, Drude weight, and spin-Hall
+conductivity which are in very good agreement with the results obtained from
+other more sophisticated approaches.
+",0,1,0,0,0,0
+1858,Correlation decay in fermionic lattice systems with power-law interactions at non-zero temperature,"  We study correlations in fermionic lattice systems with long-range
+interactions in thermal equilibrium. We prove a bound on the correlation decay
+between anti-commuting operators and generalize a long-range Lieb-Robinson type
+bound. Our results show that in these systems of spatial dimension $D$ with,
+not necessarily translation invariant, two-site interactions decaying
+algebraically with the distance with an exponent $\alpha \geq 2\,D$,
+correlations between such operators decay at least algebraically with an
+exponent arbitrarily close to $\alpha$ at any non-zero temperature. Our bound
+is asymptotically tight, which we demonstrate by a high temperature expansion
+and by numerically analyzing density-density correlations in the 1D quadratic
+(free, exactly solvable) Kitaev chain with long-range pairing.
+",0,1,0,0,0,0
+1859,Integrated Microsimulation Framework for Dynamic Pedestrian Movement Estimation in Mobility Hub,"  We present an integrated microsimulation framework to estimate the pedestrian
+movement over time and space with limited data on directional counts. Using the
+activity-based approach, simulation can compute the overall demand and
+trajectory of each agent, which are in accordance with the available partial
+observations and are in response to the initial and evolving supply conditions
+and schedules. This simulation contains a chain of processes including:
+activities generation, decision point choices, and assignment. They are
+considered in an iteratively updating loop so that the simulation can
+dynamically correct its estimates of demand. A Markov chain is constructed for
+this loop. These considerations transform the problem into a convergence
+problem. A Metropolitan Hasting algorithm is then adapted to identify the
+optimal solution. This framework can be used to fill the lack of data or to
+model the reactions of demand to exogenous changes in the scenario. Finally, we
+present a case study on Montreal Central Station, on which we tested the
+developed framework and calibrated the models. We then applied it to a possible
+future scenario for the same station.
+",0,1,1,0,0,0
+1860,Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization,"  Stochastic optimization naturally arises in machine learning. Efficient
+algorithms with provable guarantees, however, are still largely missing, when
+the objective function is nonconvex and the data points are dependent. This
+paper studies this fundamental challenge through a streaming PCA problem for
+stationary time series data. Specifically, our goal is to estimate the
+principle component of time series data with respect to the covariance matrix
+of the stationary distribution. Computationally, we propose a variant of Oja's
+algorithm combined with downsampling to control the bias of the stochastic
+gradient caused by the data dependency. Theoretically, we quantify the
+uncertainty of our proposed stochastic algorithm based on diffusion
+approximations. This allows us to prove the asymptotic rate of convergence and
+further implies near optimal asymptotic sample complexity. Numerical
+experiments are provided to support our analysis.
+",0,0,0,1,0,0
+1861,Efficient tracking of a growing number of experts,"  We consider a variation on the problem of prediction with expert advice,
+where new forecasters that were unknown until then may appear at each round. As
+often in prediction with expert advice, designing an algorithm that achieves
+near-optimal regret guarantees is straightforward, using aggregation of
+experts. However, when the comparison class is sufficiently rich, for instance
+when the best expert and the set of experts itself changes over time, such
+strategies naively require to maintain a prohibitive number of weights
+(typically exponential with the time horizon). By contrast, designing
+strategies that both achieve a near-optimal regret and maintain a reasonable
+number of weights is highly non-trivial. We consider three increasingly
+challenging objectives (simple regret, shifting regret and sparse shifting
+regret) that extend existing notions defined for a fixed expert ensemble; in
+each case, we design strategies that achieve tight regret bounds, adaptive to
+the parameters of the comparison class, while being computationally
+inexpensive. Moreover, our algorithms are anytime, agnostic to the number of
+incoming experts and completely parameter-free. Such remarkable results are
+made possible thanks to two simple but highly effective recipes: first the
+""abstention trick"" that comes from the specialist framework and enables to
+handle the least challenging notions of regret, but is limited when addressing
+more sophisticated objectives. Second, the ""muting trick"" that we introduce to
+give more flexibility. We show how to combine these two tricks in order to
+handle the most challenging class of comparison strategies.
+",1,0,0,1,0,0
+1862,Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data,"  Subsequence clustering of multivariate time series is a useful tool for
+discovering repeated patterns in temporal data. Once these patterns have been
+discovered, seemingly complicated datasets can be interpreted as a temporal
+sequence of only a small number of states, or clusters. For example, raw sensor
+data from a fitness-tracking application can be expressed as a timeline of a
+select few actions (i.e., walking, sitting, running). However, discovering
+these patterns is challenging because it requires simultaneous segmentation and
+clustering of the time series. Furthermore, interpreting the resulting clusters
+is difficult, especially when the data is high-dimensional. Here we propose a
+new method of model-based clustering, which we call Toeplitz Inverse
+Covariance-based Clustering (TICC). Each cluster in the TICC method is defined
+by a correlation network, or Markov random field (MRF), characterizing the
+interdependencies between different observations in a typical subsequence of
+that cluster. Based on this graphical representation, TICC simultaneously
+segments and clusters the time series data. We solve the TICC problem through
+alternating minimization, using a variation of the expectation maximization
+(EM) algorithm. We derive closed-form solutions to efficiently solve the two
+resulting subproblems in a scalable way, through dynamic programming and the
+alternating direction method of multipliers (ADMM), respectively. We validate
+our approach by comparing TICC to several state-of-the-art baselines in a
+series of synthetic experiments, and we then demonstrate on an automobile
+sensor dataset how TICC can be used to learn interpretable clusters in
+real-world scenarios.
+",1,0,1,0,0,0
+1863,The ellipse law: Kirchhoff meets dislocations,"  In this paper we consider a nonlocal energy $I_\alpha$ whose kernel is
+obtained by adding to the Coulomb potential an anisotropic term weighted by a
+parameter $\alpha\in \R$. The case $\alpha=0$ corresponds to purely logarithmic
+interactions, minimised by the celebrated circle law for a quadratic
+confinement; $\alpha=1$ corresponds to the energy of interacting dislocations,
+minimised by the semi-circle law. We show that for $\alpha\in (0,1)$ the
+minimiser can be computed explicitly and is the normalised characteristic
+function of the domain enclosed by an \emph{ellipse}. To prove our result we
+borrow techniques from fluid dynamics, in particular those related to
+Kirchhoff's celebrated result that domains enclosed by ellipses are rotating
+vortex patches, called \emph{Kirchhoff ellipses}. Therefore we show a
+surprising connection between vortices and dislocations.
+",0,0,1,0,0,0
+1864,SAML-QC: a Stochastic Assessment and Machine Learning based QC technique for Industrial Printing,"  Recently, the advancement in industrial automation and high-speed printing
+has raised numerous challenges related to the printing quality inspection of
+final products. This paper proposes a machine vision based technique to assess
+the printing quality of text on industrial objects. The assessment is based on
+three quality defects such as text misalignment, varying printing shades, and
+misprinted text. The proposed scheme performs the quality inspection through
+stochastic assessment technique based on the second-order statistics of
+printing. First: the text-containing area on printed product is identified
+through image processing techniques. Second: the alignment testing of the
+identified text-containing area is performed. Third: optical character
+recognition is performed to divide the text into different small boxes and only
+the intensity value of each text-containing box is taken as a random variable
+and second-order statistics are estimated to determine the varying printing
+defects in the text under one, two and three sigma thresholds. Fourth: the
+K-Nearest Neighbors based supervised machine learning is performed to provide
+the stochastic process for misprinted text detection. Finally, the technique is
+deployed on an industrial image for the printing quality assessment with
+varying values of n and m. The results have shown that the proposed SAML-QC
+technique can perform real-time automated inspection for industrial printing.
+",1,0,0,0,0,0
+1865,Probing the gravitational redshift with an Earth-orbiting satellite,"  We present an approach to testing the gravitational redshift effect using the
+RadioAstron satellite. The experiment is based on a modification of the Gravity
+Probe A scheme of nonrelativistic Doppler compensation and benefits from the
+highly eccentric orbit and ultra-stable atomic hydrogen maser frequency
+standard of the RadioAstron satellite. Using the presented techniques we expect
+to reach an accuracy of the gravitational redshift test of order $10^{-5}$, a
+magnitude better than that of Gravity Probe A. Data processing is ongoing, our
+preliminary results agree with the validity of the Einstein Equivalence
+Principle.
+",0,1,0,0,0,0
+1866,A stencil scaling approach for accelerating matrix-free finite element implementations,"  We present a novel approach to fast on-the-fly low order finite element
+assembly for scalar elliptic partial differential equations of Darcy type with
+variable coefficients optimized for matrix-free implementations. Our approach
+introduces a new operator that is obtained by appropriately scaling the
+reference stiffness matrix from the constant coefficient case. Assuming
+sufficient regularity, an a priori analysis shows that solutions obtained by
+this approach are unique and have asymptotically optimal order convergence in
+the $H^1$- and the $L^2$-norm on hierarchical hybrid grids. For the
+pre-asymptotic regime, we present a local modification that guarantees uniform
+ellipticity of the operator. Cost considerations show that our novel approach
+requires roughly one third of the floating-point operations compared to a
+classical finite element assembly scheme employing nodal integration. Our
+theoretical considerations are illustrated by numerical tests that confirm the
+expectations with respect to accuracy and run-time. A large scale application
+with more than a hundred billion ($1.6\cdot10^{11}$) degrees of freedom
+executed on 14,310 compute cores demonstrates the efficiency of the new scaling
+approach.
+",1,0,0,0,0,0
+1867,Cramér-Rao Lower Bounds for Positioning with Large Intelligent Surfaces,"  We consider the potential for positioning with a system where antenna arrays
+are deployed as a large intelligent surface (LIS). We derive
+Fisher-informations and Cramér-Rao lower bounds (CRLB) in closed-form for
+terminals along the central perpendicular line (CPL) of the LIS for all three
+Cartesian dimensions. For terminals at positions other than the CPL,
+closed-form expressions for the Fisher-informations and CRLBs seem out of
+reach, and we alternatively provide approximations (in closed-form) which are
+shown to be very accurate. We also show that under mild conditions, the CRLBs
+in general decrease quadratically in the surface-area for both the $x$ and $y$
+dimensions. For the $z$-dimension (distance from the LIS), the CRLB decreases
+linearly in the surface-area when terminals are along the CPL. However, when
+terminals move away from the CPL, the CRLB is dramatically increased and then
+also decreases quadratically in the surface-area. We also extensively discuss
+the impact of different deployments (centralized and distributed) of the LIS.
+",1,0,0,0,0,0
+1868,Asymptotic behaviour methods for the Heat Equation. Convergence to the Gaussian,"  In this expository work we discuss the asymptotic behaviour of the solutions
+of the classical heat equation posed in the whole Euclidean space.
+After an introductory review of the main facts on the existence and
+properties of solutions, we proceed with the proofs of convergence to the
+Gaussian fundamental solution, a result that holds for all integrable
+solutions, and represents in the PDE setting the Central Limit Theorem of
+probability. We present several methods of proof: first, the scaling method.
+Then several versions of the representation method. This is followed by the
+functional analysis approach that leads to the famous related equations,
+Fokker-Planck and Ornstein-Uhlenbeck. The analysis of this connection is also
+given in rather complete form here. Finally, we present the Boltzmann entropy
+method, coming from kinetic equations.
+The different methods are interesting because of the possible extension to
+prove the asymptotic behaviour or stabilization analysis for more general
+equations, linear or nonlinear. It all depends a lot on the particular
+features, and only one or some of the methods work in each case.Other settings
+of the Heat Equation are briefly discussed in Section 9 and a longer mention of
+results for different equations is done in Section 10.
+",0,0,1,0,0,0
+1869,Magnetization dynamics of weakly interacting sub-100 nm square artificial spin ices,"  Artificial Spin Ice (ASI), consisting of a two dimensional array of nanoscale
+magnetic elements, provides a fascinating opportunity to observe the physics of
+out of equilibrium systems. Initial studies concentrated on the static, frozen
+state, whilst more recent studies have accessed the out-of-equilibrium dynamic,
+fluctuating state. This opens up exciting possibilities such as the observation
+of systems exploring their energy landscape through monopole quasiparticle
+creation, potentially leading to ASI magnetricity, and to directly observe
+unconventional phase transitions. In this work we have measured and analysed
+the magnetic relaxation of thermally active ASI systems by means of SQUID
+magnetometry. We have investigated the effect of the interaction strength on
+the magnetization dynamics at different temperatures in the range where the
+nanomagnets are thermally active and have observed that they follow an
+Arrhenius-type Néel-Brown behaviour. An unexpected negative correlation of
+the average blocking temperature with the interaction strength is also
+observed, which is supported by Monte Carlo simulations. The magnetization
+relaxation measurements show faster relaxation for more strongly coupled
+nanoelements with similar dimensions. The analysis of the stretching exponents
+obtained from the measurements suggest 1-D chain-like magnetization dynamics.
+This indicates that the nature of the interactions between nanoelements lowers
+the dimensionality of the ASI from 2-D to 1-D. Finally, we present a way to
+quantify the effective interaction energy of a square ASI system, and compare
+it to the interaction energy calculated from a simple dipole model and also to
+the magnetostatic energy computed with micromagnetic simulations.
+",0,1,0,0,0,0
+1870,Filtering Tweets for Social Unrest,"  Since the events of the Arab Spring, there has been increased interest in
+using social media to anticipate social unrest. While efforts have been made
+toward automated unrest prediction, we focus on filtering the vast volume of
+tweets to identify tweets relevant to unrest, which can be provided to
+downstream users for further analysis. We train a supervised classifier that is
+able to label Arabic language tweets as relevant to unrest with high
+reliability. We examine the relationship between training data size and
+performance and investigate ways to optimize the model building process while
+minimizing cost. We also explore how confidence thresholds can be set to
+achieve desired levels of performance.
+",1,0,0,1,0,0
+1871,Structured Connectivity Augmentation,"  We initiate the algorithmic study of the following ""structured augmentation""
+question: is it possible to increase the connectivity of a given graph G by
+superposing it with another given graph H? More precisely, graph F is the
+superposition of G and H with respect to injective mapping \phi: V(H)->V(G) if
+every edge uv of F is either an edge of G, or \phi^{-1}(u)\phi^{-1}(v) is an
+edge of H. We consider the following optimization problem. Given graphs G,H,
+and a weight function \omega assigning non-negative weights to pairs of
+vertices of V(G), the task is to find \varphi of minimum weight
+\omega(\phi)=\sum_{xy\in E(H)}\omega(\phi(x)\varphi(y)) such that the edge
+connectivity of the superposition F of G and H with respect to \phi is higher
+than the edge connectivity of G. Our main result is the following ""dichotomy""
+complexity classification. We say that a class of graphs C has bounded
+vertex-cover number, if there is a constant t depending on C only such that the
+vertex-cover number of every graph from C does not exceed t. We show that for
+every class of graphs C with bounded vertex-cover number, the problems of
+superposing into a connected graph F and to 2-edge connected graph F, are
+solvable in polynomial time when H\in C. On the other hand, for any hereditary
+class C with unbounded vertex-cover number, both problems are NP-hard when H\in
+C. For the unweighted variants of structured augmentation problems, i.e. the
+problems where the task is to identify whether there is a superposition of
+graphs of required connectivity, we provide necessary and sufficient
+combinatorial conditions on the existence of such superpositions. These
+conditions imply polynomial time algorithms solving the unweighted variants of
+the problems.
+",1,0,0,0,0,0
+1872,Transition probability of Brownian motion in the octant and its application to default modeling,"  We derive a semi-analytic formula for the transition probability of
+three-dimensional Brownian motion in the positive octant with absorption at the
+boundaries. Separation of variables in spherical coordinates leads to an
+eigenvalue problem for the resulting boundary value problem in the two angular
+components. The main theoretical result is a solution to the original problem
+expressed as an expansion into special functions and an eigenvalue which has to
+be chosen to allow a matching of the boundary condition. We discuss and test
+several computational methods to solve a finite-dimensional approximation to
+this nonlinear eigenvalue problem. Finally, we apply our results to the
+computation of default probabilities and credit valuation adjustments in a
+structural credit model with mutual liabilities.
+",0,0,0,0,0,1
+1873,Block-Sparse Recurrent Neural Networks,"  Recurrent Neural Networks (RNNs) are used in state-of-the-art models in
+domains such as speech recognition, machine translation, and language
+modelling. Sparsity is a technique to reduce compute and memory requirements of
+deep learning models. Sparse RNNs are easier to deploy on devices and high-end
+server processors. Even though sparse operations need less compute and memory
+relative to their dense counterparts, the speed-up observed by using sparse
+operations is less than expected on different hardware platforms. In order to
+address this issue, we investigate two different approaches to induce block
+sparsity in RNNs: pruning blocks of weights in a layer and using group lasso
+regularization to create blocks of weights with zeros. Using these techniques,
+we demonstrate that we can create block-sparse RNNs with sparsity ranging from
+80% to 90% with small loss in accuracy. This allows us to reduce the model size
+by roughly 10x. Additionally, we can prune a larger dense network to recover
+this loss in accuracy while maintaining high block sparsity and reducing the
+overall parameter count. Our technique works with a variety of block sizes up
+to 32x32. Block-sparse RNNs eliminate overheads related to data storage and
+irregular memory accesses while increasing hardware efficiency compared to
+unstructured sparsity.
+",1,0,0,1,0,0
+1874,Equitable neighbour-sum-distinguishing edge and total colourings,"  With any (not necessarily proper) edge $k$-colouring
+$\gamma:E(G)\longrightarrow\{1,\dots,k\}$ of a graph $G$,one can associate a
+vertex colouring $\sigma\_{\gamma}$ given by $\sigma\_{\gamma}(v)=\sum\_{e\ni
+v}\gamma(e)$.A neighbour-sum-distinguishing edge $k$-colouring is an edge
+colouring whose associated vertex colouring is proper.The
+neighbour-sum-distinguishing index of a graph $G$ is then the smallest $k$ for
+which $G$ admitsa neighbour-sum-distinguishing edge $k$-colouring.These notions
+naturally extends to total colourings of graphs that assign colours to both
+vertices and edges.We study in this paper equitable
+neighbour-sum-distinguishing edge colourings andtotal colourings, that is
+colourings $\gamma$ for whichthe number of elements in any two colour classes
+of $\gamma$ differ by at most one.We determine the equitable
+neighbour-sum-distinguishing indexof complete graphs, complete bipartite graphs
+and forests,and the equitable neighbour-sum-distinguishing total chromatic
+numberof complete graphs and bipartite graphs.
+",1,0,1,0,0,0
+1875,An Oracle Property of The Nadaraya-Watson Kernel Estimator for High Dimensional Nonparametric Regression,"  The celebrated Nadaraya-Watson kernel estimator is among the most studied
+method for nonparametric regression. A classical result is that its rate of
+convergence depends on the number of covariates and deteriorates quickly as the
+dimension grows, which underscores the ""curse of dimensionality"" and has
+limited its use in high dimensional settings. In this article, we show that
+when the true regression function is single or multi-index, the effects of the
+curse of dimensionality may be mitigated for the Nadaraya-Watson kernel
+estimator. Specifically, we prove that with $K$-fold cross-validation, the
+Nadaraya-Watson kernel estimator indexed by a positive semidefinite bandwidth
+matrix has an oracle property that its rate of convergence depends on the
+number of indices of the regression function rather than the number of
+covariates. Intuitively, this oracle property is a consequence of allowing the
+bandwidths to diverge to infinity as opposed to restricting them all to
+converge to zero at certain rates as done in previous theoretical studies. Our
+result provides a theoretical perspective for the use of kernel estimation in
+high dimensional nonparametric regression and other applications such as metric
+learning when a low rank structure is anticipated. Numerical illustrations are
+given through simulations and real data examples.
+",0,0,1,1,0,0
+1876,Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe,"  We consider the problem of bandit optimization, inspired by stochastic
+optimization and online learning problems with bandit feedback. In this
+problem, the objective is to minimize a global loss function of all the
+actions, not necessarily a cumulative loss. This framework allows us to study a
+very general class of problems, with applications in statistics, machine
+learning, and other fields. To solve this problem, we analyze the
+Upper-Confidence Frank-Wolfe algorithm, inspired by techniques for bandits and
+convex optimization. We give theoretical guarantees for the performance of this
+algorithm over various classes of functions, and discuss the optimality of
+these results.
+",0,0,1,1,0,0
+1877,Highly sensitive atomic based MW interferometry,"  We theoretically study a scheme to develop an atomic based MW interferometry
+using the Rydberg states in Rb. Unlike the traditional MW interferometry, this
+scheme is not based upon the electrical circuits, hence the sensitivity of the
+phase and the amplitude/strength of the MW field is not limited by the Nyquist
+thermal noise. Further this system has great advantage due to its very high
+bandwidth, ranging from radio frequency (RF), micro wave (MW) to terahertz
+regime. In addition, this is \textbf{orders of magnitude} more sensitive to
+field strength as compared to the prior demonstrations on the MW electrometry
+using the Rydberg atomic states. However previously studied atomic systems are
+only sensitive to the field strength but not to the phase and hence this scheme
+provides a great opportunity to characterize the MW completely including the
+propagation direction and the wavefront. This study opens up a new dimension in
+the Radar technology such as in synthetic aperture radar interferometry. The MW
+interferometry is based upon a six-level loopy ladder system involving the
+Rydberg states in which two sub-systems interfere constructively or
+destructively depending upon the phase between the MW electric fields closing
+the loop.
+",0,1,0,0,0,0
+1878,The Wisdom of a Kalman Crowd,"  The Kalman Filter has been called one of the greatest inventions in
+statistics during the 20th century. Its purpose is to measure the state of a
+system by processing the noisy data received from different electronic sensors.
+In comparison, a useful resource for managers in their effort to make the right
+decisions is the wisdom of crowds. This phenomenon allows managers to combine
+judgments by different employees to get estimates that are often more accurate
+and reliable than estimates, which managers produce alone. Since harnessing the
+collective intelligence of employees, and filtering signals from multiple noisy
+sensors appear related, we looked at the possibility of using the Kalman Filter
+on estimates by people. Our predictions suggest, and our findings based on the
+Survey of Professional Forecasters reveal, that the Kalman Filter can help
+managers solve their decision-making problems by giving them stronger signals
+before they choose. Indeed, when used on a subset of forecasters identified by
+the Contribution Weighted Model, the Kalman Filter beat that rule clearly,
+across all the forecasting horizons in the survey.
+",0,0,0,0,0,1
+1879,Noisy independent component analysis of auto-correlated components,"  We present a new method for the separation of superimposed, independent,
+auto-correlated components from noisy multi-channel measurement. The presented
+method simultaneously reconstructs and separates the components, taking all
+channels into account and thereby increases the effective signal-to-noise ratio
+considerably, allowing separations even in the high noise regime.
+Characteristics of the measurement instruments can be included, allowing for
+application in complex measurement situations. Independent posterior samples
+can be provided, permitting error estimates on all desired quantities. Using
+the concept of information field theory, the algorithm is not restricted to any
+dimensionality of the underlying space or discretization scheme thereof.
+",0,1,0,1,0,0
+1880,Ages and structural and dynamical parameters of two globular clusters in the M81 group,"  GC-1 and GC-2 are two globular clusters (GCs) in the remote halo of M81 and
+M82 in the M81 group discovered by Jang et al. using the {\it Hubble Space
+Telescope} ({\it HST}) images. These two GCs were observed as part of the
+Beijing--Arizona--Taiwan--Connecticut (BATC) Multicolor Sky Survey, using 14
+intermediate-band filters covering a wavelength range of 4000--10000 \AA. We
+accurately determine these two clusters' ages and masses by comparing their
+spectral energy distributions (from 2267 to 20000~{\AA}, comprising photometric
+data in the near-ultraviolet of the {\it Galaxy Evolution Explorer}, 14 BATC
+intermediate-band, and Two Micron All Sky Survey near-infrared $JHK_{\rm s}$
+filters) with theoretical stellar population-synthesis models, resulting in
+ages of $15.50\pm3.20$ for GC-1 and $15.10\pm2.70$ Gyr for GC-2. The masses of
+GC-1 and GC-2 obtained here are $1.77-2.04\times 10^6$ and $5.20-7.11\times
+10^6 \rm~M_\odot$, respectively. In addition, the deep observations with the
+Advanced Camera for Surveys and Wide Field Camera 3 on the {\it HST} are used
+to provide the surface brightness profiles of GC-1 and GC-2. The structural and
+dynamical parameters are derived from fitting the profiles to three different
+models; in particular, the internal velocity dispersions of GC-1 and GC-2 are
+derived, which can be compared with ones obtained based on spectral
+observations in the future. For the first time, in this paper, the $r_h$ versus
+$M_V$ diagram shows that GC-2 is an ultra-compact dwarf in the M81 group.
+",0,1,0,0,0,0
+1881,Bayesian Renewables Scenario Generation via Deep Generative Networks,"  We present a method to generate renewable scenarios using Bayesian
+probabilities by implementing the Bayesian generative adversarial
+network~(Bayesian GAN), which is a variant of generative adversarial networks
+based on two interconnected deep neural networks. By using a Bayesian
+formulation, generators can be constructed and trained to produce scenarios
+that capture different salient modes in the data, allowing for better diversity
+and more accurate representation of the underlying physical process. Compared
+to conventional statistical models that are often hard to scale or sample from,
+this method is model-free and can generate samples extremely efficiently. For
+validation, we use wind and solar times-series data from NREL integration data
+sets to train the Bayesian GAN. We demonstrate that proposed method is able to
+generate clusters of wind scenarios with different variance and mean value, and
+is able to distinguish and generate wind and solar scenarios simultaneously
+even if the historical data are intentionally mixed.
+",0,0,0,1,0,0
+1882,"Graphons: A Nonparametric Method to Model, Estimate, and Design Algorithms for Massive Networks","  Many social and economic systems are naturally represented as networks, from
+off-line and on-line social networks, to bipartite networks, like Netflix and
+Amazon, between consumers and products. Graphons, developed as limits of
+graphs, form a natural, nonparametric method to describe and estimate large
+networks like Facebook and LinkedIn. Here we describe the development of the
+theory of graphons, for both dense and sparse networks, over the last decade.
+We also review theorems showing that we can consistently estimate graphons from
+massive networks in a wide variety of models. Finally, we show how to use
+graphons to estimate missing links in a sparse network, which has applications
+from estimating social and information networks in development economics, to
+rigorously and efficiently doing collaborative filtering with applications to
+movie recommendations in Netflix and product suggestions in Amazon.
+",1,1,0,0,0,0
+1883,Hopf Parametric Adjoint Objects through a 2-adjunction of the type Adj-Mnd,"  In this article Hopf parametric adjunctions are defined and analysed within
+the context of the 2-adjunction of the type $\mathbf{Adj}$-$\mathbf{Mnd}$. In
+order to do so, the definition of adjoint objects in the 2-category of
+adjunctions and in the 2-category of monads for $Cat$ are revised and
+characterized. This article finalises with the application of the obtained
+results on current categorical characterization of Hopf Monads.
+",0,0,1,0,0,0
+1884,Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning,"  Solving symmetric positive definite linear problems is a fundamental
+computational task in machine learning. The exact solution, famously, is
+cubicly expensive in the size of the matrix. To alleviate this problem, several
+linear-time approximations, such as spectral and inducing-point methods, have
+been suggested and are now in wide use. These are low-rank approximations that
+choose the low-rank space a priori and do not refine it over time. While this
+allows linear cost in the data-set size, it also causes a finite, uncorrected
+approximation error. Authors from numerical linear algebra have explored ways
+to iteratively refine such low-rank approximations, at a cost of a small number
+of matrix-vector multiplications. This idea is particularly interesting in the
+many situations in machine learning where one has to solve a sequence of
+related symmetric positive definite linear problems. From the machine learning
+perspective, such deflation methods can be interpreted as transfer learning of
+a low-rank approximation across a time-series of numerical tasks. We study the
+use of such methods for our field. Our empirical results show that, on
+regression and classification problems of intermediate size, this approach can
+interpolate between low computational cost and numerical precision.
+",1,0,0,1,0,0
+1885,Towards a Physical Oracle for the Partition Problem using Analogue Computing,"  Despite remarkable achievements in its practical tractability, the notorious
+class of NP-complete problems has been escaping all attempts to find a
+worst-case polynomial time-bound solution algorithms for any of them. The vast
+majority of work relies on Turing machines or equivalent models, all of which
+relate to digital computing. This raises the question of whether a computer
+that is (partly) non-digital could offer a new door towards an efficient
+solution. And indeed, the partition problem, which is another NP-complete
+sibling of the famous Boolean satisfiability problem SAT, might be open to
+efficient solutions using analogue computing. We investigate this hypothesis
+here, providing experimental evidence that Partition, and in turn also SAT, may
+become tractable on a combined digital and analogue computing machine. This
+work provides mostly theoretical and based on simulations, and as such does not
+exhibit a polynomial time algorithm to solve NP-complete problems. Instead, it
+is intended as a pointer to new directions of research on special-purpose
+computing architectures that may help handling the class NP efficiently.
+",1,0,0,0,0,0
+1886,Bayesian Methods in Cosmology,"  These notes aim at presenting an overview of Bayesian statistics, the
+underlying concepts and application methodology that will be useful to
+astronomers seeking to analyse and interpret a wide variety of data about the
+Universe. The level starts from elementary notions, without assuming any
+previous knowledge of statistical methods, and then progresses to more
+advanced, research-level topics. After an introduction to the importance of
+statistical inference for the physical sciences, elementary notions of
+probability theory and inference are introduced and explained. Bayesian methods
+are then presented, starting from the meaning of Bayes Theorem and its use as
+inferential engine, including a discussion on priors and posterior
+distributions. Numerical methods for generating samples from arbitrary
+posteriors (including Markov Chain Monte Carlo and Nested Sampling) are then
+covered. The last section deals with the topic of Bayesian model selection and
+how it is used to assess the performance of models, and contrasts it with the
+classical p-value approach. A series of exercises of various levels of
+difficulty are designed to further the understanding of the theoretical
+material, including fully worked out solutions for most of them.
+",0,1,0,1,0,0
+1887,Information Extraction in Illicit Domains,"  Extracting useful entities and attribute values from illicit domains such as
+human trafficking is a challenging problem with the potential for widespread
+social impact. Such domains employ atypical language models, have `long tails'
+and suffer from the problem of concept drift. In this paper, we propose a
+lightweight, feature-agnostic Information Extraction (IE) paradigm specifically
+designed for such domains. Our approach uses raw, unlabeled text from an
+initial corpus, and a few (12-120) seed annotations per domain-specific
+attribute, to learn robust IE models for unobserved pages and websites.
+Empirically, we demonstrate that our approach can outperform feature-centric
+Conditional Random Field baselines by over 18\% F-Measure on five annotated
+sets of real-world human trafficking datasets in both low-supervision and
+high-supervision settings. We also show that our approach is demonstrably
+robust to concept drift, and can be efficiently bootstrapped even in a serial
+computing environment.
+",1,0,0,0,0,0
+1888,A Tutorial on Kernel Density Estimation and Recent Advances,"  This tutorial provides a gentle introduction to kernel density estimation
+(KDE) and recent advances regarding confidence bands and geometric/topological
+features. We begin with a discussion of basic properties of KDE: the
+convergence rate under various metrics, density derivative estimation, and
+bandwidth selection. Then, we introduce common approaches to the construction
+of confidence intervals/bands, and we discuss how to handle bias. Next, we talk
+about recent advances in the inference of geometric and topological features of
+a density function using KDE. Finally, we illustrate how one can use KDE to
+estimate a cumulative distribution function and a receiver operating
+characteristic curve. We provide R implementations related to this tutorial at
+the end.
+",0,0,0,1,0,0
+1889,Optimizing expected word error rate via sampling for speech recognition,"  State-level minimum Bayes risk (sMBR) training has become the de facto
+standard for sequence-level training of speech recognition acoustic models. It
+has an elegant formulation using the expectation semiring, and gives large
+improvements in word error rate (WER) over models trained solely using
+cross-entropy (CE) or connectionist temporal classification (CTC). sMBR
+training optimizes the expected number of frames at which the reference and
+hypothesized acoustic states differ. It may be preferable to optimize the
+expected WER, but WER does not interact well with the expectation semiring, and
+previous approaches based on computing expected WER exactly involve expanding
+the lattices used during training. In this paper we show how to perform
+optimization of the expected WER by sampling paths from the lattices used
+during conventional sMBR training. The gradient of the expected WER is itself
+an expectation, and so may be approximated using Monte Carlo sampling. We show
+experimentally that optimizing WER during acoustic model training gives 5%
+relative improvement in WER over a well-tuned sMBR baseline on a 2-channel
+query recognition task (Google Home).
+",1,0,0,1,0,0
+1890,Real-Time Illegal Parking Detection System Based on Deep Learning,"  The increasing illegal parking has become more and more serious. Nowadays the
+methods of detecting illegally parked vehicles are based on background
+segmentation. However, this method is weakly robust and sensitive to
+environment. Benefitting from deep learning, this paper proposes a novel
+illegal vehicle parking detection system. Illegal vehicles captured by camera
+are firstly located and classified by the famous Single Shot MultiBox Detector
+(SSD) algorithm. To improve the performance, we propose to optimize SSD by
+adjusting the aspect ratio of default box to accommodate with our dataset
+better. After that, a tracking and analysis of movement is adopted to judge the
+illegal vehicles in the region of interest (ROI). Experiments show that the
+system can achieve a 99% accuracy and real-time (25FPS) detection with strong
+robustness in complex environments.
+",1,0,0,1,0,0
+1891,On a representation of fractional Brownian motion and the limit distributions of statistics arising in cusp statistical models,"  We discuss some extensions of results from the recent paper by Chernoyarov et
+al. (Ann. Inst. Stat. Math., October 2016) concerning limit distributions of
+Bayesian and maximum likelihood estimators in the model ""signal plus white
+noise"" with irregular cusp-type signals. Using a new representation of
+fractional Brownian motion (fBm) in terms of cusp functions we show that as the
+noise intensity tends to zero, the limit distributions are expressed in terms
+of fBm for the full range of asymmetric cusp-type signals correspondingly with
+the Hurst parameter H, 0<H<1. Simulation results for the densities and
+variances of the limit distributions of Bayesian and maximum likelihood
+estimators are also provided.
+",0,0,1,1,0,0
+1892,Stochastic Canonical Correlation Analysis,"  We tightly analyze the sample complexity of CCA, provide a learning algorithm
+that achieves optimal statistical performance in time linear in the required
+number of samples (up to log factors), as well as a streaming algorithm with
+similar guarantees.
+",1,0,0,1,0,0
+1893,Segmentation of Instances by Hashing,"  We propose a novel approach to address the Simultaneous Detection and
+Segmentation problem. Using hierarchical structures we use an efficient and
+accurate procedure that exploits the hierarchy feature information using
+Locality Sensitive Hashing. We build on recent work that utilizes convolutional
+neural networks to detect bounding boxes in an image and then use the top
+similar hierarchical region that best fits each bounding box after hashing, we
+call this approach CZ Segmentation. We then refine our final segmentation
+results by automatic hierarchy pruning. CZ Segmentation introduces a train-free
+alternative to Hypercolumns. We conduct extensive experiments on PASCAL VOC
+2012 segmentation dataset, showing that CZ gives competitive state-of-the-art
+object segmentations.
+",1,0,0,0,0,0
+1894,Grafting for Combinatorial Boolean Model using Frequent Itemset Mining,"  This paper introduces the combinatorial Boolean model (CBM), which is defined
+as the class of linear combinations of conjunctions of Boolean attributes. This
+paper addresses the issue of learning CBM from labeled data. CBM is of high
+knowledge interpretability but naïve learning of it requires exponentially
+large computation time with respect to data dimension and sample size. To
+overcome this computational difficulty, we propose an algorithm GRAB (GRAfting
+for Boolean datasets), which efficiently learns CBM within the
+$L_1$-regularized loss minimization framework. The key idea of GRAB is to
+reduce the loss minimization problem to the weighted frequent itemset mining,
+in which frequent patterns are efficiently computable. We employ benchmark
+datasets to empirically demonstrate that GRAB is effective in terms of
+computational efficiency, prediction accuracy and knowledge discovery.
+",1,0,0,1,0,0
+1895,Rapid Assessment of Damaged Homes in the Florida Keys after Hurricane Irma,"  On September 10, 2017, Hurricane Irma made landfall in the Florida Keys and
+caused significant damage. Informed by hydrodynamic storm surge and wave
+modeling and post-storm satellite imagery, a rapid damage survey was soon
+conducted for 1600+ residential buildings in Big Pine Key and Marathon. Damage
+categorizations and statistical analysis reveal distinct factors governing
+damage at these two locations. The distance from the coast is significant for
+the damage in Big Pine Key, as severely damaged buildings were located near
+narrow waterways connected to the ocean. Building type and size are critical in
+Marathon, highlighted by the near-complete destruction of trailer communities
+there. These observations raise issues of affordability and equity that need
+consideration in damage recovery and rebuilding for resilience.
+",0,0,0,1,0,0
+1896,Status maximization as a source of fairness in a networked dictator game,"  Human behavioural patterns exhibit selfish or competitive, as well as
+selfless or altruistic tendencies, both of which have demonstrable effects on
+human social and economic activity. In behavioural economics, such effects have
+traditionally been illustrated experimentally via simple games like the
+dictator and ultimatum games. Experiments with these games suggest that, beyond
+rational economic thinking, human decision-making processes are influenced by
+social preferences, such as an inclination to fairness. In this study we
+suggest that the apparent gap between competitive and altruistic human
+tendencies can be bridged by assuming that people are primarily maximising
+their status, i.e., a utility function different from simple profit
+maximisation. To this end we analyse a simple agent-based model, where
+individuals play the repeated dictator game in a social network they can
+modify. As model parameters we consider the living costs and the rate at which
+agents forget infractions by others. We find that individual strategies used in
+the game vary greatly, from selfish to selfless, and that both of the above
+parameters determine when individuals form complex and cohesive social
+networks.
+",1,0,0,0,0,1
+1897,On Dziobek Special Central Configurations,"  We study the special central configurations of the curved N-body problem in
+S^3. We show that there are special central configurations formed by N masses
+for any N >2. We then extend the concept of special central configurations to
+S^n, n>0, and study one interesting class of special central configurations in
+S^n, the Dziobek special central configurations. We obtain a criterion for them
+and reduce it to two sets of equations. Then we apply these equations to
+special central configurations of 3 bodies on S^1, 4 bodies on S^2, and 5
+bodies in S^3.
+",0,0,1,0,0,0
+1898,Laser Interferometer Space Antenna,"  Following the selection of The Gravitational Universe by ESA, and the
+successful flight of LISA Pathfinder, the LISA Consortium now proposes a 4 year
+mission in response to ESA's call for missions for L3. The observatory will be
+based on three arms with six active laser links, between three identical
+spacecraft in a triangular formation separated by 2.5 million km.
+LISA is an all-sky monitor and will offer a wide view of a dynamic cosmos
+using Gravitational Waves as new and unique messengers to unveil The
+Gravitational Universe. It provides the closest ever view of the infant
+Universe at TeV energy scales, has known sources in the form of verification
+binaries in the Milky Way, and can probe the entire Universe, from its smallest
+scales near the horizons of black holes, all the way to cosmological scales.
+The LISA mission will scan the entire sky as it follows behind the Earth in its
+orbit, obtaining both polarisations of the Gravitational Waves simultaneously,
+and will measure source parameters with astrophysically relevant sensitivity in
+a band from below $10^{-4}\,$Hz to above $10^{-1}\,$Hz.
+",0,1,0,0,0,0
+1899,Learning from a lot: Empirical Bayes in high-dimensional prediction settings,"  Empirical Bayes is a versatile approach to `learn from a lot' in two ways:
+first, from a large number of variables and second, from a potentially large
+amount of prior information, e.g. stored in public repositories. We review
+applications of a variety of empirical Bayes methods to several well-known
+model-based prediction methods including penalized regression, linear
+discriminant analysis, and Bayesian models with sparse or dense priors. We
+discuss `formal' empirical Bayes methods which maximize the marginal
+likelihood, but also more informal approaches based on other data summaries. We
+contrast empirical Bayes to cross-validation and full Bayes, and discuss hybrid
+approaches. To study the relation between the quality of an empirical Bayes
+estimator and $p$, the number of variables, we consider a simple empirical
+Bayes estimator in a linear model setting.
+We argue that empirical Bayes is particularly useful when the prior contains
+multiple parameters which model a priori information on variables, termed
+`co-data'. In particular, we present two novel examples that allow for co-data.
+First, a Bayesian spike-and-slab setting that facilitates inclusion of multiple
+co-data sources and types; second, a hybrid empirical Bayes-full Bayes ridge
+regression approach for estimation of the posterior predictive interval.
+",0,0,0,1,0,0
+1900,Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs,"  Techniques for reducing the variance of gradient estimates used in stochastic
+programming algorithms for convex finite-sum problems have received a great
+deal of attention in recent years. By leveraging dissipativity theory from
+control, we provide a new perspective on two important variance-reduction
+algorithms: SVRG and its direct accelerated variant Katyusha. Our perspective
+provides a physically intuitive understanding of the behavior of SVRG-like
+methods via a principle of energy conservation. The tools discussed here allow
+us to automate the convergence analysis of SVRG-like methods by capturing their
+essential properties in small semidefinite programs amenable to standard
+analysis and computational techniques. Our approach recovers existing
+convergence results for SVRG and Katyusha and generalizes the theory to
+alternative parameter choices. We also discuss how our approach complements the
+linear coupling technique. Our combination of perspectives leads to a better
+understanding of accelerated variance-reduced stochastic methods for finite-sum
+problems.
+",0,0,0,1,0,0
+1901,Runout transition and clustering instability observed in binary-mixture avalanche deposits,"  Binary mixtures of dry grains avalanching down a slope are experimentally
+studied in order to determine the interaction among coarse and fine grains and
+their effect on the deposit morphology. The distance travelled by the massive
+front of the avalanche over the horizontal plane of deposition area is measured
+as a function of mass content of fine particles in the mixture, grain-size
+ratio, and flume tilt. A sudden transition of the runout is detected at a
+critical content of fine particles, with a dependence on the grain-size ratio
+and flume tilt. This transition is explained as two simultaneous avalanches in
+different flowing regimes (a viscous-like one and an inertial one) competing
+against each other and provoking a full segregation and a split-off of the
+deposit into two well-defined, separated deposits. The formation of the distal
+deposit, in turn, depends on a critical amount of coarse particles. This allows
+the condensation of the pure coarse deposit around a small, initial seed
+cluster, which grows rapidly by braking and capturing subsequent colliding
+coarse particles. For different grain-size ratios and keeping a constant total
+mass, the change in the amount of fines needed for the transition to occur is
+found to be always less than 7%. For avalanches with a total mass of 4 kg we
+find that, most of the time, the runout of a binary avalanche is larger than
+the runout of monodisperse avalanches of corresponding constituent particles,
+due to lubrication on the coarse-dominated side or to drag by inertial
+particles on the fine-dominated side.
+",0,1,0,0,0,0
+1902,A Simple Convex Layers Algorithm,"  Given a set of $n$ points $P$ in the plane, the first layer $L_1$ of $P$ is
+formed by the points that appear on $P$'s convex hull. In general, a point
+belongs to layer $L_i$, if it lies on the convex hull of the set $P \setminus
+\bigcup_{j<i}\{L_j\}$. The \emph{convex layers problem} is to compute the
+convex layers $L_i$. Existing algorithms for this problem either do not achieve
+the optimal $\mathcal{O}\left(n\log n\right)$ runtime and linear space, or are
+overly complex and difficult to apply in practice. We propose a new algorithm
+that is both optimal and simple. The simplicity is achieved by independently
+computing four sets of monotone convex chains in $\mathcal{O}\left(n\log
+n\right)$ time and linear space. These are then merged in
+$\mathcal{O}\left(n\log n\right)$ time.
+",1,0,0,0,0,0
+1903,Entire Solution in an Ignition Nonlocal Dispersal Equation: Asymmetric Kernel,"  This paper mainly focus on the front-like entire solution of a classical
+nonlocal dispersal equation with ignition nonlinearity. Especially, the
+dispersal kernel function $J$ may not be symmetric here. The asymmetry of $J$
+has a great influence on the profile of the traveling waves and the sign of the
+wave speeds, which further makes the properties of the entire solution more
+diverse. We first investigate the asymptotic behavior of the traveling wave
+solutions since it plays an essential role in obtaining the front-like entire
+solution. Due to the impact of $f'(0)=0$, we can no longer use the common
+method which mainly depending on Ikehara theorem and bilateral Laplace
+transform to study the asymptotic rates of the nondecreasing traveling wave and
+the nonincreasing one tending to 0, respectively, thus we adopt another method
+to investigate them. Afterwards, we establish a new entire solution and obtain
+its qualitative properties by constructing proper supersolution and subsolution
+and by classifying the sign and size of the wave speeds.
+",0,0,1,0,0,0
+1904,Fundamental solutions for Schrodinger operators with general inverse square potentials,"  In this paper, we classify the fundamental solutions for a class of
+Schrodinger operators.
+",0,0,1,0,0,0
+1905,Making up for the deficit in a marathon run,"  To predict the final result of an athlete in a marathon run thoroughly is the
+eternal desire of each trainer. Usually, the achieved result is weaker than the
+predicted one due to the objective (e.g., environmental conditions) as well as
+subjective factors (e.g., athlete's malaise). Therefore, making up for the
+deficit between predicted and achieved results is the main ingredient of the
+analysis performed by trainers after the competition. In the analysis, they
+search for parts of a marathon course where the athlete lost time. This paper
+proposes an automatic making up for the deficit by using a Differential
+Evolution algorithm. In this case study, the results that were obtained by a
+wearable sports-watch by an athlete in a real marathon are analyzed. The first
+experiments with Differential Evolution show the possibility of using this
+method in the future.
+",1,0,0,0,0,0
+1906,An Efficient Load Balancing Method for Tree Algorithms,"  Nowadays, multiprocessing is mainstream with exponentially increasing number
+of processors. Load balancing is, therefore, a critical operation for the
+efficient execution of parallel algorithms. In this paper we consider the
+fundamental class of tree-based algorithms that are notoriously irregular, and
+hard to load-balance with existing static techniques. We propose a hybrid load
+balancing method using the utility of statistical random sampling in estimating
+the tree depth and node count distributions to uniformly partition an input
+tree. To conduct an initial performance study, we implemented the method on an
+Intel Xeon Phi accelerator system. We considered the tree traversal operation
+on both regular and irregular unbalanced trees manifested by Fibonacci and
+unbalanced (biased) randomly generated trees, respectively. The results show
+scalable performance for up to the 60 physical processors of the accelerator,
+as well as an extrapolated 128 processors case.
+",1,0,0,0,0,0
+1907,Dynamics of the spin-1/2 Heisenberg chain initialized in a domain-wall state,"  We study the dynamics of an isotropic spin-1/2 Heisenberg chain starting in a
+domain-wall initial condition, where the spins are initially up on the left
+half-line and down on the right half-line. We focus on the long-time behavior
+of the magnetization profile. We perform extensive time-dependent
+density-matrix renormalization group simulations (up to t=350) and find that
+the data are compatible with a diffusive behavior. Subleading corrections decay
+slowly blurring the emergence of the diffusive behavior. We also compare our
+results with two alternative scenarios: superdiffusive behavior and enhanced
+diffusion with a logarithmic correction. We finally discuss the evolution of
+the entanglement entropy.
+",0,1,0,0,0,0
+1908,Multi-Erasure Locally Recoverable Codes Over Small Fields For Flash Memory Array,"  Erasure codes play an important role in storage systems to prevent data loss.
+In this work, we study a class of erasure codes called Multi-Erasure Locally
+Recoverable Codes (ME-LRCs) for flash memory array. Compared to previous
+related works, we focus on the construction of ME-LRCs over small fields. We
+first develop upper and lower bounds on the minimum distance of ME-LRCs. These
+bounds explicitly take the field size into account. Our main contribution is to
+propose a general construction of ME-LRCs based on generalized tensor product
+codes, and study their erasure-correcting property. A decoding algorithm
+tailored for erasure recovery is given. We then prove that our construction
+yields optimal ME-LRCs with a wide range of code parameters. Finally, we
+present several families of ME-LRCs over different fields.
+",1,0,0,0,0,0
+1909,NSML: A Machine Learning Platform That Enables You to Focus on Your Models,"  Machine learning libraries such as TensorFlow and PyTorch simplify model
+implementation. However, researchers are still required to perform a
+non-trivial amount of manual tasks such as GPU allocation, training status
+tracking, and comparison of models with different hyperparameter settings. We
+propose a system to handle these tasks and help researchers focus on models. We
+present the requirements of the system based on a collection of discussions
+from an online study group comprising 25k members. These include automatic GPU
+allocation, learning status visualization, handling model parameter snapshots
+as well as hyperparameter modification during learning, and comparison of
+performance metrics between models via a leaderboard. We describe the system
+architecture that fulfills these requirements and present a proof-of-concept
+implementation, NAVER Smart Machine Learning (NSML). We test the system and
+confirm substantial efficiency improvements for model development.
+",1,0,0,0,0,0
+1910,High order local absorbing boundary conditions for acoustic waves in terms of farfield expansions,"  We devise a new high order local absorbing boundary condition (ABC) for
+radiating problems and scattering of time-harmonic acoustic waves from
+obstacles of arbitrary shape. By introducing an artificial boundary $S$
+enclosing the scatterer, the original unbounded domain $\Omega$ is decomposed
+into a bounded computational domain $\Omega^{-}$ and an exterior unbounded
+domain $\Omega^{+}$. Then, we define interface conditions at the artificial
+boundary $S$, from truncated versions of the well-known Wilcox and Karp
+farfield expansion representations of the exact solution in the exterior region
+$\Omega^{+}$. As a result, we obtain a new local absorbing boundary condition
+(ABC) for a bounded problem on $\Omega^{-}$, which effectively accounts for the
+outgoing behavior of the scattered field. Contrary to the low order absorbing
+conditions previously defined, the order of the error induced by this ABC can
+easily match the order of the numerical method in $\Omega^{-}$. We accomplish
+this by simply adding as many terms as needed to the truncated farfield
+expansions of Wilcox or Karp. The convergence of these expansions guarantees
+that the order of approximation of the new ABC can be increased arbitrarily
+without having to enlarge the radius of the artificial boundary. We include
+numerical results in two and three dimensions which demonstrate the improved
+accuracy and simplicity of this new formulation when compared to other
+absorbing boundary conditions.
+",0,1,1,0,0,0
+1911,Simple Necessary Conditions for the Existence of a Hamiltonian Path with Applications to Cactus Graphs,"  We describe some necessary conditions for the existence of a Hamiltonian path
+in any graph (in other words, for a graph to be traceable). These conditions
+result in a linear time algorithm to decide the Hamiltonian path problem for
+cactus graphs. We apply this algorithm to several molecular databases to report
+the numbers of graphs that are traceable cactus graphs.
+",1,0,0,0,0,0
+1912,Bootstrapping Exchangeable Random Graphs,"  We introduce two new bootstraps for exchangeable random graphs. One, the
+""empirical graphon"", is based purely on resampling, while the other, the
+""histogram stochastic block model"", is a model-based ""sieve"" bootstrap. We show
+that both of them accurately approximate the sampling distributions of motif
+densities, i.e., of the normalized counts of the number of times fixed
+subgraphs appear in the network. These densities characterize the distribution
+of (infinite) exchangeable networks. Our bootstraps therefore give, for the
+first time, a valid quantification of uncertainty in inferences about
+fundamental network statistics, and so of parameters identifiable from them.
+",0,0,0,1,0,0
+1913,A question proposed by K. Mahler on exceptional sets of transcendental functions with integer coefficients: solution of a Mahler's problem,"  In this paper, we shall prove that any subset of $\overline{\mathbb Q}\cap
+B(0,1)$, which is closed under complex conjugation and which contains the
+element $0$, is the exceptional set of uncountably many transcendental
+functions, analytic in the unit ball, with integer coefficients. This solves a
+strong version of an old question proposed by K. Mahler (1976).
+",0,0,1,0,0,0
+1914,Implementation of the Bin Hierarchy Method for restoring a smooth function from a sampled histogram,"  We present $\texttt{BHM}$, a tool for restoring a smooth function from a
+sampled histogram using the bin hierarchy method. The theoretical background of
+the method is presented in [arXiv:1707.07625]. The code automatically generates
+a smooth polynomial spline with the minimal acceptable number of knots from the
+input data. It works universally for any sufficiently regular shaped
+distribution and any level of data quality, requiring almost no external
+parameter specification. It is particularly useful for large-scale numerical
+data analysis. This paper explains the details of the implementation and the
+use of the program.
+",0,1,0,1,0,0
+1915,The Discrete Stochastic Galerkin Method for Hyperbolic Equations with Non-smooth and Random Coefficients,"  We develop a general polynomial chaos (gPC) based stochastic Galerkin (SG)
+for hyperbolic equations with random and singular coefficients. Due to the
+singu- lar nature of the solution, the standard gPC-SG methods may suffer from
+a poor or even non convergence. Taking advantage of the fact that the discrete
+solution, by the central type finite difference or finite volume approximations
+in space and time for example, is smoother, we first discretize the equation by
+a smooth finite difference or finite volume scheme, and then use the gPC-SG
+approximation to the discrete system. The jump condition at the interface is
+treated using the immersed upwind methods introduced in [8, 12]. This yields a
+method that converges with the spectral accuracy for finite mesh size and time
+step. We use a linear hyperbolic equation with discontinuous and random
+coefficient, and the Liouville equation with discontinuous and random
+potential, to illustrate our idea, with both one and second order spatial
+discretizations. Spectral convergence is established for the first equation,
+and numerical examples for both equations show the desired accu- racy of the
+method.
+",0,0,1,0,0,0
+1916,"Seasonal evolution of $\mathrm{C_2N_2}$, $\mathrm{C_3H_4}$, and $\mathrm{C_4H_2}$ abundances in Titan's lower stratosphere","  We study the seasonal evolution of Titan's lower stratosphere (around
+15~mbar) in order to better understand the atmospheric dynamics and chemistry
+in this part of the atmosphere. We analysed Cassini/CIRS far-IR observations
+from 2006 to 2016 in order to measure the seasonal variations of three
+photochemical by-products: $\mathrm{C_4H_2}$, $\mathrm{C_3H_4}$, and
+$\mathrm{C_2N_2}$. We show that the abundances of these three gases have
+evolved significantly at northern and southern high latitudes since 2006. We
+measure a sudden and steep increase of the volume mixing ratios of
+$\mathrm{C_4H_2}$, $\mathrm{C_3H_4}$, and $\mathrm{C_2N_2}$ at the south pole
+from 2012 to 2013, whereas the abundances of these gases remained approximately
+constant at the north pole over the same period. At northern mid-latitudes,
+$\mathrm{C_2N_2}$ and $\mathrm{C_4H_2}$ abundances decrease after 2012 while
+$\mathrm{C_3H_4}$ abundances stay constant. The comparison of these volume
+mixing ratio variations with the predictions of photochemical and dynamical
+models provides constraints on the seasonal evolution of atmospheric
+circulation and chemical processes at play.
+",0,1,0,0,0,0
+1917,"Systems, Actors and Agents: Operation in a multicomponent environment","  Multi-agent approach has become popular in computer science and technology.
+However, the conventional models of multi-agent and multicomponent systems
+implicitly or explicitly assume existence of absolute time or even do not
+include time in the set of defining parameters. At the same time, it is proved
+theoretically and validated experimentally that there are different times and
+time scales in a variety of real systems - physical, chemical, biological,
+social, informational, etc. Thus, the goal of this work is construction of a
+multi-agent multicomponent system models with concurrency of processes and
+diversity of actions. To achieve this goal, a mathematical system actor model
+is elaborated and its properties are studied.
+",1,0,0,0,0,0
+1918,An invitation to model theory and C*-algebras,"  We present an introductory survey to first order logic for metric structures
+and its applications to C*-algebras.
+",0,0,1,0,0,0
+1919,Random Close Packing and the Hard Sphere Percus-Yevick Theory,"  The Percus-Yevick theory for monodisperse hard spheres gives very good
+results for the pressure and structure factor of the system in a whole range of
+densities that lie within the liquid phase. However, the equation seems to lead
+to a very unacceptable result beyond that region. Namely, the Percus-Yevick
+theory predicts a smooth behavior of the pressure that diverges only when the
+volume fraction $\eta$ approaches unity. Thus, within the theory there seems to
+be no indication for the termination of the liquid phase and the transition to
+a solid or to a glass. In the present article we study the Percus-Yevick hard
+sphere pair distribution function, $g_2(r)$, for various spatial dimensions. We
+find that beyond a certain critical volume fraction $\eta_c$, the pair
+distribution function, $g_2(r)$, which should be positive definite, becomes
+negative at some distances. We also present an intriguing observation that the
+critical $\eta_c$ values we find are consistent with volume fractions where
+onsets of random close packing (or maximally random jammed states) are reported
+in the literature for various dimensions. That observation is supported by an
+intuitive argument. This work may have important implications for other systems
+for which a Percus-Yevick theory exists.
+",0,1,0,0,0,0
+1920,The Painlevé property of $\mathbb{C}P^{N-1}$ sigma models,"  We test the $\mathbb{C}P^{N-1}$ sigma models for the Painlevé property.
+While the construction of finite action solutions ensures their meromorphicity,
+the general case requires testing. The test is performed for the equations in
+the homogeneous variables, with their first component normalised to one. No
+constraints are imposed on the dimensionality of the model or the values of the
+initial exponents. This makes the test nontrivial, as the number of equations
+and dependent variables are indefinite. A $\mathbb{C}P^{N-1}$ system proves to
+have a $(4N-5)$-parameter family of solutions whose movable singularities are
+only poles, while the order of the investigated system is $4N-4$. The remaining
+degree of freedom, connected with an extra negative resonance, may correspond
+to a branching movable essential singularity. An example of such a solution is
+provided.
+",0,1,0,0,0,0
+1921,A comment on Stein's unbiased risk estimate for reduced rank estimators,"  In the framework of matrix valued observables with low rank means, Stein's
+unbiased risk estimate (SURE) can be useful for risk estimation and for tuning
+the amount of shrinkage towards low rank matrices. This was demonstrated by
+Candès et al. (2013) for singular value soft thresholding, which is a
+Lipschitz continuous estimator. SURE provides an unbiased risk estimate for an
+estimator whenever the differentiability requirements for Stein's lemma are
+satisfied. Lipschitz continuity of the estimator is sufficient, but it is
+emphasized that differentiability Lebesgue almost everywhere isn't. The reduced
+rank estimator, which gives the best approximation of the observation with a
+fixed rank, is an example of a discontinuous estimator for which Stein's lemma
+actually applies. This was observed by Mukherjee et al. (2015), but the proof
+was incomplete. This brief note gives a sufficient condition for Stein's lemma
+to hold for estimators with discontinuities, which is then shown to be
+fulfilled for a class of spectral function estimators including the reduced
+rank estimator. Singular value hard thresholding does, however, not satisfy the
+condition, and Stein's lemma does not apply to this estimator.
+",0,0,1,1,0,0
+1922,An empirical study on evaluation metrics of generative adversarial networks,"  Evaluating generative adversarial networks (GANs) is inherently challenging.
+In this paper, we revisit several representative sample-based evaluation
+metrics for GANs, and address the problem of how to evaluate the evaluation
+metrics. We start with a few necessary conditions for metrics to produce
+meaningful scores, such as distinguishing real from generated samples,
+identifying mode dropping and mode collapsing, and detecting overfitting. With
+a series of carefully designed experiments, we comprehensively investigate
+existing sample-based metrics and identify their strengths and limitations in
+practical settings. Based on these results, we observe that kernel Maximum Mean
+Discrepancy (MMD) and the 1-Nearest-Neighbor (1-NN) two-sample test seem to
+satisfy most of the desirable properties, provided that the distances between
+samples are computed in a suitable feature space. Our experiments also unveil
+interesting properties about the behavior of several popular GAN models, such
+as whether they are memorizing training samples, and how far they are from
+learning the target distribution.
+",0,0,0,1,0,0
+1923,Analytical and simulation studies of pedestrian flow at a crossing with random update rule,"  The intersecting pedestrian flow on the 2D lattice with random update rule is
+studied. Each pedestrian has three moving directions without the back step.
+Under periodic boundary conditions, an intermediate phase has been found at
+which some pedestrians could move along the border of jamming stripes. We have
+performed mean field analysis for the moving and intermediate phase
+respectively. The analytical results agree with the simulation results well.
+The empty site moves along the interface of jamming stripes when the system
+only has one empty site. The average movement of empty site in one Monte Carlo
+step (MCS) has been analyzed through the master equation. Under open boundary
+conditions, the system exhibits moving and jamming phases. The critical
+injection probability $\alpha_c$ shows nontrivially against the forward moving
+probability $q$. The analytical results of average velocity, the density and
+the flow rate against the injection probability in the moving phase also agree
+with simulation results well.
+",0,1,0,0,0,0
+1924,Static and Fluctuating Magnetic Moments in the Ferroelectric Metal LiOsO$_3$,"  LiOsO$_3$ is the first example of a new class of material called a
+ferroelectric metal. We performed zero-field and longitudinal-field $\mu$SR,
+along with a combination of electronic structure and dipole field calculations,
+to determine the magnetic ground state of LiOsO$_3$. We find that the sample
+contains both static Li nuclear moments and dynamic Os electronic moments.
+Below $\approx 0.7\,$K, the fluctuations of the Os moments slow down, though
+remain dynamic down to 0.08$\,$K. We expect this could result in a frozen-out,
+disordered ground state at even lower temperatures.
+",0,1,0,0,0,0
+1925,Compiling Deep Learning Models for Custom Hardware Accelerators,"  Convolutional neural networks (CNNs) are the core of most state-of-the-art
+deep learning algorithms specialized for object detection and classification.
+CNNs are both computationally complex and embarrassingly parallel. Two
+properties that leave room for potential software and hardware optimizations
+for embedded systems. Given a programmable hardware accelerator with a CNN
+oriented custom instructions set, the compiler's task is to exploit the
+hardware's full potential, while abiding with the hardware constraints and
+maintaining generality to run different CNN models with varying workload
+properties. Snowflake is an efficient and scalable hardware accelerator
+implemented on programmable logic devices. It implements a control pipeline for
+a custom instruction set. The goal of this paper is to present Snowflake's
+compiler that generates machine level instructions from Torch7 model
+description files. The main software design points explored in this work are:
+model structure parsing, CNN workload breakdown, loop rearrangement for memory
+bandwidth optimizations and memory access balancing. The performance achieved
+by compiler generated instructions matches against hand optimized code for
+convolution layers. Generated instructions also efficiently execute AlexNet and
+ResNet18 inference on Snowflake. Snowflake with $256$ processing units was
+synthesized on Xilinx's Zynq XC7Z045 FPGA. At $250$ MHz, AlexNet achieved in
+$93.6$ frames/s and $1.2$ GB/s of off-chip memory bandwidth, and $21.4$
+frames/s and $2.2$ GB/s for ResNet18. Total on-chip power is $5$ W.
+",1,0,0,0,0,0
+1926,Ranking with Adaptive Neighbors,"  Retrieving the most similar objects in a large-scale database for a given
+query is a fundamental building block in many application domains, ranging from
+web searches, visual, cross media, and document retrievals. State-of-the-art
+approaches have mainly focused on capturing the underlying geometry of the data
+manifolds. Graph-based approaches, in particular, define various diffusion
+processes on weighted data graphs. Despite success, these approaches rely on
+fixed-weight graphs, making ranking sensitive to the input affinity matrix. In
+this study, we propose a new ranking algorithm that simultaneously learns the
+data affinity matrix and the ranking scores. The proposed optimization
+formulation assigns adaptive neighbors to each point in the data based on the
+local connectivity, and the smoothness constraint assigns similar ranking
+scores to similar data points. We develop a novel and efficient algorithm to
+solve the optimization problem. Evaluations using synthetic and real datasets
+suggest that the proposed algorithm can outperform the existing methods.
+",0,0,0,1,0,0
+1927,Identities and central polynomials of real graded division algebras,"  Let $A$ be a finite dimensional real algebra with a division grading by a
+finite abelian group $G$. In this paper we provide finite basis for the
+$T_G$-ideal of graded identities and for the $T_G$-space of graded central
+polynomials for $A$.
+",0,0,1,0,0,0
+1928,"VB-Courant algebroids, E-Courant algebroids and generalized geometry","  In this paper, we first discuss the relation between VB-Courant algebroids
+and E-Courant algebroids and construct some examples of E-Courant algebroids.
+Then we introduce the notion of a generalized complex structure on an E-Courant
+algebroid, unifying the usual generalized complex structures on
+even-dimensional manifolds and generalized contact structures on
+odd-dimensional manifolds. Moreover, we study generalized complex structures on
+an omni-Lie algebroid in detail. In particular, we show that generalized
+complex structures on an omni-Lie algebra $\gl(V)\oplus V$ correspond to
+complex Lie algebra structures on V.
+",0,0,1,0,0,0
+1929,Majorana bound states in hybrid 2D Josephson junctions with ferromagnetic insulators,"  We consider a Josephson junction consisting of superconductor/ferromagnetic
+insulator (S/FI) bilayers as electrodes which proximizes a nearby 2D electron
+gas. By starting from a generic Josephson hybrid planar setup we present an
+exhaustive analysis of the the interplay between the superconducting and
+magnetic proximity effects and the conditions under which the structure
+undergoes transitions to a non-trivial topological phase. We address the 2D
+bound state problem using a general transfer matrix approach that reduces the
+problem to an effective 1D Hamiltonian. This allows for straightforward study
+of topological properties in different symmetry classes. As an example we
+consider a narrow channel coupled with multiple ferromagnetic superconducting
+fingers, and discuss how the Majorana bound states can be spatially controlled
+by tuning the superconducting phases. Following our approach we also show the
+energy spectrum, the free energy and finally the multiterminal Josephson
+current of the setup.
+",0,1,0,0,0,0
+1930,Two-Dimensional Large Gap Topological Insulators with Large Rashba Spin-Orbit Coupling in Group-IV films,"  Rashba spin orbit coupling in topological insulators has attracted much
+interest due to its exotic properties closely related to spintronic devices.
+The coexistence of nontrivial topology and giant Rashba splitting, however, has
+rare been observed in two-dimensional films, limiting severely its potential
+applications at room temperature. Here, we propose a series of inversion
+asymmetric group IV films, ABZ2, whose stability are confirmed by phonon
+spectrum calculations. The analyses of electronic structures reveal that they
+are intrinsic 2D TIs with a bulk gap as large as 0.74 eV, except for GeSiF2,
+SnSiCl2, GeSiCl2 and GeSiBr2 monolayers which can transform from normal to
+topological phases under appropriate tensile strains. Another prominent
+intriguing feature is the giant Rashba spin splitting with a magnitude reaching
+0.15 eV, the largest value reported in 2D films. These results present a
+platform to explore 2D TIs for room temperature device applications.
+",0,1,0,0,0,0
+1931,Topological boundary invariants for Floquet systems and quantum walks,"  A Floquet systems is a periodically driven quantum system. It can be
+described by a Floquet operator. If this unitary operator has a gap in the
+spectrum, then one can define associated topological bulk invariants which can
+either only depend on the bands of the Floquet operator or also on the time as
+a variable. It is shown how a K-theoretic result combined with the
+bulk-boundary correspondence leads to edge invariants for the half-space
+Floquet operators. These results also apply to topological quantum walks.
+",0,1,0,0,0,0
+1932,Assumption-Based Approaches to Reasoning with Priorities,"  This paper maps out the relation between different approaches for handling
+preferences in argumentation with strict rules and defeasible assumptions by
+offering translations between them. The systems we compare are: non-prioritized
+defeats i.e. attacks, preference-based defeats, and preference-based defeats
+extended with reverse defeat.
+",1,0,0,0,0,0
+1933,On the Impact of Transposition Errors in Diffusion-Based Channels,"  In this work, we consider diffusion-based molecular communication with and
+without drift between two static nano-machines. We employ type-based
+information encoding, releasing a single molecule per information bit. At the
+receiver, we consider an asynchronous detection algorithm which exploits the
+arrival order of the molecules. In such systems, transposition errors
+fundamentally undermine reliability and capacity. Thus, in this work we study
+the impact of transpositions on the system performance. Towards this, we
+present an analytical expression for the exact bit error probability (BEP)
+caused by transpositions and derive computationally tractable approximations of
+the BEP for diffusion-based channels with and without drift. Based on these
+results, we analyze the BEP when background is not negligible and derive the
+optimal bit interval that minimizes the BEP. Simulation results confirm the
+theoretical results and show the error and goodput performance for different
+parameters such as block size or noise generation rate.
+",1,0,1,0,0,0
+1934,Uniform $L^p$-improving for weighted averages on curves,"  We define variable parameter analogues of the affine arclength measure on
+curves and prove near-optimal $L^p$-improving estimates for associated
+multilinear generalized Radon transforms. Some of our results are new even in
+the convolution case.
+",0,0,1,0,0,0
+1935,Finite Sample Differentially Private Confidence Intervals,"  We study the problem of estimating finite sample confidence intervals of the
+mean of a normal population under the constraint of differential privacy. We
+consider both the known and unknown variance cases and construct differentially
+private algorithms to estimate confidence intervals. Crucially, our algorithms
+guarantee a finite sample coverage, as opposed to an asymptotic coverage.
+Unlike most previous differentially private algorithms, we do not require the
+domain of the samples to be bounded. We also prove lower bounds on the expected
+size of any differentially private confidence set showing that our the
+parameters are optimal up to polylogarithmic factors.
+",1,0,1,1,0,0
+1936,xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit,"  Extreme-scale computational science increasingly demands multiscale and
+multiphysics formulations. Combining software developed by independent groups
+is imperative: no single team has resources for all predictive science and
+decision support capabilities. Scientific libraries provide high-quality,
+reusable software components for constructing applications with improved
+robustness and portability. However, without coordination, many libraries
+cannot be easily composed. Namespace collisions, inconsistent arguments, lack
+of third-party software versioning, and additional difficulties make
+composition costly.
+The Extreme-scale Scientific Software Development Kit (xSDK) defines
+community policies to improve code quality and compatibility across
+independently developed packages (hypre, PETSc, SuperLU, Trilinos, and
+Alquimia) and provides a foundation for addressing broader issues in software
+interoperability, performance portability, and sustainability. The xSDK
+provides turnkey installation of member software and seamless combination of
+aggregate capabilities, and it marks first steps toward extreme-scale
+scientific software ecosystems from which future applications can be composed
+rapidly with assured quality and scalability.
+",1,0,0,0,0,0
+1937,Muon Spin Rotation Analysis of the Internal Magnetic Field of Heavy Fermion System Uranium Beryllium-13,"  Uranium beryllium-13 is a heavy fermion system whose anomalous behavior may
+be explained by its poorly understood internal magnetic structure. Here,
+uranium beryllium-13's magnetic distribution is probed via muon spin
+spectroscopy ($\mu$SR)-a process where positive muons localize at magnetically
+unique sites in the crystal lattice and precess at characteristic Larmor
+frequencies, providing measurements of the internal field. Muon spin
+experiments using the transverse-field technique conducted at varying
+temperatures and external magnetic field strengths are analyzed via statistical
+methods on ROOT. Two precession frequencies are observed at low temperatures
+with an amplitude ratio in the Fourier transform of 2:1, enabling muon stopping
+sites to be traced at the geometric centers of the edges of the crystal
+lattice. Characteristic strong and weak magnetic sites are deduced,
+additionally verified by mathematical relationships. Results can readily be
+applied to other heavy fermion systems, and recent identification of quantum
+critical points in a host of heavy fermion compounds show a promising future
+for the application of these systems in quantum technology. Note that this
+paper is an analysis of data, and all experiments mentioned here are conducted
+by a third party.
+",0,1,0,0,0,0
+1938,The infinity-Fucik spectrum,"  In this article we study the behavior as $p \nearrow+\infty$ of the Fucik
+spectrum for $p$-Laplace operator with zero Dirichlet boundary conditions in a
+bounded domain $\Omega\subset \mathbb{R}^n$. We characterize the limit
+equation, and we provide a description of the limit spectrum. Furthermore, we
+show some explicit computations of the spectrum for certain configurations of
+the domain.
+",0,0,1,0,0,0
+1939,Unitary Groups as Stabilizers of Orbits,"  We show that a finite unitary group which has orbits spanning the whole space
+is necessarily the setwise stabilizer of a certain orbit.
+",0,0,1,0,0,0
+1940,m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series,"  Multivariate time series (MTS) have become increasingly common in healthcare
+domains where human vital signs and laboratory results are collected for
+predictive diagnosis. Recently, there have been increasing efforts to visualize
+healthcare MTS data based on star charts or parallel coordinates. However, such
+techniques might not be ideal for visualizing a large MTS dataset, since it is
+difficult to obtain insights or interpretations due to the inherent high
+dimensionality of MTS. In this paper, we propose 'm-TSNE': a simple and novel
+framework to visualize high-dimensional MTS data by projecting them into a
+low-dimensional (2-D or 3-D) space while capturing the underlying data
+properties. Our framework is easy to use and provides interpretable insights
+for healthcare professionals to understand MTS data. We evaluate our
+visualization framework on two real-world datasets and demonstrate that the
+results of our m-TSNE show patterns that are easy to understand while the other
+methods' visualization may have limitations in interpretability.
+",1,0,0,1,0,0
+1941,On stabilization of solutions of nonlinear parabolic equations with a gradient term,"  For parabolic equations of the form $$ \frac{\partial u}{\partial t} -
+\sum_{i,j=1}^n a_{ij} (x, u) \frac{\partial^2 u}{\partial x_i \partial x_j} + f
+(x, u, D u) = 0 \quad \mbox{in } {\mathbb R}_+^{n+1}, $$ where ${\mathbb
+R}_+^{n+1} = {\mathbb R}^n \times (0, \infty)$, $n \ge 1$, $D = (\partial /
+\partial x_1, \ldots, \partial / \partial x_n)$ is the gradient operator, and
+$f$ is some function, we obtain conditions guaranteeing that every solution
+tends to zero as $t \to \infty$.
+",0,0,1,0,0,0
+1942,Deep metric learning for multi-labelled radiographs,"  Many radiological studies can reveal the presence of several co-existing
+abnormalities, each one represented by a distinct visual pattern. In this
+article we address the problem of learning a distance metric for plain
+radiographs that captures a notion of ""radiological similarity"": two chest
+radiographs are considered to be similar if they share similar abnormalities.
+Deep convolutional neural networks (DCNs) are used to learn a low-dimensional
+embedding for the radiographs that is equipped with the desired metric. Two
+loss functions are proposed to deal with multi-labelled images and potentially
+noisy labels. We report on a large-scale study involving over 745,000 chest
+radiographs whose labels were automatically extracted from free-text
+radiological reports through a natural language processing system. Using 4,500
+validated exams, we demonstrate that the methodology performs satisfactorily on
+clustering and image retrieval tasks. Remarkably, the learned metric separates
+normal exams from those having radiological abnormalities.
+",0,0,0,1,0,0
+1943,Algebras of generalized dihedral type,"  We provide a complete classification of all algebras of generalised dihedral
+type, which are natural generalizations of algebras which occurred in the study
+of blocks with dihedral defect groups. This gives a description by quivers and
+relations coming from surface triangulations.
+",0,0,1,0,0,0
+1944,Resonant particle production during inflation: a full analytical study,"  We revisit the study of the phenomenology associated to a burst of particle
+production of a field whose mass is controlled by the inflaton field and
+vanishes at one given instance during inflation. This generates a bump in the
+correlators of the primordial scalar curvature. We provide a unified formalism
+to compute various effects that have been obtained in the literature and
+confirm that the dominant effects are due to the rescattering of the produced
+particles on the inflaton condensate. We improve over existing results (based
+on numerical fits) by providing exact analytic expressions for the shape and
+height of the bump, both in the power spectrum and the equilateral bispectrum.
+We then study the regime of validity of the perturbative computations of this
+signature. Finally, we extend these computations to the case of a burst of
+particle production in a sector coupled only gravitationally to the inflaton.
+",0,1,0,0,0,0
+1945,Average whenever you meet: Opportunistic protocols for community detection,"  Consider the following asynchronous, opportunistic communication model over a
+graph $G$: in each round, one edge is activated uniformly and independently at
+random and (only) its two endpoints can exchange messages and perform local
+computations. Under this model, we study the following random process: The
+first time a vertex is an endpoint of an active edge, it chooses a random
+number, say $\pm 1$ with probability $1/2$; then, in each round, the two
+endpoints of the currently active edge update their values to their average. We
+show that, if $G$ exhibits a two-community structure (for example, two
+expanders connected by a sparse cut), the values held by the nodes will
+collectively reflect the underlying community structure over a suitable phase
+of the above process, allowing efficient and effective recovery in important
+cases.
+In more detail, we first provide a first-moment analysis showing that, for a
+large class of almost-regular clustered graphs that includes the stochastic
+block model, the expected values held by all but a negligible fraction of the
+nodes eventually reflect the underlying cut signal. We prove this property
+emerges after a mixing period of length $\mathcal O(n\log n)$. We further
+provide a second-moment analysis for a more restricted class of regular
+clustered graphs that includes the regular stochastic block model. For this
+case, we are able to show that most nodes can efficiently and locally identify
+their community of reference over a suitable time window. This results in the
+first opportunistic protocols that approximately recover community structure
+using only polylogarithmic work per node. Even for the above class of regular
+graphs, our second moment analysis requires new concentration bounds on the
+product of certain random matrices that are technically challenging and
+possibly of independent interest.
+",1,0,0,0,0,0
+1946,A polynomial-time approximation algorithm for all-terminal network reliability,"  We give a fully polynomial-time randomized approximation scheme (FPRAS) for
+the all-terminal network reliability problem, which is to determine the
+probability that, in a undirected graph, assuming each edge fails
+independently, the remaining graph is still connected. Our main contribution is
+to confirm a conjecture by Gorodezky and Pak (Random Struct. Algorithms, 2014),
+that the expected running time of the ""cluster-popping"" algorithm in
+bi-directed graphs is bounded by a polynomial in the size of the input.
+",1,0,0,0,0,0
+1947,Provably Accurate Double-Sparse Coding,"  Sparse coding is a crucial subroutine in algorithms for various signal
+processing, deep learning, and other machine learning applications. The central
+goal is to learn an overcomplete dictionary that can sparsely represent a given
+input dataset. However, a key challenge is that storage, transmission, and
+processing of the learned dictionary can be untenably high if the data
+dimension is high. In this paper, we consider the double-sparsity model
+introduced by Rubinstein et al. (2010b) where the dictionary itself is the
+product of a fixed, known basis and a data-adaptive sparse component. First, we
+introduce a simple algorithm for double-sparse coding that can be amenable to
+efficient implementation via neural architectures. Second, we theoretically
+analyze its performance and demonstrate asymptotic sample complexity and
+running time benefits over existing (provable) approaches for sparse coding. To
+our knowledge, our work introduces the first computationally efficient
+algorithm for double-sparse coding that enjoys rigorous statistical guarantees.
+Finally, we support our analysis via several numerical experiments on simulated
+data, confirming that our method can indeed be useful in problem sizes
+encountered in practical applications.
+",1,0,0,1,0,0
+1948,Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalised linear models,"  Reduced-rank regression is a dimensionality reduction method with many
+applications. The asymptotic theory for reduced rank estimators of parameter
+matrices in multivariate linear models has been studied extensively. In
+contrast, few theoretical results are available for reduced-rank multivariate
+generalised linear models. We develop M-estimation theory for concave criterion
+functions that are maximised over parameters spaces that are neither convex nor
+closed. These results are used to derive the consistency and asymptotic
+distribution of maximum likelihood estimators in reduced-rank multivariate
+generalised linear models, when the response and predictor vectors have a joint
+distribution. We illustrate our results in a real data classification problem
+with binary covariates.
+",0,0,1,1,0,0
+1949,Sequential two-fold Pearson chi-squared test and tails of the Bessel process distributions,"  We find asymptotic formulas for error probabilities of two-fold Pearson
+goodness-of-fit test as functions of two critical levels. These results may be
+reformulated in terms of tails of two-dimensional distributions of the Bessel
+process. Necessary properties of the Infeld function are obtained.
+",0,0,1,1,0,0
+1950,Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction,"  The visual focus of attention (VFOA) has been recognized as a prominent
+conversational cue. We are interested in estimating and tracking the VFOAs
+associated with multi-party social interactions. We note that in this type of
+situations the participants either look at each other or at an object of
+interest; therefore their eyes are not always visible. Consequently both gaze
+and VFOA estimation cannot be based on eye detection and tracking. We propose a
+method that exploits the correlation between eye gaze and head movements. Both
+VFOA and gaze are modeled as latent variables in a Bayesian switching
+state-space model. The proposed formulation leads to a tractable learning
+procedure and to an efficient algorithm that simultaneously tracks gaze and
+visual focus. The method is tested and benchmarked using two publicly available
+datasets that contain typical multi-party human-robot and human-human
+interactions.
+",1,0,0,0,0,0
+1951,Fully-Dynamic and Kinetic Conflict-Free Coloring of Intervals with Respect to Points,"  We introduce the fully-dynamic conflict-free coloring problem for a set $S$
+of intervals in $\mathbb{R}^1$ with respect to points, where the goal is to
+maintain a conflict-free coloring for$S$ under insertions and deletions. A
+coloring is conflict-free if for each point $p$ contained in some interval, $p$
+is contained in an interval whose color is not shared with any other interval
+containing $p$. We investigate trade-offs between the number of colors used and
+the number of intervals that are recolored upon insertion or deletion of an
+interval. Our results include:
+- a lower bound on the number of recolorings as a function of the number of
+colors, which implies that with $O(1)$ recolorings per update the worst-case
+number of colors is $\Omega(\log n/\log\log n)$, and that any strategy using
+$O(1/\varepsilon)$ colors needs $\Omega(\varepsilon n^{\varepsilon})$
+recolorings;
+- a coloring strategy that uses $O(\log n)$ colors at the cost of $O(\log n)$
+recolorings, and another strategy that uses $O(1/\varepsilon)$ colors at the
+cost of $O(n^{\varepsilon}/\varepsilon)$ recolorings;
+- stronger upper and lower bounds for special cases.
+We also consider the kinetic setting where the intervals move continuously
+(but there are no insertions or deletions); here we show how to maintain a
+coloring with only four colors at the cost of three recolorings per event and
+show this is tight.
+",1,0,0,0,0,0
+1952,Magnetic properties in ultra-thin 3d transition metal alloys II: Experimental verification of quantitative theories of damping and spin-pumping,"  A systematic experimental study of Gilbert damping is performed via
+ferromagnetic resonance for the disordered crystalline binary 3d transition
+metal alloys Ni-Co, Ni-Fe and Co-Fe over the full range of alloy compositions.
+After accounting for inhomogeneous linewidth broadening, the damping shows
+clear evidence of both interfacial damping enhancement (by spin pumping) and
+radiative damping. We quantify these two extrinsic contributions and thereby
+determine the intrinsic damping. The comparison of the intrinsic damping to
+multiple theoretical calculations yields good qualitative and quantitative
+agreement in most cases. Furthermore, the values of the damping obtained in
+this study are in good agreement with a wide range of published experimental
+and theoretical values. Additionally, we find a compositional dependence of the
+spin mixing conductance.
+",0,1,0,0,0,0
+1953,Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks,"  Learning to detect fraud in large-scale accounting data is one of the
+long-standing challenges in financial statement audits or fraud investigations.
+Nowadays, the majority of applied techniques refer to handcrafted rules derived
+from known fraud scenarios. While fairly successful, these rules exhibit the
+drawback that they often fail to generalize beyond known fraud scenarios and
+fraudsters gradually find ways to circumvent them. To overcome this
+disadvantage and inspired by the recent success of deep learning we propose the
+application of deep autoencoder neural networks to detect anomalous journal
+entries. We demonstrate that the trained network's reconstruction error
+obtainable for a journal entry and regularized by the entry's individual
+attribute probabilities can be interpreted as a highly adaptive anomaly
+assessment. Experiments on two real-world datasets of journal entries, show the
+effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A)
+and 16.95 (dataset B) and less false positive alerts compared to state of the
+art baseline methods. Initial feedback received by chartered accountants and
+fraud examiners underpinned the quality of the approach in capturing highly
+relevant accounting anomalies.
+",1,0,0,0,0,0
+1954,Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data,"  We consider a problem of learning a binary classifier only from positive data
+and unlabeled data (PU learning) and estimating the class-prior in unlabeled
+data under the case-control scenario. Most of the recent methods of PU learning
+require an estimate of the class-prior probability in unlabeled data, and it is
+estimated in advance with another method. However, such a two-step approach
+which first estimates the class prior and then trains a classifier may not be
+the optimal approach since the estimation error of the class-prior is not taken
+into account when a classifier is trained. In this paper, we propose a novel
+unified approach to estimating the class-prior and training a classifier
+alternately. Our proposed method is simple to implement and computationally
+efficient. Through experiments, we demonstrate the practical usefulness of the
+proposed method.
+",0,0,0,1,0,0
+1955,Oscillating dipole with fractional quantum source in Aharonov-Bohm electrodynamics,"  We show, in the case of a special dipolar source, that electromagnetic fields
+in fractional quantum mechanics have an unexpected space dependence:
+propagating fields may have non-transverse components, and the distinction
+between near-field zone and wave zone is blurred. We employ an extension of
+Maxwell theory, Aharonov-Bohm electrodynamics, which is compatible with
+currents $j^\nu$ conserved globally but not locally, we have derived in another
+work the field equation $\partial_\mu F^{\mu \nu}=j^\nu+i^\nu$, where $i^\nu$
+is a non-local function of $j^\nu$, called ""secondary current"". Y.\ Wei has
+recently proved that the probability current in fractional quantum mechanics is
+in general not locally conserved. We compute this current for a Gaussian wave
+packet with fractional parameter $a=3/2$ and find that in a suitable limit it
+can be approximated by our simplified dipolar source. Currents which are not
+locally conserved may be present also in other quantum systems whose wave
+functions satisfy non-local equations. The combined electromagnetic effects of
+such sources and their secondary currents are very interesting both
+theoretically and for potential applications.
+",0,1,0,0,0,0
+1956,Randomized Linear Programming Solves the Discounted Markov Decision Problem In Nearly-Linear (Sometimes Sublinear) Running Time,"  We propose a novel randomized linear programming algorithm for approximating
+the optimal policy of the discounted Markov decision problem. By leveraging the
+value-policy duality and binary-tree data structures, the algorithm adaptively
+samples state-action-state transitions and makes exponentiated primal-dual
+updates. We show that it finds an $\epsilon$-optimal policy using nearly-linear
+run time in the worst case. When the Markov decision process is ergodic and
+specified in some special data formats, the algorithm finds an
+$\epsilon$-optimal policy using run time linear in the total number of
+state-action pairs, which is sublinear in the input size. These results provide
+a new venue and complexity benchmarks for solving stochastic dynamic programs.
+",1,0,1,0,0,0
+1957,SCAV'18: Report of the 2nd International Workshop on Safe Control of Autonomous Vehicles,"  This report summarizes the discussions, open issues, take-away messages, and
+conclusions of the 2nd SCAV workshop.
+",1,0,0,0,0,0
+1958,Inference For High-Dimensional Split-Plot-Designs: A Unified Approach for Small to Large Numbers of Factor Levels,"  Statisticians increasingly face the problem to reconsider the adaptability of
+classical inference techniques. In particular, divers types of high-dimensional
+data structures are observed in various research areas; disclosing the
+boundaries of conventional multivariate data analysis. Such situations occur,
+e.g., frequently in life sciences whenever it is easier or cheaper to
+repeatedly generate a large number $d$ of observations per subject than
+recruiting many, say $N$, subjects. In this paper we discuss inference
+procedures for such situations in general heteroscedastic split-plot designs
+with $a$ independent groups of repeated measurements. These will, e.g., be able
+to answer questions about the occurrence of certain time, group and
+interactions effects or about particular profiles.
+The test procedures are based on standardized quadratic forms involving
+suitably symmetrized U-statistics-type estimators which are robust against an
+increasing number of dimensions $d$ and/or groups $a$. We then discuss its
+limit distributions in a general asymptotic framework and additionally propose
+improved small sample approximations. Finally its small sample performance is
+investigated in simulations and the applicability is illustrated by a real data
+analysis.
+",0,0,1,1,0,0
+1959,Rate Optimal Binary Linear Locally Repairable Codes with Small Availability,"  A locally repairable code with availability has the property that every code
+symbol can be recovered from multiple, disjoint subsets of other symbols of
+small size. In particular, a code symbol is said to have $(r,t)$-availability
+if it can be recovered from $t$ disjoint subsets, each of size at most $r$. A
+code with availability is said to be 'rate-optimal', if its rate is maximum
+among the class of codes with given locality, availability, and alphabet size.
+This paper focuses on rate-optimal binary, linear codes with small
+availability, and makes four contributions. First, it establishes tight upper
+bounds on the rate of binary linear codes with $(r,2)$ and $(2,3)$
+availability. Second, it establishes a uniqueness result for binary
+rate-optimal codes, showing that for certain classes of binary linear codes
+with $(r,2)$ and $(2,3)$-availability, any rate optimal code must be a direct
+sum of shorter rate optimal codes. Third, it presents novel upper bounds on the
+rates of binary linear codes with $(2,t)$ and $(r,3)$-availability. In
+particular, the main contribution here is a new method for bounding the number
+of cosets of the dual of a code with availability, using its covering
+properties. Finally, it presents a class of locally repairable linear codes
+associated with convex polyhedra, focusing on the codes associated with the
+Platonic solids. It demonstrates that these codes are locally repairable with
+$t = 2$, and that the codes associated with (geometric) dual polyhedra are
+(coding theoretic) duals of each other.
+",1,0,1,0,0,0
+1960,A general renormalization procedure on the one-dimensional lattice and decay of correlations,"  We present a general form of Renormalization operator $\mathcal{R}$ acting on
+potentials $V:\{0,1\}^\mathbb{N} \to \mathbb{R}$. We exhibit the analytical
+expression of the fixed point potential $V$ for such operator $\mathcal{R}$.
+This potential can be expressed in a naturally way in terms of a certain
+integral over the Hausdorff probability on a Cantor type set on the interval
+$[0,1]$. This result generalizes a previous one by A. Baraviera, R. Leplaideur
+and A. Lopes where the fixed point potential $V$ was of Hofbauer type.
+For the potentials of Hofbauer type (a well known case of phase transition)
+the decay is like $n^{-\gamma}$, $\gamma>0$.
+Among other things we present the estimation of the decay of correlation of
+the equilibrium probability associated to the fixed potential $V$ of our
+general renormalization procedure. In some cases we get polynomial decay like
+$n^{-\gamma}$, $\gamma>0$, and in others a decay faster than $n \,e^{ -\,
+\sqrt{n}}$, when $n \to \infty$.
+The potentials $g$ we consider here are elements of the so called family of
+Walters potentials on $\{0,1\}^\mathbb{N} $ which generalizes the potentials
+considered initially by F. Hofbauer. For these potentials some explicit
+expressions for the eigenfunctions are known.
+In a final section we also show that given any choice $d_n \to 0$ of real
+numbers varying with $n \in \mathbb{N}$ there exist a potential $g$ on the
+class defined by Walters which has a invariant probability with such numbers as
+the coefficients of correlation (for a certain explicit observable function).
+",0,1,1,0,0,0
+1961,Duality Spectral Sequences for Weierstrass Fibrations and Applications,"  We study duality spectral sequences for Weierstrass fibrations. Using these
+spectral sequences, we show that on a K-trivial Weierstrass threefold over a
+K-numerically trivial surface, any line bundle of nonzero fiber degree is taken
+by a Fourier-Mukai transform to a slope stable locally free sheaf.
+",0,0,1,0,0,0
+1962,Occupation times for the finite buffer fluid queue with phase-type ON-times,"  In this short communication we study a fluid queue with a finite buffer. The
+performance measure we are interested in is the occupation time over a finite
+time period, i.e., the fraction of time the workload process is below some
+fixed target level. We construct an alternating sequence of sojourn times
+$D_1,U_1,...$ where the pairs $(D_i,U_i)_{i\in\mathbb{N}}$ are i.i.d. random
+vectors. We use this sequence to determine the distribution function of the
+occupation time in terms of its double transform.
+",0,0,1,0,0,0
+1963,Affine forward variance models,"  We introduce the class of affine forward variance (AFV) models of which both
+the conventional Heston model and the rough Heston model are special cases. We
+show that AFV models can be characterized by the affine form of their cumulant
+generating function, which can be obtained as solution of a convolution Riccati
+equation. We further introduce the class of affine forward order flow intensity
+(AFI) models, which are structurally similar to AFV models, but driven by jump
+processes, and which include Hawkes-type models. We show that the cumulant
+generating function of an AFI model satisfies a generalized convolution Riccati
+equation and that a high-frequency limit of AFI models converges in
+distribution to the AFV model.
+",0,0,0,0,0,1
+1964,The cohomology of free loop spaces of homogeneous spaces,"  The free loops space $\Lambda X$ of a space $X$ has become an important
+object of study particularly in the case when $X$ is a manifold.The study of
+free loop spaces is motivated in particular by two main examples. The first is
+their relation to geometrically distinct periodic geodesics on a manifold,
+originally studied by Gromoll and Meyer in $1969$. More recently the study of
+string topology and in particular the Chas-Sullivan loop product has been an
+active area of research.
+A complete flag manifold is the quotient of a Lie group by its maximal torus
+and is one of the nicer examples of a homogeneous space. Both the cohomology
+and Chas-Sullivan product structure are understood for spaces $S^n$,
+$\mathbb{C}P^n$ and most simple Lie groups. Hence studying the topology of the
+free loops space on homogeneous space is a natural next step.
+In the thesis we compute the differentials in the integral Leray-Serre
+spectral sequence associated to the free loops space fibrations in the cases of
+$SU(n+1)/T^n$ and $Sp(n)/T^n$. Study in detail the structure of the third page
+of the spectral sequence in the case of $SU(n)$ and give the module structure
+of $H^*(\Lambda(SU(3)/T^2);\mathbb{Z})$ and
+$H^*(\Lambda(Sp(2)/T^2);\mathbb{Z})$.
+",0,0,1,0,0,0
+1965,Measurement of the muon-induced neutron seasonal modulation with LVD,"  Cosmic ray muons with the average energy of 280 GeV and neutrons produced by
+muons are detected with the Large Volume Detector at LNGS. We present an
+analysis of the seasonal variation of the neutron flux on the basis of the data
+obtained during 15 years. The measurement of the seasonal variation of the
+specific number of neutrons generated by muons allows to obtaine the variation
+magnitude of of the average energy of the muon flux at the depth of the LVD
+location. The source of the seasonal variation of the total neutron flux is a
+change of the intensity and the average energy of the muon flux.
+",0,1,0,0,0,0
+1966,Trail-Mediated Self-Interaction,"  A number of microorganisms leave persistent trails while moving along
+surfaces. For single-cell organisms, the trail-mediated self-interaction will
+influence its dynamics. It has been discussed recently [Kranz \textit{et al.}
+Phys. Rev. Lett. \textbf{117}, 8101 (2016)] that the self-interaction may
+localize the organism above a critical coupling $\chi_c$ to the trail. Here we
+will derive a generalized active particle model capturing the key features of
+the self-interaction and analyze its behavior for smaller couplings $\chi <
+\chi_c$. We find that fluctuations in propulsion speed shift the localization
+transition to stronger couplings.
+",0,0,0,0,1,0
+1967,A sub-super solution method for a class of nonlocal problems involving the p(x)-Laplacian operator and applications,"  In the present paper we study the existence of solutions for some nonlocal
+problems involving the p(x)-Laplacian operator. The approach is based on a new
+sub-supersolution method
+",0,0,1,0,0,0
+1968,Summability properties of Gabor expansions,"  We show that there exist complete and minimal systems of time-frequency
+shifts of Gaussians in $L^2(\mathbb{R})$ which are not strong Markushevich
+basis (do not admit the spectral synthesis). In particular, it implies that
+there is no linear summation method for general Gaussian Gabor expansions. On
+the other hand we prove that the spectral synthesis for such Gabor systems
+holds up to one dimensional defect.
+",0,0,1,0,0,0
+1969,A Las Vegas algorithm to solve the elliptic curve discrete logarithm problem,"  In this paper, we describe a new Las Vegas algorithm to solve the elliptic
+curve discrete logarithm problem. The algorithm depends on a property of the
+group of rational points of an elliptic curve and is thus not a generic
+algorithm. The algorithm that we describe has some similarities with the most
+powerful index-calculus algorithm for the discrete logarithm problem over a
+finite field.
+",1,0,1,0,0,0
+1970,Spectral sequences via examples,"  These are lecture notes for a short course about spectral sequences that was
+held at Málaga, October 18--20 (2016), during the ""Fifth Young Spanish
+Topologists Meeting"". The approach was to illustrate the basic notions via
+fully computed examples arising from Algebraic Topology and Group Theory.
+",0,0,1,0,0,0
+1971,Coherent scattering from semi-infinite non-Hermitian potentials,"  When two identical (coherent) beams are injected at a semi-infinite
+non-Hermitian medium from left and right, we show that both reflection
+$(r_L,r_R)$ and transmission $(t_L,t_R)$ amplitudes are non-reciprocal. In a
+parametric domain, there exists Spectral Singularity (SS) at a real energy
+$E=E_*$ and the determinant of the time-reversed two port S-matrix i.e.,
+$|\det(S)|=|t_L t_R-r_L r_R|$ vanishes sharply at $E=E_*$ displaying the
+phenomenon of Coherent Perfect Absorption (CPA). In the complimentary
+parametric domain, the potential becomes either left or right reflectionless at
+$E=E_z$. But we rule out the existence of Invisibility despite $r_R(E_i)=0$ and
+$t_R(E_i)=1$ in these new models. We present two simple exactly solvable models
+where the expressions for $E_*$, $E_z$, $E_i$ and the parametric conditions on
+the potential have been obtained in explicit and simple forms. Earlier, the
+novel phenomena of SS and CPA have been found to occur only in the scattering
+complex potentials which are spatially localized (vanish asymptotically) and
+having $t_L=t_R$.
+",0,1,0,0,0,0
+1972,A Higher Structure Identity Principle,"  We prove a Structure Identity Principle for theories defined on types of
+$h$-level 3 by defining a general notion of saturation for a large class of
+structures definable in the Univalent Foundations.
+",1,0,1,0,0,0
+1973,Two-Player Games for Efficient Non-Convex Constrained Optimization,"  In recent years, constrained optimization has become increasingly relevant to
+the machine learning community, with applications including Neyman-Pearson
+classification, robust optimization, and fair machine learning. A natural
+approach to constrained optimization is to optimize the Lagrangian, but this is
+not guaranteed to work in the non-convex setting, and, if using a first-order
+method, cannot cope with non-differentiable constraints (e.g. constraints on
+rates or proportions).
+The Lagrangian can be interpreted as a two-player game played between a
+player who seeks to optimize over the model parameters, and a player who wishes
+to maximize over the Lagrange multipliers. We propose a non-zero-sum variant of
+the Lagrangian formulation that can cope with non-differentiable--even
+discontinuous--constraints, which we call the ""proxy-Lagrangian"". The first
+player minimizes external regret in terms of easy-to-optimize ""proxy
+constraints"", while the second player enforces the original constraints by
+minimizing swap regret.
+For this new formulation, as for the Lagrangian in the non-convex setting,
+the result is a stochastic classifier. For both the proxy-Lagrangian and
+Lagrangian formulations, however, we prove that this classifier, instead of
+having unbounded size, can be taken to be a distribution over no more than m+1
+models (where m is the number of constraints). This is a significant
+improvement in practical terms.
+",0,0,0,1,0,0
+1974,On thin local sets of the Gaussian free field,"  We study how small a local set of the continuum Gaussian free field (GFF) in
+dimension $d$ has to be to ensure that this set is thin, which loosely speaking
+means that it captures no GFF mass on itself, in other words, that the field
+restricted to it is zero. We provide a criterion on the size of the local set
+for this to happen, and on the other hand, we show that this criterion is sharp
+by constructing small local sets that are not thin.
+",0,0,1,0,0,0
+1975,A Note on Prediction Markets,"  In a prediction market, individuals can sequentially place bets on the
+outcome of a future event. This leaves a trail of personal probabilities for
+the event, each being conditional on the current individual's private
+background knowledge and on the previously announced probabilities of other
+individuals, which give partial information about their private knowledge. By
+means of theory and examples, we revisit some results in this area. In
+particular, we consider the case of two individuals, who start with the same
+overall probability distribution but different private information, and then
+take turns in updating their probabilities. We note convergence of the
+announced probabilities to a limiting value, which may or may not be the same
+as that based on pooling their private information.
+",0,0,1,1,0,0
+1976,Dihedral angle prediction using generative adversarial networks,"  Several dihedral angles prediction methods were developed for protein
+structure prediction and their other applications. However, distribution of
+predicted angles would not be similar to that of real angles. To address this
+we employed generative adversarial networks (GAN). Generative adversarial
+networks are composed of two adversarially trained networks: a discriminator
+and a generator. A discriminator distinguishes samples from a dataset and
+generated samples while a generator generates realistic samples. Although the
+discriminator of GANs is trained to estimate density, GAN model is intractable.
+On the other hand, noise-contrastive estimation (NCE) was introduced to
+estimate a normalization constant of an unnormalized statistical model and thus
+the density function. In this thesis, we introduce noise-contrastive estimation
+generative adversarial networks (NCE-GAN) which enables explicit density
+estimation of a GAN model. And a new loss for the generator is proposed. We
+also propose residue-wise variants of auxiliary classifier GAN (AC-GAN) and
+Semi-supervised GAN to handle sequence information in a window. In our
+experiment, the conditional generative adversarial network (C-GAN), AC-GAN and
+Semi-supervised GAN were compared. And experiments done with improved
+conditions were invested. We identified a phenomenon of AC-GAN that
+distribution of its predicted angles is composed of unusual clusters. The
+distribution of the predicted angles of Semi-supervised GAN was most similar to
+the Ramachandran plot. We found that adding the output of the NCE as an
+additional input of the discriminator is helpful to stabilize the training of
+the GANs and to capture the detailed structures. Adding regression loss and
+using predicted angles by regression loss only model could improve the
+conditional generation performance of the C-GAN and AC-GAN.
+",0,0,0,1,1,0
+1977,A recurrence relation for the odd order moments of the Fabius function,"  A simple recurrence relation for the even order moments of the Fabius
+function is proven. Also, a very similar formula for the odd order moments in
+terms of the even order moments is proved. The matrices corresponding to these
+formulas (and their inverses) are multiplied so as to obtain a matrix that
+correspond to a recurrence relation for the odd order moments in terms of
+themselves. The theorem at the end gives a closed-form for the coefficients.
+",0,0,1,0,0,0
+1978,Performance of Range Separated Hybrids: Study within BECKE88 family and Semilocal Exchange Hole based Range Separated Hybrid,"  A long range corrected range separated hybrid functional is developed based
+on the density matrix expansion (DME) based semilocal exchange hole with
+Lee-Yang-Parr (LYP) correlation. An extensive study involving the proposed
+range separated hybrid for thermodynamic as well as properties related to the
+fractional occupation number is compared with different BECKE88 family
+semilocal, hybrid and range separated hybrids. It has been observed that using
+Kohn-Sham kinetic energy dependent exchange hole several properties related to
+the fractional occupation number can be improved without hindering the
+thermochemical accuracy. The newly constructed range separated hybrid
+accurately describe the hydrogen and non-hydrogen reaction barrier heights. The
+present range separated functional has been constructed using full semilocal
+meta-GGA type exchange hole having exact properties related to exchange hole
+therefore, it has a strong physical basis.
+",0,1,0,0,0,0
+1979,Manifold Mixup: Learning Better Representations by Interpolating Hidden States,"  Deep networks often perform well on the data distribution on which they are
+trained, yet give incorrect (and often very confident) answers when evaluated
+on points from off of the training distribution. This is exemplified by the
+adversarial examples phenomenon but can also be seen in terms of model
+generalization and domain shift. Ideally, a model would assign lower confidence
+to points unlike those from the training distribution. We propose a regularizer
+which addresses this issue by training with interpolated hidden states and
+encouraging the classifier to be less confident at these points. Because the
+hidden states are learned, this has an important effect of encouraging the
+hidden states for a class to be concentrated in such a way so that
+interpolations within the same class or between two different classes do not
+intersect with the real data points from other classes. This has a major
+advantage in that it avoids the underfitting which can result from
+interpolating in the input space. We prove that the exact condition for this
+problem of underfitting to be avoided by Manifold Mixup is that the
+dimensionality of the hidden states exceeds the number of classes, which is
+often the case in practice. Additionally, this concentration can be seen as
+making the features in earlier layers more discriminative. We show that despite
+requiring no significant additional computation, Manifold Mixup achieves large
+improvements over strong baselines in supervised learning, robustness to
+single-step adversarial attacks, semi-supervised learning, and Negative
+Log-Likelihood on held out samples.
+",0,0,0,1,0,0
+1980,Small Resolution Proofs for QBF using Dependency Treewidth,"  In spite of the close connection between the evaluation of quantified Boolean
+formulas (QBF) and propositional satisfiability (SAT), tools and techniques
+which exploit structural properties of SAT instances are known to fail for QBF.
+This is especially true for the structural parameter treewidth, which has
+allowed the design of successful algorithms for SAT but cannot be
+straightforwardly applied to QBF since it does not take into account the
+interdependencies between quantified variables.
+In this work we introduce and develop dependency treewidth, a new structural
+parameter based on treewidth which allows the efficient solution of QBF
+instances. Dependency treewidth pushes the frontiers of tractability for QBF by
+overcoming the limitations of previously introduced variants of treewidth for
+QBF. We augment our results by developing algorithms for computing the
+decompositions that are required to use the parameter.
+",1,0,0,0,0,0
+1981,Theoretical Analysis of Generalized Sagnac Effect in the Standard Synchronization,"  The Sagnac effect has been shown in inertial frames as well as rotating
+frames. We solve the problem of the generalized Sagnac effect in the standard
+synchronization of clocks. The speed of a light beam that traverses an optical
+fiber loop is measured with respect to the proper time of the light detector,
+and is shown to be other than the constant c, though it appears to be c if
+measured by the time standard-synchronized. The fiber loop, which can have an
+arbitrary shape, is described by an infinite number of straight lines such that
+it can be handled by the general framework of Mansouri and Sexl (MS). For a
+complete analysis of the Sagnac effect, the motion of the laboratory should be
+taken into account. The MS framework is introduced to deal with its motion
+relative to a preferred reference frame. Though the one-way speed of light is
+other than c, its two-way speed is shown to be c with respect to the proper
+time. The theoretical analysis of the generalized Sagnac effect corresponds to
+the experimental results, and shows the usefulness of the standard
+synchronization. The introduction of the standard synchrony can make
+mathematical manipulation easy and can allow us to deal with relative motions
+between inertial frames without information on their velocities relative to the
+preferred frame.
+",0,1,0,0,0,0
+1982,"Lattice thermal expansion and anisotropic displacements in urea, bromomalonic aldehyde, pentachloropyridine and naphthalene","  Anisotropic displacement parameters (ADPs) are commonly used in
+crystallography, chemistry and related fields to describe and quantify thermal
+motion of atoms. Within the very recent years, these ADPs have become
+predictable by lattice dynamics in combination with first-principles theory.
+Here, we study four very different molecular crystals, namely urea,
+bromomalonic aldehyde, pentachloropyridine, and naphthalene, by
+first-principles theory to assess the quality of ADPs calculated in the
+quasi-harmonic approximation. In addition, we predict both thermal expansion
+and thermal motion within the quasi-harmonic approximation and compare the
+predictions with experimental data. Very reliable ADPs are calculated within
+the quasi-harmonic approximation for all four cases up to at least 200 K, and
+they turn out to be in better agreement with experiment than the harmonic ones.
+In one particular case, ADPs can even reliably be predicted up to room
+temperature. Our results also hint at the importance of normal-mode
+anharmonicity in the calculation of ADPs.
+",0,1,0,0,0,0
+1983,Learning Heuristic Search via Imitation,"  Robotic motion planning problems are typically solved by constructing a
+search tree of valid maneuvers from a start to a goal configuration. Limited
+onboard computation and real-time planning constraints impose a limit on how
+large this search tree can grow. Heuristics play a crucial role in such
+situations by guiding the search towards potentially good directions and
+consequently minimizing search effort. Moreover, it must infer such directions
+in an efficient manner using only the information uncovered by the search up
+until that time. However, state of the art methods do not address the problem
+of computing a heuristic that explicitly minimizes search effort. In this
+paper, we do so by training a heuristic policy that maps the partial
+information from the search to decide which node of the search tree to expand.
+Unfortunately, naively training such policies leads to slow convergence and
+poor local minima. We present SaIL, an efficient algorithm that trains
+heuristic policies by imitating ""clairvoyant oracles"" - oracles that have full
+information about the world and demonstrate decisions that minimize search
+effort. We leverage the fact that such oracles can be efficiently computed
+using dynamic programming and derive performance guarantees for the learnt
+heuristic. We validate the approach on a spectrum of environments which show
+that SaIL consistently outperforms state of the art algorithms. Our approach
+paves the way forward for learning heuristics that demonstrate an anytime
+nature - finding feasible solutions quickly and incrementally refining it over
+time.
+",1,0,0,0,0,0
+1984,Hook removal operators on the odd Young graph,"  In this article we consider hook removal operators on odd partitions, i.e.,
+partitions labelling odd-degree irreducible characters of finite symmetric
+groups. In particular we complete the discussion, started by Isaacs, Navarro,
+Olsson and Tiep in 2016, concerning the commutativity of such operators.
+",0,0,1,0,0,0
+1985,Modal operators and toric ideals,"  In the present paper we consider modal propositional logic and look for the
+constraints that are imposed to the propositions of the special type $\Box a$
+by the structure of the relevant finite Kripke frame. We translate the usual
+language of modal propositional logic in terms of notions of commutative
+algebra, namely polynomial rings, ideals, and bases of ideals. We use
+extensively the perspective obtained in previous works in Algebraic Statistics.
+We prove that the constraints on $\Box a$ can be derived through a binomial
+ideal containing a toric ideal and we give sufficient conditions under which
+the toric ideal fully describes the constraints.
+",0,0,1,0,0,0
+1986,Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment,"  Neural network (NN) model chemistries (MCs) promise to facilitate the
+accurate exploration of chemical space and simulation of large reactive
+systems. One important path to improving these models is to add layers of
+physical detail, especially long-range forces. At short range, however, these
+models are data driven and data limited. Little is systematically known about
+how data should be sampled, and `test data' chosen randomly from some sampling
+techniques can provide poor information about generality. If the sampling
+method is narrow `test error' can appear encouragingly tiny while the model
+fails catastrophically elsewhere. In this manuscript we competitively evaluate
+two common sampling methods: molecular dynamics (MD), normal-mode sampling
+(NMS) and one uncommon alternative, Metadynamics (MetaMD), for preparing
+training geometries. We show that MD is an inefficient sampling method in the
+sense that additional samples do not improve generality. We also show MetaMD is
+easily implemented in any NNMC software package with cost that scales linearly
+with the number of atoms in a sample molecule. MetaMD is a black-box way to
+ensure samples always reach out to new regions of chemical space, while
+remaining relevant to chemistry near $k_bT$. It is one cheap tool to address
+the issue of generalization.
+",0,1,0,1,0,0
+1987,ServeNet: A Deep Neural Network for Web Service Classification,"  Automated service classification plays a crucial role in service management
+such as service discovery, selection, and composition. In recent years, machine
+learning techniques have been used for service classification. However, they
+can only predict around 10 to 20 service categories due to the quality of
+feature engineering and the imbalance problem of service dataset. In this
+paper, we present a deep neural network ServeNet with a novel dataset splitting
+algorithm to deal with these issues. ServeNet can automatically abstract
+low-level representation to high-level features, and then predict service
+classification based on the service datasets produced by the proposed splitting
+algorithm. To demonstrate the effectiveness of our approach, we conducted a
+comprehensive experimental study on 10,000 real-world services in 50
+categories. The result shows that ServeNet can achieve higher accuracy than
+other machine learning methods.
+",0,0,0,1,0,0
+1988,Photoinduced Hund excitons in the breakdown of a two-orbital Mott insulator,"  We study the photoinduced breakdown of a two-orbital Mott insulator and
+resulting metallic state. Using time-dependent density matrix renormalization
+group, we scrutinize the real-time dynamics of the half-filled two-orbital
+Hubbard model interacting with a resonant radiation field pulse. The breakdown,
+caused by production of doublon-holon pairs, is enhanced by Hund's exchange,
+which dynamically activates large orbital fluctuations. The melting of the Mott
+insulator is accompanied by a high to low spin transition with a concomitant
+reduction of antiferromagnetic spin fluctuations. Most notably, the overall
+time response is driven by the photogeneration of excitons with orbital
+character that are stabilized by Hund's coupling. These unconventional ""Hund
+excitons"" correspond to bound spin-singlet orbital-triplet doublon-holon pairs.
+We study exciton properties such as bandwidth, binding potential, and size
+within a semiclassical approach. The photometallic state results from a
+coexistence of Hund excitons and doublon-holon plasma.
+",0,1,0,0,0,0
+1989,Using Synthetic Data to Train Neural Networks is Model-Based Reasoning,"  We draw a formal connection between using synthetic training data to optimize
+neural network parameters and approximate, Bayesian, model-based reasoning. In
+particular, training a neural network using synthetic data can be viewed as
+learning a proposal distribution generator for approximate inference in the
+synthetic-data generative model. We demonstrate this connection in a
+recognition task where we develop a novel Captcha-breaking architecture and
+train it using synthetic data, demonstrating both state-of-the-art performance
+and a way of computing task-specific posterior uncertainty. Using a neural
+network trained this way, we also demonstrate successful breaking of real-world
+Captchas currently used by Facebook and Wikipedia. Reasoning from these
+empirical results and drawing connections with Bayesian modeling, we discuss
+the robustness of synthetic data results and suggest important considerations
+for ensuring good neural network generalization when training with synthetic
+data.
+",1,0,0,1,0,0
+1990,Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory,"  Learning and memory are intertwined in our brain and their relationship is at
+the core of several recent neural network models. In particular, the
+Attention-Gated MEmory Tagging model (AuGMEnT) is a reinforcement learning
+network with an emphasis on biological plausibility of memory dynamics and
+learning. We find that the AuGMEnT network does not solve some hierarchical
+tasks, where higher-level stimuli have to be maintained over a long time, while
+lower-level stimuli need to be remembered and forgotten over a shorter
+timescale. To overcome this limitation, we introduce hybrid AuGMEnT, with leaky
+or short-timescale and non-leaky or long-timescale units in memory, that allow
+to exchange lower-level information while maintaining higher-level one, thus
+solving both hierarchical and distractor tasks.
+",1,0,0,1,0,0
+1991,Dynamical structure of entangled polymers simulated under shear flow,"  The non-linear response of entangled polymers to shear flow is complicated.
+Its current understanding is framed mainly as a rheological description in
+terms of the complex viscosity. However, the full picture requires an
+assessment of the dynamical structure of individual polymer chains which give
+rise to the macroscopic observables. Here we shed new light on this problem,
+using a computer simulation based on a blob model, extended to describe shear
+flow in polymer melts and semi-dilute solutions. We examine the diffusion and
+the intermediate scattering spectra during a steady shear flow. The relaxation
+dynamics are found to speed up along the flow direction, but slow down along
+the shear gradient direction. The third axis, vorticity, shows a slowdown at
+the short scale of a tube, but reaches a net speedup at the large scale of the
+chain radius of gyration.
+",0,1,0,0,0,0
+1992,Coherent modulation up to 100 GBd 16QAM using silicon-organic hybrid (SOH) devices,"  We demonstrate the generation of higher-order modulation formats using
+silicon-based inphase/quadrature (IQ) modulators at symbol rates of up to 100
+GBd. Our devices exploit the advantages of silicon-organic hybrid (SOH)
+integration, which combines silicon-on-insulator waveguides with highly
+efficient organic electro-optic (EO) cladding materials to enable small drive
+voltages and sub-millimeter device lengths. In our experiments, we use an SOH
+IQ modulator with a {\pi}-voltage of 1.6 V to generate 100 GBd 16QAM signals.
+This is the first time that the 100 GBd mark is reached with an IQ modulator
+realized on a semiconductor substrate, leading to a single-polarization line
+rate of 400 Gbit/s. The peak-to-peak drive voltages amount to 1.5 Vpp,
+corresponding to an electrical energy dissipation in the modulator of only 25
+fJ/bit.
+",0,1,0,0,0,0
+1993,Current induced magnetization switching in PtCoCr structures with enhanced perpendicular magnetic anisotropy and spin-orbit torques,"  Magnetic trilayers having large perpendicular magnetic anisotropy (PMA) and
+high spin-orbit torques (SOTs) efficiency are the key to fabricate nonvolatile
+magnetic memory and logic devices. In this work, PMA and SOTs are
+systematically studied in Pt/Co/Cr stacks as a function of Cr thickness. An
+enhanced perpendicular anisotropy field around 10189 Oe is obtained and is
+related to the interface between Co and Cr layers. In addition, an effective
+spin Hall angle up to 0.19 is observed due to the improved antidamping-like
+torque by employing dissimilar metals Pt and Cr with opposite signs of spin
+Hall angles on opposite sides of Co layer. Finally, we observed a nearly linear
+dependence between spin Hall angle and longitudinal resistivity from their
+temperature dependent properties, suggesting that the spin Hall effect may
+arise from extrinsic skew scattering mechanism. Our results indicate that 3d
+transition metal Cr with a large negative spin Hall angle could be used to
+engineer the interfaces of trilayers to enhance PMA and SOTs.
+",0,1,0,0,0,0
+1994,Grain boundary diffusion in severely deformed Al-based alloy,"  Grain boundary diffusion in severely deformed Al-based AA5024 alloy is
+investigated. Different states are prepared by combination of equal channel
+angular processing and heat treatments, with the radioisotope $^{57}$Co being
+employed as a sensitive probe of a given grain boundary state. Its diffusion
+rates near room temperature (320~K) are utilized to quantify the effects of
+severe plastic deformation and a presumed formation of a previously reported
+deformation-modified state of grain boundaries, solute segregation at the
+interfaces, increased dislocation content after deformation and of the
+precipitation behavior on the transport phenomena along grain boundaries. The
+dominant effect of nano-sized Al$_3$Sc-based precipitates is evaluated using
+density functional theory and the Eshelby model for the determination of
+elastic stresses around the precipitates.
+",0,1,0,0,0,0
+1995,Quantum Black Holes and Atomic Nuclei are Hollow,"  The quantum Schrodinger-Newton equation is solved for a self-gravitating Bose
+gas at zero temperature. It is derived that the density is non-uniform and a
+central hollow cavity exists. The radial distribution of the particle momentum
+is uniform. It is shown that a quantum black hole can be formed only above a
+certain critical mass. The temperature effect is accounted for via the
+Schrodinger-Poisson-Boltzmann equation, where low and high temperature
+solutions are obtained. The theoretical analysis is extended to a strong
+interacting gas via the Schrodinger-Yukawa equation, showing that the atomic
+nuclei are also hollow. Hollow self-gravitating Fermi gases are described by
+the Thomas-Fermi equation.
+",0,1,0,0,0,0
+1996,Learning Non-Discriminatory Predictors,"  We consider learning a predictor which is non-discriminatory with respect to
+a ""protected attribute"" according to the notion of ""equalized odds"" proposed by
+Hardt et al. [2016]. We study the problem of learning such a non-discriminatory
+predictor from a finite training set, both statistically and computationally.
+We show that a post-hoc correction approach, as suggested by Hardt et al, can
+be highly suboptimal, present a nearly-optimal statistical procedure, argue
+that the associated computational problem is intractable, and suggest a second
+moment relaxation of the non-discrimination definition for which learning is
+tractable.
+",1,0,0,0,0,0
+1997,Time-Resolved High Spectral Resolution Observation of 2MASSW J0746425+200032AB,"  Many brown dwarfs exhibit photometric variability at levels from tenths to
+tens of percents. The photometric variability is related to magnetic activity
+or patchy cloud coverage, characteristic of brown dwarfs near the L-T
+transition. Time-resolved spectral monitoring of brown dwarfs provides
+diagnostics of cloud distribution and condensate properties. However, current
+time-resolved spectral studies of brown dwarfs are limited to low spectral
+resolution (R$\sim$100) with the exception of the study of Luhman 16 AB at
+resolution of 100,000 using the VLT$+$CRIRES. This work yielded the first map
+of brown dwarf surface inhomogeneity, highlighting the importance and unique
+contribution of high spectral resolution observations. Here, we report on the
+time-resolved high spectral resolution observations of a nearby brown dwarf
+binary, 2MASSW J0746425+200032AB. We find no coherent spectral variability that
+is modulated with rotation. Based on simulations we conclude that the coverage
+of a single spot on 2MASSW J0746425+200032AB is smaller than 1\% or 6.25\% if
+spot contrast is 50\% or 80\% of its surrounding flux, respectively. Future
+high spectral resolution observations aided by adaptive optics systems can put
+tighter constraints on the spectral variability of 2MASSW J0746425+200032AB and
+other nearby brown dwarfs.
+",0,1,0,0,0,0
+1998,"Pinned, locked, pushed, and pulled traveling waves in structured environments","  Traveling fronts describe the transition between two alternative states in a
+great number of physical and biological systems. Examples include the spread of
+beneficial mutations, chemical reactions, and the invasions by foreign species.
+In homogeneous environments, the alternative states are separated by a smooth
+front moving at a constant velocity. This simple picture can break down in
+structured environments such as tissues, patchy landscapes, and microfluidic
+devices. Habitat fragmentation can pin the front at a particular location or
+lock invasion velocities into specific values. Locked velocities are not
+sensitive to moderate changes in dispersal or growth and are determined by the
+spatial and temporal periodicity of the environment. The synchronization with
+the environment results in discontinuous fronts that propagate as periodic
+pulses. We characterize the transition from continuous to locked invasions and
+show that it is controlled by positive density-dependence in dispersal or
+growth. We also demonstrate that velocity locking is robust to demographic and
+environmental fluctuations and examine stochastic dynamics and evolution in
+locked invasions.
+",0,0,0,0,1,0
+1999,Two-pixel polarimetric camera by compressive sensing,"  We propose an original concept of compressive sensing (CS) polarimetric
+imaging based on a digital micro-mirror (DMD) array and two single-pixel
+detectors. The polarimetric sensitivity of the proposed setup is due to an
+experimental imperfection of reflecting mirrors which is exploited here to form
+an original reconstruction problem, including a CS problem and a source
+separation task. We show that a two-step approach tackling each problem
+successively is outperformed by a dedicated combined reconstruction method,
+which is explicited in this article and preferably implemented through a
+reweighted FISTA algorithm. The combined reconstruction approach is then
+further improved by including physical constraints specific to the polarimetric
+imaging context considered, which are implemented in an original constrained
+GFB algorithm. Numerical simulations demonstrate the efficiency of the 2-pixel
+CS polarimetric imaging setup to retrieve polarimetric contrast data with
+significant compression rate and good reconstruction quality. The influence of
+experimental imperfections of the DMD are also analyzed through numerical
+simulations, and 2D polarimetric imaging reconstruction results are finally
+presented.
+",1,0,0,0,0,0
+2000,A Stochastic Programming Approach for Electric Vehicle Charging Network Design,"  Advantages of electric vehicles (EV) include reduction of greenhouse gas and
+other emissions, energy security, and fuel economy. The societal benefits of
+large-scale adoption of EVs cannot be realized without adequate deployment of
+publicly accessible charging stations. We propose a two-stage stochastic
+programming model to determine the optimal network of charging stations for a
+community considering uncertainties in arrival and dwell time of vehicles,
+battery state of charge of arriving vehicles, walkable range and charging
+preferences of drivers, demand during weekdays and weekends, and rate of
+adoption of EVs within a community. We conducted studies using sample average
+approximation (SAA) method which asymptotically converges to an optimal
+solution for a two-stage stochastic problem, however it is computationally
+expensive for large-scale instances. Therefore, we developed a heuristic to
+produce near to optimal solutions quickly for our data instances. We conducted
+computational experiments using various publicly available data sources, and
+benefits of the solutions are evaluated both quantitatively and qualitatively
+for a given community.
+",1,0,1,0,0,0
+2001,Autonomy in the interactive music system VIVO,"  Interactive Music Systems (IMS) have introduced a new world of music-making
+modalities. But can we really say that they create music, as in true autonomous
+creation? Here we discuss Video Interactive VST Orchestra (VIVO), an IMS that
+considers extra-musical information by adopting a simple salience based model
+of user-system interaction when simulating intentionality in automatic music
+generation. Key features of the theoretical framework, a brief overview of
+pilot research, and a case study providing validation of the model are
+presented. This research demonstrates that a meaningful user/system interplay
+is established in what we define as reflexive multidominance.
+",1,0,0,0,0,0
+2002,Information and estimation in Fokker-Planck channels,"  We study the relationship between information- and estimation-theoretic
+quantities in time-evolving systems. We focus on the Fokker-Planck channel
+defined by a general stochastic differential equation, and show that the time
+derivatives of entropy, KL divergence, and mutual information are characterized
+by estimation-theoretic quantities involving an appropriate generalization of
+the Fisher information. Our results vastly extend De Bruijn's identity and the
+classical I-MMSE relation.
+",1,0,1,1,0,0
+2003,"The role of industry, occupation, and location specific knowledge in the survival of new firms","  How do regions acquire the knowledge they need to diversify their economic
+activities? How does the migration of workers among firms and industries
+contribute to the diffusion of that knowledge? Here we measure the industry,
+occupation, and location-specific knowledge carried by workers from one
+establishment to the next using a dataset summarizing the individual work
+history for an entire country. We study pioneer firms--firms operating in an
+industry that was not present in a region--because the success of pioneers is
+the basic unit of regional economic diversification. We find that the growth
+and survival of pioneers increase significantly when their first hires are
+workers with experience in a related industry, and with work experience in the
+same location, but not with past experience in a related occupation. We compare
+these results with new firms that are not pioneers and find that
+industry-specific knowledge is significantly more important for pioneer than
+non-pioneer firms. To address endogeneity we use Bartik instruments, which
+leverage national fluctuations in the demand for an activity as shocks for
+local labor supply. The instrumental variable estimates support the finding
+that industry-related knowledge is a predictor of the survival and growth of
+pioneer firms. These findings expand our understanding of the micro-mechanisms
+underlying regional economic diversification events.
+",0,0,0,0,0,1
+2004,Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning,"  We analyze the problem of learning a single user's preferences in an active
+learning setting, sequentially and adaptively querying the user over a finite
+time horizon. Learning is conducted via choice-based queries, where the user
+selects her preferred option among a small subset of offered alternatives.
+These queries have been shown to be a robust and efficient way to learn an
+individual's preferences. We take a parametric approach and model the user's
+preferences through a linear classifier, using a Bayesian prior to encode our
+current knowledge of this classifier. The rate at which we learn depends on the
+alternatives offered at every time epoch. Under certain noise assumptions, we
+show that the Bayes-optimal policy for maximally reducing entropy of the
+posterior distribution of this linear classifier is a greedy policy, and that
+this policy achieves a linear lower bound when alternatives can be constructed
+from the continuum. Further, we analyze a different metric called
+misclassification error, proving that the performance of the optimal policy
+that minimizes misclassification error is bounded below by a linear function of
+differential entropy. Lastly, we numerically compare the greedy entropy
+reduction policy with a knowledge gradient policy under a number of scenarios,
+examining their performance under both differential entropy and
+misclassification error.
+",1,0,0,1,0,0
+2005,Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes,"  It is widely observed that deep learning models with learned parameters
+generalize well, even with much more model parameters than the number of
+training samples. We systematically investigate the underlying reasons why deep
+neural networks often generalize well, and reveal the difference between the
+minima (with the same training error) that generalize well and those they
+don't. We show that it is the characteristics the landscape of the loss
+function that explains the good generalization capability. For the landscape of
+loss function for deep networks, the volume of basin of attraction of good
+minima dominates over that of poor minima, which guarantees optimization
+methods with random initialization to converge to good minima. We theoretically
+justify our findings through analyzing 2-layer neural networks; and show that
+the low-complexity solutions have a small norm of Hessian matrix with respect
+to model parameters. For deeper networks, extensive numerical evidence helps to
+support our arguments.
+",1,0,0,1,0,0
+2006,Benchmarking Decoupled Neural Interfaces with Synthetic Gradients,"  Artifical Neural Networks are a particular class of learning systems modeled
+after biological neural functions with an interesting penchant for Hebbian
+learning, that is ""neurons that wire together, fire together"". However, unlike
+their natural counterparts, artificial neural networks have a close and
+stringent coupling between the modules of neurons in the network. This coupling
+or locking imposes upon the network a strict and inflexible structure that
+prevent layers in the network from updating their weights until a full
+feed-forward and backward pass has occurred. Such a constraint though may have
+sufficed for a while, is now no longer feasible in the era of very-large-scale
+machine learning, coupled with the increased desire for parallelization of the
+learning process across multiple computing infrastructures. To solve this
+problem, synthetic gradients (SG) with decoupled neural interfaces (DNI) are
+introduced as a viable alternative to the backpropagation algorithm. This paper
+performs a speed benchmark to compare the speed and accuracy capabilities of
+SG-DNI as opposed to a standard neural interface using multilayer perceptron
+MLP. SG-DNI shows good promise, in that it not only captures the learning
+problem, it is also over 3-fold faster due to it asynchronous learning
+capabilities.
+",1,0,0,1,0,0
+2007,Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers,"  Macquarie University's contribution to the BioASQ challenge (Task 5b Phase B)
+focused on the use of query-based extractive summarisation techniques for the
+generation of the ideal answers. Four runs were submitted, with approaches
+ranging from a trivial system that selected the first $n$ snippets, to the use
+of deep learning approaches under a regression framework. Our experiments and
+the ROUGE results of the five test batches of BioASQ indicate surprisingly good
+results for the trivial approach. Overall, most of our runs on the first three
+test batches achieved the best ROUGE-SU4 results in the challenge.
+",1,0,0,0,0,0
+2008,Network Topology Modulation for Energy and Data Transmission in Internet of Magneto-Inductive Things,"  Internet-of-things (IoT) architectures connecting a massive number of
+heterogeneous devices need energy efficient, low hardware complexity, low cost,
+simple and secure mechanisms to realize communication among devices. One of the
+emerging schemes is to realize simultaneous wireless information and power
+transfer (SWIPT) in an energy harvesting network. Radio frequency (RF)
+solutions require special hardware and modulation methods for RF to direct
+current (DC) conversion and optimized operation to achieve SWIPT which are
+currently in an immature phase. On the other hand, magneto-inductive (MI)
+communication transceivers are intrinsically energy harvesting with potential
+for SWIPT in an efficient manner. In this article, novel modulation and
+demodulation mechanisms are presented in a combined framework with
+multiple-access channel (MAC) communication and wireless power transmission.
+The network topology of power transmitting active coils in a transceiver
+composed of a grid of coils is changed as a novel method to transmit
+information. Practical demodulation schemes are formulated and numerically
+simulated for two-user MAC topology of small size coils. The transceivers are
+suitable to attach to everyday objects to realize reliable local area network
+(LAN) communication performances with tens of meters communication ranges. The
+designed scheme is promising for future IoT applications requiring SWIPT with
+energy efficient, low cost, low power and low hardware complexity solutions.
+",1,0,1,0,0,0
+2009,Core structure of two-dimensional Fermi gas vortices in the BEC-BCS crossover region,"  We report $T=0$ diffusion Monte Carlo results for the ground-state and vortex
+excitation of unpolarized spin-1/2 fermions in a two-dimensional disk. We
+investigate how vortex core structure properties behave over the BEC-BCS
+crossover. We calculate the vortex excitation energy, density profiles, and
+vortex core properties related to the current. We find a density suppression at
+the vortex core on the BCS side of the crossover, and a depleted core on the
+BEC limit. Size-effect dependencies in the disk geometry were carefully
+studied.
+",0,1,0,0,0,0
+2010,One can hear the Euler characteristic of a simplicial complex,"  We prove that that the number p of positive eigenvalues of the connection
+Laplacian L of a finite abstract simplicial complex G matches the number b of
+even dimensional simplices in G and that the number n of negative eigenvalues
+matches the number f of odd-dimensional simplices in G. The Euler
+characteristic X(G) of G therefore can be spectrally described as X(G)=p-n.
+This is in contrast to the more classical Hodge Laplacian H which acts on the
+same Hilbert space, where X(G) is not yet known to be accessible from the
+spectrum of H. Given an ordering of G coming from a build-up as a CW complex,
+every simplex x in G is now associated to a unique eigenvector of L and the
+correspondence is computable. The Euler characteristic is now not only the
+potential energy summing over all g(x,y) with g=L^{-1} but also agrees with a
+logarithmic energy tr(log(i L)) 2/(i pi) of the spectrum of L. We also give
+here examples of L-isospectral but non-isomorphic abstract finite simplicial
+complexes. One example shows that we can not hear the cohomology of the
+complex.
+",1,0,1,0,0,0
+2011,A new complete Calabi-Yau metric on $\mathbb{C}^3$,"  Motivated by the study of collapsing Calabi-Yau threefolds with a Lefschetz
+K3 fibration, we construct a complete Calabi-Yau metric on $\mathbb{C}^3$ with
+maximal volume growth, which in the appropriate scale is expected to model the
+collapsing metric near the nodal point. This new Calabi-Yau metric has singular
+tangent cone at infinity, and its Riemannian geometry has certain non-standard
+features near the singularity of the tangent cone $\mathbb{C}^2/\mathbb{Z}_2
+\times \mathbb{C}$, which are more typical of adiabatic limit problems. The
+proof uses an existence result in H-J. Hein's PhD thesis to perturb an
+asymptotic approximate solution into an actual solution, and the main
+difficulty lies in correcting the slowly decaying error terms.
+",0,0,1,0,0,0
+2012,Weighted density fields as improved probes of modified gravity models,"  When it comes to searches for extensions to general relativity, large efforts
+are being dedicated to accurate predictions for the power spectrum of density
+perturbations. While this observable is known to be sensitive to the
+gravitational theory, its efficiency as a diagnostic for gravity is
+significantly reduced when Solar System constraints are strictly adhered to. We
+show that this problem can be overcome by studying weigthed density fields. We
+propose a transformation of the density field for which the impact of modified
+gravity on the power spectrum can be increased by more than a factor of three.
+The signal is not only amplified, but the modified gravity features are shifted
+to larger scales which are less affected by baryonic physics. Furthermore, the
+overall signal-to-noise increases, which in principle makes identifying
+signatures of modified gravity with future galaxy surveys more feasible. While
+our analysis is focused on modified gravity, the technique can be applied to
+other problems in cosmology, such as the detection of neutrinos, the effects of
+baryons or baryon acoustic oscillations.
+",0,1,0,0,0,0
+2013,Nonlinear control for an uncertain electromagnetic actuator,"  This paper presents the design of a nonlinear control law for a typical
+electromagnetic actuator system. Electromagnetic actuators are widely
+implemented in industrial applications, and especially as linear positioning
+system. In this work, we aim at taking into account a magnetic phenomenon that
+is usually neglected: flux fringing. This issue is addressed with an uncertain
+modeling approach. The proposed control law consists of two steps, a
+backstepping control regulates the mechanical part and a sliding mode approach
+controls the coil current and the magnetic force implicitly. An illustrative
+example shows the effectiveness of the presented approach.
+",1,0,0,0,0,0
+2014,Adaptation and Robust Learning of Probabilistic Movement Primitives,"  Probabilistic representations of movement primitives open important new
+possibilities for machine learning in robotics. These representations are able
+to capture the variability of the demonstrations from a teacher as a
+probability distribution over trajectories, providing a sensible region of
+exploration and the ability to adapt to changes in the robot environment.
+However, to be able to capture variability and correlations between different
+joints, a probabilistic movement primitive requires the estimation of a larger
+number of parameters compared to their deterministic counterparts, that focus
+on modeling only the mean behavior. In this paper, we make use of prior
+distributions over the parameters of a probabilistic movement primitive to make
+robust estimates of the parameters with few training instances. In addition, we
+introduce general purpose operators to adapt movement primitives in joint and
+task space. The proposed training method and adaptation operators are tested in
+a coffee preparation and in robot table tennis task. In the coffee preparation
+task we evaluate the generalization performance to changes in the location of
+the coffee grinder and brewing chamber in a target area, achieving the desired
+behavior after only two demonstrations. In the table tennis task we evaluate
+the hit and return rates, outperforming previous approaches while using fewer
+task specific heuristics.
+",1,0,0,1,0,0
+2015,Transverse spinning of light with globally unique handedness,"  Access to the transverse spin of light has unlocked new regimes in
+topological photonics and optomechanics. To achieve the transverse spin of
+nonzero longitudinal fields, various platforms that derive transversely
+confined waves based on focusing, interference, or evanescent waves have been
+suggested. Nonetheless, because of the transverse confinement inherently
+accompanying sign reversal of the field derivative, the resulting transverse
+spin handedness experiences spatial inversion, which leads to a mismatch
+between the densities of the wavefunction and its spin component and hinders
+the global observation of the transverse spin. Here, we reveal a globally pure
+transverse spin in which the wavefunction density signifies the spin
+distribution, by employing inverse molding of the eigenmode in the spin basis.
+Starting from the target spin profile, we analytically obtain the potential
+landscape and then show that the elliptic-hyperbolic transition around the
+epsilon-near-zero permittivity allows for the global conservation of transverse
+spin handedness across the topological interface between anisotropic
+metamaterials. Extending to the non-Hermitian regime, we also develop
+annihilated transverse spin modes to cover the entire Poincare sphere of the
+meridional plane. Our results enable the complete transfer of optical energy to
+transverse spinning motions and realize the classical analogy of 3-dimensional
+quantum spin states.
+",0,1,0,0,0,0
+2016,A general class of quasi-independence tests for left-truncated right-censored data,"  In survival studies, classical inferences for left-truncated data require
+quasi-independence, a property that the joint density of truncation time and
+failure time is factorizable into their marginal densities in the observable
+region. The quasi-independence hypothesis is testable; many authors have
+developed tests for left-truncated data with or without right-censoring. In
+this paper, we propose a class of test statistics for testing the
+quasi-independence which unifies the existing methods and generates new useful
+statistics such as conditional Spearman's rank correlation coefficient.
+Asymptotic normality of the proposed class of statistics is given. We show that
+a new set of tests can be powerful under certain alternatives by theoretical
+and empirical power comparison.
+",0,0,0,1,0,0
+2017,EPIC 220204960: A Quadruple Star System Containing Two Strongly Interacting Eclipsing Binaries,"  We present a strongly interacting quadruple system associated with the K2
+target EPIC 220204960. The K2 target itself is a Kp = 12.7 magnitude star at
+Teff ~ 6100 K which we designate as ""B-N"" (blue northerly image). The host of
+the quadruple system, however, is a Kp = 17 magnitude star with a composite
+M-star spectrum, which we designate as ""R-S"" (red southerly image). With a 3.2""
+separation and similar radial velocities and photometric distances, 'B-N' is
+likely physically associated with 'R-S', making this a quintuple system, but
+that is incidental to our main claim of a strongly interacting quadruple system
+in 'R-S'. The two binaries in 'R-S' have orbital periods of 13.27 d and 14.41
+d, respectively, and each has an inclination angle of >89 degrees. From our
+analysis of radial velocity measurements, and of the photometric lightcurve, we
+conclude that all four stars are very similar with masses close to 0.4 Msun.
+Both of the binaries exhibit significant ETVs where those of the primary and
+secondary eclipses 'diverge' by 0.05 days over the course of the 80-day
+observations. Via a systematic set of numerical simulations of quadruple
+systems consisting of two interacting binaries, we conclude that the outer
+orbital period is very likely to be between 300 and 500 days. If sufficient
+time is devoted to RV studies of this faint target, the outer orbit should be
+measurable within a year.
+",0,1,0,0,0,0
+2018,On the higher Cheeger problem,"  We develop the notion of higher Cheeger constants for a measurable set
+$\Omega \subset \mathbb{R}^N$. By the $k$-th Cheeger constant we mean the value
+\[h_k(\Omega) = \inf \max \{h_1(E_1), \dots, h_1(E_k)\},\] where the infimum is
+taken over all $k$-tuples of mutually disjoint subsets of $\Omega$, and
+$h_1(E_i)$ is the classical Cheeger constant of $E_i$. We prove the existence
+of minimizers satisfying additional ""adjustment"" conditions and study their
+properties. A relation between $h_k(\Omega)$ and spectral minimal
+$k$-partitions of $\Omega$ associated with the first eigenvalues of the
+$p$-Laplacian under homogeneous Dirichlet boundary conditions is stated. The
+results are applied to determine the second Cheeger constant of some planar
+domains.
+",0,0,1,0,0,0
+2019,Petri Nets and Machines of Things That Flow,"  Petri nets are an established graphical formalism for modeling and analyzing
+the behavior of systems. An important consideration of the value of Petri nets
+is their use in describing both the syntax and semantics of modeling
+formalisms. Describing a modeling notation in terms of a formal technique such
+as Petri nets provides a way to minimize ambiguity. Accordingly, it is
+imperative to develop a deep and diverse understanding of Petri nets. This
+paper is directed toward a new, but preliminary, exploration of the semantics
+of such an important tool. Specifically, the concern in this paper is with the
+semantics of Petri nets interpreted in a modeling language based on the notion
+of machines of things that flow. The semantics of several Petri net diagrams
+are analyzed in terms of flow of things. The results point to the viability of
+the approach for exploring the underlying assumptions of Petri nets.
+",1,0,0,0,0,0
+2020,Fundamental Conditions for Low-CP-Rank Tensor Completion,"  We consider the problem of low canonical polyadic (CP) rank tensor
+completion. A completion is a tensor whose entries agree with the observed
+entries and its rank matches the given CP rank. We analyze the manifold
+structure corresponding to the tensors with the given rank and define a set of
+polynomials based on the sampling pattern and CP decomposition. Then, we show
+that finite completability of the sampled tensor is equivalent to having a
+certain number of algebraically independent polynomials among the defined
+polynomials. Our proposed approach results in characterizing the maximum number
+of algebraically independent polynomials in terms of a simple geometric
+structure of the sampling pattern, and therefore we obtain the deterministic
+necessary and sufficient condition on the sampling pattern for finite
+completability of the sampled tensor. Moreover, assuming that the entries of
+the tensor are sampled independently with probability $p$ and using the
+mentioned deterministic analysis, we propose a combinatorial method to derive a
+lower bound on the sampling probability $p$, or equivalently, the number of
+sampled entries that guarantees finite completability with high probability. We
+also show that the existing result for the matrix completion problem can be
+used to obtain a loose lower bound on the sampling probability $p$. In
+addition, we obtain deterministic and probabilistic conditions for unique
+completability. It is seen that the number of samples required for finite or
+unique completability obtained by the proposed analysis on the CP manifold is
+orders-of-magnitude lower than that is obtained by the existing analysis on the
+Grassmannian manifold.
+",1,0,1,1,0,0
+2021,Multiplex Network Regression: How do relations drive interactions?,"  We introduce a statistical method to investigate the impact of dyadic
+relations on complex networks generated from repeated interactions. It is based
+on generalised hypergeometric ensembles, a class of statistical network
+ensembles developed recently. We represent different types of known relations
+between system elements by weighted graphs, separated in the different layers
+of a multiplex network. With our method we can regress the influence of each
+relational layer, the independent variables, on the interaction counts, the
+dependent variables. Moreover, we can test the statistical significance of the
+relations as explanatory variables for the observed interactions. To
+demonstrate the power of our approach and its broad applicability, we will
+present examples based on synthetic and empirical data.
+",1,1,0,1,0,0
+2022,Hall-Littlewood-PushTASEP and its KPZ limit,"  We study a new model of interactive particle systems which we call the
+randomly activated cascading exclusion process (RACEP). Particles wake up
+according to exponential clocks and then take a geometric number of steps. If
+another particle is encountered during these steps, the first particle goes to
+sleep at that location and the second is activated and proceeds accordingly. We
+consider a totally asymmetric version of this model which we refer as
+Hall-Littlewood-PushTASEP (HL-PushTASEP) on $\mathbb{Z}_{\geq 0}$ lattice where
+particles only move right and where initially particles are distributed
+according to Bernoulli product measure on $\mathbb{Z}_{\geq 0}$. We prove
+KPZ-class limit theorems for the height function fluctuations. Under a
+particular weak scaling, we also prove convergence to the solution of the KPZ
+equation.
+",0,0,1,1,0,0
+2023,Pricing for Online Resource Allocation: Intervals and Paths,"  We present pricing mechanisms for several online resource allocation problems
+which obtain tight or nearly tight approximations to social welfare. In our
+settings, buyers arrive online and purchase bundles of items; buyers' values
+for the bundles are drawn from known distributions. This problem is closely
+related to the so-called prophet-inequality of Krengel and Sucheston and its
+extensions in recent literature. Motivated by applications to cloud economics,
+we consider two kinds of buyer preferences. In the first, items correspond to
+different units of time at which a resource is available; the items are
+arranged in a total order and buyers desire intervals of items. The second
+corresponds to bandwidth allocation over a tree network; the items are edges in
+the network and buyers desire paths.
+Because buyers' preferences have complementarities in the settings we
+consider, recent constant-factor approximations via item prices do not apply,
+and indeed strong negative results are known. We develop static, anonymous
+bundle pricing mechanisms.
+For the interval preferences setting, we show that static, anonymous bundle
+pricings achieve a sublogarithmic competitive ratio, which is optimal (within
+constant factors) over the class of all online allocation algorithms, truthful
+or not. For the path preferences setting, we obtain a nearly-tight logarithmic
+competitive ratio. Both of these results exhibit an exponential improvement
+over item pricings for these settings. Our results extend to settings where the
+seller has multiple copies of each item, with the competitive ratio decreasing
+linearly with supply. Such a gradual tradeoff between supply and the
+competitive ratio for welfare was previously known only for the single item
+prophet inequality.
+",1,0,0,0,0,0
+2024,Learning best K analogies from data distribution for case-based software effort estimation,"  Case-Based Reasoning (CBR) has been widely used to generate good software
+effort estimates. The predictive performance of CBR is a dataset dependent and
+subject to extremely large space of configuration possibilities. Regardless of
+the type of adaptation technique, deciding on the optimal number of similar
+cases to be used before applying CBR is a key challenge. In this paper we
+propose a new technique based on Bisecting k-medoids clustering algorithm to
+better understanding the structure of a dataset and discovering the the optimal
+cases for each individual project by excluding irrelevant cases. Results
+obtained showed that understanding of the data characteristic prior prediction
+stage can help in automatically finding the best number of cases for each test
+project. Performance figures of the proposed estimation method are better than
+those of other regular K-based CBR methods.
+",1,0,0,0,0,0
+2025,Optimal designs for enzyme inhibition kinetic models,"  In this paper we present a new method for determining optimal designs for
+enzyme inhibition kinetic models, which are used to model the influence of the
+concentration of a substrate and an inhibition on the velocity of a reaction.
+The approach uses a nonlinear transformation of the vector of predictors such
+that the model in the new coordinates is given by an incomplete response
+surface model. Although there exist no explicit solutions of the optimal design
+problem for incomplete response surface models so far, the corresponding design
+problem in the new coordinates is substantially more transparent, such that
+explicit or numerical solutions can be determined more easily. The designs for
+the original problem can finally be found by an inverse transformation of the
+optimal designs determined for the response surface model. We illustrate the
+method determining explicit solutions for the $D$-optimal design and for the
+optimal design problem for estimating the individual coefficients in a
+non-competitive enzyme inhibition kinetic model.
+",0,0,1,1,0,0
+2026,Highly Granular Calorimeters: Technologies and Results,"  The CALICE collaboration is developing highly granular calorimeters for
+experiments at a future lepton collider primarily to establish technologies for
+particle flow event reconstruction. These technologies also find applications
+elsewhere, such as detector upgrades for the LHC. Meanwhile, the large data
+sets collected in an extensive series of beam tests have enabled detailed
+studies of the properties of hadronic showers in calorimeter systems, resulting
+in improved simulation models and development of sophisticated reconstruction
+techniques. In this proceeding, highlights are included from studies of the
+structure of hadronic showers and results on reconstruction techniques for
+imaging calorimetry. In addition, current R&D activities within CALICE are
+summarized, focusing on technological prototypes that address challenges from
+full detector system integration and production techniques amenable to mass
+production for electromagnetic and hadronic calorimeters based on silicon,
+scintillator, and gas techniques.
+",0,1,0,0,0,0
+2027,Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods,"  Hamiltonian Monte Carlo has emerged as a standard tool for posterior
+computation. In this article, we present an extension that can efficiently
+explore target distributions with discontinuous densities, which in turn
+enables efficient sampling from ordinal parameters though embedding of
+probability mass functions into continuous spaces. We motivate our approach
+through a theory of discontinuous Hamiltonian dynamics and develop a numerical
+solver of discontinuous dynamics. The proposed numerical solver is the first of
+its kind, with a remarkable ability to exactly preserve the Hamiltonian and
+thus yield a type of rejection-free proposals. We apply our algorithm to
+challenging posterior inference problems to demonstrate its wide applicability
+and competitive performance.
+",0,0,0,1,0,0
+2028,Optimal boundary gradient estimates for Lamé systems with partially infinite coefficients,"  In this paper, we derive the pointwise upper bounds and lower bounds on the
+gradients of solutions to the Lamé systems with partially infinite
+coefficients as the surface of discontinuity of the coefficients of the system
+is located very close to the boundary. When the distance tends to zero, the
+optimal blow-up rates of the gradients are established for inclusions with
+arbitrary shapes and in all dimensions.
+",0,0,1,0,0,0
+2029,On a variable step size modification of Hines' method in computational neuroscience,"  For simulating large networks of neurons Hines proposed a method which uses
+extensively the structure of the arising systems of ordinary differential
+equations in order to obtain an efficient implementation. The original method
+requires constant step sizes and produces the solution on a staggered grid. In
+the present paper a one-step modification of this method is introduced and
+analyzed with respect to their stability properties. The new method allows for
+step size control. Local error estimators are constructed. The method has been
+implemented in matlab and tested using simple Hodgkin-Huxley type models.
+Comparisons with standard state-of-the-art solvers are provided.
+",0,0,1,0,0,0
+2030,Neural Question Answering at BioASQ 5B,"  This paper describes our submission to the 2017 BioASQ challenge. We
+participated in Task B, Phase B which is concerned with biomedical question
+answering (QA). We focus on factoid and list question, using an extractive QA
+model, that is, we restrict our system to output substrings of the provided
+text snippets. At the core of our system, we use FastQA, a state-of-the-art
+neural QA system. We extended it with biomedical word embeddings and changed
+its answer layer to be able to answer list questions in addition to factoid
+questions. We pre-trained the model on a large-scale open-domain QA dataset,
+SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our
+approach, we achieve state-of-the-art results on factoid questions and
+competitive results on list questions.
+",1,0,0,0,0,0
+2031,Implementation of Smart Contracts Using Hybrid Architectures with On- and Off-Blockchain Components,"  Recently, decentralised (on-blockchain) platforms have emerged to complement
+centralised (off-blockchain) platforms for the implementation of automated,
+digital (smart) contracts. However, neither alternative can individually
+satisfy the requirements of a large class of applications. On-blockchain
+platforms suffer from scalability, performance, transaction costs and other
+limitations. Off-blockchain platforms are afflicted by drawbacks due to their
+dependence on single trusted third parties. We argue that in several
+application areas, hybrid platforms composed from the integration of on- and
+off-blockchain platforms are more able to support smart contracts that deliver
+the desired quality of service (QoS). Hybrid architectures are largely
+unexplored. To help cover the gap, in this paper we discuss the implementation
+of smart contracts on hybrid architectures. As a proof of concept, we show how
+a smart contract can be split and executed partially on an off-blockchain
+contract compliance checker and partially on the Rinkeby Ethereum network. To
+test the solution, we expose it to sequences of contractual operations
+generated mechanically by a contract validator tool.
+",1,0,0,0,0,0
+2032,Electro-mechanical control of an on-chip optical beam splitter containing an embedded quantum emitter,"  We demonstrate electro-mechanical control of an on-chip GaAs optical beam
+splitter containing a quantum dot single-photon source. The beam splitter
+consists of two nanobeam waveguides, which form a directional coupler (DC). The
+splitting ratio of the DC is controlled by varying the out-of-plane separation
+of the two waveguides using electro-mechanical actuation. We reversibly tune
+the beam splitter between an initial state, with emission into both output
+arms, and a final state with photons emitted into a single output arm. The
+device represents a compact and scalable tuning approach for use in III-V
+semiconductor integrated quantum optical circuits.
+",0,1,0,0,0,0
+2033,Data Distillation for Controlling Specificity in Dialogue Generation,"  People speak at different levels of specificity in different situations.
+Depending on their knowledge, interlocutors, mood, etc.} A conversational agent
+should have this ability and know when to be specific and when to be general.
+We propose an approach that gives a neural network--based conversational agent
+this ability. Our approach involves alternating between \emph{data
+distillation} and model training : removing training examples that are closest
+to the responses most commonly produced by the model trained from the last
+round and then retrain the model on the remaining dataset. Dialogue generation
+models trained with different degrees of data distillation manifest different
+levels of specificity.
+We then train a reinforcement learning system for selecting among this pool
+of generation models, to choose the best level of specificity for a given
+input. Compared to the original generative model trained without distillation,
+the proposed system is capable of generating more interesting and
+higher-quality responses, in addition to appropriately adjusting specificity
+depending on the context.
+Our research constitutes a specific case of a broader approach involving
+training multiple subsystems from a single dataset distinguished by differences
+in a specific property one wishes to model. We show that from such a set of
+subsystems, one can use reinforcement learning to build a system that tailors
+its output to different input contexts at test time.
+",1,0,0,0,0,0
+2034,Non-locality of the meet levels of the Trotter-Weil Hierarchy,"  We prove that the meet level $m$ of the Trotter-Weil, $\mathsf{V}_m$ is not
+local for all $m \geq 1$, as conjectured in a paper by Kufleitner and Lauser.
+In order to show this, we explicitly provide a language whose syntactic
+semigroup is in $L \mathsf{V}_m$ and not in $\mathsf{V}_m*\mathsf{D}$.
+",1,0,1,0,0,0
+2035,Particle-based and Meshless Methods with Aboria,"  Aboria is a powerful and flexible C++ library for the implementation of
+particle-based numerical methods. The particles in such methods can represent
+actual particles (e.g. Molecular Dynamics) or abstract particles used to
+discretise a continuous function over a domain (e.g. Radial Basis Functions).
+Aboria provides a particle container, compatible with the Standard Template
+Library, spatial search data structures, and a Domain Specific Language to
+specify non-linear operators on the particle set. This paper gives an overview
+of Aboria's design, an example of use, and a performance benchmark.
+",1,0,0,0,0,0
+2036,Backlund transformations and divisor doubling,"  In classical mechanics well-known cryptographic algorithms and protocols can
+be very useful for construction canonical transformations preserving form of
+Hamiltonians. We consider application of a standard generic divisor doubling
+for construction of new auto Bäcklund transformations for the Lagrange top
+and Hénon-Heiles system separable in parabolic coordinates.
+",0,1,1,0,0,0
+2037,KATE: K-Competitive Autoencoder for Text,"  Autoencoders have been successful in learning meaningful representations from
+image datasets. However, their performance on text datasets has not been widely
+studied. Traditional autoencoders tend to learn possibly trivial
+representations of text documents due to their confounding properties such as
+high-dimensionality, sparsity and power-law word distributions. In this paper,
+we propose a novel k-competitive autoencoder, called KATE, for text documents.
+Due to the competition between the neurons in the hidden layer, each neuron
+becomes specialized in recognizing specific data patterns, and overall the
+model can learn meaningful representations of textual data. A comprehensive set
+of experiments show that KATE can learn better representations than traditional
+autoencoders including denoising, contractive, variational, and k-sparse
+autoencoders. Our model also outperforms deep generative models, probabilistic
+topic models, and even word representation models (e.g., Word2Vec) in terms of
+several downstream tasks such as document classification, regression, and
+retrieval.
+",1,0,0,1,0,0
+2038,Stochastic evolution equations for large portfolios of stochastic volatility models,"  We consider a large market model of defaultable assets in which the asset
+price processes are modelled as Heston-type stochastic volatility models with
+default upon hitting a lower boundary. We assume that both the asset prices and
+their volatilities are correlated through systemic Brownian motions. We are
+interested in the loss process that arises in this setting and we prove the
+existence of a large portfolio limit for the empirical measure process of this
+system. This limit evolves as a measure valued process and we show that it will
+have a density given in terms of a solution to a stochastic partial
+differential equation of filtering type in the two-dimensional half-space, with
+a Dirichlet boundary condition. We employ Malliavin calculus to establish the
+existence of a regular density for the volatility component, and an
+approximation by models of piecewise constant volatilities combined with a
+kernel smoothing technique to obtain existence and regularity for the full
+two-dimensional filtering problem. We are able to establish good regularity
+properties for solutions, however uniqueness remains an open problem.
+",0,0,1,0,0,0
+2039,Learning to Address Health Inequality in the United States with a Bayesian Decision Network,"  Life-expectancy is a complex outcome driven by genetic, socio-demographic,
+environmental and geographic factors. Increasing socio-economic and health
+disparities in the United States are propagating the longevity-gap, making it a
+cause for concern. Earlier studies have probed individual factors but an
+integrated picture to reveal quantifiable actions has been missing. There is a
+growing concern about a further widening of healthcare inequality caused by
+Artificial Intelligence (AI) due to differential access to AI-driven services.
+Hence, it is imperative to explore and exploit the potential of AI for
+illuminating biases and enabling transparent policy decisions for positive
+social and health impact. In this work, we reveal actionable interventions for
+decreasing the longevity-gap in the United States by analyzing a County-level
+data resource containing healthcare, socio-economic, behavioral, education and
+demographic features. We learn an ensemble-averaged structure, draw inferences
+using the joint probability distribution and extend it to a Bayesian Decision
+Network for identifying policy actions. We draw quantitative estimates for the
+impact of diversity, preventive-care quality and stable-families within the
+unified framework of our decision network. Finally, we make this analysis and
+dashboard available as an interactive web-application for enabling users and
+policy-makers to validate our reported findings and to explore the impact of
+ones beyond reported in this work.
+",0,0,0,1,0,0
+2040,Asynchronous parallel primal-dual block update methods,"  Recent several years have witnessed the surge of asynchronous (async-)
+parallel computing methods due to the extremely big data involved in many
+modern applications and also the advancement of multi-core machines and
+computer clusters. In optimization, most works about async-parallel methods are
+on unconstrained problems or those with block separable constraints.
+In this paper, we propose an async-parallel method based on block coordinate
+update (BCU) for solving convex problems with nonseparable linear constraint.
+Running on a single node, the method becomes a novel randomized primal-dual BCU
+with adaptive stepsize for multi-block affinely constrained problems. For these
+problems, Gauss-Seidel cyclic primal-dual BCU needs strong convexity to have
+convergence. On the contrary, merely assuming convexity, we show that the
+objective value sequence generated by the proposed algorithm converges in
+probability to the optimal value and also the constraint residual to zero. In
+addition, we establish an ergodic $O(1/k)$ convergence result, where $k$ is the
+number of iterations. Numerical experiments are performed to demonstrate the
+efficiency of the proposed method and significantly better speed-up performance
+than its sync-parallel counterpart.
+",0,0,1,1,0,0
+2041,Towards Noncommutative Topological Quantum Field Theory: New invariants for 3-manifolds,"  We define some new invariants for 3-manifolds using the space of taut codim-1
+foliations along with various techniques from noncommutative geometry. These
+invariants originate from our attempt to generalise Topological Quantum Field
+Theories in the Noncommutative geometry / topology realm.
+",0,0,1,0,0,0
+2042,Chaotic Dynamic S Boxes Based Substitution Approach for Digital Images,"  In this paper, we propose an image encryption algorithm based on the chaos,
+substitution boxes, nonlinear transformation in Galois field and Latin square.
+Initially, the dynamic S boxes are generated using Fisher Yates shuffle method
+and piece wise linear chaotic map. The algorithm utilizes advantages of keyed
+Latin square and transformation to substitute highly correlated digital images
+and yield encrypted image with valued performance. The chaotic behavior is
+achieved using Logistic map which is used to select one of thousand S boxes and
+also decides the row and column of selected S box. The selected S box value is
+transformed using nonlinear transformation. Along with the keyed Latin square
+generated using a 256 bit external key, used to substitute secretly plain image
+pixels in cipher block chaining mode. To further strengthen the security of
+algorithm, round operation are applied to obtain final ciphered image. The
+experimental results are performed to evaluate algorithm and the anticipated
+algorithm is compared with a recent encryption scheme. The analyses demonstrate
+algorithms effectiveness in providing high security to digital media.
+",1,0,0,0,0,0
+2043,Randomized Optimal Transport on a Graph: Framework and New Distance Measures,"  The recently developed bag-of-paths framework consists in setting a
+Gibbs-Boltzmann distribution on all feasible paths of a graph. This probability
+distribution favors short paths over long ones, with a free parameter (the
+temperature $T > 0$) controlling the entropic level of the distribution. This
+formalism enables the computation of new distances or dissimilarities,
+interpolating between the shortest-path and the resistance distance, which have
+been shown to perform well in clustering and classification tasks. In this
+work, the bag-of-paths formalism is extended by adding two independent equality
+constraints fixing starting and ending nodes distributions of paths. When the
+temperature is low, this formalism is shown to be equivalent to a relaxation of
+the optimal transport problem on a network where paths carry a flow between two
+discrete distributions on nodes. The randomization is achieved by considering
+free energy minimization instead of traditional cost minimization. Algorithms
+computing the optimal free energy solution are developed for two types of
+paths: hitting (or absorbing) paths and non-hitting, regular paths, and require
+the inversion of an $n \times n$ matrix with $n$ being the number of nodes.
+Interestingly, for regular paths, the resulting optimal policy interpolates
+between the deterministic optimal transport policy ($T \rightarrow 0^{+}$) and
+the solution to the corresponding electrical circuit ($T \rightarrow \infty$).
+Two distance measures between nodes and a dissimilarity between groups of
+nodes, both integrating weights on nodes, are derived from this framework.
+",1,0,0,1,0,0
+2044,A Generative Model for Natural Sounds Based on Latent Force Modelling,"  Recent advances in analysis of subband amplitude envelopes of natural sounds
+have resulted in convincing synthesis, showing subband amplitudes to be a
+crucial component of perception. Probabilistic latent variable analysis is
+particularly revealing, but existing approaches don't incorporate prior
+knowledge about the physical behaviour of amplitude envelopes, such as
+exponential decay and feedback. We use latent force modelling, a probabilistic
+learning paradigm that incorporates physical knowledge into Gaussian process
+regression, to model correlation across spectral subband envelopes. We augment
+the standard latent force model approach by explicitly modelling correlations
+over multiple time steps. Incorporating this prior knowledge strengthens the
+interpretation of the latent functions as the source that generated the signal.
+We examine this interpretation via an experiment which shows that sounds
+generated by sampling from our probabilistic model are perceived to be more
+realistic than those generated by similar models based on nonnegative matrix
+factorisation, even in cases where our model is outperformed from a
+reconstruction error perspective.
+",0,0,0,1,0,0
+2045,Linear and Nonlinear Heat Equations on a p-Adic Ball,"  We study the Vladimirov fractional differentiation operator $D^\alpha_N$,
+$\alpha >0, N\in \mathbb Z$, on a $p$-adic ball $B_N=\{ x\in \mathbb Q_p:\
+|x|_p\le p^N\}$. To its known interpretations via restriction from a similar
+operator on $\mathbb Q_p$ and via a certain stochastic process on $B_N$, we add
+an interpretation as a pseudo-differential operator in terms of the Pontryagin
+duality on the additive group of $B_N$. We investigate the Green function of
+$D^\alpha_N$ and a nonlinear equation on $B_N$, an analog the classical porous
+medium equation.
+",0,0,1,0,0,0
+2046,PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations,"  We propose position-velocity encoders (PVEs) which learn---without
+supervision---to encode images to positions and velocities of task-relevant
+objects. PVEs encode a single image into a low-dimensional position state and
+compute the velocity state from finite differences in position. In contrast to
+autoencoders, position-velocity encoders are not trained by image
+reconstruction, but by making the position-velocity representation consistent
+with priors about interacting with the physical world. We applied PVEs to
+several simulated control tasks from pixels and achieved promising preliminary
+results.
+",1,0,0,0,0,0
+2047,Few-shot Learning by Exploiting Visual Concepts within CNNs,"  Convolutional neural networks (CNNs) are one of the driving forces for the
+advancement of computer vision. Despite their promising performances on many
+tasks, CNNs still face major obstacles on the road to achieving ideal machine
+intelligence. One is that CNNs are complex and hard to interpret. Another is
+that standard CNNs require large amounts of annotated data, which is sometimes
+hard to obtain, and it is desirable to learn to recognize objects from few
+examples. In this work, we address these limitations of CNNs by developing
+novel, flexible, and interpretable models for few-shot learning. Our models are
+based on the idea of encoding objects in terms of visual concepts (VCs), which
+are interpretable visual cues represented by the feature vectors within CNNs.
+We first adapt the learning of VCs to the few-shot setting, and then uncover
+two key properties of feature encoding using VCs, which we call category
+sensitivity and spatial pattern. Motivated by these properties, we present two
+intuitive models for the problem of few-shot learning. Experiments show that
+our models achieve competitive performances, while being more flexible and
+interpretable than alternative state-of-the-art few-shot learning methods. We
+conclude that using VCs helps expose the natural capability of CNNs for
+few-shot learning.
+",1,0,0,1,0,0
+2048,Noise Statistics Oblivious GARD For Robust Regression With Sparse Outliers,"  Linear regression models contaminated by Gaussian noise (inlier) and possibly
+unbounded sparse outliers are common in many signal processing applications.
+Sparse recovery inspired robust regression (SRIRR) techniques are shown to
+deliver high quality estimation performance in such regression models.
+Unfortunately, most SRIRR techniques assume \textit{a priori} knowledge of
+noise statistics like inlier noise variance or outlier statistics like number
+of outliers. Both inlier and outlier noise statistics are rarely known
+\textit{a priori} and this limits the efficient operation of many SRIRR
+algorithms. This article proposes a novel noise statistics oblivious algorithm
+called residual ratio thresholding GARD (RRT-GARD) for robust regression in the
+presence of sparse outliers. RRT-GARD is developed by modifying the recently
+proposed noise statistics dependent greedy algorithm for robust de-noising
+(GARD). Both finite sample and asymptotic analytical results indicate that
+RRT-GARD performs nearly similar to GARD with \textit{a priori} knowledge of
+noise statistics. Numerical simulations in real and synthetic data sets also
+point to the highly competitive performance of RRT-GARD.
+",0,0,0,1,0,0
+2049,Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation,"  This paper addresses the problem of depth estimation from a single still
+image. Inspired by recent works on multi- scale convolutional neural networks
+(CNN), we propose a deep model which fuses complementary information derived
+from multiple CNN side outputs. Different from previous methods, the
+integration is obtained by means of continuous Conditional Random Fields
+(CRFs). In particular, we propose two different variations, one based on a
+cascade of multiple CRFs, the other on a unified graphical model. By designing
+a novel CNN implementation of mean-field updates for continuous CRFs, we show
+that both proposed models can be regarded as sequential deep networks and that
+training can be performed end-to-end. Through extensive experimental evaluation
+we demonstrate the effective- ness of the proposed approach and establish new
+state of the art results on publicly available datasets.
+",1,0,0,0,0,0
+2050,On the relation between representations and computability,"  One of the fundamental results in computability is the existence of
+well-defined functions that cannot be computed. In this paper we study the
+effects of data representation on computability; we show that, while for each
+possible way of representing data there exist incomputable functions, the
+computability of a specific abstract function is never an absolute property,
+but depends on the representation used for the function domain. We examine the
+scope of this dependency and provide mathematical criteria to favour some
+representations over others. As we shall show, there are strong reasons to
+suggest that computational enumerability should be an additional axiom for
+computation models. We analyze the link between the techniques and effects of
+representation changes and those of oracle machines, showing an important
+connection between their hierarchies. Finally, these notions enable us to gain
+a new insight on the Church-Turing thesis: its interpretation as the underlying
+algebraic structure to which computation is invariant.
+",1,0,0,0,0,0
+2051,Towards Proxemic Mobile Collocated Interactions,"  Research on mobile collocated interactions has been exploring situations
+where collocated users engage in collaborative activities using their personal
+mobile devices (e.g., smartphones and tablets), thus going from
+personal/individual toward shared/multiuser experiences and interactions. The
+proliferation of ever-smaller computers that can be worn on our wrists (e.g.,
+Apple Watch) and other parts of the body (e.g., Google Glass), have expanded
+the possibilities and increased the complexity of interaction in what we term
+mobile collocated situations. Research on F-formations (or facing formations)
+has been conducted in traditional settings (e.g., home, office, parties) where
+the context and the presence of physical elements (e.g., furniture) can
+strongly influence the way people socially interact with each other. While we
+may be aware of how people arrange themselves spatially and interact with each
+other at a dinner table, in a classroom, or at a waiting room in a hospital,
+there are other less-structured, dynamic, and larger-scale spaces that present
+different types of challenges and opportunities for technology to enrich how
+people experience these (semi-) public spaces. In this article, the authors
+explore proxemic mobile collocated interactions by looking at F-formations in
+the wild. They discuss recent efforts to observe how people socially interact
+in dynamic, unstructured, non-traditional settings. The authors also report the
+results of exploratory F-formation observations conducted in the wild (i.e.,
+tourist attraction).
+",1,0,0,0,0,0
+2052,Adjusting for bias introduced by instrumental variable estimation in the Cox Proportional Hazards Model,"  Instrumental variable (IV) methods are widely used for estimating average
+treatment effects in the presence of unmeasured confounders. However, the
+capability of existing IV procedures, and most notably the two-stage residual
+inclusion (2SRI) procedure recommended for use in nonlinear contexts, to
+account for unmeasured confounders in the Cox proportional hazard model is
+unclear. We show that instrumenting an endogenous treatment induces an
+unmeasured covariate, referred to as an individual frailty in survival analysis
+parlance, which if not accounted for leads to bias. We propose a new procedure
+that augments 2SRI with an individual frailty and prove that it is consistent
+under certain conditions. The finite sample-size behavior is studied across a
+broad set of conditions via Monte Carlo simulations. Finally, the proposed
+methodology is used to estimate the average effect of carotid endarterectomy
+versus carotid artery stenting on the mortality of patients suffering from
+carotid artery disease. Results suggest that the 2SRI-frailty estimator
+generally reduces the bias of both point and interval estimators compared to
+traditional 2SRI.
+",0,0,0,1,0,0
+2053,On the length of perverse sheaves and D-modules,"  We prove that the length function for perverse sheaves and algebraic regular
+holonomic D-modules on a smooth complex algebraic variety Y is an absolute
+Q-constructible function. One consequence is: for ""any"" fixed natural (derived)
+functor F between constructible complexes or perverse sheaves on two smooth
+varieties X and Y, the loci of rank one local systems L on X whose image F(L)
+has prescribed length are Zariski constructible subsets defined over Q,
+obtained from finitely many torsion-translated complex affine algebraic subtori
+of the moduli of rank one local systems via a finite sequence of taking union,
+intersection, and complement.
+",0,0,1,0,0,0
+2054,Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks,"  Deep neural networks are commonly developed and trained in 32-bit floating
+point format. Significant gains in performance and energy efficiency could be
+realized by training and inference in numerical formats optimized for deep
+learning. Despite advances in limited precision inference in recent years,
+training of neural networks in low bit-width remains a challenging problem.
+Here we present the Flexpoint data format, aiming at a complete replacement of
+32-bit floating point format training and inference, designed to support modern
+deep network topologies without modifications. Flexpoint tensors have a shared
+exponent that is dynamically adjusted to minimize overflows and maximize
+available dynamic range. We validate Flexpoint by training AlexNet, a deep
+residual network and a generative adversarial network, using a simulator
+implemented with the neon deep learning framework. We demonstrate that 16-bit
+Flexpoint closely matches 32-bit floating point in training all three models,
+without any need for tuning of model hyperparameters. Our results suggest
+Flexpoint as a promising numerical format for future hardware for training and
+inference.
+",1,0,0,1,0,0
+2055,Bounds on the expected size of the maximum agreement subtree for a given tree shape,"  We show that the expected size of the maximum agreement subtree of two
+$n$-leaf trees, uniformly random among all trees with the shape, is
+$\Theta(\sqrt{n})$. To derive the lower bound, we prove a global structural
+result on a decomposition of rooted binary trees into subgroups of leaves
+called blobs. To obtain the upper bound, we generalize a first moment argument
+for random tree distributions that are exchangeable and not necessarily
+sampling consistent.
+",0,0,0,0,1,0
+2056,Magnetic ground state of SrRuO$_3$ thin film and applicability of standard first-principles approximations to metallic magnetism,"  A systematic first-principles study has been performed to understand the
+magnetism of thin film SrRuO$_3$ which lots of research efforts have been
+devoted to but no clear consensus has been reached about its ground state
+properties. The relative t$_{2g}$ level difference, lattice distortion as well
+as the layer thickness play together in determining the spin order. In
+particular, it is important to understand the difference between two standard
+approximations, namely LDA and GGA, in describing this metallic magnetism.
+Landau free energy analysis and the magnetization-energy-ratio plot clearly
+show the different tendency of favoring the magnetic moment formation, and it
+is magnified when applied to the thin film limit where the experimental
+information is severely limited. As a result, LDA gives a qualitatively
+different prediction from GGA in the experimentally relevant region of strain
+whereas both approximations give reasonable results for the bulk phase. We
+discuss the origin of this difference and the applicability of standard methods
+to the correlated oxide and the metallic magnetic systems.
+",0,1,0,0,0,0
+2057,No minimal tall Borel ideal in the Katětov order,"  Answering a question of the second listed author we show that there is no
+tall Borel ideal minimal among all tall Borel ideals in the Katětov order.
+",0,0,1,0,0,0
+2058,RelNN: A Deep Neural Model for Relational Learning,"  Statistical relational AI (StarAI) aims at reasoning and learning in noisy
+domains described in terms of objects and relationships by combining
+probability with first-order logic. With huge advances in deep learning in the
+current years, combining deep networks with first-order logic has been the
+focus of several recent studies. Many of the existing attempts, however, only
+focus on relations and ignore object properties. The attempts that do consider
+object properties are limited in terms of modelling power or scalability. In
+this paper, we develop relational neural networks (RelNNs) by adding hidden
+layers to relational logistic regression (the relational counterpart of
+logistic regression). We learn latent properties for objects both directly and
+through general rules. Back-propagation is used for training these models. A
+modular, layer-wise architecture facilitates utilizing the techniques developed
+within deep learning community to our architecture. Initial experiments on
+eight tasks over three real-world datasets show that RelNNs are promising
+models for relational learning.
+",1,0,0,1,0,0
+2059,$ΔN_{\text{eff}}$ and entropy production from early-decaying gravitinos,"  Gravitinos are a fundamental prediction of supergravity, their mass ($m_{G}$)
+is informative of the value of the SUSY breaking scale, and, if produced during
+reheating, their number density is a function of the reheating temperature
+($T_{\text{rh}}$). As a result, constraining their parameter space provides in
+turn significant constraints on particles physics and cosmology. We have
+previously shown that for gravitinos decaying into photons or charged particles
+during the ($\mu$ and $y$) distortion eras, upcoming CMB spectral distortions
+bounds are highly effective in constraining the $T_{\text{rh}}-m_{G}$ space.
+For heavier gravitinos (with lifetimes shorter than a few $\times10^6$ sec),
+distortions are quickly thermalized and energy injections cause a temperature
+rise for the CMB bath. If the decay occurs after neutrino decoupling, its
+overall effect is a suppression of the effective number of relativistic degrees
+of freedom ($N_{\text{eff}}$). In this paper, we utilize the observational
+bounds on $N_{\text{eff}}$ to constrain gravitino decays, and hence provide new
+constaints on gravitinos and reheating. For gravitino masses less than $\approx
+10^5$ GeV, current observations give an upper limit on the reheating scale in
+the range of $\approx 5 \times 10^{10}- 5 \times 10^{11}$GeV. For masses
+greater than $\approx 4 \times 10^3$ GeV they are more stringent than previous
+bounds from BBN constraints, coming from photodissociation of deuterium, by
+almost 2 orders of magnitude.
+",0,1,0,0,0,0
+2060,Game-Theoretic Choice of Curing Rates Against Networked SIS Epidemics by Human Decision-Makers,"  We study networks of human decision-makers who independently decide how to
+protect themselves against Susceptible-Infected-Susceptible (SIS) epidemics.
+Motivated by studies in behavioral economics showing that humans perceive
+probabilities in a nonlinear fashion, we examine the impacts of such
+misperceptions on the equilibrium protection strategies. In our setting, nodes
+choose their curing rates to minimize the infection probability under the
+degree-based mean-field approximation of the SIS epidemic plus the cost of
+their selected curing rate. We establish the existence of a degree based
+equilibrium under both true and nonlinear perceptions of infection
+probabilities (under suitable assumptions). When the per-unit cost of curing
+rate is sufficiently high, we show that true expectation minimizers choose the
+curing rate to be zero at the equilibrium, while curing rate is nonzero under
+nonlinear probability weighting.
+",1,0,0,0,0,0
+2061,A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins,"  Membrane proteins constitute a large portion of the human proteome and
+perform a variety of important functions as membrane receptors, transport
+proteins, enzymes, signaling proteins, and more. The computational studies of
+membrane proteins are usually much more complicated than those of globular
+proteins. Here we propose a new continuum model for Poisson-Boltzmann
+calculations of membrane channel proteins. Major improvements over the existing
+continuum slab model are as follows: 1) The location and thickness of the slab
+model are fine-tuned based on explicit-solvent MD simulations. 2) The highly
+different accessibility in the membrane and water regions are addressed with a
+two-step, two-probe grid labeling procedure, and 3) The water pores/channels
+are automatically identified. The new continuum membrane model is optimized (by
+adjusting the membrane probe, as well as the slab thickness and center) to best
+reproduce the distributions of buried water molecules in the membrane region as
+sampled in explicit water simulations. Our optimization also shows that the
+widely adopted water probe of 1.4 {\AA} for globular proteins is a very
+reasonable default value for membrane protein simulations. It gives an overall
+minimum number of inconsistencies between the continuum and explicit
+representations of water distributions in membrane channel proteins, at least
+in the water accessible pore/channel regions that we focus on. Finally, we
+validate the new membrane model by carrying out binding affinity calculations
+for a potassium channel, and we observe a good agreement with experiment
+results.
+",0,1,0,0,0,0
+2062,An ALMA survey of submillimetre galaxies in the Extended Chandra Deep Field South: Spectroscopic redshifts,"  We present spectroscopic redshifts of S(870)>2mJy submillimetre galaxies
+(SMGs) which have been identified from the ALMA follow-up observations of 870um
+detected sources in the Extended Chandra Deep Field South (the ALMA-LESS
+survey). We derive spectroscopic redshifts for 52 SMGs, with a median of
+z=2.4+/-0.1. However, the distribution features a high redshift tail, with ~25%
+of the SMGs at z>3. Spectral diagnostics suggest that the SMGs are young
+starbursts, and the velocity offsets between the nebular emission and UV ISM
+absorption lines suggest that many are driving winds, with velocity offsets up
+to 2000km/s. Using the spectroscopic redshifts and the extensive UV-to-radio
+photometry in this field, we produce optimised spectral energy distributions
+(SEDs) using Magphys, and use the SEDs to infer a median stellar mass of
+M*=(6+/-1)x10^{10}Msol for our SMGs with spectroscopic redshifts. By combining
+these stellar masses with the star-formation rates (measured from the
+far-infrared SEDs), we show that SMGs (on average) lie a factor ~5 above the
+main-sequence at z~2. We provide this library of 52 template fits with robust
+and well-sampled SEDs available as a resource for future studies of SMGs, and
+also release the spectroscopic catalog of ~2000 (mostly infrared-selected)
+galaxies targeted as part of the spectroscopic campaign.
+",0,1,0,0,0,0
+2063,On the structure of Hausdorff moment sequences of complex matrices,"  The paper treats several aspects of the truncated matricial
+$[\alpha,\beta]$-Hausdorff type moment problems. It is shown that each
+$[\alpha,\beta]$-Hausdorff moment sequence has a particular intrinsic
+structure. More precisely, each element of this sequence varies within a closed
+bounded matricial interval. The case that the corresponding moment coincides
+with one of the endpoints of the interval plays a particular important role.
+This leads to distinguished molecular solutions of the truncated matricial
+$[\alpha,\beta]$-Hausdorff moment problem, which satisfy some extremality
+properties. The proofs are mainly of algebraic character. The use of the
+parallel sum of matrices is an essential tool in the proofs.
+",0,0,1,0,0,0
+2064,Lower bounds on the Bergman metric near points of infinite type,"  Let $\Omega$ be a pseudoconvex domain in $\mathbb C^n$ satisfying an
+$f$-property for some function $f$. We show that the Bergman metric associated
+to $\Omega$ has the lower bound $\tilde g(\delta_\Omega(z)^{-1})$ where
+$\delta_\Omega(z)$ is the distance from $z$ to the boundary $\partial\Omega$
+and $\tilde g$ is a specific function defined by $f$. This refines
+Khanh-Zampieri's work in \cite{KZ12} with reducing the smoothness assumption of
+the boundary.
+",0,0,1,0,0,0
+2065,Minimal Approximately Balancing Weights: Asymptotic Properties and Practical Considerations,"  In observational studies and sample surveys, and regression settings,
+weighting methods are widely used to adjust for or balance observed covariates.
+Recently, a few weighting methods have been proposed that focus on directly
+balancing the covariates while minimizing the dispersion of the weights. In
+this paper, we call this class of weights minimal approximately balancing
+weights (MABW); we study their asymptotic properties and address two
+practicalities. We show that, under standard technical conditions, MABW are
+consistent estimates of the true inverse probability weights; the resulting
+weighting estimator is consistent, asymptotically normal, and
+semiparametrically efficient. For applications, we present a finite sample
+oracle inequality showing that the loss incurred by balancing too many
+functions of the covariates is limited in MABW. We also provide an algorithm
+for choosing the degree of approximate balancing in MABW. Finally, we conclude
+with numerical results that suggest approximate balancing is preferable to
+exact balancing, especially when there is limited overlap in covariate
+distributions: the root mean squared error of the weighting estimator can be
+reduced by nearly a half.
+",0,0,1,1,0,0
+2066,Phonetic-attention scoring for deep speaker features in speaker verification,"  Recent studies have shown that frame-level deep speaker features can be
+derived from a deep neural network with the training target set to discriminate
+speakers by a short speech segment. By pooling the frame-level features,
+utterance-level representations, called d-vectors, can be derived and used in
+the automatic speaker verification (ASV) task. This simple average pooling,
+however, is inherently sensitive to the phonetic content of the utterance. An
+interesting idea borrowed from machine translation is the attention-based
+mechanism, where the contribution of an input word to the translation at a
+particular time is weighted by an attention score. This score reflects the
+relevance of the input word and the present translation. We can use the same
+idea to align utterances with different phonetic contents. This paper proposes
+a phonetic-attention scoring approach for d-vector systems. By this approach,
+an attention score is computed for each frame pair. This score reflects the
+similarity of the two frames in phonetic content, and is used to weigh the
+contribution of this frame pair in the utterance-based scoring. This new
+scoring approach emphasizes the frame pairs with similar phonetic contents,
+which essentially provides a soft alignment for utterances with any phonetic
+contents. Experimental results show that compared with the naive average
+pooling, this phonetic-attention scoring approach can deliver consistent
+performance improvement in ASV tasks of both text-dependent and
+text-independent.
+",1,0,0,0,0,0
+2067,MobInsight: A Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility,"  Collective urban mobility embodies the residents' local insights on the city.
+Mobility practices of the residents are produced from their spatial choices,
+which involve various considerations such as the atmosphere of destinations,
+distance, past experiences, and preferences. The advances in mobile computing
+and the rise of geo-social platforms have provided the means for capturing the
+mobility practices; however, interpreting the residents' insights is
+challenging due to the scale and complexity of an urban environment, and its
+unique context. In this paper, we present MobInsight, a framework for making
+localized interpretations of urban mobility that reflect various aspects of the
+urbanism. MobInsight extracts a rich set of neighborhood features through
+holistic semantic aggregation, and models the mobility between all-pairs of
+neighborhoods. We evaluate MobInsight with the mobility data of Barcelona and
+demonstrate diverse localized and semantically-rich interpretations.
+",1,0,0,0,0,0
+2068,The Broad Consequences of Narrow Banking,"  We investigate the macroeconomic consequences of narrow banking in the
+context of stock-flow consistent models. We begin with an extension of the
+Goodwin-Keen model incorporating time deposits, government bills, cash, and
+central bank reserves to the base model with loans and demand deposits and use
+it to describe a fractional reserve banking system. We then characterize narrow
+banking by a full reserve requirement on demand deposits and describe the
+resulting separation between the payment system and lending functions of the
+resulting banking sector. By way of numerical examples, we explore the
+properties of fractional and full reserve versions of the model and compare
+their asymptotic properties. We find that narrow banking does not lead to any
+loss in economic growth when the models converge to a finite equilibrium, while
+allowing for more direct monitoring and prevention of financial breakdowns in
+the case of explosive asymptotic behaviour.
+",0,0,0,0,0,1
+2069,Sitatapatra: Blocking the Transfer of Adversarial Samples,"  Convolutional Neural Networks (CNNs) are widely used to solve classification
+tasks in computer vision. However, they can be tricked into misclassifying
+specially crafted `adversarial' samples -- and samples built to trick one model
+often work alarmingly well against other models trained on the same task. In
+this paper we introduce Sitatapatra, a system designed to block the transfer of
+adversarial samples. It diversifies neural networks using a key, as in
+cryptography, and provides a mechanism for detecting attacks. What's more, when
+adversarial samples are detected they can typically be traced back to the
+individual device that was used to develop them. The run-time overheads are
+minimal permitting the use of Sitatapatra on constrained systems.
+",1,0,0,1,0,0
+2070,"Neurofeedback: principles, appraisal and outstanding issues","  Neurofeedback is a form of brain training in which subjects are fed back
+information about some measure of their brain activity which they are
+instructed to modify in a way thought to be functionally advantageous. Over the
+last twenty years, NF has been used to treat various neurological and
+psychiatric conditions, and to improve cognitive function in various contexts.
+However, despite its growing popularity, each of the main steps in NF comes
+with its own set of often covert assumptions. Here we critically examine some
+conceptual and methodological issues associated with the way general objectives
+and neural targets of NF are defined, and review the neural mechanisms through
+which NF may act, and the way its efficacy is gauged. The NF process is
+characterised in terms of functional dynamics, and possible ways in which it
+may be controlled are discussed. Finally, it is proposed that improving NF will
+require better understanding of various fundamental aspects of brain dynamics
+and a more precise definition of functional brain activity and brain-behaviour
+relationships.
+",0,0,0,0,1,0
+2071,Automating Image Analysis by Annotating Landmarks with Deep Neural Networks,"  Image and video analysis is often a crucial step in the study of animal
+behavior and kinematics. Often these analyses require that the position of one
+or more animal landmarks are annotated (marked) in numerous images. The process
+of annotating landmarks can require a significant amount of time and tedious
+labor, which motivates the need for algorithms that can automatically annotate
+landmarks. In the community of scientists that use image and video analysis to
+study the 3D flight of animals, there has been a trend of developing more
+automated approaches for annotating landmarks, yet they fall short of being
+generally applicable. Inspired by the success of Deep Neural Networks (DNNs) on
+many problems in the field of computer vision, we investigate how suitable DNNs
+are for accurate and automatic annotation of landmarks in video datasets
+representative of those collected by scientists studying animals.
+Our work shows, through extensive experimentation on videos of hawkmoths,
+that DNNs are suitable for automatic and accurate landmark localization. In
+particular, we show that one of our proposed DNNs is more accurate than the
+current best algorithm for automatic localization of landmarks on hawkmoth
+videos. Moreover, we demonstrate how these annotations can be used to
+quantitatively analyze the 3D flight of a hawkmoth. To facilitate the use of
+DNNs by scientists from many different fields, we provide a self contained
+explanation of what DNNs are, how they work, and how to apply them to other
+datasets using the freely available library Caffe and supplemental code that we
+provide.
+",1,0,0,0,0,0
+2072,Weak-strong uniqueness in fluid dynamics,"  We give a survey of recent results on weak-strong uniqueness for compressible
+and incompressible Euler and Navier-Stokes equations, and also make some new
+observations. The importance of the weak-strong uniqueness principle stems, on
+the one hand, from the instances of non-uniqueness for the Euler equations
+exhibited in the past years; and on the other hand from the question of
+convergence of singular limits, for which weak-strong uniqueness represents an
+elegant tool.
+",0,1,1,0,0,0
+2073,Cognitive Subscore Trajectory Prediction in Alzheimer's Disease,"  Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of
+multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's
+Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple
+subscores that quantify different aspects of a patient's cognitive state such
+as learning, memory, and language production/comprehension. Although
+computer-aided diagnostic techniques for classification of a patient's current
+disease state exist, they provide little insight into the relationship between
+changes in brain structure and different aspects of a patient's cognitive state
+that occur over time in AD. We have developed a Convolutional Neural Network
+architecture that can concurrently predict the trajectories of the 13 subscores
+comprised by a subject's ADAS-cog examination results from a current minimally
+preprocessed structural MRI scan up to 36 months from image acquisition time
+without resorting to manual feature extraction. Mean performance metrics are
+within range of those of existing techniques that require manual feature
+selection and are limited to predicting aggregate scores.
+",1,0,0,1,0,0
+2074,Variance Regularizing Adversarial Learning,"  We introduce a novel approach for training adversarial models by replacing
+the discriminator score with a bi-modal Gaussian distribution over the
+real/fake indicator variables. In order to do this, we train the Gaussian
+classifier to match the target bi-modal distribution implicitly through
+meta-adversarial training. We hypothesize that this approach ensures a non-zero
+gradient to the generator, even in the limit of a perfect classifier. We test
+our method against standard benchmark image datasets as well as show the
+classifier output distribution is smooth and has overlap between the real and
+fake modes.
+",1,0,0,1,0,0
+2075,Improving fairness in machine learning systems: What do industry practitioners need?,"  The potential for machine learning (ML) systems to amplify social inequities
+and unfairness is receiving increasing popular and academic attention. A surge
+of recent work has focused on the development of algorithmic tools to assess
+and mitigate such unfairness. If these tools are to have a positive impact on
+industry practice, however, it is crucial that their design be informed by an
+understanding of real-world needs. Through 35 semi-structured interviews and an
+anonymous survey of 267 ML practitioners, we conduct the first systematic
+investigation of commercial product teams' challenges and needs for support in
+developing fairer ML systems. We identify areas of alignment and disconnect
+between the challenges faced by industry practitioners and solutions proposed
+in the fair ML research literature. Based on these findings, we highlight
+directions for future ML and HCI research that will better address industry
+practitioners' needs.
+",1,0,0,0,0,0
+2076,PICOSEC: Charged particle Timing to 24 picosecond Precision with MicroPattern Gas Detectors,"  The prospect of pileup induced backgrounds at the High Luminosity LHC
+(HL-LHC) has stimulated intense interest in technology for charged particle
+timing at high rates. In contrast to the role of timing for particle
+identification, which has driven incremental improvements in timing, the LHC
+timing challenge dictates a specific level of timing performance- roughly 20-30
+picoseconds. Since the elapsed time for an LHC bunch crossing (with standard
+design book parameters) has an rms spread of 170 picoseconds, the $\sim50-100$
+picosecond resolution now commonly achieved in TOF systems would be
+insufficient to resolve multiple ""in-time"" pileup. Here we present a MicroMegas
+based structure which achieves the required time precision (ie 24 picoseconds
+for 150 GeV $\mu$'s) and could potentially offer an inexpensive solution
+covering large areas with $\sim 1$ cm$^2$ pixel size. We present here a
+proof-of-principle which motivates further work in our group toward realizing a
+practical design capable of long-term survival in a high rate experiment.
+",0,1,0,0,0,0
+2077,The first result on 76Ge neutrinoless double beta decay from CDEX-1 experiment,"  We report the first result on Ge-76 neutrinoless double beta decay from
+CDEX-1 experiment at China Jinping Underground Laboratory. A mass of 994 g
+p-type point-contact high purity germanium detector has been installed to
+search the neutrinoless double beta decay events, as well as to directly detect
+dark matter particles. An exposure of 304 kg*day has been analyzed. The
+wideband spectrum from 500 keV to 3 MeV was obtained and the average event rate
+at the 2.039 MeV energy range is about 0.012 count per keV per kg per day. The
+half-life of Ge-76 neutrinoless double beta decay has been derived based on
+this result as: T 1/2 > 6.4*10^22 yr (90% C.L.). An upper limit on the
+effective Majorana-neutrino mass of 5.0 eV has been achieved. The possible
+methods to further decrease the background level have been discussed and will
+be pursued in the next stage of CDEX experiment.
+",0,1,0,0,0,0
+2078,Measuring scientific buzz,"  Keywords are important for information retrieval. They are used to classify
+and sort papers. However, these terms can also be used to study trends within
+and across fields. We want to explore the lifecycle of new keywords. How often
+do new terms come into existence and how long till they fade out? In this
+paper, we present our preliminary analysis where we measure the burstiness of
+keywords within the field of AI. We examine 150k keywords in approximately 100k
+journal and conference papers. We find that nearly 80\% of the keywords die off
+before year one for both journals and conferences but that terms last longer in
+journals versus conferences. We also observe time periods of thematic bursts in
+AI -- one where the terms are more neuroscience inspired and one more oriented
+to computational optimization. This work shows promise of using author keywords
+to better understand dynamics of buzz within science.
+",1,0,0,0,0,0
+2079,Fully Optical Spacecraft Communications: Implementing an Omnidirectional PV-Cell Receiver and 8Mb/s LED Visible Light Downlink with Deep Learning Error Correction,"  Free space optical communication techniques have been the subject of numerous
+investigations in recent years, with multiple missions expected to fly in the
+near future. Existing methods require high pointing accuracies, drastically
+driving up overall system cost. Recent developments in LED-based visible light
+communication (VLC) and past in-orbit experiments have convinced us that the
+technology has reached a critical level of maturity. On these premises, we
+propose a new optical communication system utilizing a VLC downlink and a high
+throughput, omnidirectional photovoltaic cell receiver system. By performing
+error-correction via deep learning methods and by utilizing phase-delay
+interference, the system is able to deliver data rates that match those of
+traditional laser-based solutions. A prototype of the proposed system has been
+constructed, demonstrating the scheme to be a feasible alternative to
+laser-based methods. This creates an opportunity for the full scale development
+of optical communication techniques on small spacecraft as a backup telemetry
+beacon or as a high throughput link.
+",1,0,0,0,0,0
+2080,Linear compartmental models: input-output equations and operations that preserve identifiability,"  This work focuses on the question of how identifiability of a mathematical
+model, that is, whether parameters can be recovered from data, is related to
+identifiability of its submodels. We look specifically at linear compartmental
+models and investigate when identifiability is preserved after adding or
+removing model components. In particular, we examine whether identifiability is
+preserved when an input, output, edge, or leak is added or deleted. Our
+approach, via differential algebra, is to analyze specific input-output
+equations of a model and the Jacobian of the associated coefficient map. We
+clarify a prior determinantal formula for these equations, and then use it to
+prove that, under some hypotheses, a model's input-output equations can be
+understood in terms of certain submodels we call ""output-reachable"". Our proofs
+use algebraic and combinatorial techniques.
+",0,0,0,0,1,0
+2081,"On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding","  We develop theory for nonlinear dimensionality reduction (NLDR). A number of
+NLDR methods have been developed, but there is limited understanding of how
+these methods work and the relationships between them. There is limited basis
+for using existing NLDR theory for deriving new algorithms. We provide a novel
+framework for analysis of NLDR via a connection to the statistical theory of
+linear smoothers. This allows us to both understand existing methods and derive
+new ones. We use this connection to smoothing to show that asymptotically,
+existing NLDR methods correspond to discrete approximations of the solutions of
+sets of differential equations given a boundary condition. In particular, we
+can characterize many existing methods in terms of just three limiting
+differential operators and boundary conditions. Our theory also provides a way
+to assert that one method is preferable to another; indeed, we show Local
+Tangent Space Alignment is superior within a class of methods that assume a
+global coordinate chart defines an isometric embedding of the manifold.
+",0,0,0,1,0,0
+2082,Asymmetric Deep Supervised Hashing,"  Hashing has been widely used for large-scale approximate nearest neighbor
+search because of its storage and search efficiency. Recent work has found that
+deep supervised hashing can significantly outperform non-deep supervised
+hashing in many applications. However, most existing deep supervised hashing
+methods adopt a symmetric strategy to learn one deep hash function for both
+query points and database (retrieval) points. The training of these symmetric
+deep supervised hashing methods is typically time-consuming, which makes them
+hard to effectively utilize the supervised information for cases with
+large-scale database. In this paper, we propose a novel deep supervised hashing
+method, called asymmetric deep supervised hashing (ADSH), for large-scale
+nearest neighbor search. ADSH treats the query points and database points in an
+asymmetric way. More specifically, ADSH learns a deep hash function only for
+query points, while the hash codes for database points are directly learned.
+The training of ADSH is much more efficient than that of traditional symmetric
+deep supervised hashing methods. Experiments show that ADSH can achieve
+state-of-the-art performance in real applications.
+",1,0,0,1,0,0
+2083,Pseudo-Separation for Assessment of Structural Vulnerability of a Network,"  Based upon the idea that network functionality is impaired if two nodes in a
+network are sufficiently separated in terms of a given metric, we introduce two
+combinatorial \emph{pseudocut} problems generalizing the classical min-cut and
+multi-cut problems. We expect the pseudocut problems will find broad relevance
+to the study of network reliability. We comprehensively analyze the
+computational complexity of the pseudocut problems and provide three
+approximation algorithms for these problems.
+Motivated by applications in communication networks with strict
+Quality-of-Service (QoS) requirements, we demonstrate the utility of the
+pseudocut problems by proposing a targeted vulnerability assessment for the
+structure of communication networks using QoS metrics; we perform experimental
+evaluations of our proposed approximation algorithms in this context.
+",1,0,0,0,0,0
+2084,A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes,"  In this paper we use Gaussian Process (GP) regression to propose a novel
+approach for predicting volatility of financial returns by forecasting the
+envelopes of the time series. We provide a direct comparison of their
+performance to traditional approaches such as GARCH. We compare the forecasting
+power of three approaches: GP regression on the absolute and squared returns;
+regression on the envelope of the returns and the absolute returns; and
+regression on the envelope of the negative and positive returns separately. We
+use a maximum a posteriori estimate with a Gaussian prior to determine our
+hyperparameters. We also test the effect of hyperparameter updating at each
+forecasting step. We use our approaches to forecast out-of-sample volatility of
+four currency pairs over a 2 year period, at half-hourly intervals. From three
+kernels, we select the kernel giving the best performance for our data. We use
+two published accuracy measures and four statistical loss functions to evaluate
+the forecasting ability of GARCH vs GPs. In mean squared error the GP's perform
+20% better than a random walk model, and 50% better than GARCH for the same
+data.
+",1,0,0,1,0,0
+2085,Out-of-Sample Testing for GANs,"  We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a
+test set to directly measure the reconstruction quality in the original sample
+space (no auxiliary networks are necessary), and it also computes the
+(log)likelihood for the reconstructed samples in the test set. Further, EvalGAN
+is agnostic to the GAN algorithm and the dataset. We decided to test it on
+three state-of-the-art GANs over the well-known CIFAR-10 and CelebA datasets.
+",1,0,0,1,0,0
+2086,Quantum periodicity in the critical current of superconducting rings with asymmetric link-up of current leads,"  We use superconducting rings with asymmetric link-up of current leads for
+experimental investigation of winding number change at magnetic field
+corresponding to the half of the flux quantum inside the ring. According to the
+conventional theory, the critical current of such rings should change by jump
+due to this change. Experimental data obtained at measurements of aluminum
+rings agree with theoretical prediction in magnetic flux region close to
+integer numbers of the flux quantum and disagree in the region close to the
+half of the one, where a smooth change is observed instead of the jump. First
+measurements of tantalum ring give a hope for the jump. Investigation of this
+problem may have both fundamental and practical importance.
+",0,1,0,0,0,0
+2087,Understanding Norm Change: An Evolutionary Game-Theoretic Approach (Extended Version),"  Human societies around the world interact with each other by developing and
+maintaining social norms, and it is critically important to understand how such
+norms emerge and change. In this work, we define an evolutionary game-theoretic
+model to study how norms change in a society, based on the idea that different
+strength of norms in societies translate to different game-theoretic
+interaction structures and incentives. We use this model to study, both
+analytically and with extensive agent-based simulations, the evolutionary
+relationships of the need for coordination in a society (which is related to
+its norm strength) with two key aspects of norm change: cultural inertia
+(whether or how quickly the population responds when faced with conditions that
+make a norm change desirable), and exploration rate (the willingness of agents
+to try out new strategies). Our results show that a high need for coordination
+leads to both high cultural inertia and a low exploration rate, while a low
+need for coordination leads to low cultural inertia and high exploration rate.
+This is the first work, to our knowledge, on understanding the evolutionary
+causal relationships among these factors.
+",1,0,0,0,0,0
+2088,"A Story of Parametric Trace Slicing, Garbage and Static Analysis","  This paper presents a proposal (story) of how statically detecting
+unreachable objects (in Java) could be used to improve a particular runtime
+verification approach (for Java), namely parametric trace slicing. Monitoring
+algorithms for parametric trace slicing depend on garbage collection to (i)
+cleanup data-structures storing monitored objects, ensuring they do not become
+unmanageably large, and (ii) anticipate the violation of (non-safety)
+properties that cannot be satisfied as a monitored object can no longer appear
+later in the trace. The proposal is that both usages can be improved by making
+the unreachability of monitored objects explicit in the parametric property and
+statically introducing additional instrumentation points generating related
+events. The ideas presented in this paper are still exploratory and the
+intention is to integrate the described techniques into the MarQ monitoring
+tool for quantified event automata.
+",1,0,0,0,0,0
+2089,Extended TQFT arising from enriched multi-fusion categories,"  We define a symmetric monoidal (4,3)-category with duals whose objects are
+certain enriched multi-fusion categories. For every modular tensor category
+$\mathcal{C}$, there is a self enriched multi-fusion category $\mathfrak{C}$
+giving rise to an object of this symmetric monoidal (4,3)-category. We
+conjecture that the extended 3D TQFT given by the fully dualizable object
+$\mathfrak{C}$ extends the 1-2-3-dimensional Reshetikhin-Turaev TQFT associated
+to the modular tensor category $\mathcal{C}$ down to dimension zero.
+",0,0,1,0,0,0
+2090,Multipermutation Ulam Sphere Analysis Toward Characterizing Maximal Code Size,"  Permutation codes, in the form of rank modulation, have shown promise for
+applications such as flash memory. One of the metrics recently suggested as
+appropriate for rank modulation is the Ulam metric, which measures the minimum
+translocation distance between permutations. Multipermutation codes have also
+been proposed as a generalization of permutation codes that would improve code
+size (and consequently the code rate). In this paper we analyze the Ulam metric
+in the context of multipermutations, noting some similarities and differences
+between the Ulam metric in the context of permutations. We also consider sphere
+sizes for multipermutations under the Ulam metric and resulting bounds on code
+size.
+",1,0,1,0,0,0
+2091,Thermophysical characteristics of the large main-belt asteroid (349) Dembowska,"  (349) Dembowska, a large, bright main-belt asteroid, has a fast rotation and
+oblique spin axis. It may have experienced partial melting and differentiation.
+We constrain Dembowska's thermophysical properties, e.g., thermal inertia,
+roughness fraction, geometric albedo and effective diameter within 3$\sigma$
+uncertainty of $\Gamma=20^{+12}_{-7}\rm~Jm^{-2}s^{-0.5}K^{-1}$, $f_{\rm
+r}=0.25^{+0.60}_{-0.25}$, $p_{\rm v}=0.309^{+0.026}_{-0.038}$, and $D_{\rm
+eff}=155.8^{+7.5}_{-6.2}\rm~km$, by utilizing the Advanced Thermophysical Model
+(ATPM) to analyse four sets of thermal infrared data obtained by IRAS, AKARI,
+WISE and Subaru/COMICS at different epochs. In addition, by modeling the
+thermal lightcurve observed by WISE, we obtain the rotational phases of each
+dataset. These rotationally resolved data do not reveal significant variations
+of thermal inertia and roughness across the surface, indicating the surface of
+Dembowska should be covered by a dusty regolith layer with few rocks or
+boulders. Besides, the low thermal inertia of Dembowska show no significant
+difference with other asteroids larger than 100 km, indicating the dynamical
+lives of these large asteroids are long enough to make the surface to have
+sufficiently low thermal inertia. Furthermore, based on the derived surface
+thermophysical properties, as well as the known orbital and rotational
+parameters, we can simulate Dembowska's surface and subsurface temperature
+throughout its orbital period. The surface temperature varies from $\sim40$ K
+to $\sim220$ K, showing significant seasonal variation, whereas the subsurface
+temperature achieves equilibrium temperature about $120\sim160$ K below
+$30\sim50$ cm depth.
+",0,1,0,0,0,0
+2092,CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning,"  We propose CM3, a new deep reinforcement learning method for cooperative
+multi-agent problems where agents must coordinate for joint success in
+achieving different individual goals. We restructure multi-agent learning into
+a two-stage curriculum, consisting of a single-agent stage for learning to
+accomplish individual tasks, followed by a multi-agent stage for learning to
+cooperate in the presence of other agents. These two stages are bridged by
+modular augmentation of neural network policy and value functions. We further
+adapt the actor-critic framework to this curriculum by formulating local and
+global views of the policy gradient and learning via a double critic,
+consisting of a decentralized value function and a centralized action-value
+function. We evaluated CM3 on a new high-dimensional multi-agent environment
+with sparse rewards: negotiating lane changes among multiple autonomous
+vehicles in the Simulation of Urban Mobility (SUMO) traffic simulator. Detailed
+ablation experiments show the positive contribution of each component in CM3,
+and the overall synthesis converges significantly faster to higher performance
+policies than existing cooperative multi-agent methods.
+",0,0,0,1,0,0
+2093,Adaptive twisting sliding mode control for quadrotor unmanned aerial vehicles,"  This work addresses the problem of robust attitude control of quadcopters.
+First, the mathematical model of the quadcopter is derived considering factors
+such as nonlinearity, external disturbances, uncertain dynamics and strong
+coupling. An adaptive twisting sliding mode control algorithm is then developed
+with the objective of controlling the quadcopter to track desired attitudes
+under various conditions. For this, the twisting sliding mode control law is
+modified with a proposed gain adaptation scheme to improve the control
+transient and tracking performance. Extensive simulation studies and
+comparisons with experimental data have been carried out for a Solo quadcopter.
+The results show that the proposed control scheme can achieve strong robustness
+against disturbances while is adaptable to parametric variations.
+",1,0,0,0,0,0
+2094,Dynamic Laplace: Efficient Centrality Measure for Weighted or Unweighted Evolving Networks,"  With its origin in sociology, Social Network Analysis (SNA), quickly emerged
+and spread to other areas of research, including anthropology, biology,
+information science, organizational studies, political science, and computer
+science. Being it's objective the investigation of social structures through
+the use of networks and graph theory, Social Network Analysis is, nowadays, an
+important research area in several domains. Social Network Analysis cope with
+different problems namely network metrics, models, visualization and
+information spreading, each one with several approaches, methods and
+algorithms. One of the critical areas of Social Network Analysis involves the
+calculation of different centrality measures (i.e.: the most important vertices
+within a graph). Today, the challenge is how to do this fast and efficiently,
+as many increasingly larger datasets are available. Recently, the need to apply
+such centrality algorithms to non static networks (i.e.: networks that evolve
+over time) is also a new challenge. Incremental and dynamic versions of
+centrality measures are starting to emerge (betweenness, closeness, etc). Our
+contribution is the proposal of two incremental versions of the Laplacian
+Centrality measure, that can be applied not only to large graphs but also to,
+weighted or unweighted, dynamically changing networks. The experimental
+evaluation was performed with several tests in different types of evolving
+networks, incremental or fully dynamic. Results have shown that our incremental
+versions of the algorithm can calculate node centralities in large networks,
+faster and efficiently than the corresponding batch version in both incremental
+and full dynamic network setups.
+",1,0,0,0,0,0
+2095,Fighting Accounting Fraud Through Forensic Data Analytics,"  Accounting fraud is a global concern representing a significant threat to the
+financial system stability due to the resulting diminishing of the market
+confidence and trust of regulatory authorities. Several tricks can be used to
+commit accounting fraud, hence the need for non-static regulatory interventions
+that take into account different fraudulent patterns. Accordingly, this study
+aims to improve the detection of accounting fraud via the implementation of
+several machine learning methods to better differentiate between fraud and
+non-fraud companies, and to further assist the task of examination within the
+riskier firms by evaluating relevant financial indicators. Out-of-sample
+results suggest there is a great potential in detecting falsified financial
+statements through statistical modelling and analysis of publicly available
+accounting information. The proposed methodology can be of assistance to public
+auditors and regulatory agencies as it facilitates auditing processes, and
+supports more targeted and effective examinations of accounting reports.
+",0,0,0,1,0,0
+2096,Doubly Stochastic Variational Inference for Deep Gaussian Processes,"  Gaussian processes (GPs) are a good choice for function approximation as they
+are flexible, robust to over-fitting, and provide well-calibrated predictive
+uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of
+GPs, but inference in these models has proved challenging. Existing approaches
+to inference in DGP models assume approximate posteriors that force
+independence between the layers, and do not work well in practice. We present a
+doubly stochastic variational inference algorithm, which does not force
+independence between layers. With our method of inference we demonstrate that a
+DGP model can be used effectively on data ranging in size from hundreds to a
+billion points. We provide strong empirical evidence that our inference scheme
+for DGPs works well in practice in both classification and regression.
+",0,0,0,1,0,0
+2097,Electric properties of carbon nano-onion/polyaniline composites: a combined electric modulus and ac conductivity study,"  The complex electric modulus and the ac conductivity of carbon
+nanoonion/polyaniline composites were studied from 1 mHz to 1 MHz at isothermal
+conditions ranging from 15 K to room temperature. The temperature dependence of
+the electric modulus and the dc conductivity analyses indicate a couple of
+hopping mechanisms. The distinction between thermally activated processes and
+the determination of cross-over temperature were achieved by exploring the
+temperature dependence of the fractional exponent of the dispersive ac
+conductivity and the bifurcation of the scaled ac conductivity isotherms. The
+results are analyzed by combining the granular metal model(inter-grain charge
+tunneling of extended electron states located within mesoscopic highly
+conducting polyaniline grains) and a 3D Mott variable range hopping model
+(phonon assisted tunneling within the carbon nano-onions and clusters).
+",0,1,0,0,0,0
+2098,Uniform Rates of Convergence of Some Representations of Extremes : a first approach,"  Uniform convergence rates are provided for asymptotic representations of
+sample extremes. These bounds which are universal in the sense that they do not
+depend on the extreme value index are meant to be extended to arbitrary samples
+extremes in coming papers.
+",0,0,0,1,0,0
+2099,Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting,"  Predicting Arctic sea ice extent is a notoriously difficult forecasting
+problem, even for lead times as short as one month. Motivated by Arctic
+intraannual variability phenomena such as reemergence of sea surface
+temperature and sea ice anomalies, we use a prediction approach for sea ice
+anomalies based on analog forecasting. Traditional analog forecasting relies on
+identifying a single analog in a historical record, usually by minimizing
+Euclidean distance, and forming a forecast from the analog's historical
+trajectory. Here an ensemble of analogs are used to make forecasts, where the
+ensemble weights are determined by a dynamics-adapted similarity kernel, which
+takes into account the nonlinear geometry on the underlying data manifold. We
+apply this method for forecasting pan-Arctic and regional sea ice area and
+volume anomalies from multi-century climate model data, and in many cases find
+improvement over the benchmark damped persistence forecast. Examples of success
+include the 3--6 month lead time prediction of pan-Arctic area, the winter sea
+ice area prediction of some marginal ice zone seas, and the 3--12 month lead
+time prediction of sea ice volume anomalies in many central Arctic basins. We
+discuss possible connections between KAF success and sea ice reemergence, and
+find KAF to be successful in regions and seasons exhibiting high interannual
+variability.
+",0,1,0,0,0,0
+2100,Eigensolutions and spectral analysis of a model for vertical gene transfer of plasmids,"  Plasmids are autonomously replicating genetic elements in bacteria. At cell
+division plasmids are distributed among the two daughter cells. This gene
+transfer from one generation to the next is called vertical gene transfer. We
+study the dynamics of a bacterial population carrying plasmids and are in
+particular interested in the long-time distribution of plasmids. Starting with
+a model for a bacterial population structured by the discrete number of
+plasmids, we proceed to the continuum limit in order to derive a continuous
+model. The model incorporates plasmid reproduction, division and death of
+bacteria, and distribution of plasmids at cell division. It is a hyperbolic
+integro-differential equation and a so-called growth-fragmentation-death model.
+As we are interested in the long-time distribution of plasmids we study the
+associated eigenproblem and show existence of eigensolutions. The stability of
+this solution is studied by analyzing the spectrum of the integro-differential
+operator given by the eigenproblem. By relating the spectrum with the spectrum
+of an integral operator we find a simple real dominating eigenvalue with a
+non-negative corresponding eigenfunction. Moreover, we describe an iterative
+method for the numerical construction of the eigenfunction.
+",0,0,0,0,1,0
+2101,Large-sample approximations for variance-covariance matrices of high-dimensional time series,"  Distributional approximations of (bi--) linear functions of sample
+variance-covariance matrices play a critical role to analyze vector time
+series, as they are needed for various purposes, especially to draw inference
+on the dependence structure in terms of second moments and to analyze
+projections onto lower dimensional spaces as those generated by principal
+components. This particularly applies to the high-dimensional case, where the
+dimension $d$ is allowed to grow with the sample size $n$ and may even be
+larger than $n$. We establish large-sample approximations for such bilinear
+forms related to the sample variance-covariance matrix of a high-dimensional
+vector time series in terms of strong approximations by Brownian motions. The
+results cover weakly dependent as well as many long-range dependent linear
+processes and are valid for uniformly $ \ell_1 $-bounded projection vectors,
+which arise, either naturally or by construction, in many statistical problems
+extensively studied for high-dimensional series. Among those problems are
+sparse financial portfolio selection, sparse principal components, the LASSO,
+shrinkage estimation and change-point analysis for high--dimensional time
+series, which matter for the analysis of big data and are discussed in greater
+detail.
+",0,0,1,1,0,0
+2102,Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers,"  We introduce a criterion, resilience, which allows properties of a dataset
+(such as its mean or best low rank approximation) to be robustly computed, even
+in the presence of a large fraction of arbitrary additional data. Resilience is
+a weaker condition than most other properties considered so far in the
+literature, and yet enables robust estimation in a broader variety of settings.
+We provide new information-theoretic results on robust distribution learning,
+robust estimation of stochastic block models, and robust mean estimation under
+bounded $k$th moments. We also provide new algorithmic results on robust
+distribution learning, as well as robust mean estimation in $\ell_p$-norms.
+Among our proof techniques is a method for pruning a high-dimensional
+distribution with bounded $1$st moments to a stable ""core"" with bounded $2$nd
+moments, which may be of independent interest.
+",1,0,0,1,0,0
+2103,"Non-Euclidean geometry, nontrivial topology and quantum vacuum effects","  Space out of a topological defect of the Abrikosov-Nielsen-Olesen vortex type
+is locally flat but non-Euclidean. If a spinor field is quantized in such a
+space, then a variety of quantum effects is induced in the vacuum. Basing on
+the continuum model for long-wavelength electronic excitations, originating in
+the tight-binding approximation for the nearest neighbor interaction of atoms
+in the crystal lattice, we consider quantum ground state effects in monolayer
+structures warped into nanocones by a disclination; the nonzero size of the
+disclination is taken into account, and a boundary condition at the edge of the
+disclination is chosen to ensure self-adjointness of the Dirac-Weyl Hamiltonian
+operator. In the case of carbon nanocones, we find circumstances when the
+quantum ground state effects are independent of the boundary parameter and the
+disclination size.
+",0,1,0,0,0,0
+2104,Spatial risk measures and rate of spatial diversification,"  An accurate assessment of the risk of extreme environmental events is of
+great importance for populations, authorities and the banking/insurance
+industry. Koch (2017) introduced a notion of spatial risk measure and a
+corresponding set of axioms which are well suited to analyze the risk due to
+events having a spatial extent, precisely such as environmental phenomena. The
+axiom of asymptotic spatial homogeneity is of particular interest since it
+allows one to quantify the rate of spatial diversification when the region
+under consideration becomes large. In this paper, we first investigate the
+general concepts of spatial risk measures and corresponding axioms further. We
+also explain the usefulness of this theory for the actuarial practice. Second,
+in the case of a general cost field, we especially give sufficient conditions
+such that spatial risk measures associated with expectation, variance,
+Value-at-Risk as well as expected shortfall and induced by this cost field
+satisfy the axioms of asymptotic spatial homogeneity of order 0, -2, -1 and -1,
+respectively. Last but not least, in the case where the cost field is a
+function of a max-stable random field, we mainly provide conditions on both the
+function and the max-stable field ensuring the latter properties. Max-stable
+random fields are relevant when assessing the risk of extreme events since they
+appear as a natural extension of multivariate extreme-value theory to the level
+of random fields. Overall, this paper improves our understanding of spatial
+risk measures as well as of their properties with respect to the space variable
+and generalizes many results obtained in Koch (2017).
+",0,0,0,0,0,1
+2105,Double Homotopy (Co)Limits for Relative Categories,"  We answer the question to what extent homotopy (co)limits in categories with
+weak equivalences allow for a Fubini-type interchange law. The main obstacle is
+that we do not assume our categories with weak equivalences to come equipped
+with a calculus for homotopy (co)limits, such as a derivator.
+",0,0,1,0,0,0
+2106,Theory of ground states for classical Heisenberg spin systems I,"  We formulate part I of a rigorous theory of ground states for classical,
+finite, Heisenberg spin systems. The main result is that all ground states can
+be constructed from the eigenvectors of a real, symmetric matrix with entries
+comprising the coupling constants of the spin system as well as certain
+Lagrange parameters. The eigenvectors correspond to the unique maximum of the
+minimal eigenvalue considered as a function of the Lagrange parameters.
+However, there are rare cases where all ground states obtained in this way have
+unphysical dimensions $M>3$ and the theory would have to be extended. Further
+results concern the degree of additional degeneracy, additional to the trivial
+degeneracy of ground states due to rotations or reflections. The theory is
+illustrated by a couple of elementary examples.
+",0,1,1,0,0,0
+2107,A Pliable Index Coding Approach to Data Shuffling,"  A promising research area that has recently emerged, is on how to use index
+coding to improve the communication efficiency in distributed computing
+systems, especially for data shuffling in iterative computations. In this
+paper, we posit that pliable index coding can offer a more efficient framework
+for data shuffling, as it can better leverage the many possible shuffling
+choices to reduce the number of transmissions. We theoretically analyze pliable
+index coding under data shuffling constraints, and design a hierarchical
+data-shuffling scheme that uses pliable coding as a component. We find benefits
+up to $O(ns/m)$ over index coding, where $ns/m$ is the average number of
+workers caching a message, and $m$, $n$, and $s$ are the numbers of messages,
+workers, and cache size, respectively.
+",1,0,0,0,0,0
+2108,The statistical challenge of constraining the low-mass IMF in Local Group dwarf galaxies,"  We use Monte Carlo simulations to explore the statistical challenges of
+constraining the characteristic mass ($m_c$) and width ($\sigma$) of a
+lognormal sub-solar initial mass function (IMF) in Local Group dwarf galaxies
+using direct star counts. For a typical Milky Way (MW) satellite ($M_{V} =
+-8$), jointly constraining $m_c$ and $\sigma$ to a precision of $\lesssim 20\%$
+requires that observations be complete to $\lesssim 0.2 M_{\odot}$, if the IMF
+is similar to the MW IMF. A similar statistical precision can be obtained if
+observations are only complete down to $0.4M_{\odot}$, but this requires
+measurement of nearly 100$\times$ more stars, and thus, a significantly more
+massive satellite ($M_{V} \sim -12$). In the absence of sufficiently deep data
+to constrain the low-mass turnover, it is common practice to fit a
+single-sloped power law to the low-mass IMF, or to fit $m_c$ for a lognormal
+while holding $\sigma$ fixed. We show that the former approximation leads to
+best-fit power law slopes that vary with the mass range observed and can
+largely explain existing claims of low-mass IMF variations in MW satellites,
+even if satellite galaxies have the same IMF as the MW. In addition, fixing
+$\sigma$ during fitting leads to substantially underestimated uncertainties in
+the recovered value of $m_c$ (by a factor of $\sim 4$ for typical
+observations). If the IMFs of nearby dwarf galaxies are lognormal and do vary,
+observations must reach down to $\sim m_c$ in order to robustly detect these
+variations. The high-sensitivity, near-infrared capabilities of JWST and WFIRST
+have the potential to dramatically improve constraints on the low-mass IMF. We
+present an efficient observational strategy for using these facilities to
+measure the IMFs of Local Group dwarf galaxies.
+",0,1,0,0,0,0
+2109,Spatial analysis of airborne laser scanning point clouds for predicting forest variables,"  With recent developments in remote sensing technologies, plot-level forest
+resources can be predicted utilizing airborne laser scanning (ALS). The
+prediction is often assisted by mostly vertical summaries of the ALS point
+clouds. We present a spatial analysis of the point cloud by studying the
+horizontal distribution of the pulse returns through canopy height models
+thresholded at different height levels. The resulting patterns of patches of
+vegetation and gabs on each layer are summarized to spatial ALS features. We
+propose new features based on the Euler number, which is the number of patches
+minus the number of gaps, and the empty-space function, which is a spatial
+summary function of the gab space. The empty-space function is also used to
+describe differences in the gab structure between two different layers. We
+illustrate usefulness of the proposed spatial features for predicting different
+forest variables that summarize the spatial structure of forests or their
+breast height diameter distribution. We employ the proposed spatial features,
+in addition to commonly used features from literature, in the well-known k-nn
+estimation method to predict the forest variables. We present the methodology
+on the example of a study site in Central Finland.
+",0,0,0,1,1,0
+2110,"Analytic and arithmetic properties of the $(Γ,χ)$-automorphic reproducing kernel function","  We consider the reproducing kernel function of the theta Bargmann-Fock
+Hilbert space associated to given full-rank lattice and pseudo-character, and
+we deal with some of its analytical and arithmetical properties. Specially, the
+distribution and discreteness of its zeros are examined and analytic sets
+inside a product of fundamental cells is characterized and shown to be finite
+and of cardinal less or equal to the dimension of the theta Bargmann-Fock
+Hilbert space. Moreover, we obtain some remarkable lattice sums by evaluating
+the so-called complex Hermite-Taylor coefficients. Some of them generalize some
+of the arithmetic identities established by Perelomov in the framework of
+coherent states for the specific case of von Neumann lattice. Such complex
+Hermite-Taylor coefficients are nontrivial examples of the so-called lattice's
+functions according the Serre terminology. The perfect use of the basic
+properties of the complex Hermite polynomials is crucial in this framework.
+",0,0,1,0,0,0
+2111,Concentration of $1$-Lipschitz functions on manifolds with boundary with Dirichlet boundary condition,"  In this paper, we consider a concentration of measure problem on Riemannian
+manifolds with boundary. We study concentration phenomena of non-negative
+$1$-Lipschitz functions with Dirichlet boundary condition around zero, which is
+called boundary concentration phenomena. We first examine relation between
+boundary concentration phenomena and large spectral gap phenomena of Dirichlet
+eigenvalues of Laplacian. We will obtain analogue of the Gromov-V. D. Milman
+theorem and the Funano-Shioya theorem for closed manifolds. Furthermore, to
+capture boundary concentration phenomena, we introduce a new invariant called
+the observable inscribed radius. We will formulate comparison theorems for such
+invariant under a lower Ricci curvature bound, and a lower mean curvature bound
+for the boundary. Based on such comparison theorems, we investigate various
+boundary concentration phenomena of sequences of manifolds with boundary.
+",0,0,1,0,0,0
+2112,Simulated Annealing for JPEG Quantization,"  JPEG is one of the most widely used image formats, but in some ways remains
+surprisingly unoptimized, perhaps because some natural optimizations would go
+outside the standard that defines JPEG. We show how to improve JPEG compression
+in a standard-compliant, backward-compatible manner, by finding improved
+default quantization tables. We describe a simulated annealing technique that
+has allowed us to find several quantization tables that perform better than the
+industry standard, in terms of both compressed size and image fidelity.
+Specifically, we derive tables that reduce the FSIM error by over 10% while
+improving compression by over 20% at quality level 95 in our tests; we also
+provide similar results for other quality levels. While we acknowledge our
+approach can in some images lead to visible artifacts under large
+magnification, we believe use of these quantization tables, or additional
+tables that could be found using our methodology, would significantly reduce
+JPEG file sizes with improved overall image quality.
+",1,0,0,0,0,0
+2113,Greedy Strategy Works for Clustering with Outliers and Coresets Construction,"  We study the problems of clustering with outliers in high dimension. Though a
+number of methods have been developed in the past decades, it is still quite
+challenging to design quality guaranteed algorithms with low complexities for
+the problems. Our idea is inspired by the greedy method, Gonzalez's algorithm,
+for solving the problem of ordinary $k$-center clustering. Based on some novel
+observations, we show that this greedy strategy actually can handle
+$k$-center/median/means clustering with outliers efficiently, in terms of
+qualities and complexities. We further show that the greedy approach yields
+small coreset for the problem in doubling metrics, so as to reduce the time
+complexity significantly. Moreover, a by-product is that the coreset
+construction can be applied to speedup the popular density-based clustering
+approach DBSCAN.
+",1,0,0,0,0,0
+2114,All-but-the-Top: Simple and Effective Postprocessing for Word Representations,"  Real-valued word representations have transformed NLP applications; popular
+examples are word2vec and GloVe, recognized for their ability to capture
+linguistic regularities. In this paper, we demonstrate a {\em very simple}, and
+yet counter-intuitive, postprocessing technique -- eliminate the common mean
+vector and a few top dominating directions from the word vectors -- that
+renders off-the-shelf representations {\em even stronger}. The postprocessing
+is empirically validated on a variety of lexical-level intrinsic tasks (word
+similarity, concept categorization, word analogy) and sentence-level tasks
+(semantic textural similarity and { text classification}) on multiple datasets
+and with a variety of representation methods and hyperparameter choices in
+multiple languages; in each case, the processed representations are
+consistently better than the original ones.
+",1,0,0,1,0,0
+2115,Detecting Near Duplicates in Software Documentation,"  Contemporary software documentation is as complicated as the software itself.
+During its lifecycle, the documentation accumulates a lot of near duplicate
+fragments, i.e. chunks of text that were copied from a single source and were
+later modified in different ways. Such near duplicates decrease documentation
+quality and thus hamper its further utilization. At the same time, they are
+hard to detect manually due to their fuzzy nature. In this paper we give a
+formal definition of near duplicates and present an algorithm for their
+detection in software documents. This algorithm is based on the exact software
+clone detection approach: the software clone detection tool Clone Miner was
+adapted to detect exact duplicates in documents. Then, our algorithm uses these
+exact duplicates to construct near ones. We evaluate the proposed algorithm
+using the documentation of 19 open source and commercial projects. Our
+evaluation is very comprehensive - it covers various documentation types:
+design and requirement specifications, programming guides and API
+documentation, user manuals. Overall, the evaluation shows that all kinds of
+software documentation contain a significant number of both exact and near
+duplicates. Next, we report on the performed manual analysis of the detected
+near duplicates for the Linux Kernel Documentation. We present both quantative
+and qualitative results of this analysis, demonstrate algorithm strengths and
+weaknesses, and discuss the benefits of duplicate management in software
+documents.
+",1,0,0,0,0,0
+2116,L-Graphs and Monotone L-Graphs,"  In an $\mathsf{L}$-embedding of a graph, each vertex is represented by an
+$\mathsf{L}$-segment, and two segments intersect each other if and only if the
+corresponding vertices are adjacent in the graph. If the corner of each
+$\mathsf{L}$-segment in an $\mathsf{L}$-embedding lies on a straight line, we
+call it a monotone $\mathsf{L}$-embedding. In this paper we give a full
+characterization of monotone $\mathsf{L}$-embeddings by introducing a new class
+of graphs which we call ""non-jumping"" graphs. We show that a graph admits a
+monotone $\mathsf{L}$-embedding if and only if the graph is a non-jumping
+graph. Further, we show that outerplanar graphs, convex bipartite graphs,
+interval graphs, 3-leaf power graphs, and complete graphs are subclasses of
+non-jumping graphs. Finally, we show that distance-hereditary graphs and
+$k$-leaf power graphs ($k\le 4$) admit $\mathsf{L}$-embeddings.
+",1,0,0,0,0,0
+2117,ZigZag: A new approach to adaptive online learning,"  We develop a novel family of algorithms for the online learning setting with
+regret against any data sequence bounded by the empirical Rademacher complexity
+of that sequence. To develop a general theory of when this type of adaptive
+regret bound is achievable we establish a connection to the theory of
+decoupling inequalities for martingales in Banach spaces. When the hypothesis
+class is a set of linear functions bounded in some norm, such a regret bound is
+achievable if and only if the norm satisfies certain decoupling inequalities
+for martingales. Donald Burkholder's celebrated geometric characterization of
+decoupling inequalities (1984) states that such an inequality holds if and only
+if there exists a special function called a Burkholder function satisfying
+certain restricted concavity properties. Our online learning algorithms are
+efficient in terms of queries to this function.
+We realize our general theory by giving novel efficient algorithms for
+classes including lp norms, Schatten p-norms, group norms, and reproducing
+kernel Hilbert spaces. The empirical Rademacher complexity regret bound implies
+--- when used in the i.i.d. setting --- a data-dependent complexity bound for
+excess risk after online-to-batch conversion. To showcase the power of the
+empirical Rademacher complexity regret bound, we derive improved rates for a
+supervised learning generalization of the online learning with low rank experts
+task and for the online matrix prediction task.
+In addition to obtaining tight data-dependent regret bounds, our algorithms
+enjoy improved efficiency over previous techniques based on Rademacher
+complexity, automatically work in the infinite horizon setting, and are
+scale-free. To obtain such adaptive methods, we introduce novel machinery, and
+the resulting algorithms are not based on the standard tools of online convex
+optimization.
+",1,0,1,1,0,0
+2118,Comparing Rule-Based and Deep Learning Models for Patient Phenotyping,"  Objective: We investigate whether deep learning techniques for natural
+language processing (NLP) can be used efficiently for patient phenotyping.
+Patient phenotyping is a classification task for determining whether a patient
+has a medical condition, and is a crucial part of secondary analysis of
+healthcare data. We assess the performance of deep learning algorithms and
+compare them with classical NLP approaches.
+Materials and Methods: We compare convolutional neural networks (CNNs),
+n-gram models, and approaches based on cTAKES that extract pre-defined medical
+concepts from clinical notes and use them to predict patient phenotypes. The
+performance is tested on 10 different phenotyping tasks using 1,610 discharge
+summaries extracted from the MIMIC-III database.
+Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The
+average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our
+model having an F1-score up to 37 points higher than alternative approaches. We
+additionally assess the interpretability of our model by presenting a method
+that extracts the most salient phrases for a particular prediction.
+Conclusion: We show that NLP methods based on deep learning improve the
+performance of patient phenotyping. Our CNN-based algorithm automatically
+learns the phrases associated with each patient phenotype. As such, it reduces
+the annotation complexity for clinical domain experts, who are normally
+required to develop task-specific annotation rules and identify relevant
+phrases. Our method performs well in terms of both performance and
+interpretability, which indicates that deep learning is an effective approach
+to patient phenotyping based on clinicians' notes.
+",1,0,0,1,0,0
+2119,Jackknife multiplier bootstrap: finite sample approximations to the $U$-process supremum with applications,"  This paper is concerned with finite sample approximations to the supremum of
+a non-degenerate $U$-process of a general order indexed by a function class. We
+are primarily interested in situations where the function class as well as the
+underlying distribution change with the sample size, and the $U$-process itself
+is not weakly convergent as a process. Such situations arise in a variety of
+modern statistical problems. We first consider Gaussian approximations, namely,
+approximate the $U$-process supremum by the supremum of a Gaussian process, and
+derive coupling and Kolmogorov distance bounds. Such Gaussian approximations
+are, however, not often directly applicable in statistical problems since the
+covariance function of the approximating Gaussian process is unknown. This
+motivates us to study bootstrap-type approximations to the $U$-process
+supremum. We propose a novel jackknife multiplier bootstrap (JMB) tailored to
+the $U$-process, and derive coupling and Kolmogorov distance bounds for the
+proposed JMB method. All these results are non-asymptotic, and established
+under fairly general conditions on function classes and underlying
+distributions. Key technical tools in the proofs are new local maximal
+inequalities for $U$-processes, which may be useful in other problems. We also
+discuss applications of the general approximation results to testing for
+qualitative features of nonparametric functions based on generalized local
+$U$-processes.
+",0,0,1,1,0,0
+2120,On the universality of anomalous scaling exponents of structure functions in turbulent flows,"  All previous experiments in open turbulent flows (e.g. downstream of grids,
+jet and atmospheric boundary layer) have produced quantitatively consistent
+values for the scaling exponents of velocity structure functions. The only
+measurement in closed turbulent flow (von Kármán swirling flow) using
+Taylor-hypothesis, however, produced scaling exponents that are significantly
+smaller, suggesting that the universality of these exponents are broken with
+respect to change of large scale geometry of the flow. Here, we report
+measurements of longitudinal structure functions of velocity in a von
+Kármán setup without the use of Taylor-hypothesis. The measurements are
+made using Stereo Particle Image Velocimetry at 4 different ranges of spatial
+scales, in order to observe a combined inertial subrange spanning roughly one
+and a half order of magnitude. We found scaling exponents (up to 9th order)
+that are consistent with values from open turbulent flows, suggesting that they
+might be in fact universal.
+",0,1,0,0,0,0
+2121,On the Heat Kernel and Weyl Anomaly of Schrödinger invariant theory,"  We propose a method inspired from discrete light cone quantization (DLCQ) to
+determine the heat kernel for a Schrödinger field theory (Galilean boost
+invariant with $z=2$ anisotropic scaling symmetry) living in $d+1$ dimensions,
+coupled to a curved Newton-Cartan background starting from a heat kernel of a
+relativistic conformal field theory ($z=1$) living in $d+2$ dimensions. We use
+this method to show the Schrödinger field theory of a complex scalar field
+cannot have any Weyl anomalies. To be precise, we show that the Weyl anomaly
+$\mathcal{A}^{G}_{d+1}$ for Schrödinger theory is related to the Weyl anomaly
+of a free relativistic scalar CFT $\mathcal{A}^{R}_{d+2}$ via
+$\mathcal{A}^{G}_{d+1}= 2\pi \delta (m) \mathcal{A}^{R}_{d+2}$ where $m$ is the
+charge of the scalar field under particle number symmetry. We provide further
+evidence of vanishing anomaly by evaluating Feynman diagrams in all orders of
+perturbation theory. We present an explicit calculation of the anomaly using a
+regulated Schrödinger operator, without using the null cone reduction
+technique. We generalise our method to show that a similar result holds for one
+time derivative theories with even $z>2$.
+",0,1,0,0,0,0
+2122,"Tree-based networks: characterisations, metrics, and support trees","  Phylogenetic networks generalise phylogenetic trees and allow for the
+accurate representation of the evolutionary history of a set of present-day
+species whose past includes reticulate events such as hybridisation and lateral
+gene transfer. One way to obtain such a network is by starting with a (rooted)
+phylogenetic tree $T$, called a base tree, and adding arcs between arcs of $T$.
+The class of phylogenetic networks that can be obtained in this way is called
+tree-based networks and includes the prominent classes of tree-child and
+reticulation-visible networks. Initially defined for binary phylogenetic
+networks, tree-based networks naturally extend to arbitrary phylogenetic
+networks. In this paper, we generalise recent tree-based characterisations and
+associated proximity measures for binary phylogenetic networks to arbitrary
+phylogenetic networks. These characterisations are in terms of matchings in
+bipartite graphs, path partitions, and antichains. Some of the generalisations
+are straightforward to establish using the original approach, while others
+require a very different approach. Furthermore, for an arbitrary tree-based
+network $N$, we characterise the support trees of $N$, that is, the tree-based
+embeddings of $N$. We use this characterisation to give an explicit formula for
+the number of support trees of $N$ when $N$ is binary. This formula is written
+in terms of the components of a bipartite graph.
+",1,0,0,0,0,0
+2123,Comparing People with Bibliometrics,"  Bibliometric indicators, citation counts and/or download counts are
+increasingly being used to inform personnel decisions such as hiring or
+promotions. These statistics are very often misused. Here we provide a guide to
+the factors which should be considered when using these so-called quantitative
+measures to evaluate people. Rules of thumb are given for when begin to use
+bibliometric measures when comparing otherwise similar candidates.
+",1,1,0,0,0,0
+2124,Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai,"  Unprecedented human mobility has driven the rapid urbanization around the
+world. In China, the fraction of population dwelling in cities increased from
+17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses
+challenges for policymakers and important questions for researchers. To
+investigate the process of migrant integration, we employ a one-month complete
+dataset of telecommunication metadata in Shanghai with 54 million users and 698
+million call logs. We find systematic differences between locals and migrants
+in their mobile communication networks and geographical locations. For
+instance, migrants have more diverse contacts and move around the city with a
+larger radius than locals after they settle down. By distinguishing new
+migrants (who recently moved to Shanghai) from settled migrants (who have been
+in Shanghai for a while), we demonstrate the integration process of new
+migrants in their first three weeks. Moreover, we formulate classification
+problems to predict whether a person is a migrant. Our classifier is able to
+achieve an F1-score of 0.82 when distinguishing settled migrants from locals,
+but it remains challenging to identify new migrants because of class imbalance.
+This classification setup holds promise for identifying new migrants who will
+successfully integrate into locals (new migrants that misclassified as locals).
+",1,1,0,0,0,0
+2125,A computational method for estimating Burr XII parameters with complete and multiple censored data,"  Flexibility in shape and scale of Burr XII distribution can make close
+approximation of numerous well-known probability density functions. Due to
+these capabilities, the usages of Burr XII distribution are applied in risk
+analysis, lifetime data analysis and process capability estimation. In this
+paper the Cross-Entropy (CE) method is further developed in terms of Maximum
+Likelihood Estimation (MLE) to estimate the parameters of Burr XII distribution
+for the complete data or in the presence of multiple censoring. A simulation
+study is conducted to evaluate the performance of the MLE by means of CE method
+for different parameter settings and sample sizes. The results are compared to
+other existing methods in both uncensored and censored situations.
+",0,0,0,1,0,0
+2126,Locally stationary spatio-temporal interpolation of Argo profiling float data,"  Argo floats measure seawater temperature and salinity in the upper 2,000 m of
+the global ocean. Statistical analysis of the resulting spatio-temporal dataset
+is challenging due to its nonstationary structure and large size. We propose
+mapping these data using locally stationary Gaussian process regression where
+covariance parameter estimation and spatio-temporal prediction are carried out
+in a moving-window fashion. This yields computationally tractable nonstationary
+anomaly fields without the need to explicitly model the nonstationary
+covariance structure. We also investigate Student-$t$ distributed fine-scale
+variation as a means to account for non-Gaussian heavy tails in ocean
+temperature data. Cross-validation studies comparing the proposed approach with
+the existing state-of-the-art demonstrate clear improvements in point
+predictions and show that accounting for the nonstationarity and
+non-Gaussianity is crucial for obtaining well-calibrated uncertainties. This
+approach also provides data-driven local estimates of the spatial and temporal
+dependence scales for the global ocean which are of scientific interest in
+their own right.
+",0,1,0,1,0,0
+2127,Knowledge Reuse for Customization: Metamodels in an Open Design Community for 3d Printing,"  Theories of knowledge reuse posit two distinct processes: reuse for
+replication and reuse for innovation. We identify another distinct process,
+reuse for customization. Reuse for customization is a process in which
+designers manipulate the parameters of metamodels to produce models that
+fulfill their personal needs. We test hypotheses about reuse for customization
+in Thingiverse, a community of designers that shares files for
+three-dimensional printing. 3D metamodels are reused more often than the 3D
+models they generate. The reuse of metamodels is amplified when the metamodels
+are created by designers with greater community experience. Metamodels make the
+community's design knowledge available for reuse for customization-or further
+extension of the metamodels, a kind of reuse for innovation.
+",1,0,0,0,0,0
+2128,Dynamic Rank Maximal Matchings,"  We consider the problem of matching applicants to posts where applicants have
+preferences over posts. Thus the input to our problem is a bipartite graph G =
+(A U P,E), where A denotes a set of applicants, P is a set of posts, and there
+are ranks on edges which denote the preferences of applicants over posts. A
+matching M in G is called rank-maximal if it matches the maximum number of
+applicants to their rank 1 posts, subject to this the maximum number of
+applicants to their rank 2 posts, and so on.
+We consider this problem in a dynamic setting, where vertices and edges can
+be added and deleted at any point. Let n and m be the number of vertices and
+edges in an instance G, and r be the maximum rank used by any rank-maximal
+matching in G. We give a simple O(r(m+n))-time algorithm to update an existing
+rank-maximal matching under each of these changes. When r = o(n), this is
+faster than recomputing a rank-maximal matching completely using a known
+algorithm like that of Irving et al., which takes time O(min((r + n,
+r*sqrt(n))m).
+",1,0,0,0,0,0
+2129,Early identification of important patents through network centrality,"  One of the most challenging problems in technological forecasting is to
+identify as early as possible those technologies that have the potential to
+lead to radical changes in our society. In this paper, we use the US patent
+citation network (1926-2010) to test our ability to early identify a list of
+historically significant patents through citation network analysis. We show
+that in order to effectively uncover these patents shortly after they are
+issued, we need to go beyond raw citation counts and take into account both the
+citation network topology and temporal information. In particular, an
+age-normalized measure of patent centrality, called rescaled PageRank, allows
+us to identify the significant patents earlier than citation count and PageRank
+score. In addition, we find that while high-impact patents tend to rely on
+other high-impact patents in a similar way as scientific papers, the patents'
+citation dynamics is significantly slower than that of papers, which makes the
+early identification of significant patents more challenging than that of
+significant papers.
+",1,0,0,0,0,0
+2130,Central limit theorem for the variable bandwidth kernel density estimators,"  In this paper we study the ideal variable bandwidth kernel density estimator
+introduced by McKay (1993) and Jones, McKay and Hu (1994) and the plug-in
+practical version of the variable bandwidth kernel estimator with two sequences
+of bandwidths as in Giné and Sang (2013). Based on the bias and variance
+analysis of the ideal and true variable bandwidth kernel density estimators, we
+study the central limit theorems for each of them.
+",0,0,1,1,0,0
+2131,Distance-to-Mean Continuous Conditional Random Fields to Enhance Prediction Problem in Traffic Flow Data,"  The increase of vehicle in highways may cause traffic congestion as well as
+in the normal roadways. Predicting the traffic flow in highways especially, is
+demanded to solve this congestion problem. Predictions on time-series
+multivariate data, such as in the traffic flow dataset, have been largely
+accomplished through various approaches. The approach with conventional
+prediction algorithms, such as with Support Vector Machine (SVM), is only
+capable of accommodating predictions that are independent in each time unit.
+Hence, the sequential relationships in this time series data is hardly
+explored. Continuous Conditional Random Field (CCRF) is one of Probabilistic
+Graphical Model (PGM) algorithms which can accommodate this problem. The
+neighboring aspects of sequential data such as in the time series data can be
+expressed by CCRF so that its predictions are more reliable. In this article, a
+novel approach called DM-CCRF is adopted by modifying the CCRF prediction
+algorithm to strengthen the probability of the predictions made by the baseline
+regressor. The result shows that DM-CCRF is superior in performance compared to
+CCRF. This is validated by the error decrease of the baseline up to 9%
+significance. This is twice the standard CCRF performance which can only
+decrease baseline error by 4.582% at most.
+",1,0,0,0,0,0
+2132,Submap-based Pose-graph Visual SLAM: A Robust Visual Exploration and Localization System,"  For VSLAM (Visual Simultaneous Localization and Mapping), localization is a
+challenging task, especially for some challenging situations: textureless
+frames, motion blur, etc.. To build a robust exploration and localization
+system in a given space or environment, a submap-based VSLAM system is proposed
+in this paper. Our system uses a submap back-end and a visual front-end. The
+main advantage of our system is its robustness with respect to tracking
+failure, a common problem in current VSLAM algorithms. The robustness of our
+system is compared with the state-of-the-art in terms of average tracking
+percentage. The precision of our system is also evaluated in terms of ATE
+(absolute trajectory error) RMSE (root mean square error) comparing the
+state-of-the-art. The ability of our system in solving the `kidnapped' problem
+is demonstrated. Our system can improve the robustness of visual localization
+in challenging situations.
+",1,0,0,0,0,0
+2133,Partial and Total Dielectronic Recombination Rate Coefficients for W$^{55+}$ to W$^{38+}$,"  Dielectronic recombination (DR) is the dominant mode of recombination in
+magnetically confined fusion plasmas for intermediate to low-charged ions of W.
+Complete, final-state resolved partial isonuclear W DR rate coefficient data is
+required for detailed collisional-radiative modelling for such plasmas in
+preparation for the upcoming fusion experiment ITER. To realize this
+requirement, we continue {\it The Tungsten Project} by presenting our
+calculations for tungsten ions W$^{55+}$ to W$^{38+}$. As per our prior
+calculations for W$^{73+}$ to W$^{56+}$, we use the collision package {\sc
+autostructure} to calculate partial and total DR rate coefficients for all
+relevant core-excitations in intermediate coupling (IC) and configuration
+average (CA) using $\kappa$-averaged relativistic wavefunctions. Radiative
+recombination (RR) rate coefficients are also calculated for the purpose of
+evaluating ionization fractions. Comparison of our DR rate coefficients for
+W$^{46+}$ with other authors yields agreement to within 7-19\% at peak
+abundance verifying the reliability of our method. Comparison of partial DR
+rate coefficients calculated in IC and CA yield differences of a factor
+$\sim{2}$ at peak abundance temperature, highlighting the importance of
+relativistic configuration mixing. Large differences are observed between
+ionization fractions calculated using our recombination rate coefficient data
+and that of Pütterich~\etal [Plasma Phys. and Control. Fusion 50 085016,
+(2008)]. These differences are attributed to deficiencies in the average-atom
+method used by the former to calculate their data.
+",0,1,0,0,0,0
+2134,Congenial Causal Inference with Binary Structural Nested Mean Models,"  Structural nested mean models (SNMMs) are among the fundamental tools for
+inferring causal effects of time-dependent exposures from longitudinal studies.
+With binary outcomes, however, current methods for estimating multiplicative
+and additive SNMM parameters suffer from variation dependence between the
+causal SNMM parameters and the non-causal nuisance parameters. Estimating
+methods for logistic SNMMs do not suffer from this dependence. Unfortunately,
+in contrast with the multiplicative and additive models, unbiased estimation of
+the causal parameters of a logistic SNMM rely on additional modeling
+assumptions even when the treatment probabilities are known. These difficulties
+have hindered the uptake of SNMMs in epidemiological practice, where binary
+outcomes are common. We solve the variation dependence problem for the binary
+multiplicative SNMM by a reparametrization of the non-causal nuisance
+parameters. Our novel nuisance parameters are variation independent of the
+causal parameters, and hence allows the fitting of a multiplicative SNMM by
+unconstrained maximum likelihood. It also allows one to construct true (i.e.
+congenial) doubly robust estimators of the causal parameters. Along the way, we
+prove that an additive SNMM with binary outcomes does not admit a variation
+independent parametrization, thus explaining why we restrict ourselves to the
+multiplicative SNMM.
+",0,0,0,1,0,0
+2135,Improving Search through A3C Reinforcement Learning based Conversational Agent,"  We develop a reinforcement learning based search assistant which can assist
+users through a set of actions and sequence of interactions to enable them
+realize their intent. Our approach caters to subjective search where the user
+is seeking digital assets such as images which is fundamentally different from
+the tasks which have objective and limited search modalities. Labeled
+conversational data is generally not available in such search tasks and
+training the agent through human interactions can be time consuming. We propose
+a stochastic virtual user which impersonates a real user and can be used to
+sample user behavior efficiently to train the agent which accelerates the
+bootstrapping of the agent. We develop A3C algorithm based context preserving
+architecture which enables the agent to provide contextual assistance to the
+user. We compare the A3C agent with Q-learning and evaluate its performance on
+average rewards and state values it obtains with the virtual user in validation
+episodes. Our experiments show that the agent learns to achieve higher rewards
+and better states.
+",1,0,0,0,0,0
+2136,Eco-Routing based on a Data Driven Fuel Consumption Model,"  A nonparametric fuel consumption model is developed and used for eco-routing
+algorithm development in this paper. Six months of driving information from the
+city of Ann Arbor is collected from 2,000 vehicles. The road grade information
+from more than 1,100 km of road network is modeled and the software Autonomie
+is used to calculate fuel consumption for all trips on the road network. Four
+different routing strategies including shortest distance, shortest time,
+eco-routing, and travel-time-constrained eco-routing are compared. The results
+show that eco-routing can reduce fuel consumption, but may increase travel
+time. A travel-time-constrained eco-routing algorithm is developed to keep most
+the fuel saving benefit while incurring very little increase in travel time.
+",0,0,0,1,0,0
+2137,SuperSpike: Supervised learning in multi-layer spiking neural networks,"  A vast majority of computation in the brain is performed by spiking neural
+networks. Despite the ubiquity of such spiking, we currently lack an
+understanding of how biological spiking neural circuits learn and compute
+in-vivo, as well as how we can instantiate such capabilities in artificial
+spiking circuits in-silico. Here we revisit the problem of supervised learning
+in temporally coding multi-layer spiking neural networks. First, by using a
+surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based
+three factor learning rule capable of training multi-layer networks of
+deterministic integrate-and-fire neurons to perform nonlinear computations on
+spatiotemporal spike patterns. Second, inspired by recent results on feedback
+alignment, we compare the performance of our learning rule under different
+credit assignment strategies for propagating output errors to hidden units.
+Specifically, we test uniform, symmetric and random feedback, finding that
+simpler tasks can be solved with any type of feedback, while more complex tasks
+require symmetric feedback. In summary, our results open the door to obtaining
+a better scientific understanding of learning and computation in spiking neural
+networks by advancing our ability to train them to solve nonlinear problems
+involving transformations between different spatiotemporal spike-time patterns.
+",1,0,0,1,0,0
+2138,Distributions-oriented wind forecast verification by a hidden Markov model for multivariate circular-linear data,"  Winds from the North-West quadrant and lack of precipitation are known to
+lead to an increase of PM10 concentrations over a residential neighborhood in
+the city of Taranto (Italy). In 2012 the local government prescribed a
+reduction of industrial emissions by 10% every time such meteorological
+conditions are forecasted 72 hours in advance. Wind forecasting is addressed
+using the Weather Research and Forecasting (WRF) atmospheric simulation system
+by the Regional Environmental Protection Agency. In the context of
+distributions-oriented forecast verification, we propose a comprehensive
+model-based inferential approach to investigate the ability of the WRF system
+to forecast the local wind speed and direction allowing different performances
+for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and
+direction at a relevant location are jointly modeled as a 4-dimensional time
+series with an unknown finite number of states characterized by homogeneous
+distributional behavior. The proposed model relies on a mixture of joint
+projected and skew normal distributions with time-dependent states, where the
+temporal evolution of the state membership follows a first order Markov
+process. Parameter estimates, including the number of states, are obtained by a
+Bayesian MCMC-based method. Results provide useful insights on the performance
+of WRF forecasts in relation to different combinations of wind speed and
+direction.
+",0,0,0,1,0,0
+2139,Mean Field Residual Networks: On the Edge of Chaos,"  We study randomly initialized residual networks using mean field theory and
+the theory of difference equations. Classical feedforward neural networks, such
+as those with tanh activations, exhibit exponential behavior on the average
+when propagating inputs forward or gradients backward. The exponential forward
+dynamics causes rapid collapsing of the input space geometry, while the
+exponential backward dynamics causes drastic vanishing or exploding gradients.
+We show, in contrast, that by adding skip connections, the network will,
+depending on the nonlinearity, adopt subexponential forward and backward
+dynamics, and in many cases in fact polynomial. The exponents of these
+polynomials are obtained through analytic methods and proved and verified
+empirically to be correct. In terms of the ""edge of chaos"" hypothesis, these
+subexponential and polynomial laws allow residual networks to ""hover over the
+boundary between stability and chaos,"" thus preserving the geometry of the
+input space and the gradient information flow. In our experiments, for each
+activation function we study here, we initialize residual networks with
+different hyperparameters and train them on MNIST. Remarkably, our
+initialization time theory can accurately predict test time performance of
+these networks, by tracking either the expected amount of gradient explosion or
+the expected squared distance between the images of two input vectors.
+Importantly, we show, theoretically as well as empirically, that common
+initializations such as the Xavier or the He schemes are not optimal for
+residual networks, because the optimal initialization variances depend on the
+depth. Finally, we have made mathematical contributions by deriving several new
+identities for the kernels of powers of ReLU functions by relating them to the
+zeroth Bessel function of the second kind.
+",1,1,0,0,0,0
+2140,"Automorphisms and deformations of conformally Kähler, Einstein-Maxwell metrics","  We obtain a structure theorem for the group of holomorphic automorphisms of a
+conformally Kähler, Einstein-Maxwell metric, extending the classical results
+of Matsushima, Licherowicz and Calabi in the Kähler-Einstein, cscK, and
+extremal Kähler cases. Combined with previous results of LeBrun,
+Apostolov-Maschler and Futaki-Ono, this completes the classification of the
+conformally Kähler, Einstein--Maxwell metrics on $\mathbb{CP}^1 \times
+\mathbb{CP}^1$. We also use our result in order to introduce a (relative)
+Mabuchi energy in the more general context of $(K, q, a)$-extremal Kähler
+metrics in a given Kähler class, and show that the existence of $(K, q,
+a)$-extremal Kähler metrics is stable under small deformation of the Kähler
+class, the Killing vector field $K$ and the normalization constant $a$.
+",0,0,1,0,0,0
+2141,Human-Level Intelligence or Animal-Like Abilities?,"  The vision systems of the eagle and the snake outperform everything that we
+can make in the laboratory, but snakes and eagles cannot build an eyeglass or a
+telescope or a microscope. (Judea Pearl)
+",1,0,0,1,0,0
+2142,Wembedder: Wikidata entity embedding web service,"  I present a web service for querying an embedding of entities in the Wikidata
+knowledge graph. The embedding is trained on the Wikidata dump using Gensim's
+Word2Vec implementation and a simple graph walk. A REST API is implemented.
+Together with the Wikidata API the web service exposes a multilingual resource
+for over 600'000 Wikidata items and properties.
+",1,0,0,1,0,0
+2143,Meta-Learning for Contextual Bandit Exploration,"  We describe MELEE, a meta-learning algorithm for learning a good exploration
+policy in the interactive contextual bandit setting. Here, an algorithm must
+take actions based on contexts, and learn based only on a reward signal from
+the action taken, thereby generating an exploration/exploitation trade-off.
+MELEE addresses this trade-off by learning a good exploration strategy for
+offline tasks based on synthetic data, on which it can simulate the contextual
+bandit setting. Based on these simulations, MELEE uses an imitation learning
+strategy to learn a good exploration policy that can then be applied to true
+contextual bandit tasks at test time. We compare MELEE to seven strong baseline
+contextual bandit algorithms on a set of three hundred real-world datasets, on
+which it outperforms alternatives in most settings, especially when differences
+in rewards are large. Finally, we demonstrate the importance of having a rich
+feature representation for learning how to explore.
+",1,0,0,1,0,0
+2144,Population polarization dynamics and next-generation social media algorithms,"  We present a many-body theory that explains and reproduces recent
+observations of population polarization dynamics, is supported by controlled
+human experiments, and addresses the controversy surrounding the Internet's
+impact. It predicts that whether and how a population becomes polarized is
+dictated by the nature of the underlying competition, rather than the validity
+of the information that individuals receive or their online bubbles. Building
+on this framework, we show that next-generation social media algorithms aimed
+at pulling people together, will instead likely lead to an explosive
+percolation process that generates new pockets of extremes.
+",0,1,0,0,0,0
+2145,Ride Sharing and Dynamic Networks Analysis,"  The potential of an efficient ride-sharing scheme to significantly reduce
+traffic congestion, lower emission level, as well as facilitating the
+introduction of smart cities has been widely demonstrated. This positive thrust
+however is faced with several delaying factors, one of which is the volatility
+and unpredictability of the potential benefit (or utilization) of ride-sharing
+at different times, and in different places. In this work the following
+research questions are posed: (a) Is ride-sharing utilization stable over time
+or does it undergo significant changes? (b) If ride-sharing utilization is
+dynamic, can it be correlated with some traceable features of the traffic? and
+(c) If ride-sharing utilization is dynamic, can it be predicted ahead of time?
+We analyze a dataset of over 14 Million taxi trips taken in New York City. We
+propose a dynamic travel network approach for modeling and forecasting the
+potential ride-sharing utilization over time, showing it to be highly volatile.
+In order to model the utilization's dynamics we propose a network-centric
+approach, projecting the aggregated traffic taken from continuous time periods
+into a feature space comprised of topological features of the network implied
+by this traffic. This feature space is then used to model the dynamics of
+ride-sharing utilization over time. The results of our analysis demonstrate the
+significant volatility of ride-sharing utilization over time, indicating that
+any policy, design or plan that would disregard this aspect and chose a static
+paradigm would undoubtably be either highly inefficient or provide insufficient
+resources. We show that using our suggested approach it is possible to model
+the potential utilization of ride sharing based on the topological properties
+of the rides network. We also show that using this method the potential
+utilization can be forecasting a few hours ahead of time.
+",1,1,0,0,0,0
+2146,Gas Adsorption and Dynamics in Pillared Graphene Frameworks,"  Pillared Graphene Frameworks are a novel class of microporous materials made
+by graphene sheets separated by organic spacers. One of their main features is
+that the pillar type and density can be chosen to tune the material properties.
+In this work, we present a computer simulation study of adsorption and dynamics
+of H$_{4}$, CH$_{2}$, CO$_{2}$, N$_{2}$ and O$_{2}$ and binary mixtures
+thereof, in Pillared Graphene Frameworks with nitrogen-containing organic
+spacers. In general, we find that pillar density plays the most important role
+in determining gas adsorption. In the low-pressure regime (< 10 bar) the amount
+of gas adsorbed is an increasing function of pillar density. At higher
+pressures the opposite trend is observed. Diffusion coefficients were computed
+for representative structures taking into account the framework flexibility
+that is essential in assessing the dynamical properties of the adsorbed gases.
+Good performance for the gas separation in CH$_{4}$/H$_{2}$, CO$_{2}$/H$_{2}$
+and CO$_{2}$/N$_{2}$ mixtures was found with values comparable to those of
+metal-organic frameworks and zeolites.
+",0,1,0,0,0,0
+2147,DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition,"  Recent progress in computer vision has been dominated by deep neural networks
+trained over large amounts of labeled data. Collecting such datasets is however
+a tedious, often impossible task; hence a surge in approaches relying solely on
+synthetic data for their training. For depth images however, discrepancies with
+real scans still noticeably affect the end performance. We thus propose an
+end-to-end framework which simulates the whole mechanism of these devices,
+generating realistic depth data from 3D models by comprehensively modeling
+vital factors e.g. sensor noise, material reflectance, surface geometry. Not
+only does our solution cover a wider range of sensors and achieve more
+realistic results than previous methods, assessed through extended evaluation,
+but we go further by measuring the impact on the training of neural networks
+for various recognition tasks; demonstrating how our pipeline seamlessly
+integrates such architectures and consistently enhances their performance.
+",1,0,0,0,0,0
+2148,Short Laws for Finite Groups and Residual Finiteness Growth,"  We prove that for every $n \in \mathbb{N}$ and $\delta>0$ there exists a word
+$w_n \in F_2$ of length $n^{2/3} \log(n)^{3+\delta}$ which is a law for every
+finite group of order at most $n$. This improves upon the main result of [A.
+Thom, About the length of laws for finite groups, Isr. J. Math.]. As an
+application we prove a new lower bound on the residual finiteness growth of
+non-abelian free groups.
+",0,0,1,0,0,0
+2149,"Majorana Spin Liquids, Topology and Superconductivity in Ladders","  We theoretically address spin chain analogs of the Kitaev quantum spin model
+on the honeycomb lattice. The emergent quantum spin liquid phases or Anderson
+resonating valence bond (RVB) states can be understood, as an effective model,
+in terms of p-wave superconductivity and Majorana fermions. We derive a
+generalized phase diagram for the two-leg ladder system with tunable
+interaction strengths between chains allowing us to vary the shape of the
+lattice (from square to honeycomb ribbon or brickwall ladder). We evaluate the
+winding number associated with possible emergent (topological) gapless modes at
+the edges. In the Az phase, as a result of the emergent Z2 gauge fields and
+pi-flux ground state, one may build spin-1/2 (loop) qubit operators by analogy
+to the toric code. In addition, we show how the intermediate gapless B phase
+evolves in the generalized ladder model. For the brickwall ladder, the $B$
+phase is reduced to one line, which is analyzed through perturbation theory in
+a rung tensor product states representation and bosonization. Finally, we show
+that doping with a few holes can result in the formation of hole pairs and
+leads to a mapping with the Su-Schrieffer-Heeger model in polyacetylene; a
+superconducting-insulating quantum phase transition for these hole pairs is
+accessible, as well as related topological properties.
+",0,1,0,0,0,0
+2150,Conditional Mean and Quantile Dependence Testing in High Dimension,"  Motivated by applications in biological science, we propose a novel test to
+assess the conditional mean dependence of a response variable on a large number
+of covariates. Our procedure is built on the martingale difference divergence
+recently proposed in Shao and Zhang (2014), and it is able to detect a certain
+type of departure from the null hypothesis of conditional mean independence
+without making any specific model assumptions. Theoretically, we establish the
+asymptotic normality of the proposed test statistic under suitable assumption
+on the eigenvalues of a Hermitian operator, which is constructed based on the
+characteristic function of the covariates. These conditions can be simplified
+under banded dependence structure on the covariates or Gaussian design. To
+account for heterogeneity within the data, we further develop a testing
+procedure for conditional quantile independence at a given quantile level and
+provide an asymptotic justification. Empirically, our test of conditional mean
+independence delivers comparable results to the competitor, which was
+constructed under the linear model framework, when the underlying model is
+linear. It significantly outperforms the competitor when the conditional mean
+admits a nonlinear form.
+",0,0,1,1,0,0
+2151,Finding low-tension communities,"  Motivated by applications that arise in online social media and collaboration
+networks, there has been a lot of work on community-search and team-formation
+problems. In the former class of problems, the goal is to find a subgraph that
+satisfies a certain connectivity requirement and contains a given collection of
+seed nodes. In the latter class of problems, on the other hand, the goal is to
+find individuals who collectively have the skills required for a task and form
+a connected subgraph with certain properties.
+In this paper, we extend both the community-search and the team-formation
+problems by associating each individual with a profile. The profile is a
+numeric score that quantifies the position of an individual with respect to a
+topic. We adopt a model where each individual starts with a latent profile and
+arrives to a conformed profile through a dynamic conformation process, which
+takes into account the individual's social interaction and the tendency to
+conform with one's social environment. In this framework, social tension arises
+from the differences between the conformed profiles of neighboring individuals
+as well as from differences between individuals' conformed and latent profiles.
+Given a network of individuals, their latent profiles and this conformation
+process, we extend the community-search and the team-formation problems by
+requiring the output subgraphs to have low social tension. From the technical
+point of view, we study the complexity of these problems and propose algorithms
+for solving them effectively. Our experimental evaluation in a number of social
+networks reveals the efficacy and efficiency of our methods.
+",1,1,0,0,0,0
+2152,Cohomology of the flag variety under PBW degenerations,"  PBW degenerations are a particularly nice family of flat degenerations of
+type A flag varieties. We show that the cohomology of any PBW degeneration of
+the flag variety surjects onto the cohomology of the original flag variety, and
+that this holds in an equivariant setting too. We also prove that the same is
+true in the symplectic setting when considering Feigin's linear degeneration of
+the symplectic flag variety.
+",0,0,1,0,0,0
+2153,Exact MAP inference in general higher-order graphical models using linear programming,"  This paper is concerned with the problem of exact MAP inference in general
+higher-order graphical models by means of a traditional linear programming
+relaxation approach. In fact, the proof that we have developed in this paper is
+a rather simple algebraic proof being made straightforward, above all, by the
+introduction of two novel algebraic tools. Indeed, on the one hand, we
+introduce the notion of delta-distribution which merely stands for the
+difference of two arbitrary probability distributions, and which mainly serves
+to alleviate the sign constraint inherent to a traditional probability
+distribution. On the other hand, we develop an approximation framework of
+general discrete functions by means of an orthogonal projection expressing in
+terms of linear combinations of function margins with respect to a given
+collection of point subsets, though, we rather exploit the latter approach for
+the purpose of modeling locally consistent sets of discrete functions from a
+global perspective. After that, as a first step, we develop from scratch the
+expectation optimization framework which is nothing else than a reformulation,
+on stochastic grounds, of the convex-hull approach, as a second step, we
+develop the traditional LP relaxation of such an expectation optimization
+approach, and we show that it enables to solve the MAP inference problem in
+graphical models under rather general assumptions. Last but not least, we
+describe an algorithm which allows to compute an exact MAP solution from a
+perhaps fractional optimal (probability) solution of the proposed LP
+relaxation.
+",1,0,0,0,0,0
+2154,Dissolution of topological Fermi arcs in a dirty Weyl semimetal,"  Weyl semimetals (WSMs) have recently attracted a great deal of attention as
+they provide condensed matter realization of chiral anomaly, feature
+topologically protected Fermi arc surface states and sustain sharp chiral Weyl
+quasiparticles up to a critical disorder at which a continuous quantum phase
+transition (QPT) drives the system into a metallic phase. We here numerically
+demonstrate that with increasing strength of disorder the Fermi arc gradually
+looses its sharpness, and close to the WSM-metal QPT it completely dissolves
+into the metallic bath of the bulk. Predicted topological nature of the
+WSM-metal QPT and the resulting bulk-boundary correspondence across this
+transition can directly be observed in
+angle-resolved-photo-emmision-spectroscopy (ARPES) and Fourier transformed
+scanning-tunneling-microscopy (STM) measurements by following the continuous
+deformation of the Fermi arcs with increasing disorder in recently discovered
+Weyl materials.
+",0,1,0,0,0,0
+2155,Performance Improvement in Noisy Linear Consensus Networks with Time-Delay,"  We analyze performance of a class of time-delay first-order consensus
+networks from a graph topological perspective and present methods to improve
+it. The performance is measured by network's square of H-2 norm and it is shown
+that it is a convex function of Laplacian eigenvalues and the coupling weights
+of the underlying graph of the network. First, we propose a tight convex, but
+simple, approximation of the performance measure in order to achieve lower
+complexity in our design problems by eliminating the need for
+eigen-decomposition. The effect of time-delay reincarnates itself in the form
+of non-monotonicity, which results in nonintuitive behaviors of the performance
+as a function of graph topology. Next, we present three methods to improve the
+performance by growing, re-weighting, or sparsifying the underlying graph of
+the network. It is shown that our suggested algorithms provide near-optimal
+solutions with lower complexity with respect to existing methods in literature.
+",1,0,0,0,0,0
+2156,Use of First and Third Person Views for Deep Intersection Classification,"  We explore the problem of intersection classification using monocular
+on-board passive vision, with the goal of classifying traffic scenes with
+respect to road topology. We divide the existing approaches into two broad
+categories according to the type of input data: (a) first person vision (FPV)
+approaches, which use an egocentric view sequence as the intersection is
+passed; and (b) third person vision (TPV) approaches, which use a single view
+immediately before entering the intersection. The FPV and TPV approaches each
+have advantages and disadvantages. Therefore, we aim to combine them into a
+unified deep learning framework. Experimental results show that the proposed
+FPV-TPV scheme outperforms previous methods and only requires minimal FPV/TPV
+measurements.
+",1,0,0,0,0,0
+2157,On the Liouville heat kernel for k-coarse MBRW and nonuniversality,"  We study the Liouville heat kernel (in the $L^2$ phase) associated with a
+class of logarithmically correlated Gaussian fields on the two dimensional
+torus. We show that for each $\varepsilon>0$ there exists such a field, whose
+covariance is a bounded perturbation of that of the two dimensional Gaussian
+free field, and such that the associated Liouville heat kernel satisfies the
+short time estimates, $$ \exp \left( - t^{ - \frac 1 { 1 + \frac 1 2 \gamma^2 }
+- \varepsilon } \right) \le p_t^\gamma (x, y) \le \exp \left( - t^{- \frac 1 {
+1 + \frac 1 2 \gamma^2 } + \varepsilon } \right) , $$ for $\gamma<1/2$. In
+particular, these are different from predictions, due to Watabiki, concerning
+the Liouville heat kernel for the two dimensional Gaussian free field.
+",0,0,1,0,0,0
+2158,Bounding the convergence time of local probabilistic evolution,"  Isoperimetric inequalities form a very intuitive yet powerful
+characterization of the connectedness of a state space, that has proven
+successful in obtaining convergence bounds. Since the seventies they form an
+essential tool in differential geometry, graph theory and Markov chain
+analysis. In this paper we use isoperimetric inequalities to construct a bound
+on the convergence time of any local probabilistic evolution that leaves its
+limit distribution invariant. We illustrate how this general result leads to
+new bounds on convergence times beyond the explicit Markovian setting, among
+others on quantum dynamics.
+",1,0,0,0,0,0
+2159,Excitable behaviors,"  This chapter revisits the concept of excitability, a basic system property of
+neurons. The focus is on excitable systems regarded as behaviors rather than
+dynamical systems. By this we mean open systems modulated by specific
+interconnection properties rather than closed systems classified by their
+parameter ranges. Modeling, analysis, and synthesis questions can be formulated
+in the classical language of circuit theory. The input-output characterization
+of excitability is in terms of the local sensitivity of the current-voltage
+relationship. It suggests the formulation of novel questions for non-linear
+system theory, inspired by questions from experimental neurophysiology.
+",1,0,0,0,0,0
+2160,Laplacian solitons: questions and homogeneous examples,"  We give the first examples of closed Laplacian solitons which are shrinking,
+and in particular produce closed Laplacian flow solutions with a finite-time
+singularity. Extremally Ricci pinched G2-structures (introduced by Bryant)
+which are steady Laplacian solitons have also been found. All the examples are
+left-invariant G2-structures on solvable Lie groups.
+",0,0,1,0,0,0
+2161,The Markoff Group of Transformations in Prime and Composite Moduli,"  The Markoff group of transformations is a group $\Gamma$ of affine integral
+morphisms, which is known to act transitively on the set of all positive
+integer solutions to the equation $x^{2}+y^{2}+z^{2}=xyz$. The fundamental
+strong approximation conjecture for the Markoff equation states that for every
+prime $p$, the group $\Gamma$ acts transitively on the set
+$X^{*}\left(p\right)$ of non-zero solutions to the same equation over
+$\mathbb{Z}/p\mathbb{Z}$. Recently, Bourgain, Gamburd and Sarnak proved this
+conjecture for all primes outside a small exceptional set.
+In the current paper, we study a group of permutations obtained by the action
+of $\Gamma$ on $X^{*}\left(p\right)$, and show that for most primes, it is the
+full symmetric or alternating group. We use this result to deduce that $\Gamma$
+acts transitively also on the set of non-zero solutions in a big class of
+composite moduli.
+Our result is also related to a well-known theorem of Gilman, stating that
+for any finite non-abelian simple group $G$ and $r\ge3$, the group
+$\mathrm{Aut}\left(F_{r}\right)$ acts on at least one $T_{r}$-system of $G$ as
+the alternating or symmetric group. In this language, our main result
+translates to that for most primes $p$, the group
+$\mathrm{Aut}\left(F_{2}\right)$ acts on a particular $T_{2}$-system of
+$\mathrm{PSL}\left(2,p\right)$ as the alternating or symmetric group.
+",0,0,1,0,0,0
+2162,The equivariant index of twisted dirac operators and semi-classical limits,"  Consider a spin manifold M, equipped with a line bundle L and an action of a
+compact Lie group G. We can attach to this data a family Theta(k) of
+distributions on the dual of the Lie algebra of G. The aim of this paper is to
+study the asymptotic behaviour of Theta(k) when k is large, and M possibly non
+compact, and to explore a functorial consequence of this formula for reduced
+spaces.
+",0,0,1,0,0,0
+2163,Proportionally Representative Participatory Budgeting: Axioms and Algorithms,"  Participatory budgeting is one of the exciting developments in deliberative
+grassroots democracy. We concentrate on approval elections and propose
+proportional representation axioms in participatory budgeting, by generalizing
+relevant axioms for approval-based multi-winner elections. We observe a rich
+landscape with respect to the computational complexity of identifying
+proportional budgets and computing such, and present budgeting methods that
+satisfy these axioms by identifying budgets that are representative to the
+demands of vast segments of the voters.
+",1,0,0,0,0,0
+2164,The Arctic Ocean seasonal cycles of heat and freshwater fluxes: observation-based inverse estimates,"  This paper presents the first estimate of the seasonal cycle of ocean and sea
+ice net heat and freshwater (FW) fluxes around the boundary of the Arctic
+Ocean. The ocean transports are estimated primarily using 138 moored
+instruments deployed in September 2005 to August 2006 across the four main
+Arctic gateways: Davis, Fram and Bering Straits, and the Barents Sea Opening
+(BSO). Sea ice transports are estimated from a sea ice assimilation product.
+Monthly velocity fields are calculated with a box inverse model that enforces
+volume and salinity conservation. The resulting net ocean and sea ice heat and
+FW fluxes (annual mean $\pm$ 1 standard deviation) are 175 $\pm$48 TW and 204
+$\pm$85 mSv (respectively; 1 Sv = 10$^{6} m^{3} s^{-1}$). These boundary fluxes
+accurately represent the annual means of the relevant surface fluxes. Oceanic
+net heat transport variability is driven by temperature variability in upper
+part of the water column and by volume transport variability in the Atlantic
+Water layer. Oceanic net FW transport variability is dominated by Bering Strait
+velocity variability. The net water mass transformation in the Arctic entails a
+freshening and cooling of inflowing waters by 0.62$\pm$0.23 in salinity and
+3.74$\pm$0.76C in temperature, respectively, and a reduction in density by
+0.23$\pm$0.20 kg m$^{-3}$. The volume transport into the Arctic of waters
+associated with this water mass transformation is 11.3$\pm$1.2 Sv, and the
+export is -11.4$\pm$1.1 Sv. The boundary heat and FW fluxes provide a benchmark
+data set for the validation of numerical models and atmospheric re-analyses
+products.
+",0,1,0,0,0,0
+2165,Standard Zero-Free Regions for Rankin--Selberg L-Functions via Sieve Theory,"  We give a simple proof of a standard zero-free region in the $t$-aspect for
+the Rankin--Selberg $L$-function $L(s,\pi \times \widetilde{\pi})$ for any
+unitary cuspidal automorphic representation $\pi$ of
+$\mathrm{GL}_n(\mathbb{A}_F)$ that is tempered at every nonarchimedean place
+outside a set of Dirichlet density zero.
+",0,0,1,0,0,0
+2166,Mass transfer in asymptotic-giant-branch binary systems,"  Binary stars can interact via mass transfer when one member (the primary)
+ascends onto a giant branch. The amount of gas ejected by the binary and the
+amount of gas accreted by the secondary over the lifetime of the primary
+influence the subsequent binary phenomenology. Some of the gas ejected by the
+binary will remain gravitationally bound and its distribution will be closely
+related to the formation of planetary nebulae. We investigate the nature of
+mass transfer in binary systems containing an AGB star by adding radiative
+transfer to the AstroBEAR AMR Hydro/MHD code.
+",0,1,0,0,0,0
+2167,On polar relative normalizations of ruled surfaces,"  This paper deals with skew ruled surfaces in the Euclidean space
+$\mathbb{E}^{3}$ which are equipped with polar normalizations, that is,
+relative normalizations such that the relative normal at each point of the
+ruled surface lies on the corresponding polar plane. We determine the
+invariants of a such normalized ruled surface and we study some properties of
+the Tchebychev vector field and the support vector field of a polar
+normalization. Furthermore, we study a special polar normalization, the
+relative image of which degenerates into a curve.
+",0,0,1,0,0,0
+2168,Femtosecond X-ray Fourier holography imaging of free-flying nanoparticles,"  Ultrafast X-ray imaging provides high resolution information on individual
+fragile specimens such as aerosols, metastable particles, superfluid quantum
+systems and live biospecimen, which is inaccessible with conventional imaging
+techniques. Coherent X-ray diffractive imaging, however, suffers from intrinsic
+loss of phase, and therefore structure recovery is often complicated and not
+always uniquely-defined. Here, we introduce the method of in-flight holography,
+where we use nanoclusters as reference X-ray scatterers in order to encode
+relative phase information into diffraction patterns of a virus. The resulting
+hologram contains an unambiguous three-dimensional map of a virus and two
+nanoclusters with the highest lat- eral resolution so far achieved via single
+shot X-ray holography. Our approach unlocks the benefits of holography for
+ultrafast X-ray imaging of nanoscale, non-periodic systems and paves the way to
+direct observation of complex electron dynamics down to the attosecond time
+scale.
+",0,1,0,0,0,0
+2169,The Rank Effect,"  We decompose returns for portfolios of bottom-ranked, lower-priced assets
+relative to the market into rank crossovers and changes in the relative price
+of those bottom-ranked assets. This decomposition is general and consistent
+with virtually any asset pricing model. Crossovers measure changes in rank and
+are smoothly increasing over time, while return fluctuations are driven by
+volatile relative price changes. Our results imply that in a closed,
+dividend-free market in which the relative price of bottom-ranked assets is
+approximately constant, a portfolio of those bottom-ranked assets will
+outperform the market portfolio over time. We show that bottom-ranked relative
+commodity futures prices have increased only slightly, and confirm the
+existence of substantial excess returns predicted by our theory. If these
+excess returns did not exist, then top-ranked relative prices would have had to
+be much higher in 2018 than those actually observed -- this would imply a
+radically different commodity price distribution.
+",0,0,0,0,0,1
+2170,The Generalized Label Correcting Method for Optimal Kinodynamic Motion Planning,"  Nearly all autonomous robotic systems use some form of motion planning to
+compute reference motions through their environment. An increasing use of
+autonomous robots in a broad range of applications creates a need for
+efficient, general purpose motion planning algorithms that are applicable in
+any of these new application domains.
+This thesis presents a resolution complete optimal kinodynamic motion
+planning algorithm based on a direct forward search of the set of admissible
+input signals to a dynamical model. The advantage of this generalized label
+correcting method is that it does not require a local planning subroutine as in
+the case of related methods.
+Preliminary material focuses on new topological properties of the canonical
+problem formulation that are used to show continuity of the performance
+objective. These observations are used to derive a generalization of Bellman's
+principle of optimality in the context of kinodynamic motion planning. A
+generalized label correcting algorithm is then proposed which leverages these
+results to prune candidate input signals from the search when their cost is
+greater than related signals.
+The second part of this thesis addresses admissible heuristics for
+kinodynamic motion planning. An admissibility condition is derived that can be
+used to verify the admissibility of candidate heuristics for a particular
+problem. This condition also characterizes a convex set of admissible
+heuristics.
+A linear program is formulated to obtain a heuristic which is as close to the
+optimal cost-to-go as possible while remaining admissible. This optimization is
+justified by showing its solution coincides with the solution to the
+Hamilton-Jacobi-Bellman equation. Lastly, a sum-of-squares relaxation of this
+infinite-dimensional linear program is proposed for obtaining provably
+admissible approximate solutions.
+",1,0,0,0,0,0
+2171,Learning to Compose Task-Specific Tree Structures,"  For years, recursive neural networks (RvNNs) have been shown to be suitable
+for representing text into fixed-length vectors and achieved good performance
+on several natural language processing tasks. However, the main drawback of
+RvNNs is that they require structured input, which makes data preparation and
+model implementation hard. In this paper, we propose Gumbel Tree-LSTM, a novel
+tree-structured long short-term memory architecture that learns how to compose
+task-specific tree structures only from plain text data efficiently. Our model
+uses Straight-Through Gumbel-Softmax estimator to decide the parent node among
+candidates dynamically and to calculate gradients of the discrete decision. We
+evaluate the proposed model on natural language inference and sentiment
+analysis, and show that our model outperforms or is at least comparable to
+previous models. We also find that our model converges significantly faster
+than other models.
+",1,0,0,0,0,0
+2172,"Navier-Stokes flow past a rigid body: attainability of steady solutions as limits of unsteady weak solutions, starting and landing cases","  Consider the Navier-Stokes flow in 3-dimensional exterior domains, where a
+rigid body is translating with prescribed translational velocity
+$-h(t)u_\infty$ with constant vector $u_\infty\in \mathbb R^3\setminus\{0\}$.
+Finn raised the question whether his steady slutions are attainable as limits
+for $t\to\infty$ of unsteady solutions starting from motionless state when
+$h(t)=1$ after some finite time and $h(0)=0$ (starting problem). This was
+affirmatively solved by Galdi, Heywood and Shibata for small $u_\infty$. We
+study some generalized situation in which unsteady solutions start from large
+motions being in $L^3$. We then conclude that the steady solutions for small
+$u_\infty$ are still attainable as limits of evolution of those fluid motions
+which are found as a sort of weak solutions. The opposite situation, in which
+$h(t)=0$ after some finite time and $h(0)=1$ (landing problem), is also
+discussed. In this latter case, the rest state is attainable no matter how
+large $u_\infty$ is.
+",0,0,1,0,0,0
+2173,Flux cost functions and the choice of metabolic fluxes,"  Metabolic fluxes in cells are governed by physical, biochemical,
+physiological, and economic principles. Cells may show ""economical"" behaviour,
+trading metabolic performance against the costly side-effects of high enzyme or
+metabolite concentrations. Some constraint-based flux prediction methods score
+fluxes by heuristic flux costs as proxies of enzyme investments. However,
+linear cost functions ignore enzyme kinetics and the tight coupling between
+fluxes, metabolite levels and enzyme levels. To derive more realistic cost
+functions, I define an apparent ""enzymatic flux cost"" as the minimal enzyme
+cost at which the fluxes can be realised in a given kinetic model, and a
+""kinetic flux cost"", which includes metabolite cost. I discuss the mathematical
+properties of such flux cost functions, their usage for flux prediction, and
+their importance for cells' metabolic strategies. The enzymatic flux cost
+scales linearly with the fluxes and is a concave function on the flux polytope.
+The costs of two flows are usually not additive, due to an additional
+""compromise cost"". Between flux polytopes, where fluxes change their
+directions, the enzymatic cost shows a jump. With strictly concave flux cost
+functions, cells can reduce their enzymatic cost by running different fluxes in
+different cell compartments or at different moments in time. The enzymactic
+flux cost can be translated into an approximated cell growth rate, a convex
+function on the flux polytope. Growth-maximising metabolic states can be
+predicted by Flux Cost Minimisation (FCM), a variant of FBA based on general
+flux cost functions. The solutions are flux distributions in corners of the
+flux polytope, i.e. typically elementary flux modes. Enzymatic flux costs can
+be linearly or nonlinearly approximated, providing model parameters for linear
+FBA based on kinetic parameters and extracellular concentrations, and justified
+by a kinetic model.
+",0,0,0,0,1,0
+2174,Finding Archetypal Spaces for Data Using Neural Networks,"  Archetypal analysis is a type of factor analysis where data is fit by a
+convex polytope whose corners are ""archetypes"" of the data, with the data
+represented as a convex combination of these archetypal points. While
+archetypal analysis has been used on biological data, it has not achieved
+widespread adoption because most data are not well fit by a convex polytope in
+either the ambient space or after standard data transformations. We propose a
+new approach to archetypal analysis. Instead of fitting a convex polytope
+directly on data or after a specific data transformation, we train a neural
+network (AAnet) to learn a transformation under which the data can best fit
+into a polytope. We validate this approach on synthetic data where we add
+nonlinearity. Here, AAnet is the only method that correctly identifies the
+archetypes. We also demonstrate AAnet on two biological datasets. In a T cell
+dataset measured with single cell RNA-sequencing, AAnet identifies several
+archetypal states corresponding to naive, memory, and cytotoxic T cells. In a
+dataset of gut microbiome profiles, AAnet recovers both previously described
+microbiome states and identifies novel extrema in the data. Finally, we show
+that AAnet has generative properties allowing us to uniformly sample from the
+data geometry even when the input data is not uniformly distributed.
+",1,0,0,1,0,0
+2175,Formally continuous functions on Baire space,"  A function from Baire space to the natural numbers is called formally
+continuous if it is induced by a morphism between the corresponding formal
+spaces. We compare formal continuity to two other notions of continuity on
+Baire space working in Bishop constructive mathematics: one is a function
+induced by a Brouwer-operation (i.e. inductively defined neighbourhood
+function); the other is a function uniformly continuous near every compact
+image. We show that formal continuity is equivalent to the former while it is
+strictly stronger than the latter.
+",1,0,1,0,0,0
+2176,Universal Joint Image Clustering and Registration using Partition Information,"  We consider the problem of universal joint clustering and registration of
+images and define algorithms using multivariate information functionals. We
+first study registering two images using maximum mutual information and prove
+its asymptotic optimality. We then show the shortcomings of pairwise
+registration in multi-image registration, and design an asymptotically optimal
+algorithm based on multiinformation. Further, we define a novel multivariate
+information functional to perform joint clustering and registration of images,
+and prove consistency of the algorithm. Finally, we consider registration and
+clustering of numerous limited-resolution images, defining algorithms that are
+order-optimal in scaling of number of pixels in each image with the number of
+images.
+",1,0,1,1,0,0
+2177,Quantitative statistical stability and speed of convergence to equilibrium for partially hyperbolic skew products,"  We consider a general relation between fixed point stability of suitably
+perturbed transfer operators and convergence to equilibrium (a notion which is
+strictly related to decay of correlations). We apply this relation to
+deterministic perturbations of a class of (piecewise) partially hyperbolic skew
+products whose behavior on the preserved fibration is dominated by the
+expansion of the base map. In particular we apply the results to power law
+mixing toral extensions. It turns out that in this case, the dependence of the
+physical measure on small deterministic perturbations, in a suitable
+anisotropic metric is at least Holder continuous, with an exponent which is
+explicitly estimated depending on the arithmetical properties of the system. We
+show explicit examples of toral extensions having actually Holder stability and
+non differentiable dependence of the physical measure on perturbations.
+",0,0,1,0,0,0
+2178,Minimax Rényi Redundancy,"  The redundancy for universal lossless compression of discrete memoryless
+sources in Campbell's setting is characterized as a minimax Rényi divergence,
+which is shown to be equal to the maximal $\alpha$-mutual information via a
+generalized redundancy-capacity theorem. Special attention is placed on the
+analysis of the asymptotics of minimax Rényi divergence, which is determined
+up to a term vanishing in blocklength.
+",1,0,1,0,0,0
+2179,DeepArchitect: Automatically Designing and Training Deep Architectures,"  In deep learning, performance is strongly affected by the choice of
+architecture and hyperparameters. While there has been extensive work on
+automatic hyperparameter optimization for simple spaces, complex spaces such as
+the space of deep architectures remain largely unexplored. As a result, the
+choice of architecture is done manually by the human expert through a slow
+trial and error process guided mainly by intuition. In this paper we describe a
+framework for automatically designing and training deep models. We propose an
+extensible and modular language that allows the human expert to compactly
+represent complex search spaces over architectures and their hyperparameters.
+The resulting search spaces are tree-structured and therefore easy to traverse.
+Models can be automatically compiled to computational graphs once values for
+all hyperparameters have been chosen. We can leverage the structure of the
+search space to introduce different model search algorithms, such as random
+search, Monte Carlo tree search (MCTS), and sequential model-based optimization
+(SMBO). We present experiments comparing the different algorithms on CIFAR-10
+and show that MCTS and SMBO outperform random search. In addition, these
+experiments show that our framework can be used effectively for model
+discovery, as it is possible to describe expressive search spaces and discover
+competitive models without much effort from the human expert. Code for our
+framework and experiments has been made publicly available.
+",0,0,0,1,0,0
+2180,Dynamical control of atoms with polarized bichromatic weak field,"  We propose ultranarrow dynamical control of population oscillation (PO)
+between ground states through the polarization content of an input bichromatic
+field. Appropriate engineering of classical interference between optical fields
+results in PO arising exclusively from optical pumping. Contrary to the
+expected broad spectral response associated with optical pumping, we obtain
+subnatural linewidth in complete absence of quantum interference. The
+ellipticity of the light polarizations can be used for temporal shaping of the
+PO leading to generation of multiple sidebands even at low light level.
+",0,1,0,0,0,0
+2181,Cycle packings of the complete multigraph,"  Bryant, Horsley, Maenhaut and Smith recently gave necessary and sufficient
+conditions for when the complete multigraph can be decomposed into cycles of
+specified lengths $m_1,m_2,\ldots,m_\tau$. In this paper we characterise
+exactly when there exists a packing of the complete multigraph with cycles of
+specified lengths $m_1,m_2,\ldots,m_\tau$. While cycle decompositions can give
+rise to packings by removing cycles from the decomposition, in general it is
+not known when there exists a packing of the complete multigraph with cycles of
+various specified lengths.
+",0,0,1,0,0,0
+2182,Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients,"  Modern statistical inference tasks often require iterative optimization
+methods to approximate the solution. Convergence analysis from optimization
+only tells us how well we are approximating the solution deterministically, but
+overlooks the sampling nature of the data. However, due to the randomness in
+the data, statisticians are keen to provide uncertainty quantification, or
+confidence, for the answer obtained after certain steps of optimization.
+Therefore, it is important yet challenging to understand the sampling
+distribution of the iterative optimization methods.
+This paper makes some progress along this direction by introducing a new
+stochastic optimization method for statistical inference, the moment adjusted
+stochastic gradient descent. We establish non-asymptotic theory that
+characterizes the statistical distribution of the iterative methods, with good
+optimization guarantee. On the statistical front, the theory allows for model
+misspecification, with very mild conditions on the data. For optimization, the
+theory is flexible for both the convex and non-convex cases. Remarkably, the
+moment adjusting idea motivated from ""error standardization"" in statistics
+achieves similar effect as Nesterov's acceleration in optimization, for certain
+convex problems as in fitting generalized linear models. We also demonstrate
+this acceleration effect in the non-convex setting through experiments.
+",0,0,1,1,0,0
+2183,Super cavity solitons and the coexistence of multiple nonlinear states in a tristable passive Kerr resonator,"  Passive Kerr cavities driven by coherent laser fields display a rich
+landscape of nonlinear physics, including bistability, pattern formation, and
+localised dissipative structures (solitons). Their conceptual simplicity has
+for several decades offered an unprecedented window into nonlinear cavity
+dynamics, providing insights into numerous systems and applications ranging
+from all-optical memory devices to microresonator frequency combs. Yet despite
+the decades of study, a recent theoretical study has surprisingly alluded to an
+entirely new and unexplored paradigm in the regime where nonlinearly tilted
+cavity resonances overlap with one another [T. Hansson and S. Wabnitz, J. Opt.
+Soc. Am. B 32, 1259 (2015)]. We have used synchronously driven fiber ring
+resonators to experimentally access this regime, and observed the rise of new
+nonlinear dissipative states. Specifically, we have observed, for the first
+time to the best of our knowledge, the stable coexistence of dissipative
+(cavity) solitons and extended modulation instability (Turing) patterns, and
+performed real time measurements that unveil the dynamics of the ensuing
+nonlinear structures. When operating in the regime of continuous wave
+tristability, we have further observed the coexistence of two distinct cavity
+soliton states, one of which can be identified as a ""super"" cavity soliton as
+predicted by Hansson and Wabnitz. Our experimental findings are in excellent
+agreement with theoretical analyses and numerical simulations of the
+infinite-dimensional Ikeda map that governs the cavity dynamics. The results
+from our work reveal that experimental systems can support complex combinations
+of distinct nonlinear states, and they could have practical implications to
+future microresonator-based frequency comb sources.
+",0,1,0,0,0,0
+2184,Twisting and Mixing,"  We present a framework that connects three interesting classes of groups: the
+twisted groups (also known as Suzuki-Ree groups), the mixed groups and the
+exotic pseudo-reductive groups.
+For a given characteristic p, we construct categories of twisted and mixed
+schemes. Ordinary schemes are a full subcategory of the mixed schemes. Mixed
+schemes arise from a twisted scheme by base change, although not every mixed
+scheme arises this way. The group objects in these categories are called
+twisted and mixed group schemes.
+Our main theorems state: (1) The twisted Chevalley groups ${}^2\mathsf B_2$,
+${}^2\mathsf G_2$ and ${}^2\mathsf F_4$ arise as rational points of twisted
+group schemes. (2) The mixed groups in the sense of Tits arise as rational
+points of mixed group schemes over mixed fields. (3) The exotic
+pseudo-reductive groups of Conrad, Gabber and Prasad are Weil restrictions of
+mixed group schemes.
+",0,0,1,0,0,0
+2185,KeyVec: Key-semantics Preserving Document Representations,"  Previous studies have demonstrated the empirical success of word embeddings
+in various applications. In this paper, we investigate the problem of learning
+distributed representations for text documents which many machine learning
+algorithms take as input for a number of NLP tasks.
+We propose a neural network model, KeyVec, which learns document
+representations with the goal of preserving key semantics of the input text. It
+enables the learned low-dimensional vectors to retain the topics and important
+information from the documents that will flow to downstream tasks. Our
+empirical evaluations show the superior quality of KeyVec representations in
+two different document understanding tasks.
+",1,0,0,0,0,0
+2186,Integer Factorization with a Neuromorphic Sieve,"  The bound to factor large integers is dominated by the computational effort
+to discover numbers that are smooth, typically performed by sieving a
+polynomial sequence. On a von Neumann architecture, sieving has log-log
+amortized time complexity to check each value for smoothness. This work
+presents a neuromorphic sieve that achieves a constant time check for
+smoothness by exploiting two characteristic properties of neuromorphic
+architectures: constant time synaptic integration and massively parallel
+computation. The approach is validated by modifying msieve, one of the fastest
+publicly available integer factorization implementations, to use the IBM
+Neurosynaptic System (NS1e) as a coprocessor for the sieving stage.
+",1,0,0,0,0,0
+2187,Probabilistic Assessment of PV-Battery System Impacts on LV Distribution Networks,"  The increasing uptake of residential batteries has led to suggestions that
+the prevalence of batteries on LV networks will serendipitously mitigate the
+technical problems induced by PV installations. However, in general, the
+effects of PV-battery systems on LV networks have not been well studied. Given
+this background, in this paper, we test the assertion that the uncoordinated
+operation of batteries improves network performance. In order to carry out this
+assessment, we develop a methodology for incorporating home energy management
+(HEM) operational decisions within a Monte Carlo (MC) power flow analysis
+comprising three parts. First, due to the unavailability of large number of
+load and PV traces required for MC analysis, we used a maximum a-posteriori
+Dirichlet process to generate statistically representative synthetic profiles.
+Second, a policy function approximation (PFA) that emulates the outputs of the
+HEM solver is implemented to provide battery scheduling policies for a pool of
+customers, making simulation of optimization-based HEM feasible within MC
+studies. Third, the resulting net loads are used in a MC power flow time series
+study. The efficacy of our method is shown on three typical LV feeders. Our
+assessment finds that uncoordinated PV-battery systems have little beneficial
+impact on LV networks.
+",1,0,0,0,0,0
+2188,Simulating Cellular Communications in Vehicular Networks: Making SimuLTE Interoperable with Veins,"  The evolution of cellular technologies toward 5G progressively enables
+efficient and ubiquitous communications in an increasing number of fields.
+Among these, vehicular networks are being considered as one of the most
+promising and challenging applications, requiring support for communications in
+high-speed mobility and delay-constrained information exchange in proximity. In
+this context, simulation frameworks under the OMNeT++ umbrella are already
+available: SimuLTE and Veins for cellular and vehicular systems, respectively.
+In this paper, we describe the modifications that make SimuLTE interoperable
+with Veins and INET, which leverage the OMNeT++ paradigm, and allow us to
+achieve our goal without any modification to either of the latter two. We
+discuss the limitations of the previous solution, namely VeinsLTE, which
+integrates all three in a single framework, thus preventing independent
+evolution and upgrades of each building block.
+",1,0,0,0,0,0
+2189,AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks,"  Training deep neural networks with Stochastic Gradient Descent, or its
+variants, requires careful choice of both learning rate and batch size. While
+smaller batch sizes generally converge in fewer training epochs, larger batch
+sizes offer more parallelism and hence better computational efficiency. We have
+developed a new training approach that, rather than statically choosing a
+single batch size for all epochs, adaptively increases the batch size during
+the training process. Our method delivers the convergence rate of small batch
+sizes while achieving performance similar to large batch sizes. We analyse our
+approach using the standard AlexNet, ResNet, and VGG networks operating on the
+popular CIFAR-10, CIFAR-100, and ImageNet datasets. Our results demonstrate
+that learning with adaptive batch sizes can improve performance by factors of
+up to 6.25 on 4 NVIDIA Tesla P100 GPUs while changing accuracy by less than 1%
+relative to training with fixed batch sizes.
+",1,0,0,1,0,0
+2190,Extremely broadband ultralight thermally emissive metasurfaces,"  We report the design, fabrication and characterization of ultralight highly
+emissive metaphotonic structures with record-low mass/area that emit thermal
+radiation efficiently over a broad spectral (2 to 35 microns) and angular (0-60
+degrees) range. The structures comprise one to three pairs of alternating
+nanometer-scale metallic and dielectric layers, and have measured effective 300
+K hemispherical emissivities of 0.7 to 0.9. To our knowledge, these structures,
+which are all subwavelength in thickness are the lightest reported metasurfaces
+with comparable infrared emissivity. The superior optical properties, together
+with their mechanical flexibility, low outgassing, and low areal mass, suggest
+that these metasurfaces are candidates for thermal management in applications
+demanding of ultralight flexible structures, including aerospace applications,
+ultralight photovoltaics, lightweight flexible electronics, and textiles for
+thermal insulation.
+",0,1,0,0,0,0
+2191,Wright-Fisher diffusions for evolutionary games with death-birth updating,"  We investigate spatial evolutionary games with death-birth updating in large
+finite populations. Within growing spatial structures subject to appropriate
+conditions, the density processes of a fixed type are proven to converge to the
+Wright-Fisher diffusions with drift. In addition, convergence in the
+Wasserstein distance of the laws of their occupation measures holds. The proofs
+of these results develop along an equivalence between the laws of the
+evolutionary games and certain voter models and rely on the analogous results
+of voter models on large finite sets by convergences of the Radon-Nikodym
+derivative processes. As another application of this equivalence of laws, we
+show that in a general, large population of size $N$, for which the stationary
+probabilities of the corresponding voting kernel are comparable to uniform
+probabilities, a first-derivative test among the major methods for these
+evolutionary games is applicable at least up to weak selection strengths in the
+usual biological sense (that is, selection strengths of the order $\mathcal
+O(1/N)$).
+",0,0,1,0,0,0
+2192,Quantized Compressed Sensing for Partial Random Circulant Matrices,"  We provide the first analysis of a non-trivial quantization scheme for
+compressed sensing measurements arising from structured measurements.
+Specifically, our analysis studies compressed sensing matrices consisting of
+rows selected at random, without replacement, from a circulant matrix generated
+by a random subgaussian vector. We quantize the measurements using stable,
+possibly one-bit, Sigma-Delta schemes, and use a reconstruction method based on
+convex optimization. We show that the part of the reconstruction error due to
+quantization decays polynomially in the number of measurements. This is in line
+with analogous results on Sigma-Delta quantization associated with random
+Gaussian or subgaussian matrices, and significantly better than results
+associated with the widely assumed memoryless scalar quantization. Moreover, we
+prove that our approach is stable and robust; i.e., the reconstruction error
+degrades gracefully in the presence of non-quantization noise and when the
+underlying signal is not strictly sparse. The analysis relies on results
+concerning subgaussian chaos processes as well as a variation of McDiarmid's
+inequality.
+",1,0,1,0,0,0
+2193,Optimal Scheduling of Multi-Energy Systems with Flexible Electrical and Thermal Loads,"  This paper proposes a detailed optimal scheduling model of an exemplar
+multi-energy system comprising combined cycle power plants (CCPPs), battery
+energy storage systems, renewable energy sources, boilers, thermal energy
+storage systems,electric loads and thermal loads. The proposed model considers
+the detailed start-up and shutdown power trajectories of the gas turbines,
+steam turbines and boilers. Furthermore, a practical,multi-energy load
+management scheme is proposed within the framework of the optimal scheduling
+problem. The proposed load management scheme utilizes the flexibility offered
+by system components such as flexible electrical pump loads, electrical
+interruptible loads and a flexible thermal load to reduce the overall energy
+cost of the system. The efficacy of the proposed model in reducing the energy
+cost of the system is demonstrated in the context of a day-ahead scheduling
+problem using four illustrative scenarios.
+",1,0,0,0,0,0
+2194,On the sharpness and the injective property of basic justification models,"  Justification Awareness Models, JAMs, were proposed by S.~Artemov as a tool
+for modelling epistemic scenarios like Russel's Prime Minister example. It was
+demonstrated that the sharpness and the injective property of a model play
+essential role in the epistemic usage of JAMs. The problem to axiomatize these
+properties using the propositional justification language was left opened. We
+propose the solution and define a decidable justification logic Jref that is
+sound and complete with respect to the class of all sharp injective
+justification models.
+",1,0,0,0,0,0
+2195,AFT*: Integrating Active Learning and Transfer Learning to Reduce Annotation Efforts,"  The splendid success of convolutional neural networks (CNNs) in computer
+vision is largely attributed to the availability of large annotated datasets,
+such as ImageNet and Places. However, in biomedical imaging, it is very
+challenging to create such large annotated datasets, as annotating biomedical
+images is not only tedious, laborious, and time consuming, but also demanding
+of costly, specialty-oriented skills, which are not easily accessible. To
+dramatically reduce annotation cost, this paper presents a novel method to
+naturally integrate active learning and transfer learning (fine-tuning) into a
+single framework, called AFT*, which starts directly with a pre-trained CNN to
+seek ""worthy"" samples for annotation and gradually enhance the (fine-tuned) CNN
+via continuous fine-tuning. We have evaluated our method in three distinct
+biomedical imaging applications, demonstrating that it can cut the annotation
+cost by at least half, in comparison with the state-of-the-art method. This
+performance is attributed to the several advantages derived from the advanced
+active, continuous learning capability of our method. Although AFT* was
+initially conceived in the context of computer-aided diagnosis in biomedical
+imaging, it is generic and applicable to many tasks in computer vision and
+image analysis; we illustrate the key ideas behind AFT* with the Places
+database for scene interpretation in natural images.
+",0,0,0,1,0,0
+2196,Do triangle-free planar graphs have exponentially many 3-colorings?,"  Thomassen conjectured that triangle-free planar graphs have an exponential
+number of $3$-colorings. We show this conjecture to be equivalent to the
+following statement: there exists a positive real $\alpha$ such that whenever
+$G$ is a planar graph and $A$ is a subset of its edges whose deletion makes $G$
+triangle-free, there exists a subset $A'$ of $A$ of size at least $\alpha|A|$
+such that $G-(A\setminus A')$ is $3$-colorable. This equivalence allows us to
+study restricted situations, where we can prove the statement to be true.
+",0,0,1,0,0,0
+2197,Smart TWAP trading in continuous-time equilibria,"  This paper presents a continuous-time equilibrium model of TWAP trading and
+liquidity provision in a market with multiple strategic investors with
+heterogeneous intraday trading targets. We solve the model in closed-form and
+show there are infinitely many equilibria. We compare the competitive
+equilibrium with different non-price-taking equilibria. In addition, we show
+intraday TWAP benchmarking reduces market liquidity relative to just terminal
+trading targets alone. The model is computationally tractable, and we provide a
+number of numerical illustrations. An extension to stochastic VWAP targets is
+also provided.
+",0,0,0,0,0,1
+2198,Self-sustained activity in balanced networks with low firing-rate,"  The brain can display self-sustained activity (SSA), which is the persistent
+firing of neurons in the absence of external stimuli. This spontaneous activity
+shows low neuronal firing rates and is observed in diverse in vitro and in vivo
+situations. In this work, we study the influence of excitatory/inhibitory
+balance, connection density, and network size on the self-sustained activity of
+a neuronal network model. We build a random network of adaptive exponential
+integrate-and-fire (AdEx) neuron models connected through inhibitory and
+excitatory chemical synapses. The AdEx model mimics several behaviours of
+biological neurons, such as spike initiation, adaptation, and bursting
+patterns. In an excitation/inhibition balanced state, if the mean connection
+degree (K) is fixed, the firing rate does not depend on the network size (N),
+whereas for fixed N, the firing rate decreases when K increases. However, for
+large K, SSA states can appear only for large N. We show the existence of SSA
+states with similar behaviours to those observed in experimental recordings,
+such as very low and irregular neuronal firing rates, and spike-train power
+spectra with slow fluctuations, only for balanced networks of large size.
+",0,0,0,0,1,0
+2199,Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data,"  Several important applications, such as streaming PCA and semidefinite
+programming, involve a large-scale positive-semidefinite (psd) matrix that is
+presented as a sequence of linear updates. Because of storage limitations, it
+may only be possible to retain a sketch of the psd matrix. This paper develops
+a new algorithm for fixed-rank psd approximation from a sketch. The approach
+combines the Nystrom approximation with a novel mechanism for rank truncation.
+Theoretical analysis establishes that the proposed method can achieve any
+prescribed relative error in the Schatten 1-norm and that it exploits the
+spectral decay of the input matrix. Computer experiments show that the proposed
+method dominates alternative techniques for fixed-rank psd matrix approximation
+across a wide range of examples.
+",1,0,0,1,0,0
+2200,A New Perspective on Robust $M$-Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing,"  Heavy-tailed errors impair the accuracy of the least squares estimate, which
+can be spoiled by a single grossly outlying observation. As argued in the
+seminal work of Peter Huber in 1973 [{\it Ann. Statist.} {\bf 1} (1973)
+799--821], robust alternatives to the method of least squares are sorely
+needed. To achieve robustness against heavy-tailed sampling distributions, we
+revisit the Huber estimator from a new perspective by letting the tuning
+parameter involved diverge with the sample size. In this paper, we develop
+nonasymptotic concentration results for such an adaptive Huber estimator,
+namely, the Huber estimator with the tuning parameter adapted to sample size,
+dimension, and the variance of the noise. Specifically, we obtain a
+sub-Gaussian-type deviation inequality and a nonasymptotic Bahadur
+representation when noise variables only have finite second moments. The
+nonasymptotic results further yield two conventional normal approximation
+results that are of independent interest, the Berry-Esseen inequality and
+Cramér-type moderate deviation. As an important application to large-scale
+simultaneous inference, we apply these robust normal approximation results to
+analyze a dependence-adjusted multiple testing procedure for moderately
+heavy-tailed data. It is shown that the robust dependence-adjusted procedure
+asymptotically controls the overall false discovery proportion at the nominal
+level under mild moment conditions. Thorough numerical results on both
+simulated and real datasets are also provided to back up our theory.
+",0,0,1,1,0,0
+2201,"On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications","  In this paper we analyse the benefits of incorporating interval-valued fuzzy
+sets into the Bousi-Prolog system. A syntax, declarative semantics and im-
+plementation for this extension is presented and formalised. We show, by using
+potential applications, that fuzzy logic programming frameworks enhanced with
+them can correctly work together with lexical resources and ontologies in order
+to improve their capabilities for knowledge representation and reasoning.
+",1,0,0,0,0,0
+2202,Characterization of Thermal Neutron Beam Monitors,"  Neutron beam monitors with high efficiency, low gamma sensitivity, high time
+and space resolution are required in neutron beam experiments to continuously
+diagnose the delivered beam. In this work, commercially available neutron beam
+monitors have been characterized using the R2D2 beamline at IFE (Norway) and
+using a Be-based neutron source. For the gamma sensitivity measurements
+different gamma sources have been used. The evaluation of the monitors
+includes, the study of their efficiency, attenuation, scattering and
+sensitivity to gamma. In this work we report the results of this
+characterization.
+",0,1,0,0,0,0
+2203,Detecting Galaxy-Filament Alignments in the Sloan Digital Sky Survey III,"  Previous studies have shown the filamentary structures in the cosmic web
+influence the alignments of nearby galaxies. We study this effect in the LOWZ
+sample of the Sloan Digital Sky Survey using the ""Cosmic Web Reconstruction""
+filament catalogue of Chen et al. (2016). We find that LOWZ galaxies exhibit a
+small but statistically significant alignment in the direction parallel to the
+orientation of nearby filaments. This effect is detectable even in the absence
+of nearby galaxy clusters, which suggests it is an effect from the matter
+distribution in the filament. A nonparametric regression model suggests that
+the alignment effect with filaments extends over separations of $30-40$ Mpc. We
+find that galaxies that are bright and early-forming align more strongly with
+the directions of nearby filaments than those that are faint and late-forming;
+however, trends with stellar mass are less statistically significant, within
+the narrow range of stellar mass of this sample.
+",0,0,0,1,0,0
+2204,Pandeia: A Multi-mission Exposure Time Calculator for JWST and WFIRST,"  Pandeia is the exposure time calculator (ETC) system developed for the James
+Webb Space Telescope (JWST) that will be used for creating JWST proposals. It
+includes a simulation-hybrid Python engine that calculates the two-dimensional
+pixel-by-pixel signal and noise properties of the JWST instruments. This allows
+for appropriate handling of realistic point spread functions, MULTIACCUM
+detector readouts, correlated detector readnoise, and multiple photometric and
+spectral extraction strategies. Pandeia includes support for all the JWST
+observing modes, including imaging, slitted/slitless spectroscopy, integral
+field spectroscopy, and coronagraphy. Its highly modular, data-driven design
+makes it easily adaptable to other observatories. An implementation for use
+with WFIRST is also available.
+",0,1,0,0,0,0
+2205,The numbers of edges of 5-polytopes with a given number of vertices,"  A basic combinatorial invariant of a convex polytope $P$ is its $f$-vector
+$f(P)=(f_0,f_1,\dots,f_{\dim P-1})$, where $f_i$ is the number of
+$i$-dimensional faces of $P$. Steinitz characterized all possible $f$-vectors
+of $3$-polytopes and Grünbaum characterized the pairs given by the first two
+entries of the $f$-vectors of $4$-polytopes. In this paper, we characterize the
+pairs given by the first two entries of the $f$-vectors of $5$-polytopes. The
+same result was also proved by Pineda-Villavicencio, Ugon and Yost
+independently.
+",0,0,1,0,0,0
+2206,MapExif: an image scanning and mapping tool for investigators,"  Recently, the integration of geographical coordinates into a picture has
+become more and more popular. Indeed almost all smartphones and many cameras
+today have a built-in GPS receiver that stores the location information in the
+Exif header when a picture is taken. Although the automatic embedding of
+geotags in pictures is often ignored by smart phone users as it can lead to
+endless discussions about privacy implications, these geotags could be really
+useful for investigators in analysing criminal activity. Currently, there are
+many free tools as well as commercial tools available in the market that can
+help computer forensics investigators to cover a wide range of geographic
+information related to criminal scenes or activities. However, there are not
+specific forensic tools available to deal with the geolocation of pictures
+taken by smart phones or cameras. In this paper, we propose and develop an
+image scanning and mapping tool for investigators. This tool scans all the
+files in a given directory and then displays particular photos based on
+optional filters (date, time, device, localisation) on Google Map. The file
+scanning process is not based on the file extension but its header. This tool
+can also show efficiently to users if there is more than one image on the map
+with the same GPS coordinates, or even if there are images with no GPS
+coordinates taken by the same device in the same timeline. Moreover, this new
+tool is portable; investigators can run it on any operating system without any
+installation. Another useful feature is to be able to work in a read-only
+environment, so that forensic results will not be modified. We also present and
+evaluate this tool real world application in this paper.
+",1,0,0,0,0,0
+2207,Fitch-Style Modal Lambda Calculi,"  Fitch-style modal deduction, in which modalities are eliminated by opening a
+subordinate proof, and introduced by shutting one, were investigated in the
+1990s as a basis for lambda calculi. We show that such calculi have good
+computational properties for a variety of intuitionistic modal logics.
+Semantics are given in cartesian closed categories equipped with an adjunction
+of endofunctors, with the necessity modality interpreted by the right adjoint.
+Where this functor is an idempotent comonad, a coherence result on the
+semantics allows us to present a calculus for intuitionistic S4 that is simpler
+than others in the literature. We show the calculi can be extended à la
+tense logic with the left adjoint of necessity, and are then complete for the
+categorical semantics.
+",1,0,0,0,0,0
+2208,Hot Stuff for One Year (HSOY) - A 583 million star proper motion catalogue derived from Gaia DR1 and PPMXL,"  Recently, the first installment of data from ESA's Gaia astrometric satellite
+mission (Gaia-DR1) was released, containing positions of more than 1 billion
+stars with unprecedented precision, as well as only proper motions and
+parallaxes, however only for a subset of 2 million objects. The second release,
+due in late 2017 or early 2018, will include those quantities for most objects.
+In order to provide a dataset that bridges the time gap between the Gaia-DR1
+and Gaia-DR2 releases and partly remedies the lack of proper motions in the
+former, HSOY (""Hot Stuff for One Year"") was created as a hybrid catalogue
+between Gaia-DR1 and ground-based astrometry, featuring proper motions (but no
+parallaxes) for a large fraction of the DR1 objects. While not attempting to
+compete with future Gaia releases in terms of data quality or number of
+objects, the aim of HSOY is to provide improved proper motions partly based on
+Gaia data, allowing some studies to be carried out just now or as pilot studies
+for later larger projects requiring higher-precision data. The HSOY catalogue
+was compiled using the positions taken from Gaia-DR1 combined with the input
+data from the PPMXL catalogue, employing the same weighted least-squares
+technique that was used to assemble the PPMXL catalogue itself. Results. This
+effort resulted in a four-parameter astrometric catalogue containing
+583,000,000 objects, with Gaia-DR1 quality positions and proper motions with
+precisions from significantly less than 1 mas/yr to 5 mas/yr, depending on the
+object's brightness and location on the sky.
+",0,1,0,0,0,0
+2209,Asymptotic behaviour of ground states for mixtures of ferromagnetic and antiferromagnetic interactions in a dilute regime,"  We consider randomly distributed mixtures of bonds of ferromagnetic and
+antiferromagnetic type in a two-dimensional square lattice with probability
+$1-p$ and $p$, respectively, according to an i.i.d. random variable. We study
+minimizers of the corresponding nearest-neighbour spin energy on large domains
+in ${\mathbb Z}^2$. We prove that there exists $p_0$ such that for $p\le p_0$
+such minimizers are characterized by a majority phase; i.e., they take
+identically the value $1$ or $-1$ except for small disconnected sets. A
+deterministic analogue is also proved.
+",0,0,1,0,0,0
+2210,Extended Reduced-Form Framework for Non-Life Insurance,"  In this paper we propose a general framework for modeling an insurance
+claims' information flow in continuous time, by generalizing the reduced-form
+framework for credit risk and life insurance. In particular, we assume a
+nontrivial dependence structure between the reference filtration and the
+insurance internal filtration. We apply these results for pricing non-life
+insurance liabilities in hybrid financial and insurance markets, while taking
+into account the role of inflation under the benchmark approach. This framework
+offers at the same time a general and flexible structure, and explicit and
+treatable pricing formula.
+",0,0,0,0,0,1
+2211,Overview of Recent Studies and Design Changes for the FNAL Magnetron Ion Source,"  This paper will cover several studies and design changes that will eventually
+be implemented to the Fermi National Accelerator Laboratory (FNAL) magnetron
+ion source. The topics include tungsten cathode insert, solenoid gas valves,
+current controlled arc pulser, cesium boiler redesign, gas mixtures of hydrogen
+and nitrogen, and duty factor reduction. The studies were performed on the FNAL
+test stand, with the aim to improve source lifetime, stability, and reducing
+the amount of tuning needed.
+",0,1,0,0,0,0
+2212,Complete algebraic solution of multidimensional optimization problems in tropical semifield,"  We consider multidimensional optimization problems that are formulated in the
+framework of tropical mathematics to minimize functions defined on vectors over
+a tropical semifield (a semiring with idempotent addition and invertible
+multiplication). The functions, given by a matrix and calculated through
+multiplicative conjugate transposition, are nonlinear in the tropical
+mathematics sense. We start with known results on the solution of the problems
+with irreducible matrices. To solve the problems in the case of arbitrary
+(reducible) matrices, we first derive the minimum value of the objective
+function, and find a set of solutions. We show that all solutions of the
+problem satisfy a system of vector inequalities, and then use these
+inequalities to establish characteristic properties of the solution set.
+Furthermore, all solutions of the problem are represented as a family of
+subsets, each defined by a matrix that is obtained by using a matrix
+sparsification technique. We describe a backtracking procedure that allows one
+to reduce the brute-force generation of sparsified matrices by skipping those,
+which cannot provide solutions, and thus offers an economical way to obtain all
+subsets in the family. Finally, the characteristic properties of the solution
+set are used to provide complete solutions in a closed form. We illustrate the
+results obtained with simple numerical examples.
+",1,0,1,0,0,0
+2213,Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back,"  A graph is a powerful concept for representation of relations between pairs
+of entities. Data with underlying graph structure can be found across many
+disciplines and there is a natural desire for understanding such data better.
+Deep learning (DL) has achieved significant breakthroughs in a variety of
+machine learning tasks in recent years, especially where data is structured on
+a grid, such as in text, speech, or image understanding. However, surprisingly
+little has been done to explore the applicability of DL on arbitrary
+graph-structured data directly.
+The goal of this thesis is to investigate architectures for DL on graphs and
+study how to transfer, adapt or generalize concepts that work well on
+sequential and image data to this domain. We concentrate on two important
+primitives: embedding graphs or their nodes into a continuous vector space
+representation (encoding) and, conversely, generating graphs from such vectors
+back (decoding). To that end, we make the following contributions.
+First, we introduce Edge-Conditioned Convolutions (ECC), a convolution-like
+operation on graphs performed in the spatial domain where filters are
+dynamically generated based on edge attributes. The method is used to encode
+graphs with arbitrary and varying structure.
+Second, we propose SuperPoint Graph, an intermediate point cloud
+representation with rich edge attributes encoding the contextual relationship
+between object parts. Based on this representation, ECC is employed to segment
+large-scale point clouds without major sacrifice in fine details.
+Third, we present GraphVAE, a graph generator allowing us to decode graphs
+with variable but upper-bounded number of nodes making use of approximate graph
+matching for aligning the predictions of an autoencoder with its inputs. The
+method is applied to the task of molecule generation.
+",1,0,0,1,0,0
+2214,Supermodular Optimization for Redundant Robot Assignment under Travel-Time Uncertainty,"  This paper considers the assignment of multiple mobile robots to goal
+locations under uncertain travel time estimates. Our aim is to produce optimal
+assignments, such that the average waiting time at destinations is minimized.
+Our premise is that time is the most valuable asset in the system. Hence, we
+make use of redundant robots to counter the effect of uncertainty. Since
+solving the redundant assignment problem is strongly NP-hard, we exploit
+structural properties of our problem to propose a polynomial-time, near-optimal
+solution. We demonstrate that our problem can be reduced to minimizing a
+supermodular cost function subject to a matroid constraint. This allows us to
+develop a greedy algorithm, for which we derive sub-optimality bounds. A
+comparison with the baseline non-redundant assignment shows that redundant
+assignment reduces the waiting time at goals, and that this performance gap
+increases as noise increases. Finally, we evaluate our method on a mobility
+data set (specifying vehicle availability and passenger requests), recorded in
+the area of Manhattan, New York. Our algorithm performs in real-time, and
+reduces passenger waiting times when travel times are uncertain.
+",1,0,0,0,0,0
+2215,Several classes of optimal ternary cyclic codes,"  Cyclic codes have efficient encoding and decoding algorithms over finite
+fields, so that they have practical applications in communication systems,
+consumer electronics and data storage systems. The objective of this paper is
+to give eight new classes of optimal ternary cyclic codes with parameters
+$[3^m-1,3^m-1-2m,4]$, according to a result on the non-existence of solutions
+to a certain equation over $F_{3^m}$. It is worth noticing that some recent
+conclusions on such optimal ternary cyclic codes are some special cases of our
+work. More importantly, three of the nine open problems proposed by Ding and
+Helleseth in [8] are solved completely. In addition, another one among the nine
+open problems is also promoted.
+",1,0,1,0,0,0
+2216,$Σ$-pure-injective modules for string algebras and linear relations,"  We prove that indecomposable $\Sigma$-pure-injective modules for a string
+algebra are string or band modules. The key step in our proof is a splitting
+result for infinite-dimensional linear relations.
+",0,0,1,0,0,0
+2217,Continuous Optimization of Adaptive Quadtree Structures,"  We present a novel continuous optimization method to the discrete problem of
+quadtree optimization. The optimization aims at achieving a quadtree structure
+with the highest mechanical stiffness, where the edges in the quadtree are
+interpreted as structural elements carrying mechanical loads. We formulate
+quadtree optimization as a continuous material distribution problem. The
+discrete design variables (i.e., to refine or not to refine) are replaced by
+continuous variables on multiple levels in the quadtree hierarchy. In discrete
+quadtree optimization, a cell is only eligible for refinement if its parent
+cell has been refined. We propose a continuous analogue to this dependency for
+continuous multi-level design variables, and integrate it in the iterative
+optimization process. Our results show that the continuously optimized quadtree
+structures perform much stiffer than uniform patterns and the heuristically
+optimized counterparts. We demonstrate the use of adaptive structures as
+lightweight infill for 3D printed parts, where uniform geometric patterns have
+been typically used in practice.
+",1,0,0,0,0,0
+2218,Machine Learning Meets Microeconomics: The Case of Decision Trees and Discrete Choice,"  We provide a microeconomic framework for decision trees: a popular machine
+learning method. Specifically, we show how decision trees represent a
+non-compensatory decision protocol known as disjunctions-of-conjunctions and
+how this protocol generalizes many of the non-compensatory rules used in the
+discrete choice literature so far. Additionally, we show how existing decision
+tree variants address many economic concerns that choice modelers might have.
+Beyond theoretical interpretations, we contribute to the existing literature of
+two-stage, semi-compensatory modeling and to the existing decision tree
+literature. In particular, we formulate the first bayesian model tree, thereby
+allowing for uncertainty in the estimated non-compensatory rules as well as for
+context-dependent preference heterogeneity in one's second-stage choice model.
+Using an application of bicycle mode choice in the San Francisco Bay Area, we
+estimate our bayesian model tree, and we find that it is over 1,000 times more
+likely to be closer to the true data-generating process than a multinomial
+logit model (MNL). Qualitatively, our bayesian model tree automatically finds
+the effect of bicycle infrastructure investment to be moderated by travel
+distance, socio-demographics and topography, and our model identifies
+diminishing returns from bike lane investments. These qualitative differences
+lead to bayesian model tree forecasts that directly align with the observed
+bicycle mode shares in regions with abundant bicycle infrastructure such as
+Davis, CA and the Netherlands. In comparison, MNL's forecasts are overly
+optimistic.
+",0,0,0,1,0,0
+2219,Structured Control Nets for Deep Reinforcement Learning,"  In recent years, Deep Reinforcement Learning has made impressive advances in
+solving several important benchmark problems for sequential decision making.
+Many control applications use a generic multilayer perceptron (MLP) for
+non-vision parts of the policy network. In this work, we propose a new neural
+network architecture for the policy network representation that is simple yet
+effective. The proposed Structured Control Net (SCN) splits the generic MLP
+into two separate sub-modules: a nonlinear control module and a linear control
+module. Intuitively, the nonlinear control is for forward-looking and global
+control, while the linear control stabilizes the local dynamics around the
+residual of global control. We hypothesize that this will bring together the
+benefits of both linear and nonlinear policies: improve training sample
+efficiency, final episodic reward, and generalization of learned policy, while
+requiring a smaller network and being generally applicable to different
+training methods. We validated our hypothesis with competitive results on
+simulations from OpenAI MuJoCo, Roboschool, Atari, and a custom 2D urban
+driving environment, with various ablation and generalization tests, trained
+with multiple black-box and policy gradient training methods. The proposed
+architecture has the potential to improve upon broader control tasks by
+incorporating problem specific priors into the architecture. As a case study,
+we demonstrate much improved performance for locomotion tasks by emulating the
+biological central pattern generators (CPGs) as the nonlinear part of the
+architecture.
+",1,0,0,0,0,0
+2220,PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits,"  We consider the problem of identifying any $k$ out of the best $m$ arms in an
+$n$-armed stochastic multi-armed bandit. Framed in the PAC setting, this
+particular problem generalises both the problem of `best subset selection' and
+that of selecting `one out of the best m' arms [arcsk 2017]. In applications
+such as crowd-sourcing and drug-designing, identifying a single good solution
+is often not sufficient. Moreover, finding the best subset might be hard due to
+the presence of many indistinguishably close solutions. Our generalisation of
+identifying exactly $k$ arms out of the best $m$, where $1 \leq k \leq m$,
+serves as a more effective alternative. We present a lower bound on the
+worst-case sample complexity for general $k$, and a fully sequential PAC
+algorithm, \GLUCB, which is more sample-efficient on easy instances. Also,
+extending our analysis to infinite-armed bandits, we present a PAC algorithm
+that is independent of $n$, which identifies an arm from the best $\rho$
+fraction of arms using at most an additive poly-log number of samples than
+compared to the lower bound, thereby improving over [arcsk 2017] and
+[Aziz+AKA:2018]. The problem of identifying $k > 1$ distinct arms from the best
+$\rho$ fraction is not always well-defined; for a special class of this
+problem, we present lower and upper bounds. Finally, through a reduction, we
+establish a relation between upper bounds for the `one out of the best $\rho$'
+problem for infinite instances and the `one out of the best $m$' problem for
+finite instances. We conjecture that it is more efficient to solve `small'
+finite instances using the latter formulation, rather than going through the
+former.
+",1,0,0,1,0,0
+2221,Collaborative Filtering using Denoising Auto-Encoders for Market Basket Data,"  Recommender systems (RS) help users navigate large sets of items in the
+search for ""interesting"" ones. One approach to RS is Collaborative Filtering
+(CF), which is based on the idea that similar users are interested in similar
+items. Most model-based approaches to CF seek to train a
+machine-learning/data-mining model based on sparse data; the model is then used
+to provide recommendations. While most of the proposed approaches are effective
+for small-size situations, the combinatorial nature of the problem makes it
+impractical for medium-to-large instances. In this work we present a novel
+approach to CF that works by training a Denoising Auto-Encoder (DAE) on
+corrupted baskets, i.e., baskets from which one or more items have been
+removed. The DAE is then forced to learn to reconstruct the original basket
+given its corrupted input. Due to recent advancements in optimization and other
+technologies for training neural-network models (such as DAE), the proposed
+method results in a scalable and practical approach to CF. The contribution of
+this work is twofold: (1) to identify missing items in observed baskets and,
+thus, directly providing a CF model; and, (2) to construct a generative model
+of baskets which may be used, for instance, in simulation analysis or as part
+of a more complex analytical method.
+",1,0,0,1,0,0
+2222,Stein's Method for Stationary Distributions of Markov Chains and Application to Ising Models,"  We develop a new technique, based on Stein's method, for comparing two
+stationary distributions of irreducible Markov Chains whose update rules are
+`close enough'. We apply this technique to compare Ising models on $d$-regular
+expander graphs to the Curie-Weiss model (complete graph) in terms of pairwise
+correlations and more generally $k$th order moments. Concretely, we show that
+$d$-regular Ramanujan graphs approximate the $k$th order moments of the
+Curie-Weiss model to within average error $k/\sqrt{d}$ (averaged over the size
+$k$ subsets). The result applies even in the low-temperature regime; we also
+derive some simpler approximation results for functionals of Ising models that
+hold only at high enough temperatures.
+",0,0,1,0,0,0
+2223,Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages,"  In this paper, we propose a novel and elegant solution to ""Multi-Source
+Neural Machine Translation"" (MSNMT) which only relies on preprocessing a N-way
+multilingual corpus without modifying the Neural Machine Translation (NMT)
+architecture or training procedure. We simply concatenate the source sentences
+to form a single long multi-source input sentence while keeping the target side
+sentence as it is and train an NMT system using this preprocessed corpus. We
+evaluate our method in resource poor as well as resource rich settings and show
+its effectiveness (up to 4 BLEU using 2 source languages and up to 6 BLEU using
+5 source languages) by comparing against existing methods for MSNMT. We also
+provide some insights on how the NMT system leverages multilingual information
+in such a scenario by visualizing attention.
+",1,0,0,0,0,0
+2224,Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods,"  The literature on Inverse Reinforcement Learning (IRL) typically assumes that
+humans take actions in order to minimize the expected value of a cost function,
+i.e., that humans are risk neutral. Yet, in practice, humans are often far from
+being risk neutral. To fill this gap, the objective of this paper is to devise
+a framework for risk-sensitive IRL in order to explicitly account for a human's
+risk sensitivity. To this end, we propose a flexible class of models based on
+coherent risk measures, which allow us to capture an entire spectrum of risk
+preferences from risk-neutral to worst-case. We propose efficient
+non-parametric algorithms based on linear programming and semi-parametric
+algorithms based on maximum likelihood for inferring a human's underlying risk
+measure and cost function for a rich class of static and dynamic
+decision-making settings. The resulting approach is demonstrated on a simulated
+driving game with ten human participants. Our method is able to infer and mimic
+a wide range of qualitatively different driving styles from highly risk-averse
+to risk-neutral in a data-efficient manner. Moreover, comparisons of the
+Risk-Sensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL
+framework more accurately captures observed participant behavior both
+qualitatively and quantitatively, especially in scenarios where catastrophic
+outcomes such as collisions can occur.
+",1,0,0,0,0,0
+2225,QRT maps and related Laurent systems,"  In recent work it was shown how recursive factorisation of certain QRT maps
+leads to Somos-4 and Somos-5 recurrences with periodic coefficients, and to a
+fifth-order recurrence with the Laurent property. Here we recursively factorise
+the 12-parameter symmetric QRT map, given by a second-order recurrence, to
+obtain a system of three coupled recurrences which possesses the Laurent
+property. As degenerate special cases, we first derive systems of two coupled
+recurrences corresponding to the 5-parameter multiplicative and additive
+symmetric QRT maps. In all cases, the Laurent property is established using a
+generalisation of a result due to Hickerson, and exact formulae for degree
+growth are found from ultradiscrete (tropical) analogues of the recurrences.
+For the general 18-parameter QRT map it is shown that the components of the
+iterates can be written as a ratio of quantities that satisfy the same Somos-7
+recurrence.
+",0,1,1,0,0,0
+2226,Model-based reinforcement learning in differential graphical games,"  This paper seeks to combine differential game theory with the
+actor-critic-identifier architecture to determine forward-in-time, approximate
+optimal controllers for formation tracking in multi-agent systems, where the
+agents have uncertain heterogeneous nonlinear dynamics. A continuous control
+strategy is proposed, using communication feedback from extended neighbors on a
+communication topology that has a spanning tree. A model-based reinforcement
+learning technique is developed to cooperatively control a group of agents to
+track a trajectory in a desired formation. Simulation results are presented to
+demonstrate the performance of the developed technique.
+",1,0,1,0,0,0
+2227,The depth of a finite simple group,"  We introduce the notion of the depth of a finite group $G$, defined as the
+minimal length of an unrefinable chain of subgroups from $G$ to the trivial
+subgroup. In this paper we investigate the depth of (non-abelian) finite simple
+groups. We determine the simple groups of minimal depth, and show, somewhat
+surprisingly, that alternating groups have bounded depth. We also establish
+general upper bounds on the depth of simple groups of Lie type, and study the
+relation between the depth and the much studied notion of the length of simple
+groups. The proofs of our main theorems depend (among other tools) on a deep
+number-theoretic result, namely, Helfgott's recent solution of the ternary
+Goldbach conjecture.
+",0,0,1,0,0,0
+2228,"TRAGALDABAS. First results on cosmic ray studies and their relation with the solar activity, the Earth magnetic field and the atmospheric properties","  Cosmic rays originating from extraterrestrial sources are permanently
+arriving at Earth atmosphere, where they produce up to billions of secondary
+particles. The analysis of the secondary particles reaching to the surface of
+the Earth may provide a very valuable information about the Sun activity,
+changes in the geomagnetic field and the atmosphere, among others. In this
+article, we present the first preliminary results of the analysis of the cosmic
+rays measured with a high resolution tracking detector, TRAGALDABAS, located at
+the Univ. of Santiago de Compostela, in Spain.
+",0,1,0,0,0,0
+2229,"Approximation of solutions of SDEs driven by a fractional Brownian motion, under pathwise uniqueness","  Our aim in this paper is to establish some strong stability properties of a
+solution of a stochastic differential equation driven by a fractional Brownian
+motion for which the pathwise uniqueness holds. The results are obtained using
+Skorokhod's selection theorem.
+",0,0,1,0,0,0
+2230,Smoothed GMM for quantile models,"  This paper develops theory for feasible estimators of finite-dimensional
+parameters identified by general conditional quantile restrictions, under much
+weaker assumptions than previously seen in the literature. This includes
+instrumental variables nonlinear quantile regression as a special case. More
+specifically, we consider a set of unconditional moments implied by the
+conditional quantile restrictions, providing conditions for local
+identification. Since estimators based on the sample moments are generally
+impossible to compute numerically in practice, we study feasible estimators
+based on smoothed sample moments. We propose a method of moments estimator for
+exactly identified models, as well as a generalized method of moments estimator
+for over-identified models. We establish consistency and asymptotic normality
+of both estimators under general conditions that allow for weakly dependent
+data and nonlinear structural models. Simulations illustrate the finite-sample
+properties of the methods. Our in-depth empirical application concerns the
+consumption Euler equation derived from quantile utility maximization.
+Advantages of the quantile Euler equation include robustness to fat tails,
+decoupling of risk attitude from the elasticity of intertemporal substitution,
+and log-linearization without any approximation error. For the four countries
+we examine, the quantile estimates of discount factor and elasticity of
+intertemporal substitution are economically reasonable for a range of quantiles
+above the median, even when two-stage least squares estimates are not
+reasonable.
+",0,0,1,1,0,0
+2231,Multi-district preference modelling,"  Generating realistic artificial preference distributions is an important part
+of any simulation analysis of electoral systems. While this has been discussed
+in some detail in the context of a single electoral district, many electoral
+systems of interest are based on multiple districts. Neither treating
+preferences between districts as independent nor ignoring the district
+structure yields satisfactory results. We present a model based on an extension
+of the classic Eggenberger-Pólya urn, in which each district is represented
+by an urn and there is correlation between urns. We show in detail that this
+procedure has a small number of tunable parameters, is computationally
+efficient, and produces ""realistic-looking"" distributions. We intend to use it
+in further studies of electoral systems.
+",1,1,0,0,0,0
+2232,Orientability of the moduli space of Spin(7)-instantons,"  Let $(M,\Omega)$ be a closed $8$-dimensional manifold equipped with a
+generically non-integrable $\mathrm{Spin}(7)$-structure $\Omega$. We prove that
+if $\mathrm{Hom}(H^{3}(M,\mathbb{Z}), \mathbb{Z}_{2}) = 0$ then the moduli
+space of irreducible $\mathrm{Spin}(7)$-instantons on $(M,\Omega)$ with gauge
+group $\mathrm{SU}(r)$, $r\geq 2$, is orientable.
+",0,0,1,0,0,0
+2233,Neural Text Generation: A Practical Guide,"  Deep learning methods have recently achieved great empirical success on
+machine translation, dialogue response generation, summarization, and other
+text generation tasks. At a high level, the technique has been to train
+end-to-end neural network models consisting of an encoder model to produce a
+hidden representation of the source text, followed by a decoder model to
+generate the target. While such models have significantly fewer pieces than
+earlier systems, significant tuning is still required to achieve good
+performance. For text generation models in particular, the decoder can behave
+in undesired ways, such as by generating truncated or repetitive outputs,
+outputting bland and generic responses, or in some cases producing
+ungrammatical gibberish. This paper is intended as a practical guide for
+resolving such undesired behavior in text generation models, with the aim of
+helping enable real-world applications.
+",1,0,0,1,0,0
+2234,Context Prediction for Unsupervised Deep Learning on Point Clouds,"  Point clouds provide a flexible and natural representation usable in
+countless applications such as robotics or self-driving cars. Recently, deep
+neural networks operating on raw point cloud data have shown promising results
+on supervised learning tasks such as object classification and semantic
+segmentation. While massive point cloud datasets can be captured using modern
+scanning technology, manually labelling such large 3D point clouds for
+supervised learning tasks is a cumbersome process. This necessitates effective
+unsupervised learning methods that can produce representations such that
+downstream tasks require significantly fewer annotated samples. We propose a
+novel method for unsupervised learning on raw point cloud data in which a
+neural network is trained to predict the spatial relationship between two point
+cloud segments. While solving this task, representations that capture semantic
+properties of the point cloud are learned. Our method outperforms previous
+unsupervised learning approaches in downstream object classification and
+segmentation tasks and performs on par with fully supervised methods.
+",1,0,0,1,0,0
+2235,A core-set approach for distributed quadratic programming in big-data classification,"  A new challenge for learning algorithms in cyber-physical network systems is
+the distributed solution of big-data classification problems, i.e., problems in
+which both the number of training samples and their dimension is high.
+Motivated by several problem set-ups in Machine Learning, in this paper we
+consider a special class of quadratic optimization problems involving a ""large""
+number of input data, whose dimension is ""big"". To solve these quadratic
+optimization problems over peer-to-peer networks, we propose an asynchronous,
+distributed algorithm that scales with both the number and the dimension of the
+input data (training samples in the classification problem). The proposed
+distributed optimization algorithm relies on the notion of ""core-set"" which is
+used in geometric optimization to approximate the value function associated to
+a given set of points with a smaller subset of points. By computing local
+core-sets on a smaller version of the global problem and exchanging them with
+neighbors, the nodes reach consensus on a set of active constraints
+representing an approximate solution for the global quadratic program.
+",1,0,1,0,0,0
+2236,On the Erasure Robustness Property of Random Matrices,"  The study of the restricted isometry property (RIP) for corrupted random
+matrices is particularly important in the field of compressed sensing (CS) with
+corruptions. If a matrix still satisfy RIP after a certain portion of rows are
+erased, then we say that the matrix has the strong restricted isometry property
+(SRIP. In the field of compressed sensing, random matrices satisfies certain
+moment conditions are of particular interest. Among these matrices, those with
+entries generated from i.i.d Gaussian or i.i.d $\pm1$ random variables are
+often typically considered. Recent studies have shown that a matrix generated
+from i.i.d Gaussian random variables satisfies the strong restricted isometry
+property under arbitrary erasure of rows. In the first part of this paper we
+will work on $\pm 1$ random matrices. We study the erasure robustness of $\pm
+1$ random matrices show that with overwhelming probability the SRIP will still
+hold. Moreover the analysis will also lead to the robust version of the
+Johnson-Lindenstrauss Lemma for $\pm 1$ matrices. Then in the second part of
+this paper we work on finite frames. The study of the stability of finite
+frames under corruptions shares a lot of similarity to CS with corruption. We
+will focus on the Gaussian finite frames as a starter. We will improve existing
+results and confirm that a Gaussian random frame is numerically stable under
+arbitrary erasure of rows.
+",0,0,1,0,0,0
+2237,The Weighted Kendall and High-order Kernels for Permutations,"  We propose new positive definite kernels for permutations. First we introduce
+a weighted version of the Kendall kernel, which allows to weight unequally the
+contributions of different item pairs in the permutations depending on their
+ranks. Like the Kendall kernel, we show that the weighted version is invariant
+to relabeling of items and can be computed efficiently in $O(n \ln(n))$
+operations, where $n$ is the number of items in the permutation. Second, we
+propose a supervised approach to learn the weights by jointly optimizing them
+with the function estimated by a kernel machine. Third, while the Kendall
+kernel considers pairwise comparison between items, we extend it by considering
+higher-order comparisons among tuples of items and show that the supervised
+approach of learning the weights can be systematically generalized to
+higher-order permutation kernels.
+",0,0,0,1,0,0
+2238,Synchronizing automata and the language of minimal reset words,"  We study a connection between synchronizing automata and its set $M$ of
+minimal reset words, i.e., such that no proper factor is a reset word. We first
+show that any synchronizing automaton having the set of minimal reset words
+whose set of factors does not contain a word of length at most
+$\frac{1}{4}\min\{|u|: u\in I\}+\frac{1}{16}$ has a reset word of length at
+most $(n-\frac{1}{2})^{2}$ In the last part of the paper we focus on the
+existence of synchronizing automata with a given ideal $I$ that serves as the
+set of reset words. To this end, we introduce the notion of the tail structure
+of the (not necessarily regular) ideal $I=\Sigma^{*}M\Sigma^{*}$. With this
+tool, we first show the existence of an infinite strongly connected
+synchronizing automaton $\mathcal{A}$ having $I$ as the set of reset words and
+such that every other strongly connected synchronizing automaton having $I$ as
+the set of reset words is an homomorphic image of $\mathcal{A}$. Finally, we
+show that for any non-unary regular ideal $I$ there is a strongly connected
+synchronizing automaton having $I$ as the set of reset words with at most
+$(km^{k})2^{km^{k}n}$ states, where $k=|\Sigma|$, $m$ is the length of a
+shortest word in $M$, and $n$ is the dimension of the smallest automaton
+recognizing $M$ (state complexity of $M$). This automaton is computable and we
+show an algorithm to compute it in time $\mathcal{O}((k^{2}m^{k})2^{km^{k}n})$.
+",1,0,0,0,0,0
+2239,Peephole: Predicting Network Performance Before Training,"  The quest for performant networks has been a significant force that drives
+the advancements of deep learning in recent years. While rewarding, improving
+network design has never been an easy journey. The large design space combined
+with the tremendous cost required for network training poses a major obstacle
+to this endeavor. In this work, we propose a new approach to this problem,
+namely, predicting the performance of a network before training, based on its
+architecture. Specifically, we develop a unified way to encode individual
+layers into vectors and bring them together to form an integrated description
+via LSTM. Taking advantage of the recurrent network's strong expressive power,
+this method can reliably predict the performances of various network
+architectures. Our empirical studies showed that it not only achieved accurate
+predictions but also produced consistent rankings across datasets -- a key
+desideratum in performance prediction.
+",1,0,0,1,0,0
+2240,Optimization Landscape and Expressivity of Deep CNNs,"  We analyze the loss landscape and expressiveness of practical deep
+convolutional neural networks (CNNs) with shared weights and max pooling
+layers. We show that such CNNs produce linearly independent features at a
+""wide"" layer which has more neurons than the number of training samples. This
+condition holds e.g. for the VGG network. Furthermore, we provide for such wide
+CNNs necessary and sufficient conditions for global minima with zero training
+error. For the case where the wide layer is followed by a fully connected layer
+we show that almost every critical point of the empirical loss is a global
+minimum with zero training error. Our analysis suggests that both depth and
+width are very important in deep learning. While depth brings more
+representational power and allows the network to learn high level features,
+width smoothes the optimization landscape of the loss function in the sense
+that a sufficiently wide network has a well-behaved loss surface with almost no
+bad local minima.
+",1,0,0,1,0,0
+2241,A new cosine series antialiasing function and its application to aliasing-free glottal source models for speech and singing synthesis,"  We formulated and implemented a procedure to generate aliasing-free
+excitation source signals. It uses a new antialiasing filter in the continuous
+time domain followed by an IIR digital filter for response equalization. We
+introduced a cosine-series-based general design procedure for the new
+antialiasing function. We applied this new procedure to implement the
+antialiased Fujisaki-Ljungqvist model. We also applied it to revise our
+previous implementation of the antialiased Fant-Liljencrants model. A
+combination of these signals and a lattice implementation of the time varying
+vocal tract model provides a reliable and flexible basis to test fo extractors
+and source aperiodicity analysis methods. MATLAB implementations of these
+antialiased excitation source models are available as part of our open source
+tools for speech science.
+",1,0,0,0,0,0
+2242,Defining Equations of Nilpotent Orbits for Borel Subgroups of Modality Zero in Type $A_{n}$,"  Let $G$ be a quasi-simple algebraic group defined over an algebraically
+closed field $k$ and $B$ a Borel subgroup of $G$ acting on the nilradical
+$\mathfrak{n}$ of its Lie algebra $\mathfrak{b}$ via the Adjoint
+representation. It is known that $B$ has only finitely many orbits in only five
+cases: when $G$ is of type $A_{n}$ for $n \leq 4$, and when $G$ is type
+$B_{2}$. In this paper, we elaborate on this work in the case when $G =SL_{n
++1}(k)$ (type $A_{n})$, for $n \leq 4$, by finding the polynomial defining
+equations of each orbit. Consequences of these equations include the dimension
+of the orbits and the closure ordering on the set of orbits, although these
+facts are already known. The other case, when $G$ is type $B_{2}$, can be
+approached the same way and is treated in a separate paper, where we believe
+the determination of the closure order is new.
+",0,0,1,0,0,0
+2243,Quadratically Tight Relations for Randomized Query Complexity,"  Let $f:\{0,1\}^n \rightarrow \{0,1\}$ be a Boolean function. The certificate
+complexity $C(f)$ is a complexity measure that is quadratically tight for the
+zero-error randomized query complexity $R_0(f)$: $C(f) \leq R_0(f) \leq
+C(f)^2$. In this paper we study a new complexity measure that we call
+expectational certificate complexity $EC(f)$, which is also a quadratically
+tight bound on $R_0(f)$: $EC(f) \leq R_0(f) = O(EC(f)^2)$. We prove that $EC(f)
+\leq C(f) \leq EC(f)^2$ and show that there is a quadratic separation between
+the two, thus $EC(f)$ gives a tighter upper bound for $R_0(f)$. The measure is
+also related to the fractional certificate complexity $FC(f)$ as follows:
+$FC(f) \leq EC(f) = O(FC(f)^{3/2})$. This also connects to an open question by
+Aaronson whether $FC(f)$ is a quadratically tight bound for $R_0(f)$, as
+$EC(f)$ is in fact a relaxation of $FC(f)$.
+In the second part of the work, we upper bound the distributed query
+complexity $D^\mu_\epsilon(f)$ for product distributions $\mu$ by the square of
+the query corruption bound ($\mathrm{corr}_\epsilon(f)$) which improves upon a
+result of Harsha, Jain and Radhakrishnan [2015]. A similar statement for
+communication complexity is open.
+",1,0,0,0,0,0
+2244,Parabolic subgroup orbits on finite root systems,"  Oshima's Lemma describes the orbits of parabolic subgroups of irreducible
+finite Weyl groups on crystallographic root systems. This note generalises that
+result to all root systems of finite Coxeter groups, and provides a self
+contained proof, independent of the representation theory of semisimple complex
+Lie algebras.
+",0,0,1,0,0,0
+2245,User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction,"  In human-in-the-loop machine learning, the user provides information beyond
+that in the training data. Many algorithms and user interfaces have been
+designed to optimize and facilitate this human--machine interaction; however,
+fewer studies have addressed the potential defects the designs can cause.
+Effective interaction often requires exposing the user to the training data or
+its statistics. The design of the system is then critical, as this can lead to
+double use of data and overfitting, if the user reinforces noisy patterns in
+the data. We propose a user modelling methodology, by assuming simple rational
+behaviour, to correct the problem. We show, in a user study with 48
+participants, that the method improves predictive performance in a sparse
+linear regression sentiment analysis task, where graded user knowledge on
+feature relevance is elicited. We believe that the key idea of inferring user
+knowledge with probabilistic user models has general applicability in guarding
+against overfitting and improving interactive machine learning.
+",1,0,0,1,0,0
+2246,A note on the asymptotics of the modified Bessel functions on the Stokes lines,"  We employ the exponentially improved asymptotic expansions of the confluent
+hypergeometric functions on the Stokes lines discussed by the author [Appl.
+Math. Sci. {\bf 7} (2013) 6601--6609] to give the analogous expansions of the
+modified Bessel functions $I_\nu(z)$ and $K_\nu(z)$ for large $z$ and finite
+$\nu$ on $\arg\,z=\pm\pi$ (and, in the case of $I_\nu(z)$, also on
+$\arg\,z=0$). Numerical results are presented to illustrate the accuracy of
+these expansions.
+",0,0,1,0,0,0
+2247,Fair k-Center Clustering for Data Summarization,"  In data summarization we want to choose k prototypes in order to summarize a
+data set. We study a setting where the data set comprises several demographic
+groups and we are restricted to choose k_i prototypes belonging to group i. A
+common approach to the problem without the fairness constraint is to optimize a
+centroid-based clustering objective such as k-center. A natural extension then
+is to incorporate the fairness constraint into the clustering objective.
+Existing algorithms for doing so run in time super-quadratic in the size of the
+data set. This is in contrast to the standard k-center objective that can be
+approximately optimized in linear time. In this paper, we resolve this gap by
+providing a simple approximation algorithm for the k-center problem under the
+fairness constraint with running time linear in the size of the data set and k.
+If the number of demographic groups is small, the approximation guarantee of
+our algorithm only incurs a constant-factor overhead. We demonstrate the
+applicability of our algorithm on both synthetic and real data sets.
+",1,0,0,1,0,0
+2248,Advanced Bayesian Multilevel Modeling with the R Package brms,"  The brms package allows R users to easily specify a wide range of Bayesian
+single-level and multilevel models, which are fitted with the probabilistic
+programming language Stan behind the scenes. Several response distributions are
+supported, of which all parameters (e.g., location, scale, and shape) can be
+predicted at the same time thus allowing for distributional regression.
+Non-linear relationships may be specified using non-linear predictor terms or
+semi-parametric approaches such as splines or Gaussian processes. To make all
+of these modeling options possible in a multilevel framework, brms provides an
+intuitive and powerful formula syntax, which extends the well known formula
+syntax of lme4. The purpose of the present paper is to introduce this syntax in
+detail and to demonstrate its usefulness with four examples, each showing other
+relevant aspects of the syntax.
+",0,0,0,1,0,0
+2249,"$W$-entropy, super Perelman Ricci flows and $(K, m)$-Ricci solitons","  In this paper, we prove the characterization of the $(K, \infty)$-super
+Perelman Ricci flows by various functional inequalities and gradient estimate
+for the heat semigroup generated by the Witten Laplacian on manifolds equipped
+with time dependent metrics and potentials. As a byproduct, we derive the
+Hamilton type dimension free Harnack inequality on manifolds with $(K,
+\infty)$-super Perelman Ricci flows. Based on a new second order differential
+inequality on the Boltzmann-Shannon entropy for the heat equation of the Witten
+Laplacian, we introduce a new $W$-entropy quantity and prove its monotonicity
+for the heat equation of the Witten Laplacian on complete Riemannian manifolds
+with the $CD(K, \infty)$-condition and on compact manifolds with $(K,
+\infty)$-super Perelman Ricci flows. Our results characterize the $(K,
+\infty)$-Ricci solitons and the $(K, \infty)$-Perelman Ricci flows. We also
+prove a second order differential entropy inequality on $(K, m)$-super Ricci
+flows, which can be used to characterize the $(K, m)$-Ricci solitons and the
+$(K, m)$-Ricci flows. Finally, we give a probabilistic interpretation of the
+$W$-entropy for the heat equation of the Witten Laplacian on manifolds with the
+$CD(K, m)$-condition.
+",0,0,1,0,0,0
+2250,Stellar energetic particle ionization in protoplanetary disks around T Tauri stars,"  Anomalies in the abundance measurements of short lived radionuclides in
+meteorites indicate that the protosolar nebulae was irradiated by a high amount
+of energetic particles (E$\gtrsim$10 MeV). The particle flux of the
+contemporary Sun cannot explain these anomalies. However, similar to T Tauri
+stars the young Sun was more active and probably produced enough high energy
+particles to explain those anomalies. We want to study the interaction of
+stellar energetic particles with the gas component of the disk and identify
+possible observational tracers of this interaction. We use a 2D radiation
+thermo-chemical protoplanetary disk code to model a disk representative for T
+Tauri stars. We use a particle energy distribution derived from solar flare
+observations and an enhanced stellar particle flux proposed for T Tauri stars.
+For this particle spectrum we calculate the stellar particle ionization rate
+throughout the disk with an accurate particle transport model. We study the
+impact of stellar particles for models with varying X-ray and cosmic-ray
+ionization rates. We find that stellar particle ionization has a significant
+impact on the abundances of the common disk ionization tracers HCO$^+$ and
+N$_2$H$^+$, especially in models with low cosmic-ray ionization rates. In
+contrast to cosmic rays and X-rays, stellar particles cannot reach the midplane
+of the disk. Therefore molecular ions residing in the disk surface layers are
+more affected by stellar particle ionization than molecular ions tracing the
+cold layers/midplane of the disk. Spatially resolved observations of molecular
+ions tracing different vertical layers of the disk allow to disentangle the
+contribution of stellar particle ionization from other competing ionization
+sources. Modeling such observations with a model like the one presented here
+allows to constrain the stellar particle flux in disks around T Tauri stars.
+",0,1,0,0,0,0
+2251,Extended Trust-Region Problems with One or Two Balls: Exact Copositive and Lagrangian Relaxations,"  We establish a geometric condition guaranteeing exact copositive relaxation
+for the nonconvex quadratic optimization problem under two quadratic and
+several linear constraints, and present sufficient conditions for global
+optimality in terms of generalized Karush-Kuhn-Tucker multipliers. The
+copositive relaxation is tighter than the usual Lagrangian relaxation. We
+illustrate this by providing a whole class of quadratic optimization problems
+that enjoys exactness of copositive relaxation while the usual Lagrangian
+duality gap is infinite. Finally, we also provide verifiable conditions under
+which both the usual Lagrangian relaxation and the copositive relaxation are
+exact for an extended CDT (two-ball trust-region) problem. Importantly, the
+sufficient conditions can be verified by solving linear optimization problems.
+",0,0,1,0,0,0
+2252,Weighted batch means estimators in Markov chain Monte Carlo,"  This paper proposes a family of weighted batch means variance estimators,
+which are computationally efficient and can be conveniently applied in
+practice. The focus is on Markov chain Monte Carlo simulations and estimation
+of the asymptotic covariance matrix in the Markov chain central limit theorem,
+where conditions ensuring strong consistency are provided. Finite sample
+performance is evaluated through auto-regressive, Bayesian spatial-temporal,
+and Bayesian logistic regression examples, where the new estimators show
+significant computational gains with a minor sacrifice in variance compared
+with existing methods.
+",0,0,0,1,0,0
+2253,Backpropagation through the Void: Optimizing control variates for black-box gradient estimation,"  Gradient-based optimization is the foundation of deep learning and
+reinforcement learning. Even when the mechanism being optimized is unknown or
+not differentiable, optimization using high-variance or biased gradient
+estimates is still often the best strategy. We introduce a general framework
+for learning low-variance, unbiased gradient estimators for black-box functions
+of random variables. Our method uses gradients of a neural network trained
+jointly with model parameters or policies, and is applicable in both discrete
+and continuous settings. We demonstrate this framework for training discrete
+latent-variable models. We also give an unbiased, action-conditional extension
+of the advantage actor-critic reinforcement learning algorithm.
+",1,0,0,0,0,0
+2254,Truncation-free Hybrid Inference for DPMM,"  Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian
+non-parametrics. While these models free from choosing the number of components
+a-priori, computationally attractive variational inference often reintroduces
+the need to do so, via a truncation on the variational distribution. In this
+paper we present a truncation-free hybrid inference for DPMM, combining the
+advantages of sampling-based MCMC and variational methods. The proposed
+hybridization enables more efficient variational updates, while increasing
+model complexity only if needed. We evaluate the properties of the hybrid
+updates and their empirical performance in single- as well as mixed-membership
+models. Our method is easy to implement and performs favorably compared to
+existing schemas.
+",1,0,0,1,0,0
+2255,Functors and morphisms determined by subcategories,"  We study the existence and uniqueness of minimal right determiners in various
+categories. Particularly in a Hom-finite hereditary abelian category with
+enough projectives, we prove that the Auslander-Reiten-Smal{\o}-Ringel formula
+of the minimal right determiner still holds. As an application, we give a
+formula of minimal right determiners in the category of finitely presented
+representations of strongly locally finite quivers.
+",0,0,1,0,0,0
+2256,Collective Effects in Nanolasers Explained by Generalized Rate Equations,"  We study the stationary photon output and statistics of small lasers. Our
+closed-form expressions clarify the contribution of collective effects due to
+the interaction between quantum emitters. We generalize laser rate equations
+and explain photon trapping: a decrease of the photon number output below the
+lasing threshold, derive an expression for the stationary cavity mode
+autocorrelation function $g_2$, which implies that collective effects may
+strongly influence the photon statistics. We identify conditions for coherent,
+thermal and superthermal radiation, the latter being a unique fingerprint for
+collective emission in lasers. These generic analytical results agree with
+recent experiments, complement numerical results, and provide insight into and
+design rules for nanolasers.
+",0,1,0,0,0,0
+2257,The Risk of Machine Learning,"  Many applied settings in empirical economics involve simultaneous estimation
+of a large number of parameters. In particular, applied economists are often
+interested in estimating the effects of many-valued treatments (like teacher
+effects or location effects), treatment effects for many groups, and prediction
+models with many regressors. In these settings, machine learning methods that
+combine regularized estimation and data-driven choices of regularization
+parameters are useful to avoid over-fitting. In this article, we analyze the
+performance of a class of machine learning estimators that includes ridge,
+lasso and pretest in contexts that require simultaneous estimation of many
+parameters. Our analysis aims to provide guidance to applied researchers on (i)
+the choice between regularized estimators in practice and (ii) data-driven
+selection of regularization parameters. To address (i), we characterize the
+risk (mean squared error) of regularized estimators and derive their relative
+performance as a function of simple features of the data generating process. To
+address (ii), we show that data-driven choices of regularization parameters,
+based on Stein's unbiased risk estimate or on cross-validation, yield
+estimators with risk uniformly close to the risk attained under the optimal
+(unfeasible) choice of regularization parameters. We use data from recent
+examples in the empirical economics literature to illustrate the practical
+applicability of our results.
+",0,0,0,1,0,0
+2258,Agatha: disentangling periodic signals from correlated noise in a periodogram framework,"  Periodograms are used as a key significance assessment and visualisation tool
+to display the significant periodicities in unevenly sampled time series. We
+introduce a framework of periodograms, called ""Agatha"", to disentangle periodic
+signals from correlated noise and to solve the 2-dimensional model selection
+problem: signal dimension and noise model dimension. These periodograms are
+calculated by applying likelihood maximization and marginalization and combined
+in a self-consistent way. We compare Agatha with other periodograms for the
+detection of Keplerian signals in synthetic radial velocity data produced for
+the Radial Velocity Challenge as well as in radial velocity datasets of several
+Sun-like stars. In our tests we find Agatha is able to recover signals to the
+adopted detection limit of the radial velocity challenge. Applied to real
+radial velocity, we use Agatha to confirm previous analysis of CoRoT-7 and to
+find two new planet candidates with minimum masses of 15.1 $M_\oplus$ and 7.08
+$M_\oplus$ orbiting HD177565 and HD41248, with periods of 44.5 d and 13.4 d,
+respectively. We find that Agatha outperforms other periodograms in terms of
+removing correlated noise and assessing the significances of signals with more
+robust metrics. Moreover, it can be used to select the optimal noise model and
+to test the consistency of signals in time. Agatha is intended to be flexible
+enough to be applied to time series analyses in other astronomical and
+scientific disciplines. Agatha is available at this http URL.
+",0,1,0,1,0,0
+2259,A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome,"  In this work, we consider the problem of predicting the course of a
+progressive disease, such as cancer or Alzheimer's. Progressive diseases often
+start with mild symptoms that might precede a diagnosis, and each patient
+follows their own trajectory. Patient trajectories exhibit wild variability,
+which can be associated with many factors such as genotype, age, or sex. An
+additional layer of complexity is that, in real life, the amount and type of
+data available for each patient can differ significantly. For example, for one
+patient we might have no prior history, whereas for another patient we might
+have detailed clinical assessments obtained at multiple prior time-points. This
+paper presents a probabilistic model that can handle multiple modalities
+(including images and clinical assessments) and variable patient histories with
+irregular timings and missing entries, to predict clinical scores at future
+time-points. We use a sigmoidal function to model latent disease progression,
+which gives rise to clinical observations in our generative model. We
+implemented an approximate Bayesian inference strategy on the proposed model to
+estimate the parameters on data from a large population of subjects.
+Furthermore, the Bayesian framework enables the model to automatically
+fine-tune its predictions based on historical observations that might be
+available on the test subject. We applied our method to a longitudinal
+Alzheimer's disease dataset with more than 3000 subjects [23] and present a
+detailed empirical analysis of prediction performance under different
+scenarios, with comparisons against several benchmarks. We also demonstrate how
+the proposed model can be interrogated to glean insights about temporal
+dynamics in Alzheimer's disease.
+",0,0,0,1,0,0
+2260,On the generation of the quarks through spontaneous symmetry breaking,"  In this paper we present the state of the art about the quarks: group SU(3),
+Lie algebra, the electric charge and mass. The quarks masses are generated in
+the same way as the lepton masses. It is constructed a term in the Lagrangian
+that couples the Higgs doublet to the fermion fields.
+",0,1,0,0,0,0
+2261,Distributed Holistic Clustering on Linked Data,"  Link discovery is an active field of research to support data integration in
+the Web of Data. Due to the huge size and number of available data sources,
+efficient and effective link discovery is a very challenging task. Common
+pairwise link discovery approaches do not scale to many sources with very large
+entity sets. We here propose a distributed holistic approach to link many data
+sources based on a clustering of entities that represent the same real-world
+object. Our clustering approach provides a compact and fused representation of
+entities, and can identify errors in existing links as well as many new links.
+We support a distributed execution of the clustering approach to achieve faster
+execution times and scalability for large real-world data sets. We provide a
+novel gold standard for multi-source clustering, and evaluate our methods with
+respect to effectiveness and efficiency for large data sets from the geographic
+and music domains.
+",1,0,0,0,0,0
+2262,A branch-and-bound algorithm for the minimum radius $k$-enclosing ball problem,"  The minimum $k$-enclosing ball problem seeks the ball with smallest radius
+that contains at least~$k$ of~$m$ given points in a general $n$-dimensional
+Euclidean space. This problem is NP-hard. We present a branch-and-bound
+algorithm on the tree of the subsets of~$k$ points to solve this problem. The
+nodes on the tree are ordered in a suitable way, which, complemented with a
+last-in-first-out search strategy, allows for only a small fraction of nodes to
+be explored. Additionally, an efficient dual algorithm to solve the subproblems
+at each node is employed.
+",1,0,1,0,0,0
+2263,Dependency Parsing with Dilated Iterated Graph CNNs,"  Dependency parses are an effective way to inject linguistic knowledge into
+many downstream tasks, and many practitioners wish to efficiently parse
+sentences at scale. Recent advances in GPU hardware have enabled neural
+networks to achieve significant gains over the previous best models, these
+models still fail to leverage GPUs' capability for massive parallelism due to
+their requirement of sequential processing of the sentence. In response, we
+propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for
+graph-based dependency parsing, a graph convolutional architecture that allows
+for efficient end-to-end GPU parsing. In experiments on the English Penn
+TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best
+neural network parsers.
+",1,0,0,0,0,0
+2264,Motion Planning in Irreducible Path Spaces,"  The motion of a mechanical system can be defined as a path through its
+configuration space. Computing such a path has a computational complexity
+scaling exponentially with the dimensionality of the configuration space. We
+propose to reduce the dimensionality of the configuration space by introducing
+the irreducible path --- a path having a minimal swept volume. The paper
+consists of three parts: In part I, we define the space of all irreducible
+paths and show that planning a path in the irreducible path space preserves
+completeness of any motion planning algorithm. In part II, we construct an
+approximation to the irreducible path space of a serial kinematic chain under
+certain assumptions. In part III, we conduct motion planning using the
+irreducible path space for a mechanical snake in a turbine environment, for a
+mechanical octopus with eight arms in a pipe system and for the sideways motion
+of a humanoid robot moving through a room with doors and through a hole in a
+wall. We demonstrate that the concept of an irreducible path can be applied to
+any motion planning algorithm taking curvature constraints into account.
+",1,0,0,0,0,0
+2265,Learning Neural Networks with Two Nonlinear Layers in Polynomial Time,"  We give a polynomial-time algorithm for learning neural networks with one
+layer of sigmoids feeding into any Lipschitz, monotone activation function
+(e.g., sigmoid or ReLU). We make no assumptions on the structure of the
+network, and the algorithm succeeds with respect to {\em any} distribution on
+the unit ball in $n$ dimensions (hidden weight vectors also have unit norm).
+This is the first assumption-free, provably efficient algorithm for learning
+neural networks with two nonlinear layers.
+Our algorithm-- {\em Alphatron}-- is a simple, iterative update rule that
+combines isotonic regression with kernel methods. It outputs a hypothesis that
+yields efficient oracle access to interpretable features. It also suggests a
+new approach to Boolean learning problems via real-valued conditional-mean
+functions, sidestepping traditional hardness results from computational
+learning theory.
+Along these lines, we subsume and improve many longstanding results for PAC
+learning Boolean functions to the more general, real-valued setting of {\em
+probabilistic concepts}, a model that (unlike PAC learning) requires non-i.i.d.
+noise-tolerance.
+",1,0,0,1,0,0
+2266,Sample-Derived Disjunctive Rules for Secure Power System Operation,"  Machine learning techniques have been used in the past using Monte Carlo
+samples to construct predictors of the dynamic stability of power systems. In
+this paper we move beyond the task of prediction and propose a comprehensive
+approach to use predictors, such as Decision Trees (DT), within a standard
+optimization framework for pre- and post-fault control purposes. In particular,
+we present a generalizable method for embedding rules derived from DTs in an
+operation decision-making model. We begin by pointing out the specific
+challenges entailed when moving from a prediction to a control framework. We
+proceed with introducing the solution strategy based on generalized disjunctive
+programming (GDP) as well as a two-step search method for identifying optimal
+hyper-parameters for balancing cost and control accuracy. We showcase how the
+proposed approach constructs security proxies that cover multiple contingencies
+while facing high-dimensional uncertainty with respect to operating conditions
+with the use of a case study on the IEEE 39-bus system. The method is shown to
+achieve efficient system control at a marginal increase in system price
+compared to an oracle model.
+",0,0,0,1,0,0
+2267,Satellite altimetry reveals spatial patterns of variations in the Baltic Sea wave climate,"  The main properties of the climate of waves in the seasonally ice-covered
+Baltic Sea and its decadal changes since 1990 are estimated from satellite
+altimetry data. The data set of significant wave heights (SWH) from all
+existing nine satellites, cleaned and cross-validated against in situ
+measurements, shows overall a very consistent picture. A comparison with visual
+observations shows a good correspondence with correlation coefficients of
+0.6-0.8. The annual mean SWH reveals a tentative increase of 0.005 m yr-1, but
+higher quantiles behave in a cyclic manner with a timescale of 10-15 yr.
+Changes in the basin-wide average SWH have a strong meridional pattern: an
+increase in the central and western parts of the sea and decrease in the east.
+This pattern is likely caused by a rotation of wind directions rather than by
+an increase in the wind speed.
+",0,1,0,0,0,0
+2268,Estimation of Covariance Matrices for Portfolio Optimization using Gaussian Processes,"  Estimating covariances between financial assets plays an important role in
+risk management and optimal portfolio allocation. In practice, when the sample
+size is small compared to the number of variables, i.e. when considering a wide
+universe of assets over just a few years, this poses considerable challenges
+and the empirical estimate is known to be very unstable.
+Here, we propose a novel covariance estimator based on the Gaussian Process
+Latent Variable Model (GP-LVM). Our estimator can be considered as a non-linear
+extension of standard factor models with readily interpretable parameters
+reminiscent of market betas. Furthermore, our Bayesian treatment naturally
+shrinks the sample covariance matrix towards a more structured matrix given by
+the prior and thereby systematically reduces estimation errors.
+",0,0,0,0,0,1
+2269,Zeeman interaction and Jahn-Teller effect in $Γ_8$ multiplet,"  We present a thorough analysis of the interplay of magnetic moment and the
+Jahn-Teller effect in the $\Gamma_8$ cubic multiplet. We find that in the
+presence of dynamical Jahn-Teller effect, the Zeeman interaction remains
+isotropic, whereas the $g$ and $G$ factors can change their signs. The static
+Jahn-Teller distortion also can change the sign of these $g$ factors as well as
+the nature of the magnetic anisotropy. Combining the theory with
+state-of-the-art {\it ab initio} calculations, we analyzed the magnetic
+properties of Np$^{4+}$ and Ir$^{4+}$ impurity ions in cubic environment. The
+calculated $g$ factors of Np$^{4+}$ impurity agree well with experimental data.
+The {\it ab initio} calculation predicts strong Jahn-Teller effect in Ir$^{4+}$
+ion in cubic environment and the strong vibronic reduction of $g$ and $G$
+factors.
+",0,1,0,0,0,0
+2270,Measuring the Declared SDK Versions and Their Consistency with API Calls in Android Apps,"  Android has been the most popular smartphone system, with multiple platform
+versions (e.g., KITKAT and Lollipop) active in the market. To manage the
+application's compatibility with one or more platform versions, Android allows
+apps to declare the supported platform SDK versions in their manifest files. In
+this paper, we make a first effort to study this modern software mechanism. Our
+objective is to measure the current practice of the declared SDK versions
+(which we term as DSDK versions afterwards) in real apps, and the consistency
+between the DSDK versions and their app API calls. To this end, we perform a
+three-dimensional analysis. First, we parse Android documents to obtain a
+mapping between each API and their corresponding platform versions. We then
+analyze the DSDK-API consistency for over 24K apps, among which we pre-exclude
+1.3K apps that provide different app binaries for different Android versions
+through Google Play analysis. Besides shedding light on the current DSDK
+practice, our study quantitatively measures the two side effects of
+inappropriate DSDK versions: (i) around 1.8K apps have API calls that do not
+exist in some declared SDK versions, which causes runtime crash bugs on those
+platform versions; (ii) over 400 apps, due to claiming the outdated targeted
+DSDK versions, are potentially exploitable by remote code execution. These
+results indicate the importance and difficulty of declaring correct DSDK, and
+our work can help developers fulfill this goal.
+",1,0,0,0,0,0
+2271,The Design and Implementation of Modern Online Programming Competitions,"  This paper presents a framework for the implementation of online programming
+competitions, including a set of principles for the design of the multiplayer
+game and a practical framework for the construction of the competition
+environment. The paper presents a successful example competition, the 2016-17
+Halite challenge, and briefly mentions a second competition, the Halite II
+challenge, which launched in October 2017.
+",1,0,0,0,0,0
+2272,Emergence of a spectral gap in a class of random matrices associated with split graphs,"  Motivated by the intriguing behavior displayed in a dynamic network that
+models a population of extreme introverts and extroverts (XIE), we consider the
+spectral properties of ensembles of random split graph adjacency matrices. We
+discover that, in general, a gap emerges in the bulk spectrum between -1 and 0
+that contains a single eigenvalue. An analytic expression for the bulk
+distribution is derived and verified with numerical analysis. We also examine
+their relation to chiral ensembles, which are associated with bipartite graphs.
+",0,1,1,0,0,0
+2273,Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines,"  We present a software tool that employs state-of-the-art natural language
+processing (NLP) and machine learning techniques to help newspaper editors
+compose effective headlines for online publication. The system identifies the
+most salient keywords in a news article and ranks them based on both their
+overall popularity and their direct relevance to the article. The system also
+uses a supervised regression model to identify headlines that are likely to be
+widely shared on social media. The user interface is designed to simplify and
+speed the editor's decision process on the composition of the headline. As
+such, the tool provides an efficient way to combine the benefits of automated
+predictors of engagement and search-engine optimization (SEO) with human
+judgments of overall headline quality.
+",1,0,0,0,0,0
+2274,ParaGraphE: A Library for Parallel Knowledge Graph Embedding,"  Knowledge graph embedding aims at translating the knowledge graph into
+numerical representations by transforming the entities and relations into
+continuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have
+been proposed to deal with this problem, but existing single-thread
+implementations of them are time-consuming for large-scale knowledge graphs.
+Here, we design a unified parallel framework to parallelize these methods,
+which achieves a significant time reduction without influencing the accuracy.
+We name our framework as ParaGraphE, which provides a library for parallel
+knowledge graph embedding. The source code can be downloaded from
+this https URL .
+",1,0,0,0,0,0
+2275,The Globular Cluster - Dark Matter Halo Connection,"  I present a simple phenomenological model for the observed linear scaling of
+the stellar mass in old globular clusters (GCs) with $z=0$ halo mass in which
+the stellar mass in GCs scales linearly with progenitor halo mass at $z=6$
+above a minimum halo mass for GC formation. This model reproduces the observed
+$M_{\rm GCs}-M_{\rm halo}$ relation at $z=0$ and results in a prediction for
+the minimum halo mass at $z=6$ required for hosting one GC: $M_{\rm
+min}(z=6)=1.07 \times 10^9\,M_{\odot}$. Translated to $z=0$, the mean threshold
+mass is $M_{\rm halo}(z=0) \approx 2\times 10^{10}\,M_{\odot}$. I explore the
+observability of GCs in the reionization era and their contribution to cosmic
+reionization, both of which depend sensitively on the (unknown) ratio of GC
+birth mass to present-day stellar mass, $\xi$. Based on current detections of
+$z \gtrsim 6$ objects with $M_{1500} < -17$, values of $\xi > 10$ are strongly
+disfavored; this, in turn, has potentially important implications for GC
+formation scenarios. Even for low values of $\xi$, some observed high-$z$
+galaxies may actually be GCs, complicating estimates of reionization-era galaxy
+ultraviolet luminosity functions and constraints on dark matter models. GCs are
+likely important reionization sources if $5 \lesssim \xi \lesssim 10$. I also
+explore predictions for the fraction of accreted versus in situ GCs in the
+local Universe and for descendants of systems at the halo mass threshold of GC
+formation (dwarf galaxies). An appealing feature of the model presented here is
+the ability to make predictions for GC properties based solely on dark matter
+halo merger trees.
+",0,1,0,0,0,0
+2276,NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps,"  Convolutional neural networks (CNNs) have become the dominant neural network
+architecture for solving many state-of-the-art (SOA) visual processing tasks.
+Even though Graphical Processing Units (GPUs) are most often used in training
+and deploying CNNs, their power efficiency is less than 10 GOp/s/W for
+single-frame runtime inference. We propose a flexible and efficient CNN
+accelerator architecture called NullHop that implements SOA CNNs useful for
+low-power and low-latency application scenarios. NullHop exploits the sparsity
+of neuron activations in CNNs to accelerate the computation and reduce memory
+requirements. The flexible architecture allows high utilization of available
+computing resources across kernel sizes ranging from 1x1 to 7x7. NullHop can
+process up to 128 input and 128 output feature maps per layer in a single pass.
+We implemented the proposed architecture on a Xilinx Zynq FPGA platform and
+present results showing how our implementation reduces external memory
+transfers and compute time in five different CNNs ranging from small ones up to
+the widely known large VGG16 and VGG19 CNNs. Post-synthesis simulations using
+Mentor Modelsim in a 28nm process with a clock frequency of 500 MHz show that
+the VGG19 network achieves over 450 GOp/s. By exploiting sparsity, NullHop
+achieves an efficiency of 368%, maintains over 98% utilization of the MAC
+units, and achieves a power efficiency of over 3TOp/s/W in a core area of
+6.3mm$^2$. As further proof of NullHop's usability, we interfaced its FPGA
+implementation with a neuromorphic event camera for real time interactive
+demonstrations.
+",1,0,0,0,0,0
+2277,Platooning in the Presence of a Speed Drop: A Generalized Control Model,"  The positive impacts of platooning on travel time reliability, congestion,
+emissions, and energy consumption have been shown for homogeneous roadway
+segments. However, speed limit changes frequently throughout the transportation
+network, due to either safety-related considerations (e.g., workzone
+operations) or congestion management schemes (e.g., speed harmonization
+systems). These abrupt changes in speed limit can result in shock- wave
+formation and cause travel time unreliability. Therefore, designing a
+platooning strategy for tracking a reference velocity profile is critical to
+enabling end-to-end platooning. Accordingly, this study introduces a
+generalized control model to track a desired velocity profile, while ensuring
+safety in the platoon of autonomous vehicles. We define appropriate natural
+error terms and the target curve in the state space of the control system,
+which is the set of points where all error terms vanish and corresponds to the
+case when all vehicles move with the desired velocities and in the minimum safe
+distance between them. In this way, we change the tracking velocity profile
+problem into a state- feedback stabilization problem with respect to the target
+curve. Under certain mild assumptions on the Lipschitz constant of the speed
+drop profile, we show that the stabilizing feedback can be obtained via
+introducing a natural dynamics for the maximum of the error terms for each
+vehicle. Moreover, we show that with this stabilizing feedback collisions will
+not occur if the initial state of the system of vehicles is sufficiently close
+to the target curve. We also show that the error terms remain bounded
+throughout the time and space. Two scenarios were simulated, with and without
+initial perturbations, and results confirmed the effectiveness of the proposed
+control model in tracking the speed drop while ensuring safety and string
+stability.
+",1,0,0,0,0,0
+2278,Koszul duality for Lie algebroids,"  This paper studies the role of dg-Lie algebroids in derived deformation
+theory. More precisely, we provide an equivalence between the homotopy theories
+of formal moduli problems and dg-Lie algebroids over a commutative dg-algebra
+of characteristic zero. At the level of linear objects, we show that the
+category of representations of a dg-Lie algebroid is an extension of the
+category of quasi-coherent sheaves on the corresponding formal moduli problem.
+We describe this extension geometrically in terms of pro-coherent sheaves.
+",0,0,1,0,0,0
+2279,Semi-Semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments,"  This paper presents a novel method to reduce the scale drift for indoor
+monocular simultaneous localization and mapping (SLAM). We leverage the prior
+knowledge that in the indoor environment, the line segments form tight
+clusters, e.g. many door frames in a straight corridor are of the same shape,
+size and orientation, so the same edges of these door frames form a tight line
+segment cluster. We implement our method in the popular ORB-SLAM2, which also
+serves as our baseline. In the front end we detect the line segments in each
+frame and incrementally cluster them in the 3D space. In the back end, we
+optimize the map imposing the constraint that the line segments of the same
+cluster should be the same. Experimental results show that our proposed method
+successfully reduces the scale drift for indoor monocular SLAM.
+",1,0,0,0,0,0
+2280,Direct visualization of vortex ice in a nanostructured superconductor,"  Artificial ice systems have unique physical properties promising for
+potential applications. One of the most challenging issues in this field is to
+find novel ice systems that allows a precise control over the geometries and
+many-body interactions. Superconducting vortex matter has been proposed as a
+very suitable candidate to study artificial ice, mainly due to availability of
+tunable vortex-vortex interactions and the possibility to fabricate a variety
+of nanoscale pinning potential geometries. So far, a detailed imaging of the
+local configurations in a vortex-based artificial ice system is still lacking.
+Here we present a direct visualization of the vortex ice state in a
+nanostructured superconductor. By using the scanning Hall probe microscopy, a
+large area with the vortex ice ground state configuration has been detected,
+which confirms the recent theoretical predictions for this new ice system.
+Besides the defects analogous to artificial spin ice systems, other types of
+defects have been visualized and identified. We also demonstrate the
+possibility to realize different types of defects by varying the magnetic
+field.
+",0,1,0,0,0,0
+2281,Statistical Mechanics of Node-perturbation Learning with Noisy Baseline,"  Node-perturbation learning is a type of statistical gradient descent
+algorithm that can be applied to problems where the objective function is not
+explicitly formulated, including reinforcement learning. It estimates the
+gradient of an objective function by using the change in the object function in
+response to the perturbation. The value of the objective function for an
+unperturbed output is called a baseline. Cho et al. proposed node-perturbation
+learning with a noisy baseline. In this paper, we report on building the
+statistical mechanics of Cho's model and on deriving coupled differential
+equations of order parameters that depict learning dynamics. We also show how
+to derive the generalization error by solving the differential equations of
+order parameters. On the basis of the results, we show that Cho's results are
+also apply in general cases and show some general performances of Cho's model.
+",1,0,0,1,0,0
+2282,New Generalized Fixed Point Results on $S_{b}$-Metric Spaces,"  Recently $S_{b}$-metric spaces have been introduced as the generalizations of
+metric and $S$-metric spaces. In this paper we investigate some basic
+properties of this new space. We generalize the classical Banach's contraction
+principle using the theory of a complete $S_{b}$-metric space. Also we give an
+application to linear equation systems using the $S_{b}$-metric which is
+generated by a metric.
+",0,0,1,0,0,0
+2283,A Decentralized Optimization Framework for Energy Harvesting Devices,"  Designing decentralized policies for wireless communication networks is a
+crucial problem, which has only been partially solved in the literature so far.
+In this paper, we propose the Decentralized Markov Decision Process (Dec-MDP)
+framework to analyze a wireless sensor network with multiple users which access
+a common wireless channel. We consider devices with energy harvesting
+capabilities, so that they aim at balancing the energy arrivals with the data
+departures and with the probability of colliding with other nodes. Randomly
+over time, an access point triggers a SYNC slot, wherein it recomputes the
+optimal transmission parameters of the whole network, and distributes this
+information. Every node receives its own policy, which specifies how it should
+access the channel in the future, and, thereafter, proceeds in a fully
+decentralized fashion, without interacting with other entities in the network.
+We propose a multi-layer Markov model, where an external MDP manages the jumps
+between SYNC slots, and an internal Dec-MDP computes the optimal policy in the
+near future. We numerically show that, because of the harvesting, a fully
+orthogonal scheme (e.g., TDMA-like) is suboptimal in energy harvesting
+scenarios, and the optimal trade-off lies between an orthogonal and a random
+access system.
+",1,0,1,0,0,0
+2284,Inf-sup stable finite-element methods for the Landau--Lifshitz--Gilbert and harmonic map heat flow equation,"  In this paper we propose and analyze a finite element method for both the
+harmonic map heat and Landau--Lifshitz--Gilbert equation, the time variable
+remaining continuous. Our starting point is to set out a unified saddle point
+approach for both problems in order to impose the unit sphere constraint at the
+nodes since the only polynomial function satisfying the unit sphere constraint
+everywhere are constants. A proper inf-sup condition is proved for the Lagrange
+multiplier leading to the well-posedness of the unified formulation. \emph{A
+priori} energy estimates are shown for the proposed method.
+When time integrations are combined with the saddle point finite element
+approximation some extra elaborations are required in order to ensure both
+\emph{a priori} energy estimates for the director or magnetization vector
+depending on the model and an inf-sup condition for the Lagrange multiplier.
+This is due to the fact that the unit length at the nodes is not satisfied in
+general when a time integration is performed. We will carry out a linear Euler
+time-stepping method and a non-linear Crank--Nicolson method. The latter is
+solved by using the former as a non-linear solver.
+",0,0,1,0,0,0
+2285,Power Control and Relay Selection in Full-Duplex Cognitive Relay Networks: Coherent versus Non-coherent Scenarios,"  This paper investigates power control and relay selection in Full Duplex
+Cognitive Relay Networks (FDCRNs), where the secondary-user (SU) relays can
+simultaneously receive and forward the signal from the SU source. We study both
+non-coherent and coherent scenarios. In the non-coherent case, the SU relay
+forwards the signal from the SU source without regulating the phase, while in
+the coherent scenario, the SU relay regulates the phase when forwarding the
+signal to minimize the interference at the primary-user (PU) receiver. We
+consider the problem of maximizing the transmission rate from the SU source to
+the SU destination subject to the interference constraint at the PU receiver
+and power constraints at both the SU source and SU relay. We develop
+low-complexity and high-performance joint power control and relay selection
+algorithms. The superior performance of the proposed algorithms are confirmed
+using extensive numerical evaluation. In particular, we demonstrate the
+significant gain of phase regulation at the SU relay (i.e., the gain of the
+coherent mechanism over the noncoherent mechanism).
+",1,0,1,1,0,0
+2286,Ternary and $n$-ary $f$-distributive Structures,"  We introduce and study ternary $f$-distributive structures, Ternary
+$f$-quandles and more generally their higher $n$-ary analogues. A
+classification of ternary $f$-quandles is provided in low dimensions. Moreover,
+we study extension theory and introduce a cohomology theory for ternary, and
+more generally $n$-ary, $f$-quandles. Furthermore, we give some computational
+examples.
+",0,0,1,0,0,0
+2287,"Deformations of infinite-dimensional Lie algebras, exotic cohomology, and integrable nonlinear partial differential equations","  The important unsolved problem in theory of integrable systems is to find
+conditions guaranteeing existence of a Lax representation for a given PDE. The
+use of the exotic cohomology of the symmetry algebras opens a way to formulate
+such conditions in internal terms of the PDEs under the study. In this paper we
+consider certain examples of infinite-dimensional Lie algebras with nontrivial
+second exotic cohomology groups and show that the Maurer-Cartan forms of the
+associated extensions of these Lie algebras generate Lax representations for
+integrable systems, both known and new ones.
+",0,1,0,0,0,0
+2288,Tunable high-harmonic generation by chromatic focusing of few-cycle laser pulses,"  In this work we study the impact of chromatic focusing of few-cycle laser
+pulses on high-order harmonic generation (HHG) through analysis of the emitted
+extreme ultraviolet (XUV) radiation. Chromatic focusing is usually avoided in
+the few-cycle regime, as the pulse spatio-temporal structure may be highly
+distorted by the spatiotemporal aberrations. Here, however, we demonstrate it
+as an additional control parameter to modify the generated XUV radiation. We
+present experiments where few-cycle pulses are focused by a singlet lens in a
+Kr gas jet. The chromatic distribution of focal lengths allows us to tune HHG
+spectra by changing the relative singlet-target distance. Interestingly, we
+also show that the degree of chromatic aberration needed to this control does
+not degrade substantially the harmonic conversion efficiency, still allowing
+for the generation of supercontinua with the chirped-pulse scheme, demonstrated
+previously for achromatic focussing. We back up our experiments with
+theoretical simulations reproducing the experimental HHG results depending on
+diverse parameters (input pulse spectral phase, pulse duration, focus position)
+and proving that, under the considered parameters, the attosecond pulse train
+remains very similar to the achromatic case, even showing cases of isolated
+attosecond pulse generation for near single-cycle driving pulses.
+",0,1,0,0,0,0
+2289,Gate Switchable Transport and Optical Anisotropy in 90° Twisted Bilayer Black Phosphorus,"  Anisotropy describes the directional dependence of a material's properties
+such as transport and optical response. In conventional bulk materials,
+anisotropy is intrinsically related to the crystal structure, and thus not
+tunable by the gating techniques used in modern electronics. Here we show that,
+in bilayer black phosphorus with an interlayer twist angle of 90°, the
+anisotropy of its electronic structure and optical transitions is tunable by
+gating. Using first-principles calculations, we predict that a
+laboratory-accessible gate voltage can induce a hole effective mass that is 30
+times larger along one Cartesian axis than along the other axis, and the two
+axes can be exchanged by flipping the sign of the gate voltage. This
+gate-controllable band structure also leads to a switchable optical linear
+dichroism, where the polarization of the lowest-energy optical transitions
+(absorption or luminescence) is tunable by gating. Thus, anisotropy is a
+tunable degree of freedom in twisted bilayer black phosphorus.
+",0,1,0,0,0,0
+2290,MM Algorithms for Variance Component Estimation and Selection in Logistic Linear Mixed Model,"  Logistic linear mixed model is widely used in experimental designs and
+genetic analysis with binary traits. Motivated by modern applications, we
+consider the case with many groups of random effects and each group corresponds
+to a variance component. When the number of variance components is large,
+fitting the logistic linear mixed model is challenging. We develop two
+efficient and stable minorization-maximization (MM) algorithms for the
+estimation of variance components based on the Laplace approximation of the
+logistic model. One of them leads to a simple iterative soft-thresholding
+algorithm for variance component selection using maximum penalized approximated
+likelihood. We demonstrate the variance component estimation and selection
+performance of our algorithms by simulation studies and a real data analysis.
+",0,0,0,1,0,0
+2291,Settling the query complexity of non-adaptive junta testing,"  We prove that any non-adaptive algorithm that tests whether an unknown
+Boolean function $f: \{0, 1\}^n\to \{0, 1\}$ is a $k$-junta or $\epsilon$-far
+from every $k$-junta must make $\widetilde{\Omega}(k^{3/2} / \epsilon)$ many
+queries for a wide range of parameters $k$ and $\epsilon$. Our result
+dramatically improves previous lower bounds from [BGSMdW13, STW15], and is
+essentially optimal given Blais's non-adaptive junta tester from [Blais08],
+which makes $\widetilde{O}(k^{3/2})/\epsilon$ queries. Combined with the
+adaptive tester of [Blais09] which makes $O(k\log k + k /\epsilon)$ queries,
+our result shows that adaptivity enables polynomial savings in query complexity
+for junta testing.
+",1,0,0,0,0,0
+2292,Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems,"  Language understanding is a key component in a spoken dialogue system. In
+this paper, we investigate how the language understanding module influences the
+dialogue system performance by conducting a series of systematic experiments on
+a task-oriented neural dialogue system in a reinforcement learning based
+setting. The empirical study shows that among different types of language
+understanding errors, slot-level errors can have more impact on the overall
+performance of a dialogue system compared to intent-level errors. In addition,
+our experiments demonstrate that the reinforcement learning based dialogue
+system is able to learn when and what to confirm in order to achieve better
+performance and greater robustness.
+",1,0,0,0,0,0
+2293,String stability and a delay-based spacing policy for vehicle platoons subject to disturbances,"  A novel delay-based spacing policy for the control of vehicle platoons is
+introduced together with a notion of disturbance string stability. The
+delay-based spacing policy specifies the desired inter-vehicular distance
+between vehicles and guarantees that all vehicles track the same spatially
+varying reference velocity profile, as is for example required for heavy-duty
+vehicles driving over hilly terrain. Disturbance string stability is a notion
+of string stability of vehicle platoons subject to external disturbances on all
+vehicles that guarantees that perturbations do not grow unbounded as they
+propagate through the platoon. Specifically, a control design approach in the
+spatial domain is presented that achieves tracking of the desired spacing
+policy and guarantees disturbance string stability with respect to a spatially
+varying reference velocity. The results are illustrated by means of
+simulations.
+",1,0,1,0,0,0
+2294,Reidemeister spectra for solvmanifolds in low dimensions,"  The Reidemeister number of an endomorphism of a group is the number of
+twisted conjugacy classes determined by that endomorphism. The collection of
+all Reidemeister numbers of all automorphisms of a group $G$ is called the
+Reidemeister spectrum of $G$. In this paper, we determine the Reidemeister
+spectra of all fundamental groups of solvmanifolds up to Hirsch length 4.
+",0,0,1,0,0,0
+2295,Cross-layer Optimization for Ultra-reliable and Low-latency Radio Access Networks,"  In this paper, we propose a framework for cross-layer optimization to ensure
+ultra-high reliability and ultra-low latency in radio access networks, where
+both transmission delay and queueing delay are considered. With short
+transmission time, the blocklength of channel codes is finite, and the Shannon
+Capacity cannot be used to characterize the maximal achievable rate with given
+transmission error probability. With randomly arrived packets, some packets may
+violate the queueing delay. Moreover, since the queueing delay is shorter than
+the channel coherence time in typical scenarios, the required transmit power to
+guarantee the queueing delay and transmission error probability will become
+unbounded even with spatial diversity. To ensure the required
+quality-of-service (QoS) with finite transmit power, a proactive packet
+dropping mechanism is introduced. Then, the overall packet loss probability
+includes transmission error probability, queueing delay violation probability,
+and packet dropping probability. We optimize the packet dropping policy, power
+allocation policy, and bandwidth allocation policy to minimize the transmit
+power under the QoS constraint. The optimal solution is obtained, which depends
+on both channel and queue state information. Simulation and numerical results
+validate our analysis, and show that setting packet loss probabilities equal is
+a near optimal solution.
+",1,0,0,0,0,0
+2296,Network Inference via the Time-Varying Graphical Lasso,"  Many important problems can be modeled as a system of interconnected
+entities, where each entity is recording time-dependent observations or
+measurements. In order to spot trends, detect anomalies, and interpret the
+temporal dynamics of such data, it is essential to understand the relationships
+between the different entities and how these relationships evolve over time. In
+this paper, we introduce the time-varying graphical lasso (TVGL), a method of
+inferring time-varying networks from raw time series data. We cast the problem
+in terms of estimating a sparse time-varying inverse covariance matrix, which
+reveals a dynamic network of interdependencies between the entities. Since
+dynamic network inference is a computationally expensive task, we derive a
+scalable message-passing algorithm based on the Alternating Direction Method of
+Multipliers (ADMM) to solve this problem in an efficient way. We also discuss
+several extensions, including a streaming algorithm to update the model and
+incorporate new observations in real time. Finally, we evaluate our TVGL
+algorithm on both real and synthetic datasets, obtaining interpretable results
+and outperforming state-of-the-art baselines in terms of both accuracy and
+scalability.
+",1,0,1,0,0,0
+2297,Construction of dynamical semigroups by a functional regularisation à la Kato,"  A functional version of the Kato one-parametric regularisation for the
+construction of a dynamical semigroup generator of a relative bound one
+perturbation is introduced. It does not require that the minus generator of the
+unperturbed semigroup is a positivity preserving operator. The regularisation
+is illustrated by an example of a boson-number cut-off regularisation.
+",0,0,1,0,0,0
+2298,Visual Reasoning with Multi-hop Feature Modulation,"  Recent breakthroughs in computer vision and natural language processing have
+spurred interest in challenging multi-modal tasks such as visual
+question-answering and visual dialogue. For such tasks, one successful approach
+is to condition image-based convolutional network computation on language via
+Feature-wise Linear Modulation (FiLM) layers, i.e., per-channel scaling and
+shifting. We propose to generate the parameters of FiLM layers going up the
+hierarchy of a convolutional network in a multi-hop fashion rather than all at
+once, as in prior work. By alternating between attending to the language input
+and generating FiLM layer parameters, this approach is better able to scale to
+settings with longer input sequences such as dialogue. We demonstrate that
+multi-hop FiLM generation achieves state-of-the-art for the short input
+sequence task ReferIt --- on-par with single-hop FiLM generation --- while also
+significantly outperforming prior state-of-the-art and single-hop FiLM
+generation on the GuessWhat?! visual dialogue task.
+",0,0,0,1,0,0
+2299,Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data,"  Objective: To evaluate unsupervised clustering methods for identifying
+individual-level behavioral-clinical phenotypes that relate personal biomarkers
+and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials
+and Methods: We used hierarchical clustering (HC) to identify groups of meals
+with similar nutrition and glycemic impact for 6 individuals with T2DM who
+collected self-monitoring data. We evaluated clusters on: 1) correspondence to
+gold standards generated by certified diabetes educators (CDEs) for 3
+participants; 2) face validity, rated by CDEs, and 3) impact on CDEs' ability
+to identify patterns for another 3 participants. Results: Gold standard (GS)
+included 9 patterns across 3 participants. Of these, all 9 were re-discovered
+using HC: 4 GS patterns were consistent with patterns identified by HC (over
+50% of meals in a cluster followed the pattern); another 5 were included as
+sub-groups in broader clusers. 50% (9/18) of clusters were rated over 3 on
+5-point Likert scale for validity, significance, and being actionable. After
+reviewing clusters, CDEs identified patterns that were more consistent with
+data (70% reduction in contradictions between patterns and participants'
+records). Discussion: Hierarchical clustering of blood glucose and
+macronutrient consumption appears suitable for discovering behavioral-clinical
+phenotypes in T2DM. Most clusters corresponded to gold standard and were rated
+positively by CDEs for face validity. Cluster visualizations helped CDEs
+identify more robust patterns in nutrition and glycemic impact, creating new
+possibilities for visual analytic solutions. Conclusion: Machine learning
+methods can use diabetes self-monitoring data to create personalized
+behavioral-clinical phenotypes, which may prove useful for delivering
+personalized medicine.
+",0,0,0,1,0,0
+2300,Ultrahigh capacitive energy storage in highly oriented BaZr(x)Ti(1-x)O3 thin films prepared by pulsed laser deposition,"  We report structural, optical, temperature and frequency dependent
+dielectric, and energy storage properties of pulsed laser deposited (100)
+highly textured BaZr(x)Ti(1-x)O3 (x = 0.3, 0.4 and 0.5) relaxor ferroelectric
+thin films on La0.7Sr0.3MnO3/MgO substrates which make this compound as a
+potential lead-free capacitive energy storage material for scalable electronic
+devices. A high dielectric constant of ~1400 - 3500 and a low dielectric loss
+of <0.025 were achieved at 10 kHz for all three compositions at ambient
+conditions. Ultrahigh stored and recoverable electrostatic energy densities as
+high as 214 +/- 1 and 156 +/- 1 J/cm3, respectively, were demonstrated at a
+sustained high electric field of ~3 MV/cm with an efficiency of 72.8 +/- 0.6 %
+in optimum 30% Zr substituted BaTiO3 composition.
+",0,1,0,0,0,0
+2301,Hermitian-Yang-Mills connections on collapsing elliptically fibered $K3$ surfaces,"  Let $X\rightarrow {\mathbb P}^1$ be an elliptically fibered $K3$ surface with
+a section, admitting a sequence of Ricci-flat metrics collapsing the fibers.
+Let $\mathcal E$ be a generic, holomoprhic $SU(n)$ bundle over $X$ such that
+the restriction of $\mathcal E$ to each fiber is semi-stable. Given a sequence
+$\Xi_i$ of Hermitian-Yang-Mills connections on $\mathcal E$ corresponding to
+this degeneration, we prove that, if $E$ is a given fiber away from a finite
+set, the restricted sequence $\Xi_i|_{E}$ converges to a flat connection
+uniquely determined by the holomorphic structure on $\mathcal E$.
+",0,0,1,0,0,0
+2302,Highly Efficient Human Action Recognition with Quantum Genetic Algorithm Optimized Support Vector Machine,"  In this paper we propose the use of quantum genetic algorithm to optimize the
+support vector machine (SVM) for human action recognition. The Microsoft Kinect
+sensor can be used for skeleton tracking, which provides the joints' position
+data. However, how to extract the motion features for representing the dynamics
+of a human skeleton is still a challenge due to the complexity of human motion.
+We present a highly efficient features extraction method for action
+classification, that is, using the joint angles to represent a human skeleton
+and calculating the variance of each angle during an action time window. Using
+the proposed representation, we compared the human action classification
+accuracy of two approaches, including the optimized SVM based on quantum
+genetic algorithm and the conventional SVM with grid search. Experimental
+results on the MSR-12 dataset show that the conventional SVM achieved an
+accuracy of $ 93.85\% $. The proposed approach outperforms the conventional
+method with an accuracy of $ 96.15\% $.
+",1,0,0,1,0,0
+2303,The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI,"  We present a vision-only model for gaming AI which uses a late integration
+deep convolutional network architecture trained in a purely supervised
+imitation learning context. Although state-of-the-art deep learning models for
+video game tasks generally rely on more complex methods such as deep-Q
+learning, we show that a supervised model which requires substantially fewer
+resources and training time can already perform well at human reaction speeds
+on the N64 classic game Super Smash Bros. We frame our learning task as a
+30-class classification problem, and our CNN model achieves 80% top-1 and 95%
+top-3 validation accuracy. With slight test-time fine-tuning, our model is also
+competitive during live simulation with the highest-level AI built into the
+game. We will further show evidence through network visualizations that the
+network is successfully leveraging temporal information during inference to aid
+in decision making. Our work demonstrates that supervised CNN models can
+provide good performance in challenging policy prediction tasks while being
+significantly simpler and more lightweight than alternatives.
+",1,0,0,0,0,0
+2304,Differential relations for almost Belyi maps,"  Several kinds of differential relations for polynomial components of almost
+Belyi maps are presented. Saito's theory of free divisors give particularly
+interesting (yet conjectural) logarithmic action of vector fields. The
+differential relations implied by Kitaev's construction of algebraic Painleve
+VI solutions through pull-back transformations are used to compute almost Belyi
+maps for the pull-backs giving all genus 0 and 1 Painleve VI solutions in the
+Lisovyy-Tykhyy classification.
+",0,0,1,0,0,0
+2305,Non-commutative holomorphic semicocycles,"  This paper studies holomorphic semicocycles over semigroups in the unit disk,
+which take values in an arbitrary unital Banach algebra. We prove that every
+such semicocycle is a solution to a corresponding evolution problem. We then
+investigate the linearization problem: which semicocycles are cohomologous to
+constant semicocycles? In contrast with the case of commutative semicocycles,
+in the non-commutative case non-linearizable semicocycles are shown to exist.
+Simple conditions for linearizability are derived and are shown to be sharp.
+",0,0,1,0,0,0
+2306,Comparative Study of Virtual Machines and Containers for DevOps Developers,"  In this work, we plan to develop a system to compare virtual machines with
+container technology. We would devise ways to measure the administrator effort
+of containers vs. Virtual Machines (VMs). Metrics that will be tested against
+include human efforts required, ease of migration, resource utilization and
+ease of use using containers and virtual machines.
+",1,0,0,0,0,0
+2307,Developmental tendencies in the Academic Field of Intellectual Property through the Identification of Invisible Colleges,"  The emergence of intellectual property as an academic issue opens a big gate
+to a cross-disciplinary field. Different disciplines start a dialogue in the
+framework of the international multilateral treaties in the early 90's. As
+global economy demands new knowledge on intellectual property, Science grows at
+its own pace. However, the degree of consolidation of cross-disciplinary
+academic communities is not clear. In order to know how closely related are
+these communities, this paper proposes a mixed methodology to find invisible
+colleges in the production about intellectual property. The articles examined
+in this paper were extracted from Web of Science. The analyzed period was from
+1994 to 2016, taking into account the signature of the agreement on
+Trade-Related Aspects of Intellectual Property Rights in the early 90's. A
+total amount of 1580 papers were processed through co-citation network
+analysis. An especial technique, which combine algorithms of community
+detection and defining population of articles through thresholds of shared
+references, was applied. In order to contrast the invisible colleges that
+emerged with the existence of formal institutional relations, it was made a
+qualitative tracking of the authors with respect to their institutional
+affiliation, lines of research and meeting places. Both methods show that the
+subjects of interest can be grouped into 13 different issues related to
+intellectual property field. Even though most of them are related to Laws and
+Economics, there are weak linkages between disciplines which could indicate the
+construction of a cross-disciplinary field.
+",1,1,0,0,0,0
+2308,Periodic fourth-order cubic NLS: Local well-posedness and Non-squeezing property,"  In this paper, we consider the cubic fourth-order nonlinear Schrödinger
+equation (4NLS) under the periodic boundary condition. We prove two results.
+One is the local well-posedness in $H^s$ with $-1/3 \le s < 0$ for the Cauchy
+problem of the Wick ordered 4NLS. The other one is the non-squeezing property
+for the flow map of 4NLS in the symplectic phase space $L^2(\mathbb{T})$. To
+prove the former we used the ideas introduced in [Takaoka and Tsutsumi 2004]
+and [Nakanish et al 2010], and to prove the latter we used the ideas in
+[Colliander et al 2005].
+",0,0,1,0,0,0
+2309,Feature Learning for Meta-Paths in Knowledge Graphs,"  In this thesis, we study the problem of feature learning on heterogeneous
+knowledge graphs. These features can be used to perform tasks such as link
+prediction, classification and clustering on graphs. Knowledge graphs provide
+rich semantics encoded in the edge and node types. Meta-paths consist of these
+types and abstract paths in the graph. Until now, meta-paths can only be used
+as categorical features with high redundancy and are therefore unsuitable for
+machine learning models. We propose meta-path embeddings to solve this problem
+by learning semantical and compact vector representations of them. Current
+graph embedding methods only embed nodes and edge types and therefore miss
+semantics encoded in the combination of them. Our method embeds meta-paths
+using the skipgram model with an extension to deal with the redundancy and high
+amount of meta-paths in big knowledge graphs. We critically evaluate our
+embedding approach by predicting links on Wikidata. The experiments indicate
+that we learn a sensible embedding of the meta-paths but can improve it
+further.
+",1,0,0,1,0,0
+2310,Closed-Form Exact Inverses of the Weakly Singular and Hypersingular Operators On Disks,"  We introduce new boundary integral operators which are the exact inverses of
+the weakly singular and hypersingular operators for the Laplacian on flat
+disks. Moreover, we provide explicit closed forms for them and prove the
+continuity and ellipticity of their corresponding bilinear forms in the natural
+Sobolev trace spaces. This permit us to derive new Calderón-type identities
+that can provide the foundation for optimal operator preconditioning in
+Galerkin boundary element methods.
+",0,0,1,0,0,0
+2311,Focus on Imaging Methods in Granular Physics,"  Granular materials are complex multi-particle ensembles in which macroscopic
+properties are largely determined by inter-particle interactions between their
+numerous constituents. In order to understand and to predict their macroscopic
+physical behavior, it is necessary to analyze the composition and interactions
+at the level of individual contacts and grains. To do so requires the ability
+to image individual particles and their local configurations to high precision.
+A variety of competing and complementary imaging techniques have been developed
+for that task. In this introductory paper accompanying the Focus Issue, we
+provide an overview of these imaging methods and discuss their advantages and
+drawbacks, as well as their limits of application.
+",0,1,0,0,0,0
+2312,The Stochastic Matching Problem: Beating Half with a Non-Adaptive Algorithm,"  In the stochastic matching problem, we are given a general (not necessarily
+bipartite) graph $G(V,E)$, where each edge in $E$ is realized with some
+constant probability $p > 0$ and the goal is to compute a bounded-degree
+(bounded by a function depending only on $p$) subgraph $H$ of $G$ such that the
+expected maximum matching size in $H$ is close to the expected maximum matching
+size in $G$. The algorithms in this setting are considered non-adaptive as they
+have to choose the subgraph $H$ without knowing any information about the set
+of realized edges in $G$. Originally motivated by an application to kidney
+exchange, the stochastic matching problem and its variants have received
+significant attention in recent years.
+The state-of-the-art non-adaptive algorithms for stochastic matching achieve
+an approximation ratio of $\frac{1}{2}-\epsilon$ for any $\epsilon > 0$,
+naturally raising the question that if $1/2$ is the limit of what can be
+achieved with a non-adaptive algorithm. In this work, we resolve this question
+by presenting the first algorithm for stochastic matching with an approximation
+guarantee that is strictly better than $1/2$: the algorithm computes a subgraph
+$H$ of $G$ with the maximum degree $O(\frac{\log{(1/ p)}}{p})$ such that the
+ratio of expected size of a maximum matching in realizations of $H$ and $G$ is
+at least $1/2+\delta_0$ for some absolute constant $\delta_0 > 0$. The degree
+bound on $H$ achieved by our algorithm is essentially the best possible (up to
+an $O(\log{(1/p)})$ factor) for any constant factor approximation algorithm,
+since an $\Omega(\frac{1}{p})$ degree in $H$ is necessary for a vertex to
+acquire at least one incident edge in a realization.
+",1,0,0,0,0,0
+2313,Measuring the unmeasurable - a project of domestic violence risk prediction and management,"  The prevention of domestic violence (DV) have aroused serious concerns in
+Taiwan because of the disparity between the increasing amount of reported DV
+cases that doubled over the past decade and the scarcity of social workers.
+Additionally, a large amount of data was collected when social workers use the
+predominant case management approach to document case reports information.
+However, these data were not properly stored or organized.
+To improve the efficiency of DV prevention and risk management, we worked
+with Taipei City Government and utilized the 2015 data from its DV database to
+perform a spatial pattern analysis of the reports of DV cases to build a DV
+risk map. However, during our map building process, the issue of confounding
+bias arose because we were not able to verify if reported cases truly reflected
+real violence occurrence or were simply false reports from potential victim's
+neighbors. Therefore, we used the random forest method to build a repeat
+victimization risk prediction model. The accuracy and F1-measure of our model
+were 96.3% and 62.8%. This model helped social workers differentiate the risk
+level of new cases, which further reduced their major workload significantly.
+To our knowledge, this is the first project that utilized machine learning in
+DV prevention. The research approach and results of this project not only can
+improve DV prevention process, but also be applied to other social work or
+criminal prevention areas.
+",1,0,0,0,0,0
+2314,Attaining Capacity with Algebraic Geometry Codes through the $(U|U+V)$ Construction and Koetter-Vardy Soft Decoding,"  In this paper we show how to attain the capacity of discrete symmetric
+channels with polynomial time decoding complexity by considering iterated
+$(U|U+V)$ constructions with Reed-Solomon code or algebraic geometry code
+components. These codes are decoded with a recursive computation of the {\em a
+posteriori} probabilities of the code symbols together with the Koetter-Vardy
+soft decoder used for decoding the code components in polynomial time. We show
+that when the number of levels of the iterated $(U|U+V)$ construction tends to
+infinity, we attain the capacity of any discrete symmetric channel in this way.
+This result follows from the polarization theorem together with a simple lemma
+explaining how the Koetter-Vardy decoder behaves for Reed-Solomon codes of rate
+close to $1$. However, even if this way of attaining the capacity of a
+symmetric channel is essentially the Ar{\i}kan polarization theorem, there are
+some differences with standard polar codes.
+Indeed, with this strategy we can operate succesfully close to channel
+capacity even with a small number of levels of the iterated $(U|U+V)$
+construction and the probability of error decays quasi-exponentially with the
+codelength in such a case (i.e. exponentially if we forget about the
+logarithmic terms in the exponent). We can even improve on this result by
+considering the algebraic geometry codes constructed in \cite{TVZ82}. In such a
+case, the probability of error decays exponentially in the codelength for any
+rate below the capacity of the channel. Moreover, when comparing this strategy
+to Reed-Solomon codes (or more generally algebraic geometry codes) decoded with
+the Koetter-Vardy decoding algorithm, it does not only improve the noise level
+that the code can tolerate, it also results in a significant complexity gain.
+",1,0,0,0,0,0
+2315,Embedded real-time monitoring using SystemC in IMA network,"  Avionics is one kind of domain where prevention prevails. Nonetheless fails
+occur. Sometimes due to pilot misreacting, flooded in information. Sometimes
+information itself would be better verified than trusted. To avoid some kind of
+failure, it has been thought to add,in midst of the ARINC664 aircraft data
+network, a new kind of monitoring.
+",1,0,0,0,0,0
+2316,One pixel attack for fooling deep neural networks,"  Recent research has revealed that the output of Deep Neural Networks (DNN)
+can be easily altered by adding relatively small perturbations to the input
+vector. In this paper, we analyze an attack in an extremely limited scenario
+where only one pixel can be modified. For that we propose a novel method for
+generating one-pixel adversarial perturbations based on differential
+evolution(DE). It requires less adversarial information(a black-box attack) and
+can fool more types of networks due to the inherent features of DE. The results
+show that 68.36% of the natural images in CIFAR-10 test dataset and 41.22% of
+the ImageNet (ILSVRC 2012) validation images can be perturbed to at least one
+target class by modifying just one pixel with 73.22% and 5.52% confidence on
+average. Thus, the proposed attack explores a different take on adversarial
+machine learning in an extreme limited scenario, showing that current DNNs are
+also vulnerable to such low dimension attacks. Besides, we also illustrate an
+important application of DE (or broadly speaking, evolutionary computation) in
+the domain of adversarial machine learning: creating tools that can effectively
+generate low-cost adversarial attacks against neural networks for evaluating
+robustness. The code is available on:
+this https URL
+",1,0,0,1,0,0
+2317,A computer simulation of the Volga River hydrological regime: a problem of water-retaining dam optimal location,"  We investigate of a special dam optimal location at the Volga river in area
+of the Akhtuba left sleeve beginning (7 \, km to the south of the Volga
+Hydroelectric Power Station dam). We claim that a new water-retaining dam can
+resolve the key problem of the Volga-Akhtuba floodplain related to insufficient
+water amount during the spring flooding due to the overregulation of the Lower
+Volga. By using a numerical integration of Saint-Vacant equations we study the
+water dynamics across the northern part of the Volga-Akhtuba floodplain with
+taking into account its actual topography. As the result we found an amount of
+water $V_A$ passing to the Akhtuba during spring period for a given water flow
+through the Volga Hydroelectric Power Station (so-called hydrograph which
+characterises the water flow per unit of time). By varying the location of the
+water-retaining dam $ x_d, y_d $ we obtained various values of $V_A (x_d, y_d)
+$ as well as various flow spatial structure on the territory during the flood
+period. Gradient descent method provide us the dam coordinated with the maximum
+value of ${V_A}$. Such approach to the dam location choice let us to find the
+best solution, that the value $V_A$ increases by a factor of 2. Our analysis
+demonstrate a good potential of the numerical simulations in the field of
+hydraulic works.
+",1,0,0,0,0,0
+2318,Multi-proton bunch driven hollow plasma wakefield acceleration in the nonlinear regime,"  Proton-driven plasma wakefield acceleration has been demonstrated in
+simulations to be capable of accelerating particles to the energy frontier in a
+single stage, but its potential is hindered by the fact that currently
+available proton bunches are orders of magnitude longer than the plasma
+wavelength. Fortunately, proton micro-bunching allows driving plasma waves
+resonantly. In this paper, we propose using a hollow plasma channel for
+multiple proton bunch driven plasma wakefield acceleration and demonstrate that
+it enables the operation in the nonlinear regime and resonant excitation of
+strong plasma waves. This new regime also involves beneficial features of
+hollow channels for the accelerated beam (such as emittance preservation and
+uniform accelerating field) and long buckets of stable deceleration for the
+drive beam. The regime is attained at a proper ratio among plasma skin depth,
+driver radius, hollow channel radius, and micro-bunch period.
+",0,1,0,0,0,0
+2319,Self corrective Perturbations for Semantic Segmentation and Classification,"  Convolutional Neural Networks have been a subject of great importance over
+the past decade and great strides have been made in their utility for producing
+state of the art performance in many computer vision problems. However, the
+behavior of deep networks is yet to be fully understood and is still an active
+area of research. In this work, we present an intriguing behavior: pre-trained
+CNNs can be made to improve their predictions by structurally perturbing the
+input. We observe that these perturbations - referred as Guided Perturbations -
+enable a trained network to improve its prediction performance without any
+learning or change in network weights. We perform various ablative experiments
+to understand how these perturbations affect the local context and feature
+representations. Furthermore, we demonstrate that this idea can improve
+performance of several existing approaches on semantic segmentation and scene
+labeling tasks on the PASCAL VOC dataset and supervised classification tasks on
+MNIST and CIFAR10 datasets.
+",1,0,0,1,0,0
+2320,Large-Scale Mapping of Human Activity using Geo-Tagged Videos,"  This paper is the first work to perform spatio-temporal mapping of human
+activity using the visual content of geo-tagged videos. We utilize a recent
+deep-learning based video analysis framework, termed hidden two-stream
+networks, to recognize a range of activities in YouTube videos. This framework
+is efficient and can run in real time or faster which is important for
+recognizing events as they occur in streaming video or for reducing latency in
+analyzing already captured video. This is, in turn, important for using video
+in smart-city applications. We perform a series of experiments to show our
+approach is able to accurately map activities both spatially and temporally. We
+also demonstrate the advantages of using the visual content over the
+tags/titles.
+",1,0,0,0,0,0
+2321,"Structure of a Parabolic Partial Differential Equation on Graphs and Digital spaces. Solution of PDE on Digital Spaces: a Klein Bottle, a Projective Plane, a 4D Sphere and a Moebius Band","  This paper studies the structure of a parabolic partial differential equation
+on graphs and digital n-dimensional manifolds, which are digital models of
+continuous n-manifolds. Conditions for the existence of solutions of equations
+are determined and investigated. Numerical solutions of the equation on a Klein
+bottle, a projective plane, a 4D sphere and a Moebius strip are presented.
+",1,0,1,0,0,0
+2322,GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks,"  In this paper, we generally formulate the dynamics prediction problem of
+various network systems (e.g., the prediction of mobility, traffic and
+topology) as the temporal link prediction task. Different from conventional
+techniques of temporal link prediction that ignore the potential non-linear
+characteristics and the informative link weights in the dynamic network, we
+introduce a novel non-linear model GCN-GAN to tackle the challenging temporal
+link prediction task of weighted dynamic networks. The proposed model leverages
+the benefits of the graph convolutional network (GCN), long short-term memory
+(LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics,
+topology structure and evolutionary patterns of weighted dynamic networks can
+be fully exploited to improve the temporal link prediction performance.
+Concretely, we first utilize GCN to explore the local topological
+characteristics of each single snapshot and then employ LSTM to characterize
+the evolving features of the dynamic networks. Moreover, GAN is used to enhance
+the ability of the model to generate the next weighted network snapshot, which
+can effectively tackle the sparsity and the wide-value-range problem of edge
+weights in real-life dynamic networks. To verify the model's effectiveness, we
+conduct extensive experiments on four datasets of different network systems and
+application scenarios. The experimental results demonstrate that our model
+achieves impressive results compared to the state-of-the-art competitors.
+",1,0,0,0,0,0
+2323,Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs,"  Graphs are an important tool to model data in different domains, including
+social networks, bioinformatics and the world wide web. Most of the networks
+formed in these domains are directed graphs, where all the edges have a
+direction and they are not symmetric. Betweenness centrality is an important
+index widely used to analyze networks. In this paper, first given a directed
+network $G$ and a vertex $r \in V(G)$, we propose a new exact algorithm to
+compute betweenness score of $r$. Our algorithm pre-computes a set
+$\mathcal{RV}(r)$, which is used to prune a huge amount of computations that do
+not contribute in the betweenness score of $r$. Time complexity of our exact
+algorithm depends on $|\mathcal{RV}(r)|$ and it is respectively
+$\Theta(|\mathcal{RV}(r)|\cdot|E(G)|)$ and
+$\Theta(|\mathcal{RV}(r)|\cdot|E(G)|+|\mathcal{RV}(r)|\cdot|V(G)|\log |V(G)|)$
+for unweighted graphs and weighted graphs with positive weights.
+$|\mathcal{RV}(r)|$ is bounded from above by $|V(G)|-1$ and in most cases, it
+is a small constant. Then, for the cases where $\mathcal{RV}(r)$ is large, we
+present a simple randomized algorithm that samples from $\mathcal{RV}(r)$ and
+performs computations for only the sampled elements. We show that this
+algorithm provides an $(\epsilon,\delta)$-approximation of the betweenness
+score of $r$. Finally, we perform extensive experiments over several real-world
+datasets from different domains for several randomly chosen vertices as well as
+for the vertices with the highest betweenness scores. Our experiments reveal
+that in most cases, our algorithm significantly outperforms the most efficient
+existing randomized algorithms, in terms of both running time and accuracy. Our
+experiments also show that our proposed algorithm computes betweenness scores
+of all vertices in the sets of sizes 5, 10 and 15, much faster and more
+accurate than the most efficient existing algorithms.
+",1,0,0,0,0,0
+2324,A multi-instrument non-parametric reconstruction of the electron pressure profile in the galaxy cluster CLJ1226.9+3332,"  Context: In the past decade, sensitive, resolved Sunyaev-Zel'dovich (SZ)
+studies of galaxy clusters have become common. Whereas many previous SZ studies
+have parameterized the pressure profiles of galaxy clusters, non-parametric
+reconstructions will provide insights into the thermodynamic state of the
+intracluster medium (ICM). Aims: We seek to recover the non-parametric pressure
+profiles of the high redshift ($z=0.89$) galaxy cluster CLJ 1226.9+3332 as
+inferred from SZ data from the MUSTANG, NIKA, Bolocam, and Planck instruments,
+which all probe different angular scales. Methods: Our non-parametric algorithm
+makes use of logarithmic interpolation, which under the assumption of
+ellipsoidal symmetry is analytically integrable. For MUSTANG, NIKA, and Bolocam
+we derive a non-parametric pressure profile independently and find good
+agreement among the instruments. In particular, we find that the non-parametric
+profiles are consistent with a fitted gNFW profile. Given the ability of Planck
+to constrain the total signal, we include a prior on the integrated Compton Y
+parameter as determined by Planck. Results: For a given instrument, constraints
+on the pressure profile diminish rapidly beyond the field of view. The overlap
+in spatial scales probed by these four datasets is therefore critical in
+checking for consistency between instruments. By using multiple instruments,
+our analysis of CLJ 1226.9+3332 covers a large radial range, from the central
+regions to the cluster outskirts: $0.05 R_{500} < r < 1.1 R_{500}$. This is a
+wider range of spatial scales than is typical recovered by SZ instruments.
+Similar analyses will be possible with the new generation of SZ instruments
+such as NIKA2 and MUSTANG2.
+",0,1,0,0,0,0
+2325,Recognizing Union-Find trees built up using union-by-rank strategy is NP-complete,"  Disjoint-Set forests, consisting of Union-Find trees, are data structures
+having a widespread practical application due to their efficiency. Despite them
+being well-known, no exact structural characterization of these trees is known
+(such a characterization exists for Union trees which are constructed without
+using path compression) for the case assuming union-by-rank strategy for
+merging. In this paper we provide such a characterization by means of a simple
+push operation and show that the decision problem whether a given tree (along
+with the rank info of its nodes) is a Union-Find tree is NP-complete,
+complementing our earlier similar result for the union-by-size strategy.
+",1,0,0,0,0,0
+2326,A Competitive Algorithm for Online Multi-Robot Exploration of a Translating Plume,"  In this paper, we study the problem of exploring a translating plume with a
+team of aerial robots. The shape and the size of the plume are unknown to the
+robots. The objective is to find a tour for each robot such that they
+collectively explore the plume. Specifically, the tours must be such that each
+point in the plume must be visible from the field-of-view of some robot along
+its tour. We propose a recursive Depth-First Search (DFS)-based algorithm that
+yields a constant competitive ratio for the exploration problem. The
+competitive ratio is
+$\frac{2(S_r+S_p)(R+\lfloor\log{R}\rfloor)}{(S_r-S_p)(1+\lfloor\log{R}\rfloor)}$
+where $R$ is the number of robots, and $S_r$ and $S_p$ are the robot speed and
+the plume speed, respectively. We also consider a more realistic scenario where
+the plume shape is not restricted to grid cells but an arbitrary shape. We show
+our algorithm has
+$\frac{2(S_r+S_p)(18R+\lfloor\log{R}\rfloor)}{(S_r-S_p)(1+\lfloor\log{R}\rfloor)}$
+competitive ratio under the fat condition. We empirically verify our algorithm
+using simulations.
+",1,0,0,0,0,0
+2327,Warped Riemannian metrics for location-scale models,"  The present paper shows that warped Riemannian metrics, a class of Riemannian
+metrics which play a prominent role in Riemannian geometry, are also of
+fundamental importance in information geometry. Precisely, the paper features a
+new theorem, which states that the Rao-Fisher information metric of any
+location-scale model, defined on a Riemannian manifold, is a warped Riemannian
+metric, whenever this model is invariant under the action of some Lie group.
+This theorem is a valuable tool in finding the expression of the Rao-Fisher
+information metric of location-scale models defined on high-dimensional
+Riemannian manifolds. Indeed, a warped Riemannian metric is fully determined by
+only two functions of a single variable, irrespective of the dimension of the
+underlying Riemannian manifold. Starting from this theorem, several original
+contributions are made. The expression of the Rao-Fisher information metric of
+the Riemannian Gaussian model is provided, for the first time in the
+literature. A generalised definition of the Mahalanobis distance is introduced,
+which is applicable to any location-scale model defined on a Riemannian
+manifold. The solution of the geodesic equation is obtained, for any Rao-Fisher
+information metric defined in terms of warped Riemannian metrics. Finally,
+using a mixture of analytical and numerical computations, it is shown that the
+parameter space of the von Mises-Fisher model of $n$-dimensional directional
+data, when equipped with its Rao-Fisher information metric, becomes a Hadamard
+manifold, a simply-connected complete Riemannian manifold of negative sectional
+curvature, for $n = 2,\ldots,8$. Hopefully, in upcoming work, this will be
+proved for any value of $n$.
+",0,0,1,1,0,0
+2328,Admissibility of solution estimators for stochastic optimization,"  We look at stochastic optimization problems through the lens of statistical
+decision theory. In particular, we address admissibility, in the statistical
+decision theory sense, of the natural sample average estimator for a stochastic
+optimization problem (which is also known as the empirical risk minimization
+(ERM) rule in learning literature). It is well known that for general
+stochastic optimization problems, the sample average estimator may not be
+admissible. This is known as Stein's paradox in the statistics literature. We
+show in this paper that for optimizing stochastic linear functions over compact
+sets, the sample average estimator is admissible.
+",0,0,1,1,0,0
+2329,Min-Max Regret Scheduling To Minimize the Total Weight of Late Jobs With Interval Uncertainty,"  We study the single machine scheduling problem with the objective to minimize
+the total weight of late jobs. It is assumed that the processing times of jobs
+are not exactly known at the time when a complete schedule must be dispatched.
+Instead, only interval bounds for these parameters are given. In contrast to
+the stochastic optimization approach, we consider the problem of finding a
+robust schedule, which minimizes the maximum regret of a solution. Heuristic
+algorithm based on mixed-integer linear programming is presented and examined
+through computational experiments.
+",1,0,0,0,0,0
+2330,Correcting rural building annotations in OpenStreetMap using convolutional neural networks,"  Rural building mapping is paramount to support demographic studies and plan
+actions in response to crisis that affect those areas. Rural building
+annotations exist in OpenStreetMap (OSM), but their quality and quantity are
+not sufficient for training models that can create accurate rural building
+maps. The problems with these annotations essentially fall into three
+categories: (i) most commonly, many annotations are geometrically misaligned
+with the updated imagery; (ii) some annotations do not correspond to buildings
+in the images (they are misannotations or the buildings have been destroyed);
+and (iii) some annotations are missing for buildings in the images (the
+buildings were never annotated or were built between subsequent image
+acquisitions). First, we propose a method based on Markov Random Field (MRF) to
+align the buildings with their annotations. The method maximizes the
+correlation between annotations and a building probability map while enforcing
+that nearby buildings have similar alignment vectors. Second, the annotations
+with no evidence in the building probability map are removed. Third, we present
+a method to detect non-annotated buildings with predefined shapes and add their
+annotation. The proposed methodology shows considerable improvement in accuracy
+of the OSM annotations for two regions of Tanzania and Zimbabwe, being more
+accurate than state-of-the-art baselines.
+",1,0,0,0,0,0
+2331,Closed-form Harmonic Contrast Control with Surface Impedance Coatings for Conductive Objects,"  The problem of suppressing the scattering from conductive objects is
+addressed in terms of harmonic contrast reduction. A unique compact closed-form
+solution for a surface impedance $Z_s(m,kr)$ is found in a straightforward
+manner and without any approximation as a function of the harmonic index $m$
+(scattering mode to suppress) and of the frequency regime $kr$ (product of
+wavenumber $k$ and radius $r$ of the cloaked system) at any frequency regime.
+In the quasi-static limit, mantle cloaking is obtained as a particular case for
+$kr \ll 1$ and $m=0$. In addition, beyond quasi-static regime, impedance
+coatings for a selected dominant harmonic wave can be designed with proper
+dispersive behaviour, resulting in improved reduction levels and harmonic
+filtering capability.
+",0,1,0,0,0,0
+2332,Numerical methods to prevent pressure oscillations in transcritical flows,"  The accurate and robust simulation of transcritical real-fluid effects is
+crucial for many engineering applications, such as fuel injection in internal
+combustion engines, rocket engines and gas turbines. For example, in diesel
+engines, the liquid fuel is injected into the ambient gas at a pressure that
+exceeds its critical value, and the fuel jet will be heated to a supercritical
+temperature before combustion takes place. This process is often referred to as
+transcritical injection. The largest thermodynamic gradient in the
+transcritical regime occurs as the fluid undergoes a liquid-like to a gas-like
+transition when crossing the pseudo-boiling line (Yang 2000, Oschwald et al.
+2006, Banuti 2015). The complex processes during transcritical injection are
+still not well understood. Therefore, to provide insights into high-pressure
+combustion systems, accurate and robust numerical simulation tools are required
+for the characterization of supercritical and transcritical flows.
+",0,1,0,0,0,0
+2333,Shape Convergence for Aggregate Tiles in Conformal Tilings,"  Given a substitution tiling $T$ of the plane with subdivision operator
+$\tau$, we study the conformal tilings $\mathcal{T}_n$ associated with $\tau^n
+T$. We prove that aggregate tiles within $\mathcal{T}_n$ converge in shape as
+$n\rightarrow \infty$ to their associated Euclidean tiles in $T$.
+",0,0,1,0,0,0
+2334,Performance and sensitivity of vortex coronagraphs on segmented space telescopes,"  The detection of molecular species in the atmospheres of earth-like
+exoplanets orbiting nearby stars requires an optical system that suppresses
+starlight and maximizes the sensitivity to the weak planet signals at small
+angular separations. Achieving sufficient contrast performance on a segmented
+aperture space telescope is particularly challenging due to unwanted
+diffraction within the telescope from amplitude and phase discontinuities in
+the pupil. Apodized vortex coronagraphs are a promising solution that
+theoretically meet the performance needs for high contrast imaging with future
+segmented space telescopes. We investigate the sensitivity of apodized vortex
+coronagraphs to the expected aberrations, including segment co-phasing errors
+in piston and tip/tilt as well as other low-order and mid-spatial frequency
+aberrations. Coronagraph designs and their associated telescope requirements
+are identified for conceptual HabEx and LUVOIR telescope designs.
+",0,1,0,0,0,0
+2335,Classical Spacetime Structure,"  I discuss several issues related to ""classical"" spacetime structure. I review
+Galilean, Newtonian, and Leibnizian spacetimes, and briefly describe more
+recent developments. The target audience is undergraduates and early graduate
+students in philosophy; the presentation avoids mathematical formalism as much
+as possible.
+",0,1,0,0,0,0
+2336,Fractal curves from prime trigonometric series,"  We study the convergence of the parameter family of series
+$$V_{\alpha,\beta}(t)=\sum_{p}p^{-\alpha}\exp(2\pi i p^{\beta}t),\quad
+\alpha,\beta \in \mathbb{R}_{>0},\; t \in [0,1)$$ defined over prime numbers
+$p$, and subsequently, their differentiability properties. The visible fractal
+nature of the graphs as a function of $\alpha,\beta$ is analyzed in terms of
+Hölder continuity, self similarity and fractal dimension, backed with
+numerical results. We also discuss the link of this series to random walks and
+consequently, explore numerically its random properties.
+",0,0,1,0,0,0
+2337,Facets on the convex hull of $d$-dimensional Brownian and Lévy motion,"  For stationary, homogeneous Markov processes (viz., Lévy processes,
+including Brownian motion) in dimension $d\geq 3$, we establish an exact
+formula for the average number of $(d-1)$-dimensional facets that can be
+defined by $d$ points on the process's path. This formula defines a
+universality class in that it is independent of the increments' distribution,
+and it admits a closed form when $d=3$, a case which is of particular interest
+for applications in biophysics, chemistry and polymer science.
+We also show that the asymptotical average number of facets behaves as
+$\langle \mathcal{F}_T^{(d)}\rangle \sim 2\left[\ln \left( T/\Delta
+t\right)\right]^{d-1}$, where $T$ is the total duration of the motion and
+$\Delta t$ is the minimum time lapse separating points that define a facet.
+",0,1,1,0,0,0
+2338,"Local systems on complements of arrangements of smooth, complex algebraic hypersurfaces","  We consider smooth, complex quasi-projective varieties $U$ which admit a
+compactification with a boundary which is an arrangement of smooth algebraic
+hypersurfaces. If the hypersurfaces intersect locally like hyperplanes, and the
+relative interiors of the hypersurfaces are Stein manifolds, we prove that the
+cohomology of certain local systems on $U$ vanishes. As an application, we show
+that complements of linear, toric, and elliptic arrangements are both duality
+and abelian duality spaces.
+",0,0,1,0,0,0
+2339,Some Open Problems in Random Matrix Theory and the Theory of Integrable Systems. II,"  We describe a list of open problems in random matrix theory and the theory of
+integrable systems that was presented at the conference Asymptotics in
+Integrable Systems, Random Matrices and Random Processes and Universality,
+Centre de Recherches Mathematiques, Montreal, June 7-11, 2015. We also describe
+progress that has been made on problems in an earlier list presented by the
+author on the occasion of his 60th birthday in 2005 (see [Deift P., Contemp.
+Math., Vol. 458, Amer. Math. Soc., Providence, RI, 2008, 419-430,
+arXiv:0712.0849]).
+",0,1,1,0,0,0
+2340,A semi-parametric estimation for max-mixture spatial processes,"  We proposed a semi-parametric estimation procedure in order to estimate the
+parameters of a max-mixture model and also of a max-stable model (inverse
+max-stable model) as an alternative to composite likelihood. A good estimation
+by the proposed estimator required the dependence measure to detect all
+dependence structures in the model, especially when dealing with the
+max-mixture model. We overcame this challenge by using the F-madogram. The
+semi-parametric estimation was then based on a quasi least square method, by
+minimizing the square difference between the theoretical F-madogram and an
+empirical one. We evaluated the performance of this estimator through a
+simulation study. It was shown that on an average, the estimation is performed
+well, although in some cases, it encountered some difficulties. We apply our
+estimation procedure to model the daily rainfalls over the East Australia.
+",0,0,1,1,0,0
+2341,Spectroscopic Observation and Analysis of HII regions in M33 with MMT: Temperatures and Oxygen Abundances,"  The spectra of 413 star-forming (or HII) regions in M33 (NGC 598) were
+observed by using the multifiber spectrograph of Hectospec at the 6.5-m
+Multiple Mirror Telescope (MMT). By using this homogeneous spectra sample, we
+measured the intensities of emission lines and some physical parameters, such
+as electron temperatures, electron densities, and metallicities. Oxygen
+abundances were derived via the direct method (when available) and two
+empirical strong-line methods, namely, O3N2 and N2. In the high-metallicity
+end, oxygen abundances derived from O3N2 calibration were higher than those
+derived from N2 index, indicating an inconsistency between O3N2 and N2
+calibrations. We presented a detailed analysis of the spatial distribution of
+gas-phase oxygen abundances in M33 and confirmed the existence of the
+axisymmetric global metallicity distribution widely assumed in literature.
+Local variations were also observed and subsequently associated with spiral
+structures to provide evidence of radial migration driven by arms. Our O/H
+gradient fitted out to 1.1 $R_{25}$ resulted in slopes of $-0.17\pm0.03$,
+$-0.19\pm0.01$, and $-0.16\pm0.17$ dex $R_{25}^{-1}$ utilizing abundances from
+O3N2, N2 diagnostics, and direct method, respectively.
+",0,1,0,0,0,0
+2342,Output-only parameter identification of a colored-noise-driven Van der Pol oscillator -- Thermoacoustic instabilities as an example,"  The problem of output-only parameter identification for nonlinear oscillators
+forced by colored noise is considered. In this context, it is often assumed
+that the forcing noise is white, since its actual spectral content is unknown.
+The impact of this white noise forcing assumption upon parameter identification
+is quantitatively analyzed. First, a Van der Pol oscillator forced by an
+Ornstein-Uhlenbeck process is considered. Second, the practical case of
+thermoacoustic limit cycles in combustion chambers with turbulence-induced
+forcing is investigated. It is shown that in both cases, the system parameters
+are accurately identified if time signals are appropriately band-pass filtered
+around the oscillator eigenfrequency.
+",0,1,0,0,0,0
+2343,Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy,"  Over half a million individuals are diagnosed with head and neck cancer each
+year worldwide. Radiotherapy is an important curative treatment for this
+disease, but it requires manually intensive delineation of radiosensitive
+organs at risk (OARs). This planning process can delay treatment commencement.
+While auto-segmentation algorithms offer a potentially time-saving solution,
+the challenges in defining, quantifying and achieving expert performance
+remain. Adopting a deep learning approach, we demonstrate a 3D U-Net
+architecture that achieves performance similar to experts in delineating a wide
+range of head and neck OARs. The model was trained on a dataset of 663
+deidentified computed tomography (CT) scans acquired in routine clinical
+practice and segmented according to consensus OAR definitions. We demonstrate
+its generalisability through application to an independent test set of 24 CT
+scans available from The Cancer Imaging Archive collected at multiple
+international sites previously unseen to the model, each segmented by two
+independent experts and consisting of 21 OARs commonly segmented in clinical
+practice. With appropriate validation studies and regulatory approvals, this
+system could improve the effectiveness of radiotherapy pathways.
+",0,0,0,1,0,0
+2344,Anomaly Detection in Hierarchical Data Streams under Unknown Models,"  We consider the problem of detecting a few targets among a large number of
+hierarchical data streams. The data streams are modeled as random processes
+with unknown and potentially heavy-tailed distributions. The objective is an
+active inference strategy that determines, sequentially, which data stream to
+collect samples from in order to minimize the sample complexity under a
+reliability constraint. We propose an active inference strategy that induces a
+biased random walk on the tree-structured hierarchy based on confidence bounds
+of sample statistics. We then establish its order optimality in terms of both
+the size of the search space (i.e., the number of data streams) and the
+reliability requirement. The results find applications in hierarchical heavy
+hitter detection, noisy group testing, and adaptive sampling for active
+learning, classification, and stochastic root finding.
+",1,0,0,0,0,0
+2345,Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning,"  A number of image-processing problems can be formulated as optimization
+problems. The objective function typically contains several terms specifically
+designed for different purposes. Parameters in front of these terms are used to
+control the relative weights among them. It is of critical importance to tune
+these parameters, as quality of the solution depends on their values. Tuning
+parameter is a relatively straightforward task for a human, as one can
+intelligently determine the direction of parameter adjustment based on the
+solution quality. Yet manual parameter tuning is not only tedious in many
+cases, but becomes impractical when a number of parameters exist in a problem.
+Aiming at solving this problem, this paper proposes an approach that employs
+deep reinforcement learning to train a system that can automatically adjust
+parameters in a human-like manner. We demonstrate our idea in an example
+problem of optimization-based iterative CT reconstruction with a pixel-wise
+total-variation regularization term. We set up a parameter tuning policy
+network (PTPN), which maps an CT image patch to an output that specifies the
+direction and amplitude by which the parameter at the patch center is adjusted.
+We train the PTPN via an end-to-end reinforcement learning procedure. We
+demonstrate that under the guidance of the trained PTPN for parameter tuning at
+each pixel, reconstructed CT images attain quality similar or better than in
+those reconstructed with manually tuned parameters.
+",0,1,0,0,0,0
+2346,Adversarial Examples that Fool Detectors,"  An adversarial example is an example that has been adjusted to produce a
+wrong label when presented to a system at test time. To date, adversarial
+example constructions have been demonstrated for classifiers, but not for
+detectors. If adversarial examples that could fool a detector exist, they could
+be used to (for example) maliciously create security hazards on roads populated
+with smart vehicles. In this paper, we demonstrate a construction that
+successfully fools two standard detectors, Faster RCNN and YOLO. The existence
+of such examples is surprising, as attacking a classifier is very different
+from attacking a detector, and that the structure of detectors - which must
+search for their own bounding box, and which cannot estimate that box very
+accurately - makes it quite likely that adversarial patterns are strongly
+disrupted. We show that our construction produces adversarial examples that
+generalize well across sequences digitally, even though large perturbations are
+needed. We also show that our construction yields physical objects that are
+adversarial.
+",1,0,0,0,0,0
+2347,FLUX: Progressive State Estimation Based on Zakai-type Distributed Ordinary Differential Equations,"  We propose a homotopy continuation method called FLUX for approximating
+complicated probability density functions. It is based on progressive
+processing for smoothly morphing a given density into the desired one.
+Distributed ordinary differential equations (DODEs) with an artificial time
+$\gamma \in [0,1]$ are derived for describing the evolution from the initial
+density to the desired final density. For a finite-dimensional parametrization,
+the DODEs are converted to a system of ordinary differential equations (SODEs),
+which are solved for $\gamma \in [0,1]$ and return the desired result for
+$\gamma=1$. This includes parametric representations such as Gaussians or
+Gaussian mixtures and nonparametric setups such as sample sets. In the latter
+case, we obtain a particle flow between the two densities along the artificial
+time.
+FLUX is applied to state estimation in stochastic nonlinear dynamic systems
+by gradual inclusion of measurement information. The proposed approximation
+method (1) is fast, (2) can be applied to arbitrary nonlinear systems and is
+not limited to additive noise, (3) allows for target densities that are only
+known at certain points, (4) does not require optimization, (5) does not
+require the solution of partial differential equations, and (6) works with
+standard procedures for solving SODEs. This manuscript is limited to the
+one-dimensional case and a fixed number of parameters during the progression.
+Future extensions will include consideration of higher dimensions and on the
+fly adaption of the number of parameters.
+",1,0,0,0,0,0
+2348,Direct and mediating influences of user-developer perception gaps in requirements understanding on user participation,"  User participation is considered an effective way to conduct requirements
+engineering, but user-developer perception gaps in requirements understanding
+occur frequently. Since user participation in practice is not as active as we
+expect and the requirements perception gap has been recognized as a risk that
+negatively affects projects, exploring whether user-developer perception gaps
+in requirements understanding will hinder user participation is worthwhile.
+This will help develop a greater comprehension of the intertwined relationship
+between user participation and perception gap, a topic that has not yet been
+extensively examined. This study investigates the direct and mediating
+influences of user-developer requirements perception gaps on user participation
+by integrating requirements uncertainty and top management support. Survey data
+collected from 140 subjects were examined and analyzed using structural
+equation modeling. The results indicate that perception gaps have a direct
+negative effect on user participation and negate completely the positive effect
+of top management support on user participation. Additionally, perception gaps
+do not have a mediating effect between requirements uncertainty and user
+participation because requirements uncertainty does not significantly and
+directly affect user participation, but requirements uncertainty indirectly
+influences user participation due to its significant direct effect on
+perception gaps. The theoretical and practical implications are discussed, and
+limitations and possible future research areas are identified.
+",1,0,0,0,0,0
+2349,Equivariant Schrödinger maps from two dimensional hyperbolic space,"  In this article, we consider the equivariant Schrödinger map from $\Bbb
+H^2$ to $\Bbb S^2$ which converges to the north pole of $\Bbb S^2$ at the
+origin and spatial infinity of the hyperbolic space. If the energy of the data
+is less than $4\pi$, we show that the local existence of Schrödinger map.
+Furthermore, if the energy of the data sufficiently small, we prove the
+solutions are global in time.
+",0,0,1,0,0,0
+2350,A fast numerical method for ideal fluid flow in domains with multiple stirrers,"  A collection of arbitrarily-shaped solid objects, each moving at a constant
+speed, can be used to mix or stir ideal fluid, and can give rise to interesting
+flow patterns. Assuming these systems of fluid stirrers are two-dimensional,
+the mathematical problem of resolving the flow field - given a particular
+distribution of any finite number of stirrers of specified shape and speed -
+can be formulated as a Riemann-Hilbert problem. We show that this
+Riemann-Hilbert problem can be solved numerically using a fast and accurate
+algorithm for any finite number of stirrers based around a boundary integral
+equation with the generalized Neumann kernel. Various systems of fluid stirrers
+are considered, and our numerical scheme is shown to handle highly multiply
+connected domains (i.e. systems of many fluid stirrers) with minimal
+computational expense.
+",0,0,1,0,0,0
+2351,A Short Survey on Probabilistic Reinforcement Learning,"  A reinforcement learning agent tries to maximize its cumulative payoff by
+interacting in an unknown environment. It is important for the agent to explore
+suboptimal actions as well as to pick actions with highest known rewards. Yet,
+in sensitive domains, collecting more data with exploration is not always
+possible, but it is important to find a policy with a certain performance
+guaranty. In this paper, we present a brief survey of methods available in the
+literature for balancing exploration-exploitation trade off and computing
+robust solutions from fixed samples in reinforcement learning.
+",1,0,0,1,0,0
+2352,Modification of low-temperature silicon dioxide films under the influence of technology factors,"  The structure, composition and electrophysical characteristics of
+low-temperature silicon dioxide films under influence of various technology
+factors, such as ion implantation, laser irradiation, thermal and photonic
+annealing, have been studied. Silicon dioxide films have been obtained by
+monosilane oxidation using plasma chemical method, reactive cathode sputtering,
+and tetraethoxysilane pyrolysis. In the capacity of substrates, germanium,
+silicon, gallium arsenide and gallium nitride were used. Structure and
+composition of the dielectric films were analyzed by methods of infrared
+transmission spectroscopy and frustrated internal reflectance spectroscopy.
+Analysis of modification efficiency of low-temperature silicon dioxide films
+has been made depending on the substrate type, structure and properties of the
+films, their moisture permeability, dielectric deposition technique, type and
+dose of implantation ions, temperature and kind of annealing.
+",0,1,0,0,0,0
+2353,"GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger","  This work describes the development of a high-resolution tactile-sensing
+finger for robot grasping. This finger, inspired by previous GelSight sensing
+techniques, features an integration that is slimmer, more robust, and with more
+homogeneous output than previous vision-based tactile sensors. To achieve a
+compact integration, we redesign the optical path from illumination source to
+camera by combining light guides and an arrangement of mirror reflections. We
+parameterize the optical path with geometric design variables and describe the
+tradeoffs between the finger thickness, the depth of field of the camera, and
+the size of the tactile sensing area. The sensor sustains the wear from
+continuous use -- and abuse -- in grasping tasks by combining tougher materials
+for the compliant soft gel, a textured fabric skin, a structurally rigid body,
+and a calibration process that maintains homogeneous illumination and contrast
+of the tactile images during use. Finally, we evaluate the sensor's durability
+along four metrics that track the signal quality during more than 3000 grasping
+experiments.
+",1,0,0,0,0,0
+2354,The Muon g-2 experiment at Fermilab,"  The upcoming Fermilab E989 experiment will measure the muon anomalous
+magnetic moment $a_{\mu}$ . This measurement is motivated by the previous
+measurement performed in 2001 by the BNL E821 experiment that reported a 3-4
+standard deviation discrepancy between the measured value and the Standard
+Model prediction. The new measurement at Fermilab aims to improve the precision
+by a factor of four reducing the total uncertainty from 540 parts per billion
+(BNL E821) to 140 parts per billion (Fermilab E989). This paper gives the
+status of the experiment.
+",0,1,0,0,0,0
+2355,Ringel duality as an instance of Koszul duality,"  In their previous work, S. Koenig, S. Ovsienko and the second author showed
+that every quasi-hereditary algebra is Morita equivalent to the right algebra,
+i.e. the opposite algebra of the left dual, of a coring. Let $A$ be an
+associative algebra and $V$ an $A$-coring whose right algebra $R$ is
+quasi-hereditary. In this paper, we give a combinatorial description of an
+associative algebra $B$ and a $B$-coring $W$ whose right algebra is the Ringel
+dual of $R$. We apply our results in small examples to obtain restrictions on
+the $A_\infty$-structure of the $\textrm{Ext}$-algebra of standard modules over
+a class of quasi-hereditary algebras related to birational morphisms of smooth
+surfaces.
+",0,0,1,0,0,0
+2356,Bypass Fraud Detection: Artificial Intelligence Approach,"  Telecom companies are severely damaged by bypass fraud or SIM boxing.
+However, there is a shortage of published research to tackle this problem. The
+traditional method of Test Call Generating is easily overcome by fraudsters and
+the need for more sophisticated ways is inevitable. In this work, we are
+developing intelligent algorithms that mine a huge amount of mobile operator's
+data and detect the SIMs that are used to bypass international calls. This
+method will make it hard for fraudsters to generate revenue and hinder their
+work. Also by reducing fraudulent activities, quality of service can be
+increased as well as customer satisfaction. Our technique has been evaluated
+and tested on real world mobile operator data, and proved to be very efficient.
+",1,0,0,0,0,0
+2357,Absence of cyclotron resonance in the anomalous metallic phase in InO$_x$,"  It is observed that many thin superconducting films with not too high
+disorder level (generally R$_N/\Box \leq 2000 \Omega$) placed in magnetic field
+show an anomalous metallic phase where the resistance is low but still finite
+as temperature goes to zero. Here we report in weakly disordered amorphous
+InO$_x$ thin films, that this ""Bose metal"" metal phase possesses no cyclotron
+resonance and hence non-Drude electrodynamics. Its microwave dynamical
+conductivity shows signatures of remaining short-range superconducting
+correlations and strong phase fluctuations through the whole anomalous regime.
+The absence of a finite frequency resonant mode can be associated with a
+vanishing downstream component of the vortex current parallel to the
+supercurrent and an emergent particle-hole symmetry of this anomalous metal,
+which establishes its non-Fermi liquid character.
+",0,1,0,0,0,0
+2358,Scenario Reduction Revisited: Fundamental Limits and Guarantees,"  The goal of scenario reduction is to approximate a given discrete
+distribution with another discrete distribution that has fewer atoms. We
+distinguish continuous scenario reduction, where the new atoms may be chosen
+freely, and discrete scenario reduction, where the new atoms must be chosen
+from among the existing ones. Using the Wasserstein distance as measure of
+proximity between distributions, we identify those $n$-point distributions on
+the unit ball that are least susceptible to scenario reduction, i.e., that have
+maximum Wasserstein distance to their closest $m$-point distributions for some
+prescribed $m<n$. We also provide sharp bounds on the added benefit of
+continuous over discrete scenario reduction. Finally, to our best knowledge, we
+propose the first polynomial-time constant-factor approximations for both
+discrete and continuous scenario reduction as well as the first exact
+exponential-time algorithms for continuous scenario reduction.
+",0,0,1,0,0,0
+2359,Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology,"  The relationship of scientific knowledge development to technological
+development is widely recognized as one of the most important and complex
+aspects of technological evolution. This paper adds to our understanding of the
+relationship through use of a more rigorous structure for differentiating among
+technologies based upon technological domains (defined as consisting of the
+artifacts over time that fulfill a specific generic function using a specific
+body of technical knowledge).
+",1,1,0,0,0,0
+2360,The Informativeness of $k$-Means and Dimensionality Reduction for Learning Mixture Models,"  The learning of mixture models can be viewed as a clustering problem. Indeed,
+given data samples independently generated from a mixture of distributions, we
+often would like to find the correct target clustering of the samples according
+to which component distribution they were generated from. For a clustering
+problem, practitioners often choose to use the simple k-means algorithm.
+k-means attempts to find an optimal clustering which minimizes the
+sum-of-squared distance between each point and its cluster center. In this
+paper, we provide sufficient conditions for the closeness of any optimal
+clustering and the correct target clustering assuming that the data samples are
+generated from a mixture of log-concave distributions. Moreover, we show that
+under similar or even weaker conditions on the mixture model, any optimal
+clustering for the samples with reduced dimensionality is also close to the
+correct target clustering. These results provide intuition for the
+informativeness of k-means (with and without dimensionality reduction) as an
+algorithm for learning mixture models. We verify the correctness of our
+theorems using numerical experiments and demonstrate using datasets with
+reduced dimensionality significant speed ups for the time required to perform
+clustering.
+",1,0,0,1,0,0
+2361,A Mention-Ranking Model for Abstract Anaphora Resolution,"  Resolving abstract anaphora is an important, but difficult task for text
+understanding. Yet, with recent advances in representation learning this task
+becomes a more tangible aim. A central property of abstract anaphora is that it
+establishes a relation between the anaphor embedded in the anaphoric sentence
+and its (typically non-nominal) antecedent. We propose a mention-ranking model
+that learns how abstract anaphors relate to their antecedents with an
+LSTM-Siamese Net. We overcome the lack of training data by generating
+artificial anaphoric sentence--antecedent pairs. Our model outperforms
+state-of-the-art results on shell noun resolution. We also report first
+benchmark results on an abstract anaphora subset of the ARRAU corpus. This
+corpus presents a greater challenge due to a mixture of nominal and pronominal
+anaphors and a greater range of confounders. We found model variants that
+outperform the baselines for nominal anaphors, without training on individual
+anaphor data, but still lag behind for pronominal anaphors. Our model selects
+syntactically plausible candidates and -- if disregarding syntax --
+discriminates candidates using deeper features.
+",1,0,0,1,0,0
+2362,Harmonic density interpolation methods for high-order evaluation of Laplace layer potentials in 2D and 3D,"  We present an effective harmonic density interpolation method for the
+numerical evaluation of singular and nearly singular Laplace boundary integral
+operators and layer potentials in two and three spatial dimensions. The method
+relies on the use of Green's third identity and local Taylor-like
+interpolations of density functions in terms of harmonic polynomials. The
+proposed technique effectively regularizes the singularities present in
+boundary integral operators and layer potentials, and recasts the latter in
+terms of integrands that are bounded or even more regular, depending on the
+order of the density interpolation. The resulting boundary integrals can then
+be easily, accurately, and inexpensively evaluated by means of standard
+quadrature rules. A variety of numerical examples demonstrate the effectiveness
+of the technique when used in conjunction with the classical trapezoidal rule
+(to integrate over smooth curves) in two-dimensions, and with a Chebyshev-type
+quadrature rule (to integrate over surfaces given as unions of non-overlapping
+quadrilateral patches) in three-dimensions.
+",0,1,0,0,0,0
+2363,Stochastic Low-Rank Bandits,"  Many problems in computer vision and recommender systems involve low-rank
+matrices. In this work, we study the problem of finding the maximum entry of a
+stochastic low-rank matrix from sequential observations. At each step, a
+learning agent chooses pairs of row and column arms, and receives the noisy
+product of their latent values as a reward. The main challenge is that the
+latent values are unobserved. We identify a class of non-negative matrices
+whose maximum entry can be found statistically efficiently and propose an
+algorithm for finding them, which we call LowRankElim. We derive a
+$\DeclareMathOperator{\poly}{poly} O((K + L) \poly(d) \Delta^{-1} \log n)$
+upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the
+number of columns, $d$ is the rank of the matrix, and $\Delta$ is the minimum
+gap. The bound depends on other problem-specific constants that clearly do not
+depend $K L$. To the best of our knowledge, this is the first such result in
+the literature.
+",1,0,0,1,0,0
+2364,Temporal resolution of a pre-maximum halt in a Classical Nova: V5589 Sgr observed with STEREO HI-1B,"  Classical novae show a rapid rise in optical brightness over a few hours.
+Until recently the rise phase, particularly the phenomenon of a pre-maximum
+halt, was observed sporadically. Solar observation satellites observing Coronal
+Mass Ejections enable us to observe the pre-maximum phase in unprecedented
+temporal resolution. We present observations of V5589 Sgr with STEREO HI-1B at
+a cadence of 40 min, the highest to date. We temporally resolve a pre-maximum
+halt for the first time, with two examples each rising over 40 min then
+declining within 80 min. Comparison with a grid of outburst models suggests
+this double peak, and the overall rise timescale, are consistent with a white
+dwarf mass, central temperature and accretion rate close to 1.0 solar mass,
+5x10^7 K and 10^-10 solar masses per year respectively. The modelling formally
+predicts mass loss onset at JD 2456038.2391+/-0.0139, 12 hrs before optical
+maximum. The model assumes a main-sequence donor. Observational evidence is for
+a subgiant companion; meaning the accretion rate is under-estimated.
+Post-maximum we see erratic variations commonly associated with much slower
+novae. Estimating the decline rate difficult, but we place the time to decline
+two magnitudes as 2.1 < t_2(days) < 3.9 making V5589 Sgr a ""very fast"" nova.
+The brightest point defines ""day 0"" as JD 2456038.8224+/-0.0139, although at
+this high cadence the meaning of the observed maximum becomes difficult to
+define. We suggest that such erratic variability normally goes undetected in
+faster novae due to the low cadence of typical observations; implying erratic
+behaviour is not necessarily related to the rate of decline.
+",0,1,0,0,0,0
+2365,"The Thermophysical Properties of the Bagnold Dunes, Mars: Ground-truthing Orbital Data","  In this work, we compare the thermophysical properties and particle sizes
+derived from the Mars Science Laboratory (MSL) rover's Ground Temperature
+Sensor (GTS) of the Bagnold dunes, specifically Namib dune, to those derived
+orbitally from Thermal Emission Imaging System (THEMIS), ultimately linking
+these measurements to ground-truth particle sizes determined from Mars Hand
+Lens Imager (MAHLI) images. In general, we find that all three datasets report
+consistent particle sizes for the Bagnold dunes (~110-350 microns, and are
+within measurement and model uncertainties), indicating that particle sizes of
+homogeneous materials determined from orbit are reliable. Furthermore, we
+examine the effects of two physical characteristics that could influence the
+modeled thermal inertia and particle sizes, including: 1) fine-scale (cm-m
+scale) ripples, and 2) thin layering of indurated/armored materials. To first
+order, we find small scale ripples and thin (approximately centimeter scale)
+layers do not significantly affect the determination of bulk thermal inertia
+from orbital thermal data determined from a single nighttime temperature.
+Modeling of a layer of coarse or indurated material reveals that a thin layer
+(< ~5 mm; similar to what was observed by the Curiosity rover) would not
+significantly change the observed thermal properties of the surface and would
+be dominated by the properties of the underlying material. Thermal inertia and
+grain sizes of relatively homogeneous materials derived from nighttime orbital
+data should be considered as reliable, as long as there are not significant
+sub-pixel anisothermality effects (e.g. lateral mixing of multiple
+thermophysically distinct materials).
+",0,1,0,0,0,0
+2366,Entrywise Eigenvector Analysis of Random Matrices with Low Expected Rank,"  Recovering low-rank structures via eigenvector perturbation analysis is a
+common problem in statistical machine learning, such as in factor analysis,
+community detection, ranking, matrix completion, among others. While a large
+variety of results provide tight bounds on the average errors between empirical
+and population statistics of eigenvectors, fewer results are tight for
+entrywise analyses, which are critical for a number of problems such as
+community detection and ranking.
+This paper investigates the entrywise perturbation analysis for a large class
+of random matrices whose expectations are low-rank, including community
+detection, synchronization ($\mathbb{Z}_2$-spiked Wigner model) and matrix
+completion models. Denoting by $\{u_k\}$, respectively $\{u_k^*\}$, the
+eigenvectors of a random matrix $A$, respectively $\mathbb{E} A$, the paper
+characterizes cases for which $$u_k \approx \frac{A u_k^*}{\lambda_k^*}$$
+serves as a first-order approximation under the $\ell_\infty$ norm. The fact
+that the approximation is both tight and linear in the random matrix $A$ allows
+for sharp comparisons of $u_k$ and $u_k^*$. In particular, it allows to compare
+the signs of $u_k$ and $u_k^*$ even when $\| u_k - u_k^*\|_{\infty}$ is large,
+which in turn allows to settle the conjecture in Abbe et al. (2016) that the
+spectral algorithm achieves exact recovery in the stochastic block model
+without any trimming or cleaning steps. The results are further extended to the
+perturbation of eigenspaces, providing new bounds for $\ell_\infty$-type errors
+in noisy matrix completion.
+",0,0,1,1,0,0
+2367,Algorithmic Trading with Fitted Q Iteration and Heston Model,"  We present the use of the fitted Q iteration in algorithmic trading. We show
+that the fitted Q iteration helps alleviate the dimension problem that the
+basic Q-learning algorithm faces in application to trading. Furthermore, we
+introduce a procedure including model fitting and data simulation to enrich
+training data as the lack of data is often a problem in realistic application.
+We experiment our method on both simulated environment that permits arbitrage
+opportunity and real-world environment by using prices of 450 stocks. In the
+former environment, the method performs well, implying that our method works in
+theory. To perform well in the real-world environment, the agents trained might
+require more training (iteration) and more meaningful variables with predictive
+value.
+",0,0,0,0,0,1
+2368,Superrigidity of actions on finite rank median spaces,"  Finite rank median spaces are a simultaneous generalisation of finite
+dimensional CAT(0) cube complexes and real trees. If $\Gamma$ is an irreducible
+lattice in a product of rank one simple Lie groups, we show that every action
+of $\Gamma$ on a complete, finite rank median space has a global fixed point.
+This is in sharp contrast with the behaviour of actions on infinite rank median
+spaces.
+The fixed point property is obtained as corollary to a superrigidity result;
+the latter holds for irreducible lattices in arbitrary products of compactly
+generated groups.
+In previous work, we introduced ""Roller compactifications"" of median spaces;
+these generalise a well-known construction in the case of cube complexes. We
+provide a reduced $1$-cohomology class that detects group actions with a finite
+orbit in the Roller compactification. Even for CAT(0) cube complexes, only
+second bounded cohomology classes were known with this property, due to
+Chatterji-Fernós-Iozzi. As a corollary, we observe that, in Gromov's density
+model, random groups at low density do not have Shalom's property $H_{FD}$.
+",0,0,1,0,0,0
+2369,Detecting and Explaining Causes From Text For a Time Series Event,"  Explaining underlying causes or effects about events is a challenging but
+valuable task. We define a novel problem of generating explanations of a time
+series event by (1) searching cause and effect relationships of the time series
+with textual data and (2) constructing a connecting chain between them to
+generate an explanation. To detect causal features from text, we propose a
+novel method based on the Granger causality of time series between features
+extracted from text such as N-grams, topics, sentiments, and their composition.
+The generation of the sequence of causal entities requires a commonsense
+causative knowledge base with efficient reasoning. To ensure good
+interpretability and appropriate lexical usage we combine symbolic and neural
+representations, using a neural reasoning algorithm trained on commonsense
+causal tuples to predict the next cause step. Our quantitative and human
+analysis show empirical evidence that our method successfully extracts
+meaningful causality relationships between time series with textual features
+and generates appropriate explanation between them.
+",1,0,0,0,0,0
+2370,Mailbox Types for Unordered Interactions,"  We propose a type system for reasoning on protocol conformance and deadlock
+freedom in networks of processes that communicate through unordered mailboxes.
+We model these networks in the mailbox calculus, a mild extension of the
+asynchronous {\pi}-calculus with first-class mailboxes and selective input. The
+calculus subsumes the actor model and allows us to analyze networks with
+dynamic topologies and varying number of processes possibly mixing different
+concurrency abstractions. Well-typed processes are deadlock free and never fail
+because of unexpected messages. For a non-trivial class of them, junk freedom
+is also guaranteed. We illustrate the expressiveness of the calculus and of the
+type system by encoding instances of non-uniform, concurrent objects, binary
+sessions extended with joins and forks, and some known actor benchmarks.
+",1,0,0,0,0,0
+2371,The Complexity of Counting Surjective Homomorphisms and Compactions,"  A homomorphism from a graph G to a graph H is a function from the vertices of
+G to the vertices of H that preserves edges. A homomorphism is surjective if it
+uses all of the vertices of H and it is a compaction if it uses all of the
+vertices of H and all of the non-loop edges of H. Hell and Nesetril gave a
+complete characterisation of the complexity of deciding whether there is a
+homomorphism from an input graph G to a fixed graph H. A complete
+characterisation is not known for surjective homomorphisms or for compactions,
+though there are many interesting results. Dyer and Greenhill gave a complete
+characterisation of the complexity of counting homomorphisms from an input
+graph G to a fixed graph H. In this paper, we give a complete characterisation
+of the complexity of counting surjective homomorphisms from an input graph G to
+a fixed graph H and we also give a complete characterisation of the complexity
+of counting compactions from an input graph G to a fixed graph H. In an
+addendum we use our characterisations to point out a dichotomy for the
+complexity of the respective approximate counting problems (in the connected
+case).
+",1,0,0,0,0,0
+2372,A recursive algorithm and a series expansion related to the homogeneous Boltzmann equation for hard potentials with angular cutoff,"  We consider the spatially homogeneous Boltzmann equation for hard potentials
+with angular cutoff. This equation has a unique conservative weak solution
+$(f_t)_{t\geq 0}$, once the initial condition $f_0$ with finite mass and energy
+is fixed. Taking advantage of the energy conservation, we propose a recursive
+algorithm that produces a $(0,\infty)\times\mathbb{R}^3$ random variable
+$(M_t,V_t)$ such that $E[M_t {\bf 1}_{\{V_t \in \cdot\}}]=f_t$. We also write
+down a series expansion of $f_t$. Although both the algorithm and the series
+expansion might be theoretically interesting in that they explicitly express
+$f_t$ in terms of $f_0$, we believe that the algorithm is not very efficient in
+practice and that the series expansion is rather intractable. This is a tedious
+extension to non-Maxwellian molecules of Wild's sum and of its interpretation
+by McKean.
+",0,0,1,0,0,0
+2373,Periodic auxetics: Structure and design,"  Materials science has adopted the term of auxetic behavior for structural
+deformations where stretching in some direction entails lateral widening,
+rather than lateral shrinking. Most studies, in the last three decades, have
+explored repetitive or cellular structures and used the notion of negative
+Poisson's ratio as the hallmark of auxetic behavior. However, no general
+auxetic principle has been established from this perspective. In the present
+paper, we show that a purely geometric approach to periodic auxetics is apt to
+identify essential characteristics of frameworks with auxetic deformations and
+can generate a systematic and endless series of periodic auxetic designs. The
+critical features refer to convexity properties expressed through families of
+homothetic ellipsoids.
+",0,0,1,0,0,0
+2374,Asymptotics of maximum likelihood estimation for stable law with $(M)$ parameterization,"  Asymptotics of maximum likelihood estimation for $\alpha$-stable law are
+analytically investigated with $(M)$ parameterization. The consistency and
+asymptotic normality are shown on the interior of the whole parameter space.
+Although these asymptotics have been proved with $(B)$ parameterization, there
+are several gaps between. Especially in the latter, the density, so that scores
+and their derivatives are discontinuous at $\alpha=1$ for $\beta\neq 0$ and
+usual asymptotics are impossible, whereas in $(M)$ form these quantities are
+shown to be continuous on the interior of the parameter space. We fill these
+gaps and provide a convenient theory for applied people. We numerically
+approximate the Fisher information matrix around the Cauchy law
+$(\alpha,\beta)=(1,0)$. The results exhibit continuity at $\alpha=1,\,\beta\neq
+0$ and this secures the accuracy of our calculations.
+",0,0,1,1,0,0
+2375,Enabling Massive Deep Neural Networks with the GraphBLAS,"  Deep Neural Networks (DNNs) have emerged as a core tool for machine learning.
+The computations performed during DNN training and inference are dominated by
+operations on the weight matrices describing the DNN. As DNNs incorporate more
+stages and more nodes per stage, these weight matrices may be required to be
+sparse because of memory limitations. The GraphBLAS.org math library standard
+was developed to provide high performance manipulation of sparse weight
+matrices and input/output vectors. For sufficiently sparse matrices, a sparse
+matrix library requires significantly less memory than the corresponding dense
+matrix implementation. This paper provides a brief description of the
+mathematics underlying the GraphBLAS. In addition, the equations of a typical
+DNN are rewritten in a form designed to use the GraphBLAS. An implementation of
+the DNN is given using a preliminary GraphBLAS C library. The performance of
+the GraphBLAS implementation is measured relative to a standard dense linear
+algebra library implementation. For various sizes of DNN weight matrices, it is
+shown that the GraphBLAS sparse implementation outperforms a BLAS dense
+implementation as the weight matrix becomes sparser.
+",1,0,0,0,0,0
+2376,Univariate and Bivariate Geometric Discrete Generalized Exponential Distributions,"  Marshall and Olkin (1997, Biometrika, 84, 641 - 652) introduced a very
+powerful method to introduce an additional parameter to a class of continuous
+distribution functions and hence it brings more flexibility to the model. They
+have demonstrated their method for the exponential and Weibull classes. In the
+same paper they have briefly indicated regarding its bivariate extension. The
+main aim of this paper is to introduce the same method, for the first time, to
+the class of discrete generalized exponential distributions both for the
+univariate and bivariate cases. We investigate several properties of the
+proposed univariate and bivariate classes. The univariate class has three
+parameters, whereas the bivariate class has five parameters. It is observed
+that depending on the parameter values the univariate class can be both zero
+inflated as well as heavy tailed. We propose to use EM algorithm to estimate
+the unknown parameters. Small simulation experiments have been performed to see
+the effectiveness of the proposed EM algorithm, and a bivariate data set has
+been analyzed and it is observed that the proposed models and the EM algorithm
+work quite well in practice.
+",0,0,0,1,0,0
+2377,What Can Machine Learning Teach Us about Communications?,"  Rapid improvements in machine learning over the past decade are beginning to
+have far-reaching effects. For communications, engineers with limited domain
+expertise can now use off-the-shelf learning packages to design
+high-performance systems based on simulations. Prior to the current revolution
+in machine learning, the majority of communication engineers were quite aware
+that system parameters (such as filter coefficients) could be learned using
+stochastic gradient descent. It was not at all clear, however, that more
+complicated parts of the system architecture could be learned as well. In this
+paper, we discuss the application of machine-learning techniques to two
+communications problems and focus on what can be learned from the resulting
+systems. We were pleasantly surprised that the observed gains in one example
+have a simple explanation that only became clear in hindsight. In essence, deep
+learning discovered a simple and effective strategy that had not been
+considered earlier.
+",1,0,0,1,0,0
+2378,Global stability of a network-based SIRS epidemic model with nonmonotone incidence rate,"  This paper studies the dynamics of a network-based SIRS epidemic model with
+vaccination and a nonmonotone incidence rate. This type of nonlinear incidence
+can be used to describe the psychological or inhibitory effect from the
+behavioral change of the susceptible individuals when the number of infective
+individuals on heterogeneous networks is getting larger. Using the analytical
+method, epidemic threshold $R_0$ is obtained. When $R_0$ is less than one, we
+prove the disease-free equilibrium is globally asymptotically stable and the
+disease dies out, while $R_0$ is greater than one, there exists a unique
+endemic equilibrium. By constructing a suitable Lyapunov function, we also
+prove the endemic equilibrium is globally asymptotically stable if the
+inhibitory factor $\alpha$ is sufficiently large. Numerical experiments are
+also given to support the theoretical results. It is shown both theoretically
+and numerically a larger $\alpha$ can accelerate the extinction of the disease
+and reduce the level of disease.
+",0,0,0,0,1,0
+2379,Slicewise definability in first-order logic with bounded quantifier rank,"  For every $q\in \mathbb N$ let $\textrm{FO}_q$ denote the class of sentences
+of first-order logic FO of quantifier rank at most $q$. If a graph property can
+be defined in $\textrm{FO}_q$, then it can be decided in time $O(n^q)$. Thus,
+minimizing $q$ has favorable algorithmic consequences. Many graph properties
+amount to the existence of a certain set of vertices of size $k$. Usually this
+can only be expressed by a sentence of quantifier rank at least $k$. We use the
+color-coding method to demonstrate that some (hyper)graph problems can be
+defined in $\textrm{FO}_q$ where $q$ is independent of $k$. This property of a
+graph problem is equivalent to the question of whether the corresponding
+parameterized problem is in the class $\textrm{para-AC}^0$.
+It is crucial for our results that the FO-sentences have access to built-in
+addition and multiplication. It is known that then FO corresponds to the
+circuit complexity class uniform $\textrm{AC}^0$. We explore the connection
+between the quantifier rank of FO-sentences and the depth of
+$\textrm{AC}^0$-circuits, and prove that $\textrm{FO}_q \subsetneq
+\textrm{FO}_{q+1}$ for structures with built-in addition and multiplication.
+",1,0,0,0,0,0
+2380,The efficiency of community detection by most similar node pairs,"  Community analysis is an important way to ascertain whether or not a complex
+system consists of sub-structures with different properties. In this paper, we
+give a two level community structure analysis for the SSCI journal system by
+most similar co-citation pattern. Five different strategies for the selection
+of most similar node (journal) pairs are introduced. The efficiency is checked
+by the normalized mutual information technique. Statistical properties and
+comparisons of the community results show that both of the two level detection
+could give instructional information for the community structure of complex
+systems. Further comparisons of the five strategies indicates that, the most
+efficient strategy is to assign nodes with maximum similarity into the same
+community whether the similarity information is complete or not, while random
+selection generates small world local community with no inside order. These
+results give valuable indication for efficient community detection by most
+similar node pairs.
+",1,0,0,0,0,0
+2381,Composite Adaptive Control for Bilateral Teleoperation Systems without Persistency of Excitation,"  Composite adaptive control schemes, which use both the system tracking errors
+and the prediction error to drive the update laws, have become widespread in
+achieving an improvement of system performance. However, a strong
+persistent-excitation (PE) condition should be satisfied to guarantee the
+parameter convergence. This paper proposes a novel composite adaptive control
+to guarantee parameter convergence without PE condition for nonlinear
+teleoperation systems with dynamic uncertainties and time-varying communication
+delays. The stability criteria of the closed-loop teleoperation system are
+given in terms of linear matrix inequalities. New tracking performance measures
+are proposed to evaluate the position tracking between the master and the
+slave. Simulation studies are given to show the effectiveness of the proposed
+method.
+",1,0,0,0,0,0
+2382,Infinitely many periodic orbits just above the Mañé critical value on the 2-sphere,"  We introduce a new critical value $c_\infty(L)$ for Tonelli Lagrangians $L$
+on the tangent bundle of the 2-sphere without minimizing measures supported on
+a point. We show that $c_\infty(L)$ is strictly larger than the Mañé
+critical value $c(L)$, and on every energy level $e\in(c(L),c_\infty(L))$ there
+exist infinitely many periodic orbits of the Lagrangian system of $L$, one of
+which is a local minimizer of the free-period action functional. This has
+applications to Finsler metrics of Randers type on the 2-sphere. We show that,
+under a suitable criticality assumption on a given Randers metric, after
+rescaling its magnetic part with a sufficiently large multiplicative constant,
+the new metric admits infinitely many closed geodesics, one of which is a
+waist. Examples of critical Randers metrics include the celebrated Katok
+metric.
+",0,0,1,0,0,0
+2383,Decentralized Random Walk-Based Data Collection in Networks,"  We analyze a decentralized random walk-based algorithm for data collection at
+the sink in a multi-hop sensor network. Our algorithm, Random-Collect, which
+involves data packets being passed to random neighbors in the network according
+to a random walk mechanism, requires no configuration and incurs no routing
+overhead. To analyze this method, we model the data generation process as
+independent Bernoulli arrivals at the source nodes. We analyze both latency and
+throughput in this setting, providing a theoretical lower bound for the
+throughput and a theoretical upper bound for the latency. The main contribution
+of our paper, however, is the throughput result: we present a general lower
+bound on the throughput achieved by our data collection method in terms of the
+underlying network parameters. In particular, we show that the rate at which
+our algorithm can collect data depends on the spectral gap of the given random
+walk's transition matrix and if the random walk is simple then it also depends
+on the maximum and minimum degrees of the graph modeling the network. For
+latency, we show that the time taken to collect data not only depends on the
+worst-case hitting time of the given random walk but also depends on the data
+arrival rate. In fact, our latency bound reflects the data rate-latency
+trade-off i.e., in order to achieve a higher data rate we need to compromise on
+latency and vice-versa. We also discuss some examples that demonstrate that our
+lower bound on the data rate is optimal up to constant factors, i.e., there
+exists a network topology and sink placement for which the maximum stable data
+rate is just a constant factor above our lower bound.
+",1,0,0,0,0,0
+2384,The Fan Region at 1.5 GHz. I: Polarized synchrotron emission extending beyond the Perseus Arm,"  The Fan Region is one of the dominant features in the polarized radio sky,
+long thought to be a local (distance < 500 pc) synchrotron feature. We present
+1.3-1.8 GHz polarized radio continuum observations of the region from the
+Global Magneto-Ionic Medium Survey (GMIMS) and compare them to maps of Halpha
+and polarized radio continuum intensity from 0.408-353 GHz. The high-frequency
+(> 1 GHz) and low-frequency (< 600 MHz) emission have different morphologies,
+suggesting a different physical origin. Portions of the 1.5 GHz Fan Region
+emission are depolarized by about 30% by ionized gas structures in the Perseus
+Arm, indicating that this fraction of the emission originates >2 kpc away. We
+argue for the same conclusion based on the high polarization fraction at 1.5
+GHz (about 40%). The Fan Region is offset with respect to the Galactic plane,
+covering -5° < b < +10°; we attribute this offset to the warp in the
+outer Galaxy. We discuss origins of the polarized emission, including the
+spiral Galactic magnetic field. This idea is a plausible contributing factor
+although no model to date readily reproduces all of the observations. We
+conclude that models of the Galactic magnetic field should account for the > 1
+GHz emission from the Fan Region as a Galactic-scale, not purely local,
+feature.
+",0,1,0,0,0,0
+2385,The redshift distribution of cosmological samples: a forward modeling approach,"  Determining the redshift distribution $n(z)$ of galaxy samples is essential
+for several cosmological probes including weak lensing. For imaging surveys,
+this is usually done using photometric redshifts estimated on an
+object-by-object basis. We present a new approach for directly measuring the
+global $n(z)$ of cosmological galaxy samples, including uncertainties, using
+forward modeling. Our method relies on image simulations produced using UFig
+(Ultra Fast Image Generator) and on ABC (Approximate Bayesian Computation)
+within the $MCCL$ (Monte-Carlo Control Loops) framework. The galaxy population
+is modeled using parametric forms for the luminosity functions, spectral energy
+distributions, sizes and radial profiles of both blue and red galaxies. We
+apply exactly the same analysis to the real data and to the simulated images,
+which also include instrumental and observational effects. By adjusting the
+parameters of the simulations, we derive a set of acceptable models that are
+statistically consistent with the data. We then apply the same cuts to the
+simulations that were used to construct the target galaxy sample in the real
+data. The redshifts of the galaxies in the resulting simulated samples yield a
+set of $n(z)$ distributions for the acceptable models. We demonstrate the
+method by determining $n(z)$ for a cosmic shear like galaxy sample from the
+4-band Subaru Suprime-Cam data in the COSMOS field. We also complement this
+imaging data with a spectroscopic calibration sample from the VVDS survey. We
+compare our resulting posterior $n(z)$ distributions to the one derived from
+photometric redshifts estimated using 36 photometric bands in COSMOS and find
+good agreement. This offers good prospects for applying our approach to current
+and future large imaging surveys.
+",0,1,0,0,0,0
+2386,DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks,"  In this paper we develop a novel computational sensing framework for sensing
+and recovering structured signals. When trained on a set of representative
+signals, our framework learns to take undersampled measurements and recover
+signals from them using a deep convolutional neural network. In other words, it
+learns a transformation from the original signals to a near-optimal number of
+undersampled measurements and the inverse transformation from measurements to
+signals. This is in contrast to traditional compressive sensing (CS) systems
+that use random linear measurements and convex optimization or iterative
+algorithms for signal recovery. We compare our new framework with
+$\ell_1$-minimization from the phase transition point of view and demonstrate
+that it outperforms $\ell_1$-minimization in the regions of phase transition
+plot where $\ell_1$-minimization cannot recover the exact solution. In
+addition, we experimentally demonstrate how learning measurements enhances the
+overall recovery performance, speeds up training of recovery framework, and
+leads to having fewer parameters to learn.
+",1,0,0,1,0,0
+2387,"Robotic frameworks, architectures and middleware comparison","  Nowadays, the construction of a complex robotic system requires a high level
+of specialization in a large number of diverse scientific areas. It is
+reasonable that a single researcher cannot create from scratch the entirety of
+this system, as it is impossible for him to have the necessary skills in the
+necessary fields. This obstacle is being surpassed with the existent robotic
+frameworks. This paper tries to give an extensive review of the most famous
+robotic frameworks and middleware, as well as to provide the means to
+effortlessly compare them. Additionally, we try to investigate the differences
+between the definitions of a robotic framework, a robotic middleware and a
+robotic architecture.
+",1,0,0,0,0,0
+2388,Analysis of equivalence relation in joint sparse recovery,"  The joint sparse recovery problem is a generalization of the single
+measurement vector problem which is widely studied in Compressed Sensing and it
+aims to recovery a set of jointly sparse vectors. i.e. have nonzero entries
+concentrated at common location. Meanwhile l_p-minimization subject to matrices
+is widely used in a large number of algorithms designed for this problem.
+Therefore the main contribution in this paper is two theoretical results about
+this technique. The first one is to prove that in every multiple systems of
+linear equation, there exists a constant p* such that the original unique
+sparse solution also can be recovered from a minimization in l_p quasi-norm
+subject to matrices whenever 0< p<p*. The other one is to show an analysis
+expression of such p*. Finally, we display the results of one example to
+confirm the validity of our conclusions.
+",1,0,1,0,0,0
+2389,The Stochastic Firefighter Problem,"  The dynamics of infectious diseases spread is crucial in determining their
+risk and offering ways to contain them. We study sequential vaccination of
+individuals in networks. In the original (deterministic) version of the
+Firefighter problem, a fire breaks out at some node of a given graph. At each
+time step, b nodes can be protected by a firefighter and then the fire spreads
+to all unprotected neighbors of the nodes on fire. The process ends when the
+fire can no longer spread. We extend the Firefighter problem to a probabilistic
+setting, where the infection is stochastic. We devise a simple policy that only
+vaccinates neighbors of infected nodes and is optimal on regular trees and on
+general graphs for a sufficiently large budget. We derive methods for
+calculating upper and lower bounds of the expected number of infected
+individuals, as well as provide estimates on the budget needed for containment
+in expectation. We calculate these explicitly on trees, d-dimensional grids,
+and Erdős Rényi graphs. Finally, we construct a state-dependent budget
+allocation strategy and demonstrate its superiority over constant budget
+allocation on real networks following a first order acquaintance vaccination
+policy.
+",1,0,0,0,0,0
+2390,The phase space structure of the oligopoly dynamical system by means of Darboux integrability,"  We investigate the dynamical complexity of Cournot oligopoly dynamics of
+three firms by using the qualitative methods of dynamical systems to study the
+phase structure of this model. The phase space is organized with
+one-dimensional and two-dimensional invariant submanifolds (for the monopoly
+and duopoly) and unique stable node (global attractor) in the positive quadrant
+of the phase space (Cournot equilibrium). We also study the integrability of
+the system. We demonstrate the effectiveness of the method of the Darboux
+polynomials in searching for first integrals of the oligopoly. The general
+method as well as examples of adopting this method are presented. We study
+Darboux non-integrability of the oligopoly for linear demand functions and find
+first integrals of this system for special classes of the system, in
+particular, rational integrals can be found for a quite general set of model
+parameters. We show how first integral can be useful in lowering the dimension
+of the system using the example of $n$ almost identical firms. This first
+integral also gives information about the structure of the phase space and the
+behaviour of trajectories in the neighbourhood of a Nash equilibrium
+",0,1,0,0,0,0
+2391,Generalizations of the 'Linear Chain Trick': Incorporating more flexible dwell time distributions into mean field ODE models,"  Mathematical modelers have long known of a ""rule of thumb"" referred to as the
+Linear Chain Trick (LCT; aka the Gamma Chain Trick): a technique used to
+construct mean field ODE models from continuous-time stochastic state
+transition models where the time an individual spends in a given state (i.e.,
+the dwell time) is Erlang distributed (i.e., gamma distributed with integer
+shape parameter). Despite the LCT's widespread use, we lack general theory to
+facilitate the easy application of this technique, especially for complex
+models. This has forced modelers to choose between constructing ODE models
+using heuristics with oversimplified dwell time assumptions, using time
+consuming derivations from first principles, or to instead use non-ODE models
+(like integro-differential equations or delay differential equations) which can
+be cumbersome to derive and analyze. Here, we provide analytical results that
+enable modelers to more efficiently construct ODE models using the LCT or
+related extensions. Specifically, we 1) provide novel extensions of the LCT to
+various scenarios found in applications; 2) provide formulations of the LCT and
+it's extensions that bypass the need to derive ODEs from integral or stochastic
+model equations; and 3) introduce a novel Generalized Linear Chain Trick (GLCT)
+framework that extends the LCT to a much broader family of distributions,
+including the flexible phase-type distributions which can approximate
+distributions on $\mathbb{R}^+$ and be fit to data. These results give modelers
+more flexibility to incorporate appropriate dwell time assumptions into mean
+field ODEs, including conditional dwell time distributions, and these results
+help clarify connections between individual-level stochastic model assumptions
+and the structure of corresponding mean field ODEs.
+",0,0,0,0,1,0
+2392,Life and work of Egbert Brieskorn (1936 - 2013),"  Egbert Brieskorn died on July 11, 2013, a few days after his 77th birthday.
+He was an impressive personality who has left a lasting impression on all who
+knew him, whether inside or outside of mathematics. Brieskorn was a great
+mathematician, but his interests, his knowledge, and activities ranged far
+beyond mathematics. In this contribution, which is strongly influenced by many
+years of personal connectedness of the authors with Brieskorn, we try to give a
+deeper insight into the life and work of Brieskorn. We illuminate both his
+personal commitment to peace and the environment as well as his long-term study
+of the life and work of Felix Hausdorff and the publication of Hausdorff's
+collected works. However, the main focus of the article is on the presentation
+of his remarkable and influential mathematical work.
+",0,0,1,0,0,0
+2393,Resource Allocation for a Full-Duplex Base Station Aided OFDMA System,"  Exploiting full-duplex (FD) technology on base stations (BSs) is a promising
+solution to enhancing the system performance. Motivated by this, we revisit a
+full-duplex base station (FD-BS) aided OFDMA system, which consists of one BS,
+several uplink/downlink users and multiple subcarriers. A joint 3-dimensional
+(3D) mapping scheme among subcarriers, down-link users (DUEs), uplink users
+(UUEs) is considered as well as an associated power allocation optimization. In
+detail, we first decompose the complex 3D mapping problem into three
+2-dimensional sub ones and solve them by using the iterative Hungarian method,
+respectively. Then based on the Lagrange dual method, we sequentially solve the
+power allocation and 3- dimensional mapping problem by fixing a dual point.
+Finally, the optimal solution can be obtained by utilizing the sub-gradient
+method. Unlike existing work that only solves either 3D mapping or power
+allocation problem but with a high computation complexity, we tackle both of
+them and have successfully reduced computation complexity from exponential to
+polynomial order. Numerical simulations are conducted to verify the proposed
+scheme.
+",1,0,0,0,0,0
+2394,Combining Neural Networks and Tree Search for Task and Motion Planning in Challenging Environments,"  We consider task and motion planning in complex dynamic environments for
+problems expressed in terms of a set of Linear Temporal Logic (LTL)
+constraints, and a reward function. We propose a methodology based on
+reinforcement learning that employs deep neural networks to learn low-level
+control policies as well as task-level option policies. A major challenge in
+this setting, both for neural network approaches and classical planning, is the
+need to explore future worlds of a complex and interactive environment. To this
+end, we integrate Monte Carlo Tree Search with hierarchical neural net control
+policies trained on expressive LTL specifications. This paper investigates the
+ability of neural networks to learn both LTL constraints and control policies
+in order to generate task plans in complex environments. We demonstrate our
+approach in a simulated autonomous driving setting, where a vehicle must drive
+down a road in traffic, avoid collisions, and navigate an intersection, all
+while obeying given rules of the road.
+",1,0,0,0,0,0
+2395,BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition,"  Recently, deep neural networks (DNNs) have been regarded as the
+state-of-the-art classification methods in a wide range of applications,
+especially in image classification. Despite the success, the huge number of
+parameters blocks its deployment to situations with light computing resources.
+Researchers resort to the redundancy in the weights of DNNs and attempt to find
+how fewer parameters can be chosen while preserving the accuracy at the same
+time. Although several promising results have been shown along this research
+line, most existing methods either fail to significantly compress a
+well-trained deep network or require a heavy fine-tuning process for the
+compressed network to regain the original performance. In this paper, we
+propose the \textit{Block Term} networks (BT-nets) in which the commonly used
+fully-connected layers (FC-layers) are replaced with block term layers
+(BT-layers). In BT-layers, the inputs and the outputs are reshaped into two
+low-dimensional high-order tensors, then block-term decomposition is applied as
+tensor operators to connect them. We conduct extensive experiments on benchmark
+datasets to demonstrate that BT-layers can achieve a very large compression
+ratio on the number of parameters while preserving the representation power of
+the original FC-layers as much as possible. Specifically, we can get a higher
+performance while requiring fewer parameters compared with the tensor train
+method.
+",1,0,0,1,0,0
+2396,Resampling Strategy in Sequential Monte Carlo for Constrained Sampling Problems,"  Sequential Monte Carlo (SMC) methods are a class of Monte Carlo methods that
+are used to obtain random samples of a high dimensional random variable in a
+sequential fashion. Many problems encountered in applications often involve
+different types of constraints. These constraints can make the problem much
+more challenging. In this paper, we formulate a general framework of using SMC
+for constrained sampling problems based on forward and backward pilot
+resampling strategies. We review some existing methods under the framework and
+develop several new algorithms. It is noted that all information observed or
+imposed on the underlying system can be viewed as constraints. Hence the
+approach outlined in this paper can be useful in many applications.
+",0,0,0,1,0,0
+2397,Transverse Shift in Andreev Reflection,"  An incoming electron is reflected back as a hole at a
+normal-metal-superconductor interface, a process known as Andreev reflection.
+We predict that there exists a universal transverse shift in this process due
+to the effect of spin-orbit coupling in the normal metal. Particularly, using
+both the scattering approach and the argument of angular momentum conservation,
+we demonstrate that the shifts are pronounced for lightly-doped Weyl
+semimetals, and are opposite for incoming electrons with different chirality,
+generating a chirality-dependent Hall effect for the reflected holes. The
+predicted shift is not limited to Weyl systems, but exists for a general
+three-dimensional spin-orbit- coupled metal interfaced with a superconductor.
+",0,1,0,0,0,0
+2398,Production of Entanglement Entropy by Decoherence,"  We examine the dynamics of entanglement entropy of all parts in an open
+system consisting of a two-level dimer interacting with an environment of
+oscillators. The dimer-environment interaction is almost energy conserving. We
+find the precise link between decoherence and production of entanglement
+entropy. We show that not all environment oscillators carry significant
+entanglement entropy and we identify the oscillator frequency regions which
+contribute to the production of entanglement entropy. Our results hold for
+arbitrary strengths of the dimer-environment interaction, and they are
+mathematically rigorous.
+",0,1,0,0,0,0
+2399,Aggressive Economic Incentives and Physical Activity: The Role of Choice and Technology Decision Aids,"  Aggressive incentive schemes that allow individuals to impose economic
+punishment on themselves if they fail to meet health goals present a promising
+approach for encouraging healthier behavior. However, the element of choice
+inherent in these schemes introduces concerns that only non-representative
+sectors of the population will select aggressive incentives, leaving value on
+the table for those who don't opt in. In a field experiment conducted over a 29
+week period on individuals wearing Fitbit activity trackers, we find modest and
+short lived increases in physical activity for those provided the choice of
+aggressive incentives. In contrast, we find significant and persistent
+increases for those assigned (oftentimes against their stated preference) to
+the same aggressive incentives. The modest benefits for those provided a choice
+seems to emerge because those who benefited most from the aggressive incentives
+were the least likely to choose them, and it was those who did not need them
+who opted in. These results are confirmed in a follow up lab experiment. We
+also find that benefits to individuals assigned to aggressive incentives were
+pronounced if they also updated their step target in the Fitbit mobile
+application to match the new activity goal we provided them. Our findings have
+important implications for incentive based interventions to improve health
+behavior. For firms and policy makers, our results suggest that one effective
+strategy for encouraging sustained healthy behavior combines exposure to
+aggressive incentive schemes to jolt individuals out of their comfort zones
+with technology decision aids that help individuals sustain this behavior after
+incentives end.
+",0,0,0,0,0,1
+2400,Dynamics of resonances and equilibria of Low Earth Objects,"  The nearby space surrounding the Earth is densely populated by artificial
+satellites and instruments, whose orbits are distributed within the
+Low-Earth-Orbit region (LEO), ranging between 90 and 2 000 $km$ of altitude. As
+a consequence of collisions and fragmentations, many space debris of different
+sizes are left in the LEO region. Given the threat raised by the possible
+damages which a collision of debris can provoke with operational or manned
+satellites, the study of their dynamics is nowadays mandatory. This work is
+focused on the existence of equilibria and the dynamics of resonances in LEO.
+We base our results on a simplified model which includes the geopotential and
+the atmospheric drag. Using such model, we make a qualitative study of the
+resonances and the equilibrium positions, including their location and
+stability. The dissipative effect due to the atmosphere provokes a tidal decay,
+but we give examples of different behaviors, precisely a straightforward
+passage through the resonance or rather a temporary capture. We also
+investigate the effect of the solar cycle which is responsible of fluctuations
+of the atmospheric density and we analyze the influence of Sun and Moon on LEO
+objects.
+",0,1,0,0,0,0
+2401,Nonequilibrium photonic transport and phase transition in an array of optical cavities,"  We characterize photonic transport in a boundary driven array of nonlinear
+optical cavities. We find that the output field suddenly drops when the chain
+length is increased beyond a threshold. After this threshold a highly chaotic
+and unstable regime emerges, which marks the onset of a super-diffusive
+photonic transport. We show the scaling of the threshold with pump intensity
+and nonlinearity. Finally, we address the competition of disorder and
+nonlinearity presenting a diffusive-insulator phase transition.
+",0,1,0,0,0,0
+2402,The Unusual Effectiveness of Averaging in GAN Training,"  We show empirically that the optimal strategy of parameter averaging in a
+minmax convex-concave game setting is also strikingly effective in the non
+convex-concave GAN setting, specifically alleviating the convergence issues
+associated with cycling behavior observed in GANs. We show that averaging over
+generator parameters outside of the trainig loop consistently improves
+inception and FID scores on different architectures and for different GAN
+objectives. We provide comprehensive experimental results across a range of
+datasets, bilinear games, mixture of Gaussians, CIFAR-10, STL-10, CelebA and
+ImageNet, to demonstrate its effectiveness. We achieve state-of-the-art results
+on CIFAR-10 and produce clean CelebA face images, demonstrating that averaging
+is one of the most effective techniques for training highly performant GANs.
+",0,0,0,1,0,0
+2403,A null test of General Relativity: New limits on Local Position Invariance and the variation of fundamental constants,"  We compare the long-term fractional frequency variation of four hydrogen
+masers that are part of an ensemble of clocks comprising the National Institute
+of Standards and Technology,(NIST), Boulder, timescale with the fractional
+frequencies of primary frequency standards operated by leading metrology
+laboratories in the United States, France, Germany, Italy and the United
+Kingdom for a period extending more than 14 years. The measure of the assumed
+variation of non-gravitational interaction,(LPI parameter, $\beta$)---within
+the atoms of H and Cs---over time as the earth orbits the sun, has been
+constrained to $\beta=(2.2 \pm 2.5)\times 10^{-7}$, a factor of two improvement
+over previous estimates. Using our results together with the previous best
+estimates of $\beta$ based on Rb vs. Cs, and Rb vs. H comparisons, we impose
+the most stringent limits to date on the dimensionless coupling constants that
+relate the variation of fundamental constants such as the fine-structure
+constant and the scaled quark mass with strong(QCD) interaction to the
+variation in the local gravitational potential. For any metric theory of
+gravity $\beta=0$.
+",0,1,0,0,0,0
+2404,Stellarator bootstrap current and plasma flow velocity at low collisionality,"  The bootstrap current and flow velocity of a low-collisionality stellarator
+plasma are calculated. As far as possible, the analysis is carried out in a
+uniform way across all low-collisionality regimes in general stellarator
+geometry, assuming only that the confinement is good enough that the plasma is
+approximately in local thermodynamic equilibrium. It is found that conventional
+expressions for the ion flow speed and bootstrap current in the
+low-collisionality limit are accurate only in the $1/\nu$-collisionality regime
+and need to be modified in the $\sqrt{\nu}$-regime. The correction due to
+finite collisionality is also discussed and is found to scale as $\nu^{2/5}$.
+",0,1,0,0,0,0
+2405,SG1120-1202: Mass-Quenching as Tracked by UV Emission in the Group Environment at z=0.37,"  We use the Hubble Space Telescope to obtain WFC3/F390W imaging of the
+supergroup SG1120-1202 at z=0.37, mapping the UV emission of 138
+spectroscopically confirmed members. We measure total (F390W-F814W) colors and
+visually classify the UV morphology of individual galaxies as ""clumpy"" or
+""smooth."" Approximately 30% of the members have pockets of UV emission (clumpy)
+and we identify for the first time in the group environment galaxies with UV
+morphologies similar to the jellyfish galaxies observed in massive clusters. We
+stack the clumpy UV members and measure a shallow internal color gradient,
+which indicates unobscured star formation is occurring throughout these
+galaxies. We also stack the four galaxy groups and measure a strong trend of
+decreasing UV emission with decreasing projected group distance ($R_{proj}$).
+We find that the strong correlation between decreasing UV emission and
+increasing stellar mass can fully account for the observed trend in
+(F390W-F814W) - $R_{proj}$, i.e., mass-quenching is the dominant mechanism for
+extinguishing UV emission in group galaxies. Our extensive multi-wavelength
+analysis of SG1120-1202 indicates that stellar mass is the primary predictor of
+UV emission, but that the increasing fraction of massive (red/smooth) galaxies
+at $R_{proj}$ < 2$R_{200}$ and existence of jellyfish candidates is due to the
+group environment.
+",0,1,0,0,0,0
+2406,Dynamical patterns in individual trajectories toward extremism,"  Society faces a fundamental global problem of understanding which individuals
+are currently developing strong support for some extremist entity such as ISIS
+(Islamic State) -- even if they never end up doing anything in the real world.
+The importance of online connectivity in developing intent has been confirmed
+by recent case-studies of already convicted terrorists. Here we identify
+dynamical patterns in the online trajectories that individuals take toward
+developing a high level of extremist support -- specifically, for ISIS. Strong
+memory effects emerge among individuals whose transition is fastest, and hence
+may become 'out of the blue' threats in the real world. A generalization of
+diagrammatic expansion theory helps quantify these characteristics, including
+the impact of changes in geographical location, and can facilitate prediction
+of future risks. By quantifying the trajectories that individuals follow on
+their journey toward expressing high levels of pro-ISIS support -- irrespective
+of whether they then carry out a real-world attack or not -- our findings can
+help move safety debates beyond reliance on static watch-list identifiers such
+as ethnic background or immigration status, and/or post-fact interviews with
+already-convicted individuals. Given the broad commonality of social media
+platforms, our results likely apply quite generally: for example, even on
+Telegram where (like Twitter) there is no built-in group feature as in our
+study, individuals tend to collectively build and pass through so-called
+super-group accounts.
+",1,1,0,0,0,0
+2407,Convergence rate bounds for a proximal ADMM with over-relaxation stepsize parameter for solving nonconvex linearly constrained problems,"  This paper establishes convergence rate bounds for a variant of the proximal
+alternating direction method of multipliers (ADMM) for solving nonconvex
+linearly constrained optimization problems. The variant of the proximal ADMM
+allows the inclusion of an over-relaxation stepsize parameter belonging to the
+interval $(0,2)$. To the best of our knowledge, all related papers in the
+literature only consider the case where the over-relaxation parameter lies in
+the interval $(0,(1+\sqrt{5})/2)$.
+",0,0,1,0,0,0
+2408,"Detection, Recognition and Tracking of Moving Objects from Real-time Video via Visual Vocabulary Model and Species Inspired PSO","  In this paper, we address the basic problem of recognizing moving objects in
+video images using Visual Vocabulary model and Bag of Words and track our
+object of interest in the subsequent video frames using species inspired PSO.
+Initially, the shadow free images are obtained by background modelling followed
+by foreground modeling to extract the blobs of our object of interest.
+Subsequently, we train a cubic SVM with human body datasets in accordance with
+our domain of interest for recognition and tracking. During training, using the
+principle of Bag of Words we extract necessary features of certain domains and
+objects for classification. Subsequently, matching these feature sets with
+those of the extracted object blobs that are obtained by subtracting the shadow
+free background from the foreground, we detect successfully our object of
+interest from the test domain. The performance of the classification by cubic
+SVM is satisfactorily represented by confusion matrix and ROC curve reflecting
+the accuracy of each module. After classification, our object of interest is
+tracked in the test domain using species inspired PSO. By combining the
+adaptive learning tools with the efficient classification of description, we
+achieve optimum accuracy in recognition of the moving objects. We evaluate our
+algorithm benchmark datasets: iLIDS, VIVID, Walking2, Woman. Comparative
+analysis of our algorithm against the existing state-of-the-art trackers shows
+very satisfactory and competitive results.
+",1,0,0,0,0,0
+2409,Microfluidics for Chemical Synthesis: Flow Chemistry,"  Klavs F. Jensen is Warren K. Lewis Professor in Chemical Engineering and
+Materials Science and Engineering at the Massachusetts Institute of Technology.
+Here he describes the use of microfluidics for chemical synthesis, from the
+early demonstration examples to the current efforts with automated droplet
+microfluidic screening and optimization techniques.
+",0,0,0,0,1,0
+2410,Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling,"  The complexity and size of state-of-the-art cell models have significantly
+increased in part due to the requirement that these models possess complex
+cellular functions which are thought--but not necessarily proven--to be
+important. Modern cell models often involve hundreds of parameters; the values
+of these parameters come, more often than not, from animal experiments whose
+relationship to the human physiology is weak with very little information on
+the errors in these measurements. The concomitant uncertainties in parameter
+values result in uncertainties in the model outputs or Quantities of Interest
+(QoIs). Global Sensitivity Analysis (GSA) aims at apportioning to individual
+parameters (or sets of parameters) their relative contribution to output
+uncertainty thereby introducing a measure of influence or importance of said
+parameters. New GSA approaches are required to deal with increased model size
+and complexity; a three stage methodology consisting of screening (dimension
+reduction), surrogate modeling, and computing Sobol' indices, is presented. The
+methodology is used to analyze a physiologically validated numerical model of
+neurovascular coupling which possess 160 uncertain parameters. The sensitivity
+analysis investigates three quantities of interest (QoIs), the average value of
+$K^+$ in the extracellular space, the average volumetric flow rate through the
+perfusing vessel, and the minimum value of the actin/myosin complex in the
+smooth muscle cell. GSA provides a measure of the influence of each parameter,
+for each of the three QoIs, giving insight into areas of possible physiological
+dysfunction and areas of further investigation.
+",0,0,0,0,1,0
+2411,Few new reals,"  We introduce a new method for building models of CH, together with $\Pi_2$
+statements over $H(\omega_2)$, by forcing over a model of CH. Unlike similar
+constructions in the literature, our construction adds new reals, but only
+$\aleph_1$-many of them. Using this approach, we prove that a very strong form
+of the negation of Club Guessing at $\omega_1$ known as Measuring is consistent
+together with CH, thereby answering a well-known question of Moore. The
+construction works over any model of ZFC + CH and can be described as a finite
+support forcing construction with finite systems of countable models with
+markers as side conditions and with strong symmetry constraints on both side
+conditions and working parts.
+",0,0,1,0,0,0
+2412,Boundary Hamiltonian theory for gapped topological phases on an open surface,"  In this paper we propose a Hamiltonian approach to gapped topological phases
+on an open surface with boundary. Our setting is an extension of the Levin-Wen
+model to a 2d graph on the open surface, whose boundary is part of the graph.
+We systematically construct a series of boundary Hamiltonians such that each of
+them, when combined with the usual Levin-Wen bulk Hamiltonian, gives rise to a
+gapped energy spectrum which is topologically protected; and the corresponding
+wave functions are robust under changes of the underlying graph that maintain
+the spatial topology of the system. We derive explicit ground-state
+wavefunctions of the system and show that the boundary types are classified by
+Morita-equivalent Frobenius algebras. We also construct boundary quasiparticle
+creation, measuring and hopping operators. These operators allow us to
+characterize the boundary quasiparticles by bimodules of Frobenius algebras.
+Our approach also offers a concrete set of tools for computations. We
+illustrate our approach by a few examples.
+",0,1,1,0,0,0
+2413,"Fast Inverse Nonlinear Fourier Transformation using Exponential One-Step Methods, Part I: Darboux Transformation","  This paper considers the non-Hermitian Zakharov-Shabat (ZS) scattering
+problem which forms the basis for defining the SU$(2)$-nonlinear Fourier
+transformation (NFT). The theoretical underpinnings of this generalization of
+the conventional Fourier transformation is quite well established in the
+Ablowitz-Kaup-Newell-Segur (AKNS) formalism; however, efficient numerical
+algorithms that could be employed in practical applications are still
+unavailable.
+In this paper, we present a unified framework for the forward and inverse NFT
+using exponential one-step methods which are amenable to FFT-based fast
+polynomial arithmetic. Within this discrete framework, we propose a fast
+Darboux transformation (FDT) algorithm having an operational complexity of
+$\mathscr{O}\left(KN+N\log^2N\right)$ such that the error in the computed
+$N$-samples of the $K$-soliton vanishes as $\mathscr{O}\left(N^{-p}\right)$
+where $p$ is the order of convergence of the underlying one-step method. For
+fixed $N$, this algorithm outperforms the the classical DT (CDT) algorithm
+which has a complexity of $\mathscr{O}\left(K^2N\right)$. We further present
+extension of these algorithms to the general version of DT which allows one to
+add solitons to arbitrary profiles that are admissible as scattering potentials
+in the ZS-problem. The general CDT/FDT algorithms have the same operational
+complexity as that of the $K$-soliton case and the order of convergence matches
+that of the underlying one-step method. A comparative study of these algorithms
+is presented through exhaustive numerical tests.
+",0,1,0,0,0,0
+2414,Propagation in media as a probe for topological properties,"  The central goal of this thesis is to develop methods to experimentally study
+topological phases. We do so by applying the powerful toolbox of quantum
+simulation techniques with cold atoms in optical lattices. To this day, a
+complete classification of topological phases remains elusive. In this context,
+experimental studies are key, both for studying the interplay between topology
+and complex effects and for identifying new forms of topological order. It is
+therefore crucial to find complementary means to measure topological properties
+in order to reach a fundamental understanding of topological phases. In one
+dimensional chiral systems, we suggest a new way to construct and identify
+topologically protected bound states, which are the smoking gun of these
+materials. In two dimensional Hofstadter strips (i.e: systems which are very
+short along one dimension), we suggest a new way to measure the topological
+invariant directly from the atomic dynamics.
+",0,1,0,0,0,0
+2415,Leverage Score Sampling for Faster Accelerated Regression and ERM,"  Given a matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ and a vector $b
+\in\mathbb{R}^{d}$, we show how to compute an $\epsilon$-approximate solution
+to the regression problem $ \min_{x\in\mathbb{R}^{d}}\frac{1}{2} \|\mathbf{A} x
+- b\|_{2}^{2} $ in time $ \tilde{O} ((n+\sqrt{d\cdot\kappa_{\text{sum}}})\cdot
+s\cdot\log\epsilon^{-1}) $ where
+$\kappa_{\text{sum}}=\mathrm{tr}\left(\mathbf{A}^{\top}\mathbf{A}\right)/\lambda_{\min}(\mathbf{A}^{T}\mathbf{A})$
+and $s$ is the maximum number of non-zero entries in a row of $\mathbf{A}$. Our
+algorithm improves upon the previous best running time of $ \tilde{O}
+((n+\sqrt{n \cdot\kappa_{\text{sum}}})\cdot s\cdot\log\epsilon^{-1})$.
+We achieve our result through a careful combination of leverage score
+sampling techniques, proximal point methods, and accelerated coordinate
+descent. Our method not only matches the performance of previous methods, but
+further improves whenever leverage scores of rows are small (up to
+polylogarithmic factors). We also provide a non-linear generalization of these
+results that improves the running time for solving a broader class of ERM
+problems.
+",1,0,0,1,0,0
+2416,SOTER: Programming Safe Robotics System using Runtime Assurance,"  Autonomous robots increasingly depend on third-party off-the-shelf components
+and complex machine-learning techniques. This trend makes it challenging to
+provide strong design-time certification of correct operation. To address this
+challenge, we present SOTER, a programming framework that integrates the core
+principles of runtime assurance to enable the use of uncertified controllers,
+while still providing safety guarantees.
+Runtime Assurance (RTA) is an approach used for safety-critical systems where
+design-time analysis is coupled with run-time techniques to switch between
+unverified advanced controllers and verified simple controllers. In this paper,
+we present a runtime assurance programming framework for modular design of
+provably-safe robotics software. \tool provides language primitives to
+declaratively construct a \rta module consisting of an advanced controller
+(untrusted), a safe controller (trusted), and the desired safety specification
+(S). If the RTA module is well formed then the framework provides a formal
+guarantee that it satisfies property S. The compiler generates code for
+monitoring system state and switching control between the advanced and safe
+controller in order to guarantee S. RTA allows complex systems to be
+constructed through the composition of RTA modules.
+To demonstrate the efficacy of our framework, we consider a real-world
+case-study of building a safe drone surveillance system. Our experiments both
+in simulation and on actual drones show that RTA-enabled RTA ensures safety of
+the system, including when untrusted third-party components have bugs or
+deviate from the desired behavior.
+",1,0,0,0,0,0
+2417,On the Evaluation of Silicon Photomultipliers for Use as Photosensors in Liquid Xenon Detectors,"  Silicon photomultipliers (SiPMs) are potential solid-state alternatives to
+traditional photomultiplier tubes (PMTs) for single-photon detection. In this
+paper, we report on evaluating SensL MicroFC-10035-SMT SiPMs for their
+suitability as PMT replacements. The devices were successfully operated in a
+liquid-xenon detector, which demonstrates that SiPMs can be used in noble
+element time projection chambers as photosensors. The devices were also cooled
+down to 170 K to observe dark count dependence on temperature. No dependencies
+on the direction of an applied 3.2 kV/cm electric field were observed with
+respect to dark-count rate, gain, or photon detection efficiency.
+",0,1,0,0,0,0
+2418,Reconsidering Experiments,"  Experiments may not reveal their full import at the time that they are
+performed. The scientists who perform them usually are testing a specific
+hypothesis and quite often have specific expectations limiting the possible
+inferences that can be drawn from the experiment. Nonetheless, as Hacking has
+said, experiments have lives of their own. Those lives do not end with the
+initial report of the results and consequences of the experiment. Going back
+and rethinking the consequences of the experiment in a new context, theoretical
+or empirical, has great merit as a strategy for investigation and for
+scientific problem analysis. I apply this analysis to the interplay between
+Fizeau's classic optical experiments and the building of special relativity.
+Einstein's understanding of the problems facing classical electrodynamics and
+optics, in part, was informed by Fizeau's 1851 experiments. However, between
+1851 and 1905, Fizeau's experiments were duplicated and reinterpreted by a
+succession of scientists, including Hertz, Lorentz, and Michelson. Einstein's
+analysis of the consequences of the experiments is tied closely to this
+theoretical and experimental tradition. However, Einstein's own inferences from
+the experiments differ greatly from the inferences drawn by others in that
+tradition.
+",0,1,0,0,0,0
+2419,Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features,"  We study the statistical and computational aspects of kernel principal
+component analysis using random Fourier features and show that under mild
+assumptions, $O(\sqrt{n} \log n)$ features suffices to achieve
+$O(1/\epsilon^2)$ sample complexity. Furthermore, we give a memory efficient
+streaming algorithm based on classical Oja's algorithm that achieves this rate.
+",0,0,0,1,0,0
+2420,Universal Protocols for Information Dissemination Using Emergent Signals,"  We consider a population of $n$ agents which communicate with each other in a
+decentralized manner, through random pairwise interactions. One or more agents
+in the population may act as authoritative sources of information, and the
+objective of the remaining agents is to obtain information from or about these
+source agents. We study two basic tasks: broadcasting, in which the agents are
+to learn the bit-state of an authoritative source which is present in the
+population, and source detection, in which the agents are required to decide if
+at least one source agent is present in the population or not.We focus on
+designing protocols which meet two natural conditions: (1) universality, i.e.,
+independence of population size, and (2) rapid convergence to a correct global
+state after a reconfiguration, such as a change in the state of a source agent.
+Our main positive result is to show that both of these constraints can be met.
+For both the broadcasting problem and the source detection problem, we obtain
+solutions with a convergence time of $O(\log^2 n)$ rounds, w.h.p., from any
+starting configuration. The solution to broadcasting is exact, which means that
+all agents reach the state broadcast by the source, while the solution to
+source detection admits one-sided error on a $\varepsilon$-fraction of the
+population (which is unavoidable for this problem). Both protocols are easy to
+implement in practice and have a compact formulation.Our protocols exploit the
+properties of self-organizing oscillatory dynamics. On the hardness side, our
+main structural insight is to prove that any protocol which meets the
+constraints of universality and of rapid convergence after reconfiguration must
+display a form of non-stationary behavior (of which oscillatory dynamics are an
+example). We also observe that the periodicity of the oscillatory behavior of
+the protocol, when present, must necessarily depend on the number $^\\# X$ of
+source agents present in the population. For instance, our protocols inherently
+rely on the emergence of a signal passing through the population, whose period
+is $\Theta(\log \frac{n}{^\\# X})$ rounds for most starting configurations. The
+design of clocks with tunable frequency may be of independent interest, notably
+in modeling biological networks.
+",1,0,0,0,0,0
+2421,A note on species realizations and nondegeneracy of potentials,"  In this note we show that a mutation theory of species with potential can be
+defined so that a certain class of skew-symmetrizable integer matrices have a
+species realization admitting a non-degenerate potential. This gives a partial
+affirmative answer to a question raised by Jan Geuenich and Daniel
+Labardini-Fragoso. We also provide an example of a class of skew-symmetrizable
+$4 \times 4$ integer matrices, which are not globally unfoldable nor strongly
+primitive, and that have a species realization admitting a non-degenerate
+potential.
+",0,0,1,0,0,0
+2422,A Unified Stochastic Formulation of Dissipative Quantum Dynamics. II. Beyond Linear Response of Spin Baths,"  We use the ""generalized hierarchical equation of motion"" proposed in Paper I
+to study decoherence in a system coupled to a spin bath. The present
+methodology allows a systematic incorporation of higher order anharmonic
+effects of the bath in dynamical calculations. We investigate the leading order
+corrections to the linear response approximations for spin bath models. Two
+types of spin-based environments are considered: (1) a bath of spins
+discretized from a continuous spectral density and (2) a bath of physical spins
+such as nuclear or electron spins. The main difference resides with how the
+bath frequency and the system-bath coupling parameters are chosen to represent
+an environment. When discretized from a continuous spectral density, the
+system-bath coupling typically scales as $\sim 1/\sqrt{N_B}$ where $N_B$ is the
+number of bath spins. This scaling suppresses the non-Gaussian characteristics
+of the spin bath and justify the linear response approximations in the
+thermodynamic limit. For the physical spin bath models, system-bath couplings
+are directly deduced from spin-spin interactions with no reason to obey the
+$1/\sqrt{N_B}$ scaling. It is not always possible to justify the linear
+response approximations. Furthermore, if the spin-spin Hamiltonian and/or the
+bath parameters are highly symmetrical, these additional constraints generate
+non-Markovian and persistent dynamics that is beyond the linear response
+treatments.
+",0,1,0,0,0,0
+2423,Vortex states and spin textures of rotating spin-orbit-coupled Bose-Einstein condensates in a toroidal trap,"  We consider the ground-state properties of Rashba spin-orbit-coupled
+pseudo-spin-1/2 Bose-Einstein condensates (BECs) in a rotating two-dimensional
+(2D) toroidal trap. In the absence of spin-orbit coupling (SOC), the increasing
+rotation frequency enhances the creation of giant vortices for the initially
+miscible BECs, while it can lead to the formation of semiring density patterns
+with irregular hidden vortex structures for the initially immiscible BECs.
+Without rotation, strong 2D isotropic SOC yields a heliciform-stripe phase for
+the initially immiscible BECs. Combined effects of rotation, SOC, and
+interatomic interactions on the vortex structures and typical spin textures of
+the ground state of the system are discussed systematically. In particular, for
+fixed rotation frequency above the critical value, the increasing isotropic SOC
+favors a visible vortex ring in each component which is accompanied by a hidden
+giant vortex plus a (several) hidden vortex ring(s) in the central region. In
+the case of 1D anisotropic SOC, large SOC strength results in the generation of
+hidden linear vortex string and the transition from initial phase separation
+(phase mixing) to phase mixing (phase separation). Furthermore, the peculiar
+spin textures including skyrmion lattice, skyrmion pair and skyrmion string are
+revealed in this system.
+",0,1,0,0,0,0
+2424,Semi-Global Weighted Least Squares in Image Filtering,"  Solving the global method of Weighted Least Squares (WLS) model in image
+filtering is both time- and memory-consuming. In this paper, we present an
+alternative approximation in a time- and memory- efficient manner which is
+denoted as Semi-Global Weighed Least Squares (SG-WLS). Instead of solving a
+large linear system, we propose to iteratively solve a sequence of subsystems
+which are one-dimensional WLS models. Although each subsystem is
+one-dimensional, it can take two-dimensional neighborhood information into
+account due to the proposed special neighborhood construction. We show such a
+desirable property makes our SG-WLS achieve close performance to the original
+two-dimensional WLS model but with much less time and memory cost. While
+previous related methods mainly focus on the 4-connected/8-connected
+neighborhood system, our SG-WLS can handle a more general and larger
+neighborhood system thanks to the proposed fast solution. We show such a
+generalization can achieve better performance than the 4-connected/8-connected
+neighborhood system in some applications. Our SG-WLS is $\sim20$ times faster
+than the WLS model. For an image of $M\times N$, the memory cost of SG-WLS is
+at most at the magnitude of $max\{\frac{1}{M}, \frac{1}{N}\}$ of that of the
+WLS model. We show the effectiveness and efficiency of our SG-WLS in a range of
+applications.
+",1,0,0,0,0,0
+2425,Universal elliptic Gauß sums for Atkin primes in Schoof's algorithm,"  This work builds on earlier results. We define universal elliptic Gau{\ss}
+sums for Atkin primes in Schoof's algorithm for counting points on elliptic
+curves. Subsequently, we show these quantities admit an efficiently computable
+representation in terms of the $j$-invariant and two other modular functions.
+We analyse the necessary computations in detail and derive an alternative
+approach for determining the trace of the Frobenius homomorphism for Atkin
+primes using these pre-computations. A rough run-time analysis shows, however,
+that this new method is not competitive with existing ones.
+",0,0,1,0,0,0
+2426,Deep Residual Networks and Weight Initialization,"  Residual Network (ResNet) is the state-of-the-art architecture that realizes
+successful training of really deep neural network. It is also known that good
+weight initialization of neural network avoids problem of vanishing/exploding
+gradients. In this paper, simplified models of ResNets are analyzed. We argue
+that goodness of ResNet is correlated with the fact that ResNets are relatively
+insensitive to choice of initial weights. We also demonstrate how batch
+normalization improves backpropagation of deep ResNets without tuning initial
+values of weights.
+",1,0,0,1,0,0
+2427,Wavelet graphs for the direct detection of gravitational waves,"  A second generation of gravitational wave detectors will soon come online
+with the objective of measuring for the first time the tiny gravitational
+signal from the coalescence of black hole and/or neutron star binaries. In this
+communication, we propose a new time-frequency search method alternative to
+matched filtering techniques that are usually employed to detect this signal.
+This method relies on a graph that encodes the time evolution of the signal and
+its variability by establishing links between coefficients in the multi-scale
+time-frequency decomposition of the data. We provide a proof of concept for
+this approach.
+",0,1,0,0,0,0
+2428,A Survey on Hypergraph Products (Erratum),"  A surprising diversity of different products of hypergraphs have been
+discussed in the literature. Most of the hypergraph products can be viewed as
+generalizations of one of the four standard graph products. The most widely
+studied variant, the so-called square product, does not have this property,
+however. Here we survey the literature on hypergraph products with an emphasis
+on comparing the alternative generalizations of graph products and the
+relationships among them. In this context the so-called 2-sections and
+L2-sections are considered. These constructions are closely linked to related
+colored graph structures that seem to be a useful tool for the prime factor
+decompositions w.r.t.\ specific hypergraph products. We summarize the current
+knowledge on the propagation of hypergraph invariants under the different
+hypergraph multiplications. While the overwhelming majority of the material
+concerns finite (undirected) hypergraphs, the survey also covers a summary of
+the few results on products of infinite and directed hypergraphs.
+",1,0,0,0,0,0
+2429,One- and two-channel Kondo model with logarithmic Van Hove singularity: a numerical renormalization group solution,"  Simple scaling consideration and NRG solution of the one- and two-channel
+Kondo model in the presence of a logarithmic Van Hove singularity at the Fermi
+level is given. The temperature dependences of local and impurity magnetic
+susceptibility and impurity entropy are calculated. The low-temperature
+behavior of the impurity susceptibility and impurity entropy turns out to be
+non-universal in the Kondo sense and independent of the $s-d$ coupling $J$. The
+resonant level model solution in the strong coupling regime confirms the NRG
+results. In the two-channel case the local susceptibility demonstrates a
+non-Fermi-liquid power-law behavior.
+",0,1,0,0,0,0
+2430,A deep Convolutional Neural Network for topology optimization with strong generalization ability,"  This paper proposes a deep Convolutional Neural Network(CNN) with strong
+generalization ability for structural topology optimization. The architecture
+of the neural network is made up of encoding and decoding parts, which provide
+down- and up-sampling operations. In addition, a popular technique, namely
+U-Net, was adopted to improve the performance of the proposed neural network.
+The input of the neural network is a well-designed tensor with each channel
+includes different information for the problem, and the output is the layout of
+the optimal structure. To train the neural network, a large dataset is
+generated by a conventional topology optimization approach, i.e. SIMP. The
+performance of the proposed method was evaluated by comparing its efficiency
+and accuracy with SIMP on a series of typical optimization problems. Results
+show that a significant reduction in computation cost was achieved with little
+sacrifice on the optimality of design solutions. Furthermore, the proposed
+method can intelligently solve problems under boundary conditions not being
+included in the training dataset.
+",1,0,0,1,0,0
+2431,Focused Hierarchical RNNs for Conditional Sequence Processing,"  Recurrent Neural Networks (RNNs) with attention mechanisms have obtained
+state-of-the-art results for many sequence processing tasks. Most of these
+models use a simple form of encoder with attention that looks over the entire
+sequence and assigns a weight to each token independently. We present a
+mechanism for focusing RNN encoders for sequence modelling tasks which allows
+them to attend to key parts of the input as needed. We formulate this using a
+multi-layer conditional sequence encoder that reads in one token at a time and
+makes a discrete decision on whether the token is relevant to the context or
+question being asked. The discrete gating mechanism takes in the context
+embedding and the current hidden state as inputs and controls information flow
+into the layer above. We train it using policy gradient methods. We evaluate
+this method on several types of tasks with different attributes. First, we
+evaluate the method on synthetic tasks which allow us to evaluate the model for
+its generalization ability and probe the behavior of the gates in more
+controlled settings. We then evaluate this approach on large scale Question
+Answering tasks including the challenging MS MARCO and SearchQA tasks. Our
+models shows consistent improvements for both tasks over prior work and our
+baselines. It has also shown to generalize significantly better on synthetic
+tasks as compared to the baselines.
+",0,0,0,1,0,0
+2432,The Faraday room of the CUORE Experiment,"  The paper describes the Faraday room that shields the CUORE experiment
+against electromagnetic fields, from 50 Hz up to high frequency. Practical
+contraints led to choose panels made of light shielding materials. The seams
+between panels were optimized with simulations to minimize leakage.
+Measurements of shielding performance show attenuation of a factor 15 at 50 Hz,
+and a factor 1000 above 1 KHz up to about 100 MHz.
+",0,1,0,0,0,0
+2433,Simulations and measurements of the impact of collective effects on dynamic aperture,"  We describe a benchmark study of collective and nonlinear dynamics in an APS
+storage ring. A 1-mm long bunch was assumed in the calculation of wakefield and
+element by element particle tracking with distributed wakefield component along
+the ring was performed in Elegant simulation. The result of Elegant simulation
+differed by less than 5 % from experimental measurement
+",0,1,0,0,0,0
+2434,Estimation of the asymptotic variance of univariate and multivariate random fields and statistical inference,"  Correlated random fields are a common way to model dependence struc- tures in
+high-dimensional data, especially for data collected in imaging. One important
+parameter characterizing the degree of dependence is the asymp- totic variance
+which adds up all autocovariances in the temporal and spatial domain.
+Especially, it arises in the standardization of test statistics based on
+partial sums of random fields and thus the construction of tests requires its
+estimation. In this paper we propose consistent estimators for this parameter
+for strictly stationary {\phi}-mixing random fields with arbitrary dimension of
+the domain and taking values in a Euclidean space of arbitrary dimension, thus
+allowing for multivariate random fields. We establish consistency, provide cen-
+tral limit theorems and show that distributional approximations of related test
+statistics based on sample autocovariances of random fields can be obtained by
+the subsampling approach. As in applications the spatial-temporal correlations
+are often quite local, such that a large number of autocovariances vanish or
+are negligible, we also investigate a thresholding approach where sample
+autocovariances of small magnitude are omitted. Extensive simulation studies
+show that the proposed estimators work well in practice and, when used to
+standardize image test statistics, can provide highly accurate image testing
+procedures.
+",0,0,1,1,0,0
+2435,Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation,"  Deep learning approaches such as convolutional neural nets have consistently
+outperformed previous methods on challenging tasks such as dense, semantic
+segmentation. However, the various proposed networks perform differently, with
+behaviour largely influenced by architectural choices and training settings.
+This paper explores Ensembles of Multiple Models and Architectures (EMMA) for
+robust performance through aggregation of predictions from a wide range of
+methods. The approach reduces the influence of the meta-parameters of
+individual models and the risk of overfitting the configuration to a particular
+database. EMMA can be seen as an unbiased, generic deep learning model which is
+shown to yield excellent performance, winning the first position in the BRATS
+2017 competition among 50+ participating teams.
+",1,0,0,0,0,0
+2436,Estimating Phase Duration for SPaT Messages,"  A SPaT (Signal Phase and Timing) message describes for each lane the current
+phase at a signalized intersection together with an estimate of the residual
+time of that phase. Accurate SPaT messages can be used to construct a speed
+profile for a vehicle that reduces its fuel consumption as it approaches or
+leaves an intersection. This paper presents SPaT estimation algorithms at an
+intersection with a semi-actuated signal, using real-time signal phase
+measurements. The algorithms are evaluated using high-resolution data from two
+intersections in Montgomery County, MD. The algorithms can be readily
+implemented at signal controllers. The study supports three findings. First,
+real-time information dramatically improves the accuracy of the prediction of
+the residual time compared with prediction based on historical data alone.
+Second, as time increases the prediction of the residual time may increase or
+decrease. Third, as drivers differently weight errors in predicting `end of
+green' and `end of red', drivers on two different approaches may prefer
+different estimates of the residual time of the same phase.
+",0,0,0,1,0,0
+2437,Efficient Spatial Variation Characterization via Matrix Completion,"  In this paper, we propose a novel method to estimate and characterize spatial
+variations on dies or wafers. This new technique exploits recent developments
+in matrix completion, enabling estimation of spatial variation across wafers or
+dies with a small number of randomly picked sampling points while still
+achieving fairly high accuracy. This new approach can be easily generalized,
+including for estimation of mixed spatial and structure or device type
+information.
+",1,0,0,0,0,0
+2438,TS-MPC for Autonomous Vehicles including a dynamic TS-MHE-UIO,"  In this work, a novel approach is presented to solve the problem of tracking
+trajectories in autonomous vehicles. This approach is based on the use of a
+cascade control where the external loop solves the position control using a
+novel Takagi Sugeno - Model Predictive Control (TS-MPC) approach and the
+internal loop is in charge of the dynamic control of the vehicle using a Takagi
+Sugeno - Linear Quadratic Regulator technique designed via Linear Matrix
+Inequalities (TS-LMI-LQR). Both techniques use a TS representation of the
+kinematic and dynamic models of the vehicle. In addition, a novel Takagi Sugeno
+estimator - Moving Horizon Estimator - Unknown Input Observer (TS-MHE-UIO) is
+presented. This method estimates the dynamic states of the vehicle optimally as
+well as the force of friction acting on the vehicle that is used to reduce the
+control efforts. The innovative contribution of the TS-MPC and TS-MHE-UIO
+techniques is that using the TS model formulation of the vehicle allows us to
+solve the nonlinear problem as if it were linear, reducing computation times by
+40-50 times. To demonstrate the potential of the TS-MPC we propose a comparison
+between three methods of solving the kinematic control problem: using the
+non-linear MPC formulation (NL-MPC), using TS-MPC without updating the
+prediction model and using updated TS-MPC with the references of the planner.
+",1,0,0,0,0,0
+2439,Flexibility Analysis for Smart Grid Demand Response,"  Flexibility is a key enabler for the smart grid, required to facilitate
+Demand Side Management (DSM) programs, managing electrical consumption to
+reduce peaks, balance renewable generation and provide ancillary services to
+the grid. Flexibility analysis is required to identify and quantify the
+available electrical load of a site or building which can be shed or increased
+in response to a DSM signal. A methodology for assessing flexibility is
+developed, based on flexibility formulations and optimization requirements. The
+methodology characterizes the loads, storage and on-site generation,
+incorporates site assessment using the ISO 50002:2014 energy audit standard and
+benchmarks performance against documented studies. An example application of
+the methodology is detailed using a pilot site demonstrator.
+",1,0,1,0,0,0
+2440,Duluth at SemEval-2017 Task 6: Language Models in Humor Detection,"  This paper describes the Duluth system that participated in SemEval-2017 Task
+6 #HashtagWars: Learning a Sense of Humor. The system participated in Subtasks
+A and B using N-gram language models, ranking highly in the task evaluation.
+This paper discusses the results of our system in the development and
+evaluation stages and from two post-evaluation runs.
+",1,0,0,0,0,0
+2441,Classification of grasping tasks based on EEG-EMG coherence,"  This work presents an innovative application of the well-known concept of
+cortico-muscular coherence for the classification of various motor tasks, i.e.,
+grasps of different kinds of objects. Our approach can classify objects with
+different weights (motor-related features) and different surface frictions
+(haptics-related features) with high accuracy (over 0:8). The outcomes
+presented here provide information about the synchronization existing between
+the brain and the muscles during specific activities; thus, this may represent
+a new effective way to perform activity recognition.
+",0,0,0,0,1,0
+2442,Kepler sheds new and unprecedented light on the variability of a blue supergiant: gravity waves in the O9.5Iab star HD 188209,"  Stellar evolution models are most uncertain for evolved massive stars.
+Asteroseismology based on high-precision uninterrupted space photometry has
+become a new way to test the outcome of stellar evolution theory and was
+recently applied to a multitude of stars, but not yet to massive evolved
+supergiants.Our aim is to detect, analyse and interpret the photospheric and
+wind variability of the O9.5Iab star HD 188209 from Kepler space photometry and
+long-term high-resolution spectroscopy. We used Kepler scattered-light
+photometry obtained by the nominal mission during 1460d to deduce the
+photometric variability of this O-type supergiant. In addition, we assembled
+and analysed high-resolution high signal-to-noise spectroscopy taken with four
+spectrographs during some 1800d to interpret the temporal spectroscopic
+variability of the star. The variability of this blue supergiant derived from
+the scattered-light space photometry is in full in agreement with the one found
+in the ground-based spectroscopy. We find significant low-frequency variability
+that is consistently detected in all spectral lines of HD 188209. The
+photospheric variability propagates into the wind, where it has similar
+frequencies but slightly higher amplitudes. The morphology of the frequency
+spectra derived from the long-term photometry and spectroscopy points towards a
+spectrum of travelling waves with frequency values in the range expected for an
+evolved O-type star. Convectively-driven internal gravity waves excited in the
+stellar interior offer the most plausible explanation of the detected
+variability.
+",0,1,0,0,0,0
+2443,Complexity of short Presburger arithmetic,"  We study complexity of short sentences in Presburger arithmetic (Short-PA).
+Here by ""short"" we mean sentences with a bounded number of variables,
+quantifiers, inequalities and Boolean operations; the input consists only of
+the integers involved in the inequalities. We prove that assuming Kannan's
+partition can be found in polynomial time, the satisfiability of Short-PA
+sentences can be decided in polynomial time. Furthermore, under the same
+assumption, we show that the numbers of satisfying assignments of short
+Presburger sentences can also be computed in polynomial time.
+",1,0,1,0,0,0
+2444,Deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications,"  Mean square error (MSE) has been the preferred choice as loss function in the
+current deep neural network (DNN) based speech separation techniques. In this
+paper, we propose a new cost function with the aim of optimizing the extended
+short time objective intelligibility (ESTOI) measure. We focus on applications
+where low algorithmic latency ($\leq 10$ ms) is important. We use long
+short-term memory networks (LSTM) and evaluate our proposed approach on four
+sets of two-speaker mixtures from extended Danish hearing in noise (HINT)
+dataset. We show that the proposed loss function can offer improved or at par
+objective intelligibility (in terms of ESTOI) compared to an MSE optimized
+baseline while resulting in lower objective separation performance (in terms of
+the source to distortion ratio (SDR)). We then proceed to propose an approach
+where the network is first initialized with weights optimized for MSE criterion
+and then trained with the proposed ESTOI loss criterion. This approach
+mitigates some of the losses in objective separation performance while
+preserving the gains in objective intelligibility.
+",1,0,0,0,0,0
+2445,Characterization and control of linear coupling using turn-by-turn beam position monitor data in storage rings,"  We introduce a new application of measuring symplectic generators to
+characterize and control the linear betatron coupling in storage rings. From
+synchronized and consecutive BPM (Beam Position Monitor) turn-by-turn (TbT)
+readings, symplectic Lie generators describing the coupled linear dynamics are
+extracted. Four plane-crossing terms in the generators directly characterize
+the coupling between the horizontal and the vertical planes. Coupling control
+can be accomplished by utilizing the dependency of these plane-crossing terms
+on skew quadrupoles. The method has been successfully demonstrated to reduce
+the vertical effective emittance down to the diffraction limit in the newly
+constructed National Synchrotron Light Source II (NSLS-II) storage ring. This
+method can be automatized to realize linear coupling feedback control with
+negligible disturbance on machine operation.
+",0,1,0,0,0,0
+2446,Adaptive Inferential Method for Monotone Graph Invariants,"  We consider the problem of undirected graphical model inference. In many
+applications, instead of perfectly recovering the unknown graph structure, a
+more realistic goal is to infer some graph invariants (e.g., the maximum
+degree, the number of connected subgraphs, the number of isolated nodes). In
+this paper, we propose a new inferential framework for testing nested multiple
+hypotheses and constructing confidence intervals of the unknown graph
+invariants under undirected graphical models. Compared to perfect graph
+recovery, our methods require significantly weaker conditions. This paper makes
+two major contributions: (i) Methodologically, for testing nested multiple
+hypotheses, we propose a skip-down algorithm on the whole family of monotone
+graph invariants (The invariants which are non-decreasing under addition of
+edges). We further show that the same skip-down algorithm also provides valid
+confidence intervals for the targeted graph invariants. (ii) Theoretically, we
+prove that the length of the obtained confidence intervals are optimal and
+adaptive to the unknown signal strength. We also prove generic lower bounds for
+the confidence interval length for various invariants. Numerical results on
+both synthetic simulations and a brain imaging dataset are provided to
+illustrate the usefulness of the proposed method.
+",0,0,1,1,0,0
+2447,High-dose-rate prostate brachytherapy inverse planning on dose-volume criteria by simulated annealing,"  High-dose-rate brachytherapy is a tumor treatment method where a highly
+radioactive source is brought in close proximity to the tumor. In this paper we
+develop a simulated annealing algorithm to optimize the dwell times at
+preselected dwell positions to maximize tumor coverage under dose-volume
+constraints on the organs at risk. Compared to existing algorithms, our
+algorithm has advantages in terms of speed and objective value and does not
+require an expensive general purpose solver. Its success mainly depends on
+exploiting the efficiency of matrix multiplication and a careful selection of
+the neighboring states. In this paper we outline its details and make an
+in-depth comparison with existing methods using real patient data.
+",0,1,0,0,0,0
+2448,Scaling the Scattering Transform: Deep Hybrid Networks,"  We use the scattering network as a generic and fixed ini-tialization of the
+first layers of a supervised hybrid deep network. We show that early layers do
+not necessarily need to be learned, providing the best results to-date with
+pre-defined representations while being competitive with Deep CNNs. Using a
+shallow cascade of 1 x 1 convolutions, which encodes scattering coefficients
+that correspond to spatial windows of very small sizes, permits to obtain
+AlexNet accuracy on the imagenet ILSVRC2012. We demonstrate that this local
+encoding explicitly learns invariance w.r.t. rotations. Combining scattering
+networks with a modern ResNet, we achieve a single-crop top 5 error of 11.4% on
+imagenet ILSVRC2012, comparable to the Resnet-18 architecture, while utilizing
+only 10 layers. We also find that hybrid architectures can yield excellent
+performance in the small sample regime, exceeding their end-to-end
+counterparts, through their ability to incorporate geometrical priors. We
+demonstrate this on subsets of the CIFAR-10 dataset and on the STL-10 dataset.
+",1,0,0,0,0,0
+2449,Methodological variations in lagged regression for detecting physiologic drug effects in EHR data,"  We studied how lagged linear regression can be used to detect the physiologic
+effects of drugs from data in the electronic health record (EHR). We
+systematically examined the effect of methodological variations ((i) time
+series construction, (ii) temporal parameterization, (iii) intra-subject
+normalization, (iv) differencing (lagged rates of change achieved by taking
+differences between consecutive measurements), (v) explanatory variables, and
+(vi) regression models) on performance of lagged linear methods in this
+context. We generated two gold standards (one knowledge-base derived, one
+expert-curated) for expected pairwise relationships between 7 drugs and 4 labs,
+and evaluated how the 64 unique combinations of methodological perturbations
+reproduce gold standards. Our 28 cohorts included patients in Columbia
+University Medical Center/NewYork-Presbyterian Hospital clinical database. The
+most accurate methods achieved AUROC of 0.794 for knowledge-base derived gold
+standard (95%CI [0.741, 0.847]) and 0.705 for expert-curated gold standard (95%
+CI [0.629, 0.781]). We observed a 0.633 mean AUROC (95%CI [0.610, 0.657],
+expert-curated gold standard) across all methods that re-parameterize time
+according to sequence and use either a joint autoregressive model with
+differencing or an independent lag model without differencing. The complement
+of this set of methods achieved a mean AUROC close to 0.5, indicating the
+importance of these choices. We conclude that time- series analysis of EHR data
+will likely rely on some of the beneficial pre-processing and modeling
+methodologies identified, and will certainly benefit from continued careful
+analysis of methodological perturbations. This study found that methodological
+variations, such as pre-processing and representations, significantly affect
+results, exposing the importance of evaluating these components when comparing
+machine-learning methods.
+",0,0,0,1,1,0
+2450,Leontief Meets Shannon - Measuring the Complexity of the Economic System,"  We develop a complexity measure for large-scale economic systems based on
+Shannon's concept of entropy. By adopting Leontief's perspective of the
+production process as a circular flow, we formulate the process as a Markov
+chain. Then we derive a measure of economic complexity as the average number of
+bits required to encode the flow of goods and services in the production
+process. We illustrate this measure using data from seven national economies,
+spanning several decades.
+",0,1,0,1,0,0
+2451,Exploring nucleon spin structure through neutrino neutral-current interactions in MicroBooNE,"  The net contribution of the strange quark spins to the proton spin, $\Delta
+s$, can be determined from neutral current elastic neutrino-proton interactions
+at low momentum transfer combined with data from electron-proton scattering.
+The probability of neutrino-proton interactions depends in part on the axial
+form factor, which represents the spin structure of the proton and can be
+separated into its quark flavor contributions. Low momentum transfer neutrino
+neutral current interactions can be measured in MicroBooNE, a high-resolution
+liquid argon time projection chamber (LArTPC) in its first year of running in
+the Booster Neutrino Beamline at Fermilab. The signal for these interactions in
+MicroBooNE is a single short proton track. We present our work on the automated
+reconstruction and classification of proton tracks in LArTPCs, an important
+step in the determination of neutrino- nucleon cross sections and the
+measurement of $\Delta s$.
+",0,1,0,0,0,0
+2452,A unimodular Liouville hyperbolic souvlaki --- an appendix to [arXiv:1603.06712],"  Carmesin, Federici, and Georgakopoulos [arXiv:1603.06712] constructed a
+transient hyperbolic graph that has no transient subtrees and that has the
+Liouville property for harmonic functions. We modify their construction to get
+a unimodular random graph with the same properties.
+",0,0,1,0,0,0
+2453,Comparison Based Nearest Neighbor Search,"  We consider machine learning in a comparison-based setting where we are given
+a set of points in a metric space, but we have no access to the actual
+distances between the points. Instead, we can only ask an oracle whether the
+distance between two points $i$ and $j$ is smaller than the distance between
+the points $i$ and $k$. We are concerned with data structures and algorithms to
+find nearest neighbors based on such comparisons. We focus on a simple yet
+effective algorithm that recursively splits the space by first selecting two
+random pivot points and then assigning all other points to the closer of the
+two (comparison tree). We prove that if the metric space satisfies certain
+expansion conditions, then with high probability the height of the comparison
+tree is logarithmic in the number of points, leading to efficient search
+performance. We also provide an upper bound for the failure probability to
+return the true nearest neighbor. Experiments show that the comparison tree is
+competitive with algorithms that have access to the actual distance values, and
+needs less triplet comparisons than other competitors.
+",1,0,0,1,0,0
+2454,LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances,"  Predicting the completion time of business process instances would be a very
+helpful aid when managing processes under service level agreement constraints.
+The ability to know in advance the trend of running process instances would
+allow business managers to react in time, in order to prevent delays or
+undesirable situations. However, making such accurate forecasts is not easy:
+many factors may influence the required time to complete a process instance. In
+this paper, we propose an approach based on deep Recurrent Neural Networks
+(specifically LSTMs) that is able to exploit arbitrary information associated
+to single events, in order to produce an as-accurate-as-possible prediction of
+the completion time of running instances. Experiments on real-world datasets
+confirm the quality of our proposal.
+",1,0,0,1,0,0
+2455,Message-passing algorithm of quantum annealing with nonstoquastic Hamiltonian,"  Quantum annealing (QA) is a generic method for solving optimization problems
+using fictitious quantum fluctuation. The current device performing QA involves
+controlling the transverse field; it is classically simulatable by using the
+standard technique for mapping the quantum spin systems to the classical ones.
+In this sense, the current system for QA is not powerful despite utilizing
+quantum fluctuation. Hence, we developed a system with a time-dependent
+Hamiltonian consisting of a combination of the formulated Ising model and the
+""driver"" Hamiltonian with only quantum fluctuation. In the previous study, for
+a fully connected spin model, quantum fluctuation can be addressed in a
+relatively simple way. We proved that the fully connected antiferromagnetic
+interaction can be transformed into a fluctuating transverse field and is thus
+classically simulatable at sufficiently low temperatures. Using the fluctuating
+transverse field, we established several ways to simulate part of the
+nonstoquastic Hamiltonian on classical computers. We formulated a
+message-passing algorithm in the present study. This algorithm is capable of
+assessing the performance of QA with part of the nonstoquastic Hamiltonian
+having a large number of spins. In other words, we developed a different
+approach for simulating the nonstoquastic Hamiltonian without using the quantum
+Monte Carlo technique. Our results were validated by comparison to the results
+obtained by the replica method.
+",1,0,0,1,0,0
+2456,RLE Plots: Visualising Unwanted Variation in High Dimensional Data,"  Unwanted variation can be highly problematic and so its detection is often
+crucial. Relative log expression (RLE) plots are a powerful tool for
+visualising such variation in high dimensional data. We provide a detailed
+examination of these plots, with the aid of examples and simulation, explaining
+what they are and what they can reveal. RLE plots are particularly useful for
+assessing whether a procedure aimed at removing unwanted variation, i.e. a
+normalisation procedure, has been successful. These plots, while originally
+devised for gene expression data from microarrays, can also be used to reveal
+unwanted variation in many other kinds of high dimensional data, where such
+variation can be problematic.
+",0,0,0,1,0,0
+2457,ALMA Observations of Gas-Rich Galaxies in z~1.6 Galaxy Clusters: Evidence for Higher Gas Fractions in High-Density Environments,"  We present ALMA CO (2-1) detections in 11 gas-rich cluster galaxies at z~1.6,
+constituting the largest sample of molecular gas measurements in z>1.5 clusters
+to date. The observations span three galaxy clusters, derived from the Spitzer
+Adaptation of the Red-sequence Cluster Survey. We augment the >5sigma
+detections of the CO (2-1) fluxes with multi-band photometry, yielding stellar
+masses and infrared-derived star formation rates, to place some of the first
+constraints on molecular gas properties in z~1.6 cluster environments. We
+measure sizable gas reservoirs of 0.5-2x10^11 solar masses in these objects,
+with high gas fractions and long depletion timescales, averaging 62% and 1.4
+Gyr, respectively. We compare our cluster galaxies to the scaling relations of
+the coeval field, in the context of how gas fractions and depletion timescales
+vary with respect to the star-forming main sequence. We find that our cluster
+galaxies lie systematically off the field scaling relations at z=1.6 toward
+enhanced gas fractions, at a level of ~4sigma, but have consistent depletion
+timescales. Exploiting CO detections in lower-redshift clusters from the
+literature, we investigate the evolution of the gas fraction in cluster
+galaxies, finding it to mimic the strong rise with redshift in the field. We
+emphasize the utility of detecting abundant gas-rich galaxies in high-redshift
+clusters, deeming them as crucial laboratories for future statistical studies.
+",0,1,0,0,0,0
+2458,CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN,"  Anatomical and biophysical modeling of left atrium (LA) and proximal
+pulmonary veins (PPVs) is important for clinical management of several cardiac
+diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA
+and PPVs through visualization. However, there is a strong need for an advanced
+image segmentation method to be applied to cardiac MRI for quantitative
+analysis of LA and PPVs. In this study, we address this unmet clinical need by
+exploring a new deep learning-based segmentation strategy for quantification of
+LA and PPVs with high accuracy and heightened efficiency. Our approach is based
+on a multi-view convolutional neural network (CNN) with an adaptive fusion
+strategy and a new loss function that allows fast and more accurate convergence
+of the backpropagation based optimization. After training our network from
+scratch by using more than 60K 2D MRI images (slices), we have evaluated our
+segmentation strategy to the STACOM 2013 cardiac segmentation challenge
+benchmark. Qualitative and quantitative evaluations, obtained from the
+segmentation challenge, indicate that the proposed method achieved the
+state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and
+efficiency levels (10 seconds in GPU, and 7.5 minutes in CPU).
+",1,0,0,1,0,0
+2459,Comparison of Polynomial Chaos and Gaussian Process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows,"  Data assimilation is widely used to improve flood forecasting capability,
+especially through parameter inference requiring statistical information on the
+uncertain input parameters (upstream discharge, friction coefficient) as well
+as on the variability of the water level and its sensitivity with respect to
+the inputs. For particle filter or ensemble Kalman filter, stochastically
+estimating probability density function and covariance matrices from a Monte
+Carlo random sampling requires a large ensemble of model evaluations, limiting
+their use in real-time application. To tackle this issue, fast surrogate models
+based on Polynomial Chaos and Gaussian Process can be used to represent the
+spatially distributed water level in place of solving the shallow water
+equations. This study investigates the use of these surrogates to estimate
+probability density functions and covariance matrices at a reduced
+computational cost and without the loss of accuracy, in the perspective of
+ensemble-based data assimilation. This study focuses on 1-D steady state flow
+simulated with MASCARET over the Garonne River (South-West France). Results
+show that both surrogates feature similar performance to the Monte-Carlo random
+sampling, but for a much smaller computational budget; a few MASCARET
+simulations (on the order of 10-100) are sufficient to accurately retrieve
+covariance matrices and probability density functions all along the river, even
+where the flow dynamic is more complex due to heterogeneous bathymetry. This
+paves the way for the design of surrogate strategies suitable for representing
+unsteady open-channel flows in data assimilation.
+",0,1,0,1,0,0
+2460,Thermal Sunyaev-Zel'dovich effect in the intergalactic medium with primordial magnetic fields,"  The presence of ubiquitous magnetic fields in the universe is suggested from
+observations of radiation and cosmic ray from galaxies or the intergalactic
+medium (IGM). One possible origin of cosmic magnetic fields is the
+magnetogenesis in the primordial universe. Such magnetic fields are called
+primordial magnetic fields (PMFs), and are considered to affect the evolution
+of matter density fluctuations and the thermal history of the IGM gas. Hence
+the information of PMFs is expected to be imprinted on the anisotropies of the
+cosmic microwave background (CMB) through the thermal Sunyaev-Zel'dovich (tSZ)
+effect in the IGM. In this study, given an initial power spectrum of PMFs as
+$P(k)\propto B_{\rm 1Mpc}^2 k^{n_{B}}$, we calculate dynamical and thermal
+evolutions of the IGM under the influence of PMFs, and compute the resultant
+angular power spectrum of the Compton $y$-parameter on the sky. As a result, we
+find that two physical processes driven by PMFs dominantly determine the power
+spectrum of the Compton $y$-parameter; (i) the heating due to the ambipolar
+diffusion effectively works to increase the temperature and the ionization
+fraction, and (ii) the Lorentz force drastically enhances the density contrast
+just after the recombination epoch. These facts result in making the tSZ
+angular power spectrum induced by the PMFs more remarkable at $\ell >10^4$ than
+that by galaxy clusters even with $B_{\rm 1Mpc}=0.1$ nG and $n_{B}=-1.0$
+because the contribution from galaxy clusters decreases with increasing $\ell$.
+The measurement of the tSZ angular power spectrum on high $\ell$ modes can
+provide the stringent constraint on PMFs.
+",0,1,0,0,0,0
+2461,One-dimensional model of chiral fermions with Feshbach resonant interactions,"  We study a model of two species of one-dimensional linearly dispersing
+fermions interacting via an s-wave Feshbach resonance at zero temperature.
+While this model is known to be integrable, it possesses novel features that
+have not previously been investigated. Here, we present an exact solution based
+on the coordinate Bethe Ansatz. In the limit of infinite resonance strength,
+which we term the strongly interacting limit, the two species of fermions
+behave as free Fermi gases. In the limit of infinitely weak resonance, or the
+weakly interacting limit, the gases can be in different phases depending on the
+detuning, the relative velocities of the particles, and the particle densities.
+When the molecule moves faster or slower than both species of atoms, the atomic
+velocities get renormalized and the atoms may even become non-chiral. On the
+other hand, when the molecular velocity is between that of the atoms, the
+system may behave like a weakly interacting Lieb-Liniger gas.
+",0,1,0,0,0,0
+2462,A lower bound on the positive semidefinite rank of convex bodies,"  The positive semidefinite rank of a convex body $C$ is the size of its
+smallest positive semidefinite formulation. We show that the positive
+semidefinite rank of any convex body $C$ is at least $\sqrt{\log d}$ where $d$
+is the smallest degree of a polynomial that vanishes on the boundary of the
+polar of $C$. This improves on the existing bound which relies on results from
+quantifier elimination. The proof relies on the Bézout bound applied to the
+Karush-Kuhn-Tucker conditions of optimality. We discuss the connection with the
+algebraic degree of semidefinite programming and show that the bound is tight
+(up to constant factor) for random spectrahedra of suitable dimension.
+",1,0,1,0,0,0
+2463,"Klt varieties with trivial canonical class - Holonomy, differential forms, and fundamental groups","  We investigate the holonomy group of singular Kähler-Einstein metrics on
+klt varieties with numerically trivial canonical divisor. Finiteness of the
+number of connected components, a Bochner principle for holomorphic tensors,
+and a connection between irreducibility of holonomy representations and
+stability of the tangent sheaf are established. As a consequence, known
+decompositions for tangent sheaves of varieties with trivial canonical divisor
+are refined. In particular, we show that up to finite quasi-étale covers,
+varieties with strongly stable tangent sheaf are either Calabi-Yau or
+irreducible holomorphic symplectic. These results form one building block for
+Höring-Peternell's recent proof of a singular version of the
+Beauville-Bogomolov Decomposition Theorem.
+",0,0,1,0,0,0
+2464,Selective Classification for Deep Neural Networks,"  Selective classification techniques (also known as reject option) have not
+yet been considered in the context of deep neural networks (DNNs). These
+techniques can potentially significantly improve DNNs prediction performance by
+trading-off coverage. In this paper we propose a method to construct a
+selective classifier given a trained neural network. Our method allows a user
+to set a desired risk level. At test time, the classifier rejects instances as
+needed, to grant the desired risk (with high probability). Empirical results
+over CIFAR and ImageNet convincingly demonstrate the viability of our method,
+which opens up possibilities to operate DNNs in mission-critical applications.
+For example, using our method an unprecedented 2% error in top-5 ImageNet
+classification can be guaranteed with probability 99.9%, and almost 60% test
+coverage.
+",1,0,0,0,0,0
+2465,Crowdsourcing Ground Truth for Medical Relation Extraction,"  Cognitive computing systems require human labeled data for evaluation, and
+often for training. The standard practice used in gathering this data minimizes
+disagreement between annotators, and we have found this results in data that
+fails to account for the ambiguity inherent in language. We have proposed the
+CrowdTruth method for collecting ground truth through crowdsourcing, that
+reconsiders the role of people in machine learning based on the observation
+that disagreement between annotators provides a useful signal for phenomena
+such as ambiguity in the text. We report on using this method to build an
+annotated data set for medical relation extraction for the $cause$ and $treat$
+relations, and how this data performed in a supervised training experiment. We
+demonstrate that by modeling ambiguity, labeled data gathered from crowd
+workers can (1) reach the level of quality of domain experts for this task
+while reducing the cost, and (2) provide better training data at scale than
+distant supervision. We further propose and validate new weighted measures for
+precision, recall, and F-measure, that account for ambiguity in both human and
+machine performance on this task.
+",1,0,0,0,0,0
+2466,Chaotic laser based physical random bit streaming system with a computer application interface,"  We demonstrate a random bit streaming system that uses a chaotic laser as its
+physical entropy source. By performing real-time bit manipulation for bias
+reduction, we were able to provide the memory of a personal computer with a
+constant supply of ready-to-use physical random bits at a throughput of up to 4
+Gbps. We pay special attention to the end-to-end entropy source model
+describing how the entropy from physical sources is converted into bit entropy.
+We confirmed the statistical quality of the generated random bits by revealing
+the pass rate of the NIST SP800-22 test suite to be 65 % to 75 %, which is
+commonly considered acceptable for a reliable random bit generator. We also
+confirmed the stable operation of our random bit steaming system with long-term
+bias monitoring.
+",1,1,0,0,0,0
+2467,Observation of Intrinsic Half-metallic Behavior of CrO$_2$ (100) Epitaxial Films by Bulk-sensitive Spin-resolved PES,"  We have investigated the electronic states and spin polarization of
+half-metallic ferromagnet CrO$_2$ (100) epitaxial films by bulk-sensitive
+spin-resolved photoemission spectroscopy with a focus on non-quasiparticle
+(NQP) states derived from electron-magnon interactions. We found that the
+averaged values of the spin polarization are approximately 100% and 40% at 40 K
+and 300 K, respectively. This is consistent with the previously reported result
+[H. Fujiwara et al., Appl. Phys. Lett. 106, 202404 (2015).]. At 100 K, peculiar
+spin depolarization was observed at the Fermi level ($E_{F}$), which is
+supported by theoretical calculations predicting NQP states. This suggests the
+possible appearance of NQP states in CrO$_2$. We also compare the temperature
+dependence of our spin polarizations with that of the magnetization.
+",0,1,0,0,0,0
+2468,Development and Characterisation of a Gas System and its Associated Slow-Control System for an ATLAS Small-Strip Thin Gap Chamber Testing Facility,"  A quality assurance and performance qualification laboratory was built at
+McGill University for the Canadian-made small-strip Thin Gap Chamber (sTGC)
+muon detectors produced for the 2019-2020 ATLAS experiment muon spectrometer
+upgrade. The facility uses cosmic rays as a muon source to ionise the quenching
+gas mixture of pentane and carbon dioxide flowing through the sTGC detector. A
+gas system was developed and characterised for this purpose, with a simple and
+efficient gas condenser design utilizing a Peltier thermoelectric cooler (TEC).
+The gas system was tested to provide the desired 45 vol% pentane concentration.
+For continuous operations, a state-machine system was implemented with alerting
+and remote monitoring features to run all cosmic-ray data-acquisition
+associated slow-control systems, such as high/low voltage, gas system and
+environmental monitoring, in a safe and continuous mode, even in the absence of
+an operator.
+",0,1,0,0,0,0
+2469,Convergence of extreme value statistics in a two-layer quasi-geostrophic atmospheric model,"  We search for the signature of universal properties of extreme events,
+theoretically predicted for Axiom A flows, in a chaotic and high dimensional
+dynamical system by studying the convergence of GEV (Generalized Extreme Value)
+and GP (Generalized Pareto) shape parameter estimates to a theoretical value,
+expressed in terms of partial dimensions of the attractor, which are global
+properties. We consider a two layer quasi-geostrophic (QG) atmospheric model
+using two forcing levels, and analyse extremes of different types of physical
+observables (local, zonally-averaged energy, and the average value of energy
+over the mid-latitudes). Regarding the predicted universality, we find closer
+agreement in the shape parameter estimates only in the case of strong forcing,
+producing a highly chaotic behaviour, for some observables (the local energy at
+every latitude). Due to the limited (though very large) data size and the
+presence of serial correlations, it is difficult to obtain robust statistics of
+extremes in case of the other observables. In the case of weak forcing,
+inducing a less pronounced chaotic flow with regime behaviour, we find worse
+agreement with the theory developed for Axiom A flows, which is unsurprising
+considering the properties of the system.
+",0,1,0,1,0,0
+2470,A deep search for metals near redshift 7: the line-of-sight towards ULAS J1120+0641,"  We present a search for metal absorption line systems at the highest
+redshifts to date using a deep (30h) VLT/X-Shooter spectrum of the z = 7.084
+quasi-stellar object (QSO) ULAS J1120+0641. We detect seven intervening systems
+at z > 5.5, with the highest-redshift system being a C IV absorber at z = 6.51.
+We find tentative evidence that the mass density of C IV remains flat or
+declines with redshift at z < 6, while the number density of C II systems
+remains relatively flat over 5 < z < 7. These trends are broadly consistent
+with models of chemical enrichment by star formation-driven winds that include
+a softening of the ultraviolet background towards higher redshifts. We find a
+larger number of weak ( W_rest < 0.3A ) Mg II systems over 5.9 < z < 7.0 than
+predicted by a power-law fit to the number density of stronger systems. This is
+consistent with trends in the number density of weak Mg II systems at z = 2.5,
+and suggests that the mechanisms that create these absorbers are already in
+place at z = 7. Finally, we investigate the associated narrow Si IV, C IV, and
+N V absorbers located near the QSO redshift, and find that at least one
+component shows evidence of partial covering of the continuum source.
+",0,1,0,0,0,0
+2471,Energy-Performance Trade-offs in Mobile Data Transfers,"  By year 2020, the number of smartphone users globally will reach 3 Billion
+and the mobile data traffic (cellular + WiFi) will exceed PC internet traffic
+the first time. As the number of smartphone users and the amount of data
+transferred per smartphone grow exponentially, limited battery power is
+becoming an increasingly critical problem for mobile devices which increasingly
+depend on network I/O. Despite the growing body of research in power management
+techniques for the mobile devices at the hardware layer as well as the lower
+layers of the networking stack, there has been little work focusing on saving
+energy at the application layer for the mobile systems during network I/O. In
+this paper, to the best of our knowledge, we are first to provide an in depth
+analysis of the effects of application layer data transfer protocol parameters
+on the energy consumption of mobile phones. We show that significant energy
+savings can be achieved with application layer solutions at the mobile systems
+during data transfer with no or minimal performance penalty. In many cases,
+performance increase and energy savings can be achieved simultaneously.
+",1,0,0,0,0,0
+2472,A stability result on optimal Skorokhod embedding,"  Motivated by the model- independent pricing of derivatives calibrated to the
+real market, we consider an optimization problem similar to the optimal
+Skorokhod embedding problem, where the embedded Brownian motion needs only to
+reproduce a finite number of prices of Vanilla options. We derive in this paper
+the corresponding dualities and the geometric characterization of optimizers.
+Then we show a stability result, i.e. when more and more Vanilla options are
+given, the optimization problem converges to an optimal Skorokhod embedding
+problem, which constitutes the basis of the numerical computation in practice.
+In addition, by means of different metrics on the space of probability
+measures, a convergence rate analysis is provided under suitable conditions.
+",0,0,1,0,0,0
+2473,On Symmetric Losses for Learning from Corrupted Labels,"  This paper aims to provide a better understanding of a symmetric loss. First,
+we show that using a symmetric loss is advantageous in the balanced error rate
+(BER) minimization and area under the receiver operating characteristic curve
+(AUC) maximization from corrupted labels. Second, we prove general theoretical
+properties of symmetric losses, including a classification-calibration
+condition, excess risk bound, conditional risk minimizer, and AUC-consistency
+condition. Third, since all nonnegative symmetric losses are non-convex, we
+propose a convex barrier hinge loss that benefits significantly from the
+symmetric condition, although it is not symmetric everywhere. Finally, we
+conduct experiments on BER and AUC optimization from corrupted labels to
+validate the relevance of the symmetric condition.
+",1,0,0,1,0,0
+2474,Phase matched nonlinear optics via patterning layered materials,"  The ease of integration coupled with large second-order nonlinear coefficient
+of atomically thin layered 2D materials presents a unique opportunity to
+realize second-order nonlinearity in silicon compatible integrated photonic
+system. However, the phase matching requirement for second-order nonlinear
+optical processes makes the nanophotonic design difficult. We show that by
+nano-patterning the 2D material, quasi-phase matching can be achieved. Such
+patterning based phase-matching could potentially compensate for inevitable
+fabrication errors and significantly simplify the design process of the
+nonlinear nano-photonic devices.
+",0,1,0,0,0,0
+2475,Any cyclic quadrilateral can be inscribed in any closed convex smooth curve,"  We prove that any cyclic quadrilateral can be inscribed in any closed convex
+$C^1$-curve. The smoothness condition is not required if the quadrilateral is a
+rectangle.
+",0,0,1,0,0,0
+2476,A finite Q-bad space,"  We prove that for a free noncyclic group $F$, $H_2(\hat F_\mathbb Q, \mathbb
+Q)$ is an uncountable $\mathbb Q$-vector space. Here $\hat F_\mathbb Q$ is the
+$\mathbb Q$-completion of $F$. This answers a problem of A.K. Bousfield for the
+case of rational coefficients. As a direct consequence of this result it
+follows that, a wedge of circles is $\mathbb Q$-bad in the sense of
+Bousfield-Kan. The same methods as used in the proof of the above results allow
+to show that, the homology $H_2(\hat F_\mathbb Z,\mathbb Z)$ is not divisible
+group, where $\hat F_\mathbb Z$ is the integral pronilpotent completion of $F$.
+",0,0,1,0,0,0
+2477,"On Blocking Collisions between People, Objects and other Robots","  Intentional or unintentional contacts are bound to occur increasingly more
+often due to the deployment of autonomous systems in human environments. In
+this paper, we devise methods to computationally predict imminent collisions
+between objects, robots and people, and use an upper-body humanoid robot to
+block them if they are likely to happen. We employ statistical methods for
+effective collision prediction followed by sensor-based trajectory generation
+and real-time control to attempt to stop the likely collisions using the most
+favorable part of the blocking robot. We thoroughly investigate collisions in
+various types of experimental setups involving objects, robots, and people.
+Overall, the main contribution of this paper is to devise sensor-based
+prediction, trajectory generation and control processes for highly articulated
+robots to prevent collisions against people, and conduct numerous experiments
+to validate this approach.
+",1,0,0,0,0,0
+2478,Increasing Papers' Discoverability with Precise Semantic Labeling: the sci.AI Platform,"  The number of published findings in biomedicine increases continually. At the
+same time, specifics of the domain's terminology complicates the task of
+relevant publications retrieval. In the current research, we investigate
+influence of terms' variability and ambiguity on a paper's likelihood of being
+retrieved. We obtained statistics that demonstrate significance of the issue
+and its challenges, followed by presenting the sci.AI platform, which allows
+precise terms labeling as a resolution.
+",1,0,0,0,0,0
+2479,On topological obstructions to global stabilization of an inverted pendulum,"  We consider a classical problem of control of an inverted pendulum by means
+of a horizontal motion of its pivot point. We suppose that the control law can
+be non-autonomous and non-periodic w.r.t. the position of the pendulum. It is
+shown that global stabilization of the vertical upward position of the pendulum
+cannot be obtained for any Lipschitz control law, provided some natural
+assumptions. Moreover, we show that there always exists a solution separated
+from the vertical position and along which the pendulum never becomes
+horizontal. Hence, we also prove that global stabilization cannot be obtained
+in the system where the pendulum can impact the horizontal plane (for any
+mechanical model of impact). Similar results are presented for several
+analogous systems: a pendulum on a cart, a spherical pendulum, and a pendulum
+with an additional torque control.
+",0,1,1,0,0,0
+2480,Automated Refactoring: Can They Pass The Turing Test?,"  Refactoring is a maintenance activity that aims to improve design quality
+while preserving the behavior of a system. Several (semi)automated approaches
+have been proposed to support developers in this maintenance activity, based on
+the correction of anti-patterns, which are ""poor solutions"" to recurring design
+problems. However, little quantitative evidence exists about the impact of
+automatically refactored code on program comprehension, and in which context
+automated refactoring can be as effective as manual refactoring. We performed
+an empirical study to investigate whether the use of automated refactoring
+approaches affects the understandability of systems during comprehension tasks.
+(1) We surveyed 80 developers, asking them to identify from a set of 20
+refactoring changes if they were generated by developers or by machine, and to
+rate the refactorings according to their design quality; (2) we asked 30
+developers to complete code comprehension tasks on 10 systems that were
+refactored by either a freelancer or an automated refactoring tool. We measured
+developers' performance using the NASA task load index for their effort; the
+time that they spent performing the tasks; and their percentages of correct
+answers. Results show that for 3 out the 5 types of studied anti-patterns,
+developers cannot recognize the origin of the refactoring (i.e., whether it was
+performed by a human or an automatic tool). We also observe that developers do
+not prefer human refactorings over automated refactorings, except when
+refactoring Blob classes; and that there is no statistically significant
+difference between the impact on code understandability of human refactorings
+and automated refactorings. We conclude that automated refactorings can be as
+effective as manual refactorings. However, for complex anti-patterns types like
+the Blob, the perceived quality of human refactorings is slightly higher.
+",1,0,0,0,0,0
+2481,Free fermions on a piecewise linear four-manifold. II: Pachner moves,"  This is the second in a series of papers where we construct an invariant of a
+four-dimensional piecewise linear manifold $M$ with a given middle cohomology
+class $h\in H^2(M,\mathbb C)$. This invariant is the square root of the torsion
+of unusual chain complex introduced in Part I (arXiv:1605.06498) of our work,
+multiplied by a correcting factor. Here we find this factor by studying the
+behavior of our construction under all four-dimensional Pachner moves, and show
+that it can be represented in a multiplicative form: a product of same-type
+multipliers over all 2-faces, multiplied by a product of same-type multipliers
+over all pentachora.
+",0,0,1,0,0,0
+2482,Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets,"  Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods such
+as Metropolis-Hastings is too computationally intensive to handle large
+datasets, since the cost per step usually scales like $O(n)$ in the number of
+data points $n$. We propose the Scalable Metropolis-Hastings (SMH) kernel that
+exploits Gaussian concentration of the posterior to require processing on
+average only $O(1)$ or even $O(1/\sqrt{n})$ data points per step. This scheme
+is based on a combination of factorized acceptance probabilities, procedures
+for fast simulation of Bernoulli processes, and control variate ideas. Contrary
+to many MCMC subsampling schemes such as fixed step-size Stochastic Gradient
+Langevin Dynamics, our approach is exact insofar as the invariant distribution
+is the true posterior and not an approximation to it. We characterise the
+performance of our algorithm theoretically, and give realistic and verifiable
+conditions under which it is geometrically ergodic. This theory is borne out by
+empirical results that demonstrate overall performance benefits over standard
+Metropolis-Hastings and various subsampling algorithms.
+",1,0,0,1,0,0
+2483,Regularization of the Kernel Matrix via Covariance Matrix Shrinkage Estimation,"  The kernel trick concept, formulated as an inner product in a feature space,
+facilitates powerful extensions to many well-known algorithms. While the kernel
+matrix involves inner products in the feature space, the sample covariance
+matrix of the data requires outer products. Therefore, their spectral
+properties are tightly connected. This allows us to examine the kernel matrix
+through the sample covariance matrix in the feature space and vice versa. The
+use of kernels often involves a large number of features, compared to the
+number of observations. In this scenario, the sample covariance matrix is not
+well-conditioned nor is it necessarily invertible, mandating a solution to the
+problem of estimating high-dimensional covariance matrices under small sample
+size conditions. We tackle this problem through the use of a shrinkage
+estimator that offers a compromise between the sample covariance matrix and a
+well-conditioned matrix (also known as the ""target"") with the aim of minimizing
+the mean-squared error (MSE). We propose a distribution-free kernel matrix
+regularization approach that is tuned directly from the kernel matrix, avoiding
+the need to address the feature space explicitly. Numerical simulations
+demonstrate that the proposed regularization is effective in classification
+tasks.
+",0,0,0,1,0,0
+2484,Fixing an error in Caponnetto and de Vito (2007),"  The seminal paper of Caponnetto and de Vito (2007) provides minimax-optimal
+rates for kernel ridge regression in a very general setting. Its proof,
+however, contains an error in its bound on the effective dimensionality. In
+this note, we explain the mistake, provide a correct bound, and show that the
+main theorem remains true.
+",0,0,1,1,0,0
+2485,Supervised Typing of Big Graphs using Semantic Embeddings,"  We propose a supervised algorithm for generating type embeddings in the same
+semantic vector space as a given set of entity embeddings. The algorithm is
+agnostic to the derivation of the underlying entity embeddings. It does not
+require any manual feature engineering, generalizes well to hundreds of types
+and achieves near-linear scaling on Big Graphs containing many millions of
+triples and instances by virtue of an incremental execution. We demonstrate the
+utility of the embeddings on a type recommendation task, outperforming a
+non-parametric feature-agnostic baseline while achieving 15x speedup and
+near-constant memory usage on a full partition of DBpedia. Using
+state-of-the-art visualization, we illustrate the agreement of our
+extensionally derived DBpedia type embeddings with the manually curated domain
+ontology. Finally, we use the embeddings to probabilistically cluster about 4
+million DBpedia instances into 415 types in the DBpedia ontology.
+",1,0,0,0,0,0
+2486,Flexible Attributed Network Embedding,"  Network embedding aims to find a way to encode network by learning an
+embedding vector for each node in the network. The network often has property
+information which is highly informative with respect to the node's position and
+role in the network. Most network embedding methods fail to utilize this
+information during network representation learning. In this paper, we propose a
+novel framework, FANE, to integrate structure and property information in the
+network embedding process. In FANE, we design a network to unify heterogeneity
+of the two information sources, and define a new random walking strategy to
+leverage property information and make the two information compensate. FANE is
+conceptually simple and empirically powerful. It improves over the
+state-of-the-art methods on Cora dataset classification task by over 5%, more
+than 10% on WebKB dataset classification task. Experiments also show that the
+results improve more than the state-of-the-art methods as increasing training
+size. Moreover, qualitative visualization show that our framework is helpful in
+network property information exploration. In all, we present a new way for
+efficiently learning state-of-the-art task-independent representations in
+complex attributed networks. The source code and datasets of this paper can be
+obtained from this https URL.
+",1,0,0,0,0,0
+2487,Attention Please: Consider Mockito when Evaluating Newly Proposed Automated Program Repair Techniques,"  Automated program repair (APR) has attracted widespread attention in recent
+years with substantial techniques being proposed. Meanwhile, a number of
+benchmarks have been established for evaluating the performances of APR
+techniques, among which Defects4J is one of the most wildly used benchmark.
+However, bugs in Mockito, a project augmented in a later-version of Defects4J,
+do not receive much attention by recent researches. In this paper, we aim at
+investigating the necessity of considering Mockito bugs when evaluating APR
+techniques. Our findings show that: 1) Mockito bugs are not more complex for
+repairing compared with bugs from other projects; 2) the bugs repaired by the
+state-of-the-art tools share the same repair patterns compared with those
+patterns required to repair Mockito bugs; however, 3) the state-of-the-art
+tools perform poorly on Mockito bugs (Nopol can only correctly fix one bug
+while SimFix and CapGen cannot fix any bug in Mockito even if all the buggy
+locations have been exposed). We conclude from these results that existing APR
+techniques may be overfitting to their evaluated subjects and we should
+consider Mockito, or even more bugs from other projects, when evaluating newly
+proposed APR techniques. We further find out a unique repair action required to
+repair Mockito bugs named external package addition. Importing the external
+packages from the test code associated with the source code is feasible for
+enlarging the search space and this action can be augmented with existing
+repair actions to advance existing techniques.
+",1,0,0,0,0,0
+2488,Communication Modalities for Supervised Teleoperation in Highly Dexterous Tasks - Does one size fit all?,"  This study tries to explain the connection between communication modalities
+and levels of supervision in teleoperation during a dexterous task, like
+surgery. This concept is applied to two surgical related tasks: incision and
+peg transfer. It was found that as the complexity of the task escalates, the
+combination linking human supervision with a more expressive modality shows
+better performance than other combinations of modalities and control. More
+specifically, in the peg transfer task, the combination of speech modality and
+action level supervision achieves shorter task completion time (77.1 +- 3.4 s)
+with fewer mistakes (0.20 +- 0.17 pegs dropped).
+",1,0,0,0,0,0
+2489,Learning in anonymous nonatomic games with applications to first-order mean field games,"  We introduce a model of anonymous games with the player dependent action
+sets. We propose several learning procedures based on the well-known Fictitious
+Play and Online Mirror Descent and prove their convergence to equilibrium under
+the classical monotonicity condition. Typical examples are first-order mean
+field games.
+",0,0,1,0,0,0
+2490,Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums,"  In this work, we study the problem of minimizing the sum of strongly convex
+functions split over a network of $n$ nodes. We propose the decentralized and
+asynchronous algorithm ADFS to tackle the case when local functions are
+themselves finite sums with $m$ components. ADFS converges linearly when local
+functions are smooth, and matches the rates of the best known finite sum
+algorithms when executed on a single machine. On several machines, ADFS enjoys
+a $O (\sqrt{n})$ or $O(n)$ speed-up depending on the leading complexity term as
+long as the diameter of the network is not too big with respect to $m$. This
+also leads to a $\sqrt{m}$ speed-up over state-of-the-art distributed batch
+methods, which is the expected speed-up for finite sum algorithms. In terms of
+communication times and network parameters, ADFS scales as well as optimal
+distributed batch algorithms. As a side contribution, we give a generalized
+version of the accelerated proximal coordinate gradient algorithm using
+arbitrary sampling that we apply to a well-chosen dual problem to derive ADFS.
+Yet, ADFS uses primal proximal updates that only require solving
+one-dimensional problems for many standard machine learning applications.
+Finally, ADFS can be formulated for non-smooth objectives with equally good
+scaling properties. We illustrate the improvement of ADFS over state-of-the-art
+approaches with simulations.
+",1,0,0,0,0,0
+2491,Intermediate curvatures and highly connected manifolds,"  We show that after forming a connected sum with a homotopy sphere, all
+(2j-1)-connected 2j-parallelisable manifolds in dimension 4j+1, j > 0, can be
+equipped with Riemannian metrics of 2-positive Ricci curvature. When j=1 we
+extend the above to certain classes of simply-connected non-spin 5-manifolds.
+The condition of 2-positive Ricci curvature is defined to mean that the sum of
+the two smallest eigenvalues of the Ricci tensor is positive at every point.
+This result is a counterpart to a previous result of the authors concerning the
+existence of positive Ricci curvature on highly connected manifolds in
+dimensions 4j-1 for j > 1, and in dimensions 4j+1 for j > 0 with torsion-free
+cohomology.
+",0,0,1,0,0,0
+2492,Structured Differential Learning for Automatic Threshold Setting,"  We introduce a technique that can automatically tune the parameters of a
+rule-based computer vision system comprised of thresholds, combinational logic,
+and time constants. This lets us retain the flexibility and perspicacity of a
+conventionally structured system while allowing us to perform approximate
+gradient descent using labeled data. While this is only a heuristic procedure,
+as far as we are aware there is no other efficient technique for tuning such
+systems. We describe the components of the system and the associated supervised
+learning mechanism. We also demonstrate the utility of the algorithm by
+comparing its performance versus hand tuning for an automotive headlight
+controller. Despite having over 100 parameters, the method is able to
+profitably adjust the system values given just the desired output for a number
+of videos.
+",0,0,0,1,0,0
+2493,A new fractional derivative of variable order with non-singular kernel and fractional differential equations,"  In this paper, we introduce two new non-singular kernel fractional
+derivatives and present a class of other fractional derivatives derived from
+the new formulations. We present some important results of uniformly convergent
+sequences of continuous functions, in particular the Comparison's principle,
+and others that allow, the study of the limitation of fractional nonlinear
+differential equations.
+",0,0,1,0,0,0
+2494,Safe Semi-Supervised Learning of Sum-Product Networks,"  In several domains obtaining class annotations is expensive while at the same
+time unlabelled data are abundant. While most semi-supervised approaches
+enforce restrictive assumptions on the data distribution, recent work has
+managed to learn semi-supervised models in a non-restrictive regime. However,
+so far such approaches have only been proposed for linear models. In this work,
+we introduce semi-supervised parameter learning for Sum-Product Networks
+(SPNs). SPNs are deep probabilistic models admitting inference in linear time
+in number of network edges. Our approach has several advantages, as it (1)
+allows generative and discriminative semi-supervised learning, (2) guarantees
+that adding unlabelled data can increase, but not degrade, the performance
+(safe), and (3) is computationally efficient and does not enforce restrictive
+assumptions on the data distribution. We show on a variety of data sets that
+safe semi-supervised learning with SPNs is competitive compared to
+state-of-the-art and can lead to a better generative and discriminative
+objective value than a purely supervised approach.
+",1,0,0,1,0,0
+2495,A Structured Approach to the Analysis of Remote Sensing Images,"  The number of studies for the analysis of remote sensing images has been
+growing exponentially in the last decades. Many studies, however, only report
+results---in the form of certain performance metrics---by a few selected
+algorithms on a training and testing sample. While this often yields valuable
+insights, it tells little about some important aspects. For example, one might
+be interested in understanding the nature of a study by the interaction of
+algorithm, features, and the sample as these collectively contribute to the
+outcome; among these three, which would be a more productive direction in
+improving a study; how to assess the sample quality or the value of a set of
+features etc. With a focus on land-use classification, we advocate the use of a
+structured analysis. The output of a study is viewed as the result of the
+interplay among three input dimensions: feature, sample, and algorithm.
+Similarly, another dimension, the error, can be decomposed into error along
+each input dimension. Such a structural decomposition of the inputs or error
+could help better understand the nature of the problem and potentially suggest
+directions for improvement. We use the analysis of a remote sensing image at a
+study site in Guangzhou, China, to demonstrate how such a structured analysis
+could be carried out and what insights it generates. The structured analysis
+could be applied to a new study, or as a diagnosis to an existing one. We
+expect this will inform practice in the analysis of remote sensing images, and
+help advance the state-of-the-art of land-use classification.
+",0,0,0,1,0,0
+2496,Approximations of the Restless Bandit Problem,"  The multi-armed restless bandit problem is studied in the case where the
+pay-off distributions are stationary $\varphi$-mixing. This version of the
+problem provides a more realistic model for most real-world applications, but
+cannot be optimally solved in practice, since it is known to be PSPACE-hard.
+The objective of this paper is to characterize a sub-class of the problem where
+{\em good} approximate solutions can be found using tractable approaches.
+Specifically, it is shown that under some conditions on the $\varphi$-mixing
+coefficients, a modified version of UCB can prove effective. The main challenge
+is that, unlike in the i.i.d. setting, the distributions of the sampled
+pay-offs may not have the same characteristics as those of the original bandit
+arms. In particular, the $\varphi$-mixing property does not necessarily carry
+over. This is overcome by carefully controlling the effect of a sampling policy
+on the pay-off distributions. Some of the proof techniques developed in this
+paper can be more generally used in the context of online sampling under
+dependence. Proposed algorithms are accompanied with corresponding regret
+analysis.
+",0,0,1,1,0,0
+2497,Iteratively reweighted $\ell_1$ algorithms with extrapolation,"  Iteratively reweighted $\ell_1$ algorithm is a popular algorithm for solving
+a large class of optimization problems whose objective is the sum of a
+Lipschitz differentiable loss function and a possibly nonconvex sparsity
+inducing regularizer. In this paper, motivated by the success of extrapolation
+techniques in accelerating first-order methods, we study how widely used
+extrapolation techniques such as those in [4,5,22,28] can be incorporated to
+possibly accelerate the iteratively reweighted $\ell_1$ algorithm. We consider
+three versions of such algorithms. For each version, we exhibit an explicitly
+checkable condition on the extrapolation parameters so that the sequence
+generated provably clusters at a stationary point of the optimization problem.
+We also investigate global convergence under additional Kurdyka-$\L$ojasiewicz
+assumptions on certain potential functions. Our numerical experiments show that
+our algorithms usually outperform the general iterative shrinkage and
+thresholding algorithm in [21] and an adaptation of the iteratively reweighted
+$\ell_1$ algorithm in [23, Algorithm 7] with nonmonotone line-search for
+solving random instances of log penalty regularized least squares problems in
+terms of both CPU time and solution quality.
+",0,0,0,1,0,0
+2498,Automatic generation of analysis class diagrams from use case specifications,"  In object oriented software development, the analysis modeling is concerned
+with the task of identifying problem level objects along with the relationships
+between them from software requirements. The software requirements are usually
+written in some natural language, and the analysis modeling is normally
+performed by experienced human analysts. The huge gap between the software
+requirements which are unstructured texts and analysis models which are usually
+structured UML diagrams, along with human slip-ups inevitably makes the
+transformation process error prone. The automation of this process can help in
+reducing the errors in the transformation. In this paper we propose a tool
+supported approach for automated transformation of use case specifications
+documented in English language into analysis class diagrams. The approach works
+in four steps. It first takes the textual specification of a use case as input,
+and then using a natural language parser generates type dependencies and parts
+of speech tags for each sentence in the specification. Then, it identifies the
+sentence structure of each sentence using a set of comprehensive sentence
+structure rules. Next, it applies a set of transformation rules on the type
+dependencies and parts of speech tags of the sentences to discover the problem
+level objects and the relationships between them. Finally, it generates and
+visualizes the analysis class diagram. We conducted a controlled experiment to
+compare the correctness, completeness and redundancy of the analysis class
+diagrams generated by our approach with those generated by the existing
+automated approaches. The results showed that the analysis class diagrams
+generated by our approach were more correct, more complete, and less redundant
+than those generated by the other approaches.
+",1,0,0,0,0,0
+2499,Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification,"  This Perspective provides examples of current and future applications of deep
+learning in pharmacogenomics, including: (1) identification of novel regulatory
+variants located in noncoding domains and their function as applied to
+pharmacoepigenomics; (2) patient stratification from medical records; and (3)
+prediction of drugs, targets, and their interactions. Deep learning
+encapsulates a family of machine learning algorithms that over the last decade
+has transformed many important subfields of artificial intelligence (AI) and
+has demonstrated breakthrough performance improvements on a wide range of tasks
+in biomedicine. We anticipate that in the future deep learning will be widely
+used to predict personalized drug response and optimize medication selection
+and dosing, using knowledge extracted from large and complex molecular,
+epidemiological, clinical, and demographic datasets.
+",0,0,0,1,1,0
+2500,Virtual Crystals and Nakajima Monomials,"  An explicit description of the virtualization map for the (modified) Nakajima
+monomial model for crystals is given. We give an explicit description of the
+Lusztig data for modified Nakajima monomials in type $A_n$.
+",0,0,1,0,0,0
+2501,"Memory effects, transient growth, and wave breakup in a model of paced atrium","  The mechanisms underlying cardiac fibrillation have been investigated for
+over a century, but we are still finding surprising results that change our
+view of this phenomenon. The present study focuses on the transition from
+normal rhythm to atrial fibrillation associated with a gradual increase in the
+pacing rate. While some of our findings are consistent with existing
+experimental, numerical, and theoretical studies of this problem, one result
+appears to contradict the accepted picture. Specifically we show that, in a
+two-dimensional model of paced homogeneous atrial tissue, transition from
+discordant alternans to conduction block, wave breakup, reentry, and spiral
+wave chaos is associated with transient growth of finite amplitude disturbances
+rather than a conventional instability. It is mathematically very similar to
+subcritical, or bypass, transition from laminar fluid flow to turbulence, which
+allows many of the tools developed in the context of fluid turbulence to be
+used for improving our understanding of cardiac arrhythmias.
+",0,1,0,0,0,0
+2502,"An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits","  In this paper, we propose an information-theoretic exploration strategy for
+stochastic, discrete multi-armed bandits that achieves optimal regret. Our
+strategy is based on the value of information criterion. This criterion
+measures the trade-off between policy information and obtainable rewards. High
+amounts of policy information are associated with exploration-dominant searches
+of the space and yield high rewards. Low amounts of policy information favor
+the exploitation of existing knowledge. Information, in this criterion, is
+quantified by a parameter that can be varied during search. We demonstrate that
+a simulated-annealing-like update of this parameter, with a sufficiently fast
+cooling schedule, leads to an optimal regret that is logarithmic with respect
+to the number of episodes.
+",1,0,0,1,0,0
+2503,Probabilistic Generative Adversarial Networks,"  We introduce the Probabilistic Generative Adversarial Network (PGAN), a new
+GAN variant based on a new kind of objective function. The central idea is to
+integrate a probabilistic model (a Gaussian Mixture Model, in our case) into
+the GAN framework which supports a new kind of loss function (based on
+likelihood rather than classification loss), and at the same time gives a
+meaningful measure of the quality of the outputs generated by the network.
+Experiments with MNIST show that the model learns to generate realistic images,
+and at the same time computes likelihoods that are correlated with the quality
+of the generated images. We show that PGAN is better able to cope with
+instability problems that are usually observed in the GAN training procedure.
+We investigate this from three aspects: the probability landscape of the
+discriminator, gradients of the generator, and the perfect discriminator
+problem.
+",1,0,0,1,0,0
+2504,Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo,"  The reversible jump Markov chain Monte Carlo (RJMCMC) method offers an
+across-model simulation approach for Bayesian estimation and model comparison,
+by exploring the sampling space that consists of several models of possibly
+varying dimensions. A naive implementation of RJMCMC to models like Gibbs
+random fields suffers from computational difficulties: the posterior
+distribution for each model is termed doubly-intractable since computation of
+the likelihood function is rarely available. Consequently, it is simply
+impossible to simulate a transition of the Markov chain in the presence of
+likelihood intractability. A variant of RJMCMC is presented, called noisy
+RJMCMC, where the underlying transition kernel is replaced with an
+approximation based on unbiased estimators. Based on previous theoretical
+developments, convergence guarantees for the noisy RJMCMC algorithm are
+provided. The experiments show that the noisy RJMCMC algorithm can be much more
+efficient than other exact methods, provided that an estimator with controlled
+Monte Carlo variance is used, a fact which is in agreement with the theoretical
+analysis.
+",0,0,0,1,0,0
+2505,Functorial compactification of linear spaces,"  We define compactifications of vector spaces which are functorial with
+respect to certain linear maps. These ""many-body"" compactifications are
+manifolds with corners, and the linear maps lift to b-maps in the sense of
+Melrose. We derive a simple criterion under which the lifted maps are in fact
+b-fibrations, and identify how these restrict to boundary hypersurfaces. This
+theory is an application of a general result on the iterated blow-up of cleanly
+intersecting submanifolds which extends related results in the literature.
+",0,0,1,0,0,0
+2506,Almost complex structures on connected sums of complex projective spaces,"  We show that the m-fold connected sum $m\#\mathbb{C}\mathbb{P}^{2n}$ admits
+an almost complex structure if and only if m is odd.
+",0,0,1,0,0,0
+2507,Raman Scattering by a Two-Dimensional Fermi Liquid with Spin-Orbit Coupling,"  We present a microscopic theory of Raman scattering by a two-dimensional
+Fermi liquid (FL) with Rashba and Dresselhaus types of spin-orbit coupling, and
+subject to an in-plane magnetic field (B). In the long-wavelength limit, the
+Raman spectrum probes the collective modes of such a FL: the chiral spin waves.
+The characteristic features of these modes are a linear-in-q term in the
+dispersion and the dependence of the mode frequency on the directions of both q
+and B. All of these features have been observed in recent Raman experiments on
+CdTe quantum wells.
+",0,1,0,0,0,0
+2508,Nearly Optimal Robust Subspace Tracking,"  In this work, we study the robust subspace tracking (RST) problem and obtain
+one of the first two provable guarantees for it. The goal of RST is to track
+sequentially arriving data vectors that lie in a slowly changing
+low-dimensional subspace, while being robust to corruption by additive sparse
+outliers. It can also be interpreted as a dynamic (time-varying) extension of
+robust PCA (RPCA), with the minor difference that RST also requires a short
+tracking delay. We develop a recursive projected compressive sensing algorithm
+that we call Nearly Optimal RST via ReProCS (ReProCS-NORST) because its
+tracking delay is nearly optimal. We prove that NORST solves both the RST and
+the dynamic RPCA problems under weakened standard RPCA assumptions, two simple
+extra assumptions (slow subspace change and most outlier magnitudes lower
+bounded), and a few minor assumptions.
+Our guarantee shows that NORST enjoys a near optimal tracking delay of $O(r
+\log n \log(1/\epsilon))$. Its required delay between subspace change times is
+the same, and its memory complexity is $n$ times this value. Thus both these
+are also nearly optimal. Here $n$ is the ambient space dimension, $r$ is the
+subspaces' dimension, and $\epsilon$ is the tracking accuracy. NORST also has
+the best outlier tolerance compared with all previous RPCA or RST methods, both
+theoretically and empirically (including for real videos), without requiring
+any model on how the outlier support is generated. This is possible because of
+the extra assumptions it uses.
+",0,0,0,1,0,0
+2509,"The Authority of ""Fair"" in Machine Learning","  In this paper, we argue for the adoption of a normative definition of
+fairness within the machine learning community. After characterizing this
+definition, we review the current literature of Fair ML in light of its
+implications. We end by suggesting ways to incorporate a broader community and
+generate further debate around how to decide what is fair in ML.
+",1,0,0,0,0,0
+2510,The Social Bow Tie,"  Understanding tie strength in social networks, and the factors that influence
+it, have received much attention in a myriad of disciplines for decades.
+Several models incorporating indicators of tie strength have been proposed and
+used to quantify relationships in social networks, and a standard set of
+structural network metrics have been applied to predominantly online social
+media sites to predict tie strength. Here, we introduce the concept of the
+""social bow tie"" framework, a small subgraph of the network that consists of a
+collection of nodes and ties that surround a tie of interest, forming a
+topological structure that resembles a bow tie. We also define several
+intuitive and interpretable metrics that quantify properties of the bow tie. We
+use random forests and regression models to predict categorical and continuous
+measures of tie strength from different properties of the bow tie, including
+nodal attributes. We also investigate what aspects of the bow tie are most
+predictive of tie strength in two distinct social networks: a collection of 75
+rural villages in India and a nationwide call network of European mobile phone
+users. Our results indicate several of the bow tie metrics are highly
+predictive of tie strength, and we find the more the social circles of two
+individuals overlap, the stronger their tie, consistent with previous findings.
+However, we also find that the more tightly-knit their non-overlapping social
+circles, the weaker the tie. This new finding complements our current
+understanding of what drives the strength of ties in social networks.
+",0,0,0,1,0,0
+2511,Response Regimes in Equivalent Mechanical Model of Moderately Nonlinear Liquid Sloshing,"  The paper considers non-stationary responses in reduced-order model of
+partially liquid-filled tank under external forcing. The model involves one
+common degree of freedom for the tank and the non-sloshing portion of the
+liquid, and the other one -- for the sloshing portion of the liquid. The
+coupling between these degrees of freedom is nonlinear, with the lowest-order
+potential dictated by symmetry considerations. Since the mass of the sloshing
+liquid in realistic conditions does not exceed 10% of the total mass of the
+system, the reduced-order model turns to be formally equivalent to well-studied
+oscillatory systems with nonlinear energy sinks (NES). Exploiting this analogy,
+and applying the methodology known from the studies of the systems with the
+NES, we predict a multitude of possible non-stationary responses in the
+considered model. These responses conform, at least on the qualitative level,
+to the responses observed in experimental sloshing settings, multi-modal
+theoretical models and full-scale numeric simulations.
+",0,1,0,0,0,0
+2512,Opinion evolution in time-varying social influence networks with prejudiced agents,"  Investigation of social influence dynamics requires mathematical models that
+are ""simple"" enough to admit rigorous analysis, and yet sufficiently ""rich"" to
+capture salient features of social groups. Thus, the mechanism of iterative
+opinion pooling from (DeGroot, 1974), which can explain the generation of
+consensus, was elaborated in (Friedkin and Johnsen, 1999) to take into account
+individuals' ongoing attachments to their initial opinions, or prejudices. The
+""anchorage"" of individuals to their prejudices may disable reaching consensus
+and cause disagreement in a social influence network. Further elaboration of
+this model may be achieved by relaxing its restrictive assumption of a
+time-invariant influence network. During opinion dynamics on an issue, arcs of
+interpersonal influence may be added or subtracted from the network, and the
+influence weights assigned by an individual to his/her neighbors may alter. In
+this paper, we establish new important properties of the (Friedkin and Johnsen,
+1999) opinion formation model, and also examine its extension to time-varying
+social influence networks.
+",1,1,1,0,0,0
+2513,k*-Nearest Neighbors: From Global to Local,"  The weighted k-nearest neighbors algorithm is one of the most fundamental
+non-parametric methods in pattern recognition and machine learning. The
+question of setting the optimal number of neighbors as well as the optimal
+weights has received much attention throughout the years, nevertheless this
+problem seems to have remained unsettled. In this paper we offer a simple
+approach to locally weighted regression/classification, where we make the
+bias-variance tradeoff explicit. Our formulation enables us to phrase a notion
+of optimal weights, and to efficiently find these weights as well as the
+optimal number of neighbors efficiently and adaptively, for each data point
+whose value we wish to estimate. The applicability of our approach is
+demonstrated on several datasets, showing superior performance over standard
+locally weighted methods.
+",1,0,0,1,0,0
+2514,Network Slicing for Ultra-Reliable Low Latency Communication in Industry 4.0 Scenarios,"  An important novelty of 5G is its role in transforming the industrial
+production into Industry 4.0. Specifically, Ultra-Reliable Low Latency
+Communications (URLLC) will, in many cases, enable replacement of cables with
+wireless connections and bring freedom in designing and operating
+interconnected machines, robots, and devices. However, not all industrial links
+will be of URLLC type; e.g. some applications will require high data rates.
+Furthermore, these industrial networks will be highly heterogeneous, featuring
+various communication technologies. We consider network slicing as a mechanism
+to handle the diverse set of requirements to the network. We present methods
+for slicing deterministic and packet-switched industrial communication
+protocols at an abstraction level that is decoupled from the specific
+implementation of the underlying technologies. Finally, we show how network
+calculus can be used to assess the end-to-end properties of the network slices.
+",1,0,0,0,0,0
+2515,Markov Decision Processes with Continuous Side Information,"  We consider a reinforcement learning (RL) setting in which the agent
+interacts with a sequence of episodic MDPs. At the start of each episode the
+agent has access to some side-information or context that determines the
+dynamics of the MDP for that episode. Our setting is motivated by applications
+in healthcare where baseline measurements of a patient at the start of a
+treatment episode form the context that may provide information about how the
+patient might respond to treatment decisions. We propose algorithms for
+learning in such Contextual Markov Decision Processes (CMDPs) under an
+assumption that the unobserved MDP parameters vary smoothly with the observed
+context. We also give lower and upper PAC bounds under the smoothness
+assumption. Because our lower bound has an exponential dependence on the
+dimension, we consider a tractable linear setting where the context is used to
+create linear combinations of a finite set of MDPs. For the linear setting, we
+give a PAC learning algorithm based on KWIK learning techniques.
+",1,0,0,1,0,0
+2516,Nonlinear stage of Benjamin-Feir instability in forced/damped deep water waves,"  We study a three-wave truncation of a recently proposed damped/forced
+high-order nonlinear Schrödinger equation for deep-water gravity waves under
+the effect of wind and viscosity. The evolution of the norm (wave-action) and
+spectral mean of the full model are well captured by the reduced dynamics.
+Three regimes are found for the wind-viscosity balance: we classify them
+according to the attractor in the phase-plane of the truncated system and to
+the shift of the spectral mean. A downshift can coexist with both net forcing
+and damping, i.e., attraction to period-1 or period-2 solutions. Upshift is
+associated with stronger winds, i.e., to a net forcing where the attractor is
+always a period-1 solution. The applicability of our classification to
+experiments in long wave-tanks is verified.
+",0,1,0,0,0,0
+2517,Computational Thinking in Patch,"  With the future likely to see even more pervasive computation, computational
+thinking (problem-solving skills incorporating computing knowledge) is now
+being recognized as a fundamental skill needed by all students. Computational
+thinking is conceptualizing as opposed to programming, promotes natural human
+thinking style than algorithmic reasoning, complements and combines
+mathematical and engineering thinking, and it emphasizes ideas, not artifacts.
+In this paper, we outline a new visual language, called Patch, using which
+students are able to express their solutions to eScience computational problems
+in abstract visual tools. Patch is closer to high level procedural languages
+such as C++ or Java than Scratch or Snap! but similar to them in ease of use
+and combines simplicity and expressive power in one single platform.
+",1,0,0,0,0,0
+2518,Skoda's Ideal Generation from Vanishing Theorem for Semipositive Nakano Curvature and Cauchy-Schwarz Inequality for Tensors,"  Skoda's 1972 result on ideal generation is a crucial ingredient in the
+analytic approach to the finite generation of the canonical ring and the
+abundance conjecture. Special analytic techniques developed by Skoda, other
+than applications of the usual vanishing theorems and L2 estimates for the
+d-bar equation, are required for its proof. This note (which is part of a
+lecture given in the 60th birthday conference for Lawrence Ein) gives a
+simpler, more straightforward proof of Skoda's result, which makes it a natural
+consequence of the standard techniques in vanishing theorems and solving d-bar
+equation with L2 estimates. The proof involves the following three ingredients:
+(i) one particular Cauchy-Schwarz inequality for tensors with a special factor
+which accounts for the exponent of the denominator in the formulation of the
+integral condition for Skoda's ideal generation, (ii) the nonnegativity of
+Nakano curvature of the induced metric of a special co-rank-1 subbundle of a
+trivial vector bundle twisted by a special scalar weight function, and (iii)
+the vanishing theorem and solvability of d-bar equation with L2 estimates for
+vector bundles of nonnegative Nakano curvature on a strictly pseudoconvex
+domain. Our proof gives readily other similar results on ideal generation.
+",0,0,1,0,0,0
+2519,Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data,"  One of the defining properties of deep learning is that models are chosen to
+have many more parameters than available training data. In light of this
+capacity for overfitting, it is remarkable that simple algorithms like SGD
+reliably return solutions with low test error. One roadblock to explaining
+these phenomena in terms of implicit regularization, structural properties of
+the solution, and/or easiness of the data is that many learning bounds are
+quantitatively vacuous when applied to networks learned by SGD in this ""deep
+learning"" regime. Logically, in order to explain generalization, we need
+nonvacuous bounds. We return to an idea by Langford and Caruana (2001), who
+used PAC-Bayes bounds to compute nonvacuous numerical bounds on generalization
+error for stochastic two-layer two-hidden-unit neural networks via a
+sensitivity analysis. By optimizing the PAC-Bayes bound directly, we are able
+to extend their approach and obtain nonvacuous generalization bounds for deep
+stochastic neural network classifiers with millions of parameters trained on
+only tens of thousands of examples. We connect our findings to recent and old
+work on flat minima and MDL-based explanations of generalization.
+",1,0,0,0,0,0
+2520,Method for Computationally Efficient Design of Dielectric Laser Accelerators,"  Dielectric microstructures have generated much interest in recent years as a
+means of accelerating charged particles when powered by solid state lasers. The
+acceleration gradient (or particle energy gain per unit length) is an important
+figure of merit. To design structures with high acceleration gradients, we
+explore the adjoint variable method, a highly efficient technique used to
+compute the sensitivity of an objective with respect to a large number of
+parameters. With this formalism, the sensitivity of the acceleration gradient
+of a dielectric structure with respect to its entire spatial permittivity
+distribution is calculated by the use of only two full-field electromagnetic
+simulations, the original and adjoint. The adjoint simulation corresponds
+physically to the reciprocal situation of a point charge moving through the
+accelerator gap and radiating. Using this formalism, we perform numerical
+optimizations aimed at maximizing acceleration gradients, which generate
+fabricable structures of greatly improved performance in comparison to
+previously examined geometries.
+",0,1,0,0,0,0
+2521,Computing and Using Minimal Polynomials,"  Given a zero-dimensional ideal I in a polynomial ring, many computations
+start by finding univariate polynomials in I. Searching for a univariate
+polynomial in I is a particular case of considering the minimal polynomial of
+an element in P/I. It is well known that minimal polynomials may be computed
+via elimination, therefore this is considered to be a ""resolved problem"". But
+being the key of so many computations, it is worth investigating its meaning,
+its optimization, its applications.
+",1,0,1,0,0,0
+2522,DiVM: Model Checking with LLVM and Graph Memory,"  In this paper, we introduce the concept of a virtual machine with
+graph-organised memory as a versatile backend for both explicit-state and
+abstraction-driven verification of software. Our virtual machine uses the LLVM
+IR as its instruction set, enriched with a small set of hypercalls. We show
+that the provided hypercalls are sufficient to implement a small operating
+system, which can then be linked with applications to provide a
+POSIX-compatible verification environment. Finally, we demonstrate the
+viability of the approach through a comparison with a more
+traditionally-designed LLVM model checker.
+",1,0,0,0,0,0
+2523,Uhlenbeck's decomposition in Sobolev and Morrey-Sobolev spaces,"  We present a self-contained proof of Uhlenbeck's decomposition theorem for
+$\Omega\in L^p(\mathbb{B}^n,so(m)\otimes\Lambda^1\mathbb{R}^n)$ for $p\in
+(1,n)$ with Sobolev type estimates in the case $p \in[n/2,n)$ and
+Morrey-Sobolev type estimates in the case $p\in (1,n/2)$. We also prove an
+analogous theorem in the case when $\Omega\in L^p( \mathbb{B}^n, TCO_{+}(m)
+\otimes \Lambda^1\mathbb{R}^n)$, which corresponds to Uhlenbeck's theorem with
+conformal gauge group.
+",0,0,1,0,0,0
+2524,Making Asynchronous Distributed Computations Robust to Noise,"  We consider the problem of making distributed computations robust to noise,
+in particular to worst-case (adversarial) corruptions of messages. We give a
+general distributed interactive coding scheme which simulates any asynchronous
+distributed protocol while tolerating an optimal corruption of a $\Theta(1/n)$
+fraction of all messages while incurring a moderate blowup of $O(n\log^2 n)$ in
+the communication complexity.
+Our result is the first fully distributed interactive coding scheme in which
+the topology of the communication network is not known in advance. Prior work
+required either a coordinating node to be connected to all other nodes in the
+network or assumed a synchronous network in which all nodes already know the
+complete topology of the network.
+",1,0,0,0,0,0
+2525,Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms,"  We present a straightforward source-to-source transformation that introduces
+justifications for user-defined constraints into the CHR programming language.
+Then a scheme of two rules suffices to allow for logical retraction (deletion,
+removal) of constraints during computation. Without the need to recompute from
+scratch, these rules remove not only the constraint but also undo all
+consequences of the rule applications that involved the constraint. We prove a
+confluence result concerning the rule scheme and show its correctness. When
+algorithms are written in CHR, constraints represent both data and operations.
+CHR is already incremental by nature, i.e. constraints can be added at runtime.
+Logical retraction adds decrementality. Hence any algorithm written in CHR with
+justifications will become fully dynamic. Operations can be undone and data can
+be removed at any point in the computation without compromising the correctness
+of the result. We present two classical examples of dynamic algorithms, written
+in our prototype implementation of CHR with justifications that is available
+online: maintaining the minimum of a changing set of numbers and shortest paths
+in a graph whose edges change.
+",1,0,0,0,0,0
+2526,Bose-Hubbard lattice as a controllable environment for open quantum systems,"  We investigate the open dynamics of an atomic impurity embedded in a
+one-dimensional Bose-Hubbard lattice. We derive the reduced evolution equation
+for the impurity and show that the Bose-Hubbard lattice behaves as a tunable
+engineered environment allowing to simulate both Markovian and non-Markovian
+dynamics in a controlled and experimentally realisable way. We demonstrate that
+the presence or absence of memory effects is a signature of the nature of the
+excitations induced by the impurity, being delocalized or localized in the two
+limiting cases of superfluid and Mott insulator, respectively. Furthermore, our
+findings show how the excitations supported in the two phases can be
+characterized as information carriers.
+",0,1,0,0,0,0
+2527,Semi-decidable equivalence relations obtained by composition and lattice join of decidable equivalence relations,"  Composition and lattice join (transitive closure of a union) of equivalence
+relations are operations taking pairs of decidable equivalence relations to
+relations that are semi-decidable, but not necessarily decidable. This article
+addresses the question, is every semi-decidable equivalence relation obtainable
+in those ways from a pair of decidable equivalence relations? It is shown that
+every semi-decidable equivalence relation, of which every equivalence class is
+infinite, is obtainable as both a composition and a lattice join of decidable
+equivalence relations having infinite equivalence classes. An example is
+constructed of a semi-decidable, but not decidable, equivalence relation having
+finite equivalence classes that can be obtained from decidable equivalence
+relations, both by composition and also by lattice join. Another example is
+constructed, in which such a relation cannot be obtained from decidable
+equivalence relations in either of the two ways.
+",0,0,1,0,0,0
+2528,ClipAudit: A Simple Risk-Limiting Post-Election Audit,"  We propose a simple risk-limiting audit for elections, ClipAudit. To
+determine whether candidate A (the reported winner) actually beat candidate B
+in a plurality election, ClipAudit draws ballots at random, without
+replacement, until either all cast ballots have been drawn, or until \[ a - b
+\ge \beta \sqrt{a+b}
+\] where $a$ is the number of ballots in the sample for the reported winner
+A, and $b$ is the number of ballots in the sample for opponent B, and where
+$\beta$ is a constant determined a priori as a function of the number $n$ of
+ballots cast and the risk-limit $\alpha$. ClipAudit doesn't depend on the
+unofficial margin (as does Bravo). We show how to extend ClipAudit to contests
+with multiple winners or losers, or to multiple contests.
+",1,0,0,1,0,0
+2529,"LCA(2), Weil index, and product formula","  In this paper we study the category LCA(2) of certain non-locally compact
+abelian topological groups, and extend the notion of Weil index. As
+applications we deduce some product formulas for curves over local fields and
+arithmetic surfaces.
+",0,0,1,0,0,0
+2530,A Dichotomy for Sampling Barrier-Crossing Events of Random Walks with Regularly Varying Tails,"  We study how to sample paths of a random walk up to the first time it crosses
+a fixed barrier, in the setting where the step sizes are iid with negative mean
+and have a regularly varying right tail. We introduce a desirable property for
+a change of measure to be suitable for exact simulation. We study whether the
+change of measure of Blanchet and Glynn (2008) satisfies this property and show
+that it does so if and only if the tail index $\alpha$ of the right tail lies
+in the interval $(1, \, 3/2)$.
+",0,0,1,1,0,0
+2531,Crawling migration under chemical signalling: a stochastic particle model,"  Cell migration is a fundamental process involved in physiological phenomena
+such as the immune response and morphogenesis, but also in pathological
+processes, such as the development of tumor metastasis. These functions are
+effectively ensured because cells are active systems that adapt to their
+environment. In this work, we consider a migrating cell as an active particle,
+where its intracellular activity is responsible for motion. Such system was
+already modeled in a previous model where the protrusion activity of the cell
+was described by a stochastic Markovian jump process. The model was proven able
+to capture the diversity in observed trajectories. Here, we add a description
+of the effect of an external chemical attractive signal on the protrusion
+dynamics, that may vary in time. We show that the resulting stochastic model is
+a well-posed non-homogeneous Markovian process, and provide cell trajectories
+in different settings, illustrating the effects of the signal on long-term
+trajectories.
+",0,0,0,0,1,0
+2532,Towards a Deeper Understanding of Adversarial Losses,"  Recent work has proposed various adversarial losses for training generative
+adversarial networks. Yet, it remains unclear what certain types of functions
+are valid adversarial loss functions, and how these loss functions perform
+against one another. In this paper, we aim to gain a deeper understanding of
+adversarial losses by decoupling the effects of their component functions and
+regularization terms. We first derive some necessary and sufficient conditions
+of the component functions such that the adversarial loss is a divergence-like
+measure between the data and the model distributions. In order to
+systematically compare different adversarial losses, we then propose DANTest, a
+new, simple framework based on discriminative adversarial networks. With this
+framework, we evaluate an extensive set of adversarial losses by combining
+different component functions and regularization approaches. This study leads
+to some new insights into the adversarial losses. For reproducibility, all
+source code is available at this https URL .
+",1,0,0,1,0,0
+2533,Transit Visibility Zones of the Solar System Planets,"  The detection of thousands of extrasolar planets by the transit method
+naturally raises the question of whether potential extrasolar observers could
+detect the transits of the Solar System planets. We present a comprehensive
+analysis of the regions in the sky from where transit events of the Solar
+System planets can be detected. We specify how many different Solar System
+planets can be observed from any given point in the sky, and find the maximum
+number to be three. We report the probabilities of a randomly positioned
+external observer to be able to observe single and multiple Solar System planet
+transits; specifically, we find a probability of 2.518% to be able to observe
+at least one transiting planet, 0.229% for at least two transiting planets, and
+0.027% for three transiting planets. We identify 68 known exoplanets that have
+a favourable geometric perspective to allow transit detections in the Solar
+System and we show how the ongoing K2 mission will extend this list. We use
+occurrence rates of exoplanets to estimate that there are $3.2\pm1.2$ and
+$6.6^{+1.3}_{-0.8}$ temperate Earth-sized planets orbiting GK and M dwarf stars
+brighter than $V=13$ and $V=16$ respectively, that are located in the Earth's
+transit zone.
+",0,1,0,0,0,0
+2534,Nearest-neighbour Markov point processes on graphs with Euclidean edges,"  We define nearest-neighbour point processes on graphs with Euclidean edges
+and linear networks. They can be seen as the analogues of renewal processes on
+the real line. We show that the Delaunay neighbourhood relation on a tree
+satisfies the Baddeley--M{\o}ller consistency conditions and provide a
+characterisation of Markov functions with respect to this relation. We show
+that a modified relation defined in terms of the local geometry of the graph
+satisfies the consistency conditions for all graphs with Euclidean edges.
+",0,0,1,1,0,0
+2535,A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation,"  One of the challenges in model-based control of stochastic dynamical systems
+is that the state transition dynamics are involved, and it is not easy or
+efficient to make good-quality predictions of the states. Moreover, there are
+not many representational models for the majority of autonomous systems, as it
+is not easy to build a compact model that captures the entire dynamical
+subtleties and uncertainties. In this work, we present a hierarchical Bayesian
+linear regression model with local features to learn the dynamics of a
+micro-robotic system as well as two simpler examples, consisting of a
+stochastic mass-spring damper and a stochastic double inverted pendulum on a
+cart. The model is hierarchical since we assume non-stationary priors for the
+model parameters. These non-stationary priors make the model more flexible by
+imposing priors on the priors of the model. To solve the maximum likelihood
+(ML) problem for this hierarchical model, we use the variational expectation
+maximization (EM) algorithm, and enhance the procedure by introducing hidden
+target variables. The algorithm yields parsimonious model structures, and
+consistently provides fast and accurate predictions for all our examples
+involving large training and test sets. This demonstrates the effectiveness of
+the method in learning stochastic dynamics, which makes it suitable for future
+use in a paradigm, such as model-based reinforcement learning, to compute
+optimal control policies in real time.
+",0,0,0,1,0,0
+2536,Multitask Learning and Benchmarking with Clinical Time Series Data,"  Health care is one of the most exciting frontiers in data mining and machine
+learning. Successful adoption of electronic health records (EHRs) created an
+explosion in digital clinical data available for analysis, but progress in
+machine learning for healthcare research has been difficult to measure because
+of the absence of publicly available benchmark data sets. To address this
+problem, we propose four clinical prediction benchmarks using data derived from
+the publicly available Medical Information Mart for Intensive Care (MIMIC-III)
+database. These tasks cover a range of clinical problems including modeling
+risk of mortality, forecasting length of stay, detecting physiologic decline,
+and phenotype classification. We propose strong linear and neural baselines for
+all four tasks and evaluate the effect of deep supervision, multitask training
+and data-specific architectural modifications on the performance of neural
+models.
+",1,0,0,1,0,0
+2537,Essentially Finite Vector Bundles on Normal Pseudo-proper Algebraic Stacks,"  Let $X$ be a normal, connected and projective variety over an algebraically
+closed field $k$. It is known that a vector bundle $V$ on $X$ is essentially
+finite if and only if it is trivialized by a proper surjective morphism $f:Y\to
+X$. In this paper we introduce a different approach to this problem which
+allows to extend the results to normal, connected and strongly pseudo-proper
+algebraic stack of finite type over an arbitrary field $k$.
+",0,0,1,0,0,0
+2538,Fluid flow across a wavy channel brought in contact,"  A pressure driven flow in contact interface between elastic solids with wavy
+surfaces is studied. We consider a strong coupling between the solid and the
+fluid problems, which is relevant when the fluid pressure is comparable with
+the contact pressure. An approximate analytical solution is obtained for this
+coupled problem. A finite-element monolithically coupled framework is used to
+solve the problem numerically. A good agreement is obtained between the two
+solutions within the region of the validity of the analytical one. A power-law
+interface transmissivity decay is observed near the percolation. Finally, we
+showed that the external pressure needed to seal the channel is an affine
+function of the inlet pressure and does not depend on the outlet pressure.
+",0,1,0,0,0,0
+2539,Species tree estimation using ASTRAL: how many genes are enough?,"  Species tree reconstruction from genomic data is increasingly performed using
+methods that account for sources of gene tree discordance such as incomplete
+lineage sorting. One popular method for reconstructing species trees from
+unrooted gene tree topologies is ASTRAL. In this paper, we derive theoretical
+sample complexity results for the number of genes required by ASTRAL to
+guarantee reconstruction of the correct species tree with high probability. We
+also validate those theoretical bounds in a simulation study. Our results
+indicate that ASTRAL requires $\mathcal{O}(f^{-2} \log n)$ gene trees to
+reconstruct the species tree correctly with high probability where n is the
+number of species and f is the length of the shortest branch in the species
+tree. Our simulations, which are the first to test ASTRAL explicitly under the
+anomaly zone, show trends consistent with the theoretical bounds and also
+provide some practical insights on the conditions where ASTRAL works well.
+",1,0,1,1,0,0
+2540,Two weight Commutators in the Dirichlet and Neumann Laplacian settings,"  In this paper we establish the characterization of the weighted BMO via two
+weight commutators in the settings of the Neumann Laplacian $\Delta_{N_+}$ on
+the upper half space $\mathbb{R}^n_+$ and the reflection Neumann Laplacian
+$\Delta_N$ on $\mathbb{R}^n$ with respect to the weights associated to
+$\Delta_{N_+}$ and $\Delta_{N}$ respectively. This in turn yields a weak
+factorization for the corresponding weighted Hardy spaces, where in particular,
+the weighted class associated to $\Delta_{N}$ is strictly larger than the
+Muckenhoupt weighted class and contains non-doubling weights. In our study, we
+also make contributions to the classical Muckenhoupt--Wheeden weighted Hardy
+space (BMO space respectively) by showing that it can be characterized via area
+function (Carleson measure respectively) involving the semigroup generated by
+the Laplacian on $\mathbb{R}^n$ and that the duality of these weighted Hardy
+and BMO spaces holds for Muckenhoupt $A^p$ weights with $p\in (1,2]$ while the
+previously known related results cover only $p\in (1,{n+1\over n}]$. We also
+point out that this two weight commutator theorem might not be true in the
+setting of general operators $L$, and in particular we show that it is not true
+when $L$ is the Dirichlet Laplacian $\Delta_{D_+}$ on $\mathbb{R}^n_+$.
+",0,0,1,0,0,0
+2541,Schoenberg Representations and Gramian Matrices of Matérn Functions,"  We represent Matérn functions in terms of Schoenberg's integrals which
+ensure the positive definiteness and prove the systems of translates of
+Matérn functions form Riesz sequences in $L^2(\R^n)$ or Sobolev spaces. Our
+approach is based on a new class of integral transforms that generalize Fourier
+transforms for radial functions. We also consider inverse multi-quadrics and
+obtain similar results.
+",0,0,1,0,0,0
+2542,A generalization of the Hasse-Witt matrix of a hypersurface,"  The Hasse-Witt matrix of a hypersurface in ${\mathbb P}^n$ over a finite
+field of characteristic $p$ gives essentially complete mod $p$ information
+about the zeta function of the hypersurface. But if the degree $d$ of the
+hypersurface is $\leq n$, the zeta function is trivial mod $p$ and the
+Hasse-Witt matrix is zero-by-zero. We generalize a classical formula for the
+Hasse-Witt matrix to obtain a matrix that gives a nontrivial congruence for the
+zeta function for all $d$. We also describe the differential equations
+satisfied by this matrix and prove that it is generically invertible.
+",0,0,1,0,0,0
+2543,Few-Shot Learning with Graph Neural Networks,"  We propose to study the problem of few-shot learning with the prism of
+inference on a partially observed graphical model, constructed from a
+collection of input images whose label can be either observed or not. By
+assimilating generic message-passing inference algorithms with their
+neural-network counterparts, we define a graph neural network architecture that
+generalizes several of the recently proposed few-shot learning models. Besides
+providing improved numerical performance, our framework is easily extended to
+variants of few-shot learning, such as semi-supervised or active learning,
+demonstrating the ability of graph-based models to operate well on 'relational'
+tasks.
+",1,0,0,1,0,0
+2544,High-precision measurement of the proton's atomic mass,"  We report on the precise measurement of the atomic mass of a single proton
+with a purpose-built Penning-trap system. With a precision of 32
+parts-per-trillion our result not only improves on the current CODATA
+literature value by a factor of three, but also disagrees with it at a level of
+about 3 standard deviations.
+",0,1,0,0,0,0
+2545,Prospects of detecting HI using redshifted 21 cm radiation at z ~ 3,"  Distribution of cold gas in the post-reionization era provides an important
+link between distribution of galaxies and the process of star formation.
+Redshifted 21 cm radiation from the Hyperfine transition of neutral Hydrogen
+allows us to probe the neutral component of cold gas, most of which is to be
+found in the interstellar medium of galaxies. Existing and upcoming radio
+telescopes can probe the large scale distribution of neutral Hydrogen via HI
+intensity mapping. In this paper we use an estimate of the HI power spectrum
+derived using an ansatz to compute the expected signal from the large scale HI
+distribution at z ~ 3. We find that the scale dependence of bias at small
+scales makes a significant difference to the expected signal even at large
+angular scales. We compare the predicted signal strength with the sensitivity
+of radio telescopes that can observe such radiation and calculate the
+observation time required for detecting neutral Hydrogen at these redshifts. We
+find that OWFA (Ooty Wide Field Array) offers the best possibility to detect
+neutral Hydrogen at z ~ 3 before the SKA (Square Kilometer Array) becomes
+operational. We find that the OWFA should be able to make a 3 sigma or a more
+significant detection in 2000 hours of observations at several angular scales.
+Calculations done using the Fisher matrix approach indicate that a 5 sigma
+detection of the binned HI power spectrum via measurement of the amplitude of
+the HI power spectrum is possible in 1000 hours (Sarkar, Bharadwaj and Ali,
+2017).
+",0,1,0,0,0,0
+2546,Unconditional bases of subspaces related to non-self-adjoint perturbations of self-adjoint operators,"  Assume that $T$ is a self-adjoint operator on a Hilbert space $\mathcal{H}$
+and that the spectrum of $T$ is confined in the union $\bigcup_{j\in
+J}\Delta_j$, $J\subseteq\mathbb{Z}$, of segments $\Delta_j=[\alpha_j,
+\beta_j]\subset\mathbb{R}$ such that $\alpha_{j+1}>\beta_j$ and $$ \inf_{j}
+\left(\alpha_{j+1}-\beta_j\right) = d > 0. $$ If $B$ is a bounded (in general
+non-self-adjoint) perturbation of $T$ with $\|B\|=:b<d/2$ then the spectrum of
+the perturbed operator $A=T+B$ lies in the union $\bigcup_{j\in J}
+U_{b}(\Delta_j)$ of the mutually disjoint closed $b$-neighborhoods
+$U_{b}(\Delta_j)$ of the segments $\Delta_j$ in $\mathbb{C}$. Let $Q_j$ be the
+Riesz projection onto the invariant subspace of $A$ corresponding to the part
+of the spectrum of $A$ lying in $U_{b}\left(\Delta_j\right)$, $j\in J$. Our
+main result is as follows: The subspaces $\mathcal{L}_j=Q_j(\mathcal H)$, $j\in
+J$, form an unconditional basis in the whole space $\mathcal H$.
+",0,0,1,0,0,0
+2547,Centralities of Nodes and Influences of Layers in Large Multiplex Networks,"  We formulate and propose an algorithm (MultiRank) for the ranking of nodes
+and layers in large multiplex networks. MultiRank takes into account the full
+multiplex network structure of the data and exploits the dual nature of the
+network in terms of nodes and layers. The proposed centrality of the layers
+(influences) and the centrality of the nodes are determined by a coupled set of
+equations. The basic idea consists in assigning more centrality to nodes that
+receive links from highly influential layers and from already central nodes.
+The layers are more influential if highly central nodes are active in them. The
+algorithm applies to directed/undirected as well as to weighted/unweighted
+multiplex networks. We discuss the application of MultiRank to three major
+examples of multiplex network datasets: the European Air Transportation
+Multiplex Network, the Pierre Auger Multiplex Collaboration Network and the FAO
+Multiplex Trade Network.
+",1,1,0,0,0,0
+2548,Twofold triple systems with cyclic 2-intersecting Gray codes,"  Given a combinatorial design $\mathcal{D}$ with block set $\mathcal{B}$, the
+block-intersection graph (BIG) of $\mathcal{D}$ is the graph that has
+$\mathcal{B}$ as its vertex set, where two vertices $B_{1} \in \mathcal{B}$ and
+$B_{2} \in \mathcal{B} $ are adjacent if and only if $|B_{1} \cap B_{2}| > 0$.
+The $i$-block-intersection graph ($i$-BIG) of $\mathcal{D}$ is the graph that
+has $\mathcal{B}$ as its vertex set, where two vertices $B_{1} \in \mathcal{B}$
+and $B_{2} \in \mathcal{B}$ are adjacent if and only if $|B_{1} \cap B_{2}| =
+i$. In this paper several constructions are obtained that start with twofold
+triple systems (TTSs) with Hamiltonian $2$-BIGs and result in larger TTSs that
+also have Hamiltonian $2$-BIGs. These constructions collectively enable us to
+determine the complete spectrum of TTSs with Hamiltonian $2$-BIGs (equivalently
+TTSs with cyclic $2$-intersecting Gray codes) as well as the complete spectrum
+for TTSs with $2$-BIGs that have Hamilton paths (i.e., for TTSs with
+$2$-intersecting Gray codes).
+In order to prove these spectrum results, we sometimes require ingredient
+TTSs that have large partial parallel classes; we prove lower bounds on the
+sizes of partial parallel clasess in arbitrary TTSs, and then construct larger
+TTSs with both cyclic $2$-intersecting Gray codes and parallel classes.
+",0,0,1,0,0,0
+2549,Consequences of Unhappiness While Developing Software,"  The growing literature on affect among software developers mostly reports on
+the linkage between happiness, software quality, and developer productivity.
+Understanding the positive side of happiness -- positive emotions and moods --
+is an attractive and important endeavor. Scholars in industrial and
+organizational psychology have suggested that also studying the negative side
+-- unhappiness -- could lead to cost-effective ways of enhancing working
+conditions, job performance, and to limiting the occurrence of psychological
+disorders. Our comprehension of the consequences of (un)happiness among
+developers is still too shallow, and is mainly expressed in terms of
+development productivity and software quality. In this paper, we attempt to
+uncover the experienced consequences of unhappiness among software developers.
+Using qualitative data analysis of the responses given by 181 questionnaire
+participants, we identified 49 consequences of unhappiness while doing software
+development. We found detrimental consequences on developers' mental
+well-being, the software development process, and the produced artifacts. Our
+classification scheme, available as open data, will spawn new happiness
+research opportunities of cause-effect type, and it can act as a guideline for
+practitioners for identifying damaging effects of unhappiness and for fostering
+happiness on the job.
+",1,0,0,0,0,0
+2550,Typesafe Abstractions for Tensor Operations,"  We propose a typesafe abstraction to tensors (i.e. multidimensional arrays)
+exploiting the type-level programming capabilities of Scala through
+heterogeneous lists (HList), and showcase typesafe abstractions of common
+tensor operations and various neural layers such as convolution or recurrent
+neural networks. This abstraction could lay the foundation of future typesafe
+deep learning frameworks that runs on Scala/JVM.
+",1,0,0,0,0,0
+2551,Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training,"  Exploiting sparsity enables hardware systems to run neural networks faster
+and more energy-efficiently. However, most prior sparsity-centric optimization
+techniques only accelerate the forward pass of neural networks and usually
+require an even longer training process with iterative pruning and retraining.
+We observe that artificially inducing sparsity in the gradients of the gates in
+an LSTM cell has little impact on the training quality. Further, we can enforce
+structured sparsity in the gate gradients to make the LSTM backward pass up to
+45% faster than the state-of-the-art dense approach and 168% faster than the
+state-of-the-art sparsifying method on modern GPUs. Though the structured
+sparsifying method can impact the accuracy of a model, this performance gap can
+be eliminated by mixing our sparse training method and the standard dense
+training method. Experimental results show that the mixed method can achieve
+comparable results in a shorter time span than using purely dense training.
+",0,0,0,1,0,0
+2552,Linear density-based clustering with a discrete density model,"  Density-based clustering techniques are used in a wide range of data mining
+applications. One of their most attractive features con- sists in not making
+use of prior knowledge of the number of clusters that a dataset contains along
+with their shape. In this paper we propose a new algorithm named Linear DBSCAN
+(Lin-DBSCAN), a simple approach to clustering inspired by the density model
+introduced with the well known algorithm DBSCAN. Designed to minimize the
+computational cost of density based clustering on geospatial data, Lin-DBSCAN
+features a linear time complexity that makes it suitable for real-time
+applications on low-resource devices. Lin-DBSCAN uses a discrete version of the
+density model of DBSCAN that takes ad- vantage of a grid-based scan and merge
+approach. The name of the algorithm stems exactly from its main features
+outlined above. The algorithm was tested with well known data sets.
+Experimental results prove the efficiency and the validity of this approach
+over DBSCAN in the context of spatial data clustering, enabling the use of a
+density-based clustering technique on large datasets with low computational
+cost.
+",0,0,0,1,0,0
+2553,An inexact subsampled proximal Newton-type method for large-scale machine learning,"  We propose a fast proximal Newton-type algorithm for minimizing regularized
+finite sums that returns an $\epsilon$-suboptimal point in
+$\tilde{\mathcal{O}}(d(n + \sqrt{\kappa d})\log(\frac{1}{\epsilon}))$ FLOPS,
+where $n$ is number of samples, $d$ is feature dimension, and $\kappa$ is the
+condition number. As long as $n > d$, the proposed method is more efficient
+than state-of-the-art accelerated stochastic first-order methods for non-smooth
+regularizers which requires $\tilde{\mathcal{O}}(d(n + \sqrt{\kappa
+n})\log(\frac{1}{\epsilon}))$ FLOPS. The key idea is to form the subsampled
+Newton subproblem in a way that preserves the finite sum structure of the
+objective, thereby allowing us to leverage recent developments in stochastic
+first-order methods to solve the subproblem. Experimental results verify that
+the proposed algorithm outperforms previous algorithms for $\ell_1$-regularized
+logistic regression on real datasets.
+",1,0,0,1,0,0
+2554,Future Energy Consumption Prediction Based on Grey Forecast Model,"  We use grey forecast model to predict the future energy consumption of four
+states in the U.S, and make some improvments to the model.
+",0,0,0,1,0,0
+2555,AutoPass: An Automatic Password Generator,"  Text password has long been the dominant user authentication technique and is
+used by large numbers of Internet services. If they follow recommended
+practice, users are faced with the almost insuperable problem of generating and
+managing a large number of site-unique and strong (i.e. non-guessable)
+passwords. One way of addressing this problem is through the use of a password
+generator, i.e. a client-side scheme which generates (and regenerates)
+site-specific strong passwords on demand, with the minimum of user input. This
+paper provides a detailed specification and analysis of AutoPass, a password
+generator scheme previously outlined as part of a general analysis of such
+schemes. AutoPass has been designed to address issues identified in previously
+proposed password generators, and incorporates novel techniques to address
+these issues. Unlike almost all previously proposed schemes, AutoPass enables
+the generation of passwords that meet important real-world requirements,
+including forced password changes, use of pre-specified passwords, and
+generation of passwords meeting site-specific requirements.
+",1,0,0,0,0,0
+2556,A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling,"  Randomized experiments have been critical tools of decision making for
+decades. However, subjects can show significant heterogeneity in response to
+treatments in many important applications. Therefore it is not enough to simply
+know which treatment is optimal for the entire population. What we need is a
+model that correctly customize treatment assignment base on subject
+characteristics. The problem of constructing such models from randomized
+experiments data is known as Uplift Modeling in the literature. Many algorithms
+have been proposed for uplift modeling and some have generated promising
+results on various data sets. Yet little is known about the theoretical
+properties of these algorithms. In this paper, we propose a new tree-based
+ensemble algorithm for uplift modeling. Experiments show that our algorithm can
+achieve competitive results on both synthetic and industry-provided data. In
+addition, by properly tuning the ""node size"" parameter, our algorithm is proved
+to be consistent under mild regularity conditions. This is the first consistent
+algorithm for uplift modeling that we are aware of.
+",1,0,0,1,0,0
+2557,"The cosmic shoreline: the evidence that escape determines which planets have atmospheres, and what this may mean for Proxima Centauri b","  The planets of the Solar System divide neatly between those with atmospheres
+and those without when arranged by insolation ($I$) and escape velocity
+($v_{\mathrm{esc}}$). The dividing line goes as $I \propto v_{\mathrm{esc}}^4$.
+Exoplanets with reported masses and radii are shown to crowd against the
+extrapolation of the Solar System trend, making a metaphorical cosmic shoreline
+that unites all the planets. The $I \propto v_{\mathrm{esc}}^4$ relation may
+implicate thermal escape. We therefore address the general behavior of
+hydrodynamic thermal escape models ranging from Pluto to highly-irradiated
+Extrasolar Giant Planets (EGPs). Energy-limited escape is harder to test
+because copious XUV radiation is mostly a feature of young stars, and hence
+requires extrapolating to historic XUV fluences ($I_{\mathrm{xuv}}$) using
+proxies and power laws. An energy-limited shoreline should scale as
+$I_{\mathrm{xuv}} \propto v_{\mathrm{esc}}^3\sqrt{\rho}$, which differs
+distinctly from the apparent $I_{\mathrm{xuv}} \propto v_{\mathrm{esc}}^4$
+relation. Energy-limited escape does provide good quantitative agreement to the
+highly irradiated EGPs. Diffusion-limited escape implies that no planet can
+lose more than 1% of its mass as H$_2$. Impact erosion, to the extent that
+impact velocities $v_{\mathrm{imp}}$ can be estimated for exoplanets, fits to a
+$v_{\mathrm{imp}} \approx 4\,-\,5\, v_{\mathrm{esc}}$ shoreline. The
+proportionality constant is consistent with what the collision of comet
+Shoemaker-Levy 9 showed us we should expect of modest impacts in deep
+atmospheres. With respect to the shoreline, Proxima Centauri b is on the
+metaphorical beach. Known hazards include its rapid energetic accretion, high
+impact velocities, its early life on the wrong side of the runaway greenhouse,
+and Proxima Centauri's XUV radiation. In its favor is a vast phase space of
+unknown unknowns.
+",0,1,0,0,0,0
+2558,Universal kinetics for engagement of mechanosensing pathways in cell adhesion,"  When plated onto substrates, cell morphology and even stem cell
+differentiation are influenced by the stiffness of their environment. Stiffer
+substrates give strongly spread (eventually polarized) cells with strong focal
+adhesions, and stress fibers; very soft substrates give a less developed
+cytoskeleton, and much lower cell spreading. The kinetics of this process of
+cell spreading is studied extensively, and important universal relationships
+are established on how the cell area grows with time. Here we study the
+population dynamics of spreading cells, investigating the characteristic
+processes involved in cell response to the substrate. We show that unlike the
+individual cell morphology, this population dynamics does not depend on the
+substrate stiffness. Instead, a strong activation temperature dependence is
+observed. Different cell lines on different substrates all have long-time
+statistics controlled by the thermal activation over a single energy barrier
+dG=19 kcal/mol, while the early-time kinetics follows a power law $t^5$. This
+implies that the rate of spreading depends on an internal process of
+adhesion-mechanosensing complex assembly and activation: the operational
+complex must have 5 component proteins, and the last process in the sequence
+(which we believe is the activation of focal adhesion kinase) is controlled by
+the binding energy dG.
+",0,0,0,0,1,0
+2559,Agent-based computing from multi-agent systems to agent-based Models: a visual survey,"  Agent-Based Computing is a diverse research domain concerned with the
+building of intelligent software based on the concept of ""agents"". In this
+paper, we use Scientometric analysis to analyze all sub-domains of agent-based
+computing. Our data consists of 1,064 journal articles indexed in the ISI web
+of knowledge published during a twenty year period: 1990-2010. These were
+retrieved using a topic search with various keywords commonly used in
+sub-domains of agent-based computing. In our proposed approach, we have
+employed a combination of two applications for analysis, namely Network
+Workbench and CiteSpace - wherein Network Workbench allowed for the analysis of
+complex network aspects of the domain, detailed visualization-based analysis of
+the bibliographic data was performed using CiteSpace. Our results include the
+identification of the largest cluster based on keywords, the timeline of
+publication of index terms, the core journals and key subject categories. We
+also identify the core authors, top countries of origin of the manuscripts
+along with core research institutes. Finally, our results have interestingly
+revealed the strong presence of agent-based computing in a number of
+non-computing related scientific domains including Life Sciences, Ecological
+Sciences and Social Sciences.
+",1,1,0,0,0,0
+2560,Large Spontaneous Hall Effects in Chiral Topological Magnets,"  As novel topological phases in correlated electron systems, we have found two
+examples of non-ferromagnetic states that exhibit a large anomalous Hall
+effect. One is the chiral spin liquid compound Pr$_{2}$Ir$_{2}$O$_{7}$, which
+exhibits a spontaneous Hall effect in a spin liquid state due to spin ice
+correlation. The other is the chiral antiferromagnets Mn$_{3}$Sn and Mn$_{3}$Ge
+that exhibit a large anomalous Hall effect at room temperature. The latter
+shows a sign change of the anomalous Hall effect by a small change in the
+magnetic field by a few 100 G, which should be useful for various applications.
+We will discuss that the magnetic Weyl metal states are the origin for such a
+large anomalous Hall effect observed in both the spin liquid and
+antiferromagnet that possess almost no magnetization.
+",0,1,0,0,0,0
+2561,Mott metal-insulator transition in the Doped Hubbard-Holstein model,"  Motivated by the current interest in the understanding of the Mott insulators
+away from half filling, observed in many perovskite oxides, we study the Mott
+metal-insulator transition (MIT) in the doped Hubbard-Holstein model using the
+Hatree-Fock mean field theory. The Hubbard-Holstein model is the simplest model
+containing both the Coulomb and the electron-lattice interactions, which are
+important ingredients in the physics of the perovskite oxides. In contrast to
+the half-filled Hubbard model, which always results in a single phase (either
+metallic or insulating), our results show that away from half-filling, a mixed
+phase of metallic and insulating regions occur. As the dopant concentration is
+increased, the metallic part progressively grows in volume, until it exceeds
+the percolation threshold, leading to percolative conduction. This happens
+above a critical dopant concentration $\delta_c$, which, depending on the
+strength of the electron-lattice interaction, can be a significant fraction of
+unity. This means that the material could be insulating even for a substantial
+amount of doping, in contrast to the expectation that doped holes would destroy
+the insulating behavior of the half-filled Hubbard model. Our theory provides a
+framework for the understanding of the density-driven metal-insulator
+transition observed in many complex oxides.
+",0,1,0,0,0,0
+2562,ASDA : Analyseur Syntaxique du Dialecte Alg{é}rien dans un but d'analyse s{é}mantique,"  Opinion mining and sentiment analysis in social media is a research issue
+having a great interest in the scientific community. However, before begin this
+analysis, we are faced with a set of problems. In particular, the problem of
+the richness of languages and dialects within these media. To address this
+problem, we propose in this paper an approach of construction and
+implementation of Syntactic analyzer named ASDA. This tool represents a parser
+for the Algerian dialect that label the terms of a given corpus. Thus, we
+construct a labeling table containing for each term its stem, different
+prefixes and suffixes, allowing us to determine the different grammatical parts
+a sort of POS tagging. This labeling will serve us later in the semantic
+processing of the Algerian dialect, like the automatic translation of this
+dialect or sentiment analysis
+",1,0,0,0,0,0
+2563,Latent Intention Dialogue Models,"  Developing a dialogue agent that is capable of making autonomous decisions
+and communicating by natural language is one of the long-term goals of machine
+learning research. Traditional approaches either rely on hand-crafting a small
+state-action set for applying reinforcement learning that is not scalable or
+constructing deterministic models for learning dialogue sentences that fail to
+capture natural conversational variability. In this paper, we propose a Latent
+Intention Dialogue Model (LIDM) that employs a discrete latent variable to
+learn underlying dialogue intentions in the framework of neural variational
+inference. In a goal-oriented dialogue scenario, these latent intentions can be
+interpreted as actions guiding the generation of machine responses, which can
+be further refined autonomously by reinforcement learning. The experimental
+evaluation of LIDM shows that the model out-performs published benchmarks for
+both corpus-based and human evaluation, demonstrating the effectiveness of
+discrete latent variable models for learning goal-oriented dialogues.
+",1,0,0,1,0,0
+2564,Quasiconvex elastodynamics: weak-strong uniqueness for measure-valued solutions,"  A weak-strong uniqueness result is proved for measure-valued solutions to the
+system of conservation laws arising in elastodynamics. The main novelty brought
+forward by the present work is that the underlying stored-energy function of
+the material is assumed strongly quasiconvex. The proof employs tools from the
+calculus of variations to establish general convexity-type bounds on
+quasiconvex functions and recasts them in order to adapt the relative entropy
+method to quasiconvex elastodynamics.
+",0,0,1,0,0,0
+2565,Accelerated Dual Learning by Homotopic Initialization,"  Gradient descent and coordinate descent are well understood in terms of their
+asymptotic behavior, but less so in a transient regime often used for
+approximations in machine learning. We investigate how proper initialization
+can have a profound effect on finding near-optimal solutions quickly. We show
+that a certain property of a data set, namely the boundedness of the
+correlations between eigenfeatures and the response variable, can lead to
+faster initial progress than expected by commonplace analysis. Convex
+optimization problems can tacitly benefit from that, but this automatism does
+not apply to their dual formulation. We analyze this phenomenon and devise
+provably good initialization strategies for dual optimization as well as
+heuristics for the non-convex case, relevant for deep learning. We find our
+predictions and methods to be experimentally well-supported.
+",1,0,0,0,0,0
+2566,Inverse Reinforcement Learning from Summary Data,"  Inverse reinforcement learning (IRL) aims to explain observed strategic
+behavior by fitting reinforcement learning models to behavioral data. However,
+traditional IRL methods are only applicable when the observations are in the
+form of state-action paths. This assumption may not hold in many real-world
+modeling settings, where only partial or summarized observations are available.
+In general, we may assume that there is a summarizing function $\sigma$, which
+acts as a filter between us and the true state-action paths that constitute the
+demonstration. Some initial approaches to extending IRL to such situations have
+been presented, but with very specific assumptions about the structure of
+$\sigma$, such as that only certain state observations are missing. This paper
+instead focuses on the most general case of the problem, where no assumptions
+are made about the summarizing function, except that it can be evaluated. We
+demonstrate that inference is still possible. The paper presents exact and
+approximate inference algorithms that allow full posterior inference, which is
+particularly important for assessing parameter uncertainty in this challenging
+inference situation. Empirical scalability is demonstrated to reasonably sized
+problems, and practical applicability is demonstrated by estimating the
+posterior for a cognitive science RL model based on an observed user's task
+completion time only.
+",1,0,0,1,0,0
+2567,Algorithm for Optimization and Interpolation based on Hyponormality,"  On one hand, consider the problem of finding global solutions to a polynomial
+optimization problem and, on the other hand, consider the problem of
+interpolating a set of points with a complex exponential function. This paper
+proposes a single algorithm to address both problems. It draws on the notion of
+hyponormality in operator theory. Concerning optimization, it seems to be the
+first algorithm that is capable of extracting global solutions from a
+polynomial optimization problem where the variables and data are complex
+numbers. It also applies to real polynomial optimization, a special case of
+complex polynomial optimization, and thus extends the work of Henrion and
+Lasserre implemented in GloptiPoly. Concerning interpolation, the algorithm
+provides an alternative to Prony's method based on the Autonne-Takagi
+factorization and it avoids solving a Vandermonde system. The algorithm and its
+proof are based exclusively on linear algebra. They are devoid of notions from
+algebraic geometry, contrary to existing methods for interpolation. The
+algorithm is tested on a series of examples, each illustrating a different
+facet of the approach. One of the examples demonstrates that hyponormality can
+be enforced numerically to strenghten a convex relaxation and to force its
+solution to have rank one.
+",0,0,1,0,0,0
+2568,Human experts vs. machines in taxa recognition,"  The step of expert taxa recognition currently slows down the response time of
+many bioassessments. Shifting to quicker and cheaper state-of-the-art machine
+learning approaches is still met with expert scepticism towards the ability and
+logic of machines. In our study, we investigate both the differences in
+accuracy and in the identification logic of taxonomic experts and machines. We
+propose a systematic approach utilizing deep Convolutional Neural Nets with the
+transfer learning paradigm and extensively evaluate it over a multi-label and
+multi-pose taxonomic dataset specifically created for this comparison. We also
+study the prediction accuracy on different ranks of taxonomic hierarchy in
+detail. Our results revealed that human experts using actual specimens yield
+the lowest classification error. However, our proposed, much faster, automated
+approach using deep Convolutional Neural Nets comes very close to human
+accuracy. Contrary to previous findings in the literature, we find that
+machines following a typical flat classification approach commonly used in
+machine learning performs better than forcing machines to adopt a hierarchical,
+local per parent node approach used by human taxonomic experts. Finally, we
+publicly share our unique dataset to serve as a public benchmark dataset in
+this field.
+",1,0,0,1,0,0
+2569,A micrometer-thick oxide film with high thermoelectric performance at temperature ranging from 20-400 K,"  Thermoelectric (TE) materials achieve localised conversion between thermal
+and electric energies, and the conversion efficiency is determined by a figure
+of merit zT. Up to date, two-dimensional electron gas (2DEG) related TE
+materials hold the records for zT near room-temperature. A sharp increase in zT
+up to ~2.0 was observed previously for superlattice materials such as PbSeTe,
+Bi2Te3/Sb2Te3 and SrNb0.2Ti0.8O3/SrTiO3, when the thicknesses of these TE
+materials were spatially confine within sub-nanometre scale. The
+two-dimensional confinement of carriers enlarges the density of states near the
+Fermi energy3-6 and triggers electron phonon coupling. This overcomes the
+conventional {\sigma}-S trade-off to more independently improve S, and thereby
+further increases thermoelectric power factors (PF=S2{\sigma}). Nevertheless,
+practical applications of the present 2DEG materials for high power energy
+conversions are impeded by the prerequisite of spatial confinement, as the
+amount of TE material is insufficient. Here, we report similar TE properties to
+2DEGs but achieved in SrNb0.2Ti0.8O3 films with thickness within sub-micrometer
+scale by regulating interfacial and lattice polarizations. High power factor
+(up to 103 {\mu}Wcm-1K-2) and zT value (up to 1.6) were observed for the film
+materials near room-temperature and below. Even reckon in the thickness of the
+substrate, an integrated power factor of both film and substrate approaching to
+be 102 {\mu}Wcm-1K-2 was achieved in a 2 {\mu}m-thick SrNb0.2Ti0.8O3 film grown
+on a 100 {\mu}m-thick SrTiO3 substrate. The dependence of high TE performances
+on size-confinement is reduced by ~103 compared to the conventional
+2DEG-related TE materials. As-grown oxide films are less toxic and not
+dependent on large amounts of heavy elements, potentially paving the way
+towards applications in localised refrigeration and electric power generations.
+",0,1,0,0,0,0
+2570,MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks,"  Over 50 million scholarly articles have been published: they constitute a
+unique repository of knowledge. In particular, one may infer from them
+relations between scientific concepts, such as synonyms and hyponyms.
+Artificial neural networks have been recently explored for relation extraction.
+In this work, we continue this line of work and present a system based on a
+convolutional neural network to extract relations. Our model ranked first in
+the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific
+articles (subtask C).
+",1,0,0,1,0,0
+2571,Maximizing acquisition functions for Bayesian optimization,"  Bayesian optimization is a sample-efficient approach to global optimization
+that relies on theoretically motivated value heuristics (acquisition functions)
+to guide its search process. Fully maximizing acquisition functions produces
+the Bayes' decision rule, but this ideal is difficult to achieve since these
+functions are frequently non-trivial to optimize. This statement is especially
+true when evaluating queries in parallel, where acquisition functions are
+routinely non-convex, high-dimensional, and intractable. We first show that
+acquisition functions estimated via Monte Carlo integration are consistently
+amenable to gradient-based optimization. Subsequently, we identify a common
+family of acquisition functions, including EI and UCB, whose properties not
+only facilitate but justify use of greedy approaches for their maximization.
+",0,0,0,1,0,0
+2572,Angular momentum evolution of galaxies over the past 10-Gyr: A MUSE and KMOS dynamical survey of 400 star-forming galaxies from z=0.3-1.7,"  We present a MUSE and KMOS dynamical study 405 star-forming galaxies at
+redshift z=0.28-1.65 (median redshift z=0.84). Our sample are representative of
+star-forming, main-sequence galaxies, with star-formation rates of
+SFR=0.1-30Mo/yr and stellar masses M=10^8-10^11Mo. For 49+/-4% of our sample,
+the dynamics suggest rotational support, 24+/-3% are unresolved systems and
+5+/-2% appear to be early-stage major mergers with components on 8-30kpc
+scales. The remaining 22+/-5% appear to be dynamically complex, irregular (or
+face-on systems). For galaxies whose dynamics suggest rotational support, we
+derive inclination corrected rotational velocities and show these systems lie
+on a similar scaling between stellar mass and specific angular momentum as
+local spirals with j*=J/M*\propto M^(2/3) but with a redshift evolution that
+scales as j*\propto M^{2/3}(1+z)^(-1). We identify a correlation between
+specific angular momentum and disk stability such that galaxies with the
+highest specific angular momentum, log(j*/M^(2/3))>2.5, are the most stable,
+with Toomre Q=1.10+/-0.18, compared to Q=0.53+/-0.22 for galaxies with
+log(j*/M^(2/3))<2.5. At a fixed mass, the HST morphologies of galaxies with the
+highest specific angular momentum resemble spiral galaxies, whilst those with
+low specific angular momentum are morphologically complex and dominated by
+several bright star-forming regions. This suggests that angular momentum plays
+a major role in defining the stability of gas disks: at z~1, massive galaxies
+that have disks with low specific angular momentum, appear to be globally
+unstable, clumpy and turbulent systems. In contrast, galaxies with high
+specific angular have evolved in to stable disks with spiral structures.
+",0,1,0,0,0,0
+2573,Iterative Object and Part Transfer for Fine-Grained Recognition,"  The aim of fine-grained recognition is to identify sub-ordinate categories in
+images like different species of birds. Existing works have confirmed that, in
+order to capture the subtle differences across the categories, automatic
+localization of objects and parts is critical. Most approaches for object and
+part localization relied on the bottom-up pipeline, where thousands of region
+proposals are generated and then filtered by pre-trained object/part models.
+This is computationally expensive and not scalable once the number of
+objects/parts becomes large. In this paper, we propose a nonparametric
+data-driven method for object and part localization. Given an unlabeled test
+image, our approach transfers annotations from a few similar images retrieved
+in the training set. In particular, we propose an iterative transfer strategy
+that gradually refine the predicted bounding boxes. Based on the located
+objects and parts, deep convolutional features are extracted for recognition.
+We evaluate our approach on the widely-used CUB200-2011 dataset and a new and
+large dataset called Birdsnap. On both datasets, we achieve better results than
+many state-of-the-art approaches, including a few using oracle (manually
+annotated) bounding boxes in the test images.
+",1,0,0,0,0,0
+2574,On measures of edge-uncolorability of cubic graphs: A brief survey and some new results,"  There are many hard conjectures in graph theory, like Tutte's 5-flow
+conjecture, and the 5-cycle double cover conjecture, which would be true in
+general if they would be true for cubic graphs. Since most of them are
+trivially true for 3-edge-colorable cubic graphs, cubic graphs which are not
+3-edge-colorable, often called {\em snarks}, play a key role in this context.
+Here, we survey parameters measuring how far apart a non 3-edge-colorable graph
+is from being 3-edge-colorable. We study their interrelation and prove some new
+results. Besides getting new insight into the structure of snarks, we show that
+such measures give partial results with respect to these important conjectures.
+The paper closes with a list of open problems and conjectures.
+",0,0,1,0,0,0
+2575,Gas around galaxy haloes - III: hydrogen absorption signatures around galaxies and QSOs in the Sherwood simulation suite,"  Modern theories of galaxy formation predict that galaxies impact on their
+gaseous surroundings, playing the fundamental role of regulating the amount of
+gas converted into stars. While star-forming galaxies are believed to provide
+feedback through galactic winds, Quasi-Stellar Objects (QSOs) are believed
+instead to provide feedback through the heat generated by accretion onto a
+central supermassive black hole. A quantitative difference in the impact of
+feedback on the gaseous environments of star-forming galaxies and QSOs has not
+been established through direct observations. Using the Sherwood cosmological
+simulations, we demonstrate that measurements of neutral hydrogen in the
+vicinity of star-forming galaxies and QSOs during the era of peak galaxy
+formation show excess LyA absorption extending up to comoving radii of about
+150 kpc for star-forming galaxies and 300 - 700 kpc for QSOs. Simulations
+including supernovae-driven winds with the wind velocity scaling like the
+escape velocity of the halo account for the absorption around star-forming
+galaxies but not QSOs.
+",0,1,0,0,0,0
+2576,Distributed Newton Methods for Deep Neural Networks,"  Deep learning involves a difficult non-convex optimization problem with a
+large number of weights between any two adjacent layers of a deep structure. To
+handle large data sets or complicated networks, distributed training is needed,
+but the calculation of function, gradient, and Hessian is expensive. In
+particular, the communication and the synchronization cost may become a
+bottleneck. In this paper, we focus on situations where the model is
+distributedly stored, and propose a novel distributed Newton method for
+training deep neural networks. By variable and feature-wise data partitions,
+and some careful designs, we are able to explicitly use the Jacobian matrix for
+matrix-vector products in the Newton method. Some techniques are incorporated
+to reduce the running time as well as the memory consumption. First, to reduce
+the communication cost, we propose a diagonalization method such that an
+approximate Newton direction can be obtained without communication between
+machines. Second, we consider subsampled Gauss-Newton matrices for reducing the
+running time as well as the communication cost. Third, to reduce the
+synchronization cost, we terminate the process of finding an approximate Newton
+direction even though some nodes have not finished their tasks. Details of some
+implementation issues in distributed environments are thoroughly investigated.
+Experiments demonstrate that the proposed method is effective for the
+distributed training of deep neural networks. In compared with stochastic
+gradient methods, it is more robust and may give better test accuracy.
+",0,0,0,1,0,0
+2577,Personalized advice for enhancing well-being using automated impulse response analysis --- AIRA,"  The attention for personalized mental health care is thriving. Research data
+specific to the individual, such as time series sensor data or data from
+intensive longitudinal studies, is relevant from a research perspective, as
+analyses on these data can reveal the heterogeneity among the participants and
+provide more precise and individualized results than with group-based methods.
+However, using this data for self-management and to help the individual to
+improve his or her mental health has proven to be challenging.
+The present work describes a novel approach to automatically generate
+personalized advice for the improvement of the well-being of individuals by
+using time series data from intensive longitudinal studies: Automated Impulse
+Response Analysis (AIRA). AIRA analyzes vector autoregression models of
+well-being by generating impulse response functions. These impulse response
+functions are used in simulations to determine which variables in the model
+have the largest influence on the other variables and thus on the well-being of
+the participant. The effects found can be used to support self-management.
+We demonstrate the practical usefulness of AIRA by performing analysis on
+longitudinal self-reported data about psychological variables. To evaluate its
+effectiveness and efficacy, we ran its algorithms on two data sets ($N=4$ and
+$N=5$), and discuss the results. Furthermore, we compare AIRA's output to the
+results of a previously published study and show that the results are
+comparable. By automating Impulse Response Function Analysis, AIRA fulfills the
+need for accurate individualized models of health outcomes at a low resource
+cost with the potential for upscaling.
+",1,0,0,0,0,0
+2578,Being Robust (in High Dimensions) Can Be Practical,"  Robust estimation is much more challenging in high dimensions than it is in
+one dimension: Most techniques either lead to intractable optimization problems
+or estimators that can tolerate only a tiny fraction of errors. Recent work in
+theoretical computer science has shown that, in appropriate distributional
+models, it is possible to robustly estimate the mean and covariance with
+polynomial time algorithms that can tolerate a constant fraction of
+corruptions, independent of the dimension. However, the sample and time
+complexity of these algorithms is prohibitively large for high-dimensional
+applications. In this work, we address both of these issues by establishing
+sample complexity bounds that are optimal, up to logarithmic factors, as well
+as giving various refinements that allow the algorithms to tolerate a much
+larger fraction of corruptions. Finally, we show on both synthetic and real
+data that our algorithms have state-of-the-art performance and suddenly make
+high-dimensional robust estimation a realistic possibility.
+",1,0,0,1,0,0
+2579,Properties of In-Plane Graphene/MoS2 Heterojunctions,"  The graphene/MoS2 heterojunction formed by joining the two components
+laterally in a single plane promises to exhibit a low-resistance contact
+according to the Schottky-Mott rule. Here we provide an atomic-scale
+description of the structural, electronic, and magnetic properties of this type
+of junction. We first identify the energetically favorable structures in which
+the preference of forming C-S or C-Mo bonds at the boundary depends on the
+chemical conditions. We find that significant charge transfer between graphene
+and MoS2 is localized at the boundary. We show that the abundant 1D boundary
+states substantially pin the Fermi level in the lateral contact between
+graphene and MoS2, in close analogy to the effect of 2D interfacial states in
+the contacts between 3D materials. Furthermore, we propose specific ways in
+which these effects can be exploited to achieve spin-polarized currents.
+",0,1,0,0,0,0
+2580,Semi-extraspecial groups with an abelian subgroup of maximal possible order,"  Let $p$ be a prime. A $p$-group $G$ is defined to be semi-extraspecial if for
+every maximal subgroup $N$ in $Z(G)$ the quotient $G/N$ is a an extraspecial
+group. In addition, we say that $G$ is ultraspecial if $G$ is semi-extraspecial
+and $|G:G'| = |G'|^2$. In this paper, we prove that every $p$-group of
+nilpotence class $2$ is isomorphic to a subgroup of some ultraspecial group.
+Given a prime $p$ and a positive integer $n$, we provide a framework to
+construct of all the ultraspecial groups order $p^{3n}$ that contain an abelian
+subgroup of order $p^{2n}$. In the literature, it has been proved that every
+ultraspecial group $G$ order $p^{3n}$ with at least two abelian subgroups of
+order $p^{2n}$ can be associated to a semifield. We provide a generalization of
+semifield, and then we show that every semi-extraspecial group $G$ that is the
+product of two abelian subgroups can be associated with this generalization of
+semifield.
+",0,0,1,0,0,0
+2581,Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy Diversification,"  The lack of diversity in a genetic algorithm's population may lead to a bad
+performance of the genetic operators since there is not an equilibrium between
+exploration and exploitation. In those cases, genetic algorithms present a fast
+and unsuitable convergence.
+In this paper we develop a novel hybrid genetic algorithm which attempts to
+obtain a balance between exploration and exploitation. It confronts the
+diversity problem using the named greedy diversification operator. Furthermore,
+the proposed algorithm applies a competition between parent and children so as
+to exploit the high quality visited solutions. These operators are complemented
+by a simple selection mechanism designed to preserve and take advantage of the
+population diversity.
+Additionally, we extend our proposal to the field of memetic algorithms,
+obtaining an improved model with outstanding results in practice.
+The experimental study shows the validity of the approach as well as how
+important is taking into account the exploration and exploitation concepts when
+designing an evolutionary algorithm.
+",1,0,0,0,0,0
+2582,Determinants of Mobile Money Adoption in Pakistan,"  In this work, we analyze the problem of adoption of mobile money in Pakistan
+by using the call detail records of a major telecom company as our input. Our
+results highlight the fact that different sections of the society have
+different patterns of adoption of digital financial services but user mobility
+related features are the most important one when it comes to adopting and using
+mobile money services.
+",0,0,0,1,0,0
+2583,Cherlin's conjecture for almost simple groups of Lie rank 1,"  We prove Cherlin's conjecture, concerning binary primitive permutation
+groups, for those groups with socle isomorphic to $\mathrm{PSL}_2(q)$,
+${^2\mathrm{B}_2}(q)$, ${^2\mathrm{G}_2}(q)$ or $\mathrm{PSU}_3(q)$. Our method
+uses the notion of a ""strongly non-binary action"".
+",0,0,1,0,0,0
+2584,Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations,"  This article concerns the expressive power of depth in neural nets with ReLU
+activations and bounded width. We are particularly interested in the following
+questions: what is the minimal width $w_{\text{min}}(d)$ so that ReLU nets of
+width $w_{\text{min}}(d)$ (and arbitrary depth) can approximate any continuous
+function on the unit cube $[0,1]^d$ aribitrarily well? For ReLU nets near this
+minimal width, what can one say about the depth necessary to approximate a
+given function? Our approach to this paper is based on the observation that,
+due to the convexity of the ReLU activation, ReLU nets are particularly
+well-suited for representing convex functions. In particular, we prove that
+ReLU nets with width $d+1$ can approximate any continuous convex function of
+$d$ variables arbitrarily well. These results then give quantitative depth
+estimates for the rate of approximation of any continuous scalar function on
+the $d$-dimensional cube $[0,1]^d$ by ReLU nets with width $d+3.$
+",1,0,1,1,0,0
+2585,Network Essence: PageRank Completion and Centrality-Conforming Markov Chains,"  Jiří Matoušek (1963-2015) had many breakthrough contributions in
+mathematics and algorithm design. His milestone results are not only profound
+but also elegant. By going beyond the original objects --- such as Euclidean
+spaces or linear programs --- Jirka found the essence of the challenging
+mathematical/algorithmic problems as well as beautiful solutions that were
+natural to him, but were surprising discoveries to the field.
+In this short exploration article, I will first share with readers my initial
+encounter with Jirka and discuss one of his fundamental geometric results from
+the early 1990s. In the age of social and information networks, I will then
+turn the discussion from geometric structures to network structures, attempting
+to take a humble step towards the holy grail of network science, that is to
+understand the network essence that underlies the observed
+sparse-and-multifaceted network data. I will discuss a simple result which
+summarizes some basic algebraic properties of personalized PageRank matrices.
+Unlike the traditional transitive closure of binary relations, the personalized
+PageRank matrices take ""accumulated Markovian closure"" of network data. Some of
+these algebraic properties are known in various contexts. But I hope featuring
+them together in a broader context will help to illustrate the desirable
+properties of this Markovian completion of networks, and motivate systematic
+developments of a network theory for understanding vast and ubiquitous
+multifaceted network data.
+",1,0,0,1,0,0
+2586,A Regularized Framework for Sparse and Structured Neural Attention,"  Modern neural networks are often augmented with an attention mechanism, which
+tells the network where to focus within the input. We propose in this paper a
+new framework for sparse and structured attention, building upon a smoothed max
+operator. We show that the gradient of this operator defines a mapping from
+real values to probabilities, suitable as an attention mechanism. Our framework
+includes softmax and a slight generalization of the recently-proposed sparsemax
+as special cases. However, we also show how our framework can incorporate
+modern structured penalties, resulting in more interpretable attention
+mechanisms, that focus on entire segments or groups of an input. We derive
+efficient algorithms to compute the forward and backward passes of our
+attention mechanisms, enabling their use in a neural network trained with
+backpropagation. To showcase their potential as a drop-in replacement for
+existing ones, we evaluate our attention mechanisms on three large-scale tasks:
+textual entailment, machine translation, and sentence summarization. Our
+attention mechanisms improve interpretability without sacrificing performance;
+notably, on textual entailment and summarization, we outperform the standard
+attention mechanisms based on softmax and sparsemax.
+",1,0,0,1,0,0
+2587,Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning,"  Intrinsically motivated spontaneous exploration is a key enabler of
+autonomous lifelong learning in human children. It allows them to discover and
+acquire large repertoires of skills through self-generation, self-selection,
+self-ordering and self-experimentation of learning goals. We present the
+unsupervised multi-goal reinforcement learning formal framework as well as an
+algorithmic approach called intrinsically motivated goal exploration processes
+(IMGEP) to enable similar properties of autonomous learning in machines. The
+IMGEP algorithmic architecture relies on several principles: 1) self-generation
+of goals as parameterized reinforcement learning problems; 2) selection of
+goals based on intrinsic rewards; 3) exploration with parameterized
+time-bounded policies and fast incremental goal-parameterized policy search; 4)
+systematic reuse of information acquired when targeting a goal for improving
+other goals. We present a particularly efficient form of IMGEP that uses a
+modular representation of goal spaces as well as intrinsic rewards based on
+learning progress. We show how IMGEPs automatically generate a learning
+curriculum within an experimental setup where a real humanoid robot can explore
+multiple spaces of goals with several hundred continuous dimensions. While no
+particular target goal is provided to the system beforehand, this curriculum
+allows the discovery of skills of increasing complexity, that act as stepping
+stone for learning more complex skills (like nested tool use). We show that
+learning several spaces of diverse problems can be more efficient for learning
+complex skills than only trying to directly learn these complex skills. We
+illustrate the computational efficiency of IMGEPs as these robotic experiments
+use a simple memory-based low-level policy representations and search
+algorithm, enabling the whole system to learn online and incrementally on a
+Raspberry Pi 3.
+",1,0,0,0,0,0
+2588,Sentiment Perception of Readers and Writers in Emoji use,"  Previous research has traditionally analyzed emoji sentiment from the point
+of view of the reader of the content not the author. Here, we analyze emoji
+sentiment from the point of view of the author and present a emoji sentiment
+benchmark that was built from an employee happiness dataset where emoji happen
+to be annotated with daily happiness of the author of the comment. The data
+spans over 3 years, and 4k employees of 56 companies based in Barcelona. We
+compare sentiment of writers to readers. Results indicate that, there is an 82%
+agreement in how emoji sentiment is perceived by readers and writers. Finally,
+we report that when authors use emoji they report higher levels of happiness.
+Emoji use was not found to be correlated with differences in author moodiness.
+",1,0,0,0,0,0
+2589,On C-class equations,"  The concept of a C-class of differential equations goes back to E. Cartan
+with the upshot that generic equations in a C-class can be solved without
+integration. While Cartan's definition was in terms of differential invariants
+being first integrals, all results exhibiting C-classes that we are aware of
+are based on the fact that a canonical Cartan geometry associated to the
+equations in the class descends to the space of solutions. For sufficiently low
+orders, these geometries belong to the class of parabolic geometries and the
+results follow from the general characterization of geometries descending to a
+twistor space.
+In this article we answer the question of whether a canonical Cartan geometry
+descends to the space of solutions in the case of scalar ODEs of order at least
+four and of systems of ODEs of order at least three. As in the lower order
+cases, this is characterized by the vanishing of the generalized Wilczynski
+invariants, which are defined via the linearization at a solution. The
+canonical Cartan geometries (which are not parabolic geometries) are a slight
+variation of those available in the literature based on a recent general
+construction. All the verifications needed to apply this construction for the
+classes of ODEs we study are carried out in the article, which thus also
+provides a complete alternative proof for the existence of canonical Cartan
+connections associated to higher order (systems of) ODEs.
+",0,0,1,0,0,0
+2590,Confidence Intervals for Quantiles from Histograms and Other Grouped Data,"  Interval estimation of quantiles has been treated by many in the literature.
+However, to the best of our knowledge there has been no consideration for
+interval estimation when the data are available in grouped format. Motivated by
+this, we introduce several methods to obtain confidence intervals for quantiles
+when only grouped data is available. Our preferred method for interval
+estimation is to approximate the underlying density using the Generalized
+Lambda Distribution (GLD) to both estimate the quantiles and variance of the
+quantile estimators. We compare the GLD method with some other methods that we
+also introduce which are based on a frequency approximation approach and a
+linear interpolation approximation of the density. Our methods are strongly
+supported by simulations showing that excellent coverage can be achieved for a
+wide number of distributions. These distributions include highly-skewed
+distributions such as the log-normal, Dagum and Singh-Maddala distributions. We
+also apply our methods to real data and show that inference can be carried out
+on published outcomes that have been summarized only by a histogram. Our
+methods are therefore useful for a broad range of applications. We have also
+created a web application that can be used to conveniently calculate the
+estimators.
+",0,0,0,1,0,0
+2591,Reach and speed of judgment propagation in the laboratory,"  In recent years, a large body of research has demonstrated that judgments and
+behaviors can propagate from person to person. Phenomena as diverse as
+political mobilization, health practices, altruism, and emotional states
+exhibit similar dynamics of social contagion. The precise mechanisms of
+judgment propagation are not well understood, however, because it is difficult
+to control for confounding factors such as homophily or dynamic network
+structures. We introduce a novel experimental design that renders possible the
+stringent study of judgment propagation. In this design, experimental chains of
+individuals can revise their initial judgment in a visual perception task after
+observing a predecessor's judgment. The positioning of a very good performer at
+the top of a chain created a performance gap, which triggered waves of judgment
+propagation down the chain. We evaluated the dynamics of judgment propagation
+experimentally. Despite strong social influence within pairs of individuals,
+the reach of judgment propagation across a chain rarely exceeded a social
+distance of three to four degrees of separation. Furthermore, computer
+simulations showed that the speed of judgment propagation decayed exponentially
+with the social distance from the source. We show that information distortion
+and the overweighting of other people's errors are two individual-level
+mechanisms hindering judgment propagation at the scale of the chain. Our
+results contribute to the understanding of social contagion processes, and our
+experimental method offers numerous new opportunities to study judgment
+propagation in the laboratory.
+",1,1,0,0,0,0
+2592,Texture Characterization by Using Shape Co-occurrence Patterns,"  Texture characterization is a key problem in image understanding and pattern
+recognition. In this paper, we present a flexible shape-based texture
+representation using shape co-occurrence patterns. More precisely, texture
+images are first represented by tree of shapes, each of which is associated
+with several geometrical and radiometric attributes. Then four typical kinds of
+shape co-occurrence patterns based on the hierarchical relationship of the
+shapes in the tree are learned as codewords. Three different coding methods are
+investigated to learn the codewords, with which, any given texture image can be
+encoded into a descriptive vector. In contrast with existing works, the
+proposed method not only inherits the strong ability to depict geometrical
+aspects of textures and the high robustness to variations of imaging conditions
+from the shape-based method, but also provides a flexible way to consider shape
+relationships and to compute high-order statistics on the tree. To our
+knowledge, this is the first time to use co-occurrence patterns of explicit
+shapes as a tool for texture analysis. Experiments on various texture datasets
+and scene datasets demonstrate the efficiency of the proposed method.
+",1,0,0,0,0,0
+2593,NGC 3105: A Young Cluster in the Outer Galaxy,"  Images and spectra of the open cluster NGC 3105 have been obtained with GMOS
+on Gemini South. The (i', g'-i') color-magnitude diagram (CMD) constructed from
+these data extends from the brightest cluster members to g'~23. This is 4 - 5
+mag fainter than previous CMDs at visible wavelengths and samples cluster
+members with sub-solar masses. Assuming a half-solar metallicity, comparisons
+with isochrones yield a distance of 6.6+/-0.3 kpc. An age of at least 32 Myr is
+found based on the photometric properties of the brightest stars, coupled with
+the apparent absence of pre-main sequence stars in the lower regions of the
+CMD. The luminosity function of stars between 50 and 70 arcsec from the cluster
+center is consistent with a Chabrier lognormal mass function. However, at radii
+smaller than 50 arcsec there is a higher specific frequency of the most massive
+main sequence stars than at larger radii. Photometry obtained from archival
+SPITZER images reveals that some of the brightest stars near NGC 3105 have
+excess infrared emission, presumably from warm dust envelopes. Halpha emission
+is detected in a few early-type stars in and around the cluster, building upon
+previous spectroscopic observations that found Be stars near NGC 3105. The
+equivalent width of the NaD lines in the spectra of early type stars is
+consistent with the reddening found from comparisons with isochrones. Stars
+with i'~18.5 that fall near the cluster main sequence have a spectral-type A5V,
+and a distance modulus that is consistent with that obtained by comparing
+isochrones with the CMD is found assuming solar neighborhood intrinsic
+brightnesses for these stars.
+",0,1,0,0,0,0
+2594,Exact solution of a two-species quantum dimer model for pseudogap metals,"  We present an exact ground state solution of a quantum dimer model introduced
+in Ref.[1], which features ordinary bosonic spin-singlet dimers as well as
+fermionic dimers that can be viewed as bound states of spinons and holons in a
+hole-doped resonating valence bond liquid. Interestingly, this model captures
+several essential properties of the metallic pseudogap phase in high-$T_c$
+cuprate superconductors. We identify a line in parameter space where the exact
+ground state wave functions can be constructed at an arbitrary density of
+fermionic dimers. At this exactly solvable line the ground state has a huge
+degeneracy, which can be interpreted as a flat band of fermionic excitations.
+Perturbing around the exactly solvable line, this degeneracy is lifted and the
+ground state is a fractionalized Fermi liquid with a small pocket Fermi surface
+in the low doping limit.
+",0,1,0,0,0,0
+2595,Strong instability of standing waves for nonlinear Schrödinger equations with a partial confinement,"  We study the instability of standing wave solutions for nonlinear
+Schrödinger equations with a one-dimensional harmonic potential in
+dimension $N\ge 2$. We prove that if the nonlinearity is $L^2$-critical or
+supercritical in dimension $N-1$, then any ground states are strongly unstable
+by blowup.
+",0,0,1,0,0,0
+2596,Theory and Applications of Matrix-Weighted Consensus,"  This paper proposes the matrix-weighted consensus algorithm, which is a
+generalization of the consensus algorithm in the literature. Given a networked
+dynamical system where the interconnections between agents are weighted by
+nonnegative definite matrices instead of nonnegative scalars, consensus and
+clustering phenomena naturally exist. We examine algebraic and algebraic graph
+conditions for achieving a consensus, and provide an algorithm for finding all
+clusters of a given system. Finally, we illustrate two applications of the
+proposed consensus algorithm in clustered consensus and in bearing-based
+formation control.
+",0,0,1,0,0,0
+2597,Multi-armed Bandit Problems with Strategic Arms,"  We study a strategic version of the multi-armed bandit problem, where each
+arm is an individual strategic agent and we, the principal, pull one arm each
+round. When pulled, the arm receives some private reward $v_a$ and can choose
+an amount $x_a$ to pass on to the principal (keeping $v_a-x_a$ for itself). All
+non-pulled arms get reward $0$. Each strategic arm tries to maximize its own
+utility over the course of $T$ rounds. Our goal is to design an algorithm for
+the principal incentivizing these arms to pass on as much of their private
+rewards as possible.
+When private rewards are stochastically drawn each round ($v_a^t \leftarrow
+D_a$), we show that:
+- Algorithms that perform well in the classic adversarial multi-armed bandit
+setting necessarily perform poorly: For all algorithms that guarantee low
+regret in an adversarial setting, there exist distributions $D_1,\ldots,D_k$
+and an approximate Nash equilibrium for the arms where the principal receives
+reward $o(T)$.
+- Still, there exists an algorithm for the principal that induces a game
+among the arms where each arm has a dominant strategy. When each arm plays its
+dominant strategy, the principal sees expected reward $\mu'T - o(T)$, where
+$\mu'$ is the second-largest of the means $\mathbb{E}[D_{a}]$. This algorithm
+maintains its guarantee if the arms are non-strategic ($x_a = v_a$), and also
+if there is a mix of strategic and non-strategic arms.
+",1,0,0,1,0,0
+2598,Emerging Topics in Assistive Reading Technology: From Presentation to Content Accessibility,"  With the recent focus in the accessibility field, researchers from academia
+and industry have been very active in developing innovative techniques and
+tools for assistive technology. Especially with handheld devices getting ever
+powerful and being able to recognize the user's voice, screen magnification for
+individuals with low-vision, and eye tracking devices used in studies with
+individuals with physical and intellectual disabilities, the science field is
+quickly adapting and creating conclusions as well as products to help. In this
+paper, we will focus on new technology and tools to help make reading
+easier--including reformatting document presentation (for people with physical
+vision impairments) and text simplification to make information itself easier
+to interpret (for people with intellectual disabilities). A real-world case
+study is reported based on our experience to make documents more accessible.
+",1,0,0,0,0,0
+2599,Supervised learning with quantum enhanced feature spaces,"  Machine learning and quantum computing are two technologies each with the
+potential for altering how computation is performed to address previously
+untenable problems. Kernel methods for machine learning are ubiquitous for
+pattern recognition, with support vector machines (SVMs) being the most
+well-known method for classification problems. However, there are limitations
+to the successful solution to such problems when the feature space becomes
+large, and the kernel functions become computationally expensive to estimate. A
+core element to computational speed-ups afforded by quantum algorithms is the
+exploitation of an exponentially large quantum state space through controllable
+entanglement and interference. Here, we propose and experimentally implement
+two novel methods on a superconducting processor. Both methods represent the
+feature space of a classification problem by a quantum state, taking advantage
+of the large dimensionality of quantum Hilbert space to obtain an enhanced
+solution. One method, the quantum variational classifier builds on [1,2] and
+operates through using a variational quantum circuit to classify a training set
+in direct analogy to conventional SVMs. In the second, a quantum kernel
+estimator, we estimate the kernel function and optimize the classifier
+directly. The two methods present a new class of tools for exploring the
+applications of noisy intermediate scale quantum computers [3] to machine
+learning.
+",0,0,0,1,0,0
+2600,On The Limitation of Some Fully Observable Multiple Session Resilient Shoulder Surfing Defense Mechanisms,"  Using password based authentication technique, a system maintains the login
+credentials (username, password) of the users in a password file. Once the
+password file is compromised, an adversary obtains both the login credentials.
+With the advancement of technology, even if a password is maintained in hashed
+format, then also the adversary can invert the hashed password to get the
+original one. To mitigate this threat, most of the systems nowadays store some
+system generated fake passwords (also known as honeywords) along with the
+original password of a user. This type of setup confuses an adversary while
+selecting the original password. If the adversary chooses any of these
+honeywords and submits that as a login credential, then system detects the
+attack. A large number of significant work have been done on designing
+methodologies (identified as $\text{M}^{\text{DS}}_{\text{OA}}$) that can
+protect password against observation or, shoulder surfing attack. Under this
+attack scenario, an adversary observes (or records) the login information
+entered by a user and later uses those credentials to impersonate the genuine
+user. In this paper, we have shown that because of their design principle, a
+large subset of $\text{M}^{\text{DS}}_{\text{OA}}$ (identified as
+$\text{M}^{\text{FODS}}_{\text{SOA}}$) cannot afford to store honeywords in
+password file. Thus these methods, belonging to
+$\text{M}^{\text{FODS}}_{\text{SOA}}$, are unable to provide any kind of
+security once password file gets compromised. Through our contribution in this
+paper, by still using the concept of honeywords, we have proposed few generic
+principles to mask the original password of
+$\text{M}^{\text{FODS}}_{\text{SOA}}$ category methods. We also consider few
+well-established methods like S3PAS, CHC, PAS and COP belonging to
+$\text{M}^{\text{FODS}}_{\text{SOA}}$, to show that proposed idea is
+implementable in practice.
+",1,0,0,0,0,0
+2601,Predictive and Prescriptive Analytics for Location Selection of Add-on Retail Products,"  In this paper, we study an analytical approach to selecting expansion
+locations for retailers selling add-on products whose demand is derived from
+the demand of another base product. Demand for the add-on product is realized
+only as a supplement to the demand of the base product. In our context, either
+of the two products could be subject to spatial autocorrelation where demand at
+a given location is impacted by demand at other locations. Using data from an
+industrial partner selling add-on products, we build predictive models for
+understanding the derived demand of the add-on product and establish an
+optimization framework for automating expansion decisions to maximize expected
+sales. Interestingly, spatial autocorrelation and the complexity of the
+predictive model impact the complexity and the structure of the prescriptive
+optimization model. Our results indicate that the models formulated are highly
+effective in predicting add-on product sales, and that using the optimization
+framework built on the predictive model can result in substantial increases in
+expected sales over baseline policies.
+",0,0,0,1,0,0
+2602,Algebraic characterization of regular fractions under level permutations,"  In this paper we study the behavior of the fractions of a factorial design
+under permutations of the factor levels. We focus on the notion of regular
+fraction and we introduce methods to check whether a given symmetric orthogonal
+array can or can not be transformed into a regular fraction by means of
+suitable permutations of the factor levels. The proposed techniques take
+advantage of the complex coding of the factor levels and of some tools from
+polynomial algebra. Several examples are described, mainly involving factors
+with five levels.
+",0,0,1,1,0,0
+2603,Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models,"  Biomedical events describe complex interactions between various biomedical
+entities. Event trigger is a word or a phrase which typically signifies the
+occurrence of an event. Event trigger identification is an important first step
+in all event extraction methods. However many of the current approaches either
+rely on complex hand-crafted features or consider features only within a
+window. In this paper we propose a method that takes the advantage of recurrent
+neural network (RNN) to extract higher level features present across the
+sentence. Thus hidden state representation of RNN along with word and entity
+type embedding as features avoid relying on the complex hand-crafted features
+generated using various NLP toolkits. Our experiments have shown to achieve
+state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have
+also performed category-wise analysis of the result and discussed the
+importance of various features in trigger identification task.
+",1,0,0,0,0,0
+2604,Modern-day Universities and Regional Development,"  Nowadays it is quite evident that knowledge-based society necessarily
+involves the revaluation of human and intangible assets, as the advancement of
+local economies significantly depend on the qualitative and quantitative
+characteristics of human capital[Lundvall, 2004]. As we can instantaneously
+link the universities as main actors in the creation of highly-qualified labour
+force, the role of universities increases parallel to the previously mentioned
+progresses. Universities are the general institutions of education, however i
+nthe need of adaptation to present local needs, their activities have broadened
+in the past decades [Wright et al, 2008; Etzkowitz, 2002]. Most universities
+experienced a transition period in which next to their classic activities,
+namely education and research, so called third mission activities also started
+to count, thus serving many purposes of economy and society.
+",1,0,0,0,0,0
+2605,Method of Reduction of Variables for Bilinear Matrix Inequality Problems in System and Control Designs,"  Bilinear matrix inequality (BMI) problems in system and control designs are
+investigated in this paper. A solution method of reduction of variables (MRVs)
+is proposed. This method consists of a principle of variable classification, a
+procedure for problem transformation, and a hybrid algorithm that combines
+deterministic and stochastic search engines. The classification principle is
+used to classify the decision variables of a BMI problem into two categories:
+1) external and 2) internal variables. Theoretical analysis is performed to
+show that when the classification principle is applicable, a BMI problem can be
+transformed into an unconstrained optimization problem that has fewer decision
+variables. Stochastic search and deterministic search are then applied to
+determine the decision variables of the unconstrained problem externally and
+explore the internal problem structure, respectively. The proposed method can
+address feasibility, single-objective, and multiobjective problems constrained
+by BMIs in a unified manner. A number of numerical examples in system and
+control designs are provided to validate the proposed methodology. Simulations
+show that the MRVs can outperform existing BMI solution methods in most
+benchmark problems and achieve similar levels of performance in the remaining
+problems.
+",1,0,0,0,0,0
+2606,Nonlinear transport associated with spin-density-wave dynamics in Ca$_3$Co$_{4}$O$_9$,"  We have carried out the transient nonlinear transport measurements on the
+layered cobalt oxide Ca$_3$Co$_{4}$O$_9$, in which a spin density wave (SDW)
+transition is proposed at $T_{\rm SDW} \simeq 30$ K. We find that, below
+$T_{\rm SDW}$, the electrical conductivity systematically varies with both the
+applied current and the time, indicating a close relationship between the
+observed nonlinear conduction and the SDW order in this material. The time
+dependence of the conductivity is well analyzed by considering the dynamics of
+SDW which involves a low-field deformation and a sliding motion above a
+threshold field. We also measure the transport properties of the isovalent
+Sr-substituted systems to examine an impurity effect on the nonlinear response,
+and discuss the obtained threshold fields in terms of thermal fluctuations of
+the SDW order parameter.
+",0,1,0,0,0,0
+2607,Bayesian Compression for Deep Learning,"  Compression and computational efficiency in deep learning have become a
+problem of great significance. In this work, we argue that the most principled
+and effective way to attack this problem is by adopting a Bayesian point of
+view, where through sparsity inducing priors we prune large parts of the
+network. We introduce two novelties in this paper: 1) we use hierarchical
+priors to prune nodes instead of individual weights, and 2) we use the
+posterior uncertainties to determine the optimal fixed point precision to
+encode the weights. Both factors significantly contribute to achieving the
+state of the art in terms of compression rates, while still staying competitive
+with methods designed to optimize for speed or energy efficiency.
+",1,0,0,1,0,0
+2608,The Observability Concept in a Class of Hybrid Control systems,"  In the discrete modeling approach for hybrid control systems, the continuous
+plant is reduced to a discrete event approximation, called the DES-plant, that
+is governed by a discrete event system, representing the controller. The
+observability of the DES-plant model is crucial for the synthesis of the
+controller and for the proper closed loop evolution of the hybrid control
+system. Based on a version of the framework for hybrid control systems proposed
+by Antsaklis, the paper analysis the relation between the properties of the
+cellular space of the continuous plant and a mechanism of plant-symbols
+generation, on one side, and the observability of the DES-plant automaton on
+the other side. Finally an observable discrete event abstraction of the
+continuous double integrator is presented.
+",1,0,1,0,0,0
+2609,Towards a More Reliable Privacy-preserving Recommender System,"  This paper proposes a privacy-preserving distributed recommendation
+framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the
+privacy of value, model and existence altogether. That says, not only the
+ratings from the users to the items, but also the existence of the ratings as
+well as the learned recommendation model are kept private in our framework. Our
+solution relies on a distributed client-server architecture and a two-stage
+Randomized Response algorithm, along with an implementation on the popular
+recommendation model, Matrix Factorization (MF). We further prove SDCF to meet
+the guarantee of Differential Privacy so that clients are allowed to specify
+arbitrary privacy levels. Experiments conducted on numerical rating prediction
+and one-class rating action prediction exhibit that SDCF does not sacrifice too
+much accuracy for privacy.
+",1,0,0,0,0,0
+2610,A study of posture judgement on vehicles using wearable acceleration sensor,"  We study methods to estimate drivers' posture in vehicles using acceleration
+data of wearable sensor and conduct field tests. To prevent fatal accidents,
+demands for safety management of bus and taxi are high. However, acceleration
+of vehicles is added to wearable sensor in vehicles. Therefore, we study
+methods to estimate driving posture using acceleration data acquired from shirt
+type wearable sensor hitoe and conduct field tests.
+",1,0,0,0,0,0
+2611,Smoothed nonparametric two-sample tests,"  We propose new smoothed median and the Wilcoxon's rank sum test. As is
+pointed out by Maesono et al.(2016), some nonparametric discrete tests have a
+problem with their significance probability. Because of this problem, the
+selection of the median and the Wilcoxon's test can be biased too, however, we
+show new smoothed tests are free from the problem. Significance probabilities
+and local asymptotic powers of the new tests are studied, and we show that they
+inherit good properties of the discrete tests.
+",0,0,1,1,0,0
+2612,The Complexity of Graph-Based Reductions for Reachability in Markov Decision Processes,"  We study the never-worse relation (NWR) for Markov decision processes with an
+infinite-horizon reachability objective. A state q is never worse than a state
+p if the maximal probability of reaching the target set of states from p is at
+most the same value from q, regard- less of the probabilities labelling the
+transitions. Extremal-probability states, end components, and essential states
+are all special cases of the equivalence relation induced by the NWR. Using the
+NWR, states in the same equivalence class can be collapsed. Then, actions
+leading to sub- optimal states can be removed. We show the natural decision
+problem associated to computing the NWR is coNP-complete. Finally, we ex- tend
+a previously known incomplete polynomial-time iterative algorithm to
+under-approximate the NWR.
+",1,0,0,0,0,0
+2613,A stack-vector routing protocol for automatic tunneling,"  In a network, a tunnel is a part of a path where a protocol is encapsulated
+in another one. A tunnel starts with an encapsulation and ends with the
+corresponding decapsulation. Several tunnels can be nested at some stage,
+forming a protocol stack. Tunneling is very important nowadays and it is
+involved in several tasks: IPv4/IPv6 transition, VPNs, security (IPsec, onion
+routing), etc. However, tunnel establishment is mainly performed manually or by
+script, which present obvious scalability issues. Some works attempt to
+automate a part of the process (e.g., TSP, ISATAP, etc.). However, the
+determination of the tunnel(s) endpoints is not fully automated, especially in
+the case of an arbitrary number of nested tunnels. The lack of routing
+protocols performing automatic tunneling is due to the unavailability of path
+computation algorithms taking into account encapsulations and decapsulations.
+There is a polynomial centralized algorithm to perform the task. However, to
+the best of our knowledge, no fully distributed path computation algorithm is
+known. Here, we propose the first fully distributed algorithm for path
+computation with automatic tunneling, i.e., taking into account encapsulation,
+decapsulation and conversion of protocols. Our algorithm is a generalization of
+the distributed Bellman-Ford algorithm, where the distance vector is replaced
+by a protocol stack vector. This allows to know how to route a packet with some
+protocol stack. We prove that the messages size of our algorithm is polynomial,
+even if the shortest path can be of exponential length. We also prove that the
+algorithm converges after a polynomial number of steps in a synchronized
+setting. We adapt our algorithm into a proto-protocol for routing with
+automatic tunneling and we show its efficiency through simulations.
+",1,0,0,0,0,0
+2614,Using of heterogeneous corpora for training of an ASR system,"  The paper summarizes the development of the LVCSR system built as a part of
+the Pashto speech-translation system at the SCALE (Summer Camp for Applied
+Language Exploration) 2015 workshop on ""Speech-to-text-translation for
+low-resource languages"". The Pashto language was chosen as a good ""proxy""
+low-resource language, exhibiting multiple phenomena which make the
+speech-recognition and and speech-to-text-translation systems development hard.
+Even when the amount of data is seemingly sufficient, given the fact that the
+data originates from multiple sources, the preliminary experiments reveal that
+there is little to no benefit in merging (concatenating) the corpora and more
+elaborate ways of making use of all of the data must be worked out.
+This paper concentrates only on the LVCSR part and presents a range of
+different techniques that were found to be useful in order to benefit from
+multiple different corpora
+",1,0,0,0,0,0
+2615,Inferring Narrative Causality between Event Pairs in Films,"  To understand narrative, humans draw inferences about the underlying
+relations between narrative events. Cognitive theories of narrative
+understanding define these inferences as four different types of causality,
+that include pairs of events A, B where A physically causes B (X drop, X
+break), to pairs of events where A causes emotional state B (Y saw X, Y felt
+fear). Previous work on learning narrative relations from text has either
+focused on ""strict"" physical causality, or has been vague about what relation
+is being learned. This paper learns pairs of causal events from a corpus of
+film scene descriptions which are action rich and tend to be told in
+chronological order. We show that event pairs induced using our methods are of
+high quality and are judged to have a stronger causal relation than event pairs
+from Rel-grams.
+",1,0,0,0,0,0
+2616,On Hom-Gerstenhaber algebras and Hom-Lie algebroids,"  We define the notion of hom-Batalin-Vilkovisky algebras and strong
+differential hom-Gerstenhaber algebras as a special class of hom-Gerstenhaber
+algebras and provide canonical examples associated to some well-known
+hom-structures. Representations of a hom-Lie algebroid on a hom-bundle are
+defined and a cohomology of a regular hom-Lie algebroid with coefficients in a
+representation is studied. We discuss about relationship between these classes
+of hom-Gerstenhaber algebras and geometric structures on a vector bundle. As an
+application, we associate a homology to a regular hom-Lie algebroid and then
+define a hom-Poisson homology associated to a hom-Poisson manifold.
+",0,0,1,0,0,0
+2617,Global existence in the 1D quasilinear parabolic-elliptic chemotaxis system with critical nonlinearity,"  The paper should be viewed as complement of an earlier result in [8]. In the
+paper just mentioned it is shown that 1d case of a quasilinear
+parabolic-elliptic Keller-Segel system is very special. Namely, unlike in
+higher dimensions, there is no critical nonlinearity. Indeed, for the nonlinear
+diffusion of the form 1/u all the solutions, independently on the magnitude of
+initial mass, stay bounded. However, the argument presented in [8] deals with
+the Jager-Luckhaus type system. And is very sensitive to this restriction.
+Namely, the change of variables introduced in [8], being a main step of the
+method, works only for the Jager-Luckhaus modification. It does not seem to be
+applicable in the usual version of the parabolic-elliptic Keller-Segel system.
+The present paper fulfils this gap and deals with the case of the usual
+parabolic-elliptic version. To handle it we establish a new Lyapunov-like
+functional (it is related to what was done in [8]), which leads to global
+existence of the initial-boundary value problem for any initial mass.
+",0,0,1,0,0,0
+2618,Supercongruences between truncated ${}_3F_2$ hypergeometric series,"  We establish four supercongruences between truncated ${}_3F_2$ hypergeometric
+series involving $p$-adic Gamma functions, which extend some of the
+Rodriguez-Villegas supercongruences.
+",0,0,1,0,0,0
+2619,Indoor Localization Using Visible Light Via Fusion Of Multiple Classifiers,"  A multiple classifiers fusion localization technique using received signal
+strengths (RSSs) of visible light is proposed, in which the proposed system
+transmits different intensity modulated sinusoidal signals by LEDs and the
+signals received by a Photo Diode (PD) placed at various grid points. First, we
+obtain some {\emph{approximate}} received signal strengths (RSSs) fingerprints
+by capturing the peaks of power spectral density (PSD) of the received signals
+at each given grid point. Unlike the existing RSSs based algorithms, several
+representative machine learning approaches are adopted to train multiple
+classifiers based on these RSSs fingerprints. The multiple classifiers
+localization estimators outperform the classical RSS-based LED localization
+approaches in accuracy and robustness. To further improve the localization
+performance, two robust fusion localization algorithms, namely, grid
+independent least square (GI-LS) and grid dependent least square (GD-LS), are
+proposed to combine the outputs of these classifiers. We also use a singular
+value decomposition (SVD) based LS (LS-SVD) method to mitigate the numerical
+stability problem when the prediction matrix is singular. Experiments conducted
+on intensity modulated direct detection (IM/DD) systems have demonstrated the
+effectiveness of the proposed algorithms. The experimental results show that
+the probability of having mean square positioning error (MSPE) of less than 5cm
+achieved by GD-LS is improved by 93.03\% and 93.15\%, respectively, as compared
+to those by the RSS ratio (RSSR) and RSS matching methods with the FFT length
+of 2000.
+",1,0,0,1,0,0
+2620,Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms,"  Embedding graph nodes into a vector space can allow the use of machine
+learning to e.g. predict node classes, but the study of node embedding
+algorithms is immature compared to the natural language processing field
+because of a diverse nature of graphs. We examine the performance of node
+embedding algorithms with respect to graph centrality measures that
+characterize diverse graphs, through systematic experiments with four node
+embedding algorithms, four or five graph centralities, and six datasets.
+Experimental results give insights into the properties of node embedding
+algorithms, which can be a basis for further research on this topic.
+",1,0,0,1,0,0
+2621,Data Fusion Reconstruction of Spatially Embedded Complex Networks,"  We introduce a kernel Lasso (kLasso) optimization that simultaneously
+accounts for spatial regularity and network sparsity to reconstruct spatial
+complex networks from data. Through a kernel function, the proposed approach
+exploits spatial embedding distances to penalize overabundance of spatially
+long-distance connections. Examples of both synthetic and real-world spatial
+networks show that the proposed method improves significantly upon existing
+network reconstruction techniques that mainly concerns sparsity but not spatial
+regularity. Our results highlight the promise of data fusion in the
+reconstruction of complex networks, by utilizing both microscopic node-level
+dynamics (e.g., time series data) and macroscopic network-level information
+(metadata).
+",0,1,0,1,0,0
+2622,"Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging","  We consider the problem of reconstructing signals and images from periodic
+nonlinearities. For such problems, we design a measurement scheme that supports
+efficient reconstruction; moreover, our method can be adapted to extend to
+compressive sensing-based signal and image acquisition systems. Our techniques
+can be potentially useful for reducing the measurement complexity of high
+dynamic range (HDR) imaging systems, with little loss in reconstruction
+quality. Several numerical experiments on real data demonstrate the
+effectiveness of our approach.
+",0,0,0,1,0,0
+2623,Multirole Logic (Extended Abstract),"  We identify multirole logic as a new form of logic in which
+conjunction/disjunction is interpreted as an ultrafilter on the power set of
+some underlying set (of roles) and the notion of negation is generalized to
+endomorphisms on this underlying set. We formalize both multirole logic (MRL)
+and linear multirole logic (LMRL) as natural generalizations of classical logic
+(CL) and classical linear logic (CLL), respectively, and also present a
+filter-based interpretation for intuitionism in multirole logic. Among various
+meta-properties established for MRL and LMRL, we obtain one named multiparty
+cut-elimination stating that every cut involving one or more sequents (as a
+generalization of a (binary) cut involving exactly two sequents) can be
+eliminated, thus extending the celebrated result of cut-elimination by Gentzen.
+",1,0,1,0,0,0
+2624,Interpreting Classifiers through Attribute Interactions in Datasets,"  In this work we present the novel ASTRID method for investigating which
+attribute interactions classifiers exploit when making predictions. Attribute
+interactions in classification tasks mean that two or more attributes together
+provide stronger evidence for a particular class label. Knowledge of such
+interactions makes models more interpretable by revealing associations between
+attributes. This has applications, e.g., in pharmacovigilance to identify
+interactions between drugs or in bioinformatics to investigate associations
+between single nucleotide polymorphisms. We also show how the found attribute
+partitioning is related to a factorisation of the data generating distribution
+and empirically demonstrate the utility of the proposed method.
+",1,0,0,1,0,0
+2625,A Modified Levy Jump-Diffusion Model Based on Market Sentiment Memory for Online Jump Prediction,"  In this paper, we propose a modified Levy jump diffusion model with market
+sentiment memory for stock prices, where the market sentiment comes from data
+mining implementation using Tweets on Twitter. We take the market sentiment
+process, which has memory, as the signal of Levy jumps in the stock price. An
+online learning and optimization algorithm with the Unscented Kalman filter
+(UKF) is then proposed to learn the memory and to predict possible price jumps.
+Experiments show that the algorithm provides a relatively good performance in
+identifying asset return trends.
+",1,0,0,0,0,0
+2626,Testing approximate predictions of displacements of cosmological dark matter halos,"  We present a test to quantify how well some approximate methods, designed to
+reproduce the mildly non-linear evolution of perturbations, are able to
+reproduce the clustering of DM halos once the grouping of particles into halos
+is defined and kept fixed. The following methods have been considered:
+Lagrangian Perturbation Theory (LPT) up to third order, Truncated LPT,
+Augmented LPT, MUSCLE and COLA. The test runs as follows: halos are defined by
+applying a friends-of-friends (FoF) halo finder to the output of an N-body
+simulation. The approximate methods are then applied to the same initial
+conditions of the simulation, producing for all particles displacements from
+their starting position and velocities. The position and velocity of each halo
+are computed by averaging over the particles that belong to that halo,
+according to the FoF halo finder. This procedure allows us to perform a
+well-posed test of how clustering of the matter density and halo density fields
+are recovered, without asking to the approximate method an accurate
+reconstruction of halos. We have considered the results at $z=0,0.5,1$, and we
+have analysed power spectrum in real and redshift space, object-by-object
+difference in position and velocity, density Probability Distribution Function
+(PDF) and its moments, phase difference of Fourier modes. We find that higher
+LPT orders are generally able to better reproduce the clustering of halos,
+while little or no improvement is found for the matter density field when going
+to 2LPT and 3LPT. Augmentation provides some improvement when coupled with
+2LPT, while its effect is limited when coupled with 3LPT. Little improvement is
+brought by MUSCLE with respect to Augmentation. The more expensive
+particle-mesh code COLA outperforms all LPT methods [abridged]
+",0,1,0,0,0,0
+2627,Efficient and Secure Routing Protocol for WSN-A Thesis,"  Advances in Wireless Sensor Network (WSN) have provided the availability of
+small and low-cost sensors with the capability of sensing various types of
+physical and environmental conditions, data processing, and wireless
+communication. Since WSN protocols are application specific, the focus has been
+given to the routing protocols that might differ depending on the application
+and network architecture. In this work, novel routing protocols have been
+proposed which is a cluster-based security protocol is named as Efficient and
+Secure Routing Protocol (ESRP) for WSN. The goal of ESRP is to provide an
+energy efficient routing solution with dynamic security features for clustered
+WSN. During the network formation, a node which is connected to a Personal
+Computer (PC) has been selected as a sink node. Once the sensor nodes were
+deployed, the sink node logically segregates the other nodes in a cluster
+structure and subsequently creates a WSN. This centralized cluster formation
+method is used to reduce the node level processing burden and avoid multiple
+communications. In order to ensure reliable data delivery, various security
+features have been incorporated in the proposed protocol such as Modified
+Zero-Knowledge Protocol (MZKP), Promiscuous hearing method, Trapping of
+adversaries and Mine detection. One of the unique features of this ESRP is that
+it can dynamically decide about the selection of these security methods, based
+on the residual energy of nodes.
+",1,0,0,0,0,0
+2628,Jackknife variance estimation for common mean estimators under ordered variances and general two-sample statistics,"  Samples with a common mean but possibly different, ordered variances arise in
+various fields such as interlaboratory experiments, field studies or the
+analysis of sensor data. Estimators for the common mean under ordered variances
+typically employ random weights, which depend on the sample means and the
+unbiased variance estimators. They take different forms when the sample
+estimators are in agreement with the order constraints or not, which
+complicates even basic analyses such as estimating their variance. We propose
+to use the jackknife, whose consistency is established for general smooth
+two--sample statistics induced by continuously Gâteux or Fréchet
+differentiable functionals, and, more generally, asymptotically linear
+two--sample statistics, allowing us to study a large class of common mean
+estimators. Further, it is shown that the common mean estimators under
+consideration satisfy a central limit theorem (CLT). We investigate the
+accuracy of the resulting confidence intervals by simulations and illustrate
+the approach by analyzing several data sets.
+",0,0,1,1,0,0
+2629,ISM properties of a Massive Dusty Star-Forming Galaxy discovered at z ~ 7,"  We report the discovery and constrain the physical conditions of the
+interstellar medium of the highest-redshift millimeter-selected dusty
+star-forming galaxy (DSFG) to date, SPT-S J031132-5823.4 (hereafter
+SPT0311-58), at $z=6.900 +/- 0.002$. SPT0311-58 was discovered via its 1.4mm
+thermal dust continuum emission in the South Pole Telescope (SPT)-SZ survey.
+The spectroscopic redshift was determined through an ALMA 3mm frequency scan
+that detected CO(6-5), CO(7-6) and [CI](2-1), and subsequently confirmed by
+detections of CO(3-2) with ATCA and [CII] with APEX. We constrain the
+properties of the ISM in SPT0311-58 with a radiative transfer analysis of the
+dust continuum photometry and the CO and [CI] line emission. This allows us to
+determine the gas content without ad hoc assumptions about gas mass scaling
+factors. SPT0311-58 is extremely massive, with an intrinsic gas mass of $M_{\rm
+gas} = 3.3 \pm 1.9 \times10^{11}\,M_{\odot}$. Its large mass and intense star
+formation is very rare for a source well into the Epoch of Reionization.
+",0,1,0,0,0,0
+2630,A convex formulation of traffic dynamics on transportation networks,"  This article proposes a numerical scheme for computing the evolution of
+vehicular traffic on a road network over a finite time horizon. The traffic
+dynamics on each link is modeled by the Hamilton-Jacobi (HJ) partial
+differential equation (PDE), which is an equivalent form of the
+Lighthill-Whitham-Richards PDE. The main contribution of this article is the
+construction of a single convex optimization program which computes the traffic
+flow at a junction over a finite time horizon and decouples the PDEs on
+connecting links. Compared to discretization schemes which require the
+computation of all traffic states on a time-space grid, the proposed convex
+optimization approach computes the boundary flows at the junction using only
+the initial condition on links and the boundary conditions of the network. The
+computed boundary flows at the junction specify the boundary condition for the
+HJ PDE on connecting links, which then can be separately solved using an
+existing semi-explicit scheme for single link HJ PDE. As demonstrated in a
+numerical example of ramp metering control, the proposed convex optimization
+approach also provides a natural framework for optimal traffic control
+applications.
+",0,1,1,0,0,0
+2631,Computational and informatics advances for reproducible data analysis in neuroimaging,"  The reproducibility of scientific research has become a point of critical
+concern. We argue that openness and transparency are critical for
+reproducibility, and we outline an ecosystem for open and transparent science
+that has emerged within the human neuroimaging community. We discuss the range
+of open data sharing resources that have been developed for neuroimaging data,
+and the role of data standards (particularly the Brain Imaging Data Structure)
+in enabling the automated sharing, processing, and reuse of large neuroimaging
+datasets. We outline how the open-source Python language has provided the basis
+for a data science platform that enables reproducible data analysis and
+visualization. We also discuss how new advances in software engineering, such
+as containerization, provide the basis for greater reproducibility in data
+analysis. The emergence of this new ecosystem provides an example for many
+areas of science that are currently struggling with reproducibility.
+",0,0,0,0,1,0
+2632,"HPD-invariance of the Tate, Beilinson and Parshin conjectures","  We prove that the Tate, Beilinson and Parshin conjectures are invariant under
+Homological Projective Duality (=HPD). As an application, we obtain a proof of
+these celebrated conjectures (as well as of the strong form of the Tate
+conjecture) in the new cases of linear sections of determinantal varieties and
+complete intersections of quadrics. Furthermore, we extend the original
+conjectures of Tate, Beilinson and Parshin from schemes to stacks and prove
+these extended conjectures for certain low-dimensional global orbifolds.
+",0,0,1,0,0,0
+2633,Multi-dueling Bandits with Dependent Arms,"  The dueling bandits problem is an online learning framework for learning from
+pairwise preference feedback, and is particularly well-suited for modeling
+settings that elicit subjective or implicit human feedback. In this paper, we
+study the problem of multi-dueling bandits with dependent arms, which extends
+the original dueling bandits setting by simultaneously dueling multiple arms as
+well as modeling dependencies between arms. These extensions capture key
+characteristics found in many real-world applications, and allow for the
+opportunity to develop significantly more efficient algorithms than were
+possible in the original setting. We propose the \selfsparring algorithm, which
+reduces the multi-dueling bandits problem to a conventional bandit setting that
+can be solved using a stochastic bandit algorithm such as Thompson Sampling,
+and can naturally model dependencies using a Gaussian process prior. We present
+a no-regret analysis for multi-dueling setting, and demonstrate the
+effectiveness of our algorithm empirically on a wide range of simulation
+settings.
+",1,0,0,0,0,0
+2634,New constraints on the millimetre emission of six debris disks,"  The presence of dusty debris around main sequence stars denotes the existence
+of planetary systems. Such debris disks are often identified by the presence of
+excess continuum emission at infrared and (sub-)millimetre wavelengths, with
+measurements at longer wavelengths tracing larger and cooler dust grains. The
+exponent of the slope of the disk emission at sub-millimetre wavelengths, `q',
+defines the size distribution of dust grains in the disk. This size
+distribution is a function of the rigid strength of the dust producing parent
+planetesimals. As part of the survey `PLAnetesimals around TYpical Pre-main
+seqUence Stars' (PLATYPUS) we observed six debris disks at 9-mm using the
+Australian Telescope Compact Array. We obtain marginal (~3-\sigma) detections
+of three targets: HD 105, HD 61005, and HD 131835. Upper limits for the three
+remaining disks, HD20807, HD109573, and HD109085, provide further constraint of
+the (sub-)millimetre slope of their spectral energy distributions. The values
+of q (or their limits) derived from our observations are all smaller than the
+oft-assumed steady state collisional cascade model (q = 3.5), but lie well
+within the theoretically expected range for debris disks q ~ 3 to 4. The
+measured q values for our targets are all < 3.3, consistent with both
+collisional modelling results and theoretical predictions for parent
+planetesimal bodies being `rubble piles' held together loosely by their
+self-gravity.
+",0,1,0,0,0,0
+2635,Bosonic integer quantum Hall effect as topological pumping,"  Based on a quasi-one-dimensional limit of quantum Hall states on a thin
+torus, we construct a model of interaction-induced topological pumping which
+mimics the Hall response of the bosonic integer quantum Hall (BIQH) state. The
+quasi-one-dimensional counterpart of the BIQH state is identified as the
+Haldane phase composed of two-component bosons which form effective spin-$1$
+degrees of freedom. An adiabatic change between the Haldane phase and trivial
+Mott insulators constitute {\it off-diagonal} topological pumping in which the
+translation of the lattice potential for one component induces a current in the
+other. The mechanism of this pumping is interpreted in terms of changes in
+polarizations between symmetry-protected quantized values.
+",0,1,0,0,0,0
+2636,Connected Vehicular Transportation: Data Analytics and Traffic-dependent Networking,"  With onboard operating systems becoming increasingly common in vehicles, the
+real-time broadband infotainment and Intelligent Transportation System (ITS)
+service applications in fast-motion vehicles become ever demanding, which are
+highly expected to significantly improve the efficiency and safety of our daily
+on-road lives. The emerging ITS and vehicular applications, e.g., trip
+planning, however, require substantial efforts on the real-time pervasive
+information collection and big data processing so as to provide quick decision
+making and feedbacks to the fast moving vehicles, which thus impose the
+significant challenges on the development of an efficient vehicular
+communication platform. In this article, we present TrasoNET, an integrated
+network framework to provide realtime intelligent transportation services to
+connected vehicles by exploring the data analytics and networking techniques.
+TrasoNET is built upon two key components. The first one guides vehicles to the
+appropriate access networks by exploring the information of realtime traffic
+status, specific user preferences, service applications and network conditions.
+The second component mainly involves a distributed automatic access engine,
+which enables individual vehicles to make distributed access decisions based on
+access recommender, local observation and historic information. We showcase the
+application of TrasoNET in a case study on real-time traffic sensing based on
+real traces of taxis.
+",1,0,0,0,0,0
+2637,Strongly ergodic equivalence relations: spectral gap and type III invariants,"  We obtain a spectral gap characterization of strongly ergodic equivalence
+relations on standard measure spaces. We use our spectral gap criterion to
+prove that a large class of skew-product equivalence relations arising from
+measurable $1$-cocycles with values into locally compact abelian groups are
+strongly ergodic. By analogy with the work of Connes on full factors, we
+introduce the Sd and $\tau$ invariants for type ${\rm III}$ strongly ergodic
+equivalence relations. As a corollary to our main results, we show that for any
+type ${\rm III_1}$ ergodic equivalence relation $\mathcal R$, the Maharam
+extension $\mathord{\text {c}}(\mathcal R)$ is strongly ergodic if and only if
+$\mathcal R$ is strongly ergodic and the invariant $\tau(\mathcal R)$ is the
+usual topology on $\mathbf R$. We also obtain a structure theorem for almost
+periodic strongly ergodic equivalence relations analogous to Connes' structure
+theorem for almost periodic full factors. Finally, we prove that for arbitrary
+strongly ergodic free actions of bi-exact groups (e.g. hyperbolic groups), the
+Sd and $\tau$ invariants of the orbit equivalence relation and of the
+associated group measure space von Neumann factor coincide.
+",0,0,1,0,0,0
+2638,On-the-fly Operation Batching in Dynamic Computation Graphs,"  Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer
+more flexibility for implementing models that cope with data of varying
+dimensions and structure, relative to toolkits that operate on statically
+declared computations (e.g., TensorFlow, CNTK, and Theano). However, existing
+toolkits - both static and dynamic - require that the developer organize the
+computations into the batches necessary for exploiting high-performance
+algorithms and hardware. This batching task is generally difficult, but it
+becomes a major hurdle as architectures become complex. In this paper, we
+present an algorithm, and its implementation in the DyNet toolkit, for
+automatically batching operations. Developers simply write minibatch
+computations as aggregations of single instance computations, and the batching
+algorithm seamlessly executes them, on the fly, using computationally efficient
+batched operations. On a variety of tasks, we obtain throughput similar to that
+obtained with manual batches, as well as comparable speedups over
+single-instance learning on architectures that are impractical to batch
+manually.
+",1,0,0,1,0,0
+2639,Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers,"  Clustering is the process of finding and analyzing underlying group structure
+in data. In recent years, data as become increasingly higher dimensional and,
+therefore, an increased need has arisen for dimension reduction techniques for
+clustering. Although such techniques are firmly established in the literature
+for multivariate data, there is a relative paucity in the area of matrix
+variate or three way data. Furthermore, the few methods that are available all
+assume matrix variate normality, which is not always sensible if cluster
+skewness or excess kurtosis is present. Mixtures of bilinear factor analyzers
+models using skewed matrix variate distributions are proposed. In all, four
+such mixture models are presented, based on matrix variate skew-t, generalized
+hyperbolic, variance gamma and normal inverse Gaussian distributions,
+respectively.
+",0,0,0,1,0,0
+2640,Transfer Learning to Learn with Multitask Neural Model Search,"  Deep learning models require extensive architecture design exploration and
+hyperparameter optimization to perform well on a given task. The exploration of
+the model design space is often made by a human expert, and optimized using a
+combination of grid search and search heuristics over a large space of possible
+choices. Neural Architecture Search (NAS) is a Reinforcement Learning approach
+that has been proposed to automate architecture design. NAS has been
+successfully applied to generate Neural Networks that rival the best
+human-designed architectures. However, NAS requires sampling, constructing, and
+training hundreds to thousands of models to achieve well-performing
+architectures. This procedure needs to be executed from scratch for each new
+task. The application of NAS to a wide set of tasks currently lacks a way to
+transfer generalizable knowledge across tasks. In this paper, we present the
+Multitask Neural Model Search (MNMS) controller. Our goal is to learn a
+generalizable framework that can condition model construction on successful
+model searches for previously seen tasks, thus significantly speeding up the
+search for new tasks. We demonstrate that MNMS can conduct an automated
+architecture search for multiple tasks simultaneously while still learning
+well-performing, specialized models for each task. We then show that
+pre-trained MNMS controllers can transfer learning to new tasks. By leveraging
+knowledge from previous searches, we find that pre-trained MNMS models start
+from a better location in the search space and reduce search time on unseen
+tasks, while still discovering models that outperform published human-designed
+models.
+",1,0,0,1,0,0
+2641,Hierarchical Game-Theoretic Planning for Autonomous Vehicles,"  The actions of an autonomous vehicle on the road affect and are affected by
+those of other drivers, whether overtaking, negotiating a merge, or avoiding an
+accident. This mutual dependence, best captured by dynamic game theory, creates
+a strong coupling between the vehicle's planning and its predictions of other
+drivers' behavior, and constitutes an open problem with direct implications on
+the safety and viability of autonomous driving technology. Unfortunately,
+dynamic games are too computationally demanding to meet the real-time
+constraints of autonomous driving in its continuous state and action space. In
+this paper, we introduce a novel game-theoretic trajectory planning algorithm
+for autonomous driving, that enables real-time performance by hierarchically
+decomposing the underlying dynamic game into a long-horizon ""strategic"" game
+with simplified dynamics and full information structure, and a short-horizon
+""tactical"" game with full dynamics and a simplified information structure. The
+value of the strategic game is used to guide the tactical planning, implicitly
+extending the planning horizon, pushing the local trajectory optimization
+closer to global solutions, and, most importantly, quantitatively accounting
+for the autonomous vehicle and the human driver's ability and incentives to
+influence each other. In addition, our approach admits non-deterministic models
+of human decision-making, rather than relying on perfectly rational
+predictions. Our results showcase richer, safer, and more effective autonomous
+behavior in comparison to existing techniques.
+",1,0,0,0,0,0
+2642,Observable dictionary learning for high-dimensional statistical inference,"  This paper introduces a method for efficiently inferring a high-dimensional
+distributed quantity from a few observations. The quantity of interest (QoI) is
+approximated in a basis (dictionary) learned from a training set. The
+coefficients associated with the approximation of the QoI in the basis are
+determined by minimizing the misfit with the observations. To obtain a
+probabilistic estimate of the quantity of interest, a Bayesian approach is
+employed. The QoI is treated as a random field endowed with a hierarchical
+prior distribution so that closed-form expressions can be obtained for the
+posterior distribution. The main contribution of the present work lies in the
+derivation of \emph{a representation basis consistent with the observation
+chain} used to infer the associated coefficients. The resulting dictionary is
+then tailored to be both observable by the sensors and accurate in
+approximating the posterior mean. An algorithm for deriving such an observable
+dictionary is presented. The method is illustrated with the estimation of the
+velocity field of an open cavity flow from a handful of wall-mounted point
+sensors. Comparison with standard estimation approaches relying on Principal
+Component Analysis and K-SVD dictionaries is provided and illustrates the
+superior performance of the present approach.
+",0,0,0,1,0,0
+2643,Counterintuitive Reconstruction of the Polar O-Terminated ZnO Surface With Zinc Vacancies and Hydrogen,"  Understanding the structure of ZnO surface reconstructions and their
+resultant properties is crucial to the rational design of ZnO-containing
+devices ranging from optoelectronics to catalysts. Here, we are motivated by
+recent experimental work which showed a new surface reconstruction containing
+Zn vacancies ordered in a Zn(3x3) pattern in the subsurface of (0001)-O
+terminated ZnO. A reconstruction with Zn vacancies on (0001)-O is surprising
+and counterintuitive because Zn vacancies enhance the surface dipole rather
+than reduce it. In this work, we show using Density Functional Theory (DFT)
+that subsurface Zn vacancies can form on (0001)-O when coupled with adsorption
+of surface H and are in fact stable under a wide range of common conditions. We
+also show these vacancies have a significant ordering tendency and that
+Sb-doping created subsurface inversion domain boundaries (IDBs) enhances the
+driving force of Zn vacancy alignment into large domains of the Zn(3x3)
+reconstruction.
+",0,1,0,0,0,0
+2644,A Finite-Tame-Wild Trichotomy Theorem for Tensor Diagrams,"  In this paper, we consider the problem of determining when two tensor
+networks are equivalent under a heterogeneous change of basis. In particular,
+to a string diagram in a certain monoidal category (which we call tensor
+diagrams), we formulate an associated abelian category of representations. Each
+representation corresponds to a tensor network on that diagram. We then
+classify which tensor diagrams give rise to categories that are finite, tame,
+or wild in the traditional sense of representation theory. For those tensor
+diagrams of finite and tame type, we classify the indecomposable
+representations. Our main result is that a tensor diagram is wild if and only
+if it contains a vertex of degree at least three. Otherwise, it is of tame or
+finite type.
+",0,0,1,0,0,0
+2645,Decomposing the Quantile Ratio Index with applications to Australian income and wealth data,"  The quantile ratio index introduced by Prendergast and Staudte 2017 is a
+simple and effective measure of relative inequality for income data that is
+resistant to outliers. It measures the average relative distance of a randomly
+chosen income from its symmetric quantile. Another useful property of this
+index is investigated here: given a partition of the income distribution into a
+union of sets of symmetric quantiles, one can find the conditional inequality
+for each set as measured by the quantile ratio index and readily combine them
+in a weighted average to obtain the index for the entire population. When
+applied to data for various years, one can track how these contributions to
+inequality vary over time, as illustrated here for Australian Bureau of
+Statistics income and wealth data.
+",0,0,0,1,0,0
+2646,Metamorphic Moving Horizon Estimation,"  This paper considers a practical scenario where a classical estimation method
+might have already been implemented on a certain platform when one tries to
+apply more advanced techniques such as moving horizon estimation (MHE). We are
+interested to utilize MHE to upgrade, rather than completely discard, the
+existing estimation technique. This immediately raises the question how one can
+improve the estimation performance gradually based on the pre-estimator. To
+this end, we propose a general methodology which incorporates the pre-estimator
+with a tuning parameter {\lambda} between 0 and 1 into the quadratic cost
+functions that are usually adopted in MHE. We examine the above idea in two
+standard MHE frameworks that have been proposed in the existing literature. For
+both frameworks, when {\lambda} = 0, the proposed strategy exactly matches the
+existing classical estimator; when the value of {\lambda} is increased, the
+proposed strategy exhibits a more aggressive normalized forgetting effect
+towards the old data, thereby increasing the estimation performance gradually.
+",1,0,0,0,0,0
+2647,Erosion distance for generalized persistence modules,"  The persistence diagram of Cohen-Steiner, Edelsbrunner, and Harer was
+recently generalized by Patel to the case of constructible persistence modules
+with values in a symmetric monoidal category with images. Patel also introduced
+a distance for persistence diagrams, the erosion distance. Motivated by this
+work, we extend the erosion distance to a distance of rank invariants of
+generalized persistence modules by using the generalization of the interleaving
+distance of Bubenik, de Silva, and Scott as a guideline. This extension of the
+erosion distance also gives, as a special case, a distance for multidimensional
+persistent homology groups with torsion introduced by Frosini. We show that the
+erosion distance is stable with respect to the interleaving distance, and that
+it gives a lower bound for the natural pseudo-distance in the case of sublevel
+set persistent homology of continuous functions.
+",0,0,1,0,0,0
+2648,Efficient Adjoint Computation for Wavelet and Convolution Operators,"  First-order optimization algorithms, often preferred for large problems,
+require the gradient of the differentiable terms in the objective function.
+These gradients often involve linear operators and their adjoints, which must
+be applied rapidly. We consider two example problems and derive methods for
+quickly evaluating the required adjoint operator. The first example is an image
+deblurring problem, where we must compute efficiently the adjoint of
+multi-stage wavelet reconstruction. Our formulation of the adjoint works for a
+variety of boundary conditions, which allows the formulation to generalize to a
+larger class of problems. The second example is a blind channel estimation
+problem taken from the optimization literature where we must compute the
+adjoint of the convolution of two signals. In each example, we show how the
+adjoint operator can be applied efficiently while leveraging existing software.
+",0,0,1,0,0,0
+2649,RCD: Rapid Close to Deadline Scheduling for Datacenter Networks,"  Datacenter-based Cloud Computing services provide a flexible, scalable and
+yet economical infrastructure to host online services such as multimedia
+streaming, email and bulk storage. Many such services perform geo-replication
+to provide necessary quality of service and reliability to users resulting in
+frequent large inter- datacenter transfers. In order to meet tenant service
+level agreements (SLAs), these transfers have to be completed prior to a
+deadline. In addition, WAN resources are quite scarce and costly, meaning they
+should be fully utilized. Several recently proposed schemes, such as B4,
+TEMPUS, and SWAN have focused on improving the utilization of inter-datacenter
+transfers through centralized scheduling, however, they fail to provide a
+mechanism to guarantee that admitted requests meet their deadlines. Also, in a
+recent study, authors propose Amoeba, a system that allows tenants to define
+deadlines and guarantees that the specified deadlines are met, however, to
+admit new traffic, the proposed system has to modify the allocation of already
+admitted transfers. In this paper, we propose Rapid Close to Deadline
+Scheduling (RCD), a close to deadline traffic allocation technique that is fast
+and efficient. Through simulations, we show that RCD is up to 15 times faster
+than Amoeba, provides high link utilization along with deadline guarantees, and
+is able to make quick decisions on whether a new request can be fully satisfied
+before its deadline.
+",1,0,0,0,0,0
+2650,Real representations of finite symplectic groups over fields of characteristic two,"  We prove that when $q$ is a power of $2$, every complex irreducible
+representation of $\mathrm{Sp}(2n, \mathbb{F}_q)$ may be defined over the real
+numbers, that is, all Frobenius-Schur indicators are 1. We also obtain a
+generating function for the sum of the degrees of the unipotent characters of
+$\mathrm{Sp}(2n, \mathbb{F}_q)$, or of $\mathrm{SO}(2n+1, \mathbb{F}_q)$, for
+any prime power $q$.
+",0,0,1,0,0,0
+2651,Risk measure estimation for $β$-mixing time series and applications,"  In this paper, we discuss the application of extreme value theory in the
+context of stationary $\beta$-mixing sequences that belong to the Fréchet
+domain of attraction. In particular, we propose a methodology to construct
+bias-corrected tail estimators. Our approach is based on the combination of two
+estimators for the extreme value index to cancel the bias. The resulting
+estimator is used to estimate an extreme quantile. In a simulation study, we
+outline the performance of our proposals that we compare to alternative
+estimators recently introduced in the literature. Also, we compute the
+asymptotic variance in specific examples when possible. Our methodology is
+applied to two datasets on finance and environment.
+",0,0,1,1,0,0
+2652,Transfer entropy between communities in complex networks,"  With the help of transfer entropy, we analyze information flows between
+communities of complex networks. We show that the transfer entropy provides a
+coherent description of interactions between communities, including non-linear
+interactions. To put some flesh on the bare bones, we analyze transfer
+entropies between communities of five largest financial markets, represented as
+networks of interacting stocks. Additionally, we discuss information transfer
+of rare events, which is analyzed by Rényi transfer entropy.
+",0,1,0,0,0,0
+2653,Disentangled VAE Representations for Multi-Aspect and Missing Data,"  Many problems in machine learning and related application areas are
+fundamentally variants of conditional modeling and sampling across multi-aspect
+data, either multi-view, multi-modal, or simply multi-group. For example,
+sampling from the distribution of English sentences conditioned on a given
+French sentence or sampling audio waveforms conditioned on a given piece of
+text. Central to many of these problems is the issue of missing data: we can
+observe many English, French, or German sentences individually but only
+occasionally do we have data for a sentence pair. Motivated by these
+applications and inspired by recent progress in variational autoencoders for
+grouped data, we develop factVAE, a deep generative model capable of handling
+multi-aspect data, robust to missing observations, and with a prior that
+encourages disentanglement between the groups and the latent dimensions. The
+effectiveness of factVAE is demonstrated on a variety of rich real-world
+datasets, including motion capture poses and pictures of faces captured from
+varying poses and perspectives.
+",0,0,0,1,0,0
+2654,On the spectral geometry of manifolds with conic singularities,"  In the previous article we derived a detailed asymptotic expansion of the
+heat trace for the Laplace-Beltrami operator on functions on manifolds with
+conic singularities. In this article we investigate how the terms in the
+expansion reflect the geometry of the manifold. Since the general expansion
+contains a logarithmic term, its vanishing is a necessary condition for
+smoothness of the manifold. In the two-dimensional case this implies that the
+constant term of the expansion contains a non-local term that determines the
+length of the (circular) cross section and vanishes precisely if this length
+equals $2\pi$, that is, in the smooth case. We proceed to the study of higher
+dimensions. In the four-dimensional case, the logarithmic term in the expansion
+vanishes precisely when the cross section is a spherical space form, and we
+expect that the vanishing of a further singular term will imply again
+smoothness, but this is not yet clear beyond the case of cyclic space forms. In
+higher dimensions the situation is naturally more difficult. We illustrate this
+in the case of cross sections with constant curvature. Then the logarithmic
+term becomes a polynomial in the curvature with roots that are different from
+1, which necessitates more vanishing of other terms, not isolated so far.
+",0,0,1,0,0,0
+2655,Neural Task Programming: Learning to Generalize Across Hierarchical Tasks,"  In this work, we propose a novel robot learning framework called Neural Task
+Programming (NTP), which bridges the idea of few-shot learning from
+demonstration and neural program induction. NTP takes as input a task
+specification (e.g., video demonstration of a task) and recursively decomposes
+it into finer sub-task specifications. These specifications are fed to a
+hierarchical neural program, where bottom-level programs are callable
+subroutines that interact with the environment. We validate our method in three
+robot manipulation tasks. NTP achieves strong generalization across sequential
+tasks that exhibit hierarchal and compositional structures. The experimental
+results show that NTP learns to generalize well to- wards unseen tasks with
+increasing lengths, variable topologies, and changing objectives.
+",1,0,0,0,0,0
+2656,Discovery of potential collaboration networks from open knowledge sources,"  Scientific publishing conveys the outputs of an academic or research
+activity, in this sense; it also reflects the efforts and issues in which
+people engage. To identify potential collaborative networks one of the simplest
+approaches is to leverage the co-authorship relations. In this approach,
+semantic and hierarchic relationships defined by a Knowledge Organization
+System are used in order to improve the system's ability to recommend potential
+networks beyond the lexical or syntactic analysis of the topics or concepts
+that are of interest to academics.
+",1,0,0,0,0,0
+2657,Towards Planning and Control of Hybrid Systems with Limit Cycle using LQR Trees,"  We present a multi-query recovery policy for a hybrid system with goal limit
+cycle. The sample trajectories and the hybrid limit cycle of the dynamical
+system are stabilized using locally valid Time Varying LQR controller policies
+which probabilistically cover a bounded region of state space. The original LQR
+Tree algorithm builds such trees for non-linear static and non-hybrid systems
+like a pendulum or a cart-pole. We leverage the idea of LQR trees to plan with
+a continuous control set, unlike methods that rely on discretization like
+dynamic programming to plan for hybrid dynamical systems where it is hard to
+capture the exact event of discrete transition. We test the algorithm on a
+compass gait model by stabilizing a dynamic walking hybrid limit cycle with
+point foot contact from random initial conditions. We show results from the
+simulation where the system comes back to a stable behavior with initial
+position or velocity perturbation and noise.
+",1,0,0,0,0,0
+2658,"Clustering with t-SNE, provably","  t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering and
+visualization method proposed by van der Maaten & Hinton in 2008, has rapidly
+become a standard tool in a number of natural sciences. Despite its
+overwhelming success, there is a distinct lack of mathematical foundations and
+the inner workings of the algorithm are not well understood. The purpose of
+this paper is to prove that t-SNE is able to recover well-separated clusters;
+more precisely, we prove that t-SNE in the `early exaggeration' phase, an
+optimization technique proposed by van der Maaten & Hinton (2008) and van der
+Maaten (2014), can be rigorously analyzed. As a byproduct, the proof suggests
+novel ways for setting the exaggeration parameter $\alpha$ and step size $h$.
+Numerical examples illustrate the effectiveness of these rules: in particular,
+the quality of embedding of topological structures (e.g. the swiss roll)
+improves. We also discuss a connection to spectral clustering methods.
+",1,0,0,1,0,0
+2659,"The Observable Properties of Cool Winds from Galaxies, AGN, and Star Clusters. I. Theoretical Framework","  Winds arising from galaxies, star clusters, and active galactic nuclei are
+crucial players in star and galaxy formation, but it has proven remarkably
+difficult to use observations of them to determine physical properties of
+interest, particularly mass fluxes. Much of the difficulty stems from a lack of
+a theory that links a physically-realistic model for winds' density, velocity,
+and covering factors to calculations of light emission and absorption. In this
+paper we provide such a model. We consider a wind launched from a turbulent
+region with a range of column densities, derive the differential acceleration
+of gas as a function of column density, and use this result to compute winds'
+absorption profiles, emission profiles, and emission intensity maps in both
+optically thin and optically thick species. The model is sufficiently simple
+that all required computations can be done analytically up to straightforward
+numerical integrals, rendering it suitable for the problem of deriving physical
+parameters by fitting models to observed data. We show that our model produces
+realistic absorption and emission profiles for some example cases, and argue
+that the most promising methods of deducing mass fluxes are based on
+combinations of absorption lines of different optical depths, or on combining
+absorption with measurements of molecular line emission. In the second paper in
+this series, we expand on these ideas by introducing a set of observational
+diagnostics that are significantly more robust that those commonly in use, and
+that can be used to obtain improved estimates of wind properties.
+",0,1,0,0,0,0
+2660,Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition,"  Neural models enjoy widespread use across a variety of tasks and have grown
+to become crucial components of many industrial systems. Despite their
+effectiveness and extensive popularity, they are not without their exploitable
+flaws. Initially applied to computer vision systems, the generation of
+adversarial examples is a process in which seemingly imperceptible
+perturbations are made to an image, with the purpose of inducing a deep
+learning based classifier to misclassify the image. Due to recent trends in
+speech processing, this has become a noticeable issue in speech recognition
+models. In late 2017, an attack was shown to be quite effective against the
+Speech Commands classification model. Limited-vocabulary speech classifiers,
+such as the Speech Commands model, are used quite frequently in a variety of
+applications, particularly in managing automated attendants in telephony
+contexts. As such, adversarial examples produced by this attack could have
+real-world consequences. While previous work in defending against these
+adversarial examples has investigated using audio preprocessing to reduce or
+distort adversarial noise, this work explores the idea of flooding particular
+frequency bands of an audio signal with random noise in order to detect
+adversarial examples. This technique of flooding, which does not require
+retraining or modifying the model, is inspired by work done in computer vision
+and builds on the idea that speech classifiers are relatively robust to natural
+noise. A combined defense incorporating 5 different frequency bands for
+flooding the signal with noise outperformed other existing defenses in the
+audio space, detecting adversarial examples with 91.8% precision and 93.5%
+recall.
+",1,0,0,0,0,0
+2661,Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems,"  In this paper, we propose a novel ranking framework for collaborative
+filtering with the overall aim of learning user preferences over items by
+minimizing a pairwise ranking loss. We show the minimization problem involves
+dependent random variables and provide a theoretical analysis by proving the
+consistency of the empirical risk minimization in the worst case where all
+users choose a minimal number of positive and negative items. We further derive
+a Neural-Network model that jointly learns a new representation of users and
+items in an embedded space as well as the preference relation of users over the
+pairs of items. The learning objective is based on three scenarios of ranking
+losses that control the ability of the model to maintain the ordering over the
+items induced from the users' preferences, as well as, the capacity of the
+dot-product defined in the learned embedded space to produce the ordering. The
+proposed model is by nature suitable for implicit feedback and involves the
+estimation of only very few parameters. Through extensive experiments on
+several real-world benchmarks on implicit data, we show the interest of
+learning the preference and the embedding simultaneously when compared to
+learning those separately. We also demonstrate that our approach is very
+competitive with the best state-of-the-art collaborative filtering techniques
+proposed for implicit feedback.
+",1,0,0,1,0,0
+2662,On the Sublinear Regret of Distributed Primal-Dual Algorithms for Online Constrained Optimization,"  This paper introduces consensus-based primal-dual methods for distributed
+online optimization where the time-varying system objective function
+$f_t(\mathbf{x})$ is given as the sum of local agents' objective functions,
+i.e., $f_t(\mathbf{x}) = \sum_i f_{i,t}(\mathbf{x}_i)$, and the system
+constraint function $\mathbf{g}(\mathbf{x})$ is given as the sum of local
+agents' constraint functions, i.e., $\mathbf{g}(\mathbf{x}) = \sum_i
+\mathbf{g}_i (\mathbf{x}_i) \preceq \mathbf{0}$. At each stage, each agent
+commits to an adaptive decision pertaining only to the past and locally
+available information, and incurs a new cost function reflecting the change in
+the environment. Our algorithm uses weighted averaging of the iterates for each
+agent to keep local estimates of the global constraints and dual variables. We
+show that the algorithm achieves a regret of order $O(\sqrt{T})$ with the time
+horizon $T$, in scenarios when the underlying communication topology is
+time-varying and jointly-connected. The regret is measured in regard to the
+cost function value as well as the constraint violation. Numerical results for
+online routing in wireless multi-hop networks with uncertain channel rates are
+provided to illustrate the performance of the proposed algorithm.
+",0,0,1,0,0,0
+2663,Reliability study of proportional odds family of discrete distributions,"  The proportional odds model gives a method of generating new family of
+distributions by adding a parameter, called tilt parameter, to expand an
+existing family of distributions. The new family of distributions so obtained
+is known as Marshall-Olkin family of distributions or Marshall-Olkin extended
+distributions. In this paper, we consider Marshall-Olkin family of
+distributions in discrete case with fixed tilt parameter. We study different
+ageing properties, as well as different stochastic orderings of this family of
+distributions. All the results of this paper are supported by several examples.
+",0,0,1,1,0,0
+2664,Global Orientifolded Quivers with Inflation,"  We describe global embeddings of fractional D3 branes at orientifolded
+singularities in type IIB flux compactifications. We present an explicit
+Calabi-Yau example where the chiral visible sector lives on a local
+orientifolded quiver while non-perturbative effects, $\alpha'$ corrections and
+a T-brane hidden sector lead to full closed string moduli stabilisation in a de
+Sitter vacuum. The same model can also successfully give rise to inflation
+driven by a del Pezzo divisor. Our model represents the first explicit
+Calabi-Yau example featuring both an inflationary and a chiral visible sector.
+",0,1,0,0,0,0
+2665,Discretization of Springer fibers,"  Consider a nilpotent element e in a simple complex Lie algebra. The Springer
+fibre corresponding to e admits a discretization (discrete analogue) introduced
+by the author in 1999. In this paper we propose a conjectural description of
+that discretization which is more amenable to computation.
+",0,0,1,0,0,0
+2666,On the Underapproximation of Reach Sets of Abstract Continuous-Time Systems,"  We consider the problem of proving that each point in a given set of states
+(""target set"") can indeed be reached by a given nondeterministic
+continuous-time dynamical system from some initial state. We consider this
+problem for abstract continuous-time models that can be concretized as various
+kinds of continuous and hybrid dynamical systems.
+The approach to this problem proposed in this paper is based on finding a
+suitable superset S of the target set which has the property that each partial
+trajectory of the system which lies entirely in S either is defined as the
+initial time moment, or can be locally extended backward in time, or can be
+locally modified in such a way that the resulting trajectory can be locally
+extended back in time.
+This reformulation of the problem has a relatively simple logical expression
+and is convenient for applying various local existence theorems and local
+dynamics analysis methods to proving reachability which makes it suitable for
+reasoning about the behavior of continuous and hybrid dynamical systems in
+proof assistants such as Mizar, Isabelle, etc.
+",1,0,0,0,0,0
+2667,A Bayesian nonparametric approach to log-concave density estimation,"  The estimation of a log-concave density on $\mathbb{R}$ is a canonical
+problem in the area of shape-constrained nonparametric inference. We present a
+Bayesian nonparametric approach to this problem based on an exponentiated
+Dirichlet process mixture prior and show that the posterior distribution
+converges to the log-concave truth at the (near-) minimax rate in Hellinger
+distance. Our proof proceeds by establishing a general contraction result based
+on the log-concave maximum likelihood estimator that prevents the need for
+further metric entropy calculations. We also present two computationally more
+feasible approximations and a more practical empirical Bayes approach, which
+are illustrated numerically via simulations.
+",0,0,1,1,0,0
+2668,A Complete Characterization of the 1-Dimensional Intrinsic Cech Persistence Diagrams for Metric Graphs,"  Metric graphs are special types of metric spaces used to model and represent
+simple, ubiquitous, geometric relations in data such as biological networks,
+social networks, and road networks. We are interested in giving a qualitative
+description of metric graphs using topological summaries. In particular, we
+provide a complete characterization of the 1-dimensional intrinsic Cech
+persistence diagrams for metric graphs using persistent homology. Together with
+complementary results by Adamaszek et. al, which imply results on intrinsic
+Cech persistence diagrams in all dimensions for a single cycle, our results
+constitute important steps toward characterizing intrinsic Cech persistence
+diagrams for arbitrary metric graphs across all dimensions.
+",0,0,1,0,0,0
+2669,Critical exponent $ω$ in the Gross-Neveu-Yukawa model at $O(1/N)$,"  The critcal exponent $\omega$ is evaluated at $O(1/N)$ in $d$-dimensions in
+the Gross-Neveu model using the large $N$ critical point formalism. It is shown
+to be in agreement with the recently determined three loop $\beta$-functions of
+the Gross-Neveu-Yukawa model in four dimensions. The same exponent is computed
+for the chiral Gross-Neveu and non-abelian Nambu-Jona-Lasinio universality
+classes.
+",0,1,0,0,0,0
+2670,Path Planning for Multiple Heterogeneous Unmanned Vehicles with Uncertain Service Times,"  This article presents a framework and develops a formulation to solve a path
+planning problem for multiple heterogeneous Unmanned Vehicles (UVs) with
+uncertain service times for each vehicle--target pair. The vehicles incur a
+penalty proportional to the duration of their total service time in excess of a
+preset constant. The vehicles differ in their motion constraints and are
+located at distinct depots at the start of the mission. The vehicles may also
+be equipped with disparate sensors. The objective is to find a tour for each
+vehicle that starts and ends at its respective depot such that every target is
+visited and serviced by some vehicle while minimizing the sum of the total
+travel distance and the expected penalty incurred by all the vehicles. We
+formulate the problem as a two-stage stochastic program with recourse, present
+the theoretical properties of the formulation and advantages of using such a
+formulation, as opposed to a deterministic expected value formulation, to solve
+the problem. Extensive numerical simulations also corroborate the effectiveness
+of the proposed approach.
+",1,0,1,0,0,0
+2671,Dropout-based Active Learning for Regression,"  Active learning is relevant and challenging for high-dimensional regression
+models when the annotation of the samples is expensive. Yet most of the
+existing sampling methods cannot be applied to large-scale problems, consuming
+too much time for data processing. In this paper, we propose a fast active
+learning algorithm for regression, tailored for neural network models. It is
+based on uncertainty estimation from stochastic dropout output of the network.
+Experiments on both synthetic and real-world datasets show comparable or better
+performance (depending on the accuracy metric) as compared to the baselines.
+This approach can be generalized to other deep learning architectures. It can
+be used to systematically improve a machine-learning model as it offers a
+computationally efficient way of sampling additional data.
+",0,0,0,1,0,0
+2672,Modeling Human Categorization of Natural Images Using Deep Feature Representations,"  Over the last few decades, psychologists have developed sophisticated formal
+models of human categorization using simple artificial stimuli. In this paper,
+we use modern machine learning methods to extend this work into the realm of
+naturalistic stimuli, enabling human categorization to be studied over the
+complex visual domain in which it evolved and developed. We show that
+representations derived from a convolutional neural network can be used to
+model behavior over a database of >300,000 human natural image classifications,
+and find that a group of models based on these representations perform well,
+near the reliability of human judgments. Interestingly, this group includes
+both exemplar and prototype models, contrasting with the dominance of exemplar
+models in previous work. We are able to improve the performance of the
+remaining models by preprocessing neural network representations to more
+closely capture human similarity judgments.
+",1,0,0,1,0,0
+2673,BARCHAN: Blob Alignment for Robust CHromatographic ANalysis,"  Comprehensive Two dimensional gas chromatography (GCxGC) plays a central role
+into the elucidation of complex samples. The automation of the identification
+of peak areas is of prime interest to obtain a fast and repeatable analysis of
+chromatograms. To determine the concentration of compounds or pseudo-compounds,
+templates of blobs are defined and superimposed on a reference chromatogram.
+The templates then need to be modified when different chromatograms are
+recorded. In this study, we present a chromatogram and template alignment
+method based on peak registration called BARCHAN. Peaks are identified using a
+robust mathematical morphology tool. The alignment is performed by a
+probabilistic estimation of a rigid transformation along the first dimension,
+and a non-rigid transformation in the second dimension, taking into account
+noise, outliers and missing peaks in a fully automated way. Resulting aligned
+chromatograms and masks are presented on two datasets. The proposed algorithm
+proves to be fast and reliable. It significantly reduces the time to results
+for GCxGC analysis.
+",1,1,0,0,0,0
+2674,Homogeneity Pursuit in Single Index Models based Panel Data Analysis,"  Panel data analysis is an important topic in statistics and econometrics.
+Traditionally, in panel data analysis, all individuals are assumed to share the
+same unknown parameters, e.g. the same coefficients of covariates when the
+linear models are used, and the differences between the individuals are
+accounted for by cluster effects. This kind of modelling only makes sense if
+our main interest is on the global trend, this is because it would not be able
+to tell us anything about the individual attributes which are sometimes very
+important. In this paper, we proposed a modelling based on the single index
+models embedded with homogeneity for panel data analysis, which builds the
+individual attributes in the model and is parsimonious at the same time. We
+develop a data driven approach to identify the structure of homogeneity, and
+estimate the unknown parameters and functions based on the identified
+structure. Asymptotic properties of the resulting estimators are established.
+Intensive simulation studies conducted in this paper also show the resulting
+estimators work very well when sample size is finite. Finally, the proposed
+modelling is applied to a public financial dataset and a UK climate dataset,
+the results reveal some interesting findings.
+",0,0,1,1,0,0
+2675,Feeding vs. Falling: The growth and collapse of molecular clouds in a turbulent interstellar medium,"  In order to understand the origin of observed molecular cloud properties, it
+is critical to understand how clouds interact with their environments during
+their formation, growth, and collapse. It has been suggested that
+accretion-driven turbulence can maintain clouds in a highly turbulent state,
+preventing runaway collapse, and explaining the observed non-thermal velocity
+dispersions. We present 3D, AMR, MHD simulations of a kiloparsec-scale,
+stratified, supernova-driven, self-gravitating, interstellar medium, including
+diffuse heating and radiative cooling. These simulations model the formation
+and evolution of a molecular cloud population in the turbulent interstellar
+medium. We use zoom-in techniques to focus on the dynamics of the mass
+accretion and its history for individual molecular clouds. We find that mass
+accretion onto molecular clouds proceeds as a combination of turbulent and near
+free-fall accretion of a gravitationally bound envelope. Nearby supernova
+explosions have a dual role, compressing the envelope, boosting accreted mass,
+but also disrupting parts of the envelope and eroding mass from the cloud's
+surface. It appears that the inflow rate of kinetic energy onto clouds from
+supernova explosions is insufficient to explain the net rate of charge of the
+cloud kinetic energy. In the absence of self-consistent star formation,
+conversion of gravitational potential into kinetic energy during contraction
+seems to be the main driver of non-thermal motions within clouds. We conclude
+that although clouds interact strongly with their environments, bound clouds
+are always in a state of gravitational contraction, close to runaway, and their
+properties are a natural result of this collapse.
+",0,1,0,0,0,0
+2676,Complex waveguide based on a magneto-optic layer and a dielectric photonic crystal,"  We theoretically investigate the dispersion and polarization properties of
+the electromagnetic waves in a multi-layered structure composed of a
+magneto-optic waveguide on dielectric substrate covered by one-dimensional
+dielectric photonic crystal. The numerical analysis of such a complex structure
+shows polarization filtration of TE- and TM-modes depending on geometrical
+parameters of the waveguide and photonic crystal. We consider different regimes
+of the modes propagation inside such a structure: when guiding modes propagate
+inside the magnetic film and decay in the photonic crystal; when they propagate
+in both magnetic film and photonic crystal.
+",0,1,0,0,0,0
+2677,Discriminants of complete intersection space curves,"  In this paper, we develop a new approach to the discrimi-nant of a complete
+intersection curve in the 3-dimensional projective space. By relying on the
+resultant theory, we first prove a new formula that allows us to define this
+discrimi-nant without ambiguity and over any commutative ring, in particular in
+any characteristic. This formula also provides a new method for evaluating and
+computing this discrimi-nant efficiently, without the need to introduce new
+variables as with the well-known Cayley trick. Then, we obtain new properties
+and computational rules such as the covariance and the invariance formulas.
+Finally, we show that our definition of the discriminant satisfies to the
+expected geometric property and hence yields an effective smoothness criterion
+for complete intersection space curves. Actually, we show that in the generic
+setting, it is the defining equation of the discriminant scheme if the ground
+ring is assumed to be a unique factorization domain.
+",1,0,1,0,0,0
+2678,On the Characteristic and Permanent Polynomials of a Matrix,"  There is a digraph corresponding to every square matrix over $\mathbb{C}$. We
+generate a recurrence relation using the Laplace expansion to calculate the
+characteristic, and permanent polynomials of a square matrix. Solving this
+recurrence relation, we found that the characteristic, and permanent
+polynomials can be calculated in terms of characteristic, and permanent
+polynomials of some specific induced subdigraphs of blocks in the digraph,
+respectively. Interestingly, these induced subdigraphs are vertex-disjoint and
+they partition the digraph. Similar to the characteristic, and permanent
+polynomials; the determinant, and permanent can also be calculated. Therefore,
+this article provides a combinatorial meaning of these useful quantities of the
+matrix theory. We conclude this article with a number of open problems which
+may be attempted for further research in this direction.
+",1,0,0,0,0,0
+2679,A bulk-boundary correspondence for dynamical phase transitions in one-dimensional topological insulators and superconductors,"  We study the Loschmidt echo for quenches in open one-dimensional lattice
+models with symmetry protected topological phases. For quenches where dynamical
+quantum phase transitions do occur we find that cusps in the bulk return rate
+at critical times tc are associated with sudden changes in the boundary
+contribution. For our main example, the Su-Schrieffer-Heeger model, we show
+that these sudden changes are related to the periodical appearance of two
+eigenvalues close to zero in the dynamical Loschmidt matrix. We demonstrate,
+furthermore, that the structure of the Loschmidt spectrum is linked to the
+periodic creation of long-range entanglement between the edges of the system.
+",0,1,0,0,0,0
+2680,The Consciousness Prior,"  A new prior is proposed for representation learning, which can be combined
+with other priors in order to help disentangling abstract factors from each
+other. It is inspired by the phenomenon of consciousness seen as the formation
+of a low-dimensional combination of a few concepts constituting a conscious
+thought, i.e., consciousness as awareness at a particular time instant. This
+provides a powerful constraint on the representation in that such
+low-dimensional thought vectors can correspond to statements about reality
+which are true, highly probable, or very useful for taking decisions. The fact
+that a few elements of the current state can be combined into such a predictive
+or useful statement is a strong constraint and deviates considerably from the
+maximum likelihood approaches to modelling data and how states unfold in the
+future based on an agent's actions. Instead of making predictions in the
+sensory (e.g. pixel) space, the consciousness prior allows the agent to make
+predictions in the abstract space, with only a few dimensions of that space
+being involved in each of these predictions. The consciousness prior also makes
+it natural to map conscious states to natural language utterances or to express
+classical AI knowledge in the form of facts and rules, although the conscious
+states may be richer than what can be expressed easily in the form of a
+sentence, a fact or a rule.
+",1,0,0,1,0,0
+2681,Multi-Scale Pipeline for the Search of String-Induced CMB Anisotropies,"  We propose a multi-scale edge-detection algorithm to search for the
+Gott-Kaiser-Stebbins imprints of a cosmic string (CS) network on the Cosmic
+Microwave Background (CMB) anisotropies. Curvelet decomposition and extended
+Canny algorithm are used to enhance the string detectability. Various
+statistical tools are then applied to quantify the deviation of CMB maps having
+a cosmic string contribution with respect to pure Gaussian anisotropies of
+inflationary origin. These statistical measures include the one-point
+probability density function, the weighted two-point correlation function
+(TPCF) of the anisotropies, the unweighted TPCF of the peaks and of the
+up-crossing map, as well as their cross-correlation. We use this algorithm on a
+hundred of simulated Nambu-Goto CMB flat sky maps, covering approximately
+$10\%$ of the sky, and for different string tensions $G\mu$. On noiseless sky
+maps with an angular resolution of $0.9'$, we show that our pipeline detects
+CSs with $G\mu$ as low as $G\mu\gtrsim 4.3\times 10^{-10}$. At the same
+resolution, but with a noise level typical to a CMB-S4 phase II experiment, the
+detection threshold would be to $G\mu\gtrsim 1.2 \times 10^{-7}$.
+",0,1,0,1,0,0
+2682,A simultaneous generalization of the theorems of Chevalley-Warning and Morlaye,"  Inspired by recent work of I. Baoulina, we give a simultaneous generalization
+of the theorems of Chevalley-Warning and Morlaye.
+",0,0,1,0,0,0
+2683,Some Time-changed fractional Poisson processes,"  In this paper, we study the fractional Poisson process (FPP) time-changed by
+an independent Lévy subordinator and the inverse of the Lévy subordinator,
+which we call TCFPP-I and TCFPP-II, respectively. Various distributional
+properties of these processes are established. We show that, under certain
+conditions, the TCFPP-I has the long-range dependence property and also its law
+of iterated logarithm is proved. It is shown that the TCFPP-II is a renewal
+process and its waiting time distribution is identified. Its bivariate
+distributions and also the governing difference-differential equation are
+derived. Some specific examples for both the processes are discussed. Finally,
+we present the simulations of the sample paths of these processes.
+",0,0,1,0,0,0
+2684,Fast algorithm of adaptive Fourier series,"  Adaptive Fourier decomposition (AFD, precisely 1-D AFD or Core-AFD) was
+originated for the goal of positive frequency representations of signals. It
+achieved the goal and at the same time offered fast decompositions of signals.
+There then arose several types of AFDs. AFD merged with the greedy algorithm
+idea, and in particular, motivated the so-called pre-orthogonal greedy
+algorithm (Pre-OGA) that was proven to be the most efficient greedy algorithm.
+The cost of the advantages of the AFD type decompositions is, however, the high
+computational complexity due to the involvement of maximal selections of the
+dictionary parameters. The present paper offers one formulation of the 1-D AFD
+algorithm by building the FFT algorithm into it. Accordingly, the algorithm
+complexity is reduced, from the original $\mathcal{O}(M N^2)$ to $\mathcal{O}(M
+N\log_2 N)$, where $N$ denotes the number of the discretization points on the
+unit circle and $M$ denotes the number of points in $[0,1)$. This greatly
+enhances the applicability of AFD. Experiments are carried out to show the high
+efficiency of the proposed algorithm.
+",0,0,1,0,0,0
+2685,Hybrid Indexes to Expedite Spatial-Visual Search,"  Due to the growth of geo-tagged images, recent web and mobile applications
+provide search capabilities for images that are similar to a given query image
+and simultaneously within a given geographical area. In this paper, we focus on
+designing index structures to expedite these spatial-visual searches. We start
+by baseline indexes that are straightforward extensions of the current popular
+spatial (R*-tree) and visual (LSH) index structures. Subsequently, we propose
+hybrid index structures that evaluate both spatial and visual features in
+tandem. The unique challenge of this type of query is that there are
+inaccuracies in both spatial and visual features. Therefore, different
+traversals of the index structures may produce different images as output, some
+of which more relevant to the query than the others. We compare our hybrid
+structures with a set of baseline indexes in both performance and result
+accuracy using three real world datasets from Flickr, Google Street View, and
+GeoUGV.
+",1,0,0,0,0,0
+2686,Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography,"  Electron Cryo-Tomography (ECT) enables 3D visualization of macromolecule
+structure inside single cells. Macromolecule classification approaches based on
+convolutional neural networks (CNN) were developed to separate millions of
+macromolecules captured from ECT systematically. However, given the fast
+accumulation of ECT data, it will soon become necessary to use CNN models to
+efficiently and accurately separate substantially more macromolecules at the
+prediction stage, which requires additional computational costs. To speed up
+the prediction, we compress classification models into compact neural networks
+with little in accuracy for deployment. Specifically, we propose to perform
+model compression through knowledge distillation. Firstly, a complex teacher
+network is trained to generate soft labels with better classification
+feasibility followed by training of customized student networks with simple
+architectures using the soft label to compress model complexity. Our tests
+demonstrate that our compressed models significantly reduce the number of
+parameters and time cost while maintaining similar classification accuracy.
+",0,0,0,1,1,0
+2687,Low quasiparticle coherence temperature in the one band-Hubbard model: A slave-boson approach,"  We use the Kotliar-Ruckenstein slave-boson formalism to study the temperature
+dependence of paramagnetic phases of the one-band Hubbard model for a variety
+of band structures. We calculate the Fermi liquid quasiparticle spectral weight
+$Z$ and identify the temperature at which it decreases significantly to a
+crossover to a bad metal region. Near the Mott metal-insulator transition, this
+coherence temperature $T_\textrm{coh}$ is much lower than the Fermi temperature
+of the uncorrelated Fermi gas, as is observed in a broad range of strongly
+correlated electron materials. After a proper rescaling of temperature and
+interaction, we find a universal behavior that is independent of the band
+structure of the system. We obtain the temperature-interaction phase diagram as
+a function of doping, and we compare the temperature dependence of the double
+occupancy, entropy, and charge compressibility with previous results obtained
+with Dynamical Mean-Field Theory. We analyse the stability of the method by
+calculating the charge compressibility.
+",0,1,0,0,0,0
+2688,A Note on Iterated Consistency and Infinite Proofs,"  Schmerl and Beklemishev's work on iterated reflection achieves two aims: It
+introduces the important notion of $\Pi^0_1$-ordinal, characterizing the
+$\Pi^0_1$-theorems of a theory in terms of transfinite iterations of
+consistency; and it provides an innovative calculus to compute the
+$\Pi^0_1$-ordinals for a range of theories. The present note demonstrates that
+these achievements are independent: We read off $\Pi^0_1$-ordinals from a
+Schütte-style ordinal analysis via infinite proofs, in a direct and
+transparent way.
+",0,0,1,0,0,0
+2689,Turning Internet of Things(IoT) into Internet of Vulnerabilities (IoV) : IoT Botnets,"  Internet of Things (IoT) is the next big evolutionary step in the world of
+internet. The main intention behind the IoT is to enable safer living and risk
+mitigation on different levels of life. With the advent of IoT botnets, the
+view towards IoT devices has changed from enabler of enhanced living into
+Internet of vulnerabilities for cyber criminals. IoT botnets has exposed two
+different glaring issues, 1) A large number of IoT devices are accessible over
+public Internet. 2) Security (if considered at all) is often an afterthought in
+the architecture of many wide spread IoT devices. In this article, we briefly
+outline the anatomy of the IoT botnets and their basic mode of operations. Some
+of the major DDoS incidents using IoT botnets in recent times along with the
+corresponding exploited vulnerabilities will be discussed. We also provide
+remedies and recommendations to mitigate IoT related cyber risks and briefly
+illustrate the importance of cyber insurance in the modern connected world.
+",1,0,0,0,0,0
+2690,Cwikel estimates revisited,"  In this paper, we propose a new approach to Cwikel estimates both for the
+Euclidean space and for the noncommutative Euclidean space.
+",0,0,1,0,0,0
+2691,Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power,"  Uncertainty analysis in the form of probabilistic forecasting can
+significantly improve decision making processes in the smart power grid for
+better integrating renewable energy sources such as wind. Whereas point
+forecasting provides a single expected value, probabilistic forecasts provide
+more information in the form of quantiles, prediction intervals, or full
+predictive densities. This paper analyzes the effectiveness of a novel approach
+for nonparametric probabilistic forecasting of wind power that combines a
+smooth approximation of the pinball loss function with a neural network
+architecture and a weighting initialization scheme to prevent the quantile
+cross over problem. A numerical case study is conducted using publicly
+available wind data from the Global Energy Forecasting Competition 2014.
+Multiple quantiles are estimated to form 10%, to 90% prediction intervals which
+are evaluated using a quantile score and reliability measures. Benchmark models
+such as the persistence and climatology distributions, multiple quantile
+regression, and support vector quantile regression are used for comparison
+where results demonstrate the proposed approach leads to improved performance
+while preventing the problem of overlapping quantile estimates.
+",0,0,0,1,0,0
+2692,Asynchronous Coordinate Descent under More Realistic Assumptions,"  Asynchronous-parallel algorithms have the potential to vastly speed up
+algorithms by eliminating costly synchronization. However, our understanding to
+these algorithms is limited because the current convergence of asynchronous
+(block) coordinate descent algorithms are based on somewhat unrealistic
+assumptions. In particular, the age of the shared optimization variables being
+used to update a block is assumed to be independent of the block being updated.
+Also, it is assumed that the updates are applied to randomly chosen blocks. In
+this paper, we argue that these assumptions either fail to hold or will imply
+less efficient implementations. We then prove the convergence of
+asynchronous-parallel block coordinate descent under more realistic
+assumptions, in particular, always without the independence assumption. The
+analysis permits both the deterministic (essentially) cyclic and random rules
+for block choices. Because a bound on the asynchronous delays may or may not be
+available, we establish convergence for both bounded delays and unbounded
+delays. The analysis also covers nonconvex, weakly convex, and strongly convex
+functions. We construct Lyapunov functions that directly model both objective
+progress and delays, so delays are not treated errors or noise. A
+continuous-time ODE is provided to explain the construction at a high level.
+",0,0,1,0,0,0
+2693,Zero-temperature magnetic response of small fullerene molecules at the classical and full quantum limit,"  The ground-state magnetic response of fullerene molecules with up to 36
+vertices is calculated, when spins classical or with magnitude $s=\frac{1}{2}$
+are located on their vertices and interact according to the nearest-neighbor
+antiferromagnetic Heisenberg model. The frustrated topology, which originates
+in the pentagons of the fullerenes and is enhanced by their close proximity,
+leads to a significant number of classical magnetization and susceptibility
+discontinuities, something not expected for a model lacking magnetic
+anisotropy. This establishes the classical discontinuities as a generic feature
+of fullerene molecules irrespective of their symmetry. The largest number of
+discontinuities have the molecule with 26 sites, four of the magnetization and
+two of the susceptibility, and an isomer with 34 sites, which has three each.
+In addition, for several of the fullerenes the classical zero-field lowest
+energy configuration has finite magnetization, which is unexpected for
+antiferromagnetic interactions between an even number of spins and with each
+spin having the same number of nearest-neighbors. The molecules come in
+different symmetries and topologies and there are only a few patterns of
+magnetic behavior that can be detected from such a small sample of relatively
+small fullerenes. Contrary to the classical case, in the full quantum limit
+$s=\frac{1}{2}$ there are no discontinuities for a subset of the molecules that
+was considered. This leaves the icosahedral symmetry fullerenes as the only
+ones known supporting ground-state magnetization discontinuities for
+$s=\frac{1}{2}$. It is also found that a molecule with 34 sites has a
+doubly-degenerate ground state when $s=\frac{1}{2}$.
+",0,1,0,0,0,0
+2694,Stochastic Chemical Reaction Networks for Robustly Approximating Arbitrary Probability Distributions,"  We show that discrete distributions on the $d$-dimensional non-negative
+integer lattice can be approximated arbitrarily well via the marginals of
+stationary distributions for various classes of stochastic chemical reaction
+networks. We begin by providing a class of detailed balanced networks and prove
+that they can approximate any discrete distribution to any desired accuracy.
+However, these detailed balanced constructions rely on the ability to
+initialize a system precisely, and are therefore susceptible to perturbations
+in the initial conditions. We therefore provide another construction based on
+the ability to approximate point mass distributions and prove that this
+construction is capable of approximating arbitrary discrete distributions for
+any choice of initial condition. In particular, the developed models are
+ergodic, so their limit distributions are robust to a finite number of
+perturbations over time in the counts of molecules.
+",0,0,0,0,1,0
+2695,Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes,"  The incorporation of macro-actions (temporally extended actions) into
+multi-agent decision problems has the potential to address the curse of
+dimensionality associated with such decision problems. Since macro-actions last
+for stochastic durations, multiple agents executing decentralized policies in
+cooperative environments must act asynchronously. We present an algorithm that
+modifies Generalized Advantage Estimation for temporally extended actions,
+allowing a state-of-the-art policy optimization algorithm to optimize policies
+in Dec-POMDPs in which agents act asynchronously. We show that our algorithm is
+capable of learning optimal policies in two cooperative domains, one involving
+real-time bus holding control and one involving wildfire fighting with unmanned
+aircraft. Our algorithm works by framing problems as ""event-driven decision
+processes,"" which are scenarios where the sequence and timing of actions and
+events are random and governed by an underlying stochastic process. In addition
+to optimizing policies with continuous state and action spaces, our algorithm
+also facilitates the use of event-driven simulators, which do not require time
+to be discretized into time-steps. We demonstrate the benefit of using
+event-driven simulation in the context of multiple agents taking asynchronous
+actions. We show that fixed time-step simulation risks obfuscating the sequence
+in which closely-separated events occur, adversely affecting the policies
+learned. Additionally, we show that arbitrarily shrinking the time-step scales
+poorly with the number of agents.
+",1,0,0,0,0,0
+2696,Early Salient Region Selection Does Not Drive Rapid Visual Categorization,"  The current dominant visual processing paradigm in both human and machine
+research is the feedforward, layered hierarchy of neural-like processing
+elements. Within this paradigm, visual saliency is seen by many to have a
+specific role, namely that of early selection. Early selection is thought to
+enable very fast visual performance by limiting processing to only the most
+relevant candidate portions of an image. Though this strategy has indeed led to
+improved processing time efficiency in machine algorithms, at least one set of
+critical tests of this idea has never been performed with respect to the role
+of early selection in human vision. How would the best of the current saliency
+models perform on the stimuli used by experimentalists who first provided
+evidence for this visual processing paradigm? Would the algorithms really
+provide correct candidate sub-images to enable fast categorization on those
+same images? Here, we report on a new series of tests of these questions whose
+results suggest that it is quite unlikely that such an early selection process
+has any role in human rapid visual categorization.
+",0,0,0,0,1,0
+2697,Bonsai: Synthesis-Based Reasoning for Type Systems,"  We describe algorithms for symbolic reasoning about executable models of type
+systems, supporting three queries intended for designers of type systems.
+First, we check for type soundness bugs and synthesize a counterexample program
+if such a bug is found. Second, we compare two versions of a type system,
+synthesizing a program accepted by one but rejected by the other. Third, we
+minimize the size of synthesized counterexample programs.
+These algorithms symbolically evaluate typecheckers and interpreters,
+producing formulas that characterize the set of programs that fail or succeed
+in the typechecker and the interpreter. However, symbolically evaluating
+interpreters poses efficiency challenges, which are caused by having to merge
+execution paths of the various possible input programs. Our main contribution
+is the Bonsai tree, a novel symbolic representation of programs and program
+states which addresses these challenges. Bonsai trees encode complex syntactic
+information in terms of logical constraints, enabling more efficient merging.
+We implement these algorithms in the Bonsai tool, an assistant for type
+system designers. We perform case studies on how Bonsai helps test and explore
+a variety of type systems. Bonsai efficiently synthesizes counterexamples for
+soundness bugs that have been inaccessible to automatic tools, and is the first
+automated tool to find a counterexample for the recently discovered Scala
+soundness bug SI-9633.
+",1,0,0,0,0,0
+2698,Preference-based performance measures for Time-Domain Global Similarity method,"  For Time-Domain Global Similarity (TDGS) method, which transforms the data
+cleaning problem into a binary classification problem about the physical
+similarity between channels, directly adopting common performance measures
+could only guarantee the performance for physical similarity. Nevertheless,
+practical data cleaning tasks have preferences for the correctness of original
+data sequences. To obtain the general expressions of performance measures based
+on the preferences of tasks, the mapping relations between performance of TDGS
+method about physical similarity and correctness of data sequences are
+investigated by probability theory in this paper. Performance measures for TDGS
+method in several common data cleaning tasks are set. Cases when these
+preference-based performance measures could be simplified are introduced.
+",1,0,0,0,0,0
+2699,On the Prospects for Detecting a Net Photon Circular Polarization Produced by Decaying Dark Matter,"  If dark matter interactions with Standard Model particles are $CP$-violating,
+then dark matter annihilation/decay can produce photons with a net circular
+polarization. We consider the prospects for experimentally detecting evidence
+for such a circular polarization. We identify optimal models for dark matter
+interactions with the Standard Model, from the point of view of detectability
+of the net polarization, for the case of either symmetric or asymmetric dark
+matter. We find that, for symmetric dark matter, evidence for net polarization
+could be found by a search of the Galactic Center by an instrument sensitive to
+circular polarization with an efficiency-weighted exposure of at least
+$50000~\text{cm}^2~\text{yr}$, provided the systematic detector uncertainties
+are constrained at the $1\%$ level. Better sensitivity can be obtained in the
+case of asymmetric dark matter. We discuss the prospects for achieving the
+needed level of performance using possible detector technologies.
+",0,1,0,0,0,0
+2700,Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe's Law and the LPPLS Model,"  We develop a strong diagnostic for bubbles and crashes in bitcoin, by
+analyzing the coincidence (and its absence) of fundamental and technical
+indicators. Using a generalized Metcalfe's law based on network properties, a
+fundamental value is quantified and shown to be heavily exceeded, on at least
+four occasions, by bubbles that grow and burst. In these bubbles, we detect a
+universal super-exponential unsustainable growth. We model this universal
+pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which
+parsimoniously captures diverse positive feedback phenomena, such as herding
+and imitation. The LPPLS model is shown to provide an ex-ante warning of market
+instabilities, quantifying a high crash hazard and probabilistic bracket of the
+crash time consistent with the actual corrections; although, as always, the
+precise time and trigger (which straw breaks the camel's back) being exogenous
+and unpredictable. Looking forward, our analysis identifies a substantial but
+not unprecedented overvaluation in the price of bitcoin, suggesting many months
+of volatile sideways bitcoin prices ahead (from the time of writing, March
+2018).
+",0,0,0,0,0,1
+2701,The Emission Structure of Formaldehyde MegaMasers,"  The formaldehyde MegaMaser emission has been mapped for the three host
+galaxies IC\,860. IRAS\,15107$+$0724, and Arp\,220. Elongated emission
+components are found at the nuclear centres of all galaxies with an extent
+ranging between 30 to 100 pc. These components are superposed on the peaks of
+the nuclear continuum. Additional isolated emission components are found
+superposed in the outskirts of the radio continuum structure. The brightness
+temperatures of the detected features ranges from 0.6 to 13.4 $\times 10^{4}$
+K, which confirms their masering nature. The masering scenario is interpreted
+as amplification of the radio continuum by foreground molecular gas that is
+pumped by far-infrared radiation fields in these starburst environments of the
+host galaxies.
+",0,1,0,0,0,0
+2702,Variational approach for learning Markov processes from time series data,"  Inference, prediction and control of complex dynamical systems from time
+series is important in many areas, including financial markets, power grid
+management, climate and weather modeling, or molecular dynamics. The analysis
+of such highly nonlinear dynamical systems is facilitated by the fact that we
+can often find a (generally nonlinear) transformation of the system coordinates
+to features in which the dynamics can be excellently approximated by a linear
+Markovian model. Moreover, the large number of system variables often change
+collectively on large time- and length-scales, facilitating a low-dimensional
+analysis in feature space. In this paper, we introduce a variational approach
+for Markov processes (VAMP) that allows us to find optimal feature mappings and
+optimal Markovian models of the dynamics from given time series data. The key
+insight is that the best linear model can be obtained from the top singular
+components of the Koopman operator. This leads to the definition of a family of
+score functions called VAMP-r which can be calculated from data, and can be
+employed to optimize a Markovian model. In addition, based on the relationship
+between the variational scores and approximation errors of Koopman operators,
+we propose a new VAMP-E score, which can be applied to cross-validation for
+hyper-parameter optimization and model selection in VAMP. VAMP is valid for
+both reversible and nonreversible processes and for stationary and
+non-stationary processes or realizations.
+",0,0,0,1,0,0
+2703,A unifying framework for the modelling and analysis of STR DNA samples arising in forensic casework,"  This paper presents a new framework for analysing forensic DNA samples using
+probabilistic genotyping. Specifically it presents a mathematical framework for
+specifying and combining the steps in producing forensic casework
+electropherograms of short tandem repeat loci from DNA samples. It is
+applicable to both high and low template DNA samples, that is, samples
+containing either high or low amounts DNA. A specific model is developed within
+the framework, by way of particular modelling assumptions and approximations,
+and its interpretive power presented on examples using simulated data and data
+from a publicly available dataset. The framework relies heavily on the use of
+univariate and multivariate probability generating functions. It is shown that
+these provide a succinct and elegant mathematical scaffolding to model the key
+steps in the process. A significant development in this paper is that of new
+numerical methods for accurately and efficiently evaluating the probability
+distribution of amplicons arising from the polymerase chain reaction process,
+which is modelled as a discrete multi-type branching process. Source code in
+the scripting languages Python, R and Julia is provided for illustration of
+these methods. These new developments will be of general interest to persons
+working outside the province of forensic DNA interpretation that this paper
+focuses on.
+",0,0,0,1,1,0
+2704,A new class of ferromagnetic semiconductors with high Curie temperatures,"  Ferromagnetic semiconductors (FMSs), which have the properties and
+functionalities of both semiconductors and ferromagnets, provide fascinating
+opportunities for basic research in condensed matter physics and device
+applications. Over the past two decades, however, intensive studies on various
+FMS materials, inspired by the influential mean-field Zener (MFZ) model have
+failed to realise reliable FMSs that have a high Curie temperature (Tc > 300
+K), good compatibility with semiconductor electronics, and characteristics
+superior to those of their non-magnetic host semiconductors. Here, we
+demonstrate a new n type Fe-doped narrow-gap III-V FMS, (In,Fe)Sb, in which
+ferromagnetic order is induced by electron carriers, and its Tc is unexpectedly
+high, reaching ~335 K at a modest Fe concentration of 16%. Furthermore, we show
+that by utilizing the large anomalous Hall effect of (In,Fe)Sb at room
+temperature, it is possible to obtain a Hall sensor with a very high
+sensitivity that surpasses that of the best commercially available InSb Hall
+sensor devices. Our results reveal a new design rule of FMSs that is not
+expected from the conventional MFZ model. (This work was presented at the JSAP
+Spring meeting, presentation No. E15a-501-2:
+this https URL)
+",0,1,0,0,0,0
+2705,On a Distributed Approach for Density-based Clustering,"  Efficient extraction of useful knowledge from these data is still a
+challenge, mainly when the data is distributed, heterogeneous and of different
+quality depending on its corresponding local infrastructure. To reduce the
+overhead cost, most of the existing distributed clustering approaches generate
+global models by aggregating local results obtained on each individual node.
+The complexity and quality of solutions depend highly on the quality of the
+aggregation. In this respect, we proposed for distributed density-based
+clustering that both reduces the communication overheads due to the data
+exchange and improves the quality of the global models by considering the
+shapes of local clusters. From preliminary results we show that this algorithm
+is very promising.
+",1,0,0,0,0,0
+2706,Accretion of Planetary Material onto Host Stars,"  Accretion of planetary material onto host stars may occur throughout a star's
+life. Especially prone to accretion, extrasolar planets in short-period orbits,
+while relatively rare, constitute a significant fraction of the known
+population, and these planets are subject to dynamical and atmospheric
+influences that can drive significant mass loss. Theoretical models frame
+expectations regarding the rates and extent of this planetary accretion. For
+instance, tidal interactions between planets and stars may drive complete
+orbital decay during the main sequence. Many planets that survive their stars'
+main sequence lifetime will still be engulfed when the host stars become red
+giant stars. There is some observational evidence supporting these predictions,
+such as a dearth of close-in planets around fast stellar rotators, which is
+consistent with tidal spin-up and planet accretion. There remains no clear
+chemical evidence for pollution of the atmospheres of main sequence or red
+giant stars by planetary materials, but a wealth of evidence points to active
+accretion by white dwarfs. In this article, we review the current understanding
+of accretion of planetary material, from the pre- to the post-main sequence and
+beyond. The review begins with the astrophysical framework for that process and
+then considers accretion during various phases of a host star's life, during
+which the details of accretion vary, and the observational evidence for
+accretion during these phases.
+",0,1,0,0,0,0
+2707,"High-Fidelity, Single-Shot, Quantum-Logic-Assisted Readout in a Mixed-Species Ion Chain","  We use a co-trapped ion ($^{88}\mathrm{Sr}^{+}$) to sympathetically cool and
+measure the quantum state populations of a memory-qubit ion of a different
+atomic species ($^{40}\mathrm{Ca}^{+}$) in a cryogenic, surface-electrode ion
+trap. Due in part to the low motional heating rate demonstrated here, the state
+populations of the memory ion can be transferred to the auxiliary ion by using
+the shared motion as a quantum state bus and measured with an average accuracy
+of 96(1)%. This scheme can be used in quantum information processors to reduce
+photon-scattering-induced error in unmeasured memory qubits.
+",0,1,0,0,0,0
+2708,Investigation of faint galactic carbon stars from the first Byurakan spectral survey. III. Infrared characteristics,"  Infra-Red(IR) astronomical databases, namely, IRAS, 2MASS, WISE, and Spitzer,
+are used to analyze photometric data of 126 carbon stars whose spectra are
+visible in the First Byurakan Survey low-resolution spectral plates. Among
+these, six new objects, recently confirmed on the digitized FBS plates, are
+included. For three of them, moderate-resolution CCD optical spectra are also
+presented. In this work several IR color-color diagrams are studied. Early and
+late-type C stars are separated in the JHK Near-Infra-Red(NIR) color-color
+plots, as well as in the WISE W3-W4 versus W1-W2 diagram. Late N-type
+Asymptotic Giant Branch stars are redder in W1-W2, while early-types(CH and R
+giants) are redder in W3-W4 as expected. Objects with W2-W3 > 1.0 mag. show
+double-peaked spectral energy distribution, indicating the existence of the
+circumstellar envelopes around them. 26 N-type stars have IRAS Point Source
+Catalog(PSC) associations. For FBS 1812+455 IRAS Low-Resolution Spectra in the
+wavelength range 7.7 - 22.6micron and Spitzer Space Telescope Spectra in the
+range 5 - 38micro are presented clearly showing absorption features of
+C2H2(acetylene) molecule at 7.5 and 13.7micron , and the SiC(silicone carbide)
+emission at 11.3micron. The mass-loss rates for eight Mira-type variables are
+derived from the K-[12] color and from the pulsation periods. The reddest
+object among the targets is N-type C star FBS 2213+421, which belong to the
+group of the cold post-AGB R Coronae Borealis(R CrB) variables.
+",0,1,0,0,0,0
+2709,Quantifying Interpretability and Trust in Machine Learning Systems,"  Decisions by Machine Learning (ML) models have become ubiquitous. Trusting
+these decisions requires understanding how algorithms take them. Hence
+interpretability methods for ML are an active focus of research. A central
+problem in this context is that both the quality of interpretability methods as
+well as trust in ML predictions are difficult to measure. Yet evaluations,
+comparisons and improvements of trust and interpretability require quantifiable
+measures. Here we propose a quantitative measure for the quality of
+interpretability methods. Based on that we derive a quantitative measure of
+trust in ML decisions. Building on previous work we propose to measure
+intuitive understanding of algorithmic decisions using the information transfer
+rate at which humans replicate ML model predictions. We provide empirical
+evidence from crowdsourcing experiments that the proposed metric robustly
+differentiates interpretability methods. The proposed metric also demonstrates
+the value of interpretability for ML assisted human decision making: in our
+experiments providing explanations more than doubled productivity in annotation
+tasks. However unbiased human judgement is critical for doctors, judges, policy
+makers and others. Here we derive a trust metric that identifies when human
+decisions are overly biased towards ML predictions. Our results complement
+existing qualitative work on trust and interpretability by quantifiable
+measures that can serve as objectives for further improving methods in this
+field of research.
+",1,0,0,1,0,0
+2710,The Intertropical Convergence Zone,"  This activity has been developed as a resource for the ""EU Space Awareness""
+educational programme. As part of the suite ""Our Fragile Planet"" together with
+the ""Climate Box"" it addresses aspects of weather phenomena, the Earth's
+climate and climate change as well as Earth observation efforts like in the
+European ""Copernicus"" programme. This resource consists of three parts that
+illustrate the power of the Sun driving a global air circulation system that is
+also responsible for tropical and subtropical climate zones. Through
+experiments, students learn how heated air rises above cool air and how a
+continuous heat source produces air convection streams that can even drive a
+propeller. Students then apply what they have learnt to complete a worksheet
+that presents the big picture of the global air circulation system of the
+equator region by transferring the knowledge from the previous activities in to
+a larger scale.
+",0,1,0,0,0,0
+2711,Nonconvex Sparse Logistic Regression with Weakly Convex Regularization,"  In this work we propose to fit a sparse logistic regression model by a weakly
+convex regularized nonconvex optimization problem. The idea is based on the
+finding that a weakly convex function as an approximation of the $\ell_0$
+pseudo norm is able to better induce sparsity than the commonly used $\ell_1$
+norm. For a class of weakly convex sparsity inducing functions, we prove the
+nonconvexity of the corresponding sparse logistic regression problem, and study
+its local optimality conditions and the choice of the regularization parameter
+to exclude trivial solutions. Despite the nonconvexity, a method based on
+proximal gradient descent is used to solve the general weakly convex sparse
+logistic regression, and its convergence behavior is studied theoretically.
+Then the general framework is applied to a specific weakly convex function, and
+a necessary and sufficient local optimality condition is provided. The solution
+method is instantiated in this case as an iterative firm-shrinkage algorithm,
+and its effectiveness is demonstrated in numerical experiments by both randomly
+generated and real datasets.
+",1,0,0,1,0,0
+2712,Inapproximability of the independent set polynomial in the complex plane,"  We study the complexity of approximating the independent set polynomial
+$Z_G(\lambda)$ of a graph $G$ with maximum degree $\Delta$ when the activity
+$\lambda$ is a complex number.
+This problem is already well understood when $\lambda$ is real using
+connections to the $\Delta$-regular tree $T$. The key concept in that case is
+the ""occupation ratio"" of the tree $T$. This ratio is the contribution to
+$Z_T(\lambda)$ from independent sets containing the root of the tree, divided
+by $Z_T(\lambda)$ itself. If $\lambda$ is such that the occupation ratio
+converges to a limit, as the height of $T$ grows, then there is an FPTAS for
+approximating $Z_G(\lambda)$ on a graph $G$ with maximum degree $\Delta$.
+Otherwise, the approximation problem is NP-hard.
+Unsurprisingly, the case where $\lambda$ is complex is more challenging.
+Peters and Regts identified the complex values of $\lambda$ for which the
+occupation ratio of the $\Delta$-regular tree converges. These values carve a
+cardioid-shaped region $\Lambda_\Delta$ in the complex plane. Motivated by the
+picture in the real case, they asked whether $\Lambda_\Delta$ marks the true
+approximability threshold for general complex values $\lambda$.
+Our main result shows that for every $\lambda$ outside of $\Lambda_\Delta$,
+the problem of approximating $Z_G(\lambda)$ on graphs $G$ with maximum degree
+at most $\Delta$ is indeed NP-hard. In fact, when $\lambda$ is outside of
+$\Lambda_\Delta$ and is not a positive real number, we give the stronger result
+that approximating $Z_G(\lambda)$ is actually #P-hard. If $\lambda$ is a
+negative real number outside of $\Lambda_\Delta$, we show that it is #P-hard to
+even decide whether $Z_G(\lambda)>0$, resolving in the affirmative a conjecture
+of Harvey, Srivastava and Vondrak.
+Our proof techniques are based around tools from complex analysis -
+specifically the study of iterative multivariate rational maps.
+",1,0,0,0,0,0
+2713,A giant with feet of clay: on the validity of the data that feed machine learning in medicine,"  This paper considers the use of Machine Learning (ML) in medicine by focusing
+on the main problem that this computational approach has been aimed at solving
+or at least minimizing: uncertainty. To this aim, we point out how uncertainty
+is so ingrained in medicine that it biases also the representation of clinical
+phenomena, that is the very input of ML models, thus undermining the clinical
+significance of their output. Recognizing this can motivate both medical
+doctors, in taking more responsibility in the development and use of these
+decision aids, and the researchers, in pursuing different ways to assess the
+value of these systems. In so doing, both designers and users could take this
+intrinsic characteristic of medicine more seriously and consider alternative
+approaches that do not ""sweep uncertainty under the rug"" within an objectivist
+fiction, which everyone can come up by believing as true.
+",1,0,0,1,0,0
+2714,Constraining accretion signatures of exoplanets in the TW Hya transitional disk,"  We present a near-infrared direct imaging search for accretion signatures of
+possible protoplanets around the young stellar object (YSO) TW Hya, a
+multi-ring disk exhibiting evidence of planet formation. The Pa$\beta$ line
+(1.282 $\mu$m) is an indication of accretion onto a protoplanet, and its
+intensity is much higher than that of blackbody radiation from the protoplanet.
+We focused on the Pa$\beta$ line and performed Keck/OSIRIS spectroscopic
+observations. Although spectral differential imaging (SDI) reduction detected
+no accretion signatures, the results of the present study allowed us to set
+5$\sigma$ detection limits for Pa$\beta$ emission of $5.8\times10^{-18}$ and
+$1.5\times10^{-18}$ erg/s/cm$^2$ at 0\farcs4 and 1\farcs6, respectively. We
+considered the mass of potential planets using theoretical simulations of
+circumplanetary disks and hydrogen emission. The resulting masses were $1.45\pm
+0.04$ M$_{\rm J}$ and $2.29 ^{+0.03}_{-0.04}$ M$_{\rm J}$ at 25 and 95 AU,
+respectively, which agree with the detection limits obtained from previous
+broadband imaging. The detection limits should allow the identification of
+protoplanets as small as $\sim$1 M$_{\rm J}$, which may assist in direct
+imaging searches around faint YSOs for which extreme adaptive optics
+instruments are unavailable.
+",0,1,0,0,0,0
+2715,Obstructions to planarity of contact 3-manifolds,"  We prove that if a contact 3-manifold admits an open book decomposition of
+genus 0, a certain intersection pattern cannot appear in the homology of any of
+its symplectic fillings, and morever, fillings cannot contain certain
+symplectic surfaces. Applying these obstructions to canonical contact
+structures on links of normal surface singularities, we show that links of
+isolated singularities of surfaces in the complex 3-space are planar only in
+the case of $A_n$-singularities, and in general characterize completely planar
+links of normal surface singularities (in terms of their resolution graphs). We
+also establish non-planarity of tight contact structures on certain small
+Seifert fibered L-spaces and of contact structures compatible with open books
+given by a boundary multi-twist on a page of positive genus. Additionally, we
+prove that every finitely presented group is the fundamental group of a
+Leschetz fibration with planar fibers.
+",0,0,1,0,0,0
+2716,Bounds on harmonic radius and limits of manifolds with bounded Bakry-Émery Ricci curvature,"  Under the usual condition that the volume of a geodesic ball is close to the
+Euclidean one or the injectivity radii is bounded from below, we prove a lower
+bound of the $C^{\alpha} W^{1, q}$ harmonic radius for manifolds with bounded
+Bakry-Émery Ricci curvature when the gradient of the potential is bounded.
+Under these conditions, the regularity that can be imposed on the metrics under
+harmonic coordinates is only $C^\alpha W^{1,q}$, where $q>2n$ and $n$ is the
+dimension of the manifolds. This is almost 1 order lower than that in the
+classical $C^{1,\alpha} W^{2, p}$ harmonic coordinates under bounded Ricci
+curvature condition [And]. The loss of regularity induces some difference in
+the method of proof, which can also be used to address the detail of $W^{2, p}$
+convergence in the classical case.
+Based on this lower bound and the techniques in [ChNa2] and [WZ], we extend
+Cheeger-Naber's Codimension 4 Theorem in [ChNa2] to the case where the
+manifolds have bounded Bakry-Émery Ricci curvature when the gradient of the
+potential is bounded. This result covers Ricci solitons when the gradient of
+the potential is bounded.
+During the proof, we will use a Green's function argument and adopt a linear
+algebra argument in [Bam]. A new ingradient is to show that the diagonal
+entries of the matrices in the Transformation Theorem are bounded away from 0.
+Together these seem to simplify the proof of the Codimension 4 Theorem, even in
+the case where Ricci curvature is bounded.
+",0,0,1,0,0,0
+2717,Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam,"  Uncertainty computation in deep learning is essential to design robust and
+reliable systems. Variational inference (VI) is a promising approach for such
+computation, but requires more effort to implement and execute compared to
+maximum-likelihood methods. In this paper, we propose new natural-gradient
+algorithms to reduce such efforts for Gaussian mean-field VI. Our algorithms
+can be implemented within the Adam optimizer by perturbing the network weights
+during gradient evaluations, and uncertainty estimates can be cheaply obtained
+by using the vector that adapts the learning rate. This requires lower memory,
+computation, and implementation effort than existing VI methods, while
+obtaining uncertainty estimates of comparable quality. Our empirical results
+confirm this and further suggest that the weight-perturbation in our algorithm
+could be useful for exploration in reinforcement learning and stochastic
+optimization.
+",0,0,0,1,0,0
+2718,Improving average ranking precision in user searches for biomedical research datasets,"  Availability of research datasets is keystone for health and life science
+study reproducibility and scientific progress. Due to the heterogeneity and
+complexity of these data, a main challenge to be overcome by research data
+management systems is to provide users with the best answers for their search
+queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we
+investigate a novel ranking pipeline to improve the search of datasets used in
+biomedical experiments. Our system comprises a query expansion model based on
+word embeddings, a similarity measure algorithm that takes into consideration
+the relevance of the query terms, and a dataset categorisation method that
+boosts the rank of datasets matching query constraints. The system was
+evaluated using a corpus with 800k datasets and 21 annotated user queries. Our
+system provides competitive results when compared to the other challenge
+participants. In the official run, it achieved the highest infAP among the
+participants, being +22.3% higher than the median infAP of the participant's
+best submissions. Overall, it is ranked at top 2 if an aggregated metric using
+the best official measures per participant is considered. The query expansion
+method showed positive impact on the system's performance increasing our
+baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively.
+Our similarity measure algorithm seems to be robust, in particular compared to
+Divergence From Randomness framework, having smaller performance variations
+under different training conditions. Finally, the result categorization did not
+have significant impact on the system's performance. We believe that our
+solution could be used to enhance biomedical dataset management systems. In
+particular, the use of data driven query expansion methods could be an
+alternative to the complexity of biomedical terminologies.
+",1,0,0,0,0,0
+2719,Adversarial Attacks on Neural Networks for Graph Data,"  Deep learning models for graphs have achieved strong performance for the task
+of node classification. Despite their proliferation, currently there is no
+study of their robustness to adversarial attacks. Yet, in domains where they
+are likely to be used, e.g. the web, adversaries are common. Can deep learning
+models for graphs be easily fooled? In this work, we introduce the first study
+of adversarial attacks on attributed graphs, specifically focusing on models
+exploiting ideas of graph convolutions. In addition to attacks at test time, we
+tackle the more challenging class of poisoning/causative attacks, which focus
+on the training phase of a machine learning model. We generate adversarial
+perturbations targeting the node's features and the graph structure, thus,
+taking the dependencies between instances in account. Moreover, we ensure that
+the perturbations remain unnoticeable by preserving important data
+characteristics. To cope with the underlying discrete domain we propose an
+efficient algorithm Nettack exploiting incremental computations. Our
+experimental study shows that accuracy of node classification significantly
+drops even when performing only few perturbations. Even more, our attacks are
+transferable: the learned attacks generalize to other state-of-the-art node
+classification models and unsupervised approaches, and likewise are successful
+even when only limited knowledge about the graph is given.
+",0,0,0,1,0,0
+2720,"Electromagnetic energy, momentum and forces in a dielectric medium with losses","  From the energy-momentum tensors of the electromagnetic field and the
+mechanical energy-momentum, the equations of energy conservation and balance of
+electromagnetic and mechanical forces are obtained. The equation for the
+Abraham force in a dielectric medium with losses is obtained
+",0,1,0,0,0,0
+2721,Thermotronics: toward nanocircuits to manage radiative heat flux,"  The control of electric currents in solids is at the origin of the modern
+electronics revolution which has driven our daily life since the second half of
+20th century. Surprisingly, to date, there is no thermal analog for a control
+of heat flux. Here, we summarize the very last developments carried out in this
+direction to control heat exchanges by radiation both in near and far-field in
+complex architecture networks.
+",0,1,0,0,0,0
+2722,Multiplication and Presence of Shielding Material from Time-Correlated Pulse-Height Measurements of Subcritical Plutonium Assemblies,"  We present the results from the first measurements of the Time-Correlated
+Pulse-Height (TCPH) distributions from 4.5 kg sphere of $\alpha$-phase
+weapons-grade plutonium metal in five configurations: bare, reflected by 1.27
+cm and 2.54 cm of tungsten, and 2.54 cm and 7.62 cm of polyethylene. A new
+method for characterizing source multiplication and shielding configuration is
+also demonstrated. The method relies on solving for the underlying fission
+chain timing distribution that drives the spreading of the measured TCPH
+distribution. We found that a gamma distribution fits the fission chain timing
+distribution well and that the fit parameters correlate with both
+multiplication (rate parameter) and shielding material types (shape parameter).
+The source-to-detector distance was another free parameter that we were able to
+optimize, and proved to be the most well constrained parameter. MCNPX-PoliMi
+simulations were used to complement the measurements and help illustrate trends
+in these parameters and their relation to multiplication and the amount and
+type of material coupled to the subcritical assembly.
+",0,1,0,0,0,0
+2723,General Refraction Problems with Phase Discontinuity,"  This paper provides a mathematical approach to study metasurfaces in non flat
+geometries. Analytical conditions between the curvature of the surface and the
+set of refracted directions are introduced to guarantee the existence of phase
+discontinuities. The approach contains both the near and far field cases. A
+starting point is the formulation of a vector Snell law in presence of abrupt
+discontinuities on the interfaces.
+",0,1,1,0,0,0
+2724,An Efficiently Searchable Encrypted Data Structure for Range Queries,"  At CCS 2015 Naveed et al. presented first attacks on efficiently searchable
+encryption, such as deterministic and order-preserving encryption. These
+plaintext guessing attacks have been further improved in subsequent work, e.g.
+by Grubbs et al. in 2016. Such cryptanalysis is crucially important to sharpen
+our understanding of the implications of security models. In this paper we
+present an efficiently searchable, encrypted data structure that is provably
+secure against these and even more powerful chosen plaintext attacks. Our data
+structure supports logarithmic-time search with linear space complexity. The
+indices of our data structure can be used to search by standard comparisons and
+hence allow easy retrofitting to existing database management systems. We
+implemented our scheme and show that its search time overhead is only 10
+milliseconds compared to non-secure search.
+",1,0,0,0,0,0
+2725,Hyperfine state entanglement of spinor BEC and scattering atom,"  Condensate of spin-1 atoms frozen in a unique spatial mode may possess large
+internal degrees of freedom. The scattering amplitudes of polarized cold atoms
+scattered by the condensate are obtained with the method of fractional
+parentage coefficients that treats the spin degrees of freedom rigorously.
+Channels with scattering cross sections enhanced by square of atom number of
+the condensate are found. Entanglement between the condensate and the
+propagating atom can be established by the scattering. The entanglement entropy
+is analytically obtained for arbitrary initial states. Our results also give
+hint for the establishment of quantum thermal ensembles in the hyperfine space.
+",0,1,0,0,0,0
+2726,Making Neural Programming Architectures Generalize via Recursion,"  Empirically, neural networks that attempt to learn programs from data have
+exhibited poor generalizability. Moreover, it has traditionally been difficult
+to reason about the behavior of these models beyond a certain level of input
+complexity. In order to address these issues, we propose augmenting neural
+architectures with a key abstraction: recursion. As an application, we
+implement recursion in the Neural Programmer-Interpreter framework on four
+tasks: grade-school addition, bubble sort, topological sort, and quicksort. We
+demonstrate superior generalizability and interpretability with small amounts
+of training data. Recursion divides the problem into smaller pieces and
+drastically reduces the domain of each neural network component, making it
+tractable to prove guarantees about the overall system's behavior. Our
+experience suggests that in order for neural architectures to robustly learn
+program semantics, it is necessary to incorporate a concept like recursion.
+",1,0,0,0,0,0
+2727,Bagged Empirical Null p-values: A Method to Account for Model Uncertainty in Large Scale Inference,"  When conducting large scale inference, such as genome-wide association
+studies or image analysis, nominal $p$-values are often adjusted to improve
+control over the family-wise error rate (FWER). When the majority of tests are
+null, procedures controlling the False discovery rate (Fdr) can be improved by
+replacing the theoretical global null with its empirical estimate. However,
+these other adjustment procedures remain sensitive to the working model
+assumption. Here we propose two key ideas to improve inference in this space.
+First, we propose $p$-values that are standardized to the empirical null
+distribution (instead of the theoretical null). Second, we propose model
+averaging $p$-values by bootstrap aggregation (Bagging) to account for model
+uncertainty and selection procedures. The combination of these two key ideas
+yields bagged empirical null $p$-values (BEN $p$-values) that often
+dramatically alter the rank ordering of significant findings. Moreover, we find
+that a multidimensional selection criteria based on BEN $p$-values and bagged
+model fit statistics is more likely to yield reproducible findings. A
+re-analysis of the famous Golub Leukemia data is presented to illustrate these
+ideas. We uncovered new findings in these data, not detected previously, that
+are backed by published bench work pre-dating the Gloub experiment. A
+pseudo-simulation using the leukemia data is also presented to explore the
+stability of this approach under broader conditions, and illustrates the
+superiority of the BEN $p$-values compared to the other approaches.
+",0,0,0,1,0,0
+2728,Identifying Vessel Branching from Fluid Stresses on Microscopic Robots,"  Objects moving in fluids experience patterns of stress on their surfaces
+determined by the geometry of nearby boundaries. Flows at low Reynolds number,
+as occur in microscopic vessels such as capillaries in biological tissues, have
+relatively simple relations between stresses and nearby vessel geometry. Using
+these relations, this paper shows how a microscopic robot moving with such
+flows can use changes in stress on its surface to identify when it encounters
+vessel branches.
+",1,0,0,0,0,0
+2729,Impact of Optimal Storage Allocation on Price Volatility in Electricity Markets,"  Recent studies show that the fast growing expansion of wind power generation
+may lead to extremely high levels of price volatility in wholesale electricity
+markets. Storage technologies, regardless of their specific forms e.g.
+pump-storage hydro, large-scale or distributed batteries, are capable of
+alleviating the extreme price volatility levels due to their energy usage time
+shifting, fast-ramping and price arbitrage capabilities. In this paper, we
+propose a stochastic bi-level optimization model to find the optimal nodal
+storage capacities required to achieve a certain price volatility level in a
+highly volatile electricity market. The decision on storage capacities is made
+in the upper level problem and the operation of strategic/regulated generation,
+storage and transmission players is modeled at the lower level problem using an
+extended Cournot-based stochastic game. The South Australia (SA) electricity
+market, which has recently experienced high levels of price volatility, is
+considered as the case study for the proposed storage allocation framework. Our
+numerical results indicate that 80% price volatility reduction in SA
+electricity market can be achieved by installing either 340 MWh regulated
+storage or 420 MWh strategic storage. In other words, regulated storage firms
+are more efficient in reducing the price volatility than strategic storage
+firms.
+",0,0,1,0,0,0
+2730,An independent axiomatisation for free short-circuit logic,"  Short-circuit evaluation denotes the semantics of propositional connectives
+in which the second argument is evaluated only if the first argument does not
+suffice to determine the value of the expression. Free short-circuit logic is
+the equational logic in which compound statements are evaluated from left to
+right, while atomic evaluations are not memorised throughout the evaluation,
+i.e., evaluations of distinct occurrences of an atom in a compound statement
+may yield different truth values. We provide a simple semantics for free SCL
+and an independent axiomatisation. Finally, we discuss evaluation strategies,
+some other SCLs, and side effects.
+",1,0,1,0,0,0
+2731,A Note on a Communication Game,"  We describe a communication game, and a conjecture about this game, whose
+proof would imply the well-known Sensitivity Conjecture asserting a polynomial
+relation between sensitivity and block sensitivity for Boolean functions. The
+author defined this game and observed the connection in Dec. 2013 - Jan. 2014.
+The game and connection were independently discovered by Gilmer, Koucký, and
+Saks, who also established further results about the game (not proved by us)
+and published their results in ITCS '15 [GKS15].
+This note records our independent work, including some observations that did
+not appear in [GKS15]. Namely, the main conjecture about this communication
+game would imply not only the Sensitivity Conjecture, but also a stronger
+hypothesis raised by Chung, Füredi, Graham, and Seymour [CFGS88]; and,
+another related conjecture we pose about a ""query-bounded"" variant of our
+communication game would suffice to answer a question of Aaronson, Ambainis,
+Balodis, and Bavarian [AABB14] about the query complexity of the ""Weak Parity""
+problem---a question whose resolution was previously shown by [AABB14] to
+follow from a proof of the Chung et al. hypothesis.
+",1,0,0,0,0,0
+2732,Iteration of Quadratic Polynomials Over Finite Fields,"  For a finite field of odd cardinality $q$, we show that the sequence of
+iterates of $aX^2+c$, starting at $0$, always recurs after $O(q/\log\log q)$
+steps. For $X^2+1$ the same is true for any starting value. We suggest that the
+traditional ""Birthday Paradox"" model is inappropriate for iterates of $X^3+c$,
+when $q$ is 2 mod 3.
+",0,0,1,0,0,0
+2733,Constraints on the Growth and Spin of the Supermassive Black Hole in M32 From High Cadence Visible Light Observations,"  We present 1-second cadence observations of M32 (NGC221) with the CHIMERA
+instrument at the Hale 200-inch telescope of the Palomar Observatory. Using
+field stars as a baseline for relative photometry, we are able to construct a
+light curve of the nucleus in the g-prime and r-prime band with 1sigma=36
+milli-mag photometric stability. We derive a temporal power spectrum for the
+nucleus and find no evidence for a time-variable signal above the noise as
+would be expected if the nuclear black hole were accreting gas. Thus, we are
+unable to constrain the spin of the black hole although future work will use
+this powerful instrument to target more actively accreting black holes. Given
+the black hole mass of (2.5+/-0.5)*10^6 Msun inferred from stellar kinematics,
+the absence of a contribution from a nuclear time-variable signal places an
+upper limit on the accretion rate which is 4.6*10^{-8} of the Eddington rate, a
+factor of two more stringent than past upper limits from HST. The low mass of
+the black hole despite the high stellar density suggests that the gas liberated
+by stellar interactions was primarily at early cosmic times when the low-mass
+black hole had a small Eddington luminosity. This is at least partly driven by
+a top-heavy stellar initial mass function at early cosmic times which is an
+efficient producer of stellar mass black holes. The implication is that
+supermassive black holes likely arise from seeds formed through the coalescence
+of 3-100 Msun mass black holes that then accrete gas produced through stellar
+interaction processes.
+",0,1,0,0,0,0
+2734,Sampling for Approximate Bipartite Network Projection,"  Bipartite networks manifest as a stream of edges that represent transactions,
+e.g., purchases by retail customers. Many machine learning applications employ
+neighborhood-based measures to characterize the similarity among the nodes,
+such as the pairwise number of common neighbors (CN) and related metrics. While
+the number of node pairs that share neighbors is potentially enormous, only a
+relatively small proportion of them have many common neighbors. This motivates
+finding a weighted sampling approach to preferentially sample these node pairs.
+This paper presents a new sampling algorithm that provides a fixed size
+unbiased estimate of the similarity matrix resulting from a bipartite graph
+stream projection. The algorithm has two components. First, it maintains a
+reservoir of sampled bipartite edges with sampling weights that favor selection
+of high similarity nodes. Second, arriving edges generate a stream of
+\textsl{similarity updates} based on their adjacency with the current sample.
+These updates are aggregated in a second reservoir sample-based stream
+aggregator to yield the final unbiased estimate. Experiments on real world
+graphs show that a 10% sample at each stage yields estimates of high similarity
+edges with weighted relative errors of about 1%.
+",1,0,1,0,0,0
+2735,"Navigate, Understand, Communicate: How Developers Locate Performance Bugs","  Background: Performance bugs can lead to severe issues regarding computation
+efficiency, power consumption, and user experience. Locating these bugs is a
+difficult task because developers have to judge for every costly operation
+whether runtime is consumed necessarily or unnecessarily. Objective: We wanted
+to investigate how developers, when locating performance bugs, navigate through
+the code, understand the program, and communicate the detected issues. Method:
+We performed a qualitative user study observing twelve developers trying to fix
+documented performance bugs in two open source projects. The developers worked
+with a profiling and analysis tool that visually depicts runtime information in
+a list representation and embedded into the source code view. Results: We
+identified typical navigation strategies developers used for pinpointing the
+bug, for instance, following method calls based on runtime consumption. The
+integration of visualization and code helped developers to understand the bug.
+Sketches visualizing data structures and algorithms turned out to be valuable
+for externalizing and communicating the comprehension process for complex bugs.
+Conclusion: Fixing a performance bug is a code comprehension and navigation
+problem. Flexible navigation features based on executed methods and a close
+integration of source code and performance information support the process.
+",1,0,0,0,0,0
+2736,MEXIT: Maximal un-coupling times for stochastic processes,"  Classical coupling constructions arrange for copies of the \emph{same} Markov
+process started at two \emph{different} initial states to become equal as soon
+as possible. In this paper, we consider an alternative coupling framework in
+which one seeks to arrange for two \emph{different} Markov (or other
+stochastic) processes to remain equal for as long as possible, when started in
+the \emph{same} state. We refer to this ""un-coupling"" or ""maximal agreement""
+construction as \emph{MEXIT}, standing for ""maximal exit"". After highlighting
+the importance of un-coupling arguments in a few key statistical and
+probabilistic settings, we develop an explicit \MEXIT construction for
+stochastic processes in discrete time with countable state-space. This
+construction is generalized to random processes on general state-space running
+in continuous time, and then exemplified by discussion of \MEXIT for Brownian
+motions with two different constant drifts.
+",0,0,1,0,0,0
+2737,Learning Effective Changes for Software Projects,"  The primary motivation of much of software analytics is decision making. How
+to make these decisions? Should one make decisions based on lessons that arise
+from within a particular project? Or should one generate these decisions from
+across multiple projects? This work is an attempt to answer these questions.
+Our work was motivated by a realization that much of the current generation
+software analytics tools focus primarily on prediction. Indeed prediction is a
+useful task, but it is usually followed by ""planning"" about what actions need
+to be taken. This research seeks to address the planning task by seeking
+methods that support actionable analytics that offer clear guidance on what to
+do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set
+of actionable plans within and across projects. Each of these plans, if
+followed will improve the quality of the software project.
+",1,0,0,0,0,0
+2738,Aggregating multiple types of complex data in stock market prediction: A model-independent framework,"  The increasing richness in volume, and especially types of data in the
+financial domain provides unprecedented opportunities to understand the stock
+market more comprehensively and makes the price prediction more accurate than
+before. However, they also bring challenges to classic statistic approaches
+since those models might be constrained to a certain type of data. Aiming at
+aggregating differently sourced information and offering type-free capability
+to existing models, a framework for predicting stock market of scenarios with
+mixed data, including scalar data, compositional data (pie-like) and functional
+data (curve-like), is established. The presented framework is
+model-independent, as it serves like an interface to multiple types of data and
+can be combined with various prediction models. And it is proved to be
+effective through numerical simulations. Regarding to price prediction, we
+incorporate the trading volume (scalar data), intraday return series
+(functional data), and investors' emotions from social media (compositional
+data) through the framework to competently forecast whether the market goes up
+or down at opening in the next day. The strong explanatory power of the
+framework is further demonstrated. Specifically, it is found that the intraday
+returns impact the following opening prices differently between bearish market
+and bullish market. And it is not at the beginning of the bearish market but
+the subsequent period in which the investors' ""fear"" comes to be indicative.
+The framework would help extend existing prediction models easily to scenarios
+with multiple types of data and shed light on a more systemic understanding of
+the stock market.
+",0,0,0,1,0,1
+2739,Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction,"  Taxi demand prediction is an important building block to enabling intelligent
+transportation systems in a smart city. An accurate prediction model can help
+the city pre-allocate resources to meet travel demand and to reduce empty taxis
+on streets which waste energy and worsen the traffic congestion. With the
+increasing popularity of taxi requesting services such as Uber and Didi Chuxing
+(in China), we are able to collect large-scale taxi demand data continuously.
+How to utilize such big data to improve the demand prediction is an interesting
+and critical real-world problem. Traditional demand prediction methods mostly
+rely on time series forecasting techniques, which fail to model the complex
+non-linear spatial and temporal relations. Recent advances in deep learning
+have shown superior performance on traditionally challenging tasks such as
+image classification by learning the complex features and correlations from
+large-scale data. This breakthrough has inspired researchers to explore deep
+learning techniques on traffic prediction problems. However, existing methods
+on traffic prediction have only considered spatial relation (e.g., using CNN)
+or temporal relation (e.g., using LSTM) independently. We propose a Deep
+Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial
+and temporal relations. Specifically, our proposed model consists of three
+views: temporal view (modeling correlations between future demand values with
+near time points via LSTM), spatial view (modeling local spatial correlation
+via local CNN), and semantic view (modeling correlations among regions sharing
+similar temporal patterns). Experiments on large-scale real taxi demand data
+demonstrate effectiveness of our approach over state-of-the-art methods.
+",0,0,0,1,0,0
+2740,Interstellar communication. VII. Benchmarking inscribed matter probes,"  We have explored the optimal frequency of interstellar photon communications
+and benchmarked other particles as information carriers in previous papers of
+this series. We now compare the latency and bandwidth of sending probes with
+inscribed matter. Durability requirements such as shields against dust and
+radiation, as well as data duplication, add negligible weight overhead at
+velocities <0.2c. Probes may arrive in full, while most of a photon beam is
+lost to diffraction. Probes can be more energy efficient per bit, and can have
+higher bandwidth, compared to classical communication, unless a photon receiver
+is placed in a stellar gravitational lens. The probe's advantage dominates by
+order of magnitude for long distances (kpc) and low velocities (<0.1c) at the
+cost of higher latency.
+",0,1,0,0,0,0
+2741,A description length approach to determining the number of k-means clusters,"  We present an asymptotic criterion to determine the optimal number of
+clusters in k-means. We consider k-means as data compression, and propose to
+adopt the number of clusters that minimizes the estimated description length
+after compression. Here we report two types of compression ratio based on two
+ways to quantify the description length of data after compression. This
+approach further offers a way to evaluate whether clusters obtained with
+k-means have a hierarchical structure by examining whether multi-stage
+compression can further reduce the description length. We applied our criteria
+to determine the number of clusters to synthetic data and empirical
+neuroimaging data to observe the behavior of the criteria across different
+types of data set and suitability of the two types of criteria for different
+datasets. We found that our method can offer reasonable clustering results that
+are useful for dimension reduction. While our numerical results revealed
+dependency of our criteria on the various aspects of dataset such as the
+dimensionality, the description length approach proposed here provides a useful
+guidance to determine the number of clusters in a principled manner when
+underlying properties of the data are unknown and only inferred from
+observation of data.
+",1,0,0,1,0,0
+2742,Complementary legs and rational balls,"  In this note we study the Seifert rational homology spheres with two
+complementary legs, i.e. with a pair of invariants whose fractions add up to
+one. We give a complete classification of the Seifert manifolds with 3
+exceptional fibers and two complementary legs which bound rational homology
+balls. The result translates in a statement on the sliceness of some Montesinos
+knots.
+",0,0,1,0,0,0
+2743,Gravitational Waves from Stellar Black Hole Binaries and the Impact on Nearby Sun-like Stars,"  We investigate the impact of resonant gravitational waves on quadrupole
+acoustic modes of Sun-like stars located nearby stellar black hole binary
+systems (such as GW150914 and GW151226). We find that the stimulation of the
+low-overtone modes by gravitational radiation can lead to sizeable photometric
+amplitude variations, much larger than the predictions for amplitudes driven by
+turbulent convection, which in turn are consistent with the photometric
+amplitudes observed in most Sun-like stars. For accurate stellar evolution
+models, using up-to-date stellar physics, we predict photometric amplitude
+variations of $1$ -- $10^3$ ppm for a solar mass star located at a distance
+between 1 au and 10 au from the black hole binary, and belonging to the same
+multi-star system. The observation of such a phenomenon will be within the
+reach of the Plato mission because telescope will observe several portions of
+the Milky Way, many of which are regions of high stellar density with a
+substantial mixed population of Sun-like stars and black hole binaries.
+",0,1,0,0,0,0
+2744,"Search for Exoplanets around Northern Circumpolar Stars- II. The Detection of Radial Velocity Variations in M Giant Stars HD 36384, HD 52030, and HD 208742","  We present the detection of long-period RV variations in HD 36384, HD 52030,
+and HD 208742 by using the high-resolution, fiber-fed Bohyunsan Observatory
+Echelle Spectrograph (BOES) for the precise radial velocity (RV) survey of
+about 200 northern circumpolar stars. Analyses of RV data, chromospheric
+activity indicators, and bisector variations spanning about five years suggest
+that the RV variations are compatible with planet or brown dwarf companions in
+Keplerian motion. However, HD 36384 shows photometric variations with a period
+very close to that of RV variations as well as amplitude variations in the
+weighted wavelet Z-transform (WWZ) analysis, which argues that the RV
+variations in HD~36384 are from the stellar pulsations. Assuming that the
+companion hypothesis is correct, HD~52030 hosts a companion with minimum mass
+13.3 M_Jup$ orbiting in 484 days at a distance of 1.2 AU. HD~208742 hosts a
+companion of 14.0 M_Jup at 1.5 AU with a period of 602 days. All stars are
+located at the asymptotic giant branch (AGB) stage on the H-R diagram after
+undergone the helium flash and left the giant clump.With stellar radii of 53.0
+R_Sun and 57.2 R_Sun for HD 52030 and HD 208742, respectively, these stars may
+be the largest yet, in terms of stellar radius, found to host sub-stellar
+companions. However, given possible RV amplitude variations and the fact that
+these are highly evolved stars the planet hypothesis is not yet certain.
+",0,1,0,0,0,0
+2745,Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studies,"  A personalized learning system needs a large pool of items for learners to
+solve. When working with a large pool of items, it is useful to measure the
+similarity of items. We outline a general approach to measuring the similarity
+of items and discuss specific measures for items used in introductory
+programming. Evaluation of quality of similarity measures is difficult. To this
+end, we propose an evaluation approach utilizing three levels of abstraction.
+We illustrate our approach to measuring similarity and provide evaluation using
+items from three diverse programming environments.
+",0,0,0,1,0,0
+2746,Term Models of Horn Clauses over Rational Pavelka Predicate Logic,"  This paper is a contribution to the study of the universal Horn fragment of
+predicate fuzzy logics, focusing on the proof of the existence of free models
+of theories of Horn clauses over Rational Pavelka predicate logic. We define
+the notion of a term structure associated to every consistent theory T over
+Rational Pavelka predicate logic and we prove that the term models of T are
+free on the class of all models of T. Finally, it is shown that if T is a set
+of Horn clauses, the term structure associated to T is a model of T.
+",0,0,1,0,0,0
+2747,Galaxy Rotation and Supermassive Black Hole Binary Evolution,"  Supermassive black hole (SMBH) binaries residing at the core of merging
+galaxies are recently found to be strongly affected by the rotation of their
+host galaxies. The highly eccentric orbits that form when the host is
+counterrotating emit strong bursts of gravitational waves that propel rapid
+SMBH binary coalescence. Most prior work, however, focused on planar orbits and
+a uniform rotation profile, an unlikely interaction configuration. However, the
+coupling between rotation and SMBH binary evolution appears to be such a strong
+dynamical process that it warrants further investigation. This study uses
+direct N-body simulations to isolate the effect of galaxy rotation in more
+realistic interactions. In particular, we systematically vary the SMBH orbital
+plane with respect to the galaxy rotation axis, the radial extent of the
+rotating component, and the initial eccentricity of the SMBH binary orbit. We
+find that the initial orbital plane orientation and eccentricity alone can
+change the inspiral time by an order of magnitude. Because SMBH binary inspiral
+and merger is such a loud gravitational wave source, these studies are critical
+for the future gravitational wave detector, LISA, an ESA/NASA mission currently
+set to launch by 2034.
+",0,1,0,0,0,0
+2748,A Formal Approach to Exploiting Multi-Stage Attacks based on File-System Vulnerabilities of Web Applications (Extended Version),"  Web applications require access to the file-system for many different tasks.
+When analyzing the security of a web application, secu- rity analysts should
+thus consider the impact that file-system operations have on the security of
+the whole application. Moreover, the analysis should take into consideration
+how file-system vulnerabilities might in- teract with other vulnerabilities
+leading an attacker to breach into the web application. In this paper, we first
+propose a classification of file- system vulnerabilities, and then, based on
+this classification, we present a formal approach that allows one to exploit
+file-system vulnerabilities. We give a formal representation of web
+applications, databases and file- systems, and show how to reason about
+file-system vulnerabilities. We also show how to combine file-system
+vulnerabilities and SQL-Injection vulnerabilities for the identification of
+complex, multi-stage attacks. We have developed an automatic tool that
+implements our approach and we show its efficiency by discussing several
+real-world case studies, which are witness to the fact that our tool can
+generate, and exploit, complex attacks that, to the best of our knowledge, no
+other state-of-the-art-tool for the security of web applications can find.
+",1,0,0,0,0,0
+2749,Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks,"  Fully realizing the potential of acceleration for Deep Neural Networks (DNNs)
+requires understanding and leveraging algorithmic properties. This paper builds
+upon the algorithmic insight that bitwidth of operations in DNNs can be reduced
+without compromising their classification accuracy. However, to prevent
+accuracy loss, the bitwidth varies significantly across DNNs and it may even be
+adjusted for each layer. Thus, a fixed-bitwidth accelerator would either offer
+limited benefits to accommodate the worst-case bitwidth requirements, or lead
+to a degradation in final accuracy. To alleviate these deficiencies, this work
+introduces dynamic bit-level fusion/decomposition as a new dimension in the
+design of DNN accelerators. We explore this dimension by designing Bit Fusion,
+a bit-flexible accelerator, that constitutes an array of bit-level processing
+elements that dynamically fuse to match the bitwidth of individual DNN layers.
+This flexibility in the architecture enables minimizing the computation and the
+communication at the finest granularity possible with no loss in accuracy. We
+evaluate the benefits of BitFusion using eight real-world feed-forward and
+recurrent DNNs. The proposed microarchitecture is implemented in Verilog and
+synthesized in 45 nm technology. Using the synthesis results and cycle accurate
+simulation, we compare the benefits of Bit Fusion to two state-of-the-art DNN
+accelerators, Eyeriss and Stripes. In the same area, frequency, and process
+technology, BitFusion offers 3.9x speedup and 5.1x energy savings over Eyeriss.
+Compared to Stripes, BitFusion provides 2.6x speedup and 3.9x energy reduction
+at 45 nm node when BitFusion area and frequency are set to those of Stripes.
+Scaling to GPU technology node of 16 nm, BitFusion almost matches the
+performance of a 250-Watt Titan Xp, which uses 8-bit vector instructions, while
+BitFusion merely consumes 895 milliwatts of power.
+",1,0,0,0,0,0
+2750,Multiple Access Wiretap Channel with Noiseless Feedback,"  The physical layer security in the up-link of the wireless communication
+systems is often modeled as the multiple access wiretap channel (MAC-WT), and
+recently it has received a lot attention. In this paper, the MAC-WT has been
+re-visited by considering the situation that the legitimate receiver feeds his
+received channel output back to the transmitters via two noiseless channels,
+respectively. This model is called the MAC-WT with noiseless feedback. Inner
+and outer bounds on the secrecy capacity region of this feedback model are
+provided. To be specific, we first present a decode-and-forward (DF) inner
+bound on the secrecy capacity region of this feedback model, and this bound is
+constructed by allowing each transmitter to decode the other one's transmitted
+message from the feedback, and then each transmitter uses the decoded message
+to re-encode his own messages, i.e., this DF inner bound allows the independent
+transmitters to co-operate with each other. Then, we provide a hybrid inner
+bound which is strictly larger than the DF inner bound, and it is constructed
+by using the feedback as a tool not only to allow the independent transmitters
+to co-operate with each other, but also to generate two secret keys
+respectively shared between the legitimate receiver and the two transmitters.
+Finally, we give a sato-type outer bound on the secrecy capacity region of this
+feedback model. The results of this paper are further explained via a Gaussian
+example.
+",1,0,1,0,0,0
+2751,Inter-Subject Analysis: Inferring Sparse Interactions with Dense Intra-Graphs,"  We develop a new modeling framework for Inter-Subject Analysis (ISA). The
+goal of ISA is to explore the dependency structure between different subjects
+with the intra-subject dependency as nuisance. It has important applications in
+neuroscience to explore the functional connectivity between brain regions under
+natural stimuli. Our framework is based on the Gaussian graphical models, under
+which ISA can be converted to the problem of estimation and inference of the
+inter-subject precision matrix. The main statistical challenge is that we do
+not impose sparsity constraint on the whole precision matrix and we only assume
+the inter-subject part is sparse. For estimation, we propose to estimate an
+alternative parameter to get around the non-sparse issue and it can achieve
+asymptotic consistency even if the intra-subject dependency is dense. For
+inference, we propose an ""untangle and chord"" procedure to de-bias our
+estimator. It is valid without the sparsity assumption on the inverse Hessian
+of the log-likelihood function. This inferential method is general and can be
+applied to many other statistical problems, thus it is of independent
+theoretical interest. Numerical experiments on both simulated and brain imaging
+data validate our methods and theory.
+",0,0,1,1,0,0
+2752,Impact of surface functionalisation on the quantum coherence of nitrogen vacancy centres in nanodiamond,"  Nanoscale quantum probes such as the nitrogen-vacancy centre in diamond have
+demonstrated remarkable sensing capabilities over the past decade as control
+over the fabrication and manipulation of these systems has evolved. However, as
+the size of these nanoscale quantum probes is reduced, the surface termination
+of the host material begins to play a prominent role as a source of magnetic
+and electric field noise. In this work, we show that borane-reduced nanodiamond
+surfaces can on average double the spin relaxation time of individual
+nitrogen-vacancy centres in nanodiamonds when compared to the thermally
+oxidised surfaces. Using a combination of infra-red and x-ray absorption
+spectroscopy techniques, we correlate the changes in quantum relaxation rates
+with the conversion of sp2 carbon to C-O and C-H bonds on the diamond surface.
+These findings implicate double-bonded carbon species as a dominant source of
+spin noise for near surface NV centres and show that through tailored
+engineering of the surface, we can improve the quantum properties and magnetic
+sensitivity of these nanoscale probes.
+",0,1,0,0,0,0
+2753,Nesterov's Acceleration For Approximate Newton,"  Optimization plays a key role in machine learning. Recently, stochastic
+second-order methods have attracted much attention due to their low
+computational cost in each iteration. However, these algorithms might perform
+poorly especially if it is hard to approximate the Hessian well and
+efficiently. As far as we know, there is no effective way to handle this
+problem. In this paper, we resort to Nesterov's acceleration technique to
+improve the convergence performance of a class of second-order methods called
+approximate Newton. We give a theoretical analysis that Nesterov's acceleration
+technique can improve the convergence performance for approximate Newton just
+like for first-order methods. We accordingly propose an accelerated regularized
+sub-sampled Newton. Our accelerated algorithm performs much better than the
+original regularized sub-sampled Newton in experiments, which validates our
+theory empirically. Besides, the accelerated regularized sub-sampled Newton has
+good performance comparable to or even better than classical algorithms.
+",1,0,0,0,0,0
+2754,Coverage Centrality Maximization in Undirected Networks,"  Centrality metrics are among the main tools in social network analysis. Being
+central for a user of a network leads to several benefits to the user: central
+users are highly influential and play key roles within the network. Therefore,
+the optimization problem of increasing the centrality of a network user
+recently received considerable attention. Given a network and a target user
+$v$, the centrality maximization problem consists in creating $k$ new links
+incident to $v$ in such a way that the centrality of $v$ is maximized,
+according to some centrality metric. Most of the algorithms proposed in the
+literature are based on showing that a given centrality metric is monotone and
+submodular with respect to link addition. However, this property does not hold
+for several shortest-path based centrality metrics if the links are undirected.
+In this paper we study the centrality maximization problem in undirected
+networks for one of the most important shortest-path based centrality measures,
+the coverage centrality. We provide several hardness and approximation results.
+We first show that the problem cannot be approximated within a factor greater
+than $1-1/e$, unless $P=NP$, and, under the stronger gap-ETH hypothesis, the
+problem cannot be approximated within a factor better than $1/n^{o(1)}$, where
+$n$ is the number of users. We then propose two greedy approximation
+algorithms, and show that, by suitably combining them, we can guarantee an
+approximation factor of $\Omega(1/\sqrt{n})$. We experimentally compare the
+solutions provided by our approximation algorithm with optimal solutions
+computed by means of an exact IP formulation. We show that our algorithm
+produces solutions that are very close to the optimum.
+",1,0,0,0,0,0
+2755,Color difference makes a difference: four planet candidates around tau Ceti,"  The removal of noise typically correlated in time and wavelength is one of
+the main challenges for using the radial velocity method to detect Earth
+analogues. We analyze radial velocity data of tau Ceti and find robust evidence
+for wavelength dependent noise. We find this noise can be modeled by a
+combination of moving average models and ""differential radial velocities"". We
+apply this noise model to various radial velocity data sets for tau Ceti, and
+find four periodic signals at 20.0, 49.3, 160 and 642 d which we interpret as
+planets. We identify two new signals with orbital periods of 20.0 and 49.3 d
+while the other two previously suspected signals around 160 and 600 d are
+quantified to a higher precision. The 20.0 d candidate is independently
+detected in KECK data. All planets detected in this work have minimum masses
+less than 4$M_\oplus$ with the two long period ones located around the inner
+and outer edges of the habitable zone, respectively. We find that the
+instrumental noise gives rise to a precision limit of the HARPS around 0.2 m/s.
+We also find correlation between the HARPS data and the central moments of the
+spectral line profile at around 0.5 m/s level, although these central moments
+may contain both noise and signals. The signals detected in this work have
+semi-amplitudes as low as 0.3 m/s, demonstrating the ability of the radial
+velocity technique to detect relatively weak signals.
+",0,1,0,0,0,0
+2756,Recurrent Deep Embedding Networks for Genotype Clustering and Ethnicity Prediction,"  The understanding of variations in genome sequences assists us in identifying
+people who are predisposed to common diseases, solving rare diseases, and
+finding the corresponding population group of the individuals from a larger
+population group. Although classical machine learning techniques allow
+researchers to identify groups (i.e. clusters) of related variables, the
+accuracy, and effectiveness of these methods diminish for large and
+high-dimensional datasets such as the whole human genome. On the other hand,
+deep neural network architectures (the core of deep learning) can better
+exploit large-scale datasets to build complex models. In this paper, we use the
+K-means clustering approach for scalable genomic data analysis aiming towards
+clustering genotypic variants at the population scale. Finally, we train a deep
+belief network (DBN) for predicting the geographic ethnicity. We used the
+genotype data from the 1000 Genomes Project, which covers the result of genome
+sequencing for 2504 individuals from 26 different ethnic origins and comprises
+84 million variants. Our experimental results, with a focus on accuracy and
+scalability, show the effectiveness and superiority compared to the
+state-of-the-art.
+",0,0,0,0,1,0
+2757,Measuring and avoiding side effects using relative reachability,"  How can we design reinforcement learning agents that avoid causing
+unnecessary disruptions to their environment? We argue that current approaches
+to penalizing side effects can introduce bad incentives in tasks that require
+irreversible actions, and in environments that contain sources of change other
+than the agent. For example, some approaches give the agent an incentive to
+prevent any irreversible changes in the environment, including the actions of
+other agents. We introduce a general definition of side effects, based on
+relative reachability of states compared to a default state, that avoids these
+undesirable incentives. Using a set of gridworld experiments illustrating
+relevant scenarios, we empirically compare relative reachability to penalties
+based on existing definitions and show that it is the only penalty among those
+tested that produces the desired behavior in all the scenarios.
+",0,0,0,1,0,0
+2758,Neural State Classification for Hybrid Systems,"  We introduce the State Classification Problem (SCP) for hybrid systems, and
+present Neural State Classification (NSC) as an efficient solution technique.
+SCP generalizes the model checking problem as it entails classifying each state
+$s$ of a hybrid automaton as either positive or negative, depending on whether
+or not $s$ satisfies a given time-bounded reachability specification. This is
+an interesting problem in its own right, which NSC solves using
+machine-learning techniques, Deep Neural Networks in particular. State
+classifiers produced by NSC tend to be very efficient (run in constant time and
+space), but may be subject to classification errors. To quantify and mitigate
+such errors, our approach comprises: i) techniques for certifying, with
+statistical guarantees, that an NSC classifier meets given accuracy levels; ii)
+tuning techniques, including a novel technique based on adversarial sampling,
+that can virtually eliminate false negatives (positive states classified as
+negative), thereby making the classifier more conservative. We have applied NSC
+to six nonlinear hybrid system benchmarks, achieving an accuracy of 99.25% to
+99.98%, and a false-negative rate of 0.0033 to 0, which we further reduced to
+0.0015 to 0 after tuning the classifier. We believe that this level of accuracy
+is acceptable in many practical applications, and that these results
+demonstrate the promise of the NSC approach.
+",0,0,0,1,0,0
+2759,Optimization of Tree Ensembles,"  Tree ensemble models such as random forests and boosted trees are among the
+most widely used and practically successful predictive models in applied
+machine learning and business analytics. Although such models have been used to
+make predictions based on exogenous, uncontrollable independent variables, they
+are increasingly being used to make predictions where the independent variables
+are controllable and are also decision variables. In this paper, we study the
+problem of tree ensemble optimization: given a tree ensemble that predicts some
+dependent variable using controllable independent variables, how should we set
+these variables so as to maximize the predicted value? We formulate the problem
+as a mixed-integer optimization problem. We theoretically examine the strength
+of our formulation, provide a hierarchy of approximate formulations with bounds
+on approximation quality and exploit the structure of the problem to develop
+two large-scale solution methods, one based on Benders decomposition and one
+based on iteratively generating tree split constraints. We test our methodology
+on real data sets, including two case studies in drug design and customized
+pricing, and show that our methodology can efficiently solve large-scale
+instances to near or full optimality, and outperforms solutions obtained by
+heuristic approaches. In our drug design case, we show how our approach can
+identify compounds that efficiently trade-off predicted performance and novelty
+with respect to existing, known compounds. In our customized pricing case, we
+show how our approach can efficiently determine optimal store-level prices
+under a random forest model that delivers excellent predictive accuracy.
+",1,0,1,1,0,0
+2760,Equations of state for real gases on the nuclear scale,"  The formalism to augment the classical models of equation of state for real
+gases with the quantum statistical effects is presented. It allows an arbitrary
+excluded volume procedure to model repulsive interactions, and an arbitrary
+density-dependent mean field to model attractive interactions. Variations on
+the excluded volume mechanism include van der Waals (VDW) and Carnahan-Starling
+models, while the mean fields are based on VDW, Redlich-Kwong-Soave,
+Peng-Robinson, and Clausius equations of state. The VDW parameters of the
+nucleon-nucleon interaction are fitted in each model to the properties of the
+ground state of nuclear matter, and the following range of values is obtained:
+$a = 330 - 430$ MeV fm$^3$ and $b = 2.5 - 4.4$ fm$^3$. In the context of the
+excluded-volume approach, the fits to the nuclear ground state disfavor the
+values of the effective hard-core radius of a nucleon significantly smaller
+than $0.5$ fm, at least for the nuclear matter region of the phase diagram.
+Modifications to the standard VDW repulsion and attraction terms allow to
+improve significantly the value of the nuclear incompressibility factor $K_0$,
+bringing it closer to empirical estimates. The generalization to include the
+baryon-baryon interactions into the hadron resonance gas model is performed.
+The behavior of the baryon-related lattice QCD observables at zero chemical
+potential is shown to be strongly correlated to the nuclear matter properties:
+an improved description of the nuclear incompressibility also yields an
+improved description of the lattice data at $\mu = 0$.
+",0,1,0,0,0,0
+2761,Born Again Neural Networks,"  Knowledge distillation (KD) consists of transferring knowledge from one
+machine learning model (the teacher}) to another (the student). Commonly, the
+teacher is a high-capacity model with formidable performance, while the student
+is more compact. By transferring knowledge, one hopes to benefit from the
+student's compactness. %we desire a compact model with performance close to the
+teacher's. We study KD from a new perspective: rather than compressing models,
+we train students parameterized identically to their teachers. Surprisingly,
+these {Born-Again Networks (BANs), outperform their teachers significantly,
+both on computer vision and language modeling tasks. Our experiments with BANs
+based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10
+(3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional
+experiments explore two distillation objectives: (i) Confidence-Weighted by
+Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP).
+Both methods elucidate the essential components of KD, demonstrating a role of
+the teacher outputs on both predicted and non-predicted classes. We present
+experiments with students of various capacities, focusing on the under-explored
+case where students overpower teachers. Our experiments show significant
+advantages from transferring knowledge between DenseNets and ResNets in either
+direction.
+",0,0,0,1,0,0
+2762,Exploit Kits: The production line of the Cybercrime Economy,"  The annual cost of Cybercrime to the global economy is estimated to be around
+400 billion dollar in support of which Exploit Kits have been providing
+enabling technology.This paper reviews the recent developments in Exploit Kit
+capability and how these are being applied in practice.In doing so it paves the
+way for better understanding of the exploit kits economy that may better help
+in combatting them and considers industry preparedness to respond.
+",1,0,0,0,0,0
+2763,Helicity locking in light emitted from a plasmonic nanotaper,"  Surface plasmon waves carry an intrinsic transverse spin, which is locked to
+its propagation direction. Apparently, when a singular plasmonic mode is guided
+on a conic surface this spin-locking may lead to a strong circular polarization
+of the far-field emission. Specifically, an adiabatically tapered gold nanocone
+guides an a priori excited plasmonic vortex upwards where the mode accelerates
+and finally beams out from the tip apex. The helicity of this beam is shown to
+be single-handed and stems solely from the transverse spin-locking of the
+helical plasmonic wave-front. We present a simple geometric model that fully
+predicts the emerging light spin in our system. Finally we experimentally
+demonstrate the helicity-locking phenomenon by using accurately fabricated
+nanostructures and confirm the results with the model and numerical data.
+",0,1,0,0,0,0
+2764,Declarative Statistics,"  In this work we introduce declarative statistics, a suite of declarative
+modelling tools for statistical analysis. Statistical constraints represent the
+key building block of declarative statistics. First, we introduce a range of
+relevant counting and matrix constraints and associated decompositions, some of
+which novel, that are instrumental in the design of statistical constraints.
+Second, we introduce a selection of novel statistical constraints and
+associated decompositions, which constitute a self-contained toolbox that can
+be used to tackle a wide range of problems typically encountered by
+statisticians. Finally, we deploy these statistical constraints to a wide range
+of application areas drawn from classical statistics and we contrast our
+framework against established practices.
+",1,0,0,1,0,0
+2765,Description of CRESST-II data,"  In Phase 2 of CRESST-II 18 detector modules were operated for about two years
+(July 2013 - August 2015). Together with this document we are publishing data
+from two detector modules which have been used for direct dark-matter searches.
+With these data-sets we were able to set world-leading limits on the cross
+section for spin-independent elastic scattering of dark matter particles off
+nuclei. We publish the energies of all events within the acceptance regions for
+dark-matter searches. In addition, we also publish the energies of the events
+within the electron-recoil band. This data set can be used to study
+interactions with electrons of CaWO$_4$. In this document we describe how to
+use these data sets. In particular, we explain the cut-survival probabilities
+required for comparisons of models with the data sets.
+",0,1,0,0,0,0
+2766,ABC of ladder operators for rationally extended quantum harmonic oscillator systems,"  The problem of construction of ladder operators for rationally extended
+quantum harmonic oscillator (REQHO) systems of a general form is investigated
+in the light of existence of different schemes of the Darboux-Crum-Krein-Adler
+transformations by which such systems can be generated from the quantum
+harmonic oscillator. Any REQHO system is characterized by the number of
+separated states in its spectrum, the number of `valence bands' in which the
+separated states are organized, and by the total number of the missing energy
+levels and their position. All these peculiarities of a REQHO system are shown
+to be detected and reflected by a trinity $(\mathcal{A}^\pm$,
+$\mathcal{B}^\pm$, $\mathcal{C}^\pm$) of the basic (primary) lowering and
+raising ladder operators related between themselves by certain algebraic
+identities with coefficients polynomially-dependent on the Hamiltonian. We show
+that all the secondary, higher-order ladder operators are obtainable by a
+composition of the basic ladder operators of the trinity which form the set of
+the spectrum-generating operators. Each trinity, in turn, can be constructed
+from the intertwining operators of the two complementary minimal schemes of the
+Darboux-Crum-Krein-Adler transformations.
+",0,1,1,0,0,0
+2767,On permutation-invariance of limit theorems,"  By a classical principle of probability theory, sufficiently thin
+subsequences of general sequences of random variables behave like i.i.d.\
+sequences. This observation not only explains the remarkable properties of
+lacunary trigonometric series, but also provides a powerful tool in many areas
+of analysis, such the theory of orthogonal series and Banach space theory. In
+contrast to i.i.d.\ sequences, however, the probabilistic structure of lacunary
+sequences is not permutation-invariant and the analytic properties of such
+sequences can change after rearrangement. In a previous paper we showed that
+permutation-invariance of subsequences of the trigonometric system and related
+function systems is connected with Diophantine properties of the index
+sequence. In this paper we will study permutation-invariance of subsequences of
+general r.v.\ sequences.
+",0,0,1,0,0,0
+2768,Superconductivity at 33 - 37 K in $ALn_2$Fe$_4$As$_4$O$_2$ ($A$ = K and Cs; $Ln$ = Lanthanides),"  We have synthesized 10 new iron oxyarsenides, K$Ln_2$Fe$_4$As$_4$O$_2$ ($Ln$
+= Gd, Tb, Dy, and Ho) and Cs$Ln_2$Fe$_4$As$_4$O$_2$ ($Ln$ = Nd, Sm, Gd, Tb, Dy,
+and Ho), with the aid of lattice-match [between $A$Fe$_2$As$_2$ ($A$ = K and
+Cs) and $Ln$FeAsO] approach. The resultant compounds possess hole-doped
+conducting double FeAs layers, [$A$Fe$_4$As$_4$]$^{2-}$, that are separated by
+the insulating [$Ln_2$O$_2$]$^{2+}$ slabs. Measurements of electrical
+resistivity and dc magnetic susceptibility demonstrate bulk superconductivity
+at $T_\mathrm{c}$ = 33 - 37 K. We find that $T_\mathrm{c}$ correlates with the
+axis ratio $c/a$ for all 12442-type superconductors discovered. Also,
+$T_\mathrm{c}$ tends to increase with the lattice mismatch, implying a role of
+lattice instability for the enhancement of superconductivity.
+",0,1,0,0,0,0
+2769,Live Visualization of GUI Application Code Coverage with GUITracer,"  The present paper introduces the initial implementation of a software
+exploration tool targeting graphical user interface (GUI) driven applications.
+GUITracer facilitates the comprehension of GUI-driven applications by starting
+from their most conspicuous artefact - the user interface itself. The current
+implementation of the tool can be used with any Java-based target application
+that employs one of the AWT, Swing or SWT toolkits. The tool transparently
+instruments the target application and provides real time information about the
+GUI events fired. For each event, call relations within the application are
+displayed at method, class or package level, together with detailed coverage
+information. The tool facilitates feature location, program comprehension as
+well as GUI test creation by revealing the link between the application's GUI
+and its underlying code. As such, GUITracer is intended for software
+practitioners developing or maintaining GUI-driven applications. We believe our
+tool to be especially useful for entry-level practitioners as well as students
+seeking to understand complex GUI-driven software systems. The present paper
+details the rationale as well as the technical implementation of the tool. As a
+proof-of-concept implementation, we also discuss further development that can
+lead to our tool's integration into a software development workflow.
+",1,0,0,0,0,0
+2770,3D Simulation of Electron and Ion Transmission of GEM-based Detectors,"  Time Projection Chamber (TPC) has been chosen as the main tracking system in
+several high-flux and high repetition rate experiments. These include on-going
+experiments such as ALICE and future experiments such as PANDA at FAIR and ILC.
+Different $\mathrm{R}\&\mathrm{D}$ activities were carried out on the adoption
+of Gas Electron Multiplier (GEM) as the gas amplification stage of the
+ALICE-TPC upgrade version. The requirement of low ion feedback has been
+established through these activities. Low ion feedback minimizes distortions
+due to space charge and maintains the necessary values of detector gain and
+energy resolution. In the present work, Garfield simulation framework has been
+used to study the related physical processes occurring within single, triple
+and quadruple GEM detectors. Ion backflow and electron transmission of
+quadruple GEMs, made up of foils with different hole pitch under different
+electromagnetic field configurations (the projected solutions for the ALICE
+TPC) have been studied. Finally a new triple GEM detector configuration with
+low ion backflow fraction and good electron transmission properties has been
+proposed as a simpler GEM-based alternative suitable for TPCs for future
+collider experiments.
+",0,1,0,0,0,0
+2771,Witt and Cohomological Invariants of Witt Classes,"  We classify all invariants of the functor $I^n$ (powers of the fundamental
+ideal of the Witt ring) with values in $A$, that it to say functions
+$I^n(K)\rightarrow A(K)$ compatible with field extensions, in the cases where
+$A(K)=W(K)$ is the Witt ring and $A(K)=H^*(K,\mu_2)$ is mod 2 Galois
+cohomology. This is done in terms of some invariants $f_n^d$ that behave like
+divided powers with respect to sums of Pfister forms, and we show that any
+invariant of $I^n$ can be written uniquely as a (possibly infinite) combination
+of those $f_n^d$. This in particular allows to lift operations defined on mod 2
+Milnor K-theory (or equivalently mod 2 Galois cohomology) to the level of
+$I^n$. We also study various properties of these invariants, including
+behaviour under products, similitudes, residues for discrete valuations, and
+restriction from $I^n$ to $I^{n+1}$. The goal is to use this to study
+invariants of algebras with involutions in future articles.
+",0,0,1,0,0,0
+2772,The Cooperative Output Regulation Problem of Discrete-Time Linear Multi-Agent Systems by the Adaptive Distributed Observer,"  In this paper, we first present an adaptive distributed observer for a
+discrete-time leader system. This adaptive distributed observer will provide,
+to each follower, not only the estimation of the leader's signal, but also the
+estimation of the leader's system matrix. Then, based on the estimation of the
+matrix S, we devise a discrete adaptive algorithm to calculate the solution to
+the regulator equations associated with each follower, and obtain an estimated
+feedforward control gain. Finally, we solve the cooperative output regulation
+problem for discrete-time linear multi-agent systems by both state feedback and
+output feedback adaptive distributed control laws utilizing the adaptive
+distributed observer.
+",0,0,1,0,0,0
+2773,Preferred traces on C*-algebras of self-similar groupoids arising as fixed points,"  Recent results of Laca, Raeburn, Ramagge and Whittaker show that any
+self-similar action of a groupoid on a graph determines a 1-parameter family of
+self-mappings of the trace space of the groupoid C*-algebra. We investigate the
+fixed points for these self-mappings, under the same hypotheses that Laca et
+al. used to prove that the C*-algebra of the self-similar action admits a
+unique KMS state. We prove that for any value of the parameter, the associated
+self-mapping admits a unique fixed point, which is in fact a universal
+attractor. This fixed point is precisely the trace that extends to a KMS state
+on the C*-algebra of the self-similar action.
+",0,0,1,0,0,0
+2774,Tool Breakage Detection using Deep Learning,"  In manufacture, steel and other metals are mainly cut and shaped during the
+fabrication process by computer numerical control (CNC) machines. To keep high
+productivity and efficiency of the fabrication process, engineers need to
+monitor the real-time process of CNC machines, and the lifetime management of
+machine tools. In a real manufacturing process, breakage of machine tools
+usually happens without any indication, this problem seriously affects the
+fabrication process for many years. Previous studies suggested many different
+approaches for monitoring and detecting the breakage of machine tools. However,
+there still exists a big gap between academic experiments and the complex real
+fabrication processes such as the high demands of real-time detections, the
+difficulty in data acquisition and transmission. In this work, we use the
+spindle current approach to detect the breakage of machine tools, which has the
+high performance of real-time monitoring, low cost, and easy to install. We
+analyze the features of the current of a milling machine spindle through tools
+wearing processes, and then we predict the status of tool breakage by a
+convolutional neural network(CNN). In addition, we use a BP neural network to
+understand the reliability of the CNN. The results show that our CNN approach
+can detect tool breakage with an accuracy of 93%, while the best performance of
+BP is 80%.
+",0,0,0,1,0,0
+2775,Continuous Learning in Single-Incremental-Task Scenarios,"  It was recently shown that architectural, regularization and rehearsal
+strategies can be used to train deep models sequentially on a number of
+disjoint tasks without forgetting previously acquired knowledge. However, these
+strategies are still unsatisfactory if the tasks are not disjoint but
+constitute a single incremental task (e.g., class-incremental learning). In
+this paper we point out the differences between multi-task and
+single-incremental-task scenarios and show that well-known approaches such as
+LWF, EWC and SI are not ideal for incremental task scenarios. A new approach,
+denoted as AR1, combining architectural and regularization strategies is then
+specifically proposed. AR1 overhead (in term of memory and computation) is very
+small thus making it suitable for online learning. When tested on CORe50 and
+iCIFAR-100, AR1 outperformed existing regularization strategies by a good
+margin.
+",0,0,0,1,0,0
+2776,Diagonal Rescaling For Neural Networks,"  We define a second-order neural network stochastic gradient training
+algorithm whose block-diagonal structure effectively amounts to normalizing the
+unit activations. Investigating why this algorithm lacks in robustness then
+reveals two interesting insights. The first insight suggests a new way to scale
+the stepsizes, clarifying popular algorithms such as RMSProp as well as old
+neural network tricks such as fanin stepsize scaling. The second insight
+stresses the practical importance of dealing with fast changes of the curvature
+of the cost.
+",1,0,0,1,0,0
+2777,Dynamic Bernoulli Embeddings for Language Evolution,"  Word embeddings are a powerful approach for unsupervised analysis of
+language. Recently, Rudolph et al. (2016) developed exponential family
+embeddings, which cast word embeddings in a probabilistic framework. Here, we
+develop dynamic embeddings, building on exponential family embeddings to
+capture how the meanings of words change over time. We use dynamic embeddings
+to analyze three large collections of historical texts: the U.S. Senate
+speeches from 1858 to 2009, the history of computer science ACM abstracts from
+1951 to 2014, and machine learning papers on the Arxiv from 2007 to 2015. We
+find dynamic embeddings provide better fits than classical embeddings and
+capture interesting patterns about how language changes.
+",1,0,0,1,0,0
+2778,Homotopy Decompositions of Gauge Groups over Real Surfaces,"  We analyse the homotopy types of gauge groups of principal U(n)-bundles
+associated to pseudo Real vector bundles in the sense of Atiyah. We provide
+satisfactory homotopy decompositions of these gauge groups into factors in
+which the homotopy groups are well known. Therefore, we substantially build
+upon the low dimensional homotopy groups as provided in a paper by I. Biswas,
+J. Huisman, and J. Hurtubise.
+",0,0,1,0,0,0
+2779,W-algebras associated to surfaces,"  We define an integral form of the deformed W-algebra of type gl_r, and
+construct its action on the K-theory groups of moduli spaces of rank r stable
+sheaves on a smooth projective surface S, under certain assumptions. Our
+construction generalizes the action studied by Nakajima, Grojnowski and
+Baranovsky in cohomology, although the appearance of deformed W-algebras by
+generators and relations is a new feature. Physically, this action encodes the
+AGT correspondence for 5d supersymmetric gauge theory on S x circle.
+",0,0,1,0,0,0
+2780,Comparing Classical and Relativistic Kinematics in First-Order Logic,"  The aim of this paper is to present a new logic-based understanding of the
+connection between classical kinematics and relativistic kinematics. We show
+that the axioms of special relativity can be interpreted in the language of
+classical kinematics. This means that there is a logical translation function
+from the language of special relativity to the language of classical kinematics
+which translates the axioms of special relativity into consequences of
+classical kinematics. We will also show that if we distinguish a class of
+observers (representing observers stationary with respect to the ""Ether"") in
+special relativity and exclude the non-slower-than light observers from
+classical kinematics by an extra axiom, then the two theories become
+definitionally equivalent (i.e., they become equivalent theories in the sense
+as the theory of lattices as algebraic structures is the same as the theory of
+lattices as partially ordered sets). Furthermore, we show that classical
+kinematics is definitionally equivalent to classical kinematics with only
+slower-than-light inertial observers, and hence by transitivity of definitional
+equivalence that special relativity theory extended with ""Ether"" is
+definitionally equivalent to classical kinematics. So within an axiomatic
+framework of mathematical logic, we explicitly show that the transition from
+classical kinematics to relativistic kinematics is the knowledge acquisition
+that there is no ""Ether"", accompanied by a redefinition of the concepts of time
+and space.
+",0,0,1,0,0,0
+2781,Anomalous Acoustic Plasmon Mode from Topologically Protected States,"  Plasmons, the collective excitations of electrons in the bulk or at the
+surface, play an important role in the properties of materials, and have
+generated the field of Plasmonics. We report the observation of a highly
+unusual acoustic plasmon mode on the surface of a three-dimensional topological
+insulator (TI), Bi2Se3, using momentum resolved inelastic electron scattering.
+In sharp contrast to ordinary plasmon modes, this mode exhibits almost linear
+dispersion into the second Brillouin zone and remains prominent with remarkably
+weak damping not seen in any other systems. This behavior must be associated
+with the inherent robustness of the electrons in the TI surface state, so that
+not only the surface Dirac states but also their collective excitations are
+topologically protected. On the other hand, this mode has much smaller energy
+dispersion than expected from a continuous media excitation picture, which can
+be attributed to the strong coupling with surface phonons.
+",0,1,0,0,0,0
+2782,Klein-Gordonization: mapping superintegrable quantum mechanics to resonant spacetimes,"  We describe a procedure naturally associating relativistic Klein-Gordon
+equations in static curved spacetimes to non-relativistic quantum motion on
+curved spaces in the presence of a potential. Our procedure is particularly
+attractive in application to (typically, superintegrable) problems whose energy
+spectrum is given by a quadratic function of the energy level number, since for
+such systems the spacetimes one obtains possess evenly spaced, resonant spectra
+of frequencies for scalar fields of a certain mass. This construction emerges
+as a generalization of the previously studied correspondence between the Higgs
+oscillator and Anti-de Sitter spacetime, which has been useful for both
+understanding weakly nonlinear dynamics in Anti-de Sitter spacetime and
+algebras of conserved quantities of the Higgs oscillator. Our conversion
+procedure (""Klein-Gordonization"") reduces to a nonlinear elliptic equation
+closely reminiscent of the one emerging in relation to the celebrated Yamabe
+problem of differential geometry. As an illustration, we explicitly demonstrate
+how to apply this procedure to superintegrable Rosochatius systems, resulting
+in a large family of spacetimes with resonant spectra for massless wave
+equations.
+",0,1,1,0,0,0
+2783,Towards Gene Expression Convolutions using Gene Interaction Graphs,"  We study the challenges of applying deep learning to gene expression data. We
+find experimentally that there exists non-linear signal in the data, however is
+it not discovered automatically given the noise and low numbers of samples used
+in most research. We discuss how gene interaction graphs (same pathway,
+protein-protein, co-expression, or research paper text association) can be used
+to impose a bias on a deep model similar to the spatial bias imposed by
+convolutions on an image. We explore the usage of Graph Convolutional Neural
+Networks coupled with dropout and gene embeddings to utilize the graph
+information. We find this approach provides an advantage for particular tasks
+in a low data regime but is very dependent on the quality of the graph used. We
+conclude that more work should be done in this direction. We design experiments
+that show why existing methods fail to capture signal that is present in the
+data when features are added which clearly isolates the problem that needs to
+be addressed.
+",0,0,0,1,1,0
+2784,Density-Functional Theory Study of the Optoelectronic Properties of π-Conjugated Copolymers for Organic Light-Emitting Diodes,"  Novel low-band-gap copolymer oligomers are proposed on the basis of density
+functional theory (DFT) quantum chemical calculations of photophysical
+properties. These molecules have an electron donor-accepter (D-A) architecture
+involving poly(3-hexylthiophene-2,5-diyl) (P3HT) as D units and furan, aniline,
+or hydroquinone as A units. Structural parameters, electronic properties,
+highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital
+(LUMO) gaps and molecular orbital densities are predicted. The charge transfer
+process between the D unit and the A unit one is supported by analyzing the
+optical absorption spectra of the compounds and the localization of the HOMO
+and LUMO.
+",0,1,0,0,0,0
+2785,Accurate ranking of influential spreaders in networks based on dynamically asymmetric link-impact,"  We propose an efficient and accurate measure for ranking spreaders and
+identifying the influential ones in spreading processes in networks. While the
+edges determine the connections among the nodes, their specific role in
+spreading should be considered explicitly. An edge connecting nodes i and j may
+differ in its importance for spreading from i to j and from j to i. The key
+issue is whether node j, after infected by i through the edge, would reach out
+to other nodes that i itself could not reach directly. It becomes necessary to
+invoke two unequal weights wij and wji characterizing the importance of an edge
+according to the neighborhoods of nodes i and j. The total asymmetric
+directional weights originating from a node leads to a novel measure si which
+quantifies the impact of the node in spreading processes. A s-shell
+decomposition scheme further assigns a s-shell index or weighted coreness to
+the nodes. The effectiveness and accuracy of rankings based on si and the
+weighted coreness are demonstrated by applying them to nine real-world
+networks. Results show that they generally outperform rankings based on the
+nodes' degree and k-shell index, while maintaining a low computational
+complexity. Our work represents a crucial step towards understanding and
+controlling the spread of diseases, rumors, information, trends, and
+innovations in networks.
+",1,1,0,0,0,0
+2786,Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning,"  In this paper we study how to learn stochastic, multimodal transition
+dynamics in reinforcement learning (RL) tasks. We focus on evaluating
+transition function estimation, while we defer planning over this model to
+future work. Stochasticity is a fundamental property of many task environments.
+However, discriminative function approximators have difficulty estimating
+multimodal stochasticity. In contrast, deep generative models do capture
+complex high-dimensional outcome distributions. First we discuss why, amongst
+such models, conditional variational inference (VI) is theoretically most
+appealing for model-based RL. Subsequently, we compare different VI models on
+their ability to learn complex stochasticity on simulated functions, as well as
+on a typical RL gridworld with multimodal dynamics. Results show VI
+successfully predicts multimodal outcomes, but also robustly ignores these for
+deterministic parts of the transition dynamics. In summary, we show a robust
+method to learn multimodal transitions using function approximation, which is a
+key preliminary for model-based RL in stochastic domains.
+",1,0,0,1,0,0
+2787,Publication Trends in Physics Education: A Bibliometric study,"  A publication trend in Physics Education by employing bibliometric analysis
+leads the researchers to describe current scientific movement. This paper tries
+to answer ""What do Physics education scientists concentrate in their
+publications?"" by analyzing the productivity and development of publications on
+the subject category of Physics Education in the period 1980--2013. The Web of
+Science databases in the research areas of ""EDUCATION - EDUCATIONAL RESEARCH""
+was used to extract the publication trends. The study involves 1360
+publications, including 840 articles, 503 proceedings paper, 22 reviews, 7
+editorial material, 6 Book review, and one Biographical item. Number of
+publications with ""Physical Education"" in topic increased from 0.14 % (n = 2)
+in 1980 to 16.54 % (n = 225) in 2011. Total number of receiving citations is
+8071, with approximately citations per papers of 5.93. The results show the
+publication and citations in Physic Education has increased dramatically while
+the Malaysian share is well ranked.
+",1,1,0,0,0,0
+2788,Unveiling Swarm Intelligence with Network Science$-$the Metaphor Explained,"  Self-organization is a natural phenomenon that emerges in systems with a
+large number of interacting components. Self-organized systems show robustness,
+scalability, and flexibility, which are essential properties when handling
+real-world problems. Swarm intelligence seeks to design nature-inspired
+algorithms with a high degree of self-organization. Yet, we do not know why
+swarm-based algorithms work well and neither we can compare the different
+approaches in the literature. The lack of a common framework capable of
+characterizing these several swarm-based algorithms, transcending their
+particularities, has led to a stream of publications inspired by different
+aspects of nature without much regard as to whether they are similar to already
+existing approaches. We address this gap by introducing a network-based
+framework$-$the interaction network$-$to examine computational swarm-based
+systems via the optics of social dynamics. We discuss the social dimension of
+several swarm classes and provide a case study of the Particle Swarm
+Optimization. The interaction network enables a better understanding of the
+plethora of approaches currently available by looking at them from a general
+perspective focusing on the structure of the social interactions.
+",1,0,0,0,0,0
+2789,Sphere geometry and invariants,"  A finite abstract simplicial complex G defines two finite simple graphs: the
+Barycentric refinement G1, connecting two simplices if one is a subset of the
+other and the connection graph G', connecting two simplices if they intersect.
+We prove that the Poincare-Hopf value i(x)=1-X(S(x)), where X is Euler
+characteristics and S(x) is the unit sphere of a vertex x in G1, agrees with
+the Green function value g(x,x),the diagonal element of the inverse of (1+A'),
+where A' is the adjacency matrix of G'. By unimodularity, det(1+A') is the
+product of parities (-1)^dim(x) of simplices in G, the Fredholm matrix 1+A' is
+in GL(n,Z), where n is the number of simplices in G. We show that the set of
+possible unit sphere topologies in G1 is a combinatorial invariant of the
+complex G. So, also the Green function range of G is a combinatorial invariant.
+To prove the invariance of the unit sphere topology we use that all unit
+spheres in G1 decompose as a join of a stable and unstable part. The join
+operation + renders the category X of simplicial complexes into a monoid, where
+the empty complex is the 0 element and the cone construction adds 1. The
+augmented Grothendieck group (X,+,0) contains the graph and sphere monoids
+(Graphs, +,0) and (Spheres,+,0). The Poincare-Hopf functionals i(G) as well as
+the volume are multiplicative functions on (X,+). For the sphere group, both
+i(G) as well as Fredholm characteristic are characters. The join + can be
+augmented with a product * so that we have a commutative ring (X,+,0,*,1)for
+which there are both additive and multiplicative primes and which contains as a
+subring of signed complete complexes isomorphic to the integers (Z,+,0,*,1). We
+also look at the spectrum of the Laplacian of the join of two graphs. Both for
+addition + and multiplication *, one can ask whether unique prime factorization
+holds.
+",1,0,1,0,0,0
+2790,Chaos and thermalization in small quantum systems,"  Chaos and ergodicity are the cornerstones of statistical physics and
+thermodynamics. While classically even small systems like a particle in a
+two-dimensional cavity, can exhibit chaotic behavior and thereby relax to a
+microcanonical ensemble, quantum systems formally can not. Recent theoretical
+breakthroughs and, in particular, the eigenstate thermalization hypothesis
+(ETH) however indicate that quantum systems can also thermalize. In fact ETH
+provided us with a framework connecting microscopic models and macroscopic
+phenomena, based on the notion of highly entangled quantum states. Such
+thermalization was beautifully demonstrated experimentally by A. Kaufman et.
+al. who studied relaxation dynamics of a small lattice system of interacting
+bosonic particles. By directly measuring the entanglement entropy of
+subsystems, as well as other observables, they showed that after the initial
+transient time the system locally relaxes to a thermal ensemble while globally
+maintaining a zero-entropy pure state.
+",0,1,0,0,0,0
+2791,Index Search Algorithms for Databases and Modern CPUs,"  Over the years, many different indexing techniques and search algorithms have
+been proposed, including CSS-trees, CSB+ trees, k-ary binary search, and fast
+architecture sensitive tree search. There have also been papers on how best to
+set the many different parameters of these index structures, such as the node
+size of CSB+ trees.
+These indices have been proposed because CPU speeds have been increasing at a
+dramatically higher rate than memory speeds, giving rise to the Von Neumann
+CPU--Memory bottleneck. To hide the long latencies caused by memory access, it
+has become very important to well-utilize the features of modern CPUs. In order
+to drive down the average number of CPU clock cycles required to execute CPU
+instructions, and thus increase throughput, it has become important to achieve
+a good utilization of CPU resources. Some of these are the data and instruction
+caches, and the translation lookaside buffers. But it also has become important
+to avoid branch misprediction penalties, and utilize vectorization provided by
+CPUs in the form of SIMD instructions.
+While the layout of index structures has been heavily optimized for the data
+cache of modern CPUs, the instruction cache has been neglected so far. In this
+paper, we present NitroGen, a framework for utilizing code generation for
+speeding up index traversal in main memory database systems. By bringing
+together data and code, we make index structures use the dormant resource of
+the instruction cache. We show how to combine index compilation with previous
+approaches, such as binary tree search, cache-sensitive tree search, and the
+architecture-sensitive tree search presented by Kim et al.
+",1,0,0,0,0,0
+2792,Bounding the Radius of Convergence of Analytic Functions,"  Contour integration is a crucial technique in many numeric methods of
+interest in physics ranging from differentiation to evaluating functions of
+matrices. It is often important to determine whether a given contour contains
+any poles or branch cuts, either to make use of these features or to avoid
+them. A special case of this problem is that of determining or bounding the
+radius of convergence of a function, as this provides a known circle around a
+point in which a function remains analytic. We describe a method for
+determining whether or not a circular contour of a complex-analytic function
+contains any poles. We then build on this to produce a robust method for
+bounding the radius of convergence of a complex-analytic function.
+",0,0,1,0,0,0
+2793,An Efficient Algorithm for the Multicomponent Compressible Navier-Stokes Equations in Low- and High-Mach Number Regimes,"  The goal of this study is to develop an efficient numerical algorithm
+applicable to a wide range of compressible multicomponent flows. Although many
+highly efficient algorithms have been proposed for simulating each type of the
+flows, the construction of a universal solver is known to be challenging.
+Extreme cases, such as incompressible and highly compressible flows, or
+inviscid and highly viscous flows, require different numerical treatments in
+order to maintain the efficiency, stability, and accuracy of the method.
+Linearized block implicit (LBI) factored schemes are known to provide an
+efficient way of solving the compressible Navier-Stokes equations implicitly,
+allowing us to avoid stability restrictions at low Mach number and high
+viscosity. However, the methods' splitting error has been shown to grow and
+dominate physical fluxes as the Mach number goes to zero. In this paper, a
+splitting error reduction technique is proposed to solve the issue. A novel
+finite element shock-capturing algorithm, proposed by Guermond and Popov, is
+reformulated in terms of finite differences, extended to the stiffened gas
+equation of state (SG EOS) and combined with the LBI factored scheme to
+stabilize the method around flow discontinuities at high Mach numbers. A novel
+stabilization term is proposed for low Mach number applications. The resulting
+algorithm is shown to be efficient in both low and high Mach number regimes.
+The algorithm is extended to the multicomponent case using an interface
+capturing strategy with surface tension as a continuous surface force.
+Numerical tests are presented to verify the performance and stability
+properties for a wide range of flows.
+",0,1,0,0,0,0
+2794,Goldstone-like phonon modes in a (111)-strained perovskite,"  Goldstone modes are massless particles resulting from spontaneous symmetry
+breaking. Although such modes are found in elementary particle physics as well
+as in condensed matter systems like superfluid helium, superconductors and
+magnons - structural Goldstone modes are rare. Epitaxial strain in thin films
+can induce structures and properties not accessible in bulk and has been
+intensively studied for (001)-oriented perovskite oxides. Here we predict
+Goldstone-like phonon modes in (111)-strained SrMnO3 by first-principles
+calculations. Under compressive strain the coupling between two in-plane
+rotational instabilities give rise to a Mexican hat shaped energy surface
+characteristic of a Goldstone mode. Conversely, large tensile strain induces
+in-plane polar instabilities with no directional preference, giving rise to a
+continuous polar ground state. Such phonon modes with U(1) symmetry could
+emulate structural condensed matter Higgs modes. The mass of this Higgs boson,
+given by the shape of the Mexican hat energy surface, can be tuned by strain
+through proper choice of substrate.
+",0,1,0,0,0,0
+2795,Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms,"  This paper provides a link between causal inference and machine learning
+techniques - specifically, Classification and Regression Trees (CART) - in
+observational studies where the receipt of the treatment is not randomized, but
+the assignment to the treatment can be assumed to be randomized (irregular
+assignment mechanism). The paper contributes to the growing applied machine
+learning literature on causal inference, by proposing a modified version of the
+Causal Tree (CT) algorithm to draw causal inference from an irregular
+assignment mechanism. The proposed method is developed by merging the CT
+approach with the instrumental variable framework to causal inference, hence
+the name Causal Tree with Instrumental Variable (CT-IV). As compared to CT, the
+main strength of CT-IV is that it can deal more efficiently with the
+heterogeneity of causal effects, as demonstrated by a series of numerical
+results obtained on synthetic data. Then, the proposed algorithm is used to
+evaluate a public policy implemented by the Tuscan Regional Administration
+(Italy), which aimed at easing the access to credit for small firms. In this
+context, CT-IV breaks fresh ground for target-based policies, identifying
+interesting heterogeneous causal effects.
+",0,0,0,1,0,0
+2796,SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine,"  Traditional medicine typically applies one-size-fits-all treatment for the
+entire patient population whereas precision medicine develops tailored
+treatment schemes for different patient subgroups. The fact that some factors
+may be more significant for a specific patient subgroup motivates clinicians
+and medical researchers to develop new approaches to subgroup detection and
+analysis, which is an effective strategy to personalize treatment. In this
+study, we propose a novel patient subgroup detection method, called Supervised
+Biclustring (SUBIC) using convex optimization and apply our approach to detect
+patient subgroups and prioritize risk factors for hypertension (HTN) in a
+vulnerable demographic subgroup (African-American). Our approach not only finds
+patient subgroups with guidance of a clinically relevant target variable but
+also identifies and prioritizes risk factors by pursuing sparsity of the input
+variables and encouraging similarity among the input variables and between the
+input and target variables
+",1,0,0,1,0,0
+2797,Polarization leakage in epoch of reionization windows: III. Wide-field effects of narrow-field arrays,"  Leakage of polarized Galactic diffuse emission into total intensity can
+potentially mimic the 21-cm signal coming from the epoch of reionization (EoR),
+as both of them might have fluctuating spectral structure. Although we are
+sensitive to the EoR signal only in small fields of view, chromatic sidelobes
+from further away can contaminate the inner region. Here, we explore the
+effects of leakage into the 'EoR window' of the cylindrically averaged power
+spectra (PS) within wide fields of view using both observation and simulation
+of the 3C196 and NCP fields, two observing fields of the LOFAR-EoR project. We
+present the polarization PS of two one-night observations of the two fields and
+find that the NCP field has higher fluctuations along frequency, and
+consequently exhibits more power at high-$k_\parallel$ that could potentially
+leak to Stokes $I$. Subsequently, we simulate LOFAR observations of Galactic
+diffuse polarized emission based on a model to assess what fraction of
+polarized power leaks into Stokes $I$ because of the primary beam. We find that
+the rms fractional leakage over the instrumental $k$-space is $0.35\%$ in the
+3C196 field and $0.27\%$ in the NCP field, and it does not change significantly
+within the diameters of $15^\circ$, $9^\circ$ and $4^\circ$. Based on the
+observed PS and simulated fractional leakage, we show that a similar level of
+leakage into Stokes $I$ is expected in the 3C196 and NCP fields, and the
+leakage can be considered to be a bias in the PS.
+",0,1,0,0,0,0
+2798,Image Reconstruction using Matched Wavelet Estimated from Data Sensed Compressively using Partial Canonical Identity Matrix,"  This paper proposes a joint framework wherein lifting-based, separable,
+image-matched wavelets are estimated from compressively sensed (CS) images and
+used for the reconstruction of the same. Matched wavelet can be easily designed
+if full image is available. Also matched wavelet may provide better
+reconstruction results in CS application compared to standard wavelet
+sparsifying basis. Since in CS application, we have compressively sensed image
+instead of full image, existing methods of designing matched wavelet cannot be
+used. Thus, we propose a joint framework that estimates matched wavelet from
+the compressively sensed images and also reconstructs full images. This paper
+has three significant contributions. First, lifting-based, image-matched
+separable wavelet is designed from compressively sensed images and is also used
+to reconstruct the same. Second, a simple sensing matrix is employed to sample
+data at sub-Nyquist rate such that sensing and reconstruction time is reduced
+considerably without any noticeable degradation in the reconstruction
+performance. Third, a new multi-level L-Pyramid wavelet decomposition strategy
+is provided for separable wavelet implementation on images that leads to
+improved reconstruction performance. Compared to CS-based reconstruction using
+standard wavelets with Gaussian sensing matrix and with existing wavelet
+decomposition strategy, the proposed methodology provides faster and better
+image reconstruction in compressive sensing application.
+",1,0,0,0,0,0
+2799,Python Implementation and Construction of Finite Abelian Groups,"  Here we present a working framework to establish finite abelian groups in
+python. The primary aim is to allow new A-level students to work with examples
+of finite abelian groups using open source software. We include the code used
+in the implementation of the framework. We also prove some useful results
+regarding finite abelian groups which are used to establish the functions and
+help show how number theoretic results can blend with computational power when
+studying algebra. The groups established are based modular multiplication and
+addition. We include direct products of cyclic groups meaning the user has
+access to all finite abelian groups.
+",1,0,1,0,0,0
+2800,Near-Optimal Closeness Testing of Discrete Histogram Distributions,"  We investigate the problem of testing the equivalence between two discrete
+histograms. A {\em $k$-histogram} over $[n]$ is a probability distribution that
+is piecewise constant over some set of $k$ intervals over $[n]$. Histograms
+have been extensively studied in computer science and statistics. Given a set
+of samples from two $k$-histogram distributions $p, q$ over $[n]$, we want to
+distinguish (with high probability) between the cases that $p = q$ and
+$\|p-q\|_1 \geq \epsilon$. The main contribution of this paper is a new
+algorithm for this testing problem and a nearly matching information-theoretic
+lower bound. Specifically, the sample complexity of our algorithm matches our
+lower bound up to a logarithmic factor, improving on previous work by
+polynomial factors in the relevant parameters. Our algorithmic approach applies
+in a more general setting and yields improved sample upper bounds for testing
+closeness of other structured distributions as well.
+",1,0,1,1,0,0
+2801,Insider-Attacks on Physical-Layer Group Secret-Key Generation in Wireless Networks,"  Physical-layer group secret-key (GSK) generation is an effective way of
+generating secret keys in wireless networks, wherein the nodes exploit inherent
+randomness in the wireless channels to generate group keys, which are
+subsequently applied to secure messages while broadcasting, relaying, and other
+network-level communications. While existing GSK protocols focus on securing
+the common source of randomness from external eavesdroppers, they assume that
+the legitimate nodes of the group are trusted. In this paper, we address
+insider attacks from the legitimate participants of the wireless network during
+the key generation process. Instead of addressing conspicuous attacks such as
+switching-off communication, injecting noise, or denying consensus on group
+keys, we introduce stealth attacks that can go undetected against
+state-of-the-art GSK schemes. We propose two forms of attacks, namely: (i)
+different-key attacks, wherein an insider attempts to generate different keys
+at different nodes, especially across nodes that are out of range so that they
+fail to recover group messages despite possessing the group key, and (ii)
+low-rate key attacks, wherein an insider alters the common source of randomness
+so as to reduce the key-rate. We also discuss various detection techniques,
+which are based on detecting anomalies and inconsistencies on the channel
+measurements at the legitimate nodes. Through simulations we show that GSK
+generation schemes are vulnerable to insider-threats, especially on topologies
+that cannot support additional secure links between neighbouring nodes to
+verify the attacks.
+",1,0,1,0,0,0
+2802,Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning,"  Interactive reinforcement learning (IRL) extends traditional reinforcement
+learning (RL) by allowing an agent to interact with parent-like trainers during
+a task. In this paper, we present an IRL approach using dynamic audio-visual
+input in terms of vocal commands and hand gestures as feedback. Our
+architecture integrates multi-modal information to provide robust commands from
+multiple sensory cues along with a confidence value indicating the
+trustworthiness of the feedback. The integration process also considers the
+case in which the two modalities convey incongruent information. Additionally,
+we modulate the influence of sensory-driven feedback in the IRL task using
+goal-oriented knowledge in terms of contextual affordances. We implement a
+neural network architecture to predict the effect of performed actions with
+different objects to avoid failed-states, i.e., states from which it is not
+possible to accomplish the task. In our experimental setup, we explore the
+interplay of multimodal feedback and task-specific affordances in a robot
+cleaning scenario. We compare the learning performance of the agent under four
+different conditions: traditional RL, multi-modal IRL, and each of these two
+setups with the use of contextual affordances. Our experiments show that the
+best performance is obtained by using audio-visual feedback with
+affordancemodulated IRL. The obtained results demonstrate the importance of
+multi-modal sensory processing integrated with goal-oriented knowledge in IRL
+tasks.
+",1,0,0,0,0,0
+2803,"Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America","  As South and Central American countries prepare for increased birth defects
+from Zika virus outbreaks and plan for mitigation strategies to minimize
+ongoing and future outbreaks, understanding important characteristics of Zika
+outbreaks and how they vary across regions is a challenging and important
+problem. We developed a mathematical model for the 2015 Zika virus outbreak
+dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly
+available data provided by the Pan American Health Organization, using
+Approximate Bayesian Computation to estimate parameter distributions and
+provide uncertainty quantification. An important model input is the at-risk
+susceptible population, which can vary with a number of factors including
+climate, elevation, population density, and socio-economic status. We informed
+this initial condition using the highest historically reported dengue incidence
+modified by the probable dengue reporting rates in the chosen countries. The
+model indicated that a country-level analysis was not appropriate for Colombia.
+We then estimated the basic reproduction number, or the expected number of new
+human infections arising from a single infected human, to range between 4 and 6
+for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We
+estimated the reporting rate to be around 16% in El Salvador and 18% in
+Suriname with estimated total outbreak sizes of 73,395 and 21,647 people,
+respectively. The uncertainty in parameter estimates highlights a need for
+research and data collection that will better constrain parameter ranges.
+",0,0,0,1,0,0
+2804,A systematic study of the class imbalance problem in convolutional neural networks,"  In this study, we systematically investigate the impact of class imbalance on
+classification performance of convolutional neural networks (CNNs) and compare
+frequently used methods to address the issue. Class imbalance is a common
+problem that has been comprehensively studied in classical machine learning,
+yet very limited systematic research is available in the context of deep
+learning. In our study, we use three benchmark datasets of increasing
+complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of
+imbalance on classification and perform an extensive comparison of several
+methods to address the issue: oversampling, undersampling, two-phase training,
+and thresholding that compensates for prior class probabilities. Our main
+evaluation metric is area under the receiver operating characteristic curve
+(ROC AUC) adjusted to multi-class tasks since overall accuracy metric is
+associated with notable difficulties in the context of imbalanced data. Based
+on results from our experiments we conclude that (i) the effect of class
+imbalance on classification performance is detrimental; (ii) the method of
+addressing class imbalance that emerged as dominant in almost all analyzed
+scenarios was oversampling; (iii) oversampling should be applied to the level
+that completely eliminates the imbalance, whereas the optimal undersampling
+ratio depends on the extent of imbalance; (iv) as opposed to some classical
+machine learning models, oversampling does not cause overfitting of CNNs; (v)
+thresholding should be applied to compensate for prior class probabilities when
+overall number of properly classified cases is of interest.
+",1,0,0,1,0,0
+2805,New simple lattices in products of trees and their projections,"  Let $\Gamma \leq \mathrm{Aut}(T_{d_1}) \times \mathrm{Aut}(T_{d_2})$ be a
+group acting freely and transitively on the product of two regular trees of
+degree $d_1$ and $d_2$. We develop an algorithm which computes the closure of
+the projection of $\Gamma$ on $\mathrm{Aut}(T_{d_t})$ under the hypothesis that
+$d_t \geq 6$ is even and that the local action of $\Gamma$ on $T_{d_t}$
+contains $\mathrm{Alt}(d_t)$. We show that if $\Gamma$ is torsion-free and $d_1
+= d_2 = 6$, exactly seven closed subgroups of $\mathrm{Aut}(T_6)$ arise in this
+way. We also construct two new infinite families of virtually simple lattices
+in $\mathrm{Aut}(T_{6}) \times \mathrm{Aut}(T_{4n})$ and in
+$\mathrm{Aut}(T_{2n}) \times \mathrm{Aut}(T_{2n+1})$ respectively, for all $n
+\geq 2$. In particular we provide an explicit presentation of a torsion-free
+infinite simple group on $5$ generators and $10$ relations, that splits as an
+amalgamated free product of two copies of $F_3$ over $F_{11}$. We include
+information arising from computer-assisted exhaustive searches of lattices in
+products of trees of small degrees. In an appendix by Pierre-Emmanuel Caprace,
+some of our results are used to show that abstract and relative commensurator
+groups of free groups are almost simple, providing partial answers to questions
+of Lubotzky and Lubotzky-Mozes-Zimmer.
+",0,0,1,0,0,0
+2806,Fourth-order time-stepping for stiff PDEs on the sphere,"  We present in this paper algorithms for solving stiff PDEs on the unit sphere
+with spectral accuracy in space and fourth-order accuracy in time. These are
+based on a variant of the double Fourier sphere method in coefficient space
+with multiplication matrices that differ from the usual ones, and
+implicit-explicit time-stepping schemes. Operating in coefficient space with
+these new matrices allows one to use a sparse direct solver, avoids the
+coordinate singularity and maintains smoothness at the poles, while
+implicit-explicit schemes circumvent severe restrictions on the time-steps due
+to stiffness. A comparison is made against exponential integrators and it is
+found that implicit-explicit schemes perform best. Implementations in MATLAB
+and Chebfun make it possible to compute the solution of many PDEs to high
+accuracy in a very convenient fashion.
+",0,0,1,0,0,0
+2807,On Formalizing Fairness in Prediction with Machine Learning,"  Machine learning algorithms for prediction are increasingly being used in
+critical decisions affecting human lives. Various fairness formalizations, with
+no firm consensus yet, are employed to prevent such algorithms from
+systematically discriminating against people based on certain attributes
+protected by law. The aim of this article is to survey how fairness is
+formalized in the machine learning literature for the task of prediction and
+present these formalizations with their corresponding notions of distributive
+justice from the social sciences literature. We provide theoretical as well as
+empirical critiques of these notions from the social sciences literature and
+explain how these critiques limit the suitability of the corresponding fairness
+formalizations to certain domains. We also suggest two notions of distributive
+justice which address some of these critiques and discuss avenues for
+prospective fairness formalizations.
+",1,0,0,1,0,0
+2808,The Memory Function Formalism: A Review,"  An introduction to the Zwanzig-Mori-Götze-Wölfle memory function
+formalism (or generalized Drude formalism) is presented. This formalism is used
+extensively in analyzing the experimentally obtained optical conductivity of
+strongly correlated systems like cuprates and Iron based superconductors etc.
+For a broader perspective both the generalised Langevin equation approach and
+the projection operator approach for the memory function formalism are given.
+The Götze-Wölfle perturbative expansion of memory function is presented
+and its application to the computation of the dynamical conductivity of metals
+is also reviewd. This review of the formalism contains all the mathematical
+details for pedagogical purposes.
+",0,1,0,0,0,0
+2809,RIPML: A Restricted Isometry Property based Approach to Multilabel Learning,"  The multilabel learning problem with large number of labels, features, and
+data-points has generated a tremendous interest recently. A recurring theme of
+these problems is that only a few labels are active in any given datapoint as
+compared to the total number of labels. However, only a small number of
+existing work take direct advantage of this inherent extreme sparsity in the
+label space. By the virtue of Restricted Isometry Property (RIP), satisfied by
+many random ensembles, we propose a novel procedure for multilabel learning
+known as RIPML. During the training phase, in RIPML, labels are projected onto
+a random low-dimensional subspace followed by solving a least-square problem in
+this subspace. Inference is done by a k-nearest neighbor (kNN) based approach.
+We demonstrate the effectiveness of RIPML by conducting extensive simulations
+and comparing results with the state-of-the-art linear dimensionality reduction
+based approaches.
+",1,0,0,1,0,0
+2810,Erratum to: Medial axis and singularities,"  We correct one erroneous statement made in our recent paper ""Medial axis and
+singularities"".
+",0,0,1,0,0,0
+2811,AP-initiated Multi-User Transmissions in IEEE 802.11ax WLANs,"  Next-generation 802.11ax WLANs will make extensive use of multi-user
+communications in both downlink (DL) and uplink (UL) directions to achieve high
+and efficient spectrum utilization in scenarios with many user stations per
+access point. It will become possible with the support of multi-user (MU)
+multiple input, multiple output (MIMO) and orthogonal frequency division
+multiple access (OFDMA) transmissions. In this paper, we first overview the
+novel characteristics introduced by IEEE 802.11ax to implement AP-initiated
+OFDMA and MU-MIMO transmissions in both downlink and uplink directions. Namely,
+we describe the changes made at the physical layer and at the medium access
+control layer to support OFDMA, the use of \emph{trigger frames} to schedule
+uplink multi-user transmissions, and the new \emph{multi-user RTS/CTS
+mechanism} to protect large multi-user transmissions from collisions. Then, in
+order to study the achievable throughput of an 802.11ax network, we use both
+mathematical analysis and simulations to numerically quantify the benefits of
+MU transmissions and the impact of 802.11ax overheads on the WLAN saturation
+throughput. Results show the advantages of MU transmissions in scenarios with
+many user stations, also providing some novel insights on the conditions in
+which 802.11ax WLANs are able to maximize their performance, such as the
+existence of an optimal number of active user stations in terms of throughput,
+or the need to provide strict prioritization to AP-initiated MU transmissions
+to avoid collisions with user stations.
+",1,0,0,0,0,0
+2812,What do we know about the geometry of space?,"  The belief that three dimensional space is infinite and flat in the absence
+of matter is a canon of physics that has been in place since the time of
+Newton. The assumption that space is flat at infinity has guided several modern
+physical theories. But what do we actually know to support this belief? A
+simple argument, called the ""Telescope Principle"", asserts that all that we can
+know about space is bounded by observations. Physical theories are best when
+they can be verified by observations, and that should also apply to the
+geometry of space. The Telescope Principle is simple to state, but it leads to
+very interesting insights into relativity and Yang-Mills theory via projective
+equivalences of their respective spaces.
+",0,1,0,0,0,0
+2813,Evidence of new twinning modes in magnesium questioning the shear paradigm,"  Twinning is an important deformation mode of hexagonal close-packed metals.
+The crystallographic theory is based on the 150-years old concept of simple
+shear. The habit plane of the twin is the shear plane, it is invariant. Here we
+present Electron BackScatter Diffraction observations and crystallographic
+analysis of a millimeter size twin in a magnesium single crystal whose straight
+habit plane, unambiguously determined both the parent crystal and in its twin,
+is not an invariant plane. This experimental evidence demonstrates that
+macroscopic deformation twinning can be obtained by a mechanism that is not a
+simple shear. Beside, this unconventional twin is often co-formed with a new
+conventional twin that exhibits the lowest shear magnitude ever reported in
+metals. The existence of unconventional twinning introduces a shift of paradigm
+and calls for the development of a new theory for the displacive
+transformations
+",0,1,0,0,0,0
+2814,Probing the Interatomic Potential of Solids by Strong-Field Nonlinear Phononics,"  Femtosecond optical pulses at mid-infrared frequencies have opened up the
+nonlinear control of lattice vibrations in solids. So far, all applications
+have relied on second order phonon nonlinearities, which are dominant at field
+strengths near 1 MVcm-1. In this regime, nonlinear phononics can transiently
+change the average lattice structure, and with it the functionality of a
+material. Here, we achieve an order-of-magnitude increase in field strength,
+and explore higher-order lattice nonlinearities. We drive up to five phonon
+harmonics of the A1 mode in LiNbO3. Phase-sensitive measurements of atomic
+trajectories in this regime are used to experimentally reconstruct the
+interatomic potential and to benchmark ab-initio calculations for this
+material. Tomography of the Free Energy surface by high-order nonlinear
+phononics will impact many aspects of materials research, including the study
+of classical and quantum phase transitions.
+",0,1,0,0,0,0
+2815,Assessment of learning tomography using Mie theory,"  In Optical diffraction tomography, the multiply scattered field is a
+nonlinear function of the refractive index of the object. The Rytov method is a
+linear approximation of the forward model, and is commonly used to reconstruct
+images. Recently, we introduced a reconstruction method based on the Beam
+Propagation Method (BPM) that takes the nonlinearity into account. We refer to
+this method as Learning Tomography (LT). In this paper, we carry out
+simulations in order to assess the performance of LT over the linear iterative
+method. Each algorithm has been rigorously assessed for spherical objects, with
+synthetic data generated using the Mie theory. By varying the RI contrast and
+the size of the objects, we show that the LT reconstruction is more accurate
+and robust than the reconstruction based on the linear model. In addition, we
+show that LT is able to correct distortion that is evident in Rytov
+approximation due to limitations in phase unwrapping. More importantly, the
+capacity of LT in handling multiple scattering problem are demonstrated by
+simulations of multiple cylinders using the Mie theory and confirmed by
+experimental results of two spheres.
+",0,1,0,0,0,0
+2816,Single Molecule Studies Under Constant Force Using Model Based Robust Control Design,"  Optical tweezers have enabled important insights into intracellular transport
+through the investigation of motor proteins, with their ability to manipulate
+particles at the microscale, affording femto Newton force resolution. Its use
+to realize a constant force clamp has enabled vital insights into the behavior
+of motor proteins under different load conditions. However, the varying nature
+of disturbances and the effect of thermal noise pose key challenges to force
+regulation. Furthermore, often the main aim of many studies is to determine the
+motion of the motor and the statistics related to the motion, which can be at
+odds with the force regulation objective. In this article, we propose a mixed
+objective H2-Hinfinity optimization framework using a model-based design, that
+achieves the dual goals of force regulation and real time motion estimation
+with quantifiable guarantees. Here, we minimize the Hinfinity norm for the
+force regulation and error in step estimation while maintaining the H2 norm of
+the noise on step estimate within user specified bounds. We demonstrate the
+efficacy of the framework through extensive simulations and an experimental
+implementation using an optical tweezer setup with live samples of the motor
+protein kinesin; where regulation of forces below 1 pico Newton with errors
+below 10 percent is obtained while simultaneously providing real time estimates
+of motor motion.
+",0,1,1,0,0,0
+2817,Measuring the effects of Loop Quantum Cosmology in the CMB data,"  In this Essay we investigate the observational signatures of Loop Quantum
+Cosmology (LQC) in the CMB data. First, we concentrate on the dynamics of LQC
+and we provide the basic cosmological functions. We then obtain the power
+spectrum of scalar and tensor perturbations in order to study the performance
+of LQC against the latest CMB data. We find that LQC provides a robust
+prediction for the main slow-roll parameters, like the scalar spectral index
+and the tensor-to-scalar fluctuation ratio, which are in excellent agreement
+within $1\sigma$ with the values recently measured by the Planck collaboration.
+This result indicates that LQC can be seen as an alternative scenario with
+respect to that of standard inflation.
+",0,1,0,0,0,0
+2818,Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization,"  In many modern machine learning applications, structures of underlying
+mathematical models often yield nonconvex optimization problems. Due to the
+intractability of nonconvexity, there is a rising need to develop efficient
+methods for solving general nonconvex problems with certain performance
+guarantee. In this work, we investigate the accelerated proximal gradient
+method for nonconvex programming (APGnc). The method compares between a usual
+proximal gradient step and a linear extrapolation step, and accepts the one
+that has a lower function value to achieve a monotonic decrease. In specific,
+under a general nonsmooth and nonconvex setting, we provide a rigorous argument
+to show that the limit points of the sequence generated by APGnc are critical
+points of the objective function. Then, by exploiting the
+Kurdyka-{\L}ojasiewicz (\KL) property for a broad class of functions, we
+establish the linear and sub-linear convergence rates of the function value
+sequence generated by APGnc. We further propose a stochastic variance reduced
+APGnc (SVRG-APGnc), and establish its linear convergence under a special case
+of the \KL property. We also extend the analysis to the inexact version of
+these methods and develop an adaptive momentum strategy that improves the
+numerical performance.
+",1,0,0,0,0,0
+2819,Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks,"  Correlation networks were used to detect characteristics which, although
+fixed over time, have an important influence on the evolution of prices over
+time. Potentially important features were identified using the websites and
+whitepapers of cryptocurrencies with the largest userbases. These were assessed
+using two datasets to enhance robustness: one with fourteen cryptocurrencies
+beginning from 9 November 2017, and a subset with nine cryptocurrencies
+starting 9 September 2016, both ending 6 March 2018. Separately analysing the
+subset of cryptocurrencies raised the number of data points from 115 to 537,
+and improved robustness to changes in relationships over time. Excluding USD
+Tether, the results showed a positive association between different
+cryptocurrencies that was statistically significant. Robust, strong positive
+associations were observed for six cryptocurrencies where one was a fork of the
+other; Bitcoin / Bitcoin Cash was an exception. There was evidence for the
+existence of a group of cryptocurrencies particularly associated with Cardano,
+and a separate group correlated with Ethereum. The data was not consistent with
+a token's functionality or creation mechanism being the dominant determinants
+of the evolution of prices over time but did suggest that factors other than
+speculation contributed to the price.
+",0,0,0,0,0,1
+2820,Affective Neural Response Generation,"  Existing neural conversational models process natural language primarily on a
+lexico-syntactic level, thereby ignoring one of the most crucial components of
+human-to-human dialogue: its affective content. We take a step in this
+direction by proposing three novel ways to incorporate affective/emotional
+aspects into long short term memory (LSTM) encoder-decoder neural conversation
+models: (1) affective word embeddings, which are cognitively engineered, (2)
+affect-based objective functions that augment the standard cross-entropy loss,
+and (3) affectively diverse beam search for decoding. Experiments show that
+these techniques improve the open-domain conversational prowess of
+encoder-decoder networks by enabling them to produce emotionally rich responses
+that are more interesting and natural.
+",1,0,0,0,0,0
+2821,Energy Efficient Power Allocation in Massive MIMO Systems based on Standard Interference Function,"  In this paper, energy efficient power allocation for downlink massive MIMO
+systems is investigated. A constrained non-convex optimization problem is
+formulated to maximize the energy efficiency (EE), which takes into account the
+quality of service (QoS) requirements. By exploiting the properties of
+fractional programming and the lower bound of the user data rate, the
+non-convex optimization problem is transformed into a convex optimization
+problem. The Lagrangian dual function method is utilized to convert the
+constrained convex problem into an unconstrained convex one. Due to the
+multi-variable coupling problem caused by the intra-user interference, it is
+intractable to derive an explicit solution to the above optimization problem.
+Exploiting the standard interference function, we propose an implicit iterative
+algorithm to solve the unconstrained convex optimization problem and obtain the
+optimal power allocation scheme. Simulation results show that the proposed
+iterative algorithm converges in just a few iterations, and demonstrate the
+impact of the number of users and the number of antennas on the EE.
+",1,0,0,0,0,0
+2822,Composite fermion basis for M-component Bose gases,"  The composite fermion (CF) formalism produces wave functions that are not
+always linearly independent. This is especially so in the low angular momentum
+regime in the lowest Landau level, where a subclass of CF states, known as
+simple states, gives a good description of the low energy spectrum. For the
+two-component Bose gas, explicit bases avoiding the large number of redundant
+states have been found. We generalize one of these bases to the $M$-component
+Bose gas and prove its validity. We also show that the numbers of linearly
+independent simple states for different values of angular momentum are given by
+coefficients of $q$-multinomials.
+",0,1,0,0,0,0
+2823,An FPT Algorithm Beating 2-Approximation for $k$-Cut,"  In the $k$-Cut problem, we are given an edge-weighted graph $G$ and an
+integer $k$, and have to remove a set of edges with minimum total weight so
+that $G$ has at least $k$ connected components. Prior work on this problem
+gives, for all $h \in [2,k]$, a $(2-h/k)$-approximation algorithm for $k$-cut
+that runs in time $n^{O(h)}$. Hence to get a $(2 - \varepsilon)$-approximation
+algorithm for some absolute constant $\varepsilon$, the best runtime using
+prior techniques is $n^{O(k\varepsilon)}$. Moreover, it was recently shown that
+getting a $(2 - \varepsilon)$-approximation for general $k$ is NP-hard,
+assuming the Small Set Expansion Hypothesis.
+If we use the size of the cut as the parameter, an FPT algorithm to find the
+exact $k$-Cut is known, but solving the $k$-Cut problem exactly is $W[1]$-hard
+if we parameterize only by the natural parameter of $k$. An immediate question
+is: \emph{can we approximate $k$-Cut better in FPT-time, using $k$ as the
+parameter?}
+We answer this question positively. We show that for some absolute constant
+$\varepsilon > 0$, there exists a $(2 - \varepsilon)$-approximation algorithm
+that runs in time $2^{O(k^6)} \cdot \widetilde{O} (n^4)$. This is the first FPT
+algorithm that is parameterized only by $k$ and strictly improves the
+$2$-approximation.
+",1,0,0,0,0,0
+2824,Dynamics beyond dynamic jam; unfolding the Painlevé paradox singularity,"  This paper analyses in detail the dynamics in a neighbourhood of a
+Génot-Brogliato point, colloquially termed the G-spot, which physically
+represents so-called dynamic jam in rigid body mechanics with unilateral
+contact and Coulomb friction. Such singular points arise in planar rigid body
+problems with slipping point contacts at the intersection between the
+conditions for onset of lift-off and for the Painlevé paradox. The G-spot can
+be approached in finite time by an open set of initial conditions in a general
+class of problems. The key question addressed is what happens next. In
+principle trajectories could, at least instantaneously, lift off, continue in
+slip, or undergo a so-called impact without collision. Such impacts are
+non-local in momentum space and depend on properties evaluated away from the
+G-spot. The results are illustrated on a particular physical example, namely
+the a frictional impact oscillator first studied by Leine et al.
+The answer is obtained via an analysis that involves a consistent contact
+regularisation with a stiffness proportional to $1/\varepsilon^2$. Taking a
+singular limit as $\varepsilon \to 0$, one finds an inner and an outer
+asymptotic zone in the neighbourhood of the G-spot. Two distinct cases are
+found according to whether the contact force becomes infinite or remains finite
+as the G-spot is approached. In the former case it is argued that there can be
+no such canards and so an impact without collision must occur. In the latter
+case, the canard trajectory acts as a dividing surface between trajectories
+that momentarily lift off and those that do not before taking the impact. The
+orientation of the initial condition set leading to each eventuality is shown
+to change each time a certain positive parameter $\beta$ passes through an
+integer.
+",0,1,0,0,0,0
+2825,Existence and regularity of positive solutions of quasilinear elliptic problems with singular semilinear term,"  This paper deals with existence and regularity of positive solutions of
+singular elliptic problems on a smooth bounded domain with Dirichlet boundary
+conditions involving the $\Phi$-Laplacian operator. The proof of existence is
+based on a variant of the generalized Galerkin method that we developed
+inspired on ideas by Browder and a comparison principle. By using a kind of
+Moser iteration scheme we show $L^{\infty}(\Omega)$-regularity for positive
+solutions
+",0,0,1,0,0,0
+2826,World Literature According to Wikipedia: Introduction to a DBpedia-Based Framework,"  Among the manifold takes on world literature, it is our goal to contribute to
+the discussion from a digital point of view by analyzing the representation of
+world literature in Wikipedia with its millions of articles in hundreds of
+languages. As a preliminary, we introduce and compare three different
+approaches to identify writers on Wikipedia using data from DBpedia, a
+community project with the goal of extracting and providing structured
+information from Wikipedia. Equipped with our basic set of writers, we analyze
+how they are represented throughout the 15 biggest Wikipedia language versions.
+We combine intrinsic measures (mostly examining the connectedness of articles)
+with extrinsic ones (analyzing how often articles are frequented by readers)
+and develop methods to evaluate our results. The better part of our findings
+seems to convey a rather conservative, old-fashioned version of world
+literature, but a version derived from reproducible facts revealing an implicit
+literary canon based on the editing and reading behavior of millions of people.
+While still having to solve some known issues, the introduced methods will help
+us build an observatory of world literature to further investigate its
+representativeness and biases.
+",1,0,0,0,0,0
+2827,Robust Task Clustering for Deep Many-Task Learning,"  We investigate task clustering for deep-learning based multi-task and
+few-shot learning in a many-task setting. We propose a new method to measure
+task similarities with cross-task transfer performance matrix for the deep
+learning scenario. Although this matrix provides us critical information
+regarding similarity between tasks, its asymmetric property and unreliable
+performance scores can affect conventional clustering methods adversely.
+Additionally, the uncertain task-pairs, i.e., the ones with extremely
+asymmetric transfer scores, may collectively mislead clustering algorithms to
+output an inaccurate task-partition. To overcome these limitations, we propose
+a novel task-clustering algorithm by using the matrix completion technique. The
+proposed algorithm constructs a partially-observed similarity matrix based on
+the certainty of cluster membership of the task-pairs. We then use a matrix
+completion algorithm to complete the similarity matrix. Our theoretical
+analysis shows that under mild constraints, the proposed algorithm will
+perfectly recover the underlying ""true"" similarity matrix with a high
+probability. Our results show that the new task clustering method can discover
+task clusters for training flexible and superior neural network models in a
+multi-task learning setup for sentiment classification and dialog intent
+classification tasks. Our task clustering approach also extends metric-based
+few-shot learning methods to adapt multiple metrics, which demonstrates
+empirical advantages when the tasks are diverse.
+",1,0,0,1,0,0
+2828,Detecting Learning vs Memorization in Deep Neural Networks using Shared Structure Validation Sets,"  The roles played by learning and memorization represent an important topic in
+deep learning research. Recent work on this subject has shown that the
+optimization behavior of DNNs trained on shuffled labels is qualitatively
+different from DNNs trained with real labels. Here, we propose a novel
+permutation approach that can differentiate memorization from learning in deep
+neural networks (DNNs) trained as usual (i.e., using the real labels to guide
+the learning, rather than shuffled labels). The evaluation of weather the DNN
+has learned and/or memorized, happens in a separate step where we compare the
+predictive performance of a shallow classifier trained with the features
+learned by the DNN, against multiple instances of the same classifier, trained
+on the same input, but using shuffled labels as outputs. By evaluating these
+shallow classifiers in validation sets that share structure with the training
+set, we are able to tell apart learning from memorization. Application of our
+permutation approach to multi-layer perceptrons and convolutional neural
+networks trained on image data corroborated many findings from other groups.
+Most importantly, our illustrations also uncovered interesting dynamic patterns
+about how DNNs memorize over increasing numbers of training epochs, and support
+the surprising result that DNNs are still able to learn, rather than only
+memorize, when trained with pure Gaussian noise as input.
+",0,0,0,1,0,0
+2829,Bias Correction For Paid Search In Media Mix Modeling,"  Evaluating the return on ad spend (ROAS), the causal effect of advertising on
+sales, is critical to advertisers for understanding the performance of their
+existing marketing strategy as well as how to improve and optimize it. Media
+Mix Modeling (MMM) has been used as a convenient analytical tool to address the
+problem using observational data. However it is well recognized that MMM
+suffers from various fundamental challenges: data collection, model
+specification and selection bias due to ad targeting, among others
+\citep{chan2017,wolfe2016}.
+In this paper, we study the challenge associated with measuring the impact of
+search ads in MMM, namely the selection bias due to ad targeting. Using causal
+diagrams of the search ad environment, we derive a statistically principled
+method for bias correction based on the \textit{back-door} criterion
+\citep{pearl2013causality}. We use case studies to show that the method
+provides promising results by comparison with results from randomized
+experiments. We also report a more complex case study where the advertiser had
+spent on more than a dozen media channels but results from a randomized
+experiment are not available. Both our theory and empirical studies suggest
+that in some common, practical scenarios, one may be able to obtain an
+approximately unbiased estimate of search ad ROAS.
+",0,0,0,1,0,0
+2830,Neville's algorithm revisited,"  Neville's algorithm is known to provide an efficient and numerically stable
+solution for polynomial interpolations. In this paper, an extension of this
+algorithm is presented which includes the derivatives of the interpolating
+polynomial.
+",1,0,0,0,0,0
+2831,Forecasting and Granger Modelling with Non-linear Dynamical Dependencies,"  Traditional linear methods for forecasting multivariate time series are not
+able to satisfactorily model the non-linear dependencies that may exist in
+non-Gaussian series. We build on the theory of learning vector-valued functions
+in the reproducing kernel Hilbert space and develop a method for learning
+prediction functions that accommodate such non-linearities. The method not only
+learns the predictive function but also the matrix-valued kernel underlying the
+function search space directly from the data. Our approach is based on learning
+multiple matrix-valued kernels, each of those composed of a set of input
+kernels and a set of output kernels learned in the cone of positive
+semi-definite matrices. In addition to superior predictive performance in the
+presence of strong non-linearities, our method also recovers the hidden dynamic
+relationships between the series and thus is a new alternative to existing
+graphical Granger techniques.
+",1,0,0,1,0,0
+2832,HDLTex: Hierarchical Deep Learning for Text Classification,"  The continually increasing number of documents produced each year
+necessitates ever improving information processing methods for searching,
+retrieving, and organizing text. Central to these information processing
+methods is document classification, which has become an important application
+for supervised learning. Recently the performance of these traditional
+classifiers has degraded as the number of documents has increased. This is
+because along with this growth in the number of documents has come an increase
+in the number of categories. This paper approaches this problem differently
+from current document classification methods that view the problem as
+multi-class classification. Instead we perform hierarchical classification
+using an approach we call Hierarchical Deep Learning for Text classification
+(HDLTex). HDLTex employs stacks of deep learning architectures to provide
+specialized understanding at each level of the document hierarchy.
+",1,0,0,0,0,0
+2833,Multi-task Learning with Gradient Guided Policy Specialization,"  We present a method for efficient learning of control policies for multiple
+related robotic motor skills. Our approach consists of two stages, joint
+training and specialization training. During the joint training stage, a neural
+network policy is trained with minimal information to disambiguate the motor
+skills. This forces the policy to learn a common representation of the
+different tasks. Then, during the specialization training stage we selectively
+split the weights of the policy based on a per-weight metric that measures the
+disagreement among the multiple tasks. By splitting part of the control policy,
+it can be further trained to specialize to each task. To update the control
+policy during learning, we use Trust Region Policy Optimization with
+Generalized Advantage Function (TRPOGAE). We propose a modification to the
+gradient update stage of TRPO to better accommodate multi-task learning
+scenarios. We evaluate our approach on three continuous motor skill learning
+problems in simulation: 1) a locomotion task where three single legged robots
+with considerable difference in shape and size are trained to hop forward, 2) a
+manipulation task where three robot manipulators with different sizes and joint
+types are trained to reach different locations in 3D space, and 3) locomotion
+of a two-legged robot, whose range of motion of one leg is constrained in
+different ways. We compare our training method to three baselines. The first
+baseline uses only joint training for the policy, the second trains independent
+policies for each task, and the last randomly selects weights to split. We show
+that our approach learns more efficiently than each of the baseline methods.
+",1,0,0,0,0,0
+2834,Mean square in the prime geodesic theorem,"  We prove upper bounds for the mean square of the remainder in the prime
+geodesic theorem, for every cofinite Fuchsian group, which improve on average
+on the best known pointwise bounds. The proof relies on the Selberg trace
+formula. For the modular group we prove a refined upper bound by using the
+Kuznetsov trace formula.
+",0,0,1,0,0,0
+2835,Approximation Fixpoint Theory and the Well-Founded Semantics of Higher-Order Logic Programs,"  We define a novel, extensional, three-valued semantics for higher-order logic
+programs with negation. The new semantics is based on interpreting the types of
+the source language as three-valued Fitting-monotonic functions at all levels
+of the type hierarchy. We prove that there exists a bijection between such
+Fitting-monotonic functions and pairs of two-valued-result functions where the
+first member of the pair is monotone-antimonotone and the second member is
+antimonotone-monotone. By deriving an extension of consistent approximation
+fixpoint theory (Denecker et al. 2004) and utilizing the above bijection, we
+define an iterative procedure that produces for any given higher-order logic
+program a distinguished extensional model. We demonstrate that this model is
+actually a minimal one. Moreover, we prove that our construction generalizes
+the familiar well-founded semantics for classical logic programs, making in
+this way our proposal an appealing formulation for capturing the well-founded
+semantics for higher-order logic programs. This paper is under consideration
+for acceptance in TPLP.
+",1,0,0,0,0,0
+2836,An Application of Deep Neural Networks in the Analysis of Stellar Spectra,"  Spectroscopic surveys require fast and efficient analysis methods to maximize
+their scientific impact. Here we apply a deep neural network architecture to
+analyze both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our
+convolutional neural network model (StarNet) is trained on APOGEE spectra, we
+show that the stellar parameters (temperature, gravity, and metallicity) are
+determined with similar precision and accuracy as the APOGEE pipeline. StarNet
+can also predict stellar parameters when trained on synthetic data, with
+excellent precision and accuracy for both APOGEE data and synthetic data, over
+a wide range of signal-to-noise ratios. In addition, the statistical
+uncertainties in the stellar parameter determinations are comparable to the
+differences between the APOGEE pipeline results and those determined
+independently from optical spectra. We compare StarNet to other data-driven
+methods; for example, StarNet and the Cannon 2 show similar behaviour when
+trained with the same datasets, however StarNet performs poorly on small
+training sets like those used by the original Cannon. The influence of the
+spectral features on the stellar parameters is examined via partial derivatives
+of the StarNet model results with respect to the input spectra. While StarNet
+was developed using the APOGEE observed spectra and corresponding ASSET
+synthetic data, we suggest that this technique is applicable to other
+wavelength ranges and other spectral surveys.
+",0,1,0,0,0,0
+2837,Analysis of Service-oriented Modeling Approaches for Viewpoint-specific Model-driven Development of Microservice Architecture,"  Microservice Architecture (MSA) is a novel service-based architectural style
+for distributed software systems. Compared to Service-oriented Architecture
+(SOA), MSA puts a stronger focus on self-containment of services. Each
+microservice is responsible for realizing exactly one business or technological
+capability that is distinct from other services' capabilities. Additionally, on
+the implementation and operation level, microservices are self-contained in
+that they are developed, tested, deployed and operated independently from each
+other. Next to these characteristics that distinguish MSA from SOA, both
+architectural styles rely on services as building blocks of distributed
+software architecture and hence face similar challenges regarding, e.g.,
+service identification, composition and provisioning. However, in contrast to
+MSA, SOA may rely on an extensive body of knowledge to tackle these challenges.
+Thus, due to both architectural styles being service-based, the question arises
+to what degree MSA might draw on existing findings of SOA research and
+practice. In this paper we address this question in the field of Model-driven
+Development (MDD) for design and operation of service-based architectures.
+Therefore, we present an analysis of existing MDD approaches to SOA, which
+comprises the identification and semantic clustering of modeling concepts for
+SOA design and operation. For each concept cluster, the analysis assesses its
+applicability to MDD of MSA (MSA-MDD) and assigns it to a specific modeling
+viewpoint. The goal of the presented analysis is to provide a conceptual
+foundation for an MSA-MDD metamodel.
+",1,0,0,0,0,0
+2838,RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction,"  RoboJam is a machine-learning system for generating music that assists users
+of a touchscreen music app by performing responses to their short
+improvisations. This system uses a recurrent artificial neural network to
+generate sequences of touchscreen interactions and absolute timings, rather
+than high-level musical notes. To accomplish this, RoboJam's network uses a
+mixture density layer to predict appropriate touch interaction locations in
+space and time. In this paper, we describe the design and implementation of
+RoboJam's network and how it has been integrated into a touchscreen music app.
+A preliminary evaluation analyses the system in terms of training, musical
+generation and user interaction.
+",1,0,0,0,0,0
+2839,Quasi-two-dimensional Fermi surfaces with localized $f$ electrons in the layered heavy-fermion compound CePt$_2$In$_7$,"  We report measurements of the de Haas-van Alphen effect in the layered
+heavy-fermion compound CePt$_2$In$_7$ in high magnetic fields up to 35 T. Above
+an angle-dependent threshold field, we observed several de Haas-van Alphen
+frequencies originating from almost ideally two-dimensional Fermi surfaces. The
+frequencies are similar to those previously observed to develop only above a
+much higher field of 45 T, where a clear anomaly was detected and proposed to
+originate from a change in the electronic structure [M. M. Altarawneh et al.,
+Phys. Rev. B 83, 081103 (2011)]. Our experimental results are compared with
+band structure calculations performed for both CePt$_2$In$_7$ and
+LaPt$_2$In$_7$, and the comparison suggests localized $f$ electrons in
+CePt$_2$In$_7$. This conclusion is further supported by comparing
+experimentally observed Fermi surfaces in CePt$_2$In$_7$ and PrPt$_2$In$_7$,
+which are found to be almost identical. The measured effective masses in
+CePt$_2$In$_7$ are only moderately enhanced above the bare electron mass $m_0$,
+from 2$m_0$ to 6$m_0$.
+",0,1,0,0,0,0
+2840,"Differential Forms, Linked Fields and the $u$-Invariant","  We associate an Albert form to any pair of cyclic algebras of prime degree
+$p$ over a field $F$ with $\operatorname{char}(F)=p$ which coincides with the
+classical Albert form when $p=2$. We prove that if every Albert form is
+isotropic then $H^4(F)=0$. As a result, we obtain that if $F$ is a linked field
+with $\operatorname{char}(F)=2$ then its $u$-invariant is either $0,2,4$ or
+$8$.
+",0,0,1,0,0,0
+2841,A cyclic system with delay and its characteristic equation,"  A nonlinear cyclic system with delay and the overall negative feedback is
+considered. The characteristic equation of the linearized system is studied in
+detail. Sufficient conditions for the oscillation of all solutions and for the
+existence of monotone solutions are derived in terms of roots of the
+characteristic equation.
+",0,0,1,0,0,0
+2842,Object Detection and Motion Planning for Automated Welding of Tubular Joints,"  Automatic welding of tubular TKY joints is an important and challenging task
+for the marine and offshore industry. In this paper, a framework for tubular
+joint detection and motion planning is proposed. The pose of the real tubular
+joint is detected using RGB-D sensors, which is used to obtain a
+real-to-virtual mapping for positioning the workpiece in a virtual environment.
+For motion planning, a Bi-directional Transition based Rapidly exploring Random
+Tree (BiTRRT) algorithm is used to generate trajectories for reaching the
+desired goals. The complete framework is verified with experiments, and the
+results show that the robot welding torch is able to transit without collision
+to desired goals which are close to the tubular joint.
+",1,0,0,0,0,0
+2843,Nilpotence order growth of recursion operators in characteristic p,"  We prove that the killing rate of certain degree-lowering ""recursion
+operators"" on a polynomial algebra over a finite field grows slower than
+linearly in the degree of the polynomial attacked. We also explain the
+motivating application: obtaining a lower bound for the Krull dimension of a
+local component of a big mod-p Hecke algebra in the genus-zero case. We sketch
+the application for p=2 and p=3 in level one. The case p=2 was first
+established in by Nicolas and Serre in 2012 using different methods.
+",0,0,1,0,0,0
+2844,Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems,"  Neural models have become ubiquitous in automatic speech recognition systems.
+While neural networks are typically used as acoustic models in more complex
+systems, recent studies have explored end-to-end speech recognition systems
+based on neural networks, which can be trained to directly predict text from
+input acoustic features. Although such systems are conceptually elegant and
+simpler than traditional systems, it is less obvious how to interpret the
+trained models. In this work, we analyze the speech representations learned by
+a deep end-to-end model that is based on convolutional and recurrent layers,
+and trained with a connectionist temporal classification (CTC) loss. We use a
+pre-trained model to generate frame-level features which are given to a
+classifier that is trained on frame classification into phones. We evaluate
+representations from different layers of the deep model and compare their
+quality for predicting phone labels. Our experiments shed light on important
+aspects of the end-to-end model such as layer depth, model complexity, and
+other design choices.
+",1,0,0,0,0,0
+2845,Bayesian uncertainty quantification in linear models for diffusion MRI,"  Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue
+microstructure. By fitting a model to the dMRI signal it is possible to derive
+various quantitative features. Several of the most popular dMRI signal models
+are expansions in an appropriately chosen basis, where the coefficients are
+determined using some variation of least-squares. However, such approaches lack
+any notion of uncertainty, which could be valuable in e.g. group analyses. In
+this work, we use a probabilistic interpretation of linear least-squares
+methods to recast popular dMRI models as Bayesian ones. This makes it possible
+to quantify the uncertainty of any derived quantity. In particular, for
+quantities that are affine functions of the coefficients, the posterior
+distribution can be expressed in closed-form. We simulated measurements from
+single- and double-tensor models where the correct values of several quantities
+are known, to validate that the theoretically derived quantiles agree with
+those observed empirically. We included results from residual bootstrap for
+comparison and found good agreement. The validation employed several different
+models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI)
+and Constrained Spherical Deconvolution (CSD). We also used in vivo data to
+visualize maps of quantitative features and corresponding uncertainties, and to
+show how our approach can be used in a group analysis to downweight subjects
+with high uncertainty. In summary, we convert successful linear models for dMRI
+signal estimation to probabilistic models, capable of accurate uncertainty
+quantification.
+",0,1,0,1,0,0
+2846,On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks,"  Empirical risk minimization (ERM) is ubiquitous in machine learning and
+underlies most supervised learning methods. While there has been a large body
+of work on algorithms for various ERM problems, the exact computational
+complexity of ERM is still not understood. We address this issue for multiple
+popular ERM problems including kernel SVMs, kernel ridge regression, and
+training the final layer of a neural network. In particular, we give
+conditional hardness results for these problems based on complexity-theoretic
+assumptions such as the Strong Exponential Time Hypothesis. Under these
+assumptions, we show that there are no algorithms that solve the aforementioned
+ERM problems to high accuracy in sub-quadratic time. We also give similar
+hardness results for computing the gradient of the empirical loss, which is the
+main computational burden in many non-convex learning tasks.
+",1,0,0,1,0,0
+2847,Deep Learning for Predicting Asset Returns,"  Deep learning searches for nonlinear factors for predicting asset returns.
+Predictability is achieved via multiple layers of composite factors as opposed
+to additive ones. Viewed in this way, asset pricing studies can be revisited
+using multi-layer deep learners, such as rectified linear units (ReLU) or
+long-short-term-memory (LSTM) for time-series effects. State-of-the-art
+algorithms including stochastic gradient descent (SGD), TensorFlow and dropout
+design provide imple- mentation and efficient factor exploration. To illustrate
+our methodology, we revisit the equity market risk premium dataset of Welch and
+Goyal (2008). We find the existence of nonlinear factors which explain
+predictability of returns, in particular at the extremes of the characteristic
+space. Finally, we conclude with directions for future research.
+",0,0,0,1,0,0
+2848,Analysis of Distributed ADMM Algorithm for Consensus Optimization in Presence of Error,"  ADMM is a popular algorithm for solving convex optimization problems.
+Applying this algorithm to distributed consensus optimization problem results
+in a fully distributed iterative solution which relies on processing at the
+nodes and communication between neighbors. Local computations usually suffer
+from different types of errors, due to e.g., observation or quantization noise,
+which can degrade the performance of the algorithm. In this work, we focus on
+analyzing the convergence behavior of distributed ADMM for consensus
+optimization in presence of additive node error. We specifically show that (a
+noisy) ADMM converges linearly under certain conditions and also examine the
+associated convergence point. Numerical results are provided which demonstrate
+the effectiveness of the presented analysis.
+",1,0,1,0,0,0
+2849,Jet determination of smooth CR automorphisms and generalized stationary discs,"  We prove finite jet determination for (finitely) smooth CR diffeomorphisms of
+(finitely) smooth Levi degenerate hypersurfaces in $\mathbb{C}^{n+1}$ by
+constructing generalized stationary discs glued to such hypersurfaces.
+",0,0,1,0,0,0
+2850,A Well-Tempered Landscape for Non-convex Robust Subspace Recovery,"  We present a mathematical analysis of a non-convex energy landscape for
+robust subspace recovery. We prove that an underlying subspace is the only
+stationary point and local minimizer in a specified neighborhood under
+deterministic conditions on a dataset. If the deterministic condition is
+satisfied, we further show that a geodesic gradient descent method over the
+Grassmannian manifold can exactly recover the underlying subspace when the
+method is properly initialized. Proper initialization by principal component
+analysis is guaranteed with a similar deterministic condition. Under slightly
+stronger assumptions, the gradient descent method with a special shrinking step
+size scheme achieves linear convergence. The practicality of the deterministic
+condition is demonstrated on some statistical models of data, and the method
+achieves almost state-of-the-art recovery guarantees on the Haystack Model for
+different regimes of sample size and ambient dimension. In particular, when the
+ambient dimension is fixed and the sample size is large enough, we show that
+our gradient method can exactly recover the underlying subspace for any fixed
+fraction of outliers (less than 1).
+",1,0,1,1,0,0
+2851,Biologically inspired protection of deep networks from adversarial attacks,"  Inspired by biophysical principles underlying nonlinear dendritic computation
+in neural circuits, we develop a scheme to train deep neural networks to make
+them robust to adversarial attacks. Our scheme generates highly nonlinear,
+saturated neural networks that achieve state of the art performance on gradient
+based adversarial examples on MNIST, despite never being exposed to
+adversarially chosen examples during training. Moreover, these networks exhibit
+unprecedented robustness to targeted, iterative schemes for generating
+adversarial examples, including second-order methods. We further identify
+principles governing how these networks achieve their robustness, drawing on
+methods from information geometry. We find these networks progressively create
+highly flat and compressed internal representations that are sensitive to very
+few input dimensions, while still solving the task. Moreover, they employ
+highly kurtotic weight distributions, also found in the brain, and we
+demonstrate how such kurtosis can protect even linear classifiers from
+adversarial attack.
+",1,0,0,1,0,0
+2852,Intrinsic entropies of log-concave distributions,"  The entropy of a random variable is well-known to equal the exponential
+growth rate of the volumes of its typical sets. In this paper, we show that for
+any log-concave random variable $X$, the sequence of the $\lfloor n\theta
+\rfloor^{\text{th}}$ intrinsic volumes of the typical sets of $X$ in dimensions
+$n \geq 1$ grows exponentially with a well-defined rate. We denote this rate by
+$h_X(\theta)$, and call it the $\theta^{\text{th}}$ intrinsic entropy of $X$.
+We show that $h_X(\theta)$ is a continuous function of $\theta$ over the range
+$[0,1]$, thereby providing a smooth interpolation between the values 0 and
+$h(X)$ at the endpoints 0 and 1, respectively.
+",1,0,0,0,0,0
+2853,Iteratively Linearized Reweighted Alternating Direction Method of Multipliers for a Class of Nonconvex Problems,"  In this paper, we consider solving a class of nonconvex and nonsmooth
+problems frequently appearing in signal processing and machine learning
+research. The traditional alternating direction method of multipliers
+encounters troubles in both mathematics and computations in solving the
+nonconvex and nonsmooth subproblem. In view of this, we propose a reweighted
+alternating direction method of multipliers. In this algorithm, all subproblems
+are convex and easy to solve. We also provide several guarantees for the
+convergence and prove that the algorithm globally converges to a critical point
+of an auxiliary function with the help of the Kurdyka-{\L}ojasiewicz property.
+Several numerical results are presented to demonstrate the efficiency of the
+proposed algorithm.
+",1,0,0,1,0,0
+2854,Acoustic Features Fusion using Attentive Multi-channel Deep Architecture,"  In this paper, we present a novel deep fusion architecture for audio
+classification tasks. The multi-channel model presented is formed using deep
+convolution layers where different acoustic features are passed through each
+channel. To enable dissemination of information across the channels, we
+introduce attention feature maps that aid in the alignment of frames. The
+output of each channel is merged using interaction parameters that non-linearly
+aggregate the representative features. Finally, we evaluate the performance of
+the proposed architecture on three benchmark datasets:- DCASE-2016 and LITIS
+Rouen (acoustic scene recognition), and CHiME-Home (tagging). Our experimental
+results suggest that the architecture presented outperforms the standard
+baselines and achieves outstanding performance on the task of acoustic scene
+recognition and audio tagging.
+",1,0,0,0,0,0
+2855,An EM Based Probabilistic Two-Dimensional CCA with Application to Face Recognition,"  Recently, two-dimensional canonical correlation analysis (2DCCA) has been
+successfully applied for image feature extraction. The method instead of
+concatenating the columns of the images to the one-dimensional vectors,
+directly works with two-dimensional image matrices. Although 2DCCA works well
+in different recognition tasks, it lacks a probabilistic interpretation. In
+this paper, we present a probabilistic framework for 2DCCA called probabilistic
+2DCCA (P2DCCA) and an iterative EM based algorithm for optimizing the
+parameters. Experimental results on synthetic and real data demonstrate
+superior performance in loading factor estimation for P2DCCA compared to 2DCCA.
+For real data, three subsets of AR face database and also the UMIST face
+database confirm the robustness of the proposed algorithm in face recognition
+tasks with different illumination conditions, facial expressions, poses and
+occlusions.
+",1,0,0,1,0,0
+2856,Understanding Group Event Scheduling via the OutWithFriendz Mobile Application,"  The wide adoption of smartphones and mobile applications has brought
+significant changes to not only how individuals behave in the real world, but
+also how groups of users interact with each other when organizing group events.
+Understanding how users make event decisions as a group and identifying the
+contributing factors can offer important insights for social group studies and
+more effective system and application design for group event scheduling.
+In this work, we have designed a new mobile application called
+OutWithFriendz, which enables users of our mobile app to organize group events,
+invite friends, suggest and vote on event time and venue. We have deployed
+OutWithFriendz at both Apple App Store and Google Play, and conducted a
+large-scale user study spanning over 500 users and 300 group events. Our
+analysis has revealed several important observations regarding group event
+planning process including the importance of user mobility, individual
+preferences, host preferences, and group voting process.
+",1,0,0,0,0,0
+2857,Energy Level Alignment at Hybridized Organic-metal Interfaces: the Role of Many-electron Effects,"  Hybridized molecule/metal interfaces are ubiquitous in molecular and organic
+devices. The energy level alignment (ELA) of frontier molecular levels relative
+to the metal Fermi level (EF) is critical to the conductance and functionality
+of these devices. However, a clear understanding of the ELA that includes
+many-electron self-energy effects is lacking. Here, we investigate the
+many-electron effects on the ELA using state-of-the-art, benchmark GW
+calculations on prototypical chemisorbed molecules on Au(111), in eleven
+different geometries. The GW ELA is in good agreement with photoemission for
+monolayers of benzene-diamine on Au(111). We find that in addition to static
+image charge screening, the frontier levels in most of these geometries are
+renormalized by additional screening from substrate-mediated intermolecular
+Coulomb interactions. For weakly chemisorbed systems, such as amines and
+pyridines on Au, this additional level renormalization (~1.5 eV) comes solely
+from static screened exchange energy, allowing us to suggest computationally
+more tractable schemes to predict the ELA at such interfaces. However, for more
+strongly chemisorbed thiolate layers, dynamical effects are present. Our ab
+initio results constitute an important step towards the understanding and
+manipulation of functional molecular/organic systems for both fundamental
+studies and applications.
+",0,1,0,0,0,0
+2858,Radio detection of Extensive Air Showers (ECRS 2016),"  Detection of the mostly geomagnetically generated radio emission of
+cosmic-ray air showers provides an alternative to air-Cherenkov and
+air-fluorescence detection, since it is not limited to clear nights. Like these
+established methods, the radio signal is sensitive to the calorimetric energy
+and the position of the maximum of the electromagnetic shower component. This
+makes antenna arrays an ideal extension for particle-detector arrays above a
+threshold energy of about 100 PeV of the primary cosmic-ray particles. In the
+last few years the digital radio technique for cosmic-ray air showers again
+made significant progress, and there now is a consistent picture of the
+emission mechanisms confirmed by several measurements. Recent results by the
+antenna arrays AERA and Tunka-Rex confirm that the absolute accuracy for the
+shower energy is as good as the other detection techniques. Moreover, the
+sensitivity to the shower maximum of the radio signal has been confirmed in
+direct comparison to air-Cherenkov measurements by Tunka-Rex. The dense antenna
+array LOFAR can already compete with the established techniques in accuracy for
+cosmic-ray mass-composition. In the future, a new generation of radio
+experiments might drive the field: either by providing extremely large exposure
+for inclined cosmic-ray or neutrino showers or, like the SKA core in Australia
+with its several 10,000 antennas, by providing extremely detailed measurements.
+",0,1,0,0,0,0
+2859,Bandit Regret Scaling with the Effective Loss Range,"  We study how the regret guarantees of nonstochastic multi-armed bandits can
+be improved, if the effective range of the losses in each round is small (e.g.
+the maximal difference between two losses in a given round). Despite a recent
+impossibility result, we show how this can be made possible under certain mild
+additional assumptions, such as availability of rough estimates of the losses,
+or advance knowledge of the loss of a single, possibly unspecified arm. Along
+the way, we develop a novel technique which might be of independent interest,
+to convert any multi-armed bandit algorithm with regret depending on the loss
+range, to an algorithm with regret depending only on the effective range, while
+avoiding predictably bad arms altogether.
+",1,0,0,1,0,0
+2860,Changing Fashion Cultures,"  The paper presents a novel concept that analyzes and visualizes worldwide
+fashion trends. Our goal is to reveal cutting-edge fashion trends without
+displaying an ordinary fashion style. To achieve the fashion-based analysis, we
+created a new fashion culture database (FCDB), which consists of 76 million
+geo-tagged images in 16 cosmopolitan cities. By grasping a fashion trend of
+mixed fashion styles,the paper also proposes an unsupervised fashion trend
+descriptor (FTD) using a fashion descriptor, a codeword vetor, and temporal
+analysis. To unveil fashion trends in the FCDB, the temporal analysis in FTD
+effectively emphasizes consecutive features between two different times. In
+experiments, we clearly show the analysis of fashion trends and fashion-based
+city similarity. As the result of large-scale data collection and an
+unsupervised analyzer, the proposed approach achieves world-level fashion
+visualization in a time series. The code, model, and FCDB will be publicly
+available after the construction of the project page.
+",1,0,0,0,0,0
+2861,A strong failure of aleph_0-stability for atomic classes,"  We study classes of atomic models At_T of a countable, complete first-order
+theory T . We prove that if At_T is not pcl-small, i.e., there is an atomic
+model N that realizes uncountably many types over pcl(a) for some finite tuple
+a from N, then there are 2^aleph1 non-isomorphic atomic models of T, each of
+size aleph1.
+",0,0,1,0,0,0
+2862,Sub-Gaussian estimators of the mean of a random vector,"  We study the problem of estimating the mean of a random vector $X$ given a
+sample of $N$ independent, identically distributed points. We introduce a new
+estimator that achieves a purely sub-Gaussian performance under the only
+condition that the second moment of $X$ exists. The estimator is based on a
+novel concept of a multivariate median.
+",0,0,1,1,0,0
+2863,Resource Allocation for Containing Epidemics from Temporal Network Data,"  We study the problem of containing epidemic spreading processes in temporal
+networks. We specifically focus on the problem of finding a resource allocation
+to suppress epidemic infection, provided that an empirical time-series data of
+connectivities between nodes is available. Although this problem is of
+practical relevance, it has not been clear how an empirical time-series data
+can inform our strategy of resource allocations, due to the computational
+complexity of the problem. In this direction, we present a computationally
+efficient framework for finding a resource allocation that satisfies a given
+budget constraint and achieves a given control performance. The framework is
+based on convex programming and, moreover, allows the performance measure to be
+described by a wide class of functionals called posynomials with nonnegative
+exponents. We illustrate our theoretical results using a data of temporal
+interaction networks within a primary school.
+",1,0,0,0,0,0
+2864,Towards Plan Transformations for Real-World Pick and Place Tasks,"  In this paper, we investigate the possibility of applying plan
+transformations to general manipulation plans in order to specialize them to
+the specific situation at hand. We present a framework for optimizing execution
+and achieving higher performance by autonomously transforming robot's behavior
+at runtime. We show that plans employed by robotic agents in real-world
+environments can be transformed, despite their control structures being very
+complex due to the specifics of acting in the real world. The evaluation is
+carried out on a plan of a PR2 robot performing pick and place tasks, to which
+we apply three example transformations, as well as on a large amount of
+experiments in a fast plan projection environment.
+",1,0,0,0,0,0
+2865,Learning for New Visual Environments with Limited Labels,"  In computer vision applications, such as domain adaptation (DA), few shot
+learning (FSL) and zero-shot learning (ZSL), we encounter new objects and
+environments, for which insufficient examples exist to allow for training
+""models from scratch,"" and methods that adapt existing models, trained on the
+presented training environment, to the new scenario are required. We propose a
+novel visual attribute encoding method that encodes each image as a
+low-dimensional probability vector composed of prototypical part-type
+probabilities. The prototypes are learnt to be representative of all training
+data. At test-time we utilize this encoding as an input to a classifier. At
+test-time we freeze the encoder and only learn/adapt the classifier component
+to limited annotated labels in FSL; new semantic attributes in ZSL. We conduct
+extensive experiments on benchmark datasets. Our method outperforms
+state-of-art methods trained for the specific contexts (ZSL, FSL, DA).
+",1,0,0,0,0,0
+2866,A Survey of Bandwidth and Latency Enhancement Approaches for Mobile Cloud Game Multicasting,"  Among mobile cloud applications, mobile cloud gaming has gained a significant
+popularity in the recent years. In mobile cloud games, textures, game objects,
+and game events are typically streamed from a server to the mobile client.
+One of the challenges in cloud mobile gaming is how to efficiently multicast
+gaming contents and updates in Massively Multi-player Online Games (MMOGs).
+This report surveys the state of art techniques introduced for game
+synchronization and multicasting mechanisms to decrease latency and bandwidth
+consumption, and discuss several schemes that have been proposed in this area
+that can be applied to any networked gaming context. From our point of view,
+gaming applications demand high interactivity. Therefore, concentrating on
+gaming applications will eventually cover a wide range of applications without
+violating the limited scope of this survey.
+",1,0,0,0,0,0
+2867,Modulation of High-Energy Particles and the Heliospheric Current Sheet Tilts throughout 1976-2014,"  Cosmic ray intensities (CRIs) recorded by sixteen neutron monitors have been
+used to study its dependence on the tilt angles (TA) of the heliospheric
+current sheet (HCS) during period 1976-2014, which covers three solar activity
+cycles 21, 22 and 23. The median primary rigidity covers the range 16-33 GV.
+Our results have indicated that the CRIs are directly sensitive to, and
+organized by, the interplanetary magnetic field (IMF) and its neutral sheet
+inclinations. The observed differences in the sensitivity of cosmic ray
+intensity to changes in the neutral sheet tilt angles before and after the
+reversal of interplanetary magnetic field polarity have been studied. Much
+stronger intensity-tilt angle correlation was found when the solar magnetic
+field in the North Polar Region was directed inward than it was outward. The
+rigidity dependence of sensitivities of cosmic rays differs according to the
+IMF polarity, for the periods 1981-1988 and 2001-2008 (qA < 0) it was R-1.00
+and R-1.48 respectively, while for the 1991-1998 epoch (qA > 0) it was R-1.35.
+Hysteresis loops between TA and CRIs have been examined during three solar
+activity cycles 21, 22 and 23. A consider differences in time lags during qA >
+0 and qA < 0 polarity states of the heliosphere have been observed. We also
+found that the cosmic ray intensity decreases at much faster rate with increase
+of tilt angle during qA < 0 than qA > 0, indicating stronger response to the
+tilt angle changes during qA < 0. Our results are discussed in the light of 3D
+modulation models including the gradient, curvature drifts and the tilt of the
+heliospheric current sheet.
+",0,1,0,0,0,0
+2868,Detecting the impact of public transit on the transmission of epidemics,"  In many developing countries, public transit plays an important role in daily
+life. However, few existing methods have considered the influence of public
+transit in their models. In this work, we present a dual-perspective view of
+the epidemic spreading process of the individual that involves both
+contamination in places (such as work places and homes) and public transit
+(such as buses and trains). In more detail, we consider a group of individuals
+who travel to some places using public transit, and introduce public transit
+into the epidemic spreading process. A novel modeling framework is proposed
+considering place-based infections and the public-transit-based infections. In
+the urban scenario, we investigate the public transit trip contribution rate
+(PTTCR) in the epidemic spreading process of the individual, and assess the
+impact of the public transit trip contribution rate by evaluating the volume of
+infectious people. Scenarios for strategies such as public transit and school
+closure were tested and analyzed. Our simulation results suggest that
+individuals with a high public transit trip contribution rate will increase the
+volume of infectious people when an infectious disease outbreak occurs by
+affecting the social network through the public transit trip contribution rate.
+",1,0,0,0,0,0
+2869,The Hamiltonian Dynamics of Magnetic Confinement in Toroidal Domains,"  We consider a class of magnetic fields defined over the interior of a
+manifold $M$ which go to infinity at its boundary and whose direction near the
+boundary of $M$ is controlled by a closed 1-form $\sigma_\infty \in
+\Gamma(T^*\partial M)$. We are able to show that charged particles in the
+interior of $M$ under the influence of such fields can only escape the manifold
+through the zero locus of $\sigma_\infty$. In particular in the case where the
+1-form is nowhere vanishing we conclude that the particles become confined to
+its interior for all time.
+",0,0,1,0,0,0
+2870,Airway segmentation from 3D chest CT volumes based on volume of interest using gradient vector flow,"  Some lung diseases are related to bronchial airway structures and morphology.
+Although airway segmentation from chest CT volumes is an important task in the
+computer-aided diagnosis and surgery assistance systems for the chest, complete
+3-D airway structure segmentation is a quite challenging task due to its
+complex tree-like structure. In this paper, we propose a new airway
+segmentation method from 3D chest CT volumes based on volume of interests (VOI)
+using gradient vector flow (GVF). This method segments the bronchial regions by
+applying the cavity enhancement filter (CEF) to trace the bronchial tree
+structure from the trachea. It uses the CEF in the VOI to segment each branch.
+And a tube-likeness function based on GVF and the GVF magnitude map in each VOI
+are utilized to assist predicting the positions and directions of child
+branches. By calculating the tube-likeness function based on GVF and the GVF
+magnitude map, the airway-like candidate structures are identified and their
+centrelines are extracted. Based on the extracted centrelines, we can detect
+the branch points of the bifurcations and directions of the airway branches in
+the next level. At the same time, a leakage detection is performed to avoid the
+leakage by analysing the pixel information and the shape information of airway
+candidate regions extracted in the VOI. Finally, we unify all of the extracted
+bronchial regions to form an integrated airway tree. Preliminary experiments
+using four cases of chest CT volumes demonstrated that the proposed method can
+extract more bronchial branches in comparison with other methods.
+",1,0,0,0,0,0
+2871,Multi-robot motion-formation distributed control with sensor self-calibration: experimental validation,"  In this paper, we present the design and implementation of a robust motion
+formation distributed control algorithm for a team of mobile robots. The
+primary task for the team is to form a geometric shape, which can be freely
+translated and rotated at the same time. This approach makes the robots to
+behave as a cohesive whole, which can be useful in tasks such as collaborative
+transportation. The robustness of the algorithm relies on the fact that each
+robot employs only local measurements from a laser sensor which does not need
+to be off-line calibrated. Furthermore, robots do not need to exchange any
+information with each other. Being free of sensor calibration and not requiring
+a communication channel helps the scaling of the overall system to a large
+number of robots. In addition, since the robots do not need any off-board
+localization system, but require only relative positions with respect to their
+neighbors, it can be aimed to have a full autonomous team that operates in
+environments where such localization systems are not available. The
+computational cost of the algorithm is inexpensive and the resources from a
+standard microcontroller will suffice. This fact makes the usage of our
+approach appealing as a support for other more demanding algorithms, e.g.,
+processing images from onboard cameras. We validate the performance of the
+algorithm with a team of four mobile robots equipped with low-cost commercially
+available laser scanners.
+",1,0,0,0,0,0
+2872,On the Impossibility of Supersized Machines,"  In recent years, a number of prominent computer scientists, along with
+academics in fields such as philosophy and physics, have lent credence to the
+notion that machines may one day become as large as humans. Many have further
+argued that machines could even come to exceed human size by a significant
+margin. However, there are at least seven distinct arguments that preclude this
+outcome. We show that it is not only implausible that machines will ever exceed
+human size, but in fact impossible.
+",1,1,0,0,0,0
+2873,Non-geodesic variations of Hodge structure of maximum dimension,"  There are a number of examples of variations of Hodge structure of maximum
+dimension. However, to our knowledge, those that are global on the level of the
+period domain are totally geodesic subspaces that arise from an orbit of a
+subgroup of the group of the period domain. That is, they are defined by Lie
+theory rather than by algebraic geometry. In this note, we give an example of a
+variation of maximum dimension which is nowhere tangent to a geodesic
+variation. The period domain in question, which classifies weight two Hodge
+structures with $h^{2,0} = 2$ and $h^{1,1} = 28$, is of dimension $57$. The
+horizontal tangent bundle has codimension one, thus it is an example of a
+holomorphic contact structure, with local integral manifolds of dimension 28.
+The group of the period domain is $SO(4,28)$, and one can produce global
+integral manifolds as orbits of the action of subgroups isomorphic to
+$SU(2,14)$. Our example is given by the variation of Hodge structure on the
+second cohomology of weighted projective hypersurfaces of degree $10$ in a
+weighted projective three-space with weights $1, 1, 2, 5$
+",0,0,1,0,0,0
+2874,Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective,"  Recent results in coupled or temporal graphical models offer schemes for
+estimating the relationship structure between features when the data come from
+related (but distinct) longitudinal sources. A novel application of these ideas
+is for analyzing group-level differences, i.e., in identifying if trends of
+estimated objects (e.g., covariance or precision matrices) are different across
+disparate conditions (e.g., gender or disease). Often, poor effect sizes make
+detecting the differential signal over the full set of features difficult: for
+example, dependencies between only a subset of features may manifest
+differently across groups. In this work, we first give a parametric model for
+estimating trends in the space of SPD matrices as a function of one or more
+covariates. We then generalize scan statistics to graph structures, to search
+over distinct subsets of features (graph partitions) whose temporal dependency
+structure may show statistically significant group-wise differences. We
+theoretically analyze the Family Wise Error Rate (FWER) and bounds on Type 1
+and Type 2 error. On a cohort of individuals with risk factors for Alzheimer's
+disease (but otherwise cognitively healthy), we find scientifically interesting
+group differences where the default analysis, i.e., models estimated on the
+full graph, do not survive reasonable significance thresholds.
+",1,0,0,1,0,0
+2875,A Distributed Algorithm for Computing a Common Fixed Point of a Finite Family of Paracontractions,"  A distributed algorithm is described for finding a common fixed point of a
+family of m>1 nonlinear maps M_i : R^n -> R^n assuming that each map is a
+paracontraction and that at least one such common fixed point exists. The
+common fixed point is simultaneously computed by m agents assuming each agent i
+knows only M_i, the current estimates of the fixed point generated by its
+neighbors, and nothing more. Each agent recursively updates its estimate of a
+fixed point by utilizing the current estimates generated by each of its
+neighbors. Neighbor relations are characterized by a time-varying directed
+graph N(t). It is shown under suitably general conditions on N(t), that the
+algorithm causes all agents estimates to converge to the same common fixed
+point of the m nonlinear maps.
+",1,0,1,0,0,0
+2876,The maximum of the 1-measurement of a metric measure space,"  For a metric measure space, we treat the set of distributions of 1-Lipschitz
+functions, which is called the 1-measurement. On the 1-measurement, we have a
+partial order relation by the Lipschitz order introduced by Gromov. The aim of
+this paper is to study the maximum and maximal elements of the 1-measurement
+with respect to the Lipschitz order. We present a necessary condition of a
+metric measure space for the existence of the maximum of the 1-measurement. We
+also consider a metric measure space that has the maximum of its 1-measurement.
+",0,0,1,0,0,0
+2877,Limits to Arbitrage in Markets with Stochastic Settlement Latency,"  Distributed ledger technologies rely on consensus protocols confronting
+traders with random waiting times until the transfer of ownership is
+accomplished. This time-consuming settlement process exposes arbitrageurs to
+price risk and imposes limits to arbitrage. We derive theoretical arbitrage
+boundaries under general assumptions and show that they increase with expected
+latency, latency uncertainty, spot volatility, and risk aversion. Using
+high-frequency data from the Bitcoin network, we estimate arbitrage boundaries
+due to settlement latency of on average 124 basis points, covering 88 percent
+of the observed cross-exchange price differences. Settlement through
+decentralized systems thus induces non-trivial frictions affecting market
+efficiency and price formation.
+",0,0,0,0,0,1
+2878,Normalized Information Distance and the Oscillation Hierarchy,"  We study the complexity of approximations to the normalized information
+distance. We introduce a hierarchy of computable approximations by considering
+the number of oscillations. This is a function version of the difference
+hierarchy for sets. We show that the normalized information distance is not in
+any level of this hierarchy, strengthening previous nonapproximability results.
+As an ingredient to the proof, we also prove a conditional undecidability
+result about independence.
+",1,0,1,0,0,0
+2879,Exponential Moving Average Model in Parallel Speech Recognition Training,"  As training data rapid growth, large-scale parallel training with multi-GPUs
+cluster is widely applied in the neural network model learning currently.We
+present a new approach that applies exponential moving average method in
+large-scale parallel training of neural network model. It is a non-interference
+strategy that the exponential moving average model is not broadcasted to
+distributed workers to update their local models after model synchronization in
+the training process, and it is implemented as the final model of the training
+system. Fully-connected feed-forward neural networks (DNNs) and deep
+unidirectional Long short-term memory (LSTM) recurrent neural networks (RNNs)
+are successfully trained with proposed method for large vocabulary continuous
+speech recognition on Shenma voice search data in Mandarin. The character error
+rate (CER) of Mandarin speech recognition further degrades than
+state-of-the-art approaches of parallel training.
+",1,0,0,0,0,0
+2880,Is One Hyperparameter Optimizer Enough?,"  Hyperparameter tuning is the black art of automatically finding a good
+combination of control parameters for a data miner. While widely applied in
+empirical Software Engineering, there has not been much discussion on which
+hyperparameter tuner is best for software analytics. To address this gap in the
+literature, this paper applied a range of hyperparameter optimizers (grid
+search, random search, differential evolution, and Bayesian optimization) to
+defect prediction problem. Surprisingly, no hyperparameter optimizer was
+observed to be `best' and, for one of the two evaluation measures studied here
+(F-measure), hyperparameter optimization, in 50\% cases, was no better than
+using default configurations.
+We conclude that hyperparameter optimization is more nuanced than previously
+believed. While such optimization can certainly lead to large improvements in
+the performance of classifiers used in software analytics, it remains to be
+seen which specific optimizers should be applied to a new dataset.
+",1,0,0,0,0,0
+2881,Deep Generalized Canonical Correlation Analysis,"  We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a
+method for learning nonlinear transformations of arbitrarily many views of
+data, such that the resulting transformations are maximally informative of each
+other. While methods for nonlinear two-view representation learning (Deep CCA,
+(Andrew et al., 2013)) and linear many-view representation learning
+(Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview
+representation learning technique that combines the flexibility of nonlinear
+(deep) representation learning with the statistical power of incorporating
+information from many independent sources, or views. We present the DGCCA
+formulation as well as an efficient stochastic optimization algorithm for
+solving it. We learn DGCCA representations on two distinct datasets for three
+downstream tasks: phonetic transcription from acoustic and articulatory
+measurements, and recommending hashtags and friends on a dataset of Twitter
+users. We find that DGCCA representations soundly beat existing methods at
+phonetic transcription and hashtag recommendation, and in general perform no
+worse than standard linear many-view techniques.
+",1,0,0,1,0,0
+2882,Faithfulness of Probability Distributions and Graphs,"  A main question in graphical models and causal inference is whether, given a
+probability distribution $P$ (which is usually an underlying distribution of
+data), there is a graph (or graphs) to which $P$ is faithful. The main goal of
+this paper is to provide a theoretical answer to this problem. We work with
+general independence models, which contain probabilistic independence models as
+a special case. We exploit a generalization of ordering, called preordering, of
+the nodes of (mixed) graphs. This allows us to provide sufficient conditions
+for a given independence model to be Markov to a graph with the minimum
+possible number of edges, and more importantly, necessary and sufficient
+conditions for a given probability distribution to be faithful to a graph. We
+present our results for the general case of mixed graphs, but specialize the
+definitions and results to the better-known subclasses of undirected
+(concentration) and bidirected (covariance) graphs as well as directed acyclic
+graphs.
+",0,0,1,1,0,0
+2883,On the multipliers of repelling periodic points of entire functions,"  We give a lower bound for the multipliers of repelling periodic points of
+entire functions. The bound is deduced from a bound for the multipliers of
+fixed points of composite entire functions.
+",0,0,1,0,0,0
+2884,The BCS critical temperature in a weak homogeneous magnetic field,"  We show that, within a linear approximation of BCS theory, a weak homogeneous
+magnetic field lowers the critical temperature by an explicit constant times
+the field strength, up to higher order terms. This provides a rigorous
+derivation and generalization of results obtained in the physics literature
+from WHH theory of the upper critical magnetic field. A new ingredient in our
+proof is a rigorous phase approximation to control the effects of the magnetic
+field.
+",0,1,1,0,0,0
+2885,25 Tweets to Know You: A New Model to Predict Personality with Social Media,"  Predicting personality is essential for social applications supporting
+human-centered activities, yet prior modeling methods with users written text
+require too much input data to be realistically used in the context of social
+media. In this work, we aim to drastically reduce the data requirement for
+personality modeling and develop a model that is applicable to most users on
+Twitter. Our model integrates Word Embedding features with Gaussian Processes
+regression. Based on the evaluation of over 1.3K users on Twitter, we find that
+our model achieves comparable or better accuracy than state of the art
+techniques with 8 times fewer data.
+",1,0,0,0,0,0
+2886,Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture,"  We propose a novel couple mappings method for low resolution face recognition
+using deep convolutional neural networks (DCNNs). The proposed architecture
+consists of two branches of DCNNs to map the high and low resolution face
+images into a common space with nonlinear transformations. The branch
+corresponding to transformation of high resolution images consists of 14 layers
+and the other branch which maps the low resolution face images to the common
+space includes a 5-layer super-resolution network connected to a 14-layer
+network. The distance between the features of corresponding high and low
+resolution images are backpropagated to train the networks. Our proposed method
+is evaluated on FERET data set and compared with state-of-the-art competing
+methods. Our extensive experimental results show that the proposed method
+significantly improves the recognition performance especially for very low
+resolution probe face images (11.4% improvement in recognition accuracy).
+Furthermore, it can reconstruct a high resolution image from its corresponding
+low resolution probe image which is comparable with state-of-the-art
+super-resolution methods in terms of visual quality.
+",1,0,0,0,0,0
+2887,A Comparative Study of Full-Duplex Relaying Schemes for Low Latency Applications,"  Various sectors are likely to carry a set of emerging applications while
+targeting a reliable communication with low latency transmission. To address
+this issue, upon a spectrally-efficient transmission, this paper investigates
+the performance of a one full-dulpex (FD) relay system, and considers for that
+purpose, two basic relaying schemes, namely the symbol-by-symbol transmission,
+i.e., amplify-and-forward (AF) and the block-by-block transmission, i.e.,
+selective decode-and-forward (SDF). The conducted analysis presents an
+exhaustive comparison, covering both schemes, over two different transmission
+modes, i.e., the non combining mode where the best link, direct or relay link
+is decoded and the signals combining mode, where direct and relay links are
+combined at the receiver side. While targeting latency purpose as a necessity,
+simulations show a refined results of performed comparisons, and reveal that AF
+relaying scheme is more adapted to combining mode, whereas the SDF relaying
+scheme is more suitable for non combining mode.
+",1,0,0,0,0,0
+2888,Some algebraic invariants of edge ideal of circulant graphs,"  Let $G$ be the circulant graph $C_n(S)$ with $S\subseteq\{ 1,\ldots,\left
+\lfloor\frac{n}{2}\right \rfloor\}$ and let $I(G)$ be its edge ideal in the
+ring $K[x_0,\ldots,x_{n-1}]$. Under the hypothesis that $n$ is prime we : 1)
+compute the regularity index of $R/I(G)$; 2) compute the Castelnuovo-Mumford
+regularity when $R/I(G)$ is Cohen-Macaulay; 3) prove that the circulant graphs
+with $S=\{1,\ldots,s\}$ are sequentially $S_2$ . We end characterizing the
+Cohen-Macaulay circulant graphs of Krull dimension $2$ and computing their
+Cohen-Macaulay type and Castelnuovo-Mumford regularity.
+",0,0,1,0,0,0
+2889,Efficient Pricing of Barrier Options on High Volatility Assets using Subset Simulation,"  Barrier options are one of the most widely traded exotic options on stock
+exchanges. In this paper, we develop a new stochastic simulation method for
+pricing barrier options and estimating the corresponding execution
+probabilities. We show that the proposed method always outperforms the standard
+Monte Carlo approach and becomes substantially more efficient when the
+underlying asset has high volatility, while it performs better than multilevel
+Monte Carlo for special cases of barrier options and underlying assets. These
+theoretical findings are confirmed by numerous simulation results.
+",0,0,0,1,0,1
+2890,Massively parallel multicanonical simulations,"  Generalized-ensemble Monte Carlo simulations such as the multicanonical
+method and similar techniques are among the most efficient approaches for
+simulations of systems undergoing discontinuous phase transitions or with
+rugged free- energy landscapes. As Markov chain methods, they are inherently
+serial computationally. It was demonstrated recently, however, that a
+combination of independent simulations that communicate weight updates at
+variable intervals allows for the efficient utilization of parallel
+computational resources for multicanonical simulations. Implementing this
+approach for the many-thread architecture provided by current generations of
+graphics processing units (GPUs), we show how it can be efficiently employed
+with of the order of $10^4$ parallel walkers and beyond, thus constituting a
+versatile tool for Monte Carlo simulations in the era of massively parallel
+computing. We provide the fully documented source code for the approach applied
+to the paradigmatic example of the two-dimensional Ising model as starting
+point and reference for practitioners in the field.
+",0,1,0,0,0,0
+2891,Gaia and VLT astrometry of faint stars: Precision of Gaia DR1 positions and updated VLT parallaxes of ultracool dwarfs,"  We compared positions of the Gaia first data release (DR1) secondary data set
+at its faint limit with CCD positions of stars in 20 fields observed with the
+VLT/FORS2 camera. The FORS2 position uncertainties are smaller than one
+milli-arcsecond (mas) and allowed us to perform an independent verification of
+the DR1 astrometric precision. In the fields that we observed with FORS2, we
+projected the Gaia DR1 positions into the CCD plane, performed a polynomial fit
+between the two sets of matching stars, and carried out statistical analyses of
+the residuals in positions. The residual RMS roughly matches the expectations
+given by the Gaia DR1 uncertainties, where we identified three regimes in terms
+of Gaia DR1 precision: for G = 17-20 stars we found that the formal DR1
+position uncertainties of stars with DR1 precisions in the range of 0.5-5 mas
+are underestimated by 63 +/- 5\%, whereas the DR1 uncertainties of stars in the
+range 7-10 mas are overestimated by a factor of two. For the best-measured and
+generally brighter G = 16-18 stars with DR1 positional uncertainties of <0.5
+mas, we detected 0.44 +/- 0.13 mas excess noise in the residual RMS, whose
+origin can be in both FORS2 and Gaia DR1. By adopting Gaia DR1 as the absolute
+reference frame we refined the pixel scale determination of FORS2, leading to
+minor updates to the parallaxes of 20 ultracool dwarfs that we published
+previously. We also updated the FORS2 absolute parallax of the Luhman 16 binary
+brown dwarf system to 501.42 +/- 0.11 mas
+",0,1,0,0,0,0
+2892,Parallel transport in shape analysis: a scalable numerical scheme,"  The analysis of manifold-valued data requires efficient tools from Riemannian
+geometry to cope with the computational complexity at stake. This complexity
+arises from the always-increasing dimension of the data, and the absence of
+closed-form expressions to basic operations such as the Riemannian logarithm.
+In this paper, we adapt a generic numerical scheme recently introduced for
+computing parallel transport along geodesics in a Riemannian manifold to
+finite-dimensional manifolds of diffeomorphisms. We provide a qualitative and
+quantitative analysis of its behavior on high-dimensional manifolds, and
+investigate an application with the prediction of brain structures progression.
+",0,0,1,1,0,0
+2893,Spectral Projector-Based Graph Fourier Transforms,"  The paper presents the graph Fourier transform (GFT) of a signal in terms of
+its spectral decomposition over the Jordan subspaces of the graph adjacency
+matrix $A$. This representation is unique and coordinate free, and it leads to
+unambiguous definition of the spectral components (""harmonics"") of a graph
+signal. This is particularly meaningful when $A$ has repeated eigenvalues, and
+it is very useful when $A$ is defective or not diagonalizable (as it may be the
+case with directed graphs). Many real world large sparse graphs have defective
+adjacency matrices. We present properties of the GFT and show it to satisfy a
+generalized Parseval inequality and to admit a total variation ordering of the
+spectral components. We express the GFT in terms of spectral projectors and
+present an illustrative example for a real world large urban traffic dataset.
+",1,0,0,0,0,0
+2894,Quantum Mechanical Approach to Modelling Reliability of Sensor Reports,"  Dempster-Shafer evidence theory is wildly applied in multi-sensor data
+fusion. However, lots of uncertainty and interference exist in practical
+situation, especially in the battle field. It is still an open issue to model
+the reliability of sensor reports. Many methods are proposed based on the
+relationship among collected data. In this letter, we proposed a quantum
+mechanical approach to evaluate the reliability of sensor reports, which is
+based on the properties of a sensor itself. The proposed method is used to
+modify the combining of evidences.
+",1,0,0,0,0,0
+2895,"Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes","  Could we use Computer Vision in the Internet of Things for using pictures as
+sensors? This is the principal hypothesis that we want to resolve. Currently,
+in order to create safety areas, cities, or homes, people use IP cameras.
+Nevertheless, this system needs people who watch the camera images, watch the
+recording after something occurred, or watch when the camera notifies them of
+any movement. These are the disadvantages. Furthermore, there are many Smart
+Cities and Smart Homes around the world. This is why we thought of using the
+idea of the Internet of Things to add a way of automating the use of IP
+cameras. In our case, we propose the analysis of pictures through Computer
+Vision to detect people in the analysed pictures. With this analysis, we are
+able to obtain if these pictures contain people and handle the pictures as if
+they were sensors with two possible states. Notwithstanding, Computer Vision is
+a very complicated field. This is why we needed a second hypothesis: Could we
+work with Computer Vision in the Internet of Things with a good accuracy to
+automate or semi-automate this kind of events? The demonstration of these
+hypotheses required a testing over our Computer Vision module to check the
+possibilities that we have to use this module in a possible real environment
+with a good accuracy. Our proposal, as a possible solution, is the analysis of
+entire sequence instead of isolated pictures for using pictures as sensors in
+the Internet of Things.
+",1,0,0,0,0,0
+2896,Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals,"  Interpretability of deep neural networks is a recently emerging area of
+machine learning research targeting a better understanding of how models
+perform feature selection and derive their classification decisions. In this
+paper, two neural network architectures are trained on spectrogram and raw
+waveform data for audio classification tasks on a newly created audio dataset
+and layer-wise relevance propagation (LRP), a previously proposed
+interpretability method, is applied to investigate the models' feature
+selection and decision making. It is demonstrated that the networks are highly
+reliant on feature marked as relevant by LRP through systematic manipulation of
+the input data. Our results show that by making deep audio classifiers
+interpretable, one can analyze and compare the properties and strategies of
+different models beyond classification accuracy, which potentially opens up new
+ways for model improvements.
+",1,0,0,0,0,0
+2897,On the Performance of a Canonical Labeling for Matching Correlated Erdős-Rényi Graphs,"  Graph matching in two correlated random graphs refers to the task of
+identifying the correspondence between vertex sets of the graphs. Recent
+results have characterized the exact information-theoretic threshold for graph
+matching in correlated Erdős-Rényi graphs. However, very little is known
+about the existence of efficient algorithms to achieve graph matching without
+seeds. In this work we identify a region in which a straightforward $O(n^2\log
+n)$-time canonical labeling algorithm, initially introduced in the context of
+graph isomorphism, succeeds in matching correlated Erdős-Rényi graphs.
+The algorithm has two steps. In the first step, all vertices are labeled by
+their degrees and a trivial minimum distance matching (i.e., simply sorting
+vertices according to their degrees) matches a fixed number of highest degree
+vertices in the two graphs. Having identified this subset of vertices, the
+remaining vertices are matched using a matching algorithm for bipartite graphs.
+",0,0,0,1,0,0
+2898,On semi-supervised learning,"  Semi-supervised learning deals with the problem of how, if possible, to take
+advantage of a huge amount of unclassified data, to perform a classification in
+situations when, typically, there is little labeled data. Even though this is
+not always possible (it depends on how useful, for inferring the labels, it
+would be to know the distribution of the unlabeled data), several algorithm
+have been proposed recently. %but in general they are not proved to outperform
+A new algorithm is proposed, that under almost necessary conditions, %and it is
+proved that it attains asymptotically the performance of the best theoretical
+rule as the amount of unlabeled data tends to infinity. The set of necessary
+assumptions, although reasonable, show that semi-supervised classification only
+works for very well conditioned problems. The focus is on understanding when
+and why semi-supervised learning works when the size of the initial training
+sample remains fixed and the asymptotic is on the size of the unlabeled data.
+The performance of the algorithm is assessed in the well known ""Isolet""
+real-data of phonemes, where a strong dependence on the choice of the initial
+training sample is shown.
+",0,0,0,1,0,0
+2899,Fourier dimension and spectral gaps for hyperbolic surfaces,"  We obtain an essential spectral gap for a convex co-compact hyperbolic
+surface $M=\Gamma\backslash\mathbb H^2$ which depends only on the dimension
+$\delta$ of the limit set. More precisely, we show that when $\delta>0$ there
+exists $\varepsilon_0=\varepsilon_0(\delta)>0$ such that the Selberg zeta
+function has only finitely many zeroes $s$ with $\Re s>\delta-\varepsilon_0$.
+The proof uses the fractal uncertainty principle approach developed by
+Dyatlov-Zahl [arXiv:1504.06589]. The key new component is a Fourier decay bound
+for the Patterson-Sullivan measure, which may be of independent interest. This
+bound uses the fact that transformations in the group $\Gamma$ are nonlinear,
+together with estimates on exponential sums due to Bourgain which follow from
+the discretized sum-product theorem in $\mathbb R$.
+",0,0,1,0,0,0
+2900,Semantic Evolutionary Concept Distances for Effective Information Retrieval in Query Expansion,"  In this work several semantic approaches to concept-based query expansion and
+reranking schemes are studied and compared with different ontology-based
+expansion methods in web document search and retrieval. In particular, we focus
+on concept-based query expansion schemes, where, in order to effectively
+increase the precision of web document retrieval and to decrease the users
+browsing time, the main goal is to quickly provide users with the most suitable
+query expansion. Two key tasks for query expansion in web document retrieval
+are to find the expansion candidates, as the closest concepts in web document
+domain, and to rank the expanded queries properly. The approach we propose aims
+at improving the expansion phase for better web document retrieval and
+precision. The basic idea is to measure the distance between candidate concepts
+using the PMING distance, a collaborative semantic proximity measure, i.e. a
+measure which can be computed by using statistical results from web search
+engine. Experiments show that the proposed technique can provide users with
+more satisfying expansion results and improve the quality of web document
+retrieval.
+",1,0,1,0,0,0
+2901,Emission line galaxies behind the planetary nebula IC 5148: Potential for a serendipity survey with archival data,"  During the start of a survey program using FORS2 long slit spectroscopy on
+planetary nebulae (PN) and their haloes, we serendipitously discovered six
+background emission line galaxies (ELG) with redshifts of z = 0.2057, 0.3137,
+0.37281, 0.4939, 0.7424 and 0.8668. Thus they clearly do not belong to a common
+cluster structure. We derived the major physical properties of the targets.
+Since the used long slit covers a sky area of only 570 arcsec^2, we discuss
+further potential of serendipitous discoveries in archival data, beside the
+deep systematic work of the ongoing and upcoming big surveys. We conclude that
+archival data provide a decent potential for extending the overall data on ELGs
+without any selection bias.
+",0,1,0,0,0,0
+2902,An example related to the slicing inequality for general measures,"  For $n\in \mathbb{N}$ let $S_n$ be the smallest number $S>0$ satisfying the
+inequality $$ \int_K f \le S \cdot |K|^{\frac 1n} \cdot \max_{\xi\in S^{n-1}}
+\int_{K\cap \xi^\bot} f $$ for all centrally-symmetric convex bodies $K$ in
+$\mathbb{R}^n$ and all even, continuous probability densities $f$ on $K$. Here
+$|K|$ is the volume of $K$. It was proved by the second-named author that
+$S_n\le 2\sqrt{n}$, and in analogy with Bourgain's slicing problem, it was
+asked whether $S_n$ is bounded from above by a universal constant. In this note
+we construct an example showing that $S_n\ge c\sqrt{n}/\sqrt{\log \log n},$
+where $c > 0$ is an absolute constant. Additionally, for any $0 < \alpha < 2$
+we describe a related example that satisfies the so-called
+$\psi_{\alpha}$-condition.
+",0,0,1,0,0,0
+2903,Two-level schemes for the advection equation,"  The advection equation is the basis for mathematical models of continuum
+mechanics. In the approximate solution of nonstationary problems it is
+necessary to inherit main properties of the conservatism and monotonicity of
+the solution. In this paper, the advection equation is written in the symmetric
+form, where the advection operator is the half-sum of advection operators in
+conservative (divergent) and non-conservative (characteristic) forms. The
+advection operator is skew-symmetric. Standard finite element approximations in
+space are used. The standart explicit two-level scheme for the advection
+equation is absolutly unstable. New conditionally stable regularized schemes
+are constructed, on the basis of the general theory of stability
+(well-posedness) of operator-difference schemes, the stability conditions of
+the explicit Lax-Wendroff scheme are established. Unconditionally stable and
+conservative schemes are implicit schemes of the second (Crank-Nicolson scheme)
+and fourth order. The conditionally stable implicit Lax-Wendroff scheme is
+constructed. The accuracy of the investigated explicit and implicit two-level
+schemes for an approximate solution of the advection equation is illustrated by
+the numerical results of a model two-dimensional problem.
+",1,0,0,0,0,0
+2904,Estimating functional time series by moving average model fitting,"  Functional time series have become an integral part of both functional data
+and time series analysis. Important contributions to methodology, theory and
+application for the prediction of future trajectories and the estimation of
+functional time series parameters have been made in the recent past. This paper
+continues this line of research by proposing a first principled approach to
+estimate invertible functional time series by fitting functional moving average
+processes. The idea is to estimate the coefficient operators in a functional
+linear filter. To do this a functional Innovations Algorithm is utilized as a
+starting point to estimate the corresponding moving average operators via
+suitable projections into principal directions. In order to establish
+consistency of the proposed estimators, asymptotic theory is developed for
+increasing subspaces of these principal directions. For practical purposes,
+several strategies to select the number of principal directions to include in
+the estimation procedure as well as the choice of order of the functional
+moving average process are discussed. Their empirical performance is evaluated
+through simulations and an application to vehicle traffic data.
+",0,0,0,1,0,0
+2905,Benford's law: a 'sleeping beauty' sleeping in the dirty pages of logarithmic tables,"  Benford's law is an empirical observation, first reported by Simon Newcomb in
+1881 and then independently by Frank Benford in 1938: the first significant
+digits of numbers in large data are often distributed according to a
+logarithmically decreasing function. Being contrary to intuition, the law was
+forgotten as a mere curious observation. However, in the last two decades,
+relevant literature has grown exponentially, - an evolution typical of
+""Sleeping Beauties"" (SBs) publications that go unnoticed (sleep) for a long
+time and then suddenly become center of attention (are awakened). Thus, in the
+present study, we show that Newcomb (1881) and Benford (1938) papers are
+clearly SBs. The former was in deep sleep for 110 years whereas the latter was
+in deep sleep for a comparatively lesser period of 31 years up to 1968, and in
+a state of less deep sleep for another 27 years up to 1995. Both SBs were
+awakened in the year 1995 by Hill (1995a). In so doing, we show that the waking
+prince (Hill, 1995a) is more often quoted than the SB whom he kissed, - in this
+Benford's law case, wondering whether this is a general effect, - to be
+usefully studied.
+",1,0,0,0,0,0
+2906,Characterizing correlations and synchronization in collective dynamics,"  Synchronization, that occurs both for non-chaotic and chaotic systems, is a
+striking phenomenon with many practical implications in natural phenomena.
+However, even before synchronization, strong correlations occur in the
+collective dynamics of complex systems. To characterize their nature is
+essential for the understanding of phenomena in physical and social sciences.
+The emergence of strong correlations before synchronization is illustrated in a
+few piecewise linear models. They are shown to be associated to the behavior of
+ergodic parameters which may be exactly computed in some models. The models are
+also used as a testing ground to find general methods to characterize and
+parametrize the correlated nature of collective dynamics.
+",0,1,0,0,0,0
+2907,"Soft modes and strain redistribution in continuous models of amorphous plasticity: the Eshelby paradigm, and beyond?","  The deformation of disordered solids relies on swift and localised
+rearrangements of particles. The inspection of soft vibrational modes can help
+predict the locations of these rearrangements, while the strain that they
+actually redistribute mediates collective effects. Here, we study soft modes
+and strain redistribution in a two-dimensional continuous mesoscopic model
+based on a Ginzburg-Landau free energy for perfect solids, supplemented with a
+plastic disorder potential that accounts for shear softening and
+rearrangements. Regardless of the disorder strength, our numerical simulations
+show soft modes that are always sharply peaked at the softest point of the
+material (unlike what happens for the depinning of an elastic interface).
+Contrary to widespread views, the deformation halo around this peak does not
+always have a quadrupolar (Eshelby-like) shape. Instead, for finite and
+narrowly-distributed disorder, it looks like a fracture, with a strain field
+that concentrates along some easy directions. These findings are rationalised
+with analytical calculations in the case where the plastic disorder is confined
+to a point-like `impurity'. In this case, we unveil a continuous family of
+elastic propagators, which are identical for the soft modes and for the
+equilibrium configurations. This family interpolates between the standard
+quadrupolar propagator and the fracture-like one as the anisotropy of the
+elastic medium is increased. Therefore, we expect to see a fracture-like
+propagator when extended regions on the brink of failure have already softened
+along the shear direction and thus rendered the material anisotropic, but not
+failed yet. We speculate that this might be the case in carefully aged glasses
+just before macroscopic failure.
+",0,1,0,0,0,0
+2908,On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests,"  The reproducing kernel Hilbert space (RKHS) embedding of distributions offers
+a general and flexible framework for testing problems in arbitrary domains and
+has attracted considerable amount of attention in recent years. To gain
+insights into their operating characteristics, we study here the statistical
+performance of such approaches within a minimax framework. Focusing on the case
+of goodness-of-fit tests, our analyses show that a vanilla version of the
+kernel-embedding based test could be suboptimal, and suggest a simple remedy by
+moderating the embedding. We prove that the moderated approach provides optimal
+tests for a wide range of deviations from the null and can also be made
+adaptive over a large collection of interpolation spaces. Numerical experiments
+are presented to further demonstrate the merits of our approach.
+",0,0,1,1,0,0
+2909,The influence of contrarians in the dynamics of opinion formation,"  In this work we consider the presence of contrarian agents in discrete
+3-state kinetic exchange opinion models. The contrarians are individuals that
+adopt the choice opposite to the prevailing choice of their contacts, whatever
+this choice is. We consider binary as well as three-agent interactions, with
+stochastic parameters, in a fully-connected population. Our numerical results
+suggest that the presence of contrarians destroys the absorbing state of the
+original model, changing the transition to the para-ferromagnetic type. In this
+case, the consequence for the society is that the three opinions coexist in the
+population, in both phases (ordered and disordered). Furthermore, the
+order-disorder transition is suppressed for a sufficient large fraction of
+contrarians. In some cases the transition is discontinuous, and it changes to
+continuous before it is suppressed. Some of our results are complemented by
+analytical calculations based on the master equation.
+",0,1,0,0,0,0
+2910,"Risk-neutral valuation under differential funding costs, defaults and collateralization","  We develop a unified valuation theory that incorporates credit risk
+(defaults), collateralization and funding costs, by expanding the replication
+approach to a generality that has not yet been studied previously and reaching
+valuation when replication is not assumed. This unifying theoretical framework
+clarifies the relationship between the two valuation approaches: the adjusted
+cash flows approach pioneered for example by Brigo, Pallavicini and co-authors
+([12, 13, 34]) and the classic replication approach illustrated for example by
+Bielecki and Rutkowski and co-authors ([3, 8]). In particular, results of this
+work cover most previous papers where the authors studied specific replication
+models.
+",0,0,0,0,0,1
+2911,Singular Spectrum and Recent Results on Hierarchical Operators,"  We use trace class scattering theory to exclude the possibility of absolutely
+continuous spectrum in a large class of self-adjoint operators with an
+underlying hierarchical structure and provide applications to certain random
+hierarchical operators and matrices. We proceed to contrast the localizing
+effect of the hierarchical structure in the deterministic setting with previous
+results and conjectures in the random setting. Furthermore, we survey stronger
+localization statements truly exploiting the disorder for the hierarchical
+Anderson model and report recent results concerning the spectral statistics of
+the ultrametric random matrix ensemble.
+",0,1,1,0,0,0
+2912,Weyl states and Fermi arcs in parabolic bands,"  Weyl fermions are shown to exist inside a parabolic band, where the kinetic
+energy of carriers is given by the non-relativistic Schroedinger equation.
+There are Fermi arcs as a direct consequence of the folding of a ring shaped
+Fermi surface inside the first Brillouin zone. Our results stem from the
+decomposition of the kinetic energy into the sum of the square of the Weyl
+state, the coupling to the local magnetic field and the Rashba interaction. The
+Weyl fermions break the time and reflection symmetries present in the kinetic
+energy, thus allowing for the onset of a weak three-dimensional magnetic field
+around the layer. This field brings topological stability to the current
+carrying states through a Chern number. In the special limit that the Weyl
+state becomes gapless this magnetic interaction is shown to be purely
+attractive, thus suggesting the onset of a superconducting condensate of zero
+helicity states.
+",0,1,0,0,0,0
+2913,Flexible Deep Neural Network Processing,"  The recent success of Deep Neural Networks (DNNs) has drastically improved
+the state of the art for many application domains. While achieving high
+accuracy performance, deploying state-of-the-art DNNs is a challenge since they
+typically require billions of expensive arithmetic computations. In addition,
+DNNs are typically deployed in ensemble to boost accuracy performance, which
+further exacerbates the system requirements. This computational overhead is an
+issue for many platforms, e.g. data centers and embedded systems, with tight
+latency and energy budgets. In this article, we introduce flexible DNNs
+ensemble processing technique, which achieves large reduction in average
+inference latency while incurring small to negligible accuracy drop. Our
+technique is flexible in that it allows for dynamic adaptation between quality
+of results (QoR) and execution runtime. We demonstrate the effectiveness of the
+technique on AlexNet and ResNet-50 using the ImageNet dataset. This technique
+can also easily handle other types of networks.
+",0,0,0,1,0,0
+2914,Kinetic approach to relativistic dissipation,"  Despite a long record of intense efforts, the basic mechanisms by which
+dissipation emerges from the microscopic dynamics of a relativistic fluid still
+elude a complete understanding. In particular, no unique pathway from kinetic
+theory to hydrodynamics has been identified as yet, with different approaches
+leading to different values of the transport coefficients. In this Letter, we
+approach the problem by matching data from lattice kinetic simulations with
+analytical predictions. Our numerical results provide neat evidence in favour
+of the Chapman-Enskog procedure, as suggested by recently theoretical analyses,
+along with qualitative hints at the basic reasons why the Chapman-Enskog
+expansion might be better suited than Grad's method to capture the emergence of
+dissipative effects in relativistic fluids.
+",0,1,0,0,0,0
+2915,Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization,"  This paper addresses image classification through learning a compact and
+discriminative dictionary efficiently. Given a structured dictionary with each
+atom (columns in the dictionary matrix) related to some label, we propose
+cross-label suppression constraint to enlarge the difference among
+representations for different classes. Meanwhile, we introduce group
+regularization to enforce representations to preserve label properties of
+original samples, meaning the representations for the same class are encouraged
+to be similar. Upon the cross-label suppression, we don't resort to
+frequently-used $\ell_0$-norm or $\ell_1$-norm for coding, and obtain
+computational efficiency without losing the discriminative power for
+categorization. Moreover, two simple classification schemes are also developed
+to take full advantage of the learnt dictionary. Extensive experiments on six
+data sets including face recognition, object categorization, scene
+classification, texture recognition and sport action categorization are
+conducted, and the results show that the proposed approach can outperform lots
+of recently presented dictionary algorithms on both recognition accuracy and
+computational efficiency.
+",1,0,0,1,0,0
+2916,DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding,"  Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely
+used on NLP tasks to capture the long-term and local dependencies,
+respectively. Attention mechanisms have recently attracted enormous interest
+due to their highly parallelizable computation, significantly less training
+time, and flexibility in modeling dependencies. We propose a novel attention
+mechanism in which the attention between elements from input sequence(s) is
+directional and multi-dimensional (i.e., feature-wise). A light-weight neural
+net, ""Directional Self-Attention Network (DiSAN)"", is then proposed to learn
+sentence embedding, based solely on the proposed attention without any RNN/CNN
+structure. DiSAN is only composed of a directional self-attention with temporal
+order encoded, followed by a multi-dimensional attention that compresses the
+sequence into a vector representation. Despite its simple form, DiSAN
+outperforms complicated RNN models on both prediction quality and time
+efficiency. It achieves the best test accuracy among all sentence encoding
+methods and improves the most recent best result by 1.02% on the Stanford
+Natural Language Inference (SNLI) dataset, and shows state-of-the-art test
+accuracy on the Stanford Sentiment Treebank (SST), Multi-Genre natural language
+inference (MultiNLI), Sentences Involving Compositional Knowledge (SICK),
+Customer Review, MPQA, TREC question-type classification and Subjectivity
+(SUBJ) datasets.
+",1,0,0,0,0,0
+2917,Eigenvalues of compactly perturbed operators via entropy numbers,"  We derive new estimates for the number of discrete eigenvalues of compactly
+perturbed operators on Banach spaces, assuming that the perturbing operator is
+an element of a weak entropy number ideal. Our results improve upon earlier
+results by the author and by Demuth et al. The main tool in our proofs is an
+inequality of Carl. In particular, in contrast to all previous results we do
+not rely on tools from complex analysis.
+",0,0,1,0,0,0
+2918,Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank,"  Recently low displacement rank (LDR) matrices, or so-called structured
+matrices, have been proposed to compress large-scale neural networks. Empirical
+results have shown that neural networks with weight matrices of LDR matrices,
+referred as LDR neural networks, can achieve significant reduction in space and
+computational complexity while retaining high accuracy. We formally study LDR
+matrices in deep learning. First, we prove the universal approximation property
+of LDR neural networks with a mild condition on the displacement operators. We
+then show that the error bounds of LDR neural networks are as efficient as
+general neural networks with both single-layer and multiple-layer structure.
+Finally, we propose back-propagation based training algorithm for general LDR
+neural networks.
+",1,0,0,1,0,0
+2919,A data driven trimming procedure for robust classification,"  Classification rules can be severely affected by the presence of disturbing
+observations in the training sample. Looking for an optimal classifier with
+such data may lead to unnecessarily complex rules. So, simpler effective
+classification rules could be achieved if we relax the goal of fitting a good
+rule for the whole training sample but only consider a fraction of the data. In
+this paper we introduce a new method based on trimming to produce
+classification rules with guaranteed performance on a significant fraction of
+the data. In particular, we provide an automatic way of determining the right
+trimming proportion and obtain in this setting oracle bounds for the
+classification error on the new data set.
+",0,0,1,1,0,0
+2920,The Salesman's Improved Tours for Fundamental Classes,"  Finding the exact integrality gap $\alpha$ for the LP relaxation of the
+metric Travelling Salesman Problem (TSP) has been an open problem for over
+thirty years, with little progress made. It is known that $4/3 \leq \alpha \leq
+3/2$, and a famous conjecture states $\alpha = 4/3$. For this problem,
+essentially two ""fundamental"" classes of instances have been proposed. This
+fundamental property means that in order to show that the integrality gap is at
+most $\rho$ for all instances of metric TSP, it is sufficient to show it only
+for the instances in the fundamental class. However, despite the importance and
+the simplicity of such classes, no apparent effort has been deployed for
+improving the integrality gap bounds for them. In this paper we take a natural
+first step in this endeavour, and consider the $1/2$-integer points of one such
+class. We successfully improve the upper bound for the integrality gap from
+$3/2$ to $10/7$ for a superclass of these points, as well as prove a lower
+bound of $4/3$ for the superclass. Our methods involve innovative applications
+of tools from combinatorial optimization which have the potential to be more
+broadly applied.
+",1,0,0,0,0,0
+2921,Maximum Number of Modes of Gaussian Mixtures,"  Gaussian mixture models are widely used in Statistics. A fundamental aspect
+of these distributions is the study of the local maxima of the density, or
+modes. In particular, it is not known how many modes a mixture of $k$ Gaussians
+in $d$ dimensions can have. We give a brief account of this problem's history.
+Then, we give improved lower bounds and the first upper bound on the maximum
+number of modes, provided it is finite.
+",0,0,1,1,0,0
+2922,Dynamic Clearing and Contagion in Financial Networks,"  In this paper we will consider a generalized extension of the Eisenberg-Noe
+model of financial contagion to allow for time dynamics in both discrete and
+continuous time. Derivation and interpretation of the financial implications
+will be provided. Emphasis will be placed on the continuous-time framework and
+its formulation as a differential equation driven by the operating cash flows.
+Mathematical results on existence and uniqueness of firm wealths under the
+discrete and continuous-time models will be provided. Finally, the financial
+implications of time dynamics will be considered. The focus will be on how the
+dynamic clearing solutions differ from those of the static Eisenberg-Noe model.
+",0,0,0,0,0,1
+2923,On the Semantics of Intensionality and Intensional Recursion,"  Intensionality is a phenomenon that occurs in logic and computation. In the
+most general sense, a function is intensional if it operates at a level finer
+than (extensional) equality. This is a familiar setting for computer
+scientists, who often study different programs or processes that are
+interchangeable, i.e. extensionally equal, even though they are not implemented
+in the same way, so intensionally distinct. Concomitant with intensionality is
+the phenomenon of intensional recursion, which refers to the ability of a
+program to have access to its own code. In computability theory, intensional
+recursion is enabled by Kleene's Second Recursion Theorem. This thesis is
+concerned with the crafting of a logical toolkit through which these phenomena
+can be studied. Our main contribution is a framework in which mathematical and
+computational constructions can be considered either extensionally, i.e. as
+abstract values, or intensionally, i.e. as fine-grained descriptions of their
+construction. Once this is achieved, it may be used to analyse intensional
+recursion.
+",1,0,1,0,0,0
+2924,A Theory of Solvability for Lossless Power Flow Equations -- Part I: Fixed-Point Power Flow,"  This two-part paper details a theory of solvability for the power flow
+equations in lossless power networks. In Part I, we derive a new formulation of
+the lossless power flow equations, which we term the fixed-point power flow.
+The model is stated for both meshed and radial networks, and is parameterized
+by several graph-theoretic matrices -- the power network stiffness matrices --
+which quantify the internal coupling strength of the network. The model leads
+immediately to an explicit approximation of the high-voltage power flow
+solution. For standard test cases, we find that iterates of the fixed-point
+power flow converge rapidly to the high-voltage power flow solution, with the
+approximate solution yielding accurate predictions near base case loading. In
+Part II, we leverage the fixed-point power flow to study power flow
+solvability, and for radial networks we derive conditions guaranteeing the
+existence and uniqueness of a high-voltage power flow solution. These
+conditions (i) imply exponential convergence of the fixed-point power flow
+iteration, and (ii) properly generalize the textbook two-bus system results.
+",0,0,1,0,0,0
+2925,Narcissus: Deriving Correct-By-Construction Decoders and Encoders from Binary Formats,"  It is a neat result from functional programming that libraries of parser
+combinators can support rapid construction of decoders for quite a range of
+formats. With a little more work, the same combinator program can denote both a
+decoder and an encoder. Unfortunately, the real world is full of gnarly
+formats, as with the packet formats that make up the standard Internet protocol
+stack. Most past parser-combinator approaches cannot handle these formats, and
+the few exceptions require redundancy -- one part of the natural grammar needs
+to be hand-translated into hints in multiple parts of a parser program. We show
+how to recover very natural and nonredundant format specifications, covering
+all popular network packet formats and generating both decoders and encoders
+automatically. The catch is that we use the Coq proof assistant to derive both
+kinds of artifacts using tactics, automatically, in a way that guarantees that
+they form inverses of each other. We used our approach to reimplement packet
+processing for a full Internet protocol stack, inserting our replacement into
+the OCaml-based MirageOS unikernel, resulting in minimal performance
+degradation.
+",1,0,0,0,0,0
+2926,Discrete and Continuous Green Energy on Compact Manifolds,"  In this article we study the role of the Green function for the Laplacian in
+a compact Riemannian manifold as a tool for obtaining well-distributed points.
+In particular, we prove that a sequence of minimizers for the Green energy is
+asymptotically uniformly distributed. We pay special attention to the case of
+locally harmonic manifolds.
+",0,0,1,0,0,0
+2927,"High Capacity, Secure (n, n/8) Multi Secret Image Sharing Scheme with Security Key","  The rising need of secret image sharing with high security has led to much
+advancement in lucrative exchange of important images which contain vital and
+confidential information. Multi secret image sharing system (MSIS) is an
+efficient and robust method for transmitting one or more secret images
+securely. In recent research, n secret images are encrypted into n or n+ 1
+shared images and stored in different database servers. The decoder has to
+receive all n or n+1 encrypted images to reproduce the secret image. One can
+recover partial secret information from n-1 or fewer shared images, which poses
+risk for the confidential information encrypted. In this proposed paper we
+developed a novel algorithm to increase the sharing capacity by using (n, n/8)
+multi-secret sharing scheme with increased security by generating a unique
+security key. A unrevealed comparison image is used to produce shares which
+makes the secret image invulnerable to the hackers
+",1,0,0,0,0,0
+2928,MATMPC - A MATLAB Based Toolbox for Real-time Nonlinear Model Predictive Control,"  In this paper we introduce MATMPC, an open source software built in MATLAB
+for nonlinear model predictive control (NMPC). It is designed to facilitate
+modelling, controller design and simulation for a wide class of NMPC
+applications. MATMPC has a number of algorithmic modules, including automatic
+differentiation, direct multiple shooting, condensing, linear quadratic program
+(QP) solver and globalization. It also supports a unique Curvature-like Measure
+of Nonlinearity (CMoN) MPC algorithm. MATMPC has been designed to provide
+state-of-the-art performance while making the prototyping easy, also with
+limited programming knowledge. This is achieved by writing each module directly
+in MATLAB API for C. As a result, MATMPC modules can be compiled into MEX
+functions with performance comparable to plain C/C++ solvers. MATMPC has been
+successfully used in operating systems including WINDOWS, LINUX AND OS X.
+Selected examples are shown to highlight the effectiveness of MATMPC.
+",1,0,0,0,0,0
+2929,A Copula-based Imputation Model for Missing Data of Mixed Type in Multilevel Data Sets,"  We propose a copula based method to handle missing values in multivariate
+data of mixed types in multilevel data sets. Building upon the extended rank
+likelihood of \cite{hoff2007extending} and the multinomial probit model, our
+model is a latent variable model which is able to capture the relationship
+among variables of different types as well as accounting for the clustering
+structure. We fit the model by approximating the posterior distribution of the
+parameters and the missing values through a Gibbs sampling scheme. We use the
+multiple imputation procedure to incorporate the uncertainty due to missing
+values in the analysis of the data. Our proposed method is evaluated through
+simulations to compare it with several conventional methods of handling missing
+data. We also apply our method to a data set from a cluster randomized
+controlled trial of a multidisciplinary intervention in acute stroke units. We
+conclude that our proposed copula based imputation model for mixed type
+variables achieves reasonably good imputation accuracy and recovery of
+parameters in some models of interest, and that adding random effects enhances
+performance when the clustering effect is strong.
+",0,0,0,1,0,0
+2930,Rovibrational optical cooling of a molecular beam,"  Cooling the rotation and the vibration of molecules by broadband light
+sources was possible for trapped molecular ions or ultracold molecules. Because
+of a low power spectral density, the cooling timescale has never fell below
+than a few milliseconds. Here we report on rotational and vibrational cooling
+of a supersonic beam of barium monofluoride molecules in less than 440 $\mu$s.
+Vibrational cooling was optimized by enhancing the spectral power density of a
+semiconductor light source at the underlying molecular transitions allowing us
+to transfer all the populations of $v''=1-3$ into the vibrational ground state
+($v''=0$). Rotational cooling, that requires an efficient vibrational pumping,
+was then achieved. According to a Boltzmann fit, the rotation temperature was
+reduced by almost a factor of 10. In this fashion, the population of the lowest
+rotational levels increased by more than one order of magnitude.
+",0,1,0,0,0,0
+2931,When Work Matters: Transforming Classical Network Structures to Graph CNN,"  Numerous pattern recognition applications can be formed as learning from
+graph-structured data, including social network, protein-interaction network,
+the world wide web data, knowledge graph, etc. While convolutional neural
+network (CNN) facilitates great advances in gridded image/video understanding
+tasks, very limited attention has been devoted to transform these successful
+network structures (including Inception net, Residual net, Dense net, etc.) to
+establish convolutional networks on graph, due to its irregularity and
+complexity geometric topologies (unordered vertices, unfixed number of adjacent
+edges/vertices). In this paper, we aim to give a comprehensive analysis of when
+work matters by transforming different classical network structures to graph
+CNN, particularly in the basic graph recognition problem. Specifically, we
+firstly review the general graph CNN methods, especially in its spectral
+filtering operation on the irregular graph data. We then introduce the basic
+structures of ResNet, Inception and DenseNet into graph CNN and construct these
+network structures on graph, named as G_ResNet, G_Inception, G_DenseNet. In
+particular, it seeks to help graph CNNs by shedding light on how these
+classical network structures work and providing guidelines for choosing
+appropriate graph network frameworks. Finally, we comprehensively evaluate the
+performance of these different network structures on several public graph
+datasets (including social networks and bioinformatic datasets), and
+demonstrate how different network structures work on graph CNN in the graph
+recognition task.
+",0,0,0,1,0,0
+2932,Factoring the Cycle Aging Cost of Batteries Participating in Electricity Markets,"  When participating in electricity markets, owners of battery energy storage
+systems must bid in such a way that their revenues will at least cover their
+true cost of operation. Since cycle aging of battery cells represents a
+substantial part of this operating cost, the cost of battery degradation must
+be factored in these bids. However, existing models of battery degradation
+either do not fit market clearing software or do not reflect the actual battery
+aging mechanism. In this paper we model battery cycle aging using a piecewise
+linear cost function, an approach that provides a close approximation of the
+cycle aging mechanism of electrochemical batteries and can be incorporated
+easily into existing market dispatch programs. By defining the marginal aging
+cost of each battery cycle, we can assess the actual operating profitability of
+batteries. A case study demonstrates the effectiveness of the proposed model in
+maximizing the operating profit of a battery energy storage system taking part
+in the ISO New England energy and reserve markets.
+",0,0,1,0,0,0
+2933,Asynchronous Parallel Bayesian Optimisation via Thompson Sampling,"  We design and analyse variations of the classical Thompson sampling (TS)
+procedure for Bayesian optimisation (BO) in settings where function evaluations
+are expensive, but can be performed in parallel. Our theoretical analysis shows
+that a direct application of the sequential Thompson sampling algorithm in
+either synchronous or asynchronous parallel settings yields a surprisingly
+powerful result: making $n$ evaluations distributed among $M$ workers is
+essentially equivalent to performing $n$ evaluations in sequence. Further, by
+modeling the time taken to complete a function evaluation, we show that, under
+a time constraint, asynchronously parallel TS achieves asymptotically lower
+regret than both the synchronous and sequential versions. These results are
+complemented by an experimental analysis, showing that asynchronous TS
+outperforms a suite of existing parallel BO algorithms in simulations and in a
+hyper-parameter tuning application in convolutional neural networks. In
+addition to these, the proposed procedure is conceptually and computationally
+much simpler than existing work for parallel BO.
+",1,0,0,1,0,0
+2934,Hydrodynamic signatures of stationary Marangoni-driven surfactant transport,"  We experimentally study steady Marangoni-driven surfactant transport on the
+interface of a deep water layer. Using hydrodynamic measurements, and without
+using any knowledge of the surfactant physico-chemical properties, we show that
+sodium dodecyl sulphate and Tergitol 15-S-9 introduced in low concentrations
+result in a flow driven by adsorbed surfactant. At higher surfactant
+concentration, the flow is dominated by the dissolved surfactant. Using
+Camphoric acid, whose properties are {\it a priori} unknown, we demonstrate
+this method's efficacy by showing its spreading is adsorption dominated.
+",0,1,0,0,0,0
+2935,Exponential growth of homotopy groups of suspended finite complexes,"  We study the asymptotic behavior of the homotopy groups of simply connected
+finite $p$-local complexes, and define a space to be locally hyperbolic if its
+homotopy groups have exponential growth. Under some certain conditions related
+to the functorial decomposition of loop suspension, we prove that the suspended
+finite complexes are locally hyperbolic if suitable but accessible information
+of the homotopy groups is known. In particular, we prove that Moore spaces are
+locally hyperbolic, and other candidates are also given.
+",0,0,1,0,0,0
+2936,Approximation Algorithms for Rectangle Packing Problems (PhD Thesis),"  In rectangle packing problems we are given the task of placing axis-aligned
+rectangles in a given plane region, so that they do not overlap with each
+other. In Maximum Weight Independent Set of Rectangles (MWISR), their position
+is given and we can only select which rectangles to choose, while trying to
+maximize their total weight. In Strip Packing (SP), we have to pack all the
+given rectangles in a rectangular region of fixed width, while minimizing its
+height. In 2-Dimensional Geometric Knapsack (2DGK), the target region is a
+square of a given size, and our goal is to select and pack a subset of the
+given rectangles of maximum weight. We study a generalization of MWISR and use
+it to improve the approximation for a resource allocation problem called
+bagUFP. We revisit some classical results on SP and 2DGK, by proposing a
+framework based on smaller containers that are packed with simpler rules; while
+variations of this scheme are indeed a standard technique in this area, we
+abstract away some of the problem-specific differences, obtaining simpler
+algorithms that work for different problems. We obtain improved approximations
+for SP in pseudo-polynomial time, and for a variant of 2DGK where one can to
+rotate the rectangles by 90°. For the latter, we propose the first
+algorithms with approximation factor better than 2. For the main variant of
+2DGK (without rotations), a container-based approach seems to face a natural
+barrier of 2 in the approximation factor. Thus, we consider a generalized kind
+of packing that combines container packings with another packing problem that
+we call L-packing problem, where we have to pack rectangles in an L-shaped
+region of the plane. By finding a (1 + {\epsilon})-approximation for this
+problem and exploiting the combinatorial structure of 2DGK, we obtain the first
+algorithms that break the barrier of 2 for the approximation factor of this
+problem.
+",1,0,0,0,0,0
+2937,R-boundedness Approach to linear third differential equations in a UMD Space,"  The aim of this work is to study the existence of a periodic solutions of
+third order differential equations $z'''(t) = Az(t) + f(t)$ with the periodic
+condition $x(0) = x(2\pi), x'(0) = x'(2\pi)$ and $x''(0) = x''(2\pi)$. Our
+approach is based on the R-boundedness and $L^{p}$-multiplier of linear
+operators.
+",0,0,1,0,0,0
+2938,Entanglement and quantum transport in integrable systems,"  Understanding the entanglement structure of out-of-equilibrium many-body
+systems is a challenging yet revealing task. Here we investigate the
+entanglement dynamics after a quench from a piecewise homogeneous initial state
+in integrable systems. This is the prototypical setup for studying quantum
+transport, and it consists in the sudden junction of two macroscopically
+different and homogeneous states. By exploiting the recently developed
+integrable hydrodynamic approach and the quasiparticle picture for the
+entanglement dynamics, we conjecture a formula for the entanglement production
+rate after joining two semi-infinite reservoirs, as well as the steady-state
+entanglement entropy of a finite subregion. We show that both quantities are
+determined by the quasiparticles created in the Non Equilibrium steady State
+(NESS) appearing at large times at the interface between the two reservoirs.
+Specifically, the steady-state entropy coincides with the thermodynamic entropy
+of the NESS, whereas the entropy production rate reflects its spreading into
+the bulk of the two reservoirs. Our results are numerically corroborated using
+time-dependent Density Matrix Renormalization Group (tDMRG) simulations in the
+paradigmatic XXZ spin-1/2 chain.
+",0,1,0,0,0,0
+2939,"Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking","  Transfer learning has the potential to reduce the burden of data collection
+and to decrease the unavoidable risks of the training phase. In this letter, we
+introduce a multirobot, multitask transfer learning framework that allows a
+system to complete a task by learning from a few demonstrations of another task
+executed on another system. We focus on the trajectory tracking problem where
+each trajectory represents a different task, since many robotic tasks can be
+described as a trajectory tracking problem. The proposed multirobot transfer
+learning framework is based on a combined $\mathcal{L}_1$ adaptive control and
+an iterative learning control approach. The key idea is that the adaptive
+controller forces dynamically different systems to behave as a specified
+reference model. The proposed multitask transfer learning framework uses
+theoretical control results (e.g., the concept of vector relative degree) to
+learn a map from desired trajectories to the inputs that make the system track
+these trajectories with high accuracy. This map is used to calculate the inputs
+for a new, unseen trajectory. Experimental results using two different
+quadrotor platforms and six different trajectories show that, on average, the
+proposed framework reduces the first-iteration tracking error by 74% when
+information from tracking a different single trajectory on a different
+quadrotor is utilized.
+",1,0,0,0,0,0
+2940,Exponential Bounds for the Erdős-Ginzburg-Ziv Constant,"  The Erdős-Ginzburg-Ziv constant of an abelian group $G$, denoted
+$\mathfrak{s}(G)$, is the smallest $k\in\mathbb{N}$ such that any sequence of
+elements of $G$ of length $k$ contains a zero-sum subsequence of length
+$\exp(G)$. In this paper, we use the partition rank, which generalizes the
+slice rank, to prove that for any odd prime $p$, \[
+\mathfrak{s}\left(\mathbb{F}_{p}^{n}\right)\leq(p-1)2^{p}\left(J(p)\cdot
+p\right)^{n} \] where $0.8414<J(p)<0.91837$ is the constant appearing in
+Ellenberg and Gijswijt's bound on arithmetic progression-free subsets of
+$\mathbb{F}_{p}^{n}$. For large $n$, and $p>3$, this is the first exponential
+improvement to the trivial bound. We also provide a near optimal result
+conditional on the conjecture that $\left(\mathbb{Z}/k\mathbb{Z}\right)^{n}$
+satisfies property $D$, showing that in this case \[
+\mathfrak{s}\left(\left(\mathbb{Z}/k\mathbb{Z}\right)^{n}\right)\leq(k-1)4^{n}+k.
+\]
+",0,0,1,0,0,0
+2941,Soft Weight-Sharing for Neural Network Compression,"  The success of deep learning in numerous application domains created the de-
+sire to run and train them on mobile devices. This however, conflicts with
+their computationally, memory and energy intense nature, leading to a growing
+interest in compression. Recent work by Han et al. (2015a) propose a pipeline
+that involves retraining, pruning and quantization of neural network weights,
+obtaining state-of-the-art compression rates. In this paper, we show that
+competitive compression rates can be achieved by using a version of soft
+weight-sharing (Nowlan & Hinton, 1992). Our method achieves both quantization
+and pruning in one simple (re-)training procedure. This point of view also
+exposes the relation between compression and the minimum description length
+(MDL) principle.
+",0,0,0,1,0,0
+2942,Neural Discourse Structure for Text Categorization,"  We show that discourse structure, as defined by Rhetorical Structure Theory
+and provided by an existing discourse parser, benefits text categorization. Our
+approach uses a recursive neural network and a newly proposed attention
+mechanism to compute a representation of the text that focuses on salient
+content, from the perspective of both RST and the task. Experiments consider
+variants of the approach and illustrate its strengths and weaknesses.
+",1,0,0,0,0,0
+2943,On the optimal design of grid-based binary holograms for matter wave lithography,"  Grid based binary holography (GBH) is an attractive method for patterning
+with light or matter waves. It is an approximate technique in which different
+holographic masks can be used to produce similar patterns. Here we present an
+optimal design method for GBH masks that allows for freely selecting the
+fraction of open holes in the mask from below 10% to above 90%. Open-fraction
+is an important design parameter when making masks for use in lithography
+systems. The method also includes a rescaling feature that potentially enables
+a better contrast of the generated patterns. Through simulations we investigate
+the contrast and robustness of the patterns formed by masks generated by the
+proposed optimal design method. It is demonstrated that high contrast patterns
+are achievable for a wide range of open-fractions. We conclude that reaching a
+desired open-fraction is a trade-off with the contrast of the pattern generated
+by the mask.
+",0,1,0,0,0,0
+2944,Thermal physics of the inner coma: ALMA studies of the methanol distribution and excitation in comet C/2012 K1 (PanSTARRS),"  We present spatially and spectrally-resolved observations of CH$_3$OH
+emission from comet C/2012 K1 (PanSTARRS) using The Atacama Large
+Millimeter/submillimeter Array (ALMA) on 2014 June 28-29. Two-dimensional maps
+of the line-of-sight average rotational temperature ($T_{rot}$) were derived,
+covering spatial scales $0.3''-1.8''$ (corresponding to sky-projected distances
+$\rho\sim500$-2500 km). The CH$_3$OH column density distributions are
+consistent with isotropic, uniform outflow from the nucleus, with no evidence
+for extended sources of CH$_3$OH in the coma. The $T_{rot}(\rho)$ radial
+profiles show a significant drop within a few thousand kilometers of the
+nucleus, falling from about 60 K to 20 K between $\rho=0$ and 2500 km on June
+28, whereas on June 29, $T_{rot}$ fell from about 120 K to 40 K between $\rho=$
+0 km and 1000 km. The observed $T_{rot}$ behavior is interpreted primarily as a
+result of variations in the coma kinetic temperature due to adiabatic cooling
+of the outflowing gas, as well as radiative cooling of the CH$_3$OH rotational
+levels. Our excitation model shows that radiative cooling is more important for
+the $J=7-6$ transitions (at 338 GHz) than for the $K=3-2$ transitions (at 252
+GHz), resulting in a strongly sub-thermal distribution of levels in the $J=7-6$
+band at $\rho\gtrsim1000$ km. For both bands, the observed temperature drop
+with distance is less steep than predicted by standard coma theoretical models,
+which suggests the presence of a significant source of heating in addition to
+the photolytic heat sources usually considered.
+",0,1,0,0,0,0
+2945,Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization,"  Nowadays, the availability of large-scale data in disparate application
+domains urges the deployment of sophisticated tools for extracting valuable
+knowledge out of this huge bulk of information. In that vein, low-rank
+representations (LRRs) which seek low-dimensional embeddings of data have
+naturally appeared. In an effort to reduce computational complexity and improve
+estimation performance, LRR has been viewed via a matrix factorization (MF)
+perspective. Recently, low-rank MF (LRMF) approaches have been proposed for
+tackling the inherent weakness of MF i.e., the unawareness of the dimension of
+the low-dimensional space where data reside. Herein, inspired by the merits of
+iterative reweighted schemes for rank minimization, we come up with a generic
+low-rank promoting regularization function. Then, focusing on a specific
+instance of it, we propose a regularizer that imposes column-sparsity jointly
+on the two matrix factors that result from MF, thus promoting low-rankness on
+the optimization problem. The problems of denoising, matrix completion and
+non-negative matrix factorization (NMF) are redefined according to the new LRMF
+formulation and solved via efficient Newton-type algorithms with proven
+theoretical guarantees as to their convergence and rates of convergence to
+stationary points. The effectiveness of the proposed algorithms is verified in
+diverse simulated and real data experiments.
+",1,0,0,0,0,0
+2946,"The formation of the Milky Way halo and its dwarf satellites, a NLTE-1D abundance analysis. I. Homogeneous set of atmospheric parameters","  We present a homogeneous set of accurate atmospheric parameters for a
+complete sample of very and extremely metal-poor stars in the dwarf spheroidal
+galaxies (dSphs) Sculptor, Ursa Minor, Sextans, Fornax, Boötes I, Ursa Major
+II, and Leo IV. We also deliver a Milky Way (MW) comparison sample of giant
+stars covering the -4 < [Fe/H] < -1.7 metallicity range. We show that, in the
+[Fe/H] > -3.5 regime, the non-local thermodynamic equilibrium (NLTE)
+calculations with non-spectroscopic effective temperature (Teff) and surface
+gravity (log~g) based on the photometric methods and known distance provide
+consistent abundances of the Fe I and Fe II lines. This justifies the Fe I/Fe
+II ionisation equilibrium method to determine log g for the MW halo giants with
+unknown distance. The atmospheric parameters of the dSphs and MW stars were
+checked with independent methods. In the [Fe/H] > -3.5 regime, the Ti I/Ti II
+ionisation equilibrium is fulfilled in the NLTE calculations. In the log~g -
+Teff plane, all the stars sit on the giant branch of the evolutionary tracks
+corresponding to [Fe/H] = -2 to -4, in line with their metallicities. For some
+of the most metal-poor stars of our sample, we hardly achieve consistent NLTE
+abundances from the two ionisation stages for both iron and titanium. We
+suggest that this is a consequence of the uncertainty in the Teff-colour
+relation at those metallicities. The results of these work provide the base for
+a detailed abundance analysis presented in a companion paper.
+",0,1,0,0,0,0
+2947,Fixed-Gain Augmented-State Tracking-Filters,"  A procedure for the design of fixed-gain tracking filters, using an
+augmented-state observer with signal and interference subspaces, is proposed.
+The signal subspace incorporates an integrating Newtonian model and a
+second-order maneuver model that is matched to a sustained constant-g turn; the
+deterministic interference model creates a Nyquist null for smoother track
+estimates. The selected models provide a simple means of shaping and analyzing
+the (transient and steady-state) response of tracking-filters of elevated
+order.
+",1,0,0,0,0,0
+2948,The Detectability of Radio Auroral Emission from Proxima B,"  Magnetically active stars possess stellar winds whose interaction with
+planetary magnetic fields produces radio auroral emission. We examine the
+detectability of radio auroral emission from Proxima b, the closest known
+exosolar planet orbiting our nearest neighboring star, Proxima Centauri. Using
+the Radiometric Bode's Law, we estimate the radio flux produced by the
+interaction of Proxima Centauri's stellar wind and Proxima b's magnetosphere
+for different planetary magnetic field strengths. For plausible planetary
+masses, Proxima b produces 6-83 mJy of auroral radio flux at frequencies of
+0.3-0.8 MHz for planetary magnetic field strengths of 1-3 B$_{\oplus}$.
+According to recent MHD models that vary the orbital parameters of the system,
+this emission is expected to be highly variable. This variability is due to
+large fluctuations in the size of Proxima b's magnetosphere as it crosses the
+equatorial streamer regions of the dense stellar wind and high dynamic
+pressure. Using the MHD model of Garraffo et al. 2016 for the variation of the
+magnetosphere radius during the orbit, we estimate that the observed radio flux
+can vary nearly by an order of magnitude over the 11.2 day period of Proxima b.
+The detailed amplitude variation depends on the stellar wind, orbital, and
+planetary magnetic field parameters. We discuss observing strategies for
+proposed future space-based observatories to reach frequencies below the
+ionospheric cut off ($\sim 10$ MHz) as would be required to detect the signal
+we investigate.
+",0,1,0,0,0,0
+2949,Converting topological insulators into topological metals within the tetradymite family,"  We report the electronic band structures and concomitant Fermi surfaces for a
+family of exfoliable tetradymite compounds with the formula $T_2$$Ch_2$$Pn$,
+obtained as a modification to the well-known topological insulator binaries
+Bi$_2$(Se,Te)$_3$ by replacing one chalcogen ($Ch$) with a pnictogen ($Pn$) and
+Bi with the tetravalent transition metals $T$ $=$ Ti, Zr, or Hf. This
+imbalances the electron count and results in layered metals characterized by
+relatively high carrier mobilities and bulk two-dimensional Fermi surfaces
+whose topography is well-described by first principles calculations.
+Intriguingly, slab electronic structure calculations predict Dirac-like surface
+states. In contrast to Bi$_2$Se$_3$, where the surface Dirac bands are at the
+$\Gamma-$point, for (Zr,Hf)$_2$Te$_2$(P,As) there are Dirac cones of strong
+topological character around both the $\bar {\Gamma}$- and $\bar {M}$-points
+which are above and below the Fermi energy, respectively. For Ti$_2$Te$_2$P the
+surface state is predicted to exist only around the $\bar {M}$-point. In
+agreement with these predictions, the surface states that are located below the
+Fermi energy are observed by angle resolved photoemission spectroscopy
+measurements, revealing that they coexist with the bulk metallic state. Thus,
+this family of materials provides a foundation upon which to develop novel
+phenomena that exploit both the bulk and surface states (e.g., topological
+superconductivity).
+",0,1,0,0,0,0
+2950,"When Do Birds of a Feather Flock Together? k-Means, Proximity, and Conic Programming","  Given a set of data, one central goal is to group them into clusters based on
+some notion of similarity between the individual objects. One of the most
+popular and widely-used approaches is k-means despite the computational
+hardness to find its global minimum. We study and compare the properties of
+different convex relaxations by relating them to corresponding proximity
+conditions, an idea originally introduced by Kumar and Kannan. Using conic
+duality theory, we present an improved proximity condition under which the
+Peng-Wei relaxation of k-means recovers the underlying clusters exactly. Our
+proximity condition improves upon Kumar and Kannan, and is comparable to that
+of Awashti and Sheffet where proximity conditions are established for
+projective k-means. In addition, we provide a necessary proximity condition for
+the exactness of the Peng-Wei relaxation. For the special case of equal cluster
+sizes, we establish a different and completely localized proximity condition
+under which the Amini-Levina relaxation yields exact clustering, thereby having
+addressed an open problem by Awasthi and Sheffet in the balanced case. Our
+framework is not only deterministic and model-free but also comes with a clear
+geometric meaning which allows for further analysis and generalization.
+Moreover, it can be conveniently applied to analyzing various data generative
+models such as the stochastic ball models and Gaussian mixture models. With
+this method, we improve the current minimum separation bound for the stochastic
+ball models and achieve the state-of-the-art results of learning Gaussian
+mixture models.
+",0,0,1,0,0,0
+2951,Collective Sedimentation of Squirmers under Gravity,"  Active particles, which interact hydrodynamically, display a remarkable
+variety of emergent collective phenomena. We use squirmers to model spherical
+microswimmers and explore the collective behavior of thousands of them under
+the influence of strong gravity using the method of multi-particle collision
+dynamics for simulating fluid flow. The sedimentation profile depends on the
+ratio of swimming to sedimentation velocity as well as on the squirmer type. It
+shows close packed squirmer layers at the bottom and a highly dynamic region
+with exponential density dependence towards the top. The mean vertical
+orientation of the squirmers strongly depends on height. For swimming
+velocities larger than the sedimentation velocity, squirmers show strong
+convection in the exponential region. We quantify the strength of convection
+and the extent of convection cells by the vertical current density and its
+current dipole, which are large for neutral squirmers as well as for weak
+pushers and pullers.
+",0,1,0,0,0,0
+2952,Differentially Private Bayesian Learning on Distributed Data,"  Many applications of machine learning, for example in health care, would
+benefit from methods that can guarantee privacy of data subjects. Differential
+privacy (DP) has become established as a standard for protecting learning
+results. The standard DP algorithms require a single trusted party to have
+access to the entire data, which is a clear weakness. We consider DP Bayesian
+learning in a distributed setting, where each party only holds a single sample
+or a few samples of the data. We propose a learning strategy based on a secure
+multi-party sum function for aggregating summaries from data holders and the
+Gaussian mechanism for DP. Our method builds on an asymptotically optimal and
+practically efficient DP Bayesian inference with rapidly diminishing extra
+cost.
+",1,0,0,1,0,0
+2953,Metastability and avalanche dynamics in strongly-correlated gases with long-range interactions,"  We experimentally study the stability of a bosonic Mott-insulator against the
+formation of a density wave induced by long-range interactions, and
+characterize the intrinsic dynamics between these two states. The
+Mott-insulator is created in a quantum degenerate gas of 87-Rubidium atoms,
+trapped in a three-dimensional optical lattice. The gas is located inside and
+globally coupled to an optical cavity. This causes interactions of global
+range, mediated by photons dispersively scattered between a transverse lattice
+and the cavity. The scattering comes with an atomic density modulation, which
+is measured by the photon flux leaking from the cavity. We initialize the
+system in a Mott-insulating state and then rapidly increase the global coupling
+strength. We observe that the system falls into either of two distinct final
+states. One is characterized by a low photon flux, signaling a Mott insulator,
+and the other is characterized by a high photon flux, which we associate with a
+density wave. Ramping the global coupling slowly, we observe a hysteresis loop
+between the two states - a further signature of metastability. A comparison
+with a theoretical model confirms that the metastability originates in the
+competition between short- and global-range interactions. From the increasing
+photon flux monitored during the switching process, we find that several
+thousand atoms tunnel to a neighboring site on the time scale of the single
+particle dynamics. We argue that a density modulation, initially forming in the
+compressible surface of the trapped gas, triggers an avalanche tunneling
+process in the Mott-insulating region.
+",0,1,0,0,0,0
+2954,Optical nanoscopy via quantum control,"  We present a scheme for nanoscopic imaging of a quantum mechanical two-level
+system using an optical probe in the far-field. Existing super-resolution
+schemes require more than two-levels and depend on an incoherent response to
+the lasers. Here, quantum control of the two states proceeds via rapid
+adiabatic passage. We implement this scheme on an array of semiconductor
+self-assembled quantum dots. Each quantum dot results in a bright spot in the
+image with extents down to 30 nm ({\lambda}/31). Rapid adiabatic passage is
+established as a versatile tool in the super-resolution toolbox.
+",0,1,0,0,0,0
+2955,Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks,"  We present a deep learning approach to the ISIC 2017 Skin Lesion
+Classification Challenge using a multi-scale convolutional neural network. Our
+approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset,
+which is fine-tuned for skin lesion classification using two different scales
+of input images.
+",1,0,0,0,0,0
+2956,Confidence Intervals and Hypothesis Testing for the Permutation Entropy with an application to Epilepsy,"  In nonlinear dynamics, and to a lesser extent in other fields, a widely used
+measure of complexity is the Permutation Entropy. But there is still no known
+method to determine the accuracy of this measure. There has been little
+research on the statistical properties of this quantity that characterize time
+series. The literature describes some resampling methods of quantities used in
+nonlinear dynamics - as the largest Lyapunov exponent - but all of these seems
+to fail. In this contribution we propose a parametric bootstrap methodology
+using a symbolic representation of the time series in order to obtain the
+distribution of the Permutation Entropy estimator. We perform several time
+series simulations given by well known stochastic processes: the 1=f? noise
+family, and show in each case that the proposed accuracy measure is as
+efficient as the one obtained by the frequentist approach of repeating the
+experiment. The complexity of brain electrical activity, measured by the
+Permutation Entropy, has been extensively used in epilepsy research for
+detection in dynamical changes in electroencephalogram (EEG) signal with no
+consideration of the variability of this complexity measure. An application of
+the parametric bootstrap methodology is used to compare normal and pre-ictal
+EEG signals.
+",0,1,0,1,0,0
+2957,Feature overwriting as a finite mixture process: Evidence from comprehension data,"  The ungrammatical sentence ""The key to the cabinets are on the table"" is
+known to lead to an illusion of grammaticality. As discussed in the
+meta-analysis by Jaeger et al., 2017, faster reading times are observed at the
+verb are in the agreement-attraction sentence above compared to the equally
+ungrammatical sentence ""The key to the cabinet are on the table"". One
+explanation for this facilitation effect is the feature percolation account:
+the plural feature on cabinets percolates up to the head noun key, leading to
+the illusion. An alternative account is in terms of cue-based retrieval (Lewis
+& Vasishth, 2005), which assumes that the non-subject noun cabinets is
+misretrieved due to a partial feature-match when a dependency completion
+process at the auxiliary initiates a memory access for a subject with plural
+marking. We present evidence for yet another explanation for the observed
+facilitation. Because the second sentence has two nouns with identical number,
+it is possible that these are, in some proportion of trials, more difficult to
+keep distinct, leading to slower reading times at the verb in the first
+sentence above; this is the feature overwriting account of Nairne, 1990. We
+show that the feature overwriting proposal can be implemented as a finite
+mixture process. We reanalysed ten published data-sets, fitting hierarchical
+Bayesian mixture models to these data assuming a two-mixture distribution. We
+show that in nine out of the ten studies, a mixture distribution corresponding
+to feature overwriting furnishes a superior fit over both the feature
+percolation and the cue-based retrieval accounts.
+",1,0,0,1,0,0
+2958,Photographic dataset: playing cards,"  This is a photographic dataset collected for testing image processing
+algorithms. The idea is to have images that can exploit the properties of total
+variation, therefore a set of playing cards was distributed on the scene. The
+dataset is made available at www.fips.fi/photographic_dataset2.php
+",1,1,0,0,0,0
+2959,Dynamic constraints on activity and connectivity during the learning of value,"  Human learning is a complex process in which future behavior is altered via
+the modulation of neural activity. Yet, the degree to which brain activity and
+functional connectivity during learning is constrained across subjects, for
+example by conserved anatomy and physiology or by the nature of the task,
+remains unknown. Here, we measured brain activity and functional connectivity
+in a longitudinal experiment in which healthy adult human participants learned
+the values of novel objects over the course of four days. We assessed the
+presence of constraints on activity and functional connectivity using an
+inter-subject correlation approach. Constraints on activity and connectivity
+were greater in magnitude than expected in a non-parametric permutation-based
+null model, particularly in primary sensory and motor systems, as well as in
+regions associated with the learning of value. Notably, inter-subject
+connectivity in activity and connectivity displayed marked temporal variations,
+with inter-subject correlations in activity exceeding those in connectivity
+during early learning and \emph{visa versa} in later learning. Finally,
+individual differences in performance accuracy tracked the degree to which a
+subject's connectivity, but not activity, tracked subject-general patterns.
+Taken together, our results support the notion that brain activity and
+connectivity are constrained across subjects in early learning, with
+constraints on activity, but not connectivity, decreasing in later learning.
+",0,0,0,0,1,0
+2960,A note on Weyl groups and crystallographic root lattices,"  We follow the dual approach to Coxeter systems and show for Weyl groups a
+criterium which decides whether a set of reflections is generating the group
+depending on the root and the coroot lattice. Further we study special
+generating sets involving a parabolic subgroup and show that they are very
+tame.
+",0,0,1,0,0,0
+2961,Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation,"  In the emerging advancement in the branch of autonomous robotics, the ability
+of a robot to efficiently localize and construct maps of its surrounding is
+crucial. This paper deals with utilizing thermal-infrared cameras, as opposed
+to conventional cameras as the primary sensor to capture images of the robot's
+surroundings. For localization, the images need to be further processed before
+feeding them to a navigational system. The main motivation of this paper was to
+develop an edge detection methodology capable of utilizing the low-SNR poor
+output from such a thermal camera and effectively detect smooth edges of the
+surrounding environment. The enhanced edge detector proposed in this paper
+takes the raw image from the thermal sensor, denoises the images, applies Canny
+edge detection followed by CSS method. The edges are ranked to remove any noise
+and only edges of the highest rank are kept. Then, the broken edges are linked
+by computing edge metrics and a smooth edge of the surrounding is displayed in
+a binary image. Several comparisons are also made in the paper between the
+proposed technique and the existing techniques.
+",1,0,0,1,0,0
+2962,Distributed resource allocation through utility design - Part I: optimizing the performance certificates via the price of anarchy,"  Game theory has emerged as a novel approach for the coordination of
+multiagent systems. A fundamental component of this approach is the design of a
+local utility function for each agent so that their selfish maximization
+achieves the global objective. In this paper we propose a novel framework to
+characterize and optimize the worst case performance (price of anarchy) of any
+resulting equilibrium as a function of the chosen utilities, thus providing a
+performance certificate for a large class of algorithms. More specifically, we
+consider a class of resource allocation problems, where each agent selects a
+subset of the resources with the goal of maximizing a welfare function. First,
+we show that any smoothness argument is inconclusive for the design problems
+considered. Motivated by this, we introduce a new approach providing a tight
+expression for the price of anarchy (PoA) as a function of the chosen utility
+functions. Leveraging this result, we show how to design the utilities so as to
+maximize the PoA through a tractable linear program. In Part II we specialize
+the results to submodular and supermodular welfare functions, discuss
+complexity issues and provide two applications.
+",1,0,0,0,0,0
+2963,IDK Cascades: Fast Deep Learning by Learning not to Overthink,"  Advances in deep learning have led to substantial increases in prediction
+accuracy but have been accompanied by increases in the cost of rendering
+predictions. We conjecture that fora majority of real-world inputs, the recent
+advances in deep learning have created models that effectively ""overthink"" on
+simple inputs. In this paper, we revisit the classic question of building model
+cascades that primarily leverage class asymmetry to reduce cost. We introduce
+the ""I Don't Know""(IDK) prediction cascades framework, a general framework to
+systematically compose a set of pre-trained models to accelerate inference
+without a loss in prediction accuracy. We propose two search based methods for
+constructing cascades as well as a new cost-aware objective within this
+framework. The proposed IDK cascade framework can be easily adopted in the
+existing model serving systems without additional model re-training. We
+evaluate the proposed techniques on a range of benchmarks to demonstrate the
+effectiveness of the proposed framework.
+",1,0,0,0,0,0
+2964,Learning a Unified Control Policy for Safe Falling,"  Being able to fall safely is a necessary motor skill for humanoids performing
+highly dynamic tasks, such as running and jumping. We propose a new method to
+learn a policy that minimizes the maximal impulse during the fall. The
+optimization solves for both a discrete contact planning problem and a
+continuous optimal control problem. Once trained, the policy can compute the
+optimal next contacting body part (e.g. left foot, right foot, or hands),
+contact location and timing, and the required joint actuation. We represent the
+policy as a mixture of actor-critic neural network, which consists of n control
+policies and the corresponding value functions. Each pair of actor-critic is
+associated with one of the n possible contacting body parts. During execution,
+the policy corresponding to the highest value function will be executed while
+the associated body part will be the next contact with the ground. With this
+mixture of actor-critic architecture, the discrete contact sequence planning is
+solved through the selection of the best critics while the continuous control
+problem is solved by the optimization of actors. We show that our policy can
+achieve comparable, sometimes even higher, rewards than a recursive search of
+the action space using dynamic programming, while enjoying 50 to 400 times of
+speed gain during online execution.
+",1,0,0,0,0,0
+2965,Long-Range Interactions for Hydrogen: 6P-1S and 6P-2S,"  The collisional shift of a transition constitutes an important systematic
+effect in high-precision spectroscopy. Accurate values for van der
+Waalsinteraction coefficients are required in order to evaluate the
+distance-dependent frequency shift. We here consider the interaction of excited
+hydrogen 6P atoms with metastable atoms (in the 2S state), in order to explore
+the influence of quasi-degenerate 2P, and 6S states on the dipole-dipole
+interaction. The motivation for the calculation is given by planned
+high-precision measurements of the transition. Due to the presence of
+quasi-degenerate levels, one can use the non-retarded approximation for the
+interaction terms over wide distance ranges.
+",0,1,0,0,0,0
+2966,Generating Music Medleys via Playing Music Puzzle Games,"  Generating music medleys is about finding an optimal permutation of a given
+set of music clips. Toward this goal, we propose a self-supervised learning
+task, called the music puzzle game, to train neural network models to learn the
+sequential patterns in music. In essence, such a game requires machines to
+correctly sort a few multisecond music fragments. In the training stage, we
+learn the model by sampling multiple non-overlapping fragment pairs from the
+same songs and seeking to predict whether a given pair is consecutive and is in
+the correct chronological order. For testing, we design a number of puzzle
+games with different difficulty levels, the most difficult one being music
+medley, which requiring sorting fragments from different songs. On the basis of
+state-of-the-art Siamese convolutional network, we propose an improved
+architecture that learns to embed frame-level similarity scores computed from
+the input fragment pairs to a common space, where fragment pairs in the correct
+order can be more easily identified. Our result shows that the resulting model,
+dubbed as the similarity embedding network (SEN), performs better than
+competing models across different games, including music jigsaw puzzle, music
+sequencing, and music medley. Example results can be found at our project
+website, this https URL.
+",1,0,0,1,0,0
+2967,Collisional Dynamics of Solitons in the Coupled PT symmetric Nonlocal nonlinear Schrodinger equations,"  We investigate the focusing coupled PT-symmetric nonlocal nonlinear
+Schrodinger equation employing Darboux transformation approach. We find a
+family of exact solutions including pairs of Bright-Bright, Dark-Dark and
+Bright-Dark solitons in addition to solitary waves. We show that one can
+convert bright bound state onto a dark bound state in a two-soliton solution by
+selectively fine tuning the amplitude dependent parameter. We also show that
+the energy in each mode remains conserved unlike the celebrated Manakov model.
+We also characterize the behaviour of the soliton solutions in detail. We
+emphasize that the above phenomenon occurs due to the nonlocality of the model.
+",0,1,0,0,0,0
+2968,"Frustrated spin-1/2 molecular magnetism in the mixed-valence antiferromagnets Ba3MRu2O9 (M = In, Y, Lu)","  We have performed magnetic susceptibility, heat capacity, muon spin
+relaxation, and neutron scattering measurements on three members of the family
+Ba3MRu2O9, where M = In, Y and Lu. These systems consist of mixed-valence Ru
+dimers on a triangular lattice with antiferromagnetic interdimer exchange.
+Although previous work has argued that charge order within the dimers or
+intradimer double exchange plays an important role in determining the magnetic
+properties, our results suggest that the dimers are better described as
+molecular units due to significant orbital hybridization, resulting in one
+spin-1/2 moment distributed equally over the two Ru sites. These molecular
+building blocks form a frustrated, quasi-two-dimensional triangular lattice.
+Our zero and longitudinal field muSR results indicate that the molecular
+moments develop a collective, static magnetic ground state, with oscillations
+of the zero field muon spin polarization indicative of long-range magnetic
+order in the Lu sample. The static magnetism is much more disordered in the Y
+and In samples, but they do not appear to be conventional spin glasses.
+",0,1,0,0,0,0
+2969,Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems,"  The goal of this paper is to present an end-to-end, data-driven framework to
+control Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of
+self-driving vehicles). We first model the AMoD system using a time-expanded
+network, and present a formulation that computes the optimal rebalancing
+strategy (i.e., preemptive repositioning) and the minimum feasible fleet size
+for a given travel demand. Then, we adapt this formulation to devise a Model
+Predictive Control (MPC) algorithm that leverages short-term demand forecasts
+based on historical data to compute rebalancing strategies. We test the
+end-to-end performance of this controller with a state-of-the-art LSTM neural
+network to predict customer demand and real customer data from DiDi Chuxing: we
+show that this approach scales very well for large systems (indeed, the
+computational complexity of the MPC algorithm does not depend on the number of
+customers and of vehicles in the system) and outperforms state-of-the-art
+rebalancing strategies by reducing the mean customer wait time by up to to
+89.6%.
+",1,0,0,1,0,0
+2970,Predictions of planet detections with near infrared radial velocities in the up-coming SPIRou Legacy Survey-Planet Search,"  The SPIRou near infrared spectro-polarimeter is destined to begin science
+operations at the Canada-France-Hawaii Telescope in mid-2018. One of the
+instrument's primary science goals is to discover the closest exoplanets to the
+Solar System by conducting a 3-5 year long radial velocity survey of nearby M
+dwarfs at an expected precision of $\sim 1$ m s$^{-1}$; the SPIRou Legacy
+Survey-Planet Search (SLS-PS). In this study we conduct a detailed Monte-Carlo
+simulation of the SLS-PS using our current understanding of the occurrence rate
+of M dwarf planetary systems and physical models of stellar activity. From
+simultaneous modelling of planetary signals and activity, we predict the
+population of planets detected in the SLS-PS. With our fiducial survey strategy
+and expected instrument performance over a nominal survey length of $\sim 3$
+years, we expect SPIRou to detect $85.3^{+29.3}_{-12.4}$ planets including
+$20.0^{+16.8}_{-7.2}$ habitable zone planets and $8.1^{+7.6}_{-3.2}$ Earth-like
+planets from a sample of 100 M1-M8.5 dwarfs out to 11 pc. By studying
+mid-to-late M dwarfs previously inaccessible to existing optical velocimeters,
+SPIRou will put meaningful constraints on the occurrence rate of planets around
+those stars including the value of $\eta_{\oplus}$ at an expected level of
+precision of $\lesssim 45$%. We also predict a subset of $46.7^{+16.0}_{-6.0}$
+planets may be accessible with dedicated high-contrast imagers on the next
+generation of ELTs including $4.9^{+4.7}_{-2.0}$ potentially imagable
+Earth-like planets. Lastly, we compare the results of our fiducial survey
+strategy to other foreseeable survey versions to quantify which strategy is
+optimized to reach the SLS-PS science goals. The results of our simulations are
+made available to the community on github.
+",0,1,0,0,0,0
+2971,Quantum models with energy-dependent potentials solvable in terms of exceptional orthogonal polynomials,"  We construct energy-dependent potentials for which the Schroedinger equations
+admit solu- tions in terms of exceptional orthogonal polynomials. Our method of
+construction is based on certain point transformations, applied to the
+equations of exceptional Hermite, Jacobi and Laguerre polynomials. We present
+several examples of boundary-value problems with energy-dependent potentials
+that admit a discrete spectrum and the corresponding normalizable solutions in
+closed form.
+",0,0,1,0,0,0
+2972,Constructions and classifications of projective Poisson varieties,"  This paper is intended both an introduction to the algebraic geometry of
+holomorphic Poisson brackets, and as a survey of results on the classification
+of projective Poisson manifolds that have been obtained in the past twenty
+years. It is based on the lecture series delivered by the author at the Poisson
+2016 Summer School in Geneva. The paper begins with a detailed treatment of
+Poisson surfaces, including adjunction, ruled surfaces and blowups, and leading
+to a statement of the full birational classification. We then describe several
+constructions of Poisson threefolds, outlining the classification in the
+regular case, and the case of rank-one Fano threefolds (such as projective
+space). Following a brief introduction to the notion of Poisson subspaces, we
+discuss Bondal's conjecture on the dimensions of degeneracy loci on Poisson
+Fano manifolds. We close with a discussion of log symplectic manifolds with
+simple normal crossings degeneracy divisor, including a new proof of the
+classification in the case of rank-one Fano manifolds.
+",0,0,1,0,0,0
+2973,Generalised Reichenbachian Common Cause Systems,"  The principle of the common cause claims that if an improbable coincidence
+has occurred, there must exist a common cause. This is generally taken to mean
+that positive correlations between non-causally related events should disappear
+when conditioning on the action of some underlying common cause. The extended
+interpretation of the principle, by contrast, urges that common causes should
+be called for in order to explain positive deviations between the estimated
+correlation of two events and the expected value of their correlation. The aim
+of this paper is to provide the extended reading of the principle with a
+general probabilistic model, capturing the simultaneous action of a system of
+multiple common causes. To this end, two distinct models are elaborated, and
+the necessary and sufficient conditions for their existence are determined.
+",1,0,0,1,0,0
+2974,Discovery of the most metal-poor damped Lyman-alpha system,"  We report the discovery and analysis of the most metal-poor damped
+Lyman-alpha (DLA) system currently known, based on observations made with the
+Keck HIRES spectrograph. The metal paucity of this system has only permitted
+the determination of three element abundances: [C/H] = -3.43 +/- 0.06, [O/H] =
+-3.05 +/- 0.05, and [Si/H] = -3.21 +/- 0.05, as well as an upper limit on the
+abundance of iron: [Fe/H] < -2.81. This DLA is among the most carbon-poor
+environment currently known with detectable metals. By comparing the abundance
+pattern of this DLA to detailed models of metal-free nucleosynthesis, we find
+that the chemistry of the gas is consistent with the yields of a 20.5 M_sun
+metal-free star that ended its life as a core-collapse supernova; the
+abundances we measure are inconsistent with the yields of pair-instability
+supernovae. Such a tight constraint on the mass of the progenitor Population
+III star is afforded by the well-determined C/O ratio, which we show depends
+almost monotonically on the progenitor mass when the kinetic energy of the
+supernova explosion is E_exp > 1.5x10^51 erg. We find that the DLA presented
+here has just crossed the critical 'transition discriminant' threshold,
+rendering the DLA gas now suitable for low mass star formation. We also discuss
+the chemistry of this system in the context of recent models that suggest some
+of the most metal-poor DLAs are the precursors of the 'first galaxies', and are
+the antecedents of the ultra-faint dwarf galaxies.
+",0,1,0,0,0,0
+2975,A Concurrent Perspective on Smart Contracts,"  In this paper, we explore remarkable similarities between multi-transactional
+behaviors of smart contracts in cryptocurrencies such as Ethereum and classical
+problems of shared-memory concurrency. We examine two real-world examples from
+the Ethereum blockchain and analyzing how they are vulnerable to bugs that are
+closely reminiscent to those that often occur in traditional concurrent
+programs. We then elaborate on the relation between observable contract
+behaviors and well-studied concurrency topics, such as atomicity, interference,
+synchronization, and resource ownership. The described
+contracts-as-concurrent-objects analogy provides deeper understanding of
+potential threats for smart contracts, indicate better engineering practices,
+and enable applications of existing state-of-the-art formal verification
+techniques.
+",1,0,0,0,0,0
+2976,Noise Models in the Nonlinear Spectral Domain for Optical Fibre Communications,"  Existing works on building a soliton transmission system only encode
+information using the imaginary part of the eigenvalue, which fails to make
+full use of the signal degree-of-freedoms. Motivated by this observation, we
+make the first step of encoding information using (discrete) spectral
+amplitudes by proposing analytical noise models for the spectral amplitudes of
+$N$-solitons ($N\geq 1$). To our best knowledge, this is the first work in
+building an analytical noise model for spectral amplitudes, which leads to many
+interesting information theoretic questions, such as channel capacity analysis,
+and has a potential of increasing the transmission rate. The noise statistics
+of the spectral amplitude of a soliton are also obtained without the Gaussian
+approximation.
+",1,0,1,0,0,0
+2977,Shape analysis on Lie groups and homogeneous spaces,"  In this paper we are concerned with the approach to shape analysis based on
+the so called Square Root Velocity Transform (SRVT). We propose a
+generalisation of the SRVT from Euclidean spaces to shape spaces of curves on
+Lie groups and on homogeneous manifolds. The main idea behind our approach is
+to exploit the geometry of the natural Lie group actions on these spaces.
+",0,0,1,0,0,0
+2978,Markov Models for Health Economic Evaluations: The R Package heemod,"  Health economic evaluation studies are widely used in public health to assess
+health strategies in terms of their cost-effectiveness and inform public
+policies. We developed an R package for Markov models implementing most of the
+modelling and reporting features described in reference textbooks and
+guidelines: deterministic and probabilistic sensitivity analysis, heterogeneity
+analysis, time dependency on state-time and model-time (semi-Markov and
+non-homogeneous Markov models), etc. In this paper we illustrate the features
+of heemod by building and analysing an example Markov model. We then explain
+the design and the underlying implementation of the package.
+",0,0,0,1,0,0
+2979,"Amorphous Alloys, Degradation Performance of Azo Dyes: Review","  Today freshwater is more important than ever before and it is contaminated
+from textile industry. Removal of dyes from effluent of textile using amorphous
+alloys has been studied extensively by many researchers. In this review article
+it is presented up to date development on the azo dye degradation performance
+of amorphous alloys, a new class of catalytic materials. Numerous amorphous
+alloys have been developed for increasing higher degradation efficiency in
+comparison to conventional ones for the removal of azo dyes in wastewater. One
+of the objectives of this review article is to organize the scattered available
+information on various aspects on a wide range of potentially effective in the
+removal of dyes by using amorphous alloys. This study comprises the affective
+removal factors of azo dye such as solution pH, initial dye concentration, and
+adsorbent dosage. It was concluded that Fe, Mg, Co, Al and Mn-based amorphous
+alloys with wide availability have appreciable for removing several types of
+azo dyes from wastewater. Concerning amorphous alloys for future research, some
+suggestions are proposed and conclusions have been drawn.
+",0,1,0,0,0,0
+2980,Towards the ab initio based theory of the phase transformations in iron and steel,"  Despite of the appearance of numerous new materials, the iron based alloys
+and steels continue to play an essential role in modern technology. The
+properties of a steel are determined by its structural state (ferrite,
+cementite, pearlite, bainite, martensite, and their combination) that is formed
+under thermal treatment as a result of the shear lattice reconstruction ""gamma""
+(fcc) -> ""alpha"" (bcc) and carbon diffusion redistribution. We present a review
+on a recent progress in the development of a quantitative theory of the phase
+transformations and microstructure formation in steel that is based on an ab
+initio parameterization of the Ginzburg-Landau free energy functional. The
+results of computer modeling describe the regular change of transformation
+scenario under cooling from ferritic (nucleation and diffusion-controlled
+growth of the ""alpha"" phase to martensitic (the shear lattice instability
+""gamma"" -> ""alpha""). It has been shown that the increase in short-range
+magnetic order with decreasing the temperature plays a key role in the change
+of transformation scenarios. Phase-field modeling in the framework of a
+discussed approach demonstrates the typical transformation patterns.
+",0,1,0,0,0,0
+2981,Solitons in a modified discrete nonlinear Schroedinger equation,"  We study the bulk and surface nonlinear modes of the modified one-dimensional
+discrete nonlinear Schroedinger (mDNLS) equation. A linear and a modulational
+stability analysis of the lowest-order modes is carried out. While for the
+fundamental bulk mode there is no power threshold, the fundamental surface mode
+needs a minimum power level to exist. Examination of the time evolution of
+discrete solitons in the limit of strongly localized modes, suggests ways to
+manage the Peierls- Nabarro barrier, facilitating in this way a degree of
+steering. The long-time propagation of an initially localized excitation shows
+that, at long evolution times, nonlinear effects become negligible and as a
+result, the propagation becomes ballistic. The similarity of all these results
+to the ones obtained for the DNLS equation, points out to the robustness of the
+discrete soliton phenomenology.
+",0,1,0,0,0,0
+2982,Functional renormalization-group approach to the Pokrovsky-Talapov model via modified massive Thirring fermion model,"  A possibility of the topological Kosterlitz-Thouless~(KT) transition in the
+Pokrovsky-Talapov~(PT) model is investigated by using the functional
+renormalization-group (RG) approach by Wetterich. Our main finding is that the
+nonzero misfit parameter of the model, which can be related with the linear
+gradient term (Dzyaloshinsky-Moriya interaction), makes such a transition
+impossible, what contradicts the previous consideration of this problem by
+non-perturbative RG methods. To support the conclusion the initial PT model is
+reformulated in terms of the 2D theory of relativistic fermions using an
+analogy between the 2D sine-Gordon and the massive Thirring models. In the new
+formalism the misfit parameter corresponds to an effective gauge field that
+enables to include it in the RG procedure on an equal footing with the other
+parameters of the theory. The Wetterich equation is applied to obtain flow
+equations for the parameters of the new fermionic action. We demonstrate that
+these equations reproduce the KT type of behavior if the misfit parameter is
+zero. However, any small nonzero value of the quantity rules out a possibility
+of the KT transition. To confirm the finding we develop a description of the
+problem in terms of the 2D Coulomb gas model. Within the approach the breakdown
+of the KT scenario gains a transparent meaning, the misfit gives rise to an
+effective in-plane electric field that prevents a formation of bound
+vortex-antivortex pairs.
+",0,1,0,0,0,0
+2983,Reentrant Phase Coherence in Superconducting Nanowire Composites,"  The short coherence lengths characteristic of low-dimensional superconductors
+are associated with usefully high critical fields or temperatures.
+Unfortunately, such materials are often sensitive to disorder and suffer from
+phase fluctuations in the superconducting order parameter which diverge with
+temperature $T$, magnetic field $H$ or current $I$. We propose an approach to
+overcome synthesis and fluctuation problems: building superconductors from
+inhomogeneous composites of nanofilaments. Macroscopic crystals of
+quasi-one-dimensional Na$_{2-\delta}$Mo$_6$Se$_6$ featuring Na vacancy disorder
+($\delta\approx$~0.2) are shown to behave as percolative networks of
+superconducting nanowires. Long range order is established via transverse
+coupling between individual one-dimensional filaments, yet phase coherence
+remains unstable to fluctuations and localization in the zero-($T$,$H$,$I$)
+limit. However, a region of reentrant phase coherence develops upon raising
+($T$,$H$,$I$). We attribute this phenomenon to an enhancement of the transverse
+coupling due to electron delocalization. Our observations of reentrant phase
+coherence coincide with a peak in the Josephson energy $E_J$ at non-zero
+($T$,$H$,$I$), which we estimate using a simple analytical model for a
+disordered anisotropic superconductor. Na$_{2-\delta}$Mo$_6$Se$_6$ is therefore
+a blueprint for a future generation of nanofilamentary superconductors with
+inbuilt resilience to phase fluctuations at elevated ($T$,$H$,$I$).
+",0,1,0,0,0,0
+2984,Micromagnetic Simulations for Coercivity Improvement through Nano-Structuring of Rare-Earth Free L1$_0$-FeNi Magnets,"  In this work we investigate the potential of tetragonal L1$_0$ ordered FeNi
+as candidate phase for rare earth free permanent magnets taking into account
+anisotropy values from recently synthesized, partially ordered FeNi thin films.
+In particular, we estimate the maximum energy product ($BH$)$_\mathrm{max}$ of
+L1$_0$-FeNi nanostructures using micromagnetic simulations. The maximum energy
+product is limited due to the small coercive field of partially ordered
+L1$_0$-FeNi. Nano-structured magnets consisting of 128 equi-axed, platelet-like
+and columnar-shaped grains show a theoretical maximum energy product of 228
+kJ/m$^3$, 208 kJ/m$^3$, 252 kJ/m$^3$, respectively.
+",0,1,0,0,0,0
+2985,"Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures","  The joint Value at Risk (VaR) and expected shortfall (ES) quantile regression
+model of Taylor (2017) is extended via incorporating a realized measure, to
+drive the tail risk dynamics, as a potentially more efficient driver than daily
+returns. Both a maximum likelihood and an adaptive Bayesian Markov Chain Monte
+Carlo method are employed for estimation, whose properties are assessed and
+compared via a simulation study; results favour the Bayesian approach, which is
+subsequently employed in a forecasting study of seven market indices and two
+individual assets. The proposed models are compared to a range of parametric,
+non-parametric and semi-parametric models, including GARCH, Realized-GARCH and
+the joint VaR and ES quantile regression models in Taylor (2017). The
+comparison is in terms of accuracy of one-day-ahead Value-at-Risk and Expected
+Shortfall forecasts, over a long forecast sample period that includes the
+global financial crisis in 2007-2008. The results favor the proposed models
+incorporating a realized measure, especially when employing the sub-sampled
+Realized Variance and the sub-sampled Realized Range.
+",0,0,0,0,0,1
+2986,Mobility tensor of a sphere moving on a super-hydrophobic wall: application to particle separation,"  The paper addresses the hydrodynamic behavior of a sphere close to a
+micro-patterned superhydrophobic surface described in terms of alternated
+no-slip and perfect-slip stripes. Physically, the perfect-slip stripes model
+the parallel grooves where a large gas cushion forms between fluid and solid
+wall, giving rise to slippage at the gas-liquid interface. The potential of the
+boundary element method (BEM) in dealing with mixed no-slip/perfect-slip
+boundary conditions is exploited to systematically calculate the mobility
+tensor for different particle-to-wall relative positions and for different
+particle radii. The particle hydrodynamics is characterized by a non trivial
+mobility field which presents a distinct near wall behavior where the wall
+patterning directly affects the particle motion. In the far field, the effects
+of the wall pattern can be accurately represented via an effective description
+in terms of a homogeneous wall with a suitably defined apparent slippage. The
+trajectory of the sphere under the action of an external force is also
+described in some detail. A resonant regime is found when the frequency of the
+transversal component of the force matches a characteristic crossing frequency
+imposed by the wall pattern. It is found that, under resonance, the particle
+undergoes a mean transversal drift. Since the resonance condition depends on
+the particle radius the effect can in principle be used to conceive devices for
+particle sorting based on superhydrophobic surfaces.
+",0,1,0,0,0,0
+2987,Influence of Resampling on Accuracy of Imbalanced Classification,"  In many real-world binary classification tasks (e.g. detection of certain
+objects from images), an available dataset is imbalanced, i.e., it has much
+less representatives of a one class (a minor class), than of another.
+Generally, accurate prediction of the minor class is crucial but it's hard to
+achieve since there is not much information about the minor class. One approach
+to deal with this problem is to preliminarily resample the dataset, i.e., add
+new elements to the dataset or remove existing ones. Resampling can be done in
+various ways which raises the problem of choosing the most appropriate one. In
+this paper we experimentally investigate impact of resampling on classification
+accuracy, compare resampling methods and highlight key points and difficulties
+of resampling.
+",1,0,0,1,0,0
+2988,"Literature Survey on Interplay of Topics, Information Diffusion and Connections on Social Networks","  Researchers have attempted to model information diffusion and topic trends
+and lifecycle on online social networks. They have investigated the role of
+content, social connections and communities, familiarity and behavioral
+similarity in this context. The current article presents a survey of
+representative models that perform topic analysis, capture information
+diffusion, and explore the properties of social connections in the context of
+online social networks. The article concludes with a set of outlines of open
+problems and possible directions of future research interest. This article is
+intended for researchers to identify the current literature, and explore
+possibilities to improve the art.
+",1,0,0,0,0,0
+2989,Extended B-Spline Collocation Method For KdV-Burgers Equation,"  The extended form of the classical polynomial cubic B-spline function is used
+to set up a collocation method for some initial boundary value problems derived
+for the Korteweg-de Vries-Burgers equation. Having nonexistence of third order
+derivatives of the cubic B-splines forces us to reduce the order of the term
+uxxx to give a coupled system of equations. The space discretization of this
+system is accomplished by the collocation method following the time
+discretization with Crank-Nicolson method. Two initial boundary value problems,
+one having analytical solution and the other is set up with a non analytical
+initial condition, have been simulated by the proposed method.
+",0,0,1,0,0,0
+2990,On smile properties of volatility derivatives and exotic products: understanding the VIX skew,"  We develop a method to study the implied volatility for exotic options and
+volatility derivatives with European payoffs such as VIX options. Our approach,
+based on Malliavin calculus techniques, allows us to describe the properties of
+the at-the-money implied volatility (ATMI) in terms of the Malliavin
+derivatives of the underlying process. More precisely, we study the short-time
+behaviour of the ATMI level and skew. As an application, we describe the
+short-term behavior of the ATMI of VIX and realized variance options in terms
+of the Hurst parameter of the model, and most importantly we describe the class
+of volatility processes that generate a positive skew for the VIX implied
+volatility. In addition, we find that our ATMI asymptotic formulae perform very
+well even for large maturities. Several numerical examples are provided to
+support our theoretical results.
+",0,0,0,0,0,1
+2991,Risk-Sensitive Optimal Control of Queues,"  We consider the problem of designing risk-sensitive optimal control policies
+for scheduling packet transmissions in a stochastic wireless network. A single
+client is connected to an access point (AP) through a wireless channel. Packet
+transmission incurs a cost $C$, while packet delivery yields a reward of $R$
+units. The client maintains a finite buffer of size $B$, and a penalty of $L$
+units is imposed upon packet loss which occurs due to finite queueing buffer.
+We show that the risk-sensitive optimal control policy for such a simple
+set-up is of threshold type, i.e., it is optimal to carry out packet
+transmissions only when $Q(t)$, i.e., the queue length at time $t$ exceeds a
+certain threshold $\tau$. It is also shown that the value of threshold $\tau$
+increases upon increasing the cost per unit packet transmission $C$.
+Furthermore, it is also shown that a threshold policy with threshold equal to
+$\tau$ is optimal for a set of problems in which cost $C$ lies within an
+interval $[C_l,C_u]$. Equations that need to be solved in order to obtain
+$C_l,C_u$ are also provided.
+",1,0,0,0,0,0
+2992,SPECULOOS exoplanet search and its prototype on TRAPPIST,"  One of the most significant goals of modern science is establishing whether
+life exists around other suns. The most direct path towards its achievement is
+the detection and atmospheric characterization of terrestrial exoplanets with
+potentially habitable surface conditions. The nearest ultracool dwarfs (UCDs),
+i.e. very-low-mass stars and brown dwarfs with effective temperatures lower
+than 2700 K, represent a unique opportunity to reach this goal within the next
+decade. The potential of the transit method for detecting potentially habitable
+Earth-sized planets around these objects is drastically increased compared to
+Earth-Sun analogs. Furthermore, only a terrestrial planet transiting a nearby
+UCD would be amenable for a thorough atmospheric characterization, including
+the search for possible biosignatures, with near-future facilities such as the
+James Webb Space Telescope. In this chapter, we first describe the physical
+properties of UCDs as well as the unique potential they offer for the detection
+of potentially habitable Earth-sized planets suitable for atmospheric
+characterization. Then, we present the SPECULOOS ground-based transit survey,
+that will search for Earth-sized planets transiting the nearest UCDs, as well
+as its prototype survey on the TRAPPIST telescopes. We conclude by discussing
+the prospects offered by the recent detection by this prototype survey of a
+system of seven temperate Earth-sized planets transiting a nearby UCD,
+TRAPPIST-1.
+",0,1,0,0,0,0
+2993,Small-dimensional representations of algebraic groups of type $A_l$,"  For $G$ an algebraic group of type $A_l$ over an algebraically closed field
+of characteristic $p$, we determine all irreducible rational representations of
+$G$ in defining characteristic with dimensions $\le (l+1)^s$ for $s = 3, 4$,
+provided that $l > 18$, $l > 35$ respectively. We also give explicit
+descriptions of the corresponding modules for $s = 3$.
+",0,0,1,0,0,0
+2994,Detection of methylisocyanate (CH3NCO) in a solar-type protostar,"  We report the detection of the prebiotic molecule CH3NCO in a solar-type
+protostar, IRAS16293-2422 B. A significant abundance of this species on the
+surface of the comet 67P/Churyumov-Gerasimenko has been proposed, and it has
+recently been detected in hot cores around high-mass protostars. We observed
+IRAS16293-2422 B with ALMA in the 90 GHz to 265 GHz range, and detected 8
+unblended transitions of CH3NCO. From our Local Thermodynamic Equilibrium
+analysis we derived an excitation temperature of 110+-19 K and a column density
+of (4.0+-0.3)x10^15 cm^-2 , which results in an abundance of
+<=(1.4+-0.1)x10^-10 with respect to molecular hydrogen. This implies a
+CH3NCO/HNCO and CH3NCO/NH2CHO column density ratios of ~0.08. Our modelling of
+the chemistry of CH3NCO suggests that both ice surface and gas phase formation
+reactions of this molecule are needed to explain the observations.
+",0,1,0,0,0,0
+2995,A prismatic classifying space,"  A qualgebra $G$ is a set having two binary operations that satisfy
+compatibility conditions which are modeled upon a group under conjugation and
+multiplication. We develop a homology theory for qualgebras and describe a
+classifying space for it. This space is constructed from $G$-colored prisms
+(products of simplices) and simultaneously generalizes (and includes)
+simplicial classifying spaces for groups and cubical classifying spaces for
+quandles. Degenerate cells of several types are added to the regular prismatic
+cells; by duality, these correspond to ""non-rigid"" Reidemeister moves and their
+higher dimensional analogues. Coupled with $G$-coloring techniques, our
+homology theory yields invariants of knotted trivalent graphs in $\mathbb{R}^3$
+and knotted foams in $\mathbb{R}^4$. We re-interpret these invariants as
+homotopy classes of maps from $S^2$ or $S^3$ to the classifying space of $G$.
+",0,0,1,0,0,0
+2996,Generalized Biplots for Multidimensional Scaled Projections,"  Dimension reduction and visualization is a staple of data analytics. Methods
+such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS)
+provide low dimensional (LD) projections of high dimensional (HD) data while
+preserving an HD relationship between observations. Traditional biplots assign
+meaning to the LD space of a PCA projection by displaying LD axes for the
+attributes. These axes, however, are specific to the linear projection used in
+PCA. MDS projections, which allow for arbitrary stress and dissimilarity
+functions, require special care when labeling the LD space. We propose an
+iterative scheme to plot an LD axis for each attribute based on the
+user-specified stress and dissimilarity metrics. We discuss the details of our
+general biplot methodology, its relationship with PCA-derived biplots, and
+provide examples using real data.
+",0,0,0,1,0,0
+2997,Semi-supervised Conditional GANs,"  We introduce a new model for building conditional generative models in a
+semi-supervised setting to conditionally generate data given attributes by
+adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model
+uses a pair of stacked discriminators to learn the marginal distribution of the
+data, and the conditional distribution of the attributes given the data
+respectively. In the semi-supervised setting, the marginal distribution (which
+is often harder to learn) is learned from the labeled + unlabeled data, and the
+conditional distribution is learned purely from the labeled data. Our
+experimental results demonstrate that this model performs significantly better
+compared to existing semi-supervised conditional GAN models.
+",1,0,0,1,0,0
+2998,Implicit Quantile Networks for Distributional Reinforcement Learning,"  In this work, we build on recent advances in distributional reinforcement
+learning to give a generally applicable, flexible, and state-of-the-art
+distributional variant of DQN. We achieve this by using quantile regression to
+approximate the full quantile function for the state-action return
+distribution. By reparameterizing a distribution over the sample space, this
+yields an implicitly defined return distribution and gives rise to a large
+class of risk-sensitive policies. We demonstrate improved performance on the 57
+Atari 2600 games in the ALE, and use our algorithm's implicitly defined
+distributions to study the effects of risk-sensitive policies in Atari games.
+",0,0,0,1,0,0
+2999,Looping and Clustering model for the organization of protein-DNA complexes on the bacterial genome,"  The bacterial genome is organized in a structure called the nucleoid by a
+variety of associated proteins. These proteins can form complexes on DNA that
+play a central role in various biological processes, including chromosome
+segregation. A prominent example is the large ParB-DNA complex, which forms an
+essential component of the segregation machinery in many bacteria. ChIP-Seq
+experiments show that ParB proteins localize around centromere-like parS sites
+on the DNA to which ParB binds specifically, and spreads from there over large
+sections of the chromosome. Recent theoretical and experimental studies suggest
+that DNA-bound ParB proteins can interact with each other to condense into a
+coherent 3D complex on the DNA. However, the structural organization of this
+protein-DNA complex remains unclear, and a predictive quantitative theory for
+the distribution of ParB proteins on DNA is lacking. Here, we propose the
+Looping and Clustering (LC) model, which employs a statistical physics approach
+to describe protein-DNA complexes. The LC model accounts for the extrusion of
+DNA loops from a cluster of interacting DNA-bound proteins. Conceptually, the
+structure of the protein-DNA complex is determined by a competition between
+attractive protein interactions and the configurational and loop entropy of
+this protein-DNA cluster. Indeed, we show that the protein interaction strength
+determines the ""tightness"" of the loopy protein-DNA complex. With this approach
+we consider the genomic organization of such a protein-DNA cluster around a
+single high-affinity binding site. Thus, our model provides a theoretical
+framework to quantitatively compute the binding profiles of ParB-like proteins
+around a cognate (parS) binding site.
+",0,1,0,0,0,0
+3000,Simultaneous-equation Estimation without Instrumental Variables,"  For a single equation in a system of linear equations, estimation by
+instrumental variables is the standard approach. In practice, however, it is
+often difficult to find valid instruments. This paper proposes a maximum
+likelihood method that does not require instrumental variables; it is
+illustrated by simulation and with a real data set.
+",0,0,1,1,0,0
+3001,Mean field limits for nonlinear spatially extended hawkes processes with exponential memory kernels,"  We consider spatially extended systems of interacting nonlinear Hawkes
+processes modeling large systems of neurons placed in Rd and study the
+associated mean field limits. As the total number of neurons tends to infinity,
+we prove that the evolution of a typical neuron, attached to a given spatial
+position, can be described by a nonlinear limit differential equation driven by
+a Poisson random measure. The limit process is described by a neural field
+equation. As a consequence, we provide a rigorous derivation of the neural
+field equation based on a thorough mean field analysis.
+",0,0,1,0,0,0
+3002,Bias correction in daily maximum and minimum temperature measurements through Gaussian process modeling,"  The Global Historical Climatology Network-Daily database contains, among
+other variables, daily maximum and minimum temperatures from weather stations
+around the globe. It is long known that climatological summary statistics based
+on daily temperature minima and maxima will not be accurate, if the bias due to
+the time at which the observations were collected is not accounted for. Despite
+some previous work, to our knowledge, there does not exist a satisfactory
+solution to this important problem. In this paper, we carefully detail the
+problem and develop a novel approach to address it. Our idea is to impute the
+hourly temperatures at the location of the measurements by borrowing
+information from the nearby stations that record hourly temperatures, which
+then can be used to create accurate summaries of temperature extremes. The key
+difficulty is that these imputations of the temperature curves must satisfy the
+constraint of falling between the observed daily minima and maxima, and
+attaining those values at least once in a twenty-four hour period. We develop a
+spatiotemporal Gaussian process model for imputing the hourly measurements from
+the nearby stations, and then develop a novel and easy to implement Markov
+Chain Monte Carlo technique to sample from the posterior distribution
+satisfying the above constraints. We validate our imputation model using hourly
+temperature data from four meteorological stations in Iowa, of which one is
+hidden and the data replaced with daily minima and maxima, and show that the
+imputed temperatures recover the hidden temperatures well. We also demonstrate
+that our model can exploit information contained in the data to infer the time
+of daily measurements.
+",0,0,0,1,0,0
+3003,Mechanism of the double heterostructure TiO2/ZnO/TiO2 for photocatalytic and photovoltaic applications: A theoretical study,"  Understanding the mechanism of the heterojunction is an important step
+towards controllable and tunable interfaces for photocatalytic and photovoltaic
+based devices. To this aim, we propose a thorough study of a double
+heterostructure system consisting of two semiconductors with large band gap,
+namely, wurtzite ZnO and anatase TiO2. We demonstrate via first-principle
+calculations two stable configurations of ZnO/TiO2 interfaces. Our structural
+analysis provides a key information on the nature of the complex interface and
+lattice distortions occurring when combining these materials. The study of the
+electronic properties of the sandwich nanostructure TiO2/ZnO/TiO2 reveals that
+conduction band arises mainly from Ti3d orbitals, while valence band is
+maintained by O2p of ZnO, and that the trapped states within the gap region
+frequent in single heterostructure are substantially reduced in the double
+interface system. Moreover, our work explains the origin of certain optical
+transitions observed in the experimental studies. Unexpectedly, as a
+consequence of different bond distortions, the results on the band alignments
+show electron accumulation in the left shell of TiO2 rather than the right one.
+Such behavior provides more choice for the sensitization and functionalization
+of TiO2 surfaces.
+",0,1,0,0,0,0
+3004,Maximum Number of Common Zeros of Homogeneous Polynomials over Finite Fields,"  About two decades ago, Tsfasman and Boguslavsky conjectured a formula for the
+maximum number of common zeros that $r$ linearly independent homogeneous
+polynomials of degree $d$ in $m+1$ variables with coefficients in a finite
+field with $q$ elements can have in the corresponding $m$-dimensional
+projective space. Recently, it has been shown by Datta and Ghorpade that this
+conjecture is valid if $r$ is at most $m+1$ and can be invalid otherwise.
+Moreover a new conjecture was proposed for many values of $r$ beyond $m+1$. In
+this paper, we prove that this new conjecture holds true for several values of
+$r$. In particular, this settles the new conjecture completely when $d=3$. Our
+result also includes the positive result of Datta and Ghorpade as a special
+case. Further, we determine the maximum number of zeros in certain cases not
+covered by the earlier conjectures and results, namely, the case of $d=q-1$ and
+of $d=q$. All these results are directly applicable to the determination of the
+maximum number of points on sections of Veronese varieties by linear
+subvarieties of a fixed dimension, and also the determination of generalized
+Hamming weights of projective Reed-Muller codes.
+",0,0,1,0,0,0
+3005,Wave-induced vortex recoil and nonlinear refraction,"  When a vortex refracts surface waves, the momentum flux carried by the waves
+changes direction and the waves induce a reaction force on the vortex. We study
+experimentally the resulting vortex distortion. Incoming surface gravity waves
+impinge on a steady vortex of velocity $U_0$ driven magneto-hydrodynamically at
+the bottom of a fluid layer. The waves induce a shift of the vortex center in
+the direction transverse to wave propagation, together with a decrease in
+surface vorticity. We interpret these two phenomena in the framework introduced
+by Craik and Leibovich (1976): we identify the dimensionless Stokes drift
+$S=U_s/U_0$ as the relevant control parameter, $U_s$ being the Stokes drift
+velocity of the waves. We propose a simple vortex line model which indicates
+that the shift of the vortex center originates from a balance between vorticity
+advection by the Stokes drift and self-advection of the vortex. The decrease in
+surface vorticity is interpreted as a consequence of vorticity expulsion by the
+fast Stokes drift, which confines it at depth. This purely hydrodynamic process
+is analogous to the magnetohydrodynamic expulsion of magnetic field by a
+rapidly moving conductor through the electromagnetic skin effect. We study
+vorticity expulsion in the limit of fast Stokes drift and deduce that the
+surface vorticity decreases as $1/S$, a prediction which is compatible with the
+experimental data. Such wave-induced vortex distortions have important
+consequences for the nonlinear regime of wave refraction: the refraction angle
+rapidly decreases with wave intensity.
+",0,1,0,0,0,0
+3006,Transport of Intensity Equation Microscopy for Dynamic Microtubules,"  Microtubules (MTs) are filamentous protein polymers roughly 25 nm in
+diameter. Ubiquitous in eukaryotes, MTs are well known for their structural
+role but also act as actuators, sensors, and, in association with other
+proteins, checkpoint regulators. The thin diameter and transparency of
+microtubules classifies them as sub-resolution phase objects, with concomitant
+imaging challenges. Label-free methods for imaging microtubules are preferred
+when long exposure times would lead to phototoxicity in fluorescence, or for
+retaining more native structure and activity.
+This method approaches quantitative phase imaging of MTs as an inverse
+problem based on the Transport of Intensity Equation. In a co-registered
+comparison of MT signal-to-background-noise ratio, TIE Microscopy of MTs shows
+an improvement of more than three times that of video-enhanced bright field
+imaging.
+This method avoids the anisotropy caused by prisms used in differential
+interference contrast and takes only two defocused images as input. Unlike
+other label-free techniques for imaging microtubules, in TIE microscopy
+background removal is a natural consequence of taking the difference of two
+defocused images, so the need to frequently update a background image is
+eliminated.
+",0,1,0,0,0,0
+3007,First-principles prediction of the stacking fault energy of gold at finite temperature,"  The intrinsic stacking fault energy (ISFE) $\gamma$ is a material parameter
+fundamental to the discussion of plastic deformation mechanisms in metals.
+Here, we scrutinize the temperature dependence of the ISFE of Au through
+accurate first-principles derived Helmholtz free energies employing both the
+super cell approach and the axial Ising model (AIM). A significant decrease of
+the ISFE with temperature, $-(36$-$39)$\,\% from 0 to 890\,K depending on the
+treatment of thermal expansion, is revealed, which matches the estimate based
+on the experimental temperature coefficient $d \gamma / d T $ closely. We make
+evident that this decrease predominantly originates from the excess vibrational
+entropy at the stacking fault layer, although the contribution arising from the
+static lattice expansion compensates it by approximately 60\,\%. Electronic
+excitations are found to be of minor importance for the ISFE change with
+temperature. We show that the Debye model in combination with the AIM captures
+the correct sign but significantly underestimates the magnitude of the
+vibrational contribution to $\gamma(T)$. The hexagonal close-packed (hcp) and
+double hcp structures are established as metastable phases of Au. Our results
+demonstrate that quantitative agreement with experiments can be obtained if all
+relevant temperature-induced excitations are considered in first-principles
+modeling and that the temperature dependence of the ISFE is substantial enough
+to be taken into account in crystal plasticity modeling.
+",0,1,0,0,0,0
+3008,Mathematical Programming formulations for the efficient solution of the $k$-sum approval voting problem,"  In this paper we address the problem of electing a committee among a set of
+$m$ candidates and on the basis of the preferences of a set of $n$ voters. We
+consider the approval voting method in which each voter can approve as many
+candidates as she/he likes by expressing a preference profile (boolean
+$m$-vector). In order to elect a committee, a voting rule must be established
+to `transform' the $n$ voters' profiles into a winning committee. The problem
+is widely studied in voting theory; for a variety of voting rules the problem
+was shown to be computationally difficult and approximation algorithms and
+heuristic techniques were proposed in the literature. In this paper we follow
+an Ordered Weighted Averaging approach and study the $k$-sum approval voting
+(optimization) problem in the general case $1 \leq k <n$. For this problem we
+provide different mathematical programming formulations that allow us to solve
+it in an exact solution framework. We provide computational results showing
+that our approach is efficient for medium-size test problems ($n$ up to 200,
+$m$ up to 60) since in all tested cases it was able to find the exact optimal
+solution in very short computational times.
+",1,0,1,0,0,0
+3009,DAGs with NO TEARS: Continuous Optimization for Structure Learning,"  Estimating the structure of directed acyclic graphs (DAGs, also known as
+Bayesian networks) is a challenging problem since the search space of DAGs is
+combinatorial and scales superexponentially with the number of nodes. Existing
+approaches rely on various local heuristics for enforcing the acyclicity
+constraint. In this paper, we introduce a fundamentally different strategy: We
+formulate the structure learning problem as a purely \emph{continuous}
+optimization problem over real matrices that avoids this combinatorial
+constraint entirely. This is achieved by a novel characterization of acyclicity
+that is not only smooth but also exact. The resulting problem can be
+efficiently solved by standard numerical algorithms, which also makes
+implementation effortless. The proposed method outperforms existing ones,
+without imposing any structural assumptions on the graph such as bounded
+treewidth or in-degree. Code implementing the proposed algorithm is open-source
+and publicly available at this https URL.
+",0,0,0,1,0,0
+3010,Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions,"  We consider the problem of efficient packet dissemination in wireless
+networks with point-to-multi-point wireless broadcast channels. We propose a
+dynamic policy, which achieves the broadcast capacity of the network. This
+policy is obtained by first transforming the original multi-hop network into a
+precedence-relaxed virtual single-hop network and then finding an optimal
+broadcast policy for the relaxed network. The resulting policy is shown to be
+throughput-optimal for the original wireless network using a sample-path
+argument. We also prove the NP-completeness of the finite-horizon broadcast
+problem, which is in contrast with the polynomial time solvability of the
+problem with point-to-point channels. Illustrative simulation results
+demonstrate the efficacy of the proposed broadcast policy in achieving the full
+broadcast capacity with low delay.
+",1,0,1,0,0,0
+3011,The Resilience of Life to Astrophysical Events,"  Much attention has been given in the literature to the effects of
+astrophysical events on human and land-based life. However, little has been
+discussed on the resilience of life itself. Here we instead explore the
+statistics of events that completely sterilise an Earth-like planet with planet
+radii in the range $0.5-1.5 R_{Earth}$ and temperatures of $\sim 300 \;
+\text{K}$, eradicating all forms of life. We consider the relative likelihood
+of complete global sterilisation events from three astrophysical sources --
+supernovae, gamma-ray bursts, large asteroid impacts, and passing-by stars. To
+assess such probabilities we consider what cataclysmic event could lead to the
+annihilation of not just human life, but also extremophiles, through the
+boiling of all water in Earth's oceans. Surprisingly we find that although
+human life is somewhat fragile to nearby events, the resilience of Ecdysozoa
+such as \emph{Milnesium tardigradum} renders global sterilisation an unlikely
+event.
+",0,1,0,0,0,0
+3012,Kinky DNA in solution: Small angle scattering study of a nucleosome positioning sequence,"  DNA is a flexible molecule, but the degree of its flexibility is subject to
+debate. The commonly-accepted persistence length of $l_p \approx 500\,$\AA\ is
+inconsistent with recent studies on short-chain DNA that show much greater
+flexibility but do not probe its origin. We have performed X-ray and neutron
+small-angle scattering on a short DNA sequence containing a strong nucleosome
+positioning element, and analyzed the results using a modified Kratky-Porod
+model to determine possible conformations. Our results support a hypothesis
+from Crick and Klug in 1975 that some DNA sequences in solution can have sharp
+kinks, potentially resolving the discrepancy. Our conclusions are supported by
+measurements on a radiation-damaged sample, where single-strand breaks lead to
+increased flexibility and by an analysis of data from another sequence, which
+does not have kinks, but where our method can detect a locally enhanced
+flexibility due to an $AT$-domain.
+",0,0,0,0,1,0
+3013,Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning,"  Although aviation accidents are rare, safety incidents occur more frequently
+and require a careful analysis to detect and mitigate risks in a timely manner.
+Analyzing safety incidents using operational data and producing event-based
+explanations is invaluable to airline companies as well as to governing
+organizations such as the Federal Aviation Administration (FAA) in the United
+States. However, this task is challenging because of the complexity involved in
+mining multi-dimensional heterogeneous time series data, the lack of
+time-step-wise annotation of events in a flight, and the lack of scalable tools
+to perform analysis over a large number of events. In this work, we propose a
+precursor mining algorithm that identifies events in the multidimensional time
+series that are correlated with the safety incident. Precursors are valuable to
+systems health and safety monitoring and in explaining and forecasting safety
+incidents. Current methods suffer from poor scalability to high dimensional
+time series data and are inefficient in capturing temporal behavior. We propose
+an approach by combining multiple-instance learning (MIL) and deep recurrent
+neural networks (DRNN) to take advantage of MIL's ability to learn using weakly
+supervised data and DRNN's ability to model temporal behavior. We describe the
+algorithm, the data, the intuition behind taking a MIL approach, and a
+comparative analysis of the proposed algorithm with baseline models. We also
+discuss the application to a real-world aviation safety problem using data from
+a commercial airline company and discuss the model's abilities and
+shortcomings, with some final remarks about possible deployment directions.
+",1,0,0,1,0,0
+3014,Normalized Direction-preserving Adam,"  Adaptive optimization algorithms, such as Adam and RMSprop, have shown better
+optimization performance than stochastic gradient descent (SGD) in some
+scenarios. However, recent studies show that they often lead to worse
+generalization performance than SGD, especially for training deep neural
+networks (DNNs). In this work, we identify the reasons that Adam generalizes
+worse than SGD, and develop a variant of Adam to eliminate the generalization
+gap. The proposed method, normalized direction-preserving Adam (ND-Adam),
+enables more precise control of the direction and step size for updating weight
+vectors, leading to significantly improved generalization performance.
+Following a similar rationale, we further improve the generalization
+performance in classification tasks by regularizing the softmax logits. By
+bridging the gap between SGD and Adam, we also hope to shed light on why
+certain optimization algorithms generalize better than others.
+",1,0,0,1,0,0
+3015,Learning Deep CNN Denoiser Prior for Image Restoration,"  Model-based optimization methods and discriminative learning methods have
+been the two dominant strategies for solving various inverse problems in
+low-level vision. Typically, those two kinds of methods have their respective
+merits and drawbacks, e.g., model-based optimization methods are flexible for
+handling different inverse problems but are usually time-consuming with
+sophisticated priors for the purpose of good performance; in the meanwhile,
+discriminative learning methods have fast testing speed but their application
+range is greatly restricted by the specialized task. Recent works have revealed
+that, with the aid of variable splitting techniques, denoiser prior can be
+plugged in as a modular part of model-based optimization methods to solve other
+inverse problems (e.g., deblurring). Such an integration induces considerable
+advantage when the denoiser is obtained via discriminative learning. However,
+the study of integration with fast discriminative denoiser prior is still
+lacking. To this end, this paper aims to train a set of fast and effective CNN
+(convolutional neural network) denoisers and integrate them into model-based
+optimization method to solve other inverse problems. Experimental results
+demonstrate that the learned set of denoisers not only achieve promising
+Gaussian denoising results but also can be used as prior to deliver good
+performance for various low-level vision applications.
+",1,0,0,0,0,0
+3016,The Challenge of Spin-Orbit-Tuned Ground States in Iridates,"  Effects of spin-orbit interactions in condensed matter are an important and
+rapidly evolving topic. Strong competition between spin-orbit, on-site Coulomb
+and crystalline electric field interactions in iridates drives exotic quantum
+states that are unique to this group of materials. In particular, the Jeff =
+1/2 Mott state served as an early signal that the combined effect of strong
+spin-orbit and Coulomb interactions in iridates has unique, intriguing
+consequences. In this Key Issues Review, we survey some current experimental
+studies of iridates. In essence, these materials tend to defy conventional
+wisdom: absence of conventional correlations between magnetic and insulating
+states, avoidance of metallization at high pressures, S-shaped I-V
+characteristic, emergence of an odd-parity hidden order, etc. It is
+particularly intriguing that there exist conspicuous discrepancies between
+current experimental results and theoretical proposals that address
+superconducting, topological and quantum spin liquid phases. This class of
+materials, in which the lattice degrees of freedom play a critical role seldom
+seen in other materials, evidently presents some profound intellectual
+challenges that call for more investigations both experimentally and
+theoretically. Physical properties unique to these materials may help unlock a
+world of possibilities for functional materials and devices. We emphasize that,
+given the rapidly developing nature of this field, this Key Issues Review is by
+no means an exhaustive report of the current state of experimental studies of
+iridates.
+",0,1,0,0,0,0
+3017,Derived Picard groups of preprojective algebras of Dynkin type,"  In this paper, we study two-sided tilting complexes of preprojective algebras
+of Dynkin type. We construct the most fundamental class of two-sided tilting
+complexes, which has a group structure by derived tensor products and induces a
+group of auto-equivalences of the derived category. We show that the group
+structure of the two-sided tilting complexes is isomorphic to the braid group
+of the corresponding folded graph. Moreover we show that these two-sided
+tilting complexes induce tilting mutation and any tilting complex is given as
+the derived tensor products of them. Using these results, we determine the
+derived Picard group of preprojective algebras for type $A$ and $D$.
+",0,0,1,0,0,0
+3018,Semi-supervised Learning in Network-Structured Data via Total Variation Minimization,"  We propose and analyze a method for semi-supervised learning from
+partially-labeled network-structured data. Our approach is based on a graph
+signal recovery interpretation under a clustering hypothesis that labels of
+data points belonging to the same well-connected subset (cluster) are similar
+valued. This lends naturally to learning the labels by total variation (TV)
+minimization, which we solve by applying a recently proposed primal-dual method
+for non-smooth convex optimization. The resulting algorithm allows for a highly
+scalable implementation using message passing over the underlying empirical
+graph, which renders the algorithm suitable for big data applications. By
+applying tools of compressed sensing, we derive a sufficient condition on the
+underlying network structure such that TV minimization recovers clusters in the
+empirical graph of the data. In particular, we show that the proposed
+primal-dual method amounts to maximizing network flows over the empirical graph
+of the dataset. Moreover, the learning accuracy of the proposed algorithm is
+linked to the set of network flows between data points having known labels. The
+effectiveness and scalability of our approach is verified by numerical
+experiments.
+",1,0,0,1,0,0
+3019,Susceptibility of Methicillin Resistant Staphylococcus aureus to Vancomycin using Liposomal Drug Delivery System,"  Staphylococcus aureus responsible for nosocomial infections is a significant
+threat to the public health. The increasing resistance of S.aureus to various
+antibiotics has drawn it to a prime focus for research on designing an
+appropriate drug delivery system. Emergence of Methicillin Resistant
+Staphylococcus aureus (MRSA) in 1961, necessitated the use of vancomycin ""the
+drug of last resort"" to treat these infections. Unfortunately, S.aureus has
+already started gaining resistances to vancomycin. Liposome encapsulation of
+drugs have been earlier shown to provide an efficient method of microbial
+inhibition in many cases. We have studied the effect of liposome encapsulated
+vancomycin on MRSA and evaluated the antibacterial activity of the
+liposome-entrapped drug in comparison to that of the free drug based on the
+minimum inhibitory concentration (MIC) of the drug. The MIC for liposomal
+vancomycin was found to be about half of that of free vancomycin. The growth
+response of MRSA showed that the liposomal vancomycin induced the culture to go
+into bacteriostatic state and phagocytic killing was enhanced. Administration
+of the antibiotic encapsulated in liposome thus was shown to greatly improve
+the drug delivery as well as the drug resistance caused by MRSA.
+",0,0,0,0,1,0
+3020,"Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts","  Understanding how ideas relate to each other is a fundamental question in
+many domains, ranging from intellectual history to public communication.
+Because ideas are naturally embedded in texts, we propose the first framework
+to systematically characterize the relations between ideas based on their
+occurrence in a corpus of documents, independent of how these ideas are
+represented. Combining two statistics --- cooccurrence within documents and
+prevalence correlation over time --- our approach reveals a number of different
+ways in which ideas can cooperate and compete. For instance, two ideas can
+closely track each other's prevalence over time, and yet rarely cooccur, almost
+like a ""cold war"" scenario. We observe that pairwise cooccurrence and
+prevalence correlation exhibit different distributions. We further demonstrate
+that our approach is able to uncover intriguing relations between ideas through
+in-depth case studies on news articles and research papers.
+",1,1,0,0,0,0
+3021,Deep Feature Learning for Graphs,"  This paper presents a general graph representation learning framework called
+DeepGL for learning deep node and edge representations from large (attributed)
+graphs. In particular, DeepGL begins by deriving a set of base features (e.g.,
+graphlet features) and automatically learns a multi-layered hierarchical graph
+representation where each successive layer leverages the output from the
+previous layer to learn features of a higher-order. Contrary to previous work,
+DeepGL learns relational functions (each representing a feature) that
+generalize across-networks and therefore useful for graph-based transfer
+learning tasks. Moreover, DeepGL naturally supports attributed graphs, learns
+interpretable features, and is space-efficient (by learning sparse feature
+vectors). In addition, DeepGL is expressive, flexible with many interchangeable
+components, efficient with a time complexity of $\mathcal{O}(|E|)$, and
+scalable for large networks via an efficient parallel implementation. Compared
+with the state-of-the-art method, DeepGL is (1) effective for across-network
+transfer learning tasks and attributed graph representation learning, (2)
+space-efficient requiring up to 6x less memory, (3) fast with up to 182x
+speedup in runtime performance, and (4) accurate with an average improvement of
+20% or more on many learning tasks.
+",1,0,0,1,0,0
+3022,Pseudospectral Model Predictive Control under Partially Learned Dynamics,"  Trajectory optimization of a controlled dynamical system is an essential part
+of autonomy, however many trajectory optimization techniques are limited by the
+fidelity of the underlying parametric model. In the field of robotics, a lack
+of model knowledge can be overcome with machine learning techniques, utilizing
+measurements to build a dynamical model from the data. This paper aims to take
+the middle ground between these two approaches by introducing a semi-parametric
+representation of the underlying system dynamics. Our goal is to leverage the
+considerable information contained in a traditional physics based model and
+combine it with a data-driven, non-parametric regression technique known as a
+Gaussian Process. Integrating this semi-parametric model with model predictive
+pseudospectral control, we demonstrate this technique on both a cart pole and
+quadrotor simulation with unmodeled damping and parametric error. In order to
+manage parametric uncertainty, we introduce an algorithm that utilizes Sparse
+Spectrum Gaussian Processes (SSGP) for online learning after each rollout. We
+implement this online learning technique on a cart pole and quadrator, then
+demonstrate the use of online learning and obstacle avoidance for the dubin
+vehicle dynamics.
+",1,0,0,0,0,0
+3023,Phonon-assisted oscillatory exciton dynamics in monolayer MoSe2,"  In monolayer semiconductor transition metal dichalcogenides, the
+exciton-phonon interaction is expected to strongly affect the photocarrier
+dynamics. Here, we report on an unusual oscillatory enhancement of the neutral
+exciton photoluminescence with the excitation laser frequency in monolayer
+MoSe2. The frequency of oscillation matches that of the M-point longitudinal
+acoustic phonon, LA(M). Oscillatory behavior is also observed in the
+steady-state emission linewidth and in timeresolved photoluminescence
+excitation data, which reveals variation with excitation energy in the exciton
+lifetime. These results clearly expose the key role played by phonons in the
+exciton formation and relaxation dynamics of two-dimensional van der Waals
+semiconductors.
+",0,1,0,0,0,0
+3024,Manifold Regularization for Kernelized LSTD,"  Policy evaluation or value function or Q-function approximation is a key
+procedure in reinforcement learning (RL). It is a necessary component of policy
+iteration and can be used for variance reduction in policy gradient methods.
+Therefore its quality has a significant impact on most RL algorithms. Motivated
+by manifold regularized learning, we propose a novel kernelized policy
+evaluation method that takes advantage of the intrinsic geometry of the state
+space learned from data, in order to achieve better sample efficiency and
+higher accuracy in Q-function approximation. Applying the proposed method in
+the Least-Squares Policy Iteration (LSPI) framework, we observe superior
+performance compared to widely used parametric basis functions on two standard
+benchmarks in terms of policy quality.
+",1,0,0,1,0,0
+3025,Normality of the Thue--Morse sequence along Piatetski-Shapiro sequences,"  We prove that for $1<c<4/3$ the subsequence of the Thue--Morse sequence
+$\mathbf t$ indexed by $\lfloor n^c\rfloor$ defines a normal sequence, that is,
+each finite sequence $(\varepsilon_0,\ldots,\varepsilon_{T-1})\in \{0,1\}^T$
+occurs as a contiguous subsequence of the sequence $n\mapsto \mathbf
+t\left(\lfloor n^c\rfloor\right)$ with asymptotic frequency $2^{-T}$.
+",0,0,1,0,0,0
+3026,Radio Weak Lensing Shear Measurement in the Visibility Domain - II. Source Extraction,"  This paper extends the method introduced in Rivi et al. (2016b) to measure
+galaxy ellipticities in the visibility domain for radio weak lensing surveys.
+In that paper we focused on the development and testing of the method for the
+simple case of individual galaxies located at the phase centre, and proposed to
+extend it to the realistic case of many sources in the field of view by
+isolating visibilities of each source with a faceting technique. In this second
+paper we present a detailed algorithm for source extraction in the visibility
+domain and show its effectiveness as a function of the source number density by
+running simulations of SKA1-MID observations in the band 950-1150 MHz and
+comparing original and measured values of galaxies' ellipticities. Shear
+measurements from a realistic population of 10^4 galaxies randomly located in a
+field of view of 1 deg^2 (i.e. the source density expected for the current
+radio weak lensing survey proposal with SKA1) are also performed. At SNR >= 10,
+the multiplicative bias is only a factor 1.5 worse than what found when
+analysing individual sources, and is still comparable to the bias values
+reported for similar measurement methods at optical wavelengths. The additive
+bias is unchanged from the case of individual sources, but is significantly
+larger than typically found in optical surveys. This bias depends on the shape
+of the uv coverage and we suggest that a uv-plane weighting scheme to produce a
+more isotropic shape could reduce and control additive bias.
+",0,1,0,0,0,0
+3027,Loop Representation of Wigner's Little Groups,"  Wigner's little groups are the subgroups of the Lorentz group whose
+transformations leave the momentum of a given particle invariant. They thus
+define the internal space-time symmetries of relativistic particles. These
+symmetries take different mathematical forms for massive and for massless
+particles. However, it is shown possible to construct one unified
+representation using a graphical description. This graphical approach allows us
+to describe vividly parity, time reversal, and charge conjugation of the
+internal symmetry groups. As for the language of group theory, the two-by-two
+representation is used throughout the paper. While this two-by-two
+representation is for spin-1/2 particles, it is shown possible to construct the
+representations for spin-0 particles, spin-1 particles, as well as for
+higher-spin particles, for both massive and massless cases. It is shown also
+that the four-by-four Dirac matrices constitute a two-by-two representation of
+Wigner's little group.
+",0,1,0,0,0,0
+3028,"MOA Data Reveal a New Mass, Distance, and Relative Proper Motion for Planetary System OGLE-2015-BLG-0954L","  We present the MOA Collaboration light curve data for planetary microlensing
+event OGLE-2015-BLG-0954, which was previously announced in a paper by the
+KMTNet and OGLE Collaborations. The MOA data cover the caustic exit, which was
+not covered by the KMTNet or OGLE data, and they provide a more reliable
+measurement of the finite source effect. The MOA data also provide a new source
+color measurement that reveals a lens-source relative proper motion of
+$\mu_{\rm rel} = 11.8\pm 0.8\,$mas/yr, which compares to the value of $\mu_{\rm
+rel} = 18.4\pm 1.7\,$mas/yr reported in the KMTNet-OGLE paper. This new MOA
+value for $\mu_{\rm rel}$ has an a priori probability that is a factor of $\sim
+100$ times larger than the previous value, and it does not require a lens
+system distance of $D_L < 1\,$kpc. Based on the corrected source color, we find
+that the lens system consists of a planet of mass $3.4^{+3.7}_{-1.6} M_{\rm
+Jup}$ orbiting a $0.30^{+0.34}_{-0.14}M_\odot$ star at an orbital separation of
+$2.1^{+2.2}_{-1.0}\,$AU and a distance of $1.2^{+1.1}_{-0.5}\,$kpc.
+",0,1,0,0,0,0
+3029,Ground state degeneracy in quantum spin systems protected by crystal symmetries,"  We develop a no-go theorem for two-dimensional bosonic systems with crystal
+symmetries: if there is a half-integer spin at a rotation center, where the
+point-group symmetry is $\mathbb D_{2,4,6}$, such a system must have a
+ground-state degeneracy protected by the crystal symmetry. Such a degeneracy
+indicates either a broken-symmetry state or a unconventional state of matter.
+Comparing to the Lieb-Schultz-Mattis Theorem, our result counts the spin at
+each rotation center, instead of the total spin per unit cell, and therefore
+also applies to certain systems with an even number of half-integer spins per
+unit cell.
+",0,1,0,0,0,0
+3030,Introspection: Accelerating Neural Network Training By Learning Weight Evolution,"  Neural Networks are function approximators that have achieved
+state-of-the-art accuracy in numerous machine learning tasks. In spite of their
+great success in terms of accuracy, their large training time makes it
+difficult to use them for various tasks. In this paper, we explore the idea of
+learning weight evolution pattern from a simple network for accelerating
+training of novel neural networks. We use a neural network to learn the
+training pattern from MNIST classification and utilize it to accelerate
+training of neural networks used for CIFAR-10 and ImageNet classification. Our
+method has a low memory footprint and is computationally efficient. This method
+can also be used with other optimizers to give faster convergence. The results
+indicate a general trend in the weight evolution during training of neural
+networks.
+",1,0,0,0,0,0
+3031,Single crystal polarized neutron diffraction study of the magnetic structure of HoFeO$_3$,"  Polarised neutron diffraction measurements have been made on HoFeO$_3$ single
+crystals magnetised in both the [001] and [100] directions ($Pbnm$ setting).
+The polarisation dependencies of Bragg reflection intensities were measured
+both with a high field of H = 9 T parallel to [001] at T = 70 K and with the
+lower field H = 0.5 T parallel to [100] at T = 5, 15, 25~K. A Fourier
+projection of magnetization induced parallel to [001], made using the $hk0$
+reflections measured in 9~T, indicates that almost all of it is due to
+alignment of Ho moments. Further analysis of the asymmetries of general
+reflections in these data showed that although, at 70~K, 9~T applied parallel
+to [001] hardly perturbs the antiferromagnetic order of the Fe sublattices, it
+induces significant antiferromagnetic order of the Ho sublattices in the
+$x\mhyphen y$ plane, with the antiferromagnetic components of moment having the
+same order of magnitude as the induced ferromagnetic ones. Strong intensity
+asymmetries measured in the low temperature $\Gamma_2$ structure with a lower
+field, 0.5 T $\parallel$ [100] allowed the variation of the ordered components
+of the Ho and Fe moments to be followed. Their absolute orientations, in the
+180\degree\ domain stabilised by the field were determined relative to the
+distorted perovskite structure,. This relationship fixes the sign of the
+Dzyalshinski-Moriya (D-M) interaction which leads to the weak ferromagnetism.
+Our results indicate that the combination of strong y-axis anisotropy of the Ho
+moments and Ho-Fe exchange interactions breaks the centrosymmetry of the
+structure and could lead to ferroelectric polarization.
+",0,1,0,0,0,0
+3032,Rigidity of volume-minimizing hypersurfaces in Riemannian 5-manifolds,"  In this paper we generalize the main result of [4] for manifolds that are not
+necessarily Einstein. In fact, we obtain an upper bound for the volume of a
+locally volume-minimizing closed hypersurface $\Sigma$ of a Riemannian
+5-manifold $M$ with scalar curvature bounded from below by a positive constant
+in terms of the total traceless Ricci curvature of $\Sigma$. Furthermore, if
+$\Sigma$ saturates the respective upper bound and $M$ has nonnegative Ricci
+curvature, then $\Sigma$ is isometric to $\mathbb{S}^4$ up to scaling and $M$
+splits in a neighborhood of $\Sigma$. Also, we obtain a rigidity result for the
+Riemannian cover of $M$ when $\Sigma$ minimizes the volume in its homotopy
+class and saturates the upper bound.
+",0,0,1,0,0,0
+3033,Dolha - an Efficient and Exact Data Structure for Streaming Graphs,"  A streaming graph is a graph formed by a sequence of incoming edges with time
+stamps. Unlike static graphs, the streaming graph is highly dynamic and time
+related. In the real world, the high volume and velocity streaming graphs such
+as internet traffic data, social network communication data and financial
+transfer data are bringing challenges to the classic graph data structures. We
+present a new data structure: double orthogonal list in hash table (Dolha)
+which is a high speed and high memory efficiency graph structure applicable to
+streaming graph. Dolha has constant time cost for single edge and near linear
+space cost that we can contain billions of edges information in memory size and
+process an incoming edge in nanoseconds. Dolha also has linear time cost for
+neighborhood queries, which allow it to support most algorithms in graphs
+without extra cost. We also present a persistent structure based on Dolha that
+has the ability to handle the sliding window update and time related queries.
+",1,0,0,0,0,0
+3034,"Semi-classical states for the nonlinear Choquard equations: existence, multiplicity and concentration at a potential well","  We study existence and multiplicity of semi-classical states for the
+nonlinear Choquard equation:
+$$ -\varepsilon^2\Delta v+V(x)v =
+\frac{1}{\varepsilon^\alpha}(I_\alpha*F(v))f(v) \quad \hbox{in}\ \mathbb{R}^N,
+$$ where $N\geq 3$, $\alpha\in (0,N)$, $I_\alpha(x)={A_\alpha\over
+|x|^{N-\alpha}}$ is the Riesz potential, $F\in C^1(\mathbb{R},\mathbb{R})$,
+$F'(s) = f(s)$ and $\varepsilon>0$ is a small parameter.
+We develop a new variational approach and we show the existence of a family
+of solutions concentrating, as $\varepsilon\to 0$, to a local minima of $V(x)$
+under general conditions on $F(s)$. Our result is new also for
+$f(s)=|s|^{p-2}s$ and applicable for $p\in (\frac{N+\alpha}{N},
+\frac{N+\alpha}{N-2})$. Especially, we can give the existence result for
+locally sublinear case $p\in (\frac{N+\alpha}{N}, 2)$, which gives a positive
+answer to an open problem arisen in recent works of Moroz and Van Schaftingen.
+We also study the multiplicity of positive single-peak solutions and we show
+the existence of at least $\hbox{cupl}(K)+1$ solutions concentrating around $K$
+as $\varepsilon\to 0$, where $K\subset \Omega$ is the set of minima of $V(x)$
+in a bounded potential well $\Omega$, that is, $m_0 \equiv \inf_{x\in \Omega}
+V(x) < \inf_{x\in \partial\Omega}V(x)$ and $K=\{x\in\Omega;\, V(x)=m_0\}$.
+",0,0,1,0,0,0
+3035,Results from the first cryogenic NaI detector for the COSINUS project,"  Recently there is a flourishing and notable interest in the crystalline
+scintillator material sodium iodide (NaI) as target for direct dark matter
+searches. This is mainly driven by the long-reigning contradicting situation in
+the dark matter sector: the positive evidence for the detection of a dark
+matter modulation signal claimed by the DAMA/LIBRA collaboration is (under
+so-called standard assumptions) inconsistent with the null-results reported by
+most of the other direct dark matter experiments. We present the results of a
+first prototype detector using a new experimental approach in comparison to
+\textit{conventional} single-channel NaI scintillation light detectors: a NaI
+crystal operated as a scintillating calorimeter at milli-Kelvin temperatures
+simultaneously providing a phonon (heat) plus scintillation light signal and
+particle discrimination on an event-by-event basis. We evaluate energy
+resolution, energy threshold and further performance parameters of this
+prototype detector developed within the COSINUS R&D project.
+",0,1,0,0,0,0
+3036,Minimal Representations of Lie Algebras With Non-Trivial Levi Decomposition,"  We obtain minimal dimension matrix representations for each of the Lie
+algebras of dimension five, six, seven, and eight obtained by Turkowski that
+have a non-trivial Levi decomposition. The Key technique involves using
+subspace associated to a particular representation of semi-simple Lie algebra
+to help in the construction of the radical in the putative Levi decomposition.
+",0,0,1,0,0,0
+3037,Forecasting day-ahead electricity prices in Europe: the importance of considering market integration,"  Motivated by the increasing integration among electricity markets, in this
+paper we propose two different methods to incorporate market integration in
+electricity price forecasting and to improve the predictive performance. First,
+we propose a deep neural network that considers features from connected markets
+to improve the predictive accuracy in a local market. To measure the importance
+of these features, we propose a novel feature selection algorithm that, by
+using Bayesian optimization and functional analysis of variance, evaluates the
+effect of the features on the algorithm performance. In addition, using market
+integration, we propose a second model that, by simultaneously predicting
+prices from two markets, improves the forecasting accuracy even further. As a
+case study, we consider the electricity market in Belgium and the improvements
+in forecasting accuracy when using various French electricity features. We show
+that the two proposed models lead to improvements that are statistically
+significant. Particularly, due to market integration, the predictive accuracy
+is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage
+error). In addition, we show that the proposed feature selection algorithm is
+able to perform a correct assessment, i.e. to discard the irrelevant features.
+",1,0,0,1,0,0
+3038,A Longitudinal Higher-Order Diagnostic Classification Model,"  Providing diagnostic feedback about growth is crucial to formative decisions
+such as targeted remedial instructions or interventions. This paper proposed a
+longitudinal higher-order diagnostic classification modeling approach for
+measuring growth. The new modeling approach is able to provide quantitative
+values of overall and individual growth by constructing a multidimensional
+higher-order latent structure to take into account the correlations among
+multiple latent attributes that are examined across different occasions. In
+addition, potential local item dependence among anchor (or repeated) items can
+also be taken into account. Model parameter estimation is explored in a
+simulation study. An empirical example is analyzed to illustrate the
+applications and advantages of the proposed modeling approach.
+",0,0,0,1,0,0
+3039,On Fairness and Calibration,"  The machine learning community has become increasingly concerned with the
+potential for bias and discrimination in predictive models. This has motivated
+a growing line of work on what it means for a classification procedure to be
+""fair."" In this paper, we investigate the tension between minimizing error
+disparity across different population groups while maintaining calibrated
+probability estimates. We show that calibration is compatible only with a
+single error constraint (i.e. equal false-negatives rates across groups), and
+show that any algorithm that satisfies this relaxation is no better than
+randomizing a percentage of predictions for an existing classifier. These
+unsettling findings, which extend and generalize existing results, are
+empirically confirmed on several datasets.
+",1,0,0,1,0,0
+3040,AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms,"  Simulation systems have become an essential component in the development and
+validation of autonomous driving technologies. The prevailing state-of-the-art
+approach for simulation is to use game engines or high-fidelity computer
+graphics (CG) models to create driving scenarios. However, creating CG models
+and vehicle movements (e.g., the assets for simulation) remains a manual task
+that can be costly and time-consuming. In addition, the fidelity of CG images
+still lacks the richness and authenticity of real-world images and using these
+images for training leads to degraded performance.
+In this paper we present a novel approach to address these issues: Augmented
+Autonomous Driving Simulation (AADS). Our formulation augments real-world
+pictures with a simulated traffic flow to create photo-realistic simulation
+images and renderings. More specifically, we use LiDAR and cameras to scan
+street scenes. From the acquired trajectory data, we generate highly plausible
+traffic flows for cars and pedestrians and compose them into the background.
+The composite images can be re-synthesized with different viewpoints and sensor
+models. The resulting images are photo-realistic, fully annotated, and ready
+for end-to-end training and testing of autonomous driving systems from
+perception to planning. We explain our system design and validate our
+algorithms with a number of autonomous driving tasks from detection to
+segmentation and predictions.
+Compared to traditional approaches, our method offers unmatched scalability
+and realism. Scalability is particularly important for AD simulation and we
+believe the complexity and diversity of the real world cannot be realistically
+captured in a virtual environment. Our augmented approach combines the
+flexibility in a virtual environment (e.g., vehicle movements) with the
+richness of the real world to allow effective simulation of anywhere on earth.
+",1,0,0,0,0,0
+3041,Scheme-theoretic Whitney conditions and applications to tangency of projective varieties,"  We investigate a scheme-theoretic variant of Whitney condition a. If X is a
+projec-tive variety over the field of complex numbers and Y $\subset$ X a
+subvariety, then X satisfies generically the scheme-theoretic Whitney condition
+a along Y provided that the pro-jective dual of X is smooth. We give
+applications to tangency of projective varieties over C and to convex real
+algebraic geometry. In particular, we prove a Bertini-type theorem for
+osculating plane of smooth complex space curves and a generalization of a
+Theorem of Ranestad and Sturmfels describing the algebraic boundary of an
+affine compact real variety.
+",0,0,1,0,0,0
+3042,Role of 1-D finite size Heisenberg chain in increasing metal to insulator transition temperature in hole rich VO2,"  VO2 samples are grown with different oxygen concentrations leading to
+different monoclinic, M1 and triclinic, T insulating phases which undergo a
+first order metal to insulator transition (MIT) followed by a structural phase
+transition (SPT) to rutile tetragonal phase. The metal insulator transition
+temperature (Tc) was found to be increased with increasing native defects.
+Vanadium vacancy (VV) is envisaged to create local strains in the lattice which
+prevents twisting of the V-V dimers promoting metastable monoclinic, M2 and T
+phases at intermediate temperatures. It is argued that MIT is driven by strong
+electronic correlation. The low temperature insulating phase can be considered
+as a collection of one-dimensional (1-D) half-filled band, which undergoes Mott
+transition to 1-D infinitely long Heisenberg spin 1/2 chains leading to
+structural distortion due to spin-phonon coupling. Presence of VV creates
+localized holes (d0) in the nearest neighbor, thereby fragmenting the spin 1/2
+chains at nanoscale, which in turn increase the Tc value more than that of an
+infinitely long one. The Tc value scales inversely with the average size of
+fragmented Heisenberg spin 1/2 chains following a critical exponent of 2/3,
+which is exactly the same predicted theoretically for Heisenberg spin 1/2 chain
+at nanoscale undergoing SPT (spin-Peierls transition). Thus, the observation of
+MIT and SPT at the same time in VO2 can be explained from our phenomenological
+model of reduced 1-D Heisenberg spin 1/2 chains. The reported increase
+(decrease) in Tc value of VO2 by doping with metal having valency less (more)
+than four, can also be understood easily with our unified model, for the first
+time, considering finite size scaling of Heisenberg chains.
+",0,1,0,0,0,0
+3043,Electron paramagnetic resonance g-tensors from state interaction spin-orbit coupling density matrix renormalization group,"  We present a state interaction spin-orbit coupling method to calculate
+electron paramagnetic resonance (EPR) $g$-tensors from density matrix
+renormalization group wavefunctions. We apply the technique to compute
+$g$-tensors for the \ce{TiF3} and \ce{CuCl4^2-} complexes, a [2Fe-2S] model of
+the active center of ferredoxins, and a \ce{Mn4CaO5} model of the S2 state of
+the oxygen evolving complex. These calculations raise the prospects of
+determining $g$-tensors in multireference calculations with a large number of
+open shells.
+",0,1,0,0,0,0
+3044,A multilayer multiconfiguration time-dependent Hartree study of the nonequilibrium Anderson impurity model at zero temperature,"  Quantum transport is studied for the nonequilibrium Anderson impurity model
+at zero temperature employing the multilayer multiconfiguration time-dependent
+Hartree theory within the second quantization representation (ML-MCTDH-SQR) of
+Fock space. To adress both linear and nonlinear conductance in the Kondo
+regime, two new techniques of the ML-MCTDH-SQR simulation methodology are
+introduced: (i) the use of correlated initial states, which is achieved by
+imaginary time propagation of the overall Hamiltonian at zero voltage and (ii)
+the adoption of the logarithmic discretization of the electronic continuum.
+Employing the improved methodology, the signature of the Kondo effect is
+analyzed.
+",0,1,0,0,0,0
+3045,Robust Identification of Target Genes and Outliers in Triple-negative Breast Cancer Data,"  Correct classification of breast cancer sub-types is of high importance as it
+directly affects the therapeutic options. We focus on triple-negative breast
+cancer (TNBC) which has the worst prognosis among breast cancer types. Using
+cutting edge methods from the field of robust statistics, we analyze Breast
+Invasive Carcinoma (BRCA) transcriptomic data publicly available from The
+Cancer Genome Atlas (TCGA) data portal. Our analysis identifies statistical
+outliers that may correspond to misdiagnosed patients. Furthermore, it is
+illustrated that classical statistical methods may fail in the presence of
+these outliers, prompting the need for robust statistics. Using robust sparse
+logistic regression we obtain 36 relevant genes, of which ca. 60\% have been
+previously reported as biologically relevant to TNBC, reinforcing the validity
+of the method. The remaining 14 genes identified are new potential biomarkers
+for TNBC. Out of these, JAM3, SFT2D2 and PAPSS1 were previously associated to
+breast tumors or other types of cancer. The relevance of these genes is
+confirmed by the new DetectDeviatingCells (DDC) outlier detection technique. A
+comparison of gene networks on the selected genes showed significant
+differences between TNBC and non-TNBC data. The individual role of FOXA1 in
+TNBC and non-TNBC, and the strong FOXA1-AGR2 connection in TNBC stand out. Not
+only will our results contribute to the breast cancer/TNBC understanding and
+ultimately its management, they also show that robust regression and outlier
+detection constitute key strategies to cope with high-dimensional clinical data
+such as omics data.
+",0,0,0,1,0,0
+3046,Semi-Lagrangian one-step methods for two classes of time-dependent partial differential systems,"  Semi-Lagrangian methods are numerical methods designed to find approximate
+solutions to particular time-dependent partial differential equations (PDEs)
+that describe the advection process. We propose semi-Lagrangian one-step
+methods for numerically solving initial value problems for two general systems
+of partial differential equations. Along the characteristic lines of the PDEs,
+we use ordinary differential equation (ODE) numerical methods to solve the
+PDEs. The main benefit of our methods is the efficient achievement of high
+order local truncation error through the use of Runge-Kutta methods along the
+characteristics. In addition, we investigate the numerical analysis of
+semi-Lagrangian methods applied to systems of PDEs: stability, convergence, and
+maximum error bounds.
+",0,0,1,0,0,0
+3047,Deep Learning for Forecasting Stock Returns in the Cross-Section,"  Many studies have been undertaken by using machine learning techniques,
+including neural networks, to predict stock returns. Recently, a method known
+as deep learning, which achieves high performance mainly in image recognition
+and speech recognition, has attracted attention in the machine learning field.
+This paper implements deep learning to predict one-month-ahead stock returns in
+the cross-section in the Japanese stock market and investigates the performance
+of the method. Our results show that deep neural networks generally outperform
+shallow neural networks, and the best networks also outperform representative
+machine learning models. These results indicate that deep learning shows
+promise as a skillful machine learning method to predict stock returns in the
+cross-section.
+",0,0,0,0,0,1
+3048,Efficient Nearest-Neighbor Search for Dynamical Systems with Nonholonomic Constraints,"  Nearest-neighbor search dominates the asymptotic complexity of sampling-based
+motion planning algorithms and is often addressed with k-d tree data
+structures. While it is generally believed that the expected complexity of
+nearest-neighbor queries is $O(log(N))$ in the size of the tree, this paper
+reveals that when a classic k-d tree approach is used with sub-Riemannian
+metrics, the expected query complexity is in fact $\Theta(N^p \log(N))$ for a
+number $p \in [0, 1)$ determined by the degree of nonholonomy of the system.
+These metrics arise naturally in nonholonomic mechanical systems, including
+classic wheeled robot models. To address this negative result, we propose novel
+k-d tree build and query strategies tailored to sub-Riemannian metrics and
+demonstrate significant improvements in the running time of nearest-neighbor
+search queries.
+",1,0,0,0,0,0
+3049,"Primitivity, Uniform Minimality and State Complexity of Boolean Operations","  A minimal deterministic finite automaton (DFA) is uniformly minimal if it
+always remains minimal when the final state set is replaced by a non-empty
+proper subset of the state set. We prove that a permutation DFA is uniformly
+minimal if and only if its transition monoid is a primitive group. We use this
+to study boolean operations on group languages, which are recognized by direct
+products of permutation DFAs. A direct product cannot be uniformly minimal,
+except in the trivial case where one of the DFAs in the product is a one-state
+DFA. However, non-trivial direct products can satisfy a weaker condition we
+call uniform boolean minimality, where only final state sets used to recognize
+boolean operations are considered. We give sufficient conditions for a direct
+product of two DFAs to be uniformly boolean minimal, which in turn gives
+sufficient conditions for pairs of group languages to have maximal state
+complexity under all binary boolean operations (""maximal boolean complexity"").
+In the case of permutation DFAs with one final state, we give necessary and
+sufficient conditions for pairs of group languages to have maximal boolean
+complexity. Our results demonstrate a connection between primitive groups and
+automata with strong minimality properties.
+",1,0,1,0,0,0
+3050,An Empirical Evaluation of Allgatherv on Multi-GPU Systems,"  Applications for deep learning and big data analytics have compute and memory
+requirements that exceed the limits of a single GPU. However, effectively
+scaling out an application to multiple GPUs is challenging due to the
+complexities of communication between the GPUs, particularly for collective
+communication with irregular message sizes. In this work, we provide a
+performance evaluation of the Allgatherv routine on multi-GPU systems, focusing
+on GPU network topology and the communication library used. We present results
+from the OSU-micro benchmark as well as conduct a case study for sparse tensor
+factorization, one application that uses Allgatherv with highly irregular
+message sizes. We extend our existing tensor factorization tool to run on
+systems with different node counts and varying number of GPUs per node. We then
+evaluate the communication performance of our tool when using traditional MPI,
+CUDA-aware MVAPICH and NCCL across a suite of real-world data sets on three
+different systems: a 16-node cluster with one GPU per node, NVIDIA's DGX-1 with
+8 GPUs and Cray's CS-Storm with 16 GPUs. Our results show that irregularity in
+the tensor data sets produce trends that contradict those in the OSU
+micro-benchmark, as well as trends that are absent from the benchmark.
+",1,0,0,0,0,0
+3051,Summary of Topological Study of Chaotic CBC Mode of Operation,"  In cryptography, block ciphers are the most fundamental elements in many
+symmetric-key encryption systems. The Cipher Block Chaining, denoted CBC,
+presents one of the most famous mode of operation that uses a block cipher to
+provide confidentiality or authenticity. In this research work, we intend to
+summarize our results that have been detailed in our previous series of
+articles. The goal of this series has been to obtain a complete topological
+study of the CBC block cipher mode of operation after proving his chaotic
+behavior according to the reputed definition of Devaney.
+",1,1,0,0,0,0
+3052,Benchmarking five numerical simulation techniques for computing resonance wavelengths and quality factors in photonic crystal membrane line defect cavities,"  We present numerical studies of two photonic crystal membrane microcavities,
+a short line-defect cavity with relatively low quality ($Q$) factor and a
+longer cavity with high $Q$. We use five state-of-the-art numerical simulation
+techniques to compute the cavity $Q$ factor and the resonance wavelength
+$\lambda$ for the fundamental cavity mode in both structures. For each method,
+the relevant computational parameters are systematically varied to estimate the
+computational uncertainty. We show that some methods are more suitable than
+others for treating these challenging geometries.
+",0,1,0,0,0,0
+3053,MSO+nabla is undecidable,"  This paper is about an extension of monadic second-order logic over infinite
+trees, which adds a quantifier that says ""the set of branches \pi which satisfy
+a formula \phi(\pi) has probability one"". This logic was introduced by
+Michalewski and Mio; we call it MSO+nabla following Shelah and Lehmann. The
+logic MSO+nabla subsumes many qualitative probabilistic formalisms, including
+qualitative probabilistic CTL, probabilistic LTL, or parity tree automata with
+probabilistic acceptance conditions. We consider the decision problem: decide
+if a sentence of MSO+nabla is true in the infinite binary tree? For sentences
+from the weak variant of this logic (set quantifiers range only over finite
+sets) the problem was known to be decidable, but the question for the full
+logic remained open. In this paper we show that the problem for the full logic
+MSO+nabla is undecidable.
+",1,0,0,0,0,0
+3054,Fast Nonconvex Deconvolution of Calcium Imaging Data,"  Calcium imaging data promises to transform the field of neuroscience by
+making it possible to record from large populations of neurons simultaneously.
+However, determining the exact moment in time at which a neuron spikes, from a
+calcium imaging data set, amounts to a non-trivial deconvolution problem which
+is of critical importance for downstream analyses. While a number of
+formulations have been proposed for this task in the recent literature, in this
+paper we focus on a formulation recently proposed in Jewell and Witten (2017)
+which has shown initial promising results. However, this proposal is slow to
+run on fluorescence traces of hundreds of thousands of timesteps.
+Here we develop a much faster online algorithm for solving the optimization
+problem of Jewell and Witten (2017) that can be used to deconvolve a
+fluorescence trace of 100,000 timesteps in less than a second. Furthermore,
+this algorithm overcomes a technical challenge of Jewell and Witten (2017) by
+avoiding the occurrence of so-called ""negative"" spikes. We demonstrate that
+this algorithm has superior performance relative to existing methods for spike
+deconvolution on calcium imaging datasets that were recently released as part
+of the spikefinder challenge (this http URL).
+Our C++ implementation, along with R and python wrappers, is publicly
+available on Github at this https URL.
+",0,0,0,1,1,0
+3055,Thinking Fast and Slow with Deep Learning and Tree Search,"  Sequential decision making problems, such as structured prediction, robotic
+control, and game playing, require a combination of planning policies and
+generalisation of those plans. In this paper, we present Expert Iteration
+(ExIt), a novel reinforcement learning algorithm which decomposes the problem
+into separate planning and generalisation tasks. Planning new policies is
+performed by tree search, while a deep neural network generalises those plans.
+Subsequently, tree search is improved by using the neural network policy to
+guide search, increasing the strength of new plans. In contrast, standard deep
+Reinforcement Learning algorithms rely on a neural network not only to
+generalise plans, but to discover them too. We show that ExIt outperforms
+REINFORCE for training a neural network to play the board game Hex, and our
+final tree search agent, trained tabula rasa, defeats MoHex 1.0, the most
+recent Olympiad Champion player to be publicly released.
+",1,0,0,0,0,0
+3056,Computer-aided implant design for the restoration of cranial defects,"  Patient-specific cranial implants are important and necessary in the surgery
+of cranial defect restoration. However, traditional methods of manual design of
+cranial implants are complicated and time-consuming. Our purpose is to develop
+a novel software named EasyCrania to design the cranial implants conveniently
+and efficiently. The process can be divided into five steps, which are
+mirroring model, clipping surface, surface fitting, the generation of the
+initial implant and the generation of the final implant. The main concept of
+our method is to use the geometry information of the mirrored model as the base
+to generate the final implant. The comparative studies demonstrated that the
+EasyCrania can improve the efficiency of cranial implant design significantly.
+And, the intra- and inter-rater reliability of the software were stable, which
+were 87.07+/-1.6% and 87.73+/-1.4% respectively.
+",1,0,0,0,0,0
+3057,Dimension-free Information Concentration via Exp-Concavity,"  Information concentration of probability measures have important implications
+in learning theory. Recently, it is discovered that the information content of
+a log-concave distribution concentrates around their differential entropy,
+albeit with an unpleasant dependence on the ambient dimension. In this work, we
+prove that if the potentials of the log-concave distribution are exp-concave,
+which is a central notion for fast rates in online and statistical learning,
+then the concentration of information can be further improved to depend only on
+the exp-concavity parameter, and hence, it can be dimension independent.
+Central to our proof is a novel yet simple application of the variance
+Brascamp-Lieb inequality. In the context of learning theory, our
+concentration-of-information result immediately implies high-probability
+results to many of the previous bounds that only hold in expectation.
+",0,0,0,1,0,0
+3058,A review of Dan's reduction method for multiple polylogarithms,"  In this paper we will give an account of Dan's reduction method for reducing
+the weight $ n $ multiple logarithm $ I_{1,1,\ldots,1}(x_1, x_2, \ldots, x_n) $
+to an explicit sum of lower depth multiple polylogarithms in $ \leq n - 2 $
+variables.
+We provide a detailed explanation of the method Dan outlines, and we fill in
+the missing proofs for Dan's claims. This establishes the validity of the
+method itself, and allows us to produce a corrected version of Dan's reduction
+of $ I_{1,1,1,1} $ to $ I_{3,1} $'s and $ I_4 $'s. We then use the symbol of
+multiple polylogarithms to answer Dan's question about how this reduction
+compares with his earlier reduction of $ I_{1,1,1,1} $, and his question about
+the nature of the resulting functional equation of $ I_{3,1} $.
+Finally, we apply the method to $ I_{1,1,1,1,1} $ at weight 5 to first
+produce a reduction to depth $ \leq 3 $ integrals. Using some functional
+equations from our thesis, we further reduce this to $ I_{3,1,1} $, $ I_{3,2} $
+and $ I_5 $, modulo products. We also see how to reduce $ I_{3,1,1} $ to $
+I_{3,2} $, modulo $ \delta $ (modulo products and depth 1 terms), and indicate
+how this allows us to reduce $ I_{1,1,1,1,1} $ to $ I_{3,2} $'s only, modulo $
+\delta $.
+",0,0,1,0,0,0
+3059,Improved Bayesian Compression,"  Compression of Neural Networks (NN) has become a highly studied topic in
+recent years. The main reason for this is the demand for industrial scale usage
+of NNs such as deploying them on mobile devices, storing them efficiently,
+transmitting them via band-limited channels and most importantly doing
+inference at scale. In this work, we propose to join the Soft-Weight Sharing
+and Variational Dropout approaches that show strong results to define a new
+state-of-the-art in terms of model compression.
+",0,0,0,1,0,0
+3060,Transient Response Improvement for Interconnected Linear Systems: Low-Dimensional Controller Retrofit Approach,"  In this paper, we propose a method of designing low-dimensional retrofit
+controllers for interconnected linear systems. In the proposed method, by
+retrofitting an additional low-dimensional controller to a preexisting control
+system, we aim at improving transient responses caused by spatially local state
+deflections, which can be regarded as a local fault occurring at a specific
+subsystem. It is found that a type of state-space expansion, called
+hierarchical state-space expansion, is the key to systematically designing a
+low-dimensional retrofit controller, whose action is specialized to controlling
+the corresponding subsystem. Furthermore, the state-space expansion enables
+theoretical clarification of the fact that the performance index of the
+transient response control is improved by appropriately tuning the retrofit
+controller. The efficiency of the proposed method is shown through a motivating
+example of power system control where we clarify the trade-off relation between
+the dimension of a retrofit controller and its control performance.
+",1,0,0,0,0,0
+3061,Gradient Sparsification for Communication-Efficient Distributed Optimization,"  Modern large scale machine learning applications require stochastic
+optimization algorithms to be implemented on distributed computational
+architectures. A key bottleneck is the communication overhead for exchanging
+information such as stochastic gradients among different workers. In this
+paper, to reduce the communication cost we propose a convex optimization
+formulation to minimize the coding length of stochastic gradients. To solve the
+optimal sparsification efficiently, several simple and fast algorithms are
+proposed for approximate solution, with theoretical guaranteed for sparseness.
+Experiments on $\ell_2$ regularized logistic regression, support vector
+machines, and convolutional neural networks validate our sparsification
+approaches.
+",1,0,0,1,0,0
+3062,Interactions mediated by a public good transiently increase cooperativity in growing Pseudomonas putida metapopulations,"  Bacterial communities have rich social lives. A well-established interaction
+involves the exchange of a public good in Pseudomonas populations, where the
+iron-scavenging compound pyoverdine, synthesized by some cells, is shared with
+the rest. Pyoverdine thus mediates interactions between producers and
+non-producers and can constitute a public good. This interaction is often used
+to test game theoretical predictions on the ""social dilemma"" of producers. Such
+an approach, however, underestimates the impact of specific properties of the
+public good, for example consequences of its accumulation in the environment.
+Here, we experimentally quantify costs and benefits of pyoverdine production in
+a specific environment, and build a model of population dynamics that
+explicitly accounts for the changing significance of accumulating pyoverdine as
+chemical mediator of social interactions. The model predicts that, in an
+ensemble of growing populations (metapopulation) with different initial
+producer fractions (and consequently pyoverdine contents), the global producer
+fraction initially increases. Because the benefit of pyoverdine declines at
+saturating concentrations, the increase need only be transient. Confirmed by
+experiments on metapopulations, our results show how a changing benefit of a
+public good can shape social interactions in a bacterial population.
+",0,0,0,0,1,0
+3063,Transfer results for Frobenius extensions,"  We study Frobenius extensions which are free-filtered by a totally ordered,
+finitely generated abelian group, and their free-graded counterparts. First we
+show that the Frobenius property passes up from a free-graded extension to a
+free-filtered extension, then also from a free-filtered extension to the
+extension of their Rees algebras. Our main theorem states that, under some
+natural hypotheses, a free-filtered extension of algebras is Frobenius if and
+only if the associated graded extension is Frobenius. In the final section we
+apply this theorem to provide new examples and non-examples of Frobenius
+extensions.
+",0,0,1,0,0,0
+3064,Ergodicity of spherically symmetric fluid flows outside of a Schwarzschild black hole with random boundary forcing,"  We consider the Burgers equation posed on the outer communication region of a
+Schwarzschild black hole spacetime. Assuming spherical symmetry for the fluid
+flow under consideration, we study the propagation and interaction of shock
+waves under the effect of random forcing. First of all, considering the initial
+and boundary value problem with boundary data prescribed in the vicinity of the
+horizon, we establish a generalization of the Hopf--Lax--Oleinik formula, which
+takes the curved geometry into account and allows us to establish the existence
+of bounded variation solutions. To this end, we analyze the global behavior of
+the characteristic curves in the Schwarzschild geometry, including their
+behavior near the black hole horizon. In a second part, we investigate the
+long-term statistical properties of solutions when a random forcing is imposed
+near the black hole horizon and study the ergodicity of the fluid flow under
+consideration. We prove the existence of a random global attractor and, for the
+Burgers equation outside of a Schwarzschild black hole, we are able to validate
+the so-called `one-force-one-solution' principle. Furthermore, all of our
+results are also established for a pressureless Euler model which consists of
+two balance laws and includes a transport equation satisfied by the integrated
+fluid density.
+",0,0,1,0,0,0
+3065,Recency-weighted Markovian inference,"  We describe a Markov latent state space (MLSS) model, where the latent state
+distribution is a decaying mixture over multiple past states. We present a
+simple sampling algorithm that allows to approximate such high-order MLSS with
+fixed time and memory costs.
+",1,0,0,1,0,0
+3066,A revisit on the compactness of commutators,"  A new characterization of CMO(R^n) is established by the local mean
+oscillation. Some characterizations of iterated compact commutators on weighted
+Lebesgue spaces are given, which are new even in the unweighted setting for the
+first order commutators.
+",0,0,1,0,0,0
+3067,Question Answering through Transfer Learning from Large Fine-grained Supervision Data,"  We show that the task of question answering (QA) can significantly benefit
+from the transfer learning of models trained on a different large, fine-grained
+QA dataset. We achieve the state of the art in two well-studied QA datasets,
+WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique
+from SQuAD. For WikiQA, our model outperforms the previous best model by more
+than 8%. We demonstrate that finer supervision provides better guidance for
+learning lexical and syntactic information than coarser supervision, through
+quantitative results and visual analysis. We also show that a similar transfer
+learning procedure achieves the state of the art on an entailment task.
+",1,0,0,0,0,0
+3068,Relative phantom maps,"  We define a map $f\colon X\to Y$ to be a phantom map relative to a map
+$\varphi\colon B\to Y$ if the restriction of $f$ to any finite dimensional
+skeleton of $X$ lifts to $B$ through $\varphi$, up to homotopy. There are two
+kinds of maps which are obviously relative phantom maps: (1) the composite of a
+map $X\to B$ with $\varphi$; (2) a usual phantom map $X\to Y$. A relative
+phantom map of type (1) is called trivial, and a relative phantom map out of a
+suspension which is a sum of (1) and (2) is called relatively trivial. We study
+the (relative) triviality of relative phantom maps from a suspension, and in
+particular, we give rational homotopy conditions for the (relative) triviality.
+We also give a rational homotopy condition for the triviality of relative
+phantom maps from a non-suspension to a finite Postnikov section.
+",0,0,1,0,0,0
+3069,"Concepts of Architecture, Structure and System","  The current ISO standards pertaining to the Concepts of System and
+Architecture express succinct definitions of these two key terms that lend
+themselves to practical application and can be understood through elementary
+mathematical foundations. The current work of the ISO/IEC Working Group 42 is
+seeking to refine and elaborate the existing standards. This position paper
+revisits the fundamental concepts underlying both of these key terms and offers
+an approach to: (i) refine and exemplify the term 'fundamental concepts' in the
+current ISO definition of Architecture, (ii) exploit existing standards for the
+term 'concept', and (iii) introduce a new concept, Architectural Structure,
+that can serve to unify the current terminology at a fundamental level. Precise
+elementary examples are used in to conceptualise the approach offered.
+",1,0,0,0,0,0
+3070,On mesoprimary decomposition of monoid congruences,"  We prove two main results concerning mesoprimary decomposition of monoid
+congruences, as introduced by Kahle and Miller. First, we identify which
+associated prime congruences appear in every mesoprimary decomposition, thereby
+completing the theory of mesoprimary decomposition of monoid congruences as a
+more faithful analog of primary decomposition. Second, we answer a question
+posed by Kahle and Miller by characterizing which finite posets arise as the
+set of associated prime congruences of monoid congruences.
+",0,0,1,0,0,0
+3071,Assessing the state of e-Readiness for Small and Medium Companies in Mexico: a Proposed Taxonomy and Adoption Model,"  Emerging economies frequently show a large component of their Gross Domestic
+Product to be dependant on the economic activity of small and medium
+enterprises. Nevertheless, e-business solutions are more likely designed for
+large companies. SMEs seem to follow a classical family-based management, used
+to traditional activities, rather than seeking new ways of adding value to
+their business strategy. Thus, a large portion of a nations economy may be at
+disadvantage for competition. This paper aims at assessing the state of
+e-business readiness of Mexican SMEs based on already published e-business
+evolution models and by means of a survey research design. Data is being
+collected in three cities with differing sizes and infrastructure conditions.
+Statistical results are expected to be presented. A second part of this
+research aims at applying classical adoption models to suggest potential causal
+relationships, as well as more suitable recommendations for development.
+",0,0,0,0,0,1
+3072,Densities of Hyperbolic Cusp Invariants,"  We find that cusp densities of hyperbolic knots in the 3-sphere are dense in
+[0,0.6826...] and those of links are dense in [0,0.853...]. We define a new
+invariant associated with cusp volume, the cusp crossing density, as the ratio
+between the cusp volume and the crossing number of a link, and show that cusp
+crossing density for links is bounded above by 3.1263.... Moreover, there is a
+sequence of links with cusp crossing density approaching 3. The least upper
+bound for cusp crossing density remains an open question. For two-component
+hyperbolic links, cusp crossing density is shown to be dense in the interval
+[0,1.6923...] and for all hyperbolic links, cusp crossing density is shown to
+be dense in [0, 2.120...].
+",0,0,1,0,0,0
+3073,Data Augmentation for Robust Keyword Spotting under Playback Interference,"  Accurate on-device keyword spotting (KWS) with low false accept and false
+reject rate is crucial to customer experience for far-field voice control of
+conversational agents. It is particularly challenging to maintain low false
+reject rate in real world conditions where there is (a) ambient noise from
+external sources such as TV, household appliances, or other speech that is not
+directed at the device (b) imperfect cancellation of the audio playback from
+the device, resulting in residual echo, after being processed by the Acoustic
+Echo Cancellation (AEC) system. In this paper, we propose a data augmentation
+strategy to improve keyword spotting performance under these challenging
+conditions. The training set audio is artificially corrupted by mixing in music
+and TV/movie audio, at different signal to interference ratios. Our results
+show that we get around 30-45% relative reduction in false reject rates, at a
+range of false alarm rates, under audio playback from such devices.
+",0,0,0,1,0,0
+3074,A Canonical-based NPN Boolean Matching Algorithm Utilizing Boolean Difference and Cofactor Signature,"  This paper presents a new compact canonical-based algorithm to solve the
+problem of single-output completely specified NPN Boolean matching. We propose
+a new signature vector Boolean difference and cofactor (DC) signature vector.
+Our algorithm utilizes the Boolean difference, cofactor signature and symmetry
+properties to search for canonical transformations. The use of symmetry and
+Boolean difference notably reduces the search space and speeds up the Boolean
+matching process compared to the algorithm proposed in [1]. We tested our
+algorithm on a large number of circuits. The experimental results showed that
+the average runtime of our algorithm 37% higher and its average search space
+67% smaller compared to [1] when tested on general circuits.
+",1,0,0,0,0,0
+3075,Cross ratios on boundaries of symmetric spaces and Euclidean buildings,"  We generalize the natural cross ratio on the ideal boundary of a rank one
+symmetric spaces, or even $\mathrm{CAT}(-1)$ space, to higher rank symmetric
+spaces and (non-locally compact) Euclidean buildings - we obtain vector valued
+cross ratios defined on simplices of the building at infinity. We show several
+properties of those cross ratios; for example that (under some restrictions)
+periods of hyperbolic isometries give back the translation vector. In addition,
+we show that cross ratio preserving maps on the chamber set are induced by
+isometries and vice versa - motivating that the cross ratios bring the geometry
+of the symmetric space/Euclidean building to the boundary.
+",0,0,1,0,0,0
+3076,Heterogeneous inputs to central pattern generators can shape insect gaits,"  In our previous work, we studied an interconnected bursting neuron model for
+insect locomotion, and its corresponding phase oscillator model, which at high
+speed can generate stable tripod gaits with three legs off the ground
+simultaneously in swing, and at low speed can generate stable tetrapod gaits
+with two legs off the ground simultaneously in swing. However, at low speed
+several other stable locomotion patterns, that are not typically observed as
+insect gaits, may coexist. In the present paper, by adding heterogeneous
+external input to each oscillator, we modify the bursting neuron model so that
+its corresponding phase oscillator model produces only one stable gait at each
+speed, specifically: a unique stable tetrapod gait at low speed, a unique
+stable tripod gait at high speed, and a unique branch of stable transition
+gaits connecting them. This suggests that control signals originating in the
+brain and central nervous system can modify gait patterns.
+",0,0,0,0,1,0
+3077,Link invariants derived from multiplexing of crossings,"  We introduce the multiplexing of a crossing, replacing a classical crossing
+of a virtual link diagram with multiple crossings which is a mixture of
+classical and virtual. For integers $m_{i}$ $(i=1,\ldots,n)$ and an ordered
+$n$-component virtual link diagram $D$, a new virtual link diagram
+$D(m_{1},\ldots,m_{n})$ is obtained from $D$ by the multiplexing of all
+crossings. For welded isotopic virtual link diagrams $D$ and $D'$,
+$D(m_{1},\ldots,m_{n})$ and $D'(m_{1},\ldots,m_{n})$ are welded isotopic. From
+the point of view of classical link theory, it seems very interesting that
+$D(m_{1},\ldots,m_{n})$ could not be welded isotopic to a classical link
+diagram even if $D$ is a classical one, and new classical link invariants are
+expected from known welded link invariants via the multiplexing of crossings.
+",0,0,1,0,0,0
+3078,Adaptive Algebraic Multiscale Solver for Compressible Flow in Heterogeneous Porous Media,"  This paper presents the development of an Adaptive Algebraic Multiscale
+Solver for Compressible flow (C-AMS) in heterogeneous porous media. Similar to
+the recently developed AMS for incompressible (linear) flows [Wang et al., JCP,
+2014], C-AMS operates by defining primal and dual-coarse blocks on top of the
+fine-scale grid. These coarse grids facilitate the construction of a
+conservative (finite volume) coarse-scale system and the computation of local
+basis functions, respectively. However, unlike the incompressible (elliptic)
+case, the choice of equations to solve for basis functions in compressible
+problems is not trivial. Therefore, several basis function formulations
+(incompressible and compressible, with and without accumulation) are considered
+in order to construct an efficient multiscale prolongation operator. As for the
+restriction operator, C-AMS allows for both multiscale finite volume (MSFV) and
+finite element (MSFE) methods. Finally, in order to resolve high-frequency
+errors, fine-scale (pre- and post-) smoother stages are employed. In order to
+reduce computational expense, the C-AMS operators (prolongation, restriction,
+and smoothers) are updated adaptively. In addition to this, the linear system
+in the Newton-Raphson loop is infrequently updated. Systematic numerical
+experiments are performed to determine the effect of the various options,
+outlined above, on the C-AMS convergence behaviour. An efficient C-AMS strategy
+for heterogeneous 3D compressible problems is developed based on overall CPU
+times. Finally, C-AMS is compared against an industrial-grade Algebraic
+MultiGrid (AMG) solver. Results of this comparison illustrate that the C-AMS is
+quite efficient as a nonlinear solver, even when iterated to machine accuracy.
+",1,1,0,0,0,0
+3079,Automatic differentiation of hybrid models Illustrated by Diffedge Graphic Methodology. (Survey),"  We investigate the automatic differentiation of hybrid models, viz. models
+that may contain delays, logical tests and discontinuities or loops. We
+consider differentiation with respect to parameters, initial conditions or the
+time. We emphasize the case of a small number of derivations and iterated
+differentiations are mostly treated with a foccus on high order iterations of
+the same derivation. The models we consider may involve arithmetic operations,
+elementary functions, logical tests but also more elaborate components such as
+delays, integrators, equations and differential equations solvers. This survey
+has no pretention to exhaustivity but tries to fil a gap in the litterature
+where each kind of of component may be documented, but seldom their common use.
+The general approach is illustrated by computer algebra experiments,
+stressing the interest of performing differentiation, whenever possible, on
+high level objects, before any translation in Fortran or C code. We include
+ordinary differential systems with discontinuity, with a special interest for
+those comming from discontinuous Lagrangians.
+We conclude with an overview of the graphic methodology developped in the
+Diffedge software for Simulink hybrid models. Not all possibilities are
+covered, but the methodology can be adapted. The result of automatic
+differentiation is a new block diagram and so it can be easily translated to
+produce real time embedded programs.
+We welcome any comments or suggestions of references that we may have missed.
+",1,0,0,0,0,0
+3080,Pinning down the mass of Kepler-10c: the importance of sampling and model comparison,"  Initial RV characterisation of the enigmatic planet Kepler-10c suggested a
+mass of $\sim17$ M$_\oplus$, which was remarkably high for a planet with radius
+$2.32$ R$_\oplus$; further observations and subsequent analysis hinted at a
+(possibly much) lower mass, but masses derived using RVs from two different
+spectrographs (HARPS-N and HIRES) were incompatible at a $3\sigma$-level. We
+demonstrate here how such mass discrepancies may readily arise from sub-optimal
+sampling and/or neglecting to model even a single coherent signal (stellar,
+planetary, or otherwise) that may be present in RVs. We then present a
+plausible resolution of the mass discrepancy, and ultimately characterise
+Kepler-10c as having mass $7.37_{-1.19}^{+1.32}$ M$_\oplus$, and mean density
+$3.14^{+0.63}_{-0.55}$ g cm$^{-3}$.
+",0,1,0,0,0,0
+3081,Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy,"  Building effective, enjoyable, and safe autonomous vehicles is a lot harder
+than has historically been considered. The reason is that, simply put, an
+autonomous vehicle must interact with human beings. This interaction is not a
+robotics problem nor a machine learning problem nor a psychology problem nor an
+economics problem nor a policy problem. It is all of these problems put into
+one. It challenges our assumptions about the limitations of human beings at
+their worst and the capabilities of artificial intelligence systems at their
+best. This work proposes a set of principles for designing and building
+autonomous vehicles in a human-centered way that does not run away from the
+complexity of human nature but instead embraces it. We describe our development
+of the Human-Centered Autonomous Vehicle (HCAV) as an illustrative case study
+of implementing these principles in practice.
+",1,0,0,0,0,0
+3082,Controlled trapping of single particle states on a periodic substrate by deterministic stubbing,"  A periodic array of atomic sites, described within a tight binding formalism
+is shown to be capable of trapping electronic states as it grows in size and
+gets stubbed by an atom or an atomic clusters from a side in a deterministic
+way. We prescribe a method based on a real space renormalization group method,
+that unravels a subtle correlation between the positions of the side coupled
+atoms and the energy eigenvalues for which the incoming particle finally gets
+trapped. We discuss how, in such conditions, the periodic backbone gets
+transformed into an array of infinite quantum wells in the thermodynamic limit.
+We present a case here, where the wells have a hierarchically distribution of
+widths, hosing standing wave solutions in the thermodynamic limit.
+",0,1,0,0,0,0
+3083,Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller,"  This paper proposes a deep cerebellar model articulation controller (DCMAC)
+for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by
+stacking sin-gle-layer CMAC models into multiple layers to form a DCMAC model
+and derive a modified backpropagation training algorithm to learn the DCMAC
+parameters. Com-pared with conventional CMAC, the DCMAC can characterize
+nonlinear transformations more effectively because of its deep structure.
+Experimental results confirm that the pro-posed DCMAC model outperforms the
+CMAC in terms of residual noise in an ANC task, showing that DCMAC provides
+enhanced modeling capability based on channel characteristics.
+",1,0,0,0,0,0
+3084,A Relaxation-based Network Decomposition Algorithm for Parallel Transient Stability Simulation with Improved Convergence,"  Transient stability simulation of a large-scale and interconnected electric
+power system involves solving a large set of differential algebraic equations
+(DAEs) at every simulation time-step. With the ever-growing size and complexity
+of power grids, dynamic simulation becomes more time-consuming and
+computationally difficult using conventional sequential simulation techniques.
+To cope with this challenge, this paper aims to develop a fully distributed
+approach intended for implementation on High Performance Computer (HPC)
+clusters. A novel, relaxation-based domain decomposition algorithm known as
+Parallel-General-Norton with Multiple-port Equivalent (PGNME) is proposed as
+the core technique of a two-stage decomposition approach to divide the overall
+dynamic simulation problem into a set of subproblems that can be solved
+concurrently to exploit parallelism and scalability. While the convergence
+property has traditionally been a concern for relaxation-based decomposition,
+an estimation mechanism based on multiple-port network equivalent is adopted as
+the preconditioner to enhance the convergence of the proposed algorithm. The
+proposed algorithm is illustrated using rigorous mathematics and validated both
+in terms of speed-up and capability. Moreover, a complexity analysis is
+performed to support the observation that PGNME scales well when the size of
+the subproblems are sufficiently large.
+",1,0,0,0,0,0
+3085,A Quantitative Analysis of WCAG 2.0 Compliance For Some Indian Web Portals,"  Web portals have served as an excellent medium to facilitate user centric
+services for organizations irrespective of the type, size, and domain of
+operation. The objective of these portals has been to deliver a plethora of
+services such as information dissemination, transactional services, and
+customer feedback. Therefore, the design of a web portal is crucial in order
+that it is accessible to a wide range of user community irrespective of age
+group, physical abilities, and level of literacy. In this paper, we have
+studied the compliance of WCAG 2.0 by three different categories of Indian web
+sites which are most frequently accessed by a large section of user community.
+We have provided a quantitative evaluation of different aspects of
+accessibility which we believe can pave the way for better design of web sites
+by taking care of the deficiencies inherent in the web portals.
+",1,0,0,0,0,0
+3086,Portfolio Construction Matters,"  The role of portfolio construction in the implementation of equity market
+neutral factors is often underestimated. Taking the classical momentum strategy
+as an example, we show that one can significantly improve the main strategy's
+features by properly taking care of this key step. More precisely, an optimized
+portfolio construction algorithm allows one to significantly improve the Sharpe
+Ratio, reduce sector exposures and volatility fluctuations, and mitigate the
+strategy's skewness and tail correlation with the market. These results are
+supported by long-term, world-wide simulations and will be shown to be
+universal. Our findings are quite general and hold true for a number of other
+""equity factors"". Finally, we discuss the details of a more realistic set-up
+where we also deal with transaction costs.
+",0,0,0,0,0,1
+3087,Function space analysis of deep learning representation layers,"  In this paper we propose a function space approach to Representation Learning
+and the analysis of the representation layers in deep learning architectures.
+We show how to compute a weak-type Besov smoothness index that quantifies the
+geometry of the clustering in the feature space. This approach was already
+applied successfully to improve the performance of machine learning algorithms
+such as the Random Forest and tree-based Gradient Boosting. Our experiments
+demonstrate that in well-known and well-performing trained networks, the Besov
+smoothness of the training set, measured in the corresponding hidden layer
+feature map representation, increases from layer to layer. We also contribute
+to the understanding of generalization by showing how the Besov smoothness of
+the representations, decreases as we add more mis-labeling to the training
+data. We hope this approach will contribute to the de-mystification of some
+aspects of deep learning.
+",1,0,0,1,0,0
+3088,Random group cobordisms of rank 7/4,"  We construct a model of random groups of rank 7/4, and show that in this
+model the random group has the exponential mesoscopic rank property.
+",0,0,1,0,0,0
+3089,Transport properties across the many-body localization transition in quasiperiodic and random systems,"  We theoretically study transport properties in one-dimensional interacting
+quasiperiodic systems at infinite temperature. We compare and contrast the
+dynamical transport properties across the many-body localization (MBL)
+transition in quasiperiodic and random models. Using exact diagonalization we
+compute the optical conductivity $\sigma(\omega)$ and the return probability
+$R(\tau)$ and study their average low-frequency and long-time power-law
+behavior, respectively. We show that the low-energy transport dynamics is
+markedly distinct in both the thermal and MBL phases in quasiperiodic and
+random models and find that the diffusive and MBL regimes of the quasiperiodic
+model are more robust than those in the random system. Using the distribution
+of the DC conductivity, we quantify the contribution of sample-to-sample and
+state-to-state fluctuations of $\sigma(\omega)$ across the MBL transition. We
+find that the activated dynamical scaling ansatz works poorly in the
+quasiperiodic model but holds in the random model with an estimated activation
+exponent $\psi\approx 0.9$. We argue that near the MBL transition in
+quasiperiodic systems, critical eigenstates give rise to a subdiffusive
+crossover regime on finite-size systems.
+",0,1,0,0,0,0
+3090,Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net,"  The lasso and elastic net linear regression models impose a
+double-exponential prior distribution on the model parameters to achieve
+regression shrinkage and variable selection, allowing the inference of robust
+models from large data sets. However, there has been limited success in
+deriving estimates for the full posterior distribution of regression
+coefficients in these models, due to a need to evaluate analytically
+intractable partition function integrals. Here, the Fourier transform is used
+to express these integrals as complex-valued oscillatory integrals over
+""regression frequencies"". This results in an analytic expansion and stationary
+phase approximation for the partition functions of the Bayesian lasso and
+elastic net, where the non-differentiability of the double-exponential prior
+has so far eluded such an approach. Use of this approximation leads to highly
+accurate numerical estimates for the expectation values and marginal posterior
+distributions of the regression coefficients, and allows for Bayesian inference
+of much higher dimensional models than previously possible.
+",0,0,1,1,0,0
+3091,(Un)predictability of strong El Niño events,"  The El Niño-Southern Oscillation (ENSO) is a mode of interannual
+variability in the coupled equatorial Pacific coupled atmosphere/ocean system.
+El Niño describes a state in which sea surface temperatures in the eastern
+Pacific increase and upwelling of colder, deep waters diminishes. El Niño
+events typically peak in boreal winter, but their strength varies irregularly
+on decadal time scales. There were exceptionally strong El Niño events in
+1982-83, 1997-98 and 2015-16 that affected weather on a global scale. Widely
+publicized forecasts in 2014 predicted that the 2015-16 event would occur a
+year earlier. Predicting the strength of El Niño is a matter of practical
+concern due to its effects on hydroclimate and agriculture around the world.
+This paper discusses the frequency and regularity of strong El Niño events in
+the context of chaotic dynamical systems. We discover a mechanism that limits
+their predictability in a conceptual ""recharge oscillator"" model of ENSO. Weak
+seasonal forcing or noise in this model can induce irregular switching between
+an oscillatory state that has strong El Niño events and a chaotic state that
+lacks strong events, In this regime, the timing of strong El Niño events on
+decadal time scales is unpredictable.
+",0,1,0,0,0,0
+3092,Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem,"  One of the key challenges for operations researchers solving real-world
+problems is designing and implementing high-quality heuristics to guide their
+search procedures. In the past, machine learning techniques have failed to play
+a major role in operations research approaches, especially in terms of guiding
+branching and pruning decisions. We integrate deep neural networks into a
+heuristic tree search procedure to decide which branch to choose next and to
+estimate a bound for pruning the search tree of an optimization problem. We
+call our approach Deep Learning assisted heuristic Tree Search (DLTS) and apply
+it to a well-known problem from the container terminals literature, the
+container pre-marshalling problem (CPMP). Our approach is able to learn
+heuristics customized to the CPMP solely through analyzing the solutions to
+CPMP instances, and applies this knowledge within a heuristic tree search to
+produce the highest quality heuristic solutions to the CPMP to date.
+",1,0,0,0,0,0
+3093,Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates,"  The optimization of algorithm (hyper-)parameters is crucial for achieving
+peak performance across a wide range of domains, ranging from deep neural
+networks to solvers for hard combinatorial problems. The resulting algorithm
+configuration (AC) problem has attracted much attention from the machine
+learning community. However, the proper evaluation of new AC procedures is
+hindered by two key hurdles. First, AC benchmarks are hard to set up. Second
+and even more significantly, they are computationally expensive: a single run
+of an AC procedure involves many costly runs of the target algorithm whose
+performance is to be optimized in a given AC benchmark scenario. One common
+workaround is to optimize cheap-to-evaluate artificial benchmark functions
+(e.g., Branin) instead of actual algorithms; however, these have different
+properties than realistic AC problems. Here, we propose an alternative
+benchmarking approach that is similarly cheap to evaluate but much closer to
+the original AC problem: replacing expensive benchmarks by surrogate benchmarks
+constructed from AC benchmarks. These surrogate benchmarks approximate the
+response surface corresponding to true target algorithm performance using a
+regression model, and the original and surrogate benchmark share the same
+(hyper-)parameter space. In our experiments, we construct and evaluate
+surrogate benchmarks for hyperparameter optimization as well as for AC problems
+that involve performance optimization of solvers for hard combinatorial
+problems, drawing training data from the runs of existing AC procedures. We
+show that our surrogate benchmarks capture overall important characteristics of
+the AC scenarios, such as high- and low-performing regions, from which they
+were derived, while being much easier to use and orders of magnitude cheaper to
+evaluate.
+",1,0,0,1,0,0
+3094,Multiplicative local linear hazard estimation and best one-sided cross-validation,"  This paper develops detailed mathematical statistical theory of a new class
+of cross-validation techniques of local linear kernel hazards and their
+multiplicative bias corrections. The new class of cross-validation combines
+principles of local information and recent advances in indirect
+cross-validation. A few applications of cross-validating multiplicative kernel
+hazard estimation do exist in the literature. However, detailed mathematical
+statistical theory and small sample performance are introduced via this paper
+and further upgraded to our new class of best one-sided cross-validation. Best
+one-sided cross-validation turns out to have excellent performance in its
+practical illustrations, in its small sample performance and in its
+mathematical statistical theoretical performance.
+",0,0,0,1,0,0
+3095,Random Perturbations of Matrix Polynomials,"  A sum of a large-dimensional random matrix polynomial and a fixed low-rank
+matrix polynomial is considered. The main assumption is that the resolvent of
+the random polynomial converges to some deterministic limit. A formula for the
+limit of the resolvent of the sum is derived and the eigenvalues are localised.
+Three instances are considered: a low-rank matrix perturbed by the Wigner
+matrix, a product $HX$ of a fixed diagonal matrix $H$ and the Wigner matrix $X$
+and a special matrix polynomial. The results are illustrated with various
+examples and numerical simulations.
+",0,0,1,0,0,0
+3096,Monitoring Information Quality within Web Service Composition and Execution,"  The composition of web services is a promising approach enabling flexible and
+loose integration of business applications. Numerous approaches related to web
+services composition have been developed usually following three main phases:
+the service discovery is based on the semantic description of advertised
+services, i.e. the functionality of the service, meanwhile the service
+selection is based on non- functional quality dimensions of service, and
+finally the service composition aims to support an underlying process. Most of
+those approaches explore techniques of static or dynamic design for an optimal
+service composition. One important aspect so far is mostly neglected, focusing
+on the output produced of composite web services. In this paper, in contrast to
+many prominent approaches we introduce a data quality perspective on web
+services. Based on a data quality management approach, we propose a framework
+for analyzing data produced by the composite service execution. Utilising
+process information together with data in service logs, our approach allows
+identifying problems in service composition and execution. Analyzing the
+service execution history our approach helps to improve common approaches of
+service selection and composition.
+",1,0,0,0,0,0
+3097,An open-source platform to study uniaxial stress effects on nanoscale devices,"  We present an automatic measurement platform that enables the
+characterization of nanodevices by electrical transport and optical
+spectroscopy as a function of uniaxial stress. We provide insights into and
+detailed descriptions of the mechanical device, the substrate design and
+fabrication, and the instrument control software, which is provided under
+open-source license. The capability of the platform is demonstrated by
+characterizing the piezo-resistance of an InAs nanowire device using a
+combination of electrical transport and Raman spectroscopy. The advantages of
+this measurement platform are highlighted by comparison with state-of-the-art
+piezo-resistance measurements in InAs nanowires. We envision that the
+systematic application of this methodology will provide new insights into the
+physics of nanoscale devices and novel materials for electronics, and thus
+contribute to the assessment of the potential of strain as a technology booster
+for nanoscale electronics.
+",0,1,0,0,0,0
+3098,Nonlinear probability. A theory with incompatible stochastic variables,"  In 1991 J.F. Aarnes introduced the concept of quasi-measures in a compact
+topological space $\Omega$ and established the connection between quasi-states
+on $C (\Omega)$ and quasi-measures in $\Omega$. This work solved the linearity
+problem of quasi-states on $C^*$-algebras formulated by R.V. Kadison in 1965.
+The answer is that a quasi-state need not be linear, so a quasi-state need not
+be a state. We introduce nonlinear measures in a space $\Omega$ which is a
+generalization of a measurable space. In this more general setting we are still
+able to define integration and establish a representation theorem for the
+corresponding functionals. A probabilistic language is choosen since we feel
+that the subject should be of some interest to probabilists. In particular we
+point out that the theory allows for incompatible stochastic variables. The
+need for incompatible variables is well known in quantum mechanics, but the
+need seems natural also in other contexts as we try to explain by a questionary
+example.
+Keywords and phrases: Epistemic probability, Integration with respect to mea-
+sures and other set functions, Banach algebras of continuous functions, Set
+func- tions and measures on topological spaces, States, Logical foundations of
+quantum mechanics.
+",0,0,1,1,0,0
+3099,Parametrised second-order complexity theory with applications to the study of interval computation,"  We extend the framework for complexity of operators in analysis devised by
+Kawamura and Cook (2012) to allow for the treatment of a wider class of
+representations. The main novelty is to endow represented spaces of interest
+with an additional function on names, called a parameter, which measures the
+complexity of a given name. This parameter generalises the size function which
+is usually used in second-order complexity theory and therefore also central to
+the framework of Kawamura and Cook. The complexity of an algorithm is measured
+in terms of its running time as a second-order function in the parameter, as
+well as in terms of how much it increases the complexity of a given name, as
+measured by the parameters on the input and output side.
+As an application we develop a rigorous computational complexity theory for
+interval computation. In the framework of Kawamura and Cook the representation
+of real numbers based on nested interval enclosures does not yield a reasonable
+complexity theory. In our new framework this representation is polytime
+equivalent to the usual Cauchy representation based on dyadic rational
+approximation. By contrast, the representation of continuous real functions
+based on interval enclosures is strictly smaller in the polytime reducibility
+lattice than the usual representation, which encodes a modulus of continuity.
+Furthermore, the function space representation based on interval enclosures is
+optimal in the sense that it contains the minimal amount of information amongst
+those representations which render evaluation polytime computable.
+",1,0,0,0,0,0
+3100,VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning,"  Deep generative models provide powerful tools for distributions over
+complicated manifolds, such as those of natural images. But many of these
+methods, including generative adversarial networks (GANs), can be difficult to
+train, in part because they are prone to mode collapse, which means that they
+characterize only a few modes of the true distribution. To address this, we
+introduce VEEGAN, which features a reconstructor network, reversing the action
+of the generator by mapping from data to noise. Our training objective retains
+the original asymptotic consistency guarantee of GANs, and can be interpreted
+as a novel autoencoder loss over the noise. In sharp contrast to a traditional
+autoencoder over data points, VEEGAN does not require specifying a loss
+function over the data, but rather only over the representations, which are
+standard normal by assumption. On an extensive set of synthetic and real world
+image datasets, VEEGAN indeed resists mode collapsing to a far greater extent
+than other recent GAN variants, and produces more realistic samples.
+",0,0,0,1,0,0
+3101,High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks,"  Advances in deep learning for natural images have prompted a surge of
+interest in applying similar techniques to medical images. The majority of the
+initial attempts focused on replacing the input of a deep convolutional neural
+network with a medical image, which does not take into consideration the
+fundamental differences between these two types of images. Specifically, fine
+details are necessary for detection in medical images, unlike in natural images
+where coarse structures matter most. This difference makes it inadequate to use
+the existing network architectures developed for natural images, because they
+work on heavily downscaled images to reduce the memory requirements. This hides
+details necessary to make accurate predictions. Additionally, a single exam in
+medical imaging often comes with a set of views which must be fused in order to
+reach a correct conclusion. In our work, we propose to use a multi-view deep
+convolutional neural network that handles a set of high-resolution medical
+images. We evaluate it on large-scale mammography-based breast cancer screening
+(BI-RADS prediction) using 886,000 images. We focus on investigating the impact
+of the training set size and image size on the prediction accuracy. Our results
+highlight that performance increases with the size of training set, and that
+the best performance can only be achieved using the original resolution. In the
+reader study, performed on a random subset of the test set, we confirmed the
+efficacy of our model, which achieved performance comparable to a committee of
+radiologists when presented with the same data.
+",1,0,0,1,0,0
+3102,Adaptive posterior contraction rates for the horseshoe,"  We investigate the frequentist properties of Bayesian procedures for
+estimation based on the horseshoe prior in the sparse multivariate normal means
+model. Previous theoretical results assumed that the sparsity level, that is,
+the number of signals, was known. We drop this assumption and characterize the
+behavior of the maximum marginal likelihood estimator (MMLE) of a key parameter
+of the horseshoe prior. We prove that the MMLE is an effective estimator of the
+sparsity level, in the sense that it leads to (near) minimax optimal estimation
+of the underlying mean vector generating the data. Besides this empirical Bayes
+procedure, we consider the hierarchical Bayes method of putting a prior on the
+unknown sparsity level as well. We show that both Bayesian techniques lead to
+rate-adaptive optimal posterior contraction, which implies that the horseshoe
+posterior is a good candidate for generating rate-adaptive credible sets.
+",0,0,1,1,0,0
+3103,A Convex Optimization Approach to Dynamic Programming in Continuous State and Action Spaces,"  A convex optimization-based method is proposed to numerically solve dynamic
+programs in continuous state and action spaces. This approach using a
+discretization of the state space has the following salient features. First, by
+introducing an auxiliary optimization variable that assigns the contribution of
+each grid point, it does not require an interpolation in solving an associated
+Bellman equation and constructing a control policy. Second, the proposed method
+allows us to solve the Bellman equation with a desired level of precision via
+convex programming in the case of linear systems and convex costs. We can also
+construct a control policy of which performance converges to the optimum as the
+grid resolution becomes finer in this case. Third, when a nonlinear
+control-affine system is considered, the convex optimization approach provides
+an approximate control policy with a provable suboptimality bound. Fourth, for
+general cases, the proposed convex formulation of dynamic programming operators
+can be simply modified as a nonconvex bi-level program, in which the inner
+problem is a linear program, without losing convergence properties. From our
+convex methods and analyses, we observe that convexity in dynamic programming
+deserves attention as it can play a critical role in obtaining a tractable and
+convergent numerical solution.
+",1,0,0,0,0,0
+3104,Investigating Enactive Learning for Autonomous Intelligent Agents,"  The enactive approach to cognition is typically proposed as a viable
+alternative to traditional cognitive science. Enactive cognition displaces the
+explanatory focus from the internal representations of the agent to the direct
+sensorimotor interaction with its environment. In this paper, we investigate
+enactive learning through means of artificial agent simulations. We compare the
+performances of the enactive agent to an agent operating on classical
+reinforcement learning in foraging tasks within maze environments. The
+characteristics of the agents are analysed in terms of the accessibility of the
+environmental states, goals, and exploration/exploitation tradeoffs. We confirm
+that the enactive agent can successfully interact with its environment and
+learn to avoid unfavourable interactions using intrinsically defined goals. The
+performance of the enactive agent is shown to be limited by the number of
+affordable actions.
+",1,0,0,0,0,0
+3105,Computability of semicomputable manifolds in computable topological spaces,"  We study computable topological spaces and semicomputable and computable sets
+in these spaces. In particular, we investigate conditions under which
+semicomputable sets are computable. We prove that a semicomputable compact
+manifold $M$ is computable if its boundary $\partial M$ is computable. We also
+show how this result combined with certain construction which compactifies a
+semicomputable set leads to the conclusion that some noncompact semicomputable
+manifolds in computable metric spaces are computable.
+",1,0,1,0,0,0
+3106,Acoustic streaming and its suppression in inhomogeneous fluids,"  We present a theoretical and experimental study of boundary-driven acoustic
+streaming in an inhomogeneous fluid with variations in density and
+compressibility. In a homogeneous fluid this streaming results from dissipation
+in the boundary layers (Rayleigh streaming). We show that in an inhomogeneous
+fluid, an additional non-dissipative force density acts on the fluid to
+stabilize particular inhomogeneity configurations, which markedly alters and
+even suppresses the streaming flows. Our theoretical and numerical analysis of
+the phenomenon is supported by ultrasound experiments performed with
+inhomogeneous aqueous iodixanol solutions in a glass-silicon microchip.
+",0,1,0,0,0,0
+3107,Predicting Pulsar Scintillation from Refractive Plasma Sheets,"  The dynamic and secondary spectra of many pulsars show evidence for
+long-lived, aligned images of the pulsar that are stationary on a thin
+scattering sheet. One explanation for this phenomenon considers the effects of
+wave crests along sheets in the ionized interstellar medium, such as those due
+to Alfvén waves propagating along current sheets. If these sheets are closely
+aligned to our line-of-sight to the pulsar, high bending angles arise at the
+wave crests and a selection effect causes alignment of images produced at
+different crests, similar to grazing reflection off of a lake. Using geometric
+optics, we develop a simple parameterized model of these corrugated sheets that
+can be constrained with a single observation and that makes observable
+predictions for variations in the scintillation of the pulsar over time and
+frequency. This model reveals qualitative differences between lensing from
+overdense and underdense corrugated sheets: Only if the sheet is overdense
+compared to the surrounding interstellar medium can the lensed images be
+brighter than the line-of-sight image to the pulsar, and the faint lensed
+images are closer to the pulsar at higher frequencies if the sheet is
+underdense, but at lower frequencies if the sheet is overdense.
+",0,1,0,0,0,0
+3108,Disruptive Behavior Disorder (DBD) Rating Scale for Georgian Population,"  In the presented study Parent/Teacher Disruptive Behavior Disorder (DBD)
+rating scale based on the Diagnostic and Statistical Manual of Mental Disorders
+(DSM-IV-TR [APA, 2000]) which was developed by Pelham and his colleagues
+(Pelham et al., 1992) was translated and adopted for assessment of childhood
+behavioral abnormalities, especially ADHD, ODD and CD in Georgian children and
+adolescents. The DBD rating scale was translated into Georgian language using
+back translation technique by English language philologists and checked and
+corrected by qualified psychologists and psychiatrist of Georgia. Children and
+adolescents in the age range of 6 to 16 years (N 290; Mean Age 10.50, SD=2.88)
+including 153 males (Mean Age 10.42, SD= 2.62) and 141 females (Mean Age 10.60,
+SD=3.14) were recruited from different public schools of Tbilisi and the
+Neurology Department of the Pediatric Clinic of the Tbilisi State Medical
+University. Participants objectively were assessed via interviewing
+parents/teachers and qualified psychologists in three different settings
+including school, home and clinic. In terms of DBD total scores revealed
+statistically significant differences between healthy controls (M=27.71,
+SD=17.26) and children and adolescents with ADHD (M=61.51, SD= 22.79).
+Statistically significant differences were found for inattentive subtype
+between control (M=8.68, SD=5.68) and ADHD (M=18.15, SD=6.57) groups. In
+general it was shown that children and adolescents with ADHD had high score on
+DBD in comparison to typically developed persons. In the study also was
+determined gender wise prevalence in children and adolescents with ADHD, ODD
+and CD. The research revealed prevalence of males in comparison with females in
+all investigated categories.
+",0,0,0,1,0,0
+3109,A GPU Poisson-Fermi Solver for Ion Channel Simulations,"  The Poisson-Fermi model is an extension of the classical Poisson-Boltzmann
+model to include the steric and correlation effects of ions and water treated
+as nonuniform spheres in aqueous solutions. Poisson-Boltzmann electrostatic
+calculations are essential but computationally very demanding for molecular
+dynamics or continuum simulations of complex systems in molecular biophysics
+and electrochemistry. The graphic processing unit (GPU) with enormous
+arithmetic capability and streaming memory bandwidth is now a powerful engine
+for scientific as well as industrial computing. We propose two parallel GPU
+algorithms, one for linear solver and the other for nonlinear solver, for
+solving the Poisson-Fermi equation approximated by the standard finite
+difference method in 3D to study biological ion channels with crystallized
+structures from the Protein Data Bank, for example. Numerical methods for both
+linear and nonlinear solvers in the parallel algorithms are given in detail to
+illustrate the salient features of the CUDA (compute unified device
+architecture) software platform of GPU in implementation. It is shown that the
+parallel algorithms on GPU over the sequential algorithms on CPU (central
+processing unit) can achieve 22.8x and 16.9x speedups for the linear solver
+time and total runtime, respectively.
+",0,1,0,0,0,0
+3110,Quantum Monte Carlo with variable spins: fixed-phase and fixed-node approximations,"  We study several aspects of the recently introduced fixed-phase spin-orbit
+diffusion Monte Carlo (FPSODMC) method, in particular, its relation to the
+fixed-node method and its potential use as a general approach for electronic
+structure calculations. We illustrate constructions of spinor-based wave
+functions with the full space-spin symmetry without assigning up or down spin
+labels to particular electrons, effectively ""complexifying"" even ordinary
+real-valued wave functions. Interestingly, with proper choice of the simulation
+parameters and spin variables, such fixed-phase calculations enable one to
+reach also the fixed-node limit. The fixed-phase solution provides a
+straightforward interpretation as the lowest bosonic state in a given effective
+potential generated by the many-body approximate phase. In addition, the
+divergences present at real wave function nodes are smoothed out to lower
+dimensionality, decreasing thus the variation of sampled quantities and making
+the sampling also more straightforward. We illustrate some of these properties
+on calculations of selected first-row systems that recover the fixed-node
+results with quantitatively similar levels of the corresponding biases. At the
+same time, the fixed-phase approach opens new possibilities for more general
+trial wave functions with further opportunities for increasing accuracy in
+practical calculations.
+",0,1,0,0,0,0
+3111,Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise,"  We introduce coroICA, confounding-robust independent component analysis, a
+novel ICA algorithm which decomposes linearly mixed multivariate observations
+into independent components that are corrupted (and rendered dependent) by
+hidden group-wise stationary confounding. It extends the ordinary ICA model in
+a theoretically sound and explicit way to incorporate group-wise (or
+environment-wise) confounding. We show that our general noise model allows to
+perform ICA in settings where other noisy ICA procedures fail. Additionally, it
+can be used for applications with grouped data by adjusting for different
+stationary noise within each group. We show that the noise model has a natural
+relation to causality and explain how it can be applied in the context of
+causal inference. In addition to our theoretical framework, we provide an
+efficient estimation procedure and prove identifiability of the unmixing matrix
+under mild assumptions. Finally, we illustrate the performance and robustness
+of our method on simulated data, provide audible and visual examples, and
+demonstrate the applicability to real-world scenarios by experiments on
+publicly available Antarctic ice core data as well as two EEG data sets. We
+provide a scikit-learn compatible pip-installable Python package coroICA as
+well as R and Matlab implementations accompanied by a documentation at
+this https URL.
+",0,0,0,1,1,0
+3112,Diversification-Based Learning in Computing and Optimization,"  Diversification-Based Learning (DBL) derives from a collection of principles
+and methods introduced in the field of metaheuristics that have broad
+applications in computing and optimization. We show that the DBL framework goes
+significantly beyond that of the more recent Opposition-based learning (OBL)
+framework introduced in Tizhoosh (2005), which has become the focus of numerous
+research initiatives in machine learning and metaheuristic optimization. We
+unify and extend earlier proposals in metaheuristic search (Glover, 1997,
+Glover and Laguna, 1997) to give a collection of approaches that are more
+flexible and comprehensive than OBL for creating intensification and
+diversification strategies in metaheuristic search. We also describe potential
+applications of DBL to various subfields of machine learning and optimization.
+",1,0,0,0,0,0
+3113,A Tutorial on Canonical Correlation Methods,"  Canonical correlation analysis is a family of multivariate statistical
+methods for the analysis of paired sets of variables. Since its proposition,
+canonical correlation analysis has for instance been extended to extract
+relations between two sets of variables when the sample size is insufficient in
+relation to the data dimensionality, when the relations have been considered to
+be non-linear, and when the dimensionality is too large for human
+interpretation. This tutorial explains the theory of canonical correlation
+analysis including its regularised, kernel, and sparse variants. Additionally,
+the deep and Bayesian CCA extensions are briefly reviewed. Together with the
+numerical examples, this overview provides a coherent compendium on the
+applicability of the variants of canonical correlation analysis. By bringing
+together techniques for solving the optimisation problems, evaluating the
+statistical significance and generalisability of the canonical correlation
+model, and interpreting the relations, we hope that this article can serve as a
+hands-on tool for applying canonical correlation methods in data analysis.
+",1,0,0,1,0,0
+3114,Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces,"  The field of brain-computer interfaces is poised to advance from the
+traditional goal of controlling prosthetic devices using brain signals to
+combining neural decoding and encoding within a single neuroprosthetic device.
+Such a device acts as a ""co-processor"" for the brain, with applications ranging
+from inducing Hebbian plasticity for rehabilitation after brain injury to
+reanimating paralyzed limbs and enhancing memory. We review recent progress in
+simultaneous decoding and encoding for closed-loop control and plasticity
+induction. To address the challenge of multi-channel decoding and encoding, we
+introduce a unifying framework for developing brain co-processors based on
+artificial neural networks and deep learning. These ""neural co-processors"" can
+be used to jointly optimize cost functions with the nervous system to achieve
+desired behaviors ranging from targeted neuro-rehabilitation to augmentation of
+brain function.
+",0,0,0,0,1,0
+3115,Enhanced conservation properties of Vlasov codes through coupling with conservative fluid models,"  Many phenomena in collisionless plasma physics require a kinetic description.
+The evolution of the phase space density can be modeled by means of the Vlasov
+equation, which has to be solved numerically in most of the relevant cases. One
+of the problems that often arise in such simulations is the violation of
+important physical conservation laws. Numerical diffusion in phase space
+translates into unphysical heating, which can increase the overall energy
+significantly, depending on the time scale and the plasma regime. In this
+paper, a general and straightforward way of improving conservation properties
+of Vlasov schemes is presented that can potentially be applied to a variety of
+different codes. The basic idea is to use fluid models with good conservation
+properties for correcting kinetic models. The higher moments that are missing
+in the fluid models are provided by the kinetic codes, so that both kinetic and
+fluid codes compensate the weaknesses of each other in a closed feedback loop.
+",0,1,0,0,0,0
+3116,A note on $p^λ$-convex set in a complete Riemannian manifold,"  In this paper we have generalized the notion of $\lambda$-radial contraction
+in complete Riemannian manifold and developed the concept of $p^\lambda$-convex
+function. We have also given a counter example proving the fact that in general
+$\lambda$-radial contraction of a geodesic is not necessarily a geodesic. We
+have also deduced some relations between geodesic convex sets and
+$p^\lambda$-convex sets and showed that under certain conditions they are
+equivalent.
+",0,0,1,0,0,0
+3117,Bounded cohomology and virtually free hyperbolically embedded subgroups,"  Using a probabilistic argument we show that the second bounded cohomology of
+an acylindrically hyperbolic group $G$ (e.g., a non-elementary hyperbolic or
+relatively hyperbolic group, non-exceptional mapping class group, ${\rm
+Out}(F_n)$, \dots) embeds via the natural restriction maps into the inverse
+limit of the second bounded cohomologies of its virtually free subgroups, and
+in fact even into the inverse limit of the second bounded cohomologies of its
+hyperbolically embedded virtually free subgroups. This result is new and
+non-trivial even in the case where $G$ is a (non-free) hyperbolic group. The
+corresponding statement fails in general for the third bounded cohomology, even
+for surface groups.
+",0,0,1,0,0,0
+3118,Transpiling Programmable Computable Functions to Answer Set Programs,"  Programming Computable Functions (PCF) is a simplified programming language
+which provides the theoretical basis of modern functional programming
+languages. Answer set programming (ASP) is a programming paradigm focused on
+solving search problems. In this paper we provide a translation from PCF to
+ASP. Using this translation it becomes possible to specify search problems
+using PCF.
+",1,0,0,0,0,0
+3119,Scattering polarization of the $d$-states of ions and solar magnetic field: Effects of isotropic collisions,"  Analysis of solar magnetic fields using observations as well as theoretical
+interpretations of the scattering polarization is commonly designated as a high
+priority area of the solar research. The interpretation of the observed
+polarization raises a serious theoretical challenge to the researchers involved
+in this field. In fact, realistic interpretations need detailed investigations
+of the depolarizing role of isotropic collisions with neutral hydrogen. The
+goal of this paper is to determine new relationships which allow the
+calculation of any collisional rates of the d-levels of ions by simply
+determining the value of n^* and $E_p$ without the need of determining the
+interaction potentials and treating the dynamics of collisions. The
+determination of n^* and E_p is easy and based on atomic data usually available
+online. Accurate collisional rates allow a reliable diagnostics of solar
+magnetic fields. In this work we applied our collisional FORTRAN code to a
+large number of cases involving complex and simple ions. After that, the
+results are utilized and injected in a genetic programming code developed with
+C-langugae in order to infer original relationships which will be of great help
+to solar applications. We discussed the accurarcy of our collisional rates in
+the cases of polarized complex atoms and atoms with hyperfine structure. The
+relationships are expressed on the tensorial basis and we explain how to
+include their contributions in the master equation giving the variation of the
+density matrix elements. As a test, we compared the results obtained through
+the general relationships provided in this work with the results obtained
+directly by running our code of collisions. These comparisons show a percentage
+of error of about 10% in the average value.
+",0,1,0,0,0,0
+3120,Persistence Codebooks for Topological Data Analysis,"  Topological data analysis, such as persistent homology has shown beneficial
+properties for machine learning in many tasks. Topological representations,
+such as the persistence diagram (PD), however, have a complex structure
+(multiset of intervals) which makes it difficult to combine with typical
+machine learning workflows. We present novel compact fixed-size vectorial
+representations of PDs based on clustering and bag of words encodings that cope
+well with the inherent sparsity of PDs. Our novel representations outperform
+state-of-the-art approaches from topological data analysis and are
+computationally more efficient.
+",0,0,0,1,0,0
+3121,Multivariate Analysis for Computing Maxima in High Dimensions,"  We study the problem of computing the \textsc{Maxima} of a set of $n$
+$d$-dimensional points. For dimensions 2 and 3, there are algorithms to solve
+the problem with order-oblivious instance-optimal running time. However, in
+higher dimensions there is still room for improvements. We present an algorithm
+sensitive to the structural entropy of the input set, which improves the
+running time, for large classes of instances, on the best solution for
+\textsc{Maxima} to date for $d \ge 4$.
+",1,0,0,0,0,0
+3122,"Generalized multi-Galileons, covariantized new terms, and the no-go theorem for non-singular cosmologies","  It has been pointed out that non-singular cosmological solutions in
+second-order scalar-tensor theories generically suffer from gradient
+instabilities. We extend this no-go result to second-order gravitational
+theories with an arbitrary number of interacting scalar fields. Our proof
+follows directly from the action of generalized multi-Galileons, and thus is
+different from and complementary to that based on the effective field theory
+approach. Several new terms for generalized multi-Galileons on a flat
+background were proposed recently. We find a covariant completion of them and
+confirm that they do not participate in the no-go argument.
+",0,1,0,0,0,0
+3123,Tonic activation of extrasynaptic NMDA receptors decreases intrinsic excitability and promotes bistability in a model of neuronal activity,"  NMDA receptors (NMDA-R) typically contribute to excitatory synaptic
+transmission in the central nervous system. While calcium influx through NMDA-R
+plays a critical role in synaptic plasticity, indirect experimental evidence
+also exists demonstrating actions of NMDAR-mediated calcium influx on neuronal
+excitability through the activation of calcium-activated potassium channels.
+But, so far, this mechanism has not been studied theoretically. Our theoretical
+model provide a simple description of neuronal electrical activity including
+the tonic activity of NMDA receptors and a cytosolic calcium compartment. We
+show that calcium influx through NMDA-R can directly be coupled to activation
+of calcium-activated potassium channels providing an overall inhibitory effect
+on neuronal excitability. Furthermore, the presence of tonic NMDA-R activity
+promotes bistability in electrical activity by dramatically increasing the
+stimulus interval where both a stable steady state and repetitive firing can
+exist. This results could provide an intrinsic mechanism for the constitution
+of memory traces in neuronal circuits. They also shed light on the way by which
+beta-amyloids can decrease neuronal activity when interfering with NMDA-R in
+Alzheimer's disease.
+",0,0,0,0,1,0
+3124,Using Minimum Path Cover to Boost Dynamic Programming on DAGs: Co-Linear Chaining Extended,"  Aligning sequencing reads on graph representations of genomes is an important
+ingredient of pan-genomics. Such approaches typically find a set of local
+anchors that indicate plausible matches between substrings of a read to
+subpaths of the graph. These anchor matches are then combined to form a
+(semi-local) alignment of the complete read on a subpath. Co-linear chaining is
+an algorithmically rigorous approach to combine the anchors. It is a well-known
+approach for the case of two sequences as inputs. Here we extend the approach
+so that one of the inputs can be a directed acyclic graph (DAGs), e.g. a
+splicing graph in transcriptomics or a variant graph in pan-genomics.
+This extension to DAGs turns out to have a tight connection to the minimum
+path cover problem, asking for a minimum-cardinality set of paths that cover
+all the nodes of a DAG. We study the case when the size $k$ of a minimum path
+cover is small, which is often the case in practice. First, we propose an
+algorithm for finding a minimum path cover of a DAG $(V,E)$ in $O(k|E|\log|V|)$
+time, improving all known time-bounds when $k$ is small and the DAG is not too
+dense. Second, we introduce a general technique for extending dynamic
+programming (DP) algorithms from sequences to DAGs. This is enabled by our
+minimum path cover algorithm, and works by mimicking the DP algorithm for
+sequences on each path of the minimum path cover. This technique generally
+produces algorithms that are slower than their counterparts on sequences only
+by a factor $k$. Our technique can be applied, for example, to the classical
+longest increasing subsequence and longest common subsequence problems,
+extended to labeled DAGs. Finally, we apply this technique to the co-linear
+chaining problem. We also implemented the new co-linear chaining approach.
+Experiments on splicing graphs show that the new method is efficient also in
+practice.
+",1,0,0,0,0,0
+3125,Visual Integration of Data and Model Space in Ensemble Learning,"  Ensembles of classifier models typically deliver superior performance and can
+outperform single classifier models given a dataset and classification task at
+hand. However, the gain in performance comes together with the lack in
+comprehensibility, posing a challenge to understand how each model affects the
+classification outputs and where the errors come from. We propose a tight
+visual integration of the data and the model space for exploring and combining
+classifier models. We introduce a workflow that builds upon the visual
+integration and enables the effective exploration of classification outputs and
+models. We then present a use case in which we start with an ensemble
+automatically selected by a standard ensemble selection algorithm, and show how
+we can manipulate models and alternative combinations.
+",1,0,0,1,0,0
+3126,Steady States of Rotating Stars and Galaxies,"  A rotating continuum of particles attracted to each other by gravity may be
+modeled by the Euler-Poisson system. The existence of solutions is a very
+classical problem. Here it is proven that a curve of solutions exists,
+parametrized by the rotation speed, with a fixed mass independent of the speed.
+The rotation is allowed to vary with the distance to the axis. A special case
+is when the equation of state is $p=\rho^\gamma,\ 6/5<\gamma<2,\ \gamma\ne4/3$,
+in contrast to previous variational methods which have required $4/3 < \gamma$.
+The continuum of particles may alternatively be modeled microscopically by
+the Vlasov-Poisson system. The kinetic density is a prescribed function. We
+prove an analogous theorem asserting the existence of a curve of solutions with
+constant mass. In this model the whole range $(6/5,2)$ is allowed, including
+$\gamma=4/3$.
+",0,0,1,0,0,0
+3127,Critical properties of the contact process with quenched dilution,"  We have studied the critical properties of the contact process on a square
+lattice with quenched site dilution by Monte Carlo simulations. This was
+achieved by generating in advance the percolating cluster, through the use of
+an appropriate epidemic model, and then by the simulation of the contact
+process on the top of the percolating cluster. The dynamic critical exponents
+were calculated by assuming an activated scaling relation and the static
+exponents by the usual power law behavior. Our results are in agreement with
+the prediction that the quenched diluted contact process belongs to the
+universality class of the random transverse-field Ising model. We have also
+analyzed the model and determined the phase diagram by the use of a mean-field
+theory that takes into account the correlation between neighboring sites.
+",0,1,0,0,0,0
+3128,A Unified Strouhal-Reynolds Number Relationship for Laminar Vortex Streets Generated by Different Shaped Obstacles,"  A new Strouhal-Reynolds number relationship, $St=1/(A+B/Re)$, has been
+recently proposed based on observations of laminar vortex shedding from
+circular cylinders in a flowing soap film. Since the new $St$-$Re$ relation was
+derived from a general physical consideration, it raises the possibility that
+it may be applicable to vortex shedding from bodies other than circular ones.
+The work presented herein provides experimental evidence that this is the case.
+Our measurements also show that in the asymptotic limit
+($Re\rightarrow\infty$), $St_{\infty}=1/A\simeq0.21$ is constant independent of
+rod shapes, leaving $B$ the only parameter that is shape dependent.
+",0,1,0,0,0,0
+3129,On Data-Dependent Random Features for Improved Generalization in Supervised Learning,"  The randomized-feature approach has been successfully employed in large-scale
+kernel approximation and supervised learning. The distribution from which the
+random features are drawn impacts the number of features required to
+efficiently perform a learning task. Recently, it has been shown that employing
+data-dependent randomization improves the performance in terms of the required
+number of random features. In this paper, we are concerned with the
+randomized-feature approach in supervised learning for good generalizability.
+We propose the Energy-based Exploration of Random Features (EERF) algorithm
+based on a data-dependent score function that explores the set of possible
+features and exploits the promising regions. We prove that the proposed score
+function with high probability recovers the spectrum of the best fit within the
+model class. Our empirical results on several benchmark datasets further verify
+that our method requires smaller number of random features to achieve a certain
+generalization error compared to the state-of-the-art while introducing
+negligible pre-processing overhead. EERF can be implemented in a few lines of
+code and requires no additional tuning parameters.
+",1,0,0,1,0,0
+3130,Anomalous electron spectrum and its relation to peak structure of electron scattering rate in cuprate superconductors,"  The recent discovery of a direct link between the sharp peak in the electron
+quasiparticle scattering rate of cuprate superconductors and the well-known
+peak-dip-hump structure in the electron quasiparticle excitation spectrum is
+calling for an explanation. Within the framework of the kinetic-energy driven
+superconducting mechanism, the complicated line-shape in the electron
+quasiparticle excitation spectrum of cuprate superconductors is investigated.
+It is shown that the interaction between electrons by the exchange of spin
+excitations generates a notable peak structure in the electron quasiparticle
+scattering rate around the antinodal and nodal regions. However, this peak
+structure disappears at the hot spots, which leads to that the striking
+peak-dip-hump structure is developed around the antinodal and nodal regions,
+and vanishes at the hot spots. The theory also confirms that the sharp peak
+observed in the electron quasiparticle scattering rate is directly responsible
+for the remarkable peak-dip-hump structure in the electron quasiparticle
+excitation spectrum of cuprate superconductors.
+",0,1,0,0,0,0
+3131,New goodness-of-fit diagnostics for conditional discrete response models,"  This paper proposes new specification tests for conditional models with
+discrete responses, which are key to apply efficient maximum likelihood
+methods, to obtain consistent estimates of partial effects and to get
+appropriate predictions of the probability of future events. In particular, we
+test the static and dynamic ordered choice model specifications and can cover
+infinite support distributions for e.g. count data. The traditional approach
+for specification testing of discrete response models is based on probability
+integral transforms of a jittered discrete data which leads to continuous
+uniform iid series under the true conditional distribution. Then, standard
+specification testing techniques for continuous variables could be applied to
+the transformed series, but the extra randomness from jitters affects the power
+properties of these methods. We investigate in this paper an alternative
+transformation based only on original discrete data that avoids any
+randomization. We analyze the asymptotic properties of goodness-of-fit tests
+based on this new transformation and explore the properties in finite samples
+of a bootstrap algorithm to approximate the critical values of test statistics
+which are model and parameter dependent. We show analytically and in
+simulations that our approach dominates the methods based on randomization in
+terms of power. We apply the new tests to models of the monetary policy
+conducted by the Federal Reserve.
+",0,0,1,1,0,0
+3132,Tailoring Heterovalent Interface Formation with Light,"  Integrating different semiconductor materials into an epitaxial device
+structure offers additional degrees of freedom to select for optimal material
+properties in each layer. However, interface between materials with different
+valences (i.e. III-V, II-VI and IV semiconductors) can be difficult to form
+with high quality. Using ZnSe/GaAs as a model system, we explore the use of UV
+illumination during heterovalent interface growth by molecular beam epitaxy as
+a way to modify the interface properties. We find that UV illumination alters
+the mixture of chemical bonds at the interface, permitting the formation of
+Ga-Se bonds that help to passivate the underlying GaAs layer. Illumination also
+helps to reduce defects in the ZnSe epilayer. These results suggest that
+moderate UV illumination during growth may be used as a way to improve the
+optical properties of both the GaAs and ZnSe layers on either side of the
+interface.
+",0,1,0,0,0,0
+3133,Looking backward: From Euler to Riemann,"  We survey the main ideas in the early history of the subjects on which
+Riemann worked and that led to some of his most important discoveries. The
+subjects discussed include the theory of functions of a complex variable,
+elliptic and Abelian integrals, the hypergeometric series, the zeta function,
+topology, differential geometry, integration, and the notion of space. We shall
+see that among Riemann's predecessors in all these fields, one name occupies a
+prominent place, this is Leonhard Euler. The final version of this paper will
+appear in the book \emph{From Riemann to differential geometry and relativity}
+(L. Ji, A. Papadopoulos and S. Yamada, ed.) Berlin: Springer, 2017.
+",0,0,1,0,0,0
+3134,Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper,"  In this paper we investigate an emerging application, 3D scene understanding,
+likely to be significant in the mobile space in the near future. The goal of
+this exploration is to reduce execution time while meeting our quality of
+result objectives. In previous work we showed for the first time that it is
+possible to map this application to power constrained embedded systems,
+highlighting that decision choices made at the algorithmic design-level have
+the most impact.
+As the algorithmic design space is too large to be exhaustively evaluated, we
+use a previously introduced multi-objective Random Forest Active Learning
+prediction framework dubbed HyperMapper, to find good algorithmic designs. We
+show that HyperMapper generalizes on a recent cutting edge 3D scene
+understanding algorithm and on a modern GPU-based computer architecture.
+HyperMapper is able to beat an expert human hand-tuning the algorithmic
+parameters of the class of Computer Vision applications taken under
+consideration in this paper automatically. In addition, we use crowd-sourcing
+using a 3D scene understanding Android app to show that the Pareto front
+obtained on an embedded system can be used to accelerate the same application
+on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from
+2 to over 12.
+",1,0,0,0,0,0
+3135,A comparison theorem for MW-motivic cohomology,"  We prove that for a finitely generated field over an infinite perfect field
+k, and for any integer n, the (n,n)-th MW-motivic cohomology group identifies
+with the n-th Milnor-Witt K-theory group of that field
+",0,0,1,0,0,0
+3136,Wiener Filtering for Passive Linear Quantum Systems,"  This paper considers a version of the Wiener filtering problem for
+equalization of passive quantum linear quantum systems. We demonstrate that
+taking into consideration the quantum nature of the signals involved leads to
+features typically not encountered in classical equalization problems. Most
+significantly, finding a mean-square optimal quantum equalizing filter amounts
+to solving a nonconvex constrained optimization problem. We discuss two
+approaches to solving this problem, both involving a relaxation of the
+constraint. In both cases, unlike classical equalization, there is a threshold
+on the variance of the noise below which an improvement of the mean-square
+error cannot be guaranteed.
+",1,0,0,0,0,0
+3137,Robust Navigation In GNSS Degraded Environment Using Graph Optimization,"  Robust navigation in urban environments has received a considerable amount of
+both academic and commercial interest over recent years. This is primarily due
+to large commercial organizations such as Google and Uber stepping into the
+autonomous navigation market. Most of this research has shied away from Global
+Navigation Satellite System (GNSS) based navigation. The aversion to utilizing
+GNSS data is due to the degraded nature of the data in urban environment (e.g.,
+multipath, poor satellite visibility). The degradation of the GNSS data in
+urban environments makes it such that traditional (GNSS) positioning methods
+(e.g., extended Kalman filter, particle filters) perform poorly. However,
+recent advances in robust graph theoretic based sensor fusion methods,
+primarily applied to Simultaneous Localization and Mapping (SLAM) based robotic
+applications, can also be applied to GNSS data processing. This paper will
+utilize one such method known as the factor graph in conjunction several robust
+optimization techniques to evaluate their applicability to robust GNSS data
+processing. The goals of this study are two-fold. First, for GNSS applications,
+we will experimentally evaluate the effectiveness of robust optimization
+techniques within a graph-theoretic estimation framework. Second, by releasing
+the software developed and data sets used for this study, we will introduce a
+new open-source front-end to the Georgia Tech Smoothing and Mapping (GTSAM)
+library for the purpose of integrating GNSS pseudorange observations.
+",1,0,0,0,0,0
+3138,On reproduction of On the regularization of Wasserstein GANs,"  This report has several purposes. First, our report is written to investigate
+the reproducibility of the submitted paper On the regularization of Wasserstein
+GANs (2018). Second, among the experiments performed in the submitted paper,
+five aspects were emphasized and reproduced: learning speed, stability,
+robustness against hyperparameter, estimating the Wasserstein distance, and
+various sampling method. Finally, we identify which parts of the contribution
+can be reproduced, and at what cost in terms of resources. All source code for
+reproduction is open to the public.
+",1,0,0,1,0,0
+3139,Density Estimation with Contaminated Data: Minimax Rates and Theory of Adaptation,"  This paper studies density estimation under pointwise loss in the setting of
+contamination model. The goal is to estimate $f(x_0)$ at some
+$x_0\in\mathbb{R}$ with i.i.d. observations, $$ X_1,\dots,X_n\sim
+(1-\epsilon)f+\epsilon g, $$ where $g$ stands for a contamination distribution.
+In the context of multiple testing, this can be interpreted as estimating the
+null density at a point. We carefully study the effect of contamination on
+estimation through the following model indices: contamination proportion
+$\epsilon$, smoothness of target density $\beta_0$, smoothness of contamination
+density $\beta_1$, and level of contamination $m$ at the point to be estimated,
+i.e. $g(x_0)\leq m$. It is shown that the minimax rate with respect to the
+squared error loss is of order $$
+[n^{-\frac{2\beta_0}{2\beta_0+1}}]\vee[\epsilon^2(1\wedge
+m)^2]\vee[n^{-\frac{2\beta_1}{2\beta_1+1}}\epsilon^{\frac{2}{2\beta_1+1}}], $$
+which characterizes the exact influence of contamination on the difficulty of
+the problem. We then establish the minimal cost of adaptation to contamination
+proportion, to smoothness and to both of the numbers. It is shown that some
+small price needs to be paid for adaptation in any of the three cases.
+Variations of Lepski's method are considered to achieve optimal adaptation.
+The problem is also studied when there is no smoothness assumption on the
+contamination distribution. This setting that allows for an arbitrary
+contamination distribution is recognized as Huber's $\epsilon$-contamination
+model. The minimax rate is shown to be $$
+[n^{-\frac{2\beta_0}{2\beta_0+1}}]\vee [\epsilon^{\frac{2\beta_0}{\beta_0+1}}].
+$$ The adaptation theory is also different from the smooth contamination case.
+While adaptation to either contamination proportion or smoothness only costs a
+logarithmic factor, adaptation to both numbers is proved to be impossible.
+",0,0,1,1,0,0
+3140,A uniform bound on the Brauer groups of certain log K3 surfaces,"  Let U be the complement of a smooth anticanonical divisor in a del Pezzo
+surface of degree at most 7 over a number field k. We show that there is an
+effective uniform bound for the size of the Brauer group of U in terms of the
+degree of k.
+",0,0,1,0,0,0
+3141,Composable Deep Reinforcement Learning for Robotic Manipulation,"  Model-free deep reinforcement learning has been shown to exhibit good
+performance in domains ranging from video games to simulated robotic
+manipulation and locomotion. However, model-free methods are known to perform
+poorly when the interaction time with the environment is limited, as is the
+case for most real-world robotic tasks. In this paper, we study how maximum
+entropy policies trained using soft Q-learning can be applied to real-world
+robotic manipulation. The application of this method to real-world manipulation
+is facilitated by two important features of soft Q-learning. First, soft
+Q-learning can learn multimodal exploration strategies by learning policies
+represented by expressive energy-based models. Second, we show that policies
+learned with soft Q-learning can be composed to create new policies, and that
+the optimality of the resulting policy can be bounded in terms of the
+divergence between the composed policies. This compositionality provides an
+especially valuable tool for real-world manipulation, where constructing new
+policies by composing existing skills can provide a large gain in efficiency
+over training from scratch. Our experimental evaluation demonstrates that soft
+Q-learning is substantially more sample efficient than prior model-free deep
+reinforcement learning methods, and that compositionality can be performed for
+both simulated and real-world tasks.
+",1,0,0,1,0,0
+3142,Stimulated Raman Scattering Imposes Fundamental Limits to the Duration and Bandwidth of Temporal Cavity Solitons,"  Temporal cavity solitons (CS) are optical pulses that can persist in passive
+resonators, and they play a key role in the generation of coherent
+microresonator frequency combs. In resonators made of amorphous materials, such
+as fused silica, they can exhibit a spectral red-shift due to stimulated Raman
+scattering. Here we show that this Raman-induced self-frequency-shift imposes a
+fundamental limit on the duration and bandwidth of temporal CSs. Specifically,
+we theoretically predict that stimulated Raman scattering introduces a
+previously unidentified Hopf bifurcation that leads to destabilization of CSs
+at large pump-cavity detunings, limiting the range of detunings over which they
+can exist. We have confirmed our theoretical predictions by performing
+extensive experiments in several different synchronously-driven fiber ring
+resonators, obtaining results in excellent agreement with numerical
+simulations. Our results could have significant implications for the future
+design of Kerr frequency comb systems based on amorphous microresonators.
+",0,1,0,0,0,0
+3143,Investigation of Using VAE for i-Vector Speaker Verification,"  New system for i-vector speaker recognition based on variational autoencoder
+(VAE) is investigated. VAE is a promising approach for developing accurate deep
+nonlinear generative models of complex data. Experiments show that VAE provides
+speaker embedding and can be effectively trained in an unsupervised manner. LLR
+estimate for VAE is developed. Experiments on NIST SRE 2010 data demonstrate
+its correctness. Additionally, we show that the performance of VAE-based system
+in the i-vectors space is close to that of the diagonal PLDA. Several
+interesting results are also observed in the experiments with $\beta$-VAE. In
+particular, we found that for $\beta\ll 1$, VAE can be trained to capture the
+features of complex input data distributions in an effective way, which is hard
+to obtain in the standard VAE ($\beta=1$).
+",1,0,0,1,0,0
+3144,A Unifying Framework for Convergence Analysis of Approximate Newton Methods,"  Many machine learning models are reformulated as optimization problems. Thus,
+it is important to solve a large-scale optimization problem in big data
+applications. Recently, subsampled Newton methods have emerged to attract much
+attention for optimization due to their efficiency at each iteration, rectified
+a weakness in the ordinary Newton method of suffering a high cost in each
+iteration while commanding a high convergence rate. Other efficient stochastic
+second order methods are also proposed. However, the convergence properties of
+these methods are still not well understood. There are also several important
+gaps between the current convergence theory and the performance in real
+applications. In this paper, we aim to fill these gaps. We propose a unifying
+framework to analyze local convergence properties of second order methods.
+Based on this framework, our theoretical analysis matches the performance in
+real applications.
+",1,0,0,0,0,0
+3145,A Newman property for BLD-mappings,"  We define a Newman property for BLD-mappings and study its connections to the
+porosity of the branch set in the setting of generalized manifolds equipped
+with complete path metrics.
+",0,0,1,0,0,0
+3146,Learning to Use Learners' Advice,"  In this paper, we study a variant of the framework of online learning using
+expert advice with limited/bandit feedback. We consider each expert as a
+learning entity, seeking to more accurately reflecting certain real-world
+applications. In our setting, the feedback at any time $t$ is limited in a
+sense that it is only available to the expert $i^t$ that has been selected by
+the central algorithm (forecaster), \emph{i.e.}, only the expert $i^t$ receives
+feedback from the environment and gets to learn at time $t$. We consider a
+generic black-box approach whereby the forecaster does not control or know the
+learning dynamics of the experts apart from knowing the following no-regret
+learning property: the average regret of any expert $j$ vanishes at a rate of
+at least $O(t_j^{\regretRate-1})$ with $t_j$ learning steps where $\regretRate
+\in [0, 1]$ is a parameter.
+In the spirit of competing against the best action in hindsight in
+multi-armed bandits problem, our goal here is to be competitive w.r.t. the
+cumulative losses the algorithm could receive by following the policy of always
+selecting one expert. We prove the following hardness result: without any
+coordination between the forecaster and the experts, it is impossible to design
+a forecaster achieving no-regret guarantees. In order to circumvent this
+hardness result, we consider a practical assumption allowing the forecaster to
+""guide"" the learning process of the experts by filtering/blocking some of the
+feedbacks observed by them from the environment, \emph{i.e.}, not allowing the
+selected expert $i^t$ to learn at time $t$ for some time steps. Then, we design
+a novel no-regret learning algorithm \algo for this problem setting by
+carefully guiding the feedbacks observed by experts. We prove that \algo
+achieves the worst-case expected cumulative regret of $O(\Time^\frac{1}{2 -
+\regretRate})$ after $\Time$ time steps.
+",1,0,0,0,0,0
+3147,Fast and Accurate Time Series Classification with WEASEL,"  Time series (TS) occur in many scientific and commercial applications,
+ranging from earth surveillance to industry automation to the smart grids. An
+important type of TS analysis is classification, which can, for instance,
+improve energy load forecasting in smart grids by detecting the types of
+electronic devices based on their energy consumption profiles recorded by
+automatic sensors. Such sensor-driven applications are very often characterized
+by (a) very long TS and (b) very large TS datasets needing classification.
+However, current methods to time series classification (TSC) cannot cope with
+such data volumes at acceptable accuracy; they are either scalable but offer
+only inferior classification quality, or they achieve state-of-the-art
+classification quality but cannot scale to large data volumes.
+In this paper, we present WEASEL (Word ExtrAction for time SEries
+cLassification), a novel TSC method which is both scalable and accurate. Like
+other state-of-the-art TSC methods, WEASEL transforms time series into feature
+vectors, using a sliding-window approach, which are then analyzed through a
+machine learning classifier. The novelty of WEASEL lies in its specific method
+for deriving features, resulting in a much smaller yet much more discriminative
+feature set. On the popular UCR benchmark of 85 TS datasets, WEASEL is more
+accurate than the best current non-ensemble algorithms at orders-of-magnitude
+lower classification and training times, and it is almost as accurate as
+ensemble classifiers, whose computational complexity makes them inapplicable
+even for mid-size datasets. The outstanding robustness of WEASEL is also
+confirmed by experiments on two real smart grid datasets, where it
+out-of-the-box achieves almost the same accuracy as highly tuned,
+domain-specific methods.
+",1,0,0,1,0,0
+3148,Deep Neural Network Architectures for Modulation Classification,"  In this work, we investigate the value of employing deep learning for the
+task of wireless signal modulation recognition. Recently in [1], a framework
+has been introduced by generating a dataset using GNU radio that mimics the
+imperfections in a real wireless channel, and uses 10 different modulation
+types. Further, a convolutional neural network (CNN) architecture was developed
+and shown to deliver performance that exceeds that of expert-based approaches.
+Here, we follow the framework of [1] and find deep neural network architectures
+that deliver higher accuracy than the state of the art. We tested the
+architecture of [1] and found it to achieve an accuracy of approximately 75% of
+correctly recognizing the modulation type. We first tune the CNN architecture
+of [1] and find a design with four convolutional layers and two dense layers
+that gives an accuracy of approximately 83.8% at high SNR. We then develop
+architectures based on the recently introduced ideas of Residual Networks
+(ResNet [2]) and Densely Connected Networks (DenseNet [3]) to achieve high SNR
+accuracies of approximately 83.5% and 86.6%, respectively. Finally, we
+introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to
+achieve an accuracy of approximately 88.5% at high SNR.
+",1,0,0,1,0,0
+3149,Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security,"  Online programming discussion platforms such as Stack Overflow serve as a
+rich source of information for software developers. Available information
+include vibrant discussions and oftentimes ready-to-use code snippets.
+Anecdotes report that software developers copy and paste code snippets from
+those information sources for convenience reasons. Such behavior results in a
+constant flow of community-provided code snippets into production software. To
+date, the impact of this behaviour on code security is unknown. We answer this
+highly important question by quantifying the proliferation of security-related
+code snippets from Stack Overflow in Android applications available on Google
+Play. Access to the rich source of information available on Stack Overflow
+including ready-to-use code snippets provides huge benefits for software
+developers. However, when it comes to code security there are some caveats to
+bear in mind: Due to the complex nature of code security, it is very difficult
+to provide ready-to-use and secure solutions for every problem. Hence,
+integrating a security-related code snippet from Stack Overflow into production
+software requires caution and expertise. Unsurprisingly, we observed insecure
+code snippets being copied into Android applications millions of users install
+from Google Play every day. To quantitatively evaluate the extent of this
+observation, we scanned Stack Overflow for code snippets and evaluated their
+security score using a stochastic gradient descent classifier. In order to
+identify code reuse in Android applications, we applied state-of-the-art static
+analysis. Our results are alarming: 15.4% of the 1.3 million Android
+applications we analyzed, contained security-related code snippets from Stack
+Overflow. Out of these 97.9% contain at least one insecure code snippet.
+",1,0,0,0,0,0
+3150,United Nations Digital Blue Helmets as a Starting Point for Cyber Peacekeeping,"  Prior works, such as the Tallinn manual on the international law applicable
+to cyber warfare, focus on the circumstances of cyber warfare. Many
+organizations are considering how to conduct cyber warfare, but few have
+discussed methods to reduce, or even prevent, cyber conflict. A recent series
+of publications started developing the framework of Cyber Peacekeeping (CPK)
+and its legal requirements. These works assessed the current state of
+organizations such as ITU IMPACT, NATO CCDCOE and Shanghai Cooperation
+Organization, and found that they did not satisfy requirements to effectively
+host CPK activities. An assessment of organizations currently working in the
+areas related to CPK found that the United Nations (UN) has mandates and
+organizational structures that appear to somewhat overlap the needs of CPK.
+However, the UN's current approach to Peacekeeping cannot be directly mapped to
+cyberspace. In this research we analyze the development of traditional
+Peacekeeping in the United Nations, and current initiatives in cyberspace.
+Specifically, we will compare the proposed CPK framework with the recent
+initiative of the United Nations named the 'Digital Blue Helmets' as well as
+with other projects in the UN which helps to predict and mitigate conflicts.
+Our goal is to find practical recommendations for the implementation of the CPK
+framework in the United Nations, and to examine how responsibilities defined in
+the CPK framework overlap with those of the 'Digital Blue Helmets' and the
+Global Pulse program.
+",1,0,0,0,0,0
+3151,Motives of derived equivalent K3 surfaces,"  We observe that derived equivalent K3 surfaces have isomorphic Chow motives.
+",0,0,1,0,0,0
+3152,Robust Dual View Deep Agent,"  Motivated by recent advance of machine learning using Deep Reinforcement
+Learning this paper proposes a modified architecture that produces more robust
+agents and speeds up the training process. Our architecture is based on
+Asynchronous Advantage Actor-Critic (A3C) algorithm where the total input
+dimensionality is halved by dividing the input into two independent streams. We
+use ViZDoom, 3D world software that is based on the classical first person
+shooter video game, Doom, as a test case. The experiments show that in
+comparison to single input agents, the proposed architecture succeeds to have
+the same playing performance and shows more robust behavior, achieving
+significant reduction in the number of training parameters of almost 30%.
+",0,0,0,1,0,0
+3153,Microplasma generation by slow microwave in an electromagnetically induced transparency-like metasurface,"  Microplasma generation using microwaves in an electromagnetically induced
+transparency (EIT)-like metasurface composed of two types of radiatively
+coupled cut-wire resonators with slightly different resonance frequencies is
+investigated. Microplasma is generated in either of the gaps of the cut-wire
+resonators as a result of strong enhancement of the local electric field
+associated with resonance and slow microwave effect. The threshold microwave
+power for plasma ignition is found to reach a minimum at the EIT-like
+transmission peak frequency, where the group index is maximized. A pump-probe
+measurement of the metasurface reveals that the transmission properties can be
+significantly varied by varying the properties of the generated microplasma
+near the EIT-like transmission peak frequency and the resonance frequency. The
+electron density of the microplasma is roughly estimated to be of order
+$1\times 10^{10}\,\mathrm{cm}^{-3}$ for a pump power of $15.8\,\mathrm{W}$ by
+comparing the measured transmission spectrum for the probe wave with the
+numerically calculated spectrum. In the calculation, we assumed that the plasma
+is uniformly generated in the resonator gap, that the electron temperature is
+$2\,\mathrm{eV}$, and that the elastic scattering cross section is $20 \times
+10^{-16}\,\mathrm{cm}^2$.
+",0,1,0,0,0,0
+3154,K-theory of group Banach algebras and Banach property RD,"  We investigate Banach algebras of convolution operators on the $L^p$ spaces
+of a locally compact group, and their K-theory. We show that for a discrete
+group, the corresponding K-theory groups depend continuously on $p$ in an
+inductive sense. Via a Banach version of property RD, we show that for a large
+class of groups, the K-theory groups of the Banach algebras are independent of
+$p$.
+",0,0,1,0,0,0
+3155,A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule,"  Spiking neural networks (SNNs) possess energy-efficient potential due to
+event-based computation. However, supervised training of SNNs remains a
+challenge as spike activities are non-differentiable. Previous SNNs training
+methods can basically be categorized into two classes, backpropagation-like
+training methods and plasticity-based learning methods. The former methods are
+dependent on energy-inefficient real-valued computation and non-local
+transmission, as also required in artificial neural networks (ANNs), while the
+latter either be considered biologically implausible or exhibit poor
+performance. Hence, biologically plausible (bio-plausible) high-performance
+supervised learning (SL) methods for SNNs remain deficient. In this paper, we
+proposed a novel bio-plausible SNN model for SL based on the symmetric
+spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By
+combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic
+plasticity of the dynamic threshold, our SNN model implemented SL well and
+achieved good performance in the benchmark recognition task (MNIST). To reveal
+the underlying mechanism of our SL model, we visualized both layer-based
+activities and synaptic weights using the t-distributed stochastic neighbor
+embedding (t-SNE) method after training and found that they were well
+clustered, thereby demonstrating excellent classification ability. As the
+learning rules were bio-plausible and based purely on local spike events, our
+model could be easily applied to neuromorphic hardware for online training and
+may be helpful for understanding SL information processing at the synaptic
+level in biological neural systems.
+",0,0,0,0,1,0
+3156,Learning from various labeling strategies for suicide-related messages on social media: An experimental study,"  Suicide is an important but often misunderstood problem, one that researchers
+are now seeking to better understand through social media. Due in large part to
+the fuzzy nature of what constitutes suicidal risks, most supervised approaches
+for learning to automatically detect suicide-related activity in social media
+require a great deal of human labor to train. However, humans themselves have
+diverse or conflicting views on what constitutes suicidal thoughts. So how to
+obtain reliable gold standard labels is fundamentally challenging and, we
+hypothesize, depends largely on what is asked of the annotators and what slice
+of the data they label. We conducted multiple rounds of data labeling and
+collected annotations from crowdsourcing workers and domain experts. We
+aggregated the resulting labels in various ways to train a series of supervised
+models. Our preliminary evaluations show that using unanimously agreed labels
+from multiple annotators is helpful to achieve robust machine models.
+",1,0,0,0,0,0
+3157,Modeling Information Flow Through Deep Neural Networks,"  This paper proposes a principled information theoretic analysis of
+classification for deep neural network structures, e.g. convolutional neural
+networks (CNN). The output of convolutional filters is modeled as a random
+variable Y conditioned on the object class C and network filter bank F. The
+conditional entropy (CENT) H(Y |C,F) is shown in theory and experiments to be a
+highly compact and class-informative code, that can be computed from the filter
+outputs throughout an existing CNN and used to obtain higher classification
+results than the original CNN itself. Experiments demonstrate the effectiveness
+of CENT feature analysis in two separate CNN classification contexts. 1) In the
+classification of neurodegeneration due to Alzheimer's disease (AD) and natural
+aging from 3D magnetic resonance image (MRI) volumes, 3 CENT features result in
+an AUC=94.6% for whole-brain AD classification, the highest reported accuracy
+on the public OASIS dataset used and 12% higher than the softmax output of the
+original CNN trained for the task. 2) In the context of visual object
+classification from 2D photographs, transfer learning based on a small set of
+CENT features identified throughout an existing CNN leads to AUC values
+comparable to the 1000-feature softmax output of the original network when
+classifying previously unseen object categories. The general information
+theoretical analysis explains various recent CNN design successes, e.g. densely
+connected CNN architectures, and provides insights for future research
+directions in deep learning.
+",1,0,0,1,0,0
+3158,Scalable Twin Neural Networks for Classification of Unbalanced Data,"  Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative
+to Support Vector Machines (SVM) for learning from imbalanced datasets. The
+TWSVM learns two non-parallel classifying hyperplanes by solving a couple of
+smaller sized problems. However, it is unsuitable for large datasets, as it
+involves matrix operations. In this paper, we discuss a Twin Neural Network
+(Twin NN) architecture for learning from large unbalanced datasets. The Twin NN
+also learns an optimal feature map, allowing for better discrimination between
+classes. We also present an extension of this network architecture for
+multiclass datasets. Results presented in the paper demonstrate that the Twin
+NN generalizes well and scales well on large unbalanced datasets.
+",1,0,0,0,0,0
+3159,Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition,"  Recurrent Neural Networks (RNNs) are powerful sequence modeling tools.
+However, when dealing with high dimensional inputs, the training of RNNs
+becomes computational expensive due to the large number of model parameters.
+This hinders RNNs from solving many important computer vision tasks, such as
+Action Recognition in Videos and Image Captioning. To overcome this problem, we
+propose a compact and flexible structure, namely Block-Term tensor
+decomposition, which greatly reduces the parameters of RNNs and improves their
+training efficiency. Compared with alternative low-rank approximations, such as
+tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only
+more concise (when using the same rank), but also able to attain a better
+approximation to the original RNNs with much fewer parameters. On three
+challenging tasks, including Action Recognition in Videos, Image Captioning and
+Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of
+both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes
+17,388 times fewer parameters than the standard LSTM to achieve an accuracy
+improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.
+",1,0,0,1,0,0
+3160,Deep Learning applied to Road Traffic Speed forecasting,"  In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to
+forecast a regression model for time dependent data. These algorithm's are
+designed to handle Floating Car Data (FCD) historic speeds to predict road
+traffic data. For this we aggregate the speeds into the network inputs in an
+innovative way. We compare the RMSE thus obtained with the results of a simpler
+physical model, and show that the latter achieves better RMSE accuracy. We also
+propose a new indicator, which evaluates the algorithms improvement when
+compared to a benchmark prediction. We conclude by questioning the interest of
+using deep learning methods for this specific regression task.
+",1,0,0,1,0,0
+3161,"Wait For It: Identifying ""On-Hold"" Self-Admitted Technical Debt","  Self-admitted technical debt refers to situations where a software developer
+knows that their current implementation is not optimal and indicates this using
+a source code comment. In this work, we hypothesize that it is possible to
+develop automated techniques to understand a subset of these comments in more
+detail, and to propose tool support that can help developers manage
+self-admitted technical debt more effectively. Based on a qualitative study of
+335 comments indicating self-admitted technical debt, we first identify one
+particular class of debt amenable to automated management: ""on-hold""
+self-admitted technical debt, i.e., debt which contains a condition to indicate
+that a developer is waiting for a certain event or an updated functionality
+having been implemented elsewhere. We then design and evaluate an automated
+classifier which can automatically identify these ""on-hold"" instances with a
+precision of 0.81 as well as detect the specific conditions that developers are
+waiting for. Our work presents a first step towards automated tool support that
+is able to indicate when certain instances of self-admitted technical debt are
+ready to be addressed.
+",1,0,0,0,0,0
+3162,Real time observation of granular rock analogue material deformation and failure using nonlinear laser interferometry,"  A better understanding and anticipation of natural processes such as
+landsliding or seismic fault activity requires detailed theoretical and
+experimental analysis of rock mechanics and geomaterial dynamics. These last
+decades, considerable progress has been made towards understanding deformation
+and fracture process in laboratory experiment on granular rock materials, as
+the well-known shear banding experiment. One of the reasons for this progress
+is the continuous improvement in the instrumental techniques of observation.
+But the lack of real time methods does not allow the detection of indicators of
+the upcoming fracture process and thus to anticipate the phenomenon. Here, we
+have performed uniaxial compression experiments to analyse the response of a
+granular rock material sample to different shocks. We use a novel
+interferometric laser sensor based on the nonlinear self-mixing interferometry
+technique to observe in real time the deformations of the sample and assess its
+usefulness as a diagnostic tool for the analysis of geomaterial dynamics. Due
+to the high spatial and temporal resolution of this approach, we observe both
+vibrations processes in response to a dynamic loading and the onset of failure.
+The latter is preceded by a continuous variation of vibration period of the
+material. After several shocks, the material response is no longer reversible
+and we detect a progressive accumulation of irreversible deformation leading to
+the fracture process. We demonstrate that material failure is anticipated by
+the critical slowing down of the surface vibrational motion, which may
+therefore be envisioned as an early warning signal or predictor to the
+macroscopic failure of the sample. The nonlinear self-mixing interferometry
+technique is readily extensible to fault propagation measurements. As such, it
+opens a new window of observation for the study of geomaterial deformation and
+failure.
+",0,1,0,0,0,0
+3163,Virtual link and knot invariants from non-abelian Yang-Baxter 2-cocycle pairs,"  For a given $(X,S,\beta)$, where $S,\beta\colon X\times X\to X\times X$ are
+set theoretical solutions of Yang-Baxter equation with a compatibility
+condition, we define an invariant for virtual (or classical) knots/links using
+non commutative 2-cocycles pairs $(f,g)$ that generalizes the one defined in
+[FG2]. We also define, a group $U_{nc}^{fg}=U_{nc}^{fg}(X,S,\beta)$ and
+functions $\pi_f, \pi_g\colon X\times X\to U_{nc}^{fg}(X)$ governing all
+2-cocycles in $X$. We exhibit examples of computations achieved using GAP.
+",0,0,1,0,0,0
+3164,An extensible cluster-graph taxonomy for open set sound scene analysis,"  We present a new extensible and divisible taxonomy for open set sound scene
+analysis. This new model allows complex scene analysis with tangible
+descriptors and perception labels. Its novel structure is a cluster graph such
+that each cluster (or subset) can stand alone for targeted analyses such as
+office sound event detection, whilst maintaining integrity over the whole graph
+(superset) of labels. The key design benefit is its extensibility as new labels
+are needed during new data capture. Furthermore, datasets which use the same
+taxonomy are easily augmented, saving future data collection effort. We balance
+the details needed for complex scene analysis with avoiding 'the taxonomy of
+everything' with our framework to ensure no duplicity in the superset of labels
+and demonstrate this with DCASE challenge classifications.
+",1,0,0,0,0,0
+3165,"CREATE: Multimodal Dataset for Unsupervised Learning, Generative Modeling and Prediction of Sensory Data from a Mobile Robot in Indoor Environments","  The CREATE database is composed of 14 hours of multimodal recordings from a
+mobile robotic platform based on the iRobot Create. The various sensors cover
+vision, audition, motors and proprioception. The dataset has been designed in
+the context of a mobile robot that can learn multimodal representations of its
+environment, thanks to its ability to navigate the environment. This ability
+can also be used to learn the dependencies and relationships between the
+different modalities of the robot (e.g. vision, audition), as they reflect both
+the external environment and the internal state of the robot. The provided
+multimodal dataset is expected to have multiple usages, such as multimodal
+unsupervised object learning, multimodal prediction and egomotion/causality
+detection.
+",1,0,0,0,0,0
+3166,Deep learning enhanced mobile-phone microscopy,"  Mobile-phones have facilitated the creation of field-portable, cost-effective
+imaging and sensing technologies that approach laboratory-grade instrument
+performance. However, the optical imaging interfaces of mobile-phones are not
+designed for microscopy and produce spatial and spectral distortions in imaging
+microscopic specimens. Here, we report on the use of deep learning to correct
+such distortions introduced by mobile-phone-based microscopes, facilitating the
+production of high-resolution, denoised and colour-corrected images, matching
+the performance of benchtop microscopes with high-end objective lenses, also
+extending their limited depth-of-field. After training a convolutional neural
+network, we successfully imaged various samples, including blood smears,
+histopathology tissue sections, and parasites, where the recorded images were
+highly compressed to ease storage and transmission for telemedicine
+applications. This method is applicable to other low-cost, aberrated imaging
+systems, and could offer alternatives for costly and bulky microscopes, while
+also providing a framework for standardization of optical images for clinical
+and biomedical applications.
+",1,1,0,0,0,0
+3167,On convergence of the sample correlation matrices in high-dimensional data,"  In this paper, we consider an estimation problem concerning the matrix of
+correlation coefficients in context of high dimensional data settings. In
+particular, we revisit some results in Li and Rolsalsky [Li, D. and Rolsalsky,
+A. (2006). Some strong limit theorems for the largest entries of sample
+correlation matrices, The Annals of Applied Probability, 16, 1, 423-447]. Four
+of the main theorems of Li and Rolsalsky (2006) are established in their full
+generalities and we simplify substantially some proofs of the quoted paper.
+Further, we generalize a theorem which is useful in deriving the existence of
+the pth moment as well as in studying the convergence rates in law of large
+numbers.
+",0,0,1,1,0,0
+3168,Minimal Controllability of Conjunctive Boolean Networks is NP-Complete,"  Given a conjunctive Boolean network (CBN) with $n$ state-variables, we
+consider the problem of finding a minimal set of state-variables to directly
+affect with an input so that the resulting conjunctive Boolean control network
+(CBCN) is controllable. We give a necessary and sufficient condition for
+controllability of a CBCN; an $O(n^2)$-time algorithm for testing
+controllability; and prove that nonetheless the minimal controllability problem
+for CBNs is NP-hard.
+",1,0,1,0,0,0
+3169,Consensus report on 25 years of searches for damped Ly$α$ galaxies in emission: Confirming their metallicity-luminosity relation at $z \gtrsim 2$,"  Starting from a summary of detection statistics of our recent X-shooter
+campaign, we review the major surveys, both space and ground based, for
+emission counterparts of high-redshift damped Ly$\alpha$ absorbers (DLAs)
+carried out since the first detection 25 years ago. We show that the detection
+rates of all surveys are precisely reproduced by a simple model in which the
+metallicity and luminosity of the galaxy associated to the DLA follow a
+relation of the form, ${\rm M_{UV}} = -5 \times \left(\,[{\rm M/H}] + 0.3\,
+\right) - 20.8$, and the DLA cross-section follows a relation of the form
+$\sigma_{DLA} \propto L^{0.8}$. Specifically, our spectroscopic campaign
+consists of 11 DLAs preselected based on their equivalent width of SiII
+$\lambda1526$ to have a metallicity higher than [Si/H] > -1. The targets have
+been observed with the X-shooter spectrograph at the Very Large Telescope to
+search for emission lines around the quasars. We observe a high detection rate
+of 64% (7/11), significantly higher than the typical $\sim$10% for random,
+HI-selected DLA samples. We use the aforementioned model, to simulate the
+results of our survey together with a range of previous surveys: spectral
+stacking, direct imaging (using the `double DLA' technique), long-slit
+spectroscopy, and integral field spectroscopy. Based on our model results, we
+are able to reconcile all results. Some tension is observed between model and
+data when looking at predictions of Ly$\alpha$ emission for individual targets.
+However, the object to object variations are most likely a result of the
+significant scatter in the underlying scaling relations as well as
+uncertainties in the amount of dust which affects the emission.
+",0,1,0,0,0,0
+3170,D-optimal designs for complex Ornstein-Uhlenbeck processes,"  Complex Ornstein-Uhlenbeck (OU) processes have various applications in
+statistical modelling. They play role e.g. in the description of the motion of
+a charged test particle in a constant magnetic field or in the study of
+rotating waves in time-dependent reaction diffusion systems, whereas Kolmogorov
+used such a process to model the so-called Chandler wobble, small deviation in
+the Earth's axis of rotation. In these applications parameter estimation and
+model fitting is based on discrete observations of the underlying stochastic
+process, however, the accuracy of the results strongly depend on the
+observation points.
+This paper studies the properties of D-optimal designs for estimating the
+parameters of a complex OU process with a trend. We show that in contrast with
+the case of the classical real OU process, a D-optimal design exists not only
+for the trend parameter, but also for joint estimation of the covariance
+parameters, moreover, these optimal designs are equidistant.
+",0,0,1,1,0,0
+3171,On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models,"  We consider estimating the parametric components of semi-parametric multiple
+index models in a high-dimensional and non-Gaussian setting. Such models form a
+rich class of non-linear models with applications to signal processing, machine
+learning and statistics. Our estimators leverage the score function based first
+and second-order Stein's identities and do not require the covariates to
+satisfy Gaussian or elliptical symmetry assumptions common in the literature.
+Moreover, to handle score functions and responses that are heavy-tailed, our
+estimators are constructed via carefully thresholding their empirical
+counterparts. We show that our estimator achieves near-optimal statistical rate
+of convergence in several settings. We supplement our theoretical results via
+simulation experiments that confirm the theory.
+",0,0,1,1,0,0
+3172,Self-adjointness and spectral properties of Dirac operators with magnetic links,"  We define Dirac operators on $\mathbb{S}^3$ (and $\mathbb{R}^3$) with
+magnetic fields supported on smooth, oriented links and prove self-adjointness
+of certain (natural) extensions. We then analyze their spectral properties and
+show, among other things, that these operators have discrete spectrum. Certain
+examples, such as circles in $\mathbb{S}^3$, are investigated in detail and we
+compute the dimension of the zero-energy eigenspace.
+",0,0,1,0,0,0
+3173,Latent tree models,"  Latent tree models are graphical models defined on trees, in which only a
+subset of variables is observed. They were first discussed by Judea Pearl as
+tree-decomposable distributions to generalise star-decomposable distributions
+such as the latent class model. Latent tree models, or their submodels, are
+widely used in: phylogenetic analysis, network tomography, computer vision,
+causal modeling, and data clustering. They also contain other well-known
+classes of models like hidden Markov models, Brownian motion tree model, the
+Ising model on a tree, and many popular models used in phylogenetics. This
+article offers a concise introduction to the theory of latent tree models. We
+emphasise the role of tree metrics in the structural description of this model
+class, in designing learning algorithms, and in understanding fundamental
+limits of what and when can be learned.
+",0,0,1,1,0,0
+3174,Program Synthesis from Visual Specification,"  Program synthesis is the process of automatically translating a specification
+into computer code. Traditional synthesis settings require a formal, precise
+specification. Motivated by computer education applications where a student
+learns to code simple turtle-style drawing programs, we study a novel synthesis
+setting where only a noisy user-intention drawing is specified. This allows
+students to sketch their intended output, optionally together with their own
+incomplete program, to automatically produce a completed program. We formulate
+this synthesis problem as search in the space of programs, with the score of a
+state being the Hausdorff distance between the program output and the user
+drawing. We compare several search algorithms on a corpus consisting of real
+user drawings and the corresponding programs, and demonstrate that our
+algorithms can synthesize programs optimally satisfying the specification.
+",1,0,0,0,0,0
+3175,Towards a Knowledge Graph based Speech Interface,"  Applications which use human speech as an input require a speech interface
+with high recognition accuracy. The words or phrases in the recognised text are
+annotated with a machine-understandable meaning and linked to knowledge graphs
+for further processing by the target application. These semantic annotations of
+recognised words can be represented as a subject-predicate-object triples which
+collectively form a graph often referred to as a knowledge graph. This type of
+knowledge representation facilitates to use speech interfaces with any spoken
+input application, since the information is represented in logical, semantic
+form, retrieving and storing can be followed using any web standard query
+languages. In this work, we develop a methodology for linking speech input to
+knowledge graphs and study the impact of recognition errors in the overall
+process. We show that for a corpus with lower WER, the annotation and linking
+of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight,
+a tool to interlink text documents with the linked open data is used to link
+the speech recognition output to the DBpedia knowledge graph. Such a
+knowledge-based speech recognition interface is useful for applications such as
+question answering or spoken dialog systems.
+",1,0,0,0,0,0
+3176,An asymptotic equipartition property for measures on model spaces,"  Let $G$ be a sofic group, and let $\Sigma = (\sigma_n)_{n\geq 1}$ be a sofic
+approximation to it. For a probability-preserving $G$-system, a variant of the
+sofic entropy relative to $\Sigma$ has recently been defined in terms of
+sequences of measures on its model spaces that `converge' to the system in a
+certain sense. Here we prove that, in order to study this notion, one may
+restrict attention to those sequences that have the asymptotic equipartition
+property. This may be seen as a relative of the Shannon--McMillan theorem in
+the sofic setting.
+We also give some first applications of this result, including a new formula
+for the sofic entropy of a $(G\times H)$-system obtained by co-induction from a
+$G$-system, where $H$ is any other infinite sofic group.
+",1,0,1,0,0,0
+3177,Dynamic Objects Segmentation for Visual Localization in Urban Environments,"  Visual localization and mapping is a crucial capability to address many
+challenges in mobile robotics. It constitutes a robust, accurate and
+cost-effective approach for local and global pose estimation within prior maps.
+Yet, in highly dynamic environments, like crowded city streets, problems arise
+as major parts of the image can be covered by dynamic objects. Consequently,
+visual odometry pipelines often diverge and the localization systems
+malfunction as detected features are not consistent with the precomputed 3D
+model. In this work, we present an approach to automatically detect dynamic
+object instances to improve the robustness of vision-based localization and
+mapping in crowded environments. By training a convolutional neural network
+model with a combination of synthetic and real-world data, dynamic object
+instance masks are learned in a semi-supervised way. The real-world data can be
+collected with a standard camera and requires minimal further post-processing.
+Our experiments show that a wide range of dynamic objects can be reliably
+detected using the presented method. Promising performance is demonstrated on
+our own and also publicly available datasets, which also shows the
+generalization capabilities of this approach.
+",1,0,0,0,0,0
+3178,Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size,"  A key problem in research on adversarial examples is that vulnerability to
+adversarial examples is usually measured by running attack algorithms. Because
+the attack algorithms are not optimal, the attack algorithms are prone to
+overestimating the size of perturbation needed to fool the target model. In
+other words, the attack-based methodology provides an upper-bound on the size
+of a perturbation that will fool the model, but security guarantees require a
+lower bound. CLEVER is a proposed scoring method to estimate a lower bound.
+Unfortunately, an estimate of a bound is not a bound. In this report, we show
+that gradient masking, a common problem that causes attack methodologies to
+provide only a very loose upper bound, causes CLEVER to overestimate the size
+of perturbation needed to fool the model. In other words, CLEVER does not
+resolve the key problem with the attack-based methodology, because it fails to
+provide a lower bound.
+",0,0,0,1,0,0
+3179,Gaussian process regression for forest attribute estimation from airborne laser scanning data,"  While the analysis of airborne laser scanning (ALS) data often provides
+reliable estimates for certain forest stand attributes -- such as total volume
+or basal area -- there is still room for improvement, especially in estimating
+species-specific attributes. Moreover, while information on the estimate
+uncertainty would be useful in various economic and environmental analyses on
+forests, a computationally feasible framework for uncertainty quantifying in
+ALS is still missing. In this article, the species-specific stand attribute
+estimation and uncertainty quantification (UQ) is approached using Gaussian
+process regression (GPR), which is a nonlinear and nonparametric machine
+learning method. Multiple species-specific stand attributes are estimated
+simultaneously: tree height, stem diameter, stem number, basal area, and stem
+volume. The cross-validation results show that GPR yields on average an
+improvement of 4.6\% in estimate RMSE over a state-of-the-art k-nearest
+neighbors (kNN) implementation, negligible bias and well performing UQ
+(credible intervals), while being computationally fast. The performance
+advantage over kNN and the feasibility of credible intervals persists even when
+smaller training sets are used.
+",0,0,0,1,0,0
+3180,Quantum capacitance of double-layer graphene,"  We study the ground-state properties of a double layer graphene system with
+the Coulomb interlayer electron-electron interaction modeled within the random
+phase approximation. We first obtain an expression of the quantum capacitance
+of a two layer system. In addition, we calculate the many-body
+exchange-correlation energy and quantum capacitance of the hybrid double layer
+graphene system at zero-temperature. We show an enhancement of the majority
+density layer thermodynamic density-of-states owing to an increasing interlayer
+interaction between two layers near the Dirac point. The quantum capacitance
+near the neutrality point behaves like square root of the total density,
+$\alpha \sqrt{n}$, where the coefficient $\alpha$ decreases by increasing the
+charge density imbalance between two layers. Furthermore, we show that the
+quantum capacitance changes linearly by the gate voltage. Our results can be
+verified by current experiments.
+",0,1,0,0,0,0
+3181,DeepTriangle: A Deep Learning Approach to Loss Reserving,"  We propose a novel approach for loss reserving based on deep neural networks.
+The approach allows for jointly modeling of paid losses and claims outstanding,
+and incorporation of heterogenous inputs. We validate the models on loss
+reserving data across lines of business, and show that they attain or exceed
+the predictive accuracy of existing stochastic methods. The models require
+minimal feature engineering and expert input, and can be automated to produce
+forecasts at a high frequency.
+",0,0,0,1,0,1
+3182,Distributed Time Synchronization for Networks with Random Delays and Measurement Noise,"  In this paper a new distributed asynchronous algorithm is proposed for time
+synchronization in networks with random communication delays, measurement noise
+and communication dropouts. Three different types of the drift correction
+algorithm are introduced, based on different kinds of local time increments.
+Under nonrestrictive conditions concerning network properties, it is proved
+that all the algorithm types provide convergence in the mean square sense and
+with probability one (w.p.1) of the corrected drifts of all the nodes to the
+same value (consensus). An estimate of the convergence rate of these algorithms
+is derived. For offset correction, a new algorithm is proposed containing a
+compensation parameter coping with the influence of random delays and special
+terms taking care of the influence of both linearly increasing time and drift
+correction. It is proved that the corrected offsets of all the nodes converge
+in the mean square sense and w.p.1. An efficient offset correction algorithm
+based on consensus on local compensation parameters is also proposed. It is
+shown that the overall time synchronization algorithm can also be implemented
+as a flooding algorithm with one reference node. It is proved that it is
+possible to achieve bounded error between local corrected clocks in the mean
+square sense and w.p.1. Simulation results provide an additional practical
+insight into the algorithm properties and show its advantage over the existing
+methods.
+",1,0,0,0,0,0
+3183,Using High-Rising Cities to Visualize Performance in Real-Time,"  For developers concerned with a performance drop or improvement in their
+software, a profiler allows a developer to quickly search and identify
+bottlenecks and leaks that consume much execution time. Non real-time profilers
+analyze the history of already executed stack traces, while a real-time
+profiler outputs the results concurrently with the execution of software, so
+users can know the results instantaneously. However, a real-time profiler risks
+providing overly large and complex outputs, which is difficult for developers
+to quickly analyze. In this paper, we visualize the performance data from a
+real-time profiler. We visualize program execution as a three-dimensional (3D)
+city, representing the structure of the program as artifacts in a city (i.e.,
+classes and packages expressed as buildings and districts) and their program
+executions expressed as the fluctuating height of artifacts. Through two case
+studies and using a prototype of our proposed visualization, we demonstrate how
+our visualization can easily identify performance issues such as a memory leak
+and compare performance changes between versions of a program. A demonstration
+of the interactive features of our prototype is available at
+this https URL.
+",1,0,0,0,0,0
+3184,Controller Synthesis for Discrete-time Hybrid Polynomial Systems via Occupation Measures,"  We present a novel controller synthesis approach for discrete-time hybrid
+polynomial systems, a class of systems that can model a wide variety of
+interactions between robots and their environment. The approach is rooted in
+recently developed techniques that use occupation measures to formulate the
+controller synthesis problem as an infinite-dimensional linear program. The
+relaxation of the linear program as a finite-dimensional semidefinite program
+can be solved to generate a control law. The approach has several advantages
+including that the formulation is convex, that the formulation and the
+extracted controllers are simple, and that the computational complexity is
+polynomial in the state and control input dimensions. We illustrate our
+approach on some robotics examples.
+",1,0,0,0,0,0
+3185,Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning,"  Recently, research on accelerated stochastic gradient descent methods (e.g.,
+SVRG) has made exciting progress (e.g., linear convergence for strongly convex
+problems). However, the best-known methods (e.g., Katyusha) requires at least
+two auxiliary variables and two momentum parameters. In this paper, we propose
+a fast stochastic variance reduction gradient (FSVRG) method, in which we
+design a novel update rule with the Nesterov's momentum and incorporate the
+technique of growing epoch size. FSVRG has only one auxiliary variable and one
+momentum weight, and thus it is much simpler and has much lower per-iteration
+complexity. We prove that FSVRG achieves linear convergence for strongly convex
+problems and the optimal $\mathcal{O}(1/T^2)$ convergence rate for non-strongly
+convex problems, where $T$ is the number of outer-iterations. We also extend
+FSVRG to directly solve the problems with non-smooth component functions, such
+as SVM. Finally, we empirically study the performance of FSVRG for solving
+various machine learning problems such as logistic regression, ridge
+regression, Lasso and SVM. Our results show that FSVRG outperforms the
+state-of-the-art stochastic methods, including Katyusha.
+",1,0,1,1,0,0
+3186,A vertex-weighted-Least-Squares gradient reconstruction,"  Gradient reconstruction is a key process for the spatial accuracy and
+robustness of finite volume method, especially in industrial aerodynamic
+applications in which grid quality affects reconstruction methods
+significantly. A novel gradient reconstruction method for cell-centered finite
+volume scheme is introduced. This method is composed of two successive steps.
+First, a vertex-based weighted-least-squares procedure is implemented to
+calculate vertex gradients, and then the cell-centered gradients are calculated
+by an arithmetic averaging procedure. By using these two procedures, extended
+stencils are implemented in the calculations, and the accuracy of gradient
+reconstruction is improved by the weighting procedure. In the given test cases,
+the proposed method is showing improvement on both the accuracy and
+convergence. Furthermore, the method could be extended to the calculation of
+viscous fluxes.
+",0,1,0,0,0,0
+3187,The reverse mathematics of theorems of Jordan and Lebesgue,"  The Jordan decomposition theorem states that every function $f \colon [0,1]
+\to \mathbb{R}$ of bounded variation can be written as the difference of two
+non-decreasing functions. Combining this fact with a result of Lebesgue, every
+function of bounded variation is differentiable almost everywhere in the sense
+of Lebesgue measure. We analyze the strength of these theorems in the setting
+of reverse mathematics. Over $\mathsf{RCA}_0$, a stronger version of Jordan's
+result where all functions are continuous is equivalent to $\mathsf{ACA}_0$,
+while the version stated is equivalent to $\mathsf{WKL}_0$. The result that
+every function on $[0,1]$ of bounded variation is almost everywhere
+differentiable is equivalent to $\mathsf{WWKL}_0$. To state this equivalence in
+a meaningful way, we develop a theory of Martin-Löf randomness over
+$\mathsf{RCA}_0$.
+",0,0,1,0,0,0
+3188,Semiflat Orbifold Projections,"  We compute the semiflat positive cone $K_0^{+SF}(A_\theta^\sigma)$ of the
+$K_0$-group of the irrational rotation orbifold $A_\theta^\sigma$ under the
+noncommutative Fourier transform $\sigma$ and show that it is determined by
+classes of positive trace and the vanishing of two topological invariants. The
+semiflat orbifold projections are 3-dimensional and come in three basic
+topological genera: $(2,0,0)$, $(1,1,2)$, $(0,0,2)$. (A projection is called
+semiflat when it has the form $h + \sigma(h)$ where $h$ is a flip-invariant
+projection such that $h\sigma(h)=0$.) Among other things, we also show that
+every number in $(0,1) \cap (2\mathbb Z + 2\mathbb Z\theta)$ is the trace of a
+semiflat projection in $A_\theta$. The noncommutative Fourier transform is the
+order 4 automorphism $\sigma: V \to U \to V^{-1}$ (and the flip is $\sigma^2$:
+$U \to U^{-1},\ V \to V^{-1}$), where $U,V$ are the canonical unitary
+generators of the rotation algebra $A_\theta$ satisfying $VU = e^{2\pi i\theta}
+UV$.
+",0,0,1,0,0,0
+3189,Magnetization process of the S = 1/2 two-leg organic spin-ladder compound BIP-BNO,"  We have measured the magnetization of the organic compound BIP-BNO
+(3,5'-bis(N-tert-butylaminoxyl)-3',5-dibromobiphenyl) up to 76 T where the
+magnetization is saturated. The S = 1/2 antiferromagnetic Heisenberg two-leg
+spin-ladder model accounts for the obtained experimental data regarding the
+magnetization curve, which is clarified using the quantum Monte Carlo method.
+The exchange constants on the rung and the side rail of the ladder are
+estimated to be J(rung)/kB = 65.7 K and J(leg)/kB = 14.1 K, respectively,
+deeply in the strong coupling region: J(rung)/J(leg) > 1.
+",0,1,0,0,0,0
+3190,Deep Over-sampling Framework for Classifying Imbalanced Data,"  Class imbalance is a challenging issue in practical classification problems
+for deep learning models as well as traditional models. Traditionally
+successful countermeasures such as synthetic over-sampling have had limited
+success with complex, structured data handled by deep learning models. In this
+paper, we propose Deep Over-sampling (DOS), a framework for extending the
+synthetic over-sampling method to exploit the deep feature space acquired by a
+convolutional neural network (CNN). Its key feature is an explicit, supervised
+representation learning, for which the training data presents each raw input
+sample with a synthetic embedding target in the deep feature space, which is
+sampled from the linear subspace of in-class neighbors. We implement an
+iterative process of training the CNN and updating the targets, which induces
+smaller in-class variance among the embeddings, to increase the discriminative
+power of the deep representation. We present an empirical study using public
+benchmarks, which shows that the DOS framework not only counteracts class
+imbalance better than the existing method, but also improves the performance of
+the CNN in the standard, balanced settings.
+",1,0,0,1,0,0
+3191,Failsafe Mechanism Design of Multicopters Based on Supervisory Control Theory,"  In order to handle undesirable failures of a multicopter which occur in
+either the pre-flight process or the in-flight process, a failsafe mechanism
+design method based on supervisory control theory is proposed for the
+semi-autonomous control mode. Failsafe mechanism is a control logic that guides
+what subsequent actions the multicopter should take, by taking account of
+real-time information from guidance, attitude control, diagnosis, and other
+low-level subsystems. In order to design a failsafe mechanism for multicopters,
+safety issues of multicopters are introduced. Then, user requirements including
+functional requirements and safety requirements are textually described, where
+function requirements determine a general multicopter plant, and safety
+requirements cover the failsafe measures dealing with the presented safety
+issues. In order to model the user requirements by discrete-event systems,
+several multicopter modes and events are defined. On this basis, the
+multicopter plant and control specifications are modeled by automata. Then, a
+supervisor is synthesized by monolithic supervisory control theory. In
+addition, we present three examples to demonstrate the potential blocking
+phenomenon due to inappropriate design of control specifications. Also, we
+discuss the meaning of correctness and the properties of the obtained
+supervisor. This makes the failsafe mechanism convincingly correct and
+effective. Finally, based on the obtained supervisory controller generated by
+TCT software, an implementation method suitable for multicopters is presented,
+in which the supervisory controller is transformed into decision-making codes.
+",1,0,0,0,0,0
+3192,Inverse Design of Single- and Multi-Rotor Horizontal Axis Wind Turbine Blades using Computational Fluid Dynamics,"  A method for inverse design of horizontal axis wind turbines (HAWTs) is
+presented in this paper. The direct solver for aerodynamic analysis solves the
+Reynolds Averaged Navier Stokes (RANS) equations, where the effect of the
+turbine rotor is modeled as momentum sources using the actuator disk model
+(ADM); this approach is referred to as RANS/ADM. The inverse problem is posed
+as follows: for a given selection of airfoils, the objective is to find the
+blade geometry (described as blade twist and chord distributions) which
+realizes the desired turbine aerodynamic performance at the design point; the
+desired performance is prescribed as angle of attack ($\alpha$) and axial
+induction factor ($a$) distributions along the blade. An iterative approach is
+used. An initial estimate of blade geometry is used with the direct solver
+(RANS/ADM) to obtain $\alpha$ and $a$. The differences between the calculated
+and desired values of $\alpha$ and $a$ are computed and a new estimate for the
+blade geometry (chord and twist) is obtained via nonlinear least squares
+regression using the Trust-Region-Reflective (TRF) method. This procedure is
+continued until the difference between the calculated and the desired values is
+within acceptable tolerance. The method is demonstrated for conventional,
+single-rotor HAWTs and then extended to multi-rotor, specifically dual-rotor
+wind turbines. The TRF method is also compared with the multi-dimensional
+Newton iteration method and found to provide better convergence when
+constraints are imposed in blade design, although faster convergence is
+obtained with the Newton method for unconstrained optimization.
+",0,1,0,0,0,0
+3193,The Double Galaxy Cluster Abell 2465 III. X-ray and Weak-lensing Observations,"  We report Chandra X-ray observations and optical weak-lensing measurements
+from Subaru/Suprime-Cam images of the double galaxy cluster Abell 2465
+(z=0.245). The X-ray brightness data are fit to a beta-model to obtain the
+radial gas density profiles of the northeast (NE) and southwest (SW)
+sub-components, which are seen to differ in structure. We determine core radii,
+central temperatures, the gas masses within $r_{500c}$, and the total masses
+for the broader NE and sharper SW components assuming hydrostatic equilibrium.
+The central entropy of the NE clump is about two times higher than the SW.
+Along with its structural properties, this suggests that it has undergone
+merging on its own. The weak-lensing analysis gives virial masses for each
+substructure, which compare well with earlier dynamical results. The derived
+outer mass contours of the SW sub-component from weak lensing are more
+irregular and extended than those of the NE. Although there is a weak
+enhancement and small offsets between X-ray gas and mass centers from weak
+lensing, the lack of large amounts of gas between the two sub-clusters
+indicates that Abell 2465 is in a pre-merger state. A dynamical model that is
+consistent with the observed cluster data, based on the FLASH program and the
+radial infall model, is constructed, where the subclusters currently separated
+by ~1.2Mpc are approaching each other at ~2000km/s and will meet in ~0.4Gyr.
+",0,1,0,0,0,0
+3194,Superdensity Operators for Spacetime Quantum Mechanics,"  We introduce superdensity operators as a tool for analyzing quantum
+information in spacetime. Superdensity operators encode spacetime correlation
+functions in an operator framework, and support a natural generalization of
+Hilbert space techniques and Dirac's transformation theory as traditionally
+applied to standard density operators. Superdensity operators can be measured
+experimentally, but accessing their full content requires novel procedures. We
+demonstrate these statements on several examples. The superdensity formalism
+suggests useful definitions of spacetime entropies and spacetime quantum
+channels. For example, we show that the von Neumann entropy of a superdensity
+operator is related to a quantum generalization of the Kolmogorov-Sinai
+entropy, and compute this for a many-body system. We also suggest experimental
+protocols for measuring spacetime entropies.
+",0,1,0,0,0,0
+3195,Monte Carlo study of magnetic nanoparticles adsorbed on halloysite $Al_2Si_2O_5(OH)_4$ nanotubes,"  We study properties of magnetic nanoparticles adsorbed on the halloysite
+surface. For that a distinct magnetic Hamiltonian with random distribution of
+spins on a cylindrical surface was solved by using a nonequilibrium Monte Carlo
+method. The parameters for our simulations: anisotropy constant, nanoparticle
+size distribution, saturated magnetization and geometrical parameters of the
+halloysite template were taken from recent experiments. We calculate the
+hysteresis loops and temperature dependence of the zero field cooling (ZFC)
+susceptibility, which maximum determines the blocking temperature. It is shown
+that the dipole-dipole interaction between nanoparticles moderately increases
+the blocking temperature and weakly increases the coercive force. The obtained
+hysteresis loops (e.g., the value of the coercive force) for Ni nanoparticles
+are in reasonable agreement with the experimental data. We also discuss the
+sensitivity of the hysteresis loops and ZFC susceptibilities to the change of
+anisotropy and dipole-dipole interaction, as well as the 3d-shell occupation of
+the metallic nanoparticles; in particular we predict larger coercive force for
+Fe, than for Ni nanoparticles.
+",0,1,0,0,0,0
+3196,Inference in high-dimensional linear regression models,"  We introduce an asymptotically unbiased estimator for the full
+high-dimensional parameter vector in linear regression models where the number
+of variables exceeds the number of available observations. The estimator is
+accompanied by a closed-form expression for the covariance matrix of the
+estimates that is free of tuning parameters. This enables the construction of
+confidence intervals that are valid uniformly over the parameter vector.
+Estimates are obtained by using a scaled Moore-Penrose pseudoinverse as an
+approximate inverse of the singular empirical covariance matrix of the
+regressors. The approximation induces a bias, which is then corrected for using
+the lasso. Regularization of the pseudoinverse is shown to yield narrower
+confidence intervals under a suitable choice of the regularization parameter.
+The methods are illustrated in Monte Carlo experiments and in an empirical
+example where gross domestic product is explained by a large number of
+macroeconomic and financial indicators.
+",0,0,1,1,0,0
+3197,Counterfactual Learning for Machine Translation: Degeneracies and Solutions,"  Counterfactual learning is a natural scenario to improve web-based machine
+translation services by offline learning from feedback logged during user
+interactions. In order to avoid the risk of showing inferior translations to
+users, in such scenarios mostly exploration-free deterministic logging policies
+are in place. We analyze possible degeneracies of inverse and reweighted
+propensity scoring estimators, in stochastic and deterministic settings, and
+relate them to recently proposed techniques for counterfactual learning under
+deterministic logging.
+",1,0,0,1,0,0
+3198,Conversion of Mersenne Twister to double-precision floating-point numbers,"  The 32-bit Mersenne Twister generator MT19937 is a widely used random number
+generator. To generate numbers with more than 32 bits in bit length, and
+particularly when converting into 53-bit double-precision floating-point
+numbers in $[0,1)$ in the IEEE 754 format, the typical implementation
+concatenates two successive 32-bit integers and divides them by a power of $2$.
+In this case, the 32-bit MT19937 is optimized in terms of its equidistribution
+properties (the so-called dimension of equidistribution with $v$-bit accuracy)
+under the assumption that one will mainly be using 32-bit output values, and
+hence the concatenation sometimes degrades the dimension of equidistribution
+compared with the simple use of 32-bit outputs. In this paper, we analyze such
+phenomena by investigating hidden $\mathbb{F}_2$-linear relations among the
+bits of high-dimensional outputs. Accordingly, we report that MT19937 with a
+specific lag set fails several statistical tests, such as the overlapping
+collision test, matrix rank test, and Hamming independence test.
+",1,0,0,1,0,0
+3199,BaHaMAS: A Bash Handler to Monitor and Administrate Simulations,"  Numerical QCD is often extremely resource demanding and it is not rare to run
+hundreds of simulations at the same time. Each of these can last for days or
+even months and it typically requires a job-script file as well as an input
+file with the physical parameters for the application to be run. Moreover, some
+monitoring operations (i.e. copying, moving, deleting or modifying files,
+resume crashed jobs, etc.) are often required to guarantee that the final
+statistics is correctly accumulated. Proceeding manually in handling
+simulations is probably the most error-prone way and it is deadly uncomfortable
+and inefficient! BaHaMAS was developed and successfully used in the last years
+as a tool to automatically monitor and administrate simulations.
+",0,1,0,0,0,0
+3200,Computational Study of Halide Perovskite-Derived A$_2$BX$_6$ Inorganic Compounds: Chemical Trends in Electronic Structure and Structural Stability,"  The electronic structure and energetic stability of A$_2$BX$_6$ halide
+compounds with the cubic and tetragonal variants of the perovskite-derived
+K$_2$PtCl$_6$ prototype structure are investigated computationally within the
+frameworks of density-functional-theory (DFT) and hybrid (HSE06) functionals.
+The HSE06 calculations are undertaken for seven known A$_2$BX$_6$ compounds
+with A = K, Rb and Cs, and B = Sn, Pd, Pt, Te, and X = I. Trends in band gaps
+and energetic stability are identified, which are explored further employing
+DFT calculations over a larger range of chemistries, characterized by A = K,
+Rb, Cs, B = Si, Ge, Sn, Pb, Ni, Pd, Pt, Se and Te and X = Cl, Br, I. For the
+systems investigated in this work, the band gap increases from iodide to
+bromide to chloride. Further, variations in the A site cation influences the
+band gap as well as the preferred degree of tetragonal distortion. Smaller A
+site cations such as K and Rb favor tetragonal structural distortions,
+resulting in a slightly larger band gap. For variations in the B site in the
+(Ni, Pd, Pt) group and the (Se, Te) group, the band gap increases with
+increasing cation size. However, no observed chemical trend with respect to
+cation size for band gap was found for the (Si, Sn, Ge, Pb) group. The findings
+in this work provide guidelines for the design of halide A$_2$BX$_6$ compounds
+for potential photovoltaic applications.
+",0,1,0,0,0,0
+3201,A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models,"  For high-dimensional sparse linear models, how to construct confidence
+intervals for coefficients remains a difficult question. The main reason is the
+complicated limiting distributions of common estimators such as the Lasso.
+Several confidence interval construction methods have been developed, and
+Bootstrap Lasso+OLS is notable for its simple technicality, good
+interpretability, and comparable performance with other more complicated
+methods. However, Bootstrap Lasso+OLS depends on the beta-min assumption, a
+theoretic criterion that is often violated in practice. In this paper, we
+introduce a new method called Bootstrap Lasso+Partial Ridge (LPR) to relax this
+assumption. LPR is a two-stage estimator: first using Lasso to select features
+and subsequently using Partial Ridge to refit the coefficients. Simulation
+results show that Bootstrap LPR outperforms Bootstrap Lasso+OLS when there
+exist small but non-zero coefficients, a common situation violating the
+beta-min assumption. For such coefficients, compared to Bootstrap Lasso+OLS,
+confidence intervals constructed by Bootstrap LPR have on average 50% larger
+coverage probabilities. Bootstrap LPR also has on average 35% shorter
+confidence interval lengths than the de-sparsified Lasso methods, regardless of
+whether linear models are misspecified. Additionally, we provide theoretical
+guarantees of Bootstrap LPR under appropriate conditions and implement it in
+the R package ""HDCI.""
+",0,0,0,1,0,0
+3202,The Music Streaming Sessions Dataset,"  At the core of many important machine learning problems faced by online
+streaming services is a need to model how users interact with the content.
+These problems can often be reduced to a combination of 1) sequentially
+recommending items to the user, and 2) exploiting the user's interactions with
+the items as feedback for the machine learning model. Unfortunately, there are
+no public datasets currently available that enable researchers to explore this
+topic. In order to spur that research, we release the Music Streaming Sessions
+Dataset (MSSD), which consists of approximately 150 million listening sessions
+and associated user actions. Furthermore, we provide audio features and
+metadata for the approximately 3.7 million unique tracks referred to in the
+logs. This is the largest collection of such track metadata currently available
+to the public. This dataset enables research on important problems including
+how to model user listening and interaction behaviour in streaming, as well as
+Music Information Retrieval (MIR), and session-based sequential
+recommendations.
+",1,0,0,0,0,0
+3203,CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification,"  Class imbalance classification is a challenging research problem in data
+mining and machine learning, as most of the real-life datasets are often
+imbalanced in nature. Existing learning algorithms maximise the classification
+accuracy by correctly classifying the majority class, but misclassify the
+minority class. However, the minority class instances are representing the
+concept with greater interest than the majority class instances in real-life
+applications. Recently, several techniques based on sampling methods
+(under-sampling of the majority class and over-sampling the minority class),
+cost-sensitive learning methods, and ensemble learning have been used in the
+literature for classifying imbalanced datasets. In this paper, we introduce a
+new clustering-based under-sampling approach with boosting (AdaBoost)
+algorithm, called CUSBoost, for effective imbalanced classification. The
+proposed algorithm provides an alternative to RUSBoost (random under-sampling
+with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost)
+algorithms. We evaluated the performance of CUSBoost algorithm with the
+state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost,
+SMOTEBoost on 13 imbalance binary and multi-class datasets with various
+imbalance ratios. The experimental results show that the CUSBoost is a
+promising and effective approach for dealing with highly imbalanced datasets.
+",1,0,0,1,0,0
+3204,Finding events in temporal networks: Segmentation meets densest-subgraph discovery,"  In this paper we study the problem of discovering a timeline of events in a
+temporal network. We model events as dense subgraphs that occur within
+intervals of network activity. We formulate the event-discovery task as an
+optimization problem, where we search for a partition of the network timeline
+into k non-overlapping intervals, such that the intervals span subgraphs with
+maximum total density. The output is a sequence of dense subgraphs along with
+corresponding time intervals, capturing the most interesting events during the
+network lifetime.
+A naive solution to our optimization problem has polynomial but prohibitively
+high running time complexity. We adapt existing recent work on dynamic
+densest-subgraph discovery and approximate dynamic programming to design a fast
+approximation algorithm. Next, to ensure richer structure, we adjust the
+problem formulation to encourage coverage of a larger set of nodes. This
+problem is NP-hard even for static graphs. However, on static graphs a simple
+greedy algorithm leads to approximate solution due to submodularity. We
+extended this greedy approach for the case of temporal networks. However, the
+approximation guarantee does not hold. Nevertheless, according to the
+experiments, the algorithm finds good quality solutions.
+",1,0,0,0,0,0
+3205,On the area of constrained polygonal linkages,"  We study configuration spaces of linkages whose underlying graph are polygons
+with diagonal constrains, or more general, partial two-trees. We show that
+(with an appropriate definition) the oriented area is a Bott-Morse function on
+the configuration space. Its critical points are described and Bott-Morse
+indices are computed. This paper is a generalization of analogous results for
+polygonal linkages (obtained earlier by G. Khimshiashvili, G. Panina, and A.
+Zhukova).
+",0,0,1,0,0,0
+3206,Effects of geometrical frustration on ferromagnetism in the Hubbard model on the Shastry-Sutherland lattice,"  The small-cluster exact-diagonalization calculations and the projector
+quantum Monte Carlo method are used to examine the competing effects of
+geometrical frustration and interaction on ferromagnetism in the Hubbard model
+on the Shastry-Sutherland lattice. It is shown that the geometrical frustration
+stabilizes the ferromagnetic state at high electron concentrations ($n \gtrsim
+7/4$), where strong correlations between ferromagnetism and the shape of the
+noninteracting density of states are observed. In particular, it is found that
+ferromagnetism is stabilized only for these values of frustration parameters,
+which lead to the single peaked noninterating density of states at the band
+edge. Once, two or more peaks appear in the noninteracting density of states at
+the band egde the ferromagnetic state is suppressed. This opens a new route
+towards the understanding of ferromagnetism in strongly correlated systems.
+",0,1,0,0,0,0
+3207,Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence,"  Process discovery techniques return process models that are either formal
+(precisely describing the possible behaviors) or informal (merely a ""picture""
+not allowing for any form of formal reasoning). Formal models are able to
+classify traces (i.e., sequences of events) as fitting or non-fitting. Most
+process mining approaches described in the literature produce such models. This
+is in stark contrast with the over 25 available commercial process mining tools
+that only discover informal process models that remain deliberately vague on
+the precise set of possible traces. There are two main reasons why vendors
+resort to such models: scalability and simplicity. In this paper, we propose to
+combine the best of both worlds: discovering hybrid process models that have
+formal and informal elements. As a proof of concept we present a discovery
+technique based on hybrid Petri nets. These models allow for formal reasoning,
+but also reveal information that cannot be captured in mainstream formal
+models. A novel discovery algorithm returning hybrid Petri nets has been
+implemented in ProM and has been applied to several real-life event logs. The
+results clearly demonstrate the advantages of remaining ""vague"" when there is
+not enough ""evidence"" in the data or standard modeling constructs do not ""fit"".
+Moreover, the approach is scalable enough to be incorporated in
+industrial-strength process mining tools.
+",1,0,0,0,0,0
+3208,El Lenguaje Natural como Lenguaje Formal,"  Formal languages theory is useful for the study of natural language. In
+particular, it is of interest to study the adequacy of the grammatical
+formalisms to express syntactic phenomena present in natural language. First,
+it helps to draw hypothesis about the nature and complexity of the
+speaker-hearer linguistic competence, a fundamental question in linguistics and
+other cognitive sciences. Moreover, from an engineering point of view, it
+allows the knowledge of practical limitations of applications based on those
+formalisms. In this article I introduce the adequacy problem of grammatical
+formalisms for natural language, also introducing some formal language theory
+concepts required for this discussion. Then, I review the formalisms that have
+been proposed in history, and the arguments that have been given to support or
+reject their adequacy.
+-----
+La teoría de lenguajes formales es útil para el estudio de los lenguajes
+naturales. En particular, resulta de interés estudiar la adecuación de los
+formalismos gramaticales para expresar los fenómenos sintácticos presentes
+en el lenguaje natural. Primero, ayuda a trazar hipótesis acerca de la
+naturaleza y complejidad de las competencias lingüísticas de los
+hablantes-oyentes del lenguaje, un interrogante fundamental de la
+lingüística y otras ciencias cognitivas. Además, desde el punto de vista
+de la ingeniería, permite conocer limitaciones prácticas de las
+aplicaciones basadas en dichos formalismos. En este artículo hago una
+introducción al problema de la adecuación de los formalismos gramaticales
+para el lenguaje natural, introduciendo también algunos conceptos de la
+teoría de lenguajes formales necesarios para esta discusión. Luego, hago un
+repaso de los formalismos que han sido propuestos a lo largo de la historia, y
+de los argumentos que se han dado para sostener o refutar su adecuación.
+",1,0,0,0,0,0
+3209,Bounds for the completely positive rank of a symmetric matrix over a tropical semiring,"  In this paper, we find an upper bound for the CP-rank of a matrix over a
+tropical semiring, according to the vertex clique cover of the graph prescribed
+by the pattern of the matrix. We study the graphs that beget the patterns of
+matrices with the lowest possible CP-ranks and prove that any such graph must
+have its diameter equal to 2.
+",0,0,1,0,0,0
+3210,Gaussian process classification using posterior linearisation,"  This paper proposes a new algorithm for Gaussian process classification based
+on posterior linearisation (PL). In PL, a Gaussian approximation to the
+posterior density is obtained iteratively using the best possible linearisation
+of the conditional mean of the labels and accounting for the linearisation
+error. Considering three widely-used likelihood functions, in general, PL
+provides lower classification errors in real data sets than expectation
+propagation and Laplace algorithms.
+",0,0,0,1,0,0
+3211,On Inconsistency Indices and Inconsistency Axioms in Pairwise Comparisons,"  Pairwise comparisons are an important tool of modern (multiple criteria)
+decision making. Since human judgments are often inconsistent, many studies
+focused on the ways how to express and measure this inconsistency, and several
+inconsistency indices were proposed as an alternative to Saaty inconsistency
+index and inconsistency ratio for reciprocal pairwise comparisons matrices.
+This paper aims to: firstly, introduce a new measure of inconsistency of
+pairwise comparisons and to prove its basic properties; secondly, to postulate
+an additional axiom, an upper boundary axiom, to an existing set of axioms; and
+the last, but not least, the paper provides proofs of satisfaction of this
+additional axiom by selected inconsistency indices as well as it provides their
+numerical comparison.
+",1,0,0,0,0,0
+3212,Heteroclinic traveling fronts for a generalized Fisher-Burgers equation with saturating diffusion,"  We study the existence of monotone heteroclinic traveling waves for a general
+Fisher-Burgers equation with nonlinear and possibly density-dependent
+diffusion. Such a model arises, for instance, in physical phenomena where a
+saturation effect appears for large values of the gradient. We give an estimate
+for the critical speed (namely, the first speed for which a monotone
+heteroclinic traveling wave exists) for some different shapes of the reaction
+term, and we analyze its dependence on a small real parameter when this brakes
+the diffusion, complementing our study with some numerical simulations.
+",0,0,1,0,0,0
+3213,Computer Algebra for Microhydrodynamics,"  I describe a method for computer algebra that helps with laborious
+calculations typically encountered in theoretical microhydrodynamics. The
+program mimics how humans calculate by matching patterns and making
+replacements according to the rules of algebra and calculus. This note gives an
+overview and walks through an example, while the accompanying code repository
+contains the implementation details, a tutorial, and more examples. The code
+repository is attached as supplementary material to this note, and maintained
+at this https URL
+",1,1,0,0,0,0
+3214,Rapid micro fluorescence in situ hybridization in tissue sections,"  This paper describes a micro fluorescence in situ hybridization
+({\mu}FISH)-based rapid detection of cytogenetic biomarkers on formalin-fixed
+paraffin embedded (FFPE) tissue sections. We demonstrated this method in the
+context of detecting human epidermal growth factor 2 (HER2) in breast tissue
+sections. This method uses a non-contact microfluidic scanning probe (MFP),
+which localizes FISH probes at the micrometer length-scale to selected cells of
+the tissue section. The scanning ability of the MFP allows for a versatile
+implementation of FISH on tissue sections. We demonstrated the use of
+oligonucleotide FISH probes in ethylene carbonate-based buffer enabling rapid
+hybridization within < 1 min for chromosome enumeration and 10-15 min for
+assessment of the HER2 status in FFPE sections. We further demonstrated
+recycling of FISH probes for multiple sequential tests using a defined volume
+of probes by forming hierarchical hydrodynamic flow confinements. This
+microscale method is compatible with the standard FISH protocols and with the
+Instant Quality (IQ) FISH assay, reduces the FISH probe consumption ~100-fold
+and the hybridization time 4-fold, resulting in an assay turnaround time of < 3
+h. We believe rapid {\mu}FISH has the potential of being used in pathology
+workflows as a standalone method or in combination with other molecular methods
+for diagnostic and prognostic analysis of FFPE sections.
+",0,0,0,0,1,0
+3215,Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding,"  Neural time-series data contain a wide variety of prototypical signal
+waveforms (atoms) that are of significant importance in clinical and cognitive
+research. One of the goals for analyzing such data is hence to extract such
+'shift-invariant' atoms. Even though some success has been reported with
+existing algorithms, they are limited in applicability due to their heuristic
+nature. Moreover, they are often vulnerable to artifacts and impulsive noise,
+which are typically present in raw neural recordings. In this study, we address
+these issues and propose a novel probabilistic convolutional sparse coding
+(CSC) model for learning shift-invariant atoms from raw neural signals
+containing potentially severe artifacts. In the core of our model, which we
+call $\alpha$CSC, lies a family of heavy-tailed distributions called
+$\alpha$-stable distributions. We develop a novel, computationally efficient
+Monte Carlo expectation-maximization algorithm for inference. The maximization
+step boils down to a weighted CSC problem, for which we develop a
+computationally efficient optimization algorithm. Our results show that the
+proposed algorithm achieves state-of-the-art convergence speeds. Besides,
+$\alpha$CSC is significantly more robust to artifacts when compared to three
+competing algorithms: it can extract spike bursts, oscillations, and even
+reveal more subtle phenomena such as cross-frequency coupling when applied to
+noisy neural time series.
+",0,0,0,1,0,0
+3216,Machine Learning for Drug Overdose Surveillance,"  We describe two recently proposed machine learning approaches for discovering
+emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset
+Scan enables early detection of emerging patterns in spatio-temporal data,
+accounting for both the non-iid nature of the data and the fact that detecting
+subtle patterns requires integration of information across multiple spatial
+areas and multiple time steps. We apply this approach to 17 years of
+county-aggregated data for monthly opioid overdose deaths in the New York City
+metropolitan area, showing clear advantages in the utility of discovered
+patterns as compared to typical anomaly detection approaches.
+To detect and characterize emerging overdose patterns that differentially
+affect a subpopulation of the data, including geographic, demographic, and
+behavioral patterns (e.g., which combinations of drugs are involved), we apply
+the Multidimensional Tensor Scan to 8 years of case-level overdose data from
+Allegheny County, PA. We discover previously unidentified overdose patterns
+which reveal unusual demographic clusters, show impacts of drug legislation,
+and demonstrate potential for early detection and targeted intervention. These
+approaches to early detection of overdose patterns can inform prevention and
+response efforts, as well as understanding the effects of policy changes.
+",1,0,0,1,0,0
+3217,A Capsule based Approach for Polyphonic Sound Event Detection,"  Polyphonic sound event detection (polyphonic SED) is an interesting but
+challenging task due to the concurrence of multiple sound events. Recently, SED
+methods based on convolutional neural networks (CNN) and recurrent neural
+networks (RNN) have shown promising performance. Generally, CNN are designed
+for local feature extraction while RNN are used to model the temporal
+dependency among these local features. Despite their success, it is still
+insufficient for existing deep learning techniques to separate individual sound
+event from their mixture, largely due to the overlapping characteristic of
+features. Motivated by the success of Capsule Networks (CapsNet), we propose a
+more suitable capsule based approach for polyphonic SED. Specifically, several
+capsule layers are designed to effectively select representative frequency
+bands for each individual sound event. The temporal dependency of capsule's
+outputs is then modeled by a RNN. And a dynamic threshold method is proposed
+for making the final decision based on RNN outputs. Experiments on the TUT-SED
+Synthetic 2016 dataset show that the proposed approach obtains an F1-score of
+68.8% and an error rate of 0.45, outperforming the previous state-of-the-art
+method of 66.4% and 0.48, respectively.
+",1,0,0,0,0,0
+3218,Linear simulation of ion temperature gradient driven instabilities in W7-X and LHD stellarators using GTC,"  The global gyrokinetic toroidal code (GTC) has been recently upgraded to do
+simulations in non-axisymmetric equilibrium configuration, such as
+stellarators. Linear simulation of ion temperature gradient (ITG) driven
+instabilities has been done in Wendelstein7-X (W7-X) and Large Helical Device
+(LHD) stellarators using GTC. Several results are discussed to study
+characteristics of ITG in stellarators, including toroidal grids convergence,
+nmodes number convergence, poloidal and parallel spectrums, and electrostatic
+potential mode structure on flux surface.
+",0,1,0,0,0,0
+3219,Advanced engineering of single-crystal gold nanoantennas,"  A nanofabrication process for realizing optical nanoantennas carved from a
+single-crystal gold plate is presented in this communication. The method relies
+on synthesizing two-dimensional micron-size gold crystals followed by the dry
+etching of a desired antenna layout. The fabrication of single-crystal optical
+nanoantennas with standard electron-beam lithography tool and dry etching
+reactor represents an alternative technological solution to focused ion beam
+milling of the objects. The process is exemplified by engineering nanorod
+antennas. Dark-field spectroscopy indicates that optical antennas produced from
+single crystal flakes have reduced localized surface plasmon resonance losses
+compared to amorphous designs of similar shape. The present process is easily
+applicable to other metals such as silver or copper and offers a design
+flexibility not found in crystalline particles synthesized by colloidal
+chemistry.
+",0,1,0,0,0,0
+3220,"Stick-breaking processes, clumping, and Markov chain occupation laws","  We consider the connections among `clumped' residual allocation models
+(RAMs), a general class of stick-breaking processes including Dirichlet
+processes, and the occupation laws of certain discrete space time-inhomogeneous
+Markov chains related to simulated annealing and other applications. An
+intermediate structure is introduced in a given RAM, where proportions between
+successive indices in a list are added or clumped together to form another RAM.
+In particular, when the initial RAM is a Griffiths-Engen-McCloskey (GEM)
+sequence and the indices are given by the random times that an auxiliary Markov
+chain jumps away from its current state, the joint law of the intermediate RAM
+and the locations visited in the sojourns is given in terms of a `disordered'
+GEM sequence, and an induced Markov chain. Through this joint law, we identify
+a large class of `stick breaking' processes as the limits of empirical
+occupation measures for associated time-inhomogeneous Markov chains.
+",0,0,1,1,0,0
+3221,Scaling laws of Rydberg excitons,"  Rydberg atoms have attracted considerable interest due to their huge
+interaction among each other and with external fields. They demonstrate
+characteristic scaling laws in dependence on the principal quantum number $n$
+for features such as the magnetic field for level crossing. While bearing
+striking similarities to Rydberg atoms, fundamentally new insights may be
+obtained for Rydberg excitons, as the crystal environment gives easy optical
+access to many states within an exciton multiplet. Here we study experimentally
+and theoretically the scaling of several characteristic parameters of Rydberg
+excitons with $n$. From absorption spectra in magnetic field we find for the
+first crossing of levels with adjacent principal quantum numbers a $B_r \propto
+n^{-4}$ dependence of the resonance field strength, $B_r$, due to the dominant
+paramagnetic term unlike in the atomic case where the diamagnetic contribution
+is decisive. By contrast, in electric field we find scaling laws just like for
+Rydberg atoms. The resonance electric field strength scales as $E_r \propto
+n^{-5}$. We observe anticrossings of the states belonging to multiplets with
+different principal quantum numbers. The energy splittings at the avoided
+crossings scale as $n^{-4}$ which we relate to the crystal specific deviation
+of the exciton Hamiltonian from the hydrogen model. We observe the exciton
+polarizability in the electric field to scale as $n^7$. In magnetic field the
+crossover field strength from a hydrogen-like exciton to a magnetoexciton
+dominated by electron and hole Landau level quantization scales as $n^{-3}$.
+The ionization voltages demonstrate a $n^{-4}$ scaling as for atoms. The width
+of the absorption lines remains constant before dissociation for high enough
+$n$, while for small $n \lesssim 12$ an exponential increase with the field is
+found. These results are in excellent agreement with theoretical calculations.
+",0,1,0,0,0,0
+3222,Variable selection in multivariate linear models with high-dimensional covariance matrix estimation,"  In this paper, we propose a novel variable selection approach in the
+framework of multivariate linear models taking into account the dependence that
+may exist between the responses. It consists in estimating beforehand the
+covariance matrix of the responses and to plug this estimator in a Lasso
+criterion, in order to obtain a sparse estimator of the coefficient matrix. The
+properties of our approach are investigated both from a theoretical and a
+numerical point of view. More precisely, we give general conditions that the
+estimators of the covariance matrix and its inverse have to satisfy in order to
+recover the positions of the null and non null entries of the coefficient
+matrix when the size of the covariance matrix is not fixed and can tend to
+infinity. We prove that these conditions are satisfied in the particular case
+of some Toeplitz matrices. Our approach is implemented in the R package
+MultiVarSel available from the Comprehensive R Archive Network (CRAN) and is
+very attractive since it benefits from a low computational load. We also assess
+the performance of our methodology using synthetic data and compare it with
+alternative approaches. Our numerical experiments show that including the
+estimation of the covariance matrix in the Lasso criterion dramatically
+improves the variable selection performance in many cases.
+",0,0,1,1,0,0
+3223,A Novel Model of Cancer-Induced Peripheral Neuropathy and the Role of TRPA1 in Pain Transduction,"  Background. Models of cancer-induced neuropathy are designed by injecting
+cancer cells near the peripheral nerves. The interference of tissue-resident
+immune cells does not allow a direct contact with nerve fibres which affects
+the tumor microenvironment and the invasion process. Methods. Anaplastic
+tumor-1 (AT-1) cells were inoculated within the sciatic nerves (SNs) of male
+Copenhagen rats. Lumbar dorsal root ganglia (DRGs) and the SNs were collected
+on days 3, 7, 14, and 21. SN tissues were examined for morphological changes
+and DRG tissues for immunofluorescence, electrophoretic tendency, and mRNA
+quantification. Hypersensitivities to cold, mechanical, and thermal stimuli
+were determined. HC-030031, a selective TRPA1 antagonist, was used to treat
+cold allodynia. Results. Nociception thresholds were identified on day 6.
+Immunofluorescent micrographs showed overexpression of TRPA1 on days 7 and 14
+and of CGRP on day 14 until day 21. Both TRPA1 and CGRP were coexpressed on the
+same cells. Immunoblots exhibited an increase in TRPA1 expression on day 14.
+TRPA1 mRNA underwent an increase on day 7 (normalized to 18S). Injection of
+HC-030031 transiently reversed the cold allodynia. Conclusion. A novel and a
+promising model of cancer-induced neuropathy was established, and the role of
+TRPA1 and CGRP in pain transduction was examined.
+",0,0,0,0,1,0
+3224,Statistical inference using SGD,"  We present a novel method for frequentist statistical inference in
+$M$-estimation problems, based on stochastic gradient descent (SGD) with a
+fixed step size: we demonstrate that the average of such SGD sequences can be
+used for statistical inference, after proper scaling. An intuitive analysis
+using the Ornstein-Uhlenbeck process suggests that such averages are
+asymptotically normal. From a practical perspective, our SGD-based inference
+procedure is a first order method, and is well-suited for large scale problems.
+To show its merits, we apply it to both synthetic and real datasets, and
+demonstrate that its accuracy is comparable to classical statistical methods,
+while requiring potentially far less computation.
+",1,0,1,1,0,0
+3225,Core Discovery in Hidden Graphs,"  Massive network exploration is an important research direction with many
+applications. In such a setting, the network is, usually, modeled as a graph
+$G$, whereas any structural information of interest is extracted by inspecting
+the way nodes are connected together. In the case where the adjacency matrix or
+the adjacency list of $G$ is available, one can directly apply graph mining
+algorithms to extract useful knowledge. However, there are cases where this is
+not possible because the graph is \textit{hidden} or \textit{implicit}, meaning
+that the edges are not recorded explicitly in the form of an adjacency
+representation. In such a case, the only alternative is to pose a sequence of
+\textit{edge probing queries} asking for the existence or not of a particular
+graph edge. However, checking all possible node pairs is costly (quadratic on
+the number of nodes). Thus, our objective is to pose as few edge probing
+queries as possible, since each such query is expected to be costly. In this
+work, we center our focus on the \textit{core decomposition} of a hidden graph.
+In particular, we provide an efficient algorithm to detect the maximal subgraph
+of $S_k$ of $G$ where the induced degree of every node $u \in S_k$ is at least
+$k$. Performance evaluation results demonstrate that significant performance
+improvements are achieved in comparison to baseline approaches.
+",1,0,0,0,0,0
+3226,Some exact Bradlow vortex solutions,"  We consider the Bradlow equation for vortices which was recently found by
+Manton and find a two-parameter class of analytic solutions in closed form on
+nontrivial geometries with non-constant curvature. The general solution to our
+class of metrics is given by a hypergeometric function and the area of the
+vortex domain by the Gaussian hypergeometric function.
+",0,0,1,0,0,0
+3227,Saturating sets in projective planes and hypergraph covers,"  Let $\Pi_q$ be an arbitrary finite projective plane of order $q$. A subset
+$S$ of its points is called saturating if any point outside $S$ is collinear
+with a pair of points from $S$. Applying probabilistic tools we improve the
+upper bound on the smallest possible size of the saturating set to
+$\lceil\sqrt{3q\ln{q}}\rceil+ \lceil(\sqrt{q}+1)/2\rceil$. The same result is
+presented using an algorithmic approach as well, which points out the
+connection with the transversal number of uniform multiple intersecting
+hypergraphs.
+",0,0,1,0,0,0
+3228,Accelerated Optimization in the PDE Framework: Formulations for the Active Contour Case,"  Following the seminal work of Nesterov, accelerated optimization methods have
+been used to powerfully boost the performance of first-order, gradient-based
+parameter estimation in scenarios where second-order optimization strategies
+are either inapplicable or impractical. Not only does accelerated gradient
+descent converge considerably faster than traditional gradient descent, but it
+also performs a more robust local search of the parameter space by initially
+overshooting and then oscillating back as it settles into a final
+configuration, thereby selecting only local minimizers with a basis of
+attraction large enough to contain the initial overshoot. This behavior has
+made accelerated and stochastic gradient search methods particularly popular
+within the machine learning community. In their recent PNAS 2016 paper,
+Wibisono, Wilson, and Jordan demonstrate how a broad class of accelerated
+schemes can be cast in a variational framework formulated around the Bregman
+divergence, leading to continuum limit ODE's. We show how their formulation may
+be further extended to infinite dimension manifolds (starting here with the
+geometric space of curves and surfaces) by substituting the Bregman divergence
+with inner products on the tangent space and explicitly introducing a
+distributed mass model which evolves in conjunction with the object of interest
+during the optimization process. The co-evolving mass model, which is
+introduced purely for the sake of endowing the optimization with helpful
+dynamics, also links the resulting class of accelerated PDE based optimization
+schemes to fluid dynamical formulations of optimal mass transport.
+",1,0,0,0,0,0
+3229,Limitations on Variance-Reduction and Acceleration Schemes for Finite Sum Optimization,"  We study the conditions under which one is able to efficiently apply
+variance-reduction and acceleration schemes on finite sum optimization
+problems. First, we show that, perhaps surprisingly, the finite sum structure
+by itself, is not sufficient for obtaining a complexity bound of
+$\tilde{\cO}((n+L/\mu)\ln(1/\epsilon))$ for $L$-smooth and $\mu$-strongly
+convex individual functions - one must also know which individual function is
+being referred to by the oracle at each iteration. Next, we show that for a
+broad class of first-order and coordinate-descent finite sum algorithms
+(including, e.g., SDCA, SVRG, SAG), it is not possible to get an `accelerated'
+complexity bound of $\tilde{\cO}((n+\sqrt{n L/\mu})\ln(1/\epsilon))$, unless
+the strong convexity parameter is given explicitly. Lastly, we show that when
+this class of algorithms is used for minimizing $L$-smooth and convex finite
+sums, the optimal complexity bound is $\tilde{\cO}(n+L/\epsilon)$, assuming
+that (on average) the same update rule is used in every iteration, and
+$\tilde{\cO}(n+\sqrt{nL/\epsilon})$, otherwise.
+",1,0,1,1,0,0
+3230,Stability Enhanced Large-Margin Classifier Selection,"  Stability is an important aspect of a classification procedure because
+unstable predictions can potentially reduce users' trust in a classification
+system and also harm the reproducibility of scientific conclusions. The major
+goal of our work is to introduce a novel concept of classification instability,
+i.e., decision boundary instability (DBI), and incorporate it with the
+generalization error (GE) as a standard for selecting the most accurate and
+stable classifier. Specifically, we implement a two-stage algorithm: (i)
+initially select a subset of classifiers whose estimated GEs are not
+significantly different from the minimal estimated GE among all the candidate
+classifiers; (ii) the optimal classifier is chosen as the one achieving the
+minimal DBI among the subset selected in stage (i). This general selection
+principle applies to both linear and nonlinear classifiers. Large-margin
+classifiers are used as a prototypical example to illustrate the above idea.
+Our selection method is shown to be consistent in the sense that the optimal
+classifier simultaneously achieves the minimal GE and the minimal DBI. Various
+simulations and real examples further demonstrate the advantage of our method
+over several alternative approaches.
+",0,0,0,1,0,0
+3231,Some Sphere Theorems in Linear Potential Theory,"  In this paper we analyze the capacitary potential due to a charged body in
+order to deduce sharp analytic and geometric inequalities, whose equality cases
+are saturated by domains with spherical symmetry. In particular, for a regular
+bounded domain $\Omega \subset \mathbb{R}^n$, $n\geq 3$, we prove that if the
+mean curvature $H$ of the boundary obeys the condition $$ - \bigg[
+\frac{1}{\text{Cap}(\Omega)} \bigg]^{\frac{1}{n-2}} \leq \frac{H}{n-1} \leq
+\bigg[ \frac{1}{\text{Cap}(\Omega)} \bigg]^{\frac{1}{n-2}} , $$ then $\Omega$
+is a round ball.
+",0,0,1,0,0,0
+3232,Non-Gaussian Component Analysis using Entropy Methods,"  Non-Gaussian component analysis (NGCA) is a problem in multidimensional data
+analysis which, since its formulation in 2006, has attracted considerable
+attention in statistics and machine learning. In this problem, we have a random
+variable $X$ in $n$-dimensional Euclidean space. There is an unknown subspace
+$\Gamma$ of the $n$-dimensional Euclidean space such that the orthogonal
+projection of $X$ onto $\Gamma$ is standard multidimensional Gaussian and the
+orthogonal projection of $X$ onto $\Gamma^{\perp}$, the orthogonal complement
+of $\Gamma$, is non-Gaussian, in the sense that all its one-dimensional
+marginals are different from the Gaussian in a certain metric defined in terms
+of moments. The NGCA problem is to approximate the non-Gaussian subspace
+$\Gamma^{\perp}$ given samples of $X$.
+Vectors in $\Gamma^{\perp}$ correspond to `interesting' directions, whereas
+vectors in $\Gamma$ correspond to the directions where data is very noisy. The
+most interesting applications of the NGCA model is for the case when the
+magnitude of the noise is comparable to that of the true signal, a setting in
+which traditional noise reduction techniques such as PCA don't apply directly.
+NGCA is also related to dimension reduction and to other data analysis problems
+such as ICA. NGCA-like problems have been studied in statistics for a long time
+using techniques such as projection pursuit.
+We give an algorithm that takes polynomial time in the dimension $n$ and has
+an inverse polynomial dependence on the error parameter measuring the angle
+distance between the non-Gaussian subspace and the subspace output by the
+algorithm. Our algorithm is based on relative entropy as the contrast function
+and fits under the projection pursuit framework. The techniques we develop for
+analyzing our algorithm maybe of use for other related problems.
+",0,0,0,1,0,0
+3233,Positive Geometries and Canonical Forms,"  Recent years have seen a surprising connection between the physics of
+scattering amplitudes and a class of mathematical objects--the positive
+Grassmannian, positive loop Grassmannians, tree and loop Amplituhedra--which
+have been loosely referred to as ""positive geometries"". The connection between
+the geometry and physics is provided by a unique differential form canonically
+determined by the property of having logarithmic singularities (only) on all
+the boundaries of the space, with residues on each boundary given by the
+canonical form on that boundary. In this paper we initiate an exploration of
+""positive geometries"" and ""canonical forms"" as objects of study in their own
+right in a more general mathematical setting. We give a precise definition of
+positive geometries and canonical forms, introduce general methods for finding
+forms for more complicated positive geometries from simpler ones, and present
+numerous examples of positive geometries in projective spaces, Grassmannians,
+and toric, cluster and flag varieties. We also illustrate a number of
+strategies for computing canonical forms which yield interesting
+representations for the forms associated with wide classes of positive
+geometries, ranging from the simplest Amplituhedra to new expressions for the
+volume of arbitrary convex polytopes.
+",0,0,1,0,0,0
+3234,Computational Thinking in Education: Where does it Fit? A systematic literary review,"  Computational Thinking (CT) has been described as an essential skill which
+everyone should learn and can therefore include in their skill set. Seymour
+Papert is credited as concretising Computational Thinking in 1980 but since
+Wing popularised the term in 2006 and brought it to the international
+community's attention, more and more research has been conducted on CT in
+education. The aim of this systematic literary review is to give educators and
+education researchers an overview of what work has been carried out in the
+domain, as well as potential gaps and opportunities that still exist.
+Overall it was found in this review that, although there is a lot of work
+currently being done around the world in many different educational contexts,
+the work relating to CT is still in its infancy. Along with the need to create
+an agreed-upon definition of CT lots of countries are still in the process of,
+or have not yet started, introducing CT into curriculums in all levels of
+education. It was also found that Computer Science/Computing, which could be
+the most obvious place to teach CT, has yet to become a mainstream subject in
+some countries, although this is improving. Of encouragement to educators is
+the wealth of tools and resources being developed to help teach CT as well as
+more and more work relating to curriculum development. For those teachers
+looking to incorporate CT into their schools or classes then there are
+bountiful options which include programming, hands-on exercises and more. The
+need for more detailed lesson plans and curriculum structure however, is
+something that could be of benefit to teachers.
+",1,1,0,0,0,0
+3235,Spincaloritronic signal generation in non-degenerate Si,"  Spincaloritronic signal generation due to thermal spin injection and spin
+transport is demonstrated in a non-degenerate Si spin valve. The spin-dependent
+Seebeck effect is used for the spincaloritronic signal generation, and the
+thermal gradient of about 200 mK at an interface of Fe and Si enables
+generating a spin voltage of 8 {\mu}V at room temperature. A simple expansion
+of a conventional spin drift-diffusion model with taking into account the
+spin-dependent Seebeck effect shows semiconductor materials are quite potential
+for the spincaloritronic signal generation comparing with metallic materials,
+which can allow efficient heat recycling in semiconductor spin devices.
+",0,1,0,0,0,0
+3236,Optimal fidelity multi-level Monte Carlo for quantification of uncertainty in simulations of cloud cavitation collapse,"  We quantify uncertainties in the location and magnitude of extreme pressure
+spots revealed from large scale multi-phase flow simulations of cloud
+cavitation collapse. We examine clouds containing 500 cavities and quantify
+uncertainties related to their initial spatial arrangement. The resulting
+2000-dimensional space is sampled using a non-intrusive and computationally
+efficient Multi-Level Monte Carlo (MLMC) methodology. We introduce novel
+optimal control variate coefficients to enhance the variance reduction in MLMC.
+The proposed optimal fidelity MLMC leads to more than two orders of magnitude
+speedup when compared to standard Monte Carlo methods. We identify large
+uncertainties in the location and magnitude of the peak pressure pulse and
+present its statistical correlations and joint probability density functions
+with the geometrical characteristics of the cloud. Characteristic properties of
+spatial cloud structure are identified as potential causes of significant
+uncertainties in exerted collapse pressures.
+",1,0,0,1,0,0
+3237,A New Take on Protecting Cyclists in Smart Cities,"  Pollution in urban centres is becoming a major societal problem. While
+pollution is a concern for all urban dwellers, cyclists are one of the most
+exposed groups due to their proximity to vehicle tailpipes. Consequently, new
+solutions are required to help protect citizens, especially cyclists, from the
+harmful effects of exhaust-gas emissions. In this context, hybrid vehicles
+(HVs) offer new actuation possibilities that can be exploited in this
+direction. More specifically, such vehicles when working together as a group,
+have the ability to dynamically lower the emissions in a given area, thus
+benefiting citizens, whilst still giving the vehicle owner the flexibility of
+using an Internal Combustion Engine (ICE). This paper aims to develop an
+algorithm, that can be deployed in such vehicles, whereby geofences (virtual
+geographic boundaries) are used to specify areas of low pollution around
+cyclists. The emissions level inside the geofence is controlled via a coin
+tossing algorithm to switch the HV motor into, and out of, electric mode, in a
+manner that is in some sense optimal. The optimality criterion is based on how
+polluting vehicles inside the geofence are, and the expected density of
+cyclists near each vehicle. The algorithm is triggered once a vehicle detects a
+cyclist. Implementations are presented, both in simulation, and in a real
+vehicle, and the system is tested using a Hardware-In-the-Loop (HIL) platform
+(video provided).
+",0,0,1,0,0,0
+3238,The effect upon neutrinos of core-collapse supernova accretion phase turbulence,"  During the accretion phase of a core-collapse supernovae, large amplitude
+turbulence is generated by the combination of the standing accretion shock
+instability and convection driven by neutrino heating. The turbulence directly
+affects the dynamics of the explosion, but there is also the possibility of an
+additional, indirect, feedback mechanism due to the effect turbulence can have
+upon neutrino flavor evolution and thus the neutrino heating. In this paper we
+consider the effect of turbulence during the accretion phase upon neutrino
+evolution, both numerically and analytically. Adopting representative supernova
+profiles taken from the accretion phase of a supernova simulation, we find the
+numerical calculations exhibit no effect from turbulence. We explain this
+absence using two analytic descriptions: the Stimulated Transition model and
+the Distorted Phase Effect model. In the Stimulated Transition model turbulence
+effects depend upon six different lengthscales, and three criteria must be
+satisfied between them if one is to observe a change in the flavor evolution
+due to Stimulated Transition. We further demonstrate that the Distorted Phase
+Effect depends upon the presence of multiple semi-adiabatic MSW resonances or
+discontinuities that also can be expressed as a relationship between three of
+the same lengthscales. When we examine the supernova profiles used in the
+numerical calculations we find the three Stimulated Transition criteria cannot
+be satisfied, independent of the form of the turbulence power spectrum, and
+that the same supernova profiles lack the multiple semi-adiabatic MSW
+resonances or discontinuities necessary to produce a Distorted Phase Effect.
+Thus we conclude that even though large amplitude turbulence is present in
+supernova during the accretion phase, it has no effect upon neutrino flavor
+evolution.
+",0,1,0,0,0,0
+3239,Positive-Unlabeled Learning with Non-Negative Risk Estimator,"  From only positive (P) and unlabeled (U) data, a binary classifier could be
+trained with PU learning, in which the state of the art is unbiased PU
+learning. However, if its model is very flexible, empirical risks on training
+data will go negative, and we will suffer from serious overfitting. In this
+paper, we propose a non-negative risk estimator for PU learning: when getting
+minimized, it is more robust against overfitting, and thus we are able to use
+very flexible models (such as deep neural networks) given limited P data.
+Moreover, we analyze the bias, consistency, and mean-squared-error reduction of
+the proposed risk estimator, and bound the estimation error of the resulting
+empirical risk minimizer. Experiments demonstrate that our risk estimator fixes
+the overfitting problem of its unbiased counterparts.
+",1,0,0,1,0,0
+3240,PSZ2LenS. Weak lensing analysis of the Planck clusters in the CFHTLenS and in the RCSLenS,"  The possibly unbiased selection process in surveys of the Sunyaev Zel'dovich
+effect can unveil new populations of galaxy clusters. We performed a weak
+lensing analysis of the PSZ2LenS sample, i.e. the PSZ2 galaxy clusters detected
+by the Planck mission in the sky portion covered by the lensing surveys
+CFHTLenS and RCSLenS. PSZ2LenS consists of 35 clusters and it is a
+statistically complete and homogeneous subsample of the PSZ2 catalogue. The
+Planck selected clusters appear to be unbiased tracers of the massive end of
+the cosmological haloes. The mass concentration relation of the sample is in
+excellent agreement with predictions from the Lambda cold dark matter model.
+The stacked lensing signal is detected at 14 sigma significance over the radial
+range 0.1<R<3.2 Mpc/h, and is well described by the cuspy dark halo models
+predicted by numerical simulations. We confirmed that Planck estimated masses
+are biased low by b_SZ= 27+-11(stat)+-8(sys) per cent with respect to weak
+lensing masses. The bias is higher for the cosmological subsample, b_SZ=
+40+-14+-(stat)+-8(sys) per cent.
+",0,1,0,0,0,0
+3241,GP CaKe: Effective brain connectivity with causal kernels,"  A fundamental goal in network neuroscience is to understand how activity in
+one region drives activity elsewhere, a process referred to as effective
+connectivity. Here we propose to model this causal interaction using
+integro-differential equations and causal kernels that allow for a rich
+analysis of effective connectivity. The approach combines the tractability and
+flexibility of autoregressive modeling with the biophysical interpretability of
+dynamic causal modeling. The causal kernels are learned nonparametrically using
+Gaussian process regression, yielding an efficient framework for causal
+inference. We construct a novel class of causal covariance functions that
+enforce the desired properties of the causal kernels, an approach which we call
+GP CaKe. By construction, the model and its hyperparameters have biophysical
+meaning and are therefore easily interpretable. We demonstrate the efficacy of
+GP CaKe on a number of simulations and give an example of a realistic
+application on magnetoencephalography (MEG) data.
+",0,0,0,1,0,0
+3242,On boundary behavior of mappings on Riemannian manifolds in terms of prime ends,"  A boundary behavior of ring mappings on Riemannian manifolds, which are
+generalization of quasiconformal mappings by Gehring, is investigated. In terms
+of prime ends, there are obtained theorems about continuous extension to a
+boundary of classes mentioned above. In the terms mentioned above, there are
+obtained results about equicontinuity of these classes in the closure of the
+domain.
+",0,0,1,0,0,0
+3243,Privacy Preserving Face Retrieval in the Cloud for Mobile Users,"  Recently, cloud storage and processing have been widely adopted. Mobile users
+in one family or one team may automatically backup their photos to the same
+shared cloud storage space. The powerful face detector trained and provided by
+a 3rd party may be used to retrieve the photo collection which contains a
+specific group of persons from the cloud storage server. However, the privacy
+of the mobile users may be leaked to the cloud server providers. In the
+meanwhile, the copyright of the face detector should be protected. Thus, in
+this paper, we propose a protocol of privacy preserving face retrieval in the
+cloud for mobile users, which protects the user photos and the face detector
+simultaneously. The cloud server only provides the resources of storage and
+computing and can not learn anything of the user photos and the face detector.
+We test our protocol inside several families and classes. The experimental
+results reveal that our protocol can successfully retrieve the proper photos
+from the cloud server and protect the user photos and the face detector.
+",1,0,0,0,0,0
+3244,A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares),"  This work provides a simplified proof of the statistical minimax optimality
+of (iterate averaged) stochastic gradient descent (SGD), for the special case
+of least squares. This result is obtained by analyzing SGD as a stochastic
+process and by sharply characterizing the stationary covariance matrix of this
+process. The finite rate optimality characterization captures the constant
+factors and addresses model mis-specification.
+",1,0,0,1,0,0
+3245,Averages of Unlabeled Networks: Geometric Characterization and Asymptotic Behavior,"  It is becoming increasingly common to see large collections of network data
+objects -- that is, data sets in which a network is viewed as a fundamental
+unit of observation. As a result, there is a pressing need to develop
+network-based analogues of even many of the most basic tools already standard
+for scalar and vector data. In this paper, our focus is on averages of
+unlabeled, undirected networks with edge weights. Specifically, we (i)
+characterize a certain notion of the space of all such networks, (ii) describe
+key topological and geometric properties of this space relevant to doing
+probability and statistics thereupon, and (iii) use these properties to
+establish the asymptotic behavior of a generalized notion of an empirical mean
+under sampling from a distribution supported on this space. Our results rely on
+a combination of tools from geometry, probability theory, and statistical shape
+analysis. In particular, the lack of vertex labeling necessitates working with
+a quotient space modding out permutations of labels. This results in a
+nontrivial geometry for the space of unlabeled networks, which in turn is found
+to have important implications on the types of probabilistic and statistical
+results that may be obtained and the techniques needed to obtain them.
+",0,0,1,1,0,0
+3246,The Abelian distribution,"  We define the Abelian distribution and study its basic properties. Abelian
+distributions arise in the context of neural modeling and describe the size of
+neural avalanches in fully-connected integrate-and-fire models of
+self-organized criticality in neural systems.
+",0,1,0,0,0,0
+3247,PULSEDYN - A dynamical simulation tool for studying strongly nonlinear chains,"  We introduce PULSEDYN, a particle dynamics program in $C++$, to solve
+many-body nonlinear systems in one dimension. PULSEDYN is designed to make
+computing accessible to non-specialists in the field of nonlinear dynamics of
+many-body systems and to ensure transparency and easy benchmarking of numerical
+results for an integrable model (Toda chain) and three non-integrable models
+(Fermi-Pasta-Ulam-Tsingou, Morse and Lennard-Jones). To achieve the latter, we
+have made our code open source and free to distribute. We examine (i) soliton
+propagation and two-soliton collision in the Toda system, (ii) the recurrence
+phenomenon in the Fermi-Pasta-Ulam-Tsingou system and the decay of a single
+localized nonlinear excitation in the same system through quasi-equilibrium to
+an equipartitioned state, and SW propagation in chains with (iii) Morse and
+(iv) Lennard-Jones potentials. We recover well known results from theory and
+other numerical results in the literature. We have obtained these results by
+setting up a parameter file interface which allows the code to be used as a
+black box. Therefore, we anticipate that the code would prove useful to
+students and non-specialists. At the same time, PULSEDYN provides
+scientifically accurate simulations thus making the study of rich dynamical
+processes broadly accessible.
+",0,1,0,0,0,0
+3248,Stabilization Control of the Differential Mobile Robot Using Lyapunov Function and Extended Kalman Filter,"  This paper presents the design of a control model to navigate the
+differential mobile robot to reach the desired destination from an arbitrary
+initial pose. The designed model is divided into two stages: the state
+estimation and the stabilization control. In the state estimation, an extended
+Kalman filter is employed to optimally combine the information from the system
+dynamics and measurements. Two Lyapunov functions are constructed that allow a
+hybrid feedback control law to execute the robot movements. The asymptotical
+stability and robustness of the closed loop system are assured. Simulations and
+experiments are carried out to validate the effectiveness and applicability of
+the proposed approach.
+",1,0,0,0,0,0
+3249,A Random Sample Partition Data Model for Big Data Analysis,"  Big data sets must be carefully partitioned into statistically similar data
+subsets that can be used as representative samples for big data analysis tasks.
+In this paper, we propose the random sample partition (RSP) data model to
+represent a big data set as a set of non-overlapping data subsets, called RSP
+data blocks, where each RSP data block has a probability distribution similar
+to the whole big data set. Under this data model, efficient block level
+sampling is used to randomly select RSP data blocks, replacing expensive record
+level sampling to select sample data from a big distributed data set on a
+computing cluster. We show how RSP data blocks can be employed to estimate
+statistics of a big data set and build models which are equivalent to those
+built from the whole big data set. In this approach, analysis of a big data set
+becomes analysis of few RSP data blocks which have been generated in advance on
+the computing cluster. Therefore, the new method for data analysis based on RSP
+data blocks is scalable to big data.
+",1,0,0,1,0,0
+3250,A family of transformed copulas with singular component,"  In this paper, we present a family of bivariate copulas by transforming a
+given copula function with two increasing functions, named as transformed
+copula. One distinctive characteristic of the transformed copula is its
+singular component along the main diagonal. Conditions guaranteeing the
+transformed function to be a copula function are provided, and several classes
+of the transformed copulas are given. The singular component along the main
+diagonal of the transformed copula is verified, and the tail dependence
+coefficients of the transformed copulas are obtained. Finally, some properties
+of the transformed copula are discussed, such as the totally positive of order
+2 and the concordance order.
+",0,0,1,1,0,0
+3251,Deep Learning for micro-Electrocorticographic (μECoG) Data,"  Machine learning can extract information from neural recordings, e.g.,
+surface EEG, ECoG and {\mu}ECoG, and therefore plays an important role in many
+research and clinical applications. Deep learning with artificial neural
+networks has recently seen increasing attention as a new approach in brain
+signal decoding. Here, we apply a deep learning approach using convolutional
+neural networks to {\mu}ECoG data obtained with a wireless, chronically
+implanted system in an ovine animal model. Regularized linear discriminant
+analysis (rLDA), a filter bank component spatial pattern (FBCSP) algorithm and
+convolutional neural networks (ConvNets) were applied to auditory evoked
+responses captured by {\mu}ECoG. We show that compared with rLDA and FBCSP,
+significantly higher decoding accuracy can be obtained by ConvNets trained in
+an end-to-end manner, i.e., without any predefined signal features. Deep
+learning thus proves a promising technique for {\mu}ECoG-based brain-machine
+interfacing applications.
+",0,0,0,0,1,0
+3252,Clipped Matrix Completion: A Remedy for Ceiling Effects,"  We consider the problem of recovering a low-rank matrix from its clipped
+observations. Clipping is conceivable in many scientific areas that obstructs
+statistical analyses. On the other hand, matrix completion (MC) methods can
+recover a low-rank matrix from various information deficits by using the
+principle of low-rank completion. However, the current theoretical guarantees
+for low-rank MC do not apply to clipped matrices, as the deficit depends on the
+underlying values. Therefore, the feasibility of clipped matrix completion
+(CMC) is not trivial. In this paper, we first provide a theoretical guarantee
+for the exact recovery of CMC by using a trace-norm minimization algorithm.
+Furthermore, we propose practical CMC algorithms by extending ordinary MC
+methods. Our extension is to use the squared hinge loss in place of the squared
+loss for reducing the penalty of over-estimation on clipped entries. We also
+propose a novel regularization term tailored for CMC. It is a combination of
+two trace-norm terms, and we theoretically bound the recovery error under the
+regularization. We demonstrate the effectiveness of the proposed methods
+through experiments using both synthetic and benchmark data for recommendation
+systems.
+",0,0,0,1,0,0
+3253,End-to-End Multi-Task Denoising for joint SDR and PESQ Optimization,"  Supervised learning based on a deep neural network recently has achieved
+substantial improvement on speech enhancement. Denoising networks learn mapping
+from noisy speech to clean one directly, or to a spectra mask which is the
+ratio between clean and noisy spectrum. In either case, the network is
+optimized by minimizing mean square error (MSE) between predefined labels and
+network output of spectra or time-domain signal. However, existing schemes have
+either of two critical issues: spectra and metric mismatches. The spectra
+mismatch is a well known issue that any spectra modification after short-time
+Fourier transform (STFT), in general, cannot be fully recovered after inverse
+STFT. The metric mismatch is that a conventional MSE metric is sub-optimal to
+maximize our target metrics, signal-to-distortion ratio (SDR) and perceptual
+evaluation of speech quality (PESQ). This paper presents a new end-to-end
+denoising framework with the goal of joint SDR and PESQ optimization. First,
+the network optimization is performed on the time-domain signals after ISTFT to
+avoid spectra mismatch. Second, two loss functions which have improved
+correlations with SDR and PESQ metrics are proposed to minimize metric
+mismatch. The experimental result showed that the proposed denoising scheme
+significantly improved both SDR and PESQ performance over the existing methods.
+",1,0,0,1,0,0
+3254,Scalable Importance Tempering and Bayesian Variable Selection,"  We propose a Monte Carlo algorithm to sample from high-dimensional
+probability distributions that combines Markov chain Monte Carlo (MCMC) and
+importance sampling. We provide a careful theoretical analysis, including
+guarantees on robustness to high-dimensionality, explicit comparison with
+standard MCMC and illustrations of the potential improvements in efficiency.
+Simple and concrete intuition is provided for when the novel scheme is expected
+to outperform standard schemes. When applied to Bayesian Variable Selection
+problems, the novel algorithm is orders of magnitude more efficient than
+available alternative sampling schemes and allows to perform fast and reliable
+fully Bayesian inferences with tens of thousands regressors.
+",0,0,0,1,0,0
+3255,Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals,"  Convolutional dictionary learning (CDL) estimates shift invariant basis
+adapted to multidimensional data. CDL has proven useful for image denoising or
+inpainting, as well as for pattern discovery on multivariate signals. As
+estimated patterns can be positioned anywhere in signals or images,
+optimization techniques face the difficulty of working in extremely high
+dimensions with millions of pixels or time samples, contrarily to standard
+patch-based dictionary learning. To address this optimization problem, this
+work proposes a distributed and asynchronous algorithm, employing locally
+greedy coordinate descent and an asynchronous locking mechanism that does not
+require a central server. This algorithm can be used to distribute the
+computation on a number of workers which scales linearly with the encoded
+signal's size. Experiments confirm the scaling properties which allows us to
+learn patterns on large scales images from the Hubble Space Telescope.
+",1,0,0,1,0,0
+3256,Performance Analysis of Robust Stable PID Controllers Using Dominant Pole Placement for SOPTD Process Models,"  This paper derives new formulations for designing dominant pole placement
+based proportional-integral-derivative (PID) controllers to handle second order
+processes with time delays (SOPTD). Previously, similar attempts have been made
+for pole placement in delay-free systems. The presence of the time delay term
+manifests itself as a higher order system with variable number of interlaced
+poles and zeros upon Pade approximation, which makes it difficult to achieve
+precise pole placement control. We here report the analytical expressions to
+constrain the closed loop dominant and non-dominant poles at the desired
+locations in the complex s-plane, using a third order Pade approximation for
+the delay term. However, invariance of the closed loop performance with
+different time delay approximation has also been verified using increasing
+order of Pade, representing a closed to reality higher order delay dynamics.
+The choice of the nature of non-dominant poles e.g. all being complex, real or
+a combination of them modifies the characteristic equation and influences the
+achievable stability regions. The effect of different types of non-dominant
+poles and the corresponding stability regions are obtained for nine test-bench
+processes indicating different levels of open-loop damping and lag to delay
+ratio. Next, we investigate which expression yields a wider stability region in
+the design parameter space by using Monte Carlo simulations while uniformly
+sampling a chosen design parameter space. Various time and frequency domain
+control performance parameters are investigated next, as well as their
+deviations with uncertain process parameters, using thousands of Monte Carlo
+simulations, around the robust stable solution for each of the nine test-bench
+processes.
+",0,0,0,1,0,0
+3257,On Conjugates and Adjoint Descent,"  In this note we present an $\infty$-categorical framework for descent along
+adjunctions and a general formula for counting conjugates up to equivalence
+which unifies several known formulae from different fields.
+",0,0,1,0,0,0
+3258,Learning to Parse and Translate Improves Neural Machine Translation,"  There has been relatively little attention to incorporating linguistic prior
+to neural machine translation. Much of the previous work was further
+constrained to considering linguistic prior on the source side. In this paper,
+we propose a hybrid model, called NMT+RNNG, that learns to parse and translate
+by combining the recurrent neural network grammar into the attention-based
+neural machine translation. Our approach encourages the neural machine
+translation model to incorporate linguistic prior during training, and lets it
+translate on its own afterward. Extensive experiments with four language pairs
+show the effectiveness of the proposed NMT+RNNG.
+",1,0,0,0,0,0
+3259,Boundary feedback stabilization of a flexible wing model under unsteady aerodynamic loads,"  This paper addresses the boundary stabilization of a flexible wing model,
+both in bending and twisting displacements, under unsteady aerodynamic loads,
+and in presence of a store. The wing dynamics is captured by a distributed
+parameter system as a coupled Euler-Bernoulli and Timoshenko beam model. The
+problem is tackled in the framework of semigroup theory, and a Lyapunov-based
+stability analysis is carried out to assess that the system energy, as well as
+the bending and twisting displacements, decay exponentially to zero. The
+effectiveness of the proposed boundary control scheme is evaluated based on
+simulations.
+",1,0,1,0,0,0
+3260,Out-colourings of Digraphs,"  We study vertex colourings of digraphs so that no out-neighbourhood is
+monochromatic and call such a colouring an {\bf out-colouring}. The problem of
+deciding whether a given digraph has an out-colouring with only two colours
+(called a 2-out-colouring) is ${\cal
+NP}$-complete. We show that for every choice of positive integers $r,k$ there
+exists a $k$-strong bipartite tournament which needs at least $r$ colours in
+every out-colouring. Our main results are on tournaments and semicomplete
+digraphs. We prove that, except for the Paley tournament $P_7$, every strong
+semicomplete digraph of minimum out-degree at least 3 has a 2-out-colouring.
+Furthermore, we show that every semicomplete digraph on at least 7 vertices has
+a 2-out-colouring if and only if it has a {\bf balanced} such colouring, that
+is, the difference between the number of vertices that receive colour 1 and
+colour 2 is at most one. In the second half of the paper we consider the
+generalization of 2-out-colourings to vertex partitions $(V_1,V_2)$ of a
+digraph $D$ so that each of the three digraphs induced by respectively, the
+vertices of $V_1$, the vertices of $V_2$ and all arcs between $V_1$ and $V_2$
+have minimum out-degree $k$ for a prescribed integer $k\geq 1$. Using
+probabilistic arguments we prove that there exists an absolute positive
+constant $c$ so that every semicomplete digraph of minimum out-degree at least
+$2k+c\sqrt{k}$ has such a partition. This is tight up to the value of $c$.
+",1,0,0,0,0,0
+3261,An Improved Video Analysis using Context based Extension of LSH,"  Locality Sensitive Hashing (LSH) based algorithms have already shown their
+promise in finding approximate nearest neighbors in high dimen- sional data
+space. However, there are certain scenarios, as in sequential data, where the
+proximity of a pair of points cannot be captured without considering their
+surroundings or context. In videos, as for example, a particular frame is
+meaningful only when it is seen in the context of its preceding and following
+frames. LSH has no mechanism to handle the con- texts of the data points. In
+this article, a novel scheme of Context based Locality Sensitive Hashing
+(conLSH) has been introduced, in which points are hashed together not only
+based on their closeness, but also because of similar context. The contribution
+made in this article is three fold. First, conLSH is integrated with a recently
+proposed fast optimal sequence alignment algorithm (FOGSAA) using a layered
+approach. The resultant method is applied to video retrieval for extracting
+similar sequences. The pro- posed algorithm yields more than 80% accuracy on an
+average in different datasets. It has been found to save 36.3% of the total
+time, consumed by the exhaustive search. conLSH reduces the search space to
+approximately 42% of the entire dataset, when compared with an exhaustive
+search by the aforementioned FOGSAA, Bag of Words method and the standard LSH
+implementations. Secondly, the effectiveness of conLSH is demon- strated in
+action recognition of the video clips, which yields an average gain of 12.83%
+in terms of classification accuracy over the state of the art methods using
+STIP descriptors. The last but of great significance is that this article
+provides a way of automatically annotating long and composite real life videos.
+The source code of conLSH is made available at
+this http URL
+",1,0,0,0,0,0
+3262,"Commuting graphs on Coxeter groups, Dynkin diagrams and finite subgroups of $SL(2,\mathbb{C})$","  For a group $H$ and a non empty subset $\Gamma\subseteq H$, the commuting
+graph $G=\mathcal{C}(H,\Gamma)$ is the graph with $\Gamma$ as the node set and
+where any $x,y \in \Gamma$ are joined by an edge if $x$ and $y$ commute in $H$.
+We prove that any simple graph can be obtained as a commuting graph of a
+Coxeter group, solving the realizability problem in this setup. In particular
+we can recover every Dynkin diagram of ADE type as a commuting graph. Thanks to
+the relation between the ADE classification and finite subgroups of
+$\SL(2,\C)$, we are able to rephrase results from the {\em McKay
+correspondence} in terms of generators of the corresponding Coxeter groups. We
+finish the paper studying commuting graphs $\mathcal{C}(H,\Gamma)$ for every
+finite subgroup $H\subset\SL(2,\C)$ for different subsets $\Gamma\subseteq H$,
+and investigating metric properties of them when $\Gamma=H$.
+",0,0,1,0,0,0
+3263,Exact energy stability of Bénard-Marangoni convection at infinite Prandtl number,"  Using the energy method we investigate the stability of pure conduction in
+Pearson's model for Bénard-Marangoni convection in a layer of fluid at
+infinite Prandtl number. Upon extending the space of admissible perturbations
+to the conductive state, we find an exact solution to the energy stability
+variational problem for a range of thermal boundary conditions describing
+perfectly conducting, imperfectly conducting, and insulating boundaries. Our
+analysis extends and improves previous results, and shows that with the energy
+method global stability can be proven up to the linear instability threshold
+only when the top and bottom boundaries of the fluid layer are insulating.
+Contrary to the well-known Rayleigh-Bénard convection setup, therefore,
+energy stability theory does not exclude the possibility of subcritical
+instabilities against finite-amplitude perturbations.
+",0,1,0,0,0,0
+3264,A sharpening of a problem on Bernstein polynomials and convex functions,"  We present an elementary proof of a conjecture proposed by I. Rasa in 2017
+which is an inequality involving Bernstein basis polynomials and convex
+functions. It was affirmed in positive by A. Komisarski and T. Rajba very
+recently by the use of stochastic convex orderings.
+",0,0,1,0,0,0
+3265,Local Estimate on Convexity Radius and decay of injectivity radius in a Riemannian manifold,"  In this paper we prove the following pointwise and curvature-free estimates
+on convexity radius, injectivity radius and local behavior of geodesics in a
+complete Riemannian manifold $M$: 1) the convexity radius of $p$,
+$\operatorname{conv}(p)\ge
+\min\{\frac{1}{2}\operatorname{inj}(p),\operatorname{foc}(B_{\operatorname{inj}(p)}(p))\}$,
+where $\operatorname{inj}(p)$ is the injectivity radius of $p$ and
+$\operatorname{foc}(B_r(p))$ is the focal radius of open ball centered at $p$
+with radius $r$; 2) for any two points $p,q$ in $M$, $\operatorname{inj}(q)\ge
+\min\{\operatorname{inj}(p), \operatorname{conj}(q)\}-d(p,q),$ where
+$\operatorname{conj}(q)$ is the conjugate radius of $q$; 3) for any
+$0<r<\min\{\operatorname{inj}(p),\frac{1}{2}\operatorname{conj}(B_{\operatorname{inj}(p)}(p))\}$,
+any (not necessarily minimizing) geodesic in $B_r(p)$ has length $\le 2r$. We
+also clarify two different concepts on convexity radius and give examples to
+illustrate that the one more frequently used in literature is not continuous.
+",0,0,1,0,0,0
+3266,Magnetic properties of the spin-1 chain compound NiCl$_3$C$_6$H$_5$CH$_2$CH$_2$NH$_3$,"  We report experimental results of the static magnetization, ESR and NMR
+spectroscopic measurements of the Ni-hybrid compound
+NiCl$_3$C$_6$H$_5$CH$_2$CH$_2$NH$_3$. In this material NiCl$_3$ octahedra are
+structurally arranged in chains along the crystallographic $a$-axis. According
+to the static susceptibility and ESR data Ni$^{2+}$ spins $S = 1$ are isotropic
+and are coupled antiferromagnetically (AFM) along the chain with the exchange
+constant $J = 25.5$ K. These are important prerequisites for the realization of
+the so-called Haldane spin-1 chain with the spin-singlet ground state and a
+quantum spin gap. However, experimental results evidence AFM order at $T_{\rm
+N} \approx 10$ K presumably due to small interchain couplings. Interestingly,
+frequency-, magnetic field-, and temperature-dependent ESR measurements, as
+well as the NMR data, reveal signatures which could presumably indicate an
+inhomogeneous ground state of co-existent mesoscopically spatially separated
+AFM ordered and spin-singlet state regions similar to the situation observed
+before in some spin-diluted Haldane magnets.
+",0,1,0,0,0,0
+3267,Multiple core hole formation by free-electron laser radiation in molecular nitrogen,"  We investigate the formation of multiple-core-hole states of molecular
+nitrogen interacting with a free-electron laser pulse. We obtain bound and
+continuum molecular orbitals in the single-center expansion scheme and use
+these orbitals to calculate photo-ionization and Auger decay rates. Using these
+rates, we compute the atomic ion yields generated in this interaction. We track
+the population of all states throughout this interaction and compute the
+proportion of the population which accesses different core-hole states. We also
+investigate the pulse parameters that favor the formation of these core-hole
+states for 525 eV and 1100 eV photons.
+",0,1,0,0,0,0
+3268,An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution,"  Few ideas have enjoyed as large an impact on deep learning as convolution.
+For any problem involving pixels or spatial representations, common intuition
+holds that convolutional neural networks may be appropriate. In this paper we
+show a striking counterexample to this intuition via the seemingly trivial
+coordinate transform problem, which simply requires learning a mapping between
+coordinates in (x,y) Cartesian space and one-hot pixel space. Although
+convolutional networks would seem appropriate for this task, we show that they
+fail spectacularly. We demonstrate and carefully analyze the failure first on a
+toy problem, at which point a simple fix becomes obvious. We call this solution
+CoordConv, which works by giving convolution access to its own input
+coordinates through the use of extra coordinate channels. Without sacrificing
+the computational and parametric efficiency of ordinary convolution, CoordConv
+allows networks to learn either complete translation invariance or varying
+degrees of translation dependence, as required by the end task. CoordConv
+solves the coordinate transform problem with perfect generalization and 150
+times faster with 10--100 times fewer parameters than convolution. This stark
+contrast raises the question: to what extent has this inability of convolution
+persisted insidiously inside other tasks, subtly hampering performance from
+within? A complete answer to this question will require further investigation,
+but we show preliminary evidence that swapping convolution for CoordConv can
+improve models on a diverse set of tasks. Using CoordConv in a GAN produced
+less mode collapse as the transform between high-level spatial latents and
+pixels becomes easier to learn. A Faster R-CNN detection model trained on MNIST
+showed 24% better IOU when using CoordConv, and in the RL domain agents playing
+Atari games benefit significantly from the use of CoordConv layers.
+",0,0,0,1,0,0
+3269,Uniformly recurrent subgroups and the ideal structure of reduced crossed products,"  We study the ideal structure of reduced crossed product of topological
+dynamical systems of a countable discrete group. More concretely, for a compact
+Hausdorff space $X$ with an action of a countable discrete group $\Gamma$, we
+consider the absence of a non-zero ideals in the reduced crossed product $C(X)
+\rtimes_r \Gamma$ which has a zero intersection with $C(X)$. We characterize
+this condition by a property for amenable subgroups of the stabilizer subgroups
+of $X$ in terms of the Chabauty space of $\Gamma$. This generalizes Kennedy's
+algebraic characterization of the simplicity for a reduced group
+$\mathrm{C}^{*}$-algebra of a countable discrete group.
+",0,0,1,0,0,0
+3270,TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks,"  Understanding and discovering knowledge from GPS (Global Positioning System)
+traces of human activities is an essential topic in mobility-based urban
+computing. We propose TrajectoryNet-a neural network architecture for
+point-based trajectory classification to infer real world human transportation
+modes from GPS traces. To overcome the challenge of capturing the underlying
+latent factors in the low-dimensional and heterogeneous feature space imposed
+by GPS data, we develop a novel representation that embeds the original feature
+space into another space that can be understood as a form of basis expansion.
+We also enrich the feature space via segment-based information and use Maxout
+activations to improve the predictive power of Recurrent Neural Networks
+(RNNs). We achieve over 98% classification accuracy when detecting four types
+of transportation modes, outperforming existing models without additional
+sensory data or location-based prior knowledge.
+",1,0,0,0,0,0
+3271,Modularity Matters: Learning Invariant Relational Reasoning Tasks,"  We focus on two supervised visual reasoning tasks whose labels encode a
+semantic relational rule between two or more objects in an image: the MNIST
+Parity task and the colorized Pentomino task. The objects in the images undergo
+random translation, scaling, rotation and coloring transformations. Thus these
+tasks involve invariant relational reasoning. We report uneven performance of
+various deep CNN models on these two tasks. For the MNIST Parity task, we
+report that the VGG19 model soundly outperforms a family of ResNet models.
+Moreover, the family of ResNet models exhibits a general sensitivity to random
+initialization for the MNIST Parity task. For the colorized Pentomino task, now
+both the VGG19 and ResNet models exhibit sluggish optimization and very poor
+test generalization, hovering around 30% test error. The CNN we tested all
+learn hierarchies of fully distributed features and thus encode the distributed
+representation prior. We are motivated by a hypothesis from cognitive
+neuroscience which posits that the human visual cortex is modularized, and this
+allows the visual cortex to learn higher order invariances. To this end, we
+consider a modularized variant of the ResNet model, referred to as a Residual
+Mixture Network (ResMixNet) which employs a mixture-of-experts architecture to
+interleave distributed representations with more specialized, modular
+representations. We show that very shallow ResMixNets are capable of learning
+each of the two tasks well, attaining less than 2% and 1% test error on the
+MNIST Parity and the colorized Pentomino tasks respectively. Most importantly,
+the ResMixNet models are extremely parameter efficient: generalizing better
+than various non-modular CNNs that have over 10x the number of parameters.
+These experimental results support the hypothesis that modularity is a robust
+prior for learning invariant relational reasoning.
+",0,0,0,1,1,0
+3272,Proofs of some Propositions of the semi-Intuitionistic Logic with Strong Negation,"  We offer the proofs that complete our article introducing the propositional
+calculus called semi-intuitionistic logic with strong negation.
+",0,0,1,0,0,0
+3273,Entanglement in topological systems,"  These lecture notes on entanglement in topological systems are part of the
+48th IFF Spring School 2017 on Topological Matter: Topological Insulators,
+Skyrmions and Majoranas at the Forschungszentrum Juelich, Germany. They cover a
+short discussion on topologically ordered phases and review the two main tools
+available for detecting topological order - the entanglement entropy and the
+entanglement spectrum.
+",0,1,0,0,0,0
+3274,Equivalence between Differential Inclusions Involving Prox-regular sets and maximal monotone operators,"  In this paper, we study the existence and the stability in the sense of
+Lyapunov of solutions for\ differential inclusions governed by the normal cone
+to a prox-regular set and subject to a Lipschitzian perturbation. We prove that
+such, apparently, more general nonsmooth dynamics can be indeed remodelled into
+the classical theory of differential inclusions involving maximal monotone
+operators. This result is new in the literature and permits us to make use of
+the rich and abundant achievements in this class of monotone operators to
+derive the desired existence result and stability analysis, as well as the
+continuity and differentiability properties of the solutions. This going back
+and forth between these two models of differential inclusions is made possible
+thanks to a viability result for maximal monotone operators. As an application,
+we study a Luenberger-like observer, which is shown to converge exponentially
+to the actual state when the initial value of the state's estimation remains in
+a neighborhood of the initial value of the original system.
+",0,0,1,0,0,0
+3275,Effect of iron oxide loading on magnetoferritin structure in solution as revealed by SAXS and SANS,"  Synthetic biological macromolecule of magnetoferritin containing an iron
+oxide core inside a protein shell (apoferritin) is prepared with different
+content of iron. Its structure in aqueous solution is analyzed by small-angle
+synchrotron X-ray (SAXS) and neutron (SANS) scattering. The loading factor (LF)
+defined as the average number of iron atoms per protein is varied up to LF=800.
+With an increase of the LF, the scattering curves exhibit a relative increase
+in the total scattered intensity, a partial smearing and a shift of the match
+point in the SANS contrast variation data. The analysis shows an increase in
+the polydispersity of the proteins and a corresponding effective increase in
+the relative content of magnetic material against the protein moiety of the
+shell with the LF growth. At LFs above ~150, the apoferritin shell undergoes
+structural changes, which is strongly indicative of the fact that the shell
+stability is affected by iron oxide presence.
+",0,1,0,0,0,0
+3276,Volumetric Super-Resolution of Multispectral Data,"  Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
+ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
+high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
+order to reconstruct a high-spatial/high-spectral resolution multispectral
+image volume, either the information in MS and PAN images are fused (i.e.
+pansharpening) or super-resolution reconstruction (SRR) is used with only MS
+images captured on different dates. Existing methods do not utilize temporal
+information of MS and high spatial resolution of PAN images together to improve
+the resolution. In this paper, we propose a multiframe SRR algorithm using
+pansharpened MS images, taking advantage of both temporal and spatial
+information available in multispectral imagery, in order to exceed spatial
+resolution of given PAN images. We first apply pansharpening to a set of
+multispectral images and their corresponding PAN images captured on different
+dates. Then, we use the pansharpened multispectral images as input to the
+proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The
+proposed SRR method is obtained by deriving the subband relations between
+multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images
+comparing our method to conventional techniques.
+",1,0,0,0,0,0
+3277,Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity,"  Empirical scoring functions based on either molecular force fields or
+cheminformatics descriptors are widely used, in conjunction with molecular
+docking, during the early stages of drug discovery to predict potency and
+binding affinity of a drug-like molecule to a given target. These models
+require expert-level knowledge of physical chemistry and biology to be encoded
+as hand-tuned parameters or features rather than allowing the underlying model
+to select features in a data-driven procedure. Here, we develop a general
+3-dimensional spatial convolution operation for learning atomic-level chemical
+interactions directly from atomic coordinates and demonstrate its application
+to structure-based bioactivity prediction. The atomic convolutional neural
+network is trained to predict the experimentally determined binding affinity of
+a protein-ligand complex by direct calculation of the energy associated with
+the complex, protein, and ligand given the crystal structure of the binding
+pose. Non-covalent interactions present in the complex that are absent in the
+protein-ligand sub-structures are identified and the model learns the
+interaction strength associated with these features. We test our model by
+predicting the binding free energy of a subset of protein-ligand complexes
+found in the PDBBind dataset and compare with state-of-the-art cheminformatics
+and machine learning-based approaches. We find that all methods achieve
+experimental accuracy and that atomic convolutional networks either outperform
+or perform competitively with the cheminformatics based methods. Unlike all
+previous protein-ligand prediction systems, atomic convolutional networks are
+end-to-end and fully-differentiable. They represent a new data-driven,
+physics-based deep learning model paradigm that offers a strong foundation for
+future improvements in structure-based bioactivity prediction.
+",1,1,0,1,0,0
+3278,"Random characters under the $L$-measure, I : Dirichlet characters","  We define the $L$-measure on the set of Dirichlet characters as an analogue
+of the Plancherel measure, once considered as a measure on the irreducible
+characters of the symmetric group.
+We compare the two measures and study the limit in distribution of characters
+evaluations when the size of the underlying group grows. These evaluations are
+proven to converge in law to imaginary exponentials of a Cauchy distribution in
+the same way as the rescaled windings of the complex Brownian motion. This
+contrasts with the case of the symmetric group where the renormalised
+characters converge in law to Gaussians after rescaling (Kerov Central Limit
+Theorem).
+",0,0,1,0,0,0
+3279,The Effects of Superheating Treatment on Distribution of Eutectic Silicon Particles in A357-Continuous Stainless Steel Composite,"  In the present study, superheating treatment has been applied on A357
+reinforced with 0.5 wt. % (Composite 1) and 1.0 wt.% (Composite 2) continuous
+stainless steel composite. In Composite 1 the microstructure displayed poor
+bonding between matrix and reinforcement interface. Poor bonding associated
+with large voids also can be seen in Composite 1. The results also showed that
+coarser eutectic silicon (Si) particles were less intensified around the
+matrix-reinforcement interface. From energy dispersive spectrometry (EDS)
+elemental mapping, it was clearly shown that the distribution of eutectic Si
+particles were less concentrated at poor bonding regions associated with large
+voids. Meanwhile in Composite 2, the microstructure displayed good bonding
+combined with more concentrated finer eutectic Si particles around the
+matrix-reinforcement interface. From EDS elemental mapping, it was clearly
+showed more concentrated of eutectic Si particles were distributed at the good
+bonding area. The superheating prior to casting has influenced the
+microstructure and tends to produce finer, rounded and preferred oriented
+{\alpha}-Al dendritic structures.
+",0,1,0,0,0,0
+3280,Cycle-of-Learning for Autonomous Systems from Human Interaction,"  We discuss different types of human-robot interaction paradigms in the
+context of training end-to-end reinforcement learning algorithms. We provide a
+taxonomy to categorize the types of human interaction and present our
+Cycle-of-Learning framework for autonomous systems that combines different
+human-interaction modalities with reinforcement learning. Two key concepts
+provided by our Cycle-of-Learning framework are how it handles the integration
+of the different human-interaction modalities (demonstration, intervention, and
+evaluation) and how to define the switching criteria between them.
+",1,0,0,0,0,0
+3281,Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework,"  Background: Choosing the most performing method in terms of outcome
+prediction or variables selection is a recurring problem in prognosis studies,
+leading to many publications on methods comparison. But some aspects have
+received little attention. First, most comparison studies treat prediction
+performance and variable selection aspects separately. Second, methods are
+either compared within a binary outcome setting (based on an arbitrarily chosen
+delay) or within a survival setting, but not both. In this paper, we propose a
+comparison methodology to weight up those different settings both in terms of
+prediction and variables selection, while incorporating advanced machine
+learning strategies. Methods: Using a high-dimensional case study on a
+sickle-cell disease (SCD) cohort, we compare 8 statistical methods. In the
+binary outcome setting, we consider logistic regression (LR), support vector
+machine (SVM), random forest (RF), gradient boosting (GB) and neural network
+(NN); while on the survival analysis setting, we consider the Cox Proportional
+Hazards (PH), the CURE and the C-mix models. We then compare performances of
+all methods both in terms of risk prediction and variable selection, with a
+focus on the use of Elastic-Net regularization technique. Results: Among all
+assessed statistical methods assessed, the C-mix model yields the better
+performances in both the two considered settings, as well as interesting
+interpretation aspects. There is some consistency in selected covariates across
+methods within a setting, but not much across the two settings. Conclusions: It
+appears that learning withing the survival setting first, and then going back
+to a binary prediction using the survival estimates significantly enhance
+binary predictions.
+",0,0,0,1,0,0
+3282,How the notion of ACCESS guides the organization of a European research infrastructure: the example of DARIAH,"  This contribution will show how Access play a strong role in the creation and
+structuring of DARIAH, a European Digital Research Infrastructure in Arts and
+Humanities.To achieve this goal, this contribution will develop the concept of
+Access from five examples: Interdisciplinarity point of view, Manage
+contradiction between national and international perspectives, Involve
+different communities (not only researchers stakeholders), Manage tools and
+services, Develop and use new collaboration tools. We would like to demonstrate
+that speaking about Access always implies a selection, a choice, even in the
+perspective of ""Open Access"".
+",1,0,0,0,0,0
+3283,Soliton-potential interactions for nonlinear Schrödinger equation in $\mathbb{R}^3$,"  In this work we mainly consider the dynamics and scattering of a narrow
+soliton of NLS equation with a potential in $\mathbb{R}^3$, where the
+asymptotic state of the system can be far from the initial state in parameter
+space. Specifically, if we let a narrow soliton state with initial velocity
+$\upsilon_{0}$ to interact with an extra potential $V(x)$, then the velocity
+$\upsilon_{+}$ of outgoing solitary wave in infinite time will in general be
+very different from $\upsilon_{0}$. In contrast to our present work, previous
+works proved that the soliton is asymptotically stable under the assumption
+that $\upsilon_{+}$ stays close to $\upsilon_{0}$ in a certain manner.
+",0,0,1,0,0,0
+3284,Incommensurately modulated twin structure of nyerereite Na1.64K0.36Ca(CO3)2,"  Incommensurately modulated twin structure of nyerereite Na1.64K0.36Ca(CO3)2
+has been first determined in the (3+1)D symmetry group Cmcm({\alpha}00)00s with
+modulation vector q = 0.383a*. Unit-cell values are a = 5.062(1), b = 8.790(1),
+c = 12.744(1) {\AA}. Three orthorhombic components are related by threefold
+rotation about [001]. Discontinuous crenel functions are used to describe
+occupation modulation of Ca and some CO3 groups. Strong displacive modulation
+of the oxygen atoms in vertexes of such CO3 groups is described using
+x-harmonics in crenel intervals. The Na, K atoms occupy mixed sites whose
+occupation modulation is described by two ways using either complementary
+harmonic functions or crenels. The nyerereite structure has been compared both
+with commensurately modulated structure of K-free Na2Ca(CO3)2 and with widely
+known incommensurately modulated structure of {\gamma}-Na2CO3.
+",0,1,0,0,0,0
+3285,Jensen's force and the statistical mechanics of cortical asynchronous states,"  The cortex exhibits self-sustained highly-irregular activity even under
+resting conditions, whose origin and function need to be fully understood. It
+is believed that this can be described as an ""asynchronous state"" stemming from
+the balance between excitation and inhibition, with important consequences for
+information-processing, though a competing hypothesis claims it stems from
+critical dynamics. By analyzing a parsimonious neural-network model with
+excitatory and inhibitory interactions, we elucidate a noise-induced mechanism
+called ""Jensen's force"" responsible for the emergence of a novel phase of
+arbitrarily-low but self-sustained activity, which reproduces all the
+experimental features of asynchronous states. The simplicity of our framework
+allows for a deep understanding of asynchronous states from a broad
+statistical-mechanics perspective and of the phase transitions to other
+standard phases it exhibits, opening the door to reconcile, asynchronous-state
+and critical-state hypotheses. We argue that Jensen's forces are measurable
+experimentally and might be relevant in contexts beyond neuroscience.
+",0,0,0,0,1,0
+3286,Notes on relative normalizations of ruled surfaces in the three-dimensional Euclidean space,"  This paper deals with relative normalizations of skew ruled surfaces in the
+Euclidean space $\mathbb{E}^{3}$. In section 2 we investigate some new formulae
+concerning the Pick invariant, the relative curvature, the relative mean
+curvature and the curvature of the relative metric of a relatively normalized
+ruled surface $\varPhi$ and in section 3 we introduce some special
+normalizations of it. All ruled surfaces and their corresponding normalizations
+that make $\varPhi$ an improper or a proper relative sphere are determined in
+section 4. In the last section we study ruled surfaces, which are
+\emph{centrally} normalized, i.e., their relative normals at each point lie on
+the corresponding central plane. Especially we study various properties of the
+Tchebychev vector field. We conclude the paper by the study of the central
+image of $\varPhi$.
+",0,0,1,0,0,0
+3287,Ferroelectric control of the giant Rashba spin orbit coupling in GeTe(111)/InP(111) superlattice,"  GeTe wins the renewed research interest due to its giant bulk Rashba spin
+orbit coupling (SOC), and becomes the father of a new multifunctional material,
+i.e., ferroelectric Rashba semiconductor. In the present work, we investigate
+Rashba SOC at the interface of the ferroelectric semiconductor superlattice
+GeTe(111)/InP(111) by using the first principles calculation. Contribution of
+the interface electric field and the ferroelectric field to Rashba SOC is
+revealed. A large modulation to Rashba SOC and a reversal of the spin
+polarization is obtained by switching the ferroelectric polarization. Our
+investigation about GeTe(111)/InP(111) superlattice is of great importance in
+the application of ferroelectric Rashba semiconductor in the spin field effect
+transistor.
+",0,1,0,0,0,0
+3288,Topological phase of the interlayer exchange coupling with application to magnetic switching,"  We show, theoretically, that the phase of the interlayer exchange coupling
+(IEC) undergoes a topological change of approximately $2\pi$ as the chemical
+potential of the ferromagnetic (FM) lead moves across a hybridization gap (HG).
+The effect is largely independent of the detailed parameters of the system, in
+particular the width of the gap. The implication is that for a narrow gap, a
+small perturbation in the chemical potential of the lead can give a sign
+reversal of the exchange coupling. This offers the possibility of controlling
+magnetization switching in spintronic devices such as MRAM, with little power
+consumption. Furthermore we believe that this effect has already been
+indirectly observed, in existing measurements of the IEC as a function of
+temperature and of doping of the leads.
+",0,1,0,0,0,0
+3289,New constraints on time-dependent variations of fundamental constants using Planck data,"  Observations of the CMB today allow us to answer detailed questions about the
+properties of our Universe, targeting both standard and non-standard physics.
+In this paper, we study the effects of varying fundamental constants (i.e., the
+fine-structure constant, $\alpha_{\rm EM}$, and electron rest mass, $m_{\rm
+e}$) around last scattering using the recombination codes CosmoRec and
+Recfast++. We approach the problem in a pedagogical manner, illustrating the
+importance of various effects on the free electron fraction, Thomson visibility
+function and CMB power spectra, highlighting various degeneracies. We
+demonstrate that the simpler Recfast++ treatment (based on a three-level atom
+approach) can be used to accurately represent the full computation of CosmoRec.
+We also include explicit time-dependent variations using a phenomenological
+power-law description. We reproduce previous Planck 2013 results in our
+analysis. Assuming constant variations relative to the standard values, we find
+the improved constraints $\alpha_{\rm EM}/\alpha_{\rm EM,0}=0.9993\pm 0.0025$
+(CMB only) and $m_{\rm e}/m_{\rm e,0}= 1.0039 \pm 0.0074$ (including BAO) using
+Planck 2015 data. For a redshift-dependent variation, $\alpha_{\rm
+EM}(z)=\alpha_{\rm EM}(z_0)\,[(1+z)/1100]^p$ with $\alpha_{\rm
+EM}(z_0)\equiv\alpha_{\rm EM,0}$ at $z_0=1100$, we obtain $p=0.0008\pm 0.0025$.
+Allowing simultaneous variations of $\alpha_{\rm EM}(z_0)$ and $p$ yields
+$\alpha_{\rm EM}(z_0)/\alpha_{\rm EM,0} = 0.9998\pm 0.0036$ and $p = 0.0006\pm
+0.0036$. We also discuss combined limits on $\alpha_{\rm EM}$ and $m_{\rm e}$.
+Our analysis shows that existing data is not only sensitive to the value of the
+fundamental constants around recombination but also its first time derivative.
+This suggests that a wider class of varying fundamental constant models can be
+probed using the CMB.
+",0,1,0,0,0,0
+3290,ForestClaw: A parallel algorithm for patch-based adaptive mesh refinement on a forest of quadtrees,"  We describe a parallel, adaptive, multi-block algorithm for explicit
+integration of time dependent partial differential equations on two-dimensional
+Cartesian grids. The grid layout we consider consists of a nested hierarchy of
+fixed size, non-overlapping, logically Cartesian grids stored as leaves in a
+quadtree. Dynamic grid refinement and parallel partitioning of the grids is
+done through the use of the highly scalable quadtree/octree library p4est.
+Because our concept is multi-block, we are able to easily solve on a variety of
+geometries including the cubed sphere. In this paper, we pay special attention
+to providing details of the parallel ghost-filling algorithm needed to ensure
+that both corner and edge ghost regions around each grid hold valid values.
+We have implemented this algorithm in the ForestClaw code using single-grid
+solvers from ClawPack, a software package for solving hyperbolic PDEs using
+finite volumes methods. We show weak and strong scalability results for scalar
+advection problems on two-dimensional manifold domains on 1 to 64Ki MPI
+processes, demonstrating neglible regridding overhead.
+",1,0,0,0,0,0
+3291,Distributed Triangle Counting in the Graphulo Matrix Math Library,"  Triangle counting is a key algorithm for large graph analysis. The Graphulo
+library provides a framework for implementing graph algorithms on the Apache
+Accumulo distributed database. In this work we adapt two algorithms for
+counting triangles, one that uses the adjacency matrix and another that also
+uses the incidence matrix, to the Graphulo library for server-side processing
+inside Accumulo. Cloud-based experiments show a similar performance profile for
+these different approaches on the family of power law Graph500 graphs, for
+which data skew increasingly bottlenecks. These results motivate the design of
+skew-aware hybrid algorithms that we propose for future work.
+",1,0,0,0,0,0
+3292,Ensemble Classifier for Eye State Classification using EEG Signals,"  The growing importance and utilization of measuring brain waves (e.g. EEG
+signals of eye state) in brain-computer interface (BCI) applications
+highlighted the need for suitable classification methods. In this paper, a
+comparison between three of well-known classification methods (i.e. support
+vector machine (SVM), hidden Markov map (HMM), and radial basis function (RBF))
+for EEG based eye state classification was achieved. Furthermore, a suggested
+method that is based on ensemble model was tested. The suggested (ensemble
+system) method based on a voting algorithm with two kernels: random forest (RF)
+and Kstar classification methods. The performance was tested using three
+measurement parameters: accuracy, mean absolute error (MAE), and confusion
+matrix. Results showed that the proposed method outperforms the other tested
+methods. For instance, the suggested method's performance was 97.27% accuracy
+and 0.13 MAE.
+",1,0,0,0,0,0
+3293,Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data,"  Diet design for vegetarian health is challenging due to the limited food
+repertoire of vegetarians. This challenge can be partially overcome by
+quantitative, data-driven approaches that utilise massive nutritional
+information collected for many different foods. Based on large-scale data of
+foods' nutrient compositions, the recent concept of nutritional fitness helps
+quantify a nutrient balance within each food with regard to satisfying daily
+nutritional requirements. Nutritional fitness offers prioritisation of
+recommended foods using the foods' occurrence in nutritionally adequate food
+combinations. Here, we systematically identify nutritionally recommendable
+foods for semi- to strict vegetarian diets through the computation of
+nutritional fitness. Along with commonly recommendable foods across different
+diets, our analysis reveals favourable foods specific to each diet, such as
+immature lima beans for a vegan diet as an amino acid and choline source, and
+mushrooms for ovo-lacto vegetarian and vegan diets as a vitamin D source.
+Furthermore, we find that selenium and other essential micronutrients can be
+subject to deficiency in plant-based diets, and suggest nutritionally-desirable
+dietary patterns. We extend our analysis to two hypothetical scenarios of
+highly personalised, plant-based methionine-restricted diets. Our
+nutrient-profiling approach may provide a useful guide for designing different
+types of personalised vegetarian diets.
+",1,0,0,0,1,0
+3294,Does data interpolation contradict statistical optimality?,"  We show that learning methods interpolating the training data can achieve
+optimal rates for the problems of nonparametric regression and prediction with
+square loss.
+",0,0,0,1,0,0
+3295,Spin-Frustrated Pyrochlore Chains in the Volcanic Mineral Kamchatkite (KCu3OCl(SO4)2),"  Search of new frustrated magnetic systems is of a significant importance for
+physics studying the condensed matter. The platform for geometric frustration
+of magnetic systems can be provided by copper oxocentric tetrahedra (OCu4)
+forming the base of crystalline structures of copper minerals from Tolbachik
+volcanos in Kamchatka. The present work was devoted to a new frustrated
+antiferromagnetic - kamchatkite (KCu3OCl(SO4)2). The calculation of the sign
+and strength of magnetic couplings in KCu3OCl(SO4)2 has been performed on the
+basis of structural data by the phenomenological crystal chemistry method with
+taking into account corrections on the Jahn-Teller orbital degeneracy of Cu2.
+It has been established that kamchatkite (KCu3OCl(SO4)2) contains AFM
+spin-frustrated chains of the pyrochlore type composed of cone-sharing Cu4
+tetrahedra. Strong AFM intrachain and interchain couplings compete with each
+other. Frustration of magnetic couplings in tetrahedral chains is combined with
+the presence of electric polarization.
+",0,1,0,0,0,0
+3296,Detail-revealing Deep Video Super-resolution,"  Previous CNN-based video super-resolution approaches need to align multiple
+frames to the reference. In this paper, we show that proper frame alignment and
+motion compensation is crucial for achieving high quality results. We
+accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN
+framework. Analysis and experiments show the suitability of this layer in video
+SR. The final end-to-end, scalable CNN framework effectively incorporates the
+SPMC layer and fuses multiple frames to reveal image details. Our
+implementation can generate visually and quantitatively high-quality results,
+superior to current state-of-the-arts, without the need of parameter tuning.
+",1,0,0,0,0,0
+3297,MON: Mission-optimized Overlay Networks,"  Large organizations often have users in multiple sites which are connected
+over the Internet. Since resources are limited, communication between these
+sites needs to be carefully orchestrated for the most benefit to the
+organization. We present a Mission-optimized Overlay Network (MON), a hybrid
+overlay network architecture for maximizing utility to the organization. We
+combine an offline and an online system to solve non-concave utility
+maximization problems. The offline tier, the Predictive Flow Optimizer (PFO),
+creates plans for routing traffic using a model of network conditions. The
+online tier, MONtra, is aware of the precise local network conditions and is
+able to react quickly to problems within the network. Either tier alone is
+insufficient. The PFO may take too long to react to network changes. MONtra
+only has local information and cannot optimize non-concave mission utilities.
+However, by combining the two systems, MON is robust and achieves near-optimal
+utility under a wide range of network conditions. While best-effort overlay
+networks are well studied, our work is the first to design overlays that are
+optimized for mission utility.
+",1,0,0,0,0,0
+3298,Generalized Results on Monoids as Memory,"  We show that some results from the theory of group automata and monoid
+automata still hold for more general classes of monoids and models. Extending
+previous work for finite automata over commutative groups, we demonstrate a
+context-free language that can not be recognized by any rational monoid
+automaton over a finitely generated permutable monoid. We show that the class
+of languages recognized by rational monoid automata over finitely generated
+completely simple or completely 0-simple permutable monoids is a semi-linear
+full trio. Furthermore, we investigate valence pushdown automata, and prove
+that they are only as powerful as (finite) valence automata. We observe that
+certain results proven for monoid automata can be easily lifted to the case of
+context-free valence grammars.
+",1,0,0,0,0,0
+3299,Radio Resource Allocation for Multicarrier-Low Density Spreading Multiple Access,"  Multicarrier-low density spreading multiple access (MC-LDSMA) is a promising
+multiple access technique that enables near optimum multiuser detection. In
+MC-LDSMA, each user's symbol spread on a small set of subcarriers, and each
+subcarrier is shared by multiple users. The unique structure of MC-LDSMA makes
+the radio resource allocation more challenging comparing to some well-known
+multiple access techniques. In this paper, we study the radio resource
+allocation for single-cell MC-LDSMA system. Firstly, we consider the
+single-user case, and derive the optimal power allocation and subcarriers
+partitioning schemes. Then, by capitalizing on the optimal power allocation of
+the Gaussian multiple access channel, we provide an optimal solution for
+MC-LDSMA that maximizes the users' weighted sum-rate under relaxed constraints.
+Due to the prohibitive complexity of the optimal solution, suboptimal
+algorithms are proposed based on the guidelines inferred by the optimal
+solution. The performance of the proposed algorithms and the effect of
+subcarrier loading and spreading are evaluated through Monte Carlo simulations.
+Numerical results show that the proposed algorithms significantly outperform
+conventional static resource allocation, and MC-LDSMA can improve the system
+performance in terms of spectral efficiency and fairness in comparison with
+OFDMA.
+",1,0,0,0,0,0
+3300,Estimating the Spectral Density of Large Implicit Matrices,"  Many important problems are characterized by the eigenvalues of a large
+matrix. For example, the difficulty of many optimization problems, such as
+those arising from the fitting of large models in statistics and machine
+learning, can be investigated via the spectrum of the Hessian of the empirical
+loss function. Network data can be understood via the eigenstructure of a graph
+Laplacian matrix using spectral graph theory. Quantum simulations and other
+many-body problems are often characterized via the eigenvalues of the solution
+space, as are various dynamic systems. However, naive eigenvalue estimation is
+computationally expensive even when the matrix can be represented; in many of
+these situations the matrix is so large as to only be available implicitly via
+products with vectors. Even worse, one may only have noisy estimates of such
+matrix vector products. In this work, we combine several different techniques
+for randomized estimation and show that it is possible to construct unbiased
+estimators to answer a broad class of questions about the spectra of such
+implicit matrices, even in the presence of noise. We validate these methods on
+large-scale problems in which graph theory and random matrix theory provide
+ground truth.
+",0,0,0,1,0,0
+3301,Robust 3D Distributed Formation Control with Application to Quadrotors,"  We present a distributed control strategy for a team of quadrotors to
+autonomously achieve a desired 3D formation. Our approach is based on local
+relative position measurements and does not require global position information
+or inter-vehicle communication. We assume that quadrotors have a common sense
+of direction, which is chosen as the direction of gravitational force measured
+by their onboard IMU sensors. However, this assumption is not crucial, and our
+approach is robust to inaccuracies and effects of acceleration on gravitational
+measurements. In particular, converge to the desired formation is unaffected if
+each quadrotor has a velocity vector that projects positively onto the desired
+velocity vector provided by the formation control strategy. We demonstrate the
+validity of proposed approach in an experimental setup and show that a team of
+quadrotors achieve a desired 3D formation.
+",1,0,0,0,0,0
+3302,Discrete Extremes,"  Our contribution is to widen the scope of extreme value analysis applied to
+discrete-valued data. Extreme values of a random variable $X$ are commonly
+modeled using the generalized Pareto distribution, a method that often gives
+good results in practice. When $X$ is discrete, we propose two other methods
+using a discrete generalized Pareto and a generalized Zipf distribution
+respectively. Both are theoretically motivated and we show that they perform
+well in estimating rare events in several simulated and real data cases such as
+word frequency, tornado outbreaks and multiple births.
+",0,0,1,1,0,0
+3303,Rapid Adaptation with Conditionally Shifted Neurons,"  We describe a mechanism by which artificial neural networks can learn rapid
+adaptation - the ability to adapt on the fly, with little data, to new tasks -
+that we call conditionally shifted neurons. We apply this mechanism in the
+framework of metalearning, where the aim is to replicate some of the
+flexibility of human learning in machines. Conditionally shifted neurons modify
+their activation values with task-specific shifts retrieved from a memory
+module, which is populated rapidly based on limited task experience. On
+metalearning benchmarks from the vision and language domains, models augmented
+with conditionally shifted neurons achieve state-of-the-art results.
+",1,0,0,1,0,0
+3304,JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs,"  We propose a novel approach for the generation of polyphonic music based on
+LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord
+progression based on a chord embedding. A second LSTM then generates polyphonic
+music from the predicted chord progression. The generated music sounds pleasing
+and harmonic, with only few dissonant notes. It has clear long-term structure
+that is similar to what a musician would play during a jam session. We show
+that our approach is sensible from a music theory perspective by evaluating the
+learned chord embeddings. Surprisingly, our simple model managed to extract the
+circle of fifths, an important tool in music theory, from the dataset.
+",1,0,0,1,0,0
+3305,Inflationary Primordial Black Holes as All Dark Matter,"  Following a new microlensing constraint on primordial black holes (PBHs) with
+$\sim10^{20}$--$10^{28}\,\mathrm{g}$~[1], we revisit the idea of PBH as all
+Dark Matter (DM). We have shown that the updated observational constraints
+suggest the viable mass function for PBHs as all DM to have a peak at $\simeq
+10^{20}\,\mathrm{g}$ with a small width $\sigma \lesssim 0.1$, by imposing
+observational constraints on an extended mass function in a proper way. We have
+also provided an inflation model that successfully generates PBHs as all DM
+fulfilling this requirement.
+",0,1,0,0,0,0
+3306,Condition number and matrices,"  It is well known the concept of the condition number $\kappa(A) =
+\|A\|\|A^{-1}\|$, where $A$ is a $n \times n$ real or complex matrix and the
+norm used is the spectral norm. Although it is very common to think in
+$\kappa(A)$ as ""the"" condition number of $A$, the truth is that condition
+numbers are associated to problems, not just instance of problems. Our goal is
+to clarify this difference. We will introduce the general concept of condition
+number and apply it to the particular case of real or complex matrices. After
+this, we will introduce the classic condition number $\kappa(A)$ of a matrix
+and show some known results.
+",0,0,1,0,0,0
+3307,Computational Tools in Weighted Persistent Homology,"  In this paper, we study further properties and applications of weighted
+homology and persistent homology. We introduce the Mayer-Vietoris sequence and
+generalized Bockstein spectral sequence for weighted homology. For
+applications, we show an algorithm to construct a filtration of weighted
+simplicial complexes from a weighted network. We also prove a theorem that
+allows us to calculate the mod $p^2$ weighted persistent homology given some
+information on the mod $p$ weighted persistent homology.
+",0,0,1,0,0,0
+3308,Dirac fermions in borophene,"  Honeycomb structures of group IV elements can host massless Dirac fermions
+with non-trivial Berry phases. Their potential for electronic applications has
+attracted great interest and spurred a broad search for new Dirac materials
+especially in monolayer structures. We present a detailed investigation of the
+\beta 12 boron sheet, which is a borophene structure that can form
+spontaneously on a Ag(111) surface. Our tight-binding analysis revealed that
+the lattice of the \beta 12-sheet could be decomposed into two triangular
+sublattices in a way similar to that for a honeycomb lattice, thereby hosting
+Dirac cones. Furthermore, each Dirac cone could be split by introducing
+periodic perturbations representing overlayer-substrate interactions. These
+unusual electronic structures were confirmed by angle-resolved photoemission
+spectroscopy and validated by first-principles calculations. Our results
+suggest monolayer boron as a new platform for realizing novel high-speed
+low-dissipation devices.
+",0,1,0,0,0,0
+3309,Resolving the notorious case of conical intersections for coupled cluster dynamics,"  The motion of electrons and nuclei in photochemical events often involve
+conical intersections, degeneracies between electronic states. They serve as
+funnels for nuclear relaxation - on the femtosecond scale - in processes where
+the electrons and nuclei couple nonadiabatically. Accurate ab initio quantum
+chemical models are essential for interpreting experimental measurements of
+such phenomena. In this paper we resolve a long-standing problem in coupled
+cluster theory, presenting the first formulation of the theory that correctly
+describes conical intersections between excited electronic states of the same
+symmetry. This new development demonstrates that the highly accurate coupled
+cluster theory can be applied to describe dynamics on excited electronic states
+involving conical intersections.
+",0,1,0,0,0,0
+3310,Benchmarking gate-based quantum computers,"  With the advent of public access to small gate-based quantum processors, it
+becomes necessary to develop a benchmarking methodology such that independent
+researchers can validate the operation of these processors. We explore the
+usefulness of a number of simple quantum circuits as benchmarks for gate-based
+quantum computing devices and show that circuits performing identity operations
+are very simple, scalable and sensitive to gate errors and are therefore very
+well suited for this task. We illustrate the procedure by presenting benchmark
+results for the IBM Quantum Experience, a cloud-based platform for gate-based
+quantum computing.
+",0,1,0,0,0,0
+3311,Determinant structure for tau-function of holonomic deformation of linear differential equations,"  In our previous works, a relationship between Hermite's two approximation
+problems and Schlesinger transformations of linear differential equations has
+been clarified. In this paper, we study tau-functions associated with holonomic
+deformations of linear differential equations by using Hermite's two
+approximation problems. As a result, we present a determinant formula for the
+ratio of tau-functions (tau-quotient).
+",0,1,1,0,0,0
+3312,Transfer Operator Based Approach for Optimal Stabilization of Stochastic System,"  In this paper we develop linear transfer Perron Frobenius operator-based
+approach for optimal stabilization of stochastic nonlinear system. One of the
+main highlight of the proposed transfer operator based approach is that both
+the theory and computational framework developed for the optimal stabilization
+of deterministic dynamical system in [1] carries over to the stochastic case
+with little change. The optimal stabilization problem is formulated as an
+infinite dimensional linear program. Set oriented numerical methods are
+proposed for the finite dimensional approximation of the transfer operator and
+the controller. Simulation results are presented to verify the developed
+framework.
+",1,0,1,0,0,0
+3313,CryptoDL: Deep Neural Networks over Encrypted Data,"  Machine learning algorithms based on deep neural networks have achieved
+remarkable results and are being extensively used in different domains.
+However, the machine learning algorithms requires access to raw data which is
+often privacy sensitive. To address this issue, we develop new techniques to
+provide solutions for running deep neural networks over encrypted data. In this
+paper, we develop new techniques to adopt deep neural networks within the
+practical limitation of current homomorphic encryption schemes. More
+specifically, we focus on classification of the well-known convolutional neural
+networks (CNN). First, we design methods for approximation of the activation
+functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree
+polynomials which is essential for efficient homomorphic encryption schemes.
+Then, we train convolutional neural networks with the approximation polynomials
+instead of original activation functions and analyze the performance of the
+models. Finally, we implement convolutional neural networks over encrypted data
+and measure performance of the models. Our experimental results validate the
+soundness of our approach with several convolutional neural networks with
+varying number of layers and structures. When applied to the MNIST optical
+character recognition tasks, our approach achieves 99.52\% accuracy which
+significantly outperforms the state-of-the-art solutions and is very close to
+the accuracy of the best non-private version, 99.77\%. Also, it can make close
+to 164000 predictions per hour. We also applied our approach to CIFAR-10, which
+is much more complex compared to MNIST, and were able to achieve 91.5\%
+accuracy with approximation polynomials used as activation functions. These
+results show that CryptoDL provides efficient, accurate and scalable
+privacy-preserving predictions.
+",1,0,0,0,0,0
+3314,Data-driven modeling of collaboration networks: A cross-domain analysis,"  We analyze large-scale data sets about collaborations from two different
+domains: economics, specifically 22.000 R&D alliances between 14.500 firms, and
+science, specifically 300.000 co-authorship relations between 95.000
+scientists. Considering the different domains of the data sets, we address two
+questions: (a) to what extent do the collaboration networks reconstructed from
+the data share common structural features, and (b) can their structure be
+reproduced by the same agent-based model. In our data-driven modeling approach
+we use aggregated network data to calibrate the probabilities at which agents
+establish collaborations with either newcomers or established agents. The model
+is then validated by its ability to reproduce network features not used for
+calibration, including distributions of degrees, path lengths, local clustering
+coefficients and sizes of disconnected components. Emphasis is put on comparing
+domains, but also sub-domains (economic sectors, scientific specializations).
+Interpreting the link probabilities as strategies for link formation, we find
+that in R&D collaborations newcomers prefer links with established agents,
+while in co-authorship relations newcomers prefer links with other newcomers.
+Our results shed new light on the long-standing question about the role of
+endogenous and exogenous factors (i.e., different information available to the
+initiator of a collaboration) in network formation.
+",1,1,0,0,0,0
+3315,Tunnelling in Dante's Inferno,"  We study quantum tunnelling in Dante's Inferno model of large field
+inflation. Such a tunnelling process, which will terminate inflation, becomes
+problematic if the tunnelling rate is rapid compared to the Hubble time scale
+at the time of inflation. Consequently, we constrain the parameter space of
+Dante's Inferno model by demanding a suppressed tunnelling rate during
+inflation. The constraints are derived and explicit numerical bounds are
+provided for representative examples. Our considerations are at the level of an
+effective field theory; hence, the presented constraints have to hold
+regardless of any UV completion.
+",0,1,0,0,0,0
+3316,Regulating Highly Automated Robot Ecologies: Insights from Three User Studies,"  Highly automated robot ecologies (HARE), or societies of independent
+autonomous robots or agents, are rapidly becoming an important part of much of
+the world's critical infrastructure. As with human societies, regulation,
+wherein a governing body designs rules and processes for the society, plays an
+important role in ensuring that HARE meet societal objectives. However, to
+date, a careful study of interactions between a regulator and HARE is lacking.
+In this paper, we report on three user studies which give insights into how to
+design systems that allow people, acting as the regulatory authority, to
+effectively interact with HARE. As in the study of political systems in which
+governments regulate human societies, our studies analyze how interactions
+between HARE and regulators are impacted by regulatory power and individual
+(robot or agent) autonomy. Our results show that regulator power, decision
+support, and adaptive autonomy can each diminish the social welfare of HARE,
+and hint at how these seemingly desirable mechanisms can be designed so that
+they become part of successful HARE.
+",1,0,0,0,0,0
+3317,Some theoretical results on tensor elliptical distribution,"  The multilinear normal distribution is a widely used tool in tensor analysis
+of magnetic resonance imaging (MRI). Diffusion tensor MRI provides a
+statistical estimate of a symmetric 2nd-order diffusion tensor, for each voxel
+within an imaging volume. In this article, tensor elliptical (TE) distribution
+is introduced as an extension to the multilinear normal (MLN) distribution.
+Some properties including the characteristic function and distribution of
+affine transformations are given. An integral representation connecting
+densities of TE and MLN distributions is exhibited that is used in deriving the
+expectation of any measurable function of a TE variate.
+",0,0,1,1,0,0
+3318,Applying Text Mining to Protest Stories as Voice against Media Censorship,"  Data driven activism attempts to collect, analyze and visualize data to
+foster social change. However, during media censorship it is often impossible
+to collect such data. Here we demonstrate that data from personal stories can
+also help us to gain insights about protests and activism which can work as a
+voice for the activists. We analyze protest story data by extracting location
+network from the stories and perform emotion mining to get insight about the
+protest.
+",1,0,0,0,0,0
+3319,Cost-Effective Training of Deep CNNs with Active Model Adaptation,"  Deep convolutional neural networks have achieved great success in various
+applications. However, training an effective DNN model for a specific task is
+rather challenging because it requires a prior knowledge or experience to
+design the network architecture, repeated trial-and-error process to tune the
+parameters, and a large set of labeled data to train the model. In this paper,
+we propose to overcome these challenges by actively adapting a pre-trained
+model to a new task with less labeled examples. Specifically, the pre-trained
+model is iteratively fine tuned based on the most useful examples. The examples
+are actively selected based on a novel criterion, which jointly estimates the
+potential contribution of an instance on optimizing the feature representation
+as well as improving the classification model for the target task. On one hand,
+the pre-trained model brings plentiful information from its original task,
+avoiding redesign of the network architecture or training from scratch; and on
+the other hand, the labeling cost can be significantly reduced by active label
+querying. Experiments on multiple datasets and different pre-trained models
+demonstrate that the proposed approach can achieve cost-effective training of
+DNNs.
+",0,0,0,1,0,0
+3320,Flipping growth orientation of nanographitic structures by plasma enhanced chemical vapor deposition,"  Nanographitic structures (NGSs) with multitude of morphological features are
+grown on SiO2/Si substrates by electron cyclotron resonance - plasma enhanced
+chemical vapor deposition (ECR-PECVD). CH4 is used as source gas with Ar and H2
+as dilutants. Field emission scanning electron microscopy, high resolution
+transmission electron microscopy (HRTEM) and Raman spectroscopy are used to
+study the structural and morphological features of the grown films. Herein, we
+demonstrate, how the morphology can be tuned from planar to vertical structure
+using single control parameter namely, dilution of CH4 with Ar and/or H2. Our
+results show that the competitive growth and etching processes dictate the
+morphology of the NGSs. While Ar-rich composition favors vertically oriented
+graphene nanosheets, H2-rich composition aids growth of planar films. Raman
+analysis reveals dilution of CH4 with either Ar or H2 or in combination helps
+to improve the structural quality of the films. Line shape analysis of Raman 2D
+band shows nearly symmetric Lorentzian profile which confirms the turbostratic
+nature of the grown NGSs. Further, this aspect is elucidated by HRTEM studies
+by observing elliptical diffraction pattern. Based on these experiments, a
+comprehensive understanding is obtained on the growth and structural properties
+of NGSs grown over a wide range of feedstock compositions.
+",0,1,0,0,0,0
+3321,Topological thermal Hall effect due to Weyl magnons,"  We present the first theoretical evidence of zero magnetic field topological
+(anomalous) thermal Hall effect due to Weyl magnons. Here, we consider Weyl
+magnons in stacked noncoplanar frustrated kagomé antiferromagnets recently
+proposed by Owerre, [arXiv:1708.04240]. The Weyl magnons in this system result
+from macroscopically broken time-reversal symmetry by the scalar spin chirality
+of noncoplanar chiral spin textures. Most importantly, they come from the
+lowest excitation, therefore they can be easily observed experimentally at low
+temperatures due to the population effect. Similar to electronic Weyl nodes
+close to the Fermi energy, Weyl magnon nodes in the lowest excitation are the
+most important. Indeed, we show that the topological (anomalous) thermal Hall
+effect in this system arises from nonvanishing Berry curvature due to Weyl
+magnon nodes in the lowest excitation, and it depends on their distribution
+(distance) in momentum space. The present result paves the way to directly
+probe low excitation Weyl magnons and macroscopically broken time-reversal
+symmetry in three-dimensional frustrated magnets with the anomalous thermal
+Hall effect.
+",0,1,0,0,0,0
+3322,Numerical prediction of the piezoelectric transducer response in the acoustic nearfield using a one-dimensional electromechanical finite difference approach,"  We present a simple electromechanical finite difference model to study the
+response of a piezoelectric polyvinylidenflourid (PVDF) transducer to
+optoacoustic (OA) pressure waves in the acoustic nearfield prior to thermal
+relaxation of the OA source volume. The assumption of nearfield conditions,
+i.e. the absence of acoustic diffraction, allows to treat the problem using a
+one-dimensional numerical approach. Therein, the computational domain is
+modeled as an inhomogeneous elastic medium, characterized by its local wave
+velocities and densities, allowing to explore the effect of stepwise impedance
+changes on the stress wave propagation. The transducer is modeled as a thin
+piezoelectric sensing layer and the electromechanical coupling is accomplished
+by means of the respective linear constituting equations. Considering a
+low-pass characteristic of the full experimental setup, we obtain the resulting
+transducer signal. Complementing transducer signals measured in a controlled
+laboratory experiment with numerical simulations that result from a model of
+the experimental setup, we find that, bearing in mind the apparent limitations
+of the one-dimensional approach, the simulated transducer signals can be used
+very well to predict and interpret the experimental findings.
+",0,1,0,0,0,0
+3323,A free energy landscape of the capture of CO2 by frustrated Lewis pairs,"  Frustrated Lewis pairs (FLPs) are known for its ability to capture CO2.
+Although many FLPs have been reported experimentally and several theoretical
+studies have been carried out to address the reaction mechanism, the individual
+roles of Lewis acids and bases of FLP in the capture of CO2 is still unclear.
+In this study, we employed density functional theory (DFT) based metadynamics
+simulations to investigate the complete path for the capture of CO2 by
+tBu3P/B(C6F5)3 pair, and to understand the role of the Lewis acid and base.
+Interestingly, we have found out that the Lewis acids play more important role
+than Lewis bases. Specifically, the Lewis acids are crucial for catalytical
+properties and are responsible for both kinetic and thermodynamics control. The
+Lewis bases, however, have less impact on the catalytic performance and are
+mainly responsible for the formation of FLP systems. Based on these findings,
+we propose a thumb of rule for the future synthesis of FLP-based catalyst for
+the utilization of CO2.
+",0,1,0,0,0,0
+3324,Improved Point Source Detection in Crowded Fields using Probabilistic Cataloging,"  Cataloging is challenging in crowded fields because sources are extremely
+covariant with their neighbors and blending makes even the number of sources
+ambiguous. We present the first optical probabilistic catalog, cataloging a
+crowded (~0.1 sources per pixel brighter than 22nd magnitude in F606W) Sloan
+Digital Sky Survey r band image from M2. Probabilistic cataloging returns an
+ensemble of catalogs inferred from the image and thus can capture source-source
+covariance and deblending ambiguities. By comparing to a traditional catalog of
+the same image and a Hubble Space Telescope catalog of the same region, we show
+that our catalog ensemble better recovers sources from the image. It goes more
+than a magnitude deeper than the traditional catalog while having a lower false
+discovery rate brighter than 20th magnitude. We also present an algorithm for
+reducing this catalog ensemble to a condensed catalog that is similar to a
+traditional catalog, except it explicitly marginalizes over source-source
+covariances and nuisance parameters. We show that this condensed catalog has a
+similar completeness and false discovery rate to the catalog ensemble. Future
+telescopes will be more sensitive, and thus more of their images will be
+crowded. Probabilistic cataloging performs better than existing software in
+crowded fields and so should be considered when creating photometric pipelines
+in the Large Synoptic Space Telescope era.
+",0,1,0,0,0,0
+3325,Local Convergence of Proximal Splitting Methods for Rank Constrained Problems,"  We analyze the local convergence of proximal splitting algorithms to solve
+optimization problems that are convex besides a rank constraint. For this, we
+show conditions under which the proximal operator of a function involving the
+rank constraint is locally identical to the proximal operator of its convex
+envelope, hence implying local convergence. The conditions imply that the
+non-convex algorithms locally converge to a solution whenever a convex
+relaxation involving the convex envelope can be expected to solve the
+non-convex problem.
+",1,0,0,1,0,0
+3326,On the boundary between qualitative and quantitative measures of causal effects,"  Causal relationships among variables are commonly represented via directed
+acyclic graphs. There are many methods in the literature to quantify the
+strength of arrows in a causal acyclic graph. These methods, however, have
+undesirable properties when the causal system represented by a directed acyclic
+graph is degenerate. In this paper, we characterize a degenerate causal system
+using multiplicity of Markov boundaries, and show that in this case, it is
+impossible to quantify causal effects in a reasonable fashion. We then propose
+algorithms to identify such degenerate scenarios from observed data.
+Performance of our algorithms is investigated through synthetic data analysis.
+",0,0,1,1,0,0
+3327,How Could Polyhedral Theory Harness Deep Learning?,"  The holy grail of deep learning is to come up with an automatic method to
+design optimal architectures for different applications. In other words, how
+can we effectively dimension and organize neurons along the network layers
+based on the computational resources, input size, and amount of training data?
+We outline promising research directions based on polyhedral theory and
+mixed-integer representability that may offer an analytical approach to this
+question, in contrast to the empirical techniques often employed.
+",0,0,0,1,0,0
+3328,Optimal top dag compression,"  It is shown that for a given ordered node-labelled tree of size $n$ and with
+$s$ many different node labels, one can construct in linear time a top dag of
+height $O(\log n)$ and size $O(n / \log_\sigma n) \cap O(d \cdot \log n)$,
+where $\sigma = \max\{ 2, s\}$ and $d$ is the size of the minimal dag. The size
+bound $O(n / \log_\sigma n)$ is optimal and improves on previous bounds.
+",1,0,0,0,0,0
+3329,Synthesizing SystemC Code from Delay Hybrid CSP,"  Delay is omnipresent in modern control systems, which can prompt oscillations
+and may cause deterioration of control performance, invalidate both stability
+and safety properties. This implies that safety or stability certificates
+obtained on idealized, delay-free models of systems prone to delayed coupling
+may be erratic, and further the incorrectness of the executable code generated
+from these models. However, automated methods for system verification and code
+generation that ought to address models of system dynamics reflecting delays
+have not been paid enough attention yet in the computer science community. In
+our previous work, on one hand, we investigated the verification of delay
+dynamical and hybrid systems; on the other hand, we also addressed how to
+synthesize SystemC code from a verified hybrid system modelled by Hybrid CSP
+(HCSP) without delay. In this paper, we give a first attempt to synthesize
+SystemC code from a verified delay hybrid system modelled by Delay HCSP
+(dHCSP), which is an extension of HCSP by replacing ordinary differential
+equations (ODEs) with delay differential equations (DDEs). We implement a tool
+to support the automatic translation from dHCSP to SystemC.
+",1,0,0,0,0,0
+3330,Analysis of Annual Cyclone Frequencies over Bay of Bengal: Effect of 2004 Indian Ocean Tsunami,"  This paper discusses the time series trend and variability of the cyclone
+frequencies over Bay of Bengal, particularly in order to conclude if there is
+any significant difference in the pattern visible before and after the
+disastrous 2004 Indian ocean tsunami based on the observed annual cyclone
+frequency data obtained by India Meteorological Department over the years
+1891-2015. Three different categories of cyclones- depression (<34 knots),
+cyclonic storm (34-47 knots) and severe cyclonic storm (>47 knots) have been
+analyzed separately using a non-homogeneous Poisson process approach. The
+estimated intensity functions of the Poisson processes along with their first
+two derivatives are discussed and all three categories show decreasing trend of
+the intensity functions after the tsunami. Using an exact change-point
+analysis, we show that the drops in mean intensity functions are significant
+for all three categories. As of author's knowledge, no study so far have
+discussed the relation between cyclones and tsunamis. Bay of Bengal is
+surrounded by one of the most densely populated areas of the world and any kind
+of significant change in tropical cyclone pattern has a large impact in various
+ways, for example, disaster management planning and our study is immensely
+important from that perspective.
+",0,0,0,1,0,0
+3331,Load Thresholds for Cuckoo Hashing with Overlapping Blocks,"  Dietzfelbinger and Weidling [DW07] proposed a natural variation of cuckoo
+hashing where each of $cn$ objects is assigned $k = 2$ intervals of size $\ell$
+in a linear (or cyclic) hash table of size $n$ and both start points are chosen
+independently and uniformly at random. Each object must be placed into a table
+cell within its intervals, but each cell can only hold one object. Experiments
+suggested that this scheme outperforms the variant with blocks in which
+intervals are aligned at multiples of $\ell$. In particular, the load threshold
+is higher, i.e. the load $c$ that can be achieved with high probability. For
+instance, Lehman and Panigrahy [LP09] empirically observed the threshold for
+$\ell = 2$ to be around $96.5\%$ as compared to roughly $89.7\%$ using blocks.
+They managed to pin down the asymptotics of the thresholds for large $\ell$,
+but the precise values resisted rigorous analysis.
+We establish a method to determine these load thresholds for all $\ell \geq
+2$, and, in fact, for general $k \geq 2$. For instance, for $k = \ell = 2$ we
+get $\approx 96.4995\%$. The key tool we employ is an insightful and general
+theorem due to Leconte, Lelarge, and Massoulié [LLM13], which adapts methods
+from statistical physics to the world of hypergraph orientability. In effect,
+the orientability thresholds for our graph families are determined by belief
+propagation equations for certain graph limits. As a side note we provide
+experimental evidence suggesting that placements can be constructed in linear
+time with loads close to the threshold using an adapted version of an algorithm
+by Khosla [Kho13].
+",1,0,0,0,0,0
+3332,Evaluating the hot hand phenomenon using predictive memory selection for multistep Markov Chains: LeBron James' error correcting free throws,"  Consider the problem of modeling memory for discrete-state random walks using
+higher-order Markov chains. This Letter introduces a general Bayesian framework
+under the principle of minimizing prediction error to select, from data, the
+number of prior states of recent history upon which a trajectory is
+statistically dependent. In this framework, I provide closed-form expressions
+for several alternative model selection criteria that approximate model
+prediction error for future data. Using simulations, I evaluate the statistical
+power of these criteria. These methods, when applied to data from the
+2016--2017 NBA season, demonstrate evidence of statistical dependencies in
+LeBron James' free throw shooting. In particular, a model depending on the
+previous shot (single-step Markovian) is approximately as predictive as a model
+with independent outcomes. A hybrid jagged model of two parameters, where James
+shoots a higher percentage after a missed free throw than otherwise, is more
+predictive than either model.
+",0,1,0,1,0,0
+3333,Closed almost-Kähler 4-manifolds of constant non-negative Hermitian holomorphic sectional curvature are Kähler,"  We show that a closed almost Kähler 4-manifold of globally constant
+holomorphic sectional curvature $k\geq 0$ with respect to the canonical
+Hermitian connection is automatically Kähler. The same result holds for $k<0$
+if we require in addition that the Ricci curvature is J-invariant. The proofs
+are based on the observation that such manifolds are self-dual, so that
+Chern-Weil theory implies useful integral formulas, which are then combined
+with results from Seiberg--Witten theory.
+",0,0,1,0,0,0
+3334,Pretest and Stein-Type Estimations in Quantile Regression Model,"  In this study, we consider preliminary test and shrinkage estimation
+strategies for quantile regression models. In classical Least Squares
+Estimation (LSE) method, the relationship between the explanatory and explained
+variables in the coordinate plane is estimated with a mean regression line. In
+order to use LSE, there are three main assumptions on the error terms showing
+white noise process of the regression model, also known as Gauss-Markov
+Assumptions, must be met: (1) The error terms have zero mean, (2) The variance
+of the error terms is constant and (3) The covariance between the errors is
+zero i.e., there is no autocorrelation. However, data in many areas, including
+econometrics, survival analysis and ecology, etc. does not provide these
+assumptions. First introduced by Koenker, quantile regression has been used to
+complement this deficiency of classical regression analysis and to improve the
+least square estimation. The aim of this study is to improve the performance of
+quantile regression estimators by using pre-test and shrinkage strategies. A
+Monte Carlo simulation study including a comparison with quantile $L_1$--type
+estimators such as Lasso, Ridge and Elastic Net are designed to evaluate the
+performances of the estimators. Two real data examples are given for
+illustrative purposes. Finally, we obtain the asymptotic results of suggested
+estimators
+",0,0,1,1,0,0
+3335,Outcrop fracture characterization on suppositional planes cutting through digital outcrop models (DOMs),"  Conventional fracture data collection methods are usually implemented on
+planar surfaces or assuming they are planar; these methods may introduce
+sampling errors on uneven outcrop surfaces. Consequently, data collected on
+limited types of outcrop surfaces (mainly bedding surfaces) may not be a
+sufficient representation of fracture network characteristic in outcrops.
+Recent development of techniques that obtain DOMs from outcrops and extract the
+full extent of individual fractures offers the opportunity to address the
+problem of performing the conventional sampling methods on uneven outcrop
+surfaces. In this study, we propose a new method that performs outcrop fracture
+characterization on suppositional planes cutting through DOMs. The
+suppositional plane is the best fit plane of the outcrop surface, and the
+fracture trace map is extracted on the suppositional plane so that the fracture
+network can be further characterized. The amount of sampling errors introduced
+by the conventional methods and avoided by the new method on 16 uneven outcrop
+surfaces with different roughnesses are estimated. The results show that the
+conventional sampling methods don't apply to outcrops other than bedding
+surfaces or outcrops whose roughness > 0.04 m, and that the proposed method can
+greatly extend the types of outcrop surfaces for outcrop fracture
+characterization with the suppositional plane cutting through DOMs.
+",1,1,0,0,0,0
+3336,Exploiting routinely collected severe case data to monitor and predict influenza outbreaks,"  Influenza remains a significant burden on health systems. Effective responses
+rely on the timely understanding of the magnitude and the evolution of an
+outbreak. For monitoring purposes, data on severe cases of influenza in England
+are reported weekly to Public Health England. These data are both readily
+available and have the potential to provide valuable information to estimate
+and predict the key transmission features of seasonal and pandemic influenza.
+We propose an epidemic model that links the underlying unobserved influenza
+transmission process to data on severe influenza cases. Within a Bayesian
+framework, we infer retrospectively the parameters of the epidemic model for
+each seasonal outbreak from 2012 to 2015, including: the effective reproduction
+number; the initial susceptibility; the probability of admission to intensive
+care given infection; and the effect of school closure on transmission. The
+model is also implemented in real time to assess whether early forecasting of
+the number of admission to intensive care is possible. Our model of admissions
+data allows reconstruction of the underlying transmission dynamics revealing:
+increased transmission during the season 2013/14 and a noticeable effect of
+Christmas school holiday on disease spread during season 2012/13 and 2014/15.
+When information on the initial immunity of the population is available,
+forecasts of the number of admissions to intensive care can be substantially
+improved. Readily available severe case data can be effectively used to
+estimate epidemiological characteristics and to predict the evolution of an
+epidemic, crucially allowing real-time monitoring of the transmission and
+severity of the outbreak.
+",0,1,0,1,0,0
+3337,Consistent Inter-Model Specification for Time-Homogeneous SPX Stochastic Volatility and VIX Market Models,"  This paper shows how to recover stochastic volatility models (SVMs) from
+market models for the VIX futures term structure. Market models have more
+flexibility for fitting of curves than do SVMs, and therefore they are
+better-suited for pricing VIX futures and derivatives. But the VIX itself is a
+derivative of the S&P500 (SPX) and it is common practice to price SPX
+derivatives using an SVM. Hence, a consistent model for both SPX and VIX
+derivatives would be one where the SVM is obtained by inverting the market
+model. This paper's main result is a method for the recovery of a stochastic
+volatility function as the output of an inverse problem, with the inputs given
+by a VIX futures market model. Analysis will show that some conditions need to
+be met in order for there to not be any inter-model arbitrage or mis-priced
+derivatives. Given these conditions the inverse problem can be solved. Several
+models are analyzed and explored numerically to gain a better understanding of
+the theory and its limitations.
+",0,0,0,0,0,1
+3338,Optimal Task Scheduling in Communication-Constrained Mobile Edge Computing Systems for Wireless Virtual Reality,"  Mobile edge computing (MEC) is expected to be an effective solution to
+deliver 360-degree virtual reality (VR) videos over wireless networks. In
+contrast to previous computation-constrained MEC framework, which reduces the
+computation-resource consumption at the mobile VR device by increasing the
+communication-resource consumption, we develop a communications-constrained MEC
+framework to reduce communication-resource consumption by increasing the
+computation-resource consumption and exploiting the caching resources at the
+mobile VR device in this paper. Specifically, according to the task
+modularization, the MEC server can only deliver the components which have not
+been stored in the VR device, and then the VR device uses the received
+components and the corresponding cached components to construct the task,
+resulting in low communication-resource consumption but high delay. The MEC
+server can also compute the task by itself to reduce the delay, however, it
+consumes more communication-resource due to the delivery of entire task.
+Therefore, we then propose a task scheduling strategy to decide which
+computation model should the MEC server operates, in order to minimize the
+communication-resource consumption under the delay constraint. Finally, we
+discuss the tradeoffs between communications, computing, and caching in the
+proposed system.
+",1,0,0,0,0,0
+3339,Infinitary generalizations of Deligne's completeness theorem,"  Given a regular cardinal $\kappa$ such that $\kappa^{<\kappa}=\kappa$, we
+study a class of toposes with enough points, the $\kappa$-separable toposes.
+These are equivalent to sheaf toposes over a site with $\kappa$-small limits
+that has at most $\kappa$ many objects and morphisms, the (basis for the)
+topology being generated by at most $\kappa$ many covering families, and that
+satisfy a further exactness property $T$. We prove that these toposes have
+enough $\kappa$-points, that is, points whose inverse image preserve all
+$\kappa$-small limits. This generalizes the separable toposes of Makkai and
+Reyes, that are a particular case when $\kappa=\omega$, when property $T$ is
+trivially satisfied. This result is essentially a completeness theorem for a
+certain infinitary logic that we call $\kappa$-geometric, where conjunctions of
+less than $\kappa$ formulas and existential quantification on less than
+$\kappa$ many variables is allowed. We prove that $\kappa$-geometric theories
+have a $\kappa$-classifying topos having property $T$, the universal property
+being that models of the theory in a Grothendieck topos with property $T$
+correspond to $\kappa$-geometric morphisms (geometric morphisms the inverse
+image of which preserves all $\kappa$-small limits) into that topos. Moreover,
+we prove that $\kappa$-separable toposes occur as the $\kappa$-classifying
+toposes of $\kappa$-geometric theories of at most $\kappa$ many axioms in
+canonical form, and that every such $\kappa$-classifying topos is
+$\kappa$-separable. Finally, we consider the case when $\kappa$ is weakly
+compact and study the $\kappa$-classifying topos of a $\kappa$-coherent theory
+(with at most $\kappa$ many axioms), that is, a theory where only disjunction
+of less than $\kappa$ formulas are allowed, obtaining a version of Deligne's
+theorem for $\kappa$-coherent toposes.
+",0,0,1,0,0,0
+3340,"Progressive Growing of GANs for Improved Quality, Stability, and Variation","  We describe a new training methodology for generative adversarial networks.
+The key idea is to grow both the generator and discriminator progressively:
+starting from a low resolution, we add new layers that model increasingly fine
+details as training progresses. This both speeds the training up and greatly
+stabilizes it, allowing us to produce images of unprecedented quality, e.g.,
+CelebA images at 1024^2. We also propose a simple way to increase the variation
+in generated images, and achieve a record inception score of 8.80 in
+unsupervised CIFAR10. Additionally, we describe several implementation details
+that are important for discouraging unhealthy competition between the generator
+and discriminator. Finally, we suggest a new metric for evaluating GAN results,
+both in terms of image quality and variation. As an additional contribution, we
+construct a higher-quality version of the CelebA dataset.
+",1,0,0,1,0,0
+3341,Saxion Cosmology for Thermalized Gravitino Dark Matter,"  In all supersymmetric theories, gravitinos, with mass suppressed by the
+Planck scale, are an obvious candidate for dark matter; but if gravitinos ever
+reached thermal equilibrium, such dark matter is apparently either too abundant
+or too hot, and is excluded. However, in theories with an axion, a saxion
+condensate is generated during an early era of cosmological history and its
+late decay dilutes dark matter. We show that such dilution allows previously
+thermalized gravitinos to account for the observed dark matter over very wide
+ranges of gravitino mass, keV < $m_{3/2}$ < TeV, axion decay constant, $10^9$
+GeV < $f_a$ < $10^{16}$ GeV, and saxion mass, 10 MeV < $m_s$ < 100 TeV.
+Constraints on this parameter space are studied from BBN, supersymmetry
+breaking, gravitino and axino production from freeze-in and saxion decay, and
+from axion production from both misalignment and parametric resonance
+mechanisms. Large allowed regions of $(m_{3/2}, f_a, m_s)$ remain, but differ
+for DFSZ and KSVZ theories. Superpartner production at colliders may lead to
+events with displaced vertices and kinks, and may contain saxions decaying to
+$(WW,ZZ,hh), gg, \gamma \gamma$ or a pair of Standard Model fermions. Freeze-in
+may lead to a sub-dominant warm component of gravitino dark matter, and saxion
+decay to axions may lead to dark radiation.
+",0,1,0,0,0,0
+3342,One-step and Two-step Classification for Abusive Language Detection on Twitter,"  Automatic abusive language detection is a difficult but important task for
+online social media. Our research explores a two-step approach of performing
+classification on abusive language and then classifying into specific types and
+compares it with one-step approach of doing one multi-class classification for
+detecting sexist and racist languages. With a public English Twitter corpus of
+20 thousand tweets in the type of sexism and racism, our approach shows a
+promising performance of 0.827 F-measure by using HybridCNN in one-step and
+0.824 F-measure by using logistic regression in two-steps.
+",1,0,0,0,0,0
+3343,Massive data compression for parameter-dependent covariance matrices,"  We show how the massive data compression algorithm MOPED can be used to
+reduce, by orders of magnitude, the number of simulated datasets that are
+required to estimate the covariance matrix required for the analysis of
+gaussian-distributed data. This is relevant when the covariance matrix cannot
+be calculated directly. The compression is especially valuable when the
+covariance matrix varies with the model parameters. In this case, it may be
+prohibitively expensive to run enough simulations to estimate the full
+covariance matrix throughout the parameter space. This compression may be
+particularly valuable for the next-generation of weak lensing surveys, such as
+proposed for Euclid and LSST, for which the number of summary data (such as
+band power or shear correlation estimates) is very large, $\sim 10^4$, due to
+the large number of tomographic redshift bins that the data will be divided
+into. In the pessimistic case where the covariance matrix is estimated
+separately for all points in an MCMC analysis, this may require an unfeasible
+$10^9$ simulations. We show here that MOPED can reduce this number by a factor
+of 1000, or a factor of $\sim 10^6$ if some regularity in the covariance matrix
+is assumed, reducing the number of simulations required to a manageable $10^3$,
+making an otherwise intractable analysis feasible.
+",0,1,0,1,0,0
+3344,A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure,"  Electronic medical records (EMR) contain longitudinal information about
+patients that can be used to analyze outcomes. Typically, studies on EMR data
+have worked with established variables that have already been acknowledged to
+be associated with certain outcomes. However, EMR data may also contain
+hitherto unrecognized factors for risk association and prediction of outcomes
+for a disease. In this paper, we present a scalable data-driven framework to
+analyze EMR data corpus in a disease agnostic way that systematically uncovers
+important factors influencing outcomes in patients, as supported by data and
+without expert guidance. We validate the importance of such factors by using
+the framework to predict for the relevant outcomes. Specifically, we analyze
+EMR data covering approximately 47 million unique patients to characterize
+renal failure (RF) among type 2 diabetic (T2DM) patients. We propose a
+specialized L1 regularized Cox Proportional Hazards (CoxPH) survival model to
+identify the important factors from those available from patient encounter
+history. To validate the identified factors, we use a specialized generalized
+linear model (GLM) to predict the probability of renal failure for individual
+patients within a specified time window. Our experiments indicate that the
+factors identified via our data-driven method overlap with the patient
+characteristics recognized by experts. Our approach allows for scalable,
+repeatable and efficient utilization of data available in EMRs, confirms prior
+medical knowledge and can generate new hypothesis without expert supervision.
+",1,0,0,1,0,0
+3345,Knotted solutions for linear and nonlinear theories: electromagnetism and fluid dynamics,"  We examine knotted solutions, the most simple of which is the ""Hopfion"", from
+the point of view of relations between electromagnetism and ideal fluid
+dynamics. A map between fluid dynamics and electromagnetism works for initial
+conditions or for linear perturbations, allowing us to find new knotted fluid
+solutions. Knotted solutions are also found to to be solutions of nonlinear
+generalizations of electromagnetism, and of quantum-corrected actions for
+electromagnetism coupled to other modes. For null configurations,
+electromagnetism can be described as a null pressureless fluid, for which we
+can find solutions from the knotted solutions of electromagnetism. We also map
+them to solutions of Euler's equations, obtained from a type of nonrelativistic
+reduction of the relativistic fluid equations.
+",0,1,0,0,0,0
+3346,"Implicit Media Tagging and Affect Prediction from video of spontaneous facial expressions, recorded with depth camera","  We present a method that automatically evaluates emotional response from
+spontaneous facial activity recorded by a depth camera. The automatic
+evaluation of emotional response, or affect, is a fascinating challenge with
+many applications, including human-computer interaction, media tagging and
+human affect prediction. Our approach in addressing this problem is based on
+the inferred activity of facial muscles over time, as captured by a depth
+camera recording an individual's facial activity. Our contribution is two-fold:
+First, we constructed a database of publicly available short video clips, which
+elicit a strong emotional response in a consistent manner across different
+individuals. Each video was tagged by its characteristic emotional response
+along 4 scales: \emph{Valence, Arousal, Likability} and \emph{Rewatch} (the
+desire to watch again). The second contribution is a two-step prediction
+method, based on learning, which was trained and tested using this database of
+tagged video clips. Our method was able to successfully predict the
+aforementioned 4 dimensional representation of affect, as well as to identify
+the period of strongest emotional response in the viewing recordings, in a
+method that is blind to the video clip being watch, revealing a significantly
+high agreement between the recordings of independent viewers.
+",1,0,0,0,0,0
+3347,Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities,"  Legal professionals worldwide are currently trying to get up-to-pace with the
+explosive growth in legal document availability through digital means. This
+drives a need for high efficiency Legal Information Retrieval (IR) and Question
+Answering (QA) methods. The IR task in particular has a set of unique
+challenges that invite the use of semantic motivated NLP techniques. In this
+work, a two-stage method for Legal Information Retrieval is proposed, combining
+lexical statistics and distributional sentence representations in the context
+of Competition on Legal Information Extraction/Entailment (COLIEE). The
+combination is done with the use of disambiguation rules, applied over the
+rankings obtained through n-gram statistics. After the ranking is done, its
+results are evaluated for ambiguity, and disambiguation is done if a result is
+decided to be unreliable for a given query. Competition and experimental
+results indicate small gains in overall retrieval performance using the
+proposed approach. Additionally, an analysis of error and improvement cases is
+presented for a better understanding of the contributions.
+",1,0,0,0,0,0
+3348,Modeling Study of Laser Beam Scattering by Defects on Semiconductor Wafers,"  Accurate modeling of light scattering from nanometer scale defects on Silicon
+wafers is critical for enabling increasingly shrinking semiconductor technology
+nodes of the future. Yet, such modeling of defect scattering remains unsolved
+since existing modeling techniques fail to account for complex defect and wafer
+geometries. Here, we present results of laser beam scattering from spherical
+and ellipsoidal particles located on the surface of a silicon wafer. A
+commercially available electromagnetic field solver (HFSS) was deployed on a
+multiprocessor cluster to obtain results with previously unknown accuracy down
+to light scattering intensity of -170 dB. We compute three dimensional
+scattering patterns of silicon nanospheres located on a semiconductor wafer for
+both perpendicular and parallel polarization and show the effect of sphere size
+on scattering. We further computer scattering patterns of nanometer scale
+ellipsoidal particles having different orientation angles and unveil the
+effects of ellipsoidal orientation on scattering.
+",0,1,0,0,0,0
+3349,Combinatorial and Asymptotical Results on the Neighborhood Grid,"  In 2009, Joselli et al introduced the Neighborhood Grid data structure for
+fast computation of neighborhood estimates in point clouds. Even though the
+data structure has been used in several applications and shown to be
+practically relevant, it is theoretically not yet well understood. The purpose
+of this paper is to present a polynomial-time algorithm to build the data
+structure. Furthermore, it is investigated whether the presented algorithm is
+optimal. This investigations leads to several combinatorial questions for which
+partial results are given.
+",1,0,0,0,0,0
+3350,On the structure of join tensors with applications to tensor eigenvalue problems,"  We investigate the structure of join tensors, which may be regarded as the
+multivariable extension of lattice-theoretic join matrices. Explicit formulae
+for a polyadic decomposition (i.e., a linear combination of rank-1 tensors) and
+a tensor-train decomposition of join tensors are derived on general join
+semilattices. We discuss conditions under which the obtained decompositions are
+optimal in rank, and examine numerically the storage complexity of the obtained
+decompositions for a class of LCM tensors as a special case of join tensors. In
+addition, we investigate numerically the sharpness of a theoretical upper bound
+on the tensor eigenvalues of LCM tensors.
+",0,0,1,0,0,0
+3351,Adversarial Variational Optimization of Non-Differentiable Simulators,"  Complex computer simulators are increasingly used across fields of science as
+generative models tying parameters of an underlying theory to experimental
+observations. Inference in this setup is often difficult, as simulators rarely
+admit a tractable density or likelihood function. We introduce Adversarial
+Variational Optimization (AVO), a likelihood-free inference algorithm for
+fitting a non-differentiable generative model incorporating ideas from
+generative adversarial networks, variational optimization and empirical Bayes.
+We adapt the training procedure of generative adversarial networks by replacing
+the differentiable generative network with a domain-specific simulator. We
+solve the resulting non-differentiable minimax problem by minimizing
+variational upper bounds of the two adversarial objectives. Effectively, the
+procedure results in learning a proposal distribution over simulator
+parameters, such that the JS divergence between the marginal distribution of
+the synthetic data and the empirical distribution of observed data is
+minimized. We evaluate and compare the method with simulators producing both
+discrete and continuous data.
+",1,0,0,1,0,0
+3352,Learning Light Transport the Reinforced Way,"  We show that the equations of reinforcement learning and light transport
+simulation are related integral equations. Based on this correspondence, a
+scheme to learn importance while sampling path space is derived. The new
+approach is demonstrated in a consistent light transport simulation algorithm
+that uses reinforcement learning to progressively learn where light comes from.
+As using this information for importance sampling includes information about
+visibility, too, the number of light transport paths with zero contribution is
+dramatically reduced, resulting in much less noisy images within a fixed time
+budget.
+",1,0,0,0,0,0
+3353,Posterior Asymptotic Normality for an Individual Coordinate in High-dimensional Linear Regression,"  We consider the sparse high-dimensional linear regression model
+$Y=Xb+\epsilon$ where $b$ is a sparse vector. For the Bayesian approach to this
+problem, many authors have considered the behavior of the posterior
+distribution when, in truth, $Y=X\beta+\epsilon$ for some given $\beta$. There
+have been numerous results about the rate at which the posterior distribution
+concentrates around $\beta$, but few results about the shape of that posterior
+distribution. We propose a prior distribution for $b$ such that the marginal
+posterior distribution of an individual coordinate $b_i$ is asymptotically
+normal centered around an asymptotically efficient estimator, under the truth.
+Such a result gives Bayesian credible intervals that match with the confidence
+intervals obtained from an asymptotically efficient estimator for $b_i$. We
+also discuss ways of obtaining such asymptotically efficient estimators on
+individual coordinates. We compare the two-step procedure proposed by Zhang and
+Zhang (2014) and a one-step modified penalization method.
+",0,0,1,1,0,0
+3354,Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration,"  Several fundamental problems that arise in optimization and computer science
+can be cast as follows: Given vectors $v_1,\ldots,v_m \in \mathbb{R}^d$ and a
+constraint family ${\cal B}\subseteq 2^{[m]}$, find a set $S \in \cal{B}$ that
+maximizes the squared volume of the simplex spanned by the vectors in $S$. A
+motivating example is the data-summarization problem in machine learning where
+one is given a collection of vectors that represent data such as documents or
+images. The volume of a set of vectors is used as a measure of their diversity,
+and partition or matroid constraints over $[m]$ are imposed in order to ensure
+resource or fairness constraints. Recently, Nikolov and Singh presented a
+convex program and showed how it can be used to estimate the value of the most
+diverse set when ${\cal B}$ corresponds to a partition matroid. This result was
+recently extended to regular matroids in works of Straszak and Vishnoi, and
+Anari and Oveis Gharan. The question of whether these estimation algorithms can
+be converted into the more useful approximation algorithms -- that also output
+a set -- remained open.
+The main contribution of this paper is to give the first approximation
+algorithms for both partition and regular matroids. We present novel
+formulations for the subdeterminant maximization problem for these matroids;
+this reduces them to the problem of finding a point that maximizes the absolute
+value of a nonconvex function over a Cartesian product of probability
+simplices. The technical core of our results is a new anti-concentration
+inequality for dependent random variables that allows us to relate the optimal
+value of these nonconvex functions to their value at a random point. Unlike
+prior work on the constrained subdeterminant maximization problem, our proofs
+do not rely on real-stability or convexity and could be of independent interest
+both in algorithms and complexity.
+",1,0,1,1,0,0
+3355,On the Uplink Achievable Rate of Massive MIMO System With Low-Resolution ADC and RF Impairments,"  This paper considers channel estimation and uplink achievable rate of the
+coarsely quantized massive multiple-input multiple-output (MIMO) system with
+radio frequency (RF) impairments. We utilize additive quantization noise model
+(AQNM) and extended error vector magnitude (EEVM) model to analyze the impacts
+of low-resolution analog-to-digital converters (ADCs) and RF impairments
+respectively. We show that hardware impairments cause a nonzero floor on the
+channel estimation error, which contraries to the conventional case with ideal
+hardware. The maximal-ratio combining (MRC) technique is then used at the
+receiver, and an approximate tractable expression for the uplink achievable
+rate is derived. The simulation results illustrate the appreciable
+compensations between ADCs' resolution and RF impairments. The proposed studies
+support the feasibility of equipping economical coarse ADCs and economical
+imperfect RF components in practical massive MIMO systems.
+",1,0,0,0,0,0
+3356,On The Complexity of Enumeration,"  We investigate the relationship between several enumeration complexity
+classes and focus in particular on problems having enumeration algorithms with
+incremental and polynomial delay (IncP and DelayP respectively). We show that,
+for some algorithms, we can turn an average delay into a worst case delay
+without increasing the space complexity, suggesting that IncP_1 = DelayP even
+with polynomially bounded space. We use the Exponential Time Hypothesis to
+exhibit a strict hierarchy inside IncP which gives the first separation of
+DelayP and IncP. Finally we relate the uniform generation of solutions to
+probabilistic enumeration algorithms with polynomial delay and polynomial
+space.
+",1,0,0,0,0,0
+3357,On $p$-degree of elliptic curves,"  In this note we investigate the $p$-degree function of elliptic curves over
+the field $\mathbb{Q}_p$ of $p$-adic numbers. The $p$-degree measures the least
+complexity of a non-zero $p$-torsion point on an elliptic curve. We prove some
+properties of this function and compute it explicitly in some special cases.
+",0,0,1,0,0,0
+3358,Combinatorial formulas for Kazhdan-Lusztig polynomials with respect to W-graph ideals,"  In \cite{y1} Yin generalized the definition of $W$-graph ideal $E_J$ in
+weighted Coxeter groups and introduced the weighted Kazhdan-Lusztig polynomials
+$ \left \{ P_{x,y} \mid x,y\in E_J\right \}$, where $J$ is a subset of simple
+generators $S$. In this paper, we study the combinatorial formulas for those
+polynomials, which extend the results of Deodhar \cite{v3} and Tagawa
+\cite{h1}.
+",0,0,1,0,0,0
+3359,Arithmetic statistics of modular symbols,"  Mazur, Rubin, and Stein have recently formulated a series of conjectures
+about statistical properties of modular symbols in order to understand central
+values of twists of elliptic curve $L$-functions. Two of these conjectures
+relate to the asymptotic growth of the first and second moments of the modular
+symbols. We prove these on average by using analytic properties of Eisenstein
+series twisted by modular symbols. Another of their conjectures predicts the
+Gaussian distribution of normalized modular symbols ordered according to the
+size of the denominator of the cusps. We prove this conjecture in a refined
+version that also allows restrictions on the location of the cusps.
+",0,0,1,0,0,0
+3360,Approximation Techniques for Stochastic Analysis of Biological Systems,"  There has been an increasing demand for formal methods in the design process
+of safety-critical synthetic genetic circuits. Probabilistic model checking
+techniques have demonstrated significant potential in analyzing the intrinsic
+probabilistic behaviors of complex genetic circuit designs. However, its
+inability to scale limits its applicability in practice. This chapter addresses
+the scalability problem by presenting a state-space approximation method to
+remove unlikely states resulting in a reduced, finite state representation of
+the infinite-state continuous-time Markov chain that is amenable to
+probabilistic model checking. The proposed method is evaluated on a design of a
+genetic toggle switch. Comparisons with another state-of-art tool demonstrates
+both accuracy and efficiency of the presented method.
+",1,0,0,0,0,0
+3361,Characterizing time-irreversibility in disordered fermionic systems by the effect of local perturbations,"  We study the effects of local perturbations on the dynamics of disordered
+fermionic systems in order to characterize time-irreversibility. We focus on
+three different systems, the non-interacting Anderson and Aubry-André-Harper
+(AAH-) models, and the interacting spinless disordered t-V chain. First, we
+consider the effect on the full many-body wave-functions by measuring the
+Loschmidt echo (LE). We show that in the extended/ergodic phase the LE decays
+exponentially fast with time, while in the localized phase the decay is
+algebraic. We demonstrate that the exponent of the decay of the LE in the
+localized phase diverges proportionally to the single-particle localization
+length as we approach the metal-insulator transition in the AAH model. Second,
+we probe different phases of disordered systems by studying the time
+expectation value of local observables evolved with two Hamiltonians that
+differ by a spatially local perturbation. Remarkably, we find that many-body
+localized systems could lose memory of the initial state in the long-time
+limit, in contrast to the non-interacting localized phase where some memory is
+always preserved.
+",0,1,0,0,0,0
+3362,A Lower Bound for the Number of Central Configurations on H^2,"  We study the indices of the geodesic central configurations on $\H^2$. We
+then show that central configurations are bounded away from the singularity
+set. With Morse's inequality, we get a lower bound for the number of central
+configurations on $\H^2$.
+",0,0,1,0,0,0
+3363,Scattering dominated high-temperature phase of 1T-TiSe2: an optical conductivity study,"  The controversy regarding the precise nature of the high-temperature phase of
+1T-TiSe2 lasts for decades. It has intensified in recent times when new
+evidence for the excitonic origin of the low-temperature charge-density wave
+state started to unveil. Here we address the problem of the high-temperature
+phase through precise measurements and detailed analysis of the optical
+response of 1T-TiSe2 single crystals. The separate responses of electron and
+hole subsystems are identified and followed in temperature. We show that
+neither semiconductor nor semimetal pictures can be applied in their generic
+forms as the scattering for both types of carriers is in the vicinity of the
+Ioffe-Regel limit with decay rates being comparable to or larger than the
+offsets of band extrema. The nonmetallic temperature dependence of transport
+properties comes from the anomalous temperature dependence of scattering rates.
+Near the transition temperature the heavy electrons and the light holes
+contribute equally to the conductivity. This surprising coincidence is regarded
+as the consequence of dominant intervalley scattering that precedes the
+transition. The low-frequency peak in the optical spectra is identified and
+attributed to the critical softening of the L-point collective mode.
+",0,1,0,0,0,0
+3364,Motional Ground State Cooling Outside the Lamb-Dicke Regime,"  We report Raman sideband cooling of a single sodium atom to its
+three-dimensional motional ground state in an optical tweezer. Despite a large
+Lamb-Dicke parameter, high initial temperature, and large differential light
+shifts between the excited state and the ground state, we achieve a ground
+state population of $93.5(7)$% after $53$ ms of cooling. Our technique includes
+addressing high-order sidebands, where several motional quanta are removed by a
+single laser pulse, and fast modulation of the optical tweezer intensity. We
+demonstrate that Raman sideband cooling to the 3D motional ground state is
+possible, even without tight confinement and low initial temperature.
+",0,1,0,0,0,0
+3365,Quantifying the uncertainties in an ensemble of decadal climate predictions,"  Meaningful climate predictions must be accompanied by their corresponding
+range of uncertainty. Quantifying the uncertainties is non-trivial, and
+different methods have been suggested and used in the past. Here, we propose a
+method that does not rely on any assumptions regarding the distribution of the
+ensemble member predictions. The method is tested using the CMIP5 1981-2010
+decadal predictions and is shown to perform better than two other methods
+considered here. The improved estimate of the uncertainties is of great
+importance for both practical use and for better assessing the significance of
+the effects seen in theoretical studies.
+",0,1,0,0,0,0
+3366,A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks,"  Situational awareness in vehicular networks could be substantially improved
+utilizing reliable trajectory prediction methods. More precise situational
+awareness, in turn, results in notably better performance of critical safety
+applications, such as Forward Collision Warning (FCW), as well as comfort
+applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
+vehicle trajectory prediction problem needs to be deeply investigated in order
+to come up with an end to end framework with enough precision required by the
+safety applications' controllers. This problem has been tackled in the
+literature using different methods. However, machine learning, which is a
+promising and emerging field with remarkable potential for time series
+prediction, has not been explored enough for this purpose. In this paper, a
+two-layer neural network-based system is developed which predicts the future
+values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
+the first layer and then predicts the two-dimensional, i.e. longitudinal and
+lateral, trajectory points based on the first layer's outputs. The performance
+of the proposed framework has been evaluated in realistic cut-in scenarios from
+Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
+improvement in the prediction accuracy in comparison with the kinematics model
+which is the dominant employed model by the automotive industry. Both ideal and
+nonideal communication circumstances have been investigated for our system
+evaluation. For non-ideal case, an estimation step is included in the framework
+before the parameter prediction block to handle the drawbacks of packet drops
+or sensor failures and reconstruct the time series of vehicle parameters at a
+desirable frequency.
+",1,0,0,1,0,0
+3367,Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction,"  Spatial-temporal prediction is a fundamental problem for constructing smart
+city, which is useful for tasks such as traffic control, taxi dispatching, and
+environmental policy making. Due to data collection mechanism, it is common to
+see data collection with unbalanced spatial distributions. For example, some
+cities may release taxi data for multiple years while others only release a few
+days of data; some regions may have constant water quality data monitored by
+sensors whereas some regions only have a small collection of water samples. In
+this paper, we tackle the problem of spatial-temporal prediction for the cities
+with only a short period of data collection. We aim to utilize the long-period
+data from other cities via transfer learning. Different from previous studies
+that transfer knowledge from one single source city to a target city, we are
+the first to leverage information from multiple cities to increase the
+stability of transfer. Specifically, our proposed model is designed as a
+spatial-temporal network with a meta-learning paradigm. The meta-learning
+paradigm learns a well-generalized initialization of the spatial-temporal
+network, which can be effectively adapted to target cities. In addition, a
+pattern-based spatial-temporal memory is designed to distill long-term temporal
+information (i.e., periodicity). We conduct extensive experiments on two tasks:
+traffic (taxi and bike) prediction and water quality prediction. The
+experiments demonstrate the effectiveness of our proposed model over several
+competitive baseline models.
+",1,0,0,1,0,0
+3368,Universal locally univalent functions and universal conformal metrics with constant curvature,"  We prove Runge-type theorems and universality results for locally univalent
+holomorphic and meromorphic functions. Refining a result of M. Heins, we also
+show that there is a universal bounded locally univalent function on the unit
+disk. These results are used to prove that on any hyperbolic simply connected
+plane domain there exist universal conformal metrics with prescribed constant
+curvature.
+",0,0,1,0,0,0
+3369,Towards a New Interpretation of Separable Convolutions,"  In recent times, the use of separable convolutions in deep convolutional
+neural network architectures has been explored. Several researchers, most
+notably (Chollet, 2016) and (Ghosh, 2017) have used separable convolutions in
+their deep architectures and have demonstrated state of the art or close to
+state of the art performance. However, the underlying mechanism of action of
+separable convolutions are still not fully understood. Although their
+mathematical definition is well understood as a depthwise convolution followed
+by a pointwise convolution, deeper interpretations such as the extreme
+Inception hypothesis (Chollet, 2016) have failed to provide a thorough
+explanation of their efficacy. In this paper, we propose a hybrid
+interpretation that we believe is a better model for explaining the efficacy of
+separable convolutions.
+",1,0,0,1,0,0
+3370,Numerical Simulations of Regolith Sampling Processes,"  We present recent improvements in the simulation of regolith sampling
+processes in microgravity using the numerical particle method smooth particle
+hydrodynamics (SPH). We use an elastic-plastic soil constitutive model for
+large deformation and failure flows for dynamical behaviour of regolith. In the
+context of projected small body (asteroid or small moons) sample return
+missions, we investigate the efficiency and feasibility of a particular
+material sampling method: Brushes sweep material from the asteroid's surface
+into a collecting tray. We analyze the influence of different material
+parameters of regolith such as cohesion and angle of internal friction on the
+sampling rate. Furthermore, we study the sampling process in two environments
+by varying the surface gravity (Earth's and Phobos') and we apply different
+rotation rates for the brushes. We find good agreement of our sampling
+simulations on Earth with experiments and provide estimations for the influence
+of the material properties on the collecting rate.
+",0,1,0,0,0,0
+3371,Lexical analysis of automated accounts on Twitter,"  In recent years, social bots have been using increasingly more sophisticated,
+challenging detection strategies. While many approaches and features have been
+proposed, social bots evade detection and interact much like humans making it
+difficult to distinguish real human accounts from bot accounts. For detection
+systems, various features under the broader categories of account profile,
+tweet content, network and temporal pattern have been utilised. The use of
+tweet content features is limited to analysis of basic terms such as URLs,
+hashtags, name entities and sentiment. Given a set of tweet contents with no
+obvious pattern can we distinguish contents produced by social bots from that
+of humans? We aim to answer this question by analysing the lexical richness of
+tweets produced by the respective accounts using large collections of different
+datasets. Our results show a clear margin between the two classes in lexical
+diversity, lexical sophistication and distribution of emoticons. We found that
+the proposed lexical features significantly improve the performance of
+classifying both account types. These features are useful for training a
+standard machine learning classifier for effective detection of social bot
+accounts. A new dataset is made freely available for further exploration.
+",1,0,0,0,0,0
+3372,The effect of the virial state of molecular clouds on the influence of feedback from massive stars,"  A set of Smoothed Particle Hydrodynamics simulations of the influence of
+photoionising radiation and stellar winds on a series of 10$^{4}$M$_{\odot}$
+turbulent molecular clouds with initial virial ratios of 0.7, 1.1, 1.5, 1.9 and
+2.3 and initial mean densities of 136, 1135 and 9096\,cm$^{-3}$ are presented.
+Reductions in star formation efficiency rates are found to be modest, in the
+range $30\%-50\%$ and to not vary greatly across the parameter space. In no
+case was star formation entirely terminated over the $\approx3$\,Myr duration
+of the simulations. The fractions of material unbound by feedback are in the
+range $20-60\%$, clouds with the lowest escape velocities being the most
+strongly affected.\\ Leakage of ionised gas leads to the HII regions rapidly
+becoming underpressured. The destructive effects of ionisation are thus largely
+not due to thermally--driven expansion of the HII regions, but to momentum
+transfer by photoevaporation of fresh material. Our simulations have similar
+global ionisation rates and we show that the effects of feedback upon them can
+be adequately modelled as a steady injection of momentum, resembling a
+momentum--conserving wind.
+",0,1,0,0,0,0
+3373,A contract-based method to specify stimulus-response requirements,"  A number of formal methods exist for capturing stimulus-response requirements
+in a declarative form. Someone yet needs to translate the resulting declarative
+statements into imperative programs. The present article describes a method for
+specification and verification of stimulus-response requirements in the form of
+imperative program routines with conditionals and assertions. A program prover
+then checks a candidate program directly against the stated requirements. The
+article illustrates the approach by applying it to an ASM model of the Landing
+Gear System, a widely used realistic example proposed for evaluating
+specification and verification techniques.
+",1,0,0,0,0,0
+3374,Estimation of the covariance structure of heavy-tailed distributions,"  We propose and analyze a new estimator of the covariance matrix that admits
+strong theoretical guarantees under weak assumptions on the underlying
+distribution, such as existence of moments of only low order. While estimation
+of covariance matrices corresponding to sub-Gaussian distributions is
+well-understood, much less in known in the case of heavy-tailed data. As K.
+Balasubramanian and M. Yuan write, ""data from real-world experiments oftentimes
+tend to be corrupted with outliers and/or exhibit heavy tails. In such cases,
+it is not clear that those covariance matrix estimators .. remain optimal"" and
+""..what are the other possible strategies to deal with heavy tailed
+distributions warrant further studies."" We make a step towards answering this
+question and prove tight deviation inequalities for the proposed estimator that
+depend only on the parameters controlling the ""intrinsic dimension"" associated
+to the covariance matrix (as opposed to the dimension of the ambient space); in
+particular, our results are applicable in the case of high-dimensional
+observations.
+",0,0,1,1,0,0
+3375,Deep Reinforcement Learning for De-Novo Drug Design,"  We propose a novel computational strategy for de novo design of molecules
+with desired properties termed ReLeaSE (Reinforcement Learning for Structural
+Evolution). Based on deep and reinforcement learning approaches, ReLeaSE
+integrates two deep neural networks - generative and predictive - that are
+trained separately but employed jointly to generate novel targeted chemical
+libraries. ReLeaSE employs simple representation of molecules by their SMILES
+strings only. Generative models are trained with stack-augmented memory network
+to produce chemically feasible SMILES strings, and predictive models are
+derived to forecast the desired properties of the de novo generated compounds.
+In the first phase of the method, generative and predictive models are trained
+separately with a supervised learning algorithm. In the second phase, both
+models are trained jointly with the reinforcement learning approach to bias the
+generation of new chemical structures towards those with the desired physical
+and/or biological properties. In the proof-of-concept study, we have employed
+the ReLeaSE method to design chemical libraries with a bias toward structural
+complexity or biased toward compounds with either maximal, minimal, or specific
+range of physical properties such as melting point or hydrophobicity, as well
+as to develop novel putative inhibitors of JAK2. The approach proposed herein
+can find a general use for generating targeted chemical libraries of novel
+compounds optimized for either a single desired property or multiple
+properties.
+",1,0,0,1,0,0
+3376,Batch Data Processing and Gaussian Two-Armed Bandit,"  We consider the two-armed bandit problem as applied to data processing if
+there are two alternative processing methods available with different a priori
+unknown efficiencies. One should determine the most effective method and
+provide its predominant application. Gaussian two-armed bandit describes the
+batch, and possibly parallel, processing when the same methods are applied to
+sufficiently large packets of data and accumulated incomes are used for the
+control. If the number of packets is large enough then such control does not
+deteriorate the control performance, i.e. does not increase the minimax risk.
+For example, in case of 50 packets the minimax risk is about 2% larger than
+that one corresponding to one-by-one optimal processing. However, this is
+completely true only for methods with close efficiencies because otherwise
+there may be significant expected losses at the initial stage of control when
+both actions are applied turn-by-turn. To avoid significant losses at the
+initial stage of control one should take initial packets of data having smaller
+sizes.
+",0,0,1,1,0,0
+3377,Estimating Under Five Mortality in Space and Time in a Developing World Context,"  Accurate estimates of the under-5 mortality rate (U5MR) in a developing world
+context are a key barometer of the health of a nation. This paper describes new
+models to analyze survey data on mortality in this context. We are interested
+in both spatial and temporal description, that is, wishing to estimate U5MR
+across regions and years, and to investigate the association between the U5MR
+and spatially-varying covariate surfaces. We illustrate the methodology by
+producing yearly estimates for subnational areas in Kenya over the period 1980
+- 2014 using data from demographic health surveys (DHS). We use a binomial
+likelihood with fixed effects for the urban/rural stratification to account for
+the complex survey design. We carry out smoothing using Bayesian hierarchical
+models with continuous spatial and temporally discrete components. A key
+component of the model is an offset to adjust for bias due to the effects of
+HIV epidemics. Substantively, there has been a sharp decline in U5MR in the
+period 1980 - 2014, but large variability in estimated subnational rates
+remains. A priority for future research is understanding this variability.
+Temperature, precipitation and a measure of malaria infection prevalence were
+candidates for inclusion in the covariate model.
+",0,0,0,1,0,0
+3378,Cyclotomic Construction of Strong External Difference Families in Finite Fields,"  Strong external difference family (SEDF) and its generalizations GSEDF,
+BGSEDF in a finite abelian group $G$ are combinatorial designs raised by
+Paterson and Stinson [7] in 2016 and have applications in communication theory
+to construct optimal strong algebraic manipulation detection codes. In this
+paper we firstly present some general constructions of these combinatorial
+designs by using difference sets and partial difference sets in $G$. Then, as
+applications of the general constructions, we construct series of SEDF, GSEDF
+and BGSEDF in finite fields by using cyclotomic classes.
+",1,0,1,0,0,0
+3379,Acoustic double negativity induced by position correlations within a disordered set of monopolar resonators,"  Using a Multiple Scattering Theory algorithm, we investigate numerically the
+transmission of ultrasonic waves through a disordered locally resonant
+metamaterial containing only monopolar resonators. By comparing the cases of a
+perfectly random medium with its pair correlated counterpart, we show that the
+introduction of short range correlation can substantially impact the effective
+parameters of the sample. We report, notably, the opening of an acoustic
+transparency window in the region of the hybridization band gap. Interestingly,
+the transparency window is found to be associated with negative values of both
+effective compressibility and density. Despite this feature being unexpected
+for a disordered medium of monopolar resonators, we show that it can be fully
+described analytically and that it gives rise to negative refraction of waves.
+",0,1,0,0,0,0
+3380,Interoceptive robustness through environment-mediated morphological development,"  Typically, AI researchers and roboticists try to realize intelligent behavior
+in machines by tuning parameters of a predefined structure (body plan and/or
+neural network architecture) using evolutionary or learning algorithms. Another
+but not unrelated longstanding property of these systems is their brittleness
+to slight aberrations, as highlighted by the growing deep learning literature
+on adversarial examples. Here we show robustness can be achieved by evolving
+the geometry of soft robots, their control systems, and how their material
+properties develop in response to one particular interoceptive stimulus
+(engineering stress) during their lifetimes. By doing so we realized robots
+that were equally fit but more robust to extreme material defects (such as
+might occur during fabrication or by damage thereafter) than robots that did
+not develop during their lifetimes, or developed in response to a different
+interoceptive stimulus (pressure). This suggests that the interplay between
+changes in the containing systems of agents (body plan and/or neural
+architecture) at different temporal scales (evolutionary and developmental)
+along different modalities (geometry, material properties, synaptic weights)
+and in response to different signals (interoceptive and external perception)
+all dictate those agents' abilities to evolve or learn capable and robust
+strategies.
+",1,0,0,0,0,0
+3381,Strichartz estimates for non-degenerate Schrödinger equations,"  We consider Schrödinger equation with a non-degenerate metric on the
+Euclidean space. We study local in time Strichartz estimates for the
+Schrödinger equation without loss of derivatives including the endpoint case.
+In contrast to the Riemannian metric case, we need the additional assumptions
+for the well-posedness of our Schrödinger equation and for proving Strichartz
+estimates without loss.
+",0,0,1,0,0,0
+3382,On the Diophantine equation $\sum_{j=1}^kjF_j^p=F_n^q$,"  Let $F_n$ denote the $n^{th}$ term of the Fibonacci sequence. In this paper,
+we investigate the Diophantine equation $F_1^p+2F_2^p+\cdots+kF_{k}^p=F_{n}^q$
+in the positive integers $k$ and $n$, where $p$ and $q$ are given positive
+integers. A complete solution is given if the exponents are included in the set
+$\{1,2\}$. Based on the specific cases we could solve, and a computer search
+with $p,q,k\le100$ we conjecture that beside the trivial solutions only
+$F_8=F_1+2F_2+3F_3+4F_4$, $F_4^2=F_1+2F_2+3F_3$, and
+$F_4^3=F_1^3+2F_2^3+3F_3^3$ satisfy the title equation.
+",0,0,1,0,0,0
+3383,Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks,"  Social dilemmas have been regarded as the essence of evolution game theory,
+in which the prisoner's dilemma game is the most famous metaphor for the
+problem of cooperation. Recent findings revealed people's behavior violated the
+Sure Thing Principle in such games. Classic probability methodologies have
+difficulty explaining the underlying mechanisms of people's behavior. In this
+paper, a novel quantum-like Bayesian Network was proposed to accommodate the
+paradoxical phenomenon. The special network can take interference into
+consideration, which is likely to be an efficient way to describe the
+underlying mechanism. With the assistance of belief entropy, named as Deng
+entropy, the paper proposes Belief Distance to render the model practical.
+Tested with empirical data, the proposed model is proved to be predictable and
+effective.
+",1,0,0,0,0,0
+3384,The diffusion equation with nonlocal data,"  We study the diffusion (or heat) equation on a finite 1-dimensional spatial
+domain, but we replace one of the boundary conditions with a ""nonlocal
+condition"", through which we specify a weighted average of the solution over
+the spatial interval. We provide conditions on the regularity of both the data
+and weight for the problem to admit a unique solution, and also provide a
+solution representation in terms of contour integrals. The solution and
+well-posedness results rely upon an extension of the Fokas (or unified)
+transform method to initial-nonlocal value problems for linear equations; the
+necessary extensions are described in detail. Despite arising naturally from
+the Fokas transform method, the uniqueness argument appears to be novel even
+for initial-boundary value problems.
+",0,0,1,0,0,0
+3385,Exploring Neural Transducers for End-to-End Speech Recognition,"  In this work, we perform an empirical comparison among the CTC,
+RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech
+recognition. We show that, without any language model, Seq2Seq and
+RNN-Transducer models both outperform the best reported CTC models with a
+language model, on the popular Hub5'00 benchmark. On our internal diverse
+dataset, these trends continue - RNNTransducer models rescored with a language
+model after beam search outperform our best CTC models. These results simplify
+the speech recognition pipeline so that decoding can now be expressed purely as
+neural network operations. We also study how the choice of encoder architecture
+affects the performance of the three models - when all encoder layers are
+forward only, and when encoders downsample the input representation
+aggressively.
+",1,0,0,0,0,0
+3386,Information Theory of Data Privacy,"  By combining Shannon's cryptography model with an assumption to the lower
+bound of adversaries' uncertainty to the queried dataset, we develop a secure
+Bayesian inference-based privacy model and then in some extent answer Dwork et
+al.'s question [1]: ""why Bayesian risk factors are the right measure for
+privacy loss"".
+This model ensures an adversary can only obtain little information of each
+individual from the model's output if the adversary's uncertainty to the
+queried dataset is larger than the lower bound. Importantly, the assumption to
+the lower bound almost always holds, especially for big datasets. Furthermore,
+this model is flexible enough to balance privacy and utility: by using four
+parameters to characterize the assumption, there are many approaches to balance
+privacy and utility and to discuss the group privacy and the composition
+privacy properties of this model.
+",1,0,0,0,0,0
+3387,A combined entropy and utility based generative model for large scale multiple discrete-continuous travel behaviour data,"  Generative models, either by simple clustering algorithms or deep neural
+network architecture, have been developed as a probabilistic estimation method
+for dimension reduction or to model the underlying properties of data
+structures. Although their apparent use has largely been limited to image
+recognition and classification, generative machine learning algorithms can be a
+powerful tool for travel behaviour research. In this paper, we examine the
+generative machine learning approach for analyzing multiple discrete-continuous
+(MDC) travel behaviour data to understand the underlying heterogeneity and
+correlation, increasing the representational power of such travel behaviour
+models. We show that generative models are conceptually similar to choice
+selection behaviour process through information entropy and variational
+Bayesian inference. Specifically, we consider a restricted Boltzmann machine
+(RBM) based algorithm with multiple discrete-continuous layer, formulated as a
+variational Bayesian inference optimization problem. We systematically describe
+the proposed machine learning algorithm and develop a process of analyzing
+travel behaviour data from a generative learning perspective. We show parameter
+stability from model analysis and simulation tests on an open dataset with
+multiple discrete-continuous dimensions and a size of 293,330 observations. For
+interpretability, we derive analytical methods for conditional probabilities as
+well as elasticities. Our results indicate that latent variables in generative
+models can accurately represent joint distribution consistently w.r.t multiple
+discrete-continuous variables. Lastly, we show that our model can generate
+statistically similar data distributions for travel forecasting and prediction.
+",1,0,0,1,0,0
+3388,Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation,"  We study a spectral initialization method that serves a key role in recent
+work on estimating signals in nonconvex settings. Previous analysis of this
+method focuses on the phase retrieval problem and provides only performance
+bounds. In this paper, we consider arbitrary generalized linear sensing models
+and present a precise asymptotic characterization of the performance of the
+method in the high-dimensional limit. Our analysis also reveals a phase
+transition phenomenon that depends on the ratio between the number of samples
+and the signal dimension. When the ratio is below a minimum threshold, the
+estimates given by the spectral method are no better than random guesses drawn
+from a uniform distribution on the hypersphere, thus carrying no information;
+above a maximum threshold, the estimates become increasingly aligned with the
+target signal. The computational complexity of the method, as measured by the
+spectral gap, is also markedly different in the two phases. Worked examples and
+numerical results are provided to illustrate and verify the analytical
+predictions. In particular, simulations show that our asymptotic formulas
+provide accurate predictions for the actual performance of the spectral method
+even at moderate signal dimensions.
+",1,0,0,1,0,0
+3389,Understanding GANs: the LQG Setting,"  Generative Adversarial Networks (GANs) have become a popular method to learn
+a probability model from data. In this paper, we aim to provide an
+understanding of some of the basic issues surrounding GANs including their
+formulation, generalization and stability on a simple benchmark where the data
+has a high-dimensional Gaussian distribution. Even in this simple benchmark,
+the GAN problem has not been well-understood as we observe that existing
+state-of-the-art GAN architectures may fail to learn a proper generative
+distribution owing to (1) stability issues (i.e., convergence to bad local
+solutions or not converging at all), (2) approximation issues (i.e., having
+improper global GAN optimizers caused by inappropriate GAN's loss functions),
+and (3) generalizability issues (i.e., requiring large number of samples for
+training). In this setup, we propose a GAN architecture which recovers the
+maximum-likelihood solution and demonstrates fast generalization. Moreover, we
+analyze global stability of different computational approaches for the proposed
+GAN optimization and highlight their pros and cons. Finally, we outline an
+extension of our model-based approach to design GANs in more complex setups
+than the considered Gaussian benchmark.
+",1,0,0,1,0,0
+3390,A Rosenau-type approach to the approximation of the linear Fokker--Planck equation,"  {The numerical approximation of the solution of the Fokker--Planck equation
+is a challenging problem that has been extensively investigated starting from
+the pioneering paper of Chang and Cooper in 1970. We revisit this problem at
+the light of the approximation of the solution to the heat equation proposed by
+Rosenau in 1992. Further, by means of the same idea, we address the problem of
+a consistent approximation to higher-order linear diffusion equations.
+",0,0,1,0,0,0
+3391,Analysis of Footnote Chasing and Citation Searching in an Academic Search Engine,"  In interactive information retrieval, researchers consider the user behavior
+towards systems and search tasks in order to adapt search results by analyzing
+their past interactions. In this paper, we analyze the user behavior towards
+Marcia Bates' search stratagems such as 'footnote chasing' and 'citation
+search' in an academic search engine. We performed a preliminary analysis of
+their frequency and stage of use in the social sciences search engine sowiport.
+In addition, we explored the impact of these stratagems on the whole search
+process performance. We can conclude that the appearance of these two search
+features in real retrieval sessions lead to an improvement of the precision in
+terms of positive interactions with 16% when using footnote chasing and 17% for
+the citation search stratagem.
+",1,0,0,0,0,0
+3392,Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development,"  The excited states of polyatomic systems are rather complex, and often
+exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway
+often fails to sufficiently characterize excited state motions due to their
+highly non-equilibrium nature. Here, we proposed a time series guided
+clustering algorithm to generate most relevant meta-stable patterns directly
+from ab initio dynamic trajectories. Based on the knowledge of these
+meta-stable patterns, we suggested an interpolation scheme with only a concrete
+and finite set of known patterns to accurately predict the ground and excited
+state properties of the entire dynamics trajectories. As illustrated with the
+example of sinapic acids, the estimation error for both ground and excited
+state is very close, which indicates one could predict the ground and excited
+state molecular properties with similar accuracy. These results may provide us
+some insights to construct an excited state force field with compatible energy
+terms as traditional ones.
+",0,1,0,1,0,0
+3393,You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data,"  This work offers a design of a video surveillance system based on a soft
+biometric -- gait identification from MoCap data. The main focus is on two
+substantial issues of the video surveillance scenario: (1) the walkers do not
+cooperate in providing learning data to establish their identities and (2) the
+data are often noisy or incomplete. We show that only a few examples of human
+gait cycles are required to learn a projection of raw MoCap data onto a
+low-dimensional sub-space where the identities are well separable. Latent
+features learned by Maximum Margin Criterion (MMC) method discriminate better
+than any collection of geometric features. The MMC method is also highly robust
+to noisy data and works properly even with only a fraction of joints tracked.
+The overall workflow of the design is directly applicable for a day-to-day
+operation based on the available MoCap technology and algorithms for gait
+analysis. In the concept we introduce, a walker's identity is represented by a
+cluster of gait data collected at their incidents within the surveillance
+system: They are how they walk.
+",1,0,0,0,0,0
+3394,Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network,"  Recent work by Cohen \emph{et al.} has achieved state-of-the-art results for
+learning spherical images in a rotation invariant way by using ideas from group
+representation theory and noncommutative harmonic analysis. In this paper we
+propose a generalization of this work that generally exhibits improved
+performace, but from an implementation point of view is actually simpler. An
+unusual feature of the proposed architecture is that it uses the
+Clebsch--Gordan transform as its only source of nonlinearity, thus avoiding
+repeated forward and backward Fourier transforms. The underlying ideas of the
+paper generalize to constructing neural networks that are invariant to the
+action of other compact groups.
+",0,0,0,1,0,0
+3395,Characterization of catastrophic instabilities: Market crashes as paradigm,"  Catastrophic events, though rare, do occur and when they occur, they have
+devastating effects. It is, therefore, of utmost importance to understand the
+complexity of the underlying dynamics and signatures of catastrophic events,
+such as market crashes. For deeper understanding, we choose the US and Japanese
+markets from 1985 onward, and study the evolution of the cross-correlation
+structures of stock return matrices and their eigenspectra over different short
+time-intervals or ""epochs"". A slight non-linear distortion is applied to the
+correlation matrix computed for any epoch, leading to the emerging spectrum of
+eigenvalues. The statistical properties of the emerging spectrum display: (i)
+the shape of the emerging spectrum reflects the market instability, (ii) the
+smallest eigenvalue may be able to statistically distinguish the nature of a
+market turbulence or crisis -- internal instability or external shock, and
+(iii) the time-lagged smallest eigenvalue has a statistically significant
+correlation with the mean market cross-correlation. The smallest eigenvalue
+seems to indicate that the financial market has become more turbulent in a
+similar way as the mean does. Yet we show features of the smallest eigenvalue
+of the emerging spectrum that distinguish different types of market
+instabilities related to internal or external causes. Based on the paradigmatic
+character of financial time series for other complex systems, the capacity of
+the emerging spectrum to understand the nature of instability may be a new
+feature, which can be broadly applied.
+",0,0,0,0,0,1
+3396,Discovery of Intrinsic Quantum Anomalous Hall Effect in Organic Mn-DCA Lattice,"  The quantum anomalous Hall (QAH) phase is a novel topological state of matter
+characterized by a nonzero quantized Hall conductivity without an external
+magnetic field. The realizations of QAH effect, however, are experimentally
+challengeable. Based on ab initio calculations, here we propose an intrinsic
+QAH phase in DCA Kagome lattice. The nontrivial topology in Kagome bands are
+confirmed by the nonzero chern number, quantized Hall conductivity, and gapless
+chiral edge states of Mn-DCA lattice. A tight-binding (TB) model is further
+constructed to clarify the origin of QAH effect. Furthermore, its Curie
+temperature, estimated to be ~ 253 K using Monte-Carlo simulation, is
+comparable with room temperature and higher than most of two-dimensional
+ferromagnetic thin films. Our findings present a reliable material platform for
+the observation of QAH effect in covalent-organic frameworks.
+",0,1,0,0,0,0
+3397,Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound,"  The automatic analysis of ultrasound sequences can substantially improve the
+efficiency of clinical diagnosis. In this work we present our attempt to
+automate the challenging task of measuring the vascular diameter of the fetal
+abdominal aorta from ultrasound images. We propose a neural network
+architecture consisting of three blocks: a convolutional layer for the
+extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for
+enforcing the temporal coherence across video frames and exploiting the
+temporal redundancy of a signal, and a regularized loss function, called
+\textit{CyclicLoss}, to impose our prior knowledge about the periodicity of the
+observed signal. We present experimental evidence suggesting that the proposed
+architecture can reach an accuracy substantially superior to previously
+proposed methods, providing an average reduction of the mean squared error from
+$0.31 mm^2$ (state-of-art) to $0.09 mm^2$, and a relative error reduction from
+$8.1\%$ to $5.3\%$. The mean execution speed of the proposed approach of 289
+frames per second makes it suitable for real time clinical use.
+",0,0,0,1,0,0
+3398,The Role of Data Analysis in the Development of Intelligent Energy Networks,"  Data analysis plays an important role in the development of intelligent
+energy networks (IENs). This article reviews and discusses the application of
+data analysis methods for energy big data. The installation of smart energy
+meters has provided a huge volume of data at different time resolutions,
+suggesting data analysis is required for clustering, demand forecasting, energy
+generation optimization, energy pricing, monitoring and diagnostics. The
+currently adopted data analysis technologies for IENs include pattern
+recognition, machine learning, data mining, statistics methods, etc. However,
+existing methods for data analysis cannot fully meet the requirements for
+processing the big data produced by the IENs and, therefore, more comprehensive
+data analysis methods are needed to handle the increasing amount of data and to
+mine more valuable information.
+",1,0,0,0,0,0
+3399,Quantification of the memory effect of steady-state currents from interaction-induced transport in quantum systems,"  Dynamics of a system in general depends on its initial state and how the
+system is driven, but in many-body systems the memory is usually averaged out
+during evolution. Here, interacting quantum systems without external
+relaxations are shown to retain long-time memory effects in steady states. To
+identify memory effects, we first show quasi-steady state currents form in
+finite, isolated Bose and Fermi Hubbard models driven by interaction imbalance
+and they become steady-state currents in the thermodynamic limit. By comparing
+the steady state currents from different initial states or ramping rates of the
+imbalance, long-time memory effects can be quantified. While the memory effects
+of initial states are more ubiquitous, the memory effects of switching
+protocols are mostly visible in interaction-induced transport in lattices. Our
+simulations suggest the systems enter a regime governed by a generalized Fick's
+law and memory effects lead to initial-state dependent diffusion coefficients.
+We also identify conditions for enhancing memory effects and discuss possible
+experimental implications.
+",0,1,0,0,0,0
+3400,Geometrical optimization approach to isomerization: Models and limitations,"  We study laser-driven isomerization reactions through an excited electronic
+state using the recently developed Geometrical Optimization procedure [J. Phys.
+Chem. Lett. 6, 1724 (2015)]. The goal is to analyze whether an initial wave
+packet in the ground state, with optimized amplitudes and phases, can be used
+to enhance the yield of the reaction at faster rates, exploring how the
+geometrical restrictions induced by the symmetry of the system impose
+limitations in the optimization procedure. As an example we model the
+isomerization in an oriented 2,2'-dimethyl biphenyl molecule with a simple
+quartic potential. Using long (picosecond) pulses we find that the
+isomerization can be achieved driven by a single pulse. The phase of the
+initial superposition state does not affect the yield. However, using short
+(femtosecond) pulses, one always needs a pair of pulses to force the reaction.
+High yields can only be obtained by optimizing both the initial state, and the
+wave packet prepared in the excited state, implying the well known pump-dump
+mechanism.
+",0,1,0,0,0,0
+3401,Quandle rings,"  In this paper, a theory of quandle rings is proposed for quandles analogous
+to the classical theory of group rings for groups, and interconnections between
+quandles and associated quandle rings are explored.
+",0,0,1,0,0,0
+3402,On the application of Laguerre's method to the polynomial eigenvalue problem,"  The polynomial eigenvalue problem arises in many applications and has
+received a great deal of attention over the last decade. The use of
+root-finding methods to solve the polynomial eigenvalue problem dates back to
+the work of Kublanovskaya (1969, 1970) and has received a resurgence due to the
+work of Bini and Noferini (2013). In this paper, we present a method which uses
+Laguerre iteration for computing the eigenvalues of a matrix polynomial. An
+effective method based on the numerical range is presented for computing
+initial estimates to the eigenvalues of a matrix polynomial. A detailed
+explanation of the stopping criteria is given, and it is shown that under
+suitable conditions we can guarantee the backward stability of the eigenvalues
+computed by our method. Then, robust methods are provided for computing both
+the right and left eigenvectors and the condition number of each eigenpair.
+Applications for Hessenberg and tridiagonal matrix polynomials are given and we
+show that both structures benefit from substantial computational savings.
+Finally, we present several numerical experiments to verify the accuracy of our
+method and its competitiveness for solving the roots of a polynomial and the
+tridiagonal eigenvalue problem.
+",0,0,1,0,0,0
+3403,Limiting Behaviour of the Teichmüller Harmonic Map Flow,"  In this paper we study the Teichmüller harmonic map flow as introduced by
+Rupflin and Topping [15]. It evolves pairs of maps and metrics $(u,g)$ into
+branched minimal immersions, or equivalently into weakly conformal harmonic
+maps, where $u$ maps from a fixed closed surface $M$ with metric $g$ to a
+general target manifold $N$. It arises naturally as a gradient flow for the
+Dirichlet energy functional viewed as acting on equivalence classes of such
+pairs, obtained from the invariance under diffeomorphisms and conformal changes
+of the domain metric.
+In the construction of a suitable inner product for the gradient flow a
+choice of relative weight of the map tangent directions and metric tangent
+directions is made, which manifests itself in the appearance of a coupling
+constant $\eta$ in the flow equations.
+We study limits of the flow as $\eta$ approaches 0, corresponding to slowing
+down the evolution of the metric.
+We first show that given a smooth harmonic map flow on a fixed time interval,
+the Teichmüller harmonic map flows starting at the same initial data converge
+uniformly to the underlying harmonic map flow when $\eta \downarrow 0$.
+Next we consider a rescaling of time, which increases the speed of the map
+evolution while evolving the metric at a constant rate. We show that under
+appropriate topological assumptions, in the limit the rescaled flows converge
+to a unique flow through harmonic maps with the metric evolving in the
+direction of the real part of the Hopf differential.
+",0,0,1,0,0,0
+3404,Invariant measures for the actions of the modular group,"  In this note, we give a nature action of the modular group on the ends of the
+infinite (p + 1)-cayley tree, for each prime p. We show that there is a unique
+invariant probability measure for each p.
+",0,0,1,0,0,0
+3405,Cartesian Fibrations and Representability,"  In higher category theory, we use fibrations to model presheaves. In this
+paper we introduce a new method to build such fibrations. Concretely, for
+suitable reflective subcategories of simplicial spaces, we build fibrations
+that model presheaves valued in that subcategory. Using this we can build
+Cartesian fibrations, but we can also model presheaves valued in Segal spaces.
+Additionally, using this new approach, we define representable Cartesian
+fibrations, generalizing representable presheaves valued in spaces, and show
+they have similar properties.
+",0,0,1,0,0,0
+3406,Signal coupling to embedded pitch adapters in silicon sensors,"  We have examined the effects of embedded pitch adapters on signal formation
+in n-substrate silicon microstrip sensors with data from beam tests and
+simulation. According to simulation, the presence of the pitch adapter metal
+layer changes the electric field inside the sensor, resulting in slowed signal
+formation on the nearby strips and a pick-up effect on the pitch adapter. This
+can result in an inefficiency to detect particles passing through the pitch
+adapter region. All these effects have been observed in the beam test data.
+",0,1,0,0,0,0
+3407,Winds and radiation in unison: a new semi-analytic feedback model for cloud dissolution,"  Star clusters interact with the interstellar medium (ISM) in various ways,
+most importantly in the destruction of molecular star-forming clouds, resulting
+in inefficient star formation on galactic scales. On cloud scales, ionizing
+radiation creates \hii regions, while stellar winds and supernovae drive the
+ISM into thin shells. These shells are accelerated by the combined effect of
+winds, radiation pressure and supernova explosions, and slowed down by gravity.
+Since radiative and mechanical feedback is highly interconnected, they must be
+taken into account in a self-consistent and combined manner, including the
+coupling of radiation and matter. We present a new semi-analytic
+one-dimensional feedback model for isolated massive clouds ($\geq
+10^5\,M_{\odot}$) to calculate shell dynamics and shell structure
+simultaneously. It allows us to scan a large range of physical parameters (gas
+density, star formation efficiency, metallicity) and to estimate escape
+fractions of ionizing radiation $f_{\rm{esc,i}}$, the minimum star formation
+efficiency $\epsilon_{\rm{min}}$ required to drive an outflow, and recollapse
+time scales for clouds that are not destroyed by feedback. Our results show
+that there is no simple answer to the question of what dominates cloud
+dynamics, and that each feedback process significantly influences the
+efficiency of the others. We find that variations in natal cloud density can
+very easily explain differences between dense-bound and diffuse-open star
+clusters. We also predict, as a consequence of feedback, a $4-6$ Myr age
+difference for massive clusters with multiple generations.
+",0,1,0,0,0,0
+3408,Analysis-of-marginal-Tail-Means (ATM): a robust method for discrete black-box optimization,"  We present a new method, called Analysis-of-marginal-Tail-Means (ATM), for
+effective robust optimization of discrete black-box problems. ATM has important
+applications to many real-world engineering problems (e.g., manufacturing
+optimization, product design, molecular engineering), where the objective to
+optimize is black-box and expensive, and the design space is inherently
+discrete. One weakness of existing methods is that they are not robust: these
+methods perform well under certain assumptions, but yield poor results when
+such assumptions (which are difficult to verify in black-box problems) are
+violated. ATM addresses this via the use of marginal tail means for
+optimization, which combines both rank-based and model-based methods. The
+trade-off between rank- and model-based optimization is tuned by first
+identifying important main effects and interactions, then finding a good
+compromise which best exploits additive structure. By adaptively tuning this
+trade-off from data, ATM provides improved robust optimization over existing
+methods, particularly in problems with (i) a large number of factors, (ii)
+unordered factors, or (iii) experimental noise. We demonstrate the
+effectiveness of ATM in simulations and in two real-world engineering problems:
+the first on robust parameter design of a circular piston, and the second on
+product family design of a thermistor network.
+",0,0,0,1,0,0
+3409,Modular System for Shelves and Coasts (MOSSCO v1.0) - a flexible and multi-component framework for coupled coastal ocean ecosystem modelling,"  Shelf and coastal sea processes extend from the atmosphere through the water
+column and into the sea bed. These processes are driven by physical, chemical,
+and biological interactions at local scales, and they are influenced by
+transport and cross strong spatial gradients. The linkages between domains and
+many different processes are not adequately described in current model systems.
+Their limited integration level in part reflects lacking modularity and
+flexibility; this shortcoming hinders the exchange of data and model components
+and has historically imposed supremacy of specific physical driver models. We
+here present the Modular System for Shelves and Coasts (MOSSCO,
+this http URL), a novel domain and process coupling system
+tailored---but not limited--- to the coupling challenges of and applications in
+the coastal ocean. MOSSCO builds on the existing coupling technology Earth
+System Modeling Framework and on the Framework for Aquatic Biogeochemical
+Models, thereby creating a unique level of modularity in both domain and
+process coupling; the new framework adds rich metadata, flexible scheduling,
+configurations that allow several tens of models to be coupled, and tested
+setups for coastal coupled applications. That way, MOSSCO addresses the
+technology needs of a growing marine coastal Earth System community that
+encompasses very different disciplines, numerical tools, and research
+questions.
+",0,1,0,0,0,0
+3410,"How big was Galileo's impact? Percussion in the Sixth Day of the ""Two New Sciences""","  The Giornata Sesta about the Force of Percussion is a relatively less known
+Chapter from the Galileo's masterpiece ""Discourse about Two New Sciences"". It
+was first published lately (1718), long after the first edition of the Two New
+Sciences (1638) and Galileo's death (1642). The Giornata Sesta focuses on how
+to quantify the percussion force caused by a body in movement, and describes a
+very interesting experiment known as ""the two-bucket experiment"". In this
+paper, we review this experiment reported by Galileo, develop a steady-state
+theoretical model, and solve its transient form numerically; additionally, we
+report the results from one real simplified analogous experiment. Finally, we
+discuss the conclusions drawn by Galileo -- correct, despite a probably
+unnoticeable imbalance --, showing that he did not report the thrust force
+component in his setup -- which would be fundamental for the correct
+calculation of the percussion force.
+",0,1,0,0,0,0
+3411,"Radar, without tears","  A brief introduction to radar: principles, Doppler effect, antennas,
+waveforms, power budget - and future radars. [13 pages]
+",0,1,0,0,0,0
+3412,A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation,"  One of the challenges in computational acoustics is the identification of
+models that can simulate and predict the physical behavior of a system
+generating an acoustic signal. Whenever such models are used for commercial
+applications an additional constraint is the time-to-market, making automation
+of the sound design process desirable. In previous works, a computational sound
+design approach has been proposed for the parameter estimation problem
+involving timbre matching by deep learning, which was applied to the synthesis
+of pipe organ tones. In this work we refine previous results by introducing the
+former approach in a multi-stage algorithm that also adds heuristics and a
+stochastic optimization method operating on objective cost functions based on
+psychoacoustics. The optimization method shows to be able to refine the first
+estimate given by the deep learning approach and substantially improve the
+objective metrics, with the additional benefit of reducing the sound design
+process time. Subjective listening tests are also conducted to gather
+additional insights on the results.
+",1,0,0,1,0,0
+3413,A Warped Product Splitting Theorem Through Weak KAM Theory,"  In this paper, we strengthen the splitting theorem proved in [14, 15] and
+provide a different approach using ideas from the weak KAM theory.
+",0,0,1,0,0,0
+3414,Learning Instance Segmentation by Interaction,"  We present an approach for building an active agent that learns to segment
+its visual observations into individual objects by interacting with its
+environment in a completely self-supervised manner. The agent uses its current
+segmentation model to infer pixels that constitute objects and refines the
+segmentation model by interacting with these pixels. The model learned from
+over 50K interactions generalizes to novel objects and backgrounds. To deal
+with noisy training signal for segmenting objects obtained by self-supervised
+interactions, we propose robust set loss. A dataset of robot's interactions
+along-with a few human labeled examples is provided as a benchmark for future
+research. We test the utility of the learned segmentation model by providing
+results on a downstream vision-based control task of rearranging multiple
+objects into target configurations from visual inputs alone. Videos, code, and
+robotic interaction dataset are available at
+this https URL
+",1,0,0,1,0,0
+3415,"On convergence rate of stochastic proximal point algorithm without strong convexity, smoothness or bounded gradients","  Significant parts of the recent learning literature on stochastic
+optimization algorithms focused on the theoretical and practical behaviour of
+stochastic first order schemes under different convexity properties. Due to its
+simplicity, the traditional method of choice for most supervised machine
+learning problems is the stochastic gradient descent (SGD) method. Many
+iteration improvements and accelerations have been added to the pure SGD in
+order to boost its convergence in various (strong) convexity setting. However,
+the Lipschitz gradient continuity or bounded gradients assumptions are an
+essential requirement for most existing stochastic first-order schemes. In this
+paper novel convergence results are presented for the stochastic proximal point
+algorithm in different settings. In particular, without any strong convexity,
+smoothness or bounded gradients assumptions, we show that a slightly modified
+quadratic growth assumption is sufficient to guarantee for the stochastic
+proximal point $\mathcal{O}\left(\frac{1}{k}\right)$ convergence rate, in terms
+of the distance to the optimal set. Furthermore, linear convergence is obtained
+for interpolation setting, when the optimal set of expected cost is included in
+the optimal sets of each functional component.
+",1,0,0,1,0,0
+3416,Learning Texture Manifolds with the Periodic Spatial GAN,"  This paper introduces a novel approach to texture synthesis based on
+generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the
+structure of the input noise distribution by constructing tensors with
+different types of dimensions. We call this technique Periodic Spatial GAN
+(PSGAN). The PSGAN has several novel abilities which surpass the current state
+of the art in texture synthesis. First, we can learn multiple textures from
+datasets of one or more complex large images. Second, we show that the image
+generation with PSGANs has properties of a texture manifold: we can smoothly
+interpolate between samples in the structured noise space and generate novel
+samples, which lie perceptually between the textures of the original dataset.
+In addition, we can also accurately learn periodical textures. We make multiple
+experiments which show that PSGANs can flexibly handle diverse texture and
+image data sources. Our method is highly scalable and it can generate output
+images of arbitrary large size.
+",1,0,0,1,0,0
+3417,Generating Representative Executions [Extended Abstract],"  Analyzing the behaviour of a concurrent program is made difficult by the
+number of possible executions. This problem can be alleviated by applying the
+theory of Mazurkiewicz traces to focus only on the canonical representatives of
+the equivalence classes of the possible executions of the program. This paper
+presents a generic framework that allows to specify the possible behaviours of
+the execution environment, and generate all Foata-normal executions of a
+program, for that environment, by discarding abnormal executions during the
+generation phase. The key ingredient of Mazurkiewicz trace theory, the
+dependency relation, is used in the framework in two roles: first, as part of
+the specification of which executions are allowed at all, and then as part of
+the normality checking algorithm, which is used to discard the abnormal
+executions. The framework is instantiated to the relaxed memory models of the
+SPARC hierarchy.
+",1,0,0,0,0,0
+3418,Extended periodic links and HOMFLYPT polynomial,"  Extended strongly periodic links have been introduced by Przytycki and
+Sokolov as a symmetric surgery presentation of three-manifolds on which the
+finite cyclic group acts without fixed points. The purpose of this paper is to
+prove that the symmetry of these links is reflected by the first coefficients
+of the HOMFLYPT polynomial.
+",0,0,1,0,0,0
+3419,Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency,"  Over the past decade, the idea of smart homes has been conceived as a
+potential solution to counter energy crises or to at least mitigate its
+intensive destructive consequences in the residential building sector.
+",1,0,0,0,0,0
+3420,Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters,"  Deep learning models can take weeks to train on a single GPU-equipped
+machine, necessitating scaling out DL training to a GPU-cluster. However,
+current distributed DL implementations can scale poorly due to substantial
+parameter synchronization over the network, because the high throughput of GPUs
+allows more data batches to be processed per unit time than CPUs, leading to
+more frequent network synchronization. We present Poseidon, an efficient
+communication architecture for distributed DL on GPUs. Poseidon exploits the
+layered model structures in DL programs to overlap communication and
+computation, reducing bursty network communication. Moreover, Poseidon uses a
+hybrid communication scheme that optimizes the number of bytes required to
+synchronize each layer, according to layer properties and the number of
+machines. We show that Poseidon is applicable to different DL frameworks by
+plugging Poseidon into Caffe and TensorFlow. We show that Poseidon enables
+Caffe and TensorFlow to achieve 15.5x speed-up on 16 single-GPU machines, even
+with limited bandwidth (10GbE) and the challenging VGG19-22K network for image
+classification. Moreover, Poseidon-enabled TensorFlow achieves 31.5x speed-up
+with 32 single-GPU machines on Inception-V3, a 50% improvement over the
+open-source TensorFlow (20x speed-up).
+",1,0,0,1,0,0
+3421,A closed formula for illiquid corporate bonds and an application to the European market,"  We deduce a simple closed formula for illiquid corporate coupon bond prices
+when liquid bonds with similar characteristics (e.g. maturity) are present in
+the market for the same issuer. The key model parameter is the
+time-to-liquidate a position, i.e. the time that an experienced bond trader
+takes to liquidate a given position on a corporate coupon bond.
+The option approach we propose for pricing bonds' illiquidity is reminiscent
+of the celebrated work of Longstaff (1995) on the non-marketability of some
+non-dividend-paying shares in IPOs. This approach describes a quite common
+situation in the fixed income market: it is rather usual to find issuers that,
+besides liquid benchmark bonds, issue some other bonds that either are placed
+to a small number of investors in private placements or have a limited issue
+size.
+The model considers interest rate and credit spread term structures and their
+dynamics. We show that illiquid bonds present an additional liquidity spread
+that depends on the time-to-liquidate aside from credit and interest rate
+parameters. We provide a detailed application for two issuers in the European
+market.
+",0,0,0,0,0,1
+3422,"Toric Codes, Multiplicative Structure and Decoding","  Long linear codes constructed from toric varieties over finite fields, their
+multiplicative structure and decoding. The main theme is the inherent
+multiplicative structure on toric codes. The multiplicative structure allows
+for \emph{decoding}, resembling the decoding of Reed-Solomon codes and aligns
+with decoding by error correcting pairs. We have used the multiplicative
+structure on toric codes to construct linear secret sharing schemes with
+\emph{strong multiplication} via Massey's construction generalizing the Shamir
+Linear secret sharing shemes constructed from Reed-Solomon codes. We have
+constructed quantum error correcting codes from toric surfaces by the
+Calderbank-Shor-Steane method.
+",1,0,1,0,0,0
+3423,Outlier Detection by Consistent Data Selection Method,"  Often the challenge associated with tasks like fraud and spam detection[1] is
+the lack of all likely patterns needed to train suitable supervised learning
+models. In order to overcome this limitation, such tasks are attempted as
+outlier or anomaly detection tasks. We also hypothesize that out- liers have
+behavioral patterns that change over time. Limited data and continuously
+changing patterns makes learning significantly difficult. In this work we are
+proposing an approach that detects outliers in large data sets by relying on
+data points that are consistent. The primary contribution of this work is that
+it will quickly help retrieve samples for both consistent and non-outlier data
+sets and is also mindful of new outlier patterns. No prior knowledge of each
+set is required to extract the samples. The method consists of two phases, in
+the first phase, consistent data points (non- outliers) are retrieved by an
+ensemble method of unsupervised clustering techniques and in the second phase a
+one class classifier trained on the consistent data point set is ap- plied on
+the remaining sample set to identify the outliers. The approach is tested on
+three publicly available data sets and the performance scores are competitive.
+",1,0,0,1,0,0
+3424,"Basic quantizations of $D=4$ Euclidean, Lorentz, Kleinian and quaternionic $\mathfrak{o}^{\star}(4)$ symmetries","  We construct firstly the complete list of five quantum deformations of $D=4$
+complex homogeneous orthogonal Lie algebra $\mathfrak{o}(4;\mathbb{C})\cong
+\mathfrak{o}(3;\mathbb{C})\oplus \mathfrak{o}(3;\mathbb{C})$, describing
+quantum rotational symmetry of four-dimensional complex space-time, in
+particular we provide the corresponding universal quantum $R$-matrices. Further
+applying four possible reality conditions we obtain all sixteen Hopf-algebraic
+quantum deformations for the real forms of $\mathfrak{o}(4;\mathbb{C})$:
+Euclidean $\mathfrak{o}(4)$, Lorentz $\mathfrak{o}(3,1)$, Kleinian
+$\mathfrak{o}(2,2)$ and quaternionic $\mathfrak{o}^{\star}(4)$. For
+$\mathfrak{o}(3,1)$ we only recall well-known results obtained previously by
+the authors, but for other real Lie algebras (Euclidean, Kleinian,
+quaternionic) as well as for the complex Lie algebra
+$\mathfrak{o}(4;\mathbb{C})$ we present new results.
+",0,0,1,0,0,0
+3425,Supervising Unsupervised Learning with Evolutionary Algorithm in Deep Neural Network,"  A method to control results of gradient descent unsupervised learning in a
+deep neural network by using evolutionary algorithm is proposed. To process
+crossover of unsupervisedly trained models, the algorithm evaluates pointwise
+fitness of individual nodes in neural network. Labeled training data is
+randomly sampled and breeding process selects nodes by calculating degree of
+their consistency on different sets of sampled data. This method supervises
+unsupervised training by evolutionary process. We also introduce modified
+Restricted Boltzmann Machine which contains repulsive force among nodes in a
+neural network and it contributes to isolate network nodes each other to avoid
+accidental degeneration of nodes by evolutionary process. These new methods are
+applied to document classification problem and it results better accuracy than
+a traditional fully supervised classifier implemented with linear regression
+algorithm.
+",0,0,0,1,0,0
+3426,Inductive Representation Learning in Large Attributed Graphs,"  Graphs (networks) are ubiquitous and allow us to model entities (nodes) and
+the dependencies (edges) between them. Learning a useful feature representation
+from graph data lies at the heart and success of many machine learning tasks
+such as classification, anomaly detection, link prediction, among many others.
+Many existing techniques use random walks as a basis for learning features or
+estimating the parameters of a graph model for a downstream prediction task.
+Examples include recent node embedding methods such as DeepWalk, node2vec, as
+well as graph-based deep learning algorithms. However, the simple random walk
+used by these methods is fundamentally tied to the identity of the node. This
+has three main disadvantages. First, these approaches are inherently
+transductive and do not generalize to unseen nodes and other graphs. Second,
+they are not space-efficient as a feature vector is learned for each node which
+is impractical for large graphs. Third, most of these approaches lack support
+for attributed graphs.
+To make these methods more generally applicable, we propose a framework for
+inductive network representation learning based on the notion of attributed
+random walk that is not tied to node identity and is instead based on learning
+a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute
+vector $\mathrm{\rm \bf x}$ to a type $w$. This framework serves as a basis for
+generalizing existing methods such as DeepWalk, node2vec, and many other
+previous methods that leverage traditional random walks.
+",1,0,0,1,0,0
+3427,Evidence for Two Hot Jupiter Formation Paths,"  Disk migration and high-eccentricity migration are two well-studied theories
+to explain the formation of hot Jupiters. The former predicts that these
+planets can migrate up until the planet-star Roche separation ($a_{Roche}$) and
+the latter predicts they will tidally circularize at a minimum distance of
+2$a_{Roche}$. Considering long-running radial velocity and transit surveys have
+identified a couple hundred hot Jupiters to date, we can revisit the classic
+question of hot Jupiter formation in a data-driven manner. We approach this
+problem using data from several exoplanet surveys (radial velocity, Kepler,
+HAT, and WASP) allowing for either a single population or a mixture of
+populations associated with these formation channels, and applying a
+hierarchical Bayesian mixture model of truncated power laws of the form
+$x^{\gamma-1}$ to constrain the population-level parameters of interest (e.g.,
+location of inner edges, $\gamma$, mixture fractions). Within the limitations
+of our chosen models, we find the current radial velocity and Kepler sample of
+hot Jupiters can be well explained with a single truncated power law
+distribution with a lower cutoff near 2$a_{Roche}$, a result that still holds
+after a decade, and $\gamma=-0.51\pm^{0.19}_{0.20}$. However, the HAT and WASP
+data show evidence for multiple populations (Bayes factor $\approx 10^{21}$).
+We find that $15\pm^{9}_{6}\%$ reside in a component consistent with disk
+migration ($\gamma=-0.04\pm^{0.53}_{1.27}$) and $85\pm^{6}_{9}\%$ in one
+consistent with high-eccentricity migration ($\gamma=-1.38\pm^{0.32}_{0.47}$).
+We find no immediately strong connections with some observed host star
+properties and speculate on how future exoplanet surveys could improve upon hot
+Jupiter population inference.
+",0,1,0,0,0,0
+3428,High-resolution Spectroscopy and Spectropolarimetry of Selected Delta Scuti Pulsating Variables,"  The combination of photometry, spectroscopy and spectropolarimetry of the
+chemically peculiar stars often aims to study the complex physical phenomena
+such as stellar pulsation, chemical inhomogeneity, magnetic field and their
+interplay with stellar atmosphere and circumstellar environment. The prime
+objective of the present study is to determine the atmospheric parameters of a
+set of Am stars to understand their evolutionary status. Atmospheric abundances
+and basic parameters are determined using full spectrum fitting technique by
+comparing the high-resolution spectra to the synthetic spectra. To know the
+evolutionary status we derive the effective temperature and luminosity from
+different methods and compare them with the literature. The location of these
+stars in the H-R diagram demonstrate that all the sample stars are evolved from
+the Zero-Age-Main-Sequence towards Terminal-Age-Main-Sequence and occupy the
+region of $\delta$ Sct instability strip. The abundance analysis shows that the
+light elements e.g. Ca and Sc are underabundant while iron peak elements such
+as Ba, Ce etc. are overabundant and these chemical properties are typical for
+Am stars. The results obtained from the spectropolarimetric analysis shows that
+the longitudinal magnetic fields in all the studied stars are negligible that
+gives further support their Am class of peculiarity.
+",0,1,0,0,0,0
+3429,Majority and Minority Voted Redundancy for Safety-Critical Applications,"  A new majority and minority voted redundancy (MMR) scheme is proposed that
+can provide the same degree of fault tolerance as N-modular redundancy (NMR)
+but with fewer function units and a less sophisticated voting logic. Example
+NMR and MMR circuits were implemented using a 32/28nm CMOS process and
+compared. The results show that MMR circuits dissipate less power, occupy less
+area, and encounter less critical path delay than the corresponding NMR
+circuits while providing the same degree of fault tolerance. Hence the MMR is a
+promising alternative to the NMR to efficiently implement high levels of
+redundancy in safety-critical applications.
+",1,0,0,0,0,0
+3430,Tight Analysis for the 3-Majority Consensus Dynamics,"  We present a tight analysis for the well-studied randomized 3-majority
+dynamics of stabilizing consensus, hence answering the main open question of
+Becchetti et al. [SODA'16].
+Consider a distributed system of n nodes, each initially holding an opinion
+in {1, 2, ..., k}. The system should converge to a setting where all
+(non-corrupted) nodes hold the same opinion. This consensus opinion should be
+\emph{valid}, meaning that it should be among the initially supported opinions,
+and the (fast) convergence should happen even in the presence of a malicious
+adversary who can corrupt a bounded number of nodes per round and in particular
+modify their opinions. A well-studied distributed algorithm for this problem is
+the 3-majority dynamics, which works as follows: per round, each node gathers
+three opinions --- say by taking its own and two of other nodes sampled at
+random --- and then it sets its opinion equal to the majority of this set; ties
+are broken arbitrarily, e.g., towards the node's own opinion.
+Becchetti et al. [SODA'16] showed that the 3-majority dynamics converges to
+consensus in O((k^2\sqrt{\log n} + k\log n)(k+\log n)) rounds, even in the
+presence of a limited adversary. We prove that, even with a stronger adversary,
+the convergence happens within O(k\log n) rounds. This bound is known to be
+optimal.
+",1,0,0,0,0,0
+3431,Large-scale chromosome folding versus genomic DNA sequences: A discrete double Fourier transform technique,"  Using state-of-the-art techniques combining imaging methods and
+high-throughput genomic mapping tools leaded to the significant progress in
+detailing chromosome architecture of various organisms. However, a gap still
+remains between the rapidly growing structural data on the chromosome folding
+and the large-scale genome organization. Could a part of information on the
+chromosome folding be obtained directly from underlying genomic DNA sequences
+abundantly stored in the databanks? To answer this question, we developed an
+original discrete double Fourier transform (DDFT). DDFT serves for the
+detection of large-scale genome regularities associated with domains/units at
+the different levels of hierarchical chromosome folding. The method is
+versatile and can be applied to both genomic DNA sequences and corresponding
+physico-chemical parameters such as base-pairing free energy. The latter
+characteristic is closely related to the replication and transcription and can
+also be used for the assessment of temperature or supercoiling effects on the
+chromosome folding. We tested the method on the genome of Escherichia coli K-12
+and found good correspondence with the annotated domains/units established
+experimentally. As a brief illustration of further abilities of DDFT, the study
+of large-scale genome organization for bacteriophage PHIX174 and bacterium
+Caulobacter crescentus was also added. The combined experimental, modeling, and
+bioinformatic DDFT analysis should yield more complete knowledge on the
+chromosome architecture and genome organization.
+",0,1,0,0,0,0
+3432,Superlinear scaling in the urban system of England of Wales. A comparison with US cities,"  According to the theory of urban scaling, urban indicators scale with city
+size in a predictable fashion. In particular, indicators of social and economic
+productivity are expected to have a superlinear relation. This behavior was
+verified for many urban systems, but recent findings suggest that this pattern
+may not be valid for England and Wales (E&W), where income has a linear
+relation with city size. This finding raises the question of whether the cities
+of E&W exhibit any superlinear relation with respect to quantities such as the
+level of education and occupational groups. In this paper, we evaluate the
+scaling of educational and occupational groups of E&W to see if we can detect
+superlinear relations in the number of educated and better-paid persons. As E&W
+may be unique in its linear scaling of income, we complement our analysis by
+comparing it to the urban system of the United States (US), a country for which
+superlinear scaling of income has already been demonstrated. To make the two
+urban systems comparable, we define the urban systems of both countries using
+the same method and test the sensitivity of our results to changes in the
+boundaries of cities. We find that cities of E&W exhibit patterns of
+superlinear scaling with respect to education and certain categories of
+better-paid occupations. However, the tendency of such groups to have
+superlinear scaling seems to be more consistent in the US. We show that while
+the educational and occupational distributions of US cities can partly explain
+the superlinear scaling of earnings, the distribution leads to a linear scaling
+of earnings in E&W.
+",0,1,0,0,0,0
+3433,Extensile actomyosin?,"  Living cells move thanks to assemblies of actin filaments and myosin motors
+that range from very organized striated muscle tissue to disordered
+intracellular bundles. The mechanisms powering these disordered structures are
+debated, and all models studied so far predict that they are contractile. We
+reexamine this prediction through a theoretical treatment of the interplay of
+three well-characterized internal dynamical processes in actomyosin bundles:
+actin treadmilling, the attachement-detachment dynamics of myosin and that of
+crosslinking proteins. We show that these processes enable an extensive control
+of the bundle's active mechanics, including reversals of the filaments'
+apparent velocities and the possibility of generating extension instead of
+contraction. These effects offer a new perspective on well-studied in vivo
+systems, as well as a robust criterion to experimentally elucidate the
+underpinnings of actomyosin activity.
+",0,1,0,0,0,0
+3434,Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities,"  There is an increasing interest in exploiting mobile sensing technologies and
+machine learning techniques for mental health monitoring and intervention.
+Researchers have effectively used contextual information, such as mobility,
+communication and mobile phone usage patterns for quantifying individuals' mood
+and wellbeing. In this paper, we investigate the effectiveness of neural
+network models for predicting users' level of stress by using the location
+information collected by smartphones. We characterize the mobility patterns of
+individuals using the GPS metrics presented in the literature and employ these
+metrics as input to the network. We evaluate our approach on the open-source
+StudentLife dataset. Moreover, we discuss the challenges and trade-offs
+involved in building machine learning models for digital mental health and
+highlight potential future work in this direction.
+",1,0,0,1,0,0
+3435,Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning,"  Cardiac indices estimation is of great importance during identification and
+diagnosis of cardiac disease in clinical routine. However, estimation of
+multitype cardiac indices with consistently reliable and high accuracy is still
+a great challenge due to the high variability of cardiac structures and
+complexity of temporal dynamics in cardiac MR sequences. While efforts have
+been devoted into cardiac volumes estimation through feature engineering
+followed by a independent regression model, these methods suffer from the
+vulnerable feature representation and incompatible regression model. In this
+paper, we propose a semi-automated method for multitype cardiac indices
+estimation. After manual labelling of two landmarks for ROI cropping, an
+integrated deep neural network Indices-Net is designed to jointly learn the
+representation and regression models. It comprises two tightly-coupled
+networks: a deep convolution autoencoder (DCAE) for cardiac image
+representation, and a multiple output convolution neural network (CNN) for
+indices regression. Joint learning of the two networks effectively enhances the
+expressiveness of image representation with respect to cardiac indices, and the
+compatibility between image representation and indices regression, thus leading
+to accurate and reliable estimations for all the cardiac indices.
+When applied with five-fold cross validation on MR images of 145 subjects,
+Indices-Net achieves consistently low estimation error for LV wall thicknesses
+(1.44$\pm$0.71mm) and areas of cavity and myocardium (204$\pm$133mm$^2$). It
+outperforms, with significant error reductions, segmentation method (55.1% and
+17.4%) and two-phase direct volume-only methods (12.7% and 14.6%) for wall
+thicknesses and areas, respectively. These advantages endow the proposed method
+a great potential in clinical cardiac function assessment.
+",1,0,0,0,0,0
+3436,Noncommutative hyperbolic metrics,"  We characterize certain noncommutative domains in terms of noncommutative
+holomorphic equivalence via a pseudometric that we define in purely algebraic
+terms. We prove some properties of this pseudometric and provide an application
+to free probability.
+",0,0,1,0,0,0
+3437,Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing,"  We consider the problem of estimating a regression function in the common
+situation where the number of features is small, where interpretability of the
+model is a high priority, and where simple linear or additive models fail to
+provide adequate performance. To address this problem, we present Maximum
+Variance Total Variation denoising (MVTV), an approach that is conceptually
+related both to CART and to the more recent CRISP algorithm, a state-of-the-art
+alternative method for interpretable nonlinear regression. MVTV divides the
+feature space into blocks of constant value and fits the value of all blocks
+jointly via a convex optimization routine. Our method is fully data-adaptive,
+in that it incorporates highly robust routines for tuning all hyperparameters
+automatically. We compare our approach against CART and CRISP via both a
+complexity-accuracy tradeoff metric and a human study, demonstrating that that
+MVTV is a more powerful and interpretable method.
+",0,0,0,1,0,0
+3438,Basin stability for chimera states,"  Chimera states, namely complex spatiotemporal patterns that consist of
+coexisting domains of spatially coherent and incoherent dynamics, are
+investigated in a network of coupled identical oscillators. These intriguing
+spatiotemporal patterns were first reported in nonlocally coupled phase
+oscillators, and it was shown that such mixed type behavior occurs only for
+specific initial conditions in nonlocally and globally coupled networks. The
+influence of initial conditions on chimera states has remained a fundamental
+problem since their discovery. In this report, we investigate the robustness of
+chimera states together with incoherent and coherent states in dependence on
+the initial conditions. For this, we use the basin stability method which is
+related to the volume of the basin of attraction, and we consider nonlocally
+and globally coupled time-delayed Mackey-Glass oscillators as example.
+Previously, it was shown that the existence of chimera states can be
+characterized by mean phase velocity and a statistical measure, such as the
+strength of incoherence, by using well prepared initial conditions. Here we
+show further how the coexistence of different dynamical states can be
+identified and quantified by means of the basin stability measure over a wide
+range of the parameter space.
+",0,1,0,0,0,0
+3439,On the Usage of Databases of Educational Materials in Macedonian Education,"  Technologies have become important part of our lives. The steps for
+introducing ICTs in education vary from country to country. The Republic of
+Macedonia has invested with a lot in installment of hardware and software in
+education and in teacher training. This research was aiming to determine the
+situation of usage of databases of digital educational materials and to define
+recommendation for future improvements. Teachers from urban schools were
+interviewed with a questionnaire. The findings are several: only part of the
+interviewed teachers had experience with databases of educational materials;
+all teachers still need capacity building activities focusing exactly on the
+use and benefits from databases of educational materials; preferably capacity
+building materials to be in Macedonian language; technical support and
+upgrading of software and materials should be performed on a regular basis.
+Most of the findings can be applied at both national and international level -
+with all this implemented, application of ICT in education will have much
+bigger positive impact.
+",1,0,0,0,0,0
+3440,Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching,"  We study the neural-linear bandit model for solving sequential
+decision-making problems with high dimensional side information. Neural-linear
+bandits leverage the representation power of deep neural networks and combine
+it with efficient exploration mechanisms, designed for linear contextual
+bandits, on top of the last hidden layer. Since the representation is being
+optimized during learning, information regarding exploration with ""old""
+features is lost. Here, we propose the first limited memory neural-linear
+bandit that is resilient to this phenomenon, which we term catastrophic
+forgetting. We evaluate our method on a variety of real-world data sets,
+including regression, classification, and sentiment analysis, and observe that
+our algorithm is resilient to catastrophic forgetting and achieves superior
+performance.
+",1,0,0,1,0,0
+3441,Breaking mean-motion resonances during Type I planet migration,"  We present two-dimensional hydrodynamical simulations of pairs of planets
+migrating simultaneously in the Type I regime in a protoplanetary disc.
+Convergent migration naturally leads to the trapping of these planets in
+mean-motion resonances. Once in resonance the planets' eccentricity grows
+rapidly, and disc-planet torques cause the planets to escape resonance on a
+time-scale of a few hundred orbits. The effect is more pronounced in highly
+viscous discs, but operates efficiently even in inviscid discs. We attribute
+this resonance-breaking to overstable librations driven by moderate
+eccentricity damping, but find that this mechanism operates differently in
+hydrodynamic simulations than in previous analytic calculations. Planets
+escaping resonance in this manner can potentially explain the observed paucity
+of resonances in Kepler multi-transiting systems, and we suggest that
+simultaneous disc-driven migration remains the most plausible means of
+assembling tightly-packed planetary systems.
+",0,1,0,0,0,0
+3442,The Stretch to Stray on Time: Resonant Length of Random Walks in a Transient,"  First-passage times in random walks have a vast number of diverse
+applications in physics, chemistry, biology, and finance. In general,
+environmental conditions for a stochastic process are not constant on the time
+scale of the average first-passage time, or control might be applied to reduce
+noise. We investigate moments of the first-passage time distribution under a
+transient describing relaxation of environmental conditions. We solve the
+Laplace-transformed (generalized) master equation analytically using a novel
+method that is applicable to general state schemes. The first-passage time from
+one end to the other of a linear chain of states is our application for the
+solutions. The dependence of its average on the relaxation rate obeys a power
+law for slow transients. The exponent $\nu$ depends on the chain length $N$
+like $\nu=-N/(N+1)$ to leading order. Slow transients substantially reduce the
+noise of first-passage times expressed as the coefficient of variation (CV),
+even if the average first-passage time is much longer than the transient. The
+CV has a pronounced minimum for some lengths, which we call resonant lengths.
+These results also suggest a simple and efficient noise control strategy, and
+are closely related to the timing of repetitive excitations, coherence
+resonance and information transmission by noisy excitable systems. A resonant
+number of steps from the inhibited state to the excitation threshold and slow
+recovery from negative feedback provide optimal timing noise reduction and
+information transmission.
+",0,0,0,0,1,1
+3443,Hierarchical Model for Long-term Video Prediction,"  Video prediction has been an active topic of research in the past few years.
+Many algorithms focus on pixel-level predictions, which generates results that
+blur and disintegrate within a few frames. In this project, we use a
+hierarchical approach for long-term video prediction. We aim at estimating
+high-level structure in the input frame first, then predict how that structure
+grows in the future. Finally, we use an image analogy network to recover a
+realistic image from the predicted structure. Our method is largely adopted
+from the work by Villegas et al. The method is built with a combination of
+LSTMs and analogy-based convolutional auto-encoder networks. Additionally, in
+order to generate more realistic frame predictions, we also adopt adversarial
+loss. We evaluate our method on the Penn Action dataset, and demonstrate good
+results on high-level long-term structure prediction.
+",1,0,0,0,0,0
+3444,Classical Music Clustering Based on Acoustic Features,"  In this paper we cluster 330 classical music pieces collected from MusicNet
+database based on their musical note sequence. We use shingling and chord
+trajectory matrices to create signature for each music piece and performed
+spectral clustering to find the clusters. Based on different resolution, the
+output clusters distinctively indicate composition from different classical
+music era and different composing style of the musicians.
+",1,0,0,0,0,0
+3445,Many-Body-Localization : Strong Disorder perturbative approach for the Local Integrals of Motion,"  For random quantum spin models, the strong disorder perturbative expansion of
+the Local Integrals of Motion (LIOMs) around the real-spin operators is
+revisited. The emphasis is on the links with other properties of the
+Many-Body-Localized phase, in particular the memory in the dynamics of the
+local magnetizations and the statistics of matrix elements of local operators
+in the eigenstate basis. Finally, this approach is applied to analyze the
+Many-Body-Localization transition in a toy model studied previously from the
+point of view of the entanglement entropy.
+",0,1,0,0,0,0
+3446,Semi-supervised Learning for Discrete Choice Models,"  We introduce a semi-supervised discrete choice model to calibrate discrete
+choice models when relatively few requests have both choice sets and stated
+preferences but the majority only have the choice sets. Two classic
+semi-supervised learning algorithms, the expectation maximization algorithm and
+the cluster-and-label algorithm, have been adapted to our choice modeling
+problem setting. We also develop two new algorithms based on the
+cluster-and-label algorithm. The new algorithms use the Bayesian Information
+Criterion to evaluate a clustering setting to automatically adjust the number
+of clusters. Two computational studies including a hotel booking case and a
+large-scale airline itinerary shopping case are presented to evaluate the
+prediction accuracy and computational effort of the proposed algorithms.
+Algorithmic recommendations are rendered under various scenarios.
+",0,0,0,1,0,0
+3447,An Analytic Criterion for Turbulent Disruption of Planetary Resonances,"  Mean motion commensurabilities in multi-planet systems are an expected
+outcome of protoplanetary disk-driven migration, and their relative dearth in
+the observational data presents an important challenge to current models of
+planet formation and dynamical evolution. One natural mechanism that can lead
+to the dissolution of commensurabilities is stochastic orbital forcing, induced
+by turbulent density fluctuations within the nebula. While this process is
+qualitatively promising, the conditions under which mean motion resonances can
+be broken are not well understood. In this work, we derive a simple analytic
+criterion that elucidates the relationship among the physical parameters of the
+system, and find the conditions necessary to drive planets out of resonance.
+Subsequently, we confirm our findings with numerical integrations carried out
+in the perturbative regime, as well as direct N-body simulations. Our
+calculations suggest that turbulent resonance disruption depends most
+sensitively on the planet-star mass ratio. Specifically, for a disk with
+properties comparable to the early solar nebula with $\alpha=0.01$, only planet
+pairs with cumulative mass ratios smaller than
+$(m_1+m_2)/M\lesssim10^{-5}\sim3M_{\oplus}/M_{\odot}$ are susceptible to
+breaking resonance at semi-major axis of order $a\sim0.1\,$AU. Although
+turbulence can sometimes compromise resonant pairs, an additional mechanism
+(such as suppression of resonance capture probability through disk
+eccentricity) is required to adequately explain the largely non-resonant
+orbital architectures of extrasolar planetary systems.
+",0,1,1,0,0,0
+3448,Electronic structure of transferred graphene/h-BN van der Waals heterostructures with nonzero stacking angles by nano-ARPES,"  In van der Waals heterostructures, the periodic potential from the Moiré
+superlattice can be used as a control knob to modulate the electronic structure
+of the constituent materials. Here we present a nanoscale angle-resolved
+photoemission spectroscopy (Nano-ARPES) study of transferred graphene/h-BN
+heterostructures with two different stacking angles of 2.4° and 4.3°
+respectively. Our measurements reveal six replicas of graphene Dirac cones at
+the superlattice Brillouin zone (SBZ) centers. The size of the SBZ and its
+relative rotation angle to the graphene BZ are in good agreement with Moiré
+superlattice period extracted from atomic force microscopy (AFM) measurements.
+Comparison to epitaxial graphene/h-BN with 0° stacking angles suggests
+that the interaction between graphene and h-BN decreases with increasing
+stacking angle.
+",0,1,0,0,0,0
+3449,Automatic sequences and generalised polynomials,"  We conjecture that bounded generalised polynomial functions cannot be
+generated by finite automata, except for the trivial case when they are
+ultimately periodic.
+Using methods from ergodic theory, we are able to partially resolve this
+conjecture, proving that any hypothetical counterexample is periodic away from
+a very sparse and structured set.
+In particular, we show that for a polynomial $p(n)$ with at least one
+irrational coefficient (except for the constant one) and integer $m\geq 2$, the
+sequence $\lfloor p(n) \rfloor \bmod{m}$ is never automatic.
+We also prove that the conjecture is equivalent to the claim that the set of
+powers of an integer $k\geq 2$ is not given by a generalised polynomial.
+",1,0,1,0,0,0
+3450,Robust Regulation of Infinite-Dimensional Port-Hamiltonian Systems,"  We will give general sufficient conditions under which a controller achieves
+robust regulation for a boundary control and observation system. Utilizing
+these conditions we construct a minimal order robust controller for an
+arbitrary order impedance passive linear port-Hamiltonian system. The
+theoretical results are illustrated with a numerical example where we implement
+a controller for a one-dimensional Euler-Bernoulli beam with boundary controls
+and boundary observations.
+",0,0,1,0,0,0
+3451,Existence of travelling waves and high activation energy limits for a onedimensional thermo-diffusive lean spray flame model,"  We provide a mathematical analysis of a thermo-diffusive combustion model of
+lean spray flames, for which we prove the existence of travelling waves. In the
+high activation energy singular limit we show the existence of two distinct
+combustion regimes with a sharp transition -- the diffusion limited regime and
+the vaporisation controlled regime. The latter is specific to spray flames with
+slow enough vaporisation. We give a complete characterisation of these regimes,
+including explicit velocities, profiles, and upper estimate of the size of the
+internal combustion layer. Our model is on the one hand simple enough to allow
+for explicit asymptotic limits and on the other hand rich enough to capture
+some particular aspects of spray combustion. Finally, we briefly discuss the
+cases where the vaporisation is infinitely fast, or where the spray is
+polydisperse.
+",0,0,1,0,0,0
+3452,The Motion of Small Bodies in Space-time,"  We consider the motion of small bodies in general relativity. The key result
+captures a sense in which such bodies follow timelike geodesics (or, in the
+case of charged bodies, Lorentz-force curves). This result clarifies the
+relationship between approaches that model such bodies as distributions
+supported on a curve, and those that employ smooth fields supported in small
+neighborhoods of a curve. This result also applies to ""bodies"" constructed from
+wave packets of Maxwell or Klein-Gordon fields. There follows a simple and
+precise formulation of the optical limit for Maxwell fields.
+",0,1,1,0,0,0
+3453,Extracting urban impervious surface from GF-1 imagery using one-class classifiers,"  Impervious surface area is a direct consequence of the urbanization, which
+also plays an important role in urban planning and environmental management.
+With the rapidly technical development of remote sensing, monitoring urban
+impervious surface via high spatial resolution (HSR) images has attracted
+unprecedented attention recently. Traditional multi-classes models are
+inefficient for impervious surface extraction because it requires labeling all
+needed and unneeded classes that occur in the image exhaustively. Therefore, we
+need to find a reliable one-class model to classify one specific land cover
+type without labeling other classes. In this study, we investigate several
+one-class classifiers, such as Presence and Background Learning (PBL), Positive
+Unlabeled Learning (PUL), OCSVM, BSVM and MAXENT, to extract urban impervious
+surface area using high spatial resolution imagery of GF-1, China's new
+generation of high spatial remote sensing satellite, and evaluate the
+classification accuracy based on artificial interpretation results. Compared to
+traditional multi-classes classifiers (ANN and SVM), the experimental results
+indicate that PBL and PUL provide higher classification accuracy, which is
+similar to the accuracy provided by ANN model. Meanwhile, PBL and PUL
+outperforms OCSVM, BSVM, MAXENT and SVM models. Hence, the one-class
+classifiers only need a small set of specific samples to train models without
+losing predictive accuracy, which is supposed to gain more attention on urban
+impervious surface extraction or other one specific land cover type.
+",1,0,0,0,0,0
+3454,Positive and nodal single-layered solutions to supercritical elliptic problems above the higher critical exponents,"  We study the problem% \[ -\Delta v+\lambda v=| v| ^{p-2}v\text{ in }\Omega
+,\text{\qquad}v=0\text{ on $\partial\Omega$},\text{ }% \] for
+$\lambda\in\mathbb{R}$ and supercritical exponents $p,$ in domains of the form%
+\[ \Omega:=\{(y,z)\in\mathbb{R}^{N-m-1}\times\mathbb{R}^{m+1}:(y,| z|
+)\in\Theta\}, \] where $m\geq1,$ $N-m\geq3,$ and $\Theta$ is a bounded domain
+in $\mathbb{R}% ^{N-m}$ whose closure is contained in
+$\mathbb{R}^{N-m-1}\times(0,\infty)$. Under some symmetry assumptions on
+$\Theta$, we show that this problem has infinitely many solutions for every
+$\lambda$ in an interval which contains $[0,\infty)$ and $p>2$ up to some
+number which is larger than the $(m+1)^{st}$ critical exponent
+$2_{N,m}^{\ast}:=\frac{2(N-m)}{N-m-2}$. We also exhibit domains with a
+shrinking hole, in which there are a positive and a nodal solution which
+concentrate on a sphere, developing a single layer that blows up at an
+$m$-dimensional sphere contained in the boundary of $\Omega,$ as the hole
+shrinks and $p\rightarrow2_{N,m}^{\ast}$ from above. The limit profile of the
+positive solution, in the transversal direction to the sphere of concentration,
+is a rescaling of the standard bubble, whereas that of the nodal solution is a
+rescaling of a nonradial sign-changing solution to the problem% \[ -\Delta u=|
+u| ^{2_{n}^{\ast}-2}u,\text{\qquad}u\in D^{1,2}(\mathbb{R}^{n}), \] where
+$2_{n}^{\ast}:=\frac{2n}{n-2}$ is the critical exponent in dimension
+$n.$\medskip
+",0,0,1,0,0,0
+3455,"Robust, Deep and Inductive Anomaly Detection","  PCA is a classical statistical technique whose simplicity and maturity has
+seen it find widespread use as an anomaly detection technique. However, it is
+limited in this regard by being sensitive to gross perturbations of the input,
+and by seeking a linear subspace that captures normal behaviour. The first
+issue has been dealt with by robust PCA, a variant of PCA that explicitly
+allows for some data points to be arbitrarily corrupted, however, this does not
+resolve the second issue, and indeed introduces the new issue that one can no
+longer inductively find anomalies on a test set. This paper addresses both
+issues in a single model, the robust autoencoder. This method learns a
+nonlinear subspace that captures the majority of data points, while allowing
+for some data to have arbitrary corruption. The model is simple to train and
+leverages recent advances in the optimisation of deep neural networks.
+Experiments on a range of real-world datasets highlight the model's
+effectiveness.
+",1,0,0,1,0,0
+3456,Understanding Organizational Approach towards End User Privacy,"  End user privacy is a critical concern for all organizations that collect,
+process and store user data as a part of their business. Privacy concerned
+users, regulatory bodies and privacy experts continuously demand organizations
+provide users with privacy protection. Current research lacks an understanding
+of organizational characteristics that affect an organization's motivation
+towards user privacy. This has resulted in a ""one solution fits all"" approach,
+which is incapable of providing sustainable solutions for organizational issues
+related to user privacy. In this work, we have empirically investigated 40
+diverse organizations on their motivations and approaches towards user privacy.
+Resources such as newspaper articles, privacy policies and internal privacy
+reports that display information about organizational motivations and
+approaches towards user privacy were used in the study. We could observe
+organizations to have two primary motivations to provide end users with privacy
+as voluntary driven inherent motivation, and risk driven compliance motivation.
+Building up on these findings we developed a taxonomy of organizational privacy
+approaches and further explored the taxonomy through limited exclusive
+interviews. With his work, we encourage authorities and scholars to understand
+organizational characteristics that define an organization's approach towards
+privacy, in order to effectively communicate regulations that enforce and
+encourage organizations to consider privacy within their business practices.
+",1,0,0,0,0,0
+3457,Baryonic impact on the dark matter orbital properties of Milky Way-sized haloes,"  We study the orbital properties of dark matter haloes by combining a spectral
+method and cosmological simulations of Milky Way-sized galaxies. We compare the
+dynamics and orbits of individual dark matter particles from both hydrodynamic
+and $N$-body simulations, and find that the fraction of box, tube and resonant
+orbits of the dark matter halo decreases significantly due to the effects of
+baryons. In particular, the central region of the dark matter halo in the
+hydrodynamic simulation is dominated by regular, short-axis tube orbits, in
+contrast to the chaotic, box and thin orbits dominant in the $N$-body run. This
+leads to a more spherical dark matter halo in the hydrodynamic run compared to
+a prolate one as commonly seen in the $N$-body simulations. Furthermore, by
+using a kernel based density estimator, we compare the coarse-grained
+phase-space densities of dark matter haloes in both simulations and find that
+it is lower by $\sim0.5$ dex in the hydrodynamic run due to changes in the
+angular momentum distribution, which indicates that the baryonic process that
+affects the dark matter is irreversible. Our results imply that baryons play an
+important role in determining the shape, kinematics and phase-space density of
+dark matter haloes in galaxies.
+",0,1,0,0,0,0
+3458,Extreme Value Analysis Without the Largest Values: What Can Be Done?,"  In this paper we are concerned with the analysis of heavy-tailed data when a
+portion of the extreme values is unavailable. This research was motivated by an
+analysis of the degree distributions in a large social network. The degree
+distributions of such networks tend to have power law behavior in the tails. We
+focus on the Hill estimator, which plays a starring role in heavy-tailed
+modeling. The Hill estimator for this data exhibited a smooth and increasing
+""sample path"" as a function of the number of upper order statistics used in
+constructing the estimator. This behavior became more apparent as we
+artificially removed more of the upper order statistics. Building on this
+observation we introduce a new version of the Hill estimator. It is a function
+of the number of the upper order statistics used in the estimation, but also
+depends on the number of unavailable extreme values. We establish functional
+convergence of the normalized Hill estimator to a Gaussian process. An
+estimation procedure is developed based on the limit theory to estimate the
+number of missing extremes and extreme value parameters including the tail
+index and the bias of Hill's estimator. We illustrate how this approach works
+in both simulations and real data examples.
+",0,0,1,0,0,0
+3459,Controlling light in complex media beyond the acoustic diffraction-limit using the acousto-optic transmission matrix,"  Studying the internal structure of complex samples with light is an important
+task, but a difficult challenge due to light scattering. While the complex
+optical distortions induced by multiple scattering can be effectively undone
+with the knowledge of the medium's scattering-matrix, this matrix is generally
+unknown, and cannot be measured with high resolution without the presence of
+fluorescent or absorbing probes at all points of interest. To overcome these
+limitations, we introduce here the concept of the acousto-optic transmission
+matrix (AOTM). Taking advantage of the near scattering-free propagation of
+ultrasound in complex samples, we noninvasively measure an
+ultrasonically-encoded, spatially-resolved, optical scattering-matrix. We
+demonstrate that a singular value decomposition analysis of the AOTM, acquired
+using a single or multiple ultrasonic beams, allows controlled optical focusing
+beyond the acoustic diffraction limit in scattering media. Our approach
+provides a generalized framework for analyzing acousto-optical experiments, and
+for noninvasive, high-resolution study of complex media.
+",0,1,0,0,0,0
+3460,Uniqueness and stability of Ricci flow through singularities,"  We verify a conjecture of Perelman, which states that there exists a
+canonical Ricci flow through singularities starting from an arbitrary compact
+Riemannian 3-manifold. Our main result is a uniqueness theorem for such flows,
+which, together with an earlier existence theorem of Lott and the second named
+author, implies Perelman's conjecture. We also show that this flow through
+singularities depends continuously on its initial condition and that it may be
+obtained as a limit of Ricci flows with surgery.
+Our results have applications to the study of diffeomorphism groups of three
+manifolds --- in particular to the Generalized Smale Conjecture --- which will
+appear in a subsequent paper.
+",0,0,1,0,0,0
+3461,Reciprocal space engineering with hyperuniform gold metasurfaces,"  Hyperuniform geometries feature correlated disordered topologies which follow
+from a tailored k-space design. Here we study gold plasmonic hyperuniform
+metasurfaces and we report evidence of the effectiveness of k-space engineering
+on both light scattering and light emission experiments. The metasurfaces
+possess interesting directional emission properties which are revealed by
+momentum spectroscopy as diffraction and fluorescence emission rings at
+size-specific k-vectors. The opening of these rotational-symmetric patterns
+scales with the hyperuniform correlation length parameter as predicted via the
+spectral function method.
+",0,1,0,0,0,0
+3462,STFT spectral loss for training a neural speech waveform model,"  This paper proposes a new loss using short-time Fourier transform (STFT)
+spectra for the aim of training a high-performance neural speech waveform model
+that predicts raw continuous speech waveform samples directly. Not only
+amplitude spectra but also phase spectra obtained from generated speech
+waveforms are used to calculate the proposed loss. We also mathematically show
+that training of the waveform model on the basis of the proposed loss can be
+interpreted as maximum likelihood training that assumes the amplitude and phase
+spectra of generated speech waveforms following Gaussian and von Mises
+distributions, respectively. Furthermore, this paper presents a simple network
+architecture as the speech waveform model, which is composed of uni-directional
+long short-term memories (LSTMs) and an auto-regressive structure. Experimental
+results showed that the proposed neural model synthesized high-quality speech
+waveforms.
+",1,0,0,0,0,0
+3463,Spectral Properties of Continuum Fibonacci Schrödinger Operators,"  We study continuum Schrödinger operators on the real line whose potentials
+are comprised of two compactly supported square-integrable functions
+concatenated according to an element of the Fibonacci substitution subshift
+over two letters. We show that the Hausdorff dimension of the spectrum tends to
+one in the small-coupling and high-energy regimes, regardless of the shape of
+the potential pieces.
+",0,0,1,0,0,0
+3464,Species tree inference from genomic sequences using the log-det distance,"  The log-det distance between two aligned DNA sequences was introduced as a
+tool for statistically consistent inference of a gene tree under simple
+non-mixture models of sequence evolution. Here we prove that the log-det
+distance, coupled with a distance-based tree construction method, also permits
+consistent inference of species trees under mixture models appropriate to
+aligned genomic-scale sequences data. Data may include sites from many genetic
+loci, which evolved on different gene trees due to incomplete lineage sorting
+on an ultrametric species tree, with different time-reversible substitution
+processes. The simplicity and speed of distance-based inference suggests
+log-det based methods should serve as benchmarks for judging more elaborate and
+computationally-intensive species trees inference methods.
+",0,0,0,0,1,0
+3465,Structure preserving schemes for nonlinear Fokker-Planck equations and applications,"  In this paper we focus on the construction of numerical schemes for nonlinear
+Fokker-Planck equations that preserve the structural properties, like non
+negativity of the solution, entropy dissipation and large time behavior. The
+methods here developed are second order accurate, they do not require any
+restriction on the mesh size and are capable to capture the asymptotic steady
+states with arbitrary accuracy. These properties are essential for a correct
+description of the underlying physical problem. Applications of the schemes to
+several nonlinear Fokker-Planck equations with nonlocal terms describing
+emerging collective behavior in socio-economic and life sciences are presented.
+",0,1,1,0,0,0
+3466,A Markov decision process approach to optimizing cancer therapy using multiple modalities,"  There are several different modalities, e.g., surgery, chemotherapy, and
+radiotherapy, that are currently used to treat cancer. It is common practice to
+use a combination of these modalities to maximize clinical outcomes, which are
+often measured by a balance between maximizing tumor damage and minimizing
+normal tissue side effects due to treatment. However, multi-modality treatment
+policies are mostly empirical in current practice, and are therefore subject to
+individual clinicians' experiences and intuition. We present a novel
+formulation of optimal multi-modality cancer management using a finite-horizon
+Markov decision process approach. Specifically, at each decision epoch, the
+clinician chooses an optimal treatment modality based on the patient's observed
+state, which we define as a combination of tumor progression and normal tissue
+side effect. Treatment modalities are categorized as (1) Type 1, which has a
+high risk and high reward, but is restricted in the frequency of administration
+during a treatment course, (2) Type 2, which has a lower risk and lower reward
+than Type 1, but may be repeated without restriction, and (3) Type 3, no
+treatment (surveillance), which has the possibility of reducing normal tissue
+side effect at the risk of worsening tumor progression. Numerical simulations
+using various intuitive, concave reward functions show the structural insights
+of optimal policies and demonstrate the potential applications of using a
+rigorous approach to optimizing multi-modality cancer management.
+",0,1,1,0,0,0
+3467,Complex Contagions with Timers,"  A great deal of effort has gone into trying to model social influence ---
+including the spread of behavior, norms, and ideas --- on networks. Most models
+of social influence tend to assume that individuals react to changes in the
+states of their neighbors without any time delay, but this is often not true in
+social contexts, where (for various reasons) different agents can have
+different response times. To examine such situations, we introduce the idea of
+a timer into threshold models of social influence. The presence of timers on
+nodes delays the adoption --- i.e., change of state --- of each agent, which in
+turn delays the adoptions of its neighbors. With a homogeneous-distributed
+timer, in which all nodes exhibit the same amount of delay, adoption delays are
+also homogeneous, so the adoption order of nodes remains the same. However,
+heterogeneously-distributed timers can change the adoption order of nodes and
+hence the ""adoption paths"" through which state changes spread in a network.
+Using a threshold model of social contagions, we illustrate that heterogeneous
+timers can either accelerate or decelerate the spread of adoptions compared to
+an analogous situation with homogeneous timers, and we investigate the
+relationship of such acceleration or deceleration with respect to timer
+distribution and network structure. We derive an analytical approximation for
+the temporal evolution of the fraction of adopters by modifying a pair
+approximation of the Watts threshold model, and we find good agreement with
+numerical computations. We also examine our new timer model on networks
+constructed from empirical data.
+",1,1,0,0,0,0
+3468,"Random Networks, Graphical Models, and Exchangeability","  We study conditional independence relationships for random networks and their
+interplay with exchangeability. We show that, for finitely exchangeable network
+models, the empirical subgraph densities are maximum likelihood estimates of
+their theoretical counterparts. We then characterize all possible Markov
+structures for finitely exchangeable random graphs, thereby identifying a new
+class of Markov network models corresponding to bidirected Kneser graphs. In
+particular, we demonstrate that the fundamental property of dissociatedness
+corresponds to a Markov property for exchangeable networks described by
+bidirected line graphs. Finally we study those exchangeable models that are
+also summarized in the sense that the probability of a network only depends
+onthe degree distribution, and identify a class of models that is dual to the
+Markov graphs of Frank and Strauss (1986). Particular emphasis is placed on
+studying consistency properties of network models under the process of forming
+subnetworks and we show that the only consistent systems of Markov properties
+correspond to the empty graph, the bidirected line graph of the complete graph,
+and the complete graph.
+",0,0,1,1,0,0
+3469,Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features,"  A current-aided inertial navigation framework is proposed for small
+autonomous underwater vehicles in long-duration operations (> 1 hour), where
+neither frequent surfacing nor consistent bottom-tracking are available. We
+instantiate this concept through mid-depth, underwater navigation. This
+strategy mitigates dead-reckoning uncertainty of a traditional inertial
+navigation system by comparing the estimate of local, ambient flow velocity
+with preloaded ocean current maps. The proposed navigation system is
+implemented through a marginalized particle filter where the vehicle's states
+are sequentially tracked along with sensor bias and local turbulence that is
+not resolved by general flow prediction. The performance of the proposed
+approach is first analyzed through Monte Carlo simulations in two artificial
+background flow fields, resembling real-world ocean circulation patterns,
+superposed with smaller-scale, turbulent components with Kolmogorov energy
+spectrum. The current-aided navigation scheme significantly improves the
+dead-reckoning performance of the vehicle even when unresolved, small-scale
+flow perturbations are present. For a 6-hour navigation with an
+automotive-grade inertial navigation system, the current-aided navigation
+scheme results in positioning estimates with under 3% uncertainty per distance
+traveled (UDT) in a turbulent, double-gyre flow field, and under 7.3% UDT in a
+turbulent, meandering jet flow field. Further evaluation with field test data
+and actual ocean simulation analysis demonstrates consistent performance for a
+6-hour mission, positioning result with under 25% UDT for a 24-hour navigation
+when provided direct heading measurements, and terminal positioning estimate
+with 16% UDT at the cost of increased uncertainty at an early stage of the
+navigation.
+",1,0,0,0,0,0
+3470,Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter,"  In February 2016, World Health Organization declared the Zika outbreak a
+Public Health Emergency of International Concern. With developing evidence it
+can cause birth defects, and the Summer Olympics coming up in the worst
+affected country, Brazil, the virus caught fire on social media. In this work,
+use Zika as a case study in building a tool for tracking the misinformation
+around health concerns on Twitter. We collect more than 13 million tweets --
+spanning the initial reports in February 2016 and the Summer Olympics --
+regarding the Zika outbreak and track rumors outlined by the World Health
+Organization and Snopes fact checking website. The tool pipeline, which
+incorporates health professionals, crowdsourcing, and machine learning, allows
+us to capture health-related rumors around the world, as well as clarification
+campaigns by reputable health organizations. In the case of Zika, we discover
+an extremely bursty behavior of rumor-related topics, and show that, once the
+questionable topic is detected, it is possible to identify rumor-bearing tweets
+using automated techniques. Thus, we illustrate insights the proposed tools
+provide into potentially harmful information on social media, allowing public
+health researchers and practitioners to respond with a targeted and timely
+action.
+",1,0,0,0,0,0
+3471,Monte Carlo determination of the low-energy constants for a two-dimensional spin-1 Heisenberg model with spatial anisotropy,"  The low-energy constants, namely the spin stiffness $\rho_s$, the staggered
+magnetization density ${\cal M}_s$ per area, and the spinwave velocity $c$ of
+the two-dimensional (2D) spin-1 Heisenberg model on the square and rectangular
+lattices are determined using the first principles Monte Carlo method. In
+particular, the studied models have antiferromagnetic couplings $J_1$ and $J_2$
+in the spatial 1- and 2-directions, respectively. For each considered
+$J_2/J_1$, the aspect ratio of the corresponding linear box sizes $L_2/L_1$
+used in the simulations is adjusted so that the squares of the two spatial
+winding numbers take the same values. In addition, the relevant finite-volume
+and -temperature predictions from magnon chiral perturbation theory are
+employed in extracting the numerical values of these low-energy constants. Our
+results of $\rho_{s1}$ are in quantitative agreement with those obtained by the
+series expansion method over a broad range of $J_2/J_1$. This in turn provides
+convincing numerical evidence for the quantitative correctness of our approach.
+The ${\cal M}_s$ and $c$ presented here for the spatially anisotropic models
+are new and can be used as benchmarks for future related studies.
+",0,1,0,0,0,0
+3472,Explaining Parochialism: A Causal Account for Political Polarization in Changing Economic Environments,"  Political and social polarization are a significant cause of conflict and
+poor governance in many societies, thus understanding their causes is of
+considerable importance. Here we demonstrate that shifts in socialization
+strategy similar to political polarization and/or identity politics could be a
+constructive response to periods of apparent economic decline. We start from
+the observation that economies, like ecologies are seldom at equilibrium.
+Rather, they often suffer both negative and positive shocks. We show that even
+where in an expanding economy, interacting with diverse out-groups can afford
+benefits through innovation and exploration, if that economy contracts, a
+strategy of seeking homogeneous groups can be important to maintaining
+individual solvency. This is true even where the expected value of out group
+interaction exceeds that of in group interactions. Our account unifies what
+were previously seen as conflicting explanations: identity threat versus
+economic anxiety. Our model indicates that in periods of extreme deprivation,
+cooperation with diversity again becomes the best (in fact, only viable)
+strategy. However, our model also shows that while polarization may increase
+gradually in response to shifts in the economy, gradual decrease of
+polarization may not be an available strategy; thus returning to previous
+levels of cooperation may require structural change.
+",0,0,0,0,1,1
+3473,A Secular Resonant Origin for the Loneliness of Hot Jupiters,"  Despite decades of inquiry, the origin of giant planets residing within a few
+tenths of an astronomical unit from their host stars remains unclear.
+Traditionally, these objects are thought to have formed further out before
+subsequently migrating inwards. However, the necessity of migration has been
+recently called into question with the emergence of in-situ formation models of
+close-in giant planets. Observational characterization of the transiting
+sub-sample of close-in giants has revealed that ""warm"" Jupiters, possessing
+orbital periods longer than roughly 10 days more often possess close-in,
+co-transiting planetary companions than shorter period ""hot"" Jupiters, that are
+usually lonely. This finding has previously been interpreted as evidence that
+smooth, early migration or in situ formation gave rise to warm Jupiter-hosting
+systems, whereas more violent, post-disk migration pathways sculpted hot
+Jupiter-hosting systems. In this work, we demonstrate that both classes of
+planet may arise via early migration or in-situ conglomeration, but that the
+enhanced loneliness of hot Jupiters arises due to a secular resonant
+interaction with the stellar quadrupole moment. Such an interaction tilts the
+orbits of exterior, lower mass planets, removing them from transit surveys
+where the hot Jupiter is detected. Warm Jupiter-hosting systems, in contrast,
+retain their coplanarity due to the weaker influence of the host star's
+quadrupolar potential relative to planet-disk interactions. In this way, hot
+Jupiters and warm Jupiters are placed within a unified theoretical framework
+that may be readily validated or falsified using data from upcoming missions
+such as TESS.
+",0,1,0,0,0,0
+3474,An Incremental Slicing Method for Functional Programs,"  Several applications of slicing require a program to be sliced with respect
+to more than one slicing criterion. Program specialization, parallelization and
+cohesion measurement are examples of such applications. These applications can
+benefit from an incremental static slicing method in which a significant extent
+of the computations for slicing with respect to one criterion could be reused
+for another. In this paper, we consider the problem of incremental slicing of
+functional programs. We first present a non-incremental version of the slicing
+algorithm which does a polyvariant analysis 1 of functions. Since polyvariant
+analyses tend to be costly, we compute a compact context-independent summary of
+each function and then use this summary at the call sites of the function. The
+construction of the function summary is non-trivial and helps in the
+development of the incremental version. The incremental method, on the other
+hand, consists of a one-time pre-computation step that uses the non-incremental
+version to slice the program with respect to a fixed default slicing criterion
+and processes the results further to a canonical form. Presented with an actual
+slicing criterion, the incremental step involves a low-cost computation that
+uses the results of the pre-computation to obtain the slice. We have
+implemented a prototype of the slicer for a pure subset of Scheme, with pairs
+and lists as the only algebraic data types. Our experiments show that the
+incremental step of the slicer runs orders of magnitude faster than the
+non-incremental version. We have also proved the correctness of our incremental
+algorithm with respect to the non-incremental version.
+",1,0,0,0,0,0
+3475,Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters,"  Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of
+particle collisions to build expectations of what experimental data may look
+like under different theory modeling assumptions. Petabytes of simulated data
+are needed to develop analysis techniques, though they are expensive to
+generate using existing algorithms and computing resources. The modeling of
+detectors and the precise description of particle cascades as they interact
+with the material in the calorimeter are the most computationally demanding
+steps in the simulation pipeline. We therefore introduce a deep neural
+network-based generative model to enable high-fidelity, fast, electromagnetic
+calorimeter simulation. There are still challenges for achieving precision
+across the entire phase space, but our current solution can reproduce a variety
+of particle shower properties while achieving speed-up factors of up to
+100,000$\times$. This opens the door to a new era of fast simulation that could
+save significant computing time and disk space, while extending the reach of
+physics searches and precision measurements at the LHC and beyond.
+",0,0,0,1,0,0
+3476,A study of ancient Khmer ephemerides,"  We study ancient Khmer ephemerides described in 1910 by the French engineer
+Faraut, in order to determine whether they rely on observations carried out in
+Cambodia. These ephemerides were found to be of Indian origin and have been
+adapted for another longitude, most likely in Burma. A method for estimating
+the date and place where the ephemerides were developed or adapted is described
+and applied.
+",0,1,1,0,0,0
+3477,Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure,"  Model-based compression is an effective, facilitating, and expanded model of
+neural network models with limited computing and low power. However,
+conventional models of compression techniques utilize crafted features [2,3,12]
+and explore specialized areas for exploration and design of large spaces in
+terms of size, speed, and accuracy, which usually have returns Less and time is
+up. This paper will effectively analyze deep auto compression (ADC) and
+reinforcement learning strength in an effective sample and space design, and
+improve the compression quality of the model. The results of compression of the
+advanced model are obtained without any human effort and in a completely
+automated way. With a 4- fold reduction in FLOP, the accuracy of 2.8% is higher
+than the manual compression model for VGG-16 in ImageNet.
+",0,0,0,1,0,0
+3478,Infinite Sparse Structured Factor Analysis,"  Matrix factorisation methods decompose multivariate observations as linear
+combinations of latent feature vectors. The Indian Buffet Process (IBP)
+provides a way to model the number of latent features required for a good
+approximation in terms of regularised reconstruction error. Previous work has
+focussed on latent feature vectors with independent entries. We extend the
+model to include nondiagonal latent covariance structures representing
+characteristics such as smoothness. This is done by . Using simulations we
+demonstrate that under appropriate conditions a smoothness prior helps to
+recover the true latent features, while denoising more accurately. We
+demonstrate our method on a real neuroimaging dataset, where computational
+tractability is a sufficient challenge that the efficient strategy presented
+here is essential.
+",0,0,0,1,0,0
+3479,RuntimeSearch: Ctrl+F for a Running Program,"  Developers often try to find occurrences of a certain term in a software
+system. Traditionally, a text search is limited to static source code files. In
+this paper, we introduce a simple approach, RuntimeSearch, where the given term
+is searched in the values of all string expressions in a running program. When
+a match is found, the program is paused and its runtime properties can be
+explored with a traditional debugger. The feasibility and usefulness of
+RuntimeSearch is demonstrated on a medium-sized Java project.
+",1,0,0,0,0,0
+3480,Accurate and Efficient Evaluation of Characteristic Modes,"  A new method to improve the accuracy and efficiency of characteristic mode
+(CM) decomposition for perfectly conducting bodies is presented. The method
+uses the expansion of the Green dyadic in spherical vector waves. This
+expansion is utilized in the method of moments (MoM) solution of the electric
+field integral equation to factorize the real part of the impedance matrix. The
+factorization is then employed in the computation of CMs, which improves the
+accuracy as well as the computational speed. An additional benefit is a rapid
+computation of far fields. The method can easily be integrated into existing
+MoM solvers. Several structures are investigated illustrating the improved
+accuracy and performance of the new method.
+",0,1,0,0,0,0
+3481,MUFASA: The assembly of the red sequence,"  We examine the growth and evolution of quenched galaxies in the Mufasa
+cosmological hydrodynamic simulations that include an evolving halo mass-based
+quenching prescription, with galaxy colours computed accounting for
+line-of-sight extinction to individual star particles. Mufasa reproduces the
+observed present-day red sequence reasonably well, including its slope,
+amplitude, and scatter. In Mufasa, the red sequence slope is driven entirely by
+the steep stellar mass-stellar metallicity relation, which independently agrees
+with observations. High-mass star-forming galaxies blend smoothly onto the red
+sequence, indicating the lack of a well-defined green valley at M*>10^10.5 Mo.
+The most massive galaxies quench the earliest and then grow very little in mass
+via dry merging; they attain their high masses at earlier epochs when cold
+inflows more effectively penetrate hot halos. To higher redshifts, the red
+sequence becomes increasingly contaminated with massive dusty star-forming
+galaxies; UVJ selection subtly but effectively separates these populations. We
+then examine the evolution of the mass functions of central and satellite
+galaxies split into passive and star-forming via UVJ. Massive quenched systems
+show good agreement with observations out to z~2, despite not including a rapid
+early quenching mode associated with mergers. However, low-mass quenched
+galaxies are far too numerous at z<1 in Mufasa, indicating that Mufasa strongly
+over-quenches satellites. A challenge for hydrodynamic simulations is to devise
+a quenching model that produces enough early massive quenched galaxies and
+keeps them quenched to z=0, while not being so strong as to over-quench
+satellites; Mufasa's current scheme fails at the latter.
+",0,1,0,0,0,0
+3482,Symmetric calorons and the rotation map,"  We study $SU(2)$ calorons, also known as periodic instantons, and consider
+invariance under isometries of $S^1\times\mathbb{R}^3$ coupled with a
+non-spatial isometry called the rotation map. In particular, we investigate the
+fixed points under various cyclic symmetry groups. Our approach utilises a
+construction akin to the ADHM construction of instantons -- what we call the
+monad matrix data for calorons -- derived from the work of Charbonneau and
+Hurtubise. To conclude, we present an example of how investigating these
+symmetry groups can help to construct new calorons by deriving Nahm data in the
+case of charge $2$.
+",0,0,1,0,0,0
+3483,Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks,"  Natural disasters can have catastrophic impacts on the functionality of
+infrastructure systems and cause severe physical and socio-economic losses.
+Given budget constraints, it is crucial to optimize decisions regarding
+mitigation, preparedness, response, and recovery practices for these systems.
+This requires accurate and efficient means to evaluate the infrastructure
+system reliability. While numerous research efforts have addressed and
+quantified the impact of natural disasters on infrastructure systems, typically
+using the Monte Carlo approach, they still suffer from high computational cost
+and, thus, are of limited applicability to large systems. This paper presents a
+deep learning framework for accelerating infrastructure system reliability
+analysis. In particular, two distinct deep neural network surrogates are
+constructed and studied: (1) A classifier surrogate which speeds up the
+connectivity determination of networks, and (2) An end-to-end surrogate that
+replaces a number of components such as roadway status realization,
+connectivity determination, and connectivity averaging. The proposed approach
+is applied to a simulation-based study of the two-terminal connectivity of a
+California transportation network subject to extreme probabilistic earthquake
+events. Numerical results highlight the effectiveness of the proposed approach
+in accelerating the transportation system two-terminal reliability analysis
+with extremely high prediction accuracy.
+",1,0,0,1,0,0
+3484,IMLS-SLAM: scan-to-model matching based on 3D data,"  The Simultaneous Localization And Mapping (SLAM) problem has been well
+studied in the robotics community, especially using mono, stereo cameras or
+depth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in the
+last 10 years to be very useful to perceive the environment in autonomous
+driving, but few methods exist that directly use these 3D data for odometry. We
+present a new low-drift SLAM algorithm based only on 3D LiDAR data. Our method
+relies on a scan-to-model matching framework. We first have a specific sampling
+strategy based on the LiDAR scans. We then define our model as the previous
+localized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surface
+representation. We show experiments with the Velodyne HDL32 with only 0.40%
+drift over a 4 km acquisition without any loop closure (i.e., 16 m drift after
+4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 and
+ranked among the best methods (against mono, stereo and LiDAR methods) with a
+global drift of only 0.69%.
+",1,0,0,0,0,0
+3485,Cooperative Online Learning: Keeping your Neighbors Updated,"  We study an asynchronous online learning setting with a network of agents. At
+each time step, some of the agents are activated, requested to make a
+prediction, and pay the corresponding loss. The loss function is then revealed
+to these agents and also to their neighbors in the network. When activations
+are stochastic, we show that the regret achieved by $N$ agents running the
+standard online Mirror Descent is $O(\sqrt{\alpha T})$, where $T$ is the
+horizon and $\alpha \le N$ is the independence number of the network. This is
+in contrast to the regret $\Omega(\sqrt{N T})$ which $N$ agents incur in the
+same setting when feedback is not shared. We also show a matching lower bound
+of order $\sqrt{\alpha T}$ that holds for any given network. When the pattern
+of agent activations is arbitrary, the problem changes significantly: we prove
+a $\Omega(T)$ lower bound on the regret that holds for any online algorithm
+oblivious to the feedback source.
+",1,0,0,1,0,0
+3486,FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis,"  Convolutional neural networks (CNNs) have massively impacted visual
+recognition in 2D images, and are now ubiquitous in state-of-the-art
+approaches. CNNs do not easily extend, however, to data that are not
+represented by regular grids, such as 3D shape meshes or other graph-structured
+data, to which traditional local convolution operators do not directly apply.
+To address this problem, we propose a novel graph-convolution operator to
+establish correspondences between filter weights and graph neighborhoods with
+arbitrary connectivity. The key novelty of our approach is that these
+correspondences are dynamically computed from features learned by the network,
+rather than relying on predefined static coordinates over the graph as in
+previous work. We obtain excellent experimental results that significantly
+improve over previous state-of-the-art shape correspondence results. This shows
+that our approach can learn effective shape representations from raw input
+coordinates, without relying on shape descriptors.
+",1,0,0,0,0,0
+3487,On a minimal counterexample to Brauer's $k(B)$-conjecture,"  We study Brauer's long-standing $k(B)$-conjecture on the number of characters
+in $p$-blocks for finite quasi-simple groups and show that their blocks do not
+occur as a minimal counterexample for $p\ge5$ nor in the case of abelian
+defect. For $p=3$ we obtain that the principal 3-blocks do not provide minimal
+counterexamples. We also determine the precise number of irreducible characters
+in unipotent blocks of classical groups for odd primes.
+",0,0,1,0,0,0
+3488,The best fit for the observed galaxy Counts-in-Cell distribution function,"  The Sloan Digital Sky Survey (SDSS) is the first dense redshift survey
+encompassing a volume large enough to find the best analytic probability
+density function that fits the galaxy Counts-in-Cells distribution $f_V(N)$,
+the frequency distribution of galaxy counts in a volume $V$. Different analytic
+functions have been previously proposed that can account for some of the
+observed features of the observed frequency counts, but fail to provide an
+overall good fit to this important statistical descriptor of the galaxy
+large-scale distribution. Our goal is to find the probability density function
+that better fits the observed Counts-in-Cells distribution $f_V(N)$. We have
+made a systematic study of this function applied to several samples drawn from
+the SDSS. We show the effective ways to deal with incompleteness of the sample
+(masked data) in the calculation of $f_V(N)$. We use LasDamas simulations to
+estimate the errors in the calculation. We test four different distribution
+functions to find the best fit: the Gravitational Quasi-Equilibrium
+distribution, the Negative Binomial Distribution, the Log Normal distribution
+and the Log Normal Distribution including a bias parameter. In the two latter
+cases, we apply a shot-noise correction to the distributions assuming the local
+Poisson model. We show that the best fit for the Counts-in-Cells distribution
+function is provided by the Negative Binomial distribution. In addition, at
+large scales the Log Normal distribution modified with the inclusion of the
+bias term also performs a satisfactory fit of the empirical values of $f_V(N)$.
+Our results demonstrate that the inclusion of a bias term in the Log Normal
+distribution is necessary to fit the observed galaxy Count-in-Cells
+distribution function.
+",0,1,0,0,0,0
+3489,Separator Reconnection at Earth's Dayside Magnetopause: MMS Observations Compared to Global Simulations,"  We compare a global high resolution resistive magnetohydrodynamics (MHD)
+simulation of Earth's magnetosphere with observations from the Magnetospheric
+Multiscale (MMS) constellation for a southward IMF magnetopause crossing during
+October 16, 2015 that was previously identified as an electron diffusion region
+(EDR) event. The simulation predicts a complex time-dependent magnetic topology
+consisting of multiple separators and flux ropes. Despite the topological
+complexity, the predicted distance between MMS and the primary separator is
+less than 0.5 Earth radii. These results suggest that global magnetic topology,
+rather than local magnetic geometry alone, determines the location of the
+electron diffusion region at the dayside magnetopause.
+",0,1,0,0,0,0
+3490,Semi-Supervised Overlapping Community Finding based on Label Propagation with Pairwise Constraints,"  Algorithms for detecting communities in complex networks are generally
+unsupervised, relying solely on the structure of the network. However, these
+methods can often fail to uncover meaningful groupings that reflect the
+underlying communities in the data, particularly when those structures are
+highly overlapping. One way to improve the usefulness of these algorithms is by
+incorporating additional background information, which can be used as a source
+of constraints to direct the community detection process. In this work, we
+explore the potential of semi-supervised strategies to improve algorithms for
+finding overlapping communities in networks. Specifically, we propose a new
+method, based on label propagation, for finding communities using a limited
+number of pairwise constraints. Evaluations on synthetic and real-world
+datasets demonstrate the potential of this approach for uncovering meaningful
+community structures in cases where each node can potentially belong to more
+than one community.
+",1,0,0,0,0,0
+3491,Designing Coalition-Proof Reverse Auctions over Continuous Goods,"  This paper investigates reverse auctions that involve continuous values of
+different types of goods, general nonconvex constraints, and second stage
+costs. We seek to design the payment rules and conditions under which
+coalitions of participants cannot influence the auction outcome in order to
+obtain higher collective utility. Under the incentive-compatible
+Vickrey-Clarke-Groves mechanism, we show that coalition-proof outcomes are
+achieved if the submitted bids are convex and the constraint sets are of a
+polymatroid-type. These conditions, however, do not capture the complexity of
+the general class of reverse auctions under consideration. By relaxing the
+property of incentive-compatibility, we investigate further payment rules that
+are coalition-proof without any extra conditions on the submitted bids and the
+constraint sets. Since calculating the payments directly for these mechanisms
+is computationally difficult for auctions involving many participants, we
+present two computationally efficient methods. Our results are verified with
+several case studies based on electricity market data.
+",1,0,0,0,0,0
+3492,Probabilistic Line Searches for Stochastic Optimization,"  In deterministic optimization, line searches are a standard tool ensuring
+stability and efficiency. Where only stochastic gradients are available, no
+direct equivalent has so far been formulated, because uncertain gradients do
+not allow for a strict sequence of decisions collapsing the search space. We
+construct a probabilistic line search by combining the structure of existing
+deterministic methods with notions from Bayesian optimization. Our method
+retains a Gaussian process surrogate of the univariate optimization objective,
+and uses a probabilistic belief over the Wolfe conditions to monitor the
+descent. The algorithm has very low computational cost, and no user-controlled
+parameters. Experiments show that it effectively removes the need to define a
+learning rate for stochastic gradient descent.
+",1,0,0,1,0,0
+3493,Quantum Phase transition under pressure in a heavily hydrogen-doped iron-based superconductor LaFeAsO,"  Hydrogen (H)-doped LaFeAsO is a prototypical iron-based superconductor.
+However, its phase diagram extends beyond the standard framework, where a
+superconducting (SC) phase follows an antiferromagnetic (AF) phase upon carrier
+doping; instead, the SC phase is sandwiched between two AF phases appearing in
+lightly and heavily H-doped regimes. We performed nuclear magnetic resonance
+(NMR) measurements under pressure, focusing on the second AF phase in the
+heavily H-doped regime. The second AF phase is strongly suppressed when a
+pressure of 3.0 GPa is applied, and apparently shifts to a highly H-doped
+regime, thereby a ""bare"" quantum critical point (QCP) emerges. A quantum
+critical regime emerges in a paramagnetic state near the QCP, however, the
+influence of the AF critical fluctuations to the SC phase is limited in the
+narrow doping regime near the QCP. The optimal SC condition ($T_c \sim$ 48 K)
+is unaffected by AF fluctuations.
+",0,1,0,0,0,0
+3494,Hyperboloidal similarity coordinates and a globally stable blowup profile for supercritical wave maps,"  We consider co-rotational wave maps from (1+3)-dimensional Minkowski space
+into the three-sphere. This model exhibits an explicit blowup solution and we
+prove the asymptotic nonlinear stability of this solution in the whole space
+under small perturbations of the initial data. The key ingredient is the
+introduction of a novel coordinate system that allows one to track the
+evolution past the blowup time and almost up to the Cauchy horizon of the
+singularity. As a consequence, we also obtain a result on continuation beyond
+blowup.
+",0,0,1,0,0,0
+3495,Supersymmetry in Closed Chains of Coupled Majorana Modes,"  We consider a closed chain of even number of Majorana zero modes with
+nearest-neighbour couplings which are different site by site generically, thus
+no any crystal symmetry. Instead, we demonstrate the possibility of an emergent
+supersymmetry (SUSY), which is accompanied by gapless Fermionic excitations. In
+particular, the condition can be easily satisfied by tuning only one coupling,
+regardless of how many other couplings are there. Such a system can be realized
+by four Majorana modes on two parallel Majorana nanowires with their ends
+connected by Josephson junctions and bodies connected by an external
+superconducting ring. By tuning the Josephson couplings with a magnetic flux
+$\Phi$ through the ring, we get the gapless excitations at $\Phi_{SUSY}=\pm
+f\Phi_0$ with $\Phi_0= hc/2e$, which is signaled by a zero-bias conductance
+peak in tunneling conductance. We find this $f$ generally a fractional number
+and oscillating with increasing Zeeman fields that parallel to the nanowires,
+which provide a unique experimental signature for the existence of Majorana
+modes.
+",0,1,0,0,0,0
+3496,Renormalized Hennings Invariants and 2+1-TQFTs,"  We construct non-semisimple $2+1$-TQFTs yielding mapping class group
+representations in Lyubashenko's spaces. In order to do this, we first
+generalize Beliakova, Blanchet and Geer's logarithmic Hennings invariants based
+on quantum $\mathfrak{sl}_2$ to the setting of finite-dimensional
+non-degenerate unimodular ribbon Hopf algebras. The tools used for this
+construction are a Hennings-augmented Reshetikhin-Turaev functor and modified
+traces. When the Hopf algebra is factorizable, we further show that the
+universal construction of Blanchet, Habegger, Masbaum and Vogel produces a
+$2+1$-TQFT on a not completely rigid monoidal subcategory of cobordisms.
+",0,0,1,0,0,0
+3497,On Improving Deep Reinforcement Learning for POMDPs,"  Deep Reinforcement Learning (RL) recently emerged as one of the most
+competitive approaches for learning in sequential decision making problems with
+fully observable environments, e.g., computer Go. However, very little work has
+been done in deep RL to handle partially observable environments. We propose a
+new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to
+enhance learning performance in partially observable domains. Actions are
+encoded by a fully connected layer and coupled with a convolutional observation
+to form an action-observation pair. The time series of action-observation pairs
+are then integrated by an LSTM layer that learns latent states based on which a
+fully connected layer computes Q-values as in conventional Deep Q-Networks
+(DQNs). We demonstrate the effectiveness of our new architecture in several
+partially observable domains, including flickering Atari games.
+",1,0,0,1,0,0
+3498,When to Invest in Security? Empirical Evidence and a Game-Theoretic Approach for Time-Based Security,"  Games of timing aim to determine the optimal defense against a strategic
+attacker who has the technical capability to breach a system in a stealthy
+fashion. Key questions arising are when the attack takes place, and when a
+defensive move should be initiated to reset the system resource to a known safe
+state.
+In our work, we study a more complex scenario called Time-Based Security in
+which we combine three main notions: protection time, detection time, and
+reaction time. Protection time represents the amount of time the attacker needs
+to execute the attack successfully. In other words, protection time represents
+the inherent resilience of the system against an attack. Detection time is the
+required time for the defender to detect that the system is compromised.
+Reaction time is the required time for the defender to reset the defense
+mechanisms in order to recreate a safe system state.
+In the first part of the paper, we study the VERIS Community Database (VCDB)
+and screen other data sources to provide insights into the actual timing of
+security incidents and responses. While we are able to derive distributions for
+some of the factors regarding the timing of security breaches, we assess the
+state-of-the-art regarding the collection of timing-related data as
+insufficient.
+In the second part of the paper, we propose a two-player game which captures
+the outlined Time-Based Security scenario in which both players move according
+to a periodic strategy. We carefully develop the resulting payoff functions,
+and provide theorems and numerical results to help the defender to calculate
+the best time to reset the defense mechanism by considering protection time,
+detection time, and reaction time.
+",1,0,0,0,0,0
+3499,Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models,"  Humans are not only adept in recognizing what class an input instance belongs
+to (i.e., classification task), but perhaps more remarkably, they can imagine
+(i.e., generate) plausible instances of a desired class with ease, when
+prompted. Inspired by this, we propose a framework which allows transforming
+Cascade-Correlation Neural Networks (CCNNs) into probabilistic generative
+models, thereby enabling CCNNs to generate samples from a category of interest.
+CCNNs are a well-known class of deterministic, discriminative NNs, which
+autonomously construct their topology, and have been successful in giving
+accounts for a variety of psychological phenomena. Our proposed framework is
+based on a Markov Chain Monte Carlo (MCMC) method, called the
+Metropolis-adjusted Langevin algorithm, which capitalizes on the gradient
+information of the target distribution to direct its explorations towards
+regions of high probability, thereby achieving good mixing properties. Through
+extensive simulations, we demonstrate the efficacy of our proposed framework.
+",1,0,0,1,0,0
+3500,"Semiclassical ""Divide-and-Conquer"" Method for Spectroscopic Calculations of High Dimensional Molecular Systems","  A new semiclassical ""divide-and-conquer"" method is presented with the aim of
+demonstrating that quantum dynamics simulations of high dimensional molecular
+systems are doable. The method is first tested by calculating the quantum
+vibrational power spectra of water, methane, and benzene - three molecules of
+increasing dimensionality for which benchmark quantum results are available -
+and then applied to C60, a system characterized by 174 vibrational degrees of
+freedom. Results show that the approach can accurately account for quantum
+anharmonicities, purely quantum features like overtones, and the removal of
+degeneracy when the molecular symmetry is broken.
+",0,1,0,0,0,0
+3501,Yangian Symmetry and Integrability of Planar N=4 Super-Yang-Mills Theory,"  In this letter we establish Yangian symmetry of planar N=4 super-Yang-Mills
+theory. We prove that the classical equations of motion of the model close onto
+themselves under the action of Yangian generators. Moreover we propose an
+off-shell extension of our statement which is equivalent to the invariance of
+the action and prove that it is exactly satisfied. We assert that our
+relationship serves as a criterion for integrability in planar gauge theories
+by explicitly checking that it applies to integrable ABJM theory but not to
+non-integrable N=1 super-Yang-Mills theory.
+",0,0,1,0,0,0
+3502,DaMaSCUS: The Impact of Underground Scatterings on Direct Detection of Light Dark Matter,"  Conventional dark matter direct detection experiments set stringent
+constraints on dark matter by looking for elastic scattering events between
+dark matter particles and nuclei in underground detectors. However these
+constraints weaken significantly in the sub-GeV mass region, simply because
+light dark matter does not have enough energy to trigger detectors regardless
+of the dark matter-nucleon scattering cross section. Even if future experiments
+lower their energy thresholds, they will still be blind to parameter space
+where dark matter particles interact with nuclei strongly enough that they lose
+enough energy and become unable to cause a signal above the experimental
+threshold by the time they reach the underground detector. Therefore in case
+dark matter is in the sub-GeV region and strongly interacting, possible
+underground scatterings of dark matter with terrestrial nuclei must be taken
+into account because they affect significantly the recoil spectra and event
+rates, regardless of whether the experiment probes DM via DM-nucleus or
+DM-electron interaction. To quantify this effect we present the publicly
+available Dark Matter Simulation Code for Underground Scatterings (DaMaSCUS), a
+Monte Carlo simulator of DM trajectories through the Earth taking underground
+scatterings into account. Our simulation allows the precise calculation of the
+density and velocity distribution of dark matter at any detector of given depth
+and location on Earth. The simulation can also provide the accurate recoil
+spectrum in underground detectors as well as the phase and amplitude of the
+diurnal modulation caused by this shadowing effect of the Earth, ultimately
+relating the modulations expected in different detectors, which is important to
+decisively conclude if a diurnal modulation is due to dark matter or an
+irrelevant background.
+",0,1,0,0,0,0
+3503,Allocation strategies for high fidelity models in the multifidelity regime,"  We propose a novel approach to allocating resources for expensive simulations
+of high fidelity models when used in a multifidelity framework. Allocation
+decisions that distribute computational resources across several simulation
+models become extremely important in situations where only a small number of
+expensive high fidelity simulations can be run. We identify this allocation
+decision as a problem in optimal subset selection, and subsequently regularize
+this problem so that solutions can be computed. Our regularized formulation
+yields a type of group lasso problem that has been studied in the literature to
+accomplish subset selection. Our numerical results compare performance of
+algorithms that solve the group lasso problem for algorithmic allocation
+against a variety of other strategies, including those based on classical
+linear algebraic pivoting routines and those derived from more modern machine
+learning-based methods. We demonstrate on well known synthetic problems and
+more difficult real-world simulations that this group lasso solution to the
+relaxed optimal subset selection problem performs better than the alternatives.
+",1,0,0,0,0,0
+3504,Simplicial Closure and higher-order link prediction,"  Networks provide a powerful formalism for modeling complex systems by using a
+model of pairwise interactions. But much of the structure within these systems
+involves interactions that take place among more than two nodes at once; for
+example, communication within a group rather than person-to person,
+collaboration among a team rather than a pair of coauthors, or biological
+interaction between a set of molecules rather than just two. Such higher-order
+interactions are ubiquitous, but their empirical study has received limited
+attention, and little is known about possible organizational principles of such
+structures. Here we study the temporal evolution of 19 datasets with explicit
+accounting for higher-order interactions. We show that there is a rich variety
+of structure in our datasets but datasets from the same system types have
+consistent patterns of higher-order structure. Furthermore, we find that tie
+strength and edge density are competing positive indicators of higher-order
+organization, and these trends are consistent across interactions involving
+differing numbers of nodes. To systematically further the study of theories for
+such higher-order structures, we propose higher-order link prediction as a
+benchmark problem to assess models and algorithms that predict higher-order
+structure. We find a fundamental differences from traditional pairwise link
+prediction, with a greater role for local rather than long-range information in
+predicting the appearance of new interactions.
+",1,0,0,1,0,0
+3505,Basic concepts and tools for the Toki Pona minimal and constructed language: description of the language and main issues; analysis of the vocabulary; text synthesis and syntax highlighting; Wordnet synsets,"  A minimal constructed language (conlang) is useful for experiments and
+comfortable for making tools. The Toki Pona (TP) conlang is minimal both in the
+vocabulary (with only 14 letters and 124 lemmas) and in the (about) 10 syntax
+rules. The language is useful for being a used and somewhat established minimal
+conlang with at least hundreds of fluent speakers. This article exposes current
+concepts and resources for TP, and makes available Python (and Vim) scripted
+routines for the analysis of the language, synthesis of texts, syntax
+highlighting schemes, and the achievement of a preliminary TP Wordnet. Focus is
+on the analysis of the basic vocabulary, as corpus analyses were found. The
+synthesis is based on sentence templates, relates to context by keeping track
+of used words, and renders larger texts by using a fixed number of phonemes
+(e.g. for poems) and number of sentences, words and letters (e.g. for
+paragraphs). Syntax highlighting reflects morphosyntactic classes given in the
+official dictionary and different solutions are described and implemented in
+the well-established Vim text editor. The tentative TP Wordnet is made
+available in three patterns of relations between synsets and word lemmas. In
+summary, this text holds potentially novel conceptualizations about, and tools
+and results in analyzing, synthesizing and syntax highlighting the TP language.
+",1,0,0,0,0,0
+3506,Spectral Methods for Nonparametric Models,"  Nonparametric models are versatile, albeit computationally expensive, tool
+for modeling mixture models. In this paper, we introduce spectral methods for
+the two most popular nonparametric models: the Indian Buffet Process (IBP) and
+the Hierarchical Dirichlet Process (HDP). We show that using spectral methods
+for the inference of nonparametric models are computationally and statistically
+efficient. In particular, we derive the lower-order moments of the IBP and the
+HDP, propose spectral algorithms for both models, and provide reconstruction
+guarantees for the algorithms. For the HDP, we further show that applying
+hierarchical models on dataset with hierarchical structure, which can be solved
+with the generalized spectral HDP, produces better solutions to that of flat
+models regarding likelihood performance.
+",1,0,0,1,0,0
+3507,"Setting the threshold for high throughput detectors: A mathematical approach for ensembles of dynamic, heterogeneous, probabilistic anomaly detectors","  Anomaly detection (AD) has garnered ample attention in security research, as
+such algorithms complement existing signature-based methods but promise
+detection of never-before-seen attacks. Cyber operations manage a high volume
+of heterogeneous log data; hence, AD in such operations involves multiple
+(e.g., per IP, per data type) ensembles of detectors modeling heterogeneous
+characteristics (e.g., rate, size, type) often with adaptive online models
+producing alerts in near real time. Because of high data volume, setting the
+threshold for each detector in such a system is an essential yet underdeveloped
+configuration issue that, if slightly mistuned, can leave the system useless,
+either producing a myriad of alerts and flooding downstream systems, or giving
+none. In this work, we build on the foundations of Ferragut et al. to provide a
+set of rigorous results for understanding the relationship between threshold
+values and alert quantities, and we propose an algorithm for setting the
+threshold in practice. Specifically, we give an algorithm for setting the
+threshold of multiple, heterogeneous, possibly dynamic detectors completely a
+priori, in principle. Indeed, if the underlying distribution of the incoming
+data is known (closely estimated), the algorithm provides provably manageable
+thresholds. If the distribution is unknown (e.g., has changed over time) our
+analysis reveals how the model distribution differs from the actual
+distribution, indicating a period of model refitting is necessary. We provide
+empirical experiments showing the efficacy of the capability by regulating the
+alert rate of a system with $\approx$2,500 adaptive detectors scoring over 1.5M
+events in 5 hours. Further, we demonstrate on the real network data and
+detection framework of Harshaw et al. the alternative case, showing how the
+inability to regulate alerts indicates the detection model is a bad fit to the
+data.
+",1,0,0,1,0,0
+3508,Photonic Loschmidt echo in binary waveguide lattices,"  Time reversal is one of the most intriguing yet elusive wave phenomenon of
+major interest in different areas of classical and quantum physics. Time
+reversal requires in principle to flip the sign of the Hamiltonian of the
+system, leading to a revival of the initial state (Loschmidt echo). Here it is
+shown that Loschmidt echo of photons can be observed in an optical setting
+without resorting to reversal of the Hamiltonian. We consider photonic
+propagation in a binary waveguide lattice and show that, by exchanging the two
+sublattices after some propagation distance, a Loschmidt echo can be observed.
+Examples of Loschmidt echoes for single photon and NOON states are given in
+one- and two-dimensional waveguide lattices.
+",0,1,0,0,0,0
+3509,Proof Reduction of Fair Stuttering Refinement of Asynchronous Systems and Applications,"  We present a series of definitions and theorems demonstrating how to reduce
+the requirements for proving system refinements ensuring containment of fair
+stuttering runs. A primary result of the work is the ability to reduce the
+requisite proofs on runs of a system of interacting state machines to a set of
+definitions and checks on single steps of a small number of state machines
+corresponding to the intuitive notions of freedom from starvation and deadlock.
+We further refine the definitions to afford an efficient explicit-state
+checking procedure in certain finite state cases. We demonstrate the proof
+reduction on versions of the Bakery Algorithm.
+",1,0,0,0,0,0
+3510,Steady Galactic Dynamos and Observational Consequences I: Halo Magnetic Fields,"  We study the global consequences in the halos of spiral galaxies of the
+steady, axially symmetric, mean field dynamo. We use the classical theory but
+add the possibility of using the velocity field components as parameters in
+addition to the helicity and diffusivity. The analysis is based on the simplest
+version of the theory and uses scale-invariant solutions. The velocity field
+(subject to restrictions) is a scale invariant field in a `pattern' frame, in
+place of a full dynamical theory. The `pattern frame' of reference may either
+be the systemic frame or some rigidly rotating spiral pattern frame. One type
+of solution for the magnetic field yields off-axis, spirally wound, magnetic
+field lines. These predict sign changes in the Faraday screen rotation measure
+in every quadrant of the halo of an edge-on galaxy. Such rotation measure
+oscillations have been observed in the CHANG-ES survey.
+",0,1,0,0,0,0
+3511,Efficient Decision Trees for Multi-class Support Vector Machines Using Entropy and Generalization Error Estimation,"  We propose new methods for Support Vector Machines (SVMs) using tree
+architecture for multi-class classi- fication. In each node of the tree, we
+select an appropriate binary classifier using entropy and generalization error
+estimation, then group the examples into positive and negative classes based on
+the selected classi- fier and train a new classifier for use in the
+classification phase. The proposed methods can work in time complexity between
+O(log2N) to O(N) where N is the number of classes. We compared the performance
+of our proposed methods to the traditional techniques on the UCI machine
+learning repository using 10-fold cross-validation. The experimental results
+show that our proposed methods are very useful for the problems that need fast
+classification time or problems with a large number of classes as the proposed
+methods run much faster than the traditional techniques but still provide
+comparable accuracy.
+",1,0,0,1,0,0
+3512,Semiclassical measures on hyperbolic surfaces have full support,"  We show that each limiting semiclassical measure obtained from a sequence of
+eigenfunctions of the Laplacian on a compact hyperbolic surface is supported on
+the entire cosphere bundle. The key new ingredient for the proof is the fractal
+uncertainty principle, first formulated in [arXiv:1504.06589] and proved for
+porous sets in [arXiv:1612.09040].
+",0,1,1,0,0,0
+3513,Assessment of algorithms for computing moist available potential energy,"  Atmospheric moist available potential energy (MAPE) has been traditionally
+defined as the potential energy of a moist atmosphere relative to that of the
+adiabatically sorted reference state defining a global potential energy
+minimum. Finding such a reference state was recently shown to be a linear
+assignment problem, and therefore exactly solvable. However, this is
+computationally extremely expensive, so there has been much interest in
+developing heuristic methods for computing MAPE in practice. Comparisons of the
+accuracy of such approximate algorithms have so far been limited to a small
+number of test cases; this work provides an assessment of the algorithms'
+performance across a wide range of atmospheric soundings, in two different
+locations. We determine that the divide-and-conquer algorithm is the best
+suited to practical application, but suffers from the previously overlooked
+shortcoming that it can produce a reference state with higher potential energy
+than the actual state, resulting in a negative value of MAPE. Additionally, we
+show that it is possible to construct an algorithm exploiting a theoretical
+expression linking MAPE to Convective Available Potential Energy (CAPE)
+previously derived by Kerry Emanuel. This approach has a similar accuracy to
+existing approximate sorting algorithms, whilst providing greater insight into
+the physical source of MAPE. In light of these results, we discuss how to make
+progress towards constructing a satisfactory moist APE theory for the
+atmosphere. We also outline a method for vectorising the adiabatic lifting of
+moist air parcels, which increases the computational efficiency of algorithms
+for calculating MAPE, and could be used for other applications such as
+convection schemes.
+",0,1,0,0,0,0
+3514,A Zero Knowledge Sumcheck and its Applications,"  Many seminal results in Interactive Proofs (IPs) use algebraic techniques
+based on low-degree polynomials, the study of which is pervasive in theoretical
+computer science. Unfortunately, known methods for endowing such proofs with
+zero knowledge guarantees do not retain this rich algebraic structure.
+In this work, we develop algebraic techniques for obtaining zero knowledge
+variants of proof protocols in a way that leverages and preserves their
+algebraic structure. Our constructions achieve unconditional (perfect) zero
+knowledge in the Interactive Probabilistically Checkable Proof (IPCP) model of
+Kalai and Raz [KR08] (the prover first sends a PCP oracle, then the prover and
+verifier engage in an Interactive Proof in which the verifier may query the
+PCP).
+Our main result is a zero knowledge variant of the sumcheck protocol [LFKN92]
+in the IPCP model. The sumcheck protocol is a key building block in many IPs,
+including the protocol for polynomial-space computation due to Shamir [Sha92],
+and the protocol for parallel computation due to Goldwasser, Kalai, and
+Rothblum [GKR15]. A core component of our result is an algebraic commitment
+scheme, whose hiding property is guaranteed by algebraic query complexity lower
+bounds [AW09,JKRS09]. This commitment scheme can then be used to considerably
+strengthen our previous work [BCFGRS16] that gives a sumcheck protocol with
+much weaker zero knowledge guarantees, itself using algebraic techniques based
+on algorithms for polynomial identity testing [RS05,BW04].
+We demonstrate the applicability of our techniques by deriving zero knowledge
+variants of well-known protocols based on algebraic techniques, including the
+protocols of Shamir and of Goldwasser, Kalai, and Rothblum, as well as the
+protocol of Babai, Fortnow, and Lund [BFL91].
+",1,0,0,0,0,0
+3515,Exact diagonalization and cluster mean-field study of triangular-lattice XXZ antiferromagnets near saturation,"  Quantum magnetic phases near the magnetic saturation of triangular-lattice
+antiferromagnets with XXZ anisotropy have been attracting renewed interest
+since it has been suggested that a nontrivial coplanar phase, called the
+$\pi$-coplanar or $\Psi$ phase, could be stabilized by quantum effects in a
+certain range of anisotropy parameter $J/J_z$ besides the well-known 0-coplanar
+(known also as $V$) and umbrella phases. Recently, Sellmann $et$ $al$. [Phys.
+Rev. B {\bf 91}, 081104(R) (2015)] claimed that the $\pi$-coplanar phase is
+absent for $S=1/2$ from an exact-diagonalization analysis in the sector of the
+Hilbert space with only three down-spins (three magnons). We first reconsider
+and improve this analysis by taking into account several low-lying eigenvalues
+and the associated eigenstates as a function of $J/J_z$ and by sensibly
+increasing the system sizes (up to 1296 spins). A careful identification
+analysis shows that the lowest eigenstate is a chirally antisymmetric
+combination of finite-size umbrella states for $J/J_z\gtrsim 2.218$ while it
+corresponds to a coplanar phase for $J/J_z\lesssim 2.218$. However, we
+demonstrate that the distinction between 0-coplanar and $\pi$-coplanar phases
+in the latter region is fundamentally impossible from the symmetry-preserving
+finite-size calculations with fixed magnon number.} Therefore, we also perform
+a cluster mean-field plus scaling analysis for small spins $S\leq 3/2$. The
+obtained results, together with the previous large-$S$ analysis, indicate that
+the $\pi$-coplanar phase exists for any $S$ except for the classical limit
+($S\rightarrow \infty$) and the existence range in $J/J_z$ is largest in the
+most quantum case of $S=1/2$.
+",0,1,0,0,0,0
+3516,Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions,"  We consider the problem of finding local minimizers in non-convex and
+non-smooth optimization. Under the assumption of strict saddle points, positive
+results have been derived for first-order methods. We present the first known
+results for the non-smooth case, which requires different analysis and a
+different algorithm.
+",1,0,0,1,0,0
+3517,"Don't Panic! Better, Fewer, Syntax Errors for LR Parsers","  Syntax errors are generally easy to fix for humans, but not for parsers, in
+general, and LR parsers, in particular. Traditional 'panic mode' error
+recovery, though easy to implement and applicable to any grammar, often leads
+to a cascading chain of errors that drown out the original. More advanced error
+recovery techniques suffer less from this problem but have seen little
+practical use because their typical performance was seen as poor, their worst
+case unbounded, and the repairs they reported arbitrary. In this paper we show
+two generic error recovery algorithms that fix all three problems. First, our
+algorithms are the first to report the complete set of possible repair
+sequences for a given location, allowing programmers to select the one that
+best fits their intention. Second, on a corpus of 200,000 real-world
+syntactically invalid Java programs, we show that our best performing algorithm
+is able to repair 98.71% of files within a cut-off of 0.5s. Furthermore, we are
+also able to use the complete set of repair sequences to reduce the cascading
+error problem even further than previous approaches. Our best performing
+algorithm reports 442,252.0 error locations in the corpus to the user, while
+the panic mode algorithm reports 980,848.0 error locations: in other words, our
+algorithms reduce the cascading error problem by well over half.
+",1,0,0,0,0,0
+3518,Predicting Tomorrow's Headline using Today's Twitter Deliberations,"  Predicting the popularity of news article is a challenging task. Existing
+literature mostly focused on article contents and polarity to predict
+popularity. However, existing research has not considered the users' preference
+towards a particular article. Understanding users' preference is an important
+aspect for predicting the popularity of news articles. Hence, we consider the
+social media data, from the Twitter platform, to address this research gap. In
+our proposed model, we have considered the users' involvement as well as the
+users' reaction towards an article to predict the popularity of the article. In
+short, we are predicting tomorrow's headline by probing today's Twitter
+discussion. We have considered 300 political news article from the New York
+Post, and our proposed approach has outperformed other baseline models.
+",1,0,0,0,0,0
+3519,On a problem of Bharanedhar and Ponnusamy involving planar harmonic mappings,"  In this paper, we give a negative answer to a problem presented by
+Bharanedhar and Ponnusamy (Rocky Mountain J. Math. 44: 753--777, 2014)
+concerning univalency of a class of harmonic mappings. More precisely, we show
+that for all values of the involved parameter, this class contains a
+non-univalent function. Moreover, several results on a new subclass of
+close-to-convex harmonic mappings, which is motivated by work of Ponnusamy and
+Sairam Kaliraj (Mediterr. J. Math. 12: 647--665, 2015), are obtained.
+",0,0,1,0,0,0
+3520,Projecting UK Mortality using Bayesian Generalised Additive Models,"  Forecasts of mortality provide vital information about future populations,
+with implications for pension and health-care policy as well as for decisions
+made by private companies about life insurance and annuity pricing. Stochastic
+mortality forecasts allow the uncertainty in mortality predictions to be taken
+into consideration when making policy decisions and setting product prices.
+Longer lifespans imply that forecasts of mortality at ages 90 and above will
+become more important in such calculations.
+This paper presents a Bayesian approach to the forecasting of mortality that
+jointly estimates a Generalised Additive Model (GAM) for mortality for the
+majority of the age-range and a parametric model for older ages where the data
+are sparser. The GAM allows smooth components to be estimated for age, cohort
+and age-specific improvement rates, together with a non-smoothed period effect.
+Forecasts for the United Kingdom are produced using data from the Human
+Mortality Database spanning the period 1961-2013. A metric that approximates
+predictive accuracy under Leave-One-Out cross-validation is used to estimate
+weights for the `stacking' of forecasts with different points of transition
+between the GAM and parametric elements.
+Mortality for males and females are estimated separately at first, but a
+joint model allows the asymptotic limit of mortality at old ages to be shared
+between sexes, and furthermore provides for forecasts accounting for
+correlations in period innovations. The joint and single sex model forecasts
+estimated using data from 1961-2003 are compared against observed data from
+2004-2013 to facilitate model assessment.
+",0,0,0,1,0,0
+3521,The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning,"  Machine learning is usually defined in behaviourist terms, where external
+validation is the primary mechanism of learning. In this paper, I argue for a
+more holistic interpretation in which finding more probable, efficient and
+abstract representations is as central to learning as performance. In other
+words, machine learning should be extended with strategies to reason over its
+own learning process, leading to so-called meta-cognitive machine learning. As
+such, the de facto definition of machine learning should be reformulated in
+these intrinsically multi-objective terms, taking into account not only the
+task performance but also internal learning objectives. To this end, we suggest
+a ""model entropy function"" to be defined that quantifies the efficiency of the
+internal learning processes. It is conjured that the minimization of this model
+entropy leads to concept formation. Besides philosophical aspects, some initial
+illustrations are included to support the claims.
+",1,0,0,1,0,0
+3522,Bulk viscosity model for near-equilibrium acoustic wave attenuation,"  Acoustic wave attenuation due to vibrational and rotational molecular
+relaxation, under simplifying assumptions of near-thermodynamic equilibrium and
+absence of molecular dissociations, can be accounted for by specifying a bulk
+viscosity coefficient $\mu_B$. In this paper, we propose a simple
+frequency-dependent bulk viscosity model which, under such assumptions,
+accurately captures wave attenuation rates from infrasonic to ultrasonic
+frequencies in Navier--Stokes and lattice Boltzmann simulations. The proposed
+model can be extended to any gas mixture for which molecular relaxation
+timescales and attenuation measurements are available. The performance of the
+model is assessed for air by varying the base temperature, pressure, relative
+humidity $h_r$, and acoustic frequency. Since the vibrational relaxation
+timescales of oxygen and nitrogen are a function of humidity, for certain
+frequencies an intermediate value of $h_r$ can be found which maximizes
+$\mu_B$. The contribution to bulk viscosity due to rotational relaxation is
+verified to be a function of temperature, confirming recent findings in the
+literature. While $\mu_B$ decreases with higher frequencies, its effects on
+wave attenuation become more significant, as shown via a dimensionless
+analysis. The proposed bulk viscosity model is designed for frequency-domain
+linear acoustic formulations but is also extensible to time-domain simulations
+of narrow-band frequency content flows.
+",0,1,0,0,0,0
+3523,"A Lagrangian fluctuation-dissipation relation for scalar turbulence, III. Turbulent Rayleigh-Bénard convection","  A Lagrangian fluctuation-dissipation relation has been derived in a previous
+work to describe the dissipation rate of advected scalars, both passive and
+active, in wall-bounded flows. We apply this relation here to develop a
+Lagrangian description of thermal dissipation in turbulent Rayleigh-Bénard
+convection in a right-cylindrical cell of arbitrary cross-section, with either
+imposed temperature difference or imposed heat-flux at the top and bottom
+walls. We obtain an exact relation between the steady-state thermal dissipation
+rate and the time for passive tracer particles released at the top or bottom
+wall to mix to their final uniform value near those walls. We show that an
+""ultimate regime"" with the Nusselt-number scaling predicted by Spiegel (1971)
+or, with a log-correction, by Kraichnan (1962) will occur at high Rayleigh
+numbers, unless this near-wall mixing time is asymptotically much longer than
+the free-fall time, or almost the large-scale circulation time. We suggest a
+new criterion for an ultimate regime in terms of transition to turbulence of a
+thermal ""mixing zone"", which is much wider than the standard thermal boundary
+layer. Kraichnan-Spiegel scaling may, however, not hold if the intensity and
+volume of thermal plumes decrease sufficiently rapidly with increasing Rayleigh
+number. To help resolve this issue, we suggest a program to measure the
+near-wall mixing time, which we argue is accessible both by laboratory
+experiment and by numerical simulation.
+",0,1,0,0,0,0
+3524,Model Selection for Explosive Models,"  This paper examines the limit properties of information criteria (such as
+AIC, BIC, HQIC) for distinguishing between the unit root model and the various
+kinds of explosive models. The explosive models include the local-to-unit-root
+model, the mildly explosive model and the regular explosive model. Initial
+conditions with different order of magnitude are considered. Both the OLS
+estimator and the indirect inference estimator are studied. It is found that
+BIC and HQIC, but not AIC, consistently select the unit root model when data
+come from the unit root model. When data come from the local-to-unit-root
+model, both BIC and HQIC select the wrong model with probability approaching 1
+while AIC has a positive probability of selecting the right model in the limit.
+When data come from the regular explosive model or from the mildly explosive
+model in the form of $1+n^{\alpha }/n$ with $\alpha \in (0,1)$, all three
+information criteria consistently select the true model. Indirect inference
+estimation can increase or decrease the probability for information criteria to
+select the right model asymptotically relative to OLS, depending on the
+information criteria and the true model. Simulation results confirm our
+asymptotic results in finite sample.
+",0,0,1,1,0,0
+3525,Deep Energy Estimator Networks,"  Density estimation is a fundamental problem in statistical learning. This
+problem is especially challenging for complex high-dimensional data due to the
+curse of dimensionality. A promising solution to this problem is given here in
+an inference-free hierarchical framework that is built on score matching. We
+revisit the Bayesian interpretation of the score function and the Parzen score
+matching, and construct a multilayer perceptron with a scalable objective for
+learning the energy (i.e. the unnormalized log-density), which is then
+optimized with stochastic gradient descent. In addition, the resulting deep
+energy estimator network (DEEN) is designed as products of experts. We present
+the utility of DEEN in learning the energy, the score function, and in
+single-step denoising experiments for synthetic and high-dimensional data. We
+also diagnose stability problems in the direct estimation of the score function
+that had been observed for denoising autoencoders.
+",0,0,0,1,0,0
+3526,A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis,"  Existing strategies for finite-armed stochastic bandits mostly depend on a
+parameter of scale that must be known in advance. Sometimes this is in the form
+of a bound on the payoffs, or the knowledge of a variance or subgaussian
+parameter. The notable exceptions are the analysis of Gaussian bandits with
+unknown mean and variance by Cowan and Katehakis [2015] and of uniform
+distributions with unknown support [Cowan and Katehakis, 2015]. The results
+derived in these specialised cases are generalised here to the non-parametric
+setup, where the learner knows only a bound on the kurtosis of the noise, which
+is a scale free measure of the extremity of outliers.
+",0,0,0,1,0,0
+3527,An Asynchronous Parallel Approach to Sparse Recovery,"  Asynchronous parallel computing and sparse recovery are two areas that have
+received recent interest. Asynchronous algorithms are often studied to solve
+optimization problems where the cost function takes the form $\sum_{i=1}^M
+f_i(x)$, with a common assumption that each $f_i$ is sparse; that is, each
+$f_i$ acts only on a small number of components of $x\in\mathbb{R}^n$. Sparse
+recovery problems, such as compressed sensing, can be formulated as
+optimization problems, however, the cost functions $f_i$ are dense with respect
+to the components of $x$, and instead the signal $x$ is assumed to be sparse,
+meaning that it has only $s$ non-zeros where $s\ll n$. Here we address how one
+may use an asynchronous parallel architecture when the cost functions $f_i$ are
+not sparse in $x$, but rather the signal $x$ is sparse. We propose an
+asynchronous parallel approach to sparse recovery via a stochastic greedy
+algorithm, where multiple processors asynchronously update a vector in shared
+memory containing information on the estimated signal support. We include
+numerical simulations that illustrate the potential benefits of our proposed
+asynchronous method.
+",1,0,0,0,0,0
+3528,Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples,"  State-of-the-art neural networks are vulnerable to adversarial examples; they
+can easily misclassify inputs that are imperceptibly different than their
+training and test data. In this work, we establish that the use of
+cross-entropy loss function and the low-rank features of the training data have
+responsibility for the existence of these inputs. Based on this observation, we
+suggest that addressing adversarial examples requires rethinking the use of
+cross-entropy loss function and looking for an alternative that is more suited
+for minimization with low-rank features. In this direction, we present a
+training scheme called differential training, which uses a loss function
+defined on the differences between the features of points from opposite
+classes. We show that differential training can ensure a large margin between
+the decision boundary of the neural network and the points in the training
+dataset. This larger margin increases the amount of perturbation needed to flip
+the prediction of the classifier and makes it harder to find an adversarial
+example with small perturbations. We test differential training on a binary
+classification task with CIFAR-10 dataset and demonstrate that it radically
+reduces the ratio of images for which an adversarial example could be found --
+not only in the training dataset, but in the test dataset as well.
+",1,0,0,1,0,0
+3529,Parametrizing modified gravity for cosmological surveys,"  One of the challenges in testing gravity with cosmology is the vast freedom
+opened when extending General Relativity. For linear perturbations, one
+solution consists in using the Effective Field Theory of Dark Energy (EFT of
+DE). Even then, the theory space is described in terms of a handful of free
+functions of time. This needs to be reduced to a finite number of parameters to
+be practical for cosmological surveys. We explore in this article how well
+simple parametrizations, with a small number of parameters, can fit observables
+computed from complex theories. Imposing the stability of linear perturbations
+appreciably reduces the theory space we explore. We find that observables are
+not extremely sensitive to short time-scale variations and that simple, smooth
+parametrizations are usually sufficient to describe this theory space. Using
+the Bayesian Information Criterion, we find that using two parameters for each
+function (an amplitude and a power law index) is preferred over complex models
+for 86% of our theory space.
+",0,1,0,0,0,0
+3530,Inductive Freeness of Ziegler's Canonical Multiderivations for Reflection Arrangements,"  Let $A$ be a free hyperplane arrangement. In 1989, Ziegler showed that the
+restriction $A''$ of $A$ to any hyperplane endowed with the natural
+multiplicity is then a free multiarrangement. We initiate a study of the
+stronger freeness property of inductive freeness for these canonical free
+multiarrangements and investigate them for the underlying class of reflection
+arrangements.
+More precisely, let $A = A(W)$ be the reflection arrangement of a complex
+reflection group $W$. By work of Terao, each such reflection arrangement is
+free. Thus so is Ziegler's canonical multiplicity on the restriction $A''$ of
+$A$ to a hyperplane. We show that the latter is inductively free as a
+multiarrangement if and only if $A''$ itself is inductively free.
+",0,0,1,0,0,0
+3531,Toward III-V/Si co-integration by controlling biatomic steps on hydrogenated Si(001),"  The integration of III-V on silicon is still a hot topic as it will open up a
+way to co-integrate Si CMOS logic with photonic vices. To reach this aim,
+several hurdles should be solved, and more particularly the generation of
+antiphase boundaries (APBs) at the III-V/Si(001) interface. Density functional
+theory (DFT) has been used to demonstrate the existence of a double-layer steps
+on nominal Si(001) which is formed during annealing under proper hydrogen
+chemical potential. This phenomenon could be explained by the formation of
+dimer vacancy lines which could be responsible for the preferential and
+selective etching of one type of step leading to the double step surface
+creation. To check this hypothesis, different experiments have been carried in
+an industrial 300 mm MOCVD where the total pressure during the anneal step of
+Si(001) surface has been varied. Under optimized conditions, an APBs-free GaAs
+layer was grown on a nominal Si(001) surface paving the way for III-V
+integration on silicon industrial platform.
+",0,1,0,0,0,0
+3532,Proceedings XVI Jornadas sobre Programación y Lenguajes,"  This volume contains a selection of the papers presented at the XVI Jornadas
+sobre Programación y Lenguajes (PROLE 2016), held at Salamanca, Spain, during
+September 14th-15th, 2016. Previous editions of the workshop were held in
+Santander (2015), Cádiz (2014), Madrid (2013), Almería (2012), A Coruña
+(2011), València (2010), San Sebastián (2009), Gijón (2008), Zaragoza
+(2007), Sitges (2006), Granada (2005), Málaga (2004), Alicante (2003), El
+Escorial (2002), and Almagro (2001). Programming languages provide a conceptual
+framework which is necessary for the development, analysis, optimization and
+understanding of programs and programming tasks. The aim of the PROLE series of
+conferences (PROLE stems from PROgramación y LEnguajes) is to serve as a
+meeting point for Spanish research groups which develop their work in the area
+of programming and programming languages. The organization of this series of
+events aims at fostering the exchange of ideas, experiences and results among
+these groups. Promoting further collaboration is also one of its main goals.
+",1,0,0,0,0,0
+3533,The proximal point algorithm in geodesic spaces with curvature bounded above,"  We investigate the asymptotic behavior of sequences generated by the proximal
+point algorithm for convex functions in complete geodesic spaces with curvature
+bounded above. Using the notion of resolvents of such functions, which was
+recently introduced by the authors, we show the existence of minimizers of
+convex functions under the boundedness assumptions on such sequences as well as
+the convergence of such sequences to minimizers of given functions.
+",0,0,1,0,0,0
+3534,Spatio-temporal canards in neural field equations,"  Canards are special solutions to ordinary differential equations that follow
+invariant repelling slow manifolds for long time intervals. In realistic
+biophysical single cell models, canards are responsible for several complex
+neural rhythms observed experimentally, but their existence and role in
+spatially-extended systems is largely unexplored. We describe a novel type of
+coherent structure in which a spatial pattern displays temporal canard
+behaviour. Using interfacial dynamics and geometric singular perturbation
+theory, we classify spatio-temporal canards and give conditions for the
+existence of folded-saddle and folded-node canards. We find that
+spatio-temporal canards are robust to changes in the synaptic connectivity and
+firing rate. The theory correctly predicts the existence of spatio-temporal
+canards with octahedral symmetries in a neural field model posed on the unit
+sphere.
+",0,1,1,0,0,0
+3535,"Counterfactuals, indicative conditionals, and negation under uncertainty: Are there cross-cultural differences?","  In this paper we study selected argument forms involving counterfactuals and
+indicative conditionals under uncertainty. We selected argument forms to
+explore whether people with an Eastern cultural background reason differently
+about conditionals compared to Westerners, because of the differences in the
+location of negations. In a 2x2 between-participants design, 63 Japanese
+university students were allocated to four groups, crossing indicative
+conditionals and counterfactuals, and each presented in two random task orders.
+The data show close agreement between the responses of Easterners and
+Westerners. The modal responses provide strong support for the hypothesis that
+conditional probability is the best predictor for counterfactuals and
+indicative conditionals. Finally, the grand majority of the responses are
+probabilistically coherent, which endorses the psychological plausibility of
+choosing coherence-based probability logic as a rationality framework for
+psychological reasoning research.
+",1,0,1,0,0,0
+3536,Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet,"  We propose a DTCWT ScatterNet Convolutional Neural Network (DTSCNN) formed by
+replacing the first few layers of a CNN network with a parametric log based
+DTCWT ScatterNet. The ScatterNet extracts edge based invariant representations
+that are used by the later layers of the CNN to learn high-level features. This
+improves the training of the network as the later layers can learn more complex
+patterns from the start of learning because the edge representations are
+already present. The efficient learning of the DTSCNN network is demonstrated
+on CIFAR-10 and Caltech-101 datasets. The generic nature of the ScatterNet
+front-end is shown by an equivalent performance to pre-trained CNN front-ends.
+A comparison with the state-of-the-art on CIFAR-10 and Caltech-101 datasets is
+also presented.
+",1,0,0,1,0,0
+3537,The $r$th moment of the divisor function: an elementary approach,"  Let $\tau(n)$ be the number of divisors of $n$. We give an elementary proof
+of the fact that $$ \sum_{n\le x} \tau(n)^r =xC_{r} (\log x)^{2^r-1}+O(x(\log
+x)^{2^r-2}), $$ for any integer $r\ge 2$. Here, $$ C_{r}=\frac{1}{(2^r-1)!}
+\prod_{p\ge 2}\left( \left(1-\frac{1}{p}\right)^{2^r} \left(\sum_{\alpha\ge 0}
+\frac{(\alpha+1)^r}{p^{\alpha}}\right)\right). $$
+",0,0,1,0,0,0
+3538,Liu-type Shrinkage Estimations in Linear Models,"  In this study, we present the preliminary test, Stein-type and positive part
+Liu estimators in the linear models when the parameter vector
+$\boldsymbol{\beta}$ is partitioned into two parts, namely, the main effects
+$\boldsymbol{\beta}_1$ and the nuisance effects $\boldsymbol{\beta}_2$ such
+that $\boldsymbol{\beta}=\left(\boldsymbol{\beta}_1, \boldsymbol{\beta}_2
+\right)$. We consider the case that a priori known or suspected set of the
+explanatory variables do not contribute to predict the response so that a
+sub-model may be enough for this purpose. Thus, the main interest is to
+estimate $\boldsymbol{\beta}_1$ when $\boldsymbol{\beta}_2$ is close to zero.
+Therefore, we conduct a Monte Carlo simulation study to evaluate the relative
+efficiency of the suggested estimators, where we demonstrate the superiority of
+the proposed estimators.
+",0,0,1,1,0,0
+3539,Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds,"  We present an efficient coresets-based neural network compression algorithm
+that provably sparsifies the parameters of a trained fully-connected neural
+network in a manner that approximately preserves the network's output. Our
+approach is based on an importance sampling scheme that judiciously defines a
+sampling distribution over the neural network parameters, and as a result,
+retains parameters of high importance while discarding redundant ones. We
+leverage a novel, empirical notion of sensitivity and extend traditional
+coreset constructions to the application of compressing parameters. Our
+theoretical analysis establishes guarantees on the size and accuracy of the
+resulting compressed neural network and gives rise to new generalization bounds
+that may provide novel insights on the generalization properties of neural
+networks. We demonstrate the practical effectiveness of our algorithm on a
+variety of neural network configurations and real-world data sets.
+",0,0,0,1,0,0
+3540,Active Galactic Nuclei: what's in a name?,"  Active Galactic Nuclei (AGN) are energetic astrophysical sources powered by
+accretion onto supermassive black holes in galaxies, and present unique
+observational signatures that cover the full electromagnetic spectrum over more
+than twenty orders of magnitude in frequency. The rich phenomenology of AGN has
+resulted in a large number of different ""flavours"" in the literature that now
+comprise a complex and confusing AGN ""zoo"". It is increasingly clear that these
+classifications are only partially related to intrinsic differences between
+AGN, and primarily reflect variations in a relatively small number of
+astrophysical parameters as well the method by which each class of AGN is
+selected. Taken together, observations in different electromagnetic bands as
+well as variations over time provide complementary windows on the physics of
+different sub-structures in the AGN. In this review, we present an overview of
+AGN multi-wavelength properties with the aim of painting their ""big picture""
+through observations in each electromagnetic band from radio to gamma-rays as
+well as AGN variability. We address what we can learn from each observational
+method, the impact of selection effects, the physics behind the emission at
+each wavelength, and the potential for future studies. To conclude we use these
+observations to piece together the basic architecture of AGN, discuss our
+current understanding of unification models, and highlight some open questions
+that present opportunities for future observational and theoretical progress.
+",0,1,0,0,0,0
+3541,Fixed points of Legendre-Fenchel type transforms,"  A recent result characterizes the fully order reversing operators acting on
+the class of lower semicontinuous proper convex functions in a real Banach
+space as certain linear deformations of the Legendre-Fenchel transform.
+Motivated by the Hilbert space version of this result and by the well-known
+result saying that this convex conjugation transform has a unique fixed point
+(namely, the normalized energy function), we investigate the fixed point
+equation in which the involved operator is fully order reversing and acts on
+the above-mentioned class of functions. It turns out that this nonlinear
+equation is very sensitive to the involved parameters and can have no solution,
+a unique solution, or several (possibly infinitely many) ones. Our analysis
+yields a few by-products, such as results related to positive definite
+operators, and to functional equations and inclusions involving monotone
+operators.
+",0,0,1,0,0,0
+3542,High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates,"  The optimization of composition and processing to obtain materials that
+exhibit desirable characteristics has historically relied on a combination of
+scientist intuition, trial and error, and luck. We propose a methodology that
+can accelerate this process by fitting data-driven models to experimental data
+as it is collected to suggest which experiment should be performed next. This
+methodology can guide the scientist to test the most promising candidates
+earlier, and can supplement scientific intuition and knowledge with data-driven
+insights. A key strength of the proposed framework is that it scales to
+high-dimensional parameter spaces, as are typical in materials discovery
+applications. Importantly, the data-driven models incorporate uncertainty
+analysis, so that new experiments are proposed based on a combination of
+exploring high-uncertainty candidates and exploiting high-performing regions of
+parameter space. Over four materials science test cases, our methodology led to
+the optimal candidate being found with three times fewer required measurements
+than random guessing on average.
+",0,1,0,1,0,0
+3543,Two Posets of Noncrossing Partitions Coming From Undesired Parking Spaces,"  Consider the noncrossing set partitions of an $n$-element set which either do
+not contain the block $\{n-1,n\}$, or which do not contain the singleton block
+$\{n\}$ whenever $1$ and $n-1$ are in the same block. In this article we study
+the subposet of the noncrossing partition lattice induced by these elements,
+and show that it is a supersolvable lattice, and therefore lexicographically
+shellable. We give a combinatorial model for the NBB bases of this lattice and
+derive an explicit formula for the value of its Möbius function between least
+and greatest element. This work is motivated by a recent article by M. Bruce,
+M. Dougherty, M. Hlavacek, R. Kudo, and I. Nicolas, in which they introduce a
+subposet of the noncrossing partition lattice that is determined by parking
+functions with certain forbidden entries. In particular, they conjecture that
+the resulting poset always has a contractible order complex. We prove this
+conjecture by embedding their poset into ours, and showing that it inherits the
+lexicographic shellability.
+",0,0,1,0,0,0
+3544,Semi-supervised model-based clustering with controlled clusters leakage,"  In this paper, we focus on finding clusters in partially categorized data
+sets. We propose a semi-supervised version of Gaussian mixture model, called
+C3L, which retrieves natural subgroups of given categories. In contrast to
+other semi-supervised models, C3L is parametrized by user-defined leakage
+level, which controls maximal inconsistency between initial categorization and
+resulting clustering. Our method can be implemented as a module in practical
+expert systems to detect clusters, which combine expert knowledge with true
+distribution of data. Moreover, it can be used for improving the results of
+less flexible clustering techniques, such as projection pursuit clustering. The
+paper presents extensive theoretical analysis of the model and fast algorithm
+for its efficient optimization. Experimental results show that C3L finds high
+quality clustering model, which can be applied in discovering meaningful groups
+in partially classified data.
+",1,0,0,1,0,0
+3545,Local White Matter Architecture Defines Functional Brain Dynamics,"  Large bundles of myelinated axons, called white matter, anatomically connect
+disparate brain regions together and compose the structural core of the human
+connectome. We recently proposed a method of measuring the local integrity
+along the length of each white matter fascicle, termed the local connectome. If
+communication efficiency is fundamentally constrained by the integrity along
+the entire length of a white matter bundle, then variability in the functional
+dynamics of brain networks should be associated with variability in the local
+connectome. We test this prediction using two statistical approaches that are
+capable of handling the high dimensionality of data. First, by performing
+statistical inference on distance-based correlations, we show that similarity
+in the local connectome between individuals is significantly correlated with
+similarity in their patterns of functional connectivity. Second, by employing
+variable selection using sparse canonical correlation analysis and
+cross-validation, we show that segments of the local connectome are predictive
+of certain patterns of functional brain dynamics. These results are consistent
+with the hypothesis that structural variability along axon bundles constrains
+communication between disparate brain regions.
+",0,0,0,1,1,0
+3546,An upper bound on tricolored ordered sum-free sets,"  We present a strengthening of the lemma on the lower bound of the slice rank
+by Tao (2016) motivated by the Croot-Lev-Pach-Ellenberg-Gijswijt bound on cap
+sets (2017, 2017). The Croot-Lev-Pach-Ellenberg-Gijswijt method and the lemma
+of Tao are based on the fact that the rank of a diagonal matrix is equal to the
+number of non-zero diagonal entries. Our lemma is based on the rank of
+upper-triangular matrices. This stronger lemma allows us to prove the following
+extension of the Ellenberg-Gijswijt result (2017). A tricolored ordered
+sum-free set in $\mathbb F_p^n$ is a collection
+$\{(a_i,b_i,c_i):i=1,2,\ldots,m\}$ of ordered triples in $(\mathbb F_p^n )^3$
+such that $a_i+b_i+c_i=0$ and if $a_i+b_j+c_k=0$, then $i\le j\le k$. By using
+the new lemma, we present an upper bound on the size of a tricolored ordered
+sum-free set in $\mathbb F_p^n$.
+",0,0,1,0,0,0
+3547,The effect of the spatial domain in FANOVA models with ARH(1) error term,"  Functional Analysis of Variance (FANOVA) from Hilbert-valued correlated data
+with spatial rectangular or circular supports is analyzed, when Dirichlet
+conditions are assumed on the boundary. Specifically, a Hilbert-valued fixed
+effect model with error term defined from an Autoregressive Hilbertian process
+of order one (ARH(1) process) is considered, extending the formulation given in
+Ruiz-Medina (2016). A new statistical test is also derived to contrast the
+significance of the functional fixed effect parameters. The Dirichlet
+conditions established at the boundary affect the dependence range of the
+correlated error term. While the rate of convergence to zero of the eigenvalues
+of the covariance kernels, characterizing the Gaussian functional error
+components, directly affects the stability of the generalized least-squares
+parameter estimation problem. A simulation study and a real-data application
+related to fMRI analysis are undertaken to illustrate the performance of the
+parameter estimator and statistical test derived.
+",0,0,1,1,0,0
+3548,Audio to Body Dynamics,"  We present a method that gets as input an audio of violin or piano playing,
+and outputs a video of skeleton predictions which are further used to animate
+an avatar. The key idea is to create an animation of an avatar that moves their
+hands similarly to how a pianist or violinist would do, just from audio. Aiming
+for a fully detailed correct arms and fingers motion is a goal, however, it's
+not clear if body movement can be predicted from music at all. In this paper,
+we present the first result that shows that natural body dynamics can be
+predicted at all. We built an LSTM network that is trained on violin and piano
+recital videos uploaded to the Internet. The predicted points are applied onto
+a rigged avatar to create the animation.
+",1,0,0,0,0,0
+3549,Detecting causal associations in large nonlinear time series datasets,"  Identifying causal relationships from observational time series data is a key
+problem in disciplines such as climate science or neuroscience, where
+experiments are often not possible. Data-driven causal inference is challenging
+since datasets are often high-dimensional and nonlinear with limited sample
+sizes. Here we introduce a novel method that flexibly combines linear or
+nonlinear conditional independence tests with a causal discovery algorithm that
+allows to reconstruct causal networks from large-scale time series datasets. We
+validate the method on a well-established climatic teleconnection connecting
+the tropical Pacific with extra-tropical temperatures and using large-scale
+synthetic datasets mimicking the typical properties of real data. The
+experiments demonstrate that our method outperforms alternative techniques in
+detection power from small to large-scale datasets and opens up entirely new
+possibilities to discover causal networks from time series across a range of
+research fields.
+",0,1,0,1,0,0
+3550,Controlling a remotely located Robot using Hand Gestures in real time: A DSP implementation,"  Telepresence is a necessity for present time as we can't reach everywhere and
+also it is useful in saving human life at dangerous places. A robot, which
+could be controlled from a distant location, can solve these problems. This
+could be via communication waves or networking methods. Also controlling should
+be in real time and smooth so that it can actuate on every minor signal in an
+effective way. This paper discusses a method to control a robot over the
+network from a distant location. The robot was controlled by hand gestures
+which were captured by the live camera. A DSP board TMS320DM642EVM was used to
+implement image pre-processing and fastening the whole system. PCA was used for
+gesture classification and robot actuation was done according to predefined
+procedures. Classification information was sent over the network in the
+experiment. This method is robust and could be used to control any kind of
+robot over distance.
+",1,0,0,0,0,0
+3551,Hybrid Machine Learning Approach to Popularity Prediction of Newly Released Contents for Online Video Streaming Service,"  In the industry of video content providers such as VOD and IPTV, predicting
+the popularity of video contents in advance is critical not only from a
+marketing perspective but also from a network optimization perspective. By
+predicting whether the content will be successful or not in advance, the
+content file, which is large, is efficiently deployed in the proper service
+providing server, leading to network cost optimization. Many previous studies
+have done view count prediction research to do this. However, the studies have
+been making predictions based on historical view count data from users. In this
+case, the contents had been published to the users and already deployed on a
+service server. These approaches make possible to efficiently deploy a content
+already published but are impossible to use for a content that is not be
+published. To address the problems, this research proposes a hybrid machine
+learning approach to the classification model for the popularity prediction of
+newly video contents which is not published. In this paper, we create a new
+variable based on the related content of the specific content and divide entire
+dataset by the characteristics of the contents. Next, the prediction is
+performed using XGBoosting and deep neural net based model according to the
+data characteristics of the cluster. Our model uses metadata for contents for
+prediction, so we use categorical embedding techniques to solve the sparsity of
+categorical variables and make them learn efficiently for the deep neural net
+model. As well, we use the FTRL-proximal algorithm to solve the problem of the
+view-count volatility of video content. We achieve overall better performance
+than the previous standalone method with a dataset from one of the top
+streaming service company.
+",1,0,0,1,0,0
+3552,Vaught's Two-Cardinal Theorem and Quasi-Minimality in Continuous Logic,"  We prove the following continuous analogue of Vaught's Two-Cardinal Theorem:
+if for some $\kappa>\lambda\geq \aleph_0$, a continuous theory $T$ has a model
+with density character $\kappa$ which has a definable subset of density
+character $\lambda$, then $T$ has a model with density character $\aleph_1$
+which has a separable definable subset. We also show that if we assume that $T$
+is $\omega$-stable, then if $T$ has a model of density character $\aleph_1$
+with a separable definable set, then for any uncountable $\kappa$ we can find a
+model of $T$ with density character $\kappa$ which has a separable definable
+subset. In order to prove this, we develop an approximate notion of
+quasi-minimality for the continuous setting. We apply these results to show a
+continuous version of the forward direction of the Baldwin-Lachlan
+characterization of uncountable categoricity: if a continuous theory $T$ is
+uncountably categorical, then $T$ is $\omega$-stable and has no Vaughtian
+pairs.
+",0,0,1,0,0,0
+3553,Spectral Calibration of the Fluorescence Telescopes of the Pierre Auger Observatory,"  We present a novel method to measure precisely the relative spectral response
+of the fluorescence telescopes of the Pierre Auger Observatory. We used a
+portable light source based on a xenon flasher and a monochromator to measure
+the relative spectral efficiencies of eight telescopes in steps of 5 nm from
+280 nm to 440 nm. Each point in a scan had approximately 2 nm FWHM out of the
+monochromator. Different sets of telescopes in the observatory have different
+optical components, and the eight telescopes measured represent two each of the
+four combinations of components represented in the observatory. We made an
+end-to-end measurement of the response from different combinations of optical
+components, and the monochromator setup allowed for more precise and complete
+measurements than our previous multi-wavelength calibrations. We find an
+overall uncertainty in the calibration of the spectral response of most of the
+telescopes of 1.5% for all wavelengths; the six oldest telescopes have larger
+overall uncertainties of about 2.2%. We also report changes in physics
+measureables due to the change in calibration, which are generally small.
+",0,1,0,0,0,0
+3554,Two properties of Müntz spaces,"  We show that Müntz spaces, as subspaces of $C[0,1]$, contain
+asymptotically isometric copies of $c_0$ and that their dual spaces are
+octahedral.
+",0,0,1,0,0,0
+3555,Rational approximations to the zeta function,"  This article describes a sequence of rational functions which converges
+locally uniformly to the zeta function. The numerators (and denominators) of
+these rational functions can be expressed as characteristic polynomials of
+matrices that are on the face of it very simple. As a consequence, the Riemann
+hypothesis can be restated as what looks like a rather conventional spectral
+problem but which is related to the one found by Connes in his analysis of the
+zeta function. However the point here is that the rational approximations look
+to be susceptible of quantitative estimation.
+",0,0,1,0,0,0
+3556,The COS-Halos Survey: Metallicities in the Low-Redshift Circumgalactic Medium,"  We analyze new far-ultraviolet spectra of 13 quasars from the z~0.2 COS-Halos
+survey that cover the HI Lyman limit of 14 circumgalactic medium (CGM) systems.
+These data yield precise estimates or more constraining limits than previous
+COS-Halos measurements on the HI column densities NHI. We then apply a
+Monte-Carlo Markov Chain approach on 32 systems from COS-Halos to estimate the
+metallicity of the cool (T~10^4K) CGM gas that gives rise to low-ionization
+state metal lines, under the assumption of photoionization equilibrium with the
+extragalactic UV background. The principle results are: (1) the CGM of field L*
+galaxies exhibits a declining HI surface density with impact parameter Rperp
+(at >99.5%$ confidence), (2) the transmission of ionizing radiation through CGM
+gas alone is 70+/-7%; (3) the metallicity distribution function of the cool CGM
+is unimodal with a median of 1/3 Z_Sun and a 95% interval from ~1/50 Z_Sun to
+over 3x solar. The incidence of metal poor (<1/100 Z_Sun) gas is low, implying
+any such gas discovered along quasar sightlines is typically unrelated to L*
+galaxies; (4) we find an unexpected increase in gas metallicity with declining
+NHI (at >99.9% confidence) and, therefore, also with increasing Rperp. The high
+metallicity at large radii implies early enrichment; (5) A non-parametric
+estimate of the cool CGM gas mass is M_CGM_cool = 9.2 +/- 4.3 10^10 Msun, which
+together with new mass estimates for the hot CGM may resolve the galactic
+missing baryons problem. Future analyses of halo gas should focus on the
+underlying astrophysics governing the CGM, rather than processes that simply
+expel the medium from the halo.
+",0,1,0,0,0,0
+3557,On Quaternionic Tori and their Moduli Spaces,"  Quaternionic tori are defined as quotients of the skew field $\mathbb{H}$ of
+quaternions by rank-4 lattices. Using slice regular functions, these tori are
+endowed with natural structures of quaternionic manifolds (in fact quaternionic
+curves), and a fundamental region in a $12$-dimensional real subspace is then
+constructed to classify them up to biregular diffeomorphisms. The points of the
+moduli space correspond to suitable \emph{special} bases of rank-4 lattices,
+which are studied with respect to the action of the group $GL(4, \mathbb{Z})$,
+and up to biregular diffeomeorphisms. All tori with a non trivial group of
+biregular automorphisms - and all possible groups of their biregular
+automorphisms - are then identified, and recognized to correspond to five
+different subsets of boundary points of the moduli space.
+",0,0,1,0,0,0
+3558,GIER: A Danish computer from 1961 with a role in the modern revolution of astronomy,"  A Danish computer, GIER, from 1961 played a vital role in the development of
+a new method for astrometric measurement. This method, photon counting
+astrometry, ultimately led to two satellites with a significant role in the
+modern revolution of astronomy. A GIER was installed at the Hamburg Observatory
+in 1964 where it was used to implement the entirely new method for the
+measurement of stellar positions by means of a meridian circle, then the
+fundamental instrument of astrometry. An expedition to Perth in Western
+Australia with the instrument and the computer was a success. This method was
+also implemented in space in the first ever astrometric satellite Hipparcos
+launched by ESA in 1989. The Hipparcos results published in 1997 revolutionized
+astrometry with an impact in all branches of astronomy from the solar system
+and stellar structure to cosmic distances and the dynamics of the Milky Way. In
+turn, the results paved the way for a successor, the one million times more
+powerful Gaia astrometry satellite launched by ESA in 2013. Preparations for a
+Gaia successor in twenty years are making progress.
+",0,1,0,0,0,0
+3559,Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study,"  Several recent papers investigate Active Learning (AL) for mitigating the
+data dependence of deep learning for natural language processing. However, the
+applicability of AL to real-world problems remains an open question. While in
+supervised learning, practitioners can try many different methods, evaluating
+each against a validation set before selecting a model, AL affords no such
+luxury. Over the course of one AL run, an agent annotates its dataset
+exhausting its labeling budget. Thus, given a new task, an active learner has
+no opportunity to compare models and acquisition functions. This paper provides
+a large scale empirical study of deep active learning, addressing multiple
+tasks and, for each, multiple datasets, multiple models, and a full suite of
+acquisition functions. We find that across all settings, Bayesian active
+learning by disagreement, using uncertainty estimates provided either by
+Dropout or Bayes-by Backprop significantly improves over i.i.d. baselines and
+usually outperforms classic uncertainty sampling.
+",0,0,0,1,0,0
+3560,Analogy and duality between random channel coding and lossy source coding,"  Here we write in a unified fashion (using ""R(P, Q, D)"") the random coding
+exponents in channel coding and lossy source coding. We derive their explicit
+forms and show, that, for a given random codebook distribution Q, the channel
+decoding error exponent can be viewed as an encoding success exponent in lossy
+source coding, and the channel correct-decoding exponent can be viewed as an
+encoding failure exponent in lossy source coding. We then extend the channel
+exponents to arbitrary D, which corresponds for D > 0 to erasure decoding and
+for D < 0 to list decoding. For comparison, we also derive the exact random
+coding exponent for Forney's optimum tradeoff decoder.
+",1,0,0,0,0,0
+3561,Towards Bursting Filter Bubble via Contextual Risks and Uncertainties,"  A rising topic in computational journalism is how to enhance the diversity in
+news served to subscribers to foster exploration behavior in news reading.
+Despite the success of preference learning in personalized news recommendation,
+their over-exploitation causes filter bubble that isolates readers from
+opposing viewpoints and hurts long-term user experiences with lack of
+serendipity. Since news providers can recommend neither opposite nor
+diversified opinions if unpopularity of these articles is surely predicted,
+they can only bet on the articles whose forecasts of click-through rate involve
+high variability (risks) or high estimation errors (uncertainties). We propose
+a novel Bayesian model of uncertainty-aware scoring and ranking for news
+articles. The Bayesian binary classifier models probability of success (defined
+as a news click) as a Beta-distributed random variable conditional on a vector
+of the context (user features, article features, and other contextual
+features). The posterior of the contextual coefficients can be computed
+efficiently using a low-rank version of Laplace's method via thin Singular
+Value Decomposition. Efficiencies in personalized targeting of exceptional
+articles, which are chosen by each subscriber in test period, are evaluated on
+real-world news datasets. The proposed estimator slightly outperformed existing
+training and scoring algorithms, in terms of efficiency in identifying
+successful outliers.
+",0,0,0,1,0,0
+3562,Nonlinear Loewy Factorizable Algebraic ODEs and Hayman's Conjecture,"  In this paper, we introduce certain $n$-th order nonlinear Loewy factorizable
+algebraic ordinary differential equations for the first time and study the
+growth of their meromorphic solutions in terms of the Nevanlinna characteristic
+function. It is shown that for generic cases all their meromorphic solutions
+are elliptic functions or their degenerations and hence their order of growth
+are at most two. Moreover, for the second order factorizable algebraic ODEs,
+all the meromorphic solutions of them (except for one case) are found
+explicitly. This allows us to show that a conjecture proposed by Hayman in 1996
+holds for these second order ODEs.
+",0,0,1,0,0,0
+3563,Grounding Symbols in Multi-Modal Instructions,"  As robots begin to cohabit with humans in semi-structured environments, the
+need arises to understand instructions involving rich variability---for
+instance, learning to ground symbols in the physical world. Realistically, this
+task must cope with small datasets consisting of a particular users' contextual
+assignment of meaning to terms. We present a method for processing a raw stream
+of cross-modal input---i.e., linguistic instructions, visual perception of a
+scene and a concurrent trace of 3D eye tracking fixations---to produce the
+segmentation of objects with a correspondent association to high-level
+concepts. To test our framework we present experiments in a table-top object
+manipulation scenario. Our results show our model learns the user's notion of
+colour and shape from a small number of physical demonstrations, generalising
+to identifying physical referents for novel combinations of the words.
+",1,0,0,0,0,0
+3564,Morphometric analysis in gamma-ray astronomy using Minkowski functionals: II. Joint structure quantification,"  We pursue a novel morphometric analysis to detect sources in very-high-energy
+gamma-ray counts maps by structural deviations from the background noise.
+Because the Minkowski functionals from integral geometry quantify the shape of
+the counts map itself, the morphometric analysis includes unbiased structure
+information without prior knowledge about the source. Their distribution
+provides access to intricate geometric information about the background. We
+combine techniques from stochastic geometry and statistical physics to
+determine the joint distribution of all Minkowski functionals. We achieve an
+accurate characterization of the background structure for large scan windows
+(with up to $15\times15$ pixels), where the number of microstates varies over
+up to 64 orders of magnitude. Moreover, in a detailed simulation study, we
+confirm the statistical significance of features in the background noise and
+discuss how to correct for trial effects. We also present a local correction of
+detector effects that can considerably enhance the sensitivity of the analysis.
+In the third paper of this series, we will use the here derived refined
+structure characterization for a more sensitive data analysis that can detect
+formerly undetected sources.
+",0,1,0,0,0,0
+3565,An Event-based Fast Movement Detection Algorithm for a Positioning Robot Using POWERLINK Communication,"  This work develops a tracking system based on an event-based camera. A
+bioinspired filtering algorithm to reduce noise and transmitted data while
+keeping the main features at the scene is implemented in FPGA which also serves
+as a network node. POWERLINK IEEE 61158 industrial network is used to
+communicate the FPGA with a controller connected to a self-developed two axis
+servo-controlled robot. The FPGA includes the network protocol to integrate the
+event-based camera as any other existing network node. The inverse kinematics
+for the robot is included in the controller. In addition, another network node
+is used to control pneumatic valves blowing the ball at different speed and
+trajectories. To complete the system and provide a comparison, a traditional
+frame-based camera is also connected to the controller. The imaging data for
+the tracking system are obtained either from the event-based or frame-based
+camera. Results show that the robot can accurately follow the ball using fast
+image recognition, with the intrinsic advantages of the event-based system
+(size, price, power). This works shows how the development of new equipment and
+algorithms can be efficiently integrated in an industrial system, merging
+commercial industrial equipment with the new devices so that new technologies
+can rapidly enter into the industrial field.
+",1,0,0,0,0,0
+3566,$\textsf{S}^3T$: An Efficient Score-Statistic for Spatio-Temporal Surveillance,"  We present an efficient score statistic, called the $\textsf{S}^3 \textsf{T}$
+statistic, to detect the emergence of a spatially and temporally correlated
+signal from either fixed-sample or sequential data. The signal may cause a men
+shift and/or a change in the covariance structure. The score statistic can
+capture both spatial and temporal structures of the change and hence is
+particularly powerful in detecting weak signals. The score statistic is
+computationally efficient and statistically powerful. Our main theoretical
+contribution are accurate analytical approximations on the false alarm rate of
+the detection procedures, which can be used to calibrate the threshold
+analytically. Numerical experiments on simulated and real data demonstrate the
+good performance of our procedure for solar flame detection and water quality
+monitoring.
+",0,0,1,1,0,0
+3567,Relaxing Integrity Requirements for Attack-Resilient Cyber-Physical Systems,"  The increase in network connectivity has also resulted in several
+high-profile attacks on cyber-physical systems. An attacker that manages to
+access a local network could remotely affect control performance by tampering
+with sensor measurements delivered to the controller. Recent results have shown
+that with network-based attacks, such as Man-in-the-Middle attacks, the
+attacker can introduce an unbounded state estimation error if measurements from
+a suitable subset of sensors contain false data when delivered to the
+controller. While these attacks can be addressed with the standard
+cryptographic tools that ensure data integrity, their continuous use would
+introduce significant communication and computation overhead. Consequently, we
+study effects of intermittent data integrity guarantees on system performance
+under stealthy attacks. We consider linear estimators equipped with a general
+type of residual-based intrusion detectors (including $\chi^2$ and SPRT
+detectors), and show that even when integrity of sensor measurements is
+enforced only intermittently, the attack impact is significantly limited;
+specifically, the state estimation error is bounded or the attacker cannot
+remain stealthy. Furthermore, we present methods to: (1) evaluate the effects
+of any given integrity enforcement policy in terms of reachable
+state-estimation errors for any type of stealthy attacks, and (2) design an
+enforcement policy that provides the desired estimation error guarantees under
+attack. Finally, on three automotive case studies we show that even with less
+than 10% of authenticated messages we can ensure satisfiable control
+performance in the presence of attacks.
+",1,0,1,0,0,0
+3568,Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content,"  With the increasing popularity of smart devices, rumors with multimedia
+content become more and more common on social networks. The multimedia
+information usually makes rumors look more convincing. Therefore, finding an
+automatic approach to verify rumors with multimedia content is a pressing task.
+Previous rumor verification research only utilizes multimedia as input
+features. We propose not to use the multimedia content but to find external
+information in other news platforms pivoting on it. We introduce a new features
+set, cross-lingual cross-platform features that leverage the semantic
+similarity between the rumors and the external information. When implemented,
+machine learning methods utilizing such features achieved the state-of-the-art
+rumor verification results.
+",1,0,0,0,0,0
+3569,Convergence of the Kähler-Ricci iteration,"  The Ricci iteration is a discrete analogue of the Ricci flow. According to
+Perelman, the Ricci flow converges to a Kahler-Einstein metric whenever one
+exists, and it has been conjectured that the Ricci iteration should behave
+similarly. This article confirms this conjecture. As a special case, this gives
+a new method of uniformization of the Riemann sphere.
+",0,0,1,0,0,0
+3570,Joint estimation of genetic and parent-of-origin effects using RNA-seq data from human,"  RNA sequencing allows one to study allelic imbalance of gene expression,
+which may be due to genetic factors or genomic imprinting. It is desirable to
+model both genetic and parent-of-origin effects simultaneously to avoid
+confounding and to improve the power to detect either effect. In a study of
+experimental cross, separation of genetic and parent-of-origin effects can be
+achieved by studying reciprocal cross of two inbred strains. In contrast, this
+task is much more challenging for an outbred population such as human
+population. To address this challenge, we propose a new framework to combine
+experimental strategies and novel statistical methods. Specifically, we propose
+to collect genotype data from family trios as well as RNA-seq data from the
+children of family trios. We have developed a new statistical method to
+estimate both genetic and parent-of-origin effects from such data sets. We
+demonstrated this approach by studying 30 trios of HapMap samples. Our results
+support some of previous finding of imprinted genes and also recover new
+candidate imprinted genes.
+",0,0,0,1,0,0
+3571,Consistency Analysis for Massively Inconsistent Datasets in Bound-to-Bound Data Collaboration,"  Bound-to-Bound Data Collaboration (B2BDC) provides a natural framework for
+addressing both forward and inverse uncertainty quantification problems. In
+this approach, QOI (quantity of interest) models are constrained by related
+experimental observations with interval uncertainty. A collection of such
+models and observations is termed a dataset and carves out a feasible region in
+the parameter space. If a dataset has a nonempty feasible set, it is said to be
+consistent. In real-world applications, it is often the case that collections
+of experiments and observations are inconsistent. Revealing the source of this
+inconsistency, i.e., identifying which models and/or observations are
+problematic, is essential before a dataset can be used for prediction. To
+address this issue, we introduce a constraint relaxation-based approach,
+entitled the vector consistency measure, for investigating datasets with
+numerous sources of inconsistency. The benefits of this vector consistency
+measure over a previous method of consistency analysis are demonstrated in two
+realistic gas combustion examples.
+",1,0,1,0,0,0
+3572,Hydra: a C++11 framework for data analysis in massively parallel platforms,"  Hydra is a header-only, templated and C++11-compliant framework designed to
+perform the typical bottleneck calculations found in common HEP data analyses
+on massively parallel platforms. The framework is implemented on top of the
+C++11 Standard Library and a variadic version of the Thrust library and is
+designed to run on Linux systems, using OpenMP, CUDA and TBB enabled devices.
+This contribution summarizes the main features of Hydra. A basic description of
+the overall design, functionality and user interface is provided, along with
+some code examples and measurements of performance.
+",1,1,0,0,0,0
+3573,Analysis of dropout learning regarded as ensemble learning,"  Deep learning is the state-of-the-art in fields such as visual object
+recognition and speech recognition. This learning uses a large number of
+layers, huge number of units, and connections. Therefore, overfitting is a
+serious problem. To avoid this problem, dropout learning is proposed. Dropout
+learning neglects some inputs and hidden units in the learning process with a
+probability, p, and then, the neglected inputs and hidden units are combined
+with the learned network to express the final output. We find that the process
+of combining the neglected hidden units with the learned network can be
+regarded as ensemble learning, so we analyze dropout learning from this point
+of view.
+",1,0,0,1,0,0
+3574,Spatially-resolved Brillouin spectroscopy reveals biomechanical changes in early ectatic corneal disease and post-crosslinking in vivo,"  Mounting evidence connects the biomechanical properties of tissues to the
+development of eye diseases such as keratoconus, a common disease in which the
+cornea thins and bulges into a conical shape. However, measuring biomechanical
+changes in vivo with sufficient sensitivity for disease detection has proved
+challenging. Here, we present a first large-scale study (~200 subjects,
+including normal and keratoconus patients) using Brillouin light-scattering
+microscopy to measure longitudinal modulus in corneal tissues with high
+sensitivity and spatial resolution. Our results in vivo provide evidence of
+biomechanical inhomogeneity at the onset of keratoconus and suggest that
+biomechanical asymmetry between the left and right eyes may presage disease
+development. We additionally measure the stiffening effect of corneal
+crosslinking treatment in vivo for the first time. Our results demonstrate the
+promise of Brillouin microscopy for diagnosis and treatment of keratoconus, and
+potentially other diseases.
+",0,0,0,0,1,0
+3575,Combining Symbolic Execution and Model Checking to Verify MPI Programs,"  Message Passing Interface (MPI) is the standard paradigm of programming in
+high performance computing. MPI programming takes significant effort, and is
+error-prone. Thus, effective tools for analyzing MPI programs are much needed.
+On the other hand, analyzing MPI programs itself is challenging because of
+non-determinism caused by program inputs and non-deterministic operations.
+Existing approaches for analyzing MPI programs either do not handle inputs or
+fail to support programs with mixed blocking and non-blocking operations.
+This paper presents MPI symbolic verifier (MPI-SV), the first symbolic
+execution based tool for verifying MPI programs having both blocking and
+non-blocking operations. To ensure soundness, we propose a blockingdriven
+matching algorithm to safely handle non-deterministic operations, and a method
+to soundly and completely model the equivalent behavior of a program execution
+path. The models of MPI program paths are generated on-the-fly during symbolic
+execution, and verified w.r.t. the expected properties by model checking. To
+improve scalability, MPI-SV uses the results of model checking to prune
+redundant paths.
+We have implemented MPI-SV and evaluated it on the verification of deadlock
+freedom for 108 real-world MPI tasks. The pure symbolic execution based
+technique can successfully verify 61 out of the 108 tasks (56%) within one
+hour, while in comparison, MPI-SV can verify 94 tasks (87%), a 31% improvement.
+On average, MPI-SV also achieves 7.25X speedup on verifying deadlock freedom
+and 2.64X speedup on finding deadlocks. These experimental results are
+promising, and demonstrate MPI-SV's effectiveness and efficiency.
+",1,0,0,0,0,0
+3576,An Improved Modified Cholesky Decomposition Method for Inverse Covariance Matrix Estimation,"  The modified Cholesky decomposition is commonly used for inverse covariance
+matrix estimation given a specified order of random variables. However, the
+order of variables is often not available or cannot be pre-determined. Hence,
+we propose a novel estimator to address the variable order issue in the
+modified Cholesky decomposition to estimate the sparse inverse covariance
+matrix. The key idea is to effectively combine a set of estimates obtained from
+multiple permutations of variable orders, and to efficiently encourage the
+sparse structure for the resultant estimate by the use of thresholding
+technique on the combined Cholesky factor matrix. The consistent property of
+the proposed estimate is established under some weak regularity conditions.
+Simulation studies show the superior performance of the proposed method in
+comparison with several existing approaches. We also apply the proposed method
+into the linear discriminant analysis for analyzing real-data examples for
+classification.
+",0,0,0,1,0,0
+3577,Self-exciting Point Processes: Infections and Implementations,"  This is a comment on Reinhart's ""Review of Self-Exciting Spatio-Temporal
+Point Processes and Their Applications"" (arXiv:1708.02647v1). I contribute some
+experiences from modelling the spread of infectious diseases. Furthermore, I
+try to complement the review with regard to the availability of software for
+the described models, which I think is essential in ""paving the way for new
+uses"".
+",0,0,0,1,0,0
+3578,"Magnetism and charge density waves in RNiC$_2$ (R = Ce, Pr, Nd)","  We have compared the magnetic, transport, galvanomagnetic and specific heat
+properties of CeNiC$_2$, PrNiC$_2$ and NdNiC$_2$ to study the interplay between
+charge density waves and magnetism in these compounds. The negative
+magnetoresistance in NdNiC$_2$ is discussed in terms of the partial destruction
+of charge density waves and an irreversible phase transition stabilized by the
+field induced ferromagnetic transformation is reported. For PrNiC$_2$ we
+demonstrate that the magnetic field initially weakens the CDW state, due to the
+Zeeman splitting of conduction bands. However, the Fermi surface nesting is
+enhanced at a temperature related to the magnetic anomaly.
+",0,1,0,0,0,0
+3579,Conjoined constraints on modified gravity from the expansion history and cosmic growth,"  In this paper we present conjoined constraints on several cosmological models
+from the expansion history $H(z)$ and cosmic growth $f\sigma_8(z)$. The models
+we study include the CPL $w_0w_a$ parametrization, the Holographic Dark Energy
+(HDE) model, the Time varying vacuum ($\Lambda_t$CDM) model, the Dvali,
+Gabadadze and Porrati (DGP) and Finsler-Randers (FRDE) models, a power law
+$f(T)$ model and finally the Hu-Sawicki $f(R)$ model. In all cases we perform a
+simultaneous fit to the SnIa, CMB, BAO, $H(z)$ and growth data, while also
+following the conjoined visualization of $H(z)$ and $f\sigma_8(z)$ as in Linder
+(2017). Furthermore, we introduce the Figure of Merit (FoM) in the
+$H(z)-f\sigma_8(z)$ parameter space as a way to constrain models that jointly
+fit both probes well. We use both the latest $H(z)$ and $f\sigma_8(z)$ data,
+but also LSST-like mocks with $1\%$ measurements and we find that the conjoined
+method of constraining the expansion history and cosmic growth simultaneously
+is able not only to place stringent constraints on these parameters but also to
+provide an easy visual way to discriminate cosmological models. Finally, we
+confirm the existence of a tension between the growth rate and Planck CMB data
+and we find that the FoM in the conjoined parameter space of
+$H(z)-f\sigma_8(z)$ can be used to discriminate between the $\Lambda$CDM model
+and certain classes of modified gravity models, namely the DGP and $f(T)$.
+",0,1,0,0,0,0
+3580,Coaxial collisions of a vortex ring and a sphere in an inviscid incompressible fluid,"  The dynamics of a circular thin vortex ring and a sphere moving along the
+symmetry axis of the ring in an inviscid incompressible fluid is studied on the
+basis of Euler's equations of motion. The equations of motion for position and
+radius of the vortex ring and those for position and velocity of the sphere are
+coupled by hydrodynamic interactions. The equations are cast in Hamiltonian
+form, from which it is seen that total energy and momentum are conserved. The
+four Hamiltonian equations of motion are solved numerically for a variety of
+initial conditions.
+",0,1,0,0,0,0
+3581,Cosmic viscosity as a remedy for tension between PLANCK and LSS data,"  Measurements of $\sigma_8$ from large scale structure observations show a
+discordance with the extrapolated $\sigma_8$ from Planck CMB parameters using
+$\Lambda$CDM cosmology. Similar discordance is found in the value of $H_0$ and
+$\Omega_m$. In this paper, we show that the presence of viscosity in cold dark
+matter, shear or bulk or combination of both, can remove the above mentioned
+conflicts simultaneously. This indicates that the data from Planck CMB
+observation and different LSS observations prefer small but non-zero amount of
+viscosity in cold dark matter fluid.
+",0,1,0,0,0,0
+3582,A probabilistic approach to emission-line galaxy classification,"  We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional
+emission-line classification schemes of galaxy ionization sources: the
+Baldwin-Phillips-Terlevich (BPT) and $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$
+(WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey
+Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically
+define classes of galaxies in a three-dimensional space spanned by the $\log$
+[OIII]/H$\beta$, $\log$ [NII]/H$\alpha$, and $\log$ EW(H${\alpha}$), optical
+parameters. The best-fit GMM based on several statistical criteria suggests a
+solution around four Gaussian components (GCs), which are capable to explain up
+to 97 per cent of the data variance. Using elements of information theory, we
+compare each GC to their respective astronomical counterpart. GC1 and GC4 are
+associated with star-forming galaxies, suggesting the need to define a new
+starburst subgroup. GC2 is associated with BPT's Active Galaxy Nuclei (AGN)
+class and WHAN's weak AGN class. GC3 is associated with BPT's composite class
+and WHAN's strong AGN class. Conversely, there is no statistical evidence --
+based on four GCs -- for the existence of a Seyfert/LINER dichotomy in our
+sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The
+GC5 appears associated to the LINER and Passive galaxies on the BPT and WHAN
+diagrams respectively. Subtleties aside, we demonstrate the potential of our
+methodology to recover/unravel different objects inside the wilderness of
+astronomical datasets, without lacking the ability to convey physically
+interpretable results. The probabilistic classifications from the GMM analysis
+are publicly available within the COINtoolbox
+(this https URL\_Catalogue/).
+",0,1,0,1,0,0
+3583,Quantum repeaters with individual rare-earth ions at telecommunication wavelengths,"  We present a quantum repeater scheme that is based on individual erbium and
+europium ions. Erbium ions are attractive because they emit photons at
+telecommunication wavelength, while europium ions offer exceptional spin
+coherence for long-term storage. Entanglement between distant erbium ions is
+created by photon detection. The photon emission rate of each erbium ion is
+enhanced by a microcavity with high Purcell factor, as has recently been
+demonstrated. Entanglement is then transferred to nearby europium ions for
+storage. Gate operations between nearby ions are performed using dynamically
+controlled electric-dipole coupling. These gate operations allow entanglement
+swapping to be employed in order to extend the distance over which entanglement
+is distributed. The deterministic character of the gate operations allows
+improved entanglement distribution rates in comparison to atomic ensemble-based
+protocols. We also propose an approach that utilizes multiplexing in order to
+enhance the entanglement distribution rate.
+",0,1,0,0,0,0
+3584,Questions and dependency in intuitionistic logic,"  In recent years, the logic of questions and dependencies has been
+investigated in the closely related frameworks of inquisitive logic and
+dependence logic. These investigations have assumed classical logic as the
+background logic of statements, and added formulas expressing questions and
+dependencies to this classical core. In this paper, we broaden the scope of
+these investigations by studying questions and dependency in the context of
+intuitionistic logic. We propose an intuitionistic team semantics, where teams
+are embedded within intuitionistic Kripke models. The associated logic is a
+conservative extension of intuitionistic logic with questions and dependence
+formulas. We establish a number of results about this logic, including a normal
+form result, a completeness result, and translations to classical inquisitive
+logic and modal dependence logic.
+",1,0,1,0,0,0
+3585,Translations: generalizing relative expressiveness between logics,"  There is a strong demand for precise means for the comparison of logics in
+terms of expressiveness both from theoretical and from application areas. The
+aim of this paper is to propose a sufficiently general and reasonable formal
+criterion for expressiveness, so as to apply not only to model-theoretic
+logics, but also to Tarskian and proof-theoretic logics. For model-theoretic
+logics there is a standard framework of relative expressiveness, based on the
+capacity of characterizing structures, and a straightforward formal criterion
+issuing from it. The problem is that it only allows the comparison of those
+logics defined within the same class of models. The urge for a broader
+framework of expressiveness is not new. Nevertheless, the enterprise is complex
+and a reasonable model-theoretic formal criterion is still wanting. Recently
+there appeared two criteria in this wider framework, one from García-Matos &
+Väänänen and other from L. Kuijer. We argue that they are not adequate.
+Their limitations are analyzed and we propose to move to an even broader
+framework lacking model-theoretic notions, which we call ""translational
+expressiveness"". There is already a criterion in this later framework by
+Mossakowski et al., however it turned out to be too lax. We propose some
+adequacy criteria for expressiveness and a formal criterion of translational
+expressiveness complying with them is given.
+",1,0,1,0,0,0
+3586,Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information,"  This paper considers utility optimal power control for energy harvesting
+wireless devices with a finite capacity battery. The distribution information
+of the underlying wireless environment and harvestable energy is unknown and
+only outdated system state information is known at the device controller. This
+scenario shares similarity with Lyapunov opportunistic optimization and online
+learning but is different from both. By a novel combination of Zinkevich's
+online gradient learning technique and the drift-plus-penalty technique from
+Lyapunov opportunistic optimization, this paper proposes a learning-aided
+algorithm that achieves utility within $O(\epsilon)$ of the optimal, for any
+desired $\epsilon>0$, by using a battery with an $O(1/\epsilon)$ capacity. The
+proposed algorithm has low complexity and makes power investment decisions
+based on system history, without requiring knowledge of the system state or its
+probability distribution.
+",1,0,0,0,0,0
+3587,Absence of long range order in the frustrated magnet SrDy$_2$O$_4$ due to trapped defects from a dimensionality crossover,"  Magnetic frustration and low dimensionality can prevent long range magnetic
+order and lead to exotic correlated ground states. SrDy$_2$O$_4$ consists of
+magnetic Dy$^{3+}$ ions forming magnetically frustrated zig-zag chains along
+the c-axis and shows no long range order to temperatures as low as $T=60$ mK.
+We carried out neutron scattering and AC magnetic susceptibility measurements
+using powder and single crystals of SrDy$_2$O$_4$. Diffuse neutron scattering
+indicates strong one-dimensional (1D) magnetic correlations along the chain
+direction that can be qualitatively accounted for by the axial next-nearest
+neighbour Ising (ANNNI) model with nearest-neighbor and next-nearest-neighbor
+exchange $J_1=0.3$ meV and $J_2=0.2$ meV, respectively. Three-dimensional (3D)
+correlations become important below $T^*\approx0.7$ K. At $T=60$ mK, the short
+range correlations are characterized by a putative propagation vector
+$\textbf{k}_{1/2}=(0,\frac{1}{2},\frac{1}{2})$. We argue that the absence of
+long range order arises from the presence of slowly decaying 1D domain walls
+that are trapped due to 3D correlations. This stabilizes a low-temperature
+phase without long range magnetic order, but with well-ordered chain segments
+separated by slowly-moving domain walls.
+",0,1,0,0,0,0
+3588,A class of multi-resolution approximations for large spatial datasets,"  Gaussian processes are popular and flexible models for spatial, temporal, and
+functional data, but they are computationally infeasible for large datasets. We
+discuss Gaussian-process approximations that use basis functions at multiple
+resolutions to achieve fast inference and that can (approximately) represent
+any spatial covariance structure. We consider two special cases of this
+multi-resolution-approximation framework, a taper version and a
+domain-partitioning (block) version. We describe theoretical properties and
+inference procedures, and study the computational complexity of the methods.
+Numerical comparisons and an application to satellite data are also provided.
+",0,0,0,1,0,0
+3589,Origin of Operating Voltage Increase in InGaN-based Light-emitting Diodes under High Injection: Phase Space Filling Effect on Forward Voltage Characteristics,"  As an attempt to further elucidate the operating voltage increase in
+InGaN-based light-emitting diodes (LEDs), the radiative and nonradiative
+current components are separately analyzed in combination with the Shockley
+diode equation. Through the analyses, we have shown that the increase in
+operating voltage is caused by phase space filling effect in high injection. We
+have also shown that the classical Shockley diode equation is insufficient to
+comprehensively explain the I-V curve of the LED devices since the transport
+and recombination characteristics of respective current components are
+basically different. Hence, we have proposed a modified Shockley equation
+suitable for modern LED devices. Our analysis gives a new insight on the cause
+of the wall-plug-efficiency drop influenced by such factors as the efficiency
+droop and the high operating voltage in InGaN LEDs.
+",0,1,0,0,0,0
+3590,Emergence of Selective Invariance in Hierarchical Feed Forward Networks,"  Many theories have emerged which investigate how in- variance is generated in
+hierarchical networks through sim- ple schemes such as max and mean pooling.
+The restriction to max/mean pooling in theoretical and empirical studies has
+diverted attention away from a more general way of generating invariance to
+nuisance transformations. We con- jecture that hierarchically building
+selective invariance (i.e. carefully choosing the range of the transformation
+to be in- variant to at each layer of a hierarchical network) is im- portant
+for pattern recognition. We utilize a novel pooling layer called adaptive
+pooling to find linear pooling weights within networks. These networks with the
+learnt pooling weights have performances on object categorization tasks that
+are comparable to max/mean pooling networks. In- terestingly, adaptive pooling
+can converge to mean pooling (when initialized with random pooling weights),
+find more general linear pooling schemes or even decide not to pool at all. We
+illustrate the general notion of selective invari- ance through object
+categorization experiments on large- scale datasets such as SVHN and ILSVRC
+2012.
+",1,0,0,0,0,0
+3591,Virtual retraction and Howson's theorem in pro-$p$ groups,"  We show that for every finitely generated closed subgroup $K$ of a
+non-solvable Demushkin group $G$, there exists an open subgroup $U$ of $G$
+containing $K$, and a continuous homomorphism $\tau \colon U \to K$ satisfying
+$\tau(k) = k$ for every $k \in K$. We prove that the intersection of a pair of
+finitely generated closed subgroups of a Demushkin group is finitely generated
+(giving an explicit bound on the number of generators). Furthermore, we show
+that these properties of Demushkin groups are preserved under free pro-$p$
+products, and deduce that Howson's theorem holds for the Sylow subgroups of the
+absolute Galois group of a number field. Finally, we confirm two conjectures of
+Ribes, thus classifying the finitely generated pro-$p$ M. Hall groups.
+",0,0,1,0,0,0
+3592,Convolutional Dictionary Learning: A Comparative Review and New Algorithms,"  Convolutional sparse representations are a form of sparse representation with
+a dictionary that has a structure that is equivalent to convolution with a set
+of linear filters. While effective algorithms have recently been developed for
+the convolutional sparse coding problem, the corresponding dictionary learning
+problem is substantially more challenging. Furthermore, although a number of
+different approaches have been proposed, the absence of thorough comparisons
+between them makes it difficult to determine which of them represents the
+current state of the art. The present work both addresses this deficiency and
+proposes some new approaches that outperform existing ones in certain contexts.
+A thorough set of performance comparisons indicates a very wide range of
+performance differences among the existing and proposed methods, and clearly
+identifies those that are the most effective.
+",1,0,0,1,0,0
+3593,Replication Ethics,"  Suppose some future technology enables the same consciously experienced human
+life to be repeated, identically or nearly so, N times, in series or in
+parallel. Is this roughly N times as valuable as enabling the same life once,
+because each life has value and values are additive? Or is it of roughly equal
+value as enabling the life once, because only one life is enabled, albeit in a
+physically unusual way? Does it matter whether the lives are contemporaneous or
+successive? We argue that these questions highlight a hitherto neglected facet
+of population ethics that may become relevant in the not necessarily far
+distant future.
+",1,1,0,0,0,0
+3594,PSYM-WIDE: a survey for large-separation planetary-mass companions to late spectral type members of young moving groups,"  We present the results of a direct-imaging survey for very large separation
+($>$100 au), companions around 95 nearby young K5-L5 stars and brown dwarfs.
+They are high-likelihood candidates or confirmed members of the young
+($\lessapprox$150 Myr) $\beta$ Pictoris and AB Doradus moving groups (ABDMG)
+and the TW Hya, Tucana-Horologium, Columba, Carina, and Argus associations.
+Images in $i'$ and $z'$ filters were obtained with the Gemini Multi-Object
+Spectrograph (GMOS) on Gemini South to search for companions down to an
+apparent magnitude of $z'\sim$22-24 at separations $\gtrapprox$20"" from the
+targets and in the remainder of the wide 5.5' $\times$ 5.5' GMOS field of view.
+This allowed us to probe the most distant region where planetary-mass
+companions could be gravitationally bound to the targets. This region was left
+largely unstudied by past high-contrast imaging surveys, which probed much
+closer-in separations. This survey led to the discovery of a planetary-mass
+(9-13 $\,M_{\rm{Jup}}$) companion at 2000 au from the M3V star GU Psc, a highly
+probable member of ABDMG. No other substellar companions were identified. These
+results allowed us to constrain the frequency of distant planetary-mass
+companions (5-13 $\,M_{\rm{Jup}}$) to 0.84$_{-0.66}^{+6.73}$% (95% confidence)
+at semimajor axes between 500 and 5000 au around young K5-L5 stars and brown
+dwarfs. This is consistent with other studies suggesting that gravitationally
+bound planetary-mass companions at wide separations from low-mass stars are
+relatively rare.
+",0,1,0,0,0,0
+3595,Boolean dimension and tree-width,"  The dimension is a key measure of complexity of partially ordered sets. Small
+dimension allows succinct encoding. Indeed if $P$ has dimension $d$, then to
+know whether $x \leq y$ in $P$ it is enough to check whether $x\leq y$ in each
+of the $d$ linear extensions of a witnessing realizer. Focusing on the encoding
+aspect Nešetřil and Pudlák defined a more expressive version of
+dimension. A poset $P$ has boolean dimension at most $d$ if it is possible to
+decide whether $x \leq y$ in $P$ by looking at the relative position of $x$ and
+$y$ in only $d$ permutations of the elements of $P$. We prove that posets with
+cover graphs of bounded tree-width have bounded boolean dimension. This stays
+in contrast with the fact that there are posets with cover graphs of tree-width
+three and arbitrarily large dimension. This result might be a step towards a
+resolution of the long-standing open problem: Do planar posets have bounded
+boolean dimension?
+",1,0,0,0,0,0
+3596,Drawing Planar Graphs with Few Geometric Primitives,"  We define the \emph{visual complexity} of a plane graph drawing to be the
+number of basic geometric objects needed to represent all its edges. In
+particular, one object may represent multiple edges (e.g., one needs only one
+line segment to draw a path with an arbitrary number of edges). Let $n$ denote
+the number of vertices of a graph. We show that trees can be drawn with $3n/4$
+straight-line segments on a polynomial grid, and with $n/2$ straight-line
+segments on a quasi-polynomial grid. Further, we present an algorithm for
+drawing planar 3-trees with $(8n-17)/3$ segments on an $O(n)\times O(n^2)$
+grid. This algorithm can also be used with a small modification to draw maximal
+outerplanar graphs with $3n/2$ edges on an $O(n)\times O(n^2)$ grid. We also
+study the problem of drawing maximal planar graphs with circular arcs and
+provide an algorithm to draw such graphs using only $(5n - 11)/3$ arcs. This is
+significantly smaller than the lower bound of $2n$ for line segments for a
+nontrivial graph class.
+",1,0,0,0,0,0
+3597,On the nature of the candidate T-Tauri star V501 Aurigae,"  We report new multi-colour photometry and high-resolution spectroscopic
+observations of the long-period variable V501 Aur, previously considered to be
+a weak-lined T-Tauri star belonging to the Taurus-Auriga star-forming region.
+The spectroscopic observations reveal that V501 Aur is a single-lined
+spectroscopic binary system with a 68.8-day orbital period, a slightly
+eccentric orbit (e ~ 0.03), and a systemic velocity discrepant from the mean of
+Taurus-Auriga. The photometry shows quasi-periodic variations on a different,
+~55-day timescale that we attribute to rotational modulation by spots. No
+eclipses are seen. The visible object is a rapidly rotating (vsini ~ 25 km/s)
+early K star, which along with the rotation period implies it must be large (R
+> 26.3 Rsun), as suggested also by spectroscopic estimates indicating a low
+surface gravity. The parallax from the Gaia mission and other independent
+estimates imply a distance much greater than the Taurus-Auriga region,
+consistent with the giant interpretation. Taken together, this evidence
+together with a re-evaluation of the LiI~$\lambda$6707 and H$\alpha$ lines
+shows that V501 Aur is not a T-Tauri star, but is instead a field binary with a
+giant primary far behind the Taurus-Auriga star-forming region. The large mass
+function from the spectroscopic orbit and a comparison with stellar evolution
+models suggest the secondary may be an early-type main-sequence star.
+",0,1,0,0,0,0
+3598,3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks,"  The success of various applications including robotics, digital content
+creation, and visualization demand a structured and abstract representation of
+the 3D world from limited sensor data. Inspired by the nature of human
+perception of 3D shapes as a collection of simple parts, we explore such an
+abstract shape representation based on primitives. Given a single depth image
+of an object, we present 3D-PRNN, a generative recurrent neural network that
+synthesizes multiple plausible shapes composed of a set of primitives. Our
+generative model encodes symmetry characteristics of common man-made objects,
+preserves long-range structural coherence, and describes objects of varying
+complexity with a compact representation. We also propose a method based on
+Gaussian Fields to generate a large scale dataset of primitive-based shape
+representations to train our network. We evaluate our approach on a wide range
+of examples and show that it outperforms nearest-neighbor based shape retrieval
+methods and is on-par with voxel-based generative models while using a
+significantly reduced parameter space.
+",1,0,0,1,0,0
+3599,Counting the number of distinct distances of elements in valued field extensions,"  The defect of valued field extensions is a major obstacle in open problems in
+resolution of singularities and in the model theory of valued fields, whenever
+positive characteristic is involved. We continue the detailed study of defect
+extensions through the tool of distances, which measure how well an element in
+an immediate extension can be approximated by elements from the base field. We
+show that in several situations the number of essentially distinct distances in
+fixed extensions, or even just over a fixed base field, is finite, and we
+compute upper bounds. We apply this to the special case of valued functions
+fields over perfect base fields. This provides important information used in
+forthcoming research on relative resolution problems.
+",0,0,1,0,0,0
+3600,On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses,"  Neural networks are known to be vulnerable to adversarial examples. In this
+note, we evaluate the two white-box defenses that appeared at CVPR 2018 and
+find they are ineffective: when applying existing techniques, we can reduce the
+accuracy of the defended models to 0%.
+",0,0,0,1,0,0
+3601,Statistical Inference with Local Optima,"  We study the statistical properties of an estimator derived by applying a
+gradient ascent method with multiple initializations to a multi-modal
+likelihood function. We derive the population quantity that is the target of
+this estimator and study the properties of confidence intervals (CIs)
+constructed from asymptotic normality and the bootstrap approach. In
+particular, we analyze the coverage deficiency due to finite number of random
+initializations. We also investigate the CIs by inverting the likelihood ratio
+test, the score test, and the Wald test, and we show that the resulting CIs may
+be very different. We provide a summary of the uncertainties that we need to
+consider while making inference about the population. Note that we do not
+provide a solution to the problem of multiple local maxima; instead, our goal
+is to investigate the effect from local maxima on the behavior of our
+estimator. In addition, we analyze the performance of the EM algorithm under
+random initializations and derive the coverage of a CI with a finite number of
+initializations. Finally, we extend our analysis to a nonparametric mode
+hunting problem.
+",0,0,0,1,0,0
+3602,Discovery of Shifting Patterns in Sequence Classification,"  In this paper, we investigate the multi-variate sequence classification
+problem from a multi-instance learning perspective. Real-world sequential data
+commonly show discriminative patterns only at specific time periods. For
+instance, we can identify a cropland during its growing season, but it looks
+similar to a barren land after harvest or before planting. Besides, even within
+the same class, the discriminative patterns can appear in different periods of
+sequential data. Due to such property, these discriminative patterns are also
+referred to as shifting patterns. The shifting patterns in sequential data
+severely degrade the performance of traditional classification methods without
+sufficient training data.
+We propose a novel sequence classification method by automatically mining
+shifting patterns from multi-variate sequence. The method employs a
+multi-instance learning approach to detect shifting patterns while also
+modeling temporal relationships within each multi-instance bag by an LSTM model
+to further improve the classification performance. We extensively evaluate our
+method on two real-world applications - cropland mapping and affective state
+recognition. The experiments demonstrate the superiority of our proposed method
+in sequence classification performance and in detecting discriminative shifting
+patterns.
+",1,0,0,1,0,0
+3603,Virtual Network Migration on the GENI Wide-Area SDN-Enabled Infrastructure,"  A virtual network (VN) contains a collection of virtual nodes and links
+assigned to underlying physical resources in a network substrate. VN migration
+is the process of remapping a VN's logical topology to a new set of physical
+resources to provide failure recovery, energy savings, or defense against
+attack. Providing VN migration that is transparent to running applications is a
+significant challenge. Efficient migration mechanisms are highly dependent on
+the technology deployed in the physical substrate. Prior work has considered
+migration in data centers and in the PlanetLab infrastructure. However, there
+has been little effort targeting an SDN-enabled wide-area networking
+environment - an important building block of future networking infrastructure.
+In this work, we are interested in the design, implementation and evaluation of
+VN migration in GENI as a working example of such a future network. We identify
+and propose techniques to address key challenges: the dynamic allocation of
+resources during migration, managing hosts connected to the VN, and flow table
+migration sequences to minimize packet loss. We find that GENI's virtualization
+architecture makes transparent and efficient migration challenging. We suggest
+alternatives that might be adopted in GENI and are worthy of adoption by
+virtual network providers to facilitate migration.
+",1,0,0,0,0,0
+3604,Communication-efficient Algorithm for Distributed Sparse Learning via Two-way Truncation,"  We propose a communicationally and computationally efficient algorithm for
+high-dimensional distributed sparse learning. At each iteration, local machines
+compute the gradient on local data and the master machine solves one shifted
+$l_1$ regularized minimization problem. The communication cost is reduced from
+constant times of the dimension number for the state-of-the-art algorithm to
+constant times of the sparsity number via Two-way Truncation procedure.
+Theoretically, we prove that the estimation error of the proposed algorithm
+decreases exponentially and matches that of the centralized method under mild
+assumptions. Extensive experiments on both simulated data and real data verify
+that the proposed algorithm is efficient and has performance comparable with
+the centralized method on solving high-dimensional sparse learning problems.
+",0,0,0,1,0,0
+3605,A Bayesian Estimation for the Fractional Order of the Differential Equation that Models Transport in Unconventional Hydrocarbon Reservoirs,"  The extraction of natural gas from the earth has been shown to be governed by
+differential equations concerning flow through a porous material. Recently,
+models such as fractional differential equations have been developed to model
+this phenomenon. One key issue with these models is estimating the fraction of
+the differential equation. Traditional methods such as maximum likelihood,
+least squares and even method of moments are not available to estimate this
+parameter as traditional calculus methods do not apply. We develop a Bayesian
+approach to estimate the fraction of the order of the differential equation
+that models transport in unconventional hydrocarbon reservoirs. In this paper,
+we use this approach to adequately quantify the uncertainties associated with
+the error and predictions. A simulation study is presented as well to assess
+the utility of the modeling approach.
+",0,0,0,1,0,0
+3606,Numerical simulations of magnetic billiards in a convex domain in $\mathbb{R}^2$,"  We present numerical simulations of magnetic billiards inside a convex domain
+in the plane.
+",0,1,1,0,0,0
+3607,On Lasso refitting strategies,"  A well-know drawback of l_1-penalized estimators is the systematic shrinkage
+of the large coefficients towards zero. A simple remedy is to treat Lasso as a
+model-selection procedure and to perform a second refitting step on the
+selected support. In this work we formalize the notion of refitting and provide
+oracle bounds for arbitrary refitting procedures of the Lasso solution. One of
+the most widely used refitting techniques which is based on Least-Squares may
+bring a problem of interpretability, since the signs of the refitted estimator
+might be flipped with respect to the original estimator. This problem arises
+from the fact that the Least-Squares refitting considers only the support of
+the Lasso solution, avoiding any information about signs or amplitudes. To this
+end we define a sign consistent refitting as an arbitrary refitting procedure,
+preserving the signs of the first step Lasso solution and provide Oracle
+inequalities for such estimators. Finally, we consider special refitting
+strategies: Bregman Lasso and Boosted Lasso. Bregman Lasso has a fruitful
+property to converge to the Sign-Least-Squares refitting (Least-Squares with
+sign constraints), which provides with greater interpretability. We
+additionally study the Bregman Lasso refitting in the case of orthogonal
+design, providing with simple intuition behind the proposed method. Boosted
+Lasso, in contrast, considers information about magnitudes of the first Lasso
+step and allows to develop better oracle rates for prediction. Finally, we
+conduct an extensive numerical study to show advantages of one approach over
+others in different synthetic and semi-real scenarios.
+",0,0,1,1,0,0
+3608,"Supercharacters and the discrete Fourier, cosine, and sine transforms","  Using supercharacter theory, we identify the matrices that are diagonalized
+by the discrete cosine and discrete sine transforms, respectively. Our method
+affords a combinatorial interpretation for the matrix entries.
+",0,0,1,0,0,0
+3609,A connection between MAX $κ$-CUT and the inhomogeneous Potts spin glass in the large degree limit,"  We study the asymptotic behavior of the Max $\kappa$-cut on a family of
+sparse, inhomogeneous random graphs. In the large degree limit, the leading
+term is a variational problem, involving the ground state of a constrained
+inhomogeneous Potts spin glass. We derive a Parisi type formula for the free
+energy of this model, with possible constraints on the proportions, and derive
+the limiting ground state energy by a suitable zero temperature limit.
+",0,0,1,0,0,0
+3610,Kernel method for persistence diagrams via kernel embedding and weight factor,"  Topological data analysis is an emerging mathematical concept for
+characterizing shapes in multi-scale data. In this field, persistence diagrams
+are widely used as a descriptor of the input data, and can distinguish robust
+and noisy topological properties. Nowadays, it is highly desired to develop a
+statistical framework on persistence diagrams to deal with practical data. This
+paper proposes a kernel method on persistence diagrams. A theoretical
+contribution of our method is that the proposed kernel allows one to control
+the effect of persistence, and, if necessary, noisy topological properties can
+be discounted in data analysis. Furthermore, the method provides a fast
+approximation technique. The method is applied into several problems including
+practical data in physics, and the results show the advantage compared to the
+existing kernel method on persistence diagrams.
+",0,0,1,1,0,0
+3611,Time Complexity of Constraint Satisfaction via Universal Algebra,"  The exponential-time hypothesis (ETH) states that 3-SAT is not solvable in
+subexponential time, i.e. not solvable in O(c^n) time for arbitrary c > 1,
+where n denotes the number of variables. Problems like k-SAT can be viewed as
+special cases of the constraint satisfaction problem (CSP), which is the
+problem of determining whether a set of constraints is satisfiable. In this
+paper we study thef worst-case time complexity of NP-complete CSPs. Our main
+interest is in the CSP problem parameterized by a constraint language Gamma
+(CSP(Gamma)), and how the choice of Gamma affects the time complexity. It is
+believed that CSP(Gamma) is either tractable or NP-complete, and the algebraic
+CSP dichotomy conjecture gives a sharp delineation of these two classes based
+on algebraic properties of constraint languages. Under this conjecture and the
+ETH, we first rule out the existence of subexponential algorithms for
+finite-domain NP-complete CSP(Gamma) problems. This result also extends to
+certain infinite-domain CSPs and structurally restricted CSP(Gamma) problems.
+We then begin a study of the complexity of NP-complete CSPs where one is
+allowed to arbitrarily restrict the values of individual variables, which is a
+very well-studied subclass of CSPs. For such CSPs with finite domain D, we
+identify a relation SD such that (1) CSP({SD}) is NP-complete and (2) if
+CSP(Gamma) over D is NP-complete and solvable in O(c^n) time, then CSP({SD}) is
+solvable in O(c^n) time, too. Hence, the time complexity of CSP({SD}) is a
+lower bound for all CSPs of this particular kind. We also prove that the
+complexity of CSP({SD}) is decreasing when |D| increases, unless the ETH is
+false. This implies, for instance, that for every c>1 there exists a
+finite-domain Gamma such that CSP(Gamma) is NP-complete and solvable in O(c^n)
+time.
+",1,0,0,0,0,0
+3612,Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series,"  We propose a structured low rank matrix completion algorithm to recover a
+time series of images consisting of linear combination of exponential
+parameters at every pixel, from under-sampled Fourier measurements. The spatial
+smoothness of these parameters is exploited along with the exponential
+structure of the time series at every pixel, to derive an annihilation relation
+in the $k-t$ domain. This annihilation relation translates into a structured
+low rank matrix formed from the $k-t$ samples. We demonstrate the algorithm in
+the parameter mapping setting and show significant improvement over state of
+the art methods.
+",1,0,0,0,0,0
+3613,Vector bundles and modular forms for Fuchsian groups of genus zero,"  This article lays the foundations for the study of modular forms transforming
+with respect to representations of Fuchsian groups of genus zero. More
+precisely, we define geometrically weighted graded modules of such modular
+forms, where the graded structure comes from twisting with all isomorphism
+classes of line bundles on the corresponding compactified modular curve, and we
+study their structure by relating it to the structure of vector bundles over
+orbifold curves of genus zero. We prove that these modules are free whenever
+the Fuchsian group has at most two elliptic points. For three or more elliptic
+points, we give explicit constructions of indecomposable vector bundles of rank
+two over modular orbifold curves, which give rise to non-free modules of
+geometrically weighted modular forms.
+",0,0,1,0,0,0
+3614,Indoor UAV scheduling with Restful Task Assignment Algorithm,"  Research in UAV scheduling has obtained an emerging interest from scientists
+in the optimization field. When the scheduling itself has established a strong
+root since the 19th century, works on UAV scheduling in indoor environment has
+come forth in the latest decade. Several works on scheduling UAV operations in
+indoor (two and three dimensional) and outdoor environments are reported. In
+this paper, a further study on UAV scheduling in three dimensional indoor
+environment is investigated. Dealing with indoor environment\textemdash where
+humans, UAVs, and other elements or infrastructures are likely to coexist in
+the same space\textemdash draws attention towards the safety of the operations.
+In relation to the battery level, a preserved battery level leads to safer
+operations, promoting the UAV to have a decent remaining power level. A
+methodology which consists of a heuristic approach based on Restful Task
+Assignment Algorithm, incorporated with Particle Swarm Optimization Algorithm,
+is proposed. The motivation is to preserve the battery level throughout the
+operations, which promotes less possibility in having failed UAVs on duty. This
+methodology is tested with 54 benchmark datasets stressing on 4 different
+aspects: geographical distance, number of tasks, number of predecessors, and
+slack time. The test results and their characteristics in regard to the
+proposed methodology are discussed and presented.
+",1,0,0,0,0,0
+3615,"Magnetic behavior of new compounds, Gd3RuSn6 and Tb3RuSn6","  We report temperature (T) dependence of dc magnetization, electrical
+resistivity (rho(T)), and heat-capacity of rare-earth (R) compounds, Gd3RuSn6
+and Tb3RuSn6, which are found to crystallize in the Yb3CoSn6-type orthorhombic
+structure (space group: Cmcm). The results establish that there is an onset of
+antiferromagnetic order near (T_N) 19 and 25 K respectively. In addition, we
+find that there is another magnetic transition for both the cases around 14 and
+17 K respectively. In the case of the Gd compound, the spin-scattering
+contribution to rho is found to increase below 75 K as the material is cooled
+towards T_N, thereby resulting in a minimum in the plot of rho(T) unexpected
+for Gd based systems. Isothermal magnetization at 1.8 K reveals an upward
+curvature around 50 kOe. Isothermal magnetoresistance plots show interesting
+anomalies in the magnetically ordered state. There are sign reversals in the
+plot of isothermal entropy change versus T in the magnetically ordered state,
+indicating subtle changes in the spin reorientation with T. The results reveal
+that these compounds exhibit interesting magnetic properties.
+",0,1,0,0,0,0
+3616,Stochastic Assume-Guarantee Contracts for Cyber-Physical System Design Under Probabilistic Requirements,"  We develop an assume-guarantee contract framework for the design of
+cyber-physical systems, modeled as closed-loop control systems, under
+probabilistic requirements. We use a variant of signal temporal logic, namely,
+Stochastic Signal Temporal Logic (StSTL) to specify system behaviors as well as
+contract assumptions and guarantees, thus enabling automatic reasoning about
+requirements of stochastic systems. Given a stochastic linear system
+representation and a set of requirements captured by bounded StSTL contracts,
+we propose algorithms that can check contract compatibility, consistency, and
+refinement, and generate a controller to guarantee that a contract is
+satisfied, following a stochastic model predictive control approach. Our
+algorithms leverage encodings of the verification and control synthesis tasks
+into mixed integer optimization problems, and conservative approximations of
+probabilistic constraints that produce both sound and tractable problem
+formulations. We illustrate the effectiveness of our approach on a few
+examples, including the design of embedded controllers for aircraft power
+distribution networks.
+",1,0,0,0,0,0
+3617,Ergodic Exploration of Distributed Information,"  This paper presents an active search trajectory synthesis technique for
+autonomous mobile robots with nonlinear measurements and dynamics. The
+presented approach uses the ergodicity of a planned trajectory with respect to
+an expected information density map to close the loop during search. The
+ergodic control algorithm does not rely on discretization of the search or
+action spaces, and is well posed for coverage with respect to the expected
+information density whether the information is diffuse or localized, thus
+trading off between exploration and exploitation in a single objective
+function. As a demonstration, we use a robotic electrolocation platform to
+estimate location and size parameters describing static targets in an
+underwater environment. Our results demonstrate that the ergodic exploration of
+distributed information (EEDI) algorithm outperforms commonly used
+information-oriented controllers, particularly when distractions are present.
+",1,0,0,0,0,0
+3618,Manipulating magnetism by ultrafast control of the exchange interaction,"  In recent years, the optical control of exchange interactions has emerged as
+an exciting new direction in the study of the ultrafast optical control of
+magnetic order. Here we review recent theoretical works on antiferromagnetic
+systems, devoted to i) simulating the ultrafast control of exchange
+interactions, ii) modeling the strongly nonequilibrium response of the magnetic
+order and iii) the relation with relevant experimental works developed in
+parallel. In addition to the excitation of spin precession, we discuss examples
+of rapid cooling and the control of ultrafast coherent longitudinal spin
+dynamics in response to femtosecond optically induced perturbations of exchange
+interactions. These elucidate the potential for exploiting the control of
+exchange interactions to find new scenarios for both faster and more
+energy-efficient manipulation of magnetism.
+",0,1,0,0,0,0
+3619,Towards Visual Explanations for Convolutional Neural Networks via Input Resampling,"  The predictive power of neural networks often costs model interpretability.
+Several techniques have been developed for explaining model outputs in terms of
+input features; however, it is difficult to translate such interpretations into
+actionable insight. Here, we propose a framework to analyze predictions in
+terms of the model's internal features by inspecting information flow through
+the network. Given a trained network and a test image, we select neurons by two
+metrics, both measured over a set of images created by perturbations to the
+input image: (1) magnitude of the correlation between the neuron activation and
+the network output and (2) precision of the neuron activation. We show that the
+former metric selects neurons that exert large influence over the network
+output while the latter metric selects neurons that activate on generalizable
+features. By comparing the sets of neurons selected by these two metrics, our
+framework suggests a way to investigate the internal attention mechanisms of
+convolutional neural networks.
+",1,0,0,1,0,0
+3620,FBG-Based Control of a Continuum Manipulator Interacting With Obstacles,"  Tracking and controlling the shape of continuum dexterous manipulators (CDM)
+in constraint environments is a challenging task. The imposed constraints and
+interaction with unknown obstacles may conform the CDM's shape and therefore
+demands for shape sensing methods which do not rely on direct line of sight. To
+address these issues, we integrate a novel Fiber Bragg Grating (FBG) shape
+sensing unit into a CDM, reconstruct the shape in real-time, and develop an
+optimization-based control algorithm using FBG tip position feedback. The CDM
+is designed for less-invasive treatment of osteolysis (bone degradation). To
+evaluate the performance of the feedback control algorithm when the CDM
+interacts with obstacles, we perform a set of experiments similar to the real
+scenario of the CDM interaction with soft and hard lesions during the treatment
+of osteolysis. In addition, we propose methods for identification of the CDM
+collisions with soft or hard obstacles using the jacobian information. Results
+demonstrate successful control of the CDM tip based on the FBG feedback and
+indicate repeatability and robustness of the proposed method when interacting
+with unknown obstacles.
+",1,0,0,0,0,0
+3621,Generalized variational inequalities for maximal monotone operators,"  In this paper we present some new results on the existence of solutions of
+generalized variational inequalities in real reflexive Banach spaces with
+Fréchet differentiable norms. Moreover, we also give some theorems about the
+structure of solution sets. The results obtained in this paper improve and
+extend the ones announced by Fang and Peterson [1] to infinite dimensional
+spaces.
+",0,0,1,0,0,0
+3622,Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations,"  The increasing accuracy of automatic chord estimation systems, the
+availability of vast amounts of heterogeneous reference annotations, and
+insights from annotator subjectivity research make chord label personalization
+increasingly important. Nevertheless, automatic chord estimation systems are
+historically exclusively trained and evaluated on a single reference
+annotation. We introduce a first approach to automatic chord label
+personalization by modeling subjectivity through deep learning of a harmonic
+interval-based chord label representation. After integrating these
+representations from multiple annotators, we can accurately personalize chord
+labels for individual annotators from a single model and the annotators' chord
+label vocabulary. Furthermore, we show that chord personalization using
+multiple reference annotations outperforms using a single reference annotation.
+",1,0,0,0,0,0
+3623,Classification of Questions and Learning Outcome Statements (LOS) Into Blooms Taxonomy (BT) By Similarity Measurements Towards Extracting Of Learning Outcome from Learning Material,"  Blooms Taxonomy (BT) have been used to classify the objectives of learning
+outcome by dividing the learning into three different domains; the cognitive
+domain, the effective domain and the psychomotor domain. In this paper, we are
+introducing a new approach to classify the questions and learning outcome
+statements (LOS) into Blooms taxonomy (BT) and to verify BT verb lists, which
+are being cited and used by academicians to write questions and (LOS). An
+experiment was designed to investigate the semantic relationship between the
+action verbs used in both questions and LOS to obtain more accurate
+classification of the levels of BT. A sample of 775 different action verbs
+collected from different universities allows us to measure an accurate and
+clear-cut cognitive level for the action verb. It is worth mentioning that
+natural language processing techniques were used to develop our rules as to
+induce the questions into chunks in order to extract the action verbs. Our
+proposed solution was able to classify the action verb into a precise level of
+the cognitive domain. We, on our side, have tested and evaluated our proposed
+solution using confusion matrix. The results of evaluation tests yielded 97%
+for the macro average of precision and 90% for F1. Thus, the outcome of the
+research suggests that it is crucial to analyse and verify the action verbs
+cited and used by academicians to write LOS and classify their questions based
+on blooms taxonomy in order to obtain a definite and more accurate
+classification.
+",1,0,0,0,0,0
+3624,Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence,"  The need to test whether two random vectors are independent has spawned a
+large number of competing measures of dependence. We are interested in
+nonparametric measures that are invariant under strictly increasing
+transformations, such as Kendall's tau, Hoeffding's D, and the more recently
+discovered Bergsma--Dassios sign covariance. Each of these measures exhibits
+symmetries that are not readily apparent from their definitions. Making these
+symmetries explicit, we define a new class of multivariate nonparametric
+measures of dependence that we refer to as Symmetric Rank Covariances. This new
+class generalises all of the above measures and leads naturally to multivariate
+extensions of the Bergsma--Dassios sign covariance. Symmetric Rank Covariances
+may be estimated unbiasedly using U-statistics for which we prove results on
+computational efficiency and large-sample behavior. The algorithms we develop
+for their computation include, to the best of our knowledge, the first
+efficient algorithms for the well-known Hoeffding's D statistic in the
+multivariate setting.
+",0,0,1,1,0,0
+3625,Linear magnetoresistance in the charge density wave state of quasi-two-dimensional rare-earth tritellurides,"  We report measurements of the magnetoresistance in the charge density wave
+(CDW) state of rare-earth tritellurides, namely TbTe$_3$ and HoTe$_3$. The
+magnetic field dependence of magnetoresistance exhibits a temperature dependent
+crossover between a conventional quadratic law at high $T$ and low $B$ and an
+unusual linear dependence at low $T$ and high $B$. We present a quite general
+model to explain the linear magnetoresistance taking into account the strong
+scattering of quasiparticles on CDW fluctuations in the vicinity of ""hot spots""
+of the Fermi surface (FS) where the FS reconstruction is the strongest.
+",0,1,0,0,0,0
+3626,Two classes of number fields with a non-principal Euclidean ideal,"  This paper introduces two classes of totally real quartic number fields, one
+of biquadratic extensions and one of cyclic extensions, each of which has a
+non-principal Euclidean ideal. It generalizes techniques of Graves used to
+prove that the number field $\mathbb{Q}(\sqrt{2},\sqrt{35})$ has a
+non-principal Euclidean ideal.
+",0,0,1,0,0,0
+3627,"Diamond-colored distributive lattices, move-minimizing games, and fundamental Weyl symmetric functions: The type $\mathsf{A}$ case","  We present some elementary but foundational results concerning
+diamond-colored modular and distributive lattices and connect these structures
+to certain one-player combinatorial ""move-minimizing games,"" in particular, a
+so-called ""domino game."" The objective of this game is to find, if possible,
+the least number of ""domino moves"" to get from one partition to another, where
+a domino move is, with one exception, the addition or removal of a
+domino-shaped pair of tiles. We solve this domino game by demonstrating the
+somewhat surprising fact that the associated ""game graphs"" coincide with a
+well-known family of diamond-colored distributive lattices which shall be
+referred to as the ""type $\mathsf{A}$ fundamental lattices."" These lattices
+arise as supporting graphs for the fundamental representations of the special
+linear Lie algebras and as splitting posets for type $\mathsf{A}$ fundamental
+symmetric functions, connections which are further explored in sequel papers
+for types $\mathsf{A}$, $\mathsf{C}$, and $\mathsf{B}$. In this paper, this
+connection affords a solution to the proposed domino game as well as new
+descriptions of the type $\mathsf{A}$ fundamental lattices.
+",0,0,1,0,0,0
+3628,"Hidden Truncation Hyperbolic Distributions, Finite Mixtures Thereof, and Their Application for Clustering","  A hidden truncation hyperbolic (HTH) distribution is introduced and finite
+mixtures thereof are applied for clustering. A stochastic representation of the
+HTH distribution is given and a density is derived. A hierarchical
+representation is described, which aids in parameter estimation. Finite
+mixtures of HTH distributions are presented and their identifiability is
+proved. The convexity of the HTH distribution is discussed, which is important
+in clustering applications, and some theoretical results in this direction are
+presented. The relationship between the HTH distribution and other skewed
+distributions in the literature is discussed. Illustrations are provided ---
+both of the HTH distribution and application of finite mixtures thereof for
+clustering.
+",0,0,0,1,0,0
+3629,No evidence for a significant AGN contribution to cosmic hydrogen reionization,"  We reinvestigate a claimed sample of 22 X-ray detected active galactic nuclei
+(AGN) at redshifts z > 4, which has reignited the debate as to whether young
+galaxies or AGN reionized the Universe. These sources lie within the
+GOODS-S/CANDELS field, and we examine both the robustness of the claimed X-ray
+detections (within the Chandra 4Ms imaging) and perform an independent analysis
+of the photometric redshifts of the optical/infrared counterparts. We confirm
+the reality of only 15 of the 22 reported X-ray detections, and moreover find
+that only 12 of the 22 optical/infrared counterpart galaxies actually lie
+robustly at z > 4. Combining these results we find convincing evidence for only
+7 X-ray AGN at z > 4 in the GOODS-S field, of which only one lies at z > 5. We
+recalculate the evolving far-UV (1500 Angstrom) luminosity density produced by
+AGN at high redshift, and find that it declines rapidly from z = 4 to z = 6, in
+agreement with several other recent studies of the evolving AGN luminosity
+function. The associated rapid decline in inferred hydrogen-ionizing emissivity
+contributed by AGN falls an order-of-magnitude short of the level required to
+maintain hydrogen ionization at z ~ 6. We conclude that all available evidence
+continues to favour a scenario in which young galaxies reionized the Universe,
+with AGN making, at most, a very minor contribution to cosmic hydrogen
+reionization.
+",0,1,0,0,0,0
+3630,Finding Crash-Consistency Bugs with Bounded Black-Box Crash Testing,"  We present a new approach to testing file-system crash consistency: bounded
+black-box crash testing (B3). B3 tests the file system in a black-box manner
+using workloads of file-system operations. Since the space of possible
+workloads is infinite, B3 bounds this space based on parameters such as the
+number of file-system operations or which operations to include, and
+exhaustively generates workloads within this bounded space. Each workload is
+tested on the target file system by simulating power-loss crashes while the
+workload is being executed, and checking if the file system recovers to a
+correct state after each crash. B3 builds upon insights derived from our study
+of crash-consistency bugs reported in Linux file systems in the last five
+years. We observed that most reported bugs can be reproduced using small
+workloads of three or fewer file-system operations on a newly-created file
+system, and that all reported bugs result from crashes after fsync() related
+system calls. We build two tools, CrashMonkey and ACE, to demonstrate the
+effectiveness of this approach. Our tools are able to find 24 out of the 26
+crash-consistency bugs reported in the last five years. Our tools also revealed
+10 new crash-consistency bugs in widely-used, mature Linux file systems, seven
+of which existed in the kernel since 2014. Our tools also found a
+crash-consistency bug in a verified file system, FSCQ. The new bugs result in
+severe consequences like broken rename atomicity and loss of persisted files.
+",1,0,0,0,0,0
+3631,Periodic solutions to the Cahn-Hilliard equation in the plane,"  In this paper we construct entire solutions to the Cahn-Hilliard equation
+$-\Delta(-\Delta u+W^{'}(u))+W^{""}(u)(-\Delta u+W^{'}(u))=0$ in the Euclidean
+plane, where $W(u)$ is the standard double-well potential $\frac{1}{4}
+(1-u^2)^2$. Such solutions have a non-trivial profile that shadows a Willmore
+planar curve, and converge uniformly to $\pm 1$ as $x_2 \to \pm \infty$. These
+solutions give a counterexample to the counterpart of Gibbons' conjecture for
+the fourth-order counterpart of the Allen-Cahn equation. We also study the
+$x_2$-derivative of these solutions using the special structure of Willmore's
+equation.
+",0,0,1,0,0,0
+3632,On the kinetic equation in Zakharov's wave turbulence theory for capillary waves,"  The wave turbulence equation is an effective kinetic equation that describes
+the dynamics of wave spectrum in weakly nonlinear and dispersive media. Such a
+kinetic model has been derived by physicists in the sixties, though the
+well-posedness theory remains open, due to the complexity of resonant
+interaction kernels. In this paper, we provide a global unique radial strong
+solution, the first such a result, to the wave turbulence equation for
+capillary waves.
+",0,1,1,0,0,0
+3633,Regularizing Model Complexity and Label Structure for Multi-Label Text Classification,"  Multi-label text classification is a popular machine learning task where each
+document is assigned with multiple relevant labels. This task is challenging
+due to high dimensional features and correlated labels. Multi-label text
+classifiers need to be carefully regularized to prevent the severe over-fitting
+in the high dimensional space, and also need to take into account label
+dependencies in order to make accurate predictions under uncertainty. We
+demonstrate significant and practical improvement by carefully regularizing the
+model complexity during training phase, and also regularizing the label search
+space during prediction phase. Specifically, we regularize the classifier
+training using Elastic-net (L1+L2) penalty for reducing model complexity/size,
+and employ early stopping to prevent overfitting. At prediction time, we apply
+support inference to restrict the search space to label sets encountered in the
+training set, and F-optimizer GFM to make optimal predictions for the F1
+metric. We show that although support inference only provides density
+estimations on existing label combinations, when combined with GFM predictor,
+the algorithm can output unseen label combinations. Taken collectively, our
+experiments show state of the art results on many benchmark datasets. Beyond
+performance and practical contributions, we make some interesting observations.
+Contrary to the prior belief, which deems support inference as purely an
+approximate inference procedure, we show that support inference acts as a
+strong regularizer on the label prediction structure. It allows the classifier
+to take into account label dependencies during prediction even if the
+classifiers had not modeled any label dependencies during training.
+",1,0,0,1,0,0
+3634,Network Design with Probabilistic Capacities,"  We consider a network design problem with random arc capacities and give a
+formulation with a probabilistic capacity constraint on each cut of the
+network. To handle the exponentially-many probabilistic constraints a
+separation procedure that solves a nonlinear minimum cut problem is introduced.
+For the case with independent arc capacities, we exploit the supermodularity of
+the set function defining the constraints and generate cutting planes based on
+the supermodular covering knapsack polytope. For the general correlated case,
+we give a reformulation of the constraints that allows to uncover and utilize
+the submodularity of a related function. The computational results indicate
+that exploiting the underlying submodularity and supermodularity arising with
+the probabilistic constraints provides significant advantages over the
+classical approaches.
+",0,0,1,0,0,0
+3635,Nanopteron solutions of diatomic Fermi-Pasta-Ulam-Tsingou lattices with small mass-ratio,"  Consider an infinite chain of masses, each connected to its nearest neighbors
+by a (nonlinear) spring. This is a Fermi-Pasta-Ulam-Tsingou lattice. We prove
+the existence of traveling waves in the setting where the masses alternate in
+size. In particular we address the limit where the mass ratio tends to zero.
+The problem is inherently singular and we find that the traveling waves are not
+true solitary waves but rather ""nanopterons"", which is to say, waves which
+asymptotic at spatial infinity to very small amplitude periodic waves.
+Moreover, we can only find solutions when the mass ratio lies in a certain open
+set. The difficulties in the problem all revolve around understanding Jost
+solutions of a nonlocal Schrödinger operator in its semi-classical limit.
+",0,0,1,0,0,0
+3636,Steering Orbital Optimization out of Local Minima and Saddle Points Toward Lower Energy,"  The general procedure underlying Hartree-Fock and Kohn-Sham density
+functional theory calculations consists in optimizing orbitals for a
+self-consistent solution of the Roothaan-Hall equations in an iterative
+process. It is often ignored that multiple self-consistent solutions can exist,
+several of which may correspond to minima of the energy functional. In addition
+to the difficulty sometimes encountered to converge the calculation to a
+self-consistent solution, one must ensure that the correct self-consistent
+solution was found, typically the one with the lowest electronic energy.
+Convergence to an unwanted solution is in general not trivial to detect and
+will deliver incorrect energy and molecular properties, and accordingly a
+misleading description of chemical reactivity. Wrong conclusions based on
+incorrect self-consistent field convergence are particularly cumbersome in
+automated calculations met in high-throughput virtual screening, structure
+optimizations, ab initio molecular dynamics, and in real-time explorations of
+chemical reactivity, where the vast amount of data can hardly be manually
+inspected. Here, we introduce a fast and automated approach to detect and cure
+incorrect orbital convergence, which is especially suited for electronic
+structure calculations on sequences of molecular structures. Our approach
+consists of a randomized perturbation of the converged electron density
+(matrix) intended to push orbital convergence to solutions that correspond to
+another stationary point (of potentially lower electronic energy) in the
+variational parameter space of an electronic wave function approximation.
+",0,1,0,0,0,0
+3637,Mapping stellar content to dark matter halos - III.Environmental dependence and conformity of galaxy colours,"  Recent studies suggest that the quenching properties of galaxies are
+correlated over several mega-parsecs. The large-scale ""galactic conformity""
+phenomenon around central galaxies has been regarded as a potential signature
+of ""galaxy assembly bias"" or ""pre-heating"", both of which interpret conformity
+as a result of direct environmental effects acting on galaxy formation.
+Building on the iHOD halo quenching framework developed in Zu & Mandelbaum
+(2015, 2016), we discover that our fiducial halo mass quenching model, without
+any galaxy assembly bias, can successfully explain the overall environmental
+dependence and the conformity of galaxy colours in SDSS, as measured by the
+mark correlation functions of galaxy colours and the red galaxy fractions
+around isolated primaries, respectively. Our fiducial iHOD halo quenching mock
+also correctly predicts the differences in the spatial clustering and
+galaxy-galaxy lensing signals between the more vs. less red galaxy subsamples,
+split by the red-sequence ridge-line at fixed stellar mass. Meanwhile, models
+that tie galaxy colours fully or partially to halo assembly bias have
+difficulties in matching all these observables simultaneously. Therefore, we
+demonstrate that the observed environmental dependence of galaxy colours can be
+naturally explained by the combination of 1) halo quenching and 2) the
+variation of halo mass function with environment --- an indirect environmental
+effect mediated by two separate physical processes.
+",0,1,0,0,0,0
+3638,Blue Supergiant X-ray Binaries in the Nearby Dwarf Galaxy IC 10,"  In young starburst galaxies, the X-ray population is expected to be dominated
+by the relics of the most massive and short-lived stars, black-hole and
+neutron-star high mass X-ray binaries (XRBs). In the closest such galaxy, IC
+10, we have made a multi-wavelength census of these objects. Employing a novel
+statistical correlation technique, we have matched our list of 110 X-ray point
+sources, derived from a decade of Chandra observations, against published
+photometric data. We report an 8 sigma correlation between the celestial
+coordinates of the two catalogs, with 42 X-ray sources having an optical
+counterpart. Applying an optical color-magnitude selection to isolate blue
+supergiant (SG) stars in IC 10, we find 16 matches. Both cases show a
+statistically significant overabundance versus the expectation value for chance
+alignments. The blue objects also exhibit systematically higher fx/fv ratios
+than other stars in the same magnitude range. Blue SG-XRBs include a major
+class of progenitors of double-degenerate binaries, hence their numbers are an
+important factor in modeling the rate of gravitational wave sources. We suggest
+that the anomalous features of the IC 10 stellar population are explained if
+the age of the IC 10 starburst is close to the time of the peak of interaction
+for massive binaries.
+",0,1,0,0,0,0
+3639,ConsiDroid: A Concolic-based Tool for Detecting SQL Injection Vulnerability in Android Apps,"  In this paper, we present a concolic execution technique for detecting SQL
+injection vulnerabilities in Android apps, with a new tool we called
+ConsiDroid. We extend the source code of apps with mocking technique, such that
+the execution of original source code is not affected. The extended source
+codes can be treated as Java applications and may be executed by SPF with
+concolic execution. We automatically produce a DummyMain class out of static
+analysis such that the essential functions are called sequentially and, the
+events leading to vulnerable functions are triggered. We extend SPF with taint
+analysis in ConsiDroid. For making taint analysis possible, we introduce a new
+technique of symbolic mock classes in order to ease the propagation of tainted
+values in the code. An SQL injection vulnerability is detected through
+receiving a tainted value by a vulnerable function. Besides, ConsiDroid takes
+advantage of static analysis to adjust SPF in order to inspect only suspicious
+paths. To illustrate the applicability of ConsiDroid, we have inspected
+randomly selected 140 apps from F-Droid repository. From these apps, we found
+three apps vulnerable to SQL injection. To verify their vulnerability, we
+analyzed the apps manually based on ConsiDroid's reports by using Robolectric.
+",1,0,0,0,0,0
+3640,Mesh Model (MeMo): A Systematic Approach to Agile System Engineering,"  Innovation and entrepreneurship have a very special role to play in creating
+sustainable development in the world. Engineering design plays a major role in
+innovation. These are not new facts. However this added to the fact that in
+current time knowledge seem to increase at an exponential rate, growing twice
+every few months. This creates a need to have newer methods to innovate with
+very little scope to fall short of the expectations from customers. In terms of
+reliable designing, system design tools and methodologies have been very
+helpful and have been in use in most engineering industries for decades now.
+But traditional system design is rigorous and rigid. As we can see, we need an
+innovation system that should be rigorous and flexible at the same time. We
+take our inspiration from biosphere, where some of the most rugged yet flexible
+plants are creepers which grow to create mesh. In this thematic paper we shall
+explain our approach to system engineering which we call the MeMo (Mesh Model)
+that fuses the rigor of system engineering with the flexibility of agile
+methods to create a scheme that can give rise to reliable innovation in the
+high risk market of today.
+",1,0,0,0,0,0
+3641,Alternative derivation of exact law for compressible and isothermal magnetohydrodynamics turbulence,"  The exact law for fully developed homogeneous compressible
+magnetohydrodynamics (CMHD) turbulence is derived. For an isothermal plasma,
+without the assumption of isotropy, the exact law is expressed as a function of
+the plasma velocity field, the compressible Alfvén velocity and the scalar
+density, instead of the Elsässer variables used in previous works. The
+theoretical results show four different types of terms that are involved in the
+nonlinear cascade of the total energy in the inertial range. Each category is
+examined in detail, in particular those that can be written either as source or
+flux terms. Finally, the role of the background magnetic field $B_0$ is
+highlighted and comparison with the incompressible MHD (IMHD) model is
+discussed. This point is particularly important when testing the exact law on
+numerical simulations and in situ observations in space plasmas.
+",0,1,0,0,0,0
+3642,CMS-HF Calorimeter Upgrade for Run II,"  CMS-HF Calorimeters have been undergoing a major upgrade for the last couple
+of years to alleviate the problems encountered during Run I, especially in the
+PMT and the readout systems. In this poster, the problems caused by the old
+PMTs installed in the detectors and their solutions will be explained.
+Initially, regular PMTs with thicker windows, causing large Cherenkov
+radiation, were used. Instead of the light coming through the fibers from the
+detector, stray muons passing through the PMT itself produce Cherenkov
+radiation in the PMT window, resulting in erroneously large signals. Usually,
+large signals are the result of very high-energy particles in the calorimeter
+and are tagged as important. As a result, these so-called window events
+generate false triggers. Four-anode PMTs with thinner windows were selected to
+reduce these window events. Additional channels also help eliminate such
+remaining events through algorithms comparing the output of different PMT
+channels. During the EYETS 16/17 period in the LHC operations, the final
+components of the modifications to the readout system, namely the two-channel
+front-end electronics cards, are installed. Complete upgrade of the HF
+Calorimeter, including the preparations for the Run II will be discussed in
+this poster, with possible effects on the eventual data taking.
+",0,1,0,0,0,0
+3643,Is Flat Fielding Safe for Precision CCD Astronomy?,"  The ambitious goals of precision cosmology with wide-field optical surveys
+such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope
+(LSST) demand, as their foundation, precision CCD astronomy. This in turn
+requires an understanding of previously uncharacterized sources of systematic
+error in CCD sensors, many of which manifest themselves as static effective
+variations in pixel area. Such variation renders a critical assumption behind
+the traditional procedure of flat fielding--that a sensor's pixels comprise a
+uniform grid--invalid. In this work, we present a method to infer a curl-free
+model of a sensor's underlying pixel grid from flat field images, incorporating
+the superposition of all electrostatic sensor effects--both known and
+unknown--present in flat field data. We use these pixel grid models to estimate
+the overall impact of sensor systematics on photometry, astrometry, and PSF
+shape measurements in a representative sensor from the Dark Energy Camera
+(DECam) and a prototype LSST sensor. Applying the method to DECam data recovers
+known significant sensor effects for which corrections are currently being
+developed within DES. For an LSST prototype CCD with pixel-response
+non-uniformity (PRNU) of 0.4%, we find the impact of ""improper"" flat-fielding
+on these observables is negligible in nominal .7"" seeing conditions. These
+errors scale linearly with the PRNU, so for future LSST production sensors,
+which may have larger PRNU, our method provides a way to assess whether
+pixel-level calibration beyond flat fielding will be required.
+",0,1,0,0,0,0
+3644,Gas removal in the Ursa Minor galaxy: linking hydrodynamics and chemical evolution models,"  We present results from a non-cosmological, three-dimensional hydrodynamical
+simulation of the gas in the dwarf spheroidal galaxy Ursa Minor. Assuming an
+initial baryonic-to-dark-matter ratio derived from the cosmic microwave
+background radiation, we evolved the galactic gas distribution over 3 Gyr,
+taking into account the effects of the types Ia and II supernovae. For the
+first time, we used in our simulation the instantaneous supernovae rates
+derived from a chemical evolution model applied to spectroscopic observational
+data of Ursa Minor. We show that the amount of gas that is lost in this process
+is variable with time and radius, being the highest rates observed during the
+initial 600 Myr in our simulation. Our results indicate that types Ia and II
+supernovae must be essential drivers of the gas loss in Ursa Minor galaxy (and
+probably in other similar dwarf galaxies), but it is ultimately the combination
+of galactic winds powered by these supernovae and environmental effects (e.g.,
+ram-pressure stripping) that results in the complete removal of the gas
+content.
+",0,1,0,0,0,0
+3645,Identifiability and Estimation of Structural Vector Autoregressive Models for Subsampled and Mixed Frequency Time Series,"  Causal inference in multivariate time series is challenging due to the fact
+that the sampling rate may not be as fast as the timescale of the causal
+interactions. In this context, we can view our observed series as a subsampled
+version of the desired series. Furthermore, due to technological and other
+limitations, series may be observed at different sampling rates, representing a
+mixed frequency setting. To determine instantaneous and lagged effects between
+time series at the true causal scale, we take a model-based approach based on
+structural vector autoregressive (SVAR) models. In this context, we present a
+unifying framework for parameter identifiability and estimation under both
+subsampling and mixed frequencies when the noise, or shocks, are non-Gaussian.
+Importantly, by studying the SVAR case, we are able to both provide
+identifiability and estimation methods for the causal structure of both lagged
+and instantaneous effects at the desired time scale. We further derive an exact
+EM algorithm for inference in both subsampled and mixed frequency settings. We
+validate our approach in simulated scenarios and on two real world data sets.
+",0,0,0,1,0,0
+3646,Towards Practical Differential Privacy for SQL Queries,"  Differential privacy promises to enable general data analytics while
+protecting individual privacy, but existing differential privacy mechanisms do
+not support the wide variety of features and databases used in real-world
+SQL-based analytics systems.
+This paper presents the first practical approach for differential privacy of
+SQL queries. Using 8.1 million real-world queries, we conduct an empirical
+study to determine the requirements for practical differential privacy, and
+discuss limitations of previous approaches in light of these requirements. To
+meet these requirements we propose elastic sensitivity, a novel method for
+approximating the local sensitivity of queries with general equijoins. We prove
+that elastic sensitivity is an upper bound on local sensitivity and can
+therefore be used to enforce differential privacy using any local
+sensitivity-based mechanism.
+We build FLEX, a practical end-to-end system to enforce differential privacy
+for SQL queries using elastic sensitivity. We demonstrate that FLEX is
+compatible with any existing database, can enforce differential privacy for
+real-world SQL queries, and incurs negligible (0.03%) performance overhead.
+",1,0,0,0,0,0
+3647,Adapting Engineering Education to Industrie 4.0 Vision,"  Industrie 4.0 is originally a future vision described in the high-tech
+strategy of the German government that is conceived upon the information and
+communication technologies like Cyber-Physical Systems, Internet of Things,
+Physical Internet and Internet of Services to achieve a high degree of
+flexibility in production, higher productivity rates through real-time
+monitoring and diagnosis, and a lower wastage rate of material in production.
+An important part of the tasks in the preparation for Industrie 4.0 is the
+adaption of the higher education to the requirements of this vision, in
+particular the engineering education. In this work, we introduce a road map
+consisting of three pillars describing the changes/enhancements to be conducted
+in the areas of curriculum development, lab concept, and student club
+activities. We also report our current application of this road map at the
+Turkish-German University, Istanbul.
+",1,0,0,0,0,0
+3648,A Survey on QoE-oriented Wireless Resources Scheduling,"  Future wireless systems are expected to provide a wide range of services to
+more and more users. Advanced scheduling strategies thus arise not only to
+perform efficient radio resource management, but also to provide fairness among
+the users. On the other hand, the users' perceived quality, i.e., Quality of
+Experience (QoE), is becoming one of the main drivers within the schedulers
+design. In this context, this paper starts by providing a comprehension of what
+is QoE and an overview of the evolution of wireless scheduling techniques.
+Afterwards, a survey on the most recent QoE-based scheduling strategies for
+wireless systems is presented, highlighting the application/service of the
+different approaches reported in the literature, as well as the parameters that
+were taken into account for QoE optimization. Therefore, this paper aims at
+helping readers interested in learning the basic concepts of QoE-oriented
+wireless resources scheduling, as well as getting in touch with the present
+time research frontier.
+",1,0,0,0,0,0
+3649,Wandering domains for diffeomorphisms of the k-torus: a remark on a theorem by Norton and Sullivan,"  We show that there is no C^{k+1} diffeomorphism of the k-torus which is
+semiconjugate to a minimal translation and has a wandering domain all of whose
+iterates are Euclidean balls.
+",0,0,1,0,0,0
+3650,Orthogonal Statistical Learning,"  We provide excess risk guarantees for statistical learning in the presence of
+an unknown nuisance component. We analyze a two-stage sample splitting
+meta-algorithm that takes as input two arbitrary estimation algorithms: one for
+the target model and one for the nuisance model. We show that if the population
+risk satisfies a condition called Neyman orthogonality, the impact of the first
+stage error on the excess risk bound achieved by the meta-algorithm is of
+second order. Our general theorem is agnostic to the particular algorithms used
+for the target and nuisance and only makes an assumption on their individual
+performance. This enables the use of a plethora of existing results from
+statistical learning and machine learning literature to give new guarantees for
+learning with a nuisance component. Moreover, by focusing on excess risk rather
+than parameter estimation, we can give guarantees under weaker assumptions than
+in previous works and accommodate the case where the target parameter belongs
+to a complex nonparametric class. When the nuisance and target parameters
+belong to arbitrary classes, we characterize conditions on the metric entropy
+such that oracle rates---rates of the same order as if we knew the nuisance
+model---are achieved. We also analyze the rates achieved by specific estimation
+algorithms such as variance-penalized empirical risk minimization, neural
+network estimation and sparse high-dimensional linear model estimation. We
+highlight the applicability of our results via four applications of primary
+importance: 1) heterogeneous treatment effect estimation, 2) offline policy
+optimization, 3) domain adaptation, and 4) learning with missing data.
+",1,0,1,1,0,0
+3651,Disentangling group and link persistence in Dynamic Stochastic Block models,"  We study the inference of a model of dynamic networks in which both
+communities and links keep memory of previous network states. By considering
+maximum likelihood inference from single snapshot observations of the network,
+we show that link persistence makes the inference of communities harder,
+decreasing the detectability threshold, while community persistence tends to
+make it easier. We analytically show that communities inferred from single
+network snapshot can share a maximum overlap with the underlying communities of
+a specific previous instant in time. This leads to time-lagged inference: the
+identification of past communities rather than present ones. Finally we compute
+the time lag and propose a corrected algorithm, the Lagged Snapshot Dynamic
+(LSD) algorithm, for community detection in dynamic networks. We analytically
+and numerically characterize the detectability transitions of such algorithm as
+a function of the memory parameters of the model and we make a comparison with
+a full dynamic inference.
+",1,1,0,1,0,0
+3652,A Reactive and Efficient Walking Pattern Generator for Robust Bipedal Locomotion,"  Available possibilities to prevent a biped robot from falling down in the
+presence of severe disturbances are mainly Center of Pressure (CoP) modulation,
+step location and timing adjustment, and angular momentum regulation. In this
+paper, we aim at designing a walking pattern generator which employs an optimal
+combination of these tools to generate robust gaits. In this approach, first,
+the next step location and timing are decided consistent with the commanded
+walking velocity and based on the Divergent Component of Motion (DCM)
+measurement. This stage which is done by a very small-size Quadratic Program
+(QP) uses the Linear Inverted Pendulum Model (LIPM) dynamics to adapt the
+switching contact location and time. Then, consistent with the first stage, the
+LIPM with flywheel dynamics is used to regenerate the DCM and angular momentum
+trajectories at each control cycle. This is done by modulating the CoP and
+Centroidal Momentum Pivot (CMP) to realize a desired DCM at the end of current
+step. Simulation results show the merit of this reactive approach in generating
+robust and dynamically consistent walking patterns.
+",1,0,0,0,0,0
+3653,Frequentist Consistency of Variational Bayes,"  A key challenge for modern Bayesian statistics is how to perform scalable
+inference of posterior distributions. To address this challenge, variational
+Bayes (VB) methods have emerged as a popular alternative to the classical
+Markov chain Monte Carlo (MCMC) methods. VB methods tend to be faster while
+achieving comparable predictive performance. However, there are few theoretical
+results around VB. In this paper, we establish frequentist consistency and
+asymptotic normality of VB methods. Specifically, we connect VB methods to
+point estimates based on variational approximations, called frequentist
+variational approximations, and we use the connection to prove a variational
+Bernstein-von Mises theorem. The theorem leverages the theoretical
+characterizations of frequentist variational approximations to understand
+asymptotic properties of VB. In summary, we prove that (1) the VB posterior
+converges to the Kullback-Leibler (KL) minimizer of a normal distribution,
+centered at the truth and (2) the corresponding variational expectation of the
+parameter is consistent and asymptotically normal. As applications of the
+theorem, we derive asymptotic properties of VB posteriors in Bayesian mixture
+models, Bayesian generalized linear mixed models, and Bayesian stochastic block
+models. We conduct a simulation study to illustrate these theoretical results.
+",1,0,1,1,0,0
+3654,"Quantum variance on quaternion algebras, II","  A method for determining quantum variance asymptotics on compact quotients
+attached to non-split quaternion algebras is developed in general and applied
+to ""microlocal lifts"" in the non-archimedean setting. The results obtained are
+in the spirit of recent work of Sarnak--Zhao.
+The arguments involve a careful analytic study of the theta correspondence,
+the interplay between additive and multiplicative harmonic analysis on
+quaternion algebras, the equidistribution of translates of elementary theta
+functions, and the Rallis inner product formula.
+",0,0,1,0,0,0
+3655,An intracardiac electrogram model to bridge virtual hearts and implantable cardiac devices,"  Virtual heart models have been proposed to enhance the safety of implantable
+cardiac devices through closed loop validation. To communicate with a virtual
+heart, devices have been driven by cardiac signals at specific sites. As a
+result, only the action potentials of these sites are sensed. However, the real
+device implanted in the heart will sense a complex combination of near and
+far-field extracellular potential signals. Therefore many device functions,
+such as blanking periods and refractory periods, are designed to handle these
+unexpected signals. To represent these signals, we develop an intracardiac
+electrogram (IEGM) model as an interface between the virtual heart and the
+device. The model can capture not only the local excitation but also far-field
+signals and pacing afterpotentials. Moreover, the sensing controller can
+specify unipolar or bipolar electrogram (EGM) sensing configurations and
+introduce various oversensing and undersensing modes. The simulation results
+show that the model is able to reproduce clinically observed sensing problems,
+which significantly extends the capabilities of the virtual heart model in the
+context of device validation.
+",1,1,0,0,0,0
+3656,A note on conditional versus joint unconditional weak convergence in bootstrap consistency results,"  The consistency of a bootstrap or resampling scheme is classically validated
+by weak convergence of conditional laws. However, when working with stochastic
+processes in the space of bounded functions and their weak convergence in the
+Hoffmann-J{\o}rgensen sense, an obstacle occurs: due to possible
+non-measurability, neither laws nor conditional laws are well-defined. Starting
+from an equivalent formulation of weak convergence based on the bounded
+Lipschitz metric, a classical circumvent is to formulate bootstrap consistency
+in terms of the latter distance between what might be called a
+\emph{conditional law} of the (non-measurable) bootstrap process and the law of
+the limiting process. The main contribution of this note is to provide an
+equivalent formulation of bootstrap consistency in the space of bounded
+functions which is more intuitive and easy to work with. Essentially, the
+equivalent formulation consists of (unconditional) weak convergence of the
+original process jointly with two bootstrap replicates. As a by-product, we
+provide two equivalent formulations of bootstrap consistency for statistics
+taking values in separable metric spaces: the first in terms of (unconditional)
+weak convergence of the statistic jointly with its bootstrap replicates, the
+second in terms of convergence in probability of the empirical distribution
+function of the bootstrap replicates. Finally, the asymptotic validity of
+bootstrap-based confidence intervals and tests is briefly revisited, with
+particular emphasis on the, in practice unavoidable, Monte Carlo approximation
+of conditional quantiles.
+",0,0,1,1,0,0
+3657,Dynamic scaling analysis of the long-range RKKY Ising spin glass Dy$_{x}$Y$_{1-x}$Ru$_{2}$Si$_{2}$,"  Dynamic scaling analyses of linear and nonlinear ac susceptibilities in a
+model magnet of the long-rang RKKY Ising spin glass (SG)
+Dy$_{x}$Y$_{1-x}$Ru$_{2}$Si$_{2}$ were examined. The obtained set of the
+critical exponents, $\gamma$ $\sim$ 1, $\beta$ $\sim$ 1, $\delta$ $\sim$ 2, and
+$z\nu$ $\sim$ 3.4, indicates the SG phase transition belongs to a different
+universality class from either the canonical (Heisenberg) or the short-range
+Ising SGs. The analyses also reveal a finite-temperature SG transition with the
+same critical exponents under a magnetic field and the phase transition line
+$T_{\mbox{g}}(H)$ described by $T_{\mbox{g}}(H)$ $=$
+$T_{\mbox{g}}(0)(1-AH^{2/\phi})$ with $\phi$ $\sim$ 2. The crossover exponent
+$\phi$ obeys the scaling relation $\phi$ $=$ $\gamma + \beta$ within the margin
+of errors. These results strongly suggest the spontaneous
+replica-symmetry-breaking (RSB) with a {\it non- or marginal-mean-field
+universality class} in the long-range RKKY Ising SG.
+",0,1,0,0,0,0
+3658,A Bayesian hierarchical model for related densities using Polya trees,"  Bayesian hierarchical models are used to share information between related
+samples and obtain more accurate estimates of sample-level parameters, common
+structure, and variation between samples. When the parameter of interest is the
+distribution or density of a continuous variable, a hierarchical model for
+distributions is required. A number of such models have been described in the
+literature using extensions of the Dirichlet process and related processes,
+typically as a distribution on the parameters of a mixing kernel. We propose a
+new hierarchical model based on the Polya tree, which allows direct modeling of
+densities and enjoys some computational advantages over the Dirichlet process.
+The Polya tree also allows more flexible modeling of the variation between
+samples, providing more informed shrinkage and permitting posterior inference
+on the dispersion function, which quantifies the variation among sample
+densities. We also show how the model can be extended to cluster samples in
+situations where the observed samples are believed to have been drawn from
+several latent populations.
+",0,0,0,1,0,0
+3659,Groups of fast homeomorphisms of the interval and the ping-pong argument,"  We adapt the Ping-Pong Lemma, which historically was used to study free
+products of groups, to the setting of the homeomorphism group of the unit
+interval. As a consequence, we isolate a large class of generating sets for
+subgroups of $\mathrm{Homeo}_+(I)$ for which certain finite dynamical data can
+be used to determine the marked isomorphism type of the groups which they
+generate. As a corollary, we will obtain a criteria for embedding subgroups of
+$\mathrm{Homeo}_+(I)$ into Richard Thompson's group $F$. In particular, every
+member of our class of generating sets generates a group which embeds into $F$
+and in particular is not a free product. An analogous abstract theory is also
+developed for groups of permutations of an infinite set.
+",0,0,1,0,0,0
+3660,The CodRep Machine Learning on Source Code Competition,"  CodRep is a machine learning competition on source code data. It is carefully
+designed so that anybody can enter the competition, whether professional
+researchers, students or independent scholars, without specific knowledge in
+machine learning or program analysis. In particular, it aims at being a common
+playground on which the machine learning and the software engineering research
+communities can interact. The competition has started on April 14th 2018 and
+has ended on October 14th 2018. The CodRep data is hosted at
+this https URL.
+",1,0,0,0,0,0
+3661,Flag representations of mixed volumes and mixed functionals of convex bodies,"  Mixed volumes $V(K_1,\dots, K_d)$ of convex bodies $K_1,\dots ,K_d$ in
+Euclidean space $\mathbb{R}^d$ are of central importance in the Brunn-Minkowski
+theory. Representations for mixed volumes are available in special cases, for
+example as integrals over the unit sphere with respect to mixed area measures.
+More generally, in Hug-Rataj-Weil (2013) a formula for $V(K [n], M[d-n])$,
+$n\in \{1,\dots ,d-1\}$, as a double integral over flag manifolds was
+established which involved certain flag measures of the convex bodies $K$ and
+$M$ (and required a general position of the bodies). In the following, we
+discuss the general case $V(K_1[n_1],\dots , K_k[n_k])$, $n_1+\cdots +n_k=d$,
+and show a corresponding result involving the flag measures
+$\Omega_{n_1}(K_1;\cdot),\dots, \Omega_{n_k}(K_k;\cdot)$. For this purpose, we
+first establish a curvature representation of mixed volumes over the normal
+bundles of the bodies involved.
+We also obtain a corresponding flag representation for the mixed functionals
+from translative integral geometry and a local version, for mixed (translative)
+curvature measures.
+",0,0,1,0,0,0
+3662,Learning Hard Alignments with Variational Inference,"  There has recently been significant interest in hard attention models for
+tasks such as object recognition, visual captioning and speech recognition.
+Hard attention can offer benefits over soft attention such as decreased
+computational cost, but training hard attention models can be difficult because
+of the discrete latent variables they introduce. Previous work used REINFORCE
+and Q-learning to approach these issues, but those methods can provide
+high-variance gradient estimates and be slow to train. In this paper, we tackle
+the problem of learning hard attention for a sequential task using variational
+inference methods, specifically the recently introduced VIMCO and NVIL.
+Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We
+demonstrate our method on a phoneme recognition task in clean and noisy
+environments and show that our method outperforms REINFORCE, with the
+difference being greater for a more complicated task.
+",1,0,0,1,0,0
+3663,A New Sparse and Robust Adaptive Lasso Estimator for the Independent Contamination Model,"  Many problems in signal processing require finding sparse solutions to
+under-determined, or ill-conditioned, linear systems of equations. When dealing
+with real-world data, the presence of outliers and impulsive noise must also be
+accounted for. In past decades, the vast majority of robust linear regression
+estimators has focused on robustness against rowwise contamination. Even so
+called `high breakdown' estimators rely on the assumption that a majority of
+rows of the regression matrix is not affected by outliers. Only very recently,
+the first cellwise robust regression estimation methods have been developed. In
+this paper, we define robust oracle properties, which an estimator must have in
+order to perform robust model selection for under-determined, or
+ill-conditioned linear regression models that are contaminated by cellwise
+outliers in the regression matrix. We propose and analyze a robustly weighted
+and adaptive Lasso type regularization term which takes into account cellwise
+outliers for model selection. The proposed regularization term is integrated
+into the objective function of the MM-estimator, which yields the proposed
+MM-Robust Weighted Adaptive Lasso (MM-RWAL), for which we prove that at least
+the weak robust oracle properties hold. A performance comparison to existing
+robust Lasso estimators is provided using Monte Carlo experiments. Further, the
+MM-RWAL is applied to determine the temporal releases of the European Tracer
+Experiment (ETEX) at the source location. This ill-conditioned linear inverse
+problem contains cellwise and rowwise outliers and is sparse both in the
+regression matrix and the parameter vector. The proposed RWAL penalty is not
+limited to the MM-estimator but can easily be integrated into the objective
+function of other robust estimators.
+",0,0,1,1,0,0
+3664,Systematic Quantum Mechanical Region Determination in QM/MM Simulation,"  Hybrid quantum mechanical-molecular mechanical (QM/MM) simulations are widely
+used in enzyme simulation. Over ten convergence studies of QM/MM methods have
+revealed over the past several years that key energetic and structural
+properties approach asymptotic limits with only very large (ca. 500-1000 atom)
+QM regions. This slow convergence has been observed to be due in part to
+significant charge transfer between the core active site and surrounding
+protein environment, which cannot be addressed by improvement of MM force
+fields or the embedding method employed within QM/MM. Given this slow
+convergence, it becomes essential to identify strategies for the most
+atom-economical determination of optimal QM regions and to gain insight into
+the crucial interactions captured only in large QM regions. Here, we extend and
+develop two methods for quantitative determination of QM regions. First, in the
+charge shift analysis (CSA) method, we probe the reorganization of electron
+density when core active site residues are removed completely, as determined by
+large-QM region QM/MM calculations. Second, we introduce the
+highly-parallelizable Fukui shift analysis (FSA), which identifies how
+core/substrate frontier states are altered by the presence of an additional QM
+residue on smaller initial QM regions. We demonstrate that the FSA and CSA
+approaches are complementary and consistent on three test case enzymes:
+catechol O-methyltransferase, cytochrome P450cam, and hen eggwhite lysozyme. We
+also introduce validation strategies and test sensitivities of the two methods
+to geometric structure, basis set size, and electronic structure methodology.
+Both methods represent promising approaches for the systematic, unbiased
+determination of quantum mechanical effects in enzymes and large systems that
+necessitate multi-scale modeling.
+",0,1,0,0,0,0
+3665,Comparison of electricity market designs for stable decentralized power grids,"  In this study, we develop a theoretical model of strategic equilibrium
+bidding and price-setting behaviour by heterogeneous and boundedly rational
+electricity producers and a grid operator in a single electricity market under
+uncertain information about production capabilities and electricity demand.
+We compare eight different market design variants and several levels of
+centralized electricity production that influence the spatial distribution of
+producers in the grid, their unit production and curtailment costs, and the
+mean and standard deviation of their production capabilities.
+Our market design variants differ in three aspects. Producers are either paid
+their individual bid price (""pay as bid"") or the (higher) market price set by
+the grid operator (""uniform pricing""). They are either paid for their bid
+quantity (""pay requested"") or for their actual supply (""pay supplied"") which
+may differ due to production uncertainty. Finally, excess production is either
+required to be curtailed or may be supplied to the grid.
+Overall, we find the combination of uniform pricing, paying for requested
+amounts, and required curtailment to perform best or second best in many
+respects, and to provide the best compromise between the goals of low economic
+costs, low consumer costs, positive profits, low balancing, low workload, and
+honest bidding behaviour.
+",0,1,0,0,0,0
+3666,"Look Mum, no VM Exits! (Almost)","  Multi-core CPUs are a standard component in many modern embedded systems.
+Their virtualisation extensions enable the isolation of services, and gain
+popularity to implement mixed-criticality or otherwise split systems. We
+present Jailhouse, a Linux-based, OS-agnostic partitioning hypervisor that uses
+novel architectural approaches to combine Linux, a powerful general-purpose
+system, with strictly isolated special-purpose components. Our design goals
+favour simplicity over features, establish a minimal code base, and minimise
+hypervisor activity.
+Direct assignment of hardware to guests, together with a deferred
+initialisation scheme, offloads any complex hardware handling and bootstrapping
+issues from the hypervisor to the general purpose OS. The hypervisor
+establishes isolated domains that directly access physical resources without
+the need for emulation or paravirtualisation. This retains, with negligible
+system overhead, Linux's feature-richness in uncritical parts, while frugal
+safety and real-time critical workloads execute in isolated, safe domains.
+",1,0,0,0,0,0
+3667,Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation,"  Low-rank modeling plays a pivotal role in signal processing and machine
+learning, with applications ranging from collaborative filtering, video
+surveillance, medical imaging, to dimensionality reduction and adaptive
+filtering. Many modern high-dimensional data and interactions thereof can be
+modeled as lying approximately in a low-dimensional subspace or manifold,
+possibly with additional structures, and its proper exploitations lead to
+significant reduction of costs in sensing, computation and storage. In recent
+years, there is a plethora of progress in understanding how to exploit low-rank
+structures using computationally efficient procedures in a provable manner,
+including both convex and nonconvex approaches. On one side, convex relaxations
+such as nuclear norm minimization often lead to statistically optimal
+procedures for estimating low-rank matrices, where first-order methods are
+developed to address the computational challenges; on the other side, there is
+emerging evidence that properly designed nonconvex procedures, such as
+projected gradient descent, often provide globally optimal solutions with a
+much lower computational cost in many problems. This survey article will
+provide a unified overview of these recent advances on low-rank matrix
+estimation from incomplete measurements. Attention is paid to rigorous
+characterization of the performance of these algorithms, and to problems where
+the low-rank matrix have additional structural properties that require new
+algorithmic designs and theoretical analysis.
+",0,0,0,1,0,0
+3668,Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network,"  Accurate delineation of the left ventricle (LV) is an important step in
+evaluation of cardiac function. In this paper, we present an automatic method
+for segmentation of the LV in cardiac CT angiography (CCTA) scans. Segmentation
+is performed in two stages. First, a bounding box around the LV is detected
+using a combination of three convolutional neural networks (CNNs).
+Subsequently, to obtain the segmentation of the LV, voxel classification is
+performed within the defined bounding box using a CNN. The study included CCTA
+scans of sixty patients, fifty scans were used to train the CNNs for the LV
+localization, five scans were used to train LV segmentation and the remaining
+five scans were used for testing the method. Automatic segmentation resulted in
+the average Dice coefficient of 0.85 and mean absolute surface distance of 1.1
+mm. The results demonstrate that automatic segmentation of the LV in CCTA scans
+using voxel classification with convolutional neural networks is feasible.
+",1,1,0,0,0,0
+3669,Maximal Jacobian-based Saliency Map Attack,"  The Jacobian-based Saliency Map Attack is a family of adversarial attack
+methods for fooling classification models, such as deep neural networks for
+image classification tasks. By saturating a few pixels in a given image to
+their maximum or minimum values, JSMA can cause the model to misclassify the
+resulting adversarial image as a specified erroneous target class. We propose
+two variants of JSMA, one which removes the requirement to specify a target
+class, and another that additionally does not need to specify whether to only
+increase or decrease pixel intensities. Our experiments highlight the
+competitive speeds and qualities of these variants when applied to datasets of
+hand-written digits and natural scenes.
+",0,0,0,1,0,0
+3670,Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator,"  Reinforcement learning (RL) has been successfully used to solve many
+continuous control tasks. Despite its impressive results however, fundamental
+questions regarding the sample complexity of RL on continuous problems remain
+open. We study the performance of RL in this setting by considering the
+behavior of the Least-Squares Temporal Difference (LSTD) estimator on the
+classic Linear Quadratic Regulator (LQR) problem from optimal control. We give
+the first finite-time analysis of the number of samples needed to estimate the
+value function for a fixed static state-feedback policy to within
+$\varepsilon$-relative error. In the process of deriving our result, we give a
+general characterization for when the minimum eigenvalue of the empirical
+covariance matrix formed along the sample path of a fast-mixing stochastic
+process concentrates above zero, extending a result by Koltchinskii and
+Mendelson in the independent covariates setting. Finally, we provide
+experimental evidence indicating that our analysis correctly captures the
+qualitative behavior of LSTD on several LQR instances.
+",1,0,0,1,0,0
+3671,VERITAS long term monitoring of Gamma-Ray emission from the BL Lacertae object,"  BL Lacertae is the prototype of the blazar subclass known as BL Lac type
+objects. BL Lacertae object itself is a low-frequency-peaked BL Lac(LBL). Very
+high energy (VHE) gamma ray emission from this source was discovered in 2005 by
+MAGIC observatory while the source was at a flaring state. Since then, VHE
+gamma rays from this source has been detected several times. However, all of
+those times the source was in a high activity state. Former studies suggest
+several non-thermal zones emitting in gamma-rays, then gamma-ray flare should
+be composed of a convolution. Observing the BL Lacertae object at quiescent
+states and active states is the key to disentangle these two components.
+VERITAS is monitoring the BL Lacertae object since 2011. The archival data set
+includes observations during flaring and quiescent states. This presentation
+reports on the preliminary results of the VERITAS observation between January
+2013 - December 2015, and simultaneous multiwavelength observations.
+",0,1,0,0,0,0
+3672,Spin Susceptibility of the Topological Superconductor UPt3 from Polarized Neutron Diffraction,"  Experiment and theory indicate that UPt3 is a topological superconductor in
+an odd-parity state, based in part from temperature independence of the NMR
+Knight shift. However, quasiparticle spin-flip scattering near a surface, where
+the Knight shift is measured, might be responsible. We use polarized neutron
+scattering to measure the bulk susceptibility with H||c, finding consistency
+with the Knight shift but inconsistent with theory for this field orientation.
+We infer that neither spin susceptibility nor Knight shift are a reliable
+indication of odd-parity.
+",0,1,0,0,0,0
+3673,Nano-jet Related to Bessel Beams and to Super-Resolutions in Micro-sphere Optical Experiments,"  The appearance of a Nano-jet in the micro-sphere optical experiments is
+analyzed by relating this effect to non-diffracting Bessel beams. By inserting
+a circular aperture with a radius which is in the order of subwavelength in the
+EM waist, and sending the transmitted light into a confocal microscope, EM
+fluctuations by the different Bessel beams are avoided. On this constant EM
+field evanescent waves are superposed. While this effect improves the
+optical-depth of the imaging process, the object fine-structures are obtained,
+from the modulation of the EM fields by the evanescent waves. The use of a
+combination of the micro-sphere optical system with an interferometer for phase
+contrast measurements is described.
+",0,1,0,0,0,0
+3674,Factorization systems on (stable) derivators,"  We define triangulated factorization systems on triangulated categories, and
+prove that a suitable subclass thereof (the normal triangulated torsion
+theories) corresponds bijectively to $t$-structures on the same category. This
+result is then placed in the framework of derivators regarding a triangulated
+category as the base of a stable derivator. More generally, we define derivator
+factorization systems in the 2-category $\mathrm{PDer}$, describing them as
+algebras for a suitable strict 2-monad (this result is of independent
+interest), and prove that a similar characterization still holds true: for a
+stable derivator $\mathbb D$, a suitable class of derivator factorization
+systems (the normal derivator torsion theories) correspond bijectively with
+$t$-structures on the base $\mathbb{D}(\mathbb{1})$ of the derivator. These two
+result can be regarded as the triangulated- and derivator- analogues,
+respectively, of the theorem that says that `$t$-structures are normal torsion
+theories' in the setting of stable $\infty$-categories, showing how the result
+remains true whatever the chosen model for stable homotopy theory is.
+",0,0,1,0,0,0
+3675,Dynamic Provable Data Possession Protocols with Public Verifiability and Data Privacy,"  Cloud storage services have become accessible and used by everyone.
+Nevertheless, stored data are dependable on the behavior of the cloud servers,
+and losses and damages often occur. One solution is to regularly audit the
+cloud servers in order to check the integrity of the stored data. The Dynamic
+Provable Data Possession scheme with Public Verifiability and Data Privacy
+presented in ACISP'15 is a straightforward design of such solution. However,
+this scheme is threatened by several attacks. In this paper, we carefully
+recall the definition of this scheme as well as explain how its security is
+dramatically menaced. Moreover, we proposed two new constructions for Dynamic
+Provable Data Possession scheme with Public Verifiability and Data Privacy
+based on the scheme presented in ACISP'15, one using Index Hash Tables and one
+based on Merkle Hash Trees. We show that the two schemes are secure and
+privacy-preserving in the random oracle model.
+",1,0,0,0,0,0
+3676,Robustness of persistent currents in two-dimensional Dirac systems with disorders,"  We consider two-dimensional (2D) Dirac quantum ring systems formed by the
+infinite mass constraint. When an Aharonov-Bohm magnetic flux is present, e.g.,
+through the center of the ring domain, persistent currents, i.e., permanent
+currents without dissipation, can arise. In real materials, impurities and
+defects are inevitable, raising the issue of robustness of the persistent
+currents. Using localized random potential to simulate the disorders, we
+investigate how the ensemble averaged current magnitude varies with the
+disorder density. For comparison, we study the nonrelativistic quantum
+counterpart by analyzing the solutions of the Schrödinger equation under
+the same geometrical and disorder settings. We find that, for the Dirac ring
+system, as the disorder density is systematically increased, the average
+current decreases slowly initially and then plateaus at a finite nonzero value,
+indicating remarkable robustness of the persistent currents. The physical
+mechanism responsible for the robustness is the emergence of a class of
+boundary states - whispering gallery modes. In contrast, in the Schrödinger
+ring system, such boundary states cannot form and the currents diminish rapidly
+to zero with increase in the disorder density. We develop a physical theory
+based on a quasi one-dimensional approximation to understand the striking
+contrast in the behaviors of the persistent currents in the Dirac and
+Schrödinger rings. Our 2D Dirac ring systems with disorders can be
+experimentally realized, e.g., on the surface of a topological insulator with
+natural or deliberately added impurities from the fabrication process.
+",0,1,0,0,0,0
+3677,On the arithmetically Cohen-Macaulay property for sets of points in multiprojective spaces,"  We study the arithmetically Cohen-Macaulay (ACM) property for finite sets of
+points in multiprojective spaces, especially $(\mathbb P^1)^n$. A combinatorial
+characterization, the $(\star)$-property, is known in $\mathbb P^1 \times
+\mathbb P^1$. We propose a combinatorial property, $(\star_n)$, that directly
+generalizes the $(\star)$-property to $(\mathbb P^1)^n$ for larger $n$. We show
+that $X$ is ACM if and only if it satisfies the $(\star_n)$-property. The main
+tool for several of our results is an extension to the multiprojective setting
+of certain liaison methods in projective space.
+",0,0,1,0,0,0
+3678,Fitting 3D Shapes from Partial and Noisy Point Clouds with Evolutionary Computing,"  Point clouds obtained from photogrammetry are noisy and incomplete models of
+reality. We propose an evolutionary optimization methodology that is able to
+approximate the underlying object geometry on such point clouds. This approach
+assumes a priori knowledge on the 3D structure modeled and enables the
+identification of a collection of primitive shapes approximating the scene.
+Built-in mechanisms that enforce high shape diversity and adaptive population
+size make this method suitable to modeling both simple and complex scenes. We
+focus here on the case of cylinder approximations and we describe, test, and
+compare a set of mutation operators designed for optimal exploration of their
+search space. We assess the robustness and limitations of this algorithm
+through a series of synthetic examples, and we finally demonstrate its general
+applicability on two real-life cases in vegetation and industrial settings.
+",1,0,0,0,1,0
+3679,Unique Information and Secret Key Decompositions,"  The unique information ($UI$) is an information measure that quantifies a
+deviation from the Blackwell order. We have recently shown that this quantity
+is an upper bound on the one-way secret key rate. In this paper, we prove a
+triangle inequality for the $UI$, which implies that the $UI$ is never greater
+than one of the best known upper bounds on the two-way secret key rate. We
+conjecture that the $UI$ lower bounds the two-way rate and discuss implications
+of the conjecture.
+",1,0,0,0,0,0
+3680,Decomposition Algorithm for Distributionally Robust Optimization using Wasserstein Metric,"  We study distributionally robust optimization (DRO) problems where the
+ambiguity set is defined using the Wasserstein metric. We show that this class
+of DRO problems can be reformulated as semi-infinite programs. We give an
+exchange method to solve the reformulated problem for the general nonlinear
+model, and a central cutting-surface method for the convex case, assuming that
+we have a separation oracle. We used a distributionally robust generalization
+of the logistic regression model to test our algorithm. Numerical experiments
+on the distributionally robust logistic regression models show that the number
+of oracle calls are typically 20 ? 50 to achieve 5-digit precision. The
+solution found by the model is generally better in its ability to predict with
+a smaller standard error.
+",0,0,1,0,0,0
+3681,PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography,"  Privacy is a major issue in learning from distributed data. Recently the
+cryptographic literature has provided several tools for this task. However,
+these tools either reduce the quality/accuracy of the learning
+algorithm---e.g., by adding noise---or they incur a high performance penalty
+and/or involve trusting external authorities.
+We propose a methodology for {\sl private distributed machine learning from
+light-weight cryptography} (in short, PD-ML-Lite). We apply our methodology to
+two major ML algorithms, namely non-negative matrix factorization (NMF) and
+singular value decomposition (SVD). Our resulting protocols are communication
+optimal, achieve the same accuracy as their non-private counterparts, and
+satisfy a notion of privacy---which we define---that is both intuitive and
+measurable. Our approach is to use lightweight cryptographic protocols (secure
+sum and normalized secure sum) to build learning algorithms rather than wrap
+complex learning algorithms in a heavy-cost MPC framework.
+We showcase our algorithms' utility and privacy on several applications: for
+NMF we consider topic modeling and recommender systems, and for SVD, principal
+component regression, and low rank approximation.
+",1,0,0,1,0,0
+3682,ISS Property with Respect to Boundary Disturbances for a Class of Riesz-Spectral Boundary Control Systems,"  This paper deals with the establishment of Input-to-State Stability (ISS)
+properties for infinite dimensional systems with respect to both boundary and
+distributed disturbances. First, an ISS estimate is established with respect to
+finite dimensional boundary disturbances for a class of Riesz-spectral boundary
+control systems satisfying certain eigenvalue constraints. Second, a concept of
+weak solutions is introduced in order to relax the disturbances regularity
+assumptions required to ensure the existence of strong solutions. The proposed
+concept of weak solutions, that applies to a large class of boundary control
+systems which is not limited to the Riesz-spectral ones, provides a natural
+extension of the concept of both strong and mild solutions. Assuming that an
+ISS estimate holds true for strong solutions, we show the existence, the
+uniqueness, and the ISS property of the weak solutions.
+",1,0,0,0,0,0
+3683,English-Japanese Neural Machine Translation with Encoder-Decoder-Reconstructor,"  Neural machine translation (NMT) has recently become popular in the field of
+machine translation. However, NMT suffers from the problem of repeating or
+missing words in the translation. To address this problem, Tu et al. (2017)
+proposed an encoder-decoder-reconstructor framework for NMT using
+back-translation. In this method, they selected the best forward translation
+model in the same manner as Bahdanau et al. (2015), and then trained a
+bi-directional translation model as fine-tuning. Their experiments show that it
+offers significant improvement in BLEU scores in Chinese-English translation
+task. We confirm that our re-implementation also shows the same tendency and
+alleviates the problem of repeating and missing words in the translation on a
+English-Japanese task too. In addition, we evaluate the effectiveness of
+pre-training by comparing it with a jointly-trained model of forward
+translation and back-translation.
+",1,0,0,0,0,0
+3684,Consistency Guarantees for Permutation-Based Causal Inference Algorithms,"  Bayesian networks, or directed acyclic graph (DAG) models, are widely used to
+represent complex causal systems. Since the basic task of learning a Bayesian
+network from data is NP-hard, a standard approach is greedy search over the
+space of DAGs or Markov equivalent DAGs. Since the space of DAGs on p nodes and
+the associated space of Markov equivalence classes are both much larger than
+the space of permutations, it is desirable to consider permutation-based
+searches. We here provide the first consistency guarantees, both uniform and
+high-dimensional, of a permutation-based greedy search. Geometrically, this
+search corresponds to a simplex-type algorithm on a sub-polytope of the
+permutohedron, the DAG associahedron. Every vertex in this polytope is
+associated with a DAG, and hence with a collection of permutations that are
+consistent with the DAG ordering. A walk is performed on the edges of the
+polytope maximizing the sparsity of the associated DAGs. We show based on
+simulations that this permutation search is competitive with standard
+approaches.
+",0,0,1,1,0,0
+3685,TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension,"  We present TriviaQA, a challenging reading comprehension dataset containing
+over 650K question-answer-evidence triples. TriviaQA includes 95K
+question-answer pairs authored by trivia enthusiasts and independently gathered
+evidence documents, six per question on average, that provide high quality
+distant supervision for answering the questions. We show that, in comparison to
+other recently introduced large-scale datasets, TriviaQA (1) has relatively
+complex, compositional questions, (2) has considerable syntactic and lexical
+variability between questions and corresponding answer-evidence sentences, and
+(3) requires more cross sentence reasoning to find answers. We also present two
+baseline algorithms: a feature-based classifier and a state-of-the-art neural
+network, that performs well on SQuAD reading comprehension. Neither approach
+comes close to human performance (23% and 40% vs. 80%), suggesting that
+TriviaQA is a challenging testbed that is worth significant future study. Data
+and code available at -- this http URL
+",1,0,0,0,0,0
+3686,"Optimizing Node Discovery on Networks: Problem Definitions, Fast Algorithms, and Observations","  Many people dream to become famous, YouTube video makers also wish their
+videos to have a large audience, and product retailers always hope to expose
+their products to customers as many as possible. Do these seemingly different
+phenomena share a common structure? We find that fame, popularity, or exposure,
+could be modeled as a node's discoverability on some properly defined network,
+and all of the previously mentioned phenomena can be commonly stated as a
+target node wants to be discovered easily by the other nodes in the network. In
+this work, we explicitly define a node's discoverability in a network, and
+formulate a general node discoverability optimization problem, where the goal
+is to create a budgeted set of incoming edges to the target node so as to
+optimize the target node's discoverability in the network. Although the
+optimization problem is proven to be NP-hard, we find that the defined
+discoverability measures have good properties that enable us to use a greedy
+algorithm to find provably near-optimal solutions. The computational complexity
+of a greedy algorithm is dominated by the time cost of an oracle call, i.e.,
+calculating the marginal gain of a node. To scale up the oracle call over large
+networks, we propose an estimation-and-refinement approach, that provides a
+good trade-off between estimation accuracy and computational efficiency.
+Experiments conducted on real-world networks demonstrate that our method is
+thousands of times faster than an exact method using dynamic programming,
+thereby allowing us to solve the node discoverability optimization problem on
+large networks.
+",1,0,0,0,0,0
+3687,Some properties of h-MN-convexity and Jensen's type inequalities,"  In this work, we introduce the class of $h$-${\rm{MN}}$-convex functions by
+generalizing the concept of ${\rm{MN}}$-convexity and combining it with
+$h$-convexity. Namely, Let $I,J$ be two intervals subset of
+$\left(0,\infty\right)$ such that $\left(0,1\right)\subseteq J$ and
+$\left[a,b\right]\subseteq I$. Consider a non-negative function $h:
+(0,\infty)\to \left(0,\infty\right)$ and let ${\rm{M}}:\left[0,1\right]\to
+\left[a,b\right] $ $(0<a<b)$ be a Mean function given by
+${\rm{\rm{M}}}\left(t\right)={\rm{\rm{M}}}\left( {h(t);a,b} \right)$; where
+by ${\rm{\rm{M}}}\left( {h(t);a,b} \right)$ we mean one of the following
+functions: $A_h\left( {a,b} \right):=h\left( {1 - t} \right)a + h(t) b$,
+$G_h\left( {a,b} \right)=a^{h(1-t)} b^{h(t)}$ and $H_h\left( {a,b}
+\right):=\frac{ab}{h(t) a + h\left( {1 - t} \right)b} = \frac{1}{A_h\left(
+{\frac{1}{a},\frac{1}{b}} \right)}$; with the property that
+${\rm{\rm{M}}}\left( {h(0);a,b} \right)=a$ and ${\rm{M}}\left( {h(1);a,b}
+\right)=b$.
+A function $f : I \to \left(0,\infty\right)$ is said to be
+$h$-${\rm{\rm{MN}}}$-convex (concave) if the inequality \begin{align*} f
+\left({\rm{M}}\left(t;x, y\right)\right) \le (\ge) \, {\rm{N}}\left(h(t);f (x),
+f (y)\right), \end{align*} holds for all $x,y \in I$ and $t\in [0,1]$, where M
+and N are two mean functions. In this way, nine classes of
+$h$-${\rm{MN}}$-convex functions are established and some of their analytic
+properties are explored and investigated. Characterizations of each type are
+given. Various Jensen's type inequalities and their converses are proved.
+",0,0,1,0,0,0
+3688,Impact of Traditional Sparse Optimizations on a Migratory Thread Architecture,"  Achieving high performance for sparse applications is challenging due to
+irregular access patterns and weak locality. These properties preclude many
+static optimizations and degrade cache performance on traditional systems. To
+address these challenges, novel systems such as the Emu architecture have been
+proposed. The Emu design uses light-weight migratory threads, narrow memory,
+and near-memory processing capabilities to address weak locality and reduce the
+total load on the memory system. Because the Emu architecture is fundamentally
+different than cache based hierarchical memory systems, it is crucial to
+understand the cost-benefit tradeoffs of standard sparse algorithm
+optimizations on Emu hardware. In this work, we explore sparse matrix-vector
+multiplication (SpMV) on the Emu architecture. We investigate the effects of
+different sparse optimizations such as dense vector data layouts, work
+distributions, and matrix reorderings. Our study finds that initially
+distributing work evenly across the system is inadequate to maintain load
+balancing over time due to the migratory nature of Emu threads. In severe
+cases, matrix sparsity patterns produce hot-spots as many migratory threads
+converge on a single resource. We demonstrate that known matrix reordering
+techniques can improve SpMV performance on the Emu architecture by as much as
+70% by encouraging more consistent load balancing. This can be compared with a
+performance gain of no more than 16% on a cache-memory based system.
+",1,0,0,0,0,0
+3689,Finding Bottlenecks: Predicting Student Attrition with Unsupervised Classifier,"  With pressure to increase graduation rates and reduce time to degree in
+higher education, it is important to identify at-risk students early. Automated
+early warning systems are therefore highly desirable. In this paper, we use
+unsupervised clustering techniques to predict the graduation status of declared
+majors in five departments at California State University Northridge (CSUN),
+based on a minimal number of lower division courses in each major. In addition,
+we use the detected clusters to identify hidden bottleneck courses.
+",1,0,0,1,0,0
+3690,Chemical evolution of 244Pu in the solar vicinity and its implication for the properties of r-process production,"  Meteoritic abundances of r-process elements are analyzed to deduce the
+history of chemical enrichment by r-process from the beginning of disk
+formation to the present time in the solar vicinity, by combining the abundance
+information from short-lived radioactive nuclei such as 244Pu with that from
+stable r-process nuclei such as Eu. These two types of nuclei can be associated
+with one r-process event and cumulation of events till formation of the solar
+system, respectively. With help of the observed local star formation history,
+we deduce the chemical evolution of 244Pu and obtain three main results: (i)
+the last r-process event occurred 130-140 Myr before formation of the solar
+system, (ii) the present-day low 244Pu abundance as measured in deep sea
+reservoirs results from the low recent star formation rate compared to ~4.5 - 5
+Gyr ago, and (iii) there were ~15 r-process events in the solar vicinity from
+formation of the Galaxy to the time of solar system formation and ~30 r-process
+events to the present time. Then, adopting a reasonable hypothesis that a
+neutron star merger is the r-process production site, we find that the ejected
+r-process elements are extensively spread out and mixed with interstellar
+matter with a mass of ~3.5 million solar masses, which is about 100 times
+larger than that for supernova ejecta. In addition, the event frequency of
+r-process production is estimated to be one per about 1400 core-collapse
+supernovae, which is identical to the frequency of neutron star mergers
+estimated from the analysis of stellar abundances.
+",0,1,0,0,0,0
+3691,SAVITR: A System for Real-time Location Extraction from Microblogs during Emergencies,"  We present SAVITR, a system that leverages the information posted on the
+Twitter microblogging site to monitor and analyse emergency situations. Given
+that only a very small percentage of microblogs are geo-tagged, it is essential
+for such a system to extract locations from the text of the microblogs. We
+employ natural language processing techniques to infer the locations mentioned
+in the microblog text, in an unsupervised fashion and display it on a map-based
+interface. The system is designed for efficient performance, achieving an
+F-score of 0.79, and is approximately two orders of magnitude faster than other
+available tools for location extraction.
+",1,0,0,0,0,0
+3692,Fractional Driven Damped Oscillator,"  The resonances associated with a fractional damped oscillator which is driven
+by an oscillatory external force are studied. It is shown that such resonances
+can be manipulated by tuning up either the coefficient of the fractional
+damping or the order of the corresponding fractional derivatives.
+",0,1,0,0,0,0
+3693,Bootstrap confidence sets for spectral projectors of sample covariance,"  Let $X_{1},\ldots,X_{n}$ be i.i.d. sample in $\mathbb{R}^{p}$ with zero mean
+and the covariance matrix $\mathbf{\Sigma}$. The problem of recovering the
+projector onto an eigenspace of $\mathbf{\Sigma}$ from these observations
+naturally arises in many applications. Recent technique from [Koltchinskii,
+Lounici, 2015] helps to study the asymptotic distribution of the distance in
+the Frobenius norm $\| \mathbf{P}_r - \widehat{\mathbf{P}}_r \|_{2}$ between
+the true projector $\mathbf{P}_r$ on the subspace of the $r$-th eigenvalue and
+its empirical counterpart $\widehat{\mathbf{P}}_r$ in terms of the effective
+rank of $\mathbf{\Sigma}$. This paper offers a bootstrap procedure for building
+sharp confidence sets for the true projector $\mathbf{P}_r$ from the given
+data. This procedure does not rely on the asymptotic distribution of $\|
+\mathbf{P}_r - \widehat{\mathbf{P}}_r \|_{2}$ and its moments. It could be
+applied for small or moderate sample size $n$ and large dimension $p$. The main
+result states the validity of the proposed procedure for finite samples with an
+explicit error bound for the error of bootstrap approximation. This bound
+involves some new sharp results on Gaussian comparison and Gaussian
+anti-concentration in high-dimensional spaces. Numeric results confirm a good
+performance of the method in realistic examples.
+",0,0,1,1,0,0
+3694,Locally recoverable codes from algebraic curves and surfaces,"  A locally recoverable code is a code over a finite alphabet such that the
+value of any single coordinate of a codeword can be recovered from the values
+of a small subset of other coordinates. Building on work of Barg, Tamo, and
+Vlăduţ, we present several constructions of locally recoverable codes
+from algebraic curves and surfaces.
+",1,0,1,0,0,0
+3695,Specification properties on uniform spaces,"  In the following text we introduce specification property (stroboscopical
+property) for dynamical systems on uniform space. We focus on two classes of
+dynamical systems: generalized shifts and dynamical systems with Alexandroff
+compactification of a discrete space as phase space. We prove that for a
+discrete finite topological space $X$ with at least two elements, a nonempty
+set $\Gamma$ and a self--map $\varphi:\Gamma\to\Gamma$ the generalized shift
+dynamical system $(X^\Gamma,\sigma_\varphi)$: \begin{itemize} \item has
+(almost) weak specification property if and only if $\varphi:\Gamma\to\Gamma$
+does not have any periodic point,
+\item has (uniform) stroboscopical property if and only if
+$\varphi:\Gamma\to\Gamma$
+is one-to-one. \end{itemize}
+",0,0,1,0,0,0
+3696,Tidal tails around the outer halo globular clusters Eridanus and Palomar 15,"  We report the discovery of tidal tails around the two outer halo globular
+clusters, Eridanus and Palomar 15, based on $gi$-band images obtained with
+DECam at the CTIO 4-m Blanco Telescope. The tidal tails are among the most
+remote stellar streams presently known in the Milky Way halo. Cluster members
+have been determined from the color-magnitude diagrams and used to establish
+the radial density profiles, which show, in both cases, a strong departure in
+the outer regions from the best-fit King profile. Spatial density maps reveal
+tidal tails stretching out on opposite sides of both clusters, extending over a
+length of $\sim$760 pc for Eridanus and $\sim$1160 pc for Palomar 15. The great
+circle projected from the Palomar 15 tidal tails encompasses the Galactic
+Center, while that for Eridanus passes close to four dwarf satellite galaxies,
+one of which (Sculptor) is at a comparable distance to that of Eridanus.
+",0,1,0,0,0,0
+3697,Learning by Playing - Solving Sparse Reward Tasks from Scratch,"  We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in
+the context of Reinforcement Learning (RL). SAC-X enables learning of complex
+behaviors - from scratch - in the presence of multiple sparse reward signals.
+To this end, the agent is equipped with a set of general auxiliary tasks, that
+it attempts to learn simultaneously via off-policy RL. The key idea behind our
+method is that active (learned) scheduling and execution of auxiliary policies
+allows the agent to efficiently explore its environment - enabling it to excel
+at sparse reward RL. Our experiments in several challenging robotic
+manipulation settings demonstrate the power of our approach.
+",1,0,0,1,0,0
+3698,An Infinite Hidden Markov Model With Similarity-Biased Transitions,"  We describe a generalization of the Hierarchical Dirichlet Process Hidden
+Markov Model (HDP-HMM) which is able to encode prior information that state
+transitions are more likely between ""nearby"" states. This is accomplished by
+defining a similarity function on the state space and scaling transition
+probabilities by pair-wise similarities, thereby inducing correlations among
+the transition distributions. We present an augmented data representation of
+the model as a Markov Jump Process in which: (1) some jump attempts fail, and
+(2) the probability of success is proportional to the similarity between the
+source and destination states. This augmentation restores conditional conjugacy
+and admits a simple Gibbs sampler. We evaluate the model and inference method
+on a speaker diarization task and a ""harmonic parsing"" task using four-part
+chorale data, as well as on several synthetic datasets, achieving favorable
+comparisons to existing models.
+",1,0,0,1,0,0
+3699,Methods for Estimation of Convex Sets,"  In the framework of shape constrained estimation, we review methods and works
+done in convex set estimation. These methods mostly build on stochastic and
+convex geometry, empirical process theory, functional analysis, linear
+programming, extreme value theory, etc. The statistical problems that we review
+include density support estimation, estimation of the level sets of densities
+or depth functions, nonparametric regression, etc. We focus on the estimation
+of convex sets under the Nikodym and Hausdorff metrics, which require different
+techniques and, quite surprisingly, lead to very different results, in
+particular in density support estimation. Finally, we discuss computational
+issues in high dimensions.
+",0,0,1,1,0,0
+3700,Dynamics of Charged Bulk Viscous Collapsing Cylindrical Source With Heat Flux,"  In this paper, we have explored the effects of dissipation on the dynamics of
+charged bulk viscous collapsing cylindrical source which allows the out follow
+of heat flux in the form of radiations. Misner-Sharp formulism has been
+implemented to drive the dynamical equation in term of proper time and radial
+derivatives. We have investigated the effects of charge and bulk viscosity on
+the dynamics of collapsing cylinder. To determine the effects of radial heat
+flux, we have formulated the heat transport equations in the context of
+M$\ddot{u}$ller-Israel-Stewart theory by assuming that thermodynamics
+viscous/heat coupling coefficients can be neglected within some approximations.
+In our discussion, we have introduced the viscosity by the standard
+(non-casual) thermodynamics approach. The dynamical equations have been coupled
+with the heat transport equation equation, the consequences of resulting
+coupled heat equation have been analyzed in detail.
+",0,1,0,0,0,0
+3701,An Invitation to Polynomiography via Exponential Series,"  The subject of Polynomiography deals with algorithmic visualization of
+polynomial equations, having many applications in STEM and art, see [Kal04].
+Here we consider the polynomiography of the partial sums of the exponential
+series. While the exponential function is taught in standard calculus courses,
+it is unlikely that properties of zeros of its partial sums are considered in
+such courses, let alone their visualization as science or art. The Monthly
+article Zemyan discusses some mathematical properties of these zeros. Here we
+exhibit some fractal and non-fractal polynomiographs of the partial sums while
+also presenting a brief introduction of the underlying concepts.
+Polynomiography establishes a different kind of appreciation of the
+significance of polynomials in STEM, as well as in art. It helps in the
+teaching of various topics at diverse levels. It also leads to new discoveries
+on polynomials and inspires new applications. We also present a link for the
+educator to get access to a demo polynomiography software together with a
+module that helps teach basic topics to middle and high school students, as
+well as undergraduates.
+",1,0,1,0,0,0
+3702,Towards Comfortable Cycling: A Practical Approach to Monitor the Conditions in Cycling Paths,"  This is a no brainer. Using bicycles to commute is the most sustainable form
+of transport, is the least expensive to use and are pollution-free. Towns and
+cities have to be made bicycle-friendly to encourage their wide usage.
+Therefore, cycling paths should be more convenient, comfortable, and safe to
+ride. This paper investigates a smartphone application, which passively
+monitors the road conditions during cyclists ride. To overcome the problems of
+monitoring roads, we present novel algorithms that sense the rough cycling
+paths and locate road bumps. Each event is detected in real time to improve the
+user friendliness of the application. Cyclists may keep their smartphones at
+any random orientation and placement. Moreover, different smartphones sense the
+same incident dissimilarly and hence report discrepant sensor values. We
+further address the aforementioned difficulties that limit such crowd-sourcing
+application. We evaluate our sensing application on cycling paths in Singapore,
+and show that it can successfully detect such bad road conditions.
+",1,0,0,0,0,0
+3703,Comparison moduli spaces of Riemann surfaces,"  We define a kind of moduli space of nested surfaces and mappings, which we
+call a comparison moduli space. We review examples of such spaces in geometric
+function theory and modern Teichmueller theory, and illustrate how a wide range
+of phenomena in complex analysis are captured by this notion of moduli space.
+The paper includes a list of open problems in classical and modern function
+theory and Teichmueller theory ranging from general theoretical questions to
+specific technical problems.
+",0,0,1,0,0,0
+3704,Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data,"  Manually labeled corpora are expensive to create and often not available for
+low-resource languages or domains. Automatic labeling approaches are an
+alternative way to obtain labeled data in a quicker and cheaper way. However,
+these labels often contain more errors which can deteriorate a classifier's
+performance when trained on this data. We propose a noise layer that is added
+to a neural network architecture. This allows modeling the noise and train on a
+combination of clean and noisy data. We show that in a low-resource NER task we
+can improve performance by up to 35% by using additional, noisy data and
+handling the noise.
+",0,0,0,1,0,0
+3705,Prior-aware Dual Decomposition: Document-specific Topic Inference for Spectral Topic Models,"  Spectral topic modeling algorithms operate on matrices/tensors of word
+co-occurrence statistics to learn topic-specific word distributions. This
+approach removes the dependence on the original documents and produces
+substantial gains in efficiency and provable topic inference, but at a cost:
+the model can no longer provide information about the topic composition of
+individual documents. Recently Thresholded Linear Inverse (TLI) is proposed to
+map the observed words of each document back to its topic composition. However,
+its linear characteristics limit the inference quality without considering the
+important prior information over topics. In this paper, we evaluate Simple
+Probabilistic Inverse (SPI) method and novel Prior-aware Dual Decomposition
+(PADD) that is capable of learning document-specific topic compositions in
+parallel. Experiments show that PADD successfully leverages topic correlations
+as a prior, notably outperforming TLI and learning quality topic compositions
+comparable to Gibbs sampling on various data.
+",1,0,0,0,0,0
+3706,Spectra of Earth-like Planets Through Geological Evolution Around FGKM Stars,"  Future observations of terrestrial exoplanet atmospheres will occur for
+planets at different stages of geological evolution. We expect to observe a
+wide variety of atmospheres and planets with alternative evolutionary paths,
+with some planets resembling Earth at different epochs. For an Earth-like
+atmospheric time trajectory, we simulate planets from prebiotic to current
+atmosphere based on geological data. We use a stellar grid F0V to M8V
+($T_\mathrm{eff}$ = 7000$\mskip3mu$K to 2400$\mskip3mu$K) to model four
+geological epochs of Earth's history corresponding to a prebiotic world
+(3.9$\mskip3mu$Ga), the rise of oxygen at 2.0$\mskip3mu$Ga and at
+0.8$\mskip3mu$Ga, and the modern Earth. We show the VIS - IR spectral features,
+with a focus on biosignatures through geological time for this grid of Sun-like
+host stars and the effect of clouds on their spectra.
+We find that the observability of biosignature gases reduces with increasing
+cloud cover and increases with planetary age. The observability of the visible
+O$_2$ feature for lower concentrations will partly depend on clouds, which
+while slightly reducing the feature increase the overall reflectivity thus the
+detectable flux of a planet. The depth of the IR ozone feature contributes
+substantially to the opacity at lower oxygen concentrations especially for the
+high near-UV stellar environments around F stars. Our results are a grid of
+model spectra for atmospheres representative of Earth's geological history to
+inform future observations and instrument design and are publicly available
+online.
+",0,1,0,0,0,0
+3707,Empathy in Bimatrix Games,"  Although the definition of what empathetic preferences exactly are is still
+evolving, there is a general consensus in the psychology, science and
+engineering communities that the evolution toward players' behaviors in
+interactive decision-making problems will be accompanied by the exploitation of
+their empathy, sympathy, compassion, antipathy, spitefulness, selfishness,
+altruism, and self-abnegating states in the payoffs. In this article, we study
+one-shot bimatrix games from a psychological game theory viewpoint. A new
+empathetic payoff model is calculated to fit empirical observations and both
+pure and mixed equilibria are investigated. For a realized empathy structure,
+the bimatrix game is categorized among four generic class of games. Number of
+interesting results are derived. A notable level of involvement can be observed
+in the empathetic one-shot game compared the non-empathetic one and this holds
+even for games with dominated strategies. Partial altruism can help in breaking
+symmetry, in reducing payoff-inequality and in selecting social welfare and
+more efficient outcomes. By contrast, partial spite and self-abnegating may
+worsen payoff equity. Empathetic evolutionary game dynamics are introduced to
+capture the resulting empathetic evolutionarily stable strategies under wide
+range of revision protocols including Brown-von Neumann-Nash, Smith, imitation,
+replicator, and hybrid dynamics. Finally, mutual support and Berge solution are
+investigated and their connection with empathetic preferences are established.
+We show that pure altruism is logically inconsistent, only by balancing it with
+some partial selfishness does it create a consistent psychology.
+",1,0,0,0,0,0
+3708,Free transport for convex potentials,"  We construct non-commutative analogs of transport maps among free Gibbs state
+satisfying a certain convexity condition. Unlike previous constructions, our
+approach is non-perturbative in nature and thus can be used to construct
+transport maps between free Gibbs states associated to potentials which are far
+from quadratic, i.e., states which are far from the semicircle law. An
+essential technical ingredient in our approach is the extension of free
+stochastic analysis to non-commutative spaces of functions based on the
+Haagerup tensor product.
+",0,0,1,0,0,0
+3709,The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description,"  Support vector data description (SVDD) is a popular anomaly detection
+technique. The SVDD classifier partitions the whole data space into an
+$\textit{inlier}$ region, which consists of the region $\textit{near}$ the
+training data, and an $\textit{outlier}$ region, which consists of points
+$\textit{away}$ from the training data. The computation of the SVDD classifier
+requires a kernel function, for which the Gaussian kernel is a common choice.
+The Gaussian kernel has a bandwidth parameter, and it is important to set the
+value of this parameter correctly for good results. A small bandwidth leads to
+overfitting such that the resulting SVDD classifier overestimates the number of
+anomalies, whereas a large bandwidth leads to underfitting and an inability to
+detect many anomalies. In this paper, we present a new unsupervised method for
+selecting the Gaussian kernel bandwidth. Our method, which exploits the
+low-rank representation of the kernel matrix to suggest a kernel bandwidth
+value, is competitive with existing bandwidth selection methods.
+",1,0,0,0,0,0
+3710,Chemical exfoliation of MoS2 leads to semiconducting 1T' phase and not the metallic 1T phase,"  A trigonal phase existing only as small patches on chemically exfoliated few
+layer, thermodynamically stable 1H phase of MoS2 is believed to influence
+critically properties of MoS2 based devices. This phase has been most often
+attributed to the metallic 1T phase. We investigate the electronic structure of
+chemically exfoliated MoS2 few layered systems using spatially resolved (lesser
+than 120 nm resolution) photoemission spectroscopy and Raman spectroscopy in
+conjunction with state-of-the-art electronic structure calculations. On the
+basis of these results, we establish that the ground state of this phase is a
+small gap (~90 meV) semiconductor in contrast to most claims in the literature;
+we also identify the specific trigonal (1T') structure it has among many
+suggested ones.
+",0,1,0,0,0,0
+3711,A note on the approximate admissibility of regularized estimators in the Gaussian sequence model,"  We study the problem of estimating an unknown vector $\theta$ from an
+observation $X$ drawn according to the normal distribution with mean $\theta$
+and identity covariance matrix under the knowledge that $\theta$ belongs to a
+known closed convex set $\Theta$. In this general setting, Chatterjee (2014)
+proved that the natural constrained least squares estimator is ""approximately
+admissible"" for every $\Theta$. We extend this result by proving that the same
+property holds for all convex penalized estimators as well. Moreover, we
+simplify and shorten the original proof considerably. We also provide explicit
+upper and lower bounds for the universal constant underlying the notion of
+approximate admissibility.
+",0,0,1,1,0,0
+3712,Simple labeled graph $C^*$-algebras are associated to disagreeable labeled spaces,"  By a labeled graph $C^*$-algebra we mean a $C^*$-algebra associated to a
+labeled space $(E,\mathcal L,\mathcal E)$ consisting of a labeled graph
+$(E,\mathcal L)$ and the smallest normal accommodating set $\mathcal E$ of
+vertex subsets. Every graph $C^*$-algebra $C^*(E)$ is a labeled graph
+$C^*$-algebra and it is well known that $C^*(E)$ is simple if and only if the
+graph $E$ is cofinal and satisfies Condition (L). Bates and Pask extend these
+conditions of graphs $E$ to labeled spaces, and show that if a set-finite and
+receiver set-finite labeled space $(E,\mathcal L, \mathcal E)$ is cofinal and
+disagreeable, then its $C^*$-algebra $C^*(E,\mathcal L, \mathcal E)$ is simple.
+In this paper, we show that the converse is also true.
+",0,0,1,0,0,0
+3713,Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder,"  2 Diabetes is a leading worldwide public health concern, and its increasing
+prevalence has significant health and economic importance in all nations. The
+condition is a multifactorial disorder with a complex aetiology. The genetic
+determinants remain largely elusive, with only a handful of identified
+candidate genes. Genome wide association studies (GWAS) promised to
+significantly enhance our understanding of genetic based determinants of common
+complex diseases. To date, 83 single nucleotide polymorphisms (SNPs) for type 2
+diabetes have been identified using GWAS. Standard statistical tests for single
+and multi-locus analysis such as logistic regression, have demonstrated little
+effect in understanding the genetic architecture of complex human diseases.
+Logistic regression is modelled to capture linear interactions but neglects the
+non-linear epistatic interactions present within genetic data. There is an
+urgent need to detect epistatic interactions in complex diseases as this may
+explain the remaining missing heritability in such diseases. In this paper, we
+present a novel framework based on deep learning algorithms that deal with
+non-linear epistatic interactions that exist in genome wide association data.
+Logistic association analysis under an additive genetic model, adjusted for
+genomic control inflation factor, is conducted to remove statistically
+improbable SNPs to minimize computational overheads.
+",0,0,0,1,0,0
+3714,A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication,"  In recent years, randomized methods for numerical linear algebra have
+received growing interest as a general approach to large-scale problems.
+Typically, the essential ingredient of these methods is some form of randomized
+dimension reduction, which accelerates computations, but also creates random
+approximation error. In this way, the dimension reduction step encodes a
+tradeoff between cost and accuracy. However, the exact numerical relationship
+between cost and accuracy is typically unknown, and consequently, it may be
+difficult for the user to precisely know (1) how accurate a given solution is,
+or (2) how much computation is needed to achieve a given level of accuracy. In
+the current paper, we study randomized matrix multiplication (sketching) as a
+prototype setting for addressing these general problems. As a solution, we
+develop a bootstrap method for {directly estimating} the accuracy as a function
+of the reduced dimension (as opposed to deriving worst-case bounds on the
+accuracy in terms of the reduced dimension). From a computational standpoint,
+the proposed method does not substantially increase the cost of standard
+sketching methods, and this is made possible by an ""extrapolation"" technique.
+In addition, we provide both theoretical and empirical results to demonstrate
+the effectiveness of the proposed method.
+",1,0,0,1,0,0
+3715,Results of measurements of the flux of albedo muons with NEVOD-DECOR experimental complex,"  Results of investigations of the near-horizontal muons in the range of zenith
+angles of 85-95 degrees are presented. In this range, so-called ""albedo"" muons
+(atmospheric muons scattered in the ground into the upper hemisphere) are
+detected. Albedo muons are one of the main sources of the background in
+neutrino experiments. Experimental data of two series of measurements conducted
+at the experimental complex NEVOD-DECOR with the duration of about 30 thousand
+hours ""live"" time are analyzed. The results of measurements of the muon flux
+intensity are compared with simulation results using Monte-Carlo on the basis
+of two multiple Coulomb scattering models: model of point-like nuclei and model
+taking into account finite size of nuclei.
+",0,1,0,0,0,0
+3716,Nanoscale superconducting memory based on the kinetic inductance of asymmetric nanowire loops,"  The demand for low-dissipation nanoscale memory devices is as strong as ever.
+As Moore's Law is staggering, and the demand for a low-power-consuming
+supercomputer is high, the goal of making information processing circuits out
+of superconductors is one of the central goals of modern technology and
+physics. So far, digital superconducting circuits could not demonstrate their
+immense potential. One important reason for this is that a dense
+superconducting memory technology is not yet available. Miniaturization of
+traditional superconducting quantum interference devices is difficult below a
+few micrometers because their operation relies on the geometric inductance of
+the superconducting loop. Magnetic memories do allow nanometer-scale
+miniaturization, but they are not purely superconducting (Baek et al 2014 Nat.
+Commun. 5 3888). Our approach is to make nanometer scale memory cells based on
+the kinetic inductance (and not geometric inductance) of superconducting
+nanowire loops, which have already shown many fascinating properties (Aprili
+2006 Nat. Nanotechnol. 1 15; Hopkins et al 2005 Science 308 1762). This allows
+much smaller devices and naturally eliminates magnetic-field cross-talk. We
+demonstrate that the vorticity, i.e., the winding number of the order
+parameter, of a closed superconducting loop can be used for realizing a
+nanoscale nonvolatile memory device. We demonstrate how to alter the vorticity
+in a controlled fashion by applying calibrated current pulses. A reliable
+read-out of the memory is also demonstrated. We present arguments that such
+memory can be developed to operate without energy dissipation.
+",0,1,0,0,0,0
+3717,The Autonomic Architecture of the Licas System,"  Licas (lightweight internet-based communication for autonomic services) is a
+distributed framework for building service-based systems. The framework
+provides a p2p server and more intelligent processing of information through
+its AI algorithms. Distributed communication includes XML-RPC, REST, HTTP and
+Web Services. It can now provide a robust platform for building different types
+of system, where Microservices or SOA would be possible. However, the system
+may be equally suited for the IoT, as it provides classes to connect with
+external sources and has an optional Autonomic Manager with a MAPE control loop
+integrated into the communication process. The system is also mobile-compatible
+with Android. This paper focuses in particular on the autonomic setup and how
+that might be used. A novel linking mechanism has been described previously [5]
+that can be used to dynamically link sources and this is also considered, as
+part of the autonomous framework.
+",1,0,0,0,0,0
+3718,"Some Characterizations on the Normalized Lommel, Struve and Bessel Functions of the First Kind","  In this paper, we introduce new technique for determining some necessary and
+sufficient conditions of the normalized Bessel functions $j_{\nu}$, normalized
+Struve functions $h_{\nu}$ and normalized Lommel functions $s_{\mu,\nu}$ of the
+first kind, to be in the subclasses of starlike and convex functions of order
+$\alpha$ and type $\beta$.
+",0,0,1,0,0,0
+3719,Applied Evaluative Informetrics: Part 1,"  This manuscript is a preprint version of Part 1 (General Introduction and
+Synopsis) of the book Applied Evaluative Informetrics, to be published by
+Springer in the summer of 2017. This book presents an introduction to the field
+of applied evaluative informetrics, and is written for interested scholars and
+students from all domains of science and scholarship. It sketches the field's
+history, recent achievements, and its potential and limits. It explains the
+notion of multi-dimensional research performance, and discusses the pros and
+cons of 28 citation-, patent-, reputation- and altmetrics-based indicators. In
+addition, it presents quantitative research assessment as an evaluation
+science, and focuses on the role of extra-informetric factors in the
+development of indicators, and on the policy context of their application. It
+also discusses the way forward, both for users and for developers of
+informetric tools.
+",1,0,0,0,0,0
+3720,General multilevel Monte Carlo methods for pricing discretely monitored Asian options,"  We describe general multilevel Monte Carlo methods that estimate the price of
+an Asian option monitored at $m$ fixed dates. Our approach yields unbiased
+estimators with standard deviation $O(\epsilon)$ in $O(m + (1/\epsilon)^{2})$
+expected time for a variety of processes including the Black-Scholes model,
+Merton's jump-diffusion model, the Square-Root diffusion model, Kou's double
+exponential jump-diffusion model, the variance gamma and NIG exponential Levy
+processes and, via the Milstein scheme, processes driven by scalar stochastic
+differential equations. Using the Euler scheme, our approach estimates the
+Asian option price with root mean square error $O(\epsilon)$ in
+$O(m+(\ln(\epsilon)/\epsilon)^{2})$ expected time for processes driven by
+multidimensional stochastic differential equations. Numerical experiments
+confirm that our approach outperforms the conventional Monte Carlo method by a
+factor of order $m$.
+",0,0,0,0,0,1
+3721,Medical applications of diamond magnetometry: commercial viability,"  The sensing of magnetic fields has important applications in medicine,
+particularly to the sensing of signals in the heart and brain. The fields
+associated with biomagnetism are exceptionally weak, being many orders of
+magnitude smaller than the Earth's magnetic field. To measure them requires
+that we use the most sensitive detection techniques, however, to be
+commercially viable this must be done at an affordable cost. The current state
+of the art uses costly SQUID magnetometers, although they will likely be
+superseded by less costly, but otherwise limited, alkali vapour magnetometers.
+Here, we discuss the application of diamond magnetometers to medical
+applications. Diamond magnetometers are robust, solid state devices that work
+in a broad range of environments, with the potential for sensitivity comparable
+to the leading technologies.
+",0,1,0,0,0,0
+3722,On the origin of the hydraulic jump in a thin liquid film,"  For more than a century, it has been believed that all hydraulic jumps are
+created due to gravity. However, we found that thin-film hydraulic jumps are
+not induced by gravity. This study explores the initiation of thin-film
+hydraulic jumps. For circular jumps produced by the normal impingement of a jet
+onto a solid surface, we found that the jump is formed when surface tension and
+viscous forces balance the momentum in the film and gravity plays no
+significant role. Experiments show no dependence on the orientation of the
+surface and a scaling relation balancing viscous forces and surface tension
+collapses the experimental data. Experiments on thin film planar jumps in a
+channel also show that the predominant balance is with surface tension,
+although for the thickness of the films we studied gravity also played a role
+in the jump formation. A theoretical analysis shows that the downstream
+transport of surface tension energy is the previously neglected, critical
+ingredient in these flows and that capillary waves play the role of gravity
+waves in a traditional jump in demarcating the transition from the
+supercritical to subcritical flow associated with these jumps.
+",0,1,0,0,0,0
+3723,Discovery of Complex Anomalous Patterns of Sexual Violence in El Salvador,"  When sexual violence is a product of organized crime or social imaginary, the
+links between sexual violence episodes can be understood as a latent structure.
+With this assumption in place, we can use data science to uncover complex
+patterns. In this paper we focus on the use of data mining techniques to unveil
+complex anomalous spatiotemporal patterns of sexual violence. We illustrate
+their use by analyzing all reported rapes in El Salvador over a period of nine
+years. Through our analysis, we are able to provide evidence of phenomena that,
+to the best of our knowledge, have not been previously reported in literature.
+We devote special attention to a pattern we discover in the East, where
+underage victims report their boyfriends as perpetrators at anomalously high
+rates. Finally, we explain how such analyzes could be conducted in real-time,
+enabling early detection of emerging patterns to allow law enforcement agencies
+and policy makers to react accordingly.
+",0,0,0,1,0,0
+3724,Using Social Network Information in Bayesian Truth Discovery,"  We investigate the problem of truth discovery based on opinions from multiple
+agents who may be unreliable or biased. We consider the case where agents'
+reliabilities or biases are correlated if they belong to the same community,
+which defines a group of agents with similar opinions regarding a particular
+event. An agent can belong to different communities for different events, and
+these communities are unknown a priori. We incorporate knowledge of the agents'
+social network in our truth discovery framework and develop Laplace variational
+inference methods to estimate agents' reliabilities, communities, and the event
+states. We also develop a stochastic variational inference method to scale our
+model to large social networks. Simulations and experiments on real data
+suggest that when observations are sparse, our proposed methods perform better
+than several other inference methods, including majority voting, TruthFinder,
+AccuSim, the Confidence-Aware Truth Discovery method, the Bayesian Classifier
+Combination (BCC) method, and the Community BCC method.
+",1,0,0,1,0,0
+3725,IVOA Recommendation: HiPS - Hierarchical Progressive Survey,"  This document presents HiPS, a hierarchical scheme for the description,
+storage and access of sky survey data. The system is based on hierarchical
+tiling of sky regions at finer and finer spatial resolution which facilitates a
+progressive view of a survey, and supports multi-resolution zooming and
+panning. HiPS uses the HEALPix tessellation of the sky as the basis for the
+scheme and is implemented as a simple file structure with a direct indexing
+scheme that leads to practical implementations.
+",0,1,0,0,0,0
+3726,Mammography Assessment using Multi-Scale Deep Classifiers,"  Applying deep learning methods to mammography assessment has remained a
+challenging topic. Dense noise with sparse expressions, mega-pixel raw data
+resolution, lack of diverse examples have all been factors affecting
+performance. The lack of pixel-level ground truths have especially limited
+segmentation methods in pushing beyond approximately bounding regions. We
+propose a classification approach grounded in high performance tissue
+assessment as an alternative to all-in-one localization and assessment models
+that is also capable of pinpointing the causal pixels. First, the objective of
+the mammography assessment task is formalized in the context of local tissue
+classifiers. Then, the accuracy of a convolutional neural net is evaluated on
+classifying patches of tissue with suspicious findings at varying scales, where
+highest obtained AUC is above $0.9$. The local evaluations of one such expert
+tissue classifier is used to augment the results of a heatmap regression model
+and additionally recover the exact causal regions at high resolution as a
+saliency image suitable for clinical settings.
+",0,0,0,1,0,0
+3727,Studies to Understand and Optimize the Performance of Scintillation Counters for the Mu2e Cosmic Ray Veto System,"  In order to optimize the performance of the CRV, reflection studies and aging
+studies were conducted.
+",0,1,0,0,0,0
+3728,Linear Parameter Varying Representation of a class of MIMO Nonlinear Systems,"  Linear parameter-varying (LPV) models form a powerful model class to analyze
+and control a (nonlinear) system of interest. Identifying an LPV model of a
+nonlinear system can be challenging due to the difficulty of selecting the
+scheduling variable(s) a priori, especially if a first principles based
+understanding of the system is unavailable. Converting a nonlinear model to an
+LPV form is also non-trivial and requires systematic methods to automate the
+process.
+Inspired by these challenges, a systematic LPV embedding approach starting
+from multiple-input multiple-output (MIMO) linear fractional representations
+with a nonlinear feedback block (NLFR) is proposed. This NLFR model class is
+embedded into the LPV model class by an automated factorization of the
+(possibly MIMO) static nonlinear block present in the model. As a result of the
+factorization, an LPV-LFR or an LPV state-space model with affine dependency on
+the scheduling is obtained. This approach facilitates the selection of the
+scheduling variable and the connected mapping of system variables. Such a
+conversion method enables to use nonlinear identification tools to estimate LPV
+models.
+The potential of the proposed approach is illustrated on a 2-DOF nonlinear
+mass-spring-damper example.
+",1,0,0,0,0,0
+3729,Kinetic and radiative power from optically thin accretion flows,"  We perform a set of general relativistic, radiative, magneto-hydrodynamical
+simulations (GR-RMHD) to study the transition from radiatively inefficient to
+efficient state of accretion on a non-rotating black hole. We study ion to
+electron temperature ratios ranging from $T_{\rm i}/T_{\rm e}=10$ to $100$, and
+simulate flows corresponding to accretion rates as low as $10^{-6}\dot M_{\rm
+Edd}$, and as high as $10^{-2}\dot M_{\rm Edd}$. We have found that the
+radiative output of accretion flows increases with accretion rate, and that the
+transition occurs earlier for hotter electrons (lower $T_{\rm i}/T_{\rm e}$
+ratio). At the same time, the mechanical efficiency hardly changes and accounts
+to ${\approx}\,3\%$ of the accreted rest mass energy flux, even at the highest
+simulated accretion rates. This is particularly important for the mechanical
+AGN feedback regulating massive galaxies, groups, and clusters. Comparison with
+recent observations of radiative and mechanical AGN luminosities suggests that
+the ion to electron temperature ratio in the inner, collisionless accretion
+flow should fall within $10<T_{\rm i}/T_{\rm e}<30$, i.e., the electron
+temperature should be several percent of the ion temperature.
+",0,1,0,0,0,0
+3730,First-Principles Many-Body Investigation of Correlated Oxide Heterostructures: Few-Layer-Doped SmTiO$_3$,"  Correlated oxide heterostructures pose a challenging problem in condensed
+matter research due to their structural complexity interweaved with demanding
+electron states beyond the effective single-particle picture. By exploring the
+correlated electronic structure of SmTiO$_3$ doped with few layers of SrO, we
+provide an insight into the complexity of such systems. Furthermore, it is
+shown how the advanced combination of band theory on the level of Kohn-Sham
+density functional theory with explicit many-body theory on the level of
+dynamical mean-field theory provides an adequate tool to cope with the problem.
+Coexistence of band-insulating, metallic and Mott-critical electronic regions
+is revealed in individual heterostructures with multi-orbital manifolds.
+Intriguing orbital polarizations, that qualitatively vary between the metallic
+and the Mott layers are also encountered.
+",0,1,0,0,0,0
+3731,Faster Boosting with Smaller Memory,"  The two state-of-the-art implementations of boosted trees: XGBoost and
+LightGBM, can process large training sets extremely fast. However, this
+performance requires that memory size is sufficient to hold a 2-3 multiple of
+the training set size. This paper presents an alternative approach to
+implementing boosted trees. which achieves a significant speedup over XGBoost
+and LightGBM, especially when memory size is small. This is achieved using a
+combination of two techniques: early stopping and stratified sampling, which
+are explained and analyzed in the paper. We describe our implementation and
+present experimental results to support our claims.
+",1,0,0,1,0,0
+3732,Deep Structured Learning for Facial Action Unit Intensity Estimation,"  We consider the task of automated estimation of facial expression intensity.
+This involves estimation of multiple output variables (facial action units ---
+AUs) that are structurally dependent. Their structure arises from statistically
+induced co-occurrence patterns of AU intensity levels. Modeling this structure
+is critical for improving the estimation performance; however, this performance
+is bounded by the quality of the input features extracted from face images. The
+goal of this paper is to model these structures and estimate complex feature
+representations simultaneously by combining conditional random field (CRF)
+encoded AU dependencies with deep learning. To this end, we propose a novel
+Copula CNN deep learning approach for modeling multivariate ordinal variables.
+Our model accounts for $ordinal$ structure in output variables and their
+$non$-$linear$ dependencies via copula functions modeled as cliques of a CRF.
+These are jointly optimized with deep CNN feature encoding layers using a newly
+introduced balanced batch iterative training algorithm. We demonstrate the
+effectiveness of our approach on the task of AU intensity estimation on two
+benchmark datasets. We show that joint learning of the deep features and the
+target output structure results in significant performance gains compared to
+existing deep structured models for analysis of facial expressions.
+",1,0,0,0,0,0
+3733,Placing the spotted T Tauri star LkCa 4 on an HR diagram,"  Ages and masses of young stars are often estimated by comparing their
+luminosities and effective temperatures to pre-main sequence stellar evolution
+tracks, but magnetic fields and starspots complicate both the observations and
+evolution. To understand their influence, we study the heavily-spotted
+weak-lined T-Tauri star LkCa 4 by searching for spectral signatures of
+radiation originating from the starspot or starspot groups. We introduce a new
+methodology for constraining both the starspot filling factor and the spot
+temperature by fitting two-temperature stellar atmosphere models constructed
+from Phoenix synthetic spectra to a high-resolution near-IR IGRINS spectrum.
+Clearly discernable spectral features arise from both a hot photospheric
+component $T_{\mathrm{hot}} \sim4100$ K and to a cool component
+$T_{\mathrm{cool}} \sim2700-3000$ K, which covers $\sim80\%$ of the visible
+surface. This mix of hot and cool emission is supported by analyses of the
+spectral energy distribution, rotational modulation of colors and of TiO band
+strengths, and features in low-resolution optical/near-IR spectroscopy.
+Although the revised effective temperature and luminosity make LkCa 4 appear
+much younger and lower mass than previous estimates from unspotted stellar
+evolution models, appropriate estimates will require the production and
+adoption of spotted evolutionary models. Biases from starspots likely afflict
+most fully convective young stars and contribute to uncertainties in ages and
+age spreads of open clusters. In some spectral regions starspots act as a
+featureless veiling continuum owing to high rotational broadening and heavy
+line-blanketing in cool star spectra. Some evidence is also found for an
+anti-correlation between the velocities of the warm and cool components.
+",0,1,0,0,0,0
+3734,Coexistence of pressure-induced structural phases in bulk black phosphorus: a combined x-ray diffraction and Raman study up to 18 GPa,"  We report a study of the structural phase transitions induced by pressure in
+bulk black phosphorus by using both synchrotron x-ray diffraction for pressures
+up to 12.2 GPa and Raman spectroscopy up to 18.2 GPa. Very recently black
+phosphorus attracted large attention because of the unique properties of
+fewlayers samples (phosphorene), but some basic questions are still open in the
+case of the bulk system. As concerning the presence of a Raman spectrum above
+10 GPa, which should not be observed in an elemental simple cubic system, we
+propose a new explanation by attributing a key role to the non-hydrostatic
+conditions occurring in Raman experiments. Finally, a combined analysis of
+Raman and XRD data allowed us to obtain quantitative information on presence
+and extent of coexistences between different structural phases from ~5 up to
+~15 GPa. This information can have an important role in theoretical studies on
+pressure-induced structural and electronic phase transitions in black
+phosphorus.
+",0,1,0,0,0,0
+3735,On the Complexity of the Weighted Fused Lasso,"  The solution path of the 1D fused lasso for an $n$-dimensional input is
+piecewise linear with $\mathcal{O}(n)$ segments (Hoefling et al. 2010 and
+Tibshirani et al 2011). However, existing proofs of this bound do not hold for
+the weighted fused lasso. At the same time, results for the generalized lasso,
+of which the weighted fused lasso is a special case, allow $\Omega(3^n)$
+segments (Mairal et al. 2012). In this paper, we prove that the number of
+segments in the solution path of the weighted fused lasso is
+$\mathcal{O}(n^2)$, and that, for some instances, it is $\Omega(n^2)$. We also
+give a new, very simple, proof of the $\mathcal{O}(n)$ bound for the fused
+lasso.
+",0,0,0,1,0,0
+3736,Prediction of Stable Ground-State Lithium Polyhydrides under High Pressures,"  Hydrogen-rich compounds are important for understanding the dissociation of
+dense molecular hydrogen, as well as searching for room temperature
+Bardeen-Cooper-Schrieffer (BCS) superconductors. A recent high pressure
+experiment reported the successful synthesis of novel insulating lithium
+polyhydrides when above 130 GPa. However, the results are in sharp contrast to
+previous theoretical prediction by PBE functional that around this pressure
+range all lithium polyhydrides (LiHn (n = 2-8)) should be metallic. In order to
+address this discrepancy, we perform unbiased structure search with first
+principles calculation by including the van der Waals interaction that was
+ignored in previous prediction to predict the high pressure stable structures
+of LiHn (n = 2-11, 13) up to 200 GPa. We reproduce the previously predicted
+structures, and further find novel compositions that adopt more stable
+structures. The van der Waals functional (vdW-DF) significantly alters the
+relative stability of lithium polyhydrides, and predicts that the stable
+stoichiometries for the ground-state should be LiH2 and LiH9 at 130-170 GPa,
+and LiH2, LiH8 and LiH10 at 180-200 GPa. Accurate electronic structure
+calculation with GW approximation indicates that LiH, LiH2, LiH7, and LiH9 are
+insulative up to at least 208 GPa, and all other lithium polyhydrides are
+metallic. The calculated vibron frequencies of these insulating phases are also
+in accordance with the experimental infrared (IR) data. This reconciliation
+with the experimental observation suggests that LiH2, LiH7, and LiH9 are the
+possible candidates for lithium polyhydrides synthesized in that experiment.
+Our results reinstate the credibility of density functional theory in
+description H-rich compounds, and demonstrate the importance of considering van
+der Waals interaction in this class of materials.
+",0,1,0,0,0,0
+3737,Analytical methods for vacuum simulations in high energy accelerators for future machines based on the LHC performance,"  The Future Circular Collider (FCC), currently in the design phase, will
+address many outstanding questions in particle physics. The technology to
+succeed in this 100 km circumference collider goes beyond present limits.
+Ultra-high vacuum conditions in the beam pipe is one essential requirement to
+provide a smooth operation. Different physics phenomena as photon-, ion- and
+electron- induced desorption and thermal outgassing of the chamber walls
+challenge this requirement. This paper presents an analytical model and a
+computer code PyVASCO that supports the design of a stable vacuum system by
+providing an overview of all the gas dynamics happening inside the beam pipes.
+A mass balance equation system describes the density distribution of the four
+dominating gas species $\text{H}_2, \text{CH}_4$, $\text{CO}$ and
+$\text{CO}_2$. An appropriate solving algorithm is discussed in detail and a
+validation of the model including a comparison of the output to the readings of
+LHC gauges is presented. This enables the evaluation of different designs for
+the FCC.
+",0,1,0,0,0,0
+3738,Global solutions to reaction-diffusion equations with super-linear drift and multiplicative noise,"  Let $\xi(t\,,x)$ denote space-time white noise and consider a
+reaction-diffusion equation of the form \[
+\dot{u}(t\,,x)=\tfrac12 u""(t\,,x) + b(u(t\,,x)) + \sigma(u(t\,,x))
+\xi(t\,,x), \] on $\mathbb{R}_+\times[0\,,1]$, with homogeneous Dirichlet
+boundary conditions and suitable initial data, in the case that there exists
+$\varepsilon>0$ such that $\vert b(z)\vert \ge|z|(\log|z|)^{1+\varepsilon}$ for
+all sufficiently-large values of $|z|$. When $\sigma\equiv 0$, it is well known
+that such PDEs frequently have non-trivial stationary solutions. By contrast,
+Bonder and Groisman (2009) have recently shown that there is finite-time blowup
+when $\sigma$ is a non-zero constant. In this paper, we prove that the
+Bonder--Groisman condition is unimproveable by showing that the
+reaction-diffusion equation with noise is ""typically"" well posed when $\vert
+b(z) \vert =O(|z|\log_+|z|)$ as $|z|\to\infty$. We interpret the word
+""typically"" in two essentially-different ways without altering the conclusions
+of our assertions.
+",0,0,1,0,0,0
+3739,On the semigroup rank of a group,"  For an arbitrary group $G$, it is shown that either the semigroup rank $G{\rm
+rk}S$ equals the group rank $G{\rm rk}G$, or $G{\rm rk}S = G{\rm rk}G+1$. This
+is the starting point for the rest of the article, where the semigroup rank for
+diverse kinds of groups is analysed. The semigroup rank of relatively free
+groups, for any variety of groups, is computed. For a finitely generated
+abelian group~$G$, it is proven that $G{\rm rk}S = G{\rm rk}G+1$ if and only if
+$G$ is torsion-free. In general, this is not true. Partial results are obtained
+in the nilpotent case. It is also proven that if $M$ is a connected closed
+surface, then $(\pi_1(M)){\rm rk}S = (\pi_1(M)){\rm rk}G+1$ if and only if $M$
+is orientable.
+",0,0,1,0,0,0
+3740,A time series distance measure for efficient clustering of input output signals by their underlying dynamics,"  Starting from a dataset with input/output time series generated by multiple
+deterministic linear dynamical systems, this paper tackles the problem of
+automatically clustering these time series. We propose an extension to the
+so-called Martin cepstral distance, that allows to efficiently cluster these
+time series, and apply it to simulated electrical circuits data. Traditionally,
+two ways of handling the problem are used. The first class of methods employs a
+distance measure on time series (e.g. Euclidean, Dynamic Time Warping) and a
+clustering technique (e.g. k-means, k-medoids, hierarchical clustering) to find
+natural groups in the dataset. It is, however, often not clear whether these
+distance measures effectively take into account the specific temporal
+correlations in these time series. The second class of methods uses the
+input/output data to identify a dynamic system using an identification scheme,
+and then applies a model norm-based distance (e.g. H2, H-infinity) to find out
+which systems are similar. This, however, can be very time consuming for large
+amounts of long time series data. We show that the new distance measure
+presented in this paper performs as good as when every input/output pair is
+modelled explicitly, but remains computationally much less complex. The
+complexity of calculating this distance between two time series of length N is
+O(N logN).
+",1,0,0,1,0,0
+3741,Parametric Analysis of Cherenkov Light LDF from EAS for High Energy Gamma Rays and Nuclei: Ways of Practical Application,"  In this paper we propose a 'knee-like' approximation of the lateral
+distribution of the Cherenkov light from extensive air showers in the energy
+range 30-3000 TeV and study a possibility of its practical application in high
+energy ground-based gamma-ray astronomy experiments (in particular, in
+TAIGA-HiSCORE). The approximation has a very good accuracy for individual
+showers and can be easily simplified for practical application in the HiSCORE
+wide angle timing array in the condition of a limited number of triggered
+stations.
+",0,1,0,0,0,0
+3742,On the primary spacing and microsegregation of cellular dendrites in laser deposited Ni-Nb alloys,"  In this study, an alloy phase-field model is used to simulate solidification
+microstructures at different locations within a solidified molten pool. The
+temperature gradient $G$ and the solidification velocity $V$ are obtained from
+a macroscopic heat transfer finite element simulation and provided as input to
+the phase-field model. The effects of laser beam speed and the location within
+the melt pool on the primary arm spacing and on the extent of Nb partitioning
+at the cell tips are investigated. Simulated steady-state primary spacings are
+compared with power law and geometrical models. Cell tip compositions are
+compared to a dendrite growth model. The extent of non-equilibrium interface
+partitioning of the phase-field model is investigated. Although the phase-field
+model has an anti-trapping solute flux term meant to maintain local interface
+equilibrium, we have found that during simulations it was insufficient at
+maintaining equilibrium. This is due to the fact that the additive
+manufacturing solidification conditions fall well outside the allowed limits of
+this flux term.
+",0,1,0,0,0,0
+3743,An Alternative Approach to Functional Linear Partial Quantile Regression,"  We have previously proposed the partial quantile regression (PQR) prediction
+procedure for functional linear model by using partial quantile covariance
+techniques and developed the simple partial quantile regression (SIMPQR)
+algorithm to efficiently extract PQR basis for estimating functional
+coefficients. However, although the PQR approach is considered as an attractive
+alternative to projections onto the principal component basis, there are
+certain limitations to uncovering the corresponding asymptotic properties
+mainly because of its iterative nature and the non-differentiability of the
+quantile loss function. In this article, we propose and implement an
+alternative formulation of partial quantile regression (APQR) for functional
+linear model by using block relaxation method and finite smoothing techniques.
+The proposed reformulation leads to insightful results and motivates new
+theory, demonstrating consistency and establishing convergence rates by
+applying advanced techniques from empirical process theory. Two simulations and
+two real data from ADHD-200 sample and ADNI are investigated to show the
+superiority of our proposed methods.
+",0,0,1,1,0,0
+3744,Parallel mining of time-faded heavy hitters,"  We present PFDCMSS, a novel message-passing based parallel algorithm for
+mining time-faded heavy hitters. The algorithm is a parallel version of the
+recently published FDCMSS sequential algorithm. We formally prove its
+correctness by showing that the underlying data structure, a sketch augmented
+with a Space Saving stream summary holding exactly two counters, is mergeable.
+Whilst mergeability of traditional sketches derives immediately from theory, we
+show that merging our augmented sketch is non trivial. Nonetheless, the
+resulting parallel algorithm is fast and simple to implement. To the best of
+our knowledge, PFDCMSS is the first parallel algorithm solving the problem of
+mining time-faded heavy hitters on message-passing parallel architectures.
+Extensive experimental results confirm that PFDCMSS retains the extreme
+accuracy and error bound provided by FDCMSS whilst providing excellent parallel
+scalability.
+",1,0,0,0,0,0
+3745,On a Minkowski-like inequality for asymptotically flat static manifolds,"  The Minkowski inequality is a classical inequality in differential geometry,
+giving a bound from below, on the total mean curvature of a convex surface in
+Euclidean space, in terms of its area. Recently there has been interest in
+proving versions of this inequality for manifolds other than R^n; for example,
+such an inequality holds for surfaces in spatial Schwarzschild and
+AdS-Schwarzschild manifolds. In this note, we adapt a recent analysis of Y. Wei
+to prove a Minkowski-like inequality for general static asymptotically flat
+manifolds.
+",0,0,1,0,0,0
+3746,Instantaneous Arbitrage and the CAPM,"  This paper studies the concept of instantaneous arbitrage in continuous time
+and its relation to the instantaneous CAPM. Absence of instantaneous arbitrage
+is equivalent to the existence of a trading strategy which satisfies the CAPM
+beta pricing relation in place of the market. Thus the difference between the
+arbitrage argument and the CAPM argument in Black and Scholes (1973) is this:
+the arbitrage argument assumes that there exists some portfolio satisfying the
+capm equation, whereas the CAPM argument assumes, in addition, that this
+portfolio is the market portfolio.
+",0,0,0,0,0,1
+3747,On the Complexity of Approximating Wasserstein Barycenter,"  We study the complexity of approximating Wassertein barycenter of $m$
+discrete measures, or histograms of size $n$ by contrasting two alternative
+approaches, both using entropic regularization. The first approach is based on
+the Iterative Bregman Projections (IBP) algorithm for which our novel analysis
+gives a complexity bound proportional to $\frac{mn^2}{\varepsilon^2}$ to
+approximate the original non-regularized barycenter.
+Using an alternative accelerated-gradient-descent-based approach, we obtain a
+complexity proportional to $\frac{mn^{2.5}}{\varepsilon} $. As a byproduct, we
+show that the regularization parameter in both approaches has to be
+proportional to $\varepsilon$, which causes instability of both algorithms when
+the desired accuracy is high. To overcome this issue, we propose a novel
+proximal-IBP algorithm, which can be seen as a proximal gradient method, which
+uses IBP on each iteration to make a proximal step. We also consider the
+question of scalability of these algorithms using approaches from distributed
+optimization and show that the first algorithm can be implemented in a
+centralized distributed setting (master/slave), while the second one is
+amenable to a more general decentralized distributed setting with an arbitrary
+network topology.
+",1,0,0,0,0,0
+3748,Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces,"  Modern implicit generative models such as generative adversarial networks
+(GANs) are generally known to suffer from instability and lack of
+interpretability as it is difficult to diagnose what aspects of the target
+distribution are missed by the generative model. In this work, we propose a
+theoretically grounded solution to these issues by augmenting the GAN's loss
+function with a kernel-based regularization term that magnifies local
+discrepancy between the distributions of generated and real samples. The
+proposed method relies on so-called witness points in the data space which are
+jointly trained with the generator and provide an interpretable indication of
+where the two distributions locally differ during the training procedure. In
+addition, the proposed algorithm is scaled to higher dimensions by learning the
+witness locations in a latent space of an autoencoder. We theoretically
+investigate the dynamics of the training procedure, prove that a desirable
+equilibrium point exists, and the dynamical system is locally stable around
+this equilibrium. Finally, we demonstrate different aspects of the proposed
+algorithm by numerical simulations of analytical solutions and empirical
+results for low and high-dimensional datasets.
+",1,0,0,1,0,0
+3749,Heterogeneous Transfer Learning: An Unsupervised Approach,"  Transfer learning leverages the knowledge in one domain, the source domain,
+to improve learning efficiency in another domain, the target domain. Existing
+transfer learning research is relatively well-progressed, but only in
+situations where the feature spaces of the domains are homogeneous and the
+target domain contains at least a few labeled instances. However, transfer
+learning has not been well-studied in heterogeneous settings with an unlabeled
+target domain. To contribute to the research in this emerging field, this paper
+presents: (1) an unsupervised knowledge transfer theorem that prevents negative
+transfer; and (2) a principal angle-based metric to measure the distance
+between two pairs of domains. The metric shows the extent to which homogeneous
+representations have preserved the information in original source and target
+domains. The unsupervised knowledge transfer theorem sets out the transfer
+conditions necessary to prevent negative transfer. Linear monotonic maps meet
+the transfer conditions of the theorem and, hence, are used to construct
+homogeneous representations of the heterogeneous domains, which in principle
+prevents negative transfer. The metric and the theorem have been implemented in
+an innovative transfer model, called a Grassmann-LMM-geodesic flow kernel
+(GLG), that is specifically designed for knowledge transfer across
+heterogeneous domains. The GLG model learns homogeneous representations of
+heterogeneous domains by minimizing the proposed metric. Knowledge is
+transferred through these learned representations via a geodesic flow kernel.
+Notably, the theorem presented in this paper provides the sufficient transfer
+conditions needed to guarantee that knowledge is transferred from a source
+domain to an unlabeled target domain with correctness.
+",0,0,0,1,0,0
+3750,"Diffusion transformations, Black-Scholes equation and optimal stopping","  We develop a new class of path transformations for one-dimensional diffusions
+that are tailored to alter their long-run behaviour from transient to recurrent
+or vice versa. This immediately leads to a formula for the distribution of the
+first exit times of diffusions, which is recently characterised by Karatzas and
+Ruf \cite{KR} as the minimal solution of an appropriate Cauchy problem under
+more stringent conditions. A particular limit of these transformations also
+turn out to be instrumental in characterising the stochastic solutions of
+Cauchy problems defined by the generators of strict local martingales, which
+are well-known for not having unique solutions even when one restricts
+solutions to have linear growth. Using an appropriate diffusion transformation
+we show that the aforementioned stochastic solution can be written in terms of
+the unique classical solution of an {\em alternative} Cauchy problem with
+suitable boundary conditions. This in particular resolves the long-standing
+issue of non-uniqueness with the Black-Scholes equations in derivative pricing
+in the presence of {\em bubbles}. Finally, we use these path transformations to
+propose a unified framework for solving explicitly the optimal stopping problem
+for one-dimensional diffusions with discounting, which in particular is
+relevant for the pricing and the computation of optimal exercise boundaries of
+perpetual American options.
+",0,0,1,0,0,0
+3751,Generation of concept-representative symbols,"  The visual representation of concepts or ideas through the use of simple
+shapes has always been explored in the history of Humanity, and it is believed
+to be the origin of writing. We focus on computational generation of visual
+symbols to represent concepts. We aim to develop a system that uses background
+knowledge about the world to find connections among concepts, with the goal of
+generating symbols for a given concept. We are also interested in exploring the
+system as an approach to visual dissociation and visual conceptual blending.
+This has a great potential in the area of Graphic Design as a tool to both
+stimulate creativity and aid in brainstorming in projects such as logo,
+pictogram or signage design.
+",1,0,0,0,0,0
+3752,"Quantitative results using variants of Schmidt's game: Dimension bounds, arithmetic progressions, and more","  Schmidt's game is generally used to deduce qualitative information about the
+Hausdorff dimensions of fractal sets and their intersections. However, one can
+also ask about quantitative versions of the properties of winning sets. In this
+paper we show that such quantitative information has applications to various
+questions including:
+* What is the maximal length of an arithmetic progression on the ""middle
+$\epsilon$"" Cantor set?
+* What is the smallest $n$ such that there is some element of the ternary
+Cantor set whose continued fraction partial quotients are all $\leq n$?
+* What is the Hausdorff dimension of the set of $\epsilon$-badly approximable
+numbers on the Cantor set?
+We show that a variant of Schmidt's game known as the $potential$ $game$ is
+capable of providing better bounds on the answers to these questions than the
+classical Schmidt's game. We also use the potential game to provide a new proof
+of an important lemma in the classical proof of the existence of Hall's Ray.
+",0,0,1,0,0,0
+3753,"Sublogarithmic Distributed Algorithms for Lovász Local lemma, and the Complexity Hierarchy","  Locally Checkable Labeling (LCL) problems include essentially all the classic
+problems of $\mathsf{LOCAL}$ distributed algorithms. In a recent enlightening
+revelation, Chang and Pettie [arXiv 1704.06297] showed that any LCL (on bounded
+degree graphs) that has an $o(\log n)$-round randomized algorithm can be solved
+in $T_{LLL}(n)$ rounds, which is the randomized complexity of solving (a
+relaxed variant of) the Lovász Local Lemma (LLL) on bounded degree $n$-node
+graphs. Currently, the best known upper bound on $T_{LLL}(n)$ is $O(\log n)$,
+by Chung, Pettie, and Su [PODC'14], while the best known lower bound is
+$\Omega(\log\log n)$, by Brandt et al. [STOC'16]. Chang and Pettie conjectured
+that there should be an $O(\log\log n)$-round algorithm.
+Making the first step of progress towards this conjecture, and providing a
+significant improvement on the algorithm of Chung et al. [PODC'14], we prove
+that $T_{LLL}(n)= 2^{O(\sqrt{\log\log n})}$. Thus, any $o(\log n)$-round
+randomized distributed algorithm for any LCL problem on bounded degree graphs
+can be automatically sped up to run in $2^{O(\sqrt{\log\log n})}$ rounds.
+Using this improvement and a number of other ideas, we also improve the
+complexity of a number of graph coloring problems (in arbitrary degree graphs)
+from the $O(\log n)$-round results of Chung, Pettie and Su [PODC'14] to
+$2^{O(\sqrt{\log\log n})}$. These problems include defective coloring, frugal
+coloring, and list vertex-coloring.
+",1,0,0,0,0,0
+3754,Continuous User Authentication via Unlabeled Phone Movement Patterns,"  In this paper, we propose a novel continuous authentication system for
+smartphone users. The proposed system entirely relies on unlabeled phone
+movement patterns collected through smartphone accelerometer. The data was
+collected in a completely unconstrained environment over five to twelve days.
+The contexts of phone usage were identified using k-means clustering. Multiple
+profiles, one for each context, were created for every user. Five machine
+learning algorithms were employed for classification of genuine and impostors.
+The performance of the system was evaluated over a diverse population of 57
+users. The mean equal error rates achieved by Logistic Regression, Neural
+Network, kNN, SVM, and Random Forest were 13.7%, 13.5%, 12.1%, 10.7%, and 5.6%
+respectively. A series of statistical tests were conducted to compare the
+performance of the classifiers. The suitability of the proposed system for
+different types of users was also investigated using the failure to enroll
+policy.
+",1,0,0,0,0,0
+3755,Testing isomorphism of lattices over CM-orders,"  A CM-order is a reduced order equipped with an involution that mimics complex
+conjugation. The Witt-Picard group of such an order is a certain group of ideal
+classes that is closely related to the ""minus part"" of the class group. We
+present a deterministic polynomial-time algorithm for the following problem,
+which may be viewed as a special case of the principal ideal testing problem:
+given a CM-order, decide whether two given elements of its Witt-Picard group
+are equal. In order to prevent coefficient blow-up, the algorithm operates with
+lattices rather than with ideals. An important ingredient is a technique
+introduced by Gentry and Szydlo in a cryptographic context. Our application of
+it to lattices over CM-orders hinges upon a novel existence theorem for
+auxiliary ideals, which we deduce from a result of Konyagin and Pomerance in
+elementary number theory.
+",1,0,1,0,0,0
+3756,How strong are correlations in strongly recurrent neuronal networks?,"  Cross-correlations in the activity in neural networks are commonly used to
+characterize their dynamical states and their anatomical and functional
+organizations. Yet, how these latter network features affect the spatiotemporal
+structure of the correlations in recurrent networks is not fully understood.
+Here, we develop a general theory for the emergence of correlated neuronal
+activity from the dynamics in strongly recurrent networks consisting of several
+populations of binary neurons. We apply this theory to the case in which the
+connectivity depends on the anatomical or functional distance between the
+neurons. We establish the architectural conditions under which the system
+settles into a dynamical state where correlations are strong, highly robust and
+spatially modulated. We show that such strong correlations arise if the network
+exhibits an effective feedforward structure. We establish how this feedforward
+structure determines the way correlations scale with the network size and the
+degree of the connectivity. In networks lacking an effective feedforward
+structure correlations are extremely small and only weakly depend on the number
+of connections per neuron. Our work shows how strong correlations can be
+consistent with highly irregular activity in recurrent networks, two key
+features of neuronal dynamics in the central nervous system.
+",0,0,0,0,1,0
+3757,"JSON: data model, query languages and schema specification","  Despite the fact that JSON is currently one of the most popular formats for
+exchanging data on the Web, there are very few studies on this topic and there
+are no agreement upon theoretical framework for dealing with JSON. There- fore
+in this paper we propose a formal data model for JSON documents and, based on
+the common features present in available systems using JSON, we define a
+lightweight query language allowing us to navigate through JSON documents. We
+also introduce a logic capturing the schema proposal for JSON and study the
+complexity of basic computational tasks associated with these two formalisms.
+",1,0,0,0,0,0
+3758,A six-factor asset pricing model,"  The present study introduce the human capital component to the Fama and
+French five-factor model proposing an equilibrium six-factor asset pricing
+model. The study employs an aggregate of four sets of portfolios mimicking size
+and industry with varying dimensions. The first set consists of three set of
+six portfolios each sorted on size to B/M, size to investment, and size to
+momentum. The second set comprises of five index portfolios, third, a four-set
+of twenty-five portfolios each sorted on size to B/M, size to investment, size
+to profitability, and size to momentum, and the final set constitute thirty
+industry portfolios. To estimate the parameters of six-factor asset pricing
+model for the four sets of variant portfolios, we use OLS and Generalized
+method of moments based robust instrumental variables technique (IVGMM). The
+results obtained from the relevance, endogeneity, overidentifying restrictions,
+and the Hausman's specification, tests indicate that the parameter estimates of
+the six-factor model using IVGMM are robust and performs better than the OLS
+approach. The human capital component shares equally the predictive power
+alongside the factors in the framework in explaining the variations in return
+on portfolios. Furthermore, we assess the t-ratio of the human capital
+component of each IVGMM estimates of the six-factor asset pricing model for the
+four sets of variant portfolios. The t-ratio of the human capital of the
+eighty-three IVGMM estimates are more than 3.00 with reference to the standard
+proposed by Harvey et al. (2016). This indicates the empirical success of the
+six-factor asset-pricing model in explaining the variation in asset returns.
+",0,0,0,0,0,1
+3759,Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in diffusion MRI,"  In Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion
+Imaging (HARDI), a tensor field or a spherical function field (e.g., an
+orientation distribution function field), can be estimated from measured
+diffusion weighted images. In this paper, inspired by the microscopic
+theoretical treatment of phases in liquid crystals, we introduce a novel
+mathematical framework, called Director Field Analysis (DFA), to study local
+geometric structural information of white matter based on the reconstructed
+tensor field or spherical function field: 1) We propose a set of mathematical
+tools to process general director data, which consists of dyadic tensors that
+have orientations but no direction. 2) We propose Orientational Order (OO) and
+Orientational Dispersion (OD) indices to describe the degree of alignment and
+dispersion of a spherical function in a single voxel or in a region,
+respectively; 3) We also show how to construct a local orthogonal coordinate
+frame in each voxel exhibiting anisotropic diffusion; 4) Finally, we define
+three indices to describe three types of orientational distortion (splay, bend,
+and twist) in a local spatial neighborhood, and a total distortion index to
+describe distortions of all three types. To our knowledge, this is the first
+work to quantitatively describe orientational distortion (splay, bend, and
+twist) in general spherical function fields from DTI or HARDI data. The
+proposed DFA and its related mathematical tools can be used to process not only
+diffusion MRI data but also general director field data, and the proposed
+scalar indices are useful for detecting local geometric changes of white matter
+for voxel-based or tract-based analysis in both DTI and HARDI acquisitions. The
+related codes and a tutorial for DFA will be released in DMRITool.
+",1,1,0,0,0,0
+3760,A Multi-Objective Deep Reinforcement Learning Framework,"  This paper presents a new multi-objective deep reinforcement learning (MODRL)
+framework based on deep Q-networks. We propose the use of linear and non-linear
+methods to develop the MODRL framework that includes both single-policy and
+multi-policy strategies. The experimental results on two benchmark problems
+including the two-objective deep sea treasure environment and the
+three-objective mountain car problem indicate that the proposed framework is
+able to converge to the optimal Pareto solutions effectively. The proposed
+framework is generic, which allows implementation of different deep
+reinforcement learning algorithms in different complex environments. This
+therefore overcomes many difficulties involved with standard multi-objective
+reinforcement learning (MORL) methods existing in the current literature. The
+framework creates a platform as a testbed environment to develop methods for
+solving various problems associated with the current MORL. Details of the
+framework implementation can be referred to
+this http URL.
+",0,0,0,1,0,0
+3761,Advantages of versatile neural-network decoding for topological codes,"  Finding optimal correction of errors in generic stabilizer codes is a
+computationally hard problem, even for simple noise models. While this task can
+be simplified for codes with some structure, such as topological stabilizer
+codes, developing good and efficient decoders still remains a challenge. In our
+work, we systematically study a very versatile class of decoders based on
+feedforward neural networks. To demonstrate adaptability, we apply neural
+decoders to the triangular color and toric codes under various noise models
+with realistic features, such as spatially-correlated errors. We report that
+neural decoders provide significant improvement over leading efficient decoders
+in terms of the error-correction threshold. Using neural networks simplifies
+the process of designing well-performing decoders, and does not require prior
+knowledge of the underlying noise model.
+",0,0,0,1,0,0
+3762,A retrieval-based dialogue system utilizing utterance and context embeddings,"  Finding semantically rich and computer-understandable representations for
+textual dialogues, utterances and words is crucial for dialogue systems (or
+conversational agents), as their performance mostly depends on understanding
+the context of conversations. Recent research aims at finding distributed
+vector representations (embeddings) for words, such that semantically similar
+words are relatively close within the vector-space. Encoding the ""meaning"" of
+text into vectors is a current trend, and text can range from words, phrases
+and documents to actual human-to-human conversations. In recent research
+approaches, responses have been generated utilizing a decoder architecture,
+given the vector representation of the current conversation. In this paper, the
+utilization of embeddings for answer retrieval is explored by using
+Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor
+(ANN) model, to find similar conversations in a corpus and rank possible
+candidates. Experimental results on the well-known Ubuntu Corpus (in English)
+and a customer service chat dataset (in Dutch) show that, in combination with a
+candidate selection method, retrieval-based approaches outperform generative
+ones and reveal promising future research directions towards the usability of
+such a system.
+",1,0,0,0,0,0
+3763,"Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network","  For a safe, natural and effective human-robot social interaction, it is
+essential to develop a system that allows a robot to demonstrate the
+perceivable responsive behaviors to complex human behaviors. We introduce the
+Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits
+human-like social interaction skills after 14 days of interacting with people
+in an uncontrolled real world. Each and every day during the 14 days, the
+system gathered robot interaction experiences with people through a
+hit-and-trial method and then trained the MDARQN on these experiences using
+end-to-end reinforcement learning approach. The results of interaction based
+learning indicate that the robot has learned to respond to complex human
+behaviors in a perceivable and socially acceptable manner.
+",1,0,0,1,0,0
+3764,Sparse Gaussian Processes for Continuous-Time Trajectory Estimation on Matrix Lie Groups,"  Continuous-time trajectory representations are a powerful tool that can be
+used to address several issues in many practical simultaneous localization and
+mapping (SLAM) scenarios, like continuously collected measurements distorted by
+robot motion, or during with asynchronous sensor measurements. Sparse Gaussian
+processes (GP) allow for a probabilistic non-parametric trajectory
+representation that enables fast trajectory estimation by sparse GP regression.
+However, previous approaches are limited to dealing with vector space
+representations of state only. In this technical report we extend the work by
+Barfoot et al. [1] to general matrix Lie groups, by applying constant-velocity
+prior, and defining locally linear GP. This enables using sparse GP approach in
+a large space of practical SLAM settings. In this report we give the theory and
+leave the experimental evaluation in future publications.
+",1,0,0,0,0,0
+3765,Testing homogeneity of proportions from sparse binomial data with a large number of groups,"  In this paper, we consider testing the homogeneity for proportions in
+independent binomial distributions especially when data are sparse for large
+number of groups. We provide broad aspects of our proposed tests such as
+theoretical studies, simulations and real data application. We present the
+asymptotic null distributions and asymptotic powers for our proposed tests and
+compare their performance with existing tests. Our simulation studies show that
+none of tests dominate the others, however our proposed test and a few tests
+are expected to control given sizes and obtain significant powers. We also
+present a real example regarding safety concerns associated with Avandiar
+(rosiglitazone) in Nissen and Wolsky (2007).
+",0,0,1,1,0,0
+3766,Henkin measures for the Drury-Arveson space,"  We exhibit Borel probability measures on the unit sphere in $\mathbb C^d$ for
+$d \ge 2$ which are Henkin for the multiplier algebra of the Drury-Arveson
+space, but not Henkin in the classical sense. This provides a negative answer
+to a conjecture of Clouâtre and Davidson.
+",0,0,1,0,0,0
+3767,"Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery","  PCA is one of the most widely used dimension reduction techniques. A related
+easier problem is ""subspace learning"" or ""subspace estimation"". Given
+relatively clean data, both are easily solved via singular value decomposition
+(SVD). The problem of subspace learning or PCA in the presence of outliers is
+called robust subspace learning or robust PCA (RPCA). For long data sequences,
+if one tries to use a single lower dimensional subspace to represent the data,
+the required subspace dimension may end up being quite large. For such data, a
+better model is to assume that it lies in a low-dimensional subspace that can
+change over time, albeit gradually. The problem of tracking such data (and the
+subspaces) while being robust to outliers is called robust subspace tracking
+(RST). This article provides a magazine-style overview of the entire field of
+robust subspace learning and tracking. In particular solutions for three
+problems are discussed in detail: RPCA via sparse+low-rank matrix decomposition
+(S+LR), RST via S+LR, and ""robust subspace recovery (RSR)"". RSR assumes that an
+entire data vector is either an outlier or an inlier. The S+LR formulation
+instead assumes that outliers occur on only a few data vector indices and hence
+are well modeled as sparse corruptions.
+",0,0,0,1,0,0
+3768,Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries,"  Associating image regions with text queries has been recently explored as a
+new way to bridge visual and linguistic representations. A few pioneering
+approaches have been proposed based on recurrent neural language models trained
+generatively (e.g., generating captions), but achieving somewhat limited
+localization accuracy. To better address natural-language-based visual entity
+localization, we propose a discriminative approach. We formulate a
+discriminative bimodal neural network (DBNet), which can be trained by a
+classifier with extensive use of negative samples. Our training objective
+encourages better localization on single images, incorporates text phrases in a
+broad range, and properly pairs image regions with text phrases into positive
+and negative examples. Experiments on the Visual Genome dataset demonstrate the
+proposed DBNet significantly outperforms previous state-of-the-art methods both
+for localization on single images and for detection on multiple images. We we
+also establish an evaluation protocol for natural-language visual detection.
+",1,0,0,1,0,0
+3769,The failure of rational dilation on the symmetrized $n$-disk for any $n\geq 3$,"  The open and closed \textit{symmetrized polydisc} or, \textit{symmetrized
+$n$-disc} for $n\geq 2$, are the following subsets of $\mathbb C^n$:
+\begin{align*} \mathbb G_n &=\left\{ \left(\sum_{1\leq i\leq n} z_i,\sum_{1\leq
+i<j\leq n}z_iz_j,\dots, \prod_{i=1}^n z_i \right): \,|z_i|< 1, i=1,\dots,n
+\right \}, \Gamma_n & =\left\{ \left(\sum_{1\leq i\leq n} z_i,\sum_{1\leq
+i<j\leq n}z_iz_j,\dots, \prod_{i=1}^n z_i \right): \,|z_i|\leq 1, i=1,\dots,n
+\right \}. \end{align*} A tuple of commuting $n$ operators
+$(S_1,\dots,S_{n-1},P)$ defined on a Hilbert space $\mathcal H$ for which
+$\Gamma_n$ is a spectral set is called a $\Gamma_n$-contraction. In this
+article, we show by a counter example that rational dilation fails on the
+symmetrized $n$-disc for any $n\geq 3$. We find new characterizations for the
+points in $\mathbb G_n$ and $\Gamma_n$. We also present few new
+characterizations for the $\Gamma_n$-unitaries and $\Gamma_n$-isometries.
+",0,0,1,0,0,0
+3770,Particle-without-Particle: a practical pseudospectral collocation method for linear partial differential equations with distributional sources,"  Partial differential equations with distributional sources---in particular,
+involving (derivatives of) delta distributions---have become increasingly
+ubiquitous in numerous areas of physics and applied mathematics. It is often of
+considerable interest to obtain numerical solutions for such equations, but any
+singular (""particle""-like) source modeling invariably introduces nontrivial
+computational obstacles. A common method to circumvent these is through some
+form of delta function approximation procedure on the computational grid;
+however, this often carries significant limitations on the efficiency of the
+numerical convergence rates, or sometimes even the resolvability of the problem
+at all.
+In this paper, we present an alternative technique for tackling such
+equations which avoids the singular behavior entirely: the
+""Particle-without-Particle"" method. Previously introduced in the context of the
+self-force problem in gravitational physics, the idea is to discretize the
+computational domain into two (or more) disjoint pseudospectral
+(Chebyshev-Lobatto) grids such that the ""particle"" is always at the interface
+between them; thus, one only needs to solve homogeneous equations in each
+domain, with the source effectively replaced by jump (boundary) conditions
+thereon. We prove here that this method yields solutions to any linear PDE the
+source of which is any linear combination of delta distributions and
+derivatives thereof supported on a one-dimensional subspace of the problem
+domain. We then implement it to numerically solve a variety of relevant PDEs:
+hyperbolic (with applications to neuroscience and acoustics), parabolic (with
+applications to finance), and elliptic. We generically obtain improved
+convergence rates relative to typical past implementations relying on delta
+function approximations.
+",0,0,0,0,1,1
+3771,X-Cube Fracton Model on Generic Lattices: Phases and Geometric Order,"  Fracton order is a new kind of quantum order characterized by topological
+excitations that exhibit remarkable mobility restrictions and a robust ground
+state degeneracy (GSD) which can increase exponentially with system size. In
+this paper, we present a generic lattice construction (in three dimensions) for
+a generalized X-cube model of fracton order, where the mobility restrictions of
+the subdimensional particles inherit the geometry of the lattice. This helps
+explain a previous result that lattice curvature can produce a robust GSD, even
+on a manifold with trivial topology. We provide explicit examples to show that
+the (zero temperature) phase of matter is sensitive to the lattice geometry. In
+one example, the lattice geometry confines the dimension-1 particles to small
+loops, which allows the fractons to be fully mobile charges, and the resulting
+phase is equivalent to (3+1)-dimensional toric code. However, the phase is
+sensitive to more than just lattice curvature; different lattices without
+curvature (e.g. cubic or stacked kagome lattices) also result in different
+phases of matter, which are separated by phase transitions. Unintuitively
+however, according to a previous definition of phase [Chen, Gu, Wen 2010], even
+just a rotated or rescaled cubic lattice results in different phases of matter,
+which motivates us to propose a new and coarser definition of phase for gapped
+ground states and fracton order. The new equivalence relation between ground
+states is given by the composition of a local unitary transformation and a
+quasi-isometry (which can rotate and rescale the lattice); equivalently, ground
+states are in the same phase if they can be adiabatically connected by varying
+both the Hamiltonian and the positions of the degrees of freedom (via a
+quasi-isometry). In light of the importance of geometry, we further propose
+that fracton orders should be regarded as a geometric order.
+",0,1,0,0,0,0
+3772,Genetic Algorithm for Epidemic Mitigation by Removing Relationships,"  Min-SEIS-Cluster is an optimization problem which aims at minimizing the
+infection spreading in networks. In this problem, nodes can be susceptible to
+an infection, exposed to an infection, or infectious. One of the main features
+of this problem is the fact that nodes have different dynamics when interacting
+with other nodes from the same community. Thus, the problem is characterized by
+distinct probabilities of infecting nodes from both the same and from different
+communities. This paper presents a new genetic algorithm that solves the
+Min-SEIS-Cluster problem. This genetic algorithm surpassed the current
+heuristic of this problem significantly, reducing the number of infected nodes
+during the simulation of the epidemics. The results therefore suggest that our
+new genetic algorithm is the state-of-the-art heuristic to solve this problem.
+",1,0,1,0,0,0
+3773,Angpow: a software for the fast computation of accurate tomographic power spectra,"  The statistical distribution of galaxies is a powerful probe to constrain
+cosmological models and gravity. In particular the matter power spectrum $P(k)$
+brings information about the cosmological distance evolution and the galaxy
+clustering together. However the building of $P(k)$ from galaxy catalogues
+needs a cosmological model to convert angles on the sky and redshifts into
+distances, which leads to difficulties when comparing data with predicted
+$P(k)$ from other cosmological models, and for photometric surveys like LSST.
+The angular power spectrum $C_\ell(z_1,z_2)$ between two bins located at
+redshift $z_1$ and $z_2$ contains the same information than the matter power
+spectrum, is free from any cosmological assumption, but the prediction of
+$C_\ell(z_1,z_2)$ from $P(k)$ is a costly computation when performed exactly.
+The Angpow software aims at computing quickly and accurately the auto
+($z_1=z_2$) and cross ($z_1 \neq z_2$) angular power spectra between redshift
+bins. We describe the developed algorithm, based on developments on the
+Chebyshev polynomial basis and on the Clenshaw-Curtis quadrature method. We
+validate the results with other codes, and benchmark the performance. Angpow is
+flexible and can handle any user defined power spectra, transfer functions, and
+redshift selection windows. The code is fast enough to be embedded inside
+programs exploring large cosmological parameter spaces through the
+$C_\ell(z_1,z_2)$ comparison with data. We emphasize that the Limber's
+approximation, often used to fasten the computation, gives wrong $C_\ell$
+values for cross-correlations.
+",0,1,0,0,0,0
+3774,Robust and Flexible Estimation of Stochastic Mediation Effects: A Proposed Method and Example in a Randomized Trial Setting,"  Causal mediation analysis can improve understanding of the mechanisms
+underlying epidemiologic associations. However, the utility of natural direct
+and indirect effect estimation has been limited by the assumption of no
+confounder of the mediator-outcome relationship that is affected by prior
+exposure---an assumption frequently violated in practice. We build on recent
+work that identified alternative estimands that do not require this assumption
+and propose a flexible and double robust semiparametric targeted minimum
+loss-based estimator for data-dependent stochastic direct and indirect effects.
+The proposed method treats the intermediate confounder affected by prior
+exposure as a time-varying confounder and intervenes stochastically on the
+mediator using a distribution which conditions on baseline covariates and
+marginalizes over the intermediate confounder. In addition, we assume the
+stochastic intervention is given, conditional on observed data, which results
+in a simpler estimator and weaker identification assumptions. We demonstrate
+the estimator's finite sample and robustness properties in a simple simulation
+study. We apply the method to an example from the Moving to Opportunity
+experiment. In this application, randomization to receive a housing voucher is
+the treatment/instrument that influenced moving to a low-poverty neighborhood,
+which is the intermediate confounder. We estimate the data-dependent stochastic
+direct effect of randomization to the voucher group on adolescent marijuana use
+not mediated by change in school district and the stochastic indirect effect
+mediated by change in school district. We find no evidence of mediation. Our
+estimator is easy to implement in standard statistical software, and we provide
+annotated R code to further lower implementation barriers.
+",0,0,0,1,0,0
+3775,Radial anisotropy in omega Cen limiting the room for an intermediate-mass black hole,"  Finding an intermediate-mass black hole (IMBH) in a globular cluster (or
+proving its absence) would provide valuable insights into our understanding of
+galaxy formation and evolution. However, it is challenging to identify a unique
+signature of an IMBH that cannot be accounted for by other processes.
+Observational claims of IMBH detection are indeed often based on analyses of
+the kinematics of stars in the cluster core, the most common signature being a
+rise in the velocity dispersion profile towards the centre of the system.
+Unfortunately, this IMBH signal is degenerate with the presence of
+radially-biased pressure anisotropy in the globular cluster. To explore the
+role of anisotropy in shaping the observational kinematics of clusters, we
+analyse the case of omega Cen by comparing the observed profiles to those
+calculated from the family of LIMEPY models, that account for the presence of
+anisotropy in the system in a physically motivated way. The best-fit radially
+anisotropic models reproduce the observational profiles well, and describe the
+central kinematics as derived from Hubble Space Telescope proper motions
+without the need for an IMBH.
+",0,1,0,0,0,0
+3776,The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?,"  A successful grasp requires careful balancing of the contact forces. Deducing
+whether a particular grasp will be successful from indirect measurements, such
+as vision, is therefore quite challenging, and direct sensing of contacts
+through touch sensing provides an appealing avenue toward more successful and
+consistent robotic grasping. However, in order to fully evaluate the value of
+touch sensing for grasp outcome prediction, we must understand how touch
+sensing can influence outcome prediction accuracy when combined with other
+modalities. Doing so using conventional model-based techniques is exceptionally
+difficult. In this work, we investigate the question of whether touch sensing
+aids in predicting grasp outcomes within a multimodal sensing framework that
+combines vision and touch. To that end, we collected more than 9,000 grasping
+trials using a two-finger gripper equipped with GelSight high-resolution
+tactile sensors on each finger, and evaluated visuo-tactile deep neural network
+models to directly predict grasp outcomes from either modality individually,
+and from both modalities together. Our experimental results indicate that
+incorporating tactile readings substantially improve grasping performance.
+",1,0,0,1,0,0
+3777,On the essential spectrum of elliptic differential operators,"  Let $\mathcal{A}$ be a $C^*$-algebra of bounded uniformly continuous
+functions on $X=\mathbb{R}^d$ such that $\mathcal{A}$ is stable under
+translations and contains the continuous functions that have a limit at
+infinity. Denote $\mathcal{A}^\dagger$ the boundary of $X$ in the character
+space of $\mathcal{A}$. Then the crossed product
+$\mathscr{A}=\mathcal{A}\rtimes X$ of $\mathcal{A}$ by the natural action of
+$X$ on $\mathcal{A}$ is a well defined $C^*$-algebra and to each operator
+$A\in\mathscr{A}$ one may naturally associate a family of bounded operators
+$A_\varkappa$ on $L^2(X)$ indexed by the characters
+$\varkappa\in\mathcal{A}^\dagger$. We show that the essential spectrum of $A$
+is the union of the spectra of the operators $A_\varkappa$. The applications
+cover very general classes of singular elliptic operators.
+",0,0,1,0,0,0
+3778,Shrinking Horizon Model Predictive Control with Signal Temporal Logic Constraints under Stochastic Disturbances,"  We present Shrinking Horizon Model Predictive Control (SHMPC) for
+discrete-time linear systems with Signal Temporal Logic (STL) specification
+constraints under stochastic disturbances. The control objective is to maximize
+an optimization function under the restriction that a given STL specification
+is satisfied with high probability against stochastic uncertainties. We
+formulate a general solution, which does not require precise knowledge of the
+probability distributions of the (possibly dependent) stochastic disturbances;
+only the bounded support intervals of the density functions and moment
+intervals are used. For the specific case of disturbances that are independent
+and normally distributed, we optimize the controllers further by utilizing
+knowledge of the disturbance probability distributions. We show that in both
+cases, the control law can be obtained by solving optimization problems with
+linear constraints at each step. We experimentally demonstrate effectiveness of
+this approach by synthesizing a controller for an HVAC system.
+",1,0,1,0,0,0
+3779,Novel processes and metrics for a scientific evaluation rooted in the principles of science - Version 1,"  Scientific evaluation is a determinant of how scientists, institutions and
+funders behave, and as such is a key element in the making of science. In this
+article, we propose an alternative to the current norm of evaluating research
+with journal rank. Following a well-defined notion of scientific value, we
+introduce qualitative processes that can also be quantified and give rise to
+meaningful and easy-to-use article-level metrics. In our approach, the goal of
+a scientist is transformed from convincing an editorial board through a
+vertical process to convincing peers through an horizontal one. We argue that
+such an evaluation system naturally provides the incentives and logic needed to
+constantly promote quality, reproducibility, openness and collaboration in
+science. The system is legally and technically feasible and can gradually lead
+to the self-organized reappropriation of the scientific process by the
+scholarly community and its institutions. We propose an implementation of our
+evaluation system with the platform ""the Self-Journals of Science""
+(www.sjscience.org).
+",1,0,0,0,0,0
+3780,Topological quantization of energy transport in micro- and nano-mechanical lattices,"  Topological effects typically discussed in the context of quantum physics are
+emerging as one of the central paradigms of physics. Here, we demonstrate the
+role of topology in energy transport through dimerized micro- and
+nano-mechanical lattices in the classical regime, i.e., essentially ""masses and
+springs"". We show that the thermal conductance factorizes into topological and
+non-topological components. The former takes on three discrete values and
+arises due to the appearance of edge modes that prevent good contact between
+the heat reservoirs and the bulk, giving a length-independent reduction of the
+conductance. In essence, energy input at the boundary mostly stays there, an
+effect robust against disorder and nonlinearity. These results bridge two
+seemingly disconnected disciplines of physics, namely topology and thermal
+transport, and suggest ways to engineer thermal contacts, opening a direction
+to explore the ramifications of topological properties on nanoscale technology.
+",0,1,0,0,0,0
+3781,Environmental impact assessment for climate change policy with the simulation-based integrated assessment model E3ME-FTT-GENIE,"  A high degree of consensus exists in the climate sciences over the role that
+human interference with the atmosphere is playing in changing the climate.
+Following the Paris Agreement, a similar consensus exists in the policy
+community over the urgency of policy solutions to the climate problem. The
+context for climate policy is thus moving from agenda setting, which has now
+been mostly established, to impact assessment, in which we identify policy
+pathways to implement the Paris Agreement. Most integrated assessment models
+currently used to address the economic and technical feasibility of avoiding
+climate change are based on engineering perspectives with a normative systems
+optimisation philosophy, suitable for agenda setting, but unsuitable to assess
+the socio-economic impacts of a realistic baskets of climate policies. Here, we
+introduce a fully descriptive, simulation-based integrated assessment model
+designed specifically to assess policies, formed by the combination of (1) a
+highly disaggregated macro-econometric simulation of the global economy based
+on time series regressions (E3ME), (2) a family of bottom-up evolutionary
+simulations of technology diffusion based on cross-sectional discrete choice
+models (FTT), and (3) a carbon cycle and atmosphere circulation model of
+intermediate complexity (GENIE-1). We use this combined model to create a
+detailed global and sectoral policy map and scenario that sets the economy on a
+pathway that achieves the goals of the Paris Agreement with >66% probability of
+not exceeding 2$^\circ$C of global warming. We propose a blueprint for a new
+role for integrated assessment models in this upcoming policy assessment
+context.
+",0,1,0,0,0,0
+3782,Nested Convex Bodies are Chaseable,"  In the Convex Body Chasing problem, we are given an initial point $v_0$ in
+$R^d$ and an online sequence of $n$ convex bodies $F_1, ..., F_n$. When we
+receive $F_i$, we are required to move inside $F_i$. Our goal is to minimize
+the total distance travelled. This fundamental online problem was first studied
+by Friedman and Linial (DCG 1993). They proved an $\Omega(\sqrt{d})$ lower
+bound on the competitive ratio, and conjectured that a competitive ratio
+depending only on d is possible. However, despite much interest in the problem,
+the conjecture remains wide open.
+We consider the setting in which the convex bodies are nested: $F_1 \supset
+... \supset F_n$. The nested setting is closely related to extending the online
+LP framework of Buchbinder and Naor (ESA 2005) to arbitrary linear constraints.
+Moreover, this setting retains much of the difficulty of the general setting
+and captures an essential obstacle in resolving Friedman and Linial's
+conjecture. In this work, we give the first $f(d)$-competitive algorithm for
+chasing nested convex bodies in $R^d$.
+",1,0,0,0,0,0
+3783,Analytic properties of approximate lattices,"  We introduce a notion of cocycle-induction for strong uniform approximate
+lattices in locally compact second countable groups and use it to relate
+(relative) Kazhdan- and Haagerup-type of approximate lattices to the
+corresponding properties of the ambient locally compact groups. Our approach
+applies to large classes of uniform approximate lattices (though not all of
+them) and is flexible enough to cover the $L^p$-versions of Property (FH) and
+a-(FH)-menability as well as quasified versions thereof a la Burger--Monod and
+Ozawa.
+",0,0,1,0,0,0
+3784,Auxiliary Variables in TLA+,"  Auxiliary variables are often needed for verifying that an implementation is
+correct with respect to a higher-level specification. They augment the formal
+description of the implementation without changing its semantics--that is, the
+set of behaviors that it describes. This paper explains rules for adding
+history, prophecy, and stuttering variables to TLA+ specifications, ensuring
+that the augmented specification is equivalent to the original one. The rules
+are explained with toy examples, and they are used to verify the correctness of
+a simplified version of a snapshot algorithm due to Afek et al.
+",1,0,0,0,0,0
+3785,A Homological model for the coloured Jones polynomials,"  In this paper we will present a homological model for Coloured Jones
+Polynomials. For each color $N \in \N$, we will describe the invariant
+$J_N(L,q)$ as a graded intersection pairing of certain homological classes in a
+covering of the configuration space on the punctured disk. This construction is
+based on the Lawrence representation and a result due to Kohno that relates
+quantum representations and homological representations of the braid groups.
+",0,0,1,0,0,0
+3786,Extremes of threshold-dependent Gaussian processes,"  In this contribution we are concerned with the asymptotic behaviour as $u\to
+\infty$ of $\mathbb{P}\{\sup_{t\in [0,T]} X_u(t)> u\}$, where $X_u(t),t\in
+[0,T],u>0$ is a family of centered Gaussian processes with continuous
+trajectories. A key application of our findings concerns
+$\mathbb{P}\{\sup_{t\in [0,T]} (X(t)+ g(t))> u\}$ as $u\to\infty$, for $X$ a
+centered Gaussian process and $g$ some measurable trend function. Further
+applications include the approximation of both the ruin time and the ruin
+probability of the Brownian motion risk model with constant force of interest.
+",0,0,1,1,0,0
+3787,The null hypothesis of common jumps in case of irregular and asynchronous observations,"  This paper proposes novel tests for the absence of jumps in a univariate
+semimartingale and for the absence of common jumps in a bivariate
+semimartingale. Our methods rely on ratio statistics of power variations based
+on irregular observations, sampled at different frequencies. We develop central
+limit theorems for the statistics under the respective null hypotheses and
+apply bootstrap procedures to assess the limiting distributions. Further we
+define corrected statistics to improve the finite sample performance.
+Simulations show that the test based on our corrected statistic yields good
+results and even outperforms existing tests in the case of regular
+observations.
+",0,0,1,0,0,0
+3788,Property Safety Stock Policy for Correlated Commodities Based on Probability Inequality,"  Deriving the optimal safety stock quantity with which to meet customer
+satisfaction is one of the most important topics in stock management. However,
+it is difficult to control the stock management of correlated marketable
+merchandise when using an inventory control method that was developed under the
+assumption that the demands are not correlated. For this, we propose a
+deterministic approach that uses a probability inequality to derive a
+reasonable safety stock for the case in which we know the correlation between
+various commodities. Moreover, over a given lead time, the relation between the
+appropriate safety stock and the allowable stockout rate is analytically
+derived, and the potential of our proposed procedure is validated by numerical
+experiments.
+",0,0,1,1,0,0
+3789,Carleman estimates for the time-fractional advection-diffusion equations and applications,"  In this article, we prove Carleman estimates for the generalized
+time-fractional advection-diffusion equations by considering the fractional
+derivative as perturbation for the first order time-derivative. As a direct
+application of the Carleman estimates, we show a conditional stability of a
+lateral Cauchy problem for the time-fractional advection-diffusion equation,
+and we also investigate the stability of an inverse source problem.
+",0,0,1,0,0,0
+3790,Generating and Aligning from Data Geometries with Generative Adversarial Networks,"  Unsupervised domain mapping has attracted substantial attention in recent
+years due to the success of models based on the cycle-consistency assumption.
+These models map between two domains by fooling a probabilistic discriminator,
+thereby matching the probability distributions of the real and generated data.
+Instead of this probabilistic approach, we cast the problem in terms of
+aligning the geometry of the manifolds of the two domains. We introduce the
+Manifold Geometry Matching Generative Adversarial Network (MGM GAN), which adds
+two novel mechanisms to facilitate GANs sampling from the geometry of the
+manifold rather than the density and then aligning two manifold geometries: (1)
+an importance sampling technique that reweights points based on their density
+on the manifold, making the discriminator only able to discern geometry and (2)
+a penalty adapted from traditional manifold alignment literature that
+explicitly enforces the geometry to be preserved. The MGM GAN leverages the
+manifolds arising from a pre-trained autoencoder to bridge the gap between
+formal manifold alignment literature and existing GAN work, and demonstrate the
+advantages of modeling the manifold geometry over its density.
+",1,0,0,1,0,0
+3791,Wolf-Rayet spin at low metallicity and its implication for Black Hole formation channels,"  The spin of Wolf-Rayet (WR) stars at low metallicity (Z) is most relevant for
+our understanding of gravitational wave sources such as GW 150914, as well as
+the incidence of long-duration gamma-ray bursts (GRBs). Two scenarios have been
+suggested for both phenomena: one of them involves rapid rotation and
+quasi-chemical homogeneous evolution (CHE), the other invokes classical
+evolution through mass loss in single and binary systems. WR spin rates might
+enable us to test these two scenarios. In order to obtain empirical constraints
+on black hole progenitor spin, we infer wind asymmetries in all 12 known WR
+stars in the Small Magellanic Cloud (SMC) at Z = 1/5 Zsun, as well as within a
+significantly enlarged sample of single and binary WR stars in the Large
+Magellanic Cloud (LMC at Z = 1/2 Zsun), tripling the sample of Vink (2007).
+This brings the total LMC sample to 39, making it appropriate for comparison to
+the Galactic sample. We measure WR wind asymmetries with VLT-FORS linear
+spectropolarimetry. We report the detection of new line effects in the LMC WN
+star BAT99-43 and the WC star BAT99-70, as well as the famous WR/LBV HD 5980 in
+the SMC, which might be evolving chemically homogeneously. With the previous
+reported line effects in the late-type WNL (Ofpe/WN9) objects BAT99-22 and
+BAT99-33, this brings the total LMC WR sample to 4, i.e. a frequency of ~10%.
+Perhaps surprisingly, the incidence of line effects amongst low-Z WR stars is
+not found to be any higher than amongst the Galactic WR sample, challenging the
+rotationally-induced CHE model. As WR mass loss is likely Z-dependent, our
+Magellanic Cloud line-effect WR stars may maintain their surface rotation and
+fulfill the basic conditions for producing long GRBs, both via the classical
+post-red supergiant (RSG) or luminous blue variable (LBV) channel, as well as
+resulting from CHE due to physics specific to very massive stars (VMS).
+",0,1,0,0,0,0
+3792,What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016,"  There is surprisingly little known about agenda setting for international
+development in the United Nations (UN) despite it having a significant
+influence on the process and outcomes of development efforts. This paper
+addresses this shortcoming using a novel approach that applies natural language
+processing techniques to countries' annual statements in the UN General Debate.
+Every year UN member states deliver statements during the General Debate on
+their governments' perspective on major issues in world politics. These
+speeches provide invaluable information on state preferences on a wide range of
+issues, including international development, but have largely been overlooked
+in the study of global politics. This paper identifies the main international
+development topics that states raise in these speeches between 1970 and 2016,
+and examine the country-specific drivers of international development rhetoric.
+",1,0,0,0,0,0
+3793,Randomizing growing networks with a time-respecting null model,"  Complex networks are often used to represent systems that are not static but
+grow with time: people make new friendships, new papers are published and refer
+to the existing ones, and so forth. To assess the statistical significance of
+measurements made on such networks, we propose a randomization methodology---a
+time-respecting null model---that preserves both the network's degree sequence
+and the time evolution of individual nodes' degree values. By preserving the
+temporal linking patterns of the analyzed system, the proposed model is able to
+factor out the effect of the system's temporal patterns on its structure. We
+apply the model to the citation network of Physical Review scholarly papers and
+the citation network of US movies. The model reveals that the two datasets are
+strikingly different with respect to their degree-degree correlations, and we
+discuss the important implications of this finding on the information provided
+by paradigmatic node centrality metrics such as indegree and Google's PageRank.
+The randomization methodology proposed here can be used to assess the
+significance of any structural property in growing networks, which could bring
+new insights into the problems where null models play a critical role, such as
+the detection of communities and network motifs.
+",1,1,0,0,0,0
+3794,High-dimensional posterior consistency for hierarchical non-local priors in regression,"  The choice of tuning parameter in Bayesian variable selection is a critical
+problem in modern statistics. Especially in the related work of nonlocal prior
+in regression setting, the scale parameter reflects the dispersion of the
+non-local prior density around zero, and implicitly determines the size of the
+regression coefficients that will be shrunk to zero. In this paper, we
+introduce a fully Bayesian approach with the pMOM nonlocal prior where we place
+an appropriate Inverse-Gamma prior on the tuning parameter to analyze a more
+robust model that is comparatively immune to misspecification of scale
+parameter. Under standard regularity assumptions, we extend the previous work
+where $p$ is bounded by the number of observations $n$ and establish strong
+model selection consistency when $p$ is allowed to increase at a polynomial
+rate with $n$. Through simulation studies, we demonstrate that our model
+selection procedure outperforms commonly used penalized likelihood methods in a
+range of simulation settings.
+",0,0,1,1,0,0
+3795,A metric of mutual energy and unlikely intersections for dynamical systems,"  We introduce a metric of mutual energy for adelic measures associated to the
+Arakelov-Zhang pairing. Using this metric and potential theoretic techniques
+involving discrete approximations to energy integrals, we prove an effective
+bound on a problem of Baker and DeMarco on unlikely intersections of dynamical
+systems, specifically, for the set of complex parameters $c$ for which $z=0$
+and $1$ are both preperiodic under iteration of $f_c(z)=z^2 + c$.
+",0,0,1,0,0,0
+3796,"Volvox barberi flocks, forming near-optimal, two-dimensional, polydisperse lattice packings","  Volvox barberi is a multicellular green alga forming spherical colonies of
+10000-50000 differentiated somatic and germ cells. Here, I show that these
+colonies actively self-organize over minutes into ""flocks"" that can contain
+more than 100 colonies moving and rotating collectively for hours. The colonies
+in flocks form two-dimensional, irregular, ""active crystals"", with lattice
+angles and colony diameters both following log-normal distributions. Comparison
+with a dynamical simulation of soft spheres with diameters matched to the
+Volvox samples, and a weak long-range attractive force, show that the Volvox
+flocks achieve optimal random close-packing. A dye tracer in the Volvox medium
+revealed large hydrodynamic vortices generated by colony and flock rotations,
+providing a likely source of the forces leading to flocking and optimal
+packing.
+",0,0,0,0,1,0
+3797,Learning to Identify Ambiguous and Misleading News Headlines,"  Accuracy is one of the basic principles of journalism. However, it is
+increasingly hard to manage due to the diversity of news media. Some editors of
+online news tend to use catchy headlines which trick readers into clicking.
+These headlines are either ambiguous or misleading, degrading the reading
+experience of the audience. Thus, identifying inaccurate news headlines is a
+task worth studying. Previous work names these headlines ""clickbaits"" and
+mainly focus on the features extracted from the headlines, which limits the
+performance since the consistency between headlines and news bodies is
+underappreciated. In this paper, we clearly redefine the problem and identify
+ambiguous and misleading headlines separately. We utilize class sequential
+rules to exploit structure information when detecting ambiguous headlines. For
+the identification of misleading headlines, we extract features based on the
+congruence between headlines and bodies. To make use of the large unlabeled
+data set, we apply a co-training method and gain an increase in performance.
+The experiment results show the effectiveness of our methods. Then we use our
+classifiers to detect inaccurate headlines crawled from different sources and
+conduct a data analysis.
+",1,0,0,0,0,0
+3798,Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots,"  It has recently become possible to study the dynamics of information
+diffusion in techno-social systems at scale, due to the emergence of online
+platforms, such as Twitter, with millions of users. One question that
+systematically recurs is whether information spreads according to simple or
+complex dynamics: does each exposure to a piece of information have an
+independent probability of a user adopting it (simple contagion), or does this
+probability depend instead on the number of sources of exposure, increasing
+above some threshold (complex contagion)? Most studies to date are
+observational and, therefore, unable to disentangle the effects of confounding
+factors such as social reinforcement, homophily, limited attention, or network
+community structure. Here we describe a novel controlled experiment that we
+performed on Twitter using `social bots' deployed to carry out coordinated
+attempts at spreading information. We propose two Bayesian statistical models
+describing simple and complex contagion dynamics, and test the competing
+hypotheses. We provide experimental evidence that the complex contagion model
+describes the observed information diffusion behavior more accurately than
+simple contagion. Future applications of our results include more effective
+defenses against malicious propaganda campaigns on social media, improved
+marketing and advertisement strategies, and design of effective network
+intervention techniques.
+",1,1,0,0,0,0
+3799,Birecurrent sets,"  A set is called recurrent if its minimal automaton is strongly connected and
+birecurrent if it is recurrent as well as its reversal. We prove a series of
+results concerning birecurrent sets. It is already known that any birecurrent
+set is completely reducible (that is, such that the minimal representation of
+its characteristic series is completely reducible). The main result of this
+paper characterizes completely reducible sets as linear combinations of
+birecurrent sets
+",1,0,1,0,0,0
+3800,Mobile Robotic Fabrication at 1:1 scale: the In situ Fabricator,"  This paper presents the concept of an In situ Fabricator, a mobile robot
+intended for on-site manufacturing, assembly and digital fabrication. We
+present an overview of a prototype system, its capabilities, and highlight the
+importance of high-performance control, estimation and planning algorithms for
+achieving desired construction goals. Next, we detail on two architectural
+application scenarios: first, building a full-size undulating brick wall, which
+required a number of repositioning and autonomous localisation manoeuvres.
+Second, the Mesh Mould concrete process, which shows that an In situ Fabricator
+in combination with an innovative digital fabrication tool can be used to
+enable completely novel building technologies. Subsequently, important
+limitations and disadvantages of our approach are discussed. Based on that, we
+identify the need for a new type of robotic actuator, which facilitates the
+design of novel full-scale construction robots. We provide brief insight into
+the development of this actuator and conclude the paper with an outlook on the
+next-generation In situ Fabricator, which is currently under development.
+",1,0,0,0,0,0
+3801,The path to high-energy electron-positron colliders: from Wideroe's betatron to Touschek's AdA and to LEP,"  We describe the road which led to the construction and exploitation of
+electron positron colliders, hightlighting how the young physics student Bruno
+Touschek met the Norwegian engineer Rolf Wideroe in Germany, during WWII, and
+collaborated in building the 15 MeV betatron, a secret project directed by
+Wideroe and financed by the Ministry of Aviation of the Reich. This is how
+Bruno Touschek learnt the science of making particle accelerators and was
+ready, many years later, to propose and build AdA, the first electron positron
+collider, in Frascati, Italy, in 1960. We shall then see how AdA was brought
+from Frascati to Orsay, in France. Taking advantage of the Orsay Linear
+Accelerator as injector, the Franco-Italian team was able to prove that
+collisions had taken place, opening the way to the use of particle colliders as
+a mean to explore high energy physics.
+",0,1,0,0,0,0
+3802,Lazarsfeld-Mukai Reflexive Sheaves and their Stability,"  Consider an ample and globally generated line bundle $L$ on a smooth
+projective variety $X$ of dimension $N\geq 2$ over $\mathbb{C}$. Let $D$ be a
+smooth divisor in the complete linear system of $L$. We construct reflexive
+sheaves on $X$ by an elementary transformation of a trivial bundle on $X$ along
+certain globally generated torsion-free sheaves on $D$. The dual reflexive
+sheaves are called the Lazarsfeld-Mukai reflexive sheaves. We prove the
+$\mu_L$-(semi)stability of such reflexive sheaves under certain conditions.
+",0,0,1,0,0,0
+3803,Dimensionality reduction for acoustic vehicle classification with spectral embedding,"  We propose a method for recognizing moving vehicles, using data from roadside
+audio sensors. This problem has applications ranging widely, from traffic
+analysis to surveillance. We extract a frequency signature from the audio
+signal using a short-time Fourier transform, and treat each time window as an
+individual data point to be classified. By applying a spectral embedding, we
+decrease the dimensionality of the data sufficiently for K-nearest neighbors to
+provide accurate vehicle identification.
+",1,0,0,1,0,0
+3804,Efficient Estimation of Linear Functionals of Principal Components,"  We study principal component analysis (PCA) for mean zero i.i.d. Gaussian
+observations $X_1,\dots, X_n$ in a separable Hilbert space $\mathbb{H}$ with
+unknown covariance operator $\Sigma.$ The complexity of the problem is
+characterized by its effective rank ${\bf r}(\Sigma):= \frac{{\rm
+tr}(\Sigma)}{\|\Sigma\|},$ where ${\rm tr}(\Sigma)$ denotes the trace of
+$\Sigma$ and $\|\Sigma\|$ denotes its operator norm. We develop a method of
+bias reduction in the problem of estimation of linear functionals of
+eigenvectors of $\Sigma.$ Under the assumption that ${\bf r}(\Sigma)=o(n),$ we
+establish the asymptotic normality and asymptotic properties of the risk of the
+resulting estimators and prove matching minimax lower bounds, showing their
+semi-parametric optimality.
+",0,0,1,1,0,0
+3805,A stroll in the jungle of error bounds,"  The aim of this paper is to give a short overview on error bounds and to
+provide the first bricks of a unified theory. Inspired by the works of [8, 15,
+13, 16, 10], we show indeed the centrality of the Lojasiewicz gradient
+inequality. For this, we review some necessary and sufficient conditions for
+global/local error bounds, both in the convex and nonconvex case. We also
+recall some results on quantitative error bounds which play a major role in
+convergence rate analysis and complexity theory of many optimization methods.
+",0,0,1,0,0,0
+3806,Variational obstacle avoidance problem on Riemannian manifolds,"  We introduce variational obstacle avoidance problems on Riemannian manifolds
+and derive necessary conditions for the existence of their normal extremals.
+The problem consists of minimizing an energy functional depending on the
+velocity and covariant acceleration, among a set of admissible curves, and also
+depending on a navigation function used to avoid an obstacle on the workspace,
+a Riemannian manifold.
+We study two different scenarios, a general one on a Riemannian manifold and,
+a sub-Riemannian problem. By introducing a left-invariant metric on a Lie
+group, we also study the variational obstacle avoidance problem on a Lie group.
+We apply the results to the obstacle avoidance problem of a planar rigid body
+and an unicycle.
+",1,0,1,0,0,0
+3807,An Extension of Heron's Formula,"  This paper introduces an extension of Heron's formula to approximate area of
+cyclic n-gons where the error never exceeds $\frac{\pi}{e}-1$
+",0,0,1,0,0,0
+3808,Learning Less-Overlapping Representations,"  In representation learning (RL), how to make the learned representations easy
+to interpret and less overfitted to training data are two important but
+challenging issues. To address these problems, we study a new type of
+regulariza- tion approach that encourages the supports of weight vectors in RL
+models to have small overlap, by simultaneously promoting near-orthogonality
+among vectors and sparsity of each vector. We apply the proposed regularizer to
+two models: neural networks (NNs) and sparse coding (SC), and develop an
+efficient ADMM-based algorithm for regu- larized SC. Experiments on various
+datasets demonstrate that weight vectors learned under our regularizer are more
+interpretable and have better generalization performance.
+",1,0,0,1,0,0
+3809,"LDPC Code Design for Distributed Storage: Balancing Repair Bandwidth, Reliability and Storage Overhead","  Distributed storage systems suffer from significant repair traffic generated
+due to frequent storage node failures. This paper shows that properly designed
+low-density parity-check (LDPC) codes can substantially reduce the amount of
+required block downloads for repair thanks to the sparse nature of their factor
+graph representation. In particular, with a careful construction of the factor
+graph, both low repair-bandwidth and high reliability can be achieved for a
+given code rate. First, a formula for the average repair bandwidth of LDPC
+codes is developed. This formula is then used to establish that the minimum
+repair bandwidth can be achieved by forcing a regular check node degree in the
+factor graph. Moreover, it is shown that given a fixed code rate, the variable
+node degree should also be regular to yield minimum repair bandwidth, under
+some reasonable minimum variable node degree constraint. It is also shown that
+for a given repair-bandwidth requirement, LDPC codes can yield substantially
+higher reliability than currently utilized Reed-Solomon (RS) codes. Our
+reliability analysis is based on a formulation of the general equation for the
+mean-time-to-data-loss (MTTDL) associated with LDPC codes. The formulation
+reveals that the stopping number is closely related to the MTTDL. It is further
+shown that LDPC codes can be designed such that a small loss of
+repair-bandwidth optimality may be traded for a large improvement in
+erasure-correction capability and thus the MTTDL.
+",1,0,0,0,0,0
+3810,"Structural, magnetic, and electronic properties of GdTiO3 Mott insulator thin films grown by pulsed laser deposition","  We report on the optimization process to synthesize epitaxial thin films of
+GdTiO3 on SrLaGaO4 substrates by pulsed laser deposition. Optimized films are
+free of impurity phases and are fully strained. They possess a magnetic Curie
+temperature TC = 31.8 K with a saturation magnetization of 4.2 muB per formula
+unit at 10 K. Transport measurements reveal an insulating response, as
+expected. Optical spectroscopy indicates a band gap of 0.7 eV, comparable to
+the bulk value. Our work adds ferrimagnetic orthotitanates to the palette of
+perovskite materials for the design of emergent strongly correlated states at
+oxide interfaces using a versatile growth technique such as pulsed laser
+deposition.
+",0,1,0,0,0,0
+3811,GPU-Based High-Performance Imaging for Mingantu Spectral RadioHeliograph,"  As a dedicated solar radio interferometer, the MingantU SpEctral
+RadioHeliograph (MUSER) generates massive observational data in the frequency
+range of 400 MHz -- 15 GHz. High-performance imaging forms a significantly
+important aspect of MUSER's massive data processing requirements. In this
+study, we implement a practical high-performance imaging pipeline for MUSER
+data processing. At first, the specifications of the MUSER are introduced and
+its imaging requirements are analyzed. Referring to the most commonly used
+radio astronomy software such as CASA and MIRIAD, we then implement a
+high-performance imaging pipeline based on the Graphics Processing Unit (GPU)
+technology with respect to the current operational status of the MUSER. A
+series of critical algorithms and their pseudo codes, i.e., detection of the
+solar disk and sky brightness, automatic centering of the solar disk and
+estimation of the number of iterations for clean algorithms, are proposed in
+detail. The preliminary experimental results indicate that the proposed imaging
+approach significantly increases the processing performance of MUSER and
+generates images with high-quality, which can meet the requirements of the
+MUSER data processing.
+",0,1,0,0,0,0
+3812,Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning,"  We present Deep Voice 3, a fully-convolutional attention-based neural
+text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural
+speech synthesis systems in naturalness while training ten times faster. We
+scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more
+than eight hundred hours of audio from over two thousand speakers. In addition,
+we identify common error modes of attention-based speech synthesis networks,
+demonstrate how to mitigate them, and compare several different waveform
+synthesis methods. We also describe how to scale inference to ten million
+queries per day on one single-GPU server.
+",1,0,0,0,0,0
+3813,Ubenwa: Cry-based Diagnosis of Birth Asphyxia,"  Every year, 3 million newborns die within the first month of life. Birth
+asphyxia and other breathing-related conditions are a leading cause of
+mortality during the neonatal phase. Current diagnostic methods are too
+sophisticated in terms of equipment, required expertise, and general logistics.
+Consequently, early detection of asphyxia in newborns is very difficult in many
+parts of the world, especially in resource-poor settings. We are developing a
+machine learning system, dubbed Ubenwa, which enables diagnosis of asphyxia
+through automated analysis of the infant cry. Deployed via smartphone and
+wearable technology, Ubenwa will drastically reduce the time, cost and skill
+required to make accurate and potentially life-saving diagnoses.
+",1,0,0,1,0,0
+3814,Fluid-Structure Interaction with the Entropic Lattice Boltzmann Method,"  We propose a novel fluid-structure interaction (FSI) scheme using the
+entropic multi-relaxation time lattice Boltzmann (KBC) model for the fluid
+domain in combination with a nonlinear finite element solver for the structural
+part. We show validity of the proposed scheme for various challenging set-ups
+by comparison to literature data. Beyond validation, we extend the KBC model to
+multiphase flows and couple it with FEM solver. Robustness and viability of the
+entropic multi-relaxation time model for complex FSI applications is shown by
+simulations of droplet impact on elastic superhydrophobic surfaces.
+",0,1,0,0,0,0
+3815,Two provably consistent divide and conquer clustering algorithms for large networks,"  In this article, we advance divide-and-conquer strategies for solving the
+community detection problem in networks. We propose two algorithms which
+perform clustering on a number of small subgraphs and finally patches the
+results into a single clustering. The main advantage of these algorithms is
+that they bring down significantly the computational cost of traditional
+algorithms, including spectral clustering, semi-definite programs, modularity
+based methods, likelihood based methods etc., without losing on accuracy and
+even improving accuracy at times. These algorithms are also, by nature,
+parallelizable. Thus, exploiting the facts that most traditional algorithms are
+accurate and the corresponding optimization problems are much simpler in small
+problems, our divide-and-conquer methods provide an omnibus recipe for scaling
+traditional algorithms up to large networks. We prove consistency of these
+algorithms under various subgraph selection procedures and perform extensive
+simulations and real-data analysis to understand the advantages of the
+divide-and-conquer approach in various settings.
+",0,0,1,1,0,0
+3816,Feature-based visual odometry prior for real-time semi-dense stereo SLAM,"  Robust and fast motion estimation and mapping is a key prerequisite for
+autonomous operation of mobile robots. The goal of performing this task solely
+on a stereo pair of video cameras is highly demanding and bears conflicting
+objectives: on one hand, the motion has to be tracked fast and reliably, on the
+other hand, high-level functions like navigation and obstacle avoidance depend
+crucially on a complete and accurate environment representation. In this work,
+we propose a two-layer approach for visual odometry and SLAM with stereo
+cameras that runs in real-time and combines feature-based matching with
+semi-dense direct image alignment. Our method initializes semi-dense depth
+estimation, which is computationally expensive, from motion that is tracked by
+a fast but robust keypoint-based method. Experiments on public benchmark and
+proprietary datasets show that our approach is faster than state-of-the-art
+methods without losing accuracy and yields comparable map building
+capabilities. Moreover, our approach is shown to handle large inter-frame
+motion and illumination changes much more robustly than its direct
+counterparts.
+",1,0,0,0,0,0
+3817,Effective Description of Higher-Order Scalar-Tensor Theories,"  Most existing theories of dark energy and/or modified gravity, involving a
+scalar degree of freedom, can be conveniently described within the framework of
+the Effective Theory of Dark Energy, based on the unitary gauge where the
+scalar field is uniform. We extend this effective approach by allowing the
+Lagrangian in unitary gauge to depend on the time derivative of the lapse
+function. Although this dependence generically signals the presence of an extra
+scalar degree of freedom, theories that contain only one propagating scalar
+degree of freedom, in addition to the usual tensor modes, can be constructed by
+requiring the initial Lagrangian to be degenerate. Starting from a general
+quadratic action, we derive the dispersion relations for the linear
+perturbations around Minkowski and a cosmological background. Our analysis
+directly applies to the recently introduced Degenerate Higher-Order
+Scalar-Tensor (DHOST) theories. For these theories, we find that one cannot
+recover a Poisson-like equation in the static linear regime except for the
+subclass that includes the Horndeski and so-called ""beyond Horndeski"" theories.
+We also discuss Lorentz-breaking models inspired by Horava gravity.
+",0,1,0,0,0,0
+3818,Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences,"  Video analytics requires operating with large amounts of data. Compressive
+sensing allows to reduce the number of measurements required to represent the
+video using the prior knowledge of sparsity of the original signal, but it
+imposes certain conditions on the design matrix. The Bayesian compressive
+sensing approach relaxes the limitations of the conventional approach using the
+probabilistic reasoning and allows to include different prior knowledge about
+the signal structure. This paper presents two Bayesian compressive sensing
+methods for autonomous object detection in a video sequence from a static
+camera. Their performance is compared on the real datasets with the
+non-Bayesian greedy algorithm. It is shown that the Bayesian methods can
+provide the same accuracy as the greedy algorithm but much faster; or if the
+computational time is not critical they can provide more accurate results.
+",1,0,0,1,0,0
+3819,Making Neural QA as Simple as Possible but not Simpler,"  Recent development of large-scale question answering (QA) datasets triggered
+a substantial amount of research into end-to-end neural architectures for QA.
+Increasingly complex systems have been conceived without comparison to simpler
+neural baseline systems that would justify their complexity. In this work, we
+propose a simple heuristic that guides the development of neural baseline
+systems for the extractive QA task. We find that there are two ingredients
+necessary for building a high-performing neural QA system: first, the awareness
+of question words while processing the context and second, a composition
+function that goes beyond simple bag-of-words modeling, such as recurrent
+neural networks. Our results show that FastQA, a system that meets these two
+requirements, can achieve very competitive performance compared with existing
+models. We argue that this surprising finding puts results of previous systems
+and the complexity of recent QA datasets into perspective.
+",1,0,0,0,0,0
+3820,Finite-dimensional Gaussian approximation with linear inequality constraints,"  Introducing inequality constraints in Gaussian process (GP) models can lead
+to more realistic uncertainties in learning a great variety of real-world
+problems. We consider the finite-dimensional Gaussian approach from Maatouk and
+Bay (2017) which can satisfy inequality conditions everywhere (either
+boundedness, monotonicity or convexity). Our contributions are threefold.
+First, we extend their approach in order to deal with general sets of linear
+inequalities. Second, we explore several Markov Chain Monte Carlo (MCMC)
+techniques to approximate the posterior distribution. Third, we investigate
+theoretical and numerical properties of the constrained likelihood for
+covariance parameter estimation. According to experiments on both artificial
+and real data, our full framework together with a Hamiltonian Monte Carlo-based
+sampler provides efficient results on both data fitting and uncertainty
+quantification.
+",1,0,0,1,0,0
+3821,A Generalization of Quasi-twisted Codes: Multi-twisted codes,"  Cyclic codes and their various generalizations, such as quasi-twisted (QT)
+codes, have a special place in algebraic coding theory. Among other things,
+many of the best-known or optimal codes have been obtained from these classes.
+In this work we introduce a new generalization of QT codes that we call
+multi-twisted (MT) codes and study some of their basic properties. Presenting
+several methods of constructing codes in this class and obtaining bounds on the
+minimum distances, we show that there exist codes with good parameters in this
+class that cannot be obtained as QT or constacyclic codes. This suggests that
+considering this larger class in computer searches is promising for
+constructing codes with better parameters than currently best-known linear
+codes. Working with this new class of codes motivated us to consider a problem
+about binomials over finite fields and to discover a result that is interesting
+in its own right.
+",1,0,1,0,0,0
+3822,On the Successive Cancellation Decoding of Polar Codes with Arbitrary Linear Binary Kernels,"  A method for efficiently successive cancellation (SC) decoding of polar codes
+with high-dimensional linear binary kernels (HDLBK) is presented and analyzed.
+We devise a $l$-expressions method which can obtain simplified recursive
+formulas of SC decoder in likelihood ratio form for arbitrary linear binary
+kernels to reduce the complexity of corresponding SC decoder. By considering
+the bit-channel transition probabilities $W_{G}^{(\cdot)}(\cdot|0)$ and
+$W_{G}^{(\cdot)}(\cdot|1)$ separately, a $W$-expressions method is proposed to
+further reduce the complexity of HDLBK based SC decoder. For a $m\times m$
+binary kernel, the complexity of straightforward SC decoder is $O(2^{m}N\log
+N)$. With $W$-expressions, we reduce the complexity of straightforward SC
+decoder to $O(m^{2}N\log N)$ when $m\leq 16$. Simulation results show that
+$16\times16$ kernel polar codes offer significant advantages in terms of error
+performances compared with $2\times2$ kernel polar codes under SC and list SC
+decoders.
+",1,0,1,0,0,0
+3823,OpenML: An R Package to Connect to the Machine Learning Platform OpenML,"  OpenML is an online machine learning platform where researchers can easily
+share data, machine learning tasks and experiments as well as organize them
+online to work and collaborate more efficiently. In this paper, we present an R
+package to interface with the OpenML platform and illustrate its usage in
+combination with the machine learning R package mlr. We show how the OpenML
+package allows R users to easily search, download and upload data sets and
+machine learning tasks. Furthermore, we also show how to upload results of
+experiments, share them with others and download results from other users.
+Beyond ensuring reproducibility of results, the OpenML platform automates much
+of the drudge work, speeds up research, facilitates collaboration and increases
+the users' visibility online.
+",1,0,0,1,0,0
+3824,Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning,"  Cultural activity is an inherent aspect of urban life and the success of a
+modern city is largely determined by its capacity to offer generous cultural
+entertainment to its citizens. To this end, the optimal allocation of cultural
+establishments and related resources across urban regions becomes of vital
+importance, as it can reduce financial costs in terms of planning and improve
+quality of life in the city, more generally. In this paper, we make use of a
+large longitudinal dataset of user location check-ins from the online social
+network WeChat to develop a data-driven framework for cultural planning in the
+city of Beijing. We exploit rich spatio-temporal representations on user
+activity at cultural venues and use a novel extended version of the traditional
+latent Dirichlet allocation model that incorporates temporal information to
+identify latent patterns of urban cultural interactions. Using the
+characteristic typologies of mobile user cultural activities emitted by the
+model, we determine the levels of demand for different types of cultural
+resources across urban areas. We then compare those with the corresponding
+levels of supply as driven by the presence and spatial reach of cultural venues
+in local areas to obtain high resolution maps that indicate urban regions with
+lack of cultural resources, and thus give suggestions for further urban
+cultural planning and investment optimisation.
+",1,0,0,1,0,0
+3825,Direct mapping of the temperature and velocity gradients in discs. Imaging the vertical CO snow line around IM Lupi,"  Accurate measurements of the physical structure of protoplanetary discs are
+critical inputs for planet formation models. These constraints are
+traditionally established via complex modelling of continuum and line
+observations. Instead, we present an empirical framework to locate the CO
+isotopologue emitting surfaces from high spectral and spatial resolution ALMA
+observations. We apply this framework to the disc surrounding IM Lupi, where we
+report the first direct, i.e. model independent, measurements of the radial and
+vertical gradients of temperature and velocity in a protoplanetary disc. The
+measured disc structure is consistent with an irradiated self-similar disc
+structure, where the temperature increases and the velocity decreases towards
+the disc surface. We also directly map the vertical CO snow line, which is
+located at about one gas scale height at radii between 150 and 300 au, with a
+CO freeze-out temperature of $21\pm2$ K. In the outer disc ($> 300$ au), where
+the gas surface density transitions from a power law to an exponential taper,
+the velocity rotation field becomes significantly sub-Keplerian, in agreement
+with the expected steeper pressure gradient. The sub-Keplerian velocities
+should result in a very efficient inward migration of large dust grains,
+explaining the lack of millimetre continuum emission outside of 300 au. The
+sub-Keplerian motions may also be the signature of the base of an externally
+irradiated photo-evaporative wind. In the same outer region, the measured CO
+temperature above the snow line decreases to $\approx$ 15 K because of the
+reduced gas density, which can result in a lower CO freeze-out temperature,
+photo-desorption, or deviations from local thermodynamic equilibrium.
+",0,1,0,0,0,0
+3826,Learning Rates for Kernel-Based Expectile Regression,"  Conditional expectiles are becoming an increasingly important tool in finance
+as well as in other areas of applications. We analyse a support vector machine
+type approach for estimating conditional expectiles and establish learning
+rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF
+kernels are used and the desired expectile is smooth in a Besov sense. As a
+special case, our learning rates improve the best known rates for kernel-based
+least squares regression in this scenario. Key ingredients of our statistical
+analysis are a general calibration inequality for the asymmetric least squares
+loss, a corresponding variance bound as well as an improved entropy number
+bound for Gaussian RBF kernels.
+",0,0,0,1,0,0
+3827,Critical role of electronic correlations in determining crystal structure of transition metal compounds,"  The choice that a solid system ""makes"" when adopting a crystal structure
+(stable or metastable) is ultimately governed by the interactions between
+electrons forming chemical bonds. By analyzing 6 prototypical binary
+transition-metal compounds we demonstrate here that the orbitally-selective
+strong $d$-electron correlations influence dramatically the behavior of the
+energy as a function of the spatial arrangements of the atoms. Remarkably, we
+find that the main qualitative features of this complex behavior can be traced
+back to simple electrostatics, i.e., to the fact that the strong $d$-electron
+correlations influence substantially the charge transfer mechanism, which, in
+turn, controls the electrostatic interactions. This result advances our
+understanding of the influence of strong correlations on the crystal structure,
+opens a new avenue for extending structure prediction methodologies to strongly
+correlated materials, and paves the way for predicting and studying
+metastability and polymorphism in these systems.
+",0,1,0,0,0,0
+3828,A Machine Learning Alternative to P-values,"  This paper presents an alternative approach to p-values in regression
+settings. This approach, whose origins can be traced to machine learning, is
+based on the leave-one-out bootstrap for prediction error. In machine learning
+this is called the out-of-bag (OOB) error. To obtain the OOB error for a model,
+one draws a bootstrap sample and fits the model to the in-sample data. The
+out-of-sample prediction error for the model is obtained by calculating the
+prediction error for the model using the out-of-sample data. Repeating and
+averaging yields the OOB error, which represents a robust cross-validated
+estimate of the accuracy of the underlying model. By a simple modification to
+the bootstrap data involving ""noising up"" a variable, the OOB method yields a
+variable importance (VIMP) index, which directly measures how much a specific
+variable contributes to the prediction precision of a model. VIMP provides a
+scientifically interpretable measure of the effect size of a variable, we call
+the ""predictive effect size"", that holds whether the researcher's model is
+correct or not, unlike the p-value whose calculation is based on the assumed
+correctness of the model. We also discuss a marginal VIMP index, also easily
+calculated, which measures the marginal effect of a variable, or what we call
+""the discovery effect"". The OOB procedure can be applied to both parametric and
+nonparametric regression models and requires only that the researcher can
+repeatedly fit their model to bootstrap and modified bootstrap data. We
+illustrate this approach on a survival data set involving patients with
+systolic heart failure and to a simulated survival data set where the model is
+incorrectly specified to illustrate its robustness to model misspecification.
+",1,0,0,1,0,0
+3829,Image Registration Techniques: A Survey,"  Image Registration is the process of aligning two or more images of the same
+scene with reference to a particular image. The images are captured from
+various sensors at different times and at multiple view-points. Thus to get a
+better picture of any change of a scene or object over a considerable period of
+time image registration is important. Image registration finds application in
+medical sciences, remote sensing and in computer vision. This paper presents a
+detailed review of several approaches which are classified accordingly along
+with their contributions and drawbacks. The main steps of an image registration
+procedure are also discussed. Different performance measures are presented that
+determine the registration quality and accuracy. The scope for the future
+research are presented as well.
+",1,0,0,0,0,0
+3830,Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes,"  This work initiates a general study of learning and generalization without
+the i.i.d. assumption, starting from first principles. While the standard
+approach to statistical learning theory is based on assumptions chosen largely
+for their convenience (e.g., i.i.d. or stationary ergodic), in this work we are
+interested in developing a theory of learning based only on the most
+fundamental and natural assumptions implicit in the requirements of the
+learning problem itself. We specifically study universally consistent function
+learning, where the objective is to obtain low long-run average loss for any
+target function, when the data follow a given stochastic process. We are then
+interested in the question of whether there exist learning rules guaranteed to
+be universally consistent given only the assumption that universally consistent
+learning is possible for the given data process. The reasoning that motivates
+this criterion emanates from a kind of optimist's decision theory, and so we
+refer to such learning rules as being optimistically universal. We study this
+question in three natural learning settings: inductive, self-adaptive, and
+online. Remarkably, as our strongest positive result, we find that
+optimistically universal learning rules do indeed exist in the self-adaptive
+learning setting. Establishing this fact requires us to develop new approaches
+to the design of learning algorithms. Along the way, we also identify concise
+characterizations of the family of processes under which universally consistent
+learning is possible in the inductive and self-adaptive settings. We
+additionally pose a number of enticing open problems, particularly for the
+online learning setting.
+",1,0,1,1,0,0
+3831,Obstructions for three-coloring and list three-coloring $H$-free graphs,"  A graph is $H$-free if it has no induced subgraph isomorphic to $H$. We
+characterize all graphs $H$ for which there are only finitely many minimal
+non-three-colorable $H$-free graphs. Such a characterization was previously
+known only in the case when $H$ is connected. This solves a problem posed by
+Golovach et al. As a second result, we characterize all graphs $H$ for which
+there are only finitely many $H$-free minimal obstructions for list
+3-colorability.
+",1,0,0,0,0,0
+3832,Hybrid quantum-classical modeling of quantum dot devices,"  The design of electrically driven quantum dot devices for quantum optical
+applications asks for modeling approaches combining classical device physics
+with quantum mechanics. We connect the well-established fields of
+semi-classical semiconductor transport theory and the theory of open quantum
+systems to meet this requirement. By coupling the van Roosbroeck system with a
+quantum master equation in Lindblad form, we introduce a new hybrid
+quantum-classical modeling approach, which provides a comprehensive description
+of quantum dot devices on multiple scales: It enables the calculation of
+quantum optical figures of merit and the spatially resolved simulation of the
+current flow in realistic semiconductor device geometries in a unified way. We
+construct the interface between both theories in such a way, that the resulting
+hybrid system obeys the fundamental axioms of (non-)equilibrium thermodynamics.
+We show that our approach guarantees the conservation of charge, consistency
+with the thermodynamic equilibrium and the second law of thermodynamics. The
+feasibility of the approach is demonstrated by numerical simulations of an
+electrically driven single-photon source based on a single quantum dot in the
+stationary and transient operation regime.
+",0,1,0,0,0,0
+3833,Efficient exploration with Double Uncertain Value Networks,"  This paper studies directed exploration for reinforcement learning agents by
+tracking uncertainty about the value of each available action. We identify two
+sources of uncertainty that are relevant for exploration. The first originates
+from limited data (parametric uncertainty), while the second originates from
+the distribution of the returns (return uncertainty). We identify methods to
+learn these distributions with deep neural networks, where we estimate
+parametric uncertainty with Bayesian drop-out, while return uncertainty is
+propagated through the Bellman equation as a Gaussian distribution. Then, we
+identify that both can be jointly estimated in one network, which we call the
+Double Uncertain Value Network. The policy is directly derived from the learned
+distributions based on Thompson sampling. Experimental results show that both
+types of uncertainty may vastly improve learning in domains with a strong
+exploration challenge.
+",1,0,0,1,0,0
+3834,Auxiliary Variables for Multi-Dirichlet Priors,"  Bayesian models that mix multiple Dirichlet prior parameters, called
+Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring
+mixing weights and parameters of mixed prior distributions seems tricky, as
+sums over Dirichlet parameters complicate the joint distribution of model
+parameters.
+This paper shows a novel auxiliary variable scheme which helps to simplify
+the inference for models involving hierarchical MDs and MDPs. Using this
+scheme, it is easy to derive fully collapsed inference schemes which allow for
+an efficient inference.
+",0,0,0,1,0,0
+3835,Improving and Assessing Planet Sensitivity of the GPI Exoplanet Survey with a Forward Model Matched Filter,"  We present a new matched filter algorithm for direct detection of point
+sources in the immediate vicinity of bright stars. The stellar Point Spread
+Function (PSF) is first subtracted using a Karhunen-Loéve Image Processing
+(KLIP) algorithm with Angular and Spectral Differential Imaging (ADI and SDI).
+The KLIP-induced distortion of the astrophysical signal is included in the
+matched filter template by computing a forward model of the PSF at every
+position in the image. To optimize the performance of the algorithm, we conduct
+extensive planet injection and recovery tests and tune the exoplanet spectra
+template and KLIP reduction aggressiveness to maximize the Signal-to-Noise
+Ratio (SNR) of the recovered planets. We show that only two spectral templates
+are necessary to recover any young Jovian exoplanets with minimal SNR loss. We
+also developed a complete pipeline for the automated detection of point source
+candidates, the calculation of Receiver Operating Characteristics (ROC), false
+positives based contrast curves, and completeness contours. We process in a
+uniform manner more than 330 datasets from the Gemini Planet Imager Exoplanet
+Survey (GPIES) and assess GPI typical sensitivity as a function of the star and
+the hypothetical companion spectral type. This work allows for the first time a
+comparison of different detection algorithms at a survey scale accounting for
+both planet completeness and false positive rate. We show that the new forward
+model matched filter allows the detection of $50\%$ fainter objects than a
+conventional cross-correlation technique with a Gaussian PSF template for the
+same false positive rate.
+",0,1,0,0,0,0
+3836,Numerical non-LTE 3D radiative transfer using a multigrid method,"  3D non-LTE radiative transfer problems are computationally demanding, and
+this sets limits on the size of the problems that can be solved. So far
+Multilevel Accelerated Lambda Iteration (MALI) has been to the method of choice
+to perform high-resolution computations in multidimensional problems. The
+disadvantage of MALI is that its computing time scales as $\mathcal{O}(n^2)$,
+with $n$ the number of grid points. When the grid gets finer, the computational
+cost increases quadratically. We aim to develop a 3D non-LTE radiative transfer
+code that is more efficient than MALI. We implement a non-linear multigrid,
+fast approximation storage scheme, into the existing Multi3D radiative transfer
+code. We verify our multigrid implementation by comparing with MALI
+computations. We show that multigrid can be employed in realistic problems with
+snapshots from 3D radiative-MHD simulations as input atmospheres. With
+multigrid, we obtain a factor 3.3-4.5 speedup compared to MALI. With
+full-multigrid the speed-up increases to a factor 6. The speedup is expected to
+increase for input atmospheres with more grid points and finer grid spacing.
+Solving 3D non-LTE radiative transfer problems using non-linear multigrid
+methods can be applied to realistic atmospheres with a substantial speed-up.
+",0,1,0,0,0,0
+3837,Test Case Prioritization Techniques for Model-Based Testing: A Replicated Study,"  Recently, several Test Case Prioritization (TCP) techniques have been
+proposed to order test cases for achieving a goal during test execution,
+particularly, revealing faults sooner. In the Model-Based Testing (MBT)
+context, such techniques are usually based on heuristics related to structural
+elements of the model and derived test cases. In this sense, techniques'
+performance may vary due to a number of factors. While empirical studies
+comparing the performance of TCP techniques have already been presented in
+literature, there is still little knowledge, particularly in the MBT context,
+about which factors may influence the outcomes suggested by a TCP technique. In
+a previous family of empirical studies focusing on labeled transition systems,
+we identified that the model layout, i.e. amount of branches, joins, and loops
+in the model, alone may have little influence on the performance of TCP
+techniques investigated, whereas characteristics of test cases that actually
+fail definitely influences their performance. However, we considered only
+synthetic artifacts in the study, which reduced the ability of representing
+properly the reality. In this paper, we present a replication of one of these
+studies, now with a larger and more representative selection of techniques and
+considering test suites from industrial applications as experimental objects.
+Our objective is to find out whether the results remain while increasing the
+validity in comparison to the original study. Results reinforce that there is
+no best performer among the investigated techniques and characteristics of test
+cases that fail represent an important factor, although adaptive random based
+techniques are less affected by it.
+",1,0,0,0,0,0
+3838,Magnetic order and interactions in ferrimagnetic Mn3Si2Te6,"  The magnetism in Mn$_3$Si$_2$Te$_6$ has been investigated using thermodynamic
+measurements, first principles calculations, neutron diffraction and diffuse
+neutron scattering on single crystals. These data confirm that
+Mn$_3$Si$_2$Te$_6$ is a ferrimagnet below a Curie temperature of $T_C$
+approximately 78K. The magnetism is anisotropic, with magnetization and neutron
+diffraction demonstrating that the moments lie within the basal plane of the
+trigonal structure. The saturation magnetization of approximately 1.6$\mu_B$/Mn
+at 5K originates from the different multiplicities of the two
+antiferromagnetically-aligned Mn sites. First principles calculations reveal
+antiferromagnetic exchange for the three nearest Mn-Mn pairs, which leads to a
+competition between the ferrimagnetic ground state and three other magnetic
+configurations. The ferrimagnetic state results from the energy associated with
+the third-nearest neighbor interaction, and thus long-range interactions are
+essential for the observed behavior. Diffuse magnetic scattering is observed
+around the 002 Bragg reflection at 120K, which indicates the presence of strong
+spin correlations well above $T_C$. These are promoted by the competing ground
+states that result in a relative suppression of $T_C$, and may be associated
+with a small ferromagnetic component that produces anisotropic magnetism below
+$\approx$330K.
+",0,1,0,0,0,0
+3839,On the Solution of Linear Programming Problems in the Age of Big Data,"  The Big Data phenomenon has spawned large-scale linear programming problems.
+In many cases, these problems are non-stationary. In this paper, we describe a
+new scalable algorithm called NSLP for solving high-dimensional, non-stationary
+linear programming problems on modern cluster computing systems. The algorithm
+consists of two phases: Quest and Targeting. The Quest phase calculates a
+solution of the system of inequalities defining the constraint system of the
+linear programming problem under the condition of dynamic changes in input
+data. To this end, the apparatus of Fejer mappings is used. The Targeting phase
+forms a special system of points having the shape of an n-dimensional
+axisymmetric cross. The cross moves in the n-dimensional space in such a way
+that the solution of the linear programming problem is located all the time in
+an ""-vicinity of the central point of the cross.
+",1,0,1,0,0,0
+3840,Highly accurate acoustic scattering: Isogeometric Analysis coupled with local high order Farfield Expansion ABC,"  This work is concerned with a unique combination of high order local
+absorbing boundary conditions (ABC) with a general curvilinear Finite Element
+Method (FEM) and its implementation in Isogeometric Analysis (IGA) for
+time-harmonic acoustic waves. The ABC employed were recently devised by
+Villamizar, Acosta and Dastrup [J. Comput. Phys. 333 (2017) 331] . They are
+derived from exact Farfield Expansions representations of the outgoing waves in
+the exterior of the regions enclosed by the artificial boundary. As a
+consequence, the error due to the ABC on the artificial boundary can be reduced
+conveniently such that the dominant error comes from the volume discretization
+method used in the interior of the computational domain. Reciprocally, the
+error in the interior can be made as small as the error at the artificial
+boundary by appropriate implementation of {\it p-} and {\it h}- refinement. We
+apply this novel method to cylindrical, spherical and arbitrary shape
+scatterers including a prototype submarine. Our numerical results exhibits
+spectral-like approximation and high order convergence rate. Additionally, they
+show that the proposed method can reduce both the pollution and artificial
+boundary errors to negligible levels even in very low- and high- frequency
+regimes with rather coarse discretization densities in the IGA. As a result, we
+have developed a highly accurate computational platform to numerically solve
+time-harmonic acoustic wave scattering in two- and three-dimensions.
+",1,0,0,0,0,0
+3841,Deep Domain Adaptation Based Video Smoke Detection using Synthetic Smoke Images,"  In this paper, a deep domain adaptation based method for video smoke
+detection is proposed to extract a powerful feature representation of smoke.
+Due to the smoke image samples limited in scale and diversity for deep CNN
+training, we systematically produced adequate synthetic smoke images with a
+wide variation in the smoke shape, background and lighting conditions.
+Considering that the appearance gap (dataset bias) between synthetic and real
+smoke images degrades significantly the performance of the trained model on the
+test set composed fully of real images, we build deep architectures based on
+domain adaptation to confuse the distributions of features extracted from
+synthetic and real smoke images. This approach expands the domain-invariant
+feature space for smoke image samples. With their approximate feature
+distribution off non-smoke images, the recognition rate of the trained model is
+improved significantly compared to the model trained directly on mixed dataset
+of synthetic and real images. Experimentally, several deep architectures with
+different design choices are applied to the smoke detector. The ultimate
+framework can get a satisfactory result on the test set. We believe that our
+approach is a start in the direction of utilizing deep neural networks enhanced
+with synthetic smoke images for video smoke detection.
+",1,0,0,0,0,0
+3842,NaCl crystal from salt solution with far below saturated concentration under ambient condition,"  Under ambient conditions, we directly observed NaCl crystals experimentally
+in the rGO membranes soaked in the salt solution with concentration below and
+far below the saturated concentration. Moreover, in most probability, the NaCl
+crystals show stoichiometries behavior. We attribute this unexpected
+crystallization to the cation-{\pi} interactions between the ions and the
+aromatic rings of the rGO.
+",0,1,0,0,0,0
+3843,Plasmonic properties of refractory titanium nitride,"  The development of plasmonic and metamaterial devices requires the research
+of high-performance materials, alternative to standard noble metals. Renewed as
+refractory stable compound for durable coatings, titanium nitride has been
+recently proposed as an efficient plasmonic material. Here, by using a first
+principles approach, we investigate the plasmon dispersion relations of TiN
+bulk and we predict the effect of pressure on its optoelectronic properties.
+Our results explain the main features of TiN in the visible range and prove a
+universal scaling law which relates its mechanical and plasmonic properties as
+a function of pressure. Finally, we address the formation and stability of
+surface-plasmon polaritons at different TiN/dielectric interfaces proposed by
+recent experiments. The unusual combination of plasmonics and refractory
+features paves the way for the realization of plasmonic devices able to work at
+conditions not sustainable by usual noble metals.
+",0,1,0,0,0,0
+3844,The localization transition in SU(3) gauge theory,"  We study the Anderson-like localization transition in the spectrum of the
+Dirac operator of quenched QCD. Above the deconfining transition we determine
+the temperature dependence of the mobility edge separating localized and
+delocalized eigenmodes in the spectrum. We show that the temperature where the
+mobility edge vanishes and localized modes disappear from the spectrum,
+coincides with the critical temperature of the deconfining transition. We also
+identify topological charge related close to zero modes in the Dirac spectrum
+and show that they account for only a small fraction of localized modes, a
+fraction that is rapidly falling as the temperature increases.
+",0,1,0,0,0,0
+3845,Conformation Clustering of Long MD Protein Dynamics with an Adversarial Autoencoder,"  Recent developments in specialized computer hardware have greatly accelerated
+atomic level Molecular Dynamics (MD) simulations. A single GPU-attached cluster
+is capable of producing microsecond-length trajectories in reasonable amounts
+of time. Multiple protein states and a large number of microstates associated
+with folding and with the function of the protein can be observed as
+conformations sampled in the trajectories. Clustering those conformations,
+however, is needed for identifying protein states, evaluating transition rates
+and understanding protein behavior. In this paper, we propose a novel
+data-driven generative conformation clustering method based on the adversarial
+autoencoder (AAE) and provide the associated software implementation Cong. The
+method was tested using a 208 microseconds MD simulation of the fast-folding
+peptide Trp-Cage (20 residues) obtained from the D.E. Shaw Research Group. The
+proposed clustering algorithm identifies many of the salient features of the
+folding process by grouping a large number of conformations that share common
+features not easily identifiable in the trajectory.
+",0,0,0,0,1,0
+3846,Optimal Low-Rank Dynamic Mode Decomposition,"  Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing
+the dynamics of non-linear systems from experimental datasets. Recently,
+several attempts have extended DMD to the context of low-rank approximations.
+This extension is of particular interest for reduced-order modeling in various
+applicative domains, e.g. for climate prediction, to study molecular dynamics
+or micro-electromechanical devices. This low-rank extension takes the form of a
+non-convex optimization problem. To the best of our knowledge, only sub-optimal
+algorithms have been proposed in the literature to compute the solution of this
+problem. In this paper, we prove that there exists a closed-form optimal
+solution to this problem and design an effective algorithm to compute it based
+on Singular Value Decomposition (SVD). A toy-example illustrates the gain in
+performance of the proposed algorithm compared to state-of-the-art techniques.
+",0,0,0,1,0,0
+3847,Improving Community Detection by Mining Social Interactions,"  Social relationships can be divided into different classes based on the
+regularity with which they occur and the similarity among them. Thus, rare and
+somewhat similar relationships are random and cause noise in a social network,
+thus hiding the actual structure of the network and preventing an accurate
+analysis of it. In this context, in this paper we propose a process to handle
+social network data that exploits temporal features to improve the detection of
+communities by existing algorithms. By removing random interactions, we observe
+that social networks converge to a topology with more purely social
+relationships and more modular communities.
+",1,0,0,0,0,0
+3848,The set of quantum correlations is not closed,"  We construct a linear system non-local game which can be played perfectly
+using a limit of finite-dimensional quantum strategies, but which cannot be
+played perfectly on any finite-dimensional Hilbert space, or even with any
+tensor-product strategy. In particular, this shows that the set of
+(tensor-product) quantum correlations is not closed. The constructed non-local
+game provides another counterexample to the ""middle"" Tsirelson problem, with a
+shorter proof than our previous paper (though at the loss of the universal
+embedding theorem). We also show that it is undecidable to determine if a
+linear system game can be played perfectly with a finite-dimensional strategy,
+or a limit of finite-dimensional quantum strategies.
+",0,0,1,0,0,0
+3849,Correction to the article: Floer homology and splicing knot complements,"  This note corrects the mistakes in the splicing formulas of the paper ""Floer
+homology and splicing knot complements"". The mistakes are the result of the
+incorrect assumption that for a knot $K$ inside a homology sphere $Y$, the
+involution on the knot Floer homology of $K$ which corresponds to moving the
+basepoints by one full twist around $K$ is trivial. The correction implicitly
+involves considering the contribution from this (possibly non-trivial)
+involution in a number of places.
+",0,0,1,0,0,0
+3850,Tree tribes and lower bounds for switching lemmas,"  We show tight upper and lower bounds for switching lemmas obtained by the
+action of random $p$-restrictions on boolean functions that can be expressed as
+decision trees in which every vertex is at a distance of at most $t$ from some
+leaf, also called $t$-clipped decision trees. More specifically, we show the
+following:
+$\bullet$ If a boolean function $f$ can be expressed as a $t$-clipped
+decision tree, then under the action of a random $p$-restriction $\rho$, the
+probability that the smallest depth decision tree for $f|_{\rho}$ has depth
+greater than $d$ is upper bounded by $(4p2^{t})^{d}$.
+$\bullet$ For every $t$, there exists a function $g_{t}$ that can be
+expressed as a $t$-clipped decision tree, such that under the action of a
+random $p$-restriction $\rho$, the probability that the smallest depth decision
+tree for $g_{t}|_{\rho}$ has depth greater than $d$ is lower bounded by
+$(c_{0}p2^{t})^{d}$, for $0\leq p\leq c_{p}2^{-t}$ and $0\leq d\leq
+c_{d}\frac{\log n}{2^{t}\log t}$, where $c_{0},c_{p},c_{d}$ are universal
+constants.
+",1,0,0,0,0,0
+3851,Gyrokinetic ion and drift kinetic electron model for electromagnetic simulation in the toroidal geometry,"  The kinetic effects of electrons are important to long wavelength
+magnetohydrodynamic(MHD)instabilities and short wavelength drift-Alfvenic
+instabilities responsible for turbulence transport in magnetized plasmas, since
+the non-adiabatic electron can interact with, modify and drive the low
+frequency instabilities. A novel conservative split weight scheme is proposed
+for the electromagnetic simulation with drift kinetic electrons in tokamak
+plasmas, which shows great computational advantages that there is no numerical
+constrain of electron skin depth on the perpendicular grid size without
+sacrificing any physics. Both kinetic Alfven wave and collision-less tearing
+mode are verified by using this model, which has already been implemented into
+the gyrokinetic toroidal code(GTC). This model will be used for the micro
+tearing mode and neoclassical tearing mode simulation based on the first
+principle in the future.
+",0,1,0,0,0,0
+3852,Transferring Agent Behaviors from Videos via Motion GANs,"  A major bottleneck for developing general reinforcement learning agents is
+determining rewards that will yield desirable behaviors under various
+circumstances. We introduce a general mechanism for automatically specifying
+meaningful behaviors from raw pixels. In particular, we train a generative
+adversarial network to produce short sub-goals represented through motion
+templates. We demonstrate that this approach generates visually meaningful
+behaviors in unknown environments with novel agents and describe how these
+motions can be used to train reinforcement learning agents.
+",1,0,0,1,0,0
+3853,Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications,"  Developing an intelligent vehicle which can perform human-like actions
+requires the ability to learn basic driving skills from a large amount of
+naturalistic driving data. The algorithms will become efficient if we could
+decompose the complex driving tasks into motion primitives which represent the
+elementary compositions of driving skills. Therefore, the purpose of this paper
+is to segment unlabeled trajectory data into a library of motion primitives. By
+applying a probabilistic inference based on an iterative
+Expectation-Maximization algorithm, our method segments the collected
+trajectories while learning a set of motion primitives represented by the
+dynamic movement primitives. The proposed method utilizes the mutual
+dependencies between the segmentation and representation of motion primitives
+and the driving-specific based initial segmentation. By utilizing this mutual
+dependency and the initial condition, this paper presents how we can enhance
+the performance of both the segmentation and the motion primitive library
+establishment. We also evaluate the applicability of the primitive
+representation method to imitation learning and motion planning algorithms. The
+model is trained and validated by using the driving data collected from the
+Beijing Institute of Technology intelligent vehicle platform. The results show
+that the proposed approach can find the proper segmentation and establish the
+motion primitive library simultaneously.
+",1,0,0,0,0,0
+3854,NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks,"  ""How much energy is consumed for an inference made by a convolutional neural
+network (CNN)?"" With the increased popularity of CNNs deployed on the
+wide-spectrum of platforms (from mobile devices to workstations), the answer to
+this question has drawn significant attention. From lengthening battery life of
+mobile devices to reducing the energy bill of a datacenter, it is important to
+understand the energy efficiency of CNNs during serving for making an
+inference, before actually training the model. In this work, we propose
+NeuralPower: a layer-wise predictive framework based on sparse polynomial
+regression, for predicting the serving energy consumption of a CNN deployed on
+any GPU platform. Given the architecture of a CNN, NeuralPower provides an
+accurate prediction and breakdown for power and runtime across all layers in
+the whole network, helping machine learners quickly identify the power,
+runtime, or energy bottlenecks. We also propose the ""energy-precision ratio""
+(EPR) metric to guide machine learners in selecting an energy-efficient CNN
+architecture that better trades off the energy consumption and prediction
+accuracy. The experimental results show that the prediction accuracy of the
+proposed NeuralPower outperforms the best published model to date, yielding an
+improvement in accuracy of up to 68.5%. We also assess the accuracy of
+predictions at the network level, by predicting the runtime, power, and energy
+of state-of-the-art CNN architectures, achieving an average accuracy of 88.24%
+in runtime, 88.34% in power, and 97.21% in energy. We comprehensively
+corroborate the effectiveness of NeuralPower as a powerful framework for
+machine learners by testing it on different GPU platforms and Deep Learning
+software tools.
+",1,0,0,1,0,0
+3855,"The Minor Fall, the Major Lift: Inferring Emotional Valence of Musical Chords through Lyrics","  We investigate the association between musical chords and lyrics by analyzing
+a large dataset of user-contributed guitar tablatures. Motivated by the idea
+that the emotional content of chords is reflected in the words used in
+corresponding lyrics, we analyze associations between lyrics and chord
+categories. We also examine the usage patterns of chords and lyrics in
+different musical genres, historical eras, and geographical regions. Our
+overall results confirms a previously known association between Major chords
+and positive valence. We also report a wide variation in this association
+across regions, genres, and eras. Our results suggest possible existence of
+different emotional associations for other types of chords.
+",1,0,0,0,0,0
+3856,Effective computation of $\mathrm{SO}(3)$ and $\mathrm{O}(3)$ linear representations symmetry classes,"  We propose a general algorithm to compute all the symmetry classes of any
+$\mathrm{SO}(3)$ or $\mathrm{O}(3)$ linear representation. This method relies
+on the introduction of a binary operator between sets of conjugacy classes of
+closed subgroups, called the clips. We compute explicit tables for this
+operation which allows to solve definitively the problem.
+",0,0,1,0,0,0
+3857,Subextensions for co-induced modules,"  Using cohomological methods, we prove a criterion for the embedding of a
+group extension with abelian kernel into the split extension of a co-induced
+module. This generalises some earlier similar results. We also prove an
+assertion about the conjugacy of complements in split extensions of co-induced
+modules. Both results follow from a relation between homomorphisms of certain
+cohomology groups.
+",0,0,1,0,0,0
+3858,Specht Polytopes and Specht Matroids,"  The generators of the classical Specht module satisfy intricate relations. We
+introduce the Specht matroid, which keeps track of these relations, and the
+Specht polytope, which also keeps track of convexity relations. We establish
+basic facts about the Specht polytope, for example, that the symmetric group
+acts transitively on its vertices and irreducibly on its ambient real vector
+space. A similar construction builds a matroid and polytope for a tensor
+product of Specht modules, giving ""Kronecker matroids"" and ""Kronecker
+polytopes"" instead of the usual Kronecker coefficients. We dub this process of
+upgrading numbers to matroids and polytopes ""matroidification,"" giving two more
+examples. In the course of describing these objects, we also give an elementary
+account of the construction of Specht modules different from the standard one.
+Finally, we provide code to compute with Specht matroids and their Chow rings.
+",0,0,1,0,0,0
+3859,Existence theorems for the Cauchy problem of 2D nonhomogeneous incompressible non-resistive MHD equations with vacuum,"  In this paper, we investigate the Cauchy problem of the nonhomogeneous
+incompressible non-resistive MHD on $\mathbb{R}^2$ with vacuum as far field
+density and prove that the 2D Cauchy problem has a unique local strong solution
+provided that the initial density and magnetic field decay not too slow at
+infinity. Furthermore, if the initial data satisfy some additional regularity
+and compatibility conditions, the strong solution becomes a classical one.
+",0,0,1,0,0,0
+3860,Mass Conservative and Energy Stable Finite Difference Methods for the Quasi-incompressible Navier-Stokes-Cahn-Hilliard system: Primitive Variable and Projection-Type Schemes,"  In this paper we describe two fully mass conservative, energy stable, finite
+difference methods on a staggered grid for the quasi-incompressible
+Navier-Stokes-Cahn-Hilliard (q-NSCH) system governing a binary incompressible
+fluid flow with variable density and viscosity. Both methods, namely the
+primitive method (finite difference method in the primitive variable
+formulation) and the projection method (finite difference method in a
+projection-type formulation), are so designed that the mass of the binary fluid
+is preserved, and the energy of the system equations is always non-increasing
+in time at the fully discrete level. We also present an efficient, practical
+nonlinear multigrid method - comprised of a standard FAS method for the
+Cahn-Hilliard equation, and a method based on the Vanka-type smoothing strategy
+for the Navier-Stokes equation - for solving these equations. We test the
+scheme in the context of Capillary Waves, rising droplets and Rayleigh-Taylor
+instability. Quantitative comparisons are made with existing analytical
+solutions or previous numerical results that validate the accuracy of our
+numerical schemes. Moreover, in all cases, mass of the single component and the
+binary fluid was conserved up to 10 to -8 and energy decreases in time.
+",0,0,1,0,0,0
+3861,Density-equalizing maps for simply-connected open surfaces,"  In this paper, we are concerned with the problem of creating flattening maps
+of simply-connected open surfaces in $\mathbb{R}^3$. Using a natural principle
+of density diffusion in physics, we propose an effective algorithm for
+computing density-equalizing flattening maps with any prescribed density
+distribution. By varying the initial density distribution, a large variety of
+mappings with different properties can be achieved. For instance,
+area-preserving parameterizations of simply-connected open surfaces can be
+easily computed. Experimental results are presented to demonstrate the
+effectiveness of our proposed method. Applications to data visualization and
+surface remeshing are explored.
+",1,0,1,0,0,0
+3862,Measuring the radius and mass of Planet Nine,"  Batygin and Brown (2016) have suggested the existence of a new Solar System
+planet supposed to be responsible for the perturbation of eccentric orbits of
+small outer bodies. The main challenge is now to detect and characterize this
+putative body. Here we investigate the principles of the determination of its
+physical parameters, mainly its mass and radius. For that purpose we
+concentrate on two methods, stellar occultations and gravitational microlensing
+effects (amplification, deflection and time delay). We estimate the main
+characteristics of a possible occultation or gravitational effects: flux
+variation of a background star, duration and probability of occurence. We
+investigate also additional benefits of direct imaging and of an occultation.
+",0,1,0,0,0,0
+3863,Efficient and principled score estimation with Nyström kernel exponential families,"  We propose a fast method with statistical guarantees for learning an
+exponential family density model where the natural parameter is in a
+reproducing kernel Hilbert space, and may be infinite-dimensional. The model is
+learned by fitting the derivative of the log density, the score, thus avoiding
+the need to compute a normalization constant. Our approach improves the
+computational efficiency of an earlier solution by using a low-rank,
+Nyström-like solution. The new solution retains the consistency and
+convergence rates of the full-rank solution (exactly in Fisher distance, and
+nearly in other distances), with guarantees on the degree of cost and storage
+reduction. We evaluate the method in experiments on density estimation and in
+the construction of an adaptive Hamiltonian Monte Carlo sampler. Compared to an
+existing score learning approach using a denoising autoencoder, our estimator
+is empirically more data-efficient when estimating the score, runs faster, and
+has fewer parameters (which can be tuned in a principled and interpretable
+way), in addition to providing statistical guarantees.
+",1,0,0,1,0,0
+3864,Small presentations of model categories and Vopěnka's principle,"  We prove existence results for small presentations of model categories
+generalizing a theorem of D. Dugger from combinatorial model categories to more
+general model categories. Some of these results are shown under the assumption
+of Vopěnka's principle. Our main theorem applies in particular to
+cofibrantly generated model categories where the domains of the generating
+cofibrations satisfy a slightly stronger smallness condition. As a consequence,
+assuming Vopěnka's principle, such a cofibrantly generated model category
+is Quillen equivalent to a combinatorial model category. Moreover, if there are
+generating sets which consist of presentable objects, then the same conclusion
+holds without the assumption of Vopěnka's principle. We also correct a
+mistake from previous work that made similar claims.
+",0,0,1,0,0,0
+3865,Automatic Trimap Generation for Image Matting,"  Image matting is a longstanding problem in computational photography.
+Although, it has been studied for more than two decades, yet there is a
+challenge of developing an automatic matting algorithm which does not require
+any human efforts. Most of the state-of-the-art matting algorithms require
+human intervention in the form of trimap or scribbles to generate the alpha
+matte form the input image. In this paper, we present a simple and efficient
+approach to automatically generate the trimap from the input image and make the
+whole matting process free from human-in-the-loop. We use learning based
+matting method to generate the matte from the automatically generated trimap.
+Experimental results demonstrate that our method produces good quality trimap
+which results into accurate matte estimation. We validate our results by
+replacing the automatically generated trimap by manually created trimap while
+using the same image matting algorithm.
+",1,0,0,0,0,0
+3866,X-ray spectral properties of seven heavily obscured Seyfert 2 galaxies,"  We present the combined Chandra and Swift-BAT spectral analysis of seven
+Seyfert 2 galaxies selected from the Swift-BAT 100-month catalog. We selected
+nearby (z<=0.03) sources lacking of a ROSAT counterpart and never previously
+observed with Chandra in the 0.3-10 keV energy range, and targeted these
+objects with 10 ks Chandra ACIS-S observations. The X-ray spectral fitting over
+the 0.3-150 keV energy range allows us to determine that all the objects are
+significantly obscured, having NH>=1E23 cm^(-2) at a >99% confidence level.
+Moreover, one to three sources are candidate Compton thick Active Galactic
+Nuclei (CT-AGN), i.e., have NH>=1E24 cm^(-2). We also test the recent ""spectral
+curvature"" method developed by Koss et al. (2016) to find candidate CT-AGN,
+finding a good agreement between our results and their predictions. Since the
+selection criteria we adopted have been effective in detecting highly obscured
+AGN, further observations of these and other Seyfert 2 galaxies selected from
+the Swift-BAT 100-month catalog will allow us to create a statistically
+significant sample of highly obscured AGN, therefore better understanding the
+physics of the obscuration processes.
+",0,1,0,0,0,0
+3867,Towards the LISA Backlink: Experiment design for comparing optical phase reference distribution systems,"  LISA is a proposed space-based laser interferometer detecting gravitational
+waves by measuring distances between free-floating test masses housed in three
+satellites in a triangular constellation with laser links in-between. Each
+satellite contains two optical benches that are articulated by moving optical
+subassemblies for compensating the breathing angle in the constellation. The
+phase reference distribution system, also known as backlink, forms an optical
+bi-directional path between the intra-satellite benches.
+In this work we discuss phase reference implementations with a target
+non-reciprocity of at most $2\pi\,\mathrm{\mu rad/\sqrt{Hz}}$, equivalent to
+$1\,\mathrm{pm/\sqrt{Hz}}$ for a wavelength of $1064\,\mathrm{nm}$ in the
+frequency band from $0.1\,\mathrm{mHz}$ to $1\,\mathrm{Hz}$. One phase
+reference uses a steered free beam connection, the other one a fiber together
+with additional laser frequencies. The noise characteristics of these
+implementations will be compared in a single interferometric set-up with a
+previously successfully tested direct fiber connection. We show the design of
+this interferometer created by optical simulations including ghost beam
+analysis, component alignment and noise estimation. First experimental results
+of a free beam laser link between two optical set-ups that are co-rotating by
+$\pm 1^\circ$ are presented. This experiment demonstrates sufficient thermal
+stability during rotation of less than $10^{-4}\,\mathrm{K/\sqrt{Hz}}$ at
+$1\,\mathrm{mHz}$ and operation of the free beam steering mirror control over
+more than 1 week.
+",0,1,0,0,0,0
+3868,Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs,"  Generative Adversarial Networks (GANs) have shown remarkable success as a
+framework for training models to produce realistic-looking data. In this work,
+we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to
+produce realistic real-valued multi-dimensional time series, with an emphasis
+on their application to medical data. RGANs make use of recurrent neural
+networks in the generator and the discriminator. In the case of RCGANs, both of
+these RNNs are conditioned on auxiliary information. We demonstrate our models
+in a set of toy datasets, where we show visually and quantitatively (using
+sample likelihood and maximum mean discrepancy) that they can successfully
+generate realistic time-series. We also describe novel evaluation methods for
+GANs, where we generate a synthetic labelled training dataset, and evaluate on
+a real test set the performance of a model trained on the synthetic data, and
+vice-versa. We illustrate with these metrics that RCGANs can generate
+time-series data useful for supervised training, with only minor degradation in
+performance on real test data. This is demonstrated on digit classification
+from 'serialised' MNIST and by training an early warning system on a medical
+dataset of 17,000 patients from an intensive care unit. We further discuss and
+analyse the privacy concerns that may arise when using RCGANs to generate
+realistic synthetic medical time series data.
+",1,0,0,1,0,0
+3869,Embedded eigenvalues of generalized Schrödinger operators,"  We provide examples of operators $T(D)+V$ with decaying potentials that have
+embedded eigenvalues. The decay of the potential depends on the curvature of
+the Fermi surfaces of constant kinetic energy $T$. We make the connection to
+counterexamples in Fourier restriction theory.
+",0,0,1,0,0,0
+3870,Multichannel Robot Speech Recognition Database: MChRSR,"  In real human robot interaction (HRI) scenarios, speech recognition
+represents a major challenge due to robot noise, background noise and
+time-varying acoustic channel. This document describes the procedure used to
+obtain the Multichannel Robot Speech Recognition Database (MChRSR). It is
+composed of 12 hours of multichannel evaluation data recorded in a real mobile
+HRI scenario. This database was recorded with a PR2 robot performing different
+translational and azimuthal movements. Accordingly, 16 evaluation sets were
+obtained re-recording the clean set of the Aurora 4 database in different
+movement conditions.
+",1,0,0,0,0,0
+3871,The Algorithmic Inflection of Russian and Generation of Grammatically Correct Text,"  We present a deterministic algorithm for Russian inflection. This algorithm
+is implemented in a publicly available web-service www.passare.ru which
+provides functions for inflection of single words, word matching and synthesis
+of grammatically correct Russian text. The inflectional functions have been
+tested against the annotated corpus of Russian language OpenCorpora.
+",1,0,0,0,0,0
+3872,Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures,"  Objective: A clinical decision support tool that automatically interprets
+EEGs can reduce time to diagnosis and enhance real-time applications such as
+ICU monitoring. Clinicians have indicated that a sensitivity of 95% with a
+specificity below 5% was the minimum requirement for clinical acceptance. We
+propose a highperformance classification system based on principles of big data
+and machine learning. Methods: A hybrid machine learning system that uses
+hidden Markov models (HMM) for sequential decoding and deep learning networks
+for postprocessing is proposed. These algorithms were trained and evaluated
+using the TUH EEG Corpus, which is the world's largest publicly available
+database of clinical EEG data. Results: Our approach delivers a sensitivity
+above 90% while maintaining a specificity below 5%. This system detects three
+events of clinical interest: (1) spike and/or sharp waves, (2) periodic
+lateralized epileptiform discharges, (3) generalized periodic epileptiform
+discharges. It also detects three events used to model background noise: (1)
+artifacts, (2) eye movement (3) background. Conclusions: A hybrid HMM/deep
+learning system can deliver a low false alarm rate on EEG event detection,
+making automated analysis a viable option for clinicians. Significance: The TUH
+EEG Corpus enables application of highly data consumptive machine learning
+algorithms to EEG analysis. Performance is approaching clinical acceptance for
+real-time applications.
+",1,0,0,1,0,0
+3873,Implementing a Concept Network Model,"  The same concept can mean different things or be instantiated in different
+forms depending on context, suggesting a degree of flexibility within the
+conceptual system. We propose that a compositional network model can be used to
+capture and predict this flexibility. We modeled individual concepts (e.g.,
+BANANA, BOTTLE) as graph-theoretical networks, in which properties (e.g.,
+YELLOW, SWEET) were represented as nodes and their associations as edges. In
+this framework, networks capture the within-concept statistics that reflect how
+properties correlate with each other across instances of a concept. We ran a
+classification analysis using graph eigendecomposition to validate these
+models, and find that these models can successfully discriminate between object
+concepts. We then computed formal measures from these concept networks and
+explored their relationship to conceptual structure. We find that diversity
+coefficients and core-periphery structure can be interpreted as network-based
+measures of conceptual flexibility and stability, respectively. These results
+support the feasibility of a concept network framework and highlight its
+ability to formally capture important characteristics of the conceptual system.
+",0,0,0,0,1,0
+3874,Two simple observations on representations of metaplectic groups,"  M. Hanzer and I. Matic have proved that the genuine unitary principal series
+representations of the metaplectic groups are irreducible. A simple consequence
+of that paper is a criterion for the irreducibility of the non-unitary
+principal series representations of the metaplectic groups that we give in this
+paper.
+",0,0,1,0,0,0
+3875,Spinors in Spacetime Algebra and Euclidean 4-Space,"  This article explores the geometric algebra of Minkowski spacetime, and its
+relationship to the geometric algebra of Euclidean 4-space. Both of these
+geometric algebras are algebraically isomorphic to the 2x2 matrix algebra over
+Hamilton's famous quaternions, and provide a rich geometric framework for
+various important topics in mathematics and physics, including stereographic
+projection and spinors, and both spherical and hyperbolic geometry. In
+addition, by identifying the time-like Minkowski unit vector with the extra
+dimension of Euclidean 4-space, David Hestenes' Space-Time Algebra of Minkowski
+spacetime is unified with William Baylis' Algebra of Physical Space.
+",0,0,1,0,0,0
+3876,Near-sphere lattices with constant nonlocal mean curvature,"  We are concerned with unbounded sets of $\mathbb{R}^N$ whose boundary has
+constant nonlocal (or fractional) mean curvature, which we call CNMC sets. This
+is the equation associated to critical points of the fractional perimeter
+functional under a volume constraint. We construct CNMC sets which are the
+countable union of a certain bounded domain and all its translations through a
+periodic integer lattice of dimension $M\leq N$. Our CNMC sets form a $C^2$
+branch emanating from the unit ball alone and where the parameter in the branch
+is essentially the distance to the closest lattice point. Thus, the new
+translated near-balls (or near-spheres) appear from infinity. We find their
+exact asymptotic shape as the parameter tends to infinity.
+",0,0,1,0,0,0
+3877,"Stability, convergence, and limit cycles in some human physiological processes","  Mathematical models for physiological processes aid qualitative understanding
+of the impact of various parameters on the underlying process. We analyse two
+such models for human physiological processes: the Mackey-Glass and the Lasota
+equations, which model the change in the concentration of blood cells in the
+human body. We first study the local stability of these models, and derive
+bounds on various model parameters and the feedback delay for the concentration
+to equilibrate. We then deduce conditions for non-oscillatory convergence of
+the solutions, which could ensure that the blood cell concentration does not
+oscillate. Further, we define the convergence characteristics of the solutions
+which govern the rate at which the concentration equilibrates when the system
+is stable. Owing to the possibility that physiological parameters can seldom be
+estimated precisely, we also derive bounds for robust stability\textemdash
+which enable one to ensure that the blood cell concentration equilibrates
+despite parametric uncertainty. We also highlight that when the necessary and
+sufficient condition for local stability is violated, the system transits into
+instability via a Hopf bifurcation, leading to limit cycles in the blood cell
+concentration. We then outline a framework to characterise the type of the Hopf
+bifurcation and determine the asymptotic orbital stability of limit cycles. The
+analysis is complemented with numerical examples, stability charts and
+bifurcation diagrams. The insights into the dynamical properties of the
+mathematical models may serve to guide the study of dynamical diseases.
+",1,0,0,0,0,0
+3878,Poisson multi-Bernoulli mixture filter: direct derivation and implementation,"  We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter
+for multi-target tracking with the standard point target measurements without
+using probability generating functionals or functional derivatives. We also
+establish the connection with the \delta-generalised labelled multi-Bernoulli
+(\delta-GLMB) filter, showing that a \delta-GLMB density represents a
+multi-Bernoulli mixture with labelled targets so it can be seen as a special
+case of PMBM. In addition, we propose an implementation for linear/Gaussian
+dynamic and measurement models and how to efficiently obtain typical estimators
+in the literature from the PMBM. The PMBM filter is shown to outperform other
+filters in the literature in a challenging scenario.
+",1,0,0,1,0,0
+3879,Surface magnetism in a chiral d-wave superconductor with hexagonal symmetry,"  Surface properties are examined in a chiral d-wave superconductor with
+hexagonal symmetry, whose one-body Hamiltonian possesses the intrinsic
+spin-orbit coupling identical to the one characterizing the topological nature
+of the Kane-Mele honeycomb insulator. In the normal state spin-orbit coupling
+gives rise to spontaneous surface spin currents, whereas in the superconducting
+state there exist besides the spin currents also charge surface currents, due
+to the chiral pairing symmetry. Interestingly, the combination of these two
+currents results in a surface spin polarization, whose spatial dependence is
+markedly different on the zigzag and armchair surfaces. We discuss various
+potential candidate materials, such as SrPtAs, which may exhibit these surface
+properties.
+",0,1,0,0,0,0
+3880,Penetrating a Social Network: The Follow-back Problem,"  Modern threats have emerged from the prevalence of social networks. Hostile
+actors, such as extremist groups or foreign governments, utilize these networks
+to run propaganda campaigns with different aims. For extremists, these
+campaigns are designed for recruiting new members or inciting violence. For
+foreign governments, the aim may be to create instability in rival nations.
+Proper social network counter-measures are needed to combat these threats. Here
+we present one important counter-measure: penetrating social networks. This
+means making target users connect with or follow agents deployed in the social
+network. Once such connections are established with the targets, the agents can
+influence them by sharing content which counters the influence campaign. In
+this work we study how to penetrate a social network, which we call the
+follow-back problem. The goal here is to find a policy that maximizes the
+number of targets that follow the agent.
+We conduct an empirical study to understand what behavioral and network
+features affect the probability of a target following an agent. We find that
+the degree of the target and the size of the mutual neighborhood of the agent
+and target in the network affect this probability. Based on our empirical
+findings, we then propose a model for targets following an agent. Using this
+model, we solve the follow-back problem exactly on directed acyclic graphs and
+derive a closed form expression for the expected number of follows an agent
+receives under the optimal policy. We then formulate the follow-back problem on
+an arbitrary graph as an integer program. To evaluate our integer program based
+policies, we conduct simulations on real social network topologies in Twitter.
+We find that our polices result in more effective network penetration, with
+significant increases in the expected number of targets that follow the agent.
+",1,0,0,1,0,0
+3881,Universality of density waves in p-doped La2CuO4 and n-doped Nd2CuO4+y,"  The contribution of $O^{2-}$ ions to antiferromagnetism in
+$La_{2-x}Ae_xCuO_4$ ($Ae = Sr, Ba)$ is highly sensitive to doped holes. In
+contrast, the contribution of $Cu^{2+}$ ions to antiferromagnetism in
+$Nd_{2-x}Ce_xCuO_{4+y}$ is much less sensitive to doped electrons. The
+difference causes the precarious and, respectively, robust antiferromagnetic
+phase of these cuprates. The same sensitivities affect the doping dependence of
+the incommensurability of density waves, $\delta (x)$. In the hole-doped
+compounds this gives rise to a doping offset for magnetic and charge density
+waves, $\delta_{m,c}^p(x) \propto \sqrt{x-x_{0p}^N}$. Here $x_{0p}^N$ is the
+doping concentration where the Néel temperature vanishes, $T_N(x_{0p}^N) =
+0$. No such doping offset occurs for density waves in the electron-doped
+compound. Instead, excess oxygen (necessary for stability in crystal growth) of
+concentration $y$ causes a different doping offset in the latter case,
+$\delta_{m,c}^n(x) \propto \sqrt{x- 2y}$. The square-root formulas result from
+the assumption of superlattice formation through partitioning of the $CuO_2$
+plane by pairs of itinerant charge carriers. Agreement of observed
+incommensurability $\delta(x)$ with the formulas is very good for the
+hole-doped compounds and reasonable for the electron-doped compound. The
+deviation in the latter case may be caused by residual excess oxygen.
+",0,1,0,0,0,0
+3882,The perceived assortativity of social networks: Methodological problems and solutions,"  Networks describe a range of social, biological and technical phenomena. An
+important property of a network is its degree correlation or assortativity,
+describing how nodes in the network associate based on their number of
+connections. Social networks are typically thought to be distinct from other
+networks in being assortative (possessing positive degree correlations);
+well-connected individuals associate with other well-connected individuals, and
+poorly-connected individuals associate with each other. We review the evidence
+for this in the literature and find that, while social networks are more
+assortative than non-social networks, only when they are built using
+group-based methods do they tend to be positively assortative. Non-social
+networks tend to be disassortative. We go on to show that connecting
+individuals due to shared membership of a group, a commonly used method, biases
+towards assortativity unless a large enough number of censuses of the network
+are taken. We present a number of solutions to overcoming this bias by drawing
+on advances in sociological and biological fields. Adoption of these methods
+across all fields can greatly enhance our understanding of social networks and
+networks in general.
+",1,0,0,1,0,0
+3883,Generalized Sheet Transition Conditions (GSTCs) for a Metascreen -- A Fishnet Metasurface,"  We used a multiple-scale homogenization method to derive generalized sheet
+transition conditions (GSTCs) for electromagnetic fields at the surface of a
+metascreen---a metasurface with a ""fishnet"" structure. These surfaces are
+characterized by periodically-spaced arbitrary-shaped apertures in an otherwise
+relatively impenetrable surface. The parameters in these GSTCs are interpreted
+as effective surface susceptibilities and surface porosities, which are related
+to the geometry of the apertures that constitute the metascreen. Finally, we
+emphasize the subtle but important difference between the GSTCs required for
+metascreens and those required for metafilms (a metasurface with a ""cermet""
+structure, i.e., an array of isolated (non-touching) scatterers).
+",0,1,0,0,0,0
+3884,"$k^{τ,ε}$-anonymity: Towards Privacy-Preserving Publishing of Spatiotemporal Trajectory Data","  Mobile network operators can track subscribers via passive or active
+monitoring of device locations. The recorded trajectories offer an
+unprecedented outlook on the activities of large user populations, which
+enables developing new networking solutions and services, and scaling up
+studies across research disciplines. Yet, the disclosure of individual
+trajectories raises significant privacy concerns: thus, these data are often
+protected by restrictive non-disclosure agreements that limit their
+availability and impede potential usages. In this paper, we contribute to the
+development of technical solutions to the problem of privacy-preserving
+publishing of spatiotemporal trajectories of mobile subscribers. We propose an
+algorithm that generalizes the data so that they satisfy
+$k^{\tau,\epsilon}$-anonymity, an original privacy criterion that thwarts
+attacks on trajectories. Evaluations with real-world datasets demonstrate that
+our algorithm attains its objective while retaining a substantial level of
+accuracy in the data. Our work is a step forward in the direction of open,
+privacy-preserving datasets of spatiotemporal trajectories.
+",1,0,0,0,0,0
+3885,The Taipan Galaxy Survey: Scientific Goals and Observing Strategy,"  Taipan is a multi-object spectroscopic galaxy survey starting in 2017 that
+will cover 2pi steradians over the southern sky, and obtain optical spectra for
+about two million galaxies out to z<0.4. Taipan will use the newly-refurbished
+1.2m UK Schmidt Telescope at Siding Spring Observatory with the new TAIPAN
+instrument, which includes an innovative 'Starbugs' positioning system capable
+of rapidly and simultaneously deploying up to 150 spectroscopic fibres (and up
+to 300 with a proposed upgrade) over the 6-deg diameter focal plane, and a
+purpose-built spectrograph operating from 370 to 870nm with resolving power
+R>2000. The main scientific goals of Taipan are: (i) to measure the distance
+scale of the Universe (primarily governed by the local expansion rate, H_0) to
+1% precision, and the structure growth rate of structure to 5%; (ii) to make
+the most extensive map yet constructed of the mass distribution and motions in
+the local Universe, using peculiar velocities based on improved Fundamental
+Plane distances, which will enable sensitive tests of gravitational physics;
+and (iii) to deliver a legacy sample of low-redshift galaxies as a unique
+laboratory for studying galaxy evolution as a function of mass and environment.
+The final survey, which will be completed within 5 years, will consist of a
+complete magnitude-limited sample (i<17) of about 1.2x10^6 galaxies,
+supplemented by an extension to higher redshifts and fainter magnitudes
+(i<18.1) of a luminous red galaxy sample of about 0.8x10^6 galaxies.
+Observations and data processing will be carried out remotely and in a
+fully-automated way, using a purpose-built automated 'virtual observer'
+software and an automated data reduction pipeline. The Taipan survey is
+deliberately designed to maximise its legacy value, by complementing and
+enhancing current and planned surveys of the southern sky at wavelengths from
+the optical to the radio.
+",0,1,0,0,0,0
+3886,The Externalities of Exploration and How Data Diversity Helps Exploitation,"  Online learning algorithms, widely used to power search and content
+optimization on the web, must balance exploration and exploitation, potentially
+sacrificing the experience of current users for information that will lead to
+better decisions in the future. Recently, concerns have been raised about
+whether the process of exploration could be viewed as unfair, placing too much
+burden on certain individuals or groups. Motivated by these concerns, we
+initiate the study of the externalities of exploration - the undesirable side
+effects that the presence of one party may impose on another - under the linear
+contextual bandits model. We introduce the notion of a group externality,
+measuring the extent to which the presence of one population of users impacts
+the rewards of another. We show that this impact can in some cases be negative,
+and that, in a certain sense, no algorithm can avoid it. We then study
+externalities at the individual level, interpreting the act of exploration as
+an externality imposed on the current user of a system by future users. This
+drives us to ask under what conditions inherent diversity in the data makes
+explicit exploration unnecessary. We build on a recent line of work on the
+smoothed analysis of the greedy algorithm that always chooses the action that
+currently looks optimal, improving on prior results to show that a greedy
+approach almost matches the best possible Bayesian regret rate of any other
+algorithm on the same problem instance whenever the diversity conditions hold,
+and that this regret is at most $\tilde{O}(T^{1/3})$. Returning to group-level
+effects, we show that under the same conditions, negative group externalities
+essentially vanish under the greedy algorithm. Together, our results uncover a
+sharp contrast between the high externalities that exist in the worst case, and
+the ability to remove all externalities if the data is sufficiently diverse.
+",0,0,0,1,0,0
+3887,Life-span of blowup solutions to semilinear wave equation with space-dependent critical damping,"  This paper is concerned with the blowup phenomena for initial value problem
+of semilinear wave equation with critical space-dependent damping term
+(DW:$V$). The main result of the present paper is to give a solution of the
+problem and to provide a sharp estimate for lifespan for such a solution when
+$\frac{N}{N-1}<p\leq p_S(N+V_0)$, where $p_S(N)$ is the Strauss exponent for
+(DW:$0$). The main idea of the proof is due to the technique of test functions
+for (DW:$0$) originated by Zhou--Han (2014, MR3169791). Moreover, we find a new
+threshold value $V_0=\frac{(N-1)^2}{N+1}$ for the coefficient of critical and
+singular damping $|x|^{-1}$.
+",0,0,1,0,0,0
+3888,Invariant submanifolds of (LCS)n-Manifolds with respect to quarter symmetric metric connection,"  The object of the present paper is to study invariant submanifolds of
+(LCS)n-manifolds with respect to quarter symmetric metric connection. It is
+shown that the mean curvature of an invariant submanifold of (LCS)n-manifold
+with respect to quarter symmetric metric connection and Levi-Civita connection
+are equal. An example is constructed to illustrate the results of the paper. We
+also obtain some equivalent conditions of such notion.
+",0,0,1,0,0,0
+3889,Iterated filtering methods for Markov process epidemic models,"  Dynamic epidemic models have proven valuable for public health decision
+makers as they provide useful insights into the understanding and prevention of
+infectious diseases. However, inference for these types of models can be
+difficult because the disease spread is typically only partially observed e.g.
+in form of reported incidences in given time periods. This chapter discusses
+how to perform likelihood-based inference for partially observed Markov
+epidemic models when it is relatively easy to generate samples from the Markov
+transmission model while the likelihood function is intractable. The first part
+of the chapter reviews the theoretical background of inference for partially
+observed Markov processes (POMP) via iterated filtering. In the second part of
+the chapter the performance of the method and associated practical difficulties
+are illustrated on two examples. In the first example a simulated outbreak data
+set consisting of the number of newly reported cases aggregated by week is
+fitted to a POMP where the underlying disease transmission model is assumed to
+be a simple Markovian SIR model. The second example illustrates possible model
+extensions such as seasonal forcing and over-dispersion in both, the
+transmission and observation model, which can be used, e.g., when analysing
+routinely collected rotavirus surveillance data. Both examples are implemented
+using the R-package pomp (King et al., 2016) and the code is made available
+online.
+",0,0,1,1,0,0
+3890,An Algebraic-Combinatorial Proof Technique for the GM-MDS Conjecture,"  This paper considers the problem of designing maximum distance separable
+(MDS) codes over small fields with constraints on the support of their
+generator matrices. For any given $m\times n$ binary matrix $M$, the GM-MDS
+conjecture, due to Dau et al., states that if $M$ satisfies the so-called MDS
+condition, then for any field $\mathbb{F}$ of size $q\geq n+m-1$, there exists
+an $[n,m]_q$ MDS code whose generator matrix $G$, with entries in $\mathbb{F}$,
+fits $M$ (i.e., $M$ is the support matrix of $G$). Despite all the attempts by
+the coding theory community, this conjecture remains still open in general. It
+was shown, independently by Yan et al. and Dau et al., that the GM-MDS
+conjecture holds if the following conjecture, referred to as the TM-MDS
+conjecture, holds: if $M$ satisfies the MDS condition, then the determinant of
+a transformation matrix $T$, such that $TV$ fits $M$, is not identically zero,
+where $V$ is a Vandermonde matrix with distinct parameters. In this work, we
+generalize the TM-MDS conjecture, and present an algebraic-combinatorial
+approach based on polynomial-degree reduction for proving this conjecture. Our
+proof technique's strength is based primarily on reducing inherent
+combinatorics in the proof. We demonstrate the strength of our technique by
+proving the TM-MDS conjecture for the cases where the number of rows ($m$) of
+$M$ is upper bounded by $5$. For this class of special cases of $M$ where the
+only additional constraint is on $m$, only cases with $m\leq 4$ were previously
+proven theoretically, and the previously used proof techniques are not
+applicable to cases with $m > 4$.
+",1,0,0,0,0,0
+3891,The Word Problem of $\mathbb{Z}^n$ Is a Multiple Context-Free Language,"  The \emph{word problem} of a group $G = \langle \Sigma \rangle$ can be
+defined as the set of formal words in $\Sigma^*$ that represent the identity in
+$G$. When viewed as formal languages, this gives a strong connection between
+classes of groups and classes of formal languages. For example, Anisimov showed
+that a group is finite if and only if its word problem is a regular language,
+and Muller and Schupp showed that a group is virtually-free if and only if its
+word problem is a context-free language. Above this, not much was known, until
+Salvati showed recently that the word problem of $\mathbb{Z}^2$ is a multiple
+context-free language, giving first such example. We generalize Salvati's
+result to show that the word problem of $\mathbb{Z}^n$ is a multiple
+context-free language for any $n$.
+",1,0,1,0,0,0
+3892,Rigorous proof of the Boltzmann-Gibbs distribution of money on connected graphs,"  Models in econophysics, i.e., the emerging field of statistical physics that
+applies the main concepts of traditional physics to economics, typically
+consist of large systems of economic agents who are characterized by the amount
+of money they have. In the simplest model, at each time step, one agent gives
+one dollar to another agent, with both agents being chosen independently and
+uniformly at random from the system. Numerical simulations of this model
+suggest that, at least when the number of agents and the average amount of
+money per agent are large, the distribution of money converges to an
+exponential distribution reminiscent of the Boltzmann-Gibbs distribution of
+energy in physics. The main objective of this paper is to give a rigorous proof
+of this result and show that the convergence to the exponential distribution is
+universal in the sense that it holds more generally when the economic agents
+are located on the vertices of a connected graph and interact locally with
+their neighbors rather than globally with all the other agents. We also study a
+closely related model where, at each time step, agents buy with a probability
+proportional to the amount of money they have, and prove that in this case the
+limiting distribution of money is Poissonian.
+",0,0,1,0,0,0
+3893,Stochastic population dynamics in spatially extended predator-prey systems,"  Spatially extended population dynamics models that incorporate intrinsic
+noise serve as case studies for the role of fluctuations and correlations in
+biological systems. Including spatial structure and stochastic noise in
+predator-prey competition invalidates the deterministic Lotka-Volterra picture
+of neutral population cycles. Stochastic models yield long-lived erratic
+population oscillations stemming from a resonant amplification mechanism. In
+spatially extended predator-prey systems, one observes noise-stabilized
+activity and persistent correlations. Fluctuation-induced renormalizations of
+the oscillation parameters can be analyzed perturbatively. The critical
+dynamics and the non-equilibrium relaxation kinetics at the predator extinction
+threshold are characterized by the directed percolation universality class.
+Spatial or environmental variability results in more localized patches which
+enhances both species densities. Affixing variable rates to individual
+particles and allowing for trait inheritance subject to mutations induces fast
+evolutionary dynamics for the rate distributions. Stochastic spatial variants
+of cyclic competition with rock-paper-scissors interactions illustrate
+connections between population dynamics and evolutionary game theory, and
+demonstrate how space can help maintain diversity. In two dimensions,
+three-species cyclic competition models of the May-Leonard type are
+characterized by the emergence of spiral patterns whose properties are
+elucidated by a mapping onto a complex Ginzburg-Landau equation. Extensions to
+general food networks can be classified on the mean-field level, which provides
+both a fundamental understanding of ensuing cooperativity and emergence of
+alliances. Novel space-time patterns emerge as a result of the formation of
+competing alliances, such as coarsening domains that each incorporate
+rock-paper-scissors competition games.
+",0,1,0,0,0,0
+3894,Learning Policies for Markov Decision Processes from Data,"  We consider the problem of learning a policy for a Markov decision process
+consistent with data captured on the state-actions pairs followed by the
+policy. We assume that the policy belongs to a class of parameterized policies
+which are defined using features associated with the state-action pairs. The
+features are known a priori, however, only an unknown subset of them could be
+relevant. The policy parameters that correspond to an observed target policy
+are recovered using $\ell_1$-regularized logistic regression that best fits the
+observed state-action samples. We establish bounds on the difference between
+the average reward of the estimated and the original policy (regret) in terms
+of the generalization error and the ergodic coefficient of the underlying
+Markov chain. To that end, we combine sample complexity theory and sensitivity
+analysis of the stationary distribution of Markov chains. Our analysis suggests
+that to achieve regret within order $O(\sqrt{\epsilon})$, it suffices to use
+training sample size on the order of $\Omega(\log n \cdot poly(1/\epsilon))$,
+where $n$ is the number of the features. We demonstrate the effectiveness of
+our method on a synthetic robot navigation example.
+",1,0,1,1,0,0
+3895,Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters,"  As autonomous vehicles become an every-day reality, high-accuracy pedestrian
+detection is of paramount practical importance. Pedestrian detection is a
+highly researched topic with mature methods, but most datasets focus on common
+scenes of people engaged in typical walking poses on sidewalks. But performance
+is most crucial for dangerous scenarios, such as children playing in the street
+or people using bicycles/skateboards in unexpected ways. Such ""in-the-tail""
+data is notoriously hard to observe, making both training and testing
+difficult. To analyze this problem, we have collected a novel annotated dataset
+of dangerous scenarios called the Precarious Pedestrian dataset. Even given a
+dedicated collection effort, it is relatively small by contemporary standards
+(around 1000 images). To allow for large-scale data-driven learning, we explore
+the use of synthetic data generated by a game engine. A significant challenge
+is selected the right ""priors"" or parameters for synthesis: we would like
+realistic data with poses and object configurations that mimic true Precarious
+Pedestrians. Inspired by Generative Adversarial Networks (GANs), we generate a
+massive amount of synthetic data and train a discriminative classifier to
+select a realistic subset, which we deem the Adversarial Imposters. We
+demonstrate that this simple pipeline allows one to synthesize realistic
+training data by making use of rendering/animation engines within a GAN
+framework. Interestingly, we also demonstrate that such data can be used to
+rank algorithms, suggesting that Adversarial Imposters can also be used for
+""in-the-tail"" validation at test-time, a notoriously difficult challenge for
+real-world deployment.
+",1,0,0,0,0,0
+3896,The sequence of open and closed prefixes of a Sturmian word,"  A finite word is closed if it contains a factor that occurs both as a prefix
+and as a suffix but does not have internal occurrences, otherwise it is open.
+We are interested in the {\it oc-sequence} of a word, which is the binary
+sequence whose $n$-th element is $0$ if the prefix of length $n$ of the word is
+open, or $1$ if it is closed. We exhibit results showing that this sequence is
+deeply related to the combinatorial and periodic structure of a word. In the
+case of Sturmian words, we show that these are uniquely determined (up to
+renaming letters) by their oc-sequence. Moreover, we prove that the class of
+finite Sturmian words is a maximal element with this property in the class of
+binary factorial languages. We then discuss several aspects of Sturmian words
+that can be expressed through this sequence. Finally, we provide a linear-time
+algorithm that computes the oc-sequence of a finite word, and a linear-time
+algorithm that reconstructs a finite Sturmian word from its oc-sequence.
+",1,0,1,0,0,0
+3897,Capital Regulation under Price Impacts and Dynamic Financial Contagion,"  We construct a continuous time model for price-mediated contagion
+precipitated by a common exogenous stress to the trading book of all firms in
+the financial system. In this setting, firms are constrained so as to satisfy a
+risk-weight based capital ratio requirement. We use this model to find
+analytical bounds on the risk-weights for an asset as a function of the market
+liquidity. Under these appropriate risk-weights, we find existence and
+uniqueness for the joint system of firm behavior and the asset price. We
+further consider an analytical bound on the firm liquidations, which allows us
+to construct exact formulas for stress testing the financial system with
+deterministic or random stresses. Numerical case studies are provided to
+demonstrate various implications of this model and analytical bounds.
+",0,0,0,0,0,1
+3898,On seaweed subalgebras and meander graphs in type D,"  In 2000, Dergachev and Kirillov introduced subalgebras of ""seaweed type"" in
+$\mathfrak{gl}_n$ and computed their index using certain graphs, which we call
+type-${\sf A}$ meander graphs. Then the subalgebras of seaweed type, or just
+""seaweeds"", have been defined by Panyushev (2001) for arbitrary reductive Lie
+algebras. Recently, a meander graph approach to computing the index in types
+${\sf B}$ and ${\sf C}$ has been developed by the authors. In this article, we
+consider the most difficult and interesting case of type ${\sf D}$. Some new
+phenomena occurring here are related to the fact that the Dynkin diagram has a
+branching node.
+",0,0,1,0,0,0
+3899,Singular Riemannian flows and characteristic numbers,"  Let $M$ be an even-dimensional, oriented closed manifold. We show that the
+restriction of a singular Riemannian flow on $M$ to a small tubular
+neighborhood of each connected component of its singular stratum is
+foliated-diffeomorphic to an isometric flow on the same neighborhood. We then
+prove a formula that computes characteristic numbers of $M$ as the sum of
+residues associated to the infinitesimal foliation at the components of the
+singular stratum of the flow.
+",0,0,1,0,0,0
+3900,Transformation Models in High-Dimensions,"  Transformation models are a very important tool for applied statisticians and
+econometricians. In many applications, the dependent variable is transformed so
+that homogeneity or normal distribution of the error holds. In this paper, we
+analyze transformation models in a high-dimensional setting, where the set of
+potential covariates is large. We propose an estimator for the transformation
+parameter and we show that it is asymptotically normally distributed using an
+orthogonalized moment condition where the nuisance functions depend on the
+target parameter. In a simulation study, we show that the proposed estimator
+works well in small samples. A common practice in labor economics is to
+transform wage with the log-function. In this study, we test if this
+transformation holds in CPS data from the United States.
+",0,0,1,1,0,0
+3901,Rank Two Non-Commutative Laurent Phenomenon and Pseudo-Positivity,"  We study polynomial generalizations of the Kontsevich automorphisms acting on
+the skew-field of formal rational expressions in two non-commuting variables.
+Our main result is the Laurentness and pseudo-positivity of iterations of these
+automorphisms. The resulting expressions are described combinatorially using a
+generalization of the combinatorics of compatible pairs in a maximal Dyck path
+developed by Lee, Li, and Zelevinsky. By specializing to quasi-commuting
+variables we obtain pseudo-positive expressions for rank 2 quantum generalized
+cluster variables. In the case that all internal exchange coefficients are
+zero, this quantum specialization provides a combinatorial construction of
+counting polynomials for Grassmannians of submodules in exceptional
+representations of valued quivers with two vertices.
+",0,0,1,0,0,0
+3902,Faster Coordinate Descent via Adaptive Importance Sampling,"  Coordinate descent methods employ random partial updates of decision
+variables in order to solve huge-scale convex optimization problems. In this
+work, we introduce new adaptive rules for the random selection of their
+updates. By adaptive, we mean that our selection rules are based on the dual
+residual or the primal-dual gap estimates and can change at each iteration. We
+theoretically characterize the performance of our selection rules and
+demonstrate improvements over the state-of-the-art, and extend our theory and
+algorithms to general convex objectives. Numerical evidence with hinge-loss
+support vector machines and Lasso confirm that the practice follows the theory.
+",1,0,1,1,0,0
+3903,On the role of synaptic stochasticity in training low-precision neural networks,"  Stochasticity and limited precision of synaptic weights in neural network
+models are key aspects of both biological and hardware modeling of learning
+processes. Here we show that a neural network model with stochastic binary
+weights naturally gives prominence to exponentially rare dense regions of
+solutions with a number of desirable properties such as robustness and good
+generalization performance, while typical solutions are isolated and hard to
+find. Binary solutions of the standard perceptron problem are obtained from a
+simple gradient descent procedure on a set of real values parametrizing a
+probability distribution over the binary synapses. Both analytical and
+numerical results are presented. An algorithmic extension aimed at training
+discrete deep neural networks is also investigated.
+",1,1,0,1,0,0
+3904,"Secants, bitangents, and their congruences","  A congruence is a surface in the Grassmannian $\mathrm{Gr}(1,\mathbb{P}^3)$
+of lines in projective $3$-space. To a space curve $C$, we associate the Chow
+hypersurface in $\mathrm{Gr}(1,\mathbb{P}^3)$ consisting of all lines which
+intersect $C$. We compute the singular locus of this hypersurface, which
+contains the congruence of all secants to $C$. A surface $S$ in $\mathbb{P}^3$
+defines the Hurwitz hypersurface in $\mathrm{Gr}(1,\mathbb{P}^3)$ of all lines
+which are tangent to $S$. We show that its singular locus has two components
+for general enough $S$: the congruence of bitangents and the congruence of
+inflectional tangents. We give new proofs for the bidegrees of the secant,
+bitangent and inflectional congruences, using geometric techniques such as
+duality, polar loci and projections. We also study the singularities of these
+congruences.
+",0,0,1,0,0,0
+3905,Convergence of Stochastic Approximation Monte Carlo and modified Wang-Landau algorithms: Tests for the Ising model,"  We investigate the behavior of the deviation of the estimator for the density
+of states (DOS) with respect to the exact solution in the course of Wang-Landau
+and Stochastic Approximation Monte Carlo (SAMC) simulations of the
+two-dimensional Ising model. We find that the deviation saturates in the
+Wang-Landau case. This can be cured by adjusting the refinement scheme. To this
+end, the 1/t-modification of the Wang-Landau algorithm has been suggested. A
+similar choice of refinement scheme is employed in the SAMC algorithm. The
+convergence behavior of all three algorithms is examined. It turns out that the
+convergence of the SAMC algorithm is very sensitive to the onset of the
+refinement. Finally, the internal energy and specific heat of the Ising model
+are calculated from the SAMC DOS and compared to exact values.
+",0,1,0,0,0,0
+3906,On the approximation by convolution type double singular integral operators,"  In this paper, we prove the pointwise convergence and the rate of pointwise
+convergence for a family of singular integral operators in two-dimensional
+setting in the following form: \begin{equation*} L_{\lambda }\left(
+f;x,y\right) =\underset{D}{\iint }f\left( t,s\right) K_{\lambda }\left(
+t-x,s-y\right) dsdt,\text{ }\left( x,y\right) \in D, \end{equation*} where
+$D=\left \langle a,b\right \rangle \times \left \langle c,d\right \rangle $ is
+an arbitrary closed, semi-closed or open rectangle in $\mathbb{R}^{2}$ and $%
+\lambda \in \Lambda ,$ $\Lambda $ is a set of non-negative indices with
+accumulation point $\lambda_{0}$. Also, we provide an example to support these
+theoretical results. In contrast to previous works, the kernel function
+$K_{\lambda }\left( t,s\right) $ does not have to be even, positive or 2$\pi
+-$periodic.
+",0,0,1,0,0,0
+3907,Characterization of polynomials whose large powers have all positive coefficients,"  We give a criterion which characterizes a homogeneous real multi-variate
+polynomial to have the property that all sufficiently large powers of the
+polynomial (as well as their products with any given positive homogeneous
+polynomial) have positive coefficients. Our result generalizes a result of De
+Angelis, which corresponds to the case of homogeneous bi-variate polynomials,
+as well as a classical result of Pólya, which corresponds to the case of a
+specific linear polynomial. As an application, we also give a characterization
+of certain polynomial beta functions, which are the spectral radius functions
+of the defining matrix functions of Markov chains.
+",0,0,1,0,0,0
+3908,The Moon Illusion explained by the Projective Consciousness Model,"  The Moon often appears larger near the perceptual horizon and smaller high in
+the sky though the visual angle subtended is invariant. We show how this
+illusion results from the optimization of a projective geometrical frame for
+conscious perception through free energy minimization, as articulated in the
+Projective Consciousness Model. The model accounts for all documented
+modulations of the illusion without anomalies (e.g., the size-distance
+paradox), surpasses other theories in explanatory power, makes sense of inter-
+and intra-subjective variability vis-a-vis the illusion, and yields new
+quantitative and qualitative predictions.
+",0,0,0,0,1,0
+3909,Bloch line dynamics within moving domain walls in 3D ferromagnets,"  We study field-driven magnetic domain wall dynamics in garnet strips by
+large-scale three-dimensional micromagnetic simulations. The domain wall
+propagation velocity as a function of the applied field exhibits a low-field
+linear part terminated by a sudden velocity drop at a threshold field
+magnitude, related to the onset of excitations of internal degrees of freedom
+of the domain wall magnetization. By considering a wide range of strip
+thicknesses from 30 nm to 1.89 $\mu$m, we find a non-monotonic thickness
+dependence of the threshold field for the onset of this instability, proceeding
+via nucleation and propagation of Bloch lines within the domain wall. We
+identify a critical strip thickness above which the velocity drop is due to
+nucleation of horizontal Bloch lines, while for thinner strips and depending on
+the boundary conditions employed, either generation of vertical Bloch lines, or
+close-to-uniform precession of the domain wall internal magnetization takes
+place. For strips of intermediate thicknesses, the vertical Bloch lines assume
+a deformed structure due to demagnetizing fields at the strip surfaces,
+breaking the symmetry between the top and bottom faces of the strip, and
+resulting in circulating Bloch line dynamics along the perimeter of the domain
+wall.
+",0,1,0,0,0,0
+3910,Approaching the UCT problem via crossed products of the Razak-Jacelon algebra,"  We show that the UCT problem for separable, nuclear $\mathrm C^*$-algebras
+relies only on whether the UCT holds for crossed products of certain finite
+cyclic group actions on the Razak-Jacelon algebra. This observation is
+analogous to and in fact recovers a characterization of the UCT problem in
+terms of finite group actions on the Cuntz algebra $\mathcal O_2$ established
+in previous work by the authors. Although based on a similar approach, the new
+conceptual ingredients in the finite context are the recent advances in the
+classification of stably projectionless $\mathrm C^*$-algebras, as well as a
+known characterization of the UCT problem in terms of certain tracially AF
+$\mathrm C^*$-algebras due to Dadarlat.
+",0,0,1,0,0,0
+3911,Some Remarks about the Complexity of Epidemics Management,"  Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that
+the assumptions underlying the established theory of epidemics management are
+too idealistic. For an improvement of procedures and organizations involved in
+fighting epidemics, extended models of epidemics management are required. The
+necessary extensions consist in a representation of the management loop and the
+potential frictions influencing the loop. The effects of the non-deterministic
+frictions can be taken into account by including the measures of robustness and
+risk in the assessment of management options. Thus, besides of the increased
+structural complexity resulting from the model extensions, the computational
+complexity of the task of epidemics management - interpreted as an optimization
+problem - is increased as well. This is a serious obstacle for analyzing the
+model and may require an additional pre-processing enabling a simplification of
+the analysis process. The paper closes with an outlook discussing some
+forthcoming problems.
+",0,1,0,0,0,0
+3912,Entanglement Entropy in Excited States of the Quantum Lifshitz Model,"  We investigate the entanglement properties of an infinite class of excited
+states in the quantum Lifshitz model (QLM). The presence of a conformal quantum
+critical point in the QLM makes it unusually tractable for a model above one
+spatial dimension, enabling the ground state entanglement entropy for an
+arbitrary domain to be expressed in terms of geometrical and topological
+quantities. Here we extend this result to excited states and find that the
+entanglement can be naturally written in terms of quantities which we dub
+""entanglement propagator amplitudes"" (EPAs). EPAs are geometrical probabilities
+that we explicitly calculate and interpret. A comparison of lattice and
+continuum results demonstrates that EPAs are universal. This work shows that
+the QLM is an example of a 2+1d field theory where the universal behavior of
+excited-state entanglement may be computed analytically.
+",0,1,0,0,0,0
+3913,Poisson-Fermi Formulation of Nonlocal Electrostatics in Electrolyte Solutions,"  We present a nonlocal electrostatic formulation of nonuniform ions and water
+molecules with interstitial voids that uses a Fermi-like distribution to
+account for steric and correlation effects in electrolyte solutions. The
+formulation is based on the volume exclusion of hard spheres leading to a
+steric potential and Maxwell's displacement field with Yukawa-type interactions
+resulting in a nonlocal electric potential. The classical Poisson-Boltzmann
+model fails to describe steric and correlation effects important in a variety
+of chemical and biological systems, especially in high field or large
+concentration conditions found in and near binding sites, ion channels, and
+electrodes. Steric effects and correlations are apparent when we compare
+nonlocal Poisson-Fermi results to Poisson-Boltzmann calculations in electric
+double layer and to experimental measurements on the selectivity of potassium
+channels for K+ over Na+. The present theory links atomic scale descriptions of
+the crystallized KcsA channel with macroscopic bulk conditions. Atomic
+structures and macroscopic conditions determine complex functions of great
+importance in biology, nanotechnology, and electrochemistry.
+",0,1,0,0,0,0
+3914,Learning to Transfer,"  Transfer learning borrows knowledge from a source domain to facilitate
+learning in a target domain. Two primary issues to be addressed in transfer
+learning are what and how to transfer. For a pair of domains, adopting
+different transfer learning algorithms results in different knowledge
+transferred between them. To discover the optimal transfer learning algorithm
+that maximally improves the learning performance in the target domain,
+researchers have to exhaustively explore all existing transfer learning
+algorithms, which is computationally intractable. As a trade-off, a sub-optimal
+algorithm is selected, which requires considerable expertise in an ad-hoc way.
+Meanwhile, it is widely accepted in educational psychology that human beings
+improve transfer learning skills of deciding what to transfer through
+meta-cognitive reflection on inductive transfer learning practices. Motivated
+by this, we propose a novel transfer learning framework known as Learning to
+Transfer (L2T) to automatically determine what and how to transfer are the best
+by leveraging previous transfer learning experiences. We establish the L2T
+framework in two stages: 1) we first learn a reflection function encrypting
+transfer learning skills from experiences; and 2) we infer what and how to
+transfer for a newly arrived pair of domains by optimizing the reflection
+function. Extensive experiments demonstrate the L2T's superiority over several
+state-of-the-art transfer learning algorithms and its effectiveness on
+discovering more transferable knowledge.
+",1,0,0,1,0,0
+3915,Strong Metric Subregularity of Mappings in Variational Analysis and Optimization,"  Although the property of strong metric subregularity of set-valued mappings
+has been present in the literature under various names and with various
+definitions for more than two decades, it has attracted much less attention
+than its older ""siblings"", the metric regularity and the strong metric
+regularity. The purpose of this paper is to show that the strong metric
+subregularity shares the main features of these two most popular regularity
+properties and is not less instrumental in applications. We show that the
+strong metric subregularity of a mapping F acting between metric spaces is
+stable under perturbations of the form f + F, where f is a function with a
+small calmness constant. This result is parallel to the Lyusternik-Graves
+theorem for metric regularity and to the Robinson theorem for strong
+regularity, where the perturbations are represented by a function f with a
+small Lipschitz constant. Then we study perturbation stability of the same kind
+for mappings acting between Banach spaces, where f is not necessarily
+differentiable but admits a set-valued derivative-like approximation. Strong
+metric q-subregularity is also considered, where q is a positive real constant
+appearing as exponent in the definition. Rockafellar's criterion for strong
+metric subregularity involving injectivity of the graphical derivative is
+extended to mappings acting in infinite-dimensional spaces. A sufficient
+condition for strong metric subregularity is established in terms of
+surjectivity of the Frechet coderivative. Various versions of Newton's method
+for solving generalized equations are considered including inexact and
+semismooth methods, for which superlinear convergence is shown under strong
+metric subregularity.
+",0,0,1,0,0,0
+3916,"Systematic Identification of LAEs for Visible Exploration and Reionization Research Using Subaru HSC (SILVERRUSH). I. Program Strategy and Clustering Properties of ~2,000 Lya Emitters at z=6-7 over the 0.3-0.5 Gpc$^2$ Survey Area","  We present the SILVERRUSH program strategy and clustering properties
+investigated with $\sim 2,000$ Ly$\alpha$ emitters at $z=5.7$ and $6.6$ found
+in the early data of the Hyper Suprime-Cam (HSC) Subaru Strategic Program
+survey exploiting the carefully designed narrowband filters. We derive angular
+correlation functions with the unprecedentedly large samples of LAEs at $z=6-7$
+over the large total area of $14-21$ deg$^2$ corresponding to $0.3-0.5$
+comoving Gpc$^2$. We obtain the average large-scale bias values of $b_{\rm
+avg}=4.1\pm 0.2$ ($4.5\pm 0.6$) at $z=5.7$ ($z=6.6$) for $\gtrsim L^*$ LAEs,
+indicating the weak evolution of LAE clustering from $z=5.7$ to $6.6$. We
+compare the LAE clustering results with two independent theoretical models that
+suggest an increase of an LAE clustering signal by the patchy ionized bubbles
+at the epoch of reionization (EoR), and estimate the neutral hydrogen fraction
+to be $x_{\rm HI}=0.15^{+0.15}_{-0.15}$ at $z=6.6$. Based on the halo
+occupation distribution models, we find that the $\gtrsim L^*$ LAEs are hosted
+by the dark-matter halos with the average mass of $\log (\left < M_{\rm h}
+\right >/M_\odot) =11.1^{+0.2}_{-0.4}$ ($10.8^{+0.3}_{-0.5}$) at $z=5.7$
+($6.6$) with a Ly$\alpha$ duty cycle of 1 % or less, where the results of
+$z=6.6$ LAEs may be slightly biased, due to the increase of the clustering
+signal at the EoR. Our clustering analysis reveals the low-mass nature of
+$\gtrsim L^*$ LAEs at $z=6-7$, and that these LAEs probably evolve into massive
+super-$L^*$ galaxies in the present-day universe.
+",0,1,0,0,0,0
+3917,Experimental study of mini-magnetosphere,"  Magnetosphere at ion kinetic scales, or mini-magnetosphere, possesses unusual
+features as predicted by numerical simulations. However, there are practically
+no data on the subject from space observations and the data which are available
+are far too incomplete. In the present work we describe results of laboratory
+experiment on interaction of plasma flow with magnetic dipole with parameters
+such that ion inertia length is smaller than a size of observed magnetosphere.
+A detailed structure of non-coplanar or out-of-plane component of magnetic
+field has been obtained in meridian plane. Independence of this component on
+dipole moment reversal, as was reported in previous work, has been verified. In
+the tail distinct lobes and central current sheet have been observed. It was
+found that lobe regions adjacent to boundary layer are dominated by
+non-coplanar component of magnetic field. Tail-ward oriented electric current
+in plasma associated with that component appears to be equal to ion current in
+the frontal part of magnetosphere and in the tail current sheet implying that
+electrons are stationary in those regions while ions flow by. Obtained data
+strongly support the proposed model of mini-magnetosphere based on two-fluid
+effects as described by the Hall term.
+",0,1,0,0,0,0
+3918,Matrix KP: tropical limit and Yang-Baxter maps,"  We study soliton solutions of matrix Kadomtsev-Petviashvili (KP) equations in
+a tropical limit, in which their support at fixed time is a planar graph and
+polarizations are attached to its constituting lines. There is a subclass of
+""pure line soliton solutions"" for which we find that, in this limit, the
+distribution of polarizations is fully determined by a Yang-Baxter map. For a
+vector KP equation, this map is given by an R-matrix, whereas it is a
+non-linear map in case of a more general matrix KP equation. We also consider
+the corresponding Korteweg-deVries (KdV) reduction. Furthermore, exploiting the
+fine structure of soliton interactions in the tropical limit, we obtain a new
+solution of the tetrahedron (or Zamolodchikov) equation. Moreover, a solution
+of the functional tetrahedron equation arises from the parameter-dependence of
+the vector KP R-matrix.
+",0,1,0,0,0,0
+3919,Poster Abstract: LPWA-MAC - a Low Power Wide Area network MAC protocol for cyber-physical systems,"  Low-Power Wide-Area Networks (LPWANs) are being successfully used for the
+monitoring of large-scale systems that are delay-tolerant and which have
+low-bandwidth requirements. The next step would be instrumenting these for the
+control of Cyber-Physical Systems (CPSs) distributed over large areas which
+require more bandwidth, bounded delays and higher reliability or at least more
+rigorous guarantees therein. This paper presents LPWA-MAC, a novel Low Power
+Wide-Area network MAC protocol, that ensures bounded end-to-end delays, high
+channel utility and supports many of the different traffic patterns and
+data-rates typical of CPS.
+",1,0,0,0,0,0
+3920,Asymptotic Enumeration of Compacted Binary Trees,"  A compacted tree is a graph created from a binary tree such that repeatedly
+occurring subtrees in the original tree are represented by pointers to existing
+ones, and hence every subtree is unique. Such representations form a special
+class of directed acyclic graphs. We are interested in the asymptotic number of
+compacted trees of given size, where the size of a compacted tree is given by
+the number of its internal nodes. Due to its superexponential growth this
+problem poses many difficulties. Therefore we restrict our investigations to
+compacted trees of bounded right height, which is the maximal number of edges
+going to the right on any path from the root to a leaf.
+We solve the asymptotic counting problem for this class as well as a closely
+related, further simplified class.
+For this purpose, we develop a calculus on exponential generating functions
+for compacted trees of bounded right height and for relaxed trees of bounded
+right height, which differ from compacted trees by dropping the above described
+uniqueness condition. This enables us to derive a recursively defined sequence
+of differential equations for the exponential generating functions. The
+coefficients can then be determined by performing a singularity analysis of the
+solutions of these differential equations.
+Our main results are the computation of the asymptotic numbers of relaxed as
+well as compacted trees of bounded right height and given size, when the size
+tends to infinity.
+",1,0,0,0,0,0
+3921,The STAR MAPS-based PiXeL detector,"  The PiXeL detector (PXL) for the Heavy Flavor Tracker (HFT) of the STAR
+experiment at RHIC is the first application of the state-of-the-art thin
+Monolithic Active Pixel Sensors (MAPS) technology in a collider environment.
+Custom built pixel sensors, their readout electronics and the detector
+mechanical structure are described in detail. Selected detector design aspects
+and production steps are presented. The detector operations during the three
+years of data taking (2014-2016) and the overall performance exceeding the
+design specifications are discussed in the conclusive sections of this paper.
+",0,1,0,0,0,0
+3922,Complete intersection monomial curves and the Cohen-Macaulayness of their tangent cones,"  Let $C({\bf n})$ be a complete intersection monomial curve in the
+4-dimensional affine space. In this paper we study the complete intersection
+property of the monomial curve $C({\bf n}+w{\bf v})$, where $w>0$ is an integer
+and ${\bf v} \in \mathbb{N}^{4}$. Also we investigate the Cohen-Macaulayness of
+the tangent cone of $C({\bf n}+w{\bf v})$.
+",0,0,1,0,0,0
+3923,Stable and unstable vortex knots in a trapped Bose-Einstein condensate,"  The dynamics of a quantum vortex torus knot ${\cal T}_{P,Q}$ and similar
+knots in an atomic Bose-Einstein condensate at zero temperature in the
+Thomas-Fermi regime has been considered in the hydrodynamic approximation. The
+condensate has a spatially nonuniform equilibrium density profile $\rho(z,r)$
+due to an external axisymmetric potential. It is assumed that $z_*=0$, $r_*=1$
+is a maximum point for function $r\rho(z,r)$, with $\delta
+(r\rho)\approx-(\alpha-\epsilon) z^2/2 -(\alpha+\epsilon) (\delta r)^2/2$ at
+small $z$ and $\delta r$. Configuration of knot in the cylindrical coordinates
+is specified by a complex $2\pi P$-periodic function
+$A(\varphi,t)=Z(\varphi,t)+i [R(\varphi,t)-1]$. In the case $|A|\ll 1$ the
+system is described by relatively simple approximate equations for re-scaled
+functions $W_n(\varphi)\propto A(2\pi n+\varphi)$, where $n=0,\dots,P-1$, and
+$iW_{n,t}=-(W_{n,\varphi\varphi}+\alpha W_n -\epsilon W_n^*)/2-\sum_{j\neq
+n}1/(W_n^*-W_j^*)$. At $\epsilon=0$, numerical examples of stable solutions as
+$W_n=\theta_n(\varphi-\gamma t)\exp(-i\omega t)$ with non-trivial topology have
+been found for $P=3$. Besides that, dynamics of various non-stationary knots
+with $P=3$ was simulated, and in some cases a tendency towards a finite-time
+singularity has been detected. For $P=2$ at small $\epsilon\neq 0$, rotating
+around $z$ axis configurations of the form $(W_0-W_1)\approx
+B_0\exp(i\zeta)+\epsilon C(B_0,\alpha)\exp(-i\zeta) + \epsilon
+D(B_0,\alpha)\exp(3i\zeta)$ have been investigated, where $B_0>0$ is an
+arbitrary constant, $\zeta=k_0\varphi -\Omega_0 t+\zeta_0$, $k_0=Q/2$,
+$\Omega_0=(k_0^2-\alpha)/2-2/B_0^2$. In the parameter space $(\alpha, B_0)$,
+wide stability regions for such solutions have been found. In unstable bands, a
+recurrence of the vortex knot to a weakly excited state has been noted to be
+possible.
+",0,1,0,0,0,0
+3924,Design of Configurable Sequential Circuits in Quantum-dot Cellular Automata,"  Quantum-dot cellular automata (QCA) is a likely candidate for future low
+power nano-scale electronic devices. Sequential circuits in QCA attract more
+attention due to its numerous application in digital industry. On the other
+hand, configurable devices provide low device cost and efficient utilization of
+device area. Since the fundamental building block of any sequential logic
+circuit is flip flop, hence constructing configurable, multi-purpose QCA
+flip-flops are one of the prime importance of current research. This work
+proposes a design of configurable flip-flop (CFF) which is the first of its
+kind in QCA domain. The proposed flip-flop can be configured to D, T and JK
+flip-flop by configuring its control inputs. In addition, to make more
+efficient configurable flip-flop, a clock pulse generator (CPG) is designed
+which can trigger all types of edges (falling, rising and dual) of a clock. The
+same CFF design is used to realize an edge configurable (dual/rising/falling)
+flip- flop with the help of CPG. The biggest advantage of using edge
+configurable (dual/rising/falling) flip-flop is that it can be used in 9
+different ways using the same single circuit. All the proposed designs are
+verified using QCADesigner simulator.
+",1,0,0,0,0,0
+3925,ADINE: An Adaptive Momentum Method for Stochastic Gradient Descent,"  Two major momentum-based techniques that have achieved tremendous success in
+optimization are Polyak's heavy ball method and Nesterov's accelerated
+gradient. A crucial step in all momentum-based methods is the choice of the
+momentum parameter $m$ which is always suggested to be set to less than $1$.
+Although the choice of $m < 1$ is justified only under very strong theoretical
+assumptions, it works well in practice even when the assumptions do not
+necessarily hold. In this paper, we propose a new momentum based method
+$\textit{ADINE}$, which relaxes the constraint of $m < 1$ and allows the
+learning algorithm to use adaptive higher momentum. We motivate our hypothesis
+on $m$ by experimentally verifying that a higher momentum ($\ge 1$) can help
+escape saddles much faster. Using this motivation, we propose our method
+$\textit{ADINE}$ that helps weigh the previous updates more (by setting the
+momentum parameter $> 1$), evaluate our proposed algorithm on deep neural
+networks and show that $\textit{ADINE}$ helps the learning algorithm to
+converge much faster without compromising on the generalization error.
+",1,0,0,1,0,0
+3926,Symplectic rational $G$-surfaces and equivariant symplectic cones,"  We give characterizations of a finite group $G$ acting symplectically on a
+rational surface ($\mathbb{C}P^2$ blown up at two or more points). In
+particular, we obtain a symplectic version of the dichotomy of $G$-conic
+bundles versus $G$-del Pezzo surfaces for the corresponding $G$-rational
+surfaces, analogous to a classical result in algebraic geometry. Besides the
+characterizations of the group $G$ (which is completely determined for the case
+of $\mathbb{C}P^2\# N\overline{\mathbb{C}P^2}$, $N=2,3,4$), we also investigate
+the equivariant symplectic minimality and equivariant symplectic cone of a
+given $G$-rational surface.
+",0,0,1,0,0,0
+3927,Biderivations of the twisted Heisenberg-Virasoro algebra and their applications,"  In this paper, the biderivations without the skew-symmetric condition of the
+twisted Heisenberg-Virasoro algebra are presented. We find some non-inner and
+non-skew-symmetric biderivations. As applications, the characterizations of the
+forms of linear commuting maps and the commutative post-Lie algebra structures
+on the twisted Heisenberg-Virasoro algebra are given. It also is proved that
+every biderivation of the graded twisted Heisenberg-Virasoro left-symmetric
+algebra is trivial.
+",0,0,1,0,0,0
+3928,A Data Science Approach to Understanding Residential Water Contamination in Flint,"  When the residents of Flint learned that lead had contaminated their water
+system, the local government made water-testing kits available to them free of
+charge. The city government published the results of these tests, creating a
+valuable dataset that is key to understanding the causes and extent of the lead
+contamination event in Flint. This is the nation's largest dataset on lead in a
+municipal water system.
+In this paper, we predict the lead contamination for each household's water
+supply, and we study several related aspects of Flint's water troubles, many of
+which generalize well beyond this one city. For example, we show that elevated
+lead risks can be (weakly) predicted from observable home attributes. Then we
+explore the factors associated with elevated lead. These risk assessments were
+developed in part via a crowd sourced prediction challenge at the University of
+Michigan. To inform Flint residents of these assessments, they have been
+incorporated into a web and mobile application funded by \texttt{Google.org}.
+We also explore questions of self-selection in the residential testing program,
+examining which factors are linked to when and how frequently residents
+voluntarily sample their water.
+",1,0,0,1,0,0
+3929,"Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems","  In this paper, we introduce a stochastic projected subgradient method for
+weakly convex (i.e., uniformly prox-regular) nonsmooth, nonconvex functions---a
+wide class of functions which includes the additive and convex composite
+classes. At a high-level, the method is an inexact proximal point iteration in
+which the strongly convex proximal subproblems are quickly solved with a
+specialized stochastic projected subgradient method. The primary contribution
+of this paper is a simple proof that the proposed algorithm converges at the
+same rate as the stochastic gradient method for smooth nonconvex problems. This
+result appears to be the first convergence rate analysis of a stochastic (or
+even deterministic) subgradient method for the class of weakly convex
+functions.
+",1,0,1,0,0,0
+3930,Solvable Integration Problems and Optimal Sample Size Selection,"  We compute the integral of a function or the expectation of a random variable
+with minimal cost and use, for our new algorithm and for upper bounds of the
+complexity, i.i.d. samples. Under certain assumptions it is possible to select
+a sample size based on a variance estimation, or -- more generally -- based on
+an estimation of a (central absolute) $p$-moment. That way one can guarantee a
+small absolute error with high probability, the problem is thus called
+solvable. The expected cost of the method depends on the $p$-moment of the
+random variable, which can be arbitrarily large.
+In order to prove the optimality of our algorithm we also provide lower
+bounds. These bounds apply not only to methods based on i.i.d. samples but also
+to general randomized algorithms. They show that -- up to constants -- the cost
+of the algorithm is optimal in terms of accuracy, confidence level, and norm of
+the particular input random variable. Since the considered classes of random
+variables or integrands are very large, the worst case cost would be infinite.
+Nevertheless one can define adaptive stopping rules such that for each input
+the expected cost is finite.
+We contrast these positive results with examples of integration problems that
+are not solvable.
+",0,0,0,1,0,0
+3931,Label Stability in Multiple Instance Learning,"  We address the problem of \emph{instance label stability} in multiple
+instance learning (MIL) classifiers. These classifiers are trained only on
+globally annotated images (bags), but often can provide fine-grained
+annotations for image pixels or patches (instances). This is interesting for
+computer aided diagnosis (CAD) and other medical image analysis tasks for which
+only a coarse labeling is provided. Unfortunately, the instance labels may be
+unstable. This means that a slight change in training data could potentially
+lead to abnormalities being detected in different parts of the image, which is
+undesirable from a CAD point of view. Despite MIL gaining popularity in the CAD
+literature, this issue has not yet been addressed. We investigate the stability
+of instance labels provided by several MIL classifiers on 5 different datasets,
+of which 3 are medical image datasets (breast histopathology, diabetic
+retinopathy and computed tomography lung images). We propose an unsupervised
+measure to evaluate instance stability, and demonstrate that a
+performance-stability trade-off can be made when comparing MIL classifiers.
+",1,0,0,1,0,0
+3932,New indicators for assessing the quality of in silico produced biomolecules: the case study of the aptamer-Angiopoietin-2 complex,"  Computational procedures to foresee the 3D structure of aptamers are in
+continuous progress. They constitute a crucial input to research, mainly when
+the crystallographic counterpart of the structures in silico produced is not
+present. At now, many codes are able to perform structure and binding
+prediction, although their ability in scoring the results remains rather weak.
+In this paper, we propose a novel procedure to complement the ranking outcomes
+of free docking code, by applying it to a set of anti-angiopoietin aptamers,
+whose performances are known. We rank the in silico produced configurations,
+adopting a maximum likelihood estimate, based on their topological and
+electrical properties. From the analysis, two principal kinds of conformers are
+identified, whose ability to mimick the binding features of the natural
+receptor is discussed. The procedure is easily generalizable to many biological
+biomolecules, useful for increasing chances of success in designing
+high-specificity biosensors (aptasensors).
+",0,0,0,0,1,0
+3933,A finite element method for elliptic problems with observational boundary data,"  In this paper we propose a finite element method for solving elliptic
+equations with the observational Dirichlet boundary data which may subject to
+random noises. The method is based on the weak formulation of Lagrangian
+multiplier. We show the convergence of the random finite element error in
+expectation and, when the noise is sub-Gaussian, in the Orlicz 2- norm which
+implies the probability that the finite element error estimates are violated
+decays exponentially. Numerical examples are included.
+",0,0,1,0,0,0
+3934,Sum-Product Graphical Models,"  This paper introduces a new probabilistic architecture called Sum-Product
+Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs)
+and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference
+using a class of models that incorporate context specific independence. Like
+GMs, SPGMs provide a high-level model interpretation in terms of conditional
+independence assumptions and corresponding factorizations. Thus, the new
+architecture represents a class of probability distributions that combines, for
+the first time, the semantics of graphical models with the evaluation
+efficiency of SPNs. We also propose a novel algorithm for learning both the
+structure and the parameters of SPGMs. A comparative empirical evaluation
+demonstrates competitive performances of our approach in density estimation.
+",1,0,0,1,0,0
+3935,Dependence of the Martian radiation environment on atmospheric depth: Modeling and measurement,"  The energetic particle environment on the Martian surface is influenced by
+solar and heliospheric modulation and changes in the local atmospheric pressure
+(or column depth). The Radiation Assessment Detector (RAD) on board the Mars
+Science Laboratory rover Curiosity on the surface of Mars has been measuring
+this effect for over four Earth years (about two Martian years). The
+anticorrelation between the recorded surface Galactic Cosmic Ray-induced dose
+rates and pressure changes has been investigated by Rafkin et al. (2014) and
+the long-term solar modulation has also been empirically analyzed and modeled
+by Guo et al. (2015). This paper employs the newly updated HZETRN2015 code to
+model the Martian atmospheric shielding effect on the accumulated dose rates
+and the change of this effect under different solar modulation and atmospheric
+conditions. The modeled results are compared with the most up-to-date (from 14
+August 2012 to 29 June 2016) observations of the RAD instrument on the surface
+of Mars. Both model and measurements agree reasonably well and show the
+atmospheric shielding effect under weak solar modulation conditions and the
+decline of this effect as solar modulation becomes stronger. This result is
+important for better risk estimations of future human explorations to Mars
+under different heliospheric and Martian atmospheric conditions.
+",0,1,0,0,0,0
+3936,Transactional Partitioning: A New Abstraction for Main-Memory Databases,"  The growth in variety and volume of OLTP (Online Transaction Processing)
+applications poses a challenge to OLTP systems to meet performance and cost
+demands in the existing hardware landscape. These applications are highly
+interactive (latency sensitive) and require update consistency. They target
+commodity hardware for deployment and demand scalability in throughput with
+increasing clients and data. Currently, OLTP systems used by these applications
+provide trade-offs in performance and ease of development over a variety of
+applications. In order to bridge the gap between performance and ease of
+development, we propose an intuitive, high-level programming model which allows
+OLTP applications to be modeled as a cluster of application logic units. By
+extending transactions guaranteeing full ACID semantics to provide the proposed
+model, we maintain ease of application development. The model allows the
+application developer to reason about program performance, and to influence it
+without the involvement of OLTP system designers (database designers) and/or
+DBAs. As a result, the database designer is free to focus on efficient running
+of programs to ensure optimal cluster resource utilization.
+",1,0,0,0,0,0
+3937,Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign,"  Until recently, social media was seen to promote democratic discourse on
+social and political issues. However, this powerful communication platform has
+come under scrutiny for allowing hostile actors to exploit online discussions
+in an attempt to manipulate public opinion. A case in point is the ongoing U.S.
+Congress' investigation of Russian interference in the 2016 U.S. election
+campaign, with Russia accused of using trolls (malicious accounts created to
+manipulate) and bots to spread misinformation and politically biased
+information. In this study, we explore the effects of this manipulation
+campaign, taking a closer look at users who re-shared the posts produced on
+Twitter by the Russian troll accounts publicly disclosed by U.S. Congress
+investigation. We collected a dataset with over 43 million election-related
+posts shared on Twitter between September 16 and October 21, 2016, by about 5.7
+million distinct users. This dataset included accounts associated with the
+identified Russian trolls. We use label propagation to infer the ideology of
+all users based on the news sources they shared. This method enables us to
+classify a large number of users as liberal or conservative with precision and
+recall above 90%. Conservatives retweeted Russian trolls about 31 times more
+often than liberals and produced 36x more tweets. Additionally, most retweets
+of troll content originated from two Southern states: Tennessee and Texas.
+Using state-of-the-art bot detection techniques, we estimated that about 4.9%
+and 6.2% of liberal and conservative users respectively were bots. Text
+analysis on the content shared by trolls reveals that they had a mostly
+conservative, pro-Trump agenda. Although an ideologically broad swath of
+Twitter users was exposed to Russian Trolls in the period leading up to the
+2016 U.S. Presidential election, it was mainly conservatives who helped amplify
+their message.
+",1,0,0,0,0,0
+3938,Event-Radar: Real-time Local Event Detection System for Geo-Tagged Tweet Streams,"  The local event detection is to use posting messages with geotags on social
+networks to reveal the related ongoing events and their locations. Recent
+studies have demonstrated that the geo-tagged tweet stream serves as an
+unprecedentedly valuable source for local event detection. Nevertheless, how to
+effectively extract local events from large geo-tagged tweet streams in real
+time remains challenging. A robust and efficient cloud-based real-time local
+event detection software system would benefit various aspects in the real-life
+society, from shopping recommendation for customer service providers to
+disaster alarming for emergency departments. We use the preliminary research
+GeoBurst as a starting point, which proposed a novel method to detect local
+events. GeoBurst+ leverages a novel cross-modal authority measure to identify
+several pivots in the query window. Such pivots reveal different geo-topical
+activities and naturally attract related tweets to form candidate events. It
+further summarises the continuous stream and compares the candidates against
+the historical summaries to pinpoint truly interesting local events. We mainly
+implement a website demonstration system Event-Radar with an improved algorithm
+to show the real-time local events online for public interests. Better still,
+as the query window shifts, our method can update the event list with little
+time cost, thus achieving continuous monitoring of the stream.
+",1,0,0,0,0,0
+3939,Computing metric hulls in graphs,"  We prove that, given a closure function the smallest preimage of a closed set
+can be calculated in polynomial time in the number of closed sets. This
+confirms a conjecture of Albenque and Knauer and implies that there is a
+polynomial time algorithm to compute the convex hull-number of a graph, when
+all its convex subgraphs are given as input. We then show that computing if the
+smallest preimage of a closed set is logarithmic in the size of the ground set
+is LOGSNP-complete if only the ground set is given. A special instance of this
+problem is computing the dimension of a poset given its linear extension graph,
+that was conjectured to be in P.
+The intent to show that the latter problem is LOGSNP-complete leads to
+several interesting questions and to the definition of the isometric hull,
+i.e., a smallest isometric subgraph containing a given set of vertices $S$.
+While for $|S|=2$ an isometric hull is just a shortest path, we show that
+computing the isometric hull of a set of vertices is NP-complete even if
+$|S|=3$. Finally, we consider the problem of computing the isometric
+hull-number of a graph and show that computing it is $\Sigma^P_2$ complete.
+",1,0,0,0,0,0
+3940,Evolutionary games on cycles with strong selection,"  Evolutionary games on graphs describe how strategic interactions and
+population structure determine evolutionary success, quantified by the
+probability that a single mutant takes over a population. Graph structures,
+compared to the well-mixed case, can act as amplifiers or suppressors of
+selection by increasing or decreasing the fixation probability of a beneficial
+mutant. Properties of the associated mean fixation times can be more intricate,
+especially when selection is strong. The intuition is that fixation of a
+beneficial mutant happens fast (in a dominance game), that fixation takes very
+long (in a coexistence game), and that strong selection eliminates demographic
+noise. Here we show that these intuitions can be misleading in structured
+populations. We analyze mean fixation times on the cycle graph under strong
+frequency-dependent selection for two different microscopic evolutionary update
+rules (death-birth and birth-death). We establish exact analytical results for
+fixation times under strong selection, and show that there are coexistence
+games in which fixation occurs in time polynomial in population size. Depending
+on the underlying game, we observe inherence of demographic noise even under
+strong selection, if the process is driven by random death before selection for
+birth of an offspring (death-birth update). In contrast, if selection for an
+offspring occurs before random removal (birth-death update), strong selection
+can remove demographic noise almost entirely.
+",0,1,0,0,0,0
+3941,"Epsilon-shapes: characterizing, detecting and thickening thin features in geometric models","  We focus on the analysis of planar shapes and solid objects having thin
+features and propose a new mathematical model to characterize them. Based on
+our model, that we call an epsilon-shape, we show how thin parts can be
+effectively and efficiently detected by an algorithm, and propose a novel
+approach to thicken these features while leaving all the other parts of the
+shape unchanged. When compared with state-of-the-art solutions, our proposal
+proves to be particularly flexible, efficient and stable, and does not require
+any unintuitive parameter to fine-tune the process. Furthermore, our method is
+able to detect thin features both in the object and in its complement, thus
+providing a useful tool to detect thin cavities and narrow channels. We discuss
+the importance of this kind of analysis in the design of robust structures and
+in the creation of geometry to be fabricated with modern additive manufacturing
+technology.
+",1,0,0,0,0,0
+3942,Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning,"  The learning of domain-invariant representations in the context of domain
+adaptation with neural networks is considered. We propose a new regularization
+method that minimizes the discrepancy between domain-specific latent feature
+representations directly in the hidden activation space. Although some standard
+distribution matching approaches exist that can be interpreted as the matching
+of weighted sums of moments, e.g. Maximum Mean Discrepancy (MMD), an explicit
+order-wise matching of higher order moments has not been considered before. We
+propose to match the higher order central moments of probability distributions
+by means of order-wise moment differences. Our model does not require
+computationally expensive distance and kernel matrix computations. We utilize
+the equivalent representation of probability distributions by moment sequences
+to define a new distance function, called Central Moment Discrepancy (CMD). We
+prove that CMD is a metric on the set of probability distributions on a compact
+interval. We further prove that convergence of probability distributions on
+compact intervals w.r.t. the new metric implies convergence in distribution of
+the respective random variables. We test our approach on two different
+benchmark data sets for object recognition (Office) and sentiment analysis of
+product reviews (Amazon reviews). CMD achieves a new state-of-the-art
+performance on most domain adaptation tasks of Office and outperforms networks
+trained with MMD, Variational Fair Autoencoders and Domain Adversarial Neural
+Networks on Amazon reviews. In addition, a post-hoc parameter sensitivity
+analysis shows that the new approach is stable w.r.t. parameter changes in a
+certain interval. The source code of the experiments is publicly available.
+",0,0,0,1,0,0
+3943,Understanding Geometry of Encoder-Decoder CNNs,"  Encoder-decoder networks using convolutional neural network (CNN)
+architecture have been extensively used in deep learning literatures thanks to
+its excellent performance for various inverse problems in computer vision,
+medical imaging, etc. However, it is still difficult to obtain coherent
+geometric view why such an architecture gives the desired performance. Inspired
+by recent theoretical understanding on generalizability, expressivity and
+optimization landscape of neural networks, as well as the theory of
+convolutional framelets, here we provide a unified theoretical framework that
+leads to a better understanding of geometry of encoder-decoder CNNs. Our
+unified mathematical framework shows that encoder-decoder CNN architecture is
+closely related to nonlinear basis representation using combinatorial
+convolution frames, whose expressibility increases exponentially with the
+network depth. We also demonstrate the importance of skipped connection in
+terms of expressibility, and optimization landscape.
+",1,0,0,1,0,0
+3944,AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks,"  ShuffleNet is a state-of-the-art light weight convolutional neural network
+architecture. Its basic operations include group, channel-wise convolution and
+channel shuffling. However, channel shuffling is manually designed empirically.
+Mathematically, shuffling is a multiplication by a permutation matrix. In this
+paper, we propose to automate channel shuffling by learning permutation
+matrices in network training. We introduce an exact Lipschitz continuous
+non-convex penalty so that it can be incorporated in the stochastic gradient
+descent to approximate permutation at high precision. Exact permutations are
+obtained by simple rounding at the end of training and are used in inference.
+The resulting network, referred to as AutoShuffleNet, achieved improved
+classification accuracies on CIFAR-10 and ImageNet data sets. In addition, we
+found experimentally that the standard convex relaxation of permutation
+matrices into stochastic matrices leads to poor performance. We prove
+theoretically the exactness (error bounds) in recovering permutation matrices
+when our penalty function is zero (very small). We present examples of
+permutation optimization through graph matching and two-layer neural network
+models where the loss functions are calculated in closed analytical form. In
+the examples, convex relaxation failed to capture permutations whereas our
+penalty succeeded.
+",1,0,0,1,0,0
+3945,ACDC: Altering Control Dependence Chains for Automated Patch Generation,"  Once a failure is observed, the primary concern of the developer is to
+identify what caused it in order to repair the code that induced the incorrect
+behavior. Until a permanent repair is afforded, code repair patches are
+invaluable. The aim of this work is to devise an automated patch generation
+technique that proceeds as follows: Step1) It identifies a set of
+failure-causing control dependence chains that are minimal in terms of number
+and length. Step2) It identifies a set of predicates within the chains along
+with associated execution instances, such that negating the predicates at the
+given instances would exhibit correct behavior. Step3) For each candidate
+predicate, it creates a classifier that dictates when the predicate should be
+negated to yield correct program behavior. Step4) Prior to each candidate
+predicate, the faulty program is injected with a call to its corresponding
+classifier passing it the program state and getting a return value predictively
+indicating whether to negate the predicate or not. The role of the classifiers
+is to ensure that: 1) the predicates are not negated during passing runs; and
+2) the predicates are negated at the appropriate instances within failing runs.
+We implemented our patch generation approach for the Java platform and
+evaluated our toolset using 148 defects from the Introclass and Siemens
+benchmarks. The toolset identified 56 full patches and another 46 partial
+patches, and the classification accuracy averaged 84%.
+",1,0,0,0,0,0
+3946,Proceedings Eighth Workshop on Intersection Types and Related Systems,"  This volume contains a final and revised selection of papers presented at the
+Eighth Workshop on Intersection Types and Related Systems (ITRS 2016), held on
+June 26, 2016 in Porto, in affiliation with FSCD 2016.
+",1,0,0,0,0,0
+3947,Microlensing of Extremely Magnified Stars near Caustics of Galaxy Clusters,"  Recent observations of lensed galaxies at cosmological distances have
+detected individual stars that are extremely magnified when crossing the
+caustics of lensing clusters. In idealized cluster lenses with smooth mass
+distributions, two images of a star of radius $R$ approaching a caustic
+brighten as $t^{-1/2}$ and reach a peak magnification $\sim 10^{6}\, (10\,
+R_{\odot}/R)^{1/2}$ before merging on the critical curve. We show that a mass
+fraction ($\kappa_\star \gtrsim \, 10^{-4.5}$) in microlenses inevitably
+disrupts the smooth caustic into a network of corrugated microcaustics, and
+produces light curves with numerous peaks. Using analytical calculations and
+numerical simulations, we derive the characteristic width of the network,
+caustic-crossing frequencies, and peak magnifications. For the lens parameters
+of a recent detection and a population of intracluster stars with $\kappa_\star
+\sim 0.01$, we find a source-plane width of $\sim 20 \, {\rm pc}$ for the
+caustic network, which spans $0.2 \, {\rm arcsec}$ on the image plane. A source
+star takes $\sim 2\times 10^4$ years to cross this width, with a total of $\sim
+6 \times 10^4$ crossings, each one lasting for $\sim 5\,{\rm
+hr}\,(R/10\,R_\odot)$ with typical peak magnifications of $\sim 10^{4} \left(
+R/ 10\,R_\odot \right)^{-1/2}$. The exquisite sensitivity of caustic-crossing
+events to the granularity of the lens-mass distribution makes them ideal probes
+of dark matter components, such as compact halo objects and ultralight axion
+dark matter.
+",0,1,0,0,0,0
+3948,NFFT meets Krylov methods: Fast matrix-vector products for the graph Laplacian of fully connected networks,"  The graph Laplacian is a standard tool in data science, machine learning, and
+image processing. The corresponding matrix inherits the complex structure of
+the underlying network and is in certain applications densely populated. This
+makes computations, in particular matrix-vector products, with the graph
+Laplacian a hard task. A typical application is the computation of a number of
+its eigenvalues and eigenvectors. Standard methods become infeasible as the
+number of nodes in the graph is too large. We propose the use of the fast
+summation based on the nonequispaced fast Fourier transform (NFFT) to perform
+the dense matrix-vector product with the graph Laplacian fast without ever
+forming the whole matrix. The enormous flexibility of the NFFT algorithm allows
+us to embed the accelerated multiplication into Lanczos-based eigenvalues
+routines or iterative linear system solvers and even consider other than the
+standard Gaussian kernels. We illustrate the feasibility of our approach on a
+number of test problems from image segmentation to semi-supervised learning
+based on graph-based PDEs. In particular, we compare our approach with the
+Nyström method. Moreover, we present and test an enhanced, hybrid version of
+the Nyström method, which internally uses the NFFT.
+",0,0,0,1,0,0
+3949,Convergence rate of a simulated annealing algorithm with noisy observations,"  In this paper we propose a modified version of the simulated annealing
+algorithm for solving a stochastic global optimization problem. More precisely,
+we address the problem of finding a global minimizer of a function with noisy
+evaluations. We provide a rate of convergence and its optimized parametrization
+to ensure a minimal number of evaluations for a given accuracy and a confidence
+level close to 1. This work is completed with a set of numerical
+experimentations and assesses the practical performance both on benchmark test
+cases and on real world examples.
+",0,0,1,1,0,0
+3950,Trans-allelic model for prediction of peptide:MHC-II interactions,"  Major histocompatibility complex class two (MHC-II) molecules are
+trans-membrane proteins and key components of the cellular immune system. Upon
+recognition of foreign peptides expressed on the MHC-II binding groove, helper
+T cells mount an immune response against invading pathogens. Therefore,
+mechanistic identification and knowledge of physico-chemical features that
+govern interactions between peptides and MHC-II molecules is useful for the
+design of effective epitope-based vaccines, as well as for understanding of
+immune responses. In this paper, we present a comprehensive trans-allelic
+prediction model, a generalized version of our previous biophysical model, that
+can predict peptide interactions for all three human MHC-II loci (HLA-DR,
+HLA-DP and HLA-DQ), using both peptide sequence data and structural information
+of MHC-II molecules. The advantage of this approach over other machine learning
+models is that it offers a simple and plausible physical explanation for
+peptide-MHC-II interactions. We train the model using a benchmark experimental
+dataset, and measure its predictive performance using novel data. Despite its
+relative simplicity, we find that the model has comparable performance to the
+state-of-the-art method. Focusing on the physical bases of peptide-MHC binding,
+we find support for previous theoretical predictions about the contributions of
+certain binding pockets to the binding energy. Additionally, we find that
+binding pockets P 4 and P 5 of HLA-DP, which were not previously considered as
+primary anchors, do make strong contributions to the binding energy. Together,
+the results indicate that our model can serve as a useful complement to
+alternative approaches to predicting peptide-MHC interactions.
+",0,0,0,1,0,0
+3951,Improved Speech Reconstruction from Silent Video,"  Speechreading is the task of inferring phonetic information from visually
+observed articulatory facial movements, and is a notoriously difficult task for
+humans to perform. In this paper we present an end-to-end model based on a
+convolutional neural network (CNN) for generating an intelligible and
+natural-sounding acoustic speech signal from silent video frames of a speaking
+person. We train our model on speakers from the GRID and TCD-TIMIT datasets,
+and evaluate the quality and intelligibility of reconstructed speech using
+common objective measurements. We show that speech predictions from the
+proposed model attain scores which indicate significantly improved quality over
+existing models. In addition, we show promising results towards reconstructing
+speech from an unconstrained dictionary.
+",1,0,0,0,0,0
+3952,Cluster-based Haldane state in edge-shared tetrahedral spin-cluster chain: Fedotovite K$_2$Cu$_3$O(SO$_4$)$_3$,"  Fedotovite K$_2$Cu$_3$O(SO$_4$)$_3$ is a candidate of new quantum spin
+systems, in which the edge-shared tetrahedral (EST) spin-clusters consisting of
+Cu$^{2+}$ are connected by weak inter-cluster couplings to from one-dimensional
+array. Comprehensive experimental studies by magnetic susceptibility,
+magnetization, heat capacity, and inelastic neutron scattering measurements
+reveal the presence of an effective $S$ = 1 Haldane state below $T \cong 4$ K.
+Rigorous theoretical studies provide an insight into the magnetic state of
+K$_2$Cu$_3$O(SO$_4$)$_3$: an EST cluster makes a triplet in the ground state
+and one-dimensional chain of the EST induces a cluster-based Haldane state. We
+predict that the cluster-based Haldene state emerges whenever the number of
+tetrahedra in the EST is $even$.
+",0,1,0,0,0,0
+3953,Super-Isolated Elliptic Curves and Abelian Surfaces in Cryptography,"  We call a simple abelian variety over $\mathbb{F}_p$ super-isolated if its
+($\mathbb{F}_p$-rational) isogeny class contains no other varieties. The
+motivation for considering these varieties comes from concerns about isogeny
+based attacks on the discrete log problem. We heuristically estimate that the
+number of super-isolated elliptic curves over $\mathbb{F}_p$ with prime order
+and $p \leq N$, is roughly $\tilde{\Theta}(\sqrt{N})$. In contrast, we prove
+that there are only 2 super-isolated surfaces of cryptographic size and
+near-prime order.
+",1,0,1,0,0,0
+3954,Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC,"  Multivariate techniques based on engineered features have found wide adoption
+in the identification of jets resulting from hadronic top decays at the Large
+Hadron Collider (LHC). Recent Deep Learning developments in this area include
+the treatment of the calorimeter activation as an image or supplying a list of
+jet constituent momenta to a fully connected network. This latter approach
+lends itself well to the use of Recurrent Neural Networks. In this work the
+applicability of architectures incorporating Long Short-Term Memory (LSTM)
+networks is explored. Several network architectures, methods of ordering of jet
+constituents, and input pre-processing are studied. The best performing LSTM
+network achieves a background rejection of 100 for 50% signal efficiency. This
+represents more than a factor of two improvement over a fully connected Deep
+Neural Network (DNN) trained on similar types of inputs.
+",1,0,0,1,0,0
+3955,Tangent measures of elliptic harmonic measure and applications,"  Tangent measure and blow-up methods, are powerful tools for understanding the
+relationship between the infinitesimal structure of the boundary of a domain
+and the behavior of its harmonic measure. We introduce a method for studying
+tangent measures of elliptic measures in arbitrary domains associated with
+(possibly non-symmetric) elliptic operators in divergence form whose
+coefficients have vanishing mean oscillation at the boundary. In this setting,
+we show the following for domains $ \Omega \subset \mathbb{R}^{n+1}$:
+1. We extend the results of Kenig, Preiss, and Toro [KPT09] by showing mutual
+absolute continuity of interior and exterior elliptic measures for {\it any}
+domains implies the tangent measures are a.e. flat and the elliptic measures
+have dimension $n$.
+2. We generalize the work of Kenig and Toro [KT06] and show that VMO
+equivalence of doubling interior and exterior elliptic measures for general
+domains implies the tangent measures are always elliptic polynomials.
+3. In a uniform domain that satisfies the capacity density condition and
+whose boundary is locally finite and has a.e. positive lower $n$-Hausdorff
+density, we show that if the elliptic measure is absolutely continuous with
+respect to $n$-Hausdorff measure then the boundary is rectifiable. This
+generalizes the work of Akman, Badger, Hofmann, and Martell [ABHM17].
+Finally, we generalize one of the main results of [Bad11] by showing that if
+$\omega$ is a Radon measure for which all tangent measures at a point are
+harmonic polynomials vanishing at the origin, then they are all homogeneous
+harmonic polynomials.
+",0,0,1,0,0,0
+3956,Aroma: Code Recommendation via Structural Code Search,"  Programmers often write code which have similarity to existing code written
+somewhere. A tool that could help programmers to search such similar code would
+be immensely useful. Such a tool could help programmers to extend partially
+written code snippets to completely implement necessary functionality, help to
+discover extensions to the partial code which are commonly done by other
+programmers, help to cross-check against similar code written by other
+programmers, or help to add extra code which would avoid common mistakes and
+errors. We propose Aroma, a tool and technique for code recommendation via
+structural code search. Aroma indexes a huge code corpus including thousands of
+open-source projects, takes a partial code snippet as input, searches the
+indexed method bodies which contain the partial code snippet, clusters and
+intersects the results of search to recommend a small set of succinct code
+snippets which contain the query snippet and which appears as part of several
+programs in the corpus. We evaluated Aroma on several randomly selected queries
+created from the corpus and as well as those derived from the code snippets
+obtained from Stack Overflow, a popular website for discussing code. We found
+that Aroma was able to retrieve and recommend most relevant code snippets
+efficiently.
+",1,0,0,0,0,0
+3957,On Hoffman's conjectural identity,"  In this paper, we shall prove the equality \[
+\zeta(3,\{2\}^{n},1,2)=\zeta(\{2\}^{n+3})+2\zeta(3,3,\{2\}^{n}) \] conjectured
+by Hoffman using certain identities among iterated integrals on
+$\mathbb{P}^{1}\setminus\{0,1,\infty,z\}$.
+",0,0,1,0,0,0
+3958,Parametric gain and wavelength conversion via third order nonlinear optics a CMOS compatible waveguide,"  We demonstrate sub-picosecond wavelength conversion in the C-band via four
+wave mixing in a 45cm long high index doped silica spiral waveguide. We achieve
+an on/off conversion efficiency (signal to idler) of +16.5dB as well as a
+parametric gain of +15dB for a peak pump power of 38W over a wavelength range
+of 100nm. Furthermore, we demonstrated a minimum gain of +5dB over a wavelength
+range as large as 200nm.
+",0,1,0,0,0,0
+3959,Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization,"  In this paper, we develop a new accelerated stochastic gradient method for
+efficiently solving the convex regularized empirical risk minimization problem
+in mini-batch settings. The use of mini-batches is becoming a golden standard
+in the machine learning community, because mini-batch settings stabilize the
+gradient estimate and can easily make good use of parallel computing. The core
+of our proposed method is the incorporation of our new ""double acceleration""
+technique and variance reduction technique. We theoretically analyze our
+proposed method and show that our method much improves the mini-batch
+efficiencies of previous accelerated stochastic methods, and essentially only
+needs size $\sqrt{n}$ mini-batches for achieving the optimal iteration
+complexities for both non-strongly and strongly convex objectives, where $n$ is
+the training set size. Further, we show that even in non-mini-batch settings,
+our method achieves the best known convergence rate for both non-strongly and
+strongly convex objectives.
+",1,0,1,1,0,0
+3960,Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications,"  MapReduce is a popular programming paradigm for developing large-scale,
+data-intensive computation. Many frameworks that implement this paradigm have
+recently been developed. To leverage these frameworks, however, developers must
+become familiar with their APIs and rewrite existing code. Casper is a new tool
+that automatically translates sequential Java programs into the MapReduce
+paradigm. Casper identifies potential code fragments to rewrite and translates
+them in two steps: (1) Casper uses program synthesis to search for a program
+summary (i.e., a functional specification) of each code fragment. The summary
+is expressed using a high-level intermediate language resembling the MapReduce
+paradigm and verified to be semantically equivalent to the original using a
+theorem prover. (2) Casper generates executable code from the summary, using
+either the Hadoop, Spark, or Flink API. We evaluated Casper by automatically
+converting real-world, sequential Java benchmarks to MapReduce. The resulting
+benchmarks perform up to 48.2x faster compared to the original.
+",1,0,0,0,0,0
+3961,"Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise","  In this paper, we propose a unified view of gradient-based algorithms for
+stochastic convex composite optimization. By extending the concept of estimate
+sequence introduced by Nesterov, we interpret a large class of stochastic
+optimization methods as procedures that iteratively minimize a surrogate of the
+objective. This point of view covers stochastic gradient descent (SGD), the
+variance-reduction approaches SAGA, SVRG, MISO, their proximal variants, and
+has several advantages: (i) we provide a simple generic proof of convergence
+for all of the aforementioned methods; (ii) we naturally obtain new algorithms
+with the same guarantees; (iii) we derive generic strategies to make these
+algorithms robust to stochastic noise, which is useful when data is corrupted
+by small random perturbations. Finally, we show that this viewpoint is useful
+to obtain accelerated algorithms.
+",1,0,0,1,0,0
+3962,Results of the first NaI scintillating calorimeter prototypes by COSINUS,"  Over almost three decades the TAUP conference has seen a remarkable momentum
+gain in direct dark matter search. An important accelerator were first
+indications for a modulating signal rate in the DAMA/NaI experiment reported in
+1997. Today the presence of an annual modulation, which matches in period and
+phase the expectation for dark matter, is supported at > 9$\sigma$ confidence.
+The underlying nature of dark matter, however, is still considered an open and
+fundamental question of particle physics. No other direct dark matter search
+could confirm the DAMA claim up to now; moreover, numerous null-results are in
+clear contradiction under so-called standard assumptions for the dark matter
+halo and the interaction mechanism of dark with ordinary matter. As both bear a
+dependence on the target material, resolving this controversial situation will
+convincingly only be possible with an experiment using sodium iodide (NaI) as
+target. COSINUS aims to even go a step further by combining NaI with a novel
+detection approach. COSINUS aims to operate NaI as a cryogenic calorimeter
+reading scintillation light and phonon/heat signal. Two distinct advantages
+arise from this approach, a substantially lower energy threshold for nuclear
+recoils and particle identification on an event-by-event basis. These key
+benefits will allow COSINUS to clarify a possible nuclear recoil origin of the
+DAMA signal with comparatively little exposure of O(100kg days) and, thereby,
+answer a long-standing question of particle physics. Today COSINUS is in R&D
+phase; in this contribution we show results from the 2nd prototype, albeit the
+first one of the final foreseen detector design. The key finding of this
+measurement is that pure, undoped NaI is a truly excellent scintillator at low
+temperatures: We measure 13.1% of the total deposited energy in the NaI crystal
+in the form of scintillation light (in the light detector).
+",0,1,0,0,0,0
+3963,Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data,"  Diagnosis and risk stratification of cancer and many other diseases require
+the detection of genomic breakpoints as a prerequisite of calling copy number
+alterations (CNA). This, however, is still challenging and requires
+time-consuming manual curation. As deep-learning methods outperformed classical
+state-of-the-art algorithms in various domains and have also been successfully
+applied to life science problems including medicine and biology, we here
+propose Deep SNP, a novel Deep Neural Network to learn from genomic data.
+Specifically, we used a manually curated dataset from 12 genomic single
+nucleotide polymorphism array (SNPa) profiles as truth-set and aimed at
+predicting the presence or absence of genomic breakpoints, an indicator of
+structural chromosomal variations, in windows of 40,000 probes. We compare our
+results with well-known neural network models as well as Rawcopy though this
+tool is designed to predict breakpoints and in addition genomic segments with
+high sensitivity. We show, that Deep SNP is capable of successfully predicting
+the presence or absence of a breakpoint in large genomic windows and
+outperforms state-of-the-art neural network models. Qualitative examples
+suggest that integration of a localization unit may enable breakpoint detection
+and prediction of genomic segments, even if the breakpoint coordinates were not
+provided for network training. These results warrant further evaluation of
+DeepSNP for breakpoint localization and subsequent calling of genomic segments.
+",0,0,0,0,1,0
+3964,"$(L,M)$-fuzzy convex structures","  In this paper, the notion of $(L,M)$-fuzzy convex structures is introduced.
+It is a generalization of $L$-convex structures and $M$-fuzzifying convex
+structures. In our definition of $(L,M)$-fuzzy convex structures, each
+$L$-fuzzy subset can be regarded as an $L$-convex set to some degree. The
+notion of convexity preserving functions is also generalized to lattice-valued
+case. Moreover, under the framework of $(L,M)$-fuzzy convex structures, the
+concepts of quotient structures, substructures and products are presented and
+their fundamental properties are discussed. Finally, we create a functor
+$\omega$ from $\mathbf{MYCS}$ to $\mathbf{LMCS}$ and show that there exists an
+adjunction between $\mathbf{MYCS}$ and $\mathbf{LMCS}$, where $\mathbf{MYCS}$
+and $\mathbf{LMCS}$ denote the category of $M$-fuzzifying convex structures,
+and the category of $(L,M)$-fuzzy convex structures, respectively.
+",0,0,1,0,0,0
+3965,Passive Compliance Control of Aerial Manipulators,"  This paper presents a passive compliance control for aerial manipulators to
+achieve stable environmental interactions. The main challenge is the absence of
+actuation along body-planar directions of the aerial vehicle which might be
+required during the interaction to preserve passivity. The controller proposed
+in this paper guarantees passivity of the manipulator through a proper choice
+of end-effector coordinates, and that of vehicle fuselage is guaranteed by
+exploiting time domain passivity technique. Simulation studies validate the
+proposed approach.
+",1,0,0,0,0,0
+3966,The phase retrieval problem for solutions of the Helmholtz equation,"  In this paper we consider the phase retrieval problem for Herglotz functions,
+that is, solutions of the Helmholtz equation $\Delta u+\lambda^2u=0$ on domains
+$\Omega\subset\mathbb{R}^d$, $d\geq2$. In dimension $d=2$, if $u,v$ are two
+such solutions then $|u|=|v|$ implies that either $u=cv$ or $u=c\bar v$ for
+some $c\in\mathbb{C}$ with $|c|=1$. In dimension $d\geq3$, the same conclusion
+holds under some restriction on $u$ and $v$: either they are real valued or
+zonal functions or have non vanishing mean.
+",0,0,1,0,0,0
+3967,Counting intersecting and pairs of cross-intersecting families,"  A family of subsets of $\{1,\ldots,n\}$ is called {\it intersecting} if any
+two of its sets intersect. A classical result in extremal combinatorics due to
+Erdős, Ko, and Rado determines the maximum size of an intersecting family
+of $k$-subsets of $\{1,\ldots, n\}$. In this paper we study the following
+problem: how many intersecting families of $k$-subsets of $\{1,\ldots, n\}$ are
+there? Improving a result of Balogh, Das, Delcourt, Liu, and Sharifzadeh, we
+determine this quantity asymptotically for $n\ge 2k+2+2\sqrt{k\log k}$ and
+$k\to \infty$. Moreover, under the same assumptions we also determine
+asymptotically the number of {\it non-trivial} intersecting families, that is,
+intersecting families for which the intersection of all sets is empty. We
+obtain analogous results for pairs of cross-intersecting families.
+",1,0,1,0,0,0
+3968,Zero-field Skyrmions with a High Topological Number in Itinerant Magnets,"  Magnetic skyrmions are swirling spin textures with topologically protected
+noncoplanarity. Recently, skyrmions with the topological number of unity have
+been extensively studied in both experiment and theory. We here show that a
+skyrmion crystal with an unusually high topological number of two is stabilized
+in itinerant magnets at zero magnetic field. The results are obtained for a
+minimal Kondo lattice model on a triangular lattice by an unrestricted
+large-scale numerical simulation and variational calculations. We find that the
+topological number can be switched by a magnetic field as $2\leftrightarrow
+1\leftrightarrow 0$. The skyrmion crystals are formed by the superpositions of
+three spin density waves induced by the Fermi surface effect, and hence, the
+size of skyrmions can be controlled by the band structure and electron filling.
+We also discuss the charge and spin textures of itinerant electrons in the
+skyrmion crystals which are directly obtained in our numerical simulations.
+",0,1,0,0,0,0
+3969,DoShiCo Challenge: Domain Shift in Control Prediction,"  Training deep neural network policies end-to-end for real-world applications
+so far requires big demonstration datasets in the real world or big sets
+consisting of a large variety of realistic and closely related 3D CAD models.
+These real or virtual data should, moreover, have very similar characteristics
+to the conditions expected at test time. These stringent requirements and the
+time consuming data collection processes that they entail, are currently the
+most important impediment that keeps deep reinforcement learning from being
+deployed in real-world applications. Therefore, in this work we advocate an
+alternative approach, where instead of avoiding any domain shift by carefully
+selecting the training data, the goal is to learn a policy that can cope with
+it. To this end, we propose the DoShiCo challenge: to train a model in very
+basic synthetic environments, far from realistic, in a way that it can be
+applied in more realistic environments as well as take the control decisions on
+real-world data. In particular, we focus on the task of collision avoidance for
+drones. We created a set of simulated environments that can be used as
+benchmark and implemented a baseline method, exploiting depth prediction as an
+auxiliary task to help overcome the domain shift. Even though the policy is
+trained in very basic environments, it can learn to fly without collisions in a
+very different realistic simulated environment. Of course several benchmarks
+for reinforcement learning already exist - but they never include a large
+domain shift. On the other hand, several benchmarks in computer vision focus on
+the domain shift, but they take the form of a static datasets instead of
+simulated environments. In this work we claim that it is crucial to take the
+two challenges together in one benchmark.
+",1,0,0,0,0,0
+3970,Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition,"  Interpretability has become an important issue in the machine learning field,
+along with the success of layered neural networks in various practical tasks.
+Since a trained layered neural network consists of a complex nonlinear
+relationship between large number of parameters, we failed to understand how
+they could achieve input-output mappings with a given data set. In this paper,
+we propose the non-negative task decomposition method, which applies
+non-negative matrix factorization to a trained layered neural network. This
+enables us to decompose the inference mechanism of a trained layered neural
+network into multiple principal tasks of input-output mapping, and reveal the
+roles of hidden units in terms of their contribution to each principal task.
+",0,0,0,1,0,0
+3971,Optimal Stopping for Interval Estimation in Bernoulli Trials,"  We propose an optimal sequential methodology for obtaining confidence
+intervals for a binomial proportion $\theta$. Assuming that an i.i.d. random
+sequence of Benoulli($\theta$) trials is observed sequentially, we are
+interested in designing a)~a stopping time $T$ that will decide when is the
+best time to stop sampling the process, and b)~an optimum estimator
+$\hat{\theta}_{T}$ that will provide the optimum center of the interval
+estimate of $\theta$. We follow a semi-Bayesian approach, where we assume that
+there exists a prior distribution for $\theta$, and our goal is to minimize the
+average number of samples while we guarantee a minimal coverage probability
+level. The solution is obtained by applying standard optimal stopping theory
+and computing the optimum pair $(T,\hat{\theta}_{T})$ numerically. Regarding
+the optimum stopping time component $T$, we demonstrate that it enjoys certain
+very uncommon characteristics not encountered in solutions of other classical
+optimal stopping problems. Finally, we compare our method with the optimum
+fixed-sample-size procedure but also with existing alternative sequential
+schemes.
+",0,0,0,1,0,0
+3972,Derivation of a Non-autonomous Linear Boltzmann Equation from a Heterogeneous Rayleigh Gas,"  A linear Boltzmann equation with nonautonomous collision operator is
+rigorously derived in the Boltzmann-Grad limit for the deterministic dynamics
+of a Rayleigh gas where a tagged particle is undergoing hard-sphere collisions
+with heterogeneously distributed background particles, which do not interact
+among each other. The validity of the linear Boltzmann equation holds for
+arbitrary long times under moderate assumptions on spatial continuity and
+higher moments of the initial distributions of the tagged particle and the
+heterogeneous, non-equilibrium distribution of the background. The empiric
+particle dynamics are compared to the Boltzmann dynamics using evolution
+semigroups for Kolmogorov equations of associated probability measures on
+collision histories.
+",0,0,1,0,0,0
+3973,On Blockwise Symmetric Matchgate Signatures and Higher Domain \#CSP,"  For any $n\geq 3$ and $ q\geq 3$, we prove that the {\sc Equality} function
+$(=_n)$ on $n$ variables over a domain of size $q$ cannot be realized by
+matchgates under holographic transformations. This is a consequence of our
+theorem on the structure of blockwise symmetric matchgate signatures. %due to
+the rank of the matrix form of the blockwise symmetric standard signatures,
+%where $(=_n)$ is an equality signature on domain $\{0, 1, \cdots, q-1\}$. This
+has the implication that the standard holographic algorithms based on
+matchgates, a methodology known to be universal for \#CSP over the Boolean
+domain, cannot produce P-time algorithms for planar \#CSP over any higher
+domain $q\geq 3$.
+",1,0,0,0,0,0
+3974,Deconfined quantum critical points: symmetries and dualities,"  The deconfined quantum critical point (QCP), separating the Néel and
+valence bond solid phases in a 2D antiferromagnet, was proposed as an example
+of $2+1$D criticality fundamentally different from standard
+Landau-Ginzburg-Wilson-Fisher {criticality}. In this work we present multiple
+equivalent descriptions of deconfined QCPs, and use these to address the
+possibility of enlarged emergent symmetries in the low energy limit. The
+easy-plane deconfined QCP, besides its previously discussed self-duality, is
+dual to $N_f = 2$ fermionic quantum electrodynamics (QED), which has its own
+self-duality and hence may have an O(4)$\times Z_2^T$ symmetry. We propose
+several dualities for the deconfined QCP with ${\mathrm{SU}(2)}$ spin symmetry
+which together make natural the emergence of a previously suggested $SO(5)$
+symmetry rotating the Néel and VBS orders. These emergent symmetries are
+implemented anomalously. The associated infra-red theories can also be viewed
+as surface descriptions of 3+1D topological paramagnets, giving further insight
+into the dualities. We describe a number of numerical tests of these dualities.
+We also discuss the possibility of ""pseudocritical"" behavior for deconfined
+critical points, and the meaning of the dualities and emergent symmetries in
+such a scenario.
+",0,1,0,0,0,0
+3975,Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks,"  Deep learning has become the state of the art approach in many machine
+learning problems such as classification. It has recently been shown that deep
+learning is highly vulnerable to adversarial perturbations. Taking the camera
+systems of self-driving cars as an example, small adversarial perturbations can
+cause the system to make errors in important tasks, such as classifying traffic
+signs or detecting pedestrians. Hence, in order to use deep learning without
+safety concerns a proper defense strategy is required. We propose to use
+ensemble methods as a defense strategy against adversarial perturbations. We
+find that an attack leading one model to misclassify does not imply the same
+for other networks performing the same task. This makes ensemble methods an
+attractive defense strategy against adversarial attacks. We empirically show
+for the MNIST and the CIFAR-10 data sets that ensemble methods not only improve
+the accuracy of neural networks on test data but also increase their robustness
+against adversarial perturbations.
+",0,0,0,1,0,0
+3976,The unrolled quantum group inside Lusztig's quantum group of divided powers,"  In this letter we prove that the unrolled small quantum group, appearing in
+quantum topology, is a Hopf subalgebra of Lusztig's quantum group of divided
+powers. We do so by writing down non-obvious primitive elements with the right
+adjoint action. We also construct a new larger Hopf algebra that contains the
+full unrolled quantum group. In fact this Hopf algebra contains both the
+enveloping of the Lie algebra and the ring of functions on the Lie group, and
+it should be interesting in its own right. We finally explain how this gives a
+realization of the unrolled quantum group as operators on a conformal field
+theory and match some calculations on this side. Our result extends to other
+Nichols algebras of diagonal type, including super Lie algebras.
+",0,0,1,0,0,0
+3977,Forward Collision Vehicular Radar with IEEE 802.11: Feasibility Demonstration through Measurements,"  Increasing safety and automation in transportation systems has led to the
+proliferation of radar and IEEE 802.11 dedicated short range communication
+(DSRC) in vehicles. Current implementations of vehicular radar devices,
+however, are expensive, use a substantial amount of bandwidth, and are
+susceptible to multiple security risks. Consider the feasibility of using an
+IEEE 802.11 orthogonal frequency division multiplexing (OFDM) communications
+waveform to perform radar functions. In this paper, we present an approach that
+determines the mean-normalized channel energy from frequency domain channel
+estimates and models it as a direct sinusoidal function of target range,
+enabling closest target range estimation. In addition, we propose an
+alternative to vehicular forward collision detection by extending IEEE 802.11
+dedicated short-range communications (DSRC) and WiFi technology to radar,
+providing a foundation for joint communications and radar framework.
+Furthermore, we perform an experimental demonstration using existing IEEE
+802.11 devices with minimal modification through algorithm processing on
+frequency-domain channel estimates. The results of this paper show that our
+solution delivers similar accuracy and reliability to mmWave radar devices with
+as little as 20 MHz of spectrum (doubling DSRC's 10 MHz allocation), indicating
+significant potential for industrial devices with joint vehicular
+communications and radar capabilities.
+",1,0,0,0,0,0
+3978,Link Before You Share: Managing Privacy Policies through Blockchain,"  With the advent of numerous online content providers, utilities and
+applications, each with their own specific version of privacy policies and its
+associated overhead, it is becoming increasingly difficult for concerned users
+to manage and track the confidential information that they share with the
+providers. Users consent to providers to gather and share their Personally
+Identifiable Information (PII). We have developed a novel framework to
+automatically track details about how a users' PII data is stored, used and
+shared by the provider. We have integrated our Data Privacy ontology with the
+properties of blockchain, to develop an automated access control and audit
+mechanism that enforces users' data privacy policies when sharing their data
+across third parties. We have also validated this framework by implementing a
+working system LinkShare. In this paper, we describe our framework on detail
+along with the LinkShare system. Our approach can be adopted by Big Data users
+to automatically apply their privacy policy on data operations and track the
+flow of that data across various stakeholders.
+",1,0,0,0,0,0
+3979,Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling,"  Accurately predicting and detecting interstitial lung disease (ILD) patterns
+given any computed tomography (CT) slice without any pre-processing
+prerequisites, such as manually delineated regions of interest (ROIs), is a
+clinically desirable, yet challenging goal. The majority of existing work
+relies on manually-provided ILD ROIs to extract sampled 2D image patches from
+CT slices and, from there, performs patch-based ILD categorization. Acquiring
+manual ROIs is labor intensive and serves as a bottleneck towards
+fully-automated CT imaging ILD screening over large-scale populations.
+Furthermore, despite the considerable high frequency of more than one ILD
+pattern on a single CT slice, previous works are only designed to detect one
+ILD pattern per slice or patch.
+To tackle these two critical challenges, we present multi-label deep
+convolutional neural networks (CNNs) for detecting ILDs from holistic CT slices
+(instead of ROIs or sub-images). Conventional single-labeled CNN models can be
+augmented to cope with the possible presence of multiple ILD pattern labels,
+via 1) continuous-valued deep regression based robust norm loss functions or 2)
+a categorical objective as the sum of element-wise binary logistic losses. Our
+methods are evaluated and validated using a publicly available database of 658
+patient CT scans under five-fold cross-validation, achieving promising
+performance on detecting four major ILD patterns: Ground Glass, Reticular,
+Honeycomb, and Emphysema. We also investigate the effectiveness of a CNN
+activation-based deep-feature encoding scheme using Fisher vector encoding,
+which treats ILD detection as spatially-unordered deep texture classification.
+",1,0,0,0,0,0
+3980,Convergence Results for Neural Networks via Electrodynamics,"  We study whether a depth two neural network can learn another depth two
+network using gradient descent. Assuming a linear output node, we show that the
+question of whether gradient descent converges to the target function is
+equivalent to the following question in electrodynamics: Given $k$ fixed
+protons in $\mathbb{R}^d,$ and $k$ electrons, each moving due to the attractive
+force from the protons and repulsive force from the remaining electrons,
+whether at equilibrium all the electrons will be matched up with the protons,
+up to a permutation. Under the standard electrical force, this follows from the
+classic Earnshaw's theorem. In our setting, the force is determined by the
+activation function and the input distribution. Building on this equivalence,
+we prove the existence of an activation function such that gradient descent
+learns at least one of the hidden nodes in the target network. Iterating, we
+show that gradient descent can be used to learn the entire network one node at
+a time.
+",1,1,0,0,0,0
+3981,The norm residue symbol for higher local fields,"  Since the development of higher local class field theory, several explicit
+reciprocity laws have been constructed. In particular, there are formulas
+describing the higher-dimensional Hilbert symbol given, among others, by M.
+Kurihara, A. Zinoviev and S. Vostokov. K. Kato also has explicit formulas for
+the higher-dimensional Kummer pairing associated to certain (one-dimensional)
+$p$-divisible groups.
+In this paper we construct an explicit reciprocity law describing the Kummer
+pairing associated to any (one-dimensional) formal group. The formulas are a
+generalization to higher-dimensional local fields of Kolyvagin's reciprocity
+laws. The formulas obtained describe the values of the pairing in terms of
+multidimensional $p$-adic differentiation, the logarithm of the formal group,
+the generalized trace and the norm on Milnor K-groups.
+In the second part of this paper, we will apply the results obtained here to
+give explicit formulas for the generalized Hilbert symbol and the Kummer
+pairing associated to a Lubin-Tate formal group. The results obtained in the
+second paper constitute a generalization to higher local fields, of the
+formulas of Artin-Hasse, K. Iwasawa and A. Wiles.
+",0,0,1,0,0,0
+3982,Optimizing tree decompositions in MSO,"  The classic algorithm of Bodlaender and Kloks [J. Algorithms, 1996] solves
+the following problem in linear fixed-parameter time: given a tree
+decomposition of a graph of (possibly suboptimal) width $k$, compute an
+optimum-width tree decomposition of the graph. In this work, we prove that this
+problem can also be solved in MSO in the following sense: for every positive
+integer $k$, there is an MSO transduction from tree decompositions of width $k$
+to tree decompositions of optimum width. Together with our recent results [LICS
+2016], this implies that for every $k$ there exists an MSO transduction which
+inputs a graph of treewidth $k$, and nondeterministically outputs its tree
+decomposition of optimum width. We also show that MSO transductions can be
+implemented in linear fixed-parameter time, which enables us to derive the
+algorithmic result of Bodlaender and Kloks as a corollary of our main result.
+",1,0,0,0,0,0
+3983,Nonparametric relative error estimation of the regression function for censored data,"  Let $ (T_i)_i$ be a sequence of independent identically distributed (i.i.d.)
+random variables (r.v.) of interest distributed as $ T$ and $(X_i)_i$ be a
+corresponding vector of covariates taking values on $ \mathbb{R}^d$. In
+censorship models the r.v. $T$ is subject to random censoring by another r.v.
+$C$. In this paper we built a new kernel estimator based on the so-called
+synthetic data of the mean squared relative error for the regression function.
+We establish the uniform almost sure convergence with rate over a compact set
+and its asymptotic normality. The asymptotic variance is explicitly given and
+as product we give a confidence bands. A simulation study has been conducted to
+comfort our theoretical results
+",0,0,1,1,0,0
+3984,"Intelligent Home Energy Management System for Distributed Renewable Generators, Dispatchable Residential Loads and Distributed Energy Storage Devices","  This paper presents an intelligent home energy management system integrated
+with dispatchable loads (e.g., clothes washers and dryers), distributed
+renewable generators (e.g., roof-top solar panels), and distributed energy
+storage devices (e.g., plug-in electric vehicles). The overall goal is to
+reduce the total operating costs and the carbon emissions for a future
+residential house, while satisfying the end-users comfort levels. This paper
+models a wide variety of home appliances and formulates the economic operation
+problem using mixed integer linear programming. Case studies are performed to
+validate and demonstrate the effectiveness of the proposed solution algorithm.
+Simulation results also show the positive impact of dispatchable loads,
+distributed renewable generators, and distributed energy storage devices on a
+future residential house.
+",0,0,1,0,0,0
+3985,Interpretable Structure-Evolving LSTM,"  This paper develops a general framework for learning interpretable data
+representation via Long Short-Term Memory (LSTM) recurrent neural networks over
+hierarchal graph structures. Instead of learning LSTM models over the pre-fixed
+structures, we propose to further learn the intermediate interpretable
+multi-level graph structures in a progressive and stochastic way from data
+during the LSTM network optimization. We thus call this model the
+structure-evolving LSTM. In particular, starting with an initial element-level
+graph representation where each node is a small data element, the
+structure-evolving LSTM gradually evolves the multi-level graph representations
+by stochastically merging the graph nodes with high compatibilities along the
+stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two
+connected nodes from their corresponding LSTM gate outputs, which is used to
+generate a merging probability. The candidate graph structures are accordingly
+generated where the nodes are grouped into cliques with their merging
+probabilities. We then produce the new graph structure with a
+Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in
+local optimums by stochastic sampling with an acceptance probability. Once a
+graph structure is accepted, a higher-level graph is then constructed by taking
+the partitioned cliques as its nodes. During the evolving process,
+representation becomes more abstracted in higher-levels where redundant
+information is filtered out, allowing more efficient propagation of long-range
+data dependencies. We evaluate the effectiveness of structure-evolving LSTM in
+the application of semantic object parsing and demonstrate its advantage over
+state-of-the-art LSTM models on standard benchmarks.
+",1,0,0,0,0,0
+3986,On Optimization over Tail Distributions,"  We investigate the use of optimization to compute bounds for extremal
+performance measures. This approach takes a non-parametric viewpoint that aims
+to alleviate the issue of model misspecification possibly encountered by
+conventional methods in extreme event analysis. We make two contributions
+towards solving these formulations, paying especial attention to the arising
+tail issues. First, we provide a technique in parallel to Choquet's theory, via
+a combination of integration by parts and change of measures, to transform
+shape constrained problems (e.g., monotonicity of derivatives) into families of
+moment problems. Second, we show how a moment problem cast over infinite
+support can be reformulated into a problem over compact support with an
+additional slack variable. In the context of optimization over tail
+distributions, the latter helps resolve the issue of non-convergence of
+solutions when using algorithms such as generalized linear programming. We
+further demonstrate the applicability of this result to problems with
+infinite-value constraints, which can arise in modeling heavy tails.
+",0,0,0,1,0,0
+3987,Isotropic covariance functions on graphs and their edges,"  We develop parametric classes of covariance functions on linear networks and
+their extension to graphs with Euclidean edges, i.e., graphs with edges viewed
+as line segments or more general sets with a coordinate system allowing us to
+consider points on the graph which are vertices or points on an edge. Our
+covariance functions are defined on the vertices and edge points of these
+graphs and are isotropic in the sense that they depend only on the geodesic
+distance or on a new metric called the resistance metric (which extends the
+classical resistance metric developed in electrical network theory on the
+vertices of a graph to the continuum of edge points). We discuss the advantages
+of using the resistance metric in comparison with the geodesic metric as well
+as the restrictions these metrics impose on the investigated covariance
+functions. In particular, many of the commonly used isotropic covariance
+functions in the spatial statistics literature (the power exponential,
+Mat{é}rn, generalized Cauchy, and Dagum classes) are shown to be valid with
+respect to the resistance metric for any graph with Euclidean edges, whilst
+they are only valid with respect to the geodesic metric in more special cases.
+",0,0,1,1,0,0
+3988,Towards Probabilistic Formal Modeling of Robotic Cell Injection Systems,"  Cell injection is a technique in the domain of biological cell
+micro-manipulation for the delivery of small volumes of samples into the
+suspended or adherent cells. It has been widely applied in various areas, such
+as gene injection, in-vitro fertilization (IVF), intracytoplasmic sperm
+injection (ISCI) and drug development. However, the existing manual and
+semi-automated cell injection systems require lengthy training and suffer from
+high probability of contamination and low success rate. In the recently
+introduced fully automated cell injection systems, the injection force plays a
+vital role in the success of the process since even a tiny excessive force can
+destroy the membrane or tissue of the biological cell. Traditionally, the force
+control algorithms are analyzed using simulation, which is inherently
+non-exhaustive and incomplete in terms of detecting system failures. Moreover,
+the uncertainties in the system are generally ignored in the analysis. To
+overcome these limitations, we present a formal analysis methodology based on
+probabilistic model checking to analyze a robotic cell injection system
+utilizing the impedance force control algorithm. The proposed methodology,
+developed using the PRISM model checker, allowed to find a discrepancy in the
+algorithm, which was not found by any of the previous analysis using the
+traditional methods.
+",1,0,0,0,0,0
+3989,Representations of language in a model of visually grounded speech signal,"  We present a visually grounded model of speech perception which projects
+spoken utterances and images to a joint semantic space. We use a multi-layer
+recurrent highway network to model the temporal nature of spoken speech, and
+show that it learns to extract both form and meaning-based linguistic knowledge
+from the input signal. We carry out an in-depth analysis of the representations
+used by different components of the trained model and show that encoding of
+semantic aspects tends to become richer as we go up the hierarchy of layers,
+whereas encoding of form-related aspects of the language input tends to
+initially increase and then plateau or decrease.
+",1,0,0,0,0,0
+3990,Long-Term Video Interpolation with Bidirectional Predictive Network,"  This paper considers the challenging task of long-term video interpolation.
+Unlike most existing methods that only generate few intermediate frames between
+existing adjacent ones, we attempt to speculate or imagine the procedure of an
+episode and further generate multiple frames between two non-consecutive frames
+in videos. In this paper, we present a novel deep architecture called
+bidirectional predictive network (BiPN) that predicts intermediate frames from
+two opposite directions. The bidirectional architecture allows the model to
+learn scene transformation with time as well as generate longer video
+sequences. Besides, our model can be extended to predict multiple possible
+procedures by sampling different noise vectors. A joint loss composed of clues
+in image and feature spaces and adversarial loss is designed to train our
+model. We demonstrate the advantages of BiPN on two benchmarks Moving 2D Shapes
+and UCF101 and report competitive results to recent approaches.
+",1,0,0,0,0,0
+3991,Evolutionary dynamics of cooperation in neutral populations,"  Cooperation is a difficult proposition in the face of Darwinian selection.
+Those that defect have an evolutionary advantage over cooperators who should
+therefore die out. However, spatial structure enables cooperators to survive
+through the formation of homogeneous clusters, which is the hallmark of network
+reciprocity. Here we go beyond this traditional setup and study the
+spatiotemporal dynamics of cooperation in a population of populations. We use
+the prisoner's dilemma game as the mathematical model and show that considering
+several populations simultaneously give rise to fascinating spatiotemporal
+dynamics and pattern formation. Even the simplest assumption that strategies
+between different populations are payoff-neutral with one another results in
+the spontaneous emergence of cyclic dominance, where defectors of one
+population become prey of cooperators in the other population, and vice versa.
+Moreover, if social interactions within different populations are characterized
+by significantly different temptations to defect, we observe that defectors in
+the population with the largest temptation counterintuitively vanish the
+fastest, while cooperators that hang on eventually take over the whole
+available space. Our results reveal that considering the simultaneous presence
+of different populations significantly expands the complexity of evolutionary
+dynamics in structured populations, and it allow us to understand the stability
+of cooperation under adverse conditions that could never be bridged by network
+reciprocity alone.
+",1,0,0,0,0,0
+3992,Large global-in-time solutions to a nonlocal model of chemotaxis,"  We consider the parabolic-elliptic model for the chemotaxis with fractional
+(anomalous) diffusion. Global-in-time solutions are constructed under (nearly)
+optimal assumptions on the size of radial initial data. Moreover, criteria for
+blowup of radial solutions in terms of suitable Morrey spaces norms are
+derived.
+",0,0,1,0,0,0
+3993,Microservices: Granularity vs. Performance,"  Microservice Architectures (MA) have the potential to increase the agility of
+software development. In an era where businesses require software applications
+to evolve to support software emerging requirements, particularly for Internet
+of Things (IoT) applications, we examine the issue of microservice granularity
+and explore its effect upon application latency. Two approaches to microservice
+deployment are simulated; the first with microservices in a single container,
+and the second with microservices partitioned across separate containers. We
+observed a neglibible increase in service latency for the multiple container
+deployment over a single container.
+",1,0,0,0,0,0
+3994,The strictly-correlated electron functional for spherically symmetric systems revisited,"  The strong-interaction limit of the Hohenberg-Kohn functional defines a
+multimarginal optimal transport problem with Coulomb cost. From physical
+arguments, the solution of this limit is expected to yield strictly-correlated
+particle positions, related to each other by co-motion functions (or optimal
+maps), but the existence of such a deterministic solution in the general
+three-dimensional case is still an open question. A conjecture for the
+co-motion functions for radially symmetric densities was presented in
+Phys.~Rev.~A {\bf 75}, 042511 (2007), and later used to build approximate
+exchange-correlation functionals for electrons confined in low-density quantum
+dots. Colombo and Stra [Math.~Models Methods Appl.~Sci., {\bf 26} 1025 (2016)]
+have recently shown that these conjectured maps are not always optimal. Here we
+revisit the whole issue both from the formal and numerical point of view,
+finding that even if the conjectured maps are not always optimal, they still
+yield an interaction energy (cost) that is numerically very close to the true
+minimum. We also prove that the functional built from the conjectured maps has
+the expected functional derivative also when they are not optimal.
+",0,1,0,0,0,0
+3995,A stable numerical strategy for Reynolds-Rayleigh-Plesset coupling,"  The coupling of Reynolds and Rayleigh-Plesset equations has been used in
+several works to simulate lubricated devices considering cavitation. The
+numerical strategies proposed so far are variants of a staggered strategy where
+Reynolds equation is solved considering the bubble dynamics frozen, and then
+the Rayleigh-Plesset equation is solved to update the bubble radius with the
+pressure frozen. We show that this strategy has severe stability issues and a
+stable methodology is proposed. The proposed methodology performance is
+assessed on two physical settings. The first one concerns the propagation of a
+decompression wave along a fracture considering the presence of cavitation
+nuclei. The second one is a typical journal bearing, in which the coupled model
+is compared with the Elrod-Adams model.
+",0,1,0,0,0,0
+3996,Accelerated Sparse Subspace Clustering,"  State-of-the-art algorithms for sparse subspace clustering perform spectral
+clustering on a similarity matrix typically obtained by representing each data
+point as a sparse combination of other points using either basis pursuit (BP)
+or orthogonal matching pursuit (OMP). BP-based methods are often prohibitive in
+practice while the performance of OMP-based schemes are unsatisfactory,
+especially in settings where data points are highly similar. In this paper, we
+propose a novel algorithm that exploits an accelerated variant of orthogonal
+least-squares to efficiently find the underlying subspaces. We show that under
+certain conditions the proposed algorithm returns a subspace-preserving
+solution. Simulation results illustrate that the proposed method compares
+favorably with BP-based method in terms of running time while being
+significantly more accurate than OMP-based schemes.
+",1,0,0,1,0,0
+3997,Prediction of helium vapor quality in steady state Two-phase operation for SST-1 Toroidal field magnets,"  Steady State Superconducting Tokamak (SST-1) at the Institute for Plasma
+Research (IPR) is an operational device and is the first superconducting
+Tokamak in India. Superconducting Magnets System (SCMS) in SST-1 comprises of
+sixteen Toroidal field (TF) magnets and nine Poloidal Field (PF) magnets
+manufactured using NbTi/Cu based cable-in-conduit-conductor (CICC) concept.
+SST-1, superconducting TF magnets are operated in a Cryo-stable manner being
+cooled with two-phase (TP) flow helium. The typical operating pressure of the
+TP helium is 1.6 bar (a) at corresponding saturation temperature. The SCMS has
+a typical cool-down time of about 14 days from 300 K down to 4.5 K using Helium
+plant of equivalent cooling capacity of 1350 W at 4.5 K. Using the onset of
+experimental data from the HRL, we estimated the vapor quality for the input
+heat load on to the TF magnets system. In this paper, we report the
+characteristics of two-phase flow for given thermo-hydraulic conditions during
+long steady state operation of the SST-1 TF magnets. Finally, the
+experimentally obtained results have been compared with the well-known
+correlations of two-phase flow.
+",0,1,0,0,0,0
+3998,Synthesis of Highly Anisotropic Semiconducting GaTe Nanomaterials and Emerging Properties Enabled by Epitaxy,"  Pseudo-one dimensional (pseudo-1D) materials are a new-class of materials
+where atoms are arranged in chain like structures in two-dimensions (2D).
+Examples include recently discovered black phosphorus, ReS2 and ReSe2 from
+transition metal dichalcogenides, TiS3 and ZrS3 from transition metal
+trichalcogenides and most recently GaTe. The presence of structural anisotropy
+impacts their physical properties and leads to direction dependent light-matter
+interactions, dichroic optical responses, high mobility channels, and
+anisotropic thermal conduction. Despite the numerous reports on the vapor phase
+growth of isotropic TMDCs and post transition metal chalcogenides such as MoS2
+and GaSe, the synthesis of pseudo-1D materials is particularly difficult due to
+the anisotropy in interfacial energy, which stabilizes dendritic growth rather
+than single crystalline growth with well-defined orientation. The growth of
+monoclinic GaTe has been demonstrated on flexible mica substrates with superior
+photodetecting performance. In this work, we demonstrate that pseudo-1D
+monoclinic GaTe layers can be synthesized on a variety of other substrates
+including GaAs (111), Si (111) and c-cut sapphire by physical vapor transport
+techniques. High resolution transmission electron microscopy (HRTEM)
+measurements, together with angle resolved micro-PL and micro-Raman techniques,
+provide for the very first time atomic scale resolution experiments on
+pseudo-1D structures in monoclinic GaTe and anisotropic properties.
+Interestingly, GaTe nanomaterials grown on sapphire exhibit highly efficient
+and narrow localized emission peaks below the band gap energy, which are found
+to be related to select types of line and point defects as evidenced by PL and
+Raman mapping scans. It makes the samples grown on sapphire more prominent than
+those grown on GaAs and Si, which demonstrate more regular properties.
+",0,1,0,0,0,0
+3999,Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents,"  This paper outlines a methodological approach for designing adaptive agents
+driving themselves near points of criticality. Using a synthetic approach we
+construct a conceptual model that, instead of specifying mechanistic
+requirements to generate criticality, exploits the maintenance of an
+organizational structure capable of reproducing critical behavior. Our approach
+exploits the well-known principle of universality, which classifies critical
+phenomena inside a few universality classes of systems independently of their
+specific mechanisms or topologies. In particular, we implement an artificial
+embodied agent controlled by a neural network maintaining a correlation
+structure randomly sampled from a lattice Ising model at a critical point. We
+evaluate the agent in two classical reinforcement learning scenarios: the
+Mountain Car benchmark and the Acrobot double pendulum, finding that in both
+cases the neural controller reaches a point of criticality, which coincides
+with a transition point between two regimes of the agent's behaviour,
+maximizing the mutual information between neurons and sensorimotor patterns.
+Finally, we discuss the possible applications of this synthetic approach to the
+comprehension of deeper principles connected to the pervasive presence of
+criticality in biological and cognitive systems.
+",1,1,0,0,0,0
+4000,Projection Free Rank-Drop Steps,"  The Frank-Wolfe (FW) algorithm has been widely used in solving nuclear norm
+constrained problems, since it does not require projections. However, FW often
+yields high rank intermediate iterates, which can be very expensive in time and
+space costs for large problems. To address this issue, we propose a rank-drop
+method for nuclear norm constrained problems. The goal is to generate descent
+steps that lead to rank decreases, maintaining low-rank solutions throughout
+the algorithm. Moreover, the optimization problems are constrained to ensure
+that the rank-drop step is also feasible and can be readily incorporated into a
+projection-free minimization method, e.g., Frank-Wolfe. We demonstrate that by
+incorporating rank-drop steps into the Frank-Wolfe algorithm, the rank of the
+solution is greatly reduced compared to the original Frank-Wolfe or its common
+variants.
+",0,0,0,1,0,0
+4001,Doubly autoparallel structure on the probability simplex,"  On the probability simplex, we can consider the standard information
+geometric structure with the e- and m-affine connections mutually dual with
+respect to the Fisher metric. The geometry naturally defines submanifolds
+simultaneously autoparallel for the both affine connections, which we call {\em
+doubly autoparallel submanifolds}.
+In this note we discuss their several interesting common properties. Further,
+we algebraically characterize doubly autoparallel submanifolds on the
+probability simplex and give their classification.
+",0,0,1,0,0,0
+4002,Probing Hidden Spin Order with Interpretable Machine Learning,"  The search of unconventional magnetic and non-magnetic states is a major
+topic in the study of frustrated magnetism. Canonical examples of those states
+include various spin liquids and spin nematics. However, discerning their
+existence and the correct characterization is usually challenging. Here we
+introduce a machine-learning protocol that can identify general nematic order
+and their order parameter from seemingly featureless spin configurations, thus
+providing comprehensive insight on the presence or absence of hidden orders. We
+demonstrate the capabilities of our method by extracting the analytical form of
+nematic order parameter tensors up to rank 6. This may prove useful in the
+search for novel spin states and for ruling out spurious spin liquid
+candidates.
+",0,0,0,1,0,0
+4003,Maximum a Posteriori Joint State Path and Parameter Estimation in Stochastic Differential Equations,"  A wide variety of phenomena of engineering and scientific interest are of a
+continuous-time nature and can be modeled by stochastic differential equations
+(SDEs), which represent the evolution of the uncertainty in the states of a
+system. For systems of this class, some parameters of the SDE might be unknown
+and the measured data often includes noise, so state and parameter estimators
+are needed to perform inference and further analysis using the system state
+path. The distributions of SDEs which are nonlinear or subject to non-Gaussian
+measurement noise do not admit tractable analytic expressions, so state and
+parameter estimators for these systems are often approximations based on
+heuristics, such as the extended and unscented Kalman smoothers, or the
+prediction error method using nonlinear Kalman filters. However, the Onsager
+Machlup functional can be used to obtain fictitious densities for the
+parameters and state-paths of SDEs with analytic expressions. In this thesis,
+we provide a unified theoretical framework for maximum a posteriori (MAP)
+estimation of general random variables, possibly infinite-dimensional, and show
+how the Onsager--Machlup functional can be used to construct the joint MAP
+state-path and parameter estimator for SDEs. We also prove that the minimum
+energy estimator, which is often thought to be the MAP state-path estimator,
+actually gives the state paths associated to the MAP noise paths. Furthermore,
+we prove that the discretized MAP state-path and parameter estimators, which
+have emerged recently as powerful alternatives to nonlinear Kalman smoothers,
+converge hypographically as the discretization step vanishes. Their
+hypographical limit, however, is the MAP estimator for SDEs when the
+trapezoidal discretization is used and the minimum energy estimator when the
+Euler discretization is used, associating different interpretations to each
+discretized estimate.
+",0,0,1,1,0,0
+4004,Spreading of an infectious disease between different locations,"  The endogenous adaptation of agents, that may adjust their local contact
+network in response to the risk of being infected, can have the perverse effect
+of increasing the overall systemic infectiveness of a disease. We study a
+dynamical model over two geographically distinct but interacting locations, to
+better understand theoretically the mechanism at play. Moreover, we provide
+empirical motivation from the Italian National Bovine Database, for the period
+2006-2013.
+",0,0,0,0,0,1
+4005,Observation and calculation of the quasi-bound rovibrational levels of the electronic ground state of H$_2^+$,"  Although the existence of quasi-bound rotational levels of the $X^+ \
+^2\Sigma_g^+$ ground state of H$_2^+$ has been predicted a long time ago, these
+states have never been observed. Calculated positions and widths of quasi-bound
+rotational levels located close to the top of the centrifugal barriers have not
+been reported either. Given the role that such states play in the recombination
+of H(1s) and H$^+$ to form H$_2^+$, this lack of data may be regarded as one of
+the largest unknown aspects of this otherwise accurately known fundamental
+molecular cation. We present measurements of the positions and widths of the
+lowest-lying quasi-bound rotational levels of H$_2^+$ and compare the
+experimental results with the positions and widths we calculate using a
+potential model for the $X^+$ state of H$_2^+$ which includes adiabatic,
+nonadiabatic, relativistic and radiative corrections to the Born-Oppenheimer
+approximation.
+",0,1,0,0,0,0
+4006,A case study of hurdle and generalized additive models in astronomy: the escape of ionizing radiation,"  The dark ages of the Universe end with the formation of the first generation
+of stars residing in primeval galaxies. These objects were the first to produce
+ultraviolet ionizing photons in a period when the cosmic gas changed from a
+neutral state to an ionized one, known as Epoch of Reionization (EoR). A
+pivotal aspect to comprehend the EoR is to probe the intertwined relationship
+between the fraction of ionizing photons capable to escape dark haloes, also
+known as the escape fraction ($f_{esc}$), and the physical properties of the
+galaxy. This work develops a sound statistical model suitable to account for
+such non-linear relationships and the non-Gaussian nature of $f_{esc}$. This
+model simultaneously estimates the probability that a given primordial galaxy
+starts the ionizing photon production and estimates the mean level of the
+$f_{esc}$ once it is triggered. The model was employed in the First Billion
+Years simulation suite, from which we show that the baryonic fraction and the
+rate of ionizing photons appear to have a larger impact on $f_{esc}$ than
+previously thought. A naive univariate analysis of the same problem would
+suggest smaller effects for these properties and a much larger impact for the
+specific star formation rate, which is lessened after accounting for other
+galaxy properties and non-linearities in the statistical model.
+",0,0,0,1,0,0
+4007,Out-of-focus: Learning Depth from Image Bokeh for Robotic Perception,"  In this project, we propose a novel approach for estimating depth from RGB
+images. Traditionally, most work uses a single RGB image to estimate depth,
+which is inherently difficult and generally results in poor performance, even
+with thousands of data examples. In this work, we alternatively use multiple
+RGB images that were captured while changing the focus of the camera's lens.
+This method leverages the natural depth information correlated to the different
+patterns of clarity/blur in the sequence of focal images, which helps
+distinguish objects at different depths. Since no such data set exists for
+learning this mapping, we collect our own data set using customized hardware.
+We then use a convolutional neural network for learning the depth from the
+stacked focal images. Comparative studies were conducted on both a standard
+RGBD data set and our own data set (learning from both single and multiple
+images), and results verified that stacked focal images yield better depth
+estimation than using just single RGB image.
+",1,0,0,0,0,0
+4008,Iterative Machine Teaching,"  In this paper, we consider the problem of machine teaching, the inverse
+problem of machine learning. Different from traditional machine teaching which
+views the learners as batch algorithms, we study a new paradigm where the
+learner uses an iterative algorithm and a teacher can feed examples
+sequentially and intelligently based on the current performance of the learner.
+We show that the teaching complexity in the iterative case is very different
+from that in the batch case. Instead of constructing a minimal training set for
+learners, our iterative machine teaching focuses on achieving fast convergence
+in the learner model. Depending on the level of information the teacher has
+from the learner model, we design teaching algorithms which can provably reduce
+the number of teaching examples and achieve faster convergence than learning
+without teachers. We also validate our theoretical findings with extensive
+experiments on different data distribution and real image datasets.
+",1,0,0,1,0,0
+4009,GibbsNet: Iterative Adversarial Inference for Deep Graphical Models,"  Directed latent variable models that formulate the joint distribution as
+$p(x,z) = p(z) p(x \mid z)$ have the advantage of fast and exact sampling.
+However, these models have the weakness of needing to specify $p(z)$, often
+with a simple fixed prior that limits the expressiveness of the model.
+Undirected latent variable models discard the requirement that $p(z)$ be
+specified with a prior, yet sampling from them generally requires an iterative
+procedure such as blocked Gibbs-sampling that may require many steps to draw
+samples from the joint distribution $p(x, z)$. We propose a novel approach to
+learning the joint distribution between the data and a latent code which uses
+an adversarially learned iterative procedure to gradually refine the joint
+distribution, $p(x, z)$, to better match with the data distribution on each
+step. GibbsNet is the best of both worlds both in theory and in practice.
+Achieving the speed and simplicity of a directed latent variable model, it is
+guaranteed (assuming the adversarial game reaches the virtual training criteria
+global minimum) to produce samples from $p(x, z)$ with only a few sampling
+iterations. Achieving the expressiveness and flexibility of an undirected
+latent variable model, GibbsNet does away with the need for an explicit $p(z)$
+and has the ability to do attribute prediction, class-conditional generation,
+and joint image-attribute modeling in a single model which is not trained for
+any of these specific tasks. We show empirically that GibbsNet is able to learn
+a more complex $p(z)$ and show that this leads to improved inpainting and
+iterative refinement of $p(x, z)$ for dozens of steps and stable generation
+without collapse for thousands of steps, despite being trained on only a few
+steps.
+",1,0,0,1,0,0
+4010,Characterization of 1-Tough Graphs using Factors,"  For a graph $G$, let $odd(G)$ and $\omega(G)$ denote the number of odd
+components and the number of components of $G$, respectively. Then it is
+well-known that $G$ has a 1-factor if and only if $odd(G-S)\le |S|$ for all
+$S\subset V(G)$. Also it is clear that $odd(G-S) \le \omega(G-S)$. In this
+paper we characterize a 1-tough graph $G$, which satisfies $\omega(G-S) \le
+|S|$ for all $\emptyset \ne S \subset V(G)$, using an $H$-factor of a
+set-valued function $H:V(G) \to \{ \{1\}, \{0,2\} \}$. Moreover, we generalize
+this characterization to a graph that satisfies $\omega(G-S) \le f(S)$ for all
+$\emptyset \ne S \subset V(G)$, where $f:V(G) \to \{1,3,5, \ldots\}$.
+",0,0,1,0,0,0
+4011,Optimization by gradient boosting,"  Gradient boosting is a state-of-the-art prediction technique that
+sequentially produces a model in the form of linear combinations of simple
+predictors---typically decision trees---by solving an infinite-dimensional
+convex optimization problem. We provide in the present paper a thorough
+analysis of two widespread versions of gradient boosting, and introduce a
+general framework for studying these algorithms from the point of view of
+functional optimization. We prove their convergence as the number of iterations
+tends to infinity and highlight the importance of having a strongly convex risk
+functional to minimize. We also present a reasonable statistical context
+ensuring consistency properties of the boosting predictors as the sample size
+grows. In our approach, the optimization procedures are run forever (that is,
+without resorting to an early stopping strategy), and statistical
+regularization is basically achieved via an appropriate $L^2$ penalization of
+the loss and strong convexity arguments.
+",1,0,1,1,0,0
+4012,"RDV: Register, Deposit, Vote: Secure and Decentralized Consensus Mechanism for Blockchain Networks","  A decentralized payment system is not secure if transactions are transferred
+directly between clients. In such a situation it is not possible to prevent a
+client from redeeming some coins twice in separate transactions that means a
+double-spending attack. Bitcoin uses a simple method to preventing this attack
+i.e. all transactions are published in a unique log (blockchain). This approach
+requires a global consensus on the blockchain that because of significant
+latency for transaction confirmation is vulnerable against double-spending. The
+solution is to accelerate confirmations. In this paper, we try to introduce an
+alternative for PoW because of all its major and significant problems that lead
+to collapsing decentralization of the Bitcoin, while a full decentralized
+payment system is the main goal of Bitcoin idea. As the network is growing and
+becoming larger day-today , Bitcoin is approaching this risk. The method we
+introduce is based on a distributed voting process: RDV: Register, Deposit,
+Vote.
+",1,0,0,0,0,0
+4013,The Rees algebra of a two-Borel ideal is Koszul,"  Let $M$ and $N$ be two monomials of the same degree, and let $I$ be the
+smallest Borel ideal containing $M$ and $N$. We show that the toric ring of $I$
+is Koszul by constructing a quadratic Gröbner basis for the associated toric
+ideal. Our proofs use the construction of graphs corresponding to fibers of the
+toric map. As a consequence, we conclude that the Rees algebra is also Koszul.
+",0,0,1,0,0,0
+4014,A forward--backward random process for the spectrum of 1D Anderson operators,"  We give a new expression for the law of the eigenvalues of the discrete
+Anderson model on the finite interval $[0,N]$, in terms of two random processes
+starting at both ends of the interval. Using this formula, we deduce that the
+tail of the eigenvectors behaves approximatelylike $\exp(\sigma
+B\_{|n-k|}-\gamma\frac{|n-k|}{4})$ where $B\_{s}$ is the Brownian motion and
+$k$ is uniformly chosen in $[0,N]$ independentlyof $B\_{s}$. A similar result
+has recently been shown by B. Rifkind and B. Virag in the critical case, that
+is, when the random potential is multiplied by a factor $\frac{1}{\sqrt{N}}$
+",0,0,1,0,0,0
+4015,From Curves to Tropical Jacobians and Back,"  Given a curve defined over an algebraically closed field which is complete
+with respect to a nontrivial valuation, we study its tropical Jacobian. This is
+done by first tropicalizing the curve, and then computing the Jacobian of the
+resulting weighted metric graph. In general, it is not known how to find the
+abstract tropicalization of a curve defined by polynomial equations, since an
+embedded tropicalization may not be faithful, and there is no known algorithm
+for carrying out semistable reduction in practice. We solve this problem in the
+case of hyperelliptic curves by studying admissible covers. We also describe
+how to take a weighted metric graph and compute its period matrix, which gives
+its tropical Jacobian and tropical theta divisor. Lastly, we describe the
+present status of reversing this process, namely how to compute a curve which
+has a given matrix as its period matrix.
+",0,0,1,0,0,0
+4016,Importance sampling the union of rare events with an application to power systems analysis,"  We consider importance sampling to estimate the probability $\mu$ of a union
+of $J$ rare events $H_j$ defined by a random variable $\boldsymbol{x}$. The
+sampler we study has been used in spatial statistics, genomics and
+combinatorics going back at least to Karp and Luby (1983). It works by sampling
+one event at random, then sampling $\boldsymbol{x}$ conditionally on that event
+happening and it constructs an unbiased estimate of $\mu$ by multiplying an
+inverse moment of the number of occuring events by the union bound. We prove
+some variance bounds for this sampler. For a sample size of $n$, it has a
+variance no larger than $\mu(\bar\mu-\mu)/n$ where $\bar\mu$ is the union
+bound. It also has a coefficient of variation no larger than
+$\sqrt{(J+J^{-1}-2)/(4n)}$ regardless of the overlap pattern among the $J$
+events. Our motivating problem comes from power system reliability, where the
+phase differences between connected nodes have a joint Gaussian distribution
+and the $J$ rare events arise from unacceptably large phase differences. In the
+grid reliability problems even some events defined by $5772$ constraints in
+$326$ dimensions, with probability below $10^{-22}$, are estimated with a
+coefficient of variation of about $0.0024$ with only $n=10{,}000$ sample
+values.
+",1,0,0,1,0,0
+4017,Estimating the sensitivity of centrality measures w.r.t. measurement errors,"  Most network studies rely on an observed network that differs from the
+underlying network which is obfuscated by measurement errors. It is well known
+that such errors can have a severe impact on the reliability of network
+metrics, especially on centrality measures: a more central node in the observed
+network might be less central in the underlying network.
+We introduce a metric for the reliability of centrality measures -- called
+sensitivity. Given two randomly chosen nodes, the sensitivity means the
+probability that the more central node in the observed network is also more
+central in the underlying network. The sensitivity concept relies on the
+underlying network which is usually not accessible. Therefore, we propose two
+methods to approximate the sensitivity. The iterative method, which simulates
+possible underlying networks for the estimation and the imputation method,
+which uses the sensitivity of the observed network for the estimation. Both
+methods rely on the observed network and assumptions about the underlying type
+of measurement error (e.g., the percentage of missing edges or nodes).
+Our experiments on real-world networks and random graphs show that the
+iterative method performs well in many cases. In contrast, the imputation
+method does not yield useful estimations for networks other than
+Erdős-Rényi graphs.
+",1,1,0,0,0,0
+4018,Matrix product moments in normal variables,"  Let ${\cal X }=XX^{\prime}$ be a random matrix associated with a centered
+$r$-column centered Gaussian vector $X$ with a covariance matrix $P$. In this
+article we compute expectations of matrix-products of the form $\prod_{1\leq
+i\leq n}({\cal X } P^{v_i})$ for any $n\geq 1$ and any multi-index parameters
+$v_i\in\mathbb{N}$. We derive closed form formulae and a simple sequential
+algorithm to compute these matrices w.r.t. the parameter $n$. The second part
+of the article is dedicated to a non commutative binomial formula for the
+central matrix-moments $\mathbb{E}\left(\left[{\cal X }-P\right]^n\right)$. The
+matrix product moments discussed in this study are expressed in terms of
+polynomial formulae w.r.t. the powers of the covariance matrix, with
+coefficients depending on the trace of these matrices. We also derive a series
+of estimates w.r.t. the Loewner order on quadratic forms. For instance we shall
+prove the rather crude estimate $\mathbb{E}\left(\left[{\cal X
+}-P\right]^n\right)\leq \mathbb{E}\left({\cal X }^n-P^n\right)$, for any $n\geq
+1$
+",0,0,1,1,0,0
+4019,Asymptotics and Optimal Bandwidth Selection for Nonparametric Estimation of Density Level Sets,"  Bandwidth selection is crucial in the kernel estimation of density level
+sets. Risk based on the symmetric difference between the estimated and true
+level sets is usually used to measure their proximity. In this paper we provide
+an asymptotic $L^p$ approximation to this risk, where $p$ is characterized by
+the weight function in the risk. In particular the excess risk corresponds to
+an $L^2$ type of risk, and is adopted in an optimal bandwidth selection rule
+for nonparametric level set estimation of $d$-dimensional density functions
+($d\geq 1$).
+",0,0,1,1,0,0
+4020,Population-specific design of de-immunized protein biotherapeutics,"  Immunogenicity is a major problem during the development of biotherapeutics
+since it can lead to rapid clearance of the drug and adverse reactions. The
+challenge for biotherapeutic design is therefore to identify mutants of the
+protein sequence that minimize immunogenicity in a target population whilst
+retaining pharmaceutical activity and protein function. Current approaches are
+moderately successful in designing sequences with reduced immunogenicity, but
+do not account for the varying frequencies of different human leucocyte antigen
+alleles in a specific population and in addition, since many designs are
+non-functional, require costly experimental post-screening. Here we report a
+new method for de-immunization design using multi-objective combinatorial
+optimization that simultaneously optimizes the likelihood of a functional
+protein sequence at the same time as minimizing its immunogenicity tailored to
+a target population. We bypass the need for three-dimensional protein structure
+or molecular simulations to identify functional designs by automatically
+generating sequences using probabilistic models that have been used previously
+for mutation effect prediction and structure prediction. As proof-of-principle
+we designed sequences of the C2 domain of Factor VIII and tested them
+experimentally, resulting in a good correlation with the predicted
+immunogenicity of our model.
+",1,0,0,0,0,0
+4021,Linearized Binary Regression,"  Probit regression was first proposed by Bliss in 1934 to study mortality
+rates of insects. Since then, an extensive body of work has analyzed and used
+probit or related binary regression methods (such as logistic regression) in
+numerous applications and fields. This paper provides a fresh angle to such
+well-established binary regression methods. Concretely, we demonstrate that
+linearizing the probit model in combination with linear estimators performs on
+par with state-of-the-art nonlinear regression methods, such as posterior mean
+or maximum aposteriori estimation, for a broad range of real-world regression
+problems. We derive exact, closed-form, and nonasymptotic expressions for the
+mean-squared error of our linearized estimators, which clearly separates them
+from nonlinear regression methods that are typically difficult to analyze. We
+showcase the efficacy of our methods and results for a number of synthetic and
+real-world datasets, which demonstrates that linearized binary regression finds
+potential use in a variety of inference, estimation, signal processing, and
+machine learning applications that deal with binary-valued observations or
+measurements.
+",0,0,0,1,0,0
+4022,Arithmetic properties of polynomials,"  In this paper, first, we prove that the Diophantine system
+\[f(z)=f(x)+f(y)=f(u)-f(v)=f(p)f(q)\] has infinitely many integer solutions for
+$f(X)=X(X+a)$ with nonzero integers $a\equiv 0,1,4\pmod{5}$. Second, we show
+that the above Diophantine system has an integer parametric solution for
+$f(X)=X(X+a)$ with nonzero integers $a$, if there are integers $m,n,k$ such
+that \[\begin{cases} \begin{split} (n^2-m^2) (4mnk(k+a+1) + a(m^2+2mn-n^2))
+&\equiv0\pmod{(m^2+n^2)^2},\\ (m^2+2mn-n^2) ((m^2-2mn-n^2)k(k+a+1) - 2amn)
+&\equiv0 \pmod{(m^2+n^2)^2}, \end{split} \end{cases}\] where $k\equiv0\pmod{4}$
+when $a$ is even, and $k\equiv2\pmod{4}$ when $a$ is odd. Third, we get that
+the Diophantine system \[f(z)=f(x)+f(y)=f(u)-f(v)=f(p)f(q)=\frac{f(r)}{f(s)}\]
+has a five-parameter rational solution for $f(X)=X(X+a)$ with nonzero rational
+number $a$ and infinitely many nontrivial rational parametric solutions for
+$f(X)=X(X+a)(X+b)$ with nonzero integers $a,b$ and $a\neq b$. At last, we raise
+some related questions.
+",0,0,1,0,0,0
+4023,"A Graph Analytics Framework for Ranking Authors, Papers and Venues","  A lot of scientific works are published in different areas of science,
+technology, engineering and mathematics. It is not easy, even for experts, to
+judge the quality of authors, papers and venues (conferences and journals). An
+objective measure to assign scores to these entities and to rank them is very
+useful. Although, several metrics and indexes have been proposed earlier, they
+suffer from various problems. In this paper, we propose a graph-based analytics
+framework to assign scores and to rank authors, papers and venues. Our
+algorithm considers only the link structures of the underlying graphs. It does
+not take into account other aspects, such as the associated texts and the
+reputation of these entities. In the limit of large number of iterations, the
+solution of the iterative equations gives the unique entity scores. This
+framework can be easily extended to other interdependent networks.
+",1,0,0,0,0,0
+4024,Inner Cohomology of the General Linear Group,"  The main theorem is incorrectly stated.
+",0,0,1,0,0,0
+4025,Particle-hole symmetry and composite fermions in fractional quantum Hall states,"  We study fractional quantum Hall states at filling fractions in the Jain
+sequences using the framework of composite Dirac fermions. Synthesizing
+previous work, we write down an effective field theory consistent with all
+symmetry requirements, including Galilean invariance and particle-hole
+symmetry. Employing a Fermi liquid description, we demonstrate the appearance
+of the Girvin--Macdonlald--Platzman algebra and compute the dispersion relation
+of neutral excitations and various response functions. Our results satisfy
+requirements of particle-hole symmetry. We show that while the dispersion
+relation obtained from the HLR theory is particle-hole symmetric, correlation
+functions obtained from HLR are not. The results of the Dirac theory are shown
+to be consistent with the Haldane bound on the projected structure factor,
+while those of the HLR theory violate it.
+",0,1,0,0,0,0
+4026,Large-type Artin groups are systolic,"  We prove that Artin groups from a class containing all large-type Artin
+groups are systolic. This provides a concise yet precise description of their
+geometry. Immediate consequences are new results concerning large-type Artin
+groups: biautomaticity; existence of $EZ$-boundaries; the Novikov conjecture;
+descriptions of finitely presented subgroups, of virtually solvable subgroups,
+and of centralizers for infinite order elements; the Burghelea conjecture and
+the Bass conjecture; existence of low-dimensional models for classifying spaces
+for some families of subgroups.
+",0,0,1,0,0,0
+4027,Gradient Sensing via Cell Communication,"  The chemotactic dynamics of cells and organisms that have no specialized
+gradient sensing organelles is not well understood. In fact, chemotaxis of this
+sort of organism is especially challenging to explain when the external
+chemical gradient is so small as to make variations of concentrations minute
+over the length of each of the organisms. Experimental evidence lends support
+to the conjecture that chemotactic behavior of chains of cells can be achieved
+via cell-to-cell communication. This is the chemotactic basis for the Local
+Excitation, Global Inhibition (LEGI) model.
+A generalization of the model for the communication component of the LEGI
+model is proposed. Doing so permits us to study in detail how gradient sensing
+changes as a function of the structure of the communication term. The key
+findings of this study are, an accounting of how gradient sensing is affected
+by the competition of communication and diffusive processes; the determination
+of the scale dependence of the model outcomes; the sensitivity of communication
+to parameters in the model. Together with an essential analysis of the dynamics
+of the model, these findings can prove useful in suggesting experiments aimed
+at determining the viability of a communication mechanism in chemotactic
+dynamics of chains and networks of cells exposed to a chemical concentration
+gradient.
+",0,0,0,0,1,0
+4028,Nichols Algebras and Quantum Principal Bundles,"  A general procedure for constructing Yetter-Drinfeld modules from quantum
+principal bundles is introduced. As an application a Yetter-Drinfeld structure
+is put on the cotangent space of the Heckenberger-Kolb calculi of the quantum
+Grassmannians. For the special case of quantum projective space the associated
+braiding is shown to be non-diagonal and of Hecke type. Moreover, its Nichols
+algebra is shown to be finite-dimensional and equal to the anti-holomorphic
+part of the total differential calculus.
+",0,0,1,0,0,0
+4029,Inference of signals with unknown correlation structure from nonlinear measurements,"  We present a method to reconstruct autocorrelated signals together with their
+autocorrelation structure from nonlinear, noisy measurements for arbitrary
+monotonous nonlinear instrument response. In the presented formulation the
+algorithm provides a significant speedup compared to prior implementations,
+allowing for a wider range of application. The nonlinearity can be used to
+model instrument characteristics or to enforce properties on the underlying
+signal, such as positivity. Uncertainties on any posterior quantities can be
+provided due to independent samples from an approximate posterior distribution.
+We demonstrate the methods applicability via simulated and real measurements,
+using different measurement instruments, nonlinearities and dimensionality.
+",0,1,0,1,0,0
+4030,An optimization approach for dynamical Tucker tensor approximation,"  An optimization-based approach for the Tucker tensor approximation of
+parameter-dependent data tensors and solutions of tensor differential equations
+with low Tucker rank is presented. The problem of updating the tensor
+decomposition is reformulated as fitting problem subject to the tangent space
+without relying on an orthogonality gauge condition. A discrete Euler scheme is
+established in an alternating least squares framework, where the quadratic
+subproblems reduce to trace optimization problems, that are shown to be
+explicitly solvable and accessible using SVD of small size. In the presence of
+small singular values, instability for larger ranks is reduced, since the
+method does not need the (pseudo) inverse of matricizations of the core tensor.
+Regularization of Tikhonov type can be used to compensate for the lack of
+uniqueness in the tangent space. The method is validated numerically and shown
+to be stable also for larger ranks in the case of small singular values of the
+core unfoldings. Higher order explicit integrators of Runge-Kutta type can be
+composed.
+",0,1,0,0,0,0
+4031,Optical and structural study of the pressure-induced phase transition of CdWO$_4$,"  The optical absorption of CdWO$_4$ is reported at high pressures up to 23
+GPa. The onset of a phase transition was detected at 19.5 GPa, in good
+agreement with a previous Raman spectroscopy study. The crystal structure of
+the high-pressure phase of CdWO$_4$ was solved at 22 GPa employing
+single-crystal synchrotron x-ray diffraction. The symmetry changes from space
+group $P$2/$c$ in the low-pressure wolframite phase to $P2_1/c$ in the
+high-pressure post-wolframite phase accompanied by a doubling of the unit-cell
+volume. The octahedral oxygen coordination of the tungsten and cadmium ions is
+increased to [7]-fold and [6+1]-fold, respectively, at the phase transition.
+The compressibility of the low-pressure phase of CdWO$_4$ has been reevaluated
+with powder x-ray diffraction up to 15 GPa finding a bulk modulus of $B_0$ =
+123 GPa. The direct band gap of the low-pressure phase increases with
+compression up to 16.9 GPa at 12 meV/GPa. At this point an indirect band gap
+crosses the direct band gap and decreases at -2 meV/GPa up to 19.5 GPa where
+the phase transition starts. At the phase transition the band gap collapses by
+0.7 eV and another direct band gap decreases at -50 meV/GPa up to the maximum
+measured pressure. The structural stability of the post-wolframite structure is
+confirmed by \textit{ab initio} calculations finding the post-wolframite-type
+phase to be more stable than the wolframite at 18 GPa. Lattice dynamic
+calculations based on space group $P2_1/c$ explain well the Raman-active modes
+previously measured in the high-pressure post-wolframite phase. The
+pressure-induced band gap crossing in the wolframite phase as well as the
+pressure dependence of the direct band gap in the high-pressure phase are
+further discussed with respect to the calculations.
+",0,1,0,0,0,0
+4032,Learning Context-Sensitive Convolutional Filters for Text Processing,"  Convolutional neural networks (CNNs) have recently emerged as a popular
+building block for natural language processing (NLP). Despite their success,
+most existing CNN models employed in NLP share the same learned (and static)
+set of filters for all input sentences. In this paper, we consider an approach
+of using a small meta network to learn context-sensitive convolutional filters
+for text processing. The role of meta network is to abstract the contextual
+information of a sentence or document into a set of input-aware filters. We
+further generalize this framework to model sentence pairs, where a
+bidirectional filter generation mechanism is introduced to encapsulate
+co-dependent sentence representations. In our benchmarks on four different
+tasks, including ontology classification, sentiment analysis, answer sentence
+selection, and paraphrase identification, our proposed model, a modified CNN
+with context-sensitive filters, consistently outperforms the standard CNN and
+attention-based CNN baselines. By visualizing the learned context-sensitive
+filters, we further validate and rationalize the effectiveness of proposed
+framework.
+",1,0,0,1,0,0
+4033,On right $S$-Noetherian rings and $S$-Noetherian modules,"  In this paper we study right $S$-Noetherian rings and modules, extending of
+notions introduced by Anderson and Dumitrescu in commutative algebra to
+noncommutative rings. Two characterizations of right $S$-Noetherian rings are
+given in terms of completely prime right ideals and point annihilator sets. We
+also prove an existence result for completely prime point annihilators of
+certain $S$-Noetherian modules with the following consequence in commutative
+algebra: If a module $M$ over a commutative ring is $S$-Noetherian with respect
+to a multiplicative set $S$ that contains no zero-divisors for $M$, then $M$
+has an associated prime.
+",0,0,1,0,0,0
+4034,Reconfiguration of Brain Network between Resting-state and Oddball Paradigm,"  The oddball paradigm is widely applied to the investigation of multiple
+cognitive functions. Prior studies have explored the cortical oscillation and
+power spectral differing from the resting-state conduction to oddball paradigm,
+but whether brain networks existing the significant difference is still
+unclear. Our study addressed how the brain reconfigures its architecture from a
+resting-state condition (i.e., baseline) to P300 stimulus task in the visual
+oddball paradigm. In this study, electroencephalogram (EEG) datasets were
+collected from 24 postgraduate students, who were required to only mentally
+count the number of target stimulus; afterwards the functional EEG networks
+constructed in different frequency bands were compared between baseline and
+oddball task conditions to evaluate the reconfiguration of functional network
+in the brain. Compared to the baseline, our results showed the significantly (p
+< 0.05) enhanced delta/theta EEG connectivity and decreased alpha default mode
+network in the progress of brain reconfiguration to the P300 task. Furthermore,
+the reconfigured coupling strengths were demonstrated to relate to P300
+amplitudes, which were then regarded as input features to train a classifier to
+differentiate the high and low P300 amplitudes groups with an accuracy of
+77.78%. The findings of our study help us to understand the changes of
+functional brain connectivity from resting-state to oddball stimulus task, and
+the reconfigured network pattern has the potential for the selection of good
+subjects for P300-based brain- computer interface.
+",0,0,0,0,1,0
+4035,Approximate Ranking from Pairwise Comparisons,"  A common problem in machine learning is to rank a set of n items based on
+pairwise comparisons. Here ranking refers to partitioning the items into sets
+of pre-specified sizes according to their scores, which includes identification
+of the top-k items as the most prominent special case. The score of a given
+item is defined as the probability that it beats a randomly chosen other item.
+Finding an exact ranking typically requires a prohibitively large number of
+comparisons, but in practice, approximate rankings are often adequate.
+Accordingly, we study the problem of finding approximate rankings from pairwise
+comparisons. We analyze an active ranking algorithm that counts the number of
+comparisons won, and decides whether to stop or which pair of items to compare
+next, based on confidence intervals computed from the data collected in
+previous steps. We show that this algorithm succeeds in recovering approximate
+rankings using a number of comparisons that is close to optimal up to
+logarithmic factors. We also present numerical results, showing that in
+practice, approximation can drastically reduce the number of comparisons
+required to estimate a ranking.
+",0,0,0,1,0,0
+4036,Optimised information gathering in smartphone users,"  Human activities from hunting to emailing are performed in a fractal-like
+scale invariant pattern. These patterns are considered efficient for hunting or
+foraging, but are they efficient for gathering information? Here we link the
+scale invariant pattern of inter-touch intervals on the smartphone to optimal
+strategies for information gathering. We recorded touchscreen touches in 65
+individuals for a month and categorized the activity into checking for
+information vs. sharing content. For both categories, the inter-touch intervals
+were well described by power-law fits spanning 5 orders of magnitude, from 1 s
+to several hours. The power-law exponent typically found for checking was 1.5
+and for generating it was 1.3. Next, by using computer simulations we addressed
+whether the checking pattern was efficient - in terms of minimizing futile
+attempts yielding no new information. We find that the best performing power
+law exponent depends on the duration of the assessment and the exponent of 1.5
+was the most efficient in the short-term i.e. in the few minutes range.
+Finally, we addressed whether how people generated and shared content was in
+tune with the checking pattern. We assumed that the unchecked posts must be
+minimized for maximal efficiency and according to our analysis the most
+efficient temporal pattern to share content was the exponent of 1.3 - which was
+also the pattern displayed by the smartphone users. The behavioral organization
+for content generation is different from content consumption across time
+scales. We propose that this difference is a signature of optimal behavior and
+the short-term assessments used in modern human actions.
+",1,1,0,0,0,0
+4037,On Recoverable and Two-Stage Robust Selection Problems with Budgeted Uncertainty,"  In this paper the problem of selecting $p$ out of $n$ available items is
+discussed, such that their total cost is minimized. We assume that costs are
+not known exactly, but stem from a set of possible outcomes.
+Robust recoverable and two-stage models of this selection problem are
+analyzed. In the two-stage problem, up to $p$ items is chosen in the first
+stage, and the solution is completed once the scenario becomes revealed in the
+second stage. In the recoverable problem, a set of $p$ items is selected in the
+first stage, and can be modified by exchanging up to $k$ items in the second
+stage, after a scenario reveals.
+We assume that uncertain costs are modeled through bounded uncertainty sets,
+i.e., the interval uncertainty sets with an additional linear (budget)
+constraint, in their discrete and continuous variants. Polynomial algorithms
+for recoverable and two-stage selection problems with continuous bounded
+uncertainty, and compact mixed integer formulations in the case of discrete
+bounded uncertainty are constructed.
+",1,0,1,0,0,0
+4038,"Sparsity/Undersampling Tradeoffs in Anisotropic Undersampling, with Applications in MR Imaging/Spectroscopy","  We study anisotropic undersampling schemes like those used in
+multi-dimensional NMR spectroscopy and MR imaging, which sample exhaustively in
+certain time dimensions and randomly in others.
+Our analysis shows that anisotropic undersampling schemes are equivalent to
+certain block-diagonal measurement systems. We develop novel exact formulas for
+the sparsity/undersampling tradeoffs in such measurement systems. Our formulas
+predict finite-N phase transition behavior differing substantially from the
+well known asymptotic phase transitions for classical Gaussian undersampling.
+Extensive empirical work shows that our formulas accurately describe observed
+finite-N behavior, while the usual formulas based on universality are
+substantially inaccurate.
+We also vary the anisotropy, keeping the total number of samples fixed, and
+for each variation we determine the precise sparsity/undersampling tradeoff
+(phase transition). We show that, other things being equal, the ability to
+recover a sparse object decreases with an increasing number of
+exhaustively-sampled dimensions.
+",1,0,0,0,0,0
+4039,Multi-way sparsest cut problem on trees with a control on the number of parts and outliers,"  Given a graph, the sparsest cut problem asks for a subset of vertices whose
+edge expansion (the normalized cut given by the subset) is minimized. In this
+paper, we study a generalization of this problem seeking for $ k $ disjoint
+subsets of vertices (clusters) whose all edge expansions are small and
+furthermore, the number of vertices remained in the exterior of the subsets
+(outliers) is also small. We prove that although this problem is $ NP-$hard for
+trees, it can be solved in polynomial time for all weighted trees, provided
+that we restrict the search space to subsets which induce connected subgraphs.
+The proposed algorithm is based on dynamic programming and runs in the worst
+case in $ O(k^2 n^3) $, when $ n $ is the number of vertices and $ k $ is the
+number of clusters. It also runs in linear time when the number of clusters and
+the number of outliers is bounded by a constant.
+",1,0,0,0,0,0
+4040,Gallucci's axiom revisited,"  In this paper we propose a well-justified synthetic approach of the
+projective space. We define the concepts of plane and space of incidence and
+also the Gallucci's axiom as an axiom to our classical projective space. To
+this purpose we prove from our space axioms, the theorems of Desargues, Pappus,
+the fundamental theorem of projectivities, and the fundamental theorem of
+central-axial collinearities, respectively. Our building up do not use any
+information on analytical projective geometry, as the concept of cross-ratio
+and the homogeneous coordinates of points.
+",0,0,1,0,0,0
+4041,Effect of the non-thermal Sunyaev-Zel'dovich Effect on the temperature determination of galaxy clusters,"  A recent stacking analysis of Planck HFI data of galaxy clusters (Hurier
+2016) allowed to derive the cluster temperatures by using the relativistic
+corrections to the Sunyaev-Zel'dovich effect (SZE). However, the temperatures
+of high-temperature clusters, as derived from this analysis, resulted to be
+basically higher than the temperatures derived from X-ray measurements, at a
+moderate statistical significance of $1.5\sigma$. This discrepancy has been
+attributed by Hurier (2016) to calibration issues. In this paper we discuss an
+alternative explanation for this discrepancy in terms of a non-thermal SZE
+astrophysical component. We find that this explanation can work if non-thermal
+electrons in galaxy clusters have a low value of their minimum momentum
+($p_1\sim0.5-1$), and if their pressure is of the order of $20-30\%$ of the
+thermal gas pressure. Both these conditions are hard to obtain if the
+non-thermal electrons are mixed with the hot gas in the intra cluster medium,
+but can be possibly obtained if the non-thermal electrons are mainly confined
+in bubbles with high content of non-thermal plasma and low content of thermal
+plasma, or in giant radio lobes/relics located in the outskirts of clusters. In
+order to derive more precise results on the properties of non-thermal electrons
+in clusters, and in view of more solid detections of a discrepancy between
+X-rays and SZE derived clusters temperatures that cannot be explained in other
+ways, it would be necessary to reproduce the full analysis done by Hurier
+(2016) by adding systematically the non-thermal component of the SZE.
+",0,1,0,0,0,0
+4042,ModelFactory: A Matlab/Octave based toolbox to create human body models,"  Background: Model-based analysis of movements can help better understand
+human motor control. Here, the models represent the human body as an
+articulated multi-body system that reflects the characteristics of the human
+being studied.
+Results: We present an open-source toolbox that allows for the creation of
+human models with easy-to-setup, customizable configurations. The toolbox
+scripts are written in Matlab/Octave and provide a command-based interface as
+well as a graphical interface to construct, visualize and export models.
+Built-in software modules provide functionalities such as automatic scaling of
+models based on subject height and weight, custom scaling of segment lengths,
+mass and inertia, addition of body landmarks, and addition of motion capture
+markers. Users can set up custom definitions of joints, segments and other body
+properties using the many included examples as templates. In addition to the
+human, any number of objects (e.g. exoskeletons, orthoses, prostheses, boxes)
+can be added to the modeling environment.
+Conclusions: The ModelFactory toolbox is published as open-source software
+under the permissive zLib license. The toolbox fulfills an important function
+by making it easier to create human models, and should be of interest to human
+movement researchers.
+This document is the author's version of this article.
+",1,0,0,0,1,0
+4043,Dimensionality reduction with missing values imputation,"  In this study, we propose a new statical approach for high-dimensionality
+reduction of heterogenous data that limits the curse of dimensionality and
+deals with missing values. To handle these latter, we propose to use the Random
+Forest imputation's method. The main purpose here is to extract useful
+information and so reducing the search space to facilitate the data exploration
+process. Several illustrative numeric examples, using data coming from publicly
+available machine learning repositories are also included. The experimental
+component of the study shows the efficiency of the proposed analytical
+approach.
+",1,0,0,1,0,0
+4044,An effective formalism for testing extensions to General Relativity with gravitational waves,"  The recent direct observation of gravitational waves (GW) from merging black
+holes opens up the possibility of exploring the theory of gravity in the strong
+regime at an unprecedented level. It is therefore interesting to explore which
+extensions to General Relativity (GR) could be detected. We construct an
+Effective Field Theory (EFT) satisfying the following requirements. It is
+testable with GW observations; it is consistent with other experiments,
+including short distance tests of GR; it agrees with widely accepted principles
+of physics, such as locality, causality and unitarity; and it does not involve
+new light degrees of freedom. The most general theory satisfying these
+requirements corresponds to adding to the GR Lagrangian operators constructed
+out of powers of the Riemann tensor, suppressed by a scale comparable to the
+curvature of the observed merging binaries. The presence of these operators
+modifies the gravitational potential between the compact objects, as well as
+their effective mass and current quadrupoles, ultimately correcting the
+waveform of the emitted GW.
+",0,1,0,0,0,0
+4045,On the Wiener-Hopf method for surface plasmons: Diffraction from semi-infinite metamaterial sheet,"  By formally invoking the Wiener-Hopf method, we explicitly solve a
+one-dimensional, singular integral equation for the excitation of a slowly
+decaying electromagnetic wave, called surface plasmon-polariton (SPP), of small
+wavelength on a semi-infinite, flat conducting sheet irradiated by a plane wave
+in two spatial dimensions. This setting is germane to wave diffraction by edges
+of large sheets of single-layer graphene. Our analytical approach includes: (i)
+formulation of a functional equation in the Fourier domain; (ii) evaluation of
+a split function, which is expressed by a contour integral and is a key
+ingredient of the Wiener-Hopf factorization; and (iii) extraction of the SPP as
+a simple-pole residue of a Fourier integral. Our analytical solution is in good
+agreement with a finite-element numerical computation.
+",0,0,1,0,0,0
+4046,Sim2Real View Invariant Visual Servoing by Recurrent Control,"  Humans are remarkably proficient at controlling their limbs and tools from a
+wide range of viewpoints and angles, even in the presence of optical
+distortions. In robotics, this ability is referred to as visual servoing:
+moving a tool or end-point to a desired location using primarily visual
+feedback. In this paper, we study how viewpoint-invariant visual servoing
+skills can be learned automatically in a robotic manipulation scenario. To this
+end, we train a deep recurrent controller that can automatically determine
+which actions move the end-point of a robotic arm to a desired object. The
+problem that must be solved by this controller is fundamentally ambiguous:
+under severe variation in viewpoint, it may be impossible to determine the
+actions in a single feedforward operation. Instead, our visual servoing system
+must use its memory of past movements to understand how the actions affect the
+robot motion from the current viewpoint, correcting mistakes and gradually
+moving closer to the target. This ability is in stark contrast to most visual
+servoing methods, which either assume known dynamics or require a calibration
+phase. We show how we can learn this recurrent controller using simulated data
+and a reinforcement learning objective. We then describe how the resulting
+model can be transferred to a real-world robot by disentangling perception from
+control and only adapting the visual layers. The adapted model can servo to
+previously unseen objects from novel viewpoints on a real-world Kuka IIWA
+robotic arm. For supplementary videos, see:
+this https URL
+",1,0,0,0,0,0
+4047,"The homotopy Lie algebra of symplectomorphism groups of 3-fold blow-ups of $(S^2 \times S^2, σ_{std} \oplus σ_{std}) $","  We consider the 3-point blow-up of the manifold $ (S^2 \times S^2, \sigma
+\oplus \sigma)$ where $\sigma$ is the standard symplectic form which gives area
+1 to the sphere $S^2$, and study its group of symplectomorphisms $\rm{Symp} (
+S^2 \times S^2 \#\, 3\overline{\mathbb C\mathbb P}\,\!^2, \omega)$. So far, the
+monotone case was studied by J. Evans and he proved that this group is
+contractible. Moreover, J. Li, T. J. Li and W. Wu showed that the group
+Symp$_{h}(S^2 \times S^2 \#\, 3\overline{ \mathbb C\mathbb P}\,\!^2,\omega) $
+of symplectomorphisms that act trivially on homology is always connected and
+recently they also computed its fundamental group. We describe, in full detail,
+the rational homotopy Lie algebra of this group.
+We show that some particular circle actions contained in the
+symplectomorphism group generate its full topology. More precisely, they give
+the generators of the homotopy graded Lie algebra of $\rm{Symp} (S^2 \times S^2
+\#\, 3\overline{ \mathbb C\mathbb P}\,\!^2, \omega)$. Our study depends on
+Karshon's classification of Hamiltonian circle actions and the inflation
+technique introduced by Lalonde-McDuff. As an application, we deduce the rank
+of the homotopy groups of $\rm{Symp}({\mathbb C\mathbb P}^2 \#\,
+5\overline{\mathbb C\mathbb P}\,\!^2, \tilde \omega)$, in the case of small
+blow-ups.
+",0,0,1,0,0,0
+4048,Pressure Drop and Flow development in the Entrance Region of Micro-Channels with Second Order Slip Boundary Conditions and the Requirement for Development Length,"  In the present investigation, the development of axial velocity profile, the
+requirement for development length ($L^*_{fd}=L/D_{h}$) and the pressure drop
+in the entrance region of circular and parallel plate micro-channels have been
+critically analysed for a large range of operating conditions ($10^{-2}\le
+Re\le 10^{4}$, $10^{-4}\le Kn\le 0.2$ and $0\le C_2\le 0.5$). For this purpose,
+the conventional Navier-Stokes equations have been numerically solved using the
+finite volume method on non-staggered grid, while employing the second-order
+velocity slip condition at the wall with $C_1=1$. The results indicate that
+although the magnitude of local velocity slip at the wall is always greater
+than that for the fully-developed section, the local wall shear stress,
+particularly for higher $Kn$ and $C_2$, could be considerably lower than its
+fully-developed value. This effect, which is more prominent for lower $Re$,
+significantly affects the local and the fully-developed incremental pressure
+drop number $K(x)$ and $K_{fd}$, respectively. As a result, depending upon the
+operating condition, $K_{fd}$, as well as $K(x)$, could assume negative values.
+This never reported observation implies that in the presence of enhanced
+velocity slip at the wall, the pressure gradient in the developing region could
+even be less than that in the fully-developed section. From simulated data, it
+has been observed that both $L^*_{fd}$ and $K_{fd}$ are characterised by the
+low and the high $Re$ asymptotes, using which, extremely accurate correlations
+for them have been proposed for both geometries. Although owing to the complex
+nature, no correlation could be derived for $K(x)$ and an exact knowledge of
+$K(x)$ is necessary for evaluating the actual pressure drop for a duct length
+$L^*<L^*_{fd}$, a method has been proposed that provides a conservative
+estimate of the pressure drop for both $K_{fd}>0$ and $K_{fd}\le0$.
+",0,1,0,0,0,0
+4049,Household poverty classification in data-scarce environments: a machine learning approach,"  We describe a method to identify poor households in data-scarce countries by
+leveraging information contained in nationally representative household
+surveys. It employs standard statistical learning techniques---cross-validation
+and parameter regularization---which together reduce the extent to which the
+model is over-fitted to match the idiosyncracies of observed survey data. The
+automated framework satisfies three important constraints of this development
+setting: i) The prediction model uses at most ten questions, which limits the
+costs of data collection; ii) No computation beyond simple arithmetic is needed
+to calculate the probability that a given household is poor, immediately after
+data on the ten indicators is collected; and iii) One specification of the
+model (i.e. one scorecard) is used to predict poverty throughout a country that
+may be characterized by significant sub-national differences. Using survey data
+from Zambia, the model's out-of-sample predictions distinguish poor households
+from non-poor households using information contained in ten questions.
+",0,0,0,1,0,0
+4050,Linguistic Matrix Theory,"  Recent research in computational linguistics has developed algorithms which
+associate matrices with adjectives and verbs, based on the distribution of
+words in a corpus of text. These matrices are linear operators on a vector
+space of context words. They are used to construct the meaning of composite
+expressions from that of the elementary constituents, forming part of a
+compositional distributional approach to semantics. We propose a Matrix Theory
+approach to this data, based on permutation symmetry along with Gaussian
+weights and their perturbations. A simple Gaussian model is tested against word
+matrices created from a large corpus of text. We characterize the cubic and
+quartic departures from the model, which we propose, alongside the Gaussian
+parameters, as signatures for comparison of linguistic corpora. We propose that
+perturbed Gaussian models with permutation symmetry provide a promising
+framework for characterizing the nature of universality in the statistical
+properties of word matrices. The matrix theory framework developed here
+exploits the view of statistics as zero dimensional perturbative quantum field
+theory. It perceives language as a physical system realizing a universality
+class of matrix statistics characterized by permutation symmetry.
+",1,0,0,0,0,0
+4051,"Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact","  Ponzi schemes are financial frauds where, under the promise of high profits,
+users put their money, recovering their investment and interests only if enough
+users after them continue to invest money. Originated in the offline world 150
+years ago, Ponzi schemes have since then migrated to the digital world,
+approaching first on the Web, and more recently hanging over cryptocurrencies
+like Bitcoin. Smart contract platforms like Ethereum have provided a new
+opportunity for scammers, who have now the possibility of creating
+""trustworthy"" frauds that still make users lose money, but at least are
+guaranteed to execute ""correctly"". We present a comprehensive survey of Ponzi
+schemes on Ethereum, analysing their behaviour and their impact from various
+viewpoints. Perhaps surprisingly, we identify a remarkably high number of Ponzi
+schemes, despite the hosting platform has been operating for less than two
+years.
+",1,0,0,0,0,0
+4052,On orthogonality and learning recurrent networks with long term dependencies,"  It is well known that it is challenging to train deep neural networks and
+recurrent neural networks for tasks that exhibit long term dependencies. The
+vanishing or exploding gradient problem is a well known issue associated with
+these challenges. One approach to addressing vanishing and exploding gradients
+is to use either soft or hard constraints on weight matrices so as to encourage
+or enforce orthogonality. Orthogonal matrices preserve gradient norm during
+backpropagation and may therefore be a desirable property. This paper explores
+issues with optimization convergence, speed and gradient stability when
+encouraging or enforcing orthogonality. To perform this analysis, we propose a
+weight matrix factorization and parameterization strategy through which we can
+bound matrix norms and therein control the degree of expansivity induced during
+backpropagation. We find that hard constraints on orthogonality can negatively
+affect the speed of convergence and model performance.
+",1,0,0,0,0,0
+4053,Chunk-Based Bi-Scale Decoder for Neural Machine Translation,"  In typical neural machine translation~(NMT), the decoder generates a sentence
+word by word, packing all linguistic granularities in the same time-scale of
+RNN. In this paper, we propose a new type of decoder for NMT, which splits the
+decode state into two parts and updates them in two different time-scales.
+Specifically, we first predict a chunk time-scale state for phrasal modeling,
+on top of which multiple word time-scale states are generated. In this way, the
+target sentence is translated hierarchically from chunks to words, with
+information in different granularities being leveraged. Experiments show that
+our proposed model significantly improves the translation performance over the
+state-of-the-art NMT model.
+",1,0,0,0,0,0
+4054,IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks,"  Deep neural networks (DNN) excel at extracting patterns. Through
+representation learning and automated feature engineering on large datasets,
+such models have been highly successful in computer vision and natural language
+applications. Designing optimal network architectures from a principled or
+rational approach however has been less than successful, with the best
+successful approaches utilizing an additional machine learning algorithm to
+tune the network hyperparameters. However, in many technical fields, there
+exist established domain knowledge and understanding about the subject matter.
+In this work, we develop a novel furcated neural network architecture that
+utilizes domain knowledge as high-level design principles of the network. We
+demonstrate proof-of-concept by developing IL-Net, a furcated network for
+predicting the properties of ionic liquids, which is a class of complex
+multi-chemicals entities. Compared to existing state-of-the-art approaches, we
+show that furcated networks can improve model accuracy by approximately 20-35%,
+without using additional labeled data. Lastly, we distill two key design
+principles for furcated networks that can be adapted to other domains.
+",0,0,0,1,0,0
+4055,Fast Spectral Clustering Using Autoencoders and Landmarks,"  In this paper, we introduce an algorithm for performing spectral clustering
+efficiently. Spectral clustering is a powerful clustering algorithm that
+suffers from high computational complexity, due to eigen decomposition. In this
+work, we first build the adjacency matrix of the corresponding graph of the
+dataset. To build this matrix, we only consider a limited number of points,
+called landmarks, and compute the similarity of all data points with the
+landmarks. Then, we present a definition of the Laplacian matrix of the graph
+that enable us to perform eigen decomposition efficiently, using a deep
+autoencoder. The overall complexity of the algorithm for eigen decomposition is
+$O(np)$, where $n$ is the number of data points and $p$ is the number of
+landmarks. At last, we evaluate the performance of the algorithm in different
+experiments.
+",1,0,0,1,0,0
+4056,Sufficient Markov Decision Processes with Alternating Deep Neural Networks,"  Advances in mobile computing technologies have made it possible to monitor
+and apply data-driven interventions across complex systems in real time. Markov
+decision processes (MDPs) are the primary model for sequential decision
+problems with a large or indefinite time horizon. Choosing a representation of
+the underlying decision process that is both Markov and low-dimensional is
+non-trivial. We propose a method for constructing a low-dimensional
+representation of the original decision process for which: 1. the MDP model
+holds; 2. a decision strategy that maximizes mean utility when applied to the
+low-dimensional representation also maximizes mean utility when applied to the
+original process. We use a deep neural network to define a class of potential
+process representations and estimate the process of lowest dimension within
+this class. The method is illustrated using data from a mobile study on heavy
+drinking and smoking among college students.
+",0,0,1,1,0,0
+4057,Gate-controlled magnonic-assisted switching of magnetization in ferroelectric/ferromagnetic junctions,"  Interfacing a ferromagnet with a polarized ferroelectric gate generates a
+non-uniform, interfacial spin density coupled to the ferroelectric polarization
+allowing so for an electric field control of effective transversal field to
+magnetization. Here we study the dynamic magnetization switching behavior of
+such a multilayer system based on the Landau-Lifshitz-Baryakhtar equation,
+demonstrating that interfacial magnetoelectric coupling is utilizable as a
+highly localized and efficient tool for manipulating magnetism.
+",0,1,0,0,0,0
+4058,Three-Dimensional Electronic Structure of type-II Weyl Semimetal WTe$_2$,"  By combining bulk sensitive soft-X-ray angular-resolved photoemission
+spectroscopy and accurate first-principles calculations we explored the bulk
+electronic properties of WTe$_2$, a candidate type-II Weyl semimetal featuring
+a large non-saturating magnetoresistance. Despite the layered geometry
+suggesting a two-dimensional electronic structure, we find a three-dimensional
+electronic dispersion. We report an evident band dispersion in the reciprocal
+direction perpendicular to the layers, implying that electrons can also travel
+coherently when crossing from one layer to the other. The measured Fermi
+surface is characterized by two well-separated electron and hole pockets at
+either side of the $\Gamma$ point, differently from previous more surface
+sensitive ARPES experiments that additionally found a significant quasiparticle
+weight at the zone center. Moreover, we observe a significant sensitivity of
+the bulk electronic structure of WTe$_2$ around the Fermi level to electronic
+correlations and renormalizations due to self-energy effects, previously
+neglected in first-principles descriptions.
+",0,1,0,0,0,0
+4059,Decentralized Online Learning with Kernels,"  We consider multi-agent stochastic optimization problems over reproducing
+kernel Hilbert spaces (RKHS). In this setting, a network of interconnected
+agents aims to learn decision functions, i.e., nonlinear statistical models,
+that are optimal in terms of a global convex functional that aggregates data
+across the network, with only access to locally and sequentially observed
+samples. We propose solving this problem by allowing each agent to learn a
+local regression function while enforcing consensus constraints. We use a
+penalized variant of functional stochastic gradient descent operating
+simultaneously with low-dimensional subspace projections. These subspaces are
+constructed greedily by applying orthogonal matching pursuit to the sequence of
+kernel dictionaries and weights. By tuning the projection-induced bias, we
+propose an algorithm that allows for each individual agent to learn, based upon
+its locally observed data stream and message passing with its neighbors only, a
+regression function that is close to the globally optimal regression function.
+That is, we establish that with constant step-size selections agents' functions
+converge to a neighborhood of the globally optimal one while satisfying the
+consensus constraints as the penalty parameter is increased. Moreover, the
+complexity of the learned regression functions is guaranteed to remain finite.
+On both multi-class kernel logistic regression and multi-class kernel support
+vector classification with data generated from class-dependent Gaussian mixture
+models, we observe stable function estimation and state of the art performance
+for distributed online multi-class classification. Experiments on the Brodatz
+textures further substantiate the empirical validity of this approach.
+",1,0,1,1,0,0
+4060,Enumeration of complementary-dual cyclic $\mathbb{F}_{q}$-linear $\mathbb{F}_{q^t}$-codes,"  Let $\mathbb{F}_q$ denote the finite field of order $q,$ $n$ be a positive
+integer coprime to $q$ and $t \geq 2$ be an integer. In this paper, we
+enumerate all the complementary-dual cyclic $\mathbb{F}_q$-linear
+$\mathbb{F}_{q^t}$-codes of length $n$ by placing $\ast$, ordinary and
+Hermitian trace bilinear forms on $\mathbb{F}_{q^t}^n.$
+",0,0,1,0,0,0
+4061,MUTAN: Multimodal Tucker Fusion for Visual Question Answering,"  Bilinear models provide an appealing framework for mixing and merging
+information in Visual Question Answering (VQA) tasks. They help to learn high
+level associations between question meaning and visual concepts in the image,
+but they suffer from huge dimensionality issues. We introduce MUTAN, a
+multimodal tensor-based Tucker decomposition to efficiently parametrize
+bilinear interactions between visual and textual representations. Additionally
+to the Tucker framework, we design a low-rank matrix-based decomposition to
+explicitly constrain the interaction rank. With MUTAN, we control the
+complexity of the merging scheme while keeping nice interpretable fusion
+relations. We show how our MUTAN model generalizes some of the latest VQA
+architectures, providing state-of-the-art results.
+",1,0,0,0,0,0
+4062,Nucleosynthesis Predictions and High-Precision Deuterium Measurements,"  Two new high-precision measurements of the deuterium abundance from absorbers
+along the line of sight to the quasar PKS1937--1009 were presented. The
+absorbers have lower neutral hydrogen column densities (N(HI) $\approx$
+18\,cm$^{-2}$) than for previous high-precision measurements, boding well for
+further extensions of the sample due to the plenitude of low column density
+absorbers. The total high-precision sample now consists of 12 measurements with
+a weighted average deuterium abundance of D/H = $2.55\pm0.02\times10^{-5}$. The
+sample does not favour a dipole similar to the one detected for the fine
+structure constant. The increased precision also calls for improved
+nucleosynthesis predictions. For that purpose we have updated the public
+AlterBBN code including new reactions, updated nuclear reaction rates, and the
+possibility of adding new physics such as dark matter. The standard Big Bang
+Nucleosynthesis prediction of D/H = $2.456\pm0.057\times10^{-5}$ is consistent
+with the observed value within 1.7 standard deviations.
+",0,1,0,0,0,0
+4063,Nearly-Linear Time Spectral Graph Reduction for Scalable Graph Partitioning and Data Visualization,"  This paper proposes a scalable algorithmic framework for spectral reduction
+of large undirected graphs. The proposed method allows computing much smaller
+graphs while preserving the key spectral (structural) properties of the
+original graph. Our framework is built upon the following two key components: a
+spectrum-preserving node aggregation (reduction) scheme, as well as a spectral
+graph sparsification framework with iterative edge weight scaling. We show that
+the resulting spectrally-reduced graphs can robustly preserve the first few
+nontrivial eigenvalues and eigenvectors of the original graph Laplacian. In
+addition, the spectral graph reduction method has been leveraged to develop
+much faster algorithms for multilevel spectral graph partitioning as well as
+t-distributed Stochastic Neighbor Embedding (t-SNE) of large data sets. We
+conducted extensive experiments using a variety of large graphs and data sets,
+and obtained very promising results. For instance, we are able to reduce the
+""coPapersCiteseer"" graph with 0.43 million nodes and 16 million edges to a much
+smaller graph with only 13K (32X fewer) nodes and 17K (950X fewer) edges in
+about 16 seconds; the spectrally-reduced graphs also allow us to achieve up to
+1100X speedup for spectral graph partitioning and up to 60X speedup for t-SNE
+visualization of large data sets.
+",1,0,0,0,0,0
+4064,Text Indexing and Searching in Sublinear Time,"  We introduce the first index that can be built in $o(n)$ time for a text of
+length $n$, and also queried in $o(m)$ time for a pattern of length $m$. On a
+constant-size alphabet, for example, our index uses
+$O(n\log^{1/2+\varepsilon}n)$ bits, is built in $O(n/\log^{1/2-\varepsilon} n)$
+deterministic time, and finds the $\mathrm{occ}$ pattern occurrences in time
+$O(m/\log n + \sqrt{\log n}\log\log n + \mathrm{occ})$, where $\varepsilon>0$
+is an arbitrarily small constant. As a comparison, the most recent classical
+text index uses $O(n\log n)$ bits, is built in $O(n)$ time, and searches in
+time $O(m/\log n + \log\log n + \mathrm{occ})$. We build on a novel text
+sampling based on difference covers, which enjoys properties that allow us
+efficiently computing longest common prefixes in constant time. We extend our
+results to the secondary memory model as well, where we give the first
+construction in $o(Sort(n))$ time of a data structure with suffix array
+functionality, which can search for patterns in the almost optimal time, with
+an additive penalty of $O(\sqrt{\log_{M/B} n}\log\log n)$, where $M$ is the
+size of main memory available and $B$ is the disk block size.
+",1,0,0,0,0,0
+4065,Temperature dependence of the bulk Rashba splitting in the bismuth tellurohalides,"  We study the temperature dependence of the Rashba-split bands in the bismuth
+tellurohalides BiTe$X$ $(X=$ I, Br, Cl) from first principles. We find that
+increasing temperature reduces the Rashba splitting, with the largest effect
+observed in BiTeI with a reduction of the Rashba parameter of $40$% when
+temperature increases from $0$ K to $300$ K. These results highlight the
+inadequacy of previous interpretations of the observed Rashba splitting in
+terms of static-lattice calculations alone. Notably, we find the opposite
+trend, a strengthening of the Rashba splitting with rising temperature, in the
+pressure-stabilized topological-insulator phase of BiTeI. We propose that the
+opposite trends with temperature on either side of the topological phase
+transition could be an experimental signature for identifying it. The predicted
+temperature dependence is consistent with optical conductivity measurements,
+and should also be observable using photoemission spectroscopy, which could
+provide further insights into the nature of spin splitting and topology in the
+bismuth tellurohalides.
+",0,1,0,0,0,0
+4066,Viscosity solutions and the minimal surface system,"  We give a definition of viscosity solution for the minimal surface system and
+prove a version of Allard regularity theorem in this setting.
+",0,0,1,0,0,0
+4067,Ray-tracing semiclassical low frequency acoustic modeling with local and extended reaction boundaries,"  The recently introduced acoustic ray-tracing semiclassical (RTS) method is
+validated for a set of practically relevant boundary conditions. RTS is a
+frequency domain geometrical method which directly reproduces the acoustic
+Green's function. As previously demonstrated for a rectangular room and weakly
+absorbing boundaries with a real and frequency-independent impedance, RTS is
+capable of modeling also the lowest modes of such a room, which makes it a
+useful method for low frequency sound field modeling in enclosures. In
+practice, rooms are furnished with diverse types of materials and acoustic
+elements, resulting in a frequency-dependent, phase-modifying
+absorption/reflection. In a realistic setting, we test the RTS method with two
+additional boundary conditions: a local reaction boundary simulating a
+resonating membrane absorber and an extended reaction boundary representing a
+porous layer backed by a rigid boundary described within the Delany-Bazley-Miki
+model, as well as a combination thereof. The RTS-modeled spatially dependent
+pressure response and octave band decay curves with the corresponding
+reverberation times are compared to those obtained by the finite element
+method.
+",0,1,0,0,0,0
+4068,Towards Understanding the Evolution of the WWW Conference,"  The World Wide Web conference is a well-established and mature venue with an
+already long history. Over the years it has been attracting papers reporting
+many important research achievements centered around the Web. In this work we
+aim at understanding the evolution of WWW conference series by detecting
+crucial years and important topics. We propose a simple yet novel approach
+based on tracking the classification errors of the conference papers according
+to their predicted publication years.
+",1,0,0,0,0,0
+4069,Hierarchical State Abstractions for Decision-Making Problems with Computational Constraints,"  In this semi-tutorial paper, we first review the information-theoretic
+approach to account for the computational costs incurred during the search for
+optimal actions in a sequential decision-making problem. The traditional (MDP)
+framework ignores computational limitations while searching for optimal
+policies, essentially assuming that the acting agent is perfectly rational and
+aims for exact optimality. Using the free-energy, a variational principle is
+introduced that accounts not only for the value of a policy alone, but also
+considers the cost of finding this optimal policy. The solution of the
+variational equations arising from this formulation can be obtained using
+familiar Bellman-like value iterations from dynamic programming (DP) and the
+Blahut-Arimoto (BA) algorithm from rate distortion theory. Finally, we
+demonstrate the utility of the approach for generating hierarchies of state
+abstractions that can be used to best exploit the available computational
+resources. A numerical example showcases these concepts for a path-planning
+problem in a grid world environment.
+",1,0,0,1,0,0
+4070,The generalized Milne problem in gas-dusty atmosphere,"  We consider the generalized Milne problem in non-conservative plane-parallel
+optically thick atmosphere consisting of two components - the free electrons
+and small dust particles. Recall, that the traditional Milne problem describes
+the propagation of radiation through the conservative (without absorption)
+optically thick atmosphere when the source of thermal radiation located far
+below the surface. In such case, the flux of propagating light is the same at
+every distance in an atmosphere. In the generalized Milne problem, the flux
+changes inside the atmosphere. The solutions of the both Milne problems give
+the angular distribution and polarization degree of emerging radiation. The
+considered problem depends on two dimensionless parameters W and (a+b), which
+depend on three parameters: $\eta$ - the ratio of optical depth due to free
+electrons to optical depth due to small dust grains; the absorption factor
+$\varepsilon$ of dust grains and two coefficients - $\bar b_1$ and $\bar b_2$,
+describing the averaged anisotropic dust grains. These coefficients obey the
+relation $\bar b_1+3\bar b_2=1$. The goal of the paper is to study the
+dependence of the radiation angular distribution and degree of polarization of
+emerging light on these parameters. Here we consider only continuum radiation.
+",0,1,0,0,0,0
+4071,h-multigrid agglomeration based solution strategies for discontinuous Galerkin discretizations of incompressible flow problems,"  In this work we exploit agglomeration based $h$-multigrid preconditioners to
+speed-up the iterative solution of discontinuous Galerkin discretizations of
+the Stokes and Navier-Stokes equations. As a distinctive feature $h$-coarsened
+mesh sequences are generated by recursive agglomeration of a fine grid,
+admitting arbitrarily unstructured grids of complex domains, and agglomeration
+based discontinuous Galerkin discretizations are employed to deal with
+agglomerated elements of coarse levels. Both the expense of building coarse
+grid operators and the performance of the resulting multigrid iteration are
+investigated. For the sake of efficiency coarse grid operators are inherited
+through element-by-element $L^2$ projections, avoiding the cost of numerical
+integration over agglomerated elements. Specific care is devoted to the
+projection of viscous terms discretized by means of the BR2 dG method. We
+demonstrate that enforcing the correct amount of stabilization on coarse grids
+levels is mandatory for achieving uniform convergence with respect to the
+number of levels. The numerical solution of steady and unsteady, linear and
+non-linear problems is considered tackling challenging 2D test cases and 3D
+real life computations on parallel architectures. Significant execution time
+gains are documented.
+",0,1,0,0,0,0
+4072,The content correlation of multiple streaming edges,"  We study how to detect clusters in a graph defined by a stream of edges,
+without storing the entire graph. We extend the approach to dynamic graphs
+defined by the most recent edges of the stream and to several streams. The {\em
+content correlation }of two streams $\rho(t)$ is the Jaccard similarity of
+their clusters in the windows before time $t$. We propose a simple and
+efficient method to approximate this correlation online and show that for
+dynamic random graphs which follow a power law degree distribution, we can
+guarantee a good approximation. As an application, we follow Twitter streams
+and compute their content correlations online. We then propose a {\em search by
+correlation} where answers to sets of keywords are entirely based on the small
+correlations of the streams. Answers are ordered by the correlations, and
+explanations can be traced with the stored clusters.
+",1,0,0,0,0,0
+4073,Fundamental solutions for second order parabolic systems with drift terms,"  We construct fundamental solutions of second-order parabolic systems of
+divergence form with bounded and measurable leading coefficients and divergence
+free first-order coefficients in the class of $BMO^{-1}_x$, under the
+assumption that weak solutions of the system satisfy a certain local
+boundedness estimate. We also establish Gaussian upper bound for such
+fundamental solutions under the same conditions.
+",0,0,1,0,0,0
+4074,CMB in the river frame and gauge invariance at second order,"  GAUGE INVARIANCE: The Sachs-Wolfe formula describing the Cosmic Microwave
+Background (CMB) temperature anisotropies is one of the most important
+relations in cosmology. Despite its importance, the gauge invariance of this
+formula has only been discussed at first order. Here we discuss the subtle
+issue of second-order gauge transformations on the CMB. By introducing two
+rules (needed to handle the subtle issues), we prove the gauge invariance of
+the second-order Sachs-Wolfe formula and provide several compact expressions
+which can be useful for the study of gauge transformations on cosmology. Our
+results go beyond a simple technicality: we discuss from a physical point of
+view several aspects that improve our understanding of the CMB. We also
+elucidate how crucial it is to understand gauge transformations on the CMB in
+order to avoid errors and/or misconceptions as occurred in the past. THE RIVER
+FRAME: we introduce a cosmological frame which we call the river frame. In this
+frame, photons and any object can be thought as fishes swimming in the river
+and relations are easily expressed in either the metric or the covariant
+formalism then ensuring a transparent geometric meaning. Finally, our results
+show that the river frame is useful to make perturbative and non-perturbative
+analysis. In particular, it was already used to obtain the fully nonlinear
+generalization of the Sachs-Wolfe formula and is used here to describe
+second-order perturbations.
+",0,1,0,0,0,0
+4075,Active matrix completion with uncertainty quantification,"  The noisy matrix completion problem, which aims to recover a low-rank matrix
+$\mathbf{X}$ from a partial, noisy observation of its entries, arises in many
+statistical, machine learning, and engineering applications. In this paper, we
+present a new, information-theoretic approach for active sampling (or
+designing) of matrix entries for noisy matrix completion, based on the maximum
+entropy design principle. One novelty of our method is that it implicitly makes
+use of uncertainty quantification (UQ) -- a measure of uncertainty for
+unobserved matrix entries -- to guide the active sampling procedure. The
+proposed framework reveals several novel insights on the role of compressive
+sensing (e.g., coherence) and coding design (e.g., Latin squares) on the
+sampling performance and UQ for noisy matrix completion. Using such insights,
+we develop an efficient posterior sampler for UQ, which is then used to guide a
+closed-form sampling scheme for matrix entries. Finally, we illustrate the
+effectiveness of this integrated sampling / UQ methodology in simulation
+studies and two applications to collaborative filtering.
+",0,0,0,1,0,0
+4076,Majoration du nombre de valeurs friables d'un polynôme,"  For $Q$ a polynomial with integer coefficients and $x, y \geq 2$, we prove
+upper bounds for the quantity $\Psi_Q(x, y) = |\{n\leq x: p\mid Q(n)\Rightarrow
+p\leq y\}|$.
+We apply our results to a problem of De Koninck, Doyon and Luca on integers
+divisible by the square of their largest prime factor. As a corollary to our
+arguments, we improve the known level of distribution of the set $\{n^2-D\}$
+for well-factorable moduli, previously due to Iwaniec. We also consider the
+Chebyshev problem of estimating $\max\{P^+(n^2-D), n\leq x\}$ and make
+explicit, in Deshouillers-Iwaniec's state-of-the-art result, the dependence on
+the Selberg eigenvalue conjecture.
+",0,0,1,0,0,0
+4077,General analytical solution for the electromagnetic grating diffraction problem,"  Implementing the modal method in the electromagnetic grating diffraction
+problem delivered by the curvilinear coordinate transformation yields a general
+analytical solution to the 1D grating diffraction problem in a form of a
+T-matrix. Simultaneously it is shown that the validity of the Rayleigh
+expansion is defined by the validity of the modal expansion in a transformed
+medium delivered by the coordinate transformation.
+",0,1,1,0,0,0
+4078,Synthetic geometry of differential equations: I. Jets and comonad structure,"  We give an abstract formulation of the formal theory partial differential
+equations (PDEs) in synthetic differential geometry, one that would seamlessly
+generalize the traditional theory to a range of enhanced contexts, such as
+super-geometry, higher (stacky) differential geometry, or even a combination of
+both. A motivation for such a level of generality is the eventual goal of
+solving the open problem of covariant geometric pre-quantization of locally
+variational field theories, which may include fermions and (higher) gauge
+fields. (abridged)
+",0,0,1,0,0,0
+4079,Precision Prediction for the Cosmological Density Distribution,"  The distribution of matter in the universe is, to first order, lognormal.
+Improving this approximation requires characterization of the third moment
+(skewness) of the log density field. Thus, using Millennium Simulation
+phenomenology and building on previous work, we present analytic fits for the
+mean, variance, and skewness of the log density field $A$. We further show that
+a Generalized Extreme Value (GEV) distribution accurately models $A$; we submit
+that this GEV behavior is the result of strong intrapixel correlations, without
+which the smoothed distribution would tend (by the Central Limit Theorem)
+toward a Gaussian. Our GEV model yields cumulative distribution functions
+accurate to within 1.7 per cent for near-concordance cosmologies, over a range
+of redshifts and smoothing scales.
+",0,1,0,0,0,0
+4080,Hamiltonian analogs of combustion engines: a systematic exception to adiabatic decoupling,"  Workhorse theories throughout all of physics derive effective Hamiltonians to
+describe slow time evolution, even though low-frequency modes are actually
+coupled to high-frequency modes. Such effective Hamiltonians are accurate
+because of \textit{adiabatic decoupling}: the high-frequency modes `dress' the
+low-frequency modes, and renormalize their Hamiltonian, but they do not
+steadily inject energy into the low-frequency sector. Here, however, we
+identify a broad class of dynamical systems in which adiabatic decoupling fails
+to hold, and steady energy transfer across a large gap in natural frequency
+(`steady downconversion') instead becomes possible, through nonlinear
+resonances of a certain form. Instead of adiabatic decoupling, the special
+features of multiple time scale dynamics lead in these cases to efficiency
+constraints that somewhat resemble thermodynamics.
+",0,1,0,0,0,0
+4081,Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions,"  Accurate noise modelling is important for training of deep learning
+reconstruction algorithms. While noise models are well known for traditional
+imaging techniques, the noise distribution of a novel sensor may be difficult
+to determine a priori. Therefore, we propose learning arbitrary noise
+distributions. To do so, this paper proposes a fully connected neural network
+model to map samples from a uniform distribution to samples of any explicitly
+known probability density function. During the training, the Jensen-Shannon
+divergence between the distribution of the model's output and the target
+distribution is minimized. We experimentally demonstrate that our model
+converges towards the desired state. It provides an alternative to existing
+sampling methods such as inversion sampling, rejection sampling, Gaussian
+mixture models and Markov-Chain-Monte-Carlo. Our model has high sampling
+efficiency and is easily applied to any probability distribution, without the
+need of further analytical or numerical calculations.
+",0,0,0,1,0,0
+4082,Unbiased Simulation for Optimizing Stochastic Function Compositions,"  In this paper, we introduce an unbiased gradient simulation algorithms for
+solving convex optimization problem with stochastic function compositions. We
+show that the unbiased gradient generated from the algorithm has finite
+variance and finite expected computation cost. We then combined the unbiased
+gradient simulation with two variance reduced algorithms (namely SVRG and SCSG)
+and showed that the proposed optimization algorithms based on unbiased gradient
+simulations exhibit satisfactory convergence properties. Specifically, in the
+SVRG case, the algorithm with simulated gradient can be shown to converge
+linearly to optima in expectation and almost surely under strong convexity.
+Finally, for the numerical experiment,we applied the algorithms to two
+important cases of stochastic function compositions optimization: maximizing
+the Cox's partial likelihood model and training conditional random fields.
+",0,0,0,1,0,0
+4083,Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context,"  A robot's ability to understand or ground natural language instructions is
+fundamentally tied to its knowledge about the surrounding world. We present an
+approach to grounding natural language utterances in the context of factual
+information gathered through natural-language interactions and past visual
+observations. A probabilistic model estimates, from a natural language
+utterance, the objects,relations, and actions that the utterance refers to, the
+objectives for future robotic actions it implies, and generates a plan to
+execute those actions while updating a state representation to include newly
+acquired knowledge from the visual-linguistic context. Grounding a command
+necessitates a representation for past observations and interactions; however,
+maintaining the full context consisting of all possible observed objects,
+attributes, spatial relations, actions, etc., over time is intractable.
+Instead, our model, Temporal Grounding Graphs, maintains a learned state
+representation for a belief over factual groundings, those derived from
+natural-language interactions, and lazily infers new groundings from visual
+observations using the context implied by the utterance. This work
+significantly expands the range of language that a robot can understand by
+incorporating factual knowledge and observations of its workspace in its
+inference about the meaning and grounding of natural-language utterances.
+",1,0,0,0,0,0
+4084,Nonconvex generalizations of ADMM for nonlinear equality constrained problems,"  The growing demand on efficient and distributed optimization algorithms for
+large-scale data stimulates the popularity of Alternative Direction Methods of
+Multipliers (ADMM) in numerous areas, such as compressive sensing, matrix
+completion, and sparse feature learning. While linear equality constrained
+problems have been extensively explored to be solved by ADMM, there lacks a
+generic framework for ADMM to solve problems with nonlinear equality
+constraints, which are common in practical application (e.g., orthogonality
+constraints). To address this problem, in this paper, we proposed a new generic
+ADMM framework for handling nonlinear equality constraints, called neADMM.
+First, we propose the generalized problem formulation and systematically
+provide the sufficient condition for the convergence of neADMM. Second, we
+prove a sublinear convergence rate based on variational inequality framework
+and also provide an novel accelerated strategy on the update of the penalty
+parameter. In addition, several practical applications under the generic
+framework of neADMM are provided. Experimental results on several applications
+demonstrate the usefulness of our neADMM.
+",1,0,1,0,0,0
+4085,Interpretable LSTMs For Whole-Brain Neuroimaging Analyses,"  The analysis of neuroimaging data poses several strong challenges, in
+particular, due to its high dimensionality, its strong spatio-temporal
+correlation and the comparably small sample sizes of the respective datasets.
+To address these challenges, conventional decoding approaches such as the
+searchlight reduce the complexity of the decoding problem by considering local
+clusters of voxels only. Thereby, neglecting the distributed spatial patterns
+of brain activity underlying many cognitive states. In this work, we introduce
+the DLight framework, which overcomes these challenges by utilizing a long
+short-term memory unit (LSTM) based deep neural network architecture to analyze
+the spatial dependency structure of whole-brain fMRI data. In order to maintain
+interpretability of the neuroimaging data, we adapt the layer-wise relevance
+propagation (LRP) method. Thereby, we enable the neuroscientist user to study
+the learned association of the LSTM between the data and the cognitive state of
+the individual. We demonstrate the versatility of DLight by applying it to a
+large fMRI dataset of the Human Connectome Project. We show that the decoding
+performance of our method scales better with large datasets, and moreover
+outperforms conventional decoding approaches, while still detecting
+physiologically appropriate brain areas for the cognitive states classified. We
+also demonstrate that DLight is able to detect these areas on several levels of
+data granularity (i.e., group, subject, trial, time point).
+",0,0,0,0,1,0
+4086,Effect of Isopropanol on Gold Assisted Chemical Etching of Silicon Microstructures,"  Wet etching is an essential and complex step in semiconductor device
+processing. Metal-Assisted Chemical Etching (MacEtch) is fundamentally a wet
+but anisotropic etching method. In the MacEtch technique, there are still a
+number of unresolved challenges preventing the optimal fabrication of
+high-aspect-ratio semiconductor micro- and nanostructures, such as undesired
+etching, uncontrolled catalyst movement, non-uniformity and micro-porosity in
+the metal-free areas. Here, an optimized MacEtch process using with a
+nanostructured Au catalyst is proposed for fabrication of Si high aspect ratio
+microstructures. The addition of isopropanol as surfactant in the HF-H2O2 water
+solution improves the uniformity and the control of the H2 gas release. An
+additional KOH etching removes eventually the unwanted nanowires left by the
+MacEtch through the nanoporous catalyst film. We demonstrate the benefits of
+the isopropanol addition for reducing the etching rate and the nanoporosity of
+etched structures with a monothonical decrease as a function of the isopropanol
+concentration.
+",0,1,0,0,0,0
+4087,Integrating Runtime Values with Source Code to Facilitate Program Comprehension,"  An inherently abstract nature of source code makes programs difficult to
+understand. In our research, we designed three techniques utilizing concrete
+values of variables and other expressions during program execution.
+RuntimeSearch is a debugger extension searching for a given string in all
+expressions at runtime. DynamiDoc generates documentation sentences containing
+examples of arguments, return values and state changes. RuntimeSamp augments
+source code lines in the IDE (integrated development environment) with sample
+variable values. In this post-doctoral article, we briefly describe these three
+approaches and related motivational studies, surveys and evaluations. We also
+reflect on the PhD study, providing advice for current students. Finally,
+short-term and long-term future work is described.
+",1,0,0,0,0,0
+4088,Nearest Embedded and Embedding Self-Nested Trees,"  Self-nested trees present a systematic form of redundancy in their subtrees
+and thus achieve optimal compression rates by DAG compression. A method for
+quantifying the degree of self-similarity of plants through self-nested trees
+has been introduced by Godin and Ferraro in 2010. The procedure consists in
+computing a self-nested approximation, called the nearest embedding self-nested
+tree, that both embeds the plant and is the closest to it. In this paper, we
+propose a new algorithm that computes the nearest embedding self-nested tree
+with a smaller overall complexity, but also the nearest embedded self-nested
+tree. We show from simulations that the latter is mostly the closest to the
+initial data, which suggests that this better approximation should be used as a
+privileged measure of the degree of self-similarity of plants.
+",1,0,0,0,0,0
+4089,Computing the Lusztig--Vogan Bijection,"  Let $G$ be a connected complex reductive algebraic group with Lie algebra
+$\mathfrak{g}$. The Lusztig--Vogan bijection relates two bases for the bounded
+derived category of $G$-equivariant coherent sheaves on the nilpotent cone
+$\mathcal{N}$ of $\mathfrak{g}$. One basis is indexed by $\Lambda^+$, the set
+of dominant weights of $G$, and the other by $\Omega$, the set of pairs
+$(\mathcal{O}, \mathcal{E})$ consisting of a nilpotent orbit $\mathcal{O}
+\subset \mathcal{N}$ and an irreducible $G$-equivariant vector bundle
+$\mathcal{E} \rightarrow \mathcal{O}$. The existence of the Lusztig--Vogan
+bijection $\gamma \colon \Omega \rightarrow \Lambda^+$ was proven by
+Bezrukavnikov, and an algorithm computing $\gamma$ in type $A$ was given by
+Achar. Herein we present a combinatorial description of $\gamma$ in type $A$
+that subsumes and dramatically simplifies Achar's algorithm.
+",0,0,1,0,0,0
+4090,Divide and Conquer: Variable Set Separation in Hybrid Systems Reachability Analysis,"  In this paper we propose an improvement for flowpipe-construction-based
+reachability analysis techniques for hybrid systems. Such methods apply
+iterative successor computations to pave the reachable region of the state
+space by state sets in an over-approximative manner. As the computational costs
+steeply increase with the dimension, in this work we analyse the possibilities
+for improving scalability by dividing the search space in sub-spaces and
+execute reachability computations in the sub-spaces instead of the global
+space. We formalise such an algorithm and provide experimental evaluations to
+compare the efficiency as well as the precision of our sub-space search to the
+original search in the global space.
+",1,0,0,0,0,0
+4091,The bottom of the spectrum of time-changed processes and the maximum principle of Schrödinger operators,"  We give a necessary and sufficient condition for the maximum principle of
+Schrödinger operators in terms of the bottom of the spectrum of
+time-changed processes. As a corollary, we obtain a sufficient condition for
+the Liouville property of Schrödinger operators.
+",0,0,1,0,0,0
+4092,Autocorrelation and Lower Bound on the 2-Adic Complexity of LSB Sequence of $p$-ary $m$-Sequence,"  In modern stream cipher, there are many algorithms, such as ZUC, LTE
+encryption algorithm and LTE integrity algorithm, using bit-component sequences
+of $p$-ary $m$-sequences as the input of the algorithm. Therefore, analyzing
+their statistical property (For example, autocorrelation, linear complexity and
+2-adic complexity) of bit-component sequences of $p$-ary $m$-sequences is
+becoming an important research topic. In this paper, we first derive some
+autocorrelation properties of LSB (Least Significant Bit) sequences of $p$-ary
+$m$-sequences, i.e., we convert the problem of computing autocorrelations of
+LSB sequences of period $p^n-1$ for any positive $n\geq2$ to the problem of
+determining autocorrelations of LSB sequence of period $p-1$. Then, based on
+this property and computer calculation, we list some autocorrelation
+distributions of LSB sequences of $p$-ary $m$-sequences with order $n$ for some
+small primes $p$'s, such as $p=3,5,7,11,17,31$. Additionally, using their
+autocorrelation distributions and the method inspired by Hu, we give the lower
+bounds on the 2-adic complexities of these LSB sequences. Our results show that
+the main parts of all the lower bounds on the 2-adic complexity of these LSB
+sequencesare larger than $\frac{N}{2}$, where $N$ is the period of these
+sequences. Therefor, these bounds are large enough to resist the analysis of
+RAA (Rational Approximation Algorithm) for FCSR (Feedback with Carry Shift
+Register). Especially, for a Mersenne prime $p=2^k-1$, since all its
+bit-component sequences of a $p$-ary $m$-sequence are shift equivalent, our
+results hold for all its bit-component sequences.
+",1,0,0,0,0,0
+4093,Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers,"  The telecommunications industry is highly competitive, which means that the
+mobile providers need a business intelligence model that can be used to achieve
+an optimal level of churners, as well as a minimal level of cost in marketing
+activities. Machine learning applications can be used to provide guidance on
+marketing strategies. Furthermore, data mining techniques can be used in the
+process of customer segmentation. The purpose of this paper is to provide a
+detailed analysis of the C.5 algorithm, within naive Bayesian modelling for the
+task of segmenting telecommunication customers behavioural profiling according
+to their billing and socio-demographic aspects. Results have been
+experimentally implemented.
+",1,0,0,1,0,0
+4094,A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation,"  A vital aspect in energy storage planning and operation is to accurately
+model its operational cost, which mainly comes from the battery cell
+degradation. Battery degradation can be viewed as a complex material fatigue
+process that based on stress cycles. Rainflow algorithm is a popular way for
+cycle identification in material fatigue process, and has been extensively used
+in battery degradation assessment. However, the rainflow algorithm does not
+have a closed form, which makes the major difficulty to include it in
+optimization. In this paper, we prove the rainflow cycle-based cost is convex.
+Convexity enables the proposed degradation model to be incorporated in
+different battery optimization problems and guarantees the solution quality. We
+provide a subgradient algorithm to solve the problem. A case study on PJM
+regulation market demonstrates the effectiveness of the proposed degradation
+model in maximizing the battery operating profits as well as extending its
+lifetime.
+",1,0,1,0,0,0
+4095,Demonstration of cascaded modulator-chicane micro-bunching of a relativistic electron beam,"  We present results of an experiment showing the first successful
+demonstration of a cascaded micro-bunching scheme. Two modulator-chicane
+pre-bunchers arranged in series and a high power mid-IR laser seed are used to
+modulate a 52 MeV electron beam into a train of sharp microbunches phase-locked
+to the external drive laser. This configuration allows to increase the fraction
+of electrons trapped in a strongly tapered inverse free electron laser (IFEL)
+undulator to 96\%, with up to 78\% of the particles accelerated to the final
+design energy yielding a significant improvement compared to the classical
+single buncher scheme. These results represent a critical advance in
+laser-based longitudinal phase space manipulations and find application both in
+high gradient advanced acceleration as well as in high peak and average power
+coherent radiation sources.
+",0,1,0,0,0,0
+4096,Grothendieck rigidity of 3-manifold groups,"  We show that fundamental groups of compact, orientable, irreducible
+3-manifolds with toroidal boundary are Grothendieck rigid.
+",0,0,1,0,0,0
+4097,Sobczyk's simplicial calculus does not have a proper foundation,"  The pseudoscalars in Garret Sobczyk's paper \emph{Simplicial Calculus with
+Geometric Algebra} are not well defined. Therefore his calculus does not have a
+proper foundation.
+",0,0,1,0,0,0
+4098,"Hierarchy of Information Scrambling, Thermalization, and Hydrodynamic Flow in Graphene","  We determine the information scrambling rate $\lambda_{L}$ due to
+electron-electron Coulomb interaction in graphene. $\lambda_{L}$ characterizes
+the growth of chaos and has been argued to give information about the
+thermalization and hydrodynamic transport coefficients of a many-body system.
+We demonstrate that $\lambda_{L}$ behaves for strong coupling similar to
+transport and energy relaxation rates. A weak coupling analysis, however,
+reveals that scrambling is related to dephasing or single particle relaxation.
+Furthermore, $\lambda_{L}$ is found to be parametrically larger than the
+collision rate relevant for hydrodynamic processes, such as electrical
+conduction or viscous flow, and the rate of energy relaxation, relevant for
+thermalization. Thus, while scrambling is obviously necessary for
+thermalization and quantum transport, it does generically not set the time
+scale for these processes. In addition we derive a quantum kinetic theory for
+information scrambling that resembles the celebrated Boltzmann equation and
+offers a physically transparent insight into quantum chaos in many-body
+systems.
+",0,1,0,0,0,0
+4099,Neural Collaborative Autoencoder,"  In recent years, deep neural networks have yielded state-of-the-art
+performance on several tasks. Although some recent works have focused on
+combining deep learning with recommendation, we highlight three issues of
+existing models. First, these models cannot work on both explicit and implicit
+feedback, since the network structures are specially designed for one
+particular case. Second, due to the difficulty on training deep neural
+networks, existing explicit models do not fully exploit the expressive
+potential of deep learning. Third, neural network models are easier to overfit
+on the implicit setting than shallow models. To tackle these issues, we present
+a generic recommender framework called Neural Collaborative Autoencoder (NCAE)
+to perform collaborative filtering, which works well for both explicit feedback
+and implicit feedback. NCAE can effectively capture the subtle hidden
+relationships between interactions via a non-linear matrix factorization
+process. To optimize the deep architecture of NCAE, we develop a three-stage
+pre-training mechanism that combines supervised and unsupervised feature
+learning. Moreover, to prevent overfitting on the implicit setting, we propose
+an error reweighting module and a sparsity-aware data-augmentation strategy.
+Extensive experiments on three real-world datasets demonstrate that NCAE can
+significantly advance the state-of-the-art.
+",1,0,0,1,0,0
+4100,Reexamination of Tolman's law and the Gibbs adsorption equation for curved interfaces,"  The influence of the surface curvature on the surface tension of small
+droplets in equilibrium with a surrounding vapour, or small bubbles in
+equilibrium with a surrounding liquid, can be expanded as $\gamma(R) = \gamma_0
++ c_1\gamma_0/R + O(1/R^2)$, where $R = R_\gamma$ is the radius of the surface
+of tension and $\gamma_0$ is the surface tension of the planar interface,
+corresponding to zero curvature. According to Tolman's law, the first-order
+coefficient in this expansion is assumed to be related to the planar limit
+$\delta_0$ of the Tolman length, i.e., the difference $\delta = R_\rho -
+R_\gamma$ between the equimolar radius and the radius of the surface of
+tension, by $c_1 = -2\delta_0$.
+We show here that the deduction of Tolman's law from interfacial
+thermodynamics relies on an inaccurate application of the Gibbs adsorption
+equation to dispersed phases (droplets or bubbles). A revision of the
+underlying theory reveals that the adsorption equation needs to be employed in
+an alternative manner to that suggested by Tolman. Accordingly, we develop a
+generalized Gibbs adsorption equation which consistently takes the size
+dependence of interfacial properties into account, and show that from this
+equation, a relation between the Tolman length and the influence of the size of
+the dispersed phase on the surface tension cannot be deduced, invalidating the
+argument which was put forward by Tolman [J. Chem. Phys. 17 (1949) 333].
+",0,1,0,0,0,0
+4101,Dual Supervised Learning,"  Many supervised learning tasks are emerged in dual forms, e.g.,
+English-to-French translation vs. French-to-English translation, speech
+recognition vs. text to speech, and image classification vs. image generation.
+Two dual tasks have intrinsic connections with each other due to the
+probabilistic correlation between their models. This connection is, however,
+not effectively utilized today, since people usually train the models of two
+dual tasks separately and independently. In this work, we propose training the
+models of two dual tasks simultaneously, and explicitly exploiting the
+probabilistic correlation between them to regularize the training process. For
+ease of reference, we call the proposed approach \emph{dual supervised
+learning}. We demonstrate that dual supervised learning can improve the
+practical performances of both tasks, for various applications including
+machine translation, image processing, and sentiment analysis.
+",1,0,0,1,0,0
+4102,Pentavalent symmetric graphs of order four times an odd square-free integer,"  A graph is said to be symmetric if its automorphism group is transitive on
+its arcs. Guo et al. (Electronic J. Combin. 18, \#P233, 2011) and Pan et al.
+(Electronic J. Combin. 20, \#P36, 2013) determined all pentavalent symmetric
+graphs of order $4pq$. In this paper, we shall generalize this result by
+determining all connected pentavalent symmetric graphs of order four times an
+odd square-free integer. It is shown in this paper that, for each of such
+graphs $\it\Gamma$, either the full automorphism group ${\sf Aut}\it\Gamma$ is
+isomorphic to ${\sf PSL}(2,p)$, ${\sf PGL}(2,p)$, ${\sf
+PSL}(2,p){\times}\mathbb{Z}_2$ or ${\sf PGL}(2,p){\times}\mathbb{Z}_2$, or
+$\it\Gamma$ is isomorphic to one of 8 graphs.
+",0,0,1,0,0,0
+4103,Applying the Polyhedral Model to Tile Time Loops in Devito,"  The run time of many scientific computation applications for numerical
+methods is heavily dependent on just a few multi-dimensional loop nests. Since
+these applications are often limited by memory bandwidth rather than
+computational resources they can benefit greatly from any optimizations which
+decrease the run time of their loops by improving data reuse and thus reducing
+the total memory traffic. Some of the most effective of these optimizations are
+not suitable for development by hand or require advanced software engineering
+knowledge which is beyond the level of many researchers who are not specialists
+in code optimization. Several tools exist to automate the generation of
+high-performance code for numerical methods, such as Devito which produces code
+for finite-difference approximations typically used in the seismic imaging
+domain. We present a loop-tiling optimization which can be applied to
+Devito-generated loops and improves run time by up to 27.5%, and options for
+automating this optimization in the Devito framework.
+",1,0,0,0,0,0
+4104,Deictic Image Maps: An Abstraction For Learning Pose Invariant Manipulation Policies,"  In applications of deep reinforcement learning to robotics, it is often the
+case that we want to learn pose invariant policies: policies that are invariant
+to changes in the position and orientation of objects in the world. For
+example, consider a peg-in-hole insertion task. If the agent learns to insert a
+peg into one hole, we would like that policy to generalize to holes presented
+in different poses. Unfortunately, this is a challenge using conventional
+methods. This paper proposes a novel state and action abstraction that is
+invariant to pose shifts called \textit{deictic image maps} that can be used
+with deep reinforcement learning. We provide broad conditions under which
+optimal abstract policies are optimal for the underlying system. Finally, we
+show that the method can help solve challenging robotic manipulation problems.
+",1,0,0,0,0,0
+4105,Charged Vector Particles Tunneling From 5D Black Hole and Black Ring,"  In this paper, we investigate the Hawking radiation process as a
+semiclassical quantum tunneling phenomenon from black ring and Myers-Perry
+black holes in 5-dimensional (5D) spaces. Using Lagrangian of
+Glashow-Weinberg-Salam model with background electromagnetic field (for charged
+W-bosons) and the WKB approximation, we will evaluate the tunneling
+rate/probability of charged vector particles through horizons by taking into
+account the electromagnetic vector potential. Moreover, we investigate the
+corresponding Hawking temperature values by considering Boltzmann factor for
+both cases and analyze the whole spectrum generally.
+",0,1,0,0,0,0
+4106,Implementing implicit OpenMP data sharing on GPUs,"  OpenMP is a shared memory programming model which supports the offloading of
+target regions to accelerators such as NVIDIA GPUs. The implementation in
+Clang/LLVM aims to deliver a generic GPU compilation toolchain that supports
+both the native CUDA C/C++ and the OpenMP device offloading models. There are
+situations where the semantics of OpenMP and those of CUDA diverge. One such
+example is the policy for implicitly handling local variables. In CUDA, local
+variables are implicitly mapped to thread local memory and thus become private
+to a CUDA thread. In OpenMP, due to semantics that allow the nesting of regions
+executed by different numbers of threads, variables need to be implicitly
+\emph{shared} among the threads of a contention group. In this paper we
+introduce a re-design of the OpenMP device data sharing infrastructure that is
+responsible for the implicit sharing of local variables in the Clang/LLVM
+toolchain. We introduce a new data sharing infrastructure that lowers
+implicitly shared variables to the shared memory of the GPU. We measure the
+amount of shared memory used by our scheme in cases that involve scalar
+variables and statically allocated arrays. The evaluation is carried out by
+offloading to K40 and P100 NVIDIA GPUs. For scalar variables the pressure on
+shared memory is relatively low, under 26\% of shared memory utilization for
+the K40, and does not negatively impact occupancy. The limiting occupancy
+factor in that case is register pressure. The data sharing scheme offers the
+users a simple memory model for controlling the implicit allocation of device
+shared memory.
+",1,0,0,0,0,0
+4107,Real-Time Impulse Noise Removal from MR Images for Radiosurgery Applications,"  In the recent years image processing techniques are used as a tool to improve
+detection and diagnostic capabilities in the medical applications. Medical
+applications have been so much affected by these techniques which some of them
+are embedded in medical instruments such as MRI, CT and other medical devices.
+Among these techniques, medical image enhancement algorithms play an essential
+role in removal of the noise which can be produced by medical instruments and
+during image transfer. It has been proved that impulse noise is a major type of
+noise, which is produced during medical operations, such as MRI, CT, and
+angiography, by their image capturing devices. An embeddable hardware module
+which is able to denoise medical images before and during surgical operations
+could be very helpful. In this paper an accurate algorithm is proposed for
+real-time removal of impulse noise in medical images. All image blocks are
+divided into three categories of edge, smooth, and disordered areas. A
+different reconstruction method is applied to each category of blocks for the
+purpose of noise removal. The proposed method is tested on MR images.
+Simulation results show acceptable denoising accuracy for various levels of
+noise. Also an FPAG implementation of our denoising algorithm shows acceptable
+hardware resource utilization. Hence, the algorithm is suitable for embedding
+in medical hardware instruments such as radiosurgery devices.
+",1,1,0,0,0,0
+4108,Pitfalls of Graph Neural Network Evaluation,"  Semi-supervised node classification in graphs is a fundamental problem in
+graph mining, and the recently proposed graph neural networks (GNNs) have
+achieved unparalleled results on this task. Due to their massive success, GNNs
+have attracted a lot of attention, and many novel architectures have been put
+forward. In this paper we show that existing evaluation strategies for GNN
+models have serious shortcomings. We show that using the same
+train/validation/test splits of the same datasets, as well as making
+significant changes to the training procedure (e.g. early stopping criteria)
+precludes a fair comparison of different architectures. We perform a thorough
+empirical evaluation of four prominent GNN models and show that considering
+different splits of the data leads to dramatically different rankings of
+models. Even more importantly, our findings suggest that simpler GNN
+architectures are able to outperform the more sophisticated ones if the
+hyperparameters and the training procedure are tuned fairly for all models.
+",1,0,0,0,0,0
+4109,Randomized CP Tensor Decomposition,"  The CANDECOMP/PARAFAC (CP) tensor decomposition is a popular
+dimensionality-reduction method for multiway data. Dimensionality reduction is
+often sought since many high-dimensional tensors have low intrinsic rank
+relative to the dimension of the ambient measurement space. However, the
+emergence of `big data' poses significant computational challenges for
+computing this fundamental tensor decomposition. Leveraging modern randomized
+algorithms, we demonstrate that the coherent structure can be learned from a
+smaller representation of the tensor in a fraction of the time. Moreover, the
+high-dimensional signal can be faithfully approximated from the compressed
+measurements. Thus, this simple but powerful algorithm enables one to compute
+the approximate CP decomposition even for massive tensors. The approximation
+error can thereby be controlled via oversampling and the computation of power
+iterations. In addition to theoretical results, several empirical results
+demonstrate the performance of the proposed algorithm.
+",1,0,0,1,0,0
+4110,App Store 2.0: From Crowd Information to Actionable Feedback in Mobile Ecosystems,"  Given the increasing competition in mobile app ecosystems, improving the
+experience of users has become a major goal for app vendors. This article
+introduces a visionary app store, called APP STORE 2.0, which exploits
+crowdsourced information about apps, devices and users to increase the overall
+quality of the delivered mobile apps. We sketch a blueprint architecture of the
+envisioned app stores and discuss the different kinds of actionable feedbacks
+that app stores can generate using crowdsourced information.
+",1,0,0,0,0,0
+4111,An Information Matrix Approach for State Secrecy,"  This paper studies the problem of remote state estimation in the presence of
+a passive eavesdropper. A sensor measures a linear plant's state and transmits
+it to an authorized user over a packet-dropping channel, which is susceptible
+to eavesdropping. Our goal is to design a coding scheme such that the
+eavesdropper cannot infer the plant's current state, while the user
+successfully decodes the sent messages. We employ a novel class of codes,
+termed State-Secrecy Codes, which are fast and efficient for dynamical systems.
+They apply linear time-varying transformations to the current and past states
+received by the user. In this way, they force the eavesdropper's information
+matrix to decrease with asymptotically the same rate as in the open-loop
+prediction case, i.e. when the eavesdropper misses all messages. As a result,
+the eavesdropper's minimum mean square error (mmse) for the unstable states
+grows unbounded, while the respective error for the stable states converges to
+the open-loop prediction one. These secrecy guarantees are achieved under
+minimal conditions, which require that, at least once, the user receives the
+corresponding packet while the eavesdropper fails to intercept it. Meanwhile,
+the user's estimation performance remains optimal. The theoretical results are
+illustrated in simulations.
+",1,0,0,0,0,0
+4112,Adversarial Attacks on Node Embeddings,"  The goal of network representation learning is to learn low-dimensional node
+embeddings that capture the graph structure and are useful for solving
+downstream tasks. However, despite the proliferation of such methods there is
+currently no study of their robustness to adversarial attacks. We provide the
+first adversarial vulnerability analysis on the widely used family of methods
+based on random walks. We derive efficient adversarial perturbations that
+poison the network structure and have a negative effect on both the quality of
+the embeddings and the downstream tasks. We further show that our attacks are
+transferable -- they generalize to many models -- and are successful even when
+the attacker has restricted actions.
+",1,0,0,0,0,0
+4113,Time-reversed magnetically controlled perturbation (TRMCP) optical focusing inside scattering media,"  Manipulating and focusing light deep inside biological tissue and tissue-like
+complex media has been desired for long yet considered challenging. One
+feasible strategy is through optical wavefront engineering, where the optical
+scattering-induced phase distortions are time reversed or pre-compensated so
+that photons travel along different optical paths interfere constructively at
+the targeted position within a scattering medium. To define the targeted
+position, an internal guidestar is needed to guide or provide a feedback for
+wavefront engineering. It could be injected or embedded probes such as
+fluorescence or nonlinear microspheres, ultrasonic modulation, as well as
+absorption perturbation. Here we propose to use a magnetically controlled
+optical absorbing microsphere as the internal guidestar. Using a digital
+optical phase conjugation system, we obtained sharp optical focusing within
+scattering media through time-reversing the scattered light perturbed by the
+magnetic microshpere. Since the object is magnetically controlled, dynamic
+optical focusing is allowed with a relatively large field-of-view by scanning
+the magnetic field externally. Moreover, the magnetic microsphere can be
+packaged with an organic membrane, using biological or chemical means to serve
+as a carrier. Therefore the technique may find particular applications for
+enhanced targeted drug delivery, and imaging and photoablation of angiogenic
+vessels in tumours.
+",0,1,0,0,0,0
+4114,Dimensionality-strain phase diagram of strontium iridates,"  The competition between spin-orbit coupling, bandwidth ($W$) and
+electron-electron interaction ($U$) makes iridates highly susceptible to small
+external perturbations, which can trigger the onset of novel types of
+electronic and magnetic states. Here we employ {\em first principles}
+calculations based on density functional theory and on the constrained random
+phase approximation to study how dimensionality and strain affect the strength
+of $U$ and $W$ in (SrIrO$_3$)$_m$/(SrTiO$_3$) superlattices. The result is a
+phase diagram explaining two different types of controllable magnetic and
+electronic transitions, spin-flop and insulator-to-metal, connected with the
+disruption of the $J_{eff}=1/2$ state which cannnot be understood within a
+simplified local picture.
+",0,1,0,0,0,0
+4115,Herschel survey and modelling of externally-illuminated photoevaporating protoplanetary disks,"  Protoplanetary disks undergo substantial mass-loss by photoevaporation, a
+mechanism which is crucial to their dynamical evolution. However, the processes
+regulating the gas energetics have not been well constrained by observations so
+far. We aim at studying the processes involved in disk photoevaporation when it
+is driven by far-UV photons. We present a unique Herschel survey and new ALMA
+observations of four externally-illuminated photoevaporating disks (a.k.a.
+proplyds). For the analysis of these data, we developed a 1D model of the
+photodissociation region (PDR) of a proplyd, based on the Meudon PDR code and
+computed the far infrared line emission. We successfully reproduce most of the
+observations and derive key physical parameters, i.e. densities at the disk
+surface of about $10^{6}$ cm$^{-3}$ and local gas temperatures of about 1000 K.
+Our modelling suggests that all studied disks are found in a transitional
+regime resulting from the interplay between several heating and cooling
+processes that we identify. These differ from those dominating in classical
+PDRs, i.e. grain photo-electric effect and cooling by [OI] and [CII] FIR lines.
+This energetic regime is associated to an equilibrium dynamical point of the
+photoevaporation flow: the mass-loss rate is self-regulated to set the envelope
+column density at a value that maintains the temperature at the disk surface
+around 1000 K. From our best-fit models, we estimate mass-loss rates - of the
+order of $10^{-7}$ $\mathrm{M}_\odot$/yr - that are in agreement with earlier
+spectroscopic observation of ionised gas tracers. This holds only if we assume
+an evaporation flow launched from the disk surface at sound speed
+(supercritical regime). We have identified the energetic regime regulating
+FUV-photoevaporation in proplyds. This regime could be implemented into models
+of the dynamical evolution of protoplanetary disks.
+",0,1,0,0,0,0
+4116,Observation of Spatio-temporal Instability of Femtosecond Pulses in Normal Dispersion Multimode Graded-Index Fiber,"  We study the spatio-temporal instability generated by a universal unstable
+attractor in normal dispersion graded-index multimode fiber (GRIN MMF) for
+femtosecond pulses. Our results present the generation of geometric parametric
+instability (GPI) sidebands with ultrashort input pulse for the first time.
+Observed GPI sidebands are 91 THz detuned from the pump wavelength, 800 nm.
+Detailed analysis carried out numerically by employing coupled-mode pulse
+propagation model including optical shock and Raman nonlinearity terms. A
+simplified theoretical model and numerically calculated spectra are
+well-aligned with experimental results. For input pulses of 200-fs duration,
+formation and evolution of GPI are shown in both spatial and temporal domains.
+The spatial intensity distribution of the total field and GPI sidebands are
+calculated. Numerically and experimentally obtained beam shapes of first GPI
+features a Gaussian-like beam profile. Our numerical results verify the unique
+feature of GPI and generated sidebands preserve their inherited spatial
+intensity profile from the input pulse for different propagation distances
+particularly for focused and spread the total field inside the GRIN MMF.
+",0,1,0,0,0,0
+4117,Spectral curves for the rogue waves,"  Here we find the spectral curves, corresponding to the known rational or
+quasi-rational solutions of AKNS hierarchy equations, ultimately connected with
+the modeling of the rogue waves events in the optical waveguides and in
+hydrodynamics. We also determine spectral curves for the multi-phase
+trigonometric, hyperbolic and elliptic solutions for the same hierarchy. It
+seams that the nature of the related spectral curves was not sufficiently
+discussed in existing literature.
+",0,1,1,0,0,0
+4118,Volume functional of compact manifolds with a prescribed boundary metric,"  We prove that a critical metric of the volume functional on a
+four-dimensional compact manifold with boundary satisfying a second-order
+vanishing condition on the Weyl tensor must be isometric to a geodesic ball in
+a simply connected space form $\mathbb{R}^{4}$, $\mathbb{H}^{4}$ or
+$\mathbb{S}^{4}.$ Moreover, we provide an integral curvature estimate involving
+the Yamabe constant for critical metrics of the volume functional, which allows
+us to get a rigidity result for such critical metrics on four-dimensional
+manifolds.
+",0,0,1,0,0,0
+4119,Deep Multitask Learning for Semantic Dependency Parsing,"  We present a deep neural architecture that parses sentences into three
+semantic dependency graph formalisms. By using efficient, nearly arc-factored
+inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
+base system is able to significantly improve the state of the art for semantic
+dependency parsing, without using hand-engineered features or syntax. We then
+explore two multitask learning approaches---one that shares parameters across
+formalisms, and one that uses higher-order structures to predict the graphs
+jointly. We find that both approaches improve performance across formalisms on
+average, achieving a new state of the art. Our code is open-source and
+available at this https URL.
+",1,0,0,0,0,0
+4120,Chentsov's theorem for exponential families,"  Chentsov's theorem characterizes the Fisher information metric on statistical
+models as essentially the only Riemannian metric that is invariant under
+sufficient statistics. This implies that each statistical model is naturally
+equipped with a geometry, so Chentsov's theorem explains why many statistical
+properties can be described in geometric terms. However, despite being one of
+the foundational theorems of statistics, Chentsov's theorem has only been
+proved previously in very restricted settings or under relatively strong
+regularity and invariance assumptions. We therefore prove a version of this
+theorem for the important case of exponential families. In particular, we
+characterise the Fisher information metric as the only Riemannian metric (up to
+rescaling) on an exponential family and its derived families that is invariant
+under independent and identically distributed extensions and canonical
+sufficient statistics. Our approach is based on the central limit theorem, so
+it gives a unified proof for both discrete and continuous exponential families,
+and it is less technical than previous approaches.
+",1,0,1,1,0,0
+4121,Dark trions and biexcitons in WS2 and WSe2 made bright by e-e scattering,"  The direct band gap character and large spin-orbit splitting of the valence
+band edges (at the K and K' valleys) in monolayer transition metal
+dichalcogenides have put these two-dimensional materials under the spot-light
+of intense experimental and theoretical studies. In particular, for Tungsten
+dichalcogenides it has been found that the sign of spin splitting of conduction
+band edges makes ground state excitons radiatively inactive (dark) due to spin
+and momentum mismatch between the constituent electron and hole. One might
+similarly assume that the ground states of charged excitons and biexcitons in
+these monolayers are also dark. Here, we show that the intervalley
+K$\leftrightarrows$K' electron-electron scattering mixes bright and dark states
+of these complexes, and estimate the radiative lifetimes in the ground states
+of these ""semi-dark"" trions and biexcitons to be ~ 10ps, and analyse how these
+complexes appear in the temperature-dependent photoluminescence spectra of WS2
+and WSe2 monolayers.
+",0,1,0,0,0,0
+4122,SGDLibrary: A MATLAB library for stochastic gradient descent algorithms,"  We consider the problem of finding the minimizer of a function $f:
+\mathbb{R}^d \rightarrow \mathbb{R}$ of the finite-sum form $\min f(w) =
+1/n\sum_{i}^n f_i(w)$. This problem has been studied intensively in recent
+years in the field of machine learning (ML). One promising approach for
+large-scale data is to use a stochastic optimization algorithm to solve the
+problem. SGDLibrary is a readable, flexible and extensible pure-MATLAB library
+of a collection of stochastic optimization algorithms. The purpose of the
+library is to provide researchers and implementers a comprehensive evaluation
+environment for the use of these algorithms on various ML problems.
+",1,0,0,1,0,0
+4123,Structured Uncertainty Prediction Networks,"  This paper is the first work to propose a network to predict a structured
+uncertainty distribution for a synthesized image. Previous approaches have been
+mostly limited to predicting diagonal covariance matrices. Our novel model
+learns to predict a full Gaussian covariance matrix for each reconstruction,
+which permits efficient sampling and likelihood evaluation.
+We demonstrate that our model can accurately reconstruct ground truth
+correlated residual distributions for synthetic datasets and generate plausible
+high frequency samples for real face images. We also illustrate the use of
+these predicted covariances for structure preserving image denoising.
+",0,0,0,1,0,0
+4124,"Shannon's entropy and its Generalizations towards Statistics, Reliability and Information Science during 1948-2018","  Starting from the pioneering works of Shannon and Weiner in 1948, a plethora
+of works have been reported on entropy in different directions. Entropy-related
+review work in the direction of statistics, reliability and information
+science, to the best of our knowledge, has not been reported so far. Here we
+have tried to collect all possible works in this direction during the period
+1948-2018 so that people interested in entropy, specially the new researchers,
+get benefited.
+",0,0,0,1,0,0
+4125,Understanding MIDI: A Painless Tutorial on Midi Format,"  A short overview demystifying the midi audio format is presented. The goal is
+to explain the file structure and how the instructions are used to produce a
+music signal, both in the case of monophonic signals as for polyphonic signals.
+",1,0,0,0,0,0
+4126,"Optimization, fast and slow: optimally switching between local and Bayesian optimization","  We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects
+between multiple alternative acquisition functions and traditional local
+optimization at each step. This is combined with a novel stopping condition
+based on expected regret. This pairing allows us to obtain the best
+characteristics of both local and Bayesian optimization, making efficient use
+of function evaluations while yielding superior convergence to the global
+minimum on a selection of optimization problems, and also halting optimization
+once a principled and intuitive stopping condition has been fulfilled.
+",0,0,0,1,0,0
+4127,Optimization of exposure time division for wide field observations,"  The optical observations of wide fields of view encounter the problem of
+selection of best exposure time. As there are usually plenty of objects
+observed simultaneously, the quality of photometry of the brightest ones is
+always better than of the dimmer ones. Frequently all of them are equally
+interesting for the astronomers and thus it is desired to have all of them
+measured with the highest possible accuracy.
+In this paper we present a novel optimization algorithm dedicated for the
+division of exposure time into sub-exposures, which allows to perform
+photometry with more balanced noise budget. Thanks to the proposed technique,
+the photometric precision of dimmer objects is increased at the expense of the
+measurement fidelity of the brightest ones. We tested the method on real
+observations using two telescope setups demonstrating its usefulness and good
+agreement with the theoretical expectations. The main application of our
+approach is a wide range of sky surveys, including the ones performed by the
+space telescopes. The method can be applied for planning virtually any
+photometric observations, in which the objects of interest show a wide range of
+magnitudes.
+",0,1,0,0,0,0
+4128,Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder,"  Multi-Entity Dependence Learning (MEDL) explores conditional correlations
+among multiple entities. The availability of rich contextual information
+requires a nimble learning scheme that tightly integrates with deep neural
+networks and has the ability to capture correlation structures among
+exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional
+multivariate distribution as a generating process. As a result, the variational
+lower bound of the joint likelihood can be optimized via a conditional
+variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was
+motivated by two real-world applications in computational sustainability: one
+studies the spatial correlation among multiple bird species using the eBird
+data and the other models multi-dimensional landscape composition and human
+footprint in the Amazon rainforest with satellite images. We show that
+MEDL_CVAE captures rich dependency structures, scales better than previous
+methods, and further improves on the joint likelihood taking advantage of very
+large datasets that are beyond the capacity of previous methods.
+",1,0,0,1,0,0
+4129,A recognition algorithm for simple-triangle graphs,"  A simple-triangle graph is the intersection graph of triangles that are
+defined by a point on a horizontal line and an interval on another horizontal
+line. The time complexity of the recognition problem for simple-triangle graphs
+was a longstanding open problem, which was recently settled. This paper
+provides a new recognition algorithm for simple-triangle graphs to improve the
+time bound from $O(n^2 \overline{m})$ to $O(nm)$, where $n$, $m$, and
+$\overline{m}$ are the number of vertices, edges, and non-edges of the graph,
+respectively. The algorithm uses the vertex ordering characterization that a
+graph is a simple-triangle graph if and only if there is a linear ordering of
+the vertices containing both an alternating orientation of the graph and a
+transitive orientation of the complement of the graph. We also show, as a
+byproduct, that an alternating orientation can be obtained in $O(nm)$ time for
+cocomparability graphs, and it is NP-complete to decide whether a graph has an
+orientation that is alternating and acyclic.
+",1,0,0,0,0,0
+4130,A normalized gradient flow method with attractive-repulsive splitting for computing ground states of Bose-Einstein condensates with higher-order interaction,"  In this paper, we generalize the normalized gradient flow method to compute
+the ground states of Bose-Einstein condensates (BEC) with higher order
+interactions (HOI), which is modelled via the modified Gross-Pitaevskii
+equation (MGPE). Schemes constructed in naive ways suffer from severe stability
+problems due to the high restrictions on time steps. To build an efficient and
+stable scheme, we split the HOI term into two parts with each part treated
+separately. The part corresponding to a repulsive/positive energy is treated
+semi-implicitly while the one corresponding to an attractive/negative energy is
+treated fully explicitly. Based on the splitting, we construct the
+BEFD-splitting and BESP-splitting schemes. A variety of numerical experiments
+shows that the splitting will improve the stability of the schemes
+significantly. Besides, we will show that the methods can be applied to
+multidimensional problems and to the computation of the first excited state as
+well.
+",0,1,0,0,0,0
+4131,Truncation in Hahn Fields is Undecidable and Wild,"  We show that in any nontrivial Hahn field with truncation as a primitive
+operation we can interpret the monadic second-order logic of the additive
+monoid of natural numbers and are thus undecidable. We also specify a definable
+binary relation on such a structure that has $\SOP$ and $\TP$.
+",0,0,1,0,0,0
+4132,Direct measurement of laser aberration and ahead point from ARTEMIS satellite through strong clouds,"  Laser communication has advances in compared with radio frequency
+communication as result of much high carrier frequency from ultraviolet to near
+infrared. Very narrow laser beam is possible to form with very high power
+density. But laser beam has high destruction and attenuation on clouds,
+turbulence, scattering on aerosols and molecules of the atmosphere. Low Earth
+orbits (LEO), Middling Earth orbits (MEO) and partly Geosynchronous Earth orbit
+(GSO) satellites moving on the sky and laser light from satellites moves across
+different turbulence conditions of the atmosphere, clouds, molecules of the
+atmosphere H2O, O2, N2, CO, O3 and other. We performed unique experiments with
+propagation of laser beams from beacon of OPALE terminal of ARTEMIS satellite
+through thin clouds. We have found that small part of laser radiation is
+received from ahead point there the satellite will be after time of propagation
+of laser radiation from the satellite to telescope. It is in accordance with
+theory of relativity for aberration of light during transition from moving to
+not moving coordinate systems. It is positive effect for laser communication
+through the atmosphere and clouds because will be possible to develop a system
+for reduce of the atmosphere turbulence during of laser communication from
+ground to the satellites. The interest is what will be during propagation of
+laser radiation from the satellite through strong clouds. The detail
+descriptions of laser experiment with ARTEMIS GSO satellite through strong
+clouds and estimations of the laser power through strong clouds are presented
+in this paper. Accordingly we must search the optimal wave lengths and power of
+lasers for performs laser communication in different cloudy conditions.
+",0,1,0,0,0,0
+4133,DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem,"  This paper introduces the first deep neural network-based estimation metric
+for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network
+predicts whether or not they should be adjacent in the correct assembly of the
+puzzle, using nothing but the pixels of each piece. The proposed metric
+exhibits an extremely high precision even though no manual feature extraction
+is performed. When incorporated into an existing puzzle solver, the solution's
+accuracy increases significantly, achieving thereby a new state-of-the-art
+standard.
+",1,0,0,1,0,0
+4134,Attack Analysis for Distributed Control Systems: An Internal Model Principle Approach,"  Although adverse effects of attacks have been acknowledged in many
+cyber-physical systems, there is no system-theoretic comprehension of how a
+compromised agent can leverage communication capabilities to maximize the
+damage in distributed multi-agent systems. A rigorous analysis of
+cyber-physical attacks enables us to increase the system awareness against
+attacks and design more resilient control protocols. To this end, we will take
+the role of the attacker to identify the worst effects of attacks on root nodes
+and non-root nodes in a distributed control system. More specifically, we show
+that a stealthy attack on root nodes can mislead the entire network to a wrong
+understanding of the situation and even destabilize the synchronization
+process. This will be called the internal model principle for the attacker and
+will intensify the urgency of designing novel control protocols to mitigate
+these types of attacks.
+",1,0,0,0,0,0
+4135,A conservative scheme for electromagnetic simulation of magnetized plasmas with kinetic electrons,"  A conservative scheme has been formulated and verified for gyrokinetic
+particle simulations of electromagnetic waves and instabilities in magnetized
+plasmas. An electron continuity equation derived from drift kinetic equation is
+used to time advance electron density perturbation by using the perturbed
+mechanical flow calculated from the parallel vector potential, and the parallel
+vector potential is solved by using the perturbed canonical flow from the
+perturbed distribution function. In gyrokinetic particle simulations using this
+new scheme, shear Alfvén wave dispersion relation in shearless slab and
+continuum damping in sheared cylinder have been recovered. The new scheme
+overcomes the stringent requirement in conventional perturbative simulation
+method that perpendicular grid size needs to be as small as electron
+collisionless skin depth even for the long wavelength Alfvén waves. The new
+scheme also avoids the problem in conventional method that an unphysically
+large parallel electric field arises due to the inconsistency between
+electrostatic potential calculated from the perturbed density and vector
+potential calculated from the perturbed canonical flow. Finally, the
+gyrokinetic particle simulations of the Alfvén waves in sheared cylinder have
+superior numerical properties compared with the fluid simulations, which suffer
+from numerical difficulties associated with singular mode structures.
+",0,1,0,0,0,0
+4136,Corrupt Bandits for Preserving Local Privacy,"  We study a variant of the stochastic multi-armed bandit (MAB) problem in
+which the rewards are corrupted. In this framework, motivated by privacy
+preservation in online recommender systems, the goal is to maximize the sum of
+the (unobserved) rewards, based on the observation of transformation of these
+rewards through a stochastic corruption process with known parameters. We
+provide a lower bound on the expected regret of any bandit algorithm in this
+corrupted setting. We devise a frequentist algorithm, KLUCB-CF, and a Bayesian
+algorithm, TS-CF and give upper bounds on their regret. We also provide the
+appropriate corruption parameters to guarantee a desired level of local privacy
+and analyze how this impacts the regret. Finally, we present some experimental
+results that confirm our analysis.
+",1,0,0,1,0,0
+4137,Deep Reinforcement Learning based Optimal Control of Hot Water Systems,"  Energy consumption for hot water production is a major draw in high
+efficiency buildings. Optimizing this has typically been approached from a
+thermodynamics perspective, decoupled from occupant influence. Furthermore,
+optimization usually presupposes existence of a detailed dynamics model for the
+hot water system. These assumptions lead to suboptimal energy efficiency in the
+real world. In this paper, we present a novel reinforcement learning based
+methodology which optimizes hot water production. The proposed methodology is
+completely generalizable, and does not require an offline step or human domain
+knowledge to build a model for the hot water vessel or the heating element.
+Occupant preferences too are learnt on the fly. The proposed system is applied
+to a set of 32 houses in the Netherlands where it reduces energy consumption
+for hot water production by roughly 20% with no loss of occupant comfort.
+Extrapolating, this translates to absolute savings of roughly 200 kWh for a
+single household on an annual basis. This performance can be replicated to any
+domestic hot water system and optimization objective, given that the fairly
+minimal requirements on sensor data are met. With millions of hot water systems
+operational worldwide, the proposed framework has the potential to reduce
+energy consumption in existing and new systems on a multi Gigawatt-hour scale
+in the years to come.
+",0,0,0,1,0,0
+4138,A Model-Based Fuzzy Control Approach to Achieving Adaptation with Contextual Uncertainties,"  Self-adaptive system (SAS) is capable of adjusting its behavior in response
+to meaningful changes in the operational context and itself. Due to the
+inherent volatility of the open and changeable environment in which SAS is
+embedded, the ability of adaptation is highly demanded by many
+software-intensive systems. Two concerns, i.e., the requirements uncertainty
+and the context uncertainty are most important among others. An essential issue
+to be addressed is how to dynamically adapt non-functional requirements (NFRs)
+and task configurations of SASs with context uncertainty. In this paper, we
+propose a model-based fuzzy control approach that is underpinned by the
+feedforward-feedback control mechanism. This approach identifies and represents
+NFR uncertainties, task uncertainties and context uncertainties with linguistic
+variables, and then designs an inference structure and rules for the fuzzy
+controller based on the relations between the requirements model and the
+context model. The adaptation of NFRs and task configurations is achieved
+through fuzzification, inference, defuzzification and readaptation. Our
+approach is demonstrated with a mobile computing application and is evaluated
+through a series of simulation experiments.
+",1,0,0,0,0,0
+4139,See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS,"  The Intelligent Transportation System (ITS) targets to a coordinated traffic
+system by applying the advanced wireless communication technologies for road
+traffic scheduling. Towards an accurate road traffic control, the short-term
+traffic forecasting to predict the road traffic at the particular site in a
+short period is often useful and important. In existing works, Seasonal
+Autoregressive Integrated Moving Average (SARIMA) model is a popular approach.
+The scheme however encounters two challenges: 1) the analysis on related data
+is insufficient whereas some important features of data may be neglected; and
+2) with data presenting different features, it is unlikely to have one
+predictive model that can fit all situations. To tackle above issues, in this
+work, we develop a hybrid model to improve accuracy of SARIMA. In specific, we
+first explore the autocorrelation and distribution features existed in traffic
+flow to revise structure of the time series model. Based on the Gaussian
+distribution of traffic flow, a hybrid model with a Bayesian learning algorithm
+is developed which can effectively expand the application scenarios of SARIMA.
+We show the efficiency and accuracy of our proposal using both analysis and
+experimental studies. Using the real-world trace data, we show that the
+proposed predicting approach can achieve satisfactory performance in practice.
+",1,0,0,1,0,0
+4140,Single-Crystal N-polar GaN p-n Diodes by Plasma-Assisted Molecular Beam Epitaxy,"  N-polar GaN p-n diodes are realized on single-crystal N-polar GaN bulk wafers
+by plasma-assisted molecular beam epitaxy growth. The current-voltage
+characteristics show high-quality rectification and electroluminescence
+characteristics with a high on/off current ratio and interband photon emission.
+The measured electroluminescence spectrum is dominated by strong near-band edge
+emission, while deep level luminescence is greatly suppressed. A very low
+dislocation density leads to a high reverse breakdown electric field. The low
+leakage current N-polar diodes open up several potential applications in
+polarization-engineered photonic and electronic devices.
+",0,1,0,0,0,0
+4141,Particle Identification with the TOP and ARICH detectors at Belle II,"  Particle identification at the Belle II experiment will be provided by two
+ring imaging Cherenkov devices, the time of propagation counters in the central
+region and the proximity focusing RICH with aerogel radiator in the forward
+end-cap region. The key features of these two detectors, the performance
+studies, and the construction progress is presented.
+",0,1,0,0,0,0
+4142,Face Detection and Face Recognition In the Wild Using Off-the-Shelf Freely Available Components,"  This paper presents an easy and efficient face detection and face recognition
+approach using free software components from the internet. Face detection and
+face recognition problems have wide applications in home and office security.
+Therefore this work will helpful for those searching for a free face
+off-the-shelf face detection system. Using this system, faces can be detected
+in uncontrolled environments. In the detection phase, every individual face is
+detected and in the recognition phase the detected faces are compared with the
+faces in a given data set and recognized.
+",1,0,0,0,0,0
+4143,End-to-end Learning of Deterministic Decision Trees,"  Conventional decision trees have a number of favorable properties, including
+interpretability, a small computational footprint and the ability to learn from
+little training data. However, they lack a key quality that has helped fuel the
+deep learning revolution: that of being end-to-end trainable, and to learn from
+scratch those features that best allow to solve a given supervised learning
+problem. Recent work (Kontschieder 2015) has addressed this deficit, but at the
+cost of losing a main attractive trait of decision trees: the fact that each
+sample is routed along a small subset of tree nodes only. We here propose a
+model and Expectation-Maximization training scheme for decision trees that are
+fully probabilistic at train time, but after a deterministic annealing process
+become deterministic at test time. We also analyze the learned oblique split
+parameters on image datasets and show that Neural Networks can be trained at
+each split node. In summary, we present the first end-to-end learning scheme
+for deterministic decision trees and present results on par with or superior to
+published standard oblique decision tree algorithms.
+",1,0,0,1,0,0
+4144,Conceptualization of Object Compositions Using Persistent Homology,"  A topological shape analysis is proposed and utilized to learn concepts that
+reflect shape commonalities. Our approach is two-fold: i) a spatial topology
+analysis of point cloud segment constellations within objects. Therein
+constellations are decomposed and described in an hierarchical manner - from
+single segments to segment groups until a single group reflects an entire
+object. ii) a topology analysis of the description space in which segment
+decompositions are exposed in. Inspired by Persistent Homology, hidden groups
+of shape commonalities are revealed from object segment decompositions.
+Experiments show that extracted persistent groups of commonalities can
+represent semantically meaningful shape concepts. We also show the
+generalization capability of the proposed approach considering samples of
+external datasets.
+",1,0,0,0,0,0
+4145,Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks,"  Existing methods for arterial blood pressure (BP) estimation directly map the
+input physiological signals to output BP values without explicitly modeling the
+underlying temporal dependencies in BP dynamics. As a result, these models
+suffer from accuracy decay over a long time and thus require frequent
+calibration. In this work, we address this issue by formulating BP estimation
+as a sequence prediction problem in which both the input and target are
+temporal sequences. We propose a novel deep recurrent neural network (RNN)
+consisting of multilayered Long Short-Term Memory (LSTM) networks, which are
+incorporated with (1) a bidirectional structure to access larger-scale context
+information of input sequence, and (2) residual connections to allow gradients
+in deep RNN to propagate more effectively. The proposed deep RNN model was
+tested on a static BP dataset, and it achieved root mean square error (RMSE) of
+3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction
+respectively, surpassing the accuracy of traditional BP prediction models. On a
+multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81
+mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP
+prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction,
+respectively, which outperforms all previous models with notable improvement.
+The experimental results suggest that modeling the temporal dependencies in BP
+dynamics significantly improves the long-term BP prediction accuracy.
+",1,0,0,1,0,0
+4146,Risks for life on habitable planets from superflares of their host stars,"  We explore some of the ramifications arising from superflares on the
+evolutionary history of Earth, other planets in the Solar system, and
+exoplanets. We propose that the most powerful superflares can serve as
+plausible drivers of extinction events, and that their periodicity could
+correspond to certain patterns in the terrestrial fossil diversity record. On
+the other hand, weaker superflares may play a positive role in enabling the
+origin of life through the formation of key organic compounds. Superflares
+could also prove to be quite detrimental to the evolution of complex life on
+present-day Mars and exoplanets in the habitable zone of M- and K-dwarfs. We
+conclude that the risk posed by superflares has not been sufficiently
+appreciated, and that humanity might potentially witness a superflare event in
+the next $\sim 10^3$ years leading to devastating economic and technological
+losses. In light of the many uncertainties and assumptions associated with our
+analysis, we recommend that these results should be viewed with due caution.
+",0,1,0,0,0,0
+4147,Thermodynamics of Spin-1/2 Kagomé Heisenberg Antiferromagnet: Algebraic Paramagnetic Liquid and Finite-Temperature Phase Diagram,"  Quantum fluctuations from frustration can trigger quantum spin liquids (QSLs)
+at zero temperature. However, it is unclear how thermal fluctuations affect a
+QSL. We employ state-of-the-art tensor network-based methods to explore the
+ground state and thermodynamic properties of the spin-1/2 kagome Heisenberg
+antiferromagnet (KHA). Its ground state is shown to be consistent with a
+gapless QSL by observing the absence of zero-magnetization plateau as well as
+the algebraic behaviors of susceptibility and specific heat at low
+temperatures, respectively. We show that there exists an \textit{algebraic
+paramagnetic liquid} (APL) that possesses both the paramagnetic properties and
+the algebraic behaviors inherited from the QSL. The APL is induced under the
+interplay between quantum fluctuations from geometrical frustration and thermal
+fluctuations. By studying the temperature-dependent behaviors of specific heat
+and magnetic susceptibility, a finite-temperature phase diagram in a magnetic
+field is suggested, where various phases are identified. This present study
+gains useful insight into the thermodynamic properties of the spin-1/2 KHA with
+or without a magnetic field and is helpful for relevant experimental studies.
+",0,1,0,0,0,0
+4148,Fast Rigid 3D Registration Solution: A Simple Method Free of SVD and Eigen-Decomposition,"  A novel solution is obtained to solve the rigid 3D registration problem,
+motivated by previous eigen-decomposition approaches. Different from existing
+solvers, the proposed algorithm does not require sophisticated matrix
+operations e.g. singular value decomposition or eigenvalue decomposition.
+Instead, the optimal eigenvector of the point cross-covariance matrix can be
+computed within several iterations. It is also proven that the optimal rotation
+matrix can be directly computed for cases without need of quaternion. The
+simple framework provides very easy approach of integer-implementation on
+embedded platforms. Simulations on noise-corrupted point clouds have verified
+the robustness and computation speed of the proposed method. The final results
+indicate that the proposed algorithm is accurate, robust and owns over $60\%
+\sim 80\%$ less computation time than representatives. It has also been applied
+to real-world applications for faster relative robotic navigation.
+",1,0,0,0,0,0
+4149,Massive MIMO 5G Cellular Networks: mm-wave vs. μ-wave Frequencies,"  Enhanced mobile broadband (eMBB) is one of the key use-cases for the
+development of the new standard 5G New Radio for the next generation of mobile
+wireless networks. Large-scale antenna arrays, a.k.a. Massive MIMO, the usage
+of carrier frequencies in the range 10-100 GHz, the so-called millimeter wave
+(mm-wave) band, and the network densification with the introduction of
+small-sized cells are the three technologies that will permit implementing eMBB
+services and realizing the Gbit/s mobile wireless experience. This paper is
+focused on the massive MIMO technology; initially conceived for conventional
+cellular frequencies in the sub-6 GHz range (\mu-wave), the massive MIMO
+concept has been then progressively extended to the case in which mm-wave
+frequencies are used. However, due to different propagation mechanisms in urban
+scenarios, the resulting MIMO channel models at \mu-wave and mm-wave are
+radically different. Six key basic differences are pinpointed in this paper,
+along with the implications that they have on the architecture and algorithms
+of the communication transceivers and on the attainable performance in terms of
+reliability and multiplexing capabilities.
+",1,0,0,0,0,0
+4150,Independent Set Size Approximation in Graph Streams,"  We study the problem of estimating the size of independent sets in a graph
+$G$ defined by a stream of edges. Our approach relies on the Caro-Wei bound,
+which expresses the desired quantity in terms of a sum over nodes of the
+reciprocal of their degrees, denoted by $\beta(G)$. Our results show that
+$\beta(G)$ can be approximated accurately, based on a provided lower bound on
+$\beta$. Stronger results are possible when the edges are promised to arrive
+grouped by an incident node. In this setting, we obtain a value that is at most
+a logarithmic factor below the true value of $\beta$ and no more than the true
+independent set size. To justify the form of this bound, we also show an
+$\Omega(n/\beta)$ lower bound on any algorithm that approximates $\beta$ up to
+a constant factor.
+",1,0,0,0,0,0
+4151,Two-Person Zero-Sum Games with Unbounded Payoff Functions and Uncertain Expected Payoffs,"  This paper provides sufficient conditions for the existence of values and
+solutions for two-person zero-sum one-step games with possibly noncompact
+action sets for both players and possibly unbounded payoff functions, which may
+be neither convex nor concave. For such games payoffs may not be defined for
+some pairs of strategies. In addition to the existence of values and solutions,
+this paper investigates continuity properties of the value functions and
+solution multifunctions for families of games with possibly noncompact action
+sets and unbounded payoff functions, when action sets and payoffs depend on a
+parameter.
+",0,0,1,0,0,0
+4152,Scale relativistic formulation of non-differentiable mechanics II: The Schroedinger picture,"  This article is the second in a series of two presenting the Scale
+Relativistic approach to non-differentiability in mechanics and its relation to
+quantum mechanics. Here, we show Schroedinger's equation to be a reformulation
+of Newton's fundamental relation of dynamics as generalized to
+non-differentiable geometries in the first paper \cite{paper1}. It motivates an
+alternative interpretation of the other axioms of standard quantum mechanics in
+a coherent picture. This exercise validates the Scale Relativistic approach
+and, at the same time, it allows to identify macroscopic chaotic systems
+considered at time scales exceeding their horizon of predictability as
+candidates in which to search for quantum-like structuring or behavior.
+",0,1,0,0,0,0
+4153,Compact arrangement for femtosecond laser induced generation of broadband hard x-ray pulses,"  We present a simple apparatus for femtosecond laser induced generation of
+X-rays. The apparatus consists of a vacuum chamber containing an off-axis
+parabolic focusing mirror, a reel system, a debris protection setup, a quartz
+window for the incoming laser beam, and an X-ray window. Before entering the
+vacuum chamber, the femtosecond laser is expanded with an all reflective
+telescope design to minimize laser intensity losses and pulse broadening while
+allowing for focusing as well as peak intensity optimization. The laser pulse
+duration was characterized by second-harmonic generation frequency resolved
+optical gating. A high spatial resolution knife-edge technique was implemented
+to characterize the beam size at the focus of the X-ray generation apparatus.
+We have characterized x-ray spectra obtained with three different samples:
+titanium, iron:chromium alloy, and copper. In all three cases, the femtosecond
+laser generated X-rays give spectral lines consistent with literature reports.
+We present a rms amplitude analysis of the generated X-ray pulses, and provide
+an upper bound for the duration of the X-ray pulses.
+",0,1,0,0,0,0
+4154,Hierarchical Summarization of Metric Changes,"  We study changes in metrics that are defined on a cartesian product of trees.
+Such metrics occur naturally in many practical applications, where a global
+metric (such as revenue) can be broken down along several hierarchical
+dimensions (such as location, gender, etc).
+Given a change in such a metric, our goal is to identify a small set of
+non-overlapping data segments that account for the change. An organization
+interested in improving the metric can then focus their attention on these data
+segments.
+Our key contribution is an algorithm that mimics the operation of a
+hierarchical organization of analysts. The algorithm has been successfully
+applied, for example within Google Adwords to help advertisers triage the
+performance of their advertising campaigns.
+We show that the algorithm is optimal for two dimensions, and has an
+approximation ratio $\log^{d-2}(n+1)$ for $d \geq 3$ dimensions, where $n$ is
+the number of input data segments. For the Adwords application, we can show
+that our algorithm is in fact a $2$-approximation.
+Mathematically, we identify a certain data pattern called a \emph{conflict}
+that both guides the design of the algorithm, and plays a central role in the
+hardness results. We use these conflicts to both derive a lower bound of
+$1.144^{d-2}$ (again $d\geq3$) for our algorithm, and to show that the problem
+is NP-hard, justifying the focus on approximation.
+",1,0,0,0,0,0
+4155,Manifold learning with bi-stochastic kernels,"  In this paper we answer the following question: what is the infinitesimal
+generator of the diffusion process defined by a kernel that is normalized such
+that it is bi-stochastic with respect to a specified measure? More precisely,
+under the assumption that data is sampled from a Riemannian manifold we
+determine how the resulting infinitesimal generator depends on the potentially
+nonuniform distribution of the sample points, and the specified measure for the
+bi-stochastic normalization. In a special case, we demonstrate a connection to
+the heat kernel. We consider both the case where only a single data set is
+given, and the case where a data set and a reference set are given. The
+spectral theory of the constructed operators is studied, and Nyström
+extension formulas for the gradients of the eigenfunctions are computed.
+Applications to discrete point sets and manifold learning are discussed.
+",0,0,1,1,0,0
+4156,The importance of the weak: Interaction modifiers in artificial spin ices,"  The modification of geometry and interactions in two-dimensional magnetic
+nanosystems has enabled a range of studies addressing the magnetic order,
+collective low-energy dynamics, and emergent magnetic properties, in e.g.
+artificial spin ice structures. The common denominator of all these
+investigations is the use of Ising-like mesospins as building blocks, in the
+form of elongated magnetic islands. Here we introduce a new approach: single
+interaction modifiers, using slave-mesospins in the form of discs, within which
+the mesospin is free to rotate in the disc plane. We show that by placing these
+on the vertices of square artificial spin ice arrays and varying their
+diameter, it is possible to tailor the strength and the ratio of the
+interaction energies. We demonstrate the existence of degenerate ice-rule
+obeying states in square artificial spin ice structures, enabling the
+exploration of thermal dynamics in a spin liquid manifold. Furthermore, we even
+observe the emergence of flux lattices on larger length-scales, when the energy
+landscape of the vertices is reversed. The work highlights the potential of a
+design strategy for two-dimensional magnetic nano-architectures, through which
+mixed dimensionality of mesospins can be used to promote thermally emergent
+mesoscale magnetic states.
+",0,1,0,0,0,0
+4157,How To Extract Fashion Trends From Social Media? A Robust Object Detector With Support For Unsupervised Learning,"  With the proliferation of social media, fashion inspired from celebrities,
+reputed designers as well as fashion influencers has shortened the cycle of
+fashion design and manufacturing. However, with the explosion of fashion
+related content and large number of user generated fashion photos, it is an
+arduous task for fashion designers to wade through social media photos and
+create a digest of trending fashion. This necessitates deep parsing of fashion
+photos on social media to localize and classify multiple fashion items from a
+given fashion photo. While object detection competitions such as MSCOCO have
+thousands of samples for each of the object categories, it is quite difficult
+to get large labeled datasets for fast fashion items. Moreover,
+state-of-the-art object detectors do not have any functionality to ingest large
+amount of unlabeled data available on social media in order to fine tune object
+detectors with labeled datasets. In this work, we show application of a generic
+object detector, that can be pretrained in an unsupervised manner, on 24
+categories from recently released Open Images V4 dataset. We first train the
+base architecture of the object detector using unsupervisd learning on 60K
+unlabeled photos from 24 categories gathered from social media, and then
+subsequently fine tune it on 8.2K labeled photos from Open Images V4 dataset.
+On 300 X 300 image inputs, we achieve 72.7% mAP on a test dataset of 2.4K
+photos while performing 11% to 17% better as compared to the state-of-the-art
+object detectors. We show that this improvement is due to our choice of
+architecture that lets us do unsupervised learning and that performs
+significantly better in identifying small objects.
+",0,0,0,1,0,0
+4158,Multi-Task Learning Using Neighborhood Kernels,"  This paper introduces a new and effective algorithm for learning kernels in a
+Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here,
+our approach can be easily applied to standard single task learning, as well.
+As shown by our empirical results, our algorithm consistently outperforms the
+traditional kernel learning algorithms such as uniform combination solution,
+convex combinations of base kernels as well as some kernel alignment-based
+models, which have been proven to give promising results in the past. We
+present a Rademacher complexity bound based on which a new Multi-Task Multiple
+Kernel Learning (MT-MKL) model is derived. In particular, we propose a Support
+Vector Machine-regularized model in which, for each task, an optimal kernel is
+learned based on a neighborhood-defining kernel that is not restricted to be
+positive semi-definite. Comparative experimental results are showcased that
+underline the merits of our neighborhood-defining framework in both
+classification and regression problems.
+",1,0,0,1,0,0
+4159,Optimized Bucket Wheel Design for Asteroid Excavation,"  Current spacecraft need to launch with all of their required fuel for travel.
+This limits the system performance, payload capacity, and mission flexibility.
+One compelling alternative is to perform In-Situ Resource Utilization (ISRU) by
+extracting fuel from small bodies in local space such as asteroids or small
+satellites. Compared to the Moon or Mars, the microgravity on an asteroid
+demands a fraction of the energy for digging and accessing hydrated regolith
+just below the surface. Previous asteroid excavation efforts have focused on
+discrete capture events (an extension of sampling technology) or whole-asteroid
+capture and processing. This paper proposes an optimized bucket wheel design
+for surface excavation of an asteroid or small-body. Asteroid regolith is
+excavated and water extracted for use as rocket propellant. Our initial study
+focuses on system design, bucket wheel mechanisms, and capture dynamics applied
+to ponded materials known to exist on asteroids like Itokawa and Eros and small
+satellites like Phobos and Deimos. For initial evaluation of
+material-spacecraft dynamics and mechanics, we assume lunar-like regolith for
+bulk density, particle size and cohesion. We shall present our estimates for
+the energy balance of excavation and processing versus fuel gained.
+Conventional electrolysis of water is used to produce hydrogen and oxygen. It
+is compared with steam for propulsion and both show significant delta-v. We
+show that a return trip from Deimos to Earth is possible for a 12 kg craft
+using ISRU processed fuel.
+",1,1,0,0,0,0
+4160,Fraction of the X-ray selected AGNs with optical emission lines in galaxy groups,"  Compared with numerous X-ray dominant active galactic nuclei (AGNs) without
+emission-line signatures in their optical spectra, the X-ray selected AGNs with
+optical emission lines are probably still in the high-accretion phase of black
+hole growth. This paper presents an investigation on the fraction of these
+X-ray detected AGNs with optical emission-line spectra in 198 galaxy groups at
+$z<1$ in a rest frame 0.1-2.4 keV luminosity range 41.3 <log(L_X/erg s-1) <
+44.1 within the COSMOS field, as well as its variations with redshift and group
+richness. For various selection criteria of member galaxies, the numbers of
+galaxies and the AGNs with optical emission lines in each galaxy group are
+obtained. It is found that, in total 198 X-ray groups, there are 27 AGNs
+detected in 26 groups. AGN fraction is on everage less than $4.6 (\pm 1.2)\%$
+for individual groups hosting at least one AGN. The corrected overall AGN
+fraction for whole group sample is less than $0.98 (\pm 0.11) \%$. The
+normalized locations of group AGNs show that 15 AGNs are found to be located in
+group centers, including all 6 low-luminosity group AGNs. A week rising
+tendency with $z$ are found: overall AGN fraction is 0.30-0.43% for the groups
+at $z<0.5$, and 0.55-0.64% at 0.5 < z < 1.0. For the X-ray groups at $z>0.5$,
+most member AGNs are X-ray bright, optically dull, which results in a lower AGN
+fractions at higher redshifts. The AGN fraction in isolated fields also
+exhibits a rising trend with redshift, and the slope is consistent with that in
+groups. The environment of galaxy groups seems to make no difference in
+detection probability of the AGNs with emission lines. Additionally, a larger
+AGN fractions are found in poorer groups, which implies that the AGNs in poorer
+groups might still be in the high-accretion phase, whereas the AGN population
+in rich clusters is mostly in the low-accretion, X-ray dominant phase.
+",0,1,0,0,0,0
+4161,A local search 2.917-approximation algorithm for duo-preservation string mapping,"  We study the {\em maximum duo-preservation string mapping} ({\sc Max-Duo})
+problem, which is the complement of the well studied {\em minimum common string
+partition} ({\sc MCSP}) problem. Both problems have applications in many fields
+including text compression and bioinformatics. Motivated by an earlier local
+search algorithm, we present an improved approximation and show that its
+performance ratio is no greater than ${35}/{12} < 2.917$. This beats the
+current best $3.25$-approximation for {\sc Max-Duo}. The performance analysis
+of our algorithm is done through a complex yet interesting amortization. Two
+lower bounds on the locality gap of our algorithm are also provided.
+",1,0,0,0,0,0
+4162,Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code,"  The current trends in next-generation exascale systems go towards integrating
+a wide range of specialized (co-)processors into traditional supercomputers.
+Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per
+surface unit, opening the access of heterogeneous platforms to a wider range of
+users is an important problem to be tackled. However, heterogeneous platforms
+limit the portability of the applications and increase development complexity
+due to the programming skills required. Program transformation can help make
+programming heterogeneous systems easier by defining a step-wise transformation
+process that translates a given initial code into a semantically equivalent
+final code, but adapted to a specific platform. Program transformation systems
+require the definition of efficient transformation strategies to tackle the
+combinatorial problem that emerges due to the large set of transformations
+applicable at each step of the process. In this paper we propose a machine
+learning-based approach to learn heuristics to define program transformation
+strategies. Our approach proposes a novel combination of reinforcement learning
+and classification methods to efficiently tackle the problems inherent to this
+type of systems. Preliminary results demonstrate the suitability of this
+approach.
+",1,0,0,0,0,0
+4163,Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning,"  We propose a general approach to modeling semi-supervised learning (SSL)
+algorithms. Specifically, we present a declarative language for modeling both
+traditional supervised classification tasks and many SSL heuristics, including
+both well-known heuristics such as co-training and novel domain-specific
+heuristics. In addition to representing individual SSL heuristics, we show that
+multiple heuristics can be automatically combined using Bayesian optimization
+methods. We experiment with two classes of tasks, link-based text
+classification and relation extraction. We show modest improvements on
+well-studied link-based classification benchmarks, and state-of-the-art results
+on relation-extraction tasks for two realistic domains.
+",1,0,0,1,0,0
+4164,Fabrication of a centimeter-long cavity on a nanofiber for cavity QED,"  We report the fabrication of a 1.2 cm long cavity directly on a nanofiber
+using femtosecond laser ablation. The cavity modes with finesse value in the
+range 200-400 can still maintain the transmission between 40-60%, which can
+enable ""strong-coupling"" regime of cavity QED for a single atom trapped 200 nm
+away from the fiber surface. For such cavity modes, we estimate the one-pass
+intra-cavity transmission to be 99.53%. Other cavity modes, which can enable
+high cooperativity in the range 3-10, show transmission over 60-85% and are
+suitable for fiber-based single photon sources and quantum nonlinear optics in
+the ""Purcell"" regime.
+",0,1,0,0,0,0
+4165,Optimized Deformed Laplacian for Spectrum-based Community Detection in Sparse Heterogeneous Graphs,"  Spectral clustering is one of the most popular, yet still incompletely
+understood, methods for community detection on graphs. In this article we study
+spectral clustering based on the deformed Laplacian matrix $D-rA$, for sparse
+heterogeneous graphs (following a two-class degree-corrected stochastic block
+model). For a specific value $r = \zeta$, we show that, unlike competing
+methods such as the Bethe Hessian or non-backtracking operator approaches,
+clustering is insensitive to the graph heterogeneity. Based on heuristic
+arguments, we study the behavior of the informative eigenvector of $D-\zeta A$
+and, as a result, we accurately predict the clustering accuracy. Via extensive
+simulations and application to real networks, the resulting clustering
+algorithm is validated and observed to systematically outperform
+state-of-the-art competing methods.
+",1,0,0,1,0,0
+4166,High dimensional deformed rectangular matrices with applications in matrix denoising,"  We consider the recovery of a low rank $M \times N$ matrix $S$ from its noisy
+observation $\tilde{S}$ in two different regimes. Under the assumption that $M$
+is comparable to $N$, we propose two consistent estimators for $S$. Our
+analysis relies on the local behavior of the large dimensional rectangular
+matrices with finite rank perturbation. We also derive the convergent limits
+and rates for the singular values and vectors of such matrices.
+",0,0,1,1,0,0
+4167,Validation of the 3-under-2 principle of cell wall growth in Gram-positive bacteria by simulation of a simple coarse-grained model,"  The aim of this work is to propose a first coarse-grained model of Bacillus
+subtilis cell wall, handling explicitly the existence of multiple layers of
+peptidoglycans. In this first work, we aim at the validation of the recently
+proposed ""three under two"" principle.
+",0,1,0,0,0,0
+4168,Self-bound quantum droplets in atomic mixtures,"  Self-bound quantum droplets are a newly discovered phase in the context of
+ultracold atoms. In this work we report their experimental realization
+following the original proposal by Petrov [Phys. Rev. Lett. 115, 155302
+(2015)], using an attractive bosonic mixture. In this system spherical droplets
+form due to the balance of competing attractive and repulsive forces, provided
+by the mean-field energy close to the collapse threshold and the first-order
+correction due to quantum fluctuations. Thanks to an optical levitating
+potential with negligible residual confinement we observe self-bound droplets
+in free space and we characterize the conditions for their formation as well as
+their equilibrium properties. This work sets the stage for future studies on
+quantum droplets, from the measurement of their peculiar excitation spectrum,
+to the exploration of their superfluid nature.
+",0,1,0,0,0,0
+4169,Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs,"  This paper is concerned with the problem of stochastic control of gene
+regulatory networks (GRNs) observed indirectly through noisy measurements and
+with uncertainty in the intervention inputs. The partial observability of the
+gene states and uncertainty in the intervention process are accounted for by
+modeling GRNs using the partially-observed Boolean dynamical system (POBDS)
+signal model with noisy gene expression measurements. Obtaining the optimal
+infinite-horizon control strategy for this problem is not attainable in
+general, and we apply reinforcement learning and Gaussian process techniques to
+find a near-optimal solution. The POBDS is first transformed to a
+directly-observed Markov Decision Process in a continuous belief space, and the
+Gaussian process is used for modeling the cost function over the belief and
+intervention spaces. Reinforcement learning then is used to learn the cost
+function from the available gene expression data. In addition, we employ
+sparsification, which enables the control of large partially-observed GRNs. The
+performance of the resulting algorithm is studied through a comprehensive set
+of numerical experiments using synthetic gene expression data generated from a
+melanoma gene regulatory network.
+",0,0,0,1,0,0
+4170,On Memory System Design for Stochastic Computing,"  Growing uncertainty in design parameters (and therefore, in design
+functionality) renders stochastic computing particularly promising, which
+represents and processes data as quantized probabilities. However, due to the
+difference in data representation, integrating conventional memory (designed
+and optimized for non-stochastic computing) in stochastic computing systems
+inevitably incurs a significant data conversion overhead. Barely any stochastic
+computing proposal to-date covers the memory impact. In this paper, as the
+first study of its kind to the best of our knowledge, we rethink the memory
+system design for stochastic computing. The result is a seamless stochastic
+system, StochMem, which features analog memory to trade the energy and area
+overhead of data conversion for computation accuracy. In this manner StochMem
+can reduce the energy (area) overhead by up-to 52.8% (93.7%) at the cost of at
+most 0.7% loss in computation accuracy.
+",1,0,0,0,0,0
+4171,Strongly convex stochastic online optimization on a unit simplex with application to the mixing least square regression,"  In this paper we propose a new approach to obtain mixing least square
+regression estimate by means of stochastic online mirror descent in
+non-euclidian set-up.
+",0,0,1,0,0,0
+4172,Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos,"  Despite rapid advances in face recognition, there remains a clear gap between
+the performance of still image-based face recognition and video-based face
+recognition, due to the vast difference in visual quality between the domains
+and the difficulty of curating diverse large-scale video datasets. This paper
+addresses both of those challenges, through an image to video feature-level
+domain adaptation approach, to learn discriminative video frame
+representations. The framework utilizes large-scale unlabeled video data to
+reduce the gap between different domains while transferring discriminative
+knowledge from large-scale labeled still images. Given a face recognition
+network that is pretrained in the image domain, the adaptation is achieved by
+(i) distilling knowledge from the network to a video adaptation network through
+feature matching, (ii) performing feature restoration through synthetic data
+augmentation and (iii) learning a domain-invariant feature through a domain
+adversarial discriminator. We further improve performance through a
+discriminator-guided feature fusion that boosts high-quality frames while
+eliminating those degraded by video domain-specific factors. Experiments on the
+YouTube Faces and IJB-A datasets demonstrate that each module contributes to
+our feature-level domain adaptation framework and substantially improves video
+face recognition performance to achieve state-of-the-art accuracy. We
+demonstrate qualitatively that the network learns to suppress diverse artifacts
+in videos such as pose, illumination or occlusion without being explicitly
+trained for them.
+",1,0,0,0,0,0
+4173,Unifying DAGs and UGs,"  We introduce a new class of graphical models that generalizes
+Lauritzen-Wermuth-Frydenberg chain graphs by relaxing the semi-directed
+acyclity constraint so that only directed cycles are forbidden. Moreover, up to
+two edges are allowed between any pair of nodes. Specifically, we present
+local, pairwise and global Markov properties for the new graphical models and
+prove their equivalence. We also present an equivalent factorization property.
+Finally, we present a causal interpretation of the new models.
+",1,0,0,1,0,0
+4174,Program Completionin the Input Language of GRINGO,"  We argue that turning a logic program into a set of completed definitions can
+be sometimes thought of as the ""reverse engineering"" process of generating a
+set of conditions that could serve as a specification for it. Accordingly, it
+may be useful to define completion for a large class of ASP programs and to
+automate the process of generating and simplifying completion formulas.
+Examining the output produced by this kind of software may help programmers to
+see more clearly what their program does, and to what degree its behavior
+conforms with their expectations. As a step toward this goal, we propose here a
+definition of program completion for a large class of programs in the input
+language of the ASP grounder GRINGO, and study its properties. This note is
+under consideration for publication in Theory and Practice of Logic
+Programming.
+",1,0,0,0,0,0
+4175,Numerical modelling of surface water wave interaction with a moving wall,"  In the present manuscript, we consider the practical problem of wave
+interaction with a vertical wall. However, the novelty here consists in the
+fact that the wall can move horizontally due to a system of springs. The water
+wave evolution is described with the free surface potential flow model. Then, a
+semi-analytical numerical method is presented. It is based on a mapping
+technique and a finite difference scheme in the transformed domain. The idea is
+to pose the equations on a fixed domain. This method is thoroughly tested and
+validated in our study. By choosing specific values of spring parameters, this
+system can be used to damp (or in other words to extract the energy of)
+incident water waves.
+",1,1,0,0,0,0
+4176,Dynamic Curriculum Learning for Imbalanced Data Classification,"  Human attribute analysis is a challenging task in the field of computer
+vision, since the data is largely imbalance-distributed. Common techniques such
+as re-sampling and cost-sensitive learning require prior-knowledge to train the
+system. To address this problem, we propose a unified framework called Dynamic
+Curriculum Learning (DCL) to online adaptively adjust the sampling strategy and
+loss learning in single batch, which resulting in better generalization and
+discrimination. Inspired by the curriculum learning, DCL consists of two level
+curriculum schedulers: (1) sampling scheduler not only manages the data
+distribution from imbalanced to balanced but also from easy to hard; (2) loss
+scheduler controls the learning importance between classification and metric
+learning loss. Learning from these two schedulers, we demonstrate our DCL
+framework with the new state-of-the-art performance on the widely used face
+attribute dataset CelebA and pedestrian attribute dataset RAP.
+",1,0,0,0,0,0
+4177,Causal Discovery in the Presence of Measurement Error: Identifiability Conditions,"  Measurement error in the observed values of the variables can greatly change
+the output of various causal discovery methods. This problem has received much
+attention in multiple fields, but it is not clear to what extent the causal
+model for the measurement-error-free variables can be identified in the
+presence of measurement error with unknown variance. In this paper, we study
+precise sufficient identifiability conditions for the measurement-error-free
+causal model and show what information of the causal model can be recovered
+from observed data. In particular, we present two different sets of
+identifiability conditions, based on the second-order statistics and
+higher-order statistics of the data, respectively. The former was inspired by
+the relationship between the generating model of the
+measurement-error-contaminated data and the factor analysis model, and the
+latter makes use of the identifiability result of the over-complete independent
+component analysis problem.
+",1,0,0,1,0,0
+4178,Elliptic fibrations on covers of the elliptic modular surface of level 5,"  We consider the K3 surfaces that arise as double covers of the elliptic
+modular surface of level 5, $R_{5,5}$. Such surfaces have a natural elliptic
+fibration induced by the fibration on $R_{5,5}$. Moreover, they admit several
+other elliptic fibrations. We describe such fibrations in terms of linear
+systems of curves on $R_{5,5}$. This has a major advantage over other methods
+of classification of elliptic fibrations, namely, a simple algorithm that has
+as input equations of linear systems of curves in the projective plane yields a
+Weierstrass equation for each elliptic fibration. We deal in detail with the
+cases for which the double cover is branched over the two reducible fibers of
+type $I_5$ and for which it is branched over two smooth fibers, giving a
+complete list of elliptic fibrations for these two scenarios.
+",0,0,1,0,0,0
+4179,A wide field-of-view crossed Dragone optical system using the anamorphic aspherical surfaces,"  A side-fed crossed Dragone telescope provides a wide field-of-view. This type
+of a telescope is commonly employed in the measurement of cosmic microwave
+background (CMB) polarization, which requires an image-space telecentric
+telescope with a large focal plane over broadband coverage. We report the
+design of the wide field-of-view crossed Dragone optical system using the
+anamorphic aspherical surfaces with correction terms up to the 10th order. We
+achieved the Strehl ratio larger than 0.95 over 32 by 18 square degrees at 150
+GHz. This design is an image-space telecentric and fully diffraction-limited
+system below 400 GHz. We discuss the optical performance in the uniformity of
+the axially symmetric point spread function and telecentricity over the
+field-of-view. We also address the analysis to evaluate the polarization
+properties, including the instrumental polarization, extinction rate, and
+polarization angle rotation. This work is a part of programs to design a
+compact multi-color wide field-of-view telescope for LiteBIRD, which is a next
+generation CMB polarization satellite.
+",0,1,0,0,0,0
+4180,Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording,"  Individual Neurons in the nervous systems exploit various dynamics. To
+capture these dynamics for single neurons, we tune the parameters of an
+electrophysiological model of nerve cells, to fit experimental data obtained by
+calcium imaging. A search for the biophysical parameters of this model is
+performed by means of a genetic algorithm, where the model neuron is exposed to
+a predefined input current representing overall inputs from other parts of the
+nervous system. The algorithm is then constrained for keeping the ion-channel
+currents within reasonable ranges, while producing the best fit to a calcium
+imaging time series of the AVA interneuron, from the brain of the soil-worm, C.
+elegans. Our settings enable us to project a set of biophysical parameters to
+the the neuron kinetics observed in neuronal imaging.
+",1,0,0,0,0,0
+4181,Matrix elements of irreducible representations of $\mathrm{SU}(n+1)\times\mathrm{SU}(n+1)$ and multivariable matrix-valued orthogonal polynomials,"  In Part 1 we study the spherical functions on compact symmetric pairs of
+arbitrary rank under a suitable multiplicity freeness assumption and additional
+conditions on the branching rules. The spherical functions are taking values in
+the spaces of linear operators of a finite dimensional representation of the
+subgroup, so the spherical functions are matrix-valued. Under these assumptions
+these functions can be described in terms of matrix-valued orthogonal
+polynomials in several variables, where the number of variables is the rank of
+the compact symmetric pair. Moreover, these polynomials are uniquely determined
+as simultaneous eigenfunctions of a commutative algebra of differential
+operators.
+In Part 2 we verify that the group case $\mathrm{SU}(n+1)$ meets all the
+conditions that we impose in Part 1. For any $k\in\mathbb{N}_{0}$ we obtain
+families of orthogonal polynomials in $n$ variables with values in the $N\times
+N$-matrices, where $N=\binom{n+k}{k}$. The case $k=0$ leads to the classical
+Heckman-Opdam polynomials of type $A_{n}$ with geometric parameter. For $k=1$
+we obtain the most complete results. In this case we give an explicit
+expression of the matrix weight, which we show to be irreducible whenever
+$n\ge2$. We also give explicit expressions of the spherical functions that
+determine the matrix weight for $k=1$. These expressions are used to calculate
+the spherical functions that determine the matrix weight for general $k$ up to
+invertible upper-triangular matrices. This generalizes and gives a new proof of
+a formula originally obtained by Koornwinder for the case $n=1$. The commuting
+family of differential operators that have the matrix-valued polynomials as
+simultaneous eigenfunctions contains an element of order one. We give explicit
+formulas for differential operators of order one and two for $(n,k)$ equal to
+$(2,1)$ and $(3,1)$.
+",0,0,1,0,0,0
+4182,On the variance of internode distance under the multispecies coalescent,"  We consider the problem of estimating species trees from unrooted gene tree
+topologies in the presence of incomplete lineage sorting, a common phenomenon
+that creates gene tree heterogeneity in multilocus datasets. One popular class
+of reconstruction methods in this setting is based on internode distances, i.e.
+the average graph distance between pairs of species across gene trees. While
+statistical consistency in the limit of large numbers of loci has been
+established in some cases, little is known about the sample complexity of such
+methods. Here we make progress on this question by deriving a lower bound on
+the worst-case variance of internode distance which depends linearly on the
+corresponding graph distance in the species tree. We also discuss some
+algorithmic implications.
+",0,0,0,0,1,0
+4183,DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN,"  Recently, the introduction of the generative adversarial network (GAN) and
+its variants has enabled the generation of realistic synthetic samples, which
+has been used for enlarging training sets. Previous work primarily focused on
+data augmentation for semi-supervised and supervised tasks. In this paper, we
+instead focus on unsupervised anomaly detection and propose a novel generative
+data augmentation framework optimized for this task. In particular, we propose
+to oversample infrequent normal samples - normal samples that occur with small
+probability, e.g., rare normal events. We show that these samples are
+responsible for false positives in anomaly detection. However, oversampling of
+infrequent normal samples is challenging for real-world high-dimensional data
+with multimodal distributions. To address this challenge, we propose to use a
+GAN variant known as the adversarial autoencoder (AAE) to transform the
+high-dimensional multimodal data distributions into low-dimensional unimodal
+latent distributions with well-defined tail probability. Then, we
+systematically oversample at the `edge' of the latent distributions to increase
+the density of infrequent normal samples. We show that our oversampling
+pipeline is a unified one: it is generally applicable to datasets with
+different complex data distributions. To the best of our knowledge, our method
+is the first data augmentation technique focused on improving performance in
+unsupervised anomaly detection. We validate our method by demonstrating
+consistent improvements across several real-world datasets.
+",0,0,0,1,0,0
+4184,Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors,"  Gestures are a natural communication modality for humans. The ability to
+interpret gestures is fundamental for robots aiming to naturally interact with
+humans. Wearable sensors are promising to monitor human activity, in particular
+the usage of triaxial accelerometers for gesture recognition have been
+explored. Despite this, the state of the art presents lack of systems for
+reliable online gesture recognition using accelerometer data. The article
+proposes SLOTH, an architecture for online gesture recognition, based on a
+wearable triaxial accelerometer, a Recurrent Neural Network (RNN) probabilistic
+classifier and a procedure for continuous gesture detection, relying on
+modelling gesture probabilities, that guarantees (i) good recognition results
+in terms of precision and recall, (ii) immediate system reactivity.
+",1,0,0,0,0,0
+4185,Conditional Variance Penalties and Domain Shift Robustness,"  When training a deep network for image classification, one can broadly
+distinguish between two types of latent features of images that will drive the
+classification. Following the notation of Gong et al. (2016), we can divide
+latent features into (i) ""core"" features $X^\text{core}$ whose distribution
+$X^\text{core}\vert Y$ does not change substantially across domains and (ii)
+""style"" features $X^{\text{style}}$ whose distribution $X^{\text{style}}\vert
+Y$ can change substantially across domains. These latter orthogonal features
+would generally include features such as rotation, image quality or brightness
+but also more complex ones like hair color or posture for images of persons.
+Guarding against future adversarial domain shifts implies that the influence of
+the second type of style features in the prediction has to be limited. We
+assume that the domain itself is not observed and hence a latent variable. We
+do assume, however, that we can sometimes observe a typically discrete
+identifier or $\mathrm{ID}$ variable. We know in some applications, for
+example, that two images show the same person, and $\mathrm{ID}$ then refers to
+the identity of the person. The method requires only a small fraction of images
+to have an $\mathrm{ID}$ variable. We group data samples if they share the same
+class and identifier $(Y,\mathrm{ID})=(y,\mathrm{id})$ and penalize the
+conditional variance of the prediction if we condition on $(Y,\mathrm{ID})$.
+Using this approach is shown to protect against shifts in the distribution of
+the style variables for both regression and classification models.
+Specifically, the conditional variance penalty CoRe is shown to be equivalent
+to minimizing the risk under noise interventions in a regression setting and is
+shown to lead to adversarial risk consistency in a partially linear
+classification setting.
+",1,0,0,1,0,0
+4186,The First Optical Spectra of Wolf Rayet Stars in M101 Revealed with Gemini/GMOS,"  Deep narrow-band HST imaging of the iconic spiral galaxy M101 has revealed
+over a thousand new Wolf Rayet (WR) candidates. We report spectrographic
+confirmation of 10 HeII emission line sources hosting 15 WR stars. We find WR
+stars present at both sub- and super-solar metalicities with WC stars favouring
+more metal-rich regions compared to WN stars. We investigate the association of
+WR stars with HII regions using archival HST imaging and conclude that the
+majority of WR stars are in or associated with HII regions. Of the 10 emission
+lines sources, only one appears to be unassociated with a star-forming region.
+Our spectroscopic survey provides confidence that our narrow-band photometric
+candidates are in fact bonafide WR stars, which will allow us to characterise
+the progenitors of any core-collapse supernovae that erupt in the future in
+M101.
+",0,1,0,0,0,0
+4187,Machine Learning Topological Invariants with Neural Networks,"  In this Letter we supervisedly train neural networks to distinguish different
+topological phases in the context of topological band insulators. After
+training with Hamiltonians of one-dimensional insulators with chiral symmetry,
+the neural network can predict their topological winding numbers with nearly
+100% accuracy, even for Hamiltonians with larger winding numbers that are not
+included in the training data. These results show a remarkable success that the
+neural network can capture the global and nonlinear topological features of
+quantum phases from local inputs. By opening up the neural network, we confirm
+that the network does learn the discrete version of the winding number formula.
+We also make a couple of remarks regarding the role of the symmetry and the
+opposite effect of regularization techniques when applying machine learning to
+physical systems.
+",1,1,0,0,0,0
+4188,Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification,"  We propose a Label Propagation based algorithm for weakly supervised text
+classification. We construct a graph where each document is represented by a
+node and edge weights represent similarities among the documents. Additionally,
+we discover underlying topics using Latent Dirichlet Allocation (LDA) and
+enrich the document graph by including the topics in the form of additional
+nodes. The edge weights between a topic and a text document represent level of
+""affinity"" between them. Our approach does not require document level
+labelling, instead it expects manual labels only for topic nodes. This
+significantly minimizes the level of supervision needed as only a few topics
+are observed to be enough for achieving sufficiently high accuracy. The Label
+Propagation Algorithm is employed on this enriched graph to propagate labels
+among the nodes. Our approach combines the advantages of Label Propagation
+(through document-document similarities) and Topic Modelling (for minimal but
+smart supervision). We demonstrate the effectiveness of our approach on various
+datasets and compare with state-of-the-art weakly supervised text
+classification approaches.
+",1,0,0,0,0,0
+4189,Parallel Markov Chain Monte Carlo for the Indian Buffet Process,"  Indian Buffet Process based models are an elegant way for discovering
+underlying features within a data set, but inference in such models can be
+slow. Inferring underlying features using Markov chain Monte Carlo either
+relies on an uncollapsed representation, which leads to poor mixing, or on a
+collapsed representation, which leads to a quadratic increase in computational
+complexity. Existing attempts at distributing inference have introduced
+additional approximation within the inference procedure. In this paper we
+present a novel algorithm to perform asymptotically exact parallel Markov chain
+Monte Carlo inference for Indian Buffet Process models. We take advantage of
+the fact that the features are conditionally independent under the
+beta-Bernoulli process. Because of this conditional independence, we can
+partition the features into two parts: one part containing only the finitely
+many instantiated features and the other part containing the infinite tail of
+uninstantiated features. For the finite partition, parallel inference is simple
+given the instantiation of features. But for the infinite tail, performing
+uncollapsed MCMC leads to poor mixing and hence we collapse out the features.
+The resulting hybrid sampler, while being parallel, produces samples
+asymptotically from the true posterior.
+",0,0,0,1,0,0
+4190,The Robot Routing Problem for Collecting Aggregate Stochastic Rewards,"  We propose a new model for formalizing reward collection problems on graphs
+with dynamically generated rewards which may appear and disappear based on a
+stochastic model. The *robot routing problem* is modeled as a graph whose nodes
+are stochastic processes generating potential rewards over discrete time. The
+rewards are generated according to the stochastic process, but at each step, an
+existing reward disappears with a given probability. The edges in the graph
+encode the (unit-distance) paths between the rewards' locations. On visiting a
+node, the robot collects the accumulated reward at the node at that time, but
+traveling between the nodes takes time. The optimization question asks to
+compute an optimal (or epsilon-optimal) path that maximizes the expected
+collected rewards.
+We consider the finite and infinite-horizon robot routing problems. For
+finite-horizon, the goal is to maximize the total expected reward, while for
+infinite horizon we consider limit-average objectives. We study the
+computational and strategy complexity of these problems, establish NP-lower
+bounds and show that optimal strategies require memory in general. We also
+provide an algorithm for computing epsilon-optimal infinite paths for arbitrary
+epsilon > 0.
+",1,0,1,0,0,0
+4191,Estimating occupation time functionals,"  We study the estimation of integral type functionals $\int_{0}^{t}f(X_{r})dr$
+for a function $f$ and a $d$-dimensional càdlàg process $X$ with respect to
+discrete observations by a Riemann-sum estimator. Based on novel semimartingale
+approximations in the Fourier domain, central limit theorems are proved for
+$L^{2}$-Sobolev functions $f$ with fractional smoothness and continuous Itô
+semimartingales $X$. General $L^{2}(\mathbb{P})$-upper bounds on the error for
+càdlàg processes are given under weak assumptions. These bounds combine and
+generalize all previously obtained results in the literature and apply also to
+non-Markovian processes. Several detailed examples are discussed. As
+application the approximation of local times for fractional Brownian motion is
+studied. The optimality of the $L^{2}(\mathbb{P})$-upper bounds is shown by
+proving the corresponding lower bounds in case of Brownian motion.
+",0,0,1,1,0,0
+4192,Bootstrapping a Lexicon for Emotional Arousal in Software Engineering,"  Emotional arousal increases activation and performance but may also lead to
+burnout in software development. We present the first version of a Software
+Engineering Arousal lexicon (SEA) that is specifically designed to address the
+problem of emotional arousal in the software developer ecosystem. SEA is built
+using a bootstrapping approach that combines word embedding model trained on
+issue-tracking data and manual scoring of items in the lexicon. We show that
+our lexicon is able to differentiate between issue priorities, which are a
+source of emotional activation and then act as a proxy for arousal. The best
+performance is obtained by combining SEA (428 words) with a previously created
+general purpose lexicon by Warriner et al. (13,915 words) and it achieves
+Cohen's d effect sizes up to 0.5.
+",1,0,0,0,0,0
+4193,Early Solar System irradiation quantified by linked vanadium and beryllium isotope variations in meteorites,"  X-ray emission in young stellar objects (YSOs) is orders of magnitude more
+intense than in main sequence stars1,2, suggestive of cosmic ray irradiation of
+surrounding accretion disks. Protoplanetary disk irradiation has been detected
+around YSOs by HERSCHEL3. In our solar system, short-lived 10Be (half-life =
+1.39 My4), which cannot be produced by stellar nucleosynthesis, was discovered
+in the oldest solar system solids, the calcium-aluminium-rich inclusions
+(CAIs)5. The high 10Be abundance, as well as detection of other irradiation
+tracers6,7, suggest 10Be likely originates from cosmic ray irradiation caused
+by solar flares8. Nevertheless, the nature of these flares (gradual or
+impulsive), the target (gas or dust), and the duration and location of
+irradiation remain unknown. Here we use the vanadium isotopic composition,
+together with initial 10Be abundance to quantify irradiation conditions in the
+early Solar System9. For the initial 10Be abundances recorded in CAIs, 50V
+excesses of a few per mil relative to chondrites have been predicted10,11. We
+report 50V excesses in CAIs up to 4.4 per mil that co-vary with 10Be abundance.
+Their co-variation dictates that excess 50V and 10Be were synthesised through
+irradiation of refractory dust. Modelling of the production rate of 50V and
+10Be demonstrates that the dust was exposed to solar cosmic rays produced by
+gradual flares for less than 300 years at about 0.1 au from the protoSun.
+",0,1,0,0,0,0
+4194,Two classes of nonlocal Evolution Equations related by a shared Traveling Wave Problem,"  We consider reaction-diffusion equations and Korteweg-de Vries-Burgers (KdVB)
+equations, i.e. scalar conservation laws with diffusive-dispersive
+regularization. We review the existence of traveling wave solutions for these
+two classes of evolution equations. For classical equations the traveling wave
+problem (TWP) for a local KdVB equation can be identified with the TWP for a
+reaction-diffusion equation. In this article we study this relationship for
+these two classes of evolution equations with nonlocal diffusion/dispersion.
+This connection is especially useful, if the TW equation is not studied
+directly, but the existence of a TWS is proven using one of the evolution
+equations instead. Finally, we present three models from fluid dynamics and
+discuss the TWP via its link to associated reaction-diffusion equations.
+",0,0,1,0,0,0
+4195,Analyzing IO Amplification in Linux File Systems,"  We present the first systematic analysis of read, write, and space
+amplification in Linux file systems. While many researchers are tackling write
+amplification in key-value stores, IO amplification in file systems has been
+largely unexplored. We analyze data and metadata operations on five widely-used
+Linux file systems: ext2, ext4, XFS, btrfs, and F2FS. We find that data
+operations result in significant write amplification (2-32X) and that metadata
+operations have a large IO cost. For example, a single rename requires 648 KB
+write IO in btrfs. We also find that small random reads result in read
+amplification of 2-13X. Based on these observations, we present the CReWS
+conjecture about the relationship between IO amplification, consistency, and
+storage space utilization. We hope this paper spurs people to design future
+file systems with less IO amplification, especially for non-volatile memory
+technologies.
+",1,0,0,0,0,0
+4196,Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS,"  The features of collaboration patterns are often considered to be different
+from discipline to discipline. Meanwhile, collaborating among disciplines is an
+obvious feature emerged in modern scientific research, which incubates several
+interdisciplines. The features of collaborations in and among the disciplines
+of biological, physical and social sciences are analyzed based on 52,803 papers
+published in a multidisciplinary journal PNAS during 1999 to 2013. From those
+data, we found similar transitivity and assortativity of collaboration patterns
+as well as the identical distribution type of collaborators per author and that
+of papers per author, namely a mixture of generalized Poisson and power-law
+distributions. In addition, we found that interdisciplinary research is
+undertaken by a considerable fraction of authors, not just those with many
+collaborators or those with many papers. This case study provides a window for
+understanding aspects of multidisciplinary and interdisciplinary collaboration
+patterns.
+",1,1,0,0,0,0
+4197,"Inflationary magneto-(non)genesis, increasing kinetic couplings, and the strong coupling problem","  We study the generation of magnetic fields during inflation making use of a
+coupling of the inflaton and moduli fields to electromagnetism via the photon
+kinetic term, and assuming that the coupling is an increasing function of time.
+We demonstrate that the strong coupling problem of inflationary magnetogenesis
+can be avoided by incorporating the destabilization of moduli fields after
+inflation. The magnetic field always dominates over the electric one, and thus
+the severe constraints on the latter from backreaction, which are the demanding
+obstacles in the case of a decreasing coupling function, do not apply to the
+current scenario. However, we show that this loophole to the strong coupling
+problem comes at a price: the normalization of the amplitude of magnetic fields
+is determined by this coupling term and is therefore suppressed by a large
+factor after the moduli destabilization completes. From this we conclude that
+there is no self-consistent and generic realization of primordial
+magnetogenesis producing scale-invariant fields in the case of an increasing
+kinetic coupling.
+",0,1,0,0,0,0
+4198,HornDroid: Practical and Sound Static Analysis of Android Applications by SMT Solving,"  We present HornDroid, a new tool for the static analysis of information flow
+properties in Android applications. The core idea underlying HornDroid is to
+use Horn clauses for soundly abstracting the semantics of Android applications
+and to express security properties as a set of proof obligations that are
+automatically discharged by an off-the-shelf SMT solver. This approach makes it
+possible to fine-tune the analysis in order to achieve a high degree of
+precision while still using off-the-shelf verification tools, thereby
+leveraging the recent advances in this field. As a matter of fact, HornDroid
+outperforms state-of-the-art Android static analysis tools on benchmarks
+proposed by the community. Moreover, HornDroid is the first static analysis
+tool for Android to come with a formal proof of soundness, which covers the
+core of the analysis technique: besides yielding correctness assurances, this
+proof allowed us to identify some critical corner-cases that affect the
+soundness guarantees provided by some of the previous static analysis tools for
+Android.
+",1,0,0,0,0,0
+4199,Assessing the reliability polynomial based on percolation theory,"  In this paper, we study the robustness of network topologies. We use the
+concept of percolation as measuring tool to assess the reliability polynomial
+of those systems which can be modeled as a general inhomogeneous random graph
+as well as scale-free random graph.
+",0,0,1,1,0,0
+4200,Interval Exchange Transformations and Low-Discrepancy,"  In [Mas82] and [Vee78] it was proved independently that almost every interval
+exchange transformation is uniquely ergodic. The Birkhoff ergodic theorem
+implies that these maps mainly have uniformly distributed orbits. This raises
+the question under which conditions the orbits yield low-discrepancy sequences.
+The case of $n=2$ intervals corresponds to circle rotation, where conditions
+for low-discrepancy are well-known. In this paper, we give corresponding
+conditions in the case $n=3$. Furthermore, we construct infinitely many
+interval exchange transformations with low-discrepancy orbits for $n \geq 4$.
+We also show that these examples do not coincide with $LS$-sequences if $S \geq
+2$.
+",0,0,1,0,0,0
+4201,Metric-Optimized Example Weights,"  Real-world machine learning applications often have complex test metrics, and
+may have training and test data that follow different distributions. We propose
+addressing these issues by using a weighted loss function with a standard
+convex loss, but with weights on the training examples that are learned to
+optimize the test metric of interest on the validation set. These
+metric-optimized example weights can be learned for any test metric, including
+black box losses and customized metrics for specific applications. We
+illustrate the performance of our proposal with public benchmark datasets and
+real-world applications with domain shift and custom loss functions that
+balance multiple objectives, impose fairness policies, and are non-convex and
+non-decomposable.
+",0,0,0,1,0,0
+4202,Interior Eigensolver for Sparse Hermitian Definite Matrices Based on Zolotarev's Functions,"  This paper proposes an efficient method for computing selected generalized
+eigenpairs of a sparse Hermitian definite matrix pencil (A, B). Based on
+Zolotarev's best rational function approximations of the signum function and
+conformal mapping techniques, we construct the best rational function
+approximation of a rectangular function supported on an arbitrary interval.
+This new best rational function approximation is applied to construct spectrum
+filters of (A, B). Combining fast direct solvers and the shift-invariant GMRES,
+a hybrid fast algorithm is proposed to apply spectral filters efficiently.
+Compared to the state-of-the-art algorithm FEAST, the proposed rational
+function approximation is proved to be optimal among a larger function class,
+and the numerical implementation of the proposed method is also faster. The
+efficiency and stability of the proposed method are demonstrated by numerical
+examples from computational chemistry.
+",1,0,1,0,0,0
+4203,A Polya-Vinogradov-type inequality on $\mathbb{Z}[i]$,"  We establish a Polya-Vinogradov-type bound for finite periodic multipicative
+characters on the Gaussian integers.
+",0,0,1,0,0,0
+4204,Distinction of representations via Bruhat-Tits buildings of p-adic groups,"  Introductory and pedagogical treatmeant of the article : P. Broussous
+""Distinction of the Steinberg representation"", with an appendix by François
+Courtès, IMRN 2014, no 11, 3140-3157. To appear in Proceedings of Chaire Jean
+Morlet, Dipendra Prasad, Volker Heiermann Ed. 2017. Contains modified and
+simplified proofs of loc. cit. This article is written in memory of
+François Courtès who passed away in september 2016.
+",0,0,1,0,0,0
+4205,Face Super-Resolution Through Wasserstein GANs,"  Generative adversarial networks (GANs) have received a tremendous amount of
+attention in the past few years, and have inspired applications addressing a
+wide range of problems. Despite its great potential, GANs are difficult to
+train. Recently, a series of papers (Arjovsky & Bottou, 2017a; Arjovsky et al.
+2017b; and Gulrajani et al. 2017) proposed using Wasserstein distance as the
+training objective and promised easy, stable GAN training across architectures
+with minimal hyperparameter tuning. In this paper, we compare the performance
+of Wasserstein distance with other training objectives on a variety of GAN
+architectures in the context of single image super-resolution. Our results
+agree that Wasserstein GAN with gradient penalty (WGAN-GP) provides stable and
+converging GAN training and that Wasserstein distance is an effective metric to
+gauge training progress.
+",1,0,0,1,0,0
+4206,Network-based protein structural classification,"  Experimental determination of protein function is resource-consuming. As an
+alternative, computational prediction of protein function has received
+attention. In this context, protein structural classification (PSC) can help,
+by allowing for determining structural classes of currently unclassified
+proteins based on their features, and then relying on the fact that proteins
+with similar structures have similar functions. Existing PSC approaches rely on
+sequence-based or direct (""raw"") 3-dimensional (3D) structure-based protein
+features. In contrast, we first model 3D structures as protein structure
+networks (PSNs). Then, we use (""processed"") network-based features for PSC. We
+propose the use of graphlets, state-of-the-art features in many domains of
+network science, in the task of PSC. Moreover, because graphlets can deal only
+with unweighted PSNs, and because accounting for edge weights when constructing
+PSNs could improve PSC accuracy, we also propose a deep learning framework that
+automatically learns network features from the weighted PSNs. When evaluated on
+a large set of ~9,400 CATH and ~12,800 SCOP protein domains (spanning 36 PSN
+sets), our proposed approaches are superior to existing PSC approaches in terms
+of accuracy, with comparable running time.
+",0,0,0,1,1,0
+4207,Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments,"  Eliminating the negative effect of non-stationary environmental noise is a
+long-standing research topic for automatic speech recognition that stills
+remains an important challenge. Data-driven supervised approaches, including
+ones based on deep neural networks, have recently emerged as potential
+alternatives to traditional unsupervised approaches and with sufficient
+training, can alleviate the shortcomings of the unsupervised methods in various
+real-life acoustic environments. In this light, we review recently developed,
+representative deep learning approaches for tackling non-stationary additive
+and convolutional degradation of speech with the aim of providing guidelines
+for those involved in the development of environmentally robust speech
+recognition systems. We separately discuss single- and multi-channel techniques
+developed for the front-end and back-end of speech recognition systems, as well
+as joint front-end and back-end training frameworks.
+",1,0,0,0,0,0
+4208,The function field Sathé-Selberg formula in arithmetic progressions and `short intervals',"  We use a function field analogue of a method of Selberg to derive an
+asymptotic formula for the number of (square-free) monic polynomials in
+$\mathbb{F}_q[X]$ of degree $n$ with precisely $k$ irreducible factors, in the
+limit as $n$ tends to infinity. We then adapt this method to count such
+polynomials in arithmetic progressions and short intervals, and by making use
+of Weil's `Riemann hypothesis' for curves over $\mathbb{F}_q$, obtain better
+ranges for these formulae than are currently known for their analogues in the
+number field setting. Finally, we briefly discuss the regime in which $q$ tends
+to infinity.
+",0,0,1,0,0,0
+4209,Polynomial Relations Between Matrices of Graphs,"  We derive a correspondence between the eigenvalues of the adjacency matrix
+$A$ and the signless Laplacian matrix $Q$ of a graph $G$ when $G$ is
+$(d_1,d_2)$-biregular by using the relation $A^2=(Q-d_1I)(Q-d_2I)$. This
+motivates asking when it is possible to have $X^r=f(Y)$ for $f$ a polynomial,
+$r>0$, and $X,\ Y$ matrices associated to a graph $G$. It turns out that,
+essentially, this can only happen if $G$ is either regular or biregular.
+",0,0,1,0,0,0
+4210,Random active path model of deep neural networks with diluted binary synapses,"  Deep learning has become a powerful and popular tool for a variety of machine
+learning tasks. However, it is challenging to understand the mechanism of deep
+learning from a theoretical perspective. In this work, we propose a random
+active path model to study collective properties of deep neural networks with
+binary synapses, under the removal perturbation of connections between layers.
+In the model, the path from input to output is randomly activated, and the
+corresponding input unit constrains the weights along the path into the form of
+a $p$-weight interaction glass model. A critical value of the perturbation is
+observed to separate a spin glass regime from a paramagnetic regime, with the
+transition being of the first order. The paramagnetic phase is conjectured to
+have a poor generalization performance.
+",1,0,0,1,0,0
+4211,Super Generalized Central Limit Theorem: Limit distributions for sums of non-identical random variables with power-laws,"  In nature or societies, the power-law is present ubiquitously, and then it is
+important to investigate the mathematical characteristics of power-laws in the
+recent era of big data. In this paper we prove the superposition of
+non-identical stochastic processes with power-laws converges in density to a
+unique stable distribution. This property can be used to explain the
+universality of stable laws such that the sums of the logarithmic return of
+non-identical stock price fluctuations follow stable distributions.
+",0,0,1,1,0,0
+4212,On the Adjacency Spectra of Hypertrees,"  We extend the results of Zhang et al. to show that $\lambda$ is an eigenvalue
+of a $k$-uniform hypertree $(k \geq 3)$ if and only if it is a root of a
+particular matching polynomial for a connected induced subtree. We then use
+this to provide a spectral characterization for power hypertrees. Notably, the
+situation is quite different from that of ordinary trees, i.e., $2$-uniform
+trees. We conclude by presenting an example (an $11$ vertex, $3$-uniform
+non-power hypertree) illustrating these phenomena.
+",0,0,1,0,0,0
+4213,Coherent structures and spectral energy transfer in turbulent plasma: a space-filter approach,"  Plasma turbulence at scales of the order of the ion inertial length is
+mediated by several mechanisms, including linear wave damping, magnetic
+reconnection, formation and dissipation of thin current sheets, stochastic
+heating. It is now understood that the presence of localized coherent
+structures enhances the dissipation channels and the kinetic features of the
+plasma. However, no formal way of quantifying the relationship between
+scale-to-scale energy transfer and the presence of spatial structures has so
+far been presented. In this letter we quantify such relationship analyzing the
+results of a two-dimensional high-resolution Hall-MHD simulation. In
+particular, we employ the technique of space-filtering to derive a spectral
+energy flux term which defines, in any point of the computational domain, the
+signed flux of spectral energy across a given wavenumber. The characterization
+of coherent structures is performed by means of a traditional two-dimensional
+wavelet transformation. By studying the correlation between the spectral energy
+flux and the wavelet amplitude, we demonstrate the strong relationship between
+scale-to-scale transfer and coherent structures. Furthermore, by conditioning
+one quantity with respect to the other, we are able for the first time to
+quantify the inhomogeneity of the turbulence cascade induced by topological
+structures in the magnetic field. Taking into account the low filling-factor of
+coherent structures (i.e. they cover a small portion of space), it emerges that
+80% of the spectral energy transfer (both in the direct and inverse cascade
+directions) is localized in about 50% of space, and 50% of the energy transfer
+is localized in only 25% of space.
+",0,1,0,0,0,0
+4214,Bayesian Sparsification of Recurrent Neural Networks,"  Recurrent neural networks show state-of-the-art results in many text analysis
+tasks but often require a lot of memory to store their weights. Recently
+proposed Sparse Variational Dropout eliminates the majority of the weights in a
+feed-forward neural network without significant loss of quality. We apply this
+technique to sparsify recurrent neural networks. To account for recurrent
+specifics we also rely on Binary Variational Dropout for RNN. We report 99.5%
+sparsity level on sentiment analysis task without a quality drop and up to 87%
+sparsity level on language modeling task with slight loss of accuracy.
+",1,0,0,1,0,0
+4215,Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning,"  We build a deep reinforcement learning (RL) agent that can predict the
+likelihood of an individual testing positive for malaria by asking questions
+about their household. The RL agent learns to determine which survey question
+to ask next and when to stop to make a prediction about their likelihood of
+malaria based on their responses hitherto. The agent incurs a small penalty for
+each question asked, and a large reward/penalty for making the correct/wrong
+prediction; it thus has to learn to balance the length of the survey with the
+accuracy of its final predictions. Our RL agent is a Deep Q-network that learns
+a policy directly from the responses to the questions, with an action defined
+for each possible survey question and for each possible prediction class. We
+focus on Kenya, where malaria is a massive health burden, and train the RL
+agent on a dataset of 6481 households from the Kenya Malaria Indicator Survey
+2015. To investigate the importance of having survey questions be adaptive to
+responses, we compare our RL agent to a supervised learning (SL) baseline that
+fixes its set of survey questions a priori. We evaluate on prediction accuracy
+and on the number of survey questions asked on a holdout set and find that the
+RL agent is able to predict with 80% accuracy, using only 2.5 questions on
+average. In addition, the RL agent learns to survey adaptively to responses and
+is able to match the SL baseline in prediction accuracy while significantly
+reducing survey length.
+",1,0,0,1,0,0
+4216,Curious Minds Wonder Alike: Studying Multimodal Behavioral Dynamics to Design Social Scaffolding of Curiosity,"  Curiosity is the strong desire to learn or know more about something or
+someone. Since learning is often a social endeavor, social dynamics in
+collaborative learning may inevitably influence curiosity. There is a scarcity
+of research, however, focusing on how curiosity can be evoked in group learning
+contexts. Inspired by a recently proposed theoretical framework that
+articulates an integrated socio-cognitive infrastructure of curiosity, in this
+work, we use data-driven approaches to identify fine-grained social scaffolding
+of curiosity in child-child interaction, and propose how they can be used to
+elicit and maintain curiosity in technology-enhanced learning environments. For
+example, we discovered sequential patterns of multimodal behaviors across group
+members and we describe those that maximize an individual's utility, or
+likelihood, of demonstrating curiosity during open-ended problem-solving in
+group work. We also discovered, and describe here, behaviors that directly or
+in a mediated manner cause curiosity related conversational behaviors in the
+interaction, with twice as many interpersonal causal influences compared to
+intrapersonal ones. We explain how these findings form a solid foundation for
+developing curiosity-increasing learning technologies or even assisting a human
+coach to induce curiosity among learners.
+",1,0,0,0,0,0
+4217,Communication Complexity of Discrete Fair Division,"  We initiate the study of the communication complexity of fair division with
+indivisible goods. We focus on some of the most well-studied fairness notions
+(envy-freeness, proportionality, and approximations thereof) and valuation
+classes (submodular, subadditive and unrestricted). Within these parameters,
+our results completely resolve whether the communication complexity of
+computing a fair allocation (or determining that none exist) is polynomial or
+exponential (in the number of goods), for every combination of fairness notion,
+valuation class, and number of players, for both deterministic and randomized
+protocols.
+",1,0,0,0,0,0
+4218,A proof of the Muir-Suffridge conjecture for convex maps of the unit ball in $\mathbb C^n$,"  We prove (and improve) the Muir-Suffridge conjecture for holomorphic convex
+maps. Namely, let $F:\mathbb B^n\to \mathbb C^n$ be a univalent map from the
+unit ball whose image $D$ is convex. Let $\mathcal S\subset \partial \mathbb
+B^n$ be the set of points $\xi$ such that $\lim_{z\to \xi}\|F(z)\|=\infty$.
+Then we prove that $\mathcal S$ is either empty, or contains one or two points
+and $F$ extends as a homeomorphism $\tilde{F}:\overline{\mathbb B^n}\setminus
+\mathcal S\to \overline{D}$. Moreover, $\mathcal S=\emptyset$ if $D$ is
+bounded, $\mathcal S$ has one point if $D$ has one connected component at
+$\infty$ and $\mathcal S$ has two points if $D$ has two connected components at
+$\infty$ and, up to composition with an affine map, $F$ is an extension of the
+strip map in the plane to higher dimension.
+",0,0,1,0,0,0
+4219,Continuous Functional Calculus for Quaternionic Bounded Normal Operators,"  In this article we give an approach to define continuous functional calculus
+for bounded quaternionic normal operators defined on a right quaternionic
+Hilbert space.
+",0,0,1,0,0,0
+4220,"Evolution of structure, magnetism and electronic transport in doped pyrochlore iridate Y$_2$Ir$_{2-x}$Ru$_{x}$O$_7$","  The interplay between spin-orbit coupling (SOC) and electron correlation
+($U$) is considered for many exotic phenomena in iridium oxides. We have
+investigated the evolution of structural, magnetic and electronic properties in
+pyrochlore iridate Y$_2$Ir$_{2-x}$Ru$_{x}$O$_7$ where the substitution of Ru
+has been aimed to tune this interplay. The Ru substitution does not introduce
+any structural phase transition, however, we do observe an evolution of lattice
+parameters with the doping level $x$. X-ray photoemission spectroscopy (XPS)
+study indicates Ru adopts charge state of Ru$^{4+}$ and replaces the Ir$^{4+}$
+accordingly. Magnetization data reveal both the onset of magnetic
+irreversibility and the magnetic moment decreases with progressive substitution
+of Ru. These materials show non-equilibrium low temperature magnetic state as
+revealed by magnetic relaxation data. Interestingly, we find magnetic
+relaxation rate increases with substitution of Ru. The electrical resistivity
+shows an insulating behavior in whole temperature range, however, resistivity
+decreases with substitution of Ru. Nature of electronic conduction has been
+found to follow power-law behavior for all the materials.
+",0,1,0,0,0,0
+4221,Cieliebak's Invariance Theorem and contact structures via connected sums,"  We present a strong version of Abouzaid's No-Escape Lemma, which allows
+varying contact forms on the boundary and which can be used instead of the
+Maximum Principle. Moreover, we give a clarified proof of Cieliebak's
+Invariance Theorem for Symplectic homology under subcritical handle attachment.
+Finally, we introduce the notion of asymptotically finitely generated contact
+structures, which states essentially that the Symplectic homology in a certain
+degree of any filling of such contact manifolds is uniformly generated by only
+finitely many Reeb orbits. This property is then used to show that a large
+class of manifolds carries infinitely many exactly fillable contact structures.
+",0,0,1,0,0,0
+4222,Learning Graph Weighted Models on Pictures,"  Graph Weighted Models (GWMs) have recently been proposed as a natural
+generalization of weighted automata over strings and trees to arbitrary
+families of labeled graphs (and hypergraphs). A GWM generically associates a
+labeled graph with a tensor network and computes a value by successive
+contractions directed by its edges. In this paper, we consider the problem of
+learning GWMs defined over the graph family of pictures (or 2-dimensional
+words). As a proof of concept, we consider regression and classification tasks
+over the simple Bars & Stripes and Shifting Bits picture languages and provide
+an experimental study investigating whether these languages can be learned in
+the form of a GWM from positive and negative examples using gradient-based
+methods. Our results suggest that this is indeed possible and that
+investigating the use of gradient-based methods to learn picture series and
+functions computed by GWMs over other families of graphs could be a fruitful
+direction.
+",0,0,0,1,0,0
+4223,Solutions for biharmonic equations with steep potential wells,"  In this paper, we are concerned with the existence of least energy solutions
+for the following biharmonic equations: $$\Delta^2 u+(\lambda
+V(x)-\delta)u=|u|^{p-2}u \quad in\quad \mathbb{R}^N$$ where $N\geq 5,
+2<p\leq\frac{2N}{N-4}, \lambda>0$ is a parameter, $V(x)$ is a nonnegative
+potential function with nonempty zero sets $\mbox{int} V^{-1}(0)$,
+$0<\delta<\mu_0$ and $\mu_0$ is the principle eigenvalue of $\Delta^2$ in the
+zero sets $\mbox{int} V^{-1}(0)$ of $V(x)$. Here $\mbox{int} V^{-1}(0)$ denotes
+the interior part of the set $V^{-1}(0):=\{x\in \mathbb{R}^N: V(x)=0\}$. We
+prove that the above equation admits a least energy solution which is trapped
+near the zero sets $\mbox{int} V^{-1}(0)$ for $\lambda>0$ large.
+",0,0,1,0,0,0
+4224,General three and four person two color Hat Game,"  N distinguishable players are randomly fitted with a white or black hat,
+where the probabilities of getting a white or black hat may be different for
+each player, but known to all the players. All players guess simultaneously the
+color of their own hat observing only the hat colors of the other N-1 players.
+It is also allowed for each player to pass: no color is guessed. The team wins
+if at least one player guesses his hat color correctly and none of the players
+has an incorrect guess. No communication of any sort is allowed, except for an
+initial strategy session before the game begins. Our goal is to maximize the
+probability of winning the game and to describe winning strategies, using the
+concept of an adequate set. We find explicit solutions in case of N =3 and N
+=4.
+",1,0,0,0,0,0
+4225,Pencilled regular parallelisms,"  Over any field $\mathbb K$, there is a bijection between regular spreads of
+the projective space ${\rm PG}(3,{\mathbb K})$ and $0$-secant lines of the
+Klein quadric in ${\rm PG}(5,{\mathbb K})$. Under this bijection, regular
+parallelisms of ${\rm PG}(3,{\mathbb K})$ correspond to hyperflock determining
+line sets (hfd line sets) with respect to the Klein quadric. An hfd line set is
+defined to be \emph{pencilled} if it is composed of pencils of lines. We
+present a construction of pencilled hfd line sets, which is then shown to
+determine all such sets. Based on these results, we describe the corresponding
+regular parallelisms. These are also termed as being \emph{pencilled}. Any
+Clifford parallelism is regular and pencilled. From this, we derive necessary
+and sufficient algebraic conditions for the existence of pencilled hfd line
+sets.
+",0,0,1,0,0,0
+4226,The loss surface of deep and wide neural networks,"  While the optimization problem behind deep neural networks is highly
+non-convex, it is frequently observed in practice that training deep networks
+seems possible without getting stuck in suboptimal points. It has been argued
+that this is the case as all local minima are close to being globally optimal.
+We show that this is (almost) true, in fact almost all local minima are
+globally optimal, for a fully connected network with squared loss and analytic
+activation function given that the number of hidden units of one layer of the
+network is larger than the number of training points and the network structure
+from this layer on is pyramidal.
+",1,0,0,1,0,0
+4227,Gap Acceptance During Lane Changes by Large-Truck Drivers-An Image-Based Analysis,"  This paper presents an analysis of rearward gap acceptance characteristics of
+drivers of large trucks in highway lane change scenarios. The range between the
+vehicles was inferred from camera images using the estimated lane width
+obtained from the lane tracking camera as the reference. Six-hundred lane
+change events were acquired from a large-scale naturalistic driving data set.
+The kinematic variables from the image-based gap analysis were filtered by the
+weighted linear least squares in order to extrapolate them at the lane change
+time. In addition, the time-to-collision and required deceleration were
+computed, and potential safety threshold values are provided. The resulting
+range and range rate distributions showed directional discrepancies, i.e., in
+left lane changes, large trucks are often slower than other vehicles in the
+target lane, whereas they are usually faster in right lane changes. Video
+observations have confirmed that major motivations for changing lanes are
+different depending on the direction of move, i.e., moving to the left (faster)
+lane occurs due to a slower vehicle ahead or a merging vehicle on the
+right-hand side, whereas right lane changes are frequently made to return to
+the original lane after passing.
+",1,0,0,0,0,0
+4228,Diversity-Sensitive Conditional Generative Adversarial Networks,"  We propose a simple yet highly effective method that addresses the
+mode-collapse problem in the Conditional Generative Adversarial Network (cGAN).
+Although conditional distributions are multi-modal (i.e., having many modes) in
+practice, most cGAN approaches tend to learn an overly simplified distribution
+where an input is always mapped to a single output regardless of variations in
+latent code. To address such issue, we propose to explicitly regularize the
+generator to produce diverse outputs depending on latent codes. The proposed
+regularization is simple, general, and can be easily integrated into most
+conditional GAN objectives. Additionally, explicit regularization on generator
+allows our method to control a balance between visual quality and diversity. We
+demonstrate the effectiveness of our method on three conditional generation
+tasks: image-to-image translation, image inpainting, and future video
+prediction. We show that simple addition of our regularization to existing
+models leads to surprisingly diverse generations, substantially outperforming
+the previous approaches for multi-modal conditional generation specifically
+designed in each individual task.
+",1,0,0,1,0,0
+4229,Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis,"  Global Style Tokens (GSTs) are a recently-proposed method to learn latent
+disentangled representations of high-dimensional data. GSTs can be used within
+Tacotron, a state-of-the-art end-to-end text-to-speech synthesis system, to
+uncover expressive factors of variation in speaking style. In this work, we
+introduce the Text-Predicted Global Style Token (TP-GST) architecture, which
+treats GST combination weights or style embeddings as ""virtual"" speaking style
+labels within Tacotron. TP-GST learns to predict stylistic renderings from text
+alone, requiring neither explicit labels during training nor auxiliary inputs
+for inference. We show that, when trained on a dataset of expressive speech,
+our system generates audio with more pitch and energy variation than two
+state-of-the-art baseline models. We further demonstrate that TP-GSTs can
+synthesize speech with background noise removed, and corroborate these analyses
+with positive results on human-rated listener preference audiobook tasks.
+Finally, we demonstrate that multi-speaker TP-GST models successfully factorize
+speaker identity and speaking style. We provide a website with audio samples
+for each of our findings.
+",1,0,0,1,0,0
+4230,Independence of Sources in Social Networks,"  Online social networks are more and more studied. The links between users of
+a social network are important and have to be well qualified in order to detect
+communities and find influencers for example. In this paper, we present an
+approach based on the theory of belief functions to estimate the degrees of
+cognitive independence between users in a social network. We experiment the
+proposed method on a large amount of data gathered from the Twitter social
+network.
+",1,0,0,0,0,0
+4231,Classical and quantum systems: transport due to rare events,"  We review possible mechanisms for energy transfer based on 'rare' or
+'non-perturbative' effects, in physical systems that present a many-body
+localized phenomenology. The main focus is on classical systems, with or
+without quenched disorder. For non-quantum systems, the breakdown of
+localization is usually not regarded as an issue, and we thus aim at
+identifying the fastest channels for transport. Next, we contemplate the
+possibility of applying the same mechanisms in quantum systems, including
+disorder free systems (e.g. Bose-Hubbard chain), disordered many-body localized
+systems with mobility edges at energies below the edge, and strongly disordered
+lattice systems in $d>1$. For quantum mechanical systems, the relevance of
+these considerations for transport is currently a matter of debate.
+",0,1,0,0,0,0
+4232,An a Priori Exponential Tail Bound for k-Folds Cross-Validation,"  We consider a priori generalization bounds developed in terms of
+cross-validation estimates and the stability of learners. In particular, we
+first derive an exponential Efron-Stein type tail inequality for the
+concentration of a general function of n independent random variables. Next,
+under some reasonable notion of stability, we use this exponential tail bound
+to analyze the concentration of the k-fold cross-validation (KFCV) estimate
+around the true risk of a hypothesis generated by a general learning rule.
+While the accumulated literature has often attributed this concentration to the
+bias and variance of the estimator, our bound attributes this concentration to
+the stability of the learning rule and the number of folds k. This insight
+raises valid concerns related to the practical use of KFCV and suggests
+research directions to obtain reliable empirical estimates of the actual risk.
+",1,0,0,1,0,0
+4233,Relaxed Wasserstein with Applications to GANs,"  We propose a novel class of statistical divergences called \textit{Relaxed
+Wasserstein} (RW) divergence. RW divergence generalizes Wasserstein divergence
+and is parametrized by a class of strictly convex and differentiable functions.
+We establish for RW divergence several probabilistic properties, which are
+critical for the success of Wasserstein divergence. In particular, we show that
+RW divergence is dominated by Total Variation (TV) and Wasserstein-$L^2$
+divergence, and that RW divergence has continuity, differentiability and
+duality representation. Finally, we provide a nonasymptotic moment estimate and
+a concentration inequality for RW divergence.
+Our experiments on the image generation task demonstrate that RW divergence
+is a suitable choice for GANs. Indeed, the performance of RWGANs with
+Kullback-Leibler (KL) divergence is very competitive with other
+state-of-the-art GANs approaches. Furthermore, RWGANs possess better
+convergence properties than the existing WGANs with competitive inception
+scores. To the best of our knowledge, our new conceptual framework is the first
+to not only provide the flexibility in designing effective GANs scheme, but
+also the possibility in studying different losses under a unified mathematical
+framework.
+",1,0,0,1,0,0
+4234,"Extremal functions for the Moser--Trudinger inequality of Adimurthi--Druet type in $W^{1,N}(\mathbb R^N)$","  We study the existence and nonexistence of maximizers for variational problem
+concerning to the Moser--Trudinger inequality of Adimurthi--Druet type in
+$W^{1,N}(\mathbb R^N)$ \[ MT(N,\beta, \alpha) =\sup_{u\in W^{1,N}(\mathbb R^N),
+\|\nabla u\|_N^N + \|u\|_N^N\leq 1} \int_{\mathbb R^N} \Phi_N(\beta(1+\alpha
+\|u\|_N^N)^{\frac1{N-1}} |u|^{\frac N{N-1}}) dx, \] where $\Phi_N(t) =e^{t}
+-\sum_{k=0}^{N-2} \frac{t^k}{k!}$, $0\leq \alpha < 1$ both in the subcritical
+case $\beta < \beta_N$ and critical case $\beta =\beta_N$ with $\beta_N = N
+\omega_{N-1}^{\frac1{N-1}}$ and $\omega_{N-1}$ denotes the surface area of the
+unit sphere in $\mathbb R^N$. We will show that $MT(N,\beta,\alpha)$ is
+attained in the subcritical case if $N\geq 3$ or $N=2$ and $\beta \in
+(\frac{2(1+2\alpha)}{(1+\alpha)^2 B_2},\beta_2)$ with $B_2$ is the best
+constant in a Gagliardo--Nirenberg inequality in $W^{1,2}(\mathbb R^2)$. We
+also show that $MT(2,\beta,\alpha)$ is not attained for $\beta$ small which is
+different from the context of bounded domains. In the critical case, we prove
+that $MT(N,\beta_N,\alpha)$ is attained for $\alpha\geq 0$ small enough. To
+prove our results, we first establish a lower bound for $MT(N,\beta,\alpha)$
+which excludes the concentrating or vanishing behaviors of their maximizer
+sequences. This implies the attainability of $MT(N,\beta,\alpha)$ in the
+subcritical case. The proof in the critical case is based on the blow-up
+analysis method. Finally, by using the Moser sequence together the scaling
+argument, we show that $MT(N,\beta_N,1) =\infty$. Our results settle the
+questions left open in \cite{doO2015,doO2016}.
+",0,0,1,0,0,0
+4235,Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning,"  This paper introduces Dex, a reinforcement learning environment toolkit
+specialized for training and evaluation of continual learning methods as well
+as general reinforcement learning problems. We also present the novel continual
+learning method of incremental learning, where a challenging environment is
+solved using optimal weight initialization learned from first solving a similar
+easier environment. We show that incremental learning can produce vastly
+superior results than standard methods by providing a strong baseline method
+across ten Dex environments. We finally develop a saliency method for
+qualitative analysis of reinforcement learning, which shows the impact
+incremental learning has on network attention.
+",1,0,0,1,0,0
+4236,The first order partial differential equations resolved with any derivatives,"  In this paper we discuss the first order partial differential equations
+resolved with any derivatives. At first, we transform the first order partial
+differential equation resolved with respect to a time derivative into a system
+of linear equations. Secondly, we convert it into a system of the first order
+linear partial differential equations with constant coefficients and nonlinear
+algebraic equations. Thirdly, we solve them by the Fourier transform and
+convert them into the equivalent integral equations. At last, we extend to
+discuss the first order partial differential equations resolved with respect to
+time derivatives and the general scenario resolved with any derivatives.
+",0,0,1,0,0,0
+4237,The Genus-One Global Mirror Theorem for the Quintic Threefold,"  We prove the genus-one restriction of the all-genus
+Landau-Ginzburg/Calabi-Yau conjecture of Chiodo and Ruan, stated in terms of
+the geometric quantization of an explicit symplectomorphism determined by
+genus-zero invariants. This provides the first evidence supporting the
+higher-genus Landau-Ginzburg/Calabi-Yau correspondence for the quintic
+threefold, and exhibits the first instance of the ""genus zero controls higher
+genus"" principle, in the sense of Givental's quantization formalism, for
+non-semisimple cohomological field theories.
+",0,0,1,0,0,0
+4238,Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Based Interaction,"  We present a novel approach for mobile manipulator self-calibration using
+contact information. Our method, based on point cloud registration, is applied
+to estimate the extrinsic transform between a fixed vision sensor mounted on a
+mobile base and an end effector. Beyond sensor calibration, we demonstrate that
+the method can be extended to include manipulator kinematic model parameters,
+which involves a non-rigid registration process. Our procedure uses on-board
+sensing exclusively and does not rely on any external measurement devices,
+fiducial markers, or calibration rigs. Further, it is fully automatic in the
+general case. We experimentally validate the proposed method on a custom mobile
+manipulator platform, and demonstrate centimetre-level post-calibration
+accuracy in positioning of the end effector using visual guidance only. We also
+discuss the stability properties of the registration algorithm, in order to
+determine the conditions under which calibration is possible.
+",1,0,0,0,0,0
+4239,Towards Open Data for the Citation Content Analysis,"  The paper presents first results of the CitEcCyr project funded by RANEPA.
+The project aims to create a source of open citation data for research papers
+written in Russian. Compared to existing sources of citation data, CitEcCyr is
+working to provide the following added values: a) a transparent and distributed
+architecture of a technology that generates the citation data; b) an openness
+of all built/used software and created citation data; c) an extended set of
+citation data sufficient for the citation content analysis; d) services for
+public control over a quality of the citation data and a citing activity of
+researchers.
+",1,0,0,0,0,0
+4240,Real Time Collision Detection and Identification for Robotic Manipulators,"  The majority of everyday tasks involve interacting with unstructured
+environments. This implies that, in order for robots to be truly useful they
+must be able to handle contacts. This paper explores how a particle filter can
+be used to localize a contact point and estimate the external force. We
+demonstrate the capability of the particle filter on a simulated 4DoF planar
+robotic arm, and compare it to a well-established analytical approach.
+",1,0,0,0,0,0
+4241,Time-dynamic inference for non-Markov transition probabilities under independent right-censoring,"  In this article, weak convergence of the general non-Markov state transition
+probability estimator by Titman (2015) is established which, up to now, has not
+been verified yet for other general non-Markov estimators. A similar theorem is
+shown for the bootstrap, yielding resampling-based inference methods for
+statistical functionals. Formulas of the involved covariance functions are
+presented in detail. Particular applications include the conditional expected
+length of stay in a specific state, given occupation of another state in the
+past, as well as the construction of time-simultaneous confidence bands for the
+transition probabilities. The expected lengths of stay in the two-sample liver
+cirrhosis data-set by Andersen et al. (1993) are compared and confidence
+intervals for their difference are constructed. With borderline significance
+and in comparison to the placebo group, the treatment group has an elevated
+expected length of stay in the healthy state given an earlier disease state
+occupation. In contrast, the Aalen-Johansen estimator-based confidence
+interval, which relies on a Markov assumption, leads to a drastically different
+conclusion. Also, graphical illustrations of confidence bands for the
+transition probabilities demonstrate the biasedness of the Aalen-Johansen
+estimator in this data example. The reliability of these results is assessed in
+a simulation study.
+",0,0,1,1,0,0
+4242,Multitask Learning with CTC and Segmental CRF for Speech Recognition,"  Segmental conditional random fields (SCRFs) and connectionist temporal
+classification (CTC) are two sequence labeling methods used for end-to-end
+training of speech recognition models. Both models define a transcription
+probability by marginalizing decisions about latent segmentation alternatives
+to derive a sequence probability: the former uses a globally normalized joint
+model of segment labels and durations, and the latter classifies each frame as
+either an output symbol or a ""continuation"" of the previous label. In this
+paper, we train a recognition model by optimizing an interpolation between the
+SCRF and CTC losses, where the same recurrent neural network (RNN) encoder is
+used for feature extraction for both outputs. We find that this multitask
+objective improves recognition accuracy when decoding with either the SCRF or
+CTC models. Additionally, we show that CTC can also be used to pretrain the RNN
+encoder, which improves the convergence rate when learning the joint model.
+",1,0,0,0,0,0
+4243,Electronic structure and non-linear optical properties of organic photovoltaic systems with potential applications on solar cell devices: A DFT approach,"  The use of eco-friendly materials for the environment has been addressed as a
+critical issue in the development of systems for renewable energy applications.
+In this regard, the investigation of organic photovoltaic (OPV) molecules for
+the implementation in solar cells, has become a subject of intense research in
+the last years. The present work is a systematic study at the B3LYP level of
+theory performed for a series of 50 OPV materials. Full geometry optimizations
+revealed that those systems with a twisted geometry are the most energetically
+stable. Nuclear independent Chemical shifts (NICS) values show a strong
+aromatic character along the series, indicating a possible polymerization in
+solid-state, via a {\pi}-{\pi} stacking, which may be relevant in the design of
+a solar cell device. The absorption spectra in the series was also computed
+using Time Dependent DFT at the same level of theory, indicating that all
+spectra are red-shifted along the series. This is a promissory property that
+may be directly implemented in a photovoltaic material, since it is possible to
+absorb a larger range of visible light. The computed HOMO-LUMO gaps as a
+measurement of the band gap in semiconductors, show a reasonable agreement with
+those found in experiment, predicting candidate materials that may be directly
+used in photovoltaic applications. Non-linear optical (NLO) properties were
+also estimated with the aid of a PCBM molecule as a model of an acceptor, and a
+final set of optimal systems was identified as potential candidates to be
+implemented as photovoltaic materials. The methodological approach presented in
+this work may aid in the in silico assisted-design of OPV materials.
+",0,1,0,0,0,0
+4244,Connectivity Learning in Multi-Branch Networks,"  While much of the work in the design of convolutional networks over the last
+five years has revolved around the empirical investigation of the importance of
+depth, filter sizes, and number of feature channels, recent studies have shown
+that branching, i.e., splitting the computation along parallel but distinct
+threads and then aggregating their outputs, represents a new promising
+dimension for significant improvements in performance. To combat the complexity
+of design choices in multi-branch architectures, prior work has adopted simple
+strategies, such as a fixed branching factor, the same input being fed to all
+parallel branches, and an additive combination of the outputs produced by all
+branches at aggregation points.
+In this work we remove these predefined choices and propose an algorithm to
+learn the connections between branches in the network. Instead of being chosen
+a priori by the human designer, the multi-branch connectivity is learned
+simultaneously with the weights of the network by optimizing a single loss
+function defined with respect to the end task. We demonstrate our approach on
+the problem of multi-class image classification using three different datasets
+where it yields consistently higher accuracy compared to the state-of-the-art
+""ResNeXt"" multi-branch network given the same learning capacity.
+",1,0,0,0,0,0
+4245,A multiple timescales approach to bridging spiking- and population-level dynamics,"  A rigorous bridge between spiking-level and macroscopic quantities is an
+on-going and well-developed story for asynchronously firing neurons, but focus
+has shifted to include neural populations exhibiting varying synchronous
+dynamics. Recent literature has used the Ott--Antonsen ansatz (2008) to great
+effect, allowing a rigorous derivation of an order parameter for large
+oscillator populations. The ansatz has been successfully applied using several
+models including networks of Kuramoto oscillators, theta models, and
+integrate-and-fire neurons, along with many types of network topologies. In the
+present study, we take a converse approach: given the mean field dynamics of
+slow synapses, predict the synchronization properties of finite neural
+populations. The slow synapse assumption is amenable to averaging theory and
+the method of multiple timescales. Our proposed theory applies to two
+heterogeneous populations of N excitatory n-dimensional and N inhibitory
+m-dimensional oscillators with homogeneous synaptic weights. We then
+demonstrate our theory using two examples. In the first example we take a
+network of excitatory and inhibitory theta neurons and consider the case with
+and without heterogeneous inputs. In the second example we use Traub models
+with calcium for the excitatory neurons and Wang-Buzs{á}ki models for the
+inhibitory neurons. We accurately predict phase drift and phase locking in each
+example even when the slow synapses exhibit non-trivial mean-field dynamics.
+",0,0,0,0,1,0
+4246,Two-Stream RNN/CNN for Action Recognition in 3D Videos,"  The recognition of actions from video sequences has many applications in
+health monitoring, assisted living, surveillance, and smart homes. Despite
+advances in sensing, in particular related to 3D video, the methodologies to
+process the data are still subject to research. We demonstrate superior results
+by a system which combines recurrent neural networks with convolutional neural
+networks in a voting approach. The gated-recurrent-unit-based neural networks
+are particularly well-suited to distinguish actions based on long-term
+information from optical tracking data; the 3D-CNNs focus more on detailed,
+recent information from video data. The resulting features are merged in an SVM
+which then classifies the movement. In this architecture, our method improves
+recognition rates of state-of-the-art methods by 14% on standard data sets.
+",1,0,0,0,0,0
+4247,DNA translocation through alpha-haemolysin nano-pores with potential application to macromolecular data storage,"  Digital information can be encoded in the building-block sequence of
+macromolecules, such as RNA and single-stranded DNA. Methods of ""writing"" and
+""reading"" macromolecular strands are currently available, but they are slow and
+expensive. In an ideal molecular data storage system, routine operations such
+as write, read, erase, store, and transfer must be done reliably and at high
+speed within an integrated chip. As a first step toward demonstrating the
+feasibility of this concept, we report preliminary results of DNA readout
+experiments conducted in miniaturized chambers that are scalable to even
+smaller dimensions. We show that translocation of a single-stranded DNA
+molecule (consisting of 50 adenosine bases followed by 100 cytosine bases)
+through an ion-channel yields a characteristic signal that is attributable to
+the 2-segment structure of the molecule. We also examine the dependence of the
+translocation rate and speed on the adjustable parameters of the experiment.
+",1,1,0,0,0,0
+4248,Contextuality from missing and versioned data,"  Traditionally categorical data analysis (e.g. generalized linear models)
+works with simple, flat datasets akin to a single table in a database with no
+notion of missing data or conflicting versions. In contrast, modern data
+analysis must deal with distributed databases with many partial local tables
+that need not always agree. The computational agents tabulating these tables
+are spatially separated, with binding speed-of-light constraints and data
+arriving too rapidly for these distributed views ever to be fully informed and
+globally consistent. Contextuality is a mathematical property which describes a
+kind of inconsistency arising in quantum mechanics (e.g. in Bell's theorem). In
+this paper we show how contextuality can arise in common data collection
+scenarios, including missing data and versioning (as in low-latency distributed
+databases employing snapshot isolation). In the companion paper, we develop
+statistical models adapted to this regime.
+",1,0,0,1,0,0
+4249,Reveal the Mantle and K-40 Components of Geoneutrinos with Liquid Scintillator Cherenkov Neutrino Detectors,"  In this article we present an idea of using liquid scintillator Cherenkov
+neutrino detectors to detect the mantle and K-40 components of geoneutrinos.
+Liquid scintillator Cherenkov detectors feature both energy and direction
+measurement for charge particles. Geoneutrinos can be detected with the elastic
+scattering process of neutrino and electron. With the directionality, the
+dominant intrinsic background originated from solar neutrinos in common liquid
+scintillator detectors can be suppressed. The mantle geoneutrinos can be
+distinguished because they come mainly underneath. The K-40 geoneutrinos can
+also be identified, if the detection threshold for direction measurement can be
+lower than, for example, 0.8 MeV. According to our calculation, a moderate,
+kilo-ton scale, detector can observe tens of candidates, and is a practical
+start for an experiment.
+",0,1,0,0,0,0
+4250,Improving Stock Movement Prediction with Adversarial Training,"  This paper contributes a new machine learning solution for stock movement
+prediction, which aims to predict whether the price of a stock will be up or
+down in the near future. The key novelty is that we propose to employ
+adversarial training to improve the generalization of a recurrent neural
+network model. The rationality of adversarial training here is that the input
+features to stock prediction are typically based on stock price, which is
+essentially a stochastic variable and continuously changed with time by nature.
+As such, normal training with stationary price-based features (e.g. the closing
+price) can easily overfit the data, being insufficient to obtain reliable
+models. To address this problem, we propose to add perturbations to simulate
+the stochasticity of continuous price variable, and train the model to work
+well under small yet intentional perturbations. Extensive experiments on two
+real-world stock data show that our method outperforms the state-of-the-art
+solution with 3.11% relative improvements on average w.r.t. accuracy, verifying
+the usefulness of adversarial training for stock prediction task. Codes will be
+made available upon acceptance.
+",0,0,0,0,0,1
+4251,Weighted estimates for positive operators and Doob maximal operators on filtered measure spaces,"  We characterize strong type and weak type inequalities with two weights for
+positive operators on filtered measure spaces. These estimates are
+probabilistic analogues of two-weight inequalities for positive operators
+associated to the dyadic cubes in $\mathbb R^n$ due to Lacey, Sawyer and
+Uriarte-Tuero \cite{LaSaUr}. Several mixed bounds for the Doob maximal operator
+on filtered measure spaces are also obtained. In fact, Hytönen-Pérez
+type and Lerner-Moen type norm estimates for Doob maximal operator are
+established. Our approaches are mainly based on the construction of principal
+sets.
+",0,0,1,0,0,0
+4252,Sentiment Identification in Code-Mixed Social Media Text,"  Sentiment analysis is the Natural Language Processing (NLP) task dealing with
+the detection and classification of sentiments in texts. While some tasks deal
+with identifying the presence of sentiment in the text (Subjectivity analysis),
+other tasks aim at determining the polarity of the text categorizing them as
+positive, negative and neutral. Whenever there is a presence of sentiment in
+the text, it has a source (people, group of people or any entity) and the
+sentiment is directed towards some entity, object, event or person. Sentiment
+analysis tasks aim to determine the subject, the target and the polarity or
+valence of the sentiment. In our work, we try to automatically extract
+sentiment (positive or negative) from Facebook posts using a machine learning
+approach.While some works have been done in code-mixed social media data and in
+sentiment analysis separately, our work is the first attempt (as of now) which
+aims at performing sentiment analysis of code-mixed social media text. We have
+used extensive pre-processing to remove noise from raw text. Multilayer
+Perceptron model has been used to determine the polarity of the sentiment. We
+have also developed the corpus for this task by manually labeling Facebook
+posts with their associated sentiments.
+",1,0,0,0,0,0
+4253,SVSGAN: Singing Voice Separation via Generative Adversarial Network,"  Separating two sources from an audio mixture is an important task with many
+applications. It is a challenging problem since only one signal channel is
+available for analysis. In this paper, we propose a novel framework for singing
+voice separation using the generative adversarial network (GAN) with a
+time-frequency masking function. The mixture spectra is considered to be a
+distribution and is mapped to the clean spectra which is also considered a
+distribtution. The approximation of distributions between mixture spectra and
+clean spectra is performed during the adversarial training process. In contrast
+with current deep learning approaches for source separation, the parameters of
+the proposed framework are first initialized in a supervised setting and then
+optimized by the training procedure of GAN in an unsupervised setting.
+Experimental results on three datasets (MIR-1K, iKala and DSD100) show that
+performance can be improved by the proposed framework consisting of
+conventional networks.
+",1,0,0,0,0,0
+4254,Microscopic theory of refractive index applied to metamaterials: Effective current response tensor corresponding to standard relation $n^2 = \varepsilon_{\mathrm{eff}} μ_{\mathrm{eff}}$,"  In this article, we first derive the wavevector- and frequency-dependent,
+microscopic current response tensor which corresponds to the ""macroscopic""
+ansatz $\vec D = \varepsilon_0 \varepsilon_{\mathrm{eff}} \vec E$ and $\vec B =
+\mu_0 \mu_{\mathrm{eff}} \vec H$ with wavevector- and frequency-independent,
+""effective"" material constants $\varepsilon_{\mathrm{eff}}$ and
+$\mu_{\mathrm{eff}}$. We then deduce the electromagnetic and optical properties
+of this effective material model by employing exact, microscopic response
+relations. In particular, we argue that for recovering the standard relation
+$n^2 = \varepsilon_{\mathrm{eff}} \mu_{\mathrm{eff}}$ between the refractive
+index and the effective material constants, it is imperative to start from the
+microscopic wave equation in terms of the transverse dielectric function,
+$\varepsilon_{\mathrm{T}}(\vec k, \omega) = 0$. On the phenomenological side,
+our result is especially relevant for metamaterials research, which draws
+directly on the standard relation for the refractive index in terms of
+effective material constants. Since for a wide class of materials the current
+response tensor can be calculated from first principles and compared to the
+model expression derived here, this work also paves the way for a systematic
+search for new metamaterials.
+",0,1,0,0,0,0
+4255,Semidefinite Relaxation-Based Optimization of Multiple-Input Wireless Power Transfer Systems,"  An optimization procedure for multi-transmitter (MISO) wireless power
+transfer (WPT) systems based on tight semidefinite relaxation (SDR) is
+presented. This method ensures physical realizability of MISO WPT systems
+designed via convex optimization -- a robust, semi-analytical and intuitive
+route to optimizing such systems. To that end, the nonconvex constraints
+requiring that power is fed into rather than drawn from the system via all
+transmitter ports are incorporated in a convex semidefinite relaxation, which
+is efficiently and reliably solvable by dedicated algorithms. A test of the
+solution then confirms that this modified problem is equivalent (tight
+relaxation) to the original (nonconvex) one and that the true global optimum
+has been found. This is a clear advantage over global optimization methods
+(e.g. genetic algorithms), where convergence to the true global optimum cannot
+be ensured or tested. Discussions of numerical results yielded by both the
+closed-form expressions and the refined technique illustrate the importance and
+practicability of the new method. It, is shown that this technique offers a
+rigorous optimization framework for a broad range of current and emerging WPT
+applications.
+",1,0,1,0,0,0
+4256,Counterfactual Reasoning with Disjunctive Knowledge in a Linear Structural Equation Model,"  We consider the problem of estimating counterfactual quantities when prior
+knowledge is available in the form of disjunctive statements. These include
+disjunction of conditions (e.g., ""the patient is more than 60 years of age"") as
+well as disjuction of antecedants (e.g., ""had the patient taken either drug A
+or drug B""). Focusing on linear structural equation models (SEM) and imperfect
+control plans, we extend the counterfactual framework of Balke and Pearl (1995)
+, Chen and Pearl (2015), and Pearl (2009, pp. 389-391) from unconditional to
+conditional plans, from a univariate treatment to a set of treatments, and from
+point type knowledge to disjunctive knowledge. Finally, we provide improved
+matrix representations of the resulting counterfactual parameters, and improved
+computational methods of their evaluation.
+",0,0,0,1,0,0
+4257,Post-edit Analysis of Collective Biography Generation,"  Text generation is increasingly common but often requires manual post-editing
+where high precision is critical to end users. However, manual editing is
+expensive so we want to ensure this effort is focused on high-value tasks. And
+we want to maintain stylistic consistency, a particular challenge in crowd
+settings. We present a case study, analysing human post-editing in the context
+of a template-based biography generation system. An edit flow visualisation
+combined with manual characterisation of edits helps identify and prioritise
+work for improving end-to-end efficiency and accuracy.
+",1,0,0,0,0,0
+4258,The SysML/KAOS Domain Modeling Approach,"  A means of building safe critical systems consists of formally modeling the
+requirements formulated by stakeholders and ensuring their consistency with
+respect to application domain properties. This paper proposes a metamodel for
+an ontology modeling formalism based on OWL and PLIB. This modeling formalism
+is part of a method for modeling the domain of systems whose requirements are
+captured through SysML/KAOS. The formal semantics of SysML/KAOS goals are
+represented using Event-B specifications. Goals provide the set of events,
+while domain models will provide the structure of the system state of the
+Event-B specification. Our proposal is illustrated through a case study dealing
+with a Cycab localization component specification. The case study deals with
+the specification of a localization software component that uses GPS,Wi-Fi and
+sensor technologies for the realtime localization of the Cycab vehicle, an
+autonomous ground transportation system designed to be robust and completely
+independent.
+",1,0,0,0,0,0
+4259,Neighborhood selection with application to social networks,"  The topic of this paper is modeling and analyzing dependence in stochastic
+social networks. Using a latent variable block model allows the analysis of
+dependence between blocks via the analysis of a latent graphical model. Our
+approach to the analysis of the graphical model then is based on the idea
+underlying the neighborhood selection scheme put forward by Meinshausen and
+Bühlmann (2006). However, because of the latent nature of our model,
+estimates have to be used in lieu of the unobserved variables. This leads to a
+novel analysis of graphical models under uncertainty, in the spirit of
+Rosenbaum et al. (2010), or Belloni et al. (2017). Lasso-based selectors, and a
+class of Dantzig-type selectors are studied.
+",0,0,1,1,0,0
+4260,Spinless hourglass nodal-line semimetals,"  Nodal-line semimetals, one of the topological semimetals, have degeneracy
+along nodal lines where the band gap is closed. In many cases, the nodal lines
+appear accidentally, and in such cases it is impossible to determine whether
+the nodal lines appear or not, only from the crystal symmetry and the electron
+filling. In this paper, for spinless systems, we show that in specific space
+groups at $4N+2$ fillings ($8N+4$ fillings including the spin degree of
+freedom), presence of the nodal lines is required regardless of the details of
+the systems. Here, the spinless systems refer to crystals where the spin-orbit
+coupling is negligible and the spin degree of freedom can be omitted because of
+the SU(2) spin degeneracy. In this case the shape of the band structure around
+these nodal lines is like an hourglass, and we call this a spinless hourglass
+nodal-line semimetal. We construct a model Hamiltonian as an example and we
+show that it is always in the spinless hourglass nodal-line semimetal phase
+even when the model parameters are changed without changing the symmetries of
+the system. We also establish a list of all the centrosymmetric space groups,
+under which spinless systems always have hourglass nodal lines, and illustrate
+where the nodal lines are located. We propose that Al$_3$FeSi$_2$, whose
+space-group symmetry is Pbcn (No. 60), is one of the nodal-line semimetals
+arising from this mechanism.
+",0,1,0,0,0,0
+4261,Average-radius list-recovery of random linear codes: it really ties the room together,"  We analyze the list-decodability, and related notions, of random linear
+codes. This has been studied extensively before: there are many different
+parameter regimes and many different variants. Previous works have used
+complementary styles of arguments---which each work in their own parameter
+regimes but not in others---and moreover have left some gaps in our
+understanding of the list-decodability of random linear codes. In particular,
+none of these arguments work well for list-recovery, a generalization of
+list-decoding that has been useful in a variety of settings.
+In this work, we present a new approach, which works across parameter regimes
+and further generalizes to list-recovery. Our main theorem can establish better
+list-decoding and list-recovery results for low-rate random linear codes over
+large fields; list-recovery of high-rate random linear codes; and it can
+recover the rate bounds of Guruswami, Hastad, and Kopparty for constant-rate
+random linear codes (although with large list sizes).
+",1,0,0,0,0,0
+4262,Insights on representational similarity in neural networks with canonical correlation,"  Comparing different neural network representations and determining how
+representations evolve over time remain challenging open questions in our
+understanding of the function of neural networks. Comparing representations in
+neural networks is fundamentally difficult as the structure of representations
+varies greatly, even across groups of networks trained on identical tasks, and
+over the course of training. Here, we develop projection weighted CCA
+(Canonical Correlation Analysis) as a tool for understanding neural networks,
+building off of SVCCA, a recently proposed method (Raghu et al., 2017). We
+first improve the core method, showing how to differentiate between signal and
+noise, and then apply this technique to compare across a group of CNNs,
+demonstrating that networks which generalize converge to more similar
+representations than networks which memorize, that wider networks converge to
+more similar solutions than narrow networks, and that trained networks with
+identical topology but different learning rates converge to distinct clusters
+with diverse representations. We also investigate the representational dynamics
+of RNNs, across both training and sequential timesteps, finding that RNNs
+converge in a bottom-up pattern over the course of training and that the hidden
+state is highly variable over the course of a sequence, even when accounting
+for linear transforms. Together, these results provide new insights into the
+function of CNNs and RNNs, and demonstrate the utility of using CCA to
+understand representations.
+",0,0,0,1,0,0
+4263,Complex Hadamard matrices with noncommutative entries,"  We axiomatize and study the matrices of type $H\in M_N(A)$, having unitary
+entries, $H_{ij}\in U(A)$, and whose rows and columns are subject to
+orthogonality type conditions. Here $A$ can be any $C^*$-algebra, for instance
+$A=\mathbb C$, where we obtain the usual complex Hadamard matrices, or
+$A=C(X)$, where we obtain the continuous families of complex Hadamard matrices.
+Our formalism allows the construction of a quantum permutation group $G\subset
+S_N^+$, whose structure and computation is discussed here.
+",0,0,1,0,0,0
+4264,Subregular Complexity and Deep Learning,"  This paper argues that the judicial use of formal language theory and
+grammatical inference are invaluable tools in understanding how deep neural
+networks can and cannot represent and learn long-term dependencies in temporal
+sequences. Learning experiments were conducted with two types of Recurrent
+Neural Networks (RNNs) on six formal languages drawn from the Strictly Local
+(SL) and Strictly Piecewise (SP) classes. The networks were Simple RNNs
+(s-RNNs) and Long Short-Term Memory RNNs (LSTMs) of varying sizes. The SL and
+SP classes are among the simplest in a mathematically well-understood hierarchy
+of subregular classes. They encode local and long-term dependencies,
+respectively. The grammatical inference algorithm Regular Positive and Negative
+Inference (RPNI) provided a baseline. According to earlier research, the LSTM
+architecture should be capable of learning long-term dependencies and should
+outperform s-RNNs. The results of these experiments challenge this narrative.
+First, the LSTMs' performance was generally worse in the SP experiments than in
+the SL ones. Second, the s-RNNs out-performed the LSTMs on the most complex SP
+experiment and performed comparably to them on the others.
+",1,0,0,0,0,0
+4265,Observation of oscillatory relaxation in the Sn-terminated surface of epitaxial rock-salt SnSe $\{111\}$ topological crystalline insulator,"  Topological crystalline insulators have been recently predicted and observed
+in rock-salt structure SnSe $\{111\}$ thin films. Previous studies have
+suggested that the Se-terminated surface of this thin film with hydrogen
+passivation, has a reduced surface energy and is thus a preferred
+configuration. In this paper, synchrotron-based angle-resolved photoemission
+spectroscopy, along with density functional theory calculations, are used to
+demonstrate conclusively that a rock-salt SnSe $\{111\}$ thin film
+epitaxially-grown on \ce{Bi2Se3} has a stable Sn-terminated surface. These
+observations are supported by low energy electron diffraction (LEED)
+intensity-voltage measurements and dynamical LEED calculations, which further
+show that the Sn-terminated SnSe $\{111\}$ thin film has undergone a surface
+structural relaxation of the interlayer spacing between the Sn and Se atomic
+planes. In sharp contrast to the Se-terminated counterpart, the observed Dirac
+surface state in the Sn-terminated SnSe $\{111\}$ thin film is shown to yield a
+high Fermi velocity, $0.50\times10^6$m/s, which suggests a potential mechanism
+of engineering the Dirac surface state of topological materials by tuning the
+surface configuration.
+",0,1,0,0,0,0
+4266,Chaotic Dynamics of Inner Ear Hair Cells,"  Experimental records of active bundle motility are used to demonstrate the
+presence of a low-dimensional chaotic attractor in hair cell dynamics.
+Dimensionality tests from dynamic systems theory are applied to estimate the
+number of independent variables sufficient for modeling the hair cell response.
+Poincare maps are constructed to observe a quasiperiodic transition from chaos
+to order with increasing amplitudes of mechanical forcing. The onset of this
+transition is accompanied by a reduction of Kolmogorov entropy in the system
+and an increase in mutual information between the stimulus and the hair bundle,
+indicative of signal detection. A simple theoretical model is used to describe
+the observed chaotic dynamics. The model exhibits an enhancement of sensitivity
+to weak stimuli when the system is poised in the chaotic regime. We propose
+that chaos may play a role in the hair cell's ability to detect low-amplitude
+sounds.
+",0,1,0,0,0,0
+4267,From Dirac semimetals to topological phases in three dimensions: a coupled wire construction,"  Weyl and Dirac (semi)metals in three dimensions have robust gapless
+electronic band structures. Their massless single-body energy spectra are
+protected by symmetries such as lattice translation, (screw) rotation and time
+reversal. In this manuscript, we discuss many-body interactions in these
+systems. We focus on strong interactions that preserve symmetries and are
+outside the single-body mean-field regime. By mapping a Dirac (semi)metal to a
+model based on a three dimensional array of coupled Dirac wires, we show (1)
+the Dirac (semi)metal can acquire a many-body excitation energy gap without
+breaking the relevant symmetries, and (2) interaction can enable an anomalous
+Weyl (semi)metallic phase that is otherwise forbidden by symmetries in the
+single-body setting and can only be present holographically on the boundary of
+a four dimensional weak topological insulator. Both of these topological states
+support fractional gapped (gapless) bulk (resp. boundary) quasiparticle
+excitations.
+",0,1,0,0,0,0
+4268,CHIME FRB: An application of FFT beamforming for a radio telescope,"  We have developed FFT beamforming techniques for the CHIME radio telescope,
+to search for and localize the astrophysical signals from Fast Radio Bursts
+(FRBs) over a large instantaneous field-of-view (FOV) while maintaining the
+full angular resolution of CHIME. We implement a hybrid beamforming pipeline in
+a GPU correlator, synthesizing 256 FFT-formed beams in the North-South
+direction by four formed beams along East-West via exact phasing, tiling a sky
+area of ~250 square degrees. A zero-padding approximation is employed to
+improve chromatic beam alignment across the wide bandwidth of 400 to 800 MHz.
+We up-channelize the data in order to achieve fine spectral resolution of
+$\Delta\nu$=24 kHz and time cadence of 0.983 ms, desirable for detecting
+transient and dispersed signals such as those from FRBs.
+",0,1,0,0,0,0
+4269,MRI-PET Registration with Automated Algorithm in Pre-clinical Studies,"  Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET)
+automatic 3-D registration is implemented and validated for small animal image
+volumes so that the high-resolution anatomical MRI information can be fused
+with the low spatial resolution of functional PET information for the
+localization of lesion that is currently in high demand in the study of tumor
+of cancer (oncology) and its corresponding preparation of pharmaceutical drugs.
+Though many registration algorithms are developed and applied on human brain
+volumes, these methods may not be as efficient on small animal datasets due to
+lack of intensity information and often the high anisotropy in voxel
+dimensions. Therefore, a fully automatic registration algorithm which can
+register not only assumably rigid small animal volumes such as brain but also
+deformable organs such as kidney, cardiac and chest is developed using a
+combination of global affine and local B-spline transformation models in which
+mutual information is used as a similarity criterion. The global affine
+registration uses a multi-resolution pyramid on image volumes of 3 levels
+whereas in local B-spline registration, a multi-resolution scheme is applied on
+the B-spline grid of 2 levels on the finest resolution of the image volumes in
+which only the transform itself is affected rather than the image volumes.
+Since mutual information lacks sufficient spatial information, PCA is used to
+inject it by estimating initial translation and rotation parameters. It is
+computationally efficient since it is implemented using C++ and ITK library,
+and is qualitatively and quantitatively shown that this PCA-initialized global
+registration followed by local registration is in close agreement with expert
+manual registration and outperforms the one without PCA initialization tested
+on small animal brain and kidney.
+",1,0,0,0,0,0
+4270,Generalization of Special Functions and its Applications to Multiplicative and Ordinary Fractional Derivatives,"  The goal of this paper is to extend the classical and multiplicative
+fractional derivatives. For this purpose, it is introduced the new extended
+modified Bessel function and also given an important relation between this new
+function I(v,q;x) and the confluent hypergeometric function. Besides, it is
+used to generalize the hypergeometric, the confluent hypergeometric and the
+extended beta functions by using the new extended modified Bessel function.
+Also, the asymptotic formulae and the generating function of the extended
+modified Bessel function are obtained. The extensions of classical and
+multiplicative fractional derivatives are defined via extended modified Bessel
+function and, first time the fractional derivative of rational functions is
+explicitly given via complex partial fraction decomposition.
+",0,0,1,0,0,0
+4271,Incident Light Frequency-based Image Defogging Algorithm,"  Considering the problem of color distortion caused by the defogging algorithm
+based on dark channel prior, an improved algorithm was proposed to calculate
+the transmittance of all channels respectively. First, incident light
+frequency's effect on the transmittance of various color channels was analyzed
+according to the Beer-Lambert's Law, from which a proportion among various
+channel transmittances was derived; afterwards, images were preprocessed by
+down-sampling to refine transmittance, and then the original size was restored
+to enhance the operational efficiency of the algorithm; finally, the
+transmittance of all color channels was acquired in accordance with the
+proportion, and then the corresponding transmittance was used for image
+restoration in each channel. The experimental results show that compared with
+the existing algorithm, this improved image defogging algorithm could make
+image colors more natural, solve the problem of slightly higher color
+saturation caused by the existing algorithm, and shorten the operation time by
+four to nine times.
+",1,0,0,0,0,0
+4272,Tangent points of d-lower content regular sets and $β$ numbers,"  We present a generalisation of C. Bishop and P. Jones' result in [BJ1], where
+they give a characterisation of the tangent points of a Jordan curve in terms
+of $\beta$ numbers. Instead of the $L^\infty$ Jones' $\beta$ numbers, we use an
+averaged version of them, firstly introduced by J. Azzam and R. Schul in [AS1].
+A fundamental tool in the proof will be the Reifenberg parameterisation Theorem
+of G. David and T. Toro (see [DT1]).
+",0,0,1,0,0,0
+4273,Quasi-Oracle Estimation of Heterogeneous Treatment Effects,"  Flexible estimation of heterogeneous treatment effects lies at the heart of
+many statistical challenges, such as personalized medicine and optimal resource
+allocation. In this paper, we develop a general class of two-step algorithms
+for heterogeneous treatment effect estimation in observational studies. We
+first estimate marginal effects and treatment propensities in order to form an
+objective function that isolates the causal component of the signal. Then, we
+optimize this data-adaptive objective function. Our approach has several
+advantages over existing methods. From a practical perspective, our method is
+flexible and easy to use: In both steps, we can use any loss-minimization
+method, e.g., penalized regression, deep neutral networks, or boosting;
+moreover, these methods can be fine-tuned by cross validation. Meanwhile, in
+the case of penalized kernel regression, we show that our method has a
+quasi-oracle property: Even if the pilot estimates for marginal effects and
+treatment propensities are not particularly accurate, we achieve the same error
+bounds as an oracle who has a priori knowledge of these two nuisance
+components. We implement variants of our approach based on both penalized
+regression and boosting in a variety of simulation setups, and find promising
+performance relative to existing baselines.
+",0,0,1,1,0,0
+4274,Adaptive clustering procedure for continuous gravitational wave searches,"  In hierarchical searches for continuous gravitational waves, clustering of
+candidates is an important postprocessing step because it reduces the number of
+noise candidates that are followed-up at successive stages [1][7][12]. Previous
+clustering procedures bundled together nearby candidates ascribing them to the
+same root cause (be it a signal or a disturbance), based on a predefined
+cluster volume. In this paper, we present a procedure that adapts the cluster
+volume to the data itself and checks for consistency of such volume with what
+is expected from a signal. This significantly improves the noise rejection
+capabilities at fixed detection threshold, and at fixed computing resources for
+the follow-up stages, this results in an overall more sensitive search. This
+new procedure was employed in the first Einstein@Home search on data from the
+first science run of the advanced LIGO detectors (O1) [11].
+",0,1,1,0,0,0
+4275,The heat trace for the drifting Laplacian and Schrödinger operators on manifolds,"  We study the heat trace for both the drifting Laplacian as well as
+Schrödinger operators on compact Riemannian manifolds. In the case of a
+finite regularity potential or weight function, we prove the existence of a
+partial (six term) asymptotic expansion of the heat trace for small times as
+well as a suitable remainder estimate. We also demonstrate that the more
+precise asymptotic behavior of the remainder is determined by and conversely
+distinguishes higher (Sobolev) regularity on the potential or weight function.
+In the case of a smooth weight function, we determine the full asymptotic
+expansion of the heat trace for the drifting Laplacian for small times. We then
+use the heat trace to study the asymptotics of the eigenvalue counting
+function. In both cases the Weyl law coincides with the Weyl law for the
+Riemannian manifold with the standard Laplace-Beltrami operator. We conclude by
+demonstrating isospectrality results for the drifting Laplacian on compact
+manifolds.
+",0,0,1,0,0,0
+4276,Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data,"  As online systems based on machine learning are offered to public or paid
+subscribers via application programming interfaces (APIs), they become
+vulnerable to frequent exploits and attacks. This paper studies adversarial
+machine learning in the practical case when there are rate limitations on API
+calls. The adversary launches an exploratory (inference) attack by querying the
+API of an online machine learning system (in particular, a classifier) with
+input data samples, collecting returned labels to build up the training data,
+and training an adversarial classifier that is functionally equivalent and
+statistically close to the target classifier. The exploratory attack with
+limited training data is shown to fail to reliably infer the target classifier
+of a real text classifier API that is available online to the public. In
+return, a generative adversarial network (GAN) based on deep learning is built
+to generate synthetic training data from a limited number of real training data
+samples, thereby extending the training data and improving the performance of
+the inferred classifier. The exploratory attack provides the basis to launch
+the causative attack (that aims to poison the training process) and evasion
+attack (that aims to fool the classifier into making wrong decisions) by
+selecting training and test data samples, respectively, based on the confidence
+scores obtained from the inferred classifier. These stealth attacks with small
+footprint (using a small number of API calls) make adversarial machine learning
+practical under the realistic case with limited training data available to the
+adversary.
+",1,0,0,1,0,0
+4277,"Coloring ($P_6$, diamond, $K_4$)-free graphs","  We show that every ($P_6$, diamond, $K_4$)-free graph is $6$-colorable.
+Moreover, we give an example of a ($P_6$, diamond, $K_4$)-free graph $G$ with
+$\chi(G) = 6$. This generalizes some known results in the literature.
+",1,0,0,0,0,0
+4278,Habitable Climate Scenarios for Proxima Centauri b With a Dynamic Ocean,"  The nearby exoplanet Proxima Centauri b will be a prime future target for
+characterization, despite questions about its retention of water. Climate
+models with static oceans suggest that an Earth-like Proxima b could harbor a
+small dayside region of surface liquid water at fairly warm temperatures
+despite its weak instellation. We present the first 3-dimensional climate
+simulations of Proxima b with a dynamic ocean. We find that an ocean-covered
+Proxima b could have a much broader area of surface liquid water but at much
+colder temperatures than previously suggested, due to ocean heat transport and
+depression of the freezing point by salinity. Elevated greenhouse gas
+concentrations do not necessarily produce more open ocean area because of
+possible dynamic regime transitions. For an evolutionary path leading to a
+highly saline present ocean, Proxima b could conceivably be an inhabited,
+mostly open ocean planet dominated by halophilic life. For an ocean planet in
+3:2 spin-orbit resonance, a permanent tropical waterbelt exists for moderate
+eccentricity. Simulations of Proxima Centauri b may also be a model for the
+habitability of planets receiving similar instellation from slightly cooler or
+warmer stars, e.g., in the TRAPPIST-1, LHS 1140, GJ 273, and GJ 3293 systems.
+",0,1,0,0,0,0
+4279,Level bounds for exceptional quantum subgroups in rank two,"  There is a long-standing belief that the modular tensor categories
+$\mathcal{C}(\mathfrak{g},k)$, for $k\in\mathbb{Z}_{\geq1}$ and
+finite-dimensional simple complex Lie algebras $\mathfrak{g}$, contain
+exceptional connected étale algebras at only finitely many levels $k$. This
+premise has known implications for the study of relations in the Witt group of
+nondegenerate braided fusion categories, modular invariants of conformal field
+theories, and the classification of subfactors in the theory of von Neumann
+algebras. Here we confirm this conjecture when $\mathfrak{g}$ has rank 2,
+contributing proofs and explicit bounds when $\mathfrak{g}$ is of type $B_2$ or
+$G_2$, adding to the previously known positive results for types $A_1$ and
+$A_2$.
+",0,0,1,0,0,0
+4280,Convolution Forgetting Curve Model for Repeated Learning,"  Most of mathematic forgetting curve models fit well with the forgetting data
+under the learning condition of one time rather than repeated. In the paper, a
+convolution model of forgetting curve is proposed to simulate the memory
+process during learning. In this model, the memory ability (i.e. the central
+procedure in the working memory model) and learning material (i.e. the input in
+the working memory model) is regarded as the system function and the input
+function, respectively. The status of forgetting (i.e. the output in the
+working memory model) is regarded as output function or the convolution result
+of the memory ability and learning material. The model is applied to simulate
+the forgetting curves in different situations. The results show that the model
+is able to simulate the forgetting curves not only in one time learning
+condition but also in multi-times condition. The model is further verified in
+the experiments of Mandarin tone learning for Japanese learners. And the
+predicted curve fits well on the test points.
+",1,0,0,0,1,0
+4281,Efficient computation of multidimensional theta functions,"  An important step in the efficient computation of multi-dimensional theta
+functions is the construction of appropriate symplectic transformations for a
+given Riemann matrix assuring a rapid convergence of the theta series. An
+algorithm is presented to approximately map the Riemann matrix to the Siegel
+fundamental domain. The shortest vector of the lattice generated by the Riemann
+matrix is identified exactly, and the algorithm ensures that its length is
+larger than $\sqrt{3}/2$. The approach is based on a previous algorithm by
+Deconinck et al. using the LLL algorithm for lattice reductions. Here, the LLL
+algorithm is replaced by exact Minkowski reductions for small genus and an
+exact identification of the shortest lattice vector for larger values of the
+genus.
+",0,1,1,0,0,0
+4282,The careless use of language in quantum information,"  An imperative aspect of modern science is that scientific institutions act
+for the benefit of a common scientific enterprise, rather than for the personal
+gain of individuals within them. This implies that science should not
+perpetuate existing or historical unequal social orders. Some scientific
+terminology, though, gives a very different impression. I will give two
+examples of terminology invented recently for the field of quantum information
+which use language associated with subordination, slavery, and racial
+segregation: 'ancilla qubit' and 'quantum supremacy'.
+",0,1,0,0,0,0
+4283,Origin of meteoritic stardust unveiled by a revised proton-capture rate of $^{17}$O,"  Stardust grains recovered from meteorites provide high-precision snapshots of
+the isotopic composition of the stellar environment in which they formed.
+Attributing their origin to specific types of stars, however, often proves
+difficult. Intermediate-mass stars of 4-8 solar masses are expected to
+contribute a large fraction of meteoritic stardust. However, no grains have
+been found with characteristic isotopic compositions expected from such stars.
+This is a long-standing puzzle, which points to serious gaps in our
+understanding of the lifecycle of stars and dust in our Galaxy. Here we show
+that the increased proton-capture rate of $^{17}$O reported by a recent
+underground experiment leads to $^{17}$O/$^{16}$O isotopic ratios that match
+those observed in a population of stardust grains, for proton-burning
+temperatures of 60-80 million K. These temperatures are indeed achieved at the
+base of the convective envelope during the late evolution of intermediate-mass
+stars of 4-8 solar masses, which reveals them as the most likely site of origin
+of the grains. This result provides the first direct evidence that these stars
+contributed to the dust inventory from which the Solar System formed.
+",0,1,0,0,0,0
+4284,Some Connections Between Cycles and Permutations that Fix a Set and Touchard Polynomials and Covers of Multisets,"  We present a new proof of a fundamental result concerning cycles of random
+permutations which gives some intuition for the connection between Touchard
+polynomials and the Poisson distribution. We also introduce a rather novel
+permutation statistic and study its distribution. This quantity, indexed by
+$m$, is the number of sets of size $m$ fixed by the permutation. This leads to
+a new and simpler derivation of the exponential generating function for the
+number of covers of certain multisets.
+",0,0,1,0,0,0
+4285,Generalizing the first-difference correlated random walk for marine animal movement data,"  Animal telemetry data are often analysed with discrete time movement models
+assuming rotation in the movement. These models are defined with equidistant
+distant time steps. However, telemetry data from marine animals are observed
+irregularly. To account for irregular data, a time-irregularised
+first-difference correlated random walk model with drift is introduced. The
+model generalizes the commonly used first-difference correlated random walk
+with regular time steps by allowing irregular time steps, including a drift
+term, and by allowing different autocorrelation in the two coordinates. The
+model is applied to data from a ringed seal collected through the Argos
+satellite system, and is compared to related movement models through
+simulations. Accounting for irregular data in the movement model results in
+accurate parameter estimates and reconstruction of movement paths. Measured by
+distance, the introduced model can provide more accurate movement paths than
+the regular time counterpart. Extracting accurate movement paths from uncertain
+telemetry data is important for evaluating space use patterns for marine
+animals, which in turn is crucial for management. Further, handling irregular
+data directly in the movement model allows efficient simultaneous analysis of
+several animals.
+",0,0,0,0,1,0
+4286,Low spin wave damping in the insulating chiral magnet Cu$_{2}$OSeO$_{3}$,"  Chiral magnets with topologically nontrivial spin order such as Skyrmions
+have generated enormous interest in both fundamental and applied sciences. We
+report broadband microwave spectroscopy performed on the insulating chiral
+ferrimagnet Cu$_{2}$OSeO$_{3}$. For the damping of magnetization dynamics we
+find a remarkably small Gilbert damping parameter of about $1\times10^{-4}$ at
+5 K. This value is only a factor of 4 larger than the one reported for the best
+insulating ferrimagnet yttrium iron garnet. We detect a series of sharp
+resonances and attribute them to confined spin waves in the mm-sized samples.
+Considering the small damping, insulating chiral magnets turn out to be
+promising candidates when exploring non-collinear spin structures for high
+frequency applications.
+",0,1,0,0,0,0
+4287,Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation,"  For human pose estimation in monocular images, joint occlusions and
+overlapping upon human bodies often result in deviated pose predictions. Under
+these circumstances, biologically implausible pose predictions may be produced.
+In contrast, human vision is able to predict poses by exploiting geometric
+constraints of joint inter-connectivity. To address the problem by
+incorporating priors about the structure of human bodies, we propose a novel
+structure-aware convolutional network to implicitly take such priors into
+account during training of the deep network. Explicit learning of such
+constraints is typically challenging. Instead, we design discriminators to
+distinguish the real poses from the fake ones (such as biologically implausible
+ones). If the pose generator (G) generates results that the discriminator fails
+to distinguish from real ones, the network successfully learns the priors.
+",1,0,0,0,0,0
+4288,Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection,"  Among underwater perceptual sensors, imaging sonar has been highlighted for
+its perceptual robustness underwater. The major challenge of imaging sonar,
+however, arises from the difficulty in defining visual features despite limited
+resolution and high noise levels. Recent developments in deep learning provide
+a powerful solution for computer-vision researches using optical images.
+Unfortunately, deep learning-based approaches are not well established for
+imaging sonars, mainly due to the scant data in the training phase. Unlike the
+abundant publically available terrestrial images, obtaining underwater images
+is often costly, and securing enough underwater images for training is not
+straightforward. To tackle this issue, this paper presents a solution to this
+field's lack of data by introducing a novel end-to-end image-synthesizing
+method in the training image preparation phase. The proposed method present
+image synthesizing scheme to the images captured by an underwater simulator.
+Our synthetic images are based on the sonar imaging models and noisy
+characteristics to represent the real data obtained from the sea. We validate
+the proposed scheme by training using a simulator and by testing the simulated
+images with real underwater sonar images obtained from a water tank and the
+sea.
+",1,0,0,0,0,0
+4289,Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression,"  Genome-wide association studies (GWAS) have achieved great success in the
+genetic study of Alzheimer's disease (AD). Collaborative imaging genetics
+studies across different research institutions show the effectiveness of
+detecting genetic risk factors. However, the high dimensionality of GWAS data
+poses significant challenges in detecting risk SNPs for AD. Selecting relevant
+features is crucial in predicting the response variable. In this study, we
+propose a novel Distributed Feature Selection Framework (DFSF) to conduct the
+large-scale imaging genetics studies across multiple institutions. To speed up
+the learning process, we propose a family of distributed group Lasso screening
+rules to identify irrelevant features and remove them from the optimization.
+Then we select the relevant group features by performing the group Lasso
+feature selection process in a sequence of parameters. Finally, we employ the
+stability selection to rank the top risk SNPs that might help detect the early
+stage of AD. To the best of our knowledge, this is the first distributed
+feature selection model integrated with group Lasso feature selection as well
+as detecting the risk genetic factors across multiple research institutions
+system. Empirical studies are conducted on 809 subjects with 5.9 million SNPs
+which are distributed across several individual institutions, demonstrating the
+efficiency and effectiveness of the proposed method.
+",1,0,0,1,0,0
+4290,Strong Functional Representation Lemma and Applications to Coding Theorems,"  This paper shows that for any random variables $X$ and $Y$, it is possible to
+represent $Y$ as a function of $(X,Z)$ such that $Z$ is independent of $X$ and
+$I(X;Z|Y)\le\log(I(X;Y)+1)+4$ bits. We use this strong functional
+representation lemma (SFRL) to establish a bound on the rate needed for
+one-shot exact channel simulation for general (discrete or continuous) random
+variables, strengthening the results by Harsha et al. and Braverman and Garg,
+and to establish new and simple achievability results for one-shot
+variable-length lossy source coding, multiple description coding and Gray-Wyner
+system. We also show that the SFRL can be used to reduce the channel with state
+noncausally known at the encoder to a point-to-point channel, which provides a
+simple achievability proof of the Gelfand-Pinsker theorem.
+",1,0,1,0,0,0
+4291,Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation,"  Infants are experts at playing, with an amazing ability to generate novel
+structured behaviors in unstructured environments that lack clear extrinsic
+reward signals. We seek to replicate some of these abilities with a neural
+network that implements curiosity-driven intrinsic motivation. Using a simple
+but ecologically naturalistic simulated environment in which the agent can move
+and interact with objects it sees, the agent learns a world model predicting
+the dynamic consequences of its actions. Simultaneously, the agent learns to
+take actions that adversarially challenge the developing world model, pushing
+the agent to explore novel and informative interactions with its environment.
+We demonstrate that this policy leads to the self-supervised emergence of a
+spectrum of complex behaviors, including ego motion prediction, object
+attention, and object gathering. Moreover, the world model that the agent
+learns supports improved performance on object dynamics prediction and
+localization tasks. Our results are a proof-of-principle that computational
+models of intrinsic motivation might account for key features of developmental
+visuomotor learning in infants.
+",0,0,0,1,0,0
+4292,Predicting shim gaps in aircraft assembly with machine learning and sparse sensing,"  A modern aircraft may require on the order of thousands of custom shims to
+fill gaps between structural components in the airframe that arise due to
+manufacturing tolerances adding up across large structures. These shims are
+necessary to eliminate gaps, maintain structural performance, and minimize
+pull-down forces required to bring the aircraft into engineering nominal
+configuration for peak aerodynamic efficiency. Gap filling is a time-consuming
+process, involving either expensive by-hand inspection or computations on vast
+quantities of measurement data from increasingly sophisticated metrology
+equipment. Either case amounts to significant delays in production, with much
+of the time spent in the critical path of aircraft assembly. This work presents
+an alternative strategy for predictive shimming, based on machine learning and
+sparse sensing to first learn gap distributions from historical data, and then
+design optimized sparse sensing strategies to streamline data collection and
+processing. This new approach is based on the assumption that patterns exist in
+shim distributions across aircraft, which may be mined and used to reduce the
+burden of data collection and processing in future aircraft. Specifically,
+robust principal component analysis is used to extract low-dimensional patterns
+in the gap measurements while rejecting outliers. Next, optimized sparse
+sensors are obtained that are most informative about the dimensions of a new
+aircraft in these low-dimensional principal components. We demonstrate the
+success of the proposed approach, called PIXel Identification Despite
+Uncertainty in Sensor Technology (PIXI-DUST), on historical production data
+from 54 representative Boeing commercial aircraft. Our algorithm successfully
+predicts $99\%$ of shim gaps within the desired measurement tolerance using
+$3\%$ of the laser scan points typically required; all results are
+cross-validated.
+",0,0,0,1,0,0
+4293,Multivariate central limit theorems for Rademacher functionals with applications,"  Quantitative multivariate central limit theorems for general functionals of
+possibly non-symmetric and non-homogeneous infinite Rademacher sequences are
+proved by combining discrete Malliavin calculus with the smart path method for
+normal approximation. In particular, a discrete multivariate second-order
+Poincaré inequality is developed. As a first application, the normal
+approximation of vectors of subgraph counting statistics in the
+Erdős-Rényi random graph is considered. In this context, we further
+specialize to the normal approximation of vectors of vertex degrees. In a
+second application we prove a quantitative multivariate central limit theorem
+for vectors of intrinsic volumes induced by random cubical complexes.
+",0,0,1,0,0,0
+4294,Modification of Social Dominance in Social Networks by Selective Adjustment of Interpersonal Weights,"  According to the DeGroot-Friedkin model of a social network, an individual's
+social power evolves as the network discusses individual opinions over a
+sequence of issues. Under mild assumptions on the connectivity of the network,
+the social power of every individual converges to a constant strictly positive
+value as the number of issues discussed increases. If the network has a special
+topology, termed ""star topology"", then all social power accumulates with the
+individual at the centre of the star. This paper studies the strategic
+introduction of new individuals and/or interpersonal relationships into a
+social network with star topology to reduce the social power of the centre
+individual. In fact, several strategies are proposed. For each strategy, we
+derive necessary and sufficient conditions on the strength of the new
+interpersonal relationships, based on local information, which ensures that the
+centre individual no longer has the greatest social power within the social
+network. Interpretations of these conditions show that the strategies are
+remarkably intuitive and that certain strategies are favourable compared to
+others, all of which is sociologically expected.
+",1,0,0,0,0,0
+4295,On Abrikosov Lattice Solutions of the Ginzburg-Landau Equations,"  We prove existence of Abrikosov vortex lattice solutions of the
+Ginzburg-Landau equations of superconductivity, with multiple magnetic flux
+quanta per a fundamental cell. We also revisit the existence proof for the
+Abrikosov vortex lattices, streamlining some arguments and providing some
+essential details missing in earlier proofs for a single magnetic flux quantum
+per a fundamental cell.
+",0,0,1,0,0,0
+4296,$0.7-2.5~μ$m spectra of Hilda asteroids,"  The Hilda asteroids are primitive bodies in resonance with Jupiter whose
+origin and physical properties are not well understood. Current models posit
+that these asteroids formed in the outer Solar System and were scattered along
+with the Jupiter Trojans into their present-day positions during a chaotic
+episode of dynamical restructuring. In order to explore the surface composition
+of these enigmatic objects in comparison with an analogous study of Trojans
+(Emery et al. 2011), we present new near-infrared spectra (0.7-2.5 $\mu$m) of
+25 Hilda asteroids. No discernible absorption features are apparent in the
+data. Synthesizing the bimodalities in optical color and infrared reflectivity
+reported in previous studies, we classify 26 of the 28 Hildas in our spectral
+sample into the so-called less-red and red sub-populations and find that the
+two sub-populations have distinct average spectral shapes. Combining our
+results with visible spectra, we find that Trojans and Hildas possess similar
+overall spectral shapes, suggesting that the two minor body populations share a
+common progenitor population. A more detailed examination reveals that while
+the red Trojans and Hildas have nearly identical spectra, less-red Hildas are
+systematically bluer in the visible and redder in the near-infrared than
+less-red Trojans, indicating a putative broad, shallow absorption feature
+between 0.5 and 1.0 $\mu$m. We argue that the less-red and red objects found in
+both Hildas and Trojans represent two distinct surface chemistries and
+attribute the small discrepancy between less-red Hildas and Trojans to the
+difference in surface temperatures between the two regions.
+",0,1,0,0,0,0
+4297,Ground-state properties of unitary bosons: from clusters to matter,"  The properties of cold Bose gases at unitarity have been extensively
+investigated in the last few years both theoretically and experimentally. In
+this paper we use a family of interactions tuned to two-body unitarity and very
+weak three-body binding to demonstrate the universal properties of both
+clusters and matter. We determine the universal properties of finite clusters
+up to 60 particles and, for the first time, explicitly demonstrate the
+saturation of energy and density with particle number and compare with bulk
+properties. At saturation in the bulk we determine the energy, density, two-
+and three-body contacts and the condensate fraction. We find that uniform
+matter is more bound than three-body clusters by nearly two orders of
+magnitude, the two-body contact is very large in absolute terms, and yet the
+condensate fraction is also very large, greater than 90%. Equilibrium
+properties of these systems may be experimentally accessible through rapid
+quenching of weakly-interacting boson superfluids.
+",0,1,0,0,0,0
+4298,Lower bounds on the Noether number,"  The best known method to give a lower bound for the Noether number of a given
+finite group is to use the fact that it is greater than or equal to the Noether
+number of any of the subgroups or factor groups. The results of the present
+paper show in particular that these inequalities are strict for proper
+subgroups or factor groups. This is established by studying the algebra of
+coinvariants of a representation induced from a representation of a subgroup.
+",0,0,1,0,0,0
+4299,A Survey on the Adoption of Cloud Computing in Education Sector,"  Education is a key factor in ensuring economic growth, especially for
+countries with growing economies. Today, students have become more
+technologically savvy as teaching and learning uses more advance technology day
+in, day out. Due to virtualize resources through the Internet, as well as
+dynamic scalability, cloud computing has continued to be adopted by more
+organizations. Despite the looming financial crisis, there has been increasing
+pressure for educational institutions to deliver better services using minimal
+resources. Leaning institutions, both public and private can utilize the
+potential advantage of cloud computing to ensure high quality service
+regardless of the minimal resources available. Cloud computing is taking a
+center stage in academia because of its various benefits. Various learning
+institutions use different cloud-based applications provided by the service
+providers to ensure that their students and other users can perform both
+academic as well as business-related tasks. Thus, this research will seek to
+establish the benefits associated with the use of cloud computing in learning
+institutions. The solutions provided by the cloud technology ensure that the
+research and development, as well as the teaching is more sustainable and
+efficient, thus positively influencing the quality of learning and teaching
+within educational institutions. This has led to various learning institutions
+adopting cloud technology as a solution to various technological challenges
+they face on a daily routine.
+",1,0,0,0,0,0
+4300,Deep Neural Networks as Gaussian Processes,"  It has long been known that a single-layer fully-connected neural network
+with an i.i.d. prior over its parameters is equivalent to a Gaussian process
+(GP), in the limit of infinite network width. This correspondence enables exact
+Bayesian inference for infinite width neural networks on regression tasks by
+means of evaluating the corresponding GP. Recently, kernel functions which
+mimic multi-layer random neural networks have been developed, but only outside
+of a Bayesian framework. As such, previous work has not identified that these
+kernels can be used as covariance functions for GPs and allow fully Bayesian
+prediction with a deep neural network.
+In this work, we derive the exact equivalence between infinitely wide deep
+networks and GPs. We further develop a computationally efficient pipeline to
+compute the covariance function for these GPs. We then use the resulting GPs to
+perform Bayesian inference for wide deep neural networks on MNIST and CIFAR-10.
+We observe that trained neural network accuracy approaches that of the
+corresponding GP with increasing layer width, and that the GP uncertainty is
+strongly correlated with trained network prediction error. We further find that
+test performance increases as finite-width trained networks are made wider and
+more similar to a GP, and thus that GP predictions typically outperform those
+of finite-width networks. Finally we connect the performance of these GPs to
+the recent theory of signal propagation in random neural networks.
+",1,0,0,1,0,0
+4301,The Brown-Peterson spectrum is not $E_{2(p^2+2)}$ at odd primes,"  Recently, Lawson has shown that the 2-primary Brown-Peterson spectrum does
+not admit the structure of an $E_{12}$ ring spectrum, thus answering a question
+of May in the negative. We extend Lawson's result to odd primes by proving that
+the p-primary Brown-Peterson spectrum does not admit the structure of an
+$E_{2(p^2+2)}$ ring spectrum. We also show that there can be no map $MU \to BP$
+of $E_{2p+3}$ ring spectra at any prime.
+",0,0,1,0,0,0
+4302,Beliefs in Markov Trees - From Local Computations to Local Valuation,"  This paper is devoted to expressiveness of hypergraphs for which uncertainty
+propagation by local computations via Shenoy/Shafer method applies. It is
+demonstrated that for this propagation method for a given joint belief
+distribution no valuation of hyperedges of a hypergraph may provide with
+simpler hypergraph structure than valuation of hyperedges by conditional
+distributions. This has vital implication that methods recovering belief
+networks from data have no better alternative for finding the simplest
+hypergraph structure for belief propagation. A method for recovery
+tree-structured belief networks has been developed and specialized for
+Dempster-Shafer belief functions
+",1,0,0,0,0,0
+4303,A model for random fire induced tree-grass coexistence in savannas,"  Tree-grass coexistence in savanna ecosystems depends strongly on
+environmental disturbances out of which crucial is fire. Most modeling attempts
+in the literature lack stochastic approach to fire occurrences which is
+essential to reflect their unpredictability. Existing models that actually
+include stochasticity of fire are usually analyzed only numerically. We
+introduce new minimalistic model of tree-grass coexistence where fires occur
+according to stochastic process. We use the tools of linear semigroup theory to
+provide more careful mathematical analysis of the model. Essentially we show
+that there exists a unique stationary distribution of tree and grass biomasses.
+",0,0,0,0,1,0
+4304,A depth-based method for functional time series forecasting,"  An approach is presented for making predictions about functional time series.
+The method is applied to data coming from periodically correlated processes and
+electricity demand, obtaining accurate point forecasts and narrow prediction
+bands that cover high proportions of the forecasted functional datum, for a
+given confidence level. The method is computationally efficient and
+substantially different to other functional time series methods, offering a new
+insight for the analysis of these data structures.
+",0,0,0,1,0,0
+4305,Dihedral Molecular Configurations Interacting by Lennard-Jones and Coulomb Forces,"  In this paper, we investigate periodic vibrations of a group of particles
+with a dihedral configuration in the plane governed by the Lennard-Jones and
+Coulomb forces. Using the gradient equivariant degree, we provide a full
+topological classification of the periodic solutions with both temporal and
+spatial symmetries. In the process, we provide with general formulae for the
+spectrum of the linearized system which allows us to obtain the critical
+frequencies of the particle motions which indicate the set of all critical
+periods of small amplitude periodic solutions emerging from a given stationary
+symmetric orbit of solutions.
+",0,0,1,0,0,0
+4306,Understanding news story chains using information retrieval and network clustering techniques,"  Content analysis of news stories (whether manual or automatic) is a
+cornerstone of the communication studies field. However, much research is
+conducted at the level of individual news articles, despite the fact that news
+events (especially significant ones) are frequently presented as ""stories"" by
+news outlets: chains of connected articles covering the same event from
+different angles. These stories are theoretically highly important in terms of
+increasing public recall of news items and enhancing the agenda-setting power
+of the press. Yet thus far, the field has lacked an efficient method for
+detecting groups of articles which form stories in a way that enables their
+analysis.
+In this work, we present a novel, automated method for identifying linked
+news stories from within a corpus of articles. This method makes use of
+techniques drawn from the field of information retrieval to identify textual
+closeness of pairs of articles, and then clustering techniques taken from the
+field of network analysis to group these articles into stories. We demonstrate
+the application of the method to a corpus of 61,864 articles, and show how it
+can efficiently identify valid story clusters within the corpus. We use the
+results to make observations about the prevalence and dynamics of stories
+within the UK news media, showing that more than 50% of news production takes
+place within stories.
+",1,0,0,0,0,0
+4307,Fair Kernel Learning,"  New social and economic activities massively exploit big data and machine
+learning algorithms to do inference on people's lives. Applications include
+automatic curricula evaluation, wage determination, and risk assessment for
+credits and loans. Recently, many governments and institutions have raised
+concerns about the lack of fairness, equity and ethics in machine learning to
+treat these problems. It has been shown that not including sensitive features
+that bias fairness, such as gender or race, is not enough to mitigate the
+discrimination when other related features are included. Instead, including
+fairness in the objective function has been shown to be more efficient.
+We present novel fair regression and dimensionality reduction methods built
+on a previously proposed fair classification framework. Both methods rely on
+using the Hilbert Schmidt independence criterion as the fairness term. Unlike
+previous approaches, this allows us to simplify the problem and to use multiple
+sensitive variables simultaneously. Replacing the linear formulation by kernel
+functions allows the methods to deal with nonlinear problems. For both linear
+and nonlinear formulations the solution reduces to solving simple matrix
+inversions or generalized eigenvalue problems. This simplifies the evaluation
+of the solutions for different trade-off values between the predictive error
+and fairness terms. We illustrate the usefulness of the proposed methods in toy
+examples, and evaluate their performance on real world datasets to predict
+income using gender and/or race discrimination as sensitive variables, and
+contraceptive method prediction under demographic and socio-economic sensitive
+descriptors.
+",0,0,0,1,0,0
+4308,Underwater Surveying via Bearing only Cooperative Localization,"  Bearing only cooperative localization has been used successfully on aerial
+and ground vehicles. In this paper we present an extension of the approach to
+the underwater domain. The focus is on adapting the technique to handle the
+challenging visibility conditions underwater. Furthermore, data from inertial,
+magnetic, and depth sensors are utilized to improve the robustness of the
+estimation. In addition to robotic applications, the presented technique can be
+used for cave mapping and for marine archeology surveying, both by human
+divers. Experimental results from different environments, including a fresh
+water, low visibility, lake in South Carolina; a cavern in Florida; and coral
+reefs in Barbados during the day and during the night, validate the robustness
+and the accuracy of the proposed approach.
+",1,0,0,0,0,0
+4309,Sterile Neutrinos and B-L Symmetry,"  We revisit the relation between the neutrino masses and the spontaneous
+breaking of the B-L gauge symmetry. We discuss the main scenarios for Dirac and
+Majorana neutrinos and point out two simple mechanisms for neutrino masses. In
+this context the neutrino masses can be generated either at tree level or at
+quantum level and one predicts the existence of very light sterile neutrinos
+with masses below the eV scale. The predictions for lepton number violating
+processes such as mu to e and mu to e gamma are discussed in detail. The impact
+from the cosmological constraints on the effective number of relativistic
+degree of freedom is investigated.
+",0,1,0,0,0,0
+4310,Effects of parametric uncertainties in cascaded open quantum harmonic oscillators and robust generation of Gaussian invariant states,"  This paper is concerned with the generation of Gaussian invariant states in
+cascades of open quantum harmonic oscillators governed by linear quantum
+stochastic differential equations. We carry out infinitesimal perturbation
+analysis of the covariance matrix for the invariant Gaussian state of such a
+system and the related purity functional subject to inaccuracies in the energy
+and coupling matrices of the subsystems. This leads to the problem of balancing
+the state-space realizations of the component oscillators through symplectic
+similarity transformations in order to minimize the mean square sensitivity of
+the purity functional to small random perturbations of the parameters. This
+results in a quadratic optimization problem with an effective solution in the
+case of cascaded one-mode oscillators, which is demonstrated by a numerical
+example. We also discuss a connection of the sensitivity index with classical
+statistical distances and outline infinitesimal perturbation analysis for
+translation invariant cascades of identical oscillators. The findings of the
+paper are applicable to robust state generation in quantum stochastic networks.
+",1,0,1,0,0,0
+4311,A computer-based recursion algorithm for automatic charge of power device of electric vehicles carrying electromagnet,"  This paper proposes a computer-based recursion algorithm for automatic charge
+of power device of electric vehicles carrying electromagnet. The charging
+system includes charging cable with one end connecting gang socket,
+electromagnetic gear driving the connecting socket and a charging pile breaking
+or closing, and detecting part for detecting electric vehicle static call or
+start state. The gang socket mentioned above is linked to electromagnetic gear,
+and the detecting part is connected with charging management system containing
+the intelligent charging power module which controls the electromagnetic drive
+action to close socket with a charging pile at static state and to break at
+start state. Our work holds an electric automobile with convenience, safety low
+maintenance cost.
+",1,0,0,0,0,0
+4312,Abelian Tensor Models on the Lattice,"  We consider a chain of Abelian Klebanov-Tarnopolsky fermionic tensor models
+coupled through quartic nearest-neighbor interactions. We characterize the
+gauge-singlet spectrum for small chains ($L=2,3,4,5$) and observe that the
+spectral statistics exhibits strong evidences in favor of quasi-many body
+localization.
+",0,1,0,0,0,0
+4313,Distance-based Confidence Score for Neural Network Classifiers,"  The reliable measurement of confidence in classifiers' predictions is very
+important for many applications and is, therefore, an important part of
+classifier design. Yet, although deep learning has received tremendous
+attention in recent years, not much progress has been made in quantifying the
+prediction confidence of neural network classifiers. Bayesian models offer a
+mathematically grounded framework to reason about model uncertainty, but
+usually come with prohibitive computational costs. In this paper we propose a
+simple, scalable method to achieve a reliable confidence score, based on the
+data embedding derived from the penultimate layer of the network. We
+investigate two ways to achieve desirable embeddings, by using either a
+distance-based loss or Adversarial Training. We then test the benefits of our
+method when used for classification error prediction, weighting an ensemble of
+classifiers, and novelty detection. In all tasks we show significant
+improvement over traditional, commonly used confidence scores.
+",1,0,0,1,0,0
+4314,Benford analysis of quantum critical phenomena: First digit provides high finite-size scaling exponent while first two and further are not much better,"  Benford's law is an empirical edict stating that the lower digits appear more
+often than higher ones as the first few significant digits in statistics of
+natural phenomena and mathematical tables. A marked proportion of such analyses
+is restricted to the first significant digit. We employ violation of Benford's
+law, up to the first four significant digits, for investigating magnetization
+and correlation data of paradigmatic quantum many-body systems to detect
+cooperative phenomena, focusing on the finite-size scaling exponents thereof.
+We find that for the transverse field quantum XY model, behavior of the very
+first significant digit of an observable, at an arbitrary point of the
+parameter space, is enough to capture the quantum phase transition in the model
+with a relatively high scaling exponent. A higher number of significant digits
+do not provide an appreciable further advantage, in particular, in terms of an
+increase in scaling exponents. Since the first significant digit of a physical
+quantity is relatively simple to obtain in experiments, the results have
+potential implications for laboratory observations in noisy environments.
+",0,1,0,0,0,0
+4315,A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI,"  Two popular classes of methods for approximate inference are Markov chain
+Monte Carlo (MCMC) and variational inference. MCMC tends to be accurate if run
+for a long enough time, while variational inference tends to give better
+approximations at shorter time horizons. However, the amount of time needed for
+MCMC to exceed the performance of variational methods can be quite high,
+motivating more fine-grained tradeoffs. This paper derives a distribution over
+variational parameters, designed to minimize a bound on the divergence between
+the resulting marginal distribution and the target, and gives an example of how
+to sample from this distribution in a way that interpolates between the
+behavior of existing methods based on Langevin dynamics and stochastic gradient
+variational inference (SGVI).
+",1,0,0,1,0,0
+4316,Making Sense of Bell's Theorem and Quantum Nonlocality,"  Bell's theorem has fascinated physicists and philosophers since his 1964
+paper, which was written in response to the 1935 paper of Einstein, Podolsky,
+and Rosen. Bell's theorem and its many extensions have led to the claim that
+quantum mechanics and by inference nature herself are nonlocal in the sense
+that a measurement on a system by an observer at one location has an immediate
+effect on a distant ""entangled"" system (one with which the original system has
+previously interacted). Einstein was repulsed by such ""spooky action at a
+distance"" and was led to question whether quantum mechanics could provide a
+complete description of physical reality. In this paper I argue that quantum
+mechanics does not require spooky action at a distance of any kind and yet it
+is entirely reasonable to question the assumption that quantum mechanics can
+provide a complete description of physical reality. The magic of entangled
+quantum states has little to do with entanglement and everything to do with
+superposition, a property of all quantum systems and a foundational tenet of
+quantum mechanics.
+",0,1,0,0,0,0
+4317,On Some properties of dyadic operators,"  In this paper, the objects of our investigation are some dyadic operators,
+including dyadic shifts, multilinear paraproducts and multilinear Haar
+multipliers. We mainly focus on the continuity and compactness of these
+operators. First, we consider the continuity properties of these operators.
+Then, by the Fréchet-Kolmogorov-Riesz-Tsuji theorem, the non-compactness
+properties of these dyadic operators will be studied. Moreover, we show that
+their commutators are compact with \textit{CMO} functions, which is quite
+different from the non-compaceness properties of these dyadic operators. These
+results are similar to those for Calderón-Zygmund singular integral
+operators.
+",0,0,1,0,0,0
+4318,Rationalizability and Epistemic Priority Orderings,"  At the beginning of a dynamic game, players may have exogenous theories about
+how the opponents are going to play. Suppose that these theories are commonly
+known. Then, players will refine their first-order beliefs, and challenge their
+own theories, through strategic reasoning. I develop and characterize
+epistemically a new solution concept, Selective Rationalizability, which
+accomplishes this task under the following assumption: when the observed
+behavior is not compatible with the beliefs in players' rationality and
+theories of all orders, players keep the orders of belief in rationality that
+are per se compatible with the observed behavior, and drop the incompatible
+beliefs in the theories. Thus, Selective Rationalizability captures Common
+Strong Belief in Rationality (Battigalli and Siniscalchi, 2002) and refines
+Extensive-Form Rationalizability (Pearce, 1984; BS, 2002), whereas
+Strong-$\Delta$-Rationalizability (Battigalli, 2003; Battigalli and
+Siniscalchi, 2003) captures the opposite epistemic priority choice. Selective
+Rationalizability can be extended to encompass richer epistemic priority
+orderings among different theories of opponents' behavior. This allows to
+establish a surprising connection with strategic stability (Kohlberg and
+Mertens, 1986).
+",1,0,0,0,0,0
+4319,Communications for Wearable Devices,"  Wearable devices are transforming computing and the human-computer
+interaction and they are a primary means for motion recognition of reflexive
+systems. We review basic wearable deployments and their open wireless
+communications. An algorithm that uses accelerometer data to provide a control
+and communication signal is described. Challenges in the further deployment of
+wearable device in the field of body area network and biometric verification
+are discussed.
+",1,0,0,0,0,0
+4320,Nonlocal Nonlinear Schrödinger Equations and Their Soliton Solutions,"  We study standard and nonlocal nonlinear Schrödinger (NLS) equations
+obtained from the coupled NLS system of equations (Ablowitz-Kaup-Newell-Segur
+(AKNS) equations) by using standard and nonlocal reductions respectively. By
+using the Hirota bilinear method we first find soliton solutions of the coupled
+NLS system of equations then using the reduction formulas we find the soliton
+solutions of the standard and nonlocal NLS equations. We give examples for
+particular values of the parameters and plot the function $|q(t,x)|^2$ for the
+standard and nonlocal NLS equations.
+",0,1,0,0,0,0
+4321,Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning,"  Prediction is an appealing objective for self-supervised learning of
+behavioral skills, particularly for autonomous robots. However, effectively
+utilizing predictive models for control, especially with raw image inputs,
+poses a number of major challenges. How should the predictions be used? What
+happens when they are inaccurate? In this paper, we tackle these questions by
+proposing a method for learning robotic skills from raw image observations,
+using only autonomously collected experience. We show that even an imperfect
+model can complete complex tasks if it can continuously retry, but this
+requires the model to not lose track of the objective (e.g., the object of
+interest). To enable a robot to continuously retry a task, we devise a
+self-supervised algorithm for learning image registration, which can keep track
+of objects of interest for the duration of the trial. We demonstrate that this
+idea can be combined with a video-prediction based controller to enable complex
+behaviors to be learned from scratch using only raw visual inputs, including
+grasping, repositioning objects, and non-prehensile manipulation. Our
+real-world experiments demonstrate that a model trained with 160 robot hours of
+autonomously collected, unlabeled data is able to successfully perform complex
+manipulation tasks with a wide range of objects not seen during training.
+",1,0,0,0,0,0
+4322,S-Isomap++: Multi Manifold Learning from Streaming Data,"  Manifold learning based methods have been widely used for non-linear
+dimensionality reduction (NLDR). However, in many practical settings, the need
+to process streaming data is a challenge for such methods, owing to the high
+computational complexity involved. Moreover, most methods operate under the
+assumption that the input data is sampled from a single manifold, embedded in a
+high dimensional space. We propose a method for streaming NLDR when the
+observed data is either sampled from multiple manifolds or irregularly sampled
+from a single manifold. We show that existing NLDR methods, such as Isomap,
+fail in such situations, primarily because they rely on smoothness and
+continuity of the underlying manifold, which is violated in the scenarios
+explored in this paper. However, the proposed algorithm is able to learn
+effectively in presence of multiple, and potentially intersecting, manifolds,
+while allowing for the input data to arrive as a massive stream.
+",1,0,0,1,0,0
+4323,Extreme radio-wave scattering associated with hot stars,"  We use data on extreme radio scintillation to demonstrate that this
+phenomenon is associated with hot stars in the solar neighbourhood. The ionized
+gas responsible for the scattering is found at distances up to 1.75pc from the
+host star, and on average must comprise 1.E5 distinct structures per star. We
+detect azimuthal velocities of the plasma, relative to the host star, up to 9.7
+km/s, consistent with warm gas expanding at the sound speed. The circumstellar
+plasma structures that we infer are similar in several respects to the cometary
+knots seen in the Helix, and in other planetary nebulae. There the ionized gas
+appears as a skin around tiny molecular clumps. Our analysis suggests that
+molecular clumps are ubiquitous circumstellar features, unrelated to the
+evolutionary state of the star. The total mass in such clumps is comparable to
+the stellar mass.
+",0,1,0,0,0,0
+4324,Tracking network dynamics: a survey of distances and similarity metrics,"  From longitudinal biomedical studies to social networks, graphs have emerged
+as a powerful framework for describing evolving interactions between agents in
+complex systems. In such studies, after pre-processing, the data can be
+represented by a set of graphs, each representing a system's state at different
+points in time. The analysis of the system's dynamics depends on the selection
+of the appropriate analytical tools. After characterizing similarities between
+states, a critical step lies in the choice of a distance between graphs capable
+of reflecting such similarities. While the literature offers a number of
+distances that one could a priori choose from, their properties have been
+little investigated and no guidelines regarding the choice of such a distance
+have yet been provided. In particular, most graph distances consider that the
+nodes are exchangeable and do not take into account node identities. Accounting
+for the alignment of the graphs enables us to enhance these distances'
+sensitivity to perturbations in the network and detect important changes in
+graph dynamics. Thus the selection of an adequate metric is a decisive --yet
+delicate--practical matter.
+In the spirit of Goldenberg, Zheng and Fienberg's seminal 2009 review, the
+purpose of this article is to provide an overview of commonly-used graph
+distances and an explicit characterization of the structural changes that they
+are best able to capture. We use as a guiding thread to our discussion the
+application of these distances to the analysis of both a longitudinal
+microbiome dataset and a brain fMRI study. We show examples of using
+permutation tests to detect the effect of covariates on the graphs'
+variability. Synthetic examples provide intuition as to the qualities and
+drawbacks of the different distances. Above all, we provide some guidance for
+choosing one distance over another in certain types of applications.
+",1,0,0,1,0,0
+4325,Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces,"  Both neural networks and decision trees are popular machine learning methods
+and are widely used to solve problems from diverse domains. These two
+classifiers are commonly used base classifiers in an ensemble framework. In
+this paper, we first present a new variant of oblique decision tree based on a
+linear classifier, then construct an ensemble classifier based on the fusion of
+a fast neural network, random vector functional link network and oblique
+decision trees. Random Vector Functional Link Network has an elegant closed
+form solution with extremely short training time. The neural network partitions
+each training bag (obtained using bagging) at the root level into C subsets
+where C is the number of classes in the dataset and subsequently, C oblique
+decision trees are trained on such partitions. The proposed method provides a
+rich insight into the data by grouping the confusing or hard to classify
+samples for each class and thus, provides an opportunity to employ fine-grained
+classification rule over the data. The performance of the ensemble classifier
+is evaluated on several multi-class datasets where it demonstrates a superior
+performance compared to other state-of- the-art classifiers.
+",0,0,0,1,0,0
+4326,Graded components of local cohomology modules,"  Let $A$ be a regular ring containing a field of characteristic zero and let
+$R = A[X_1,\ldots, X_m]$. Consider $R$ as standard graded with $deg \ A = 0$
+and $deg \ X_i = 1$ for all $i$. In this paper we present a comprehensive study
+of graded components of local cohomology modules $H^i_I(R)$ where $I$ is an
+\emph{arbitrary} homogeneous ideal in $R$. Our study seems to be the first in
+this regard.
+",0,0,1,0,0,0
+4327,Drug Selection via Joint Push and Learning to Rank,"  Selecting the right drugs for the right patients is a primary goal of
+precision medicine. In this manuscript, we consider the problem of cancer drug
+selection in a learning-to-rank framework. We have formulated the cancer drug
+selection problem as to accurately predicting 1). the ranking positions of
+sensitive drugs and 2). the ranking orders among sensitive drugs in cancer cell
+lines based on their responses to cancer drugs. We have developed a new
+learning-to-rank method, denoted as pLETORg , that predicts drug ranking
+structures in each cell line via using drug latent vectors and cell line latent
+vectors. The pLETORg method learns such latent vectors through explicitly
+enforcing that, in the drug ranking list of each cell line, the sensitive drugs
+are pushed above insensitive drugs, and meanwhile the ranking orders among
+sensitive drugs are correct. Genomics information on cell lines is leveraged in
+learning the latent vectors. Our experimental results on a benchmark cell
+line-drug response dataset demonstrate that the new pLETORg significantly
+outperforms the state-of-the-art method in prioritizing new sensitive drugs.
+",0,0,0,1,0,0
+4328,Two-dimensional electron gas at the interface of the ferroelectric-antiferromagnetic heterostructure Ba_0.8Sr_0.2TiO_3/LaMnO_3,"  The temperature dependence of the electrical resistivity of the
+heterostructures consisting of single crystalline LaMnO$_3$ samples with
+different crystallographic orientations covered by the epitaxial ferroelectric
+Ba$_{0.8}$Sr$_{0.2}$TiO$_3$ film has been studied. Results obtained for the
+heterostructure have been compared with the electrical resistivity of the
+single crystalline LaMnO$_3$ without the film. It was found that for the
+samples with the films where the polarization axis is perpendicular to the
+crystal surface the electrical resistivity strongly decreases, and at the
+temperature below ~160 K undergoes the insulator-metal transition. Ab-initio
+calculations were also performed for the structural and electronic properties
+of the BaTiO$_3$/LaMnO$_3$ heterostructure. Transition to the 2D electron gas
+at the interface is shown.
+",0,1,0,0,0,0
+4329,Linear Discriminant Generative Adversarial Networks,"  We develop a novel method for training of GANs for unsupervised and class
+conditional generation of images, called Linear Discriminant GAN (LD-GAN). The
+discriminator of an LD-GAN is trained to maximize the linear separability
+between distributions of hidden representations of generated and targeted
+samples, while the generator is updated based on the decision hyper-planes
+computed by performing LDA over the hidden representations. LD-GAN provides a
+concrete metric of separation capacity for the discriminator, and we
+experimentally show that it is possible to stabilize the training of LD-GAN
+simply by calibrating the update frequencies between generators and
+discriminators in the unsupervised case, without employment of normalization
+methods and constraints on weights. In the class conditional generation tasks,
+the proposed method shows improved training stability together with better
+generalization performance compared to WGAN that employs an auxiliary
+classifier.
+",1,0,0,1,0,0
+4330,Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy,"  Monoclonal antibodies constitute one of the most important strategies to
+treat patients suffering from cancers such as hematological malignancies and
+solid tumors. In order to guarantee the quality of those preparations prepared
+at hospital, quality control has to be developed. The aim of this study was to
+explore a noninvasive, nondestructive, and rapid analytical method to ensure
+the quality of the final preparation without causing any delay in the process.
+We analyzed four mAbs (Inlfiximab, Bevacizumab, Ramucirumab and Rituximab)
+diluted at therapeutic concentration in chloride sodium 0.9% using Raman
+spectroscopy. To reduce the prediction errors obtained with traditional
+chemometric data analysis, we explored a data-driven approach using statistical
+machine learning methods where preprocessing and predictive models are jointly
+optimized. We prepared a data analytics workflow and submitted the problem to a
+collaborative data challenge platform called Rapid Analytics and Model
+Prototyping (RAMP). This allowed to use solutions from about 300 data
+scientists during five days of collaborative work. The prediction of the four
+mAbs samples was considerably improved with a misclassification rate and the
+mean error rate of 0.8% and 4%, respectively.
+",1,0,0,0,0,0
+4331,Sequential noise-induced escapes for oscillatory network dynamics,"  It is well known that the addition of noise in a multistable system can
+induce random transitions between stable states. The rate of transition can be
+characterised in terms of the noise-free system's dynamics and the added noise:
+for potential systems in the presence of asymptotically low noise the
+well-known Kramers' escape time gives an expression for the mean escape time.
+This paper examines some general properties and examples of transitions between
+local steady and oscillatory attractors within networks: the transition rates
+at each node may be affected by the dynamics at other nodes. We use first
+passage time theory to explain some properties of scalings noted in the
+literature for an idealised model of initiation of epileptic seizures in small
+systems of coupled bistable systems with both steady and oscillatory
+attractors. We focus on the case of sequential escapes where a steady attractor
+is only marginally stable but all nodes start in this state. As the nodes
+escape to the oscillatory regime, we assume that the transitions back are very
+infrequent in comparison. We quantify and characterise the resulting sequences
+of noise-induced escapes. For weak enough coupling we show that a master
+equation approach gives a good quantitative understanding of sequential
+escapes, but for strong coupling this description breaks down.
+",0,1,0,0,0,0
+4332,Confluence of Conditional Term Rewrite Systems via Transformations,"  Conditional term rewriting is an intuitive yet complex extension of term
+rewriting. In order to benefit from the simpler framework of unconditional
+rewriting, transformations have been defined to eliminate the conditions of
+conditional term rewrite systems.
+Recent results provide confluence criteria for conditional term rewrite
+systems via transformations, yet they are restricted to CTRSs with certain
+syntactic properties like weak left-linearity. These syntactic properties imply
+that the transformations are sound for the given CTRS.
+This paper shows how to use transformations to prove confluence of
+operationally terminating, right-stable deterministic conditional term rewrite
+systems without the necessity of soundness restrictions. For this purpose, it
+is shown that certain rewrite strategies, in particular almost U-eagerness and
+innermost rewriting, always imply soundness.
+",1,0,0,0,0,0
+4333,Observation of Skyrmions at Room Temperature in Co2FeAl Heusler Alloy Ultrathin Films,"  Magnetic skyrmions are topological spin structures having immense potential
+for energy efficient spintronic devices. However, observations of skyrmions at
+room temperature are limited to patterned nanostructures. Here, we report the
+observation of stable skyrmions in unpatterned Ta/Co2FeAl(CFA)/MgO thin film
+heterostructures at room temperature and in zero external magnetic field
+employing magnetic force microscopy. The skyrmions are observed in a trilayer
+structure comprised of heavy metal (HM)/ferromagnet (FM)/Oxide interfaces which
+result in strong interfacial Dzyaloshinskii-Moriya interaction (i-DMI) as
+evidenced by Brillouin light scattering measurements, in agreement with the
+results of micromagnetic simulations. We also emphasize on room temperature
+observation of multiple skyrmions which can be stabilized for suitable choices
+of CFA layer thickness, perpendicular magnetic anisotropy, and i-DMI. These
+results open up a new paradigm for designing room temperature spintronic
+devices based on skyrmions in FM continuous thin films.
+",0,1,0,0,0,0
+4334,Bayesian Nonparametric Spectral Estimation,"  Spectral estimation (SE) aims to identify how the energy of a signal (e.g., a
+time series) is distributed across different frequencies. This can become
+particularly challenging when only partial and noisy observations of the signal
+are available, where current methods fail to handle uncertainty appropriately.
+In this context, we propose a joint probabilistic model for signals,
+observations and spectra, where SE is addressed as an exact inference problem.
+Assuming a Gaussian process prior over the signal, we apply Bayes' rule to find
+the analytic posterior distribution of the spectrum given a set of
+observations. Besides its expressiveness and natural account of spectral
+uncertainty, the proposed model also provides a functional-form representation
+of the power spectral density, which can be optimised efficiently. Comparison
+with previous approaches, in particular against Lomb-Scargle, is addressed
+theoretically and also experimentally in three different scenarios. Code and
+demo available at this https URL.
+",0,0,0,1,0,0
+4335,Imprecise dynamic walking with time-projection control,"  We present a new walking foot-placement controller based on 3LP, a 3D model
+of bipedal walking that is composed of three pendulums to simulate falling,
+swing and torso dynamics. Taking advantage of linear equations and closed-form
+solutions of the 3LP model, our proposed controller projects intermediate
+states of the biped back to the beginning of the phase for which a discrete LQR
+controller is designed. After the projection, a proper control policy is
+generated by this LQR controller and used at the intermediate time. This
+control paradigm reacts to disturbances immediately and includes rules to
+account for swing dynamics and leg-retraction. We apply it to a simulated Atlas
+robot in position-control, always commanded to perform in-place walking. The
+stance hip joint in our robot keeps the torso upright to let the robot
+naturally fall, and the swing hip joint tracks the desired footstep location.
+Combined with simple Center of Pressure (CoP) damping rules in the low-level
+controller, our foot-placement enables the robot to recover from strong pushes
+and produce periodic walking gaits when subject to persistent sources of
+disturbance, externally or internally. These gaits are imprecise, i.e.,
+emergent from asymmetry sources rather than precisely imposing a desired
+velocity to the robot. Also in extreme conditions, restricting linearity
+assumptions of the 3LP model are often violated, but the system remains robust
+in our simulations. An extensive analysis of closed-loop eigenvalues, viable
+regions and sensitivity to push timings further demonstrate the strengths of
+our simple controller.
+",1,0,0,0,0,0
+4336,Interplay of dilution and magnetic field in the nearest-neighbor spin-ice model on the pyrochlore lattice,"  We study the magnetic field effects on the diluted spin-ice materials using
+the replica-exchange Monte Carlo simulation. We observe five plateaus in the
+magnetization curve of the diluted nearest-neighbor spin-ice model on the
+pyrochlore lattice when a magnetic field is applied in the [111] direction.
+This is in contrast to the case of the pure model with two plateaus. The origin
+of five plateaus is investigated from the spin configuration of two
+corner-sharing tetrahedra in the case of the diluted model.
+",0,1,0,0,0,0
+4337,RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process,"  An RNN-based forecasting approach is used to early detect anomalies in
+industrial multivariate time series data from a simulated Tennessee Eastman
+Process (TEP) with many cyber-attacks. This work continues a previously
+proposed LSTM-based approach to the fault detection in simpler data. It is
+considered necessary to adapt the RNN network to deal with data containing
+stochastic, stationary, transitive and a rich variety of anomalous behaviours.
+There is particular focus on early detection with special NAB-metric. A
+comparison with the DPCA approach is provided. The generated data set is made
+publicly available.
+",1,0,0,0,0,0
+4338,Modelling of limitations of bulk heterojunction architecture in organic solar cells,"  Polymer solar cells are considered as very promising candidates for
+development of photovoltaics of the future. They are cheap and easy to
+fabricate, however, up to now, they possess fundamental drawback, low
+effectiveness. In the most popular BHJ (bulk heterojunction) architecture the
+actual record of efficiency is about 13 percent. One ask the question how
+fundamental this limitation is. In our paper we propose the simple model which
+examines the limitations of efficiency by analysis of geometrical aspects of
+the BHJ architecture. In this paper we considered two dimensional model. We
+calculated the effective length of the donor-acceptor border in the random
+mixture of donor and acceptor nanocrystals and further compared it with an
+ideal comb architecture. It turns out that in the BHJ architecture, this
+effective length is about 2 times smaller than in the comb architecture.
+",0,1,0,0,0,0
+4339,Detecting singular weak-dissipation limit for flutter onset in reversible systems,"  A `flutter machine' is introduced for the investigation of a singular
+interface between the classical and reversible Hopf bifurcations that is
+theoretically predicted to be generic in nonconservative reversible systems
+with vanishing dissipation. In particular, such a singular interface exists for
+the Pflüger viscoelastic column moving in a resistive medium, which is proven
+by means of the perturbation theory of multiple eigenvalues with the Jordan
+block. The laboratory setup, consisting of a cantilevered viscoelastic rod
+loaded by a positional force with non-zero curl produced by dry friction,
+demonstrates high sensitivity of the classical Hopf bifurcation onset {to the
+ratio between} the weak air drag and Kelvin-Voigt damping in the Pflüger
+column. Thus, the Whitney umbrella singularity is experimentally confirmed,
+responsible for discontinuities accompanying dissipation-induced instabilities
+in a broad range of physical contexts.
+",0,1,0,0,0,0
+4340,A Hand Combining Two Simple Grippers to Pick up and Arrange Objects for Assembly,"  This paper proposes a novel robotic hand design for assembly tasks. The idea
+is to combine two simple grippers -- an inner gripper which is used for precise
+alignment, and an outer gripper which is used for stable holding. Conventional
+robotic hands require complicated compliant mechanisms or complicated control
+strategy and force sensing to conduct assemble tasks, which makes them costly
+and difficult to pick and arrange small objects like screws or washers.
+Compared to the conventional hands, the proposed design provides a low-cost
+solution for aligning, picking up, and arranging various objects by taking
+advantages of the geometric constraints of the positioning fingers and gravity.
+It is able to deal with small screws and washers, and eliminate the position
+errors of cylindrical objects or objects with cylindrical holes. In the
+experiments, both real-world tasks and quantitative analysis are performed to
+validate the aligning, picking, and arrangements abilities of the design.
+",1,0,0,0,0,0
+4341,A Time Hierarchy Theorem for the LOCAL Model,"  The celebrated Time Hierarchy Theorem for Turing machines states, informally,
+that more problems can be solved given more time. The extent to which a time
+hierarchy-type theorem holds in the distributed LOCAL model has been open for
+many years. It is consistent with previous results that all natural problems in
+the LOCAL model can be classified according to a small constant number of
+complexities, such as $O(1),O(\log^* n), O(\log n), 2^{O(\sqrt{\log n})}$, etc.
+In this paper we establish the first time hierarchy theorem for the LOCAL
+model and prove that several gaps exist in the LOCAL time hierarchy.
+1. We define an infinite set of simple coloring problems called Hierarchical
+$2\frac{1}{2}$-Coloring}. A correctly colored graph can be confirmed by simply
+checking the neighborhood of each vertex, so this problem fits into the class
+of locally checkable labeling (LCL) problems. However, the complexity of the
+$k$-level Hierarchical $2\frac{1}{2}$-Coloring problem is $\Theta(n^{1/k})$,
+for $k\in\mathbb{Z}^+$. The upper and lower bounds hold for both general graphs
+and trees, and for both randomized and deterministic algorithms.
+2. Consider any LCL problem on bounded degree trees. We prove an
+automatic-speedup theorem that states that any randomized $n^{o(1)}$-time
+algorithm solving the LCL can be transformed into a deterministic $O(\log
+n)$-time algorithm. Together with a previous result, this establishes that on
+trees, there are no natural deterministic complexities in the ranges
+$\omega(\log^* n)$---$o(\log n)$ or $\omega(\log n)$---$n^{o(1)}$.
+3. We expose a gap in the randomized time hierarchy on general graphs. Any
+randomized algorithm that solves an LCL problem in sublogarithmic time can be
+sped up to run in $O(T_{LLL})$ time, which is the complexity of the distributed
+Lovasz local lemma problem, currently known to be $\Omega(\log\log n)$ and
+$O(\log n)$.
+",1,0,0,0,0,0
+4342,Second sound in systems of one-dimensional fermions,"  We study sound in Galilean invariant systems of one-dimensional fermions. At
+low temperatures, we find a broad range of frequencies in which in addition to
+the waves of density there is a second sound corresponding to ballistic
+propagation of heat in the system. The damping of the second sound mode is
+weak, provided the frequency is large compared to a relaxation rate that is
+exponentially small at low temperatures. At lower frequencies the second sound
+mode is damped, and the propagation of heat is diffusive.
+",0,1,0,0,0,0
+4343,The equational theory of the natural join and inner union is decidable,"  The natural join and the inner union operations combine relations of a
+database. Tropashko and Spight [24] realized that these two operations are the
+meet and join operations in a class of lattices, known by now as the relational
+lattices. They proposed then lattice theory as an algebraic approach to the
+theory of databases, alternative to the relational algebra. Previous works [17,
+22] proved that the quasiequational theory of these lattices-that is, the set
+of definite Horn sentences valid in all the relational lattices-is undecidable,
+even when the signature is restricted to the pure lattice signature. We prove
+here that the equational theory of relational lattices is decidable. That, is
+we provide an algorithm to decide if two lattice theoretic terms t, s are made
+equal under all intepretations in some relational lattice. We achieve this goal
+by showing that if an inclusion t $\le$ s fails in any of these lattices, then
+it fails in a relational lattice whose size is bound by a triple exponential
+function of the sizes of t and s.
+",1,0,1,0,0,0
+4344,Doping-induced spin-orbit splitting in Bi-doped ZnO nanowires,"  Our predictions, based on density-functional calculations, reveal that
+surface doping of ZnO nanowires with Bi leads to a linear-in-$k$ splitting of
+the conduction-band states, through spin-orbit interaction, due to the lowering
+of the symmetry in the presence of the dopant. This finding implies that spin
+polarization of the conduction electrons in Bi-doped ZnO nanowires could be
+controlled with applied electric (as opposed to magnetic) fields, making them
+candidate materials for spin-orbitronic applications. Our findings also show
+that the degree of spin splitting could be tuned by adjusting the dopant
+concentration. Defect calculations and ab initio molecular dynamics simulations
+indicate that stable doping configurations exhibiting the foregoing
+linear-in-$k$ splitting could be realized under reasonable thermodynamic
+conditions.
+",0,1,0,0,0,0
+4345,A robotic vision system to measure tree traits,"  The autonomous measurement of tree traits, such as branching structure,
+branch diameters, branch lengths, and branch angles, is required for tasks such
+as robotic pruning of trees as well as structural phenotyping. We propose a
+robotic vision system called the Robotic System for Tree Shape Estimation
+(RoTSE) to determine tree traits in field settings. The process is composed of
+the following stages: image acquisition with a mobile robot unit, segmentation,
+reconstruction, curve skeletonization, conversion to a graph representation,
+and then computation of traits. Quantitative and qualitative results on apple
+trees are shown in terms of accuracy, computation time, and robustness.
+Compared to ground truth measurements, the RoTSE produced the following
+estimates: branch diameter (mean-squared error $0.99$ mm), branch length
+(mean-squared error $45.64$ mm), and branch angle (mean-squared error $10.36$
+degrees). The average run time was 8.47 minutes when the voxel resolution was
+$3$ mm$^3$.
+",1,0,0,0,0,0
+4346,Resolution enhancement in in-line holography by numerical compensation of vibrations,"  Mechanical vibrations of components of the optical system is one of the
+sources of blurring of interference pattern in coherent imaging systems. The
+problem is especially important in holography where the resolution of the
+reconstructed objects depends on the effective size of the hologram, that is on
+the extent of the interference pattern, and on the contrast of the interference
+fringes. We discuss the mathematical relation between the vibrations, the
+hologram contrast and the reconstructed object. We show how vibrations can be
+post-filtered out from the hologram or from the reconstructed object assuming a
+Gaussian distribution of the vibrations. We also provide a numerical example of
+compensation for directional motion blur. We demonstrate our approach for light
+optical and electron holograms, acquired with both, plane- as well as
+spherical-waves. As a result of such hologram deblurring, the resolution of the
+reconstructed objects is enhanced by almost a factor of 2. We believe that our
+approach opens up a new venue of post-experimental resolution enhancement in
+in-line holography by adapting the rich database/catalogue of motion deblurring
+algorithms developed for photography and image restoration applications.
+",0,1,0,0,0,0
+4347,Embodied Question Answering,"  We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
+an agent is spawned at a random location in a 3D environment and asked a
+question (""What color is the car?""). In order to answer, the agent must first
+intelligently navigate to explore the environment, gather information through
+first-person (egocentric) vision, and then answer the question (""orange"").
+This challenging task requires a range of AI skills -- active perception,
+language understanding, goal-driven navigation, commonsense reasoning, and
+grounding of language into actions. In this work, we develop the environments,
+end-to-end-trained reinforcement learning agents, and evaluation protocols for
+EmbodiedQA.
+",1,0,0,0,0,0
+4348,Toward Unsupervised Text Content Manipulation,"  Controlled generation of text is of high practical use. Recent efforts have
+made impressive progress in generating or editing sentences with given textual
+attributes (e.g., sentiment). This work studies a new practical setting of text
+content manipulation. Given a structured record, such as `(PLAYER: Lebron,
+POINTS: 20, ASSISTS: 10)', and a reference sentence, such as `Kobe easily
+dropped 30 points', we aim to generate a sentence that accurately describes the
+full content in the record, with the same writing style (e.g., wording,
+transitions) of the reference. The problem is unsupervised due to lack of
+parallel data in practice, and is challenging to minimally yet effectively
+manipulate the text (by rewriting/adding/deleting text portions) to ensure
+fidelity to the structured content. We derive a dataset from a basketball game
+report corpus as our testbed, and develop a neural method with unsupervised
+competing objectives and explicit content coverage constraints. Automatic and
+human evaluations show superiority of our approach over competitive methods
+including a strong rule-based baseline and prior approaches designed for style
+transfer.
+",1,0,0,0,0,0
+4349,Unsupervised learning of object landmarks by factorized spatial embeddings,"  Learning automatically the structure of object categories remains an
+important open problem in computer vision. In this paper, we propose a novel
+unsupervised approach that can discover and learn landmarks in object
+categories, thus characterizing their structure. Our approach is based on
+factorizing image deformations, as induced by a viewpoint change or an object
+deformation, by learning a deep neural network that detects landmarks
+consistently with such visual effects. Furthermore, we show that the learned
+landmarks establish meaningful correspondences between different object
+instances in a category without having to impose this requirement explicitly.
+We assess the method qualitatively on a variety of object types, natural and
+man-made. We also show that our unsupervised landmarks are highly predictive of
+manually-annotated landmarks in face benchmark datasets, and can be used to
+regress these with a high degree of accuracy.
+",1,0,0,1,0,0
+4350,Data-Efficient Design Exploration through Surrogate-Assisted Illumination,"  Design optimization techniques are often used at the beginning of the design
+process to explore the space of possible designs. In these domains illumination
+algorithms, such as MAP-Elites, are promising alternatives to classic
+optimization algorithms because they produce diverse, high-quality solutions in
+a single run, instead of only a single near-optimal solution. Unfortunately,
+these algorithms currently require a large number of function evaluations,
+limiting their applicability. In this article we introduce a new illumination
+algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate
+modeling techniques to create a map of the design space according to
+user-defined features while minimizing the number of fitness evaluations. On a
+2-dimensional airfoil optimization problem SAIL produces hundreds of diverse
+but high-performing designs with several orders of magnitude fewer evaluations
+than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of
+producing maps of high-performing designs in realistic 3-dimensional
+aerodynamic tasks with an accurate flow simulation. Data-efficient design
+exploration with SAIL can help designers understand what is possible, beyond
+what is optimal, by considering more than pure objective-based optimization.
+",0,0,0,1,0,0
+4351,Stochastic seismic waveform inversion using generative adversarial networks as a geological prior,"  We present an application of deep generative models in the context of
+partial-differential equation (PDE) constrained inverse problems. We combine a
+generative adversarial network (GAN) representing an a priori model that
+creates subsurface geological structures and their petrophysical properties,
+with the numerical solution of the PDE governing the propagation of acoustic
+waves within the earth's interior. We perform Bayesian inversion using an
+approximate Metropolis-adjusted Langevin algorithm (MALA) to sample from the
+posterior given seismic observations. Gradients with respect to the model
+parameters governing the forward problem are obtained by solving the adjoint of
+the acoustic wave equation. Gradients of the mismatch with respect to the
+latent variables are obtained by leveraging the differentiable nature of the
+deep neural network used to represent the generative model. We show that
+approximate MALA sampling allows efficient Bayesian inversion of model
+parameters obtained from a prior represented by a deep generative model,
+obtaining a diverse set of realizations that reflect the observed seismic
+response.
+",0,0,0,1,0,0
+4352,A Capillary Surface with No Radial Limits,"  In 1996, Kirk Lancaster and David Siegel investigated the existence and
+behavior of radial limits at a corner of the boundary of the domain of
+solutions of capillary and other prescribed mean curvature problems with
+contact angle boundary data. In Theorem 3, they provide an example of a
+capillary surface in a unit disk $D$ which has no radial limits at
+$(0,0)\in\partial D.$ In their example, the contact angle ($\gamma$) cannot be
+bounded away from zero and $\pi.$
+Here we consider a domain $\Omega$ with a convex corner at $(0,0)$ and find a
+capillary surface $z=f(x,y)$ in $\Omega\times\mathbb{R}$ which has no radial
+limits at $(0,0)\in\partial\Omega$ such that $\gamma$ is bounded away from $0$
+and $\pi.$
+",0,0,1,0,0,0
+4353,Brain Computer Interface for Gesture Control of a Social Robot: an Offline Study,"  Brain computer interface (BCI) provides promising applications in
+neuroprosthesis and neurorehabilitation by controlling computers and robotic
+devices based on the patient's intentions. Here, we have developed a novel BCI
+platform that controls a personalized social robot using noninvasively acquired
+brain signals. Scalp electroencephalogram (EEG) signals are collected from a
+user in real-time during tasks of imaginary movements. The imagined body
+kinematics are decoded using a regression model to calculate the user-intended
+velocity. Then, the decoded kinematic information is mapped to control the
+gestures of a social robot. The platform here may be utilized as a
+human-robot-interaction framework by combining with neurofeedback mechanisms to
+enhance the cognitive capability of persons with dementia.
+",1,0,0,0,0,0
+4354,Multi-agent Gaussian Process Motion Planning via Probabilistic Inference,"  This paper deals with motion planning for multiple agents by representing the
+problem as a simultaneous optimization of every agent's trajectory. Each
+trajectory is considered as a sample from a one-dimensional continuous-time
+Gaussian process (GP) generated by a linear time-varying stochastic
+differential equation driven by white noise. By formulating the planning
+problem as probabilistic inference on a factor graph, the structure of the
+pertaining GP can be exploited to find the solution efficiently using numerical
+optimization. In contrast to planning each agent's trajectory individually,
+where only the current poses of other agents are taken into account, we propose
+simultaneous planning of multiple trajectories that works in a predictive
+manner. It takes into account the information about each agent's whereabouts at
+every future time instant, since full trajectories of each agent are found
+jointly during a single optimization procedure. We compare the proposed method
+to an individual trajectory planning approach, demonstrating significant
+improvement in both success rate and computational efficiency.
+",1,0,0,0,0,0
+4355,Faster Algorithms for Mean-Payoff Parity Games,"  Graph games provide the foundation for modeling and synthesis of reactive
+processes. Such games are played over graphs where the vertices are controlled
+by two adversarial players. We consider graph games where the objective of the
+first player is the conjunction of a qualitative objective (specified as a
+parity condition) and a quantitative objective (specified as a mean-payoff
+condition). There are two variants of the problem, namely, the threshold
+problem where the quantitative goal is to ensure that the mean-payoff value is
+above a threshold, and the value problem where the quantitative goal is to
+ensure the optimal mean-payoff value; in both cases ensuring the qualitative
+parity objective. The previous best-known algorithms for game graphs with $n$
+vertices, $m$ edges, parity objectives with $d$ priorities, and maximal
+absolute reward value $W$ for mean-payoff objectives, are as follows:
+$O(n^{d+1} \cdot m \cdot W)$ for the threshold problem, and $O(n^{d+2} \cdot m
+\cdot W)$ for the value problem. Our main contributions are faster algorithms,
+and the running times of our algorithms are as follows: $O(n^{d-1} \cdot m
+\cdot W)$ for the threshold problem, and $O(n^{d} \cdot m \cdot W \cdot \log
+(n\cdot W))$ for the value problem. For mean-payoff parity objectives with two
+priorities, our algorithms match the best-known bounds of the algorithms for
+mean-payoff games (without conjunction with parity objectives). Our results are
+relevant in synthesis of reactive systems with both functional requirement
+(given as a qualitative objective) and performance requirement (given as a
+quantitative objective).
+",1,0,0,0,0,0
+4356,Similarity Function Tracking using Pairwise Comparisons,"  Recent work in distance metric learning has focused on learning
+transformations of data that best align with specified pairwise similarity and
+dissimilarity constraints, often supplied by a human observer. The learned
+transformations lead to improved retrieval, classification, and clustering
+algorithms due to the better adapted distance or similarity measures. Here, we
+address the problem of learning these transformations when the underlying
+constraint generation process is nonstationary. This nonstationarity can be due
+to changes in either the ground-truth clustering used to generate constraints
+or changes in the feature subspaces in which the class structure is apparent.
+We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD),
+a general adaptive, online approach for learning and tracking optimal metrics
+as they change over time that is highly robust to a variety of nonstationary
+behaviors in the changing metric. We apply the OCELAD framework to an ensemble
+of online learners. Specifically, we create a retro-initialized composite
+objective mirror descent (COMID) ensemble (RICE) consisting of a set of
+parallel COMID learners with different learning rates, and demonstrate
+parameter-free RICE-OCELAD metric learning on both synthetic data and a highly
+nonstationary Twitter dataset. We show significant performance improvements and
+increased robustness to nonstationary effects relative to previously proposed
+batch and online distance metric learning algorithms.
+",1,0,0,1,0,0
+4357,Deep Boosted Regression for MR to CT Synthesis,"  Attenuation correction is an essential requirement of positron emission
+tomography (PET) image reconstruction to allow for accurate quantification.
+However, attenuation correction is particularly challenging for PET-MRI as
+neither PET nor magnetic resonance imaging (MRI) can directly image tissue
+attenuation properties. MRI-based computed tomography (CT) synthesis has been
+proposed as an alternative to physics based and segmentation-based approaches
+that assign a population-based tissue density value in order to generate an
+attenuation map. We propose a novel deep fully convolutional neural network
+that generates synthetic CTs in a recursive manner by gradually reducing the
+residuals of the previous network, increasing the overall accuracy and
+generalisability, while keeping the number of trainable parameters within
+reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT
+pairs and a four-fold random bootstrapped validation with a 80:20 split is
+performed. Quantitative results show that the proposed framework outperforms a
+state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE)
+from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction
+error from 14.3% to 7.2%.
+",0,0,0,1,0,0
+4358,Discretisation of regularity structures,"  We introduce a general framework allowing to apply the theory of regularity
+structures to discretisations of stochastic PDEs. The approach pursued in this
+article is that we do not focus on any one specific discretisation procedure.
+Instead, we assume that we are given a scale $\varepsilon > 0$ and a ""black
+box"" describing the behaviour of our discretised objects at scales below
+$\varepsilon $.
+",0,0,1,0,0,0
+4359,Optimization of Wireless Power Transfer Systems Enhanced by Passive Elements and Metasurfaces,"  This paper presents a rigorous optimization technique for wireless power
+transfer (WPT) systems enhanced by passive elements, ranging from simple
+reflectors and intermedi- ate relays all the way to general electromagnetic
+guiding and focusing structures, such as metasurfaces and metamaterials. At its
+core is a convex semidefinite relaxation formulation of the otherwise nonconvex
+optimization problem, of which tightness and optimality can be confirmed by a
+simple test of its solutions. The resulting method is rigorous, versatile, and
+general -- it does not rely on any assumptions. As shown in various examples,
+it is able to efficiently and reliably optimize such WPT systems in order to
+find their physical limitations on performance, optimal operating parameters
+and inspect their working principles, even for a large number of active
+transmitters and passive elements.
+",1,0,1,0,0,0
+4360,Sharpening Jensen's Inequality,"  This paper proposes a new sharpened version of the Jensen's inequality. The
+proposed new bound is simple and insightful, is broadly applicable by imposing
+minimum assumptions, and provides fairly accurate result in spite of its simple
+form. Applications to the moment generating function, power mean inequalities,
+and Rao-Blackwell estimation are presented. This presentation can be
+incorporated in any calculus-based statistical course.
+",0,0,1,1,0,0
+4361,"Knotted solutions, from electromagnetism to fluid dynamics","  Knotted solutions to electromagnetism and fluid dynamics are investigated,
+based on relations we find between the two subjects. We can write fluid
+dynamics in electromagnetism language, but only on an initial surface, or for
+linear perturbations, and we use this map to find knotted fluid solutions, as
+well as new electromagnetic solutions. We find that knotted solutions of
+Maxwell electromagnetism are also solutions of more general nonlinear theories,
+like Born-Infeld, and including ones which contain quantum corrections from
+couplings with other modes, like Euler-Heisenberg and string theory DBI. Null
+configurations in electromagnetism can be described as a null pressureless
+fluid, and from this map we can find null fluid knotted solutions. A type of
+nonrelativistic reduction of the relativistic fluid equations is described,
+which allows us to find also solutions of the (nonrelativistic) Euler's
+equations.
+",0,1,0,0,0,0
+4362,On the intersection graph of ideals of $\mathbb{Z}_m$,"  Let $m>1$ be an integer, and let $I(\mathbb{Z}_m)^*$ be the set of all
+non-zero proper ideals of $\mathbb{Z}_m$. The intersection graph of ideals of
+$\mathbb{Z}_m$, denoted by $G(\mathbb{Z}_m)$, is a graph with vertices
+$I(\mathbb{Z}_m)^*$ and two distinct vertices $I,J\in I(\mathbb{Z}_m)^*$ are
+adjacent if and only if $I\cap J\neq 0$. Let $n>1$ be an integer and
+$\mathbb{Z}_n$ be a $\mathbb{Z}_m$-module. In this paper, we introduce and
+study a kind of graph structure of $\mathbb{Z}_m$, denoted by
+$G_n(\mathbb{Z}_m)$. It is the undirected graph with the vertex set
+$I(\mathbb{Z}_m)^*$, and two distinct vertices $I$ and $J$ are adjacent if and
+only if $I\mathbb{Z}_n\cap J\mathbb{Z}_n\neq 0$. Clearly,
+$G_m(\mathbb{Z}_m)=G(\mathbb{Z}_m)$. We obtain some graph theoretical
+properties of $G_n(\mathbb{Z}_m)$ and we compute some of its numerical
+invariants, namely girth, independence number, domination number, maximum
+degree and chromatic index. We also determine all integer numbers $n$ and $m$
+for which $G_n(\mathbb{Z}_m)$ is Eulerian.
+",0,0,1,0,0,0
+4363,Optimization of Executable Formal Interpreters developed in Higher-order Theorem Proving Systems,"  In recent publications, we presented a novel formal symbolic process virtual
+machine (FSPVM) framework that combined higher-order theorem proving and
+symbolic execution for verifying the reliability and security of smart
+contracts developed in the Ethereum blockchain system without suffering the
+standard issues surrounding reusability, consistency, and automation. A
+specific FSPVM, denoted as FSPVM-E, was developed in Coq based on a general,
+extensible, and reusable formal memory (GERM) framework, an extensible and
+universal formal intermediate programming language, denoted as Lolisa, which is
+a large subset of the Solidity programming language that uses generalized
+algebraic datatypes, and a corresponding formally verified interpreter for
+Lolisa, denoted as FEther, which serves as a crucial component of FSPVM-E.
+However, our past work has demonstrated that the execution efficiency of the
+standard development of FEther is extremely low. As a result, FSPVM-E fails to
+achieve its expected verification effect. The present work addresses this issue
+by first identifying three root causes of the low execution efficiency of
+formal interpreters. We then build abstract models of these causes, and present
+respective optimization schemes for rectifying the identified conditions.
+Finally, we apply these optimization schemes to FEther, and demonstrate that
+its execution efficiency has been improved significantly.
+",1,0,0,0,0,0
+4364,Healthcare Robotics,"  Robots have the potential to be a game changer in healthcare: improving
+health and well-being, filling care gaps, supporting care givers, and aiding
+health care workers. However, before robots are able to be widely deployed, it
+is crucial that both the research and industrial communities work together to
+establish a strong evidence-base for healthcare robotics, and surmount likely
+adoption barriers. This article presents a broad contextualization of robots in
+healthcare by identifying key stakeholders, care settings, and tasks; reviewing
+recent advances in healthcare robotics; and outlining major challenges and
+opportunities to their adoption.
+",1,0,0,0,0,0
+4365,Personalized Thread Recommendation for MOOC Discussion Forums,"  Social learning, i.e., students learning from each other through social
+interactions, has the potential to significantly scale up instruction in online
+education. In many cases, such as in massive open online courses (MOOCs),
+social learning is facilitated through discussion forums hosted by course
+providers. In this paper, we propose a probabilistic model for the process of
+learners posting on such forums, using point processes. Different from existing
+works, our method integrates topic modeling of the post text, timescale
+modeling of the decay in post activity over time, and learner topic interest
+modeling into a single model, and infers this information from user data. Our
+method also varies the excitation levels induced by posts according to the
+thread structure, to reflect typical notification settings in discussion
+forums. We experimentally validate the proposed model on three real-world MOOC
+datasets, with the largest one containing up to 6,000 learners making 40,000
+posts in 5,000 threads. Results show that our model excels at thread
+recommendation, achieving significant improvement over a number of baselines,
+thus showing promise of being able to direct learners to threads that they are
+interested in more efficiently. Moreover, we demonstrate analytics that our
+model parameters can provide, such as the timescales of different topic
+categories in a course.
+",1,0,0,1,0,0
+4366,CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model,"  Background: Widespread adoption of electronic health records (EHRs) has
+enabled secondary use of EHR data for clinical research and healthcare
+delivery. Natural language processing (NLP) techniques have shown promise in
+their capability to extract the embedded information in unstructured clinical
+data, and information retrieval (IR) techniques provide flexible and scalable
+solutions that can augment the NLP systems for retrieving and ranking relevant
+records. Methods: In this paper, we present the implementation of Cohort
+Retrieval Enhanced by Analysis of Text from EHRs (CREATE), a cohort retrieval
+system that can execute textual cohort selection queries on both structured and
+unstructured EHR data. CREATE is a proof-of-concept system that leverages a
+combination of structured queries and IR techniques on NLP results to improve
+cohort retrieval performance while adopting the Observational Medical Outcomes
+Partnership (OMOP) Common Data Model (CDM) to enhance model portability. The
+NLP component empowered by cTAKES is used to extract CDM concepts from textual
+queries. We design a hierarchical index in Elasticsearch to support CDM concept
+search utilizing IR techniques and frameworks. Results: Our case study on 5
+cohort identification queries evaluated using the IR metric, P@5 (Precision at
+5) at both the patient-level and document-level, demonstrates that CREATE
+achieves an average P@5 of 0.90, which outperforms systems using only
+structured data or only unstructured data with average P@5s of 0.54 and 0.74,
+respectively.
+",1,0,0,0,0,0
+4367,"The same strain of Piscine orthoreovirus (PRV-1) is involved with the development of different, but related, diseases in Atlantic and Pacific Salmon in British Columbia","  Piscine orthoreovirus Strain PRV-1 is the causative agent of heart and
+skeletal muscle inflammation (HSMI) in Atlantic salmon (Salmo salar). Given its
+high prevalence in net pen salmon, debate has arisen on whether PRV poses a
+risk to migratory salmon, especially in British Columbia (BC) where
+commercially important wild Pacific salmon are in decline. Various strains of
+PRV have been associated with diseases in Pacific salmon, including
+erythrocytic inclusion body syndrome (EIBS), HSMI-like disease, and
+jaundice/anemia in Japan, Norway, Chile and Canada. We examine the
+developmental pathway of HSMI and jaundice/anemia associated with PRV-1 in
+farmed Atlantic and Chinook (Oncorhynchus tshawytscha) salmon in BC,
+respectively. In situ hybridization localized PRV-1 within developing lesions
+in both diseases. The two diseases showed dissimilar pathological pathways,
+with inflammatory lesions in heart and skeletal muscle in Atlantic salmon, and
+degenerative-necrotic lesions in kidney and liver in Chinook salmon, plausibly
+explained by differences in PRV load tolerance in red blood cells. Viral genome
+sequencing revealed no consistent differences in PRV-1 variants intimately
+involved in the development of both diseases, suggesting that migratory Chinook
+salmon may be at more than a minimal risk of disease from exposure to the high
+levels of PRV occurring on salmon farms.
+",0,0,0,0,1,0
+4368,Charge transfer and metallicity in LaNiO$_3$/LaMnO$_3$ superlattices,"  Motivated by recent experiments, we use the $+U$ extension of the generalized
+gradient approximation to density functional theory to study superlattices
+composed of alternating layers of LaNiO$_3$ and LaMnO$_3$. For comparison we
+also study a rocksalt ((111) double perovskite) structure and bulk LaNiO$_3$
+and LaMnO$_3$. A Wannier function analysis indicates that band parameters are
+transferable from bulk to superlattice situations with the exception of the
+transition metal d-level energy, which has a contribution from the change in
+d-shell occupancy. The charge transfer from Mn to Ni is found to be moderate in
+the superlattice, indicating metallic behavior, in contrast to the insulating
+behavior found in recent experiments, while the rocksalt structure is found to
+be insulating with a large Mn-Ni charge transfer. We suggest a high density of
+cation antisite defects may account for the insulating behavior experimentally
+observed in short-period superlattices.
+",0,1,0,0,0,0
+4369,On Learning the $cμ$ Rule in Single and Parallel Server Networks,"  We consider learning-based variants of the $c \mu$ rule for scheduling in
+single and parallel server settings of multi-class queueing systems.
+In the single server setting, the $c \mu$ rule is known to minimize the
+expected holding-cost (weighted queue-lengths summed over classes and a fixed
+time horizon). We focus on the problem where the service rates $\mu$ are
+unknown with the holding-cost regret (regret against the $c \mu$ rule with
+known $\mu$) as our objective. We show that the greedy algorithm that uses
+empirically learned service rates results in a constant holding-cost regret
+(the regret is independent of the time horizon). This free exploration can be
+explained in the single server setting by the fact that any work-conserving
+policy obtains the same number of samples in a busy cycle.
+In the parallel server setting, we show that the $c \mu$ rule may result in
+unstable queues, even for arrival rates within the capacity region. We then
+present sufficient conditions for geometric ergodicity under the $c \mu$ rule.
+Using these results, we propose an almost greedy algorithm that explores only
+when the number of samples falls below a threshold. We show that this algorithm
+delivers constant holding-cost regret because a free exploration condition is
+eventually satisfied.
+",1,0,0,0,0,0
+4370,Report: Performance comparison between C2075 and P100 GPU cards using cosmological correlation functions,"  In this report, some cosmological correlation functions are used to evaluate
+the differential performance between C2075 and P100 GPU cards. In the past, the
+correlation functions used in this work have been widely studied and exploited
+on some previous GPU architectures. The analysis of the performance indicates
+that a speedup in the range from 13 to 15 is achieved without any additional
+optimization process for the P100 card.
+",1,1,0,0,0,0
+4371,Crafting Adversarial Examples For Speech Paralinguistics Applications,"  Computational paralinguistic analysis is increasingly being used in a wide
+range of cyber applications, including security-sensitive applications such as
+speaker verification, deceptive speech detection, and medical diagnostics.
+While state-of-the-art machine learning techniques, such as deep neural
+networks, can provide robust and accurate speech analysis, they are susceptible
+to adversarial attacks. In this work, we propose an end-to-end scheme to
+generate adversarial examples for computational paralinguistic applications by
+perturbing directly the raw waveform of an audio recording rather than specific
+acoustic features. Our experiments show that the proposed adversarial
+perturbation can lead to a significant performance drop of state-of-the-art
+deep neural networks, while only minimally impairing the audio quality.
+",1,0,0,1,0,0
+4372,On the Analysis of Bacterial Cooperation with a Characterization of 2D Signal Propagation,"  The exchange of small molecular signals within microbial populations is
+generally referred to as quorum sensing (QS). QS is ubiquitous in nature and
+enables microorganisms to respond to fluctuations of living environments by
+working together. In this work, a QS-based communication system within a
+microbial population in a two-dimensional (2D) environment is analytically
+modeled. Notably, the diffusion and degradation of signaling molecules within
+the population is characterized. Microorganisms are randomly distributed on a
+2D circle where each one releases molecules at random times. The number of
+molecules observed at each randomly-distributed bacterium is analyzed. Using
+this analysis and some approximation, the expected density of cooperating
+bacteria is derived. The analytical results are validated via a particle-based
+simulation method. The model can be used to predict and control behavioral
+dynamics of microscopic populations that have imperfect signal propagation.
+",0,0,0,0,1,0
+4373,The Ebb and Flow of Controversial Debates on Social Media,"  We explore how the polarization around controversial topics evolves on
+Twitter - over a long period of time (2011 to 2016), and also as a response to
+major external events that lead to increased related activity. We find that
+increased activity is typically associated with increased polarization;
+however, we find no consistent long-term trend in polarization over time among
+the topics we study.
+",1,1,0,0,0,0
+4374,Training of Deep Neural Networks based on Distance Measures using RMSProp,"  The vanishing gradient problem was a major obstacle for the success of deep
+learning. In recent years it was gradually alleviated through multiple
+different techniques. However the problem was not really overcome in a
+fundamental way, since it is inherent to neural networks with activation
+functions based on dot products. In a series of papers, we are going to analyze
+alternative neural network structures which are not based on dot products. In
+this first paper, we revisit neural networks built up of layers based on
+distance measures and Gaussian activation functions. These kinds of networks
+were only sparsely used in the past since they are hard to train when using
+plain stochastic gradient descent methods. We show that by using Root Mean
+Square Propagation (RMSProp) it is possible to efficiently learn multi-layer
+neural networks. Furthermore we show that when appropriately initialized these
+kinds of neural networks suffer much less from the vanishing and exploding
+gradient problem than traditional neural networks even for deep networks.
+",1,0,0,1,0,0
+4375,Generalized Concomitant Multi-Task Lasso for sparse multimodal regression,"  In high dimension, it is customary to consider Lasso-type estimators to
+enforce sparsity. For standard Lasso theory to hold, the regularization
+parameter should be proportional to the noise level, yet the latter is
+generally unknown in practice. A possible remedy is to consider estimators,
+such as the Concomitant/Scaled Lasso, which jointly optimize over the
+regression coefficients as well as over the noise level, making the choice of
+the regularization independent of the noise level. However, when data from
+different sources are pooled to increase sample size, or when dealing with
+multimodal datasets, noise levels typically differ and new dedicated estimators
+are needed. In this work we provide new statistical and computational solutions
+to deal with such heteroscedastic regression models, with an emphasis on
+functional brain imaging with combined magneto- and electroencephalographic
+(M/EEG) signals. Adopting the formulation of Concomitant Lasso-type estimators,
+we propose a jointly convex formulation to estimate both the regression
+coefficients and the (square root of the) noise covariance. When our framework
+is instantiated to de-correlated noise, it leads to an efficient algorithm
+whose computational cost is not higher than for the Lasso and Concomitant
+Lasso, while addressing more complex noise structures. Numerical experiments
+demonstrate that our estimator yields improved prediction and support
+identification while correctly estimating the noise (square root) covariance.
+Results on multimodal neuroimaging problems with M/EEG data are also reported.
+",0,0,1,1,0,0
+4376,A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging,"  We propose a novel automatic method for accurate segmentation of the prostate
+in T2-weighted magnetic resonance imaging (MRI). Our method is based on
+convolutional neural networks (CNNs). Because of the large variability in the
+shape, size, and appearance of the prostate and the scarcity of annotated
+training data, we suggest training two separate CNNs. A global CNN will
+determine a prostate bounding box, which is then resampled and sent to a local
+CNN for accurate delineation of the prostate boundary. This way, the local CNN
+can effectively learn to segment the fine details that distinguish the prostate
+from the surrounding tissue using the small amount of available training data.
+To fully exploit the training data, we synthesize additional data by deforming
+the training images and segmentations using a learned shape model. We apply the
+proposed method on the PROMISE12 challenge dataset and achieve state of the art
+results. Our proposed method generates accurate, smooth, and artifact-free
+segmentations. On the test images, we achieve an average Dice score of 90.6
+with a small standard deviation of 2.2, which is superior to all previous
+methods. Our two-step segmentation approach and data augmentation strategy may
+be highly effective in segmentation of other organs from small amounts of
+annotated medical images.
+",1,0,0,1,0,0
+4377,A note on signature of Lefschetz fibrations with planar fiber,"  Using theorems of Eliashberg and McDuff, Etnyre [Et] proved that the
+intersection form of a symplectic filling of a contact 3-manifold supported by
+planar open book is negative definite.
+In this paper, we prove a signature formula for allowable Lefschetz
+fibrations over $D^2$ with planar fiber by computing Maslov index appearing in
+Wall's non-additivity formula.
+The signature formula leads to an alternative proof of Etnyre's theorem via
+works of Niederkrüger and Wendl [NWe] and Wendl [We].
+Conversely, Etnyre's theorem, together with the existence theorem of Stein
+structures on Lefschetz fibrations over $D^2$ with bordered fiber by Loi and
+Piergallini [LP], implies the formula.
+",0,0,1,0,0,0
+4378,Wild Bootstrapping Rank-Based Procedures: Multiple Testing in Nonparametric Split-Plot Designs,"  Split-plot or repeated measures designs are frequently used for planning
+experiments in the life or social sciences. Typical examples include the
+comparison of different treatments over time, where both factors may possess an
+additional factorial structure. For such designs, the statistical analysis
+usually consists of several steps. If the global null is rejected, multiple
+comparisons are usually performed. Usually, general factorial repeated measures
+designs are inferred by classical linear mixed models. Common underlying
+assumptions, such as normality or variance homogeneity are often not met in
+real data. Furthermore, to deal even with, e.g., ordinal or ordered categorical
+data, adequate effect sizes should be used. Here, multiple contrast tests and
+simultaneous confidence intervals for general factorial split-plot designs are
+developed and equipped with a novel asymptotically correct wild bootstrap
+approach.
+Because the regulatory authorities typically require the calculation of
+confidence intervals, this work also provides simultaneous confidence intervals
+for single contrasts and for the ratio of different contrasts in meaningful
+effects. Extensive simulations are conducted to foster the theoretical
+findings. Finally, two different datasets exemplify the applicability of the
+novel procedure.
+",0,0,1,1,0,0
+4379,Graphical Sequent Calculi for Modal Logics,"  The syntax of modal graphs is defined in terms of the continuous cut and
+broken cut following Charles Peirce's notation in the gamma part of his
+graphical logic of existential graphs. Graphical calculi for normal modal
+logics are developed based on a reformulation of the graphical calculus for
+classical propositional logic. These graphical calculi are of the nature of
+deep inference. The relationship between graphical calculi and sequent calculi
+for modal logics is shown by translations between graphs and modal formulas.
+",1,0,0,0,0,0
+4380,Engineering a flux-dependent mobility edge in disordered zigzag chains,"  There has been great interest in realizing quantum simulators of charged
+particles in artificial gauge fields. Here, we perform the first quantum
+simulation explorations of the combination of artificial gauge fields and
+disorder. Using synthetic lattice techniques based on parametrically-coupled
+atomic momentum states, we engineer zigzag chains with a tunable homogeneous
+flux. The breaking of time-reversal symmetry by the applied flux leads to
+analogs of spin-orbit coupling and spin-momentum locking, which we observe
+directly through the chiral dynamics of atoms initialized to single lattice
+sites. We additionally introduce precisely controlled disorder in the site
+energy landscape, allowing us to explore the interplay of disorder and large
+effective magnetic fields. The combination of correlated disorder and
+controlled intra- and inter-row tunneling in this system naturally supports
+energy-dependent localization, relating to a single-particle mobility edge. We
+measure the localization properties of the extremal eigenstates of this system,
+the ground state and the most-excited state, and demonstrate clear evidence for
+a flux-dependent mobility edge. These measurements constitute the first direct
+evidence for energy-dependent localization in a lower-dimensional system, as
+well as the first explorations of the combined influence of artificial gauge
+fields and engineered disorder. Moreover, we provide direct evidence for
+interaction shifts of the localization transitions for both low- and
+high-energy eigenstates in correlated disorder, relating to the presence of a
+many-body mobility edge. The unique combination of strong interactions,
+controlled disorder, and tunable artificial gauge fields present in this
+synthetic lattice system should enable myriad explorations into intriguing
+correlated transport phenomena.
+",0,1,0,0,0,0
+4381,On the tensor semigroup of affine kac-moody lie algebras,"  In this paper, we are interested in the decomposition of the tensor product
+of two representations of a symmetrizable Kac-Moody Lie algebra $\mathfrak g$.
+Let $P\_+$ be the set of dominant integral weights. For $\lambda\in P\_+$ ,
+$L(\lambda)$ denotes the irreducible, integrable, highest weight representation
+of g with highest weight $\lambda$. Let $P\_{+,\mathbb Q}$ be the rational
+convex cone generated by $P\_+$. Consider the tensor cone $\Gamma(\mathfrak g)
+:= \{(\lambda\_1 ,\lambda\_2, \mu) $\in$ P\_{+,\mathbb Q}^3\,| \exists N
+\textgreater{} 1 L(N\mu) \subset L(N \lambda\_1)\otimes L(N \lambda\_2)\}$. If
+$\mathfrak g$ is finite dimensional, $\Gamma(\mathfrak g)$ is a polyhedral
+convex cone described in 2006 by Belkale-Kumar by an explicit finite list of
+inequalities. In general, $\Gamma(\mathfrak g)$ is nor polyhedral, nor closed.
+In this article we describe the closure of $\Gamma(\mathfrak g)$ by an explicit
+countable family of linear inequalities, when $\mathfrak g$ is untwisted
+affine. This solves a Brown-Kumar's conjecture in this case. We also obtain
+explicit saturation factors for the semigroup of triples $(\lambda\_1,
+\lambda\_2 , \mu) $\in$ P\_+^3$ such that $L(\mu) $\subset$ L(\lambda\_1)
+\otimes L(\lambda\_2)$. Note that even the existence of such saturation factors
+is not obvious since the semigroup is not finitely generated. For example, in
+type $A , we prove that any integer $d\geq 2$ is a saturation factor,
+generalizing the case ${\tilde A}\_1$ shown by Brown-Kumar.
+",0,0,1,0,0,0
+4382,Electronic and Thermodynamic Properties of the Amino- and Carboxamido-Functionalized C-60-Based Fullerenes: Towards Non-Volatile Carbon Dioxide Scavengers,"  Development of new greenhouse gas scavengers is actively pursued nowadays.
+Volatility caused solvent consumption and significant regeneration costs
+associated with the aqueous amine solutions motivate search for more
+technologically and economically advanced solutions. We hereby used hybrid
+density functional theory to characterize thermodynamics, structure, electronic
+and solvation properties of amino and carboxamido functionalized C60 fullerene.
+C60 is non-volatile and supports a large density of amino groups on its
+surface. Attachment of polar groups to fullerene C60 adjusts its dipole moment
+and band gap quite substantially, ultimately resulting in systematically better
+hydration thermodynamics. Reaction of polyaminofullerenes with CO2 is favored
+enthalpically, but prohibited entropically at standard conditions. Free energy
+of the CO2 capture by polyaminofullerenes is non-sensitive to the number of
+amino groups per fullerene. This result fosters consideration of
+polyaminofullerenes for CO2 fixation.
+",0,1,0,0,0,0
+4383,Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask,"  Singing voice separation based on deep learning relies on the usage of
+time-frequency masking. In many cases the masking process is not a learnable
+function or is not encapsulated into the deep learning optimization.
+Consequently, most of the existing methods rely on a post processing step using
+the generalized Wiener filtering. This work proposes a method that learns and
+optimizes (during training) a source-dependent mask and does not need the
+aforementioned post processing step. We introduce a recurrent inference
+algorithm, a sparse transformation step to improve the mask generation process,
+and a learned denoising filter. Obtained results show an increase of 0.49 dB
+for the signal to distortion ratio and 0.30 dB for the signal to interference
+ratio, compared to previous state-of-the-art approaches for monaural singing
+voice separation.
+",1,0,0,0,0,0
+4384,Confidence Bands for Coefficients in High Dimensional Linear Models with Error-in-variables,"  We study high-dimensional linear models with error-in-variables. Such models
+are motivated by various applications in econometrics, finance and genetics.
+These models are challenging because of the need to account for measurement
+errors to avoid non-vanishing biases in addition to handle the high
+dimensionality of the parameters. A recent growing literature has proposed
+various estimators that achieve good rates of convergence. Our main
+contribution complements this literature with the construction of simultaneous
+confidence regions for the parameters of interest in such high-dimensional
+linear models with error-in-variables.
+These confidence regions are based on the construction of moment conditions
+that have an additional orthogonal property with respect to nuisance
+parameters. We provide a construction that requires us to estimate an
+additional high-dimensional linear model with error-in-variables for each
+component of interest. We use a multiplier bootstrap to compute critical values
+for simultaneous confidence intervals for a subset $S$ of the components. We
+show its validity despite of possible model selection mistakes, and allowing
+for the cardinality of $S$ to be larger than the sample size.
+We apply and discuss the implications of our results to two examples and
+conduct Monte Carlo simulations to illustrate the performance of the proposed
+procedure.
+",0,0,1,1,0,0
+4385,On the Power of Truncated SVD for General High-rank Matrix Estimation Problems,"  We show that given an estimate $\widehat{A}$ that is close to a general
+high-rank positive semi-definite (PSD) matrix $A$ in spectral norm (i.e.,
+$\|\widehat{A}-A\|_2 \leq \delta$), the simple truncated SVD of $\widehat{A}$
+produces a multiplicative approximation of $A$ in Frobenius norm. This
+observation leads to many interesting results on general high-rank matrix
+estimation problems, which we briefly summarize below ($A$ is an $n\times n$
+high-rank PSD matrix and $A_k$ is the best rank-$k$ approximation of $A$):
+(1) High-rank matrix completion: By observing
+$\Omega(\frac{n\max\{\epsilon^{-4},k^2\}\mu_0^2\|A\|_F^2\log
+n}{\sigma_{k+1}(A)^2})$ elements of $A$ where $\sigma_{k+1}\left(A\right)$ is
+the $\left(k+1\right)$-th singular value of $A$ and $\mu_0$ is the incoherence,
+the truncated SVD on a zero-filled matrix satisfies $\|\widehat{A}_k-A\|_F \leq
+(1+O(\epsilon))\|A-A_k\|_F$ with high probability.
+(2)High-rank matrix de-noising: Let $\widehat{A}=A+E$ where $E$ is a Gaussian
+random noise matrix with zero mean and $\nu^2/n$ variance on each entry. Then
+the truncated SVD of $\widehat{A}$ satisfies $\|\widehat{A}_k-A\|_F \leq
+(1+O(\sqrt{\nu/\sigma_{k+1}(A)}))\|A-A_k\|_F + O(\sqrt{k}\nu)$.
+(3) Low-rank Estimation of high-dimensional covariance: Given $N$
+i.i.d.~samples $X_1,\cdots,X_N\sim\mathcal N_n(0,A)$, can we estimate $A$ with
+a relative-error Frobenius norm bound? We show that if $N =
+\Omega\left(n\max\{\epsilon^{-4},k^2\}\gamma_k(A)^2\log N\right)$ for
+$\gamma_k(A)=\sigma_1(A)/\sigma_{k+1}(A)$, then $\|\widehat{A}_k-A\|_F \leq
+(1+O(\epsilon))\|A-A_k\|_F$ with high probability, where
+$\widehat{A}=\frac{1}{N}\sum_{i=1}^N{X_iX_i^\top}$ is the sample covariance.
+",0,0,1,1,0,0
+4386,Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition,"  When a human drives a car along a road for the first time, they later
+recognize where they are on the return journey typically without needing to
+look in their rear-view mirror or turn around to look back, despite significant
+viewpoint and appearance change. Such navigation capabilities are typically
+attributed to our semantic visual understanding of the environment [1] beyond
+geometry to recognizing the types of places we are passing through such as
+""passing a shop on the left"" or ""moving through a forested area"". Humans are in
+effect using place categorization [2] to perform specific place recognition
+even when the viewpoint is 180 degrees reversed. Recent advances in deep neural
+networks have enabled high-performance semantic understanding of visual places
+and scenes, opening up the possibility of emulating what humans do. In this
+work, we develop a novel methodology for using the semantics-aware higher-order
+layers of deep neural networks for recognizing specific places from within a
+reference database. To further improve the robustness to appearance change, we
+develop a descriptor normalization scheme that builds on the success of
+normalization schemes for pure appearance-based techniques such as SeqSLAM [3].
+Using two different datasets - one road-based, one pedestrian-based, we
+evaluate the performance of the system in performing place recognition on
+reverse traversals of a route with a limited field of view camera and no
+turn-back-and-look behaviours, and compare to existing state-of-the-art
+techniques and vanilla off-the-shelf features. The results demonstrate
+significant improvements over the existing state of the art, especially for
+extreme perceptual challenges that involve both great viewpoint change and
+environmental appearance change. We also provide experimental analyses of the
+contributions of the various system components.
+",1,0,0,0,0,0
+4387,Ultra-broadband On-chip Twisted Light Emitter,"  On-chip twisted light emitters are essential components for orbital angular
+momentum (OAM) communication devices, which could address the growing demand
+for high-capacity communication systems by providing an additional degree of
+freedom for wavelength/frequency division multiplexing (WDM/FDM). Although
+whispering gallery mode enabled OAM emitters have been shown to possess some
+advantages, such as being compact and phase accurate, their inherent narrow
+bandwidth prevents them from being compatible with WDM/FDM techniques. Here, we
+demonstrate an ultra-broadband multiplexed OAM emitter that utilizes a novel
+joint path-resonance phase control concept. The emitter has a micron sized
+radius and nanometer sized features. Coaxial OAM beams are emitted across the
+entire telecommunication band from 1450 to 1650 nm. We applied the emitter for
+OAM communication with a data rate of 1.2 Tbit/s assisted by 30-channel optical
+frequency combs (OFC). The emitter provides a new solution to further increase
+of the capacity in the OFC communication scenario.
+",0,1,0,0,0,0
+4388,Linear Additive Markov Processes,"  We introduce LAMP: the Linear Additive Markov Process. Transitions in LAMP
+may be influenced by states visited in the distant history of the process, but
+unlike higher-order Markov processes, LAMP retains an efficient
+parametrization. LAMP also allows the specific dependence on history to be
+learned efficiently from data. We characterize some theoretical properties of
+LAMP, including its steady-state and mixing time. We then give an algorithm
+based on alternating minimization to learn LAMP models from data. Finally, we
+perform a series of real-world experiments to show that LAMP is more powerful
+than first-order Markov processes, and even holds its own against deep
+sequential models (LSTMs) with a negligible increase in parameter complexity.
+",1,0,0,1,0,0
+4389,Radial and circular synchronization clusters in extended starlike network of van der Pol oscillators,"  We consider extended starlike networks where the hub node is coupled with
+several chains of nodes representing star rays. Assuming that nodes of the
+network are occupied by nonidentical self-oscillators we study various forms of
+their cluster synchronization. Radial cluster emerges when the nodes are
+synchronized along a ray, while circular cluster is formed by nodes without
+immediate connections but located on identical distances to the hub. By its
+nature the circular synchronization is a new manifestation of so called remote
+synchronization [Phys. Rev. E 85 (2012), 026208]. We report its long-range form
+when the synchronized nodes interact through at least three intermediate nodes.
+Forms of long-range remote synchronization are elements of scenario of
+transition to the total synchronization of the network. We observe that the far
+ends of rays synchronize first. Then more circular clusters appear involving
+closer to hub nodes. Subsequently the clusters merge and, finally, all network
+become synchronous. Behavior of the extended starlike networks is found to be
+strongly determined by the ray length, while varying the number of rays
+basically affects fine details of a dynamical picture. Symmetry of the star
+also extensively influences the dynamics. In an asymmetric star circular
+cluster mainly vanish in favor of radial ones, however, long-range remote
+synchronization survives.
+",1,1,0,0,0,0
+4390,On the Fourth Power Moment of Fourier Coefficients of Cusp Form,"  Let $a(n)$ be the Fourier coefficients of a holomorphic cusp form of weight
+$\kappa=2n\geqslant12$ for the full modular group and
+$A(x)=\sum\limits_{n\leqslant x}a(n)$. In this paper, we establish an
+asymptotic formula of the fourth power moment of $A(x)$ and prove that
+\begin{equation*}
+\int_1^TA^4(x)\mathrm{d}x=\frac{3}{64\kappa\pi^4}s_{4;2}(\tilde{a})
+T^{2\kappa}+O\big(T^{2\kappa-\delta_4+\varepsilon}\big) \end{equation*} with
+$\delta_4=1/8$, which improves the previous result.
+",0,0,1,0,0,0
+4391,Provable benefits of representation learning,"  There is general consensus that learning representations is useful for a
+variety of reasons, e.g. efficient use of labeled data (semi-supervised
+learning), transfer learning and understanding hidden structure of data.
+Popular techniques for representation learning include clustering, manifold
+learning, kernel-learning, autoencoders, Boltzmann machines, etc.
+To study the relative merits of these techniques, it's essential to formalize
+the definition and goals of representation learning, so that they are all
+become instances of the same definition. This paper introduces such a formal
+framework that also formalizes the utility of learning the representation. It
+is related to previous Bayesian notions, but with some new twists. We show the
+usefulness of our framework by exhibiting simple and natural settings -- linear
+mixture models and loglinear models, where the power of representation learning
+can be formally shown. In these examples, representation learning can be
+performed provably and efficiently under plausible assumptions (despite being
+NP-hard), and furthermore: (i) it greatly reduces the need for labeled data
+(semi-supervised learning) and (ii) it allows solving classification tasks when
+simpler approaches like nearest neighbors require too much data (iii) it is
+more powerful than manifold learning methods.
+",1,0,0,1,0,0
+4392,TherML: Thermodynamics of Machine Learning,"  In this work we offer a framework for reasoning about a wide class of
+existing objectives in machine learning. We develop a formal correspondence
+between this work and thermodynamics and discuss its implications.
+",0,0,0,1,0,0
+4393,Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach,"  With the advent of Big Data, nowadays in many applications databases
+containing large quantities of similar time series are available. Forecasting
+time series in these domains with traditional univariate forecasting procedures
+leaves great potentials for producing accurate forecasts untapped. Recurrent
+neural networks (RNNs), and in particular Long Short-Term Memory (LSTM)
+networks, have proven recently that they are able to outperform
+state-of-the-art univariate time series forecasting methods in this context
+when trained across all available time series. However, if the time series
+database is heterogeneous, accuracy may degenerate, so that on the way towards
+fully automatic forecasting methods in this space, a notion of similarity
+between the time series needs to be built into the methods. To this end, we
+present a prediction model that can be used with different types of RNN models
+on subgroups of similar time series, which are identified by time series
+clustering techniques. We assess our proposed methodology using LSTM networks,
+a widely popular RNN variant. Our method achieves competitive results on
+benchmarking datasets under competition evaluation procedures. In particular,
+in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM
+model and outperforms all other methods on the CIF2016 forecasting competition
+dataset.
+",1,0,0,1,0,0
+4394,Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information,"  We introduce a new virtual environment for simulating a card game known as
+""Big 2"". This is a four-player game of imperfect information with a relatively
+complicated action space (being allowed to play 1,2,3,4 or 5 card combinations
+from an initial starting hand of 13 cards). As such it poses a challenge for
+many current reinforcement learning methods. We then use the recently proposed
+""Proximal Policy Optimization"" algorithm to train a deep neural network to play
+the game, purely learning via self-play, and find that it is able to reach a
+level which outperforms amateur human players after only a relatively short
+amount of training time and without needing to search a tree of future game
+states.
+",0,0,0,1,0,0
+4395,Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms,"  We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images
+of 70,000 fashion products from 10 categories, with 7,000 images per category.
+The training set has 60,000 images and the test set has 10,000 images.
+Fashion-MNIST is intended to serve as a direct drop-in replacement for the
+original MNIST dataset for benchmarking machine learning algorithms, as it
+shares the same image size, data format and the structure of training and
+testing splits. The dataset is freely available at
+this https URL
+",1,0,0,1,0,0
+4396,Experimental Evaluation of Book Drawing Algorithms,"  A $k$-page book drawing of a graph $G=(V,E)$ consists of a linear ordering of
+its vertices along a spine and an assignment of each edge to one of the $k$
+pages, which are half-planes bounded by the spine. In a book drawing, two edges
+cross if and only if they are assigned to the same page and their vertices
+alternate along the spine. Crossing minimization in a $k$-page book drawing is
+NP-hard, yet book drawings have multiple applications in visualization and
+beyond. Therefore several heuristic book drawing algorithms exist, but there is
+no broader comparative study on their relative performance. In this paper, we
+propose a comprehensive benchmark set of challenging graph classes for book
+drawing algorithms and provide an extensive experimental study of the
+performance of existing book drawing algorithms.
+",1,0,0,0,0,0
+4397,Probing magnetism in the vortex phase of PuCoGa$_5$ by X-ray magnetic circular dichroism,"  We have measured X-ray magnetic circular dichroism (XMCD) spectra at the Pu
+$M_{4,5}$ absorption edges from a newly-prepared high-quality single crystal of
+the heavy fermion superconductor $^{242}$PuCoGa$_{5}$, exhibiting a critical
+temperature $T_{c} = 18.7~{\rm K}$. The experiment probes the vortex phase
+below $T_{c}$ and shows that an external magnetic field induces a Pu 5$f$
+magnetic moment at 2 K equal to the temperature-independent moment measured in
+the normal phase up to 300 K by a SQUID device. This observation is in
+agreement with theoretical models claiming that the Pu atoms in PuCoGa$_{5}$
+have a nonmagnetic singlet ground state resulting from the hybridization of the
+conduction electrons with the intermediate-valence 5$f$ electronic shell.
+Unexpectedly, XMCD spectra show that the orbital component of the $5f$ magnetic
+moment increases significantly between 30 and 2 K; the antiparallel spin
+component increases as well, leaving the total moment practically constant. We
+suggest that this indicates a low-temperature breakdown of the complete
+Kondo-like screening of the local 5$f$ moment.
+",0,1,0,0,0,0
+4398,Joint secrecy over the K-Transmitter Multiple Access Channel,"  This paper studies the problem of secure communication over a K-transmitter
+multiple access channel in the presence of an external eavesdropper, subject to
+a joint secrecy constraint (i.e., information leakage rate from the collection
+of K messages to an eavesdropper is made vanishing). As a result, we establish
+the joint secrecy achievable rate region. To this end, our results build upon
+two techniques in addition to the standard information-theoretic methods. The
+first is a generalization of Chia-El Gamal's lemma on entropy bound for a set
+of codewords given partial information. The second is to utilize a compact
+representation of a list of sets that, together with properties of mutual
+information, leads to an efficient Fourier-Motzkin elimination. These two
+approaches could also be of independent interests in other contexts.
+",1,0,0,0,0,0
+4399,Nonlinear Flexoelectricity in Non-centrosymmetric Crystals,"  We analytically derive the elastic, dielectric, piezoelectric, and the
+flexoelectric phenomenological coefficients as functions of microscopic model
+parameters such as ionic positions and spring constants in the two-dimensional
+square-lattice model with rock-salt-type ionic arrangement. Monte-Carlo
+simulation reveals that a difference in the given elastic constants of the
+diagonal springs, each of which connects the same cations or anions, is
+responsible for the linear flexoelectric effect in the model. We show the
+quadratic flexoelectric effect is present only in non-centrosymmetric systems
+and it can overwhelm the linear effect in feasibly large strain gradients.
+",0,1,0,0,0,0
+4400,Well-posedness of nonlinear transport equation by stochastic perturbation,"  We are concerned with multidimensional nonlinear stochastic transport
+equation driven by Brownian motions. For irregular fluxes, by using stochastic
+BGK approximations and commutator estimates, we gain the existence and
+uniqueness of stochastic entropy solutions. Besides, for $BV$ initial data, the
+$BV$ and Hölder regularities are also derived for the unique stochastic
+entropy solution. Particularly, for the transport equation, we gain a
+regularization result, i.e. while the existence fails for the transport
+equation, we prove that a multiplicative stochastic perturbation of Brownian
+type is enough to render the equation well-posed. This seems to be another
+explicit example (the first example is given in [22]) of a PDE of fluid
+dynamics that becomes well-posed under the influence of a multiplicative
+Brownian type noise.
+",0,0,1,0,0,0
+4401,Massively parallel lattice-Boltzmann codes on large GPU clusters,"  This paper describes a massively parallel code for a state-of-the art thermal
+lattice- Boltzmann method. Our code has been carefully optimized for
+performance on one GPU and to have a good scaling behavior extending to a large
+number of GPUs. Versions of this code have been already used for large-scale
+studies of convective turbulence. GPUs are becoming increasingly popular in HPC
+applications, as they are able to deliver higher performance than traditional
+processors. Writing efficient programs for large clusters is not an easy task
+as codes must adapt to increasingly parallel architectures, and the overheads
+of node-to-node communications must be properly handled. We describe the
+structure of our code, discussing several key design choices that were guided
+by theoretical models of performance and experimental benchmarks. We present an
+extensive set of performance measurements and identify the corresponding main
+bot- tlenecks; finally we compare the results of our GPU code with those
+measured on other currently available high performance processors. Our results
+are a production-grade code able to deliver a sustained performance of several
+tens of Tflops as well as a design and op- timization methodology that can be
+used for the development of other high performance applications for
+computational physics.
+",1,0,0,0,0,0
+4402,A Large Dimensional Study of Regularized Discriminant Analysis Classifiers,"  This article carries out a large dimensional analysis of standard regularized
+discriminant analysis classifiers designed on the assumption that data arise
+from a Gaussian mixture model with different means and covariances. The
+analysis relies on fundamental results from random matrix theory (RMT) when
+both the number of features and the cardinality of the training data within
+each class grow large at the same pace. Under mild assumptions, we show that
+the asymptotic classification error approaches a deterministic quantity that
+depends only on the means and covariances associated with each class as well as
+the problem dimensions. Such a result permits a better understanding of the
+performance of regularized discriminant analsysis, in practical large but
+finite dimensions, and can be used to determine and pre-estimate the optimal
+regularization parameter that minimizes the misclassification error
+probability. Despite being theoretically valid only for Gaussian data, our
+findings are shown to yield a high accuracy in predicting the performances
+achieved with real data sets drawn from the popular USPS data base, thereby
+making an interesting connection between theory and practice.
+",0,0,0,1,0,0
+4403,Non-Spherical Szekeres models in the language of Cosmological Perturbations,"  We study the differences and equivalences between the non-perturbative
+description of the evolution of cosmic structure furnished by the Szekeres dust
+models (a non-spherical exact solution of Einstein's equations) and the
+dynamics of Cosmological Perturbation Theory (CPT) for dust sources in a
+$\Lambda$CDM background. We show how the dynamics of Szekeres models can be
+described by evolution equations given in terms of ""exact fluctuations"" that
+identically reduce (at all orders) to evolution equations of CPT in the
+comoving isochronous gauge. We explicitly show how Szekeres linearised exact
+fluctuations are specific (deterministic) realisations of standard linear
+perturbations of CPT given as random fields but, as opposed to the latter
+perturbations, they can be evolved exactly into the full non-linear regime. We
+prove two important results: (i) the conservation of the curvature perturbation
+(at all scales) also holds for the appropriate approximation of the exact
+Szekeres fluctuations in a $\Lambda$CDM background, and (ii) the different
+collapse morphologies of Szekeres models yields, at nonlinear order, different
+functional forms for the growth factor that follows from the study of redshift
+space distortions. The metric based potentials used in linear CPT are computed
+in terms of the parameters of the linearised Szekeres models, thus allowing us
+to relate our results to linear CPT results in other gauges. We believe that
+these results provide a solid starting stage to examine the role of
+non-perturbative General Relativity in current cosmological research.
+",0,1,0,0,0,0
+4404,Smart materials and structures for energy harvesters,"  Vibrational energy harvesters capture mechanical energy from ambient
+vibrations and convert the mechanical energy into electrical energy to power
+wireless electronic systems. Challenges exist in the process of capturing
+mechanical energy from ambient vibrations. For example, resonant harvesters may
+be used to improve power output near their resonance, but their narrow
+bandwidth makes them less suitable for applications with varying vibrational
+frequencies. Higher operating frequencies can increase harvesters power output,
+but many vibrational sources are characterized by lower frequencies, such as
+human motions. This paper provides a thorough review of state of the art energy
+harvesters based on various energy sources such as solar, thermal,
+electromagnetic and mechanical energy, as well as smart materials including
+piezoelectric materials and carbon nanotubes. The paper will then focus on
+vibrational energy harvesters to review harvesters using typical transduction
+mechanisms and various techniques to address the challenges in capturing
+mechanical energy and delivering it to the transducers.
+",0,1,0,0,0,0
+4405,DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout,"  The paper presents a novel, principled approach to train recurrent neural
+networks from the Reservoir Computing family that are robust to missing part of
+the input features at prediction time. By building on the ensembling properties
+of Dropout regularization, we propose a methodology, named DropIn, which
+efficiently trains a neural model as a committee machine of subnetworks, each
+capable of predicting with a subset of the original input features. We discuss
+the application of the DropIn methodology in the context of Reservoir Computing
+models and targeting applications characterized by input sources that are
+unreliable or prone to be disconnected, such as in pervasive wireless sensor
+networks and ambient intelligence. We provide an experimental assessment using
+real-world data from such application domains, showing how the Dropin
+methodology allows to maintain predictive performances comparable to those of a
+model without missing features, even when 20\%-50\% of the inputs are not
+available.
+",1,0,0,1,0,0
+4406,A Single-Channel Architecture for Algebraic Integer Based 8$\times$8 2-D DCT Computation,"  An area efficient row-parallel architecture is proposed for the real-time
+implementation of bivariate algebraic integer (AI) encoded 2-D discrete cosine
+transform (DCT) for image and video processing. The proposed architecture
+computes 8$\times$8 2-D DCT transform based on the Arai DCT algorithm. An
+improved fast algorithm for AI based 1-D DCT computation is proposed along with
+a single channel 2-D DCT architecture. The design improves on the 4-channel AI
+DCT architecture that was published recently by reducing the number of integer
+channels to one and the number of 8-point 1-D DCT cores from 5 down to 2. The
+architecture offers exact computation of 8$\times$8 blocks of the 2-D DCT
+coefficients up to the FRS, which converts the coefficients from the AI
+representation to fixed-point format using the method of expansion factors.
+Prototype circuits corresponding to FRS blocks based on two expansion factors
+are realized, tested, and verified on FPGA-chip, using a Xilinx Virtex-6
+XC6VLX240T device. Post place-and-route results show a 20% reduction in terms
+of area compared to the 2-D DCT architecture requiring five 1-D AI cores. The
+area-time and area-time${}^2$ complexity metrics are also reduced by 23% and
+22% respectively for designs with 8-bit input word length. The digital
+realizations are simulated up to place and route for ASICs using 45 nm CMOS
+standard cells. The maximum estimated clock rate is 951 MHz for the CMOS
+realizations indicating 7.608$\cdot$10$^9$ pixels/seconds and a 8$\times$8
+block rate of 118.875 MHz.
+",1,0,0,0,0,0
+4407,More lessons from the six box toy experiment,"  Following a paper in which the fundamental aspects of probabilistic inference
+were introduced by means of a toy experiment, details of the analysis of
+simulated long sequences of extractions are shown here. In fact, the striking
+performance of probability-based inference and forecasting, compared to those
+obtained by simple `rules', might impress those practitioners who are usually
+underwhelmed by the philosophical foundation of the different methods. The
+analysis of the sequences also shows how the smallness of the probability of
+what has been actually observed, given the hypotheses of interest, is
+irrelevant for the purpose of inference.
+",0,1,1,0,0,0
+4408,A Biomechanical Study on the Use of Curved Drilling Technique for Treatment of Osteonecrosis of Femoral Head,"  Osteonecrosis occurs due to the loss of blood supply to the bone, leading to
+spontaneous death of the trabecular bone. Delayed treatment of the involved
+patients results in collapse of the femoral head, which leads to a need for
+total hip arthroplasty surgery. Core decompression, as the most popular
+technique for treatment of the osteonecrosis, includes removal of the lesion
+area by drilling a straight tunnel to the lesion, debriding the dead bone and
+replacing it with bone substitutes. However, there are two drawbacks for this
+treatment method. First, due to the rigidity of the instruments currently used
+during core decompression, lesions cannot be completely removed and/or
+excessive healthy bone may also be removed with the lesion. Second, the use of
+bone substitutes, despite its biocompatibility and osteoconductivity, may not
+provide sufficient mechanical strength and support for the bone. To address
+these shortcomings, a novel robot-assisted curved core decompression (CCD)
+technique is introduced to provide surgeons with direct access to the lesions
+causing minimal damage to the healthy bone. In this study, with the aid of
+finite element (FE) simulations, we investigate biomechanical performance of
+core decompression using the curved drilling technique in the presence of
+normal gait loading. In this regard, we compare the result of the CCD using
+bone substitutes and flexible implants with other conventional core
+decompression techniques. The study finding shows that the maximum principal
+stress occurring at the superior domain of the neck is smaller in the CCD
+techniques (i.e. 52.847 MPa) compared to the other core decompression methods.
+",0,0,0,0,1,0
+4409,Output feedback exponential stabilization for 1-D unstable wave equations with boundary control matched disturbance,"  We study the output feedback exponential stabilization of a one-dimensional
+unstable wave equation, where the boundary input, given by the Neumann trace at
+one end of the domain, is the sum of the control input and the total
+disturbance. The latter is composed of a nonlinear uncertain feedback term and
+an external bounded disturbance. Using the two boundary displacements as output
+signals, we design a disturbance estimator that does not use high gain. It is
+shown that the disturbance estimator can estimate the total disturbance in the
+sense that the estimation error signal is in $L^2[0,\infty)$. Using the
+estimated total disturbance, we design an observer whose state is exponentially
+convergent to the state of original system. Finally, we design an
+observer-based output feedback stabilizing controller. The total disturbance is
+approximately canceled in the feedback loop by its estimate. The closed-loop
+system is shown to be exponentially stable while guaranteeing that all the
+internal signals are uniformly bounded.
+",0,0,1,0,0,0
+4410,Bio-inspired Tensegrity Soft Modular Robots,"  In this paper, we introduce a design principle to develop novel soft modular
+robots based on tensegrity structures and inspired by the cytoskeleton of
+living cells. We describe a novel strategy to realize tensegrity structures
+using planar manufacturing techniques, such as 3D printing. We use this
+strategy to develop icosahedron tensegrity structures with programmable
+variable stiffness that can deform in a three-dimensional space. We also
+describe a tendon-driven contraction mechanism to actively control the
+deformation of the tensegrity mod-ules. Finally, we validate the approach in a
+modular locomotory worm as a proof of concept.
+",1,0,0,0,0,0
+4411,How proper are Bayesian models in the astronomical literature?,"  The well-known Bayes theorem assumes that a posterior distribution is a
+probability distribution. However, the posterior distribution may no longer be
+a probability distribution if an improper prior distribution (non-probability
+measure) such as an unbounded uniform prior is used. Improper priors are often
+used in the astronomical literature to reflect a lack of prior knowledge, but
+checking whether the resulting posterior is a probability distribution is
+sometimes neglected. It turns out that 23 articles out of 75 articles (30.7%)
+published online in two renowned astronomy journals (ApJ and MNRAS) between Jan
+1, 2017 and Oct 15, 2017 make use of Bayesian analyses without rigorously
+establishing posterior propriety. A disturbing aspect is that a Gibbs-type
+Markov chain Monte Carlo (MCMC) method can produce a seemingly reasonable
+posterior sample even when the posterior is not a probability distribution
+(Hobert and Casella, 1996). In such cases, researchers may erroneously make
+probabilistic inferences without noticing that the MCMC sample is from a
+non-existing probability distribution. We review why checking posterior
+propriety is fundamental in Bayesian analyses, and discuss how to set up
+scientifically motivated proper priors.
+",0,1,0,0,0,0
+4412,Diffusive Tidal Evolution for Migrating hot Jupiters,"  I consider a Jovian planet on a highly eccentric orbit around its host star,
+a situation produced by secular interactions with its planetary or stellar
+companions. The tidal interactions at every periastron passage exchange energy
+between the orbit and the planet's degree-2 fundamental-mode. Starting from
+zero energy, the f-mode can diffusively grow to large amplitudes if its
+one-kick energy gain > 10^-5 of the orbital energy. This requires a pericentre
+distance of < 4 tidal radii (or 1.6 Roche radii). If the f-mode has a
+non-negligible initial energy, diffusive evolution can occur at a lower
+threshold. The first effect can stall the secular migration as the f-mode can
+absorb orbital energy and decouple the planet from its secular perturbers,
+parking all migrating jupiters safely outside the zone of tidal disruption. The
+second effect leads to rapid orbit circularization as it allows an excited
+f-mode to continuously absorb orbital energy as the orbit eccentricity
+decreases. So without any explicit dissipation, other than the fact that the
+f-mode will damp nonlinearly when its amplitude reaches unity, the planet can
+be transported from a few AU to ~ 0.2 AU in ~ 10^4 yrs. Such a rapid
+circularization is equivalent to a dissipation factor Q ~ 1, and it explains
+the observed deficit of super-eccentric Jovian planets. Lastly, the repeated
+f-mode breaking likely deposit energy and angular momentum in the outer
+envelope, and avoid thermally ablating the planet.
+Overall, this work boosts the case for forming hot Jupiters through
+high-eccentricity secular migration.
+",0,1,0,0,0,0
+4413,"Von Neumann dimension, Hodge index theorem and geometric applications","  This note contains a reformulation of the Hodge index theorem within the
+framework of Atiyah's $L^2$-index theory. More precisely, given a compact
+Kähler manifold $(M,h)$ of even complex dimension $2m$, we prove that
+$$\sigma(M)=\sum_{p,q=0}^{2m}(-1)^ph_{(2),\Gamma}^{p,q}(M)$$ where $\sigma(M)$
+is the signature of $M$ and $h_{(2),\Gamma}^{p,q}(M)$ are the $L^2$-Hodge
+numbers of $M$ with respect to a Galois covering having $\Gamma$ as group of
+Deck transformations. Likewise we also prove an $L^2$-version of the
+Frölicher index theorem. Afterwards we give some applications of these two
+theorems and finally we conclude this paper by collecting other properties of
+the $L^2$-Hodge numbers.
+",0,0,1,0,0,0
+4414,Relativistic asymmetries in the galaxy cross-correlation function,"  We study the asymmetry in the two-point cross-correlation function of two
+populations of galaxies focusing in particular on the relativistic effects that
+include the gravitational redshift. We derive the cross-correlation function on
+small and large scales using two different approaches: General Relativistic and
+Newtonian perturbation theory. Following recent work by Bonvin et al.,
+Gaztanaga et al. and Croft, we calculate the dipole and the shell estimator
+with the two procedures and we compare our results. We find that while General
+Relativistic Perturbation Theory (GRPT) is able to make predictions of
+relativistic effects on very large, obviously linear scales (r > 50 Mpc/h), the
+presence of non-linearities physically occurring on much smaller scales (down
+to those describing galactic potential wells) can strongly affect the asymmetry
+estimators. These can lead to cancellations of the relativistic terms, and sign
+changes in the estimators on scales up to r ~ 50 Mpc/h. On the other hand, with
+an appropriate non-linear gravitational potential, the results obtained using
+Newtonian theory can successfully describe the asymmetry on smaller, non-linear
+scales (r < 20 Mpc/h) where gravitational redshift is the dominant term. On
+larger scales the asymmetry is much smaller in magnitude, and measurement is
+not within reach of current observations. This is in agreement with the
+observational results obtained by Gaztnaga et al. and the first detection of
+relativistic effects (on (r < 20 Mpc/h) scales) by Alam et al.
+",0,1,0,0,0,0
+4415,Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units,"  In this paper, we present an end-to-end automatic speech recognition system,
+which successfully employs subword units in a hybrid CTC-Attention based
+system. The subword units are obtained by the byte-pair encoding (BPE)
+compression algorithm. Compared to using words as modeling units, using
+characters or subword units does not suffer from the out-of-vocabulary (OOV)
+problem. Furthermore, using subword units further offers a capability in
+modeling longer context than using characters. We evaluate different systems
+over the LibriSpeech 1000h dataset. The subword-based hybrid CTC-Attention
+system obtains 6.8% word error rate (WER) on the test_clean subset without any
+dictionary or external language model. This represents a significant
+improvement (a 12.8% WER relative reduction) over the character-based hybrid
+CTC-Attention system.
+",1,0,0,0,0,0
+4416,Harmonic analysis and distribution-free inference for spherical distributions,"  Fourier analysis and representation of circular distributions in terms of
+their Fourier coefficients, is quite commonly discussed and used for model-free
+inference such as testing uniformity and symmetry etc. in dealing with
+2-dimensional directions. However a similar discussion for spherical
+distributions, which are used to model 3-dimensional directional data, has not
+been fully developed in the literature in terms of their harmonics. This paper,
+in what we believe is the first such attempt, looks at the probability
+distributions on a unit sphere, through the perspective of spherical harmonics,
+analogous to the Fourier analysis for distributions on a unit circle. Harmonic
+representations of many currently used spherical models are presented and
+discussed. A very general family of spherical distributions is then introduced,
+special cases of which yield many known spherical models. Through the prism of
+harmonic analysis, one can look at the mean direction, dispersion, and various
+forms of symmetry for these models in a generic setting. Aspects of
+distribution free inference such as estimation and large-sample tests for these
+symmetries, are provided. The paper concludes with a real-data example
+analyzing the longitudinal sunspot activity.
+",0,0,0,1,0,0
+4417,Free-form modelling of galaxy clusters: a Bayesian and data-driven approach,"  A new method is presented for modelling the physical properties of galaxy
+clusters. Our technique moves away from the traditional approach of assuming
+specific parameterised functional forms for the variation of physical
+quantities within the cluster, and instead allows for a 'free-form'
+reconstruction, but one for which the level of complexity is determined
+automatically by the observational data and may depend on position within the
+cluster. This is achieved by representing each independent cluster property as
+some interpolating or approximating function that is specified by a set of
+control points, or 'nodes', for which the number of nodes, together with their
+positions and amplitudes, are allowed to vary and are inferred in a Bayesian
+manner from the data. We illustrate our nodal approach in the case of a
+spherical cluster by modelling the electron pressure profile Pe(r) in analyses
+both of simulated Sunyaev-Zel'dovich (SZ) data from the Arcminute MicroKelvin
+Imager (AMI) and of real AMI observations of the cluster MACS J0744+3927 in the
+CLASH sample. We demonstrate that one may indeed determine the complexity
+supported by the data in the reconstructed Pe(r), and that one may constrain
+two very important quantities in such an analysis: the cluster total volume
+integrated Comptonisation parameter (Ytot) and the extent of the gas
+distribution in the cluster (rmax). The approach is also well-suited to
+detecting clusters in blind SZ surveys.
+",0,1,0,0,0,0
+4418,Multi-focus Attention Network for Efficient Deep Reinforcement Learning,"  Deep reinforcement learning (DRL) has shown incredible performance in
+learning various tasks to the human level. However, unlike human perception,
+current DRL models connect the entire low-level sensory input to the
+state-action values rather than exploiting the relationship between and among
+entities that constitute the sensory input. Because of this difference, DRL
+needs vast amount of experience samples to learn. In this paper, we propose a
+Multi-focus Attention Network (MANet) which mimics human ability to spatially
+abstract the low-level sensory input into multiple entities and attend to them
+simultaneously. The proposed method first divides the low-level input into
+several segments which we refer to as partial states. After this segmentation,
+parallel attention layers attend to the partial states relevant to solving the
+task. Our model estimates state-action values using these attended partial
+states. In our experiments, MANet attains highest scores with significantly
+less experience samples. Additionally, the model shows higher performance
+compared to the Deep Q-network and the single attention model as benchmarks.
+Furthermore, we extend our model to attentive communication model for
+performing multi-agent cooperative tasks. In multi-agent cooperative task
+experiments, our model shows 20% faster learning than existing state-of-the-art
+model.
+",1,0,0,1,0,0
+4419,Weak lensing power spectrum reconstruction by counting galaxies.-- I: the ABS method,"  We propose an Analytical method of Blind Separation (ABS) of cosmic
+magnification from the intrinsic fluctuations of galaxy number density in the
+observed galaxy number density distribution. The ABS method utilizes the
+different dependences of the signal (cosmic magnification) and contamination
+(galaxy intrinsic clustering) on galaxy flux, to separate the two. It works
+directly on the measured cross galaxy angular power spectra between different
+flux bins. It determines/reconstructs the lensing power spectrum analytically,
+without assumptions of galaxy intrinsic clustering and cosmology. It is
+unbiased in the limit of infinite number of galaxies. In reality the lensing
+reconstruction accuracy depends on survey configurations, galaxy biases, and
+other complexities, due to finite number of galaxies and the resulting shot
+noise fluctuations in the cross galaxy power spectra. We estimate its
+performance (systematic and statistical errors) in various cases. We find that,
+stage IV dark energy surveys such as SKA and LSST are capable of reconstructing
+the lensing power spectrum at $z\simeq 1$ and $\ell\la 5000$ accurately. This
+lensing reconstruction only requires counting galaxies, and is therefore highly
+complementary to the cosmic shear measurement by the same surveys.
+",0,1,0,0,0,0
+4420,Optimal Output Consensus of High-Order Multi-Agent Systems with Embedded Technique,"  In this paper, we study an optimal output consensus problem for a multi-agent
+network with agents in the form of multi-input multi-output minimum-phase
+dynamics. Optimal output consensus can be taken as an extended version of the
+existing output consensus problem for higher-order agents with an optimization
+requirement, where the output variables of agents are driven to achieve a
+consensus on the optimal solution of a global cost function. To solve this
+problem, we first construct an optimal signal generator, and then propose an
+embedded control scheme by embedding the generator in the feedback loop. We
+give two kinds of algorithms based on different available information along
+with both state feedback and output feedback, and prove that these algorithms
+with the embedded technique can guarantee the solvability of the problem for
+high-order multi-agent systems under standard assumptions.
+",1,0,1,0,0,0
+4421,Equilibrium selection via Optimal transport,"  We propose a new dynamics for equilibrium selection of finite player discrete
+strategy games. The dynamics is motivated by optimal transportation, and models
+individual players' myopicity, greedy and uncertainty when making decisions.
+The stationary measure of the dynamics provides each pure Nash equilibrium a
+probability by which it is ranked. For potential games, its dynamical
+properties are characterized by entropy and Fisher information.
+",0,0,1,0,0,0
+4422,Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks,"  This paper presents a new method for medical diagnosis of neurodegenerative
+diseases, such as Parkinson's, by extracting and using latent information from
+trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs).
+In particular, our approach adopts a combination of transfer learning, k-means
+clustering and k-Nearest Neighbour classification of deep neural network
+learned representations to provide enriched prediction of the disease based on
+MRI and/or DaT Scan data. A new loss function is introduced and used in the
+training of the DNNs, so as to perform adaptation of the generated learned
+representations between data from different medical environments. Results are
+presented using a recently published database of Parkinson's related
+information, which was generated and evaluated in a hospital environment.
+",1,0,0,1,0,0
+4423,Representation Theorems for Solvable Sesquilinear Forms,"  New results are added to the paper [4] about q-closed and solvable
+sesquilinear forms. The structure of the Banach space
+$\mathcal{D}[||\cdot||_\Omega]$ defined on the domain $\mathcal{D}$ of a
+q-closed sesquilinear form $\Omega$ is unique up to isomorphism, and the
+adjoint of a sesquilinear form has the same property of q-closure or of
+solvability. The operator associated to a solvable sesquilinear form is the
+greatest which represents the form and it is self-adjoint if, and only if, the
+form is symmetric. We give more criteria of solvability for q-closed
+sesquilinear forms. Some of these criteria are related to the numerical range,
+and we analyse in particular the forms which are solvable with respect to inner
+products. The theory of solvable sesquilinear forms generalises those of many
+known sesquilinear forms in literature.
+",0,0,1,0,0,0
+4424,Using Continuous Power Modulation for Exchanging Local Channel State Information,"  This letter provides a simple but efficient technique, which allows each
+transmitter of an interference network, to exchange local channel state
+information with the other transmitters. One salient feature of the proposed
+technique is that a transmitter only needs measurements of the signal power at
+its intended receiver to implement it, making direct inter-transmitter
+signaling channels unnecessary. The key idea to achieve this is to use a
+transient period during which the continuous power level of a transmitter is
+taken to be the linear combination of the channel gains to be exchanged.
+",1,0,0,0,0,0
+4425,Spectral Efficient and Energy Aware Clustering in Cellular Networks,"  The current and envisaged increase of cellular traffic poses new challenges
+to Mobile Network Operators (MNO), who must densify their Radio Access Networks
+(RAN) while maintaining low Capital Expenditure and Operational Expenditure to
+ensure long-term sustainability. In this context, this paper analyses optimal
+clustering solutions based on Device-to-Device (D2D) communications to mitigate
+partially or completely the need for MNOs to carry out extremely dense RAN
+deployments. Specifically, a low complexity algorithm that enables the creation
+of spectral efficient clusters among users from different cells, denoted as
+enhanced Clustering Optimization for Resources' Efficiency (eCORE) is
+presented. Due to the imbalance between uplink and downlink traffic, a
+complementary algorithm, known as Clustering algorithm for Load Balancing
+(CaLB), is also proposed to create non-spectral efficient clusters when they
+result in a capacity increase. Finally, in order to alleviate the energy
+overconsumption suffered by cluster heads, the Clustering Energy Efficient
+algorithm (CEEa) is also designed to manage the trade-off between the capacity
+enhancement and the early battery drain of some users. Results show that the
+proposed algorithms increase the network capacity and outperform existing
+solutions, while, at the same time, CEEa is able to handle the cluster heads
+energy overconsumption.
+",1,0,0,0,0,0
+4426,Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition,"  Recurrent neural network (RNN) language models (LMs) and Long Short Term
+Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform
+traditional N-gram LMs on speech recognition tasks. However, these models are
+computationally more expensive than N-gram LMs for decoding, and thus,
+challenging to integrate into speech recognizers. Recent research has proposed
+the use of lattice-rescoring algorithms using RNNLMs and LSTMLMs as an
+efficient strategy to integrate these models into a speech recognition system.
+In this paper, we evaluate existing lattice rescoring algorithms along with new
+variants on a YouTube speech recognition task. Lattice rescoring using LSTMLMs
+reduces the word error rate (WER) for this task by 8\% relative to the WER
+obtained using an N-gram LM.
+",1,0,0,1,0,0
+4427,Vanishing of Littlewood-Richardson polynomials is in P,"  J. DeLoera-T. McAllister and K. D. Mulmuley-H. Narayanan-M. Sohoni
+independently proved that determining the vanishing of Littlewood-Richardson
+coefficients has strongly polynomial time computational complexity. Viewing
+these as Schubert calculus numbers, we prove the generalization to the
+Littlewood-Richardson polynomials that control equivariant cohomology of
+Grassmannians. We construct a polytope using the edge-labeled tableau rule of
+H. Thomas-A. Yong. Our proof then combines a saturation theorem of D.
+Anderson-E. Richmond-A. Yong, a reading order independence property, and E.
+Tardos' algorithm for combinatorial linear programming.
+",1,0,1,0,0,0
+4428,Multi-task memory networks for category-specific aspect and opinion terms co-extraction,"  In aspect-based sentiment analysis, most existing methods either focus on
+aspect/opinion terms extraction or aspect terms categorization. However, each
+task by itself only provides partial information to end users. To generate more
+detailed and structured opinion analysis, we propose a finer-grained problem,
+which we call category-specific aspect and opinion terms extraction. This
+problem involves the identification of aspect and opinion terms within each
+sentence, as well as the categorization of the identified terms. To this end,
+we propose an end-to-end multi-task attention model, where each task
+corresponds to aspect/opinion terms extraction for a specific category. Our
+model benefits from exploring the commonalities and relationships among
+different tasks to address the data sparsity issue. We demonstrate its
+state-of-the-art performance on three benchmark datasets.
+",1,0,0,0,0,0
+4429,Discussion on Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data by Bradley et al,"  I begin my discussion by summarizing the methodology proposed and new
+distributional results on multivariate log-Gamma derived in the paper. Then, I
+draw an interesting connection between their work with mean field variational
+Bayes. Lastly, I make some comments on the simulation results and the
+performance of the proposed Poisson multivariate spatio-temporal mixed effects
+model (P-MSTM).
+",0,0,1,1,0,0
+4430,Elucidation of the helical spin structure of FeAs,"  We present the results of resonant x-ray scattering measurements and
+electronic structure calculations on the monoarsenide FeAs. We elucidate
+details of the magnetic structure, showing the ratio of ellipticity of the spin
+helix is larger than previously thought, at 2.58(3), and reveal both a
+right-handed chirality and an out of plane component of the magnetic moments in
+the spin helix. We find that electronic structure calculations and analysis of
+the spin-orbit interaction are able to qualitatively account for this canting.
+",0,1,0,0,0,0
+4431,A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications,"  Rapid popularity of Internet of Things (IoT) and cloud computing permits
+neuroscientists to collect multilevel and multichannel brain data to better
+understand brain functions, diagnose diseases, and devise treatments. To ensure
+secure and reliable data communication between end-to-end (E2E) devices
+supported by current IoT and cloud infrastructure, trust management is needed
+at the IoT and user ends. This paper introduces a Neuro-Fuzzy based
+Brain-inspired trust management model (TMM) to secure IoT devices and relay
+nodes, and to ensure data reliability. The proposed TMM utilizes node
+behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference
+System and weighted-additive methods respectively to assess the nodes
+trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2
+simulation results confirm the robustness and accuracy of the proposed TMM in
+identifying malicious nodes in the communication network. With the growing
+usage of cloud based IoT frameworks in Neuroscience research, integrating the
+proposed TMM into the existing infrastructure will assure secure and reliable
+data communication among the E2E devices.
+",0,0,0,0,1,0
+4432,The topography of the environment alters the optimal search strategy for active particles,"  In environments with scarce resources, adopting the right search strategy can
+make the difference between succeeding and failing, even between life and
+death. At different scales, this applies to molecular encounters in the cell
+cytoplasm, to animals looking for food or mates in natural landscapes, to
+rescuers during search-and-rescue operations in disaster zones, as well as to
+genetic computer algorithms exploring parameter spaces. When looking for sparse
+targets in a homogeneous environment, a combination of ballistic and diffusive
+steps is considered optimal; in particular, more ballistic Lévy flights with
+exponent {\alpha} <= 1 are generally believed to optimize the search process.
+However, most search spaces present complex topographies, with boundaries,
+barriers and obstacles. What is the best search strategy in these more
+realistic scenarios? Here we show that the topography of the environment
+significantly alters the optimal search strategy towards less ballistic and
+more Brownian strategies. We consider an active particle performing a blind
+search in a two-dimensional space with steps drawn from a Lévy distribution
+with exponent varying from {\alpha} = 1 to {\alpha} = 2 (Brownian). We
+demonstrate that the optimal search strategy depends on the topography of the
+environment, with {\alpha} assuming intermediate values in the whole range
+under consideration. We interpret these findings in terms of a simple
+theoretical model, and discuss their robustness to the addition of Brownian
+diffusion to the searcher's motion. Our results are relevant for search
+problems at different length scales, from animal and human foraging to
+microswimmers' taxis, to biochemical rates of reaction.
+",0,1,0,0,0,0
+4433,A Neural Network Approach for Mixing Language Models,"  The performance of Neural Network (NN)-based language models is steadily
+improving due to the emergence of new architectures, which are able to learn
+different natural language characteristics. This paper presents a novel
+framework, which shows that a significant improvement can be achieved by
+combining different existing heterogeneous models in a single architecture.
+This is done through 1) a feature layer, which separately learns different
+NN-based models and 2) a mixture layer, which merges the resulting model
+features. In doing so, this architecture benefits from the learning
+capabilities of each model with no noticeable increase in the number of model
+parameters or the training time. Extensive experiments conducted on the Penn
+Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
+significant reduction of the perplexity when compared to state-of-the-art
+feedforward as well as recurrent neural network architectures.
+",1,0,0,0,0,0
+4434,Iron Intercalated Covalent-Organic Frameworks: First Crystalline Porous Thermoelectric Materials,"  Covalent-organic frameworks (COFs) are intriguing platforms for designing
+functional molecular materials. Here, we present a computational study based on
+van der Waals dispersion-corrected hybrid density functional theory
+calculations to analyze the material properties of boroxine-linked and
+triazine-linked intercalated-COFs. The effect of Fe atoms on the electronic
+band structures near the Fermi energy level of the intercalated-COFs have been
+investigated. The density of states (DOSs) computations have been performed to
+analyze the material properties of these kind of intercalated-COFs. We predict
+that COFs's electronic properties can be fine tuned by adding Fe atoms between
+two organic layers in their structures. The new COFs are predicted to be
+thermoelectric materials. These intercalated-COFs provide a new strategy to
+create thermoelectric materials within a rigid porous network in a highly
+controlled and predictable manner.
+",0,1,0,0,0,0
+4435,DCFNet: Deep Neural Network with Decomposed Convolutional Filters,"  Filters in a Convolutional Neural Network (CNN) contain model parameters
+learned from enormous amounts of data. In this paper, we suggest to decompose
+convolutional filters in CNN as a truncated expansion with pre-fixed bases,
+namely the Decomposed Convolutional Filters network (DCFNet), where the
+expansion coefficients remain learned from data. Such a structure not only
+reduces the number of trainable parameters and computation, but also imposes
+filter regularity by bases truncation. Through extensive experiments, we
+consistently observe that DCFNet maintains accuracy for image classification
+tasks with a significant reduction of model parameters, particularly with
+Fourier-Bessel (FB) bases, and even with random bases. Theoretically, we
+analyze the representation stability of DCFNet with respect to input
+variations, and prove representation stability under generic assumptions on the
+expansion coefficients. The analysis is consistent with the empirical
+observations.
+",0,0,0,1,0,0
+4436,"Symmetries, Invariants and Generating Functions: Higher-order Statistics of Biased Tracers","  Gravitationally collapsed objects are known to be biased tracers of an
+underlying density contrast. Using symmetry arguments, generalised biasing
+schemes have recently been developed to relate the halo density contrast
+$\delta_h$ with the underlying density contrast $\delta$, divergence of
+velocity $\theta$ and their higher-order derivatives. This is done by
+constructing invariants such as $s, t, \psi,\eta$. We show how the generating
+function formalism in Eulerian standard perturbation theory (SPT) can be used
+to show that many of the additional terms based on extended Galilean and
+Lifshitz symmetry actually do not make any contribution to the higher-order
+statistics of biased tracers. Other terms can also be drastically simplified
+allowing us to write the vertices associated with $\delta_h$ in terms of the
+vertices of $\delta$ and $\theta$, the higher-order derivatives and the bias
+coefficients. We also compute the cumulant correlators (CCs) for two different
+tracer populations. These perturbative results are valid for tree-level
+contributions but at an arbitrary order. We also take into account the
+stochastic nature bias in our analysis. Extending previous results of a local
+polynomial model of bias, we express the one-point cumulants ${\cal S}_N$ and
+their two-point counterparts, the CCs i.e. ${\cal C}_{pq}$, of biased tracers
+in terms of that of their underlying density contrast counterparts. As a
+by-product of our calculation we also discuss the results using approximations
+based on Lagrangian perturbation theory (LPT).
+",0,1,0,0,0,0
+4437,"Reversible Sequences of Cardinals, Reversible Equivalence Relations, and Similar Structures","  A relational structure ${\mathbb X}$ is said to be reversible iff every
+bijective endomorphism $f:X\rightarrow X$ is an automorphism. We define a
+sequence of non-zero cardinals $\langle \kappa_i :i\in I\rangle$ to be
+reversible iff each surjection $f :I\rightarrow I$ such that $\kappa_j
+=\sum_{i\in f^{-1}[\{ j \}]}\kappa_i$, for all $j\in I $, is a bijection, and
+characterize such sequences: either $\langle \kappa_i :i\in I\rangle$ is a
+finite-to-one sequence, or $\kappa_i\in {\mathbb N}$, for all $i\in I$, $K:=\{
+m\in {\mathbb N} : \kappa_i =m $, for infinitely many $i\in I \}$ is a
+non-empty independent set, and $\gcd (K)$ divides at most finitely many
+elements of the set $\{ \kappa_i :i\in I \}$. We isolate a class of binary
+structures such that a structure from the class is reversible iff the sequence
+of cardinalities of its connectivity components is reversible. In particular,
+we characterize reversible equivalence relations, reversible posets which are
+disjoint unions of cardinals $\leq \omega$, and some similar structures. In
+addition, we show that a poset with linearly ordered connectivity components is
+reversible, if the corresponding sequence of cardinalities is reversible and,
+using this fact, detect a wide class of examples of reversible posets and
+topological spaces.
+",0,0,1,0,0,0
+4438,A Longitudinal Study of Google Play,"  The difficulty of large scale monitoring of app markets affects our
+understanding of their dynamics. This is particularly true for dimensions such
+as app update frequency, control and pricing, the impact of developer actions
+on app popularity, as well as coveted membership in top app lists. In this
+paper we perform a detailed temporal analysis on two datasets we have collected
+from the Google Play Store, one consisting of 160,000 apps and the other of
+87,223 newly released apps. We have monitored and collected data about these
+apps over more than 6 months. Our results show that a high number of these apps
+have not been updated over the monitoring interval. Moreover, these apps are
+controlled by a few developers that dominate the total number of app downloads.
+We observe that infrequently updated apps significantly impact the median app
+price. However, a changing app price does not correlate with the download
+count. Furthermore, we show that apps that attain higher ranks have better
+stability in top app lists. We show that app market analytics can help detect
+emerging threat vectors, and identify search rank fraud and even malware.
+Further, we discuss the research implications of app market analytics on
+improving developer and user experiences.
+",1,0,0,0,0,0
+4439,Nonparametric Variational Auto-encoders for Hierarchical Representation Learning,"  The recently developed variational autoencoders (VAEs) have proved to be an
+effective confluence of the rich representational power of neural networks with
+Bayesian methods. However, most work on VAEs use a rather simple prior over the
+latent variables such as standard normal distribution, thereby restricting its
+applications to relatively simple phenomena. In this work, we propose
+hierarchical nonparametric variational autoencoders, which combines
+tree-structured Bayesian nonparametric priors with VAEs, to enable infinite
+flexibility of the latent representation space. Both the neural parameters and
+Bayesian priors are learned jointly using tailored variational inference. The
+resulting model induces a hierarchical structure of latent semantic concepts
+underlying the data corpus, and infers accurate representations of data
+instances. We apply our model in video representation learning. Our method is
+able to discover highly interpretable activity hierarchies, and obtain improved
+clustering accuracy and generalization capacity based on the learned rich
+representations.
+",1,0,0,1,0,0
+4440,Corpus-compressed Streaming and the Spotify Problem,"  In this work, we describe a problem which we refer to as the \textbf{Spotify
+problem} and explore a potential solution in the form of what we call
+corpus-compressed streaming schemes.
+Inspired by the problem of constrained bandwidth during use of the popular
+Spotify application on mobile networks, the Spotify problem applies in any
+number of practical domains where devices may be periodically expected to
+experience degraded communication or storage capacity. One obvious solution
+candidate which comes to mind immediately is standard compression. Though
+obviously applicable, standard compression does not in any way exploit all
+characteristics of the problem; in particular, standard compression is
+oblivious to the fact that a decoder has a period of virtually unrestrained
+communication. Towards applying compression in a manner which attempts to
+stretch the benefit of periods of higher communication capacity into periods of
+restricted capacity, we introduce as a solution the idea of a corpus-compressed
+streaming scheme.
+This report begins with a formal definition of a corpus-compressed streaming
+scheme. Following a discussion of how such schemes apply to the Spotify
+problem, we then give a survey of specific corpus-compressed scheming schemes
+guided by an exploration of different measures of description complexity within
+the Chomsky hierarchy of languages.
+",1,0,0,0,0,0
+4441,An intrinsic parallel transport in Wasserstein space,"  If M is a smooth compact connected Riemannian manifold, let P(M) denote the
+Wasserstein space of probability measures on M. We describe a geometric
+construction of parallel transport of some tangent cones along geodesics in
+P(M). We show that when everything is smooth, the geometric parallel transport
+agrees with earlier formal calculations.
+",0,0,1,0,0,0
+4442,Factor Analysis for Spectral Estimation,"  Power spectrum estimation is an important tool in many applications, such as
+the whitening of noise. The popular multitaper method enjoys significant
+success, but fails for short signals with few samples. We propose a statistical
+model where a signal is given by a random linear combination of fixed, yet
+unknown, stochastic sources. Given multiple such signals, we estimate the
+subspace spanned by the power spectra of these fixed sources. Projecting
+individual power spectrum estimates onto this subspace increases estimation
+accuracy. We provide accuracy guarantees for this method and demonstrate it on
+simulated and experimental data from cryo-electron microscopy.
+",0,0,1,1,0,0
+4443,Effects of Images with Different Levels of Familiarity on EEG,"  Evaluating human brain potentials during watching different images can be
+used for memory evaluation, information retrieving, guilty-innocent
+identification and examining the brain response. In this study, the effects of
+watching images, with different levels of familiarity, on subjects'
+Electroencephalogram (EEG) have been studied. Three different groups of images
+with three familiarity levels of ""unfamiliar"", ""familiar"" and ""very familiar""
+have been considered for this study. EEG signals of 21 subjects (14 men) were
+recorded. After signal acquisition, pre-processing, including noise and
+artifact removal, were performed on epochs of data. Features, including
+spatial-statistical, wavelet, frequency and harmonic parameters, and also
+correlation between recording channels, were extracted from the data. Then, we
+evaluated the efficiency of the extracted features by using p-value and also an
+orthogonal feature selection method (combination of Gram-Schmitt method and
+Fisher discriminant ratio) for feature dimensional reduction. As the final step
+of feature selection, we used 'add-r take-away l' method for choosing the most
+discriminative features. For data classification, including all two-class and
+three-class cases, we applied Support Vector Machine (SVM) on the extracted
+features. The correct classification rates (CCR) for ""unfamiliar-familiar"",
+""unfamiliar-very familiar"" and ""familiar-very familiar"" cases were 85.6%,
+92.6%, and 70.6%, respectively. The best results of classifications were
+obtained in pre-frontal and frontal regions of brain. Also, wavelet, frequency
+and harmonic features were among the most discriminative features. Finally, in
+three-class case, the best CCR was 86.8%.
+",0,0,0,1,0,0
+4444,Image synthesis with graph cuts: a fast model proposal mechanism in probabilistic inversion,"  Geophysical inversion should ideally produce geologically realistic
+subsurface models that explain the available data. Multiple-point statistics is
+a geostatistical approach to construct subsurface models that are consistent
+with site-specific data, but also display the same type of patterns as those
+found in a training image. The training image can be seen as a conceptual model
+of the subsurface and is used as a non-parametric model of spatial variability.
+Inversion based on multiple-point statistics is challenging due to high
+nonlinearity and time-consuming geostatistical resimulation steps that are
+needed to create new model proposals. We propose an entirely new model proposal
+mechanism for geophysical inversion that is inspired by texture synthesis in
+computer vision. Instead of resimulating pixels based on higher-order patterns
+in the training image, we identify a suitable patch of the training image that
+replace a corresponding patch in the current model without breaking the
+patterns found in the training image, that is, remaining consistent with the
+given prior. We consider three cross-hole ground-penetrating radar examples in
+which the new model proposal mechanism is employed within an extended
+Metropolis Markov chain Monte Carlo (MCMC) inversion. The model proposal step
+is about 40 times faster than state-of-the-art multiple-point statistics
+resimulation techniques, the number of necessary MCMC steps is lower and the
+quality of the final model realizations is of similar quality. The model
+proposal mechanism is presently limited to 2-D fields, but the method is
+general and can be applied to a wide range of subsurface settings and
+geophysical data types.
+",0,1,0,0,0,0
+4445,Graded super duality for general linear Lie superalgebras,"  We provide a new proof of the super duality equivalence between infinite-rank
+parabolic BGG categories of general linear Lie (super) algebras conjectured by
+Cheng and Wang and first proved by Cheng and Lam. We do this by establishing a
+new uniqueness theorem for tensor product categorifications motivated by work
+of Brundan, Losev, and Webster. Moreover we show that these BGG categories have
+Koszul graded lifts and super duality can be lifted to a graded equivalence.
+",0,0,1,0,0,0
+4446,On a Formal Model of Safe and Scalable Self-driving Cars,"  In recent years, car makers and tech companies have been racing towards self
+driving cars. It seems that the main parameter in this race is who will have
+the first car on the road. The goal of this paper is to add to the equation two
+additional crucial parameters. The first is standardization of safety assurance
+--- what are the minimal requirements that every self-driving car must satisfy,
+and how can we verify these requirements. The second parameter is scalability
+--- engineering solutions that lead to unleashed costs will not scale to
+millions of cars, which will push interest in this field into a niche academic
+corner, and drive the entire field into a ""winter of autonomous driving"". In
+the first part of the paper we propose a white-box, interpretable, mathematical
+model for safety assurance, which we call Responsibility-Sensitive Safety
+(RSS). In the second part we describe a design of a system that adheres to our
+safety assurance requirements and is scalable to millions of cars.
+",1,0,0,1,0,0
+4447,A Low-power Reversible Alkali Atom Source,"  An electrically-controllable, solid-state, reversible device for sourcing and
+sinking alkali vapor is presented. When placed inside an alkali vapor cell,
+both an increase and decrease of the rubidium vapor density by a factor of two
+are demonstrated through laser absorption spectroscopy on 10 to 15 s time
+scales. The device requires low voltage (5 V), low power (<3.4 mW peak power),
+and low energy (<10.7 mJ per 10 s pulse). The absence of oxygen emission during
+operation is shown through residual gas analysis, indicating Rb is not lost
+through chemical reaction but rather by ion transport through the designed
+channel. This device is of interest for atomic physics experiments and, in
+particular, for portable cold-atom systems where dynamic control of alkali
+vapor density can enable advances in science and technology.
+",0,1,0,0,0,0
+4448,GoT-WAVE: Temporal network alignment using graphlet-orbit transitions,"  Global pairwise network alignment (GPNA) aims to find a one-to-one node
+mapping between two networks that identifies conserved network regions. GPNA
+algorithms optimize node conservation (NC) and edge conservation (EC). NC
+quantifies topological similarity between nodes. Graphlet-based degree vectors
+(GDVs) are a state-of-the-art topological NC measure. Dynamic GDVs (DGDVs) were
+used as a dynamic NC measure within the first-ever algorithms for GPNA of
+temporal networks: DynaMAGNA++ and DynaWAVE. The latter is superior for larger
+networks. We recently developed a different graphlet-based measure of temporal
+node similarity, graphlet-orbit transitions (GoTs). Here, we use GoTs instead
+of DGDVs as a new dynamic NC measure within DynaWAVE, resulting in a new
+approach, GoT-WAVE.
+On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 25% and speed
+by 64%. On real networks, when optimizing only dynamic NC, each method is
+superior ~50% of the time. While DynaWAVE benefits more from also optimizing
+dynamic EC, only GoT-WAVE can support directed edges. Hence, GoT-WAVE is a
+promising new temporal GPNA algorithm, which efficiently optimizes dynamic NC.
+Future work on better incorporating dynamic EC may yield further improvements.
+",1,0,0,1,0,0
+4449,Randomness-induced quantum spin liquid on honeycomb lattice,"  We present a quantu spin liquid state in a spin-1/2 honeycomb lattice with
+randomness in the exchange interaction. That is, we successfully introduce
+randomness into the organic radial-based complex and realize a random-singlet
+(RS) state. All magnetic and thermodynamic experimental results indicate the
+liquid-like behaviors, which are consistent with those expected in the RS
+state. These results demonstrate that the randomness or inhomogeneity in the
+actual systems stabilize the RS state and yield liquid-like behavior.
+",0,1,0,0,0,0
+4450,Symmetric structure for the endomorphism algebra of projective-injective module in parabolic category,"  We show that for any singular dominant integral weight $\lambda$ of a complex
+semisimple Lie algebra $\mathfrak{g}$, the endomorphism algebra $B$ of any
+projective-injective module of the parabolic BGG category
+$\mathcal{O}_\lambda^{\mathfrak{p}}$ is a symmetric algebra (as conjectured by
+Khovanov) extending the results of Mazorchuk and Stroppel for the regular
+dominant integral weight. Moreover, the endomorphism algebra $B$ is equipped
+with a homogeneous (non-degenerate) symmetrizing form. In the appendix, there
+is a short proof due to K. Coulembier and V. Mazorchuk showing that the
+endomorphism algebra $B_\lambda^{\mathfrak{p}}$ of the basic
+projective-injective module of $\mathcal{O}_\lambda^{\mathfrak{p}}$ is a
+symmetric algebra.
+",0,0,1,0,0,0
+4451,Cantor series and rational numbers,"  The article is devoted to the investigation of representation of rational
+numbers by Cantor series. Necessary and sufficient conditions for a rational
+number to be representable by a positive Cantor series are formulated for the
+case of an arbitrary sequence $(q_k)$ and some its corollaries are considered.
+Results of this article were presented by the author of this article on the
+International Conference on Algebra dedicated to 100th anniversary of S. M.
+Chernikov (www.researchgate.net/publication/311415815,
+www.researchgate.net/publication/301849984). This investigation was also
+presented in some reports (links to the reports:
+www.researchgate.net/publication/303736670,
+www.researchgate.net/publication/303720573, etc.).
+",0,0,1,0,0,0
+4452,PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks,"  Many real-world systems are profitably described as complex networks that
+grow over time. Preferential attachment and node fitness are two simple growth
+mechanisms that not only explain certain structural properties commonly
+observed in real-world systems, but are also tied to a number of applications
+in modeling and inference. While there are statistical packages for estimating
+various parametric forms of the preferential attachment function, there is no
+such package implementing non-parametric estimation procedures. The
+non-parametric approach to the estimation of the preferential attachment
+function allows for comparatively finer-grained investigations of the
+`rich-get-richer' phenomenon that could lead to novel insights in the search to
+explain certain nonstandard structural properties observed in real-world
+networks. This paper introduces the R package PAFit, which implements
+non-parametric procedures for estimating the preferential attachment function
+and node fitnesses in a growing network, as well as a number of functions for
+generating complex networks from these two mechanisms. The main computational
+part of the package is implemented in C++ with OpenMP to ensure scalability to
+large-scale networks. We first introduce the main functionalities of PAFit
+through simulated examples, and then use the package to analyze a collaboration
+network between scientists in the field of complex networks. The results
+indicate the joint presence of `rich-get-richer' and `fit-get-richer' phenomena
+in the collaboration network. The estimated attachment function is observed to
+be near-linear, which we interpret as meaning that the chance an author gets a
+new collaborator is proportional to their current number of collaborators.
+Furthermore, the estimated author fitnesses reveal a host of familiar faces
+from the complex networks community among the field's topmost fittest network
+scientists.
+",1,1,0,1,0,0
+4453,Kähler metrics via Lorentzian Geometry in dimension four,"  Given a semi-Riemannian $4$-manifold $(M,g)$ with two distinguished vector
+fields satisfying properties determined by their shear, twist and various Lie
+bracket relations, a family of Kähler metrics $g_K$ is constructed, defined
+on an open set in $M$, which coincides with $M$ in many typical examples. Under
+certain conditions $g$ and $g_K$ share various properties, such as a Killing
+vector field or a vector field with a geodesic flow. In some cases the Kähler
+metrics are complete. The Ricci and scalar curvatures of $g_K$ are computed
+under certain assumptions in terms of data associated to $g$. Many examples are
+described, including classical spacetimes in warped products, for instance de
+Sitter spacetime, as well as gravitational plane waves, metrics of Petrov type
+$D$ such as Kerr and NUT metrics, and metrics for which $g_K$ is an SKR metric.
+For the latter an inverse ansatz is described, constructing $g$ from the SKR
+metric.
+",0,0,1,0,0,0
+4454,z-Classes and Rational Conjugacy Classes in Alternating Groups,"  In this paper, we compute the number of z-classes (conjugacy classes of
+centralizers of elements) in the symmetric group S_n, when n is greater or
+equal to 3 and alternating group A_n, when n is greater or equal to 4. It turns
+out that the difference between the number of conjugacy classes and the number
+of z-classes for S_n is determined by those restricted partitions of n-2 in
+which 1 and 2 do not appear as its part. And, in the case of alternating
+groups, it is determined by those restricted partitions of n-3 which has all
+its parts distinct, odd and in which 1 (and 2) does not appear as its part,
+along with an error term. The error term is given by those partitions of n
+which have each of its part distinct, odd and perfect square. Further, we prove
+that the number of rational-valued irreducible complex characters for A_n is
+same as the number of conjugacy classes which are rational.
+",0,0,1,0,0,0
+4455,Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device,"  This paper proposes a practical approach for automatic sleep stage
+classification based on a multi-level feature learning framework and Recurrent
+Neural Network (RNN) classifier using heart rate and wrist actigraphy derived
+from a wearable device. The feature learning framework is designed to extract
+low- and mid-level features. Low-level features capture temporal and frequency
+domain properties and mid-level features learn compositions and structural
+information of signals. Since sleep staging is a sequential problem with
+long-term dependencies, we take advantage of RNNs with Bidirectional Long
+Short-Term Memory (BLSTM) architectures for sequence data learning. To simulate
+the actual situation of daily sleep, experiments are conducted with a resting
+group in which sleep is recorded in resting state, and a comprehensive group in
+which both resting sleep and non-resting sleep are included.We evaluate the
+algorithm based on an eight-fold cross validation to classify five sleep stages
+(W, N1, N2, N3, and REM). The proposed algorithm achieves weighted precision,
+recall and F1 score of 58.0%, 60.3%, and 58.2% in the resting group and 58.5%,
+61.1%, and 58.5% in the comprehensive group, respectively. Various comparison
+experiments demonstrate the effectiveness of feature learning and BLSTM. We
+further explore the influence of depth and width of RNNs on performance. Our
+method is specially proposed for wearable devices and is expected to be
+applicable for long-term sleep monitoring at home. Without using too much prior
+domain knowledge, our method has the potential to generalize sleep disorder
+detection.
+",1,0,0,1,0,0
+4456,Martin David Kruskal: a biographical memoir,"  Martin David Kruskal was one of the most versatile theoretical physicists of
+his generation and is distinguished for his enduring work in several different
+areas, most notably plasma physics, a memorable detour into relativity, and his
+pioneering work in nonlinear waves. In the latter, together with Norman
+Zabusky, he invented the concept of the soliton and, with others, developed its
+application to classes of partial differential equations of physical
+significance.
+",0,1,0,0,0,0
+4457,Unified theory for finite Markov chains,"  We provide a unified framework to compute the stationary distribution of any
+finite irreducible Markov chain or equivalently of any irreducible random walk
+on a finite semigroup $S$. Our methods use geometric finite semigroup theory
+via the Karnofsky-Rhodes and the McCammond expansions of finite semigroups with
+specified generators; this does not involve any linear algebra. The original
+Tsetlin library is obtained by applying the expansions to $P(n)$, the set of
+all subsets of an $n$ element set. Our set-up generalizes previous
+groundbreaking work involving left-regular bands (or $\mathscr{R}$-trivial
+bands) by Brown and Diaconis, extensions to $\mathscr{R}$-trivial semigroups by
+Ayyer, Steinberg, Thiéry and the second author, and important recent work by
+Chung and Graham. The Karnofsky-Rhodes expansion of the right Cayley graph of
+$S$ in terms of generators yields again a right Cayley graph. The McCammond
+expansion provides normal forms for elements in the expanded $S$. Using our
+previous results with Silva based on work by Berstel, Perrin, Reutenauer, we
+construct (infinite) semaphore codes on which we can define Markov chains.
+These semaphore codes can be lumped using geometric semigroup theory. Using
+normal forms and associated Kleene expressions, they yield formulas for the
+stationary distribution of the finite Markov chain of the expanded $S$ and the
+original $S$. Analyzing the normal forms also provides an estimate on the
+mixing time.
+",0,0,1,0,0,0
+4458,A Simulator for Hedonic Games,"  Hedonic games are meant to model how coalitions of people form and break
+apart in the real world. However, it is difficult to run simulations when
+everything must be done by hand on paper. We present an online software that
+allows fast and visual simulation of several types of hedonic games.
+this http URL
+",1,0,0,0,0,0
+4459,Estimating Large Precision Matrices via Modified Cholesky Decomposition,"  We introduce the $k$-banded Cholesky prior for estimating a high-dimensional
+bandable precision matrix via the modified Cholesky decomposition. The bandable
+assumption is imposed on the Cholesky factor of the decomposition. We obtained
+the P-loss convergence rate under the spectral norm and the matrix
+$\ell_{\infty}$ norm and the minimax lower bounds. Since the P-loss convergence
+rate (Lee and Lee (2017)) is stronger than the posterior convergence rate, the
+rates obtained are also posterior convergence rates. Furthermore, when the true
+precision matrix is a $k_0$-banded matrix with some finite $k_0$, the obtained
+P-loss convergence rates coincide with the minimax rates. The established
+convergence rates are slightly slower than the minimax lower bounds, but these
+are the fastest rates for bandable precision matrices among the existing
+Bayesian approaches. A simulation study is conducted to compare the performance
+to the other competitive estimators in various scenarios.
+",0,0,1,1,0,0
+4460,High order conformal symplectic and ergodic schemes for stochastic Langevin equation via generating functions,"  In this paper, we consider the stochastic Langevin equation with additive
+noises, which possesses both conformal symplectic geometric structure and
+ergodicity. We propose a methodology of constructing high weak order conformal
+symplectic schemes by converting the equation into an equivalent autonomous
+stochastic Hamiltonian system and modifying the associated generating function.
+To illustrate this approach, we construct a specific second order numerical
+scheme, and prove that its symplectic form dissipates exponentially. Moreover,
+for the linear case, the proposed scheme is also shown to inherit the
+ergodicity of the original system, and the temporal average of the numerical
+solution is a proper approximation of the ergodic limit over long time.
+Numerical experiments are given to verify these theoretical results.
+",0,0,1,0,0,0
+4461,Crowd ideation of supervised learning problems,"  Crowdsourcing is an important avenue for collecting machine learning data,
+but crowdsourcing can go beyond simple data collection by employing the
+creativity and wisdom of crowd workers. Yet crowd participants are unlikely to
+be experts in statistics or predictive modeling, and it is not clear how well
+non-experts can contribute creatively to the process of machine learning. Here
+we study an end-to-end crowdsourcing algorithm where groups of non-expert
+workers propose supervised learning problems, rank and categorize those
+problems, and then provide data to train predictive models on those problems.
+Problem proposal includes and extends feature engineering because workers
+propose the entire problem, not only the input features but also the target
+variable. We show that workers without machine learning experience can
+collectively construct useful datasets and that predictive models can be
+learned on these datasets. In our experiments, the problems proposed by workers
+covered a broad range of topics, from politics and current events to problems
+capturing health behavior, demographics, and more. Workers also favored
+questions showing positively correlated relationships, which has interesting
+implications given many supervised learning methods perform as well with strong
+negative correlations. Proper instructions are crucial for non-experts, so we
+also conducted a randomized trial to understand how different instructions may
+influence the types of problems proposed by workers. In general, shifting the
+focus of machine learning tasks from designing and training individual
+predictive models to problem proposal allows crowdsourcers to design
+requirements for problems of interest and then guide workers towards
+contributing to the most suitable problems.
+",0,0,0,1,0,0
+4462,Joins in the strong Weihrauch degrees,"  The Weihrauch degrees and strong Weihrauch degrees are partially ordered
+structures representing degrees of unsolvability of various mathematical
+problems. Their study has been widely applied in computable analysis,
+complexity theory, and more recently, also in computable combinatorics. We
+answer an open question about the algebraic structure of the strong Weihrauch
+degrees, by exhibiting a join operation that turns these degrees into a
+lattice. Previously, the strong Weihrauch degrees were only known to form a
+lower semi-lattice. We then show that unlike the Weihrauch degrees, which are
+known to form a distributive lattice, the lattice of strong Weihrauch degrees
+is not distributive. Therefore, the two structures are not isomorphic.
+",1,0,1,0,0,0
+4463,Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition,"  Long short-term memory (LSTM) is normally used in recurrent neural network
+(RNN) as basic recurrent unit. However,conventional LSTM assumes that the state
+at current time step depends on previous time step. This assumption constraints
+the time dependency modeling capability. In this study, we propose a new
+variation of LSTM, advanced LSTM (A-LSTM), for better temporal context
+modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The
+A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based
+weighted pooling RNN can also complement the state-of-the-art emotion
+classification framework. This shows the advantage of A-LSTM.
+",1,0,0,1,0,0
+4464,A combinatorial model for the free loop fibration,"  We introduce the abstract notion of a closed necklical set in order to
+describe a functorial combinatorial model of the free loop fibration $\Omega
+Y\rightarrow \Lambda Y\rightarrow Y$ over the geometric realization $Y=|X|$ of
+a path connected simplicial set $X.$ In particular, to any path connected
+simplicial set $X$ we associate a closed necklical set
+$\widehat{\mathbf{\Lambda}}X$ such that its geometric realization
+$|\widehat{\mathbf{\Lambda}}X|$, a space built out of gluing ""freehedrical"" and
+""cubical"" cells, is homotopy equivalent to the free loop space $\Lambda Y$ and
+the differential graded module of chains $C_*(\widehat{\mathbf{\Lambda}}X)$
+generalizes the coHochschild chain complex of the chain coalgebra $C_\ast(X).$
+",0,0,1,0,0,0
+4465,On the number of circular orders on a group,"  We give a classification and complete algebraic description of groups
+allowing only finitely many (left multiplication invariant) circular orders. In
+particular, they are all solvable groups with a specific semi-direct product
+decomposition. This allows us to also show that the space of circular orders of
+any group is either finite or uncountable. As a special case and first step, we
+show that the space of circular orderings of an infinite Abelian group has no
+isolated points, hence is homeomorphic to a cantor set.
+",0,0,1,0,0,0
+4466,Preparation and Measurement in Quantum Memory Models,"  Quantum Cognition has delivered a number of models for semantic memory, but
+to date these have tended to assume pure states and projective measurement.
+Here we relax these assumptions. A quantum inspired model of human word
+association experiments will be extended using a density matrix representation
+of human memory and a POVM based upon non-ideal measurements. Our formulation
+allows for a consideration of key terms like measurement and contextuality
+within a rigorous modern approach. This approach both provides new conceptual
+advances and suggests new experimental protocols.
+",0,0,0,0,1,0
+4467,Detailed proof of Nazarov's inequality,"  The purpose of this note is to provide a detailed proof of Nazarov's
+inequality stated in Lemma A.1 in Chernozhukov, Chetverikov, and Kato (2017,
+Annals of Probability).
+",0,0,1,1,0,0
+4468,TRPL+K: Thick-Restart Preconditioned Lanczos+K Method for Large Symmetric Eigenvalue Problems,"  The Lanczos method is one of the standard approaches for computing a few
+eigenpairs of a large, sparse, symmetric matrix. It is typically used with
+restarting to avoid unbounded growth of memory and computational requirements.
+Thick-restart Lanczos is a popular restarted variant because of its simplicity
+and numerically robustness. However, convergence can be slow for highly
+clustered eigenvalues so more effective restarting techniques and the use of
+preconditioning is needed. In this paper, we present a thick-restart
+preconditioned Lanczos method, TRPL+K, that combines the power of locally
+optimal restarting (+K) and preconditioning techniques with the efficiency of
+the thick-restart Lanczos method. TRPL+K employs an inner-outer scheme where
+the inner loop applies Lanczos on a preconditioned operator while the outer
+loop augments the resulting Lanczos subspace with certain vectors from the
+previous restart cycle to obtain eigenvector approximations with which it thick
+restarts the outer subspace. We first identify the differences from various
+relevant methods in the literature. Then, based on an optimization perspective,
+we show an asymptotic global quasi-optimality of a simplified TRPL+K method
+compared to an unrestarted global optimal method. Finally, we present extensive
+experiments showing that TRPL+K either outperforms or matches other
+state-of-the-art eigenmethods in both matrix-vector multiplications and
+computational time.
+",1,0,0,0,0,0
+4469,Behavior of l-bits near the many-body localization transition,"  Eigenstates of fully many-body localized (FMBL) systems are described by
+quasilocal operators $\tau_i^z$ (l-bits), which are conserved exactly under
+Hamiltonian time evolution. The algebra of the operators $\tau_i^z$ and
+$\tau_i^x$ associated with l-bits ($\boldsymbol{\tau}_i$) completely defines
+the eigenstates and the matrix elements of local operators between eigenstates
+at all energies. We develop a non-perturbative construction of the full set of
+l-bit algebras in the many-body localized phase for the canonical model of MBL.
+Our algorithm to construct the Pauli-algebra of l-bits combines exact
+diagonalization and a tensor network algorithm developed for efficient
+diagonalization of large FMBL Hamiltonians. The distribution of localization
+lengths of the l-bits is evaluated in the MBL phase and used to characterize
+the MBL-to-thermal transition.
+",0,1,0,0,0,0
+4470,ROPE: high-dimensional network modeling with robust control of edge FDR,"  Network modeling has become increasingly popular for analyzing genomic data,
+to aid in the interpretation and discovery of possible mechanistic components
+and therapeutic targets. However, genomic-scale networks are high-dimensional
+models and are usually estimated from a relatively small number of samples.
+Therefore, their usefulness is hampered by estimation instability. In addition,
+the complexity of the models is controlled by one or more penalization (tuning)
+parameters where small changes to these can lead to vastly different networks,
+thus making interpretation of models difficult. This necessitates the
+development of techniques to produce robust network models accompanied by
+estimation quality assessments.
+We introduce Resampling of Penalized Estimates (ROPE): a novel statistical
+method for robust network modeling. The method utilizes resampling-based
+network estimation and integrates results from several levels of penalization
+through a constrained, over-dispersed beta-binomial mixture model. ROPE
+provides robust False Discovery Rate (FDR) control of network estimates and
+each edge is assigned a measure of validity, the q-value, corresponding to the
+FDR-level for which the edge would be included in the network model. We apply
+ROPE to several simulated data sets as well as genomic data from The Cancer
+Genome Atlas. We show that ROPE outperforms state-of-the-art methods in terms
+of FDR control and robust performance across data sets. We illustrate how to
+use ROPE to make a principled model selection for which genomic associations to
+study further. ROPE is available as an R package on CRAN.
+",0,0,0,1,0,0
+4471,Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior,"  Automated vehicles can change the society by improved safety, mobility and
+fuel efficiency. However, due to the higher cost and change in business model,
+over the coming decades, the highly automated vehicles likely will continue to
+interact with many human-driven vehicles. In the past, the control/design of
+the highly automated (robotic) vehicles mainly considers safety and efficiency
+but failed to address the ""driving culture"" of surrounding human-driven
+vehicles. Thus, the robotic vehicles may demonstrate behaviors very different
+from other vehicles. We study this ""driving etiquette"" problem in this paper.
+As the first step, we report the key behavior parameters of human driven
+vehicles derived from a large naturalistic driving database. The results can be
+used to guide future algorithm design of highly automated vehicles or to
+develop realistic human-driven vehicle behavior model in simulations.
+",1,0,0,0,0,0
+4472,e-Fair: Aggregation in e-Commerce for Exploiting Economies of Scale,"  In recent years, many new and interesting models of successful online
+business have been developed, including competitive models such as auctions,
+where the product price tends to rise, and group-buying, where users cooperate
+obtaining a dynamic price that tends to go down. We propose the e-fair as a
+business model for social commerce, where both sellers and buyers are grouped
+to maximize benefits. e-Fairs extend the group-buying model aggregating demand
+and supply for price optimization as well as consolidating shipments and
+optimize withdrawals for guaranteeing additional savings. e-Fairs work upon
+multiple dimensions: time to aggregate buyers, their geographical distribution,
+price/quantity curves provided by sellers, and location of withdrawal points.
+We provide an analytical model for time and spatial optimization and simulate
+realistic scenarios using both real purchase data from an Italian marketplace
+and simulated ones. Experimental results demonstrate the potentials offered by
+e-fairs and show benefits for all the involved actors.
+",1,0,0,0,0,0
+4473,Stable spike clusters for the precursor Gierer-Meinhardt system in R2,"  We consider the Gierer-Meinhardt system with small inhibitor diffusivity,
+very small activator diffusivity and a precursor inhomogeneity.
+For any given positive integer k we construct a spike cluster consisting of
+$k$ spikes which all approach the same nondegenerate local minimum point of the
+precursor inhomogeneity. We show that this spike cluster can be linearly
+stable. In particular, we show the existence of spike clusters for spikes
+located at the vertices of a polygon with or without centre. Further, the
+cluster without centre is stable for up to three spikes, whereas the cluster
+with centre is stable for up to six spikes.
+The main idea underpinning these stable spike clusters is the following: due
+to the small inhibitor diffusivity the interaction between spikes is repulsive,
+and the spikes are attracted towards the local minimum point of the precursor
+inhomogeneity. Combining these two effects can lead to an equilibrium of spike
+positions within the cluster such that the cluster is linearly stable.
+",0,0,1,0,0,0
+4474,Solvability of curves on surfaces,"  In this article, we study subloci of solvable curves in $\mathcal{M}_g$ which
+are contained in either a K3-surface or a quadric or a cubic surface. We give a
+bound on the dimension of such subloci. In the case of complete intersection
+genus $g$ curves in a cubic surface, we show that a general such curve is
+solvable.
+",0,0,1,0,0,0
+4475,Scaling Limits for Super--replication with Transient Price Impact,"  We prove limit theorems for the super-replication cost of European options in
+a Binomial model with transient price impact. We show that if the time step
+goes to zero and the effective resilience between consecutive trading times
+remains constant then the limit of the super--replication prices coincide with
+the scaling limit for temporary price impact with a modified market depth.
+",0,0,0,0,0,1
+4476,Kernel-based Inference of Functions over Graphs,"  The study of networks has witnessed an explosive growth over the past decades
+with several ground-breaking methods introduced. A particularly interesting --
+and prevalent in several fields of study -- problem is that of inferring a
+function defined over the nodes of a network. This work presents a versatile
+kernel-based framework for tackling this inference problem that naturally
+subsumes and generalizes the reconstruction approaches put forth recently by
+the signal processing on graphs community. Both the static and the dynamic
+settings are considered along with effective modeling approaches for addressing
+real-world problems. The herein analytical discussion is complemented by a set
+of numerical examples, which showcase the effectiveness of the presented
+techniques, as well as their merits related to state-of-the-art methods.
+",1,0,0,1,0,0
+4477,Noncommutative products of Euclidean spaces,"  We present natural families of coordinate algebras of noncommutative products
+of Euclidean spaces. These coordinate algebras are quadratic ones associated
+with an R-matrix which is involutive and satisfies the Yang-Baxter equations.
+As a consequence they enjoy a list of nice properties, being regular of finite
+global dimension. Notably, we have eight-dimensional noncommutative euclidean
+spaces which are particularly well behaved and are deformations parametrised by
+a two-dimensional sphere. Quotients include noncommutative seven-spheres as
+well as noncommutative ""quaternionic tori"". There is invariance for an action
+of $SU(2) \times SU(2)$ in parallel with the action of $U(1) \times U(1)$ on a
+""complex"" noncommutative torus which allows one to construct quaternionic toric
+noncommutative manifolds. Additional classes of solutions are disjoint from the
+classical case.
+",0,0,1,0,0,0
+4478,X-ray and Optical Study of the Gamma-ray Source 3FGL J0838.8$-$2829: Identification of a Candidate Millisecond Pulsar Binary and an Asynchronous Polar,"  We observed the field of the Fermi source 3FGL J0838.8-2829 in optical and
+X-rays, initially motivated by the cataclysmic variable (CV) 1RXS
+J083842.1-282723 that lies within its error circle. Several X-ray sources first
+classified as CVs have turned out to be gamma-ray emitting millisecond pulsars
+(MSPs). We find that 1RXS J083842.1-282723 is in fact an unusual CV, a
+stream-fed asynchronous polar in which accretion switches between magnetic
+poles (that are $\approx$120$^{\circ}$ apart) when the accretion rate is at
+minimum. High-amplitude X-ray modulation at periods of 94.8$\pm$0.4 minutes and
+14.7$\pm$1.2 hr are seen. The former appears to be the spin period, while
+latter is inferred to be one-third of the beat period between the spin and the
+orbit, implying an orbital period of 98.3$\pm$0.5 minutes. We also measure an
+optical emission-line spectroscopic period of 98.413$\pm$0.004 minutes which is
+consistent with the orbital period inferred from the X-rays. In any case, this
+system is unlikely to be the gamma-ray source. Instead, we find a fainter
+variable X-ray and optical source, XMMU J083850.38-282756.8, that is modulated
+on a time scale of hours in addition to exhibiting occasional sharp flares. It
+resembles the black widow or redback pulsars that have been discovered as
+counterparts of Fermi sources, with the optical modulation due to heating of
+the photosphere of a low-mass companion star by, in this case, an as-yet
+undetected MSP. We propose XMMU J083850.38-282756.8 as the MSP counterpart of
+3FGL J0838.8-2829.
+",0,1,0,0,0,0
+4479,Adversarial Variational Inference and Learning in Markov Random Fields,"  Markov random fields (MRFs) find applications in a variety of machine
+learning areas, while the inference and learning of such models are challenging
+in general. In this paper, we propose the Adversarial Variational Inference and
+Learning (AVIL) algorithm to solve the problems with a minimal assumption about
+the model structure of an MRF. AVIL employs two variational distributions to
+approximately infer the latent variables and estimate the partition function,
+respectively. The variational distributions, which are parameterized as neural
+networks, provide an estimate of the negative log likelihood of the MRF. On one
+hand, the estimate is in an intuitive form of approximate contrastive free
+energy. On the other hand, the estimate is a minimax optimization problem,
+which is solved by stochastic gradient descent in an alternating manner. We
+apply AVIL to various undirected generative models in a fully black-box manner
+and obtain better results than existing competitors on several real datasets.
+",1,0,0,1,0,0
+4480,Geometric k-nearest neighbor estimation of entropy and mutual information,"  Nonparametric estimation of mutual information is used in a wide range of
+scientific problems to quantify dependence between variables. The k-nearest
+neighbor (knn) methods are consistent, and therefore expected to work well for
+large sample size. These methods use geometrically regular local volume
+elements. This practice allows maximum localization of the volume elements, but
+can also induce a bias due to a poor description of the local geometry of the
+underlying probability measure. We introduce a new class of knn estimators that
+we call geometric knn estimators (g-knn), which use more complex local volume
+elements to better model the local geometry of the probability measures. As an
+example of this class of estimators, we develop a g-knn estimator of entropy
+and mutual information based on elliptical volume elements, capturing the local
+stretching and compression common to a wide range of dynamical systems
+attractors. A series of numerical examples in which the thickness of the
+underlying distribution and the sample sizes are varied suggest that local
+geometry is a source of problems for knn methods such as the
+Kraskov-Stögbauer-Grassberger (KSG) estimator when local geometric effects
+cannot be removed by global preprocessing of the data. The g-knn method
+performs well despite the manipulation of the local geometry. In addition, the
+examples suggest that the g-knn estimators can be of particular relevance to
+applications in which the system is large, but data size is limited.
+",0,0,1,1,0,0
+4481,Resonant Electron Impact Excitation of 3d levels in Fe$^{14+}$ and Fe$^{15+}$,"  We present laboratory spectra of the $3p$--$3d$ transitions in Fe$^{14+}$ and
+Fe$^{15+}$ excited with a mono-energetic electron beam. In the energy dependent
+spectra obtained by sweeping the electron energy, resonant excitation is
+confirmed as an intensity enhancement at specific electron energies. The
+experimental results are compared with theoretical cross sections calculated
+based on fully relativistic wave functions and the distorted-wave
+approximation. Comparisons between the experimental and theoretical results
+show good agreement for the resonance strength. A significant discrepancy is,
+however, found for the non-resonant cross section in Fe$^{14+}$. %, which can
+be considered as a fundamental cause of the line intensity ratio problem that
+has often been found in both observatory and laboratory measurements. This
+discrepancy is considered to be the fundamental cause of the previously
+reported inconsistency of the model with the observed intensity ratio between
+the $^3\!P_2$ -- $^3\!D_3$ and $^1\!P_1$ -- $^1\!D_2$ transitions.
+",0,1,0,0,0,0
+4482,Automated Top View Registration of Broadcast Football Videos,"  In this paper, we propose a novel method to register football broadcast video
+frames on the static top view model of the playing surface. The proposed method
+is fully automatic in contrast to the current state of the art which requires
+manual initialization of point correspondences between the image and the static
+model. Automatic registration using existing approaches has been difficult due
+to the lack of sufficient point correspondences. We investigate an alternate
+approach exploiting the edge information from the line markings on the field.
+We formulate the registration problem as a nearest neighbour search over a
+synthetically generated dictionary of edge map and homography pairs. The
+synthetic dictionary generation allows us to exhaustively cover a wide variety
+of camera angles and positions and reduce this problem to a minimal per-frame
+edge map matching procedure. We show that the per-frame results can be improved
+in videos using an optimization framework for temporal camera stabilization. We
+demonstrate the efficacy of our approach by presenting extensive results on a
+dataset collected from matches of football World Cup 2014.
+",1,0,0,0,0,0
+4483,Reservoir Computing for Detection of Steady State in Performance Tests of Compressors,"  Fabrication of devices in industrial plants often includes undergoing quality
+assurance tests or tests that seek to determine some attributes or capacities
+of the device. For instance, in testing refrigeration compressors, we want to
+find the true refrigeration capacity of the compressor being tested. Such test
+(also called an episode) may take up to four hours, being an actual hindrance
+to applying it to the total number of compressors produced. This work seeks to
+reduce the time spent on such industrial trials by employing Recurrent Neural
+Networks (RNNs) as dynamical models for detecting when a test is entering the
+so-called steady-state region. Specifically, we use Reservoir Computing (RC)
+networks which simplify the learning of RNNs by speeding up training time and
+showing convergence to a global optimum. Also, this work proposes a
+self-organized subspace projection method for RC networks which uses
+information from the beginning of the episode to define a cluster to which the
+episode belongs to. This assigned cluster defines a particular binary input
+that shifts the operating point of the reservoir to a subspace of trajectories
+for the duration of the episode. This new method is shown to turn the RC model
+robust in performance with respect to varying combination of reservoir
+parameters, such as spectral radius and leak rate, when compared to a standard
+RC network.
+",1,0,0,0,0,0
+4484,Fast swaption pricing in Gaussian term structure models,"  We propose a fast and accurate numerical method for pricing European
+swaptions in multi-factor Gaussian term structure models. Our method can be
+used to accelerate the calibration of such models to the volatility surface.
+The pricing of an interest rate option in such a model involves evaluating a
+multi-dimensional integral of the payoff of the claim on a domain where the
+payoff is positive. In our method, we approximate the exercise boundary of the
+state space by a hyperplane tangent to the maximum probability point on the
+boundary and simplify the multi-dimensional integration into an analytical
+form. The maximum probability point can be determined using the gradient
+descent method. We demonstrate that our method is superior to previous methods
+by comparing the results to the price obtained by numerical integration.
+",0,0,0,0,0,1
+4485,Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?,"  In this article, we extend the conventional framework of
+convolutional-Restricted-Boltzmann-Machine to learn highly abstract features
+among abitrary number of time related input maps by constructing a layer of
+multiplicative units, which capture the relations among inputs. In many cases,
+more than two maps are strongly related, so it is wise to make multiplicative
+unit learn relations among more input maps, in other words, to find the optimal
+relational-order of each unit. In order to enable our machine to learn
+relational order, we developed a reinforcement-learning method whose optimality
+is proven to train the network.
+",1,0,0,1,0,0
+4486,Predicting how and when hidden neurons skew measured synaptic interactions,"  A major obstacle to understanding neural coding and computation is the fact
+that experimental recordings typically sample only a small fraction of the
+neurons in a circuit. Measured neural properties are skewed by interactions
+between recorded neurons and the ""hidden"" portion of the network. To properly
+interpret neural data and determine how biological structure gives rise to
+neural circuit function, we thus need a better understanding of the
+relationships between measured effective neural properties and the true
+underlying physiological properties. Here, we focus on how the effective
+spatiotemporal dynamics of the synaptic interactions between neurons are
+reshaped by coupling to unobserved neurons. We find that the effective
+interactions from a pre-synaptic neuron $r'$ to a post-synaptic neuron $r$ can
+be decomposed into a sum of the true interaction from $r'$ to $r$ plus
+corrections from every directed path from $r'$ to $r$ through unobserved
+neurons. Importantly, the resulting formula reveals when the hidden units
+have---or do not have---major effects on reshaping the interactions among
+observed neurons. As a particular example of interest, we derive a formula for
+the impact of hidden units in random networks with ""strong""
+coupling---connection weights that scale with $1/\sqrt{N}$, where $N$ is the
+network size, precisely the scaling observed in recent experiments. With this
+quantitative relationship between measured and true interactions, we can study
+how network properties shape effective interactions, which properties are
+relevant for neural computations, and how to manipulate effective interactions.
+",0,1,0,0,0,0
+4487,Humanoid Robots as Agents of Human Consciousness Expansion,"  The ""Loving AI"" project involves developing software enabling humanoid robots
+to interact with people in loving and compassionate ways, and to promote
+people' self-understanding and self-transcendence. Currently the project
+centers on the Hanson Robotics robot ""Sophia"" -- specifically, on supplying
+Sophia with personality content and cognitive, linguistic, perceptual and
+behavioral content aimed at enabling loving interactions supportive of human
+self-transcendence. In September 2017 a small pilot study was conducted,
+involving the Sophia robot leading human subjects through dialogues and
+exercises focused on meditation, visualization and relaxation. The pilot was an
+apparent success, qualitatively demonstrating the viability of the approach and
+the ability of appropriate human-robot interaction to increase human well-being
+and advance human consciousness.
+",1,0,0,0,0,0
+4488,Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training,"  In this paper we study leveraging confidence information induced by
+adversarial training to reinforce adversarial robustness of a given
+adversarially trained model. A natural measure of confidence is $\|F({\bf
+x})\|_\infty$ (i.e. how confident $F$ is about its prediction?). We start by
+analyzing an adversarial training formulation proposed by Madry et al.. We
+demonstrate that, under a variety of instantiations, an only somewhat good
+solution to their objective induces confidence to be a discriminator, which can
+distinguish between right and wrong model predictions in a neighborhood of a
+point sampled from the underlying distribution. Based on this, we propose
+Highly Confident Near Neighbor (${\tt HCNN}$), a framework that combines
+confidence information and nearest neighbor search, to reinforce adversarial
+robustness of a base model. We give algorithms in this framework and perform a
+detailed empirical study. We report encouraging experimental results that
+support our analysis, and also discuss problems we observed with existing
+adversarial training.
+",1,0,0,1,0,0
+4489,Stateless Puzzles for Real Time Online Fraud Preemption,"  The profitability of fraud in online systems such as app markets and social
+networks marks the failure of existing defense mechanisms. In this paper, we
+propose FraudSys, a real-time fraud preemption approach that imposes
+Bitcoin-inspired computational puzzles on the devices that post online system
+activities, such as reviews and likes. We introduce and leverage several novel
+concepts that include (i) stateless, verifiable computational puzzles, that
+impose minimal performance overhead, but enable the efficient verification of
+their authenticity, (ii) a real-time, graph-based solution to assign fraud
+scores to user activities, and (iii) mechanisms to dynamically adjust puzzle
+difficulty levels based on fraud scores and the computational capabilities of
+devices. FraudSys does not alter the experience of users in online systems, but
+delays fraudulent actions and consumes significant computational resources of
+the fraudsters. Using real datasets from Google Play and Facebook, we
+demonstrate the feasibility of FraudSys by showing that the devices of honest
+users are minimally impacted, while fraudster controlled devices receive daily
+computational penalties of up to 3,079 hours. In addition, we show that with
+FraudSys, fraud does not pay off, as a user equipped with mining hardware
+(e.g., AntMiner S7) will earn less than half through fraud than from honest
+Bitcoin mining.
+",1,0,0,0,0,0
+4490,Multi-Period Flexibility Forecast for Low Voltage Prosumers,"  Near-future electric distribution grids operation will have to rely on
+demand-side flexibility, both by implementation of demand response strategies
+and by taking advantage of the intelligent management of increasingly common
+small-scale energy storage. The Home energy management system (HEMS), installed
+at low voltage residential clients, will play a crucial role on the flexibility
+provision to both system operators and market players like aggregators.
+Modeling and forecasting multi-period flexibility from residential prosumers,
+such as battery storage and electric water heater, while complying with
+internal constraints (comfort levels, data privacy) and uncertainty is a
+complex task. This papers describes a computational method that is capable of
+efficiently learn and define the feasibility flexibility space from
+controllable resources connected to a HEMS. An Evolutionary Particle Swarm
+Optimization (EPSO) algorithm is adopted and reshaped to derive a set of
+feasible temporal trajectories for the residential net-load, considering
+storage, flexible appliances, and predefined costumer preferences, as well as
+load and photovoltaic (PV) forecast uncertainty. A support vector data
+description (SVDD) algorithm is used to build models capable of classifying
+feasible and non-feasible HEMS operating trajectories upon request from an
+optimization/control algorithm operated by a DSO or market player.
+",1,0,0,0,0,0
+4491,Assessing the Economics of Customer-Sited Multi-Use Energy Storage,"  This paper presents an approach to assess the economics of customer-sited
+energy storage systems (ESSs) which are owned and operated by a customer. The
+ESSs can participate in frequency regulation and spinning reserve markets, and
+are used to help the customer consume available renewable energy and reduce
+electricity bill. A rolling-horizon approach is developed to optimize the
+service schedule, and the resulting costs and revenues are used to assess
+economics of the ESSs. The economic assessment approach is illustrated with
+case studies, from which we obtain some new observations on profitability of
+the customer- sited multi-use ESSs.
+",0,0,1,0,0,0
+4492,A Hamiltonian approach for the Thermodynamics of AdS black holes,"  In this work we study the Thermodynamics of D-dimensional Schwarzschild-anti
+de Sitter (SAdS) black holes. The minimal Thermodynamics of the SAdS spacetime
+is briefly discussed, highlighting some of its strong points and shortcomings.
+The minimal SAdS Thermodynamics is extended within a Hamiltonian approach, by
+means of the introduction of an additional degree of freedom. We demonstrate
+that the cosmological constant can be introduced in the thermodynamic
+description of the SAdS black hole with a canonical transformation of the
+Schwarzschild problem, closely related to the introduction of an anti-de Sitter
+thermodynamic volume. The treatment presented is consistent, in the sense that
+it is compatible with the introduction of new thermodynamic potentials, and
+respects the laws of black hole Thermodynamics. By demanding homogeneity of the
+thermodynamic variables, we are able to construct a new equation of state that
+completely characterizes the Thermodynamics of SAdS black holes. The treatment
+naturally generates phenomenological constants that can be associated with
+different boundary conditions in underlying microscopic theories. A whole new
+set of phenomena can be expected from the proposed generalization of SAdS
+Thermodynamics.
+",0,1,1,0,0,0
+4493,Dzyaloshinskii Moriya interaction across antiferromagnet / ferromagnet interface,"  The antiferromagnet (AFM) / ferromagnet (FM) interfaces are of central
+importance in recently developed pure electric or ultrafast control of FM
+spins, where the underlying mechanisms remain unresolved. Here we report the
+direct observation of Dzyaloshinskii Moriya interaction (DMI) across the AFM/FM
+interface of IrMn/CoFeB thin films. The interfacial DMI is quantitatively
+measured from the asymmetric spin wave dispersion in the FM layer using
+Brillouin light scattering. The DMI strength is enhanced by a factor of 7 with
+increasing IrMn layer thickness in the range of 1- 7.5 nm. Our findings provide
+deeper insight into the coupling at AFM/FM interface and may stimulate new
+device concepts utilizing chiral spin textures such as magnetic skyrmions in
+AFM/FM heterostructures.
+",0,1,0,0,0,0
+4494,Pore cross-talk in colloidal filtration,"  Blockage of pores by particles is found in many processes, including
+filtration and oil extraction. We present filtration experiments through a
+linear array of ten channels with one dimension which is sub-micron, through
+which a dilute dispersion of Brownian polystyrene spheres flows under the
+action of a fixed pressure drop. The growth rate of a clog formed by particles
+at a pore entrance systematically increases with the number of already
+saturated (entirely clogged) pores, indicating that there is an interaction or
+""cross-talk"" between the pores. This observation is interpreted based on a
+phenomenological model, stating that a diffusive redistribution of particles
+occurs along the membrane, from clogged to free pores. This one-dimensional
+model could be extended to two-dimensional membranes.
+",0,1,0,0,0,0
+4495,Temporal Markov Processes for Transport in Porous Media: Random Lattice Networks,"  Monte Carlo (MC) simulations of transport in random porous networks indicate
+that for high variances of the log-normal permeability distribution, the
+transport of a passive tracer is non-Fickian. Here we model this non-Fickian
+dispersion in random porous networks using discrete temporal Markov models. We
+show that such temporal models capture the spreading behavior accurately. This
+is true despite the fact that the slow velocities are strongly correlated in
+time, and some studies have suggested that the persistence of low velocities
+would render the temporal Markovian model inapplicable. Compared to previously
+proposed temporal stochastic differential equations with case specific drift
+and diffusion terms, the models presented here require fewer modeling
+assumptions. Moreover, we show that discrete temporal Markov models can be used
+to represent dispersion in unstructured networks, which are widely used to
+model porous media. A new method is proposed to extend the state space of
+temporal Markov models to improve the model predictions in the presence of
+extremely low velocities in particle trajectories and extend the applicability
+of the model to higher temporal resolutions. Finally, it is shown that by
+combining multiple transitions, temporal models are more efficient for
+computing particle evolution compared to correlated CTRW with spatial
+increments that are equal to the lengths of the links in the network.
+",1,1,0,0,0,0
+4496,Defect Properties of Na and K in Cu2ZnSnS4 from Hybrid Functional Calculation,"  In-growth or post-deposition treatment of $Cu_{2}ZnSnS_{4}$ (CZTS) absorber
+layer had led to improved photovoltaic efficiency, however, the underlying
+physical mechanism of such improvements are less studied. In this study, the
+thermodynamics of Na and K related defects in CZTS are investigated from first
+principle approach using hybrid functional, with chemical potential of Na and K
+established from various phases of their polysulphides. Both Na and K
+predominantly substitute on Cu sites similar to their behavior in
+$Cu(In,Ga)Se_{2}$, in contrast to previous results using the generalized
+gradient approximation (GGA). All substitutional and interstitial defects are
+shown to be either shallow levels or highly energetically unfavorable. Defect
+complexing between Na and abundant intrinsic defects did not show possibility
+of significant incorporation enhancement or introducing deep n-type levels. The
+possible benefit of Na incorporation on enhancing photovoltaic efficiency is
+discussed. The negligible defect solubility of K in CZTS also suggests possible
+surfactant candidate.
+",0,1,0,0,0,0
+4497,"Explicit minimisation of a convex quadratic under a general quadratic constraint: a global, analytic approach","  A novel approach is introduced to a very widely occurring problem, providing
+a complete, explicit resolution of it: minimisation of a convex quadratic under
+a general quadratic, equality or inequality, constraint. Completeness comes via
+identification of a set of mutually exclusive and exhaustive special cases.
+Explicitness, via algebraic expressions for each solution set. Throughout,
+underlying geometry illuminates and informs algebraic development. In
+particular, centrally to this new approach, affine equivalence is exploited to
+re-express the same problem in simpler coordinate systems. Overall, the
+analysis presented provides insight into the diverse forms taken both by the
+problem itself and its solution set, showing how each may be intrinsically
+unstable. Comparisons of this global, analytic approach with the, intrinsically
+complementary, local, computational approach of (generalised) trust region
+methods point to potential synergies between them. Points of contact with
+simultaneous diagonalisation results are noted.
+",0,0,1,1,0,0
+4498,Morphisms of open games,"  We define a notion of morphisms between open games, exploiting a surprising
+connection between lenses in computer science and compositional game theory.
+This extends the more intuitively obvious definition of globular morphisms as
+mappings between strategy profiles that preserve best responses, and hence in
+particular preserve Nash equilibria. We construct a symmetric monoidal double
+category in which the horizontal 1-cells are open games, vertical 1-morphisms
+are lenses, and 2-cells are morphisms of open games. States (morphisms out of
+the monoidal unit) in the vertical category give a flexible solution concept
+that includes both Nash and subgame perfect equilibria. Products in the
+vertical category give an external choice operator that is reminiscent of
+products in game semantics, and is useful in practical examples. We illustrate
+the above two features with a simple worked example from microeconomics, the
+market entry game.
+",1,0,0,0,0,0
+4499,U(1)$\times$SU(2) Gauge Invariance Made Simple for Density Functional Approximations,"  A semi-relativistic density-functional theory that includes spin-orbit
+couplings and Zeeman fields on equal footing with the electromagnetic
+potentials, is an appealing framework to develop a unified first-principles
+computational approach for non-collinear magnetism, spintronics, orbitronics,
+and topological states. The basic variables of this theory include the
+paramagnetic current and the spin-current density, besides the particle and the
+spin density, and the corresponding exchange-correlation (xc) energy functional
+is invariant under local U(1)$\times$SU(2) gauge transformations. The xc-energy
+functional must be approximated to enable practical applications, but, contrary
+to the case of the standard density functional theory, finding simple
+approximations suited to deal with realistic atomistic inhomogeneities has been
+a long-standing challenge. Here, we propose a way out of this impasse by
+showing that approximate gauge-invariant functionals can be easily generated
+from existing approximate functionals of ordinary density-functional theory by
+applying a simple {\it minimal substitution} on the kinetic energy density,
+which controls the short-range behavior of the exchange hole. Our proposal
+opens the way to the construction of approximate, yet non-empirical
+functionals, which do not assume weak inhomogeneity and should therefore have a
+wide range of applicability in atomic, molecular and condensed matter physics.
+",0,1,0,0,0,0
+4500,L^2-Betti numbers of rigid C*-tensor categories and discrete quantum groups,"  We compute the $L^2$-Betti numbers of the free $C^*$-tensor categories, which
+are the representation categories of the universal unitary quantum groups
+$A_u(F)$. We show that the $L^2$-Betti numbers of the dual of a compact quantum
+group $G$ are equal to the $L^2$-Betti numbers of the representation category
+$Rep(G)$ and thus, in particular, invariant under monoidal equivalence. As an
+application, we obtain several new computations of $L^2$-Betti numbers for
+discrete quantum groups, including the quantum permutation groups and the free
+wreath product groups. Finally, we obtain upper bounds for the first
+$L^2$-Betti number in terms of a generating set of a $C^*$-tensor category.
+",0,0,1,0,0,0
+4501,Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding,"  Recently, there is increasing interest and research on the interpretability
+of machine learning models, for example how they transform and internally
+represent EEG signals in Brain-Computer Interface (BCI) applications. This can
+help to understand the limits of the model and how it may be improved, in
+addition to possibly provide insight about the data itself. Schirrmeister et
+al. (2017) have recently reported promising results for EEG decoding with deep
+convolutional neural networks (ConvNets) trained in an end-to-end manner and,
+with a causal visualization approach, showed that they learn to use spectral
+amplitude changes in the input. In this study, we investigate how ConvNets
+represent spectral features through the sequence of intermediate stages of the
+network. We show higher sensitivity to EEG phase features at earlier stages and
+higher sensitivity to EEG amplitude features at later stages. Intriguingly, we
+observed a specialization of individual stages of the network to the classical
+EEG frequency bands alpha, beta, and high gamma. Furthermore, we find first
+evidence that particularly in the last convolutional layer, the network learns
+to detect more complex oscillatory patterns beyond spectral phase and
+amplitude, reminiscent of the representation of complex visual features in
+later layers of ConvNets in computer vision tasks. Our findings thus provide
+insights into how ConvNets hierarchically represent spectral EEG features in
+their intermediate layers and suggest that ConvNets can exploit and might help
+to better understand the compositional structure of EEG time series.
+",1,0,0,1,0,0
+4502,Global weak solutions in a three-dimensional Keller-Segel-Navier-Stokes system with nonlinear diffusion,"  The coupled quasilinear Keller-Segel-Navier-Stokes system is considered under
+Neumann boundary conditions for $n$ and $c$ and no-slip boundary conditions for
+$u$ in three-dimensional bounded domains $\Omega\subseteq \mathbb{R}^3$ with
+smooth boundary, where $m>0,\kappa\in \mathbb{R}$ are given constants, $\phi\in
+W^{1,\infty}(\Omega)$. If $ m> 2$, then for all reasonably regular initial
+data, a corresponding initial-boundary value problem for $(KSNF)$ possesses a
+globally defined weak solution.
+",0,0,1,0,0,0
+4503,On Consistency of Compressive Spectral Clustering,"  Spectral clustering is one of the most popular methods for community
+detection in graphs. A key step in spectral clustering algorithms is the eigen
+decomposition of the $n{\times}n$ graph Laplacian matrix to extract its $k$
+leading eigenvectors, where $k$ is the desired number of clusters among $n$
+objects. This is prohibitively complex to implement for very large datasets.
+However, it has recently been shown that it is possible to bypass the eigen
+decomposition by computing an approximate spectral embedding through graph
+filtering of random signals. In this paper, we analyze the working of spectral
+clustering performed via graph filtering on the stochastic block model.
+Specifically, we characterize the effects of sparsity, dimensionality and
+filter approximation error on the consistency of the algorithm in recovering
+planted clusters.
+",1,0,0,1,0,0
+4504,Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases,"  $\textbf{Objective}$: To assess the validity of an automatic EEG arousal
+detection algorithm using large patient samples and different heterogeneous
+databases
+$\textbf{Methods}$: Automatic scorings were confronted with results from
+human expert scorers on a total of 2768 full-night PSG recordings obtained from
+two different databases. Of them, 472 recordings were obtained during clinical
+routine at our sleep center and were subdivided into two subgroups of 220
+(HMC-S) and 252 (HMC-M) recordings each, attending to the procedure followed by
+the clinical expert during the visual review (semi-automatic or purely manual,
+respectively). In addition, 2296 recordings from the public SHHS-2 database
+were evaluated against the respective manual expert scorings.
+$\textbf{Results}$: Event-by-event epoch-based validation resulted in an
+overall Cohen kappa agreement K = 0.600 (HMC-S), 0.559 (HMC-M), and 0.573
+(SHHS-2). Estimated inter-scorer variability on the datasets was, respectively,
+K = 0.594, 0.561 and 0.543. Analyses of the corresponding Arousal Index scores
+showed associated automatic-human repeatability indices ranging in 0.693-0.771
+(HMC-S), 0.646-0.791 (HMC-M), and 0.759-0.791 (SHHS-2).
+$\textbf{Conclusions}$: Large-scale validation of our automatic EEG arousal
+detector on different databases has shown robust performance and good
+generalization results comparable to the expected levels of human agreement.
+Special emphasis has been put on allowing reproducibility of the results and
+implementation of our method has been made accessible online as open source
+code
+",0,0,0,0,1,0
+4505,"Story of the Developments in Statistical Physics of Fracture, Breakdown \& Earthquake: A Personal Account","  We review the developments of the statistical physics of fracture and
+earthquake over the last four decades. We argue that major progress has been
+made in this field and that the key concepts should now become integral part of
+the (under-) graduate level text books in condensed matter physics. For arguing
+in favor of this, we compare the development (citations) with the same for some
+other related topics in condensed matter, for which Nobel prizes have already
+been awarded.
+",0,1,0,0,0,0
+4506,LocalNysation: A bottom up approach to efficient localized kernel regression,"  We consider a localized approach in the well-established setting of
+reproducing kernel learning under random design. The input space $X$ is
+partitioned into local disjoint subsets $X_j$ ($j=1,...,m$) equipped with a
+local reproducing kernel $K_j$. It is then straightforward to define local KRR
+estimates. Our first main contribution is in showing that minimax optimal rates
+of convergence are preserved if the number $m$ of partitions grows sufficiently
+slowly with the sample size, under locally different degrees on smoothness
+assumptions on the regression function. As a byproduct, we show that low
+smoothness on exceptional sets of small probability does not contribute,
+leading to a faster rate of convergence. Our second contribution lies in
+showing that the partitioning approach for KRR can be efficiently combined with
+local Nyström subsampling, improving computational cost twofold. If the
+number of locally subsampled inputs grows sufficiently fast with the sample
+size, minimax optimal rates of convergence are maintained.
+",0,0,1,1,0,0
+4507,Evolutionary Acyclic Graph Partitioning,"  Directed graphs are widely used to model data flow and execution dependencies
+in streaming applications. This enables the utilization of graph partitioning
+algorithms for the problem of parallelizing computation for multiprocessor
+architectures. However due to resource restrictions, an acyclicity constraint
+on the partition is necessary when mapping streaming applications to an
+embedded multiprocessor. Here, we contribute a multi-level algorithm for the
+acyclic graph partitioning problem. Based on this, we engineer an evolutionary
+algorithm to further reduce communication cost, as well as to improve load
+balancing and the scheduling makespan on embedded multiprocessor architectures.
+",1,0,0,0,0,0
+4508,Mechanisms of near-surface structural evolution in nanocrystalline materials during sliding contact,"  The wear-driven structural evolution of nanocrystalline Cu was simulated with
+molecular dynamics under constant normal loads, followed by a quantitative
+analysis. While the microstructure far away from the sliding contact remains
+unchanged, grain growth accompanied by partial dislocations and twin formation
+was observed near the contact surface, with more rapid coarsening promoted by
+higher applied normal loads. The structural evolution continues with increasing
+number of sliding cycles and eventually saturates to a stable distinct layer of
+coarsened grains, separated from the finer matrix by a steep gradient in grain
+size. The coarsening process is balanced by the rate of material removal when
+the normal load is high enough. The observed structural evolution leads to an
+increase in hardness and decrease in friction coefficient, which also saturate
+after a number of sliding cycles. This work provides important mechanistic
+understanding of nanocrystalline wear, while also introducing a methodology for
+atomistic simulations of cyclic wear damage under constant applied normal
+loads.
+",0,1,0,0,0,0
+4509,Generalized Hölder's inequality on Morrey spaces,"  The aim of this paper is to present necessary and sufficient conditions for
+generalized Hölder's inequality on generalized Morrey spaces. We also
+obtain similar results on weak Morrey spaces and on generalized weak Morrey
+spaces. The necessary and sufficient conditions for the generalized
+Hölder's inequality on these spaces are obtained through estimates for
+characteristic functions of balls in $\mathbb{R}^d$.
+",0,0,1,0,0,0
+4510,Endomorphism Algebras of Abelian varieties with special reference to Superelliptic Jacobians,"  This is (mostly) a survey article. We use an information about Galois
+properties of points of small order on an abelian variety in order to describe
+its endomorphism algebra over an algebraic closure of the ground field. We
+discuss in detail applications to jacobians of cyclic covers of the projective
+line.
+",0,0,1,0,0,0
+4511,Ranks of rational points of the Jacobian varieties of hyperelliptic curves,"  In this paper, we obtain bounds for the Mordell-Weil ranks over cyclotomic
+extensions of a wide range of abelian varieties defined over a number field $F$
+whose primes above $p$ are totally ramified over $F/\mathbb{Q}$. We assume that
+the abelian varieties may have good non-ordinary reduction at those primes. Our
+work is a generalization of \cite{Kim}, in which the second author generalized
+Perrin-Riou's Iwasawa theory for elliptic curves over $\mathbb{Q}$ with
+supersingular reduction (\cite{Perrin-Riou}) to elliptic curves defined over
+the above-mentioned number field $F$. On top of non-ordinary reduction and the
+ramification of the field $F$, we deal with the additional difficulty that the
+dimensions of the abelian varieties can be any number bigger than 1 which
+causes a variety of issues. As a result, we obtain bounds for the ranks over
+cyclotomic extensions $\mathbb{Q}(\mu_{p^{\max(M,N)+n}})$ of the Jacobian
+varieties of {\it ramified} hyperelliptic curves $y^{2p^M}=x^{3p^N}+ax^{p^N}+b$
+among others.
+",0,0,1,0,0,0
+4512,A Generalized Function defined by the Euler first kind integral and its connection with the Dirac delta function,"  We have shown that in some region where the Euler integral of the first kind
+diverges, the Euler formula defines a generalized function. The connected of
+this generalized function with the Dirac delta function is found.
+",0,0,1,0,0,0
+4513,Optical emission of graphene and electron-hole pair production induced by a strong THz field,"  We report on the first experimental observation of graphene optical emission
+induced by the intense THz pulse. P-doped CVD graphene with the initial Fermi
+energy of about 200 meV was used, optical photons was detected in the
+wavelength range of 340-600 nm. Emission started when THz field amplitude
+exceeded 100 kV/cm. For THz fields from 200 to 300 kV/cm the temperature of
+optical radiation was constant, while the number of emitted photons increased
+several dozen times. This fact clearly indicates multiplication of
+electron-hole pairs induced by an external field itself and not due to electron
+heating. The experimental data are in a good agreement with the theory of
+Landau-Zener interband transitions. It is shown theoretically that Landau-Zener
+transitions are possible even in the case of heavily doped graphene because the
+strong THz field removes quasiparticles from the region of interband
+transitions during several femtoseconds, which cancels the Pauli blocking
+effect.
+",0,1,0,0,0,0
+4514,Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environments,"  Navigating safely in urban environments remains a challenging problem for
+autonomous vehicles. Occlusion and limited sensor range can pose significant
+challenges to safely navigate among pedestrians and other vehicles in the
+environment. Enabling vehicles to quantify the risk posed by unseen regions
+allows them to anticipate future possibilities, resulting in increased safety
+and ride comfort. This paper proposes an algorithm that takes advantage of the
+known road layouts to forecast, quantify, and aggregate risk associated with
+occlusions and limited sensor range. This allows us to make predictions of risk
+induced by unobserved vehicles even in heavily occluded urban environments. The
+risk can then be used either by a low-level planning algorithm to generate
+better trajectories, or by a high-level one to plan a better route. The
+proposed algorithm is evaluated on intersection layouts from real-world map
+data with up to five other vehicles in the scene, and verified to reduce
+collision rates by 4.8x comparing to a baseline method while improving driving
+comfort.
+",1,0,0,0,0,0
+4515,Quantum oscillations and Dirac-Landau levels in Weyl superconductors,"  When magnetic field is applied to metals and semimetals quantum oscillations
+appear as individual Landau levels cross the Fermi level. Quantum oscillations
+generally do not occur in superconductors (SC) because magnetic field is either
+expelled from the sample interior or, if strong enough, drives the material
+into the normal state. In addition, elementary excitations of a superconductor
+-- Bogoliubov quasiparticles -- do not carry a well defined electric charge and
+therefore do not couple in a simple way to the applied magnetic field. We
+predict here that in Weyl superconductors certain types of elastic strain have
+the ability to induce chiral pseudo-magnetic field which can reorganize the
+electronic states into Dirac-Landau levels with linear band crossings at low
+energy. The resulting quantum oscillations in the quasiparticle density of
+states and thermal conductivity can be experimentally observed under a bending
+deformation of a thin film Weyl SC and provide new insights into this
+fascinating family of materials.
+",0,1,0,0,0,0
+4516,Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models,"  The emergence and development of cancer is a consequence of the accumulation
+over time of genomic mutations involving a specific set of genes, which
+provides the cancer clones with a functional selective advantage. In this work,
+we model the order of accumulation of such mutations during the progression,
+which eventually leads to the disease, by means of probabilistic graphic
+models, i.e., Bayesian Networks (BNs). We investigate how to perform the task
+of learning the structure of such BNs, according to experimental evidence,
+adopting a global optimization meta-heuristics. In particular, in this work we
+rely on Genetic Algorithms, and to strongly reduce the execution time of the
+inference -- which can also involve multiple repetitions to collect
+statistically significant assessments of the data -- we distribute the
+calculations using both multi-threading and a multi-node architecture. The
+results show that our approach is characterized by good accuracy and
+specificity; we also demonstrate its feasibility, thanks to a 84x reduction of
+the overall execution time with respect to a traditional sequential
+implementation.
+",1,0,0,1,0,0
+4517,"A note on the paper ""Contraction mappings in $b$-metric spaces"" by Czerwik","  In this paper we correct an inaccuracy that appears in the proof of Theorem
+1. in Czerwik's article ""Contraction mappings in $b$-metric spaces."", Acta
+Math. Inform. Univ. Ostraviensis, 1:5--11, 1993.
+",0,0,1,0,0,0
+4518,Floquet Analysis of Kuznetsov--Ma breathers: A Path Towards Spectral Stability of Rogue Waves,"  In the present work, we aim at taking a step towards the spectral stability
+analysis of Peregrine solitons, i.e., wave structures that are used to emulate
+extreme wave events. Given the space-time localized nature of Peregrine
+solitons, this is a priori a non-trivial task. Our main tool in this effort
+will be the study of the spectral stability of the periodic generalization of
+the Peregrine soliton in the evolution variable, namely the Kuznetsov--Ma
+breather. Given the periodic structure of the latter, we compute the
+corresponding Floquet multipliers, and examine them in the limit where the
+period of the orbit tends to infinity. This way, we extrapolate towards the
+stability of the limiting structure, namely the Peregrine soliton. We find that
+multiple unstable modes of the background are enhanced, yet no additional
+unstable eigenmodes arise as the Peregrine limit is approached. We explore the
+instability evolution also in direct numerical simulations.
+",0,1,0,0,0,0
+4519,Playing Music in Just Intonation - A Dynamically Adapting Tuning Scheme,"  We investigate a dynamically adapting tuning scheme for microtonal tuning of
+musical instruments, allowing the performer to play music in just intonation in
+any key. Unlike other methods, which are based on a procedural analysis of the
+chordal structure, the tuning scheme continually solves a system of linear
+equations without making explicit decisions. In complex situations, where not
+all intervals of a chord can be tuned according to just frequency ratios, the
+method automatically yields a tempered compromise. We outline the
+implementation of the algorithm in an open-source software project that we have
+provided in order to demonstrate the feasibility of the tuning method.
+",0,1,0,0,0,0
+4520,Photon propagation through linearly active dimers,"  We provide an analytic propagator for non-Hermitian dimers showing linear
+gain or losses in the quantum regime. In particular, we focus on experimentally
+feasible realizations of the $\mathcal{PT}$-symmetric dimer and provide their
+mean photon number and second order two-point correlation. We study the
+propagation of vacuum, single photon spatially-separable, and two-photon
+spatially-entangled states. We show that each configuration produces a
+particular signature that might signal their possible uses as photon switches,
+semi-classical intensity-tunable sources, or spatially entangled sources to
+mention a few possible applications.
+",0,1,0,0,0,0
+4521,End-to-End Learning of Geometry and Context for Deep Stereo Regression,"  We propose a novel deep learning architecture for regressing disparity from a
+rectified pair of stereo images. We leverage knowledge of the problem's
+geometry to form a cost volume using deep feature representations. We learn to
+incorporate contextual information using 3-D convolutions over this volume.
+Disparity values are regressed from the cost volume using a proposed
+differentiable soft argmin operation, which allows us to train our method
+end-to-end to sub-pixel accuracy without any additional post-processing or
+regularization. We evaluate our method on the Scene Flow and KITTI datasets and
+on KITTI we set a new state-of-the-art benchmark, while being significantly
+faster than competing approaches.
+",1,0,0,0,0,0
+4522,BLADYG: A Graph Processing Framework for Large Dynamic Graphs,"  Recently, distributed processing of large dynamic graphs has become very
+popular, especially in certain domains such as social network analysis, Web
+graph analysis and spatial network analysis. In this context, many
+distributed/parallel graph processing systems have been proposed, such as
+Pregel, GraphLab, and Trinity. These systems can be divided into two
+categories: (1) vertex-centric and (2) block-centric approaches. In
+vertex-centric approaches, each vertex corresponds to a process, and message
+are exchanged among vertices. In block-centric approaches, the unit of
+computation is a block, a connected subgraph of the graph, and message
+exchanges occur among blocks. In this paper, we are considering the issues of
+scale and dynamism in the case of block-centric approaches. We present bladyg,
+a block-centric framework that addresses the issue of dynamism in large-scale
+graphs. We present an implementation of BLADYG on top of akka framework. We
+experimentally evaluate the performance of the proposed framework.
+",1,0,0,0,0,0
+4523,Variational Inference via Transformations on Distributions,"  Variational inference methods often focus on the problem of efficient model
+optimization, with little emphasis on the choice of the approximating
+posterior. In this paper, we review and implement the various methods that
+enable us to develop a rich family of approximating posteriors. We show that
+one particular method employing transformations on distributions results in
+developing very rich and complex posterior approximation. We analyze its
+performance on the MNIST dataset by implementing with a Variational Autoencoder
+and demonstrate its effectiveness in learning better posterior distributions.
+",1,0,0,1,0,0
+4524,VLSI Computational Architectures for the Arithmetic Cosine Transform,"  The discrete cosine transform (DCT) is a widely-used and important signal
+processing tool employed in a plethora of applications. Typical fast algorithms
+for nearly-exact computation of DCT require floating point arithmetic, are
+multiplier intensive, and accumulate round-off errors. Recently proposed fast
+algorithm arithmetic cosine transform (ACT) calculates the DCT exactly using
+only additions and integer constant multiplications, with very low area
+complexity, for null mean input sequences. The ACT can also be computed
+non-exactly for any input sequence, with low area complexity and low power
+consumption, utilizing the novel architecture described. However, as a
+trade-off, the ACT algorithm requires 10 non-uniformly sampled data points to
+calculate the 8-point DCT. This requirement can easily be satisfied for
+applications dealing with spatial signals such as image sensors and biomedical
+sensor arrays, by placing sensor elements in a non-uniform grid. In this work,
+a hardware architecture for the computation of the null mean ACT is proposed,
+followed by a novel architectures that extend the ACT for non-null mean
+signals. All circuits are physically implemented and tested using the Xilinx
+XC6VLX240T FPGA device and synthesized for 45 nm TSMC standard-cell library for
+performance assessment.
+",1,0,0,0,0,0
+4525,Simulated Tornado Optimization,"  We propose a swarm-based optimization algorithm inspired by air currents of a
+tornado. Two main air currents - spiral and updraft - are mimicked. Spiral
+motion is designed for exploration of new search areas and updraft movements is
+deployed for exploitation of a promising candidate solution. Assignment of just
+one search direction to each particle at each iteration, leads to low
+computational complexity of the proposed algorithm respect to the conventional
+algorithms. Regardless of the step size parameters, the only parameter of the
+proposed algorithm, called tornado diameter, can be efficiently adjusted by
+randomization. Numerical results over six different benchmark cost functions
+indicate comparable and, in some cases, better performance of the proposed
+algorithm respect to some other metaheuristics.
+",1,0,1,0,0,0
+4526,On a backward problem for multidimensional Ginzburg-Landau equation with random data,"  In this paper, we consider a backward in time problem for Ginzburg-Landau
+equation in multidimensional domain associated with some random data. The
+problem is ill-posed in the sense of Hadamard. To regularize the instable
+solution, we develop a new regularized method combined with statistical
+approach to solve this problem. We prove a upper bound, on the rate of
+convergence of the mean integrated squared error in $L^2 $ norm and $H^1$ norm.
+",0,0,1,0,0,0
+4527,Electronic and atomic kinetics in solids irradiated with free-electron lasers or swift-heavy ions,"  In this brief review we discuss the transient processes in solids under
+irradiation with femtosecond X-ray free-electron-laser (FEL) pulses and
+swift-heavy ions (SHI). Both kinds of irradiation produce highly excited
+electrons in a target on extremely short timescales. Transfer of the excess
+electronic energy into the lattice may lead to observable target modifications
+such as phase transitions and damage formation. Transient kinetics of material
+excitation and relaxation under FEL or SHI irradiation are comparatively
+discussed. The same origin for the electronic and atomic relaxation in both
+cases is demonstrated. Differences in these kinetics introduced by the
+geometrical effects ({\mu}m-size of a laser spot vs nm-size of an ion track)
+and initial irradiation (photoabsorption vs an ion impact) are analyzed. The
+basic mechanisms of electron transport and electron-lattice coupling are
+addressed. Appropriate models and their limitations are presented.
+Possibilities of thermal and nonthermal melting of materials under FEL and SHI
+irradiation are discussed.
+",0,1,0,0,0,0
+4528,Network analysis of the COSMOS galaxy field,"  The galaxy data provided by COSMOS survey for 1 by 1 degree field of sky are
+analysed by methods of complex networks. Three galaxy samples (slices) with
+redshifts ranging within intervals 0.88-0.91, 0.91-0.94 and 0.94-0.97 are
+studied as two-dimensional projections for the spatial distributions of
+galaxies. We construct networks and calculate network measures for each sample,
+in order to analyse the network similarity of different samples, distinguish
+various topological environments, and find associations between galaxy
+properties (colour index and stellar mass) and their topological environments.
+Results indicate a high level of similarity between geometry and topology for
+different galaxy samples and no clear evidence of evolutionary trends in
+network measures. The distribution of local clustering coefficient C manifests
+three modes which allow for discrimination between stand-alone singlets and
+dumbbells (0 <= C <= 0.1), intermediately (0 < C < 0.9) and clique (0.9 <= C <=
+1) like galaxies. Analysing astrophysical properties of galaxies (colour index
+and stellar masses), we show that distributions are similar in all slices,
+however weak evolutionary trends can also be seen across redshift slices. To
+specify different topological environments we have extracted selections of
+galaxies from each sample according to different modes of C distribution. We
+have found statistically significant associations between evolutionary
+parameters of galaxies and selections of C: the distribution of stellar mass
+for galaxies with interim C differ from the corresponding distributions for
+stand-alone and clique galaxies, and this difference holds for all redshift
+slices. The colour index realises somewhat different behaviour.
+",0,1,0,0,0,0
+4529,A Functional Taxonomy of Music Generation Systems,"  Digital advances have transformed the face of automatic music generation
+since its beginnings at the dawn of computing. Despite the many breakthroughs,
+issues such as the musical tasks targeted by different machines and the degree
+to which they succeed remain open questions. We present a functional taxonomy
+for music generation systems with reference to existing systems. The taxonomy
+organizes systems according to the purposes for which they were designed. It
+also reveals the inter-relatedness amongst the systems. This design-centered
+approach contrasts with predominant methods-based surveys and facilitates the
+identification of grand challenges to set the stage for new breakthroughs.
+",1,0,0,0,0,0
+4530,Covariant representations for singular actions on C*-algebras,"  Singular actions on C*-algebras are automorphic group actions on C*-algebras,
+where the group need not be locally compact, or the action need not be strongly
+continuous. We study the covariant representation theory of such actions. In
+the usual case of strongly continuous actions of locally compact groups on
+C*-algebras, this is done via crossed products, but this approach is not
+available for singular C*-actions (this was our path in a previous paper). The
+literature regarding covariant representations for singular actions is already
+large and scattered, and in need of some consolidation. We collect in this
+survey a range of results in this field, mostly known. We improve some proofs
+and elucidate some interconnections. These include existence theorems by
+Borchers and Halpern, Arveson spectra, the Borchers-Arveson theorem, standard
+representations and Stinespring dilations as well as ground states, KMS states
+and ergodic states and the spatial structure of their GNS representations.
+",0,0,1,0,0,0
+4531,Control Synthesis for Permutation-Symmetric High-Dimensional Systems With Counting Constraints,"  General purpose correct-by-construction synthesis methods are limited to
+systems with low dimensionality or simple specifications. In this work we
+consider highly symmetrical counting problems and exploit the symmetry to
+synthesize provably correct controllers for systems with tens of thousands of
+states. The key ingredients of the solution are an aggregate abstraction
+procedure for mildly heterogeneous systems and a formulation of counting
+constraints as linear inequalities.
+",1,0,1,0,0,0
+4532,Commutativity and Commutative Pairs of Some Differential Equations,"  In this study, explicit differential equations representing commutative pairs
+of some well-known second-order linear time-varying systems have been derived.
+The commutativity of these systems are investigated by considering 30
+second-order linear differential equations with variable coefficients. It is
+shown that the system modeled by each one of these equations has a commutative
+pair with (or without) some conditions or not. There appear special cases such
+that both, only one or neither of the original system and its commutative pair
+has explicit analytic solution. Some benefits of commutativity have already
+been mentioned in the literature but a new application for in cryptology for
+obscuring transmitted signals in telecommunication is illustrated in this
+paper.
+",1,0,0,0,0,0
+4533,Astrophysical signatures of leptonium,"  More than 10^43 positrons annihilate every second in the centre of our Galaxy
+yet, despite four decades of observations, their origin is still unknown. Many
+candidates have been proposed, such as supernovae and low mass X-ray binaries.
+However, these models are difficult to reconcile with the distribution of
+positrons, which are highly concentrated in the Galactic bulge, and therefore
+require specific propagation of the positrons through the interstellar medium.
+Alternative sources include dark matter decay, or the supermassive black hole,
+both of which would have a naturally high bulge-to-disc ratio.
+The chief difficulty in reconciling models with the observations is the
+intrinsically poor angular resolution of gamma-ray observations, which cannot
+resolve point sources. Essentially all of the positrons annihilate via the
+formation of positronium. This gives rise to the possibility of observing
+recombination lines of positronium emitted before the atom annihilates. These
+emission lines would be in the UV and the NIR, giving an increase in angular
+resolution of a factor of 10^4 compared to gamma ray observations, and allowing
+the discrimination between point sources and truly diffuse emission.
+Analogously to the formation of positronium, it is possible to form atoms of
+true muonium and true tauonium. Since muons and tauons are intrinsically
+unstable, the formation of such leptonium atoms will be localised to their
+places of origin. Thus observations of true muonium or true tauonium can
+provide another way to distinguish between truly diffuse sources such as dark
+matter decay, and an unresolved distribution of point sources.
+",0,1,0,0,0,0
+4534,Enhancing Stratified Graph Sampling Algorithms based on Approximate Degree Distribution,"  Sampling technique has become one of the recent research focuses in the
+graph-related fields. Most of the existing graph sampling algorithms tend to
+sample the high degree or low degree nodes in the complex networks because of
+the characteristic of scale-free. Scale-free means that degrees of different
+nodes are subject to a power law distribution. So, there is a significant
+difference in the degrees between the overall sampling nodes. In this paper, we
+propose an idea of approximate degree distribution and devise a stratified
+strategy using it in the complex networks. We also develop two graph sampling
+algorithms combining the node selection method with the stratified strategy.
+The experimental results show that our sampling algorithms preserve several
+properties of different graphs and behave more accurately than other
+algorithms. Further, we prove the proposed algorithms are superior to the
+off-the-shelf algorithms in terms of the unbiasedness of the degrees and more
+efficient than state-of-the-art FFS and ES-i algorithms.
+",1,0,0,0,0,0
+4535,Testing for Balance in Social Networks,"  Friendship and antipathy exist in concert with one another in real social
+networks. Despite the role they play in social interactions, antagonistic ties
+are poorly understood and infrequently measured. One important theory of
+negative ties that has received relatively little empirical evaluation is
+balance theory, the codification of the adage `the enemy of my enemy is my
+friend' and similar sayings. Unbalanced triangles are those with an odd number
+of negative ties, and the theory posits that such triangles are rare. To test
+for balance, previous works have utilized a permutation test on the edge signs.
+The flaw in this method, however, is that it assumes that negative and positive
+edges are interchangeable. In reality, they could not be more different. Here,
+we propose a novel test of balance that accounts for this discrepancy and show
+that our test is more accurate at detecting balance. Along the way, we prove
+asymptotic normality of the test statistic under our null model, which is of
+independent interest. Our case study is a novel dataset of signed networks we
+collected from 32 isolated, rural villages in Honduras. Contrary to previous
+results, we find that there is only marginal evidence for balance in social tie
+formation in this setting.
+",1,0,0,0,0,0
+4536,Empirical Recurrence Rates for Seismic Signals on Planetary Surfaces,"  We review the recurrence intervals as a function of ground motion amplitude
+at several terrestrial locations, and make the first interplanetary comparison
+with measurements on the Moon, Mars, Venus and Titan. This empirical approach
+gives an intuitive guide to the relative seismicity of these locations, without
+invoking interior models and specific sources: for example a Venera-14
+observation of possible ground motion indicates a microseismic environment
+mid-way between noisy and quiet terrestrial locations; quiet terrestrial
+regions see a peak velocity amplitude in mm/s roughly equal to 0.4*N(-0.7),
+where N is the number of events observed per year. The Apollo data show signals
+for a given recurrence rate are typically about 10,000 times smaller in
+amplitude than a quiet site on Earth, while Viking data masked for low-wind
+periods appears comparable with a quiet terrestrial site. Recurrence rate plots
+from in-situ measurements provide a convenient guide to expectations for
+seismic instrumentation on future planetary missions : while small geophones
+can discriminate terrestrial activity rates, observations with guidance
+accelerometers are typically too insensitive to provide meaningful constraints
+unless operated for long periods.
+",0,1,0,0,0,0
+4537,Landau Damping of Beam Instabilities by Electron Lenses,"  Modern and future particle accelerators employ increasingly higher intensity
+and brighter beams of charged particles and become operationally limited by
+coherent beam instabilities. Usual methods to control the instabilities, such
+as octupole magnets, beam feedback dampers and use of chromatic effects, become
+less effective and insufficient. We show that, in contrast, Lorentz forces of a
+low-energy, a magnetically stabilized electron beam, or ""electron lens"", easily
+introduces transverse nonlinear focusing sufficient for Landau damping of
+transverse beam instabilities in accelerators. It is also important that,
+unlike other nonlinear elements, the electron lens provides the frequency
+spread mainly at the beam core, thus allowing much higher frequency spread
+without lifetime degradation. For the parameters of the Future Circular
+Collider, a single conventional electron lens a few meters long would provide
+stabilization superior to tens of thousands of superconducting octupole
+magnets.
+",0,1,0,0,0,0
+4538,"On Algebraic Characterization of SSC of the Jahangir's Graph $\mathcal{J}_{n,m}$","  In this paper, some algebraic and combinatorial characterizations of the
+spanning simplicial complex $\Delta_s(\mathcal{J}_{n,m})$ of the Jahangir's
+graph $\mathcal{J}_{n,m}$ are explored. We show that
+$\Delta_s(\mathcal{J}_{n,m})$ is pure, present the formula for $f$-vectors
+associated to it and hence deduce a recipe for computing the Hilbert series of
+the Face ring $k[\Delta_s(\mathcal{J}_{n,m})]$. Finaly, we show that the face
+ring of $\Delta_s(\mathcal{J}_{n,m})$ is Cohen-Macaulay and give some open
+scopes of the current work.
+",0,0,1,0,0,0
+4539,Accelerated Gossip via Stochastic Heavy Ball Method,"  In this paper we show how the stochastic heavy ball method (SHB) -- a popular
+method for solving stochastic convex and non-convex optimization problems
+--operates as a randomized gossip algorithm. In particular, we focus on two
+special cases of SHB: the Randomized Kaczmarz method with momentum and its
+block variant. Building upon a recent framework for the design and analysis of
+randomized gossip algorithms, [Loizou Richtarik, 2016] we interpret the
+distributed nature of the proposed methods. We present novel protocols for
+solving the average consensus problem where in each step all nodes of the
+network update their values but only a subset of them exchange their private
+values. Numerical experiments on popular wireless sensor networks showing the
+benefits of our protocols are also presented.
+",1,0,0,0,0,0
+4540,Attention-Based Models for Text-Dependent Speaker Verification,"  Attention-based models have recently shown great performance on a range of
+tasks, such as speech recognition, machine translation, and image captioning
+due to their ability to summarize relevant information that expands through the
+entire length of an input sequence. In this paper, we analyze the usage of
+attention mechanisms to the problem of sequence summarization in our end-to-end
+text-dependent speaker recognition system. We explore different topologies and
+their variants of the attention layer, and compare different pooling methods on
+the attention weights. Ultimately, we show that attention-based models can
+improves the Equal Error Rate (EER) of our speaker verification system by
+relatively 14% compared to our non-attention LSTM baseline model.
+",1,0,0,1,0,0
+4541,Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds,"  Similarity and metric learning provides a principled approach to construct a
+task-specific similarity from weakly supervised data. However, these methods
+are subject to the curse of dimensionality: as the number of features grows
+large, poor generalization is to be expected and training becomes intractable
+due to high computational and memory costs. In this paper, we propose a
+similarity learning method that can efficiently deal with high-dimensional
+sparse data. This is achieved through a parameterization of similarity
+functions by convex combinations of sparse rank-one matrices, together with the
+use of a greedy approximate Frank-Wolfe algorithm which provides an efficient
+way to control the number of active features. We show that the convergence rate
+of the algorithm, as well as its time and memory complexity, are independent of
+the data dimension. We further provide a theoretical justification of our
+modeling choices through an analysis of the generalization error, which depends
+logarithmically on the sparsity of the solution rather than on the number of
+features. Our experiments on datasets with up to one million features
+demonstrate the ability of our approach to generalize well despite the high
+dimensionality as well as its superiority compared to several competing
+methods.
+",1,0,0,1,0,0
+4542,Automata-Guided Hierarchical Reinforcement Learning for Skill Composition,"  Skills learned through (deep) reinforcement learning often generalizes poorly
+across domains and re-training is necessary when presented with a new task. We
+present a framework that combines techniques in \textit{formal methods} with
+\textit{reinforcement learning} (RL). The methods we provide allows for
+convenient specification of tasks with logical expressions, learns hierarchical
+policies (meta-controller and low-level controllers) with well-defined
+intrinsic rewards, and construct new skills from existing ones with little to
+no additional exploration. We evaluate the proposed methods in a simple grid
+world simulation as well as a more complicated kitchen environment in AI2Thor
+",1,0,0,0,0,0
+4543,Emergent transport in a many-body open system driven by interacting quantum baths,"  We analyze an open many-body system that is strongly coupled at its
+boundaries to interacting quantum baths. We show that the two-body interactions
+inside the baths induce emergent phenomena in the spin transport. The system
+and baths are modeled as independent spin chains resulting in a global
+non-homogeneous XXZ model. The evolution of the system-bath state is simulated
+using matrix-product-states methods. We present two phase transitions induced
+by bath interactions. For weak bath interactions we observe ballistic and
+insulating phases. However, for strong bath interactions a diffusive phase
+emerges with a distinct power-law decay of the time-dependent spin current
+$Q\propto t^{-\alpha}$. Furthermore, we investigate long-lasting current
+oscillations arising from the non-Markovian dynamics in the homogeneous case,
+and find a sharp change in their frequency scaling coinciding with the triple
+point of the phase diagram.
+",0,1,0,0,0,0
+4544,Experimental Investigation of Optimum Beam Size for FSO Uplink,"  In this paper, the effect of transmitter beam size on the performance of free
+space optical (FSO) communication has been determined experimentally.
+Irradiance profile for varying turbulence strength is obtained using optical
+turbulence generating (OTG) chamber inside laboratory environment. Based on the
+results, an optimum beam size is investigated using the semi-analytical method.
+Moreover, the combined effects of atmospheric scintillation and beam wander
+induced pointing errors are considered in order to determine the optimum beam
+size that minimizes the bit error rate (BER) of the system for a fixed
+transmitter power and link length. The results show that the optimum beam size
+increases with the increase in zenith angle but has negligible effect with the
+increase in fade threshold level at low turbulence levels and has a marginal
+effect at high turbulence levels. Finally, the obtained outcome is useful for
+FSO system design and BER performance analysis.
+",1,1,0,0,0,0
+4545,The role of surface water in the geometry of Mars' valley networks and its climatic implications,"  Mars' surface bears the imprint of valley networks formed billions of years
+ago and their relicts can still be observed today. However, whether these
+networks were formed by groundwater sapping, ice melt, or fluvial runoff has
+been continuously debated. These different scenarios have profoundly different
+implications for Mars' climatic history, and thus for its habitability in the
+distant past. Recent studies on Earth revealed that channel networks in arid
+landscapes with more surface runoff branch at narrower angles, while in humid
+environments with more groundwater flow, branching angles are much wider. We
+find that valley networks on Mars generally tend to branch at narrow angles
+similar to those found in arid landscapes on Earth. This result supports the
+inference that Mars once had an active hydrologic cycle and that Mars' valley
+networks were formed primarily by overland flow erosion with groundwater
+seepage playing only a minor role.
+",0,1,0,0,0,0
+4546,An All-in-One Network for Dehazing and Beyond,"  This paper proposes an image dehazing model built with a convolutional neural
+network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed
+based on a re-formulated atmospheric scattering model. Instead of estimating
+the transmission matrix and the atmospheric light separately as most previous
+models did, AOD-Net directly generates the clean image through a light-weight
+CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other
+deep models, e.g., Faster R-CNN, for improving high-level task performance on
+hazy images. Experimental results on both synthesized and natural hazy image
+datasets demonstrate our superior performance than the state-of-the-art in
+terms of PSNR, SSIM and the subjective visual quality. Furthermore, when
+concatenating AOD-Net with Faster R-CNN and training the joint pipeline from
+end to end, we witness a large improvement of the object detection performance
+on hazy images.
+",1,0,0,0,0,0
+4547,GdPtPb: A non collinear antiferromagnet with distorted Kagomé lattice,"  In the spirit of searching for Gd-based, frustrated, rare earth magnets, we
+have found antiferomagnetism (AF) in GdPtPb which crystallizes in the
+ZrNiAl-type structure that has a distorted Kagomé lattice of Gd-triangles.
+Single crystals were grown and investigated using structural, magnetic,
+transport and thermodynamic measurements. GdPtPb orders antiferromagnetically
+at 15.5 K arguably with a planar, non-collinear structure. The high temperature
+magnetic susceptibility data reveal an ""anti-frustration"" behavior having a
+frustration parameter, $|f|$ = $|\Theta|$/ $T_N$ = 0.25, which can be explained
+by mean field theory (MFT) within a two sub-lattice model. Study of the
+magnetic phase diagram down to $T$ = 1.8 K reveals a change of magnetic
+structure through a metamagnetic transition at around 20 kOe and the
+disappearance of the AF ordering near 140 kOe. In total, our work indicates
+that, GdPtPb can serve as an example of a planar, non collinear, AF with a
+distorted Kagomé magnetic sub-lattice.
+",0,1,0,0,0,0
+4548,Belitskii's canonical forms of linear dynamical systems,"  In the note, all indecomposable canonical forms of linear systems with
+dimension less than or equal to $4$ are determined based on Belitskii's
+algorithm. As an application, an effective way to calculate dimensions of
+equivalence classes of linear systems is given by using Belitskii's canonical
+forms.
+",0,0,1,0,0,0
+4549,The Geometry of Nodal Sets and Outlier Detection,"  Let $(M,g)$ be a compact manifold and let $-\Delta \phi_k = \lambda_k \phi_k$
+be the sequence of Laplacian eigenfunctions. We present a curious new
+phenomenon which, so far, we only managed to understand in a few highly
+specialized cases: the family of functions $f_N:M \rightarrow \mathbb{R}_{\geq
+0}$ $$ f_N(x) = \sum_{k \leq N}{ \frac{1}{\sqrt{\lambda_k}}
+\frac{|\phi_k(x)|}{\|\phi_k\|_{L^{\infty}(M)}}}$$ seems strangely suited for
+the detection of anomalous points on the manifold. It may be heuristically
+interpreted as the sum over distances to the nearest nodal line and potentially
+hints at a new phenomenon in spectral geometry. We give rigorous statements on
+the unit square $[0,1]^2$ (where minima localize in $\mathbb{Q}^2$) and on
+Paley graphs (where $f_N$ recovers the geometry of quadratic residues of the
+underlying finite field $\mathbb{F}_p$). Numerical examples show that the
+phenomenon seems to arise on fairly generic manifolds.
+",0,0,1,1,0,0
+4550,Multimodal Clustering for Community Detection,"  Multimodal clustering is an unsupervised technique for mining interesting
+patterns in $n$-adic binary relations or $n$-mode networks. Among different
+types of such generalized patterns one can find biclusters and formal concepts
+(maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode
+case, closed $n$-sets for $n$-mode case, etc. Object-attribute biclustering
+(OA-biclustering) for mining large binary datatables (formal contexts or 2-mode
+networks) arose by the end of the last decade due to intractability of
+computation problems related to formal concepts; this type of patterns was
+proposed as a meaningful and scalable approximation of formal concepts. In this
+paper, our aim is to present recent advance in OA-biclustering and its
+extensions to mining multi-mode communities in SNA setting. We also discuss
+connection between clustering coefficients known in SNA community for 1-mode
+and 2-mode networks and OA-bicluster density, the main quality measure of an
+OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks
+show that this type of patterns is suitable for community detection in
+multi-mode cases within reasonable time even though the number of corresponding
+$n$-cliques is still unknown due to computation difficulties. An interpretation
+of OA-biclusters for 1-mode networks is provided as well.
+",1,0,0,1,0,0
+4551,Identification of a space varying coefficient of a linear viscoelastic string of Maxwell-Boltzman type,"  In this paper we solve the problem of the identification of a coefficient
+which appears in the model of a distributed system with persistent memory
+encountered in linear viscoelasticity (and in diffusion processes with memory).
+The additional data used in the identification are subsumed in the input output
+map from the deformation to the traction on the boundary. We extend a dynamical
+approach to identification introduced by Belishev in the case of purely elastic
+(memoryless) bodies and based on a special equation due to Blagoveshchenskii.
+So, in particular, we extend Blagoveshchenskii equation to our class of systems
+with persistent memory.
+",1,0,1,0,0,0
+4552,Efficient four-wave mixing at the nanofocus of integrated organic gap plasmon waveguides on silicon,"  Nonlinear optics, especially frequency mixing, underpins modern optical
+technology and scientific exploration in quantum optics, materials and life
+sciences, and optical communications. Since nonlinear effects are weak,
+efficient frequency mixing must accumulate over large interaction lengths
+restricting the integration of nonlinear photonics with electronics and
+establishing limitations on mixing processes due to the requirement of phase
+matching. In this work we report efficient four-wave mixing over micron-scale
+interaction lengths at telecoms wavelengths. We use an integrated plasmonic gap
+waveguide on silicon that strongly confines light within a nonlinear organic
+polymer in the gap. Our approach is so effective because the gap waveguide
+intensifies light by efficiently nanofocusing it to a mode cross-section of a
+few tens of nanometres, generating a nonlinear response so strong that
+efficient four-wave mixing accumulates in just a micron. This is significant as
+our technique opens up nonlinear optics to a regime where phase matching and
+dispersion considerations are relaxed, giving rise to the possibility of
+compact, broadband, and efficient frequency mixing on a platform that can be
+integrated with silicon photonics.
+",0,1,0,0,0,0
+4553,Weakly nonergodic dynamics in the Gross--Pitaevskii lattice,"  The microcanonical Gross--Pitaevskii (aka semiclassical Bose-Hubbard) lattice
+model dynamics is characterized by a pair of energy and norm densities. The
+grand canonical Gibbs distribution fails to describe a part of the density
+space, due to the boundedness of its kinetic energy spectrum. We define
+Poincare equilibrium manifolds and compute the statistics of microcanonical
+excursion times off them. The tails of the distribution functions quantify the
+proximity of the many-body dynamics to a weakly-nonergodic phase, which occurs
+when the average excursion time is infinite. We find that a crossover to
+weakly-nonergodic dynamics takes place inside the nonGibbs phase, being
+unnoticed by the largest Lyapunov exponent. In the ergodic part of the
+non-Gibbs phase, the Gibbs distribution should be replaced by an unknown
+modified one. We relate our findings to the corresponding integrable limit,
+close to which the actions are interacting through a short range coupling
+network.
+",0,1,0,0,0,0
+4554,Does agricultural subsidies foster Italian southern farms? A Spatial Quantile Regression Approach,"  During the last decades, public policies become a central pillar in
+supporting and stabilising agricultural sector. In 1962, EU policy-makers
+developed the so-called Common Agricultural Policy (CAP) to ensure
+competitiveness and a common market organisation for agricultural products,
+while 2003 reform decouple the CAP from the production to focus only on income
+stabilization and the sustainability of agricultural sector. Notwithstanding
+farmers are highly dependent to public support, literature on the role played
+by the CAP in fostering agricultural performances is still scarce and
+fragmented. Actual CAP policies increases performance differentials between
+Northern Central EU countries and peripheral regions. This paper aims to
+evaluate the effectiveness of CAP in stimulate performances by focusing on
+Italian lagged Regions. Moreover, agricultural sector is deeply rooted in
+place-based production processes. In this sense, economic analysis which omit
+the presence of spatial dependence produce biased estimates of the
+performances. Therefore, this paper, using data on subsidies and economic
+results of farms from the RICA dataset which is part of the Farm Accountancy
+Data Network (FADN), proposes a spatial Augmented Cobb-Douglas Production
+Function to evaluate the effects of subsidies on farm's performances. The major
+innovation in this paper is the implementation of a micro-founded quantile
+version of a spatial lag model to examine how the impact of the subsidies may
+vary across the conditional distribution of agricultural performances. Results
+show an increasing shape which switch from negative to positive at the median
+and becomes statistical significant for higher quantiles. Additionally, spatial
+autocorrelation parameter is positive and significant across all the
+conditional distribution, suggesting the presence of significant spatial
+spillovers in agricultural performances.
+",0,0,0,1,0,0
+4555,Proton fire hose instabilities in the expanding solar wind,"  Using two-dimensional hybrid expanding box simulations we study the
+competition between the continuously driven parallel proton temperature
+anisotropy and fire hose instabilities in collisionless homogeneous plasmas.
+For quasi radial ambient magnetic field the expansion drives
+$T_{\mathrm{p}\|}>T_{\mathrm{p}\perp}$ and the system becomes eventually
+unstable with respect to the dominant parallel fire hose instability. This
+instability is generally unable to counteract the induced anisotropization and
+the system typically becomes unstable with respect to the oblique fire hose
+instability later on. The oblique instability efficiently reduces the
+anisotropy and the system rapidly stabilizes while a significant part of the
+generated electromagnetic fluctuations is damped to protons. As long as the
+magnetic field is in the quasi radial direction, this evolution repeats itself
+and the electromagnetic fluctuations accumulate. For sufficiently oblique
+magnetic field the expansion drives $T_{\mathrm{p}\perp}>T_{\mathrm{p}\|}$ and
+brings the system to the stable region with respect to the fire hose
+instabilities.
+",0,1,0,0,0,0
+4556,Minimax Optimal Rates of Estimation in Functional ANOVA Models with Derivatives,"  We establish minimax optimal rates of convergence for nonparametric
+estimation in functional ANOVA models when data from first-order partial
+derivatives are available. Our results reveal that partial derivatives can
+improve convergence rates for function estimation with deterministic or random
+designs. In particular, for full $d$-interaction models, the optimal rates with
+first-order partial derivatives on $p$ covariates are identical to those for
+$(d-p)$-interaction models without partial derivatives. For additive models,
+the rates by using all first-order partial derivatives are root-$n$ to achieve
+the ""parametric rate"". We also investigate the minimax optimal rates for
+first-order partial derivative estimations when derivative data are available.
+Those rates coincide with the optimal rate for estimating the first-order
+derivative of a univariate function.
+",0,0,1,1,0,0
+4557,Phase Retrieval via Randomized Kaczmarz: Theoretical Guarantees,"  We consider the problem of phase retrieval, i.e. that of solving systems of
+quadratic equations. A simple variant of the randomized Kaczmarz method was
+recently proposed for phase retrieval, and it was shown numerically to have a
+computational edge over state-of-the-art Wirtinger flow methods. In this paper,
+we provide the first theoretical guarantee for the convergence of the
+randomized Kaczmarz method for phase retrieval. We show that it is sufficient
+to have as many Gaussian measurements as the dimension, up to a constant
+factor. Along the way, we introduce a sufficient condition on measurement sets
+for which the randomized Kaczmarz method is guaranteed to work. We show that
+Gaussian sampling vectors satisfy this property with high probability; this is
+proved using a chaining argument coupled with bounds on VC dimension and metric
+entropy.
+",1,0,1,1,0,0
+4558,Beyond perturbation 1: de Rham spaces,"  It is shown that if one uses the notion of infinity nilpotent elements due to
+Moerdijk and Reyes, instead of the usual definition of nilpotents to define
+reduced $C^\infty$-schemes, the resulting de Rham spaces are given as quotients
+by actions of germs of diagonals, instead of the formal neighbourhoods of the
+diagonals.
+",0,0,1,0,0,0
+4559,Semantic Web Prefetching Using Semantic Relatedness between Web pages,"  Internet as become the way of life in the fast growing digital life.Even with
+the increase in the internet speed, higher latency time is still a challenge.
+To reduce latency, caching and pre fetching techniques can be used. However,
+caching fails for dynamic websites which keeps on changing rapidly. Another
+technique is web prefetching, which prefetches the web pages that the user is
+likely to request for in the future. Semantic web prefetching makes use of
+keywords and descriptive texts like anchor text, titles, text surrounding
+anchor text of the present web pages for predicting users future requests.
+Semantic information is embedded within the web pages during their designing
+for the purpose of reflecting the relationship between the web pages. The
+client can fetch this information from the server. However, this technique
+involves load on web designers for adding external tags and on server for
+providing this information along with the desired page, which is not desirable.
+This paper is an effort to find the semantic relation between web pages using
+the keywords provided by the user and the anchor texts of the hyperlinks on the
+present web page.It provides algorithms for sequential and similar semantic
+relations. These algorithms will be implemented on the client side which will
+not cause overhead on designers and load on server for semantic information.
+",1,0,0,0,0,0
+4560,Learning Certifiably Optimal Rule Lists for Categorical Data,"  We present the design and implementation of a custom discrete optimization
+technique for building rule lists over a categorical feature space. Our
+algorithm produces rule lists with optimal training performance, according to
+the regularized empirical risk, with a certificate of optimality. By leveraging
+algorithmic bounds, efficient data structures, and computational reuse, we
+achieve several orders of magnitude speedup in time and a massive reduction of
+memory consumption. We demonstrate that our approach produces optimal rule
+lists on practical problems in seconds. Our results indicate that it is
+possible to construct optimal sparse rule lists that are approximately as
+accurate as the COMPAS proprietary risk prediction tool on data from Broward
+County, Florida, but that are completely interpretable. This framework is a
+novel alternative to CART and other decision tree methods for interpretable
+modeling.
+",0,0,0,1,0,0
+4561,Cold-Start Reinforcement Learning with Softmax Policy Gradient,"  Policy-gradient approaches to reinforcement learning have two common and
+undesirable overhead procedures, namely warm-start training and sample variance
+reduction. In this paper, we describe a reinforcement learning method based on
+a softmax value function that requires neither of these procedures. Our method
+combines the advantages of policy-gradient methods with the efficiency and
+simplicity of maximum-likelihood approaches. We apply this new cold-start
+reinforcement learning method in training sequence generation models for
+structured output prediction problems. Empirical evidence validates this method
+on automatic summarization and image captioning tasks.
+",1,0,0,0,0,0
+4562,Implicit Cooperative Positioning in Vehicular Networks,"  Absolute positioning of vehicles is based on Global Navigation Satellite
+Systems (GNSS) combined with on-board sensors and high-resolution maps. In
+Cooperative Intelligent Transportation Systems (C-ITS), the positioning
+performance can be augmented by means of vehicular networks that enable
+vehicles to share location-related information. This paper presents an Implicit
+Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle
+(V2V) connectivity in an innovative manner, avoiding the use of explicit V2V
+measurements such as ranging. In the ICP approach, vehicles jointly localize
+non-cooperative physical features (such as people, traffic lights or inactive
+cars) in the surrounding areas, and use them as common noisy reference points
+to refine their location estimates. Information on sensed features are fused
+through V2V links by a consensus procedure, nested within a message passing
+algorithm, to enhance the vehicle localization accuracy. As positioning does
+not rely on explicit ranging information between vehicles, the proposed ICP
+method is amenable to implementation with off-the-shelf vehicular communication
+hardware. The localization algorithm is validated in different traffic
+scenarios, including a crossroad area with heterogeneous conditions in terms of
+feature density and V2V connectivity, as well as a real urban area by using
+Simulation of Urban MObility (SUMO) for traffic data generation. Performance
+results show that the proposed ICP method can significantly improve the vehicle
+location accuracy compared to the stand-alone GNSS, especially in harsh
+environments, such as in urban canyons, where the GNSS signal is highly
+degraded or denied.
+",1,0,0,1,0,0
+4563,A Generalized Accelerated Composite Gradient Method: Uniting Nesterov's Fast Gradient Method and FISTA,"  We demonstrate that the augmented estimate sequence framework unites the most
+popular primal first-order schemes for large-scale problems: the Fast Gradient
+Method (FGM) and the Fast Iterative Shrinkage Thresholding Algorithm (FISTA).
+We further showcase the flexibility of the augmented estimate sequence by
+deriving a Generalized Accelerated Composite Gradient Method endowed with
+monotonicity alongside a versatile line-search procedure. The new method
+surpasses both FGM and FISTA in terms of robustness and usability. In
+particular, it is guaranteed to converge without requiring any quantitative
+prior information on the problem. Additional information, if available, leads
+to an improvement in performance at least on par with the state-of-the-art. We
+support our findings with simulation results.
+",0,0,1,0,0,0
+4564,Piezoresponse of ferroelectric films in ferroionic states: time and voltage dynamics,"  The interplay between electrochemical surface charges and bulk
+ferroelectricity in thin films gives rise to a continuum of coupled ferro-ionic
+states. These states are exquisitely sensitive to chemical and electric
+conditions at the surfaces, applied voltage, and oxygen pressure. Using the
+analytical approach combining the Ginzburg-Landau-Devonshire description of the
+ferroelectricity with Langmuir adsorption isotherm for the ions at the film
+surface, we have studied the temperature-, time- and field- dependent
+polarization changes and electromechanical response of the ferro-ionic states.
+The responses are found to be inseparable in thermodynamic equilibrium and at
+low frequencies of applied voltage. The states become separable in high
+frequency dynamic mode due to the several orders of magnitude difference in the
+relaxation times of ferroelectric polarization and surface ions charge density.
+These studies provide an insight into dynamic behavior of nanoscale
+ferroelectrics with open surface exposed to different kinds of
+electrochemically active gaseous surrounding.
+",0,1,0,0,0,0
+4565,Penalty Alternating Direction Methods for Mixed-Integer Optimization: A New View on Feasibility Pumps,"  Feasibility pumps are highly effective primal heuristics for mixed-integer
+linear and nonlinear optimization. However, despite their success in practice
+there are only few works considering their theoretical properties. We show that
+feasibility pumps can be seen as alternating direction methods applied to
+special reformulations of the original problem, inheriting the convergence
+theory of these methods. Moreover, we propose a novel penalty framework that
+encompasses this alternating direction method, which allows us to refrain from
+random perturbations that are applied in standard versions of feasibility pumps
+in case of failure. We present a convergence theory for the new penalty based
+alternating direction method and compare the new variant of the feasibility
+pump with existing versions in an extensive numerical study for mixed-integer
+linear and nonlinear problems.
+",0,0,1,0,0,0
+4566,Lorentz covariant and gauge invariant description of orbital and spin angular momentum and the non-symmetric energy momentum tensor,"  Starting from covariant expressions, a gauge independent separation of
+orbital and spin angular momentum for electrodynamics is presented. This
+results from the non-symmetric canonical energy momentum tensor of the
+electromagnetic field. The origin of the difficulty is discussed and a
+covariant gauge invariant spin vector is derived. The paradox concerning the
+spin angular momentum of a plane wave finds a natural solution.
+",0,1,0,0,0,0
+4567,Dirichlet's theorem and Jacobsthal's function,"  If $a$ and $d$ are relatively prime, we refer to the set of integers
+congruent to $a$ mod $d$ as an `eligible' arithmetic progression. A theorem of
+Dirichlet says that every eligible arithmetic progression contains infinitely
+many primes; the theorem follows from the assertion that every eligible
+arithmetic progression contains at least one prime. The Jacobsthal function
+$g(n)$ is defined as the smallest positive integer such that every sequence of
+$g(n)$ consecutive integers contains an integer relatively prime to $n$. In
+this paper, we show by a combinatorial argument that every eligible arithmetic
+progression with $d\le76$ contains at least one prime, and we show that certain
+plausible bounds on the Jacobsthal function of primorials would imply that
+every eligible arithmetic progression contains at least one prime. That is,
+certain plausible bounds on the Jacobsthal function would lead to an elementary
+proof of Dirichlet's theorem.
+",0,0,1,0,0,0
+4568,Asymptotic control theory for a closed string,"  We develop an asymptotical control theory for one of the simplest distributed
+oscillating systems, namely, for a closed string under a bounded load applied
+to a single distinguished point. We find exact classes of string states that
+admit complete damping and an asymptotically exact value of the required time.
+By using approximate reachable sets instead of exact ones, we design a
+dry-friction like feedback control, which turns out to be asymptotically
+optimal. We prove the existence of motion under the control using a rather
+explicit solution of a nonlinear wave equation. Remarkably, the solution is
+determined via purely algebraic operations. The main result is a proof of
+asymptotic optimality of the control thus constructed.
+",0,0,1,0,0,0
+4569,Large Scale Replication Projects in Contemporary Psychological Research,"  Replication is complicated in psychological research because studies of a
+given psychological phenomenon can never be direct or exact replications of one
+another, and thus effect sizes vary from one study of the phenomenon to the
+next--an issue of clear importance for replication. Current large scale
+replication projects represent an important step forward for assessing
+replicability, but provide only limited information because they have thus far
+been designed in a manner such that heterogeneity either cannot be assessed or
+is intended to be eliminated. Consequently, the non-trivial degree of
+heterogeneity found in these projects represents a lower bound on
+heterogeneity. We recommend enriching large scale replication projects going
+forward by em- bracing heterogeneity. We argue this is key for assessing
+replicability: if effect sizes are sufficiently heterogeneous--even if the sign
+of the effect is consistent--the phenomenon in question does not seem
+particularly replicable and the theory underlying it seems poorly constructed
+and in need of enrichment. Uncovering why and revising theory in light of it
+will lead to improved theory that explains heterogeneity and in- creases
+replicability. Given this, large scale replication projects can play an
+important role not only in assessing replicability but also in advancing
+theory.
+",0,0,0,1,0,0
+4570,Intrinsic pinning by naturally occurring correlated defects in FeSe$_\text{1-x}$Te$_\text{x}$ superconductors,"  We study the angular dependence of the dissipation in the superconducting
+state of FeSe and Fe(Se$_\text{1-x}$Te$_\text{x}$) through electrical transport
+measurements, using crystalline intergrown materials. We reveal the key role of
+the inclusions of the non superconducting magnetic phase
+Fe$_\text{1-y}$(Se$_\text{1-x}$Te$_\text{x}$), growing into the
+Fe(Se$_\text{1-x}$Te$_\text{x}$) pure $\beta$-phase, in the development of a
+correlated defect structure. The matching of both atomic structures defines the
+growth habit of the crystalline material as well as the correlated planar
+defects orientation.
+",0,1,0,0,0,0
+4571,Models for the Displacement Calculus,"  The displacement calculus $\mathbf{D}$ is a conservative extension of the
+Lambek calculus $\mathbf{L1}$ (with empty antecedents allowed in sequents).
+$\mathbf{L1}$ can be said to be the logic of concatenation, while $\mathbf{D}$
+can be said to be the logic of concatenation and intercalation. In many senses,
+it can be claimed that $\mathbf{D}$ mimics $\mathbf{L1}$ in that the proof
+theory, generative capacity and complexity of the former calculus are natural
+extensions of the latter calculus. In this paper, we strengthen this claim. We
+present the appropriate classes of models for $\mathbf{D}$ and prove some
+completeness results; strikingly, we see that these results and proofs are
+natural extensions of the corresponding ones for $\mathbf{L1}$.
+",1,0,0,0,0,0
+4572,Labeled homology of higher-dimensional automata,"  We construct labeling homomorphisms on the cubical homology of
+higher-dimensional automata and show that they are natural with respect to
+cubical dimaps and compatible with the tensor product of HDAs. We also indicate
+two possible applications of labeled homology in concurrency theory.
+",1,0,1,0,0,0
+4573,Magnetic Actuation and Feedback Cooling of a Cavity Optomechanical Torque Sensor,"  We demonstrate the integration of a mesoscopic ferromagnetic needle with a
+cavity optomechanical torsional resonator, and its use for quantitative
+determination of the needle's magnetic properties, as well as amplification and
+cooling of the resonator motion. With this system we measure torques as small
+as 32 zNm, corresponding to sensing an external magnetic field of 0.12 A/m (150
+nT). Furthermore, we are able to extract the magnetization (1710 kA/m) of the
+magnetic sample, not known a priori, demonstrating this system's potential for
+studies of nanomagnetism. Finally, we show that we can magnetically drive the
+torsional resonator into regenerative oscillations, and dampen its mechanical
+mode temperature from room temperature to 11.6 K, without sacrificing torque
+sensitivity.
+",0,1,0,0,0,0
+4574,A Maximum Matching Algorithm for Basis Selection in Spectral Learning,"  We present a solution to scale spectral algorithms for learning sequence
+functions. We are interested in the case where these functions are sparse (that
+is, for most sequences they return 0). Spectral algorithms reduce the learning
+problem to the task of computing an SVD decomposition over a special type of
+matrix called the Hankel matrix. This matrix is designed to capture the
+relevant statistics of the training sequences. What is crucial is that to
+capture long range dependencies we must consider very large Hankel matrices.
+Thus the computation of the SVD becomes a critical bottleneck. Our solution
+finds a subset of rows and columns of the Hankel that realizes a compact and
+informative Hankel submatrix. The novelty lies in the way that this subset is
+selected: we exploit a maximal bipartite matching combinatorial algorithm to
+look for a sub-block with full structural rank, and show how computation of
+this sub-block can be further improved by exploiting the specific structure of
+Hankel matrices.
+",1,0,0,1,0,0
+4575,Bäcklund Transformations for the Boussinesq Equation and Merging Solitons,"  The Bäcklund transformation (BT) for the ""good"" Boussinesq equation and its
+superposition principles are presented and applied. Unlike many other standard
+integrable equations, the Boussinesq equation does not have a strictly
+algebraic superposition principle for 2 BTs, but it does for 3. We present
+associated lattice systems. Applying the BT to the trivial solution generates
+standard solitons but also what we call ""merging solitons"" --- solutions in
+which two solitary waves (with related speeds) merge into a single one. We use
+the superposition principles to generate a variety of interesting solutions,
+including superpositions of a merging soliton with $1$ or $2$ regular solitons,
+and solutions that develop a singularity in finite time which then disappears
+at some later finite time. We prove a Wronskian formula for the solutions
+obtained by applying a general sequence of BTs on the trivial solution.
+Finally, we show how to obtain the standard conserved quantities of the
+Boussinesq equation from the BT, and how the hierarchy of local symmetries
+follows in a simple manner from the superposition principle for 3 BTs.
+",0,1,1,0,0,0
+4576,Towards Evolutional Compression,"  Compressing convolutional neural networks (CNNs) is essential for
+transferring the success of CNNs to a wide variety of applications to mobile
+devices. In contrast to directly recognizing subtle weights or filters as
+redundant in a given CNN, this paper presents an evolutionary method to
+automatically eliminate redundant convolution filters. We represent each
+compressed network as a binary individual of specific fitness. Then, the
+population is upgraded at each evolutionary iteration using genetic operations.
+As a result, an extremely compact CNN is generated using the fittest
+individual. In this approach, either large or small convolution filters can be
+redundant, and filters in the compressed network are more distinct. In
+addition, since the number of filters in each convolutional layer is reduced,
+the number of filter channels and the size of feature maps are also decreased,
+naturally improving both the compression and speed-up ratios. Experiments on
+benchmark deep CNN models suggest the superiority of the proposed algorithm
+over the state-of-the-art compression methods.
+",1,0,0,1,0,0
+4577,Spin inversion in fluorinated graphene n-p junction,"  We consider a dilute fluorinated graphene nanoribbon as a spin-active
+element. The fluorine adatoms introduce a local spin-orbit Rashba interaction
+that induces spin-precession for electron passing by. In the absence of the
+external magnetic field the transport is dominated by multiple scattering by
+adatoms which cancels the spin precession effects, since the direction of the
+spin precession depends on the electron momentum. Accumulation of the spin
+precession effects is possible provided that the Fermi level electron passes
+many times near the same adatom with the same momentum. In order to arrange for
+these conditions a circular n-p junction can be introduced to the ribbon by
+e.g. potential of the tip of an atomic force microscope. In the quantum Hall
+conditions the electron current gets confined along the junction. The electron
+spin interaction with the local Rashba field changes with the lifetime of the
+quasi-bound states that is controlled with the coupling of the junction to the
+edge of the ribbon. We demonstrate that the spin-flip probability can be
+increased in this manner by as much as three orders of magnitude.
+",0,1,0,0,0,0
+4578,Brunn-Minkowski inequalities in product metric measure spaces,"  Given one metric measure space $X$ satisfying a linear Brunn-Minkowski
+inequality, and a second one $Y$ satisfying a Brunn-Minkowski inequality with
+exponent $p\ge -1$, we prove that the product $X\times Y$ with the standard
+product distance and measure satisfies a Brunn-Minkowski inequality of order
+$1/(1+p^{-1})$ under mild conditions on the measures and the assumption that
+the distances are strictly intrinsic. The same result holds when we consider
+restricted classes of sets. We also prove that a linear Brunn-Minkowski
+inequality is obtained in $X\times Y$ when $Y$ satisfies a Prékopa-Leindler
+inequality.
+In particular, we show that the classical Brunn-Minkowski inequality holds
+for any pair of weakly unconditional sets in $\mathbb{R}^n$ (i.e., those
+containing the projection of every point in the set onto every coordinate
+subspace) when we consider the standard distance and the product measure of $n$
+one-dimensional real measures with positively decreasing densities. This yields
+an improvement of the class of sets satisfying the Gaussian Brunn-Minkowski
+inequality.
+Furthermore, associated isoperimetric inequalities as well as recently
+obtained Brunn-Minkowski's inequalities are derived from our results.
+",0,0,1,0,0,0
+4579,Deep Learning with Low Precision by Half-wave Gaussian Quantization,"  The problem of quantizing the activations of a deep neural network is
+considered. An examination of the popular binary quantization approach shows
+that this consists of approximating a classical non-linearity, the hyperbolic
+tangent, by two functions: a piecewise constant sign function, which is used in
+feedforward network computations, and a piecewise linear hard tanh function,
+used in the backpropagation step during network learning. The problem of
+approximating the ReLU non-linearity, widely used in the recent deep learning
+literature, is then considered. An half-wave Gaussian quantizer (HWGQ) is
+proposed for forward approximation and shown to have efficient implementation,
+by exploiting the statistics of of network activations and batch normalization
+operations commonly used in the literature. To overcome the problem of gradient
+mismatch, due to the use of different forward and backward approximations,
+several piece-wise backward approximators are then investigated. The
+implementation of the resulting quantized network, denoted as HWGQ-Net, is
+shown to achieve much closer performance to full precision networks, such as
+AlexNet, ResNet, GoogLeNet and VGG-Net, than previously available low-precision
+networks, with 1-bit binary weights and 2-bit quantized activations.
+",1,0,0,0,0,0
+4580,Degenerate cyclotomic Hecke algebras and higher level Heisenberg categorification,"  We associate a monoidal category $\mathcal{H}^\lambda$ to each dominant
+integral weight $\lambda$ of $\widehat{\mathfrak{sl}}_p$ or
+$\mathfrak{sl}_\infty$. These categories, defined in terms of planar diagrams,
+act naturally on categories of modules for the degenerate cyclotomic Hecke
+algebras associated to $\lambda$. We show that, in the $\mathfrak{sl}_\infty$
+case, the level $d$ Heisenberg algebra embeds into the Grothendieck ring of
+$\mathcal{H}^\lambda$, where $d$ is the level of $\lambda$. The categories
+$\mathcal{H}^\lambda$ can be viewed as a graphical calculus describing
+induction and restriction functors between categories of modules for degenerate
+cyclotomic Hecke algebras, together with their natural transformations. As an
+application of this tool, we prove a new result concerning centralizers for
+degenerate cyclotomic Hecke algebras.
+",0,0,1,0,0,0
+4581,Intense automorphisms of finite groups,"  Let $G$ be a group. An automorphism of $G$ is called intense if it sends each
+subgroup of $G$ to a conjugate; the collection of such automorphisms is denoted
+by $\mathrm{Int}(G)$. In the special case in which $p$ is a prime number and
+$G$ is a finite $p$-group, one can show that $\mathrm{Int}(G)$ is the
+semidirect product of a normal $p$-Sylow and a cyclic subgroup of order
+dividing $p-1$. In this thesis we classify the finite $p$-groups whose groups
+of intense automorphisms are not themselves $p$-groups. It emerges from our
+investigation that the structure of such groups is almost completely determined
+by their nilpotency class: for $p>3$, they share a quotient, growing with their
+class, with a uniquely determined infinite $2$-generated pro-$p$ group.
+",0,0,1,0,0,0
+4582,The spectra of harmonic layer potential operators on domains with rotationally symmetric conical points,"  We study the adjoint of the double layer potential associated with the
+Laplacian (the adjoint of the Neumann-Poincaré operator), as a map on the
+boundary surface $\Gamma$ of a domain in $\mathbb{R}^3$ with conical points.
+The spectrum of this operator directly reflects the well-posedness of related
+transmission problems across $\Gamma$. In particular, if the domain is
+understood as an inclusion with complex permittivity $\epsilon$, embedded in a
+background medium with unit permittivity, then the polarizability tensor of the
+domain is well-defined when $(\epsilon+1)/(\epsilon-1)$ belongs to the
+resolvent set in energy norm. We study surfaces $\Gamma$ that have a finite
+number of conical points featuring rotational symmetry. On the energy space, we
+show that the essential spectrum consists of an interval. On $L^2(\Gamma)$,
+i.e. for square-integrable boundary data, we show that the essential spectrum
+consists of a countable union of curves, outside of which the Fredholm index
+can be computed as a winding number with respect to the essential spectrum. We
+provide explicit formulas, depending on the opening angles of the conical
+points. We reinforce our study with very precise numerical experiments,
+computing the energy space spectrum and the spectral measures of the
+polarizability tensor in two different examples. Our results indicate that the
+densities of the spectral measures may approach zero extremely rapidly in the
+continuous part of the energy space spectrum.
+",0,0,1,0,0,0
+4583,Experimental Determination of the Structural Coefficient of Restitution of a Bouncing Asteroid Lander,"  The structural coefficient of restitution describes the kinetic energy
+dissipation upon low-velocity (~0.1 m/s) impact of a small asteroid lander,
+MASCOT, against a hard, ideally elastic plane surface. It is a crucial
+worst-case input for mission analysis for landing MACOT on a 1km asteroid in
+2018. We conducted pendulum tests and describe their analysis and the results.
+",0,1,0,0,0,0
+4584,Onset of a modulational instability in trapped dipolar Bose-Einstein condensates,"  We explore the phase diagram of a finite-sized dysprosium dipolar
+Bose-Einstein condensate in a cylindrical harmonic trap. We monitor the final
+state after the scattering length is lowered from the repulsive BEC regime to
+the quantum droplet regime. Either an adiabatic transformation between a BEC
+and a quantum droplet is obtained or, above a critical trap aspect ratio
+$\lambda_{\rm c}=1.87(14)$, a modulational instability results in the formation
+of multiple droplets. This is in full agreement with the predicted structure of
+the phase diagram with a crossover region below $\lambda_{\rm c}$ and a
+multistable region above. Our results provide the missing piece connecting the
+previously explored regimes resulting in a single or multiple dipolar quantum
+droplets.
+",0,1,0,0,0,0
+4585,Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology,"  Topological data analysis is an emerging area in exploratory data analysis
+and data mining. Its main tool, persistent homology, has become a popular
+technique to study the structure of complex, high-dimensional data. In this
+paper, we propose a novel method using persistent homology to quantify
+structural changes in time-varying graphs. Specifically, we transform each
+instance of the time-varying graph into metric spaces, extract topological
+features using persistent homology, and compare those features over time. We
+provide a visualization that assists in time-varying graph exploration and
+helps to identify patterns of behavior within the data. To validate our
+approach, we conduct several case studies on real world data sets and show how
+our method can find cyclic patterns, deviations from those patterns, and
+one-time events in time-varying graphs. We also examine whether
+persistence-based similarity measure as a graph metric satisfies a set of
+well-established, desirable properties for graph metrics.
+",1,0,0,0,0,0
+4586,Controller Synthesis for Discrete-Time Polynomial Systems via Occupation Measures,"  In this paper, we design nonlinear state feedback controllers for
+discrete-time polynomial dynamical systems via the occupation measure approach.
+We propose the discrete-time controlled Liouville equation, and use it to
+formulate the controller synthesis problem as an infinite-dimensional linear
+programming problem on measures, which is then relaxed as finite-dimensional
+semidefinite programming problems on moments of measures and their duals on
+sums-of-squares polynomials. Nonlinear controllers can be extracted from the
+solutions to the relaxed problems. The advantage of the occupation measure
+approach is that we solve convex problems instead of generally non-convex
+problems, and the computational complexity is polynomial in the state and input
+dimensions, and hence the approach is more scalable. In addition, we show that
+the approach can be applied to over-approximating the backward reachable set of
+discrete-time autonomous polynomial systems and the controllable set of
+discrete-time polynomial systems under known state feedback control laws. We
+illustrate our approach on several dynamical systems.
+",1,0,0,0,0,0
+4587,Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion,"  Computed Tomography (CT) reconstruction is a fundamental component to a wide
+variety of applications ranging from security, to healthcare. The classical
+techniques require measuring projections, called sinograms, from a full
+180$^\circ$ view of the object. This is impractical in a limited angle
+scenario, when the viewing angle is less than 180$^\circ$, which can occur due
+to different factors including restrictions on scanning time, limited
+flexibility of scanner rotation, etc. The sinograms obtained as a result, cause
+existing techniques to produce highly artifact-laden reconstructions. In this
+paper, we propose to address this problem through implicit sinogram completion,
+on a challenging real world dataset containing scans of common checked-in
+luggage. We propose a system, consisting of 1D and 2D convolutional neural
+networks, that operates on a limited angle sinogram to directly produce the
+best estimate of a reconstruction. Next, we use the x-ray transform on this
+reconstruction to obtain a ""completed"" sinogram, as if it came from a full
+180$^\circ$ measurement. We feed this to standard analytical and iterative
+reconstruction techniques to obtain the final reconstruction. We show with
+extensive experimentation that this combined strategy outperforms many
+competitive baselines. We also propose a measure of confidence for the
+reconstruction that enables a practitioner to gauge the reliability of a
+prediction made by our network. We show that this measure is a strong indicator
+of quality as measured by the PSNR, while not requiring ground truth at test
+time. Finally, using a segmentation experiment, we show that our reconstruction
+preserves the 3D structure of objects effectively.
+",0,0,0,1,0,0
+4588,A three-dimensional symmetry result for a phase transition equation in the genuinely nonlocal regime,"  We consider bounded solutions of the nonlocal Allen-Cahn equation $$
+(-\Delta)^s u=u-u^3\qquad{\mbox{ in }}{\mathbb{R}}^3,$$ under the monotonicity
+condition $\partial_{x_3}u>0$ and in the genuinely nonlocal regime in
+which~$s\in\left(0,\frac12\right)$. Under the limit assumptions $$
+\lim_{x_n\to-\infty} u(x',x_n)=-1\quad{\mbox{ and }}\quad \lim_{x_n\to+\infty}
+u(x',x_n)=1,$$ it has been recently shown that~$u$ is necessarily $1$D, i.e. it
+depends only on one Euclidean variable. The goal of this paper is to obtain a
+similar result without assuming such limit conditions. This type of results can
+be seen as nonlocal counterparts of the celebrated conjecture formulated by
+Ennio De Giorgi.
+",0,0,1,0,0,0
+4589,The growth of bismuth on Bi$_2$Se$_3$ and the stability of the first bilayer,"  Bi(0001) films with thicknesses up to several bilayers (BLs) are grown on
+Se-terminated Bi$_2$Se$_3$(0001) surfaces, and low energy electron diffraction
+(LEED), low energy ion scattering (LEIS) and atomic force microscopy (AFM) are
+used to investigate the surface composition, topography and atomic structure.
+For a single deposited Bi BL, the lattice constant matches that of the
+substrate and the Bi atoms adjacent to the uppermost Se atoms are located at
+fcc-like sites. When a 2nd Bi bilayer is deposited, it is incommensurate with
+the substrate. As the thickness of the deposited Bi film increases further, the
+lattice parameter evolves to that of bulk Bi(0001). After annealing a multiple
+BL film at 120°C, the first commensurate Bi BL remains intact, but the
+additional BLs aggregate to form thicker islands of Bi. These results show that
+a single Bi BL on Bi$_2$Se$_3$ is a particularly stable structure. After
+annealing to 490°C, all of the excess Bi desorbs and the Se-terminated
+Bi$_2$Se$_3$ surface is restored.
+",0,1,0,0,0,0
+4590,Introduction to Tensor Decompositions and their Applications in Machine Learning,"  Tensors are multidimensional arrays of numerical values and therefore
+generalize matrices to multiple dimensions. While tensors first emerged in the
+psychometrics community in the $20^{\text{th}}$ century, they have since then
+spread to numerous other disciplines, including machine learning. Tensors and
+their decompositions are especially beneficial in unsupervised learning
+settings, but are gaining popularity in other sub-disciplines like temporal and
+multi-relational data analysis, too.
+The scope of this paper is to give a broad overview of tensors, their
+decompositions, and how they are used in machine learning. As part of this, we
+are going to introduce basic tensor concepts, discuss why tensors can be
+considered more rigid than matrices with respect to the uniqueness of their
+decomposition, explain the most important factorization algorithms and their
+properties, provide concrete examples of tensor decomposition applications in
+machine learning, conduct a case study on tensor-based estimation of mixture
+models, talk about the current state of research, and provide references to
+available software libraries.
+",1,0,0,1,0,0
+4591,Sampling-based vs. Design-based Uncertainty in Regression Analysis,"  Consider a researcher estimating the parameters of a regression function
+based on data for all 50 states in the United States or on data for all visits
+to a website. What is the interpretation of the estimated parameters and the
+standard errors? In practice, researchers typically assume that the sample is
+randomly drawn from a large population of interest and report standard errors
+that are designed to capture sampling variation. This is common practice, even
+in applications where it is difficult to articulate what that population of
+interest is, and how it differs from the sample. In this article, we explore an
+alternative approach to inference, which is partly design-based. In a
+design-based setting, the values of some of the regressors can be manipulated,
+perhaps through a policy intervention. Design-based uncertainty emanates from
+lack of knowledge about the values that the regression outcome would have taken
+under alternative interventions. We derive standard errors that account for
+design-based uncertainty instead of, or in addition to, sampling-based
+uncertainty. We show that our standard errors in general are smaller than the
+infinite-population sampling-based standard errors and provide conditions under
+which they coincide.
+",0,0,1,1,0,0
+4592,Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction,"  Missing data and noisy observations pose significant challenges for reliably
+predicting events from irregularly sampled multivariate time series
+(longitudinal) data. Imputation methods, which are typically used for
+completing the data prior to event prediction, lack a principled mechanism to
+account for the uncertainty due to missingness. Alternatively, state-of-the-art
+joint modeling techniques can be used for jointly modeling the longitudinal and
+event data and compute event probabilities conditioned on the longitudinal
+observations. These approaches, however, make strong parametric assumptions and
+do not easily scale to multivariate signals with many observations. Our
+proposed approach consists of several key innovations. First, we develop a
+flexible and scalable joint model based upon sparse multiple-output Gaussian
+processes. Unlike state-of-the-art joint models, the proposed model can explain
+highly challenging structure including non-Gaussian noise while scaling to
+large data. Second, we derive an optimal policy for predicting events using the
+distribution of the event occurrence estimated by the joint model. The derived
+policy trades-off the cost of a delayed detection versus incorrect assessments
+and abstains from making decisions when the estimated event probability does
+not satisfy the derived confidence criteria. Experiments on a large dataset
+show that the proposed framework significantly outperforms state-of-the-art
+techniques in event prediction.
+",1,0,0,1,0,0
+4593,"Concurrent Coding: A Reason to Think Differently About Encoding Against Noise, Burst Errors and Jamming","  Concurrent coding is an unconventional encoding technique that simultaneously
+provides protection against noise, burst errors and interference. This
+simple-to-understand concept is investigated by distinguishing 2 types of code,
+open and closed, with the majority of the investigation concentrating on closed
+codes. Concurrent coding is shown to possess an inherent method of
+synchronisation thus requiring no additional synchronisation signals to be
+added. This enables an isolated codeword transmission to be synchronised and
+decoded in the presence of noise and burst errors. Comparisons are made with
+the spread spectrum technique CDMA. With a like-for-like comparison concurrent
+coding performs comparably against random noise effects, performs better
+against burst errors and is far superior in terms of transmitted energy
+efficiency
+",1,0,0,0,0,0
+4594,Traveling dark-bright solitons in a reduced spin-orbit coupled system: application to Bose-Einstein condensates,"  In the present work, we explore the potential of spin-orbit (SO) coupled
+Bose-Einstein condensates to support multi-component solitonic states in the
+form of dark-bright (DB) solitons. In the case where Raman linear coupling
+between components is absent, we use a multiscale expansion method to reduce
+the model to the integrable Mel'nikov system. The soliton solutions of the
+latter allow us to reconstruct approximate traveling DB solitons for the
+reduced SO coupled system. For small values of the formal perturbation
+parameter, the resulting waveforms propagate undistorted, while for large
+values thereof, they shed some dispersive radiation, and subsequently distill
+into a robust propagating structure. After quantifying the relevant radiation
+effect, we also study the dynamics of DB solitons in a parabolic trap,
+exploring how their oscillation frequency varies as a function of the bright
+component mass and the Raman laser wavenumber.
+",0,1,0,0,0,0
+4595,On methods to determine bounds on the Q-factor for a given directivity,"  This paper revisit and extend the interesting case of bounds on the Q-factor
+for a given directivity for a small antenna of arbitrary shape. A higher
+directivity in a small antenna is closely connected with a narrow impedance
+bandwidth. The relation between bandwidth and a desired directivity is still
+not fully understood, not even for small antennas. Initial investigations in
+this direction has related the radius of a circumscribing sphere to the
+directivity, and bounds on the Q-factor has also been derived for a partial
+directivity in a given direction. In this paper we derive lower bounds on the
+Q-factor for a total desired directivity for an arbitrarily shaped antenna in a
+given direction as a convex problem using semi-definite relaxation techniques
+(SDR). We also show that the relaxed solution is also a solution of the
+original problem of determining the lower Q-factor bound for a total desired
+directivity.
+SDR can also be used to relax a class of other interesting non-convex
+constraints in antenna optimization such as tuning, losses, front-to-back
+ratio. We compare two different new methods to determine the lowest Q-factor
+for arbitrary shaped antennas for a given total directivity. We also compare
+our results with full EM-simulations of a parasitic element antenna with high
+directivity.
+",0,1,1,0,0,0
+4596,Nano-optical imaging of monolayer MoSe2 using tip-enhanced photoluminescence,"  Band gap tuning in two-dimensional transitional metal dichalcogenides (TMDs)
+is crucial in fabricating new optoelectronic devices. High resolution
+photoluminescence (PL) microscopy is needed for accurate band gap
+characterization. We performed tip-enhanced photoluminescence (TEPL)
+measurements of monolayer MoSe2 with nanoscale spatial resolution, providing an
+improved characterization of the band gap correlated with the topography
+compared with the conventional far field spectroscopy. We also observed PL
+shifts at the edges and investigated the spatial dependence of the TEPL
+enhancement factors.
+",0,1,0,0,0,0
+4597,Electron paramagnetic resonance and photochromism of $\mathrm{N}_{3}\mathrm{V}^{0}$ in diamond,"  The defect in diamond formed by a vacancy surrounded by three
+nearest-neighbor nitrogen atoms and one carbon atom,
+$\mathrm{N}_{3}\mathrm{V}$, is found in $\approx98\%$ of natural diamonds.
+Despite $\mathrm{N}_{3}\mathrm{V}^{0}$ being the earliest electron paramagnetic
+resonance spectrum observed in diamond, to date no satisfactory simulation of
+the spectrum for an arbitrary magnetic field direction has been produced due to
+its complexity. In this work, $\mathrm{N}_{3}\mathrm{V}^{0}$ is identified in
+$^{15}\mathrm{N}$-doped synthetic diamond following irradiation and annealing.
+The $\mathrm{^{15}N}_{3}\mathrm{V}^{0}$ spin Hamiltonian parameters are revised
+and used to refine the parameters for $\mathrm{^{14}N}_{3}\mathrm{V}^{0}$,
+enabling the latter to be accurately simulated and fitted for an arbitrary
+magnetic field direction. Study of $\mathrm{^{15}N}_{3}\mathrm{V}^{0}$ under
+excitation with green light indicates charge transfer between
+$\mathrm{N}_{3}\mathrm{V}$ and $\mathrm{N_s}$. It is argued that this charge
+transfer is facilitated by direct ionization of $\mathrm{N}_{3}\mathrm{V}^{-}$,
+an as-yet unobserved charge state of $\mathrm{N}_{3}\mathrm{V}$.
+",0,1,0,0,0,0
+4598,On central leaves of Hodge-type Shimura varieties with parahoric level structure,"  Kisin and Pappas constructed integral models of Hodge-type Shimura varieties
+with parahoric level structure at $p>2$, such that the formal neighbourhood of
+a mod~$p$ point can be interpreted as a deformation space of $p$-divisible
+group with some Tate cycles (generalising Faltings' construction). In this
+paper, we study the central leaf and the closed Newton stratum in the formal
+neighbourhoods of mod~$p$ points of Kisin-Pappas integral models with parahoric
+level structure; namely, we obtain the dimension of central leaves and the
+almost product structure of Newton strata. In the case of hyperspecial level
+strucure (i.e., in the good reduction case), our main results were already
+obtained by Hamacher, and the result of this paper holds for ramified groups as
+well.
+",0,0,1,0,0,0
+4599,The sequential loss of allelic diversity,"  This paper gives a new flavor of what Peter Jagers and his co-authors call
+`the path to extinction'. In a neutral population with constant size $N$, we
+assume that each individual at time $0$ carries a distinct type, or allele. We
+consider the joint dynamics of these $N$ alleles, for example the dynamics of
+their respective frequencies and more plainly the nonincreasing process
+counting the number of alleles remaining by time $t$. We call this process the
+extinction process. We show that in the Moran model, the extinction process is
+distributed as the process counting (in backward time) the number of common
+ancestors to the whole population, also known as the block counting process of
+the $N$-Kingman coalescent. Stimulated by this result, we investigate: (1)
+whether it extends to an identity between the frequencies of blocks in the
+Kingman coalescent and the frequencies of alleles in the extinction process,
+both evaluated at jump times; (2) whether it extends to the general case of
+$\Lambda$-Fleming-Viot processes.
+",0,0,0,0,1,0
+4600,Online classification of imagined speech using functional near-infrared spectroscopy signals,"  Most brain-computer interfaces (BCIs) based on functional near-infrared
+spectroscopy (fNIRS) require that users perform mental tasks such as motor
+imagery, mental arithmetic, or music imagery to convey a message or to answer
+simple yes or no questions. These cognitive tasks usually have no direct
+association with the communicative intent, which makes them difficult for users
+to perform. In this paper, a 3-class intuitive BCI is presented which enables
+users to directly answer yes or no questions by covertly rehearsing the word
+'yes' or 'no' for 15 s. The BCI also admits an equivalent duration of
+unconstrained rest which constitutes the third discernable task. Twelve
+participants each completed one offline block and six online blocks over the
+course of 2 sessions. The mean value of the change in oxygenated hemoglobin
+concentration during a trial was calculated for each channel and used to train
+a regularized linear discriminant analysis (RLDA) classifier. By the final
+online block, 9 out of 12 participants were performing above chance (p<0.001),
+with a 3-class accuracy of 83.8+9.4%. Even when considering all participants,
+the average online 3-class accuracy over the last 3 blocks was 64.1+20.6%, with
+only 3 participants scoring below chance (p<0.001). For most participants,
+channels in the left temporal and temporoparietal cortex provided the most
+discriminative information. To our knowledge, this is the first report of an
+online fNIRS 3-class imagined speech BCI. Our findings suggest that imagined
+speech can be used as a reliable activation task for selected users for the
+development of more intuitive BCIs for communication.
+",0,0,0,0,1,0
+4601,A Parsimonious Dynamical Model for Structural Learning in the Human Brain,"  The human brain is capable of diverse feats of intelligence. A particularly
+salient example is the ability to deduce structure from time-varying auditory
+and visual stimuli, enabling humans to master the rules of language and to
+build rich expectations of their physical environment. The broad relevance of
+this ability for human cognition motivates the need for a first-principles
+model explicating putative mechanisms. Here we propose a general framework for
+structural learning in the brain, composed of an evolving, high-dimensional
+dynamical system driven by external stimuli or internal processes. We
+operationalize the scenario in which humans learn the rules that generate a
+sequence of stimuli, rather than the exemplar stimuli themselves. We model
+external stimuli as seemingly disordered chaotic time series generated by
+complex dynamical systems; the underlying structure being deduced is then that
+of the corresponding chaotic attractor. This approach allows us to demonstrate
+and theoretically explain the emergence of five distinct phenomena reminiscent
+of cognitive functions: (i) learning the structure of a chaotic system purely
+from time series, (ii) generating new streams of stimuli from a chaotic system,
+(iii) switching stream generation among multiple learned chaotic systems,
+either spontaneously or in response to external perturbations, (iv) inferring
+missing data from sparse observations of the chaotic system, and (v)
+deciphering superimposed input from different chaotic systems. Numerically, we
+show that these phenomena emerge naturally from a recurrent neural network of
+Erdos-Renyi topology in which the synaptic strengths adapt in a Hebbian-like
+manner. Broadly, our work blends chaotic theory and artificial neural networks
+to answer the long standing question of how neural systems can learn the
+structure underlying temporal sequences of stimuli.
+",0,0,0,0,1,0
+4602,Estimation of mean residual life,"  Yang (1978) considered an empirical estimate of the mean residual life
+function on a fixed finite interval. She proved it to be strongly uniformly
+consistent and (when appropriately standardized) weakly convergent to a
+Gaussian process. These results are extended to the whole half line, and the
+variance of the the limiting process is studied. Also, nonparametric
+simultaneous confidence bands for the mean residual life function are obtained
+by transforming the limiting process to Brownian motion.
+",0,0,1,1,0,0
+4603,Trace-free characters and abelian knot contact homology II,"  We calculate ghost characters for the (5,6)-torus knot, and using them we
+show that the (5,6)-torus knot gives a counter-example of Ng's conjecture
+concerned with the relationship between degree 0 abelian knot contact homology
+and the character variety of the 2-fold branched covering of the 3-sphere
+branched along the knot.
+",0,0,1,0,0,0
+4604,Surface Plasmon Excitation of Second Harmonic light: Emission and Absorption,"  We aim to clarify the role that absorption plays in nonlinear optical
+processes in a variety of metallic nanostructures and show how it relates to
+emission and conversion efficiency. We define a figure of merit that
+establishes the structure's ability to either favor or impede second harmonic
+generation. Our findings suggest that, despite the best efforts embarked upon
+to enhance local fields and light coupling via plasmon excitation, nearly
+always the absorbed harmonic energy far surpasses the harmonic energy emitted
+in the far field. Qualitative and quantitative understanding of absorption
+processes is crucial in the evaluation of practical designs of plasmonic
+nanostructures for the purpose of frequency mixing.
+",0,1,0,0,0,0
+4605,Fault diagnosability of data center networks,"  The data center networks $D_{n,k}$, proposed in 2008, has many desirable
+features such as high network capacity. A kind of generalization of
+diagnosability for network $G$ is $g$-good-neighbor diagnosability which is
+denoted by $t_g(G)$. Let $\kappa^g(G)$ be the $R^g$-connectivity. Lin et. al.
+in [IEEE Trans. on Reliability, 65 (3) (2016) 1248--1262] and Xu et. al in
+[Theor. Comput. Sci. 659 (2017) 53--63] gave the same problem independently
+that: the relationship between the $R^g$-connectivity $\kappa^g(G)$ and
+$t_g(G)$ of a general graph $G$ need to be studied in the future. In this
+paper, this open problem is solved for general regular graphs. We firstly
+establish the relationship of $\kappa^g(G)$ and $t_g(G)$, and obtain that
+$t_g(G)=\kappa^g(G)+g$ under some conditions. Secondly, we obtain the
+$g$-good-neighbor diagnosability of $D_{k,n}$ which are
+$t_g(D_{k,n})=(g+1)(k-1)+n+g$ for $1\leq g\leq n-1$ under the PMC model and the
+MM model, respectively. Further more, we show that $D_{k,n}$ is tightly super
+$(n+k-1)$-connected for $n\geq 2$ and $k\geq 2$ and we also prove that the
+largest connected component of the survival graph contains almost all of the
+remaining vertices in $D_{k,n}$ when $2k+n-2$ vertices removed.
+",1,0,0,0,0,0
+4606,The Effects of Protostellar Disk Turbulence on CO Emission Lines: A Comparison Study of Disks with Constant CO Abundance vs. Chemically Evolving Disks,"  Turbulence is the leading candidate for angular momentum transport in
+protoplanetary disks and therefore influences disk lifetimes and planet
+formation timescales. However, the turbulent properties of protoplanetary disks
+are poorly constrained observationally. Recent studies have found turbulent
+speeds smaller than what fully-developed MRI would produce (Flaherty et al.
+2015, 2017). However, existing studies assumed a constant CO/H2 ratio of 0.0001
+in locations where CO is not frozen-out or photo-dissociated. Our previous
+studies of evolving disk chemistry indicate that CO is depleted by
+incorporation into complex organic molecules well inside the freeze-out radius
+of CO. We consider the effects of this chemical depletion on measurements of
+turbulence. Simon et al. (2015) suggested that the ratio of the peak line flux
+to the flux at line center of the CO J=3-2 transition is a reasonable
+diagnostic of turbulence, so we focus on that metric, while adding some
+analysis of the more complex effects on spatial distribution. We simulate the
+emission lines of CO based on chemical evolution models presented in Yu et al.
+(2016), and find that the peak-to-trough ratio changes as a function of time as
+CO is destroyed. Specifically, a CO-depleted disk with high turbulent velocity
+mimics the peak-to-trough ratios of a non-CO-depleted disk with lower turbulent
+velocity. We suggest that disk observers and modelers take into account the
+possibility of CO depletion when using line peak-to-trough ratios to constrain
+the degree of turbulence in disks. Assuming that CO/H2 = 0.0001 at all disk
+radii can lead to underestimates of turbulent speeds in the disk by at least
+0.2 km/s.
+",0,1,0,0,0,0
+4607,A General Framework of Multi-Armed Bandit Processes by Arm Switch Restrictions,"  This paper proposes a general framework of multi-armed bandit (MAB) processes
+by introducing a type of restrictions on the switches among arms evolving in
+continuous time.
+The Gittins index process is constructed for any single arm subject to the
+restrictions on switches and then the optimality of the corresponding Gittins
+index rule is established. The Gittins indices defined in this paper are
+consistent with the ones for MAB processes in continuous time, integer time,
+semi-Markovian setting as well as general discrete time setting, so that the
+new theory covers the classical models as special cases and also applies to
+many other situations that have not yet been touched in the literature. While
+the proof of the optimality of Gittins index policies benefits from ideas in
+the existing theory of MAB processes in continuous time, new techniques are
+introduced which drastically simplify the proof.
+",0,0,0,1,0,0
+4608,Autonomous Urban Localization and Navigation with Limited Information,"  Urban environments offer a challenging scenario for autonomous driving.
+Globally localizing information, such as a GPS signal, can be unreliable due to
+signal shadowing and multipath errors. Detailed a priori maps of the
+environment with sufficient information for autonomous navigation typically
+require driving the area multiple times to collect large amounts of data,
+substantial post-processing on that data to obtain the map, and then
+maintaining updates on the map as the environment changes. This paper addresses
+the issue of autonomous driving in an urban environment by investigating
+algorithms and an architecture to enable fully functional autonomous driving
+with limited information. An algorithm to autonomously navigate urban roadways
+with little to no reliance on an a priori map or GPS is developed. Localization
+is performed with an extended Kalman filter with odometry, compass, and sparse
+landmark measurement updates. Navigation is accomplished by a compass-based
+navigation control law. Key results from Monte Carlo studies show success rates
+of urban navigation under different environmental conditions. Experiments
+validate the simulated results and demonstrate that, for given test conditions,
+an expected range can be found for a given success rate.
+",1,0,0,0,0,0
+4609,Iterative Refinement for $\ell_p$-norm Regression,"  We give improved algorithms for the $\ell_{p}$-regression problem, $\min_{x}
+\|x\|_{p}$ such that $A x=b,$ for all $p \in (1,2) \cup (2,\infty).$ Our
+algorithms obtain a high accuracy solution in $\tilde{O}_{p}(m^{\frac{|p-2|}{2p
++ |p-2|}}) \le \tilde{O}_{p}(m^{\frac{1}{3}})$ iterations, where each iteration
+requires solving an $m \times m$ linear system, $m$ being the dimension of the
+ambient space.
+By maintaining an approximate inverse of the linear systems that we solve in
+each iteration, we give algorithms for solving $\ell_{p}$-regression to $1 /
+\text{poly}(n)$ accuracy that run in time $\tilde{O}_p(m^{\max\{\omega,
+7/3\}}),$ where $\omega$ is the matrix multiplication constant. For the current
+best value of $\omega > 2.37$, we can thus solve $\ell_{p}$ regression as fast
+as $\ell_{2}$ regression, for all constant $p$ bounded away from $1.$
+Our algorithms can be combined with fast graph Laplacian linear equation
+solvers to give minimum $\ell_{p}$-norm flow / voltage solutions to $1 /
+\text{poly}(n)$ accuracy on an undirected graph with $m$ edges in
+$\tilde{O}_{p}(m^{1 + \frac{|p-2|}{2p + |p-2|}}) \le
+\tilde{O}_{p}(m^{\frac{4}{3}})$ time.
+For sparse graphs and for matrices with similar dimensions, our iteration
+counts and running times improve on the $p$-norm regression algorithm by
+[Bubeck-Cohen-Lee-Li STOC`18] and general-purpose convex optimization
+algorithms. At the core of our algorithms is an iterative refinement scheme for
+$\ell_{p}$-norms, using the smoothed $\ell_{p}$-norms introduced in the work of
+Bubeck et al. Given an initial solution, we construct a problem that seeks to
+minimize a quadratically-smoothed $\ell_{p}$ norm over a subspace, such that a
+crude solution to this problem allows us to improve the initial solution by a
+constant factor, leading to algorithms with fast convergence.
+",1,0,0,1,0,0
+4610,Minimal free resolution of the associated graded ring of certain monomial curves,"  In this article, we give the explicit minimal free resolution of the
+associated graded ring of certain affine monomial curves in affine 4-space
+based on the standard basis theory. As a result, we give the minimal graded
+free resolution and compute the Hilbert function of the tangent cone of these
+families.
+",0,0,1,0,0,0
+4611,Knowledge distillation using unlabeled mismatched images,"  Current approaches for Knowledge Distillation (KD) either directly use
+training data or sample from the training data distribution. In this paper, we
+demonstrate effectiveness of 'mismatched' unlabeled stimulus to perform KD for
+image classification networks. For illustration, we consider scenarios where
+this is a complete absence of training data, or mismatched stimulus has to be
+used for augmenting a small amount of training data. We demonstrate that
+stimulus complexity is a key factor for distillation's good performance. Our
+examples include use of various datasets for stimulating MNIST and CIFAR
+teachers.
+",1,0,0,1,0,0
+4612,Forbidden triads and Creative Success in Jazz: The Miles Davis Factor,"  This article argues for the importance of forbidden triads - open triads with
+high-weight edges - in predicting success in creative fields. Forbidden triads
+had been treated as a residual category beyond closed and open triads, yet I
+argue that these structures provide opportunities to combine socially evolved
+styles in new ways. Using data on the entire history of recorded jazz from 1896
+to 2010, I show that observed collaborations have tolerated the openness of
+high weight triads more than expected, observed jazz sessions had more
+forbidden triads than expected, and the density of forbidden triads contributed
+to the success of recording sessions, measured by the number of record releases
+of session material. The article also shows that the sessions of Miles Davis
+had received an especially high boost from forbidden triads.
+",0,1,0,1,0,0
+4613,Structural analysis of rubble-pile asteroids applied to collisional evolution,"  Solar system small bodies come in a wide variety of shapes and sizes, which
+are achieved following very individual evolutional paths through billions of
+years. This paper focuses on the reshaping process of rubble-pile asteroids
+driven by meteorite impacts. In our study, numerous possible equilibrium
+configurations are obtained via Monte Carlo simulation, and the structural
+stability of these configurations is determined via eigen analysis of the
+geometric constructions. The eigen decomposition reveals a connection between
+the cluster's reactions and the types of external disturbance. Numerical
+simulations are performed to verify the analytical results. The gravitational
+N-body code pkdgrav is used to mimic the responses of the cluster under
+intermittent non-dispersive impacts. We statistically confirm that the
+stability index, the total gravitational potential and the volume of inertia
+ellipsoid show consistent tendency of variation. A common regime is found in
+which the clusters tend towards crystallization under intermittent impacts,
+i.e., only the configurations with high structural stability survive under the
+external disturbances. The results suggest the trivial non-disruptive impacts
+might play an important role in the rearrangement of the constituent blocks,
+which may strengthen these rubble piles and help to build a robust structure
+under impacts of similar magnitude. The final part of this study consists of
+systematic simulations over two parameters, the projectile momentum and the
+rotational speed of the cluster. The results show a critical value exists for
+the projectile momentum, as predicted by theory, below which all clusters
+become responseless to external disturbances; and the rotation proves to be
+significant for it exhibits an ""enhancing"" effect on loose-packed clusters,
+which coincides with the observation that several fast-spinning asteroids have
+low bulk densities.
+",0,1,0,0,0,0
+4614,The Imani Periodic Functions: Genesis and Preliminary Results,"  The Leah-Hamiltonian, $H(x,y)=y^2/2+3x^{4/3}/4$, is introduced as a
+functional equation for $x(t)$ and $y(t)$. By means of a nonlinear
+transformation to new independent variables, we show that this functional
+equation has a special class of periodic solutions which we designate the Imani
+functions. The explicit construction of these functions is done such that they
+possess many of the general properties of the standard trigonometric cosine and
+sine functions. We conclude by providing a listing of a number of currently
+unresolved issues relating to the Imani functions.
+",0,0,1,0,0,0
+4615,Towards fully automated protein structure elucidation with NMR spectroscopy,"  Nuclear magnetic resonance (NMR) spectroscopy is one of the leading
+techniques for protein studies. The method features a number of properties,
+allowing to explain macromolecular interactions mechanistically and resolve
+structures with atomic resolution. However, due to laborious data analysis, a
+full potential of NMR spectroscopy remains unexploited. Here we present an
+approach aiming at automation of two major bottlenecks in the analysis
+pipeline, namely, peak picking and chemical shift assignment. Our approach
+combines deep learning, non-parametric models and combinatorial optimization,
+and is able to detect signals of interest in a multidimensional NMR data with
+high accuracy and match them with atoms in medium-length protein sequences,
+which is a preliminary step to solve protein spatial structure.
+",0,0,0,1,0,0
+4616,On families of fibred knots with equal Seifert forms,"  For every genus $g\geq 2$, we construct an infinite family of strongly
+quasipositive fibred knots having the same Seifert form as the torus knot
+$T(2,2g+1)$. In particular, their signatures and four-genera are maximal and
+their homological monodromies (hence their Alexander module structures) agree.
+On the other hand, the geometric stretching factors are pairwise distinct and
+the knots are pairwise not ribbon concordant.
+",0,0,1,0,0,0
+4617,Robust Computation in 2D Absolute EIT (a-EIT) Using D-bar Methods with the `exp' Approximation,"  Objective: Absolute images have important applications in medical Electrical
+Impedance Tomography (EIT) imaging, but the traditional minimization and
+statistical based computations are very sensitive to modeling errors and noise.
+In this paper, it is demonstrated that D-bar reconstruction methods for
+absolute EIT are robust to such errors. Approach: The effects of errors in
+domain shape and electrode placement on absolute images computed with 2D D-bar
+reconstruction algorithms are studied on experimental data. Main Results: It is
+demonstrated with tank data from several EIT systems that these methods are
+quite robust to such modeling errors, and furthermore the artefacts arising
+from such modeling errors are similar to those occurring in classic
+time-difference EIT imaging. Significance: This study is promising for clinical
+applications where absolute EIT images are desirable, but previously thought
+impossible.
+",1,0,0,0,0,0
+4618,A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data,"  Epidemic outbreaks are an important healthcare challenge, especially in
+developing countries where they represent one of the major causes of mortality.
+Approaches that can rapidly target subpopulations for surveillance and control
+are critical for enhancing containment processes during epidemics.
+Using a real-world dataset from Ivory Coast, this work presents an attempt to
+unveil the socio-geographical heterogeneity of disease transmission dynamics.
+By employing a spatially explicit meta-population epidemic model derived from
+mobile phone Call Detail Records (CDRs), we investigate how the differences in
+mobility patterns may affect the course of a realistic infectious disease
+outbreak. We consider different existing measures of the spatial dimension of
+human mobility and interactions, and we analyse their relevance in identifying
+the highest risk sub-population of individuals, as the best candidates for
+isolation countermeasures. The approaches presented in this paper provide
+further evidence that mobile phone data can be effectively exploited to
+facilitate our understanding of individuals' spatial behaviour and its
+relationship with the risk of infectious diseases' contagion. In particular, we
+show that CDRs-based indicators of individuals' spatial activities and
+interactions hold promise for gaining insight of contagion heterogeneity and
+thus for developing containment strategies to support decision-making during
+country-level pandemics.
+",1,1,0,0,0,0
+4619,Attosecond Streaking in the Water Window: A New Regime of Attosecond Pulse Characterization,"  We report on the first streaking measurement of water-window attosecond
+pulses generated via high harmonic generation, driven by sub-2-cycle,
+CEP-stable, 1850 nm laser pulses. Both the central photon energy and the energy
+bandwidth far exceed what has been demonstrated thus far, warranting the
+investigation of the attosecond streaking technique for the soft X-ray regime
+and the limits of the FROGCRAB retrieval algorithm under such conditions. We
+also discuss the problem of attochirp compensation and issues regarding much
+lower photo-ionization cross sections compared with the XUV in addition to the
+fact that several shells of target gases are accessed simultaneously. Based on
+our investigation, we caution that the vastly different conditions in the soft
+X-ray regime warrant a diligent examination of the fidelity of the measurement
+and the retrieval procedure.
+",0,1,0,0,0,0
+4620,The intrinsic Baldwin effect in broad Balmer lines of six long-term monitored AGNs,"  We investigate the intrinsic Baldwin effect (Beff) of the broad H$\alpha$ and
+H$\beta$ emission lines for six Type 1 active galactic nuclei (AGNs) with
+different broad line characteristics: two Seyfert 1 (NGC 4151 and NGC 5548),
+two AGNs with double-peaked broad line profiles (3C 390.3 and Arp 102B), one
+narrow line Seyfert 1 (Ark 564), and one high-luminosity quasar with highly red
+asymmetric broad line profiles (E1821+643). We found that a significant
+intrinsic Beff was present in all Type 1 AGNs in our sample. Moreover, we do
+not see strong difference in intrinsic Beff slopes in different types of AGNs
+which probably have different physical properties, such as inclination, broad
+line region geometry, or accretion rate. Additionally, we found that the
+intrinsic Beff was not connected with the global one, which, instead, could not
+be detected in the broad H$\alpha$ or H$\beta$ emission lines. In the case of
+NGC 4151, the detected variation of the Beff slope could be due to the change
+in the site of line formation in the BLR. Finally, the intrinsic Beff might be
+caused by the additional optical continuum component that is not part of the
+ionization continuum.
+",0,1,0,0,0,0
+4621,A Novel Formal Agent-based Simulation Modeling Framework of an AIDS Complex Adaptive System,"  HIV/AIDS spread depends upon complex patterns of interaction among various
+sub-sets emerging at population level. This added complexity makes it difficult
+to study and model AIDS and its dynamics. AIDS is therefore a natural candidate
+to be modeled using agent-based modeling, a paradigm well-known for modeling
+Complex Adaptive Systems (CAS). While agent-based models are also well-known to
+effectively model CAS, often times models can tend to be ambiguous and the use
+of purely text-based specifications (such as ODD) can make models difficult to
+be replicated. Previous work has shown how formal specification may be used in
+conjunction with agent-based modeling to develop models of various CAS.
+However, to the best of our knowledge, no such model has been developed in
+conjunction with AIDS. In this paper, we present a Formal Agent-Based
+Simulation modeling framework (FABS-AIDS) for an AIDS-based CAS. FABS-AIDS
+employs the use of a formal specification model in conjunction with an
+agent-based model to reduce ambiguity as well as improve clarity in the model
+definition. The proposed model demonstrates the effectiveness of using formal
+specification in conjunction with agent-based simulation for developing models
+of CAS in general and, social network-based agent-based models, in particular.
+",1,1,0,0,0,0
+4622,Interplay of Fluorescence and Phosphorescence in Organic Biluminescent Emitters,"  Biluminescent organic emitters show simultaneous fluorescence and
+phosphorescence at room temperature. So far, the optimization of the room
+temperature phosphorescence (RTP) in these materials has drawn the attention of
+research. However, the continuous wave operation of these emitters will
+consequently turn them into systems with vastly imbalanced singlet and triplet
+populations, which is due to the respective excited state lifetimes. This study
+reports on the exciton dynamics of the biluminophore NPB
+(N,N-di(1-naphthyl)-N,N-diphenyl-(1,1-biphenyl)-4,4-diamine). In the extreme
+case, the singlet and triplet exciton lifetimes stretch from 3 ns to 300 ms,
+respectively. Through sample engineering and oxygen quenching experiments, the
+triplet exciton density can be controlled over several orders of magnitude
+allowing to studying exciton interactions between singlet and triplet
+manifolds. The results show, that singlet-triplet annihilation reduces the
+overall biluminescence efficiency already at moderate excitation levels.
+Additionally, the presented system represents an illustrative role model to
+study excitonic effects in organic materials.
+",0,1,0,0,0,0
+4623,Ion-impact-induced multifragmentation of liquid droplets,"  An instability of a liquid droplet traversed by an energetic ion is explored.
+This instability is brought about by the predicted shock wave induced by the
+ion. An observation of multifragmentation of small droplets traversed by ions
+with high linear energy transfer is suggested to demonstrate the existence of
+shock waves. A number of effects are analysed in effort to find the conditions
+for such an experiment to be signifying. The presence of shock waves crucially
+affects the scenario of radiation damage with ions since the shock waves
+significantly contribute to the thermomechanical damage of biomolecules as well
+as the transport of reactive species. While the scenario has been upheld by
+analyses of biological experiments, the shock waves have not yet been observed
+directly, regardless of a number of ideas of experiments to detect them were
+exchanged at conferences.
+",0,1,0,0,0,0
+4624,Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction,"  Navigating in search and rescue environments is challenging, since a variety
+of terrains has to be considered. Hybrid driving-stepping locomotion, as
+provided by our robot Momaro, is a promising approach. Similar to other
+locomotion methods, it incorporates many degrees of freedom---offering high
+flexibility but making planning computationally expensive for larger
+environments.
+We propose a navigation planning method, which unifies different levels of
+representation in a single planner. In the vicinity of the robot, it provides
+plans with a fine resolution and a high robot state dimensionality. With
+increasing distance from the robot, plans become coarser and the robot state
+dimensionality decreases. We compensate this loss of information by enriching
+coarser representations with additional semantics. Experiments show that the
+proposed planner provides plans for large, challenging scenarios in feasible
+time.
+",1,0,0,0,0,0
+4625,Blackbody Radiation in Classical Physics: A Historical Perspective,"  We point out that current textbooks of modern physics are a century
+out-of-date in their treatment of blackbody radiation within classical physics.
+Relativistic classical electrodynamics including classical electromagnetic
+zero-point radiation gives the Planck spectrum with zero-point radiation as the
+blackbody radiation spectrum. In contrast, nonrelativistic mechanics cannot
+support the idea of zero-point energy; therefore if nonrelativistic classical
+statistical mechanics or nonrelativistic mechanical scatterers are invoked for
+radiation equilibrium, one arrives at only the low-frequency Rayleigh-Jeans
+part of the spectrum which involves no zero-point energy, and does not include
+the high-frequency part of the spectrum involving relativistically-invariant
+classical zero-point radiation. Here we first discuss the correct understanding
+of blackbody radiation within relativistic classical physics, and then we
+review the historical treatment. Finally, we point out how the presence of
+Lorentz-invariant classical zero-point radiation and the use of relativistic
+particle interactions transform the previous historical arguments so as now to
+give the Planck spectrum including classical zero-point radiation. Within
+relativistic classical electromagnetic theory, Planck's constant h appears as
+the scale of source-free zero-point radiation.
+",0,1,0,0,0,0
+4626,"The Lyman Continuum escape fraction of faint galaxies at z~3.3 in the CANDELS/GOODS-North, EGS, and COSMOS fields with LBC","  The reionization of the Universe is one of the most important topics of
+present day astrophysical research. The most plausible candidates for the
+reionization process are star-forming galaxies, which according to the
+predictions of the majority of the theoretical and semi-analytical models
+should dominate the HI ionizing background at z~3. We aim at measuring the
+Lyman continuum escape fraction, which is one of the key parameters to compute
+the contribution of star-forming galaxies to the UV background. We have used
+ultra-deep U-band imaging (U=30.2mag at 1sigma) by LBC/LBT in the
+CANDELS/GOODS-North field, as well as deep imaging in COSMOS and EGS fields, in
+order to estimate the Lyman continuum escape fraction of 69 star-forming
+galaxies with secure spectroscopic redshifts at 3.27<z<3.40 to faint magnitude
+limits (L=0.2L*, or equivalently M1500~-19). We have measured through stacks a
+stringent upper limit (<1.7% at 1sigma) for the relative escape fraction of HI
+ionizing photons from bright galaxies (L>L*), while for the faint population
+(L=0.2L*) the limit to the escape fraction is ~10%. We have computed the
+contribution of star-forming galaxies to the observed UV background at z~3 and
+we have found that it is not enough to keep the Universe ionized at these
+redshifts, unless their escape fraction increases significantly (>10%) at low
+luminosities (M1500>-19). We compare our results on the Lyman continuum escape
+fraction of high-z galaxies with recent estimates in the literature and discuss
+future prospects to shed light on the end of the Dark Ages. In the future,
+strong gravitational lensing will be fundamental to measure the Lyman continuum
+escape fraction down to faint magnitudes (M1500~-16) which are inaccessible
+with the present instrumentation on blank fields.
+",0,1,0,0,0,0
+4627,The effect of stellar and AGN feedback on the low redshift Lyman-$α$ forest in the Sherwood simulation suite,"  We study the effect of different feedback prescriptions on the properties of
+the low redshift ($z\leq1.6$) Ly$\alpha$ forest using a selection of
+hydrodynamical simulations drawn from the Sherwood simulation suite. The
+simulations incorporate stellar feedback, AGN feedback and a simplified scheme
+for efficiently modelling the low column density Ly$\alpha$ forest. We confirm
+a discrepancy remains between Cosmic Origins Spectrograph (COS) observations of
+the Ly$\alpha$ forest column density distribution function (CDDF) at $z \simeq
+0.1$ for high column density systems ($N_{\rm HI}>10^{14}\rm\,cm^{-2}$), as
+well as Ly$\alpha$ velocity widths that are too narrow compared to the COS
+data. Stellar or AGN feedback -- as currently implemented in our simulations --
+have only a small effect on the CDDF and velocity width distribution. We
+conclude that resolving the discrepancy between the COS data and simulations
+requires an increase in the temperature of overdense gas with $\Delta=4$--$40$,
+either through additional He$\,\rm \scriptstyle II\ $ photo-heating at $z>2$ or
+fine-tuned feedback that ejects overdense gas into the IGM at just the right
+temperature for it to still contribute significantly to the Ly$\alpha$ forest.
+Alternatively a larger, currently unresolved turbulent component to the line
+width could resolve the discrepancy.
+",0,1,0,0,0,0
+4628,Asteroid 2017 FZ2 et al.: signs of recent mass-shedding from YORP?,"  The first direct detection of the asteroidal YORP effect, a phenomenon that
+changes the spin states of small bodies due to thermal reemission of sunlight
+from their surfaces, was obtained for (54509) YORP 2000 PH5. Such an alteration
+can slowly increase the rotation rate of asteroids, driving them to reach their
+fission limit and causing their disruption. This process can produce binaries
+and unbound asteroid pairs. Secondary fission opens the door to the eventual
+formation of transient but genetically-related groupings. Here, we show that
+the small near-Earth asteroid (NEA) 2017 FZ2 was a co-orbital of our planet of
+the quasi-satellite type prior to their close encounter on 2017 March 23.
+Because of this flyby with the Earth, 2017 FZ2 has become a non-resonant NEA.
+Our N-body simulations indicate that this object may have experienced
+quasi-satellite engagements with our planet in the past and it may return as a
+co-orbital in the future. We identify a number of NEAs that follow similar
+paths, the largest named being YORP, which is also an Earth's co-orbital. An
+apparent excess of NEAs moving in these peculiar orbits is studied within the
+framework of two orbit population models. A possibility that emerges from this
+analysis is that such an excess, if real, could be the result of mass shedding
+from YORP itself or a putative larger object that produced YORP. Future
+spectroscopic observations of 2017 FZ2 during its next visit in 2018 (and of
+related objects when feasible) may be able to confirm or reject this
+interpretation.
+",0,1,0,0,0,0
+4629,On Increasing Self-Confidence in Non-Bayesian Social Learning over Time-Varying Directed Graphs,"  We study the convergence of the log-linear non-Bayesian social learning
+update rule, for a group of agents that collectively seek to identify a
+parameter that best describes a joint sequence of observations. Contrary to
+recent literature, we focus on the case where agents assign decaying weights to
+its neighbors, and the network is not connected at every time instant but over
+some finite time intervals. We provide a necessary and sufficient condition for
+the rate at which agents decrease the weights and still guarantees social
+learning.
+",1,0,0,0,0,0
+4630,Exploring a search for long-duration transient gravitational waves associated with magnetar bursts,"  Soft gamma repeaters and anomalous X-ray pulsars are thought to be magnetars,
+neutron stars with strong magnetic fields of order $\mathord{\sim}
+10^{13}$--$10^{15} \, \mathrm{gauss}$. These objects emit intermittent bursts
+of hard X-rays and soft gamma rays. Quasiperiodic oscillations in the X-ray
+tails of giant flares imply the existence of neutron star oscillation modes
+which could emit gravitational waves powered by the magnetar's magnetic energy
+reservoir. We describe a method to search for transient gravitational-wave
+signals associated with magnetar bursts with durations of 10s to 1000s of
+seconds. The sensitivity of this method is estimated by adding simulated
+waveforms to data from the sixth science run of Laser Interferometer
+Gravitational-wave Observatory (LIGO). We find a search sensitivity in terms of
+the root sum square strain amplitude of $h_{\mathrm{rss}} = 1.3 \times 10^{-21}
+\, \mathrm{Hz}^{-1/2}$ for a half sine-Gaussian waveform with a central
+frequency $f_0 = 150 \, \mathrm{Hz}$ and a characteristic time $\tau = 400 \,
+\mathrm{s}$. This corresponds to a gravitational wave energy of
+$E_{\mathrm{GW}} = 4.3 \times 10^{46} \, \mathrm{erg}$, the same order of
+magnitude as the 2004 giant flare which had an estimated electromagnetic energy
+of $E_{\mathrm{EM}} = \mathord{\sim} 1.7 \times 10^{46} (d/ 8.7 \,
+\mathrm{kpc})^2 \, \mathrm{erg}$, where $d$ is the distance to SGR 1806-20. We
+present an extrapolation of these results to Advanced LIGO, estimating a
+sensitivity to a gravitational wave energy of $E_{\mathrm{GW}} = 3.2 \times
+10^{43} \, \mathrm{erg}$ for a magnetar at a distance of $1.6 \, \mathrm{kpc}$.
+These results suggest this search method can probe significantly below the
+energy budgets for magnetar burst emission mechanisms such as crust cracking
+and hydrodynamic deformation.
+",0,1,0,0,0,0
+4631,Optomechanical characterization of silicon nitride membrane arrays,"  We report on the optical and mechanical characterization of arrays of
+parallel micromechanical membranes. Pairs of high-tensile stress, 100 nm-thick
+silicon nitride membranes are assembled parallel with each other with
+separations ranging from 8.5 to 200 $\mu$m. Their optical properties are
+accurately determined using a combination of broadband and monochromatic
+illuminations and the lowest vibrational mode frequencies and mechanical
+quality factors are determined interferometrically. The results and techniques
+demonstrated are promising for investigations of collective phenomena in
+optomechanical arrays.
+",0,1,0,0,0,0
+4632,Coupling between a charge density wave and magnetism in an Heusler material,"  The Prototypical magnetic memory shape alloy Ni$_2$MnGa undergoes various
+phase transitions as a function of temperature, pressure, and doping. In the
+low-temperature phases below 260 K, an incommensurate structural modulation
+occurs along the [110] direction which is thought to arise from softening of a
+phonon mode. It is not at present clear how this phenomenon is related, if at
+all, to the magnetic memory effect. Here we report time-resolved measurements
+which track both the structural and magnetic components of the phase transition
+from the modulated cubic phase as it is brought into the high-symmetry phase.
+The results suggest that the photoinduced demagnetization modifies the Fermi
+surface in regions that couple strongly to the periodicity of the structural
+modulation through the nesting vector. The amplitude of the periodic lattice
+distortion, however, appears to be less affected by the demagnetizaton.
+",0,1,0,0,0,0
+4633,Best-Effort FPGA Programming: A Few Steps Can Go a Long Way,"  FPGA-based heterogeneous architectures provide programmers with the ability
+to customize their hardware accelerators for flexible acceleration of many
+workloads. Nonetheless, such advantages come at the cost of sacrificing
+programmability. FPGA vendors and researchers attempt to improve the
+programmability through high-level synthesis (HLS) technologies that can
+directly generate hardware circuits from high-level language descriptions.
+However, reading through recent publications on FPGA designs using HLS, one
+often gets the impression that FPGA programming is still hard in that it leaves
+programmers to explore a very large design space with many possible
+combinations of HLS optimization strategies.
+In this paper we make two important observations and contributions. First, we
+demonstrate a rather surprising result: FPGA programming can be made easy by
+following a simple best-effort guideline of five refinement steps using HLS. We
+show that for a broad class of accelerator benchmarks from MachSuite, the
+proposed best-effort guideline improves the FPGA accelerator performance by
+42-29,030x. Compared to the baseline CPU performance, the FPGA accelerator
+performance is improved from an average 292.5x slowdown to an average 34.4x
+speedup. Moreover, we show that the refinement steps in the best-effort
+guideline, consisting of explicit data caching, customized pipelining,
+processing element duplication, computation/communication overlapping and
+scratchpad reorganization, correspond well to the best practice guidelines for
+multicore CPU programming. Although our best-effort guideline may not always
+lead to the optimal solution, it substantially simplifies the FPGA programming
+effort, and will greatly support the wide adoption of FPGA-based acceleration
+by the software programming community.
+",1,0,0,0,0,0
+4634,Towards the study of least squares estimators with convex penalty,"  Penalized least squares estimation is a popular technique in high-dimensional
+statistics. It includes such methods as the LASSO, the group LASSO, and the
+nuclear norm penalized least squares. The existing theory of these methods is
+not fully satisfying since it allows one to prove oracle inequalities with
+fixed high probability only for the estimators depending on this probability.
+Furthermore, the control of compatibility factors appearing in the oracle
+bounds is often not explicit. Some very recent developments suggest that the
+theory of oracle inequalities can be revised in an improved way. In this paper,
+we provide an overview of ideas and tools leading to such an improved theory.
+We show that, along with overcoming the disadvantages mentioned above, the
+methodology extends to the hilbertian framework and it applies to a large class
+of convex penalties. This paper is partly expository. In particular, we provide
+adapted proofs of some results from other recent work.
+",0,0,1,1,0,0
+4635,A Multi-traffic Inter-cell Interference Coordination Scheme in Dense Cellular Networks,"  This paper proposes a novel semi-distributed and practical ICIC scheme based
+on the Almost Blank SubFrame (ABSF) approach specified by 3GPP. We define two
+mathematical programming problems for the cases of guaranteed and best-effort
+traffic, and use game theory to study the properties of the derived ICIC
+distributed schemes, which are compared in detail against unaffordable
+centralized schemes. Based on the analysis of the proposed models, we define
+Distributed Multi-traffic Scheduling (DMS), a unified distributed framework for
+adaptive interference-aware scheduling of base stations in future cellular
+networks which accounts for both guaranteed and best-effort traffic. DMS
+follows a two-tier approach, consisting of local ABSF schedulers, which perform
+the resource distribution between guaranteed and best effort traffic, and a
+lightweight local supervisor, which coordinates ABSF local decisions. As a
+result of such a two-tier design, DMS requires very light signaling to drive
+the local schedulers to globally efficient operating points. As shown by means
+of numerical results, DMS allows to (i) maximize radio resources reuse, (ii)
+provide requested quality for guaranteed traffic, (iii) minimize the time
+dedicated to guaranteed traffic to leave room for best-effort traffic, and (iv)
+maximize resource utilization efficiency for best-effort traffic.
+",1,0,0,0,0,0
+4636,Distributional Adversarial Networks,"  We propose a framework for adversarial training that relies on a sample
+rather than a single sample point as the fundamental unit of discrimination.
+Inspired by discrepancy measures and two-sample tests between probability
+distributions, we propose two such distributional adversaries that operate and
+predict on samples, and show how they can be easily implemented on top of
+existing models. Various experimental results show that generators trained with
+our distributional adversaries are much more stable and are remarkably less
+prone to mode collapse than traditional models trained with pointwise
+prediction discriminators. The application of our framework to domain
+adaptation also results in considerable improvement over recent
+state-of-the-art.
+",1,0,0,0,0,0
+4637,First order magneto-structural transition and magnetocaloric effect in MnNiGe$_{0.9}$Ga$_{0.1}$,"  The first order magneto-structural transition ($T_t\simeq95$ K) and
+magnetocaloric effect in MnNiGe$_{0.9}$Ga$_{0.1}$ are studied via powder x-ray
+diffraction and magnetization measurements. Temperature dependent x-ray
+diffraction measurements reveal that the magneto-structural transition remains
+incomplete down to 23 K, resulting in a coexistence of antiferromagnetic and
+ferromagnetic phases at low temperatures. The fraction of the high temperature
+Ni$_2$In-type hexagonal ferromagnetic and low temperature TiNiSi-type
+orthorhombic antiferromagnetic phases is estimated to be $\sim 40\%$ and $\sim
+60\%$, respectively at 23 K. The ferromagnetic phase fraction increases with
+increasing field which is found to be in non-equilibrium state and gives rise
+to a weak re-entrant transition while warming under field-cooled condition. It
+shows a large inverse magnetocaloric effect across the magneto-structural
+transition and a conventional magnetocaloric effect across the second order
+paramagnetic to ferromagnetic transition. The relative cooling power which
+characterizes the performance of a magnetic refrigerant material is found to be
+reasonably high compared to the other reported magnetocaloric alloys.
+",0,1,0,0,0,0
+4638,Parametric Gaussian Process Regression for Big Data,"  This work introduces the concept of parametric Gaussian processes (PGPs),
+which is built upon the seemingly self-contradictory idea of making Gaussian
+processes parametric. Parametric Gaussian processes, by construction, are
+designed to operate in ""big data"" regimes where one is interested in
+quantifying the uncertainty associated with noisy data. The proposed
+methodology circumvents the well-established need for stochastic variational
+inference, a scalable algorithm for approximating posterior distributions. The
+effectiveness of the proposed approach is demonstrated using an illustrative
+example with simulated data and a benchmark dataset in the airline industry
+with approximately 6 million records.
+",0,0,0,1,0,0
+4639,Achieving non-discrimination in prediction,"  Discrimination-aware classification is receiving an increasing attention in
+data science fields. The pre-process methods for constructing a
+discrimination-free classifier first remove discrimination from the training
+data, and then learn the classifier from the cleaned data. However, they lack a
+theoretical guarantee for the potential discrimination when the classifier is
+deployed for prediction. In this paper, we fill this gap by mathematically
+bounding the probability of the discrimination in prediction being within a
+given interval in terms of the training data and classifier. We adopt the
+causal model for modeling the data generation mechanism, and formally defining
+discrimination in population, in a dataset, and in prediction. We obtain two
+important theoretical results: (1) the discrimination in prediction can still
+exist even if the discrimination in the training data is completely removed;
+and (2) not all pre-process methods can ensure non-discrimination in prediction
+even though they can achieve non-discrimination in the modified training data.
+Based on the results, we develop a two-phase framework for constructing a
+discrimination-free classifier with a theoretical guarantee. The experiments
+demonstrate the theoretical results and show the effectiveness of our two-phase
+framework.
+",1,0,0,1,0,0
+4640,Fixing and almost fixing a planar convex body,"  A set of points a 1 ,. .. , a n fixes a planar convex body K if the points
+are on bdK, the boundary of K, and if any small move of K brings some point of
+the set in intK, the interior of K. The points a 1 ,. .. , a n $\in$ bdK almost
+fix K if, for any neighbourhoods V i of a i (i = 1,. .. , n), there are pairs
+of points a i , a i $\in$ V i $\cap$ bdK such that a 1 , a 1 ,. .. , a n fix K.
+This note compares several definitions of these notions and gives first order
+conditions for a 1 ,. .. , a n $\in$ bdK to fix, and to almost fix, K.
+",0,0,1,0,0,0
+4641,Undersampled dynamic X-ray tomography with dimension reduction Kalman filter,"  In this paper, we consider prior-based dimension reduction Kalman filter for
+undersampled dynamic X-ray tomography. With this method, the X-ray
+reconstructions are parameterized by a low-dimensional basis. Thus, the
+proposed method is a) computationally very light; and b) extremely robust as
+all the computations can be done explicitly. With real and simulated
+measurement data, we show that the method provides accurate reconstructions
+even with very limited number of angular directions.
+",0,0,0,1,0,0
+4642,End-to-End Sound Source Separation Conditioned On Instrument Labels,"  Can we perform an end-to-end sound source separation (SSS) with a variable
+number of sources using a deep learning model? This paper presents an extension
+of the Wave-U-Net model which allows end-to-end monaural source separation with
+a non-fixed number of sources. Furthermore, we propose multiplicative
+conditioning with instrument labels at the bottleneck of the Wave-U-Net and
+show its effect on the separation results. This approach can be further
+extended to other types of conditioning such as audio-visual SSS and
+score-informed SSS.
+",1,0,0,0,0,0
+4643,Group analysis of general Burgers-Korteweg-de Vries equations,"  The complete group classification problem for the class of (1+1)-dimensional
+$r$th order general variable-coefficient Burgers-Korteweg-de Vries equations is
+solved for arbitrary values of $r$ greater than or equal to two. We find the
+equivalence groupoids of this class and its various subclasses obtained by
+gauging equation coefficients with equivalence transformations. Showing that
+this class and certain gauged subclasses are normalized in the usual sense, we
+reduce the complete group classification problem for the entire class to that
+for the selected maximally gauged subclass, and it is the latter problem that
+is solved efficiently using the algebraic method of group classification.
+Similar studies are carried out for the two subclasses of equations with
+coefficients depending at most on the time or space variable, respectively.
+Applying an original technique, we classify Lie reductions of equations from
+the class under consideration with respect to its equivalence group. Studying
+of alternative gauges for equation coefficients with equivalence
+transformations allows us not only to justify the choice of the most
+appropriate gauge for the group classification but also to construct for the
+first time classes of differential equations with nontrivial generalized
+equivalence group such that equivalence-transformation components corresponding
+to equation variables locally depend on nonconstant arbitrary elements of the
+class. For the subclass of equations with coefficients depending at most on the
+time variable, which is normalized in the extended generalized sense, we
+explicitly construct its extended generalized equivalence group in a rigorous
+way. The new notion of effective generalized equivalence group is introduced.
+",0,1,1,0,0,0
+4644,Evolutionary Generative Adversarial Networks,"  Generative adversarial networks (GAN) have been effective for learning
+generative models for real-world data. However, existing GANs (GAN and its
+variants) tend to suffer from training problems such as instability and mode
+collapse. In this paper, we propose a novel GAN framework called evolutionary
+generative adversarial networks (E-GAN) for stable GAN training and improved
+generative performance. Unlike existing GANs, which employ a pre-defined
+adversarial objective function alternately training a generator and a
+discriminator, we utilize different adversarial training objectives as mutation
+operations and evolve a population of generators to adapt to the environment
+(i.e., the discriminator). We also utilize an evaluation mechanism to measure
+the quality and diversity of generated samples, such that only well-performing
+generator(s) are preserved and used for further training. In this way, E-GAN
+overcomes the limitations of an individual adversarial training objective and
+always preserves the best offspring, contributing to progress in and the
+success of GANs. Experiments on several datasets demonstrate that E-GAN
+achieves convincing generative performance and reduces the training problems
+inherent in existing GANs.
+",0,0,0,1,0,0
+4645,Extended Sammon Projection and Wavelet Kernel Extreme Learning Machine for Gait-Based Legitimate User Identification on Smartphones,"  Smartphones have ubiquitously integrated into our home and work environments,
+however, users normally rely on explicit but inefficient identification
+processes in a controlled environment. Therefore, when a device is stolen, a
+thief can have access to the owner's personal information and services against
+the stored password/s. As a result of this potential scenario, this work
+demonstrates the possibilities of legitimate user identification in a
+semi-controlled environment through the built-in smartphones motion dynamics
+captured by two different sensors. This is a two-fold process: sub-activity
+recognition followed by user/impostor identification. Prior to the
+identification; Extended Sammon Projection (ESP) method is used to reduce the
+redundancy among the features. To validate the proposed system, we first
+collected data from four users walking with their device freely placed in one
+of their pants pockets. Through extensive experimentation, we demonstrate that
+together time and frequency domain features optimized by ESP to train the
+wavelet kernel based extreme learning machine classifier is an effective system
+to identify the legitimate user or an impostor with \(97\%\) accuracy.
+",1,0,0,0,0,0
+4646,On the free path length distribution for linear motion in an n-dimensional box,"  We consider the distribution of free path lengths, or the distance between
+consecutive bounces of random particles, in an n-dimensional rectangular box.
+If each particle travels a distance R, then, as R tends to infinity the free
+path lengths coincides with the distribution of the length of the intersection
+of a random line with the box (for a natural ensemble of random lines) and we
+give an explicit formula (piecewise real analytic) for the probability density
+function in dimension two and three.
+In dimension two we also consider a closely related model where each particle
+is allowed to bounce N times, as N tends to infinity, and give an explicit
+(again piecewise real analytic) formula for its probability density function.
+Further, in both models we can recover the side lengths of the box from the
+location of the discontinuities of the probability density functions.
+",0,0,1,0,0,0
+4647,Spin-Orbit Misalignments of Three Jovian Planets via Doppler Tomography,"  We present measurements of the spin-orbit misalignments of the hot Jupiters
+HAT-P-41 b and WASP-79 b, and the aligned warm Jupiter Kepler-448 b. We
+obtained these measurements with Doppler tomography, where we spectroscopically
+resolve the line profile perturbation during the transit due to the
+Rossiter-McLaughlin effect. We analyze time series spectra obtained during
+portions of five transits of HAT-P-41 b, and find a value of the spin-orbit
+misalignment of $\lambda = -22.1_{-6.0}^{+0.8 \circ}$. We reanalyze the radial
+velocity Rossiter-McLaughlin data on WASP-79 b obtained by Addison et al.
+(2013) using Doppler tomographic methodology. We measure
+$\lambda=-99.1_{-3.9}^{+4.1\circ}$, consistent with but more precise than the
+value found by Addison et al. (2013). For Kepler-448 b we perform a joint fit
+to the Kepler light curve, Doppler tomographic data, and a radial velocity
+dataset from Lillo-Box et al. (2015). We find an approximately aligned orbit
+($\lambda=-7.1^{+4.2 \circ}_{-2.8}$), in modest disagreement with the value
+found by Bourrier et al. (2015). Through analysis of the Kepler light curve we
+measure a stellar rotation period of $P_{\mathrm{rot}}=1.27 \pm 0.11$ days, and
+use this to argue that the full three-dimensional spin-orbit misalignment is
+small, $\psi\sim0^{\circ}$.
+",0,1,0,0,0,0
+4648,Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes,"  This paper introduces a novel parameter estimation method for the probability
+tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet
+processes (HDPs). The main result of this paper is to show that improved
+parameter estimation allows BNCs to outperform leading learning methods such as
+Random Forest for both 0-1 loss and RMSE, albeit just on categorical datasets.
+As data assets become larger, entering the hyped world of ""big"", efficient
+accurate classification requires three main elements: (1) classifiers with
+low-bias that can capture the fine-detail of large datasets (2) out-of-core
+learners that can learn from data without having to hold it all in main memory
+and (3) models that can classify new data very efficiently.
+The latest Bayesian network classifiers (BNCs) satisfy these requirements.
+Their bias can be controlled easily by increasing the number of parents of the
+nodes in the graph. Their structure can be learned out of core with a limited
+number of passes over the data. However, as the bias is made lower to
+accurately model classification tasks, so is the accuracy of their parameters'
+estimates, as each parameter is estimated from ever decreasing quantities of
+data. In this paper, we introduce the use of Hierarchical Dirichlet Processes
+for accurate BNC parameter estimation.
+We conduct an extensive set of experiments on 68 standard datasets and
+demonstrate that our resulting classifiers perform very competitively with
+Random Forest in terms of prediction, while keeping the out-of-core capability
+and superior classification time.
+",1,0,0,1,0,0
+4649,Modeling and Simulation of the Dynamics of the Quick Return Mechanism: A Bond Graph Approach,"  This paper applies the multibond graph approach for rigid multibody systems
+to model the dynamics of general spatial mechanisms. The commonly used quick
+return mechanism which comprises of revolute as well as prismatic joints has
+been chosen as a representative example to demonstrate the application of this
+technique and its resulting advantages. In this work, the links of the quick
+return mechanism are modeled as rigid bodies. The rigid links are then coupled
+at the joints based on the nature of constraint. This alternative method of
+formulation of system dynamics, using Bond Graphs, offers a rich set of
+features that include pictorial representation of the dynamics of translation
+and rotation for each link of the mechanism in the inertial frame,
+representation and handling of constraints at the joints, depiction of
+causality, obtaining dynamic reaction forces and moments at various locations
+in the mechanism and so on. Yet another advantage of this approach is that the
+coding for simulation can be carried out directly from the Bond Graph in an
+algorithmic manner, without deriving system equations. In this work, the
+program code for simulation is written in MATLAB. The vector and tensor
+operations are conveniently represented in MATLAB, resulting in a compact and
+optimized code. The simulation results are plotted and discussed in detail.
+",1,0,0,0,0,0
+4650,Nauticle: a general-purpose particle-based simulation tool,"  Nauticle is a general-purpose simulation tool for the flexible and highly
+configurable application of particle-based methods of either discrete or
+continuum phenomena. It is presented that Nauticle has three distinct layers
+for users and developers, then the top two layers are discussed in detail. The
+paper introduces the Symbolic Form Language (SFL) of Nauticle, which
+facilitates the formulation of user-defined numerical models at the top level
+in text-based configuration files and provides simple application examples of
+use. On the other hand, at the intermediate level, it is shown that the SFL can
+be intuitively extended with new particle methods without tedious recoding or
+even the knowledge of the bottom level. Finally, the efficiency of the code is
+also tested through a performance benchmark.
+",1,1,0,0,0,0
+4651,Local and non-local energy spectra of superfluid $^3$He turbulence,"  Below the phase transition temperature $Tc \simeq 10^{-3}$K He-3B has a
+mixture of normal and superfluid components. Turbulence in this material is
+carried predominantly by the superfluid component. We explore the statistical
+properties of this quantum turbulence, stressing the differences from the
+better known classical counterpart. To this aim we study the time-honored
+Hall-Vinen-Bekarevich-Khalatnikov coarse-grained equations of superfluid
+turbulence. We combine pseudo-spectral direct numerical simulations with
+analytic considerations based on an integral closure for the energy flux. We
+avoid the assumption of locality of the energy transfer which was used
+previously in both analytic and numerical studies of the superfluid He-3B
+turbulence. For T<0.37 Tc, with relatively weak mutual friction, we confirm the
+previously found ""subcritical"" energy spectrum E(k), given by a superposition
+of two power laws that can be approximated as $E(k)~ k^{-x}$ with an apparent
+scaling exponent 5/3 <x(k)< 3. For T>0.37 Tc and with strong mutual friction,
+we observed numerically and confirmed analytically the scale-invariant spectrum
+$E(k)~ k^{-x}$ with a (k-independent) exponent x > 3 that gradually increases
+with the temperature and reaches a value $x\simeq 9$ for $T\approx 0.72 Tc$. In
+the near-critical regimes we discover a strong enhancement of intermittency
+which exceeds by an order of magnitude the corresponding level in classical
+hydrodynamic turbulence.
+",0,1,0,0,0,0
+4652,Microwave SQUID Multiplexer demonstration for Cosmic Microwave Background Imagers,"  Key performance characteristics are demonstrated for the microwave SQUID
+multiplexer ($\mu$MUX) coupled to transition edge sensor (TES) bolometers that
+have been optimized for cosmic microwave background (CMB) observations. In a
+64-channel demonstration, we show that the $\mu$MUX produces a white, input
+referred current noise level of 29~pA$/\sqrt{\mathrm{Hz}}$ at -77~dB microwave
+probe tone power, which is well below expected fundamental detector and photon
+noise sources for a ground-based CMB-optimized bolometer. Operated with
+negligible photon loading, we measure 98~pA$/\sqrt{\mathrm{Hz}}$ in the
+TES-coupled channels biased at 65% of the sensor normal resistance. This noise
+level is consistent with that predicted from bolometer thermal fluctuation
+(i.e., phonon) noise. Furthermore, the power spectral density exhibits a white
+spectrum at low frequencies ($\sim$~100~mHz), which enables CMB mapping on
+large angular scales that constrain the physics of inflation. Additionally, we
+report cross-talk measurements that indicate a level below 0.3%, which is less
+than the level of cross-talk from multiplexed readout systems in deployed CMB
+imagers. These measurements demonstrate the $\mu$MUX as a viable readout
+technique for future CMB imaging instruments.
+",0,1,0,0,0,0
+4653,Preserving Differential Privacy Between Features in Distributed Estimation,"  Privacy is crucial in many applications of machine learning. Legal, ethical
+and societal issues restrict the sharing of sensitive data making it difficult
+to learn from datasets that are partitioned between many parties. One important
+instance of such a distributed setting arises when information about each
+record in the dataset is held by different data owners (the design matrix is
+""vertically-partitioned"").
+In this setting few approaches exist for private data sharing for the
+purposes of statistical estimation and the classical setup of differential
+privacy with a ""trusted curator"" preparing the data does not apply. We work
+with the notion of $(\epsilon,\delta)$-distributed differential privacy which
+extends single-party differential privacy to the distributed,
+vertically-partitioned case. We propose PriDE, a scalable framework for
+distributed estimation where each party communicates perturbed random
+projections of their locally held features ensuring
+$(\epsilon,\delta)$-distributed differential privacy is preserved. For
+$\ell_2$-penalized supervised learning problems PriDE has bounded estimation
+error compared with the optimal estimates obtained without privacy constraints
+in the non-distributed setting. We confirm this empirically on real world and
+synthetic datasets.
+",1,0,0,1,0,0
+4654,Parallel implementation of a vehicle rail dynamical model for multi-core systems,"  This research presents a model of a complex dynamic object running on a
+multi-core system. Discretization and numerical integration for multibody
+models of vehicle rail elements in the vertical longitudinal plane fluctuations
+is considered. The implemented model and solution of the motion differential
+equations allow estimating the basic processes occurring in the system with
+various external influences. Hence the developed programming model can be used
+for performing analysis and comparing new vehicle designs.
+Keywords-dynamic model; multi-core system; SMP system; rolling stock.
+",1,0,0,0,0,0
+4655,Towards Neural Phrase-based Machine Translation,"  In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our
+method explicitly models the phrase structures in output sequences using
+Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence
+modeling method. To mitigate the monotonic alignment requirement of SWAN, we
+introduce a new layer to perform (soft) local reordering of input sequences.
+Different from existing neural machine translation (NMT) approaches, NPMT does
+not use attention-based decoding mechanisms. Instead, it directly outputs
+phrases in a sequential order and can decode in linear time. Our experiments
+show that NPMT achieves superior performances on IWSLT 2014
+German-English/English-German and IWSLT 2015 English-Vietnamese machine
+translation tasks compared with strong NMT baselines. We also observe that our
+method produces meaningful phrases in output languages.
+",1,0,0,1,0,0
+4656,Towards Accurate Multi-person Pose Estimation in the Wild,"  We propose a method for multi-person detection and 2-D pose estimation that
+achieves state-of-art results on the challenging COCO keypoints task. It is a
+simple, yet powerful, top-down approach consisting of two stages.
+In the first stage, we predict the location and scale of boxes which are
+likely to contain people; for this we use the Faster RCNN detector. In the
+second stage, we estimate the keypoints of the person potentially contained in
+each proposed bounding box. For each keypoint type we predict dense heatmaps
+and offsets using a fully convolutional ResNet. To combine these outputs we
+introduce a novel aggregation procedure to obtain highly localized keypoint
+predictions. We also use a novel form of keypoint-based Non-Maximum-Suppression
+(NMS), instead of the cruder box-level NMS, and a novel form of keypoint-based
+confidence score estimation, instead of box-level scoring.
+Trained on COCO data alone, our final system achieves average precision of
+0.649 on the COCO test-dev set and the 0.643 test-standard sets, outperforming
+the winner of the 2016 COCO keypoints challenge and other recent state-of-art.
+Further, by using additional in-house labeled data we obtain an even higher
+average precision of 0.685 on the test-dev set and 0.673 on the test-standard
+set, more than 5% absolute improvement compared to the previous best performing
+method on the same dataset.
+",1,0,0,0,0,0
+4657,Global band topology of simple and double Dirac-point (semi-)metals,"  We combine space group representation theory together with scanning of closed
+subdomains of the Brillouin zone with Wilson loops to algebraically determine
+global band structure topology. Considering space group #19 as a case study, we
+show that the energy ordering of the irreducible representations at the
+high-symmetry points $\{\Gamma,S,T,U\}$ fully determines the global band
+topology, with all topological classes characterized through their simple and
+double Dirac-points.
+",0,1,0,0,0,0
+4658,Topological Perspectives on Statistical Quantities I,"  In statistics cumulants are defined to be functions that measure the linear
+independence of random variables. In the non-communicative case the Boolean
+cumulants can be described as functions that measure deviation of a map between
+algebras from being an algebra morphism. In Algebraic topology maps that are
+homotopic to being algebra morphisms are studied using the theory of $A_\infty$
+algebras. In this paper we will explore the link between these two points of
+views on maps between algebras that are not algebra maps.
+",0,0,1,0,0,0
+4659,A Python Calculator for Supernova Remnant Evolution,"  A freely available Python code for modelling SNR evolution has been created.
+This software is intended for two purposes: to understand SNR evolution; and to
+use in modelling observations of SNR for obtaining good estimates of SNR
+properties. It includes all phases for the standard path of evolution for
+spherically symmetric SNRs. In addition, alternate evolutionary models are
+available, including evolution in a cloudy ISM, the fractional energy loss
+model, and evolution in a hot low-density ISM. The graphical interface takes in
+various parameters and produces outputs such as shock radius and velocity vs.
+time, SNR surface brightness profile and spectrum. Some interesting properties
+of SNR evolution are demonstrated using the program.
+",0,1,0,0,0,0
+4660,Über die Präzision interprozeduraler Analysen,"  In this work, we examine two approaches to interprocedural data-flow analysis
+of Sharir and Pnueli in terms of precision: the functional and the call-string
+approach. In doing so, not only the theoretical best, but all solutions are
+regarded which occur when using abstract interpretation or widening
+additionally. It turns out that the solutions of both approaches coincide. This
+property is preserved when using abstract interpretation; in the case of
+widening, a comparison of the results is not always possible.
+",1,0,0,0,0,0
+4661,Gee-Haw Whammy Diddle,"  Gee-Haw Whammy Diddle is a seemingly simple mechanical toy consisting of a
+wooden stick and a second stick that is made up of a series of notches with a
+propeller at its end. When the wooden stick is pulled over the notches, the
+propeller starts to rotate. In spite of its simplicity, physical principles
+governing the motion of the stick and the propeller are rather complicated and
+interesting. Here we provide a thorough analysis of the system and parameters
+influencing the motion. We show that contrary to the results published on this
+topic so far, neither elliptic motion of the stick nor frequency
+synchronization is needed for starting the motion of the propeller.
+",0,1,0,0,0,0
+4662,Cancellation theorem for Grothendieck-Witt-correspondences and Witt-correspondences,"  The cancellation theorem for Grothendieck-Witt-correspondences and
+Witt-correspondences between smooth varieties over an infinite prefect field
+$k$, $char k \neq 2$, is proved, the isomorphism
+$$Hom_{\mathbf{DM}^\mathrm{GW}_\mathrm{eff}}(A^\bullet,B^\bullet) \simeq
+Hom_{\mathbf{DM}^\mathrm{GW}_\mathrm{eff}}(A^\bullet(1),B^\bullet(1)),$$ for
+$A^\bullet,B^\bullet\in \mathbf{DM}^\mathrm{GW}_\mathrm{eff}(k)$ in the
+category of effective Grothendieck-Witt-motives constructed in
+\cite{AD_DMGWeff} is obtained (and similarly for Witt-motives).
+This implies that the canonical functor $\Sigma_{\mathbb G_m^{\wedge
+1}}^\infty\colon \mathbf{DM}^\mathrm{GW}_\mathrm{eff}(k)\to
+\mathbf{DM}^\mathrm{GW}(k)$ is fully faithful, where
+$\mathbf{DM}^\mathrm{GW}(k)$ is the category of non-effective GW-motives
+(defined by stabilization of $\mathbf{DM}^\mathrm{GW}_\mathrm{eff}(k)$ along
+$\mathbb G_m^{\wedge 1}$) and yields the main property of motives of smooth
+varieties in the category $\mathbf{DM}^\mathrm{GW}(k)$: $$
+Hom_{\mathbf{DM}^\mathrm{GW}(k)}(M^{GW}(X), \Sigma_{\mathbb G_m^{\wedge
+1}}^\infty\mathcal F[i]) \simeq H^i_{Nis}(X,\mathcal F) ,$$ for any smooth
+variety $X$ and homotopy invariant sheave with GW-transfers $\mathcal F$ (and
+similarly for $\mathbf{DM}^\mathrm{W}(k)$).
+",0,0,1,0,0,0
+4663,Jumping across biomedical contexts using compressive data fusion,"  Motivation: The rapid growth of diverse biological data allows us to consider
+interactions between a variety of objects, such as genes, chemicals, molecular
+signatures, diseases, pathways and environmental exposures. Often, any pair of
+objects--such as a gene and a disease--can be related in different ways, for
+example, directly via gene-disease associations or indirectly via functional
+annotations, chemicals and pathways. Different ways of relating these objects
+carry different semantic meanings. However, traditional methods disregard these
+semantics and thus cannot fully exploit their value in data modeling.
+Results: We present Medusa, an approach to detect size-k modules of objects
+that, taken together, appear most significant to another set of objects. Medusa
+operates on large-scale collections of heterogeneous data sets and explicitly
+distinguishes between diverse data semantics. It advances research along two
+dimensions: it builds on collective matrix factorization to derive different
+semantics, and it formulates the growing of the modules as a submodular
+optimization program. Medusa is flexible in choosing or combining semantic
+meanings and provides theoretical guarantees about detection quality. In a
+systematic study on 310 complex diseases, we show the effectiveness of Medusa
+in associating genes with diseases and detecting disease modules. We
+demonstrate that in predicting gene-disease associations Medusa compares
+favorably to methods that ignore diverse semantic meanings. We find that the
+utility of different semantics depends on disease categories and that, overall,
+Medusa recovers disease modules more accurately when combining different
+semantics.
+",1,0,0,1,0,0
+4664,Memories of a Theoretical Physicist,"  While I was dealing with a brain injury and finding it difficult to work, two
+friends (Derek Westen, a friend of the KITP, and Steve Shenker, with whom I was
+recently collaborating), suggested that a new direction might be good. Steve in
+particular regarded me as a good writer and suggested that I try that. I
+quickly took to Steve's suggestion. Having only two bodies of knowledge, myself
+and physics, I decided to write an autobiography about my development as a
+theoretical physicist.
+This is not written for any particular audience, but just to give myself a
+goal. It will probably have too much physics for a nontechnical reader, and too
+little for a physicist, but perhaps there with be different things for each.
+Parts may be tedious. But it is somewhat unique, I think, a blow-by-blow
+history of where I started and where I got to.
+Probably the target audience is theoretical physicists, especially young
+ones, who may enjoy comparing my struggles with their own. Some disclaimers:
+This is based on my own memories, jogged by the arXiv and Inspire. There will
+surely be errors and omissions. And note the title: this is about my memories,
+which will be different for other people. Also, it would not be possible for me
+to mention all the authors whose work might intersect mine, so this should not
+be treated as a reference work.
+",0,1,0,0,0,0
+4665,"Connecting the dots between mechanosensitive channel abundance, osmotic shock, and survival at single-cell resolution","  Rapid changes in extracellular osmolarity are one of many insults microbial
+cells face on a daily basis. To protect against such shocks, Escherichia coli
+and other microbes express several types of transmembrane channels which open
+and close in response to changes in membrane tension. In E. coli, one of the
+most abundant channels is the mechanosensitive channel of large conductance
+(MscL). While this channel has been heavily characterized through structural
+methods, electrophysiology, and theoretical modeling, our understanding of its
+physiological role in preventing cell death by alleviating high membrane
+tension remains tenuous. In this work, we examine the contribution of MscL
+alone to cell survival after osmotic shock at single cell resolution using
+quantitative fluorescence microscopy. We conduct these experiments in an E.
+coli strain which is lacking all mechanosensitive channel genes save for MscL
+whose expression is tuned across three orders of magnitude through
+modifications of the Shine-Dalgarno sequence. While theoretical models suggest
+that only a few MscL channels would be needed to alleviate even large changes
+in osmotic pressure, we find that between 500 and 700 channels per cell are
+needed to convey upwards of 80% survival. This number agrees with the average
+MscL copy number measured in wild-type E. coli cells through proteomic studies
+and quantitative Western blotting. Furthermore, we observe zero survival events
+in cells with less than 100 channels per cell. This work opens new questions
+concerning the contribution of other mechanosensitive channels to survival as
+well as regulation of their activity.
+",0,0,0,0,1,0
+4666,Optical Mapping Near-eye Three-dimensional Display with Correct Focus Cues,"  We present an optical mapping near-eye (OMNI) three-dimensional display
+method for wearable devices. By dividing a display screen into different
+sub-panels and optically mapping them to various depths, we create a multiplane
+volumetric image with correct focus cues for depth perception. The resultant
+system can drive the eye's accommodation to the distance that is consistent
+with binocular stereopsis, thereby alleviating the vergence-accommodation
+conflict, the primary cause for eye fatigue and discomfort. Compared with the
+previous methods, the OMNI display offers prominent advantages in adaptability,
+image dynamic range, and refresh rate.
+",1,1,0,0,0,0
+4667,Quantum anomalous Hall state from spatially decaying interactions on the decorated honeycomb lattice,"  Topological phases typically encode topology at the level of the single
+particle band structure. But a remarkable class of models shows that quantum
+anomalous Hall effects can be driven exclusively by interactions, while the
+parent non-interacting band structure is topologically trivial. Unfortunately,
+these models have so far relied on interactions that do not spatially decay and
+are therefore unphysical. We study a model of spinless fermions on a decorated
+honeycomb lattice. Using complementary methods, mean-field theory and exact
+diagonalization, we find a robust quantum anomalous Hall phase arising from
+spatially decaying interactions. Our finding paves the way for observing the
+quantum anomalous Hall effect driven entirely by interactions.
+",0,1,0,0,0,0
+4668,Poisson--Gamma Dynamical Systems,"  We introduce a new dynamical system for sequentially observed multivariate
+count data. This model is based on the gamma--Poisson construction---a natural
+choice for count data---and relies on a novel Bayesian nonparametric prior that
+ties and shrinks the model parameters, thus avoiding overfitting. We present an
+efficient MCMC inference algorithm that advances recent work on augmentation
+schemes for inference in negative binomial models. Finally, we demonstrate the
+model's inductive bias using a variety of real-world data sets, showing that it
+exhibits superior predictive performance over other models and infers highly
+interpretable latent structure.
+",1,0,0,1,0,0
+4669,Best-Choice Edge Grafting for Efficient Structure Learning of Markov Random Fields,"  Incremental methods for structure learning of pairwise Markov random fields
+(MRFs), such as grafting, improve scalability by avoiding inference over the
+entire feature space in each optimization step. Instead, inference is performed
+over an incrementally grown active set of features. In this paper, we address
+key computational bottlenecks that current incremental techniques still suffer
+by introducing best-choice edge grafting, an incremental, structured method
+that activates edges as groups of features in a streaming setting. The method
+uses a reservoir of edges that satisfy an activation condition, approximating
+the search for the optimal edge to activate. It also reorganizes the search
+space using search-history and structure heuristics. Experiments show a
+significant speedup for structure learning and a controllable trade-off between
+the speed and quality of learning.
+",1,0,0,1,0,0
+4670,Cavity-enhanced transport of charge,"  We theoretically investigate charge transport through electronic bands of a
+mesoscopic one-dimensional system, where inter-band transitions are coupled to
+a confined cavity mode, initially prepared close to its vacuum. This coupling
+leads to light-matter hybridization where the dressed fermionic bands interact
+via absorption and emission of dressed cavity-photons. Using a self-consistent
+non-equilibrium Green's function method, we compute electronic transmissions
+and cavity photon spectra and demonstrate how light-matter coupling can lead to
+an enhancement of charge conductivity in the steady-state. We find that
+depending on cavity loss rate, electronic bandwidth, and coupling strength, the
+dynamics involves either an individual or a collective response of Bloch
+states, and explain how this affects the current enhancement. We show that the
+charge conductivity enhancement can reach orders of magnitudes under
+experimentally relevant conditions.
+",0,1,0,0,0,0
+4671,Isoparameteric hypersurfaces in a Randers sphere of constant flag curvature,"  In this paper, I study the isoparametric hypersurfaces in a Randers sphere
+$(S^n,F)$ of constant flag curvature, with the navigation datum $(h,W)$. I
+prove that an isoparametric hypersurface $M$ for the standard round sphere
+$(S^n,h)$ which is tangent to $W$ remains isoparametric for $(S^n,F)$ after the
+navigation process. This observation provides a special class of isoparametric
+hypersurfaces in $(S^n,F)$, which can be equivalently described as the regular
+level sets of isoparametric functions $f$ satisfying $-f$ is transnormal. I
+provide a classification for these special isoparametric hypersurfaces $M$,
+together with their ambient metric $F$ on $S^n$, except the case that $M$ is of
+the OT-FKM type with the multiplicities $(m_1,m_2)=(8,7)$. I also give a
+complete classificatoin for all homogeneous hypersurfaces in $(S^n,F)$. They
+all belong to these special isoparametric hypersurfaces. Because of the extra
+$W$, the number of distinct principal curvature can only be 1,2 or 4, i.e.
+there are less homogeneous hypersurfaces for $(S^n,F)$ than those for
+$(S^n,h)$.
+",0,0,1,0,0,0
+4672,Bright-field microscopy of transparent objects: a ray tracing approach,"  Formation of a bright-field microscopic image of a transparent phase object
+is described in terms of elementary geometrical optics. Our approach is based
+on the premise that image replicates the intensity distribution (real or
+virtual) at the front focal plane of the objective. The task is therefore
+reduced to finding the change in intensity at the focal plane caused by the
+object. This can be done by ray tracing complemented with the requirement of
+conservation of the number of rays. Despite major simplifications involved in
+such an analysis, it reproduces some results from the paraxial wave theory.
+Additionally, our analysis suggests two ways of extracting quantitative phase
+information from bright-field images: by vertically shifting the focal plane
+(the approach used in the transport-of-intensity analysis) or by varying the
+angle of illumination. In principle, information thus obtained should allow
+reconstruction of the object morphology.
+",0,1,0,0,0,0
+4673,Entropy Production Rate is Maximized in Non-Contractile Actomyosin,"  The actin cytoskeleton is an active semi-flexible polymer network whose
+non-equilibrium properties coordinate both stable and contractile behaviors to
+maintain or change cell shape. While myosin motors drive the actin cytoskeleton
+out-of-equilibrium, the role of myosin-driven active stresses in the
+accumulation and dissipation of mechanical energy is unclear. To investigate
+this, we synthesize an actomyosin material in vitro whose active stress content
+can tune the network from stable to contractile. Each increment in activity
+determines a characteristic spectrum of actin filament fluctuations which is
+used to calculate the total mechanical work and the production of entropy in
+the material. We find that the balance of work and entropy does not increase
+monotonically and, surprisingly, the entropy production rate is maximized in
+the non-contractile, stable state. Our study provides evidence that the origins
+of system entropy production and activity-dependent dissipation arise from
+disorder in the molecular interactions between actin and myosin
+",0,0,0,0,1,0
+4674,Intrinsic resolving power of XUV diffraction gratings measured with Fizeau interferometry,"  We introduce a method for using Fizeau interferometry to measure the
+intrinsic resolving power of a diffraction grating. This method is more
+accurate than traditional techniques based on a long-trace profiler (LTP),
+since it is sensitive to long-distance phase errors not revealed by a d-spacing
+map. We demonstrate 50,400 resolving power for a mechanically ruled XUV grating
+from Inprentus, Inc.
+",0,1,0,0,0,0
+4675,When do we have the power to detect biological interactions in spatial point patterns?,"  Determining the relative importance of environmental factors, biotic
+interactions and stochasticity in assembling and maintaining species-rich
+communities remains a major challenge in ecology. In plant communities,
+interactions between individuals of different species are expected to leave a
+spatial signature in the form of positive or negative spatial correlations over
+distances relating to the spatial scale of interaction. Most studies using
+spatial point process tools have found relatively little evidence for
+interactions between pairs of species. More interactions tend to be detected in
+communities with fewer species. However, there is currently no understanding of
+how the power to detect spatial interactions may change with sample size, or
+the scale and intensity of interactions.
+We use a simple 2-species model where the scale and intensity of interactions
+are controlled to simulate point pattern data. In combination with an
+approximation to the variance of the spatial summary statistics that we sample,
+we investigate the power of current spatial point pattern methods to correctly
+reject the null model of bivariate species independence.
+We show that the power to detect interactions is positively related to the
+abundances of the species tested, and the intensity and scale of interactions.
+Increasing imbalance in abundances has a negative effect on the power to detect
+interactions. At population sizes typically found in currently available
+datasets for species-rich plant communities we find only a very low power to
+detect interactions. Differences in power may explain the increased frequency
+of interactions in communities with fewer species. Furthermore, the
+community-wide frequency of detected interactions is very sensitive to a
+minimum abundance criterion for including species in the analyses.
+",0,0,0,0,1,0
+4676,Spectral Algorithms for Computing Fair Support Vector Machines,"  Classifiers and rating scores are prone to implicitly codifying biases, which
+may be present in the training data, against protected classes (i.e., age,
+gender, or race). So it is important to understand how to design classifiers
+and scores that prevent discrimination in predictions. This paper develops
+computationally tractable algorithms for designing accurate but fair support
+vector machines (SVM's). Our approach imposes a constraint on the covariance
+matrices conditioned on each protected class, which leads to a nonconvex
+quadratic constraint in the SVM formulation. We develop iterative algorithms to
+compute fair linear and kernel SVM's, which solve a sequence of relaxations
+constructed using a spectral decomposition of the nonconvex constraint. Its
+effectiveness in achieving high prediction accuracy while ensuring fairness is
+shown through numerical experiments on several data sets.
+",1,0,0,1,0,0
+4677,Random matrix approach for primal-dual portfolio optimization problems,"  In this paper, we revisit the portfolio optimization problems of the
+minimization/maximization of investment risk under constraints of budget and
+investment concentration (primal problem) and the maximization/minimization of
+investment concentration under constraints of budget and investment risk (dual
+problem) for the case that the variances of the return rates of the assets are
+identical. We analyze both optimization problems by using the Lagrange
+multiplier method and the random matrix approach. Thereafter, we compare the
+results obtained from our proposed approach with the results obtained in
+previous work. Moreover, we use numerical experiments to validate the results
+obtained from the replica approach and the random matrix approach as methods
+for analyzing both the primal and dual portfolio optimization problems.
+",1,1,0,0,0,0
+4678,On a method for constructing the Lax pairs for integrable models via quadratic ansatz,"  A method for constructing the Lax pairs for nonlinear integrable models is
+suggested. First we look for a nonlinear invariant manifold to the
+linearization of the given equation. Examples show that such invariant manifold
+does exist and can effectively be found. Actually it is defined by a quadratic
+form. As a result we get a nonlinear Lax pair consisting of the linearized
+equation and the invariant manifold. Our second step consists of finding an
+appropriate change of the variables to linearize the found nonlinear Lax pair.
+The desired change of the variables is again defined by a quadratic form. The
+method is illustrated by the well-known KdV equation and the modified Volterra
+chain. New Lax pairs are found. The formal asymptotic expansions for their
+eigenfunctions are constructed around the singular values of the spectral
+parameter. By applying the method of the formal diagonalization to these Lax
+pairs the infinite series of the local conservation laws are obtained for the
+corresponding nonlinear models.
+",0,1,0,0,0,0
+4679,Insight into the modeling of seismic waves for detection of underground cavities,"  Motivated by the need to detect an underground cavity within the procedure of
+an On-Site-Inspection (OSI), of the Comprehensive Nuclear Test Ban Treaty
+Organization, the aim of this paper is to present results on the comparison of
+our numerical simulations with an analytic solution. The accurate numerical
+modeling can facilitate the development of proper analysis techniques to detect
+the remnants of an underground nuclear test. The larger goal is to help set a
+rigorous scientific base of OSI and to contribute to bringing the Treaty into
+force. For our 3D numerical simulations, we use the discontinuous Galerkin
+Spectral Element Code SPEED jointly developed at MOX (The Laboratory for
+Modeling and Scientific Computing, Department of Mathematics) and at DICA
+(Department of Civil and Environmental Engineering) of the Politecnico di
+Milano.
+",0,1,0,0,0,0
+4680,Uncorrelated far AGN flaring with their delayed UHECRs events,"  The most distant AGN, within the allowed GZK cut-off radius, have been
+recently candidate by many authors as the best location for observed UHECR
+origination. Indeed, the apparent homogeneity and isotropy of recent UHECR
+signals seems to require a far cosmic isotropic and homogeneous scenario
+involving a proton UHECR courier: our galaxy or nearest local group or super
+galactic plane (ruled by Virgo cluster) are too much near and apparently too
+much anisotropic in disagreement with PAO and TA almost homogeneous sample
+data. However, the few and mild observed UHECR clustering, the North and South
+Hot Spots, are smeared in wide solid angles. Their consequent random walk
+flight from most far GZK UHECR sources, nearly at 100 Mpc, must be delayed
+(with respect to a straight AGN photon gamma flaring arrival trajectory) at
+least by a million years. During this time, the AGN jet blazing signal, its
+probable axis deflection (such as the helical jet in Mrk501), its miss
+alignment or even its almost certain exhaust activity may lead to a complete
+misleading correlation between present UHECR events and a much earlier active
+AGN ejection. UHECR maps maybe anyway related to galactic or nearest (Cen A,
+M82) AGN extragalactic UHECR sources shining in twin Hot Spot. Therefore we
+defend our (quite different) scenarios where UHECR are mostly made by lightest
+UHECR nuclei originated by nearby AGN sources, or few galactic sources, whose
+delayed signals reach us within few thousand years in the observed smeared sky
+areas.
+",0,1,0,0,0,0
+4681,How to centralize and normalize quandle extensions,"  We show that quandle coverings in the sense of Eisermann form a (regular
+epi)-reflective subcategory of the category of surjective quandle
+homomorphisms, both by using arguments coming from categorical Galois theory
+and by constructing concretely a centralization congruence. Moreover, we show
+that a similar result holds for normal quandle extensions.
+",0,0,1,0,0,0
+4682,Geometric Fluctuation Theorem,"  We derive an extended fluctuation theorem for a geometric pumping in a
+spin-boson system under a periodic control of environmental temperatures by
+using a Markovian quantum master equation. We perform the Monte-Carlo
+simulation and obtain the current distribution, the average current and the
+fluctuation. Using the extended fluctuation theorem we try to explain the
+results of our simulation. The fluctuation theorem leads to the fluctuation
+dissipation relations but the absence of the conventional reciprocal relation.
+",0,1,0,0,0,0
+4683,Opinion Dynamics via Search Engines (and other Algorithmic Gatekeepers),"  Ranking algorithms are the information gatekeepers of the Internet era. We
+develop a stylized model to study the effects of ranking algorithms on opinion
+dynamics. We consider a search engine that uses an algorithm based on
+popularity and on personalization. We find that popularity-based rankings
+generate an advantage of the fewer effect: fewer websites reporting a given
+signal attract relatively more traffic overall. This highlights a novel,
+ranking-driven channel that explains the diffusion of misinformation, as
+websites reporting incorrect information may attract an amplified amount of
+traffic precisely because they are few. Furthermore, when individuals provide
+sufficiently positive feedback to the ranking algorithm, popularity-based
+rankings tend to aggregate information while personalization acts in the
+opposite direction.
+",1,0,0,0,0,1
+4684,Exploring home robot capabilities by medium fidelity prototyping,"  In order for autonomous robots to be able to support people's well-being in
+homes and everyday environments, new interactive capabilities will be required,
+as exemplified by the soft design used for Disney's recent robot character
+Baymax in popular fiction. Home robots will be required to be easy to interact
+with and intelligent--adaptive, fun, unobtrusive and involving little effort to
+power and maintain--and capable of carrying out useful tasks both on an
+everyday level and during emergencies. The current article adopts an
+exploratory medium fidelity prototyping approach for testing some new robotic
+capabilities in regard to recognizing people's activities and intentions and
+behaving in a way which is transparent to people. Results are discussed with
+the aim of informing next designs.
+",1,0,0,0,0,0
+4685,Unsupervised Domain Adaptation Based on Source-guided Discrepancy,"  Unsupervised domain adaptation is the problem setting where data generating
+distributions in the source and target domains are different, and labels in the
+target domain are unavailable. One important question in unsupervised domain
+adaptation is how to measure the difference between the source and target
+domains. A previously proposed discrepancy that does not use the source domain
+labels requires high computational cost to estimate and may lead to a loose
+generalization error bound in the target domain. To mitigate these problems, we
+propose a novel discrepancy called source-guided discrepancy (S-disc), which
+exploits labels in the source domain. As a consequence, S-disc can be computed
+efficiently with a finite sample convergence guarantee. In addition, we show
+that S-disc can provide a tighter generalization error bound than the one based
+on an existing discrepancy. Finally, we report experimental results that
+demonstrate the advantages of S-disc over the existing discrepancies.
+",0,0,0,1,0,0
+4686,"On structured surfaces with defects: geometry, strain incompatibility, internal stress, and natural shapes","  Given a distribution of defects on a structured surface, such as those
+represented by 2-dimensional crystalline materials, liquid crystalline
+surfaces, and thin sandwiched shells, what is the resulting stress field and
+the deformed shape? Motivated by this concern, we first classify, and quantify,
+the translational, rotational, and metrical defects allowable over a broad
+class of structured surfaces. With an appropriate notion of strain, the defect
+densities are then shown to appear as sources of strain incompatibility. The
+strain incompatibility relations, with appropriate kinematical assumptions on
+the decomposition of strain into elastic and plastic parts, and the stress
+equilibrium relations, with a suitable choice of material response, provide the
+necessary equations for determining both the internal stress field and the
+deformed shape. We demonstrate this by applying our theory to Kirchhoff-Love
+shells with a kinematics which allows for small in-surface strains but
+moderately large rotations.
+",0,1,1,0,0,0
+4687,Management system for the SND experiments,"  A new management system for the SND detector experiments (at VEPP-2000
+collider in Novosibirsk) is developed. We describe here the interaction between
+a user and the SND databases. These databases contain experiment configuration,
+conditions and metadata. The new system is designed in client-server
+architecture. It has several logical layers corresponding to the users roles. A
+new template engine is created. A web application is implemented using Node.js
+framework. At the time the application provides: showing and editing
+configuration; showing experiment metadata and experiment conditions data
+index; showing SND log (prototype).
+",1,1,0,0,0,0
+4688,Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate Codes,"  Spiking neural networks (SNNs) could play a key role in unsupervised machine
+learning applications, by virtue of strengths related to learning from the fine
+temporal structure of event-based signals. However, some spike-timing-related
+strengths of SNNs are hindered by the sensitivity of spike-timing-dependent
+plasticity (STDP) rules to input spike rates, as fine temporal correlations may
+be obstructed by coarser correlations between firing rates. In this article, we
+propose a spike-timing-dependent learning rule that allows a neuron to learn
+from the temporally-coded information despite the presence of rate codes. Our
+long-term plasticity rule makes use of short-term synaptic fatigue dynamics. We
+show analytically that, in contrast to conventional STDP rules, our fatiguing
+STDP (FSTDP) helps learn the temporal code, and we derive the necessary
+conditions to optimize the learning process. We showcase the effectiveness of
+FSTDP in learning spike-timing correlations among processes of different rates
+in synthetic data. Finally, we use FSTDP to detect correlations in real-world
+weather data from the United States in an experimental realization of the
+algorithm that uses a neuromorphic hardware platform comprising phase-change
+memristive devices. Taken together, our analyses and demonstrations suggest
+that FSTDP paves the way for the exploitation of the spike-based strengths of
+SNNs in real-world applications.
+",1,0,0,1,0,0
+4689,Kernel-Based Learning for Smart Inverter Control,"  Distribution grids are currently challenged by frequent voltage excursions
+induced by intermittent solar generation. Smart inverters have been advocated
+as a fast-responding means to regulate voltage and minimize ohmic losses. Since
+optimal inverter coordination may be computationally challenging and preset
+local control rules are subpar, the approach of customized control rules
+designed in a quasi-static fashion features as a golden middle. Departing from
+affine control rules, this work puts forth non-linear inverter control
+policies. Drawing analogies to multi-task learning, reactive control is posed
+as a kernel-based regression task. Leveraging a linearized grid model and given
+anticipated data scenarios, inverter rules are jointly designed at the feeder
+level to minimize a convex combination of voltage deviations and ohmic losses
+via a linearly-constrained quadratic program. Numerical tests using real-world
+data on a benchmark feeder demonstrate that nonlinear control rules driven also
+by a few non-local readings can attain near-optimal performance.
+",1,0,0,1,0,0
+4690,Temperature fluctuations in a changing climate: an ensemble-based experimental approach,"  There is an ongoing debate in the literature about whether the present global
+warming is increasing local and global temperature variability. The central
+methodological issues of this debate relate to the proper treatment of
+normalised temperature anomalies and trends in the studied time series which
+may be difficult to separate from time-evolving fluctuations. Some argue that
+temperature variability is indeed increasing globally, whereas others conclude
+it is decreasing or remains practically unchanged. Meanwhile, a consensus
+appears to emerge that local variability in certain regions (e.g. Western
+Europe and North America) has indeed been increasing in the past 40 years. Here
+we investigate the nature of connections between external forcing and climate
+variability conceptually by using a laboratory-scale minimal model of
+mid-latitude atmospheric thermal convection subject to continuously decreasing
+`equator-to-pole' temperature contrast, mimicking climate change. The analysis
+of temperature records from an ensemble of experimental runs (`realisations')
+all driven by identical time-dependent external forcing reveals that the
+collective variability of the ensemble and that of individual realisations may
+be markedly different -- a property to be considered when interpreting climate
+records.
+",0,1,0,0,0,0
+4691,Matrix factorizations for quantum complete intersections,"  We introduce twisted matrix factorizations for quantum complete intersections
+of codimension two. For such an algebra, we show that in a given dimension,
+almost all the indecomposable modules with bounded minimal projective
+resolutions correspond to such matrix factorizations.
+",0,0,1,0,0,0
+4692,Coresets for Vector Summarization with Applications to Network Graphs,"  We provide a deterministic data summarization algorithm that approximates the
+mean $\bar{p}=\frac{1}{n}\sum_{p\in P} p$ of a set $P$ of $n$ vectors in
+$\REAL^d$, by a weighted mean $\tilde{p}$ of a \emph{subset} of $O(1/\eps)$
+vectors, i.e., independent of both $n$ and $d$. We prove that the squared
+Euclidean distance between $\bar{p}$ and $\tilde{p}$ is at most $\eps$
+multiplied by the variance of $P$. We use this algorithm to maintain an
+approximated sum of vectors from an unbounded stream, using memory that is
+independent of $d$, and logarithmic in the $n$ vectors seen so far. Our main
+application is to extract and represent in a compact way friend groups and
+activity summaries of users from underlying data exchanges. For example, in the
+case of mobile networks, we can use GPS traces to identify meetings, in the
+case of social networks, we can use information exchange to identify friend
+groups. Our algorithm provably identifies the {\it Heavy Hitter} entries in a
+proximity (adjacency) matrix. The Heavy Hitters can be used to extract and
+represent in a compact way friend groups and activity summaries of users from
+underlying data exchanges. We evaluate the algorithm on several large data
+sets.
+",1,0,0,0,0,0
+4693,Two-dimensional matter-wave solitons and vortices in competing cubic-quintic nonlinear lattices,"  The nonlinear lattice---a new and nonlinear class of periodic
+potentials---was recently introduced to generate various nonlinear localized
+modes. Several attempts failed to stabilize two-dimensional (2D) solitons
+against their intrinsic critical collapse in Kerr media. Here, we provide a
+possibility for supporting 2D matter-wave solitons and vortices in an extended
+setting---the cubic and quintic model---by introducing another nonlinear
+lattice whose period is controllable and can be different from its cubic
+counterpart, to its quintic nonlinearity, therefore making a fully `nonlinear
+quasi-crystal'.
+A variational approximation based on Gaussian ansatz is developed for the
+fundamental solitons and in particular, their stability exactly follows the
+inverted \textit{Vakhitov-Kolokolov} stability criterion, whereas the vortex
+solitons are only studied by means of numerical methods. Stability regions for
+two types of localized mode---the fundamental and vortex solitons---are
+provided. A noteworthy feature of the localized solutions is that the vortex
+solitons are stable only when the period of the quintic nonlinear lattice is
+the same as the cubic one or when the quintic nonlinearity is constant, while
+the stable fundamental solitons can be created under looser conditions. Our
+physical setting (cubic-quintic model) is in the framework of the
+Gross-Pitaevskii equation (GPE) or nonlinear Schrödinger equation, the
+predicted localized modes thus may be implemented in Bose-Einstein condensates
+and nonlinear optical media with tunable cubic and quintic nonlinearities.
+",0,1,0,0,0,0
+4694,RELink: A Research Framework and Test Collection for Entity-Relationship Retrieval,"  Improvements of entity-relationship (E-R) search techniques have been
+hampered by a lack of test collections, particularly for complex queries
+involving multiple entities and relationships. In this paper we describe a
+method for generating E-R test queries to support comprehensive E-R search
+experiments. Queries and relevance judgments are created from content that
+exists in a tabular form where columns represent entity types and the table
+structure implies one or more relationships among the entities. Editorial work
+involves creating natural language queries based on relationships represented
+by the entries in the table. We have publicly released the RELink test
+collection comprising 600 queries and relevance judgments obtained from a
+sample of Wikipedia List-of-lists-of-lists tables. The latter comprise tuples
+of entities that are extracted from columns and labelled by corresponding
+entity types and relationships they represent. In order to facilitate research
+in complex E-R retrieval, we have created and released as open source the
+RELink Framework that includes Apache Lucene indexing and search specifically
+tailored to E-R retrieval. RELink includes entity and relationship indexing
+based on the ClueWeb-09-B Web collection with FACC1 text span annotations
+linked to Wikipedia entities. With ready to use search resources and a
+comprehensive test collection, we support community in pursuing E-R research at
+scale.
+",1,0,0,0,0,0
+4695,NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot,"  Humanoid robotics research depends on capable robot platforms, but recently
+developed advanced platforms are often not available to other research groups,
+expensive, dangerous to operate, or closed-source. The lack of available
+platforms forces researchers to work with smaller robots, which have less
+strict dynamic constraints or with simulations, which lack many real-world
+effects. We developed NimbRo-OP2X to address this need. At a height of 135 cm
+our robot is large enough to interact in a human environment. Its low weight of
+only 19 kg makes the operation of the robot safe and easy, as no special
+operational equipment is necessary. Our robot is equipped with a fast onboard
+computer and a GPU to accelerate parallel computations. We extend our already
+open-source software by a deep-learning based vision system and gait parameter
+optimisation. The NimbRo-OP2X was evaluated during RoboCup 2018 in Montréal,
+Canada, where it won all possible awards in the Humanoid AdultSize class.
+",1,0,0,0,0,0
+4696,Optical bandgap engineering in nonlinear silicon nitride waveguides,"  Silicon nitride is awell-established material for photonic devices and
+integrated circuits. It displays a broad transparency window spanning from the
+visible to the mid-IR and waveguides can be manufactured with low losses. An
+absence of nonlinear multi-photon absorption in the erbium lightwave
+communications band has enabled various nonlinear optic applications in the
+past decade. Silicon nitride is a dielectric material whose optical and
+mechanical properties strongly depend on the deposition conditions. In
+particular, the optical bandgap can be modified with the gas flow ratio during
+low-pressure chemical vapor deposition (LPCVD). Here we show that this
+parameter can be controlled in a highly reproducible manner, providing an
+approach to synthesize the nonlinear Kerr coefficient of the material. This
+holistic empirical study provides relevant guidelines to optimize the
+properties of LPCVD silicon nitride waveguides for nonlinear optics
+applications that rely on the Kerr effect.
+",0,1,0,0,0,0
+4697,Approximate Steepest Coordinate Descent,"  We propose a new selection rule for the coordinate selection in coordinate
+descent methods for huge-scale optimization. The efficiency of this novel
+scheme is provably better than the efficiency of uniformly random selection,
+and can reach the efficiency of steepest coordinate descent (SCD), enabling an
+acceleration of a factor of up to $n$, the number of coordinates. In many
+practical applications, our scheme can be implemented at no extra cost and
+computational efficiency very close to the faster uniform selection. Numerical
+experiments with Lasso and Ridge regression show promising improvements, in
+line with our theoretical guarantees.
+",1,0,1,0,0,0
+4698,How Robust are Deep Neural Networks?,"  Convolutional and Recurrent, deep neural networks have been successful in
+machine learning systems for computer vision, reinforcement learning, and other
+allied fields. However, the robustness of such neural networks is seldom
+apprised, especially after high classification accuracy has been attained. In
+this paper, we evaluate the robustness of three recurrent neural networks to
+tiny perturbations, on three widely used datasets, to argue that high accuracy
+does not always mean a stable and a robust (to bounded perturbations,
+adversarial attacks, etc.) system. Especially, normalizing the spectrum of the
+discrete recurrent network to bound the spectrum (using power method, Rayleigh
+quotient, etc.) on a unit disk produces stable, albeit highly non-robust neural
+networks. Furthermore, using the $\epsilon$-pseudo-spectrum, we show that
+training of recurrent networks, say using gradient-based methods, often result
+in non-normal matrices that may or may not be diagonalizable. Therefore, the
+open problem lies in constructing methods that optimize not only for accuracy
+but also for the stability and the robustness of the underlying neural network,
+a criterion that is distinct from the other.
+",0,0,0,1,0,0
+4699,Learning Latent Representations for Speech Generation and Transformation,"  An ability to model a generative process and learn a latent representation
+for speech in an unsupervised fashion will be crucial to process vast
+quantities of unlabelled speech data. Recently, deep probabilistic generative
+models such as Variational Autoencoders (VAEs) have achieved tremendous success
+in modeling natural images. In this paper, we apply a convolutional VAE to
+model the generative process of natural speech. We derive latent space
+arithmetic operations to disentangle learned latent representations. We
+demonstrate the capability of our model to modify the phonetic content or the
+speaker identity for speech segments using the derived operations, without the
+need for parallel supervisory data.
+",1,0,0,1,0,0
+4700,Approximate Profile Maximum Likelihood,"  We propose an efficient algorithm for approximate computation of the profile
+maximum likelihood (PML), a variant of maximum likelihood maximizing the
+probability of observing a sufficient statistic rather than the empirical
+sample. The PML has appealing theoretical properties, but is difficult to
+compute exactly. Inspired by observations gleaned from exactly solvable cases,
+we look for an approximate PML solution, which, intuitively, clumps comparably
+frequent symbols into one symbol. This amounts to lower-bounding a certain
+matrix permanent by summing over a subgroup of the symmetric group rather than
+the whole group during the computation. We extensively experiment with the
+approximate solution, and find the empirical performance of our approach is
+competitive and sometimes significantly better than state-of-the-art
+performance for various estimation problems.
+",1,0,0,1,0,0
+4701,On the generalized nonlinear Camassa-Holm equation,"  In this paper, a generalized nonlinear Camassa-Holm equation with time- and
+space-dependent coefficients is considered. We show that the control of the
+higher order dispersive term is possible by using an adequate weight function
+to define the energy. The existence and uniqueness of solutions are obtained
+via a Picard iterative method.
+",0,0,1,0,0,0
+4702,802.11 Wireless Simulation and Anomaly Detection using HMM and UBM,"  Despite the growing popularity of 802.11 wireless networks, users often
+suffer from connectivity problems and performance issues due to unstable radio
+conditions and dynamic user behavior among other reasons. Anomaly detection and
+distinction are in the thick of major challenges that network managers
+encounter. Complication of monitoring the broaden and complex WLANs, that often
+requires heavy instrumentation of the user devices, makes the anomaly detection
+analysis even harder. In this paper we exploit 802.11 access point usage data
+and propose an anomaly detection technique based on Hidden Markov Model (HMM)
+and Universal Background Model (UBM) on data that is inexpensive to obtain. We
+then generate a number of network anomalous scenarios in OMNeT++/INET network
+simulator and compare the detection outcomes with those in baseline approaches
+(RawData and PCA). The experimental results show the superiority of HMM and
+HMM-UBM models in detection precision and sensitivity.
+",1,0,0,0,0,0
+4703,Early MFCC And HPCP Fusion for Robust Cover Song Identification,"  While most schemes for automatic cover song identification have focused on
+note-based features such as HPCP and chord profiles, a few recent papers
+surprisingly showed that local self-similarities of MFCC-based features also
+have classification power for this task. Since MFCC and HPCP capture
+complementary information, we design an unsupervised algorithm that combines
+normalized, beat-synchronous blocks of these features using cross-similarity
+fusion before attempting to locally align a pair of songs. As an added bonus,
+our scheme naturally incorporates structural information in each song to fill
+in alignment gaps where both feature sets fail. We show a striking jump in
+performance over MFCC and HPCP alone, achieving a state of the art mean
+reciprocal rank of 0.87 on the Covers80 dataset. We also introduce a new
+medium-sized hand designed benchmark dataset called ""Covers 1000,"" which
+consists of 395 cliques of cover songs for a total of 1000 songs, and we show
+that our algorithm achieves an MRR of 0.9 on this dataset for the first
+correctly identified song in a clique. We provide the precomputed HPCP and MFCC
+features, as well as beat intervals, for all songs in the Covers 1000 dataset
+for use in further research.
+",1,0,0,0,0,0
+4704,Optimally Gathering Two Robots,"  We present an algorithm that ensures in finite time the gathering of two
+robots in the non-rigid ASYNC model. To circumvent established impossibility
+results, we assume robots are equipped with 2-colors lights and are able to
+measure distances between one another. Aside from its light, a robot has no
+memory of its past actions, and its protocol is deterministic. Since, in the
+same model, gathering is impossible when lights have a single color, our
+solution is optimal with respect to the number of used colors.
+",1,0,0,0,0,0
+4705,On Grauert-Riemenschneider type criterions,"  Let $(X,\omega)$ be a compact Hermitian manifold of complex dimension $n$. In
+this article, we first survey recent progress towards Grauert-Riemenschneider
+type criterions. Secondly, we give a simplified proof of Boucksom's conjecture
+given by the author under the assumption that the Hermitian metric $\omega$
+satisfies $\partial\overline{\partial}\omega^l=$ for all $l$, i.e., if $T$ is a
+closed positive current on $X$ such that $\int_XT_{ac}^n>0$, then the class
+$\{T\}$ is big and $X$ is Kähler. Finally, as an easy observation, we point
+out that Nguyen's result can be generalized as follows: if
+$\partial\overline{\partial}\omega=0$, and $T$ is a closed positive current
+with analytic singularities, such that $\int_XT^n_{ac}>0$, then the class
+$\{T\}$ is big and $X$ is Kähler.
+",0,0,1,0,0,0
+4706,P-Governance Technology: Using Big Data for Political Party Management,"  Information and Communication Technology (ICT) has been playing a pivotal
+role since the last decade in developing countries that brings citizen services
+to the doorsteps and connecting people. With this aspiration ICT has introduced
+several technologies of citizen services towards all categories of people. The
+purpose of this study is to examine the Governance technology perspectives for
+political party, emphasizing on the basic critical steps through which it could
+be operationalized. We call it P-Governance. P-Governance shows technologies to
+ensure governance, management, interaction communication in a political party
+by improving decision making processes using big data. P-Governance challenges
+the competence perspective to apply itself more assiduously to
+operationalization, including the need to choose and give definition to one or
+more units of analysis (of which the routine is a promising candidate). This
+paper is to focus on research challenges posed by competence to which
+P-Governance can and should respond include different strategy issues faced by
+particular sections. Both the qualitative as well as quantitative research
+approaches were conducted. The standard of citizen services, choice &
+consultation, courtesy & consultation, entrance & information, and value for
+money have found the positive relation with citizen's satisfaction. This study
+results how can be technology make important roles on political movements in
+developing countries using big data.
+",1,0,0,0,0,0
+4707,The Forgettable-Watcher Model for Video Question Answering,"  A number of visual question answering approaches have been proposed recently,
+aiming at understanding the visual scenes by answering the natural language
+questions. While the image question answering has drawn significant attention,
+video question answering is largely unexplored.
+Video-QA is different from Image-QA since the information and the events are
+scattered among multiple frames. In order to better utilize the temporal
+structure of the videos and the phrasal structures of the answers, we propose
+two mechanisms: the re-watching and the re-reading mechanisms and combine them
+into the forgettable-watcher model. Then we propose a TGIF-QA dataset for video
+question answering with the help of automatic question generation. Finally, we
+evaluate the models on our dataset. The experimental results show the
+effectiveness of our proposed models.
+",1,0,0,0,0,0
+4708,Analogues of the $p^n$th Hilbert symbol in characteristic $p$ (updated),"  The $p$th degree Hilbert symbol $(\cdot,\cdot )_p:K^\times/K^{\times p}\times
+K^\times/K^{\times p}\to{}_p{\rm Br}(K)$ from characteristic $\neq p$ has two
+analogues in characteristic $p$, $$[\cdot,\cdot )_p:K/\wp (K)\times
+K^\times/K^{\times p}\to{}_p{\rm Br}(K),$$ where $\wp$ is the Artin-Schreier
+map $x\mapsto x^p-x$, and $$((\cdot,\cdot ))_p:K/K^p\times K/K^p\to{}_p{\rm
+Br}(K).$$
+The symbol $[\cdot,\cdot )_p$ generalizes to an analogue of $(\cdot,\cdot
+)_{p^n}$ via the Witt vectors, $$[\cdot,\cdot )_{p^n}:W_n(K)/\wp (W_n(K))\times
+K^\times/K^{\times p^n}\to{}_{p^n}{\rm Br}(K).$$
+Here $W_n(K)$ is the truncation of length $n$ of the ring of $p$-typical Witt
+wectors, i.e. $W_{\{1,p,\ldots,p^{n-1}\}}(K)$.
+In this paper we construct similar generalizations for $((\cdot,\cdot ))_p$.
+Our construction involves Witt vectors and Weyl algebras. In the process we
+obtain a new kind of Weyl algebras in characteristic $p$, with many interesting
+properties.
+The symbols we introduce, $((\cdot,\cdot ))_{p^n}$ and, more generally,
+$((\cdot,\cdot ))_{p^m,p^n}$, which here are defined in terms of central simple
+algebras, coincide with the homonymous symbols we introduced in
+[arXiv:1711.00980] in terms of the symbols $[\cdot,\cdot )_{p^n}$. This will be
+proved in a future paper. In the present paper we only introduce the symbols
+and we prove that they have the same properties with the symbols from
+[arXiv:1711.00980]. These properies are enough to obtain the representation
+theorem for ${}_{p^n}{\rm Br}(K)$ from [arXiv:1711.00980], Theorem 4.10.
+",0,0,1,0,0,0
+4709,"GAMER-2: a GPU-accelerated adaptive mesh refinement code -- accuracy, performance, and scalability","  We present GAMER-2, a GPU-accelerated adaptive mesh refinement (AMR) code for
+astrophysics. It provides a rich set of features, including adaptive
+time-stepping, several hydrodynamic schemes, magnetohydrodynamics,
+self-gravity, particles, star formation, chemistry and radiative processes with
+GRACKLE, data analysis with yt, and memory pool for efficient object
+allocation. GAMER-2 is fully bitwise reproducible. For the performance
+optimization, it adopts hybrid OpenMP/MPI/GPU parallelization and utilizes
+overlapping CPU computation, GPU computation, and CPU-GPU communication. Load
+balancing is achieved using a Hilbert space-filling curve on a level-by-level
+basis without the need to duplicate the entire AMR hierarchy on each MPI
+process. To provide convincing demonstrations of the accuracy and performance
+of GAMER-2, we directly compare with Enzo on isolated disk galaxy simulations
+and with FLASH on galaxy cluster merger simulations. We show that the physical
+results obtained by different codes are in very good agreement, and GAMER-2
+outperforms Enzo and FLASH by nearly one and two orders of magnitude,
+respectively, on the Blue Waters supercomputers using $1-256$ nodes. More
+importantly, GAMER-2 exhibits similar or even better parallel scalability
+compared to the other two codes. We also demonstrate good weak and strong
+scaling using up to 4096 GPUs and 65,536 CPU cores, and achieve a uniform
+resolution as high as $10{,}240^3$ cells. Furthermore, GAMER-2 can be adopted
+as an AMR+GPUs framework and has been extensively used for the wave dark matter
+($\psi$DM) simulations. GAMER-2 is open source (available at
+this https URL) and new contributions are welcome.
+",0,1,0,0,0,0
+4710,Partition algebras $\mathsf{P}_k(n)$ with $2k>n$ and the fundamental theorems of invariant theory for the symmetric group $\mathsf{S}_n$,"  Assume $\mathsf{M}_n$ is the $n$-dimensional permutation module for the
+symmetric group $\mathsf{S}_n$, and let $\mathsf{M}_n^{\otimes k}$ be its
+$k$-fold tensor power. The partition algebra $\mathsf{P}_k(n)$ maps
+surjectively onto the centralizer algebra
+$\mathsf{End}_{\mathsf{S}_n}(\mathsf{M}_n^{\otimes k})$ for all $k, n \in
+\mathbb{Z}_{\ge 1}$ and isomorphically when $n \ge 2k$. We describe the image
+of the surjection $\Phi_{k,n}:\mathsf{P}_k(n) \to
+\mathsf{End}_{\mathsf{S}_n}(\mathsf{M}_n^{\otimes k})$ explicitly in terms of
+the orbit basis of $\mathsf{P}_k(n)$ and show that when $2k > n$ the kernel of
+$\Phi_{k,n}$ is generated by a single essential idempotent $\mathsf{e}_{k,n}$,
+which is an orbit basis element. We obtain a presentation for
+$\mathsf{End}_{\mathsf{S}_n}(\mathsf{M}_n^{\otimes k})$ by imposing one
+additional relation, $\mathsf{e}_{k,n} = 0$, to the standard presentation of
+the partition algebra $\mathsf{P}_k(n)$ when $2k > n$. As a consequence, we
+obtain the fundamental theorems of invariant theory for the symmetric group
+$\mathsf{S}_n$. We show under the natural embedding of the partition algebra
+$\mathsf{P}_n(n)$ into $\mathsf{P}_k(n)$ for $k \ge n$ that the essential
+idempotent $\mathsf{e}_{n,n}$ generates the kernel of $\Phi_{k,n}$. Therefore,
+the relation $\mathsf{e}_{n,n} = 0$ can replace $\mathsf{e}_{k,n} = 0$ when $k
+\ge n$.
+",0,0,1,0,0,0
+4711,Distributed sub-optimal resource allocation over weight-balanced graph via singular perturbation,"  In this paper, we consider distributed optimization design for resource
+allocation problems over weight-balanced graphs. With the help of singular
+perturbation analysis, we propose a simple sub-optimal continuous-time
+optimization algorithm. Moreover, we prove the existence and uniqueness of the
+algorithm equilibrium, and then show the convergence with an exponential rate.
+Finally, we verify the sub-optimality of the algorithm, which can approach the
+optimal solution as an adjustable parameter tends to zero.
+",0,0,1,0,0,0
+4712,Decentralized P2P Energy Trading under Network Constraints in a Low-Voltage Network,"  The increasing uptake of distributed energy resources (DERs) in distribution
+systems and the rapid advance of technology have established new scenarios in
+the operation of low-voltage networks. In particular, recent trends in
+cryptocurrencies and blockchain have led to a proliferation of peer-to-peer
+(P2P) energy trading schemes, which allow the exchange of energy between the
+neighbors without any intervention of a conventional intermediary in the
+transactions. Nevertheless, far too little attention has been paid to the
+technical constraints of the network under this scenario. A major challenge to
+implementing P2P energy trading is that of ensuring that network constraints
+are not violated during the energy exchange. This paper proposes a methodology
+based on sensitivity analysis to assess the impact of P2P transactions on the
+network and to guarantee an exchange of energy that does not violate network
+constraints. The proposed method is tested on a typical UK low-voltage network.
+The results show that our method ensures that energy is exchanged between users
+under the P2P scheme without violating the network constraints, and that users
+can still capture the economic benefits of the P2P architecture.
+",1,0,0,0,0,0
+4713,Fictitious GAN: Training GANs with Historical Models,"  Generative adversarial networks (GANs) are powerful tools for learning
+generative models. In practice, the training may suffer from lack of
+convergence. GANs are commonly viewed as a two-player zero-sum game between two
+neural networks. Here, we leverage this game theoretic view to study the
+convergence behavior of the training process. Inspired by the fictitious play
+learning process, a novel training method, referred to as Fictitious GAN, is
+introduced. Fictitious GAN trains the deep neural networks using a mixture of
+historical models. Specifically, the discriminator (resp. generator) is updated
+according to the best-response to the mixture outputs from a sequence of
+previously trained generators (resp. discriminators). It is shown that
+Fictitious GAN can effectively resolve some convergence issues that cannot be
+resolved by the standard training approach. It is proved that asymptotically
+the average of the generator outputs has the same distribution as the data
+samples.
+",0,0,0,1,0,0
+4714,Suppression of Hall number due to charge density wave order in high-$T_c$ cuprates,"  Understanding the pseudogap phase in hole-doped high temperature cuprate
+superconductors remains a central challenge in condensed matter physics. From a
+host of recent experiments there is now compelling evidence of translational
+symmetry breaking charge density wave (CDW) order in a wide range of doping
+inside this phase. Two distinct types of incommensurate charge order --
+bidirectional at zero or low magnetic fields and unidirectional at high
+magnetic fields close to the upper critical field $H_{c2}$ -- have been
+reported so far in approximately the same doping range between $p\simeq 0.08$
+and $p\simeq 0.16$. In concurrent developments, recent high field Hall
+experiments have also revealed two indirect but striking signatures of Fermi
+surface reconstruction in the pseudogap phase, namely, a sign change of the
+Hall coefficient to negative values at low temperatures at intermediate range
+of hole doping and a rapid suppression of the positive Hall number without
+change in sign near optimal doping $p \sim 0.19$. We show that the assumption
+of a unidirectional incommensurate CDW (with or without a coexisting weak
+bidirectional order) at high magnetic fields near optimal doping and a
+coexistence of both types of orders of approximately equal magnitude at high
+magnetic fields at intermediate range of doping may help explain the striking
+behavior of low temperature Hall effect in the entire pseudogap phase.
+",0,1,0,0,0,0
+4715,The Wavefunction of the Collapsing Bose-Einstein Condensate,"  Bose-Einstein condensates with tunable interatomic interactions have been
+studied intensely in recent experiments. The investigation of the collapse of a
+condensate following a sudden change in the nature of the interaction from
+repulsive to attractive has led to the observation of a remnant condensate that
+did not undergo further collapse. We suggest that this high-density remnant is
+in fact the absolute minimum of the energy, if the attractive atomic
+interactions are nonlocal, and is therefore inherently stable. We show that a
+variational trial function consisting of a superposition of two distinct
+gaussians is an accurate representation of the wavefunction of the ground state
+of the conventional local Gross-Pitaevskii field equation for an attractive
+condensate and gives correctly the points of emergence of instability. We then
+use such a superposition of two gaussians as a variational trial function in
+order to calculate the minima of the energy when it includes a nonlocal
+interaction term. We use experimental data in order to study the long range of
+the nonlocal interaction, showing that they agree very well with a
+dimensionally derived expression for this range.
+",0,1,0,0,0,0
+4716,Not-So-Random Features,"  We propose a principled method for kernel learning, which relies on a
+Fourier-analytic characterization of translation-invariant or
+rotation-invariant kernels. Our method produces a sequence of feature maps,
+iteratively refining the SVM margin. We provide rigorous guarantees for
+optimality and generalization, interpreting our algorithm as online
+equilibrium-finding dynamics in a certain two-player min-max game. Evaluations
+on synthetic and real-world datasets demonstrate scalability and consistent
+improvements over related random features-based methods.
+",1,0,0,1,0,0
+4717,Dual-Primal Graph Convolutional Networks,"  In recent years, there has been a surge of interest in developing deep
+learning methods for non-Euclidean structured data such as graphs. In this
+paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture
+that alternates convolution-like operations on the graph and its dual. Our
+approach allows to learn both vertex- and edge features and generalizes the
+previous graph attention (GAT) model. We provide extensive experimental
+validation showing state-of-the-art results on a variety of tasks tested on
+established graph benchmarks, including CORA and Citeseer citation networks as
+well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender
+systems.
+",0,0,0,1,0,0
+4718,CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq,"  Direct cDNA preamplification protocols developed for single-cell RNA-seq
+(scRNA-seq) have enabled transcriptome profiling of rare cells without having
+to pool multiple samples or to perform RNA extraction. We term this approach
+limiting-cell RNA-seq (lcRNA-seq). Unlike scRNA-seq, which focuses on
+'cell-atlasing', lcRNA-seq focuses on identifying differentially expressed
+genes (DEGs) between experimental groups. This requires accounting for systems
+noise which can obscure biological differences. We present CLEAR, a workflow
+that identifies robust transcripts in lcRNA-seq data for between-group
+comparisons. To develop CLEAR, we compared DEGs from RNA extracted from
+FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia
+patient diluted to input RNA levels of 10-, 100- and 1,000pg. Data quality at
+ultralow input levels are known to be noisy. When using CLEAR transcripts vs.
+using all available transcripts, downstream analyses reveal more shared DEGs,
+improved Principal Component Analysis separation of cell type, and increased
+similarity between results across different input RNA amounts. CLEAR was
+applied to two publicly available ultralow input RNA-seq data and an in-house
+murine neural cell lcRNA-seq dataset. CLEAR provides a novel way to visualize
+the public datasets while validates cell phenotype markers for astrocytes,
+neural stem and progenitor cells.
+",0,0,0,0,1,0
+4719,Training Deep AutoEncoders for Collaborative Filtering,"  This paper proposes a novel model for the rating prediction task in
+recommender systems which significantly outperforms previous state-of-the art
+models on a time-split Netflix data set. Our model is based on deep autoencoder
+with 6 layers and is trained end-to-end without any layer-wise pre-training. We
+empirically demonstrate that: a) deep autoencoder models generalize much better
+than the shallow ones, b) non-linear activation functions with negative parts
+are crucial for training deep models, and c) heavy use of regularization
+techniques such as dropout is necessary to prevent over-fiting. We also propose
+a new training algorithm based on iterative output re-feeding to overcome
+natural sparseness of collaborate filtering. The new algorithm significantly
+speeds up training and improves model performance. Our code is available at
+this https URL
+",1,0,0,1,0,0
+4720,Unit circle rectification of the MVDR beamformer,"  The sample matrix inversion (SMI) beamformer implements Capon's minimum
+variance distortionless (MVDR) beamforming using the sample covariance matrix
+(SCM). In a snapshot limited environment, the SCM is poorly conditioned
+resulting in a suboptimal performance from the SMI beamformer. Imposing
+structural constraints on the SCM estimate to satisfy known theoretical
+properties of the ensemble MVDR beamformer mitigates the impact of limited
+snapshots on the SMI beamformer performance. Toeplitz rectification and
+bounding the norm of weight vector are common approaches for such constrains.
+This paper proposes the unit circle rectification technique which constraints
+the SMI beamformer to satisfy a property of the ensemble MVDR beamformer: for
+narrowband planewave beamforming on a uniform linear array, the zeros of the
+MVDR weight array polynomial must fall on the unit circle. Numerical
+simulations show that the resulting unit circle MVDR (UC MVDR) beamformer
+frequently improves the suppression of both discrete interferers and white
+background noise compared to the classic SMI beamformer. Moreover, the UC MVDR
+beamformer is shown to suppress discrete interferers better than the MVDR
+beamformer diagonally loaded to maximize the SINR.
+",1,0,0,0,0,0
+4721,Reducing Storage of Global Wind Ensembles with Stochastic Generators,"  Wind has the potential to make a significant contribution to future energy
+resources. Locating the sources of this renewable energy on a global scale is
+however extremely challenging, given the difficulty to store very large data
+sets generated by modern computer models. We propose a statistical model that
+aims at reproducing the data-generating mechanism of an ensemble of runs via a
+Stochastic Generator (SG) of global annual wind data. We introduce an
+evolutionary spectrum approach with spatially varying parameters based on
+large-scale geographical descriptors such as altitude to better account for
+different regimes across the Earth's orography. We consider a multi-step
+conditional likelihood approach to estimate the parameters that explicitly
+accounts for nonstationary features while also balancing memory storage and
+distributed computation. We apply the proposed model to more than 18 million
+points of yearly global wind speed. The proposed SG requires orders of
+magnitude less storage for generating surrogate ensemble members from wind than
+does creating additional wind fields from the climate model, even if an
+effective lossy data compression algorithm is applied to the simulation output.
+",0,0,0,1,0,0
+4722,Jackknife Empirical Likelihood-based inference for S-Gini indices,"  Widely used income inequality measure, Gini index is extended to form a
+family of income inequality measures known as Single-Series Gini (S-Gini)
+indices. In this study, we develop empirical likelihood (EL) and jackknife
+empirical likelihood (JEL) based inference for S-Gini indices. We prove that
+the limiting distribution of both EL and JEL ratio statistics are Chi-square
+distribution with one degree of freedom. Using the asymptotic distribution we
+construct EL and JEL based confidence intervals for realtive S-Gini indices. We
+also give bootstrap-t and bootstrap calibrated empirical likelihood confidence
+intervals for S-Gini indices. A numerical study is carried out to compare the
+performances of the proposed confidence interval with the bootstrap methods. A
+test for S-Gini indices based on jackknife empirical likelihood ratio is also
+proposed. Finally we illustrate the proposed method using an income data.
+",0,0,0,1,0,0
+4723,Continual One-Shot Learning of Hidden Spike-Patterns with Neural Network Simulation Expansion and STDP Convergence Predictions,"  This paper presents a constructive algorithm that achieves successful
+one-shot learning of hidden spike-patterns in a competitive detection task. It
+has previously been shown (Masquelier et al., 2008) that spike-timing-dependent
+plasticity (STDP) and lateral inhibition can result in neurons competitively
+tuned to repeating spike-patterns concealed in high rates of overall
+presynaptic activity. One-shot construction of neurons with synapse weights
+calculated as estimates of converged STDP outcomes results in immediate
+selective detection of hidden spike-patterns. The capability of continual
+learning is demonstrated through the successful one-shot detection of new sets
+of spike-patterns introduced after long intervals in the simulation time.
+Simulation expansion (Lightheart et al., 2013) has been proposed as an approach
+to the development of constructive algorithms that are compatible with
+simulations of biological neural networks. A simulation of a biological neural
+network may have orders of magnitude fewer neurons and connections than the
+related biological neural systems; therefore, simulated neural networks can be
+assumed to be a subset of a larger neural system. The constructive algorithm is
+developed using simulation expansion concepts to perform an operation
+equivalent to the exchange of neurons between the simulation and the larger
+hypothetical neural system. The dynamic selection of neurons to simulate within
+a larger neural system (hypothetical or stored in memory) may be a starting
+point for a wide range of developments and applications in machine learning and
+the simulation of biology.
+",0,0,0,1,0,0
+4724,Interpretable High-Dimensional Inference Via Score Projection with an Application in Neuroimaging,"  In the fields of neuroimaging and genetics, a key goal is testing the
+association of a single outcome with a very high-dimensional imaging or genetic
+variable. Often, summary measures of the high-dimensional variable are created
+to sequentially test and localize the association with the outcome. In some
+cases, the results for summary measures are significant, but subsequent tests
+used to localize differences are underpowered and do not identify regions
+associated with the outcome. Here, we propose a generalization of Rao's score
+test based on projecting the score statistic onto a linear subspace of a
+high-dimensional parameter space. In addition, we provide methods to localize
+signal in the high-dimensional space by projecting the scores to the subspace
+where the score test was performed. This allows for inference in the
+high-dimensional space to be performed on the same degrees of freedom as the
+score test, effectively reducing the number of comparisons. Simulation results
+demonstrate the test has competitive power relative to others commonly used. We
+illustrate the method by analyzing a subset of the Alzheimer's Disease
+Neuroimaging Initiative dataset. Results suggest cortical thinning of the
+frontal and temporal lobes may be a useful biological marker of Alzheimer's
+risk.
+",0,0,1,1,0,0
+4725,A maximum principle for free boundary minimal varieties of arbitrary codimension,"  We establish a boundary maximum principle for free boundary minimal
+submanifolds in a Riemannian manifold with boundary, in any dimension and
+codimension. Our result holds more generally in the context of varifolds.
+",0,0,1,0,0,0
+4726,Drawing Big Graphs using Spectral Sparsification,"  Spectral sparsification is a general technique developed by Spielman et al.
+to reduce the number of edges in a graph while retaining its structural
+properties. We investigate the use of spectral sparsification to produce good
+visual representations of big graphs. We evaluate spectral sparsification
+approaches on real-world and synthetic graphs. We show that spectral
+sparsifiers are more effective than random edge sampling. Our results lead to
+guidelines for using spectral sparsification in big graph visualization.
+",1,0,0,0,0,0
+4727,Optimal paths on the road network as directed polymers,"  We analyze the statistics of the shortest and fastest paths on the road
+network between randomly sampled end points. To a good approximation, these
+optimal paths are found to be directed in that their lengths (at large scales)
+are linearly proportional to the absolute distance between them. This motivates
+comparisons to universal features of directed polymers in random media. There
+are similarities in scalings of fluctuations in length/time and transverse
+wanderings, but also important distinctions in the scaling exponents, likely
+due to long-range correlations in geographic and man-made features. At short
+scales the optimal paths are not directed due to circuitous excursions governed
+by a fat-tailed (power-law) probability distribution.
+",0,1,0,0,0,0
+4728,Optimal proportional reinsurance and investment for stochastic factor models,"  In this work we investigate the optimal proportional reinsurance-investment
+strategy of an insurance company which wishes to maximize the expected
+exponential utility of its terminal wealth in a finite time horizon. Our goal
+is to extend the classical Cramer-Lundberg model introducing a stochastic
+factor which affects the intensity of the claims arrival process, described by
+a Cox process, as well as the insurance and reinsurance premia. Using the
+classical stochastic control approach based on the Hamilton-Jacobi-Bellman
+equation we characterize the optimal strategy and provide a verification result
+for the value function via classical solutions of two backward partial
+differential equations. Existence and uniqueness of these solutions are
+discussed. Results under various premium calculation principles are illustrated
+and a new premium calculation rule is proposed in order to get more realistic
+strategies and to better fit our stochastic factor model. Finally, numerical
+simulations are performed to obtain sensitivity analyses.
+",0,0,0,0,0,1
+4729,Formal Black-Box Analysis of Routing Protocol Implementations,"  The Internet infrastructure relies entirely on open standards for its routing
+protocols. However, the majority of routers on the Internet are closed-source.
+Hence, there is no straightforward way to analyze them. Specifically, one
+cannot easily identify deviations of a router's routing functionality from the
+routing protocol's standard. Such deviations (either deliberate or inadvertent)
+are particularly important to identify since they may degrade the security or
+resiliency of the network.
+A model-based testing procedure is a technique that allows to systematically
+generate tests based on a model of the system to be tested; thereby finding
+deviations in the system compared to the model. However, applying such an
+approach to a complex multi-party routing protocol requires a prohibitively
+high number of tests to cover the desired functionality. We propose efficient
+and practical optimizations to the model-based testing procedure that are
+tailored to the analysis of routing protocols. These optimizations allow to
+devise a formal black-box method to unearth deviations in closed-source routing
+protocols' implementations. The method relies only on the ability to test the
+targeted protocol implementation and observe its output. Identification of the
+deviations is fully automatic.
+We evaluate our method against one of the complex and widely used routing
+protocols on the Internet -- OSPF. We search for deviations in the OSPF
+implementation of Cisco. Our evaluation identified numerous significant
+deviations that can be abused to compromise the security of a network. The
+deviations were confirmed by Cisco. We further employed our method to analyze
+the OSPF implementation of the Quagga Routing Suite. The analysis revealed one
+significant deviation. Subsequent to the disclosure of the deviations some of
+them were also identified by IBM, Lenovo and Huawei in their own products.
+",1,0,0,0,0,0
+4730,Finding polynomial loop invariants for probabilistic programs,"  Quantitative loop invariants are an essential element in the verification of
+probabilistic programs. Recently, multivariate Lagrange interpolation has been
+applied to synthesizing polynomial invariants. In this paper, we propose an
+alternative approach. First, we fix a polynomial template as a candidate of a
+loop invariant. Using Stengle's Positivstellensatz and a transformation to a
+sum-of-squares problem, we find sufficient conditions on the coefficients.
+Then, we solve a semidefinite programming feasibility problem to synthesize the
+loop invariants. If the semidefinite program is unfeasible, we backtrack after
+increasing the degree of the template. Our approach is semi-complete in the
+sense that it will always lead us to a feasible solution if one exists and
+numerical errors are small. Experimental results show the efficiency of our
+approach.
+",1,0,0,0,0,0
+4731,Active Decision Boundary Annotation with Deep Generative Models,"  This paper is on active learning where the goal is to reduce the data
+annotation burden by interacting with a (human) oracle during training.
+Standard active learning methods ask the oracle to annotate data samples.
+Instead, we take a profoundly different approach: we ask for annotations of the
+decision boundary. We achieve this using a deep generative model to create
+novel instances along a 1d line. A point on the decision boundary is revealed
+where the instances change class. Experimentally we show on three data sets
+that our method can be plugged-in to other active learning schemes, that human
+oracles can effectively annotate points on the decision boundary, that our
+method is robust to annotation noise, and that decision boundary annotations
+improve over annotating data samples.
+",1,0,0,0,0,0
+4732,Multifrequency Excitation and Detection Scheme in Apertureless Scattering Near Field Scanning Optical Microscopy,"  We theoretically and experimentally demonstrate a multifrequency excitation
+and detection scheme in apertureless near field optical microscopy, that
+exceeds current state of the art sensitivity and background suppression. By
+exciting the AFM tip at its two first flexural modes, and demodulating the
+detected signal at the harmonics of their sum, we extract a near field signal
+with a twofold improved sensitivity and deep sub-wavelength resolution,
+reaching $\lambda/230$. Furthermore, the method offers rich control over
+experimental degrees of freedom, expanding the parameter space for achieving
+complete optical background suppression. This approach breaks the ground for
+non-interferometric complete phase and amplitude retrieval of the near field
+signal, and is suitable for any multimodal excitation and higher harmonic
+demodulation.
+",0,1,0,0,0,0
+4733,Extended Kitaev chain with longer-range hopping and pairing,"  We consider the Kitaev chain model with finite and infinite range in the
+hopping and pairing parameters, looking in particular at the appearance of
+Majorana zero energy modes and massive edge modes. We study the system both in
+the presence and in the absence of time reversal symmetry, by means of
+topological invariants and exact diagonalization, disclosing very rich phase
+diagrams. In particular, for extended hopping and pairing terms, we can get as
+many Majorana modes at each end of the chain as the neighbors involved in the
+couplings. Finally we generalize the transfer matrix approach useful to
+calculate the zero-energy Majorana modes at the edges for a generic number of
+coupled neighbors.
+",0,1,0,0,0,0
+4734,Global entropy solutions to the compressible Euler equations in the isentropic nozzle flow for large data: Application of the modified Godunov scheme and the generalized invariant regions,"  We study the motion of isentropic gas in nozzles. This is a major subject in
+fluid dynamics. In fact, the nozzle is utilized to increase the thrust of
+rocket engines. Moreover, the nozzle flow is closely related to astrophysics.
+These phenomena are governed by the compressible Euler equation, which is one
+of crucial equations in inhomogeneous conservation laws.
+In this paper, we consider its unsteady flow and devote to proving the global
+existence and stability of solutions to the Cauchy problem for the general
+nozzle. The theorem has been proved in (Tsuge in Arch. Ration. Mech. Anal.
+209:365-400 (2013)). However, this result is limited to small data. Our aim in
+the present paper is to remove this restriction, that is, we consider large
+data. Although the subject is important in Mathematics, Physics and
+engineering, it remained open for a long time. The problem seems to lie in a
+bounded estimate of approximate solutions, because we have only method to
+investigate the behavior with respect to the time variable. To solve this, we
+first introduce a generalized invariant region. Compared with the existing
+ones, its upper and lower bounds are extended constants to functions of the
+space variable. However, we cannot apply the new invariant region to the
+traditional difference method. Therefore, we invent the modified Godunov
+scheme. The approximate solutions consist of some functions corresponding to
+the upper and lower bounds of the invariant regions. These methods enable us to
+investigate the behavior of approximate solutions with respect to the space
+variable. The ideas are also applicable to other nonlinear problems involving
+similar difficulties.
+",0,0,1,0,0,0
+4735,Effects of atrial fibrillation on the arterial fluid dynamics: a modelling perspective,"  Atrial fibrillation (AF) is the most common form of arrhythmia with
+accelerated and irregular heart rate (HR), leading to both heart failure and
+stroke and being responsible for an increase in cardiovascular morbidity and
+mortality. In spite of its importance, the direct effects of AF on the arterial
+hemodynamic patterns are not completely known to date. Based on a multiscale
+modelling approach, the proposed work investigates the effects of AF on the
+local arterial fluid dynamics. AF and normal sinus rhythm (NSR) conditions are
+simulated extracting 2000 $\mathrm{RR}$ heartbeats and comparing the most
+relevant cardiac and vascular parameters at the same HR (75 bpm). Present
+outcomes evidence that the arterial system is not able to completely absorb the
+AF-induced variability, which can be even amplified towards the peripheral
+circulation. AF is also able to locally alter the wave dynamics, by modifying
+the interplay between forward and backward signals. The sole heart rhythm
+variation (i.e., from NSR to AF) promotes an alteration of the regular dynamics
+at the arterial level which, in terms of pressure and peripheral perfusion,
+suggests a modification of the physiological phenomena ruled by periodicity
+(e.g., regular organ perfusion)and a possible vascular dysfunction due to the
+prolonged exposure to irregular and extreme values. The present study
+represents a first modeling approach to characterize the variability of
+arterial hemodynamics in presence of AF, which surely deserves further clinical
+investigation.
+",0,0,0,0,1,0
+4736,Controllability and optimal control of the transport equation with a localized vector field,"  We study controllability of a Partial Differential Equation of transport
+type, that arises in crowd models. We are interested in controlling such system
+with a control being a Lipschitz vector field on a fixed control set $\omega$.
+We prove that, for each initial and final configuration, one can steer one to
+another with such class of controls only if the uncontrolled dynamics allows to
+cross the control set $\omega$. We also prove a minimal time result for such
+systems. We show that the minimal time to steer one initial configuration to
+another is related to the condition of having enough mass in $\omega$ to feed
+the desired final configuration.
+",0,0,1,0,0,0
+4737,Gravitational instabilities in a protosolar-like disc II: continuum emission and mass estimates,"  Gravitational instabilities (GIs) are most likely a fundamental process
+during the early stages of protoplanetary disc formation. Recently, there have
+been detections of spiral features in young, embedded objects that appear
+consistent with GI-driven structure. It is crucial to perform hydrodynamic and
+radiative transfer simulations of gravitationally unstable discs in order to
+assess the validity of GIs in such objects, and constrain optimal targets for
+future observations. We utilise the radiative transfer code LIME to produce
+continuum emission maps of a $0.17\,\mathrm{M}_{\odot}$ self-gravitating
+protosolar-like disc. We note the limitations of using LIME as is and explore
+methods to improve upon the default gridding. We use CASA to produce synthetic
+observations of 270 continuum emission maps generated across different
+frequencies, inclinations and dust opacities. We find that the spiral structure
+of our protosolar-like disc model is distinguishable across the majority of our
+parameter space after 1 hour of observation, and is especially prominent at
+230$\,$GHz due to the favourable combination of angular resolution and
+sensitivity. Disc mass derived from the observations is sensitive to the
+assumed dust opacities and temperatures, and therefore can be underestimated by
+a factor of at least 30 at 850$\,$GHz and 2.5 at 90$\,$GHz. As a result, this
+effect could retrospectively validate GIs in discs previously thought not
+massive enough to be gravitationally unstable, which could have a significant
+impact on the understanding of the formation and evolution of protoplanetary
+discs.
+",0,1,0,0,0,0
+4738,Information Processing by Networks of Quantum Decision Makers,"  We suggest a model of a multi-agent society of decision makers taking
+decisions being based on two criteria, one is the utility of the prospects and
+the other is the attractiveness of the considered prospects. The model is the
+generalization of quantum decision theory, developed earlier for single
+decision makers realizing one-step decisions, in two principal aspects. First,
+several decision makers are considered simultaneously, who interact with each
+other through information exchange. Second, a multistep procedure is treated,
+when the agents exchange information many times. Several decision makers
+exchanging information and forming their judgement, using quantum rules, form a
+kind of a quantum information network, where collective decisions develop in
+time as a result of information exchange. In addition to characterizing
+collective decisions that arise in human societies, such networks can describe
+dynamical processes occurring in artificial quantum intelligence composed of
+several parts or in a cluster of quantum computers. The practical usage of the
+theory is illustrated on the dynamic disjunction effect for which three
+quantitative predictions are made: (i) the probabilistic behavior of decision
+makers at the initial stage of the process is described; (ii) the decrease of
+the difference between the initial prospect probabilities and the related
+utility factors is proved; (iii) the existence of a common consensus after
+multiple exchange of information is predicted. The predicted numerical values
+are in very good agreement with empirical data.
+",1,0,0,0,0,0
+4739,The Value of Sharing Intermittent Spectrum,"  Recent initiatives by regulatory agencies to increase spectrum resources
+available for broadband access include rules for sharing spectrum with
+high-priority incumbents. We study a model in which wireless Service Providers
+(SPs) charge for access to their own exclusive-use (licensed) band along with
+access to an additional shared band. The total, or delivered price in each band
+is the announced price plus a congestion cost, which depends on the load, or
+total users normalized by the bandwidth. The shared band is intermittently
+available with some probability, due to incumbent activity, and when
+unavailable, any traffic carried on that band must be shifted to licensed
+bands. The SPs then compete for quantity of users. We show that the value of
+the shared band depends on the relative sizes of the SPs: large SPs with more
+bandwidth are better able to absorb the variability caused by intermittency
+than smaller SPs. However, as the amount of shared spectrum increases, the
+large SPs may not make use of it. In that scenario shared spectrum creates more
+value than splitting it among the SPs for exclusive use. We also show that
+fixing the average amount of available shared bandwidth, increasing the
+reliability of the band is preferable to increasing the bandwidth.
+",1,0,0,0,0,0
+4740,Origin of layer dependence in band structures of two-dimensional materials,"  We study the origin of layer dependence in band structures of two-dimensional
+materials. We find that the layer dependence, at the density functional theory
+(DFT) level, is a result of quantum confinement and the non-linearity of the
+exchange-correlation functional. We use this to develop an efficient scheme for
+performing DFT and GW calculations of multilayer systems. We show that the DFT
+and quasiparticle band structures of a multilayer system can be derived from a
+single calculation on a monolayer of the material. We test this scheme on
+multilayers of MoS$_2$, graphene and phosphorene. This new scheme yields
+results in excellent agreement with the standard methods at a fraction of the
+computation cost. This helps overcome the challenge of performing fully
+converged GW calculations on multilayers of 2D materials, particularly in the
+case of transition metal dichalcogenides which involve very stringent
+convergence parameters.
+",0,1,0,0,0,0
+4741,From Strings to Sets,"  A complete proof is given of relative interpretability of Adjunctive Set
+Theory with Extensionality in an elementary concatenation theory.
+",0,0,1,0,0,0
+4742,Sharp measure contraction property for generalized H-type Carnot groups,"  We prove that H-type Carnot groups of rank $k$ and dimension $n$ satisfy the
+$\mathrm{MCP}(K,N)$ if and only if $K\leq 0$ and $N \geq k+3(n-k)$. The latter
+integer coincides with the geodesic dimension of the Carnot group. The same
+result holds true for the larger class of generalized H-type Carnot groups
+introduced in this paper, and for which we compute explicitly the optimal
+synthesis. This constitutes the largest class of Carnot groups for which the
+curvature exponent coincides with the geodesic dimension. We stress that
+generalized H-type Carnot groups have step 2, include all corank 1 groups and,
+in general, admit abnormal minimizing curves.
+As a corollary, we prove the absolute continuity of the Wasserstein geodesics
+for the quadratic cost on all generalized H-type Carnot groups.
+",0,0,1,0,0,0
+4743,Twisted Quantum Double Model of Topological Orders with Boundaries,"  We generalize the twisted quantum double model of topological orders in two
+dimensions to the case with boundaries by systematically constructing the
+boundary Hamiltonians. Given the bulk Hamiltonian defined by a gauge group $G$
+and a three-cocycle in the third cohomology group of $G$ over $U(1)$, a
+boundary Hamiltonian can be defined by a subgroup $K$ of $G$ and a two-cochain
+in the second cochain group of $K$ over $U(1)$. The consistency between the
+bulk and boundary Hamiltonians is dictated by what we call the Frobenius
+condition that constrains the two-cochain given the three-cocyle. We offer a
+closed-form formula computing the ground state degeneracy of the model on a
+cylinder in terms of the input data only, which can be naturally generalized to
+surfaces with more boundaries. We also explicitly write down the ground-state
+wavefunction of the model on a disk also in terms of the input data only.
+",0,1,1,0,0,0
+4744,Evolutionary game of coalition building under external pressure,"  We study the fragmentation-coagulation (or merging and splitting)
+evolutionary control model as introduced recently by one of the authors, where
+$N$ small players can form coalitions to resist to the pressure exerted by the
+principal. It is a Markov chain in continuous time and the players have a
+common reward to optimize. We study the behavior as $N$ grows and show that the
+problem converges to a (one player) deterministic optimization problem in
+continuous time, in the infinite dimensional state space.
+",0,0,1,0,0,0
+4745,Nearly resolution V plans on blocks of small size,"  In Bagchi (2010) main effect plans ""orthogonal through the block factor""
+(POTB) have been constructed. The main advantages of a POTB are that (a) it may
+exist in a set up where an ""usual"" orthogonal main effect plan (OMEP) cannot
+exist and (b) the data analysis is nearly as simple as an OMEP. In the present
+paper we extend this idea and define the concept of orthogonality between a
+pair of factorial effects ( main effects or interactions) ""through the block
+factor"" in the context of a symmetrical experiment. We consider plans generated
+from an initial plan by adding runs. For such a plan we have derived necessary
+and sufficient conditions for a pair of effects to be orthogonal through the
+block factor in terms of the generators. We have also derived a sufficient
+condition on the generators so as to turn a pair of effects aliased in the
+initial plan separated in the final plan. The theory developed is illustrated
+with plans for experiments with three-level factors in situations where
+interactions between three or more factors are absent. We have constructed
+plans with blocks of size four and fewer runs than a resolution $V$ plan
+estimating all main effects and all but at most one two-factor interactions.
+",0,0,1,1,0,0
+4746,Graphene-based electron transport layers in perovskite solar cells: a step-up for an efficient carrier collection,"  The electron transport layer (ETL) plays a fundamental role in perovskite
+solar cells. Recently, graphene-based ETLs have been proved to be good
+candidate for scalable fabrication processes and to achieve higher carrier
+injection with respect to most commonly used ETLs. In this work we
+experimentally study the effects of different graphene-based ETLs in sensitized
+MAPI solar cells. By means of time-integrated and picosecond time-resolved
+photoluminescence techniques, the carrier recombination dynamics in MAPI films
+embedded in different ETLs is investigated. Using graphene doped mesoporous
+TiO2 (G+mTiO2) with the addition of a lithium-neutralized graphene oxide
+(GO-Li) interlayer as ETL, we find that the carrier collection efficiency is
+increased by about a factor two with respect to standard mTiO2. Taking
+advantage of the absorption coefficient dispersion, we probe the MAPI layer
+morphology, along the thickness, finding that the MAPI embedded in the ETL
+composed by G+mTiO2 plus GO-Li brings to a very good crystalline quality of the
+MAPI layer with a trap density about one order of magnitude lower than that
+found with the other ETLs. In addition, this ETL freezes MAPI at the tetragonal
+phase, regardless of the temperature. Graphene-based ETLs can open the way to
+significant improvement of perovskite solar cells.
+",0,1,0,0,0,0
+4747,Learning and Transferring IDs Representation in E-commerce,"  Many machine intelligence techniques are developed in E-commerce and one of
+the most essential components is the representation of IDs, including user ID,
+item ID, product ID, store ID, brand ID, category ID etc. The classical
+encoding based methods (like one-hot encoding) are inefficient in that it
+suffers sparsity problems due to its high dimension, and it cannot reflect the
+relationships among IDs, either homogeneous or heterogeneous ones. In this
+paper, we propose an embedding based framework to learn and transfer the
+representation of IDs. As the implicit feedbacks of users, a tremendous amount
+of item ID sequences can be easily collected from the interactive sessions. By
+jointly using these informative sequences and the structural connections among
+IDs, all types of IDs can be embedded into one low-dimensional semantic space.
+Subsequently, the learned representations are utilized and transferred in four
+scenarios: (i) measuring the similarity between items, (ii) transferring from
+seen items to unseen items, (iii) transferring across different domains, (iv)
+transferring across different tasks. We deploy and evaluate the proposed
+approach in Hema App and the results validate its effectiveness.
+",1,0,0,1,0,0
+4748,Differentiable Supervector Extraction for Encoding Speaker and Phrase Information in Text Dependent Speaker Verification,"  In this paper, we propose a new differentiable neural network alignment
+mechanism for text-dependent speaker verification which uses alignment models
+to produce a supervector representation of an utterance. Unlike previous works
+with similar approaches, we do not extract the embedding of an utterance from
+the mean reduction of the temporal dimension. Our system replaces the mean by a
+phrase alignment model to keep the temporal structure of each phrase which is
+relevant in this application since the phonetic information is part of the
+identity in the verification task. Moreover, we can apply a convolutional
+neural network as front-end, and thanks to the alignment process being
+differentiable, we can train the whole network to produce a supervector for
+each utterance which will be discriminative with respect to the speaker and the
+phrase simultaneously. As we show, this choice has the advantage that the
+supervector encodes the phrase and speaker information providing good
+performance in text-dependent speaker verification tasks. In this work, the
+process of verification is performed using a basic similarity metric, due to
+simplicity, compared to other more elaborate models that are commonly used. The
+new model using alignment to produce supervectors was tested on the
+RSR2015-Part I database for text-dependent speaker verification, providing
+competitive results compared to similar size networks using the mean to extract
+embeddings.
+",1,0,0,0,0,0
+4749,In situ accretion of gaseous envelopes on to planetary cores embedded in evolving protoplanetary discs,"  The core accretion hypothesis posits that planets with significant gaseous
+envelopes accreted them from their protoplanetary discs after the formation of
+rocky/icy cores. Observations indicate that such exoplanets exist at a broad
+range of orbital radii, but it is not known whether they accreted their
+envelopes in situ, or originated elsewhere and migrated to their current
+locations. We consider the evolution of solid cores embedded in evolving
+viscous discs that undergo gaseous envelope accretion in situ with orbital
+radii in the range $0.1-10\rm au$. Additionally, we determine the long-term
+evolution of the planets that had no runaway gas accretion phase after disc
+dispersal. We find: (i) Planets with $5 \rm M_{\oplus}$ cores never undergo
+runaway accretion. The most massive envelope contained $2.8 \rm M_{\oplus}$
+with the planet orbiting at $10 \rm au$. (ii) Accretion is more efficient onto
+$10 \rm M_{\oplus}$ and $15 \rm M_{\oplus}$ cores. For orbital radii $a_{\rm p}
+\ge 0.5 \rm au$, $15 \rm M_{\oplus}$ cores always experienced runaway gas
+accretion. For $a_{\rm p} \ge 5 \rm au$, all but one of the $10 \rm M_{\oplus}$
+cores experienced runaway gas accretion. No planets experienced runaway growth
+at $a_{\rm p} = 0.1 \rm au$. (iii) We find that, after disc dispersal, planets
+with significant gaseous envelopes cool and contract on Gyr time-scales, the
+contraction time being sensitive to the opacity assumed. Our results indicate
+that Hot Jupiters with core masses $\lesssim 15 \rm M_{\oplus}$ at $\lesssim
+0.1 \rm au$ likely accreted their gaseous envelopes at larger distances and
+migrated inwards. Consistently with the known exoplanet population,
+Super-Earths and mini-Neptunes at small radii during the disc lifetime, accrete
+only modest gaseous envelopes.
+",0,1,0,0,0,0
+4750,New Two Step Laplace Adam-Bashforth Method for Integer an Non integer Order Partial Differential Equations,"  This paper presents a novel method that allows to generalise the use of the
+Adam-Bashforth to Partial Differential Equations with local and non local
+operator. The Method derives a two step Adam-Bashforth numerical scheme in
+Laplace space and the solution is taken back into the real space via inverse
+Laplace transform. The method yields a powerful numerical algorithm for
+fractional order derivative where the usually very difficult to manage
+summation in the numerical scheme disappears. Error Analysis of the method is
+also presented. Applications of the method and numerical simulations are
+presented on a wave-equation like, and on a fractional order diffusion
+equation.
+",0,0,1,0,0,0
+4751,The Impact of Alternation,"  Alternating automata have been widely used to model and verify systems that
+handle data from finite domains, such as communication protocols or hardware.
+The main advantage of the alternating model of computation is that
+complementation is possible in linear time, thus allowing to concisely encode
+trace inclusion problems that occur often in verification. In this paper we
+consider alternating automata over infinite alphabets, whose transition rules
+are formulae in a combined theory of booleans and some infinite data domain,
+that relate past and current values of the data variables. The data theory is
+not fixed, but rather it is a parameter of the class. We show that union,
+intersection and complementation are possible in linear time in this model and,
+though the emptiness problem is undecidable, we provide two efficient
+semi-algorithms, inspired by two state-of-the-art abstraction refinement model
+checking methods: lazy predicate abstraction \cite{HJMS02} and the \impact~
+semi-algorithm \cite{mcmillan06}. We have implemented both methods and report
+the results of an experimental comparison.
+",1,0,0,0,0,0
+4752,Partial-wave Coulomb t-matrices for like-charged particles at ground-state energy,"  We study a special case at which the analytical solution of the
+Lippmann-Schwinger integral equation for the partial wave two-body Coulomb
+transition matrix for likely charged particles at negative energy is possible.
+With the use of the Fock's method of the stereographic projection of the
+momentum space onto the four-dimensional unit sphere, the analytical
+expressions for s-, p- and d-wave partial Coulomb transition matrices for
+repulsively interacting particles at bound-state energy have been derived.
+",0,1,0,0,0,0
+4753,Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness,"  Causal effect estimation from observational data is an important and much
+studied research topic. The instrumental variable (IV) and local causal
+discovery (LCD) patterns are canonical examples of settings where a closed-form
+expression exists for the causal effect of one variable on another, given the
+presence of a third variable. Both rely on faithfulness to infer that the
+latter only influences the target effect via the cause variable. In reality, it
+is likely that this assumption only holds approximately and that there will be
+at least some form of weak interaction. This brings about the paradoxical
+situation that, in the large-sample limit, no predictions are made, as
+detecting the weak edge invalidates the setting. We introduce an alternative
+approach by replacing strict faithfulness with a prior that reflects the
+existence of many 'weak' (irrelevant) and 'strong' interactions. We obtain a
+posterior distribution over the target causal effect estimator which shows
+that, in many cases, we can still make good estimates. We demonstrate the
+approach in an application on a simple linear-Gaussian setting, using the
+MultiNest sampling algorithm, and compare it with established techniques to
+show our method is robust even when strict faithfulness is violated.
+",1,0,0,1,0,0
+4754,The Cross-section of a Spherical Double Cone,"  We show that the poset of $SL(n)$-orbit closures in the product of two
+partial flag varieties is a lattice if the action of $SL(n)$ is spherical.
+",0,0,1,0,0,0
+4755,Understanding looping kinetics of a long polymer molecule in solution. Exact solution for delocalized sink model,"  The fundamental understanding of loop formation of long polymer chains in
+solution has been an important thread of research for several theoretical and
+experimental studies. Loop formations are important phenomenological parameters
+in many important biological processes. Here we give a general method for
+finding an exact analytical solution for the occurrence of looping of a long
+polymer chains in solution modeled by using a Smoluchowski-like equation with a
+delocalized sink. The average rate constant for the delocalized sink is
+explicitly expressed in terms of the corresponding rate constants for localized
+sinks with different initial conditions. Simple analytical expressions are
+provided for average rate constant.
+",0,1,0,0,0,0
+4756,SESA: Supervised Explicit Semantic Analysis,"  In recent years supervised representation learning has provided state of the
+art or close to the state of the art results in semantic analysis tasks
+including ranking and information retrieval. The core idea is to learn how to
+embed items into a latent space such that they optimize a supervised objective
+in that latent space. The dimensions of the latent space have no clear
+semantics, and this reduces the interpretability of the system. For example, in
+personalization models, it is hard to explain why a particular item is ranked
+high for a given user profile. We propose a novel model of representation
+learning called Supervised Explicit Semantic Analysis (SESA) that is trained in
+a supervised fashion to embed items to a set of dimensions with explicit
+semantics. The model learns to compare two objects by representing them in this
+explicit space, where each dimension corresponds to a concept from a knowledge
+base. This work extends Explicit Semantic Analysis (ESA) with a supervised
+model for ranking problems. We apply this model to the task of Job-Profile
+relevance in LinkedIn in which a set of skills defines our explicit dimensions
+of the space. Every profile and job are encoded to this set of skills their
+similarity is calculated in this space. We use RNNs to embed text input into
+this space. In addition to interpretability, our model makes use of the
+web-scale collaborative skills data that is provided by users for each LinkedIn
+profile. Our model provides state of the art result while it remains
+interpretable.
+",1,0,0,0,0,0
+4757,High-Level Concepts for Affective Understanding of Images,"  This paper aims to bridge the affective gap between image content and the
+emotional response of the viewer it elicits by using High-Level Concepts
+(HLCs). In contrast to previous work that relied solely on low-level features
+or used convolutional neural network (CNN) as a black-box, we use HLCs
+generated by pretrained CNNs in an explicit way to investigate the
+relations/associations between these HLCs and a (small) set of Ekman's
+emotional classes. As a proof-of-concept, we first propose a linear admixture
+model for modeling these relations, and the resulting computational framework
+allows us to determine the associations between each emotion class and certain
+HLCs (objects and places). This linear model is further extended to a nonlinear
+model using support vector regression (SVR) that aims to predict the viewer's
+emotional response using both low-level image features and HLCs extracted from
+images. These class-specific regressors are then assembled into a regressor
+ensemble that provide a flexible and effective predictor for predicting
+viewer's emotional responses from images. Experimental results have
+demonstrated that our results are comparable to existing methods, with a clear
+view of the association between HLCs and emotional classes that is ostensibly
+missing in most existing work.
+",1,0,0,0,0,0
+4758,A Network of Networks Approach to Interconnected Power Grids,"  We present two different approaches to model power grids as interconnected
+networks of networks. Both models are derived from a model for spatially
+embedded mono-layer networks and are generalised to handle an arbitrary number
+of network layers. The two approaches are distinguished by their use case. The
+static glue stick construction model yields a multi-layer network from a
+predefined layer interconnection scheme, i.e. different layers are attached
+with transformer edges. It is especially suited to construct multi-layer power
+grids with a specified number of nodes in and transformers between layers. We
+contrast it with a genuine growth model which we label interconnected layer
+growth model.
+",0,1,0,0,0,0
+4759,A Nonparametric Method for Producing Isolines of Bivariate Exceedance Probabilities,"  We present a method for drawing isolines indicating regions of equal joint
+exceedance probability for bivariate data. The method relies on bivariate
+regular variation, a dependence framework widely used for extremes. This
+framework enables drawing isolines corresponding to very low exceedance
+probabilities and these lines may lie beyond the range of the data. The method
+we utilize for characterizing dependence in the tail is largely nonparametric.
+Furthermore, we extend this method to the case of asymptotic independence and
+propose a procedure which smooths the transition from asymptotic independence
+in the interior to the first-order behavior on the axes. We propose a
+diagnostic plot for assessing isoline estimate and choice of smoothing, and a
+bootstrap procedure to visually assess uncertainty.
+",0,0,0,1,0,0
+4760,Finite element error analysis for measure-valued optimal control problems governed by a 1D wave equation with variable coefficients,"  This work is concerned with the optimal control problems governed by a 1D
+wave equation with variable coefficients and the control spaces $\mathcal M_T$
+of either measure-valued functions $L_{w^*}^2(I,\mathcal M(\Omega))$ or vector
+measures $\mathcal M(\Omega,L^2(I))$. The cost functional involves the standard
+quadratic tracking terms and the regularization term $\alpha\|u\|_{\mathcal
+M_T}$ with $\alpha>0$. We construct and study three-level in time bilinear
+finite element discretizations for this class of problems. The main focus lies
+on the derivation of error estimates for the optimal state variable and the
+error measured in the cost functional. The analysis is mainly based on some
+previous results of the authors. The numerical results are included.
+",0,0,1,0,0,0
+4761,Marked points on translation surfaces,"  We show that all GL(2,R) equivariant point markings over orbit closures of
+translation surfaces arise from branched covering constructions and periodic
+points, completely classify such point markings over strata of quadratic
+differentials, and give applications to the finite blocking problem.
+",0,0,1,0,0,0
+4762,Multitask Learning for Fundamental Frequency Estimation in Music,"  Fundamental frequency (f0) estimation from polyphonic music includes the
+tasks of multiple-f0, melody, vocal, and bass line estimation. Historically
+these problems have been approached separately, and only recently, using
+learning-based approaches. We present a multitask deep learning architecture
+that jointly estimates outputs for various tasks including multiple-f0, melody,
+vocal and bass line estimation, and is trained using a large,
+semi-automatically annotated dataset. We show that the multitask model
+outperforms its single-task counterparts, and explore the effect of various
+design decisions in our approach, and show that it performs better or at least
+competitively when compared against strong baseline methods.
+",1,0,0,1,0,0
+4763,Real eigenvalues of a non-self-adjoint perturbation of the self-adjoint Zakharov-Shabat operator,"  We study the eigenvalues of the self-adjoint Zakharov-Shabat operator
+corresponding to the defocusing nonlinear Schrodinger equation in the inverse
+scattering method. Real eigenvalues exist when the square of the potential has
+a simple well. We derive two types of quantization condition for the
+eigenvalues by using the exact WKB method, and show that the eigenvalues stay
+real for a sufficiently small non-self-adjoint perturbation when the potential
+has some PT-like symmetry.
+",0,0,1,0,0,0
+4764,Planning with Multiple Biases,"  Recent work has considered theoretical models for the behavior of agents with
+specific behavioral biases: rather than making decisions that optimize a given
+payoff function, the agent behaves inefficiently because its decisions suffer
+from an underlying bias. These approaches have generally considered an agent
+who experiences a single behavioral bias, studying the effect of this bias on
+the outcome.
+In general, however, decision-making can and will be affected by multiple
+biases operating at the same time. How do multiple biases interact to produce
+the overall outcome? Here we consider decisions in the presence of a pair of
+biases exhibiting an intuitively natural interaction: present bias -- the
+tendency to value costs incurred in the present too highly -- and sunk-cost
+bias -- the tendency to incorporate costs experienced in the past into one's
+plans for the future.
+We propose a theoretical model for planning with this pair of biases, and we
+show how certain natural behavioral phenomena can arise in our model only when
+agents exhibit both biases. As part of our model we differentiate between
+agents that are aware of their biases (sophisticated) and agents that are
+unaware of them (naive). Interestingly, we show that the interaction between
+the two biases is quite complex: in some cases, they mitigate each other's
+effects while in other cases they might amplify each other. We obtain a number
+of further results as well, including the fact that the planning problem in our
+model for an agent experiencing and aware of both biases is computationally
+hard in general, though tractable under more relaxed assumptions.
+",1,1,0,0,0,0
+4765,Zhu reduction for Jacobi $n$-point functions and applications,"  We establish precise Zhu reduction formulas for Jacobi $n$-point functions
+which show the absence of any possible poles arising in these formulas. We then
+exploit this to produce results concerning the structure of strongly regular
+vertex operator algebras, and also to motivate new differential operators
+acting on Jacobi forms. Finally, we apply the reduction formulas to the Fermion
+model in order to create polynomials of quasi-Jacobi forms which are Jacobi
+forms.
+",0,0,1,0,0,0
+4766,Fourier-like multipliers and applications for integral operators,"  Timelimited functions and bandlimited functions play a fundamental role in
+signal and image processing. But by the uncertainty principles, a signal cannot
+be simultaneously time and bandlimited. A natural assumption is thus that a
+signal is almost time and almost bandlimited. The aim of this paper is to prove
+that the set of almost time and almost bandlimited signals is not excluded from
+the uncertainty principles. The transforms under consideration are integral
+operators with bounded kernels for which there is a Parseval Theorem. Then we
+define the wavelet multipliers for this class of operators, and study their
+boundedness and Schatten class properties. We show that the wavelet multiplier
+is unitary equivalent to a scalar multiple of the phase space restriction
+operator. Moreover we prove that a signal which is almost time and almost
+bandlimited can be approximated by its projection on the span of the first
+eigenfunctions of the phase space restriction operator, corresponding to the
+largest eigenvalues which are close to one.
+",0,0,1,0,0,0
+4767,Inferring Properties of the ISM from Supernova Remnant Size Distributions,"  We model the size distribution of supernova remnants to infer the surrounding
+ISM density. Using simple, yet standard SNR evolution models, we find that the
+distribution of ambient densities is remarkably narrow; either the standard
+assumptions about SNR evolution are wrong, or observable SNRs are biased to a
+narrow range of ambient densities. We show that the size distributions are
+consistent with log-normal, which severely limits the number of model
+parameters in any SNR population synthesis model. Simple Monte Carlo
+simulations demonstrate that the size distribution is indistinguishable from
+log-normal when the SNR sample size is less than 600. This implies that these
+SNR distributions provide only information on the mean and variance, yielding
+additional information only when the sample size grows larger than $\sim{600}$
+SNRs. To infer the parameters of the ambient density, we use Bayesian
+statistical inference under the assumption that SNR evolution is dominated by
+the Sedov phase. In particular, we use the SNR sizes and explosion energies to
+estimate the mean and variance of the ambient medium surrounding SNR
+progenitors. We find that the mean ISM particle density around our sample of
+SNRs is $\mu_{\log{n}} = -1.33$, in $\log_{10}$ of particles per cubic
+centimeter, with variance $\sigma^2_{\log{n}} = 0.49$. If interpreted at face
+value, this implies that most SNRs result from supernovae propagating in the
+warm, ionized medium. However, it is also likely that either SNR evolution is
+not dominated by the simple Sedov evolution or SNR samples are biased to the
+warm, ionized medium (WIM).
+",0,1,0,0,0,0
+4768,Mining Target Attribute Subspace and Set of Target Communities in Large Attributed Networks,"  Community detection provides invaluable help for various applications, such
+as marketing and product recommendation. Traditional community detection
+methods designed for plain networks may not be able to detect communities with
+homogeneous attributes inside on attributed networks with attribute
+information. Most of recent attribute community detection methods may fail to
+capture the requirements of a specific application and not be able to mine the
+set of required communities for a specific application. In this paper, we aim
+to detect the set of target communities in the target subspace which has some
+focus attributes with large importance weights satisfying the requirements of a
+specific application. In order to improve the university of the problem, we
+address the problem in an extreme case where only two sample nodes in any
+potential target community are provided. A Target Subspace and Communities
+Mining (TSCM) method is proposed. In TSCM, a sample information extension
+method is designed to extend the two sample nodes to a set of exemplar nodes
+from which the target subspace is inferred. Then the set of target communities
+are located and mined based on the target subspace. Experiments on synthetic
+datasets demonstrate the effectiveness and efficiency of our method and
+applications on real-world datasets show its application values.
+",1,1,0,0,0,0
+4769,Achieving rental harmony with a secretive roommate,"  Given the subjective preferences of n roommates in an n-bedroom apartment,
+one can use Sperner's lemma to find a division of the rent such that each
+roommate is content with a distinct room. At the given price distribution, no
+roommate has a strictly stronger preference for a different room. We give a new
+elementary proof that the subjective preferences of only n-1 of the roommates
+actually suffice to achieve this envy-free rent division. Our proof, in
+particular, yields an algorithm to find such a fair division of rent. The
+techniques also give generalizations of Sperner's lemma including a new proof
+of a conjecture of the third author.
+",0,0,1,0,0,0
+4770,A Channel-Based Perspective on Conjugate Priors,"  A desired closure property in Bayesian probability is that an updated
+posterior distribution be in the same class of distributions --- say Gaussians
+--- as the prior distribution. When the updating takes place via a statistical
+model, one calls the class of prior distributions the `conjugate priors' of the
+model. This paper gives (1) an abstract formulation of this notion of conjugate
+prior, using channels, in a graphical language, (2) a simple abstract proof
+that such conjugate priors yield Bayesian inversions, and (3) a logical
+description of conjugate priors that highlights the required closure of the
+priors under updating. The theory is illustrated with several standard
+examples, also covering multiple updating.
+",1,0,0,0,0,0
+4771,Cascaded Coded Distributed Computing on Heterogeneous Networks,"  Coded distributed computing (CDC) introduced by Li et al. in 2015 offers an
+efficient approach to trade computing power to reduce the communication load in
+general distributed computing frameworks such as MapReduce. For the more
+general cascaded CDC, Map computations are repeated at $r$ nodes to
+significantly reduce the communication load among nodes tasked with computing
+$Q$ Reduce functions $s$ times. While an achievable cascaded CDC scheme was
+proposed, it only operates on homogeneous networks, where the storage,
+computation load and communication load of each computing node is the same. In
+this paper, we address this limitation by proposing a novel combinatorial
+design which operates on heterogeneous networks where nodes have varying
+storage and computing capabilities. We provide an analytical characterization
+of the computation-communication trade-off and show that it is optimal within a
+constant factor and could outperform the state-of-the-art homogeneous schemes.
+",1,0,0,0,0,0
+4772,A FEL Based on a Superlattice,"  The motion and photon emission of electrons in a superlattice may be
+described as in an undulator. Therefore, there is a close analogy between
+ballistic electrons in a superlattice and electrons in a free electron laser
+(FEL). Touching upon this analogy the intensity of photon emission in the IR
+region and the gain are calculated. It is shown that the amplification can be
+significant, reaching tens of percent.
+",0,1,0,0,0,0
+4773,Thermal and non-thermal emission from the cocoon of a gamma-ray burst jet,"  We present hydrodynamic simulations of the hot cocoon produced when a
+relativistic jet passes through the gamma-ray burst (GRB) progenitor star and
+its environment, and we compute the lightcurve and spectrum of the radiation
+emitted by the cocoon. The radiation from the cocoon has a nearly thermal
+spectrum with a peak in the X-ray band, and it lasts for a few minutes in the
+observer frame; the cocoon radiation starts at roughly the same time as when
+$\gamma$-rays from a burst trigger detectors aboard GRB satellites. The
+isotropic cocoon luminosity ($\sim 10^{47}$ erg s$^{-1}$) is of the same order
+of magnitude as the X-ray luminosity of a typical long-GRB afterglow during the
+plateau phase. This radiation should be identifiable in the Swift data because
+of its nearly thermal spectrum which is distinct from the somewhat brighter
+power-law component. The detection of this thermal component would provide
+information regarding the size and density stratification of the GRB progenitor
+star. Photons from the cocoon are also inverse-Compton (IC) scattered by
+electrons in the relativistic jet. We present the IC lightcurve and spectrum,
+by post-processing the results of the numerical simulations. The IC spectrum
+lies in 10 keV--MeV band for typical GRB parameters. The detection of this IC
+component would provide an independent measurement of GRB jet Lorentz factor
+and it would also help to determine the jet magnetisation parameter.
+",0,1,0,0,0,0
+4774,Least Squares Polynomial Chaos Expansion: A Review of Sampling Strategies,"  As non-institutive polynomial chaos expansion (PCE) techniques have gained
+growing popularity among researchers, we here provide a comprehensive review of
+major sampling strategies for the least squares based PCE. Traditional sampling
+methods, such as Monte Carlo, Latin hypercube, quasi-Monte Carlo, optimal
+design of experiments (ODE), Gaussian quadratures, as well as more recent
+techniques, such as coherence-optimal and randomized quadratures are discussed.
+We also propose a hybrid sampling method, dubbed alphabetic-coherence-optimal,
+that employs the so-called alphabetic optimality criteria used in the context
+of ODE in conjunction with coherence-optimal samples. A comparison between the
+empirical performance of the selected sampling methods applied to three
+numerical examples, including high-order PCE's, high-dimensional problems, and
+low oversampling ratios, is presented to provide a road map for practitioners
+seeking the most suitable sampling technique for a problem at hand. We observed
+that the alphabetic-coherence-optimal technique outperforms other sampling
+methods, specially when high-order ODE are employed and/or the oversampling
+ratio is low.
+",0,0,0,1,0,0
+4775,Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks,"  Recognizing human activities in a sequence is a challenging area of research
+in ubiquitous computing. Most approaches use a fixed size sliding window over
+consecutive samples to extract features---either handcrafted or learned
+features---and predict a single label for all samples in the window. Two key
+problems emanate from this approach: i) the samples in one window may not
+always share the same label. Consequently, using one label for all samples
+within a window inevitably lead to loss of information; ii) the testing phase
+is constrained by the window size selected during training while the best
+window size is difficult to tune in practice. We propose an efficient algorithm
+that can predict the label of each sample, which we call dense labeling, in a
+sequence of human activities of arbitrary length using a fully convolutional
+network. In particular, our approach overcomes the problems posed by the
+sliding window step. Additionally, our algorithm learns both the features and
+classifier automatically. We release a new daily activity dataset based on a
+wearable sensor with hospitalized patients. We conduct extensive experiments
+and demonstrate that our proposed approach is able to outperform the
+state-of-the-arts in terms of classification and label misalignment measures on
+three challenging datasets: Opportunity, Hand Gesture, and our new dataset.
+",1,0,0,0,0,0
+4776,Higher Tetrahedral Algebras,"  We introduce and study the higher tetrahedral algebras, an exotic family of
+finite-dimensional tame symmetric algebras over an algebraically closed field.
+The Gabriel quiver of such an algebra is the triangulation quiver associated to
+the coherent orientation of the tetrahedron. Surprisingly, these algebras
+occurred in the classification of all algebras of generalised quaternion type,
+but are not weighted surface algebras. We prove that a higher tetrahedral
+algebra is periodic if and only if it is non-singular.
+",0,0,1,0,0,0
+4777,Adaptive multi-penalty regularization based on a generalized Lasso path,"  For many algorithms, parameter tuning remains a challenging and critical
+task, which becomes tedious and infeasible in a multi-parameter setting.
+Multi-penalty regularization, successfully used for solving undetermined sparse
+regression of problems of unmixing type where signal and noise are additively
+mixed, is one of such examples. In this paper, we propose a novel algorithmic
+framework for an adaptive parameter choice in multi-penalty regularization with
+a focus on the correct support recovery. Building upon the theory of
+regularization paths and algorithms for single-penalty functionals, we extend
+these ideas to a multi-penalty framework by providing an efficient procedure
+for the construction of regions containing structurally similar solutions,
+i.e., solutions with the same sparsity and sign pattern, over the whole range
+of parameters. Combining this with a model selection criterion, we can choose
+regularization parameters in a data-adaptive manner. Another advantage of our
+algorithm is that it provides an overview on the solution stability over the
+whole range of parameters. This can be further exploited to obtain additional
+insights into the problem of interest. We provide a numerical analysis of our
+method and compare it to the state-of-the-art single-penalty algorithms for
+compressed sensing problems in order to demonstrate the robustness and power of
+the proposed algorithm.
+",1,0,0,1,0,0
+4778,Shape Generation using Spatially Partitioned Point Clouds,"  We propose a method to generate 3D shapes using point clouds. Given a
+point-cloud representation of a 3D shape, our method builds a kd-tree to
+spatially partition the points. This orders them consistently across all
+shapes, resulting in reasonably good correspondences across all shapes. We then
+use PCA analysis to derive a linear shape basis across the spatially
+partitioned points, and optimize the point ordering by iteratively minimizing
+the PCA reconstruction error. Even with the spatial sorting, the point clouds
+are inherently noisy and the resulting distribution over the shape coefficients
+can be highly multi-modal. We propose to use the expressive power of neural
+networks to learn a distribution over the shape coefficients in a
+generative-adversarial framework. Compared to 3D shape generative models
+trained on voxel-representations, our point-based method is considerably more
+light-weight and scalable, with little loss of quality. It also outperforms
+simpler linear factor models such as Probabilistic PCA, both qualitatively and
+quantitatively, on a number of categories from the ShapeNet dataset.
+Furthermore, our method can easily incorporate other point attributes such as
+normal and color information, an additional advantage over voxel-based
+representations.
+",1,0,0,0,0,0
+4779,Open Vocabulary Scene Parsing,"  Recognizing arbitrary objects in the wild has been a challenging problem due
+to the limitations of existing classification models and datasets. In this
+paper, we propose a new task that aims at parsing scenes with a large and open
+vocabulary, and several evaluation metrics are explored for this problem. Our
+proposed approach to this problem is a joint image pixel and word concept
+embeddings framework, where word concepts are connected by semantic relations.
+We validate the open vocabulary prediction ability of our framework on ADE20K
+dataset which covers a wide variety of scenes and objects. We further explore
+the trained joint embedding space to show its interpretability.
+",1,0,0,0,0,0
+4780,Parameter Estimation in Mean Reversion Processes with Periodic Functional Tendency,"  This paper describes the procedure to estimate the parameters in mean
+reversion processes with functional tendency defined by a periodic continuous
+deterministic function, expressed as a series of truncated Fourier. Two phases
+of estimation are defined, in the first phase through Gaussian techniques using
+the Euler-Maruyama discretization, we obtain the maximum likelihood function,
+that will allow us to find estimators of the external parameters and an
+estimation of the expected value of the process. In the second phase, a
+reestimate of the periodic functional tendency with it's parameters of phase
+and amplitude is carried out, this will allow, improve the initial estimation.
+Some experimental result using simulated data sets are graphically illustrated.
+",0,0,0,1,0,0
+4781,User Interface (UI) Design Issues for the Multilingual Users: A Case Study,"  A multitude of web and desktop applications are now widely available in
+diverse human languages. This paper explores the design issues that are
+specifically relevant for multilingual users. It reports on the continued
+studies of Information System (IS) issues and users' behaviour across
+cross-cultural and transnational boundaries. Taking the BBC website as a model
+that is internationally recognised, usability tests were conducted to compare
+different versions of the website. The dependant variables derived from the
+questionnaire were analysed (via descriptive statistics) to elucidate the
+multilingual UI design issues. Using Principal Component Analysis (PCA), five
+de-correlated variables were identified which were then used for hypotheses
+tests. A modified version of Herzberg's Hygiene-motivational Theory about the
+Workplace was applied to assess the components used in the website. Overall, it
+was concluded that the English versions of the website gave superior usability
+results and this implies the need for deeper study of the problems in usability
+of the translated versions.
+",1,0,0,0,0,0
+4782,NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media,"  Nowadays, a big part of people rely on available content in social media in
+their decisions (e.g. reviews and feedback on a topic or product). The
+possibility that anybody can leave a review provide a golden opportunity for
+spammers to write spam reviews about products and services for different
+interests. Identifying these spammers and the spam content is a hot topic of
+research and although a considerable number of studies have been done recently
+toward this end, but so far the methodologies put forth still barely detect
+spam reviews, and none of them show the importance of each extracted feature
+type. In this study, we propose a novel framework, named NetSpam, which
+utilizes spam features for modeling review datasets as heterogeneous
+information networks to map spam detection procedure into a classification
+problem in such networks. Using the importance of spam features help us to
+obtain better results in terms of different metrics experimented on real-world
+review datasets from Yelp and Amazon websites. The results show that NetSpam
+outperforms the existing methods and among four categories of features;
+including review-behavioral, user-behavioral, reviewlinguistic,
+user-linguistic, the first type of features performs better than the other
+categories.
+",1,1,0,0,0,0
+4783,Proving Non-Deterministic Computations in Agda,"  We investigate proving properties of Curry programs using Agda. First, we
+address the functional correctness of Curry functions that, apart from some
+syntactic and semantic differences, are in the intersection of the two
+languages. Second, we use Agda to model non-deterministic functions with two
+distinct and competitive approaches incorporating the non-determinism. The
+first approach eliminates non-determinism by considering the set of all
+non-deterministic values produced by an application. The second approach
+encodes every non-deterministic choice that the application could perform. We
+consider our initial experiment a success. Although proving properties of
+programs is a notoriously difficult task, the functional logic paradigm does
+not seem to add any significant layer of difficulty or complexity to the task.
+",1,0,0,0,0,0
+4784,NeuroRule: A Connectionist Approach to Data Mining,"  Classification, which involves finding rules that partition a given data set
+into disjoint groups, is one class of data mining problems. Approaches proposed
+so far for mining classification rules for large databases are mainly decision
+tree based symbolic learning methods. The connectionist approach based on
+neural networks has been thought not well suited for data mining. One of the
+major reasons cited is that knowledge generated by neural networks is not
+explicitly represented in the form of rules suitable for verification or
+interpretation by humans. This paper examines this issue. With our newly
+developed algorithms, rules which are similar to, or more concise than those
+generated by the symbolic methods can be extracted from the neural networks.
+The data mining process using neural networks with the emphasis on rule
+extraction is described. Experimental results and comparison with previously
+published works are presented.
+",1,0,0,0,0,0
+4785,Contextual Explanation Networks,"  Modern learning algorithms excel at producing accurate but complex models of
+the data. However, deploying such models in the real-world requires extra care:
+we must ensure their reliability, robustness, and absence of undesired biases.
+This motivates the development of models that are equally accurate but can be
+also easily inspected and assessed beyond their predictive performance. To this
+end, we introduce contextual explanation networks (CENs)---a class of
+architectures that learn to predict by generating and utilizing intermediate,
+simplified probabilistic models. Specifically, CENs generate parameters for
+intermediate graphical models which are further used for prediction and play
+the role of explanations. Contrary to the existing post-hoc model-explanation
+tools, CENs learn to predict and to explain jointly. Our approach offers two
+major advantages: (i) for each prediction, valid, instance-specific
+explanations are generated with no computational overhead and (ii) prediction
+via explanation acts as a regularizer and boosts performance in low-resource
+settings. We analyze the proposed framework theoretically and experimentally.
+Our results on image and text classification and survival analysis tasks
+demonstrate that CENs are not only competitive with the state-of-the-art
+methods but also offer additional insights behind each prediction, that are
+valuable for decision support. We also show that while post-hoc methods may
+produce misleading explanations in certain cases, CENs are always consistent
+and allow to detect such cases systematically.
+",1,0,0,1,0,0
+4786,Inference for Differential Equation Models using Relaxation via Dynamical Systems,"  Statistical regression models whose mean functions are represented by
+ordinary differential equations (ODEs) can be used to describe phenomenons
+dynamical in nature, which are abundant in areas such as biology, climatology
+and genetics. The estimation of parameters of ODE based models is essential for
+understanding its dynamics, but the lack of an analytical solution of the ODE
+makes the parameter estimation challenging. The aim of this paper is to propose
+a general and fast framework of statistical inference for ODE based models by
+relaxation of the underlying ODE system. Relaxation is achieved by a properly
+chosen numerical procedure, such as the Runge-Kutta, and by introducing
+additive Gaussian noises with small variances. Consequently, filtering methods
+can be applied to obtain the posterior distribution of the parameters in the
+Bayesian framework. The main advantage of the proposed method is computation
+speed. In a simulation study, the proposed method was at least 14 times faster
+than the other methods. Theoretical results which guarantee the convergence of
+the posterior of the approximated dynamical system to the posterior of true
+model are presented. Explicit expressions are given that relate the order and
+the mesh size of the Runge-Kutta procedure to the rate of convergence of the
+approximated posterior as a function of sample size.
+",0,0,0,1,0,0
+4787,Joint Trajectory and Communication Design for UAV-Enabled Multiple Access,"  Unmanned aerial vehicles (UAVs) have attracted significant interest recently
+in wireless communication due to their high maneuverability, flexible
+deployment, and low cost. This paper studies a UAV-enabled wireless network
+where the UAV is employed as an aerial mobile base station (BS) to serve a
+group of users on the ground. To achieve fair performance among users, we
+maximize the minimum throughput over all ground users by jointly optimizing the
+multiuser communication scheduling and UAV trajectory over a finite horizon.
+The formulated problem is shown to be a mixed integer non-convex optimization
+problem that is difficult to solve in general. We thus propose an efficient
+iterative algorithm by applying the block coordinate descent and successive
+convex optimization techniques, which is guaranteed to converge to at least a
+locally optimal solution. To achieve fast convergence and stable throughput, we
+further propose a low-complexity initialization scheme for the UAV trajectory
+design based on the simple circular trajectory. Extensive simulation results
+are provided which show significant throughput gains of the proposed design as
+compared to other benchmark schemes.
+",1,0,1,0,0,0
+4788,Imaging the Schwarzschild-radius-scale Structure of M87 with the Event Horizon Telescope using Sparse Modeling,"  We propose a new imaging technique for radio and optical/infrared
+interferometry. The proposed technique reconstructs the image from the
+visibility amplitude and closure phase, which are standard data products of
+short-millimeter very long baseline interferometers such as the Event Horizon
+Telescope (EHT) and optical/infrared interferometers, by utilizing two
+regularization functions: the $\ell_1$-norm and total variation (TV) of the
+brightness distribution. In the proposed method, optimal regularization
+parameters, which represent the sparseness and effective spatial resolution of
+the image, are derived from data themselves using cross validation (CV). As an
+application of this technique, we present simulated observations of M87 with
+the EHT based on four physically motivated models. We confirm that $\ell_1$+TV
+regularization can achieve an optimal resolution of $\sim 20-30$% of the
+diffraction limit $\lambda/D_{\rm max}$, which is the nominal spatial
+resolution of a radio interferometer. With the proposed technique, the EHT can
+robustly and reasonably achieve super-resolution sufficient to clearly resolve
+the black hole shadow. These results make it promising for the EHT to provide
+an unprecedented view of the event-horizon-scale structure in the vicinity of
+the super-massive black hole in M87 and also the Galactic center Sgr A*.
+",0,1,0,0,0,0
+4789,The process of purely event-driven programs,"  Using process algebra, this paper describes the formalisation of the
+process/semantics behind the purely event-driven programming language.
+",1,0,0,0,0,0
+4790,"Partial dust obscuration in active galactic nuclei as a cause of broad-line profile and lag variability, and apparent accretion disc inhomogeneities","  The profiles of the broad emission lines of active galactic nuclei (AGNs) and
+the time delays in their response to changes in the ionizing continuum (""lags"")
+give information about the structure and kinematics of the inner regions of
+AGNs. Line profiles are also our main way of estimating the masses of the
+supermassive black holes (SMBHs). However, the profiles often show
+ill-understood, asymmetric structure and velocity-dependent lags vary with
+time. Here we show that partial obscuration of the broad-line region (BLR) by
+outflowing, compact, dusty clumps produces asymmetries and velocity-dependent
+lags similar to those observed. Our model explains previously inexplicable
+changes in the ratios of the hydrogen lines with time and velocity, the lack of
+correlation of changes in line profiles with variability of the central engine,
+the velocity dependence of lags, and the change of lags with time. We propose
+that changes on timescales longer than the light-crossing time do not come from
+dynamical changes in the BLR, but are a natural result of the effect of
+outflowing dusty clumps driven by radiation pressure acting on the dust. The
+motion of these clumps offers an explanation of long-term changes in
+polarization. The effects of the dust complicate the study of the structure and
+kinematics of the BLR and the search for sub-parsec SMBH binaries. Partial
+obscuration of the accretion disc can also provide the local fluctuations in
+luminosity that can explain sizes deduced from microlensing.
+",0,1,0,0,0,0
+4791,From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields,"  Agricultural robots are expected to increase yields in a sustainable way and
+automate precision tasks, such as weeding and plant monitoring. At the same
+time, they move in a continuously changing, semi-structured field environment,
+in which features can hardly be found and reproduced at a later time.
+Challenges for Lidar and visual detection systems stem from the fact that
+plants can be very small, overlapping and have a steadily changing appearance.
+Therefore, a popular way to localize vehicles with high accuracy is based on
+ex- pensive global navigation satellite systems and not on natural landmarks.
+The contribution of this work is a novel image- based plant localization
+technique that uses the time-invariant stem emerging point as a reference. Our
+approach is based on a fully convolutional neural network that learns landmark
+localization from RGB and NIR image input in an end-to-end manner. The network
+performs pose regression to generate a plant location likelihood map. Our
+approach allows us to cope with visual variances of plants both for different
+species and different growth stages. We achieve high localization accuracies as
+shown in detailed evaluations of a sugar beet cultivation phase. In experiments
+with our BoniRob we demonstrate that detections can be robustly reproduced with
+centimeter accuracy.
+",1,0,0,0,0,0
+4792,There's more to the multimedia effect than meets the eye: is seeing pictures believing?,"  Textbooks in applied mathematics often use graphs to explain the meaning of
+formulae, even though their benefit is still not fully explored. To test
+processes underlying this assumed multimedia effect we collected performance
+scores, eye movements, and think-aloud protocols from students solving problems
+in vector calculus with and without graphs. Results showed no overall
+multimedia effect, but instead an effect to confirm statements that were
+accompanied by graphs, irrespective of whether these statements were true or
+false. Eye movement and verbal data shed light on this surprising finding.
+Students looked proportionally less at the text and the problem statement when
+a graph was present. Moreover, they experienced more mental effort with the
+graph, as indicated by more silent pauses in thinking aloud. Hence, students
+actively processed the graphs. This, however, was not sufficient. Further
+analysis revealed that the more students looked at the statement, the better
+they performed. Thus, in the multimedia condition the graph drew students'
+attention and cognitive capacities away from focusing on the statement. A good
+alternative strategy in the multimedia condition was to frequently look between
+graph and problem statement, and thus to integrate their information. In
+conclusion, graphs influence where students look and what they process, and may
+even mislead them into believing accompanying information. Thus, teachers and
+textbook designers should be very critical on when to use graphs and carefully
+consider how the graphs are integrated with other parts of the problem.
+",0,1,1,0,0,0
+4793,Methods of Enumerating Two Vertex Maps of Arbitrary Genus,"  This paper provides an alternate proof to parts of the Goulden-Slofstra
+formula for enumerating two vertex maps by genus, which is an extension of the
+famous Harer-Zagier formula that computes the Euler characteristic of the
+moduli space of curves. This paper also shows a further simplification to the
+Goulden-Slofstra formula. Portions of this alternate proof will be used in a
+subsequent paper, where it forms a basis for a more general result that applies
+for a certain class of maps with an arbitrary number of vertices.
+",0,0,1,0,0,0
+4794,Higher Theory and the Three Problems of Physics,"  According to the Butterfield--Isham proposal, to understand quantum gravity
+we must revise the way we view the universe of mathematics. However, this paper
+demonstrates that the current elaborations of this programme neglect quantum
+interactions. The paper then introduces the Faddeev--Mickelsson anomaly which
+obstructs the renormalization of Yang--Mills theory, suggesting that to
+theorise on many-particle systems requires a many-topos view of mathematics
+itself: higher theory. As our main contribution, the topos theoretic framework
+is used to conceptualise the fact that there are principally three different
+quantisation problems, the differences of which have been ignored not just by
+topos physicists but by most philosophers of science. We further argue that if
+higher theory proves out to be necessary for understanding quantum gravity, its
+implications to philosophy will be foundational: higher theory challenges the
+propositional concept of truth and thus the very meaning of theorising in
+science.
+",0,1,1,0,0,0
+4795,"Light emission by accelerated electric, toroidal and anapole dipolar sources","  Emission of electromagnetic radiation by accelerated particles with electric,
+toroidal and anapole dipole moments is analyzed. It is shown that ellipticity
+of the emitted light can be used to differentiate between electric and toroidal
+dipole sources, and that anapoles, elementary neutral non-radiating
+configurations, which consist of electric and toroidal dipoles, can emit light
+under uniform acceleration. The existence of non-radiating configurations in
+electrodynamics implies that it is impossible to fully determine the internal
+makeup of the emitter given only the distribution of the emitted light. Here we
+demonstrate that there is a loop-hole in this `inverse source problem'. Our
+results imply that there may be a whole range of new phenomena to be discovered
+by studying the electromagnetic response of matter under acceleration.
+",0,1,0,0,0,0
+4796,Bayesian Approaches to Distribution Regression,"  Distribution regression has recently attracted much interest as a generic
+solution to the problem of supervised learning where labels are available at
+the group level, rather than at the individual level. Current approaches,
+however, do not propagate the uncertainty in observations due to sampling
+variability in the groups. This effectively assumes that small and large groups
+are estimated equally well, and should have equal weight in the final
+regression. We account for this uncertainty with a Bayesian distribution
+regression formalism, improving the robustness and performance of the model
+when group sizes vary. We frame our models in a neural network style, allowing
+for simple MAP inference using backpropagation to learn the parameters, as well
+as MCMC-based inference which can fully propagate uncertainty. We demonstrate
+our approach on illustrative toy datasets, as well as on a challenging problem
+of predicting age from images.
+",1,0,0,1,0,0
+4797,"Atomic-Scale Structure Relaxation, Chemistry and Charge Distribution of Dislocation Cores in SrTiO3","  By using the state-of-the-art microscopy and spectroscopy in
+aberration-corrected scanning transmission electron microscopes, we determine
+the atomic arrangements, occupancy, elemental distribution, and the electronic
+structures of dislocation cores in the 10°tilted SrTiO3 bicrystal. We
+identify that there are two different types of oxygen deficient dislocation
+cores, i.e., the SrO plane terminated Sr0.82Ti0.85O3-x (Ti3.67+, 0.48<x<0.91)
+and TiO2 plane terminated Sr0.63Ti0.90O3-y (Ti3.60+, 0.57<y<1). They have the
+same Burgers vector of a[100] but different atomic arrangements and chemical
+properties. Besides the oxygen vacancies, Sr vacancies and rocksalt-like
+titanium oxide reconstruction are also identified in the dislocation core with
+TiO2 plane termination. Our atomic-scale study reveals the true atomic
+structures and chemistry of individual dislocation cores, providing useful
+insights into understanding the properties of dislocations and grain
+boundaries.
+",0,1,0,0,0,0
+4798,Transição de fase no sistema de Hénon-Heiles (Phase transition in the Henon-Heiles system),"  The Henon-Heiles system was originally proposed to describe the dynamical
+behavior of galaxies, but this system has been widely applied in dynamical
+systems by exhibit great details in phase space. This work presents the
+formalism to describe Henon-Heiles system and a qualitative approach of
+dynamics behavior. The growth of chaotic region in phase space is observed by
+Poincare Surface of Section when the total energy increases. Island of
+regularity remain around stable points and relevants phenomena appear, such as
+sticky.
+",0,1,0,0,0,0
+4799,Self-similar solutions of fragmentation equations revisited,"  We study the large time behaviour of the mass (size) of particles described
+by the fragmentation equation with homogeneous breakup kernel. We give
+necessary and sufficient conditions for the convergence of solutions to the
+unique self-similar solution.
+",0,0,1,0,0,0
+4800,Refining Source Representations with Relation Networks for Neural Machine Translation,"  Although neural machine translation (NMT) with the encoder-decoder framework
+has achieved great success in recent times, it still suffers from some
+drawbacks: RNNs tend to forget old information which is often useful and the
+encoder only operates through words without considering word relationship. To
+solve these problems, we introduce a relation networks (RN) into NMT to refine
+the encoding representations of the source. In our method, the RN first
+augments the representation of each source word with its neighbors and reasons
+all the possible pairwise relations between them. Then the source
+representations and all the relations are fed to the attention module and the
+decoder together, keeping the main encoder-decoder architecture unchanged.
+Experiments on two Chinese-to-English data sets in different scales both show
+that our method can outperform the competitive baselines significantly.
+",1,0,0,0,0,0
+4801,On the Statistical Efficiency of Optimal Kernel Sum Classifiers,"  We propose a novel combination of optimization tools with learning theory
+bounds in order to analyze the sample complexity of optimal kernel sum
+classifiers. This contrasts the typical learning theoretic results which hold
+for all (potentially suboptimal) classifiers. Our work also justifies
+assumptions made in prior work on multiple kernel learning. As a byproduct of
+our analysis, we also provide a new form of Rademacher complexity for
+hypothesis classes containing only optimal classifiers.
+",1,0,0,1,0,0
+4802,Ultracold atoms in multiple-radiofrequency dressed adiabatic potentials,"  We present the first experimental demonstration of a multiple-radiofrequency
+dressed potential for the configurable magnetic confinement of ultracold atoms.
+We load cold $^{87}$Rb atoms into a double well potential with an adjustable
+barrier height, formed by three radiofrequencies applied to atoms in a static
+quadrupole magnetic field. Our multiple-radiofrequency approach gives precise
+control over the double well characteristics, including the depth of individual
+wells and the height of the barrier, and enables reliable transfer of atoms
+between the available trapping geometries. We have characterised the
+multiple-radiofrequency dressed system using radiofrequency spectroscopy,
+finding good agreement with the eigenvalues numerically calculated using
+Floquet theory. This method creates trapping potentials that can be
+reconfigured by changing the amplitudes, polarizations and frequencies of the
+applied dressing fields, and easily extended with additional dressing
+frequencies.
+",0,1,0,0,0,0
+4803,Distribution Matching in Variational Inference,"  We show that Variational Autoencoders consistently fail to learn marginal
+distributions in latent and visible space. We ask whether this is a consequence
+of matching conditional distributions, or a limitation of explicit model and
+posterior distributions. We explore alternatives provided by marginal
+distribution matching and implicit distributions through the use of Generative
+Adversarial Networks in variational inference. We perform a large-scale
+evaluation of several VAE-GAN hybrids and explore the implications of class
+probability estimation for learning distributions. We conclude that at present
+VAE-GAN hybrids have limited applicability: they are harder to scale, evaluate,
+and use for inference compared to VAEs; and they do not improve over the
+generation quality of GANs.
+",0,0,0,1,0,0
+4804,Henkin constructions of models with size continuum,"  We survey the technique of constructing customized models of size continuum
+in omega steps and illustrate the method by giving new proofs of mostly old
+results within this rubric. One new theorem, which is joint with Saharon
+Shelah, is that a pseudominimal theory has an atomic model of size continuum.
+",0,0,1,0,0,0
+4805,Orthogonal groups in characteristic 2 acting on polytopes of high rank,"  We show that for all integers $m\geq 2$, and all integers $k\geq 2$, the
+orthogonal groups $\Orth^{\pm}(2m,\Fk)$ act on abstract regular polytopes of
+rank $2m$, and the symplectic groups $\Sp(2m,\Fk)$ act on abstract regular
+polytopes of rank $2m+1$.
+",0,0,1,0,0,0
+4806,Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes,"  Full autonomy for fixed-wing unmanned aerial vehicles (UAVs) requires the
+capability to autonomously detect potential landing sites in unknown and
+unstructured terrain, allowing for self-governed mission completion or handling
+of emergency situations. In this work, we propose a perception system
+addressing this challenge by detecting landing sites based on their texture and
+geometric shape without using any prior knowledge about the environment. The
+proposed method considers hazards within the landing region such as terrain
+roughness and slope, surrounding obstacles that obscure the landing approach
+path, and the local wind field that is estimated by the on-board EKF. The
+latter enables applicability of the proposed method on small-scale autonomous
+planes without landing gear. A safe approach path is computed based on the UAV
+dynamics, expected state estimation and actuator uncertainty, and the on-board
+computed elevation map. The proposed framework has been successfully tested on
+photo-realistic synthetic datasets and in challenging real-world environments.
+",1,0,0,0,0,0
+4807,Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language,"  We address the problem of efficient acoustic-model refinement (continuous
+retraining) using semi-supervised and active learning for a low resource Indian
+language, wherein the low resource constraints are having i) a small labeled
+corpus from which to train a baseline `seed' acoustic model and ii) a large
+training corpus without orthographic labeling or from which to perform a data
+selection for manual labeling at low costs. The proposed semi-supervised
+learning decodes the unlabeled large training corpus using the seed model and
+through various protocols, selects the decoded utterances with high reliability
+using confidence levels (that correlate to the WER of the decoded utterances)
+and iterative bootstrapping. The proposed active learning protocol uses
+confidence level based metric to select the decoded utterances from the large
+unlabeled corpus for further labeling. The semi-supervised learning protocols
+can offer a WER reduction, from a poorly trained seed model, by as much as 50%
+of the best WER-reduction realizable from the seed model's WER, if the large
+corpus were labeled and used for acoustic-model training. The active learning
+protocols allow that only 60% of the entire training corpus be manually
+labeled, to reach the same performance as the entire data.
+",1,0,0,0,0,0
+4808,Energy spectrum of cascade showers generated by cosmic ray muons in water,"  The spatial distribution of Cherenkov radiation from cascade showers
+generated by muons in water has been measured with Cherenkov water calorimeter
+(CWC) NEVOD. This result allowed to improve the techniques of treating cascade
+showers with unknown axes by means of CWC response analysis. The techniques of
+selecting the events with high energy cascade showers and reconstructing their
+parameters are discussed. Preliminary results of measurements of the spectrum
+of cascade showers in the energy range 100 GeV - 20 TeV generated by cosmic ray
+muons at large zenith angles and their comparison with expectation are
+presented.
+",0,1,0,0,0,0
+4809,The limit point of the pentagram map,"  The pentagram map is a discrete dynamical system defined on the space of
+polygons in the plane. In the first paper on the subject, R. Schwartz proved
+that the pentagram map produces from each convex polygon a sequence of
+successively smaller polygons that converges exponentially to a point. We
+investigate the limit point itself, giving an explicit description of its
+Cartesian coordinates as roots of certain degree three polynomials.
+",0,0,1,0,0,0
+4810,Reconstruction formulas for Photoacoustic Imaging in Attenuating Media,"  In this paper we study the problem of photoacoustic inversion in a weakly
+attenuating medium. We present explicit reconstruction formulas in such media
+and show that the inversion based on such formulas is moderately ill--posed.
+Moreover, we present a numerical algorithm for imaging and demonstrate in
+numerical experiments the feasibility of this approach.
+",0,0,1,0,0,0
+4811,Rank Determination for Low-Rank Data Completion,"  Recently, fundamental conditions on the sampling patterns have been obtained
+for finite completability of low-rank matrices or tensors given the
+corresponding ranks. In this paper, we consider the scenario where the rank is
+not given and we aim to approximate the unknown rank based on the location of
+sampled entries and some given completion. We consider a number of data models,
+including single-view matrix, multi-view matrix, CP tensor, tensor-train tensor
+and Tucker tensor. For each of these data models, we provide an upper bound on
+the rank when an arbitrary low-rank completion is given. We characterize these
+bounds both deterministically, i.e., with probability one given that the
+sampling pattern satisfies certain combinatorial properties, and
+probabilistically, i.e., with high probability given that the sampling
+probability is above some threshold. Moreover, for both single-view matrix and
+CP tensor, we are able to show that the obtained upper bound is exactly equal
+to the unknown rank if the lowest-rank completion is given. Furthermore, we
+provide numerical experiments for the case of single-view matrix, where we use
+nuclear norm minimization to find a low-rank completion of the sampled data and
+we observe that in most of the cases the proposed upper bound on the rank is
+equal to the true rank.
+",1,0,0,1,0,0
+4812,Network structure from rich but noisy data,"  Driven by growing interest in the sciences, industry, and among the broader
+public, a large number of empirical studies have been conducted in recent years
+of the structure of networks ranging from the internet and the world wide web
+to biological networks and social networks. The data produced by these
+experiments are often rich and multimodal, yet at the same time they may
+contain substantial measurement error. In practice, this means that the true
+network structure can differ greatly from naive estimates made from the raw
+data, and hence that conclusions drawn from those naive estimates may be
+significantly in error. In this paper we describe a technique that circumvents
+this problem and allows us to make optimal estimates of the true structure of
+networks in the presence of both richly textured data and significant
+measurement uncertainty. We give example applications to two different social
+networks, one derived from face-to-face interactions and one from self-reported
+friendships.
+",1,1,0,0,0,0
+4813,Algebraic Foundations of Proof Refinement,"  We contribute a general apparatus for dependent tactic-based proof refinement
+in the LCF tradition, in which the statements of subgoals may express a
+dependency on the proofs of other subgoals; this form of dependency is
+extremely useful and can serve as an algorithmic alternative to extensions of
+LCF based on non-local instantiation of schematic variables. Additionally, we
+introduce a novel behavioral distinction between refinement rules and tactics
+based on naturality. Our framework, called Dependent LCF, is already deployed
+in the nascent RedPRL proof assistant for computational cubical type theory.
+",1,0,0,0,0,0
+4814,Transfer Learning for Neural Semantic Parsing,"  The goal of semantic parsing is to map natural language to a machine
+interpretable meaning representation language (MRL). One of the constraints
+that limits full exploration of deep learning technologies for semantic parsing
+is the lack of sufficient annotation training data. In this paper, we propose
+using sequence-to-sequence in a multi-task setup for semantic parsing with a
+focus on transfer learning. We explore three multi-task architectures for
+sequence-to-sequence modeling and compare their performance with an
+independently trained model. Our experiments show that the multi-task setup
+aids transfer learning from an auxiliary task with large labeled data to a
+target task with smaller labeled data. We see absolute accuracy gains ranging
+from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging
+from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and
+semantic auxiliary tasks.
+",1,0,0,0,0,0
+4815,Definable Valuations induced by multiplicative subgroups and NIP Fields,"  We study the algebraic implications of the non-independence property (NIP)
+and variants thereof (dp-minimality) on infinite fields, motivated by the
+conjecture that all such fields which are neither real closed nor separably
+closed admit a definable henselian valuation. Our results mainly focus on Hahn
+fields and build up on Will Johnson's preprint ""dp-minimal fields"", arXiv:
+1507.02745v1, July 2015.
+",0,0,1,0,0,0
+4816,Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning,"  Layout hotpot detection is one of the main steps in modern VLSI design. A
+typical hotspot detection flow is extremely time consuming due to the
+computationally expensive mask optimization and lithographic simulation. Recent
+researches try to facilitate the procedure with a reduced flow including
+feature extraction, training set generation and hotspot detection, where
+feature extraction methods and hotspot detection engines are deeply studied.
+However, the performance of hotspot detectors relies highly on the quality of
+reference layout libraries which are costly to obtain and usually predetermined
+or randomly sampled in previous works. In this paper, we propose an active
+learning-based layout pattern sampling and hotspot detection flow, which
+simultaneously optimizes the machine learning model and the training set that
+aims to achieve similar or better hotspot detection performance with much
+smaller number of training instances. Experimental results show that our
+proposed method can significantly reduce lithography simulation overhead while
+attaining satisfactory detection accuracy on designs under both DUV and EUV
+lithography technologies.
+",0,0,0,1,0,0
+4817,iCorr : Complex correlation method to detect origin of replication in prokaryotic and eukaryotic genomes,"  Computational prediction of origin of replication (ORI) has been of great
+interest in bioinformatics and several methods including GC Skew, Z curve,
+auto-correlation etc. have been explored in the past. In this paper, we have
+extended the auto-correlation method to predict ORI location with much higher
+resolution for prokaryotes. The proposed complex correlation method (iCorr)
+converts the genome sequence into a sequence of complex numbers by mapping the
+nucleotides to {+1,-1,+i,-i} instead of {+1,-1} used in the auto-correlation
+method (here, 'i' is square root of -1). Thus, the iCorr method uses
+information about the positions of all the four nucleotides unlike the earlier
+auto-correlation method which uses the positional information of only one
+nucleotide. Also, this earlier method required visual inspection of the
+obtained graphs to identify the location of origin of replication. The proposed
+iCorr method does away with this need and is able to identify the origin
+location simply by picking the peak in the iCorr graph. The iCorr method also
+works for a much smaller segment size compared to the earlier auto-correlation
+method, which can be very helpful in experimental validation of the
+computational predictions. We have also developed a variant of the iCorr method
+to predict ORI location in eukaryotes and have tested it with the
+experimentally known origin locations of S. cerevisiae with an average accuracy
+of 71.76%.
+",0,1,0,0,0,0
+4818,On the Power Spectral Density Applied to the Analysis of Old Canvases,"  A routine task for art historians is painting diagnostics, such as dating or
+attribution. Signal processing of the X-ray image of a canvas provides useful
+information about its fabric. However, previous methods may fail when very old
+and deteriorated artworks or simply canvases of small size are studied. We
+present a new framework to analyze and further characterize the paintings from
+their radiographs. First, we start from a general analysis of lattices and
+provide new unifying results about the theoretical spectra of weaves. Then, we
+use these results to infer the main structure of the fabric, like the type of
+weave and the thread densities. We propose a practical estimation of these
+theoretical results from paintings with the averaged power spectral density
+(PSD), which provides a more robust tool. Furthermore, we found that the PSD
+provides a fingerprint that characterizes the whole canvas. We search and
+discuss some distinctive features we may find in that fingerprint. We apply
+these results to several masterpieces of the 17th and 18th centuries from the
+Museo Nacional del Prado to show that this approach yields accurate results in
+thread counting and is very useful for paintings comparison, even in situations
+where previous methods fail.
+",1,0,1,0,0,0
+4819,Simons' type formula for slant submanifolds of complex space form,"  In this paper, we study a slant submanifold of a complex space form. We also
+obtain an integral formula of Simons' type for a Kaehlerian slant submanifold
+in a complex space form and apply it to prove our main result.
+",0,0,1,0,0,0
+4820,Eco-evolutionary feedbacks - theoretical models and perspectives,"  1. Theoretical models pertaining to feedbacks between ecological and
+evolutionary processes are prevalent in multiple biological fields. An
+integrative overview is currently lacking, due to little crosstalk between the
+fields and the use of different methodological approaches.
+2. Here we review a wide range of models of eco-evolutionary feedbacks and
+highlight their underlying assumptions. We discuss models where feedbacks occur
+both within and between hierarchical levels of ecosystems, including
+populations, communities, and abiotic environments, and consider feedbacks
+across spatial scales.
+3. Identifying the commonalities among feedback models, and the underlying
+assumptions, helps us better understand the mechanistic basis of
+eco-evolutionary feedbacks. Eco-evolutionary feedbacks can be readily modelled
+by coupling demographic and evolutionary formalisms. We provide an overview of
+these approaches and suggest future integrative modelling avenues.
+4. Our overview highlights that eco-evolutionary feedbacks have been
+incorporated in theoretical work for nearly a century. Yet, this work does not
+always include the notion of rapid evolution or concurrent ecological and
+evolutionary time scales. We discuss the importance of density- and
+frequency-dependent selection for feedbacks, as well as the importance of
+dispersal as a central linking trait between ecology and evolution in a spatial
+context.
+",0,0,0,0,1,0
+4821,Unusual behavior of cuprates explained by heterogeneous charge localization,"  The cuprate high-temperature superconductors are among the most intensively
+studied materials, yet essential questions regarding their principal phases and
+the transitions between them remain unanswered. Generally thought of as doped
+charge-transfer insulators, these complex lamellar oxides exhibit pseudogap,
+strange-metal, superconducting and Fermi-liquid behaviour with increasing
+hole-dopant concentration. Here we propose a simple inhomogeneous Mott-like
+(de)localization model wherein exactly one hole per copper-oxygen unit is
+gradually delocalized with increasing doping and temperature. The model is
+percolative in nature, with parameters that are experimentally constrained. It
+comprehensively captures pivotal unconventional experimental results, including
+the temperature and doping dependence of the pseudogap phenomenon, the
+strange-metal linear temperature dependence of the planar resistivity, and the
+doping dependence of the superfluid density. The success and simplicity of our
+model greatly demystify the cuprate phase diagram and point to a local
+superconducting pairing mechanism involving the (de)localized hole.
+",0,1,0,0,0,0
+4822,An Agile Software Engineering Method to Design Blockchain Applications,"  Cryptocurrencies and their foundation technology, the Blockchain, are
+reshaping finance and economics, allowing a decentralized approach enabling
+trusted applications with no trusted counterpart. More recently, the Blockchain
+and the programs running on it, called Smart Contracts, are also finding more
+and more applications in all fields requiring trust and sound certifications.
+Some people have come to the point of saying that the ""Blockchain revolution""
+can be compared to that of the Internet and the Web in their early days. As a
+result, all the software development revolving around the Blockchain technology
+is growing at a staggering rate. The feeling of many software engineers about
+such huge interest in Blockchain technologies is that of unruled and hurried
+software development, a sort of competition on a first-come-first-served basis
+which does not assure neither software quality, nor that the basic concepts of
+software engineering are taken into account. This paper tries to cope with this
+issue, proposing a software development process to gather the requirement,
+analyze, design, develop, test and deploy Blockchain applications. The process
+is based on several Agile practices, such as User Stories and iterative and
+incremental development based on them. However, it makes also use of more
+formal notations, such as some UML diagrams describing the design of the
+system, with additions to represent specific concepts found in Blockchain
+development. The method is described in good detail, and an example is given to
+show how it works.
+",1,0,0,0,0,0
+4823,Optimizing Prediction Intervals by Tuning Random Forest via Meta-Validation,"  Recent studies have shown that tuning prediction models increases prediction
+accuracy and that Random Forest can be used to construct prediction intervals.
+However, to our best knowledge, no study has investigated the need to, and the
+manner in which one can, tune Random Forest for optimizing prediction intervals
+{ this paper aims to fill this gap. We explore a tuning approach that combines
+an effectively exhaustive search with a validation technique on a single Random
+Forest parameter. This paper investigates which, out of eight validation
+techniques, are beneficial for tuning, i.e., which automatically choose a
+Random Forest configuration constructing prediction intervals that are reliable
+and with a smaller width than the default configuration. Additionally, we
+present and validate three meta-validation techniques to determine which are
+beneficial, i.e., those which automatically chose a beneficial validation
+technique. This study uses data from our industrial partner (Keymind Inc.) and
+the Tukutuku Research Project, related to post-release defect prediction and
+Web application effort estimation, respectively. Results from our study
+indicate that: i) the default configuration is frequently unreliable, ii) most
+of the validation techniques, including previously successfully adopted ones
+such as 50/50 holdout and bootstrap, are counterproductive in most of the
+cases, and iii) the 75/25 holdout meta-validation technique is always
+beneficial; i.e., it avoids the likely counterproductive effects of validation
+techniques.
+",0,0,0,1,0,0
+4824,Explicit construction of RIP matrices is Ramsey-hard,"  Matrices $\Phi\in\R^{n\times p}$ satisfying the Restricted Isometry Property
+(RIP) are an important ingredient of the compressive sensing methods. While it
+is known that random matrices satisfy the RIP with high probability even for
+$n=\log^{O(1)}p$, the explicit construction of such matrices defied the
+repeated efforts, and the most known approaches hit the so-called $\sqrt{n}$
+sparsity bottleneck. The notable exception is the work by Bourgain et al
+\cite{bourgain2011explicit} constructing an $n\times p$ RIP matrix with
+sparsity $s=\Theta(n^{{1\over 2}+\epsilon})$, but in the regime
+$n=\Omega(p^{1-\delta})$.
+In this short note we resolve this open question in a sense by showing that
+an explicit construction of a matrix satisfying the RIP in the regime
+$n=O(\log^2 p)$ and $s=\Theta(n^{1\over 2})$ implies an explicit construction
+of a three-colored Ramsey graph on $p$ nodes with clique sizes bounded by
+$O(\log^2 p)$ -- a question in the extremal combinatorics which has been open
+for decades.
+",0,0,0,1,0,0
+4825,Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net,"  Models applied on real time response task, like click-through rate (CTR)
+prediction model, require high accuracy and rigorous response time. Therefore,
+top-performing deep models of high depth and complexity are not well suited for
+these applications with the limitations on the inference time. In order to
+further improve the neural networks' performance given the time and
+computational limitations, we propose an approach that exploits a cumbersome
+net to help train the lightweight net for prediction. We dub the whole process
+rocket launching, where the cumbersome booster net is used to guide the
+learning of the target light net throughout the whole training process. We
+analyze different loss functions aiming at pushing the light net to behave
+similarly to the booster net, and adopt the loss with best performance in our
+experiments. We use one technique called gradient block to improve the
+performance of the light net and booster net further. Experiments on benchmark
+datasets and real-life industrial advertisement data present that our light
+model can get performance only previously achievable with more complex models.
+",1,0,0,1,0,0
+4826,The CMS HGCAL detector for HL-LHC upgrade,"  The High Luminosity LHC (HL-LHC) will integrate 10 times more luminosity than
+the LHC, posing significant challenges for radiation tolerance and event pileup
+on detectors, especially for forward calorimetry, and hallmarks the issue for
+future colliders. As part of its HL-LHC upgrade program, the CMS collaboration
+is designing a High Granularity Calorimeter to replace the existing endcap
+calorimeters. It features unprecedented transverse and longitudinal
+segmentation for both electromagnetic (ECAL) and hadronic (HCAL) compartments.
+This will facilitate particle-flow calorimetry, where the fine structure of
+showers can be measured and used to enhance pileup rejection and particle
+identification, whilst still achieving good energy resolution. The ECAL and a
+large fraction of HCAL will be based on hexagonal silicon sensors of
+0.5-1cm$^{2}$ cell size, with the remainder of the HCAL based on
+highly-segmented scintillators with SiPM readout. The intrinsic high-precision
+timing capabilities of the silicon sensors will add an extra dimension to event
+reconstruction, especially in terms of pileup rejection. An overview of the
+HGCAL project is presented, covering motivation, engineering design, readout
+and trigger concepts, and performance (simulated and from beam tests).
+",0,1,0,0,0,0
+4827,Complete Classification of Generalized Santha-Vazirani Sources,"  Let $\mathcal{F}$ be a finite alphabet and $\mathcal{D}$ be a finite set of
+distributions over $\mathcal{F}$. A Generalized Santha-Vazirani (GSV) source of
+type $(\mathcal{F}, \mathcal{D})$, introduced by Beigi, Etesami and Gohari
+(ICALP 2015, SICOMP 2017), is a random sequence $(F_1, \dots, F_n)$ in
+$\mathcal{F}^n$, where $F_i$ is a sample from some distribution $d \in
+\mathcal{D}$ whose choice may depend on $F_1, \dots, F_{i-1}$.
+We show that all GSV source types $(\mathcal{F}, \mathcal{D})$ fall into one
+of three categories: (1) non-extractable; (2) extractable with error
+$n^{-\Theta(1)}$; (3) extractable with error $2^{-\Omega(n)}$. This rules out
+other error rates like $1/\log n$ or $2^{-\sqrt{n}}$.
+We provide essentially randomness-optimal extraction algorithms for
+extractable sources. Our algorithm for category (2) sources extracts with error
+$\varepsilon$ from $n = \mathrm{poly}(1/\varepsilon)$ samples in time linear in
+$n$. Our algorithm for category (3) sources extracts $m$ bits with error
+$\varepsilon$ from $n = O(m + \log 1/\varepsilon)$ samples in time
+$\min\{O(nm2^m),n^{O(\lvert\mathcal{F}\rvert)}\}$.
+We also give algorithms for classifying a GSV source type $(\mathcal{F},
+\mathcal{D})$: Membership in category (1) can be decided in $\mathrm{NP}$,
+while membership in category (3) is polynomial-time decidable.
+",1,0,0,0,0,0
+4828,"Hermann Hankel's ""On the general theory of motion of fluids"", an essay including an English translation of the complete Preisschrift from 1861","  The present is a companion paper to ""A contemporary look at Hermann Hankel's
+1861 pioneering work on Lagrangian fluid dynamics"" by Frisch, Grimberg and
+Villone (2017). Here we present the English translation of the 1861 prize
+manuscript from Göttingen University ""Zur allgemeinen Theorie der Bewegung
+der Flüssigkeiten"" (On the general theory of the motion of the fluids) of
+Hermann Hankel (1839-1873), which was originally submitted in Latin and then
+translated into German by the Author for publication. We also provide the
+English translation of two important reports on the manuscript, one written by
+Bernhard Riemann and the other by Wilhelm Eduard Weber, during the assessment
+process for the prize. Finally we give a short biography of Hermann Hankel with
+his complete bibliography.
+",0,1,1,0,0,0
+4829,Targeted Damage to Interdependent Networks,"  The giant mutually connected component (GMCC) of an interdependent or
+multiplex network collapses with a discontinuous hybrid transition under random
+damage to the network. If the nodes to be damaged are selected in a targeted
+way, the collapse of the GMCC may occur significantly sooner. Finding the
+minimal damage set which destroys the largest mutually connected component of a
+given interdependent network is a computationally prohibitive simultaneous
+optimization problem. We introduce a simple heuristic strategy -- Effective
+Multiplex Degree -- for targeted attack on interdependent networks that
+leverages the indirect damage inherent in multiplex networks to achieve a
+damage set smaller than that found by any other non computationally intensive
+algorithm. We show that the intuition from single layer networks that decycling
+(damage of the $2$-core) is the most effective way to destroy the giant
+component, does not carry over to interdependent networks, and in fact such
+approaches are worse than simply removing the highest degree nodes.
+",1,0,0,0,0,0
+4830,The limit of the Hermitian-Yang-Mills flow on reflexive sheaves,"  In this paper, we study the asymptotic behavior of the Hermitian-Yang-Mills
+flow on a reflexive sheaf. We prove that the limiting reflexive sheaf is
+isomorphic to the double dual of the graded sheaf associated to the
+Harder-Narasimhan-Seshadri filtration, this answers a question by Bando and
+Siu.
+",0,0,1,0,0,0
+4831,High-accuracy phase-field models for brittle fracture based on a new family of degradation functions,"  Phase-field approaches to fracture based on energy minimization principles
+have been rapidly gaining popularity in recent years, and are particularly
+well-suited for simulating crack initiation and growth in complex fracture
+networks. In the phase-field framework, the surface energy associated with
+crack formation is calculated by evaluating a functional defined in terms of a
+scalar order parameter and its gradients, which in turn describe the fractures
+in a diffuse sense following a prescribed regularization length scale. Imposing
+stationarity of the total energy leads to a coupled system of partial
+differential equations, one enforcing stress equilibrium and another governing
+phase-field evolution. The two equations are coupled through an energy
+degradation function that models the loss of stiffness in the bulk material as
+it undergoes damage. In the present work, we introduce a new parametric family
+of degradation functions aimed at increasing the accuracy of phase-field models
+in predicting critical loads associated with crack nucleation as well as the
+propagation of existing fractures. An additional goal is the preservation of
+linear elastic response in the bulk material prior to fracture. Through the
+analysis of several numerical examples, we demonstrate the superiority of the
+proposed family of functions to the classical quadratic degradation function
+that is used most often in the literature.
+",0,1,1,0,0,0
+4832,Straggler Mitigation in Distributed Optimization Through Data Encoding,"  Slow running or straggler tasks can significantly reduce computation speed in
+distributed computation. Recently, coding-theory-inspired approaches have been
+applied to mitigate the effect of straggling, through embedding redundancy in
+certain linear computational steps of the optimization algorithm, thus
+completing the computation without waiting for the stragglers. In this paper,
+we propose an alternate approach where we embed the redundancy directly in the
+data itself, and allow the computation to proceed completely oblivious to
+encoding. We propose several encoding schemes, and demonstrate that popular
+batch algorithms, such as gradient descent and L-BFGS, applied in a
+coding-oblivious manner, deterministically achieve sample path linear
+convergence to an approximate solution of the original problem, using an
+arbitrarily varying subset of the nodes at each iteration. Moreover, this
+approximation can be controlled by the amount of redundancy and the number of
+nodes used in each iteration. We provide experimental results demonstrating the
+advantage of the approach over uncoded and data replication strategies.
+",1,0,0,1,0,0
+4833,Inference Trees: Adaptive Inference with Exploration,"  We introduce inference trees (ITs), a new class of inference methods that
+build on ideas from Monte Carlo tree search to perform adaptive sampling in a
+manner that balances exploration with exploitation, ensures consistency, and
+alleviates pathologies in existing adaptive methods. ITs adaptively sample from
+hierarchical partitions of the parameter space, while simultaneously learning
+these partitions in an online manner. This enables ITs to not only identify
+regions of high posterior mass, but also maintain uncertainty estimates to
+track regions where significant posterior mass may have been missed. ITs can be
+based on any inference method that provides a consistent estimate of the
+marginal likelihood. They are particularly effective when combined with
+sequential Monte Carlo, where they capture long-range dependencies and yield
+improvements beyond proposal adaptation alone.
+",0,0,0,1,0,0
+4834,Application of backpropagation neural networks to both stages of fingerprinting based WIPS,"  We propose a scheme to employ backpropagation neural networks (BPNNs) for
+both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal
+strengths (FWIPS): radio map construction during the offline stage, and
+localization during the online stage. Given a training radio map (TRM), i.e., a
+set of coordinate vectors and associated WLAN/WiFi signal strengths of the
+available access points, a BPNN can be trained to output the expected signal
+strengths for any input position within the region of interest (BPNN-RM). This
+can be used to provide a continuous representation of the radio map and to
+filter, densify or decimate a discrete radio map. Correspondingly, the TRM can
+also be used to train another BPNN to output the expected position within the
+region of interest for any input vector of recorded signal strengths and thus
+carry out localization (BPNN-LA).Key aspects of the design of such artificial
+neural networks for a specific application are the selection of design
+parameters like the number of hidden layers and nodes within the network, and
+the training procedure. Summarizing extensive numerical simulations, based on
+real measurements in a testbed, we analyze the impact of these design choices
+on the performance of the BPNN and compare the results in particular to those
+obtained using the $k$ nearest neighbors ($k$NN) and weighted $k$ nearest
+neighbors approaches to FWIPS.
+",1,0,0,1,0,0
+4835,Bayesian Bootstraps for Massive Data,"  Recently, two scalable adaptations of the bootstrap have been proposed: the
+bag of little bootstraps (BLB; Kleiner et al., 2014) and the subsampled double
+bootstrap (SDB; Sengupta et al., 2016). In this paper, we introduce Bayesian
+bootstrap analogues to the BLB and SDB that have similar theoretical and
+computational properties, a strategy to perform lossless inference for a class
+of functionals of the Bayesian bootstrap, and briefly discuss extensions for
+Dirichlet Processes.
+",0,0,0,1,0,0
+4836,"Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner","  Impressive image captioning results are achieved in domains with plenty of
+training image and sentence pairs (e.g., MSCOCO). However, transferring to a
+target domain with significant domain shifts but no paired training data
+(referred to as cross-domain image captioning) remains largely unexplored. We
+propose a novel adversarial training procedure to leverage unpaired data in the
+target domain. Two critic networks are introduced to guide the captioner,
+namely domain critic and multi-modal critic. The domain critic assesses whether
+the generated sentences are indistinguishable from sentences in the target
+domain. The multi-modal critic assesses whether an image and its generated
+sentence are a valid pair. During training, the critics and captioner act as
+adversaries -- captioner aims to generate indistinguishable sentences, whereas
+critics aim at distinguishing them. The assessment improves the captioner
+through policy gradient updates. During inference, we further propose a novel
+critic-based planning method to select high-quality sentences without
+additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source
+domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k)
+as the target domains. Our method consistently performs well on all datasets.
+In particular, on CUB-200-2011, we achieve 21.8% CIDEr-D improvement after
+adaptation. Utilizing critics during inference further gives another 4.5%
+boost.
+",1,0,0,0,0,0
+4837,Faster Fuzzing: Reinitialization with Deep Neural Models,"  We improve the performance of the American Fuzzy Lop (AFL) fuzz testing
+framework by using Generative Adversarial Network (GAN) models to reinitialize
+the system with novel seed files. We assess performance based on the temporal
+rate at which we produce novel and unseen code paths. We compare this approach
+to seed file generation from a random draw of bytes observed in the training
+seed files. The code path lengths and variations were not sufficiently diverse
+to fully replace AFL input generation. However, augmenting native AFL with
+these additional code paths demonstrated improvements over AFL alone.
+Specifically, experiments showed the GAN was faster and more effective than the
+LSTM and out-performed a random augmentation strategy, as measured by the
+number of unique code paths discovered. GAN helps AFL discover 14.23% more code
+paths than the random strategy in the same amount of CPU time, finds 6.16% more
+unique code paths, and finds paths that are on average 13.84% longer. Using GAN
+shows promise as a reinitialization strategy for AFL to help the fuzzer
+exercise deep paths in software.
+",1,0,0,0,0,0
+4838,Contego: An Adaptive Framework for Integrating Security Tasks in Real-Time Systems,"  Embedded real-time systems (RTS) are pervasive. Many modern RTS are exposed
+to unknown security flaws, and threats to RTS are growing in both number and
+sophistication. However, until recently, cyber-security considerations were an
+afterthought in the design of such systems. Any security mechanisms integrated
+into RTS must (a) co-exist with the real- time tasks in the system and (b)
+operate without impacting the timing and safety constraints of the control
+logic. We introduce Contego, an approach to integrating security tasks into RTS
+without affecting temporal requirements. Contego is specifically designed for
+legacy systems, viz., the real-time control systems in which major alterations
+of the system parameters for constituent tasks is not always feasible. Contego
+combines the concept of opportunistic execution with hierarchical scheduling to
+maintain compatibility with legacy systems while still providing flexibility by
+allowing security tasks to operate in different modes. We also define a metric
+to measure the effectiveness of such integration. We evaluate Contego using
+synthetic workloads as well as with an implementation on a realistic embedded
+platform (an open- source ARM CPU running real-time Linux).
+",1,0,0,0,0,0
+4839,Second Order Analysis for Joint Source-Channel Coding with Markovian Source,"  We derive the second order rates of joint source-channel coding, whose source
+obeys an irreducible and ergodic Markov process when the channel is a discrete
+memoryless, while a previous study solved it only in a special case. We also
+compare the joint source-channel scheme with the separation scheme in the
+second order regime while a previous study made a notable comparison only with
+numerical calculation. To make these two notable progress, we introduce two
+kinds of new distribution families, switched Gaussian convolution distribution
+and *-product distribution, which are defined by modifying the Gaussian
+distribution.
+",1,0,1,0,0,0
+4840,Interface currents and magnetization in singlet-triplet superconducting heterostructures: Role of chiral and helical domains,"  Chiral and helical domain walls are generic defects of topological
+spin-triplet superconductors. We study theoretically the magnetic and transport
+properties of superconducting singlet-triplet-singlet heterostructure as a
+function of the phase difference between the singlet leads in the presence of
+chiral and helical domains inside the spin-triplet region. The local inversion
+symmetry breaking at the singlet-triplet interface allows the emergence of a
+static phase-controlled magnetization, and generally yields both spin and
+charge currents flowing along the edges. The parity of the domain wall number
+affects the relative orientation of the interface moments and currents, while
+in some cases the domain walls themselves contribute to spin and charge
+transport. We demonstrate that singlet-triplet heterostructures are a generic
+prototype to generate and control non-dissipative spin and charge effects,
+putting them in a broader class of systems exhibiting spin-Hall, anomalous Hall
+effects and similar phenomena. Features of the electron transport and magnetic
+effects at the interfaces can be employed to assess the presence of domains in
+chiral/helical superconductors.
+",0,1,0,0,0,0
+4841,Implicit Weight Uncertainty in Neural Networks,"  Modern neural networks tend to be overconfident on unseen, noisy or
+incorrectly labelled data and do not produce meaningful uncertainty measures.
+Bayesian deep learning aims to address this shortcoming with variational
+approximations (such as Bayes by Backprop or Multiplicative Normalising Flows).
+However, current approaches have limitations regarding flexibility and
+scalability. We introduce Bayes by Hypernet (BbH), a new method of variational
+approximation that interprets hypernetworks as implicit distributions. It
+naturally uses neural networks to model arbitrarily complex distributions and
+scales to modern deep learning architectures. In our experiments, we
+demonstrate that our method achieves competitive accuracies and predictive
+uncertainties on MNIST and a CIFAR5 task, while being the most robust against
+adversarial attacks.
+",1,0,0,1,0,0
+4842,A systematic analysis of the XMM-Newton background: III. Impact of the magnetospheric environment,"  A detailed characterization of the particle induced background is fundamental
+for many of the scientific objectives of the Athena X-ray telescope, thus an
+adequate knowledge of the background that will be encountered by Athena is
+desirable. Current X-ray telescopes have shown that the intensity of the
+particle induced background can be highly variable. Different regions of the
+magnetosphere can have very different environmental conditions, which can, in
+principle, differently affect the particle induced background detected by the
+instruments. We present results concerning the influence of the magnetospheric
+environment on the background detected by EPIC instrument onboard XMM-Newton
+through the estimate of the variation of the in-Field-of-View background excess
+along the XMM-Newton orbit. An important contribution to the XMM background,
+which may affect the Athena background as well, comes from soft proton flares.
+Along with the flaring component a low-intensity component is also present. We
+find that both show modest variations in the different magnetozones and that
+the soft proton component shows a strong trend with the distance from Earth.
+",0,1,0,0,0,0
+4843,DeepPermNet: Visual Permutation Learning,"  We present a principled approach to uncover the structure of visual data by
+solving a novel deep learning task coined visual permutation learning. The goal
+of this task is to find the permutation that recovers the structure of data
+from shuffled versions of it. In the case of natural images, this task boils
+down to recovering the original image from patches shuffled by an unknown
+permutation matrix. Unfortunately, permutation matrices are discrete, thereby
+posing difficulties for gradient-based methods. To this end, we resort to a
+continuous approximation of these matrices using doubly-stochastic matrices
+which we generate from standard CNN predictions using Sinkhorn iterations.
+Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet,
+an end-to-end CNN model for this task. The utility of DeepPermNet is
+demonstrated on two challenging computer vision problems, namely, (i) relative
+attributes learning and (ii) self-supervised representation learning. Our
+results show state-of-the-art performance on the Public Figures and OSR
+benchmarks for (i) and on the classification and segmentation tasks on the
+PASCAL VOC dataset for (ii).
+",1,0,0,0,0,0
+4844,ADE String Chains and Mirror Symmetry,"  6d superconformal field theories (SCFTs) are the SCFTs in the highest
+possible dimension. They can be geometrically engineered in F-theory by
+compactifying on non-compact elliptic Calabi-Yau manifolds. In this paper we
+focus on the class of SCFTs whose base geometry is determined by $-2$ curves
+intersecting according to ADE Dynkin diagrams and derive the corresponding
+mirror Calabi-Yau manifold. The mirror geometry is uniquely determined in terms
+of the mirror curve which has also an interpretation in terms of the
+Seiberg-Witten curve of the four-dimensional theory arising from torus
+compactification. Adding the affine node of the ADE quiver to the base
+geometry, we connect to recent results on SYZ mirror symmetry for the $A$ case
+and provide a physical interpretation in terms of little string theory. Our
+results, however, go beyond this case as our construction naturally covers the
+$D$ and $E$ cases as well.
+",0,0,1,0,0,0
+4845,(non)-automaticity of completely multiplicative sequences having negligible many non-trivial prime factors,"  In this article we consider the completely multiplicative sequences $(a_n)_{n
+\in \mathbf{N}}$ defined on a field $\mathbf{K}$ and satisfying $$\sum_{p| p
+\leq n, a_p \neq 1, p \in \mathbf{P}}\frac{1}{p}<\infty,$$ where $\mathbf{P}$
+is the set of prime numbers. We prove that if such sequences are automatic then
+they cannot have infinitely many prime numbers $p$ such that $a_{p}\neq 1$.
+Using this fact, we prove that if a completely multiplicative sequence
+$(a_n)_{n \in \mathbf{N}}$, vanishing or not, can be written in the form
+$a_n=b_n\chi_n$ such that $(b_n)_{n \in \mathbf{N}}$ is a non ultimately
+periodic, completely multiplicative automatic sequence satisfying the above
+condition, and $(\chi_n)_{n \in \mathbf{N}}$ is a Dirichlet character or a
+constant sequence, then there exists only one prime number $p$ such that $b_p
+\neq 1$ or $0$.
+",0,0,1,0,0,0
+4846,Timing Aware Dummy Metal Fill Methodology,"  In this paper, we analyzed parasitic coupling capacitance coming from dummy
+metal fill and its impact on timing. Based on the modeling, we proposed two
+approaches to minimize the timing impact from dummy metal fill. The first
+approach applies more spacing between critical nets and metal fill, while the
+second approach leverages the shielding effects of reference nets. Experimental
+results show consistent improvement compared to traditional metal fill method.
+",1,0,0,0,0,0
+4847,Asymptotic efficiency of the proportional compensation scheme for a large number of producers,"  We consider a manager, who allocates some fixed total payment amount between
+$N$ rational agents in order to maximize the aggregate production. The profit
+of $i$-th agent is the difference between the compensation (reward) obtained
+from the manager and the production cost. We compare (i) the \emph{normative}
+compensation scheme, where the manager enforces the agents to follow an optimal
+cooperative strategy; (ii) the \emph{linear piece rates} compensation scheme,
+where the manager announces an optimal reward per unit good; (iii) the
+\emph{proportional} compensation scheme, where agent's reward is proportional
+to his contribution to the total output. Denoting the correspondent total
+production levels by $s^*$, $\hat s$ and $\overline s$ respectively, where the
+last one is related to the unique Nash equilibrium, we examine the limits of
+the prices of anarchy $\mathscr A_N=s^*/\overline s$, $\mathscr A_N'=\hat
+s/\overline s$ as $N\to\infty$. These limits are calculated for the cases of
+identical convex costs with power asymptotics at the origin, and for power
+costs, corresponding to the Coob-Douglas and generalized CES production
+functions with decreasing returns to scale. Our results show that
+asymptotically no performance is lost in terms of $\mathscr A'_N$, and in terms
+of $\mathscr A_N$ the loss does not exceed $31\%$.
+",1,0,0,0,0,0
+4848,Non-equilibrium statistical mechanics of continuous attractors,"  Continuous attractors have been used to understand recent neuroscience
+experiments where persistent activity patterns encode internal representations
+of external attributes like head direction or spatial location. However, the
+conditions under which the emergent bump of neural activity in such networks
+can be manipulated by space and time-dependent external sensory or motor
+signals are not understood. Here, we find fundamental limits on how rapidly
+internal representations encoded along continuous attractors can be updated by
+an external signal. We apply these results to place cell networks to derive a
+velocity-dependent non-equilibrium memory capacity in neural networks.
+",0,0,0,0,1,0
+4849,Some results on the existence of t-all-or-nothing transforms over arbitrary alphabets,"  A $(t, s, v)$-all-or-nothing transform is a bijective mapping defined on
+$s$-tuples over an alphabet of size $v$, which satisfies the condition that the
+values of any $t$ input co-ordinates are completely undetermined, given only
+the values of any $s-t$ output co-ordinates. The main question we address in
+this paper is: for which choices of parameters does a $(t, s,
+v)$-all-or-nothing transform (AONT) exist? More specifically, if we fix $t$ and
+$v$, we want to determine the maximum integer $s$ such that a $(t, s, v)$-AONT
+exists. We mainly concentrate on the case $t=2$ for arbitrary values of $v$,
+where we obtain various necessary as well as sufficient conditions for
+existence of these objects. We consider both linear and general (linear or
+nonlinear) AONT. We also show some connections between AONT, orthogonal arrays
+and resilient functions.
+",1,0,1,0,0,0
+4850,Exhaustive Exploration of the Failure-oblivious Computing Search Space,"  High-availability of software systems requires automated handling of crashes
+in presence of errors. Failure-oblivious computing is one technique that aims
+to achieve high availability. We note that failure-obliviousness has not been
+studied in depth yet, and there is very few study that helps understand why
+failure-oblivious techniques work. In order to make failure-oblivious computing
+to have an impact in practice, we need to deeply understand failure-oblivious
+behaviors in software. In this paper, we study, design and perform an
+experiment that analyzes the size and the diversity of the failure-oblivious
+behaviors. Our experiment consists of exhaustively computing the search space
+of 16 field failures of large-scale open-source Java software. The outcome of
+this experiment is a much better understanding of what really happens when
+failure-oblivious computing is used, and this opens new promising research
+directions.
+",1,0,0,0,0,0
+4851,Theoretical Accuracy in Cosmological Growth Estimation,"  We elucidate the importance of the consistent treatment of gravity-model
+specific non-linearities when estimating the growth of cosmological structures
+from redshift space distortions (RSD). Within the context of standard
+perturbation theory (SPT), we compare the predictions of two theoretical
+templates with redshift space data from COLA (COmoving Lagrangian Acceleration)
+simulations in the normal branch of DGP gravity (nDGP) and General Relativity
+(GR). Using COLA for these comparisons is validated using a suite of full
+N-body simulations for the same theories. The two theoretical templates
+correspond to the standard general relativistic perturbation equations and
+those same equations modelled within nDGP. Gravitational clustering non-linear
+effects are accounted for by modelling the power spectrum up to one loop order
+and redshift space clustering anisotropy is modelled using the Taruya,
+Nishimichi and Saito (TNS) RSD model. Using this approach, we attempt to
+recover the simulation's fiducial logarithmic growth parameter $f$. By
+assigning the simulation data with errors representing an idealised survey with
+a volume of $10\mbox{Gpc}^3/h^3$, we find the GR template is unable to recover
+fiducial $f$ to within 1$\sigma$ at $z=1$ when we match the data up to $k_{\rm
+max}=0.195h$/Mpc. On the other hand, the DGP template recovers the fiducial
+value within $1\sigma$. Further, we conduct the same analysis for sets of mock
+data generated for generalised models of modified gravity using SPT, where
+again we analyse the GR template's ability to recover the fiducial value. We
+find that for models with enhanced gravitational non-linearity, the theoretical
+bias of the GR template becomes significant for stage IV surveys. Thus, we show
+that for the future large data volume galaxy surveys, the self-consistent
+modelling of non-GR gravity scenarios will be crucial in constraining theory
+parameters.
+",0,1,0,0,0,0
+4852,Model-Robust Counterfactual Prediction Method,"  We develop a novel method for counterfactual analysis based on observational
+data using prediction intervals for units under different exposures. Unlike
+methods that target heterogeneous or conditional average treatment effects of
+an exposure, the proposed approach aims to take into account the irreducible
+dispersions of counterfactual outcomes so as to quantify the relative impact of
+different exposures. The prediction intervals are constructed in a
+distribution-free and model-robust manner based on the conformal prediction
+approach. The computational obstacles to this approach are circumvented by
+leveraging properties of a tuning-free method that learns sparse additive
+predictor models for counterfactual outcomes. The method is illustrated using
+both real and synthetic data.
+",0,0,1,1,0,0
+4853,Exponentiated Generalized Pareto Distribution: Properties and applications towards Extreme Value Theory,"  The Generalized Pareto Distribution (GPD) plays a central role in modelling
+heavy tail phenomena in many applications. Applying the GPD to actual datasets
+however is a non-trivial task. One common way suggested in the literature to
+investigate the tail behaviour is to take logarithm to the original dataset in
+order to reduce the sample variability. Inspired by this, we propose and study
+the Exponentiated Generalized Pareto Distribution (exGPD), which is created via
+log-transform of the GPD variable. After introducing the exGPD we derive
+various distributional quantities, including the moment generating function,
+tail risk measures. As an application we also develop a plot as an alternative
+to the Hill plot to identify the tail index of heavy tailed datasets, based on
+the moment matching for the exGPD. Various numerical analyses with both
+simulated and actual datasets show that the proposed plot works well.
+",0,0,1,1,0,0
+4854,Learning with Average Top-k Loss,"  In this work, we introduce the {\em average top-$k$} (\atk) loss as a new
+aggregate loss for supervised learning, which is the average over the $k$
+largest individual losses over a training dataset. We show that the \atk loss
+is a natural generalization of the two widely used aggregate losses, namely the
+average loss and the maximum loss, but can combine their advantages and
+mitigate their drawbacks to better adapt to different data distributions.
+Furthermore, it remains a convex function over all individual losses, which can
+lead to convex optimization problems that can be solved effectively with
+conventional gradient-based methods. We provide an intuitive interpretation of
+the \atk loss based on its equivalent effect on the continuous individual loss
+functions, suggesting that it can reduce the penalty on correctly classified
+data. We further give a learning theory analysis of \matk learning on the
+classification calibration of the \atk loss and the error bounds of \atk-SVM.
+We demonstrate the applicability of minimum average top-$k$ learning for binary
+classification and regression using synthetic and real datasets.
+",1,0,0,1,0,0
+4855,Reflexive polytopes arising from perfect graphs,"  Reflexive polytopes form one of the distinguished classes of lattice
+polytopes. Especially reflexive polytopes which possess the integer
+decomposition property are of interest. In the present paper, by virtue of the
+algebraic technique on Grönbner bases, a new class of reflexive polytopes
+which possess the integer decomposition property and which arise from perfect
+graphs will be presented. Furthermore, the Ehrhart $\delta$-polynomials of
+these polytopes will be studied.
+",0,0,1,0,0,0
+4856,Meta Networks,"  Neural networks have been successfully applied in applications with a large
+amount of labeled data. However, the task of rapid generalization on new
+concepts with small training data while preserving performances on previously
+learned ones still presents a significant challenge to neural network models.
+In this work, we introduce a novel meta learning method, Meta Networks
+(MetaNet), that learns a meta-level knowledge across tasks and shifts its
+inductive biases via fast parameterization for rapid generalization. When
+evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve
+a near human-level performance and outperform the baseline approaches by up to
+6% accuracy. We demonstrate several appealing properties of MetaNet relating to
+generalization and continual learning.
+",1,0,0,1,0,0
+4857,Variable selection for clustering with Gaussian mixture models: state of the art,"  The mixture models have become widely used in clustering, given its
+probabilistic framework in which its based, however, for modern databases that
+are characterized by their large size, these models behave disappointingly in
+setting out the model, making essential the selection of relevant variables for
+this type of clustering. After recalling the basics of clustering based on a
+model, this article will examine the variable selection methods for model-based
+clustering, as well as presenting opportunities for improvement of these
+methods.
+",1,0,0,1,0,0
+4858,Analysing Magnetism Using Scanning SQUID Microscopy,"  Scanning superconducting quantum interference device microscopy (SSM) is a
+scanning probe technique that images local magnetic flux, which allows for
+mapping of magnetic fields with high field and spatial accuracy. Many studies
+involving SSM have been published in the last decades, using SSM to make
+qualitative statements about magnetism. However, quantitative analysis using
+SSM has received less attention. In this work, we discuss several aspects of
+interpreting SSM images and methods to improve quantitative analysis. First, we
+analyse the spatial resolution and how it depends on several factors. Second,
+we discuss the analysis of SSM scans and the information obtained from the SSM
+data. Using simulations, we show how signals evolve as a function of changing
+scan height, SQUID loop size, magnetization strength and orientation. We also
+investigated 2-dimensional autocorrelation analysis to extract information
+about the size, shape and symmetry of magnetic features. Finally, we provide an
+outlook on possible future applications and improvements.
+",0,1,0,0,0,0
+4859,Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems,"  Machine Learning models incorporating multiple layered learning networks have
+been seen to provide effective models for various classification problems. The
+resulting optimization problem to solve for the optimal vector minimizing the
+empirical risk is, however, highly nonconvex. This alone presents a challenge
+to application and development of appropriate optimization algorithms for
+solving the problem. However, in addition, there are a number of interesting
+problems for which the objective function is non- smooth and nonseparable. In
+this paper, we summarize the primary challenges involved, the state of the art,
+and present some numerical results on an interesting and representative class
+of problems.
+",0,0,0,1,0,0
+4860,On the essential self-adjointness of singular sub-Laplacians,"  We prove a general essential self-adjointness criterion for sub-Laplacians on
+complete sub-Riemannian manifolds, defined with respect to singular measures.
+As a consequence, we show that the intrinsic sub-Laplacian (i.e. defined w.r.t.
+Popp's measure) is essentially self-adjoint on the equiregular connected
+components of a sub-Riemannian manifold. This result holds under mild
+regularity assumptions on the singular region, and when the latter does not
+contain characteristic points.
+",0,0,1,0,0,0
+4861,Are Saddles Good Enough for Deep Learning?,"  Recent years have seen a growing interest in understanding deep neural
+networks from an optimization perspective. It is understood now that converging
+to low-cost local minima is sufficient for such models to become effective in
+practice. However, in this work, we propose a new hypothesis based on recent
+theoretical findings and empirical studies that deep neural network models
+actually converge to saddle points with high degeneracy. Our findings from this
+work are new, and can have a significant impact on the development of gradient
+descent based methods for training deep networks. We validated our hypotheses
+using an extensive experimental evaluation on standard datasets such as MNIST
+and CIFAR-10, and also showed that recent efforts that attempt to escape
+saddles finally converge to saddles with high degeneracy, which we define as
+`good saddles'. We also verified the famous Wigner's Semicircle Law in our
+experimental results.
+",1,0,0,1,0,0
+4862,Monotonicity and enclosure methods for the p-Laplace equation,"  We show that the convex hull of a monotone perturbation of a homogeneous
+background conductivity in the $p$-conductivity equation is determined by
+knowledge of the nonlinear Dirichlet-Neumann operator. We give two independent
+proofs, one of which is based on the monotonicity method and the other on the
+enclosure method. Our results are constructive and require no jump or
+smoothness properties on the conductivity perturbation or its support.
+",0,0,1,0,0,0
+4863,Tension and chemical efficiency of Myosin-II motors,"  Recent experiments demonstrate that molecular motors from the Myosin II
+family serve as cross-links inducing active tension in the cytoskeletal
+network. Here we revise the Brownian ratchet model, previously studied in the
+context of active transport along polymer tracks, in setups resembling a motor
+in a polymer network, also taking into account the effect of electrostatic
+changes in the motor heads. We explore important mechanical quantities and show
+that such a model is also capable of mechanosensing. Finally, we introduce a
+novel efficiency based on excess heat production by the chemical cycle which is
+directly related to the active tension the motor exerts. The chemical
+efficiencies differ considerably for motors with a different number of heads,
+while their mechanical properties remain qualitatively similar. For motors with
+a small number of heads, the chemical efficiency is maximal when they are
+frustrated, a trait that is not found in larger motors.
+",0,1,0,0,0,0
+4864,Token-based Function Computation with Memory,"  In distributed function computation, each node has an initial value and the
+goal is to compute a function of these values in a distributed manner. In this
+paper, we propose a novel token-based approach to compute a wide class of
+target functions to which we refer as ""Token-based function Computation with
+Memory"" (TCM) algorithm. In this approach, node values are attached to tokens
+and travel across the network. Each pair of travelling tokens would coalesce
+when they meet, forming a token with a new value as a function of the original
+token values. In contrast to the Coalescing Random Walk (CRW) algorithm, where
+token movement is governed by random walk, meeting of tokens in our scheme is
+accelerated by adopting a novel chasing mechanism. We proved that, compared to
+the CRW algorithm, the TCM algorithm results in a reduction of time complexity
+by a factor of at least $\sqrt{n/\log(n)}$ in Erdös-Renyi and complete
+graphs, and by a factor of $\log(n)/\log(\log(n))$ in torus networks.
+Simulation results show that there is at least a constant factor improvement in
+the message complexity of TCM algorithm in all considered topologies.
+Robustness of the CRW and TCM algorithms in the presence of node failure is
+analyzed. We show that their robustness can be improved by running multiple
+instances of the algorithms in parallel.
+",1,0,0,1,0,0
+4865,Simple property of heterogeneous aspiration dynamics: Beyond weak selection,"  How individuals adapt their behavior in cultural evolution remains elusive.
+Theoretical studies have shown that the update rules chosen to model individual
+decision making can dramatically modify the evolutionary outcome of the
+population as a whole. This hints at the complexities of considering the
+personality of individuals in a population, where each one uses its own rule.
+Here, we investigate whether and how heterogeneity in the rules of behavior
+update alters the evolutionary outcome. We assume that individuals update
+behaviors by aspiration-based self-evaluation and they do so in their own ways.
+Under weak selection, we analytically reveal a simple property that holds for
+any two-strategy multi-player games in well-mixed populations and on regular
+graphs: the evolutionary outcome in a population with heterogeneous update
+rules is the weighted average of the outcomes in the corresponding homogeneous
+populations, and the associated weights are the frequencies of each update rule
+in the heterogeneous population. Beyond weak selection, we show that this
+property holds for public goods games. Our finding implies that heterogeneous
+aspiration dynamics is additive. This additivity greatly reduces the complexity
+induced by the underlying individual heterogeneity. Our work thus provides an
+efficient method to calculate evolutionary outcomes under heterogeneous update
+rules.
+",0,0,0,0,1,0
+4866,Warm dark matter and the ionization history of the Universe,"  In warm dark matter scenarios structure formation is suppressed on small
+scales with respect to the cold dark matter case, reducing the number of
+low-mass halos and the fraction of ionized gas at high redshifts and thus,
+delaying reionization. This has an impact on the ionization history of the
+Universe and measurements of the optical depth to reionization, of the
+evolution of the global fraction of ionized gas and of the thermal history of
+the intergalactic medium, can be used to set constraints on the mass of the
+dark matter particle. However, the suppression of the fraction of ionized
+medium in these scenarios can be partly compensated by varying other
+parameters, as the ionization efficiency or the minimum mass for which halos
+can host star-forming galaxies. Here we use different data sets regarding the
+ionization and thermal histories of the Universe and, taking into account the
+degeneracies from several astrophysical parameters, we obtain a lower bound on
+the mass of thermal warm dark matter candidates of $m_X > 1.3$ keV, or $m_s >
+5.5$ keV for the case of sterile neutrinos non-resonantly produced in the early
+Universe, both at 90\% confidence level.
+",0,1,0,0,0,0
+4867,High quality factor manganese-doped aluminum lumped-element kinetic inductance detectors sensitive to frequencies below 100 GHz,"  Aluminum lumped-element kinetic inductance detectors (LEKIDs) sensitive to
+millimeter-wave photons have been shown to exhibit high quality factors, making
+them highly sensitive and multiplexable. The superconducting gap of aluminum
+limits aluminum LEKIDs to photon frequencies above 100 GHz. Manganese-doped
+aluminum (Al-Mn) has a tunable critical temperature and could therefore be an
+attractive material for LEKIDs sensitive to frequencies below 100 GHz if the
+internal quality factor remains sufficiently high when manganese is added to
+the film. To investigate, we measured some of the key properties of Al-Mn
+LEKIDs. A prototype eight-element LEKID array was fabricated using a 40 nm
+thick film of Al-Mn deposited on a 500 {\mu}m thick high-resistivity,
+float-zone silicon substrate. The manganese content was 900 ppm, the measured
+$T_c = 694\pm1$ mK, and the resonance frequencies were near 150 MHz. Using
+measurements of the forward scattering parameter $S_{21}$ at various bath
+temperatures between 65 and 250 mK, we determined that the Al-Mn LEKIDs we
+fabricated have internal quality factors greater than $2 \times 10^5$, which is
+high enough for millimeter-wave astrophysical observations. In the dark
+conditions under which these devices were measured, the fractional frequency
+noise spectrum shows a shallow slope that depends on bath temperature and probe
+tone amplitude, which could be two-level system noise. The anticipated white
+photon noise should dominate this level of low-frequency noise when the
+detectors are illuminated with millimeter-waves in future measurements. The
+LEKIDs responded to light pulses from a 1550 nm light-emitting diode, and we
+used these light pulses to determine that the quasiparticle lifetime is 60
+{\mu}s.
+",0,1,0,0,0,0
+4868,Tetramer Bound States in Heteronuclear Systems,"  We calculate the universal spectrum of trimer and tetramer states in
+heteronuclear mixtures of ultracold atoms with different masses in the vicinity
+of the heavy-light dimer threshold. To extract the energies, we solve the
+three- and four-body problem for simple two- and three-body potentials tuned to
+the universal region using the Gaussian expansion method. We focus on the case
+of one light particle of mass $m$ and two or three heavy bosons of mass $M$
+with resonant heavy-light interactions. We find that trimer and tetramer cross
+into the heavy-light dimer threshold at almost the same point and that as the
+mass ratio $M/m$ decreases, the distance between the thresholds for trimer and
+tetramer states becomes smaller. We also comment on the possibility of
+observing exotic three-body states consisting of a dimer and two atoms in this
+region and compare with previous work.
+",0,1,0,0,0,0
+4869,Dark Energy Cosmological Models with General forms of Scale Factor,"  In this paper, we have constructed dark energy models in an anisotropic
+Bianchi-V space-time and studied the role of anisotropy in the evolution of
+dark energy. We have considered anisotropic dark energy fluid with different
+pressure gradients along different spatial directions. In order to obtain a
+deterministic solution, we have considered three general forms of scale factor.
+The different forms of scale factors considered here produce time varying
+deceleration parameters in all the cases that simulates the cosmic transition.
+The variable equation of state (EoS) parameter, skewness parameters for all the
+models are obtained and analyzed. The physical properties of the models are
+also discussed.
+",0,1,0,0,0,0
+4870,Mutual Kernel Matrix Completion,"  With the huge influx of various data nowadays, extracting knowledge from them
+has become an interesting but tedious task among data scientists, particularly
+when the data come in heterogeneous form and have missing information. Many
+data completion techniques had been introduced, especially in the advent of
+kernel methods. However, among the many data completion techniques available in
+the literature, studies about mutually completing several incomplete kernel
+matrices have not been given much attention yet. In this paper, we present a
+new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that
+tackles this problem of mutually inferring the missing entries of multiple
+kernel matrices by combining the notions of data fusion and kernel matrix
+completion, applied on biological data sets to be used for classification task.
+We first introduced an objective function that will be minimized by exploiting
+the EM algorithm, which in turn results to an estimate of the missing entries
+of the kernel matrices involved. The completed kernel matrices are then
+combined to produce a model matrix that can be used to further improve the
+obtained estimates. An interesting result of our study is that the E-step and
+the M-step are given in closed form, which makes our algorithm efficient in
+terms of time and memory. After completion, the (completed) kernel matrices are
+then used to train an SVM classifier to test how well the relationships among
+the entries are preserved. Our empirical results show that the proposed
+algorithm bested the traditional completion techniques in preserving the
+relationships among the data points, and in accurately recovering the missing
+kernel matrix entries. By far, MKMC offers a promising solution to the problem
+of mutual estimation of a number of relevant incomplete kernel matrices.
+",1,0,0,1,0,0
+4871,Quantum Klein Space and Superspace,"  We give an algebraic quantization, in the sense of quantum groups, of the
+complex Minkowski space, and we examine the real forms corresponding to the
+signatures $(3,1)$, $(2,2)$, $(4,0)$, constructing the corresponding quantum
+metrics and providing an explicit presentation of the quantized coordinate
+algebras. In particular, we focus on the Kleinian signature $(2,2)$. The
+quantizations of the complex and real spaces come together with a coaction of
+the quantizations of the respective symmetry groups. We also extend such
+quantizations to the $\mathcal{N}=1$ supersetting.
+",0,0,1,0,0,0
+4872,Bayesian Lasso Posterior Sampling via Parallelized Measure Transport,"  It is well known that the Lasso can be interpreted as a Bayesian posterior
+mode estimate with a Laplacian prior. Obtaining samples from the full posterior
+distribution, the Bayesian Lasso, confers major advantages in performance as
+compared to having only the Lasso point estimate. Traditionally, the Bayesian
+Lasso is implemented via Gibbs sampling methods which suffer from lack of
+scalability, unknown convergence rates, and generation of samples that are
+necessarily correlated. We provide a measure transport approach to generate
+i.i.d samples from the posterior by constructing a transport map that
+transforms a sample from the Laplacian prior into a sample from the posterior.
+We show how the construction of this transport map can be parallelized into
+modules that iteratively solve Lasso problems and perform closed-form linear
+algebra updates. With this posterior sampling method, we perform maximum
+likelihood estimation of the Lasso regularization parameter via the EM
+algorithm. We provide comparisons to traditional Gibbs samplers using the
+diabetes dataset of Efron et al. Lastly, we give an example implementation on a
+computing system that leverages parallelization, a graphics processing unit,
+whose execution time has much less dependence on dimension as compared to a
+standard implementation.
+",0,0,0,1,0,0
+4873,Endogeneous Dynamics of Intraday Liquidity,"  In this paper we investigate the endogenous information contained in four
+liquidity variables at a five minutes time scale on equity markets around the
+world: the traded volume, the bid-ask spread, the volatility and the volume at
+first limits of the orderbook. In the spirit of Granger causality, we measure
+the level of information by the level of accuracy of linear autoregressive
+models. This empirical study is carried out on a dataset of more than 300
+stocks from four different markets (US, UK, Japan and Hong Kong) from a period
+of over five years. We discuss the obtained performances of autoregressive (AR)
+models on stationarized versions of the variables, focusing on explaining the
+observed differences between stocks.
+Since empirical studies are often conducted at this time scale, we believe it
+is of paramount importance to document endogenous dynamics in a simple
+framework with no addition of supplemental information. Our study can hence be
+used as a benchmark to identify exogenous effects. On the other hand, most
+optimal trading frameworks (like the celebrated Almgren and Chriss one), focus
+on computing an optimal trading speed at a frequency close to the one we
+consider. Such frameworks very often take i.i.d. assumptions on liquidity
+variables; this paper document the auto-correlations emerging from real data,
+opening the door to new developments in optimal trading.
+",0,0,0,0,0,1
+4874,Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks,"  Data diversity is critical to success when training deep learning models.
+Medical imaging data sets are often imbalanced as pathologic findings are
+generally rare, which introduces significant challenges when training deep
+learning models. In this work, we propose a method to generate synthetic
+abnormal MRI images with brain tumors by training a generative adversarial
+network using two publicly available data sets of brain MRI. We demonstrate two
+unique benefits that the synthetic images provide. First, we illustrate
+improved performance on tumor segmentation by leveraging the synthetic images
+as a form of data augmentation. Second, we demonstrate the value of generative
+models as an anonymization tool, achieving comparable tumor segmentation
+results when trained on the synthetic data versus when trained on real subject
+data. Together, these results offer a potential solution to two of the largest
+challenges facing machine learning in medical imaging, namely the small
+incidence of pathological findings, and the restrictions around sharing of
+patient data.
+",0,0,0,1,0,0
+4875,Adaptive Feature Representation for Visual Tracking,"  Robust feature representation plays significant role in visual tracking.
+However, it remains a challenging issue, since many factors may affect the
+experimental performance. The existing method which combine different features
+by setting them equally with the fixed weight could hardly solve the issues,
+due to the different statistical properties of different features across
+various of scenarios and attributes. In this paper, by exploiting the internal
+relationship among these features, we develop a robust method to construct a
+more stable feature representation. More specifically, we utilize a co-training
+paradigm to formulate the intrinsic complementary information of multi-feature
+template into the efficient correlation filter framework. We test our approach
+on challenging se- quences with illumination variation, scale variation,
+deformation etc. Experimental results demonstrate that the proposed method
+outperforms state-of-the-art methods favorably.
+",1,0,0,0,0,0
+4876,An analysis of the SPARSEVA estimate for the finite sample data case,"  In this paper, we develop an upper bound for the SPARSEVA (SPARSe Estimation
+based on a VAlidation criterion) estimation error in a general scheme, i.e.,
+when the cost function is strongly convex and the regularized norm is
+decomposable for a pair of subspaces. We show how this general bound can be
+applied to a sparse regression problem to obtain an upper bound for the
+traditional SPARSEVA problem. Numerical results are used to illustrate the
+effectiveness of the suggested bound.
+",0,0,1,1,0,0
+4877,The Remarkable Similarity of Massive Galaxy Clusters From z~0 to z~1.9,"  We present the results of a Chandra X-ray survey of the 8 most massive galaxy
+clusters at z>1.2 in the South Pole Telescope 2500 deg^2 survey. We combine
+this sample with previously-published Chandra observations of 49 massive
+X-ray-selected clusters at 0<z<0.1 and 90 SZ-selected clusters at 0.25<z<1.2 to
+constrain the evolution of the intracluster medium (ICM) over the past ~10 Gyr.
+We find that the bulk of the ICM has evolved self similarly over the full
+redshift range probed here, with the ICM density at r>0.2R500 scaling like
+E(z)^2. In the centers of clusters (r<0.1R500), we find significant deviations
+from self similarity (n_e ~ E(z)^{0.1+/-0.5}), consistent with no redshift
+dependence. When we isolate clusters with over-dense cores (i.e., cool cores),
+we find that the average over-density profile has not evolved with redshift --
+that is, cool cores have not changed in size, density, or total mass over the
+past ~9-10 Gyr. We show that the evolving ""cuspiness"" of clusters in the X-ray,
+reported by several previous studies, can be understood in the context of a
+cool core with fixed properties embedded in a self similarly-evolving cluster.
+We find no measurable evolution in the X-ray morphology of massive clusters,
+seemingly in tension with the rapidly-rising (with redshift) rate of major
+mergers predicted by cosmological simulations. We show that these two results
+can be brought into agreement if we assume that the relaxation time after a
+merger is proportional to the crossing time, since the latter is proportional
+to H(z)^(-1).
+",0,1,0,0,0,0
+4878,Rigorous estimates for the relegation algorithm,"  We revisit the relegation algorithm by Deprit et al. (Celest. Mech. Dyn.
+Astron. 79:157-182, 2001) in the light of the rigorous Nekhoroshev's like
+theory. This relatively recent algorithm is nowadays widely used for
+implementing closed form analytic perturbation theories, as it generalises the
+classical Birkhoff normalisation algorithm. The algorithm, here briefly
+explained by means of Lie transformations, has been so far introduced and used
+in a formal way, i.e. without providing any rigorous convergence or asymptotic
+estimates. The overall aim of this paper is to find such quantitative estimates
+and to show how the results about stability over exponentially long times can
+be recovered in a simple and effective way, at least in the non-resonant case.
+",0,1,0,0,0,0
+4879,Linear Pentapods with a Simple Singularity Variety,"  There exists a bijection between the configuration space of a linear pentapod
+and all points $(u,v,w,p_x,p_y,p_z)\in\mathbb{R}^{6}$ located on the singular
+quadric $\Gamma: u^2+v^2+w^2=1$, where $(u,v,w)$ determines the orientation of
+the linear platform and $(p_x,p_y,p_z)$ its position. Then the set of all
+singular robot configurations is obtained by intersecting $\Gamma$ with a cubic
+hypersurface $\Sigma$ in $\mathbb{R}^{6}$, which is only quadratic in the
+orientation variables and position variables, respectively. This article
+investigates the restrictions to be imposed on the design of this mechanism in
+order to obtain a reduction in degree. In detail we study the cases where
+$\Sigma$ is (1) linear in position variables, (2) linear in orientation
+variables and (3) quadratic in total. The resulting designs of linear pentapods
+have the advantage of considerably simplified computation of singularity-free
+spheres in the configuration space. Finally we propose three kinematically
+redundant designs of linear pentapods with a simple singularity surface.
+",1,0,0,0,0,0
+4880,Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error,"  Neural networks, a central tool in machine learning, have demonstrated
+remarkable, high fidelity performance on image recognition and classification
+tasks. These successes evince an ability to accurately represent high
+dimensional functions, potentially of great use in computational and applied
+mathematics. That said, there are few rigorous results about the representation
+error and trainability of neural networks. Here we characterize both the error
+and the scaling of the error with the size of the network by reinterpreting the
+standard optimization algorithm used in machine learning applications,
+stochastic gradient descent, as the evolution of a particle system with
+interactions governed by a potential related to the objective or ""loss""
+function used to train the network. We show that, when the number $n$ of
+parameters is large, the empirical distribution of the particles descends on a
+convex landscape towards a minimizer at a rate independent of $n$. We establish
+a Law of Large Numbers and a Central Limit Theorem for the empirical
+distribution, which together show that the approximation error of the network
+universally scales as $O(n^{-1})$. Remarkably, these properties do not depend
+on the dimensionality of the domain of the function that we seek to represent.
+Our analysis also quantifies the scale and nature of the noise introduced by
+stochastic gradient descent and provides guidelines for the step size and batch
+size to use when training a neural network. We illustrate our findings on
+examples in which we train neural network to learn the energy function of the
+continuous 3-spin model on the sphere. The approximation error scales as our
+analysis predicts in as high a dimension as $d=25$.
+",0,0,0,1,0,0
+4881,Adaptive Similar Triangles Method: a Stable Alternative to Sinkhorn's Algorithm for Regularized Optimal Transport,"  In this paper, we are motivated by two important applications:
+entropy-regularized optimal transport problem and road or IP traffic demand
+matrix estimation by entropy model. Both of them include solving a special type
+of optimization problem with linear equality constraints and objective given as
+a sum of an entropy regularizer and a linear function. It is known that the
+state-of-the-art solvers for this problem, which are based on Sinkhorn's method
+(also known as RSA or balancing method), can fail to work, when the
+entropy-regularization parameter is small. We consider the above optimization
+problem as a particular instance of a general strongly convex optimization
+problem with linear constraints. We propose a new algorithm to solve this
+general class of problems. Our approach is based on the transition to the dual
+problem. First, we introduce a new accelerated gradient method with adaptive
+choice of gradient's Lipschitz constant. Then, we apply this method to the dual
+problem and show, how to reconstruct an approximate solution to the primal
+problem with provable convergence rate. We prove the rate $O(1/k^2)$, $k$ being
+the iteration counter, both for the absolute value of the primal objective
+residual and constraints infeasibility. Our method has similar to Sinkhorn's
+method complexity of each iteration, but is faster and more stable numerically,
+when the regularization parameter is small. We illustrate the advantage of our
+method by numerical experiments for the two mentioned applications. We show
+that there exists a threshold, such that, when the regularization parameter is
+smaller than this threshold, our method outperforms the Sinkhorn's method in
+terms of computation time.
+",0,0,1,0,0,0
+4882,Images of Ideals under Derivations and $\mathcal E$-Derivations of Univariate Polynomial Algebras over a Field of Characteristic Zero,"  Let $K$ be a field of characteristic zero and $x$ a free variable. A
+$K$-$\mathcal E$-derivation of $K[x]$ is a $K$-linear map of the form
+$\operatorname{I}-\phi$ for some $K$-algebra endomorphism $\phi$ of $K[x]$,
+where $\operatorname{I}$ denotes the identity map of $K[x]$. In this paper we
+study the image of an ideal of $K[x]$ under some $K$-derivations and
+$K$-$\mathcal E$-derivations of $K[x]$. We show that the LFED conjecture
+proposed in [Z4] holds for all $K$-$\mathcal E$-derivations and all locally
+finite $K$-derivations of $K[x]$. We also show that the LNED conjecture
+proposed in [Z4] holds for all locally nilpotent $K$-derivations of $K[x]$, and
+also for all locally nilpotent $K$-$\mathcal E$-derivations of $K[x]$ and the
+ideals $uK[x]$ such that either $u=0$, or $\operatorname{deg}\, u\le 1$, or $u$
+has at least one repeated root in the algebraic closure of $K$. As a
+bi-product, the homogeneous Mathieu subspaces (Mathieu-Zhao spaces) of the
+univariate polynomial algebra over an arbitrary field have also been
+classified.
+",0,0,1,0,0,0
+4883,Maximum genus of the Jenga like configurations,"  We treat the boundary of the union of blocks in the Jenga game as a surface
+with a polyhedral structure and consider its genus. We generalize the game and
+determine the maximum genus of the generalized game.
+",0,0,1,0,0,0
+4884,A Decidable Very Expressive Description Logic for Databases (Extended Version),"  We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally
+closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples
+(generalising the positional notation), projections of relations, and global
+and local objectification of relations, able to express inclusion, functional,
+key, and external uniqueness dependencies. The logic is equipped with both TBox
+and ABox axioms. We show how a simple syntactic restriction on the appearance
+of projections sharing common attributes in a $\mathcal{DLR}^+$ knowledge base
+makes reasoning in the language decidable with the same computational
+complexity as $\mathcal{DLR}$. The obtained $\mathcal{DLR}^\pm$ n-ary
+description logic is able to encode more thoroughly conceptual data models such
+as EER, UML, and ORM.
+",1,0,0,0,0,0
+4885,Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction,"  Developing a Brain-Computer Interface~(BCI) for seizure prediction can help
+epileptic patients have a better quality of life. However, there are many
+difficulties and challenges in developing such a system as a real-life support
+for patients. Because of the nonstationary nature of EEG signals, normal and
+seizure patterns vary across different patients. Thus, finding a group of
+manually extracted features for the prediction task is not practical. Moreover,
+when using implanted electrodes for brain recording massive amounts of data are
+produced. This big data calls for the need for safe storage and high
+computational resources for real-time processing. To address these challenges,
+a cloud-based BCI system for the analysis of this big EEG data is presented.
+First, a dimensionality-reduction technique is developed to increase
+classification accuracy as well as to decrease the communication bandwidth and
+computation time. Second, following a deep-learning approach, a stacked
+autoencoder is trained in two steps for unsupervised feature extraction and
+classification. Third, a cloud-computing solution is proposed for real-time
+analysis of big EEG data. The results on a benchmark clinical dataset
+illustrate the superiority of the proposed patient-specific BCI as an
+alternative method and its expected usefulness in real-life support of epilepsy
+patients.
+",1,0,0,1,0,0
+4886,Centrality measures for graphons: Accounting for uncertainty in networks,"  As relational datasets modeled as graphs keep increasing in size and their
+data-acquisition is permeated by uncertainty, graph-based analysis techniques
+can become computationally and conceptually challenging. In particular, node
+centrality measures rely on the assumption that the graph is perfectly known --
+a premise not necessarily fulfilled for large, uncertain networks. Accordingly,
+centrality measures may fail to faithfully extract the importance of nodes in
+the presence of uncertainty. To mitigate these problems, we suggest a
+statistical approach based on graphon theory: we introduce formal definitions
+of centrality measures for graphons and establish their connections to
+classical graph centrality measures. A key advantage of this approach is that
+centrality measures defined at the modeling level of graphons are inherently
+robust to stochastic variations of specific graph realizations. Using the
+theory of linear integral operators, we define degree, eigenvector, Katz and
+PageRank centrality functions for graphons and establish concentration
+inequalities demonstrating that graphon centrality functions arise naturally as
+limits of their counterparts defined on sequences of graphs of increasing size.
+The same concentration inequalities also provide high-probability bounds
+between the graphon centrality functions and the centrality measures on any
+sampled graph, thereby establishing a measure of uncertainty of the measured
+centrality score. The same concentration inequalities also provide
+high-probability bounds between the graphon centrality functions and the
+centrality measures on any sampled graph, thereby establishing a measure of
+uncertainty of the measured centrality score.
+",1,0,1,1,0,0
+4887,A time-periodic mechanical analog of the quantum harmonic oscillator,"  We theoretically investigate the stability and linear oscillatory behavior of
+a naturally unstable particle whose potential energy is harmonically modulated.
+We find this fundamental dynamical system is analogous in time to a quantum
+harmonic oscillator. In a certain modulation limit, a.k.a. the Kapitza regime,
+the modulated oscillator can behave like an effective classic harmonic
+oscillator. But in the overlooked opposite limit, the stable modes of
+vibrations are quantized in the modulation parameter space. By analogy with the
+statistical interpretation of quantum physics, those modes can be characterized
+by the time-energy uncertainty relation of a quantum harmonic oscillator.
+Reducing the almost-periodic vibrational modes of the particle to their
+periodic eigenfunctions, one can transform the original equation of motion to a
+dimensionless Schrödinger stationary wave equation with a harmonic potential.
+This reduction process introduces two features reminiscent of the quantum
+realm: a wave-particle duality and a loss of causality that could legitimate a
+statistical interpretation of the computed eigenfunctions. These results shed
+new light on periodically time-varying linear dynamical systems and open an
+original path in the recently revived field of quantum mechanical analogs.
+",0,1,0,0,0,0
+4888,Anharmonicity and the isotope effect in superconducting lithium at high pressures: a first-principles approach,"  Recent experiments [Schaeffer 2015] have shown that lithium presents an
+extremely anomalous isotope effect in the 15-25 GPa pressure range. In this
+article we have calculated the anharmonic phonon dispersion of $\mathrm{^7Li}$
+and $\mathrm{^6Li}$ under pressure, their superconducting transition
+temperatures, and the associated isotope effect. We have found a huge
+anharmonic renormalization of a transverse acoustic soft mode along $\Gamma$K
+in the fcc phase, the expected structure at the pressure range of interest. In
+fact, the anharmonic correction dynamically stabilizes the fcc phase above 25
+GPa. However, we have not found any anomalous scaling of the superconducting
+temperature with the isotopic mass. Additionally, we have also analyzed whether
+the two lithium isotopes adopting different structures could explain the
+observed anomalous behavior. According to our enthalpy calculations including
+zero-point motion and anharmonicity it would not be possible in a stable
+regime.
+",0,1,0,0,0,0
+4889,Time-resolved polarimetry of the superluminous SN 2015bn with the Nordic Optical Telescope,"  We present imaging polarimetry of the superluminous supernova SN 2015bn,
+obtained over nine epochs between $-$20 and $+$46 days with the Nordic Optical
+Telescope. This was a nearby, slowly-evolving Type I superluminous supernova
+that has been studied extensively and for which two epochs of
+spectropolarimetry are also available. Based on field stars, we determine the
+interstellar polarisation in the Galaxy to be negligible. The polarisation of
+SN 2015bn shows a statistically significant increase during the last epochs,
+confirming previous findings. Our well-sampled imaging polarimetry series
+allows us to determine that this increase (from $\sim 0.54\%$ to $\gtrsim
+1.10\%$) coincides in time with rapid changes that took place in the optical
+spectrum. We conclude that the supernova underwent a `phase transition' at
+around $+$20 days, when the photospheric emission shifted from an outer layer,
+dominated by natal C and O, to a more aspherical inner core, dominated by
+freshly nucleosynthesized material. This two-layered model might account for
+the characteristic appearance and properties of Type I superluminous
+supernovae.
+",0,1,0,0,0,0
+4890,Deep Bayesian Active Learning with Image Data,"  Even though active learning forms an important pillar of machine learning,
+deep learning tools are not prevalent within it. Deep learning poses several
+difficulties when used in an active learning setting. First, active learning
+(AL) methods generally rely on being able to learn and update models from small
+amounts of data. Recent advances in deep learning, on the other hand, are
+notorious for their dependence on large amounts of data. Second, many AL
+acquisition functions rely on model uncertainty, yet deep learning methods
+rarely represent such model uncertainty. In this paper we combine recent
+advances in Bayesian deep learning into the active learning framework in a
+practical way. We develop an active learning framework for high dimensional
+data, a task which has been extremely challenging so far, with very sparse
+existing literature. Taking advantage of specialised models such as Bayesian
+convolutional neural networks, we demonstrate our active learning techniques
+with image data, obtaining a significant improvement on existing active
+learning approaches. We demonstrate this on both the MNIST dataset, as well as
+for skin cancer diagnosis from lesion images (ISIC2016 task).
+",1,0,0,1,0,0
+4891,Robust Optical Flow Estimation in Rainy Scenes,"  Optical flow estimation in the rainy scenes is challenging due to background
+degradation introduced by rain streaks and rain accumulation effects in the
+scene. Rain accumulation effect refers to poor visibility of remote objects due
+to the intense rainfall. Most existing optical flow methods are erroneous when
+applied to rain sequences because the conventional brightness constancy
+constraint (BCC) and gradient constancy constraint (GCC) generally break down
+in this situation. Based on the observation that the RGB color channels receive
+raindrop radiance equally, we introduce a residue channel as a new data
+constraint to reduce the effect of rain streaks. To handle rain accumulation,
+our method decomposes the image into a piecewise-smooth background layer and a
+high-frequency detail layer. It also enforces the BCC on the background layer
+only. Results on both synthetic dataset and real images show that our algorithm
+outperforms existing methods on different types of rain sequences. To our
+knowledge, this is the first optical flow method specifically dealing with
+rain.
+",1,0,0,0,0,0
+4892,"Thermophysical Phenomena in Metal Additive Manufacturing by Selective Laser Melting: Fundamentals, Modeling, Simulation and Experimentation","  Among the many additive manufacturing (AM) processes for metallic materials,
+selective laser melting (SLM) is arguably the most versatile in terms of its
+potential to realize complex geometries along with tailored microstructure.
+However, the complexity of the SLM process, and the need for predictive
+relation of powder and process parameters to the part properties, demands
+further development of computational and experimental methods. This review
+addresses the fundamental physical phenomena of SLM, with a special emphasis on
+the associated thermal behavior. Simulation and experimental methods are
+discussed according to three primary categories. First, macroscopic approaches
+aim to answer questions at the component level and consider for example the
+determination of residual stresses or dimensional distortion effects prevalent
+in SLM. Second, mesoscopic approaches focus on the detection of defects such as
+excessive surface roughness, residual porosity or inclusions that occur at the
+mesoscopic length scale of individual powder particles. Third, microscopic
+approaches investigate the metallurgical microstructure evolution resulting
+from the high temperature gradients and extreme heating and cooling rates
+induced by the SLM process. Consideration of physical phenomena on all of these
+three length scales is mandatory to establish the understanding needed to
+realize high part quality in many applications, and to fully exploit the
+potential of SLM and related metal AM processes.
+",1,1,0,0,0,0
+4893,Numerical Methods for Pulmonary Image Registration,"  Due to complexity and invisibility of human organs, diagnosticians need to
+analyze medical images to determine where the lesion region is, and which kind
+of disease is, in order to make precise diagnoses. For satisfying clinical
+purposes through analyzing medical images, registration plays an essential
+role. For instance, in Image-Guided Interventions (IGI) and computer-aided
+surgeries, patient anatomy is registered to preoperative images to guide
+surgeons complete procedures. Medical image registration is also very useful in
+surgical planning, monitoring disease progression and for atlas construction.
+Due to the significance, the theories, methods, and implementation method of
+image registration constitute fundamental knowledge in educational training for
+medical specialists. In this chapter, we focus on image registration of a
+specific human organ, i.e. the lung, which is prone to be lesioned. For
+pulmonary image registration, the improvement of the accuracy and how to obtain
+it in order to achieve clinical purposes represents an important problem which
+should seriously be addressed. In this chapter, we provide a survey which
+focuses on the role of image registration in educational training together with
+the state-of-the-art of pulmonary image registration. In the first part, we
+describe clinical applications of image registration introducing artificial
+organs in Simulation-based Education. In the second part, we summarize the
+common methods used in pulmonary image registration and analyze popular papers
+to obtain a survey of pulmonary image registration.
+",0,1,0,0,0,0
+4894,Solving Non-parametric Inverse Problem in Continuous Markov Random Field using Loopy Belief Propagation,"  In this paper, we address the inverse problem, or the statistical machine
+learning problem, in Markov random fields with a non-parametric pair-wise
+energy function with continuous variables. The inverse problem is formulated by
+maximum likelihood estimation. The exact treatment of maximum likelihood
+estimation is intractable because of two problems: (1) it includes the
+evaluation of the partition function and (2) it is formulated in the form of
+functional optimization. We avoid Problem (1) by using Bethe approximation.
+Bethe approximation is an approximation technique equivalent to the loopy
+belief propagation. Problem (2) can be solved by using orthonormal function
+expansion. Orthonormal function expansion can reduce a functional optimization
+problem to a function optimization problem. Our method can provide an analytic
+form of the solution of the inverse problem within the framework of Bethe
+approximation.
+",1,1,0,1,0,0
+4895,Topology Adaptive Graph Convolutional Networks,"  Spectral graph convolutional neural networks (CNNs) require approximation to
+the convolution to alleviate the computational complexity, resulting in
+performance loss. This paper proposes the topology adaptive graph convolutional
+network (TAGCN), a novel graph convolutional network defined in the vertex
+domain. We provide a systematic way to design a set of fixed-size learnable
+filters to perform convolutions on graphs. The topologies of these filters are
+adaptive to the topology of the graph when they scan the graph to perform
+convolution. The TAGCN not only inherits the properties of convolutions in CNN
+for grid-structured data, but it is also consistent with convolution as defined
+in graph signal processing. Since no approximation to the convolution is
+needed, TAGCN exhibits better performance than existing spectral CNNs on a
+number of data sets and is also computationally simpler than other recent
+methods.
+",1,0,0,1,0,0
+4896,Suspensions of finite-size neutrally-buoyant spheres in turbulent duct flow,"  We study the turbulent square duct flow of dense suspensions of
+neutrally-buoyant spherical particles. Direct numerical simulations (DNS) are
+performed in the range of volume fractions $\phi=0-0.2$, using the immersed
+boundary method (IBM) to account for the dispersed phase. Based on the
+hydraulic diameter a Reynolds number of $5600$ is considered. We report flow
+features and particle statistics specific to this geometry, and compare the
+results to the case of two-dimensional channel flows. In particular, we observe
+that for $\phi=0.05$ and $0.1$, particles preferentially accumulate on the
+corner bisectors, close to the duct corners as also observed for laminar square
+duct flows of same duct-to-particle size ratios. At the highest volume
+fraction, particles preferentially accumulate in the core region. For channel
+flows, in the absence of lateral confinement particles are found instead to be
+uniformily distributed across the channel. We also observe that the intensity
+of the cross-stream secondary flows increases (with respect to the unladen
+case) with the volume fraction up to $\phi=0.1$, as a consequence of the high
+concentration of particles along the corner bisector. For $\phi=0.2$ the
+turbulence activity is strongly reduced and the intensity of the secondary
+flows reduces below that of the unladen case. The friction Reynolds number
+increases with $\phi$ in dilute conditions, as observed for channel flows.
+However, for $\phi=0.2$ the mean friction Reynolds number decreases below the
+value for $\phi=0.1$.
+",0,1,0,0,0,0
+4897,Far-from-equilibrium transport of excited carriers in nanostructures,"  Transport of charged carriers in regimes of strong non-equilibrium is
+critical in a wide array of applications ranging from solar energy conversion
+and semiconductor devices to quantum information. Plasmonic hot-carrier science
+brings this regime of transport physics to the forefront since photo-excited
+carriers must be extracted far from equilibrium to harvest their energy
+efficiently. Here, we present a theoretical and computational framework,
+Non-Equilibrium Scattering in Space and Energy (NESSE), to predict the spatial
+evolution of carrier energy distributions that combines the best features of
+phase-space (Boltzmann) and particle-based (Monte Carlo) methods. Within the
+NESSE framework, we bridge first-principles electronic structure predictions of
+plasmon decay and carrier collision integrals at the atomic scale, with
+electromagnetic field simulations at the nano- to mesoscale. Finally, we apply
+NESSE to predict spatially-resolved energy distributions of photo-excited
+carriers that impact the surface of experimentally realizable plasmonic
+nanostructures, enabling first-principles design of hot carrier devices.
+",0,1,0,0,0,0
+4898,"On annihilators of bounded $(\frak g, \frak k)$-modules","  Let $\frak g$ be a semisimple Lie algebra and $\frak k\subset\frak g$ be a
+reductive subalgebra. We say that a $\frak g$-module $M$ is a bounded $(\frak
+g, \frak k)$-module if $M$ is a direct sum of simple finite-dimensional $\frak
+k$-modules and the multiplicities of all simple $\frak k$-modules in that
+direct sum are universally bounded.
+The goal of this article is to show that the ""boundedness"" property for a
+simple $(\frak g, \frak k)$-module $M$ is equivalent to a property of the
+associated variety of the annihilator of $M$ (this is the closure of a
+nilpotent coadjoint orbit inside $\frak g^*$) under the assumption that the
+main field is algebraically closed and of characteristic 0. In particular this
+implies that if $M_1, M_2$ are simple $(\frak g, \frak k)$-modules such that
+$M_1$ is bounded and the associated varieties of the annihilators of $M_1$ and
+$M_2$ coincide then $M_2$ is also bounded. This statement is a geometric
+analogue of a purely algebraic fact due to I. Penkov and V. Serganova and it
+was posed as a conjecture in my Ph.D. thesis.
+",0,0,1,0,0,0
+4899,Neutron Star Planets: Atmospheric processes and habitability,"  Of the roughly 3000 neutron stars known, only a handful have sub-stellar
+companions. The most famous of these are the low-mass planets around the
+millisecond pulsar B1257+12. New evidence indicates that observational biases
+could still hide a wide variety of planetary systems around most neutron stars.
+We consider the environment and physical processes relevant to neutron star
+planets, in particular the effect of X-ray irradiation and the relativistic
+pulsar wind on the planetary atmosphere. We discuss the survival time of planet
+atmospheres and the planetary surface conditions around different classes of
+neutron stars, and define a neutron star habitable zone. Depending on as-yet
+poorly constrained aspects of the pulsar wind, both Super-Earths around
+B1257+12 could lie within its habitable zone.
+",0,1,0,0,0,0
+4900,Discrete Time-Crystalline Order in Cavity and Circuit QED Systems,"  Discrete time crystals are a recently proposed and experimentally observed
+out-of-equilibrium dynamical phase of Floquet systems, where the stroboscopic
+evolution of a local observable repeats itself at an integer multiple of the
+driving period. We address this issue in a driven-dissipative setup, focusing
+on the modulated open Dicke model, which can be implemented by cavity or
+circuit QED systems. In the thermodynamic limit, we employ semiclassical
+approaches and find rich dynamical phases on top of the discrete
+time-crystalline order. In a deep quantum regime with few qubits, we find clear
+signatures of a transient discrete time-crystalline behavior, which is absent
+in the isolated counterpart. We establish a phenomenology of dissipative
+discrete time crystals by generalizing the Landau theory of phase transitions
+to Floquet open systems.
+",0,1,0,0,0,0
+4901,On the number of solutions of some transcendental equations,"  We give upper and lower bounds for the number of solutions of the equation
+$p(z)\log|z|+q(z)=0$ with polynomials $p$ and $q$.
+",0,0,1,0,0,0
+4902,Relaxation of p-growth integral functionals under space-dependent differential constraints,"  A representation formula for the relaxation of integral energies
+$$(u,v)\mapsto\int_{\Omega} f(x,u(x),v(x))\,dx,$$ is obtained, where $f$
+satisfies $p$-growth assumptions, $1<p<+\infty$, and the fields $v$ are
+subjected to space-dependent first order linear differential constraints in the
+framework of $\mathscr{A}$-quasiconvexity with variable coefficients.
+",0,0,1,0,0,0
+4903,Simultaneous shot inversion for nonuniform geometries using fast data interpolation,"  Stochastic optimization is key to efficient inversion in PDE-constrained
+optimization. Using 'simultaneous shots', or random superposition of source
+terms, works very well in simple acquisition geometries where all sources see
+all receivers, but this rarely occurs in practice. We develop an approach that
+interpolates data to an ideal acquisition geometry while solving the inverse
+problem using simultaneous shots. The approach is formulated as a joint inverse
+problem, combining ideas from low-rank interpolation with full-waveform
+inversion. Results using synthetic experiments illustrate the flexibility and
+efficiency of the approach.
+",0,0,0,1,0,0
+4904,Critical neural networks with short and long term plasticity,"  In recent years self organised critical neuronal models have provided
+insights regarding the origin of the experimentally observed avalanching
+behaviour of neuronal systems. It has been shown that dynamical synapses, as a
+form of short-term plasticity, can cause critical neuronal dynamics. Whereas
+long-term plasticity, such as hebbian or activity dependent plasticity, have a
+crucial role in shaping the network structure and endowing neural systems with
+learning abilities. In this work we provide a model which combines both
+plasticity mechanisms, acting on two different time-scales. The measured
+avalanche statistics are compatible with experimental results for both the
+avalanche size and duration distribution with biologically observed percentages
+of inhibitory neurons. The time-series of neuronal activity exhibits temporal
+bursts leading to 1/f decay in the power spectrum. The presence of long-term
+plasticity gives the system the ability to learn binary rules such as XOR,
+providing the foundation of future research on more complicated tasks such as
+pattern recognition.
+",0,1,0,0,0,0
+4905,Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening,"  Music, being a multifaceted stimulus evolving at multiple timescales,
+modulates brain function in a manifold way that encompasses not only the
+distinct stages of auditory perception but also higher cognitive processes like
+memory and appraisal. Network theory is apparently a promising approach to
+describe the functional reorganization of brain oscillatory dynamics during
+music listening. However, the music induced changes have so far been examined
+within the functional boundaries of isolated brain rhythms. Using naturalistic
+music, we detected the functional segregation patterns associated with
+different cortical rhythms, as these were reflected in the surface EEG
+measurements. The emerged structure was compared across frequency bands to
+quantify the interplay among rhythms. It was also contrasted against the
+structure from the rest and noise listening conditions to reveal the specific
+components stemming from music listening. Our methodology includes an efficient
+graph-partitioning algorithm, which is further utilized for mining prototypical
+modular patterns, and a novel algorithmic procedure for identifying switching
+nodes that consistently change module during music listening. Our results
+suggest the multiplex character of the music-induced functional reorganization
+and particularly indicate the dependence between the networks reconstructed
+from the {\delta} and {\beta}H rhythms. This dependence is further justified
+within the framework of nested neural oscillations and fits perfectly within
+the context of recently introduced cortical entrainment to music. Considering
+its computational efficiency, and in conjunction with the flexibility of in
+situ electroencephalography, it may lead to novel assistive tools for real-life
+applications.
+",0,0,0,0,1,0
+4906,Some characterizations of the preimage of $A_{\infty}$ for the Hardy-Littlewood maximal operator and consequences,"  The purpose of this paper is to give some characterizations of the weight
+functions $w$ such that $Mw$ is in $A_{\infty}$. We show that for those weights
+to be in $A_{\infty}$ ensures to be in $A_{1}$. We give a criterion in terms of
+the local maximal functions $m_{\lambda}$ and we present a pair of
+applications, among them someone similar to the Coifman-Rochberg
+characterization of $A_{1}$ but using functions of the form $(f^{\#})^{\delta}$
+and $(m_{\lambda}u)^{\delta}$ instead of $(Mf)^{\delta}$.
+",0,0,1,0,0,0
+4907,Magma oceans and enhanced volcanism on TRAPPIST-1 planets due to induction heating,"  Low-mass M stars are plentiful in the Universe and often host small, rocky
+planets detectable with the current instrumentation. Recently, seven small
+planets have been discovered orbiting the ultracool dwarf
+TRAPPIST-1\cite{Gillon16,Gillon17}. We examine the role of electromagnetic
+induction heating of these planets, caused by the star's rotation and the
+planet's orbital motion. If the stellar rotation and magnetic dipole axes are
+inclined with respect to each other, induction heating can melt the upper
+mantle and enormously increase volcanic activity, sometimes producing a magma
+ocean below the planetary surface. We show that induction heating leads the
+three innermost planets, one of which is in the habitable zone, to either
+evolve towards a molten mantle planet, or to experience increased outgassing
+and volcanic activity, while the four outermost planets remain mostly
+unaffected.
+",0,1,0,0,0,0
+4908,Coqatoo: Generating Natural Language Versions of Coq Proofs,"  Due to their numerous advantages, formal proofs and proof assistants, such as
+Coq, are becoming increasingly popular. However, one disadvantage of using
+proof assistants is that the resulting proofs can sometimes be hard to read and
+understand, particularly for less-experienced users. To address this issue, we
+have implemented a tool capable of generating natural language versions of Coq
+proofs called Coqatoo, which we present in this paper.
+",1,0,0,0,0,0
+4909,Buildup of Speaking Skills in an Online Learning Community: A Network-Analytic Exploration,"  In this study, we explore peer-interaction effects in online networks on
+speaking skill development. In particular, we present an evidence for gradual
+buildup of skills in a small-group setting that has not been reported in the
+literature. We introduce a novel dataset of six online communities consisting
+of 158 participants focusing on improving their speaking skills. They
+video-record speeches for 5 prompts in 10 days and exchange comments and
+performance-ratings with their peers. We ask (i) whether the participants'
+ratings are affected by their interaction patterns with peers, and (ii) whether
+there is any gradual buildup of speaking skills in the communities towards
+homogeneity. To analyze the data, we employ tools from the emerging field of
+Graph Signal Processing (GSP). GSP enjoys a distinction from Social Network
+Analysis in that the latter is concerned primarily with the connection
+structures of graphs, while the former studies signals on top of graphs. We
+study the performance ratings of the participants as graph signals atop
+underlying interaction topologies. Total variation analysis of the graph
+signals show that the participants' rating differences decrease with time
+(slope=-0.04, p<0.01), while average ratings increase (slope=0.07,
+p<0.05)--thereby gradually building up the ratings towards community-wide
+homogeneity. We provide evidence for peer-influence through a prediction
+formulation. Our consensus-based prediction model outperforms baseline
+network-agnostic regression models by about 23% in predicting performance
+ratings. This, in turn, shows that participants' ratings are affected by their
+peers' ratings and the associated interaction patterns, corroborating previous
+findings. Then, we formulate a consensus-based diffusion model that captures
+these observations of peer-influence from our analyses.
+",1,0,0,0,0,0
+4910,A Note on Kaldi's PLDA Implementation,"  Some explanations to Kaldi's PLDA implementation to make formula derivation
+easier to catch.
+",0,0,0,1,0,0
+4911,Breakdown of the Chiral Anomaly in Weyl Semimetals in a Strong Magnetic Field,"  The low-energy quasiparticles of Weyl semimetals are a condensed-matter
+realization of the Weyl fermions introduced in relativistic field theory.
+Chiral anomaly, the nonconservation of the chiral charge under parallel
+electric and magnetic fields, is arguably the most important phenomenon of Weyl
+semimetals and has been explained as an imbalance between the occupancies of
+the gapless, zeroth Landau levels with opposite chiralities. This widely
+accepted picture has served as the basis for subsequent studies. Here we report
+the breakdown of the chiral anomaly in Weyl semimetals in a strong magnetic
+field based on ab initio calculations. A sizable energy gap that depends
+sensitively on the direction of the magnetic field may open up due to the
+mixing of the zeroth Landau levels associated with the opposite-chirality Weyl
+points that are away from each other in the Brillouin zone. Our study provides
+a theoretical framework for understanding a wide range of phenomena closely
+related to the chiral anomaly in topological semimetals, such as
+magnetotransport, thermoelectric responses, and plasmons, to name a few.
+",0,1,0,0,0,0
+4912,Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network,"  In Chinese societies, superstition is of paramount importance, and vehicle
+license plates with desirable numbers can fetch very high prices in auctions.
+Unlike other valuable items, license plates are not allocated an estimated
+price before auction. I propose that the task of predicting plate prices can be
+viewed as a natural language processing (NLP) task, as the value depends on the
+meaning of each individual character on the plate and its semantics. I
+construct a deep recurrent neural network (RNN) to predict the prices of
+vehicle license plates in Hong Kong, based on the characters on a plate. I
+demonstrate the importance of having a deep network and of retraining.
+Evaluated on 13 years of historical auction prices, the deep RNN outperforms
+previous models by a significant margin.
+",1,0,0,1,0,0
+4913,Magnetic field--induced modification of selection rules for Rb D$_2$ line monitored by selective reflection from a vapor nanocell,"  Magnetic field-induced giant modification of the probabilities of five
+transitions of $5S_{1/2}, F_g=2 \rightarrow 5P_{3/2}, F_e=4$ of $^{85}$Rb and
+three transitions of $5S_{1/2}, F_g=1 \rightarrow 5P_{3/2}, F_e=3$ of $^{87}$Rb
+forbidden by selection rules for zero magnetic field has been observed
+experimentally and described theoretically for the first time. For the case of
+excitation with circularly-polarized ($\sigma^+$) laser radiation, the
+probability of $F_g=2, ~m_F=-2 \rightarrow F_e=4, ~m_F=-1$ transition becomes
+the largest among the seventeen transitions of $^{85}$Rb $F_g=2 \rightarrow
+F_e=1,2,3,4$ group, and the probability of $F_g=1,~m_F=-1 \rightarrow
+F_e=3,~m_F=0$ transition becomes the largest among the nine transitions of
+$^{87}$Rb $F_g=1 \rightarrow F_e=0,1,2,3$ group, in a wide range of magnetic
+field 200 -- 1000 G. Complete frequency separation of individual Zeeman
+components was obtained by implementation of derivative selective reflection
+technique with a 300 nm-thick nanocell filled with Rb, allowing formation of
+narrow optical resonances. Possible applications are addressed. The theoretical
+model is perfectly consistent with the experimental results.
+",0,1,0,0,0,0
+4914,"An Adaptive, Multivariate Partitioning Algorithm for Global Optimization of Nonconvex Programs","  In this work, we develop an adaptive, multivariate partitioning algorithm for
+solving mixed-integer nonlinear programs (MINLP) with multi-linear terms to
+global optimality. This iterative algorithm primarily exploits the advantages
+of piecewise polyhedral relaxation approaches via disjunctive formulations to
+solve MINLPs to global optimality in contrast to the conventional spatial
+branch-and-bound approaches. In order to maintain relatively small-scale
+mixed-integer linear programs at every iteration of the algorithm, we
+adaptively partition the variable domains appearing in the multi-linear terms.
+We also provide proofs on convergence guarantees of the proposed algorithm to a
+global solution. Further, we discuss a few algorithmic enhancements based on
+the sequential bound-tightening procedure as a presolve step, where we observe
+the importance of solving piecewise relaxations compared to basic convex
+relaxations to speed-up the convergence of the algorithm to global optimality.
+We demonstrate the effectiveness of our disjunctive formulations and the
+algorithm on well-known benchmark problems (including Pooling and Blending
+instances) from MINLPLib and compare with state-of-the-art global optimization
+solvers. With this novel approach, we solve several large-scale instances which
+are, in some cases, intractable by the global optimization solver. We also
+shrink the best known optimality gap for one of the hard, generalized pooling
+problem instance.
+",1,0,1,0,0,0
+4915,Twists of quantum Borel algebras,"  We classify Drinfeld twists for the quantum Borel subalgebra u_q(b) in the
+Frobenius-Lusztig kernel u_q(g), where g is a simple Lie algebra over C and q
+an odd root of unity. More specifically, we show that alternating forms on the
+character group of the group of grouplikes for u_q(b) generate all twists for
+u_q(b), under a certain algebraic group action. This implies a simple
+classification of Hopf algebras whose categories of representations are tensor
+equivalent to that of u_q(b). We also show that cocycle twists for the
+corresponding De Concini-Kac algebra are in bijection with alternating forms on
+the aforementioned character group.
+",0,0,1,0,0,0
+4916,Distributions and Statistical Power of Optimal Signal-Detection Methods In Finite Cases,"  In big data analysis for detecting rare and weak signals among $n$ features,
+some grouping-test methods such as Higher Criticism test (HC), Berk-Jones test
+(B-J), and $\phi$-divergence test share the similar asymptotical optimality
+when $n \rightarrow \infty$. However, in practical data analysis $n$ is
+frequently small and moderately large at most. In order to properly apply these
+optimal tests and wisely choose them for practical studies, it is important to
+know how to get the p-values and statistical power of them. To address this
+problem in an even broader context, this paper provides analytical solutions
+for a general family of goodness-of-fit (GOF) tests, which covers these optimal
+tests. For any given i.i.d. and continuous distributions of the input test
+statistics of the $n$ features, both p-value and statistical power of such a
+GOF test can be calculated. By calculation we compared the finite-sample
+performances of asymptotically optimal tests under the normal mixture
+alternative. Results show that HC is the best choice when signals are rare,
+while B-J is more robust over various signal patterns. In the application to a
+real genome-wide association study, results illustrate that the p-value
+calculation works well, and the optimal tests have potentials for detecting
+novel disease genes with weak genetic effects. The calculations have been
+implemented in an R package SetTest and published on the CRAN.
+",0,0,1,1,0,0
+4917,Cubical-like geometry of quasi-median graphs and applications to geometric group theory,"  The class of quasi-median graphs is a generalisation of median graphs, or
+equivalently of CAT(0) cube complexes. The purpose of this thesis is to
+introduce these graphs in geometric group theory. In the first part of our
+work, we extend the definition of hyperplanes from CAT(0) cube complexes, and
+we show that the geometry of a quasi-median graph essentially reduces to the
+combinatorics of its hyperplanes. In the second part, we exploit the specific
+structure of the hyperplanes to state combination results. The main idea is
+that if a group acts in a suitable way on a quasi-median graph so that
+clique-stabilisers satisfy some non-positively curved property $\mathcal{P}$,
+then the whole group must satisfy $\mathcal{P}$ as well. The properties we are
+interested in are mainly (relative) hyperbolicity, (equivariant)
+$\ell^p$-compressions, CAT(0)-ness and cubicality. In the third part, we apply
+our general criteria to several classes of groups, including graph products,
+Guba and Sapir's diagram products, some wreath products, and some graphs of
+groups. Graph products are our most natural examples, where the link between
+the group and its quasi-median graph is particularly strong and explicit; in
+particular, we are able to determine precisely when a graph product is
+relatively hyperbolic.
+",0,0,1,0,0,0
+4918,Doubly Nested Network for Resource-Efficient Inference,"  We propose doubly nested network(DNNet) where all neurons represent their own
+sub-models that solve the same task. Every sub-model is nested both layer-wise
+and channel-wise. While nesting sub-models layer-wise is straight-forward with
+deep-supervision as proposed in \cite{xie2015holistically}, channel-wise
+nesting has not been explored in the literature to our best knowledge.
+Channel-wise nesting is non-trivial as neurons between consecutive layers are
+all connected to each other. In this work, we introduce a technique to solve
+this problem by sorting channels topologically and connecting neurons
+accordingly. For the purpose, channel-causal convolutions are used. Slicing
+doubly nested network gives a working sub-network. The most notable application
+of our proposed network structure with slicing operation is resource-efficient
+inference. At test time, computing resources such as time and memory available
+for running the prediction algorithm can significantly vary across devices and
+applications. Given a budget constraint, we can slice the network accordingly
+and use a sub-model for inference within budget, requiring no additional
+computation such as training or fine-tuning after deployment. We demonstrate
+the effectiveness of our approach in several practical scenarios of utilizing
+available resource efficiently.
+",0,0,0,1,0,0
+4919,Structural and bonding character of potassium-doped p-terphenyl superconductors,"  Recently, there is a series of reports by Wang et al. on the
+superconductivity in K-doped p-terphenyl (KxC18H14) with the transition
+temperatures range from 7 to 123 Kelvin. Identifying the structural and bonding
+character is the key to understand the superconducting phases and the related
+properties. Therefore we carried out an extensive study on the crystal
+structures with different doping levels and investigate the thermodynamic
+stability, structural, electronic, and magnetic properties by the
+first-principles calculations. Our calculated structures capture most features
+of the experimentally observed X-ray diffraction patterns. The K doping
+concentration is constrained to within the range of 2 and 3. The obtained
+formation energy indicates that the system at x = 2.5 is more stable. The
+strong ionic bonding interaction is found in between K atoms and organic
+molecules. The charge transfer accounts for the metallic feature of the doped
+materials. For a small amount of charge transferred, the tilting force between
+the two successive benzenes drives the system to stabilize at the
+antiferromagnetic ground state, while the system exhibits non-magnetic behavior
+with increasing charge transfer. The multiformity of band structures near the
+Fermi level indicates that the driving force for superconductivity is
+complicated.
+",0,1,0,0,0,0
+4920,Influence of the Forward Difference Scheme for the Time Derivative on the Stability of Wave Equation Numerical Solution,"  Research on numerical stability of difference equations has been quite
+intensive in the past century. The choice of difference schemes for the
+derivative terms in these equations contributes to a wide range of the
+stability analysis issues - one of which is how a chosen scheme may directly or
+indirectly contribute to such stability. In the present paper, how far the
+forward difference scheme for the time derivative in the wave equation
+influences the stability of the equation numerical solution, is particularly
+investigated. The stability analysis of the corresponding difference equation
+involving four schemes, namely Lax's, central, forward, and rearward
+differences, were carried out, and the resulting stability criteria were
+compared. The results indicate that the instability of the solution of wave
+equation is not always due to the forward difference scheme for the time
+derivative. Rather, it is shown in this paper that the stability criterion is
+still possible when the spatial derivative is represented by an appropriate
+difference scheme. This sheds light on the degree of applicability of a
+difference scheme for a hyperbolic equation.
+",1,0,0,0,0,0
+4921,Uniqueness of the von Neumann continuous factor,"  For a division ring $D$, denote by $\mathcal M_D$ the $D$-ring obtained as
+the completion of the direct limit $\varinjlim_n M_{2^n}(D)$ with respect to
+the metric induced by its unique rank function. We prove that, for any
+ultramatricial $D$-ring $\mathcal B$ and any non-discrete extremal pseudo-rank
+function $N$ on $\mathcal B$, there is an isomorphism of $D$-rings
+$\overline{\mathcal B} \cong \mathcal M_D$, where $\overline{\mathcal B}$
+stands for the completion of $\mathcal B$ with respect to the pseudo-metric
+induced by $N$. This generalizes a result of von Neumann. We also show a
+corresponding uniqueness result for $*$-algebras over fields $F$ with positive
+definite involution, where the algebra $\mathcal M_F$ is endowed with its
+natural involution coming from the $*$-transpose involution on each of the
+factors $M_{2^n}(F)$.
+",0,0,1,0,0,0
+4922,A hybrid finite volume -- finite element method for bulk--surface coupled problems,"  The paper develops a hybrid method for solving a system of
+advection--diffusion equations in a bulk domain coupled to advection--diffusion
+equations on an embedded surface. A monotone nonlinear finite volume method for
+equations posed in the bulk is combined with a trace finite element method for
+equations posed on the surface. In our approach, the surface is not fitted by
+the mesh and is allowed to cut through the background mesh in an arbitrary way.
+Moreover, a triangulation of the surface into regular shaped elements is not
+required. The background mesh is an octree grid with cubic cells. As an example
+of an application, we consider the modeling of contaminant transport in
+fractured porous media. One standard model leads to a coupled system of
+advection--diffusion equations in a bulk (matrix) and along a surface
+(fracture). A series of numerical experiments with both steady and unsteady
+problems and different embedded geometries illustrate the numerical properties
+of the hybrid approach. The method demonstrates great flexibility in handling
+curvilinear or branching lower dimensional embedded structures.
+",0,1,1,0,0,0
+4923,From Quenched Disorder to Continuous Time Random Walk,"  This work focuses on quantitative representation of transport in systems with
+quenched disorder. Explicit mapping of the quenched trap model to continuous
+time random walk is presented. Linear temporal transformation: $t\to
+t/\Lambda^{1/\alpha}$ for transient process on translationally invariant
+lattice, in the sub-diffusive regime, is sufficient for asymptotic mapping.
+Exact form of the constant $\Lambda^{1/\alpha}$ is established. Disorder
+averaged position probability density function for quenched trap model is
+obtained and analytic expressions for the diffusion coefficient and drift are
+provided.
+",0,1,0,0,0,0
+4924,Network Flows that Solve Least Squares for Linear Equations,"  This paper presents a first-order {distributed continuous-time algorithm} for
+computing the least-squares solution to a linear equation over networks. Given
+the uniqueness of the solution, with nonintegrable and diminishing step size,
+convergence results are provided for fixed graphs. The exact rate of
+convergence is also established for various types of step size choices falling
+into that category. For the case where non-unique solutions exist, convergence
+to one such solution is proved for constantly connected switching graphs with
+square integrable step size, and for uniformly jointly connected switching
+graphs under the boundedness assumption on system states. Validation of the
+results and illustration of the impact of step size on the convergence speed
+are made using a few numerical examples.
+",1,0,0,0,0,0
+4925,A Framework for Evaluating Model-Driven Self-adaptive Software Systems,"  In the last few years, Model Driven Development (MDD), Component-based
+Software Development (CBSD), and context-oriented software have become
+interesting alternatives for the design and construction of self-adaptive
+software systems. In general, the ultimate goal of these technologies is to be
+able to reduce development costs and effort, while improving the modularity,
+flexibility, adaptability, and reliability of software systems. An analysis of
+these technologies shows them all to include the principle of the separation of
+concerns, and their further integration is a key factor to obtaining
+high-quality and self-adaptable software systems. Each technology identifies
+different concerns and deals with them separately in order to specify the
+design of the self-adaptive applications, and, at the same time, support
+software with adaptability and context-awareness. This research studies the
+development methodologies that employ the principles of model-driven
+development in building self-adaptive software systems. To this aim, this
+article proposes an evaluation framework for analysing and evaluating the
+features of model-driven approaches and their ability to support software with
+self-adaptability and dependability in highly dynamic contextual environment.
+Such evaluation framework can facilitate the software developers on selecting a
+development methodology that suits their software requirements and reduces the
+development effort of building self-adaptive software systems. This study
+highlights the major drawbacks of the propped model-driven approaches in the
+related works, and emphasise on considering the volatile aspects of
+self-adaptive software in the analysis, design and implementation phases of the
+development methodologies. In addition, we argue that the development
+methodologies should leave the selection of modelling languages and modelling
+tools to the software developers.
+",1,0,0,0,0,0
+4926,Second order necessary and sufficient optimality conditions for singular solutions of partially-affine control problems,"  In this article we study optimal control problems for systems that are affine
+with respect to some of the control variables and nonlinear in relation to the
+others. We consider finitely many equality and inequality constraints on the
+initial and final values of the state. We investigate singular optimal
+solutions for this class of problems, for which we obtain second order
+necessary and sufficient conditions for weak optimality in integral form. We
+also derive Goh pointwise necessary optimality conditions. We show an example
+to illustrate the results.
+",0,0,1,0,0,0
+4927,Bipartite Envy-Free Matching,"  Bipartite Envy-Free Matching (BEFM) is a relaxation of perfect matching. In a
+bipartite graph with parts X and Y, a BEFM is a matching of some vertices in X
+to some vertices in Y, such that each unmatched vertex in X is not adjacent to
+any matched vertex in Y (so the unmatched vertices do not ""envy"" the matched
+ones). The empty matching is always a BEFM. This paper presents sufficient and
+necessary conditions for the existence of a non-empty BEFM. These conditions
+are based on cardinality of neighbor-sets, similarly to Hall's condition for
+the existence of a perfect matching. The conditions can be verified in
+polynomial time, and in case they are satisfied, a non-empty BEFM can be found
+by a polynomial-time algorithm. The paper presents some applications of BEFM as
+a subroutine in fair division algorithms.
+",1,0,0,0,0,0
+4928,Phase diagram of a generalized off-diagonal Aubry-André model with p-wave pairing,"  Off-diagonal Aubry-André (AA) model has recently attracted a great deal
+of attention as they provide condensed matter realization of topological
+phases. We numerically study a generalized off-diagonal AA model with p-wave
+superfluid pairing in the presence of both commensurate and incommensurate
+hopping modulations. The phase diagram as functions of the modulation strength
+of incommensurate hopping and the strength of the p-wave pairing is obtained by
+using the multifractal analysis. We show that with the appearance of the p-wave
+pairing, the system exhibits mobility-edge phases and critical phases with
+various number of topologically protected zero-energy modes. Predicted
+topological nature of these exotic phases can be realized in a cold atomic
+system of incommensurate bichromatic optical lattice with induced p-wave
+superfluid pairing by using a Raman laser in proximity to a molecular
+Bose-Einstein condensation.
+",0,1,0,0,0,0
+4929,Around Average Behavior: 3-lambda Network Model,"  The analysis of networks affects the research of many real phenomena. The
+complex network structure can be viewed as a network's state at the time of the
+analysis or as a result of the process through which the network arises.
+Research activities focus on both and, thanks to them, we know not only many
+measurable properties of networks but also the essence of some phenomena that
+occur during the evolution of networks. One typical research area is the
+analysis of co-authorship networks and their evolution. In our paper, the
+analysis of one real-world co-authorship network and inspiration from existing
+models form the basis of the hypothesis from which we derive new 3-lambda
+network model. This hypothesis works with the assumption that regular behavior
+of nodes revolves around an average. However, some anomalies may occur. The
+3-lambda model is stochastic and uses the three parameters associated with the
+average behavior of the nodes. The growth of the network based on this model
+assumes that one step of the growth is an interaction in which both new and
+existing nodes are participating. In the paper we present the results of the
+analysis of a co-authorship network and formulate a hypothesis and a model
+based on this hypothesis. Later in the paper, we examine the outputs from the
+network generator based on the 3-lambda model and show that generated networks
+have characteristics known from the environment of real-world networks.
+",1,1,0,0,0,0
+4930,Hierarchical star formation across the grand design spiral NGC1566,"  We investigate how star formation is spatially organized in the grand-design
+spiral NGC 1566 from deep HST photometry with the Legacy ExtraGalactic UV
+Survey (LEGUS). Our contour-based clustering analysis reveals 890 distinct
+stellar conglomerations at various levels of significance. These star-forming
+complexes are organized in a hierarchical fashion with the larger congregations
+consisting of smaller structures, which themselves fragment into even smaller
+and more compact stellar groupings. Their size distribution, covering a wide
+range in length-scales, shows a power-law as expected from scale-free
+processes. We explain this shape with a simple ""fragmentation and enrichment""
+model. The hierarchical morphology of the complexes is confirmed by their
+mass--size relation which can be represented by a power-law with a fractional
+exponent, analogous to that determined for fractal molecular clouds. The
+surface stellar density distribution of the complexes shows a log-normal shape
+similar to that for supersonic non-gravitating turbulent gas. Between 50 and 65
+per cent of the recently-formed stars, as well as about 90 per cent of the
+young star clusters, are found inside the stellar complexes, located along the
+spiral arms. We find an age-difference between young stars inside the complexes
+and those in their direct vicinity in the arms of at least 10 Myr. This
+timescale may relate to the minimum time for stellar evaporation, although we
+cannot exclude the in situ formation of stars. As expected, star formation
+preferentially occurs in spiral arms. Our findings reveal turbulent-driven
+hierarchical star formation along the arms of a grand-design galaxy.
+",0,1,0,0,0,0
+4931,Informed Asymptotically Optimal Anytime Search,"  Path planning in robotics often requires finding high-quality solutions to
+continuously valued and/or high-dimensional problems. These problems are
+challenging and most planning algorithms instead solve simplified
+approximations. Popular approximations include graphs and random samples, as
+respectively used by informed graph-based searches and anytime sampling-based
+planners. Informed graph-based searches, such as A*, traditionally use
+heuristics to search a priori graphs in order of potential solution quality.
+This makes their search efficient but leaves their performance dependent on the
+chosen approximation. If its resolution is too low then they may not find a
+(suitable) solution but if it is too high then they may take a prohibitively
+long time to do so. Anytime sampling-based planners, such as RRT*,
+traditionally use random sampling to approximate the problem domain
+incrementally. This allows them to increase resolution until a suitable
+solution is found but makes their search dependent on the order of
+approximation. Arbitrary sequences of random samples expand the approximation
+in every direction and fill the problem domain but may be prohibitively
+inefficient at containing a solution. This paper unifies and extends these two
+approaches to develop Batch Informed Trees (BIT*), an informed, anytime
+sampling-based planner. BIT* solves continuous path planning problems
+efficiently by using sampling and heuristics to alternately approximate and
+search the problem domain. Its search is ordered by potential solution quality,
+as in A*, and its approximation improves indefinitely with additional
+computational time, as in RRT*. It is shown analytically to be almost-surely
+asymptotically optimal and experimentally to outperform existing sampling-based
+planners, especially on high-dimensional planning problems.
+",1,0,0,0,0,0
+4932,Certifying coloring algorithms for graphs without long induced paths,"  Let $P_k$ be a path, $C_k$ a cycle on $k$ vertices, and $K_{k,k}$ a complete
+bipartite graph with $k$ vertices on each side of the bipartition. We prove
+that (1) for any integers $k, t>0$ and a graph $H$ there are finitely many
+subgraph minimal graphs with no induced $P_k$ and $K_{t,t}$ that are not
+$H$-colorable and (2) for any integer $k>4$ there are finitely many subgraph
+minimal graphs with no induced $P_k$ that are not $C_{k-2}$-colorable.
+The former generalizes the result of Hell and Huang [Complexity of coloring
+graphs without paths and cycles, Discrete Appl. Math. 216: 211--232 (2017)] and
+the latter extends a result of Bruce, Hoang, and Sawada [A certifying algorithm
+for 3-colorability of $P_5$-Free Graphs, ISAAC 2009: 594--604]. Both our
+results lead to polynomial-time certifying algorithms for the corresponding
+coloring problems.
+",1,0,0,0,0,0
+4933,Efficient Estimation for Dimension Reduction with Censored Data,"  We propose a general index model for survival data, which generalizes many
+commonly used semiparametric survival models and belongs to the framework of
+dimension reduction. Using a combination of geometric approach in
+semiparametrics and martingale treatment in survival data analysis, we devise
+estimation procedures that are feasible and do not require
+covariate-independent censoring as assumed in many dimension reduction methods
+for censored survival data. We establish the root-$n$ consistency and
+asymptotic normality of the proposed estimators and derive the most efficient
+estimator in this class for the general index model. Numerical experiments are
+carried out to demonstrate the empirical performance of the proposed estimators
+and an application to an AIDS data further illustrates the usefulness of the
+work.
+",0,0,1,1,0,0
+4934,Analysis of error control in large scale two-stage multiple hypothesis testing,"  When dealing with the problem of simultaneously testing a large number of
+null hypotheses, a natural testing strategy is to first reduce the number of
+tested hypotheses by some selection (screening or filtering) process, and then
+to simultaneously test the selected hypotheses. The main advantage of this
+strategy is to greatly reduce the severe effect of high dimensions. However,
+the first screening or selection stage must be properly accounted for in order
+to maintain some type of error control. In this paper, we will introduce a
+selection rule based on a selection statistic that is independent of the test
+statistic when the tested hypothesis is true. Combining this selection rule and
+the conventional Bonferroni procedure, we can develop a powerful and valid
+two-stage procedure. The introduced procedure has several nice properties: (i)
+it completely removes the selection effect; (ii) it reduces the multiplicity
+effect; (iii) it does not ""waste"" data while carrying out both selection and
+testing. Asymptotic power analysis and simulation studies illustrate that this
+proposed method can provide higher power compared to usual multiple testing
+methods while controlling the Type 1 error rate. Optimal selection thresholds
+are also derived based on our asymptotic analysis.
+",0,0,1,1,0,0
+4935,Subdifferential characterization of probability functions under Gaussian distribution,"  Probability functions figure prominently in optimization problems of
+engineering. They may be nonsmooth even if all input data are smooth.This fact
+motivates the consideration of subdifferentials for such typically just
+continuous functions. The aim of this paper is to provide subdifferential
+formulae in the case of Gaussian distributions for possibly
+infinite-dimensional decision variables and nonsmooth (locally Lipschitzian)
+input data. These formulae are based on the spheric-radial decomposition of
+Gaussian random vectors on the one hand and on a cone of directions of moderate
+growth on the other. By successively adding additional hypotheses, conditions
+are satisfied under which the probability function is locally Lipschitzian or
+even differentiable.
+",0,0,1,0,0,0
+4936,XES Tensorflow - Process Prediction using the Tensorflow Deep-Learning Framework,"  Predicting the next activity of a running process is an important aspect of
+process management. Recently, artificial neural networks, so called
+deep-learning approaches, have been proposed to address this challenge. This
+demo paper describes a software application that applies the Tensorflow
+deep-learning framework to process prediction. The software application reads
+industry-standard XES files for training and presents the user with an
+easy-to-use graphical user interface for both training and prediction. The
+system provides several improvements over earlier work. This demo paper focuses
+on the software implementation and describes the architecture and user
+interface.
+",1,0,0,0,0,0
+4937,EPTL - A temporal logic for weakly consistent systems,"  The high availability and scalability of weakly-consistent systems attracts
+system designers. Yet, writing correct application code for this type of
+systems is difficult; even how to specify the intended behavior of such systems
+is still an open question. There has not been established any standard method
+to specify the intended dynamic behavior of a weakly consistent system. There
+exist specifications of various consistency models for distributed and
+concurrent systems; and the semantics of replicated datatypes like CRDTs have
+been specified in axiomatic and operational models based on visibility
+relations.
+In this paper, we present a temporal logic, EPTL, that is tailored to specify
+properties of weakly consistent systems. In contrast to LTL and CTL, EPTL takes
+into account that operations of weakly consistent systems are in many cases not
+serializable and have to be treated respectively to capture the behavior. We
+embed our temporal logic in Isabelle/HOL and can thereby leverage strong
+semi-automatic proving capabilities.
+",1,0,0,0,0,0
+4938,An initial-boundary value problem for the integrable spin-1 Gross-Pitaevskii equations with a 4x4 Lax pair on the half-line,"  We investigate the initial-boundary value problem for the integrable spin-1
+Gross-Pitaevskii (GP) equations with a 4x4 Lax pair on the half-line. The
+solution of this system can be obtained in terms of the solution of a 4x4
+matrix Riemann-Hilbert (RH) problem formulated in the complex k-plane. The
+relevant jump matrices of the RH problem can be explicitly found using the two
+spectral functions s(k) and S(k), which can be defined by the initial data, the
+Dirichlet-Neumann boundary data at x=0. The global relation is established
+between the two dependent spectral functions. The general mappings between
+Dirichlet and Neumann boundary values are analyzed in terms of the global
+relation.
+",0,1,1,0,0,0
+4939,4-DoF Tracking for Robot Fine Manipulation Tasks,"  This paper presents two visual trackers from the different paradigms of
+learning and registration based tracking and evaluates their application in
+image based visual servoing. They can track object motion with four degrees of
+freedom (DoF) which, as we will show here, is sufficient for many fine
+manipulation tasks. One of these trackers is a newly developed learning based
+tracker that relies on learning discriminative correlation filters while the
+other is a refinement of a recent 8 DoF RANSAC based tracker adapted with a new
+appearance model for tracking 4 DoF motion.
+Both trackers are shown to provide superior performance to several state of
+the art trackers on an existing dataset for manipulation tasks. Further, a new
+dataset with challenging sequences for fine manipulation tasks captured from
+robot mounted eye-in-hand (EIH) cameras is also presented. These sequences have
+a variety of challenges encountered during real tasks including jittery camera
+movement, motion blur, drastic scale changes and partial occlusions.
+Quantitative and qualitative results on these sequences are used to show that
+these two trackers are robust to failures while providing high precision that
+makes them suitable for such fine manipulation tasks.
+",1,0,0,0,0,0
+4940,Absence of chaos in Digital Memcomputing Machines with solutions,"  Digital memcomputing machines (DMMs) are non-linear dynamical systems
+designed so that their equilibrium points are solutions of the Boolean problem
+they solve. In a previous work [Chaos 27, 023107 (2017)] it was argued that
+when DMMs support solutions of the associated Boolean problem then strange
+attractors cannot coexist with such equilibria. In this work, we demonstrate
+such conjecture. In particular, we show that both topological transitivity and
+the strongest property of topological mixing are inconsistent with the point
+dissipative property of DMMs when equilibrium points are present. This is true
+for both the whole phase space and the global attractor. Absence of topological
+transitivity is enough to imply absence of chaotic behavior. In a similar vein,
+we prove that if DMMs do not have equilibrium points, the only attractors
+present are invariant tori/periodic orbits with periods that may possibly
+increase with system size (quasi-attractors).
+",1,1,0,0,0,0
+4941,New ideas for tests of Lorentz invariance with atomic systems,"  We describe a broadly applicable experimental proposal to search for the
+violation of local Lorentz invariance (LLI) with atomic systems. The new scheme
+uses dynamic decoupling and can be implemented in current atomic clocks
+experiments, both with single ions and arrays of neutral atoms. Moreover, the
+scheme can be performed on systems with no optical transitions, and therefore
+it is also applicable to highly charged ions which exhibit particularly high
+sensitivity to Lorentz invariance violation. We show the results of an
+experiment measuring the expected signal of this proposal using a two-ion
+crystal of $^{88}$Sr$^+$ ions. We also carry out a systematic study of the
+sensitivity of highly charged ions to LLI to identify the best candidates for
+the LLI tests.
+",0,1,0,0,0,0
+4942,Fine-resolution analysis of exoplanetary distributions by wavelets: hints of an overshooting iceline accumulation,"  We investigate 1D exoplanetary distributions using a novel analysis algorithm
+based on the continuous wavelet transform. The analysis pipeline includes an
+estimation of the wavelet transform of the probability density function
+(p.d.f.) without pre-binning, use of optimized wavelets, a rigorous
+significance testing of the patterns revealed in the p.d.f., and an optimized
+minimum-noise reconstruction of the p.d.f. via matching pursuit iterations.
+In the distribution of orbital periods, $P$, our analysis revealed a narrow
+subfamily of exoplanets within the broad family of ""warm jupiters"", or massive
+giants with $P\gtrsim 300$~d, which are often deemed to be related with the
+iceline accumulation in a protoplanetary disk. We detected a p.d.f. pattern
+that represents an upturn followed by an overshooting peak spanning $P\sim
+300-600$~d, right beyond the ""period valley"". It is separated from the other
+planets by p.d.f. concavities from both sides. It has at least two-sigma
+significance.
+In the distribution of planet radii, $R$, and using the California Kepler
+Survey sample properly cleaned, we confirm the hints of a bimodality with two
+peaks about $R=1.3 R_\oplus$ and $R=2.4 R_\oplus$, and the ""evaporation valley""
+between them. However, we obtain just a modest significance for this pattern,
+two-sigma only at the best. Besides, our follow-up application of the Hartigan
+& Hartigan dip test for unimodality returns $3$ per cent false alarm
+probability (merely $2.2$-sigma significance), contrary to $0.14$ per cent (or
+$3.2$-sigma), as claimed by Fulton et al. (2017).
+",0,1,0,0,0,0
+4943,Small sets in dense pairs,"  Let $\widetilde{\mathcal M}=\langle \mathcal M, P\rangle$ be an expansion of
+an o-minimal structure $\mathcal M$ by a dense set $P\subseteq M$, such that
+three tameness conditions hold. We prove that the induced structure on $P$ by
+$\mathcal M$ eliminates imaginaries. As a corollary, we obtain that every small
+set $X$ definable in $\widetilde{\mathcal M}$ can be definably embedded into
+some $P^l$, uniformly in parameters, settling a question from [10]. We verify
+the tameness conditions in three examples: dense pairs of real closed fields,
+expansions of $\mathcal M$ by a dense independent set, and expansions by a
+dense divisible multiplicative group with the Mann property. Along the way, we
+point out a gap in the proof of a relevant elimination of imaginaries result in
+Wencel [17]. The above results are in contrast to recent literature, as it is
+known in general that $\widetilde{\mathcal M}$ does not eliminate imaginaries,
+and neither it nor the induced structure on $P$ admits definable Skolem
+functions.
+",0,0,1,0,0,0
+4944,Meta-Learning MCMC Proposals,"  Effective implementations of sampling-based probabilistic inference often
+require manually constructed, model-specific proposals. Inspired by recent
+progresses in meta-learning for training learning agents that can generalize to
+unseen environments, we propose a meta-learning approach to building effective
+and generalizable MCMC proposals. We parametrize the proposal as a neural
+network to provide fast approximations to block Gibbs conditionals. The learned
+neural proposals generalize to occurrences of common structural motifs across
+different models, allowing for the construction of a library of learned
+inference primitives that can accelerate inference on unseen models with no
+model-specific training required. We explore several applications including
+open-universe Gaussian mixture models, in which our learned proposals
+outperform a hand-tuned sampler, and a real-world named entity recognition
+task, in which our sampler yields higher final F1 scores than classical
+single-site Gibbs sampling.
+",1,0,0,1,0,0
+4945,Putting Self-Supervised Token Embedding on the Tables,"  Information distribution by electronic messages is a privileged means of
+transmission for many businesses and individuals, often under the form of
+plain-text tables. As their number grows, it becomes necessary to use an
+algorithm to extract text and numbers instead of a human. Usual methods are
+focused on regular expressions or on a strict structure in the data, but are
+not efficient when we have many variations, fuzzy structure or implicit labels.
+In this paper we introduce SC2T, a totally self-supervised model for
+constructing vector representations of tokens in semi-structured messages by
+using characters and context levels that address these issues. It can then be
+used for an unsupervised labeling of tokens, or be the basis for a
+semi-supervised information extraction system.
+",1,0,0,0,0,0
+4946,Enhanced ferromagnetic transition temperature induced by a microscopic structural rearrangement in the diluted magnetic semiconductor Ge$_{1-x}$Mn$_{x}$Te,"  The correlation between magnetic properties and microscopic structural
+aspects in the diluted magnetic semiconductor Ge$_{1-x}$Mn$_{x}$Te is
+investigated by x-ray diffraction and magnetization as a function of the Mn
+concentration $x$. The occurrence of high ferromagnetic-transition temperatures
+in the rhombohedrally distorted phase of slowly-cooled Ge$_{1-x}$Mn$_{x}$Te is
+shown to be directly correlated with the formation and coexistence of
+strongly-distorted Mn-poor and weakly-distorted Mn-rich regions. It is
+demonstrated that the weakly-distorted phase fraction is responsible for the
+occurrence of high-transition temperatures in Ge$_{1-x}$Mn$_{x}$Te. When the Mn
+concentration becomes larger, the Mn-rich regions start to switch into the
+undistorted cubic structure, and the transition temperature is suppressed
+concurrently. By identifying suitable annealing conditions, we successfully
+increased the transition temperature to above 200 K for Mn concentrations close
+to the cubic phase. Structural data indicate that the weakly-distorted phase
+fraction can be restored at the expense of the cubic regions upon the
+enhancement of the transition temperature, clearly establishing the direct link
+between high-transition temperatures and the weakly-distorted Mn-rich phase
+fraction.
+",0,1,0,0,0,0
+4947,Getting the public involved in Quantum Error Correction,"  The Decodoku project seeks to let users get hands-on with cutting-edge
+quantum research through a set of simple puzzle games. The design of these
+games is explicitly based on the problem of decoding qudit variants of surface
+codes. This problem is presented such that it can be tackled by players with no
+prior knowledge of quantum information theory, or any other high-level physics
+or mathematics. Methods devised by the players to solve the puzzles can then
+directly be incorporated into decoding algorithms for quantum computation. In
+this paper we give a brief overview of the novel decoding methods devised by
+players, and provide short postmortem for Decodoku v1.0-v4.1.
+",0,1,0,0,0,0
+4948,Courcelle's Theorem Made Dynamic,"  Dynamic complexity is concerned with updating the output of a problem when
+the input is slightly changed. We study the dynamic complexity of model
+checking a fixed monadic second-order formula over evolving subgraphs of a
+fixed maximal graph having bounded tree-width; here the subgraph evolves by
+losing or gaining edges (from the maximal graph). We show that this problem is
+in DynFO (with LOGSPACE precomputation), via a reduction to a Dyck reachability
+problem on an acyclic automaton.
+",1,0,0,0,0,0
+4949,PorePy: An Open-Source Simulation Tool for Flow and Transport in Deformable Fractured Rocks,"  Fractures are ubiquitous in the subsurface and strongly affect flow and
+deformation. The physical shape of the fractures, they are long and thin
+objects, puts strong limitations on how the effect of this dynamics can be
+incorporated into standard reservoir simulation tools. This paper reports the
+development of an open-source software framework, termed PorePy, which is aimed
+at simulation of flow and transport in three-dimensional fractured reservoirs,
+as well as deformation of the reservoir due to shearing along fracture and
+fault planes. Starting from a description of fractures as polygons embedded in
+a 3D domain, PorePy provides semi-automatic gridding to construct a
+discrete-fracture-matrix model, which forms the basis for subsequent
+simulations. PorePy allows for flow and transport in all lower-dimensional
+objects, including planes (2D) representing fractures, and lines (1D) and
+points (0D), representing fracture intersections. Interaction between processes
+in neighboring domains of different dimension is implemented as a sequence of
+couplings of objects one dimension apart. This readily allows for handling of
+complex fracture geometries compared to capabilities of existing software. In
+addition to flow and transport, PorePy provides models for rock mechanics,
+poro-elasticity and coupling with fracture deformation models. The software is
+fully open, and can serve as a framework for transparency and reproducibility
+of simulations. We describe the design principles of PorePy from a user
+perspective, with focus on possibilities within gridding, covered physical
+processes and available discretizations. The power of the framework is
+illustrated with two sets of simulations; involving respectively coupled flow
+and transport in a fractured porous medium, and low-pressure stimulation of a
+geothermal reservoir.
+",1,1,0,0,0,0
+4950,Second-grade fluids in curved pipes,"  This paper is concerned with the application of finite element methods to
+obtain solutions for steady fully developed second-grade flows in a curved pipe
+of circular cross-section and arbitrary curvature ratio, under a given axial
+pressure gradient. The qualitative and quantitative behavior of the secondary
+flows is analyzed with respect to inertia and viscoelasticity.
+",0,1,1,0,0,0
+4951,Designing nearly tight window for improving time-frequency masking,"  Many audio signal processing methods are formulated in the time-frequency
+(T-F) domain which is obtained by the short-time Fourier transform (STFT). The
+property of STFT is fully characterized by window function, and thus designing
+a better window is important for improving the performance of the processing
+especially when a less redundant T-F representation is desirable. While many
+window functions have been proposed in the literature, they are designed to
+have a good frequency response for analysis, which may not perform well in
+terms of signal processing. The window design must take the effect of the
+reconstruction (from the T-F domain into the time domain) into account for
+improving the performance. In this paper, an optimization-based design method
+of a nearly tight window is proposed to obtain a window performing well for the
+T-F domain signal processing.
+",1,0,0,0,0,0
+4952,Asymptotic Properties of Recursive Maximum Likelihood Estimation in Non-Linear State-Space Models,"  Using stochastic gradient search and the optimal filter derivative, it is
+possible to perform recursive (i.e., online) maximum likelihood estimation in a
+non-linear state-space model. As the optimal filter and its derivative are
+analytically intractable for such a model, they need to be approximated
+numerically. In [Poyiadjis, Doucet and Singh, Biometrika 2018], a recursive
+maximum likelihood algorithm based on a particle approximation to the optimal
+filter derivative has been proposed and studied through numerical simulations.
+Here, this algorithm and its asymptotic behavior are analyzed theoretically. We
+show that the algorithm accurately estimates maxima to the underlying (average)
+log-likelihood when the number of particles is sufficiently large. We also
+derive (relatively) tight bounds on the estimation error. The obtained results
+hold under (relatively) mild conditions and cover several classes of non-linear
+state-space models met in practice.
+",0,0,0,1,0,0
+4953,A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning,"  For safe and efficient planning and control in autonomous driving, we need a
+driving policy which can achieve desirable driving quality in long-term horizon
+with guaranteed safety and feasibility. Optimization-based approaches, such as
+Model Predictive Control (MPC), can provide such optimal policies, but their
+computational complexity is generally unacceptable for real-time
+implementation. To address this problem, we propose a fast integrated planning
+and control framework that combines learning- and optimization-based approaches
+in a two-layer hierarchical structure. The first layer, defined as the ""policy
+layer"", is established by a neural network which learns the long-term optimal
+driving policy generated by MPC. The second layer, called the ""execution
+layer"", is a short-term optimization-based controller that tracks the reference
+trajecotries given by the ""policy layer"" with guaranteed short-term safety and
+feasibility. Moreover, with efficient and highly-representative features, a
+small-size neural network is sufficient in the ""policy layer"" to handle many
+complicated driving scenarios. This renders online imitation learning with
+Dataset Aggregation (DAgger) so that the performance of the ""policy layer"" can
+be improved rapidly and continuously online. Several exampled driving scenarios
+are demonstrated to verify the effectiveness and efficiency of the proposed
+framework.
+",1,0,0,0,0,0
+4954,Microservices in Practice: A Survey Study,"  Microservices architectures have become largely popular in the last years.
+However, we still lack empirical evidence about the use of microservices and
+the practices followed by practitioners. Thereupon, in this paper, we report
+the results of a survey with 122 professionals who work with microservices. We
+report how the industry is using this architectural style and whether the
+perception of practitioners regarding the advantages and challenges of
+microservices is according to the literature.
+",1,0,0,0,0,0
+4955,Predicted novel insulating electride compound between alkali metals lithium and sodium under high pressure,"  The application of high pressure can fundamentally modify the crystalline and
+electronic structures of elements as well as their chemical reactivity, which
+could lead to the formation of novel materials. Here, we explore the reactivity
+of lithium with sodium under high pressure, using a swarm structure searching
+techniques combined with first-principles calculations, which identify a
+thermodynamically stable LiNa compound adopting an orthorhombic oP8 phase at
+pressure above 355 GPa. The formation of LiNa may be a consequence of strong
+concentration of electrons transfer from the lithium and the sodium atoms into
+the interstitial sites, which also leads to opening a relatively wide band gap
+for LiNa-op8. This is substantially different from the picture that share or
+exchange electrons in common compounds and alloys. In addition, lattice-dynamic
+calculations indicate that LiNa-op8 remains dynamically stable when pressure
+decompresses down to 70 GPa.
+",0,1,0,0,0,0
+4956,The relationship between $k$-forcing and $k$-power domination,"  Zero forcing and power domination are iterative processes on graphs where an
+initial set of vertices are observed, and additional vertices become observed
+based on some rules. In both cases, the goal is to eventually observe the
+entire graph using the fewest number of initial vertices. Chang et al.
+introduced $k$-power domination in [Generalized power domination in graphs,
+{\it Discrete Applied Math.} 160 (2012) 1691-1698] as a generalization of power
+domination and standard graph domination. Independently, Amos et al. defined
+$k$-forcing in [Upper bounds on the $k$-forcing number of a graph, {\it
+Discrete Applied Math.} 181 (2015) 1-10] to generalize zero forcing. In this
+paper, we combine the study of $k$-forcing and $k$-power domination, providing
+a new approach to analyze both processes. We give a relationship between the
+$k$-forcing and the $k$-power domination numbers of a graph that bounds one in
+terms of the other. We also obtain results using the contraction of subgraphs
+that allow the parallel computation of $k$-forcing and $k$-power dominating
+sets.
+",0,0,1,0,0,0
+4957,The transition matrix between the Specht and web bases is unipotent with additional vanishing entries,"  We compare two important bases of an irreducible representation of the
+symmetric group: the web basis and the Specht basis. The web basis has its
+roots in the Temperley-Lieb algebra and knot-theoretic considerations. The
+Specht basis is a classic algebraic and combinatorial construction of symmetric
+group representations which arises in this context through the geometry of
+varieties called Springer fibers. We describe a graph that encapsulates
+combinatorial relations between each of these bases, prove that there is a
+unique way (up to scaling) to map the Specht basis into the web representation,
+and use this to recover a result of Garsia-McLarnan that the transition matrix
+between the Specht and web bases is upper-triangular with ones along the
+diagonal. We then strengthen their result to prove vanishing of certain
+additional entries unless a nesting condition on webs is satisfied. In fact we
+conjecture that the entries of the transition matrix are nonnegative and are
+nonzero precisely when certain directed paths exist in the web graph.
+",0,0,1,0,0,0
+4958,Robotic Wireless Sensor Networks,"  In this chapter, we present a literature survey of an emerging, cutting-edge,
+and multi-disciplinary field of research at the intersection of Robotics and
+Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
+Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
+that aims to achieve certain sensing goals while meeting and maintaining
+certain communication performance requirements, through cooperative control,
+learning and adaptation. While both of the component areas, i.e., Robotics and
+WSN, are very well-known and well-explored, there exist a whole set of new
+opportunities and research directions at the intersection of these two fields
+which are relatively or even completely unexplored. One such example would be
+the use of a set of robotic routers to set up a temporary communication path
+between a sender and a receiver that uses the controlled mobility to the
+advantage of packet routing. We find that there exist only a limited number of
+articles to be directly categorized as RWSN related works whereas there exist a
+range of articles in the robotics and the WSN literature that are also relevant
+to this new field of research. To connect the dots, we first identify the core
+problems and research trends related to RWSN such as connectivity,
+localization, routing, and robust flow of information. Next, we classify the
+existing research on RWSN as well as the relevant state-of-the-arts from
+robotics and WSN community according to the problems and trends identified in
+the first step. Lastly, we analyze what is missing in the existing literature,
+and identify topics that require more research attention in the future.
+",1,0,0,0,0,0
+4959,Using Mode Connectivity for Loss Landscape Analysis,"  Mode connectivity is a recently introduced frame- work that empirically
+establishes the connected- ness of minima by finding a high accuracy curve
+between two independently trained models. To investigate the limits of this
+setup, we examine the efficacy of this technique in extreme cases where the
+input models are trained or initialized differently. We find that the procedure
+is resilient to such changes. Given this finding, we propose using the
+framework for analyzing loss surfaces and training trajectories more generally,
+and in this direction, study SGD with cosine annealing and restarts (SGDR). We
+report that while SGDR moves over barriers in its trajectory, propositions
+claiming that it converges to and escapes from multiple local minima are not
+substantiated by our empirical results.
+",0,0,0,1,0,0
+4960,Lifelong Generative Modeling,"  Lifelong learning is the problem of learning multiple consecutive tasks in a
+sequential manner where knowledge gained from previous tasks is retained and
+used for future learning. It is essential towards the development of
+intelligent machines that can adapt to their surroundings. In this work we
+focus on a lifelong learning approach to generative modeling where we
+continuously incorporate newly observed distributions into our learnt model. We
+do so through a student-teacher Variational Autoencoder architecture which
+allows us to learn and preserve all the distributions seen so far without the
+need to retain the past data nor the past models. Through the introduction of a
+novel cross-model regularizer, inspired by a Bayesian update rule, the student
+model leverages the information learnt by the teacher, which acts as a summary
+of everything seen till now. The regularizer has the additional benefit of
+reducing the effect of catastrophic interference that appears when we learn
+over sequences of distributions. We demonstrate its efficacy in learning
+sequentially observed distributions as well as its ability to learn a common
+latent representation across a complex transfer learning scenario.
+",1,0,0,1,0,0
+4961,Finding Root Causes of Floating Point Error with Herbgrind,"  Floating-point arithmetic plays a central role in science, engineering, and
+finance by enabling developers to approximate real arithmetic. To address
+numerical issues in large floating-point applications, developers must identify
+root causes, which is difficult because floating-point errors are generally
+non-local, non-compositional, and non-uniform.
+This paper presents Herbgrind, a tool to help developers identify and address
+root causes in numerical code written in low-level C/C++ and Fortran. Herbgrind
+dynamically tracks dependencies between operations and program outputs to avoid
+false positives and abstracts erroneous computations to a simplified program
+fragment whose improvement can reduce output error. We perform several case
+studies applying Herbgrind to large, expert-crafted numerical programs and show
+that it scales to applications spanning hundreds of thousands of lines,
+correctly handling the low-level details of modern floating point hardware and
+mathematical libraries, and tracking error across function boundaries and
+through the heap.
+",1,0,0,0,0,0
+4962,Catalog of Candidates for Quasars at 3 < z < 5.5 Selected among X-Ray Sources from the 3XMM-DR4 Survey of the XMM-Newton Observatory,"  We have compiled a catalog of 903 candidates for type 1 quasars at redshifts
+3<z<5.5 selected among the X-ray sources of the serendipitous XMM-Newton survey
+presented in the 3XMM-DR4 catalog (the median X-ray flux is 5x10^{-15}
+erg/s/cm^2 the 0.5-2 keV energy band) and located at high Galactic latitudes
+>20 deg in Sloan Digital Sky Survey (SDSS) fields with a total area of about
+300 deg^2. Photometric SDSS data as well infrared 2MASS and WISE data were used
+to select the objects. We selected the point sources from the photometric SDSS
+catalog with a magnitude error Delta z<0.2 and a color i-z<0.6 (to first
+eliminate the M-type stars). For the selected sources, we have calculated the
+dependences chi^2(z) for various spectral templates from the library that we
+compiled for these purposes using the EAZY software. Based on these data, we
+have rejected the objects whose spectral energy distributions are better
+described by the templates of stars at z=0 and obtained a sample of quasars
+with photometric redshift estimates 2.75<zphot<5.5. The selection completeness
+of known quasars at z>3 in the investigated fields is shown to be about 80%.
+The normalized median absolute deviation is 0.07, while the outlier fraction is
+eta= 9. The number of objects per unit area in our sample exceeds the number of
+quasars in the spectroscopic SDSS sample at the same redshifts approximately by
+a factor of 1.5. The subsequent spectroscopic testing of the redshifts of our
+selected candidates for quasars at 3<z<5.5 will allow the purity of this sample
+to be estimated more accurately.
+",0,1,0,0,0,0
+4963,The Theta Number of Simplicial Complexes,"  We introduce a generalization of the celebrated Lovász theta number of a
+graph to simplicial complexes of arbitrary dimension. Our generalization takes
+advantage of real simplicial cohomology theory, in particular combinatorial
+Laplacians, and provides a semidefinite programming upper bound of the
+independence number of a simplicial complex. We consider properties of the
+graph theta number such as the relationship to Hoffman's ratio bound and to the
+chromatic number and study how they extend to higher dimensions. Like in the
+case of graphs, the higher dimensional theta number can be extended to a
+hierarchy of semidefinite programming upper bounds reaching the independence
+number. We analyze the value of the theta number and of the hierarchy for dense
+random simplicial complexes.
+",1,0,1,0,0,0
+4964,Prediction Scores as a Window into Classifier Behavior,"  Most multi-class classifiers make their prediction for a test sample by
+scoring the classes and selecting the one with the highest score. Analyzing
+these prediction scores is useful to understand the classifier behavior and to
+assess its reliability. We present an interactive visualization that
+facilitates per-class analysis of these scores. Our system, called Classilist,
+enables relating these scores to the classification correctness and to the
+underlying samples and their features. We illustrate how such analysis reveals
+varying behavior of different classifiers. Classilist is available for use
+online, along with source code, video tutorials, and plugins for R, RapidMiner,
+and KNIME at this https URL.
+",1,0,0,1,0,0
+4965,Effective perturbation theory for linear operators,"  We propose a new approach to the spectral theory of perturbed linear
+operators , in the case of a simple isolated eigenvalue. We obtain two kind of
+results: ""radius bounds"" which ensure perturbation theory applies for
+perturbations up to an explicit size, and ""regularity bounds"" which control the
+variations of eigendata to any order. Our method is based on the Implicit
+Function Theorem and proceeds by establishing differential inequalities on two
+natural quantities: the norm of the projection to the eigendirection, and the
+norm of the reduced resolvent. We obtain completely explicit results without
+any assumption on the underlying Banach space. In companion articles, on the
+one hand we apply the regularity bounds to Markov chains, obtaining
+non-asymptotic concentration and Berry-Ess{é}en inequalities with explicit
+constants, and on the other hand we apply the radius bounds to transfer
+operator of intermittent maps, obtaining explicit high-temperature regimes
+where a spectral gap occurs.
+",0,0,1,0,0,0
+4966,I-MMSE relations in random linear estimation and a sub-extensive interpolation method,"  Consider random linear estimation with Gaussian measurement matrices and
+noise. One can compute infinitesimal variations of the mutual information under
+infinitesimal variations of the signal-to-noise ratio or of the measurement
+rate. We discuss how each variation is related to the minimum mean-square error
+and deduce that the two variations are directly connected through a very simple
+identity. The main technical ingredient is a new interpolation method called
+""sub-extensive interpolation method"". We use it to provide a new proof of an
+I-MMSE relation recently found by Reeves and Pfister [1] when the measurement
+rate is varied. Our proof makes it clear that this relation is intimately
+related to another I-MMSE relation also recently proved in [2]. One can
+directly verify that the identity relating the two types of variation of mutual
+information is indeed consistent with the one letter replica symmetric formula
+for the mutual information, first derived by Tanaka [3] for binary signals, and
+recently proved in more generality in [1,2,4,5] (by independent methods).
+However our proof is independent of any knowledge of Tanaka's formula.
+",1,1,0,0,0,0
+4967,Non-convex Finite-Sum Optimization Via SCSG Methods,"  We develop a class of algorithms, as variants of the stochastically
+controlled stochastic gradient (SCSG) methods (Lei and Jordan, 2016), for the
+smooth non-convex finite-sum optimization problem. Assuming the smoothness of
+each component, the complexity of SCSG to reach a stationary point with
+$\mathbb{E} \|\nabla f(x)\|^{2}\le \epsilon$ is $O\left (\min\{\epsilon^{-5/3},
+\epsilon^{-1}n^{2/3}\}\right)$, which strictly outperforms the stochastic
+gradient descent. Moreover, SCSG is never worse than the state-of-the-art
+methods based on variance reduction and it significantly outperforms them when
+the target accuracy is low. A similar acceleration is also achieved when the
+functions satisfy the Polyak-Lojasiewicz condition. Empirical experiments
+demonstrate that SCSG outperforms stochastic gradient methods on training
+multi-layers neural networks in terms of both training and validation loss.
+",1,0,1,0,0,0
+4968,Minority carrier diffusion lengths and mobilities in low-doped n-InGaAs for focal plane array applications,"  The hole diffusion length in n-InGaAs is extracted for two samples of
+different doping concentrations using a set of long and thin diffused junction
+diodes separated by various distances on the order of the diffusion length. The
+methodology is described, including the ensuing analysis which yields diffusion
+lengths between 70 - 85 um at room temperature for doping concentrations in the
+range of 5 - 9 x 10^15 cm-3. The analysis also provides insight into the
+minority carrier mobility which is a parameter not commonly reported in the
+literature. Hole mobilities on the order of 500 - 750 cm2/Vs are reported for
+the aforementioned doping range, which are comparable albeit longer than the
+majority hole mobility for the same doping magnitude in p-InGaAs. A radiative
+recombination coefficient of (0.5-0.2)x10^-10 cm^-3s^-1 is also extracted from
+the ensuing analysis for an InGaAs thickness of 2.7 um. Preliminary evidence is
+also given for both heavy and light hole diffusion. The dark current of
+InP/InGaAs p-i-n photodetectors with 25 and 15 um pitches are then calibrated
+to device simulations and correlated to the extracted diffusion lengths and
+doping concentrations. An effective Shockley-Read-Hall lifetime of between
+90-200 us provides the best fit to the dark current of these structures.
+",0,1,0,0,0,0
+4969,Layered semi-convection and tides in giant planet interiors - I. Propagation of internal waves,"  Layered semi-convection is a possible candidate to explain Saturn's
+luminosity excess and the abnormally large radius of some hot Jupiters. In
+giant planet interiors, it could lead to the creation of density staircases,
+which are convective layers separated by thin stably stratified interfaces. We
+study the propagation of internal waves in a region of layered semi-convection,
+with the aim to predict energy transport by internal waves incident upon a
+density staircase. The goal is then to understand the resulting tidal
+dissipation when these waves are excited by other bodies such as moons in giant
+planets systems. We use a local Cartesian analytical model, taking into account
+the complete Coriolis acceleration at any latitude, thus generalizing previous
+works. We find transmission of incident internal waves to be strongly affected
+by the presence of a density staircase, even if these waves are initially pure
+inertial waves (which are restored by the Coriolis acceleration). In
+particular, low-frequency waves of all wavelengths are perfectly transmitted
+near the critical latitude. Otherwise, short-wavelength waves are only
+efficiently transmitted if they are resonant with a free mode (interfacial
+gravity wave or short-wavelength inertial mode) of the staircase. In all other
+cases, waves are primarily reflected unless their wavelengths are longer than
+the vertical extent of the entire staircase (not just a single step). We expect
+incident internal waves to be strongly affected by the presence of a density
+staircase in a frequency-, latitude- and wavelength-dependent manner. First,
+this could lead to new criteria to probe the interior of giant planets by
+seismology; and second, this may have important consequences for tidal
+dissipation and our understanding of the evolution of giant planet systems.
+",0,1,0,0,0,0
+4970,A new Weber type integral equation related to the Weber-Titchmarsh problem,"  We derive solvability conditions and closed-form solution for the Weber type
+integral equation, related to the familiar Weber-Orr integral transforms and
+the old Weber-Titchmarsh problem (posed in Proc. Lond. Math. Soc. 22 (2)
+(1924), pp.15, 16), recently solved by the author. Our method involves
+properties of the inverse Mellin transform of integrable functions. The
+Mellin-Parseval equality and some integrals, involving the Gauss hypergeometric
+function are used.
+",0,0,1,0,0,0
+4971,Non-commutative Discretize-then-Optimize Algorithms for Elliptic PDE-Constrained Optimal Control Problems,"  In this paper, we analyze the convergence of several discretize-then-optimize
+algorithms, based on either a second-order or a fourth-order finite difference
+discretization, for solving elliptic PDE-constrained optimization or optimal
+control problems. To ensure the convergence of a discretize-then-optimize
+algorithm, one well-accepted criterion is to choose or redesign the
+discretization scheme such that the resultant discretize-then-optimize
+algorithm commutes with the corresponding optimize-then-discretize algorithm.
+In other words, both types of algorithms would give rise to exactly the same
+discrete optimality system. However, such an approach is not trivial. In this
+work, by investigating a simple distributed elliptic optimal control problem,
+we first show that enforcing such a stringent condition of commutative property
+is only sufficient but not necessary for achieving the desired convergence. We
+then propose to add some suitable $H_1$ semi-norm penalty/regularization terms
+to recover the lost convergence due to the inconsistency caused by the loss of
+commutativity. Numerical experiments are carried out to verify our theoretical
+analysis and also validate the effectiveness of our proposed regularization
+techniques.
+",0,0,1,0,0,0
+4972,Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks,"  The paper evaluates three variants of the Gated Recurrent Unit (GRU) in
+recurrent neural networks (RNN) by reducing parameters in the update and reset
+gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and
+show that these GRU-RNN variant models perform as well as the original GRU RNN
+model while reducing the computational expense.
+",1,0,0,1,0,0
+4973,Some results on the annihilators and attached primes of local cohomology modules,"  Let $(R, \frak m)$ be a local ring and $M$ a finitely generated $R$-module.
+It is shown that if $M$ is relative Cohen-Macaulay with respect to an ideal
+$\frak a$ of $R$, then $\text{Ann}_R(H_{\mathfrak{a}}^{\text{cd}(\mathfrak{a},
+M)}(M))=\text{Ann}_RM/L=\text{Ann}_RM$ and
+$\text{Ass}_R(R/\text{Ann}_RM)\subseteq \{\mathfrak{p} \in \text{Ass}_R
+M|\,{\rm cd}(\mathfrak{a}, R/\mathfrak{p})=\text{cd}(\mathfrak{a}, M)\},$ where
+$L$ is the largest submodule of $M$ such that ${\rm cd}(\mathfrak{a}, L)< {\rm
+cd}(\mathfrak{a}, M)$. We also show that if $H^{\dim M}_{\mathfrak{a}}(M)=0$,
+then $\text{Att}_R(H^{\dim M-1}_{\mathfrak{a}}(M))= \{\mathfrak{p} \in
+\text{Supp} (M)|\,{\rm cd}(\mathfrak{a}, R/\mathfrak{p})=\dim M-1\},$ and so
+the attached primes of $H^{\dim M-1}_{\mathfrak{a}}(M)$ depends only on
+$\text{Supp} (M)$. Finally, we prove that if $M$ is an arbitrary module (not
+necessarily finitely generated) over a Noetherian ring $R$ with ${\rm
+cd}(\mathfrak{a}, M)={\rm cd}(\mathfrak{a}, R/\text{Ann}_RM)$, then
+$\text{Att}_R(H^{{\rm cd}(\mathfrak{a},
+M)}_{\mathfrak{a}}(M))\subseteq\{\mathfrak{p} \in V(\text{Ann}_RM)|\,{\rm
+cd}(\mathfrak{a}, R/\mathfrak{p})={\rm cd}(\mathfrak{a}, M)\}.$
+As a consequence of this it is shown that if $\dim M=\dim R$, then
+$\text{Att}_R(H^{\dim M}_{\mathfrak{a}}(M))\subseteq\{\mathfrak{p} \in
+\text{Ass}_R M|\,{\rm cd}(\mathfrak{a}, R/\mathfrak{p})=\dim M\}.$
+",0,0,1,0,0,0
+4974,Translation matrix elements for spherical Gauss-Laguerre basis functions,"  Spherical Gauss-Laguerre (SGL) basis functions, i.e., normalized functions of
+the type $L_{n-l-1}^{(l + 1/2)}(r^2) r^{l} Y_{lm}(\vartheta,\varphi)$, $|m|
+\leq l < n \in \mathbb{N}$, constitute an orthonormal polynomial basis of the
+space $L^{2}$ on $\mathbb{R}^{3}$ with radial Gaussian weight $\exp(-r^{2})$.
+We have recently described reliable fast Fourier transforms for the SGL basis
+functions. The main application of the SGL basis functions and our fast
+algorithms is in solving certain three-dimensional rigid matching problems,
+where the center is prioritized over the periphery. For this purpose, so-called
+SGL translation matrix elements are required, which describe the spectral
+behavior of the SGL basis functions under translations. In this paper, we
+derive a closed-form expression of these translation matrix elements, allowing
+for a direct computation of these quantities in practice.
+",0,0,1,0,0,0
+4975,A theoretical analysis of extending frequency-bin entanglement from photon-photon to atom-photon hybrid systems,"  Inspired by the recent developments in the research of atom-photon quantum
+interface and energy-time entanglement between single photon pulses, we propose
+to establish the concept of a special energy-time entanglement between a single
+photon pulse and internal states of a single atom, which is analogous to the
+frequency-bin entanglement between single photon pulses. We show that this type
+of entanglement arises naturally in the interaction between frequency-bin
+entangled single photon pulse pair and a single atom, via straightforward
+atom-photon phase gate operations. We also discuss the properties of this type
+of entanglement and show a preliminary example of its potential application in
+quantum networking. Moreover, a quantum entanglement witness is constructed to
+detect such entanglement from a reasonably large set of separable states.
+",0,1,0,0,0,0
+4976,Parallel Concatenation of Bayesian Filters: Turbo Filtering,"  In this manuscript a method for developing novel filtering algorithms through
+the parallel concatenation of two Bayesian filters is illustrated. Our
+description of this method, called turbo filtering, is based on a new graphical
+model; this allows us to efficiently describe both the processing accomplished
+inside each of the constituent filter and the interactions between them. This
+model is exploited to develop two new filtering algorithms for conditionally
+linear Gaussian systems. Numerical results for a specific dynamic system
+evidence that such filters can achieve a better complexity-accuracy tradeoff
+than marginalized particle filtering.
+",0,0,0,1,0,0
+4977,Shot noise in ultrathin superconducting wires,"  Quantum phase slips (QPS) may produce non-equilibrium voltage fluctuations in
+current-biased superconducting nanowires. Making use of the Keldysh technique
+and employing the phase-charge duality arguments we investigate such
+fluctuations within the four-point measurement scheme and demonstrate that shot
+noise of the voltage detected in such nanowires may essentially depend on the
+particular measurement setup. In long wires the shot noise power decreases with
+increasing frequency $\Omega$ and vanishes beyond a threshold value of $\Omega$
+at $T \to 0$
+",0,1,0,0,0,0
+4978,Linearity of stability conditions,"  We study different concepts of stability for modules over a finite
+dimensional algebra: linear stability, given by a ""central charge"", and
+nonlinear stability given by the wall-crossing sequence of a ""green path"". Two
+other concepts, finite Harder-Narasimhan stratification of the module category
+and maximal forward hom-orthogonal sequences of Schurian modules, which are
+always equivalent to each other, are shown to be equivalent to nonlinear
+stability and to a maximal green sequence, defined using Fomin-Zelevinsky
+quiver mutation, in the case the algebra is hereditary.
+This is the first of a series of three papers whose purpose is to determine
+all maximal green sequences of maximal length for quivers of affine type
+$\tilde A$ and determine which are linear. The complete answer will be given in
+the final paper [1].
+",0,0,1,0,0,0
+4979,Urban Data Streams and Machine Learning: A Case of Swiss Real Estate Market,"  In this paper, we show how using publicly available data streams and machine
+learning algorithms one can develop practical data driven services with no
+input from domain experts as a form of prior knowledge. We report the initial
+steps toward development of a real estate portal in Switzerland. Based on
+continuous web crawling of publicly available real estate advertisements and
+using building data from Open Street Map, we developed a system, where we
+roughly estimate the rental and sale price indexes of 1.7 million buildings
+across the country. In addition to these rough estimates, we developed a web
+based API for accurate automated valuation of rental prices of individual
+properties and spatial sensitivity analysis of rental market. We tested several
+established function approximation methods against the test data to check the
+quality of the rental price estimations and based on our experiments, Random
+Forest gives very reasonable results with the median absolute relative error of
+6.57 percent, which is comparable with the state of the art in the industry. We
+argue that while recently there have been successful cases of real estate
+portals, which are based on Big Data, majority of the existing solutions are
+expensive, limited to certain users and mostly with non-transparent underlying
+systems. As an alternative we discuss, how using the crawled data sets and
+other open data sets provided from different institutes it is easily possible
+to develop data driven services for spatial and temporal sensitivity analysis
+in the real estate market to be used for different stakeholders. We believe
+that this kind of digital literacy can disrupt many other existing business
+concepts across many domains.
+",1,0,0,1,0,0
+4980,BinPro: A Tool for Binary Source Code Provenance,"  Enforcing open source licenses such as the GNU General Public License (GPL),
+analyzing a binary for possible vulnerabilities, and code maintenance are all
+situations where it is useful to be able to determine the source code
+provenance of a binary. While previous work has either focused on computing
+binary-to-binary similarity or source-to-source similarity, BinPro is the first
+work we are aware of to tackle the problem of source-to-binary similarity.
+BinPro can match binaries with their source code even without knowing which
+compiler was used to produce the binary, or what optimization level was used
+with the compiler. To do this, BinPro utilizes machine learning to compute
+optimal code features for determining binary-to-source similarity and a static
+analysis pipeline to extract and compute similarity based on those features.
+Our experiments show that on average BinPro computes a similarity of 81% for
+matching binaries and source code of the same applications, and an average
+similarity of 25% for binaries and source code of similar but different
+applications. This shows that BinPro's similarity score is useful for
+determining if a binary was derived from a particular source code.
+",1,0,0,0,0,0
+4981,The placement of the head that maximizes predictability. An information theoretic approach,"  The minimization of the length of syntactic dependencies is a
+well-established principle of word order and the basis of a mathematical theory
+of word order. Here we complete that theory from the perspective of information
+theory, adding a competing word order principle: the maximization of
+predictability of a target element. These two principles are in conflict: to
+maximize the predictability of the head, the head should appear last, which
+maximizes the costs with respect to dependency length minimization. The
+implications of such a broad theoretical framework to understand the
+optimality, diversity and evolution of the six possible orderings of subject,
+object and verb are reviewed.
+",1,1,0,0,0,0
+4982,Nonparametric regression using deep neural networks with ReLU activation function,"  Consider the multivariate nonparametric regression model. It is shown that
+estimators based on sparsely connected deep neural networks with ReLU
+activation function and properly chosen network architecture achieve the
+minimax rates of convergence (up to log n-factors) under a general composition
+assumption on the regression function. The framework includes many well-studied
+structural constraints such as (generalized) additive models. While there is a
+lot of flexibility in the network architecture, the tuning parameter is the
+sparsity of the network. Specifically, we consider large networks with number
+of potential network parameters exceeding the sample size. The analysis gives
+some insights why multilayer feedforward neural networks perform well in
+practice. Interestingly, the depth (number of layers) of the neural network
+architectures plays an important role and our theory suggests that for
+nonparametric regression scaling the network depth with the logarithm of the
+sample size is natural. It is also shown that under the composition assumption
+wavelet estimators can only achieve suboptimal rates.
+",1,0,1,1,0,0
+4983,Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract),"  This paper presents an unsupervised method to learn a neural network, namely
+an explainer, to interpret a pre-trained convolutional neural network (CNN),
+i.e., the explainer uses interpretable visual concepts to explain features in
+middle conv-layers of a CNN. Given feature maps of a conv-layer of the CNN, the
+explainer performs like an auto-encoder, which decomposes the feature maps into
+object-part features. The object-part features are learned to reconstruct CNN
+features without much loss of information. We can consider the disentangled
+representations of object parts a paraphrase of CNN features, which help people
+understand the knowledge encoded by the CNN. More crucially, we learn the
+explainer via knowledge distillation without using any annotations of object
+parts or textures for supervision. In experiments, our method was widely used
+to interpret features of different benchmark CNNs, and explainers significantly
+boosted the feature interpretability without hurting the discrimination power
+of the CNNs.
+",1,0,0,1,0,0
+4984,Measuring Cognitive Conflict in Virtual Reality with Feedback-Related Negativity,"  As virtual reality (VR) emerges as a mainstream platform, designers have
+started to experiment new interaction techniques to enhance the user
+experience. This is a challenging task because designers not only strive to
+provide designs with good performance but also carefully ensure not to disrupt
+users' immersive experience. There is a dire need for a new evaluation tool
+that extends beyond traditional quantitative measurements to assist designers
+in the design process. We propose an EEG-based experiment framework that
+evaluates interaction techniques in VR by measuring intentionally elicited
+cognitive conflict. Through the analysis of the feedback-related negativity
+(FRN) as well as other quantitative measurements, this framework allows
+designers to evaluate the effect of the variables of interest. We studied the
+framework by applying it to the fundamental task of 3D object selection using
+direct 3D input, i.e. tracked hand in VR. The cognitive conflict is
+intentionally elicited by manipulating the selection radius of the target
+object. Our first behavior experiment validated the framework in line with the
+findings of conflict-induced behavior adjustments like those reported in other
+classical psychology experiment paradigms. Our second EEG-based experiment
+examines the effect of the appearance of virtual hands. We found that the
+amplitude of FRN correlates with the level of realism of the virtual hands,
+which concurs with the Uncanny Valley theory.
+",1,0,0,0,0,0
+4985,Strong deformations of DNA: Effect on the persistence length,"  Extreme deformations of the DNA double helix attracted a lot of attention
+during the past decades. Particularly, the determination of the persistence
+length of DNA with extreme local disruptions, or kinks, has become a crucial
+problem in the studies of many important biological processes. In this paper we
+review an approach to calculate the persistence length of the double helix by
+taking into account the formation of kinks of arbitrary configuration. The
+reviewed approach improves the Kratky--Porod model to determine the type and
+nature of kinks that occur in the double helix, by measuring a reduction of the
+persistence length of the kinkable DNA.
+",0,0,0,0,1,0
+4986,BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters,"  Many real-world objects are designed by smooth curves, especially in the
+domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and
+hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design
+process of those objects, we propose a deep learning based generative model
+that can synthesize smooth curves. The model maps a low-dimensional latent
+representation to a sequence of discrete points sampled from a rational
+Bézier curve. We demonstrate the performance of our method in completing both
+synthetic and real-world generative tasks. Results show that our method can
+generate diverse and realistic curves, while preserving consistent shape
+variation in the latent space, which is favorable for latent space design
+optimization or design space exploration.
+",0,0,0,1,0,0
+4987,Raman scattering study of tetragonal magnetic phase in Sr$_{1-x}$Na$_x$Fe$_2$As$_2$: structural symmetry and electronic gap,"  We use inelastic light scattering to study Sr$_{1-x}$Na$_x$Fe$_2$As$_2$
+($x\approx0.34$), which exhibits a robust tetragonal magnetic phase that
+restores the four-fold rotation symmetry inside the orthorhombic magnetic
+phase. With cooling, we observe splitting and recombination of an $E_g$ phonon
+peak upon entering the orthorhombic and tetragonal magnetic phases,
+respectively, consistent with the reentrant phase behavior. Our electronic
+Raman data reveal a pronounced feature that is clearly associated with the
+tetragonal magnetic phase, suggesting the opening of an electronic gap. No
+phonon back-folding behavior can be detected above the noise level, which
+implies that any lattice translation symmetry breaking in the tetragonal
+magnetic phase must be very weak.
+",0,1,0,0,0,0
+4988,Learning Mixture of Gaussians with Streaming Data,"  In this paper, we study the problem of learning a mixture of Gaussians with
+streaming data: given a stream of $N$ points in $d$ dimensions generated by an
+unknown mixture of $k$ spherical Gaussians, the goal is to estimate the model
+parameters using a single pass over the data stream. We analyze a streaming
+version of the popular Lloyd's heuristic and show that the algorithm estimates
+all the unknown centers of the component Gaussians accurately if they are
+sufficiently separated. Assuming each pair of centers are $C\sigma$ distant
+with $C=\Omega((k\log k)^{1/4}\sigma)$ and where $\sigma^2$ is the maximum
+variance of any Gaussian component, we show that asymptotically the algorithm
+estimates the centers optimally (up to constants); our center separation
+requirement matches the best known result for spherical Gaussians
+\citep{vempalawang}. For finite samples, we show that a bias term based on the
+initial estimate decreases at $O(1/{\rm poly}(N))$ rate while variance
+decreases at nearly optimal rate of $\sigma^2 d/N$.
+Our analysis requires seeding the algorithm with a good initial estimate of
+the true cluster centers for which we provide an online PCA based clustering
+algorithm. Indeed, the asymptotic per-step time complexity of our algorithm is
+the optimal $d\cdot k$ while space complexity of our algorithm is $O(dk\log
+k)$.
+In addition to the bias and variance terms which tend to $0$, the
+hard-thresholding based updates of streaming Lloyd's algorithm is agnostic to
+the data distribution and hence incurs an approximation error that cannot be
+avoided. However, by using a streaming version of the classical
+(soft-thresholding-based) EM method that exploits the Gaussian distribution
+explicitly, we show that for a mixture of two Gaussians the true means can be
+estimated consistently, with estimation error decreasing at nearly optimal
+rate, and tending to $0$ for $N\rightarrow \infty$.
+",1,0,0,1,0,0
+4989,Dust in the reionization era: ALMA observations of a $z$=8.38 Galaxy,"  We report on the detailed analysis of a gravitationally-lensed Y-band
+dropout, A2744_YD4, selected from deep Hubble Space Telescope imaging in the
+Frontier Field cluster Abell 2744. Band 7 observations with the Atacama Large
+Millimeter Array (ALMA) indicate the proximate detection of a significant 1mm
+continuum flux suggesting the presence of dust for a star-forming galaxy with a
+photometric redshift of $z\simeq8$. Deep X-SHOOTER spectra confirms the high
+redshift identity of A2744_YD4 via the detection of Lyman $\alpha$ emission at
+a redshift $z$=8.38. The association with the ALMA detection is confirmed by
+the presence of [OIII] 88$\mu$m emission at the same redshift. Although both
+emission features are only significant at the 4 $\sigma$ level, we argue their
+joint detection and the positional coincidence with a high redshift dropout in
+the HST images confirms the physical association. Analysis of the available
+photometric data and the modest gravitational magnification ($\mu\simeq2$)
+indicates A2744_YD4 has a stellar mass of $\sim$ 2$\times$10$^9$ M$_{\odot}$, a
+star formation rate of $\sim20$ M$_{\odot}$/yr and a dust mass of
+$\sim$6$\times$10$^{6}$ M$_{\odot}$. We discuss the implications of the
+formation of such a dust mass only $\simeq$200 Myr after the onset of cosmic
+reionisation.
+",0,1,0,0,0,0
+4990,Green-Blue Stripe Pattern for Range Sensing from a Single Image,"  In this paper, we present a novel method for rapid high-resolution range
+sensing using green-blue stripe pattern. We use green and blue for designing
+high-frequency stripe projection pattern. For accurate and reliable range
+recovery, we identify the stripe patterns by our color-stripe segmentation and
+unwrapping algorithms. The experimental result for a naked human face shows the
+effectiveness of our method.
+",1,0,0,0,0,0
+4991,It Takes (Only) Two: Adversarial Generator-Encoder Networks,"  We present a new autoencoder-type architecture that is trainable in an
+unsupervised mode, sustains both generation and inference, and has the quality
+of conditional and unconditional samples boosted by adversarial learning.
+Unlike previous hybrids of autoencoders and adversarial networks, the
+adversarial game in our approach is set up directly between the encoder and the
+generator, and no external mappings are trained in the process of learning. The
+game objective compares the divergences of each of the real and the generated
+data distributions with the prior distribution in the latent space. We show
+that direct generator-vs-encoder game leads to a tight coupling of the two
+components, resulting in samples and reconstructions of a comparable quality to
+some recently-proposed more complex architectures.
+",1,0,0,1,0,0
+4992,Electrostatic gyrokinetic simulation of global tokamak boundary plasma and the generation of nonlinear intermittent turbulence,"  Boundary plasma physics plays an important role in tokamak confinement, but
+is difficult to simulate in a gyrokinetic code due to the scale-inseparable
+nonlocal multi-physics in magnetic separatrix and open magnetic field geometry.
+Neutral particles are also an important part of the boundary plasma physics. In
+the present paper, noble electrostatic gyrokinetic techniques to simulate the
+flux-driven, low-beta electrostatic boundary plasma is reported. Gyrokinetic
+ions and drift-kinetic electrons are utilized without scale-separation between
+the neoclassical and turbulence dynamics. It is found that the nonlinear
+intermittent turbulence is a natural gyrokinetic phenomenon in the boundary
+plasma in the vicinity of the magnetic separatrix surface and in the scrape-off
+layer.
+",0,1,0,0,0,0
+4993,The Montecinos-Balsara ADER-FV Polynomial Basis: Convergence Properties & Extension to Non-Conservative Multidimensional Systems,"  Hyperbolic systems of PDEs can be solved to arbitrary orders of accuracy by
+using the ADER Finite Volume method. These PDE systems may be non-conservative
+and non-homogeneous, and contain stiff source terms. ADER-FV requires a
+spatio-temporal polynomial reconstruction of the data in each spacetime cell,
+at each time step. This reconstruction is obtained as the root of a nonlinear
+system, resulting from the use of a Galerkin method. It was proved in Jackson
+[7] that for traditional choices of basis polynomials, the eigenvalues of
+certain matrices appearing in these nonlinear systems are always 0, regardless
+of the number of spatial dimensions of the PDEs or the chosen order of accuracy
+of the ADER-FV method. This guarantees fast convergence to the Galerkin root
+for certain classes of PDEs.
+In Montecinos and Balsara [9] a new, more efficient class of basis
+polynomials for the one-dimensional ADER-FV method was presented. This new
+class of basis polynomials, originally presented for conservative systems, is
+extended to multidimensional, non-conservative systems here, and the
+corresponding property regarding the eigenvalues of the Galerkin matrices is
+proved.
+",0,1,0,0,0,0
+4994,Reducibility of the Quantum Harmonic Oscillator in $d$-dimensions with Polynomial Time Dependent Perturbation,"  We prove a reducibility result for a quantum harmonic oscillator in arbitrary
+dimensions with arbitrary frequencies perturbed by a linear operator which is a
+polynomial of degree two in $x_j$, $-i \partial_j$ with coefficients which
+depend quasiperiodically on time.
+",0,0,1,0,0,0
+4995,Model Order Selection Rules For Covariance Structure Classification,"  The adaptive classification of the interference covariance matrix structure
+for radar signal processing applications is addressed in this paper. This
+represents a key issue because many detection architectures are synthesized
+assuming a specific covariance structure which may not necessarily coincide
+with the actual one due to the joint action of the system and environment
+uncertainties. The considered classification problem is cast in terms of a
+multiple hypotheses test with some nested alternatives and the theory of Model
+Order Selection (MOS) is exploited to devise suitable decision rules. Several
+MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria
+are adopted and the corresponding merits and drawbacks are discussed. At the
+analysis stage, illustrating examples for the probability of correct model
+selection are presented showing the effectiveness of the proposed rules.
+",0,0,1,1,0,0
+4996,Enhancing Interpretability of Black-box Soft-margin SVM by Integrating Data-based Priors,"  The lack of interpretability often makes black-box models difficult to be
+applied to many practical domains. For this reason, the current work, from the
+black-box model input port, proposes to incorporate data-based prior
+information into the black-box soft-margin SVM model to enhance its
+interpretability. The concept and incorporation mechanism of data-based prior
+information are successively developed, based on which the interpretable or
+partly interpretable SVM optimization model is designed and then solved through
+handily rewriting the optimization problem as a nonlinear quadratic programming
+problem. An algorithm for mining data-based linear prior information from data
+set is also proposed, which generates a linear expression with respect to two
+appropriate inputs identified from all inputs of system. At last, the proposed
+interpretability enhancement strategy is applied to eight benchmark examples
+for effectiveness exhibition.
+",1,0,0,1,0,0
+4997,CollaGAN : Collaborative GAN for Missing Image Data Imputation,"  In many applications requiring multiple inputs to obtain a desired output, if
+any of the input data is missing, it often introduces large amounts of bias.
+Although many techniques have been developed for imputing missing data, the
+image imputation is still difficult due to complicated nature of natural
+images. To address this problem, here we proposed a novel framework for missing
+image data imputation, called Collaborative Generative Adversarial Network
+(CollaGAN). CollaGAN converts an image imputation problem to a multi-domain
+images-to-image translation task so that a single generator and discriminator
+network can successfully estimate the missing data using the remaining clean
+data set. We demonstrate that CollaGAN produces the images with a higher visual
+quality compared to the existing competing approaches in various image
+imputation tasks.
+",1,0,0,1,0,0
+4998,A Kuroda-style j-translation,"  In topos theory it is well-known that any nucleus j gives rise to a
+translation of intuitionistic logic into itself in a way which generalises the
+Goedel-Gentzen negative translation. Here we show that there exists a similar
+j-translation which is more in the spirit of Kuroda's negative translation. The
+key is to apply the nucleus not only to the entire formula and universally
+quantified subformulas, but to conclusions of implications as well. The
+development is entirely syntactic and no knowledge of topos theory is required
+to read this small note.
+",0,0,1,0,0,0
+4999,Electrical 2π phase control of infrared light in a 350nm footprint using graphene plasmons,"  Modulating the amplitude and phase of light is at the heart of many
+applications such as wavefront shaping, transformation optics, phased arrays,
+modulators and sensors. Performing this task with high efficiency and small
+footprint is a formidable challenge. Metasurfaces and plasmonics are promising
+, but metals exhibit weak electro-optic effects. Two-dimensional materials,
+such as graphene, have shown great performance as modulators with small drive
+voltages. Here we show a graphene plasmonic phase modulator which is capable of
+tuning the phase between 0 and 2{\pi} in situ. With a footprint of 350nm it is
+more than 30 times smaller than the 10.6$\mu$m free space wavelength. The
+modulation is achieved by spatially controlling the plasmon phase velocity in a
+device where the spatial carrier density profile is tunable. We provide a
+scattering theory for plasmons propagating through spatial density profiles.
+This work constitutes a first step towards two-dimensional transformation
+optics for ultra-compact modulators and biosensing.
+",0,1,0,0,0,0
+5000,Existence of closed geodesics through a regular point on translation surfaces,"  We show that on any translation surface, if a regular point is contained in a
+simple closed geodesic, then it is contained in infinitely many simple closed
+geodesics, whose directions are dense in the unit circle. Moreover, the set of
+points that are not contained in any simple closed geodesic is finite. We also
+construct explicit examples showing that such points exist. For a surface in
+any hyperelliptic component, we show that this finite exceptional set is
+actually empty. The proofs of our results use Apisa's classifications of
+periodic points and of $\GL(2,\R)$ orbit closures in hyperelliptic components,
+as well as a recent result of Eskin-Filip-Wright.
+",0,0,1,0,0,0
+5001,Mathematical and numerical validation of the simplified spherical harmonics approach for time-dependent anisotropic-scattering transport problems in homogeneous media,"  In this work, we extend the solid harmonics derivation, which was used by
+Ackroyd et al to derive the steady-state SP$_N$ equations, to transient
+problems. The derivation expands the angular flux in ordinary surface harmonics
+but uses harmonic polynomials to generate additional surface spherical harmonic
+terms to be used in Galerkin projection. The derivation shows the equivalence
+between the SP$_N$ and the P$_N$ approximation. Also, we use the line source
+problem and McClarren's ""box"" problem to demonstrate such equivalence
+numerically. Both problems were initially proposed for isotropic scattering,
+but here we add higher-order scattering moments to them. Results show that the
+difference between the SP$_N$ and P$_N$ scalar flux solution is at the roundoff
+level.
+",0,1,0,0,0,0
+5002,L1-norm Principal-Component Analysis of Complex Data,"  L1-norm Principal-Component Analysis (L1-PCA) of real-valued data has
+attracted significant research interest over the past decade. However, L1-PCA
+of complex-valued data remains to date unexplored despite the many possible
+applications (e.g., in communication systems). In this work, we establish
+theoretical and algorithmic foundations of L1-PCA of complex-valued data
+matrices. Specifically, we first show that, in contrast to the real-valued case
+for which an optimal polynomial-cost algorithm was recently reported by
+Markopoulos et al., complex L1-PCA is formally NP-hard in the number of data
+points. Then, casting complex L1-PCA as a unimodular optimization problem, we
+present the first two suboptimal algorithms in the literature for its solution.
+Our experimental studies illustrate the sturdy resistance of complex L1-PCA
+against faulty measurements/outliers in the processed data.
+",1,0,0,0,0,0
+5003,Viscous dissipation of surface waves and its relevance to analogue gravity experiments,"  We consider dissipation of surface waves on fluids, with a view to its
+effects on analogue gravity experiments. We begin by reviewing some general
+properties of wave dissipation, before restricting our attention to surface
+waves and the dissipative role played by viscosity there. Finally, with
+particular focus on water, we consider several experimental setups inspired by
+analogue gravity: the analogue Hawking effect, the black hole laser, the
+analogue wormhole, and double bouncing at the wormhole entrance. Dissipative
+effects are considered in each, and we give estimates for their optimized
+experimental parameters.
+",0,1,0,0,0,0
+5004,Neural Optimizer Search with Reinforcement Learning,"  We present an approach to automate the process of discovering optimization
+methods, with a focus on deep learning architectures. We train a Recurrent
+Neural Network controller to generate a string in a domain specific language
+that describes a mathematical update equation based on a list of primitive
+functions, such as the gradient, running average of the gradient, etc. The
+controller is trained with Reinforcement Learning to maximize the performance
+of a model after a few epochs. On CIFAR-10, our method discovers several update
+rules that are better than many commonly used optimizers, such as Adam,
+RMSProp, or SGD with and without Momentum on a ConvNet model. We introduce two
+new optimizers, named PowerSign and AddSign, which we show transfer well and
+improve training on a variety of different tasks and architectures, including
+ImageNet classification and Google's neural machine translation system.
+",1,0,0,1,0,0
+5005,"Hybrid Sterility Can Only be Primary When Acting as a Reproductive Barrier for Sympatric Speciation,","  Parental gametes unite to form a zygote that develops into an adult with
+gonads that, in turn, produce gametes. Interruption of this germinal cycle by
+prezygotic or postzygotic reproductive barriers can result in two independent
+cycles, each with the potential to evolve into a new species. When the
+speciation process is complete, members of each species are fully
+reproductively isolated from those of the other. During speciation a primary
+barrier may be supported and eventually superceded by a later appearing
+secondary barrier. For those holding certain cases of prezygotic isolation to
+be primary (e.g. elephant cannot copulate with mouse), the onus is to show that
+they had not been preceded over evolutionary time by periods of postzygotic
+hybrid inviability (genically determined) or sterility (genically or
+chromosomally determined). Likewise, the onus is upon those holding cases of
+hybrid inviability to be primary (e.g. Dobzhansky-Muller epistatic
+incompatibilities), to show that they had not been preceded by periods, however
+brief, of hybrid sterility. The latter, when acting as a sympatric barrier
+causing reproductive isolation, can only be primary. In many cases, hybrid
+sterility may result from incompatibilities between parental chromosomes that
+attempt to pair during meiosis in the gonad of their offspring
+(Winge-Crowther-Bateson incompatibilities). While WCB incompatibilities have
+long been observed on a microscopic scale, there is growing evidence for a role
+of dispersed finer DNA sequence differences.
+",0,0,0,0,1,0
+5006,Point-Cloud-Based Aerial Fragmentation Analysis for Application in the Minerals Industry,"  This work investigates the application of Unmanned Aerial Vehicle (UAV)
+technology for measurement of rock fragmentation without placement of scale
+objects in the scene to determine image scale. Commonly practiced image-based
+rock fragmentation analysis requires a technician to walk to a rock pile, place
+a scale object of known size in the area of interest, and capture individual 2D
+images. Our previous work has used UAV technology for the first time to acquire
+real-time rock fragmentation data and has shown comparable quality of results;
+however, it still required the (potentially dangerous) placement of scale
+objects, and continued to make the assumption that the rock pile surface is
+planar and that the scale objects lie on the surface plane. This work improves
+our UAV-based approach to enable rock fragmentation measurement without
+placement of scale objects and without the assumption of planarity. This is
+achieved by first generating a point cloud of the rock pile from 2D images,
+taking into account intrinsic and extrinsic camera parameters, and then taking
+2D images for fragmentation analysis. This work represents an important step
+towards automating post-blast rock fragmentation analysis. In experiments, a
+rock pile with known size distribution was photographed by the UAV with and
+without using scale objects. For fragmentation analysis without scale objects,
+a point cloud of the rock pile was generated and used to compute image scale.
+Comparison of the rock size distributions show that this point-cloud-based
+method enables producing measurements with better or comparable accuracy
+(within 10% of the ground truth) to the manual method with scale objects.
+",1,0,0,0,0,0
+5007,DyNet: The Dynamic Neural Network Toolkit,"  We describe DyNet, a toolkit for implementing neural network models based on
+dynamic declaration of network structure. In the static declaration strategy
+that is used in toolkits like Theano, CNTK, and TensorFlow, the user first
+defines a computation graph (a symbolic representation of the computation), and
+then examples are fed into an engine that executes this computation and
+computes its derivatives. In DyNet's dynamic declaration strategy, computation
+graph construction is mostly transparent, being implicitly constructed by
+executing procedural code that computes the network outputs, and the user is
+free to use different network structures for each input. Dynamic declaration
+thus facilitates the implementation of more complicated network architectures,
+and DyNet is specifically designed to allow users to implement their models in
+a way that is idiomatic in their preferred programming language (C++ or
+Python). One challenge with dynamic declaration is that because the symbolic
+computation graph is defined anew for every training example, its construction
+must have low overhead. To achieve this, DyNet has an optimized C++ backend and
+lightweight graph representation. Experiments show that DyNet's speeds are
+faster than or comparable with static declaration toolkits, and significantly
+faster than Chainer, another dynamic declaration toolkit. DyNet is released
+open-source under the Apache 2.0 license and available at
+this http URL.
+",1,0,0,1,0,0
+5008,Polyteam Semantics,"  Team semantics is the mathematical framework of modern logics of dependence
+and independence in which formulae are interpreted by sets of assignments
+(teams) instead of single assignments as in first-order logic. In order to
+deepen the fruitful interplay between team semantics and database dependency
+theory, we define ""Polyteam Semantics"" in which formulae are evaluated over a
+family of teams. We begin by defining a novel polyteam variant of dependence
+atoms and give a finite axiomatisation for the associated implication problem.
+We also characterise the expressive power of poly-dependence logic by
+properties of polyteams that are downward closed and definable in existential
+second-order logic (ESO). The analogous result is shown to hold for
+poly-independence logic and all ESO-definable properties.
+",1,0,0,0,0,0
+5009,"Where Classification Fails, Interpretation Rises","  An intriguing property of deep neural networks is their inherent
+vulnerability to adversarial inputs, which significantly hinders their
+application in security-critical domains. Most existing detection methods
+attempt to use carefully engineered patterns to distinguish adversarial inputs
+from their genuine counterparts, which however can often be circumvented by
+adaptive adversaries. In this work, we take a completely different route by
+leveraging the definition of adversarial inputs: while deceiving for deep
+neural networks, they are barely discernible for human visions. Building upon
+recent advances in interpretable models, we construct a new detection framework
+that contrasts an input's interpretation against its classification. We
+validate the efficacy of this framework through extensive experiments using
+benchmark datasets and attacks. We believe that this work opens a new direction
+for designing adversarial input detection methods.
+",1,0,0,1,0,0
+5010,Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling,"  Latent Dirichlet Allocation (LDA) models trained without stopword removal
+often produce topics with high posterior probabilities on uninformative words,
+obscuring the underlying corpus content. Even when canonical stopwords are
+manually removed, uninformative words common in that corpus will still dominate
+the most probable words in a topic. In this work, we first show how the
+standard topic quality measures of coherence and pointwise mutual information
+act counter-intuitively in the presence of common but irrelevant words, making
+it difficult to even quantitatively identify situations in which topics may be
+dominated by stopwords. We propose an additional topic quality metric that
+targets the stopword problem, and show that it, unlike the standard measures,
+correctly correlates with human judgements of quality. We also propose a
+simple-to-implement strategy for generating topics that are evaluated to be of
+much higher quality by both human assessment and our new metric. This approach,
+a collection of informative priors easily introduced into most LDA-style
+inference methods, automatically promotes terms with domain relevance and
+demotes domain-specific stop words. We demonstrate this approach's
+effectiveness in three very different domains: Department of Labor accident
+reports, online health forum posts, and NIPS abstracts. Overall we find that
+current practices thought to solve this problem do not do so adequately, and
+that our proposal offers a substantial improvement for those interested in
+interpreting their topics as objects in their own right.
+",1,0,0,0,0,0
+5011,Buckling in Armored Droplets,"  The issue of the buckling mechanism in droplets stabilized by solid particles
+(armored droplets) is tackled at a mesoscopic level using dissipative particle
+dynamics simulations. We consider spherical water droplet in a decane solvent
+coated with nanoparticle monolayers of two different types: Janus and
+homogeneous. The chosen particles yield comparable initial three-phase contact
+angles, chosen to maximize the adsorption energy at the interface. We study the
+interplay between the evolution of droplet shape, layering of the particles,
+and their distribution at the interface when the volume of the droplets is
+reduced. We show that Janus particles affect strongly the shape of the droplet
+with the formation of a crater-like depression. This evolution is actively
+controlled by a close-packed particle monolayer at the curved interface. On the
+contrary, homogeneous particles follow passively the volume reduction of the
+droplet, whose shape does not deviate too much from spherical, even when a
+nanoparticle monolayer/bilayer transition is detected at the interface. We
+discuss how these buckled armored droplets might be of relevance in various
+applications including potential drug delivery systems and biomimetic design of
+functional surfaces.
+",0,1,0,0,0,0
+5012,Density of Analytic Polynomials in Abstract Hardy Spaces,"  Let $X$ be a separable Banach function space on the unit circle $\mathbb{T}$
+and $H[X]$ be the abstract Hardy space built upon $X$. We show that the set of
+analytic polynomials is dense in $H[X]$ if the Hardy-Littlewood maximal
+operator is bounded on the associate space $X'$. This result is specified to
+the case of variable Lebesgue spaces.
+",0,0,1,0,0,0
+5013,On non-Abelian Lie Bracket of Generalized Covariant Hamilton Systems,"  This is a theoretical paper, which is a continuation of [arXiv:1710.10597],
+it considers the non-abelian Lie algebra $\mathcal{G}$ of Lie groups for
+$\left[ {{X}_{i}},{{X}_{j}} \right]=c_{ij}^{k}{{X}_{k}}\in \mathcal{G}$ on the
+foundation of the GCHS, where $c_{ij}^{k}\in {{C}^{\infty }}\left( U,R \right)$
+are the structure constants. The GPWB [arXiv:1710.10597] is nonlinear bracket
+applying to the non-Euclidean space, the second order (2,0) form antisymmetric
+curvature tensor ${{F}_{ij}}=c_{ij}^{k}{{D}_{k}}$, and Qsu quantity
+${{q}_{i}}=w_{i}^{k}{{D}_{k}}$ are accordingly obtained by using the
+non-abelian Lie bracket. The GCHS $\left\{ H,f \right\}\in {{C}^{\infty
+}}\left( M,\mathbb{R} \right)$ holds for the non-symplectic vector field
+$X_{H}^{M}\in \mathcal{G}$ and $f\in {{C}^{\infty }}\left( M,\mathbb{R}
+\right)$ that implies the covariant evolution equation consists of two parts,
+NGHS and W dynamics along with the second order invariant operator
+$\frac{{\mathcal{D}^{2}}}{d{{t}^{2}}}=\frac{{{d}^{2}}}{d{{t}^{2}}}+2w\frac{d}{dt}+\beta$.
+",0,0,1,0,0,0
+5014,The Topology of Statistical Verifiability,"  Topological models of empirical and formal inquiry are increasingly
+prevalent. They have emerged in such diverse fields as domain theory [1, 16],
+formal learning theory [18], epistemology and philosophy of science [10, 15, 8,
+9, 2], statistics [6, 7] and modal logic [17, 4]. In those applications, open
+sets are typically interpreted as hypotheses deductively verifiable by true
+propositional information that rules out relevant possibilities. However, in
+statistical data analysis, one routinely receives random samples logically
+compatible with every statistical hypothesis. We bridge the gap between
+propositional and statistical data by solving for the unique topology on
+probability measures in which the open sets are exactly the statistically
+verifiable hypotheses. Furthermore, we extend that result to a topological
+characterization of learnability in the limit from statistical data.
+",1,0,0,0,0,0
+5015,A spectral approach to the linking number in the 3-torus,"  Given a closed Riemannian manifold and a pair of multi-curves in it, we give
+a formula relating the linking number of the later to the spectral theory of
+the Laplace operator acting on differential one forms. As an application, we
+compute the linking number of any two multi-geodesics of the flat torus of
+dimension 3, generalising a result of P. Dehornoy.
+",0,0,1,0,0,0
+5016,Volkov-Pankratov states in topological heterojunctions,"  We show that a smooth interface between two insulators of opposite
+topological Z2 indices possesses multiple surface states, both massless and
+massive. While the massless surface state is non-degenerate, chiral and
+insensitive to the interface potential, the massive surface states only appear
+for a sufficiently smooth heterojunction. The surface states are particle-hole
+symmetric and a voltage drop reveals their intrinsic relativistic nature,
+similarly to Landau bands of Dirac electrons in a magnetic field. We discuss
+the relevance of the massive Dirac surface states in recent ARPES and transport
+experiments.
+",0,1,0,0,0,0
+5017,Sampling-based Estimation of In-degree Distribution with Applications to Directed Complex Networks,"  The focus of this work is on estimation of the in-degree distribution in
+directed networks from sampling network nodes or edges. A number of sampling
+schemes are considered, including random sampling with and without replacement,
+and several approaches based on random walks with possible jumps. When sampling
+nodes, it is assumed that only the out-edges of that node are visible, that is,
+the in-degree of that node is not observed. The suggested estimation of the
+in-degree distribution is based on two approaches. The inversion approach
+exploits the relation between the original and sample in-degree distributions,
+and can estimate the bulk of the in-degree distribution, but not the tail of
+the distribution. The tail of the in-degree distribution is estimated through
+an asymptotic approach, which itself has two versions: one assuming a power-law
+tail and the other for a tail of general form. The two estimation approaches
+are examined on synthetic and real networks, with good performance results,
+especially striking for the asymptotic approach.
+",1,0,0,0,0,0
+5018,Phase I results with the Large Angle Beamstrahlung Monitor (LABM) with SuperKEKB beams,"  We report on the SuperKEKB Phase I operations of the Large Angle
+Beamstrhalung Monitor (LABM). The detector is described and its performance
+characterized using the synchrotron radiation backgrounds from the last Beam
+Line magnets. The backgrounds are also used to determine the expected position
+of the Interaction Point (IP), and the expected background rates during Phase
+II.
+",0,1,0,0,0,0
+5019,Network flow of mobile agents enhances the evolution of cooperation,"  We study the effect of contingent movement on the persistence of cooperation
+on complex networks with empty nodes. Each agent plays Prisoner's Dilemma game
+with its neighbors and then it either updates the strategy depending on the
+payoff difference with neighbors or it moves to another empty node if not
+satisfied with its own payoff. If no neighboring node is empty, each agent
+stays at the same site. By extensive evolutionary simulations, we show that the
+medium density of agents enhances cooperation where the network flow of mobile
+agents is also medium. Moreover, if the movements of agents are more frequent
+than the strategy updating, cooperation is further promoted. In scale-free
+networks, the optimal density for cooperation is lower than other networks
+because agents get stuck at hubs. Our study suggests that keeping a smooth
+network flow is significant for the persistence of cooperation in ever-changing
+societies.
+",0,0,0,0,1,0
+5020,Neural Face Editing with Intrinsic Image Disentangling,"  Traditional face editing methods often require a number of sophisticated and
+task specific algorithms to be applied one after the other --- a process that
+is tedious, fragile, and computationally intensive. In this paper, we propose
+an end-to-end generative adversarial network that infers a face-specific
+disentangled representation of intrinsic face properties, including shape (i.e.
+normals), albedo, and lighting, and an alpha matte. We show that this network
+can be trained on ""in-the-wild"" images by incorporating an in-network
+physically-based image formation module and appropriate loss functions. Our
+disentangling latent representation allows for semantically relevant edits,
+where one aspect of facial appearance can be manipulated while keeping
+orthogonal properties fixed, and we demonstrate its use for a number of facial
+editing applications.
+",1,0,0,0,0,0
+5021,Data Reduction and Image Reconstruction Techniques for Non-Redundant Masking,"  The technique of non-redundant masking (NRM) transforms a conventional
+telescope into an interferometric array. In practice, this provides a much
+better constrained point spread function than a filled aperture and thus higher
+resolution than traditional imaging methods. Here we describe an NRM data
+reduction pipeline. We discuss strategies for NRM observations regarding
+dithering patterns and calibrator selection. We describe relevant image
+calibrations and use example Large Binocular Telescope datasets to show their
+effects on the scatter in the Fourier measurements. We also describe the
+various ways to calculate Fourier quantities, and discuss different calibration
+strategies. We present the results of image reconstructions from simulated
+observations where we adjust prior images, weighting schemes, and error bar
+estimation. We compare two imaging algorithms and discuss implications for
+reconstructing images from real observations. Finally, we explore how the
+current state of the art compares to next generation Extremely Large
+Telescopes.
+",0,1,0,0,0,0
+5022,Dynamic transport in a quantum wire driven by spin-orbit interaction,"  We consider a gated one-dimensional (1D) quantum wire disturbed in a
+contactless manner by an alternating electric field produced by a tip of a
+scanning probe microscope. In this schematic 1D electrons are driven not by a
+pulling electric field but rather by a non-stationary spin-orbit interaction
+(SOI) created by the tip. We show that a charge current appears in the wire in
+the presence of the Rashba SOI produced by the gate net charge and image
+charges of 1D electrons induced on the gate (iSOI). The iSOI contributes to the
+charge susceptibility by breaking the spin-charge separation between the
+charge- and spin collective excitations, generated by the probe. The velocity
+of the excitations is strongly renormalized by SOI, which opens a way to
+fine-tune the charge and spin response of 1D electrons by changing the gate
+potential. One of the modes softens upon increasing the gate potential to
+enhance the current response as well as the power dissipated in the system.
+",0,1,0,0,0,0
+5023,A Nonlinear Kernel Support Matrix Machine for Matrix Learning,"  In many problems of supervised tensor learning (STL), real world data such as
+face images or MRI scans are naturally represented as matrices, which are also
+called as second order tensors. Most existing classifiers based on tensor
+representation, such as support tensor machine (STM) need to solve iteratively
+which occupy much time and may suffer from local minima. In this paper, we
+present a kernel support matrix machine (KSMM) to perform supervised learning
+when data are represented as matrices. KSMM is a general framework for the
+construction of matrix-based hyperplane to exploit structural information. We
+analyze a unifying optimization problem for which we propose an asymptotically
+convergent algorithm. Theoretical analysis for the generalization bounds is
+derived based on Rademacher complexity with respect to a probability
+distribution. We demonstrate the merits of the proposed method by exhaustive
+experiments on both simulation study and a number of real-word datasets from a
+variety of application domains.
+",1,0,0,1,0,0
+5024,Noisy Networks for Exploration,"  We introduce NoisyNet, a deep reinforcement learning agent with parametric
+noise added to its weights, and show that the induced stochasticity of the
+agent's policy can be used to aid efficient exploration. The parameters of the
+noise are learned with gradient descent along with the remaining network
+weights. NoisyNet is straightforward to implement and adds little computational
+overhead. We find that replacing the conventional exploration heuristics for
+A3C, DQN and dueling agents (entropy reward and $\epsilon$-greedy respectively)
+with NoisyNet yields substantially higher scores for a wide range of Atari
+games, in some cases advancing the agent from sub to super-human performance.
+",1,0,0,1,0,0
+5025,Global stability of the Rate Control Protocol (RCP) and some implications for protocol design,"  The Rate Control Protocol (RCP) is a congestion control protocol that relies
+on explicit feedback from routers. RCP estimates the flow rate using two forms
+of feedback: rate mismatch and queue size. However, it remains an open design
+question whether queue size feedback in RCP is useful, given the presence of
+rate mismatch. The model we consider has RCP flows operating over a single
+bottleneck, with heterogeneous time delays. We first derive a sufficient
+condition for global stability, and then highlight how this condition favors
+the design choice of having only rate mismatch in the protocol definition.
+",1,0,0,0,0,0
+5026,Performance Evaluation of Channel Decoding With Deep Neural Networks,"  With the demand of high data rate and low latency in fifth generation (5G),
+deep neural network decoder (NND) has become a promising candidate due to its
+capability of one-shot decoding and parallel computing. In this paper, three
+types of NND, i.e., multi-layer perceptron (MLP), convolution neural network
+(CNN) and recurrent neural network (RNN), are proposed with the same parameter
+magnitude. The performance of these deep neural networks are evaluated through
+extensive simulation. Numerical results show that RNN has the best decoding
+performance, yet at the price of the highest computational overhead. Moreover,
+we find there exists a saturation length for each type of neural network, which
+is caused by their restricted learning abilities.
+",1,0,0,0,0,0
+5027,Trading Bounds for Memory in Games with Counters,"  We study two-player games with counters, where the objective of the first
+player is that the counter values remain bounded. We investigate the existence
+of a trade-off between the size of the memory and the bound achieved on the
+counters, which has been conjectured by Colcombet and Loeding.
+We show that unfortunately this conjecture does not hold: there is no
+trade-off between bounds and memory, even for finite arenas. On the positive
+side, we prove the existence of a trade-off for the special case of thin tree
+arenas. This allows to extend the theory of regular cost functions over thin
+trees, and obtain as a corollary the decidability of cost monadic second-order
+logic over thin trees.
+",1,0,0,0,0,0
+5028,On the Tropical Discs Counting on Elliptic K3 Surfaces with General Singular Fibres,"  Using Lagrangian Floer theory, we study the tropical geometry of K3 surfaces
+with general singular fibres. In particular, we give the local models for the
+type $I_n$, $II$, $III$ and $IV$ singular fibres in the Kodaira's
+classification and generalize the correspondence theorem between open
+Gromov-Witten invariants/tropical discs counting to these cases.
+",0,0,1,0,0,0
+5029,The Frobenius problem for four numerical semigroups,"  The greatest integer that does not belong to a numerical semigroup $S$ is
+called the Frobenius number of $S$ and finding the Frobenius number is called
+the Frobenius problem. In this paper, we introduce the Frobenius problem for
+numerical semigroups generated by Thabit number base b and Thabit number of the
+second kind base b which are motivated by the Frobenius problem for Thabit
+numerical semigroups. Also, we introduce the Frobenius problem for numerical
+semigroups generated by Cunningham number and Fermat number base $b$
+",0,0,1,0,0,0
+5030,Coherence and its Role in Excitation Energy Transfer in Fenna-Mathews-Olson Complex,"  We show that the coherence between different bacteriochlorophyll-a (BChla)
+sites in the Fenna-Mathews-Olson complex is an essential ingredient for
+excitation energy transfer between various sites. The coherence delocalizes the
+excitation energy, which results in the redistribution of excitation among all
+the BChla sites in the steady state. We further show that the system remains
+partially coherent at the steady state. In our numerical simulation of the
+non-Markovian density matrix equation, we consider both the inhomogeneity of
+the protein environment and the effect of active vibronic modes.
+",0,1,0,0,0,0
+5031,Optimal Caching and Scheduling for Cache-enabled D2D Communications,"  To maximize offloading gain of cache-enabled device-to-device (D2D)
+communications, content placement and delivery should be jointly designed. In
+this letter, we jointly optimize caching and scheduling policies to maximize
+successful offloading probability, defined as the probability that a user can
+obtain desired file in local cache or via D2D link with data rate larger than a
+given threshold. We obtain the optimal scheduling factor for a random
+scheduling policy that can control interference in a distributed manner, and a
+low complexity solution to compute caching distribution. We show that the
+offloading gain can be remarkably improved by the joint optimization.
+",1,0,0,0,0,0
+5032,Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model,"  Many interesting natural phenomena are sparsely distributed and discrete.
+Locating the hotspots of such sparsely distributed phenomena is often difficult
+because their density gradient is likely to be very noisy. We present a novel
+approach to this search problem, where we model the co-occurrence relations
+between a robot's observations with a Bayesian nonparametric topic model. This
+approach makes it possible to produce a robust estimate of the spatial
+distribution of the target, even in the absence of direct target observations.
+We apply the proposed approach to the problem of finding the spatial locations
+of the hotspots of a specific phytoplankton taxon in the ocean. We use
+classified image data from Imaging FlowCytobot (IFCB), which automatically
+measures individual microscopic cells and colonies of cells. Given these
+individual taxon-specific observations, we learn a phytoplankton community
+model that characterizes the co-occurrence relations between taxa. We present
+experiments with simulated robot missions drawn from real observation data
+collected during a research cruise traversing the US Atlantic coast. Our
+results show that the proposed approach outperforms nearest neighbor and
+k-means based methods for predicting the spatial distribution of hotspots from
+in-situ observations.
+",1,0,0,1,0,0
+5033,"Effective field theory for dissipative fluids (II): classical limit, dynamical KMS symmetry and entropy current","  In this paper we further develop the fluctuating hydrodynamics proposed in
+arXiv:1511.03646 in a number of ways. We first work out in detail the classical
+limit of the hydrodynamical action, which exhibits many simplifications. In
+particular, this enables a transparent formulation of the action in physical
+spacetime in the presence of arbitrary external fields. It also helps to
+clarify issues related to field redefinitions and frame choices. We then
+propose that the action is invariant under a $Z_2$ symmetry to which we refer
+as the dynamical KMS symmetry. The dynamical KMS symmetry is physically
+equivalent to the previously proposed local KMS condition in the classical
+limit, but is more convenient to implement and more general. It is applicable
+to any states in local equilibrium rather than just thermal density matrix
+perturbed by external background fields. Finally we elaborate the formulation
+for a conformal fluid, which contains some new features, and work out the
+explicit form of the entropy current to second order in derivatives for a
+neutral conformal fluid.
+",0,1,1,0,0,0
+5034,Quantum non demolition measurements: parameter estimation for mixtures of multinomials,"  In Quantum Non Demolition measurements, the sequence of observations is
+distributed as a mixture of multinomial random variables. Parameters of the
+dynamics are naturally encoded into this family of distributions. We show the
+local asymptotic mixed normality of the underlying statistical model and the
+consistency of the maximum likelihood estimator. Furthermore, we prove the
+asymptotic optimality of this estimator as it saturates the usual Cramér Rao
+bound.
+",0,0,1,1,0,0
+5035,Why We Need New Evaluation Metrics for NLG,"  The majority of NLG evaluation relies on automatic metrics, such as BLEU . In
+this paper, we motivate the need for novel, system- and data-independent
+automatic evaluation methods: We investigate a wide range of metrics, including
+state-of-the-art word-based and novel grammar-based ones, and demonstrate that
+they only weakly reflect human judgements of system outputs as generated by
+data-driven, end-to-end NLG. We also show that metric performance is data- and
+system-specific. Nevertheless, our results also suggest that automatic metrics
+perform reliably at system-level and can support system development by finding
+cases where a system performs poorly.
+",1,0,0,0,0,0
+5036,First constraints on fuzzy dark matter from Lyman-$α$ forest data and hydrodynamical simulations,"  We present constraints on the masses of extremely light bosons dubbed fuzzy
+dark matter from Lyman-$\alpha$ forest data. Extremely light bosons with a De
+Broglie wavelength of $\sim 1$ kpc have been suggested as dark matter
+candidates that may resolve some of the current small scale problems of the
+cold dark matter model. For the first time we use hydrodynamical simulations to
+model the Lyman-$\alpha$ flux power spectrum in these models and compare with
+the observed flux power spectrum from two different data sets: the XQ-100 and
+HIRES/MIKE quasar spectra samples. After marginalization over nuisance and
+physical parameters and with conservative assumptions for the thermal history
+of the IGM that allow for jumps in the temperature of up to $5000\rm\,K$,
+XQ-100 provides a lower limit of 7.1$\times 10^{-22}$ eV, HIRES/MIKE returns a
+stronger limit of 14.3$\times 10^{-22}$ eV, while the combination of both data
+sets results in a limit of 20 $\times 10^{-22}$ eV (2$\sigma$ C.L.). The limits
+for the analysis of the combined data sets increases to 37.5$\times 10^{-22}$
+eV (2$\sigma$ C.L.) when a smoother thermal history is assumed where the
+temperature of the IGM evolves as a power-law in redshift. Light boson masses
+in the range $1-10 \times10^{-22}$ eV are ruled out at high significance by our
+analysis, casting strong doubts that FDM helps solve the ""small scale crisis""
+of the cold dark matter models.
+",0,1,0,0,0,0
+5037,On functionals involving the torsional rigidity related to some classes of nonlinear operators,"  In this paper we study optimal estimates for two functionals involving the
+anisotropic $p$-torsional rigidity $T_p(\Omega)$, $1<p<+\infty$. More
+precisely, we study $\Phi(\Omega)=\frac{T_p(\Omega)}{|\Omega|M(\Omega)}$ and
+$\Psi(\Omega)=\frac{T_p(\Omega)}{|\Omega|[R_{F}(\Omega)]^{\frac{p}{p-1}}}$,
+where $M(\Omega)$ is the maximum of the torsion function $u_{\Omega}$ and
+$R_F(\Omega)$ is the anisotropic inradius of $\Omega$.
+",0,0,1,0,0,0
+5038,Quasars Probing Quasars IX. The Kinematics of the Circumgalactic Medium Surrounding z ~ 2 Quasars,"  We examine the kinematics of the gas in the environments of galaxies hosting
+quasars at $z\sim2$. We employ 148 projected quasar pairs to study the
+circumgalactic gas of the foreground quasars in absorption. The sample selects
+foreground quasars with precise redshift measurements, using emission-lines
+with precision $\lesssim300\,{\rm km\,s^{-1}}$ and average offsets from the
+systemic redshift $\lesssim|100\,{\rm km\,s^{-1}}|$. We stack the background
+quasar spectra at the foreground quasar's systemic redshift to study the mean
+absorption in \ion{C}{2}, \ion{C}{4}, and \ion{Mg}{2}. We find that the mean
+absorptions exhibit large velocity widths $\sigma_v\approx300\,{\rm
+km\,s^{-1}}$. Further, the mean absorptions appear to be asymmetric about the
+systemic redshifts. The mean absorption centroids exhibit small redshift
+relative to the systemic $\delta v\approx+200\,{\rm km\,s^{-1}}$, with large
+intrinsic scatter in the centroid velocities of the individual absorption
+systems. We find the observed widths are consistent with gas in gravitational
+motion and Hubble flow. However, while the observation of large widths alone
+does not require galactic-scale outflows, the observed offsets suggest that the
+gas is on average outflowing from the galaxy. The observed offsets also suggest
+that the ionizing radiation from the foreground quasars is anisotropic and/or
+intermittent.
+",0,1,0,0,0,0
+5039,Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification,"  The computer-aided analysis of medical scans is a longstanding goal in the
+medical imaging field. Currently, deep learning has became a dominant
+methodology for supporting pathologists and radiologist. Deep learning
+algorithms have been successfully applied to digital pathology and radiology,
+nevertheless, there are still practical issues that prevent these tools to be
+widely used in practice. The main obstacles are low number of available cases
+and large size of images (a.k.a. the small n, large p problem in machine
+learning), and a very limited access to annotation at a pixel level that can
+lead to severe overfitting and large computational requirements. We propose to
+handle these issues by introducing a framework that processes a medical image
+as a collection of small patches using a single, shared neural network. The
+final diagnosis is provided by combining scores of individual patches using a
+permutation-invariant operator (combination). In machine learning community
+such approach is called a multi-instance learning (MIL).
+",1,0,0,1,0,0
+5040,Reeb dynamics inspired by Katok's example in Finsler geometry,"  Inspired by Katok's examples of Finsler metrics with a small number of closed
+geodesics, we present two results on Reeb flows with finitely many periodic
+orbits. The first result is concerned with a contact-geometric description of
+magnetic flows on the 2-sphere found recently by Benedetti. We give a simple
+interpretation of that work in terms of a quaternionic symmetry. In the second
+part, we use Hamiltonian circle actions on symplectic manifolds to produce
+compact, connected contact manifolds in dimension at least five with
+arbitrarily large numbers of periodic Reeb orbits. This contrasts sharply with
+recent work by Cristofaro-Gardiner, Hutchings and Pomerleano on Reeb flows in
+dimension three. With the help of Hamiltonian plugs and a surgery construction
+due to Laudenbach we reprove a result of Cieliebak: one can produce Hamiltonian
+flows in dimension at least five with any number of periodic orbits; in
+dimension three, with any number greater than one.
+",0,0,1,0,0,0
+5041,Pressure-induced magnetic collapse and metallization of $\mathrm{TlF}{\mathrm{e}}_{1.6}\mathrm{S}{\mathrm{e}}_{2}$,"  The crystal structure, magnetic ordering, and electrical resistivity of
+TlFe1.6Se2 were studied at high pressures. Below ~7 GPa, TlFe1.6Se2 is an
+antiferromagnetically ordered semiconductor with a ThCr2Si2-type structure. The
+insulator-to-metal transformation observed at a pressure of ~ 7 GPa is
+accompanied by a loss of magnetic ordering and an isostructural phase
+transition. In the pressure range ~ 7.5 - 11 GPa a remarkable downturn in
+resistivity, which resembles a superconducting transition, is observed below 15
+K. We discuss this feature as the possible onset of superconductivity
+originating from a phase separation in a small fraction of the sample in the
+vicinity of the magnetic transition.
+",0,1,0,0,0,0
+5042,Real-Time Adaptive Image Compression,"  We present a machine learning-based approach to lossy image compression which
+outperforms all existing codecs, while running in real-time.
+Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG
+2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of
+generic images across all quality levels. At the same time, our codec is
+designed to be lightweight and deployable: for example, it can encode or decode
+the Kodak dataset in around 10ms per image on GPU.
+Our architecture is an autoencoder featuring pyramidal analysis, an adaptive
+coding module, and regularization of the expected codelength. We also
+supplement our approach with adversarial training specialized towards use in a
+compression setting: this enables us to produce visually pleasing
+reconstructions for very low bitrates.
+",1,0,0,1,0,0
+5043,Probabilistic Graphical Modeling approach to dynamic PET direct parametric map estimation and image reconstruction,"  In the context of dynamic emission tomography, the conventional processing
+pipeline consists of independent image reconstruction of single time frames,
+followed by the application of a suitable kinetic model to time activity curves
+(TACs) at the voxel or region-of-interest level. The relatively new field of 4D
+PET direct reconstruction, by contrast, seeks to move beyond this scheme and
+incorporate information from multiple time frames within the reconstruction
+task. Existing 4D direct models are based on a deterministic description of
+voxels' TACs, captured by the chosen kinetic model, considering the photon
+counting process the only source of uncertainty. In this work, we introduce a
+new probabilistic modeling strategy based on the key assumption that activity
+time course would be subject to uncertainty even if the parameters of the
+underlying dynamic process were known. This leads to a hierarchical Bayesian
+model, which we formulate using the formalism of Probabilistic Graphical
+Modeling (PGM). The inference of the joint probability density function arising
+from PGM is addressed using a new gradient-based iterative algorithm, which
+presents several advantages compared to existing direct methods: it is flexible
+to an arbitrary choice of linear and nonlinear kinetic model; it enables the
+inclusion of arbitrary (sub)differentiable priors for parametric maps; it is
+simpler to implement and suitable to integration in computing frameworks for
+machine learning. Computer simulations and an application to real patient scan
+showed how the proposed approach allows us to weight the importance of the
+kinetic model, providing a bridge between indirect and deterministic direct
+methods.
+",0,0,0,1,0,0
+5044,"Perturbative Thermodynamic Geometry of Nonextensive Ideal Classical, Bose and Fermi Gases","  We investigate perturbative thermodynamic geometry of nonextensive ideal
+Classical, Bose and Fermi gases.We show that the intrinsic statistical
+interaction of nonextensive Bose (Fermi) gas is attractive (repulsive) similar
+to the extensive case but the value of thermodynamic curvature is changed by
+nonextensive parameter. In contrary to the extensive ideal classical gas, the
+nonextensive one may be divided to two different regimes. According to
+deviation parameter of the system to the nonextensive case, one can find a
+special value of fugacity, $z^{*}$, where the sign of thermodynamic curvature
+is changed. Therefore, we argue that the nonextensive parameter induces an
+attractive (repulsive) statistical interaction for $z<z^{*}$ ($z>z^{*}$) for an
+ideal classical gas. Also, according to the singular point of thermodynamic
+curvature, we consider the condensation of nonextensive Boson gas.
+",0,1,0,0,0,0
+5045,On some mellin transforms for the Riemann zeta function in the critical strip,"  We offer two new Mellin transform evaluations for the Riemann zeta function
+in the region $0<\Re(s)<1.$ Some discussion is offered in the way of evaluating
+some further Fourier integrals involving the Riemann xi function.
+",0,0,1,0,0,0
+5046,Timelike surfaces in Minkowski space with a canonical null direction,"  Given a constant vector field $Z$ in Minkowski space, a timelike surface is
+said to have a canonical null direction with respect to $Z$ if the projection
+of $Z$ on the tangent space of the surface gives a lightlike vector field. In
+this paper we describe these surfaces in the ruled case. For example when the
+Minkowski space has three dimensions then a surface with a canonical null
+direction is minimal and flat. On the other hand, we describe several
+properties in the non ruled case and we partially describe these surfaces in
+four-dimensional Minkowski space. We give different ways for building these
+surfaces in four-dimensional Minkowski space and we finally use the Gauss map
+for describe another properties of these surfaces.
+",0,0,1,0,0,0
+5047,The existence and global exponential stability of almost periodic solutions for neutral type CNNs on time scales,"  In this paper, a class of neutral type competitive neural networks with mixed
+time-varying delays and leakage delays on time scales is proposed. Based on the
+exponential dichotomy of linear dynamic equations on time scales, Banach's
+fixed point theorem and the theory of calculus on time scales, some sufficient
+conditions that are independent of the backwards graininess function of the
+time scale are obtained for the existence and global exponential stability of
+almost periodic solutions for this class of neural networks. The obtained
+results are completely new and indicate that both the continuous time and the
+discrete time cases of the networks share the same dynamical behavior. Finally,
+an examples is given to show the effectiveness of the obtained results.
+",0,0,1,0,0,0
+5048,"Fluid-Structure Interaction for the Classroom: Interpolation, Hearts, and Swimming!","  While students may find spline interpolation easily digestible, based on
+their familiarity with continuity of a function and its derivatives, some of
+its inherent value may be missed when students only see it applied to standard
+data interpolation exercises. In this paper, we offer alternatives where
+students can qualitatively and quantitatively witness the resulting dynamical
+differences when objects are driven through a fluid using different spline
+interpolation methods. They say, seeing is believing; here we showcase the
+differences between linear and cubic spline interpolation using examples from
+fluid pumping and aquatic locomotion. Moreover, students can define their own
+interpolation functions and visualize the dynamics unfold. To solve the
+fluid-structure interaction system, the open source software IB2d is used. In
+that vein, all simulation codes, analysis scripts, and movies are provided for
+streamlined use.
+",0,0,0,0,1,0
+5049,Modelling the evaporation of nanoparticle suspensions from heterogeneous surfaces,"  We present a Monte Carlo (MC) grid-based model for the drying of drops of a
+nanoparticle suspension upon a heterogeneous surface. The model consists of a
+generalised lattice-gas in which the interaction parameters in the Hamiltonian
+can be varied to model different properties of the materials involved. We show
+how to choose correctly the interactions, to minimise the effects of the
+underlying grid so that hemispherical droplets form. We also include the
+effects of surface roughness to examine the effects of contact-line pinning on
+the dynamics. When there is a `lid' above the system, which prevents
+evaporation, equilibrium drops form on the surface, which we use to determine
+the contact angle and how it varies as the parameters of the model are changed.
+This enables us to relate the interaction parameters to the materials used in
+applications. The model has also been applied to drying on heterogeneous
+surfaces, in particular to the case where the suspension is deposited on a
+surface consisting of a pair of hydrophilic conducting metal surfaces that are
+either side of a band of hydrophobic insulating polymer. This situation occurs
+when using inkjet printing to manufacture electrical connections between the
+metallic parts of the surface. The process is not always without problems,
+since the liquid can dewet from the hydrophobic part of the surface, breaking
+the bridge before the drying process is complete. The MC model reproduces the
+observed dewetting, allowing the parameters to be varied so that the conditions
+for the best connection can be established. We show that if the hydrophobic
+portion of the surface is located at a step below the height of the
+neighbouring metal, the chance of dewetting of the liquid during the drying
+process is significantly reduced.
+",0,1,0,0,0,0
+5050,Image retargeting via Beltrami representation,"  Image retargeting aims to resize an image to one with a prescribed aspect
+ratio. Simple scaling inevitably introduces unnatural geometric distortions on
+the important content of the image. In this paper, we propose a simple and yet
+effective method to resize an image, which preserves the geometry of the
+important content, using the Beltrami representation. Our algorithm allows
+users to interactively label content regions as well as line structures. Image
+resizing can then be achieved by warping the image by an orientation-preserving
+bijective warping map with controlled distortion. The warping map is
+represented by its Beltrami representation, which captures the local geometric
+distortion of the map. By carefully prescribing the values of the Beltrami
+representation, images with different complexity can be effectively resized.
+Our method does not require solving any optimization problems and tuning
+parameters throughout the process. This results in a simple and efficient
+algorithm to solve the image retargeting problem. Extensive experiments have
+been carried out, which demonstrate the efficacy of our proposed method.
+",1,0,0,0,0,0
+5051,Coupling Load-Following Control with OPF,"  In this paper, the optimal power flow (OPF) problem is augmented to account
+for the costs associated with the load-following control of a power network.
+Load-following control costs are expressed through the linear quadratic
+regulator (LQR). The power network is described by a set of nonlinear
+differential algebraic equations (DAEs). By linearizing the DAEs around a known
+equilibrium, a linearized OPF that accounts for steady-state operational
+constraints is formulated first. This linearized OPF is then augmented by a set
+of linear matrix inequalities that are algebraically equivalent to the
+implementation of an LQR controller. The resulting formulation, termed LQR-OPF,
+is a semidefinite program which furnishes optimal steady-state setpoints and an
+optimal feedback law to steer the system to the new steady state with minimum
+load-following control costs. Numerical tests demonstrate that the setpoints
+computed by LQR-OPF result in lower overall costs and frequency deviations
+compared to the setpoints of a scheme where OPF and load-following control are
+considered separately.
+",1,0,1,0,0,0
+5052,A cellular algebra with specific decomposition of the unity,"  Let $ \mathbb{A}$ be a cellular algebra over a field $\mathbb{F}$ with a
+decomposition of the identity $ 1_{\mathbb{A}} $ into orthogonal idempotents $
+e_i$, $i \in I$ (for some finite set $I$) satisfying some properties. We
+describe the entire Loewy structure of cell modules of the algebra $ \mathbb{A}
+$ by using the representation theory of the algebra $ e_i \mathbb{A} e_i $ for
+each $ i $. Moreover, we also study the block theory of $\mathbb{A}$ by using
+this decomposition.
+",0,0,1,0,0,0
+5053,On relation between discrete Frenet frames and the bi-Hamiltonian structure of the discrete nonlinear Schrödinger equation,"  The discrete Frenet equation entails a local framing of a discrete, piecewise
+linear polygonal chain in terms of its bond and torsion angles. In particular,
+the tangent vector of a segment is akin the classical O(3) spin variable. Thus
+there is a relation to the lattice Heisenberg model, that can be used to model
+physical properties of the chain. On the other hand, the Heisenberg model is
+closely related to the discrete nonlinear Schrödinger (DNLS) equation. Here
+we apply these interrelations to develop a perspective on discrete chains
+dynamics: We employ the properties of a discrete chain in terms of a spinorial
+representation of the discrete Frenet equation, to introduce a bi-hamiltonian
+structure for the discrete nonlinear Schrödinger equation (DNLSE), which we
+then use to produce integrable chain dynamics.
+",0,1,1,0,0,0
+5054,A stronger version of a question proposed by K. Mahler,"  In 1902, P. Stäckel proved the existence of a transcendental function
+$f(z)$, analytic in a neighbourhood of the origin, and with the property that
+both $f(z)$ and its inverse function assume, in this neighbourhood, algebraic
+values at all algebraic points. Based on this result, in 1976, K. Mahler raised
+the question of the existence of such functions which are analytic in
+$\mathbb{C}$. Recently, the authors answered positively this question. In this
+paper, we prove a much stronger version of this result by considering other
+subsets of $\mathbb{C}$.
+",0,0,1,0,0,0
+5055,"Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models","  We consider classifiers for high-dimensional data under the strongly spiked
+eigenvalue (SSE) model. We first show that high-dimensional data often have the
+SSE model. We consider a distance-based classifier using eigenstructures for
+the SSE model. We apply the noise reduction methodology to estimation of the
+eigenvalues and eigenvectors in the SSE model. We create a new distance-based
+classifier by transforming data from the SSE model to the non-SSE model. We
+give simulation studies and discuss the performance of the new classifier.
+Finally, we demonstrate the new classifier by using microarray data sets.
+",0,0,0,1,0,0
+5056,Group Field theory and Tensor Networks: towards a Ryu-Takayanagi formula in full quantum gravity,"  We establish a dictionary between group field theory (thus, spin networks and
+random tensors) states and generalized random tensor networks. Then, we use
+this dictionary to compute the Rényi entropy of such states and recover the
+Ryu-Takayanagi formula, in two different cases corresponding to two different
+truncations/approximations, suggested by the established correspondence.
+",0,1,0,0,0,0
+5057,Behavior of Accelerated Gradient Methods Near Critical Points of Nonconvex Functions,"  We examine the behavior of accelerated gradient methods in smooth nonconvex
+unconstrained optimization, focusing in particular on their behavior near
+strict saddle points. Accelerated methods are iterative methods that typically
+step along a direction that is a linear combination of the previous step and
+the gradient of the function evaluated at a point at or near the current
+iterate. (The previous step encodes gradient information from earlier stages in
+the iterative process.) We show by means of the stable manifold theorem that
+the heavy-ball method method is unlikely to converge to strict saddle points,
+which are points at which the gradient of the objective is zero but the Hessian
+has at least one negative eigenvalue. We then examine the behavior of the
+heavy-ball method and other accelerated gradient methods in the vicinity of a
+strict saddle point of a nonconvex quadratic function, showing that both
+methods can diverge from this point more rapidly than the steepest-descent
+method.
+",0,0,1,0,0,0
+5058,"Distinguishing differential susceptibility, diathesis-stress and vantage sensitivity: beyond the single gene and environment model","  Currently, two main approaches exist to distinguish differential
+susceptibility from diathesis-stress and vantage sensitivity in genotype x
+environment interaction (GxE) research: Regions of significance (RoS) and
+competitive-confirmatory approaches. Each is limited by their
+single-gene/single-environment foci given that most phenotypes are the product
+of multiple interacting genetic and environmental factors. We thus addressed
+these two concerns in a recently developed R package (LEGIT) for constructing
+GxE interaction models with latent genetic and environmental scores using
+alternating optimization. Herein we test, by means of computer simulation,
+diverse GxE models in the context of both single and multiple genes and
+environments. Results indicate that the RoS and competitive-confirmatory
+approaches were highly accurate when the sample size was large, whereas the
+latter performed better in small samples and for small effect sizes. The
+confirmatory approach generally had good accuracy (a) when effect size was
+moderate and N >= 500 and (b) when effect size was large and N >= 250, whereas
+RoS performed poorly. Computational tools to determine the type of GxE of
+multiple genes and environments are provided as extensions in our LEGIT R
+package.
+",0,0,0,1,0,0
+5059,A weak type estimate for rough singular integrals,"  We obtain a weak type $(1,1)$ estimate for a maximal operator associated with
+the classical rough homogeneous singular integrals $T_{\Omega}$. In particular,
+this provides a different approach to a sparse domination for $T_{\Omega}$
+obtained recently by Conde-Alonso, Culiuc, Di Plinio and Ou.
+",0,0,1,0,0,0
+5060,Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System,"  The Internet of things (IoT) is still in its infancy and has attracted much
+interest in many industrial sectors including medical fields, logistics
+tracking, smart cities and automobiles. However as a paradigm, it is
+susceptible to a range of significant intrusion threats. This paper presents a
+threat analysis of the IoT and uses an Artificial Neural Network (ANN) to
+combat these threats. A multi-level perceptron, a type of supervised ANN, is
+trained using internet packet traces, then is assessed on its ability to thwart
+Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the
+classification of normal and threat patterns on an IoT Network. The ANN
+procedure is validated against a simulated IoT network. The experimental
+results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS
+attacks.
+",1,0,0,0,0,0
+5061,An experimental study of Bitcoin fluctuation using machine learning methods,"  In this paper, we study the ability to make the short-term prediction of the
+exchange price fluctuations towards the United States dollar for the Bitcoin
+market. We use the data of realized volatility collected from one of the
+largest Bitcoin digital trading offices in 2016 and 2017 as well as order
+information. Experiments are performed to evaluate a variety of statistical and
+machine learning approaches.
+",0,0,0,1,0,0
+5062,"A unified theory for exact stochastic modelling of univariate and multivariate processes with continuous, mixed type, or discrete marginal distributions and any correlation structure","  Hydroclimatic processes are characterized by heterogeneous spatiotemporal
+correlation structures and marginal distributions that can be continuous,
+mixed-type, discrete or even binary. Simulating exactly such processes can
+greatly improve hydrological analysis and design. Yet this challenging task is
+accomplished often by ad hoc and approximate methodologies that are devised for
+specific variables and purposes. In this study, a single framework is proposed
+allowing the exact simulation of processes with any marginal and any
+correlation structure. We unify, extent, and improve of a general-purpose
+modelling strategy based on the assumption that any process can emerge by
+transforming a parent Gaussian process with a specific correlation structure. A
+novel mathematical representation of the parent-Gaussian scheme provides a
+consistent and fully general description that supersedes previous specific
+parameterizations, resulting in a simple, fast and efficient simulation
+procedure for every spatiotemporal process. In particular, introducing a simple
+but flexible procedure we obtain a parametric expression of the correlation
+transformation function, allowing to assess the correlation structure of the
+parent-Gaussian process that yields the prescribed correlation of the target
+process after marginal back transformation. The same framework is also
+applicable for cyclostationary and multivariate modelling. The simulation of a
+variety of hydroclimatic variables with very different correlation structures
+and marginals, such as precipitation, stream flow, wind speed, humidity,
+extreme events per year, etc., as well as a multivariate application,
+highlights the flexibility, advantages, and complete generality of the proposed
+methodology.
+",0,0,1,1,0,0
+5063,"The Guiding Influence of Stanley Mandelstam, from S-Matrix Theory to String Theory","  The guiding influence of some of Stanley Mandelstam's key contributions to
+the development of theoretical high energy physics is discussed, from the
+motivation for the study of the analytic properties of the scattering matrix
+through to dual resonance models and their evolution into string theory.
+",0,1,0,0,0,0
+5064,Radiation-driven turbulent accretion onto massive black holes,"  Accretion of gas and interaction of matter and radiation are at the heart of
+many questions pertaining to black hole (BH) growth and coevolution of massive
+BHs and their host galaxies. To answer them it is critical to quantify how the
+ionizing radiation that emanates from the innermost regions of the BH accretion
+flow couples to the surrounding medium and how it regulates the BH fueling. In
+this work we use high resolution 3-dimensional (3D) radiation-hydrodynamic
+simulations with the code Enzo, equipped with adaptive ray tracing module
+Moray, to investigate radiation-regulated BH accretion of cold gas. Our
+simulations reproduce findings from an earlier generation of 1D/2D simulations:
+the accretion powered UV and X-ray radiation forms a highly ionized bubble,
+which leads to suppression of BH accretion rate characterized by quasi-periodic
+outbursts. A new feature revealed by the 3D simulations is the highly turbulent
+nature of the gas flow in vicinity of the ionization front. During quiescent
+periods between accretion outbursts, the ionized bubble shrinks in size and the
+gas density that precedes the ionization front increases. Consequently, the 3D
+simulations show oscillations in the accretion rate of only ~2-3 orders of
+magnitude, significantly smaller than 1D/2D models. We calculate the energy
+budget of the gas flow and find that turbulence is the main contributor to the
+kinetic energy of the gas but corresponds to less than 10% of its thermal
+energy and thus does not contribute significantly to the pressure support of
+the gas.
+",0,1,0,0,0,0
+5065,Bayesian Inference of Log Determinants,"  The log-determinant of a kernel matrix appears in a variety of machine
+learning problems, ranging from determinantal point processes and generalized
+Markov random fields, through to the training of Gaussian processes. Exact
+calculation of this term is often intractable when the size of the kernel
+matrix exceeds a few thousand. In the spirit of probabilistic numerics, we
+reinterpret the problem of computing the log-determinant as a Bayesian
+inference problem. In particular, we combine prior knowledge in the form of
+bounds from matrix theory and evidence derived from stochastic trace estimation
+to obtain probabilistic estimates for the log-determinant and its associated
+uncertainty within a given computational budget. Beyond its novelty and
+theoretic appeal, the performance of our proposal is competitive with
+state-of-the-art approaches to approximating the log-determinant, while also
+quantifying the uncertainty due to budget-constrained evidence.
+",1,0,0,1,0,0
+5066,Investigating prescriptions for artificial resistivity in smoothed particle magnetohydrodynamics,"  In numerical simulations, artificial terms are applied to the evolution
+equations for stability. To prove their validity, these terms are thoroughly
+tested in test problems where the results are well known. However, they are
+seldom tested in production-quality simulations at high resolution where they
+interact with a plethora of physical and numerical algorithms. We test three
+artificial resistivities in both the Orszag-Tang vortex and in a star formation
+simulation. From the Orszag-Tang vortex, the Price et. al. (2017) artificial
+resistivity is the least dissipative thus captures the density and magnetic
+features; in the star formation algorithm, each artificial resistivity
+algorithm interacts differently with the sink particle to produce various
+results, including gas bubbles, dense discs, and migrating sink particles. The
+star formation simulations suggest that it is important to rely upon physical
+resistivity rather than artificial resistivity for convergence.
+",0,1,0,0,0,0
+5067,"A Planning and Control Framework for Humanoid Systems: Robust, Optimal, and Real-time Performance","  Humanoid robots are increasingly demanded to operate in interactive and
+human-surrounded environments while achieving sophisticated locomotion and
+manipulation tasks. To accomplish these tasks, roboticists unremittingly seek
+for advanced methods that generate whole-body coordination behaviors and
+meanwhile fulfill various planning and control objectives. Undoubtedly, these
+goals pose fundamental challenges to the robotics and control community. To
+take an incremental step towards reducing the performance gap between
+theoretical foundations and real implementations, we present a planning and
+control framework for the humanoid, especially legged robots, for achieving
+high performance and generating agile motions. A particular concentration is on
+the robust, optimal and real-time performance. This framework constitutes three
+hierarchical layers: First, we present a robust optimal phase-space planning
+framework for dynamic legged locomotion over rough terrain. This framework is a
+hybrid motion planner incorporating a series of pivotal components. Second, we
+take a step toward formally synthesizing high-level reactive planners for
+whole-body locomotion in constrained environments. We formulate a two-player
+temporal logic game between the contact planner and its possibly-adversarial
+environment. Third, we propose a distributed control architecture for the
+latency-prone humanoid robotic systems. A central experimental phenomenon is
+observed that the stability of high impedance distributed controllers is highly
+sensitive to damping feedback delay but much less to stiffness feedback delay.
+We pursue a detailed analysis of the distributed controllers where damping
+feedback effort is executed in proximity to the control plant, and stiffness
+feedback effort is implemented in a latency-prone centralized control process.
+",1,0,1,0,0,0
+5068,Attention-based Wav2Text with Feature Transfer Learning,"  Conventional automatic speech recognition (ASR) typically performs
+multi-level pattern recognition tasks that map the acoustic speech waveform
+into a hierarchy of speech units. But, it is widely known that information loss
+in the earlier stage can propagate through the later stages. After the
+resurgence of deep learning, interest has emerged in the possibility of
+developing a purely end-to-end ASR system from the raw waveform to the
+transcription without any predefined alignments and hand-engineered models.
+However, the successful attempts in end-to-end architecture still used
+spectral-based features, while the successful attempts in using raw waveform
+were still based on the hybrid deep neural network - Hidden Markov model
+(DNN-HMM) framework. In this paper, we construct the first end-to-end
+attention-based encoder-decoder model to process directly from raw speech
+waveform to the text transcription. We called the model as ""Attention-based
+Wav2Text"". To assist the training process of the end-to-end model, we propose
+to utilize a feature transfer learning. Experimental results also reveal that
+the proposed Attention-based Wav2Text model directly with raw waveform could
+achieve a better result in comparison with the attentional encoder-decoder
+model trained on standard front-end filterbank features.
+",1,0,0,0,0,0
+5069,Amplitude death and resurgence of oscillation in network of mobile oscillators,"  The phenomenon of amplitude death has been explored using a variety of
+different coupling strategies in the last two decades. In most of the work, the
+basic coupling arrangement is considered to be static over time, although many
+realistic systems exhibit significant changes in the interaction pattern as
+time varies. In this article, we study the emergence of amplitude death in a
+dynamical network composed of time-varying interaction amidst a collection of
+random walkers in a finite region of three dimensional space. We consider an
+oscillator for each walker and demonstrate that depending upon the network
+parameters and hence the interaction between them, global oscillation in the
+network gets suppressed. In this framework, vision range of each oscillator
+decides the number of oscillators with which it interacts. In addition, with
+the use of an appropriate feedback parameter in the coupling strategy, we
+articulate how the suppressed oscillation can be resurrected in the systems'
+parameter space. The phenomenon of amplitude death and the resurgence of
+oscillation is investigated taking limit cycle and chaotic oscillators for
+broad ranges of parameters, like interaction strength k between the entities,
+vision range r and the speed of movement v.
+",0,1,0,0,0,0
+5070,Quintessential Inflation with $α$-attractors,"  A novel approach to quintessential inflation model building is studied,
+within the framework of $\alpha$-attractors, motivated by supergravity
+theories. Inflationary observables are in excellent agreement with the latest
+CMB observations, while quintessence explains the dark energy observations
+without any fine-tuning. The model is kept intentionally minimal, avoiding the
+introduction of many degrees of freedom, couplings and mass scales. In stark
+contrast to $\Lambda$CDM, for natural values of the parameters, the model
+attains transient accelerated expansion, which avoids the future horizon
+problem, while it maintains the field displacement mildly sub-Planckian such
+that the flatness of the quintessential tail is not lifted by radiative
+corrections and violations of the equivalence principle (fifth force) are under
+control. In particular, the required value of the cosmological constant is near
+the eletroweak scale. Attention is paid to the reheating of the Universe, which
+avoids gravitino overproduction and respects nucleosynthesis constraints.
+Kination is treated in a model independent way. A spike in gravitational waves,
+due to kination, is found not to disturb nucleosynthesis as well.
+",0,1,0,0,0,0
+5071,Degenerate and chiral states in the extended Heisenberg model in the kagome lattice,"  We present a study of the low temperature phases of the antiferromagnetic
+extended classical Heisenberg model in the kagome lattice, up to third nearest
+neighbors. First, we focus on the degenerate lines in the boundaries of the
+well-known staggered chiral phases. These boundaries have either semi-extensive
+or extensive degeneracy, and we discuss the partial selection of states by
+thermal fluctuations. Then, we study the model under an external magnetic field
+on these lines and in the staggered chiral phases. We pay particular attention
+to the highly frustrated point, where the three exchange couplings are equal.
+We show that this point can me mapped to a model with spin liquid behavior and
+non-zero chirality. Finally, we explore the effect of Dzyaloshinskii-Moriya
+(DM) interactions in two ways: an homogeneous and a staggered DM interaction.
+In both cases, there is a rich low temperature phase diagram, with different
+spontaneously broken symmetries and non trivial chiral phases.
+",0,1,0,0,0,0
+5072,Enhancing Blood Glucose Prediction with Meal Absorption and Physical Exercise Information,"  Objective: Numerous glucose prediction algorithm have been proposed to
+empower type 1 diabetes (T1D) management. Most of these algorithms only account
+for input such as glucose, insulin and carbohydrate, which limits their
+performance. Here, we present a novel glucose prediction algorithm which, in
+addition to standard inputs, accounts for meal absorption and physical exercise
+information to enhance prediction accuracy. Methods: a compartmental model of
+glucose-insulin dynamics combined with a deconvolution technique for state
+estimation is employed for glucose prediction. In silico data corresponding
+from the 10 adult subjects of UVa-Padova simulator, and clinical data from 10
+adults with T1D were used. Finally, a comparison against a validated glucose
+prediction algorithm based on a latent variable with exogenous input (LVX)
+model is provided. Results: For a prediction horizon of 60 minutes, accounting
+for meal absorption and physical exercise improved glucose forecasting
+accuracy. In particular, root mean square error (mg/dL) went from 26.68 to
+23.89, p<0.001 (in silico data); and from 37.02 to 35.96, p<0.001 (clinical
+data - only meal information). Such improvement in accuracy was translated into
+significant improvements on hypoglycaemia and hyperglycaemia prediction.
+Finally, the performance of the proposed algorithm is statistically superior to
+that of the LVX algorithm (26.68 vs. 32.80, p<0.001 (in silico data); 37.02 vs.
+49.17, p<0.01 (clinical data). Conclusion: Taking into account meal absorption
+and physical exercise information improves glucose prediction accuracy.
+",1,0,0,0,1,0
+5073,The fraction of cool-core clusters in X-ray vs. SZ samples using Chandra observations,"  We derive and compare the fractions of cool-core clusters in the {\em Planck}
+Early Sunyaev-Zel'dovich sample of 164 clusters with $z \leq 0.35$ and in a
+flux-limited X-ray sample of 100 clusters with $z \leq 0.30$, using {\em
+Chandra} observations. We use four metrics to identify cool-core clusters: 1)
+the concentration parameter: the ratio of the integrated emissivity profile
+within 0.15 $r_{500}$ to that within $r_{500}$, and 2) the ratio of the
+integrated emissivity profile within 40 kpc to that within 400 kpc, 3) the
+cuspiness of the gas density profile: the negative of the logarithmic
+derivative of the gas density with respect to the radius, measured at 0.04
+$r_{500}$, and 4) the central gas density, measured at 0.01 $r_{500}$. We find
+that the sample of X-ray selected clusters, as characterized by each of these
+metrics, contains a significantly larger fraction of cool-core clusters
+compared to the sample of SZ selected clusters (44$\pm$7\% vs. 28$\pm$4\% using
+the concentration parameter in the 0.15--1.0 $r_{500}$ range, 61$\pm$8\% vs.
+36$\pm$5\% using the concentration parameter in the 40--400 kpc range,
+64$\pm$8\% vs. 38$\pm$5\% using the cuspiness, and 53$\pm$7\% vs. 39$\pm$5\%
+using the central gas density). Qualitatively, cool-core clusters are more
+X-ray luminous at fixed mass. Hence, our X-ray flux-limited sample, compared to
+the approximately mass-limited SZ sample, is over-represented with cool-core
+clusters. We describe a simple quantitative model that uses the excess
+luminosity of cool-core clusters compared to non-cool-core clusters at fixed
+mass to successfully predict the observed fraction of cool-core clusters in
+X-ray selected samples.
+",0,1,0,0,0,0
+5074,Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers,"  Online Multi-Object Tracking (MOT) from videos is a challenging computer
+vision task which has been extensively studied for decades. Most of the
+existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm
+combined with popular machine learning approaches which largely reduce the
+human effort to tune algorithm parameters. However, the commonly used
+supervised learning approaches require the labeled data (e.g., bounding boxes),
+which is expensive for videos. Also, the TBD framework is usually suboptimal
+since it is not end-to-end, i.e., it considers the task as detection and
+tracking, but not jointly. To achieve both label-free and end-to-end learning
+of MOT, we propose a Tracking-by-Animation framework, where a differentiable
+neural model first tracks objects from input frames and then animates these
+objects into reconstructed frames. Learning is then driven by the
+reconstruction error through backpropagation. We further propose a
+Reprioritized Attentive Tracking to improve the robustness of data association.
+Experiments conducted on both synthetic and real video datasets show the
+potential of the proposed model.
+",0,0,0,1,0,0
+5075,Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation,"  Generating novel graph structures that optimize given objectives while
+obeying some given underlying rules is fundamental for chemistry, biology and
+social science research. This is especially important in the task of molecular
+graph generation, whose goal is to discover novel molecules with desired
+properties such as drug-likeness and synthetic accessibility, while obeying
+physical laws such as chemical valency. However, designing models to find
+molecules that optimize desired properties while incorporating highly complex
+and non-differentiable rules remains to be a challenging task. Here we propose
+Graph Convolutional Policy Network (GCPN), a general graph convolutional
+network based model for goal-directed graph generation through reinforcement
+learning. The model is trained to optimize domain-specific rewards and
+adversarial loss through policy gradient, and acts in an environment that
+incorporates domain-specific rules. Experimental results show that GCPN can
+achieve 61% improvement on chemical property optimization over state-of-the-art
+baselines while resembling known molecules, and achieve 184% improvement on the
+constrained property optimization task.
+",0,0,0,1,0,0
+5076,Impact of the positive ion current on large size neutrino detectors and delayed photon emission,"  Given their small mobility coefficient in liquid argon with respect to the
+electrons, the ions spend a considerably longer time in the active volume. We
+studied the effects of the positive ion current in a liquid argon time
+projection chamber, in the context of massive argon experiments for neutrino
+physics. The constant recombination between free ions and electrons produces a
+quenching of the charge signal and a constant emission of photons, uncorrelated
+in time and space to the physical interactions. The predictions evidence some
+potential concerns for multi-ton argon detectors, particularly when operated on
+surface
+",0,1,0,0,0,0
+5077,On Multilevel Coding Schemes Based on Non-Binary LDPC Codes,"  We address the problem of constructing of coding schemes for the channels
+with high-order modulations. It is known, that non-binary LDPC codes are
+especially good for such channels and significantly outperform their binary
+counterparts. Unfortunately, their decoding complexity is still large. In order
+to reduce the decoding complexity we consider multilevel coding schemes based
+on non-binary LDPC codes (NB-LDPC-MLC schemes) over smaller fields. The use of
+such schemes gives us a reasonable gain in complexity. At the same time the
+performance of NB-LDPC-MLC schemes is practically the same as the performance
+of LDPC codes over the field matching the modulation order. In particular by
+means of simulations we showed that the performance of NB-LDPC-MLC schemes over
+GF(16) is the same as the performance of non-binary LDPC codes over GF(64) and
+GF(256) in AWGN channel with QAM64 and QAM256 accordingly. We also perform a
+comparison with binary LDPC codes.
+",1,0,0,0,0,0
+5078,Tunable coupling-induced resonance splitting in self-coupled Silicon ring cavity with robust spectral characteristics,"  We propose and demonstrate a self-coupled microring resonator for resonance
+splitting by mutual mode coupling of cavity mode and counter-propagating mode
+in Silicon-on-Insulator platform The resonator is constructed with a
+self-coupling region that can excite counter-propagating mode. We
+experimentally study the effect of self-coupling on the resonance splitting,
+resonance extinction, and quality-factor evolution and stability. Based on the
+coupling, we achieve 72% of FSR splitting for a cavity with FSR 2.1 nm with <
+5% variation in the cavity quality factor. The self-coupled resonance splitting
+shows highly robust spectral characteristic that can be exploited for sensing
+and optical signal processing.
+",0,1,0,0,0,0
+5079,High-precision measurements and theoretical calculations of indium excited-state polarizabilities,"  We report measurements of the $^{115}$In $7p_{1/2}$ and $7p_{3/2}$ scalar and
+tensor polarizabilities using two-step diode laser spectroscopy in an atomic
+beam. The scalar polarizabilities are one to two orders of magnitude larger
+than for lower lying indium states due to the close proximity of the $7p$ and
+$6d$ states. For the scalar polarizabilities, we find values (in atomic units)
+of $1.811(4) \times 10^5$ $a_0^3$ and $2.876(6) \times 10^5$ $a_0^3$ for the
+$7p_{1/2}$ and $7p_{3/2}$ states respectively. We estimate the smaller tensor
+polarizability component of the $7p_{3/2}$ state to be $-1.43(18) \times 10^4$
+$a_0^3$. These measurements represent the first high-precision benchmarks of
+transition properties of such high excited states of trivalent atomic systems.
+We also present new ab initio calculations of these quantities and other In
+polarizabilities using two high-precision relativistic methods to make a global
+comparison of the accuracies of the two approaches. The precision of the
+experiment is sufficient to differentiate between the two theoretical methods
+as well as to allow precise determination of the indium $7p-6d$ matrix
+elements. The results obtained in this work are applicable to other heavier and
+more complicated systems, and provide much needed guidance for the development
+of even more precise theoretical approaches.
+",0,1,0,0,0,0
+5080,2D reductions of the equation $u_{yy} = u_{tx} + u_yu_{xx} - u_xu_{xy}$ and their nonlocal symmetries,"  We consider the 3D equation $u_{yy} = u_{tx} + u_yu_{xx} - u_xu_{xy}$ and its
+2D reductions: (1) $u_{yy} = (u_y+y)u_{xx}-u_xu_{xy}-2$ (which is equivalent to
+the Gibbons-Tsarev equation) and (2) $u_{yy} = (u_y+2x)u_{xx} + (y-u_x)u_{xy}
+-u_x$. Using reduction of the known Lax pair for the 3D equation, we describe
+nonlocal symmetries of~(1) and~(2) and show that the Lie algebras of these
+symmetries are isomorphic to the Witt algebra.
+",0,1,0,0,0,0
+5081,An improved Belief Propagation algorithm finds many Bethe states in the random field Ising model on random graphs,"  We first present an empirical study of the Belief Propagation (BP) algorithm,
+when run on the random field Ising model defined on random regular graphs in
+the zero temperature limit. We introduce the notion of maximal solutions for
+the BP equations and we use them to fix a fraction of spins in their ground
+state configuration. At the phase transition point the fraction of
+unconstrained spins percolates and their number diverges with the system size.
+This in turn makes the associated optimization problem highly non trivial in
+the critical region. Using the bounds on the BP messages provided by the
+maximal solutions we design a new and very easy to implement BP scheme which is
+able to output a large number of stable fixed points. On one side this new
+algorithm is able to provide the minimum energy configuration with high
+probability in a competitive time. On the other side we found that the number
+of fixed points of the BP algorithm grows with the system size in the critical
+region. This unexpected feature poses new relevant questions on the physics of
+this class of models.
+",1,1,0,0,0,0
+5082,Large-scale diversity estimation through surname origin inference,"  The study of surnames as both linguistic and geographical markers of the past
+has proven valuable in several research fields spanning from biology and
+genetics to demography and social mobility. This article builds upon the
+existing literature to conceive and develop a surname origin classifier based
+on a data-driven typology. This enables us to explore a methodology to describe
+large-scale estimates of the relative diversity of social groups, especially
+when such data is scarcely available. We subsequently analyze the
+representativeness of surname origins for 15 socio-professional groups in
+France.
+",0,0,0,1,0,0
+5083,Schur $Q$-functions and the Capelli eigenvalue problem for the Lie superalgebra $\mathfrak q(n)$,"  Let $\mathfrak l:= \mathfrak q(n)\times\mathfrak q(n)$, where $\mathfrak
+q(n)$ denotes the queer Lie superalgebra. The associative superalgebra $V$ of
+type $Q(n)$ has a left and right action of $\mathfrak q(n)$, and hence is
+equipped with a canonical $\mathfrak l$-module structure. We consider a
+distinguished basis $\{D_\lambda\}$ of the algebra of $\mathfrak l$-invariant
+super-polynomial differential operators on $V$, which is indexed by strict
+partitions of length at most $n$. We show that the spectrum of the operator
+$D_\lambda$, when it acts on the algebra $\mathscr P(V)$ of super-polynomials
+on $V$, is given by the factorial Schur $Q$-function of Okounkov and Ivanov.
+This constitutes a refinement and a new proof of a result of Nazarov, who
+computed the top-degree homogeneous part of the Harish-Chandra image of
+$D_\lambda$. As a further application, we show that the radial projections of
+the spherical super-polynomials corresponding to the diagonal symmetric pair
+$(\mathfrak l,\mathfrak m)$, where $\mathfrak m:=\mathfrak q(n)$, of
+irreducible $\mathfrak l$-submodules of $\mathscr P(V)$ are the classical Schur
+$Q$-functions.
+",0,0,1,0,0,0
+5084,Dynamics of cosmological perturbations in modified Brans-Dicke cosmology with matter-scalar field interaction,"  In this work we focus on a novel completion of the well-known Brans-Dicke
+theory that introduces an interaction between the dark energy and dark matter
+sectors, known as complete Brans-Dicke (CBD) theory. We obtain viable
+cosmological accelerating solutions that fit Supernovae observations with great
+precision without any scalar potential $V(\phi)$. We use these solutions to
+explore the impact of the CBD theory on the large scale structure by studying
+the dynamics of its linear perturbations. We observe a growing behavior of the
+lensing potential $\Phi_{+}$ at late-times, while the growth rate is actually
+suppressed relatively to $\Lambda$CDM, which allows the CBD theory to provide a
+competitive fit to current RSD measurements of $f\sigma_{8}$. However, we also
+observe that the theory exhibits a pathological change of sign in the effective
+gravitational constant concerning the perturbations on sub-horizon scales that
+could pose a challenge to its validity.
+",0,1,0,0,0,0
+5085,"Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech","  Speech emotion recognition is an important and challenging task in the realm
+of human-computer interaction. Prior work proposed a variety of models and
+feature sets for training a system. In this work, we conduct extensive
+experiments using an attentive convolutional neural network with multi-view
+learning objective function. We compare system performance using different
+lengths of the input signal, different types of acoustic features and different
+types of emotion speech (improvised/scripted). Our experimental results on the
+Interactive Emotional Motion Capture (IEMOCAP) database reveal that the
+recognition performance strongly depends on the type of speech data independent
+of the choice of input features. Furthermore, we achieved state-of-the-art
+results on the improvised speech data of IEMOCAP.
+",1,0,0,0,0,0
+5086,"Men Are from Mars, Women Are from Venus: Evaluation and Modelling of Verbal Associations","  We present a quantitative analysis of human word association pairs and study
+the types of relations presented in the associations. We put our main focus on
+the correlation between response types and respondent characteristics such as
+occupation and gender by contrasting syntagmatic and paradigmatic associations.
+Finally, we propose a personalised distributed word association model and show
+the importance of incorporating demographic factors into the models commonly
+used in natural language processing.
+",1,0,0,0,0,0
+5087,Galaxies as High-Resolution Telescopes,"  Recent observations show a population of active galaxies with milliarcseconds
+offsets between optical and radio emission. Such offsets can be an indication
+of extreme phenomena associated with supermassive black holes including
+relativistic jets, binary supermassive black holes, or even recoiling
+supermassive black holes. However, the multi-wavelength structure of active
+galaxies at a few milliarcseconds cannot be fathomed with direct observations.
+We propose using strong gravitational lensing to elucidate the multi-wavelength
+structure of sources. When sources are located close to the caustic of lensing
+galaxy, even small offset in the position of the sources results in a drastic
+difference in the position and magnification of mirage images. We show that the
+angular offset in the position of the sources can be amplified more than 50
+times in the observed position of mirage images. We find that at least 8% of
+the observed gravitationally lensed quasars will be in the caustic
+configuration. The synergy between SKA and Euclid will provide an ideal set of
+observations for thousands of gravitationally lensed sources in the caustic
+configuration, which will allow us to elucidate the multi-wavelength structure
+for a large ensemble of sources, and study the physical origin of radio
+emissions, their connection to supermassive black holes, and their cosmic
+evolution.
+",0,1,0,0,0,0
+5088,Continuous CM-regularity of semihomogeneous vector bundles,"  We show that if $X$ is an abelian variety of dimension $g \geq 1$ and
+${\mathcal E}$ is an M-regular coherent sheaf on $X$, the Castelnuovo-Mumford
+regularity of ${\mathcal E}$ with respect to an ample and globally generated
+line bundle ${\mathcal O}(1)$ on $X$ is at most $g$, and that equality is
+obtained when ${\mathcal E}^{\vee}(1)$ is continuously globally generated. As
+an application, we give a numerical characterization of ample semihomogeneous
+vector bundles for which this bound is attained.
+",0,0,1,0,0,0
+5089,Factorisation of the product of Dirichlet series of completely multiplicative functions,"  In the first chapter, we will present a computation of the square value of
+the module of L functions associated to a Dirichlet character. This computation
+suggests to ask if a certain ring of arithmetic multiplicative functions exists
+and if it is unique. This search has led to the construction of that ring in
+chapter two. Finally, in the third chapter, we will present some propositions
+associated with this ring. The result below is one of the main results of this
+work :
+For F and G two completely multiplicative functions, $ s $ a complex number
+such as the dirichlet series $ D(F,s) $ and $ D(G,s) $ converge :
+$ \forall F,G \in \mathbb{M}_{c} : D(F,s) \times D(G,s) = D(F \times G,2s)
+\times D(F \square G,s) $
+where the operation $ \square $ is defined in chapter two as the sum of the
+previously mentioned ring. Here are some similar versions, with $ s = x+iy $ :
+$ \forall F, G \in \mathbb{M}_{c} : ~ D(F,s) \times D(G,\overline{s}) = D(F
+\times G,2x) \times D(\frac{F}{\text{Id}_{e}^{iy}} \square
+\frac{G}{\text{Id}_{e}^{-iy}}, x) $
+$ \forall F, G \in \mathbb{M}_{c} : ~ |D(F,s)|^{2} = D(|F|^{2},2x) \times
+D(\frac{F}{\text{Id}_{e}^{iy}} \square \overline{\frac{F}{\text{Id}_{e}^{iy}}},
+x) $
+",0,0,1,0,0,0
+5090,LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables,"  Many inverse problems involve two or more sets of variables that represent
+different physical quantities but are tightly coupled with each other. For
+example, image super-resolution requires joint estimation of the image and
+motion parameters from noisy measurements. Exploiting this structure is key for
+efficiently solving these large-scale optimization problems, which are often
+ill-conditioned.
+In this paper, we present a new method called Linearize And Project (LAP)
+that offers a flexible framework for solving inverse problems with coupled
+variables. LAP is most promising for cases when the subproblem corresponding to
+one of the variables is considerably easier to solve than the other. LAP is
+based on a Gauss-Newton method, and thus after linearizing the residual, it
+eliminates one block of variables through projection. Due to the linearization,
+this block can be chosen freely. Further, LAP supports direct, iterative, and
+hybrid regularization as well as constraints. Therefore LAP is attractive,
+e.g., for ill-posed imaging problems. These traits differentiate LAP from
+common alternatives for this type of problem such as variable projection
+(VarPro) and block coordinate descent (BCD). Our numerical experiments compare
+the performance of LAP to BCD and VarPro using three coupled problems whose
+forward operators are linear with respect to one block and nonlinear for the
+other set of variables.
+",1,0,1,0,0,0
+5091,Obtaining the Current-Flux Relations of the Saturated PMSM by Signal Injection,"  This paper proposes a method based on signal injection to obtain the
+saturated current-flux relations of a PMSM from locked-rotor experiments. With
+respect to the classical method based on time integration, it has the main
+advantage of being completely independent of the stator resistance; moreover,
+it is less sensitive to voltage biases due to the power inverter, as the
+injected signal may be fairly large.
+",1,0,0,0,0,0
+5092,"Approximation properties of (p,q)-Meyer-Konig-Zeller Durrmeyer operators","  In this paper, we introduce Durrmeyer type modification of Meyer-Konig-Zeller
+operators based on (p,q)-integers. Rate of convergence of these operators are
+explored with the help of Korovkin type theorems. We establish some direct
+results for proposed operators. We also obtain statistical approximation
+properties of operators. In last section, we show rate of convergence of
+(p,q)-Meyer-Konig-Zeller Durrmeyer operators for some functions by means of
+Matlab programming.
+",0,0,1,0,0,0
+5093,Deep Graph Infomax,"  We present Deep Graph Infomax (DGI), a general approach for learning node
+representations within graph-structured data in an unsupervised manner. DGI
+relies on maximizing mutual information between patch representations and
+corresponding high-level summaries of graphs---both derived using established
+graph convolutional network architectures. The learnt patch representations
+summarize subgraphs centered around nodes of interest, and can thus be reused
+for downstream node-wise learning tasks. In contrast to most prior approaches
+to unsupervised learning with GCNs, DGI does not rely on random walk
+objectives, and is readily applicable to both transductive and inductive
+learning setups. We demonstrate competitive performance on a variety of node
+classification benchmarks, which at times even exceeds the performance of
+supervised learning.
+",1,0,0,1,0,0
+5094,Solitons and breathers for nonisospectral mKdV equation with Darboux transformation,"  Under investigation in this paper is the nonisospectral and variable
+coefficients modified Kortweg-de Vries (vc-mKdV) equation, which manifests in
+diverse areas of physics such as fluid dynamics, ion acoustic solitons and
+plasma mechanics. With the degrees of restriction reduced, a simplified
+constraint is introduced, under which the vc-mKdV equation is an integrable
+system and the spectral flow is time-varying. The Darboux transformation for
+such equation is constructed, which gives rise to the generation of variable
+kinds of solutions including the double-breather coherent structure, periodical
+soliton-breather and localized solitons and breathers. In addition, the effect
+of variable coefficients and initial phases is discussed in terms of the
+soliton amplitude, polarity, velocity and width, which might provide feasible
+soliton management with certain conditions taken into account.
+",0,1,0,0,0,0
+5095,Sequential detection of low-rank changes using extreme eigenvalues,"  We study the problem of detecting an abrupt change to the signal covariance
+matrix. In particular, the covariance changes from a ""white"" identity matrix to
+an unknown spiked or low-rank matrix. Two sequential change-point detection
+procedures are presented, based on the largest and the smallest eigenvalues of
+the sample covariance matrix. To control false-alarm-rate, we present an
+accurate theoretical approximation to the average-run-length (ARL) and expected
+detection delay (EDD) of the detection, leveraging the extreme eigenvalue
+distributions from random matrix theory and by capturing a non-negligible
+temporal correlation in the sequence of scan statistics due to the sliding
+window approach. Real data examples demonstrate the good performance of our
+method for detecting behavior change of a swarm.
+",0,0,1,1,0,0
+5096,Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework,"  In this paper, we argue that the future of Artificial Intelligence research
+resides in two keywords: integration and embodiment. We support this claim by
+analyzing the recent advances of the field. Regarding integration, we note that
+the most impactful recent contributions have been made possible through the
+integration of recent Machine Learning methods (based in particular on Deep
+Learning and Recurrent Neural Networks) with more traditional ones (e.g.
+Monte-Carlo tree search, goal babbling exploration or addressable memory
+systems). Regarding embodiment, we note that the traditional benchmark tasks
+(e.g. visual classification or board games) are becoming obsolete as
+state-of-the-art learning algorithms approach or even surpass human performance
+in most of them, having recently encouraged the development of first-person 3D
+game platforms embedding realistic physics. Building upon this analysis, we
+first propose an embodied cognitive architecture integrating heterogenous
+sub-fields of Artificial Intelligence into a unified framework. We demonstrate
+the utility of our approach by showing how major contributions of the field can
+be expressed within the proposed framework. We then claim that benchmarking
+environments need to reproduce ecologically-valid conditions for bootstrapping
+the acquisition of increasingly complex cognitive skills through the concept of
+a cognitive arms race between embodied agents.
+",1,0,0,0,0,0
+5097,Does a growing static length scale control the glass transition?,"  Several theories of the glass transition propose that the structural
+relaxation time {\tau}{\alpha} is controlled by a growing static length scale
+{\xi} that is determined by the free energy landscape but not by the local
+dynamical rules governing its exploration. We argue, based on recent
+simulations using particle-radius-swap dynamics, that only a modest factor in
+the increase in {\tau}{\alpha} on approach to the glass transition may stem
+from the growth of a static length, with a vastly larger contribution
+attributable instead to a slowdown of local dynamics. This reinforces arguments
+that we base on the observed strong coupling of particle diffusion and density
+fluctuations in real glasses
+",0,1,0,0,0,0
+5098,Canonical models of arithmetic $(1; \infty)$ curves,"  In 1983 Takeuchi showed that up to conjugation there are exactly 4 arithmetic
+subgroups of $\textrm{PSL}_2 (\mathbb{R})$ with signature $(1; \infty)$.
+Shinichi Mochizuki gave a purely geometric characterization of the
+corresponding arithmetic $(1; \infty)$-curves, which also arise naturally in
+the context of his recent work on inter-universal Teichmüller theory.
+Using Bely\u{\i} maps, we explicitly determine the canonical models of these
+curves. We also study their arithmetic properties and modular interpretations.
+",0,0,1,0,0,0
+5099,State observation and sensor selection for nonlinear networks,"  A large variety of dynamical systems, such as chemical and biomolecular
+systems, can be seen as networks of nonlinear entities. Prediction, control,
+and identification of such nonlinear networks require knowledge of the state of
+the system. However, network states are usually unknown, and only a fraction of
+the state variables are directly measurable. The observability problem concerns
+reconstructing the network state from this limited information. Here, we
+propose a general optimization-based approach for observing the states of
+nonlinear networks and for optimally selecting the observed variables. Our
+results reveal several fundamental limitations in network observability, such
+as the trade-off between the fraction of observed variables and the observation
+length on one side, and the estimation error on the other side. We also show
+that owing to the crucial role played by the dynamics, purely graph- theoretic
+observability approaches cannot provide conclusions about one's practical
+ability to estimate the states. We demonstrate the effectiveness of our methods
+by finding the key components in biological and combustion reaction networks
+from which we determine the full system state. Our results can lead to the
+design of novel sensing principles that can greatly advance prediction and
+control of the dynamics of such networks.
+",1,0,1,0,0,0
+5100,A Faster Implementation of Online Run-Length Burrows-Wheeler Transform,"  Run-length encoding Burrows-Wheeler Transformed strings, resulting in
+Run-Length BWT (RLBWT), is a powerful tool for processing highly repetitive
+strings. We propose a new algorithm for online RLBWT working in run-compressed
+space, which runs in $O(n\lg r)$ time and $O(r\lg n)$ bits of space, where $n$
+is the length of input string $S$ received so far and $r$ is the number of runs
+in the BWT of the reversed $S$. We improve the state-of-the-art algorithm for
+online RLBWT in terms of empirical construction time. Adopting the dynamic list
+for maintaining a total order, we can replace rank queries in a dynamic wavelet
+tree on a run-length compressed string by the direct comparison of labels in a
+dynamic list. The empirical result for various benchmarks show the efficiency
+of our algorithm, especially for highly repetitive strings.
+",1,0,0,0,0,0
+5101,Maximal fluctuations of confined actomyosin gels: dynamics of the cell nucleus,"  We investigate the effect of stress fluctuations on the stochastic dynamics
+of an inclusion embedded in a viscous gel. We show that, in non-equilibrium
+systems, stress fluctuations give rise to an effective attraction towards the
+boundaries of the confining domain, which is reminiscent of an active Casimir
+effect. We apply this generic result to the dynamics of deformations of the
+cell nucleus and we demonstrate the appearance of a fluctuation maximum at a
+critical level of activity, in agreement with recent experiments [E. Makhija,
+D. S. Jokhun, and G. V. Shivashankar, Proc. Natl. Acad. Sci. U.S.A. 113, E32
+(2016)].
+",0,1,0,0,0,0
+5102,Age-at-harvest models as monitoring and harvest management tools for Wisconsin carnivores,"  Quantifying and estimating wildlife population sizes is a foundation of
+wildlife management. However, many carnivore species are cryptic, leading to
+innate difficulties in estimating their populations. We evaluated the potential
+for using more rigorous statistical models to estimate the populations of black
+bears (Ursus americanus) in Wisconisin, and their applicability to other
+furbearers such as bobcats (Lynx rufus). To estimate black bear populations, we
+developed an AAH state-space model in a Bayesian framework based on Norton
+(2015) that can account for variation in harvest and population demographics
+over time. Our state-space model created an accurate estimate of the black bear
+population in Wisconsin based on age-at-harvest data and improves on previous
+models by using little demographic data, no auxiliary data, and not being
+sensitive to initial population size. The increased accuracy of the AAH
+state-space models should allow for better management by being able to set
+accurate quotas to ensure a sustainable harvest for the black bear population
+in each zone. We also evaluated the demography and annual harvest data of
+bobcats in Wisconsin to determine trends in harvest, method, and hunter
+participation in Wisconsin. Each trait of harvested bobcats that we tested
+(mass, male:female sex ratio, and age) changed over time, and because these
+were interrelated, and we inferred that harvest selection for larger size
+biased harvests in favor of a) larger, b) older, and c) male bobcats by hound
+hunters. We also found an increase in the proportion of bobcats that were
+harvested by hound hunting compared to trapping, and that hound hunters were
+more likely to create taxidermy mounts than trappers. We also found that
+decreasing available bobcat tags and increasing success have occurred over
+time, and correlate with substantially increasing both hunter populations and
+hunter interest.
+",0,0,0,0,1,0
+5103,Information Planning for Text Data,"  Information planning enables faster learning with fewer training examples. It
+is particularly applicable when training examples are costly to obtain. This
+work examines the advantages of information planning for text data by focusing
+on three supervised models: Naive Bayes, supervised LDA and deep neural
+networks. We show that planning based on entropy and mutual information
+outperforms random selection baseline and therefore accelerates learning.
+",0,0,0,1,0,0
+5104,Development of Si-CMOS hybrid detectors towards electron tracking based Compton imaging in semiconductor detectors,"  Electron tracking based Compton imaging is a key technique to improve the
+sensitivity of Compton cameras by measuring the initial direction of recoiled
+electrons. To realize this technique in semiconductor Compton cameras, we
+propose a new detector concept, Si-CMOS hybrid detector. It is a Si detector
+bump-bonded to a CMOS readout integrated circuit to obtain electron trajectory
+images. To acquire the energy and the event timing, signals from N-side are
+also read out in this concept. By using an ASIC for the N-side readout, the
+timing resolution of few us is achieved. In this paper, we present the results
+of two prototypes with 20 um pitch pixels. The images of the recoiled electron
+trajectories are obtained with them successfully. The energy resolutions (FWHM)
+are 4.1 keV (CMOS) and 1.4 keV (N-side) at 59.5 keV. In addition, we confirmed
+that the initial direction of the electron is determined using the
+reconstruction algorithm based on the graph theory approach. These results show
+that Si-CMOS hybrid detectors can be used for electron tracking based Compton
+imaging.
+",0,1,0,0,0,0
+5105,Sharp constant of an anisotropic Gagliardo-Nirenberg-type inequality and applications,"  In this paper we establish the best constant of an anisotropic
+Gagliardo-Nirenberg-type inequality related to the
+Benjamin-Ono-Zakharov-Kuznetsov equation. As an application of our results, we
+prove the uniform bound of solutions for such a equation in the energy space.
+",0,0,1,0,0,0
+5106,"Treatment Effect Quantification for Time-to-event Endpoints -- Estimands, Analysis Strategies, and beyond","  A draft addendum to ICH E9 has been released for public consultation in
+August 2017. The addendum focuses on two topics particularly relevant for
+randomized confirmatory clinical trials: estimands and sensitivity analyses.
+The need to amend ICH E9 grew out of the realization of a lack of alignment
+between the objectives of a clinical trial stated in the protocol and the
+accompanying quantification of the ""treatment effect"" reported in a regulatory
+submission. We embed time-to-event endpoints in the estimand framework, and
+discuss how the four estimand attributes described in the addendum apply to
+time-to-event endpoints. We point out that if the proportional hazards
+assumption is not met, the estimand targeted by the most prevalent methods used
+to analyze time-to-event endpoints, logrank test and Cox regression, depends on
+the censoring distribution. We discuss for a large randomized clinical trial
+how the analyses for the primary and secondary endpoints as well as the
+sensitivity analyses actually performed in the trial can be seen in the context
+of the addendum. To the best of our knowledge, this is the first attempt to do
+so for a trial with a time-to-event endpoint. Questions that remain open with
+the addendum for time-to-event endpoints and beyond are formulated, and
+recommendations for planning of future trials are given. We hope that this will
+provide a contribution to developing a common framework based on the final
+version of the addendum that can be applied to design, protocols, statistical
+analysis plans, and clinical study reports in the future.
+",0,0,0,1,0,0
+5107,A character of Siegel modular group of level 2 from theta constants,"  Given a characteristic, we define a character of the Siegel modular group of
+level 2, the computations of their values are also obtained. By using our
+theorems, some key theorems of Igusa [1] can be recovered.
+",0,0,1,0,0,0
+5108,Antiferromagnetic Chern insulators in non-centrosymmetric systems,"  We investigate a new class of topological antiferromagnetic (AF) Chern
+insulators driven by electronic interactions in two-dimensional systems without
+inversion symmetry. Despite the absence of a net magnetization, AF Chern
+insulators (AFCI) possess a nonzero Chern number $C$ and exhibit the quantum
+anomalous Hall effect (QAHE). Their existence is guaranteed by the bifurcation
+of the boundary line of Weyl points between a quantum spin Hall insulator and a
+topologically trivial phase with the emergence of AF long-range order. As a
+concrete example, we study the phase structure of the honeycomb lattice
+Kane-Mele model as a function of the inversion-breaking ionic potential and the
+Hubbard interaction. We find an easy $z$-axis $C=1$ AFCI phase and a spin-flop
+transition to a topologically trivial $xy$-plane collinear antiferromagnet. We
+propose experimental realizations of the AFCI and QAHE in correlated electron
+materials and cold atom systems.
+",0,1,0,0,0,0
+5109,The concentration-mass relation of clusters of galaxies from the OmegaWINGS survey,"  The relation between a cosmological halo concentration and its mass (cMr) is
+a powerful tool to constrain cosmological models of halo formation and
+evolution. On the scale of galaxy clusters the cMr has so far been determined
+mostly with X-ray and gravitational lensing data. The use of independent
+techniques is helpful in assessing possible systematics. Here we provide one of
+the few determinations of the cMr by the dynamical analysis of the
+projected-phase-space distribution of cluster members. Based on the WINGS and
+OmegaWINGS data sets, we used the Jeans analysis with the MAMPOSSt technique to
+determine masses and concentrations for 49 nearby clusters, each of which has
+~60 spectroscopic members or more within the virial region, after removal of
+substructures. Our cMr is in statistical agreement with theoretical predictions
+based on LambdaCDM cosmological simulations. Our cMr is different from most
+previous observational determinations because of its flatter slope and lower
+normalization. It is however in agreement with two recent cMr obtained using
+the lensing technique on the CLASH and LoCuSS cluster data sets. In the future
+we will extend our analysis to galaxy systems of lower mass and at higher
+redshifts.
+",0,1,0,0,0,0
+5110,Effects of the structural distortion on the electronic band structure of {\boldmath $\rm Na Os O_3$} studied within density functional theory and a three-orbital model,"  Effects of the structural distortion associated with the $\rm OsO_6$
+octahedral rotation and tilting on the electronic band structure and magnetic
+anisotropy energy for the $5d^3$ compound NaOsO$_3$ are investigated using the
+density functional theory (DFT) and within a three-orbital model. Comparison of
+the essential features of the DFT band structures with the three-orbital model
+for both the undistorted and distorted structures provides insight into the
+orbital and directional asymmetry in the electron hopping terms resulting from
+the structural distortion. The orbital mixing terms obtained in the transformed
+hopping Hamiltonian resulting from the octahedral rotations are shown to
+account for the fine features in the DFT band structure. Staggered
+magnetization and the magnetic character of states near the Fermi energy
+indicate weak coupling behavior.
+",0,1,0,0,0,0
+5111,On the Joint Distribution Of $\mathrm{Sel}_ϕ(E/\mathbb{Q})$ and $\mathrm{Sel}_{\hatϕ}(E^\prime/\mathbb{Q})$ in Quadratic Twist Families,"  If $E$ is an elliptic curve with a point of order two, then work of Klagsbrun
+and Lemke Oliver shows that the distribution of
+$\dim_{\mathbb{F}_2}\mathrm{Sel}_\phi(E^d/\mathbb{Q}) - \dim_{\mathbb{F}_2}
+\mathrm{Sel}_{\hat\phi}(E^{\prime d}/\mathbb{Q})$ within the quadratic twist
+family tends to the discrete normal distribution $\mathcal{N}(0,\frac{1}{2}
+\log \log X)$ as $X \rightarrow \infty$.
+We consider the distribution of $\mathrm{dim}_{\mathbb{F}_2}
+\mathrm{Sel}_\phi(E^d/\mathbb{Q})$ within such a quadratic twist family when
+$\dim_{\mathbb{F}_2} \mathrm{Sel}_\phi(E^d/\mathbb{Q}) - \dim_{\mathbb{F}_2}
+\mathrm{Sel}_{\hat\phi}(E^{\prime d}/\mathbb{Q})$ has a fixed value $u$.
+Specifically, we show that for every $r$, the limiting probability that
+$\dim_{\mathbb{F}_2} \mathrm{Sel}_\phi(E^d/\mathbb{Q}) = r$ is given by an
+explicit constant $\alpha_{r,u}$. The constants $\alpha_{r,u}$ are closely
+related to the $u$-probabilities introduced in Cohen and Lenstra's work on the
+distribution of class groups, and thus provide a connection between the
+distribution of Selmer groups of elliptic curves and random abelian groups.
+Our analysis of this problem has two steps. The first step uses algebraic and
+combinatorial methods to directly relate the ranks of the Selmer groups in
+question to the dimensions of the kernels of random $\mathbb{F}_2$-matrices.
+This proves that the density of twists with a given $\phi$-Selmer rank $r$ is
+given by $\alpha_{r,u}$ for an unusual notion of density. The second step of
+the analysis utilizes techniques from analytic number theory to show that this
+result implies the correct asymptotics in terms of the natural notion of
+density.
+",0,0,1,0,0,0
+5112,Learning What Data to Learn,"  Machine learning is essentially the sciences of playing with data. An
+adaptive data selection strategy, enabling to dynamically choose different data
+at various training stages, can reach a more effective model in a more
+efficient way. In this paper, we propose a deep reinforcement learning
+framework, which we call \emph{\textbf{N}eural \textbf{D}ata \textbf{F}ilter}
+(\textbf{NDF}), to explore automatic and adaptive data selection in the
+training process. In particular, NDF takes advantage of a deep neural network
+to adaptively select and filter important data instances from a sequential
+stream of training data, such that the future accumulative reward (e.g., the
+convergence speed) is maximized. In contrast to previous studies in data
+selection that is mainly based on heuristic strategies, NDF is quite generic
+and thus can be widely suitable for many machine learning tasks. Taking neural
+network training with stochastic gradient descent (SGD) as an example,
+comprehensive experiments with respect to various neural network modeling
+(e.g., multi-layer perceptron networks, convolutional neural networks and
+recurrent neural networks) and several applications (e.g., image classification
+and text understanding) demonstrate that NDF powered SGD can achieve comparable
+accuracy with standard SGD process by using less data and fewer iterations.
+",1,0,0,1,0,0
+5113,Deformation estimation of an elastic object by partial observation using a neural network,"  Deformation estimation of elastic object assuming an internal organ is
+important for the computer navigation of surgery. The aim of this study is to
+estimate the deformation of an entire three-dimensional elastic object using
+displacement information of very few observation points. A learning approach
+with a neural network was introduced to estimate the entire deformation of an
+object. We applied our method to two elastic objects; a rectangular
+parallelepiped model, and a human liver model reconstructed from computed
+tomography data. The average estimation error for the human liver model was
+0.041 mm when the object was deformed up to 66.4 mm, from only around 3 %
+observations. These results indicate that the deformation of an entire elastic
+object can be estimated with an acceptable level of error from limited
+observations by applying a trained neural network to a new deformation.
+",1,0,0,1,0,0
+5114,Comparative analysis of two discretizations of Ricci curvature for complex networks,"  We have performed an empirical comparison of two distinct notions of discrete
+Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and
+Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci
+curvature were developed based on different properties of the classical smooth
+notion, and thus, the two notions shed light on different aspects of network
+structure and behavior. Nevertheless, our extensive computational analysis in a
+wide range of both model and real-world networks shows that the two
+discretizations of Ricci curvature are highly correlated in many networks.
+Moreover, we show that if one considers the augmented Forman-Ricci curvature
+which also accounts for the two-dimensional simplicial complexes arising in
+graphs, the observed correlation between the two discretizations is even
+higher, especially, in real networks. Besides the potential theoretical
+implications of these observations, the close relationship between the two
+discretizations has practical implications whereby Forman-Ricci curvature can
+be employed in place of Ollivier-Ricci curvature for faster computation in
+larger real-world networks whenever coarse analysis suffices.
+",1,0,1,0,0,0
+5115,Zero-Shot Learning via Class-Conditioned Deep Generative Models,"  We present a deep generative model for learning to predict classes not seen
+at training time. Unlike most existing methods for this problem, that represent
+each class as a point (via a semantic embedding), we represent each seen/unseen
+class using a class-specific latent-space distribution, conditioned on class
+attributes. We use these latent-space distributions as a prior for a supervised
+variational autoencoder (VAE), which also facilitates learning highly
+discriminative feature representations for the inputs. The entire framework is
+learned end-to-end using only the seen-class training data. The model infers
+corresponding attributes of a test image by maximizing the VAE lower bound; the
+inferred attributes may be linked to labels not seen when training. We further
+extend our model to a (1) semi-supervised/transductive setting by leveraging
+unlabeled unseen-class data via an unsupervised learning module, and (2)
+few-shot learning where we also have a small number of labeled inputs from the
+unseen classes. We compare our model with several state-of-the-art methods
+through a comprehensive set of experiments on a variety of benchmark data sets.
+",1,0,0,0,0,0
+5116,On the vanishing viscosity approximation of a nonlinear model for tumor growth,"  We investigate the dynamics of a nonlinear system modeling tumor growth with
+drug application. The tumor is viewed as a mixture consisting of proliferating,
+quiescent and dead cells as well as a nutrient in the presence of a drug. The
+system is given by a multi-phase flow model: the densities of the different
+cells are governed by a set of transport equations, the density of the nutrient
+and the density of the drug are governed by rather general diffusion equations,
+while the velocity of the tumor is given by Darcy's equation. The domain
+occupied by the tumor in this setting is a growing continuum $\Omega$ with
+boundary $\partial \Omega$ both of which evolve in time. Global-in-time weak
+solutions are obtained using an approach based on the vanishing viscosity of
+the Brinkman's regularization. Both the solutions and the domain are rather
+general, no symmetry assumption is required and the result holds for large
+initial data.
+",0,0,1,0,0,0
+5117,Noise-gating to clean astrophysical image data,"  I present a family of algorithms to reduce noise in astrophysical im- ages
+and image sequences, preserving more information from the original data than is
+retained by conventional techniques. The family uses locally adaptive filters
+(""noise gates"") in the Fourier domain, to separate coherent image structure
+from background noise based on the statistics of local neighborhoods in the
+image. Processing of solar data limited by simple shot noise or by additive
+noise reveals image structure not easily visible in the originals, preserves
+photometry of observable features, and reduces shot noise by a factor of 10 or
+more with little to no apparent loss of resolution, revealing faint features
+that were either not directly discernible or not sufficiently strongly detected
+for quantitative analysis. The method works best on image sequences containing
+related subjects, for example movies of solar evolution, but is also applicable
+to single images provided that there are enough pixels. The adaptive filter
+uses the statistical properties of noise and of local neighborhoods in the
+data, to discriminate between coherent features and incoherent noise without
+reference to the specific shape or evolution of the those features. The
+technique can potentially be modified in a straightforward way to exploit
+additional a priori knowledge about the functional form of the noise.
+",0,1,0,0,0,0
+5118,Variational Bayesian dropout: pitfalls and fixes,"  Dropout, a stochastic regularisation technique for training of neural
+networks, has recently been reinterpreted as a specific type of approximate
+inference algorithm for Bayesian neural networks. The main contribution of the
+reinterpretation is in providing a theoretical framework useful for analysing
+and extending the algorithm. We show that the proposed framework suffers from
+several issues; from undefined or pathological behaviour of the true posterior
+related to use of improper priors, to an ill-defined variational objective due
+to singularity of the approximating distribution relative to the true
+posterior. Our analysis of the improper log uniform prior used in variational
+Gaussian dropout suggests the pathologies are generally irredeemable, and that
+the algorithm still works only because the variational formulation annuls some
+of the pathologies. To address the singularity issue, we proffer Quasi-KL (QKL)
+divergence, a new approximate inference objective for approximation of
+high-dimensional distributions. We show that motivations for variational
+Bernoulli dropout based on discretisation and noise have QKL as a limit.
+Properties of QKL are studied both theoretically and on a simple practical
+example which shows that the QKL-optimal approximation of a full rank Gaussian
+with a degenerate one naturally leads to the Principal Component Analysis
+solution.
+",0,0,0,1,0,0
+5119,Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power,"  Wind energy forecasting helps to manage power production, and hence, reduces
+energy cost. Deep Neural Networks (DNN) mimics hierarchical learning in the
+human brain and thus possesses hierarchical, distributed, and multi-task
+learning capabilities. Based on aforementioned characteristics, we report Deep
+Belief Network (DBN) based forecast engine for wind power prediction because of
+its good generalization and unsupervised pre-training attributes. The proposed
+DBN-WP forecast engine, which exhibits stochastic feature generation
+capabilities and is composed of multiple Restricted Boltzmann Machines,
+generates suitable features for wind power prediction using atmospheric
+properties as input. DBN-WP, due to its unsupervised pre-training of RBM layers
+and generalization capabilities, is able to learn the fluctuations in the
+meteorological properties and thus is able to perform effective mapping of the
+wind power. In the deep network, a regression layer is appended at the end to
+predict sort-term wind power. It is experimentally shown that the deep learning
+and unsupervised pre-training capabilities of DBN based model has comparable
+and in some cases better results than hybrid and complex learning techniques
+proposed for wind power prediction. The proposed prediction system based on
+DBN, achieves mean values of RMSE, MAE and SDE as 0.124, 0.083 and 0.122,
+respectively. Statistical analysis of several independent executions of the
+proposed DBN-WP wind power prediction system demonstrates the stability of the
+system. The proposed DBN-WP architecture is easy to implement and offers
+generalization as regards the change in location of the wind farm is concerned.
+",0,0,0,1,0,0
+5120,Control Interpretations for First-Order Optimization Methods,"  First-order iterative optimization methods play a fundamental role in large
+scale optimization and machine learning. This paper presents control
+interpretations for such optimization methods. First, we give loop-shaping
+interpretations for several existing optimization methods and show that they
+are composed of basic control elements such as PID and lag compensators. Next,
+we apply the small gain theorem to draw a connection between the convergence
+rate analysis of optimization methods and the input-output gain computations of
+certain complementary sensitivity functions. These connections suggest that
+standard classical control synthesis tools may be brought to bear on the design
+of optimization algorithms.
+",1,0,1,0,0,0
+5121,Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction,"  Machine understanding of complex images is a key goal of artificial
+intelligence. One challenge underlying this task is that visual scenes contain
+multiple inter-related objects, and that global context plays an important role
+in interpreting the scene. A natural modeling framework for capturing such
+effects is structured prediction, which optimizes over complex labels, while
+modeling within-label interactions. However, it is unclear what principles
+should guide the design of a structured prediction model that utilizes the
+power of deep learning components. Here we propose a design principle for such
+architectures that follows from a natural requirement of permutation
+invariance. We prove a necessary and sufficient characterization for
+architectures that follow this invariance, and discuss its implication on model
+design. Finally, we show that the resulting model achieves new state of the art
+results on the Visual Genome scene graph labeling benchmark, outperforming all
+recent approaches.
+",0,0,0,1,0,0
+5122,Identifying Similarities in Epileptic Patients for Drug Resistance Prediction,"  Currently, approximately 30% of epileptic patients treated with antiepileptic
+drugs (AEDs) remain resistant to treatment (known as refractory patients). This
+project seeks to understand the underlying similarities in refractory patients
+vs. other epileptic patients, identify features contributing to drug resistance
+across underlying phenotypes for refractory patients, and develop predictive
+models for drug resistance in epileptic patients. In this study, epileptic
+patient data was examined to attempt to observe discernable similarities or
+differences in refractory patients (case) and other non-refractory patients
+(control) to map underlying mechanisms in causality. For the first part of the
+study, unsupervised algorithms such as Kmeans, Spectral Clustering, and
+Gaussian Mixture Models were used to examine patient features projected into a
+lower dimensional space. Results from this study showed a high degree of
+non-linearity in the underlying feature space. For the second part of this
+study, classification algorithms such as Logistic Regression, Gradient Boosted
+Decision Trees, and SVMs, were tested on the reduced-dimensionality features,
+with accuracy results of 0.83(+/-0.3) testing using 7 fold cross validation.
+Observations of test results indicate using a radial basis function kernel PCA
+to reduce features ingested by a Gradient Boosted Decision Tree Ensemble lead
+to gains in improved accuracy in mapping a binary decision to highly non-linear
+features collected from epileptic patients.
+",1,0,0,1,0,0
+5123,Variational Inference of Disentangled Latent Concepts from Unlabeled Observations,"  Disentangled representations, where the higher level data generative factors
+are reflected in disjoint latent dimensions, offer several benefits such as
+ease of deriving invariant representations, transferability to other tasks,
+interpretability, etc. We consider the problem of unsupervised learning of
+disentangled representations from large pool of unlabeled observations, and
+propose a variational inference based approach to infer disentangled latent
+factors. We introduce a regularizer on the expectation of the approximate
+posterior over observed data that encourages the disentanglement. We also
+propose a new disentanglement metric which is better aligned with the
+qualitative disentanglement observed in the decoder's output. We empirically
+observe significant improvement over existing methods in terms of both
+disentanglement and data likelihood (reconstruction quality).
+",1,0,0,1,0,0
+5124,Designing the color of hot-dip galvanized steel sheet through destructive light interference using a Zn-Ti liquid metallic bath,"  The color of hot-dip galvanized steel sheet was adjusted in a reproducible
+way using a liquid Zn-Ti metallic bath, air atmosphere, and controlling the
+bath temperature as the only experimental parameter. Coloring was found only
+for sample s cooled in air and dipped into Ti-containing liquid Zn. For samples
+dipped into a 0.15 wt pct Ti-containing Zn bath, the color remained metallic
+(gray) below a 792 K (519 C) bath temperature; it was yellow at 814 K, violet
+at 847 K, and blue at 873 K. With the increasing bath temperature, the
+thickness of the adhered Zn-Ti layer gradually decreased from 52 to 32
+micrometers, while the thickness of the outer TiO2 layer gradually increased
+from 24 to 69 nm. Due to small Al contamination of the Zn bath, a thin (around
+2 nm) alumina-rich layer is found between the outer TiO2 layer and the inner
+macroscopic Zn layer. It is proven that the color change was governed by the
+formation of thin outer TiO2 layer; different colors appear depending on the
+thickness of this layer, mostly due to the destructive interference of visible
+light on this transparent nano-layer. A complex model was built to explain the
+results using known relationships of chemical thermodynamics, adhesion, heat
+flow, kinetics of chemical reactions, diffusion, and optics.
+",0,1,0,0,0,0
+5125,Counting Multiplicities in a Hypersurface over a Number Field,"  We fix a counting function of multiplicities of algebraic points in a
+projective hypersurface over a number field, and take the sum over all
+algebraic points of bounded height and fixed degree. An upper bound for the sum
+with respect to this counting function will be given in terms of the degree of
+the hypersurface, the dimension of the singular locus, the upper bounds of
+height, and the degree of the field of definition.
+",0,0,1,0,0,0
+5126,Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis,"  We consider the task of fine-grained sentiment analysis from the perspective
+of multiple instance learning (MIL). Our neural model is trained on document
+sentiment labels, and learns to predict the sentiment of text segments, i.e.
+sentences or elementary discourse units (EDUs), without segment-level
+supervision. We introduce an attention-based polarity scoring method for
+identifying positive and negative text snippets and a new dataset which we call
+SPOT (as shorthand for Segment-level POlariTy annotations) for evaluating
+MIL-style sentiment models like ours. Experimental results demonstrate superior
+performance against multiple baselines, whereas a judgement elicitation study
+shows that EDU-level opinion extraction produces more informative summaries
+than sentence-based alternatives.
+",1,0,0,0,0,0
+5127,Eva-CiM: A System-Level Energy Evaluation Framework for Computing-in-Memory Architectures,"  Computing-in-Memory (CiM) architectures aim to reduce costly data transfers
+by performing arithmetic and logic operations in memory and hence relieve the
+pressure due to the memory wall. However, determining whether a given workload
+can really benefit from CiM, which memory hierarchy and what device technology
+should be adopted by a CiM architecture requires in-depth study that is not
+only time consuming but also demands significant expertise in architectures and
+compilers. This paper presents an energy evaluation framework, Eva-CiM, for
+systems based on CiM architectures. Eva-CiM encompasses a multi-level (from
+device to architecture) comprehensive tool chain by leveraging existing
+modeling and simulation tools such as GEM5, McPAT [2] and DESTINY [3]. To
+support high-confidence prediction, rapid design space exploration and ease of
+use, Eva-CiM introduces several novel modeling/analysis approaches including
+models for capturing memory access and dependency-aware ISA traces, and for
+quantifying interactions between the host CPU and CiM modules. Eva-CiM can
+readily produce energy estimates of the entire system for a given program, a
+processor architecture, and the CiM array and technology specifications.
+Eva-CiM is validated by comparing with DESTINY [3] and [4], and enables
+findings including practical contributions from CiM-supported accesses,
+CiM-sensitive benchmarking as well as the pros and cons of increased memory
+size for CiM. Eva-CiM also enables exploration over different configurations
+and device technologies, showing 1.3-6.0X energy improvement for SRAM and
+2.0-7.9X for FeFET-RAM, respectively.
+",1,0,0,0,0,0
+5128,Converging Shock Flows for a Mie-Grüneisen Equation of State,"  Previous work has shown that the one-dimensional (1D) inviscid compressible
+flow (Euler) equations admit a wide variety of scale-invariant solutions
+(including the famous Noh, Sedov, and Guderley shock solutions) when the
+included equation of state (EOS) closure model assumes a certain
+scale-invariant form. However, this scale-invariant EOS class does not include
+even simple models used for shock compression of crystalline solids, including
+many broadly applicable representations of Mie-Grüneisen EOS. Intuitively,
+this incompatibility naturally arises from the presence of multiple dimensional
+scales in the Mie-Grüneisen EOS, which are otherwise absent from
+scale-invariant models that feature only dimensionless parameters (such as the
+adiabatic index in the ideal gas EOS). The current work extends previous
+efforts intended to rectify this inconsistency, by using a scale-invariant EOS
+model to approximate a Mie- Grüneisen EOS form. To this end, the adiabatic
+bulk modulus for the Mie-Grüneisen EOS is constructed, and its key features
+are used to motivate the selection of a scale-invariant approximation form. The
+remaining surrogate model parameters are selected through enforcement of the
+Rankine-Hugoniot jump conditions for an infinitely strong shock in a
+Mie-Grüneisen material. Finally, the approximate EOS is used in conjunction
+with the 1D inviscid Euler equations to calculate a semi-analytical,
+Guderley-like imploding shock solution in a metal sphere, and to determine if
+and when the solution may be valid for the underlying Mie-Grüneisen EOS.
+",0,1,0,0,0,0
+5129,Robust estimation of tree structured Gaussian Graphical Model,"  Consider jointly Gaussian random variables whose conditional independence
+structure is specified by a graphical model. If we observe realizations of the
+variables, we can compute the covariance matrix, and it is well known that the
+support of the inverse covariance matrix corresponds to the edges of the
+graphical model. Instead, suppose we only have noisy observations. If the noise
+at each node is independent, we can compute the sum of the covariance matrix
+and an unknown diagonal. The inverse of this sum is (in general) dense. We ask:
+can the original independence structure be recovered? We address this question
+for tree structured graphical models. We prove that this problem is
+unidentifiable, but show that this unidentifiability is limited to a small
+class of candidate trees. We further present additional constraints under which
+the problem is identifiable. Finally, we provide an O(n^3) algorithm to find
+this equivalence class of trees.
+",1,0,0,1,0,0
+5130,What Propels Celebrity Follower Counts? Language Use or Social Connectivity,"  Follower count is a factor that quantifies the popularity of celebrities. It
+is a reflection of their power, prestige and overall social reach. In this
+paper we investigate whether the social connectivity or the language choice is
+more correlated to the future follower count of a celebrity. We collect data
+about tweets, retweets and mentions of 471 Indian celebrities with verified
+Twitter accounts. We build two novel networks to approximate social
+connectivity of the celebrities. We study various structural properties of
+these two networks and observe their correlations with future follower counts.
+In parallel, we analyze the linguistic structure of the tweets (LIWC features,
+syntax and sentiment features and style and readability features) and observe
+the correlations of each of these with the future follower count of a
+celebrity. As a final step we use there features to classify a celebrity in a
+specific bucket of future follower count (HIGH, MID or LOW). We observe that
+the network features alone achieve an accuracy of 0.52 while the linguistic
+features alone achieve an accuracy of 0.69 grossly outperforming the network
+features. The network and linguistic features in conjunction produce an
+accuracy of 0.76. We also discuss some final insights that we obtain from
+further data analysis celebrities with larger follower counts post tweets that
+have (i) more words from friend and family LIWC categories, (ii) more positive
+sentiment laden words, (iii) have better language constructs and are (iv) more
+readable.
+",1,0,0,0,0,0
+5131,Monochromatic metrics are generalized Berwald,"  We show that monochromatic Finsler metrics, i.e., Finsler metrics such that
+each two tangent spaces are isomorphic as normed spaces, are generalized
+Berwald metrics, i.e., there exists an affine connection, possibly with
+torsion, that preserves the Finsler function
+",0,0,1,0,0,0
+5132,CTCF Degradation Causes Increased Usage of Upstream Exons in Mouse Embryonic Stem Cells,"  Transcriptional repressor CTCF is an important regulator of chromatin 3D
+structure, facilitating the formation of topologically associating domains
+(TADs). However, its direct effects on gene regulation is less well understood.
+Here, we utilize previously published ChIP-seq and RNA-seq data to investigate
+the effects of CTCF on alternative splicing of genes with CTCF sites. We
+compared the amount of RNA-seq signals in exons upstream and downstream of
+binding sites following auxin-induced degradation of CTCF in mouse embryonic
+stem cells. We found that changes in gene expression following CTCF depletion
+were significant, with a general increase in the presence of upstream exons. We
+infer that a possible mechanism by which CTCF binding contributes to
+alternative splicing is by causing pauses in the transcription mechanism during
+which splicing elements are able to concurrently act on upstream exons already
+transcribed into RNA.
+",0,0,0,0,1,0
+5133,"The stability of tightly-packed, evenly-spaced systems of Earth-mass planets orbiting a Sun-like star","  Many of the multi-planet systems discovered to date have been notable for
+their compactness, with neighbouring planets closer together than any in the
+Solar System. Interestingly, planet-hosting stars have a wide range of ages,
+suggesting that such compact systems can survive for extended periods of time.
+We have used numerical simulations to investigate how quickly systems go
+unstable in relation to the spacing between planets, focusing on hypothetical
+systems of Earth-mass planets on evenly-spaced orbits (in mutual Hill radii).
+In general, the further apart the planets are initially, the longer it takes
+for a pair of planets to undergo a close encounter. We recover the results of
+previous studies, showing a linear trend in the initial planet spacing between
+3 and 8 mutual Hill radii and the logarithm of the stability time.
+Investigating thousands of simulations with spacings up to 13 mutual Hill radii
+reveals distinct modulations superimposed on this relationship in the vicinity
+of first and second-order mean motion resonances of adjacent and next-adjacent
+planets. We discuss the impact of this structure and the implications on the
+stability of compact multi-planet systems. Applying the outcomes of our
+simulations, we show that isolated systems of up to five Earth-mass planets can
+fit in the habitable zone of a Sun-like star without close encounters for at
+least $10^9$ orbits.
+",0,1,0,0,0,0
+5134,GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units,"  We present GPUQT, a quantum transport code fully implemented on graphics
+processing units. Using this code, one can obtain intrinsic electronic
+transport properties of large systems described by a real-space tight-binding
+Hamiltonian together with one or more types of disorder. The DC Kubo
+conductivity is represented as a time integral of the velocity auto-correlation
+or a time derivative of the mean square displacement. Linear scaling (with
+respect to the total number of orbitals in the system) computation time and
+memory usage are achieved by using various numerical techniques, including
+sparse matrix-vector multiplication, random phase approximation of trace,
+Chebyshev expansion of quantum evolution operator, and kernel polynomial method
+for quantum resolution operator. We describe the inputs and outputs of GPUQT
+and give two examples to demonstrate its usage, paying attention to the
+interpretations of the results.
+",0,1,0,0,0,0
+5135,Full-Duplex Cooperative Cognitive Radio Networks with Wireless Energy Harvesting,"  This paper proposes and analyzes a new full-duplex (FD) cooperative cognitive
+radio network with wireless energy harvesting (EH). We consider that the
+secondary receiver is equipped with a FD radio and acts as a FD hybrid access
+point (HAP), which aims to collect information from its associated EH secondary
+transmitter (ST) and relay the signals. The ST is assumed to be equipped with
+an EH unit and a rechargeable battery such that it can harvest and accumulate
+energy from radio frequency (RF) signals transmitted by the primary transmitter
+(PT) and the HAP. We develop a novel cooperative spectrum sharing (CSS)
+protocol for the considered system. In the proposed protocol, thanks to its FD
+capability, the HAP can receive the PT's signals and transmit energy-bearing
+signals to charge the ST simultaneously, or forward the PT's signals and
+receive the ST's signals at the same time. We derive analytical expressions for
+the achievable throughput of both primary and secondary links by characterizing
+the dynamic charging/discharging behaviors of the ST battery as a finite-state
+Markov chain. We present numerical results to validate our theoretical analysis
+and demonstrate the merits of the proposed protocol over its non-cooperative
+counterpart.
+",1,0,0,0,0,0
+5136,Inkjet printing-based volumetric display projecting multiple full-colour 2D patterns,"  In this study, a method to construct a full-colour volumetric display is
+presented using a commercially available inkjet printer. Photoreactive
+luminescence materials are minutely and automatically printed as the volume
+elements, and volumetric displays are constructed with high resolution using
+easy-to-fabricate means that exploit inkjet printing technologies. The results
+experimentally demonstrate the first prototype of an inkjet printing-based
+volumetric display composed of multiple layers of transparent films that yield
+a full-colour three-dimensional (3D) image. Moreover, we propose a design
+algorithm with 3D structures that provide multiple different 2D full-colour
+patterns when viewed from different directions and experimentally demonstrates
+prototypes. It is considered that these types of 3D volumetric structures and
+their fabrication methods based on widely deployed existing printing
+technologies can be utilised as novel information display devices and systems,
+including digital signage, media art, entertainment and security.
+",1,0,0,0,0,0
+5137,Analysis of luminosity distributions of strong lensing galaxies: subtraction of diffuse lensed signal,"  Strong gravitational lensing gives access to the total mass distribution of
+galaxies. It can unveil a great deal of information about the lenses dark
+matter content when combined with the study of the lenses light profile.
+However, gravitational lensing galaxies, by definition, appear surrounded by
+point-like and diffuse lensed signal that is irrelevant to the lens flux.
+Therefore, the observer is most often restricted to studying the innermost
+portions of the galaxy, where classical fitting methods show some
+instabilities. We aim at subtracting that lensed signal and at characterising
+some lenses light profile by computing their shape parameters. Our objective is
+to evaluate the total integrated flux in an aperture the size of the Einstein
+ring in order to obtain a robust estimate of the quantity of ordinary matter in
+each system. We are expanding the work we started in a previous paper that
+consisted in subtracting point-like lensed images and in independently
+measuring each shape parameter. We improve it by designing a subtraction of the
+diffuse lensed signal, based only on one simple hypothesis of symmetry. This
+extra step improves our study of the shape parameters and we refine it even
+more by upgrading our half-light radius measurement. We also calculate the
+impact of our specific image processing on the error bars. The diffuse lensed
+signal subtraction makes it possible to study a larger portion of relevant
+galactic flux, as the radius of the fitting region increases by on average
+17\%. We retrieve new half-light radii values that are on average 11\% smaller
+than in our previous work, although the uncertainties overlap in most cases.
+This shows that not taking the diffuse lensed signal into account may lead to a
+significant overestimate of the half-light radius. We are also able to measure
+the flux within the Einstein radius and to compute secure error bars to all of
+our results.
+",0,1,0,0,0,0
+5138,Support Estimation via Regularized and Weighted Chebyshev Approximations,"  We introduce a new framework for estimating the support size of an unknown
+distribution which improves upon known approximation-based techniques. Our main
+contributions include describing a rigorous new weighted Chebyshev polynomial
+approximation method and introducing regularization terms into the problem
+formulation that provably improve the performance of state-of-the-art
+approximation-based approaches. In particular, we present both theoretical and
+computer simulation results that illustrate the utility and performance
+improvements of our method. The theoretical analysis relies on jointly
+optimizing the bias and variance components of the risk, and combining new
+weighted minmax polynomial approximation techniques with discretized
+semi-infinite programming solvers. Such a setting allows for casting the
+estimation problem as a linear program (LP) with a small number of variables
+and constraints that may be solved as efficiently as the original Chebyshev
+approximation-based problem. The described approach also applies to the support
+coverage and entropy estimation problems. Our newly developed technique is
+tested on synthetic data and used to estimate the number of bacterial species
+in the human gut. On synthetic datasets, we observed up to five-fold
+improvements in the value of the worst-case risk. For the bioinformatics
+application, metagenomic data from the NIH Human Gut and the American Gut
+Microbiome was combined and processed to obtain lists of bacterial taxonomies.
+These were subsequently used to compute the bacterial species histograms and
+estimate the number of bacterial species in the human gut to roughly 2350, with
+the species being represented by trillions of cells.
+",1,0,0,1,0,0
+5139,Two-photon superbunching of pseudothermal light in a Hanbury Brown-Twiss interferometer,"  Two-photon superbunching of pseudothermal light is observed with single-mode
+continuous-wave laser light in a linear optical system. By adding more
+two-photon paths via three rotating ground glasses,g(2)(0) = 7.10 is
+experimentally observed. The second-order temporal coherence function of
+superbunching pseudothermal light is theoretically and experimentally studied
+in detail. It is predicted that the degree of coherence of light can be
+increased dramatically by adding more multi-photon paths. For instance, the
+degree of the second- and third-order coherence of the superbunching
+pseudothermal light with five rotating ground glasses can reach 32 and 7776,
+respectively. The results are helpful to understand the physics of
+superbunching and to improve the visibility of thermal light ghost imaging.
+",0,1,0,0,0,0
+5140,Non Relativistic Limit of Integrable QFT with fermionic excitations,"  The aim of this paper is to investigate the non-relativistic limit of
+integrable quantum field theories with fermionic fields, such as the O(N)
+Gross-Neveu model, the supersymmetric Sinh-Gordon and non-linear sigma models.
+The non-relativistic limit of these theories is implemented by a double scaling
+limit which consists of sending the speed of light c to infinity and rescaling
+at the same time the relevant coupling constant of the model in such a way to
+have finite energy excitations. For the general purpose of mapping the space of
+continuous non-relativistic integrable models, this paper completes and
+integrates the analysis done in Ref.[1] on the non-relativistic limit of purely
+bosonic theories.
+",0,1,0,0,0,0
+5141,Extensions of the Benson-Solomon fusion systems,"  The Benson-Solomon systems comprise the only known family of simple saturated
+fusion systems at the prime two that do not arise as the fusion system of any
+finite group. We determine the automorphism groups and the possible almost
+simple extensions of these systems and of their centric linking systems.
+",0,0,1,0,0,0
+5142,Run-Wise Simulations for Imaging Atmospheric Cherenkov Telescope Arrays,"  We present a new paradigm for the simulation of arrays of Imaging Atmospheric
+Cherenkov Telescopes (IACTs) which overcomes limitations of current approaches.
+Up to now, all major IACT experiments rely on the same Monte-Carlo simulation
+strategy, using predefined observation and instrument settings. Simulations
+with varying parameters are generated to provide better estimates of the
+Instrument Response Functions (IRFs) of different observations. However, a
+large fraction of the simulation configuration remains preserved, leading to
+complete negligence of all related influences. Additionally, the simulation
+scheme relies on interpolations between different array configurations, which
+are never fully reproducing the actual configuration for a given observation.
+Interpolations are usually performed on zenith angles, off-axis angles, array
+multiplicity, and the optical response of the instrument. With the advent of
+hybrid systems consisting of a large number of IACTs with different sizes,
+types, and camera configurations, the complexity of the interpolation and the
+size of the phase space becomes increasingly prohibitive. Going beyond the
+existing approaches, we introduce a new simulation and analysis concept which
+takes into account the actual observation conditions as well as individual
+telescope configurations of each observation run of a given data set. These
+run-wise simulations (RWS) thus exhibit considerably reduced systematic
+uncertainties compared to the existing approach, and are also more
+computationally efficient and simple. The RWS framework has been implemented in
+the H.E.S.S. software and tested, and is already being exploited in science
+analysis.
+",0,1,0,0,0,0
+5143,Multi-party Poisoning through Generalized $p$-Tampering,"  In a poisoning attack against a learning algorithm, an adversary tampers with
+a fraction of the training data $T$ with the goal of increasing the
+classification error of the constructed hypothesis/model over the final test
+distribution. In the distributed setting, $T$ might be gathered gradually from
+$m$ data providers $P_1,\dots,P_m$ who generate and submit their shares of $T$
+in an online way.
+In this work, we initiate a formal study of $(k,p)$-poisoning attacks in
+which an adversary controls $k\in[n]$ of the parties, and even for each
+corrupted party $P_i$, the adversary submits some poisoned data $T'_i$ on
+behalf of $P_i$ that is still ""$(1-p)$-close"" to the correct data $T_i$ (e.g.,
+$1-p$ fraction of $T'_i$ is still honestly generated). For $k=m$, this model
+becomes the traditional notion of poisoning, and for $p=1$ it coincides with
+the standard notion of corruption in multi-party computation.
+We prove that if there is an initial constant error for the generated
+hypothesis $h$, there is always a $(k,p)$-poisoning attacker who can decrease
+the confidence of $h$ (to have a small error), or alternatively increase the
+error of $h$, by $\Omega(p \cdot k/m)$. Our attacks can be implemented in
+polynomial time given samples from the correct data, and they use no wrong
+labels if the original distributions are not noisy.
+At a technical level, we prove a general lemma about biasing bounded
+functions $f(x_1,\dots,x_n)\in[0,1]$ through an attack model in which each
+block $x_i$ might be controlled by an adversary with marginal probability $p$
+in an online way. When the probabilities are independent, this coincides with
+the model of $p$-tampering attacks, thus we call our model generalized
+$p$-tampering. We prove the power of such attacks by incorporating ideas from
+the context of coin-flipping attacks into the $p$-tampering model and
+generalize the results in both of these areas.
+",0,0,0,1,0,0
+5144,Spectral edge behavior for eventually monotone Jacobi and Verblunsky coefficients,"  We consider Jacobi matrices with eventually increasing sequences of diagonal
+and off-diagonal Jacobi parameters. We describe the asymptotic behavior of the
+subordinate solution at the top of the essential spectrum, and the asymptotic
+behavior of the spectral density at the top of the essential spectrum.
+In particular, allowing on both diagonal and off-diagonal Jacobi parameters
+perturbations of the free case of the form $- \sum_{j=1}^J c_j n^{-\tau_j} +
+o(n^{-\tau_1-1})$ with $0 < \tau_1 < \tau_2 < \dots < \tau_J$ and $c_1>0$, we
+find the asymptotic behavior of the $\log$ of spectral density to order
+$O(\log(2-x))$ as $x$ approaches $2$.
+Apart from its intrinsic interest, the above results also allow us to
+describe the asymptotics of the spectral density for orthogonal polynomials on
+the unit circle with real-valued Verblunsky coefficients of the same form.
+",0,0,1,0,0,0
+5145,Web Video in Numbers - An Analysis of Web-Video Metadata,"  Web video is often used as a source of data in various fields of study. While
+specialized subsets of web video, mainly earmarked for dedicated purposes, are
+often analyzed in detail, there is little information available about the
+properties of web video as a whole. In this paper we present insights gained
+from the analysis of the metadata associated with more than 120 million videos
+harvested from two popular web video platforms, vimeo and YouTube, in 2016 and
+compare their properties with the ones found in commonly used video
+collections. This comparison has revealed that existing collections do not (or
+no longer) properly reflect the properties of web video ""in the wild"".
+",1,0,0,0,0,0
+5146,Bernoulli Correlations and Cut Polytopes,"  Given $n$ symmetric Bernoulli variables, what can be said about their
+correlation matrix viewed as a vector? We show that the set of those vectors
+$R(\mathcal{B}_n)$ is a polytope and identify its vertices. Those extreme
+points correspond to correlation vectors associated to the discrete uniform
+distributions on diagonals of the cube $[0,1]^n$. We also show that the
+polytope is affinely isomorphic to a well-known cut polytope ${\rm CUT}(n)$
+which is defined as a convex hull of the cut vectors in a complete graph with
+vertex set $\{1,\ldots,n\}$. The isomorphism is obtained explicitly as
+$R(\mathcal{B}_n)= {\mathbf{1}}-2~{\rm CUT}(n)$. As a corollary of this work,
+it is straightforward using linear programming to determine if a particular
+correlation matrix is realizable or not. Furthermore, a sampling method for
+multivariate symmetric Bernoullis with given correlation is obtained. In some
+cases the method can also be used for general, not exclusively Bernoulli,
+marginals.
+",0,0,1,1,0,0
+5147,Tensor products of NCDL-C*-algebras and the C*-algebra of the Heisenberg motion groups,"  We show that the tensor product $A\otimes B$ over $\mathbb{C}$ of two $C^*
+$-algebras satisfying the \textit{NCDL} conditions has again the same property.
+We use this result to describe the $C^* $-algebra of the Heisenberg motion
+groups $G_n = \mathbb{T}^n \ltimes \mathbb{H}_n$ as algebra of operator fields
+defined over the spectrum of $G_n $.
+",0,0,1,0,0,0
+5148,ViP-CNN: Visual Phrase Guided Convolutional Neural Network,"  As the intermediate level task connecting image captioning and object
+detection, visual relationship detection started to catch researchers'
+attention because of its descriptive power and clear structure. It detects the
+objects and captures their pair-wise interactions with a
+subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each
+visual relationship is considered as a phrase with three components. We
+formulate the visual relationship detection as three inter-connected
+recognition problems and propose a Visual Phrase guided Convolutional Neural
+Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a
+Phrase-guided Message Passing Structure (PMPS) to establish the connection
+among relationship components and help the model consider the three problems
+jointly. Corresponding non-maximum suppression method and model training
+strategy are also proposed. Experimental results show that our ViP-CNN
+outperforms the state-of-art method both in speed and accuracy. We further
+pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is
+found to perform better than the pretraining on the ImageNet for this task.
+",1,0,0,0,0,0
+5149,Haantjes Algebras and Diagonalization,"  We propose the notion of Haantjes algebra, which consists of an assignment of
+a family of fields of operators over a differentiable manifold, with vanishing
+Haantjes torsion and satisfying suitable compatibility conditions among each
+others. Haantjes algebras naturally generalize several known interesting
+geometric structures, arising in Riemannian geometry and in the theory of
+integrable systems. At the same time, they play a crucial role in the theory of
+diagonalization of operators on differentiable manifolds.
+Whenever the elements of an Haantjes algebra are semisimple and commute, we
+shall prove that there exists a set of local coordinates where all operators
+can be diagonalized simultaneously. Moreover, in the non-semisimple case, they
+acquire simultaneously a block-diagonal form.
+",0,1,1,0,0,0
+5150,Multipair Massive MIMO Relaying Systems with One-Bit ADCs and DACs,"  This paper considers a multipair amplify-and-forward massive MIMO relaying
+system with one-bit ADCs and one-bit DACs at the relay. The channel state
+information is estimated via pilot training, and then utilized by the relay to
+perform simple maximum-ratio combining/maximum-ratio transmission processing.
+Leveraging on the Bussgang decomposition, an exact achievable rate is derived
+for the system with correlated quantization noise. Based on this, a closed-form
+asymptotic approximation for the achievable rate is presented, thereby enabling
+efficient evaluation of the impact of key parameters on the system performance.
+Furthermore, power scaling laws are characterized to study the potential energy
+efficiency associated with deploying massive one-bit antenna arrays at the
+relay. In addition, a power allocation strategy is designed to compensate for
+the rate degradation caused by the coarse quantization. Our results suggest
+that the quality of the channel estimates depends on the specific orthogonal
+pilot sequences that are used, contrary to unquantized systems where any set of
+orthogonal pilot sequences gives the same result. Moreover, the sum rate gap
+between the double-quantized relay system and an ideal non-quantized system is
+a moderate factor of $4/\pi^2$ in the low power regime.
+",1,0,0,0,0,0
+5151,Bayesian Methods for Exoplanet Science,"  Exoplanet research is carried out at the limits of the capabilities of
+current telescopes and instruments. The studied signals are weak, and often
+embedded in complex systematics from instrumental, telluric, and astrophysical
+sources. Combining repeated observations of periodic events, simultaneous
+observations with multiple telescopes, different observation techniques, and
+existing information from theory and prior research can help to disentangle the
+systematics from the planetary signals, and offers synergistic advantages over
+analysing observations separately. Bayesian inference provides a
+self-consistent statistical framework that addresses both the necessity for
+complex systematics models, and the need to combine prior information and
+heterogeneous observations. This chapter offers a brief introduction to
+Bayesian inference in the context of exoplanet research, with focus on time
+series analysis, and finishes with an overview of a set of freely available
+programming libraries.
+",0,1,0,0,0,0
+5152,Feature importance scores and lossless feature pruning using Banzhaf power indices,"  Understanding the influence of features in machine learning is crucial to
+interpreting models and selecting the best features for classification. In this
+work we propose the use of principles from coalitional game theory to reason
+about importance of features. In particular, we propose the use of the Banzhaf
+power index as a measure of influence of features on the outcome of a
+classifier. We show that features having Banzhaf power index of zero can be
+losslessly pruned without damage to classifier accuracy. Computing the power
+indices does not require having access to data samples. However, if samples are
+available, the indices can be empirically estimated. We compute Banzhaf power
+indices for a neural network classifier on real-life data, and compare the
+results with gradient-based feature saliency, and coefficients of a logistic
+regression model with $L_1$ regularization.
+",1,0,0,1,0,0
+5153,Robust clustering of languages across Wikipedia growth,"  Wikipedia is the largest existing knowledge repository that is growing on a
+genuine crowdsourcing support. While the English Wikipedia is the most
+extensive and the most researched one with over five million articles,
+comparatively little is known about the behavior and growth of the remaining
+283 smaller Wikipedias, the smallest of which, Afar, has only one article. Here
+we use a subset of this data, consisting of 14962 different articles, each of
+which exists in 26 different languages, from Arabic to Ukrainian. We study the
+growth of Wikipedias in these languages over a time span of 15 years. We show
+that, while an average article follows a random path from one language to
+another, there exist six well-defined clusters of Wikipedias that share common
+growth patterns. The make-up of these clusters is remarkably robust against the
+method used for their determination, as we verify via four different clustering
+methods. Interestingly, the identified Wikipedia clusters have little
+correlation with language families and groups. Rather, the growth of Wikipedia
+across different languages is governed by different factors, ranging from
+similarities in culture to information literacy.
+",1,0,0,1,0,0
+5154,Attractive Heaviside-Maxwellian (Vector) Gravity from Special Relativity and Quantum Field Theory,"  Adopting two independent approaches (a) Lorentz-invariance of physical laws
+and (b) local phase invariance of quantum field theory applied to the Dirac
+Lagrangian for massive electrically neutral Dirac particles, we rediscovered
+the fundamental field equations of Heaviside Gravity (HG) of 1893 and
+Maxwellian Gravity (MG), which look different from each other due to a sign
+difference in some terms of their respective field equations. However, they are
+shown to represent two mathematical representations of a single physical theory
+of vector gravity that we name here as Heaviside-Maxwellian Gravity (HMG), in
+which the speed of gravitational waves in vacuum is uniquely found to be equal
+to the speed of light in vacuum. We also corrected a sign error in Heaviside's
+speculative gravitational analogue of the Lorentz force law. This spin-1 HMG is
+shown to produce attractive force between like masses under static condition,
+contrary to the prevalent view of field theorists. Galileo's law of
+universality of free fall is a consequence of HMG, without any initial
+assumption of the equality of gravitational mass with velocity-dependent mass.
+We also note a new set of Lorentz-Maxwell's equations having the same physical
+effects as the standard set - a byproduct of our present study.
+",0,1,0,0,0,0
+5155,Critical pairing fluctuations in the normal state of a superconductor: pseudogap and quasi-particle damping,"  We study the effect of critical pairing fluctuations on the electronic
+properties in the normal state of a clean superconductor in three dimensions.
+Using a functional renormalization group approach to take the non-Gaussian
+nature of critical fluctuations into account, we show microscopically that in
+the BCS regime, where the inverse coherence length is much smaller than the
+Fermi wavevector, critical pairing fluctuations give rise to a non-analytic
+contribution to the quasi-particle damping of order $ T_c \sqrt{Gi} \ln ( 80 /
+Gi )$, where the Ginzburg-Levanyuk number $Gi$ is a dimensionless measure for
+the width of the critical region. As a consequence, there is a temperature
+window above $T_c$ where the quasiparticle damping due to critical pairing
+fluctuations can be larger than the usual $T^2$-Fermi liquid damping due to
+non-critical scattering processes. On the other hand, in the strong coupling
+regime where $Gi$ is of order unity, we find that the quasiparticle damping due
+to critical pairing fluctuations is proportional to the temperature. Moreover,
+we show that in the vicinity of the critical temperature $T_c$ the electronic
+density of states exhibits a fluctuation-induced pseudogap. We also use
+functional renormalization group methods to derive and classify various types
+of processes induced by the pairing interaction in Fermi systems close to the
+superconducting instability.
+",0,1,0,0,0,0
+5156,The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints,"  In this paper, we propose a low-rank coordinate descent approach to
+structured semidefinite programming with diagonal constraints. The approach,
+which we call the Mixing method, is extremely simple to implement, has no free
+parameters, and typically attains an order of magnitude or better improvement
+in optimization performance over the current state of the art. We show that the
+method is strictly decreasing, converges to a critical point, and further that
+for sufficient rank all non-optimal critical points are unstable. Moreover, we
+prove that with a step size, the Mixing method converges to the global optimum
+of the semidefinite program almost surely in a locally linear rate under random
+initialization. This is the first low-rank semidefinite programming method that
+has been shown to achieve a global optimum on the spherical manifold without
+assumption. We apply our algorithm to two related domains: solving the maximum
+cut semidefinite relaxation, and solving a maximum satisfiability relaxation
+(we also briefly consider additional applications such as learning word
+embeddings). In all settings, we demonstrate substantial improvement over the
+existing state of the art along various dimensions, and in total, this work
+expands the scope and scale of problems that can be solved using semidefinite
+programming methods.
+",1,0,1,1,0,0
+5157,GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks,"  The Fisher information metric is an important foundation of information
+geometry, wherein it allows us to approximate the local geometry of a
+probability distribution. Recurrent neural networks such as the
+Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield
+state-of-the-art performance on speech translation or image captioning have so
+far ignored the geometry of the latent embedding, that they iteratively learn.
+We propose the information geometric Seq2Seq (GeoSeq2Seq) network which
+abridges the gap between deep recurrent neural networks and information
+geometry. Specifically, the latent embedding offered by a recurrent network is
+encoded as a Fisher kernel of a parametric Gaussian Mixture Model, a formalism
+common in computer vision. We utilise such a network to predict the shortest
+routes between two nodes of a graph by learning the adjacency matrix using the
+GeoSeq2Seq formalism; our results show that for such a problem the
+probabilistic representation of the latent embedding supersedes the
+non-probabilistic embedding by 10-15\%.
+",1,0,0,1,0,0
+5158,Stochastic graph Voronoi tessellation reveals community structure,"  Given a network, the statistical ensemble of its graph-Voronoi diagrams with
+randomly chosen cell centers exhibits properties convertible into information
+on the network's large scale structures. We define a node-pair level measure
+called {\it Voronoi cohesion} which describes the probability for sharing the
+same Voronoi cell, when randomly choosing $g$ centers in the network. This
+measure provides information based on the global context (the network in its
+entirety) a type of information that is not carried by other similarity
+measures. We explore the mathematical background of this phenomenon and several
+of its potential applications. A special focus is laid on the possibilities and
+limitations pertaining to the exploitation of the phenomenon for community
+detection purposes.
+",1,1,0,0,0,0
+5159,Recursion for the smallest eigenvalue density of $β$-Wishart-Laguerre ensemble,"  The statistics of the smallest eigenvalue of Wishart-Laguerre ensemble is
+important from several perspectives. The smallest eigenvalue density is
+typically expressible in terms of determinants or Pfaffians. These results are
+of utmost significance in understanding the spectral behavior of
+Wishart-Laguerre ensembles and, among other things, unveil the underlying
+universality aspects in the asymptotic limits. However, obtaining exact and
+explicit expressions by expanding determinants or Pfaffians becomes impractical
+if large dimension matrices are involved. For the real matrices ($\beta=1$)
+Edelman has provided an efficient recurrence scheme to work out exact and
+explicit results for the smallest eigenvalue density which does not involve
+determinants or matrices. Very recently, an analogous recurrence scheme has
+been obtained for the complex matrices ($\beta=2$). In the present work we
+extend this to $\beta$-Wishart-Laguerre ensembles for the case when exponent
+$\alpha$ in the associated Laguerre weight function, $\lambda^\alpha
+e^{-\beta\lambda/2}$, is a non-negative integer, while $\beta$ is positive
+real. This also gives access to the smallest eigenvalue density of fixed trace
+$\beta$-Wishart-Laguerre ensemble, as well as moments for both cases. Moreover,
+comparison with earlier results for the smallest eigenvalue density in terms of
+certain hypergeometric function of matrix argument results in an effective way
+of evaluating these explicitly. Exact evaluations for large values of $n$ (the
+matrix dimension) and $\alpha$ also enable us to compare with Tracy-Widom
+density and large deviation results of Katzav and Castillo. We also use our
+result to obtain the density of the largest of the proper delay times which are
+eigenvalues of the Wigner-Smith matrix and are relevant to the problem of
+quantum chaotic scattering.
+",0,0,0,1,0,0
+5160,Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation,"  Advances in image processing and computer vision in the latest years have
+brought about the use of visual features in artwork recommendation. Recent
+works have shown that visual features obtained from pre-trained deep neural
+networks (DNNs) perform very well for recommending digital art. Other recent
+works have shown that explicit visual features (EVF) based on attractiveness
+can perform well in preference prediction tasks, but no previous work has
+compared DNN features versus specific attractiveness-based visual features
+(e.g. brightness, texture) in terms of recommendation performance. In this
+work, we study and compare the performance of DNN and EVF features for the
+purpose of physical artwork recommendation using transactional data from
+UGallery, an online store of physical paintings. In addition, we perform an
+exploratory analysis to understand if DNN embedded features have some relation
+with certain EVF. Our results show that DNN features outperform EVF, that
+certain EVF features are more suited for physical artwork recommendation and,
+finally, we show evidence that certain neurons in the DNN might be partially
+encoding visual features such as brightness, providing an opportunity for
+explaining recommendations based on visual neural models.
+",1,0,0,0,0,0
+5161,DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds,"  We propose DeepMapping, a novel registration framework using deep neural
+networks (DNNs) as auxiliary functions to align multiple point clouds from
+scratch to a globally consistent frame. We use DNNs to model the highly
+non-convex mapping process that traditionally involves hand-crafted data
+association, sensor pose initialization, and global refinement. Our key novelty
+is that properly defining unsupervised losses to ""train"" these DNNs through
+back-propagation is equivalent to solving the underlying registration problem,
+yet enables fewer dependencies on good initialization as required by ICP. Our
+framework contains two DNNs: a localization network that estimates the poses
+for input point clouds, and a map network that models the scene structure by
+estimating the occupancy status of global coordinates. This allows us to
+convert the registration problem to a binary occupancy classification, which
+can be solved efficiently using gradient-based optimization. We further show
+that DeepMapping can be readily extended to address the problem of Lidar SLAM
+by imposing geometric constraints between consecutive point clouds. Experiments
+are conducted on both simulated and real datasets. Qualitative and quantitative
+comparisons demonstrate that DeepMapping often enables more robust and accurate
+global registration of multiple point clouds than existing techniques. Our code
+is available at this http URL.
+",1,0,0,0,0,0
+5162,The First Comparison Between Swarm-C Accelerometer-Derived Thermospheric Densities and Physical and Empirical Model Estimates,"  The first systematic comparison between Swarm-C accelerometer-derived
+thermospheric density and both empirical and physics-based model results using
+multiple model performance metrics is presented. This comparison is performed
+at the satellite's high temporal 10-s resolution, which provides a meaningful
+evaluation of the models' fidelity for orbit prediction and other space weather
+forecasting applications. The comparison against the physical model is
+influenced by the specification of the lower atmospheric forcing, the
+high-latitude ionospheric plasma convection, and solar activity. Some insights
+into the model response to thermosphere-driving mechanisms are obtained through
+a machine learning exercise. The results of this analysis show that the
+short-timescale variations observed by Swarm-C during periods of high solar and
+geomagnetic activity were better captured by the physics-based model than the
+empirical models. It is concluded that Swarm-C data agree well with the
+climatologies inherent within the models and are, therefore, a useful data set
+for further model validation and scientific research.
+",0,1,0,0,0,0
+5163,Gaussian Parsimonious Clustering Models with Covariates,"  We consider model-based clustering methods for continuous, correlated data
+that account for external information available in the presence of mixed-type
+fixed covariates by proposing the MoEClust suite of models. These allow
+covariates influence the component weights and/or component densities by
+modelling the parameters of the mixture as functions of the covariates. A
+familiar range of constrained eigen-decomposition parameterisations of the
+component covariance matrices are also accommodated. This paper thus addresses
+the equivalent aims of including covariates in Gaussian Parsimonious Clustering
+Models and incorporating parsimonious covariance structures into the Gaussian
+mixture of experts framework. The MoEClust models demonstrate significant
+improvement from both perspectives in applications to univariate and
+multivariate data sets.
+",0,0,0,1,0,0
+5164,The Rice-Shapiro theorem in Computable Topology,"  We provide requirements on effectively enumerable topological spaces which
+guarantee that the Rice-Shapiro theorem holds for the computable elements of
+these spaces. We show that the relaxation of these requirements leads to the
+classes of effectively enumerable topological spaces where the Rice-Shapiro
+theorem does not hold. We propose two constructions that generate effectively
+enumerable topological spaces with particular properties from wn--families and
+computable trees without computable infinite paths. Using them we propose
+examples that give a flavor of this class.
+",1,0,1,0,0,0
+5165,Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications,"  This paper studies the problem of multivariate linear regression where a
+portion of the observations is grossly corrupted or is missing, and the
+magnitudes and locations of such occurrences are unknown in priori. To deal
+with this problem, we propose a new approach by explicitly consider the error
+source as well as its sparseness nature. An interesting property of our
+approach lies in its ability of allowing individual regression output elements
+or tasks to possess their unique noise levels. Moreover, despite working with a
+non-smooth optimization problem, our approach still guarantees to converge to
+its optimal solution. Experiments on synthetic data demonstrate the
+competitiveness of our approach compared with existing multivariate regression
+models. In addition, empirically our approach has been validated with very
+promising results on two exemplar real-world applications: The first concerns
+the prediction of \textit{Big-Five} personality based on user behaviors at
+social network sites (SNSs), while the second is 3D human hand pose estimation
+from depth images. The implementation of our approach and comparison methods as
+well as the involved datasets are made publicly available in support of the
+open-source and reproducible research initiatives.
+",1,0,0,1,0,0
+5166,A Deep Learning Approach for Population Estimation from Satellite Imagery,"  Knowing where people live is a fundamental component of many decision making
+processes such as urban development, infectious disease containment, evacuation
+planning, risk management, conservation planning, and more. While bottom-up,
+survey driven censuses can provide a comprehensive view into the population
+landscape of a country, they are expensive to realize, are infrequently
+performed, and only provide population counts over broad areas. Population
+disaggregation techniques and population projection methods individually
+address these shortcomings, but also have shortcomings of their own. To jointly
+answer the questions of ""where do people live"" and ""how many people live
+there,"" we propose a deep learning model for creating high-resolution
+population estimations from satellite imagery. Specifically, we train
+convolutional neural networks to predict population in the USA at a
+$0.01^{\circ} \times 0.01^{\circ}$ resolution grid from 1-year composite
+Landsat imagery. We validate these models in two ways: quantitatively, by
+comparing our model's grid cell estimates aggregated at a county-level to
+several US Census county-level population projections, and qualitatively, by
+directly interpreting the model's predictions in terms of the satellite image
+inputs. We find that aggregating our model's estimates gives comparable results
+to the Census county-level population projections and that the predictions made
+by our model can be directly interpreted, which give it advantages over
+traditional population disaggregation methods. In general, our model is an
+example of how machine learning techniques can be an effective tool for
+extracting information from inherently unstructured, remotely sensed data to
+provide effective solutions to social problems.
+",1,0,0,0,0,0
+5167,Trends in the Diffusion of Misinformation on Social Media,"  We measure trends in the diffusion of misinformation on Facebook and Twitter
+between January 2015 and July 2018. We focus on stories from 570 sites that
+have been identified as producers of false stories. Interactions with these
+sites on both Facebook and Twitter rose steadily through the end of 2016.
+Interactions then fell sharply on Facebook while they continued to rise on
+Twitter, with the ratio of Facebook engagements to Twitter shares falling by
+approximately 60 percent. We see no similar pattern for other news, business,
+or culture sites, where interactions have been relatively stable over time and
+have followed similar trends on the two platforms both before and after the
+election.
+",1,0,0,0,0,1
+5168,SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications,"  We describe the SemEval task of extracting keyphrases and relations between
+them from scientific documents, which is crucial for understanding which
+publications describe which processes, tasks and materials. Although this was a
+new task, we had a total of 26 submissions across 3 evaluation scenarios. We
+expect the task and the findings reported in this paper to be relevant for
+researchers working on understanding scientific content, as well as the broader
+knowledge base population and information extraction communities.
+",1,0,0,1,0,0
+5169,Well-posedness of the Two-dimensional Nonlinear Schrödinger Equation with Concentrated Nonlinearity,"  We consider a two-dimensional nonlinear Schrödinger equation with
+concentrated nonlinearity. In both the focusing and defocusing case we prove
+local well-posedness, i.e., existence and uniqueness of the solution for short
+times, as well as energy and mass conservation. In addition, we prove that this
+implies global existence in the defocusing case, irrespective of the power of
+the nonlinearity, while in the focusing case blowing-up solutions may arise.
+",0,0,1,0,0,0
+5170,The nilpotent variety of $W(1;n)_{p}$ is irreducible,"  In the late 1980s, Premet conjectured that the nilpotent variety of any
+finite dimensional restricted Lie algebra over an algebraically closed field of
+characteristic $p>0$ is irreducible. This conjecture remains open, but it is
+known to hold for a large class of simple restricted Lie algebras, e.g. for Lie
+algebras of connected reductive algebraic groups, and for Cartan series $W, S$
+and $H$. In this paper, with the assumption that $p>3$, we confirm this
+conjecture for the minimal $p$-envelope $W(1;n)_p$ of the Zassenhaus algebra
+$W(1;n)$ for all $n\geq 2$.
+",0,0,1,0,0,0
+5171,Solitons in Bose-Einstein Condensates with Helicoidal Spin-Orbit Coupling,"  We report on the existence and stability of freely moving solitons in a
+spatially inhomogeneous Bose- Einstein condensate with helicoidal spin-orbit
+(SO) coupling. In spite of the periodically varying parameters, the system
+allows for the existence of stable propagating solitons. Such states are found
+in the rotating frame, where the helicoidal SO coupling is reduced to a
+homogeneous one. In the absence of the Zeeman splitting, the coupled
+Gross-Pitaevskii equations describing localized states feature many properties
+of the integrable systems. In particular, four-parametric families of solitons
+can be obtained in the exact form. Such solitons interact elastically. Zeeman
+splitting still allows for the existence of two families of moving solitons,
+but makes collisions of solitons inelastic.
+",0,1,0,0,0,0
+5172,Possible heights of graph transformation groups,"  In the following text we prove that for all finite $p\geq0$ there exists a
+topological graph $X$ such that $\{p,p+1,p+2,\ldots\}\cup\{+\infty\}$ is the
+collection of all possible heights for transformation groups with phase space
+$X$. Moreover for all topological graph $X$ with $p$ as height of
+transformation group $(Homeo(X),X)$, $\{p,p+1,p+2,\ldots\}\cup\{+\infty\}$
+again is the collection of all possible heights for transformation groups with
+phase space $X$.
+",0,0,1,0,0,0
+5173,Dependencies: Formalising Semantic Catenae for Information Retrieval,"  Building machines that can understand text like humans is an AI-complete
+problem. A great deal of research has already gone into this, with astounding
+results, allowing everyday people to discuss with their telephones, or have
+their reading materials analysed and classified by computers. A prerequisite
+for processing text semantics, common to the above examples, is having some
+computational representation of text as an abstract object. Operations on this
+representation practically correspond to making semantic inferences, and by
+extension simulating understanding text. The complexity and granularity of
+semantic processing that can be realised is constrained by the mathematical and
+computational robustness, expressiveness, and rigour of the tools used.
+This dissertation contributes a series of such tools, diverse in their
+mathematical formulation, but common in their application to model semantic
+inferences when machines process text. These tools are principally expressed in
+nine distinct models that capture aspects of semantic dependence in highly
+interpretable and non-complex ways. This dissertation further reflects on
+present and future problems with the current research paradigm in this area,
+and makes recommendations on how to overcome them.
+The amalgamation of the body of work presented in this dissertation advances
+the complexity and granularity of semantic inferences that can be made
+automatically by machines.
+",1,0,0,0,0,0
+5174,A New Pseudo-color Technique Based on Intensity Information Protection for Passive Sensor Imagery,"  Remote sensing image processing is so important in geo-sciences. Images which
+are obtained by different types of sensors might initially be unrecognizable.
+To make an acceptable visual perception in the images, some pre-processing
+steps (for removing noises and etc) are preformed which they affect the
+analysis of images. There are different types of processing according to the
+types of remote sensing images. The method that we are going to introduce in
+this paper is to use virtual colors to colorize the gray-scale images of
+satellite sensors. This approach helps us to have a better analysis on a sample
+single-band image which has been taken by Landsat-8 (OLI) sensor (as a
+multi-band sensor with natural color bands, its images' natural color can be
+compared to synthetic color by our approach). A good feature of this method is
+the original image reversibility in order to keep the suitable resolution of
+output images.
+",1,0,0,0,0,0
+5175,Tunneling anisotropic magnetoresistance driven by magnetic phase transition,"  The independent control of two magnetic electrodes and spin-coherent
+transport in magnetic tunnel junctions are strictly required for tunneling
+magnetoresistance, while junctions with only one ferromagnetic electrode
+exhibit tunneling anisotropic magnetoresistance dependent on the anisotropic
+density of states with no room temperature performance so far. Here we report
+an alternative approach to obtaining tunneling anisotropic magnetoresistance in
+alfa-FeRh-based junctions driven by the magnetic phase transition of alfa-FeRh
+and resultantly large variation of the density of states in the vicinity of MgO
+tunneling barrier, referred to as phase transition tunneling anisotropic
+magnetoresistance. The junctions with only one alfa-FeRh magnetic electrode
+show a magnetoresistance ratio up to 20% at room temperature. Both the polarity
+and magnitude of the phase transition tunneling anisotropic magnetoresistance
+can be modulated by interfacial engineering at the alfa-FeRh/MgO interface.
+Besides the fundamental significance, our finding might add a different
+dimension to magnetic random access memory and antiferromagnet spintronics.
+",0,1,0,0,0,0
+5176,Finding Efficient Swimming Strategies in a Three Dimensional Chaotic Flow by Reinforcement Learning,"  We apply a reinforcement learning algorithm to show how smart particles can
+learn approximately optimal strategies to navigate in complex flows. In this
+paper we consider microswimmers in a paradigmatic three-dimensional case given
+by a stationary superposition of two Arnold-Beltrami-Childress flows with
+chaotic advection along streamlines. In such a flow, we study the evolution of
+point-like particles which can decide in which direction to swim, while keeping
+the velocity amplitude constant. We show that it is sufficient to endow the
+swimmers with a very restricted set of actions (six fixed swimming directions
+in our case) to have enough freedom to find efficient strategies to move upward
+and escape local fluid traps. The key ingredient is the
+learning-from-experience structure of the algorithm, which assigns positive or
+negative rewards depending on whether the taken action is, or is not,
+profitable for the predetermined goal in the long term horizon. This is another
+example supporting the efficiency of the reinforcement learning approach to
+learn how to accomplish difficult tasks in complex fluid environments.
+",1,1,0,0,0,0
+5177,Trapped imbalanced fermionic superfluids in one dimension: A variational approach,"  We propose and analyze a variational wave function for a
+population-imbalanced one-dimensional Fermi gas that allows for
+Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) type pairing correlations among the two
+fermion species, while also accounting for the harmonic confining potential. In
+the strongly interacting regime, we find large spatial oscillations of the
+order parameter, indicative of an FFLO state. The obtained density profiles
+versus imbalance are consistent with recent experimental results as well as
+with theoretical calculations based on combining Bethe ansatz with the local
+density approximation. Although we find no signature of the FFLO state in the
+densities of the two fermion species, we show that the oscillations of the
+order parameter appear in density-density correlations, both in-situ and after
+free expansion. Furthermore, above a critical polarization, the value of which
+depends on the interaction, we find the unpaired Fermi-gas state to be
+energetically more favorable.
+",0,1,0,0,0,0
+5178,Prospects of dynamical determination of General Relativity parameter beta and solar quadrupole moment J2 with asteroid radar astronomy,"  We evaluated the prospects of quantifying the parameterized post-Newtonian
+parameter beta and solar quadrupole moment J2 with observations of near-Earth
+asteroids with large orbital precession rates (9 to 27 arcsec century$^{-1}$).
+We considered existing optical and radar astrometry, as well as radar
+astrometry that can realistically be obtained with the Arecibo planetary radar
+in the next five years. Our sensitivity calculations relied on a traditional
+covariance analysis and Monte Carlo simulations. We found that independent
+estimates of beta and J2 can be obtained with precisions of $6\times10^{-4}$
+and $3\times10^{-8}$, respectively. Because we assumed rather conservative
+observational uncertainties, as is the usual practice when reporting radar
+astrometry, it is likely that the actual precision will be closer to
+$2\times10^{-4}$ and $10^{-8}$, respectively. A purely dynamical determination
+of solar oblateness with asteroid radar astronomy may therefore rival the
+helioseismology determination.
+",0,1,0,0,0,0
+5179,Modelling and Using Response Times in Online Courses,"  Each time a learner in a self-paced online course is trying to answer an
+assessment question, it takes some time to submit the answer, and if multiple
+attempts are allowed and the first answer was incorrect, it takes some time to
+submit the second attempt, and so on. Here we study the distribution of such
+""response times"". We find that the log-normal statistical model for such times,
+previously suggested in the literature, holds for online courses qualitatively.
+Users who, according to this model, tend to take longer on submits are more
+likely to complete the course, have a higher level of engagement and achieve a
+higher grade. This finding can be the basis for designing interventions in
+online courses, such as MOOCs, which would encourage some users to slow down.
+",1,0,0,0,0,0
+5180,Univalent Foundations and the UniMath Library,"  We give a concise presentation of the Univalent Foundations of mathematics
+outlining the main ideas, followed by a discussion of the UniMath library of
+formalized mathematics implementing the ideas of the Univalent Foundations
+(section 1), and the challenges one faces in attempting to design a large-scale
+library of formalized mathematics (section 2). This leads us to a general
+discussion about the links between architecture and mathematics where a meeting
+of minds is revealed between architects and mathematicians (section 3). On the
+way our odyssey from the foundations to the ""horizon"" of mathematics will lead
+us to meet the mathematicians David Hilbert and Nicolas Bourbaki as well as the
+architect Christopher Alexander.
+",1,0,1,0,0,0
+5181,Distributed methods for synchronization of orthogonal matrices over graphs,"  This paper addresses the problem of synchronizing orthogonal matrices over
+directed graphs. For synchronized transformations (or matrices), composite
+transformations over loops equal the identity. We formulate the synchronization
+problem as a least-squares optimization problem with nonlinear constraints. The
+synchronization problem appears as one of the key components in applications
+ranging from 3D-localization to image registration. The main contributions of
+this work can be summarized as the introduction of two novel algorithms; one
+for symmetric graphs and one for graphs that are possibly asymmetric. Under
+general conditions, the former has guaranteed convergence to the solution of a
+spectral relaxation to the synchronization problem. The latter is stable for
+small step sizes when the graph is quasi-strongly connected. The proposed
+methods are verified in numerical simulations.
+",1,0,1,0,0,0
+5182,Stochastic Model of SIR Epidemic Modelling,"  Threshold theorem is probably the most important development of mathematical
+epidemic modelling. Unfortunately, some models may not behave according to the
+threshold. In this paper, we will focus on the final outcome of SIR model with
+demography. The behaviour of the model approached by deteministic and
+stochastic models will be introduced, mainly using simulations. Furthermore, we
+will also investigate the dynamic of susceptibles in population in absence of
+infective. We have successfully showed that both deterministic and stochastic
+models performed similar results when $R_0 \leq 1$. That is, the disease-free
+stage in the epidemic. But when $R_0 > 1$, the deterministic and stochastic
+approaches had different interpretations.
+",0,0,0,1,1,0
+5183,Parent Oriented Teacher Selection Causes Language Diversity,"  An evolutionary model for emergence of diversity in language is developed. We
+investigated the effects of two real life observations, namely, people prefer
+people that they communicate with well, and people interact with people that
+are physically close to each other. Clearly these groups are relatively small
+compared to the entire population. We restrict selection of the teachers from
+such small groups, called imitation sets, around parents. Then the child learns
+language from a teacher selected within the imitation set of her parent. As a
+result, there are subcommunities with their own languages developed. Within
+subcommunity comprehension is found to be high. The number of languages is
+related to the relative size of imitation set by a power law.
+",1,0,0,0,0,0
+5184,Learning Role-based Graph Embeddings,"  Random walks are at the heart of many existing network embedding methods.
+However, such algorithms have many limitations that arise from the use of
+random walks, e.g., the features resulting from these methods are unable to
+transfer to new nodes and graphs as they are tied to vertex identity. In this
+work, we introduce the Role2Vec framework which uses the flexible notion of
+attributed random walks, and serves as a basis for generalizing existing
+methods such as DeepWalk, node2vec, and many others that leverage random walks.
+Our proposed framework enables these methods to be more widely applicable for
+both transductive and inductive learning as well as for use on graphs with
+attributes (if available). This is achieved by learning functions that
+generalize to new nodes and graphs. We show that our proposed framework is
+effective with an average AUC improvement of 16.55% while requiring on average
+853x less space than existing methods on a variety of graphs.
+",1,0,0,1,0,0
+5185,Quantum Emulation of Extreme Non-equilibrium Phenomena with Trapped Atoms,"  Ultracold atomic physics experiments offer a nearly ideal context for the
+investigation of quantum systems far from equilibrium. We describe three
+related emerging directions of research into extreme non-equilibrium phenomena
+in atom traps: quantum emulation of ultrafast atom-light interactions, coherent
+phasonic spectroscopy in tunable quasicrystals, and realization of Floquet
+matter in strongly-driven lattice systems. We show that all three should enable
+quantum emulation in parameter regimes inaccessible in solid-state experiments,
+facilitating a complementary approach to open problems in non-equilibrium
+condensed matter.
+",0,1,0,0,0,0
+5186,A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach,"  We consider solving convex-concave saddle point problems. We focus on two
+variants of gradient decent-ascent algorithms, Extra-gradient (EG) and
+Optimistic Gradient (OGDA) methods, and show that they admit a unified analysis
+as approximations of the classical proximal point method for solving
+saddle-point problems. This viewpoint enables us to generalize EG (in terms of
+extrapolation steps) and OGDA (in terms of parameters) and obtain new
+convergence rate results for these algorithms for the bilinear case as well as
+the strongly convex-concave case.
+",1,0,0,1,0,0
+5187,Adaptive Multi-Step Prediction based EKF to Power System Dynamic State Estimation,"  Power system dynamic state estimation is essential to monitoring and
+controlling power system stability. Kalman filtering approaches are predominant
+in estimation of synchronous machine dynamic states (i.e. rotor angle and rotor
+speed). This paper proposes an adaptive multi-step prediction (AMSP) approach
+to improve the extended Kalman filter s (EKF) performance in estimating the
+dynamic states of a synchronous machine. The proposed approach consists of
+three major steps. First, two indexes are defined to quantify the non-linearity
+levels of the state transition function and measurement function, respectively.
+Second, based on the non-linearity indexes, a multi prediction factor (Mp) is
+defined to determine the number of prediction steps. And finally, to mitigate
+the non-linearity impact on dynamic state estimation (DSE) accuracy, the
+prediction step repeats a few time based on Mp before performing the correction
+step. The two-area four-machine system is used to evaluate the effectiveness of
+the proposed AMSP approach. It is shown through the Monte-Carlo method that a
+good trade-off between estimation accuracy and computational time can be
+achieved effectively through the proposed AMSP approach.
+",1,1,0,0,0,0
+5188,Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality,"  A number of statistical estimation problems can be addressed by semidefinite
+programs (SDP). While SDPs are solvable in polynomial time using interior point
+methods, in practice generic SDP solvers do not scale well to high-dimensional
+problems. In order to cope with this problem, Burer and Monteiro proposed a
+non-convex rank-constrained formulation, which has good performance in practice
+but is still poorly understood theoretically.
+In this paper we study the rank-constrained version of SDPs arising in MaxCut
+and in synchronization problems. We establish a Grothendieck-type inequality
+that proves that all the local maxima and dangerous saddle points are within a
+small multiplicative gap from the global maximum. We use this structural
+information to prove that SDPs can be solved within a known accuracy, by
+applying the Riemannian trust-region method to this non-convex problem, while
+constraining the rank to be of order one. For the MaxCut problem, our
+inequality implies that any local maximizer of the rank-constrained SDP
+provides a $ (1 - 1/(k-1)) \times 0.878$ approximation of the MaxCut, when the
+rank is fixed to $k$.
+We then apply our results to data matrices generated according to the
+Gaussian ${\mathbb Z}_2$ synchronization problem, and the two-groups stochastic
+block model with large bounded degree. We prove that the error achieved by
+local maximizers undergoes a phase transition at the same threshold as for
+information-theoretically optimal methods.
+",0,0,1,1,0,0
+5189,Poisson-Nernst-Planck equations with steric effects - non-convexity and multiple stationary solutions,"  We study the existence and stability of stationary solutions of
+Poisson-Nernst- Planck equations with steric effects (PNP-steric equations)
+with two counter-charged species. These equations describe steady current
+through open ionic channels quite well. The current levels in open ionic
+channels are known to switch between `open' or `closed' states in a spontaneous
+stochastic process called gating, suggesting that their governing equations
+should give rise to multiple stationary solutions that enable such multi-stable
+behavior. We show that within a range of parameters, steric effects give rise
+to multiple stationary solutions that are smooth. These solutions, however, are
+all unstable under PNP-steric dynamics. Following these findings, we introduce
+a novel PNP-Cahn-Hilliard model, and show that it admits multiple stationary
+solutions that are smooth and stable. The various branches of stationary
+solutions and their stability are mapped utilizing bifurcation analysis and
+numerical continuation methods.
+",0,1,1,0,0,0
+5190,A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices,"  In this paper we present a new algorithm for compressive sensing that makes
+use of binary measurement matrices and achieves exact recovery of ultra sparse
+vectors, in a single pass and without any iterations. Due to its noniterative
+nature, our algorithm is hundreds of times faster than $\ell_1$-norm
+minimization, and methods based on expander graphs, both of which require
+multiple iterations. Our algorithm can accommodate nearly sparse vectors, in
+which case it recovers index set of the largest components, and can also
+accommodate burst noise measurements. Compared to compressive sensing methods
+that are guaranteed to achieve exact recovery of all sparse vectors, our method
+requires fewer measurements However, methods that achieve statistical recovery,
+that is, recovery of almost all but not all sparse vectors, can require fewer
+measurements than our method.
+",1,0,0,0,0,0
+5191,Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions,"  An important goal common to domain adaptation and causal inference is to make
+accurate predictions when the distributions for the source (or training)
+domain(s) and target (or test) domain(s) differ. In many cases, these different
+distributions can be modeled as different contexts of a single underlying
+system, in which each distribution corresponds to a different perturbation of
+the system, or in causal terms, an intervention. We focus on a class of such
+causal domain adaptation problems, where data for one or more source domains
+are given, and the task is to predict the distribution of a certain target
+variable from measurements of other variables in one or more target domains. We
+propose an approach for solving these problems that exploits causal inference
+and does not rely on prior knowledge of the causal graph, the type of
+interventions or the intervention targets. We demonstrate our approach by
+evaluating a possible implementation on simulated and real world data.
+",1,0,0,1,0,0
+5192,Fitting ReLUs via SGD and Quantized SGD,"  In this paper we focus on the problem of finding the optimal weights of the
+shallowest of neural networks consisting of a single Rectified Linear Unit
+(ReLU). These functions are of the form $\mathbf{x}\rightarrow
+\max(0,\langle\mathbf{w},\mathbf{x}\rangle)$ with $\mathbf{w}\in\mathbb{R}^d$
+denoting the weight vector. We focus on a planted model where the inputs are
+chosen i.i.d. from a Gaussian distribution and the labels are generated
+according to a planted weight vector. We first show that mini-batch stochastic
+gradient descent when suitably initialized, converges at a geometric rate to
+the planted model with a number of samples that is optimal up to numerical
+constants. Next we focus on a parallel implementation where in each iteration
+the mini-batch gradient is calculated in a distributed manner across multiple
+processors and then broadcast to a master or all other processors. To reduce
+the communication cost in this setting we utilize a Quanitzed Stochastic
+Gradient Scheme (QSGD) where the partial gradients are quantized. Perhaps
+unexpectedly, we show that QSGD maintains the fast convergence of SGD to a
+globally optimal model while significantly reducing the communication cost. We
+further corroborate our numerical findings via various experiments including
+distributed implementations over Amazon EC2.
+",1,0,0,1,0,0
+5193,The meet operation in the imbalance lattice of maximal instantaneous codes: alternative proof of existence,"  An alternative proof is given of the existence of greatest lower bounds in
+the imbalance order of binary maximal instantaneous codes of a given size.
+These codes are viewed as maximal antichains of a given size in the infinite
+binary tree of 0-1 words. The proof proposed makes use of a single balancing
+operation instead of expansion and contraction as in the original proof of the
+existence of glb.
+",0,0,1,0,0,0
+5194,Approximating Partition Functions in Constant Time,"  We study approximations of the partition function of dense graphical models.
+Partition functions of graphical models play a fundamental role is statistical
+physics, in statistics and in machine learning. Two of the main methods for
+approximating the partition function are Markov Chain Monte Carlo and
+Variational Methods. An impressive body of work in mathematics, physics and
+theoretical computer science provides conditions under which Markov Chain Monte
+Carlo methods converge in polynomial time. These methods often lead to
+polynomial time approximation algorithms for the partition function in cases
+where the underlying model exhibits correlation decay. There are very few
+theoretical guarantees for the performance of variational methods. One
+exception is recent results by Risteski (2016) who considered dense graphical
+models and showed that using variational methods, it is possible to find an
+$O(\epsilon n)$ additive approximation to the log partition function in time
+$n^{O(1/\epsilon^2)}$ even in a regime where correlation decay does not hold.
+We show that under essentially the same conditions, an $O(\epsilon n)$
+additive approximation of the log partition function can be found in constant
+time, independent of $n$. In particular, our results cover dense Ising and
+Potts models as well as dense graphical models with $k$-wise interaction. They
+also apply for low threshold rank models.
+",1,0,0,1,0,0
+5195,Stability of a Volterra Integral Equation on Time Scales,"  In this paper, we study Hyers-Ulam stability for integral equation of
+Volterra type in time scale setting. Moreover we study the stability of the
+considered equation in Hyers-Ulam-Rassias sense. Our technique depends on
+successive approximation method, and we use time scale variant of induction
+principle to show that equation (1.1) is stable on unbounded domains in
+Hyers-Ulam-Rassias sense.
+",0,0,1,0,0,0
+5196,Near-IR period-luminosity relations for pulsating stars in $ω$ Centauri (NGC 5139),"  $\omega$ Centauri (NGC 5139) hosts hundreds of pulsating variable stars of
+different types, thus representing a treasure trove for studies of their
+corresponding period-luminosity (PL) relations. Our goal in this study is to
+obtain the PL relations for RR Lyrae, and SX Phoenicis stars in the field of
+the cluster, based on high-quality, well-sampled light curves in the
+near-infrared (IR). $\omega$ Centauri was observed using VIRCAM mounted on
+VISTA. A total of 42 epochs in $J$ and 100 epochs in $K_{\rm S}$ were obtained,
+spanning 352 days. Point-spread function photometry was performed using DoPhot
+and DAOPHOT in the outer and inner regions of the cluster, respectively. Based
+on the comprehensive catalogue of near-IR light curves thus secured, PL
+relations were obtained for the different types of pulsators in the cluster,
+both in the $J$ and $K_{\rm S}$ bands. This includes the first PL relations in
+the near-IR for fundamental-mode SX Phoenicis stars. The near-IR magnitudes and
+periods of Type II Cepheids and RR Lyrae stars were used to derive an updated
+true distance modulus to the cluster, with a resulting value of $(m-M)_0 =
+13.708 \pm 0.035 \pm 0.10$ mag, where the error bars correspond to the adopted
+statistical and systematic errors, respectively. Adding the errors in
+quadrature, this is equivalent to a heliocentric distance of $5.52\pm 0.27$
+kpc.
+",0,1,0,0,0,0
+5197,Pseudo-deterministic Proofs,"  We introduce pseudo-deterministic interactive proofs (psdAM): interactive
+proof systems for search problems where the verifier is guaranteed with high
+probability to output the same output on different executions. As in the case
+with classical interactive proofs, the verifier is a probabilistic polynomial
+time algorithm interacting with an untrusted powerful prover.
+We view pseudo-deterministic interactive proofs as an extension of the study
+of pseudo-deterministic randomized polynomial time algorithms: the goal of the
+latter is to find canonical solutions to search problems whereas the goal of
+the former is to prove that a solution to a search problem is canonical to a
+probabilistic polynomial time verifier. Alternatively, one may think of the
+powerful prover as aiding the probabilistic polynomial time verifier to find
+canonical solutions to search problems, with high probability over the
+randomness of the verifier. The challenge is that pseudo-determinism should
+hold not only with respect to the randomness, but also with respect to the
+prover: a malicious prover should not be able to cause the verifier to output a
+solution other than the unique canonical one.
+",1,0,0,0,0,0
+5198,The Mechanism behind Erosive Bursts in Porous Media,"  Erosion and deposition during flow through porous media can lead to large
+erosive bursts that manifest as jumps in permeability and pressure loss. Here
+we reveal that the cause of these bursts is the re-opening of clogged pores
+when the pressure difference between two opposite sites of the pore surpasses a
+certain threshold. We perform numerical simulations of flow through porous
+media and compare our predictions to experimental results, recovering with
+excellent agreement shape and power-law distribution of pressure loss jumps,
+and the behavior of the permeability jumps as function of particle
+concentration. Furthermore, we find that erosive bursts only occur for pressure
+gradient thresholds within the range of two critical values, independent on how
+the flow is driven. Our findings provide a better understanding of sudden sand
+production in oil wells and breakthrough in filtration.
+",0,1,0,0,0,0
+5199,The Maximum Likelihood Degree of Toric Varieties,"  We study the maximum likelihood degree (ML degree) of toric varieties, known
+as discrete exponential models in statistics. By introducing scaling
+coefficients to the monomial parameterization of the toric variety, one can
+change the ML degree. We show that the ML degree is equal to the degree of the
+toric variety for generic scalings, while it drops if and only if the scaling
+vector is in the locus of the principal $A$-determinant. We also illustrate how
+to compute the ML estimate of a toric variety numerically via homotopy
+continuation from a scaled toric variety with low ML degree. Throughout, we
+include examples motivated by algebraic geometry and statistics. We compute the
+ML degree of rational normal scrolls and a large class of Veronese-type
+varieties. In addition, we investigate the ML degree of scaled Segre varieties,
+hierarchical loglinear models, and graphical models.
+",0,0,1,1,0,0
+5200,Low Mach number limit of a pressure correction MAC scheme for compressible barotropic flows,"  We study the incompressible limit of a pressure correction MAC scheme [3] for
+the unstationary compressible barotropic Navier-Stokes equations. Provided the
+initial data are well-prepared, the solution of the numerical scheme converges,
+as the Mach number tends to zero, towards the solution of the classical
+pressure correction inf-sup stable MAC scheme for the incompressible
+Navier-Stokes equations.
+",0,1,1,0,0,0
+5201,Objective Bayesian Analysis for Change Point Problems,"  In this paper we present a loss-based approach to change point analysis. In
+particular, we look at the problem from two perspectives. The first focuses on
+the definition of a prior when the number of change points is known a priori.
+The second contribution aims to estimate the number of change points by using a
+loss-based approach recently introduced in the literature. The latter considers
+change point estimation as a model selection exercise. We show the performance
+of the proposed approach on simulated data and real data sets.
+",0,0,1,1,0,0
+5202,On the Mechanism of Large Amplitude Flapping of Inverted Foil in a Uniform Flow,"  An elastic foil interacting with a uniform flow with its trailing edge
+clamped, also known as the inverted foil, exhibits a wide range of complex
+self-induced flapping regimes such as large amplitude flapping (LAF), deformed
+and flipped flapping. Here, we perform three-dimensional numerical experiments
+to examine the role of vortex shedding and the vortex-vortex interaction on the
+LAF response at Reynolds number Re=30,000. Here we investigate the dynamics of
+the inverted foil for a novel configuration wherein we introduce a fixed
+splitter plate at the trailing edge to suppress the vortex shedding from
+trailing edge and inhibit the interaction between the counter-rotating
+vortices. We find that the inhibition of the interaction has an insignificant
+effect on the transverse flapping amplitudes, due to a relatively weaker
+coupling between the counter-rotating vortices emanating from the leading edge
+and trailing edge. However, the inhibition of the trailing edge vortex reduces
+the streamwise flapping amplitude, the flapping frequency and the net strain
+energy of foil. To further generalize our understanding of the LAF, we next
+perform low-Reynolds number (Re$\in[0.1,50]$) simulations for the identical
+foil properties to realize the impact of vortex shedding on the large amplitude
+flapping. Due to the absence of vortex shedding process in the low-$Re$ regime,
+the inverted foil no longer exhibits the periodic flapping. However, the
+flexible foil still loses its stability through divergence instability to
+undergo a large static deformation. Finally, we introduce an analogous
+analytical model for the LAF based on the dynamics of an elastically mounted
+flat plate undergoing flow-induced pitching oscillations in a uniform stream.
+",0,1,0,0,0,0
+5203,Ultrafast Epitaxial Growth of Metre-Sized Single-Crystal Graphene on Industrial Cu Foil,"  A foundation of the modern technology that uses single-crystal silicon has
+been the growth of high-quality single-crystal Si ingots with diameters up to
+12 inches or larger. For many applications of graphene, large-area high-quality
+(ideally of single-crystal) material will be enabling. Since the first growth
+on copper foil a decade ago, inch-sized single-crystal graphene has been
+achieved. We present here the growth, in 20 minutes, of a graphene film of 5 x
+50 cm2 dimension with > 99% ultra-highly oriented grains. This growth was
+achieved by: (i) synthesis of sub-metre-sized single-crystal Cu(111) foil as
+substrate; (ii) epitaxial growth of graphene islands on the Cu(111) surface;
+(iii) seamless merging of such graphene islands into a graphene film with high
+single crystallinity and (iv) the ultrafast growth of graphene film. These
+achievements were realized by a temperature-driven annealing technique to
+produce single-crystal Cu(111) from industrial polycrystalline Cu foil and the
+marvellous effects of a continuous oxygen supply from an adjacent oxide. The
+as-synthesized graphene film, with very few misoriented grains (if any), has a
+mobility up to ~ 23,000 cm2V-1s-1 at 4 K and room temperature sheet resistance
+of ~ 230 ohm/square. It is very likely that this approach can be scaled up to
+achieve exceptionally large and high-quality graphene films with single
+crystallinity, and thus realize various industrial-level applications at a low
+cost.
+",0,1,0,0,0,0
+5204,Multidimensional Sampling of Isotropically Bandlimited Signals,"  A new lower bound on the average reconstruction error variance of
+multidimensional sampling and reconstruction is presented. It applies to
+sampling on arbitrary lattices in arbitrary dimensions, assuming a stochastic
+process with constant, isotropically bandlimited spectrum and reconstruction by
+the best linear interpolator. The lower bound is exact for any lattice at
+sufficiently high and low sampling rates. The two threshold rates where the
+error variance deviates from the lower bound gives two optimality criteria for
+sampling lattices. It is proved that at low rates, near the first threshold,
+the optimal lattice is the dual of the best sphere-covering lattice, which for
+the first time establishes a rigorous relation between optimal sampling and
+optimal sphere covering. A previously known result is confirmed at high rates,
+near the second threshold, namely, that the optimal lattice is the dual of the
+best sphere-packing lattice. Numerical results quantify the performance of
+various lattices for sampling and support the theoretical optimality criteria.
+",1,0,1,1,0,0
+5205,Asymptotic structure of almost eigenfunctions of drift Laplacians on conical ends,"  We use a weighted variant of the frequency functions introduced by Almgren to
+prove sharp asymptotic estimates for almost eigenfunctions of the drift
+Laplacian associated to the Gaussian weight on an asymptotically conical end.
+As a consequence, we obtain a purely elliptic proof of a result of L. Wang on
+the uniqueness of self-shrinkers of the mean curvature flow asymptotic to a
+given cone. Another consequence is a unique continuation property for
+self-expanders of the mean curvature flow that flow from a cone.
+",0,0,1,0,0,0
+5206,Palomar Optical Spectrum of Hyperbolic Near-Earth Object A/2017 U1,"  We present optical spectroscopy of the recently discovered hyperbolic
+near-Earth object A/2017 U1, taken on 25 Oct 2017 at Palomar Observatory.
+Although our data are at a very low signal-to-noise, they indicate a very red
+surface at optical wavelengths without significant absorption features.
+",0,1,0,0,0,0
+5207,Electrically driven quantum light emission in electromechanically-tuneable photonic crystal cavities,"  A single quantum dot deterministically coupled to a photonic crystal
+environment constitutes an indispensable elementary unit to both generate and
+manipulate single-photons in next-generation quantum photonic circuits. To
+date, the scaling of the number of these quantum nodes on a fully-integrated
+chip has been prevented by the use of optical pumping strategies that require a
+bulky off-chip laser along with the lack of methods to control the energies of
+nano-cavities and emitters. Here, we concurrently overcome these limitations by
+demonstrating electrical injection of single excitonic lines within a
+nano-electro-mechanically tuneable photonic crystal cavity. When an
+electrically-driven dot line is brought into resonance with a photonic crystal
+mode, its emission rate is enhanced. Anti-bunching experiments reveal the
+quantum nature of these on-demand sources emitting in the telecom range. These
+results represent an important step forward in the realization of integrated
+quantum optics experiments featuring multiple electrically-triggered
+Purcell-enhanced single-photon sources embedded in a reconfigurable
+semiconductor architecture.
+",0,1,0,0,0,0
+5208,The Odyssey Approach for Optimizing Federated SPARQL Queries,"  Answering queries over a federation of SPARQL endpoints requires combining
+data from more than one data source. Optimizing queries in such scenarios is
+particularly challenging not only because of (i) the large variety of possible
+query execution plans that correctly answer the query but also because (ii)
+there is only limited access to statistics about schema and instance data of
+remote sources. To overcome these challenges, most federated query engines rely
+on heuristics to reduce the space of possible query execution plans or on
+dynamic programming strategies to produce optimal plans. Nevertheless, these
+plans may still exhibit a high number of intermediate results or high execution
+times because of heuristics and inaccurate cost estimations. In this paper, we
+present Odyssey, an approach that uses statistics that allow for a more
+accurate cost estimation for federated queries and therefore enables Odyssey to
+produce better query execution plans. Our experimental results show that
+Odyssey produces query execution plans that are better in terms of data
+transfer and execution time than state-of-the-art optimizers. Our experiments
+using the FedBench benchmark show execution time gains of at least 25 times on
+average.
+",1,0,0,0,0,0
+5209,A variant of Gromov's problem on Hölder equivalence of Carnot groups,"  It is unknown if there exists a locally $\alpha$-Hölder homeomorphism
+$f:\mathbb{R}^3\to \mathbb{H}^1$ for any $\frac{1}{2}< \alpha\le \frac{2}{3}$,
+although the identity map $\mathbb{R}^3\to \mathbb{H}^1$ is locally
+$\frac{1}{2}$-Hölder. More generally, Gromov asked: Given $k$ and a Carnot
+group $G$, for which $\alpha$ does there exist a locally $\alpha$-Hölder
+homeomorphism $f:\mathbb{R}^k\to G$? Here, we equip a Carnot group $G$ with the
+Carnot-Carathéodory metric. In 2014, Balogh, Hajlasz, and Wildrick considered
+a variant of this problem. These authors proved that if $k>n$, there does not
+exist an injective, $(\frac{1}{2}+)$-Hölder mapping $f:\mathbb{R}^k\to
+\mathbb{H}^n$ that is also locally Lipschitz as a mapping into
+$\mathbb{R}^{2n+1}$. For their proof, they use the fact that $\mathbb{H}^n$ is
+purely $k$-unrectifiable for $k>n$. In this paper, we will extend their result
+from the Heisenberg group to model filiform groups and Carnot groups of step at
+most three. We will now require that the Carnot group is purely
+$k$-unrectifiable. The main key to our proof will be showing that
+$(\frac{1}{2}+)$-Hölder maps $f:\mathbb{R}^k\to G$ that are locally Lipschitz
+into Euclidean space, are weakly contact. Proving weak contactness in these two
+settings requires understanding the relationship between the algebraic and
+metric structures of the Carnot group. We will use coordinates of the first and
+second kind for Carnot groups.
+",0,0,1,0,0,0
+5210,The Trees of Hanoi,"  The game of the Towers of Hanoi is generalized to binary trees. First, a
+straightforward solution of the game is discussed. Second, a shorter solution
+is presented, which is then shown to be optimal.
+",1,0,1,0,0,0
+5211,On the risk of convex-constrained least squares estimators under misspecification,"  We consider the problem of estimating the mean of a noisy vector. When the
+mean lies in a convex constraint set, the least squares projection of the
+random vector onto the set is a natural estimator. Properties of the risk of
+this estimator, such as its asymptotic behavior as the noise tends to zero,
+have been well studied. We instead study the behavior of this estimator under
+misspecification, that is, without the assumption that the mean lies in the
+constraint set. For appropriately defined notions of risk in the misspecified
+setting, we prove a generalization of a low noise characterization of the risk
+due to Oymak and Hassibi in the case of a polyhedral constraint set. An
+interesting consequence of our results is that the risk can be much smaller in
+the misspecified setting than in the well-specified setting. We also discuss
+consequences of our result for isotonic regression.
+",0,0,1,1,0,0
+5212,Benchmarks for Image Classification and Other High-dimensional Pattern Recognition Problems,"  A good classification method should yield more accurate results than simple
+heuristics. But there are classification problems, especially high-dimensional
+ones like the ones based on image/video data, for which simple heuristics can
+work quite accurately; the structure of the data in such problems is easy to
+uncover without any sophisticated or computationally expensive method. On the
+other hand, some problems have a structure that can only be found with
+sophisticated pattern recognition methods. We are interested in quantifying the
+difficulty of a given high-dimensional pattern recognition problem. We consider
+the case where the patterns come from two pre-determined classes and where the
+objects are represented by points in a high-dimensional vector space. However,
+the framework we propose is extendable to an arbitrarily large number of
+classes. We propose classification benchmarks based on simple random projection
+heuristics. Our benchmarks are 2D curves parameterized by the classification
+error and computational cost of these simple heuristics. Each curve divides the
+plane into a ""positive- gain"" and a ""negative-gain"" region. The latter contains
+methods that are ill-suited for the given classification problem. The former is
+divided into two by the curve asymptote; methods that lie in the small region
+under the curve but right of the asymptote merely provide a computational gain
+but no structural advantage over the random heuristics. We prove that the curve
+asymptotes are optimal (i.e. at Bayes error) in some cases, and thus no
+sophisticated method can provide a structural advantage over the random
+heuristics. Such classification problems, an example of which we present in our
+numerical experiments, provide poor ground for testing new pattern
+classification methods.
+",0,0,0,1,0,0
+5213,On the Binary Lossless Many-Help-One Problem with Independently Degraded Helpers,"  Although the rate region for the lossless many-help-one problem with
+independently degraded helpers is already ""solved"", its solution is given in
+terms of a convex closure over a set of auxiliary random variables. Thus, for
+any such a problem in particular, an optimization over the set of auxiliary
+random variables is required to truly solve the rate region. Providing the
+solution is surprisingly difficult even for an example as basic as binary
+sources. In this work, we derive a simple and tight inner bound on the rate
+region's lower boundary for the lossless many-help-one problem with
+independently degraded helpers when specialized to sources that are binary,
+uniformly distributed, and interrelated through symmetric channels. This
+scenario finds important applications in emerging cooperative communication
+schemes in which the direct-link transmission is assisted via multiple lossy
+relaying links. Numerical results indicate that the derived inner bound proves
+increasingly tight as the helpers become more degraded.
+",1,0,0,0,0,0
+5214,Efficient Correlated Topic Modeling with Topic Embedding,"  Correlated topic modeling has been limited to small model and problem sizes
+due to their high computational cost and poor scaling. In this paper, we
+propose a new model which learns compact topic embeddings and captures topic
+correlations through the closeness between the topic vectors. Our method
+enables efficient inference in the low-dimensional embedding space, reducing
+previous cubic or quadratic time complexity to linear w.r.t the topic size. We
+further speedup variational inference with a fast sampler to exploit sparsity
+of topic occurrence. Extensive experiments show that our approach is capable of
+handling model and data scales which are several orders of magnitude larger
+than existing correlation results, without sacrificing modeling quality by
+providing competitive or superior performance in document classification and
+retrieval.
+",1,0,0,1,0,0
+5215,Structure-aware error bounds for linear classification with the zero-one loss,"  We prove risk bounds for binary classification in high-dimensional settings
+when the sample size is allowed to be smaller than the dimensionality of the
+training set observations. In particular, we prove upper bounds for both
+'compressive learning' by empirical risk minimization (ERM) (that is when the
+ERM classifier is learned from data that have been projected from
+high-dimensions onto a randomly selected low-dimensional subspace) as well as
+uniform upper bounds in the full high-dimensional space. A novel tool we employ
+in both settings is the 'flipping probability' of Durrant and Kaban (ICML 2013)
+which we use to capture benign geometric structures that make a classification
+problem 'easy' in the sense of demanding a relatively low sample size for
+guarantees of good generalization. Furthermore our bounds also enable us to
+explain or draw connections between several existing successful classification
+algorithms. Finally we show empirically that our bounds are informative enough
+in practice to serve as the objective function for learning a classifier (by
+using them to do so).
+",0,0,1,1,0,0
+5216,Dimensional reduction and the equivariant Chern character,"  We propose a dimensional reduction procedure in the Stolz--Teichner framework
+of supersymmetric Euclidean field theories (EFTs) that is well-suited in the
+presence of a finite gauge group or, more generally, for field theories over an
+orbifold. As an illustration, we give a geometric interpretation of the Chern
+character for manifolds with an action by a finite group.
+",0,0,1,0,0,0
+5217,Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions,"  We present a practical approach for processing mobile sensor time series data
+for continual deep learning predictions. The approach comprises data cleaning,
+normalization, capping, time-based compression, and finally classification with
+a recurrent neural network. We demonstrate the effectiveness of the approach in
+a case study with 279 participants. On the basis of sparse sensor events, the
+network continually predicts whether the participants would attend to a
+notification within 10 minutes. Compared to a random baseline, the classifier
+achieves a 40% performance increase (AUC of 0.702) on a withheld test set. This
+approach allows to forgo resource-intensive, domain-specific, error-prone
+feature engineering, which may drastically increase the applicability of
+machine learning to mobile phone sensor data.
+",1,0,0,0,0,0
+5218,Formal Privacy for Functional Data with Gaussian Perturbations,"  Motivated by the rapid rise in statistical tools in Functional Data Analysis,
+we consider the Gaussian mechanism for achieving differential privacy with
+parameter estimates taking values in a, potentially infinite-dimensional,
+separable Banach space. Using classic results from probability theory, we show
+how densities over function spaces can be utilized to achieve the desired
+differential privacy bounds. This extends prior results of Hall et al (2013) to
+a much broader class of statistical estimates and summaries, including ""path
+level"" summaries, nonlinear functionals, and full function releases. By
+focusing on Banach spaces, we provide a deeper picture of the challenges for
+privacy with complex data, especially the role regularization plays in
+balancing utility and privacy. Using an application to penalized smoothing, we
+explicitly highlight this balance in the context of mean function estimation.
+Simulations and an application to diffusion tensor imaging are briefly
+presented, with extensive additions included in a supplement.
+",0,0,1,1,0,0
+5219,An algorithm to reconstruct convex polyhedra from their face normals and areas,"  A well-known result in the study of convex polyhedra, due to Minkowski, is
+that a convex polyhedron is uniquely determined (up to translation) by the
+directions and areas of its faces. The theorem guarantees existence of the
+polyhedron associated to given face normals and areas, but does not provide a
+constructive way to find it explicitly. This article provides an algorithm to
+reconstruct 3D convex polyhedra from their face normals and areas, based on an
+method by Lasserre to compute the volume of a convex polyhedron in
+$\mathbb{R}^n$. A Python implementation of the algorithm is available at
+this https URL.
+",0,1,0,0,0,0
+5220,Electron affinities of water clusters from density-functional and many-body-perturbation theory,"  In this work, we assess the accuracy of dielectric-dependent hybrid density
+functionals and many-body perturbation theory methods for the calculation of
+electron affinities of small water clusters, including hydrogen-bonded water
+dimer and water hexamer isomers. We show that many-body perturbation theory in
+the G$_0$W$_0$ approximation starting with the dielectric-dependent hybrid
+functionals predicts electron affinities of clusters within 0.1 eV of the
+coupled-cluster results with single, double, and perturbative triple
+excitations.
+",0,1,0,0,0,0
+5221,Grid-converged Solution and Analysis of the Unsteady Viscous Flow in a Two-dimensional Shock Tube,"  The flow in a shock tube is extremely complex with dynamic multi-scale
+structures of sharp fronts, flow separation, and vortices due to the
+interaction of the shock wave, the contact surface, and the boundary layer over
+the side wall of the tube. Prediction and understanding of the complex fluid
+dynamics is of theoretical and practical importance. It is also an extremely
+challenging problem for numerical simulation, especially at relatively high
+Reynolds numbers. Daru & Tenaud (Daru, V. & Tenaud, C. 2001 Evaluation of TVD
+high resolution schemes for unsteady viscous shocked flows. Computers & Fluids
+30, 89-113) proposed a two-dimensional model problem as a numerical test case
+for high-resolution schemes to simulate the flow field in a square closed shock
+tube. Though many researchers have tried this problem using a variety of
+computational methods, there is not yet an agreed-upon grid-converged solution
+of the problem at the Reynolds number of 1000. This paper presents a rigorous
+grid-convergence study and the resulting grid-converged solutions for this
+problem by using a newly-developed, efficient, and high-order gas-kinetic
+scheme. Critical data extracted from the converged solutions are documented as
+benchmark data. The complex fluid dynamics of the flow at Re = 1000 are
+discussed and analysed in detail. Major phenomena revealed by the numerical
+computations include the downward concentration of the fluid through the curved
+shock, the formation of the vortices, the mechanism of the shock wave
+bifurcation, the structure of the jet along the bottom wall, and the
+Kelvin-Helmholtz instability near the contact surface.
+",0,1,0,0,0,0
+5222,Exact zero modes in twisted Kitaev chains,"  We study the Kitaev chain under generalized twisted boundary conditions, for
+which both the amplitudes and the phases of the boundary couplings can be tuned
+at will. We explicitly show the presence of exact zero modes for large chains
+belonging to the topological phase in the most general case, in spite of the
+absence of ""edges"" in the system. For specific values of the phase parameters,
+we rigorously obtain the condition for the presence of the exact zero modes in
+finite chains, and show that the zero modes obtained are indeed localized. The
+full spectrum of the twisted chains with zero chemical potential is
+analytically presented. Finally, we demonstrate the persistence of zero modes
+(level crossing) even in the presence of disorder or interactions.
+",0,1,0,0,0,0
+5223,Generative Temporal Models with Memory,"  We consider the general problem of modeling temporal data with long-range
+dependencies, wherein new observations are fully or partially predictable based
+on temporally-distant, past observations. A sufficiently powerful temporal
+model should separate predictable elements of the sequence from unpredictable
+elements, express uncertainty about those unpredictable elements, and rapidly
+identify novel elements that may help to predict the future. To create such
+models, we introduce Generative Temporal Models augmented with external memory
+systems. They are developed within the variational inference framework, which
+provides both a practical training methodology and methods to gain insight into
+the models' operation. We show, on a range of problems with sparse, long-term
+temporal dependencies, that these models store information from early in a
+sequence, and reuse this stored information efficiently. This allows them to
+perform substantially better than existing models based on well-known recurrent
+neural networks, like LSTMs.
+",1,0,0,1,0,0
+5224,Global research collaboration: Networks and partners in South East Asia,"  This is an empirical paper that addresses the role of bilateral and
+multilateral international co-authorships in the six leading science systems
+among the ASEAN group of countries (ASEAN6). The paper highlights the different
+ways that bilateral and multilateral co-authorships structure global networks
+and the collaborations of the ASEAN6. The paper looks at the influence of the
+collaboration styles of major collaborating countries of the ASEAN6,
+particularly the USA and Japan. It also highlights the role of bilateral and
+multilateral co-authorships in the production of knowledge in the leading
+specialisations of the ASEAN6. The discussion section offers some tentative
+explanations for major dynamics evident in the results and summarises the next
+steps in this research.
+",1,0,0,0,0,0
+5225,Spontaneous and stimulus-induced coherent states of dynamically balanced neuronal networks,"  How the information microscopically processed by individual neurons is
+integrated and used in organising the macroscopic behaviour of an animal is a
+central question in neuroscience. Coherence of dynamics over different scales
+has been suggested as a clue to the mechanisms underlying this integration.
+Balanced excitation and inhibition amplify microscopic fluctuations to a
+macroscopic level and may provide a mechanism for generating coherent dynamics
+over the two scales. Previous theories of brain dynamics, however, have been
+restricted to cases in which population-averaged activities have been
+constrained to constant values, that is, to cases with no macroscopic degrees
+of freedom. In the present study, we investigate balanced neuronal networks
+with a nonzero number of macroscopic degrees of freedom coupled to microscopic
+degrees of freedom. In these networks, amplified microscopic fluctuations drive
+the macroscopic dynamics, while the macroscopic dynamics determine the
+statistics of the microscopic fluctuations. We develop a novel type of
+mean-field theory applicable to this class of interscale interactions, for
+which an analytical approach has previously been unknown. Irregular macroscopic
+rhythms similar to those observed in the brain emerge spontaneously as a result
+of such interactions. Microscopic inputs to a small number of neurons
+effectively entrain the whole network through the amplification mechanism.
+Neuronal responses become coherent as the magnitude of either the balanced
+excitation and inhibition or the external inputs is increased. Our mean-field
+theory successfully predicts the behaviour of the model. Our numerical results
+further suggest that the coherent dynamics can be used for selective read-out
+of information. In conclusion, our results show a novel form of neuronal
+information processing that bridges different scales, and advance our
+understanding of the brain.
+",0,1,0,0,0,0
+5226,Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors,"  Pairwise association measure is an important operation in data analytics.
+Kendall's tau coefficient is one widely used correlation coefficient
+identifying non-linear relationships between ordinal variables. In this paper,
+we investigated a parallel algorithm accelerating all-pairs Kendall's tau
+coefficient computation via single instruction multiple data (SIMD) vectorized
+sorting on Intel Xeon Phis by taking advantage of many processing cores and
+512-bit SIMD vector instructions. To facilitate workload balancing and overcome
+on-chip memory limitation, we proposed a generic framework for symmetric
+all-pairs computation by building provable bijective functions between job
+identifier and coordinate space. Performance evaluation demonstrated that our
+algorithm on one 5110P Phi achieves two orders-of-magnitude speedups over
+16-threaded MATLAB and three orders-of-magnitude speedups over sequential R,
+both running on high-end CPUs. Besides, our algorithm exhibited rather good
+distributed computing scalability with respect to number of Phis. Source code
+and datasets are publicly available at this http URL.
+",1,0,0,0,0,0
+5227,Effect of Blast Exposure on Gene-Gene Interactions,"  Repeated exposure to low-level blast may initiate a range of adverse health
+problem such as traumatic brain injury (TBI). Although many studies
+successfully identified genes associated with TBI, yet the cellular mechanisms
+underpinning TBI are not fully elucidated. In this study, we investigated
+underlying relationship among genes through constructing transcript Bayesian
+networks using RNA-seq data. The data for pre- and post-blast transcripts,
+which were collected on 33 individuals in Army training program, combined with
+our system approach provide unique opportunity to investigate the effect of
+blast-wave exposure on gene-gene interactions. Digging into the networks, we
+identified four subnetworks related to immune system and inflammatory process
+that are disrupted due to the exposure. Among genes with relatively high fold
+change in their transcript expression level, ATP6V1G1, B2M, BCL2A1, PELI,
+S100A8, TRIM58 and ZNF654 showed major impact on the dysregulation of the
+gene-gene interactions. This study reveals how repeated exposures to traumatic
+conditions increase the level of fold change of transcript expression and
+hypothesizes new targets for further experimental studies.
+",0,0,0,0,1,0
+5228,A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields,"  Gaussian Markov random fields are used in a large number of disciplines in
+machine vision and spatial statistics. The models take advantage of sparsity in
+matrices introduced through the Markov assumptions, and all operations in
+inference and prediction use sparse linear algebra operations that scale well
+with dimensionality. Yet, for very high-dimensional models, exact computation
+of predictive variances of linear combinations of variables is generally
+computationally prohibitive, and approximate methods (generally interpolation
+or conditional simulation) are typically used instead. A set of conditions are
+established under which the variances of linear combinations of random
+variables can be computed exactly using the Takahashi recursions. The ensuing
+computational simplification has wide applicability and may be used to enhance
+several software packages where model fitting is seated in a maximum-likelihood
+framework. The resulting algorithm is ideal for use in a variety of spatial
+statistical applications, including \emph{LatticeKrig} modelling, statistical
+downscaling, and fixed rank kriging. It can compute hundreds of thousands exact
+predictive variances of linear combinations on a standard desktop with ease,
+even when large spatial GMRF models are used.
+",0,0,0,1,0,0
+5229,Rational motivic path spaces and Kim's relative unipotent section conjecture,"  We initiate a study of path spaces in the nascent context of ""motivic dga's"",
+under development in doctoral work by Gabriella Guzman. This enables us to
+reconstruct the unipotent fundamental group of a pointed scheme from the
+associated augmented motivic dga, and provides us with a factorization of Kim's
+relative unipotent section conjecture into several smaller conjectures with a
+homotopical flavor. Based on a conversation with Joseph Ayoub, we prove that
+the path spaces of the punctured projective line over a number field are
+concentrated in degree zero with respect to Levine's t-structure for mixed Tate
+motives. This constitutes a step in the direction of Kim's conjecture.
+",0,0,1,0,0,0
+5230,Convergence Rates of Latent Topic Models Under Relaxed Identifiability Conditions,"  In this paper we study the frequentist convergence rate for the Latent
+Dirichlet Allocation (Blei et al., 2003) topic models. We show that the maximum
+likelihood estimator converges to one of the finitely many equivalent
+parameters in Wasserstein's distance metric at a rate of $n^{-1/4}$ without
+assuming separability or non-degeneracy of the underlying topics and/or the
+existence of more than three words per document, thus generalizing the previous
+works of Anandkumar et al. (2012, 2014) from an information-theoretical
+perspective. We also show that the $n^{-1/4}$ convergence rate is optimal in
+the worst case.
+",1,0,0,1,0,0
+5231,Subspace Clustering of Very Sparse High-Dimensional Data,"  In this paper we consider the problem of clustering collections of very short
+texts using subspace clustering. This problem arises in many applications such
+as product categorisation, fraud detection, and sentiment analysis. The main
+challenge lies in the fact that the vectorial representation of short texts is
+both high-dimensional, due to the large number of unique terms in the corpus,
+and extremely sparse, as each text contains a very small number of words with
+no repetition. We propose a new, simple subspace clustering algorithm that
+relies on linear algebra to cluster such datasets. Experimental results on
+identifying product categories from product names obtained from the US Amazon
+website indicate that the algorithm can be competitive against state-of-the-art
+clustering algorithms.
+",1,0,0,1,0,0
+5232,Quarnet inference rules for level-1 networks,"  An important problem in phylogenetics is the construction of phylogenetic
+trees. One way to approach this problem, known as the supertree method,
+involves inferring a phylogenetic tree with leaves consisting of a set $X$ of
+species from a collection of trees, each having leaf-set some subset of $X$. In
+the 1980's characterizations, certain inference rules were given for when a
+collection of 4-leaved trees, one for each 4-element subset of $X$, can all be
+simultaneously displayed by a single supertree with leaf-set $X$. Recently, it
+has become of interest to extend such results to phylogenetic networks. These
+are a generalization of phylogenetic trees which can be used to represent
+reticulate evolution (where species can come together to form a new species).
+It has been shown that a certain type of phylogenetic network, called a level-1
+network, can essentially be constructed from 4-leaved trees. However, the
+problem of providing appropriate inference rules for such networks remains
+unresolved. Here we show that by considering 4-leaved networks, called
+quarnets, as opposed to 4-leaved trees, it is possible to provide such rules.
+In particular, we show that these rules can be used to characterize when a
+collection of quarnets, one for each 4-element subset of $X$, can all be
+simultaneously displayed by a level-1 network with leaf-set $X$. The rules are
+an intriguing mixture of tree inference rules, and an inference rule for
+building up a cyclic ordering of $X$ from orderings on subsets of $X$ of size
+4. This opens up several new directions of research for inferring phylogenetic
+networks from smaller ones, which could yield new algorithms for solving the
+supernetwork problem in phylogenetics.
+",1,0,0,0,0,0
+5233,21 cm Angular Power Spectrum from Minihalos as a Probe of Primordial Spectral Runnings,"  Measurements of 21 cm line fluctuations from minihalos have been discussed as
+a powerful probe of a wide range of cosmological models. However, previous
+studies have taken into account only the pixel variance, where contributions
+from different scales are integrated. In order to sort out information from
+different scales, we formulate the angular power spectrum of 21 cm line
+fluctuations from minihalos at different redshifts, which can enhance the
+constraining power enormously. By adopting this formalism, we investigate
+expected constraints on parameters characterizing the primordial power
+spectrum, particularly focusing on the spectral index $n_s$ and its runnings
+$\alpha_s$ and $\beta_s$. We show that future observations of 21 cm line
+fluctuations from minihalos, in combination with cosmic microwave background,
+can potentially probe these runnings as $\alpha_s \sim {\cal O}(10^{-3})$ and
+$\beta_s \sim {\cal O}(10^{-4})$. Its implications to the test of inflationary
+models are also discussed.
+",0,1,0,0,0,0
+5234,The IRX-Beta Dust Attenuation Relation in Cosmological Galaxy Formation Simulations,"  We utilise a series of high-resolution cosmological zoom simulations of
+galaxy formation to investigate the relationship between the ultraviolet (UV)
+slope, beta, and the ratio of the infrared luminosity to UV luminosity (IRX) in
+the spectral energy distributions (SEDs) of galaxies. We employ dust radiative
+transfer calculations in which the SEDs of the stars in galaxies propagate
+through the dusty interstellar medium. Our main goals are to understand the
+origin of, and scatter in the IRX-beta relation; to assess the efficacy of
+simplified stellar population synthesis screen models in capturing the
+essential physics in the IRX-beta relation; and to understand systematic
+deviations from the canonical local IRX-beta relations in particular
+populations of high-redshift galaxies. Our main results follow. Galaxies that
+have young stellar populations with relatively cospatial UV and IR emitting
+regions and a Milky Way-like extinction curve fall on or near the standard
+Meurer relation. This behaviour is well captured by simplified screen models.
+Scatter in the IRX-beta relation is dominated by three major effects: (i) older
+stellar populations drive galaxies below the relations defined for local
+starbursts due to a reddening of their intrinsic UV SEDs; (ii) complex
+geometries in high-z heavily star forming galaxies drive galaxies toward blue
+UV slopes owing to optically thin UV sightlines; (iii) shallow extinction
+curves drive galaxies downward in the IRX-beta plane due to lowered NUV/FUV
+extinction ratios. We use these features of the UV slopes of galaxies to derive
+a fitting relation that reasonably collapses the scatter back toward the
+canonical local relation. Finally, we use these results to develop an
+understanding for the location of two particularly enigmatic populations of
+galaxies in the IRX-beta plane: z~2-4 dusty star forming galaxies, and z>5 star
+forming galaxies.
+",0,1,0,0,0,0
+5235,Strict convexity of the Mabuchi functional for energy minimizers,"  There are two parts of this paper. First, we discovered an explicit formula
+for the complex Hessian of the weighted log-Bergman kernel on a parallelogram
+domain, and utilised this formula to give a new proof about the strict
+convexity of the Mabuchi functional along a smooth geodesic. Second, when a
+C^{1,1}-geodesic connects two non-degenerate energy minimizers, we also proved
+this strict convexity, by showing that such a geodesic must be non-degenerate
+and smooth.
+",0,0,1,0,0,0
+5236,Anonymous Variables in Imperative Languages,"  In this paper, we bring anonymous variables into imperative languages.
+Anonymous variables represent don't-care values and have proven useful in logic
+programming. To bring the same level of benefits into imperative languages, we
+describe an extension to C wth anonymous variables.
+",1,0,0,0,0,0
+5237,Simultaneously constraining the astrophysics of reionisation and the epoch of heating with 21CMMC,"  The cosmic 21 cm signal is set to revolutionise our understanding of the
+early Universe, allowing us to probe the 3D temperature and ionisation
+structure of the intergalactic medium (IGM). It will open a window onto the
+unseen first galaxies, showing us how their UV and X-ray photons drove the
+cosmic milestones of the epoch of reionisation (EoR) and epoch of heating
+(EoH). To facilitate parameter inference from the 21 cm signal, we previously
+developed 21CMMC: a Monte Carlo Markov Chain sampler of 3D EoR simulations.
+Here we extend 21CMMC to include simultaneous modelling of the EoH, resulting
+in a complete Bayesian inference framework for the astrophysics dominating the
+observable epochs of the cosmic 21 cm signal. We demonstrate that second
+generation interferometers, the Hydrogen Epoch of Reionisation Array (HERA) and
+Square Kilometre Array (SKA) will be able to constrain ionising and X-ray
+source properties of the first galaxies with a fractional precision of order
+$\sim1$-10 per cent (1$\sigma$). The ionisation history of the Universe can be
+constrained to within a few percent. Using our extended framework, we quantify
+the bias in EoR parameter recovery incurred by the common simplification of a
+saturated spin temperature in the IGM. Depending on the extent of overlap
+between the EoR and EoH, the recovered astrophysical parameters can be biased
+by $\sim3-10\sigma$.
+",0,1,0,0,0,0
+5238,Non-penalized variable selection in high-dimensional linear model settings via generalized fiducial inference,"  Standard penalized methods of variable selection and parameter estimation
+rely on the magnitude of coefficient estimates to decide which variables to
+include in the final model. However, coefficient estimates are unreliable when
+the design matrix is collinear. To overcome this challenge an entirely new
+perspective on variable selection is presented within a generalized fiducial
+inference framework. This new procedure is able to effectively account for
+linear dependencies among subsets of covariates in a high-dimensional setting
+where $p$ can grow almost exponentially in $n$, as well as in the classical
+setting where $p \le n$. It is shown that the procedure very naturally assigns
+small probabilities to subsets of covariates which include redundancies by way
+of explicit $L_{0}$ minimization. Furthermore, with a typical sparsity
+assumption, it is shown that the proposed method is consistent in the sense
+that the probability of the true sparse subset of covariates converges in
+probability to 1 as $n \to \infty$, or as $n \to \infty$ and $p \to \infty$.
+Very reasonable conditions are needed, and little restriction is placed on the
+class of possible subsets of covariates to achieve this consistency result.
+",0,0,0,1,0,0
+5239,DNN Filter Bank Cepstral Coefficients for Spoofing Detection,"  With the development of speech synthesis techniques, automatic speaker
+verification systems face the serious challenge of spoofing attack. In order to
+improve the reliability of speaker verification systems, we develop a new
+filter bank based cepstral feature, deep neural network filter bank cepstral
+coefficients (DNN-FBCC), to distinguish between natural and spoofed speech. The
+deep neural network filter bank is automatically generated by training a filter
+bank neural network (FBNN) using natural and synthetic speech. By adding
+restrictions on the training rules, the learned weight matrix of FBNN is
+band-limited and sorted by frequency, similar to the normal filter bank. Unlike
+the manually designed filter bank, the learned filter bank has different filter
+shapes in different channels, which can capture the differences between natural
+and synthetic speech more effectively. The experimental results on the ASVspoof
+{2015} database show that the Gaussian mixture model maximum-likelihood
+(GMM-ML) classifier trained by the new feature performs better than the
+state-of-the-art linear frequency cepstral coefficients (LFCC) based
+classifier, especially on detecting unknown attacks.
+",1,0,0,0,0,0
+5240,The existence of positive least energy solutions for a class of Schrodinger-Poisson systems involving critical nonlocal term with general nonlinearity,"  The present study is concerned with the following Schrödinger-Poisson
+system involving critical nonlocal term with general nonlinearity: $$ \left\{
+\begin{array}{ll} -\Delta u+V(x)u- \phi |u|^3u= f(u), & x\in\mathbb{R}^3,
+-\Delta \phi= |u|^5, & x\in\mathbb{R}^3,\\ \end{array} \right. $$ Under certain
+assumptions on non-constant $V(x)$, the existence of a positive least energy
+solution is obtained by using some new analytical skills and Pohožaev type
+manifold. In particular, the Ambrosetti-Rabinowitz type condition or
+monotonicity assumption on the nonlinearity is not necessary.
+",0,0,1,0,0,0
+5241,Up-down colorings of virtual-link diagrams and the necessity of Reidemeister moves of type II,"  We introduce an up-down coloring of a virtual-link diagram. The
+colorabilities give a lower bound of the minimum number of Reidemeister moves
+of type II which are needed between two 2-component virtual-link diagrams. By
+using the notion of a quandle cocycle invariant, we determine the necessity of
+Reidemeister moves of type II for a pair of diagrams of the trivial
+virtual-knot. This implies that for any virtual-knot diagram $D$, there exists
+a diagram $D'$ representing the same virtual-knot such that any sequence of
+generalized Reidemeister moves between them includes at least one Reidemeister
+move of type II.
+",0,0,1,0,0,0
+5242,Comparison of SMT and RBMT; The Requirement of Hybridization for Marathi-Hindi MT,"  We present in this paper our work on comparison between Statistical Machine
+Translation (SMT) and Rule-based machine translation for translation from
+Marathi to Hindi. Rule Based systems although robust take lots of time to
+build. On the other hand statistical machine translation systems are easier to
+create, maintain and improve upon. We describe the development of a basic
+Marathi-Hindi SMT system and evaluate its performance. Through a detailed error
+analysis, we, point out the relative strengths and weaknesses of both systems.
+Effectively, we shall see that even with a small amount of training corpus a
+statistical machine translation system has many advantages for high quality
+domain specific machine translation over that of a rule-based counterpart.
+",1,0,0,0,0,0
+5243,Introduction to the Special Issue on Approaches to Control Biological and Biologically Inspired Networks,"  The emerging field at the intersection of quantitative biology, network
+modeling, and control theory has enjoyed significant progress in recent years.
+This Special Issue brings together a selection of papers on complementary
+approaches to observe, identify, and control biological and biologically
+inspired networks. These approaches advance the state of the art in the field
+by addressing challenges common to many such networks, including high
+dimensionality, strong nonlinearity, uncertainty, and limited opportunities for
+observation and intervention. Because these challenges are not unique to
+biological systems, it is expected that many of the results presented in these
+contributions will also find applications in other domains, including physical,
+social, and technological networks.
+",1,0,0,0,1,0
+5244,Towards Classification of Web ontologies using the Horizontal and Vertical Segmentation,"  The new era of the Web is known as the semantic Web or the Web of data. The
+semantic Web depends on ontologies that are seen as one of its pillars. The
+bigger these ontologies, the greater their exploitation. However, when these
+ontologies become too big other problems may appear, such as the complexity to
+charge big files in memory, the time it needs to download such files and
+especially the time it needs to make reasoning on them. We discuss in this
+paper approaches for segmenting such big Web ontologies as well as its
+usefulness. The segmentation method extracts from an existing ontology a
+segment that represents a layer or a generation in the existing ontology; i.e.
+a horizontally extraction. The extracted segment should be itself an ontology.
+",1,0,0,0,0,0
+5245,An Efficient Version of the Bombieri-Vaaler Lemma,"  In their celebrated paper ""On Siegel's Lemma"", Bombieri and Vaaler found an
+upper bound on the height of integer solutions of systems of linear Diophantine
+equations. Calculating the bound directly, however, requires exponential time.
+In this paper, we present the bound in a different form that can be computed in
+polynomial time. We also give an elementary (and arguably simpler) proof for
+the bound.
+",1,0,1,0,0,0
+5246,Personalized and Private Peer-to-Peer Machine Learning,"  The rise of connected personal devices together with privacy concerns call
+for machine learning algorithms capable of leveraging the data of a large
+number of agents to learn personalized models under strong privacy
+requirements. In this paper, we introduce an efficient algorithm to address the
+above problem in a fully decentralized (peer-to-peer) and asynchronous fashion,
+with provable convergence rate. We show how to make the algorithm
+differentially private to protect against the disclosure of information about
+the personal datasets, and formally analyze the trade-off between utility and
+privacy. Our experiments show that our approach dramatically outperforms
+previous work in the non-private case, and that under privacy constraints, we
+can significantly improve over models learned in isolation.
+",1,0,0,1,0,0
+5247,Interacting Chaplygin gas revisited,"  The implications of considering interaction between Chaplygin gas and a
+barotropic fluid with constant equation of state have been explored. The unique
+feature of this work is that assuming an interaction $Q \propto H\rho_d$,
+analytic expressions for the energy density and pressure have been derived in
+terms of the Hypergeometric $_2\text{F}_1$ function. It is worthwhile to
+mention that an interacting Chaplygin gas model was considered in 2006 by Zhang
+and Zhu, nevertheless, analytic solutions for the continuity equations could
+not be determined assuming an interaction proportional to $H$ times the sum of
+the energy densities of Chaplygin gas and dust. Our model can successfully
+explain the transition from the early decelerating phase to the present phase
+of cosmic acceleration. Arbitrary choice of the free parameters of our model
+through trial and error show at recent observational data strongly favors
+$w_m=0$ and $w_m=-\frac{1}{3}$ over the $w_m=\frac{1}{3}$ case. Interestingly,
+the present model also incorporates the transition of dark energy into the
+phantom domain, however, future deceleration is forbidden.
+",0,1,0,0,0,0
+5248,GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations,"  We present GALARIO, a computational library that exploits the power of modern
+graphical processing units (GPUs) to accelerate the analysis of observations
+from radio interferometers like ALMA or the VLA. GALARIO speeds up the
+computation of synthetic visibilities from a generic 2D model image or a radial
+brightness profile (for axisymmetric sources). On a GPU, GALARIO is 150 faster
+than standard Python and 10 times faster than serial C++ code on a CPU. Highly
+modular, easy to use and to adopt in existing code, GALARIO comes as two
+compiled libraries, one for Nvidia GPUs and one for multicore CPUs, where both
+have the same functions with identical interfaces. GALARIO comes with Python
+bindings but can also be directly used in C or C++. The versatility and the
+speed of GALARIO open new analysis pathways that otherwise would be
+prohibitively time consuming, e.g. fitting high resolution observations of
+large number of objects, or entire spectral cubes of molecular gas emission. It
+is a general tool that can be applied to any field that uses radio
+interferometer observations. The source code is available online at
+this https URL under the open source GNU Lesser General
+Public License v3.
+",0,1,0,0,0,0
+5249,Focused time-lapse inversion of radio and audio magnetotelluric data,"  Geoelectrical techniques are widely used to monitor groundwater processes,
+while surprisingly few studies have considered audio (AMT) and radio (RMT)
+magnetotellurics for such purposes. In this numerical investigation, we analyze
+to what extent inversion results based on AMT and RMT monitoring data can be
+improved by (1) time-lapse difference inversion; (2) incorporation of
+statistical information about the expected model update (i.e., the model
+regularization is based on a geostatistical model); (3) using alternative model
+norms to quantify temporal changes (i.e., approximations of l1 and Cauchy norms
+using iteratively reweighted least-squares), (4) constraining model updates to
+predefined ranges (i.e., using Lagrange Multipliers to only allow either
+increases or decreases of electrical resistivity with respect to background
+conditions). To do so, we consider a simple illustrative model and a more
+realistic test case related to seawater intrusion. The results are encouraging
+and show significant improvements when using time-lapse difference inversion
+with non l2 model norms. Artifacts that may arise when imposing compactness of
+regions with temporal changes can be suppressed through inequality constraints
+to yield models without oscillations outside the true region of temporal
+changes. Based on these results, we recommend approximate l1-norm solutions as
+they can resolve both sharp and smooth interfaces within the same model.
+",0,1,0,0,0,0
+5250,Deep Exploration via Randomized Value Functions,"  We study the use of randomized value functions to guide deep exploration in
+reinforcement learning. This offers an elegant means for synthesizing
+statistically and computationally efficient exploration with common practical
+approaches to value function learning. We present several reinforcement
+learning algorithms that leverage randomized value functions and demonstrate
+their efficacy through computational studies. We also prove a regret bound that
+establishes statistical efficiency with a tabular representation.
+",1,0,0,1,0,0
+5251,Spectral Norm Regularization for Improving the Generalizability of Deep Learning,"  We investigate the generalizability of deep learning based on the sensitivity
+to input perturbation. We hypothesize that the high sensitivity to the
+perturbation of data degrades the performance on it. To reduce the sensitivity
+to perturbation, we propose a simple and effective regularization method,
+referred to as spectral norm regularization, which penalizes the high spectral
+norm of weight matrices in neural networks. We provide supportive evidence for
+the abovementioned hypothesis by experimentally confirming that the models
+trained using spectral norm regularization exhibit better generalizability than
+other baseline methods.
+",1,0,0,1,0,0
+5252,Estimating the chromospheric magnetic field from a revised NLTE modeling: the case of HR7428,"  In this work we use the semi-empirical atmospheric modeling method to obtain
+the chro-mospheric temperature, pressure, density and magnetic field
+distribution versus height in the K2 primary component of the RS CVn binary
+system HR 7428. While temperature, pressure, density are the standard output of
+the semi-empirical modeling technique, the chromospheric magnetic field
+estimation versus height comes from considering the possibility of not
+im-posing hydrostatic equilibrium in the semi-empirical computation. The
+stability of the best non-hydrostatic equilibrium model, implies the presence
+of an additive (toward the center of the star) pressure, that decreases in
+strength from the base of the chromosphere toward the outer layers.
+Interpreting the additive pressure as magnetic pressure we estimated a magnetic
+field intensity of about 500 gauss at the base of the chromosphere.
+",0,1,0,0,0,0
+5253,An accurate approximation formula for gamma function,"  In this paper, we present a very accurate approximation for gamma function:
+\begin{equation*} \Gamma \left( x+1\right) \thicksim \sqrt{2\pi x}\left(
+\dfrac{x}{e}\right) ^{x}\left( x\sinh \frac{1}{x}\right) ^{x/2}\exp \left(
+\frac{7}{324}\frac{1}{ x^{3}\left( 35x^{2}+33\right) }\right) =W_{2}\left(
+x\right) \end{equation*} as $x\rightarrow \infty $, and prove that the function
+$x\mapsto \ln \Gamma \left( x+1\right) -\ln W_{2}\left( x\right) $ is strictly
+decreasing and convex from $\left( 1,\infty \right) $ onto $\left( 0,\beta
+\right) $, where \begin{equation*} \beta =\frac{22\,025}{22\,032}-\ln
+\sqrt{2\pi \sinh 1}\approx 0.00002407. \end{equation*}
+",0,0,1,0,0,0
+5254,Reconciling Bayesian and Total Variation Methods for Binary Inversion,"  A central theme in classical algorithms for the reconstruction of
+discontinuous functions from observational data is perimeter regularization. On
+the other hand, sparse or noisy data often demands a probabilistic approach to
+the reconstruction of images, to enable uncertainty quantification; the
+Bayesian approach to inversion is a natural framework in which to carry this
+out. The link between Bayesian inversion methods and perimeter regularization,
+however, is not fully understood. In this paper two links are studied: (i) the
+MAP objective function of a suitably chosen phase-field Bayesian approach is
+shown to be closely related to a least squares plus perimeter regularization
+objective; (ii) sample paths of a suitably chosen Bayesian level set
+formulation are shown to possess finite perimeter and to have the ability to
+learn about the true perimeter. Furthermore, the level set approach is shown to
+lead to faster algorithms for uncertainty quantification than the phase field
+approach.
+",0,0,1,1,0,0
+5255,Perception Driven Texture Generation,"  This paper investigates a novel task of generating texture images from
+perceptual descriptions. Previous work on texture generation focused on either
+synthesis from examples or generation from procedural models. Generating
+textures from perceptual attributes have not been well studied yet. Meanwhile,
+perceptual attributes, such as directionality, regularity and roughness are
+important factors for human observers to describe a texture. In this paper, we
+propose a joint deep network model that combines adversarial training and
+perceptual feature regression for texture generation, while only random noise
+and user-defined perceptual attributes are required as input. In this model, a
+preliminary trained convolutional neural network is essentially integrated with
+the adversarial framework, which can drive the generated textures to possess
+given perceptual attributes. An important aspect of the proposed model is that,
+if we change one of the input perceptual features, the corresponding appearance
+of the generated textures will also be changed. We design several experiments
+to validate the effectiveness of the proposed method. The results show that the
+proposed method can produce high quality texture images with desired perceptual
+properties.
+",1,0,0,0,0,0
+5256,Computational modeling approaches in gonadotropin signaling,"  Follicle-stimulating hormone (FSH) and luteinizing hormone (LH) play
+essential roles in animal reproduction. They exert their function through
+binding to their cognate receptors, which belong to the large family of G
+protein-coupled receptors (GPCRs). This recognition at the plasma membrane
+triggers a plethora of cellular events, whose processing and integration
+ultimately lead to an adapted biological response. Understanding the nature and
+the kinetics of these events is essential for innovative approaches in drug
+discovery. The study and manipulation of such complex systems requires the use
+of computational modeling approaches combined with robust in vitro functional
+assays for calibration and validation. Modeling brings a detailed understanding
+of the system and can also be used to understand why existing drugs do not work
+as well as expected, and how to design more efficient ones.
+",0,0,0,0,1,0
+5257,SEP-Nets: Small and Effective Pattern Networks,"  While going deeper has been witnessed to improve the performance of
+convolutional neural networks (CNN), going smaller for CNN has received
+increasing attention recently due to its attractiveness for mobile/embedded
+applications. It remains an active and important topic how to design a small
+network while retaining the performance of large and deep CNNs (e.g., Inception
+Nets, ResNets). Albeit there are already intensive studies on compressing the
+size of CNNs, the considerable drop of performance is still a key concern in
+many designs. This paper addresses this concern with several new contributions.
+First, we propose a simple yet powerful method for compressing the size of deep
+CNNs based on parameter binarization. The striking difference from most
+previous work on parameter binarization/quantization lies at different
+treatments of $1\times 1$ convolutions and $k\times k$ convolutions ($k>1$),
+where we only binarize $k\times k$ convolutions into binary patterns. The
+resulting networks are referred to as pattern networks. By doing this, we show
+that previous deep CNNs such as GoogLeNet and Inception-type Nets can be
+compressed dramatically with marginal drop in performance. Second, in light of
+the different functionalities of $1\times 1$ (data projection/transformation)
+and $k\times k$ convolutions (pattern extraction), we propose a new block
+structure codenamed the pattern residual block that adds transformed feature
+maps generated by $1\times 1$ convolutions to the pattern feature maps
+generated by $k\times k$ convolutions, based on which we design a small network
+with $\sim 1$ million parameters. Combining with our parameter binarization, we
+achieve better performance on ImageNet than using similar sized networks
+including recently released Google MobileNets.
+",1,0,0,0,0,0
+5258,Fundamental Limitations of Cavity-assisted Atom Interferometry,"  Atom interferometers employing optical cavities to enhance the beam splitter
+pulses promise significant advances in science and technology, notably for
+future gravitational wave detectors. Long cavities, on the scale of hundreds of
+meters, have been proposed in experiments aiming to observe gravitational waves
+with frequencies below 1 Hz, where laser interferometers, such as LIGO, have
+poor sensitivity. Alternatively, short cavities have also been proposed for
+enhancing the sensitivity of more portable atom interferometers. We explore the
+fundamental limitations of two-mirror cavities for atomic beam splitting, and
+establish upper bounds on the temperature of the atomic ensemble as a function
+of cavity length and three design parameters: the cavity g-factor, the
+bandwidth, and the optical suppression factor of the first and second order
+spatial modes. A lower bound to the cavity bandwidth is found which avoids
+elongation of the interaction time and maximizes power enhancement. An upper
+limit to cavity length is found for symmetric two-mirror cavities, restricting
+the practicality of long baseline detectors. For shorter cavities, an upper
+limit on the beam size was derived from the geometrical stability of the
+cavity. These findings aim to aid the design of current and future
+cavity-assisted atom interferometers.
+",0,1,0,0,0,0
+5259,Markov chain aggregation and its application to rule-based modelling,"  Rule-based modelling allows to represent molecular interactions in a compact
+and natural way. The underlying molecular dynamics, by the laws of stochastic
+chemical kinetics, behaves as a continuous-time Markov chain. However, this
+Markov chain enumerates all possible reaction mixtures, rendering the analysis
+of the chain computationally demanding and often prohibitive in practice. We
+here describe how it is possible to efficiently find a smaller, aggregate
+chain, which preserves certain properties of the original one. Formal methods
+and lumpability notions are used to define algorithms for automated and
+efficient construction of such smaller chains (without ever constructing the
+original ones). We here illustrate the method on an example and we discuss the
+applicability of the method in the context of modelling large signalling
+pathways.
+",1,0,0,0,1,0
+5260,Shape and Positional Geometry of Multi-Object Configurations,"  In previous work, we introduced a method for modeling a configuration of
+objects in 2D and 3D images using a mathematical ""medial/skeletal linking
+structure."" In this paper, we show how these structures allow us to capture
+positional properties of a multi-object configuration in addition to the shape
+properties of the individual objects. In particular, we introduce numerical
+invariants for positional properties which measure the closeness of neighboring
+objects, including identifying the parts of the objects which are close, and
+the ""relative significance"" of objects compared with the other objects in the
+configuration. Using these numerical measures, we introduce a hierarchical
+ordering and relations between the individual objects, and quantitative
+criteria for identifying subconfigurations. In addition, the invariants provide
+a ""proximity matrix"" which yields a unique set of weightings measuring overall
+proximity of objects in the configuration. Furthermore, we show that these
+invariants, which are volumetrically defined and involve external regions, may
+be computed via integral formulas in terms of ""skeletal linking integrals""
+defined on the internal skeletal structures of the objects.
+",1,0,0,0,0,0
+5261,Interface mediated mechanisms of plastic strain recovery in AgCu alloy,"  Through the combination of transmission electron microscopy analysis of the
+deformed microstructure and molecular dynamics computer simulations of the
+deformation processes, the mechanisms of plastic strain recovery in bulk AgCu
+eutectic with either incoherent twin or cube-on-cube interfaces between the Ag
+and Cu layers and a bilayer thickness of 500 nm have been revealed. The
+character of the incoherent twin interfaces changed uniquely after dynamic
+compressive loading for samples that exhibited plastic strain recovery and was
+found to drive the recovery, which is due to dislocation retraction and
+rearrangement of the interfaces. The magnitude of the recovery decreased with
+increasing strain as dislocation tangles and dislocation cell structures
+formed. No change in the orientation relationship was found at cube-on-cube
+interfaces and these exhibited a lesser amount of plastic strain recovery in
+the simulations and none experimentally in samples with larger layer
+thicknesses with predominantly cube-on-cube interfaces. Molecular dynamics
+computer simulations verified the importance of the change in the incoherent
+twin interface structure as the driving force for dislocation annihilation at
+the interfaces and the plastic strain recovery.
+",0,1,0,0,0,0
+5262,Refounding legitimacy towards Aethogenesis,"  The fusion of humans and technology takes us into an unknown world described
+by some authors as populated by quasi living species that would relegate us -
+ordinary humans - to the rank of alienated agents emptied of our identity and
+consciousness. I argue instead that our world is woven of simple though
+invisible perspectives which - if we become aware of them - may renew our
+ability for making judgments and enhance our autonomy. I became aware of these
+invisible perspectives by observing and practicing a real time collective net
+art experiment called the Poietic Generator. As the perspectives unveiled by
+this experiment are invisible I have called them anoptical perspectives i.e.
+non-optical by analogy with the optical perspective of the Renaissance. Later I
+have come to realize that these perspectives obtain their cognitive structure
+from the political origins of our language. Accordingly it is possible to
+define certain cognitive criteria for assessing the legitimacy of the anoptical
+perspectives just like some artists and architects of the Renaissance defined
+the geometrical criteria that established the legitimacy of the optical one.
+",1,0,0,0,0,0
+5263,FELIX-2.0: New version of the finite element solver for the time dependent generator coordinate method with the Gaussian overlap approximation,"  The time-dependent generator coordinate method (TDGCM) is a powerful method
+to study the large amplitude collective motion of quantum many-body systems
+such as atomic nuclei. Under the Gaussian Overlap Approximation (GOA), the
+TDGCM leads to a local, time-dependent Schrödinger equation in a
+multi-dimensional collective space. In this paper, we present the version 2.0
+of the code FELIX that solves the collective Schrödinger equation in a finite
+element basis. This new version features: (i) the ability to solve a
+generalized TDGCM+GOA equation with a metric term in the collective
+Hamiltonian, (ii) support for new kinds of finite elements and different types
+of quadrature to compute the discretized Hamiltonian and overlap matrices,
+(iii) the possibility to leverage the spectral element scheme, (iv) an explicit
+Krylov approximation of the time propagator for time integration instead of the
+implicit Crank-Nicolson method implemented in the first version, (v) an
+entirely redesigned workflow. We benchmark this release on an analytic problem
+as well as on realistic two-dimensional calculations of the low-energy fission
+of Pu240 and Fm256. Low to moderate numerical precision calculations are most
+efficiently performed with simplex elements with a degree 2 polynomial basis.
+Higher precision calculations should instead use the spectral element method
+with a degree 4 polynomial basis. We emphasize that in a realistic calculation
+of fission mass distributions of Pu240, FELIX-2.0 is about 20 times faster than
+its previous release (within a numerical precision of a few percents).
+",0,1,0,0,0,0
+5264,Scattering in the energy space for Boussinesq equations,"  In this note we show that all small solutions in the energy space of the
+generalized 1D Boussinesq equation must decay to zero as time tends to
+infinity, strongly on slightly proper subsets of the space-time light cone. Our
+result does not require any assumption on the power of the nonlinearity,
+working even for the supercritical range of scattering. No parity assumption on
+the initial data is needed.
+",0,0,1,0,0,0
+5265,A Data-Driven Supply-Side Approach for Measuring Cross-Border Internet Purchases,"  The digital economy is a highly relevant item on the European Union's policy
+agenda. Cross-border internet purchases are part of the digital economy, but
+their total value can currently not be accurately measured or estimated.
+Traditional approaches based on consumer surveys or business surveys are shown
+to be inadequate for this purpose, due to language bias and sampling issues,
+respectively. We address both problems by proposing a novel approach based on
+supply-side data, namely tax returns. The proposed data-driven record-linkage
+techniques and machine learning algorithms utilize two additional open data
+sources: European business registers and internet data. Our main finding is
+that the value of total cross-border internet purchases within the European
+Union by Dutch consumers was over EUR 1.3 billion in 2016. This is more than 6
+times as high as current estimates. Our finding motivates the implementation of
+the proposed methodology in other EU member states. Ultimately, it could lead
+to more accurate estimates of cross-border internet purchases within the entire
+European Union.
+",0,0,0,1,0,0
+5266,Low Rank Magnetic Resonance Fingerprinting,"  Purpose: Magnetic Resonance Fingerprinting (MRF) is a relatively new approach
+that provides quantitative MRI measures using randomized acquisition.
+Extraction of physical quantitative tissue parameters is performed off-line,
+without the need of patient presence, based on acquisition with varying
+parameters and a dictionary generated according to the Bloch equation
+simulations. MRF uses hundreds of radio frequency (RF) excitation pulses for
+acquisition, and therefore a high undersampling ratio in the sampling domain
+(k-space) is required for reasonable scanning time. This undersampling causes
+spatial artifacts that hamper the ability to accurately estimate the tissue's
+quantitative values. In this work, we introduce a new approach for quantitative
+MRI using MRF, called magnetic resonance Fingerprinting with LOw Rank (FLOR).
+Methods: We exploit the low rank property of the concatenated temporal
+imaging contrasts, on top of the fact that the MRF signal is sparsely
+represented in the generated dictionary domain. We present an iterative scheme
+that consists of a gradient step followed by a low rank projection using the
+singular value decomposition.
+Results: Experimental results consist of retrospective sampling, that allows
+comparison to a well defined reference, and prospective sampling that shows the
+performance of FLOR for a real-data sampling scenario. Both experiments
+demonstrate improved parameter accuracy compared to other compressed-sensing
+and low-rank based methods for MRF at 5% and 9% sampling ratios, for the
+retrospective and prospective experiments, respectively.
+Conclusions: We have shown through retrospective and prospective experiments
+that by exploiting the low rank nature of the MRF signal, FLOR recovers the MRF
+temporal undersampled images and provides more accurate parameter maps compared
+to previous iterative methods.
+",1,1,0,0,0,0
+5267,SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient,"  In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH),
+as well as its practical variant SARAH+, as a novel approach to the finite-sum
+minimization problems. Different from the vanilla SGD and other modern
+stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple
+recursive framework for updating stochastic gradient estimates; when comparing
+to SAG/SAGA, SARAH does not require a storage of past gradients. The linear
+convergence rate of SARAH is proven under strong convexity assumption. We also
+prove a linear convergence rate (in the strongly convex case) for an inner loop
+of SARAH, the property that SVRG does not possess. Numerical experiments
+demonstrate the efficiency of our algorithm.
+",1,0,1,1,0,0
+5268,A criterion related to the Riemann Hypothesis,"  A crucial role in the Nyman-Beurling-Báez-Duarte approach to the Riemann
+Hypothesis is played by the distance \[
+d_N^2:=\inf_{A_N}\frac{1}{2\pi}\int_{-\infty}^\infty\left|1-\zeta
+A_N\left(\frac{1}{2}+it\right)\right|^2\frac{dt}{\frac{1}{4}+t^2}\:, \] where
+the infimum is over all Dirichlet polynomials
+$$A_N(s)=\sum_{n=1}^{N}\frac{a_n}{n^s}$$ of length $N$. In this paper we
+investigate $d_N^2$ under the assumption that the Riemann zeta function has
+four non-trivial zeros off the critical line. Thus we obtain a criterion for
+the non validity of the Riemann Hypothesis.
+",0,0,1,0,0,0
+5269,Non-Stationary Spectral Kernels,"  We propose non-stationary spectral kernels for Gaussian process regression.
+We propose to model the spectral density of a non-stationary kernel function as
+a mixture of input-dependent Gaussian process frequency density surfaces. We
+solve the generalised Fourier transform with such a model, and present a family
+of non-stationary and non-monotonic kernels that can learn input-dependent and
+potentially long-range, non-monotonic covariances between inputs. We derive
+efficient inference using model whitening and marginalized posterior, and show
+with case studies that these kernels are necessary when modelling even rather
+simple time series, image or geospatial data with non-stationary
+characteristics.
+",1,0,0,1,0,0
+5270,Spectral and Energy Efficiency of Uplink D2D Underlaid Massive MIMO Cellular Networks,"  One of key 5G scenarios is that device-to-device (D2D) and massive
+multiple-input multiple-output (MIMO) will be co-existed. However, interference
+in the uplink D2D underlaid massive MIMO cellular networks needs to be
+coordinated, due to the vast cellular and D2D transmissions. To this end, this
+paper introduces a spatially dynamic power control solution for mitigating the
+cellular-to-D2D and D2D-to-cellular interference. In particular, the proposed
+D2D power control policy is rather flexible including the special cases of no
+D2D links or using maximum transmit power. Under the considered power control,
+an analytical approach is developed to evaluate the spectral efficiency (SE)
+and energy efficiency (EE) in such networks. Thus, the exact expressions of SE
+for a cellular user or D2D transmitter are derived, which quantify the impacts
+of key system parameters such as massive MIMO antennas and D2D density.
+Moreover, the D2D scale properties are obtained, which provide the sufficient
+conditions for achieving the anticipated SE. Numerical results corroborate our
+analysis and show that the proposed power control solution can efficiently
+mitigate interference between the cellular and D2D tier. The results
+demonstrate that there exists the optimal D2D density for maximizing the area
+SE of D2D tier. In addition, the achievable EE of a cellular user can be
+comparable to that of a D2D user.
+",1,0,1,0,0,0
+5271,Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations,"  Neural networks are among the most accurate supervised learning methods in
+use today, but their opacity makes them difficult to trust in critical
+applications, especially when conditions in training differ from those in test.
+Recent work on explanations for black-box models has produced tools (e.g. LIME)
+to show the implicit rules behind predictions, which can help us identify when
+models are right for the wrong reasons. However, these methods do not scale to
+explaining entire datasets and cannot correct the problems they reveal. We
+introduce a method for efficiently explaining and regularizing differentiable
+models by examining and selectively penalizing their input gradients, which
+provide a normal to the decision boundary. We apply these penalties both based
+on expert annotation and in an unsupervised fashion that encourages diverse
+models with qualitatively different decision boundaries for the same
+classification problem. On multiple datasets, we show our approach generates
+faithful explanations and models that generalize much better when conditions
+differ between training and test.
+",1,0,0,1,0,0
+5272,The ANTARES Collaboration: Contributions to ICRC 2017 Part II: The multi-messenger program,"  Papers on the ANTARES multi-messenger program, prepared for the 35th
+International Cosmic Ray Conference (ICRC 2017, Busan, South Korea) by the
+ANTARES Collaboration
+",0,1,0,0,0,0
+5273,Hamiltonian structure of peakons as weak solutions for the modified Camassa-Holm equation,"  The modified Camassa-Holm (mCH) equation is a bi-Hamiltonian system
+possessing $N$-peakon weak solutions, for all $N\geq 1$, in the setting of an
+integral formulation which is used in analysis for studying local
+well-posedness, global existence, and wave breaking for non-peakon solutions.
+Unlike the original Camassa-Holm equation, the two Hamiltonians of the mCH
+equation do not reduce to conserved integrals (constants of motion) for
+$2$-peakon weak solutions. This perplexing situation is addressed here by
+finding an explicit conserved integral for $N$-peakon weak solutions for all
+$N\geq 2$. When $N$ is even, the conserved integral is shown to provide a
+Hamiltonian structure with the use of a natural Poisson bracket that arises
+from reduction of one of the Hamiltonian structures of the mCH equation. But
+when $N$ is odd, the Hamiltonian equations of motion arising from the conserved
+integral using this Poisson bracket are found to differ from the dynamical
+equations for the mCH $N$-peakon weak solutions. Moreover, the lack of
+conservation of the two Hamiltonians of the mCH equation when they are reduced
+to $2$-peakon weak solutions is shown to extend to $N$-peakon weak solutions
+for all $N\geq 2$. The connection between this loss of integrability structure
+and related work by Chang and Szmigielski on the Lax pair for the mCH equation
+is discussed.
+",0,1,0,0,0,0
+5274,Orbital-dependent correlations in PuCoGa$_5$,"  We investigate the normal state of the superconducting compound PuCoGa$_5$
+using the combination of density functional theory (DFT) and dynamical mean
+field theory (DMFT), with the continuous time quantum Monte Carlo (CTQMC) and
+the vertex-corrected one-crossing approximation (OCA) as the impurity solvers.
+Our DFT+DMFT(CTQMC) calculations suggest a strong tendency of Pu-5$f$ orbitals
+to differentiate at low temperatures. The renormalized 5$f_{5/2}$ states
+exhibit a Fermi-liquid behavior whereas one electron in the 5$f_{7/2}$ states
+is at the edge of a Mott localization. We find that the orbital differentiation
+is manifested as the removing of 5$f_{7/2}$ spectral weight from the Fermi
+level relative to DFT. We corroborate these conclusions with DFT+DMFT(OCA)
+calculations which demonstrate that 5$f_{5/2}$ electrons have a much larger
+Kondo scale than the 5$f_{7/2}$.
+",0,1,0,0,0,0
+5275,A variational derivation of the nonequilibrium thermodynamics of a moist atmosphere with rain process and its pseudoincompressible approximation,"  Irreversible processes play a major role in the description and prediction of
+atmospheric dynamics. In this paper, we present a variational derivation of the
+evolution equations for a moist atmosphere with rain process and subject to the
+irreversible processes of viscosity, heat conduction, diffusion, and phase
+transition. This derivation is based on a general variational formalism for
+nonequilibrium thermodynamics which extends Hamilton's principle to
+incorporates irreversible processes. It is valid for any state equation and
+thus also covers the case of the atmosphere of other planets. In this approach,
+the second law of thermodynamics is understood as a nonlinear constraint
+formulated with the help of new variables, called thermodynamic displacements,
+whose time derivative coincides with the thermodynamic force of the
+irreversible process. The formulation is written both in the Lagrangian and
+Eulerian descriptions and can be directly adapted to oceanic dynamics. We
+illustrate the efficiency of our variational formulation as a modeling tool in
+atmospheric thermodynamics, by deriving a pseudoincompressible model for moist
+atmospheric thermodynamics with general equations of state and subject to the
+irreversible processes of viscosity, heat conduction, diffusion, and phase
+transition.
+",0,1,1,0,0,0
+5276,On certain weighted 7-colored partitions,"  Inspired by Andrews' 2-colored generalized Frobenius partitions, we consider
+certain weighted 7-colored partition functions and establish some interesting
+Ramanujan-type identities and congruences. Moreover, we provide combinatorial
+interpretations of some congruences modulo 5 and 7. Finally, we study the
+properties of weighted 7-colored partitions weighted by the parity of certain
+partition statistics.
+",0,0,1,0,0,0
+5277,Employee turnover prediction and retention policies design: a case study,"  This paper illustrates the similarities between the problems of customer
+churn and employee turnover. An example of employee turnover prediction model
+leveraging classical machine learning techniques is developed. Model outputs
+are then discussed to design \& test employee retention policies. This type of
+retention discussion is, to our knowledge, innovative and constitutes the main
+value of this paper.
+",1,0,0,1,0,0
+5278,Do You Want Your Autonomous Car To Drive Like You?,"  With progress in enabling autonomous cars to drive safely on the road, it is
+time to start asking how they should be driving. A common answer is that they
+should be adopting their users' driving style. This makes the assumption that
+users want their autonomous cars to drive like they drive - aggressive drivers
+want aggressive cars, defensive drivers want defensive cars. In this paper, we
+put that assumption to the test. We find that users tend to prefer a
+significantly more defensive driving style than their own. Interestingly, they
+prefer the style they think is their own, even though their actual driving
+style tends to be more aggressive. We also find that preferences do depend on
+the specific driving scenario, opening the door for new ways of learning
+driving style preference.
+",1,0,0,0,0,0
+5279,Now Playing: Continuous low-power music recognition,"  Existing music recognition applications require a connection to a server that
+performs the actual recognition. In this paper we present a low-power music
+recognizer that runs entirely on a mobile device and automatically recognizes
+music without user interaction. To reduce battery consumption, a small music
+detector runs continuously on the mobile device's DSP chip and wakes up the
+main application processor only when it is confident that music is present.
+Once woken, the recognizer on the application processor is provided with a few
+seconds of audio which is fingerprinted and compared to the stored fingerprints
+in the on-device fingerprint database of tens of thousands of songs. Our
+presented system, Now Playing, has a daily battery usage of less than 1% on
+average, respects user privacy by running entirely on-device and can passively
+recognize a wide range of music.
+",1,0,0,0,0,0
+5280,COLA: Decentralized Linear Learning,"  Decentralized machine learning is a promising emerging paradigm in view of
+global challenges of data ownership and privacy. We consider learning of linear
+classification and regression models, in the setting where the training data is
+decentralized over many user devices, and the learning algorithm must run
+on-device, on an arbitrary communication network, without a central
+coordinator. We propose COLA, a new decentralized training algorithm with
+strong theoretical guarantees and superior practical performance. Our framework
+overcomes many limitations of existing methods, and achieves communication
+efficiency, scalability, elasticity as well as resilience to changes in data
+and participating devices.
+",0,0,0,1,0,0
+5281,Improving the Expected Improvement Algorithm,"  The expected improvement (EI) algorithm is a popular strategy for information
+collection in optimization under uncertainty. The algorithm is widely known to
+be too greedy, but nevertheless enjoys wide use due to its simplicity and
+ability to handle uncertainty and noise in a coherent decision theoretic
+framework. To provide rigorous insight into EI, we study its properties in a
+simple setting of Bayesian optimization where the domain consists of a finite
+grid of points. This is the so-called best-arm identification problem, where
+the goal is to allocate measurement effort wisely to confidently identify the
+best arm using a small number of measurements. In this framework, one can show
+formally that EI is far from optimal. To overcome this shortcoming, we
+introduce a simple modification of the expected improvement algorithm.
+Surprisingly, this simple change results in an algorithm that is asymptotically
+optimal for Gaussian best-arm identification problems, and provably outperforms
+standard EI by an order of magnitude.
+",1,0,0,1,0,0
+5282,Performance Limits of Solutions to Network Utility Maximization Problems,"  We study performance limits of solutions to utility maximization problems
+(e.g., max-min problems) in wireless networks as a function of the power budget
+$\bar{p}$ available to transmitters. Special focus is devoted to the utility
+and the transmit energy efficiency (i.e., utility over transmit power) of the
+solution. Briefly, we show tight bounds for the general class of network
+utility optimization problems that can be solved by computing conditional
+eigenvalues of standard interference mappings. The proposed bounds, which are
+based on the concept of asymptotic functions, are simple to compute, provide us
+with good estimates of the performance of networks for any value of $\bar{p}$
+in many real-world applications, and enable us to determine points in which
+networks move from a noise limited regime to an interference limited regime.
+Furthermore, they also show that the utility and the transmit energy efficiency
+scales as $\Theta(1)$ and $\Theta(1/\bar{p})$, respectively, as
+$\bar{p}\to\infty$.
+",1,0,0,0,0,0
+5283,Toeplitz Order,"  A new approach to problems of the Uncertainty Principle in Harmonic Analysis,
+based on the use of Toeplitz operators, has brought progress to some of the
+classical problems in the area. The goal of this paper is to develop and
+systematize the function theoretic component of the Toeplitz approach by
+introducing a partial order on the set of inner functions induced by the action
+of Toeplitz operators. We study connections of the new order with some of the
+classical problems and known results. We discuss remaining problems and
+possible directions for further research.
+",0,0,1,0,0,0
+5284,Definably compact groups definable in real closed fields. I,"  We study definably compact definably connected groups definable in a
+sufficiently saturated real closed field $R$. We introduce the notion of
+group-generic point for $\bigvee$-definable groups and show the existence of
+group-generic points for definably compact groups definable in a sufficiently
+saturated o-minimal expansion of a real closed field. We use this notion along
+with some properties of generic sets to prove that for every definably compact
+definably connected group $G$ definable in $R$ there are a connected
+$R$-algebraic group $H$, a definable injective map $\phi$ from a generic
+definable neighborhood of the identity of $G$ into the group $H\left(R\right)$
+of $R$-points of $H$ such that $\phi$ acts as a group homomorphism inside its
+domain. This result is used in [2] to prove that the o-minimal universal
+covering group of an abelian connected definably compact group definable in a
+sufficiently saturated real closed field $R$ is, up to locally definable
+isomorphisms, an open connected locally definable subgroup of the o-minimal
+universal covering group of the $R$-points of some $R$-algebraic group.
+",0,0,1,0,0,0
+5285,The unreasonable effectiveness of small neural ensembles in high-dimensional brain,"  Despite the widely-spread consensus on the brain complexity, sprouts of the
+single neuron revolution emerged in neuroscience in the 1970s. They brought
+many unexpected discoveries, including grandmother or concept cells and sparse
+coding of information in the brain.
+In machine learning for a long time, the famous curse of dimensionality
+seemed to be an unsolvable problem. Nevertheless, the idea of the blessing of
+dimensionality becomes gradually more and more popular. Ensembles of
+non-interacting or weakly interacting simple units prove to be an effective
+tool for solving essentially multidimensional problems. This approach is
+especially useful for one-shot (non-iterative) correction of errors in large
+legacy artificial intelligence systems.
+These simplicity revolutions in the era of complexity have deep fundamental
+reasons grounded in geometry of multidimensional data spaces. To explore and
+understand these reasons we revisit the background ideas of statistical
+physics. In the course of the 20th century they were developed into the
+concentration of measure theory. New stochastic separation theorems reveal the
+fine structure of the data clouds.
+We review and analyse biological, physical, and mathematical problems at the
+core of the fundamental question: how can high-dimensional brain organise
+reliable and fast learning in high-dimensional world of data by simple tools?
+Two critical applications are reviewed to exemplify the approach: one-shot
+correction of errors in intellectual systems and emergence of static and
+associative memories in ensembles of single neurons.
+",0,0,0,0,1,0
+5286,Explicit cocycle formulas on finite abelian groups with applications to braided linear Gr-categories and Dijkgraaf-Witten invariants,"  We provide explicit and unified formulas for the cocycles of all degrees on
+the normalized bar resolutions of finite abelian groups. This is achieved by
+constructing a chain map from the normalized bar resolution to a Koszul-like
+resolution for any given finite abelian group. With a help of the obtained
+cocycle formulas, we determine all the braided linear Gr-categories and compute
+the Dijkgraaf-Witten Invariants of the $n$-torus for all $n$.
+",0,0,1,0,0,0
+5287,Threshold Selection for Multivariate Heavy-Tailed Data,"  Regular variation is often used as the starting point for modeling
+multivariate heavy-tailed data. A random vector is regularly varying if and
+only if its radial part $R$ is regularly varying and is asymptotically
+independent of the angular part $\Theta$ as $R$ goes to infinity. The
+conditional limiting distribution of $\Theta$ given $R$ is large characterizes
+the tail dependence of the random vector and hence its estimation is the
+primary goal of applications. A typical strategy is to look at the angular
+components of the data for which the radial parts exceed some threshold. While
+a large class of methods has been proposed to model the angular distribution
+from these exceedances, the choice of threshold has been scarcely discussed in
+the literature. In this paper, we describe a procedure for choosing the
+threshold by formally testing the independence of $R$ and $\Theta$ using a
+measure of dependence called distance covariance. We generalize the limit
+theorem for distance covariance to our unique setting and propose an algorithm
+which selects the threshold for $R$. This algorithm incorporates a subsampling
+scheme that is also applicable to weakly dependent data. Moreover, it avoids
+the heavy computation in the calculation of the distance covariance, a typical
+limitation for this measure. The performance of our method is illustrated on
+both simulated and real data.
+",0,0,1,1,0,0
+5288,An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms,"  Deep learning (DL) has recently achieved tremendous success in a variety of
+cutting-edge applications, e.g., image recognition, speech and natural language
+processing, and autonomous driving. Besides the available big data and hardware
+evolution, DL frameworks and platforms play a key role to catalyze the
+research, development, and deployment of DL intelligent solutions. However, the
+difference in computation paradigm, architecture design and implementation of
+existing DL frameworks and platforms brings challenges for DL software
+development, deployment, maintenance, and migration. Up to the present, it
+still lacks a comprehensive study on how current diverse DL frameworks and
+platforms influence the DL software development process.
+In this paper, we initiate the first step towards the investigation on how
+existing state-of-the-art DL frameworks (i.e., TensorFlow, Theano, and Torch)
+and platforms (i.e., server/desktop, web, and mobile) support the DL software
+development activities. We perform an in-depth and comparative evaluation on
+metrics such as learning accuracy, DL model size, robustness, and performance,
+on state-of-the-art DL frameworks across platforms using two popular datasets
+MNIST and CIFAR-10. Our study reveals that existing DL frameworks still suffer
+from compatibility issues, which becomes even more severe when it comes to
+different platforms. We pinpoint the current challenges and opportunities
+towards developing high quality and compatible DL systems. To ignite further
+investigation along this direction to address urgent industrial demands of
+intelligent solutions, we make all of our assembled feasible toolchain and
+dataset publicly available.
+",1,0,0,0,0,0
+5289,On the complexity of topological conjugacy of compact metrizable $G$-ambits,"  In this note, we analyze the classification problem for compact metrizable
+$G$-ambits for a countable discrete group $G$ from the point of view of
+descriptive set theory. More precisely, we prove that the topological conjugacy
+relation on the standard Borel space of compact metrizable $G$-ambits is Borel
+for every countable discrete group $G$.
+",0,0,1,0,0,0
+5290,Classical affine W-superalgebras via generalized Drinfeld-Sokolov reductions and related integrable systems,"  The purpose of this article is to investigate relations between
+W-superalgebras and integrable super-Hamiltonian systems. To this end, we
+introduce the generalized Drinfel'd-Sokolov (D-S) reduction associated to a Lie
+superalgebra $g$ and its even nilpotent element $f$, and we find a new
+definition of the classical affine W-superalgebra $W(g,f,k)$ via the D-S
+reduction. This new construction allows us to find free generators of
+$W(g,f,k)$, as a differential superalgebra, and two independent Lie brackets on
+$W(g,f,k)/\partial W(g,f,k).$ Moreover, we describe super-Hamiltonian systems
+with the Poisson vertex algebras theory. A W-superalgebra with certain
+properties can be understood as an underlying differential superalgebra of a
+series of integrable super-Hamiltonian systems.
+",0,0,1,0,0,0
+5291,Temporally Identity-Aware SSD with Attentional LSTM,"  Temporal object detection has attracted significant attention, but most
+popular detection methods can not leverage the rich temporal information in
+videos. Very recently, many different algorithms have been developed for video
+detection task, but real-time online approaches are frequently deficient. In
+this paper, based on attention mechanism and convolutional long short-term
+memory (ConvLSTM), we propose a temporal signal-shot detector (TSSD) for
+real-world detection. Distinct from previous methods, we take aim at temporally
+integrating pyramidal feature hierarchy using ConvLSTM, and design a novel
+structure including a low-level temporal unit as well as a high-level one
+(HL-TU) for multi-scale feature maps. Moreover, we develop a creative temporal
+analysis unit, namely, attentional ConvLSTM (AC-LSTM), in which a temporal
+attention module is specially tailored for background suppression and scale
+suppression while a ConvLSTM integrates attention-aware features through time.
+An association loss is designed for temporal coherence. Besides, online tubelet
+analysis (OTA) is exploited for identification. Finally, our method is
+evaluated on ImageNet VID dataset and 2DMOT15 dataset. Extensive comparisons on
+the detection and tracking capability validate the superiority of the proposed
+approach. Consequently, the developed TSSD-OTA is fairly faster and achieves an
+overall competitive performance in terms of detection and tracking. The source
+code will be made available.
+",1,0,0,0,0,0
+5292,Carleman Estimate for Surface in Euclidean Space at Infinity,"  This paper develops a Carleman type estimate for immersed surface in
+Euclidean space at infinity. With this estimate, we obtain an unique
+continuation property for harmonic functions on immersed surfaces vanishing at
+infinity, which leads to rigidity results in geometry.
+",0,0,1,0,0,0
+5293,Formalizing Timing Diagram Requirements in Discrete Duration Calulus,"  Several temporal logics have been proposed to formalise timing diagram
+requirements over hardware and embedded controllers. These include LTL,
+discrete time MTL and the recent industry standard PSL. However, succintness
+and visual structure of a timing diagram are not adequately captured by their
+formulae. Interval temporal logic QDDC is a highly succint and visual notation
+for specifying patterns of behaviours.
+In this paper, we propose a practically useful notation called SeCeCntnl
+which enhances negation free fragment of QDDC with features of nominals and
+limited liveness. We show that timing diagrams can be naturally
+(compositionally) and succintly formalized in SeCeCntnl as compared with PSL
+and MTL. We give a linear time translation from timing diagrams to SeCeCntnl.
+As our second main result, we propose a linear time translation of SeCeCntnl
+into QDDC. This allows QDDC tools such as DCVALID and DCSynth to be used for
+checking consistency of timing diagram requirements as well as for automatic
+synthesis of property monitors and controllers. We give examples of a minepump
+controller and a bus arbiter to illustrate our tools. Giving a theoretical
+analysis, we show that for the proposed SeCeCntnl, the satisfiability and model
+checking have elementary complexity as compared to the non-elementary
+complexity for the full logic QDDC.
+",1,0,0,0,0,0
+5294,Cubical Covers of Sets in $\mathbb{R}^n$,"  Wild sets in $\mathbb{R}^n$ can be tamed through the use of various
+representations though sometimes this taming removes features considered
+important. Finding the wildest sets for which it is still true that the
+representations faithfully inform us about the original set is the focus of
+this rather playful, expository paper that we hope will stimulate interest in
+cubical coverings as well as the other two ideas we explore briefly: Jones'
+$\beta$ numbers and varifolds from geometric measure theory.
+",0,0,1,0,0,0
+5295,Mitochondrial network fragmentation modulates mutant mtDNA accumulation independently of absolute fission-fusion rates,"  Mitochondrial DNA (mtDNA) mutations cause severe congenital diseases but may
+also be associated with healthy aging. MtDNA is stochastically replicated and
+degraded, and exists within organelles which undergo dynamic fusion and
+fission. The role of the resulting mitochondrial networks in determining the
+time evolution of the cellular proportion of mutated mtDNA molecules
+(heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy
+variance), remains incompletely understood. Heteroplasmy variance is
+particularly important since it modulates the number of pathological cells in a
+tissue. Here, we provide the first wide-reaching mathematical treatment which
+bridges mitochondrial network and genetic states. We show that, for a range of
+models, the rate of increase in heteroplasmy variance, and the rate of
+\textit{de novo} mutation, is proportionately modulated by the fraction of
+unfused mitochondria, independently of the absolute fission-fusion rate. In the
+context of selective fusion, we show that intermediate fusion/fission ratios
+are optimal for the clearance of mtDNA mutants. Our findings imply that
+modulating network state, mitophagy rate and copy number to slow down
+heteroplasmy dynamics when mean heteroplasmy is low, could have therapeutic
+advantages for mitochondrial disease and healthy aging.
+",0,0,0,0,1,0
+5296,Depth Separation for Neural Networks,"  Let $f:\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}\to\mathbb{S}$ be a function of
+the form $f(\mathbf{x},\mathbf{x}') = g(\langle\mathbf{x},\mathbf{x}'\rangle)$
+for $g:[-1,1]\to \mathbb{R}$. We give a simple proof that shows that poly-size
+depth two neural networks with (exponentially) bounded weights cannot
+approximate $f$ whenever $g$ cannot be approximated by a low degree polynomial.
+Moreover, for many $g$'s, such as $g(x)=\sin(\pi d^3x)$, the number of neurons
+must be $2^{\Omega\left(d\log(d)\right)}$. Furthermore, the result holds
+w.r.t.\ the uniform distribution on $\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$.
+As many functions of the above form can be well approximated by poly-size depth
+three networks with poly-bounded weights, this establishes a separation between
+depth two and depth three networks w.r.t.\ the uniform distribution on
+$\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$.
+",1,0,0,1,0,0
+5297,An Accurate Interconnect Test Structure for Parasitic Validation in On-Chip Machine Learning Accelerators,"  For nanotechnology nodes, the feature size is shrunk rapidly, the wire
+becomes narrow and thin, it leads to high RC parasitic, especially for
+resistance. The overall system performance are dominated by interconnect rather
+than device. As such, it is imperative to accurately measure and model
+interconnect parasitic in order to predict interconnect performance on silicon.
+Despite many test structures developed in the past to characterize device
+models and layout effects, only few of them are available for interconnects.
+Nevertheless, they are either not suitable for real chip implementation or too
+complicated to be embedded. A compact yet comprehensive test structure to
+capture all interconnect parasitic in a real chip is needed. To address this
+problem, this paper describes a set of test structures that can be used to
+study the timing performance (i.e. propagation delay and crosstalk) of various
+interconnect configurations. Moreover, an empirical model is developed to
+estimate the actual RC parasitic. Compared with the state-of-the-art
+interconnect test structures, the new structure is compact in size and can be
+easily embedded on die as a parasitic variation monitor. We have validated the
+proposed structure on a test chip in TSMC 28nm HPM process. Recently, the test
+structure is further modified to identify the serious interconnect process
+issues for critical path design using TSMC 7nm FF process.
+",1,0,0,0,0,0
+5298,A New Compton-thick AGN in our Cosmic Backyard: Unveiling the Buried Nucleus in NGC 1448 with NuSTAR,"  NGC 1448 is one of the nearest luminous galaxies ($L_{8-1000\mu m} >$ 10$^{9}
+L_{\odot}$) to ours ($z$ $=$ 0.00390), and yet the active galactic nucleus
+(AGN) it hosts was only recently discovered, in 2009. In this paper, we present
+an analysis of the nuclear source across three wavebands: mid-infrared (MIR)
+continuum, optical, and X-rays. We observed the source with the Nuclear
+Spectroscopic Telescope Array (NuSTAR), and combined this data with archival
+Chandra data to perform broadband X-ray spectral fitting ($\approx$0.5-40 keV)
+of the AGN for the first time. Our X-ray spectral analysis reveals that the AGN
+is buried under a Compton-thick (CT) column of obscuring gas along our
+line-of-sight, with a column density of $N_{\rm H}$(los) $\gtrsim$ 2.5 $\times$
+10$^{24}$ cm$^{-2}$. The best-fitting torus models measured an intrinsic 2-10
+keV luminosity of $L_{2-10\rm{,int}}$ $=$ (3.5-7.6) $\times$ 10$^{40}$ erg
+s$^{-1}$, making NGC 1448 one of the lowest luminosity CTAGNs known. In
+addition to the NuSTAR observation, we also performed optical spectroscopy for
+the nucleus in this edge-on galaxy using the European Southern Observatory New
+Technology Telescope. We re-classify the optical nuclear spectrum as a Seyfert
+on the basis of the Baldwin-Philips-Terlevich diagnostic diagrams, thus
+identifying the AGN at optical wavelengths for the first time. We also present
+high spatial resolution MIR observations of NGC 1448 with Gemini/T-ReCS, in
+which a compact nucleus is clearly detected. The absorption-corrected 2-10 keV
+luminosity measured from our X-ray spectral analysis agrees with that predicted
+from the optical [OIII]$\lambda$5007\AA\ emission line and the MIR 12$\mu$m
+continuum, further supporting the CT nature of the AGN.
+",0,1,0,0,0,0
+5299,Blowup constructions for Lie groupoids and a Boutet de Monvel type calculus,"  We present natural and general ways of building Lie groupoids, by using the
+classical procedures of blowups and of deformations to the normal cone. Our
+constructions are seen to recover many known ones involved in index theory. The
+deformation and blowup groupoids obtained give rise to several extensions of
+$C^*$-algebras and to full index problems. We compute the corresponding
+K-theory maps. Finally, the blowup of a manifold sitting in a transverse way in
+the space of objects of a Lie groupoid leads to a calculus, quite similar to
+the Boutet de Monvel calculus for manifolds with boundary.
+",0,0,1,0,0,0
+5300,Possible spin gapless semiconductor type behaviour in CoFeMnSi epitaxial thin films,"  Spin-gapless semiconductors with their unique band structures have recently
+attracted much attention due to their interesting transport properties that can
+be utilized in spintronics applications. We have successfully deposited the
+thin films of quaternary spin-gapless semiconductor CoFeMnSi Heusler alloy on
+MgO (001) substrates using a pulsed laser deposition system. These films show
+epitaxial growth along (001) direction and display uniform and smooth
+crystalline surface. The magnetic properties reveal that the film is
+ferromagnetically soft along the in-plane direction and its Curie temperature
+is well above 400 K. The electrical conductivity of the film is low and
+exhibits a nearly temperature independent semiconducting behaviour. The
+estimated temperature coefficient of resistivity for the film is -7x10^-10
+Ohm.m/K, which is comparable to the values reported for spin-gapless
+semiconductors.
+",0,1,0,0,0,0
+5301,Simulated JWST/NIRISS Transit Spectroscopy of Anticipated TESS Planets Compared to Select Discoveries from Space-Based and Ground-Based Surveys,"  The Transiting Exoplanet Survey Satellite (TESS) will embark in 2018 on a
+2-year wide-field survey mission, discovering over a thousand terrestrial,
+super-Earth and sub-Neptune-sized exoplanets potentially suitable for follow-up
+observations using the James Webb Space Telescope (JWST). This work aims to
+understand the suitability of anticipated TESS planet discoveries for
+atmospheric characterization by JWST's Near InfraRed Imager and Slitless
+Spectrograph (NIRISS) by employing a simulation tool to estimate the
+signal-to-noise (S/N) achievable in transmission spectroscopy. We applied this
+tool to Monte Carlo predictions of the TESS expected planet yield and then
+compared the S/N for anticipated TESS discoveries to our estimates of S/N for
+18 known exoplanets. We analyzed the sensitivity of our results to planetary
+composition, cloud cover, and presence of an observational noise floor. We
+found that several hundred anticipated TESS discoveries with radii from 1.5 to
+2.5 times the Earth's radius will produce S/N higher than currently known
+exoplanets in this radius regime, such as K2-3b or K2-3c. In the terrestrial
+planet regime, we found that only a few anticipated TESS discoveries will
+result in higher S/N than currently known exoplanets, such as the TRAPPIST-1
+planets, GJ1132b, and LHS1140b. However, we emphasize that this outcome is
+based upon Kepler-derived occurrence rates, and that co-planar compact
+multi-planet systems (e.g., TRAPPIST-1) may be under-represented in the
+predicted TESS planet yield. Finally, we apply our calculations to estimate the
+required magnitude of a JWST follow-up program devoted to mapping the
+transition region between hydrogen-dominated and high molecular weight
+atmospheres. We find that a modest observing program of between 60 to 100 hours
+of charged JWST time can define the nature of that transition (e.g., step
+function versus a power law).
+",0,1,0,0,0,0
+5302,The general linear 2-groupoid,"  We deal with the symmetries of a (2-term) graded vector space or bundle. Our
+first theorem shows that they define a (strict) Lie 2-groupoid in a natural
+way. Our second theorem explores the construction of nerves for Lie
+2-categories, showing that it yields simplicial manifolds if the 2-cells are
+invertible. Finally, our third and main theorem shows that smooth
+pseudofunctors into our general linear 2-groupoid classify 2-term
+representations up to homotopy of Lie groupoids.
+",0,0,1,0,0,0
+5303,DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction,"  Traffic flow prediction is an important research issue to avoid traffic
+congestion in transportation systems. Traffic congestion avoiding can be
+achieved by knowing traffic flow and then conducting transportation planning.
+Achieving traffic flow prediction is challenging as the prediction is affected
+by many complex factors such as inter-region traffic, vehicles' relations, and
+sudden events. However, as the mobile data of vehicles has been widely
+collected by sensor-embedded devices in transportation systems, it is possible
+to predict the traffic flow by analysing mobile data. This study proposes a
+deep learning based prediction algorithm, DeepTFP, to collectively predict the
+traffic flow on each and every traffic road of a city. This algorithm uses
+three deep residual neural networks to model temporal closeness, period, and
+trend properties of traffic flow. Each residual neural network consists of a
+branch of residual convolutional units. DeepTFP aggregates the outputs of the
+three residual neural networks to optimize the parameters of a time series
+prediction model. Contrast experiments on mobile time series data from the
+transportation system of England demonstrate that the proposed DeepTFP
+outperforms the Long Short-Term Memory (LSTM) architecture based method in
+prediction accuracy.
+",1,0,0,0,0,0
+5304,Non-equilibrium Optical Conductivity: General Theory and Application to Transient Phases,"  A non-equilibrium theory of optical conductivity of dirty-limit
+superconductors and commensurate charge density wave is presented. We discuss
+the current response to different experimentally relevant light-field probe
+pulses and show that a single frequency definition of the optical conductivity
+$\sigma(\omega)\equiv j(\omega)/E(\omega)$ is difficult to interpret out of the
+adiabatic limit. We identify characteristic time domain signatures
+distinguishing between superconducting, normal metal and charge density wave
+states. We also suggest a route to directly address the instantaneous
+superfluid stiffness of a superconductor by shaping the probe light field.
+",0,1,0,0,0,0
+5305,The Momentum Distribution of Liquid $^4$He,"  We report high-resolution neutron Compton scattering measurements of liquid
+$^4$He under saturated vapor pressure. There is excellent agreement between the
+observed scattering and ab initio predictions of its lineshape. Quantum Monte
+Carlo calculations predict that the Bose condensate fraction is zero in the
+normal fluid, builds up rapidly just below the superfluid transition
+temperature, and reaches a value of approximately $7.5\%$ below 1 K. We also
+used model fit functions to obtain from the scattering data empirical estimates
+for the average atomic kinetic energy and Bose condensate fraction. These
+quantities are also in excellent agreement with ab initio calculations. The
+convergence between the scattering data and Quantum Monte Carlo calculations is
+strong evidence for a Bose broken symmetry in superfluid $^4$He.
+",0,1,0,0,0,0
+5306,Semantic Code Repair using Neuro-Symbolic Transformation Networks,"  We study the problem of semantic code repair, which can be broadly defined as
+automatically fixing non-syntactic bugs in source code. The majority of past
+work in semantic code repair assumed access to unit tests against which
+candidate repairs could be validated. In contrast, the goal here is to develop
+a strong statistical model to accurately predict both bug locations and exact
+fixes without access to information about the intended correct behavior of the
+program. Achieving such a goal requires a robust contextual repair model, which
+we train on a large corpus of real-world source code that has been augmented
+with synthetically injected bugs. Our framework adopts a two-stage approach
+where first a large set of repair candidates are generated by rule-based
+processors, and then these candidates are scored by a statistical model using a
+novel neural network architecture which we refer to as Share, Specialize, and
+Compete. Specifically, the architecture (1) generates a shared encoding of the
+source code using an RNN over the abstract syntax tree, (2) scores each
+candidate repair using specialized network modules, and (3) then normalizes
+these scores together so they can compete against one another in comparable
+probability space. We evaluate our model on a real-world test set gathered from
+GitHub containing four common categories of bugs. Our model is able to predict
+the exact correct repair 41\% of the time with a single guess, compared to 13\%
+accuracy for an attentional sequence-to-sequence model.
+",1,0,0,0,0,0
+5307,Hints on the gradual re-sizing of the torus in AGN by decomposing IRS/Spitzer spectra,"  Several authors have claimed that the less luminous active galactic nuclei
+(AGN) are not capable of sustaining the dusty torus structure. Thus, a gradual
+re-sizing of the torus is expected when the AGN luminosity decreases. Our aim
+is to confront mid-infrared observations of local AGN of different luminosities
+with this scenario. We decomposed about ~100 IRS/Spitzer spectra of LLAGN and
+powerful Seyferts in order to decontaminate the torus component from other
+contributors. We have used the affinity propagation (AP) method to cluster the
+data into five groups within the sample according to torus contribution to the
+5-15 um range (Ctorus) and bolometric luminosity. The AP groups show a
+progressively higher torus contribution and an increase of the bolometric
+luminosity, from Group 1 (Ctorus~ 0% and logLbol ~ 41) and up to Group 5
+(Ctorus ~80% and log(Lbol) ~44). We have fitted the average spectra of each of
+the AP groups to clumpy models. The torus is no longer present in Group 1,
+supporting the disappearance at low-luminosities. We were able to fit the
+average spectra for the torus component in Groups 3 (Ctorus~ 40% and log(Lbol)~
+42.6), 4 (Ctorus~ 60% and log(Lbol)~ 43.7), and 5 to Clumpy torus models. We
+did not find a good fitting to Clumpy torus models for Group 2 (Ctorus~ 18% and
+log(Lbol)~ 42). This might suggest a different configuration and/or composition
+of the clouds for Group 2, which is consistent with a different gas content
+seen in Groups 1, 2, and 3, according to the detections of H2 molecular lines.
+Groups 3, 4, and 5 show a trend to decrease of the width of the torus (which
+yields to a likely decrease of the geometrical covering factor), although we
+cannot confirm it with the present data. Finally, Groups 3, 4, and 5 show an
+increase on the outer radius of the torus for higher luminosities, consistent
+with a re-sizing of the torus according to the AGN luminosity.
+",0,1,0,0,0,0
+5308,Self-similar minimizers of a branched transport functional,"  We solve here completely an irrigation problem from a Dirac mass to the
+Lebesgue measure. The functional we consider is a two dimensional analog of a
+functional previously derived in the study of branched patterns in type-I
+superconductors. The minimizer we obtain is a self-similar tree.
+",0,0,1,0,0,0
+5309,S-OHEM: Stratified Online Hard Example Mining for Object Detection,"  One of the major challenges in object detection is to propose detectors with
+highly accurate localization of objects. The online sampling of high-loss
+region proposals (hard examples) uses the multitask loss with equal weight
+settings across all loss types (e.g, classification and localization, rigid and
+non-rigid categories) and ignores the influence of different loss distributions
+throughout the training process, which we find essential to the training
+efficacy. In this paper, we present the Stratified Online Hard Example Mining
+(S-OHEM) algorithm for training higher efficiency and accuracy detectors.
+S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling
+technique, to choose the training examples according to this influence during
+hard example mining, and thus enhance the performance of object detectors. We
+show through systematic experiments that S-OHEM yields an average precision
+(AP) improvement of 0.5% on rigid categories of PASCAL VOC 2007 for both the
+IoU threshold of 0.6 and 0.7. For KITTI 2012, both results of the same metric
+are 1.6%. Regarding the mean average precision (mAP), a relative increase of
+0.3% and 0.5% (1% and 0.5%) is observed for VOC07 (KITTI12) using the same set
+of IoU threshold. Also, S-OHEM is easy to integrate with existing region-based
+detectors and is capable of acting with post-recognition level regressors.
+",1,0,0,0,0,0
+5310,Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time,"  Dynamic topic modeling facilitates the identification of topical trends over
+time in temporal collections of unstructured documents. We introduce a novel
+unsupervised neural dynamic topic model named as Recurrent Neural
+Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each
+time influence the topic discovery in the subsequent time steps. We account for
+the temporal ordering of documents by explicitly modeling a joint distribution
+of latent topical dependencies over time, using distributional estimators with
+temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP
+research, we demonstrate that compared to state-of-the art topic models, RNNRSM
+shows better generalization, topic interpretation, evolution and trends. We
+also introduce a metric (named as SPAN) to quantify the capability of dynamic
+topic model to capture word evolution in topics over time.
+",1,0,0,0,0,0
+5311,Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions,"  Embedding complex objects as vectors in low dimensional spaces is a
+longstanding problem in machine learning. We propose in this work an extension
+of that approach, which consists in embedding objects as elliptical probability
+distributions, namely distributions whose densities have elliptical level sets.
+We endow these measures with the 2-Wasserstein metric, with two important
+benefits: (i) For such measures, the squared 2-Wasserstein metric has a closed
+form, equal to a weighted sum of the squared Euclidean distance between means
+and the squared Bures metric between covariance matrices. The latter is a
+Riemannian metric between positive semi-definite matrices, which turns out to
+be Euclidean on a suitable factor representation of such matrices, which is
+valid on the entire geodesic between these matrices. (ii) The 2-Wasserstein
+distance boils down to the usual Euclidean metric when comparing Diracs, and
+therefore provides a natural framework to extend point embeddings. We show that
+for these reasons Wasserstein elliptical embeddings are more intuitive and
+yield tools that are better behaved numerically than the alternative choice of
+Gaussian embeddings with the Kullback-Leibler divergence. In particular, and
+unlike previous work based on the KL geometry, we learn elliptical
+distributions that are not necessarily diagonal. We demonstrate the advantages
+of elliptical embeddings by using them for visualization, to compute embeddings
+of words, and to reflect entailment or hypernymy.
+",0,0,0,1,0,0
+5312,Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines,"  This paper describes our participation in Task 5 track 2 of SemEval 2017 to
+predict the sentiment of financial news headlines for a specific company on a
+continuous scale between -1 and 1. We tackled the problem using a number of
+approaches, utilising a Support Vector Regression (SVR) and a Bidirectional
+Long Short-Term Memory (BLSTM). We found an improvement of 4-6% using the LSTM
+model over the SVR and came fourth in the track. We report a number of
+different evaluations using a finance specific word embedding model and reflect
+on the effects of using different evaluation metrics.
+",1,0,0,0,0,0
+5313,Bootstrapping for multivariate linear regression models,"  The multivariate linear regression model is an important tool for
+investigating relationships between several response variables and several
+predictor variables. The primary interest is in inference about the unknown
+regression coefficient matrix. We propose multivariate bootstrap techniques as
+a means for making inferences about the unknown regression coefficient matrix.
+These bootstrapping techniques are extensions of those developed in Freedman
+(1981), which are only appropriate for univariate responses. Extensions to the
+multivariate linear regression model are made without proof. We formalize this
+extension and prove its validity. A real data example and two simulated data
+examples which offer some finite sample verification of our theoretical results
+are provided.
+",0,0,1,1,0,0
+5314,Long coherence times for edge spins,"  We show that in certain one-dimensional spin chains with open boundary
+conditions, the edge spins retain memory of their initial state for very long
+times. The long coherence times do not require disorder, only an ordered phase.
+In the integrable Ising and XYZ chains, the presence of a strong zero mode
+means the coherence time is infinite, even at infinite temperature. When Ising
+is perturbed by interactions breaking the integrability, the coherence time
+remains exponentially long in the perturbing couplings. We show that this is a
+consequence of an edge ""almost"" strong zero mode that almost commutes with the
+Hamiltonian. We compute this operator explicitly, allowing us to estimate
+accurately the plateau value of edge spin autocorrelator.
+",0,1,1,0,0,0
+5315,Exploring light mediators with low-threshold direct detection experiments,"  We explore the potential of future cryogenic direct detection experiments to
+determine the properties of the mediator that communicates the interactions
+between dark matter and nuclei. Due to their low thresholds and large
+exposures, experiments like CRESST-III, SuperCDMS SNOLAB and EDELWEISS-III will
+have excellent capability to reconstruct mediator masses in the MeV range for a
+large class of models. Combining the information from several experiments
+further improves the parameter reconstruction, even when taking into account
+additional nuisance parameters related to background uncertainties and the dark
+matter velocity distribution. These observations may offer the intriguing
+possibility of studying dark matter self-interactions with direct detection
+experiments.
+",0,1,0,0,0,0
+5316,DR/DZ equivalence conjecture and tautological relations,"  In this paper we present a family of conjectural relations in the
+tautological ring of the moduli spaces of stable curves which implies the
+strong double ramification/Dubrovin-Zhang equivalence conjecture. Our
+tautological relations have the form of an equality between two different
+families of tautological classes, only one of which involves the double
+ramification cycle. We prove that both families behave the same way upon
+pullback and pushforward with respect to forgetting a marked point. We also
+prove that our conjectural relations are true in genus $0$ and $1$ and also
+when first pushed forward from $\overline{\mathcal{M}}_{g,n+m}$ to
+$\overline{\mathcal{M}}_{g,n}$ and then restricted to $\mathcal{M}_{g,n}$, for
+any $g,n,m\geq 0$. Finally we show that, for semisimple CohFTs, the DR/DZ
+equivalence only depends on a subset of our relations, finite in each genus,
+which we prove for $g\leq 2$. As an application we find a new formula for the
+class $\lambda_g$ as a linear combination of dual trees intersected with kappa
+and psi classes, and we check it for $g \leq 3$.
+",0,0,1,0,0,0
+5317,Surface Networks,"  We study data-driven representations for three-dimensional triangle meshes,
+which are one of the prevalent objects used to represent 3D geometry. Recent
+works have developed models that exploit the intrinsic geometry of manifolds
+and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants,
+which learn from the local metric tensor via the Laplacian operator. Despite
+offering excellent sample complexity and built-in invariances, intrinsic
+geometry alone is invariant to isometric deformations, making it unsuitable for
+many applications. To overcome this limitation, we propose several upgrades to
+GNNs to leverage extrinsic differential geometry properties of
+three-dimensional surfaces, increasing its modeling power.
+In particular, we propose to exploit the Dirac operator, whose spectrum
+detects principal curvature directions --- this is in stark contrast with the
+classical Laplace operator, which directly measures mean curvature. We coin the
+resulting models \emph{Surface Networks (SN)}. We prove that these models
+define shape representations that are stable to deformation and to
+discretization, and we demonstrate the efficiency and versatility of SNs on two
+challenging tasks: temporal prediction of mesh deformations under non-linear
+dynamics and generative models using a variational autoencoder framework with
+encoders/decoders given by SNs.
+",1,0,0,1,0,0
+5318,Forward Flux Sampling Calculation of Homogeneous Nucleation Rates from Aqueous NaCl Solutions,"  We used molecular dynamics simulations and the path sampling technique known
+as forward flux sampling to study homogeneous nucleation of NaCl crystals from
+supersaturated aqueous solutions at 298 K and 1 bar. Nucleation rates were
+obtained for a range of salt concentrations for the Joung-Cheatham NaCl force
+field combined with the SPC/E water model. The calculated nucleation rates are
+significantly lower than available experimental measurements. The estimates for
+the nucleation rates in this work do not rely on classical nucleation theory,
+but the pathways observed in the simulations suggest that the nucleation
+process is better described by classical nucleation theory than an alternative
+interpretation based on Ostwald's step rule, in contrast to some prior
+simulations of related models. In addition to the size of NaCl nucleus, we find
+that the crystallinity of a nascent cluster plays an important role in the
+nucleation process. Nuclei with high crystallinity were found to have higher
+growth probability and longer lifetimes, possibly because they are less exposed
+to hydration water.
+",0,1,0,0,0,0
+5319,Driving an Ornstein--Uhlenbeck Process to Desired First-Passage Time Statistics,"  First-passage time (FPT) of an Ornstein-Uhlenbeck (OU) process is of immense
+interest in a variety of contexts. This paper considers an OU process with two
+boundaries, one of which is absorbing while the other one could be either
+reflecting or absorbing, and studies the control strategies that can lead to
+desired FPT moments. Our analysis shows that the FPT distribution of an OU
+process is scale invariant with respect to the drift parameter, i.e., the drift
+parameter just controls the mean FPT and doesn't affect the shape of the
+distribution. This allows to independently control the mean and coefficient of
+variation (CV) of the FPT. We show that that increasing the threshold may
+increase or decrease CV of the FPT, depending upon whether or not one of the
+threshold is reflecting. We also explore the effect of control parameters on
+the FPT distribution, and find parameters that minimize the distance between
+the FPT distribution and a desired distribution.
+",0,0,1,0,0,0
+5320,The self-consistent Dyson equation and self-energy functionals: failure or new opportunities?,"  Perturbation theory using self-consistent Green's functions is one of the
+most widely used approaches to study many-body effects in condensed matter. On
+the basis of general considerations and by performing analytical calculations
+for the specific example of the Hubbard atom, we discuss some key features of
+this approach. We show that when the domain of the functionals that are used to
+realize the map between the non-interacting and the interacting Green's
+functions is properly defined, there exists a class of self-energy functionals
+for which the self-consistent Dyson equation has only one solution, which is
+the physical one. We also show that manipulation of the perturbative expansion
+of the interacting Green's function may lead to a wrong self-energy as
+functional of the interacting Green's function, at least for some regions of
+the parameter space. These findings confirm and explain numerical results of
+Kozik et al. for the widely used skeleton series of Luttinger and Ward [Phys.
+Rev. Lett. 114, 156402]. Our study shows that it is important to distinguish
+between the maps between sets of functions and the functionals that realize
+those maps. We demonstrate that the self-consistent Green's functions approach
+itself is not problematic, whereas the functionals that are widely used may
+have a limited range of validity.
+",0,1,0,0,0,0
+5321,Statistical Implications of the Revenue Transfer Methodology in the Affordable Care Act,"  The Affordable Care Act (ACA) includes a permanent revenue transfer
+methodology which provides financial incentives to health insurance plans that
+have higher than average actuarial risk. In this paper, we derive some
+statistical implications of the revenue transfer methodology in the ACA. We
+treat as random variables the revenue transfers between individual insurance
+plans in a given marketplace, where each plan's revenue transfer amount is
+measured as a percentage of the plan's total premium. We analyze the means and
+variances of those random variables, and deduce from the zero sum nature of the
+revenue transfers that there is no limit to the magnitude of revenue transfer
+payments relative to plans' total premiums. Using data provided by the American
+Academy of Actuaries and by the Centers for Medicare and Medicaid Services, we
+obtain an explanation for empirical phenomena that revenue transfers were more
+variable and can be substantially greater for insurance plans with smaller
+market shares. We show that it is often the case that an insurer which has
+decreasing market share will also have increased volatility in its revenue
+transfers.
+",0,0,0,1,0,0
+5322,Chance-Constrained Combinatorial Optimization with a Probability Oracle and Its Application to Probabilistic Partial Set Covering,"  We investigate a class of chance-constrained combinatorial optimization
+problems. Given a pre-specified risk level $\epsilon \in [0,1]$, the
+chance-constrained program aims to find the minimum cost selection of a vector
+of binary decisions $x$ such that a desirable event $\mathcal{B}(x)$ occurs
+with probability at least $ 1-\epsilon$. In this paper, we assume that we have
+an oracle that computes $\mathbb P( \mathcal{B}(x))$ exactly. Using this
+oracle, we propose a general exact method for solving the chance-constrained
+problem. In addition, we show that if the chance-constrained program is solved
+approximately by a sampling-based approach, then the oracle can be used as a
+tool for checking and fixing the feasibility of the optimal solution given by
+this approach. We demonstrate the effectiveness of our proposed methods on a
+variant of the probabilistic set covering problem (PSC), which admits an
+efficient probability oracle. We give a compact mixed-integer program that
+solves PSC optimally (without sampling) for a special case. For large-scale
+instances for which the exact methods exhibit slow convergence, we propose a
+sampling-based approach that exploits the special structure of PSC. In
+particular, we introduce a new class of facet-defining inequalities for a
+submodular substructure of PSC, and show that a sampling-based algorithm
+coupled with the probability oracle solves the large-scale test instances
+effectively.
+",0,0,1,0,0,0
+5323,Optimal Input Design for Affine Model Discrimination with Applications in Intention-Aware Vehicles,"  This paper considers the optimal design of input signals for the purpose of
+discriminating among a finite number of affine models with uncontrolled inputs
+and noise. Each affine model represents a different system operating mode,
+corresponding to unobserved intents of other drivers or robots, or to fault
+types or attack strategies, etc. The input design problem aims to find optimal
+separating/discriminating (controlled) inputs such that the output trajectories
+of all the affine models are guaranteed to be distinguishable from each other,
+despite uncertainty in the initial condition and uncontrolled inputs as well as
+the presence of process and measurement noise. We propose a novel formulation
+to solve this problem, with an emphasis on guarantees for model discrimination
+and optimality, in contrast to a previously proposed conservative formulation
+using robust optimization. This new formulation can be recast as a bilevel
+optimization problem and further reformulated as a mixed-integer linear program
+(MILP). Moreover, our fairly general problem setting allows the incorporation
+of objectives and/or responsibilities among rational agents. For instance, each
+driver has to obey traffic rules, while simultaneously optimizing for safety,
+comfort and energy efficiency. Finally, we demonstrate the effectiveness of our
+approach for identifying the intention of other vehicles in several driving
+scenarios.
+",1,0,0,0,0,0
+5324,Stable Unitary Integrators for the Numerical Implementation of Continuous Unitary Transformations,"  The technique of continuous unitary transformations has recently been used to
+provide physical insight into a diverse array of quantum mechanical systems.
+However, the question of how to best numerically implement the flow equations
+has received little attention. The most immediately apparent approach, using
+standard Runge-Kutta numerical integration algorithms, suffers from both severe
+inefficiency due to stiffness and the loss of unitarity. After reviewing the
+formalism of continuous unitary transformations and Wegner's original choice
+for the infinitesimal generator of the flow, we present a number of approaches
+to resolving these issues including a choice of generator which induces what we
+call the ""uniform tangent decay flow"" and three numerical integrators
+specifically designed to perform continuous unitary transformations efficiently
+while preserving the unitarity of flow. We conclude by applying one of the flow
+algorithms to a simple calculation that visually demonstrates the many-body
+localization transition.
+",1,1,0,0,0,0
+5325,Sparse and Smooth Prior for Bayesian Linear Regression with Application to ETEX Data,"  Sparsity of the solution of a linear regression model is a common
+requirement, and many prior distributions have been designed for this purpose.
+A combination of the sparsity requirement with smoothness of the solution is
+also common in application, however, with considerably fewer existing prior
+models. In this paper, we compare two prior structures, the Bayesian fused
+lasso (BFL) and least-squares with adaptive prior covariance matrix (LS-APC).
+Since only variational solution was published for the latter, we derive a Gibbs
+sampling algorithm for its inference and Bayesian model selection. The method
+is designed for high dimensional problems, therefore, we discuss numerical
+issues associated with evaluation of the posterior. In simulation, we show that
+the LS-APC prior achieves results comparable to that of the Bayesian Fused
+Lasso for piecewise constant parameter and outperforms the BFL for parameters
+of more general shapes. Another advantage of the LS-APC priors is revealed in
+real application to estimation of the release profile of the European Tracer
+Experiment (ETEX). Specifically, the LS-APC model provides more conservative
+uncertainty bounds when the regressor matrix is not informative.
+",0,0,0,1,0,0
+5326,Deep Learning: Generalization Requires Deep Compositional Feature Space Design,"  Generalization error defines the discriminability and the representation
+power of a deep model. In this work, we claim that feature space design using
+deep compositional function plays a significant role in generalization along
+with explicit and implicit regularizations. Our claims are being established
+with several image classification experiments. We show that the information
+loss due to convolution and max pooling can be marginalized with the
+compositional design, improving generalization performance. Also, we will show
+that learning rate decay acts as an implicit regularizer in deep model
+training.
+",1,0,0,1,0,0
+5327,First detection of sign-reversed linear polarization from the forbidden [O I] 630.03 nm line,"  We report on the detection of linear polarization of the forbidden [O i]
+630.03 nm spectral line. The observations were carried out in the broader
+context of the determination of the solar oxygen abundance, an important
+problem in astrophysics that still remains unresolved. We obtained
+spectro-polarimetric data of the forbidden [O i] line at 630.03 nm as well as
+other neighboring permitted lines with the Solar Optical Telescope of the
+Hinode satellite. A novel averaging technique was used, yielding very high
+signal-to-noise ratios in excess of $10^5$. We confirm that the linear
+polarization is sign-reversed compared to permitted lines as a result of the
+line being dominated by a magnetic dipole transition. Our observations open a
+new window for solar oxygen abundance studies, offering an alternative method
+to disentangle the Ni i blend from the [O i] line at 630.03 nm that has the
+advantage of simple LTE formation physics.
+",0,1,0,0,0,0
+5328,Certifying Some Distributional Robustness with Principled Adversarial Training,"  Neural networks are vulnerable to adversarial examples and researchers have
+proposed many heuristic attack and defense mechanisms. We address this problem
+through the principled lens of distributionally robust optimization, which
+guarantees performance under adversarial input perturbations. By considering a
+Lagrangian penalty formulation of perturbing the underlying data distribution
+in a Wasserstein ball, we provide a training procedure that augments model
+parameter updates with worst-case perturbations of training data. For smooth
+losses, our procedure provably achieves moderate levels of robustness with
+little computational or statistical cost relative to empirical risk
+minimization. Furthermore, our statistical guarantees allow us to efficiently
+certify robustness for the population loss. For imperceptible perturbations,
+our method matches or outperforms heuristic approaches.
+",1,0,0,1,0,0
+5329,The minimal hidden computer needed to implement a visible computation,"  Master equations are commonly used to model the dynamics of physical systems.
+Surprisingly, many deterministic maps $x \rightarrow f(x)$ cannot be
+implemented by any master equation, even approximately. This raises the
+question of how they arise in real-world systems like digital computers. We
+show that any deterministic map over some ""visible"" states can be implemented
+with a master equation--but only if additional ""hidden"" states are dynamically
+coupled to those visible states. We also show that any master equation
+implementing a given map can be decomposed into a sequence of ""hidden""
+timesteps, demarcated by changes in what transitions are allowed under the rate
+matrix. Often there is a real-world cost for each additional hidden state, and
+for each additional hidden timestep. We derive the associated ""space/time""
+tradeoff between the numbers of hidden states and of hidden timesteps needed to
+implement any given $f(x)$.
+",1,1,0,0,0,0
+5330,A multi-task convolutional neural network for mega-city analysis using very high resolution satellite imagery and geospatial data,"  Mega-city analysis with very high resolution (VHR) satellite images has been
+drawing increasing interest in the fields of city planning and social
+investigation. It is known that accurate land-use, urban density, and
+population distribution information is the key to mega-city monitoring and
+environmental studies. Therefore, how to generate land-use, urban density, and
+population distribution maps at a fine scale using VHR satellite images has
+become a hot topic. Previous studies have focused solely on individual tasks
+with elaborate hand-crafted features and have ignored the relationship between
+different tasks. In this study, we aim to propose a universal framework which
+can: 1) automatically learn the internal feature representation from the raw
+image data; and 2) simultaneously produce fine-scale land-use, urban density,
+and population distribution maps. For the first target, a deep convolutional
+neural network (CNN) is applied to learn the hierarchical feature
+representation from the raw image data. For the second target, a novel
+CNN-based universal framework is proposed to process the VHR satellite images
+and generate the land-use, urban density, and population distribution maps. To
+the best of our knowledge, this is the first CNN-based mega-city analysis
+method which can process a VHR remote sensing image with such a large data
+volume. A VHR satellite image (1.2 m spatial resolution) of the center of Wuhan
+covering an area of 2606 km2 was used to evaluate the proposed method. The
+experimental results confirm that the proposed method can achieve a promising
+accuracy for land-use, urban density, and population distribution maps.
+",1,0,0,0,0,0
+5331,Exploiting Multi-layer Graph Factorization for Multi-attributed Graph Matching,"  Multi-attributed graph matching is a problem of finding correspondences
+between two sets of data while considering their complex properties described
+in multiple attributes. However, the information of multiple attributes is
+likely to be oversimplified during a process that makes an integrated
+attribute, and this degrades the matching accuracy. For that reason, a
+multi-layer graph structure-based algorithm has been proposed recently. It can
+effectively avoid the problem by separating attributes into multiple layers.
+Nonetheless, there are several remaining issues such as a scalability problem
+caused by the huge matrix to describe the multi-layer structure and a
+back-projection problem caused by the continuous relaxation of the quadratic
+assignment problem. In this work, we propose a novel multi-attributed graph
+matching algorithm based on the multi-layer graph factorization. We reformulate
+the problem to be solved with several small matrices that are obtained by
+factorizing the multi-layer structure. Then, we solve the problem using a
+convex-concave relaxation procedure for the multi-layer structure. The proposed
+algorithm exhibits better performance than state-of-the-art algorithms based on
+the single-layer structure.
+",1,0,0,0,0,0
+5332,Secure Search on the Cloud via Coresets and Sketches,"  \emph{Secure Search} is the problem of retrieving from a database table (or
+any unsorted array) the records matching specified attributes, as in SQL SELECT
+queries, but where the database and the query are encrypted. Secure search has
+been the leading example for practical applications of Fully Homomorphic
+Encryption (FHE) starting in Gentry's seminal work; however, to the best of our
+knowledge all state-of-the-art secure search algorithms to date are realized by
+a polynomial of degree $\Omega(m)$ for $m$ the number of records, which is
+typically too slow in practice even for moderate size $m$.
+In this work we present the first algorithm for secure search that is
+realized by a polynomial of degree polynomial in $\log m$. We implemented our
+algorithm in an open source library based on HELib implementation for the
+Brakerski-Gentry-Vaikuntanthan's FHE scheme, and ran experiments on Amazon's
+EC2 cloud. Our experiments show that we can retrieve the first match in a
+database of millions of entries in less than an hour using a single machine;
+the time reduced almost linearly with the number of machines.
+Our result utilizes a new paradigm of employing coresets and sketches, which
+are modern data summarization techniques common in computational geometry and
+machine learning, for efficiency enhancement for homomorphic encryption. As a
+central tool we design a novel sketch that returns the first positive entry in
+a (not necessarily sparse) array; this sketch may be of independent interest.
+",1,0,0,0,0,0
+5333,LATTES: a novel detector concept for a gamma-ray experiment in the Southern hemisphere,"  The Large Array Telescope for Tracking Energetic Sources (LATTES), is a novel
+concept for an array of hybrid EAS array detectors, composed of a Resistive
+Plate Counter array coupled to a Water Cherenkov Detector, planned to cover
+gamma rays from less than 100 GeV up to 100 TeVs. This experiment, to be
+installed at high altitude in South America, could cover the existing gap in
+sensitivity between satellite and ground arrays.
+The low energy threshold, large duty cycle and wide field of view of LATTES
+makes it a powerful tool to detect transient phenomena and perform long term
+observations of variable sources. Moreover, given its characteristics, it would
+be fully complementary to the planned Cherenkov Telescope Array (CTA) as it
+would be able to issue alerts.
+In this talk, a description of its main features and capabilities, as well as
+results on its expected performance, and sensitivity, will be presented.
+",0,1,0,0,0,0
+5334,A general model for plane-based clustering with loss function,"  In this paper, we propose a general model for plane-based clustering. The
+general model contains many existing plane-based clustering methods, e.g.,
+k-plane clustering (kPC), proximal plane clustering (PPC), twin support vector
+clustering (TWSVC) and its extensions. Under this general model, one may obtain
+an appropriate clustering method for specific purpose. The general model is a
+procedure corresponding to an optimization problem, where the optimization
+problem minimizes the total loss of the samples. Thereinto, the loss of a
+sample derives from both within-cluster and between-cluster. In theory, the
+termination conditions are discussed, and we prove that the general model
+terminates in a finite number of steps at a local or weak local optimal point.
+Furthermore, based on this general model, we propose a plane-based clustering
+method by introducing a new loss function to capture the data distribution
+precisely. Experimental results on artificial and public available datasets
+verify the effectiveness of the proposed method.
+",1,0,0,1,0,0
+5335,Renormalization of quasiparticle band gap in doped two-dimensional materials from many-body calculations,"  Doped free carriers can substantially renormalize electronic self-energy and
+quasiparticle band gaps of two-dimensional (2D) materials. However, it is still
+challenging to quantitatively calculate this many-electron effect, particularly
+at the low doping density that is most relevant to realistic experiments and
+devices. Here we develop a first-principles-based effective-mass model within
+the GW approximation and show a dramatic band gap renormalization of a few
+hundred meV for typical 2D semiconductors. Moreover, we reveal the roles of
+different many-electron interactions: The Coulomb-hole contribution is dominant
+for low doping densities while the screened-exchange contribution is dominant
+for high doping densities. Three prototypical 2D materials are studied by this
+method, h-BN, MoS2, and black phosphorus, covering insulators to
+semiconductors. Especially, anisotropic black phosphorus exhibits a
+surprisingly large band gap renormalization because of its smaller
+density-of-state that enhances the screened-exchange interactions. Our work
+demonstrates an efficient way to accurately calculate band gap renormalization
+and provides quantitative understanding of doping-dependent many-electron
+physics of general 2D semiconductors.
+",0,1,0,0,0,0
+5336,Hyperbolicity as an obstruction to smoothability for one-dimensional actions,"  Ghys and Sergiescu proved in the $80$s that Thompson's group $T$, and hence
+$F$, admits actions by $C^{\infty}$ diffeomorphisms of the circle . They proved
+that the standard actions of these groups are topologically conjugate to a
+group of $C^\infty$ diffeomorphisms. Monod defined a family of groups of
+piecewise projective homeomorphisms, and Lodha-Moore defined finitely
+presentable groups of piecewise projective homeomorphisms. These groups are of
+particular interest because they are nonamenable and contain no free subgroup.
+In contrast to the result of Ghys-Sergiescu, we prove that the groups of Monod
+and Lodha-Moore are not topologically conjugate to a group of $C^1$
+diffeomorphisms.
+Furthermore, we show that the group of Lodha-Moore has no nonabelian $C^1$
+action on the interval. We also show that many Monod's groups $H(A)$, for
+instance when $A$ is such that $\mathsf{PSL}(2,A)$ contains a rational
+homothety $x\mapsto \tfrac{p}{q}x$, do not admit a $C^1$ action on the
+interval. The obstruction comes from the existence of hyperbolic fixed points
+for $C^1$ actions. With slightly different techniques, we also show that some
+groups of piecewise affine homeomorphisms of the interval or the circle are not
+smoothable.
+",0,0,1,0,0,0
+5337,Lasso ANOVA Decompositions for Matrix and Tensor Data,"  Consider the problem of estimating the entries of an unknown mean matrix or
+tensor given a single noisy realization. In the matrix case, this problem can
+be addressed by decomposing the mean matrix into a component that is additive
+in the rows and columns, i.e.\ the additive ANOVA decomposition of the mean
+matrix, plus a matrix of elementwise effects, and assuming that the elementwise
+effects may be sparse. Accordingly, the mean matrix can be estimated by solving
+a penalized regression problem, applying a lasso penalty to the elementwise
+effects. Although solving this penalized regression problem is straightforward,
+specifying appropriate values of the penalty parameters is not. Leveraging the
+posterior mode interpretation of the penalized regression problem, moment-based
+empirical Bayes estimators of the penalty parameters can be defined. Estimation
+of the mean matrix using these these moment-based empirical Bayes estimators
+can be called LANOVA penalization, and the corresponding estimate of the mean
+matrix can be called the LANOVA estimate. The empirical Bayes estimators are
+shown to be consistent. Additionally, LANOVA penalization is extended to
+accommodate sparsity of row and column effects and to estimate an unknown mean
+tensor. The behavior of the LANOVA estimate is examined under misspecification
+of the distribution of the elementwise effects, and LANOVA penalization is
+applied to several datasets, including a matrix of microarray data, a three-way
+tensor of fMRI data and a three-way tensor of wheat infection data.
+",0,0,0,1,0,0
+5338,NetSciEd: Network Science and Education for the Interconnected World,"  This short article presents a summary of the NetSciEd (Network Science and
+Education) initiative that aims to address the need for curricula, resources,
+accessible materials, and tools for introducing K-12 students and the general
+public to the concept of networks, a crucial framework in understanding
+complexity. NetSciEd activities include (1) the NetSci High educational
+outreach program (since 2010), which connects high school students and their
+teachers with regional university research labs and provides them with the
+opportunity to work on network science research projects; (2) the NetSciEd
+symposium series (since 2012), which brings network science researchers and
+educators together to discuss how network science can help and be integrated
+into formal and informal education; and (3) the Network Literacy: Essential
+Concepts and Core Ideas booklet (since 2014), which was created collaboratively
+and subsequently translated into 18 languages by an extensive group of network
+science researchers and educators worldwide.
+",1,1,0,0,0,0
+5339,A cup product lemma for continuous plurisubharmonic functions,"  A version of Gromov's cup product lemma in which one factor is the (1,0)-part
+of the differential of a continuous plurisubharmonic function is obtained. As
+an application, it is shown that a connected noncompact complete Kaehler
+manifold that has exactly one end and admits a continuous plurisubharmonic
+function that is strictly plurisubharmonic along some germ of a 2-dimensional
+complex analytic set at some point has the Bochner-Hartogs property; that is,
+the first compactly supported cohomology with values in the structure sheaf
+vanishes.
+",0,0,1,0,0,0
+5340,Near-perfect spin filtering and negative differential resistance in an Fe(II)S complex,"  Density functional theory and nonequilibrium Green's function calculations
+have been used to explore spin-resolved transport through the high-spin state
+of an iron(II)sulfur single molecular magnet. Our results show that this
+molecule exhibits near-perfect spin filtering, where the spin-filtering
+efficiency is above 99%, as well as significant negative differential
+resistance centered at a low bias voltage. The rise in the spin-up conductivity
+up to the bias voltage of 0.4 V is dominated by a conductive lowest unoccupied
+molecular orbital, and this is accompanied by a slight increase in the magnetic
+moment of the Fe atom. The subsequent drop in the spin-up conductivity is
+because the conductive channel moves to the highest occupied molecular orbital
+which has a lower conductance contribution. This is accompanied by a drop in
+the magnetic moment of the Fe atom. These two exceptional properties, and the
+fact that the onset of negative differential resistance occurs at low bias
+voltage, suggests the potential of the molecule in nanoelectronic and
+nanospintronic applications.
+",0,1,0,0,0,0
+5341,Search for magnetic inelastic dark matter with XENON100,"  We present the first search for dark matter-induced delayed coincidence
+signals in a dual-phase xenon time projection chamber, using the 224.6 live
+days of the XENON100 science run II. This very distinct signature is predicted
+in the framework of magnetic inelastic dark matter which has been proposed to
+reconcile the modulation signal reported by the DAMA/LIBRA collaboration with
+the null results from other direct detection experiments. No candidate event
+has been found in the region of interest and upper limits on the WIMP's
+magnetic dipole moment are derived. The scenarios proposed to explain the
+DAMA/LIBRA modulation signal by magnetic inelastic dark matter interactions of
+WIMPs with masses of 58.0 GeV/c$^2$ and 122.7 GeV/c$^2$ are excluded at 3.3
+$\sigma$ and 9.3 $\sigma$, respectively.
+",0,1,0,0,0,0
+5342,"Structural, elastic, electronic, and bonding properties of intermetallic Nb3Pt and Nb3Os compounds: a DFT study","  Theoretical investigation of structural, elastic, electronic and bonding
+properties of A-15 Nb-based intermetallic compounds Nb3B (B = Pt, Os) have been
+performed using first principles calculations based on the density functional
+theory (DFT). Optimized cell parameters are found to be in good agreement with
+available experimental and theoretical results. The elastic constants at zero
+pressure and temperature are calculated and the anisotropic behaviors of the
+compounds are studied. Both the compounds are mechanically stable and ductile
+in nature. Other elastic properties such as Pugh's ratio, Cauchy pressure,
+machinability index are derived for the first time. Nb3Os is expected to have
+good lubricating properties compared to Nb3Pt. The electronic band structure
+and energy density of states (DOS) have been studied with and without
+spin-orbit coupling (SOC). The band structures of both the compounds are spin
+symmetric. Electronic band structure and DOS reveal that both the compounds are
+metallic and the conductivity mainly arise from the Nb 4d states. The Fermi
+surface features have been studied for the first time. The Fermi surfaces of
+Nb3B contain both hole- and electron-like sheets which change as one replaces
+Pt with Os. The electronic charge density distribution shows that Nb3Pt and
+Nb3Os both have a mixture of ionic and covalent bonding. The charge transfer
+between atomic species in these compounds has been explained by the Mulliken
+bond population analysis.
+",0,1,0,0,0,0
+5343,Clustering and Model Selection via Penalized Likelihood for Different-sized Categorical Data Vectors,"  In this study, we consider unsupervised clustering of categorical vectors
+that can be of different size using mixture. We use likelihood maximization to
+estimate the parameters of the underlying mixture model and a penalization
+technique to select the number of mixture components. Regardless of the true
+distribution that generated the data, we show that an explicit penalty, known
+up to a multiplicative constant, leads to a non-asymptotic oracle inequality
+with the Kullback-Leibler divergence on the two sides of the inequality. This
+theoretical result is illustrated by a document clustering application. To this
+aim a novel robust expectation-maximization algorithm is proposed to estimate
+the mixture parameters that best represent the different topics. Slope
+heuristics are used to calibrate the penalty and to select a number of
+clusters.
+",0,0,1,1,0,0
+5344,Topology reveals universal features for network comparison,"  The topology of any complex system is key to understanding its structure and
+function. Fundamentally, algebraic topology guarantees that any system
+represented by a network can be understood through its closed paths. The length
+of each path provides a notion of scale, which is vitally important in
+characterizing dominant modes of system behavior. Here, by combining topology
+with scale, we prove the existence of universal features which reveal the
+dominant scales of any network. We use these features to compare several
+canonical network types in the context of a social media discussion which
+evolves through the sharing of rumors, leaks and other news. Our analysis
+enables for the first time a universal understanding of the balance between
+loops and tree-like structure across network scales, and an assessment of how
+this balance interacts with the spreading of information online. Crucially, our
+results allow networks to be quantified and compared in a purely model-free way
+that is theoretically sound, fully automated, and inherently scalable.
+",1,0,1,1,0,0
+5345,Gated Recurrent Networks for Seizure Detection,"  Recurrent Neural Networks (RNNs) with sophisticated units that implement a
+gating mechanism have emerged as powerful technique for modeling sequential
+signals such as speech or electroencephalography (EEG). The latter is the focus
+on this paper. A significant big data resource, known as the TUH EEG Corpus
+(TUEEG), has recently become available for EEG research, creating a unique
+opportunity to evaluate these recurrent units on the task of seizure detection.
+In this study, we compare two types of recurrent units: long short-term memory
+units (LSTM) and gated recurrent units (GRU). These are evaluated using a state
+of the art hybrid architecture that integrates Convolutional Neural Networks
+(CNNs) with RNNs. We also investigate a variety of initialization methods and
+show that initialization is crucial since poorly initialized networks cannot be
+trained. Furthermore, we explore regularization of these convolutional gated
+recurrent networks to address the problem of overfitting. Our experiments
+revealed that convolutional LSTM networks can achieve significantly better
+performance than convolutional GRU networks. The convolutional LSTM
+architecture with proper initialization and regularization delivers 30%
+sensitivity at 6 false alarms per 24 hours.
+",0,0,0,1,0,0
+5346,Non-convex Conditional Gradient Sliding,"  We investigate a projection free method, namely conditional gradient sliding
+on batched, stochastic and finite-sum non-convex problem. CGS is a smart
+combination of Nesterov's accelerated gradient method and Frank-Wolfe (FW)
+method, and outperforms FW in the convex setting by saving gradient
+computations. However, the study of CGS in the non-convex setting is limited.
+In this paper, we propose the non-convex conditional gradient sliding (NCGS)
+which surpasses the non-convex Frank-Wolfe method in batched, stochastic and
+finite-sum setting.
+",0,0,1,0,0,0
+5347,Multinomial Sum Formulas of Multiple Zeta Values,"  For a pair of positive integers $n,k$ with $n\geq 2$, in this paper we prove
+that $$ \sum_{r=1}^k\sum_{|\bf\alpha|=k}{k\choose\bf\alpha}
+\zeta(n\bf\alpha)=\zeta(n)^k =\sum^k_{r=1}\sum_{|\bf\alpha|=k}
+{k\choose\bf\alpha}(-1)^{k-r}\zeta^\star(n\bf\alpha), $$ where
+$\bf\alpha=(\alpha_1,\alpha_2,\ldots,\alpha_r)$ is a $r$-tuple of positive
+integers. Moreover, we give an application to combinatorics and get the
+following identity: $$ \sum^{2k}_{r=1}r!{2k\brace
+r}=\sum^k_{p=1}\sum^k_{q=1}{k\brace p}{k\brace q} p!q!D(p,q), $$ where
+${k\brace p}$ is the Stirling numbers of the second kind and $D(p,q)$ is the
+Delannoy number.
+",0,0,1,0,0,0
+5348,Copolar convexity,"  We introduce a new operation, copolar addition, on unbounded convex subsets
+of the positive orthant of real euclidean space and establish convexity of the
+covolumes of the corresponding convex combinations. The proof is based on a
+technique of geodesics of plurisubharmonic functions. As an application, we
+show that there are no relative extremal functions inside a non-constant
+geodesic curve between two toric relative extremal functions.
+",0,0,1,0,0,0
+5349,Go with the Flow: Compositional Abstractions for Concurrent Data Structures (Extended Version),"  Concurrent separation logics have helped to significantly simplify
+correctness proofs for concurrent data structures. However, a recurring problem
+in such proofs is that data structure abstractions that work well in the
+sequential setting are much harder to reason about in a concurrent setting due
+to complex sharing and overlays. To solve this problem, we propose a novel
+approach to abstracting regions in the heap by encoding the data structure
+invariant into a local condition on each individual node. This condition may
+depend on a quantity associated with the node that is computed as a fixpoint
+over the entire heap graph. We refer to this quantity as a flow. Flows can
+encode both structural properties of the heap (e.g. the reachable nodes from
+the root form a tree) as well as data invariants (e.g. sortedness). We then
+introduce the notion of a flow interface, which expresses the relies and
+guarantees that a heap region imposes on its context to maintain the local flow
+invariant with respect to the global heap. Our main technical result is that
+this notion leads to a new semantic model of separation logic. In this model,
+flow interfaces provide a general abstraction mechanism for describing complex
+data structures. This abstraction mechanism admits proof rules that generalize
+over a wide variety of data structures. To demonstrate the versatility of our
+approach, we show how to extend the logic RGSep with flow interfaces. We have
+used this new logic to prove linearizability and memory safety of nontrivial
+concurrent data structures. In particular, we obtain parametric linearizability
+proofs for concurrent dictionary algorithms that abstract from the details of
+the underlying data structure representation. These proofs cannot be easily
+expressed using the abstraction mechanisms provided by existing separation
+logics.
+",1,0,0,0,0,0
+5350,A Liouville Theorem for Mean Curvature Flow,"  Ancient solutions arise in the study of parabolic blow-ups. If we can
+categorize ancient solutions, we can better understand blow-up limits. Based on
+an argument of Giga and Kohn, we give a Liouville-type theorem restricting
+ancient, type-I, non-collapsing two- dimensional mean curvature flows to either
+spheres or cylinders.
+",0,0,1,0,0,0
+5351,FeSe(en)0.3 - Separated FeSe layers with stripe-type crystal structure by intercalation of neutral spacer molecules,"  Solvothermal intercalation of ethylenediamine molecules into FeSe separates
+the layers by 1078 pm and creates a different stacking. FeSe(en)0.3 is not
+superconducting although each layer exhibits the stripe-type crystal structure
+and the Fermi surface topology of superconducting FeSe. FeSe(en)0.3 requires
+electron-doping for high-Tc similar to monolayers of FeSe@SrTiO3, whose much
+higher Tc may arise from the proximity of the oxide surface.
+",0,1,0,0,0,0
+5352,Coexistence of quantum and classical flows in quantum turbulence in the $T=0$ limit,"  Tangles of quantized vortex line of initial density ${\cal L}(0) \sim 6\times
+10^3$\,cm$^{-2}$ and variable amplitude of fluctuations of flow velocity $U(0)$
+at the largest length scale were generated in superfluid $^4$He at $T=0.17$\,K,
+and their free decay ${\cal L}(t)$ was measured. If $U(0)$ is small, the excess
+random component of vortex line length firstly decays as ${\cal L} \propto
+t^{-1}$ until it becomes comparable with the structured component responsible
+for the classical velocity field, and the decay changes to ${\cal L} \propto
+t^{-3/2}$. The latter regime always ultimately prevails, provided the classical
+description of $U$ holds. A quantitative model of coexisting cascades of
+quantum and classical energies describes all regimes of the decay.
+",0,1,0,0,0,0
+5353,Four-dimensional Lens Space Index from Two-dimensional Chiral Algebra,"  We study the supersymmetric partition function on $S^1 \times L(r, 1)$, or
+the lens space index of four-dimensional $\mathcal{N}=2$ superconformal field
+theories and their connection to two-dimensional chiral algebras. We primarily
+focus on free theories as well as Argyres-Douglas theories of type $(A_1, A_k)$
+and $(A_1, D_k)$. We observe that in specific limits, the lens space index is
+reproduced in terms of the (refined) character of an appropriately twisted
+module of the associated two-dimensional chiral algebra or a generalized vertex
+operator algebra. The particular twisted module is determined by the choice of
+discrete holonomies for the flavor symmetry in four-dimensions.
+",0,0,1,0,0,0
+5354,Lions' formula for RKHSs of real harmonic functions on Lipschitz domains,"  Let $ \Omega$ be a bounded Lipschitz domain of $ \mathbb{R}^{d}.$ The purpose
+of this paper is to establish Lions' formula for reproducing kernel Hilbert
+spaces $\mathcal H^s(\Omega)$ of real harmonic functions elements of the usual
+Sobolev space $H^s(\Omega)$ for $s\geq 0.$ To this end, we provide a functional
+characterization of $\mathcal H^s(\Omega)$ via some new families of positive
+self-adjoint operators, describe their trace data and discuss the values of $s$
+for which they are RKHSs. Also a construction of an orthonormal basis of
+$\mathcal H^s(\Omega)$ is established.
+",0,0,1,0,0,0
+5355,Optimization and Performance of Bifacial Solar Modules: A Global Perspective,"  With the rapidly growing interest in bifacial photovoltaics (PV), a worldwide
+map of their potential performance can help assess and accelerate the global
+deployment of this emerging technology. However, the existing literature only
+highlights optimized bifacial PV for a few geographic locations or develops
+worldwide performance maps for very specific configurations, such as the
+vertical installation. It is still difficult to translate these location- and
+configuration-specific conclusions to a general optimized performance of this
+technology. In this paper, we present a global study and optimization of
+bifacial solar modules using a rigorous and comprehensive modeling framework.
+Our results demonstrate that with a low albedo of 0.25, the bifacial gain of
+ground-mounted bifacial modules is less than 10% worldwide. However, increasing
+the albedo to 0.5 and elevating modules 1 m above the ground can boost the
+bifacial gain to 30%. Moreover, we derive a set of empirical design rules,
+which optimize bifacial solar modules across the world, that provide the
+groundwork for rapid assessment of the location-specific performance. We find
+that ground-mounted, vertical, east-west-facing bifacial modules will
+outperform their south-north-facing, optimally tilted counterparts by up to 15%
+below the latitude of 30 degrees, for an albedo of 0.5. The relative energy
+output is the reverse of this in latitudes above 30 degrees. A detailed and
+systematic comparison with experimental data from Asia, Europe, and North
+America validates the model presented in this paper. An online simulation tool
+(this https URL) based on the model developed in this paper is
+also available for a user to predict and optimize bifacial modules in any
+arbitrary location across the globe.
+",0,1,0,0,0,0
+5356,Wave propagation modelling in various microearthquake environments using a spectral-element method,"  Simulation of wave propagation in a microearthquake environment is often
+challenging due to small-scale structural and material heterogeneities. We
+simulate wave propagation in three different real microearthquake environments
+using a spectral-element method. In the first example, we compute the full
+wavefield in 2D and 3D models of an underground ore mine, namely the Pyhaesalmi
+mine in Finland. In the second example, we simulate wave propagation in a
+homogeneous velocity model including the actual topography of an unstable rock
+slope at Aaknes in western Norway. Finally, we compute the full wavefield for a
+weakly anisotropic cylindrical sample at laboratory scale, which was used for
+an acoustic emission experiment under triaxial loading. We investigate the
+characteristic features of wave propagation in those models and compare
+synthetic waveforms with observed waveforms wherever possible. We illustrate
+the challenges associated with the spectral-element simulation in those models.
+",0,1,0,0,0,0
+5357,Fast Snapshottable Concurrent Braun Heaps,"  This paper proposes a new concurrent heap algorithm, based on a stateless
+shape property, which efficiently maintains balance during insert and removeMin
+operations implemented with hand-over-hand locking. It also provides a O(1)
+linearizable snapshot operation based on lazy copy-on-write semantics. Such
+snapshots can be used to provide consistent views of the heap during iteration,
+as well as to make speculative updates (which can later be dropped).
+The simplicity of the algorithm allows it to be easily proven correct, and
+the choice of shape property provides priority queue performance which is
+competitive with highly optimized skiplist implementations (and has stronger
+bounds on worst-case time complexity).
+A Scala reference implementation is provided.
+",1,0,0,0,0,0
+5358,"GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings","  This article presents GuideR, a user-guided rule induction algorithm, which
+overcomes the largest limitation of the existing methods-the lack of the
+possibility to introduce user's preferences or domain knowledge to the rule
+learning process. Automatic selection of attributes and attribute ranges often
+leads to the situation in which resulting rules do not contain interesting
+information. We propose an induction algorithm which takes into account user's
+requirements. Our method uses the sequential covering approach and is suitable
+for classification, regression, and survival analysis problems. The
+effectiveness of the algorithm in all these tasks has been verified
+experimentally, confirming guided rule induction to be a powerful data analysis
+tool.
+",0,0,0,1,0,0
+5359,Frequency analysis and the representation of slowly diffusing planetary solutions,"  Over short time intervals planetary ephemerides have been traditionally
+represented in analytical form as finite sums of periodic terms or sums of
+Poisson terms that are periodic terms with polynomial amplitudes. Nevertheless,
+this representation is not well adapted for the evolution of the planetary
+orbits in the solar system over million of years as they present drifts in
+their main frequencies, due to the chaotic nature of their dynamics. The aim of
+the present paper is to develop a numerical algorithm for slowly diffusing
+solutions of a perturbed integrable Hamiltonian system that will apply to the
+representation of the chaotic planetary motions with varying frequencies. By
+simple analytical considerations, we first argue that it is possible to recover
+exactly a single varying frequency. Then, a function basis involving
+time-dependent fundamental frequencies is formulated in a semi-analytical way.
+Finally, starting from a numerical solution, a recursive algorithm is used to
+numerically decompose the solution on the significant elements of the function
+basis. Simple examples show that this algorithm can be used to give compact
+representations of different types of slowly diffusing solutions. As a test
+example, we show how this algorithm can be successfully applied to obtain a
+very compact approximation of the La2004 solution of the orbital motion of the
+Earth over 40 Myr ([-35Myr,5Myr]). This example has been chosen as this
+solution is widely used for the reconstruction of the climates of the past.
+",0,1,0,0,0,0
+5360,Geometric clustering in normed planes,"  Given two sets of points $A$ and $B$ in a normed plane, we prove that there
+are two linearly separable sets $A'$ and $B'$ such that $\mathrm{diam}(A')\leq
+\mathrm{diam}(A)$, $\mathrm{diam}(B')\leq \mathrm{diam}(B)$, and $A'\cup
+B'=A\cup B.$ This extends a result for the Euclidean distance to symmetric
+convex distance functions. As a consequence, some Euclidean $k$-clustering
+algorithms are adapted to normed planes, for instance, those that minimize the
+maximum, the sum, or the sum of squares of the $k$ cluster diameters. The
+2-clustering problem when two different bounds are imposed to the diameters is
+also solved. The Hershberger-Suri's data structure for managing ball hulls can
+be useful in this context.
+",0,0,1,0,0,0
+5361,Spectrum Sharing for LTE-A Network in TV White Space,"  Rural areas in the developing countries are predominantly devoid of Internet
+access as it is not viable for operators to provide broadband service in these
+areas. To solve this problem, we propose a middle mile Long erm Evolution
+Advanced (LTE-A) network operating in TV white space to connect villages to an
+optical Point of Presence (PoP) located in the vicinity of a rural area. We
+study the problem of spectrum sharing for the middle mile networks deployed by
+multiple operators. A graph theory based Fairness Constrained Channel
+Allocation (FCCA) algorithm is proposed, employing Carrier Aggregation (CA) and
+Listen Before Talk (LBT) features of LTE-A. We perform extensive system level
+simulations to demonstrate that FCCA not only increases spectral efficiency but
+also improves system fairness.
+",1,0,0,0,0,0
+5362,Instantons for 4-manifolds with periodic ends and an obstruction to embeddings of 3-manifolds,"  We construct an obstruction for the existence of embeddings of homology
+$3$-sphere into homology $S^3\times S^1$ under some cohomological condition.
+The obstruction is defined as an element in the filtered version of the
+instanton Floer cohomology due to R.Fintushel-R.Stern. We make use of the
+$\mathbb{Z}$-fold covering space of homology $S^3\times S^1$ and the instantons
+on it.
+",0,0,1,0,0,0
+5363,"Laplacian networks: growth, local symmetry and shape optimization","  Inspired by river networks and other structures formed by Laplacian growth,
+we use the Loewner equation to investigate the growth of a network of thin
+fingers in a diffusion field. We first review previous contributions to
+illustrate how this formalism reduces the network's expansion to three rules,
+which respectively govern the velocity, the direction, and the nucleation of
+its growing branches. This framework allows us to establish the mathematical
+equivalence between three formulations of the direction rule, namely geodesic
+growth, growth that maintains local symmetry and growth that maximizes flux
+into tips for a given amount of growth. Surprisingly, we find that this growth
+rule may result in a network different from the static configuration that
+optimizes flux into tips.
+",0,1,0,0,0,0
+5364,Dropping Convexity for More Efficient and Scalable Online Multiview Learning,"  Multiview representation learning is very popular for latent factor analysis.
+It naturally arises in many data analysis, machine learning, and information
+retrieval applications to model dependent structures among multiple data
+sources. For computational convenience, existing approaches usually formulate
+the multiview representation learning as convex optimization problems, where
+global optima can be obtained by certain algorithms in polynomial time.
+However, many pieces of evidence have corroborated that heuristic nonconvex
+approaches also have good empirical computational performance and convergence
+to the global optima, although there is a lack of theoretical justification.
+Such a gap between theory and practice motivates us to study a nonconvex
+formulation for multiview representation learning, which can be efficiently
+solved by a simple stochastic gradient descent (SGD) algorithm. We first
+illustrate the geometry of the nonconvex formulation; Then, we establish
+asymptotic global rates of convergence to the global optima by diffusion
+approximations. Numerical experiments are provided to support our theory.
+",0,0,1,1,0,0
+5365,Automatic Vector-based Road Structure Mapping Using Multi-beam LiDAR,"  In this paper, we studied a SLAM method for vector-based road structure
+mapping using multi-beam LiDAR. We propose to use the polyline as the primary
+mapping element instead of grid cell or point cloud, because the vector-based
+representation is precise and lightweight, and it can directly generate
+vector-based High-Definition (HD) driving map as demanded by autonomous driving
+systems. We explored: 1) the extraction and vectorization of road structures
+based on local probabilistic fusion. 2) the efficient vector-based matching
+between frames of road structures. 3) the loop closure and optimization based
+on the pose-graph. In this study, we took a specific road structure, the road
+boundary, as an example. We applied the proposed matching method in three
+different scenes and achieved the average absolute matching error of 0.07. We
+further applied the mapping system to the urban road with the length of 860
+meters and achieved an average global accuracy of 0.466 m without the help of
+high precision GPS.
+",1,0,0,0,0,0
+5366,"Schwarzian derivatives, projective structures, and the Weil-Petersson gradient flow for renormalized volume","  To a complex projective structure $\Sigma$ on a surface, Thurston associates
+a locally convex pleated surface. We derive bounds on the geometry of both in
+terms of the norms $\|\phi_\Sigma\|_\infty$ and $\|\phi_\Sigma\|_2$ of the
+quadratic differential $\phi_\Sigma$ of $\Sigma$ given by the Schwarzian
+derivative of the associated locally univalent map. We show that these give a
+unifying approach that generalizes a number of important, well known results
+for convex cocompact hyperbolic structures on 3-manifolds, including bounds on
+the Lipschitz constant for the nearest-point retraction and the length of the
+bending lamination. We then use these bounds to begin a study of the
+Weil-Petersson gradient flow of renormalized volume on the space $CC(N)$ of
+convex cocompact hyperbolic structures on a compact manifold $N$ with
+incompressible boundary, leading to a proof of the conjecture that the
+renormalized volume has infimum given by one-half the simplicial volume of
+$DN$, the double of $N$.
+",0,0,1,0,0,0
+5367,A Deep Network Model for Paraphrase Detection in Short Text Messages,"  This paper is concerned with paraphrase detection. The ability to detect
+similar sentences written in natural language is crucial for several
+applications, such as text mining, text summarization, plagiarism detection,
+authorship authentication and question answering. Given two sentences, the
+objective is to detect whether they are semantically identical. An important
+insight from this work is that existing paraphrase systems perform well when
+applied on clean texts, but they do not necessarily deliver good performance
+against noisy texts. Challenges with paraphrase detection on user generated
+short texts, such as Twitter, include language irregularity and noise. To cope
+with these challenges, we propose a novel deep neural network-based approach
+that relies on coarse-grained sentence modeling using a convolutional neural
+network and a long short-term memory model, combined with a specific
+fine-grained word-level similarity matching model. Our experimental results
+show that the proposed approach outperforms existing state-of-the-art
+approaches on user-generated noisy social media data, such as Twitter texts,
+and achieves highly competitive performance on a cleaner corpus.
+",1,0,0,0,0,0
+5368,"Organic-inorganic Copper(II)-based Material: a Low-Toxic, Highly Stable Light Absorber beyond Organolead Perovskites","  Lead halide perovskite solar cells have recently emerged as a very promising
+photovoltaic technology due to their excellent power conversion efficiencies;
+however, the toxicity of lead and the poor stability of perovskite materials
+remain two main challenges that need to be addressed. Here, for the first time,
+we report a lead-free, highly stable C6H4NH2CuBr2I compound. The C6H4NH2CuBr2I
+films exhibit extraordinary hydrophobic behavior with a contact angle of
+approximately 90 degree, and their X-ray diffraction patterns remain unchanged
+even after four hours of water immersion. UV-Vis absorption spectrum shows that
+C6H4NH2CuBr2I compound has an excellent optical absorption over the entire
+visible spectrum. We applied this copper-based light absorber in printable
+mesoscopic solar cell for the initial trial and achieved a power conversion
+efficiency of 0.5%. Our study represents an alternative pathway to develop
+low-toxic and highly stable organic-inorganic hybrid materials for photovoltaic
+application.
+",0,1,0,0,0,0
+5369,"Acyclic cluster algebras, reflection groups, and curves on a punctured disc","  We establish a bijective correspondence between certain non-self-intersecting
+curves in an $n$-punctured disc and positive ${\mathbf c}$-vectors of acyclic
+cluster algebras whose quivers have multiple arrows between every pair of
+vertices. As a corollary, we obtain a proof of a conjecture by K.-H. Lee and K.
+Lee (arXiv:1703.09113) on the combinatorial description of real Schur roots for
+acyclic quivers with multiple arrows, and give a combinatorial characterization
+of seeds in terms of curves in an $n$-punctured disc.
+",0,0,1,0,0,0
+5370,Inferring Structural Characteristics of Networks with Strong and Weak Ties from Fixed-Choice Surveys,"  Knowing the structure of an offline social network facilitates a variety of
+analyses, including studying the rate at which infectious diseases may spread
+and identifying a subset of actors to immunize in order to reduce, as much as
+possible, the rate of spread. Offline social network topologies are typically
+estimated by surveying actors and asking them to list their neighbours. While
+identifying close friends and family (i.e., strong ties) can typically be done
+reliably, listing all of one's acquaintances (i.e., weak ties) is subject to
+error due to respondent fatigue. This issue is commonly circumvented through
+the use of so-called ""fixed choice"" surveys where respondents are asked to name
+a fixed, small number of their weak ties (e.g., two or ten). Of course, the
+resulting crude observed network will omit many ties, and using this crude
+network to infer properties of the network, such as its degree distribution or
+clustering coefficient, will lead to biased estimates. This paper develops
+estimators, based on the method of moments, for a number of network
+characteristics including those related to the first and second moments of the
+degree distribution as well as the network size, using fixed-choice survey
+data. Experiments with simulated data illustrate that the proposed estimators
+perform well across a variety of network topologies and measurement scenarios,
+and the resulting estimates are significantly more accurate than those obtained
+directly using the crude observed network, which are commonly used in the
+literature. We also describe a variation of the Jackknife procedure that can be
+used to obtain an estimate of the estimator variance.
+",1,1,0,0,0,0
+5371,A Method Of Detecting Gravitational Wave Based On Time-frequency Analysis And Convolutional Neural Networks,"  This work investigated the detection of gravitational wave (GW) from
+simulated damped sinusoid signals contaminated with Gaussian noise. We proposed
+to treat it as a classification problem with one class bearing our special
+attentions. Two successive steps of the proposed scheme are as following:
+first, decompose the data using a wavelet packet and represent the GW signal
+and noise using the derived decomposition coefficients; Second, detect the
+existence of GW using a convolutional neural network (CNN). To reflect our
+special attention on searching GW signals, the performance is evaluated using
+not only the traditional classification accuracy (correct ratio), but also
+receiver operating characteristic (ROC) curve, and experiments show excelllent
+performances on both evaluation measures. The generalization of a proposed
+searching scheme on GW model parameter and possible extensions to other data
+analysis tasks are crucial for a machine learning based approach. On this
+aspect, experiments shows that there is no significant difference between GW
+model parameters on identification performances by our proposed scheme.
+Therefore, the proposed scheme has excellent generalization and could be used
+to search for non-trained and un-known GW signals or glitches in the future GW
+astronomy era.
+",0,1,0,0,0,0
+5372,The connection between zero chromaticity and long in-plane polarization lifetime in a magnetic storage ring,"  In this paper, we demonstrate the connection between a magnetic storage ring
+with additional sextupole fields set so that the x and y chromaticities vanish
+and the maximizing of the lifetime of in-plane polarization (IPP) for a
+0.97-GeV/c deuteron beam. The IPP magnitude was measured by continuously
+monitoring the down-up scattering asymmetry (sensitive to sideways
+polarization) in an in-beam, carbon-target polarimeter and unfolding the
+precession of the IPP due to the magnetic anomaly of the deuteron. The optimum
+operating conditions for a long IPP lifetime were made by scanning the field of
+the storage ring sextupole magnet families while observing the rate of IPP loss
+during storage of the beam. The beam was bunched and electron cooled. The IPP
+losses appear to arise from the change of the orbit circumference, and
+consequently the particle speed and spin tune, due to the transverse betatron
+oscillations of individual particles in the beam. The effects of these changes
+are canceled by an appropriate sextupole field setting.
+",0,1,0,0,0,0
+5373,Phonemic and Graphemic Multilingual CTC Based Speech Recognition,"  Training automatic speech recognition (ASR) systems requires large amounts of
+data in the target language in order to achieve good performance. Whereas large
+training corpora are readily available for languages like English, there exists
+a long tail of languages which do suffer from a lack of resources. One method
+to handle data sparsity is to use data from additional source languages and
+build a multilingual system. Recently, ASR systems based on recurrent neural
+networks (RNNs) trained with connectionist temporal classification (CTC) have
+gained substantial research interest. In this work, we extended our previous
+approach towards training CTC-based systems multilingually. Our systems feature
+a global phone set, based on the joint phone sets of each source language. We
+evaluated the use of different language combinations as well as the addition of
+Language Feature Vectors (LFVs). As contrastive experiment, we built systems
+based on graphemes as well. Systems having a multilingual phone set are known
+to suffer in performance compared to their monolingual counterparts. With our
+proposed approach, we could reduce the gap between these mono- and multilingual
+setups, using either graphemes or phonemes.
+",1,0,0,0,0,0
+5374,Model-Based Clustering of Time-Evolving Networks through Temporal Exponential-Family Random Graph Models,"  Dynamic networks are a general language for describing time-evolving complex
+systems, and discrete time network models provide an emerging statistical
+technique for various applications. It is a fundamental research question to
+detect the community structure in time-evolving networks. However, due to
+significant computational challenges and difficulties in modeling communities
+of time-evolving networks, there is little progress in the current literature
+to effectively find communities in time-evolving networks. In this work, we
+propose a novel model-based clustering framework for time-evolving networks
+based on discrete time exponential-family random graph models. To choose the
+number of communities, we use conditional likelihood to construct an effective
+model selection criterion. Furthermore, we propose an efficient variational
+expectation-maximization (EM) algorithm to find approximate maximum likelihood
+estimates of network parameters and mixing proportions. By using variational
+methods and minorization-maximization (MM) techniques, our method has appealing
+scalability for large-scale time-evolving networks. The power of our method is
+demonstrated in simulation studies and empirical applications to international
+trade networks and the collaboration networks of a large American research
+university.
+",0,0,0,1,0,0
+5375,Multi-agent Time-based Decision-making for the Search and Action Problem,"  Many robotic applications, such as search-and-rescue, require multiple agents
+to search for and perform actions on targets. However, such missions present
+several challenges, including cooperative exploration, task selection and
+allocation, time limitations, and computational complexity. To address this, we
+propose a decentralized multi-agent decision-making framework for the search
+and action problem with time constraints. The main idea is to treat time as an
+allocated budget in a setting where each agent action incurs a time cost and
+yields a certain reward. Our approach leverages probabilistic reasoning to make
+near-optimal decisions leading to maximized reward. We evaluate our method in
+the search, pick, and place scenario of the Mohamed Bin Zayed International
+Robotics Challenge (MBZIRC), by using a probability density map and reward
+prediction function to assess actions. Extensive simulations show that our
+algorithm outperforms benchmark strategies, and we demonstrate system
+integration in a Gazebo-based environment, validating the framework's readiness
+for field application.
+",1,0,0,0,0,0
+5376,Anisotropic twicing for single particle reconstruction using autocorrelation analysis,"  The missing phase problem in X-ray crystallography is commonly solved using
+the technique of molecular replacement, which borrows phases from a previously
+solved homologous structure, and appends them to the measured Fourier
+magnitudes of the diffraction patterns of the unknown structure. More recently,
+molecular replacement has been proposed for solving the missing orthogonal
+matrices problem arising in Kam's autocorrelation analysis for single particle
+reconstruction using X-ray free electron lasers and cryo-EM. In classical
+molecular replacement, it is common to estimate the magnitudes of the unknown
+structure as twice the measured magnitudes minus the magnitudes of the
+homologous structure, a procedure known as `twicing'. Mathematically, this is
+equivalent to finding an unbiased estimator for a complex-valued scalar. We
+generalize this scheme for the case of estimating real or complex valued
+matrices arising in single particle autocorrelation analysis. We name this
+approach ""Anisotropic Twicing"" because unlike the scalar case, the unbiased
+estimator is not obtained by a simple magnitude isotropic correction. We
+compare the performance of the least squares, twicing and anisotropic twicing
+estimators on synthetic and experimental datasets. We demonstrate 3D homology
+modeling in cryo-EM directly from experimental data without iterative
+refinement or class averaging, for the first time.
+",1,0,0,1,0,0
+5377,Epi-two-dimensional fluid flow: a new topological paradigm for dimensionality,"  While a variety of fundamental differences are known to separate
+two-dimensional (2D) and three-dimensional (3D) fluid flows, it is not well
+understood how they are related. Conventionally, dimensional reduction is
+justified by an \emph{a priori} geometrical framework; i.e., 2D flows occur
+under some geometrical constraint such as shallowness. However, deeper inquiry
+into 3D flow often finds the presence of local 2D-like structures without such
+a constraint, where 2D-like behavior may be identified by the integrability of
+vortex lines or vanishing local helicity. Here we propose a new paradigm of
+flow structure by introducing an intermediate class, termed epi-2-dimensional
+flow, and thereby build a topological bridge between 2D and 3D flows. The
+epi-2D property is local, and is preserved in fluid elements obeying ideal
+(inviscid and barotropic) mechanics; a local epi-2D flow may be regarded as a
+`particle' carrying a generalized enstrophy as its charge. A finite viscosity
+may cause `fusion' of two epi-2D particles, generating helicity from their
+charges giving rise to 3D flow.
+",0,1,0,0,0,0
+5378,Dimensional reduction and its breakdown in the driven random field O(N) model,"  The critical behavior of the random field $O(N)$ model driven at a uniform
+velocity is investigated at zero-temperature. From naive phenomenological
+arguments, we introduce a dimensional reduction property, which relates the
+large-scale behavior of the $D$-dimensional driven random field $O(N)$ model to
+that of the $(D-1)$-dimensional pure $O(N)$ model. This is an analogue of the
+dimensional reduction property in equilibrium cases, which states that the
+large-scale behavior of $D$-dimensional random field models is identical to
+that of $(D-2)$-dimensional pure models. However, the dimensional reduction
+property breaks down in low enough dimensions due to the presence of multiple
+meta-stable states. By employing the non-perturbative renormalization group
+approach, we calculate the critical exponents of the driven random field $O(N)$
+model near three-dimensions and determine the range of $N$ in which the
+dimensional reduction breaks down.
+",0,1,0,0,0,0
+5379,Statistical Properties of Loss Rate Estimators in Tree Topology (2),"  Four types of explicit estimators are proposed here to estimate the loss
+rates of the links in a network with the tree topology and all of them are
+derived by the maximum likelihood principle. One of the four is developed from
+an estimator that was used but neglected because it was suspected to have a
+higher variance. All of the estimators are proved to be either unbiased or
+asymptotic unbiased. In addition, a set of formulae are derived to compute the
+efficiencies and variances of the estimates obtained by the estimators. One of
+the formulae shows that if a path is divided into two segments, the variance of
+the estimates obtained for the pass rate of a segment is equal to the variance
+of the pass rate of the path divided by the square of the pass rate of the
+other segment. A number of theorems and corollaries are derived from the
+formulae that can be used to evaluate the performance of an estimator. Using
+the theorems and corollaries, we find the estimators from the neglected one are
+the best estimator for the networks with the tree topology in terms of
+efficiency and computation complexity.
+",1,0,0,0,0,0
+5380,On The Communication Complexity of High-Dimensional Permutations,"  We study the multiparty communication complexity of high dimensional
+permutations, in the Number On the Forehead (NOF) model. This model is due to
+Chandra, Furst and Lipton (CFL) who also gave a nontrivial protocol for the
+Exactly-n problem where three players receive integer inputs and need to decide
+if their inputs sum to a given integer $n$. There is a considerable body of
+literature dealing with the same problem, where $(\mathbb{N},+)$ is replaced by
+some other abelian group. Our work can be viewed as a far-reaching extension of
+this line of work.
+We show that the known lower bounds for that group-theoretic problem apply to
+all high dimensional permutations. We introduce new proof techniques that
+appeal to recent advances in Additive Combinatorics and Ramsey theory. We
+reveal new and unexpected connections between the NOF communication complexity
+of high dimensional permutations and a variety of well known and thoroughly
+studied problems in combinatorics.
+Previous protocols for Exactly-n all rely on the construction of large sets
+of integers without a 3-term arithmetic progression. No direct algorithmic
+protocol was previously known for the problem, and we provide the first such
+algorithm. This suggests new ways to significantly improve the CFL protocol.
+Many new open questions are presented throughout.
+",1,0,0,0,0,0
+5381,Moonshine: Distilling with Cheap Convolutions,"  Many engineers wish to deploy modern neural networks in memory-limited
+settings; but the development of flexible methods for reducing memory use is in
+its infancy, and there is little knowledge of the resulting cost-benefit. We
+propose structural model distillation for memory reduction using a strategy
+that produces a student architecture that is a simple transformation of the
+teacher architecture: no redesign is needed, and the same hyperparameters can
+be used. Using attention transfer, we provide Pareto curves/tables for
+distillation of residual networks with four benchmark datasets, indicating the
+memory versus accuracy payoff. We show that substantial memory savings are
+possible with very little loss of accuracy, and confirm that distillation
+provides student network performance that is better than training that student
+architecture directly on data.
+",1,0,0,1,0,0
+5382,A New Wiretap Channel Model and its Strong Secrecy Capacity,"  In this paper, a new wiretap channel model is proposed, where the legitimate
+transmitter and receiver communicate over a discrete memoryless channel. The
+wiretapper has perfect access to a fixed-length subset of the transmitted
+codeword symbols of her choosing. Additionally, she observes the remainder of
+the transmitted symbols through a discrete memoryless channel. This new model
+subsumes the classical wiretap channel and wiretap channel II with noisy main
+channel as its special cases. The strong secrecy capacity of the proposed
+channel model is identified. Achievability is established by solving a dual
+secret key agreement problem in the source model, and converting the solution
+to the original channel model using probability distribution approximation
+arguments. In the dual problem, a source encoder and decoder, who observe
+random sequences independent and identically distributed according to the input
+and output distributions of the legitimate channel in the original problem,
+communicate a confidential key over a public error-free channel using a single
+forward transmission, in the presence of a compound wiretapping source who has
+perfect access to the public discussion. The security of the key is guaranteed
+for the exponentially many possibilities of the subset chosen at wiretapper by
+deriving a lemma which provides a doubly-exponential convergence rate for the
+probability that, for a fixed choice of the subset, the key is uniform and
+independent from the public discussion and the wiretapping source's
+observation. The converse is derived by using Sanov's theorem to upper bound
+the secrecy capacity of the new wiretap channel model by the secrecy capacity
+when the tapped subset is randomly chosen by nature.
+",1,0,0,0,0,0
+5383,One-to-One Matching of RTT and Path Changes,"  Route selection based on performance measurements is an essential task in
+inter-domain Traffic Engineering. It can benefit from the detection of
+significant changes in RTT measurements and the understanding on potential
+causes of change. Among the extensive works on change detection methods and
+their applications in various domains, few focus on RTT measurements. It is
+thus unclear which approach works the best on such data.
+In this paper, we present an evaluation framework for change detection on RTT
+times series, consisting of: 1) a carefully labelled 34,008-hour RTT dataset as
+ground truth; 2) a scoring method specifically tailored for RTT measurements.
+Furthermore, we proposed a data transformation that improves the detection
+performance of existing methods. Path changes are as well attended to. We fix
+shortcomings of previous works by distinguishing path changes due to routing
+protocols (IGP and BGP) from those caused by load balancing.
+Finally, we apply our change detection methods to a large set of measurements
+from RIPE Atlas. The characteristics of both RTT and path changes are analyzed;
+the correlation between the two are also illustrated. We identify extremely
+frequent AS path changes yet with few consequences on RTT, which has not been
+reported before.
+",1,0,0,0,0,0
+5384,Infinite horizon asymptotic average optimality for large-scale parallel server networks,"  We study infinite-horizon asymptotic average optimality for parallel server
+network with multiple classes of jobs and multiple server pools in the
+Halfin-Whitt regime. Three control formulations are considered: 1) minimizing
+the queueing and idleness cost, 2) minimizing the queueing cost under a
+constraints on idleness at each server pool, and 3) fairly allocating the idle
+servers among different server pools. For the third problem, we consider a
+class of bounded-queue, bounded-state (BQBS) stable networks, in which any
+moment of the state is bounded by that of the queue only (for both the limiting
+diffusion and diffusion-scaled state processes). We show that the optimal
+values for the diffusion-scaled state processes converge to the corresponding
+values of the ergodic control problems for the limiting diffusion. We present a
+family of state-dependent Markov balanced saturation policies (BSPs) that
+stabilize the controlled diffusion-scaled state processes. It is shown that
+under these policies, the diffusion-scaled state process is exponentially
+ergodic, provided that at least one class of jobs has a positive abandonment
+rate. We also establish useful moment bounds, and study the ergodic properties
+of the diffusion-scaled state processes, which play a crucial role in proving
+the asymptotic optimality.
+",1,0,1,0,0,0
+5385,Energy fluxes and spectra for turbulent and laminar flows,"  Two well-known turbulence models to describe the inertial and dissipative
+ranges simultaneously are by Pao~[Phys. Fluids {\bf 8}, 1063 (1965)] and
+Pope~[{\em Turbulent Flows.} Cambridge University Press, 2000]. In this paper,
+we compute energy spectrum $E(k)$ and energy flux $\Pi(k)$ using spectral
+simulations on grids up to $4096^3$, and show consistency between the numerical
+results and predictions by the aforementioned models. We also construct a model
+for laminar flows that predicts $E(k)$ and $\Pi(k)$ to be of the form
+$\exp(-k)$, and verify the model predictions using numerical simulations. The
+shell-to-shell energy transfers for the turbulent flows are {\em forward and
+local} for both inertial and dissipative range, but those for the laminar flows
+are {\em forward and nonlocal}.
+",0,1,0,0,0,0
+5386,Understanding low-temperature bulk transport in samarium hexaboride without relying on in-gap bulk states,"  We present a new model to explain the difference between the transport and
+spectroscopy gaps in samarium hexaboride (SmB$_6$), which has been a mystery
+for some time. We propose that SmB$_6$ can be modeled as an intrinsic
+semiconductor with a depletion length that diverges at cryogenic temperatures.
+In this model, we find a self-consistent solution to Poisson's equation in the
+bulk, with boundary conditions based on Fermi energy pinning due to surface
+charges. The solution yields band bending in the bulk; this explains the
+difference between the two gaps because spectroscopic methods measure the gap
+near the surface, while transport measures the average over the bulk. We also
+connect the model to transport parameters, including the Hall coefficient and
+thermopower, using semiclassical transport theory. The divergence of the
+depletion length additionally explains the 10-12 K feature in data for these
+parameters, demonstrating a crossover from bulk dominated transport above this
+temperature to surface-dominated transport below this temperature. We find good
+agreement between our model and a collection of transport data from 4-40 K.
+This model can also be generalized to materials with similar band structure.
+",0,1,0,0,0,0
+5387,Towards Optimal Strategy for Adaptive Probing in Incomplete Networks,"  We investigate a graph probing problem in which an agent has only an
+incomplete view $G' \subsetneq G$ of the network and wishes to explore the
+network with least effort. In each step, the agent selects a node $u$ in $G'$
+to probe. After probing $u$, the agent gains the information about $u$ and its
+neighbors. All the neighbors of $u$ become \emph{observed} and are
+\emph{probable} in the subsequent steps (if they have not been probed). What is
+the best probing strategy to maximize the number of nodes explored in $k$
+probes? This problem serves as a fundamental component for other
+decision-making problems in incomplete networks such as information harvesting
+in social networks, network crawling, network security, and viral marketing
+with incomplete information.
+While there are a few methods proposed for the problem, none can perform
+consistently well across different network types. In this paper, we establish a
+strong (in)approximability for the problem, proving that no algorithm can
+guarantees finite approximation ratio unless P=NP. On the bright side, we
+design learning frameworks to capture the best probing strategies for
+individual network. Our extensive experiments suggest that our framework can
+learn efficient probing strategies that \emph{consistently} outperform previous
+heuristics and metric-based approaches.
+",1,1,0,0,0,0
+5388,Generalised Discount Functions applied to a Monte-Carlo AImu Implementation,"  In recent years, work has been done to develop the theory of General
+Reinforcement Learning (GRL). However, there are few examples demonstrating
+these results in a concrete way. In particular, there are no examples
+demonstrating the known results regarding gener- alised discounting. We have
+added to the GRL simulation platform AIXIjs the functionality to assign an
+agent arbitrary discount functions, and an environment which can be used to
+determine the effect of discounting on an agent's policy. Using this, we
+investigate how geometric, hyperbolic and power discounting affect an informed
+agent in a simple MDP. We experimentally reproduce a number of theoretical
+results, and discuss some related subtleties. It was found that the agent's
+behaviour followed what is expected theoretically, assuming appropriate
+parameters were chosen for the Monte-Carlo Tree Search (MCTS) planning
+algorithm.
+",1,0,0,0,0,0
+5389,One year of monitoring the Vela pulsar using a Phased Array Feed,"  We have observed the Vela pulsar for one year using a Phased Array Feed (PAF)
+receiver on the 12-metre antenna of the Parkes Test-Bed Facility. These
+observations have allowed us to investigate the stability of the PAF
+beam-weights over time, to demonstrate that pulsars can be timed over long
+periods using PAF technology and to detect and study the most recent glitch
+event that occurred on 12 December 2016. The beam-weights are shown to be
+stable to 1% on time scales on the order of three weeks. We discuss the
+implications of this for monitoring pulsars using PAFs on single dish
+telescopes.
+",0,1,0,0,0,0
+5390,Using Multiple Seasonal Holt-Winters Exponential Smoothing to Predict Cloud Resource Provisioning,"  Elasticity is one of the key features of cloud computing that attracts many
+SaaS providers to minimize their services' cost. Cost is minimized by
+automatically provision and release computational resources depend on actual
+computational needs. However, delay of starting up new virtual resources can
+cause Service Level Agreement violation. Consequently, predicting cloud
+resources provisioning gains a lot of attention to scale computational
+resources in advance. However, most of current approaches do not consider
+multi-seasonality in cloud workloads. This paper proposes cloud resource
+provisioning prediction algorithm based on Holt-Winters exponential smoothing
+method. The proposed algorithm extends Holt-Winters exponential smoothing
+method to model cloud workload with multi-seasonal cycles. Prediction accuracy
+of the proposed algorithm has been improved by employing Artificial Bee Colony
+algorithm to optimize its parameters. Performance of the proposed algorithm has
+been evaluated and compared with double and triple exponential smoothing
+methods. Our results have shown that the proposed algorithm outperforms other
+methods.
+",1,0,0,0,0,0
+5391,Possible evidence for spin-transfer torque induced by spin-triplet supercurrent,"  Cooper pairs in superconductors are normally spin singlet. Nevertheless,
+recent studies suggest that spin-triplet Cooper pairs can be created at
+carefully engineered superconductor-ferromagnet interfaces. If Cooper pairs are
+spin-polarized they would transport not only charge but also a net spin
+component, but without dissipation, and therefore minimize the heating effects
+associated with spintronic devices. Although it is now established that triplet
+supercurrents exist, their most interesting property - spin - is only inferred
+indirectly from transport measurements. In conventional spintronics, it is well
+known that spin currents generate spin-transfer torques that alter
+magnetization dynamics and switch magnetic moments. The observation of similar
+effects due to spin-triplet supercurrents would not only confirm the net spin
+of triplet pairs but also pave the way for applications of superconducting
+spintronics. Here, we present a possible evidence for spin-transfer torques
+induced by triplet supercurrents in superconductor/ferromagnet/superconductor
+(S/F/S) Josephson junctions. Below the superconducting transition temperature
+T_c, the ferromagnetic resonance (FMR) field at X-band (~ 9.0 GHz) shifts
+rapidly to a lower field with decreasing temperature due to the spin-transfer
+torques induced by triplet supercurrents. In contrast, this phenomenon is
+absent in ferromagnet/superconductor (F/S) bilayers and
+superconductor/insulator/ferromagnet/superconductor (S/I/F/S) multilayers where
+no supercurrents pass through the ferromagnetic layer. These experimental
+observations are discussed with theoretical predictions for ferromagnetic
+Josephson junctions with precessing magnetization.
+",0,1,0,0,0,0
+5392,Robust Guaranteed-Cost Adaptive Quantum Phase Estimation,"  Quantum parameter estimation plays a key role in many fields like quantum
+computation, communication and metrology. Optimal estimation allows one to
+achieve the most precise parameter estimates, but requires accurate knowledge
+of the model. Any inevitable uncertainty in the model parameters may heavily
+degrade the quality of the estimate. It is therefore desired to make the
+estimation process robust to such uncertainties. Robust estimation was
+previously studied for a varying phase, where the goal was to estimate the
+phase at some time in the past, using the measurement results from both before
+and after that time within a fixed time interval up to current time. Here, we
+consider a robust guaranteed-cost filter yielding robust estimates of a varying
+phase in real time, where the current phase is estimated using only past
+measurements. Our filter minimizes the largest (worst-case) variance in the
+allowable range of the uncertain model parameter(s) and this determines its
+guaranteed cost. It outperforms in the worst case the optimal Kalman filter
+designed for the model with no uncertainty, that corresponds to the center of
+the possible range of the uncertain parameter(s). Moreover, unlike the Kalman
+filter, our filter in the worst case always performs better than the best
+achievable variance for heterodyne measurements, that we consider as the
+tolerable threshold for our system. Furthermore, we consider effective quantum
+efficiency and effective noise power, and show that our filter provides the
+best results by these measures in the worst case.
+",1,0,1,0,0,0
+5393,End-to-End Multi-View Networks for Text Classification,"  We propose a multi-view network for text classification. Our method
+automatically creates various views of its input text, each taking the form of
+soft attention weights that distribute the classifier's focus among a set of
+base features. For a bag-of-words representation, each view focuses on a
+different subset of the text's words. Aggregating many such views results in a
+more discriminative and robust representation. Through a novel architecture
+that both stacks and concatenates views, we produce a network that emphasizes
+both depth and width, allowing training to converge quickly. Using our
+multi-view architecture, we establish new state-of-the-art accuracies on two
+benchmark tasks.
+",1,0,0,0,0,0
+5394,Collaborative similarity analysis of multilayer developer-project bipartite network,"  To understand the multiple relations between developers and projects on
+GitHub as a whole, we model them as a multilayer bipartite network and analyze
+the degree distributions, the nearest neighbors' degree distributions and their
+correlations with degree, and the collaborative similarity distributions and
+their correlations with degree. Our results show that all degree distributions
+have a power-law form, especially, the degree distribution of projects in
+watching layer has double power-law form. Negative correlations between nearest
+neighbors' degree and degree for both developers and projects are observed in
+both layers, exhibiting a disassortative mixing pattern. The collaborative
+similarity of both developers and projects negatively correlates with degree in
+watching layer, while a positive correlations is observed for developers in
+forking layer and no obvious correlation is observed for projects in forking
+layer.
+",1,1,0,0,0,0
+5395,Evaluation of equity-based debt obligations,"  We consider a class of participation rights, i.e. obligations issued by a
+company to investors who are interested in performance-based compensation.
+Albeit having desirable economic properties equity-based debt obligations
+(EbDO) pose challenges in accounting and contract pricing. We formulate and
+solve the associated mathematical problem in a discrete time, as well as a
+continuous time setting. In the latter case the problem is reduced to a
+forward-backward stochastic differential equation (FBSDE) and solved using the
+method of decoupling fields.
+",0,0,0,0,0,1
+5396,Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP,"  Online sparse linear regression is an online problem where an algorithm
+repeatedly chooses a subset of coordinates to observe in an adversarially
+chosen feature vector, makes a real-valued prediction, receives the true label,
+and incurs the squared loss. The goal is to design an online learning algorithm
+with sublinear regret to the best sparse linear predictor in hindsight. Without
+any assumptions, this problem is known to be computationally intractable. In
+this paper, we make the assumption that data matrix satisfies restricted
+isometry property, and show that this assumption leads to computationally
+efficient algorithms with sublinear regret for two variants of the problem. In
+the first variant, the true label is generated according to a sparse linear
+model with additive Gaussian noise. In the second, the true label is chosen
+adversarially.
+",1,0,0,0,0,0
+5397,Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences,"  Image-guided radiation therapy can benefit from accurate motion tracking by
+ultrasound imaging, in order to minimize treatment margins and radiate moving
+anatomical targets, e.g., due to breathing. One way to formulate this tracking
+problem is the automatic localization of given tracked anatomical landmarks
+throughout a temporal ultrasound sequence. For this, we herein propose a
+fully-convolutional Siamese network that learns the similarity between pairs of
+image regions containing the same landmark. Accordingly, it learns to localize
+and thus track arbitrary image features, not only predefined anatomical
+structures. We employ a temporal consistency model as a location prior, which
+we combine with the network-predicted location probability map to track a
+target iteratively in ultrasound sequences. We applied this method on the
+dataset of the Challenge on Liver Ultrasound Tracking (CLUST) with competitive
+results, where our work is the first to effectively apply CNNs on this tracking
+problem, thanks to our temporal regularization.
+",1,0,0,0,0,0
+5398,Single-Shot 3D Diffractive Imaging of Core-Shell Nanoparticles with Elemental Specificity,"  We report 3D coherent diffractive imaging of Au/Pd core-shell nanoparticles
+with 6 nm resolution on 5-6 femtosecond timescales. We measured single-shot
+diffraction patterns of core-shell nanoparticles using very intense and short
+x-ray free electron laser pulses. By taking advantage of the curvature of the
+Ewald sphere and the symmetry of the nanoparticle, we reconstructed the 3D
+electron density of 34 core-shell structures from single-shot diffraction
+patterns. We determined the size of the Au core and the thickness of the Pd
+shell to be 65.0 +/- 1.0 nm and 4.0 +/- 0.5 nm, respectively, and identified
+the 3D elemental distribution inside the nanoparticles with an accuracy better
+than 2%. We anticipate this method can be used for quantitative 3D imaging of
+symmetrical nanostructures and virus particles.
+",0,1,0,0,0,0
+5399,SEPIA - a new single pixel receiver at the APEX Telescope,"  Context: We describe the new SEPIA (Swedish-ESO PI Instrument for APEX)
+receiver, which was designed and built by the Group for Advanced Receiver
+Development (GARD), at Onsala Space Observatory (OSO) in collaboration with
+ESO. It was installed and commissioned at the APEX telescope during 2015 with
+an ALMA Band 5 receiver channel and updated with a new frequency channel (ALMA
+Band 9) in February 2016. Aims: This manuscript aims to provide, for observers
+who use the SEPIA receiver, a reference in terms of the hardware description,
+optics and performance as well as the commissioning results. Methods: Out of
+three available receiver cartridge positions in SEPIA, the two current
+frequency channels, corresponding to ALMA Band 5, the RF band 158--211 GHz, and
+Band 9, the RF band 600--722 GHz, provide state-of-the-art dual polarization
+receivers. The Band 5 frequency channel uses 2SB SIS mixers with an average SSB
+noise temperature around 45K with IF (intermediate frequency) band 4--8 GHz for
+each sideband providing total 4x4 GHz IF band. The Band 9 frequency channel
+uses DSB SIS mixers with a noise temperature of 75--125K with IF band 4--12 GHz
+for each polarization. Results: Both current SEPIA receiver channels are
+available to all APEX observers.
+",0,1,0,0,0,0
+5400,Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting,"  We present an algorithm to identify sparse dependence structure in continuous
+and non-Gaussian probability distributions, given a corresponding set of data.
+The conditional independence structure of an arbitrary distribution can be
+represented as an undirected graph (or Markov random field), but most
+algorithms for learning this structure are restricted to the discrete or
+Gaussian cases. Our new approach allows for more realistic and accurate
+descriptions of the distribution in question, and in turn better estimates of
+its sparse Markov structure. Sparsity in the graph is of interest as it can
+accelerate inference, improve sampling methods, and reveal important
+dependencies between variables. The algorithm relies on exploiting the
+connection between the sparsity of the graph and the sparsity of transport
+maps, which deterministically couple one probability measure to another.
+",1,0,0,1,0,0
+5401,QuanFuzz: Fuzz Testing of Quantum Program,"  Nowadays, quantum program is widely used and quickly developed. However, the
+absence of testing methodology restricts their quality. Different input format
+and operator from traditional program make this issue hard to resolve.
+In this paper, we present QuanFuzz, a search-based test input generator for
+quantum program. We define the quantum sensitive information to evaluate test
+input for quantum program and use matrix generator to generate test cases with
+higher coverage. First, we extract quantum sensitive information -- measurement
+operations on those quantum registers and the sensitive branches associated
+with those measurement results, from the quantum source code. Then, we use the
+sensitive information guided algorithm to mutate the initial input matrix and
+select those matrices which improve the probability weight for a value of the
+quantum register to trigger the sensitive branch. The process keeps iterating
+until the sensitive branch triggered. We tested QuanFuzz on benchmarks and
+acquired 20% - 60% more coverage compared to traditional testing input
+generation.
+",1,0,0,0,0,0
+5402,Option Pricing Models Driven by the Space-Time Fractional Diffusion: Series Representation and Applications,"  In this paper, we focus on option pricing models based on space-time
+fractional diffusion. We briefly revise recent results which show that the
+option price can be represented in the terms of rapidly converging
+double-series and apply these results to the data from real markets. We focus
+on estimation of model parameters from the market data and estimation of
+implied volatility within the space-time fractional option pricing models.
+",0,0,0,0,0,1
+5403,Anticipation: an effective evolutionary strategy for a sub-optimal population in a cyclic environment,"  We built a two-state model of an asexually reproducing organism in a periodic
+environment endowed with the capability to anticipate an upcoming environmental
+change and undergo pre-emptive switching. By virtue of these anticipatory
+transitions, the organism oscillates between its two states that is a time
+$\theta$ out of sync with the environmental oscillation. We show that an
+anticipation-capable organism increases its long-term fitness over an organism
+that oscillates in-sync with the environment, provided $\theta$ does not exceed
+a threshold. We also show that the long-term fitness is maximized for an
+optimal anticipation time that decreases approximately as $1/n$, $n$ being the
+number of cell divisions in time $T$. Furthermore, we demonstrate that optimal
+""anticipators"" outperforms ""bet-hedgers"" in the range of parameters considered.
+For a sub-optimal ensemble of anticipators, anticipation performs better to
+bet-hedging only when the variance in anticipation is small compared to the
+mean and the rate of pre-emptive transition is high. Taken together, our work
+suggests that anticipation increases overall fitness of an organism in a
+periodic environment and it is a viable alternative to bet-hedging provided the
+error in anticipation is small.
+",0,0,0,0,1,0
+5404,Unravelling Airbnb Predicting Price for New Listing,"  This paper analyzes Airbnb listings in the city of San Francisco to better
+understand how different attributes such as bedrooms, location, house type
+amongst others can be used to accurately predict the price of a new listing
+that optimal in terms of the host's profitability yet affordable to their
+guests. This model is intended to be helpful to the internal pricing tools that
+Airbnb provides to its hosts. Furthermore, additional analysis is performed to
+ascertain the likelihood of a listings availability for potential guests to
+consider while making a booking. The analysis begins with exploring and
+examining the data to make necessary transformations that can be conducive for
+a better understanding of the problem at large while helping us make
+hypothesis. Moving further, machine learning models are built that are
+intuitive to use to validate the hypothesis on pricing and availability and run
+experiments in that context to arrive at a viable solution. The paper then
+concludes with a discussion on the business implications, associated risks and
+future scope.
+",0,0,0,0,0,1
+5405,"Finding, Hitting and Packing Cycles in Subexponential Time on Unit Disk Graphs","  We give algorithms with running time $2^{O({\sqrt{k}\log{k}})} \cdot
+n^{O(1)}$ for the following problems. Given an $n$-vertex unit disk graph $G$
+and an integer $k$, decide whether $G$ contains (1) a path on exactly/at least
+$k$ vertices, (2) a cycle on exactly $k$ vertices, (3) a cycle on at least $k$
+vertices, (4) a feedback vertex set of size at most $k$, and (5) a set of $k$
+pairwise vertex-disjoint cycles. For the first three problems, no
+subexponential time parameterized algorithms were previously known. For the
+remaining two problems, our algorithms significantly outperform the previously
+best known parameterized algorithms that run in time $2^{O(k^{0.75}\log{k})}
+\cdot n^{O(1)}$. Our algorithms are based on a new kind of tree decompositions
+of unit disk graphs where the separators can have size up to $k^{O(1)}$ and
+there exists a solution that crosses every separator at most $O(\sqrt{k})$
+times. The running times of our algorithms are optimal up to the $\log{k}$
+factor in the exponent, assuming the Exponential Time Hypothesis.
+",1,0,0,0,0,0
+5406,The self-referring DNA and protein: a remark on physical and geometrical aspects,"  All known life forms are based upon a hierarchy of interwoven feedback loops,
+operating over a cascade of space, time and energy scales. Among the most basic
+loops are those connecting DNA and proteins. For example, in genetic networks,
+DNA genes are expressed as proteins, which may bind near the same genes and
+thereby control their own expression. In this molecular type of self-reference,
+information is mapped from the DNA sequence to the protein and back to DNA.
+There is a variety of dynamic DNA-protein self-reference loops, and the purpose
+of this remark is to discuss certain geometrical and physical aspects related
+to the back and forth mapping between DNA and proteins. The discussion raises
+basic questions regarding the nature of DNA and proteins as self-referring
+matter, which are examined in a simple toy model.
+",0,0,0,0,1,0
+5407,Optimal input design for system identification using spectral decomposition,"  The aim of this paper is to design a band-limited optimal input with power
+constraints for identifying a linear multi-input multi-output system. It is
+assumed that the nominal system parameters are specified. The key idea is to
+use the spectral decomposition theorem and write the power spectrum as
+$\phi_{u}(j\omega)=\frac{1}{2}H(j\omega)H^*(j\omega)$. The matrix $H(j\omega)$
+is expressed in terms of a truncated basis for
+$\mathcal{L}^2\left(\left[-\omega_{\mbox{cut-off}},\omega_{\mbox{cut-off}}\right]\right)$.
+With this parameterization, the elements of the Fisher Information Matrix and
+the power constraints turn out to be homogeneous quadratics in the basis
+coefficients. The optimality criterion used are the well-known
+$\mathcal{D}-$optimality, $\mathcal{A}-$optimality, $\mathcal{T}-$optimality
+and $\mathcal{E}-$optimality. The resulting optimization problem is non-convex
+in general. A lower bound on the optimum is obtained through a bi-linear
+formulation of the problem, while an upper bound is obtained through a convex
+relaxation. These bounds can be computed efficiently as the associated problems
+are convex. The lower bound is used as a sub-optimal solution, the
+sub-optimality of which is determined by the difference in the bounds.
+Interestingly, the bounds match in many instances and thus, the global optimum
+is achieved. A discussion on the non-convexity of the optimization problem is
+also presented. Simulations are provided for corroboration.
+",1,0,1,0,0,0
+5408,Creating a Web Analysis and Visualization Environment,"  Due to the rapid growth of the World Wide Web, resource discovery becomes an
+increasing problem. As an answer to the demand for information management, a
+third generation of World-Wide Web tools will evolve: information gathering and
+processing agents. This paper describes WAVE (Web Analysis and Visualization
+Environment), a 3D interface for World-Wide Web information visualization and
+browsing. It uses the mathematical theory of concept analysis to conceptually
+cluster objects, and to create a three-dimensional layout of information nodes.
+So-called ""conceptual scales"" for attributes, such as location, title,
+keywords, topic, size, or modification time, provide a formal mechanism that
+automatically classifies and categorizes documents, creating a conceptual
+information space. A visualization shell serves as an ergonomically sound user
+interface for exploring this information space.
+",1,0,0,0,0,0
+5409,Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners,"  Supervised learning has been very successful for automatic segmentation of
+images from a single scanner. However, several papers report deteriorated
+performances when using classifiers trained on images from one scanner to
+segment images from other scanners. We propose a transfer learning classifier
+that adapts to differences between training and test images. This method uses a
+weighted ensemble of classifiers trained on individual images. The weight of
+each classifier is determined by the similarity between its training image and
+the test image.
+We examine three unsupervised similarity measures, which can be used in
+scenarios where no labeled data from a newly introduced scanner or scanning
+protocol is available. The measures are based on a divergence, a bag distance,
+and on estimating the labels with a clustering procedure. These measures are
+asymmetric. We study whether the asymmetry can improve classification. Out of
+the three similarity measures, the bag similarity measure is the most robust
+across different studies and achieves excellent results on four brain tissue
+segmentation datasets and three white matter lesion segmentation datasets,
+acquired at different centers and with different scanners and scanning
+protocols. We show that the asymmetry can indeed be informative, and that
+computing the similarity from the test image to the training images is more
+appropriate than the opposite direction.
+",1,0,0,1,0,0
+5410,Service Providers of the Sharing Economy: Who Joins and Who Benefits?,"  Many ""sharing economy"" platforms, such as Uber and Airbnb, have become
+increasingly popular, providing consumers with more choices and suppliers a
+chance to make profit. They, however, have also brought about emerging issues
+regarding regulation, tax obligation, and impact on urban environment, and have
+generated heated debates from various interest groups. Empirical studies
+regarding these issues are limited, partly due to the unavailability of
+relevant data. Here we aim to understand service providers of the sharing
+economy, investigating who joins and who benefits, using the Airbnb market in
+the United States as a case study. We link more than 211 thousand Airbnb
+listings owned by 188 thousand hosts with demographic, socio-economic status
+(SES), housing, and tourism characteristics. We show that income and education
+are consistently the two most influential factors that are linked to the
+joining of Airbnb, regardless of the form of participation or year. Areas with
+lower median household income, or higher fraction of residents who have
+Bachelor's and higher degrees, tend to have more hosts. However, when
+considering the performance of listings, as measured by number of newly
+received reviews, we find that income has a positive effect for entire-home
+listings; listings located in areas with higher median household income tend to
+have more new reviews. Our findings demonstrate empirically that the
+disadvantage of SES-disadvantaged areas and the advantage of SES-advantaged
+areas may be present in the sharing economy.
+",1,0,0,0,0,0
+5411,"The generalized Fermat equation with exponents 2, 3, n","  We study the Generalized Fermat Equation $x^2 + y^3 = z^p$, to be solved in
+coprime integers, where $p \ge 7$ is prime. Using modularity and level lowering
+techniques, the problem can be reduced to the determination of the sets of
+rational points satisfying certain 2-adic and 3-adic conditions on a finite set
+of twists of the modular curve $X(p)$.
+We first develop new local criteria to decide if two elliptic curves with
+certain types of potentially good reduction at 2 and 3 can have symplectically
+or anti-symplectically isomorphic $p$-torsion modules. Using these criteria we
+produce the minimal list of twists of $X(p)$ that have to be considered, based
+on local information at 2 and 3; this list depends on $p \bmod 24$. Using
+recent results on mod $p$ representations with image in the normalizer of a
+split Cartan subgroup, the list can be further reduced in some cases.
+Our second main result is the complete solution of the equation when $p =
+11$, which previously was the smallest unresolved $p$. One relevant new
+ingredient is the use of the `Selmer group Chabauty' method introduced by the
+third author in a recent preprint, applied in an Elliptic Curve Chabauty
+context, to determine relevant points on $X_0(11)$ defined over certain number
+fields of degree 12. This result is conditional on GRH, which is needed to show
+correctness of the computation of the class groups of five specific number
+fields of degree 36.
+We also give some partial results for the case $p = 13$.
+",0,0,1,0,0,0
+5412,On the image of the almost strict Morse n-category under almost strict n-functors,"  In an earlier work, we constructed the almost strict Morse $n$-category
+$\mathcal X$ which extends Cohen $\&$ Jones $\&$ Segal's flow category. In this
+article, we define two other almost strict $n$-categories $\mathcal V$ and
+$\mathcal W$ where $\mathcal V$ is based on homomorphisms between real vector
+spaces and $\mathcal W$ consists of tuples of positive integers. The Morse
+index and the dimension of the Morse moduli spaces give rise to almost strict
+$n$-category functors $\mathcal F : \mathcal X \to \mathcal V$ and $\mathcal G
+: \mathcal X \to \mathcal W$.
+",0,0,1,0,0,0
+5413,Short-term Memory of Deep RNN,"  The extension of deep learning towards temporal data processing is gaining an
+increasing research interest. In this paper we investigate the properties of
+state dynamics developed in successive levels of deep recurrent neural networks
+(RNNs) in terms of short-term memory abilities. Our results reveal interesting
+insights that shed light on the nature of layering as a factor of RNN design.
+Noticeably, higher layers in a hierarchically organized RNN architecture
+results to be inherently biased towards longer memory spans even prior to
+training of the recurrent connections. Moreover, in the context of Reservoir
+Computing framework, our analysis also points out the benefit of a layered
+recurrent organization as an efficient approach to improve the memory skills of
+reservoir models.
+",0,0,0,1,0,0
+5414,Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge,"  We consider the use of Deep Learning methods for modeling complex phenomena
+like those occurring in natural physical processes. With the large amount of
+data gathered on these phenomena the data intensive paradigm could begin to
+challenge more traditional approaches elaborated over the years in fields like
+maths or physics. However, despite considerable successes in a variety of
+application domains, the machine learning field is not yet ready to handle the
+level of complexity required by such problems. Using an example application,
+namely Sea Surface Temperature Prediction, we show how general background
+knowledge gained from physics could be used as a guideline for designing
+efficient Deep Learning models. In order to motivate the approach and to assess
+its generality we demonstrate a formal link between the solution of a class of
+differential equations underlying a large family of physical phenomena and the
+proposed model. Experiments and comparison with series of baselines including a
+state of the art numerical approach is then provided.
+",1,0,0,1,0,0
+5415,Neutron Stars in Screened Modified Gravity: Chameleon vs Dilaton,"  We consider the scalar field profile around relativistic compact objects such
+as neutron stars for a range of modified gravity models with screening
+mechanisms of the chameleon and Damour-Polyakov types. We focus primarily on
+inverse power law chameleons and the environmentally dependent dilaton as
+examples of both mechanisms. We discuss the modified Tolman-Oppenheimer-Volkoff
+equation and then implement a relaxation algorithm to solve for the scalar
+profiles numerically. We find that chameleons and dilatons behave in a similar
+manner and that there is a large degeneracy between the modified gravity
+parameters and the neutron star equation of state. This is exemplified by the
+modifications to the mass-radius relationship for a variety of model
+parameters.
+",0,1,0,0,0,0
+5416,Spin pumping into superconductors: A new probe of spin dynamics in a superconducting thin film,"  Spin pumping refers to the microwave-driven spin current injection from a
+ferromagnet into the adjacent target material. We theoretically investigate the
+spin pumping into superconductors by fully taking account of impurity
+spin-orbit scattering that is indispensable to describe diffusive spin
+transport with finite spin diffusion length. We calculate temperature
+dependence of the spin pumping signal and show that a pronounced coherence peak
+appears immediately below the superconducting transition temperature Tc, which
+survives even in the presence of the spin-orbit scattering. The phenomenon
+provides us with a new way of studying the dynamic spin susceptibility in a
+superconducting thin film. This is contrasted with the nuclear magnetic
+resonance technique used to study a bulk superconductor.
+",0,1,0,0,0,0
+5417,Evidence of Eta Aquariid Outbursts Recorded in the Classic Maya Hieroglyphic Script Using Orbital Integrations,"  No firm evidence has existed that the ancient Maya civilization recorded
+specific occurrences of meteor showers or outbursts in the corpus of Maya
+hieroglyphic inscriptions. In fact, there has been no evidence of any
+pre-Hispanic civilization in the Western Hemisphere recording any observations
+of any meteor showers on any specific dates.
+The authors numerically integrated meteoroid-sized particles released by
+Comet Halley as early as 1404 BC to identify years within the Maya Classic
+Period, AD 250-909, when Eta Aquariid outbursts might have occurred. Outbursts
+determined by computer model were then compared to specific events in the Maya
+record to see if any correlation existed between the date of the event and the
+date of the outburst. The model was validated by successfully explaining
+several outbursts around the same epoch in the Chinese record. Some outbursts
+observed by the Maya were due to recent revolutions of Comet Halley, within a
+few centuries, and some to resonant behavior in older Halley trails, of the
+order of a thousand years. Examples were found of several different Jovian mean
+motion resonances as well as the 1:3 Saturnian resonance that have controlled
+the dynamical evolution of meteoroids in apparently observed outbursts.
+",0,1,0,0,0,0
+5418,Degenerations of NURBS curves while all of weights approaching infinity,"  NURBS curve is widely used in Computer Aided Design and Computer Aided
+Geometric Design. When a single weight approaches infinity, the limit of a
+NURBS curve tends to the corresponding control point. In this paper, a kind of
+control structure of a NURBS curve, called regular control curve, is defined.
+We prove that the limit of the NURBS curve is exactly its regular control curve
+when all of weights approach infinity, where each weight is multiplied by a
+certain one-parameter function tending to infinity, different for each control
+point. Moreover, some representative examples are presented to show this
+property and indicate its application for shape deformation.
+",1,0,0,0,0,0
+5419,A Convex Parametrization of a New Class of Universal Kernel Functions for use in Kernel Learning,"  We propose a new class of universal kernel functions which admit a linear
+parametrization using positive semidefinite matrices. These kernels are
+generalizations of the Sobolev kernel and are defined by piecewise-polynomial
+functions. The class of kernels is termed ""tessellated"" as the resulting
+discriminant is defined piecewise with hyper-rectangular domains whose corners
+are determined by the training data. The kernels have scalable complexity, but
+each instance is universal in the sense that its hypothesis space is dense in
+$L_2$. Using numerical testing, we show that for the soft margin SVM, this
+class can eliminate the need for Gaussian kernels. Furthermore, we demonstrate
+that when the ratio of the number of training data to features is high, this
+method will significantly outperform other kernel learning algorithms. Finally,
+to reduce the complexity associated with SDP-based kernel learning methods, we
+use a randomized basis for the positive matrices to integrate with existing
+multiple kernel learning algorithms such as SimpleMKL.
+",1,0,0,1,0,0
+5420,Hessian-based Analysis of Large Batch Training and Robustness to Adversaries,"  Large batch size training of Neural Networks has been shown to incur accuracy
+loss when trained with the current methods. The exact underlying reasons for
+this are still not completely understood. Here, we study large batch size
+training through the lens of the Hessian operator and robust optimization. In
+particular, we perform a Hessian based study to analyze exactly how the
+landscape of the loss function changes when training with large batch size. We
+compute the true Hessian spectrum, without approximation, by back-propagating
+the second derivative. Extensive experiments on multiple networks show that
+saddle-points are not the cause for generalization gap of large batch size
+training, and the results consistently show that large batch converges to
+points with noticeably higher Hessian spectrum. Furthermore, we show that
+robust training allows one to favor flat areas, as points with large Hessian
+spectrum show poor robustness to adversarial perturbation. We further study
+this relationship, and provide empirical and theoretical proof that the inner
+loop for robust training is a saddle-free optimization problem \textit{almost
+everywhere}. We present detailed experiments with five different network
+architectures, including a residual network, tested on MNIST, CIFAR-10, and
+CIFAR-100 datasets. We have open sourced our method which can be accessed at
+[1].
+",0,0,0,1,0,0
+5421,Sparse-Group Bayesian Feature Selection Using Expectation Propagation for Signal Recovery and Network Reconstruction,"  We present a Bayesian method for feature selection in the presence of
+grouping information with sparsity on the between- and within group level.
+Instead of using a stochastic algorithm for parameter inference, we employ
+expectation propagation, which is a deterministic and fast algorithm. Available
+methods for feature selection in the presence of grouping information have a
+number of short-comings: on one hand, lasso methods, while being fast,
+underestimate the regression coefficients and do not make good use of the
+grouping information, and on the other hand, Bayesian approaches, while
+accurate in parameter estimation, often rely on the stochastic and slow Gibbs
+sampling procedure to recover the parameters, rendering them infeasible e.g.
+for gene network reconstruction. Our approach of a Bayesian sparse-group
+framework with expectation propagation enables us to not only recover accurate
+parameter estimates in signal recovery problems, but also makes it possible to
+apply this Bayesian framework to large-scale network reconstruction problems.
+The presented method is generic but in terms of application we focus on gene
+regulatory networks. We show on simulated and experimental data that the method
+constitutes a good choice for network reconstruction regarding the number of
+correctly selected features, prediction on new data and reasonable computing
+time.
+",0,0,0,1,0,0
+5422,"A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography","  Brain CT has become a standard imaging tool for emergent evaluation of brain
+condition, and measurement of midline shift (MLS) is one of the most important
+features to address for brain CT assessment. We present a simple method to
+estimate MLS and propose a new alternative parameter to MLS: the ratio of MLS
+over the maximal width of intracranial region (MLS/ICWMAX). Three neurosurgeons
+and our automated system were asked to measure MLS and MLS/ICWMAX in the same
+sets of axial CT images obtained from 41 patients admitted to ICU under
+neurosurgical service. A weighted midline (WML) was plotted based on individual
+pixel intensities, with higher weighted given to the darker portions. The MLS
+could then be measured as the distance between the WML and ideal midline (IML)
+near the foramen of Monro. The average processing time to output an automatic
+MLS measurement was around 10 seconds. Our automated system achieved an overall
+accuracy of 90.24% when the CT images were calibrated automatically, and
+performed better when the calibrations of head rotation were done manually
+(accuracy: 92.68%). MLS/ICWMAX and MLS both gave results in same confusion
+matrices and produced similar ROC curve results. We demonstrated a simple, fast
+and accurate automated system of MLS measurement and introduced a new parameter
+(MLS/ICWMAX) as a good alternative to MLS in terms of estimating the degree of
+brain deformation, especially when non-DICOM images (e.g. JPEG) are more easily
+accessed.
+",1,1,0,0,0,0
+5423,Anisotropic Dielectric Relaxation in Single Crystal H$_{2}$O Ice Ih from 80-250 K,"  Three properties of the dielectric relaxation in ultra-pure single
+crystalline H$_{2}$O ice Ih were probed at temperatures between 80-250 K; the
+thermally stimulated depolarization current, static electrical conductivity,
+and dielectric relaxation time. The measurements were made with a guarded
+parallel-plate capacitor constructed of fused quartz with Au electrodes. The
+data agree with relaxation-based models and provide for the determination of
+activation energies, which suggest that relaxation in ice is dominated by
+Bjerrum defects below 140 K. Furthermore, anisotropy in the dielectric
+relaxation data reveals that molecular reorientations along the
+crystallographic $c$-axis are energetically favored over those along the
+$a$-axis between 80-140 K. These results lend support for the postulate of a
+shared origin between the dielectric relaxation dynamics and the thermodynamic
+partial proton-ordering in ice near 100 K, and suggest a preference for
+ordering along the $c$-axis.
+",0,1,0,0,0,0
+5424,Design of Improved Quasi-Cyclic Protograph-Based Raptor-Like LDPC Codes for Short Block-Lengths,"  Protograph-based Raptor-like low-density parity-check codes (PBRL codes) are
+a recently proposed family of easily encodable and decodable rate-compatible
+LDPC (RC-LDPC) codes. These codes have an excellent iterative decoding
+threshold and performance across all design rates. PBRL codes designed thus
+far, for both long and short block-lengths, have been based on optimizing the
+iterative decoding threshold of the protograph of the RC code family at various
+design rates.
+In this work, we propose a design method to obtain better quasi-cyclic (QC)
+RC-LDPC codes with PBRL structure for short block-lengths (of a few hundred
+bits). We achieve this by maximizing an upper bound on the minimum distance of
+any QC-LDPC code that can be obtained from the protograph of a PBRL ensemble.
+The obtained codes outperform the original PBRL codes at short block-lengths by
+significantly improving the error floor behavior at all design rates.
+Furthermore, we identify a reduction in complexity of the design procedure,
+facilitated by the general structure of a PBRL ensemble.
+",1,0,0,0,0,0
+5425,Comparing the Finite-Time Performance of Simulation-Optimization Algorithms,"  We empirically evaluate the finite-time performance of several
+simulation-optimization algorithms on a testbed of problems with the goal of
+motivating further development of algorithms with strong finite-time
+performance. We investigate if the observed performance of the algorithms can
+be explained by properties of the problems, e.g., the number of decision
+variables, the topology of the objective function, or the magnitude of the
+simulation error.
+",0,0,1,1,0,0
+5426,On the Constituent Attributes of Software and Organisational Resilience,"  Our societies are increasingly dependent on services supplied by computers &
+their software. New technology only exacerbates this dependence by increasing
+the number, performance, and degree of autonomy and inter-connectivity of
+software-empowered computers and cyber-physical ""things"", which translates into
+unprecedented scenarios of interdependence. As a consequence, guaranteeing the
+persistence-of-identity of individual & collective software systems and
+software-backed organisations becomes an important prerequisite toward
+sustaining the safety, security, & quality of the computer services supporting
+human societies. Resilience is the term used to refer to the ability of a
+system to retain its functional and non-functional identity. In this article we
+conjecture that a better understanding of resilience may be reached by
+decomposing it into ancillary constituent properties, the same way as a better
+insight in system dependability was obtained by breaking it down into
+sub-properties. 3 of the main sub-properties of resilience proposed here refer
+respectively to the ability to perceive environmental changes; understand the
+implications introduced by those changes; and plan & enact adjustments intended
+to improve the system-environment fit. A fourth property characterises the way
+the above abilities manifest themselves in computer systems. The 4 properties
+are then analyzed in 3 families of case studies, each consisting of 3 software
+systems that embed different resilience methods. Our major conclusion is that
+reasoning in terms of resilience sub-properties may help revealing the
+characteristics and limitations of classic methods and tools meant to achieve
+system and organisational resilience. We conclude by suggesting that our method
+may prelude to meta-resilient systems -- systems, that is, able to adjust
+optimally their own resilience with respect to changing environmental
+conditions.
+",1,0,0,0,0,0
+5427,"Borcherds-Bozec algebras, root multiplicities and the Schofield construction","  Using the twisted denominator identity, we derive a closed form root
+multiplicity formula for all symmetrizable Borcherds-Bozec algebras and discuss
+its applications including the case of Monster Borcherds-Bozec algebra. In the
+second half of the paper, we provide the Schofield constuction of symmetric
+Borcherds-Bozec algebras.
+",0,0,1,0,0,0
+5428,Pressure-induced spin pairing transition of Fe$^{3+}$ in oxygen octahedra,"  High pressure can provoke spin transitions in transition metal-bearing
+compounds. These transitions are of high interest not only for fundamental
+physics and chemistry, but also may have important implications for
+geochemistry and geophysics of the Earth and planetary interiors. Here we have
+carried out a comparative study of the pressure-induced spin transition in
+compounds with trivalent iron, octahedrally coordinated by oxygen.
+High-pressure single-crystal Mössbauer spectroscopy data for FeBO$_3$,
+Fe$_2$O$_3$ and Fe$_3$(Fe$_{1.766(2)}$Si$_{0.234(2)}$)(SiO$_4$)$_3$ are
+presented together with detailed analysis of hyperfine parameter behavior. We
+argue that $\zeta$-Fe$_2$O$_3$ is an intermediate phase in the reconstructive
+phase transition between $\iota$-Fe$_2$O$_3$ and $\theta$-Fe$_2$O$_3$ and
+question the proposed perovskite-type structure for $\zeta$-Fe$_2$O$_3$.The
+structural data show that the spin transition is closely related to the volume
+of the iron octahedron. The transition starts when volumes reach 8.9-9.3
+\AA$^3$, which corresponds to pressures of 45-60 GPa, depending on the
+compound. Based on phenomenological arguments we conclude that the spin
+transition can proceed only as a first-order phase transition in
+magnetically-ordered compounds. An empirical rule for prediction of cooperative
+behavior at the spin transition is proposed. The instability of iron octahedra,
+together with strong interactions between them in the vicinity of the critical
+volume, may trigger a phase transition in the metastable phase. We find that
+the isomer shift of high spin iron ions depends linearly on the octahedron
+volume with approximately the same coefficient, independent of the particular
+compounds and/or oxidation state. For eight-fold coordinated Fe$^{2+}$ we
+observe a significantly weaker nonlinear volume dependence.
+",0,1,0,0,0,0
+5429,LSH on the Hypercube Revisited,"  LSH (locality sensitive hashing) had emerged as a powerful technique in
+nearest-neighbor search in high dimensions [IM98, HIM12]. Given a point set $P$
+in a metric space, and given parameters $r$ and $\varepsilon > 0$, the task is
+to preprocess the point set, such that given a query point $q$, one can quickly
+decide if $q$ is in distance at most $\leq r$ or $\geq (1+\varepsilon)r$ from
+the point set $P$. Once such a near-neighbor data-structure is available, one
+can reduce the general nearest-neighbor search to logarithmic number of queries
+in such structures [IM98, Har01, HIM12].
+In this note, we revisit the most basic settings, where $P$ is a set of
+points in the binary hypercube $\{0,1\}^d$, under the $L_1$/Hamming metric, and
+present a short description of the LSH scheme in this case. We emphasize that
+there is no new contribution in this note, except (maybe) the presentation
+itself, which is inspired by the authors recent work [HM17].
+",1,0,0,0,0,0
+5430,A Novel Metamaterial-Inspired RF-coil for Preclinical Dual-Nuclei MRI,"  In this paper we propose, design and test a new dual-nuclei RF-coil inspired
+by wire metamaterial structures. The coil operates due to resonant excitation
+of hybridized eigenmodes in multimode flat periodic structures comprising
+several coupled thin metal strips. It was shown that the field distribution of
+the coil (i.e. penetration depth) can be controlled independently at two
+different Larmor frequencies by selecting a proper eigenmode in each of two
+mutually orthogonal periodic structures. The proposed coil requires no lumped
+capacitors for tuning and matching. In order to demonstrate the performance of
+the new design, an experimental preclinical coil for $^{19}$F/$^{1}$H imaging
+of small animals at 7.05T was engineered and tested on a homogeneous liquid
+phantom and in-vivo. The presented results demonstrate that the coil was well
+tuned and matched simultaneously at two Larmor frequencies and capable of image
+acquisition with both the nuclei reaching large homogeneity area along with a
+sufficient signal-to-noise ratio. In an in-vivo experiment it has been shown
+that without retuning the setup it was possible to obtain anatomical $^{1}$H
+images of a mouse under anesthesia consecutively with $^{19}$F images of a tiny
+tube filled with a fluorine-containing liquid and attached to the body of the
+mouse.
+",0,1,0,0,0,0
+5431,A biofilm and organomineralisation model for the growth and limiting size of ooids,"  Ooids are typically spherical sediment grains characterised by concentric
+layers encapsulating a core. There is no universally accepted explanation for
+ooid genesis, though factors such as agitation, abiotic and/or microbial
+mineralisation and size limitation have been variously invoked. We develop a
+mathematical model for ooid growth, inspired by work on avascular brain
+tumours, that assumes mineralisation in a biofilm to form a central core and
+concentric growth of laminations. The model predicts a limiting size with the
+sequential width variation of growth rings comparing favourably with those
+observed in experimentally grown ooids generated from biomicrospheres. In
+reality, this model pattern may be complicated during growth by syngenetic
+aggrading neomorphism of the unstable mineral phase, followed by diagenetic
+recrystallisation that further complicates the structure. Our model provides a
+potential key to understanding the genetic archive preserved in the internal
+structures of naturally occurring ooids.
+",0,1,0,0,0,0
+5432,Spectral Filtering for General Linear Dynamical Systems,"  We give a polynomial-time algorithm for learning latent-state linear
+dynamical systems without system identification, and without assumptions on the
+spectral radius of the system's transition matrix. The algorithm extends the
+recently introduced technique of spectral filtering, previously applied only to
+systems with a symmetric transition matrix, using a novel convex relaxation to
+allow for the efficient identification of phases.
+",0,0,0,1,0,0
+5433,Markov $L_2$-inequality with the Laguerre weight,"  Let $w_\alpha(t) := t^{\alpha}\,e^{-t}$, where $\alpha > -1$, be the Laguerre
+weight function, and let $\|\cdot\|_{w_\alpha}$ be the associated $L_2$-norm,
+$$ \|f\|_{w_\alpha} = \left\{\int_{0}^{\infty} |f(x)|^2
+w_\alpha(x)\,dx\right\}^{1/2}\,. $$ By $\mathcal{P}_n$ we denote the set of
+algebraic polynomials of degree $\le n$.
+We study the best constant $c_n(\alpha)$ in the Markov inequality in this
+norm $$ \|p_n'\|_{w_\alpha} \le c_n(\alpha) \|p_n\|_{w_\alpha}\,,\qquad p_n \in
+\mathcal{P}_n\,, $$ namely the constant $$ c_n(\alpha) := \sup_{p_n \in
+\mathcal{P}_n} \frac{\|p_n'\|_{w_\alpha}}{\|p_n\|_{w_\alpha}}\,. $$ We derive
+explicit lower and upper bounds for the Markov constant $c_n(\alpha)$, as well
+as for the asymptotic Markov constant $$
+c(\alpha)=\lim_{n\rightarrow\infty}\frac{c_n(\alpha)}{n}\,. $$
+",0,0,1,0,0,0
+5434,Intrusion Prevention and Detection in Grid Computing - The ALICE Case,"  Grids allow users flexible on-demand usage of computing resources through
+remote communication networks. A remarkable example of a Grid in High Energy
+Physics (HEP) research is used in the ALICE experiment at European Organization
+for Nuclear Research CERN. Physicists can submit jobs used to process the huge
+amount of particle collision data produced by the Large Hadron Collider (LHC).
+Grids face complex security challenges. They are interesting targets for
+attackers seeking for huge computational resources. Since users can execute
+arbitrary code in the worker nodes on the Grid sites, special care should be
+put in this environment. Automatic tools to harden and monitor this scenario
+are required. Currently, there is no integrated solution for such requirement.
+This paper describes a new security framework to allow execution of job
+payloads in a sandboxed context. It also allows process behavior monitoring to
+detect intrusions, even when new attack methods or zero day vulnerabilities are
+exploited, by a Machine Learning approach. We plan to implement the proposed
+framework as a software prototype that will be tested as a component of the
+ALICE Grid middleware.
+",1,0,0,0,0,0
+5435,Evolution-Preserving Dense Trajectory Descriptors,"  Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve
+state-of-the-art human action recognition results on a number of datasets. This
+paper improves their performance by applying rank pooling to each trajectory,
+encoding the temporal evolution of deep learning features computed along the
+trajectory. This leads to Evolution-Preserving Trajectory (EPT) descriptors, a
+novel type of video descriptor that significantly outperforms Trajectory-pooled
+Deep-learning Descriptors. EPT descriptors are defined based on dense
+trajectories, and they provide complimentary benefits to video descriptors that
+are not based on trajectories. In particular, we show that the combination of
+EPT descriptors and VideoDarwin leads to state-of-the-art performance on
+Hollywood2 and UCF101 datasets.
+",1,0,0,0,0,0
+5436,Multilingual and Cross-lingual Timeline Extraction,"  In this paper we present an approach to extract ordered timelines of events,
+their participants, locations and times from a set of multilingual and
+cross-lingual data sources. Based on the assumption that event-related
+information can be recovered from different documents written in different
+languages, we extend the Cross-document Event Ordering task presented at
+SemEval 2015 by specifying two new tasks for, respectively, Multilingual and
+Cross-lingual Timeline Extraction. We then develop three deterministic
+algorithms for timeline extraction based on two main ideas. First, we address
+implicit temporal relations at document level since explicit time-anchors are
+too scarce to build a wide coverage timeline extraction system. Second, we
+leverage several multilingual resources to obtain a single, inter-operable,
+semantic representation of events across documents and across languages. The
+result is a highly competitive system that strongly outperforms the current
+state-of-the-art. Nonetheless, further analysis of the results reveals that
+linking the event mentions with their target entities and time-anchors remains
+a difficult challenge. The systems, resources and scorers are freely available
+to facilitate its use and guarantee the reproducibility of results.
+",1,0,0,0,0,0
+5437,Mixture modeling on related samples by $ψ$-stick breaking and kernel perturbation,"  There has been great interest recently in applying nonparametric kernel
+mixtures in a hierarchical manner to model multiple related data samples
+jointly. In such settings several data features are commonly present: (i) the
+related samples often share some, if not all, of the mixture components but
+with differing weights, (ii) only some, not all, of the mixture components vary
+across the samples, and (iii) often the shared mixture components across
+samples are not aligned perfectly in terms of their location and spread, but
+rather display small misalignments either due to systematic cross-sample
+difference or more often due to uncontrolled, extraneous causes. Properly
+incorporating these features in mixture modeling will enhance the efficiency of
+inference, whereas ignoring them not only reduces efficiency but can jeopardize
+the validity of the inference due to issues such as confounding. We introduce
+two techniques for incorporating these features in modeling related data
+samples using kernel mixtures. The first technique, called $\psi$-stick
+breaking, is a joint generative process for the mixing weights through the
+breaking of both a stick shared by all the samples for the components that do
+not vary in size across samples and an idiosyncratic stick for each sample for
+those components that do vary in size. The second technique is to imbue random
+perturbation into the kernels, thereby accounting for cross-sample
+misalignment. These techniques can be used either separately or together in
+both parametric and nonparametric kernel mixtures. We derive efficient Bayesian
+inference recipes based on MCMC sampling for models featuring these techniques,
+and illustrate their work through both simulated data and a real flow cytometry
+data set in prediction/estimation, cross-sample calibration, and testing
+multi-sample differences.
+",0,0,0,1,0,0
+5438,Optimal segmentation of directed graph and the minimum number of feedback arcs,"  The minimum feedback arc set problem asks to delete a minimum number of arcs
+(directed edges) from a digraph (directed graph) to make it free of any
+directed cycles. In this work we approach this fundamental cycle-constrained
+optimization problem by considering a generalized task of dividing the digraph
+into D layers of equal size. We solve the D-segmentation problem by the
+replica-symmetric mean field theory and belief-propagation heuristic
+algorithms. The minimum feedback arc density of a given random digraph ensemble
+is then obtained by extrapolating the theoretical results to the limit of large
+D. A divide-and-conquer algorithm (nested-BPR) is devised to solve the minimum
+feedback arc set problem with very good performance and high efficiency.
+",1,1,0,0,0,0
+5439,Ensemble of Neural Classifiers for Scoring Knowledge Base Triples,"  This paper describes our approach for the triple scoring task at the WSDM Cup
+2017. The task required participants to assign a relevance score for each pair
+of entities and their types in a knowledge base in order to enhance the ranking
+results in entity retrieval tasks. We propose an approach wherein the outputs
+of multiple neural network classifiers are combined using a supervised machine
+learning model. The experimental results showed that our proposed method
+achieved the best performance in one out of three measures (i.e., Kendall's
+tau), and performed competitively in the other two measures (i.e., accuracy and
+average score difference).
+",1,0,0,0,0,0
+5440,A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation,"  In this paper, we present an efficient computational framework with the
+purpose of generating weighted pseudo-measurements to improve the quality of
+Distribution System State Estimation (DSSE) and provide observability with
+Advanced Metering Infrastructure (AMI) against unobservable customers and
+missing data. The proposed technique is based on a game-theoretic expansion of
+Relevance Vector Machines (RVM). This platform is able to estimate the customer
+power consumption data and quantify its uncertainty while reducing the
+prohibitive computational burden of model training for large AMI datasets. To
+achieve this objective, the large training set is decomposed and distributed
+among multiple parallel learning entities. The resulting estimations from the
+parallel RVMs are then combined using a game-theoretic model based on the idea
+of repeated games with vector payoff. It is observed that through this approach
+and by exploiting the seasonal changes in customers' behavior the accuracy of
+pseudo-measurements can be considerably improved, while introducing robustness
+against bad training data samples. The proposed pseudo-measurement generation
+model is integrated into a DSSE using a closed-loop information system, which
+takes advantage of a Branch Current State Estimator (BCSE) data to further
+improve the performance of the designed machine learning framework. This method
+has been tested on a practical distribution feeder model with smart meter data
+for verification.
+",1,0,0,0,0,0
+5441,Nonsparse learning with latent variables,"  As a popular tool for producing meaningful and interpretable models,
+large-scale sparse learning works efficiently when the underlying structures
+are indeed or close to sparse. However, naively applying the existing
+regularization methods can result in misleading outcomes due to model
+misspecification. In particular, the direct sparsity assumption on coefficient
+vectors has been questioned in real applications. Therefore, we consider
+nonsparse learning with the conditional sparsity structure that the coefficient
+vector becomes sparse after taking out the impacts of certain unobservable
+latent variables. A new methodology of nonsparse learning with latent variables
+(NSL) is proposed to simultaneously recover the significant observable
+predictors and latent factors as well as their effects. We explore a common
+latent family incorporating population principal components and derive the
+convergence rates of both sample principal components and their score vectors
+that hold for a wide class of distributions. With the properly estimated latent
+variables, properties including model selection consistency and oracle
+inequalities under various prediction and estimation losses are established for
+the proposed methodology. Our new methodology and results are evidenced by
+simulation and real data examples.
+",0,0,1,1,0,0
+5442,The Role of Network Analysis in Industrial and Applied Mathematics,"  Many problems in industry --- and in the social, natural, information, and
+medical sciences --- involve discrete data and benefit from approaches from
+subjects such as network science, information theory, optimization,
+probability, and statistics. The study of networks is concerned explicitly with
+connectivity between different entities, and it has become very prominent in
+industrial settings, an importance that has intensified amidst the modern data
+deluge. In this commentary, we discuss the role of network analysis in
+industrial and applied mathematics, and we give several examples of network
+science in industry. We focus, in particular, on discussing a
+physical-applied-mathematics approach to the study of networks. We also discuss
+several of our own collaborations with industry on projects in network
+analysis.
+",1,1,0,0,0,0
+5443,"Automated Detection, Exploitation, and Elimination of Double-Fetch Bugs using Modern CPU Features","  Double-fetch bugs are a special type of race condition, where an unprivileged
+execution thread is able to change a memory location between the time-of-check
+and time-of-use of a privileged execution thread. If an unprivileged attacker
+changes the value at the right time, the privileged operation becomes
+inconsistent, leading to a change in control flow, and thus an escalation of
+privileges for the attacker. More severely, such double-fetch bugs can be
+introduced by the compiler, entirely invisible on the source-code level.
+We propose novel techniques to efficiently detect, exploit, and eliminate
+double-fetch bugs. We demonstrate the first combination of state-of-the-art
+cache attacks with kernel-fuzzing techniques to allow fully automated
+identification of double fetches. We demonstrate the first fully automated
+reliable detection and exploitation of double-fetch bugs, making manual
+analysis as in previous work superfluous. We show that cache-based triggers
+outperform state-of-the-art exploitation techniques significantly, leading to
+an exploitation success rate of up to 97%. Our modified fuzzer automatically
+detects double fetches and automatically narrows down this candidate set for
+double-fetch bugs to the exploitable ones. We present the first generic
+technique based on hardware transactional memory, to eliminate double-fetch
+bugs in a fully automated and transparent manner. We extend defensive
+programming techniques by retrofitting arbitrary code with automated
+double-fetch prevention, both in trusted execution environments as well as in
+syscalls, with a performance overhead below 1%.
+",1,0,0,0,0,0
+5444,Fano resonances and fluorescence enhancement of a dipole emitter near a plasmonic nanoshell,"  We analytically study the spontaneous emission of a single optical dipole
+emitter in the vicinity of a plasmonic nanoshell, based on the Lorenz-Mie
+theory. We show that the fluorescence enhancement due to the coupling between
+optical emitter and sphere can be tuned by the aspect ratio of the core-shell
+nanosphere and by the distance between the quantum emitter and its surface. In
+particular, we demonstrate that both the enhancement and quenching of the
+fluorescence intensity are associated with plasmonic Fano resonances induced by
+near- and far-field interactions. These Fano resonances have asymmetry
+parameters whose signs depend on the orientation of the dipole with respect to
+the spherical nanoshell. We also show that if the atomic dipole is oriented
+tangentially to the nanoshell, the interaction exhibits saddle points in the
+near-field energy flow. This results in a Lorentzian fluorescence enhancement
+response in the near field and a Fano line-shape in the far field. The
+signatures of this interaction may have interesting applications for sensing
+the presence and the orientation of optical emitters in close proximity to
+plasmonic nanoshells.
+",0,1,0,0,0,0
+5445,Unoriented Spectral Triples,"  Any oriented Riemannian manifold with a Spin-structure defines a spectral
+triple, so the spectral triple can be regarded as a noncommutative
+Spin-manifold. Otherwise for any unoriented Riemannian manifold there is the
+two-fold covering by oriented Riemannian manifold. Moreover there are
+noncommutative generalizations of finite-fold coverings. This circumstances
+yield a notion of unoriented spectral triple which is covered by oriented one.
+",0,0,1,0,0,0
+5446,Gradient-enhanced kriging for high-dimensional problems,"  Surrogate models provide a low computational cost alternative to evaluating
+expensive functions. The construction of accurate surrogate models with large
+numbers of independent variables is currently prohibitive because it requires a
+large number of function evaluations. Gradient-enhanced kriging has the
+potential to reduce the number of function evaluations for the desired accuracy
+when efficient gradient computation, such as an adjoint method, is available.
+However, current gradient-enhanced kriging methods do not scale well with the
+number of sampling points due to the rapid growth in the size of the
+correlation matrix where new information is added for each sampling point in
+each direction of the design space. They do not scale well with the number of
+independent variables either due to the increase in the number of
+hyperparameters that needs to be estimated. To address this issue, we develop a
+new gradient-enhanced surrogate model approach that drastically reduced the
+number of hyperparameters through the use of the partial-least squares method
+that maintains accuracy. In addition, this method is able to control the size
+of the correlation matrix by adding only relevant points defined through the
+information provided by the partial-least squares method. To validate our
+method, we compare the global accuracy of the proposed method with conventional
+kriging surrogate models on two analytic functions with up to 100 dimensions,
+as well as engineering problems of varied complexity with up to 15 dimensions.
+We show that the proposed method requires fewer sampling points than
+conventional methods to obtain the desired accuracy, or provides more accuracy
+for a fixed budget of sampling points. In some cases, we get over 3 times more
+accurate models than a bench of surrogate models from the literature, and also
+over 3200 times faster than standard gradient-enhanced kriging models.
+",1,0,0,1,0,0
+5447,Contagion dynamics of extremist propaganda in social networks,"  Recent terrorist attacks carried out on behalf of ISIS on American and
+European soil by lone wolf attackers or sleeper cells remind us of the
+importance of understanding the dynamics of radicalization mediated by social
+media communication channels. In this paper, we shed light on the social media
+activity of a group of twenty-five thousand users whose association with ISIS
+online radical propaganda has been manually verified. By using a computational
+tool known as dynamical activity-connectivity maps, based on network and
+temporal activity patterns, we investigate the dynamics of social influence
+within ISIS supporters. We finally quantify the effectiveness of ISIS
+propaganda by determining the adoption of extremist content in the general
+population and draw a parallel between radical propaganda and epidemics
+spreading, highlighting that information broadcasters and influential ISIS
+supporters generate highly-infectious cascades of information contagion. Our
+findings will help generate effective countermeasures to combat the group and
+other forms of online extremism.
+",1,1,0,0,0,0
+5448,Estimate exponential memory decay in Hidden Markov Model and its applications,"  Inference in hidden Markov model has been challenging in terms of scalability
+due to dependencies in the observation data. In this paper, we utilize the
+inherent memory decay in hidden Markov models, such that the forward and
+backward probabilities can be carried out with subsequences, enabling efficient
+inference over long sequences of observations. We formulate this forward
+filtering process in the setting of the random dynamical system and there exist
+Lyapunov exponents in the i.i.d random matrices production. And the rate of the
+memory decay is known as $\lambda_2-\lambda_1$, the gap of the top two Lyapunov
+exponents almost surely. An efficient and accurate algorithm is proposed to
+numerically estimate the gap after the soft-max parametrization. The length of
+subsequences $B$ given the controlled error $\epsilon$ is
+$B=\log(\epsilon)/(\lambda_2-\lambda_1)$. We theoretically prove the validity
+of the algorithm and demonstrate the effectiveness with numerical examples. The
+method developed here can be applied to widely used algorithms, such as
+mini-batch stochastic gradient method. Moreover, the continuity of Lyapunov
+spectrum ensures the estimated $B$ could be reused for the nearby parameter
+during the inference.
+",0,0,0,1,0,0
+5449,Fabrication of antenna-coupled KID array for Cosmic Microwave Background detection,"  Kinetic Inductance Detectors (KIDs) have become an attractive alternative to
+traditional bolometers in the sub-mm and mm observing community due to their
+innate frequency multiplexing capabilities and simple lithographic processes.
+These advantages make KIDs a viable option for the $O(500,000)$ detectors
+needed for the upcoming Cosmic Microwave Background - Stage 4 (CMB-S4)
+experiment. We have fabricated antenna-coupled MKID array in the 150GHz band
+optimized for CMB detection. Our design uses a twin slot antenna coupled to
+inverted microstrip made from a superconducting Nb/Al bilayer and SiN$_x$,
+which is then coupled to an Al KID grown on high resistivity Si. We present the
+fabrication process and measurements of SiN$_x$ microstrip resonators.
+",0,1,0,0,0,0
+5450,The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R,"  Penalized regression models such as the lasso have been extensively applied
+to analyzing high-dimensional data sets. However, due to memory limitations,
+existing R packages like glmnet and ncvreg are not capable of fitting
+lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are
+increasingly seen in many areas such as genetics, genomics, biomedical imaging,
+and high-frequency finance. In this research, we implement an R package called
+biglasso that tackles this challenge. biglasso utilizes memory-mapped files to
+store the massive data on the disk, only reading data into memory when
+necessary during model fitting, and is thus able to handle out-of-core
+computation seamlessly. Moreover, it's equipped with newly proposed, more
+efficient feature screening rules, which substantially accelerate the
+computation. Benchmarking experiments show that our biglasso package, as
+compared to existing popular ones like glmnet, is much more memory- and
+computation-efficient. We further analyze a 31 GB real data set on a laptop
+with only 16 GB RAM to demonstrate the out-of-core computation capability of
+biglasso in analyzing massive data sets that cannot be accommodated by existing
+R packages.
+",0,0,0,1,0,0
+5451,Run-and-Inspect Method for Nonconvex Optimization and Global Optimality Bounds for R-Local Minimizers,"  Many optimization algorithms converge to stationary points. When the
+underlying problem is nonconvex, they may get trapped at local minimizers and
+occasionally stagnate near saddle points. We propose the Run-and-Inspect
+Method, which adds an ""inspect"" phase to existing algorithms that helps escape
+from non-global stationary points. The inspection samples a set of points in a
+radius $R$ around the current point. When a sample point yields a sufficient
+decrease in the objective, we move there and resume an existing algorithm. If
+no sufficient decrease is found, the current point is called an approximate
+$R$-local minimizer. We show that an $R$-local minimizer is globally optimal,
+up to a specific error depending on $R$, if the objective function can be
+implicitly decomposed into a smooth convex function plus a restricted function
+that is possibly nonconvex, nonsmooth. For high-dimensional problems, we
+introduce blockwise inspections to overcome the curse of dimensionality while
+still maintaining optimality bounds up to a factor equal to the number of
+blocks. Our method performs well on a set of artificial and realistic nonconvex
+problems by coupling with gradient descent, coordinate descent, EM, and
+prox-linear algorithms.
+",1,0,0,1,0,0
+5452,Hierarchical Adversarially Learned Inference,"  We propose a novel hierarchical generative model with a simple Markovian
+structure and a corresponding inference model. Both the generative and
+inference model are trained using the adversarial learning paradigm. We
+demonstrate that the hierarchical structure supports the learning of
+progressively more abstract representations as well as providing semantically
+meaningful reconstructions with different levels of fidelity. Furthermore, we
+show that minimizing the Jensen-Shanon divergence between the generative and
+inference network is enough to minimize the reconstruction error. The resulting
+semantically meaningful hierarchical latent structure discovery is exemplified
+on the CelebA dataset. There, we show that the features learned by our model in
+an unsupervised way outperform the best handcrafted features. Furthermore, the
+extracted features remain competitive when compared to several recent deep
+supervised approaches on an attribute prediction task on CelebA. Finally, we
+leverage the model's inference network to achieve state-of-the-art performance
+on a semi-supervised variant of the MNIST digit classification task.
+",0,0,0,1,0,0
+5453,Demonstration of a quantum key distribution network in urban fibre-optic communication lines,"  We report the results of the implementation of a quantum key distribution
+(QKD) network using standard fibre communication lines in Moscow. The developed
+QKD network is based on the paradigm of trusted repeaters and allows a common
+secret key to be generated between users via an intermediate trusted node. The
+main feature of the network is the integration of the setups using two types of
+encoding, i.e. polarisation encoding and phase encoding. One of the possible
+applications of the developed QKD network is the continuous key renewal in
+existing symmetric encryption devices with a key refresh time of up to 14 s.
+",1,0,0,0,0,0
+5454,ZhuSuan: A Library for Bayesian Deep Learning,"  In this paper we introduce ZhuSuan, a python probabilistic programming
+library for Bayesian deep learning, which conjoins the complimentary advantages
+of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike
+existing deep learning libraries, which are mainly designed for deterministic
+neural networks and supervised tasks, ZhuSuan is featured for its deep root
+into Bayesian inference, thus supporting various kinds of probabilistic models,
+including both the traditional hierarchical Bayesian models and recent deep
+generative models. We use running examples to illustrate the probabilistic
+programming on ZhuSuan, including Bayesian logistic regression, variational
+auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural
+networks.
+",1,0,0,1,0,0
+5455,UntrimmedNets for Weakly Supervised Action Recognition and Detection,"  Current action recognition methods heavily rely on trimmed videos for model
+training. However, it is expensive and time-consuming to acquire a large-scale
+trimmed video dataset. This paper presents a new weakly supervised
+architecture, called UntrimmedNet, which is able to directly learn action
+recognition models from untrimmed videos without the requirement of temporal
+annotations of action instances. Our UntrimmedNet couples two important
+components, the classification module and the selection module, to learn the
+action models and reason about the temporal duration of action instances,
+respectively. These two components are implemented with feed-forward networks,
+and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit
+the learned models for action recognition (WSR) and detection (WSD) on the
+untrimmed video datasets of THUMOS14 and ActivityNet. Although our UntrimmedNet
+only employs weak supervision, our method achieves performance superior or
+comparable to that of those strongly supervised approaches on these two
+datasets.
+",1,0,0,0,0,0
+5456,Flatness of Minima in Random Inflationary Landscapes,"  We study the likelihood which relative minima of random polynomial potentials
+support the slow-roll conditions for inflation. Consistent with
+renormalizability and boundedness, the coefficients that appear in the
+potential are chosen to be order one with respect to the energy scale at which
+inflation transpires. Investigation of the single field case illustrates a
+window in which the potentials satisfy the slow-roll conditions. When there are
+two scalar fields, we find that the probability depends on the choice of
+distribution for the coefficients. A uniform distribution yields a $0.05\%$
+probability of finding a suitable minimum in the random potential whereas a
+maximum entropy distribution yields a $0.1\%$ probability.
+",0,1,0,0,0,0
+5457,Deconvolutional Latent-Variable Model for Text Sequence Matching,"  A latent-variable model is introduced for text matching, inferring sentence
+representations by jointly optimizing generative and discriminative objectives.
+To alleviate typical optimization challenges in latent-variable models for
+text, we employ deconvolutional networks as the sequence decoder (generator),
+providing learned latent codes with more semantic information and better
+generalization. Our model, trained in an unsupervised manner, yields stronger
+empirical predictive performance than a decoder based on Long Short-Term Memory
+(LSTM), with less parameters and considerably faster training. Further, we
+apply it to text sequence-matching problems. The proposed model significantly
+outperforms several strong sentence-encoding baselines, especially in the
+semi-supervised setting.
+",1,0,0,1,0,0
+5458,Magneto-elastic coupling model of deformable anisotropic superconductors,"  We develop a magneto-elastic (ME) coupling model for the interaction between
+the vortex lattice and crystal elasticity. The theory extends the Kogan-Clem's
+anisotropic Ginzburg-Landau (GL) model to include the elasticity effect. The
+anisotropies in superconductivity and elasticity are simultaneously considered
+in the GL theory frame. We compare the field and angular dependences of the
+magnetization to the relevant experiments. The contribution of the ME
+interaction to the magnetization is comparable to the vortex-lattice energy, in
+materials with relatively strong pressure dependence of the critical
+temperature. The theory can give the appropriate slope of the field dependence
+of magnetization near the upper critical field. The magnetization ratio along
+different vortex frame axes is independent with the ME interaction. The
+theoretical description of the magnetization ratio is applicable only if the
+applied field moderately close to the upper critical field.
+",0,1,0,0,0,0
+5459,Sparse Phase Retrieval via Sparse PCA Despite Model Misspecification: A Simplified and Extended Analysis,"  We consider the problem of high-dimensional misspecified phase retrieval.
+This is where we have an $s$-sparse signal vector $\mathbf{x}_*$ in
+$\mathbb{R}^n$, which we wish to recover using sampling vectors
+$\textbf{a}_1,\ldots,\textbf{a}_m$, and measurements $y_1,\ldots,y_m$, which
+are related by the equation $f(\left<\textbf{a}_i,\textbf{x}_*\right>) = y_i$.
+Here, $f$ is an unknown link function satisfying a positive correlation with
+the quadratic function. This problem was analyzed in a recent paper by Neykov,
+Wang and Liu, who provided recovery guarantees for a two-stage algorithm with
+sample complexity $m = O(s^2\log n)$. In this paper, we show that the first
+stage of their algorithm suffices for signal recovery with the same sample
+complexity, and extend the analysis to non-Gaussian measurements. Furthermore,
+we show how the algorithm can be generalized to recover a signal vector
+$\textbf{x}_*$ efficiently given geometric prior information other than
+sparsity.
+",1,0,1,0,0,0
+5460,Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing,"  We consider the problem of minimizing a convex objective function $F$ when
+one can only evaluate its noisy approximation $\hat{F}$. Unless one assumes
+some structure on the noise, $\hat{F}$ may be an arbitrary nonconvex function,
+making the task of minimizing $F$ intractable. To overcome this, prior work has
+often focused on the case when $F(x)-\hat{F}(x)$ is uniformly-bounded. In this
+paper we study the more general case when the noise has magnitude $\alpha F(x)
++ \beta$ for some $\alpha, \beta > 0$, and present a polynomial time algorithm
+that finds an approximate minimizer of $F$ for this noise model. Previously,
+Markov chains, such as the stochastic gradient Langevin dynamics, have been
+used to arrive at approximate solutions to these optimization problems.
+However, for the noise model considered in this paper, no single temperature
+allows such a Markov chain to both mix quickly and concentrate near the global
+minimizer. We bypass this by combining ""simulated annealing"" with the
+stochastic gradient Langevin dynamics, and gradually decreasing the temperature
+of the chain in order to approach the global minimizer. As a corollary one can
+approximately minimize a nonconvex function that is close to a convex function;
+however, the closeness can deteriorate as one moves away from the optimum.
+",1,0,0,1,0,0
+5461,Online $^{222}$Rn removal by cryogenic distillation in the XENON100 experiment,"  We describe the purification of xenon from traces of the radioactive noble
+gas radon using a cryogenic distillation column. The distillation column is
+integrated into the gas purification loop of the XENON100 detector for online
+radon removal. This enabled us to significantly reduce the constant $^{222}$Rn
+background originating from radon emanation. After inserting an auxiliary
+$^{222}$Rn emanation source in the gas loop, we determined a radon reduction
+factor of R > 27 (95% C.L.) for the distillation column by monitoring the
+$^{222}$Rn activity concentration inside the XENON100 detector.
+",0,1,0,0,0,0
+5462,The Kite Graph is Determined by Its Adjacency Spectrum,"  The Kite graph $Kite_{p}^{q}$ is obtained by appending the complete graph
+$K_{p}$ to a pendant vertex of the path $P_{q}$. In this paper, the kite graph
+is proved to be determined by the spectrum of its adjacency matrix.
+",0,0,1,0,0,0
+5463,Matchability of heterogeneous networks pairs,"  We consider the problem of graph matchability in non-identically distributed
+networks. In a general class of edge-independent networks, we demonstrate that
+graph matchability is almost surely lost when matching the networks directly,
+and is almost perfectly recovered when first centering the networks using
+Universal Singular Value Thresholding before matching. These theoretical
+results are then demonstrated in both real and synthetic simulation settings.
+We also recover analogous core-matchability results in a very general core-junk
+network model, wherein some vertices do not correspond between the graph pair.
+",1,0,1,1,0,0
+5464,Visual Progression Analysis of Student Records Data,"  University curriculum, both on a campus level and on a per-major level, are
+affected in a complex way by many decisions of many administrators and faculty
+over time. As universities across the United States share an urgency to
+significantly improve student success and success retention, there is a
+pressing need to better understand how the student population is progressing
+through the curriculum, and how to provide better supporting infrastructure and
+refine the curriculum for the purpose of improving student outcomes. This work
+has developed a visual knowledge discovery system called eCamp that pulls
+together a variety of populationscale data products, including student grades,
+major descriptions, and graduation records. These datasets were previously
+disconnected and only available to and maintained by independent campus
+offices. The framework models and analyzes the multi-level relationships hidden
+within these data products, and visualizes the student flow patterns through
+individual majors as well as through a hierarchy of majors. These results
+support analytical tasks involving student outcomes, student retention, and
+curriculum design. It is shown how eCamp has revealed student progression
+information that was previously unavailable.
+",1,0,0,0,0,0
+5465,A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction,"  While linear mixed model (LMM) has shown a competitive performance in
+correcting spurious associations raised by population stratification, family
+structures, and cryptic relatedness, more challenges are still to be addressed
+regarding the complex structure of genotypic and phenotypic data. For example,
+geneticists have discovered that some clusters of phenotypes are more
+co-expressed than others. Hence, a joint analysis that can utilize such
+relatedness information in a heterogeneous data set is crucial for genetic
+modeling.
+We proposed the sparse graph-structured linear mixed model (sGLMM) that can
+incorporate the relatedness information from traits in a dataset with
+confounding correction. Our method is capable of uncovering the genetic
+associations of a large number of phenotypes together while considering the
+relatedness of these phenotypes. Through extensive simulation experiments, we
+show that the proposed model outperforms other existing approaches and can
+model correlation from both population structure and shared signals. Further,
+we validate the effectiveness of sGLMM in the real-world genomic dataset on two
+different species from plants and humans. In Arabidopsis thaliana data, sGLMM
+behaves better than all other baseline models for 63.4% traits. We also discuss
+the potential causal genetic variation of Human Alzheimer's disease discovered
+by our model and justify some of the most important genetic loci.
+",1,0,0,1,0,0
+5466,Capacity Releasing Diffusion for Speed and Locality,"  Diffusions and related random walk procedures are of central importance in
+many areas of machine learning, data analysis, and applied mathematics. Because
+they spread mass agnostically at each step in an iterative manner, they can
+sometimes spread mass ""too aggressively,"" thereby failing to find the ""right""
+clusters. We introduce a novel Capacity Releasing Diffusion (CRD) Process,
+which is both faster and stays more local than the classical spectral diffusion
+process. As an application, we use our CRD Process to develop an improved local
+algorithm for graph clustering. Our local graph clustering method can find
+local clusters in a model of clustering where one begins the CRD Process in a
+cluster whose vertices are connected better internally than externally by an
+$O(\log^2 n)$ factor, where $n$ is the number of nodes in the cluster. Thus,
+our CRD Process is the first local graph clustering algorithm that is not
+subject to the well-known quadratic Cheeger barrier. Our result requires a
+certain smoothness condition, which we expect to be an artifact of our
+analysis. Our empirical evaluation demonstrates improved results, in particular
+for realistic social graphs where there are moderately good---but not very
+good---clusters.
+",1,0,0,0,0,0
+5467,Two-term spectral asymptotics for the Dirichlet pseudo-relativistic kinetic energy operator on a bounded domain,"  Continuing the series of works following Weyl's one-term asymptotic formula
+for the counting function $N(\lambda)=\sum_{n=1}^\infty(\lambda_n{-}\lambda)_-$
+of the eigenvalues of the Dirichlet Laplacian and the much later found two-term
+expansion on domains with highly regular boundary by Ivrii and Melrose, we
+prove a two-term asymptotic expansion of the $N$-th Cesàro mean of the
+eigenvalues of $\sqrt{-\Delta + m^2} - m$ for $m>0$ with Dirichlet boundary
+condition on a bounded domain $\Omega\subset\mathbb R^d$ for $d\geq 2$,
+extending a result by Frank and Geisinger for the fractional Laplacian ($m=0$)
+and improving upon the small-time asymptotics of the heat trace $Z(t) =
+\sum_{n=1}^\infty e^{-t \lambda_n}$ by Bañuelos et al. and Park and Song.
+",0,0,1,0,0,0
+5468,Exact Good-Turing characterization of the two-parameter Poisson-Dirichlet superpopulation model,"  Large sample size equivalence between the celebrated {\it approximated}
+Good-Turing estimator of the probability to discover a species already observed
+a certain number of times (Good, 1953) and the modern Bayesian nonparametric
+counterpart has been recently established by virtue of a particular smoothing
+rule based on the two-parameter Poisson-Dirichlet model. Here we improve on
+this result showing that, for any finite sample size, when the population
+frequencies are assumed to be selected from a superpopulation with
+two-parameter Poisson-Dirichlet distribution, then Bayesian nonparametric
+estimation of the discovery probabilities corresponds to Good-Turing {\it
+exact} estimation. Moreover under general superpopulation hypothesis the
+Good-Turing solution admits an interpretation as a modern Bayesian
+nonparametric estimator under partial information.
+",0,0,1,1,0,0
+5469,Uniform deviation and moment inequalities for random polytopes with general densities in arbitrary convex bodies,"  We prove an exponential deviation inequality for the convex hull of a finite
+sample of i.i.d. random points with a density supported on an arbitrary convex
+body in $\R^d$, $d\geq 2$. When the density is uniform, our result yields rate
+optimal upper bounds for all the moments of the missing volume of the convex
+hull, uniformly over all convex bodies of $\R^d$: We make no restrictions on
+their volume, location in the space or smoothness of their boundary. After
+extending an identity due to Efron, we also prove upper bounds for the moments
+of the number of vertices of the random polytope. Surprisingly, these bounds do
+not depend on the underlying density and we prove that the growth rates that we
+obtain are tight in a certain sense.
+",0,0,1,1,0,0
+5470,On the Efficiency of Connection Charges---Part II: Integration of Distributed Energy Resources,"  This two-part paper addresses the design of retail electricity tariffs for
+distribution systems with distributed energy resources (DERs). Part I presents
+a framework to optimize an ex-ante two-part tariff for a regulated monopolistic
+retailer who faces stochastic wholesale prices on the one hand and stochastic
+demand on the other. In Part II, the integration of DERs is addressed by
+analyzing their endogenous effect on the optimal two-part tariff and the
+induced welfare gains. Two DER integration models are considered: (i) a
+decentralized model involving behind-the-meter DERs in a net metering setting,
+and (ii) a centralized model involving DERs integrated by the retailer. It is
+shown that DERs integrated under either model can achieve the same social
+welfare and the net-metering tariff structure is optimal. The retail prices
+under both integration models are equal and reflect the expected wholesale
+prices. The connection charges differ and are affected by the retailer's fixed
+costs as well as the statistical dependencies between wholesale prices and
+behind-the-meter DERs. In particular, the connection charge of the
+decentralized model is generally higher than that of the centralized model. An
+empirical analysis is presented to estimate the impact of DER on welfare
+distribution and inter-class cross-subsidies using real price and demand data
+and simulations. The analysis shows that, with the prevailing retail pricing
+and net-metering, consumer welfare decreases with the level of DER integration.
+Issues of cross-subsidy and practical drawbacks of decentralized integration
+are also discussed.
+",0,0,1,0,0,0
+5471,Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks,"  The standard LSTM recurrent neural networks while very powerful in long-range
+dependency sequence applications have highly complex structure and relatively
+large (adaptive) parameters. In this work, we present empirical comparison
+between the standard LSTM recurrent neural network architecture and three new
+parameter-reduced variants obtained by eliminating combinations of the input
+signal, bias, and hidden unit signals from individual gating signals. The
+experiments on two sequence datasets show that the three new variants, called
+simply as LSTM1, LSTM2, and LSTM3, can achieve comparable performance to the
+standard LSTM model with less (adaptive) parameters.
+",1,0,0,1,0,0
+5472,Transition Jitter in Heat Assisted Magnetic Recording by Micromagnetic Simulation,"  In this paper we apply an extended Landau-Lifschitz equation, as introduced
+by Baňas et al. for the simulation of heat-assisted magnetic recording.
+This equation has similarities with the Landau-Lifshitz-Bloch equation. The
+Baňas equation is supposed to be used in a continuum setting with sub-grain
+discretization by the finite-element method. Thus, local geometric features and
+nonuniform magnetic states during switching are taken into account. We
+implement the Baňas model and test its capability for predicting the
+recording performance in a realistic recording scenario. By performing
+recording simulations on 100 media slabs with randomized granular structure and
+consecutive read back calculation, the write position shift and transition
+jitter for bit lengths of 10nm, 12nm, and 20nm are calculated.
+",0,1,0,0,0,0
+5473,Complexity of human response delay in intermittent control: The case of virtual stick balancing,"  Response delay is an inherent and essential part of human actions. In the
+context of human balance control, the response delay is traditionally modeled
+using the formalism of delay-differential equations, which adopts the
+approximation of fixed delay. However, experimental studies revealing
+substantial variability, adaptive anticipation, and non-stationary dynamics of
+response delay provide evidence against this approximation. In this paper, we
+call for development of principally new mathematical formalism describing human
+response delay. To support this, we present the experimental data from a simple
+virtual stick balancing task. Our results demonstrate that human response delay
+is a widely distributed random variable with complex properties, which can
+exhibit oscillatory and adaptive dynamics characterized by long-range
+correlations. Given this, we argue that the fixed-delay approximation ignores
+essential properties of human response, and conclude with possible directions
+for future developments of new mathematical notions describing human control.
+",0,0,0,0,1,0
+5474,Algebraic cycles on some special hyperkähler varieties,"  This note contains some examples of hyperkähler varieties $X$ having a
+group $G$ of non-symplectic automorphisms, and such that the action of $G$ on
+certain Chow groups of $X$ is as predicted by Bloch's conjecture. The examples
+range in dimension from $6$ to $132$. For each example, the quotient $Y=X/G$ is
+a Calabi-Yau variety which has interesting Chow-theoretic properties; in
+particular, the variety $Y$ satisfies (part of) a strong version of the
+Beauville-Voisin conjecture.
+",0,0,1,0,0,0
+5475,On recurrence in G-spaces,"  We introduce and analyze the following general concept of recurrence. Let $G$
+be a group and let $X$ be a G-space with the action $G\times X\longrightarrow
+X$, $(g,x)\longmapsto gx$. For a family $\mathfrak{F}$ of subset of $X$ and
+$A\in \mathfrak{F}$, we denote $\Delta_{\mathfrak{F}}(A)=\{g\in G: gB\subseteq
+A$ for some $B\in \mathfrak{F}, \ B\subseteq A\}$, and say that a subset $R$ of
+$G$ is $\mathfrak{F}$-recurrent if $R\bigcap \Delta_{\mathfrak{F}}
+(A)\neq\emptyset$ for each $A\in \mathfrak{F}$.
+",0,0,1,0,0,0
+5476,Deep adversarial neural decoding,"  Here, we present a novel approach to solve the problem of reconstructing
+perceived stimuli from brain responses by combining probabilistic inference
+with deep learning. Our approach first inverts the linear transformation from
+latent features to brain responses with maximum a posteriori estimation and
+then inverts the nonlinear transformation from perceived stimuli to latent
+features with adversarial training of convolutional neural networks. We test
+our approach with a functional magnetic resonance imaging experiment and show
+that it can generate state-of-the-art reconstructions of perceived faces from
+brain activations.
+",1,0,0,1,0,0
+5477,Optimally Guarding 2-Reflex Orthogonal Polyhedra by Reflex Edge Guards,"  We study the problem of guarding an orthogonal polyhedron having reflex edges
+in just two directions (as opposed to three) by placing guards on reflex edges
+only.
+We show that (r - g)/2 + 1 reflex edge guards are sufficient, where r is the
+number of reflex edges in a given polyhedron and g is its genus. This bound is
+tight for g=0. We thereby generalize a classic planar Art Gallery theorem of
+O'Rourke, which states that the same upper bound holds for vertex guards in an
+orthogonal polygon with r reflex vertices and g holes.
+Then we give a similar upper bound in terms of m, the total number of edges
+in the polyhedron. We prove that (m - 4)/8 + g reflex edge guards are
+sufficient, whereas the previous best known bound was 11m/72 + g/6 - 1 edge
+guards (not necessarily reflex).
+We also discuss the setting in which guards are open (i.e., they are segments
+without the endpoints), proving that the same results hold even in this more
+challenging case.
+Finally, we show how to compute guard locations in O(n log n) time.
+",1,0,0,0,0,0
+5478,Strongly regular decompositions and symmetric association schemes of a power of two,"  For any positive integer $m$, the complete graph on $2^{2m}(2^m+2)$ vertices
+is decomposed into $2^m+1$ commuting strongly regular graphs, which give rise
+to a symmetric association scheme of class $2^{m+2}-2$. Furthermore, the
+eigenmatrices of the symmetric association schemes are determined explicitly.
+As an application, the eigenmatrix of the commutative strongly regular
+decomposition obtained from the strongly regular graphs is derived.
+",0,0,1,0,0,0
+5479,Resilient Non-Submodular Maximization over Matroid Constraints,"  The control and sensing of large-scale systems results in combinatorial
+problems not only for sensor and actuator placement but also for scheduling or
+observability/controllability. Such combinatorial constraints in system design
+and implementation can be captured using a structure known as matroids. In
+particular, the algebraic structure of matroids can be exploited to develop
+scalable algorithms for sensor and actuator selection, along with quantifiable
+approximation bounds. However, in large-scale systems, sensors and actuators
+may fail or may be (cyber-)attacked. The objective of this paper is to focus on
+resilient matroid-constrained problems arising in control and sensing but in
+the presence of sensor and actuator failures. In general, resilient
+matroid-constrained problems are computationally hard. Contrary to the
+non-resilient case (with no failures), even though they often involve objective
+functions that are monotone or submodular, no scalable approximation algorithms
+are known for their solution. In this paper, we provide the first algorithm,
+that also has the following properties: First, it achieves system-wide
+resiliency, i.e., the algorithm is valid for any number of denial-of-service
+attacks or failures. Second, it is scalable, as our algorithm terminates with
+the same running time as state-of-the-art algorithms for (non-resilient)
+matroid-constrained optimization. Third, it provides provable approximation
+bounds on the system performance, since for monotone objective functions our
+algorithm guarantees a solution close to the optimal. We quantify our
+algorithm's approximation performance using a notion of curvature for monotone
+(not necessarily submodular) set functions. Finally, we support our theoretical
+analyses with numerical experiments, by considering a control-aware sensor
+selection scenario, namely, sensing-constrained robot navigation.
+",1,0,0,1,0,0
+5480,Tests for comparing time-invariant and time-varying spectra based on the Anderson-Darling statistic,"  Based on periodogram-ratios of two univariate time series at different
+frequency points, two tests are proposed for comparing their spectra. One is an
+Anderson-Darling-like statistic for testing the equality of two time-invariant
+spectra. The other is the maximum of Anderson-Darling-like statistics for
+testing the equality of two spectra no matter that they are time-invariant and
+time-varying. Both of two tests are applicable for independent or dependent
+time series. Several simulation examples show that the proposed statistics
+outperform those that are also based on periodogram-ratios but constructed by
+the Pearson-like statistics.
+",0,0,0,1,0,0
+5481,Temperley-Lieb and Birman-Murakami-Wenzl like relations from multiplicity free semi-simple tensor system,"  In this article we consider conditions under which projection operators in
+multiplicity free semi-simple tensor categories satisfy Temperley-Lieb like
+relations. This is then used as a stepping stone to prove sufficient conditions
+for obtaining a representation of the Birman-Murakami-Wenzl algebra from a
+braided multiplicity free semi-simple tensor category. The results are found by
+utalising the data of the categories. There is considerable overlap with the
+results found in arXiv:1607.08908, where proofs are shown by manipulating
+diagrams.
+",0,0,1,0,0,0
+5482,A Nash Type result for Divergence Parabolic Equation related to Hormander's vector fields,"  In this paper we consider the divergence parabolic equation with bounded and
+measurable coefficients related to Hormander's vector fields and establish a
+Nash type result, i.e., the local Holder regularity for weak solutions. After
+deriving the parabolic Sobolev inequality, (1,1) type Poincaré inequality of
+Hormander's vector fields and a De Giorgi type Lemma, the Holder regularity
+of weak solutions to the equation is proved based on the estimates of
+oscillations of solutions and the isomorphism between parabolic Campanato space
+and parabolic Holder space. As a consequence, we give the Harnack inequality
+of weak solutions by showing an extension property of positivity for functions
+in the De Giorgi class.
+",0,0,1,0,0,0
+5483,"Wick order, spreadability and exchangeability for monotone commutation relations","  We exhibit a Hamel basis for the concrete $*$-algebra $\mathfrak{M}_o$
+associated to monotone commutation relations realised on the monotone Fock
+space, mainly composed by Wick ordered words of annihilators and creators. We
+apply such a result to investigate spreadability and exchangeability of the
+stochastic processes arising from such commutation relations. In particular, we
+show that spreadability comes from a monoidal action implementing a dissipative
+dynamics on the norm closure $C^*$-algebra $\mathfrak{M} =
+\overline{\mathfrak{M}_o}$. Moreover, we determine the structure of spreadable
+and exchangeable monotone stochastic processes using their correspondence with
+sp\-reading invariant and symmetric monotone states, respectively.
+",0,0,1,0,0,0
+5484,Visualizing Time-Varying Particle Flows with Diffusion Geometry,"  The tasks of identifying separation structures and clusters in flow data are
+fundamental to flow visualization. Significant work has been devoted to these
+tasks in flow represented by vector fields, but there are unique challenges in
+addressing these tasks for time-varying particle data. The unstructured nature
+of particle data, nonuniform and sparse sampling, and the inability to access
+arbitrary particles in space-time make it difficult to define separation and
+clustering for particle data. We observe that weaker notions of separation and
+clustering through continuous measures of these structures are meaningful when
+coupled with user exploration. We achieve this goal by defining a measure of
+particle similarity between pairs of particles. More specifically, separation
+occurs when spatially-localized particles are dissimilar, while clustering is
+characterized by sets of particles that are similar to one another. To be
+robust to imperfections in sampling we use diffusion geometry to compute
+particle similarity. Diffusion geometry is parameterized by a scale that allows
+a user to explore separation and clustering in a continuous manner. We
+illustrate the benefits of our technique on a variety of 2D and 3D flow
+datasets, from particles integrated in fluid simulations based on time-varying
+vector fields, to particle-based simulations in astrophysics.
+",1,0,0,0,0,0
+5485,Factorization tricks for LSTM networks,"  We present two simple ways of reducing the number of parameters and
+accelerating the training of large Long Short-Term Memory (LSTM) networks: the
+first one is ""matrix factorization by design"" of LSTM matrix into the product
+of two smaller matrices, and the second one is partitioning of LSTM matrix, its
+inputs and states into the independent groups. Both approaches allow us to
+train large LSTM networks significantly faster to the near state-of the art
+perplexity while using significantly less RNN parameters.
+",1,0,0,1,0,0
+5486,Pure Rough Mereology and Counting,"  The study of mereology (parts and wholes) in the context of formal approaches
+to vagueness can be approached in a number of ways. In the context of rough
+sets, mereological concepts with a set-theoretic or valuation based ontology
+acquire complex and diverse behavior. In this research a general rough set
+framework called granular operator spaces is extended and the nature of
+parthood in it is explored from a minimally intrusive point of view. This is
+used to develop counting strategies that help in classifying the framework. The
+developed methodologies would be useful for drawing involved conclusions about
+the nature of data (and validity of assumptions about it) from antichains
+derived from context. The problem addressed is also about whether counting
+procedures help in confirming that the approximations involved in formation of
+data are indeed rough approximations?
+",1,0,1,0,0,0
+5487,Relaxation of nonlinear elastic energies involving deformed configuration and applications to nematic elastomers,"  We start from a variational model for nematic elastomers that involves two
+energies: mechanical and nematic. The first one consists of a nonlinear elastic
+energy which is influenced by the orientation of the molecules of the nematic
+elastomer. The nematic energy is an Oseen--Frank energy in the deformed
+configuration. The constraint of the positivity of the determinant of the
+deformation gradient is imposed. The functionals are not assumed to have the
+usual polyconvexity or quasiconvexity assumptions to be lower semicontinuous.
+We instead compute its relaxation, that is, the lower semicontinuous envelope,
+which turns out to be the quasiconvexification of the mechanical term plus the
+tangential quasiconvexification of the nematic term. The main assumptions are
+that the quasiconvexification of the mechanical term is polyconvex and that the
+deformation is in the Sobolev space $W^{1,p}$ (with $p>n-1$ and $n$ the
+dimension of the space) and does not present cavitation.
+",0,0,1,0,0,0
+5488,A Scalable Framework for Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters with Weight and Workload Balancing,"  Deep Neural Networks (DNNs) have revolutionized numerous applications, but
+the demand for ever more performance remains unabated. Scaling DNN computations
+to larger clusters is generally done by distributing tasks in batch mode using
+methods such as distributed synchronous SGD. Among the issues with this
+approach is that to make the distributed cluster work with high utilization,
+the workload distributed to each node must be large, which implies nontrivial
+growth in the SGD mini-batch size.
+In this paper, we propose a framework called FPDeep, which uses a hybrid of
+model and layer parallelism to configure distributed reconfigurable clusters to
+train DNNs. This approach has numerous benefits. First, the design does not
+suffer from batch size growth. Second, novel workload and weight partitioning
+leads to balanced loads of both among nodes. And third, the entire system is a
+fine-grained pipeline. This leads to high parallelism and utilization and also
+minimizes the time features need to be cached while waiting for
+back-propagation. As a result, storage demand is reduced to the point where
+only on-chip memory is used for the convolution layers. We evaluate FPDeep with
+the Alexnet, VGG-16, and VGG-19 benchmarks. Experimental results show that
+FPDeep has good scalability to a large number of FPGAs, with the limiting
+factor being the FPGA-to-FPGA bandwidth. With 6 transceivers per FPGA, FPDeep
+shows linearity up to 83 FPGAs. Energy efficiency is evaluated with respect to
+GOPs/J. FPDeep provides, on average, 6.36x higher energy efficiency than
+comparable GPU servers.
+",1,0,0,0,0,0
+5489,Toward Incorporation of Relevant Documents in word2vec,"  Recent advances in neural word embedding provide significant benefit to
+various information retrieval tasks. However as shown by recent studies,
+adapting the embedding models for the needs of IR tasks can bring considerable
+further improvements. The embedding models in general define the term
+relatedness by exploiting the terms' co-occurrences in short-window contexts.
+An alternative (and well-studied) approach in IR for related terms to a query
+is using local information i.e. a set of top-retrieved documents. In view of
+these two methods of term relatedness, in this work, we report our study on
+incorporating the local information of the query in the word embeddings. One
+main challenge in this direction is that the dense vectors of word embeddings
+and their estimation of term-to-term relatedness remain difficult to interpret
+and hard to analyze. As an alternative, explicit word representations propose
+vectors whose dimensions are easily interpretable, and recent methods show
+competitive performance to the dense vectors. We introduce a neural-based
+explicit representation, rooted in the conceptual ideas of the word2vec
+Skip-Gram model. The method provides interpretable explicit vectors while
+keeping the effectiveness of the Skip-Gram model. The evaluation of various
+explicit representations on word association collections shows that the newly
+proposed method out- performs the state-of-the-art explicit representations
+when tasked with ranking highly similar terms. Based on the introduced ex-
+plicit representation, we discuss our approaches on integrating local documents
+in globally-trained embedding models and discuss the preliminary results.
+",1,0,0,0,0,0
+5490,Randomized Load Balancing on Networks with Stochastic Inputs,"  Iterative load balancing algorithms for indivisible tokens have been studied
+intensively in the past. Complementing previous worst-case analyses, we study
+an average-case scenario where the load inputs are drawn from a fixed
+probability distribution. For cycles, tori, hypercubes and expanders, we obtain
+almost matching upper and lower bounds on the discrepancy, the difference
+between the maximum and the minimum load. Our bounds hold for a variety of
+probability distributions including the uniform and binomial distribution but
+also distributions with unbounded range such as the Poisson and geometric
+distribution. For graphs with slow convergence like cycles and tori, our
+results demonstrate a substantial difference between the convergence in the
+worst- and average-case. An important ingredient in our analysis is new upper
+bound on the t-step transition probability of a general Markov chain, which is
+derived by invoking the evolving set process.
+",1,0,0,0,0,0
+5491,The classification of Lagrangians nearby the Whitney immersion,"  The Whitney immersion is a Lagrangian sphere inside the four-dimensional
+symplectic vector space which has a single transverse double point of
+self-intersection index $+1.$ This Lagrangian also arises as the Weinstein
+skeleton of the complement of a binodal cubic curve inside the projective
+plane, and the latter Weinstein manifold is thus the `standard' neighbourhood
+of Lagrangian immersions of this type. We classify the Lagrangians inside such
+a neighbourhood which are homologous to the Whitney immersion, and which either
+are embedded or immersed with a single double point; they are shown to be
+Hamiltonian isotopic to either product tori, Chekanov tori, or rescalings of
+the Whitney immersion.
+",0,0,1,0,0,0
+5492,"Simulation study of energy resolution, position resolution and $π^0$-$γ$ separation of a sampling electromagnetic calorimeter at high energies","  A simulation study of energy resolution, position resolution, and
+$\pi^0$-$\gamma$ separation using multivariate methods of a sampling
+calorimeter is presented. As a realistic example, the geometry of the
+calorimeter is taken from the design geometry of the Shashlik calorimeter which
+was considered as a candidate for CMS endcap for the phase II of LHC running.
+The methods proposed in this paper can be easily adapted to various geometrical
+layouts of a sampling calorimeter. Energy resolution is studied for different
+layouts and different absorber-scintillator combinations of the Shashlik
+detector. It is shown that a boosted decision tree using fine grained
+information of the calorimeter can perform three times better than a cut-based
+method for separation of $\pi^0$ from $\gamma$ over a large energy range of 20
+GeV-200 GeV.
+",0,1,0,0,0,0
+5493,Multi-Round Influence Maximization (Extended Version),"  In this paper, we study the Multi-Round Influence Maximization (MRIM)
+problem, where influence propagates in multiple rounds independently from
+possibly different seed sets, and the goal is to select seeds for each round to
+maximize the expected number of nodes that are activated in at least one round.
+MRIM problem models the viral marketing scenarios in which advertisers conduct
+multiple rounds of viral marketing to promote one product. We consider two
+different settings: 1) the non-adaptive MRIM, where the advertiser needs to
+determine the seed sets for all rounds at the very beginning, and 2) the
+adaptive MRIM, where the advertiser can select seed sets adaptively based on
+the propagation results in the previous rounds. For the non-adaptive setting,
+we design two algorithms that exhibit an interesting tradeoff between
+efficiency and effectiveness: a cross-round greedy algorithm that selects seeds
+at a global level and achieves $1/2 - \varepsilon$ approximation ratio, and a
+within-round greedy algorithm that selects seeds round by round and achieves
+$1-e^{-(1-1/e)}-\varepsilon \approx 0.46 - \varepsilon$ approximation ratio but
+saves running time by a factor related to the number of rounds. For the
+adaptive setting, we design an adaptive algorithm that guarantees
+$1-e^{-(1-1/e)}-\varepsilon$ approximation to the adaptive optimal solution. In
+all cases, we further design scalable algorithms based on the reverse influence
+sampling approach and achieve near-linear running time. We conduct experiments
+on several real-world networks and demonstrate that our algorithms are
+effective for the MRIM task.
+",1,0,0,0,0,0
+5494,Generalisation dynamics of online learning in over-parameterised neural networks,"  Deep neural networks achieve stellar generalisation on a variety of problems,
+despite often being large enough to easily fit all their training data. Here we
+study the generalisation dynamics of two-layer neural networks in a
+teacher-student setup, where one network, the student, is trained using
+stochastic gradient descent (SGD) on data generated by another network, called
+the teacher. We show how for this problem, the dynamics of SGD are captured by
+a set of differential equations. In particular, we demonstrate analytically
+that the generalisation error of the student increases linearly with the
+network size, with other relevant parameters held constant. Our results
+indicate that achieving good generalisation in neural networks depends on the
+interplay of at least the algorithm, its learning rate, the model architecture,
+and the data set.
+",1,0,0,1,0,0
+5495,Nonparametric Testing for Differences in Electricity Prices: The Case of the Fukushima Nuclear Accident,"  This work is motivated by the problem of testing for differences in the mean
+electricity prices before and after Germany's abrupt nuclear phaseout after the
+nuclear disaster in Fukushima Daiichi, Japan, in mid-March 2011. Taking into
+account the nature of the data and the auction design of the electricity
+market, we approach this problem using a Local Linear Kernel (LLK) estimator
+for the nonparametric mean function of sparse covariate-adjusted functional
+data. We build upon recent theoretical work on the LLK estimator and propose a
+two-sample test statistics using a finite sample correction to avoid size
+distortions. Our nonparametric test results on the price differences point to a
+Simpson's paradox explaining an unexpected result recently reported in the
+literature.
+",0,0,0,1,0,0
+5496,Dynamic coupling of ferromagnets via spin Hall magnetoresistance,"  The synchronized magnetization dynamics in ferromagnets on a nonmagnetic
+heavy metal caused by the spin Hall effect is investigated theoretically. The
+direct and inverse spin Hall effects near the ferromagnetic/nonmagnetic
+interface generate longitudinal and transverse electric currents. The
+phenomenon is known as the spin Hall magnetoresistance effect, whose magnitude
+depends on the magnetization direction in the ferromagnet due to the spin
+transfer effect. When another ferromagnet is placed onto the same nonmagnet,
+these currents are again converted to the spin current by the spin Hall effect
+and excite the spin torque to this additional ferromagnet, resulting in the
+excitation of the coupled motions of the magnetizations. The in-phase or
+antiphase synchronization of the magnetization oscillations, depending on the
+value of the Gilbert damping constant and the field-like torque strength, is
+found in the transverse geometry by solving the Landau-Lifshitz-Gilbert
+equation numerically. On the other hand, in addition to these synchronizations,
+the synchronization having a phase difference of a quarter of a period is also
+found in the longitudinal geometry. The analytical theory clarifying the
+relation among the current, frequency, and phase difference is also developed,
+where it is shown that the phase differences observed in the numerical
+simulations correspond to that giving the fixed points of the energy supplied
+by the coupling torque.
+",0,1,0,0,0,0
+5497,Exact Combinatorial Inference for Brain Images,"  The permutation test is known as the exact test procedure in statistics.
+However, often it is not exact in practice and only an approximate method since
+only a small fraction of every possible permutation is generated. Even for a
+small sample size, it often requires to generate tens of thousands
+permutations, which can be a serious computational bottleneck. In this paper,
+we propose a novel combinatorial inference procedure that enumerates all
+possible permutations combinatorially without any resampling. The proposed
+method is validated against the standard permutation test in simulation studies
+with the ground truth. The method is further applied in twin DTI study in
+determining the genetic contribution of the minimum spanning tree of the
+structural brain connectivity.
+",0,0,0,1,1,0
+5498,Laser annealing heals radiation damage in avalanche photodiodes,"  Avalanche photodiodes (APDs) are a practical option for space-based quantum
+communications requiring single-photon detection. However, radiation damage to
+APDs significantly increases their dark count rates and reduces their useful
+lifetimes in orbit. We show that high-power laser annealing of irradiated APDs
+of three different models (Excelitas C30902SH, Excelitas SLiK, and Laser
+Components SAP500S2) heals the radiation damage and substantially restores low
+dark count rates. Of nine samples, the maximum dark count rate reduction factor
+varies between 5.3 and 758 when operating at minus 80 degrees Celsius. The
+illumination power to reach these reduction factors ranges from 0.8 to 1.6 W.
+Other photon detection characteristics, such as photon detection efficiency,
+timing jitter, and afterpulsing probability, remain mostly unaffected. These
+results herald a promising method to extend the lifetime of a quantum satellite
+equipped with APDs.
+",0,1,0,0,0,0
+5499,Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification,"  We are interested in the development of surrogate models for uncertainty
+quantification and propagation in problems governed by stochastic PDEs using a
+deep convolutional encoder-decoder network in a similar fashion to approaches
+considered in deep learning for image-to-image regression tasks. Since normal
+neural networks are data intensive and cannot provide predictive uncertainty,
+we propose a Bayesian approach to convolutional neural nets. A recently
+introduced variational gradient descent algorithm based on Stein's method is
+scaled to deep convolutional networks to perform approximate Bayesian inference
+on millions of uncertain network parameters. This approach achieves state of
+the art performance in terms of predictive accuracy and uncertainty
+quantification in comparison to other approaches in Bayesian neural networks as
+well as techniques that include Gaussian processes and ensemble methods even
+when the training data size is relatively small. To evaluate the performance of
+this approach, we consider standard uncertainty quantification benchmark
+problems including flow in heterogeneous media defined in terms of limited
+data-driven permeability realizations. The performance of the surrogate model
+developed is very good even though there is no underlying structure shared
+between the input (permeability) and output (flow/pressure) fields as is often
+the case in the image-to-image regression models used in computer vision
+problems. Studies are performed with an underlying stochastic input
+dimensionality up to $4,225$ where most other uncertainty quantification
+methods fail. Uncertainty propagation tasks are considered and the predictive
+output Bayesian statistics are compared to those obtained with Monte Carlo
+estimates.
+",0,0,0,1,0,0
+5500,A symmetric monoidal and equivariant Segal infinite loop space machine,"  In [MMO] (arXiv:1704.03413), we reworked and generalized equivariant infinite
+loop space theory, which shows how to construct $G$-spectra from $G$-spaces
+with suitable structure. In this paper, we construct a new variant of the
+equivariant Segal machine that starts from the category $\scr{F}$ of finite
+sets rather than from the category ${\scr{F}}_G$ of finite $G$-sets and which
+is equivalent to the machine studied by Shimakawa and in [MMO]. In contrast to
+the machine in [MMO], the new machine gives a lax symmetric monoidal functor
+from the symmetric monoidal category of $\scr{F}$-$G$-spaces to the symmetric
+monoidal category of orthogonal $G$-spectra. We relate it multiplicatively to
+suspension $G$-spectra and to Eilenberg-MacLane $G$-spectra via lax symmetric
+monoidal functors from based $G$-spaces and from abelian groups to
+$\scr{F}$-$G$-spaces. Even non-equivariantly, this gives an appealing new
+variant of the Segal machine. This new variant makes the equivariant
+generalization of the theory essentially formal, hence is likely to be
+applicable in other contexts.
+",0,0,1,0,0,0
+5501,Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials,"  Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from
+the parietal and occipital regions of the brain that are evoked from flickering
+visual stimuli. SSVEPs are robust signals measurable in the
+electroencephalogram (EEG) and are commonly used in brain-computer interfaces
+(BCIs). However, methods for high-accuracy decoding of SSVEPs usually require
+hand-crafted approaches that leverage domain-specific knowledge of the stimulus
+signals, such as specific temporal frequencies in the visual stimuli and their
+relative spatial arrangement. When this knowledge is unavailable, such as when
+SSVEP signals are acquired asynchronously, such approaches tend to fail. In
+this paper, we show how a compact convolutional neural network (Compact-CNN),
+which only requires raw EEG signals for automatic feature extraction, can be
+used to decode signals from a 12-class SSVEP dataset without the need for any
+domain-specific knowledge or calibration data. We report across subject mean
+accuracy of approximately 80% (chance being 8.3%) and show this is
+substantially better than current state-of-the-art hand-crafted approaches
+using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, we
+analyze our Compact-CNN to examine the underlying feature representation,
+discovering that the deep learner extracts additional phase and amplitude
+related features associated with the structure of the dataset. We discuss how
+our Compact-CNN shows promise for BCI applications that allow users to freely
+gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as
+provides a method for analyzing SSVEP signals in a way that might augment our
+understanding about the basic processing in the visual cortex.
+",0,0,0,1,1,0
+5502,Long-range proximity effect in Nb-based heterostructures induced by a magnetically inhomogeneous permalloy layer,"  Odd-frequency triplet Cooper pairs are believed to be the carriers of
+long-range superconducting correlations in ferromagnets. Such triplet pairs are
+generated by inhomogeneous magnetism at the interface between a superconductor
+(S) and a ferromagnet (F). So far, reproducible long-range effects were
+reported only in complex layered structures designed to provide the magnetic
+inhomogeneity. Here we show that spin triplet pair formation can be found in
+simple unstructured Nb/Permalloy (Py = Ni_0.8Fe_0.2)/Nb trilayers and Nb/Py
+bilayers, but only when the thickness of the ferromagnetic layer ranges between
+140 and 250 nm. The effect is related to the emergence of an intrinsically
+inhomogeneous magnetic state, which is a precursor of the well-known stripe
+regime in Py that in our samples sets in at thickness larger than 300 nm.
+",0,1,0,0,0,0
+5503,ASK/PSK-correspondence and the r-map,"  We formulate a correspondence between affine and projective special Kähler
+manifolds of the same dimension. As an application, we show that, under this
+correspondence, the affine special Kähler manifolds in the image of the rigid
+r-map are mapped to one-parameter deformations of projective special Kähler
+manifolds in the image of the supergravity r-map. The above one-parameter
+deformations are interpreted as perturbative $\alpha'$-corrections in heterotic
+and type-II string compactifications with $N=2$ supersymmetry. Also affine
+special Kähler manifolds with quadratic prepotential are mapped to
+one-parameter families of projective special Kähler manifolds with quadratic
+prepotential. We show that the completeness of the deformed supergravity r-map
+metric depends solely on the (well-understood) completeness of the undeformed
+metric and the sign of the deformation parameter.
+",0,0,1,0,0,0
+5504,Trust Region Value Optimization using Kalman Filtering,"  Policy evaluation is a key process in reinforcement learning. It assesses a
+given policy using estimation of the corresponding value function. When using a
+parameterized function to approximate the value, it is common to optimize the
+set of parameters by minimizing the sum of squared Bellman Temporal Differences
+errors. However, this approach ignores certain distributional properties of
+both the errors and value parameters. Taking these distributions into account
+in the optimization process can provide useful information on the amount of
+confidence in value estimation. In this work we propose to optimize the value
+by minimizing a regularized objective function which forms a trust region over
+its parameters. We present a novel optimization method, the Kalman Optimization
+for Value Approximation (KOVA), based on the Extended Kalman Filter. KOVA
+minimizes the regularized objective function by adopting a Bayesian perspective
+over both the value parameters and noisy observed returns. This distributional
+property provides information on parameter uncertainty in addition to value
+estimates. We provide theoretical results of our approach and analyze the
+performance of our proposed optimizer on domains with large state and action
+spaces.
+",1,0,0,1,0,0
+5505,Simplified Long Short-term Memory Recurrent Neural Networks: part I,"  We present five variants of the standard Long Short-term Memory (LSTM)
+recurrent neural networks by uniformly reducing blocks of adaptive parameters
+in the gating mechanisms. For simplicity, we refer to these models as LSTM1,
+LSTM2, LSTM3, LSTM4, and LSTM5, respectively. Such parameter-reduced variants
+enable speeding up data training computations and would be more suitable for
+implementations onto constrained embedded platforms. We comparatively evaluate
+and verify our five variant models on the classical MNIST dataset and
+demonstrate that these variant models are comparable to a standard
+implementation of the LSTM model while using less number of parameters.
+Moreover, we observe that in some cases the standard LSTM's accuracy
+performance will drop after a number of epochs when using the ReLU
+nonlinearity; in contrast, however, LSTM3, LSTM4 and LSTM5 will retain their
+performance.
+",1,0,0,0,0,0
+5506,Contracts as specifications for dynamical systems in driving variable form,"  This paper introduces assume/guarantee contracts on continuous-time control
+systems, hereby extending contract theories for discrete systems to certain new
+model classes and specifications. Contracts are regarded as formal
+characterizations of control specifications, providing an alternative to
+specifications in terms of dissipativity properties or set-invariance. The
+framework has the potential to capture a richer class of specifications more
+suitable for complex engineering systems. The proposed contracts are supported
+by results that enable the verification of contract implementation and the
+comparison of contracts. These results are illustrated by an example of a
+vehicle following system.
+",1,0,0,0,0,0
+5507,Visualizing the Phase-Space Dynamics of an External Cavity Semiconductor Laser,"  We map the phase-space trajectories of an external-cavity semiconductor laser
+using phase portraits. This is both a visualization tool as well as a
+thoroughly quantitative approach enabling unprecedented insight into the
+dynamical regimes, from continuous-wave through coherence collapse as feedback
+is increased. Namely, the phase portraits in the intensity versus laser-diode
+terminal-voltage (serving as a surrogate for inversion) plane are mapped out.
+We observe a route to chaos interrupted by two types of limit cycles, a
+subharmonic regime and period-doubled dynamics at the edge of chaos. The
+transition of the dynamics are analyzed utilizing bifurcation diagrams for both
+the optical intensity and the laser-diode terminal voltage. These observations
+provide visual insight into the dynamics in these systems.
+",0,1,0,0,0,0
+5508,On decision regions of narrow deep neural networks,"  We show that for neural network functions that have width less or equal to
+the input dimension all connected components of decision regions are unbounded.
+The result holds for continuous and strictly monotonic activation functions as
+well as for ReLU activation. This complements recent results on approximation
+capabilities of [Hanin 2017 Approximating] and connectivity of decision regions
+of [Nguyen 2018 Neural] for such narrow neural networks. Further, we give an
+example that negatively answers the question posed in [Nguyen 2018 Neural]
+whether one of their main results still holds for ReLU activation. Our results
+are illustrated by means of numerical experiments.
+",0,0,0,1,0,0
+5509,An adelic arithmeticity theorem for lattices in products,"  We prove that, under mild assumptions, a lattice in a product of semi-simple
+Lie group and a totally disconnected locally compact group is, in a certain
+sense, arithmetic. We do not assume the lattice to be finitely generated or the
+ambient group to be compactly generated.
+",0,0,1,0,0,0
+5510,Quadratic automaton algebras and intermediate growth,"  We present an example of a quadratic algebra given by three generators and
+three relations, which is automaton (the set of normal words forms a regular
+language) and such that its ideal of relations does not possess a finite
+Gröbner basis with respect to any choice of generators and any choice of a
+well-ordering of monomials compatible with multiplication. This answers a
+question of Ufnarovski.
+Another result is a simple example (4 generators and 7 relations) of a
+quadratic algebra of intermediate growth.
+",0,0,1,0,0,0
+5511,Homotopy types of gauge groups related to $S^3$-bundles over $S^4$,"  Let $M_{l,m}$ be the total space of the $S^3$-bundle over $S^4$ classified by
+the element $l\sigma+m\rho\in{\pi_4(SO(4))}$, $l,m\in\mathbb Z$. In this paper
+we study the homotopy theory of gauge groups of principal $G$-bundles over
+manifolds $M_{l,m}$ when $G$ is a simply connected simple compact Lie group
+such that $\pi_6(G)=0$. That is, $G$ is one of the following groups: $SU(n)$
+$(n\geq4)$, $Sp(n)$ $(n\geq2)$, $Spin(n)$ $(n\geq5)$, $F_4$, $E_6$, $E_7$,
+$E_8$. If the integral homology of $M_{l,m}$ is torsion-free, we describe the
+homotopy type of the gauge groups over $M_{l,m}$ as products of recognisable
+spaces. For any manifold $M_{l,m}$ with non-torsion-free homology, we give a
+$p$-local homotopy decomposition, for a prime $p\geq 5$, of the loop space of
+the gauge groups.
+",0,0,1,0,0,0
+5512,Optical quality assurance of GEM foils,"  An analysis software was developed for the high aspect ratio optical scanning
+system in the Detec- tor Laboratory of the University of Helsinki and the
+Helsinki Institute of Physics. The system is used e.g. in the quality assurance
+of the GEM-TPC detectors being developed for the beam diagnostics system of the
+SuperFRS at future FAIR facility. The software was tested by analyzing five
+CERN standard GEM foils scanned with the optical scanning system. The
+measurement uncertainty of the diameter of the GEM holes and the pitch of the
+hole pattern was found to be 0.5 {\mu}m and 0.3 {\mu}m, respectively. The
+software design and the performance are discussed. The correlation between the
+GEM hole size distribution and the corresponding gain variation was studied by
+comparing them against a detailed gain mapping of a foil and a set of six lower
+precision control measurements. It can be seen that a qualitative estimation of
+the behavior of the local variation in gain across the GEM foil can be made
+based on the measured sizes of the outer and inner holes.
+",0,1,0,0,0,0
+5513,On the Global Continuity of the Roots of Families of Monic Polynomials (in Russian),"  We raise a question on the existence of continuous roots of families of monic
+polynomials (by the root of a family of polynomials we mean a function of the
+coefficients of polynomials of a given family that maps each tuple of
+coefficients to a root of the polynomial with these coefficients). We prove
+that the family of monic second-degree polynomials with complex coefficients
+and the families of monic fourth-degree and fifth-degree polynomials with real
+coefficients have no continuous root. We also prove that the family of monic
+second-degree polynomials with real coefficients has continuous roots and we
+describe the set of all such roots.
+",0,0,1,0,0,0
+5514,On Number of Rich Words,"  Any finite word $w$ of length $n$ contains at most $n+1$ distinct palindromic
+factors. If the bound $n+1$ is reached, the word $w$ is called rich. The number
+of rich words of length $n$ over an alphabet of cardinality $q$ is denoted
+$R_n(q)$. For binary alphabet, Rubinchik and Shur deduced that ${R_n(2)}\leq c
+1.605^n $ for some constant $c$. We prove that $\lim\limits_{n\rightarrow
+\infty }\sqrt[n]{R_n(q)}=1$ for any $q$, i.e. $R_n(q)$ has a subexponential
+growth on any alphabet.
+",0,0,1,0,0,0
+5515,A Geometric Analysis of Power System Loadability Regions,"  Understanding the feasible power flow region is of central importance to
+power system analysis. In this paper, we propose a geometric view of the power
+system loadability problem. By using rectangular coordinates for complex
+voltages, we provide an integrated geometric understanding of active and
+reactive power flow equations on loadability boundaries. Based on such an
+understanding, we develop a linear programming framework to 1) verify if an
+operating point is on the loadability boundary, 2) compute the margin of an
+operating point to the loadability boundary, and 3) calculate a loadability
+boundary point of any direction. The proposed method is computationally more
+efficient than existing methods since it does not require solving nonlinear
+optimization problems or calculating the eigenvalues of the power flow
+Jacobian. Standard IEEE test cases demonstrate the capability of the new method
+compared to the current state-of-the-art methods.
+",1,0,1,0,0,0
+5516,Model Selection Confidence Sets by Likelihood Ratio Testing,"  The traditional activity of model selection aims at discovering a single
+model superior to other candidate models. In the presence of pronounced noise,
+however, multiple models are often found to explain the same data equally well.
+To resolve this model selection ambiguity, we introduce the general approach of
+model selection confidence sets (MSCSs) based on likelihood ratio testing. A
+MSCS is defined as a list of models statistically indistinguishable from the
+true model at a user-specified level of confidence, which extends the familiar
+notion of confidence intervals to the model-selection framework. Our approach
+guarantees asymptotically correct coverage probability of the true model when
+both sample size and model dimension increase. We derive conditions under which
+the MSCS contains all the relevant information about the true model structure.
+In addition, we propose natural statistics based on the MSCS to measure
+importance of variables in a principled way that accounts for the overall model
+uncertainty. When the space of feasible models is large, MSCS is implemented by
+an adaptive stochastic search algorithm which samples MSCS models with high
+probability. The MSCS methodology is illustrated through numerical experiments
+on synthetic data and real data examples.
+",0,0,1,1,0,0
+5517,Analysis of Dirichlet forms on graphs,"  In this thesis, we study connections between metric and combinatorial graphs
+from a Dirichlet space point of view.
+",0,0,1,0,0,0
+5518,Designing and building the mlpack open-source machine learning library,"  mlpack is an open-source C++ machine learning library with an emphasis on
+speed and flexibility. Since its original inception in 2007, it has grown to be
+a large project implementing a wide variety of machine learning algorithms,
+from standard techniques such as decision trees and logistic regression to
+modern techniques such as deep neural networks as well as other
+recently-published cutting-edge techniques not found in any other library.
+mlpack is quite fast, with benchmarks showing mlpack outperforming other
+libraries' implementations of the same methods. mlpack has an active community,
+with contributors from around the world---including some from PUST. This short
+paper describes the goals and design of mlpack, discusses how the open-source
+community functions, and shows an example usage of mlpack for a simple data
+science problem.
+",1,0,0,0,0,0
+5519,A Vietoris-Smale mapping theorem for the homotopy of hyperdefinable sets,"  Results of Smale (1957) and Dugundji (1969) allow to compare the homotopy
+groups of two topological spaces $X$ and $Y$ whenever a map $f:X\to Y$ with
+strong connectivity conditions on the fibers is given. We apply similar
+techniques in o-minimal expansions of fields to compare the o-minimal homotopy
+of a definable set $X$ with the homotopy of some of its bounded hyperdefinable
+quotients $X/E$. Under suitable assumption, we show that $\pi_{n}(X)^{\rm
+def}\cong\pi_{n}(X/E)$ and $\dim(X)=\dim_{\mathbb R}(X/E)$. As a special case,
+given a definably compact group, we obtain a new proof of Pillay's group
+conjecture ""$\dim(G)=\dim_{\mathbb R}(G/G^{00}$)"" largely independent of the
+group structure of $G$. We also obtain different proofs of various comparison
+results between classical and o-minimal homotopy.
+",0,0,1,0,0,0
+5520,Characterization of multivariate Bernoulli distributions with given margins,"  We express each Fréchet class of multivariate Bernoulli distributions with
+given margins as the convex hull of a set of densities, which belong to the
+same Fréchet class. This characterisation allows us to establish whether a
+given correlation matrix is compatible with the assigned margins and, if it is,
+to easily construct one of the corresponding joint densities. % Such
+%representation is based on a polynomial expression of the distributions of a
+Fréchet class. We reduce the problem of finding a density belonging to a
+Fréchet class and with given correlation matrix to the solution of a linear
+system of equations. Our methodology also provides the bounds that each
+correlation must satisfy to be compatible with the assigned margins. An
+algorithm and its use in some examples is shown.
+",0,0,1,1,0,0
+5521,Note on character varieties and cluster algebras,"  We use Bonahon-Wong's trace map to study character varieties of the
+once-punctured torus and of the 4-punctured sphere. We clarify a relationship
+with cluster algebra associated with ideal triangulations of surfaces, and we
+show that the Goldman Poisson algebra of loops on surfaces is recovered from
+the Poisson structure of cluster algebra. It is also shown that cluster
+mutations give the automorphism of the character varieties. Motivated by a work
+of Chekhov-Mazzocco-Rubtsov, we revisit confluences of punctures on sphere from
+cluster algebraic viewpoint, and we obtain associated affine cubic surfaces
+constructed by van der Put-Saito based on the Riemann-Hilbert correspondence.
+Further studied are quantizations of character varieties by use of quantum
+cluster algebra.
+",0,0,1,0,0,0
+5522,Privacy-Preserving Multi-Period Demand Response: A Game Theoretic Approach,"  We study a multi-period demand response problem in the smart grid with
+multiple companies and their consumers. We model the interactions by a
+Stackelberg game, where companies are the leaders and consumers are the
+followers. It is shown that this game has a unique equilibrium at which the
+companies set prices to maximize their revenues while the consumers respond
+accordingly to maximize their utilities subject to their local constraints.
+Billing minimization is achieved as an outcome of our method. Closed-form
+expressions are provided for the strategies of all players. Based on these
+solutions, a power allocation game has been formulated, and which is shown to
+admit a unique pure-strategy Nash equilibrium, for which closed-form
+expressions are provided. For privacy, we provide a distributed algorithm for
+the computation of all strategies. We study the asymptotic behavior of
+equilibrium strategies when the numbers of periods and consumers grow. We find
+an appropriate company-to-user ratio for the large population regime.
+Furthermore, it is shown, both analytically and numerically, that the
+multi-period scheme, compared with the single-period one, provides more
+incentives for energy consumers to participate in demand response. We have also
+carried out case studies on real life data to demonstrate the benefits of our
+approach, including billing savings of up to 30\%.
+",1,0,0,0,0,0
+5523,An ALMA survey of submillimetre galaxies in the COSMOS field: The extent of the radio-emitting region revealed by 3 GHz imaging with the Very Large Array,"  We determine the radio size distribution of a large sample of 152 SMGs in
+COSMOS that were detected with ALMA at 1.3 mm. For this purpose, we used the
+observations taken by the VLA-COSMOS 3 GHz Large Project. One hundred and
+fifteen of the 152 target SMGs were found to have a 3 GHz counterpart. The
+median value of the major axis FWHM at 3 GHz is derived to be $4.6\pm0.4$ kpc.
+The radio sizes show no evolutionary trend with redshift, or difference between
+different galaxy morphologies. We also derived the spectral indices between 1.4
+and 3 GHz, and 3 GHz brightness temperatures for the sources, and the median
+values were found to be $\alpha=-0.67$ and $T_{\rm B}=12.6\pm2$ K. Three of the
+target SMGs, which are also detected with the VLBA, show clearly higher
+brightness temperatures than the typical values. Although the observed radio
+emission appears to be predominantly powered by star formation and supernova
+activity, our results provide a strong indication of the presence of an AGN in
+the VLBA and X-ray-detected SMG AzTEC/C61. The median radio-emitting size we
+have derived is 1.5-3 times larger than the typical FIR dust-emitting sizes of
+SMGs, but similar to that of the SMGs' molecular gas component traced through
+mid-$J$ line emission of CO. The physical conditions of SMGs probably render
+the diffusion of cosmic-ray electrons inefficient, and hence an unlikely
+process to lead to the observed extended radio sizes. Instead, our results
+point towards a scenario where SMGs are driven by galaxy interactions and
+mergers. Besides triggering vigorous starbursts, galaxy collisions can also
+pull out the magnetised fluids from the interacting disks, and give rise to a
+taffy-like synchrotron-emitting bridge. This provides an explanation for the
+spatially extended radio emission of SMGs, and can also cause a deviation from
+the well-known IR-radio correlation.
+",0,1,0,0,0,0
+5524,Negative differential resistance and magnetoresistance in zigzag borophene nanoribbons,"  We investigate the transport properties of pristine zigzag-edged borophene
+nanoribbons (ZBNRs) of different widths, using the fist-principles
+calculations. We choose ZBNRs with widths of 5 and 6 as odd and even widths.
+The differences of the quantum transport properties are found, where even-N
+BNRs and odd-N BNRs have different current-voltage relationships. Moreover, the
+negative differential resistance (NDR) can be observed within certain bias
+range in 5-ZBNR, while 6-ZBNR behaves as metal whose current rises with the
+increase of the voltage. The spin filter effect of 36% can be revealed when the
+two electrodes have opposite magnetization direction. Furthermore, the
+magnetoresistance effect appears to be in even-N ZBNRs, and the maximum value
+can reach 70%.
+",0,1,0,0,0,0
+5525,Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications,"  The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs)
+of a graph Laplacian matrix have been widely used in spectral clustering and
+community detection. However, in real-life applications the number of clusters
+or communities (say, $K$) is generally unknown a-priori. Consequently, the
+majority of the existing methods either choose $K$ heuristically or they repeat
+the clustering method with different choices of $K$ and accept the best
+clustering result. The first option, more often, yields suboptimal result,
+while the second option is computationally expensive. In this work, we propose
+an incremental method for constructing the eigenspectrum of the graph Laplacian
+matrix. This method leverages the eigenstructure of graph Laplacian matrix to
+obtain the $K$-th smallest eigenpair of the Laplacian matrix given a collection
+of all previously computed $K-1$ smallest eigenpairs. Our proposed method
+adapts the Laplacian matrix such that the batch eigenvalue decomposition
+problem transforms into an efficient sequential leading eigenpair computation
+problem. As a practical application, we consider user-guided spectral
+clustering. Specifically, we demonstrate that users can utilize the proposed
+incremental method for effective eigenpair computation and for determining the
+desired number of clusters based on multiple clustering metrics.
+",1,0,0,1,0,0
+5526,Audio-replay attack detection countermeasures,"  This paper presents the Speech Technology Center (STC) replay attack
+detection systems proposed for Automatic Speaker Verification Spoofing and
+Countermeasures Challenge 2017. In this study we focused on comparison of
+different spoofing detection approaches. These were GMM based methods, high
+level features extraction with simple classifier and deep learning frameworks.
+Experiments performed on the development and evaluation parts of the challenge
+dataset demonstrated stable efficiency of deep learning approaches in case of
+changing acoustic conditions. At the same time SVM classifier with high level
+features provided a substantial input in the efficiency of the resulting STC
+systems according to the fusion systems results.
+",1,0,0,1,0,0
+5527,Joint Scheduling and Transmission Power Control in Wireless Ad Hoc Networks,"  In this paper, we study how to determine concurrent transmissions and the
+transmission power level of each link to maximize spectrum efficiency and
+minimize energy consumption in a wireless ad hoc network. The optimal joint
+transmission packet scheduling and power control strategy are determined when
+the node density goes to infinity and the network area is unbounded. Based on
+the asymptotic analysis, we determine the fundamental capacity limits of a
+wireless network, subject to an energy consumption constraint. We propose a
+scheduling and transmission power control mechanism to approach the optimal
+solution to maximize spectrum and energy efficiencies in a practical network.
+The distributed implementation of the proposed scheduling and transmission
+power control scheme is presented based on our MAC framework proposed in [1].
+Simulation results demonstrate that the proposed scheme achieves 40% higher
+throughput than existing schemes. Also, the energy consumption using the
+proposed scheme is about 20% of the energy consumed using existing power saving
+MAC protocols.
+",1,0,0,0,0,0
+5528,Proceedings 15th International Conference on Automata and Formal Languages,"  The 15th International Conference on Automata and Formal Languages (AFL 2017)
+was held in Debrecen, Hungary, from September 4 to 6, 2017. The conference was
+organized by the Faculty of Informatics of the University of Debrecen and the
+Faculty of Informatics of the Eötvös Loránd University of Budapest.
+Topics of interest covered all aspects of automata and formal languages,
+including theory and applications.
+",1,0,0,0,0,0
+5529,Correlative cellular ptychography with functionalized nanoparticles at the Fe L-edge,"  Precise localization of nanoparticles within a cell is crucial to the
+understanding of cell-particle interactions and has broad applications in
+nanomedicine. Here, we report a proof-of-principle experiment for imaging
+individual functionalized nanoparticles within a mammalian cell by correlative
+microscopy. Using a chemically-fixed, HeLa cell labeled with fluorescent
+core-shell nanoparticles as a model system, we implemented a graphene-oxide
+layer as a substrate to significantly reduce background scattering. We
+identified cellular features of interest by fluorescence microscopy, followed
+by scanning transmission X-ray tomography to localize the particles in 3D, and
+ptychographic coherent diffractive imaging of the fine features in the region
+at high resolution. By tuning the X-ray energy to the Fe L-edge, we
+demonstrated sensitive detection of nanoparticles composed of a 22 nm magnetic
+Fe3O4 core encased by a 25-nm-thick fluorescent silica (SiO2) shell. These
+fluorescent core-shell nanoparticles act as landmarks and offer clarity in a
+cellular context. Our correlative microscopy results confirmed a subset of
+particles to be fully internalized, and high-contrast ptychographic images
+showed two oxidation states of individual nanoparticles with a resolution of
+~16.5 nm. The ability to precisely localize individual fluorescent
+nanoparticles within mammalian cells will expand our understanding of the
+structure/function relationships for functionalized nanoparticles.
+",0,1,0,0,0,0
+5530,A Dynamic-Adversarial Mining Approach to the Security of Machine Learning,"  Operating in a dynamic real world environment requires a forward thinking and
+adversarial aware design for classifiers, beyond fitting the model to the
+training data. In such scenarios, it is necessary to make classifiers - a)
+harder to evade, b) easier to detect changes in the data distribution over
+time, and c) be able to retrain and recover from model degradation. While most
+works in the security of machine learning has concentrated on the evasion
+resistance (a) problem, there is little work in the areas of reacting to
+attacks (b and c). Additionally, while streaming data research concentrates on
+the ability to react to changes to the data distribution, they often take an
+adversarial agnostic view of the security problem. This makes them vulnerable
+to adversarial activity, which is aimed towards evading the concept drift
+detection mechanism itself. In this paper, we analyze the security of machine
+learning, from a dynamic and adversarial aware perspective. The existing
+techniques of Restrictive one class classifier models, Complex learning models
+and Randomization based ensembles, are shown to be myopic as they approach
+security as a static task. These methodologies are ill suited for a dynamic
+environment, as they leak excessive information to an adversary, who can
+subsequently launch attacks which are indistinguishable from the benign data.
+Based on empirical vulnerability analysis against a sophisticated adversary, a
+novel feature importance hiding approach for classifier design, is proposed.
+The proposed design ensures that future attacks on classifiers can be detected
+and recovered from. The proposed work presents motivation, by serving as a
+blueprint, for future work in the area of Dynamic-Adversarial mining, which
+combines lessons learned from Streaming data mining, Adversarial learning and
+Cybersecurity.
+",0,0,0,1,0,0
+5531,Software stage-effort estimation based on association rule mining and fuzzy set theory,"  Relaying on early effort estimation to predict the required number of
+resources is not often sufficient, and could lead to under or over estimation.
+It is widely acknowledge that that software development process should be
+refined regularly and that software prediction made at early stage of software
+development is yet kind of guesses. Even good predictions are not sufficient
+with inherent uncertainty and risks. The stage-effort estimation allows project
+manager to re-allocate correct number of resources, re-schedule project and
+control project progress to finish on time and within budget. In this paper we
+propose an approach to utilize prior effort records to predict stage effort.
+The proposed model combines concepts of Fuzzy set theory and association rule
+mining. The results were good in terms of prediction accuracy and have
+potential to deliver good stage-effort estimation.
+",1,0,0,0,0,0
+5532,Phase Transitions in Approximate Ranking,"  We study the problem of approximate ranking from observations of pairwise
+interactions. The goal is to estimate the underlying ranks of $n$ objects from
+data through interactions of comparison or collaboration. Under a general
+framework of approximate ranking models, we characterize the exact optimal
+statistical error rates of estimating the underlying ranks. We discover
+important phase transition boundaries of the optimal error rates. Depending on
+the value of the signal-to-noise ratio (SNR) parameter, the optimal rate, as a
+function of SNR, is either trivial, polynomial, exponential or zero. The four
+corresponding regimes thus have completely different error behaviors. To the
+best of our knowledge, this phenomenon, especially the phase transition between
+the polynomial and the exponential rates, has not been discovered before.
+",0,0,1,1,0,0
+5533,Finding Influential Training Samples for Gradient Boosted Decision Trees,"  We address the problem of finding influential training samples for a
+particular case of tree ensemble-based models, e.g., Random Forest (RF) or
+Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this
+problem is studying how the model's predictions change upon leave-one-out
+retraining, leaving out each individual training sample. Recent work has shown
+that, for parametric models, this analysis can be conducted in a
+computationally efficient way. We propose several ways of extending this
+framework to non-parametric GBDT ensembles under the assumption that tree
+structures remain fixed. Furthermore, we introduce a general scheme of
+obtaining further approximations to our method that balance the trade-off
+between performance and computational complexity. We evaluate our approaches on
+various experimental setups and use-case scenarios and demonstrate both the
+quality of our approach to finding influential training samples in comparison
+to the baselines and its computational efficiency.
+",0,0,0,1,0,0
+5534,Vulnerability and co-susceptibility determine the size of network cascades,"  In a network, a local disturbance can propagate and eventually cause a
+substantial part of the system to fail, in cascade events that are easy to
+conceptualize but extraordinarily difficult to predict. Here, we develop a
+statistical framework that can predict cascade size distributions by
+incorporating two ingredients only: the vulnerability of individual components
+and the co-susceptibility of groups of components (i.e., their tendency to fail
+together). Using cascades in power grids as a representative example, we show
+that correlations between component failures define structured and often
+surprisingly large groups of co-susceptible components. Aside from their
+implications for blackout studies, these results provide insights and a new
+modeling framework for understanding cascades in financial systems, food webs,
+and complex networks in general.
+",1,1,0,0,0,0
+5535,Transfer learning for music classification and regression tasks,"  In this paper, we present a transfer learning approach for music
+classification and regression tasks. We propose to use a pre-trained convnet
+feature, a concatenated feature vector using the activations of feature maps of
+multiple layers in a trained convolutional network. We show how this convnet
+feature can serve as general-purpose music representation. In the experiments,
+a convnet is trained for music tagging and then transferred to other
+music-related classification and regression tasks. The convnet feature
+outperforms the baseline MFCC feature in all the considered tasks and several
+previous approaches that are aggregating MFCCs as well as low- and high-level
+music features.
+",1,0,0,0,0,0
+5536,Dynamic Transition in Symbiotic Evolution Induced by Growth Rate Variation,"  In a standard bifurcation of a dynamical system, the stationary points (or
+more generally attractors) change qualitatively when varying a control
+parameter. Here we describe a novel unusual effect, when the change of a
+parameter, e.g. a growth rate, does not influence the stationary states, but
+nevertheless leads to a qualitative change of dynamics. For instance, such a
+dynamic transition can be between the convergence to a stationary state and a
+strong increase without stationary states, or between the convergence to one
+stationary state and that to a different state. This effect is illustrated for
+a dynamical system describing two symbiotic populations, one of which exhibits
+a growth rate larger than the other one. We show that, although the stationary
+states of the dynamical system do not depend on the growth rates, the latter
+influence the boundary of the basins of attraction. This change of the basins
+of attraction explains this unusual effect of the quantitative change of
+dynamics by growth rate variation.
+",0,1,0,0,0,0
+5537,Scale-invariant magnetoresistance in a cuprate superconductor,"  The anomalous metallic state in high-temperature superconducting cuprates is
+masked by the onset of superconductivity near a quantum critical point. Use of
+high magnetic fields to suppress superconductivity has enabled a detailed study
+of the ground state in these systems. Yet, the direct effect of strong magnetic
+fields on the metallic behavior at low temperatures is poorly understood,
+especially near critical doping, $x=0.19$. Here we report a high-field
+magnetoresistance study of thin films of \LSCO cuprates in close vicinity to
+critical doping, $0.161\leq x\leq0.190$. We find that the metallic state
+exposed by suppressing superconductivity is characterized by a
+magnetoresistance that is linear in magnetic field up to the highest measured
+fields of $80$T. The slope of the linear-in-field resistivity is
+temperature-independent at very high fields. It mirrors the magnitude and
+doping evolution of the linear-in-temperature resistivity that has been
+ascribed to Planckian dissipation near a quantum critical point. This
+establishes true scale-invariant conductivity as the signature of the strange
+metal state in the high-temperature superconducting cuprates.
+",0,1,0,0,0,0
+5538,Thermoelectric Devices: Principles and Future Trends,"  The principles of the thermoelectric phenomenon, including Seebeck effect,
+Peltier effect, and Thomson effect are discussed. The dependence of the
+thermoelectric devices on the figure of merit, Seebeck coefficient, electrical
+conductivity, and thermal conductivity are explained in details. The paper
+provides an overview of the different types of thermoelectric materials,
+explains the techniques used to grow thin films for these materials, and
+discusses future research and development trends for this technology.
+",0,1,0,0,0,0
+5539,Exploring one particle orbitals in large Many-Body Localized systems,"  Strong disorder in interacting quantum systems can give rise to the
+phenomenon of Many-Body Localization (MBL), which defies thermalization due to
+the formation of an extensive number of quasi local integrals of motion. The
+one particle operator content of these integrals of motion is related to the
+one particle orbitals of the one particle density matrix and shows a strong
+signature across the MBL transition as recently pointed out by Bera et al.
+[Phys. Rev. Lett. 115, 046603 (2015); Ann. Phys. 529, 1600356 (2017)]. We study
+the properties of the one particle orbitals of many-body eigenstates of an MBL
+system in one dimension. Using shift-and-invert MPS (SIMPS), a matrix product
+state method to target highly excited many-body eigenstates introduced in
+[Phys. Rev. Lett. 118, 017201 (2017)], we are able to obtain accurate results
+for large systems of sizes up to L = 64. We find that the one particle orbitals
+drawn from eigenstates at different energy densities have high overlap and
+their occupations are correlated with the energy of the eigenstates. Moreover,
+the standard deviation of the inverse participation ratio of these orbitals is
+maximal at the nose of the mobility edge. Also, the one particle orbitals decay
+exponentially in real space, with a correlation length that increases at low
+disorder. In addition, we find a 1/f distribution of the coupling constants of
+a certain range of the number operators of the OPOs, which is related to their
+exponential decay.
+",0,1,0,0,0,0
+5540,On transient waves in linear viscoelasticity,"  The aim of this paper is to present a comprehensive review of method of the
+wave-front expansion, also known in the literature as the Buchen-Mainardi
+algorithm. In particular, many applications of this technique to the
+fundamental models of both ordinary and fractional linear viscoelasticity are
+thoroughly presented and discussed.
+",0,1,0,0,0,0
+5541,Detection of an Optical Counterpart to the ALFALFA Ultra-compact High Velocity Cloud AGC 249525,"  We report on the detection at $>$98% confidence of an optical counterpart to
+AGC 249525, an Ultra-Compact High Velocity Cloud (UCHVC) discovered by the
+ALFALFA blind neutral hydrogen survey. UCHVCs are compact, isolated HI clouds
+with properties consistent with their being nearby low-mass galaxies, but
+without identified counterparts in extant optical surveys. Analysis of the
+resolved stellar sources in deep $g$- and $i$-band imaging from the WIYN pODI
+camera reveals a clustering of possible Red Giant Branch stars associated with
+AGC 249525 at a distance of 1.64$\pm$0.45 Mpc. Matching our optical detection
+with the HI synthesis map of AGC 249525 from Adams et al. (2016) shows that the
+stellar overdensity is exactly coincident with the highest-density HI contour
+from that study. Combining our optical photometry and the HI properties of this
+object yields an absolute magnitude of $-7.1 \leq M_V \leq -4.5$, a stellar
+mass between $2.2\pm0.6\times10^4 M_{\odot}$ and $3.6\pm1.0\times10^5
+M_{\odot}$, and an HI to stellar mass ratio between 9 and 144. This object has
+stellar properties within the observed range of gas-poor Ultra-Faint Dwarfs in
+the Local Group, but is gas-dominated.
+",0,1,0,0,0,0
+5542,Boosting the power factor with resonant states: a model study,"  A particularly promising pathway to enhance the efficiency of thermoelectric
+materials lies in the use of resonant states, as suggested by experimentalists
+and theorists alike. In this paper, we go over the mechanisms used in the
+literature to explain how resonant levels affect the thermoelectric properties,
+and we suggest that the effects of hybridization are crucial yet
+ill-understood. In order to get a good grasp of the physical picture and to
+draw guidelines for thermoelectric enhancement, we use a tight-binding model
+containing a conduction band hybridized with a flat band. We find that the
+conductivity is suppressed in a wide energy range near the resonance, but that
+the Seebeck coefficient can be boosted for strong enough hybridization, thus
+allowing for a significant increase of the power factor. The Seebeck
+coefficient can also display a sign change as the Fermi level crosses the
+resonance. Our results suggest that in order to boost the power factor, the
+hybridization strength must not be too low, the resonant level must not be too
+close to the conduction (or valence) band edge, and the Fermi level must be
+located around, but not inside, the resonant peak.
+",0,1,0,0,0,0
+5543,Activation cross-section data for alpha-particle induced nuclear reactions on natural ytterbium for some longer lived radioisotopes,"  Additional experimental cross sections were deduced for the long half-life
+activation products (172Hf and 173Lu) from the alpha particle induced reactions
+on ytterbium up to 38 MeV from late, long measurements and for 175Yb, 167Tm
+from a re-evaluation of earlier measured spectra. The cross-sections are
+compared with the earlier experimental datasets and with the data based on the
+TALYS theoretical nuclear reaction model (available in the TENDL-2014 and 2015
+libraries) and the ALICE-IPPE code.
+",0,0,0,1,0,0
+5544,Congestion-Aware Distributed Network Selection for Integrated Cellular and Wi-Fi Networks,"  Intelligent network selection plays an important role in achieving an
+effective data offloading in the integrated cellular and Wi-Fi networks.
+However, previously proposed network selection schemes mainly focused on
+offloading as much data traffic to Wi-Fi as possible, without systematically
+considering the Wi-Fi network congestion and the ping-pong effect, both of
+which may lead to a poor overall user quality of experience. Thus, in this
+paper, we study a more practical network selection problem by considering both
+the impacts of the network congestion and switching penalties. More
+specifically, we formulate the users' interactions as a Bayesian network
+selection game (NSG) under the incomplete information of the users' mobilities.
+We prove that it is a Bayesian potential game and show the existence of a pure
+Bayesian Nash equilibrium that can be easily reached. We then propose a
+distributed network selection (DNS) algorithm based on the network congestion
+statistics obtained from the operator. Furthermore, we show that computing the
+optimal centralized network allocation is an NP-hard problem, which further
+justifies our distributed approach. Simulation results show that the DNS
+algorithm achieves the highest user utility and a good fairness among users, as
+compared with the on-the-spot offloading and cellular-only benchmark schemes.
+",1,0,0,0,0,0
+5545,Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification,"  High-resolution satellite imagery have been increasingly used on remote
+sensing classification problems. One of the main factors is the availability of
+this kind of data. Even though, very little effort has been placed on the zebra
+crossing classification problem. In this letter, crowdsourcing systems are
+exploited in order to enable the automatic acquisition and annotation of a
+large-scale satellite imagery database for crosswalks related tasks. Then, this
+dataset is used to train deep-learning-based models in order to accurately
+classify satellite images that contains or not zebra crossings. A novel dataset
+with more than 240,000 images from 3 continents, 9 countries and more than 20
+cities was used in the experiments. Experimental results showed that freely
+available crowdsourcing data can be used to accurately (97.11%) train robust
+models to perform crosswalk classification on a global scale.
+",1,0,0,1,0,0
+5546,Complexity of the Regularized Newton Method,"  Newton's method for finding an unconstrained minimizer for strictly convex
+functions, generally speaking, does not converge from any starting point.
+We introduce and study the damped regularized Newton's method (DRNM). It
+converges globally for any strictly convex function, which has a minimizer in
+$R^n$.
+Locally DRNM converges with a quadratic rate. We characterize the
+neighborhood of the minimizer, where the quadratic rate occurs. Based on it we
+estimate the number of DRNM's steps required for finding an $\varepsilon$-
+approximation for the minimizer.
+",0,0,1,0,0,0
+5547,Quantifying Program Bias,"  With the range and sensitivity of algorithmic decisions expanding at a
+break-neck speed, it is imperative that we aggressively investigate whether
+programs are biased. We propose a novel probabilistic program analysis
+technique and apply it to quantifying bias in decision-making programs.
+Specifically, we (i) present a sound and complete automated verification
+technique for proving quantitative properties of probabilistic programs; (ii)
+show that certain notions of bias, recently proposed in the fairness
+literature, can be phrased as quantitative correctness properties; and (iii)
+present FairSquare, the first verification tool for quantifying program bias,
+and evaluate it on a range of decision-making programs.
+",1,0,0,0,0,0
+5548,A Theory of Exoplanet Transits with Light Scattering,"  Exoplanet transit spectroscopy enables the characterization of distant
+worlds, and will yield key results for NASA's James Webb Space Telescope.
+However, transit spectra models are often simplified, omitting potentially
+important processes like refraction and multiple scattering. While the former
+process has seen recent development, the effects of light multiple scattering
+on exoplanet transit spectra has received little attention. Here, we develop a
+detailed theory of exoplanet transit spectroscopy that extends to the full
+refracting and multiple scattering case. We explore the importance of
+scattering for planet-wide cloud layers, where the relevant parameters are the
+slant scattering optical depth, the scattering asymmetry parameter, and the
+angular size of the host star. The latter determines the size of the ""target""
+for a photon that is back-mapped from an observer. We provide results that
+straightforwardly indicate the potential importance of multiple scattering for
+transit spectra. When the orbital distance is smaller than 10-20 times the
+stellar radius, multiple scattering effects for aerosols with asymmetry
+parameters larger than 0.8-0.9 can become significant. We provide examples of
+the impacts of cloud/haze multiple scattering on transit spectra of a hot
+Jupiter-like exoplanet. For cases with a forward and conservatively scattering
+cloud/haze, differences due to multiple scattering effects can exceed 200 ppm,
+but shrink to zero at wavelength ranges corresponding to strong gas absorption
+or when the slant optical depth of the cloud exceeds several tens. We conclude
+with a discussion of types of aerosols for which multiple scattering in transit
+spectra may be important.
+",0,1,0,0,0,0
+5549,Tuning Majorana zero modes with temperature in $π$-phase Josephson junctions,"  We study a superconductor-normal state-superconductor (SNS) Josephson
+junction along the edge of a quantum spin Hall insulator (QSHI) with a
+superconducting $\pi$-phase across the junction. We solve self-consistently for
+the superconducting order parameter and find both real junctions, where the
+order parameter is fully real throughout the system, and junctions where the
+order parameter has a complex phase winding. Real junctions host two Majorana
+zero modes (MZMs), while phase-winding junctions have no subgap states close to
+zero energy. At zero temperature we find that the phase-winding solution always
+has the lowest free energy, which we establish being due to a strong
+proximity-effect into the N region. With increasing temperature this
+proximity-effect is dramatically decreased and we find a phase transition into
+a real junction with two MZMs.
+",0,1,0,0,0,0
+5550,A repulsive skyrmion chain as guiding track for a race track memory,"  A skyrmion racetrack design is proposed that allows for thermally stable
+skyrmions to code information and dynamical pinning sites that move with the
+applied current. This concept solves the problem of intrinsic distributions of
+pinning times and pinning currents of skyrmions at static geometrical or
+magnetic pinning sites. The dynamical pinning sites are realized by a skyrmion
+carrying wire, where the skyrmion repulsion is used in order to keep the
+skyrmions at equal distances. The information is coded by an additional layer
+where the presence and absence of a skyrmion is used to code the information.
+The lowest energy barrier for a data loss is calculated to be DE = 55 kBT300
+which is sufficient for long time thermal stability.
+",0,1,0,0,0,0
+5551,Compressive Sensing-Based Detection with Multimodal Dependent Data,"  Detection with high dimensional multimodal data is a challenging problem when
+there are complex inter- and intra- modal dependencies. While several
+approaches have been proposed for dependent data fusion (e.g., based on copula
+theory), their advantages come at a high price in terms of computational
+complexity. In this paper, we treat the detection problem with compressive
+sensing (CS) where compression at each sensor is achieved via low dimensional
+random projections. CS has recently been exploited to solve detection problems
+under various assumptions on the signals of interest, however, its potential
+for dependent data fusion has not been explored adequately. We exploit the
+capability of CS to capture statistical properties of uncompressed data in
+order to compute decision statistics for detection in the compressed domain.
+First, a Gaussian approximation is employed to perform likelihood ratio (LR)
+based detection with compressed data. In this approach, inter-modal dependence
+is captured via a compressed version of the covariance matrix of the
+concatenated (temporally and spatially) uncompressed data vector. We show that,
+under certain conditions, this approach with a small number of compressed
+measurements per node leads to enhanced performance compared to detection with
+uncompressed data using widely considered suboptimal approaches. Second, we
+develop a nonparametric approach where a decision statistic based on the second
+order statistics of uncompressed data is computed in the compressed domain. The
+second approach is promising over other related nonparametric approaches and
+the first approach when multimodal data is highly correlated at the expense of
+slightly increased computational complexity.
+",1,0,0,1,0,0
+5552,Factors in Recommending Contrarian Content on Social Media,"  Polarization is a troubling phenomenon that can lead to societal divisions
+and hurt the democratic process. It is therefore important to develop methods
+to reduce it.
+We propose an algorithmic solution to the problem of reducing polarization.
+The core idea is to expose users to content that challenges their point of
+view, with the hope broadening their perspective, and thus reduce their
+polarity. Our method takes into account several aspects of the problem, such as
+the estimated polarity of the user, the probability of accepting the
+recommendation, the polarity of the content, and popularity of the content
+being recommended.
+We evaluate our recommendations via a large-scale user study on Twitter users
+that were actively involved in the discussion of the US elections results.
+Results shows that, in most cases, the factors taken into account in the
+recommendation affect the users as expected, and thus capture the essential
+features of the problem.
+",1,0,0,0,0,0
+5553,Generating the Log Law of the Wall with Superposition of Standing Waves,"  Turbulence remains an unsolved multidisciplinary science problem. As one of
+the most well-known examples in turbulent flows, knowledge of the logarithmic
+mean velocity profile (MVP), so called the log law of the wall, plays an
+important role everywhere turbulent flow meets the solid wall, such as fluids
+in any kind of channels, skin friction of all types of transportations, the
+atmospheric wind on a planetary ground, and the oceanic current on the seabed.
+However, the mechanism of how this log-law MVP is formed under the multiscale
+nature of turbulent shears remains one of the greatest interests of turbulence
+puzzles. To untangle the multiscale coupling of turbulent shear stresses, we
+explore for a known fundamental tool in physics. Here we present how to
+reproduce the log-law MVP with the even harmonic modes of fixed-end standing
+waves. We find that when these harmonic waves of same magnitude are considered
+as the multiscale turbulent shear stresses, the wave envelope of their
+superposition simulates the mean shear stress profile of the wall-bounded flow.
+It implies that the log-law MVP is not expectedly related to the turbulent
+scales in the inertial subrange associated with the Kolmogorov energy cascade,
+revealing the dissipative nature of all scales involved. The MVP with reduced
+harmonic modes also shows promising connection to the understanding of flow
+transition to turbulence. The finding here suggests the simple harmonic waves
+as good agents to help unravel the complex turbulent dynamics in wall-bounded
+flow.
+",0,1,0,0,0,0
+5554,Adaptive p-value weighting with power optimality,"  Weighting the p-values is a well-established strategy that improves the power
+of multiple testing procedures while dealing with heterogeneous data. However,
+how to achieve this task in an optimal way is rarely considered in the
+literature. This paper contributes to fill the gap in the case of
+group-structured null hypotheses, by introducing a new class of procedures
+named ADDOW (for Adaptive Data Driven Optimal Weighting) that adapts both to
+the alternative distribution and to the proportion of true null hypotheses. We
+prove the asymptotical FDR control and power optimality among all weighted
+procedures of ADDOW, which shows that it dominates all existing procedures in
+that framework. Some numerical experiments show that the proposed method
+preserves its optimal properties in the finite sample setting when the number
+of tests is moderately large.
+",0,0,1,1,0,0
+5555,Topical homophily in online social systems,"  Understanding the dynamics of social interactions is crucial to comprehend
+human behavior. The emergence of online social media has enabled access to data
+regarding people relationships at a large scale. Twitter, specifically, is an
+information oriented network, with users sharing and consuming information. In
+this work, we study whether users tend to be in contact with people interested
+in similar topics, i.e., topical homophily. To do so, we propose an approach
+based on the use of hashtags to extract information topics from Twitter
+messages and model users' interests. Our results show that, on average, users
+are connected with other users similar to them and stronger relationships are
+due to a higher topical similarity. Furthermore, we show that topical homophily
+provides interesting information that can eventually allow inferring users'
+connectivity. Our work, besides providing a way to assess the topical
+similarity of users, quantifies topical homophily among individuals,
+contributing to a better understanding of how complex social systems are
+structured.
+",1,0,0,0,0,0
+5556,Perceptual Context in Cognitive Hierarchies,"  Cognition does not only depend on bottom-up sensor feature abstraction, but
+also relies on contextual information being passed top-down. Context is higher
+level information that helps to predict belief states at lower levels. The main
+contribution of this paper is to provide a formalisation of perceptual context
+and its integration into a new process model for cognitive hierarchies. Several
+simple instantiations of a cognitive hierarchy are used to illustrate the role
+of context. Notably, we demonstrate the use context in a novel approach to
+visually track the pose of rigid objects with just a 2D camera.
+",1,0,0,0,0,0
+5557,Coherence for braided and symmetric pseudomonoids,"  Presentations for unbraided, braided and symmetric pseudomonoids are defined.
+Biequivalences characterising the semistrict bicategories generated by these
+presentations are proven. It is shown that these biequivalences categorify
+results in the theory of monoids and commutative monoids, and generalise
+standard coherence theorems for braided and symmetric monoidal categories.
+",1,0,1,0,0,0
+5558,Linear Optimal Power Flow Using Cycle Flows,"  Linear optimal power flow (LOPF) algorithms use a linearization of the
+alternating current (AC) load flow equations to optimize generator dispatch in
+a network subject to the loading constraints of the network branches. Common
+algorithms use the voltage angles at the buses as optimization variables, but
+alternatives can be computationally advantageous. In this article we provide a
+review of existing methods and describe a new formulation that expresses the
+loading constraints directly in terms of the flows themselves, using a
+decomposition of the network graph into a spanning tree and closed cycles. We
+provide a comprehensive study of the computational performance of the various
+formulations, in settings that include computationally challenging applications
+such as multi-period LOPF with storage dispatch and generation capacity
+expansion. We show that the new formulation of the LOPF solves up to 7 times
+faster than the angle formulation using a commercial linear programming solver,
+while another existing cycle-based formulation solves up to 20 times faster,
+with an average speed-up of factor 3 for the standard networks considered here.
+If generation capacities are also optimized, the average speed-up rises to a
+factor of 12, reaching up to factor 213 in a particular instance. The speed-up
+is largest for networks with many buses and decentral generators throughout the
+network, which is highly relevant given the rise of distributed renewable
+generation and the computational challenge of operation and planning in such
+networks.
+",1,1,0,0,0,0
+5559,"Toward construction of a consistent field theory with Poincare covariance in terms of step-function-type basis functions showing confinement/deconfinement, mass-gap and Regge trajectory for non-pure/pure non-Abelian gauge fields","  This article is a review by the authors concerning the construction of a
+Poincar${\rm \acute{e}}$ covariant (owing to spacetime continuum)
+field-theoretic formalism in terms of step-function-type basis functions
+without ultraviolet divergences. This formalism analytically derives
+confinement/deconfinement, mass-gap and Regge trajectory for non-Abelian gauge
+fields, and gives solutions for self-interacting scalar fields. Fields
+propagate in spacetime continuum and fields with finite degrees of freedom
+toward continuum limit have no ultraviolet divergence. Basis functions defined
+in a parameter spacetime are mapped to real spacetime. The authors derive a new
+solution comprised of classical fields as a vacuum and quantum fluctuations,
+leading to the linear potential between the particle and antiparticle from the
+Wilson loop. The Polyakov line gives finite binding energies and reveals the
+deconfining property at high temperatures. The quantum action yields positive
+mass from the classical fields and quantum fluctuations produces the Coulomb
+potential. Pure Yang-Mills fields show the same mass-gap owing to the
+particle-antiparticle pair creation. The Dirac equation under linear potential
+is analytically solved in this formalism, reproducing the principal properties
+of Regge trajectories at a quantum level. Further outlook mentions a
+possibility of the difference between conventional continuum and present wave
+functions responsible for the cosmological constant.
+",0,1,0,0,0,0
+5560,The Exact Solution to Rank-1 L1-norm TUCKER2 Decomposition,"  We study rank-1 {L1-norm-based TUCKER2} (L1-TUCKER2) decomposition of 3-way
+tensors, treated as a collection of $N$ $D \times M$ matrices that are to be
+jointly decomposed. Our contributions are as follows. i) We prove that the
+problem is equivalent to combinatorial optimization over $N$ antipodal-binary
+variables. ii) We derive the first two algorithms in the literature for its
+exact solution. The first algorithm has cost exponential in $N$; the second one
+has cost polynomial in $N$ (under a mild assumption). Our algorithms are
+accompanied by formal complexity analysis. iii) We conduct numerical studies to
+compare the performance of exact L1-TUCKER2 (proposed) with standard HOSVD,
+HOOI, GLRAM, PCA, L1-PCA, and TPCA-L1. Our studies show that L1-TUCKER2
+outperforms (in tensor approximation) all the above counterparts when the
+processed data are outlier corrupted.
+",1,0,0,1,0,0
+5561,A Statistical Comparative Planetology Approach to the Hunt for Habitable Exoplanets and Life Beyond the Solar System,"  The search for habitable exoplanets and life beyond the Solar System is one
+of the most compelling scientific opportunities of our time. Nevertheless, the
+high cost of building facilities that can address this topic and the keen
+public interest in the results of such research requires the rigorous
+development of experiments that can deliver a definitive advance in our
+understanding. Most work to date in this area has focused on a ""systems
+science"" approach of obtaining and interpreting comprehensive data for
+individual planets to make statements about their habitability and the
+possibility that they harbor life. This strategy is challenging because of the
+diversity of exoplanets, both observed and expected, and the limited
+information that can be obtained with astronomical instruments. Here we propose
+a complementary approach that is based on performing surveys of key planetary
+characteristics and using statistical marginalization to answer broader
+questions than can be addressed with a small sample of objects. The fundamental
+principle of this comparative planetology approach is maximizing what can be
+learned from each type of measurement by applying it widely rather than
+requiring that multiple kinds of observations be brought to bear on a single
+object. As a proof of concept, we outline a survey of terrestrial exoplanet
+atmospheric water and carbon dioxide abundances that would test the habitable
+zone hypothesis and lead to a deeper understanding of the frequency of
+habitable planets. We also discuss ideas for additional surveys that could be
+developed to test other foundational hypotheses is this area.
+",0,1,0,0,0,0
+5562,Fast Generation for Convolutional Autoregressive Models,"  Convolutional autoregressive models have recently demonstrated
+state-of-the-art performance on a number of generation tasks. While fast,
+parallel training methods have been crucial for their success, generation is
+typically implemented in a naïve fashion where redundant computations are
+unnecessarily repeated. This results in slow generation, making such models
+infeasible for production environments. In this work, we describe a method to
+speed up generation in convolutional autoregressive models. The key idea is to
+cache hidden states to avoid redundant computation. We apply our fast
+generation method to the Wavenet and PixelCNN++ models and achieve up to
+$21\times$ and $183\times$ speedups respectively.
+",1,0,0,1,0,0
+5563,Variable domain N-linked glycosylation and negative surface charge are key features of monoclonal ACPA: implications for B-cell selection,"  Autoreactive B cells have a central role in the pathogenesis of rheumatoid
+arthritis (RA), and recent findings have proposed that anti-citrullinated
+protein autoantibodies (ACPA) may be directly pathogenic. Herein, we
+demonstrate the frequency of variable-region glycosylation in single-cell
+cloned mAbs. A total of 14 ACPA mAbs were evaluated for predicted N-linked
+glycosylation motifs in silico and compared to 452 highly-mutated mAbs from RA
+patients and controls. Variable region N-linked motifs (N-X-S/T) were
+strikingly prevalent within ACPA (100%) compared to somatically hypermutated
+(SHM) RA bone marrow plasma cells (21%), and synovial plasma cells from
+seropositive (39%) and seronegative RA (7%). When normalized for SHM, ACPA
+still had significantly higher frequency of N-linked motifs compared to all
+studied mAbs including highly-mutated HIV broadly-neutralizing and
+malaria-associated mAbs. The Fab glycans of ACPA-mAbs were highly sialylated,
+contributed to altered charge, but did not influence antigen binding. The
+analysis revealed evidence of unusual B-cell selection pressure and
+SHM-mediated decreased in surface charge and isoelectric point in ACPA. It is
+still unknown how these distinct features of anti-citrulline immunity may have
+an impact on pathogenesis. However, it is evident that they offer selective
+advantages for ACPA+ B cells, possibly also through non-antigen driven
+mechanisms.
+",0,0,0,0,1,0
+5564,Periodic Airy process and equilibrium dynamics of edge fermions in a trap,"  We establish an exact mapping between (i) the equilibrium (imaginary time)
+dynamics of non-interacting fermions trapped in a harmonic potential at
+temperature $T=1/\beta$ and (ii) non-intersecting Ornstein-Uhlenbeck (OU)
+particles constrained to return to their initial positions after time $\beta$.
+Exploiting the determinantal structure of the process we compute the universal
+correlation functions both in the bulk and at the edge of the trapped Fermi
+gas. The latter corresponds to the top path of the non-intersecting OU
+particles, and leads us to introduce and study the time-periodic Airy$_2$
+process, ${\cal A}^b_2(u)$, depending on a single parameter, the period $b$.
+The standard Airy$_2$ process is recovered for $b=+\infty$. We discuss
+applications of our results to the real time quantum dynamics of trapped
+fermions.
+",0,1,1,0,0,0
+5565,Quantum machine learning: a classical perspective,"  Recently, increased computational power and data availability, as well as
+algorithmic advances, have led machine learning techniques to impressive
+results in regression, classification, data-generation and reinforcement
+learning tasks. Despite these successes, the proximity to the physical limits
+of chip fabrication alongside the increasing size of datasets are motivating a
+growing number of researchers to explore the possibility of harnessing the
+power of quantum computation to speed-up classical machine learning algorithms.
+Here we review the literature in quantum machine learning and discuss
+perspectives for a mixed readership of classical machine learning and quantum
+computation experts. Particular emphasis will be placed on clarifying the
+limitations of quantum algorithms, how they compare with their best classical
+counterparts and why quantum resources are expected to provide advantages for
+learning problems. Learning in the presence of noise and certain
+computationally hard problems in machine learning are identified as promising
+directions for the field. Practical questions, like how to upload classical
+data into quantum form, will also be addressed.
+",1,0,0,1,0,0
+5566,Robot Assisted Tower Construction - A Resource Distribution Task to Study Human-Robot Collaboration and Interaction with Groups of People,"  Research on human-robot collaboration or human-robot teaming, has focused
+predominantly on understanding and enabling collaboration between a single
+robot and a single human. Extending human-robot collaboration research beyond
+the dyad, raises novel questions about how a robot should distribute resources
+among group members and about what the social and task related consequences of
+the distribution are. Methodological advances are needed to allow researchers
+to collect data about human robot collaboration that involves multiple people.
+This paper presents Tower Construction, a novel resource distribution task that
+allows researchers to examine collaboration between a robot and groups of
+people. By focusing on the question of whether and how a robot's distribution
+of resources (wooden blocks required for a building task) affects collaboration
+dynamics and outcomes, we provide a case of how this task can be applied in a
+laboratory study with 124 participants to collect data about human robot
+collaboration that involves multiple humans. We highlight the kinds of insights
+the task can yield. In particular we find that the distribution of resources
+affects perceptions of performance, and interpersonal dynamics between human
+team-members.
+",1,0,0,0,0,0
+5567,Counterexample Guided Inductive Optimization,"  This paper describes three variants of a counterexample guided inductive
+optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT)
+solvers. In particular, CEGIO relies on iterative executions to constrain a
+verification procedure, in order to perform inductive generalization, based on
+counterexamples extracted from SMT solvers. CEGIO is able to successfully
+optimize a wide range of functions, including non-linear and non-convex
+optimization problems based on SMT solvers, in which data provided by
+counterexamples are employed to guide the verification engine, thus reducing
+the optimization domain. The present algorithms are evaluated using a large set
+of benchmarks typically employed for evaluating optimization techniques.
+Experimental results show the efficiency and effectiveness of the proposed
+algorithms, which find the optimal solution in all evaluated benchmarks, while
+traditional techniques are usually trapped by local minima.
+",1,0,0,0,0,0
+5568,Bounds for the difference between two Čebyšev functionals,"  In this work, a generalization of pre-Grüss inequality is established.
+Several bounds for the difference between two Čebyšev functional are
+proved.
+",0,0,1,0,0,0
+5569,Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals,"  An electroencephalography (EEG) based Brain Computer Interface (BCI) enables
+people to communicate with the outside world by interpreting the EEG signals of
+their brains to interact with devices such as wheelchairs and intelligent
+robots. More specifically, motor imagery EEG (MI-EEG), which reflects a
+subjects active intent, is attracting increasing attention for a variety of BCI
+applications. Accurate classification of MI-EEG signals while essential for
+effective operation of BCI systems, is challenging due to the significant noise
+inherent in the signals and the lack of informative correlation between the
+signals and brain activities. In this paper, we propose a novel deep neural
+network based learning framework that affords perceptive insights into the
+relationship between the MI-EEG data and brain activities. We design a joint
+convolutional recurrent neural network that simultaneously learns robust
+high-level feature presentations through low-dimensional dense embeddings from
+raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various
+artifacts such as background activities. The proposed approach has been
+evaluated extensively on a large- scale public MI-EEG dataset and a limited but
+easy-to-deploy dataset collected in our lab. The results show that our approach
+outperforms a series of baselines and the competitive state-of-the- art
+methods, yielding a classification accuracy of 95.53%. The applicability of our
+proposed approach is further demonstrated with a practical BCI system for
+typing.
+",1,0,0,0,0,0
+5570,Model Checking of Cache for WCET Analysis Refinement,"  On real-time systems running under timing constraints, scheduling can be
+performed when one is aware of the worst case execution time (WCET) of tasks.
+Usually, the WCET of a task is unknown and schedulers make use of safe
+over-approximations given by static WCET analysis. To reduce the
+over-approximation, WCET analysis has to gain information about the underlying
+hardware behavior, such as pipelines and caches. In this paper, we focus on the
+cache analysis, which classifies memory accesses as hits/misses according to
+the set of possible cache states. We propose to refine the results of classical
+cache analysis using a model checker, introducing a new cache model for the
+least recently used (LRU) policy.
+",1,0,0,0,0,0
+5571,Rational Solutions of the Painlevé-II Equation Revisited,"  The rational solutions of the Painlevé-II equation appear in several
+applications and are known to have many remarkable algebraic and analytic
+properties. They also have several different representations, useful in
+different ways for establishing these properties. In particular,
+Riemann-Hilbert representations have proven to be useful for extracting the
+asymptotic behavior of the rational solutions in the limit of large degree
+(equivalently the large-parameter limit). We review the elementary properties
+of the rational Painlevé-II functions, and then we describe three different
+Riemann-Hilbert representations of them that have appeared in the literature: a
+representation by means of the isomonodromy theory of the Flaschka-Newell Lax
+pair, a second representation by means of the isomonodromy theory of the
+Jimbo-Miwa Lax pair, and a third representation found by Bertola and Bothner
+related to pseudo-orthogonal polynomials. We prove that the Flaschka-Newell and
+Bertola-Bothner Riemann-Hilbert representations of the rational Painlevé-II
+functions are explicitly connected to each other. Finally, we review recent
+results describing the asymptotic behavior of the rational Painlevé-II
+functions obtained from these Riemann-Hilbert representations by means of the
+steepest descent method.
+",0,1,1,0,0,0
+5572,PHAST: Protein-like heteropolymer analysis by statistical thermodynamics,"  PHAST is a software package written in standard Fortran, with MPI and CUDA
+extensions, able to efficiently perform parallel multicanonical Monte Carlo
+simulations of single or multiple heteropolymeric chains, as coarse-grained
+models for proteins. The outcome data can be straightforwardly analyzed within
+its microcanonical Statistical Thermodynamics module, which allows for
+computing the entropy, caloric curve, specific heat and free energies. As a
+case study, we investigate the aggregation of heteropolymers bioinspired on
+$A\beta_{25-33}$ fragments and their cross-seeding with $IAPP_{20-29}$
+isoforms. Excellent parallel scaling is observed, even under numerically
+difficult first-order like phase transitions, which are properly described by
+the built-in fully reconfigurable force fields. Still, the package is free and
+open source, this shall motivate users to readily adapt it to specific
+purposes.
+",0,1,0,0,0,0
+5573,An Information-Theoretic Analysis for Thompson Sampling with Many Actions,"  Information-theoretic Bayesian regret bounds of Russo and Van Roy capture the
+dependence of regret on prior uncertainty. However, this dependence is through
+entropy, which can become arbitrarily large as the number of actions increases.
+We establish new bounds that depend instead on a notion of rate-distortion.
+Among other things, this allows us to recover through information-theoretic
+arguments a near-optimal bound for the linear bandit. We also offer a bound for
+the logistic bandit that dramatically improves on the best previously
+available, though this bound depends on an information-theoretic statistic that
+we have only been able to quantify via computation.
+",0,0,0,1,0,0
+5574,Optimal portfolio selection in an Itô-Markov additive market,"  We study a portfolio selection problem in a continuous-time Itô-Markov
+additive market with prices of financial assets described by Markov additive
+processes which combine Lévy processes and regime switching models. Thus the
+model takes into account two sources of risk: the jump diffusion risk and the
+regime switching risk. For this reason the market is incomplete. We complete
+the market by enlarging it with the use of a set of Markovian jump securities,
+Markovian power-jump securities and impulse regime switching securities.
+Moreover, we give conditions under which the market is
+asymptotic-arbitrage-free. We solve the portfolio selection problem in the
+Itô-Markov additive market for the power utility and the logarithmic utility.
+",0,0,0,0,0,1
+5575,SPIRITS: Uncovering Unusual Infrared Transients With Spitzer,"  We present an ongoing, systematic search for extragalactic infrared
+transients, dubbed SPIRITS --- SPitzer InfraRed Intensive Transients Survey. In
+the first year, using Spitzer/IRAC, we searched 190 nearby galaxies with
+cadence baselines of one month and six months. We discovered over 1958
+variables and 43 transients. Here, we describe the survey design and highlight
+14 unusual infrared transients with no optical counterparts to deep limits,
+which we refer to as SPRITEs (eSPecially Red Intermediate Luminosity Transient
+Events). SPRITEs are in the infrared luminosity gap between novae and
+supernovae, with [4.5] absolute magnitudes between -11 and -14 (Vega-mag) and
+[3.6]-[4.5] colors between 0.3 mag and 1.6 mag. The photometric evolution of
+SPRITEs is diverse, ranging from < 0.1 mag/yr to > 7 mag/yr. SPRITEs occur in
+star-forming galaxies. We present an in-depth study of one of them, SPIRITS
+14ajc in Messier 83, which shows shock-excited molecular hydrogen emission.
+This shock may have been triggered by the dynamic decay of a non-hierarchical
+system of massive stars that led to either the formation of a binary or a
+proto-stellar merger.
+",0,1,0,0,0,0
+5576,Is the annual growth rate in balance of trade time series for Ireland nonlinear,"  We describe the Time Series Multivariate Adaptive Regressions Splines
+(TSMARS) method. This method is useful for identifying nonlinear structure in a
+time series. We use TSMARS to model the annual change in the balance of trade
+for Ireland from 1970 to 2007. We compare the TSMARS estimate with long memory
+ARFIMA estimates and long-term parsimonious linear models. We show that the
+change in the balance of trade is nonlinear and possesses weakly long range
+effects. Moreover, we compare the period prior to the introduction of the
+Intrastat system in 1993 with the period from 1993 onward. Here we show that in
+the earlier period the series had a substantial linear signal embedded in it
+suggesting that estimation efforts in the earlier period may have resulted in
+an over-smoothed series.
+",0,0,0,1,0,0
+5577,Sparse-View X-Ray CT Reconstruction Using $\ell_1$ Prior with Learned Transform,"  A major challenge in X-ray computed tomography (CT) is reducing radiation
+dose while maintaining high quality of reconstructed images. To reduce the
+radiation dose, one can reduce the number of projection views (sparse-view CT);
+however, it becomes difficult to achieve high quality image reconstruction as
+the number of projection views decreases. Researchers have applied the concept
+of learning sparse representations from (high-quality) CT image dataset to the
+sparse-view CT reconstruction. We propose a new statistical CT reconstruction
+model that combines penalized weighted-least squares (PWLS) and $\ell_1$
+regularization with learned sparsifying transform (PWLS-ST-$\ell_1$), and an
+algorithm for PWLS-ST-$\ell_1$. Numerical experiments for sparse-view 2D
+fan-beam CT and 3D axial cone-beam CT show that the $\ell_1$ regularizer
+significantly improves the sharpness of edges of reconstructed images compared
+to the CT reconstruction methods using edge-preserving regularizer and $\ell_2$
+regularization with learned ST.
+",1,1,0,1,0,0
+5578,Adversarial classification: An adversarial risk analysis approach,"  Classification problems in security settings are usually contemplated as
+confrontations in which one or more adversaries try to fool a classifier to
+obtain a benefit. Most approaches to such adversarial classification problems
+have focused on game theoretical ideas with strong underlying common knowledge
+assumptions, which are actually not realistic in security domains. We provide
+an alternative framework to such problem based on adversarial risk analysis,
+which we illustrate with several examples. Computational and implementation
+issues are discussed.
+",0,0,0,1,0,0
+5579,Volume growth in the component of fibered twists,"  For a Liouville domain $W$ whose boundary admits a periodic Reeb flow, we can
+consider the connected component $[\tau] \in \pi_0(\text{Symp}^c(\widehat W))$
+of fibered twists. In this paper, we investigate an entropy-type invariant,
+called the slow volume growth, of the component $[\tau]$ and give a uniform
+lower bound of the growth using wrapped Floer homology. We also show that
+$[\tau]$ has infinite order in $\pi_0(\text{Symp}^c(\widehat W))$ if there is
+an admissible Lagrangian $L$ in $W$ whose wrapped Floer homology is infinite
+dimensional. We apply our results to fibered twists coming from the Milnor
+fibers of $A_k$-type singularities and complements of a symplectic hypersurface
+in a real symplectic manifold. They admit so-called real Lagrangians, and we
+can explicitly compute wrapped Floer homology groups using a version of
+Morse-Bott spectral sequences.
+",0,0,1,0,0,0
+5580,Scalar Reduction of a Neural Field Model with Spike Frequency Adaptation,"  We study a deterministic version of a one- and two-dimensional attractor
+neural network model of hippocampal activity first studied by Itskov et al
+2011. We analyze the dynamics of the system on the ring and torus domain with
+an even periodized weight matrix, assum- ing weak and slow spike frequency
+adaptation and a weak stationary input current. On these domains, we find
+transitions from spatially localized stationary solutions (""bumps"") to
+(periodically modulated) solutions (""sloshers""), as well as constant and
+non-constant velocity traveling bumps depending on the relative strength of
+external input current and adaptation. The weak and slow adaptation allows for
+a reduction of the system from a distributed partial integro-differential
+equation to a system of scalar Volterra integro-differential equations
+describing the movement of the centroid of the bump solution. Using this
+reduction, we show that on both domains, sloshing solutions arise through an
+Andronov-Hopf bifurcation and derive a normal form for the Hopf bifurcation on
+the ring. We also show existence and stability of constant velocity solutions
+on both domains using Evans functions. In contrast to existing studies, we
+assume a general weight matrix of Mexican-hat type in addition to a smooth
+firing rate function.
+",0,0,0,0,1,0
+5581,Revisiting Elementary Denotational Semantics,"  Operational semantics have been enormously successful, in large part due to
+its flexibility and simplicity, but they are not compositional. Denotational
+semantics, on the other hand, are compositional but the lattice-theoretic
+models are complex and difficult to scale to large languages. However, there
+are elementary models of the $\lambda$-calculus that are much less complex: by
+Coppo, Dezani-Ciancaglini, and Salle (1979), Engeler (1981), and Plotkin
+(1993).
+This paper takes first steps toward answering the question: can elementary
+models be good for the day-to-day work of language specification,
+mechanization, and compiler correctness? The elementary models in the
+literature are simple, but they are not as intuitive as they could be. To
+remedy this, we create a new model that represents functions literally as
+finite graphs. Regarding mechanization, we give the first machine-checked proof
+of soundness and completeness of an elementary model with respect to an
+operational semantics. Regarding compiler correctness, we define a polyvariant
+inliner for the call-by-value $\lambda$-calculus and prove that its output is
+contextually equivalent to its input. Toward scaling elementary models to
+larger languages, we formulate our semantics in a monadic style, give a
+semantics for System F with general recursion, and mechanize the proof of type
+soundness.
+",1,0,0,0,0,0
+5582,Dirac Composite Fermion - A Particle-Hole Spinor,"  The particle-hole (PH) symmetry at half-filled Landau level requires the
+relationship between the flux number N_phi and the particle number N on a
+sphere to be exactly N_phi - 2(N-1) = 1. The wave functions of composite
+fermions with 1/2 ""orbital spin"", which contributes to the shift ""1"" in the
+N_phi and N relationship, are proposed, shown to be PH symmetric, and validated
+with exact finite system results. It is shown the many-body composite electron
+and composite hole wave functions at half-filling can be formed from the two
+components of the same spinor wave function of a massless Dirac fermion at
+zero-magnetic field. It is further shown that away from half-filling, the
+many-body composite electron wave function at filling factor nu and its PH
+conjugated composite hole wave function at 1-nu can be formed from the two
+components of the very same spinor wave functions of a massless Dirac fermion
+at non-zero magnetic field. This relationship leads to the proposal of a very
+simple Dirac composite fermion effective field theory, where the two-component
+Dirac fermion field is a particle-hole spinor field coupled to the same
+emergent gauge field, with one field component describing the composite
+electrons and the other describing the PH conjugated composite holes. As such,
+the density of the Dirac spinor field is the density sum of the composite
+electron and hole field components, and therefore is equal to the degeneracy of
+the Lowest Landau level. On the other hand, the charge density coupled to the
+external magnetic field is the density difference between the composite
+electron and hole field components, and is therefore neutral at exactly
+half-filling. It is shown that the proposed particle-hole spinor effective
+field theory gives essentially the same electromagnetic responses as Son's
+Dirac composite fermion theory does.
+",0,1,0,0,0,0
+5583,Safe Adaptive Importance Sampling,"  Importance sampling has become an indispensable strategy to speed up
+optimization algorithms for large-scale applications. Improved adaptive
+variants - using importance values defined by the complete gradient information
+which changes during optimization - enjoy favorable theoretical properties, but
+are typically computationally infeasible. In this paper we propose an efficient
+approximation of gradient-based sampling, which is based on safe bounds on the
+gradient. The proposed sampling distribution is (i) provably the best sampling
+with respect to the given bounds, (ii) always better than uniform sampling and
+fixed importance sampling and (iii) can efficiently be computed - in many
+applications at negligible extra cost. The proposed sampling scheme is generic
+and can easily be integrated into existing algorithms. In particular, we show
+that coordinate-descent (CD) and stochastic gradient descent (SGD) can enjoy
+significant a speed-up under the novel scheme. The proven efficiency of the
+proposed sampling is verified by extensive numerical testing.
+",1,0,0,0,0,0
+5584,Secure Grouping Protocol Using a Deck of Cards,"  We consider a problem, which we call secure grouping, of dividing a number of
+parties into some subsets (groups) in the following manner: Each party has to
+know the other members of his/her group, while he/she may not know anything
+about how the remaining parties are divided (except for certain public
+predetermined constraints, such as the number of parties in each group). In
+this paper, we construct an information-theoretically secure protocol using a
+deck of physical cards to solve the problem, which is jointly executable by the
+parties themselves without a trusted third party. Despite the non-triviality
+and the potential usefulness of the secure grouping, our proposed protocol is
+fairly simple to describe and execute. Our protocol is based on algebraic
+properties of conjugate permutations. A key ingredient of our protocol is our
+new techniques to apply multiplication and inverse operations to hidden
+permutations (i.e., those encoded by using face-down cards), which would be of
+independent interest and would have various potential applications.
+",1,0,0,0,0,0
+5585,Categorically closed topological groups,"  Let $\mathcal C$ be a subcategory of the category of topologized semigroups
+and their partial continuous homomorphisms. An object $X$ of the category
+${\mathcal C}$ is called ${\mathcal C}$-closed if for each morphism $f:X\to Y$
+of the category ${\mathcal C}$ the image $f(X)$ is closed in $Y$. In the paper
+we detect topological groups which are $\mathcal C$-closed for the categories
+$\mathcal C$ whose objects are Hausdorff topological (semi)groups and whose
+morphisms are isomorphic topological embeddings, injective continuous
+homomorphisms, continuous homomorphisms, or partial continuous homomorphisms
+with closed domain.
+",0,0,1,0,0,0
+5586,Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections,"  According to the Eurobarometer report about EU media use of May 2018, the
+number of European citizens who consult on-line social networks for accessing
+information is considerably increasing. In this work we analyze approximately
+$10^6$ tweets exchanged during the last Italian elections. By using an
+entropy-based null model discounting the activity of the users, we first
+identify potential political alliances within the group of verified accounts:
+if two verified users are retweeted more than expected by the non-verified
+ones, they are likely to be related. Then, we derive the users' affiliation to
+a coalition measuring the polarization of unverified accounts. Finally, we
+study the bipartite directed representation of the tweets and retweets network,
+in which tweets and users are collected on the two layers. Users with the
+highest out-degree identify the most popular ones, whereas highest out-degree
+posts are the most ""viral"". We identify significant content spreaders by
+statistically validating the connections that cannot be explained by users'
+tweeting activity and posts' virality by using an entropy-based null model as
+benchmark. The analysis of the directed network of validated retweets reveals
+signals of the alliances formed after the elections, highlighting commonalities
+of interests before the event of the national elections.
+",1,0,0,0,0,0
+5587,Annealed Generative Adversarial Networks,"  We introduce a novel framework for adversarial training where the target
+distribution is annealed between the uniform distribution and the data
+distribution. We posited a conjecture that learning under continuous annealing
+in the nonparametric regime is stable irrespective of the divergence measures
+in the objective function and proposed an algorithm, dubbed {\ss}-GAN, in
+corollary. In this framework, the fact that the initial support of the
+generative network is the whole ambient space combined with annealing are key
+to balancing the minimax game. In our experiments on synthetic data, MNIST, and
+CelebA, {\ss}-GAN with a fixed annealing schedule was stable and did not suffer
+from mode collapse.
+",1,0,0,1,0,0
+5588,Attitude Control of Spacecraft Formations Subject To Distributed Communication Delays,"  This paper considers the problem of achieving attitude consensus in
+spacecraft formations with bounded, time-varying communication delays between
+spacecraft connected as specified by a strongly connected topology. A state
+feedback con- troller is proposed and investigated using a time domain approach
+(via LMIs) and a frequency domain approach (via the small-gain theorem) to
+obtain delay depen- dent stability criteria to achieve the desired consensus.
+Simulations are presented to demonstrate the application of the strategy in a
+specific scenario.
+",1,0,0,0,0,0
+5589,Cloud Radiative Effect Study Using Sky Camera,"  The analysis of clouds in the earth's atmosphere is important for a variety
+of applications, viz. weather reporting, climate forecasting, and solar energy
+generation. In this paper, we focus our attention on the impact of cloud on the
+total solar irradiance reaching the earth's surface. We use weather station to
+record the total solar irradiance. Moreover, we employ collocated ground-based
+sky camera to automatically compute the instantaneous cloud coverage. We
+analyze the relationship between measured solar irradiance and computed cloud
+coverage value, and conclude that higher cloud coverage greatly impacts the
+total solar irradiance. Such studies will immensely help in solar energy
+generation and forecasting.
+",1,1,0,0,0,0
+5590,On Testing Machine Learning Programs,"  Nowadays, we are witnessing a wide adoption of Machine learning (ML) models
+in many safety-critical systems, thanks to recent breakthroughs in deep
+learning and reinforcement learning. Many people are now interacting with
+systems based on ML every day, e.g., voice recognition systems used by virtual
+personal assistants like Amazon Alexa or Google Home. As the field of ML
+continues to grow, we are likely to witness transformative advances in a wide
+range of areas, from finance, energy, to health and transportation. Given this
+growing importance of ML-based systems in our daily life, it is becoming
+utterly important to ensure their reliability. Recently, software researchers
+have started adapting concepts from the software testing domain (e.g., code
+coverage, mutation testing, or property-based testing) to help ML engineers
+detect and correct faults in ML programs. This paper reviews current existing
+testing practices for ML programs. First, we identify and explain challenges
+that should be addressed when testing ML programs. Next, we report existing
+solutions found in the literature for testing ML programs. Finally, we identify
+gaps in the literature related to the testing of ML programs and make
+recommendations of future research directions for the scientific community. We
+hope that this comprehensive review of software testing practices will help ML
+engineers identify the right approach to improve the reliability of their
+ML-based systems. We also hope that the research community will act on our
+proposed research directions to advance the state of the art of testing for ML
+programs.
+",1,0,0,0,0,0
+5591,Scalable and Efficient Statistical Inference with Estimating Functions in the MapReduce Paradigm for Big Data,"  The theory of statistical inference along with the strategy of
+divide-and-conquer for large- scale data analysis has recently attracted
+considerable interest due to great popularity of the MapReduce programming
+paradigm in the Apache Hadoop software framework. The central analytic task in
+the development of statistical inference in the MapReduce paradigm pertains to
+the method of combining results yielded from separately mapped data batches.
+One seminal solution based on the confidence distribution has recently been
+established in the setting of maximum likelihood estimation in the literature.
+This paper concerns a more general inferential methodology based on estimating
+functions, termed as the Rao-type confidence distribution, of which the maximum
+likelihood is a special case. This generalization provides a unified framework
+of statistical inference that allows regression analyses of massive data sets
+of important types in a parallel and scalable fashion via a distributed file
+system, including longitudinal data analysis, survival data analysis, and
+quantile regression, which cannot be handled using the maximum likelihood
+method. This paper investigates four important properties of the proposed
+method: computational scalability, statistical optimality, methodological
+generality, and operational robustness. In particular, the proposed method is
+shown to be closely connected to Hansen's generalized method of moments (GMM)
+and Crowder's optimality. An interesting theoretical finding is that the
+asymptotic efficiency of the proposed Rao-type confidence distribution
+estimator is always greater or equal to the estimator obtained by processing
+the full data once. All these properties of the proposed method are illustrated
+via numerical examples in both simulation studies and real-world data analyses.
+",0,0,0,1,0,0
+5592,Instability of pulses in gradient reaction-diffusion systems: A symplectic approach,"  In a scalar reaction-diffusion equation, it is known that the stability of a
+steady state can be determined from the Maslov index, a topological invariant
+that counts the state's critical points. In particular, this implies that pulse
+solutions are unstable. We extend this picture to pulses in reaction-diffusion
+systems with gradient nonlinearity. In particular, we associate a Maslov index
+to any asymptotically constant state, generalizing existing definitions of the
+Maslov index for homoclinic orbits. It is shown that this index equals the
+number of unstable eigenvalues for the linearized evolution equation. Finally,
+we use a symmetry argument to show that any pulse solution must have nonzero
+Maslov index, and hence be unstable.
+",0,0,1,0,0,0
+5593,Consistency of the plug-in functional predictor of the Ornstein-Uhlenbeck process in Hilbert and Banach spaces,"  New results on functional prediction of the Ornstein-Uhlenbeck process in an
+autoregressive Hilbert-valued and Banach-valued frameworks are derived.
+Specifically, consistency of the maximum likelihood estimator of the
+autocorrelation operator, and of the associated plug-in predictor is obtained
+in both frameworks.
+",0,0,1,1,0,0
+5594,Analysis of Sequence Polymorphism of LINEs and SINEs in Entamoeba histolytica,"  The goal of this dissertation is to study the sequence polymorphism in
+retrotransposable elements of Entamoeba histolytica. The Quasispecies theory, a
+concept of equilibrium (stationary), has been used to understand the behaviour
+of these elements. Two datasets of retrotransposons of Entamoeba histolytica
+have been used. We present results from both datasets of retrotransposons
+(SINE1s) of E. histolytica. We have calculated the mutation rate of EhSINE1s
+for both datasets and drawn a phylogenetic tree for newly determined EhSINE1s
+(dataset II). We have also discussed the variation in lengths of EhSINE1s for
+both datasets. Using the quasispecies model, we have shown how sequences of
+SINE1s vary within the population. The outputs of the quasispecies model are
+discussed in the presence and the absence of back mutation by taking different
+values of fitness. From our study of Non-long terminal repeat retrotransposons
+(LINEs and their non-autonomous partner's SINEs) of Entamoeba histolytica, we
+can conclude that an active EhSINE can generate very similar copies of itself
+by retrotransposition. Due to this reason, it increases mutations which give
+the result of sequence polymorphism. We have concluded that the mutation rate
+of SINE is very high. This high mutation rate provides an idea for the
+existence of SINEs, which may affect the genetic analysis of EhSINE1
+ancestries, and calculation of phylogenetic distances.
+",0,0,0,0,1,0
+5595,Classification of digital affine noncommutative geometries,"  It is known that connected translation invariant $n$-dimensional
+noncommutative differentials $d x^i$ on the algebra $k[x^1,\cdots,x^n]$ of
+polynomials in $n$-variables over a field $k$ are classified by commutative
+algebras $V$ on the vector space spanned by the coordinates. This data also
+applies to construct differentials on the Heisenberg algebra `spacetime' with
+relations $[x^\mu,x^\nu]=\lambda\Theta^{\mu\nu}$ where $ \Theta$ is an
+antisymmetric matrix as well as to Lie algebras with pre-Lie algebra
+structures. We specialise the general theory to the field $k={\ \mathbb{F}}_2$
+of two elements, in which case translation invariant metrics (i.e. with
+constant coefficients) are equivalent to making $V$ a Frobenius algebras. We
+classify all of these and their quantum Levi-Civita bimodule connections for
+$n=2,3$, with partial results for $n=4$. For $n=2$ we find 3 inequivalent
+differential structures admitting 1,2 and 3 invariant metrics respectively. For
+$n=3$ we find 6 differential structures admitting $0,1,2,3,4,7$ invariant
+metrics respectively. We give some examples for $n=4$ and general $n$.
+Surprisingly, not all our geometries for $n\ge 2$ have zero quantum Riemann
+curvature. Quantum gravity is normally seen as a weighted `sum' over all
+possible metrics but our results are a step towards a deeper approach in which
+we must also `sum' over differential structures. Over ${\mathbb{F}}_2$ we
+construct some of our algebras and associated structures by digital gates,
+opening up the possibility of `digital geometry'.
+",0,0,1,0,0,0
+5596,The vectorial Ribaucour transformation for submanifolds of constant sectional curvature,"  We obtain a reduction of the vectorial Ribaucour transformation that
+preserves the class of submanifolds of constant sectional curvature of space
+forms, which we call the $L$-transformation. It allows to construct a family of
+such submanifolds starting with a given one and a vector-valued solution of a
+system of linear partial differential equations. We prove a decomposition
+theorem for the $L$-transformation, which is a far-reaching generalization of
+the classical permutability formula for the Ribaucour transformation of
+surfaces of constant curvature in Euclidean three space. As a consequence, we
+derive a Bianchi-cube theorem, which allows to produce, from $k$ initial scalar
+$L$-transforms of a given submanifold of constant curvature, a whole
+$k$-dimensional cube all of whose remaining $2^k-(k+1)$ vertices are
+submanifolds with the same constant sectional curvature given by explicit
+algebraic formulae. We also obtain further reductions, as well as corresponding
+decomposition and Bianchi-cube theorems, for the classes of $n$-dimensional
+flat Lagrangian submanifolds of $\mathbb{C}^n$ and $n$-dimensional Lagrangian
+submanifolds with constant curvature $c$ of the complex projective space
+$\mathbb C\mathbb P^n(4c)$ or the complex hyperbolic space $\mathbb C\mathbb
+H^n(4c)$ of complex dimension $n$ and constant holomorphic curvature~4c.
+",0,0,1,0,0,0
+5597,Social Networks through the Prism of Cognition,"  Human relations are driven by social events - people interact, exchange
+information, share knowledge and emotions, or gather news from mass media.
+These events leave traces in human memory. The initial strength of a trace
+depends on cognitive factors such as emotions or attention span. Each trace
+continuously weakens over time unless another related event activity
+strengthens it. Here, we introduce a novel Cognition-driven Social Network
+(CogSNet) model that accounts for cognitive aspects of social perception and
+explicitly represents human memory dynamics. For validation, we apply our model
+to NetSense data on social interactions among university students. The results
+show that CogSNet significantly improves quality of modeling of human
+interactions in social networks.
+",1,0,0,0,0,0
+5598,Metriplectic Integrators for the Landau Collision Operator,"  We present a novel framework for addressing the nonlinear Landau collision
+integral in terms of finite element and other subspace projection methods. We
+employ the underlying metriplectic structure of the Landau collision integral
+and, using a Galerkin discretization for the velocity space, we transform the
+infinite-dimensional system into a finite-dimensional, time-continuous
+metriplectic system. Temporal discretization is accomplished using the concept
+of discrete gradients. The conservation of energy, momentum, and particle
+densities, as well as the production of entropy is demonstrated algebraically
+for the fully discrete system. Due to the generality of our approach, the
+conservation properties and the monotonic behavior of entropy are guaranteed
+for finite element discretizations in general, independently of the mesh
+configuration.
+",0,1,0,0,0,0
+5599,Eliminating higher-multiplicity intersections in the metastable dimension range,"  The $r$-fold analogues of Whitney trick were `in the air' since 1960s.
+However, only in this century they were stated, proved and applied to obtain
+interesting results, most notably by Mabillard and Wagner. Here we prove and
+apply a version of the $r$-fold Whitney trick when general position $r$-tuple
+intersections have positive dimension.
+Theorem. Assume that $D=D_1\sqcup\ldots\sqcup D_r$ is disjoint union of
+$k$-dimensional disks, $rd\ge (r+1)k+3$, and $f:D\to B^d$ a proper PL (smooth)
+map such that $f\partial D_1\cap\ldots\cap f\partial D_r=\emptyset$. If the map
+$$f^r:\partial(D_1\times\ldots\times D_r)\to
+(B^d)^r-\{(x,x,\ldots,x)\in(B^d)^r\ |\ x\in B^d\}$$ extends to
+$D_1\times\ldots\times D_r$, then there is a proper PL (smooth) map $\overline
+f:D\to B^d$ such that $\overline f=f$ on $\partial D$ and $\overline
+fD_1\cap\ldots\cap \overline fD_r=\emptyset$.
+",1,0,1,0,0,0
+5600,Lagrangian Transport Through Surfaces in Compressible Flows,"  A material-based, i.e., Lagrangian, methodology for exact integration of flux
+by volume-preserving flows through a surface has been developed recently in
+[Karrasch, SIAM J. Appl. Math., 76 (2016), pp. 1178-1190]. In the present
+paper, we first generalize this framework to general compressible flows,
+thereby solving the donating region problem in full generality. Second, we
+demonstrate the efficacy of this approach on a slightly idealized version of a
+classic two-dimensional mixing problem: transport in a cross-channel
+micromixer, as considered recently in [Balasuriya, SIAM J. Appl. Dyn. Syst., 16
+(2017), pp. 1015-1044].
+",1,1,1,0,0,0
+5601,Manipulative Elicitation -- A New Attack on Elections with Incomplete Preferences,"  Lu and Boutilier proposed a novel approach based on ""minimax regret"" to use
+classical score based voting rules in the setting where preferences can be any
+partial (instead of complete) orders over the set of alternatives. We show here
+that such an approach is vulnerable to a new kind of manipulation which was not
+present in the classical (where preferences are complete orders) world of
+voting. We call this attack ""manipulative elicitation."" More specifically, it
+may be possible to (partially) elicit the preferences of the agents in a way
+that makes some distinguished alternative win the election who may not be a
+winner if we elicit every preference completely. More alarmingly, we show that
+the related computational task is polynomial time solvable for a large class of
+voting rules which includes all scoring rules, maximin, Copeland$^\alpha$ for
+every $\alpha\in[0,1]$, simplified Bucklin voting rules, etc. We then show that
+introducing a parameter per pair of alternatives which specifies the minimum
+number of partial preferences where this pair of alternatives must be
+comparable makes the related computational task of manipulative elicitation
+\NPC for all common voting rules including a class of scoring rules which
+includes the plurality, $k$-approval, $k$-veto, veto, and Borda voting rules,
+maximin, Copeland$^\alpha$ for every $\alpha\in[0,1]$, and simplified Bucklin
+voting rules. Hence, in this work, we discover a fundamental vulnerability in
+using minimax regret based approach in partial preferential setting and propose
+a novel way to tackle it.
+",1,0,0,0,0,0
+5602,Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks,"  Event sequence, asynchronously generated with random timestamp, is ubiquitous
+among applications. The precise and arbitrary timestamp can carry important
+clues about the underlying dynamics, and has lent the event data fundamentally
+different from the time-series whereby series is indexed with fixed and equal
+time interval. One expressive mathematical tool for modeling event is point
+process. The intensity functions of many point processes involve two
+components: the background and the effect by the history. Due to its inherent
+spontaneousness, the background can be treated as a time series while the other
+need to handle the history events. In this paper, we model the background by a
+Recurrent Neural Network (RNN) with its units aligned with time series indexes
+while the history effect is modeled by another RNN whose units are aligned with
+asynchronous events to capture the long-range dynamics. The whole model with
+event type and timestamp prediction output layers can be trained end-to-end.
+Our approach takes an RNN perspective to point process, and models its
+background and history effect. For utility, our method allows a black-box
+treatment for modeling the intensity which is often a pre-defined parametric
+form in point processes. Meanwhile end-to-end training opens the venue for
+reusing existing rich techniques in deep network for point process modeling. We
+apply our model to the predictive maintenance problem using a log dataset by
+more than 1000 ATMs from a global bank headquartered in North America.
+",1,0,0,1,0,0
+5603,A note on relative amenable of finite von Neumann algebras,"  Let $M$ be a finite von Neumann algebra (resp. a type II$_{1}$ factor) and
+let $N\subset M$ be a II$_{1}$ factor (resp. $N\subset M$ have an atomic part).
+We prove that the inclusion $N\subset M$ is amenable implies the identity map
+on $M$ has an approximate factorization through $M_m(\mathbb{C})\otimes N $ via
+trace preserving normal unital completely positive maps, which is a
+generalization of a result of Haagerup. We also prove two permanence properties
+for amenable inclusions. One is weak Haagerup property, the other is weak
+exactness.
+",0,0,1,0,0,0
+5604,Powerful numbers in $(1^{\ell}+q^{\ell})(2^{\ell}+q^{\ell})\cdots (n^{\ell}+q^{\ell})$,"  Let $q$ be a positive integer. Recently, Niu and Liu proved that if $n\ge
+\max\{q,1198-q\}$, then the product $(1^3+q^3)(2^3+q^3)\cdots (n^3+q^3)$ is not
+a powerful number. In this note, we prove that (i) for any odd prime power
+$\ell$ and $n\ge \max\{q,11-q\}$, the product
+$(1^{\ell}+q^{\ell})(2^{\ell}+q^{\ell})\cdots (n^{\ell}+q^{\ell})$ is not a
+powerful number; (2) for any positive odd integer $\ell$, there exists an
+integer $N_{q,\ell}$ such that for any positive integer $n\ge N_{q,\ell}$, the
+product $(1^{\ell}+q^{\ell})(2^{\ell}+q^{\ell})\cdots (n^{\ell}+q^{\ell})$ is
+not a powerful number.
+",0,0,1,0,0,0
+5605,LandmarkBoost: Efficient Visual Context Classifiers for Robust Localization,"  The growing popularity of autonomous systems creates a need for reliable and
+efficient metric pose retrieval algorithms. Currently used approaches tend to
+rely on nearest neighbor search of binary descriptors to perform the 2D-3D
+matching and guarantee realtime capabilities on mobile platforms. These methods
+struggle, however, with the growing size of the map, changes in viewpoint or
+appearance, and visual aliasing present in the environment. The rigidly defined
+descriptor patterns only capture a limited neighborhood of the keypoint and
+completely ignore the overall visual context.
+We propose LandmarkBoost - an approach that, in contrast to the conventional
+2D-3D matching methods, casts the search problem as a landmark classification
+task. We use a boosted classifier to classify landmark observations and
+directly obtain correspondences as classifier scores. We also introduce a
+formulation of visual context that is flexible, efficient to compute, and can
+capture relationships in the entire image plane. The original binary
+descriptors are augmented with contextual information and informative features
+are selected by the boosting framework. Through detailed experiments, we
+evaluate the retrieval quality and performance of LandmarkBoost, demonstrating
+that it outperforms common state-of-the-art descriptor matching methods.
+",1,0,0,0,0,0
+5606,On stably trivial spin torsors over low-dimensional schemes,"  The paper discusses stably trivial torsors for spin and orthogonal groups
+over smooth affine schemes over infinite perfect fields of characteristic
+unequal to 2. We give a complete description of all the invariants relevant for
+the classification of such objects over schemes of dimension at most $3$, along
+with many examples. The results are based on the
+$\mathbb{A}^1$-representability theorem for torsors and transfer of known
+computations of $\mathbb{A}^1$-homotopy sheaves along the sporadic isomorphisms
+to spin groups.
+",0,0,1,0,0,0
+5607,"On the relation between dependency distance, crossing dependencies, and parsing. Comment on ""Dependency distance: a new perspective on syntactic patterns in natural languages"" by Haitao Liu et al","  Liu et al. (2017) provide a comprehensive account of research on dependency
+distance in human languages. While the article is a very rich and useful report
+on this complex subject, here I will expand on a few specific issues where
+research in computational linguistics (specifically natural language
+processing) can inform DDM research, and vice versa. These aspects have not
+been explored much in the article by Liu et al. or elsewhere, probably due to
+the little overlap between both research communities, but they may provide
+interesting insights for improving our understanding of the evolution of human
+languages, the mechanisms by which the brain processes and understands
+language, and the construction of effective computer systems to achieve this
+goal.
+",1,0,0,0,0,0
+5608,Edge Erasures and Chordal Graphs,"  We prove several results about chordal graphs and weighted chordal graphs by
+focusing on exposed edges. These are edges that are properly contained in a
+single maximal complete subgraph. This leads to a characterization of chordal
+graphs via deletions of a sequence of exposed edges from a complete graph. Most
+interesting is that in this context the connected components of the
+edge-induced subgraph of exposed edges are 2-edge connected. We use this latter
+fact in the weighted case to give a modified version of Kruskal's second
+algorithm for finding a minimum spanning tree in a weighted chordal graph. This
+modified algorithm benefits from being local in an important sense.
+",1,0,1,0,0,0
+5609,One-dimensional plasmonic hotspots located between silver nanowire dimers evaluated by surface-enhanced resonance Raman scattering,"  Hotspots of surface-enhanced resonance Raman scattering (SERRS) are localized
+within 1 nm at gaps or crevices of plasmonic nanoparticle (NP) dimers. We
+demonstrate SERRS hotspots with volumes that are extended in one dimension tens
+of thousand times compared to standard zero-dimensional hotspots using gaps or
+crevices of plasmonic nanowire (NW) dimers.
+",0,1,0,0,0,0
+5610,An efficient methodology for the analysis and modeling of computer experiments with large number of inputs,"  Complex computer codes are often too time expensive to be directly used to
+perform uncertainty, sensitivity, optimization and robustness analyses. A
+widely accepted method to circumvent this problem consists in replacing
+cpu-time expensive computer models by cpu inexpensive mathematical functions,
+called metamodels. For example, the Gaussian process (Gp) model has shown
+strong capabilities to solve practical problems , often involving several
+interlinked issues. However, in case of high dimensional experiments (with
+typically several tens of inputs), the Gp metamodel building process remains
+difficult, even unfeasible, and application of variable selection techniques
+cannot be avoided. In this paper, we present a general methodology allowing to
+build a Gp metamodel with large number of inputs in a very efficient manner.
+While our work focused on the Gp metamodel, its principles are fully generic
+and can be applied to any types of metamodel. The objective is twofold:
+estimating from a minimal number of computer experiments a highly predictive
+metamodel. This methodology is successfully applied on an industrial computer
+code.
+",0,0,1,1,0,0
+5611,Knowledge Adaptation: Teaching to Adapt,"  Domain adaptation is crucial in many real-world applications where the
+distribution of the training data differs from the distribution of the test
+data. Previous Deep Learning-based approaches to domain adaptation need to be
+trained jointly on source and target domain data and are therefore unappealing
+in scenarios where models need to be adapted to a large number of domains or
+where a domain is evolving, e.g. spam detection where attackers continuously
+change their tactics.
+To fill this gap, we propose Knowledge Adaptation, an extension of Knowledge
+Distillation (Bucilua et al., 2006; Hinton et al., 2015) to the domain
+adaptation scenario. We show how a student model achieves state-of-the-art
+results on unsupervised domain adaptation from multiple sources on a standard
+sentiment analysis benchmark by taking into account the domain-specific
+expertise of multiple teachers and the similarities between their domains.
+When learning from a single teacher, using domain similarity to gauge
+trustworthiness is inadequate. To this end, we propose a simple metric that
+correlates well with the teacher's accuracy in the target domain. We
+demonstrate that incorporating high-confidence examples selected by this metric
+enables the student model to achieve state-of-the-art performance in the
+single-source scenario.
+",1,0,0,0,0,0
+5612,A Datamining Approach for Emotions Extraction and Discovering Cricketers performance from Stadium to Sensex,"  Microblogging sites are the direct platform for the users to express their
+views. It has been observed from previous studies that people are viable to
+flaunt their emotions for events (eg. natural catastrophes, sports, academics
+etc.), for persons (actor/actress, sports person, scientist) and for the places
+they visit. In this study we focused on a sport event, particularly the cricket
+tournament and collected the emotions of the fans for their favorite players
+using their tweets. Further, we acquired the stock market performance of the
+brands which are either endorsing the players or sponsoring the match in the
+tournament. It has been observed that performance of the player triggers the
+users to flourish their emotions over social media therefore, we observed
+correlation between players performance and fans' emotions. Therefore, we found
+the direct connection between player's performance with brand's behavior on
+stock market.
+",1,0,0,0,0,0
+5613,Local Nonparametric Estimation for Second-Order Jump-Diffusion Model Using Gamma Asymmetric Kernels,"  This paper discusses the local linear smoothing to estimate the unknown first
+and second infinitesimal moments in second-order jump-diffusion model based on
+Gamma asymmetric kernels. Under the mild conditions, we obtain the weak
+consistency and the asymptotic normality of these estimators for both interior
+and boundary design points. Besides the standard properties of the local linear
+estimation such as simple bias representation and boundary bias correction, the
+local linear smoothing using Gamma asymmetric kernels possess some extra
+advantages such as variable bandwidth, variance reduction and resistance to
+sparse design, which is validated through finite sample simulation study.
+Finally, we employ the estimators for the return of some high frequency
+financial data.
+",0,0,1,1,0,0
+5614,Ergodic actions of the compact quantum group $O_{-1}(2)$,"  Among the ergodic actions of a compact quantum group $\mathbb{G}$ on possibly
+non-commutative spaces, those that are {\it embeddable} are the natural
+analogues of actions of a compact group on its homogeneous spaces. These can be
+realized as {\it coideal subalgebras} of the function algebra
+$\mathcal{O}(\mathbb{G})$ attached to the compact quantum group.
+We classify the embeddable ergodic actions of the compact quantum group
+$O_{-1}(2)$, basing our analysis on the bijective correspondence between all
+ergodic actions of the classical group $O(2)$ and those of its quantum twist
+resulting from the monoidal equivalence between their respective tensor
+categories of unitary representations.
+In the last section we give counterexamples showing that in general we cannot
+expect a bijective correspondence between embeddable ergodic actions of two
+monoidally equivalent compact quantum groups.
+",0,0,1,0,0,0
+5615,On Vector ARMA Models Consistent with a Finite Matrix Covariance Sequence,"  We formulate the so called ""VARMA covariance matching problem"" and
+demonstrate the existence of a solution using the degree theory from
+differential topology.
+",0,0,1,1,0,0
+5616,Edge states in non-Fermi liquids,"  We devise an approach to the calculation of scaling dimensions of generic
+operators describing scattering within multi-channel Luttinger liquid. The
+local impurity scattering in an arbitrary configuration of conducting and
+insulating channels is investigated and the problem is reduced to a single
+algebraic matrix equation. In particular, the solution to this equation is
+found for a finite array of chains described by Luttinger liquid models. It is
+found that for a weak inter-chain hybridisation and intra-channel
+electron-electron attraction the edge wires are robust against disorder whereas
+bulk wires, on contrary, become insulating. Thus, the edge state may exist in a
+finite sliding Luttinger liquid without time-reversal symmetry breaking
+(quantum Hall systems) or spin-orbit interaction (topological insulators).
+",0,1,0,0,0,0
+5617,RobustFill: Neural Program Learning under Noisy I/O,"  The problem of automatically generating a computer program from some
+specification has been studied since the early days of AI. Recently, two
+competing approaches for automatic program learning have received significant
+attention: (1) neural program synthesis, where a neural network is conditioned
+on input/output (I/O) examples and learns to generate a program, and (2) neural
+program induction, where a neural network generates new outputs directly using
+a latent program representation.
+Here, for the first time, we directly compare both approaches on a
+large-scale, real-world learning task. We additionally contrast to rule-based
+program synthesis, which uses hand-crafted semantics to guide the program
+generation. Our neural models use a modified attention RNN to allow encoding of
+variable-sized sets of I/O pairs. Our best synthesis model achieves 92%
+accuracy on a real-world test set, compared to the 34% accuracy of the previous
+best neural synthesis approach. The synthesis model also outperforms a
+comparable induction model on this task, but we more importantly demonstrate
+that the strength of each approach is highly dependent on the evaluation metric
+and end-user application. Finally, we show that we can train our neural models
+to remain very robust to the type of noise expected in real-world data (e.g.,
+typos), while a highly-engineered rule-based system fails entirely.
+",1,0,0,0,0,0
+5618,Review of flexible and transparent thin-film transistors based on zinc oxide and related materials,"  Flexible and transparent electronics presents a new era of electronic
+technologies. Ubiquitous applications involve wearable electronics, biosensors,
+flexible transparent displays, radio-frequency identifications (RFIDs),
+etc.Zinc oxide (ZnO) and related materials are the most commonly used inorganic
+semiconductors in flexible and transparent devices, owing to their high
+electrical performance, together with low processing temperature and good
+optical transparency.In this paper, we review recent advances in flexible and
+transparent thin-film transistors (TFTs) based on ZnO and related
+materials.After a brief introduction, the main progresses on the preparation of
+each component (substrate, electrodes, channel and dielectrics) are summarized
+and discussed. Then, the effect of mechanical bending on electrical performance
+was highlighted. Finally, we suggest the challenges and opportunities in future
+investigations.
+",0,1,0,0,0,0
+5619,Updated physics design of the DAEdALUS and IsoDAR coupled cyclotrons for high intensity H2+ beam production,"  The Decay-At-rest Experiment for delta-CP violation At a Laboratory for
+Underground Science (DAEdALUS) and the Isotope Decay-At-Rest experiment
+(IsoDAR) are proposed experiments to search for CP violation in the neutrino
+sector, and ""sterile"" neutrinos, respectively. In order to be decisive within 5
+years, the neutrino flux and, consequently, the driver beam current (produced
+by chained cyclotrons) must be high. H2+ was chosen as primary beam ion in
+order to reduce the electrical current and thus space charge. This has the
+added advantage of allowing for stripping extraction at the exit of the
+DAEdALUS Superconducting Ring Cyclotron (DSRC). The primary beam current is
+higher than current cyclotrons have demonstrated which has led to a substantial
+R&D effort of our collaboration in the last years. We present the results of
+this research, including tests of prototypes and highly realistic beam
+simulations, which led to the latest physics-based design. The presented
+results suggest that it is feasible, albeit challenging, to accelerate 5 mA of
+H2+ to 60 MeV/amu in a compact cyclotron and boost it to 800 MeV/amu in the
+DSRC with clean extraction in both cases.
+",0,1,0,0,0,0
+5620,Scalable Gaussian Process Computations Using Hierarchical Matrices,"  We present a kernel-independent method that applies hierarchical matrices to
+the problem of maximum likelihood estimation for Gaussian processes. The
+proposed approximation provides natural and scalable stochastic estimators for
+its gradient and Hessian, as well as the expected Fisher information matrix,
+that are computable in quasilinear $O(n \log^2 n)$ complexity for a large range
+of models. To accomplish this, we (i) choose a specific hierarchical
+approximation for covariance matrices that enables the computation of their
+exact derivatives and (ii) use a stabilized form of the Hutchinson stochastic
+trace estimator. Since both the observed and expected information matrices can
+be computed in quasilinear complexity, covariance matrices for MLEs can also be
+estimated efficiently. After discussing the associated mathematics, we
+demonstrate the scalability of the method, discuss details of its
+implementation, and validate that the resulting MLEs and confidence intervals
+based on the inverse Fisher information matrix faithfully approach those
+obtained by the exact likelihood.
+",0,0,0,1,0,0
+5621,Evolutionary Data Systems,"  Anyone in need of a data system today is confronted with numerous complex
+options in terms of system architectures, such as traditional relational
+databases, NoSQL and NewSQL solutions as well as several sub-categories like
+column-stores, row-stores etc. This overwhelming array of choices makes
+bootstrapping data-driven applications difficult and time consuming, requiring
+expertise often not accessible due to cost issues (e.g., to scientific labs or
+small businesses). In this paper, we present the vision of evolutionary data
+systems that free systems architects and application designers from the
+complex, cumbersome and expensive process of designing and tuning specialized
+data system architectures that fit only a single, static application scenario.
+Setting up an evolutionary system is as simple as identifying the data. As new
+data and queries come in, the system automatically evolves so that its
+architecture matches the properties of the incoming workload at all times.
+Inspired by the theory of evolution, at any given point in time, an
+evolutionary system may employ multiple competing solutions down at the low
+level of database architectures -- characterized as combinations of data
+layouts, access methods and execution strategies. Over time, ""the fittest wins""
+and becomes the dominant architecture until the environment (workload) changes.
+In our initial prototype, we demonstrate solutions that can seamlessly evolve
+(back and forth) between a key-value store and a column-store architecture in
+order to adapt to changing workloads.
+",1,0,0,0,0,0
+5622,Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks,"  We consider the problem of sequentially making decisions that are rewarded by
+""successes"" and ""failures"" which can be predicted through an unknown
+relationship that depends on a partially controllable vector of attributes for
+each instance. The learner takes an active role in selecting samples from the
+instance pool. The goal is to maximize the probability of success in either
+offline (training) or online (testing) phases. Our problem is motivated by
+real-world applications where observations are time-consuming and/or expensive.
+We develop a knowledge gradient policy using an online Bayesian linear
+classifier to guide the experiment by maximizing the expected value of
+information of labeling each alternative. We provide a finite-time analysis of
+the estimated error and show that the maximum likelihood estimator based
+produced by the KG policy is consistent and asymptotically normal. We also show
+that the knowledge gradient policy is asymptotically optimal in an offline
+setting. This work further extends the knowledge gradient to the setting of
+contextual bandits. We report the results of a series of experiments that
+demonstrate its efficiency.
+",0,0,0,1,0,0
+5623,Determination of hysteresis in finite-state random walks using Bayesian cross validation,"  Consider the problem of modeling hysteresis for finite-state random walks
+using higher-order Markov chains. This Letter introduces a Bayesian framework
+to determine, from data, the number of prior states of recent history upon
+which a trajectory is statistically dependent. The general recommendation is to
+use leave-one-out cross validation, using an easily-computable formula that is
+provided in closed form. Importantly, Bayes factors using flat model priors are
+biased in favor of too-complex a model (more hysteresis) when a large amount of
+data is present and the Akaike information criterion (AIC) is biased in favor
+of too-sparse a model (less hysteresis) when few data are present.
+",0,1,0,1,0,0
+5624,Moyennes effectives de fonctions multiplicatives complexes,"  We establish effective mean-value estimates for a wide class of
+multiplicative arithmetic functions, thereby providing (essentially optimal)
+quantitative versions of Wirsing's classical estimates and extending those of
+Halász. Several applications are derived, including: estimates for the
+difference of mean-values of so-called pretentious functions, local laws for
+the distribution of prime factors in an arbitrary set, and weighted
+distribution of additive functions.
+",0,0,1,0,0,0
+5625,Magnetic Excitations and Continuum of a Field-Induced Quantum Spin Liquid in $α$-RuCl$_3$,"  We report on terahertz spectroscopy of quantum spin dynamics in
+$\alpha$-RuCl$_3$, a system proximate to the Kitaev honeycomb model, as a
+function of temperature and magnetic field. An extended magnetic continuum
+develops below the structural phase transition at $T_{s2}=62$K. With the onset
+of a long-range magnetic order at $T_N=6.5$K, spectral weight is transferred to
+a well-defined magnetic excitation at $\hbar \omega_1 = 2.48$meV, which is
+accompanied by a higher-energy band at $\hbar \omega_2 = 6.48$meV. Both
+excitations soften in magnetic field, signaling a quantum phase transition at
+$B_c=7$T where we find a broad continuum dominating the dynamical response.
+Above $B_c$, the long-range order is suppressed, and on top of the continuum,
+various emergent magnetic excitations evolve. These excitations follow clear
+selection rules and exhibit distinct field dependencies, characterizing the
+dynamical properties of the field-induced quantum spin liquid.
+",0,1,0,0,0,0
+5626,Wireless Network-Level Partial Relay Cooperation: A Stable Throughput Analysis,"  In this work, we study the benefit of partial relay cooperation. We consider
+a two-node system consisting of one source and one relay node transmitting
+information to a common destination. The source and the relay have external
+traffic and in addition, the relay is equipped with a flow controller to
+regulate the incoming traffic from the source node. The cooperation is
+performed at the network level. A collision channel with erasures is
+considered. We provide an exact characterization of the stability region of the
+system and we also prove that the system with partial cooperation is always
+better or at least equal to the system without the flow controller.
+",1,0,0,0,0,0
+5627,Improving Development Practices through Experimentation: an Industrial TDD Case,"  Test-Driven Development (TDD), an agile development approach that enforces
+the construction of software systems by means of successive micro-iterative
+testing coding cycles, has been widely claimed to increase external software
+quality. In view of this, some managers at Paf-a Nordic gaming entertainment
+company-were interested in knowing how would TDD perform at their premises.
+Eventually, if TDD outperformed their traditional way of coding (i.e., YW,
+short for Your Way), it would be possible to switch to TDD considering the
+empirical evidence achieved at the company level. We conduct an experiment at
+Paf to evaluate the performance of TDD, YW and the reverse approach of TDD
+(i.e., ITL, short for Iterative-Test Last) on external quality. TDD outperforms
+YW and ITL at Paf. Despite the encouraging results, we cannot recommend Paf to
+immediately adopt TDD as the difference in performance between YW and TDD is
+small. However, as TDD looks promising at Paf, we suggest to move some
+developers to TDD and to run a future experiment to compare the performance of
+TDD and YW. TDD slightly outperforms ITL in controlled experiments for TDD
+novices. However, more industrial experiments are still needed to evaluate the
+performance of TDD in real-life contexts.
+",1,0,0,0,0,0
+5628,An explicit Gross-Zagier formula related to the Sylvester Conjecture,"  Let $p\equiv 4,7\mod 9$ be a rational prime number such that $3\mod p$ is not
+a cubic residue. In this paper we prove the 3-part of the product of the full
+BSD conjectures for $E_p$ and $E_{3p^3}$ is true using an explicit Gross-Zagier
+formula, where $E_p: x^3+y^3=p$ and $E_{3p^2}: x^3+y^3=3p^2$ are the elliptic
+curves related to the Sylvester conjecture and cube sum problems.
+",0,0,1,0,0,0
+5629,Case Studies of Exocomets in the System of HD 10180,"  The aim of our study is to investigate the dynamics of possible comets in the
+HD 10180 system. This investigation is motivated by the discovery of exocomets
+in various systems, especially $\beta$ Pictoris, as well as in at least ten
+other systems. Detailed theoretical studies about the formation and evolution
+of star--planet systems indicate that exocomets should be quite common. Further
+observational results are expected in the foreseeable future, in part due to
+the availability of the Large Synoptic Survey Telescope. Nonetheless, the Solar
+System represents the best studied example for comets, thus serving as a prime
+motivation for investigating comets in HD 10180 as well. HD 10180 is strikingly
+similar to the Sun. This system contains six confirmed planets and (at least)
+two additional planets subject to final verification. In our studies, we
+consider comets of different inclinations and eccentricities and find an array
+of different outcomes such as encounters with planets, captures, and escapes.
+Comets with relatively large eccentricities are able to enter the inner region
+of the system facing early planetary encounters. Stable comets experience
+long-term evolution of orbital elements, as expected. We also tried to
+distinguish cometary families akin to our Solar System but no clear distinction
+between possible families was found. Generally, theoretical and observational
+studies of exoplanets have a large range of ramifications, involving the
+origin, structure and evolution of systems as well as the proliferation of
+water and prebiotic compounds to terrestrial planets, which will increase their
+chances of being habitable.
+",0,1,0,0,0,0
+5630,3D ab initio modeling in cryo-EM by autocorrelation analysis,"  Single-Particle Reconstruction (SPR) in Cryo-Electron Microscopy (cryo-EM) is
+the task of estimating the 3D structure of a molecule from a set of noisy 2D
+projections, taken from unknown viewing directions. Many algorithms for SPR
+start from an initial reference molecule, and alternate between refining the
+estimated viewing angles given the molecule, and refining the molecule given
+the viewing angles. This scheme is called iterative refinement. Reliance on an
+initial, user-chosen reference introduces model bias, and poor initialization
+can lead to slow convergence. Furthermore, since no ground truth is available
+for an unsolved molecule, it is difficult to validate the obtained results.
+This creates the need for high quality ab initio models that can be quickly
+obtained from experimental data with minimal priors, and which can also be used
+for validation. We propose a procedure to obtain such an ab initio model
+directly from raw data using Kam's autocorrelation method. Kam's method has
+been known since 1980, but it leads to an underdetermined system, with missing
+orthogonal matrices. Until now, this system has been solved only for special
+cases, such as highly symmetric molecules or molecules for which a homologous
+structure was already available. In this paper, we show that knowledge of just
+two clean projections is sufficient to guarantee a unique solution to the
+system. This system is solved by an optimization-based heuristic. For the first
+time, we are then able to obtain a low-resolution ab initio model of an
+asymmetric molecule directly from raw data, without 2D class averaging and
+without tilting. Numerical results are presented on both synthetic and
+experimental data.
+",0,0,0,1,0,0
+5631,Reinforcement Learning with a Corrupted Reward Channel,"  No real-world reward function is perfect. Sensory errors and software bugs
+may result in RL agents observing higher (or lower) rewards than they should.
+For example, a reinforcement learning agent may prefer states where a sensory
+error gives it the maximum reward, but where the true reward is actually small.
+We formalise this problem as a generalised Markov Decision Problem called
+Corrupt Reward MDP. Traditional RL methods fare poorly in CRMDPs, even under
+strong simplifying assumptions and when trying to compensate for the possibly
+corrupt rewards. Two ways around the problem are investigated. First, by giving
+the agent richer data, such as in inverse reinforcement learning and
+semi-supervised reinforcement learning, reward corruption stemming from
+systematic sensory errors may sometimes be completely managed. Second, by using
+randomisation to blunt the agent's optimisation, reward corruption can be
+partially managed under some assumptions.
+",1,0,0,1,0,0
+5632,Divisor-sum fibers,"  Let $s(\cdot)$ denote the sum-of-proper-divisors function, that is, $s(n) =
+\sum_{d\mid n,~d<n}d$. Erdős-Granville-Pomerance-Spiro conjectured that for
+any set $\mathcal{A}$ of asymptotic density zero, the preimage set
+$s^{-1}(\mathcal{A})$ also has density zero. We prove a weak form of this
+conjecture: If $\epsilon(x)$ is any function tending to $0$ as $x\to\infty$,
+and $\mathcal{A}$ is a set of integers of cardinality at most
+$x^{\frac12+\epsilon(x)}$, then the number of integers $n\le x$ with $s(n) \in
+\mathcal{A}$ is $o(x)$, as $x\to\infty$. In particular, the EGPS conjecture
+holds for infinite sets with counting function $O(x^{\frac12 + \epsilon(x)})$.
+We also disprove a hypothesis from the same paper of EGPS by showing that for
+any positive numbers $\alpha$ and $\epsilon$, there are integers $n$ with
+arbitrarily many $s$-preimages lying between $\alpha(1-\epsilon)n$ and
+$\alpha(1+\epsilon)n$. Finally, we make some remarks on solutions $n$ to
+congruences of the form $\sigma(n) \equiv a\pmod{n}$, proposing a modification
+of a conjecture appearing in recent work of the first two authors. We also
+improve a previous upper bound for the number of solutions $n \leq x$, making
+it uniform in $a$.
+",0,0,1,0,0,0
+5633,New zirconium hydrides predicted by structure search method based on first principles calculations,"  The formation of precipitated zirconium (Zr) hydrides is closely related to
+the hydrogen embrittlement problem for the cladding materials of pressured
+water reactors (PWR). In this work, we systematically investigated the crystal
+structures of zirconium hydride (ZrHx) with different hydrogen concentrations
+(x = 0~2, atomic ratio) by combining the basin hopping algorithm with first
+principles calculations. We conclude that the P3m1 {\zeta}-ZrH0.5 is
+dynamically unstable, while a novel dynamically stable P3m1 ZrH0.5 structure
+was discovered in the structure search. The stability of bistable P42/nnm
+ZrH1.5 structures and I4/mmm ZrH2 structures are also revisited. We find that
+the P42/nnm (c/a > 1) ZrH1.5 is dynamically unstable, while the I4/mmm (c/a =
+1.57) ZrH2 is dynamically stable.The P42/nnm (c/a < 1) ZrH1.5 might be a key
+intermediate phase for the transition of {\gamma}->{\delta}->{\epsilon} phases.
+Additionally, by using the thermal dynamic simulations, we find that
+{\delta}-ZrH1.5 is the most stable structure at high temperature while ZrH2 is
+the most stable hydride at low temperature. Slow cooling process will promote
+the formation of {\delta}-ZrH1.5, and fast cooling process will promote the
+formation of {\gamma}-ZrH. These results may help to understand the phase
+transitions of zirconium hydrides.
+",0,1,0,0,0,0
+5634,Exchange striction driven magnetodielectric effect and potential photovoltaic effect in polar CaOFeS,"  CaOFeS is a semiconducting oxysulfide with polar layered triangular
+structure. Here a comprehensive theoretical study has been performed to reveal
+its physical properties, including magnetism, electronic structure, phase
+transition, magnetodielectric effect, as well as optical absorption. Our
+calculations confirm the Ising-like G-type antiferromagnetic ground state
+driven by the next-nearest neighbor exchanges, which breaks the trigonal
+symmetry and is responsible for the magnetodielectric effect driven by exchange
+striction. In addition, a large coefficient of visible light absorption is
+predicted, which leads to promising photovoltaic effect with the maximum
+light-to-electricity energy conversion efficiency up to 24.2%.
+",0,1,0,0,0,0
+5635,Studying Magnetic Fields using Low-frequency Pulsar Observations,"  Low-frequency polarisation observations of pulsars, facilitated by
+next-generation radio telescopes, provide powerful probes of astrophysical
+plasmas that span many orders of magnitude in magnetic field strength and
+scale: from pulsar magnetospheres to intervening magneto-ionic plasmas
+including the ISM and the ionosphere. Pulsar magnetospheres with teragauss
+field strengths can be explored through their numerous emission phenomena
+across multiple frequencies, the mechanism behind which remains elusive.
+Precise dispersion and Faraday rotation measurements towards a large number of
+pulsars probe the three-dimensional large-scale (and eventually small-scale)
+structure of the Galactic magnetic field, which plays a role in many
+astrophysical processes, but is not yet well understood, especially towards the
+Galactic halo. We describe some results and ongoing work from the Low Frequency
+Array (LOFAR) and the Murchison Widefield Array (MWA) radio telescopes in these
+areas. These and other pathfinder and precursor telescopes have reinvigorated
+low-frequency science and build towards the Square Kilometre Array (SKA), which
+will make significant advancements in studies of astrophysical magnetic fields
+in the next 50 years.
+",0,1,0,0,0,0
+5636,Two-sided Facility Location,"  Recent years have witnessed the rise of many successful e-commerce
+marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and
+Upwork, where a central platform mediates economic transactions between buyers
+and sellers. Motivated by these platforms, we formulate a set of facility
+location problems that we term Two-sided Facility location. In our model,
+agents arrive at nodes in an underlying metric space, where the metric distance
+between any buyer and seller captures the quality of the corresponding match.
+The platform posts prices and wages at the nodes, and opens a set of facilities
+to route the agents to. The agents at any facility are assumed to be matched.
+The platform ensures high match quality by imposing a distance constraint
+between a node and the facilities it is routed to. It ensures high service
+availability by ensuring flow to the facility is at least a pre-specified lower
+bound. Subject to these constraints, the goal of the platform is to maximize
+the social surplus (or gains from trade) subject to weak budget balance, i.e.,
+profit being non-negative.
+We present an approximation algorithm for this problem that yields a $(1 +
+\epsilon)$ approximation to surplus for any constant $\epsilon > 0$, while
+relaxing the match quality (i.e., maximum distance of any match) by a constant
+factor. We use an LP rounding framework that easily extends to other objectives
+such as maximizing volume of trade or profit.
+We justify our models by considering a dynamic marketplace setting where
+agents arrive according to a stochastic process and have finite patience (or
+deadlines) for being matched. We perform queueing analysis to show that for
+policies that route agents to facilities and match them, ensuring a low
+abandonment probability of agents reduces to ensuring sufficient flow arrives
+at each facility.
+",1,0,0,0,0,0
+5637,Optimal hedging under fast-varying stochastic volatility,"  In a market with a rough or Markovian mean-reverting stochastic volatility
+there is no perfect hedge. Here it is shown how various delta-type hedging
+strategies perform and can be evaluated in such markets. A precise
+characterization of the hedging cost, the replication cost caused by the
+volatility fluctuations, is presented in an asymptotic regime of rapid mean
+reversion for the volatility fluctuations. The optimal dynamic asset based
+hedging strategy in the considered regime is identified as the so-called
+`practitioners' delta hedging scheme. It is moreover shown that the
+performances of the delta-type hedging schemes are essentially independent of
+the regularity of the volatility paths in the considered regime and that the
+hedging costs are related to a vega risk martingale whose magnitude is
+proportional to a new market risk parameter.
+",0,0,0,0,0,1
+5638,Personal Food Computer: A new device for controlled-environment agriculture,"  Due to their interdisciplinary nature, devices for controlled-environment
+agriculture have the possibility to turn into ideal tools not only to conduct
+research on plant phenology but also to create curricula in a wide range of
+disciplines. Controlled-environment devices are increasing their
+functionalities as well as improving their accessibility. Traditionally,
+building one of these devices from scratch implies knowledge in fields such as
+mechanical engineering, digital electronics, programming, and energy
+management. However, the requirements of an effective controlled environment
+device for personal use brings new constraints and challenges. This paper
+presents the OpenAg Personal Food Computer (PFC); a low cost desktop size
+platform, which not only targets plant phenology researchers but also
+hobbyists, makers, and teachers from elementary to high-school levels (K-12).
+The PFC is completely open-source and it is intended to become a tool that can
+be used for collective data sharing and plant growth analysis. Thanks to its
+modular design, the PFC can be used in a large spectrum of activities.
+",1,0,0,0,0,0
+5639,Quenched Noise and Nonlinear Oscillations in Bistable Multiscale Systems,"  Nonlinear oscillators are a key modelling tool in many applications. The
+influence of annealed noise on nonlinear oscillators has been studied
+intensively. It can induce effects in nonlinear oscillators not present in the
+deterministic setting. Yet, there is no theory regarding the quenched noise
+scenario of random parameters sampled on fixed time intervals, although this
+situation is often a lot more natural. Here we study a paradigmatic nonlinear
+oscillator of van-der-Pol/FitzHugh-Nagumo type under quenched noise as a
+piecewise-deterministic Markov process. There are several interesting effects
+such as period shifts and new different trapped types of small-amplitude
+oscillations, which can be captured analytically. Furthermore, we numerically
+discover quenched resonance and show that it differs significantly from
+previous finite-noise optimality resonance effects. This demonstrates that
+quenched oscillatorscan be viewed as a new building block of nonlinear
+dynamics.
+",0,1,1,0,0,0
+5640,Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning,"  The recently proposed Multi-Layer Convolutional Sparse Coding (ML-CSC) model,
+consisting of a cascade of convolutional sparse layers, provides a new
+interpretation of Convolutional Neural Networks (CNNs). Under this framework,
+the computation of the forward pass in a CNN is equivalent to a pursuit
+algorithm aiming to estimate the nested sparse representation vectors -- or
+feature maps -- from a given input signal. Despite having served as a pivotal
+connection between CNNs and sparse modeling, a deeper understanding of the
+ML-CSC is still lacking: there are no pursuit algorithms that can serve this
+model exactly, nor are there conditions to guarantee a non-empty model. While
+one can easily obtain signals that approximately satisfy the ML-CSC
+constraints, it remains unclear how to simply sample from the model and, more
+importantly, how one can train the convolutional filters from real data.
+In this work, we propose a sound pursuit algorithm for the ML-CSC model by
+adopting a projection approach. We provide new and improved bounds on the
+stability of the solution of such pursuit and we analyze different practical
+alternatives to implement this in practice. We show that the training of the
+filters is essential to allow for non-trivial signals in the model, and we
+derive an online algorithm to learn the dictionaries from real data,
+effectively resulting in cascaded sparse convolutional layers. Last, but not
+least, we demonstrate the applicability of the ML-CSC model for several
+applications in an unsupervised setting, providing competitive results. Our
+work represents a bridge between matrix factorization, sparse dictionary
+learning and sparse auto-encoders, and we analyze these connections in detail.
+",1,0,0,1,0,0
+5641,Coble's group and the integrability of the Gosset-Elte polytopes and tessellations,"  This paper considers the planar figure of a combinatorial polytope or
+tessellation identified by the Coxeter symbol $k_{i,j}$ , inscribed in a conic,
+satisfying the geometric constraint that each octahedral cell has a centre.
+This realisation exists, and is movable, on account of some constraints being
+satisfied as a consequence of the others. A close connection to the birational
+group found originally by Coble in the different context of invariants for sets
+of points in projective space, allows to specify precisely a determining subset
+of vertices that may be freely chosen. This gives a unified geometric view of
+certain integrable discrete systems in one, two and three dimensions. Making
+contact with previous geometric accounts in the case of three dimensions, it is
+shown how the figure also manifests as a configuration of circles generalising
+the Clifford lattices, and how it can be applied to construct the spatial
+point-line configurations called the Desargues maps.
+",0,1,1,0,0,0
+5642,Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples,"  Deep Learning (DL) methods show very good performance when trained on large,
+balanced data sets. However, many practical problems involve imbalanced data
+sets, or/and classes with a small number of training samples. The performance
+of DL methods as well as more traditional classifiers drops significantly in
+such settings. Most of the existing solutions for imbalanced problems focus on
+customizing the data for training. A more principled solution is to use mixed
+Hinge-Minimax risk [19] specifically designed to solve binary problems with
+imbalanced training sets. Here we propose a Latent Hinge Minimax (LHM) risk and
+a training algorithm that generalizes this paradigm to an ensemble of
+hyperplanes that can form arbitrary complex, piecewise linear boundaries. To
+extract good features, we combine LHM model with CNN via transfer learning. To
+solve multi-class problem we map pre-trained category-specific LHM classifiers
+to a multi-class neural network and adjust the weights with very fast tuning.
+LHM classifier enables the use of unlabeled data in its training and the
+mapping allows for multi-class inference, resulting in a classifier that
+performs better than alternatives when trained on a small number of training
+samples.
+",1,0,0,0,0,0
+5643,Autonomous Vehicle Speed Control for Safe Navigation of Occluded Pedestrian Crosswalk,"  Both humans and the sensors on an autonomous vehicle have limited sensing
+capabilities. When these limitations coincide with scenarios involving
+vulnerable road users, it becomes important to account for these limitations in
+the motion planner. For the scenario of an occluded pedestrian crosswalk, the
+speed of the approaching vehicle should be a function of the amount of
+uncertainty on the roadway. In this work, the longitudinal controller is
+formulated as a partially observable Markov decision process and dynamic
+programming is used to compute the control policy. The control policy scales
+the speed profile to be used by a model predictive steering controller.
+",1,0,0,0,0,0
+5644,Statistical inference methods for cumulative incidence function curves at a fixed point in time,"  Competing risks data arise frequently in clinical trials. When the
+proportional subdistribution hazard assumption is violated or two cumulative
+incidence function (CIF) curves cross, rather than comparing the overall
+treatment effects, researchers may be interested in focusing on a comparison of
+clinical utility at some fixed time points. This paper extend a series of tests
+that are constructed based on a pseudo-value regression technique or different
+transformation functions for CIFs and their variances based on Gaynor's or
+Aalen's work, and the differences among CIFs at a given time point are
+compared.
+",0,0,0,1,0,0
+5645,Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines,"  We compare and contrast the statistical physics and quantum physics inspired
+approaches for unsupervised generative modeling of classical data. The two
+approaches represent probabilities of observed data using energy-based models
+and quantum states respectively.Classical and quantum information patterns of
+the target datasets therefore provide principled guidelines for structural
+design and learning in these two approaches. Taking the restricted Boltzmann
+machines (RBM) as an example, we analyze the information theoretical bounds of
+the two approaches. We verify our reasonings by comparing the performance of
+RBMs of various architectures on the standard MNIST datasets.
+",0,0,0,1,0,0
+5646,A Cofibration Category on Closure Spaces,"  We construct a cofibration category structure on the category of closure
+spaces $\mathbf{Cl}$, the category whose objects are sets endowed with a
+Čech closure operator and whose morphisms are the continuous maps between
+them. We then study various closure structures on metric spaces, graphs, and
+simplicial complexes, showing how each case gives rise to an interesting
+homotopy theory. In particular, we show that there exists a natural family of
+closure structures on metric spaces which produces a non-trivial homotopy
+theory for finite metric spaces, i.e. point clouds, the spaces of interest in
+topological data analysis. We then give a closure structure to graphs and
+simplicial complexes which may be used to construct a new combinatorial (as
+opposed to topological) homotopy theory for each skeleton of those spaces. We
+show that there is a Seifert-van Kampen theorem for closure spaces, a
+well-defined notion of persistent homotopy and an associated interleaving
+distance, and, as an illustration of the difference with the topological
+setting, we calculate the fundamental group for the circle and the wedge of
+circles endowed with different closure structures.
+",0,0,1,0,0,0
+5647,XSAT of Linear CNF Formulas,"  Open questions with respect to the computational complexity of linear CNF
+formulas in connection with regularity and uniformity are addressed. In
+particular it is proven that any l-regular monotone CNF formula is
+XSAT-unsatisfiable if its number of clauses m is not a multiple of l. For exact
+linear formulas one finds surprisingly that l-regularity implies k-uniformity,
+with m = 1 + k(l-1)) and allowed k-values obey k(k-1) = 0 (mod l). Then the
+computational complexity of the class of monotone exact linear and l-regular
+CNF formulas with respect to XSAT can be determined: XSAT-satisfiability is
+either trivial, if m is not a multiple of l, or it can be decided in
+sub-exponential time, namely O(exp(n^^1/2)). Sub-exponential time behaviour for
+the wider class of regular and uniform linear CNF formulas can be shown for
+certain subclasses.
+",1,0,1,0,0,0
+5648,Drone Squadron Optimization: a Self-adaptive Algorithm for Global Numerical Optimization,"  This paper proposes Drone Squadron Optimization, a new self-adaptive
+metaheuristic for global numerical optimization which is updated online by a
+hyper-heuristic. DSO is an artifact-inspired technique, as opposed to many
+algorithms used nowadays, which are nature-inspired. DSO is very flexible
+because it is not related to behaviors or natural phenomena. DSO has two core
+parts: the semi-autonomous drones that fly over a landscape to explore, and the
+Command Center that processes the retrieved data and updates the drones'
+firmware whenever necessary. The self-adaptive aspect of DSO in this work is
+the perturbation/movement scheme, which is the procedure used to generate
+target coordinates. This procedure is evolved by the Command Center during the
+global optimization process in order to adapt DSO to the search landscape. DSO
+was evaluated on a set of widely employed benchmark functions. The statistical
+analysis of the results shows that the proposed method is competitive with the
+other methods in the comparison, the performance is promising, but several
+future improvements are planned.
+",1,0,1,0,0,0
+5649,A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets,"  The original ImageNet dataset is a popular large-scale benchmark for training
+Deep Neural Networks. Since the cost of performing experiments (e.g, algorithm
+design, architecture search, and hyperparameter tuning) on the original dataset
+might be prohibitive, we propose to consider a downsampled version of ImageNet.
+In contrast to the CIFAR datasets and earlier downsampled versions of ImageNet,
+our proposed ImageNet32$\times$32 (and its variants ImageNet64$\times$64 and
+ImageNet16$\times$16) contains exactly the same number of classes and images as
+ImageNet, with the only difference that the images are downsampled to
+32$\times$32 pixels per image (64$\times$64 and 16$\times$16 pixels for the
+variants, respectively). Experiments on these downsampled variants are
+dramatically faster than on the original ImageNet and the characteristics of
+the downsampled datasets with respect to optimal hyperparameters appear to
+remain similar. The proposed datasets and scripts to reproduce our results are
+available at this http URL and
+this https URL
+",1,0,0,0,0,0
+5650,Stability of Conditional Sequential Monte Carlo,"  The particle Gibbs (PG) sampler is a Markov Chain Monte Carlo (MCMC)
+algorithm, which uses an interacting particle system to perform the Gibbs
+steps. Each Gibbs step consists of simulating a particle system conditioned on
+one particle path. It relies on a conditional Sequential Monte Carlo (cSMC)
+method to create the particle system. We propose a novel interpretation of the
+cSMC algorithm as a perturbed Sequential Monte Carlo (SMC) method and apply
+telescopic decompositions developed for the analysis of SMC algorithms
+\cite{delmoral2004} to derive a bound for the distance between the expected
+sampled path from cSMC and the target distribution of the MCMC algorithm. This
+can be used to get a uniform ergodicity result. In particular, we can show that
+the mixing rate of cSMC can be kept constant by increasing the number of
+particles linearly with the number of observations. Based on our decomposition,
+we also prove a central limit theorem for the cSMC Algorithm, which cannot be
+done using the approaches in \cite{Andrieu2013} and \cite{Lindsten2014}.
+",0,0,0,1,0,0
+5651,Diffusion along chains of normally hyperbolic cylinders,"  The present paper is part of a series of articles dedicated to the existence
+of Arnold diffusion for cusp-residual perturbations of Tonelli Hamiltonians on
+$\mathbb{A}^3$. Our goal here is to construct an abstract geometric framework
+that can be used to prove the existence of diffusing orbits in the so-called a
+priori stable setting, once the preliminary geometric reductions are preformed.
+Our framework also applies, rather directly, to the a priori unstable setting.
+The main geometric objects of interest are $3$-dimensional normally
+hyperbolic invariant cylinders with boundary, which in particular admit
+well-defined stable and unstable manifolds. These enable us to define, in our
+setting, chains of cylinders, i.e., finite, ordered families of cylinders in
+which each cylinder admits homoclinic connections, and any two consecutive
+elements in the family admit heteroclinic connections.
+Our main result is the existence of diffusing orbits drifting along such
+chains, under precise conditions on the dynamics on the cylinders, and on their
+homoclinic and heteroclinic structure.
+",0,1,1,0,0,0
+5652,Multi-wavelength Spectral Analysis of Ellerman Bombs Observed by FISS and IRIS,"  Ellerman bombs (EBs) are a kind of solar activities that is suggested to
+occur in the lower atmosphere. Recent observations using the Interface Region
+Imaging Spectrograph (IRIS) show connections of EBs and IRIS bombs (IBs),
+implying that EBs might be heated to a much higher temperature ($8\times10^{4}$
+K) than previous results. Here we perform a spectral analysis of the EBs
+simultaneously observed by the Fast Imaging Solar Spectrograph (FISS) and IRIS.
+The observational results show clear evidence of heating in the lower
+atmosphere, indicated by the wing enhancement in H$\alpha$, Ca II 8542 Å
+and Mg II triplet lines, and also by brightenings in the images of 1700 Å
+and 2832 Å ultraviolet continuum channels. Additionally, the Mg II triplet
+line intensity is correlated with that of H$\alpha$ when the EB occurs,
+indicating the possibility to use the triplet as an alternative way to identify
+EBs. However, we do not find any signal in IRIS hotter lines (C II and Si IV).
+For further analysis, we employ a two-cloud model to fit the two chromospheric
+lines (H$\alpha$ and Ca II 8542 Å) simultaneously, and obtain a temperature
+enhancement of 2300 K for a strong EB. This temperature is among the highest of
+previous modeling results while still insufficient to produce IB signatures at
+ultraviolet wavelengths.
+",0,1,0,0,0,0
+5653,Numerical study of the Kadomtsev--Petviashvili equation and dispersive shock waves,"  A detailed numerical study of the long time behaviour of dispersive shock
+waves in solutions to the Kadomtsev-Petviashvili (KP) I equation is presented.
+It is shown that modulated lump solutions emerge from the dispersive shock
+waves. For the description of dispersive shock waves, Whitham modulation
+equations for KP are obtained. It is shown that the modulation equations near
+the soliton line are hyperbolic for the KPII equation while they are elliptic
+for the KPI equation leading to a focusing effect and the formation of lumps.
+Such a behaviour is similar to the appearance of breathers for the focusing
+nonlinear Schrodinger equation in the semiclassical limit.
+",0,1,1,0,0,0
+5654,Asymptotic power of Rao's score test for independence in high dimensions,"  Let ${\bf R}$ be the Pearson correlation matrix of $m$ normal random
+variables. The Rao's score test for the independence hypothesis $H_0 : {\bf R}
+= {\bf I}_m$, where ${\bf I}_m$ is the identity matrix of dimension $m$, was
+first considered by Schott (2005) in the high dimensional setting. In this
+paper, we study the asymptotic minimax power function of this test, under an
+asymptotic regime in which both $m$ and the sample size $n$ tend to infinity
+with the ratio $m/n$ upper bounded by a constant. In particular, our result
+implies that the Rao's score test is rate-optimal for detecting the dependency
+signal $\|{\bf R} - {\bf I}_m\|_F$ of order $\sqrt{m/n}$, where $\|\cdot\|_F$
+is the matrix Frobenius norm.
+",0,0,1,1,0,0
+5655,Sensing and Modeling Human Behavior Using Social Media and Mobile Data,"  In the past years we have witnessed the emergence of the new discipline of
+computational social science, which promotes a new data-driven and
+computation-based approach to social sciences. In this article we discuss how
+the availability of new technologies such as online social media and mobile
+smartphones has allowed researchers to passively collect human behavioral data
+at a scale and a level of granularity that were just unthinkable some years
+ago. We also discuss how these digital traces can then be used to prove (or
+disprove) existing theories and develop new models of human behavior.
+",1,1,0,0,0,0
+5656,Interior transmission eigenvalue problems on compact manifolds with boundary conductivity parameters,"  In this paper, we consider an interior transmission eigenvalue (ITE) problem
+on some compact $C^{\infty }$-Riemannian manifolds with a common smooth
+boundary. In particular, these manifolds may have different topologies, but we
+impose some conditions of Riemannian metrics, indices of refraction and
+boundary conductivity parameters on the boundary. Then we prove the
+discreteness of the set of ITEs, the existence of infinitely many ITEs, and its
+Weyl type lower bound. For our settings, we can adopt the argument by
+Lakshtanov and Vainberg, considering the Dirichlet-to-Neumann map. As an
+application, we derive the existence of non-scattering energies for
+time-harmonic acoustic equations. For the sake of simplicity, we consider the
+scattering theory on the Euclidean space. However, the argument is applicable
+for certain kinds of non-compact manifolds with ends on which we can define the
+scattering matrix.
+",0,0,1,0,0,0
+5657,A superpolynomial lower bound for the size of non-deterministic complement of an unambiguous automaton,"  Unambiguous non-deterministic finite automata have intermediate expressive
+power and succinctness between deterministic and non-deterministic automata. It
+has been conjectured that every unambiguous non-deterministic one-way finite
+automaton (1UFA) recognizing some language L can be converted into a 1UFA
+recognizing the complement of the original language L with polynomial increase
+in the number of states. We disprove this conjecture by presenting a family of
+1UFAs on a single-letter alphabet such that recognizing the complements of the
+corresponding languages requires superpolynomial increase in the number of
+states even for generic non-deterministic one-way finite automata. We also note
+that both the languages and their complements can be recognized by sweeping
+deterministic automata with a linear increase in the number of states.
+",1,0,0,0,0,0
+5658,Taxonomy Induction using Hypernym Subsequences,"  We propose a novel, semi-supervised approach towards domain taxonomy
+induction from an input vocabulary of seed terms. Unlike all previous
+approaches, which typically extract direct hypernym edges for terms, our
+approach utilizes a novel probabilistic framework to extract hypernym
+subsequences. Taxonomy induction from extracted subsequences is cast as an
+instance of the minimumcost flow problem on a carefully designed directed
+graph. Through experiments, we demonstrate that our approach outperforms
+stateof- the-art taxonomy induction approaches across four languages.
+Importantly, we also show that our approach is robust to the presence of noise
+in the input vocabulary. To the best of our knowledge, no previous approaches
+have been empirically proven to manifest noise-robustness in the input
+vocabulary.
+",1,0,0,0,0,0
+5659,Identifying Clickbait Posts on Social Media with an Ensemble of Linear Models,"  The purpose of a clickbait is to make a link so appealing that people click
+on it. However, the content of such articles is often not related to the title,
+shows poor quality, and at the end leaves the reader unsatisfied.
+To help the readers, the organizers of the clickbait challenge
+(this http URL) asked the participants to build a machine
+learning model for scoring articles with respect to their ""clickbaitness"".
+In this paper we propose to solve the clickbait problem with an ensemble of
+Linear SVM models, and our approach was tested successfully in the challenge:
+it showed great performance of 0.036 MSE and ranked 3rd among all the solutions
+to the contest.
+",1,0,0,0,0,0
+5660,WYS*: A DSL for Verified Secure Multi-party Computations,"  Secure multi-party computation (MPC) enables a set of mutually distrusting
+parties to cooperatively compute, using a cryptographic protocol, a function
+over their private data. This paper presents Wys*, a new domain-specific
+language (DSL) for writing mixed-mode MPCs. Wys* is an embedded DSL hosted in
+F*, a verification-oriented, effectful programming language. Wys* source
+programs are essentially F* programs written in a custom MPC effect, meaning
+that the programmers can use F*'s logic to verify the correctness and security
+properties of their programs. To reason about the distributed runtime semantics
+of these programs, we formalize a deep embedding of Wys*, also in F*. We
+mechanize the necessary metatheory to prove that the properties verified for
+the Wys* source programs carry over to the distributed, multi-party semantics.
+Finally, we use F*'s extraction to extract an interpreter that we have proved
+matches this semantics, yielding a partially verified implementation. Wys* is
+the first DSL to enable formal verification of MPC programs. We have
+implemented several MPC protocols in Wys*, including private set intersection,
+joint median, and an MPC card dealing application, and have verified their
+correctness and security.
+",1,0,0,0,0,0
+5661,Probabilities of causation of climate changes,"  Multiple changes in Earth's climate system have been observed over the past
+decades. Determining how likely each of these changes are to have been caused
+by human influence, is important for decision making on mitigation and
+adaptation policy. Here we describe an approach for deriving the probability
+that anthropogenic forcings have caused a given observed change. The proposed
+approach is anchored into causal counterfactual theory (Pearl 2009) which has
+been introduced recently, and was in fact partly used already, in the context
+of extreme weather event attribution (EA). We argue that these concepts are
+also relevant, and can be straightforwardly extended to, the context of
+detection and attribution of long term trends associated to climate change
+(D&A). For this purpose, and in agreement with the principle of
+""fingerprinting"" applied in the conventional D&A framework, a trajectory of
+change is converted into an event occurrence defined by maximizing the causal
+evidence associated to the forcing under scrutiny. Other key assumptions used
+in the conventional D&A framework, in particular those related to numerical
+models error, can also be adapted conveniently to this approach. Our proposal
+thus allows to bridge the conventional framework with the standard causal
+theory, in an attempt to improve the quantification of causal probabilities. An
+illustration suggests that our approach is prone to yield a significantly
+higher estimate of the probability that anthropogenic forcings have caused the
+observed temperature change, thus supporting more assertive causal claims.
+",0,0,0,1,0,0
+5662,A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs,"  The field of Distributed Constraint Optimization has gained momentum in
+recent years thanks to its ability to address various applications related to
+multi-agent cooperation. While techniques to solve Distributed Constraint
+Optimization Problems (DCOPs) are abundant and have matured substantially since
+the field inception, the number of DCOP realistic applications and benchmark
+used to asses the performance of DCOP algorithms is lagging behind. To contrast
+this background we (i) introduce the Smart Home Device Scheduling (SHDS)
+problem, which describe the problem of coordinating smart devices schedules
+across multiple homes as a multi-agent system, (ii) detail the physical models
+adopted to simulate smart sensors, smart actuators, and homes environments, and
+(iii) introduce a DCOP realistic benchmark for SHDS problems.
+",1,0,0,0,0,0
+5663,The extra scalar degrees of freedom from the two Higgs doublet model for dark energy,"  In principle a minimal extension of the standard model of Particle Physics,
+the two Higgs doublet model, can be invoked to explain the scalar field
+responsible of dark energy. The two doublets are in general mixed. After
+diagonalization, the lightest CP-even Higgs and CP-odd Higgs are jointly taken
+to be the dark energy candidate. The dark energy obtained from Higgs fields in
+this case is indistinguishable from the cosmological constant.
+",0,1,0,0,0,0
+5664,Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience,"  We consider the problem of transferring policies to the real world by
+training on a distribution of simulated scenarios. Rather than manually tuning
+the randomization of simulations, we adapt the simulation parameter
+distribution using a few real world roll-outs interleaved with policy training.
+In doing so, we are able to change the distribution of simulations to improve
+the policy transfer by matching the policy behavior in simulation and the real
+world. We show that policies trained with our method are able to reliably
+transfer to different robots in two real world tasks: swing-peg-in-hole and
+opening a cabinet drawer. The video of our experiments can be found at
+this https URL
+",1,0,0,0,0,0
+5665,Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs,"  We address the problem of learning vector representations for entities and
+relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This
+problem has received significant attention in the past few years and multiple
+methods have been proposed. Most of the existing methods in the literature use
+a predefined characteristic scoring function for evaluating the correctness of
+KG triples. These scoring functions distinguish correct triples (high score)
+from incorrect ones (low score). However, their performance vary across
+different datasets. In this work, we demonstrate that a simple neural network
+based score function can consistently achieve near start-of-the-art performance
+on multiple datasets. We also quantitatively demonstrate biases in standard
+benchmark datasets, and highlight the need to perform evaluation spanning
+various datasets.
+",1,0,0,1,0,0
+5666,On the tail behavior of a class of multivariate conditionally heteroskedastic processes,"  Conditions for geometric ergodicity of multivariate autoregressive
+conditional heteroskedasticity (ARCH) processes, with the so-called BEKK (Baba,
+Engle, Kraft, and Kroner) parametrization, are considered. We show for a class
+of BEKK-ARCH processes that the invariant distribution is regularly varying. In
+order to account for the possibility of different tail indices of the
+marginals, we consider the notion of vector scaling regular variation, in the
+spirit of Perfekt (1997, Advances in Applied Probability, 29, pp. 138-164). The
+characterization of the tail behavior of the processes is used for deriving the
+asymptotic properties of the sample covariance matrices.
+",0,0,1,1,0,0
+5667,On the Complexity of Detecting Convexity over a Box,"  It has recently been shown that the problem of testing global convexity of
+polynomials of degree four is {strongly} NP-hard, answering an open question of
+N.Z. Shor. This result is minimal in the degree of the polynomial when global
+convexity is of concern. In a number of applications however, one is interested
+in testing convexity only over a compact region, most commonly a box (i.e.,
+hyper-rectangle). In this paper, we show that this problem is also strongly
+NP-hard, in fact for polynomials of degree as low as three. This result is
+minimal in the degree of the polynomial and in some sense justifies why
+convexity detection in nonlinear optimization solvers is limited to quadratic
+functions or functions with special structure. As a byproduct, our proof shows
+that the problem of testing whether all matrices in an interval family are
+positive semidefinite is strongly NP-hard. This problem, which was previously
+shown to be (weakly) NP-hard by Nemirovski, is of independent interest in the
+theory of robust control.
+",0,0,0,1,0,0
+5668,Variance-Reduced Stochastic Learning under Random Reshuffling,"  Several useful variance-reduced stochastic gradient algorithms, such as SVRG,
+SAGA, Finito, and SAG, have been proposed to minimize empirical risks with
+linear convergence properties to the exact minimizer. The existing convergence
+results assume uniform data sampling with replacement. However, it has been
+observed in related works that random reshuffling can deliver superior
+performance over uniform sampling and, yet, no formal proofs or guarantees of
+exact convergence exist for variance-reduced algorithms under random
+reshuffling. This paper makes two contributions. First, it resolves this open
+issue and provides the first theoretical guarantee of linear convergence under
+random reshuffling for SAGA; the argument is also adaptable to other
+variance-reduced algorithms. Second, under random reshuffling, the paper
+proposes a new amortized variance-reduced gradient (AVRG) algorithm with
+constant storage requirements compared to SAGA and with balanced gradient
+computations compared to SVRG. AVRG is also shown analytically to converge
+linearly.
+",1,0,0,1,0,0
+5669,CELIO: An application development framework for interactive spaces,"  Developing applications for interactive space is different from developing
+cross-platform applications for personal computing. Input, output, and
+architectural variations in each interactive space introduce big overhead in
+terms of cost and time for developing, deploying and maintaining applications
+for interactive spaces. Often, these applications become on-off experience tied
+to the deployed spaces. To alleviate this problem and enable rapid responsive
+space design applications similar to responsive web design, we present CELIO
+application development framework for interactive spaces. The framework is
+micro services based and neatly decouples application and design specifications
+from hardware and architecture specifications of an interactive space. In this
+paper, we describe this framework and its implementation details. Also, we
+briefly discuss the use cases developed using this framework.
+",1,0,0,0,0,0
+5670,Single Iteration Conditional Based DSE Considering Spatial and Temporal Correlation,"  The increasing complexity of distribution network calls for advancement in
+distribution system state estimation (DSSE) to monitor the operating conditions
+more accurately. Sufficient number of measurements is imperative for a reliable
+and accurate state estimation. The limitation on the measurement devices is
+generally tackled with using the so-called pseudo measured data. However, the
+errors in pseudo data by cur-rent techniques are quite high leading to a poor
+DSSE. As customer loads in distribution networks show high cross-correlation in
+various locations and over successive time steps, it is plausible that
+deploying the spatial-temporal dependencies can improve the pseudo data
+accuracy and estimation. Although, the role of spatial dependency in DSSE has
+been addressed in the literature, one can hardly find an efficient DSSE
+framework capable of incorporating temporal dependencies present in customer
+loads. Consequently, to obtain a more efficient and accurate state estimation,
+we propose a new non-iterative DSSE framework to involve spatial-temporal
+dependencies together. The spatial-temporal dependencies are modeled by
+conditional multivariate complex Gaussian distributions and are studied for
+both static and real-time state estimations, where information at preceding
+time steps are employed to increase the accuracy of DSSE. The efficiency of the
+proposed approach is verified based on quality and accuracy indices, standard
+deviation and computational time. Two balanced medium voltage (MV) and one
+unbalanced low voltage (LV) distribution case studies are used for evaluations.
+",0,0,0,1,0,0
+5671,Enhanced Network Embeddings via Exploiting Edge Labels,"  Network embedding methods aim at learning low-dimensional latent
+representation of nodes in a network. While achieving competitive performance
+on a variety of network inference tasks such as node classification and link
+prediction, these methods treat the relations between nodes as a binary
+variable and ignore the rich semantics of edges. In this work, we attempt to
+learn network embeddings which simultaneously preserve network structure and
+relations between nodes. Experiments on several real-world networks illustrate
+that by considering different relations between different node pairs, our
+method is capable of producing node embeddings of higher quality than a number
+of state-of-the-art network embedding methods, as evaluated on a challenging
+multi-label node classification task.
+",1,0,0,0,0,0
+5672,Virtual Constraints and Hybrid Zero Dynamics for Realizing Underactuated Bipedal Locomotion,"  Underactuation is ubiquitous in human locomotion and should be ubiquitous in
+bipedal robotic locomotion as well. This chapter presents a coherent theory for
+the design of feedback controllers that achieve stable walking gaits in
+underactuated bipedal robots. Two fundamental tools are introduced, virtual
+constraints and hybrid zero dynamics. Virtual constraints are relations on the
+state variables of a mechanical model that are imposed through a time-invariant
+feedback controller. One of their roles is to synchronize the robot's joints to
+an internal gait phasing variable. A second role is to induce a low dimensional
+system, the zero dynamics, that captures the underactuated aspects of a robot's
+model, without any approximations. To enhance intuition, the relation between
+physical constraints and virtual constraints is first established. From here,
+the hybrid zero dynamics of an underactuated bipedal model is developed, and
+its fundamental role in the design of asymptotically stable walking motions is
+established. The chapter includes numerous references to robots on which the
+highlighted techniques have been implemented.
+",1,0,1,0,0,0
+5673,Selective insulators and anomalous responses in correlated fermions with synthetic extra dimensions,"  We study a three-component fermionic fluid in an optical lattice in a regime
+of intermediate-to- strong interactions allowing for Raman processes connecting
+the different components, similar to those used to create artificial gauge
+fields (AGF). Using Dynamical Mean-Field Theory we show that the combined
+effect of interactions and AGFs induces a variety of anomalous phases in which
+different components of the fermionic fluid display qualitative differences,
+i.e., the physics is flavor-selective. Remarkably, the different components can
+display huge differences in the correlation effects, measured by their
+effective masses and non-monotonic behavior of their occupation number as a
+function of the chemical potential, signaling a sort of selective instability
+of the overall stable quantum fluid.
+",0,1,0,0,0,0
+5674,Computation of optimal transport and related hedging problems via penalization and neural networks,"  This paper presents a widely applicable approach to solving (multi-marginal,
+martingale) optimal transport and related problems via neural networks. The
+core idea is to penalize the optimization problem in its dual formulation and
+reduce it to a finite dimensional one which corresponds to optimizing a neural
+network with smooth objective function. We present numerical examples from
+optimal transport, martingale optimal transport, portfolio optimization under
+uncertainty and generative adversarial networks that showcase the generality
+and effectiveness of the approach.
+",0,0,0,1,0,1
+5675,Active particles in periodic lattices,"  Both natural and artificial small-scale swimmers may often self-propel in
+environments subject to complex geometrical constraints. While most past
+theoretical work on low-Reynolds number locomotion addressed idealised
+geometrical situations, not much is known on the motion of swimmers in
+heterogeneous environments. As a first theoretical model, we investigate
+numerically the behaviour of a single spherical micro-swimmer located in an
+infinite, periodic body-centred cubic lattice consisting of rigid inert spheres
+of the same size as the swimmer. Running a large number of simulations we
+uncover the phase diagram of possible trajectories as a function of the
+strength of the swimming actuation and the packing density of the lattice. We
+then use hydrodynamic theory to rationalise our computational results and show
+in particular how the far-field nature of the swimmer (pusher vs. puller)
+governs even the behaviour at high volume fractions.
+",0,1,0,0,0,0
+5676,First measeurements in search for keV-sterile neutrino in tritium beta-decay by Troitsk nu-mass experiment,"  We present the first measurements of tritium beta-decay spectrum in the
+electron energy range 16-18.6 keV. The goal is to find distortions which may
+correspond to the presence of a heavy sterile neutrinos. A possible
+contribution of this kind would manifest itself as a kink in the spectrum with
+a similar shape but with end point shifted by the value of a heavy neutrino
+mass. We set a new upper limits to the neutrino mixing matrix element U^2_{e4}
+which improve existing limits by a factor from 2 to 5 in the mass range 0.1-2
+keV.
+",0,1,0,0,0,0
+5677,Fractional Abelian topological phases of matter for fermions in two-dimensional space,"  These notes constitute chapter 7 from ""l'Ecole de Physique des Houches""
+Session CIII, August 2014 dedicated to Topological Aspects of Condensed matter
+physics. The tenfold way in quasi-one-dimensional space is presented. The
+method of chiral Abelian bosonization is reviewed. It is then applied to the
+stability analysis for the edge theory in symmetry class AII, and for the
+construction of two-dimensional topological phases from coupled wires.
+",0,1,0,0,0,0
+5678,A new construction of universal spaces for asymptotic dimension,"  For each $n$, we construct a separable metric space $\mathbb{U}_n$ that is
+universal in the coarse category of separable metric spaces with asymptotic
+dimension ($\mathop{asdim}$) at most $n$ and universal in the uniform category
+of separable metric spaces with uniform dimension ($\mathop{udim}$) at most
+$n$. Thus, $\mathbb{U}_n$ serves as a universal space for dimension $n$ in both
+the large-scale and infinitesimal topology. More precisely, we prove:
+\[
+\mathop{asdim} \mathbb{U}_n = \mathop{udim} \mathbb{U}_n = n
+\] and such that for each separable metric space $X$,
+a) if $\mathop{asdim} X \leq n$, then $X$ is coarsely equivalent to a subset
+of $\mathbb{U}_n$;
+b) if $\mathop{udim} X \leq n$, then $X$ is uniformly homeomorphic to a
+subset of $\mathbb{U}_n$.
+",0,0,1,0,0,0
+5679,A Comprehensive Framework for Dynamic Bike Rebalancing in a Large Bike Sharing Network,"  Bike sharing is a vital component of a modern multi-modal transportation
+system. However, its implementation can lead to bike supply-demand imbalance
+due to fluctuating spatial and temporal demands. This study proposes a
+comprehensive framework to develop optimal dynamic bike rebalancing strategies
+in a large bike sharing network. It consists of three components, including a
+station-level pick-up/drop-off prediction model, station clustering model, and
+capacitated location-routing optimization model. For the first component, we
+propose a powerful deep learning model called graph convolution neural network
+model (GCNN) with data-driven graph filter (DDGF), which can automatically
+learn the hidden spatial-temporal correlations among stations to provide more
+accurate predictions; for the second component, we apply a graph clustering
+algorithm labeled the Community Detection algorithm to cluster stations that
+locate geographically close to each other and have a small net demand gap;
+last, a capacitated location-routing problem (CLRP) is solved to deal with the
+combination of two types of decision variables: the locations of bike
+distribution centers and the design of distribution routes for each cluster.
+",0,0,0,1,0,0
+5680,On Thin Air Reads: Towards an Event Structures Model of Relaxed Memory,"  To model relaxed memory, we propose confusion-free event structures over an
+alphabet with a justification relation. Executions are modeled by justified
+configurations, where every read event has a justifying write event.
+Justification alone is too weak a criterion, since it allows cycles of the kind
+that result in so-called thin-air reads. Acyclic justification forbids such
+cycles, but also invalidates event reorderings that result from compiler
+optimizations and dynamic instruction scheduling. We propose the notion of
+well-justification, based on a game-like model, which strikes a middle ground.
+We show that well-justified configurations satisfy the DRF theorem: in any
+data-race free program, all well-justified configurations are sequentially
+consistent. We also show that rely-guarantee reasoning is sound for
+well-justified configurations, but not for justified configurations. For
+example, well-justified configurations are type-safe.
+Well-justification allows many, but not all reorderings performed by relaxed
+memory. In particular, it fails to validate the commutation of independent
+reads. We discuss variations that may address these shortcomings.
+",1,0,0,0,0,0
+5681,Tensor ring decomposition,"  Tensor decompositions such as the canonical format and the tensor train
+format have been widely utilized to reduce storage costs and operational
+complexities for high-dimensional data, achieving linear scaling with the input
+dimension instead of exponential scaling. In this paper, we investigate even
+lower storage-cost representations in the tensor ring format, which is an
+extension of the tensor train format with variable end-ranks. Firstly, we
+introduce two algorithms for converting a tensor in full format to tensor ring
+format with low storage cost. Secondly, we detail a rounding operation for
+tensor rings and show how this requires new definitions of common linear
+algebra operations in the format to obtain storage-cost savings. Lastly, we
+introduce algorithms for transforming the graph structure of graph-based tensor
+formats, with orders of magnitude lower complexity than existing literature.
+The efficiency of all algorithms is demonstrated on a number of numerical
+examples, and we achieve up to more than an order of magnitude higher
+compression ratios than previous approaches to using the tensor ring format.
+",1,0,0,0,0,0
+5682,Combining Information from Multiple Forecasters: Inefficiency of Central Tendency,"  Even though the forecasting literature agrees that aggregating multiple
+predictions of some future outcome typically outperforms the individual
+predictions, there is no general consensus about the right way to do this. Most
+common aggregators are means, defined loosely as aggregators that always remain
+between the smallest and largest predictions. Examples include the arithmetic
+mean, trimmed means, median, mid-range, and many other measures of central
+tendency. If the forecasters use different information, the aggregator ideally
+combines their information into a consensus without losing or distorting any of
+it. An aggregator that achieves this is considered efficient. Unfortunately,
+our results show that if the forecasters use their information accurately, an
+aggregator that always remains strictly between the smallest and largest
+predictions is never efficient in practice. A similar result holds even if the
+ideal predictions are distorted with random error that is centered at zero. If
+these noisy predictions are aggregated with a similar notion of centrality,
+then, under some mild conditions, the aggregator is asymptotically inefficient.
+",0,0,1,1,0,0
+5683,QCD-Aware Recursive Neural Networks for Jet Physics,"  Recent progress in applying machine learning for jet physics has been built
+upon an analogy between calorimeters and images. In this work, we present a
+novel class of recursive neural networks built instead upon an analogy between
+QCD and natural languages. In the analogy, four-momenta are like words and the
+clustering history of sequential recombination jet algorithms is like the
+parsing of a sentence. Our approach works directly with the four-momenta of a
+variable-length set of particles, and the jet-based tree structure varies on an
+event-by-event basis. Our experiments highlight the flexibility of our method
+for building task-specific jet embeddings and show that recursive architectures
+are significantly more accurate and data efficient than previous image-based
+networks. We extend the analogy from individual jets (sentences) to full events
+(paragraphs), and show for the first time an event-level classifier operating
+on all the stable particles produced in an LHC event.
+",0,1,0,1,0,0
+5684,A conjecture on $C$-matrices of cluster algebras,"  For a skew-symmetrizable cluster algebra $\mathcal A_{t_0}$ with principal
+coefficients at $t_0$, we prove that each seed $\Sigma_t$ of $\mathcal A_{t_0}$
+is uniquely determined by its {\bf C-matrix}, which was proposed by Fomin and
+Zelevinsky in \cite{FZ3} as a conjecture. Our proof is based on the fact that
+the positivity of cluster variables and sign-coherence of $c$-vectors hold for
+$\mathcal A_{t_0}$, which was actually verified in \cite{GHKK}. More discussion
+is given in the sign-skew-symmetric case so as to obtain a conclusion as weak
+version of the conjecture in this general case.
+",0,0,1,0,0,0
+5685,Attacking Similarity-Based Link Prediction in Social Networks,"  Link prediction is one of the fundamental problems in computational social
+science. A particularly common means to predict existence of unobserved links
+is via structural similarity metrics, such as the number of common neighbors;
+node pairs with higher similarity are thus deemed more likely to be linked.
+However, a number of applications of link prediction, such as predicting links
+in gang or terrorist networks, are adversarial, with another party incentivized
+to minimize its effectiveness by manipulating observed information about the
+network. We offer a comprehensive algorithmic investigation of the problem of
+attacking similarity-based link prediction through link deletion, focusing on
+two broad classes of such approaches, one which uses only local information
+about target links, and another which uses global network information. While we
+show several variations of the general problem to be NP-Hard for both local and
+global metrics, we exhibit a number of well-motivated special cases which are
+tractable. Additionally, we provide principled and empirically effective
+algorithms for the intractable cases, in some cases proving worst-case
+approximation guarantees.
+",1,0,0,0,0,0
+5686,On algebraic branching programs of small width,"  In 1979 Valiant showed that the complexity class VP_e of families with
+polynomially bounded formula size is contained in the class VP_s of families
+that have algebraic branching programs (ABPs) of polynomially bounded size.
+Motivated by the problem of separating these classes we study the topological
+closure VP_e-bar, i.e. the class of polynomials that can be approximated
+arbitrarily closely by polynomials in VP_e. We describe VP_e-bar with a
+strikingly simple complete polynomial (in characteristic different from 2)
+whose recursive definition is similar to the Fibonacci numbers. Further
+understanding this polynomial seems to be a promising route to new formula
+lower bounds.
+Our methods are rooted in the study of ABPs of small constant width. In 1992
+Ben-Or and Cleve showed that formula size is polynomially equivalent to width-3
+ABP size. We extend their result (in characteristic different from 2) by
+showing that approximate formula size is polynomially equivalent to approximate
+width-2 ABP size. This is surprising because in 2011 Allender and Wang gave
+explicit polynomials that cannot be computed by width-2 ABPs at all! The
+details of our construction lead to the aforementioned characterization of
+VP_e-bar.
+As a natural continuation of this work we prove that the class VNP can be
+described as the class of families that admit a hypercube summation of
+polynomially bounded dimension over a product of polynomially many affine
+linear forms. This gives the first separations of algebraic complexity classes
+from their nondeterministic analogs.
+",1,0,0,0,0,0
+5687,A lightweight thermal heat switch for redundant cryocooling on satellites,"  A previously designed cryogenic thermal heat switch for space applications
+has been optimized for low mass, high structural stability, and reliability.
+The heat switch makes use of the large linear thermal expansion coefficient
+(CTE) of the thermoplastic UHMW-PE for actuation. A structure model, which
+includes the temperature dependent properties of the actuator, is derived to be
+able to predict the contact pressure between the switch parts. This pressure
+was used in a thermal model in order to predict the switch performance under
+different heat loads and operating temperatures. The two models were used to
+optimize the mass and stability of the switch. Its reliability was proven by
+cyclic actuation of the switch and by shaker tests.
+",0,1,0,0,0,0
+5688,Fast Automatic Smoothing for Generalized Additive Models,"  Multiple generalized additive models (GAMs) are a type of distributional
+regression wherein parameters of probability distributions depend on predictors
+through smooth functions, with selection of the degree of smoothness via $L_2$
+regularization. Multiple GAMs allow finer statistical inference by
+incorporating explanatory information in any or all of the parameters of the
+distribution. Owing to their nonlinearity, flexibility and interpretability,
+GAMs are widely used, but reliable and fast methods for automatic smoothing in
+large datasets are still lacking, despite recent advances. We develop a general
+methodology for automatically learning the optimal degree of $L_2$
+regularization for multiple GAMs using an empirical Bayes approach. The smooth
+functions are penalized by different amounts, which are learned simultaneously
+by maximization of a marginal likelihood through an approximate
+expectation-maximization algorithm that involves a double Laplace approximation
+at the E-step, and leads to an efficient M-step. Empirical analysis shows that
+the resulting algorithm is numerically stable, faster than all existing methods
+and achieves state-of-the-art accuracy. For illustration, we apply it to an
+important and challenging problem in the analysis of extremal data.
+",0,0,0,1,0,0
+5689,Stabilization and control of Majorana bound states with elongated skyrmions,"  We show that elongated magnetic skyrmions can host Majorana bound states in a
+proximity-coupled two-dimensional electron gas sandwiched between a chiral
+magnet and an $s$-wave superconductor. Our proposal requires stable skyrmions
+with unit topological charge, which can be realized in a wide range of
+multilayer magnets, and allows quantum information transfer by using standard
+methods in spintronics via skyrmion motion. We also show how braiding
+operations can be realized in our proposal.
+",0,1,0,0,0,0
+5690,Meromorphic functions with small Schwarzian derivative,"  We consider the family of all meromorphic functions $f$ of the form $$
+f(z)=\frac{1}{z}+b_0+b_1z+b_2z^2+\cdots $$ analytic and locally univalent in
+the puncture disk $\mathbb{D}_0:=\{z\in\mathbb{C}:\,0<|z|<1\}$. Our first
+objective in this paper is to find a sufficient condition for $f$ to be
+meromorphically convex of order $\alpha$, $0\le \alpha<1$, in terms of the fact
+that the absolute value of the well-known Schwarzian derivative $S_f (z)$ of
+$f$ is bounded above by a smallest positive root of a non-linear equation.
+Secondly, we consider a family of functions $g$ of the form
+$g(z)=z+a_2z^2+a_3z^3+\cdots$ analytic and locally univalent in the open unit
+disk $\mathbb{D}:=\{z\in\mathbb{C}:\,|z|<1\}$, and show that $g$ is belonging
+to a family of functions convex in one direction if $|S_g(z)|$ is bounded above
+by a small positive constant depending on the second coefficient $a_2$. In
+particular, we show that such functions $g$ are also contained in the starlike
+and close-to-convex family.
+",0,0,1,0,0,0
+5691,Measure-geometric Laplacians for discrete distributions,"  In 2002 Freiberg and Zähle introduced and developed a harmonic calculus for
+measure-geometric Laplacians associated to continuous distributions. We show
+their theory can be extended to encompass distributions with finite support and
+give a matrix representation for the resulting operators. In the case of a
+uniform discrete distribution we make use of this matrix representation to
+explicitly determine the eigenvalues and the eigenfunctions of the associated
+Laplacian.
+",0,0,1,0,0,0
+5692,Optimal heat transfer and optimal exit times,"  A heat exchanger can be modeled as a closed domain containing an
+incompressible fluid. The moving fluid has a temperature distribution obeying
+the advection-diffusion equation, with zero temperature boundary conditions at
+the walls. Starting from a positive initial temperature distribution in the
+interior, the goal is to flux the heat through the walls as efficiently as
+possible. Here we consider a distinct but closely related problem, that of the
+integrated mean exit time of Brownian particles starting inside the domain.
+Since flows favorable to rapid heat exchange should lower exit times, we
+minimize a norm of the exit time. This is a time-independent optimization
+problem that we solve analytically in some limits, and numerically otherwise.
+We find an (at least locally) optimal velocity field that cools the domain on a
+mechanical time scale, in the sense that the integrated mean exit time is
+independent on molecular diffusivity in the limit of large-energy flows.
+",0,1,0,0,0,0
+5693,"A Semantics Comparison Workbench for a Concurrent, Asynchronous, Distributed Programming Language","  A number of high-level languages and libraries have been proposed that offer
+novel and simple to use abstractions for concurrent, asynchronous, and
+distributed programming. The execution models that realise them, however, often
+change over time---whether to improve performance, or to extend them to new
+language features---potentially affecting behavioural and safety properties of
+existing programs. This is exemplified by SCOOP, a message-passing approach to
+concurrent object-oriented programming that has seen multiple changes proposed
+and implemented, with demonstrable consequences for an idiomatic usage of its
+core abstraction. We propose a semantics comparison workbench for SCOOP with
+fully and semi-automatic tools for analysing and comparing the state spaces of
+programs with respect to different execution models or semantics. We
+demonstrate its use in checking the consistency of properties across semantics
+by applying it to a set of representative programs, and highlighting a
+deadlock-related discrepancy between the principal execution models of SCOOP.
+Furthermore, we demonstrate the extensibility of the workbench by generalising
+the formalisation of an execution model to support recently proposed extensions
+for distributed programming. Our workbench is based on a modular and
+parameterisable graph transformation semantics implemented in the GROOVE tool.
+We discuss how graph transformations are leveraged to atomically model
+intricate language abstractions, how the visual yet algebraic nature of the
+model can be used to ascertain soundness, and highlight how the approach could
+be applied to similar languages.
+",1,0,0,0,0,0
+5694,Simulating Linear Logic in 1-Only Linear Logic,"  Linear Logic was introduced by Girard as a resource-sensitive refinement of
+classical logic. It turned out that full propositional Linear Logic is
+undecidable (Lincoln, Mitchell, Scedrov, and Shankar) and, hence, it is more
+expressive than (modalized) classical or intuitionistic logic. In this paper we
+focus on the study of the simplest fragments of Linear Logic, such as the
+one-literal and constant-only fragments (the latter contains no literals at
+all). Here we demonstrate that all these extremely simple fragments of Linear
+Logic (one-literal, $\bot$-only, and even unit-only) are exactly of the same
+expressive power as the corresponding full versions. We present also a complete
+computational interpretation (in terms of acyclic programs with stack) for
+bottom-free Intuitionistic Linear Logic. Based on this interpretation, we prove
+the fairness of our encodings and establish the foregoing complexity results.
+",1,0,0,0,0,0
+5695,Adaptive Network Coding Schemes for Satellite Communications,"  In this paper, we propose two novel physical layer aware adaptive network
+coding and coded modulation schemes for time variant channels. The proposed
+schemes have been applied to different satellite communications scenarios with
+different Round Trip Times (RTT). Compared to adaptive network coding, and
+classical non-adaptive network coding schemes for time variant channels, as
+benchmarks, the proposed schemes demonstrate that adaptation of packet
+transmission based on the channel variation and corresponding erasures allows
+for significant gains in terms of throughput, delay and energy efficiency. We
+shed light on the trade-off between energy efficiency and delay-throughput
+gains, demonstrating that conservative adaptive approaches that favors less
+transmission under high erasures, might cause higher delay and less throughput
+gains in comparison to non-conservative approaches that favor more transmission
+to account for high erasures.
+",1,0,0,0,0,0
+5696,A Symbolic Computation Framework for Constitutive Modelling Based On Entropy Principles,"  The entropy principle in the formulation of Müller and Liu is a common
+tool used in constitutive modelling for the development of restrictions on the
+unknown constitutive functions describing material properties of various
+physical continua. In the current work, a symbolic software implementation of
+the Liu algorithm, based on \verb|Maple| software and the \verb|GeM| package,
+is presented. The computational framework is used to algorithmically perform
+technically demanding symbolic computations related to the entropy principle,
+to simplify and reduce Liu's identities, and ultimately to derive explicit
+formulas describing classes of constitutive functions that do not violate the
+entropy principle. Detailed physical examples are presented and discussed.
+",0,1,0,0,0,0
+5697,Measuring bot and human behavioral dynamics,"  Bots, social media accounts controlled by software rather than by humans,
+have recently been under the spotlight for their association with various forms
+of online manipulation. To date, much work has focused on social bot detection,
+but little attention has been devoted to the characterization and measurement
+of the behavior and activity of bots, as opposed to humans'. Over the course of
+the years, bots have become more sophisticated, and capable to reflect some
+short-term behavior, emulating that of human users. The goal of this paper is
+to study the behavioral dynamics that bots exhibit over the course of one
+activity session, and highlight if and how these differ from human activity
+signatures. By using a large Twitter dataset associated with recent political
+events, we first separate bots and humans, then isolate their activity
+sessions. We compile a list of quantities to be measured, like the propensity
+of users to engage in social interactions or to produce content. Our analysis
+highlights the presence of short-term behavioral trends in humans, which can be
+associated with a cognitive origin, that are absent in bots, intuitively due to
+their automated activity. These findings are finally codified to create and
+evaluate a machine learning algorithm to detect activity sessions produced by
+bots and humans, to allow for more nuanced bot detection strategies.
+",1,0,0,0,0,0
+5698,Optimal control of elliptic equations with positive measures,"  Optimal control problems without control costs in general do not possess
+solutions due to the lack of coercivity. However, unilateral constraints
+together with the assumption of existence of strictly positive solutions of a
+pre-adjoint state equation, are sufficient to obtain existence of optimal
+solutions in the space of Radon measures. Optimality conditions for these
+generalized minimizers can be obtained using Fenchel duality, which requires a
+non-standard perturbation approach if the control-to-observation mapping is not
+continuous (e.g., for Neumann boundary control in three dimensions). Combining
+a conforming discretization of the measure space with a semismooth Newton
+method allows the numerical solution of the optimal control problem.
+",0,0,1,0,0,0
+5699,Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases,"  Text representations using neural word embeddings have proven effective in
+many NLP applications. Recent researches adapt the traditional word embedding
+models to learn vectors of multiword expressions (concepts/entities). However,
+these methods are limited to textual knowledge bases (e.g., Wikipedia). In this
+paper, we propose a novel and simple technique for integrating the knowledge
+about concepts from two large scale knowledge bases of different structure
+(Wikipedia and Probase) in order to learn concept representations. We adapt the
+efficient skip-gram model to seamlessly learn from the knowledge in Wikipedia
+text and Probase concept graph. We evaluate our concept embedding models on two
+tasks: (1) analogical reasoning, where we achieve a state-of-the-art
+performance of 91% on semantic analogies, (2) concept categorization, where we
+achieve a state-of-the-art performance on two benchmark datasets achieving
+categorization accuracy of 100% on one and 98% on the other. Additionally, we
+present a case study to evaluate our model on unsupervised argument type
+identification for neural semantic parsing. We demonstrate the competitive
+accuracy of our unsupervised method and its ability to better generalize to out
+of vocabulary entity mentions compared to the tedious and error prone methods
+which depend on gazetteers and regular expressions.
+",1,0,0,0,0,0
+5700,Fraunhofer diffraction at the two-dimensional quadratically distorted (QD) Grating,"  A two-dimensional (2D) mathematical model of quadratically distorted (QD)
+grating is established with the principles of Fraunhofer diffraction and
+Fourier optics. Discrete sampling and bisection algorithm are applied for
+finding numerical solution of the diffraction pattern of QD grating. This 2D
+mathematical model allows the precise design of QD grating and improves the
+optical performance of simultaneous multiplane imaging system.
+",0,1,0,0,0,0
+5701,High order surface radiation conditions for time-harmonic waves in exterior domains,"  We formulate a new family of high order on-surface radiation conditions to
+approximate the outgoing solution to the Helmholtz equation in exterior
+domains. Motivated by the pseudo-differential expansion of the
+Dirichlet-to-Neumann operator developed by Antoine et al. (J. Math. Anal. Appl.
+229:184-211, 1999), we design a systematic procedure to apply
+pseudo-differential symbols of arbitrarily high order. Numerical results are
+presented to illustrate the performance of the proposed method for solving both
+the Dirichlet and the Neumann boundary value problems. Possible improvements
+and extensions are also discussed.
+",0,1,1,0,0,0
+5702,Probabilistic PARAFAC2,"  The PARAFAC2 is a multimodal factor analysis model suitable for analyzing
+multi-way data when one of the modes has incomparable observation units, for
+example because of differences in signal sampling or batch sizes. A fully
+probabilistic treatment of the PARAFAC2 is desirable in order to improve
+robustness to noise and provide a well founded principle for determining the
+number of factors, but challenging because the factor loadings are constrained
+to be orthogonal. We develop two probabilistic formulations of the PARAFAC2
+along with variational procedures for inference: In the one approach, the mean
+values of the factor loadings are orthogonal leading to closed form variational
+updates, and in the other, the factor loadings themselves are orthogonal using
+a matrix Von Mises-Fisher distribution. We contrast our probabilistic
+formulation to the conventional direct fitting algorithm based on maximum
+likelihood. On simulated data and real fluorescence spectroscopy and gas
+chromatography-mass spectrometry data, we compare our approach to the
+conventional PARAFAC2 model estimation and find that the probabilistic
+formulation is more robust to noise and model order misspecification. The
+probabilistic PARAFAC2 thus forms a promising framework for modeling multi-way
+data accounting for uncertainty.
+",0,0,0,1,0,0
+5703,Quantizing Euclidean motions via double-coset decomposition,"  Concepts from mathematical crystallography and group theory are used here to
+quantize the group of rigid-body motions, resulting in a ""motion alphabet"" with
+which to express robot motion primitives. From these primitives it is possible
+to develop a dictionary of physical actions. Equipped with an alphabet of the
+sort developed here, intelligent actions of robots in the world can be
+approximated with finite sequences of characters, thereby forming the
+foundation of a language in which to articulate robot motion. In particular, we
+use the discrete handedness-preserving symmetries of macromolecular crystals
+(known in mathematical crystallography as Sohncke space groups) to form a
+coarse discretization of the space $\rm{SE}(3)$ of rigid-body motions. This
+discretization is made finer by subdividing using the concept of double-coset
+decomposition. More specifically, a very efficient, equivolumetric quantization
+of spatial motion can be defined using the group-theoretic concept of a
+double-coset decomposition of the form $\Gamma \backslash \rm{SE}(3) / \Delta$,
+where $\Gamma$ is a Sohncke space group and $\Delta$ is a finite group of
+rotational symmetries such as those of the icosahedron. The resulting discrete
+alphabet is based on a very uniform sampling of $\rm{SE}(3)$ and is a tool for
+describing the continuous trajectories of robots and humans. The general
+""signals to symbols"" problem in artificial intelligence is cast in this
+framework for robots moving continuously in the world, and we present a
+coarse-to-fine search scheme here to efficiently solve this decoding problem in
+practice.
+",1,0,0,0,0,0
+5704,Evolutionary Stability of Reputation Management System in Peer to Peer Networks,"  Each participant in peer-to-peer network prefers to free-ride on the
+contribution of other participants. Reputation based resource sharing is a way
+to control the free riding. Instead of classical game theory we use
+evolutionary game theory to analyse the reputation based resource sharing in
+peer to peer system. Classical game-theoretical approach requires global
+information of the population. However, the evolutionary games only assumes
+light cognitive capabilities of users, that is, each user imitates the behavior
+of other user with better payoff. We find that without any extra benefit
+reputation strategy is not stable in the system. We also find the fraction of
+users who calculate the reputation for controlling the free riding in
+equilibrium. In this work first we made a game theoretical model for the
+reputation system and then we calculate the threshold of the fraction of users
+with which the reputation strategy is sustainable in the system. We found that
+in simplistic conditions reputation calculation is not evolutionarily stable
+strategy but if we impose some initial payment to all users and then distribute
+that payment among the users who are calculating reputation then reputation is
+evolutionary stable strategy.
+",1,0,0,0,0,0
+5705,A Study of FOSS'2013 Survey Data Using Clustering Techniques,"  FOSS is an acronym for Free and Open Source Software. The FOSS 2013 survey
+primarily targets FOSS contributors and relevant anonymized dataset is publicly
+available under CC by SA license. In this study, the dataset is analyzed from a
+critical perspective using statistical and clustering techniques (especially
+multiple correspondence analysis) with a strong focus on women contributors
+towards discovering hidden trends and facts. Important inferences are drawn
+about development practices and other facets of the free software and OSS
+worlds.
+",1,0,0,1,0,0
+5706,Intermetallic Nanocrystals: Syntheses and Catalytic Applications,"  At the forefront of nanochemistry, there exists a research endeavor centered
+around intermetallic nanocrystals, which are unique in terms of long-range
+atomic ordering, well-defined stoichiometry, and controlled crystal structure.
+In contrast to alloy nanocrystals with no atomic ordering, it has been
+challenging to synthesize intermetallic nanocrystals with a tight control over
+their size and shape. This review article highlights recent progress in the
+synthesis of intermetallic nanocrystals with controllable sizes and
+well-defined shapes. We begin with a simple analysis and some insights key to
+the selection of experimental conditions for generating intermetallic
+nanocrystals. We then present examples to highlight the viable use of
+intermetallic nanocrystals as electrocatalysts or catalysts for various
+reactions, with a focus on the enhanced performance relative to their alloy
+counterparts that lack atomic ordering. We conclude with perspectives on future
+developments in the context of synthetic control, structure-property
+relationship, and application.
+",0,1,0,0,0,0
+5707,Linear-scaling electronic structure theory: Electronic temperature in the Kernel Polynomial Method,"  Linear-scaling electronic structure methods based on the calculation of
+moments of the underlying electronic Hamiltonian offer a computationally
+efficient and numerically robust scheme to drive large-scale atomistic
+simulations, in which the quantum-mechanical nature of the electrons is
+explicitly taken into account. We compare the kernel polynomial method to the
+Fermi operator expansion method and establish a formal connection between the
+two approaches. We show that the convolution of the kernel polynomial method
+may be understood as an effective electron temperature. The results of a number
+of possible kernels are formally examined, and then applied to a representative
+tight-binding model.
+",0,1,0,0,0,0
+5708,Characterizing the 2016 Russian IRA Influence Campaign,"  Until recently, social media were seen to promote democratic discourse on
+social and political issues. However, this powerful communication ecosystem has
+come under scrutiny for allowing hostile actors to exploit online discussions
+in an attempt to manipulate public opinion. A case in point is the ongoing U.S.
+Congress investigation of Russian interference in the 2016 U.S. election
+campaign, with Russia accused of, among other things, using trolls (malicious
+accounts created for the purpose of manipulation) and bots (automated accounts)
+to spread propaganda and politically biased information. In this study, we
+explore the effects of this manipulation campaign, taking a closer look at
+users who re-shared the posts produced on Twitter by the Russian troll accounts
+publicly disclosed by U.S. Congress investigation. We collected a dataset of 13
+million election-related posts shared on Twitter in the year of 2016 by over a
+million distinct users. This dataset includes accounts associated with the
+identified Russian trolls as well as users sharing posts in the same time
+period on a variety of topics around the 2016 elections. We use label
+propagation to infer the users' ideology based on the news sources they share.
+We are able to classify a large number of users as liberal or conservative with
+precision and recall above 84%. Conservative users who retweet Russian trolls
+produced significantly more tweets than liberal ones, about 8 times as many in
+terms of tweets. Additionally, trolls' position in the retweet network is
+stable over time, unlike users who retweet them who form the core of the
+election-related retweet network by the end of 2016. Using state-of-the-art bot
+detection techniques, we estimate that about 5% and 11% of liberal and
+conservative users are bots, respectively.
+",1,0,0,0,0,0
+5709,Nonlinear Mapping Convergence and Application to Social Networks,"  This paper discusses discrete-time maps of the form $x(k + 1) = F(x(k))$,
+focussing on equilibrium points of such maps. Under some circumstances,
+Lefschetz fixed-point theory can be used to establish the existence of a single
+locally attractive equilibrium (which is sometimes globally attractive) when a
+general property of local attractivity is known for any equilibrium. Problems
+in social networks often involve such discrete-time systems, and we make an
+application to one such problem.
+",1,0,1,0,0,0
+5710,Correlating Cellular Features with Gene Expression using CCA,"  To understand the biology of cancer, joint analysis of multiple data
+modalities, including imaging and genomics, is crucial. The involved nature of
+gene-microenvironment interactions necessitates the use of algorithms which
+treat both data types equally. We propose the use of canonical correlation
+analysis (CCA) and a sparse variant as a preliminary discovery tool for
+identifying connections across modalities, specifically between gene expression
+and features describing cell and nucleus shape, texture, and stain intensity in
+histopathological images. Applied to 615 breast cancer samples from The Cancer
+Genome Atlas, CCA revealed significant correlation of several image features
+with expression of PAM50 genes, known to be linked to outcome, while Sparse CCA
+revealed associations with enrichment of pathways implicated in cancer without
+leveraging prior biological understanding. These findings affirm the utility of
+CCA for joint phenotype-genotype analysis of cancer.
+",0,0,0,1,1,0
+5711,Links with nontrivial Alexander polynomial which are topologically concordant to the Hopf link,"  We give infinitely many $2$-component links with unknotted components which
+are topologically concordant to the Hopf link, but not smoothly concordant to
+any $2$-component link with trivial Alexander polynomial. Our examples are
+pairwise non-concordant.
+",0,0,1,0,0,0
+5712,Shape optimization in laminar flow with a label-guided variational autoencoder,"  Computational design optimization in fluid dynamics usually requires to solve
+non-linear partial differential equations numerically. In this work, we explore
+a Bayesian optimization approach to minimize an object's drag coefficient in
+laminar flow based on predicting drag directly from the object shape. Jointly
+training an architecture combining a variational autoencoder mapping shapes to
+latent representations and Gaussian process regression allows us to generate
+improved shapes in the two dimensional case we consider.
+",1,0,0,0,0,0
+5713,An Empirical Analysis of Vulnerabilities in Python Packages for Web Applications,"  This paper examines software vulnerabilities in common Python packages used
+particularly for web development. The empirical dataset is based on the PyPI
+package repository and the so-called Safety DB used to track vulnerabilities in
+selected packages within the repository. The methodological approach builds on
+a release-based time series analysis of the conditional probabilities for the
+releases of the packages to be vulnerable. According to the results, many of
+the Python vulnerabilities observed seem to be only modestly severe; input
+validation and cross-site scripting have been the most typical vulnerabilities.
+In terms of the time series analysis based on the release histories, only the
+recent past is observed to be relevant for statistical predictions; the
+classical Markov property holds.
+",1,0,0,0,0,0
+5714,A combination chaotic system and application in color image encryption,"  In this paper, by using Logistic, Sine and Tent systems we define a
+combination chaotic system. Some properties of the chaotic system are studied
+by using figures and numerical results. A color image encryption algorithm is
+introduced based on new chaotic system. Also this encryption algorithm can be
+used for gray scale or binary images.
+The experimental results of the encryption algorithm show that the encryption
+algorithm is secure and practical.
+",1,0,0,0,0,0
+5715,A Hybrid MILP and IPM for Dynamic Economic Dispatch with Valve Point Effect,"  Dynamic economic dispatch with valve-point effect (DED-VPE) is a non-convex
+and non-differentiable optimization problem which is difficult to solve
+efficiently. In this paper, a hybrid mixed integer linear programming (MILP)
+and interior point method (IPM), denoted by MILP-IPM, is proposed to solve such
+a DED-VPE problem, where the complicated transmission loss is also included.
+Due to the non-differentiable characteristic of DED-VPE, the classical
+derivative-based optimization methods can not be used any more. With the help
+of model reformulation, a differentiable non-linear programming (NLP)
+formulation which can be directly solved by IPM is derived. However, if the
+DED-VPE is solved by IPM in a single step, the optimization will easily trap in
+a poor local optima due to its non-convex and multiple local minima
+characteristics. To exploit a better solution, an MILP method is required to
+solve the DED-VPE without transmission loss, yielding a good initial point for
+IPM to improve the quality of the solution. Simulation results demonstrate the
+validity and effectiveness of the proposed MILP-IPM in solving DED-VPE.
+",0,0,1,0,0,0
+5716,Using Big Data Technologies for HEP Analysis,"  The HEP community is approaching an era were the excellent performances of
+the particle accelerators in delivering collision at high rate will force the
+experiments to record a large amount of information. The growing size of the
+datasets could potentially become a limiting factor in the capability to
+produce scientific results timely and efficiently. Recently, new technologies
+and new approaches have been developed in industry to answer to the necessity
+to retrieve information as quickly as possible to analyze PB and EB datasets.
+Providing the scientists with these modern computing tools will lead to
+rethinking the principles of data analysis in HEP, making the overall
+scientific process faster and smoother.
+In this paper, we are presenting the latest developments and the most recent
+results on the usage of Apache Spark for HEP analysis. The study aims at
+evaluating the efficiency of the application of the new tools both
+quantitatively, by measuring the performances, and qualitatively, focusing on
+the user experience. The first goal is achieved by developing a data reduction
+facility: working together with CERN Openlab and Intel, CMS replicates a real
+physics search using Spark-based technologies, with the ambition of reducing 1
+PB of public data in 5 hours, collected by the CMS experiment, to 1 TB of data
+in a format suitable for physics analysis.
+The second goal is achieved by implementing multiple physics use-cases in
+Apache Spark using as input preprocessed datasets derived from official CMS
+data and simulation. By performing different end-analyses up to the publication
+plots on different hardware, feasibility, usability and portability are
+compared to the ones of a traditional ROOT-based workflow.
+",1,0,0,0,0,0
+5717,Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network,"  Brains need to predict how the body reacts to motor commands. It is an open
+question how networks of spiking neurons can learn to reproduce the non-linear
+body dynamics caused by motor commands, using local, online and stable learning
+rules. Here, we present a supervised learning scheme for the feedforward and
+recurrent connections in a network of heterogeneous spiking neurons. The error
+in the output is fed back through fixed random connections with a negative
+gain, causing the network to follow the desired dynamics, while an online and
+local rule changes the weights. The rule for Feedback-based Online Local
+Learning Of Weights (FOLLOW) is local in the sense that weight changes depend
+on the presynaptic activity and the error signal projected onto the
+postsynaptic neuron. We provide examples of learning linear, non-linear and
+chaotic dynamics, as well as the dynamics of a two-link arm. Using the Lyapunov
+method, and under reasonable assumptions and approximations, we show that
+FOLLOW learning is stable uniformly, with the error going to zero
+asymptotically.
+",1,0,0,0,0,0
+5718,Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits,"  In this paper, we propose and study opportunistic bandits - a new variant of
+bandits where the regret of pulling a suboptimal arm varies under different
+environmental conditions, such as network load or produce price. When the
+load/price is low, so is the cost/regret of pulling a suboptimal arm (e.g.,
+trying a suboptimal network configuration). Therefore, intuitively, we could
+explore more when the load/price is low and exploit more when the load/price is
+high. Inspired by this intuition, we propose an Adaptive Upper-Confidence-Bound
+(AdaUCB) algorithm to adaptively balance the exploration-exploitation tradeoff
+for opportunistic bandits. We prove that AdaUCB achieves $O(\log T)$ regret
+with a smaller coefficient than the traditional UCB algorithm. Furthermore,
+AdaUCB achieves $O(1)$ regret with respect to $T$ if the exploration cost is
+zero when the load level is below a certain threshold. Last, based on both
+synthetic data and real-world traces, experimental results show that AdaUCB
+significantly outperforms other bandit algorithms, such as UCB and TS (Thompson
+Sampling), under large load/price fluctuations.
+",1,0,0,1,0,0
+5719,A Quantile Estimate Based on Local Curve Fitting,"  Quantile estimation is a problem presented in fields such as quality control,
+hydrology, and economics. There are different techniques to estimate such
+quantiles. Nevertheless, these techniques use an overall fit of the sample when
+the quantiles of interest are usually located in the tails of the distribution.
+Regression Approach for Quantile Estimation (RAQE) is a method based on
+regression techniques and the properties of the empirical distribution to
+address this problem. The method was first presented for the problem of
+capability analysis. In this paper, a generalization of the method is
+presented, extended to the multiple sample scenario, and data from real
+examples is used to illustrate the proposed approaches. In addition,
+theoretical framework is presented to support the extension for multiple
+homogeneous samples and the use of the uncertainty of the estimated
+probabilities as a weighting factor in the analysis.
+",0,0,0,1,0,0
+5720,Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking,"  Dynamical downscaling with high-resolution regional climate models may offer
+the possibility of realistically reproducing precipitation and weather events
+in climate simulations. As resolutions fall to order kilometers, the use of
+explicit rather than parametrized convection may offer even greater fidelity.
+However, these increased model resolutions both allow and require increasingly
+complex diagnostics for evaluating model fidelity. In this study we use a suite
+of dynamically downscaled simulations of the summertime U.S. (WRF driven by
+NCEP) with systematic variations in parameters and treatment of convection as a
+test case for evaluation of model precipitation. In particular, we use a novel
+rainstorm identification and tracking algorithm that allocates essentially all
+rainfall to individual precipitation events (Chang et al. 2016). This approach
+allows multiple insights, including that, at least in these runs, model wet
+bias is driven by excessive areal extent of precipitating events. Biases are
+time-dependent, producing excessive diurnal cycle amplitude. We show that this
+effect is produced not by new production of events but by excessive enlargement
+of long-lived precipitation events during daytime, and that in the domain
+average, precipitation biases appear best represented as additive offsets. Of
+all model configurations evaluated, convection-permitting simulations most
+consistently reduced biases in precipitation event characteristics.
+",0,1,0,1,0,0
+5721,An integral formula for Riemannian $G$-structures with applications to almost hermitian and almost contact structures,"  For a Riemannian $G$-structure, we compute the divergence of the vector field
+induced by the intrinsic torsion. Applying the Stokes theorem, we obtain the
+integral formula on a closed oriented Riemannian manifold, which we interpret
+in certain cases. We focus on almost harmitian and almost contact metric
+structures.
+",0,0,1,0,0,0
+5722,Putin's peaks: Russian election data revisited,"  We study the anomalous prevalence of integer percentages in the last
+parliamentary (2016) and presidential (2018) Russian elections. We show how
+this anomaly in Russian federal elections has evolved since 2000.
+",0,0,0,1,0,0
+5723,On rate of convergence in non-central limit theorems,"  The main result of this paper is the rate of convergence to Hermite-type
+distributions in non-central limit theorems. To the best of our knowledge, this
+is the first result in the literature on rates of convergence of functionals of
+random fields to Hermite-type distributions with ranks greater than 2. The
+results were obtained under rather general assumptions on the spectral
+densities of random fields. These assumptions are even weaker than in the known
+convergence results for the case of Rosenblatt distributions. Additionally,
+Lévy concentration functions for Hermite-type distributions were
+investigated.
+",0,0,1,0,0,0
+5724,"Optimal Output Regulation for Square, Over-Actuated and Under-Actuated Linear Systems","  This paper considers two different problems in trajectory tracking control
+for linear systems. First, if the control is not unique which is most input
+energy efficient. Second, if exact tracking is infeasible which control
+performs most accurately. These are typical challenges for over-actuated
+systems and for under-actuated systems, respectively. We formulate both goals
+as optimal output regulation problems. Then we contribute two new sets of
+regulator equations to output regulation theory that provide the desired
+solutions. A thorough study indicates solvability and uniqueness under weak
+assumptions. E.g., we can always determine the solution of the classical
+regulator equations that is most input energy efficient. This is of great value
+if there are infinitely many solutions. We derive our results by a linear
+quadratic tracking approach and establish a useful link to output regulation
+theory.
+",1,0,0,0,0,0
+5725,Pattern recognition techniques for Boson Sampling validation,"  The difficulty of validating large-scale quantum devices, such as Boson
+Samplers, poses a major challenge for any research program that aims to show
+quantum advantages over classical hardware. To address this problem, we propose
+a novel data-driven approach wherein models are trained to identify common
+pathologies using unsupervised machine learning methods. We illustrate this
+idea by training a classifier that exploits K-means clustering to distinguish
+between Boson Samplers that use indistinguishable photons from those that do
+not. We train the model on numerical simulations of small-scale Boson Samplers
+and then validate the pattern recognition technique on larger numerical
+simulations as well as on photonic chips in both traditional Boson Sampling and
+scattershot experiments. The effectiveness of such method relies on
+particle-type-dependent internal correlations present in the output
+distributions. This approach performs substantially better on the test data
+than previous methods and underscores the ability to further generalize its
+operation beyond the scope of the examples that it was trained on.
+",1,0,0,0,0,0
+5726,Conformal k-NN Anomaly Detector for Univariate Data Streams,"  Anomalies in time-series data give essential and often actionable information
+in many applications. In this paper we consider a model-free anomaly detection
+method for univariate time-series which adapts to non-stationarity in the data
+stream and provides probabilistic abnormality scores based on the conformal
+prediction paradigm. Despite its simplicity the method performs on par with
+complex prediction-based models on the Numenta Anomaly Detection benchmark and
+the Yahoo! S5 dataset.
+",1,0,0,1,0,0
+5727,Applications of Trajectory Data from the Perspective of a Road Transportation Agency: Literature Review and Maryland Case Study,"  Transportation agencies have an opportunity to leverage
+increasingly-available trajectory datasets to improve their analyses and
+decision-making processes. However, this data is typically purchased from
+vendors, which means agencies must understand its potential benefits beforehand
+in order to properly assess its value relative to the cost of acquisition.
+While the literature concerned with trajectory data is rich, it is naturally
+fragmented and focused on technical contributions in niche areas, which makes
+it difficult for government agencies to assess its value across different
+transportation domains. To overcome this issue, the current paper explores
+trajectory data from the perspective of a road transportation agency interested
+in acquiring trajectories to enhance its analyses. The paper provides a
+literature review illustrating applications of trajectory data in six areas of
+road transportation systems analysis: demand estimation, modeling human
+behavior, designing public transit, traffic performance measurement and
+prediction, environment and safety. In addition, it visually explores 20
+million GPS traces in Maryland, illustrating existing and suggesting new
+applications of trajectory data.
+",1,0,0,1,0,0
+5728,Towards the dual motivic Steenrod algebra in positive characteristic,"  The dual motivic Steenrod algebra with mod $\ell$ coefficients was computed
+by Voevodsky over a base field of characteristic zero, and by Hoyois, Kelly,
+and {\O}stv{\ae}r over a base field of characteristic $p \neq \ell$. In the
+case $p = \ell$, we show that the conjectured answer is a retract of the actual
+answer. We also describe the slices of the algebraic cobordism spectrum $MGL$:
+we show that the conjectured form of $s_n MGL$ is a retract of the actual
+answer.
+",0,0,1,0,0,0
+5729,Toeplitz Quantization and Convexity,"  Let $T^m_f $ be the Toeplitz quantization of a real $ C^{\infty}$ function
+defined on the sphere $ \mathbb{CP}(1)$. $T^m_f $ is therefore a Hermitian
+matrix with spectrum $\lambda^m= (\lambda_0^m,\ldots,\lambda_m^m)$. Schur's
+theorem says that the diagonal of a Hermitian matrix $A$ that has the same
+spectrum of $ T^m_f $ lies inside a finite dimensional convex set whose extreme
+points are $\{( \lambda_{\sigma(0)}^m,\ldots,\lambda_{\sigma(m)}^m)\}$, where
+$\sigma$ is any permutation of $(m+1)$ elements. In this paper, we prove that
+these convex sets ""converge"" to a huge convex set in $L^2([0,1])$ whose extreme
+points are $ f^*\circ \phi$, where $ f^*$ is the decreasing rearrangement of $
+f$ and $ \phi $ ranges over the set of measure preserving transformations of
+the unit interval $ [0,1]$.
+",0,0,1,0,0,0
+5730,"The Paulsen Problem, Continuous Operator Scaling, and Smoothed Analysis","  The Paulsen problem is a basic open problem in operator theory: Given vectors
+$u_1, \ldots, u_n \in \mathbb R^d$ that are $\epsilon$-nearly satisfying the
+Parseval's condition and the equal norm condition, is it close to a set of
+vectors $v_1, \ldots, v_n \in \mathbb R^d$ that exactly satisfy the Parseval's
+condition and the equal norm condition? Given $u_1, \ldots, u_n$, the squared
+distance (to the set of exact solutions) is defined as $\inf_{v} \sum_{i=1}^n
+\| u_i - v_i \|_2^2$ where the infimum is over the set of exact solutions.
+Previous results show that the squared distance of any $\epsilon$-nearly
+solution is at most $O({\rm{poly}}(d,n,\epsilon))$ and there are
+$\epsilon$-nearly solutions with squared distance at least $\Omega(d\epsilon)$.
+The fundamental open question is whether the squared distance can be
+independent of the number of vectors $n$.
+We answer this question affirmatively by proving that the squared distance of
+any $\epsilon$-nearly solution is $O(d^{13/2} \epsilon)$. Our approach is based
+on a continuous version of the operator scaling algorithm and consists of two
+parts. First, we define a dynamical system based on operator scaling and use it
+to prove that the squared distance of any $\epsilon$-nearly solution is $O(d^2
+n \epsilon)$. Then, we show that by randomly perturbing the input vectors, the
+dynamical system will converge faster and the squared distance of an
+$\epsilon$-nearly solution is $O(d^{5/2} \epsilon)$ when $n$ is large enough
+and $\epsilon$ is small enough. To analyze the convergence of the dynamical
+system, we develop some new techniques in lower bounding the operator capacity,
+a concept introduced by Gurvits to analyze the operator scaling algorithm.
+",1,0,1,0,0,0
+5731,On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation,"  We study mechanisms to characterize how the asymptotic convergence of
+backpropagation in deep architectures, in general, is related to the network
+structure, and how it may be influenced by other design choices including
+activation type, denoising and dropout rate. We seek to analyze whether network
+architecture and input data statistics may guide the choices of learning
+parameters and vice versa. Given the broad applicability of deep architectures,
+this issue is interesting both from theoretical and a practical standpoint.
+Using properties of general nonconvex objectives (with first-order
+information), we first build the association between structural, distributional
+and learnability aspects of the network vis-à-vis their interaction with
+parameter convergence rates. We identify a nice relationship between feature
+denoising and dropout, and construct families of networks that achieve the same
+level of convergence. We then derive a workflow that provides systematic
+guidance regarding the choice of network sizes and learning parameters often
+mediated4 by input statistics. Our technical results are corroborated by an
+extensive set of evaluations, presented in this paper as well as independent
+empirical observations reported by other groups. We also perform experiments
+showing the practical implications of our framework for choosing the best
+fully-connected design for a given problem.
+",1,0,1,1,0,0
+5732,Iron Snow in the Martian Core?,"  The decline of Mars' global magnetic field some 3.8-4.1 billion years ago is
+thought to reflect the demise of the dynamo that operated in its liquid core.
+The dynamo was probably powered by planetary cooling and so its termination is
+intimately tied to the thermochemical evolution and present-day physical state
+of the Martian core. Bottom-up growth of a solid inner core, the
+crystallization regime for Earth's core, has been found to produce a long-lived
+dynamo leading to the suggestion that the Martian core remains entirely liquid
+to this day. Motivated by the experimentally-determined increase in the Fe-S
+liquidus temperature with decreasing pressure at Martian core conditions, we
+investigate whether Mars' core could crystallize from the top down. We focus on
+the ""iron snow"" regime, where newly-formed solid consists of pure Fe and is
+therefore heavier than the liquid. We derive global energy and entropy
+equations that describe the long-timescale thermal and magnetic history of the
+core from a general theory for two-phase, two-component liquid mixtures,
+assuming that the snow zone is in phase equilibrium and that all solid falls
+out of the layer and remelts at each timestep. Formation of snow zones occurs
+for a wide range of interior and thermal properties and depends critically on
+the initial sulfur concentration, x0. Release of gravitational energy and
+latent heat during growth of the snow zone do not generate sufficient entropy
+to restart the dynamo unless the snow zone occupies at least 400 km of the
+core. Snow zones can be 1.5-2 Gyrs old, though thermal stratification of the
+uppermost core, not included in our model, likely delays onset. Models that
+match the available magnetic and geodetic constraints have x0~10% and snow
+zones that occupy approximately the top 100 km of the present-day Martian core.
+",0,1,0,0,0,0
+5733,Motion Segmentation via Global and Local Sparse Subspace Optimization,"  In this paper, we propose a new framework for segmenting feature-based moving
+objects under affine subspace model. Since the feature trajectories in practice
+are high-dimensional and contain a lot of noise, we firstly apply the sparse
+PCA to represent the original trajectories with a low-dimensional global
+subspace, which consists of the orthogonal sparse principal vectors.
+Subsequently, the local subspace separation will be achieved via automatically
+searching the sparse representation of the nearest neighbors for each projected
+data. In order to refine the local subspace estimation result and deal with the
+missing data problem, we propose an error estimation to encourage the projected
+data that span a same local subspace to be clustered together. In the end, the
+segmentation of different motions is achieved through the spectral clustering
+on an affinity matrix, which is constructed with both the error estimation and
+sparse neighbors optimization. We test our method extensively and compare it
+with state-of-the-art methods on the Hopkins 155 dataset and Freiburg-Berkeley
+Motion Segmentation dataset. The results show that our method is comparable
+with the other motion segmentation methods, and in many cases exceed them in
+terms of precision and computation time.
+",1,0,0,0,0,0
+5734,Topological strings linking with quasi-particle exchange in superconducting Dirac semimetals,"  We demonstrate a topological classification of vortices in three dimensional
+time-reversal invariant topological superconductors based on superconducting
+Dirac semimetals with an s-wave superconducting order parameter by means of a
+pair of numbers $(N_\Phi,N)$, accounting how many units $N_\Phi$ of magnetic
+fluxes $hc/4e$ and how many $N$ chiral Majorana modes the vortex carries. From
+these quantities, we introduce a topological invariant which further classifies
+the properties of such vortices under linking processes. While such processes
+are known to be related to instanton processes in a field theoretic
+description, we demonstrate here that they are, in fact, also equivalent to the
+fractional Josephson effect on junctions based at the edges of quantum spin
+Hall systems. This allows one to consider microscopically the effects of
+interactions in the linking problem. We therefore demonstrate that associated
+to links between vortices, one has the exchange of quasi-particles, either
+Majorana zero-modes or $e/2$ quasi-particles, which allows for a topological
+classification of vortices in these systems, seen to be $\mathbb{Z}_8$
+classified. While $N_\Phi$ and $N$ are shown to be both even or odd in the
+weakly-interacting limit, in the strongly interacting scenario one loosens this
+constraint. In this case, one may have further fractionalization possibilities
+for the vortices, whose excitations are described by $SO(3)_3$-like conformal
+field theories with quasi-particle exchanges of more exotic types.
+",0,1,0,0,0,0
+5735,Self-Imitation Learning,"  This paper proposes Self-Imitation Learning (SIL), a simple off-policy
+actor-critic algorithm that learns to reproduce the agent's past good
+decisions. This algorithm is designed to verify our hypothesis that exploiting
+past good experiences can indirectly drive deep exploration. Our empirical
+results show that SIL significantly improves advantage actor-critic (A2C) on
+several hard exploration Atari games and is competitive to the state-of-the-art
+count-based exploration methods. We also show that SIL improves proximal policy
+optimization (PPO) on MuJoCo tasks.
+",0,0,0,1,0,0
+5736,Controllability to Equilibria of the 1-D Fokker-Planck Equation with Zero-Flux Boundary Condition,"  We consider the problem of controlling the spatiotemporal probability
+distribution of a robotic swarm that evolves according to a reflected diffusion
+process, using the space- and time-dependent drift vector field parameter as
+the control variable. In contrast to previous work on control of the
+Fokker-Planck equation, a zero-flux boundary condition is imposed on the
+partial differential equation that governs the swarm probability distribution,
+and only bounded vector fields are considered to be admissible as control
+parameters. Under these constraints, we show that any initial probability
+distribution can be transported to a target probability distribution under
+certain assumptions on the regularity of the target distribution. In
+particular, we show that if the target distribution is (essentially) bounded,
+has bounded first-order and second-order partial derivatives, and is bounded
+from below by a strictly positive constant, then this distribution can be
+reached exactly using a drift vector field that is bounded in space and time.
+Our proof is constructive and based on classical linear semigroup theoretic
+concepts.
+",1,0,1,0,0,0
+5737,Buy your coffee with bitcoin: Real-world deployment of a bitcoin point of sale terminal,"  In this paper we discuss existing approaches for Bitcoin payments, as
+suitable for a small business for small-value transactions. We develop an
+evaluation framework utilizing security, usability, deployability criteria,,
+examine several existing systems, tools. Following a requirements engineering
+approach, we designed, implemented a new Point of Sale (PoS) system that
+satisfies an optimal set of criteria within our evaluation framework. Our open
+source system, Aunja PoS, has been deployed in a real world cafe since October
+2014.
+",1,0,0,0,0,0
+5738,Tackling non-linearities with the effective field theory of dark energy and modified gravity,"  We present the extension of the effective field theory framework to the
+mildly non-linear scales. The effective field theory approach has been
+successfully applied to the late time cosmic acceleration phenomenon and it has
+been shown to be a powerful method to obtain predictions about cosmological
+observables on linear scales. However, mildly non-linear scales need to be
+consistently considered when testing gravity theories because a large part of
+the data comes from those scales. Thus, non-linear corrections to predictions
+on observables coming from the linear analysis can help in discriminating among
+different gravity theories. We proceed firstly by identifying the necessary
+operators which need to be included in the effective field theory Lagrangian in
+order to go beyond the linear order in perturbations and then we construct the
+corresponding non-linear action. Moreover, we present the complete recipe to
+map any single field dark energy and modified gravity models into the
+non-linear effective field theory framework by considering a general action in
+the Arnowitt-Deser-Misner formalism. In order to illustrate this recipe we
+proceed to map the beyond-Horndeski theory and low-energy Horava gravity into
+the effective field theory formalism. As a final step we derived the 4th order
+action in term of the curvature perturbation. This allowed us to identify the
+non-linear contributions coming from the linear order perturbations which at
+the next order act like source terms. Moreover, we confirm that the stability
+requirements, ensuring the positivity of the kinetic term and the speed of
+propagation for scalar mode, are automatically satisfied once the viability of
+the theory is demanded at linear level. The approach we present here will allow
+to construct, in a model independent way, all the relevant predictions on
+observables at mildly non-linear scales.
+",0,1,0,0,0,0
+5739,A recommender system to restore images with impulse noise,"  We build a collaborative filtering recommender system to restore images with
+impulse noise for which the noisy pixels have been previously identified. We
+define this recommender system in terms of a new color image representation
+using three matrices that depend on the noise-free pixels of the image to
+restore, and two parameters: $k$, the number of features; and $\lambda$, the
+regularization factor. We perform experiments on a well known image database to
+test our algorithm and we provide image quality statistics for the results
+obtained. We discuss the roles of bias and variance in the performance of our
+algorithm as determined by the values of $k$ and $\lambda$, and provide
+guidance on how to choose the values of these parameters. Finally, we discuss
+the possibility of using our collaborative filtering recommender system to
+perform image inpainting and super-resolution.
+",1,0,0,1,0,0
+5740,A Parallel Simulator for Massive Reservoir Models Utilizing Distributed-Memory Parallel Systems,"  This paper presents our work on developing parallel computational methods for
+two-phase flow on modern parallel computers, where techniques for linear
+solvers and nonlinear methods are studied and the standard and inexact Newton
+methods are investigated. A multi-stage preconditioner for two-phase flow is
+applied and advanced matrix processing strategies are studied. A local
+reordering method is developed to speed the solution of linear systems.
+Numerical experiments show that these computational methods are effective and
+scalable, and are capable of computing large-scale reservoir simulation
+problems using thousands of CPU cores on parallel computers. The nonlinear
+techniques, preconditioner and matrix processing strategies can also be applied
+to three-phase black oil, compositional and thermal models.
+",1,0,0,0,0,0
+5741,Bifurcation structure of cavity soliton dynamics in a VCSEL with saturable absorber and time-delayed feedback,"  We consider a wide-aperture surface-emitting laser with a saturable absorber
+section subjected to time-delayed feedback. We adopt the mean-field approach
+assuming a single longitudinal mode operation of the solitary VCSEL. We
+investigate cavity soliton dynamics under the effect of time- delayed feedback
+in a self-imaging configuration where diffraction in the external cavity is
+negligible. Using bifurcation analysis, direct numerical simulations and
+numerical path continuation methods, we identify the possible bifurcations and
+map them in a plane of feedback parameters. We show that for both the
+homogeneous and localized stationary lasing solutions in one spatial dimension
+the time-delayed feedback induces complex spatiotemporal dynamics, in
+particular a period doubling route to chaos, quasiperiodic oscillations and
+multistability of the stationary solutions.
+",0,1,0,0,0,0
+5742,Early warning signal for interior crises in excitable systems,"  The ability to reliably predict critical transitions in dynamical systems is
+a long-standing goal of diverse scientific communities. Previous work focused
+on early warning signals related to local bifurcations (critical slowing down)
+and non-bifurcation type transitions. We extend this toolbox and report on a
+characteristic scaling behavior (critical attractor growth) which is indicative
+of an impending global bifurcation, an interior crisis in excitable systems. We
+demonstrate our early warning signal in a conceptual climate model as well as
+in a model of coupled neurons known to exhibit extreme events. We observed
+critical attractor growth prior to interior crises of chaotic as well as
+strange-nonchaotic attractors. These observations promise to extend the classes
+of transitions that can be predicted via early warning signals.
+",0,1,0,0,0,0
+5743,Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery,"  We propose a new iteratively reweighted least squares (IRLS) algorithm for
+the recovery of a matrix $X \in \mathbb{C}^{d_1\times d_2}$ of rank $r
+\ll\min(d_1,d_2)$ from incomplete linear observations, solving a sequence of
+low complexity linear problems. The easily implementable algorithm, which we
+call harmonic mean iteratively reweighted least squares (HM-IRLS), optimizes a
+non-convex Schatten-$p$ quasi-norm penalization to promote low-rankness and
+carries three major strengths, in particular for the matrix completion setting.
+First, we observe a remarkable global convergence behavior of the algorithm's
+iterates to the low-rank matrix for relevant, interesting cases, for which any
+other state-of-the-art optimization approach fails the recovery. Secondly,
+HM-IRLS exhibits an empirical recovery probability close to $1$ even for a
+number of measurements very close to the theoretical lower bound $r (d_1 +d_2
+-r)$, i.e., already for significantly fewer linear observations than any other
+tractable approach in the literature. Thirdly, HM-IRLS exhibits a locally
+superlinear rate of convergence (of order $2-p$) if the linear observations
+fulfill a suitable null space property. While for the first two properties we
+have so far only strong empirical evidence, we prove the third property as our
+main theoretical result.
+",1,0,1,0,0,0
+5744,The Italian Pension Gap: a Stochastic Optimal Control Approach,"  We study the gap between the state pension provided by the Italian pension
+system pre-Dini reform and post-Dini reform. The goal is to fill the gap
+between the old and the new pension by joining a defined contribution pension
+scheme and adopting an optimal investment strategy that is target-based. We
+find that it is possible to cover, at least partially, this gap with the
+additional income of the pension scheme, especially in the presence of late
+retirement and in the presence of stagnant career. Workers with dynamic career
+and workers who retire early are those who are most penalised by the reform.
+Results are intuitive and in line with previous studies on the subject.
+",0,0,0,0,0,1
+5745,Immigration-induced phase transition in a regulated multispecies birth-death process,"  Power-law-distributed species counts or clone counts arise in many biological
+settings such as multispecies cell populations, population genetics, and
+ecology. This empirical observation that the number of species $c_{k}$
+represented by $k$ individuals scales as negative powers of $k$ is also
+supported by a series of theoretical birth-death-immigration (BDI) models that
+consistently predict many low-population species, a few intermediate-population
+species, and very high-population species. However, we show how a simple global
+population-dependent regulation in a neutral BDI model destroys the power law
+distributions. Simulation of the regulated BDI model shows a high probability
+of observing a high-population species that dominates the total population.
+Further analysis reveals that the origin of this breakdown is associated with
+the failure of a mean-field approximation for the expected species abundance
+distribution. We find an accurate estimate for the expected distribution
+$\langle c_k \rangle$ by mapping the problem to a lower-dimensional Moran
+process, allowing us to also straightforwardly calculate the covariances
+$\langle c_k c_\ell \rangle$. Finally, we exploit the concepts associated with
+energy landscapes to explain the failure of the mean-field assumption by
+identifying a phase transition in the quasi-steady-state species counts
+triggered by a decreasing immigration rate.
+",0,0,0,0,1,0
+5746,Nanoscale assembly of superconducting vortices with scanning tunnelling microscope tip,"  Vortices play a crucial role in determining the properties of superconductors
+as well as their applications. Therefore, characterization and manipulation of
+vortices, especially at the single vortex level, is of great importance. Among
+many techniques to study single vortices, scanning tunneling microscopy (STM)
+stands out as a powerful tool, due to its ability to detect the local
+electronic states and high spatial resolution. However, local control of
+superconductivity as well as the manipulation of individual vortices with the
+STM tip is still lacking. Here we report a new function of the STM, namely to
+control the local pinning in a superconductor through the heating effect. Such
+effect allows us to quench the superconducting state at nanoscale, and leads to
+the growth of vortex-clusters whose size can be controlled by the bias voltage.
+We also demonstrate the use of an STM tip to assemble single quantum vortices
+into desired nanoscale configurations.
+",0,1,0,0,0,0
+5747,STACCATO: A Novel Solution to Supernova Photometric Classification with Biased Training Sets,"  We present a new solution to the problem of classifying Type Ia supernovae
+from their light curves alone given a spectroscopically confirmed but biased
+training set, circumventing the need to obtain an observationally expensive
+unbiased training set. We use Gaussian processes (GPs) to model the
+supernovae's (SN) light curves, and demonstrate that the choice of covariance
+function has only a small influence on the GPs ability to accurately classify
+SNe. We extend and improve the approach of Richards et al (2012} -- a diffusion
+map combined with a random forest classifier -- to deal specifically with the
+case of biassed training sets. We propose a novel method, called STACCATO
+(SynThetically Augmented Light Curve ClassificATiOn') that synthetically
+augments a biased training set by generating additional training data from the
+fitted GPs. Key to the success of the method is the partitioning of the
+observations into subgroups based on their propensity score of being included
+in the training set. Using simulated light curve data, we show that STACCATO
+increases performance, as measured by the area under the Receiver Operating
+Characteristic curve (AUC), from 0.93 to 0.96, close to the AUC of 0.977
+obtained using the 'gold standard' of an unbiased training set and
+significantly improving on the previous best result of 0.88. STACCATO also
+increases the true positive rate for SNIa classification by up to a factor of
+50 for high-redshift/low brightness SNe.
+",0,1,0,0,0,0
+5748,Coupling of multiscale and multi-continuum approaches,"  Simulating complex processes in fractured media requires some type of model
+reduction. Well-known approaches include multi-continuum techniques, which have
+been commonly used in approximating subgrid effects for flow and transport in
+fractured media. Our goal in this paper is to (1) show a relation between
+multi-continuum approaches and Generalized Multiscale Finite Element Method
+(GMsFEM) and (2) to discuss coupling these approaches for solving problems in
+complex multiscale fractured media. The GMsFEM, a systematic approach,
+constructs multiscale basis functions via local spectral decomposition in
+pre-computed snapshot spaces. We show that GMsFEM can automatically identify
+separate fracture networks via local spectral problems. We discuss the relation
+between these basis functions and continuums in multi-continuum methods. The
+GMsFEM can automatically detect each continuum and represent the interaction
+between the continuum and its surrounding (matrix). For problems with
+simplified fracture networks, we propose a simplified basis construction with
+the GMsFEM. This simplified approach is effective when the fracture networks
+are known and have simplified geometries. We show that this approach can
+achieve a similar result compared to the results using the GMsFEM with spectral
+basis functions. Further, we discuss the coupling between the GMsFEM and
+multi-continuum approaches. In this case, many fractures are resolved while for
+unresolved fractures, we use a multi-continuum approach with local
+Representative Volume Element (RVE) information. As a result, the method deals
+with a system of equations on a coarse grid, where each equation represents one
+of the continua on the fine grid. We present various basis construction
+mechanisms and numerical results.
+",0,0,1,0,0,0
+5749,Spaces which invert weak homotopy equivalences,"  It is well known that if $X$ is a CW-complex, then for every weak homotopy
+equivalence $f:A\to B$, the map $f_*:[X,A]\to [X,B]$ induced in homotopy
+classes is a bijection. For which spaces $X$ is $f^*:[B,X]\to [A,X]$ a
+bijection for every weak equivalence $f$? This question was considered by J.
+Strom and T. Goodwillie. In this note we prove that a non-empty space inverts
+weak equivalences if and only if it is contractible.
+",0,0,1,0,0,0
+5750,Minimal surfaces in the 3-sphere by desingularizing intersecting Clifford tori,"  For each integer $k \geq 2$, we apply gluing methods to construct sequences
+of minimal surfaces embedded in the round $3$-sphere. We produce two types of
+sequences, all desingularizing collections of intersecting Clifford tori.
+Sequences of the first type converge to a collection of $k$ Clifford tori
+intersecting with maximal symmetry along these two circles. Near each of the
+circles, after rescaling, the sequences converge smoothly on compact subsets to
+a Karcher-Scherk tower of order $k$. Sequences of the second type desingularize
+a collection of the same $k$ Clifford tori supplemented by an additional
+Clifford torus equidistant from the original two circles of intersection, so
+that the latter torus orthogonally intersects each of the former $k$ tori along
+a pair of disjoint orthogonal circles, near which the corresponding rescaled
+sequences converge to a singly periodic Scherk surface. The simpler examples of
+the first type resemble surfaces constructed by Choe and Soret \cite{CS} by
+different methods where the number of handles desingularizing each circle is
+the same. There is a plethora of new examples which are more complicated and on
+which the number of handles for the two circles differs. Examples of the second
+type are new as well.
+",0,0,1,0,0,0
+5751,Results from EDGES High-Band: I. Constraints on Phenomenological Models for the Global $21$ cm Signal,"  We report constraints on the global $21$ cm signal due to neutral hydrogen at
+redshifts $14.8 \geq z \geq 6.5$. We derive our constraints from low foreground
+observations of the average sky brightness spectrum conducted with the EDGES
+High-Band instrument between September $7$ and October $26$, $2015$.
+Observations were calibrated by accounting for the effects of antenna beam
+chromaticity, antenna and ground losses, signal reflections, and receiver
+parameters. We evaluate the consistency between the spectrum and
+phenomenological models for the global $21$ cm signal. For tanh-based
+representations of the ionization history during the epoch of reionization, we
+rule out, at $\geq2\sigma$ significance, models with duration of up to $\Delta
+z = 1$ at $z\approx8.5$ and higher than $\Delta z = 0.4$ across most of the
+observed redshift range under the usual assumption that the $21$ cm spin
+temperature is much larger than the temperature of the cosmic microwave
+background (CMB) during reionization. We also investigate a `cold' IGM scenario
+that assumes perfect Ly$\alpha$ coupling of the $21$ cm spin temperature to the
+temperature of the intergalactic medium (IGM), but that the IGM is not heated
+by early stars or stellar remants. Under this assumption, we reject tanh-based
+reionization models of duration $\Delta z \lesssim 2$ over most of the observed
+redshift range. Finally, we explore and reject a broad range of Gaussian models
+for the $21$ cm absorption feature expected in the First Light era. As an
+example, we reject $100$ mK Gaussians with duration (full width at half
+maximum) $\Delta z \leq 4$ over the range $14.2\geq z\geq 6.5$ at $\geq2\sigma$
+significance.
+",0,1,0,0,0,0
+5752,Novel paradigms for advanced distribution grid energy management,"  The electricity distribution grid was not designed to cope with load dynamics
+imposed by high penetration of electric vehicles, neither to deal with the
+increasing deployment of distributed Renewable Energy Sources. Distribution
+System Operators (DSO) will increasingly rely on flexible Distributed Energy
+Resources (flexible loads, controllable generation and storage) to keep the
+grid stable and to ensure quality of supply. In order to properly integrate
+demand-side flexibility, DSOs need new energy management architectures, capable
+of fostering collaboration with wholesale market actors and pro-sumers. We
+propose the creation of Virtual Distribution Grids (VDG) over a common physical
+infrastructure , to cope with heterogeneity of resources and actors, and with
+the increasing complexity of distribution grid management and related resources
+allocation problems. Focusing on residential VDG, we propose an agent-based
+hierarchical architecture for providing Demand-Side Management services through
+a market-based approach, where households transact their surplus/lack of energy
+and their flexibility with neighbours, aggregators, utilities and DSOs. For
+implementing the overall solution, we consider fine-grained control of smart
+homes based on Inter-net of Things technology. Homes seamlessly transact
+self-enforcing smart contracts over a blockchain-based generic platform.
+Finally, we extend the architecture to solve existing problems on smart home
+control, beyond energy management.
+",1,0,0,0,0,0
+5753,Uniformly Bounded Sets in Quasiperiodically Forced Dynamical Systems,"  This paper addresses structures of state space in quasiperiodically forced
+dynamical systems. We develop a theory of ergodic partition of state space in a
+class of measure-preserving and dissipative flows, which is a natural extension
+of the existing theory for measure-preserving maps. The ergodic partition
+result is based on eigenspace at eigenvalue 0 of the associated Koopman
+operator, which is realized via time-averages of observables, and provides a
+constructive way to visualize a low-dimensional slice through a
+high-dimensional invariant set. We apply the result to the systems with a
+finite number of attractors and show that the time-average of a continuous
+observable is well-defined and reveals the invariant sets, namely, a finite
+number of basins of attraction. We provide a characterization of invariant sets
+in the quasiperiodically forced systems. A theorem on uniform boundedness of
+the invariant sets is proved. The series of analytical results enables
+numerical analysis of invariant sets in the quasiperiodically forced systems
+based on the ergodic partition and time-averages. Using this, we analyze a
+nonlinear model of complex power grids that represents the short-term swing
+instability, named the coherent swing instability. We show that our analytical
+results can be used to understand stability regions in such complex systems.
+",1,0,0,0,0,0
+5754,"Understand Functionality and Dimensionality of Vector Embeddings: the Distributional Hypothesis, the Pairwise Inner Product Loss and Its Bias-Variance Trade-off","  Vector embedding is a foundational building block of many deep learning
+models, especially in natural language processing. In this paper, we present a
+theoretical framework for understanding the effect of dimensionality on vector
+embeddings. We observe that the distributional hypothesis, a governing
+principle of statistical semantics, requires a natural unitary-invariance for
+vector embeddings. Motivated by the unitary-invariance observation, we propose
+the Pairwise Inner Product (PIP) loss, a unitary-invariant metric on the
+similarity between two embeddings. We demonstrate that the PIP loss captures
+the difference in functionality between embeddings, and that the PIP loss is
+tightly connect with two basic properties of vector embeddings, namely
+similarity and compositionality. By formulating the embedding training process
+as matrix factorization with noise, we reveal a fundamental bias-variance
+trade-off between the signal spectrum and noise power in the dimensionality
+selection process. This bias-variance trade-off sheds light on many empirical
+observations which have not been thoroughly explained, for example the
+existence of an optimal dimensionality. Moreover, we discover two new results
+about vector embeddings, namely their robustness against over-parametrization
+and their forward stability. The bias-variance trade-off of the PIP loss
+explicitly answers the fundamental open problem of dimensionality selection for
+vector embeddings.
+",0,0,0,1,0,0
+5755,Strongly correlated one-dimensional Bose-Fermi quantum mixtures: symmetry and correlations,"  We consider multi-component quantum mixtures (bosonic, fermionic, or mixed)
+with strongly repulsive contact interactions in a one-dimensional harmonic
+trap. In the limit of infinitely strong repulsion and zero temperature, using
+the class-sum method, we study the symmetries of the spatial wave function of
+the mixture. We find that the ground state of the system has the most symmetric
+spatial wave function allowed by the type of mixture. This provides an example
+of the generalized Lieb-Mattis theorem. Furthermore, we show that the symmetry
+properties of the mixture are embedded in the large-momentum tails of the
+momentum distribution, which we evaluate both at infinite repulsion by an exact
+solution and at finite interactions using a numerical DMRG approach. This
+implies that an experimental measurement of the Tan's contact would allow to
+unambiguously determine the symmetry of any kind of multi-component mixture.
+",0,1,0,0,0,0
+5756,Joint Pose and Principal Curvature Refinement Using Quadrics,"  In this paper we present a novel joint approach for optimising surface
+curvature and pose alignment. We present two implementations of this joint
+optimisation strategy, including a fast implementation that uses two frames and
+an offline multi-frame approach. We demonstrate an order of magnitude
+improvement in simulation over state of the art dense relative point-to-plane
+Iterative Closest Point (ICP) pose alignment using our dense joint
+frame-to-frame approach and show comparable pose drift to dense point-to-plane
+ICP bundle adjustment using low-cost depth sensors. Additionally our improved
+joint quadric based approach can be used to more accurately estimate surface
+curvature on noisy point clouds than previous approaches.
+",1,0,0,0,0,0
+5757,Stable basic sets for finite special linear and unitary group,"  In this paper we show, using Deligne-Lusztig theory and Kawanaka's theory of
+generalised Gelfand-Graev representations, that the decomposition matrix of the
+special linear and unitary group in non defining characteristic can be made
+unitriangular with respect to a basic set that is stable under the action of
+automorphisms.
+",0,0,1,0,0,0
+5758,Correspondences without a Core,"  We study the formal properties of correspondences of curves without a core,
+focusing on the case of étale correspondences. The motivating examples come
+from Hecke correspondences of Shimura curves. Given a correspondence without a
+core, we construct an infinite graph $\mathcal{G}_{gen}$ together with a large
+group of ""algebraic"" automorphisms $A$. The graph $\mathcal{G}_{gen}$ measures
+the ""generic dynamics"" of the correspondence. We construct specialization maps
+$\mathcal{G}_{gen}\rightarrow\mathcal{G}_{phys}$ to the ""physical dynamics"" of
+the correspondence. We also prove results on the number of bounded étale
+orbits, in particular generalizing a recent theorem of Hallouin and Perret. We
+use a variety of techniques: Galois theory, the theory of groups acting on
+infinite graphs, and finite group schemes.
+",0,0,1,0,0,0
+5759,Oxidative species-induced excitonic transport in tubulin aromatic networks: Potential implications for neurodegenerative disease,"  Oxidative stress is a pathological hallmark of neurodegenerative tauopathic
+disorders such as Alzheimer's disease and Parkinson's disease-related dementia,
+which are characterized by altered forms of the microtubule-associated protein
+(MAP) tau. MAP tau is a key protein in stabilizing the microtubule architecture
+that regulates neuron morphology and synaptic strength. The precise role of
+reactive oxygen species (ROS) in the tauopathic disease process, however, is
+poorly understood. It is known that the production of ROS by mitochondria can
+result in ultraweak photon emission (UPE) within cells. One likely absorber of
+these photons is the microtubule cytoskeleton, as it forms a vast network
+spanning neurons, is highly co-localized with mitochondria, and shows a high
+density of aromatic amino acids. Functional microtubule networks may traffic
+this ROS-generated endogenous photon energy for cellular signaling, or they may
+serve as dissipaters/conduits of such energy. Experimentally, after in vitro
+exposure to exogenous photons, microtubules have been shown to reorient and
+reorganize in a dose-dependent manner with the greatest effect being observed
+around 280 nm, in the tryptophan and tyrosine absorption range. In this paper,
+recent modeling efforts based on ambient temperature experiment are presented,
+showing that tubulin polymers can feasibly absorb and channel these
+photoexcitations via resonance energy transfer, on the order of dendritic
+length scales. Since microtubule networks are compromised in tauopathic
+diseases, patients with these illnesses would be unable to support effective
+channeling of these photons for signaling or dissipation. Consequent emission
+surplus due to increased UPE production or decreased ability to absorb and
+transfer may lead to increased cellular oxidative damage, thus hastening the
+neurodegenerative process.
+",0,1,0,0,0,0
+5760,On radial Schroedinger operators with a Coulomb potential,"  This paper presents a thorough analysis of 1-dimensional Schroedinger
+operators whose potential is a linear combination of the Coulomb term 1/r and
+the centrifugal term 1/r^2. We allow both coupling constants to be complex.
+Using natural boundary conditions at 0, a two parameter holomorphic family of
+closed operators is introduced. We call them the Whittaker operators, since in
+the mathematical literature their eigenvalue equation is called the Whittaker
+equation. Spectral and scattering theory for Whittaker operators is studied.
+Whittaker operators appear in quantum mechanics as the radial part of the
+Schroedinger operator with a Coulomb potential.
+",0,0,1,0,0,0
+5761,Extrema-weighted feature extraction for functional data,"  Motivation: Although there is a rich literature on methods for assessing the
+impact of functional predictors, the focus has been on approaches for dimension
+reduction that can fail dramatically in certain applications. Examples of
+standard approaches include functional linear models, functional principal
+components regression, and cluster-based approaches, such as latent trajectory
+analysis. This article is motivated by applications in which the dynamics in a
+predictor, across times when the value is relatively extreme, are particularly
+informative about the response. For example, physicians are interested in
+relating the dynamics of blood pressure changes during surgery to post-surgery
+adverse outcomes, and it is thought that the dynamics are more important when
+blood pressure is significantly elevated or lowered.
+Methods: We propose a novel class of extrema-weighted feature (XWF)
+extraction models. Key components in defining XWFs include the marginal density
+of the predictor, a function up-weighting values at high quantiles of this
+marginal, and functionals characterizing local dynamics. Algorithms are
+proposed for fitting of XWF-based regression and classification models, and are
+compared with current methods for functional predictors in simulations and a
+blood pressure during surgery application.
+Results: XWFs find features of intraoperative blood pressure trajectories
+that are predictive of postoperative mortality. By their nature, most of these
+features cannot be found by previous methods.
+",0,0,0,1,0,0
+5762,"A representation theorem for stochastic processes with separable covariance functions, and its implications for emulation","  Many applications require stochastic processes specified on two- or
+higher-dimensional domains; spatial or spatial-temporal modelling, for example.
+In these applications it is attractive, for conceptual simplicity and
+computational tractability, to propose a covariance function that is separable;
+e.g., the product of a covariance function in space and one in time. This paper
+presents a representation theorem for such a proposal, and shows that all
+processes with continuous separable covariance functions are second-order
+identical to the product of second-order uncorrelated processes. It discusses
+the implications of separable or nearly separable prior covariances for the
+statistical emulation of complicated functions such as computer codes, and
+critically reexamines the conventional wisdom concerning emulator structure,
+and size of design.
+",0,0,1,1,0,0
+5763,Facial Recognition Enabled Smart Door Using Microsoft Face API,"  Privacy and Security are two universal rights and, to ensure that in our
+daily life we are secure, a lot of research is going on in the field of home
+security, and IoT is the turning point for the industry, where we connect
+everyday objects to share data for our betterment. Facial recognition is a
+well-established process in which the face is detected and identified out of
+the image. We aim to create a smart door, which secures the gateway on the
+basis of who we are. In our proof of concept of a smart door we have used a
+live HD camera on the front side of setup attached to a display monitor
+connected to the camera to show who is standing in front of the door, also the
+whole system will be able to give voice outputs by processing text them on the
+Raspberry Pi ARM processor used and show the answers as output on the screen.
+We are using a set of electromagnets controlled by the microcontroller, which
+will act as a lock. So a person can open the smart door with the help of facial
+recognition and at the same time also be able to interact with it. The facial
+recognition is done by Microsoft face API but our state of the art desktop
+application operating over Microsoft Visual Studio IDE reduces the
+computational time by detecting the face out of the photo and giving that as
+the output to Microsoft Face API, which is hosted over Microsoft Azure cloud
+support.
+",1,0,0,0,0,0
+5764,Veamy: an extensible object-oriented C++ library for the virtual element method,"  This paper summarizes the development of Veamy, an object-oriented C++
+library for the virtual element method (VEM) on general polygonal meshes, whose
+modular design is focused on its extensibility. The linear elastostatic and
+Poisson problems in two dimensions have been chosen as the starting stage for
+the development of this library. The theory of the VEM, upon which Veamy is
+built, is presented using a notation and a terminology that resemble the
+language of the finite element method (FEM) in engineering analysis. Several
+examples are provided to demonstrate the usage of Veamy, and in particular, one
+of them features the interaction between Veamy and the polygonal mesh generator
+PolyMesher. A computational performance comparison between VEM and FEM is also
+conducted. Veamy is free and open source software.
+",1,0,0,0,0,0
+5765,Composition by Conversation,"  Most musical programming languages are developed purely for coding virtual
+instruments or algorithmic compositions. Although there has been some work in
+the domain of musical query languages for music information retrieval, there
+has been little attempt to unify the principles of musical programming and
+query languages with cognitive and natural language processing models that
+would facilitate the activity of composition by conversation. We present a
+prototype framework, called MusECI, that merges these domains, permitting
+score-level algorithmic composition in a text editor while also supporting
+connectivity to existing natural language processing frameworks.
+",1,0,0,0,0,0
+5766,Electrostatic and induction effects in the solubility of water in alkanes,"  Experiments show that at 298~K and 1 atm pressure the transfer free energy,
+$\mu^{\rm ex}$, of water from its vapor to liquid normal alkanes $C_nH_{2n+2}$
+($n=5\ldots12$) is negative. Earlier it was found that with the united-atom
+TraPPe model for alkanes and the SPC/E model for water, one had to artificially
+enhance the attractive alkane-water cross interaction to capture this behavior.
+Here we revisit the calculation of $\mu^{\rm ex}$ using the polarizable AMOEBA
+and the non-polarizable Charmm General (CGenFF) forcefields. We test both the
+AMOEBA03 and AMOEBA14 water models; the former has been validated with the
+AMOEBA alkane model while the latter is a revision of AMOEBA03 to better
+describe liquid water. We calculate $\mu^{\rm ex}$ using the test particle
+method. With CGenFF, $\mu^{\rm ex}$ is positive and the error relative to
+experiments is about 1.5 $k_{\rm B}T$. With AMOEBA, $\mu^{\rm ex}$ is negative
+and deviations relative to experiments are between 0.25 $k_{\rm B}T$ (AMOEBA14)
+and 0.5 $k_{\rm B}T$ (AMOEBA03). Quantum chemical calculations in a continuum
+solvent suggest that zero point effects may account for some of the deviation.
+Forcefield limitations notwithstanding, electrostatic and induction effects,
+commonly ignored in considerations of water-alkane interactions, appear to be
+decisive in the solubility of water in alkanes.
+",0,1,0,0,0,0
+5767,Impact of theoretical priors in cosmological analyses: the case of single field quintessence,"  We investigate the impact of general conditions of theoretical stability and
+cosmological viability on dynamical dark energy models. As a powerful example,
+we study whether minimally coupled, single field Quintessence models that are
+safe from ghost instabilities, can source the CPL expansion history recently
+shown to be mildly favored by a combination of CMB (Planck) and Weak Lensing
+(KiDS) data. We find that in their most conservative form, the theoretical
+conditions impact the analysis in such a way that smooth single field
+Quintessence becomes significantly disfavored with respect to the standard LCDM
+cosmological model. This is due to the fact that these conditions cut a
+significant portion of the (w0;wa) parameter space for CPL, in particular
+eliminating the region that would be favored by weak lensing data. Within the
+scenario of a smooth dynamical dark energy parametrized with CPL, weak lensing
+data favors a region that would require multiple fields to ensure gravitational
+stability.
+",0,1,0,0,0,0
+5768,Spin tracking of polarized protons in the Main Injector at Fermilab,"  The Main Injector (MI) at Fermilab currently produces high-intensity beams of
+protons at energies of 120 GeV for a variety of physics experiments.
+Acceleration of polarized protons in the MI would provide opportunities for a
+rich spin physics program at Fermilab. To achieve polarized proton beams in the
+Fermilab accelerator complex, detailed spin tracking simulations with realistic
+parameters based on the existing facility are required. This report presents
+studies at the MI using a single 4-twist Siberian snake to determine the
+depolarizing spin resonances for the relevant synchrotrons. Results will be
+presented first for a perfect MI lattice, followed by a lattice that includes
+the real MI imperfections, such as the measured magnet field errors and
+quadrupole misalignments. The tolerances of each of these factors in
+maintaining polarization in the Main Injector will be discussed.
+",0,1,0,0,0,0
+5769,Wormholes and masses for Goldstone bosons,"  There exist non-trivial stationary points of the Euclidean action for an
+axion particle minimally coupled to Einstein gravity, dubbed wormholes. They
+explicitly break the continuos global shift symmetry of the axion in a
+non-perturbative way, and generate an effective potential that may compete with
+QCD depending on the value of the axion decay constant. In this paper, we
+explore both theoretical and phenomenological aspects of this issue. On the
+theory side, we address the problem of stability of the wormhole solutions, and
+we show that the spectrum of the quadratic action features only positive
+eigenvalues. On the phenomenological side, we discuss, beside the obvious
+application to the QCD axion, relevant consequences for models with ultralight
+dark matter, black hole superradiance, and the relaxation of the electroweak
+scale. We conclude discussing wormhole solutions for a generic coset and the
+potential they generate.
+",0,1,0,0,0,0
+5770,A class of states supporting diffusive spin dynamics in the isotropic Heisenberg model,"  The spin transport in isotropic Heisenberg model in the sector with zero
+magnetization is generically super-diffusive. Despite that, we here demonstrate
+that for a specific set of domain-wall-like initial product states it can
+instead be diffusive. We theoretically explain the time evolution of such
+states by showing that in the limiting regime of weak spatial modulation they
+are approximately product states for very long times, and demonstrate that even
+in the case of larger spatial modulation the bipartite entanglement entropy
+grows only logarithmically in time. In the limiting regime we derive a simple
+closed equation governing the dynamics, which in the continuum limit and for
+the initial step magnetization profile results in a solution expressed in terms
+of Fresnel integrals.
+",0,1,0,0,0,0
+5771,Learning to Invert: Signal Recovery via Deep Convolutional Networks,"  The promise of compressive sensing (CS) has been offset by two significant
+challenges. First, real-world data is not exactly sparse in a fixed basis.
+Second, current high-performance recovery algorithms are slow to converge,
+which limits CS to either non-real-time applications or scenarios where massive
+back-end computing is available. In this paper, we attack both of these
+challenges head-on by developing a new signal recovery framework we call {\em
+DeepInverse} that learns the inverse transformation from measurement vectors to
+signals using a {\em deep convolutional network}. When trained on a set of
+representative images, the network learns both a representation for the signals
+(addressing challenge one) and an inverse map approximating a greedy or convex
+recovery algorithm (addressing challenge two). Our experiments indicate that
+the DeepInverse network closely approximates the solution produced by
+state-of-the-art CS recovery algorithms yet is hundreds of times faster in run
+time. The tradeoff for the ultrafast run time is a computationally intensive,
+off-line training procedure typical to deep networks. However, the training
+needs to be completed only once, which makes the approach attractive for a host
+of sparse recovery problems.
+",1,0,0,1,0,0
+5772,Multi-message Authentication over Noisy Channel with Secure Channel Codes,"  In this paper, we investigate multi-message authentication to combat
+adversaries with infinite computational capacity. An authentication framework
+over a wiretap channel $(W_1,W_2)$ is proposed to achieve information-theoretic
+security with the same key. The proposed framework bridges the two research
+areas in physical (PHY) layer security: secure transmission and message
+authentication. Specifically, the sender Alice first transmits message $M$ to
+the receiver Bob over $(W_1,W_2)$ with an error correction code; then Alice
+employs a hash function (i.e., $\varepsilon$-AWU$_2$ hash functions) to
+generate a message tag $S$ of message $M$ using key $K$, and encodes $S$ to a
+codeword $X^n$ by leveraging an existing strongly secure channel coding with
+exponentially small (in code length $n$) average probability of error; finally,
+Alice sends $X^n$ over $(W_1,W_2)$ to Bob who authenticates the received
+messages. We develop a theorem regarding the requirements/conditions for the
+authentication framework to be information-theoretic secure for authenticating
+a polynomial number of messages in terms of $n$. Based on this theorem, we
+propose an authentication protocol that can guarantee the security
+requirements, and prove its authentication rate can approach infinity when $n$
+goes to infinity. Furthermore, we design and implement an efficient and
+feasible authentication protocol over binary symmetric wiretap channel (BSWC)
+by using \emph{Linear Feedback Shifting Register} based (LFSR-based) hash
+functions and strong secure polar code. Through extensive experiments, it is
+demonstrated that the proposed protocol can achieve low time cost, high
+authentication rate, and low authentication error rate.
+",1,0,0,0,0,0
+5773,The Garden of Eden theorem: old and new,"  We review topics in the theory of cellular automata and dynamical systems
+that are related to the Moore-Myhill Garden of Eden theorem.
+",0,1,1,0,0,0
+5774,Bosonization in Non-Relativistic CFTs,"  We demonstrate explicitly the correspondence between all protected operators
+in a 2+1 dimensional non-supersymmetric bosonization duality in the
+non-relativistic limit. Roughly speaking we consider $SU(N)$ Chern-Simons field
+theory at level $k$ with $N_f$ flavours of fundamental boson, and match its
+chiral sector to that of a $SU(k)$ theory at level $N$ with $N_f$ fundamental
+fermions. We present the matching at the level of indices and individual
+operators, seeing the mechanism of failure for $N_f > N$, and point out that
+the non-relativistic setting is a particularly friendly setting for studying
+interesting questions about such dualities.
+",0,1,0,0,0,0
+5775,Learning latent representations for style control and transfer in end-to-end speech synthesis,"  In this paper, we introduce the Variational Autoencoder (VAE) to an
+end-to-end speech synthesis model, to learn the latent representation of
+speaking styles in an unsupervised manner. The style representation learned
+through VAE shows good properties such as disentangling, scaling, and
+combination, which makes it easy for style control. Style transfer can be
+achieved in this framework by first inferring style representation through the
+recognition network of VAE, then feeding it into TTS network to guide the style
+in synthesizing speech. To avoid Kullback-Leibler (KL) divergence collapse in
+training, several techniques are adopted. Finally, the proposed model shows
+good performance of style control and outperforms Global Style Token (GST)
+model in ABX preference tests on style transfer.
+",1,0,0,0,0,0
+5776,Directed negative-weight percolation,"  We consider a directed variant of the negative-weight percolation model in a
+two-dimensional, periodic, square lattice. The problem exhibits edge weights
+which are taken from a distribution that allows for both positive and negative
+values. Additionally, in this model variant all edges are directed. For a given
+realization of the disorder, a minimally weighted loop/path configuration is
+determined by performing a non-trivial transformation of the original lattice
+into a minimum weight perfect matching problem. For this problem, fast
+polynomial-time algorithms are available, thus we could study large systems
+with high accuracy. Depending on the fraction of negatively and positively
+weighted edges in the lattice, a continuous phase transition can be identified,
+whose characterizing critical exponents we have estimated by a finite-size
+scaling analyses of the numerically obtained data. We observe a strong change
+of the universality class with respect to standard directed percolation, as
+well as with respect to undirected negative-weight percolation. Furthermore,
+the relation to directed polymers in random media is illustrated.
+",0,1,0,0,0,0
+5777,Integrating Lipschitzian Dynamical Systems using Piecewise Algorithmic Differentiation,"  In this article we analyze a generalized trapezoidal rule for initial value
+problems with piecewise smooth right hand side \(F:\R^n\to\R^n\). When applied
+to such a problem the classical trapezoidal rule suffers from a loss of
+accuracy if the solution trajectory intersects a nondifferentiability of \(F\).
+The advantage of the proposed generalized trapezoidal rule is threefold:
+Firstly we can achieve a higher convergence order than with the classical
+method. Moreover, the method is energy preserving for piecewise linear
+Hamiltonian systems. Finally, in analogy to the classical case we derive a
+third order interpolation polynomial for the numerical trajectory. In the
+smooth case the generalized rule reduces to the classical one. Hence, it is a
+proper extension of the classical theory. An error estimator is given and
+numerical results are presented.
+",0,0,1,0,0,0
+5778,Effects of Planetesimal Accretion on the Thermal and Structural Evolution of Sub-Neptunes,"  A remarkable discovery of NASA's Kepler mission is the wide diversity in the
+average densities of planets of similar mass. After gas disk dissipation, fully
+formed planets could interact with nearby planetesimals from a remnant
+planetesimal disk. These interactions would often lead to planetesimal
+accretion due to the relatively high ratio between the planet size and the hill
+radius for typical planets. We present calculations using the open-source
+stellar evolution toolkit MESA (Modules for Experiments in Stellar
+Astrophysics) modified to include the deposition of planetesimals into the H/He
+envelopes of sub-Neptunes (~1-20 MEarth). We show that planetesimal accretion
+can alter the mass-radius isochrones for these planets. The same initial planet
+as a result of the same total accreted planetesimal mass can have up to ~5%
+difference in mean densities several Gyr after the last accretion due to
+inherent stochasticity of the accretion process. During the phase of rapid
+accretion these differences are more dramatic. The additional energy deposition
+from the accreted planetesimals increase the ratio between the planet's radius
+to that of the core during rapid accretion, which in turn leads to enhanced
+loss of atmospheric mass. As a result, the same initial planet can end up with
+very different envelope mass fractions. These differences manifest as
+differences in mean densities long after accretion stops. These effects are
+particularly important for planets initially less massive than ~10 MEarth and
+with envelope mass fraction less than ~10%, thought to be the most common type
+of planets discovered by Kepler.
+",0,1,0,0,0,0
+5779,Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions,"  We obtain estimation error rates and sharp oracle inequalities for
+regularization procedures of the form \begin{equation*}
+\hat f \in argmin_{f\in
+F}\left(\frac{1}{N}\sum_{i=1}^N\ell(f(X_i), Y_i)+\lambda \|f\|\right)
+\end{equation*} when $\|\cdot\|$ is any norm, $F$ is a convex class of
+functions and $\ell$ is a Lipschitz loss function satisfying a Bernstein
+condition over $F$. We explore both the bounded and subgaussian stochastic
+frameworks for the distribution of the $f(X_i)$'s, with no assumption on the
+distribution of the $Y_i$'s. The general results rely on two main objects: a
+complexity function, and a sparsity equation, that depend on the specific
+setting in hand (loss $\ell$ and norm $\|\cdot\|$).
+As a proof of concept, we obtain minimax rates of convergence in the
+following problems: 1) matrix completion with any Lipschitz loss function,
+including the hinge and logistic loss for the so-called 1-bit matrix completion
+instance of the problem, and quantile losses for the general case, which
+enables to estimate any quantile on the entries of the matrix; 2) logistic
+LASSO and variants such as the logistic SLOPE; 3) kernel methods, where the
+loss is the hinge loss, and the regularization function is the RKHS norm.
+",0,0,1,1,0,0
+5780,On closed Lie ideals of certain tensor products of $C^*$-algebras,"  For a simple $C^*$-algebra $A$ and any other $C^*$-algebra $B$, it is proved
+that every closed ideal of $A \otimes^{\min} B$ is a product ideal if either
+$A$ is exact or $B$ is nuclear. Closed commutator of a closed ideal in a Banach
+algebra whose every closed ideal possesses a quasi-central approximate identity
+is described in terms of the commutator of the Banach algebra. If $\alpha$ is
+either the Haagerup norm, the operator space projective norm or the
+$C^*$-minimal norm, then this allows us to identify all closed Lie ideals of $A
+\otimes^{\alpha} B$, where $A$ and $B$ are simple, unital $C^*$-algebras with
+one of them admitting no tracial functionals, and to deduce that every
+non-central closed Lie ideal of $B(H) \otimes^{\alpha} B(H)$ contains the
+product ideal $K(H) \otimes^{\alpha} K(H)$. Closed Lie ideals of $A
+\otimes^{\min} C(X)$ are also determined, $A$ being any simple unital
+$C^*$-algebra with at most one tracial state and $X$ any compact Hausdorff
+space. And, it is shown that closed Lie ideals of $A \otimes^{\alpha} K(H)$ are
+precisely the product ideals, where $A$ is any unital $C^*$-algebra and
+$\alpha$ any completely positive uniform tensor norm.
+",0,0,1,0,0,0
+5781,On the Chemistry of the Young Massive Protostellar core NGC 2264 CMM3,"  We present the first gas-grain astrochemical model of the NGC 2264 CMM3
+protostellar core. The chemical evolution of the core is affected by changing
+its physical parameters such as the total density and the amount of
+gas-depletion onto grain surfaces as well as the cosmic ray ionisation rate,
+$\zeta$. We estimated $\zeta_{\text {CMM3}}$ = 1.6 $\times$ 10$^{-17}$
+s$^{-1}$. This value is 1.3 times higher than the standard CR ionisation rate,
+$\zeta_{\text {ISM}}$ = 1.3 $\times$ 10$^{-17}$ s$^{-1}$. Species response
+differently to changes into the core physical conditions, but they are more
+sensitive to changes in the depletion percentage and CR ionisation rate than to
+variations in the core density. Gas-phase models highlighted the importance of
+surface reactions as factories of large molecules and showed that for sulphur
+bearing species depletion is important to reproduce observations.
+Comparing the results of the reference model with the most recent millimeter
+observations of the NGC 2264 CMM3 core showed that our model is capable of
+reproducing the observed abundances of most of the species during early stages
+($\le$ 3$\times$10$^4$ yrs) of their chemical evolution. Models with variations
+in the core density between 1 - 20 $\times$ 10$^6$ cm$^{-3}$ are also in good
+agreement with observations during the early time interval 1 $\times$ 10$^4 <$
+t (yr) $<$ 5 $\times$ 10$^4$. In addition, models with higher CR ionisation
+rates (5 - 10) $\times \zeta_{\text {ISM}}$ are often overestimating the
+fractional abundances of the species. However, models with $\zeta_{\text
+{CMM3}}$ = 5 $\zeta_{\text {ISM}}$ may best fit observations at times $\sim$ 2
+$\times$ 10$^4$ yrs. Our results suggest that CMM3 is (1 - 5) $\times$ 10$^4$
+yrs old. Therefore, the core is chemically young and it may host a Class 0
+object as suggested by previous studies.
+",0,1,0,0,0,0
+5782,Uniform Consistency in Stochastic Block Model with Continuous Community Label,"  \cite{bickel2009nonparametric} developed a general framework to establish
+consistency of community detection in stochastic block model (SBM). In most
+applications of this framework, the community label is discrete. For example,
+in \citep{bickel2009nonparametric,zhao2012consistency} the degree corrected SBM
+is assumed to have a discrete degree parameter. In this paper, we generalize
+the method of \cite{bickel2009nonparametric} to give consistency analysis of
+maximum likelihood estimator (MLE) in SBM with continuous community label. We
+show that there is a standard procedure to transform the $||\cdot||_2$ error
+bound to the uniform error bound. We demonstrate the application of our general
+results by proving the uniform consistency (strong consistency) of the MLE in
+the exponential network model with interaction effect. Unfortunately, in the
+continuous parameter case, the condition ensuring uniform consistency we
+obtained is much stronger than that in the discrete parameter case, namely
+$n\mu_n^5/(\log n)^{8}\rightarrow\infty$ versus $n\mu_n/\log
+n\rightarrow\infty$. Where $n\mu_n$ represents the average degree of the
+network. But continuous is the limit of discrete. So it is not surprising as we
+show that by discretizing the community label space into sufficiently small
+(but not too small) pieces and applying the MLE on the discretized community
+label space, uniform consistency holds under almost the same condition as in
+discrete community label space. Such a phenomenon is surprising since the
+discretization does not depend on the data or the model. This reminds us of the
+thresholding method.
+",0,0,0,1,0,0
+5783,Fine-scale population structure analysis in Armadillidium vulgare (Isopoda: Oniscidea) reveals strong female philopatry,"  In the last decades, dispersal studies have benefitted from the use of
+molecular markers for detecting patterns differing between categories of
+individuals, and have highlighted sex-biased dispersal in several species. To
+explain this phenomenon, sex-related handicaps such as parental care have been
+recently proposed as a hypothesis. Herein we tested this hypothesis in
+Armadillidium vulgare, a terrestrial isopod in which females bear the totality
+of the high parental care costs. We performed a fine-scale analysis of
+sex-specific dispersal patterns, using males and females originating from five
+sampling points located within 70 meters of each other. Based on microsatellite
+markers and both F-statistics and spatial autocorrelation analyses, our results
+revealed that while males did not present a significant structure at this
+geographic scale, females were significantly more similar to each other when
+they were collected in the same sampling point. These results support the
+sex-handicap hypothesis, and we suggest that widening dispersal studies to
+other isopods or crustaceans, displaying varying levels of parental care but
+differing in their ecology or mating system, might shed light on the processes
+underlying the evolution of sex-biased dispersal.
+",0,0,0,0,1,0
+5784,Fast transforms over finite fields of characteristic two,"  An additive fast Fourier transform over a finite field of characteristic two
+efficiently evaluates polynomials at every element of an $\mathbb{F}_2$-linear
+subspace of the field. We view these transforms as performing a change of basis
+from the monomial basis to the associated Lagrange basis, and consider the
+problem of performing the various conversions between these two bases, the
+associated Newton basis, and the '' novel '' basis of Lin, Chung and Han (FOCS
+2014). Existing algorithms are divided between two families, those designed for
+arbitrary subspaces and more efficient algorithms designed for specially
+constructed subspaces of fields with degree equal to a power of two. We
+generalise techniques from both families to provide new conversion algorithms
+that may be applied to arbitrary subspaces, but which benefit equally from the
+specially constructed subspaces. We then construct subspaces of fields with
+smooth degree for which our algorithms provide better performance than existing
+algorithms.
+",1,0,0,0,0,0
+5785,Universality of group embeddability,"  Working in the framework of Borel reducibility, we study various notions of
+embeddability between groups. We prove that the embeddability between countable
+groups, the topological embeddability between (discrete) Polish groups, and the
+isometric embeddability between separable groups with a bounded bi-invariant
+complete metric are all invariantly universal analytic quasi-orders. This
+strengthens some results from [Wil14] and [FLR09].
+",0,0,1,0,0,0
+5786,Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency,"  In order to fully function in human environments, robot perception will need
+to account for the uncertainty caused by translucent materials. Translucency
+poses several open challenges in the form of transparent objects (e.g.,
+drinking glasses), refractive media (e.g., water), and diffuse partial
+occlusions (e.g., objects behind stained glass panels). This paper presents
+Plenoptic Monte Carlo Localization (PMCL) as a method for localizing object
+poses in the presence of translucency using plenoptic (light-field)
+observations. We propose a new depth descriptor, the Depth Likelihood Volume
+(DLV), and its use within a Monte Carlo object localization algorithm. We
+present results of localizing and manipulating objects with translucent
+materials and objects occluded by layers of translucency. Our PMCL
+implementation uses observations from a Lytro first generation light field
+camera to allow a Michigan Progress Fetch robot to perform grasping.
+",1,0,0,0,0,0
+5787,"CUR Decompositions, Similarity Matrices, and Subspace Clustering","  A general framework for solving the subspace clustering problem using the CUR
+decomposition is presented. The CUR decomposition provides a natural way to
+construct similarity matrices for data that come from a union of unknown
+subspaces $\mathscr{U}=\underset{i=1}{\overset{M}\bigcup}S_i$. The similarity
+matrices thus constructed give the exact clustering in the noise-free case.
+Additionally, this decomposition gives rise to many distinct similarity
+matrices from a given set of data, which allow enough flexibility to perform
+accurate clustering of noisy data. We also show that two known methods for
+subspace clustering can be derived from the CUR decomposition. An algorithm
+based on the theoretical construction of similarity matrices is presented, and
+experiments on synthetic and real data are presented to test the method.
+Additionally, an adaptation of our CUR based similarity matrices is utilized
+to provide a heuristic algorithm for subspace clustering; this algorithm yields
+the best overall performance to date for clustering the Hopkins155 motion
+segmentation dataset.
+",1,0,0,1,0,0
+5788,Approximating Geometric Knapsack via L-packings,"  We study the two-dimensional geometric knapsack problem (2DK) in which we are
+given a set of n axis-aligned rectangular items, each one with an associated
+profit, and an axis-aligned square knapsack. The goal is to find a
+(non-overlapping) packing of a maximum profit subset of items inside the
+knapsack (without rotating items). The best-known polynomial-time approximation
+factor for this problem (even just in the cardinality case) is (2 + \epsilon)
+[Jansen and Zhang, SODA 2004].
+In this paper, we break the 2 approximation barrier, achieving a
+polynomial-time (17/9 + \epsilon) < 1.89 approximation, which improves to
+(558/325 + \epsilon) < 1.72 in the cardinality case. Essentially all prior work
+on 2DK approximation packs items inside a constant number of rectangular
+containers, where items inside each container are packed using a simple greedy
+strategy. We deviate for the first time from this setting: we show that there
+exists a large profit solution where items are packed inside a constant number
+of containers plus one L-shaped region at the boundary of the knapsack which
+contains items that are high and narrow and items that are wide and thin. As a
+second major and the main algorithmic contribution of this paper, we present a
+PTAS for this case. We believe that this will turn out to be useful in future
+work in geometric packing problems.
+We also consider the variant of the problem with rotations (2DKR), where
+items can be rotated by 90 degrees. Also, in this case, the best-known
+polynomial-time approximation factor (even for the cardinality case) is (2 +
+\epsilon) [Jansen and Zhang, SODA 2004]. Exploiting part of the machinery
+developed for 2DK plus a few additional ideas, we obtain a polynomial-time (3/2
++ \epsilon)-approximation for 2DKR, which improves to (4/3 + \epsilon) in the
+cardinality case.
+",1,0,0,0,0,0
+5789,Impact and mitigation strategy for future solar flares,"  It is widely established that extreme space weather events associated with
+solar flares are capable of causing widespread technological damage. We develop
+a simple mathematical model to assess the economic losses arising from these
+phenomena over time. We demonstrate that the economic damage is characterized
+by an initial period of power-law growth, followed by exponential amplification
+and eventual saturation. We outline a mitigation strategy to protect our planet
+by setting up a magnetic shield to deflect charged particles at the Lagrange
+point L$_1$, and demonstrate that this approach appears to be realizable in
+terms of its basic physical parameters. We conclude our analysis by arguing
+that shielding strategies adopted by advanced civilizations will lead to
+technosignatures that are detectable by upcoming missions.
+",0,1,0,0,0,0
+5790,Empirical Bayes Matrix Completion,"  We develop an empirical Bayes (EB) algorithm for the matrix completion
+problems. The EB algorithm is motivated from the singular value shrinkage
+estimator for matrix means by Efron and Morris (1972). Since the EB algorithm
+is essentially the EM algorithm applied to a simple model, it does not require
+heuristic parameter tuning other than tolerance. Numerical results demonstrated
+that the EB algorithm achieves a good trade-off between accuracy and efficiency
+compared to existing algorithms and that it works particularly well when the
+difference between the number of rows and columns is large. Application to real
+data also shows the practical utility of the EB algorithm.
+",0,0,1,1,0,0
+5791,Excitonic Instability and Pseudogap Formation in Nodal Line Semimetal ZrSiS,"  Electron correlation effects are studied in ZrSiS using a combination of
+first-principles and model approaches. We show that basic electronic properties
+of ZrSiS can be described within a two-dimensional lattice model of two nested
+square lattices. High degree of electron-hole symmetry characteristic for ZrSiS
+is one of the key features of this model. Having determined model parameters
+from first-principles calculations, we then explicitly take electron-electron
+interactions into account and show that at moderately low temperatures ZrSiS
+exhibits excitonic instability, leading to the formation of a pseudogap in the
+electronic spectrum. The results can be understood in terms of
+Coulomb-interaction-assisted pairing of electrons and holes reminiscent to that
+of an excitonic insulator. Our finding allows us to provide a physical
+interpretation to the unusual mass enhancement of charge carriers in ZrSiS
+recently observed experimentally.
+",0,1,0,0,0,0
+5792,The Riemannian Geometry of Deep Generative Models,"  Deep generative models learn a mapping from a low dimensional latent space to
+a high-dimensional data space. Under certain regularity conditions, these
+models parameterize nonlinear manifolds in the data space. In this paper, we
+investigate the Riemannian geometry of these generated manifolds. First, we
+develop efficient algorithms for computing geodesic curves, which provide an
+intrinsic notion of distance between points on the manifold. Second, we develop
+an algorithm for parallel translation of a tangent vector along a path on the
+manifold. We show how parallel translation can be used to generate analogies,
+i.e., to transport a change in one data point into a semantically similar
+change of another data point. Our experiments on real image data show that the
+manifolds learned by deep generative models, while nonlinear, are surprisingly
+close to zero curvature. The practical implication is that linear paths in the
+latent space closely approximate geodesics on the generated manifold. However,
+further investigation into this phenomenon is warranted, to identify if there
+are other architectures or datasets where curvature plays a more prominent
+role. We believe that exploring the Riemannian geometry of deep generative
+models, using the tools developed in this paper, will be an important step in
+understanding the high-dimensional, nonlinear spaces these models learn.
+",1,0,0,1,0,0
+5793,"Portable, high-performance containers for HPC","  Building and deploying software on high-end computing systems is a
+challenging task. High performance applications have to reliably run across
+multiple platforms and environments, and make use of site-specific resources
+while resolving complicated software-stack dependencies. Containers are a type
+of lightweight virtualization technology that attempt to solve this problem by
+packaging applications and their environments into standard units of software
+that are: portable, easy to build and deploy, have a small footprint, and low
+runtime overhead. In this work we present an extension to the container runtime
+of Shifter that provides containerized applications with a mechanism to access
+GPU accelerators and specialized networking from the host system, effectively
+enabling performance portability of containers across HPC resources. The
+presented extension makes possible to rapidly deploy high-performance software
+on supercomputers from containerized applications that have been developed,
+built, and tested in non-HPC commodity hardware, e.g. the laptop or workstation
+of a researcher.
+",1,0,0,0,0,0
+5794,Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution,"  Adversarially trained deep neural networks have significantly improved
+performance of single image super resolution, by hallucinating photorealistic
+local textures, thereby greatly reducing the perception difference between a
+real high resolution image and its super resolved (SR) counterpart. However,
+application to medical imaging requires preservation of diagnostically relevant
+features while refraining from introducing any diagnostically confusing
+artifacts. We propose using a deep convolutional super resolution network
+(SRNet) trained for (i) minimising reconstruction loss between the real and SR
+images, and (ii) maximally confusing learned relativistic visual Turing test
+(rVTT) networks to discriminate between (a) pair of real and SR images (T1) and
+(b) pair of patches in real and SR selected from region of interest (T2). The
+adversarial loss of T1 and T2 while backpropagated through SRNet helps it learn
+to reconstruct pathorealism in the regions of interest such as white blood
+cells (WBC) in peripheral blood smears or epithelial cells in histopathology of
+cancerous biopsy tissues, which are experimentally demonstrated here.
+Experiments performed for measuring signal distortion loss using peak signal to
+noise ratio (pSNR) and structural similarity (SSIM) with variation of SR scale
+factors, impact of rVTT adversarial losses, and impact on reporting using SR on
+a commercially available artificial intelligence (AI) digital pathology system
+substantiate our claims.
+",1,0,0,0,0,0
+5795,Quantum dynamics of a hydrogen-like atom in a time-dependent box: non-adiabatic regime,"  We consider a hydrogen atom confined in time-dependent trap created by a
+spherical impenetrable box with time-dependent radius. For such model we study
+the behavior of atomic electron under the (non-adiabatic) dynamical confinement
+caused by the rapidly moving wall of the box. The expectation values of the
+total and kinetic energy, average force, pressure and coordinate are analyzed
+as a function of time for linearly expanding, contracting and harmonically
+breathing boxes. It is shown that linearly extending box leads to de-excitation
+of the atom, while the rapidly contracting box causes the creation of very high
+pressure on the atom and transition of the atomic electron into the unbound
+state. In harmonically breathing box diffusive excitation of atomic electron
+may occur in analogy with that for atom in a microwave field.
+",0,1,0,0,0,0
+5796,Richardson's solutions in the real- and complex-energy spectrum,"  The constant pairing Hamiltonian holds exact solutions worked out by
+Richardson in the early Sixties. This exact solution of the pairing Hamiltonian
+regained interest at the end of the Nineties. The discret complex-energy states
+had been included in the Richardson's solutions by Hasegawa et al. [1]. In this
+contribution we reformulate the problem of determining the exact eigenenergies
+of the pairing Hamiltonian when the continuum is included through the single
+particle level density. The solutions with discret complex-energy states is
+recovered by analytic continuation of the equations to the complex energy
+plane. This formulation may be applied to loosely bound system where the
+correlations with the continuum-spectrum of energy is really important. Some
+details are given to show how the many-body eigenenergy emerges as sum of the
+pair-energies.
+",0,1,0,0,0,0
+5797,Complexity Results for MCMC derived from Quantitative Bounds,"  This paper considers how to obtain MCMC quantitative convergence bounds which
+can be translated into tight complexity bounds in high-dimensional setting. We
+propose a modified drift-and-minorization approach, which establishes a
+generalized drift condition defined in a subset of the state space. The subset
+is called the ""large set"", and is chosen to rule out some ""bad"" states which
+have poor drift property when the dimension gets large. Using the ""large set""
+together with a ""centered"" drift function, a quantitative bound can be obtained
+which can be translated into a tight complexity bound. As a demonstration, we
+analyze a certain realistic Gibbs sampler algorithm and obtain a complexity
+upper bound for the mixing time, which shows that the number of iterations
+required for the Gibbs sampler to converge is constant. It is our hope that
+this modified drift-and-minorization approach can be employed in many other
+specific examples to obtain complexity bounds for high-dimensional Markov
+chains.
+",0,0,0,1,0,0
+5798,Symmetries and regularity for holomorphic maps between balls,"  Let $f:{\mathbb B}^n \to {\mathbb B}^N$ be a holomorphic map. We study
+subgroups $\Gamma_f \subseteq {\rm Aut}({\mathbb B}^n)$ and $T_f \subseteq {\rm
+Aut}({\mathbb B}^N)$. When $f$ is proper, we show both these groups are Lie
+subgroups. When $\Gamma_f$ contains the center of ${\bf U}(n)$, we show that
+$f$ is spherically equivalent to a polynomial. When $f$ is minimal we show that
+there is a homomorphism $\Phi:\Gamma_f \to T_f$ such that $f$ is equivariant
+with respect to $\Phi$. To do so, we characterize minimality via the triviality
+of a third group $H_f$. We relate properties of ${\rm Ker}(\Phi)$ to older
+results on invariant proper maps between balls. When $f$ is proper but
+completely non-rational, we show that either both $\Gamma_f$ and $T_f$ are
+finite or both are noncompact.
+",0,0,1,0,0,0
+5799,On the treatment of $\ell$-changing proton-hydrogen Rydberg atom collisions,"  Energy-conserving, angular momentum-changing collisions between protons and
+highly excited Rydberg hydrogen atoms are important for precise understanding
+of atomic recombination at the photon decoupling era, and the elemental
+abundance after primordial nucleosynthesis. Early approaches to $\ell$-changing
+collisions used perturbation theory for only dipole-allowed ($\Delta \ell=\pm
+1$) transitions. An exact non-perturbative quantum mechanical treatment is
+possible, but it comes at computational cost for highly excited Rydberg states.
+In this note we show how to obtain a semi-classical limit that is accurate and
+simple, and develop further physical insights afforded by the non-perturbative
+quantum mechanical treatment.
+",0,1,0,0,0,0
+5800,Complex Economic Activities Concentrate in Large Cities,"  Why do some economic activities agglomerate more than others? And, why does
+the agglomeration of some economic activities continue to increase despite
+recent developments in communication and transportation technologies? In this
+paper, we present evidence that complex economic activities concentrate more in
+large cities. We find this to be true for technologies, scientific
+publications, industries, and occupations. Using historical patent data, we
+show that the urban concentration of complex economic activities has been
+continuously increasing since 1850. These findings suggest that the increasing
+urban concentration of jobs and innovation might be a consequence of the
+growing complexity of the economy.
+",1,0,0,0,0,0
+5801,A 588 Gbps LDPC Decoder Based on Finite-Alphabet Message Passing,"  An ultra-high throughput low-density parity check (LDPC) decoder with an
+unrolled full-parallel architecture is proposed, which achieves the highest
+decoding throughput compared to previously reported LDPC decoders in the
+literature. The decoder benefits from a serial message-transfer approach
+between the decoding stages to alleviate the well-known routing congestion
+problem in parallel LDPC decoders. Furthermore, a finite-alphabet message
+passing algorithm is employed to replace the variable node update rule of the
+standard min-sum decoder with look-up tables, which are designed in a way that
+maximizes the mutual information between decoding messages. The proposed
+algorithm results in an architecture with reduced bit-width messages, leading
+to a significantly higher decoding throughput and to a lower area as compared
+to a min-sum decoder when serial message-transfer is used. The architecture is
+placed and routed for the standard min-sum reference decoder and for the
+proposed finite-alphabet decoder using a custom pseudo-hierarchical backend
+design strategy to further alleviate routing congestions and to handle the
+large design. Post-layout results show that the finite-alphabet decoder with
+the serial message-transfer architecture achieves a throughput as large as 588
+Gbps with an area of 16.2 mm$^2$ and dissipates an average power of 22.7 pJ per
+decoded bit in a 28 nm FD-SOI library. Compared to the reference min-sum
+decoder, this corresponds to 3.1 times smaller area and 2 times better energy
+efficiency.
+",1,0,0,0,0,0
+5802,On q-analogues of quadratic Euler sums,"  In this paper we define the generalized q-analogues of Euler sums and present
+a new family of identities for q-analogues of Euler sums by using the method of
+Jackson q-integral rep- resentations of series. We then apply it to obtain a
+family of identities relating quadratic Euler sums to linear sums and
+q-polylogarithms. Furthermore, we also use certain stuffle products to evaluate
+several q-series with q-harmonic numbers. Some interesting new results and
+illustrative examples are considered. Finally, we can obtain some explicit
+relations for the classical Euler sums when q approaches to 1.
+",0,0,1,0,0,0
+5803,An iterative aggregation and disaggregation approach to the calculation of steady-state distributions of continuous processes,"  A mapping of the process on a continuous configuration space to the symbolic
+representation of the motion on a discrete state space will be combined with an
+iterative aggregation and disaggregation (IAD) procedure to obtain steady state
+distributions of the process. The IAD speeds up the convergence to the unit
+eigenvector, which is the steady state distribution, by forming smaller
+aggregated matrices whose unit eigenvector solutions are used to refine
+approximations of the steady state vector until convergence is reached. This
+method works very efficiently and can be used together with distributed or
+parallel computing methods to obtain high resolution images of the steady state
+distribution of complex atomistic or energy landscape type problems. The method
+is illustrated in two numerical examples. In the first example the transition
+matrix is assumed to be known. The second example represents an overdamped
+Brownian motion process subject to a dichotomously changing external potential.
+",0,1,0,0,0,0
+5804,Reynolds number dependence of the structure functions in homogeneous turbulence,"  We compare the predictions of stochastic closure theory (SCT) with
+experimental measurements of homogeneous turbulence made in the Variable
+Density Turbulence Tunnel (VDTT) at the Max Planck Institute for Dynamics and
+Self-Organization in Gottingen. While the general form of SCT contains
+infinitely many free parameters, the data permit us to reduce the number to
+seven, only three of which are active over the entire inertial range. Of these
+three, one parameter characterizes the variance of the mean field noise in SCT
+and another characterizes the rate in the large deviations of the mean. The
+third parameter is the decay exponent of the Fourier variables in the Fourier
+expansion of the noise, which characterizes the smoothness of the turbulent
+velocity. SCT compares favorably with velocity structure functions measured in
+the experiment. We considered even-order structure functions ranging in order
+from two to eight as well as the third-order structure functions at five
+Taylor-Reynolds numbers (Rl) between 110 and 1450. The comparisons highlight
+several advantages of the SCT, which include explicit predictions for the
+structure functions at any scale and for any Reynolds number. We observed that
+finite-Rl corrections, for instance, are important even at the highest Reynolds
+numbers produced in the experiments. SCT gives us the correct basis function to
+express all the moments of the velocity differences in turbulence in Fourier
+space. The SCT produces the coefficients of the series and so determines the
+statistical quantities that characterize the small scales in turbulence. It
+also characterizes the random force acting on the fluid in the stochastic
+Navier-Stokes equation, as described in the paper.
+",0,1,0,0,0,0
+5805,Power and Energy-efficiency Roofline Model for GPUs,"  Energy consumption has been a great deal of concern in recent years and
+developers need to take energy-efficiency into account when they design
+algorithms. Their design needs to be energy-efficient and low-power while it
+tries to achieve attainable performance provided by underlying hardware.
+However, different optimization techniques have different effects on power and
+energy-efficiency and a visual model would assist in the selection process.
+In this paper, we extended the roofline model and provided a visual
+representation of optimization strategies for power consumption. Our model is
+composed of various ceilings regarding each strategy we included in our models.
+One roofline model for computational performance and one for memory performance
+is introduced. We assembled our models based on some optimization strategies
+for two widespread GPUs from NVIDIA: Geforce GTX 970 and Tesla K80.
+",1,0,0,0,0,0
+5806,Equivalence of estimates on domain and its boundary,"  Let $\Omega$ be a pseudoconvex domain in $\mathbb C^n$ with smooth boundary
+$b\Omega$. We define general estimates $(f\text{-}\mathcal M)^k_{\Omega}$ and
+$(f\text{-}\mathcal M)^k_{b\Omega}$ on $k$-forms for the complex Laplacian
+$\Box$ on $\Omega$ and the Kohn-Laplacian $\Box_b$ on $b\Omega$. For $1\le k\le
+n-2$, we show that $(f\text{-}\mathcal M)^k_{b\Omega}$ holds if and only if
+$(f\text{-}\mathcal M)^k_{\Omega}$ and $(f\text{-}\mathcal M)^{n-k-1}_{\Omega}$
+hold. Our proof relies on Kohn's method in [Ann. of Math. (2), 156(1):213--248,
+2002].
+",0,0,1,0,0,0
+5807,Waveform and Spectrum Management for Unmanned Aerial Systems Beyond 2025,"  The application domains of civilian unmanned aerial systems (UASs) include
+agriculture, exploration, transportation, and entertainment. The expected
+growth of the UAS industry brings along new challenges: Unmanned aerial vehicle
+(UAV) flight control signaling requires low throughput, but extremely high
+reliability, whereas the data rate for payload data can be significant. This
+paper develops UAV number projections and concludes that small and micro UAVs
+will dominate the US airspace with accelerated growth between 2028 and 2032. We
+analyze the orthogonal frequency division multiplexing (OFDM) waveform because
+it can provide the much needed flexibility, spectral efficiency, and,
+potentially, reliability and derive suitable OFDM waveform parameters as a
+function of UAV flight characteristics. OFDM also lends itself to agile
+spectrum access. Based on our UAV growth predictions, we conclude that dynamic
+spectrum access is needed and discuss the applicability of spectrum sharing
+techniques for future UAS communications.
+",1,0,0,0,0,0
+5808,Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election,"  Opinion polls have been the bridge between public opinion and politicians in
+elections. However, developing surveys to disclose people's feedback with
+respect to economic issues is limited, expensive, and time-consuming. In recent
+years, social media such as Twitter has enabled people to share their opinions
+regarding elections. Social media has provided a platform for collecting a
+large amount of social media data. This paper proposes a computational public
+opinion mining approach to explore the discussion of economic issues in social
+media during an election. Current related studies use text mining methods
+independently for election analysis and election prediction; this research
+combines two text mining methods: sentiment analysis and topic modeling. The
+proposed approach has effectively been deployed on millions of tweets to
+analyze economic concerns of people during the 2012 US presidential election.
+",1,0,0,1,0,0
+5809,The igus Humanoid Open Platform: A Child-sized 3D Printed Open-Source Robot for Research,"  The use of standard robotic platforms can accelerate research and lower the
+entry barrier for new research groups. There exist many affordable humanoid
+standard platforms in the lower size ranges of up to 60cm, but larger humanoid
+robots quickly become less affordable and more difficult to operate, maintain
+and modify. The igus Humanoid Open Platform is a new and affordable, fully
+open-source humanoid platform. At 92cm in height, the robot is capable of
+interacting in an environment meant for humans, and is equipped with enough
+sensors, actuators and computing power to support researchers in many fields.
+The structure of the robot is entirely 3D printed, leading to a lightweight and
+visually appealing design. The main features of the platform are described in
+this article.
+",1,0,0,0,0,0
+5810,Sports stars: analyzing the performance of astronomers at visualization-based discovery,"  In this data-rich era of astronomy, there is a growing reliance on automated
+techniques to discover new knowledge. The role of the astronomer may change
+from being a discoverer to being a confirmer. But what do astronomers actually
+look at when they distinguish between ""sources"" and ""noise?"" What are the
+differences between novice and expert astronomers when it comes to visual-based
+discovery? Can we identify elite talent or coach astronomers to maximize their
+potential for discovery? By looking to the field of sports performance
+analysis, we consider an established, domain-wide approach, where the expertise
+of the viewer (i.e. a member of the coaching team) plays a crucial role in
+identifying and determining the subtle features of gameplay that provide a
+winning advantage. As an initial case study, we investigate whether the
+SportsCode performance analysis software can be used to understand and document
+how an experienced HI astronomer makes discoveries in spectral data cubes. We
+find that the process of timeline-based coding can be applied to spectral cube
+data by mapping spectral channels to frames within a movie. SportsCode provides
+a range of easy to use methods for annotation, including feature-based codes
+and labels, text annotations associated with codes, and image-based drawing.
+The outputs, including instance movies that are uniquely associated with coded
+events, provide the basis for a training program or team-based analysis that
+could be used in unison with discipline specific analysis software. In this
+coordinated approach to visualization and analysis, SportsCode can act as a
+visual notebook, recording the insight and decisions in partnership with
+established analysis methods. Alternatively, in situ annotation and coding of
+features would be a valuable addition to existing and future visualisation and
+analysis packages.
+",0,1,0,0,0,0
+5811,Chimera states in complex networks: interplay of fractal topology and delay,"  Chimera states are an example of intriguing partial synchronization patterns
+emerging in networks of identical oscillators. They consist of spatially
+coexisting domains of coherent (synchronized) and incoherent (desynchronized)
+dynamics. We analyze chimera states in networks of Van der Pol oscillators with
+hierarchical connectivities, and elaborate the role of time delay introduced in
+the coupling term. In the parameter plane of coupling strength and delay time
+we find tongue-like regions of existence of chimera states alternating with
+regions of existence of coherent travelling waves. We demonstrate that by
+varying the time delay one can deliberately stabilize desired spatio-temporal
+patterns in the system.
+",0,1,0,0,0,0
+5812,Dense 3D Facial Reconstruction from a Single Depth Image in Unconstrained Environment,"  With the increasing demands of applications in virtual reality such as 3D
+films, virtual Human-Machine Interactions and virtual agents, the analysis of
+3D human face analysis is considered to be more and more important as a
+fundamental step for those virtual reality tasks. Due to information provided
+by an additional dimension, 3D facial reconstruction enables aforementioned
+tasks to be achieved with higher accuracy than those based on 2D facial
+analysis. The denser the 3D facial model is, the more information it could
+provide. However, most existing dense 3D facial reconstruction methods require
+complicated processing and high system cost. To this end, this paper presents a
+novel method that simplifies the process of dense 3D facial reconstruction by
+employing only one frame of depth data obtained with an off-the-shelf RGB-D
+sensor. The experiments showed competitive results with real world data.
+",1,0,0,0,0,0
+5813,Concentration phenomena for a fractional Schrödinger-Kirchhoff type equation,"  In this paper we deal with the multiplicity and concentration of positive
+solutions for the following fractional Schrödinger-Kirchhoff type equation
+\begin{equation*} M\left(\frac{1}{\varepsilon^{3-2s}}
+\iint_{\mathbb{R}^{6}}\frac{|u(x)- u(y)|^{2}}{|x-y|^{3+2s}} dxdy +
+\frac{1}{\varepsilon^{3}} \int_{\mathbb{R}^{3}} V(x)u^{2}
+dx\right)[\varepsilon^{2s} (-\Delta)^{s}u+ V(x)u]= f(u) \, \mbox{in}
+\mathbb{R}^{3} \end{equation*} where $\varepsilon>0$ is a small parameter,
+$s\in (\frac{3}{4}, 1)$, $(-\Delta)^{s}$ is the fractional Laplacian, $M$ is a
+Kirchhoff function, $V$ is a continuous positive potential and $f$ is a
+superlinear continuous function with subcritical growth. By using penalization
+techniques and Ljusternik-Schnirelmann theory, we investigate the relation
+between the number of positive solutions with the topology of the set where the
+potential attains its minimum.
+",0,0,1,0,0,0
+5814,Inferactive data analysis,"  We describe inferactive data analysis, so-named to denote an interactive
+approach to data analysis with an emphasis on inference after data analysis.
+Our approach is a compromise between Tukey's exploratory (roughly speaking
+""model free"") and confirmatory data analysis (roughly speaking classical and
+""model based""), also allowing for Bayesian data analysis. We view this approach
+as close in spirit to current practice of applied statisticians and data
+scientists while allowing frequentist guarantees for results to be reported in
+the scientific literature, or Bayesian results where the data scientist may
+choose the statistical model (and hence the prior) after some initial
+exploratory analysis. While this approach to data analysis does not cover every
+scenario, and every possible algorithm data scientists may use, we see this as
+a useful step in concrete providing tools (with frequentist statistical
+guarantees) for current data scientists. The basis of inference we use is
+selective inference [Lee et al., 2016, Fithian et al., 2014], in particular its
+randomized form [Tian and Taylor, 2015a]. The randomized framework, besides
+providing additional power and shorter confidence intervals, also provides
+explicit forms for relevant reference distributions (up to normalization)
+through the {\em selective sampler} of Tian et al. [2016]. The reference
+distributions are constructed from a particular conditional distribution formed
+from what we call a DAG-DAG -- a Data Analysis Generative DAG. As sampling
+conditional distributions in DAGs is generally complex, the selective sampler
+is crucial to any practical implementation of inferactive data analysis. Our
+principal goal is in reviewing the recent developments in selective inference
+as well as describing the general philosophy of selective inference.
+",0,0,1,1,0,0
+5815,Promising Accurate Prefix Boosting for sequence-to-sequence ASR,"  In this paper, we present promising accurate prefix boosting (PAPB), a
+discriminative training technique for attention based sequence-to-sequence
+(seq2seq) ASR. PAPB is devised to unify the training and testing scheme in an
+effective manner. The training procedure involves maximizing the score of each
+partial correct sequence obtained during beam search compared to other
+hypotheses. The training objective also includes minimization of token
+(character) error rate. PAPB shows its efficacy by achieving 10.8\% and 3.8\%
+WER with and without RNNLM respectively on Wall Street Journal dataset.
+",1,0,0,0,0,0
+5816,Tunable GMM Kernels,"  The recently proposed ""generalized min-max"" (GMM) kernel can be efficiently
+linearized, with direct applications in large-scale statistical learning and
+fast near neighbor search. The linearized GMM kernel was extensively compared
+in with linearized radial basis function (RBF) kernel. On a large number of
+classification tasks, the tuning-free GMM kernel performs (surprisingly) well
+compared to the best-tuned RBF kernel. Nevertheless, one would naturally expect
+that the GMM kernel ought to be further improved if we introduce tuning
+parameters.
+In this paper, we study three simple constructions of tunable GMM kernels:
+(i) the exponentiated-GMM (or eGMM) kernel, (ii) the powered-GMM (or pGMM)
+kernel, and (iii) the exponentiated-powered-GMM (epGMM) kernel. The pGMM kernel
+can still be efficiently linearized by modifying the original hashing procedure
+for the GMM kernel. On about 60 publicly available classification datasets, we
+verify that the proposed tunable GMM kernels typically improve over the
+original GMM kernel. On some datasets, the improvements can be astonishingly
+significant.
+For example, on 11 popular datasets which were used for testing deep learning
+algorithms and tree methods, our experiments show that the proposed tunable GMM
+kernels are strong competitors to trees and deep nets. The previous studies
+developed tree methods including ""abc-robust-logitboost"" and demonstrated the
+excellent performance on those 11 datasets (and other datasets), by
+establishing the second-order tree-split formula and new derivatives for
+multi-class logistic loss. Compared to tree methods like
+""abc-robust-logitboost"" (which are slow and need substantial model sizes), the
+tunable GMM kernels produce largely comparable results.
+",1,0,0,1,0,0
+5817,Synchrotron radiation induced magnetization in magnetically-doped and pristine topological insulators,"  Quantum mechanics postulates that any measurement influences the state of the
+investigated system. Here, by means of angle-, spin-, and time-resolved
+photoemission experiments and ab initio calculations we demonstrate how
+non-equal depopulation of the Dirac cone (DC) states with opposite momenta in
+V-doped and pristine topological insulators (TIs) created by a photoexcitation
+by linearly polarized synchrotron radiation (SR) is followed by the
+hole-generated uncompensated spin accumulation and the SR-induced magnetization
+via the spin-torque effect. We show that the photoexcitation of the DC is
+asymmetric, that it varies with the photon energy, and that it practically does
+not change during the relaxation. We find a relation between the
+photoexcitation asymmetry, the generated spin accumulation and the induced spin
+polarization of the DC and V 3d states. Experimentally the SR-generated
+in-plane and out-of-plane magnetization is confirmed by the
+$k_{\parallel}$-shift of the DC position and by the splitting of the states at
+the Dirac point even above the Curie temperature. Theoretical predictions and
+estimations of the measurable physical quantities substantiate the experimental
+results.
+",0,1,0,0,0,0
+5818,The linear nature of pseudowords,"  Given a pseudoword over suitable pseudovarieties, we associate to it a
+labeled linear order determined by the factorizations of the pseudoword. We
+show that, in the case of the pseudovariety of aperiodic finite semigroups, the
+pseudoword can be recovered from the labeled linear order.
+",0,0,1,0,0,0
+5819,"Health Care Expenditures, Financial Stability, and Participation in the Supplemental Nutrition Assistance Program (SNAP)","  This paper examines the association between household healthcare expenses and
+participation in the Supplemental Nutrition Assistance Program (SNAP) when
+moderated by factors associated with financial stability of households. Using a
+large longitudinal panel encompassing eight years, this study finds that an
+inter-temporal increase in out-of-pocket medical expenses increased the
+likelihood of household SNAP participation in the current period. Financially
+stable households with precautionary financial assets to cover at least 6
+months worth of household expenses were significantly less likely to
+participate in SNAP. The low income households who recently experienced an
+increase in out of pocket medical expenses but had adequate precautionary
+savings were less likely than similar households who did not have precautionary
+savings to participate in SNAP. Implications for economists, policy makers, and
+household finance professionals are discussed.
+",0,0,0,0,0,1
+5820,Improved estimates for polynomial Roth type theorems in finite fields,"  We prove that, under certain conditions on the function pair $\varphi_1$ and
+$\varphi_2$, bilinear average $p^{-1}\sum_{y\in
+\mathbb{F}_p}f_1(x+\varphi_1(y)) f_2(x+\varphi_2(y))$ along curve $(\varphi_1,
+\varphi_2)$ satisfies certain decay estimate. As a consequence, Roth type
+theorems hold in the setting of finite fields. In particular, if
+$\varphi_1,\varphi_2\in \mathbb{F}_p[X]$ with $\varphi_1(0)=\varphi_2(0)=0$ are
+linearly independent polynomials, then for any $A\subset \mathbb{F}_p,
+|A|=\delta p$ with $\delta>c p^{-\frac{1}{12}}$, there are $\gtrsim
+\delta^3p^2$ triplets $x,x+\varphi_1(y), x+\varphi_2(y)\in A$. This extends a
+recent result of Bourgain and Chang who initiated this type of problems, and
+strengthens the bound in a result of Peluse, who generalized Bourgain and
+Chang's work. The proof uses discrete Fourier analysis and algebraic geometry.
+",0,0,1,0,0,0
+5821,Molecules cooled below the Doppler limit,"  The ability to cool atoms below the Doppler limit -- the minimum temperature
+reachable by Doppler cooling -- has been essential to most experiments with
+quantum degenerate gases, optical lattices and atomic fountains, among many
+other applications. A broad set of new applications await ultracold molecules,
+and the extension of laser cooling to molecules has begun. A molecular
+magneto-optical trap has been demonstrated, where molecules approached the
+Doppler limit. However, the sub-Doppler temperatures required for most
+applications have not yet been reached. Here we cool molecules to 50 uK, well
+below the Doppler limit, using a three-dimensional optical molasses. These
+ultracold molecules could be loaded into optical tweezers to trap arbitrary
+arrays for quantum simulation, launched into a molecular fountain for testing
+fundamental physics, and used to study ultracold collisions and ultracold
+chemistry.
+",0,1,0,0,0,0
+5822,On purely generated $α$-smashing weight structures and weight-exact localizations,"  This paper is dedicated to new methods of constructing weight structures and
+weight-exact localizations; our arguments generalize their bounded versions
+considered in previous papers of the authors. We start from a class of objects
+$P$ of triangulated category $C$ that satisfies a certain negativity condition
+(there are no $C$-extensions of positive degrees between elements of $P$; we
+actually need a somewhat stronger condition of this sort) to obtain a weight
+structure both ""halves"" of which are closed either with respect to
+$C$-coproducts of less than $\alpha$ objects (for $\alpha$ being a fixed
+regular cardinal) or with respect to all coproducts (provided that $C$ is
+closed with respect to coproducts of this sort). This construction gives all
+""reasonable"" weight structures satisfying the latter condition. In particular,
+we obtain certain weight structures on spectra (in $SH$) consisting of less
+than $\alpha$ cells and on certain localizations of $SH$; these results are
+new.
+",0,0,1,0,0,0
+5823,Evaluating Overfit and Underfit in Models of Network Community Structure,"  A common data mining task on networks is community detection, which seeks an
+unsupervised decomposition of a network into structural groups based on
+statistical regularities in the network's connectivity. Although many methods
+exist, the No Free Lunch theorem for community detection implies that each
+makes some kind of tradeoff, and no algorithm can be optimal on all inputs.
+Thus, different algorithms will over or underfit on different inputs, finding
+more, fewer, or just different communities than is optimal, and evaluation
+methods that use a metadata partition as a ground truth will produce misleading
+conclusions about general accuracy. Here, we present a broad evaluation of over
+and underfitting in community detection, comparing the behavior of 16
+state-of-the-art community detection algorithms on a novel and structurally
+diverse corpus of 406 real-world networks. We find that (i) algorithms vary
+widely both in the number of communities they find and in their corresponding
+composition, given the same input, (ii) algorithms can be clustered into
+distinct high-level groups based on similarities of their outputs on real-world
+networks, and (iii) these differences induce wide variation in accuracy on link
+prediction and link description tasks. We introduce a new diagnostic for
+evaluating overfitting and underfitting in practice, and use it to roughly
+divide community detection methods into general and specialized learning
+algorithms. Across methods and inputs, Bayesian techniques based on the
+stochastic block model and a minimum description length approach to
+regularization represent the best general learning approach, but can be
+outperformed under specific circumstances. These results introduce both a
+theoretically principled approach to evaluate over and underfitting in models
+of network community structure and a realistic benchmark by which new methods
+may be evaluated and compared.
+",1,0,0,1,1,0
+5824,Named Entity Evolution Recognition on the Blogosphere,"  Advancements in technology and culture lead to changes in our language. These
+changes create a gap between the language known by users and the language
+stored in digital archives. It affects user's possibility to firstly find
+content and secondly interpret that content. In previous work we introduced our
+approach for Named Entity Evolution Recognition~(NEER) in newspaper
+collections. Lately, increasing efforts in Web preservation lead to increased
+availability of Web archives covering longer time spans. However, language on
+the Web is more dynamic than in traditional media and many of the basic
+assumptions from the newspaper domain do not hold for Web data. In this paper
+we discuss the limitations of existing methodology for NEER. We approach these
+by adapting an existing NEER method to work on noisy data like the Web and the
+Blogosphere in particular. We develop novel filters that reduce the noise and
+make use of Semantic Web resources to obtain more information about terms. Our
+evaluation shows the potentials of the proposed approach.
+",1,0,0,0,0,0
+5825,Truncated Cramér-von Mises test of normality,"  A new test of normality based on a standardised empirical process is
+introduced in this article.
+The first step is to introduce a Cramér-von Mises type statistic with
+weights equal to the inverse of the standard normal density function supported
+on a symmetric interval $[-a_n,a_n]$ depending on the sample size $n.$ The
+sequence of end points $a_n$ tends to infinity, and is chosen so that the
+statistic goes to infinity at the speed of $\ln \ln n.$ After substracting the
+mean, a suitable test statistic is obtained, with the same asymptotic law as
+the well-known Shapiro-Wilk statistic. The performance of the new test is
+described and compared with three other well-known tests of normality, namely,
+Shapiro-Wilk, Anderson-Darling and that of del Barrio-Matrán, Cuesta
+Albertos, and Rodr\'{\i}guez Rodr\'{\i}guez, by means of power calculations
+under many alternative hypotheses.
+",0,0,1,1,0,0
+5826,Bayesian fairness,"  We consider the problem of how decision making can be fair when the
+underlying probabilistic model of the world is not known with certainty. We
+argue that recent notions of fairness in machine learning need to explicitly
+incorporate parameter uncertainty, hence we introduce the notion of {\em
+Bayesian fairness} as a suitable candidate for fair decision rules. Using
+balance, a definition of fairness introduced by Kleinberg et al (2016), we show
+how a Bayesian perspective can lead to well-performing, fair decision rules
+even under high uncertainty.
+",1,0,0,1,0,0
+5827,A boundary integral equation method for mode elimination and vibration confinement in thin plates with clamped points,"  We consider the bi-Laplacian eigenvalue problem for the modes of vibration of
+a thin elastic plate with a discrete set of clamped points. A high-order
+boundary integral equation method is developed for efficient numerical
+determination of these modes in the presence of multiple localized defects for
+a wide range of two-dimensional geometries. The defects result in
+eigenfunctions with a weak singularity that is resolved by decomposing the
+solution as a superposition of Green's functions plus a smooth regular part.
+This method is applied to a variety of regular and irregular domains and two
+key phenomena are observed. First, careful placement of clamping points can
+entirely eliminate particular eigenvalues and suggests a strategy for
+manipulating the vibrational characteristics of rigid bodies so that
+undesirable frequencies are removed. Second, clamping of the plate can result
+in partitioning of the domain so that vibrational modes are largely confined to
+certain spatial regions. This numerical method gives a precision tool for
+tuning the vibrational characteristics of thin elastic plates.
+",0,0,1,0,0,0
+5828,Noether's Problem on Semidirect Product Groups,"  Let $K$ be a field, $G$ a finite group. Let $G$ act on the function field $L
+= K(x_{\sigma} : \sigma \in G)$ by $\tau \cdot x_{\sigma} = x_{\tau\sigma}$ for
+any $\sigma, \tau \in G$. Denote the fixed field of the action by $K(G) = L^{G}
+= \left\{ \frac{f}{g} \in L : \sigma(\frac{f}{g}) = \frac{f}{g}, \forall \sigma
+\in G \right\}$. Noether's problem asks whether $K(G)$ is rational (purely
+transcendental) over $K$. It is known that if $G = C_m \rtimes C_n$ is a
+semidirect product of cyclic groups $C_m$ and $C_n$ with $\mathbb{Z}[\zeta_n]$
+a unique factorization domain, and $K$ contains an $e$th primitive root of
+unity, where $e$ is the exponent of $G$, then $K(G)$ is rational over $K$. In
+this paper, we give another criteria to determine whether $K(C_m \rtimes C_n)$
+is rational over $K$. In particular, if $p, q$ are prime numbers and there
+exists $x \in \mathbb{Z}[\zeta_q]$ such that the norm
+$N_{\mathbb{Q}(\zeta_q)/\mathbb{Q}}(x) = p$, then $\mathbb{C}(C_{p} \rtimes
+C_{q})$ is rational over $\mathbb{C}$.
+",0,0,1,0,0,0
+5829,A note on the uniqueness of models in social abstract argumentation,"  Social abstract argumentation is a principled way to assign values to
+conflicting (weighted) arguments. In this note we discuss the important
+property of the uniqueness of the model.
+",1,0,0,0,0,0
+5830,On Optimal Generalizability in Parametric Learning,"  We consider the parametric learning problem, where the objective of the
+learner is determined by a parametric loss function. Employing empirical risk
+minimization with possibly regularization, the inferred parameter vector will
+be biased toward the training samples. Such bias is measured by the cross
+validation procedure in practice where the data set is partitioned into a
+training set used for training and a validation set, which is not used in
+training and is left to measure the out-of-sample performance. A classical
+cross validation strategy is the leave-one-out cross validation (LOOCV) where
+one sample is left out for validation and training is done on the rest of the
+samples that are presented to the learner, and this process is repeated on all
+of the samples. LOOCV is rarely used in practice due to the high computational
+complexity. In this paper, we first develop a computationally efficient
+approximate LOOCV (ALOOCV) and provide theoretical guarantees for its
+performance. Then we use ALOOCV to provide an optimization algorithm for
+finding the regularizer in the empirical risk minimization framework. In our
+numerical experiments, we illustrate the accuracy and efficiency of ALOOCV as
+well as our proposed framework for the optimization of the regularizer.
+",1,0,0,1,0,0
+5831,A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse,"  This study addresses the problem of identifying the meaning of unknown words
+or entities in a discourse with respect to the word embedding approaches used
+in neural language models. We proposed a method for on-the-fly construction and
+exploitation of word embeddings in both the input and output layers of a neural
+model by tracking contexts. This extends the dynamic entity representation used
+in Kobayashi et al. (2016) and incorporates a copy mechanism proposed
+independently by Gu et al. (2016) and Gulcehre et al. (2016). In addition, we
+construct a new task and dataset called Anonymized Language Modeling for
+evaluating the ability to capture word meanings while reading. Experiments
+conducted using our novel dataset show that the proposed variant of RNN
+language model outperformed the baseline model. Furthermore, the experiments
+also demonstrate that dynamic updates of an output layer help a model predict
+reappearing entities, whereas those of an input layer are effective to predict
+words following reappearing entities.
+",1,0,0,0,0,0
+5832,Bounds on the Size and Asymptotic Rate of Subblock-Constrained Codes,"  The study of subblock-constrained codes has recently gained attention due to
+their application in diverse fields. We present bounds on the size and
+asymptotic rate for two classes of subblock-constrained codes. The first class
+is binary constant subblock-composition codes (CSCCs), where each codeword is
+partitioned into equal sized subblocks, and every subblock has the same fixed
+weight. The second class is binary subblock energy-constrained codes (SECCs),
+where the weight of every subblock exceeds a given threshold. We present novel
+upper and lower bounds on the code sizes and asymptotic rates for binary CSCCs
+and SECCs. For a fixed subblock length and small relative distance, we show
+that the asymptotic rate for CSCCs (resp. SECCs) is strictly lower than the
+corresponding rate for constant weight codes (CWCs) (resp. heavy weight codes
+(HWCs)). Further, for codes with high weight and low relative distance, we show
+that the asymptotic rates for CSCCs is strictly lower than that of SECCs, which
+contrasts that the asymptotic rate for CWCs is equal to that of HWCs. We also
+provide a correction to an earlier result by Chee et al. (2014) on the
+asymptotic CSCC rate. Additionally, we present several numerical examples
+comparing the rates for CSCCs and SECCs with those for constant weight codes
+and heavy weight codes.
+",1,0,0,0,0,0
+5833,Extreme values of the Riemann zeta function on the 1-line,"  We prove that there are arbitrarily large values of $t$ such that
+$|\zeta(1+it)| \geq e^{\gamma} (\log_2 t + \log_3 t) + \mathcal{O}(1)$. This
+essentially matches the prediction for the optimal lower bound in a conjecture
+of Granville and Soundararajan. Our proof uses a new variant of the ""long
+resonator"" method. While earlier implementations of this method crucially
+relied on a ""sparsification"" technique to control the mean-square of the
+resonator function, in the present paper we exploit certain self-similarity
+properties of a specially designed resonator function.
+",0,0,1,0,0,0
+5834,Robust two-qubit gates in a linear ion crystal using a frequency-modulated driving force,"  In an ion trap quantum computer, collective motional modes are used to
+entangle two or more qubits in order to execute multi-qubit logical gates. Any
+residual entanglement between the internal and motional states of the ions
+results in loss of fidelity, especially when there are many spectator ions in
+the crystal. We propose using a frequency-modulated (FM) driving force to
+minimize such errors. In simulation, we obtained an optimized FM two-qubit gate
+that can suppress errors to less than 0.01\% and is robust against frequency
+drifts over $\pm$1 kHz. Experimentally, we have obtained a two-qubit gate
+fidelity of $98.3(4)\%$, a state-of-the-art result for two-qubit gates with 5
+ions.
+",0,1,0,0,0,0
+5835,Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks,"  We investigate models of the mitogenactivated protein kinases (MAPK) network,
+with the aim of determining where in parameter space there exist multiple
+positive steady states. We build on recent progress which combines various
+symbolic computation methods for mixed systems of equalities and inequalities.
+We demonstrate that those techniques benefit tremendously from a newly
+implemented graph theoretical symbolic preprocessing method. We compare
+computation times and quality of results of numerical continuation methods with
+our symbolic approach before and after the application of our preprocessing.
+",1,0,0,0,0,0
+5836,Computer Self-efficacy and Its Relationship with Web Portal Usage: Evidence from the University of the East,"  The University of the East Web Portal is an academic, web based system that
+provides educational electronic materials and e-learning services. To fully
+optimize its usage, it is imperative to determine the factors that relate to
+its usage. Thus, this study, to determine the computer self-efficacy of the
+faculty members of the University of the East and its relationship with their
+web portal usage, was conceived. Using a validated questionnaire, the profile
+of the respondents, their computer self-efficacy, and web portal usage were
+gathered. Data showed that the respondents were relatively young (M = 40 years
+old), majority had masters degree (f = 85, 72%), most had been using the web
+portal for four semesters (f = 60, 51%), and the large part were intermediate
+web portal users (f = 69, 59%). They were highly skilled in using the computer
+(M = 4.29) and skilled in using the Internet (M = 4.28). E-learning services (M
+= 3.29) and online library resources (M = 3.12) were only used occasionally.
+Pearson correlation revealed that age was positively correlated with online
+library resources (r = 0.267, p < 0.05) and a negative relationship existed
+between perceived skill level in using the portal and online library resources
+usage (r = -0.206, p < 0.05). A 2x2 chi square revealed that the highest
+educational attainment had a significant relationship with online library
+resources (chi square = 5.489, df = 1, p < 0.05). Basic computer (r = 0.196, p
+< 0.05) and Internet skills (r = 0.303, p < 0.05) were significantly and
+positively related with e-learning services usage but not with online library
+resources usage. Other individual factors such as attitudes towards the web
+portal and anxiety towards using the web portal can be investigated.
+",1,0,0,0,0,0
+5837,Normal forms of dispersive scalar Poisson brackets with two independent variables,"  We classify the dispersive Poisson brackets with one dependent variable and
+two independent variables, with leading order of hydrodynamic type, up to Miura
+transformations. We show that, in contrast to the case of a single independent
+variable for which a well known triviality result exists, the Miura equivalence
+classes are parametrised by an infinite number of constants, which we call
+numerical invariants of the brackets. We obtain explicit formulas for the first
+few numerical invariants.
+",0,1,1,0,0,0
+5838,A Liouville theorem for indefinite fractional diffusion equations and its application to existence of solutions,"  In this work we obtain a Liouville theorem for positive, bounded solutions of
+the equation $$ (-\Delta)^s u= h(x_N)f(u) \quad \hbox{in }\mathbb{R}^{N} $$
+where $(-\Delta)^s$ stands for the fractional Laplacian with $s\in (0,1)$, and
+the functions $h$ and $f$ are nondecreasing. The main feature is that the
+function $h$ changes sign in $\mathbb{R}$, therefore the problem is sometimes
+termed as indefinite. As an application we obtain a priori bounds for positive
+solutions of some boundary value problems, which give existence of such
+solutions by means of bifurcation methods.
+",0,0,1,0,0,0
+5839,Semiparametric Contextual Bandits,"  This paper studies semiparametric contextual bandits, a generalization of the
+linear stochastic bandit problem where the reward for an action is modeled as a
+linear function of known action features confounded by an non-linear
+action-independent term. We design new algorithms that achieve
+$\tilde{O}(d\sqrt{T})$ regret over $T$ rounds, when the linear function is
+$d$-dimensional, which matches the best known bounds for the simpler
+unconfounded case and improves on a recent result of Greenewald et al. (2017).
+Via an empirical evaluation, we show that our algorithms outperform prior
+approaches when there are non-linear confounding effects on the rewards.
+Technically, our algorithms use a new reward estimator inspired by
+doubly-robust approaches and our proofs require new concentration inequalities
+for self-normalized martingales.
+",0,0,0,1,0,0
+5840,Robustness of Quantum-Enhanced Adaptive Phase Estimation,"  As all physical adaptive quantum-enhanced metrology schemes operate under
+noisy conditions with only partially understood noise characteristics, so a
+practical control policy must be robust even for unknown noise. We aim to
+devise a test to evaluate the robustness of AQEM policies and assess the
+resource used by the policies. The robustness test is performed on QEAPE by
+simulating the scheme under four phase-noise models corresponding to
+normal-distribution noise, random-telegraph noise, skew-normal-distribution
+noise, and log-normal-distribution noise. Control policies are devised either
+by an evolutionary algorithm under the same noisy conditions, albeit ignorant
+of its properties, or a Bayesian-based feedback method that assumes no noise.
+Our robustness test and resource comparison method can be used to determining
+the efficacy and selecting a suitable policy.
+",1,0,0,1,0,0
+5841,Six operations formalism for generalized operads,"  This paper shows that generalizations of operads equipped with their
+respective bar/cobar dualities are related by a six operations formalism
+analogous to that of classical contexts in algebraic geometry. As a consequence
+of our constructions, we prove intertwining theorems which govern derived
+Koszul duality of push-forwards and pull-backs.
+",0,0,1,0,0,0
+5842,How Complex is your classification problem? A survey on measuring classification complexity,"  Extracting characteristics from the training datasets of classification
+problems has proven effective in a number of meta-analyses. Among them,
+measures of classification complexity can estimate the difficulty in separating
+the data points into their expected classes. Descriptors of the spatial
+distribution of the data and estimates of the shape and size of the decision
+boundary are among the existent measures for this characterization. This
+information can support the formulation of new data-driven pre-processing and
+pattern recognition techniques, which can in turn be focused on challenging
+characteristics of the problems. This paper surveys and analyzes measures which
+can be extracted from the training datasets in order to characterize the
+complexity of the respective classification problems. Their use in recent
+literature is also reviewed and discussed, allowing to prospect opportunities
+for future work in the area. Finally, descriptions are given on an R package
+named Extended Complexity Library (ECoL) that implements a set of complexity
+measures and is made publicly available.
+",0,0,0,1,0,0
+5843,Constraining Dark Energy Dynamics in Extended Parameter Space,"  Dynamical dark energy has been recently suggested as a promising and physical
+way to solve the 3.4 sigma tension on the value of the Hubble constant $H_0$
+between the direct measurement of Riess et al. (2016) (R16, hereafter) and the
+indirect constraint from Cosmic Microwave Anisotropies obtained by the Planck
+satellite under the assumption of a $\Lambda$CDM model. In this paper, by
+parameterizing dark energy evolution using the $w_0$-$w_a$ approach, and
+considering a $12$ parameter extended scenario, we find that: a) the tension on
+the Hubble constant can indeed be solved with dynamical dark energy, b) a
+cosmological constant is ruled out at more than $95 \%$ c.l. by the Planck+R16
+dataset, and c) all of the standard quintessence and half of the ""downward
+going"" dark energy model space (characterized by an equation of state that
+decreases with time) is also excluded at more than $95 \%$ c.l. These results
+are further confirmed when cosmic shear, CMB lensing, or SN~Ia luminosity
+distance data are also included. However, tension remains with the BAO dataset.
+A cosmological constant and small portion of the freezing quintessence models
+are still in agreement with the Planck+R16+BAO dataset at between 68\% and 95\%
+c.l. Conversely, for Planck plus a phenomenological $H_0$ prior, both thawing
+and freezing quintessence models prefer a Hubble constant of less than 70
+km/s/Mpc. The general conclusions hold also when considering models with
+non-zero spatial curvature.
+",0,1,0,0,0,0
+5844,Evaluation of matrix factorisation approaches for muscle synergy extraction,"  The muscle synergy concept provides a widely-accepted paradigm to break down
+the complexity of motor control. In order to identify the synergies, different
+matrix factorisation techniques have been used in a repertoire of fields such
+as prosthesis control and biomechanical and clinical studies. However, the
+relevance of these matrix factorisation techniques is still open for discussion
+since there is no ground truth for the underlying synergies. Here, we evaluate
+factorisation techniques and investigate the factors that affect the quality of
+estimated synergies. We compared commonly used matrix factorisation methods:
+Principal component analysis (PCA), Independent component analysis (ICA),
+Non-negative matrix factorization (NMF) and second-order blind identification
+(SOBI). Publicly available real data were used to assess the synergies
+extracted by each factorisation method in the classification of wrist
+movements. Synthetic datasets were utilised to explore the effect of muscle
+synergy sparsity, level of noise and number of channels on the extracted
+synergies. Results suggest that the sparse synergy model and a higher number of
+channels would result in better-estimated synergies. Without dimensionality
+reduction, SOBI showed better results than other factorisation methods. This
+suggests that SOBI would be an alternative when a limited number of electrodes
+is available but its performance was still poor in that case. Otherwise, NMF
+had the best performance when the number of channels was higher than the number
+of synergies. Therefore, NMF would be the best method for muscle synergy
+extraction.
+",0,0,0,0,1,0
+5845,Comparison of two classifications of a class of ODE's in the case of general position,"  Two classifications of second order ODE's cubic with respect to the first
+order derivative are compared in the case of general position, which is common
+for both classifications. The correspondence of vectorial, pseudovectorial,
+scalar, and pseudoscalar invariants is established.
+",0,0,1,0,0,0
+5846,Learning linear structural equation models in polynomial time and sample complexity,"  The problem of learning structural equation models (SEMs) from data is a
+fundamental problem in causal inference. We develop a new algorithm --- which
+is computationally and statistically efficient and works in the
+high-dimensional regime --- for learning linear SEMs from purely observational
+data with arbitrary noise distribution. We consider three aspects of the
+problem: identifiability, computational efficiency, and statistical efficiency.
+We show that when data is generated from a linear SEM over $p$ nodes and
+maximum degree $d$, our algorithm recovers the directed acyclic graph (DAG)
+structure of the SEM under an identifiability condition that is more general
+than those considered in the literature, and without faithfulness assumptions.
+In the population setting, our algorithm recovers the DAG structure in
+$\mathcal{O}(p(d^2 + \log p))$ operations. In the finite sample setting, if the
+estimated precision matrix is sparse, our algorithm has a smoothed complexity
+of $\widetilde{\mathcal{O}}(p^3 + pd^7)$, while if the estimated precision
+matrix is dense, our algorithm has a smoothed complexity of
+$\widetilde{\mathcal{O}}(p^5)$. For sub-Gaussian noise, we show that our
+algorithm has a sample complexity of $\mathcal{O}(\frac{d^8}{\varepsilon^2}
+\log (\frac{p}{\sqrt{\delta}}))$ to achieve $\varepsilon$ element-wise additive
+error with respect to the true autoregression matrix with probability at most
+$1 - \delta$, while for noise with bounded $(4m)$-th moment, with $m$ being a
+positive integer, our algorithm has a sample complexity of
+$\mathcal{O}(\frac{d^8}{\varepsilon^2} (\frac{p^2}{\delta})^{1/m})$.
+",1,0,0,1,0,0
+5847,On Bousfield's problem for solvable groups of finite Prüfer rank,"  For a group $G$ and $R=\mathbb Z,\mathbb Z/p,\mathbb Q$ we denote by $\hat
+G_R$ the $R$-completion of $G.$ We study the map $H_n(G,K)\to H_n(\hat G_R,K),$
+where $(R,K)=(\mathbb Z,\mathbb Z/p),(\mathbb Z/p,\mathbb Z/p),(\mathbb
+Q,\mathbb Q).$ We prove that $H_2(G,K)\to H_2(\hat G_R,K)$ is an epimorphism
+for a finitely generated solvable group $G$ of finite Prüfer rank. In
+particular, Bousfield's $HK$-localisation of such groups coincides with the
+$K$-completion for $K=\mathbb Z/p,\mathbb Q.$ Moreover, we prove that
+$H_n(G,K)\to H_n(\hat G_R,K)$ is an epimorphism for any $n$ if $G$ is a
+finitely presented group of the form $G=M\rtimes C,$ where $C$ is the infinite
+cyclic group and $M$ is a $C$-module.
+",0,0,1,0,0,0
+5848,Approximate Analytical Solution of a Cancer Immunotherapy Model by the Application of Differential Transform and Adomian Decomposition Methods,"  Immunotherapy plays a major role in tumour treatment, in comparison with
+other methods of dealing with cancer. The Kirschner-Panetta (KP) model of
+cancer immunotherapy describes the interaction between tumour cells, effector
+cells and interleukin-2 which are clinically utilized as medical treatment. The
+model selects a rich concept of immune-tumour dynamics. In this paper,
+approximate analytical solutions to KP model are represented by using the
+differential transform and Adomian decomposition. The complicated nonlinearity
+of the KP system causes the application of these two methods to require more
+involved calculations. The approximate analytical solutions to the model are
+compared with the results obtained by numerical fourth order Runge-Kutta
+method.
+",0,0,0,0,1,0
+5849,"Point-hyperplane frameworks, slider joints, and rigidity preserving transformations","  A one-to-one correspondence between the infinitesimal motions of bar-joint
+frameworks in $\mathbb{R}^d$ and those in $\mathbb{S}^d$ is a classical
+observation by Pogorelov, and further connections among different rigidity
+models in various different spaces have been extensively studied. In this
+paper, we shall extend this line of research to include the infinitesimal
+rigidity of frameworks consisting of points and hyperplanes. This enables us to
+understand correspondences between point-hyperplane rigidity, classical
+bar-joint rigidity, and scene analysis.
+Among other results, we derive a combinatorial characterization of graphs
+that can be realized as infinitesimally rigid frameworks in the plane with a
+given set of points collinear. This extends a result by Jackson and Jordán,
+which deals with the case when three points are collinear.
+",0,0,1,0,0,0
+5850,Spectrum of signless 1-Laplacian on simplicial complexes,"  We first develop a general framework for signless 1-Laplacian defined in
+terms of the combinatorial structure of a simplicial complex. The structure of
+the eigenvectors and the complex feature of eigenvalues are studied. The
+Courant nodal domain theorem for partial differential equation is extended to
+the signless 1-Laplacian on complex. We also study the effects of a wedge sum
+and a duplication of a motif on the spectrum of the signless 1-Laplacian, and
+identify some of the combinatorial features of a simplicial complex that are
+encoded in its spectrum. A special result is that the independent number and
+clique covering number on a complex provide lower and upper bounds of the
+multiplicity of the largest eigenvalue of signless 1-Laplacian, respectively,
+which has no counterpart of $p$-Laplacian for any $p>1$.
+",0,0,1,0,0,0
+5851,Inter-Area Oscillation Damping With Non-Synchronized Wide-Area Power System Stabilizer,"  One of the major issues in an interconnected power system is the low damping
+of inter-area oscillations which significantly reduces the power transfer
+capability. Advances in Wide-Area Measurement System (WAMS) makes it possible
+to use the information from geographical distant location to improve power
+system dynamics and performances. A speed deviation based Wide-Area Power
+System Stabilizer (WAPSS) is known to be effective in damping inter-area modes.
+However, the involvement of wide-area signals gives rise to the problem of
+time-delay, which may degrade the system performance. In general, time-stamped
+synchronized signals from Phasor Data Concentrator (PDC) are used for WAPSS, in
+which delays are introduced in both local and remote signals. One can opt for a
+feedback of remote signal only from PDC and uses the local signal as it is
+available, without time synchronization. This paper utilizes configurations of
+time-matched synchronized and nonsychronized feedback and provides the
+guidelines to design the controller. The controllers are synthesized using
+$H_\infty$ control with regional pole placement for ensuring adequate dynamic
+performance. To show the effectiveness of the proposed approach, two power
+system models have been used for the simulations. It is shown that the
+controllers designed based on the nonsynchronized signals are more robust to
+time time delay variations than the controllers using synchronized signal.
+",1,0,0,0,0,0
+5852,Genetic Algorithms for Evolving Computer Chess Programs,"  This paper demonstrates the use of genetic algorithms for evolving: 1) a
+grandmaster-level evaluation function, and 2) a search mechanism for a chess
+program, the parameter values of which are initialized randomly. The evaluation
+function of the program is evolved by learning from databases of (human)
+grandmaster games. At first, the organisms are evolved to mimic the behavior of
+human grandmasters, and then these organisms are further improved upon by means
+of coevolution. The search mechanism is evolved by learning from tactical test
+suites. Our results show that the evolved program outperforms a two-time world
+computer chess champion and is at par with the other leading computer chess
+programs.
+",1,0,0,1,0,0
+5853,Low-cost Autonomous Navigation System Based on Optical Flow Classification,"  This work presents a low-cost robot, controlled by a Raspberry Pi, whose
+navigation system is based on vision. The strategy used consisted of
+identifying obstacles via optical flow pattern recognition. Its estimation was
+done using the Lucas-Kanade algorithm, which can be executed by the Raspberry
+Pi without harming its performance. Finally, an SVM-based classifier was used
+to identify patterns of this signal associated with obstacles movement. The
+developed system was evaluated considering its execution over an optical flow
+pattern dataset extracted from a real navigation environment. In the end, it
+was verified that the acquisition cost of the system was inferior to that
+presented by most of the cited works, while its performance was similar to
+theirs.
+",1,0,0,0,0,0
+5854,"NVIDIA Tensor Core Programmability, Performance & Precision","  The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called
+""Tensor Core"" that performs one matrix-multiply-and-accumulate on 4x4 matrices
+per clock cycle. The NVIDIA Tesla V100 accelerator, featuring the Volta
+microarchitecture, provides 640 Tensor Cores with a theoretical peak
+performance of 125 Tflops/s in mixed precision. In this paper, we investigate
+current approaches to program NVIDIA Tensor Cores, their performances and the
+precision loss due to computation in mixed precision.
+Currently, NVIDIA provides three different ways of programming
+matrix-multiply-and-accumulate on Tensor Cores: the CUDA Warp Matrix Multiply
+Accumulate (WMMA) API, CUTLASS, a templated library based on WMMA, and cuBLAS
+GEMM. After experimenting with different approaches, we found that NVIDIA
+Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100
+GPU, seven and three times the performance in single and half precision
+respectively. A WMMA implementation of batched GEMM reaches a performance of 4
+Tflops/s. While precision loss due to matrix multiplication with half precision
+input might be critical in many HPC applications, it can be considerably
+reduced at the cost of increased computation. Our results indicate that HPC
+applications using matrix multiplications can strongly benefit from using of
+NVIDIA Tensor Cores.
+",1,0,0,0,0,0
+5855,A spectroscopic survey of Orion KL between 41.5 and 50 GHz,"  Orion KL is one of the most frequently observed sources in the Galaxy, and
+the site where many molecular species have been discovered for the first time.
+With the availability of powerful wideband backends, it is nowadays possible to
+complete spectral surveys in the entire mm-range to obtain a spectroscopically
+unbiased chemical picture of the region. In this paper we present a sensitive
+spectral survey of Orion KL, made with one of the 34m antennas of the Madrid
+Deep Space Communications Complex in Robledo de Chavela, Spain. The spectral
+range surveyed is from 41.5 to 50 GHz, with a frequency spacing of 180 kHz
+(equivalent to about 1.2 km/s, depending on the exact frequency). The rms
+achieved ranges from 8 to 12 mK. The spectrum is dominated by the J=1-0 SiO
+maser lines and by radio recombination lines (RRLs), which were detected up to
+Delta_n=11. Above a 3-sigma level, we identified 66 RRLs and 161 molecular
+lines corresponding to 39 isotopologues from 20 molecules; a total of 18 lines
+remain unidentified, two of them above a 5-sigma level. Results of radiative
+modelling of the detected molecular lines (excluding masers) are presented. At
+this frequency range, this is the most sensitive survey and also the one with
+the widest band. Although some complex molecules like CH_3CH_2CN and CH_2CHCN
+arise from the hot core, most of the detected molecules originate from the low
+temperature components in Orion KL.
+",0,1,0,0,0,0
+5856,Restricted Boltzmann Machines for Robust and Fast Latent Truth Discovery,"  We address the problem of latent truth discovery, LTD for short, where the
+goal is to discover the underlying true values of entity attributes in the
+presence of noisy, conflicting or incomplete information. Despite a multitude
+of algorithms to address the LTD problem that can be found in literature, only
+little is known about their overall performance with respect to effectiveness
+(in terms of truth discovery capabilities), efficiency and robustness. A
+practical LTD approach should satisfy all these characteristics so that it can
+be applied to heterogeneous datasets of varying quality and degrees of
+cleanliness.
+We propose a novel algorithm for LTD that satisfies the above requirements.
+The proposed model is based on Restricted Boltzmann Machines, thus coined
+LTD-RBM. In extensive experiments on various heterogeneous and publicly
+available datasets, LTD-RBM is superior to state-of-the-art LTD techniques in
+terms of an overall consideration of effectiveness, efficiency and robustness.
+",0,0,0,1,0,0
+5857,Effects of the Mach number on the evolution of vortex-surface fields in compressible Taylor--Green flows,"  We investigate the evolution of vortex-surface fields (VSFs) in compressible
+Taylor--Green flows at Mach numbers ($Ma$) ranging from 0.5 to 2.0 using direct
+numerical simulation. The formulation of VSFs in incompressible flows is
+extended to compressible flows, and a mass-based renormalization of VSFs is
+used to facilitate characterizing the evolution of a particular vortex surface.
+The effects of the Mach number on the VSF evolution are different in three
+stages. In the early stage, the jumps of the compressive velocity component
+near shocklets generate sinks to contract surrounding vortex surfaces, which
+shrink vortex volume and distort vortex surfaces. The subsequent reconnection
+of vortex surfaces, quantified by the minimal distance between approaching
+vortex surfaces and the exchange of vorticity fluxes, occurs earlier and has a
+higher reconnection degree for larger $Ma$ owing to the dilatational
+dissipation and shocklet-induced reconnection of vortex lines. In the late
+stage, the positive dissipation rate and negative pressure work accelerate the
+loss of kinetic energy and suppress vortex twisting with increasing $Ma$.
+",0,1,0,0,0,0
+5858,Above-threshold ionization (ATI) in multicenter molecules: the role of the initial state,"  A possible route to extract electronic and nuclear dynamics from molecular
+targets with attosecond temporal and nanometer spatial resolution is to employ
+recolliding electrons as `probes'. The recollision process in molecules is,
+however, very challenging to treat using {\it ab initio} approaches. Even for
+the simplest diatomic systems, such as H$_2$, today's computational
+capabilities are not enough to give a complete description of the electron and
+nuclear dynamics initiated by a strong laser field. As a consequence,
+approximate qualitative descriptions are called to play an important role. In
+this contribution we extend the work presented in N. Suárez {\it et al.},
+Phys.~Rev. A {\bf 95}, 033415 (2017), to three-center molecular targets.
+Additionally, we incorporate a more accurate description of the molecular
+ground state, employing information extracted from quantum chemistry software
+packages. This step forward allows us to include, in a detailed way, both the
+molecular symmetries and nodes present in the high-occupied molecular orbital.
+We are able to, on the one hand, keep our formulation as analytical as in the
+case of diatomics, and, on the other hand, to still give a complete description
+of the underlying physics behind the above-threshold ionization process. The
+application of our approach to complex multicenter - with more than 3 centers,
+targets appears to be straightforward.
+",0,1,0,0,0,0
+5859,"Detailed, accurate, human shape estimation from clothed 3D scan sequences","  We address the problem of estimating human pose and body shape from 3D scans
+over time. Reliable estimation of 3D body shape is necessary for many
+applications including virtual try-on, health monitoring, and avatar creation
+for virtual reality. Scanning bodies in minimal clothing, however, presents a
+practical barrier to these applications. We address this problem by estimating
+body shape under clothing from a sequence of 3D scans. Previous methods that
+have exploited body models produce smooth shapes lacking personalized details.
+We contribute a new approach to recover a personalized shape of the person. The
+estimated shape deviates from a parametric model to fit the 3D scans. We
+demonstrate the method using high quality 4D data as well as sequences of
+visual hulls extracted from multi-view images. We also make available BUFF, a
+new 4D dataset that enables quantitative evaluation
+(this http URL). Our method outperforms the state of the art in
+both pose estimation and shape estimation, qualitatively and quantitatively.
+",1,0,0,0,0,0
+5860,The Picard groups for unital inclusions of unital $C^*$-algebras,"  We shall introduce the notion of the Picard group for an inclusion of
+$C^*$-algebras. We shall also study its basic properties and the relation
+between the Picard group for an inclusion of $C^*$-algebras and the ordinary
+Picard group. Furthermore, we shall give some examples of the Picard groups for
+unital inclusions of unital $C^*$-algebras.
+",0,0,1,0,0,0
+5861,Generic Singularities of 3D Piecewise Smooth Dynamical Systems,"  The aim of this paper is to provide a discussion on current directions of
+research involving typical singularities of 3D nonsmooth vector fields. A brief
+survey of known results is presented. The main purpose of this work is to
+describe the dynamical features of a fold-fold singularity in its most basic
+form and to give a complete and detailed proof of its local structural
+stability (or instability). In addition, classes of all topological types of a
+fold-fold singularity are intrinsically characterized. Such proof essentially
+follows firstly from some lines laid out by Colombo, García, Jeffrey,
+Teixeira and others and secondly offers a rigorous mathematical treatment under
+clear and crisp assumptions and solid arguments. One should to highlight that
+the geometric-topological methods employed lead us to the completely
+mathematical understanding of the dynamics around a T-singularity. This
+approach lends itself to applications in generic bifurcation theory. It is
+worth to say that such subject is still poorly understood in higher dimension.
+",0,0,1,0,0,0
+5862,A Big Data Analysis Framework Using Apache Spark and Deep Learning,"  With the spreading prevalence of Big Data, many advances have recently been
+made in this field. Frameworks such as Apache Hadoop and Apache Spark have
+gained a lot of traction over the past decades and have become massively
+popular, especially in industries. It is becoming increasingly evident that
+effective big data analysis is key to solving artificial intelligence problems.
+Thus, a multi-algorithm library was implemented in the Spark framework, called
+MLlib. While this library supports multiple machine learning algorithms, there
+is still scope to use the Spark setup efficiently for highly time-intensive and
+computationally expensive procedures like deep learning. In this paper, we
+propose a novel framework that combines the distributive computational
+abilities of Apache Spark and the advanced machine learning architecture of a
+deep multi-layer perceptron (MLP), using the popular concept of Cascade
+Learning. We conduct empirical analysis of our framework on two real world
+datasets. The results are encouraging and corroborate our proposed framework,
+in turn proving that it is an improvement over traditional big data analysis
+methods that use either Spark or Deep learning as individual elements.
+",1,0,0,1,0,0
+5863,Multi-Layer Generalized Linear Estimation,"  We consider the problem of reconstructing a signal from multi-layered
+(possibly) non-linear measurements. Using non-rigorous but standard methods
+from statistical physics we present the Multi-Layer Approximate Message Passing
+(ML-AMP) algorithm for computing marginal probabilities of the corresponding
+estimation problem and derive the associated state evolution equations to
+analyze its performance. We also give the expression of the asymptotic free
+energy and the minimal information-theoretically achievable reconstruction
+error. Finally, we present some applications of this measurement model for
+compressed sensing and perceptron learning with structured matrices/patterns,
+and for a simple model of estimation of latent variables in an auto-encoder.
+",1,1,0,1,0,0
+5864,The Trace and the Mass of subcritical GJMS Operators,"  Let $L_g$ be the subcritical GJMS operator on an even-dimensional compact
+manifold $(X, g)$ and consider the zeta-regularized trace
+$\mathrm{Tr}_\zeta(L_g^{-1})$ of its inverse. We show that if $\ker L_g = 0$,
+then the supremum of this quantity, taken over all metrics $g$ of fixed volume
+in the conformal class, is always greater than or equal to the corresponding
+quantity on the standard sphere. Moreover, we show that in the case that it is
+strictly larger, the supremum is attained by a metric of constant mass. Using
+positive mass theorems, we give some geometric conditions for this to happen.
+",0,0,1,0,0,0
+5865,Two-photon imaging assisted by a dynamic random medium,"  Random scattering is usually viewed as a serious nuisance in optical imaging,
+and needs to be prevented in the conventional imaging scheme based on
+single-photon interference. Here we proposed a two-photon imaging scheme with
+the widely used lens replaced by a dynamic random medium. In contrast to
+destroying imaging process, the dynamic random medium in our scheme works as a
+crucial imaging element to bring constructive interference, and allows us to
+image an object from light field scattered by this dynamic random medium. On
+the one hand, our imaging scheme with incoherent two-photon illumination
+enables us to achieve super-resolution imaging with the resolution reaching
+Heisenberg limit. On the other hand, with coherent two-photon illumination, the
+image of a pure-phase object can be obtained in our imaging scheme. These
+results show new possibilities to overcome bottleneck of widely used
+single-photon imaging by developing imaging method based on multi-photon
+interference.
+",0,1,0,0,0,0
+5866,Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm,"  Deployment of deep neural networks (DNNs) in safety- or security-critical
+systems requires provable guarantees on their correct behaviour. A common
+requirement is robustness to adversarial perturbations in a neighbourhood
+around an input. In this paper we focus on the $L_0$ norm and aim to compute,
+for a trained DNN and an input, the maximal radius of a safe norm ball around
+the input within which there are no adversarial examples. Then we define global
+robustness as an expectation of the maximal safe radius over a test data set.
+We first show that the problem is NP-hard, and then propose an approximate
+approach to iteratively compute lower and upper bounds on the network's
+robustness. The approach is \emph{anytime}, i.e., it returns intermediate
+bounds and robustness estimates that are gradually, but strictly, improved as
+the computation proceeds; \emph{tensor-based}, i.e., the computation is
+conducted over a set of inputs simultaneously, instead of one by one, to enable
+efficient GPU computation; and has \emph{provable guarantees}, i.e., both the
+bounds and the robustness estimates can converge to their optimal values.
+Finally, we demonstrate the utility of the proposed approach in practice to
+compute tight bounds by applying and adapting the anytime algorithm to a set of
+challenging problems, including global robustness evaluation, competitive $L_0$
+attacks, test case generation for DNNs, and local robustness evaluation on
+large-scale ImageNet DNNs. We release the code of all case studies via GitHub.
+",0,0,0,1,0,0
+5867,Evolution of an eroding cylinder in single and lattice arrangements,"  The coupled evolution of an eroding cylinder immersed in a fluid within the
+subcritical Reynolds range is explored with scale resolving simulations.
+Erosion of the cylinder is driven by fluid shear stress. Kármán vortex
+shedding features in the wake and these oscillations occur on a significantly
+smaller time scale compared to the slowly eroding cylinder boundary. Temporal
+and spatial averaging across the cylinder span allows mean wall statistics such
+as wall shear to be evaluated; with geometry evolving in 2-D and the flow field
+simulated in 3-D. The cylinder develops into a rounded triangular body with
+uniform wall shear stress which is in agreement with existing theory and
+experiments. We introduce a node shuffle algorithm to reposition nodes around
+the cylinder boundary with a uniform distribution such that the mesh quality is
+preserved under high boundary deformation. A cylinder is then modelled within
+an infinite array of other cylinders by simulating a repeating unit cell and
+their profile evolution is studied. A similar terminal form is discovered for
+large cylinder spacings with consistent flow conditions and an intermediate
+profile was found with a closely packed lattice before reaching the common
+terminal form.
+",0,1,0,0,0,0
+5868,FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference,"  A classical problem in causal inference is that of matching, where treatment
+units need to be matched to control units. Some of the main challenges in
+developing matching methods arise from the tension among (i) inclusion of as
+many covariates as possible in defining the matched groups, (ii) having matched
+groups with enough treated and control units for a valid estimate of Average
+Treatment Effect (ATE) in each group, and (iii) computing the matched pairs
+efficiently for large datasets. In this paper we propose a fast method for
+approximate and exact matching in causal analysis called FLAME (Fast
+Large-scale Almost Matching Exactly). We define an optimization objective for
+match quality, which gives preferences to matching on covariates that can be
+useful for predicting the outcome while encouraging as many matches as
+possible. FLAME aims to optimize our match quality measure, leveraging
+techniques that are natural for query processing in the area of database
+management. We provide two implementations of FLAME using SQL queries and
+bit-vector techniques.
+",1,0,0,1,0,0
+5869,Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications,"  We propose a stochastic extension of the primal-dual hybrid gradient
+algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems
+that are separable in the dual variable. The analysis is carried out for
+general convex-concave saddle point problems and problems that are either
+partially smooth / strongly convex or fully smooth / strongly convex. We
+perform the analysis for arbitrary samplings of dual variables, and obtain
+known deterministic results as a special case. Several variants of our
+stochastic method significantly outperform the deterministic variant on a
+variety of imaging tasks.
+",1,0,1,0,0,0
+5870,Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications,"  We study combinatorial multi-armed bandit with probabilistically triggered
+arms (CMAB-T) and semi-bandit feedback. We resolve a serious issue in the prior
+CMAB-T studies where the regret bounds contain a possibly exponentially large
+factor of $1/p^*$, where $p^*$ is the minimum positive probability that an arm
+is triggered by any action. We address this issue by introducing a triggering
+probability modulated (TPM) bounded smoothness condition into the general
+CMAB-T framework, and show that many applications such as influence
+maximization bandit and combinatorial cascading bandit satisfy this TPM
+condition. As a result, we completely remove the factor of $1/p^*$ from the
+regret bounds, achieving significantly better regret bounds for influence
+maximization and cascading bandits than before. Finally, we provide lower bound
+results showing that the factor $1/p^*$ is unavoidable for general CMAB-T
+problems, suggesting that the TPM condition is crucial in removing this factor.
+",1,0,0,1,0,0
+5871,Multiscale Hierarchical Convolutional Networks,"  Deep neural network algorithms are difficult to analyze because they lack
+structure allowing to understand the properties of underlying transforms and
+invariants. Multiscale hierarchical convolutional networks are structured deep
+convolutional networks where layers are indexed by progressively higher
+dimensional attributes, which are learned from training data. Each new layer is
+computed with multidimensional convolutions along spatial and attribute
+variables. We introduce an efficient implementation of such networks where the
+dimensionality is progressively reduced by averaging intermediate layers along
+attribute indices. Hierarchical networks are tested on CIFAR image data bases
+where they obtain comparable precisions to state of the art networks, with much
+fewer parameters. We study some properties of the attributes learned from these
+databases.
+",1,0,0,1,0,0
+5872,Caveats for information bottleneck in deterministic scenarios,"  Information bottleneck (IB) is a method for extracting information from one
+random variable $X$ that is relevant for predicting another random variable
+$Y$. To do so, IB identifies an intermediate ""bottleneck"" variable $T$ that has
+low mutual information $I(X;T)$ and high mutual information $I(Y;T)$. The ""IB
+curve"" characterizes the set of bottleneck variables that achieve maximal
+$I(Y;T)$ for a given $I(X;T)$, and is typically explored by maximizing the ""IB
+Lagrangian"", $I(Y;T) - \beta I(X;T)$. In some cases, $Y$ is a deterministic
+function of $X$, including many classification problems in supervised learning
+where the output class $Y$ is a deterministic function of the input $X$. We
+demonstrate three caveats when using IB in any situation where $Y$ is a
+deterministic function of $X$: (1) the IB curve cannot be recovered by
+maximizing the IB Lagrangian for different values of $\beta$; (2) there are
+""uninteresting"" trivial solutions at all points of the IB curve; and (3) for
+multi-layer classifiers that achieve low prediction error, different layers
+cannot exhibit a strict trade-off between compression and prediction, contrary
+to a recent proposal. We also show that when $Y$ is a small perturbation away
+from being a deterministic function of $X$, these three caveats arise in an
+approximate way. To address problem (1), we propose a functional that, unlike
+the IB Lagrangian, can recover the IB curve in all cases. We demonstrate the
+three caveats on the MNIST dataset.
+",0,0,0,1,0,0
+5873,Monte Carlo Simulation of Charge Transport in Graphene (Simulazione Monte Carlo per il trasporto di cariche nel grafene),"  Simulations of charge transport in graphene are presented by implementing a
+recent method published on the paper: V. Romano, A. Majorana, M. Coco, ""DSMC
+method consistent with the Pauli exclusion principle and comparison with
+deterministic solutions for charge transport in graphene"", Journal of
+Computational Physics 302 (2015) 267-284. After an overview of the most
+important aspects of the semiclassical transport model for the dynamics of
+electrons in monolayer graphene, it is made a comparison in computational time
+between MATLAB and Fortran implementations of the algorithms. Therefore it is
+studied the case of graphene on substrates which it is produced original
+results by introducing models for the distribution of distances between
+graphene's atoms and impurities. Finally simulations, by choosing different
+kind of substrates, are done.
+-----
+Le simulazioni per il trasporto di cariche nel grafene sono presentate
+implementando un recente metodo pubblicato nell'articolo: V. Romano, A.
+Majorana, M. Coco, ""DSMC method consistent with the Pauli exclusion principle
+and comparison with deterministic solutions for charge transport in graphene"",
+Journal of Computational Physics 302 (2015) 267-284. Dopo una panoramica sugli
+aspetti più importanti del modello di trasporto semiclassico per la dinamica
+degli elettroni nel grafene sospeso, è stato effettuato un confronto del
+tempo computazionale tra le implementazioni MATLAB e Fortran dell'algoritmo.
+Inoltre è stato anche studiato il caso del grafene su substrato su cui sono
+stati prodotti dei risultati originali considerando dei modelli per la
+distribuzione delle distanze tra gli atomi del grafene e le impurezze. Infine
+sono state effettuate delle simulazioni scegliendo substrati di diversa natura.
+",1,1,0,0,0,0
+5874,Embedded-Graph Theory,"  In this paper, we propose a new type of graph, denoted as ""embedded-graph"",
+and its theory, which employs a distributed representation to describe the
+relations on the graph edges. Embedded-graphs can express linguistic and
+complicated relations, which cannot be expressed by the existing edge-graphs or
+weighted-graphs. We introduce the mathematical definition of embedded-graph,
+translation, edge distance, and graph similarity. We can transform an
+embedded-graph into a weighted-graph and a weighted-graph into an edge-graph by
+the translation method and by threshold calculation, respectively. The edge
+distance of an embedded-graph is a distance based on the components of a target
+vector, and it is calculated through cosine similarity with the target vector.
+The graph similarity is obtained considering the relations with linguistic
+complexity. In addition, we provide some examples and data structures for
+embedded-graphs in this paper.
+",1,0,0,0,0,0
+5875,A duality principle for the multi-block entanglement entropy of free fermion systems,"  The analysis of the entanglement entropy of a subsystem of a one-dimensional
+quantum system is a powerful tool for unravelling its critical nature. For
+instance, the scaling behaviour of the entanglement entropy determines the
+central charge of the associated Virasoro algebra. For a free fermion system,
+the entanglement entropy depends essentially on two sets, namely the set $A$ of
+sites of the subsystem considered and the set $K$ of excited momentum modes. In
+this work we make use of a general duality principle establishing the
+invariance of the entanglement entropy under exchange of the sets $A$ and $K$
+to tackle complex problems by studying their dual counterparts. The duality
+principle is also a key ingredient in the formulation of a novel conjecture for
+the asymptotic behavior of the entanglement entropy of a free fermion system in
+the general case in which both sets $A$ and $K$ consist of an arbitrary number
+of blocks. We have verified that this conjecture reproduces the numerical
+results with excellent precision for all the configurations analyzed. We have
+also applied the conjecture to deduce several asymptotic formulas for the
+mutual and $r$-partite information generalizing the known ones for the single
+block case.
+",0,1,1,0,0,0
+5876,Game Efficiency through Linear Programming Duality,"  The efficiency of a game is typically quantified by the price of anarchy
+(PoA), defined as the worst ratio of the objective function value of an
+equilibrium --- solution of the game --- and that of an optimal outcome. Given
+the tremendous impact of tools from mathematical programming in the design of
+algorithms and the similarity of the price of anarchy and different measures
+such as the approximation and competitive ratios, it is intriguing to develop a
+duality-based method to characterize the efficiency of games.
+In the paper, we present an approach based on linear programming duality to
+study the efficiency of games. We show that the approach provides a general
+recipe to analyze the efficiency of games and also to derive concepts leading
+to improvements. The approach is particularly appropriate to bound the PoA.
+Specifically, in our approach the dual programs naturally lead to competitive
+PoA bounds that are (almost) optimal for several classes of games. The approach
+indeed captures the smoothness framework and also some current non-smooth
+techniques/concepts. We show the applicability to the wide variety of games and
+environments, from congestion games to Bayesian welfare, from full-information
+settings to incomplete-information ones.
+",1,0,0,0,0,0
+5877,An Equation-By-Equation Method for Solving the Multidimensional Moment Constrained Maximum Entropy Problem,"  An equation-by-equation (EBE) method is proposed to solve a system of
+nonlinear equations arising from the moment constrained maximum entropy problem
+of multidimensional variables. The design of the EBE method combines ideas from
+homotopy continuation and Newton's iterative methods. Theoretically, we
+establish the local convergence under appropriate conditions and show that the
+proposed method, geometrically, finds the solution by searching along the
+surface corresponding to one component of the nonlinear problem. We will
+demonstrate the robustness of the method on various numerical examples,
+including: (1) A six-moment one-dimensional entropy problem with an explicit
+solution that contains components of order $10^0-10^3$ in magnitude; (2)
+Four-moment multidimensional entropy problems with explicit solutions where the
+resulting systems to be solved ranging from $70-310$ equations; (3) Four- to
+eight-moment of a two-dimensional entropy problem, which solutions correspond
+to the densities of the two leading EOFs of the wind stress-driven large-scale
+oceanic model. In this case, we find that the EBE method is more accurate
+compared to the classical Newton's method, the MATLAB generic solver, and the
+previously developed BFGS-based method, which was also tested on this problem.
+(4) Four-moment constrained of up to five-dimensional entropy problems which
+solutions correspond to multidimensional densities of the components of the
+solutions of the Kuramoto-Sivashinsky equation. For the higher dimensional
+cases of this example, the EBE method is superior because it automatically
+selects a subset of the prescribed moment constraints from which the maximum
+entropy solution can be estimated within the desired tolerance. This selection
+feature is particularly important since the moment constrained maximum entropy
+problems do not necessarily have solutions in general.
+",0,0,1,0,0,0
+5878,On reductions of the discrete Kadomtsev--Petviashvili-type equations,"  The reduction by restricting the spectral parameters $k$ and $k'$ on a
+generic algebraic curve of degree $\mathcal{N}$ is performed for the discrete
+AKP, BKP and CKP equations, respectively. A variety of two-dimensional discrete
+integrable systems possessing a more general solution structure arise from the
+reduction, and in each case a unified formula for generic positive integer
+$\mathcal{N}\geq 2$ is given to express the corresponding reduced integrable
+lattice equations. The obtained extended two-dimensional lattice models give
+rise to many important integrable partial difference equations as special
+degenerations. Some new integrable lattice models such as the discrete
+Sawada--Kotera, Kaup--Kupershmidt and Hirota--Satsuma equations in extended
+form are given as examples within the framework.
+",0,1,0,0,0,0
+5879,Exploring the Space of Black-box Attacks on Deep Neural Networks,"  Existing black-box attacks on deep neural networks (DNNs) so far have largely
+focused on transferability, where an adversarial instance generated for a
+locally trained model can ""transfer"" to attack other learning models. In this
+paper, we propose novel Gradient Estimation black-box attacks for adversaries
+with query access to the target model's class probabilities, which do not rely
+on transferability. We also propose strategies to decouple the number of
+queries required to generate each adversarial sample from the dimensionality of
+the input. An iterative variant of our attack achieves close to 100%
+adversarial success rates for both targeted and untargeted attacks on DNNs. We
+carry out extensive experiments for a thorough comparative evaluation of
+black-box attacks and show that the proposed Gradient Estimation attacks
+outperform all transferability based black-box attacks we tested on both MNIST
+and CIFAR-10 datasets, achieving adversarial success rates similar to well
+known, state-of-the-art white-box attacks. We also apply the Gradient
+Estimation attacks successfully against a real-world Content Moderation
+classifier hosted by Clarifai. Furthermore, we evaluate black-box attacks
+against state-of-the-art defenses. We show that the Gradient Estimation attacks
+are very effective even against these defenses.
+",1,0,0,0,0,0
+5880,Convergence diagnostics for stochastic gradient descent with constant step size,"  Many iterative procedures in stochastic optimization exhibit a transient
+phase followed by a stationary phase. During the transient phase the procedure
+converges towards a region of interest, and during the stationary phase the
+procedure oscillates in that region, commonly around a single point. In this
+paper, we develop a statistical diagnostic test to detect such phase transition
+in the context of stochastic gradient descent with constant learning rate. We
+present theory and experiments suggesting that the region where the proposed
+diagnostic is activated coincides with the convergence region. For a class of
+loss functions, we derive a closed-form solution describing such region.
+Finally, we suggest an application to speed up convergence of stochastic
+gradient descent by halving the learning rate each time stationarity is
+detected. This leads to a new variant of stochastic gradient descent, which in
+many settings is comparable to state-of-art.
+",1,0,1,1,0,0
+5881,Strong Khovanov-Floer Theories and Functoriality,"  We provide a unified framework for proving Reidemeister-invariance and
+functoriality for a wide range of link homology theories. These include Lee
+homology, Heegaard Floer homology of branched double covers, singular instanton
+homology, and \Szabo's geometric link homology theory. We follow Baldwin,
+Hedden, and Lobb (arXiv:1509.04691) in leveraging the relationships between
+these theories and Khovanov homology. We obtain stronger functoriality results
+by avoiding spectral sequences and instead showing that each theory factors
+through Bar-Natan's cobordism-theoretic link homology theory.
+",0,0,1,0,0,0
+5882,Formal Verification of Neural Network Controlled Autonomous Systems,"  In this paper, we consider the problem of formally verifying the safety of an
+autonomous robot equipped with a Neural Network (NN) controller that processes
+LiDAR images to produce control actions. Given a workspace that is
+characterized by a set of polytopic obstacles, our objective is to compute the
+set of safe initial conditions such that a robot trajectory starting from these
+initial conditions is guaranteed to avoid the obstacles. Our approach is to
+construct a finite state abstraction of the system and use standard
+reachability analysis over the finite state abstraction to compute the set of
+the safe initial states. The first technical problem in computing the finite
+state abstraction is to mathematically model the imaging function that maps the
+robot position to the LiDAR image. To that end, we introduce the notion of
+imaging-adapted sets as partitions of the workspace in which the imaging
+function is guaranteed to be affine. We develop a polynomial-time algorithm to
+partition the workspace into imaging-adapted sets along with computing the
+corresponding affine imaging functions. Given this workspace partitioning, a
+discrete-time linear dynamics of the robot, and a pre-trained NN controller
+with Rectified Linear Unit (ReLU) nonlinearity, the second technical challenge
+is to analyze the behavior of the neural network. To that end, we utilize a
+Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible
+segments of different ReLUs. SMC solvers then use a Boolean satisfiability
+solver and a convex programming solver and decompose the problem into smaller
+subproblems. To accelerate this process, we develop a pre-processing algorithm
+that could rapidly prune the space feasible ReLU segments. Finally, we
+demonstrate the efficiency of the proposed algorithms using numerical
+simulations with increasing complexity of the neural network controller.
+",1,0,0,0,0,0
+5883,Nonvanishing of central $L$-values of Maass forms,"  With the method of moments and the mollification method, we study the central
+$L$-values of GL(2) Maass forms of weight $0$ and level $1$ and establish a
+positive-proportional nonvanishing result of such values in the aspect of large
+spectral parameter in short intervals, which is qualitatively optimal in view
+of Weyl's law. As an application of this result and a formula of Katok--Sarnak,
+we give a nonvanishing result on the first Fourier coefficients of Maass forms
+of weight $\frac{1}{2}$ and level $4$ in the Kohnen plus space.
+",0,0,1,0,0,0
+5884,A Universally Optimal Multistage Accelerated Stochastic Gradient Method,"  We study the problem of minimizing a strongly convex and smooth function when
+we have noisy estimates of its gradient. We propose a novel multistage
+accelerated algorithm that is universally optimal in the sense that it achieves
+the optimal rate both in the deterministic and stochastic case and operates
+without knowledge of noise characteristics. The algorithm consists of stages
+that use a stochastic version of Nesterov's accelerated algorithm with a
+specific restart and parameters selected to achieve the fastest reduction in
+the bias-variance terms in the convergence rate bounds.
+",1,0,0,1,0,0
+5885,Radio Observation of Venus at Meter Wavelengths using the GMRT,"  The Venusian surface has been studied by measuring radar reflections and
+thermal radio emission over a wide spectral region of several centimeters to
+meter wavelengths from the Earth-based as well as orbiter platforms. The
+radiometric observations, in the decimeter (dcm) wavelength regime showed a
+decreasing trend in the observed brightness temperature (Tb) with increasing
+wavelength. The thermal emission models available at present have not been able
+to explain the radiometric observations at longer wavelength (dcm) to a
+satisfactory level. This paper reports the first interferometric imaging
+observations of Venus below 620 MHz. They were carried out at 606, 332.9 and
+239.9 MHz using the Giant Meterwave Radio Telescope (GMRT). The Tb values
+derived at the respective frequencies are 526 K, 409 K and < 426 K, with errors
+of ~7% which are generally consistent with the reported Tb values at 608 MHz
+and 430 MHz by previous investigators, but are much lower than those derived
+from high-frequency observations at 1.38-22.46 GHz using the VLA.
+",0,1,0,0,0,0
+5886,Measurements of the depth of maximum of air-shower profiles at the Pierre Auger Observatory and their composition implications,"  Air-showers measured by the Pierre Auger Observatory were analyzed in order
+to extract the depth of maximum (Xmax).The results allow the analysis of the
+Xmax distributions as a function of energy ($> 10^{17.8}$ eV). The Xmax
+distributions, their mean and standard deviation are analyzed with the help of
+shower simulations with the aim of interpreting the mass composition. The mean
+and standard deviation were used to derive <ln A> and its variance as a
+function of energy. The fraction of four components (p, He, N and Fe) were fit
+to the Xmax distributions. Regardless of the hadronic model used the data is
+better described by a mix of light, intermediate and heavy primaries. Also,
+independent of the hadronic models, a decrease of the proton flux with energy
+is observed. No significant contribution of iron nuclei is derived in the
+entire energy range studied.
+",0,1,0,0,0,0
+5887,Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML,"  Reward augmented maximum likelihood (RAML), a simple and effective learning
+framework to directly optimize towards the reward function in structured
+prediction tasks, has led to a number of impressive empirical successes. RAML
+incorporates task-specific reward by performing maximum-likelihood updates on
+candidate outputs sampled according to an exponentiated payoff distribution,
+which gives higher probabilities to candidates that are close to the reference
+output. While RAML is notable for its simplicity, efficiency, and its
+impressive empirical successes, the theoretical properties of RAML, especially
+the behavior of the exponentiated payoff distribution, has not been examined
+thoroughly. In this work, we introduce softmax Q-distribution estimation, a
+novel theoretical interpretation of RAML, which reveals the relation between
+RAML and Bayesian decision theory. The softmax Q-distribution can be regarded
+as a smooth approximation of the Bayes decision boundary, and the Bayes
+decision rule is achieved by decoding with this Q-distribution. We further show
+that RAML is equivalent to approximately estimating the softmax Q-distribution,
+with the temperature $\tau$ controlling approximation error. We perform two
+experiments, one on synthetic data of multi-class classification and one on
+real data of image captioning, to demonstrate the relationship between RAML and
+the proposed softmax Q-distribution estimation method, verifying our
+theoretical analysis. Additional experiments on three structured prediction
+tasks with rewards defined on sequential (named entity recognition), tree-based
+(dependency parsing) and irregular (machine translation) structures show
+notable improvements over maximum likelihood baselines.
+",1,0,0,1,0,0
+5888,Vision-based Autonomous Landing in Catastrophe-Struck Environments,"  Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving
+technology that can enable identification of survivors under collapsed
+buildings in the aftermath of natural disasters such as earthquakes or gas
+explosions. However, these UAVs have to be able to autonomously land on debris
+piles in order to accurately locate the survivors. This problem is extremely
+challenging as the structure of these debris piles is often unknown and no
+prior knowledge can be leveraged. In this work, we propose a computationally
+efficient system that is able to reliably identify safe landing sites and
+autonomously perform the landing maneuver. Specifically, our algorithm computes
+costmaps based on several hazard factors including terrain flatness, steepness,
+depth accuracy and energy consumption information. We first estimate dense
+candidate landing sites from the resulting costmap and then employ clustering
+to group neighboring sites into a safe landing region. Finally, a minimum-jerk
+trajectory is computed for landing considering the surrounding obstacles and
+the UAV dynamics. We demonstrate the efficacy of our system using experiments
+from a city scale hyperrealistic simulation environment and in real-world
+scenarios with collapsed buildings.
+",1,0,0,0,0,0
+5889,Data-Driven Sparse Sensor Placement for Reconstruction,"  Optimal sensor placement is a central challenge in the design, prediction,
+estimation, and control of high-dimensional systems. High-dimensional states
+can often leverage a latent low-dimensional representation, and this inherent
+compressibility enables sparse sensing. This article explores optimized sensor
+placement for signal reconstruction based on a tailored library of features
+extracted from training data. Sparse point sensors are discovered using the
+singular value decomposition and QR pivoting, which are two ubiquitous matrix
+computations that underpin modern linear dimensionality reduction. Sparse
+sensing in a tailored basis is contrasted with compressed sensing, a universal
+signal recovery method in which an unknown signal is reconstructed via a sparse
+representation in a universal basis. Although compressed sensing can recover a
+wider class of signals, we demonstrate the benefits of exploiting known
+patterns in data with optimized sensing. In particular, drastic reductions in
+the required number of sensors and improved reconstruction are observed in
+examples ranging from facial images to fluid vorticity fields. Principled
+sensor placement may be critically enabling when sensors are costly and
+provides faster state estimation for low-latency, high-bandwidth control.
+MATLAB code is provided for all examples.
+",1,0,1,0,0,0
+5890,Fast trimers in one-dimensional extended Fermi-Hubbard model,"  We consider a one-dimensional two component extended Fermi-Hubbard model with
+nearest neighbor interactions and mass imbalance between the two species. We
+study the stability of trimers, various observables for detecting them, and
+expansion dynamics. We generalize the definition of the trimer gap to include
+the formation of different types of clusters originating from nearest neighbor
+interactions. Expansion dynamics reveal rapidly propagating trimers, with
+speeds exceeding doublon propagation in strongly interacting regime. We present
+a simple model for understanding this unique feature of the movement of the
+trimers, and we discuss the potential for experimental realization.
+",0,1,0,0,0,0
+5891,A Geometric Approach for Real-time Monitoring of Dynamic Large Scale Graphs: AS-level graphs illustrated,"  The monitoring of large dynamic networks is a major chal- lenge for a wide
+range of application. The complexity stems from properties of the underlying
+graphs, in which slight local changes can lead to sizable variations of global
+prop- erties, e.g., under certain conditions, a single link cut that may be
+overlooked during monitoring can result in splitting the graph into two
+disconnected components. Moreover, it is often difficult to determine whether a
+change will propagate globally or remain local. Traditional graph theory
+measure such as the centrality or the assortativity of the graph are not
+satisfying to characterize global properties of the graph. In this paper, we
+tackle the problem of real-time monitoring of dynamic large scale graphs by
+developing a geometric approach that leverages notions of geometric curvature
+and recent development in graph embeddings using Ollivier-Ricci curvature [47].
+We illustrate the use of our method by consid- ering the practical case of
+monitoring dynamic variations of global Internet using topology changes
+information provided by combining several BGP feeds. In particular, we use our
+method to detect major events and changes via the geometry of the embedding of
+the graph.
+",1,0,0,1,0,0
+5892,"On general $(α, β)$-metrics of weak Landsberg type","  In this paper, we study general $(\alpha,\beta)$-metrics which $\alpha$ is a
+Riemannian metric and $\beta$ is an one-form. We have proven that every weak
+Landsberg general $(\alpha,\beta)$-metric is a Berwald metric, where $\beta$ is
+a closed and conformal one-form. This show that there exist no generalized
+unicorn metric in this class of general $(\alpha,\beta)$-metric. Further, We
+show that $F$ is a Landsberg general $(\alpha,\beta)$-metric if and only if it
+is weak Landsberg general $(\alpha,\beta)$-metric, where $\beta$ is a closed
+and conformal one-form.
+",0,0,1,0,0,0
+5893,An Intersectional Definition of Fairness,"  We introduce a measure of fairness for algorithms and data with regard to
+multiple protected attributes. Our proposed definition, differential fairness,
+is informed by the framework of intersectionality, which analyzes how
+interlocking systems of power and oppression affect individuals along
+overlapping dimensions including race, gender, sexual orientation, class, and
+disability. We show that our criterion behaves sensibly for any subset of the
+set of protected attributes, and we illustrate links to differential privacy. A
+case study on census data demonstrates the utility of our approach.
+",0,0,0,1,0,0
+5894,Technological Parasitism,"  Technological parasitism is a new theory to explain the evolution of
+technology in society. In this context, this study proposes a model to analyze
+the interaction between a host technology (system) and a parasitic technology
+(subsystem) to explain evolutionary pathways of technologies as complex
+systems. The coefficient of evolutionary growth of the model here indicates the
+typology of evolution of parasitic technology in relation to host technology:
+i.e., underdevelopment, growth and development. This approach is illustrated
+with realistic examples using empirical data of product and process
+technologies. Overall, then, the theory of technological parasitism can be
+useful for bringing a new perspective to explain and generalize the evolution
+of technology and predict which innovations are likely to evolve rapidly in
+society.
+",0,0,0,0,0,1
+5895,On the sample mean after a group sequential trial,"  A popular setting in medical statistics is a group sequential trial with
+independent and identically distributed normal outcomes, in which interim
+analyses of the sum of the outcomes are performed. Based on a prescribed
+stopping rule, one decides after each interim analysis whether the trial is
+stopped or continued. Consequently, the actual length of the study is a random
+variable. It is reported in the literature that the interim analyses may cause
+bias if one uses the ordinary sample mean to estimate the location parameter.
+For a generic stopping rule, which contains many classical stopping rules as a
+special case, explicit formulas for the expected length of the trial, the bias,
+and the mean squared error (MSE) are provided. It is deduced that, for a fixed
+number of interim analyses, the bias and the MSE converge to zero if the first
+interim analysis is performed not too early. In addition, optimal rates for
+this convergence are provided. Furthermore, under a regularity condition,
+asymptotic normality in total variation distance for the sample mean is
+established. A conclusion for naive confidence intervals based on the sample
+mean is derived. It is also shown how the developed theory naturally fits in
+the broader framework of likelihood theory in a group sequential trial setting.
+A simulation study underpins the theoretical findings.
+",0,0,1,1,0,0
+5896,A New UGV Teleoperation Interface for Improved Awareness of Network Connectivity and Physical Surroundings,"  A reliable wireless connection between the operator and the teleoperated
+Unmanned Ground Vehicle (UGV) is critical in many Urban Search and Rescue
+(USAR) missions. Unfortunately, as was seen in e.g. the Fukushima disaster, the
+networks available in areas where USAR missions take place are often severely
+limited in range and coverage. Therefore, during mission execution, the
+operator needs to keep track of not only the physical parts of the mission,
+such as navigating through an area or searching for victims, but also the
+variations in network connectivity across the environment. In this paper, we
+propose and evaluate a new teleoperation User Interface (UI) that includes a
+way of estimating the Direction of Arrival (DoA) of the Radio Signal Strength
+(RSS) and integrating the DoA information in the interface. The evaluation
+shows that using the interface results in more objects found, and less aborted
+missions due to connectivity problems, as compared to a standard interface. The
+proposed interface is an extension to an existing interface centered around the
+video stream captured by the UGV. But instead of just showing the network
+signal strength in terms of percent and a set of bars, the additional
+information of DoA is added in terms of a color bar surrounding the video feed.
+With this information, the operator knows what movement directions are safe,
+even when moving in regions close to the connectivity threshold.
+",1,0,0,0,0,0
+5897,Practical Integer-to-Binary Mapping for Quantum Annealers,"  Recent advancements in quantum annealing hardware and numerous studies in
+this area suggests that quantum annealers have the potential to be effective in
+solving unconstrained binary quadratic programming problems. Naturally, one may
+desire to expand the application domain of these machines to problems with
+general discrete variables. In this paper, we explore the possibility of
+employing quantum annealers to solve unconstrained quadratic programming
+problems over a bounded integer domain. We present an approach for encoding
+integer variables into binary ones, thereby representing unconstrained integer
+quadratic programming problems as unconstrained binary quadratic programming
+problems. To respect some of the limitations of the currently developed quantum
+annealers, we propose an integer encoding, named bounded- coefficient encoding,
+in which we limit the size of the coefficients that appear in the encoding.
+Furthermore, we propose an algorithm for finding the upper bound on the
+coefficients of the encoding using the precision of the machine and the
+coefficients of the original integer problem. Finally, we experimentally show
+that this approach is far more resilient to the noise of the quantum annealers
+compared to traditional approaches for the encoding of integers in base two.
+",1,0,1,0,0,0
+5898,Nonequilibrium entropic bounds for Darwinian replicators,"  Life evolved on our planet by means of a combination of Darwinian selection
+and innovations leading to higher levels of complexity. The emergence and
+selection of replicating entities is a central problem in prebiotic evolution.
+Theoretical models have shown how populations of different types of replicating
+entities exclude or coexist with other classes of replicators. Models are
+typically kinetic, based on standard replicator equations. On the other hand,
+the presence of thermodynamical constrains for these systems remain an open
+question. This is largely due to the lack of a general theory of out of
+statistical methods for systems far from equilibrium. Nonetheless, a first
+approach to this problem has been put forward in a series of novel
+developements in non-equilibrium physics, under the rubric of the extended
+second law of thermodynamics. The work presented here is twofold: firstly, we
+review this theoretical framework and provide a brief description of the three
+fundamental replicator types in prebiotic evolution: parabolic, malthusian and
+hyperbolic. Finally, we employ these previously mentioned techinques to explore
+how replicators are constrained by thermodynamics.
+",0,1,0,0,0,0
+5899,Cluster decomposition of full configuration interaction wave functions: a tool for chemical interpretation of systems with strong correlation,"  Approximate full configuration interaction (FCI) calculations have recently
+become tractable for systems of unforeseen size thanks to stochastic and
+adaptive approximations to the exponentially scaling FCI problem. The result of
+an FCI calculation is a weighted set of electronic configurations, which can
+also be expressed in terms of excitations from a reference configuration. The
+excitation amplitudes contain information on the complexity of the electronic
+wave function, but this information is contaminated by contributions from
+disconnected excitations, i.e. those excitations that are just products of
+independent lower-level excitations. The unwanted contributions can be removed
+via a cluster decomposition procedure, making it possible to examine the
+importance of connected excitations in complicated multireference molecules
+which are outside the reach of conventional algorithms. We present an
+implementation of the cluster decomposition analysis and apply it to both true
+FCI wave functions, as well as wave functions generated from the adaptive
+sampling CI (ASCI) algorithm. The cluster decomposition is useful for
+interpreting calculations in chemical studies, as a diagnostic for the
+convergence of various excitation manifolds, as well as as a guidepost for
+polynomially scaling electronic structure models. Applications are presented
+for (i) the double dissociation of water, (ii) the carbon dimer, (iii) the
+{\pi} space of polyacenes, as well as (iv) the chromium dimer. While the
+cluster amplitudes exhibit rapid decay with increasing rank for the first three
+systems, even connected octuple excitations still appear important in Cr$_2$,
+suggesting that spin-restricted single-reference coupled-cluster approaches may
+not be tractable for some problems in transition metal chemistry.
+",0,1,0,0,0,0
+5900,Possible Quasi-Periodic modulation in the z = 1.1 $γ$-ray blazar PKS 0426-380,"  We search for $\gamma$-ray and optical periodic modulations in a distant flat
+spectrum radio quasar (FSRQ) PKS 0426-380 (the redshift $z=1.1$). Using two
+techniques (i.e., the maximum likelihood optimization and the exposure-weighted
+aperture photometry), we obtain $\gamma$-ray light curves from \emph{Fermi}-LAT
+Pass 8 data covering from 2008 August to 2016 December. We then analyze the
+light curves with the Lomb-Scargle Periodogram (LSP) and the Weighted Wavelet
+Z-transform (WWZ). A $\gamma$-ray quasi-periodicity with a period of 3.35 $\pm$
+0.68 years is found at the significance-level of $\simeq3.6\ \sigma$. The
+optical-UV flux covering from 2005 August to 2013 April provided by ASI SCIENCE
+DATA CENTER is also analyzed, but no significant quasi-periodicity is found. It
+should be pointed out that the result of the optical-UV data could be tentative
+because of the incomplete of the data. Further long-term multiwavelength
+monitoring of this FSRQ is needed to confirm its quasi-periodicity.
+",0,1,0,0,0,0
+5901,Comment on Jackson's analysis of electric charge quantization due to interaction with Dirac's magnetic monopole,"  In J.D. Jackson's Classical Electrodynamics textbook, the analysis of Dirac's
+charge quantization condition in the presence of a magnetic monopole has a
+mathematical omission and an all too brief physical argument that might mislead
+some students. This paper presents a detailed derivation of Jackson's main
+result, explains the significance of the missing term, and highlights the close
+connection between Jackson's findings and Dirac's original argument.
+",0,1,0,0,0,0
+5902,Study of charged hadron multiplicities in charged-current neutrino-lead interactions in the OPERA detector,"  The OPERA experiment was designed to search for $\nu_{\mu} \rightarrow
+\nu_{\tau}$ oscillations in appearance mode through the direct observation of
+tau neutrinos in the CNGS neutrino beam. In this paper, we report a study of
+the multiplicity of charged particles produced in charged-current neutrino
+interactions in lead. We present charged hadron average multiplicities, their
+dispersion and investigate the KNO scaling in different kinematical regions.
+The results are presented in detail in the form of tables that can be used in
+the validation of Monte Carlo generators of neutrino-lead interactions.
+",0,1,0,0,0,0
+5903,On the coefficients of the Alekseev Torossian associator,"  This paper explains a method to calculate the coefficients of the
+Alekseev-Torossian associator as linear combinations of iterated integrals of
+Kontsevich weight forms of Lie graphs.
+",0,0,1,0,0,0
+5904,Exact spectral decomposition of a time-dependent one-particle reduced density matrix,"  We determine the exact time-dependent non-idempotent one-particle reduced
+density matrix and its spectral decomposition for a harmonically confined
+two-particle correlated one-dimensional system when the interaction terms in
+the Schrödinger Hamiltonian are changed abruptly. Based on this matrix in
+coordinate space we derivea precise condition for the equivalence of the purity
+and the overlap-square of the correlated and non-correlated wave functions as
+the system evolves in time. This equivalence holds only if the interparticle
+interactions are affected, while the confinement terms are unaffected within
+the stability range of the system. Under this condition we also analyze various
+time-dependent measures of entanglement and demonstrate that, depending on the
+magnitude of the changes made in the Schrödinger Hamiltonian, periodic,
+logarithmically incresing or constant value behavior of the von Neumann entropy
+can occur.
+",0,1,0,0,0,0
+5905,A short variational proof of equivalence between policy gradients and soft Q learning,"  Two main families of reinforcement learning algorithms, Q-learning and policy
+gradients, have recently been proven to be equivalent when using a softmax
+relaxation on one part, and an entropic regularization on the other. We relate
+this result to the well-known convex duality of Shannon entropy and the softmax
+function. Such a result is also known as the Donsker-Varadhan formula. This
+provides a short proof of the equivalence. We then interpret this duality
+further, and use ideas of convex analysis to prove a new policy inequality
+relative to soft Q-learning.
+",1,0,0,0,0,0
+5906,Non-parametric Message Important Measure: Storage Code Design and Transmission Planning for Big Data,"  Storage and transmission in big data are discussed in this paper, where
+message importance is taken into account. Similar to Shannon Entropy and Renyi
+Entropy, we define non-parametric message important measure (NMIM) as a measure
+for the message importance in the scenario of big data, which can characterize
+the uncertainty of random events. It is proved that the proposed NMIM can
+sufficiently describe two key characters of big data: rare events finding and
+large diversities of events. Based on NMIM, we first propose an effective
+compressed encoding mode for data storage, and then discuss the channel
+transmission over some typical channel models. Numerical simulation results
+show that using our proposed strategy occupies less storage space without
+losing too much message importance, and there are growth region and saturation
+region for the maximum transmission, which contributes to designing of better
+practical communication system.
+",0,0,1,1,0,0
+5907,Graphene and its elemental analogue: A molecular dynamics view of fracture phenomenon,"  Graphene and some graphene like two dimensional materials; hexagonal boron
+nitride (hBN) and silicene have unique mechanical properties which severely
+limit the suitability of conventional theories used for common brittle and
+ductile materials to predict the fracture response of these materials. This
+study revealed the fracture response of graphene, hBN and silicene nanosheets
+under different tiny crack lengths by molecular dynamics (MD) simulations using
+LAMMPS. The useful strength of these large area two dimensional materials are
+determined by their fracture toughness. Our study shows a comparative analysis
+of mechanical properties among the elemental analogues of graphene and
+suggested that hBN can be a good substitute for graphene in terms of mechanical
+properties. We have also found that the pre-cracked sheets fail in brittle
+manner and their failure is governed by the strength of the atomic bonds at the
+crack tip. The MD prediction of fracture toughness shows significant difference
+with the fracture toughness determined by Griffth's theory of brittle failure
+which restricts the applicability of Griffith's criterion for these materials
+in case of nano-cracks. Moreover, the strengths measured in armchair and zigzag
+directions of nanosheets of these materials implied that the bonds in armchair
+direction has the stronger capability to resist crack propagation compared to
+zigzag direction.
+",0,1,0,0,0,0
+5908,Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning,"  Skilled robotic manipulation benefits from complex synergies between
+non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing
+can help rearrange cluttered objects to make space for arms and fingers;
+likewise, grasping can help displace objects to make pushing movements more
+precise and collision-free. In this work, we demonstrate that it is possible to
+discover and learn these synergies from scratch through model-free deep
+reinforcement learning. Our method involves training two fully convolutional
+networks that map from visual observations to actions: one infers the utility
+of pushes for a dense pixel-wise sampling of end effector orientations and
+locations, while the other does the same for grasping. Both networks are
+trained jointly in a Q-learning framework and are entirely self-supervised by
+trial and error, where rewards are provided from successful grasps. In this
+way, our policy learns pushing motions that enable future grasps, while
+learning grasps that can leverage past pushes. During picking experiments in
+both simulation and real-world scenarios, we find that our system quickly
+learns complex behaviors amid challenging cases of clutter, and achieves better
+grasping success rates and picking efficiencies than baseline alternatives
+after only a few hours of training. We further demonstrate that our method is
+capable of generalizing to novel objects. Qualitative results (videos), code,
+pre-trained models, and simulation environments are available at
+this http URL
+",1,0,0,1,0,0
+5909,A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods,"  Evaluation and validation of complicated control systems are crucial to
+guarantee usability and safety. Usually, failure happens in some very rarely
+encountered situations, but once triggered, the consequence is disastrous.
+Accelerated Evaluation is a methodology that efficiently tests those
+rarely-occurring yet critical failures via smartly-sampled test cases. The
+distribution used in sampling is pivotal to the performance of the method, but
+building a suitable distribution requires case-by-case analysis. This paper
+proposes a versatile approach for constructing sampling distribution using
+kernel method. The approach uses statistical learning tools to approximate the
+critical event sets and constructs distributions based on the unique properties
+of Gaussian distributions. We applied the method to evaluate the automated
+vehicles. Numerical experiments show proposed approach can robustly identify
+the rare failures and significantly reduce the evaluation time.
+",1,0,0,1,0,0
+5910,Decoupled Access-Execute on ARM big.LITTLE,"  Energy-efficiency plays a significant role given the battery lifetime
+constraints in embedded systems and hand-held devices. In this work we target
+the ARM big.LITTLE, a heterogeneous platform that is dominant in the mobile and
+embedded market, which allows code to run transparently on different
+microarchitectures with individual energy and performance characteristics. It
+allows to se more energy efficient cores to conserve power during simple tasks
+and idle times and switch over to faster, more power hungry cores when
+performance is needed. This proposal explores the power-savings and the
+performance gains that can be achieved by utilizing the ARM big.LITTLE core in
+combination with Decoupled Access-Execute (DAE). DAE is a compiler technique
+that splits code regions into two distinct phases: a memory-bound Access phase
+and a compute-bound Execute phase. By scheduling the memory-bound phase on the
+LITTLE core, and the compute-bound phase on the big core, we conserve energy
+while caching data from main memory and perform computations at maximum
+performance. Our preliminary findings show that applying DAE on ARM big.LITTLE
+has potential. By prefetching data in Access we can achieve an IPC improvement
+of up to 37% in the Execute phase, and manage to shift more than half of the
+program runtime to the LITTLE core. We also provide insight into advantages and
+disadvantages of our approach, present preliminary results and discuss
+potential solutions to overcome locking overhead.
+",1,0,0,0,0,0
+5911,Closure structures parameterized by systems of isotone Galois connections,"  We study properties of classes of closure operators and closure systems
+parameterized by systems of isotone Galois connections. The parameterizations
+express stronger requirements on idempotency and monotony conditions of closure
+operators. The present approach extends previous approaches to fuzzy closure
+operators which appeared in analysis of object-attribute data with graded
+attributes and reasoning with if-then rules in graded setting and is also
+related to analogous results developed in linear temporal logic. In the paper,
+we present foundations of the operators and include examples of general
+problems in data analysis where such operators appear.
+",1,0,0,0,0,0
+5912,High Isolation Improvement in a Compact UWB MIMO Antenna,"  A compact multiple-input-multiple-output (MIMO) antenna with very high
+isolation is proposed for ultrawide-band (UWB) applications. The antenna with a
+compact size of 30.1x20.5 mm^2 (0.31${\lambda}_0$ x0.21${\lambda}_0$ ) consists
+of two planar-monopole antenna elements. It is found that isolation of more
+than 25 dB can be achieved between two parallel monopole antenna elements. For
+the low-frequency isolation, an efficient technique of bending the feed-line
+and applying a new protruded ground is introduced. To increase isolation, a
+design based on suppressing surface wave, near-field, and far-field coupling is
+applied. The simulation and measurement results of the proposed antenna with
+the good agreement are presented and show a bandwidth with S 11 < -10 dB, S 12
+< -25 dB ranged from 3.1 to 10.6 GHz making the proposed antenna a good
+candidate for UWB MIMO systems.
+",0,1,0,0,0,0
+5913,Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition,"  Tensor decompositions are used in various data mining applications from
+social network to medical applications and are extremely useful in discovering
+latent structures or concepts in the data. Many real-world applications are
+dynamic in nature and so are their data. To deal with this dynamic nature of
+data, there exist a variety of online tensor decomposition algorithms. A
+central assumption in all those algorithms is that the number of latent
+concepts remains fixed throughout the entire stream. However, this need not be
+the case. Every incoming batch in the stream may have a different number of
+latent concepts, and the difference in latent concepts from one tensor batch to
+another can provide insights into how our findings in a particular application
+behave and deviate over time. In this paper, we define ""concept"" and ""concept
+drift"" in the context of streaming tensor decomposition, as the manifestation
+of the variability of latent concepts throughout the stream. Furthermore, we
+introduce SeekAndDestroy, an algorithm that detects concept drift in streaming
+tensor decomposition and is able to produce results robust to that drift. To
+the best of our knowledge, this is the first work that investigates concept
+drift in streaming tensor decomposition. We extensively evaluate SeekAndDestroy
+on synthetic datasets, which exhibit a wide variety of realistic drift. Our
+experiments demonstrate the effectiveness of SeekAndDestroy, both in the
+detection of concept drift and in the alleviation of its effects, producing
+results with similar quality to decomposing the entire tensor in one shot.
+Additionally, in real datasets, SeekAndDestroy outperforms other streaming
+baselines, while discovering novel useful components.
+",0,0,0,1,0,0
+5914,Handling Adversarial Concept Drift in Streaming Data,"  Classifiers operating in a dynamic, real world environment, are vulnerable to
+adversarial activity, which causes the data distribution to change over time.
+These changes are traditionally referred to as concept drift, and several
+approaches have been developed in literature to deal with the problem of drift
+handling and detection. However, most concept drift handling techniques,
+approach it as a domain independent task, to make them applicable to a wide
+gamut of reactive systems. These techniques were developed from an adversarial
+agnostic perspective, where they are naive and assume that drift is a benign
+change, which can be fixed by updating the model. However, this is not the case
+when an active adversary is trying to evade the deployed classification system.
+In such an environment, the properties of concept drift are unique, as the
+drift is intended to degrade the system and at the same time designed to avoid
+detection by traditional concept drift detection techniques. This special
+category of drift is termed as adversarial drift, and this paper analyzes its
+characteristics and impact, in a streaming environment. A novel framework for
+dealing with adversarial concept drift is proposed, called the Predict-Detect
+streaming framework. Experimental evaluation of the framework, on generated
+adversarial drifting data streams, demonstrates that this framework is able to
+provide reliable unsupervised indication of drift, and is able to recover from
+drifts swiftly. While traditional partially labeled concept drift detection
+methodologies fail to detect adversarial drifts, the proposed framework is able
+to detect such drifts and operates with <6% labeled data, on average. Also, the
+framework provides benefits for active learning over imbalanced data streams,
+by innately providing for feature space honeypots, where minority class
+adversarial samples may be captured.
+",0,0,0,1,0,0
+5915,Real intersection homology,"  We present a definition of intersection homology for real algebraic varieties
+that is analogous to Goresky and MacPherson's original definition of
+intersection homology for complex varieties.
+",0,0,1,0,0,0
+5916,High Contrast Observations of Bright Stars with a Starshade,"  Starshades are a leading technology to enable the direct detection and
+spectroscopic characterization of Earth-like exoplanets. In an effort to
+advance starshade technology through system level demonstrations, the
+McMath-Pierce Solar Telescope was adapted to enable the suppression of
+astronomical sources with a starshade. The long baselines achievable with the
+heliostat provide measurements of starshade performance at a flight-like
+Fresnel number and resolution, aspects critical to the validation of optical
+models. The heliostat has provided the opportunity to perform the first
+astronomical observations with a starshade and has made science accessible in a
+unique parameter space, high contrast at moderate inner working angles. On-sky
+images are valuable for developing the experience and tools needed to extract
+science results from future starshade observations. We report on high contrast
+observations of nearby stars provided by a starshade. We achieve 5.6e-7
+contrast at 30 arcseconds inner working angle on the star Vega and provide new
+photometric constraints on background stars near Vega.
+",0,1,0,0,0,0
+5917,Unconstrained inverse quadratic programming problem,"  The paper covers a formulation of the inverse quadratic programming problem
+in terms of unconstrained optimization where it is required to find the unknown
+parameters (the matrix of the quadratic form and the vector of the quasi-linear
+part of the quadratic form) provided that approximate estimates of the optimal
+solution of the direct problem and those of the target function to be minimized
+in the form of pairs of values lying in the corresponding neighborhoods are
+only known. The formulation of the inverse problem and its solution are based
+on the least squares method. In the explicit form the inverse problem solution
+has been derived in the form a system of linear equations. The parameters
+obtained can be used for reconstruction of the direct quadratic programming
+problem and determination of the optimal solution and the extreme value of the
+target function, which were not known formerly. It is possible this approach
+opens new ways in over applications, for example, in neurocomputing and quadric
+surfaces fitting. Simple numerical examples have been demonstrated. A scenario
+in the Octave/MATLAB programming language has been proposed for practical
+implementation of the method.
+",1,0,1,0,0,0
+5918,Families of Thue equations associated with a rank one subgroup of the unit group of a number field,"  Twisting a binary form $F_0(X,Y)\in{\mathbb{Z}}[X,Y]$ of degree $d\ge 3$ by
+powers $\upsilon^a$ ($a\in{\mathbb{Z}}$) of an algebraic unit $\upsilon$ gives
+rise to a binary form $F_a(X,Y)\in{\mathbb{Z}}[X,Y]$. More precisely, when $K$
+is a number field of degree $d$, $\sigma_1,\sigma_2,\dots,\sigma_d$ the
+embeddings of $K$ into $\mathbb{C}$, $\alpha$ a nonzero element in $K$,
+$a_0\in{\mathbb{Z}}$, $a_0>0$ and $$ F_0(X,Y)=a_0\displaystyle\prod_{i=1}^d
+(X-\sigma_i(\alpha) Y), $$ then for $a\in{\mathbb{Z}}$ we set $$
+F_a(X,Y)=\displaystyle a_0\prod_{i=1}^d (X-\sigma_i(\alpha\upsilon^a) Y). $$
+Given $m\ge 0$, our main result is an effective upper bound for the solutions
+$(x,y,a)\in{\mathbb{Z}}^3$ of the Diophantine inequalities $$ 0<|F_a(x,y)|\le m
+$$ for which $xy\not=0$ and ${\mathbb{Q}}(\alpha \upsilon^a)=K$. Our estimate
+involves an effectively computable constant depending only on $d$; it is
+explicit in terms of $m$, in terms of the heights of $F_0$ and of $\upsilon$,
+and in terms of the regulator of the number field $K$.
+",0,0,1,0,0,0
+5919,On Defects Between Gapped Boundaries in Two-Dimensional Topological Phases of Matter,"  Defects between gapped boundaries provide a possible physical realization of
+projective non-abelian braid statistics. A notable example is the projective
+Majorana/parafermion braid statistics of boundary defects in fractional quantum
+Hall/topological insulator and superconductor heterostructures. In this paper,
+we develop general theories to analyze the topological properties and
+projective braiding of boundary defects of topological phases of matter in two
+spatial dimensions. We present commuting Hamiltonians to realize defects
+between gapped boundaries in any $(2+1)D$ untwisted Dijkgraaf-Witten theory,
+and use these to describe their topological properties such as their quantum
+dimension. By modeling the algebraic structure of boundary defects through
+multi-fusion categories, we establish a bulk-edge correspondence between
+certain boundary defects and symmetry defects in the bulk. Even though it is
+not clear how to physically braid the defects, this correspondence elucidates
+the projective braid statistics for many classes of boundary defects, both
+amongst themselves and with bulk anyons. Specifically, three such classes of
+importance to condensed matter physics/topological quantum computation are
+studied in detail: (1) A boundary defect version of Majorana and parafermion
+zero modes, (2) a similar version of genons in bilayer theories, and (3)
+boundary defects in $\mathfrak{D}(S_3)$.
+",0,1,1,0,0,0
+5920,"Arbitrary order 2D virtual elements for polygonal meshes: Part II, inelastic problem","  The present paper is the second part of a twofold work, whose first part is
+reported in [3], concerning a newly developed Virtual Element Method (VEM) for
+2D continuum problems. The first part of the work proposed a study for linear
+elastic problem. The aim of this part is to explore the features of the VEM
+formulation when material nonlinearity is considered, showing that the accuracy
+and easiness of implementation discovered in the analysis inherent to the first
+part of the work are still retained. Three different nonlinear constitutive
+laws are considered in the VEM formulation. In particular, the generalized
+viscoplastic model, the classical Mises plasticity with isotropic/kinematic
+hardening and a shape memory alloy (SMA) constitutive law are implemented. The
+versatility with respect to all the considered nonlinear material constitutive
+laws is demonstrated through several numerical examples, also remarking that
+the proposed 2D VEM formulation can be straightforwardly implemented as in a
+standard nonlinear structural finite element method (FEM) framework.
+",0,0,1,0,0,0
+5921,Reconstruction of Hidden Representation for Robust Feature Extraction,"  This paper aims to develop a new and robust approach to feature
+representation. Motivated by the success of Auto-Encoders, we first theoretical
+summarize the general properties of all algorithms that are based on
+traditional Auto-Encoders: 1) The reconstruction error of the input can not be
+lower than a lower bound, which can be viewed as a guiding principle for
+reconstructing the input. Additionally, when the input is corrupted with
+noises, the reconstruction error of the corrupted input also can not be lower
+than a lower bound. 2) The reconstruction of a hidden representation achieving
+its ideal situation is the necessary condition for the reconstruction of the
+input to reach the ideal state. 3) Minimizing the Frobenius norm of the
+Jacobian matrix of the hidden representation has a deficiency and may result in
+a much worse local optimum value. We believe that minimizing the reconstruction
+error of the hidden representation is more robust than minimizing the Frobenius
+norm of the Jacobian matrix of the hidden representation. Based on the above
+analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs),
+which uses corruption and reconstruction on both the input and the hidden
+representation. We demonstrate that the proposed model is highly flexible and
+extensible and has a potentially better capability to learn invariant and
+robust feature representations. We also show that our model is more robust than
+Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features.
+Furthermore, we detail how to train DDAEs with two different pre-training
+methods by optimizing the objective function in a combined and separate manner,
+respectively. Comparative experiments illustrate that the proposed model is
+significantly better for representation learning than the state-of-the-art
+models.
+",1,0,0,1,0,0
+5922,HONE: Higher-Order Network Embeddings,"  This paper describes a general framework for learning Higher-Order Network
+Embeddings (HONE) from graph data based on network motifs. The HONE framework
+is highly expressive and flexible with many interchangeable components. The
+experimental results demonstrate the effectiveness of learning higher-order
+network representations. In all cases, HONE outperforms recent embedding
+methods that are unable to capture higher-order structures with a mean relative
+gain in AUC of $19\%$ (and up to $75\%$ gain) across a wide variety of networks
+and embedding methods.
+",1,0,0,1,0,0
+5923,Cosmological Evolution and Exact Solutions in a Fourth-order Theory of Gravity,"  A fourth-order theory of gravity is considered which in terms of dynamics has
+the same degrees of freedom and number of constraints as those of scalar-tensor
+theories. In addition it admits a canonical point-like Lagrangian description.
+We study the critical points of the theory and we show that it can describe the
+matter epoch of the universe and that two accelerated phases can be recovered
+one of which describes a de Sitter universe. Finally for some models exact
+solutions are presented.
+",0,1,1,0,0,0
+5924,Linear Progress with Exponential Decay in Weakly Hyperbolic Groups,"  A random walk $w_n$ on a separable, geodesic hyperbolic metric space $X$
+converges to the boundary $\partial X$ with probability one when the step
+distribution supports two independent loxodromics. In particular, the random
+walk makes positive linear progress. Progress is known to be linear with
+exponential decay when (1) the step distribution has exponential tail and (2)
+the action on $X$ is acylindrical. We extend exponential decay to the
+non-acylindrical case.
+",0,0,1,0,0,0
+5925,"Talbot-enhanced, maximum-visibility imaging of condensate interference","  Nearly two centuries ago Talbot first observed the fascinating effect whereby
+light propagating through a periodic structure generates a `carpet' of image
+revivals in the near field. Here we report the first observation of the spatial
+Talbot effect for light interacting with periodic Bose-Einstein condensate
+interference fringes. The Talbot effect can lead to dramatic loss of fringe
+visibility in images, degrading precision interferometry, however we
+demonstrate how the effect can also be used as a tool to enhance visibility, as
+well as extend the useful focal range of matter wave detection systems by
+orders of magnitude. We show that negative optical densities arise from
+matter-wave induced lensing of detuned imaging light -- yielding
+Talbot-enhanced single-shot interference visibility of >135% compared to the
+ideal visibility for resonant light.
+",0,1,0,0,0,0
+5926,A numerical study of the F-model with domain-wall boundaries,"  We perform a numerical study of the F-model with domain-wall boundary
+conditions. Various exact results are known for this particular case of the
+six-vertex model, including closed expressions for the partition function for
+any system size as well as its asymptotics and leading finite-size corrections.
+To complement this picture we use a full lattice multi-cluster algorithm to
+study equilibrium properties of this model for systems of moderate size, up to
+L=512. We compare the energy to its exactly known large-L asymptotics. We
+investigate the model's infinite-order phase transition by means of finite-size
+scaling for an observable derived from the staggered polarization in order to
+test the method put forward in our recent joint work with Duine and Barkema. In
+addition we analyse local properties of the model. Our data are perfectly
+consistent with analytical expressions for the arctic curves. We investigate
+the structure inside the temperate region of the lattice, confirming the
+oscillations in vertex densities that were first observed by Sylju{\aa}sen and
+Zvonarev, and recently studied by Lyberg et al. We point out
+'(anti)ferroelectric' oscillations close to the corresponding frozen regions as
+well as 'higher-order' oscillations forming an intricate pattern with
+saddle-point-like features.
+",0,1,0,0,0,0
+5927,Visualizing the Loss Landscape of Neural Nets,"  Neural network training relies on our ability to find ""good"" minimizers of
+highly non-convex loss functions. It is well-known that certain network
+architecture designs (e.g., skip connections) produce loss functions that train
+easier, and well-chosen training parameters (batch size, learning rate,
+optimizer) produce minimizers that generalize better. However, the reasons for
+these differences, and their effects on the underlying loss landscape, are not
+well understood. In this paper, we explore the structure of neural loss
+functions, and the effect of loss landscapes on generalization, using a range
+of visualization methods. First, we introduce a simple ""filter normalization""
+method that helps us visualize loss function curvature and make meaningful
+side-by-side comparisons between loss functions. Then, using a variety of
+visualizations, we explore how network architecture affects the loss landscape,
+and how training parameters affect the shape of minimizers.
+",1,0,0,1,0,0
+5928,Hydrogen bonding characterization in water and small molecules,"  The prototypical Hydrogen bond in water dimer and Hydrogen bonds in the
+protonated water dimer, in other small molecules, in water cyclic clusters, and
+in ice, covering a wide range of bond strengths, are theoretically investigated
+by first-principles calculations based on the Density Functional Theory,
+considering a standard Generalized Gradient Approximation functional but also,
+for the water dimer, hybrid and van-der-Waals corrected functionals. We compute
+structural, energetic, and electrostatic (induced molecular dipole moments)
+properties. In particular, Hydrogen bonds are characterized in terms of
+differential electron densities distributions and profiles, and of the shifts
+of the centres of Maximally localized Wannier Functions. The information from
+the latter quantities can be conveyed into a single geometric bonding parameter
+that appears to be correlated to the Mayer bond order parameter and can be
+taken as an estimate of the covalent contribution to the Hydrogen bond. By
+considering the cyclic water hexamer and the hexagonal phase of ice we also
+elucidate the importance of cooperative/anticooperative effects in
+Hydrogen-bonding formation.
+",0,1,0,0,0,0
+5929,A Calculus of Truly Concurrent Mobile Processes,"  We make a mixture of Milner's $\pi$-calculus and our previous work on truly
+concurrent process algebra, which is called $\pi_{tc}$. We introduce syntax and
+semantics of $\pi_{tc}$, its properties based on strongly truly concurrent
+bisimilarities. Also, we include an axiomatization of $\pi_{tc}$. $\pi_{tc}$
+can be used as a formal tool in verifying mobile systems in a truly concurrent
+flavor.
+",1,0,0,0,0,0
+5930,Gigahertz optomechanical modulation by split-ring-resonator nanophotonic meta-atom arrays,"  Using polarization-resolved transient reflection spectroscopy, we investigate
+the ultrafast modulation of light interacting with a metasurface consisting of
+coherently vibrating nanophotonic meta-atoms in the form of U-shaped split-ring
+resonators, that exhibit co-localized optical and mechanical resonances. With a
+two-dimensional square-lattice array of these resonators formed of gold on a
+glass substrate, we monitor the visible-pump-pulse induced gigahertz
+oscillations in intensity of reflected linearly-polarized infrared probe light
+pulses, modulated by the resonators effectively acting as miniature tuning
+forks. A multimodal vibrational response involving the opening and closing
+motion of the split rings is detected in this way. Numerical simulations of the
+associated transient deformations and strain fields elucidate the complex
+nanomechanical dynamics contributing to the ultrafast optical modulation, and
+point to the role of acousto-plasmonic interactions through the opening and
+closing motion of the SRR gaps as the dominant effect. Applications include
+ultrafast acoustooptic modulator design and sensing.
+",0,1,0,0,0,0
+5931,Guaranteed Fault Detection and Isolation for Switched Affine Models,"  This paper considers the problem of fault detection and isolation (FDI) for
+switched affine models. We first study the model invalidation problem and its
+application to guaranteed fault detection. Novel and intuitive
+optimization-based formulations are proposed for model invalidation and
+T-distinguishability problems, which we demonstrate to be computationally more
+efficient than an earlier formulation that required a complicated change of
+variables. Moreover, we introduce a distinguishability index as a measure of
+separation between the system and fault models, which offers a practical method
+for finding the smallest receding time horizon that is required for fault
+detection, and for finding potential design recommendations for ensuring
+T-distinguishability. Then, we extend our fault detection guarantees to the
+problem of fault isolation with multiple fault models, i.e., the identification
+of the type and location of faults, by introducing the concept of
+I-isolability. An efficient way to implement the FDI scheme is also proposed,
+whose run-time does not grow with the number of fault models that are
+considered. Moreover, we derive bounds on detection and isolation delays and
+present an adaptive scheme for reducing isolation delays. Finally, the
+effectiveness of the proposed method is illustrated using several examples,
+including an HVAC system model with multiple faults.
+",1,0,1,0,0,0
+5932,NOOP: A Domain-Theoretic Model of Nominally-Typed OOP,"  The majority of industrial-strength object-oriented (OO) software is written
+using nominally-typed OO programming languages. Extant domain-theoretic models
+of OOP developed to analyze OO type systems miss, however, a crucial feature of
+these mainstream OO languages: nominality. This paper presents the construction
+of NOOP as the first domain-theoretic model of OOP that includes full
+class/type names information found in nominally-typed OOP. Inclusion of nominal
+information in objects of NOOP and asserting that type inheritance in
+statically-typed OO programming languages is an inherently nominal notion allow
+readily proving that type inheritance and subtyping are completely identified
+in these languages. This conclusion is in full agreement with intuitions of
+developers and language designers of these OO languages, and contrary to the
+belief that ""inheritance is not subtyping,"" which came from assuming
+non-nominal (a.k.a., structural) models of OOP.
+To motivate the construction of NOOP, this paper briefly presents the
+benefits of nominal-typing to mainstream OO developers and OO language
+designers, as compared to structural-typing. After presenting NOOP, the paper
+further briefly compares NOOP to the most widely known domain-theoretic models
+of OOP. Leveraging the development of NOOP, the comparisons presented in this
+paper provide clear, brief and precise technical and mathematical accounts for
+the relation between nominal and structural OO type systems. NOOP, thus,
+provides a firmer semantic foundation for analyzing and progressing
+nominally-typed OO programming languages.
+",1,0,0,0,0,0
+5933,Modelling wave-induced sea ice breakup in the marginal ice zone,"  A model of ice floe breakup under ocean wave forcing in the marginal ice zone
+(MIZ) is proposed to investigate how floe size distribution (FSD) evolves under
+repeated wave breakup events. A three-dimensional linear model of ocean wave
+scattering by a finite array of compliant circular ice floes is coupled to a
+flexural failure model, which breaks a floe into two floes provided the
+two-dimensional stress field satisfies a breakup criterion. A closed-feedback
+loop algorithm is devised, which (i)~solves wave scattering problem for a given
+FSD under time-harmonic plane wave forcing, (ii)~computes the stress field in
+all the floes, (iii)~fractures the floes satisfying the breakup criterion and
+(iv)~generates an updated FSD, initialising the geometry for the next iteration
+of the loop.The FSD after 50 breakup events is uni-modal and near normal, or
+bi-modal. Multiple scattering is found to enhance breakup for long waves and
+thin ice, but to reduce breakup for short waves and thick ice. A breakup front
+marches forward in the latter regime, as wave-induced fracture weakens the ice
+cover allowing waves to travel deeper into the MIZ.
+",0,1,0,0,0,0
+5934,An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference,"  Hidden Markov models (HMMs) are popular time series model in many fields
+including ecology, economics and genetics. HMMs can be defined over discrete or
+continuous time, though here we only cover the former. In the field of movement
+ecology in particular, HMMs have become a popular tool for the analysis of
+movement data because of their ability to connect observed movement data to an
+underlying latent process, generally interpreted as the animal's unobserved
+behavior. Further, we model the tendency to persist in a given behavior over
+time. Notation presented here will generally follow the format of Zucchini et
+al. (2016) and cover HMMs applied in an unsupervised case to animal movement
+data, specifically positional data. We provide Stan code to analyze movement
+data of the wild haggis as presented first in Michelot et al. (2016).
+",0,0,0,1,1,0
+5935,Sampling a Network to Find Nodes of Interest,"  The focus of the current research is to identify people of interest in social
+networks. We are especially interested in studying dark networks, which
+represent illegal or covert activity. In such networks, people are unlikely to
+disclose accurate information when queried. We present REDLEARN, an algorithm
+for sampling dark networks with the goal of identifying as many nodes of
+interest as possible. We consider two realistic lying scenarios, which describe
+how individuals in a dark network may attempt to conceal their connections. We
+test and present our results on several real-world multilayered networks, and
+show that REDLEARN achieves up to a 340% improvement over the next best
+strategy.
+",1,1,0,0,0,0
+5936,Representation Mixing for TTS Synthesis,"  Recent character and phoneme-based parametric TTS systems using deep learning
+have shown strong performance in natural speech generation. However, the choice
+between character or phoneme input can create serious limitations for practical
+deployment, as direct control of pronunciation is crucial in certain cases. We
+demonstrate a simple method for combining multiple types of linguistic
+information in a single encoder, named representation mixing, enabling flexible
+choice between character, phoneme, or mixed representations during inference.
+Experiments and user studies on a public audiobook corpus show the efficacy of
+our approach.
+",1,0,0,0,0,0
+5937,Safe Open-Loop Strategies for Handling Intermittent Communications in Multi-Robot Systems,"  In multi-robot systems where a central decision maker is specifying the
+movement of each individual robot, a communication failure can severely impair
+the performance of the system. This paper develops a motion strategy that
+allows robots to safely handle critical communication failures for such
+multi-robot architectures. For each robot, the proposed algorithm computes a
+time horizon over which collisions with other robots are guaranteed not to
+occur. These safe time horizons are included in the commands being transmitted
+to the individual robots. In the event of a communication failure, the robots
+execute the last received velocity commands for the corresponding safe time
+horizons leading to a provably safe open-loop motion strategy. The resulting
+algorithm is computationally effective and is agnostic to the task that the
+robots are performing. The efficacy of the strategy is verified in simulation
+as well as on a team of differential-drive mobile robots.
+",1,0,0,0,0,0
+5938,Scientific co-authorship networks,"  The paper addresses the stability of the co-authorship networks in time. The
+analysis is done on the networks of Slovenian researchers in two time periods
+(1991-2000 and 2001-2010). Two researchers are linked if they published at
+least one scientific bibliographic unit in a given time period. As proposed by
+Kronegger et al. (2011), the global network structures are examined by
+generalized blockmodeling with the assumed
+multi-core--semi-periphery--periphery blockmodel type. The term core denotes a
+group of researchers who published together in a systematic way with each
+other.
+The obtained blockmodels are comprehensively analyzed by visualizations and
+through considering several statistics regarding the global network structure.
+To measure the stability of the obtained blockmodels, different adjusted
+modified Rand and Wallace indices are applied. Those enable to distinguish
+between the splitting and merging of cores when operationalizing the stability
+of cores. Also, the adjusted modified indices can be used when new researchers
+occur in the second time period (newcomers) and when some researchers are no
+longer present in the second time period (departures). The research disciplines
+are described and clustered according to the values of these indices.
+Considering the obtained clusters, the sources of instability of the research
+disciplines are studied (e.g., merging or splitting of cores, newcomers or
+departures). Furthermore, the differences in the stability of the obtained
+cores on the level of scientific disciplines are studied by linear regression
+analysis where some personal characteristics of the researchers (e.g., age,
+gender), are also considered.
+",1,0,0,1,0,0
+5939,Projection Based Weight Normalization for Deep Neural Networks,"  Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned
+problem. We observe that the scaling-based weight space symmetry property in
+rectified nonlinear network will cause this negative effect. Therefore, we
+propose to constrain the incoming weights of each neuron to be unit-norm, which
+is formulated as an optimization problem over Oblique manifold. A simple yet
+efficient method referred to as projection based weight normalization (PBWN) is
+also developed to solve this problem. PBWN executes standard gradient updates,
+followed by projecting the updated weight back to Oblique manifold. This
+proposed method has the property of regularization and collaborates well with
+the commonly used batch normalization technique. We conduct comprehensive
+experiments on several widely-used image datasets including CIFAR-10,
+CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art
+convolutional neural networks, such as Inception, VGG and residual networks.
+The results show that our method is able to improve the performance of DNNs
+with different architectures consistently. We also apply our method to Ladder
+network for semi-supervised learning on permutation invariant MNIST dataset,
+and our method outperforms the state-of-the-art methods: we obtain test errors
+as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples,
+respectively.
+",1,0,0,0,0,0
+5940,Mapping stable direct and retrograde orbits around the triple system of asteroids (45) Eugenia,"  It is well accepted that knowing the composition and the orbital evolution of
+asteroids may help us to understand the process of formation of the Solar
+System. It is also known that asteroids can represent a threat to our planet.
+Such important role made space missions to asteroids a very popular topic in
+the current astrodynamics and astronomy studies. By taking into account the
+increasingly interest in space missions to asteroids, especially to multiple
+systems, we present a study aimed to characterize the stable and unstable
+regions around the triple system of asteroids (45) Eugenia. The goal is to
+characterize unstable and stable regions of this system and compare with the
+system 2001 SN263 - the target of the ASTER mission. Besides, Prado (2014) used
+a new concept for mapping orbits considering the disturbance received by the
+spacecraft from all the perturbing forces individually. This method was also
+applied to (45) Eugenia. We present the stable and unstable regions for
+particles with relative inclination between 0 and 180 degrees. We found that
+(45) Eugenia presents larger stable regions for both, prograde and retrograde
+cases. This is mainly because the satellites of this system are small when
+compared to the primary body, and because they are not so close to each other.
+We also present a comparison between those two triple systems, and a discussion
+on how these results may guide us in the planning of future missions.
+",0,1,0,0,0,0
+5941,Zinc oxide induces the stringent response and major reorientations in the central metabolism of Bacillus subtilis,"  Microorganisms, such as bacteria, are one of the first targets of
+nanoparticles in the environment. In this study, we tested the effect of two
+nanoparticles, ZnO and TiO2, with the salt ZnSO4 as the control, on the
+Gram-positive bacterium Bacillus subtilis by 2D gel electrophoresis-based
+proteomics. Despite a significant effect on viability (LD50), TiO2 NPs had no
+detectable effect on the proteomic pattern, while ZnO NPs and ZnSO4
+significantly modified B. subtilis metabolism. These results allowed us to
+conclude that the effects of ZnO observed in this work were mainly attributable
+to Zn dissolution in the culture media. Proteomic analysis highlighted twelve
+modulated proteins related to central metabolism: MetE and MccB (cysteine
+metabolism), OdhA, AspB, IolD, AnsB, PdhB and YtsJ (Krebs cycle) and XylA,
+YqjI, Drm and Tal (pentose phosphate pathway). Biochemical assays, such as free
+sulfhydryl, CoA-SH and malate dehydrogenase assays corroborated the observed
+central metabolism reorientation and showed that Zn stress induced oxidative
+stress, probably as a consequence of thiol chelation stress by Zn ions. The
+other patterns affected by ZnO and ZnSO4 were the stringent response and the
+general stress response. Nine proteins involved in or controlled by the
+stringent response showed a modified expression profile in the presence of ZnO
+NPs or ZnSO4: YwaC, SigH, YtxH, YtzB, TufA, RplJ, RpsB, PdhB and Mbl. An
+increase in the ppGpp concentration confirmed the involvement of the stringent
+response during a Zn stress. All these metabolic reorientations in response to
+Zn stress were probably the result of complex regulatory mechanisms including
+at least the stringent response via YwaC.
+",0,0,0,0,1,0
+5942,Learning what matters - Sampling interesting patterns,"  In the field of exploratory data mining, local structure in data can be
+described by patterns and discovered by mining algorithms. Although many
+solutions have been proposed to address the redundancy problems in pattern
+mining, most of them either provide succinct pattern sets or take the interests
+of the user into account-but not both. Consequently, the analyst has to invest
+substantial effort in identifying those patterns that are relevant to her
+specific interests and goals. To address this problem, we propose a novel
+approach that combines pattern sampling with interactive data mining. In
+particular, we introduce the LetSIP algorithm, which builds upon recent
+advances in 1) weighted sampling in SAT and 2) learning to rank in interactive
+pattern mining. Specifically, it exploits user feedback to directly learn the
+parameters of the sampling distribution that represents the user's interests.
+We compare the performance of the proposed algorithm to the state-of-the-art in
+interactive pattern mining by emulating the interests of a user. The resulting
+system allows efficient and interleaved learning and sampling, thus
+user-specific anytime data exploration. Finally, LetSIP demonstrates favourable
+trade-offs concerning both quality-diversity and exploitation-exploration when
+compared to existing methods.
+",1,0,0,1,0,0
+5943,Proofs as Relational Invariants of Synthesized Execution Grammars,"  The automatic verification of programs that maintain unbounded low-level data
+structures is a critical and open problem. Analyzers and verifiers developed in
+previous work can synthesize invariants that only describe data structures of
+heavily restricted forms, or require an analyst to provide predicates over
+program data and structure that are used in a synthesized proof of correctness.
+In this work, we introduce a novel automatic safety verifier of programs that
+maintain low-level data structures, named LTTP. LTTP synthesizes proofs of
+program safety represented as a grammar of a given program's control paths,
+annotated with invariants that relate program state at distinct points within
+its path of execution. LTTP synthesizes such proofs completely automatically,
+using a novel inductive-synthesis algorithm.
+We have implemented LTTP as a verifier for JVM bytecode and applied it to
+verify the safety of a collection of verification benchmarks. Our results
+demonstrate that LTTP can be applied to automatically verify the safety of
+programs that are beyond the scope of previously-developed verifiers.
+",1,0,0,0,0,0
+5944,Orthogonal involutions and totally singular quadratic forms in characteristic two,"  We associate to every central simple algebra with involution of orthogonal
+type in characteristic two a totally singular quadratic form which reflects
+certain anisotropy properties of the involution. It is shown that this
+quadratic form can be used to classify totally decomposable algebras with
+orthogonal involution. Also, using this form, a criterion is obtained for an
+orthogonal involution on a split algebra to be conjugated to the transpose
+involution.
+",0,0,1,0,0,0
+5945,"Coastal flood implications of 1.5 °C, 2.0 °C, and 2.5 °C temperature stabilization targets in the 21st and 22nd century","  Sea-level rise (SLR) is magnifying the frequency and severity of coastal
+flooding. The rate and amount of global mean sea-level (GMSL) rise is a
+function of the trajectory of global mean surface temperature (GMST).
+Therefore, temperature stabilization targets (e.g., 1.5 °C and 2.0 °C
+of warming above pre-industrial levels, as from the Paris Agreement) have
+important implications for coastal flood risk. Here, we assess differences in
+the return periods of coastal floods at a global network of tide gauges between
+scenarios that stabilize GMST warming at 1.5 °C, 2.0 °C, and 2.5
+°C above pre-industrial levels. We employ probabilistic, localized SLR
+projections and long-term hourly tide gauge records to construct estimates of
+the return levels of current and future flood heights for the 21st and 22nd
+centuries. By 2100, under 1.5 °C, 2.0 °C, and 2.5 °C GMST
+stabilization, median GMSL is projected to rise 47 cm with a very likely range
+of 28-82 cm (90% probability), 55 cm (very likely 30-94 cm), and 58 cm (very
+likely 36-93 cm), respectively. As an independent comparison, a semi-empirical
+sea level model calibrated to temperature and GMSL over the past two millennia
+estimates median GMSL will rise within < 13% of these projections. By 2150,
+relative to the 2.0 °C scenario, GMST stabilization of 1.5 °C
+inundates roughly 5 million fewer inhabitants that currently occupy lands,
+including 40,000 fewer individuals currently residing in Small Island
+Developing States. Relative to a 2.0 °C scenario, the reduction in the
+amplification of the frequency of the 100-yr flood arising from a 1.5 °C
+GMST stabilization is greatest in the eastern United States and in Europe, with
+flood frequency amplification being reduced by about half.
+",0,1,0,0,0,0
+5946,Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data,"  We present a factorized hierarchical variational autoencoder, which learns
+disentangled and interpretable representations from sequential data without
+supervision. Specifically, we exploit the multi-scale nature of information in
+sequential data by formulating it explicitly within a factorized hierarchical
+graphical model that imposes sequence-dependent priors and sequence-independent
+priors to different sets of latent variables. The model is evaluated on two
+speech corpora to demonstrate, qualitatively, its ability to transform speakers
+or linguistic content by manipulating different sets of latent variables; and
+quantitatively, its ability to outperform an i-vector baseline for speaker
+verification and reduce the word error rate by as much as 35% in mismatched
+train/test scenarios for automatic speech recognition tasks.
+",1,0,0,1,0,0
+5947,De-blending Deep Herschel Surveys: A Multi-wavelength Approach,"  Cosmological surveys in the far infrared are known to suffer from confusion.
+The Bayesian de-blending tool, XID+, currently provides one of the best ways to
+de-confuse deep Herschel SPIRE images, using a flat flux density prior. This
+work is to demonstrate that existing multi-wavelength data sets can be
+exploited to improve XID+ by providing an informed prior, resulting in more
+accurate and precise extracted flux densities. Photometric data for galaxies in
+the COSMOS field were used to constrain spectral energy distributions (SEDs)
+using the fitting tool CIGALE. These SEDs were used to create Gaussian prior
+estimates in the SPIRE bands for XID+. The multi-wavelength photometry and the
+extracted SPIRE flux densities were run through CIGALE again to allow us to
+compare the performance of the two priors. Inferred ALMA flux densities
+(F$^i$), at 870$\mu$m and 1250$\mu$m, from the best fitting SEDs from the
+second CIGALE run were compared with measured ALMA flux densities (F$^m$) as an
+independent performance validation. Similar validations were conducted with the
+SED modelling and fitting tool MAGPHYS and modified black body functions to
+test for model dependency. We demonstrate a clear improvement in agreement
+between the flux densities extracted with XID+ and existing data at other
+wavelengths when using the new informed Gaussian prior over the original
+uninformed prior. The residuals between F$^m$ and F$^i$ were calculated. For
+the Gaussian prior, these residuals, expressed as a multiple of the ALMA error
+($\sigma$), have a smaller standard deviation, 7.95$\sigma$ for the Gaussian
+prior compared to 12.21$\sigma$ for the flat prior, reduced mean, 1.83$\sigma$
+compared to 3.44$\sigma$, and have reduced skew to positive values, 7.97
+compared to 11.50. These results were determined to not be significantly model
+dependent. This results in statistically more reliable SPIRE flux densities.
+",0,1,0,0,0,0
+5948,Room-Temperature Ionic Liquids Meet Bio-Membranes: the State-of-the- Art,"  Room-temperature ionic liquids (RTIL) are a new class of organic salts whose
+melting temperature falls below the conventional limit of 100C. Their low vapor
+pressure, moreover, has made these ionic compounds the solvents of choice of
+the so-called green chemistry. For these and other peculiar characteristics,
+they are increasingly used in industrial applications. However, studies of
+their interaction with living organisms have highlighted mild to severe health
+hazards. Since their cytotoxicity shows a positive correlation with their
+lipo-philicity, several chemical-physical studies of their interaction with
+biomembranes have been carried out in the last few years, aiming to identify
+the microscopic mechanisms behind their toxicity. Cation chain length and anion
+nature have been seen to affect the lipo-philicity and, in turn, the toxicity
+of RTILs. The emerging picture, however, raises new questions, points to the
+need to assess toxicity on a case-by-case basis, but also suggests a potential
+positive role of RTILs in pharmacology, bio-medicine, and, more in general,
+bio-nano-technology. Here, we review this new subject of research, and comment
+on the future and the potential importance of this new field of study.
+",0,1,0,0,0,0
+5949,"The use of Charts, Pivot Tables, and Array Formulas in two Popular Spreadsheet Corpora","  The use of spreadsheets in industry is widespread. Companies base decisions
+on information coming from spreadsheets. Unfortunately, spreadsheets are
+error-prone and this increases the risk that companies base their decisions on
+inaccurate information, which can lead to incorrect decisions and loss of
+money. In general, spreadsheet research is aimed to reduce the error-proneness
+of spreadsheets. Most research is concentrated on the use of formulas. However,
+there are other constructions in spreadsheets, like charts, pivot tables, and
+array formulas, that are also used to present decision support information to
+the user. There is almost no research about how these constructions are used.
+To improve spreadsheet quality it is important to understand how spreadsheets
+are used and to obtain a complete understanding, the use of charts, pivot
+tables, and array formulas should be included in research. In this paper, we
+analyze two popular spreadsheet corpora: Enron and EUSES on the use of the
+aforementioned constructions.
+",1,0,0,0,0,0
+5950,"Disordered statistical physics in low dimensions: extremes, glass transition, and localization","  This thesis presents original results in two domains of disordered
+statistical physics: logarithmic correlated Random Energy Models (logREMs), and
+localization transitions in long-range random matrices.
+In the first part devoted to logREMs, we show how to characterise their
+common properties and model--specific data. Then we develop their replica
+symmetry breaking treatment, which leads to the freezing scenario of their free
+energy distribution and the general description of their minima process, in
+terms of decorated Poisson point process. We also report a series of new
+applications of the Jack polynomials in the exact predictions of some
+observables in the circular model and its variants. Finally, we present the
+recent progress on the exact connection between logREMs and the Liouville
+conformal field theory.
+The goal of the second part is to introduce and study a new class of banded
+random matrices, the broadly distributed class, which is characterid an
+effective sparseness. We will first study a specific model of the class, the
+Beta Banded random matrices, inspired by an exact mapping to a recently studied
+statistical model of long--range first--passage percolation/epidemics dynamics.
+Using analytical arguments based on the mapping and numerics, we show the
+existence of localization transitions with mobility edges in the
+""stretch--exponential"" parameter--regime of the statistical models. Then, using
+a block--diagonalization renormalization approach, we argue that such
+localization transitions occur generically in the broadly distributed class.
+",0,1,0,0,0,0
+5951,HyperMinHash: MinHash in LogLog space,"  In this extended abstract, we describe and analyze a lossy compression of
+MinHash from buckets of size $O(\log n)$ to buckets of size $O(\log\log n)$ by
+encoding using floating-point notation. This new compressed sketch, which we
+call HyperMinHash, as we build off a HyperLogLog scaffold, can be used as a
+drop-in replacement of MinHash. Unlike comparable Jaccard index fingerprinting
+algorithms in sub-logarithmic space (such as b-bit MinHash), HyperMinHash
+retains MinHash's features of streaming updates, unions, and cardinality
+estimation. For a multiplicative approximation error $1+ \epsilon$ on a Jaccard
+index $ t $, given a random oracle, HyperMinHash needs $O\left(\epsilon^{-2}
+\left( \log\log n + \log \frac{1}{ t \epsilon} \right)\right)$ space.
+HyperMinHash allows estimating Jaccard indices of 0.01 for set cardinalities on
+the order of $10^{19}$ with relative error of around 10\% using 64KiB of
+memory; MinHash can only estimate Jaccard indices for cardinalities of
+$10^{10}$ with the same memory consumption.
+",1,0,0,0,0,0
+5952,Asynchronous stochastic price pump,"  We propose a model for equity trading in a population of agents where each
+agent acts to achieve his or her target stock-to-bond ratio, and, as a feedback
+mechanism, follows a market adaptive strategy. In this model only a fraction of
+agents participates in buying and selling stock during a trading period, while
+the rest of the group accepts the newly set price. Using numerical simulations
+we show that the stochastic process settles on a stationary regime for the
+returns. The mean return can be greater or less than the return on the bond and
+it is determined by the parameters of the adaptive mechanism. When the number
+of interacting agents is fixed, the distribution of the returns follows the
+log-normal density. In this case, we give an analytic formula for the mean rate
+of return in terms of the rate of change of agents' risk levels and confirm the
+formula by numerical simulations. However, when the number of interacting
+agents per period is random, the distribution of returns can significantly
+deviate from the log-normal, especially as the variance of the distribution for
+the number of interacting agents increases.
+",0,0,0,0,0,1
+5953,"Character Distributions of Classical Chinese Literary Texts: Zipf's Law, Genres, and Epochs","  We collect 14 representative corpora for major periods in Chinese history in
+this study. These corpora include poetic works produced in several dynasties,
+novels of the Ming and Qing dynasties, and essays and news reports written in
+modern Chinese. The time span of these corpora ranges between 1046 BCE and 2007
+CE. We analyze their character and word distributions from the viewpoint of the
+Zipf's law, and look for factors that affect the deviations and similarities
+between their Zipfian curves. Genres and epochs demonstrated their influences
+in our analyses. Specifically, the character distributions for poetic works of
+between 618 CE and 1644 CE exhibit striking similarity. In addition, although
+texts of the same dynasty may tend to use the same set of characters, their
+character distributions still deviate from each other.
+",1,0,0,0,0,0
+5954,Improving Bi-directional Generation between Different Modalities with Variational Autoencoders,"  We investigate deep generative models that can exchange multiple modalities
+bi-directionally, e.g., generating images from corresponding texts and vice
+versa. A major approach to achieve this objective is to train a model that
+integrates all the information of different modalities into a joint
+representation and then to generate one modality from the corresponding other
+modality via this joint representation. We simply applied this approach to
+variational autoencoders (VAEs), which we call a joint multimodal variational
+autoencoder (JMVAE). However, we found that when this model attempts to
+generate a large dimensional modality missing at the input, the joint
+representation collapses and this modality cannot be generated successfully.
+Furthermore, we confirmed that this difficulty cannot be resolved even using a
+known solution. Therefore, in this study, we propose two models to prevent this
+difficulty: JMVAE-kl and JMVAE-h. Results of our experiments demonstrate that
+these methods can prevent the difficulty above and that they generate
+modalities bi-directionally with equal or higher likelihood than conventional
+VAE methods, which generate in only one direction. Moreover, we confirm that
+these methods can obtain the joint representation appropriately, so that they
+can generate various variations of modality by moving over the joint
+representation or changing the value of another modality.
+",0,0,0,1,0,0
+5955,Matching neural paths: transfer from recognition to correspondence search,"  Many machine learning tasks require finding per-part correspondences between
+objects. In this work we focus on low-level correspondences - a highly
+ambiguous matching problem. We propose to use a hierarchical semantic
+representation of the objects, coming from a convolutional neural network, to
+solve this ambiguity. Training it for low-level correspondence prediction
+directly might not be an option in some domains where the ground-truth
+correspondences are hard to obtain. We show how transfer from recognition can
+be used to avoid such training. Our idea is to mark parts as ""matching"" if
+their features are close to each other at all the levels of convolutional
+feature hierarchy (neural paths). Although the overall number of such paths is
+exponential in the number of layers, we propose a polynomial algorithm for
+aggregating all of them in a single backward pass. The empirical validation is
+done on the task of stereo correspondence and demonstrates that we achieve
+competitive results among the methods which do not use labeled target domain
+data.
+",1,0,0,0,0,0
+5956,On the affine random walk on the torus,"  Let $\mu$ be a borelian probability measure on
+$\mathbf{G}:=\mathrm{SL}_d(\mathbb{Z}) \ltimes \mathbb{T}^d$. Define, for $x\in
+\mathbb{T}^d$, a random walk starting at $x$ denoting for $n\in \mathbb{N}$, \[
+\left\{\begin{array}{rcl} X_0 &=&x\\ X_{n+1} &=& a_{n+1} X_n + b_{n+1}
+\end{array}\right. \] where $((a_n,b_n))\in \mathbf{G}^\mathbb{N}$ is an iid
+sequence of law $\mu$.
+Then, we denote by $\mathbb{P}_x$ the measure on $(\mathbb{T}^d)^\mathbb{N}$
+that is the image of $\mu^{\otimes \mathbb{N}}$ by the map $\left((g_n) \mapsto
+(x,g_1 x, g_2 g_1 x, \dots , g_n \dots g_1 x, \dots)\right)$ and for any
+$\varphi \in \mathrm{L}^1((\mathbb{T}^d)^\mathbb{N}, \mathbb{P}_x)$, we set
+$\mathbb{E}_x \varphi((X_n)) = \int \varphi((X_n))
+\mathrm{d}\mathbb{P}_x((X_n))$.
+Bourgain, Furmann, Lindenstrauss and Mozes studied this random walk when
+$\mu$ is concentrated on $\mathrm{SL}_d(\mathbb{Z}) \ltimes\{0\}$ and this
+allowed us to study, for any hölder-continuous function $f$ on the torus, the
+sequence $(f(X_n))$ when $x$ is not too well approximable by rational points.
+In this article, we are interested in the case where $\mu$ is not
+concentrated on $\mathrm{SL}_d(\mathbb{Z}) \ltimes \mathbb{Q}^d/\mathbb{Z}^d$
+and we prove that, under assumptions on the group spanned by the support of
+$\mu$, the Lebesgue's measure $\nu$ on the torus is the only stationary
+probability measure and that for any hölder-continuous function $f$ on the
+torus, $\mathbb{E}_x f(X_n)$ converges exponentially fast to $\int
+f\mathrm{d}\nu$.
+Then, we use this to prove the law of large numbers, a non-concentration
+inequality, the functional central limit theorem and it's almost-sure version
+for the sequence $(f(X_n))$.
+In the appendix, we state a non-concentration inequality for products of
+random matrices without any irreducibility assumption.
+",0,0,1,0,0,0
+5957,StackInsights: Cognitive Learning for Hybrid Cloud Readiness,"  Hybrid cloud is an integrated cloud computing environment utilizing a mix of
+public cloud, private cloud, and on-premise traditional IT infrastructures.
+Workload awareness, defined as a detailed full range understanding of each
+individual workload, is essential in implementing the hybrid cloud. While it is
+critical to perform an accurate analysis to determine which workloads are
+appropriate for on-premise deployment versus which workloads can be migrated to
+a cloud off-premise, the assessment is mainly performed by rule or policy based
+approaches. In this paper, we introduce StackInsights, a novel cognitive system
+to automatically analyze and predict the cloud readiness of workloads for an
+enterprise. Our system harnesses the critical metrics across the entire stack:
+1) infrastructure metrics, 2) data relevance metrics, and 3) application
+taxonomy, to identify workloads that have characteristics of a) low sensitivity
+with respect to business security, criticality and compliance, and b) low
+response time requirements and access patterns. Since the capture of the data
+relevance metrics involves an intrusive and in-depth scanning of the content of
+storage objects, a machine learning model is applied to perform the business
+relevance classification by learning from the meta level metrics harnessed
+across stack. In contrast to traditional methods, StackInsights significantly
+reduces the total time for hybrid cloud readiness assessment by orders of
+magnitude.
+",1,0,0,0,0,0
+5958,Risk-averse model predictive control,"  Risk-averse model predictive control (MPC) offers a control framework that
+allows one to account for ambiguity in the knowledge of the underlying
+probability distribution and unifies stochastic and worst-case MPC. In this
+paper we study risk-averse MPC problems for constrained nonlinear Markovian
+switching systems using generic cost functions, and derive Lyapunov-type
+risk-averse stability conditions by leveraging the properties of risk-averse
+dynamic programming operators. We propose a controller design procedure to
+design risk-averse stabilizing terminal conditions for constrained nonlinear
+Markovian switching systems. Lastly, we cast the resulting risk-averse optimal
+control problem in a favorable form which can be solved efficiently and thus
+deems risk-averse MPC suitable for applications.
+",0,0,1,0,0,0
+5959,Neutral evolution and turnover over centuries of English word popularity,"  Here we test Neutral models against the evolution of English word frequency
+and vocabulary at the population scale, as recorded in annual word frequencies
+from three centuries of English language books. Against these data, we test
+both static and dynamic predictions of two neutral models, including the
+relation between corpus size and vocabulary size, frequency distributions, and
+turnover within those frequency distributions. Although a commonly used Neutral
+model fails to replicate all these emergent properties at once, we find that
+modified two-stage Neutral model does replicate the static and dynamic
+properties of the corpus data. This two-stage model is meant to represent a
+relatively small corpus (population) of English books, analogous to a `canon',
+sampled by an exponentially increasing corpus of books in the wider population
+of authors. More broadly, this mode -- a smaller neutral model within a larger
+neutral model -- could represent more broadly those situations where mass
+attention is focused on a small subset of the cultural variants.
+",1,1,0,0,0,0
+5960,On Statistically-Secure Quantum Homomorphic Encryption,"  Homomorphic encryption is an encryption scheme that allows computations to be
+evaluated on encrypted inputs without knowledge of their raw messages. Recently
+Ouyang et al. constructed a quantum homomorphic encryption (QHE) scheme for
+Clifford circuits with statistical security (or information-theoretic security
+(IT-security)). It is desired to see whether an
+information-theoretically-secure (ITS) quantum FHE exists. If not, what other
+nontrivial class of quantum circuits can be homomorphically evaluated with
+IT-security? We provide a limitation for the first question that an ITS quantum
+FHE necessarily incurs exponential overhead. As for the second one, we propose
+a QHE scheme for the instantaneous quantum polynomial-time (IQP) circuits. Our
+QHE scheme for IQP circuits follows from the one-time pad.
+",1,0,0,0,0,0
+5961,Monte Carlo Tree Search for Asymmetric Trees,"  We present an extension of Monte Carlo Tree Search (MCTS) that strongly
+increases its efficiency for trees with asymmetry and/or loops. Asymmetric
+termination of search trees introduces a type of uncertainty for which the
+standard upper confidence bound (UCB) formula does not account. Our first
+algorithm (MCTS-T), which assumes a non-stochastic environment, backs-up tree
+structure uncertainty and leverages it for exploration in a modified UCB
+formula. Results show vastly improved efficiency in a well-known asymmetric
+domain in which MCTS performs arbitrarily bad. Next, we connect the ideas about
+asymmetric termination to the presence of loops in the tree, where the same
+state appears multiple times in a single trace. An extension to our algorithm
+(MCTS-T+), which in addition to non-stochasticity assumes full state
+observability, further increases search efficiency for domains with loops as
+well. Benchmark testing on a set of OpenAI Gym and Atari 2600 games indicates
+that our algorithms always perform better than or at least equivalent to
+standard MCTS, and could be first-choice tree search algorithms for
+non-stochastic, fully-observable environments.
+",0,0,0,1,0,0
+5962,On the difference-to-sum power ratio of speech and wind noise based on the Corcos model,"  The difference-to-sum power ratio was proposed and used to suppress wind
+noise under specific acoustic conditions. In this contribution, a general
+formulation of the difference-to-sum power ratio associated with a mixture of
+speech and wind noise is proposed and analyzed. In particular, it is assumed
+that the complex coherence of convective turbulence can be modelled by the
+Corcos model. In contrast to the work in which the power ratio was first
+presented, the employed Corcos model holds for every possible air stream
+direction and takes into account the lateral coherence decay rate. The obtained
+expression is subsequently validated with real data for a dual microphone
+set-up. Finally, the difference-to- sum power ratio is exploited as a spatial
+feature to indicate the frame-wise presence of wind noise, obtaining improved
+detection performance when compared to an existing multi-channel wind noise
+detection approach.
+",1,0,0,0,0,0
+5963,Quantum torus algebras and B(C) type Toda systems,"  In this paper, we construct a new even constrained B(C) type Toda hierarchy
+and derive its B(C) type Block type additional symmetry. Also we generalize the
+B(C) type Toda hierarchy to the $N$-component B(C) type Toda hierarchy which is
+proved to have symmetries of a coupled $\bigotimes^NQT_+ $ algebra ( $N$-folds
+direct product of the positive half of the quantum torus algebra $QT$).
+",0,1,1,0,0,0
+5964,A Decidable Intuitionistic Temporal Logic,"  We introduce the logic $\sf ITL^e$, an intuitionistic temporal logic based on
+structures $(W,\preccurlyeq,S)$, where $\preccurlyeq$ is used to interpret
+intuitionistic implication and $S$ is a $\preccurlyeq$-monotone function used
+to interpret temporal modalities. Our main result is that the satisfiability
+and validity problems for $\sf ITL^e$ are decidable. We prove this by showing
+that the logic enjoys the strong finite model property. In contrast, we also
+consider a `persistent' version of the logic, $\sf ITL^p$, whose models are
+similar to Cartesian products. We prove that, unlike $\sf ITL^e$, $\sf ITL^p$
+does not have the finite model property.
+",0,0,1,0,0,0
+5965,A Re-weighted Joint Spatial-Radon Domain CT Image Reconstruction Model for Metal Artifact Reduction,"  High density implants such as metals often lead to serious artifacts in the
+reconstructed CT images which hampers the accuracy of image based diagnosis and
+treatment planning. In this paper, we propose a novel wavelet frame based CT
+image reconstruction model to reduce metal artifacts. This model is built on a
+joint spatial and Radon (projection) domain (JSR) image reconstruction
+framework with a built-in weighting and re-weighting mechanism in Radon domain
+to repair degraded projection data. The new weighting strategy used in the
+proposed model not only makes the regularization in Radon domain by wavelet
+frame transform more effective, but also makes the commonly assumed linear
+model for CT imaging a more accurate approximation of the nonlinear physical
+problem. The proposed model, which will be referred to as the re-weighted JSR
+model, combines the ideas of the recently proposed wavelet frame based JSR
+model \cite{Dong2013} and the normalized metal artifact reduction model
+\cite{meyer2010normalized}, and manages to achieve noticeably better CT
+reconstruction quality than both methods. To solve the proposed re-weighted JSR
+model, an efficient alternative iteration algorithm is proposed with guaranteed
+convergence. Numerical experiments on both simulated and real CT image data
+demonstrate the effectiveness of the re-weighted JSR model and its advantage
+over some of the state-of-the-art methods.
+",0,1,1,0,0,0
+5966,A simple proof that the $(n^2-1)$-puzzle is hard,"  The 15 puzzle is a classic reconfiguration puzzle with fifteen uniquely
+labeled unit squares within a $4 \times 4$ board in which the goal is to slide
+the squares (without ever overlapping) into a target configuration. By
+generalizing the puzzle to an $n \times n$ board with $n^2-1$ squares, we can
+study the computational complexity of problems related to the puzzle; in
+particular, we consider the problem of determining whether a given end
+configuration can be reached from a given start configuration via at most a
+given number of moves. This problem was shown NP-complete in Ratner and Warmuth
+(1990). We provide an alternative simpler proof of this fact by reduction from
+the rectilinear Steiner tree problem.
+",1,0,0,0,0,0
+5967,Is Proxima Centauri b habitable? -- A study of atmospheric loss,"  We address the important question of whether the newly discovered exoplanet,
+Proxima Centauri b (PCb), is capable of retaining an atmosphere over long
+periods of time. This is done by adapting a sophisticated multi-species MHD
+model originally developed for Venus and Mars, and computing the ion escape
+losses from PCb. The results suggest that the ion escape rates are about two
+orders of magnitude higher than the terrestrial planets of our Solar system if
+PCb is unmagnetized. In contrast, if the planet does have an intrinsic dipole
+magnetic field, the rates are lowered for certain values of the stellar wind
+dynamic pressure, but they are still higher than the observed values for our
+Solar system's terrestrial planets. These results must be interpreted with due
+caution, since most of the relevant parameters for PCb remain partly or wholly
+unknown.
+",0,1,0,0,0,0
+5968,A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies,"  Joint analysis of multiple phenotypes can increase statistical power in
+genetic association studies. Principal component analysis, as a popular
+dimension reduction method, especially when the number of phenotypes is
+high-dimensional, has been proposed to analyze multiple correlated phenotypes.
+It has been empirically observed that the first PC, which summarizes the
+largest amount of variance, can be less powerful than higher order PCs and
+other commonly used methods in detecting genetic association signals. In this
+paper, we investigate the properties of PCA-based multiple phenotype analysis
+from a geometric perspective by introducing a novel concept called principal
+angle. A particular PC is powerful if its principal angle is $0^o$ and is
+powerless if its principal angle is $90^o$. Without prior knowledge about the
+true principal angle, each PC can be powerless. We propose linear, non-linear
+and data-adaptive omnibus tests by combining PCs. We show that the omnibus PC
+test is robust and powerful in a wide range of scenarios. We study the
+properties of the proposed methods using power analysis and eigen-analysis. The
+subtle differences and close connections between these combined PC methods are
+illustrated graphically in terms of their rejection boundaries. Our proposed
+tests have convex acceptance regions and hence are admissible. The $p$-values
+for the proposed tests can be efficiently calculated analytically and the
+proposed tests have been implemented in a publicly available R package {\it
+MPAT}. We conduct simulation studies in both low and high dimensional settings
+with various signal vectors and correlation structures. We apply the proposed
+tests to the joint analysis of metabolic syndrome related phenotypes with data
+sets collected from four international consortia to demonstrate the
+effectiveness of the proposed combined PC testing procedures.
+",0,0,0,1,0,0
+5969,A Design Based on Stair-case Band Alignment of Electron Transport Layer for Improving Performance and Stability in Planar Perovskite Solar Cells,"  Among the n-type metal oxide materials used in the planar perovskite solar
+cells, zinc oxide (ZnO) is a promising candidate to replace titanium dioxide
+(TiO2) due to its relatively high electron mobility, high transparency, and
+versatile nanostructures. Here, we present the application of low temperature
+solution processed ZnO/Al-doped ZnO (AZO) bilayer thin film as electron
+transport layers (ETLs) in the inverted perovskite solar cells, which provide a
+stair-case band profile. Experimental results revealed that the power
+conversion efficiency (PCE) of perovskite solar cells were significantly
+increased from 12.25 to 16.07% by employing the AZO thin film as the buffer
+layer. Meanwhile, the short-circuit current density (Jsc), open-circuit voltage
+(Voc), and fill factor (FF) were improved to 20.58 mA/cm2, 1.09V, and 71.6%,
+respectively. The enhancement in performance is attributed to the modified
+interface in ETL with stair-case band alignment of ZnO/AZO/CH3NH3PbI3, which
+allows more efficient extraction of photogenerated electrons in the CH3NH3PbI3
+active layer. Thus, it is demonstrated that the ZnO/AZO bilayer ETLs would
+benefit the electron extraction and contribute in enhancing the performance of
+perovskite solar cells.
+",0,1,0,0,0,0
+5970,Statistics on functional data and covariance operators in linear inverse problems,"  We introduce a framework for the statistical analysis of functional data in a
+setting where these objects cannot be fully observed, but only indirect and
+noisy measurements are available, namely an inverse problem setting. The
+proposed methodology can be applied either to the analysis of indirectly
+observed functional data or to the associated covariance operators,
+representing second-order information, and thus lying on a non-Euclidean space.
+To deal with the ill-posedness of the inverse problem, we exploit the spatial
+structure of the sample data by introducing a flexible regularizing term
+embedded in the model. Thanks to its efficiency, the proposed model is applied
+to MEG data, leading to a novel statistical approach to the investigation of
+functional connectivity.
+",0,0,0,1,0,0
+5971,Development of ICA and IVA Algorithms with Application to Medical Image Analysis,"  Independent component analysis (ICA) is a widely used BSS method that can
+uniquely achieve source recovery, subject to only scaling and permutation
+ambiguities, through the assumption of statistical independence on the part of
+the latent sources. Independent vector analysis (IVA) extends the applicability
+of ICA by jointly decomposing multiple datasets through the exploitation of the
+dependencies across datasets. Though both ICA and IVA algorithms cast in the
+maximum likelihood (ML) framework enable the use of all available statistical
+information in reality, they often deviate from their theoretical optimality
+properties due to improper estimation of the probability density function
+(PDF). This motivates the development of flexible ICA and IVA algorithms that
+closely adhere to the underlying statistical description of the data. Although
+it is attractive minimize the assumptions, important prior information about
+the data, such as sparsity, is usually available. If incorporated into the ICA
+model, use of this additional information can relax the independence
+assumption, resulting in an improvement in the overall separation performance.
+Therefore, the development of a unified mathematical framework that can take
+into account both statistical independence and sparsity is of great interest.
+In this work, we first introduce a flexible ICA algorithm that uses an
+effective PDF estimator to accurately capture the underlying statistical
+properties of the data. We then discuss several techniques to accurately
+estimate the parameters of the multivariate generalized Gaussian distribution,
+and how to integrate them into the IVA model. Finally, we provide a
+mathematical framework that enables direct control over the influence of
+statistical independence and sparsity, and use this framework to develop an
+effective ICA algorithm that can jointly exploit these two forms of diversity.
+",0,0,0,1,0,0
+5972,Observability of characteristic binary-induced structures in circumbinary disks,"  Context: A substantial fraction of protoplanetary disks forms around stellar
+binaries. The binary system generates a time-dependent non-axisymmetric
+gravitational potential, inducing strong tidal forces on the circumbinary disk.
+This leads to a change in basic physical properties of the circumbinary disk,
+which should in turn result in unique structures that are potentially
+observable with the current generation of instruments.
+Aims: The goal of this study is to identify these characteristic structures,
+to constrain the physical conditions that cause them, and to evaluate the
+feasibility to observe them in circumbinary disks.
+Methods: To achieve this, at first two-dimensional hydrodynamic simulations
+are performed. The resulting density distributions are post-processed with a 3D
+radiative transfer code to generate re-emission and scattered light maps. Based
+on these, we study the influence of various parameters, such as the mass of the
+stellar components, the mass of the disk and the binary separation on
+observable features in circumbinary disks.
+Results: We find that the Atacama Large (sub-)Millimetre Array (ALMA) as well
+as the European Extremely Large Telescope (E-ELT) are capable of tracing
+asymmetries in the inner region of circumbinary disks which are affected most
+by the binary-disk interaction. Observations at submillimetre/millimetre
+wavelengths will allow the detection of the density waves at the inner rim of
+the disk and the inner cavity. With the E-ELT one can partially resolve the
+innermost parts of the disk in the infrared wavelength range, including the
+disk's rim, accretion arms and potentially the expected circumstellar disks
+around each of the binary components.
+",0,1,0,0,0,0
+5973,Sound Event Detection in Synthetic Audio: Analysis of the DCASE 2016 Task Results,"  As part of the 2016 public evaluation challenge on Detection and
+Classification of Acoustic Scenes and Events (DCASE 2016), the second task
+focused on evaluating sound event detection systems using synthetic mixtures of
+office sounds. This task, which follows the `Event Detection - Office
+Synthetic' task of DCASE 2013, studies the behaviour of tested algorithms when
+facing controlled levels of audio complexity with respect to background noise
+and polyphony/density, with the added benefit of a very accurate ground truth.
+This paper presents the task formulation, evaluation metrics, submitted
+systems, and provides a statistical analysis of the results achieved, with
+respect to various aspects of the evaluation dataset.
+",1,0,0,1,0,0
+5974,Pressure-induced Superconductivity in the Three-component Fermion Topological Semimetal Molybdenum Phosphide,"  Topological semimetal, a novel state of quantum matter hosting exotic
+emergent quantum phenomena dictated by the non-trivial band topology, has
+emerged as a new frontier in condensed-matter physics. Very recently, a
+coexistence of triply degenerate points of band crossing and Weyl points near
+the Fermi level was theoretically predicted and immediately experimentally
+verified in single crystalline molybdenum phosphide (MoP). Here we show in this
+material the high-pressure electronic transport and synchrotron X-ray
+diffraction (XRD) measurements, combined with density functional theory (DFT)
+calculations. We report the emergence of pressure-induced superconductivity in
+MoP with a critical temperature Tc of about 2 K at 27.6 GPa, rising to 3.7 K at
+the highest pressure of 95.0 GPa studied. No structural phase transitions is
+detected up to 60.6 GPa from the XRD. Meanwhile, the Weyl points and triply
+degenerate points topologically protected by the crystal symmetry are retained
+at high pressure as revealed by our DFT calculations. The coexistence of
+three-component fermion and superconductivity in heavily pressurized MoP offers
+an excellent platform to study the interplay between topological phase of
+matter and superconductivity.
+",0,1,0,0,0,0
+5975,Chaotic zones around rotating small bodies,"  Small bodies of the Solar system, like asteroids, trans-Neptunian objects,
+cometary nuclei, planetary satellites, with diameters smaller than one thousand
+kilometers usually have irregular shapes, often resembling dumb-bells, or
+contact binaries. The spinning of such a gravitating dumb-bell creates around
+it a zone of chaotic orbits. We determine its extent analytically and
+numerically. We find that the chaotic zone swells significantly if the rotation
+rate is decreased, in particular, the zone swells more than twice if the
+rotation rate is decreased ten times with respect to the ""centrifugal breakup""
+threshold. We illustrate the properties of the chaotic orbital zones in
+examples of the global orbital dynamics about asteroid 243 Ida (which has a
+moon, Dactyl, orbiting near the edge of the chaotic zone) and asteroid 25143
+Itokawa.
+",0,1,0,0,0,0
+5976,Collective excitations and supersolid behavior of bosonic atoms inside two crossed optical cavities,"  We discuss the nature of symmetry breaking and the associated collective
+excitations for a system of bosons coupled to the electromagnetic field of two
+optical cavities. For the specific configuration realized in a recent
+experiment at ETH, we show that, in absence of direct intercavity scattering
+and for parameters chosen such that the atoms couple symmetrically to both
+cavities, the system possesses an approximate $U(1)$ symmetry which holds
+asymptotically for vanishing cavity field intensity. It corresponds to the
+invariance with respect to redistributing the total intensity $I=I_1+I_2$
+between the two cavities. The spontaneous breaking of this symmetry gives rise
+to a broken continuous translation-invariance for the atoms, creating a
+supersolid-like order in the presence of a Bose-Einstein condensate. In
+particular, we show that atom-mediated scattering between the two cavities,
+which favors the state with equal light intensities $I_1=I_2$ and reduces the
+symmetry to $\mathbf{Z}_2\otimes \mathbf{Z}_2$, gives rise to a finite value
+$\sim \sqrt{I}$ of the effective Goldstone mass. For strong atom driving, this
+low energy mode is clearly separated from an effective Higgs excitation
+associated with changes of the total intensity $I$. In addition, we compute the
+spectral distribution of the cavity light field and show that both the Higgs
+and Goldstone mode acquire a finite lifetime due to Landau damping at non-zero
+temperature.
+",0,1,0,0,0,0
+5977,Generalized Value Iteration Networks: Life Beyond Lattices,"  In this paper, we introduce a generalized value iteration network (GVIN),
+which is an end-to-end neural network planning module. GVIN emulates the value
+iteration algorithm by using a novel graph convolution operator, which enables
+GVIN to learn and plan on irregular spatial graphs. We propose three novel
+differentiable kernels as graph convolution operators and show that the
+embedding based kernel achieves the best performance. We further propose
+episodic Q-learning, an improvement upon traditional n-step Q-learning that
+stabilizes training for networks that contain a planning module. Lastly, we
+evaluate GVIN on planning problems in 2D mazes, irregular graphs, and
+real-world street networks, showing that GVIN generalizes well for both
+arbitrary graphs and unseen graphs of larger scale and outperforms a naive
+generalization of VIN (discretizing a spatial graph into a 2D image).
+",1,0,0,0,0,0
+5978,Structure and Evolution of Internally Heated Hot Jupiters,"  Hot Jupiters receive strong stellar irradiation, producing equilibrium
+temperatures of $1000 - 2500 \ \mathrm{Kelvin}$. Incoming irradiation directly
+heats just their thin outer layer, down to pressures of $\sim 0.1 \
+\mathrm{bars}$. In standard irradiated evolution models of hot Jupiters,
+predicted transit radii are too small. Previous studies have shown that deeper
+heating -- at a small fraction of the heating rate from irradiation -- can
+explain observed radii. Here we present a suite of evolution models for HD
+209458b where we systematically vary both the depth and intensity of internal
+heating, without specifying the uncertain heating mechanism(s). Our models
+start with a hot, high entropy planet whose radius decreases as the convective
+interior cools. The applied heating suppresses this cooling. We find that very
+shallow heating -- at pressures of $1 - 10 \ \mathrm{bars}$ -- does not
+significantly suppress cooling, unless the total heating rate is $\gtrsim 10\%$
+of the incident stellar power. Deeper heating, at $100 \ \mathrm{bars}$,
+requires heating at only $1\%$ of the stellar irradiation to explain the
+observed transit radius of $1.4 R_{\rm Jup}$ after 5 Gyr of cooling. In
+general, more intense and deeper heating results in larger hot Jupiter radii.
+Surprisingly, we find that heat deposited at $10^4 \ \mathrm{bars}$ -- which is
+exterior to $\approx 99\%$ of the planet's mass -- suppresses planetary cooling
+as effectively as heating at the center. In summary, we find that relatively
+shallow heating is required to explain the radii of most hot Jupiters, provided
+that this heat is applied early and persists throughout their evolution.
+",0,1,0,0,0,0
+5979,Inference-Based Distributed Channel Allocation in Wireless Sensor Networks,"  Interference-aware resource allocation of time slots and frequency channels
+in single-antenna, halfduplex radio wireless sensor networks (WSN) is
+challenging. Devising distributed algorithms for such task further complicates
+the problem. This work studiesWSN joint time and frequency channel allocation
+for a given routing tree, such that: a) allocation is performed in a fully
+distributed way, i.e., information exchange is only performed among neighboring
+WSN terminals, within communication up to two hops, and b) detection of
+potential interfering terminals is simplified and can be practically realized.
+The algorithm imprints space, time, frequency and radio hardware constraints
+into a loopy factor graph and performs iterative message passing/ loopy belief
+propagation (BP) with randomized initial priors. Sufficient conditions for
+convergence to a valid solution are offered, for the first time in the
+literature, exploiting the structure of the proposed factor graph. Based on
+theoretical findings, modifications of BP are devised that i) accelerate
+convergence to a valid solution and ii) reduce computation cost. Simulations
+reveal promising throughput results of the proposed distributed algorithm, even
+though it utilizes simplified interfering terminals set detection. Future work
+could modify the constraints such that other disruptive wireless technologies
+(e.g., full-duplex radios or network coding) could be accommodated within the
+same inference framework.
+",1,0,0,0,0,0
+5980,Switch Functions,"  We define a switch function to be a function from an interval to $\{1,-1\}$
+with a finite number of sign changes. (Special cases are the Walsh functions.)
+By a topological argument, we prove that, given $n$ real-valued functions,
+$f_1, \dots, f_n$, in $L^1[0,1]$, there exists a switch function, $\sigma$,
+with at most $n$ sign changes that is simultaneously orthogonal to all of them
+in the sense that $\int_0^1 \sigma(t)f_i(t)dt=0$, for all $i = 1, \dots , n$.
+Moreover, we prove that, for each $\lambda \in (-1,1)$, there exists a unique
+switch function, $\sigma$, with $n$ switches such that $\int_0^1 \sigma(t) p(t)
+dt = \lambda \int_0^1 p(t)dt$ for every real polynomial $p$ of degree at most
+$n-1$. We also prove the same statement holds for every real even polynomial of
+degree at most $2n-2$. Furthermore, for each of these latter results, we write
+down, in terms of $\lambda$ and $n$, a degree $n$ polynomial whose roots are
+the switch points of $\sigma$; we are thereby able to compute these switch
+functions.
+",0,0,1,0,0,0
+5981,Schrödinger operators periodic in octants,"  We consider Schrödinger operators with periodic potentials in the positive
+quadrant for dim $>1$ with Dirichlet boundary condition. We show that for any
+integer $N$ and any interval $I$ there exists a periodic potential such that
+the Schrödinger operator has $N$ eigenvalues counted with the multiplicity on
+this interval and there is no other spectrum on the interval. Furthermore, to
+the right and to the left of it there is a essential spectrum.
+Moreover, we prove similar results for Schrödinger operators for other
+domains. The proof is based on the inverse spectral theory for Hill operators
+on the real line.
+",0,0,1,0,0,0
+5982,First international comparison of fountain primary frequency standards via a long distance optical fiber link,"  We report on the first comparison of distant caesium fountain primary
+frequency standards (PFSs) via an optical fiber link. The 1415 km long optical
+link connects two PFSs at LNE-SYRTE (Laboratoire National de métrologie et
+d'Essais - SYstème de Références Temps-Espace) in Paris (France)
+with two at PTB (Physikalisch-Technische Bundesanstalt) in Braunschweig
+(Germany). For a long time, these PFSs have been major contributors to accuracy
+of the International Atomic Time (TAI), with stated accuracies of around
+$3\times 10^{-16}$. They have also been the references for a number of absolute
+measurements of clock transition frequencies in various optical frequency
+standards in view of a future redefinition of the second. The phase coherent
+optical frequency transfer via a stabilized telecom fiber link enables far
+better resolution than any other means of frequency transfer based on satellite
+links. The agreement for each pair of distant fountains compared is well within
+the combined uncertainty of a few 10$^{-16}$ for all the comparisons, which
+fully supports the stated PFSs' uncertainties. The comparison also includes a
+rubidium fountain frequency standard participating in the steering of TAI and
+enables a new absolute determination of the $^{87}$Rb ground state hyperfine
+transition frequency with an uncertainty of $3.1\times 10^{-16}$.
+This paper is dedicated to the memory of André Clairon, who passed away
+on the 24$^{th}$ of December 2015, for his pioneering and long-lasting efforts
+in atomic fountains. He also pioneered optical links from as early as 1997.
+",0,1,0,0,0,0
+5983,"Hardy inequalities, Rellich inequalities and local Dirichlet forms","  First the Hardy and Rellich inequalities are defined for the submarkovian
+operator associated with a local Dirichlet form. Secondly, two general
+conditions are derived which are sufficient to deduce the Rellich inequality
+from the Hardy inequality. In addition the Rellich constant is calculated from
+the Hardy constant. Thirdly, we establish that the criteria for the Rellich
+inequality are verified for a large class of weighted second-order operators on
+a domain $\Omega\subseteq \Ri^d$. The weighting near the boundary $\partial
+\Omega$ can be different from the weighting at infinity. Finally these results
+are applied to weighted second-order operators on $\Ri^d\backslash\{0\}$ and to
+a general class of operators of Grushin type.
+",0,0,1,0,0,0
+5984,Optimized Quantification of Spin Relaxation Times in the Hybrid State,"  Purpose: The analysis of optimized spin ensemble trajectories for relaxometry
+in the hybrid state.
+Methods: First, we constructed visual representations to elucidate the
+differential equation that governs spin dynamics in hybrid state. Subsequently,
+numerical optimizations were performed to find spin ensemble trajectories that
+minimize the Cramér-Rao bound for $T_1$-encoding, $T_2$-encoding, and their
+weighted sum, respectively, followed by a comparison of the Cramér-Rao bounds
+obtained with our optimized spin-trajectories, as well as Look-Locker and
+multi-spin-echo methods. Finally, we experimentally tested our optimized spin
+trajectories with in vivo scans of the human brain.
+Results: After a nonrecurring inversion segment on the southern hemisphere of
+the Bloch sphere, all optimized spin trajectories pursue repetitive loops on
+the northern half of the sphere in which the beginning of the first and the end
+of the last loop deviate from the others. The numerical results obtained in
+this work align well with intuitive insights gleaned directly from the
+governing equation. Our results suggest that hybrid-state sequences outperform
+traditional methods. Moreover, hybrid-state sequences that balance $T_1$- and
+$T_2$-encoding still result in near optimal signal-to-noise efficiency. Thus,
+the second parameter can be encoded at virtually no extra cost.
+Conclusion: We provide insights regarding the optimal encoding processes of
+spin relaxation times in order to guide the design of robust and efficient
+pulse sequences. We find that joint acquisitions of $T_1$ and $T_2$ in the
+hybrid state are substantially more efficient than sequential encoding
+techniques.
+",0,1,0,0,0,0
+5985,On the generation of drift flows in wall-bounded flows transiting to turbulence,"  Despite recent progress, laminar-turbulent coexistence in transitional planar
+wall-bounded shear flows is still not well understood. Contrasting with the
+processes by which chaotic flow inside turbulent patches is sustained at the
+local (minimal flow unit) scale, the mechanisms controlling the obliqueness of
+laminar-turbulent interfaces typically observed all along the coexistence range
+are still mysterious. An extension of Waleffe's approach [Phys. Fluids 9 (1997)
+883--900] is used to show that, already at the local scale, drift flows
+breaking the problem's spanwise symmetry are generated just by slightly
+detuning the modes involved in the self-sustainment process. This opens
+perspectives for theorizing the formation of laminar-turbulent patterns.
+",0,1,0,0,0,0
+5986,Goldbach's Function Approximation Using Deep Learning,"  Goldbach conjecture is one of the most famous open mathematical problems. It
+states that every even number, bigger than two, can be presented as a sum of 2
+prime numbers. % In this work we present a deep learning based model that
+predicts the number of Goldbach partitions for a given even number.
+Surprisingly, our model outperforms all state-of-the-art analytically derived
+estimations for the number of couples, while not requiring prime factorization
+of the given number. We believe that building a model that can accurately
+predict the number of couples brings us one step closer to solving one of the
+world most famous open problems. To the best of our knowledge, this is the
+first attempt to consider machine learning based data-driven methods to
+approximate open mathematical problems in the field of number theory, and hope
+that this work will encourage such attempts.
+",0,0,0,1,0,0
+5987,Estimation of a Continuous Distribution on a Real Line by Discretization Methods -- Complete Version--,"  For an unknown continuous distribution on a real line, we consider the
+approximate estimation by the discretization. There are two methods for the
+discretization. First method is to divide the real line into several intervals
+before taking samples (""fixed interval method"") . Second method is dividing the
+real line using the estimated percentiles after taking samples (""moving
+interval method""). In either way, we settle down to the estimation problem of a
+multinomial distribution. We use (symmetrized) $f$-divergence in order to
+measure the discrepancy of the true distribution and the estimated one. Our
+main result is the asymptotic expansion of the risk (i.e. expected divergence)
+up to the second-order term in the sample size. We prove theoretically that the
+moving interval method is asymptotically superior to the fixed interval method.
+We also observe how the presupposed intervals (fixed interval method) or
+percentiles (moving interval method) affect the asymptotic risk.
+",0,0,1,1,0,0
+5988,Reviving and Improving Recurrent Back-Propagation,"  In this paper, we revisit the recurrent back-propagation (RBP) algorithm,
+discuss the conditions under which it applies as well as how to satisfy them in
+deep neural networks. We show that RBP can be unstable and propose two variants
+based on conjugate gradient on the normal equations (CG-RBP) and Neumann series
+(Neumann-RBP). We further investigate the relationship between Neumann-RBP and
+back propagation through time (BPTT) and its truncated version (TBPTT). Our
+Neumann-RBP has the same time complexity as TBPTT but only requires constant
+memory, whereas TBPTT's memory cost scales linearly with the number of
+truncation steps. We examine all RBP variants along with BPTT and TBPTT in
+three different application domains: associative memory with continuous
+Hopfield networks, document classification in citation networks using graph
+neural networks and hyperparameter optimization for fully connected networks.
+All experiments demonstrate that RBPs, especially the Neumann-RBP variant, are
+efficient and effective for optimizing convergent recurrent neural networks.
+",0,0,0,1,0,0
+5989,A family of Dirichlet-Morrey spaces,"  To each weighted Dirichlet space $\mathcal{D}_p$, $0<p<1$, we associate a
+family of Morrey-type spaces ${\mathcal{D}}_p^{\lambda}$, $0< \lambda < 1$,
+constructed by imposing growth conditions on the norm of hyperbolic translates
+of functions. We indicate some of the properties of these spaces, mention the
+characterization in terms of boundary values, and study integration and
+multiplication operators on them.
+",0,0,1,0,0,0
+5990,Merging real and virtual worlds: An analysis of the state of the art and practical evaluation of Microsoft Hololens,"  Achieving a symbiotic blending between reality and virtuality is a dream that
+has been lying in the minds of many people for a long time. Advances in various
+domains constantly bring us closer to making that dream come true. Augmented
+reality as well as virtual reality are in fact trending terms and are expected
+to further progress in the years to come.
+This master's thesis aims to explore these areas and starts by defining
+necessary terms such as augmented reality (AR) or virtual reality (VR). Usual
+taxonomies to classify and compare the corresponding experiences are then
+discussed.
+In order to enable those applications, many technical challenges need to be
+tackled, such as accurate motion tracking with 6 degrees of freedom (positional
+and rotational), that is necessary for compelling experiences and to prevent
+user sickness. Additionally, augmented reality experiences typically rely on
+image processing to position the superimposed content. To do so, ""paper""
+markers or features extracted from the environment are often employed. Both
+sets of techniques are explored and common solutions and algorithms are
+presented.
+After investigating those technical aspects, I carry out an objective
+comparison of the existing state-of-the-art and state-of-the-practice in those
+domains, and I discuss present and potential applications in these areas. As a
+practical validation, I present the results of an application that I have
+developed using Microsoft HoloLens, one of the more advanced affordable
+technologies for augmented reality that is available today. Based on the
+experience and lessons learned during this development, I discuss the
+limitations of current technologies and present some avenues of future
+research.
+",1,0,0,0,0,0
+5991,Fast Characterization of Segmental Duplications in Genome Assemblies,"  Segmental duplications (SDs), or low-copy repeats (LCR), are segments of DNA
+greater than 1 Kbp with high sequence identity that are copied to other regions
+of the genome. SDs are among the most important sources of evolution, a common
+cause of genomic structural variation, and several are associated with diseases
+of genomic origin. Despite their functional importance, SDs present one of the
+major hurdles for de novo genome assembly due to the ambiguity they cause in
+building and traversing both state-of-the-art overlap-layout-consensus and de
+Bruijn graphs. This causes SD regions to be misassembled, collapsed into a
+unique representation, or completely missing from assembled reference genomes
+for various organisms. In turn, this missing or incorrect information limits
+our ability to fully understand the evolution and the architecture of the
+genomes. Despite the essential need to accurately characterize SDs in
+assemblies, there is only one tool that has been developed for this purpose,
+called Whole Genome Assembly Comparison (WGAC). WGAC is comprised of several
+steps that employ different tools and custom scripts, which makes it difficult
+and time consuming to use. Thus there is still a need for algorithms to
+characterize within-assembly SDs quickly, accurately, and in a user friendly
+manner.
+Here we introduce a SEgmental Duplication Evaluation Framework (SEDEF) to
+rapidly detect SDs through sophisticated filtering strategies based on Jaccard
+similarity and local chaining. We show that SEDEF accurately detects SDs while
+maintaining substantial speed up over WGAC that translates into practical run
+times of minutes instead of weeks. Notably, our algorithm captures up to 25%
+pairwise error between segments, where previous studies focused on only 10%,
+allowing us to more deeply track the evolutionary history of the genome.
+SEDEF is available at this https URL
+",0,0,0,0,1,0
+5992,Cross validation for locally stationary processes,"  We propose an adaptive bandwidth selector via cross validation for local
+M-estimators in locally stationary processes. We prove asymptotic optimality of
+the procedure under mild conditions on the underlying parameter curves. The
+results are applicable to a wide range of locally stationary processes such
+linear and nonlinear processes. A simulation study shows that the method works
+fairly well also in misspecified situations.
+",0,0,1,1,0,0
+5993,"Weyl nodes in Andreev spectra of multiterminal Josephson junctions: Chern numbers, conductances and supercurrents","  We consider mesoscopic four-terminal Josephson junctions and study emergent
+topological properties of the Andreev subgap bands. We use symmetry-constrained
+analysis for Wigner-Dyson classes of scattering matrices to derive band
+dispersions. When scattering matrix of the normal region connecting
+superconducting leads is energy-independent, the determinant formula for
+Andreev spectrum can be reduced to a palindromic equation that admits a
+complete analytical solution. Band topology manifests with an appearance of the
+Weyl nodes which serve as monopoles of finite Berry curvature. The
+corresponding fluxes are quantified by Chern numbers that translate into a
+quantized nonlocal conductance that we compute explicitly for the
+time-reversal-symmetric scattering matrix. The topological regime can be also
+identified by supercurrents as Josephson current-phase relationships exhibit
+pronounced nonanalytic behavior and discontinuities near Weyl points that can
+be controllably accessed in experiments.
+",0,1,0,0,0,0
+5994,Injectivity of the connecting homomorphisms,"  Let $A$ be the inductive limit of a sequence $$A_1\, \xrightarrow{\phi_{1,2}}
+\,A_2\,\xrightarrow{\phi_{2,3}} \,A_3\rightarrow\cdots$$ with
+$A_n=\oplus_{i=1}^{n_i}A_{[n,i]}$, where all the $A_{[n,i]}$ are
+Elliott-Thomsen algebras and $\phi_{n,n+1}$ are homomorphisms, in this paper,
+we will prove that $A$ can be written as another inductive limit
+$$B_1\,\xrightarrow{\psi_{1,2}} \,B_2\,\xrightarrow{\psi_{2,3}}
+\,B_3\rightarrow\cdots$$ with $B_n=\oplus_{i=1}^{n_i}B_{[n,i]}$, where all the
+$B_{[n,i]}$ are Elliott-Thomsen building blocks and with the extra condition
+that all the $\phi_{n,n+1}$ are injective.
+",0,0,1,0,0,0
+5995,Selective Inference for Change Point Detection in Multi-dimensional Sequences,"  We study the problem of detecting change points (CPs) that are characterized
+by a subset of dimensions in a multi-dimensional sequence. A method for
+detecting those CPs can be formulated as a two-stage method: one for selecting
+relevant dimensions, and another for selecting CPs. It has been difficult to
+properly control the false detection probability of these CP detection methods
+because selection bias in each stage must be properly corrected. Our main
+contribution in this paper is to formulate a CP detection problem as a
+selective inference problem, and show that exact (non-asymptotic) inference is
+possible for a class of CP detection methods. We demonstrate the performances
+of the proposed selective inference framework through numerical simulations and
+its application to our motivating medical data analysis problem.
+",0,0,0,1,0,0
+5996,RPC: A Large-Scale Retail Product Checkout Dataset,"  Over recent years, emerging interest has occurred in integrating computer
+vision technology into the retail industry. Automatic checkout (ACO) is one of
+the critical problems in this area which aims to automatically generate the
+shopping list from the images of the products to purchase. The main challenge
+of this problem comes from the large scale and the fine-grained nature of the
+product categories as well as the difficulty for collecting training images
+that reflect the realistic checkout scenarios due to continuous update of the
+products. Despite its significant practical and research value, this problem is
+not extensively studied in the computer vision community, largely due to the
+lack of a high-quality dataset. To fill this gap, in this work we propose a new
+dataset to facilitate relevant research. Our dataset enjoys the following
+characteristics: (1) It is by far the largest dataset in terms of both product
+image quantity and product categories. (2) It includes single-product images
+taken in a controlled environment and multi-product images taken by the
+checkout system. (3) It provides different levels of annotations for the
+check-out images. Comparing with the existing datasets, ours is closer to the
+realistic setting and can derive a variety of research problems. Besides the
+dataset, we also benchmark the performance on this dataset with various
+approaches. The dataset and related resources can be found at
+\url{this https URL}.
+",1,0,0,0,0,0
+5997,Anyonic self-induced disorder in a stabilizer code: quasi-many body localization in a translational invariant model,"  We enquire into the quasi-many-body localization in topologically ordered
+states of matter, revolving around the case of Kitaev toric code on ladder
+geometry, where different types of anyonic defects carry different masses
+induced by environmental errors. Our study verifies that random arrangement of
+anyons generates a complex energy landscape solely through braiding statistics,
+which suffices to suppress the diffusion of defects in such multi-component
+anyonic liquid. This non-ergodic dynamic suggests a promising scenario for
+investigation of quasi-many-body localization. Computing standard diagnostics
+evidences that, in such disorder-free many-body system, a typical initial
+inhomogeneity of anyons gives birth to a glassy dynamics with an exponentially
+diverging time scale of the full relaxation. A by-product of this dynamical
+effect is manifested by the slow growth of entanglement entropy, with
+characteristic time scales bearing resemblance to those of inhomogeneity
+relaxation. This setting provides a new platform which paves the way toward
+impeding logical errors by self-localization of anyons in a generic, high
+energy state, originated in their exotic statistics.
+",0,1,0,0,0,0
+5998,KiDS-450: Tomographic Cross-Correlation of Galaxy Shear with {\it Planck} Lensing,"  We present the tomographic cross-correlation between galaxy lensing measured
+in the Kilo Degree Survey (KiDS-450) with overlapping lensing measurements of
+the cosmic microwave background (CMB), as detected by Planck 2015. We compare
+our joint probe measurement to the theoretical expectation for a flat
+$\Lambda$CDM cosmology, assuming the best-fitting cosmological parameters from
+the KiDS-450 cosmic shear and Planck CMB analyses. We find that our results are
+consistent within $1\sigma$ with the KiDS-450 cosmology, with an amplitude
+re-scaling parameter $A_{\rm KiDS} = 0.86 \pm 0.19$. Adopting a Planck
+cosmology, we find our results are consistent within $2\sigma$, with $A_{\it
+Planck} = 0.68 \pm 0.15$. We show that the agreement is improved in both cases
+when the contamination to the signal by intrinsic galaxy alignments is
+accounted for, increasing $A$ by $\sim 0.1$. This is the first tomographic
+analysis of the galaxy lensing -- CMB lensing cross-correlation signal, and is
+based on five photometric redshift bins. We use this measurement as an
+independent validation of the multiplicative shear calibration and of the
+calibrated source redshift distribution at high redshifts. We find that
+constraints on these two quantities are strongly correlated when obtained from
+this technique, which should therefore not be considered as a stand-alone
+competitive calibration tool.
+",0,1,0,0,0,0
+5999,Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network,"  Earthquake Early Warning (EEW) systems can effectively reduce fatalities,
+injuries, and damages caused by earthquakes. Current EEW systems are mostly
+based on traditional seismic and geodetic networks, and exist only in a few
+countries due to the high cost of installing and maintaining such systems. The
+MyShake system takes a different approach and turns people's smartphones into
+portable seismic sensors to detect earthquake-like motions. However, to issue
+EEW messages with high accuracy and low latency in the real world, we need to
+address a number of challenges related to mobile computing. In this paper, we
+first summarize our experience building and deploying the MyShake system, then
+focus on two key challenges for smartphone-based EEW (sensing heterogeneity and
+user/system dynamics) and some preliminary exploration. We also discuss other
+challenges and new research directions associated with smartphone-based seismic
+network.
+",1,0,0,0,0,0
+6000,Gate-error analysis in simulations of quantum computers with transmon qubits,"  In the model of gate-based quantum computation, the qubits are controlled by
+a sequence of quantum gates. In superconducting qubit systems, these gates can
+be implemented by voltage pulses. The success of implementing a particular gate
+can be expressed by various metrics such as the average gate fidelity, the
+diamond distance, and the unitarity. We analyze these metrics of gate pulses
+for a system of two superconducting transmon qubits coupled by a resonator, a
+system inspired by the architecture of the IBM Quantum Experience. The metrics
+are obtained by numerical solution of the time-dependent Schrödinger equation
+of the transmon system. We find that the metrics reflect systematic errors that
+are most pronounced for echoed cross-resonance gates, but that none of the
+studied metrics can reliably predict the performance of a gate when used
+repeatedly in a quantum algorithm.
+",0,1,0,0,0,0
+6001,Tidal disruptions by rotating black holes: relativistic hydrodynamics with Newtonian codes,"  We propose an approximate approach for studying the relativistic regime of
+stellar tidal disruptions by rotating massive black holes. It combines an exact
+relativistic description of the hydrodynamical evolution of a test fluid in a
+fixed curved spacetime with a Newtonian treatment of the fluid's self-gravity.
+Explicit expressions for the equations of motion are derived for Kerr spacetime
+using two different coordinate systems. We implement the new methodology within
+an existing Newtonian Smoothed Particle Hydrodynamics code and show that
+including the additional physics involves very little extra computational cost.
+We carefully explore the validity of the novel approach by first testing its
+ability to recover geodesic motion, and then by comparing the outcome of tidal
+disruption simulations against previous relativistic studies. We further
+compare simulations in Boyer--Lindquist and Kerr--Schild coordinates and
+conclude that our approach allows accurate simulation even of tidal disruption
+events where the star penetrates deeply inside the tidal radius of a rotating
+black hole. Finally, we use the new method to study the effect of the black
+hole spin on the morphology and fallback rate of the debris streams resulting
+from tidal disruptions, finding that while the spin has little effect on the
+fallback rate, it does imprint heavily on the stream morphology, and can even
+be a determining factor in the survival or disruption of the star itself. Our
+methodology is discussed in detail as a reference for future astrophysical
+applications.
+",0,1,0,0,0,0
+6002,A promise checked is a promise kept: Inspection Testing,"  Occasionally, developers need to ensure that the compiler treats their code
+in a specific way that is only visible by inspecting intermediate or final
+compilation artifacts. This is particularly common with carefully crafted
+compositional libraries, where certain usage patterns are expected to trigger
+an intricate sequence of compiler optimizations -- stream fusion is a
+well-known example.
+The developer of such a library has to manually inspect build artifacts and
+check for the expected properties. Because this is too tedious to do often, it
+will likely go unnoticed if the property is broken by a change to the library
+code, its dependencies or the compiler. The lack of automation has led to
+released versions of such libraries breaking their documented promises.
+This indicates that there is an unrecognized need for a new testing paradigm,
+inspection testing, where the programmer declaratively describes non-functional
+properties of an compilation artifact and the compiler checks these properties.
+We define inspection testing abstractly, implement it in the context of Haskell
+and show that it increases the quality of such libraries.
+",1,0,0,0,0,0
+6003,Linear-Time Sequence Classification using Restricted Boltzmann Machines,"  Classification of sequence data is the topic of interest for dynamic Bayesian
+models and Recurrent Neural Networks (RNNs). While the former can explicitly
+model the temporal dependencies between class variables, the latter have a
+capability of learning representations. Several attempts have been made to
+improve performance by combining these two approaches or increasing the
+processing capability of the hidden units in RNNs. This often results in
+complex models with a large number of learning parameters. In this paper, a
+compact model is proposed which offers both representation learning and
+temporal inference of class variables by rolling Restricted Boltzmann Machines
+(RBMs) and class variables over time. We address the key issue of
+intractability in this variant of RBMs by optimising a conditional
+distribution, instead of a joint distribution. Experiments reported in the
+paper on melody modelling and optical character recognition show that the
+proposed model can outperform the state-of-the-art. Also, the experimental
+results on optical character recognition, part-of-speech tagging and text
+chunking demonstrate that our model is comparable to recurrent neural networks
+with complex memory gates while requiring far fewer parameters.
+",1,0,0,1,0,0
+6004,A central $U(1)$-extension of a double Lie groupoid,"  In this paper, we introduce a notion of a central $U(1)$-extension of a
+double Lie groupoid and show that it defines a cocycle in the certain triple
+complex.
+",0,0,1,0,0,0
+6005,Common fixed point theorems under an implicit contractive condition on metric spaces endowed with an arbitrary binary relation and an application,"  The aim of this paper is to establish some metrical coincidence and common
+fixed point theorems with an arbitrary relation under an implicit contractive
+condition which is general enough to cover a multitude of well known
+contraction conditions in one go besides yielding several new ones. We also
+provide an example to demonstrate the generality of our results over several
+well known corresponding results of the existing literature. Finally, we
+utilize our results to prove an existence theorem for ensuring the solution of
+an integral equation.
+",0,0,1,0,0,0
+6006,The NIEP,"  The nonnegative inverse eigenvalue problem (NIEP) asks which lists of $n$
+complex numbers (counting multiplicity) occur as the eigenvalues of some
+$n$-by-$n$ entry-wise nonnegative matrix. The NIEP has a long history and is a
+known hard (perhaps the hardest in matrix analysis?) and sought after problem.
+Thus, there are many subproblems and relevant results in a variety of
+directions. We survey most work on the problem and its several variants, with
+an emphasis on recent results, and include 130 references. The survey is
+divided into: a) the single eigenvalue problems; b) necessary conditions; c)
+low dimensional results; d) sufficient conditions; e) appending 0's to achieve
+realizability; f) the graph NIEP's; g) Perron similarities; and h) the
+relevance of Jordan structure.
+",0,0,1,0,0,0
+6007,Operationalizing Conflict and Cooperation between Automated Software Agents in Wikipedia: A Replication and Expansion of 'Even Good Bots Fight',"  This paper replicates, extends, and refutes conclusions made in a study
+published in PLoS ONE (""Even Good Bots Fight""), which claimed to identify
+substantial levels of conflict between automated software agents (or bots) in
+Wikipedia using purely quantitative methods. By applying an integrative
+mixed-methods approach drawing on trace ethnography, we place these alleged
+cases of bot-bot conflict into context and arrive at a better understanding of
+these interactions. We found that overwhelmingly, the interactions previously
+characterized as problematic instances of conflict are typically better
+characterized as routine, productive, even collaborative work. These results
+challenge past work and show the importance of qualitative/quantitative
+collaboration. In our paper, we present quantitative metrics and qualitative
+heuristics for operationalizing bot-bot conflict. We give thick descriptions of
+kinds of events that present as bot-bot reverts, helping distinguish conflict
+from non-conflict. We computationally classify these kinds of events through
+patterns in edit summaries. By interpreting found/trace data in the
+socio-technical contexts in which people give that data meaning, we gain more
+from quantitative measurements, drawing deeper understandings about the
+governance of algorithmic systems in Wikipedia. We have also released our data
+collection, processing, and analysis pipeline, to facilitate computational
+reproducibility of our findings and to help other researchers interested in
+conducting similar mixed-method scholarship in other platforms and contexts.
+",1,0,0,0,0,0
+6008,Synthesizing Bijective Lenses,"  Bidirectional transformations between different data representations occur
+frequently in modern software systems. They appear as serializers and
+deserializers, as database views and view updaters, and more. Manually building
+bidirectional transformations---by writing two separate functions that are
+intended to be inverses---is tedious and error prone. A better approach is to
+use a domain-specific language in which both directions can be written as a
+single expression. However, these domain-specific languages can be difficult to
+program in, requiring programmers to manage fiddly details while working in a
+complex type system.
+To solve this, we present Optician, a tool for type-directed synthesis of
+bijective string transformers. The inputs to Optician are two ordinary regular
+expressions representing two data formats and a few concrete examples for
+disambiguation. The output is a well-typed program in Boomerang (a
+bidirectional language based on the theory of lenses). The main technical
+challenge involves navigating the vast program search space efficiently enough.
+Unlike most prior work on type-directed synthesis, our system operates in the
+context of a language with a rich equivalence relation on types (the theory of
+regular expressions). We synthesize terms of a equivalent language and convert
+those generated terms into our lens language. We prove the correctness of our
+synthesis algorithm. We also demonstrate empirically that our new language
+changes the synthesis problem from one that admits intractable solutions to one
+that admits highly efficient solutions. We evaluate Optician on a benchmark
+suite of 39 examples including both microbenchmarks and realistic examples
+derived from other data management systems including Flash Fill, a tool for
+synthesizing string transformations in spreadsheets, and Augeas, a tool for
+bidirectional processing of Linux system configuration files.
+",1,0,0,0,0,0
+6009,Tuning the piezoelectric and mechanical properties of the AlN system via alloying with YN and BN,"  Recent advances in microelectromechanical systems often require
+multifunctional materials, which are designed so as to optimize more than one
+property. Using density functional theory calculations for alloyed nitride
+systems, we illustrate how co-alloying a piezoelectric material (AlN) with
+different nitrides helps tune both its piezoelectric and mechanical properties
+simultaneously. Wurtzite AlN-YN alloys display increased piezoelectric response
+with YN concentration, accompanied by mechanical softening along the
+crystallographic c direction. Both effects increase the electromechanical
+coupling coefficients relevant for transducers and actuators. Resonator
+applications, however, require superior stiffness, thus leading to the need to
+decouple the increased piezoelectric response from a softened lattice. We show
+that co-alloying of AlN with YN and BN results in improved elastic properties
+while retaining most of the piezoelectric enhancements from YN alloying. This
+finding may lead to new avenues for tuning the design properties of
+piezoelectrics through composition-property maps.
+Keywords: piezoelectricity, electromechanical coupling, density functional
+theory, co-alloying
+",0,1,0,0,0,0
+6010,Embedding for bulk systems using localized atomic orbitals,"  We present an embedding approach for semiconductors and insulators based on
+or- bital rotations in the space of occupied Kohn-Sham orbitals. We have
+implemented our approach in the popular VASP software package. We demonstrate
+its power for defect structures in silicon and polaron formation in titania,
+two challenging cases for conventional Kohn-Sham density functional theory.
+",0,1,0,0,0,0
+6011,Simple Surveys: Response Retrieval Inspired by Recommendation Systems,"  In the last decade, the use of simple rating and comparison surveys has
+proliferated on social and digital media platforms to fuel recommendations.
+These simple surveys and their extrapolation with machine learning algorithms
+shed light on user preferences over large and growing pools of items, such as
+movies, songs and ads. Social scientists have a long history of measuring
+perceptions, preferences and opinions, often over smaller, discrete item sets
+with exhaustive rating or ranking surveys. This paper introduces simple surveys
+for social science application. We ran experiments to compare the predictive
+accuracy of both individual and aggregate comparative assessments using four
+types of simple surveys: pairwise comparisons and ratings on 2, 5 and
+continuous point scales in three distinct contexts: perceived Safety of Google
+Streetview Images, Likeability of Artwork, and Hilarity of Animal GIFs. Across
+contexts, we find that continuous scale ratings best predict individual
+assessments but consume the most time and cognitive effort. Binary choice
+surveys are quick and perform best to predict aggregate assessments, useful for
+collective decision tasks, but poorly predict personalized preferences, for
+which they are currently used by Netflix to recommend movies. Pairwise
+comparisons, by contrast, perform well to predict personal assessments, but
+poorly predict aggregate assessments despite being widely used to crowdsource
+ideas and collective preferences. We demonstrate how findings from these
+surveys can be visualized in a low-dimensional space that reveals distinct
+respondent interpretations of questions asked in each context. We conclude by
+reflecting on differences between sparse, incomplete simple surveys and their
+traditional survey counterparts in terms of efficiency, information elicited
+and settings in which knowing less about more may be critical for social
+science.
+",1,0,0,1,0,0
+6012,A Reduction for the Distinct Distances Problem in ${\mathbb R}^d$,"  We introduce a reduction from the distinct distances problem in ${\mathbb
+R}^d$ to an incidence problem with $(d-1)$-flats in ${\mathbb R}^{2d-1}$.
+Deriving the conjectured bound for this incidence problem (the bound predicted
+by the polynomial partitioning technique) would lead to a tight bound for the
+distinct distances problem in ${\mathbb R}^d$. The reduction provides a large
+amount of information about the $(d-1)$-flats, and a framework for deriving
+more restrictions that these satisfy. Our reduction is based on introducing a
+Lie group that is a double cover of the special Euclidean group. This group can
+be seen as a variant of the Spin group, and a large part of our analysis
+involves studying its properties.
+",0,0,1,0,0,0
+6013,Local electronic properties of the graphene-protected giant Rashba-split BiAg$_2$ surface,"  We report the preparation of the interface between graphene and the strong
+Rashba-split BiAg$_2$ surface alloy and investigatigation of its structure as
+well as the electronic properties by means of scanning tunneling
+microscopy/spectroscopy and density functional theory calculations. Upon
+evaluation of the quasiparticle interference patterns the unpertrubated linear
+dispersion for the $\pi$ band of $n$-doped graphene is observed. Our results
+also reveal the intact nature of the giant Rashba-split surface states of the
+BiAg$_2$ alloy, which demonstrate only a moderate downward energy shift upon
+the presence of graphene. This effect is explained in the framework of density
+functional theory by an inward relaxation of the Bi atoms at the interface and
+subsequent delocalisation of the wave function of the surface states. Our
+findings demonstrate a realistic pathway to prepare a graphene protected giant
+Rashba-split BiAg$_2$ for possible spintronic applications.
+",0,1,0,0,0,0
+6014,Particle trapping and conveying using an optical Archimedes' screw,"  Trapping and manipulation of particles using laser beams has become an
+important tool in diverse fields of research. In recent years, particular
+interest is given to the problem of conveying optically trapped particles over
+extended distances either down or upstream the direction of the photons
+momentum flow. Here, we propose and demonstrate experimentally an optical
+analogue of the famous Archimedes' screw where the rotation of a
+helical-intensity beam is transferred to the axial motion of optically-trapped
+micro-meter scale airborne carbon based particles. With this optical screw,
+particles were easily conveyed with controlled velocity and direction, upstream
+or downstream the optical flow, over a distance of half a centimeter. Our
+results offer a very simple optical conveyor that could be adapted to a wide
+range of optical trapping scenarios.
+",0,1,0,0,0,0
+6015,Scalable Inference for Nested Chinese Restaurant Process Topic Models,"  Nested Chinese Restaurant Process (nCRP) topic models are powerful
+nonparametric Bayesian methods to extract a topic hierarchy from a given text
+corpus, where the hierarchical structure is automatically determined by the
+data. Hierarchical Latent Dirichlet Allocation (hLDA) is a popular instance of
+nCRP topic models. However, hLDA has only been evaluated at small scale,
+because the existing collapsed Gibbs sampling and instantiated weight
+variational inference algorithms either are not scalable or sacrifice inference
+quality with mean-field assumptions. Moreover, an efficient distributed
+implementation of the data structures, such as dynamically growing count
+matrices and trees, is challenging.
+In this paper, we propose a novel partially collapsed Gibbs sampling (PCGS)
+algorithm, which combines the advantages of collapsed and instantiated weight
+algorithms to achieve good scalability as well as high model quality. An
+initialization strategy is presented to further improve the model quality.
+Finally, we propose an efficient distributed implementation of PCGS through
+vectorization, pre-processing, and a careful design of the concurrent data
+structures and communication strategy.
+Empirical studies show that our algorithm is 111 times more efficient than
+the previous open-source implementation for hLDA, with comparable or even
+better model quality. Our distributed implementation can extract 1,722 topics
+from a 131-million-document corpus with 28 billion tokens, which is 4-5 orders
+of magnitude larger than the previous largest corpus, with 50 machines in 7
+hours.
+",1,0,0,1,0,0
+6016,"Equations of $\,\overline{M}_{0,n}$","  Following work of Keel and Tevelev, we give explicit polynomials in the Cox
+ring of $\mathbb{P}^1\times\cdots\times\mathbb{P}^{n-3}$ that, conjecturally,
+determine $\overline{M}_{0,n}$ as a subscheme. Using Macaulay2, we prove that
+these equations generate the ideal for $n=5, 6, 7, 8$. For $n \leq 6$ we give a
+cohomological proof that these polynomials realize $\overline{M}_{0,n}$ as a
+projective variety, embedded in $\mathbb{P}^{(n-2)!-1}$ by the complete log
+canonical linear system.
+",0,0,1,0,0,0
+6017,Lattice Boltzmann simulation of viscous fingering of immiscible displacement in a channel using an improved wetting scheme,"  An improved wetting boundary implementation strategy is proposed based on
+lattice Boltzmann color-gradient model in this paper. In this strategy, an
+extra interface force condition is demonstrated based on the diffuse interface
+assumption and is employed in contact line region. It has been validated by
+three benchmark problems: static droplet wetting on a flat surface and a curved
+surface, and dynamic capillary filling. Good performances are shown in all
+three cases. Relied on the strict validation to our scheme, the viscous
+fingering phenomenon of immiscible fluids displacement in a two-dimensional
+channel has been restudied in this paper. High viscosity ratio, wide range
+contact angle, accurate moving contact line and mutual independence between
+surface tension and viscosity are the obvious advantages of our model. We find
+the linear relationship between the contact angle and displacement velocity or
+variation of finger length. When the viscosity ratio is smaller than 20, the
+displacement velocity is increasing with increasing viscosity ratio and
+reducing capillary number, and when the viscosity ratio is larger than 20, the
+displacement velocity tends to a specific constant. A similar conclusion is
+obtained on the variation of finger length.
+",0,1,0,0,0,0
+6018,Implementation of Control Strategies for Sterile Insect Techniques,"  In this paper, we propose a sex-structured entomological model that serves as
+a basis for design of control strategies relying on releases of sterile male
+mosquitoes (Aedes spp) and aiming at elimination of the wild vector population
+in some target locality. We consider different types of releases (constant and
+periodic impulsive), providing necessary conditions to reach elimination.
+However, the main part of the paper is focused on the study of the periodic
+impulsive control in different situations. When the size of wild mosquito
+population cannot be assessed in real time, we propose the so-called open-loop
+control strategy that relies on periodic impulsive releases of sterile males
+with constant release size. Under this control mode, global convergence towards
+the mosquito-free equilibrium is proved on the grounds of sufficient condition
+that relates the size and frequency of releases. If periodic assessments
+(either synchronized with releases or more sparse) of the wild population size
+are available in real time, we propose the so-called closed-loop control
+strategy, which is adjustable in accordance with reliable estimations of the
+wild population sizes. Under this control mode, global convergence to the
+mosquito-free equilibrium is proved on the grounds of another sufficient
+condition that relates not only the size and frequency of periodic releases but
+also the frequency of sparse measurements taken on wild populations. Finally,
+we propose a mixed control strategy that combines open-loop and closed-loop
+strategies. This control mode renders the best result, in terms of overall time
+needed to reach elimination and the number of releases to be effectively
+carried out during the whole release campaign, while requiring for a reasonable
+amount of released sterile insects.
+",0,0,0,0,1,0
+6019,Convergence and submeasures in Boolean algebras,"  A Boolean algebra carries a strictly positive exhaustive submeasure if and
+only if it has a sequential topology that is uniformly Frechet.
+",0,0,1,0,0,0
+6020,Characterizing a CCD detector for astronomical purposes: OAUNI Project,"  This work verifies the instrumental characteristics of the CCD detector which
+is part of the UNI astronomical observatory. We measured the linearity of the
+CCD detector of the SBIG STXL6303E camera, along with the associated gain and
+readout noise. The linear response to the incident light of the detector is
+extremely linear (R2 =99.99%), its effective gain is 1.65 +/- 0.01 e-/ADU and
+its readout noise is 12.2 e-. These values are in agreement with the
+manufacturer. We confirm that this detector is extremely precise to make
+measurements for astronomical purposes.
+",0,1,0,0,0,0
+6021,"Genetic algorithm-based control of birefringent filtering for self-tuning, self-pulsing fiber lasers","  Polarization-based filtering in fiber lasers is well-known to enable spectral
+tunability and a wide range of dynamical operating states. This effect is
+rarely exploited in practical systems, however, because optimization of cavity
+parameters is non-trivial and evolves due to environmental sensitivity. Here,
+we report a genetic algorithm-based approach, utilizing electronic control of
+the cavity transfer function, to autonomously achieve broad wavelength tuning
+and the generation of Q-switched pulses with variable repetition rate and
+duration. The practicalities and limitations of simultaneous spectral and
+temporal self-tuning from a simple fiber laser are discussed, paving the way to
+on-demand laser properties through algorithmic control and machine learning
+schemes.
+",0,1,0,0,0,0
+6022,"Public discourse and news consumption on online social media: A quantitative, cross-platform analysis of the Italian Referendum","  The rising attention to the spreading of fake news and unsubstantiated rumors
+on online social media and the pivotal role played by confirmation bias led
+researchers to investigate different aspects of the phenomenon. Experimental
+evidence showed that confirmatory information gets accepted even if containing
+deliberately false claims while dissenting information is mainly ignored or
+might even increase group polarization. It seems reasonable that, to address
+misinformation problem properly, we have to understand the main determinants
+behind content consumption and the emergence of narratives on online social
+media. In this paper we address such a challenge by focusing on the discussion
+around the Italian Constitutional Referendum by conducting a quantitative,
+cross-platform analysis on both Facebook public pages and Twitter accounts. We
+observe the spontaneous emergence of well-separated communities on both
+platforms. Such a segregation is completely spontaneous, since no
+categorization of contents was performed a priori. By exploring the dynamics
+behind the discussion, we find that users tend to restrict their attention to a
+specific set of Facebook pages/Twitter accounts. Finally, taking advantage of
+automatic topic extraction and sentiment analysis techniques, we are able to
+identify the most controversial topics inside and across both platforms. We
+measure the distance between how a certain topic is presented in the
+posts/tweets and the related emotional response of users. Our results provide
+interesting insights for the understanding of the evolution of the core
+narratives behind different echo chambers and for the early detection of
+massive viral phenomena around false claims.
+",1,1,0,0,0,0
+6023,Testing small scale gravitational wave detectors with dynamical mass distributions,"  The recent discovery of gravitational waves by the LIGO-Virgo collaboration
+created renewed interest in the investigation of alternative gravitational
+detector designs, such as small scale resonant detectors. In this article, it
+is shown how proposed small scale detectors can be tested by generating
+dynamical gravitational fields with appropriate distributions of moving masses.
+A series of interesting experiments will be possible with this setup. In
+particular, small scale detectors can be tested very early in the development
+phase and tests can be used to progress quickly in their development. This
+could contribute to the emerging field of gravitational wave astronomy.
+",0,1,0,0,0,0
+6024,AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms,"  Approximate probabilistic inference algorithms are central to many fields.
+Examples include sequential Monte Carlo inference in robotics, variational
+inference in machine learning, and Markov chain Monte Carlo inference in
+statistics. A key problem faced by practitioners is measuring the accuracy of
+an approximate inference algorithm on a specific data set. This paper
+introduces the auxiliary inference divergence estimator (AIDE), an algorithm
+for measuring the accuracy of approximate inference algorithms. AIDE is based
+on the observation that inference algorithms can be treated as probabilistic
+models and the random variables used within the inference algorithm can be
+viewed as auxiliary variables. This view leads to a new estimator for the
+symmetric KL divergence between the approximating distributions of two
+inference algorithms. The paper illustrates application of AIDE to algorithms
+for inference in regression, hidden Markov, and Dirichlet process mixture
+models. The experiments show that AIDE captures the qualitative behavior of a
+broad class of inference algorithms and can detect failure modes of inference
+algorithms that are missed by standard heuristics.
+",1,0,0,1,0,0
+6025,Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filters,"  Data-driven spatial filtering algorithms optimize scores such as the contrast
+between two conditions to extract oscillatory brain signal components. Most
+machine learning approaches for filter estimation, however, disregard
+within-trial temporal dynamics and are extremely sensitive to changes in
+training data and involved hyperparameters. This leads to highly variable
+solutions and impedes the selection of a suitable candidate for,
+e.g.,~neurotechnological applications. Fostering component introspection, we
+propose to embrace this variability by condensing the functional signatures of
+a large set of oscillatory components into homogeneous clusters, each
+representing specific within-trial envelope dynamics.
+The proposed method is exemplified by and evaluated on a complex hand force
+task with a rich within-trial structure. Based on electroencephalography data
+of 18 healthy subjects, we found that the components' distinct temporal
+envelope dynamics are highly subject-specific. On average, we obtained seven
+clusters per subject, which were strictly confined regarding their underlying
+frequency bands. As the analysis method is not limited to a specific spatial
+filtering algorithm, it could be utilized for a wide range of
+neurotechnological applications, e.g., to select and monitor functionally
+relevant features for brain-computer interface protocols in stroke
+rehabilitation.
+",0,0,0,1,1,0
+6026,Neural Architecture Search with Bayesian Optimisation and Optimal Transport,"  Bayesian Optimisation (BO) refers to a class of methods for global
+optimisation of a function $f$ which is only accessible via point evaluations.
+It is typically used in settings where $f$ is expensive to evaluate. A common
+use case for BO in machine learning is model selection, where it is not
+possible to analytically model the generalisation performance of a statistical
+model, and we resort to noisy and expensive training and validation procedures
+to choose the best model. Conventional BO methods have focused on Euclidean and
+categorical domains, which, in the context of model selection, only permits
+tuning scalar hyper-parameters of machine learning algorithms. However, with
+the surge of interest in deep learning, there is an increasing demand to tune
+neural network \emph{architectures}. In this work, we develop NASBOT, a
+Gaussian process based BO framework for neural architecture search. To
+accomplish this, we develop a distance metric in the space of neural network
+architectures which can be computed efficiently via an optimal transport
+program. This distance might be of independent interest to the deep learning
+community as it may find applications outside of BO. We demonstrate that NASBOT
+outperforms other alternatives for architecture search in several cross
+validation based model selection tasks on multi-layer perceptrons and
+convolutional neural networks.
+",0,0,0,1,0,0
+6027,An improved parametric model for hysteresis loop approximation,"  A number of improvements have been added to the existing analytical model of
+hysteresis loop defined in parametric form. In particular, three phase shifts
+are included in the model, which permits to tilt the hysteresis loop smoothly
+by the required angle at the split point as well as to smoothly change the
+curvature of the loop. As a result, the error of approximation of a hysteresis
+loop by the improved model does not exceed 1%, which is several times less than
+the error of the existing model. The improved model is capable of approximating
+most of the known types of rate-independent symmetrical hysteresis loops
+encountered in the practice of physical measurements. The model allows building
+smooth, piecewise-linear, hybrid, minor, mirror-reflected, inverse, reverse,
+double and triple loops. One of the possible applications of the model
+developed is linearization of a probe microscope piezoscanner. The improved
+model can be found useful for the tasks of simulation of scientific instruments
+that contain hysteresis elements.
+",1,1,1,0,0,0
+6028,Kidnapping Model: An Extension of Selten's Game,"  Selten's game is a kidnapping model where the probability of capturing the
+kidnapper is independent of whether the hostage has been released or executed.
+Most often, in view of the elevated sensitivities involved, authorities put
+greater effort and resources into capturing the kidnapper if the hostage has
+been executed, in contrast to the case when a ransom is paid to secure the
+hostage's release. In this paper, we study the asymmetric game when the
+probability of capturing the kidnapper depends on whether the hostage has been
+executed or not and find a new uniquely determined perfect equilibrium point in
+Selten's game.
+",1,0,0,0,0,0
+6029,"To prune, or not to prune: exploring the efficacy of pruning for model compression","  Model pruning seeks to induce sparsity in a deep neural network's various
+connection matrices, thereby reducing the number of nonzero-valued parameters
+in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep
+networks at the cost of only a marginal loss in accuracy and achieve a sizable
+reduction in model size. This hints at the possibility that the baseline models
+in these experiments are perhaps severely over-parameterized at the outset and
+a viable alternative for model compression might be to simply reduce the number
+of hidden units while maintaining the model's dense connection structure,
+exposing a similar trade-off in model size and accuracy. We investigate these
+two distinct paths for model compression within the context of energy-efficient
+inference in resource-constrained environments and propose a new gradual
+pruning technique that is simple and straightforward to apply across a variety
+of models/datasets with minimal tuning and can be seamlessly incorporated
+within the training process. We compare the accuracy of large, but pruned
+models (large-sparse) and their smaller, but dense (small-dense) counterparts
+with identical memory footprint. Across a broad range of neural network
+architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find
+large-sparse models to consistently outperform small-dense models and achieve
+up to 10x reduction in number of non-zero parameters with minimal loss in
+accuracy.
+",1,0,0,1,0,0
+6030,Connecting Software Metrics across Versions to Predict Defects,"  Accurate software defect prediction could help software practitioners
+allocate test resources to defect-prone modules effectively and efficiently. In
+the last decades, much effort has been devoted to build accurate defect
+prediction models, including developing quality defect predictors and modeling
+techniques. However, current widely used defect predictors such as code metrics
+and process metrics could not well describe how software modules change over
+the project evolution, which we believe is important for defect prediction. In
+order to deal with this problem, in this paper, we propose to use the
+Historical Version Sequence of Metrics (HVSM) in continuous software versions
+as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN),
+a popular modeling technique, to take HVSM as the input to build software
+prediction models. The experimental results show that, in most cases, the
+proposed HVSM-based RNN model has a significantly better effort-aware ranking
+effectiveness than the commonly used baseline models.
+",1,0,0,0,0,0
+6031,Semiparametric panel data models using neural networks,"  This paper presents an estimator for semiparametric models that uses a
+feed-forward neural network to fit the nonparametric component. Unlike many
+methodologies from the machine learning literature, this approach is suitable
+for longitudinal/panel data. It provides unbiased estimation of the parametric
+component of the model, with associated confidence intervals that have
+near-nominal coverage rates. Simulations demonstrate (1) efficiency, (2) that
+parametric estimates are unbiased, and (3) coverage properties of estimated
+intervals. An application section demonstrates the method by predicting
+county-level corn yield using daily weather data from the period 1981-2015,
+along with parametric time trends representing technological change. The method
+is shown to out-perform linear methods such as OLS and ridge/lasso, as well as
+random forest. The procedures described in this paper are implemented in the R
+package panelNNET.
+",0,0,0,1,0,0
+6032,Split and Rephrase,"  We propose a new sentence simplification task (Split-and-Rephrase) where the
+aim is to split a complex sentence into a meaning preserving sequence of
+shorter sentences. Like sentence simplification, splitting-and-rephrasing has
+the potential of benefiting both natural language processing and societal
+applications. Because shorter sentences are generally better processed by NLP
+systems, it could be used as a preprocessing step which facilitates and
+improves the performance of parsers, semantic role labellers and machine
+translation systems. It should also be of use for people with reading
+disabilities because it allows the conversion of longer sentences into shorter
+ones. This paper makes two contributions towards this new task. First, we
+create and make available a benchmark consisting of 1,066,115 tuples mapping a
+single complex sentence to a sequence of sentences expressing the same meaning.
+Second, we propose five models (vanilla sequence-to-sequence to
+semantically-motivated models) to understand the difficulty of the proposed
+task.
+",1,0,0,0,0,0
+6033,Constructive Euler hydrodynamics for one-dimensional attractive particle systems,"  We review a (constructive) approach first introduced in [6] and further
+developed in [7, 8, 38, 9] for hydrodynamic limits of asymmetric attractive
+particle systems, in a weak or in a strong (that is, almost sure) sense, in an
+homogeneous or in a quenched disordered setting.
+",0,0,1,0,0,0
+6034,Cyber Insurance for Heterogeneous Wireless Networks,"  Heterogeneous wireless networks (HWNs) composed of densely deployed base
+stations of different types with various radio access technologies have become
+a prevailing trend to accommodate ever-increasing traffic demand in enormous
+volume. Nowadays, users rely heavily on HWNs for ubiquitous network access that
+contains valuable and critical information such as financial transactions,
+e-health, and public safety. Cyber risks, representing one of the most
+significant threats to network security and reliability, are increasing in
+severity. To address this problem, this article introduces the concept of cyber
+insurance to transfer the cyber risk (i.e., service outage, as a consequence of
+cyber risks in HWNs) to a third party insurer. Firstly, a review of the
+enabling technologies for HWNs and their vulnerabilities to cyber risks is
+presented. Then, the fundamentals of cyber insurance are introduced, and
+subsequently, a cyber insurance framework for HWNs is presented. Finally, open
+issues are discussed and the challenges are highlighted for integrating cyber
+insurance as a service of next generation HWNs.
+",1,0,0,0,0,0
+6035,Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition,"  This paper focuses on multi-scale approaches for variational methods and
+corresponding gradient flows. Recently, for convex regularization functionals
+such as total variation, new theory and algorithms for nonlinear eigenvalue
+problems via nonlinear spectral decompositions have been developed. Those
+methods open new directions for advanced image filtering. However, for an
+effective use in image segmentation and shape decomposition, a clear
+interpretation of the spectral response regarding size and intensity scales is
+needed but lacking in current approaches. In this context, $L^1$ data
+fidelities are particularly helpful due to their interesting multi-scale
+properties such as contrast invariance. Hence, the novelty of this work is the
+combination of $L^1$-based multi-scale methods with nonlinear spectral
+decompositions. We compare $L^1$ with $L^2$ scale-space methods in view of
+spectral image representation and decomposition. We show that the contrast
+invariant multi-scale behavior of $L^1-TV$ promotes sparsity in the spectral
+response providing more informative decompositions. We provide a numerical
+method and analyze synthetic and biomedical images at which decomposition leads
+to improved segmentation.
+",1,0,1,0,0,0
+6036,PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks,"  We present a novel approach for the prediction of anticancer compound
+sensitivity by means of multi-modal attention-based neural networks (PaccMann).
+In our approach, we integrate three key pillars of drug sensitivity, namely,
+the molecular structure of compounds, transcriptomic profiles of cancer cells
+as well as prior knowledge about interactions among proteins within cells. Our
+models ingest a drug-cell pair consisting of SMILES encoding of a compound and
+the gene expression profile of a cancer cell and predicts an IC50 sensitivity
+value. Gene expression profiles are encoded using an attention-based encoding
+mechanism that assigns high weights to the most informative genes. We present
+and study three encoders for SMILES string of compounds: 1) bidirectional
+recurrent 2) convolutional 3) attention-based encoders. We compare our devised
+models against a baseline model that ingests engineered fingerprints to
+represent the molecular structure. We demonstrate that using our
+attention-based encoders, we can surpass the baseline model. The use of
+attention-based encoders enhance interpretability and enable us to identify
+genes, bonds and atoms that were used by the network to make a prediction.
+",0,0,0,0,1,0
+6037,Evaporation and scattering of momentum- and velocity-dependent dark matter in the Sun,"  Dark matter with momentum- or velocity-dependent interactions with nuclei has
+shown significant promise for explaining the so-called Solar Abundance Problem,
+a longstanding discrepancy between solar spectroscopy and helioseismology. The
+best-fit models are all rather light, typically with masses in the range of 3-5
+GeV. This is exactly the mass range where dark matter evaporation from the Sun
+can be important, but to date no detailed calculation of the evaporation of
+such models has been performed. Here we carry out this calculation, for the
+first time including arbitrary velocity- and momentum-dependent interactions,
+thermal effects, and a completely general treatment valid from the optically
+thin limit all the way through to the optically thick regime. We find that
+depending on the dark matter mass, interaction strength and type, the mass
+below which evaporation is relevant can vary from 1 to 4 GeV. This has the
+effect of weakening some of the better-fitting solutions to the Solar Abundance
+Problem, but also improving a number of others. As a by-product, we also
+provide an improved derivation of the capture rate that takes into account
+thermal and optical depth effects, allowing the standard result to be smoothly
+matched to the well-known saturation limit.
+",0,1,0,0,0,0
+6038,ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features,"  With the rapid development of spaceborne imaging techniques, object detection
+in optical remote sensing imagery has drawn much attention in recent decades.
+While many advanced works have been developed with powerful learning
+algorithms, the incomplete feature representation still cannot meet the demand
+for effectively and efficiently handling image deformations, particularly
+objective scaling and rotation. To this end, we propose a novel object
+detection framework, called optical remote sensing imagery detector (ORSIm
+detector), integrating diverse channel features extraction, feature learning,
+fast image pyramid matching, and boosting strategy. ORSIm detector adopts a
+novel spatial-frequency channel feature (SFCF) by jointly considering the
+rotation-invariant channel features constructed in frequency domain and the
+original spatial channel features (e.g., color channel, gradient magnitude).
+Subsequently, we refine SFCF using learning-based strategy in order to obtain
+the high-level or semantically meaningful features. In the test phase, we
+achieve a fast and coarsely-scaled channel computation by mathematically
+estimating a scaling factor in the image domain. Extensive experimental results
+conducted on the two different airborne datasets are performed to demonstrate
+the superiority and effectiveness in comparison with previous state-of-the-art
+methods.
+",1,0,0,0,0,0
+6039,"Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained ""Hard Faces""","  Large-scale variations still pose a challenge in unconstrained face
+detection. To the best of our knowledge, no current face detection algorithm
+can detect a face as large as 800 x 800 pixels while simultaneously detecting
+another one as small as 8 x 8 pixels within a single image with equally high
+accuracy. We propose a two-stage cascaded face detection framework, Multi-Path
+Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a
+deep neural network with a classic learning strategy, to tackle this challenge.
+The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes
+faces at three different scales. It simultaneously utilizes three parallel
+outputs of the convolutional feature maps to predict multi-scale candidate face
+regions. The ""atrous"" convolution trick (convolution with up-sampled filters)
+and a newly proposed sampling layer for ""hard"" examples are embedded in MP-RPN
+to further boost its performance. The second stage is a Boosted Forests
+classifier, which utilizes deep facial features pooled from inside the
+candidate face regions as well as deep contextual features pooled from a larger
+region surrounding the candidate face regions. This step is included to further
+remove hard negative samples. Experiments show that this approach achieves
+state-of-the-art face detection performance on the WIDER FACE dataset ""hard""
+partition, outperforming the former best result by 9.6% for the Average
+Precision.
+",1,0,0,0,0,0
+6040,Naturally occurring $^{32}$Si and low-background silicon dark matter detectors,"  The naturally occurring radioisotope $^{32}$Si represents a potentially
+limiting background in future dark matter direct-detection experiments. We
+investigate sources of $^{32}$Si and the vectors by which it comes to reside in
+silicon crystals used for fabrication of radiation detectors. We infer that the
+$^{32}$Si concentration in commercial single-crystal silicon is likely
+variable, dependent upon the specific geologic and hydrologic history of the
+source (or sources) of silicon ""ore"" and the details of the silicon-refinement
+process. The silicon production industry is large, highly segmented by refining
+step, and multifaceted in terms of final product type, from which we conclude
+that production of $^{32}$Si-mitigated crystals requires both targeted silicon
+material selection and a dedicated refinement-through-crystal-production
+process. We review options for source material selection, including quartz from
+an underground source and silicon isotopically reduced in $^{32}$Si. To
+quantitatively evaluate the $^{32}$Si content in silicon metal and precursor
+materials, we propose analytic methods employing chemical processing and
+radiometric measurements. Ultimately, it appears feasible to produce silicon
+detectors with low levels of $^{32}$Si, though significant assay method
+development is required to validate this claim and thereby enable a quality
+assurance program during an actual controlled silicon-detector production
+cycle.
+",0,1,0,0,0,0
+6041,Synergies between Exoplanet Surveys and Variable Star Research,"  With the discovery of the first transiting extrasolar planetary system back
+to 1999, a great number of projects started to hunt for other similar systems.
+Because of the incidence rate of such systems was unknown and the length of the
+shallow transit events is only a few percent of the orbital period, the goal
+was to monitor continuously as many stars as possible for at least a period of
+a few months. Small aperture, large field of view automated telescope systems
+have been installed with a parallel development of new data reduction and
+analysis methods, leading to better than 1% per data point precision for
+thousands of stars. With the successful launch of the photometric satellites
+CoRot and Kepler, the precision increased further by one-two orders of
+magnitude. Millions of stars have been analyzed and searched for transits. In
+the history of variable star astronomy this is the biggest undertaking so far,
+resulting in photometric time series inventories immensely valuable for the
+whole field. In this review we briefly discuss the methods of data analysis
+that were inspired by the main science driver of these surveys and highlight
+some of the most interesting variable star results that impact the field of
+variable star astronomy.
+",0,1,0,0,0,0
+6042,Improved Set-based Symbolic Algorithms for Parity Games,"  Graph games with {\omega}-regular winning conditions provide a mathematical
+framework to analyze a wide range of problems in the analysis of reactive
+systems and programs (such as the synthesis of reactive systems, program
+repair, and the verification of branching time properties). Parity conditions
+are canonical forms to specify {\omega}-regular winning conditions. Graph games
+with parity conditions are equivalent to {\mu}-calculus model checking, and
+thus a very important algorithmic problem. Symbolic algorithms are of great
+significance because they provide scalable algorithms for the analysis of large
+finite-state systems, as well as algorithms for the analysis of infinite-state
+systems with finite quotient. A set-based symbolic algorithm uses the basic set
+operations and the one-step predecessor operators. We consider graph games with
+$n$ vertices and parity conditions with $c$ priorities. While many explicit
+algorithms exist for graph games with parity conditions, for set-based symbolic
+algorithms there are only two algorithms (notice that we use space to refer to
+the number of sets stored by a symbolic algorithm): (a) the basic algorithm
+that requires $O(n^c)$ symbolic operations and linear space; and (b) an
+improved algorithm that requires $O(n^{c/2+1})$ symbolic operations but also
+$O(n^{c/2+1})$ space (i.e., exponential space). In this work we present two
+set-based symbolic algorithms for parity games: (a) our first algorithm
+requires $O(n^{c/2+1})$ symbolic operations and only requires linear space; and
+(b) developing on our first algorithm, we present an algorithm that requires
+$O(n^{c/3+1})$ symbolic operations and only linear space. We also present the
+first linear space set-based symbolic algorithm for parity games that requires
+at most a sub-exponential number of symbolic operations.
+",1,0,0,0,0,0
+6043,A Syllable-based Technique for Word Embeddings of Korean Words,"  Word embedding has become a fundamental component to many NLP tasks such as
+named entity recognition and machine translation. However, popular models that
+learn such embeddings are unaware of the morphology of words, so it is not
+directly applicable to highly agglutinative languages such as Korean. We
+propose a syllable-based learning model for Korean using a convolutional neural
+network, in which word representation is composed of trained syllable vectors.
+Our model successfully produces morphologically meaningful representation of
+Korean words compared to the original Skip-gram embeddings. The results also
+show that it is quite robust to the Out-of-Vocabulary problem.
+",1,0,0,0,0,0
+6044,Existence of Evolutionarily Stable Strategies Remains Hard to Decide for a Wide Range of Payoff Values,"  The concept of an evolutionarily stable strategy (ESS), introduced by Smith
+and Price, is a refinement of Nash equilibrium in 2-player symmetric games in
+order to explain counter-intuitive natural phenomena, whose existence is not
+guaranteed in every game. The problem of deciding whether a game possesses an
+ESS has been shown to be $\Sigma_{2}^{P}$-complete by Conitzer using the
+preceding important work by Etessami and Lochbihler. The latter, among other
+results, proved that deciding the existence of ESS is both NP-hard and
+coNP-hard. In this paper we introduce a ""reduction robustness"" notion and we
+show that deciding the existence of an ESS remains coNP-hard for a wide range
+of games even if we arbitrarily perturb within some intervals the payoff values
+of the game under consideration. In contrast, ESS exist almost surely for large
+games with random and independent payoffs chosen from the same distribution.
+",1,0,0,0,0,0
+6045,Compound Poisson approximation to estimate the Lévy density,"  We construct an estimator of the Lévy density of a pure jump Lévy
+process, possibly of infinite variation, from the discrete observation of one
+trajectory at high frequency. The novelty of our procedure is that we directly
+estimate the Lévy density relying on a pathwise strategy, whereas existing
+procedures rely on spectral techniques. By taking advantage of a compound
+Poisson approximation of the Lévy density, we circumvent the use of spectral
+techniques and in particular of the Lévy-Khintchine formula. A linear wavelet
+estimators is built and its performance is studied in terms of $L_p$ loss
+functions, $p\geq 1$, over Besov balls. The resulting rates are minimax-optimal
+for a large class of Lévy processes. We discuss the robustness of the
+procedure to the presence of a Brownian part and to the estimation set getting
+close to the critical value 0.
+",0,0,1,1,0,0
+6046,On the non-vanishing of certain Dirichlet series,"  Given $k\in\mathbb N$, we study the vanishing of the Dirichlet series
+$$D_k(s,f):=\sum_{n\geq1} d_k(n)f(n)n^{-s}$$ at the point $s=1$, where $f$ is a
+periodic function modulo a prime $p$. We show that if $(k,p-1)=1$ or
+$(k,p-1)=2$ and $p\equiv 3\mod 4$, then there are no odd rational-valued
+functions $f\not\equiv 0$ such that $D_k(1,f)=0$, whereas in all other cases
+there are examples of odd functions $f$ such that $D_k(1,f)=0$.
+As a consequence, we obtain, for example, that the set of values
+$L(1,\chi)^2$, where $\chi$ ranges over odd characters mod $p$, are linearly
+independent over $\mathbb Q$.
+",0,0,1,0,0,0
+6047,On discrete homology of a free pro-$p$-group,"  For a prime $p$, let $\hat F_p$ be a finitely generated free pro-$p$-group of
+rank $\geq 2$. We show that the second discrete homology group $H_2(\hat
+F_p,\mathbb Z/p)$ is an uncountable $\mathbb Z/p$-vector space. This answers a
+problem of A.K. Bousfield.
+",0,0,1,0,0,0
+6048,Conceptual Modeling of Inventory Management Processes as a Thinging Machine,"  A control model is typically classified into three forms: conceptual,
+mathematical and simulation (computer). This paper analyzes a conceptual
+modeling application with respect to an inventory management system. Today,
+most organizations utilize computer systems for inventory control that provide
+protection when interruptions or breakdowns occur within work processes.
+Modeling the inventory processes is an active area of research that utilizes
+many diagrammatic techniques, including data flow diagrams, Universal Modeling
+Language (UML) diagrams and Integration DEFinition (IDEF). We claim that
+current conceptual modeling frameworks lack uniform notions and have inability
+to appeal to designers and analysts. We propose modeling an inventory system as
+an abstract machine, called a Thinging Machine (TM), with five operations:
+creation, processing, receiving, releasing and transferring. The paper provides
+side-by-side contrasts of some existing examples of conceptual modeling
+methodologies that apply to TM. Additionally, TM is applied in a case study of
+an actual inventory system that uses IBM Maximo. The resulting conceptual
+depictions point to the viability of FM as a valuable tool for developing a
+high-level representation of inventory processes.
+",1,0,0,0,0,0
+6049,Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks,"  We present the first approach for 3D point-cloud to image translation based
+on conditional Generative Adversarial Networks (cGAN). The model handles
+multi-modal information sources from different domains, i.e. raw point-sets and
+images. The generator is capable of processing three conditions, whereas the
+point-cloud is encoded as raw point-set and camera projection. An image
+background patch is used as constraint to bias environmental texturing. A
+global approximation function within the generator is directly applied on the
+point-cloud (Point-Net). Hence, the representative learning model incorporates
+global 3D characteristics directly at the latent feature space. Conditions are
+used to bias the background and the viewpoint of the generated image. This
+opens up new ways in augmenting or texturing 3D data to aim the generation of
+fully individual images. We successfully evaluated our method on the Kitti and
+SunRGBD dataset with an outstanding object detection inception score.
+",1,0,0,0,0,0
+6050,11 T Dipole for the Dispersion Suppressor Collimators,"  Chapter 11 in High-Luminosity Large Hadron Collider (HL-LHC) : Preliminary
+Design Report. The Large Hadron Collider (LHC) is one of the largest scientific
+instruments ever built. Since opening up a new energy frontier for exploration
+in 2010, it has gathered a global user community of about 7,000 scientists
+working in fundamental particle physics and the physics of hadronic matter at
+extreme temperature and density. To sustain and extend its discovery potential,
+the LHC will need a major upgrade in the 2020s. This will increase its
+luminosity (rate of collisions) by a factor of five beyond the original design
+value and the integrated luminosity (total collisions created) by a factor ten.
+The LHC is already a highly complex and exquisitely optimised machine so this
+upgrade must be carefully conceived and will require about ten years to
+implement. The new configuration, known as High Luminosity LHC (HL-LHC), will
+rely on a number of key innovations that push accelerator technology beyond its
+present limits. Among these are cutting-edge 11-12 tesla superconducting
+magnets, compact superconducting cavities for beam rotation with ultra-precise
+phase control, new technology and physical processes for beam collimation and
+300 metre-long high-power superconducting links with negligible energy
+dissipation. The present document describes the technologies and components
+that will be used to realise the project and is intended to serve as the basis
+for the detailed engineering design of HL-LHC.
+",0,1,0,0,0,0
+6051,Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case,"  Jets from boosted heavy particles have a typical angular scale which can be
+used to distinguish them from QCD jets. We introduce a machine learning
+strategy for jet substructure analysis using a spectral function on the angular
+scale. The angular spectrum allows us to scan energy deposits over the angle
+between a pair of particles in a highly visual way. We set up an artificial
+neural network (ANN) to find out characteristic shapes of the spectra of the
+jets from heavy particle decays. By taking the Higgs jets and QCD jets as
+examples, we show that the ANN of the angular spectrum input has similar
+performance to existing taggers. In addition, some improvement is seen when
+additional extra radiations occur. Notably, the new algorithm automatically
+combines the information of the multi-point correlations in the jet.
+",0,0,0,1,0,0
+6052,UTD-CRSS Submission for MGB-3 Arabic Dialect Identification: Front-end and Back-end Advancements on Broadcast Speech,"  This study presents systems submitted by the University of Texas at Dallas,
+Center for Robust Speech Systems (UTD-CRSS) to the MGB-3 Arabic Dialect
+Identification (ADI) subtask. This task is defined to discriminate between five
+dialects of Arabic, including Egyptian, Gulf, Levantine, North African, and
+Modern Standard Arabic. We develop multiple single systems with different
+front-end representations and back-end classifiers. At the front-end level,
+feature extraction methods such as Mel-frequency cepstral coefficients (MFCCs)
+and two types of bottleneck features (BNF) are studied for an i-Vector
+framework. As for the back-end level, Gaussian back-end (GB), and Generative
+Adversarial Networks (GANs) classifiers are applied alternately. The best
+submission (contrastive) is achieved for the ADI subtask with an accuracy of
+76.94% by augmenting the randomly chosen part of the development dataset.
+Further, with a post evaluation correction in the submitted system, final
+accuracy is increased to 79.76%, which represents the best performance achieved
+so far for the challenge on the test dataset.
+",1,0,0,0,0,0
+6053,Parametrization and Generation of Geological Models with Generative Adversarial Networks,"  One of the main challenges in the parametrization of geological models is the
+ability to capture complex geological structures often observed in subsurface
+fields. In recent years, Generative Adversarial Networks (GAN) were proposed as
+an efficient method for the generation and parametrization of complex data,
+showing state-of-the-art performances in challenging computer vision tasks such
+as reproducing natural images (handwritten digits, human faces, etc.). In this
+work, we study the application of Wasserstein GAN for the parametrization of
+geological models. The effectiveness of the method is assessed for uncertainty
+propagation tasks using several test cases involving different permeability
+patterns and subsurface flow problems. Results show that GANs are able to
+generate samples that preserve the multipoint statistical features of the
+geological models both visually and quantitatively. The generated samples
+reproduce both the geological structures and the flow properties of the
+reference data.
+",0,1,0,1,0,0
+6054,Finite element procedures for computing normals and mean curvature on triangulated surfaces and their use for mesh refinement,"  In this paper we consider finite element approaches to computing the mean
+curvature vector and normal at the vertices of piecewise linear triangulated
+surfaces. In particular, we adopt a stabilization technique which allows for
+first order $L^2$-convergence of the mean curvature vector and apply this
+stabilization technique also to the computation of continuous, recovered,
+normals using $L^2$-projections of the piecewise constant face normals.
+Finally, we use our projected normals to define an adaptive mesh refinement
+approach to geometry resolution where we also employ spline techniques to
+reconstruct the surface before refinement. We compare or results to previously
+proposed approaches.
+",0,0,1,0,0,0
+6055,Basis Adaptive Sample Efficient Polynomial Chaos (BASE-PC),"  For a large class of orthogonal basis functions, there has been a recent
+identification of expansion methods for computing accurate, stable
+approximations of a quantity of interest. This paper presents, within the
+context of uncertainty quantification, a practical implementation using basis
+adaptation, and coherence motivated sampling, which under assumptions has
+satisfying guarantees. This implementation is referred to as Basis Adaptive
+Sample Efficient Polynomial Chaos (BASE-PC). A key component of this is the use
+of anisotropic polynomial order which admits evolving global bases for
+approximation in an efficient manner, leading to consistently stable
+approximation for a practical class of smooth functionals. This fully adaptive,
+non-intrusive method, requires no a priori information of the solution, and has
+satisfying theoretical guarantees of recovery. A key contribution to stability
+is the use of a presented correction sampling for coherence-optimal sampling in
+order to improve stability and accuracy within the adaptive basis scheme.
+Theoretically, the method may dramatically reduce the impact of dimensionality
+in function approximation, and numerically the method is demonstrated to
+perform well on problems with dimension up to 1000.
+",0,0,1,1,0,0
+6056,Maximum a posteriori estimation through simulated annealing for binary asteroid orbit determination,"  This paper considers a new method for the binary asteroid orbit determination
+problem. The method is based on the Bayesian approach with a global
+optimisation algorithm. The orbital parameters to be determined are modelled
+through an a posteriori distribution made of a priori and likelihood terms. The
+first term constrains the parameters space and it allows the introduction of
+available knowledge about the orbit. The second term is based on given
+observations and it allows us to use and compare different observational error
+models. Once the a posteriori model is built, the estimator of the orbital
+parameters is computed using a global optimisation procedure: the simulated
+annealing algorithm. The maximum a posteriori (MAP) techniques are verified
+using simulated and real data. The obtained results validate the proposed
+method. The new approach guarantees independence of the initial parameters
+estimation and theoretical convergence towards the global optimisation
+solution. It is particularly useful in these situations, whenever a good
+initial orbit estimation is difficult to get, whenever observations are not
+well-sampled, and whenever the statistical behaviour of the observational
+errors cannot be stated Gaussian like.
+",0,1,0,1,0,0
+6057,Extrapolating Expected Accuracies for Large Multi-Class Problems,"  The difficulty of multi-class classification generally increases with the
+number of classes. Using data from a subset of the classes, can we predict how
+well a classifier will scale with an increased number of classes? Under the
+assumptions that the classes are sampled identically and independently from a
+population, and that the classifier is based on independently learned scoring
+functions, we show that the expected accuracy when the classifier is trained on
+k classes is the (k-1)st moment of a certain distribution that can be estimated
+from data. We present an unbiased estimation method based on the theory, and
+demonstrate its application on a facial recognition example.
+",1,0,0,1,0,0
+6058,Deep Learning Methods for Efficient Large Scale Video Labeling,"  We present a solution to ""Google Cloud and YouTube-8M Video Understanding
+Challenge"" that ranked 5th place. The proposed model is an ensemble of three
+model families, two frame level and one video level. The training was performed
+on augmented dataset, with cross validation.
+",1,0,0,1,0,0
+6059,RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data,"  Remote sensing image classification is a fundamental task in remote sensing
+image processing. Remote sensing field still lacks of such a large-scale
+benchmark compared to ImageNet, Place2. We propose a remote sensing image
+classification benchmark (RSI-CB) based on crowd-source data which is massive,
+scalable, and diversity. Using crowdsource data, we can efficiently annotate
+ground objects in remotes sensing image by point of interests, vectors data
+from OSM or other crowd-source data. Based on this method, we construct a
+worldwide large-scale benchmark for remote sensing image classification. In
+this benchmark, there are two sub datasets with 256 * 256 and 128 * 128 size
+respectively since different convolution neural networks requirement different
+image size. The former sub dataset contains 6 categories with 35 subclasses
+with total of more than 24,000 images; the later one contains 6 categories with
+45 subclasses with total of more than 36,000 images. The six categories are
+agricultural land, construction land and facilities, transportation and
+facilities, water and water conservancy facilities, woodland and other land,
+and each category has several subclasses. This classification system is defined
+according to the national standard of land use classification in China, and is
+inspired by the hierarchy mechanism of ImageNet. Finally, we have done a large
+number of experiments to compare RSI-CB with SAT-4, UC-Merced datasets on
+handcrafted features, such as such as SIFT, and classical CNN models, such as
+AlexNet, VGG, GoogleNet, and ResNet. We also show CNN models trained by RSI-CB
+have good performance when transfer to other dataset, i.e. UC-Merced, and good
+generalization ability. The experiments show that RSI-CB is more suitable as a
+benchmark for remote sensing image classification task than other ones in big
+data era, and can be potentially used in practical applications.
+",1,0,0,0,0,0
+6060,"Quantum groups, Yang-Baxter maps and quasi-determinants","  For any quasi-triangular Hopf algebra, there exists the universal R-matrix,
+which satisfies the Yang-Baxter equation. It is known that the adjoint action
+of the universal R-matrix on the elements of the tensor square of the algebra
+constitutes a quantum Yang-Baxter map, which satisfies the set-theoretic
+Yang-Baxter equation. The map has a zero curvature representation among
+L-operators defined as images of the universal R-matrix. We find that the zero
+curvature representation can be solved by the Gauss decomposition of a product
+of L-operators. Thereby obtained a quasi-determinant expression of the quantum
+Yang-Baxter map associated with the quantum algebra $U_{q}(gl(n))$. Moreover,
+the map is identified with products of quasi-Plücker coordinates over a
+matrix composed of the L-operators. We also consider the quasi-classical limit,
+where the underlying quantum algebra reduces to a Poisson algebra. The
+quasi-determinant expression of the quantum Yang-Baxter map reduces to ratios
+of determinants, which give a new expression of a classical Yang-Baxter map.
+",0,1,1,0,0,0
+6061,Low-level Active Visual Navigation: Increasing robustness of vision-based localization using potential fields,"  This paper proposes a low-level visual navigation algorithm to improve visual
+localization of a mobile robot. The algorithm, based on artificial potential
+fields, associates each feature in the current image frame with an attractive
+or neutral potential energy, with the objective of generating a control action
+that drives the vehicle towards the goal, while still favoring feature rich
+areas within a local scope, thus improving the localization performance. One
+key property of the proposed method is that it does not rely on mapping, and
+therefore it is a lightweight solution that can be deployed on miniaturized
+aerial robots, in which memory and computational power are major constraints.
+Simulations and real experimental results using a mini quadrotor equipped with
+a downward looking camera demonstrate that the proposed method can effectively
+drive the vehicle to a designated goal through a path that prevents
+localization failure.
+",1,0,0,0,0,0
+6062,A Brownian Motion Model and Extreme Belief Machine for Modeling Sensor Data Measurements,"  As the title suggests, we will describe (and justify through the presentation
+of some of the relevant mathematics) prediction methodologies for sensor
+measurements. This exposition will mainly be concerned with the mathematics
+related to modeling the sensor measurements.
+",1,0,0,0,0,0
+6063,On the nature of the magnetic phase transition in a Weyl semimetal,"  We investigate the nature of the magnetic phase transition induced by the
+short-ranged electron-electron interactions in a Weyl semimetal by using the
+perturbative renormalization-group method. We find that the critical point
+associated with the quantum phase transition is characterized by a Gaussian
+fixed point perturbed by a dangerously irrelevant operator. Although the
+low-energy and long-distance physics is governed by a free theory, the
+velocities of the fermionic quasiparticles and the magnetic excitations suffer
+from nontrivial renormalization effects. In particular, their ratio approaches
+one, which indicates an emergent Lorentz symmetry at low energies. We further
+investigate the stability of the fixed point in the presence of weak disorder.
+We show that while the fixed point is generally stable against weak disorder,
+among those disorders that are consistent with the emergent chiral symmetry of
+the clean system, a moderately strong random chemical potential and/or random
+vector potential may induce a quantum phase transition towards a
+disorder-dominated phase. We propose a global phase diagram of the Weyl
+semimetal in the presence of both electron-electron interactions and disorder
+based on our results.
+",0,1,0,0,0,0
+6064,Detecting Cyber-Physical Attacks in Additive Manufacturing using Digital Audio Signing,"  Additive Manufacturing (AM, or 3D printing) is a novel manufacturing
+technology that is being adopted in industrial and consumer settings. However,
+the reliance of this technology on computerization has raised various security
+concerns. In this paper we address sabotage via tampering with the 3D printing
+process. We present an object verification system using side-channel
+emanations: sound generated by onboard stepper motors. The contributions of
+this paper are following. We present two algorithms: one which generates a
+master audio fingerprint for the unmodified printing process, and one which
+computes the similarity between other print recordings and the master audio
+fingerprint. We then evaluate the deviation due to tampering, focusing on the
+detection of minimal tampering primitives. By detecting the deviation at the
+time of its occurrence, we can stop the printing process for compromised
+objects, thus save time and prevent material waste. We discuss impacts on the
+method by aspects like background noise, or different audio recorder positions.
+We further outline our vision with use cases incorporating our approach.
+",1,0,0,0,0,0
+6065,Short-wavelength out-of-band EUV emission from Sn laser-produced plasma,"  We present the results of spectroscopic measurements in the extreme
+ultraviolet (EUV) regime (7-17 nm) of molten tin microdroplets illuminated by a
+high-intensity 3-J, 60-ns Nd:YAG laser pulse. The strong 13.5 nm emission from
+this laser-produced plasma is of relevance for next-generation nanolithography
+machines. Here, we focus on the shorter wavelength features between 7 and 12 nm
+which have so far remained poorly investigated despite their diagnostic
+relevance. Using flexible atomic code calculations and local thermodynamic
+equilibrium arguments, we show that the line features in this region of the
+spectrum can be explained by transitions from high-lying configurations within
+the Sn$^{8+}$-Sn$^{15+}$ ions. The dominant transitions for all ions but
+Sn$^{8+}$ are found to be electric-dipole transitions towards the $n$=4 ground
+state from the core-excited configuration in which a 4$p$ electron is promoted
+to the 5$s$ sub-shell. Our results resolve some long-standing spectroscopic
+issues and provide reliable charge state identification for Sn laser-produced
+plasma, which could be employed as a useful tool for diagnostic purposes.
+",0,1,0,0,0,0
+6066,Compact Cardinals and Eight Values in Cichoń's Diagram,"  Assuming three strongly compact cardinals, it is consistent that \[ \aleph_1
+< \mathrm{add}(\mathrm{null}) < \mathrm{cov}(\mathrm{null}) < \mathfrak{b} <
+\mathfrak{d} < \mathrm{non}(\mathrm{null}) < \mathrm{cof}(\mathrm{null}) <
+2^{\aleph_0}.\] Under the same assumption, it is consistent that \[ \aleph_1 <
+\mathrm{add}(\mathrm{null}) < \mathrm{cov}(\mathrm{null}) <
+\mathrm{non}(\mathrm{meager}) < \mathrm{cov}(\mathrm{meager}) <
+\mathrm{non}(\mathrm{null}) < \mathrm{cof}(\mathrm{null}) < 2^{\aleph_0}.\]
+",0,0,1,0,0,0
+6067,Analysis of Peer Review Effectiveness for Academic Journals Based on Distributed Parallel System,"  A simulation model based on parallel systems is established, aiming to
+explore the relation between the number of submissions and the overall quality
+of academic journals within a similar discipline under peer review. The model
+can effectively simulate the submission, review and acceptance behaviors of
+academic journals, in a distributed manner. According to the simulation
+experiments, it could possibly happen that the overall standard of academic
+journals may deteriorate due to excessive submissions.
+",1,0,0,0,0,0
+6068,Observational signatures of linear warps in circumbinary discs,"  In recent years an increasing number of observational studies have hinted at
+the presence of warps in protoplanetary discs, however a general comprehensive
+description of observational diagnostics of warped discs was missing. We
+performed a series of 3D SPH hydrodynamic simulations and combined them with 3D
+radiative transfer calculations to study the observability of warps in
+circumbinary discs, whose plane is misaligned with respect to the orbital plane
+of the central binary. Our numerical hydrodynamic simulations confirm previous
+analytical results on the dependence of the warp structure on the viscosity and
+the initial misalignment between the binary and the disc. To study the
+observational signatures of warps we calculate images in the continuum at
+near-infrared and sub-millimetre wavelengths and in the pure rotational
+transition of CO in the sub-millimetre. Warped circumbinary discs show surface
+brightness asymmetry in near-infrared scattered light images as well as in
+optically thick gas lines at sub-millimetre wavelengths. The asymmetry is
+caused by self-shadowing of the disc by the inner warped regions, thus the
+strength of the asymmetry depends on the strength of the warp. The projected
+velocity field, derived from line observations, shows characteristic
+deviations, twists and a change in the slope of the rotation curve, from that
+of an unperturbed disc. In extreme cases even the direction of rotation appears
+to change in the disc inwards of a characteristic radius. The strength of the
+kinematical signatures of warps decreases with increasing inclination. The
+strength of all warp signatures decreases with decreasing viscosity.
+",0,1,0,0,0,0
+6069,Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures,"  Separating an audio scene into isolated sources is a fundamental problem in
+computer audition, analogous to image segmentation in visual scene analysis.
+Source separation systems based on deep learning are currently the most
+successful approaches for solving the underdetermined separation problem, where
+there are more sources than channels. Traditionally, such systems are trained
+on sound mixtures where the ground truth decomposition is already known. Since
+most real-world recordings do not have such a decomposition available, this
+limits the range of mixtures one can train on, and the range of mixtures the
+learned models may successfully separate. In this work, we use a simple blind
+spatial source separation algorithm to generate estimated decompositions of
+stereo mixtures. These estimates, together with a weighting scheme in the
+time-frequency domain, based on confidence in the separation quality, are used
+to train a deep learning model that can be used for single-channel separation,
+where no source direction information is available. This demonstrates how a
+simple cue such as the direction of origin of source can be used to bootstrap a
+model for source separation that can be used in situations where that cue is
+not available.
+",1,0,0,0,0,0
+6070,Multi-parameter One-Sided Monitoring Test,"  Multi-parameter one-sided hypothesis test problems arise naturally in many
+applications. We are particularly interested in effective tests for monitoring
+multiple quality indices in forestry products. Our search reveals that there
+are many effective statistical methods in the literature for normal data, and
+that they can easily be adapted for non-normal data. We find that the beautiful
+likelihood ratio test is unsatisfactory, because in order to control the size,
+it must cope with the least favorable distributions at the cost of power. In
+this paper, we find a novel way to slightly ease the size control, obtaining a
+much more powerful test. Simulation confirms that the new test retains good
+control of the type I error and is markedly more powerful than the likelihood
+ratio test as well as many competitors based on normal data. The new method
+performs well in the context of monitoring multiple quality indices.
+",0,0,1,1,0,0
+6071,Statistical mechanics of low-rank tensor decomposition,"  Often, large, high dimensional datasets collected across multiple modalities
+can be organized as a higher order tensor. Low-rank tensor decomposition then
+arises as a powerful and widely used tool to discover simple low dimensional
+structures underlying such data. However, we currently lack a theoretical
+understanding of the algorithmic behavior of low-rank tensor decompositions. We
+derive Bayesian approximate message passing (AMP) algorithms for recovering
+arbitrarily shaped low-rank tensors buried within noise, and we employ dynamic
+mean field theory to precisely characterize their performance. Our theory
+reveals the existence of phase transitions between easy, hard and impossible
+inference regimes, and displays an excellent match with simulations. Moreover,
+it reveals several qualitative surprises compared to the behavior of symmetric,
+cubic tensor decomposition. Finally, we compare our AMP algorithm to the most
+commonly used algorithm, alternating least squares (ALS), and demonstrate that
+AMP significantly outperforms ALS in the presence of noise.
+",0,0,0,0,1,0
+6072,A path integral based model for stocks and order dynamics,"  We introduce a model for the short-term dynamics of financial assets based on
+an application to finance of quantum gauge theory, developing ideas of Ilinski.
+We present a numerical algorithm for the computation of the probability
+distribution of prices and compare the results with APPLE stocks prices and the
+S&P500 index.
+",0,0,0,0,0,1
+6073,A New Algorithm to Automate Inductive Learning of Default Theories,"  In inductive learning of a broad concept, an algorithm should be able to
+distinguish concept examples from exceptions and noisy data. An approach
+through recursively finding patterns in exceptions turns out to correspond to
+the problem of learning default theories. Default logic is what humans employ
+in common-sense reasoning. Therefore, learned default theories are better
+understood by humans. In this paper, we present new algorithms to learn default
+theories in the form of non-monotonic logic programs. Experiments reported in
+this paper show that our algorithms are a significant improvement over
+traditional approaches based on inductive logic programming.
+",1,0,0,0,0,0
+6074,Origin of the pressure-dependent T$_c$ valley in superconducting simple cubic phosphorus,"  Motivated by recent experiments, we investigate the pressure-dependent
+electronic structure and electron-phonon (\emph{e-ph}) coupling for simple
+cubic phosphorus by performing first-principle calculations within the full
+potential linearized augmented plane wave method. As a function of increasing
+pressure, our calculations show a valley feature in T$_c$, followed by an
+eventual decrease for higher pressures. We demonstrate that this T$_c$ valley
+at low pressures is due to two nearby Lifshitz transitions, as we analyze the
+band-resolved contributions to the \emph{e-ph} coupling. Below the first
+Lifshitz transition, the phonon hardening and shrinking of the $\gamma$ Fermi
+surface with $s$ orbital character results in a decreased T$_c$ with increasing
+pressure. After the second Lifshitz transition, the appearance of $\delta$
+Fermi surfaces with $3d$ orbital character generate strong \emph{e-ph}
+inter-band couplings in $\alpha\delta$ and $\beta\delta$ channels, and hence
+lead to an increase of T$_c$. For higher pressures, the phonon hardening
+finally dominates, and T$_c$ decreases again. Our study reveals that the
+intriguing T$_c$} valley discovered in experiment can be attributed to Lifshitz
+transitions, while the plateau of T$_c$ detected at intermediate pressures
+appears to be beyond the scope of our analysis. This strongly suggests that
+besides \emph{e-ph} coupling, electronic correlations along with plasmonic
+contributions may be relevant for simple cubic phosphorous. Our findings hint
+at the notion that increasing pressure can shift the low-energy orbital weight
+towards $d$ character, and as such even trigger an enhanced importance of
+orbital-selective electronic correlations despite an increase of the overall
+bandwidth.
+",0,1,0,0,0,0
+6075,V-cycle multigrid algorithms for discontinuous Galerkin methods on non-nested polytopic meshes,"  In this paper we analyse the convergence properties of V-cycle multigrid
+algorithms for the numerical solution of the linear system of equations arising
+from discontinuous Galerkin discretization of second-order elliptic partial
+differential equations on polytopal meshes. Here, the sequence of spaces that
+stands at the basis of the multigrid scheme is possibly non nested and is
+obtained based on employing agglomeration with possible edge/face coarsening.
+We prove that the method converges uniformly with respect to the granularity of
+the grid and the polynomial approximation degree p, provided that the number of
+smoothing steps, which depends on p, is chosen sufficiently large.
+",1,0,0,0,0,0
+6076,On a result of Fel'dman on linear forms in the values of some E-functions,"  We shall consider a result of Fel'dman, where a sharp Baker-type lower bound
+is obtained for linear forms in the values of some E-functions. Fel'dman's
+proof is based on an explicit construction of Padé approximations of the
+first kind for these functions. In the present paper we introduce Padé
+approximations of the second kind for the same functions and use these to
+obtain a slightly improved version of Fel'dman's result.
+",0,0,1,0,0,0
+6077,Learning Low-shot facial representations via 2D warping,"  In this work, we mainly study the influence of the 2D warping module for
+one-shot face recognition.
+",1,0,0,0,0,0
+6078,Catalyzed bimolecular reactions in responsive nanoreactors,"  We describe a general theory for surface-catalyzed bimolecular reactions in
+responsive nanoreactors, catalytically active nanoparticles coated by a
+stimuli-responsive 'gating' shell, whose permeability controls the activity of
+the process. We address two archetypal scenarios encountered in this system:
+The first, where two species diffusing from a bulk solution react at the
+catalyst's surface; the second where only one of the reactants diffuses from
+the bulk while the other one is produced at the nanoparticle surface, e.g., by
+light conversion. We find that in both scenarios the total catalytic rate has
+the same mathematical structure, once diffusion rates are properly redefined.
+Moreover, the diffusional fluxes of the different reactants are strongly
+coupled, providing a richer behavior than that arising in unimolecular
+reactions. We also show that in stark contrast to bulk reactions, the
+identification of a limiting reactant is not simply determined by the relative
+bulk concentrations but controlled by the nanoreactor shell permeability.
+Finally, we describe an application of our theory by analyzing experimental
+data on the reaction between hexacyanoferrate (III) and borohydride ions in
+responsive hydrogel-based core-shell nanoreactors.
+",0,1,0,0,0,0
+6079,Average Case Constant Factor Time and Distance Optimal Multi-Robot Path Planning in Well-Connected Environments,"  Fast algorithms for optimal multi-robot path planning are sought after in
+many real-world applications. Known methods, however, generally do not
+simultaneously guarantee good solution optimality and fast run time (e.g.,
+polynomial). In this work, we develop a low-polynomial running time algorithm,
+called SplitAndGroup (SAG),that solves the multi-robot path planning problem on
+grids and grid-like environments and produces constant factor makespan-optimal
+solutions in the average case. That is, SAG is an average case
+O(1)-approximation algorithm. SAG computes solutions with sub-linear makespan
+and is capable of handling cases when the density of robots is extremely high -
+in a graph-theoretic setting, the algorithm supports cases where all vertices
+of the underlying graph are occupied by robots. SAG attains its desirable
+properties through a careful combination of divide-and-conquer technique and
+network flow based methods for routing the robots. Solutions from SAG, in a
+weaker sense, is also a constant factor approximation on total distance
+optimality.
+",1,0,0,0,0,0
+6080,Hierarchical Learning for Modular Robots,"  We argue that hierarchical methods can become the key for modular robots
+achieving reconfigurability. We present a hierarchical approach for modular
+robots that allows a robot to simultaneously learn multiple tasks. Our
+evaluation results present an environment composed of two different modular
+robot configurations, namely 3 degrees-of-freedom (DoF) and 4DoF with two
+corresponding targets. During the training, we switch between configurations
+and targets aiming to evaluate the possibility of training a neural network
+that is able to select appropriate motor primitives and robot configuration to
+achieve the target. The trained neural network is then transferred and executed
+on a real robot with 3DoF and 4DoF configurations. We demonstrate how this
+technique generalizes to robots with different configurations and tasks.
+",1,0,0,0,0,0
+6081,Online Learning with Diverse User Preferences,"  In this paper, we investigate the impact of diverse user preference on
+learning under the stochastic multi-armed bandit (MAB) framework. We aim to
+show that when the user preferences are sufficiently diverse and each arm can
+be optimal for certain users, the O(log T) regret incurred by exploring the
+sub-optimal arms under the standard stochastic MAB setting can be reduced to a
+constant. Our intuition is that to achieve sub-linear regret, the number of
+times an optimal arm being pulled should scale linearly in time; when all arms
+are optimal for certain users and pulled frequently, the estimated arm
+statistics can quickly converge to their true values, thus reducing the need of
+exploration dramatically. We cast the problem into a stochastic linear bandits
+model, where both the users preferences and the state of arms are modeled as
+{independent and identical distributed (i.i.d)} d-dimensional random vectors.
+After receiving the user preference vector at the beginning of each time slot,
+the learner pulls an arm and receives a reward as the linear product of the
+preference vector and the arm state vector. We also assume that the state of
+the pulled arm is revealed to the learner once its pulled. We propose a
+Weighted Upper Confidence Bound (W-UCB) algorithm and show that it can achieve
+a constant regret when the user preferences are sufficiently diverse. The
+performance of W-UCB under general setups is also completely characterized and
+validated with synthetic data.
+",1,0,0,1,0,0
+6082,Gaussian Process based Passivation of a Class of Nonlinear Systems with Unknown Dynamics,"  The paper addresses the problem of passivation of a class of nonlinear
+systems where the dynamics are unknown. For this purpose, we use the highly
+flexible, data-driven Gaussian process regression for the identification of the
+unknown dynamics for feed-forward compensation. The closed loop system of the
+nonlinear system, the Gaussian process model and a feedback control law is
+guaranteed to be semi-passive with a specific probability. The predicted
+variance of the Gaussian process regression is used to bound the model error
+which additionally allows to specify the state space region where the
+closed-loop system behaves passive. Finally, the theoretical results are
+illustrated by a simulation.
+",1,0,0,0,0,0
+6083,A Bayesian Method for Joint Clustering of Vectorial Data and Network Data,"  We present a new model-based integrative method for clustering objects given
+both vectorial data, which describes the feature of each object, and network
+data, which indicates the similarity of connected objects. The proposed general
+model is able to cluster the two types of data simultaneously within one
+integrative probabilistic model, while traditional methods can only handle one
+data type or depend on transforming one data type to another. Bayesian
+inference of the clustering is conducted based on a Markov chain Monte Carlo
+algorithm. A special case of the general model combining the Gaussian mixture
+model and the stochastic block model is extensively studied. We used both
+synthetic data and real data to evaluate this new method and compare it with
+alternative methods. The results show that our simultaneous clustering method
+performs much better. This improvement is due to the power of the model-based
+probabilistic approach for efficiently integrating information.
+",1,0,0,1,0,0
+6084,Pointwise-generalized-inverses of linear maps between C$^*$-algebras and JB$^*$-triples,"  We study pointwise-generalized-inverses of linear maps between
+C$^*$-algebras. Let $\Phi$ and $\Psi$ be linear maps between complex Banach
+algebras $A$ and $B$. We say that $\Psi$ is a pointwise-generalized-inverse of
+$\Phi$ if $\Phi(aba)=\Phi(a)\Psi(b)\Phi(a),$ for every $a,b\in A$. The pair
+$(\Phi,\Psi)$ is Jordan-triple multiplicative if $\Phi$ is a
+pointwise-generalized-inverse of $\Psi$ and the latter is a
+pointwise-generalized-inverse of $\Phi$. We study the basic properties of this
+maps in connection with Jordan homomorphism, triple homomorphisms and strongly
+preservers. We also determine conditions to guarantee the automatic continuity
+of the pointwise-generalized-inverse of continuous operator between
+C$^*$-algebras. An appropriate generalization is introduced in the setting of
+JB$^*$-triples.
+",0,0,1,0,0,0
+6085,The cohomology of rank two stable bundle moduli: mod two nilpotency & skew Schur polynomials,"  We compute cup product pairings in the integral cohomology ring of the moduli
+space of rank two stable bundles with odd determinant over a Riemann surface
+using methods of Zagier. The resulting formula is related to a generating
+function for certain skew Schur polynomials. As an application, we compute the
+nilpotency degree of a distinguished degree two generator in the mod two
+cohomology ring. We then give descriptions of the mod two cohomology rings in
+low genus, and describe the subrings invariant under the mapping class group
+action.
+",0,0,1,0,0,0
+6086,Automated Formal Synthesis of Digital Controllers for State-Space Physical Plants,"  We present a sound and automated approach to synthesize safe digital feedback
+controllers for physical plants represented as linear, time invariant models.
+Models are given as dynamical equations with inputs, evolving over a continuous
+state space and accounting for errors due to the digitalization of signals by
+the controller. Our approach has two stages, leveraging counterexample guided
+inductive synthesis (CEGIS) and reachability analysis. CEGIS synthesizes a
+static feedback controller that stabilizes the system under restrictions given
+by the safety of the reach space. Safety is verified either via BMC or abstract
+acceleration; if the verification step fails, we refine the controller by
+generalizing the counterexample. We synthesize stable and safe controllers for
+intricate physical plant models from the digital control literature.
+",1,0,0,0,0,0
+6087,A constrained control-planning strategy for redundant manipulators,"  This paper presents an interconnected control-planning strategy for redundant
+manipulators, subject to system and environmental constraints. The method
+incorporates low-level control characteristics and high-level planning
+components into a robust strategy for manipulators acting in complex
+environments, subject to joint limits. This strategy is formulated using an
+adaptive control rule, the estimated dynamic model of the robotic system and
+the nullspace of the linearized constraints. A path is generated that takes
+into account the capabilities of the platform. The proposed method is
+computationally efficient, enabling its implementation on a real multi-body
+robotic system. Through experimental results with a 7 DOF manipulator, we
+demonstrate the performance of the method in real-world scenarios.
+",1,0,0,0,0,0
+6088,Lagrangian for RLC circuits using analogy with the classical mechanics concepts,"  We study and formulate the Lagrangian for the LC, RC, RL, and RLC circuits by
+using the analogy concept with the mechanical problem in classical mechanics
+formulations. We found that the Lagrangian for the LC and RLC circuits are
+governed by two terms i. e. kinetic energy-like and potential energy-like
+terms. The Lagrangian for the RC circuit is only a contribution from the
+potential energy-like term and the Lagrangian for the RL circuit is only from
+the kinetic energy-like term.
+",0,1,0,0,0,0
+6089,A note on recent criticisms to Birnbaum's theorem,"  In this note, we provide critical commentary on two articles that cast doubt
+on the validity and implications of Birnbaum's theorem: Evans (2013) and Mayo
+(2014). In our view, the proof is correct and the consequences of the theorem
+are alive and well.
+",0,0,1,1,0,0
+6090,High efficiently numerical simulation of the TDGL equation with reticular free energy in hydrogel,"  In this paper, we focus on the numerical simulation of phase separation about
+macromolecule microsphere composite (MMC) hydrogel. The model equation is based
+on Time-Dependent Ginzburg-Landau (TDGL) equation with reticular free energy.
+We have put forward two $L^2$ stable schemes to simulate simplified TDGL
+equation. In numerical experiments, we observe that simulating the whole
+process of phase separation requires a considerably long time. We also notice
+that the total free energy changes significantly in initial time and varies
+slightly in the following time. Based on these properties, we introduce an
+adaptive strategy based on one of stable scheme mentioned. It is found that the
+introduction of the time adaptivity cannot only resolve the dynamical changes
+of the solution accurately but also can significantly save CPU time for the
+long time simulation.
+",0,1,0,0,0,0
+6091,Entanglement verification protocols for distributed systems based on the Quantum Recursive Network Architecture,"  In distributed systems based on the Quantum Recursive Network Architecture,
+quantum channels and quantum memories are used to establish entangled quantum
+states between node pairs. Such systems are robust against attackers that
+interact with the quantum channels. Conversely, weaknesses emerge when an
+attacker takes full control of a node and alters the configuration of the local
+quantum memory, either to make a denial-of-service attack or to reprogram the
+node. In such a scenario, entanglement verification over quantum memories is a
+means for detecting the intruder. Usually, entanglement verification approaches
+focus either on untrusted sources of entangled qubits (photons, in most cases)
+or on eavesdroppers that interfere with the quantum channel while entangled
+qubits are transmitted. Instead, in this work we assume that the source of
+entanglement is trusted, but parties may be dishonest. Looking for efficient
+entanglement verification protocols that only require classical channels and
+local quantum operations to work, we thoroughly analyze the one proposed by
+Nagy and Akl, that we denote as NA2010 for simplicity, and we define and
+analyze two entanglement verification protocols based on teleportation (denoted
+as AC1 and AC2), characterized by increasing efficiency in terms of intrusion
+detection probability versus sacrificed quantum resources.
+",1,0,0,0,0,0
+6092,On the radius of spatial analyticity for the quartic generalized KdV equation,"  Lower bound on the rate of decrease in time of the uniform radius of spatial
+analyticity of solutions to the quartic generalized KdV equation is derived,
+which improves an earlier result by Bona, Grujić and Kalisch.
+",0,0,1,0,0,0
+6093,Calibrating Noise to Variance in Adaptive Data Analysis,"  Datasets are often used multiple times and each successive analysis may
+depend on the outcome of previous analyses. Standard techniques for ensuring
+generalization and statistical validity do not account for this adaptive
+dependence. A recent line of work studies the challenges that arise from such
+adaptive data reuse by considering the problem of answering a sequence of
+""queries"" about the data distribution where each query may depend arbitrarily
+on answers to previous queries.
+The strongest results obtained for this problem rely on differential privacy
+-- a strong notion of algorithmic stability with the important property that it
+""composes"" well when data is reused. However the notion is rather strict, as it
+requires stability under replacement of an arbitrary data element. The simplest
+algorithm is to add Gaussian (or Laplace) noise to distort the empirical
+answers. However, analysing this technique using differential privacy yields
+suboptimal accuracy guarantees when the queries have low variance. Here we
+propose a relaxed notion of stability that also composes adaptively. We
+demonstrate that a simple and natural algorithm based on adding noise scaled to
+the standard deviation of the query provides our notion of stability. This
+implies an algorithm that can answer statistical queries about the dataset with
+substantially improved accuracy guarantees for low-variance queries. The only
+previous approach that provides such accuracy guarantees is based on a more
+involved differentially private median-of-means algorithm and its analysis
+exploits stronger ""group"" stability of the algorithm.
+",1,0,0,0,0,0
+6094,Testing Equality of Autocovariance Operators for Functional Time Series,"  We consider strictly stationary stochastic processes of Hilbert space-valued
+random variables and focus on tests of the equality of the lag-zero
+autocovariance operators of several independent functional time series. A
+moving block bootstrap-based testing procedure is proposed which generates
+pseudo random elements that satisfy the null hypothesis of interest. It is
+based on directly bootstrapping the time series of tensor products which
+overcomes some common difficulties associated with applications of the
+bootstrap to related testing problems. The suggested methodology can be
+potentially applied to a broad range of test statistics of the hypotheses of
+interest. As an example, we establish validity for approximating the
+distribution under the null of a fully functional test statistic based on the
+Hilbert-Schmidt distance of the corresponding sample lag-zero autocovariance
+operators, and show consistency under the alternative. As a prerequisite, we
+prove a central limit theorem for the moving block bootstrap procedure applied
+to the sample autocovariance operator which is of interest on its own. The
+finite sample size and power performance of the suggested moving block
+bootstrap-based testing procedure is illustrated through simulations and an
+application to a real-life dataset is discussed.
+",0,0,1,1,0,0
+6095,The Lyman-alpha forest power spectrum from the XQ-100 Legacy Survey,"  We present the Lyman-$\alpha$ flux power spectrum measurements of the XQ-100
+sample of quasar spectra obtained in the context of the European Southern
+Observatory Large Programme ""Quasars and their absorption lines: a legacy
+survey of the high redshift universe with VLT/XSHOOTER"". Using $100$ quasar
+spectra with medium resolution and signal-to-noise ratio we measure the power
+spectrum over a range of redshifts $z = 3 - 4.2$ and over a range of scales $k
+= 0.003 - 0.06\,\mathrm{s\,km^{-1}}$. The results agree well with the
+measurements of the one-dimensional power spectrum found in the literature. The
+data analysis used in this paper is based on the Fourier transform and has been
+tested on synthetic data. Systematic and statistical uncertainties of our
+measurements are estimated, with a total error (statistical and systematic)
+comparable to the one of the BOSS data in the overlapping range of scales, and
+smaller by more than $50\%$ for higher redshift bins ($z>3.6$) and small scales
+($k > 0.01\,\mathrm{s\,km^{-1}}$). The XQ-100 data set has the unique feature
+of having signal-to-noise ratios and resolution intermediate between the two
+data sets that are typically used to perform cosmological studies, i.e. BOSS
+and high-resolution spectra (e.g. UVES/VLT or HIRES). More importantly, the
+measured flux power spectra span the high redshift regime which is usually more
+constraining for structure formation models.
+",0,1,0,0,0,0
+6096,AdS4 backgrounds with N>16 supersymmetries in 10 and 11 dimensions,"  We explore all warped $AdS_4\times_w M^{D-4}$ backgrounds with the most
+general allowed fluxes that preserve more than 16 supersymmetries in $D=10$-
+and $11$-dimensional supergravities. After imposing the assumption that either
+the internal space $M^{D-4}$ is compact without boundary or the isometry
+algebra of the background decomposes into that of AdS$_4$ and that of
+$M^{D-4}$, we find that there are no such backgrounds in IIB supergravity.
+Similarly in IIA supergravity, there is a unique such background with 24
+supersymmetries locally isometric to $AdS_4\times \mathbb{CP}^3$, and in $D=11$
+supergravity all such backgrounds are locally isometric to the maximally
+supersymmetric $AdS_4\times S^7$ solution.
+",0,0,1,0,0,0
+6097,Room Temperature Polariton Lasing in All-Inorganic Perovskites,"  Polariton lasing is the coherent emission arising from a macroscopic
+polariton condensate first proposed in 1996. Over the past two decades,
+polariton lasing has been demonstrated in a few inorganic and organic
+semiconductors in both low and room temperatures. Polariton lasing in inorganic
+materials significantly relies on sophisticated epitaxial growth of crystalline
+gain medium layers sandwiched by two distributed Bragg reflectors in which
+combating the built-in strain and mismatched thermal properties is nontrivial.
+On the other hand, organic active media usually suffer from large threshold
+density and weak nonlinearity due to the Frenkel exciton nature. Further
+development of polariton lasing towards technologically significant
+applications demand more accessible materials, ease of device fabrication and
+broadly tunable emission at room temperature. Herein, we report the
+experimental realization of room-temperature polariton lasing based on an
+epitaxy-free all-inorganic cesium lead chloride perovskite microcavity.
+Polariton lasing is unambiguously evidenced by a superlinear power dependence,
+macroscopic ground state occupation, blueshift of ground state emission,
+narrowing of the linewidth and the build-up of long-range spatial coherence.
+Our work suggests considerable promise of lead halide perovskites towards
+large-area, low-cost, high performance room temperature polariton devices and
+coherent light sources extending from the ultraviolet to near infrared range.
+",0,1,0,0,0,0
+6098,"Conditional Independence, Conditional Mean Independence, and Zero Conditional Covariance","  Investigation of the reversibility of the directional hierarchy in the
+interdependency among the notions of conditional independence, conditional mean
+independence, and zero conditional covariance, for two random variables X and Y
+given a conditioning element Z which is not constrained by any topological
+restriction on its range, reveals that if the first moments of X, Y, and XY
+exist, then conditional independence implies conditional mean independence and
+conditional mean independence implies zero conditional covariance, but the
+direction of the hierarchy is not reversible in general. If the conditional
+expectation of Y given X and Z is ""affine in X,"" which happens when X is
+Bernoulli, then the ""intercept"" and ""slope"" of the conditional expectation
+(that is, the nonparametric regression function) equal the ""intercept"" and
+""slope"" of the ""least-squares linear regression function"", as a result of which
+zero conditional covariance implies conditional mean independence.
+",0,0,1,1,0,0
+6099,Probabilistic Sensor Fusion for Ambient Assisted Living,"  There is a widely-accepted need to revise current forms of health-care
+provision, with particular interest in sensing systems in the home. Given a
+multiple-modality sensor platform with heterogeneous network connectivity, as
+is under development in the Sensor Platform for HEalthcare in Residential
+Environment (SPHERE) Interdisciplinary Research Collaboration (IRC), we face
+specific challenges relating to the fusion of the heterogeneous sensor
+modalities.
+We introduce Bayesian models for sensor fusion, which aims to address the
+challenges of fusion of heterogeneous sensor modalities. Using this approach we
+are able to identify the modalities that have most utility for each particular
+activity, and simultaneously identify which features within that activity are
+most relevant for a given activity.
+We further show how the two separate tasks of location prediction and
+activity recognition can be fused into a single model, which allows for
+simultaneous learning an prediction for both tasks.
+We analyse the performance of this model on data collected in the SPHERE
+house, and show its utility. We also compare against some benchmark models
+which do not have the full structure,and show how the proposed model compares
+favourably to these methods
+",1,0,0,1,0,0
+6100,Bounded time computation on metric spaces and Banach spaces,"  We extend the framework by Kawamura and Cook for investigating computational
+complexity for operators occurring in analysis. This model is based on
+second-order complexity theory for functions on the Baire space, which is
+lifted to metric spaces by means of representations. Time is measured in terms
+of the length of the input encodings and the required output precision. We
+propose the notions of a complete representation and of a regular
+representation. We show that complete representations ensure that any
+computable function has a time bound. Regular representations generalize
+Kawamura and Cook's more restrictive notion of a second-order representation,
+while still guaranteeing fast computability of the length of the encodings.
+Applying these notions, we investigate the relationship between purely metric
+properties of a metric space and the existence of a representation such that
+the metric is computable within bounded time. We show that a bound on the
+running time of the metric can be straightforwardly translated into size bounds
+of compact subsets of the metric space. Conversely, for compact spaces and for
+Banach spaces we construct a family of admissible, complete, regular
+representations that allow for fast computation of the metric and provide short
+encodings. Here it is necessary to trade the time bound off against the length
+of encodings.
+",1,0,1,0,0,0
+6101,Gradient descent GAN optimization is locally stable,"  Despite the growing prominence of generative adversarial networks (GANs),
+optimization in GANs is still a poorly understood topic. In this paper, we
+analyze the ""gradient descent"" form of GAN optimization i.e., the natural
+setting where we simultaneously take small gradient steps in both generator and
+discriminator parameters. We show that even though GAN optimization does not
+correspond to a convex-concave game (even for simple parameterizations), under
+proper conditions, equilibrium points of this optimization procedure are still
+\emph{locally asymptotically stable} for the traditional GAN formulation. On
+the other hand, we show that the recently proposed Wasserstein GAN can have
+non-convergent limit cycles near equilibrium. Motivated by this stability
+analysis, we propose an additional regularization term for gradient descent GAN
+updates, which \emph{is} able to guarantee local stability for both the WGAN
+and the traditional GAN, and also shows practical promise in speeding up
+convergence and addressing mode collapse.
+",1,0,1,1,0,0
+6102,Transition rates and radiative lifetimes of Ca I,"  We tabulate spontaneous emission rates for all possible 811
+electric-dipole-allowed transitions between the 75 lowest-energy states of Ca
+I. These involve the $4sns$ ($n=4-8$), $4snp$ ($n=4-7$), $4snd$ ($n=3-6$),
+$4snf$ ($n=4-6$), $4p^2$, and $3d4p$ electronic configurations. We compile the
+transition rates by carrying out ab initio relativistic calculations using the
+combined method of configuration interaction and many-body perturbation theory.
+The results are compared to the available literature values. The tabulated
+rates can be useful in various applications, such as optimizing laser cooling
+in magneto-optical traps, estimating various systematic effects in optical
+clocks and evaluating static or dynamic polarizabilities and long-range
+atom-atom interaction coefficients and related atomic properties.
+",0,1,0,0,0,0
+6103,Defect entropies and enthalpies in Barium Fluoride,"  Various experimental techniques, have revealed that the predominant intrinsic
+point defects in BaF$_2$ are anion Frenkel defects. Their formation enthalpy
+and entropy as well as the corresponding parameters for the fluorine vacancy
+and fluorine interstitial motion have been determined. In addition, low
+temperature dielectric relaxation measurements in BaF$_2$ doped with uranium
+leads to the parameters {\tau}$_0$, E in the Arrhenius relation
+{\tau}={\tau}$_0$exp(E/kBT) for the relaxation time {\tau}. For the relaxation
+peak associated with a single tetravalent uranium, the migration entropy
+deduced from the pre-exponential factor {\tau}$_0$, is smaller than the anion
+Frenkel defect formation entropy by almost two orders of magnitude. We show
+that, despite their great variation, the defect entropies and enthalpies are
+interconnected through a model based on anharmonic properties of the bulk
+material that have been recently studied by employing density-functional theory
+and density-functional perturbation theory.
+",0,1,0,0,0,0
+6104,Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning,"  Exploration in complex domains is a key challenge in reinforcement learning,
+especially for tasks with very sparse rewards. Recent successes in deep
+reinforcement learning have been achieved mostly using simple heuristic
+exploration strategies such as $\epsilon$-greedy action selection or Gaussian
+control noise, but there are many tasks where these methods are insufficient to
+make any learning progress. Here, we consider more complex heuristics:
+efficient and scalable exploration strategies that maximize a notion of an
+agent's surprise about its experiences via intrinsic motivation. We propose to
+learn a model of the MDP transition probabilities concurrently with the policy,
+and to form intrinsic rewards that approximate the KL-divergence of the true
+transition probabilities from the learned model. One of our approximations
+results in using surprisal as intrinsic motivation, while the other gives the
+$k$-step learning progress. We show that our incentives enable agents to
+succeed in a wide range of environments with high-dimensional state spaces and
+very sparse rewards, including continuous control tasks and games in the Atari
+RAM domain, outperforming several other heuristic exploration techniques.
+",1,0,0,0,0,0
+6105,Local and collective magnetism of EuFe$_2$As$_2$,"  We present an experimental study of the local and collective magnetism of
+$\mathrm{EuFe_2As_2}$, that is isostructural with the high temperature
+superconductor parent compound $\mathrm{BaFe_2As_2}$. In contrast to
+$\mathrm{BaFe_2As_2}$, where only Fe spins order, $\mathrm{EuFe_2As_2}$ has an
+additional magnetic transition below 20 K due to the ordering of the Eu$^{2+}$
+spins ($J =7/2$, with $L=0$ and $S=7/2$) in an A-type antiferromagnetic texture
+(ferromagnetic layers stacked antiferromagnetically). This may potentially
+affect the FeAs layer and its local and correlated magnetism. Fe-K$_\beta$
+x-ray emission experiments on $\mathrm{EuFe_2As_2}$ single crystals reveal a
+local magnetic moment of 1.3$\pm0.15~\mu_B$ at 15 K that slightly increases to
+1.45$\pm0.15~\mu_B$ at 300 K. Resonant inelastic x-ray scattering (RIXS)
+experiments performed on the same crystals show dispersive broad (in energy)
+magnetic excitations along $\mathrm{(0, 0)\rightarrow(1, 0)}$ and $\mathrm{(0,
+0)\rightarrow(1, 1)}$ with a bandwidth on the order of 170-180 meV. These
+results on local and collective magnetism are in line with other parent
+compounds of the $\mathrm{AFe_2As_2}$ series ($A=$ Ba, Ca, and Sr), especially
+the well characterized $\mathrm{BaFe_2As_2}$. Thus, our experiments lead us to
+the conclusion that the effect of the high magnetic moment of Eu on the
+magnitude of both Fe local magnetic moment and spin excitations is small and
+confined to low energy excitations.
+",0,1,0,0,0,0
+6106,Newton slopes for twisted Artin--Schreier--Witt Towers,"  We fix a monic polynomial $f(x) \in \mathbb F_q[x]$ over a finite field of
+characteristic $p$ of degree relatively prime to $p$. Let $a\mapsto \omega(a)$
+be the Teichmüller lift of $\mathbb F_q$, and let $\chi:\mathbb{Z}\to \mathbb
+C_p^\times$ be a finite character of $\mathbb Z_p$. The $L$-function associated
+to the polynomial $f$ and the so-called twisted character $\omega^u\times \chi$
+is denoted by $L_f(\omega^u,\chi,s)$. We prove that, when the conductor of the
+character is large enough, the $p$-adic Newton slopes of this $L$-function form
+arithmetic progressions.
+",0,0,1,0,0,0
+6107,Active model learning and diverse action sampling for task and motion planning,"  The objective of this work is to augment the basic abilities of a robot by
+learning to use new sensorimotor primitives to enable the solution of complex
+long-horizon problems. Solving long-horizon problems in complex domains
+requires flexible generative planning that can combine primitive abilities in
+novel combinations to solve problems as they arise in the world. In order to
+plan to combine primitive actions, we must have models of the preconditions and
+effects of those actions: under what circumstances will executing this
+primitive achieve some particular effect in the world?
+We use, and develop novel improvements on, state-of-the-art methods for
+active learning and sampling. We use Gaussian process methods for learning the
+conditions of operator effectiveness from small numbers of expensive training
+examples collected by experimentation on a robot. We develop adaptive sampling
+methods for generating diverse elements of continuous sets (such as robot
+configurations and object poses) during planning for solving a new task, so
+that planning is as efficient as possible. We demonstrate these methods in an
+integrated system, combining newly learned models with an efficient
+continuous-space robot task and motion planner to learn to solve long horizon
+problems more efficiently than was previously possible.
+",1,0,0,1,0,0
+6108,End-to-end distance and contour length distribution functions of DNA helices,"  We present a computational method to evaluate the end-to-end and the contour
+length distribution functions of short DNA molecules described by a mesoscopic
+Hamiltonian. The method generates a large statistical ensemble of possible
+configurations for each dimer in the sequence, selects the global equilibrium
+twist conformation for the molecule and determines the average base pair
+distances along the molecule backbone. Integrating over the base pair radial
+and angular fluctuations, we derive the room temperature distribution functions
+as a function of the sequence length. The obtained values for the most probable
+end-to-end distance and contour length distance, providing a measure of the
+global molecule size, are used to examine the DNA flexibility at short length
+scales. It is found that, also in molecules with less than $\sim 60$ base
+pairs, coiled configurations maintain a large statistical weight and,
+consistently, the persistence lengths may be much smaller than in kilo-base
+DNA.
+",0,0,0,0,1,0
+6109,Online Estimation of Multiple Dynamic Graphs in Pattern Sequences,"  Many time-series data including text, movies, and biological signals can be
+represented as sequences of correlated binary patterns. These patterns may be
+described by weighted combinations of a few dominant structures that underpin
+specific interactions among the binary elements. To extract the dominant
+correlation structures and their contributions to generating data in a
+time-dependent manner, we model the dynamics of binary patterns using the
+state-space model of an Ising-type network that is composed of multiple
+undirected graphs. We provide a sequential Bayes algorithm to estimate the
+dynamics of weights on the graphs while gaining the graph structures online.
+This model can uncover overlapping graphs underlying the data better than a
+traditional orthogonal decomposition method, and outperforms an original
+time-dependent full Ising model. We assess the performance of the method by
+simulated data, and demonstrate that spontaneous activity of cultured
+hippocampal neurons is represented by dynamics of multiple graphs.
+",1,0,0,1,1,0
+6110,"Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm","  We introduce a community detection algorithm (Fluid Communities) based on the
+idea of fluids interacting in an environment, expanding and contracting as a
+result of that interaction. Fluid Communities is based on the propagation
+methodology, which represents the state-of-the-art in terms of computational
+cost and scalability. While being highly efficient, Fluid Communities is able
+to find communities in synthetic graphs with an accuracy close to the current
+best alternatives. Additionally, Fluid Communities is the first
+propagation-based algorithm capable of identifying a variable number of
+communities in network. To illustrate the relevance of the algorithm, we
+evaluate the diversity of the communities found by Fluid Communities, and find
+them to be significantly different from the ones found by alternative methods.
+",1,1,0,0,0,0
+6111,Identification of Dynamic Systems with Interval Arithmetic,"  This paper aims to identify three electrical systems: a series RLC circuit, a
+motor/generator coupled system, and the Duffing-Ueda oscillator. In order to
+obtain the system's models was used the error reduction ratio and the Akaike
+information criterion. Our approach to handle the numerical errors was the
+interval arithmetic by means of the resolution of the least squares estimation.
+The routines was implemented in Intlab, a Matlab toolbox devoted to arithmetic
+interval. Finally, the interval RMSE was calculated to verify the quality of
+the obtained models. The applied methodology was satisfactory, since the
+obtained intervals encompass the system's data and allow to demonstrate how the
+numerical errors affect the answers.
+",1,0,0,0,0,0
+6112,Four Fundamental Questions in Probability Theory and Statistics,"  This study has the purpose of addressing four questions that lie at the base
+of the probability theory and statistics, and includes two main steps. As
+first, we conduct the textual analysis of the most significant works written by
+eminent probability theorists. The textual analysis turns out to be a rather
+innovative method of study in this domain, and shows how the sampled writers,
+no matter he is a frequentist or a subjectivist, share a similar approach. Each
+author argues on the multifold aspects of probability then he establishes the
+mathematical theory on the basis of his intellectual conclusions. It may be
+said that mathematics ranks second. Hilbert foresees an approach far different
+from that used by the sampled authors. He proposes to axiomatize the
+probability calculus notably to describe the probability concepts using purely
+mathematical criteria. In the second stage of the present research we address
+the four issues of the probability theory and statistics following the
+recommendations of Hilbert. Specifically, we use two theorems that prove how
+the frequentist and the subjectivist models are not incompatible as many
+believe. Probability has distinct meanings under different hypotheses, and in
+turn classical statistics and Bayesian statistics are available for adoption in
+different circumstances. Subsequently, these results are commented upon,
+followed by our conclusions
+",0,0,0,1,0,0
+6113,Connections on parahoric torsors over curves,"  We define parahoric $\cG$--torsors for certain Bruhat--Tits group scheme
+$\cG$ on a smooth complex projective curve $X$ when the weights are real, and
+also define connections on them. We prove that a $\cG$--torsor is given by a
+homomorphism from $\pi_1(X\setminus D)$ to a maximal compact subgroup of $G$,
+where $D\, \subset\, X$ is the parabolic divisor, if and only if the torsor is
+polystable.
+",0,0,1,0,0,0
+6114,On Reduced Input-Output Dynamic Mode Decomposition,"  The identification of reduced-order models from high-dimensional data is a
+challenging task, and even more so if the identified system should not only be
+suitable for a certain data set, but generally approximate the input-output
+behavior of the data source. In this work, we consider the input-output dynamic
+mode decomposition method for system identification. We compare excitation
+approaches for the data-driven identification process and describe an
+optimization-based stabilization strategy for the identified systems.
+",1,0,0,0,0,0
+6115,Safety-Aware Apprenticeship Learning,"  Apprenticeship learning (AL) is a kind of Learning from Demonstration
+techniques where the reward function of a Markov Decision Process (MDP) is
+unknown to the learning agent and the agent has to derive a good policy by
+observing an expert's demonstrations. In this paper, we study the problem of
+how to make AL algorithms inherently safe while still meeting its learning
+objective. We consider a setting where the unknown reward function is assumed
+to be a linear combination of a set of state features, and the safety property
+is specified in Probabilistic Computation Tree Logic (PCTL). By embedding
+probabilistic model checking inside AL, we propose a novel
+counterexample-guided approach that can ensure safety while retaining
+performance of the learnt policy. We demonstrate the effectiveness of our
+approach on several challenging AL scenarios where safety is essential.
+",1,0,0,0,0,0
+6116,Solitary wave solutions and their interactions for fully nonlinear water waves with surface tension in the generalized Serre equations,"  Some effects of surface tension on fully-nonlinear, long, surface water waves
+are studied by numerical means. The differences between various solitary waves
+and their interactions in subcritical and supercritical surface tension regimes
+are presented. Analytical expressions for new peaked travelling wave solutions
+are presented in the case of critical surface tension. The numerical
+experiments were performed using a high-accurate finite element method based on
+smooth cubic splines and the four-stage, classical, explicit Runge-Kutta method
+of order four.
+",0,1,1,0,0,0
+6117,Relative weak mixing of W*-dynamical systems via joinings,"  A characterization of relative weak mixing in W*-dynamical systems in terms
+of a relatively independent joining is proven.
+",0,0,1,0,0,0
+6118,An assessment of Fe XX - Fe XXII emission lines in SDO/EVE data as diagnostics for high density solar flare plasmas using EUVE stellar observations,"  The Extreme Ultraviolet Variability Experiment (EVE) on the Solar Dynamics
+Observatory obtains extreme-ultraviolet (EUV) spectra of the full-disk Sun at a
+spectral resolution of ~1 A and cadence of 10 s. Such a spectral resolution
+would normally be considered to be too low for the reliable determination of
+electron density (N_e) sensitive emission line intensity ratios, due to
+blending. However, previous work has shown that a limited number of Fe XXI
+features in the 90-60 A wavelength region of EVE do provide useful
+N_e-diagnostics at relatively low flare densities (N_e ~ 10^11-10^12 cm^-3).
+Here we investigate if additional highly ionised Fe line ratios in the EVE
+90-160 A range may be reliably employed as N_e-diagnostics. In particular, the
+potential for such diagnostics to provide density estimates for high N_e
+(~10^13 cm^-3) flare plasmas is assessed. Our study employs EVE spectra for
+X-class flares, combined with observations of highly active late-type stars
+from the Extreme Ultraviolet Explorer (EUVE) satellite plus experimental data
+for well-diagnosed tokamak plasmas, both of which are similar in wavelength
+coverage and spectral resolution to those from EVE. Several ratios are
+identified in EVE data which yield consistent values of electron density,
+including Fe XX 113.35/121.85 and Fe XXII 114.41/135.79, with confidence in
+their reliability as N_e-diagnostics provided by the EUVE and tokamak results.
+These ratios also allow the determination of density in solar flare plasmas up
+to values of ~10^13 cm^-3.
+",0,1,0,0,0,0
+6119,Multi-Scale Spatially Weighted Local Histograms in O(1),"  Weighting pixel contribution considering its location is a key feature in
+many fundamental image processing tasks including filtering, object modeling
+and distance matching. Several techniques have been proposed that incorporate
+Spatial information to increase the accuracy and boost the performance of
+detection, tracking and recognition systems at the cost of speed. But, it is
+still not clear how to efficiently ex- tract weighted local histograms in
+constant time using integral histogram. This paper presents a novel algorithm
+to compute accurately multi-scale Spatially weighted local histograms in
+constant time using Weighted Integral Histogram (SWIH) for fast search. We
+applied our spatially weighted integral histogram approach for fast tracking
+and obtained more accurate and robust target localization result in comparison
+with using plain histogram.
+",1,0,0,0,0,0
+6120,A note on minimal dispersion of point sets in the unit cube,"  We study the dispersion of a point set, a notion closely related to the
+discrepancy. Given a real $r\in (0,1)$ and an integer $d\geq 2$, let $N(r,d)$
+denote the minimum number of points inside the $d$-dimensional unit cube
+$[0,1]^d$ such that they intersect every axis-aligned box inside $[0,1]^d$ of
+volume greater than $r$. We prove an upper bound on $N(r,d)$, matching a lower
+bound of Aistleitner et al. up to a multiplicative constant depending only on
+$r$. This fully determines the rate of growth of $N(r,d)$ if $r\in(0,1)$ is
+fixed.
+",1,0,0,0,0,0
+6121,Limit theorems in bi-free probability theory,"  In this paper additive bi-free convolution is defined for general Borel
+probability measures, and the limiting distributions for sums of bi-free pairs
+of selfadjoint commuting random variables in an infinitesimal triangular array
+are determined. These distributions are characterized by their bi-freely
+infinite divisibility, and moreover, a transfer principle is established for
+limit theorems in classical probability theory and Voiculescu's bi-free
+probability theory. Complete descriptions of bi-free stability and fullness of
+planar probability distributions are also set down. All these results reveal
+one important feature about the theory of bi-free probability that it parallels
+the classical theory perfectly well. The emphasis in the whole work is not on
+the tool of bi-free combinatorics but only on the analytic machinery.
+",0,0,1,0,0,0
+6122,Axiomatisability and hardness for universal Horn classes of hypergraphs,"  We characterise finite axiomatisability and intractability of deciding
+membership for universal Horn classes generated by finite loop-free
+hypergraphs.
+",1,0,1,0,0,0
+6123,Snyder Like Modified Gravity in Newton's Spacetime,"  This work is focused on searching a geodesic interpretation of the dynamics
+of a particle under the effects of a Snyder like deformation in the background
+of the Kepler problem. In order to accomplish that task, a newtonian spacetime
+is used. Newtonian spacetime is not a metric manifold, but allows to introduce
+a torsion free connection in order to interpret the dynamic equations of the
+deformed Kepler problem as geodesics in a curved spacetime. These geodesics and
+the curvature terms of the Riemann and Ricci tensors show a mass and a
+fundamental length dependence as expected, but are velocity independent. In
+this sense, the effect of introducing a deformed algebra is examinated and the
+corresponding curvature terms calculated, as well as the modifications of the
+integrals of motion.
+",0,1,0,0,0,0
+6124,Dissipatively Coupled Waveguide Networks for Coherent Diffusive Photonics,"  A photonic circuit is generally described as a structure in which light
+propagates by unitary exchange and transfers reversibly between channels. In
+contrast, the term `diffusive' is more akin to a chaotic propagation in
+scattering media, where light is driven out of coherence towards a thermal
+mixture. Based on the dynamics of open quantum systems, the combination of
+these two opposites can result in novel techniques for coherent light control.
+The crucial feature of these photonic structures is dissipative coupling
+between modes, via an interaction with a common reservoir. Here, we demonstrate
+experimentally that such systems can perform optical equalisation to smooth
+multimode light, or act as a distributor, guiding it into selected channels.
+Quantum thermodynamically, these systems can act as catalytic coherent
+reservoirs by performing perfect non-Landauer erasure. For lattice structures,
+localised stationary states can be supported in the continuum, similar to
+compacton-like states in conventional flat band lattices.
+",0,1,0,0,0,0
+6125,Inadequate Risk Analysis Might Jeopardize The Functional Safety of Modern Systems,"  In the early 90s, researchers began to focus on security as an important
+property to address in combination with safety. Over the years, researchers
+have proposed approaches to harmonize activities within the safety and security
+disciplines. Despite the academic efforts to identify interdependencies and to
+propose combined approaches for safety and security, there is still a lack of
+integration between safety and security practices in the industrial context, as
+they have separate standards and independent processes often addressed and
+assessed by different organizational teams and authorities. Specifically,
+security concerns are generally not covered in any detail in safety standards
+potentially resulting in successfully safety-certified systems that still are
+open for security threats from e.g., malicious intents from internal and
+external personnel and hackers that may jeopardize safety. In recent years
+security has again received an increasing attention of being an important issue
+also in safety assurance, as the open interconnected nature of emerging systems
+makes them susceptible to security threats at a much higher degree than
+existing more confined products.This article presents initial ideas on how to
+extend safety work to include aspects of security during the context
+establishment and initial risk assessment procedures. The ambition of our
+proposal is to improve safety and increase efficiency and effectiveness of the
+safety work within the frames of the current safety standards, i.e., raised
+security awareness in compliance with the current safety standards. We believe
+that our proposal is useful to raise the security awareness in industrial
+contexts, although it is not a complete harmonization of safety and security
+disciplines, as it merely provides applicable guidance to increase security
+awareness in a safety context.
+",1,0,0,0,0,0
+6126,Simultaneous determination of the drift and diffusion coefficients in stochastic differential equations,"  In this work, we consider a one-dimensional It{ô} diffusion process X t
+with possibly nonlinear drift and diffusion coefficients. We show that, when
+the diffusion coefficient is known, the drift coefficient is uniquely
+determined by an observation of the expectation of the process during a small
+time interval, and starting from values X 0 in a given subset of R. With the
+same type of observation, and given the drift coefficient, we also show that
+the diffusion coefficient is uniquely determined. When both coefficients are
+unknown, we show that they are simultaneously uniquely determined by the
+observation of the expectation and variance of the process, during a small time
+interval, and starting again from values X 0 in a given subset of R. To derive
+these results, we apply the Feynman-Kac theorem which leads to a linear
+parabolic equation with unknown coefficients in front of the first and second
+order terms. We then solve the corresponding inverse problem with PDE technics
+which are mainly based on the strong parabolic maximum principle.
+",0,0,1,0,0,0
+6127,A compactness theorem for four-dimensional shrinking gradient Ricci solitons,"  Haslhofer and Müller proved a compactness Theorem for four-dimensional
+shrinking gradient Ricci solitons, with the only assumption being that the
+entropy is uniformly bounded from below. However, the limit in their result
+could possibly be an orbifold Ricci shrinker. In this paper we prove a
+compactness theorem for noncompact four-dimensional shrinking gradient Ricci
+solitons with a topological restriction and a noncollapsing assumption, that
+is, we consider Ricci shrinkers that can be embedded in a closed four-manifold
+with vanishing second homology group over every field and are strongly
+$\kappa$-noncollapsed with respect to a universal $\kappa$. In particular, we
+do not need any curvature assumption and the limit is still a smooth nonflat
+shrinking gradient Ricci soliton.
+",0,0,1,0,0,0
+6128,Demand-Independent Optimal Tolls,"  Wardrop equilibria in nonatomic congestion games are in general inefficient
+as they do not induce an optimal flow that minimizes the total travel time.
+Network tolls are a prominent and popular way to induce an optimum flow in
+equilibrium. The classical approach to find such tolls is marginal cost pricing
+which requires the exact knowledge of the demand on the network. In this paper,
+we investigate under which conditions demand-independent optimum tolls exist
+that induce the system optimum flow for any travel demand in the network. We
+give several characterizations for the existence of such tolls both in terms of
+the cost structure and the network structure of the game. Specifically we show
+that demand-independent optimum tolls exist if and only if the edge cost
+functions are shifted monomials as used by the Bureau of Public Roads.
+Moreover, non-negative demand-independent optimum tolls exist when the network
+is a directed acyclic multi-graph. Finally, we show that any network with a
+single origin-destination pair admits demand-independent optimum tolls that,
+although not necessarily non-negative, satisfy a budget constraint.
+",1,0,0,0,0,0
+6129,An analytic formulation for positive-unlabeled learning via weighted integral probability metric,"  We consider the problem of learning a binary classifier from only positive
+and unlabeled observations (PU learning). Although recent research in PU
+learning has succeeded in showing theoretical and empirical performance, most
+existing algorithms need to solve either a convex or a non-convex optimization
+problem and thus are not suitable for large-scale datasets. In this paper, we
+propose a simple yet theoretically grounded PU learning algorithm by extending
+the previous work proposed for supervised binary classification (Sriperumbudur
+et al., 2012). The proposed PU learning algorithm produces a closed-form
+classifier when the hypothesis space is a closed ball in reproducing kernel
+Hilbert space. In addition, we establish upper bounds of the estimation error
+and the excess risk. The obtained estimation error bound is sharper than
+existing results and the excess risk bound does not rely on an approximation
+error term. To the best of our knowledge, we are the first to explicitly derive
+the excess risk bound in the field of PU learning. Finally, we conduct
+extensive numerical experiments using both synthetic and real datasets,
+demonstrating improved accuracy, scalability, and robustness of the proposed
+algorithm.
+",1,0,0,1,0,0
+6130,Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor,"  Knights Landing (KNL) is the code name for the second-generation Intel Xeon
+Phi product family. KNL has generated significant interest in the data analysis
+and machine learning communities because its new many-core architecture targets
+both of these workloads. The KNL many-core vector processor design enables it
+to exploit much higher levels of parallelism. At the Lincoln Laboratory
+Supercomputing Center (LLSC), the majority of users are running data analysis
+applications such as MATLAB and Octave. More recently, machine learning
+applications, such as the UC Berkeley Caffe deep learning framework, have
+become increasingly important to LLSC users. Thus, the performance of these
+applications on KNL systems is of high interest to LLSC users and the broader
+data analysis and machine learning communities. Our data analysis benchmarks of
+these application on the Intel KNL processor indicate that single-core
+double-precision generalized matrix multiply (DGEMM) performance on KNL systems
+has improved by ~3.5x compared to prior Intel Xeon technologies. Our data
+analysis applications also achieved ~60% of the theoretical peak performance.
+Also a performance comparison of a machine learning application, Caffe, between
+the two different Intel CPUs, Xeon E5 v3 and Xeon Phi 7210, demonstrated a 2.7x
+improvement on a KNL node.
+",1,1,0,0,0,0
+6131,A Proof of the Conjecture of Lehmer and of the Conjecture of Schinzel-Zassenhaus,"  The conjecture of Lehmer is proved to be true. The proof mainly relies upon:
+(i) the properties of the Parry Upper functions $f_{house(\alpha)}(z)$
+associated with the dynamical zeta functions $\zeta_{house(\alpha)}(z)$ of the
+Rényi--Parry arithmetical dynamical systems, for $\alpha$ an algebraic
+integer $\alpha$ of house ""$house(\alpha)$"" greater than 1, (ii) the discovery
+of lenticuli of poles of $\zeta_{house(\alpha)}(z)$ which uniformly
+equidistribute at the limit on a limit ""lenticular"" arc of the unit circle,
+when $house(\alpha)$ tends to $1^+$, giving rise to a continuous lenticular
+minorant ${\rm M}_{r}(house(\alpha))$ of the Mahler measure ${\rm M}(\alpha)$,
+(iii) the Poincaré asymptotic expansions of these poles and of this minorant
+${\rm M}_{r}(house(\alpha))$ as a function of the dynamical degree. With the
+same arguments the conjecture of Schinzel-Zassenhaus is proved to be true. An
+inequality improving those of Dobrowolski and Voutier ones is obtained. The set
+of Salem numbers is shown to be bounded from below by the Perron number
+$\theta_{31}^{-1} = 1.08545\ldots$, dominant root of the trinomial $-1 - z^{30}
++ z^{31}$. Whether Lehmer's number is the smallest Salem number remains open. A
+lower bound for the Weil height of nonzero totally real algebraic numbers,
+$\neq \pm 1$, is obtained (Bogomolov property). For sequences of algebraic
+integers of Mahler measure smaller than the smallest Pisot number, whose houses
+have a dynamical degree tending to infinity, the Galois orbit measures of
+conjugates are proved to converge towards the Haar measure on $|z|=1$ (limit
+equidistribution).
+",0,0,1,0,0,0
+6132,Pseudo asymptotically periodic solutions for fractional integro-differential neutral equations,"  In this paper, we study the existence and uniqueness of pseudo
+$S$-asymptotically $\omega$-periodic mild solutions of class $r$ for fractional
+integro-differential neutral equations. An example is presented to illustrate
+the application of the abstract results.
+",0,0,1,0,0,0
+6133,Updating the silent speech challenge benchmark with deep learning,"  The 2010 Silent Speech Challenge benchmark is updated with new results
+obtained in a Deep Learning strategy, using the same input features and
+decoding strategy as in the original article. A Word Error Rate of 6.4% is
+obtained, compared to the published value of 17.4%. Additional results
+comparing new auto-encoder-based features with the original features at reduced
+dimensionality, as well as decoding scenarios on two different language models,
+are also presented. The Silent Speech Challenge archive has been updated to
+contain both the original and the new auto-encoder features, in addition to the
+original raw data.
+",1,0,0,0,0,0
+6134,Magnetic field control of cycloidal domains and electric polarization in multiferroic BiFeO$_3$,"  The magnetic field induced rearrangement of the cycloidal spin structure in
+ferroelectric mono-domain single crystals of the room-temperature multiferroic
+BiFeO$_3$ is studied using small-angle neutron scattering (SANS). The cycloid
+propagation vectors are observed to rotate when magnetic fields applied
+perpendicular to the rhombohedral (polar) axis exceed a pinning threshold value
+of $\sim$5\,T. In light of these experimental results, a phenomenological model
+is proposed that captures the rearrangement of the cycloidal domains, and we
+revisit the microscopic origin of the magnetoelectric effect. A new coupling
+between the magnetic anisotropy and the polarization is proposed that explains
+the recently discovered magnetoelectric polarization to the rhombohedral axis.
+",0,1,0,0,0,0
+6135,DepQBF 6.0: A Search-Based QBF Solver Beyond Traditional QCDCL,"  We present the latest major release version 6.0 of the quantified Boolean
+formula (QBF) solver DepQBF, which is based on QCDCL. QCDCL is an extension of
+the conflict-driven clause learning (CDCL) paradigm implemented in state of the
+art propositional satisfiability (SAT) solvers. The Q-resolution calculus
+(QRES) is a QBF proof system which underlies QCDCL. QCDCL solvers can produce
+QRES proofs of QBFs in prenex conjunctive normal form (PCNF) as a byproduct of
+the solving process. In contrast to traditional QCDCL based on QRES, DepQBF 6.0
+implements a variant of QCDCL which is based on a generalization of QRES. This
+generalization is due to a set of additional axioms and leaves the original
+Q-resolution rules unchanged. The generalization of QRES enables QCDCL to
+potentially produce exponentially shorter proofs than the traditional variant.
+We present an overview of the features implemented in DepQBF and report on
+experimental results which demonstrate the effectiveness of generalized QRES in
+QCDCL.
+",1,0,0,0,0,0
+6136,On Optimization of Radiative Dipole Body Array Coils for 7 Tesla MRI,"  In this contribution we present numerical and experimental results of a
+parametric study of radiative dipole antennas in a phased array configuration
+for efficient body magnetic resonance imaging at 7T via parallel transmit. For
+magnetic resonance imaging (MRI) at ultrahigh fields (7T and higher) dipole
+antennas are commonly used in phased arrays, particularly for body imaging
+targets. This study reveals the effects of dipole positioning in the array
+(elevation of dipoles above the subject and inter-dipole spacing) on their
+mutual coupling, $B_1^{+}$ per unit power and $B_1^{+}$ per maximum local SAR
+efficiencies as well as the RF-shimming capability. The results demonstrate the
+trade-off between low maximum local SAR and sensitivity to the subject
+variation and provide the working parameter range for practical body arrays
+composed of recently suggested fractionated dipoles.
+",0,1,0,0,0,0
+6137,Speaking Style Authentication Using Suprasegmental Hidden Markov Models,"  The importance of speaking style authentication from human speech is gaining
+an increasing attention and concern from the engineering community. The
+importance comes from the demand to enhance both the naturalness and efficiency
+of spoken language human-machine interface. Our work in this research focuses
+on proposing, implementing, and testing speaker-dependent and text-dependent
+speaking style authentication (verification) systems that accept or reject the
+identity claim of a speaking style based on suprasegmental hidden Markov models
+(SPHMMs). Based on using SPHMMs, our results show that the average speaking
+style authentication performance is: 99%, 37%, 85%, 60%, 61%, 59%, 41%, 61%,
+and 57% belonging respectively to the speaking styles: neutral, shouted, slow,
+loud, soft, fast, angry, happy, and fearful.
+",1,0,0,0,0,0
+6138,A fixed point formula and Harish-Chandra's character formula,"  The main result in this paper is a fixed point formula for equivariant
+indices of elliptic differential operators, for proper actions by connected
+semisimple Lie groups on possibly noncompact manifolds, with compact quotients.
+For compact groups and manifolds, this reduces to the Atiyah-Segal-Singer fixed
+point formula. Other special cases include an index theorem by Connes and
+Moscovici for homogeneous spaces, and an earlier index theorem by the second
+author, both in cases where the group acting is connected and semisimple. As an
+application of this fixed point formula, we give a new proof of
+Harish-Chandra's character formula for discrete series representations.
+",0,0,1,0,0,0
+6139,The phonon softening due to melting of the ferromagnetic order in elemental iron,"  We study the fundamental question of the lattice dynamics of a metallic
+ferromagnet in the regime where the static long range magnetic order is
+replaced by the fluctuating local moments embedded in a metallic host. We use
+the \textit{ab initio} Density Functional Theory(DFT)+embedded Dynamical
+Mean-Field Theory(eDMFT) functional approach to address the dynamic stability
+of iron polymorphs and the phonon softening with increased temperature. We show
+that the non-harmonic and inhomogeneous phonon softening measured in iron is a
+result of the melting of the long range ferromagnetic order, and is unrelated
+to the first order structural transition from the BCC to the FCC phase, as is
+usually assumed. We predict that the BCC structure is dynamically stable at all
+temperatures at normal pressure, and is only thermodynamically unstable between
+the BCC-$\alpha$ and the BCC-$\delta$ phase of iron.
+",0,1,0,0,0,0
+6140,Decomposing manifolds into Cartesian products,"  The decomposability of a Cartesian product of two nondecomposable manifolds
+into products of lower dimensional manifolds is studied. For 3-manifolds we
+obtain an analog of a result due to Borsuk for surfaces, and in higher
+dimensions we show that similar analogs do not exist unless one imposes further
+restrictions such as simple connectivity.
+",0,0,1,0,0,0
+6141,Performance of the MAGIC telescopes under moonlight,"  MAGIC, a system of two imaging atmospheric Cherenkov telescopes, achieves its
+best performance under dark conditions, i.e. in absence of moonlight or
+twilight. Since operating the telescopes only during dark time would severely
+limit the duty cycle, observations are also performed when the Moon is present
+in the sky. Here we present a dedicated Moon-adapted analysis and characterize
+the performance of MAGIC under moonlight. We evaluate energy threshold, angular
+resolution and sensitivity of MAGIC under different background light levels,
+based on Crab Nebula observations and tuned Monte Carlo simulations. This study
+includes observations taken under non-standard hardware configurations, such as
+reducing the camera photomultiplier tubes gain by a factor $\sim$1.7 (reduced
+HV settings) with respect to standard settings (nominal HV) or using UV-pass
+filters to strongly reduce the amount of moonlight reaching the telescopes
+cameras. The Crab Nebula spectrum is correctly reconstructed in all the studied
+illumination levels, that reach up to 30 times brighter than under dark
+conditions. The main effect of moonlight is an increase in the analysis energy
+threshold and in the systematic uncertainties on the flux normalization. The
+sensitivity degradation is constrained to be below 10\%, within 15-30\% and
+between 60 and 80\% for nominal HV, reduced HV and UV-pass filter observations,
+respectively. No worsening of the angular resolution was found. Thanks to
+observations during moonlight, the duty cycle can be doubled, suppressing the
+need to stop observations around full Moon.
+",0,1,0,0,0,0
+6142,Mastering Heterogeneous Behavioural Models,"  Heterogeneity is one important feature of complex systems, leading to the
+complexity of their construction and analysis. Moving the heterogeneity at
+model level helps in mastering the difficulty of composing heterogeneous models
+which constitute a large system. We propose a method made of an algebra and
+structure morphisms to deal with the interaction of behavioural models,
+provided that they are compatible. We prove that heterogeneous models can
+interact in a safe way, and therefore complex heterogeneous systems can be
+built and analysed incrementally. The Uppaal tool is targeted for
+experimentations.
+",1,0,0,0,0,0
+6143,Approximation and Convergence Properties of Generative Adversarial Learning,"  Generative adversarial networks (GAN) approximate a target data distribution
+by jointly optimizing an objective function through a ""two-player game"" between
+a generator and a discriminator. Despite their empirical success, however, two
+very basic questions on how well they can approximate the target distribution
+remain unanswered. First, it is not known how restricting the discriminator
+family affects the approximation quality. Second, while a number of different
+objective functions have been proposed, we do not understand when convergence
+to the global minima of the objective function leads to convergence to the
+target distribution under various notions of distributional convergence.
+In this paper, we address these questions in a broad and unified setting by
+defining a notion of adversarial divergences that includes a number of recently
+proposed objective functions. We show that if the objective function is an
+adversarial divergence with some additional conditions, then using a restricted
+discriminator family has a moment-matching effect. Additionally, we show that
+for objective functions that are strict adversarial divergences, convergence in
+the objective function implies weak convergence, thus generalizing previous
+results.
+",1,0,0,1,0,0
+6144,Driver Distraction Identification with an Ensemble of Convolutional Neural Networks,"  The World Health Organization (WHO) reported 1.25 million deaths yearly due
+to road traffic accidents worldwide and the number has been continuously
+increasing over the last few years. Nearly fifth of these accidents are caused
+by distracted drivers. Existing work of distracted driver detection is
+concerned with a small set of distractions (mostly, cell phone usage).
+Unreliable ad-hoc methods are often used.In this paper, we present the first
+publicly available dataset for driver distraction identification with more
+distraction postures than existing alternatives. In addition, we propose a
+reliable deep learning-based solution that achieves a 90% accuracy. The system
+consists of a genetically-weighted ensemble of convolutional neural networks,
+we show that a weighted ensemble of classifiers using a genetic algorithm
+yields in a better classification confidence. We also study the effect of
+different visual elements in distraction detection by means of face and hand
+localizations, and skin segmentation. Finally, we present a thinned version of
+our ensemble that could achieve 84.64% classification accuracy and operate in a
+real-time environment.
+",1,0,0,1,0,0
+6145,Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network,"  Advanced driver assistance systems (ADAS) can be significantly improved with
+effective driver action prediction (DAP). Predicting driver actions early and
+accurately can help mitigate the effects of potentially unsafe driving
+behaviors and avoid possible accidents. In this paper, we formulate driver
+action prediction as a timeseries anomaly prediction problem. While the anomaly
+(driver actions of interest) detection might be trivial in this context,
+finding patterns that consistently precede an anomaly requires searching for or
+extracting features across multi-modal sensory inputs. We present such a driver
+action prediction system, including a real-time data acquisition, processing
+and learning framework for predicting future or impending driver action. The
+proposed system incorporates camera-based knowledge of the driving environment
+and the driver themselves, in addition to traditional vehicle dynamics. It then
+uses a deep bidirectional recurrent neural network (DBRNN) to learn the
+correlation between sensory inputs and impending driver behavior achieving
+accurate and high horizon action prediction. The proposed system performs
+better than other existing systems on driver action prediction tasks and can
+accurately predict key driver actions including acceleration, braking, lane
+change and turning at durations of 5sec before the action is executed by the
+driver.
+",1,0,0,1,0,0
+6146,A simple neural network module for relational reasoning,"  Relational reasoning is a central component of generally intelligent
+behavior, but has proven difficult for neural networks to learn. In this paper
+we describe how to use Relation Networks (RNs) as a simple plug-and-play module
+to solve problems that fundamentally hinge on relational reasoning. We tested
+RN-augmented networks on three tasks: visual question answering using a
+challenging dataset called CLEVR, on which we achieve state-of-the-art,
+super-human performance; text-based question answering using the bAbI suite of
+tasks; and complex reasoning about dynamic physical systems. Then, using a
+curated dataset called Sort-of-CLEVR we show that powerful convolutional
+networks do not have a general capacity to solve relational questions, but can
+gain this capacity when augmented with RNs. Our work shows how a deep learning
+architecture equipped with an RN module can implicitly discover and learn to
+reason about entities and their relations.
+",1,0,0,0,0,0
+6147,Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD,"  We propose a new algorithm for the computation of a singular value
+decomposition (SVD) low-rank approximation of a matrix in the Matrix Product
+Operator (MPO) format, also called the Tensor Train Matrix format. Our tensor
+network randomized SVD (TNrSVD) algorithm is an MPO implementation of the
+randomized SVD algorithm that is able to compute dominant singular values and
+their corresponding singular vectors. In contrast to the state-of-the-art
+tensor-based alternating least squares SVD (ALS-SVD) and modified alternating
+least squares SVD (MALS-SVD) matrix approximation methods, TNrSVD can be up to
+17 times faster while achieving the same accuracy. In addition, our TNrSVD
+algorithm also produces accurate approximations in particular cases where both
+ALS-SVD and MALS-SVD fail to converge. We also propose a new algorithm for the
+fast conversion of a sparse matrix into its corresponding MPO form, which is up
+to 509 times faster than the standard Tensor Train SVD (TT-SVD) method while
+achieving machine precision accuracy. The efficiency and accuracy of both
+algorithms are demonstrated in numerical experiments.
+",1,0,0,0,0,0
+6148,Path Cover and Path Pack Inequalities for the Capacitated Fixed-Charge Network Flow Problem,"  Capacitated fixed-charge network flows are used to model a variety of
+problems in telecommunication, facility location, production planning and
+supply chain management. In this paper, we investigate capacitated path
+substructures and derive strong and easy-to-compute \emph{path cover and path
+pack inequalities}. These inequalities are based on an explicit
+characterization of the submodular inequalities through a fast computation of
+parametric minimum cuts on a path, and they generalize the well-known flow
+cover and flow pack inequalities for the single-node relaxations of
+fixed-charge flow models. We provide necessary and sufficient facet conditions.
+Computational results demonstrate the effectiveness of the inequalities when
+used as cuts in a branch-and-cut algorithm.
+",1,0,1,0,0,0
+6149,Bridging the Gap Between Value and Policy Based Reinforcement Learning,"  We establish a new connection between value and policy based reinforcement
+learning (RL) based on a relationship between softmax temporal value
+consistency and policy optimality under entropy regularization. Specifically,
+we show that softmax consistent action values correspond to optimal entropy
+regularized policy probabilities along any action sequence, regardless of
+provenance. From this observation, we develop a new RL algorithm, Path
+Consistency Learning (PCL), that minimizes a notion of soft consistency error
+along multi-step action sequences extracted from both on- and off-policy
+traces. We examine the behavior of PCL in different scenarios and show that PCL
+can be interpreted as generalizing both actor-critic and Q-learning algorithms.
+We subsequently deepen the relationship by showing how a single model can be
+used to represent both a policy and the corresponding softmax state values,
+eliminating the need for a separate critic. The experimental evaluation
+demonstrates that PCL significantly outperforms strong actor-critic and
+Q-learning baselines across several benchmarks.
+",1,0,0,1,0,0
+6150,Chalcogenide Glass-on-Graphene Photonics,"  Two-dimensional (2-D) materials are of tremendous interest to integrated
+photonics given their singular optical characteristics spanning light emission,
+modulation, saturable absorption, and nonlinear optics. To harness their
+optical properties, these atomically thin materials are usually attached onto
+prefabricated devices via a transfer process. In this paper, we present a new
+route for 2-D material integration with planar photonics. Central to this
+approach is the use of chalcogenide glass, a multifunctional material which can
+be directly deposited and patterned on a wide variety of 2-D materials and can
+simultaneously function as the light guiding medium, a gate dielectric, and a
+passivation layer for 2-D materials. Besides claiming improved fabrication
+yield and throughput compared to the traditional transfer process, our
+technique also enables unconventional multilayer device geometries optimally
+designed for enhancing light-matter interactions in the 2-D layers.
+Capitalizing on this facile integration method, we demonstrate a series of
+high-performance glass-on-graphene devices including ultra-broadband on-chip
+polarizers, energy-efficient thermo-optic switches, as well as graphene-based
+mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators.
+",0,1,0,0,0,0
+6151,Speaker verification using end-to-end adversarial language adaptation,"  In this paper we investigate the use of adversarial domain adaptation for
+addressing the problem of language mismatch between speaker recognition
+corpora. In the context of speaker verification, adversarial domain adaptation
+methods aim at minimizing certain divergences between the distribution that the
+utterance-level features follow (i.e. speaker embeddings) when drawn from
+source and target domains (i.e. languages), while preserving their capacity in
+recognizing speakers. Neural architectures for extracting utterance-level
+representations enable us to apply adversarial adaptation methods in an
+end-to-end fashion and train the network jointly with the standard
+cross-entropy loss. We examine several configurations, such as the use of
+(pseudo-)labels on the target domain as well as domain labels in the feature
+extractor, and we demonstrate the effectiveness of our method on the
+challenging NIST SRE16 and SRE18 benchmarks.
+",1,0,0,0,0,0
+6152,Real elliptic curves and cevian geometry,"  We study the elliptic curve $E_a: (ax+1)y^2+(ax+1)(x-1)y+x^2-x=0$, which we
+call the geometric normal form of an elliptic curve. We show that any elliptic
+curve whose $j$-invariant is real is isomorphic to a curve $E_a$ in geometric
+normal form, and show that for $a \notin \{0, -1, -9\}$, the points on $E_a$,
+minus a set of $6$ points, can be characterized in terms of the cevian geometry
+of a triangle.
+",0,0,1,0,0,0
+6153,Omni $n$-Lie algebras and linearization of higher analogues of Courant algebroids,"  In this paper, we introduce the notion of an omni $n$-Lie algebra and show
+that they are linearization of higher analogues of standard Courant algebroids.
+We also introduce the notion of a nonabelian omni $n$-Lie algebra and show that
+they are linearization of higher analogues of Courant algebroids associated to
+Nambu-Poisson manifolds.
+",0,0,1,0,0,0
+6154,Musical Instrument Recognition Using Their Distinctive Characteristics in Artificial Neural Networks,"  In this study an Artificial Neural Network was trained to classify musical
+instruments, using audio samples transformed to the frequency domain. Different
+features of the sound, in both time and frequency domain, were analyzed and
+compared in relation to how much information that could be derived from that
+limited data. The study concluded that in comparison with the base experiment,
+that had an accuracy of 93.5%, using the attack only resulted in 80.2% and the
+initial 100 Hz in 64.2%.
+",1,0,0,1,0,0
+6155,Revealing structure components of the retina by deep learning networks,"  Deep convolutional neural networks (CNNs) have demonstrated impressive
+performance on visual object classification tasks. In addition, it is a useful
+model for predication of neuronal responses recorded in visual system. However,
+there is still no clear understanding of what CNNs learn in terms of visual
+neuronal circuits. Visualizing CNN's features to obtain possible connections to
+neuronscience underpinnings is not easy due to highly complex circuits from the
+retina to higher visual cortex. Here we address this issue by focusing on
+single retinal ganglion cells with a simple model and electrophysiological
+recordings from salamanders. By training CNNs with white noise images to
+predicate neural responses, we found that convolutional filters learned in the
+end are resembling to biological components of the retinal circuit. Features
+represented by these filters tile the space of conventional receptive field of
+retinal ganglion cells. These results suggest that CNN could be used to reveal
+structure components of neuronal circuits.
+",0,0,0,1,0,0
+6156,Generalized Yangians and their Poisson counterparts,"  By a generalized Yangian we mean a Yangian-like algebra of one of two
+classes. One of these classes consists of the so-called braided Yangians,
+introduced in our previous paper. The braided Yangians are in a sense similar
+to the reflection equation algebra. The generalized Yangians of second class,
+called the Yangians of RTT type, are defined by the same formulae as the usual
+Yangians are but with other quantum $R$-matrices. If such an $R$-matrix is the
+simplest trigonometrical $R$-matrix, the corresponding Yangian of RTT type is
+the so-called q-Yangian. We claim that each generalized Yangian is a
+deformation of the commutative algebra ${\rm Sym}(gl(m)[t^{-1}])$ provided that
+the corresponding $R$-matrix is a deformation of the flip. Also, we exhibit the
+corresponding Poisson brackets.
+",0,0,1,0,0,0
+6157,Asymptotics of Hankel determinants with a one-cut regular potential and Fisher-Hartwig singularities,"  We obtain asymptotics of large Hankel determinants whose weight depends on a
+one-cut regular potential and any number of Fisher-Hartwig singularities. This
+generalises two results: 1) a result of Berestycki, Webb and Wong [5] for
+root-type singularities, and 2) a result of Its and Krasovsky [37] for a
+Gaussian weight with a single jump-type singularity. We show that when we apply
+a piecewise constant thinning on the eigenvalues of a random Hermitian matrix
+drawn from a one-cut regular ensemble, the gap probability in the thinned
+spectrum, as well as correlations of the characteristic polynomial of the
+associated conditional point process, can be expressed in terms of these
+determinants.
+",0,0,1,0,0,0
+6158,Beyond linear galaxy alignments,"  Galaxy intrinsic alignments (IA) are a critical uncertainty for current and
+future weak lensing measurements. We describe a perturbative expansion of IA,
+analogous to the treatment of galaxy biasing. From an astrophysical
+perspective, this model includes the expected large-scale alignment mechanisms
+for galaxies that are pressure-supported (tidal alignment) and
+rotation-supported (tidal torquing) as well as the cross-correlation between
+the two. Alternatively, this expansion can be viewed as an effective model
+capturing all relevant effects up to the given order. We include terms up to
+second order in the density and tidal fields and calculate the resulting IA
+contributions to two-point statistics at one-loop order. For fiducial
+amplitudes of the IA parameters, we find the quadratic alignment and
+linear-quadratic cross terms can contribute order-unity corrections to the
+total intrinsic alignment signal at $k\sim0.1\,h^{-1}{\rm Mpc}$, depending on
+the source redshift distribution. These contributions can lead to significant
+biases on inferred cosmological parameters in Stage IV photometric weak lensing
+surveys. We perform forecasts for an LSST-like survey, finding that use of the
+standard ""NLA"" model for intrinsic alignments cannot remove these large
+parameter biases, even when allowing for a more general redshift dependence.
+The model presented here will allow for more accurate and flexible IA treatment
+in weak lensing and combined probes analyses, and an implementation is made
+available as part of the public FAST-PT code. The model also provides a more
+advanced framework for understanding the underlying IA processes and their
+relationship to fundamental physics.
+",0,1,0,0,0,0
+6159,Modelling thermo-electro-mechanical effects in orthotropic cardiac tissue,"  In this paper we introduce a new mathematical model for the active
+contraction of cardiac muscle, featuring different thermo-electric and
+nonlinear conductivity properties. The passive hyperelastic response of the
+tissue is described by an orthotropic exponential model, whereas the ionic
+activity dictates active contraction incorporated through the concept of
+orthotropic active strain. We use a fully incompressible formulation, and the
+generated strain modifies directly the conductivity mechanisms in the medium
+through the pull-back transformation. We also investigate the influence of
+thermo-electric effects in the onset of multiphysics emergent spatiotemporal
+dynamics, using nonlinear diffusion. It turns out that these ingredients have a
+key role in reproducing pathological chaotic dynamics such as ventricular
+fibrillation during inflammatory events, for instance. The specific structure
+of the governing equations suggests to cast the problem in mixed-primal form
+and we write it in terms of Kirchhoff stress, displacements, solid pressure,
+electric potential, activation generation, and ionic variables. We also propose
+a new mixed-primal finite element method for its numerical approximation, and
+we use it to explore the properties of the model and to assess the importance
+of coupling terms, by means of a few computational experiments in 3D.
+",0,0,0,0,1,0
+6160,Debt-Prone Bugs: Technical Debt in Software Maintenance,"  Fixing bugs is an important phase in software development and maintenance. In
+practice, the process of bug fixing may conflict with the release schedule.
+Such confliction leads to a trade-off between software quality and release
+schedule, which is known as the technical debt metaphor. In this article, we
+propose the concept of debt-prone bugs to model the technical debt in software
+maintenance. We identify three types of debt-prone bugs, namely tag bugs,
+reopened bugs, and duplicate bugs. A case study on Mozilla is conducted to
+examine the impact of debt-prone bugs in software products. We investigate the
+correlation between debt-prone bugs and the product quality. For a product
+under development, we build prediction models based on historical products to
+predict the time cost of fixing bugs. The result shows that identifying
+debt-prone bugs can assist in monitoring and improving software quality.
+",1,0,0,0,0,0
+6161,Khovanov-Rozansky homology and higher Catalan sequences,"  We give a simple recursion which computes the triply graded Khovanov-Rozansky
+homology of several infinite families of knots and links, including the
+$(n,nm\pm 1)$ and $(n,nm)$ torus links for $n,m\geq 1$. We interpret our
+results in terms of Catalan combinatorics, proving a conjecture of Gorsky's.
+Our computations agree with predictions coming from Hilbert schemes and
+rational DAHA, which also proves the Gorsky-Oblomkov-Rasmussen-Shende
+conjectures in these cases. Additionally, our results suggest a topological
+interpretation of the symmetric functions which appear in the context of the
+$m$-shuffle conjecture of Haglund-Haiman-Loehr-Remmel-Ulyanov.
+",0,0,1,0,0,0
+6162,New face of multifractality: Multi-branched left-sidedness and phase transitions in multifractality of interevent times,"  We develop an extended multifractal analysis based on the Legendre-Fenchel
+transform rather than the routinely used Legendre transform. We apply this
+analysis to studying time series consisting of inter-event times. As a result,
+we discern the non-monotonic behavior of the generalized Hurst exponent - the
+fundamental exponent studied by us - and hence a multi-branched left-sided
+spectrum of dimensions. This kind of multifractality is a direct result of the
+non-monotonic behavior of the generalized Hurst exponent and is not caused by
+non-analytic behavior as has been previously suggested. We examine the main
+thermodynamic consequences of the existence of this type of multifractality
+related to the thermal stable, metastable, and unstable phases within a
+hierarchy of fluctuations, and also to the first and second order phase
+transitions between them.
+",0,0,0,0,0,1
+6163,Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions,"  By building up on the recent theory that established the connection between
+implicit generative modeling and optimal transport, in this study, we propose a
+novel parameter-free algorithm for learning the underlying distributions of
+complicated datasets and sampling from them. The proposed algorithm is based on
+a functional optimization problem, which aims at finding a measure that is
+close to the data distribution as much as possible and also expressive enough
+for generative modeling purposes. We formulate the problem as a gradient flow
+in the space of probability measures. The connections between gradient flows
+and stochastic differential equations let us develop a computationally
+efficient algorithm for solving the optimization problem, where the resulting
+algorithm resembles the recent dynamics-based Markov Chain Monte Carlo
+algorithms. We provide formal theoretical analysis where we prove finite-time
+error guarantees for the proposed algorithm. Our experimental results support
+our theory and shows that our algorithm is able to capture the structure of
+challenging distributions.
+",0,0,0,1,0,0
+6164,Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization,"  Iterative Hard Thresholding (IHT) is a class of projected gradient descent
+methods for optimizing sparsity-constrained minimization models, with the best
+known efficiency and scalability in practice. As far as we know, the existing
+IHT-style methods are designed for sparse minimization in primal form. It
+remains open to explore duality theory and algorithms in such a non-convex and
+NP-hard problem setting. In this paper, we bridge this gap by establishing a
+duality theory for sparsity-constrained minimization with $\ell_2$-regularized
+loss function and proposing an IHT-style algorithm for dual maximization. Our
+sparse duality theory provides a set of sufficient and necessary conditions
+under which the original NP-hard/non-convex problem can be equivalently solved
+in a dual formulation. The proposed dual IHT algorithm is a super-gradient
+method for maximizing the non-smooth dual objective. An interesting finding is
+that the sparse recovery performance of dual IHT is invariant to the Restricted
+Isometry Property (RIP), which is required by virtually all the existing primal
+IHT algorithms without sparsity relaxation. Moreover, a stochastic variant of
+dual IHT is proposed for large-scale stochastic optimization. Numerical results
+demonstrate the superiority of dual IHT algorithms to the state-of-the-art
+primal IHT-style algorithms in model estimation accuracy and computational
+efficiency.
+",1,0,0,1,0,0
+6165,Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?,"  Binary neural networks (BNN) have been studied extensively since they run
+dramatically faster at lower memory and power consumption than floating-point
+networks, thanks to the efficiency of bit operations. However, contemporary
+BNNs whose weights and activations are both single bits suffer from severe
+accuracy degradation. To understand why, we investigate the representation
+ability, speed and bias/variance of BNNs through extensive experiments. We
+conclude that the error of BNNs is predominantly caused by the intrinsic
+instability (training time) and non-robustness (train & test time). Inspired by
+this investigation, we propose the Binary Ensemble Neural Network (BENN) which
+leverages ensemble methods to improve the performance of BNNs with limited
+efficiency cost. While ensemble techniques have been broadly believed to be
+only marginally helpful for strong classifiers such as deep neural networks,
+our analyses and experiments show that they are naturally a perfect fit to
+boost BNNs. We find that our BENN, which is faster and much more robust than
+state-of-the-art binary networks, can even surpass the accuracy of the
+full-precision floating number network with the same architecture.
+",0,0,0,1,0,0
+6166,Generalized connected sum formula for the Arnold invariants of generic plane curves,"  We define the generalized connected sum for generic closed plane curves,
+generalizing the strange sum defined by Arnold, and completely describe how the
+Arnold invariants $J^{\pm}$ and $\mathit{St}$ behave under the generalized
+connected sums.
+",0,0,1,0,0,0
+6167,Smart patterned surfaces with programmable thermal emissivity and their design through combinatorial strategies,"  The emissivity of common materials remains constant with temperature
+variations, and cannot drastically change. However, it is possible to design
+its entire behaviour as a function of temperature, and to significantly modify
+the thermal emissivity of a surface through the combination of different
+materials and patterns. Here, we show that smart patterned surfaces consisting
+of smaller structures (motifs) may be designed to respond uniquely through
+combinatorial design strategies by transforming themselves from 2D to 3D
+complex structures with a two-way shape memory effect. The smart surfaces can
+passively manipulate thermal radiation without-the use of controllers and power
+supplies-because their modus operandi has already been programmed and
+integrated into their intrinsic characteristics; the environment provides the
+energy required for their activation. Each motif emits thermal radiation in a
+certain manner, as it changes its geometry; however, the spatial distribution
+of these motifs causes them to interact with each other. Therefore, their
+combination and interaction determine the global behaviour of the surfaces,
+thus enabling their a priori design. The emissivity behaviour is not random; it
+is determined by two fundamental parameters, namely the combination of
+orientations in which the motifs open (n-fold rotational symmetry (rn)) and the
+combination of materials (colours) on the motifs; these generate functions
+which fully determine the dependency of the emissivity on the temperature.
+",0,1,0,0,0,0
+6168,STAR: Spatio-Temporal Altimeter Waveform Retracking using Sparse Representation and Conditional Random Fields,"  Satellite radar altimetry is one of the most powerful techniques for
+measuring sea surface height variations, with applications ranging from
+operational oceanography to climate research. Over open oceans, altimeter
+return waveforms generally correspond to the Brown model, and by inversion,
+estimated shape parameters provide mean surface height and wind speed. However,
+in coastal areas or over inland waters, the waveform shape is often distorted
+by land influence, resulting in peaks or fast decaying trailing edges. As a
+result, derived sea surface heights are then less accurate and waveforms need
+to be reprocessed by sophisticated algorithms. To this end, this work suggests
+a novel Spatio-Temporal Altimetry Retracking (STAR) technique. We show that
+STAR enables the derivation of sea surface heights over the open ocean as well
+as over coastal regions of at least the same quality as compared to existing
+retracking methods, but for a larger number of cycles and thus retaining more
+useful data. Novel elements of our method are (a) integrating information from
+spatially and temporally neighboring waveforms through a conditional random
+field approach, (b) sub-waveform detection, where relevant sub-waveforms are
+separated from corrupted or non-relevant parts through a sparse representation
+approach, and (c) identifying the final best set of sea surfaces heights from
+multiple likely heights using Dijkstra's algorithm. We apply STAR to data from
+the Jason-1, Jason-2 and Envisat missions for study sites in the Gulf of
+Trieste, Italy and in the coastal region of the Ganges-Brahmaputra-Meghna
+estuary, Bangladesh. We compare to several established and recent retracking
+methods, as well as to tide gauge data. Our experiments suggest that the
+obtained sea surface heights are significantly less affected by outliers when
+compared to results obtained by other approaches.
+",0,1,0,0,0,0
+6169,Magnetization jump in one dimensional $J-Q_{2}$ model with anisotropic exchange,"  We investigate the adiabatic magnetization process of the one-dimensional
+$J-Q_{2}$ model with XXZ anisotropy $g$ in an external magnetic field $h$ by
+using density matrix renormalization group (DMRG) method. According to the
+characteristic of the magnetization curves, we draw a magnetization phase
+diagram consisting of four phases. For a fixed nonzero pair coupling $Q$, i)
+when $g<-1$, the ground state is always ferromagnetic in spite of $h$; ii) when
+$g>-1$ but still small, the whole magnetization curve is continuous and smooth;
+iii) if further increasing $g$, there is a macroscopic magnetization jump from
+partially- to fully-polarized state; iv) for a sufficiently large $g$, the
+magnetization jump is from non- to fully-polarized state. By examining the
+energy per magnon and the correlation function, we find that the origin of the
+magnetization jump is the condensation of magnons and the formation of magnetic
+domains. We also demonstrate that while the experienced states are
+Heisenberg-like without long-range order, all the \textit{jumped-over} states
+have antiferromagnetic or Néel long-range orders, or their mixing.
+",0,1,0,0,0,0
+6170,CoMID: Context-based Multi-Invariant Detection for Monitoring Cyber-Physical Software,"  Cyber-physical software continually interacts with its physical environment
+for adaptation in order to deliver smart services. However, the interactions
+can be subject to various errors when the software's assumption on its
+environment no longer holds, thus leading to unexpected misbehavior or even
+failure. To address this problem, one promising way is to conduct runtime
+monitoring of invariants, so as to prevent cyber-physical software from
+entering such errors (a.k.a. abnormal states). To effectively detect abnormal
+states, we in this article present an approach, named Context-based
+Multi-Invariant Detection (CoMID), which consists of two techniques:
+context-based trace grouping and multi-invariant detection. The former infers
+contexts to distinguish different effective scopes for CoMID's derived
+invariants, and the latter conducts ensemble evaluation of multiple invariants
+to detect abnormal states. We experimentally evaluate CoMID on real-world
+cyber-physical software. The results show that CoMID achieves a 5.7-28.2%
+higher true-positive rate and a 6.8-37.6% lower false-positive rate in
+detecting abnormal states, as compared with state-of-the-art approaches (i.e.,
+Daikon and ZoomIn). When deployed in field tests, CoMID's runtime monitoring
+improves the success rate of cyber-physical software in its task executions by
+15.3-31.7%.
+",1,0,0,0,0,0
+6171,Asymptotics of multivariate contingency tables with fixed marginals,"  We consider the asymptotic distribution of a cell in a 2 x ... x 2
+contingency table as the fixed marginal totals tend to infinity. The asymptotic
+order of the cell variance is derived and a useful diagnostic is given for
+determining whether the cell has a Poisson limit or a Gaussian limit. There are
+three forms of Poisson convergence. The exact form is shown to be determined by
+the growth rates of the two smallest marginal totals. The results are
+generalized to contingency tables with arbitrary sizes and are further
+complemented with concrete examples.
+",0,0,1,1,0,0
+6172,On the Faithfulness of 1-dimensional Topological Quantum Field Theories,"  This paper explores 1-dimensional topological quantum field theories. We
+separately deal with strict and strong 1-dimensional topological quantum field
+theories. The strict one is regarded as a symmetric monoidal functor between
+the category of 1-cobordisms and the category of matrices, and the strong one
+is a symmetric monoidal functor between the category of 1-cobordisms and the
+category of finite dimensional vector spaces. It has been proved that both
+strict and strong 1-dimensional topological quantum field theories are
+faithful.
+",0,0,1,0,0,0
+6173,Distribution of the periodic points of the Farey map,"  We expand the cross section of the geodesic flow in the tangent bundle of the
+modular surface given by Series to produce another section whose return map
+under the geodesic flow is a double cover of the natural extension of the Farey
+map. We use this cross section to extend the correspondence between the closed
+geodesics on the modular surface and the periodic points of the Gauss map to
+include the periodic points of the Farey map. Then, analogous to the work of
+Pollicott, we prove an equidistribution result for the periodic points of the
+Farey map when they are ordered according to the length of their corresponding
+closed geodesics.
+",0,0,1,0,0,0
+6174,Marangoni effects on a thin liquid film coating a sphere with axial or radial thermal gradients,"  We study the time evolution of a thin liquid film coating the outer surface
+of a sphere in the presence of gravity, surface tension and thermal gradients.
+We derive the fourth-order nonlinear partial differential equation that models
+the thin film dynamics, including Marangoni terms arising from the dependence
+of surface tension on temperature. We consider two different imposed
+temperature distributions with axial or radial thermal gradients. We analyze
+the stability of a uniform coating under small perturbations and carry out
+numerical simulations in COMSOL for a range of parameter values. In the case of
+an axial temperature gradient, we find steady states with either uniform film
+thickness, or with the fluid accumulating at the bottom or near the top of the
+sphere, depending on the total volume of liquid in the film, dictating whether
+gravity or Marangoni effects dominate. In the case of a radial temperature
+gradient, a stability analysis reveals the most unstable non-axisymmetric modes
+on an initially uniform coating film.
+",0,1,0,0,0,0
+6175,Fooling the classifier: Ligand antagonism and adversarial examples,"  Machine learning algorithms are sensitive to so-called adversarial
+perturbations. This is reminiscent of cellular decision-making where antagonist
+ligands may prevent correct signaling, like during the early immune response.
+We draw a formal analogy between neural networks used in machine learning and
+the general class of adaptive proofreading networks. We then apply simple
+adversarial strategies from machine learning to models of ligand
+discrimination. We show how kinetic proofreading leads to ""boundary tilting""
+and identify three types of perturbation (adversarial, non adversarial and
+ambiguous). We then use a gradient-descent approach to compare different
+adaptive proofreading models, and we reveal the existence of two qualitatively
+different regimes characterized by the presence or absence of a critical point.
+These regimes are reminiscent of the ""feature-to-prototype"" transition
+identified in machine learning, corresponding to two strategies in ligand
+antagonism (broad vs. specialized). Overall, our work connects evolved cellular
+decision-making to classification in machine learning, showing that behaviours
+close to the decision boundary can be understood through the same mechanisms.
+",0,0,0,1,1,0
+6176,A Simplified Approach to Analyze Complementary Sensitivity Trade-offs in Continuous-Time and Discrete-Time Systems,"  A simplified approach is proposed to investigate the continuous-time and
+discrete-time complementary sensitivity Bode integrals (CSBIs) in this note.
+For continuous-time feedback systems with unbounded frequency domain, the CSBI
+weighted by $1/\omega^2$ is considered, where this simplified method reveals a
+more explicit relationship between the value of CSBI and the structure of the
+open-loop transfer function. With a minor modification of this method, the CSBI
+of discrete-time system is derived, and illustrative examples are provided.
+Compared with the existing results on CSBI, neither Cauchy integral theorem nor
+Poisson integral formula are used throughout the analysis, and the analytic
+constraint on the integrand is removed.
+",1,0,0,0,0,0
+6177,Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures,"  In this paper, we propose a family of graph partition similarity measures
+that take the topology of the graph into account. These graph-aware measures
+are alternatives to using set partition similarity measures that are not
+specifically designed for graph partitions. The two types of measures,
+graph-aware and set partition measures, are shown to have opposite behaviors
+with respect to resolution issues and provide complementary information
+necessary to assess that two graph partitions are similar.
+",0,0,0,1,0,0
+6178,Competitive Resource Allocation in HetNets: the Impact of Small-cell Spectrum Constraints and Investment Costs,"  Heterogeneous wireless networks with small-cell deployments in licensed and
+unlicensed spectrum bands are a promising approach for expanding wireless
+connectivity and service. As a result, wireless service providers (SPs) are
+adding small-cells to augment their existing macro-cell deployments. This added
+flexibility complicates network management, in particular, service pricing and
+spectrum allocations across macro- and small-cells. Further, these decisions
+depend on the degree of competition among SPs. Restrictions on shared spectrum
+access imposed by regulators, such as low power constraints that lead to
+small-cell deployments, along with the investment cost needed to add small
+cells to an existing network, also impact strategic decisions and market
+efficiency. If the revenue generated by small-cells does not cover the
+investment cost, then there will be no deployment even if it increases social
+welfare. We study the implications of such spectrum constraints and investment
+costs on resource allocation and pricing decisions by competitive SPs, along
+with the associated social welfare. Our results show that while the optimal
+resource allocation taking constraints and investment into account can be
+uniquely determined, adding those features with strategic SPs can have a
+substantial effect on the equilibrium market structure.
+",1,0,0,0,0,0
+6179,Gradient Reversal Against Discrimination,"  No methods currently exist for making arbitrary neural networks fair. In this
+work we introduce GRAD, a new and simplified method to producing fair neural
+networks that can be used for auto-encoding fair representations or directly
+with predictive networks. It is easy to implement and add to existing
+architectures, has only one (insensitive) hyper-parameter, and provides
+improved individual and group fairness. We use the flexibility of GRAD to
+demonstrate multi-attribute protection.
+",0,0,0,1,0,0
+6180,Non-commutative crepant resolutions for some toric singularities I,"  We give a criterion for the existence of non-commutative crepant resolutions
+(NCCR's) for certain toric singularities. In particular we recover Broomhead's
+result that a 3-dimensional toric Gorenstein singularity has a NCCR. Our result
+also yields the existence of a NCCR for a 4-dimensional toric Gorenstein
+singularity which is known to have no toric NCCR.
+",0,0,1,0,0,0
+6181,High temperature pairing in a strongly interacting two-dimensional Fermi gas,"  We observe many-body pairing in a two-dimensional gas of ultracold fermionic
+atoms at temperatures far above the critical temperature for superfluidity. For
+this, we use spatially resolved radio-frequency spectroscopy to measure pairing
+energies spanning a wide range of temperatures and interaction strengths. In
+the strongly interacting regime where the scattering length between fermions is
+on the same order as the inter-particle spacing, the pairing energy in the
+normal phase significantly exceeds the intrinsic two-body binding energy of the
+system and shows a clear dependence on local density. This implies that pairing
+in this regime is driven by many-body correlations, rather than two-body
+physics. We find this effect to persist at temperatures close to the Fermi
+temperature which demonstrates that pairing correlations in strongly
+interacting two-dimensional fermionic systems are remarkably robust against
+thermal fluctuations.
+",0,1,0,0,0,0
+6182,Untangling the hairball: fitness based asymptotic reduction of biological networks,"  Complex mathematical models of interaction networks are routinely used for
+prediction in systems biology. However, it is difficult to reconcile network
+complexities with a formal understanding of their behavior. Here, we propose a
+simple procedure (called $\bar \varphi$) to reduce biological models to
+functional submodules, using statistical mechanics of complex systems combined
+with a fitness-based approach inspired by $\textit{in silico}$ evolution. $\bar
+\varphi$ works by putting parameters or combination of parameters to some
+asymptotic limit, while keeping (or slightly improving) the model performance,
+and requires parameter symmetry breaking for more complex models. We illustrate
+$\bar \varphi$ on biochemical adaptation and on different models of immune
+recognition by T cells. An intractable model of immune recognition with close
+to a hundred individual transition rates is reduced to a simple two-parameter
+model. $\bar \varphi$ extracts three different mechanisms for early immune
+recognition, and automatically discovers similar functional modules in
+different models of the same process, allowing for model classification and
+comparison. Our procedure can be applied to biological networks based on rate
+equations using a fitness function that quantifies phenotypic performance.
+",0,1,0,0,0,0
+6183,Exotica and the status of the strong cosmic censor conjecture in four dimensions,"  An immense class of physical counterexamples to the four dimensional strong
+cosmic censor conjecture---in its usual broad formulation---is exhibited. More
+precisely, out of any closed and simply connected 4-manifold an open Ricci-flat
+Lorentzian 4-manifold is constructed which is not globally hyperbolic and no
+perturbation of it, in any sense, can be globally hyperbolic. This very stable
+non-global-hyperbolicity is the consequence of our open spaces having a
+""creased end"" i.e., an end diffeomorphic to an exotic ${\mathbb R}^4$. Open
+manifolds having an end like this is a typical phenomenon in four dimensions.
+The construction is based on a collection of results of Gompf and Taubes on
+exotic and self-dual spaces, respectively, as well as applying Penrose'
+non-linear graviton construction (i.e., twistor theory) to solve the Riemannian
+Einstein's equation. These solutions then are converted into stably
+non-globally-hyperbolic Lorentzian vacuum solutions. It follows that the
+plethora of vacuum solutions we found cannot be obtained via the initial value
+formulation of the Einstein's equation because they are ""too long"" in a certain
+sense (explained in the text). This different (i.e., not based on the initial
+value formulation but twistorial) technical background might partially explain
+why the existence of vacuum solutions of this kind have not been realized so
+far in spite of the fact that, apparently, their superabundance compared to the
+well-known globally hyperbolic vacuum solutions is overwhelming.
+",0,0,1,0,0,0
+6184,Centralities in Simplicial Complexes,"  Complex networks can be used to represent complex systems which originate in
+the real world. Here we study a transformation of these complex networks into
+simplicial complexes, where cliques represent the simplices of the complex. We
+extend the concept of node centrality to that of simplicial centrality and
+study several mathematical properties of degree, closeness, betweenness,
+eigenvector, Katz, and subgraph centrality for simplicial complexes. We study
+the degree distributions of these centralities at the different levels. We also
+compare and describe the differences between the centralities at the different
+levels. Using these centralities we study a method for detecting essential
+proteins in PPI networks of cells and explain the varying abilities of the
+centrality measures at the different levels in identifying these essential
+proteins. The paper is written in a self-contained way, such that it can be
+used by practitioners of network theory as a basis for further developments.
+",1,1,0,0,0,0
+6185,The Samuel realcompactification,"  For a uniform space (X, $\mu$), we introduce a realcompactification of X by
+means of the family $U_{\mu}(X)$ of all the real-valued uniformly continuous
+functions, in the same way that the known Samuel compactification is given by
+$U^{*}_{\mu}(X)$ the set of all the bounded functions in $U_{\mu}(X)$. We will
+call it ""the Samuel realcompactification"" by several resemblances to the Samuel
+compactification. In this note, we present different ways to construct such
+realcompactification as well as we study the corresponding problem of knowing
+when a uniform space is Samuel realcompact, that is, it coincides with its
+Samuel realcompactification. At this respect we obtain as main result a theorem
+of Katětov-Shirota type, by means of a new property of completeness
+recently introduced by the authors, called Bourbaki-completeness.
+",0,0,1,0,0,0
+6186,Segmented Terahertz Electron Accelerator and Manipulator (STEAM),"  Acceleration and manipulation of ultrashort electron bunches are the basis
+behind electron and X-ray devices used for ultrafast, atomic-scale imaging and
+spectroscopy. Using laser-generated THz drivers enables intrinsic
+synchronization as well as dramatic gains in field strengths, field gradients
+and component compactness, leading to shorter electron bunches, higher
+spatio-temporal resolution and smaller infrastructures. We present a segmented
+THz electron accelerator and manipulator (STEAM) with extended interaction
+lengths capable of performing multiple high-field operations on the energy and
+phase-space of ultrashort bunches with moderate charge. With this single
+device, powered by few-microjoule, single-cycle, 0.3 THz pulses, we demonstrate
+record THz-device acceleration of >30 keV, streaking with <10 fs resolution,
+focusing with >2 kT/m strengths, compression to ~100 fs as well as real-time
+switching between these modes of operation. The STEAM device demonstrates the
+feasibility of future THz-based compact electron guns, accelerators, ultrafast
+electron diffractometers and Free-Electron Lasers with transformative impact.
+",0,1,0,0,0,0
+6187,The $2$-nd Hessian type equation on almost Hermitian manifolds,"  In this paper, we derive the second order estimate to the $2$-nd Hessian type
+equation on a compact almost Hermitian manifold.
+",0,0,1,0,0,0
+6188,Learning graphs from data: A signal representation perspective,"  The construction of a meaningful graph topology plays a crucial role in the
+effective representation, processing, analysis and visualization of structured
+data. When a natural choice of the graph is not readily available from the data
+sets, it is thus desirable to infer or learn a graph topology from the data. In
+this tutorial overview, we survey solutions to the problem of graph learning,
+including classical viewpoints from statistics and physics, and more recent
+approaches that adopt a graph signal processing (GSP) perspective. We further
+emphasize the conceptual similarities and differences between classical and
+GSP-based graph inference methods, and highlight the potential advantage of the
+latter in a number of theoretical and practical scenarios. We conclude with
+several open issues and challenges that are keys to the design of future signal
+processing and machine learning algorithms for learning graphs from data.
+",1,0,0,1,0,0
+6189,Universal edge transport in interacting Hall systems,"  We study the edge transport properties of $2d$ interacting Hall systems,
+displaying single-mode chiral edge currents. For this class of many-body
+lattice models, including for instance the interacting Haldane model, we prove
+the quantization of the edge charge conductance and the bulk-edge
+correspondence. Instead, the edge Drude weight and the edge susceptibility are
+interaction-dependent; nevertheless, they satisfy exact universal scaling
+relations, in agreement with the chiral Luttinger liquid theory. Moreover,
+charge and spin excitations differ in their velocities, giving rise to the
+spin-charge separation phenomenon. The analysis is based on exact
+renormalization group methods, and on a combination of lattice and emergent
+Ward identities. The invariance of the emergent chiral anomaly under the
+renormalization group flow plays a crucial role in the proof.
+",0,1,0,0,0,0
+6190,Brownian dynamics of elongated particles in a quasi-2D isotropic liquid,"  We demonstrate experimentally that the long-range hydrodynamic interactions
+in an incompressible quasi 2D isotropic fluid result in an anisotropic viscous
+drag acting on elongated particles. The anisotropy of the drag is increasing
+with increasing ratio of the particle length to the hydrodynamic scale given by
+the Saffman-Delbrück length. The micro-rheology data for translational and
+rotational drags collected over three orders of magnitude of the effective
+particle length demonstrate the validity of the current theoretical approaches
+to the hydrodynamics in restricted geometry. The results also demonstrate
+crossovers between the hydrodynamical regimes determined by the characteristic
+length scales.
+",0,1,0,0,0,0
+6191,Photonic Band Structure of Two-dimensional Atomic Lattices,"  Two-dimensional atomic arrays exhibit a number of intriguing quantum optical
+phenomena, including subradiance, nearly perfect reflection of radiation and
+long-lived topological edge states. Studies of emission and scattering of
+photons in such lattices require complete treatment of the radiation pattern
+from individual atoms, including long-range interactions. We describe a
+systematic approach to perform the calculations of collective energy shifts and
+decay rates in the presence of such long-range interactions for arbitrary
+two-dimensional atomic lattices. As applications of our method, we investigate
+the topological properties of atomic lattices both in free-space and near
+plasmonic surfaces.
+",0,1,0,0,0,0
+6192,Multifractal analysis of the time series of daily means of wind speed in complex regions,"  In this paper, we applied the multifractal detrended fluctuation analysis to
+the daily means of wind speed measured by 119 weather stations distributed over
+the territory of Switzerland. The analysis was focused on the inner time
+fluctuations of wind speed, which could be more linked with the local
+conditions of the highly varying topography of Switzerland. Our findings point
+out to a persistent behaviour of all the measured wind speed series (indicated
+by a Hurst exponent significantly larger than 0.5), and to a high
+multifractality degree indicating a relative dominance of the large
+fluctuations in the dynamics of wind speed, especially in the Swiss plateau,
+which is comprised between the Jura and Alp mountain ranges. The study
+represents a contribution to the understanding of the dynamical mechanisms of
+wind speed variability in mountainous regions.
+",0,0,0,1,0,0
+6193,Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning,"  We present a new deep meta reinforcement learner, which we call Deep Episodic
+Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity
+metric for a non-parametric model-based reinforcement learning algorithm. Our
+model is trained end-to-end via back-propagation. Despite being trained using
+the model-free Q-learning objective, we show that DEVI's model-based internal
+structure provides `one-shot' transfer to changes in reward and transition
+structure, even for tasks with very high-dimensional state spaces.
+",1,0,0,1,0,0
+6194,"Disproval of the validated planets K2-78b, K2-82b, and K2-92b","  Transiting super-Earths orbiting bright stars in short orbital periods are
+interesting targets for the study of planetary atmospheres. While selecting
+super-Earths suitable for further characterization from the ground among a list
+of confirmed and validated exoplanets detected by K2, we found some suspicious
+cases that led to us re-assessing the nature of the detected transiting signal.
+We did a photometric analysis of the K2 light curves and centroid motions of
+the photometric barycenters. Our study shows that the validated planets K2-78b,
+K2-82b, and K2-92b are actually not planets but background eclipsing binaries.
+The eclipsing binaries are inside the Kepler photometric aperture, but outside
+the ground-based high resolution images used for validation. We advise extreme
+care on the validation of candidate planets discovered by space missions. It is
+important that all the assumptions in the validation process are carefully
+checked. An independent confirmation is mandatory in order to avoid wasting
+valuable resources on further characterization of non-existent targets.
+",0,1,0,0,0,0
+6195,Long-lived mesoscopic entanglement between two damped infinite harmonic chains,"  We consider two chains, each made of $N$ independent oscillators, immersed in
+a common thermal bath and study the dynamics of their mutual quantum
+correlations in the thermodynamic, large-$N$ limit. We show that dissipation
+and noise due to the presence of the external environment are able to generate
+collective quantum correlations between the two chains at the mesoscopic level.
+The created collective quantum entanglement between the two many-body systems
+turns out to be rather robust, surviving for asymptotically long times even for
+non vanishing bath temperatures.
+",0,1,0,0,0,0
+6196,Measured Multiseries and Integration,"  A paper by Bruno Salvy and the author introduced measured multiseries and
+gave an algorithm to compute these for a large class of elementary functions,
+modulo a zero-equivalence method for constants. This gave a theoretical
+background for the implementation that Salvy was developing at that time. The
+main result of the present article is an algorithm to calculate measured
+multiseries for integrals of functions of the form h*sin G, where h and G
+belong to a Hardy field. The process can reiterated with the resulting algebra,
+and also applied to solutions of a second order differential equation of a
+particular form.
+",1,0,0,0,0,0
+6197,Spectral Approach to Verifying Non-linear Arithmetic Circuits,"  This paper presents a fast and effective computer algebraic method for
+analyzing and verifying non-linear integer arithmetic circuits using a novel
+algebraic spectral model. It introduces a concept of algebraic spectrum, a
+numerical form of polynomial expression; it uses the distribution of
+coefficients of the monomials to determine the type of arithmetic function
+under verification. In contrast to previous works, the proof of functional
+correctness is achieved by computing an algebraic spectrum combined with a
+local rewriting of word-level polynomials. The speedup is achieved by
+propagating coefficients through the circuit using And-Inverter Graph (AIG)
+datastructure. The effectiveness of the method is demonstrated with experiments
+including standard and Booth multipliers, and other synthesized non-linear
+arithmetic circuits up to 1024 bits containing over 12 million gates.
+",1,0,0,0,0,0
+6198,Signal-based Bayesian Seismic Monitoring,"  Detecting weak seismic events from noisy sensors is a difficult perceptual
+task. We formulate this task as Bayesian inference and propose a generative
+model of seismic events and signals across a network of spatially distributed
+stations. Our system, SIGVISA, is the first to directly model seismic
+waveforms, allowing it to incorporate a rich representation of the physics
+underlying the signal generation process. We use Gaussian processes over
+wavelet parameters to predict detailed waveform fluctuations based on
+historical events, while degrading smoothly to simple parametric envelopes in
+regions with no historical seismicity. Evaluating on data from the western US,
+we recover three times as many events as previous work, and reduce mean
+location errors by a factor of four while greatly increasing sensitivity to
+low-magnitude events.
+",1,1,0,0,0,0
+6199,Multi-Label Learning with Global and Local Label Correlation,"  It is well-known that exploiting label correlations is important to
+multi-label learning. Existing approaches either assume that the label
+correlations are global and shared by all instances; or that the label
+correlations are local and shared only by a data subset. In fact, in the
+real-world applications, both cases may occur that some label correlations are
+globally applicable and some are shared only in a local group of instances.
+Moreover, it is also a usual case that only partial labels are observed, which
+makes the exploitation of the label correlations much more difficult. That is,
+it is hard to estimate the label correlations when many labels are absent. In
+this paper, we propose a new multi-label approach GLOCAL dealing with both the
+full-label and the missing-label cases, exploiting global and local label
+correlations simultaneously, through learning a latent label representation and
+optimizing label manifolds. The extensive experimental studies validate the
+effectiveness of our approach on both full-label and missing-label data.
+",1,0,0,0,0,0
+6200,Complexity of Verifying Nonblockingness in Modular Supervisory Control,"  Complexity analysis becomes a common task in supervisory control. However,
+many results of interest are spread across different topics. The aim of this
+paper is to bring several interesting results from complexity theory and to
+illustrate their relevance to supervisory control by proving new nontrivial
+results concerning nonblockingness in modular supervisory control of discrete
+event systems modeled by finite automata.
+",1,0,0,0,0,0
+6201,Predicting signatures of anisotropic resonance energy transfer in dye-functionalized nanoparticles,"  Resonance energy transfer (RET) is an inherently anisotropic process. Even
+the simplest, well-known Förster theory, based on the transition
+dipole-dipole coupling, implicitly incorporates the anisotropic character of
+RET. In this theoretical work, we study possible signatures of the fundamental
+anisotropic character of RET in hybrid nanomaterials composed of a
+semiconductor nanoparticle (NP) decorated with molecular dyes. In particular,
+by means of a realistic kinetic model, we show that the analysis of the dye
+photoluminescence difference for orthogonal input polarizations reveals the
+anisotropic character of the dye-NP RET which arises from the intrinsic
+anisotropy of the NP lattice. In a prototypical core/shell wurtzite CdSe/ZnS NP
+functionalized with cyanine dyes (Cy3B), this difference is predicted to be as
+large as 75\% and it is strongly dependent in amplitude and sign on the dye-NP
+distance. We account for all the possible RET processes within the system,
+together with competing decay pathways in the separate segments. In addition,
+we show that the anisotropic signature of RET is persistent up to a large
+number of dyes per NP.
+",0,1,0,0,0,0
+6202,Matrix Completion and Performance Guarantees for Single Individual Haplotyping,"  Single individual haplotyping is an NP-hard problem that emerges when
+attempting to reconstruct an organism's inherited genetic variations using data
+typically generated by high-throughput DNA sequencing platforms. Genomes of
+diploid organisms, including humans, are organized into homologous pairs of
+chromosomes that differ from each other in a relatively small number of variant
+positions. Haplotypes are ordered sequences of the nucleotides in the variant
+positions of the chromosomes in a homologous pair; for diploids, haplotypes
+associated with a pair of chromosomes may be conveniently represented by means
+of complementary binary sequences. In this paper, we consider a binary matrix
+factorization formulation of the single individual haplotyping problem and
+efficiently solve it by means of alternating minimization. We analyze the
+convergence properties of the alternating minimization algorithm and establish
+theoretical bounds for the achievable haplotype reconstruction error. The
+proposed technique is shown to outperform existing methods when applied to
+synthetic as well as real-world Fosmid-based HapMap NA12878 datasets.
+",0,0,0,1,1,0
+6203,Simulation and analysis of $γ$-Ni cellular growth during laser powder deposition of Ni-based superalloys,"  Cellular or dendritic microstructures that result as a function of additive
+manufacturing solidification conditions in a Ni-based melt pool are simulated
+in the present work using three-dimensional phase-field simulations. A
+macroscopic thermal model is used to obtain the temperature gradient $G$ and
+the solidification velocity $V$ which are provided as inputs to the phase-field
+model. We extract the cell spacings, cell core compositions, and cell tip as
+well as mushy zone temperatures from the simulated microstructures as a
+function of $V$. Cell spacings are compared with different scaling laws that
+correlate to the solidification conditions and approximated by $G^{-m}V^{-n}$.
+Cell core compositions are compared with the analytical solutions of a dendrite
+growth theory and found to be in good agreement. Through analysis of the mushy
+zone, we extract a characteristic bridging plane, where the primary $\gamma$
+phase coalesces across the intercellular liquid channels at a $\gamma$ fraction
+between 0.6 and 0.7. The temperature and the $\gamma$ fraction in this plane
+are found to decrease with increasing $V$. The simulated microstructural
+features are significant as they can be used as inputs for the simulation of
+subsequent heat treatment processes.
+",0,1,0,0,0,0
+6204,Localization of Extended Quantum Objects,"  A quantum system of particles can exist in a localized phase, exhibiting
+ergodicity breaking and maintaining forever a local memory of its initial
+conditions. We generalize this concept to a system of extended objects, such as
+strings and membranes, arguing that such a system can also exhibit localization
+in the presence of sufficiently strong disorder (randomness) in the
+Hamiltonian. We show that localization of large extended objects can be mapped
+to a lower-dimensional many-body localization problem. For example, motion of a
+string involves propagation of point-like signals down its length to keep the
+different segments in causal contact. For sufficiently strong disorder, all
+such internal modes will exhibit many-body localization, resulting in the
+localization of the entire string. The eigenstates of the system can then be
+constructed perturbatively through a convergent 'string locator expansion.' We
+propose a type of out-of-time-order string correlator as a diagnostic of such a
+string localized phase. Localization of other higher-dimensional objects, such
+as membranes, can also be studied through a hierarchical construction by
+mapping onto localization of lower-dimensional objects. Our arguments are
+'asymptotic' ($i.e.$ valid up to rare regions) but they extend the notion of
+localization (and localization protected order) to a host of settings where
+such ideas previously did not apply. These include high-dimensional
+ferromagnets with domain wall excitations, three-dimensional topological phases
+with loop-like excitations, and three-dimensional type-II superconductors with
+flux line excitations. In type-II superconductors, localization of flux lines
+could stabilize superconductivity at energy densities where a normal state
+would arise in thermal equilibrium.
+",0,1,0,0,0,0
+6205,The next-to-minimal weights of binary projective Reed-Muller codes,"  Projective Reed-Muller codes were introduced by Lachaud, in 1988 and their
+dimension and minimum distance were determined by Serre and S{\o}rensen in
+1991. In coding theory one is also interested in the higher Hamming weights, to
+study the code performance. Yet, not many values of the higher Hamming weights
+are known for these codes, not even the second lowest weight (also known as
+next-to-minimal weight) is completely determined. In this paper we determine
+all the values of the next-to-minimal weight for the binary projective
+Reed-Muller codes, which we show to be equal to the next-to-minimal weight of
+Reed-Muller codes in most, but not all, cases.
+",1,0,1,0,0,0
+6206,Big Data Classification Using Augmented Decision Trees,"  We present an algorithm for classification tasks on big data. Experiments
+conducted as part of this study indicate that the algorithm can be as accurate
+as ensemble methods such as random forests or gradient boosted trees. Unlike
+ensemble methods, the models produced by the algorithm can be easily
+interpreted. The algorithm is based on a divide and conquer strategy and
+consists of two steps. The first step consists of using a decision tree to
+segment the large dataset. By construction, decision trees attempt to create
+homogeneous class distributions in their leaf nodes. However, non-homogeneous
+leaf nodes are usually produced. The second step of the algorithm consists of
+using a suitable classifier to determine the class labels for the
+non-homogeneous leaf nodes. The decision tree segment provides a coarse segment
+profile while the leaf level classifier can provide information about the
+attributes that affect the label within a segment.
+",1,0,0,1,0,0
+6207,Special tilting modules for algebras with positive dominant dimension,"  We study a set of uniquely determined tilting and cotilting modules for an
+algebra with positive dominant dimension, with the property that they are
+generated or cogenerated (and usually both) by projective-injectives. These
+modules have various interesting properties, for example that their
+endomorphism algebras always have global dimension at most that of the original
+algebra. We characterise d-Auslander-Gorenstein algebras and d-Auslander
+algebras via the property that the relevant tilting and cotilting modules
+coincide. By the Morita-Tachikawa correspondence, any algebra of dominant
+dimension at least 2 may be expressed (essentially uniquely) as the
+endomorphism algebra of a generator-cogenerator for another algebra, and we
+also study our special tilting and cotilting modules from this point of view,
+via the theory of recollements and intermediate extension functors.
+",0,0,1,0,0,0
+6208,The dynamical structure of political corruption networks,"  Corruptive behaviour in politics limits economic growth, embezzles public
+funds, and promotes socio-economic inequality in modern democracies. We analyse
+well-documented political corruption scandals in Brazil over the past 27 years,
+focusing on the dynamical structure of networks where two individuals are
+connected if they were involved in the same scandal. Our research reveals that
+corruption runs in small groups that rarely comprise more than eight people, in
+networks that have hubs and a modular structure that encompasses more than one
+corruption scandal. We observe abrupt changes in the size of the largest
+connected component and in the degree distribution, which are due to the
+coalescence of different modules when new scandals come to light or when
+governments change. We show further that the dynamical structure of political
+corruption networks can be used for successfully predicting partners in future
+scandals. We discuss the important role of network science in detecting and
+mitigating political corruption.
+",1,0,0,1,0,0
+6209,Maxent-Stress Optimization of 3D Biomolecular Models,"  Knowing a biomolecule's structure is inherently linked to and a prerequisite
+for any detailed understanding of its function. Significant effort has gone
+into developing technologies for structural characterization. These
+technologies do not directly provide 3D structures; instead they typically
+yield noisy and erroneous distance information between specific entities such
+as atoms or residues, which have to be translated into consistent 3D models.
+Here we present an approach for this translation process based on
+maxent-stress optimization. Our new approach extends the original graph drawing
+method for the new application's specifics by introducing additional
+constraints and confidence values as well as algorithmic components. Extensive
+experiments demonstrate that our approach infers structural models (i. e.,
+sensible 3D coordinates for the molecule's atoms) that correspond well to the
+distance information, can handle noisy and error-prone data, and is
+considerably faster than established tools. Our results promise to allow domain
+scientists nearly-interactive structural modeling based on distance
+constraints.
+",1,1,0,0,0,0
+6210,Enhanced Quantum Synchronization via Quantum Machine Learning,"  We study the quantum synchronization between a pair of two-level systems
+inside two coupled cavities. By using a digital-analog decomposition of the
+master equation that rules the system dynamics, we show that this approach
+leads to quantum synchronization between both two-level systems. Moreover, we
+can identify in this digital-analog block decomposition the fundamental
+elements of a quantum machine learning protocol, in which the agent and the
+environment (learning units) interact through a mediating system, namely, the
+register. If we can additionally equip this algorithm with a classical feedback
+mechanism, which consists of projective measurements in the register,
+reinitialization of the register state and local conditional operations on the
+agent and environment subspace, a powerful and flexible quantum machine
+learning protocol emerges. Indeed, numerical simulations show that this
+protocol enhances the synchronization process, even when every subsystem
+experience different loss/decoherence mechanisms, and give us the flexibility
+to choose the synchronization state. Finally, we propose an implementation
+based on current technologies in superconducting circuits.
+",1,0,0,1,0,0
+6211,Engineering phonon leakage in nanomechanical resonators,"  We propose and experimentally demonstrate a technique for coupling phonons
+out of an optomechanical crystal cavity. By designing a perturbation that
+breaks a symmetry in the elastic structure, we selectively induce phonon
+leakage without affecting the optical properties. It is shown experimentally
+via cryogenic measurements that the proposed cavity perturbation causes loss of
+phonons into mechanical waves on the surface of silicon, while leaving photon
+lifetimes unaffected. This demonstrates that phonon leakage can be engineered
+in on-chip optomechanical systems. We experimentally observe large fluctuations
+in leakage rates that we attribute to fabrication disorder and verify this
+using simulations. Our technique opens the way to engineering more complex
+on-chip phonon networks utilizing guided mechanical waves to connect quantum
+systems.
+",0,1,0,0,0,0
+6212,Entropy generation and momentum transfer in the superconductor-normal and normal-superconductor phase transformations and the consistency of the conventional theory of superconductivity,"  Since the discovery of the Meissner effect the superconductor to normal (S-N)
+phase transition in the presence of a magnetic field is understood to be a
+first order phase transformation that is reversible under ideal conditions and
+obeys the laws of thermodynamics. The reverse (N-S) transition is the Meissner
+effect. This implies in particular that the kinetic energy of the supercurrent
+is not dissipated as Joule heat in the process where the superconductor becomes
+normal and the supercurrent stops. In this paper we analyze the entropy
+generation and the momentum transfer between the supercurrent and the body in
+the S-N transition and the N-S transition as described by the conventional
+theory of superconductivity. We find that it is impossible to explain the
+transition in a way that is consistent with the laws of thermodynamics unless
+the momentum transfer between the supercurrent and the body occurs with zero
+entropy generation, for which the conventional theory of superconductivity
+provides no mechanism. Instead, we point out that the alternative theory of
+hole superconductivity does not encounter such difficulties.
+",0,1,0,0,0,0
+6213,A Local Faber-Krahn inequality and Applications to Schrödinger's Equation,"  We prove a local Faber-Krahn inequality for solutions $u$ to the Dirichlet
+problem for $\Delta + V$ on an arbitrary domain $\Omega$ in $\mathbb{R}^n$.
+Suppose a solution $u$ assumes a global maximum at some point $x_0 \in \Omega$
+and $u(x_0)>0$. Let $T(x_0)$ be the smallest time at which a Brownian motion,
+started at $x_0$, has exited the domain $\Omega$ with probability $\ge 1/2$.
+For nice (e.g., convex) domains, $T(x_0) \asymp d(x_0,\partial\Omega)^2$ but we
+make no assumption on the geometry of the domain. Our main result is that there
+exists a ball $B$ of radius $\asymp T(x_0)^{1/2}$ such that $$ \| V
+\|_{L^{\frac{n}{2}, 1}(\Omega \cap B)} \ge c_n > 0, $$ provided that $n \ge 3$.
+In the case $n = 2$, the above estimate fails and we obtain a substitute
+result. The Laplacian may be replaced by a uniformly elliptic operator in
+divergence form. This result both unifies and strenghtens a series of earlier
+results.
+",0,0,1,0,0,0
+6214,Étale groupoids and their $C^*$-algebras,"  These notes were written as supplementary material for a five-hour lecture
+series presented at the Centre de Recerca Mathemàtica at the Universitat
+Autònoma de Barcelona from the 13th to the 17th of March 2017. The intention
+of these notes is to give a brief overview of some key topics in the area of
+$C^*$-algebras associated to étale groupoids. The scope has been deliberately
+contained to the case of étale groupoids with the intention that much of the
+representation-theoretic technology and measure-theoretic analysis required to
+handle general groupoids can be suppressed in this simpler setting.
+A published version of these notes will appear in the volume tentatively
+titled ""Operator algebras and dynamics: groupoids, crossed products and Rokhlin
+dimension"" by Gabor Szabo, Dana P. Williams and myself, and edited by Francesc
+Perera, in the series ""Advanced Courses in Mathematics. CRM Barcelona."" The
+pagination of this arXiv version is not identical to Birkhäuser's style, but
+I have tried to make it close. The theorem numbering should be correct. I'm
+grateful to the CRM and Birkhäuser for allowing me to post a version on
+arXiv.
+",0,0,1,0,0,0
+6215,Semi-classical limit of the Levy-Lieb functional in Density Functional Theory,"  In a recent work, Bindini and De Pascale have introduced a regularization of
+$N$-particle symmetric probabilities which preserves their one-particle
+marginals. In this short note, we extend their construction to mixed quantum
+fermionic states. This enables us to prove the convergence of the Levy-Lieb
+functional in Density Functional Theory , to the corresponding multi-marginal
+optimal transport in the semi-classical limit. Our result holds for mixed
+states of any particle number $N$, with or without spin.
+",0,1,1,0,0,0
+6216,A characterization of cellular motivic spectra,"  Let $ \alpha: \mathcal{C} \to \mathcal{D}$ be a symmetric monoidal functor
+from a stable presentable symmetric monoidal $\infty$-category $\mathcal{C} $
+compactly generated by the tensorunit to a stable presentable symmetric
+monoidal $\infty$-category $ \mathcal{D} $ with compact tensorunit. Let $\beta:
+\mathcal{D} \to \mathcal{C}$ be a right adjoint of $\alpha$ and $ \mathrm{X}:
+\mathcal{B} \to \mathcal{D} $ a symmetric monoidal functor starting at a small
+rigid symmetric monoidal $\infty$-category $ \mathcal{B}$. We construct a
+symmetric monoidal equivalence between modules in the $\infty$-category of
+functors $ \mathcal{B} \to \mathcal{C} $ over the $ \mathrm{E}_\infty$-algebra
+$\beta \circ \mathrm{X} $ and the full subcategory of $\mathcal{D}$ compactly
+generated by the essential image of $\mathrm{X}$. Especially for every motivic
+$ \mathrm{E}_\infty$-ring spectrum $\mathrm{A}$ we obtain a symmetric monoidal
+equivalence between the $\infty$-category of cellular motivic
+$\mathrm{A}$-module spectra and modules in the $\infty$-category of functors
+$\mathrm{QS}$ to spectra over some $ \mathrm{E}_\infty$-algebra, where
+$\mathrm{QS}$ denotes the 0th space of the sphere spectrum.
+",0,0,1,0,0,0
+6217,The Impedance of Flat Metallic Plates with Small Corrugations,"  Summarizes recent work on the wakefields and impedances of flat, metallic
+plates with small corrugations
+",0,1,0,0,0,0
+6218,"The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness","  Community detection in graphs is the problem of finding groups of vertices
+which are more densely connected than they are to the rest of the graph. This
+problem has a long history, but it is undergoing a resurgence of interest due
+to the need to analyze social and biological networks. While there are many
+ways to formalize it, one of the most popular is as an inference problem, where
+there is a ""ground truth"" community structure built into the graph somehow. The
+task is then to recover the ground truth knowing only the graph.
+Recently it was discovered, first heuristically in physics and then
+rigorously in probability and computer science, that this problem has a phase
+transition at which it suddenly becomes impossible. Namely, if the graph is too
+sparse, or the probabilistic process that generates it is too noisy, then no
+algorithm can find a partition that is correlated with the planted one---or
+even tell if there are communities, i.e., distinguish the graph from a purely
+random one with high probability. Above this information-theoretic threshold,
+there is a second threshold beyond which polynomial-time algorithms are known
+to succeed; in between, there is a regime in which community detection is
+possible, but conjectured to require exponential time.
+For computer scientists, this field offers a wealth of new ideas and open
+questions, with connections to probability and combinatorics, message-passing
+algorithms, and random matrix theory. Perhaps more importantly, it provides a
+window into the cultures of statistical physics and statistical inference, and
+how those cultures think about distributions of instances, landscapes of
+solutions, and hardness.
+",1,1,1,0,0,0
+6219,Characterizing the spread of exaggerated news content over social media,"  In this paper, we consider a dataset comprising press releases about health
+research from different universities in the UK along with a corresponding set
+of news articles. First, we do an exploratory analysis to understand how the
+basic information published in the scientific journals get exaggerated as they
+are reported in these press releases or news articles. This initial analysis
+shows that some news agencies exaggerate almost 60\% of the articles they
+publish in the health domain; more than 50\% of the press releases from certain
+universities are exaggerated; articles in topics like lifestyle and childhood
+are heavily exaggerated. Motivated by the above observation we set the central
+objective of this paper to investigate how exaggerated news spreads over an
+online social network like Twitter. The LIWC analysis points to a remarkable
+observation these late tweets are essentially laden in words from opinion and
+realize categories which indicates that, given sufficient time, the wisdom of
+the crowd is actually able to tell apart the exaggerated news. As a second step
+we study the characteristics of the users who never or rarely post exaggerated
+news content and compare them with those who post exaggerated news content more
+frequently. We observe that the latter class of users have less retweets or
+mentions per tweet, have significantly more number of followers, use more slang
+words, less hyperbolic words and less word contractions. We also observe that
+the LIWC categories like bio, health, body and negative emotion are more
+pronounced in the tweets posted by the users in the latter class. As a final
+step we use these observations as features and automatically classify the two
+groups achieving an F1 score of 0.83.
+",1,0,0,0,0,0
+6220,Aerodynamic noise from rigid trailing edges with finite porous extensions,"  This paper investigates the effects of finite flat porous extensions to
+semi-infinite impermeable flat plates in an attempt to control trailing-edge
+noise through bio-inspired adaptations. Specifically the problem of sound
+generated by a gust convecting in uniform mean steady flow scattering off the
+trailing edge and permeable-impermeable junction is considered. This setup
+supposes that any realistic trailing-edge adaptation to a blade would be
+sufficiently small so that the turbulent boundary layer encapsulates both the
+porous edge and the permeable-impermeable junction, and therefore the
+interaction of acoustics generated at these two discontinuous boundaries is
+important. The acoustic problem is tackled analytically through use of the
+Wiener-Hopf method. A two-dimensional matrix Wiener-Hopf problem arises due to
+the two interaction points (the trailing edge and the permeable-impermeable
+junction). This paper discusses a new iterative method for solving this matrix
+Wiener-Hopf equation which extends to further two-dimensional problems in
+particular those involving analytic terms that exponentially grow in the upper
+or lower half planes. This method is an extension of the commonly used ""pole
+removal"" technique and avoids the needs for full matrix factorisation.
+Convergence of this iterative method to an exact solution is shown to be
+particularly fast when terms neglected in the second step are formally smaller
+than all other terms retained. The final acoustic solution highlights the
+effects of the permeable-impermeable junction on the generated noise, in
+particular how this junction affects the far-field noise generated by
+high-frequency gusts by creating an interference to typical trailing-edge
+scattering. This effect results in partially porous plates predicting a lower
+noise reduction than fully porous plates when compared to fully impermeable
+plates.
+",0,1,1,0,0,0
+6221,99% of Parallel Optimization is Inevitably a Waste of Time,"  It is well known that many optimization methods, including SGD, SAGA, and
+Accelerated SGD for over-parameterized models, do not scale linearly in the
+parallel setting. In this paper, we present a new version of block coordinate
+descent that solves this issue for a number of methods. The core idea is to
+make the sampling of coordinate blocks on each parallel unit independent of the
+others. Surprisingly, we prove that the optimal number of blocks to be updated
+by each of $n$ units in every iteration is equal to $m/n$, where $m$ is the
+total number of blocks. As an illustration, this means that when $n=100$
+parallel units are used, $99\%$ of work is a waste of time. We demonstrate that
+with $m/n$ blocks used by each unit the iteration complexity often remains the
+same. Among other applications which we mention, this fact can be exploited in
+the setting of distributed optimization to break the communication bottleneck.
+Our claims are justified by numerical experiments which demonstrate almost a
+perfect match with our theory on a number of datasets.
+",1,0,0,1,0,0
+6222,Tuning the effective spin-orbit coupling in molecular semiconductors,"  The control of spins and spin to charge conversion in organics requires
+understanding the molecular spin-orbit coupling (SOC), and a means to tune its
+strength. However, quantifying SOC strengths indirectly through spin relaxation
+effects has proven diffi- cult due to competing relaxation mechanisms. Here we
+present a systematic study of the g-tensor shift in molecular semiconductors
+and link it directly to the SOC strength in a series of high mobility molecular
+semiconductors with strong potential for future devices. The results
+demonstrate a rich variability of the molecular g-shifts with the effective
+SOC, depending on subtle aspects of molecular composition and structure. We
+correlate the above g -shifts to spin-lattice relaxation times over four orders
+of magnitude, from 200 {\mu}s to 0.15 {\mu}s, for isolated molecules in
+solution and relate our findings for isolated molecules in solution to the spin
+relaxation mechanisms that are likely to be relevant in solid state systems.
+",0,1,0,0,0,0
+6223,How Deep Are Deep Gaussian Processes?,"  Recent research has shown the potential utility of Deep Gaussian Processes.
+These deep structures are probability distributions, designed through
+hierarchical construction, which are conditionally Gaussian. In this paper, the
+current published body of work is placed in a common framework and, through
+recursion, several classes of deep Gaussian processes are defined. The
+resulting samples generated from a deep Gaussian process have a Markovian
+structure with respect to the depth parameter, and the effective depth of the
+resulting process is interpreted in terms of the ergodicity, or non-ergodicity,
+of the resulting Markov chain. For the classes of deep Gaussian processes
+introduced, we provide results concerning their ergodicity and hence their
+effective depth. We also demonstrate how these processes may be used for
+inference; in particular we show how a Metropolis-within-Gibbs construction
+across the levels of the hierarchy can be used to derive sampling tools which
+are robust to the level of resolution used to represent the functions on a
+computer. For illustration, we consider the effect of ergodicity in some simple
+numerical examples.
+",0,0,1,1,0,0
+6224,Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors,"  While single measurement vector (SMV) models have been widely studied in
+signal processing, there is a surging interest in addressing the multiple
+measurement vectors (MMV) problem. In the MMV setting, more than one
+measurement vector is available and the multiple signals to be recovered share
+some commonalities such as a common support. Applications in which MMV is a
+naturally occurring phenomenon include online streaming, medical imaging, and
+video recovery. This work presents a stochastic iterative algorithm for the
+support recovery of jointly sparse corrupted MMV. We present a variant of the
+Sparse Randomized Kaczmarz algorithm for corrupted MMV and compare our proposed
+method with an existing Kaczmarz type algorithm for MMV problems. We also
+showcase the usefulness of our approach in the online (streaming) setting and
+provide empirical evidence that suggests the robustness of the proposed method
+to the distribution of the corruption and the number of corruptions occurring.
+",1,0,0,0,0,0
+6225,Exploiting ITO colloidal nanocrystals for ultrafast pulse generation,"  Dynamical materials that capable of responding to optical stimuli have always
+been pursued for designing novel photonic devices and functionalities, of which
+the response speed and amplitude as well as integration adaptability and energy
+effectiveness are especially critical. Here we show ultrafast pulse generation
+by exploiting the ultrafast and sensitive nonlinear dynamical processes in
+tunably solution-processed colloidal epsilon-near-zero (ENZ) transparent
+conducting oxide (TCO) nanocrystals (NCs), of which the potential respond
+response speed is >2 THz and modulation depth is ~23% pumped at ~0.7 mJ/cm2,
+benefiting from the highly confined geometry in addition to the ENZ enhancement
+effect. These ENZ NCs may offer a scalable and printable material solution for
+dynamic photonic and optoelectronic devices.
+",0,1,0,0,0,0
+6226,A quantum dynamic belief model to explain the interference effects of categorization on decision making,"  Categorization is necessary for many decision making tasks. However, the
+categorization process may interfere the decision making result and the law of
+total probability can be violated in some situations. To predict the
+interference effect of categorization, some model based on quantum probability
+has been proposed. In this paper, a new quantum dynamic belief (QDB) model is
+proposed. Considering the precise decision may not be made during the process,
+the concept of uncertainty is introduced in our model to simulate real human
+thinking process. Then the interference effect categorization can be predicted
+by handling the uncertain information. The proposed model is applied to a
+categorization decision-making experiment to explain the interference effect of
+categorization. Compared with other models, our model is relatively more
+succinct and the result shows the correctness and effectiveness of our model.
+",1,0,0,0,0,0
+6227,Throughput Optimal Beam Alignment in Millimeter Wave Networks,"  Millimeter wave communications rely on narrow-beam transmissions to cope with
+the strong signal attenuation at these frequencies, thus demanding precise beam
+alignment between transmitter and receiver. The communication overhead incurred
+to achieve beam alignment may become a severe impairment in mobile networks.
+This paper addresses the problem of optimizing beam alignment acquisition, with
+the goal of maximizing throughput. Specifically, the algorithm jointly
+determines the portion of time devoted to beam alignment acquisition, as well
+as, within this portion of time, the optimal beam search parameters, using the
+framework of Markov decision processes. It is proved that a bisection search
+algorithm is optimal, and that it outperforms exhaustive and iterative search
+algorithms proposed in the literature. The duration of the beam alignment phase
+is optimized so as to maximize the overall throughput. The numerical results
+show that the throughput, optimized with respect to the duration of the beam
+alignment phase, achievable under the exhaustive algorithm is 88.3% lower than
+that achievable under the bisection algorithm. Similarly, the throughput
+achievable by the iterative search algorithm for a division factor of 4 and 8
+is, respectively, 12.8% and 36.4% lower than that achievable by the bisection
+algorithm.
+",1,0,0,0,0,0
+6228,Behavioural Change Support Intelligent Transportation Applications,"  This workshop invites researchers and practitioners to participate in
+exploring behavioral change support intelligent transportation applications. We
+welcome submissions that explore intelligent transportation systems (ITS),
+which interact with travelers in order to persuade them or nudge them towards
+sustainable transportation behaviors and decisions. Emerging opportunities
+including the use of data and information generated by ITS and users' mobile
+devices in order to render personalized, contextualized and timely transport
+behavioral change interventions are in our focus. We invite submissions and
+ideas from domains of ITS including, but not limited to, multi-modal journey
+planners, advanced traveler information systems and in-vehicle systems. The
+expected outcome will be a deeper understanding of the challenges and future
+research directions with respect to behavioral change support through ITS.
+",1,0,0,0,0,0
+6229,SU(2) Pfaffian systems and gauge theory,"  Motivated by the description of Nurowski's conformal structure for maximally
+symmetric homogeneous examples of bracket-generating rank 2 distributions in
+dimension 5, aka $(2,3,5)$-distributions, we consider a rank $3$ Pfaffian
+system in dimension 5 with $SU(2)$ symmetry. We find the conditions for which
+this Pfaffian system has the maximal symmetry group (in the real case this is
+the split real form of $G_2$), and give the associated Nurowski's conformal
+classes. We also present a $SU(2)$ gauge-theoretic interpretation of the
+results obtained.
+",0,0,1,0,0,0
+6230,Correlation effects in superconducting quantum dot systems,"  We study the effect of electron correlations on a system consisting of a
+single-level quantum dot with local Coulomb interaction attached to two
+superconducting leads. We use the single-impurity Anderson model with BCS
+superconducting baths to study the interplay between the proximity induced
+electron pairing and the local Coulomb interaction. We show how to solve the
+model using the continuous-time hybridization-expansion quantum Monte Carlo
+method. The results obtained for experimentally relevant parameters are
+compared with results of self-consistent second order perturbation theory as
+well as with the numerical renormalization group method.
+",0,1,0,0,0,0
+6231,Learning non-parametric Markov networks with mutual information,"  We propose a method for learning Markov network structures for continuous
+data without invoking any assumptions about the distribution of the variables.
+The method makes use of previous work on a non-parametric estimator for mutual
+information which is used to create a non-parametric test for multivariate
+conditional independence. This independence test is then combined with an
+efficient constraint-based algorithm for learning the graph structure. The
+performance of the method is evaluated on several synthetic data sets and it is
+shown to learn considerably more accurate structures than competing methods
+when the dependencies between the variables involve non-linearities.
+",1,0,0,1,0,0
+6232,Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling,"  In this work, we design a machine learning based method, online adaptive
+primal support vector regression (SVR), to model the implied volatility surface
+(IVS). The algorithm proposed is the first derivation and implementation of an
+online primal kernel SVR. It features enhancements that allow efficient online
+adaptive learning by embedding the idea of local fitness and budget maintenance
+to dynamically update support vectors upon pattern drifts. For algorithm
+acceleration, we implement its most computationally intensive parts in a Field
+Programmable Gate Arrays hardware, where a 132x speedup over CPU is achieved
+during online prediction. Using intraday tick data from the E-mini S&P 500
+options market, we show that the Gaussian kernel outperforms the linear kernel
+in regulating the size of support vectors, and that our empirical IVS algorithm
+beats two competing online methods with regards to model complexity and
+regression errors (the mean absolute percentage error of our algorithm is up to
+13%). Best results are obtained at the center of the IVS grid due to its larger
+number of adjacent support vectors than the edges of the grid. Sensitivity
+analysis is also presented to demonstrate how hyper parameters affect the error
+rates and model complexity.
+",1,0,0,1,0,0
+6233,Hybrid control strategy for a semi active suspension system using fuzzy logic and bio-inspired chaotic fruit fly algorithm,"  This study proposes a control strategy for the efficient semi active
+suspension systems utilizing a novel hybrid PID-fuzzy logic control scheme .In
+the control architecture, we employ the Chaotic Fruit Fly Algorithm for PID
+tuning since it can avoid local minima by chaotic search. A novel linguistic
+rule based fuzzy logic controller is developed to aid the PID.A quarter car
+model with a non-linear spring system is used to test the performance of the
+proposed control approach. A road terrain is chosen where the comfort and
+handling parameters are tested specifically in the regions of abrupt changes.
+The results suggest that the suspension systems controlled by the hybrid
+strategy has the potential to offer more comfort and handling by reducing the
+peak acceleration and suspension distortion by 83.3 % and 28.57% respectively
+when compared to the active suspension systems. Also, compared to the
+performance of similar suspension control strategies optimized by stochastic
+algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO)
+and Bacterial Foraging Optimization (BFO), reductions in peak acceleration and
+suspension distortion are found to be 25%, 32.3%, 54.6% and 23.35 %, 22.5%, 5.4
+% respectively.The details of the solution methodology have been presented in
+the paper.
+",1,0,0,0,0,0
+6234,On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms,"  We demonstrate that, for a range of state-of-the-art machine learning
+algorithms, the differences in generalisation performance obtained using
+default parameter settings and using parameters tuned via cross-validation can
+be similar in magnitude to the differences in performance observed between
+state-of-the-art and uncompetitive learning systems. This means that fair and
+rigorous evaluation of new learning algorithms requires performance comparison
+against benchmark methods with best-practice model selection procedures, rather
+than using default parameter settings. We investigate the sensitivity of three
+key machine learning algorithms (support vector machine, random forest and
+rotation forest) to their default parameter settings, and provide guidance on
+determining sensible default parameter values for implementations of these
+algorithms. We also conduct an experimental comparison of these three
+algorithms on 121 classification problems and find that, perhaps surprisingly,
+rotation forest is significantly more accurate on average than both random
+forest and a support vector machine.
+",1,0,0,1,0,0
+6235,"Coaction functors, II","  In further study of the application of crossed-product functors to the
+Baum-Connes Conjecture, Buss, Echterhoff, and Willett introduced various other
+properties that crossed-product functors may have. Here we introduce and study
+analogues of these properties for coaction functors, making sure that the
+properties are preserved when the coaction functors are composed with the full
+crossed product to make a crossed-product functor. The new properties for
+coaction functors studied here are functoriality for generalized homomorphisms
+and the correspondence property. We particularly study the connections with the
+ideal property. The study of functoriality for generalized homomorphisms
+requires a detailed development of the Fischer construction of maximalization
+of coactions with regard to possibly degenerate homomorphisms into multiplier
+algebras. We verify that all ""KLQ"" functors arising from large ideals of the
+Fourier-Stieltjes algebra $B(G)$ have all the properties we study, and at the
+opposite extreme we give an example of a coaction functor having none of the
+properties.
+",0,0,1,0,0,0
+6236,Classification of Casimirs in 2D hydrodynamics,"  We describe a complete list of Casimirs for 2D Euler hydrodynamics on a
+surface without boundary: we define generalized enstrophies which, along with
+circulations, form a complete set of invariants for coadjoint orbits of
+area-preserving diffeomorphisms on a surface. We also outline a possible
+extension of main notions to the boundary case and formulate several open
+questions in that setting.
+",0,1,1,0,0,0
+6237,Novel Compliant omnicrawler-wheel transforming module,"  This paper presents a novel design of a crawler robot which is capable of
+transforming its chassis from an Omni crawler mode to a large-sized wheel mode
+using a novel mechanism. The transformation occurs without any additional
+actuators. Interestingly the robot can transform into a large diameter and
+small width wheel which enhances its maneuverability like small turning radius
+and fast/efficient locomotion. This paper contributes on improving the
+locomotion mode of previously developed hybrid compliant omnicrawler robot
+CObRaSO. In addition to legged and tracked mechanism, CObRaSO can now display
+large wheel mode which contributes to its locomotion capabilities. Mechanical
+design of the robot has been explained in a detailed manner in this paper and
+also the transforming experiment and torque analysis has been shown clearly
+",1,0,0,0,0,0
+6238,Bifurcation of solutions to Hamiltonian boundary value problems,"  A bifurcation is a qualitative change in a family of solutions to an equation
+produced by varying parameters. In contrast to the local bifurcations of
+dynamical systems that are often related to a change in the number or stability
+of equilibria, bifurcations of boundary value problems are global in nature and
+may not be related to any obvious change in dynamical behaviour. Catastrophe
+theory is a well-developed framework which studies the bifurcations of critical
+points of functions. In this paper we study the bifurcations of solutions of
+boundary-value problems for symplectic maps, using the language of
+(finite-dimensional) singularity theory. We associate certain such problems
+with a geometric picture involving the intersection of Lagrangian submanifolds,
+and hence with the critical points of a suitable generating function. Within
+this framework, we then study the effect of three special cases: (i) some
+common boundary conditions, such as Dirichlet boundary conditions for
+second-order systems, restrict the possible types of bifurcations (for example,
+in generic planar systems only the A-series beginning with folds and cusps can
+occur); (ii) integrable systems, such as planar Hamiltonian systems, can
+exhibit a novel periodic pitchfork bifurcation; and (iii) systems with
+Hamiltonian symmetries or reversing symmetries can exhibit restricted
+bifurcations associated with the symmetry. This approach offers an alternative
+to the analysis of critical points in function spaces, typically used in the
+study of bifurcation of variational problems, and opens the way to the
+detection of more exotic bifurcations than the simple folds and cusps that are
+often found in examples.
+",0,0,1,0,0,0
+6239,Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video,"  Manual annotations of temporal bounds for object interactions (i.e. start and
+end times) are typical training input to recognition, localization and
+detection algorithms. For three publicly available egocentric datasets, we
+uncover inconsistencies in ground truth temporal bounds within and across
+annotators and datasets. We systematically assess the robustness of
+state-of-the-art approaches to changes in labeled temporal bounds, for object
+interaction recognition. As boundaries are trespassed, a drop of up to 10% is
+observed for both Improved Dense Trajectories and Two-Stream Convolutional
+Neural Network.
+We demonstrate that such disagreement stems from a limited understanding of
+the distinct phases of an action, and propose annotating based on the Rubicon
+Boundaries, inspired by a similarly named cognitive model, for consistent
+temporal bounds of object interactions. Evaluated on a public dataset, we
+report a 4% increase in overall accuracy, and an increase in accuracy for 55%
+of classes when Rubicon Boundaries are used for temporal annotations.
+",1,0,0,0,0,0
+6240,Dynamical transport measurement of the Luttinger parameter in helical edges states of 2D topological insulators,"  One-dimensional (1D) electron systems in the presence of Coulomb interaction
+are described by Luttinger liquid theory. The strength of Coulomb interaction
+in the Luttinger liquid, as parameterized by the Luttinger parameter K, is in
+general difficult to measure. This is because K is usually hidden in powerlaw
+dependencies of observables as a function of temperature or applied bias. We
+propose a dynamical way to measure K on the basis of an electronic
+time-of-flight experiment. We argue that the helical Luttinger liquid at the
+edge of a 2D topological insulator constitutes a preeminently suited
+realization of a 1D system to test our proposal. This is based on the
+robustness of helical liquids against elastic backscattering in the presence of
+time reversal symmetry.
+",0,1,0,0,0,0
+6241,Controlling thermal emission of phonon by magnetic metasurfaces,"  Our experiment shows that the thermal emission of phonon can be controlled by
+magnetic resonance (MR) mode in a metasurface (MTS). Through changing the
+structural parameter of metasurface, the MR wavelength can be tuned to the
+phonon resonance wavelength. This introduces a strong coupling between phonon
+and MR, which results in an anticrossing phonon-plasmons mode. In the process,
+we can manipulate the polarization and angular radiation of thermal emission of
+phonon. Such metasurface provides a new kind of thermal emission structures for
+various thermal management applications.
+",0,1,0,0,0,0
+6242,Distributed Average Tracking of Heterogeneous Physical Second-order Agents With No Input Signals Constraint,"  This paper addresses distributed average tracking of physical second-order
+agents with heterogeneous nonlinear dynamics, where there is no constraint on
+input signals. The nonlinear terms in agents' dynamics are heterogeneous,
+satisfying a Lipschitz-like condition that will be defined later and is more
+general than the Lipschitz condition. In the proposed algorithm, a control
+input and a filter are designed for each agent. Each agent's filter has two
+outputs and the idea is that the first output estimates the average of the
+input signals and the second output estimates the average of the input
+velocities asymptotically. In parallel, each agent's position and velocity are
+driven to track, respectively, the first and the second outputs. Having
+heterogeneous nonlinear terms in agents' dynamics necessitates designing the
+filters for agents. Since the nonlinear terms in agents' dynamics can be
+unbounded and the input signals are arbitrary, novel state-dependent
+time-varying gains are employed in agents' filters and control inputs to
+overcome these unboundedness effects. Finally the results are improved to
+achieve the distributed average tracking for a group of double-integrator
+agents, where there is no constraint on input signals and the filter is not
+required anymore. Numerical simulations are also presented to illustrate the
+theoretical results.
+",0,0,1,0,0,0
+6243,Bound states of the two-dimensional Dirac equation for an energy-dependent hyperbolic Scarf potential,"  We study the two-dimensional massless Dirac equation for a potential that is
+allowed to depend on the energy and on one of the spatial variables. After
+determining a modified orthogonality relation and norm for such systems, we
+present an application involving an energy-dependent version of the hyperbolic
+Scarf potential. We construct closed-form bound state solutions of the
+associated Dirac equation.
+",0,1,0,0,0,0
+6244,Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild,"  Humans take advantage of real world symmetries for various tasks, yet
+capturing their superb symmetry perception mechanism with a computational model
+remains elusive. Motivated by a new study demonstrating the extremely high
+inter-person accuracy of human perceived symmetries in the wild, we have
+constructed the first deep-learning neural network for reflection and rotation
+symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common
+Object in COntext) dataset with nearly 11K consistent symmetry-labels from more
+than 400 human observers. We employ novel methods to convert discrete human
+labels into symmetry heatmaps, capture symmetry densely in an image and
+quantitatively evaluate Sym-NET against multiple existing computer vision
+algorithms. On CVPR 2013 symmetry competition testsets and unseen MS-COCO
+photos, Sym-NET significantly outperforms all other competitors. Beyond
+mathematically well-defined symmetries on a plane, Sym-NET demonstrates
+abilities to identify viewpoint-varied 3D symmetries, partially occluded
+symmetrical objects, and symmetries at a semantic level.
+",1,0,0,1,0,0
+6245,Sharp Threshold of Blow-up and Scattering for the fractional Hartree equation,"  We consider the fractional Hartree equation in the $L^2$-supercritical case,
+and we find a sharp threshold of the scattering versus blow-up dichotomy for
+radial data: If $
+M[u_{0}]^{\frac{s-s_c}{s_c}}E[u_{0}<M[Q]^{\frac{s-s_c}{s_c}}E[Q]$ and
+$M[u_{0}]^{\frac{s-s_c}{s_c}}\|u_{0}\|^2_{\dot H^s}<M[Q]^{\frac{s-s_c}{s_c}}\|
+Q\|^2_{\dot H^s}$, then the solution $u(t)$ is globally well-posed and
+scatters; if $
+M[u_{0}]^{\frac{s-s_c}{s_c}}E[u_{0}]<M[Q]^{\frac{s-s_c}{s_c}}E[Q]$ and
+$M[u_{0}]^{\frac{s-s_c}{s_c}}\|u_{0}\|^2_{\dot H^s}>M[Q]^{\frac{s-s_c}{s_c}}\|
+Q\|^2_{\dot H^s}$, the solution $u(t)$ blows up in finite time. This condition
+is sharp in the sense that the solitary wave solution $e^{it}Q(x)$ is global
+but not scattering, which satisfies the equality in the above conditions. Here,
+$Q$ is the ground-state solution for the fractional Hartree equation.
+",0,0,1,0,0,0
+6246,GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs,"  This work studies the problem of stochastic dynamic filtering and state
+propagation with complex beliefs. The main contribution is GP-SUM, a filtering
+algorithm tailored to dynamic systems and observation models expressed as
+Gaussian Processes (GP), and to states represented as a weighted sum of
+Gaussians. The key attribute of GP-SUM is that it does not rely on
+linearizations of the dynamic or observation models, or on unimodal Gaussian
+approximations of the belief, hence enables tracking complex state
+distributions. The algorithm can be seen as a combination of a sampling-based
+filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by
+sampling the state distribution and propagating each sample through the dynamic
+system and observation models. On the other hand, it achieves effective
+sampling and accurate probabilistic propagation by relying on the GP form of
+the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM
+outperforms several GP-Bayes and Particle Filters on a standard benchmark. We
+also demonstrate its use in a pushing task, predicting with experimental
+accuracy the naturally occurring non-Gaussian distributions.
+",1,0,0,1,0,0
+6247,A Dynamic Programming Principle for Distribution-Constrained Optimal Stopping,"  We consider an optimal stopping problem where a constraint is placed on the
+distribution of the stopping time. Reformulating the problem in terms of
+so-called measure-valued martingales allows us to transform the marginal
+constraint into an initial condition and view the problem as a stochastic
+control problem; we establish the corresponding dynamic programming principle.
+",0,0,1,0,0,0
+6248,Spectral algebra models of unstable v_n-periodic homotopy theory,"  We give a survey of a generalization of Quillen-Sullivan rational homotopy
+theory which gives spectral algebra models of unstable v_n-periodic homotopy
+types. In addition to describing and contextualizing our original approach, we
+sketch two other recent approaches which are of a more conceptual nature, due
+to Arone-Ching and Heuts. In the process, we also survey many relevant concepts
+which arise in the study of spectral algebra over operads, including
+topological André-Quillen cohomology, Koszul duality, and Goodwillie
+calculus.
+",0,0,1,0,0,0
+6249,The length of excitable knots,"  The FitzHugh-Nagumo equation provides a simple mathematical model of cardiac
+tissue as an excitable medium hosting spiral wave vortices. Here we present
+extensive numerical simulations studying long-term dynamics of knotted vortex
+string solutions for all torus knots up to crossing number 11. We demonstrate
+that FitzHugh-Nagumo evolution preserves the knot topology for all the examples
+presented, thereby providing a novel field theory approach to the study of
+knots. Furthermore, the evolution yields a well-defined minimal length for each
+knot that is comparable to the ropelength of ideal knots. We highlight the role
+of the medium boundary in stabilizing the length of the knot and discuss the
+implications beyond torus knots. By applying Moffatt's test we are able to show
+that there is not a unique attractor within a given knot topology.
+",0,1,1,0,0,0
+6250,The Bane of Low-Dimensionality Clustering,"  In this paper, we give a conditional lower bound of $n^{\Omega(k)}$ on
+running time for the classic k-median and k-means clustering objectives (where
+n is the size of the input), even in low-dimensional Euclidean space of
+dimension four, assuming the Exponential Time Hypothesis (ETH). We also
+consider k-median (and k-means) with penalties where each point need not be
+assigned to a center, in which case it must pay a penalty, and extend our lower
+bound to at least three-dimensional Euclidean space.
+This stands in stark contrast to many other geometric problems such as the
+traveling salesman problem, or computing an independent set of unit spheres.
+While these problems benefit from the so-called (limited) blessing of
+dimensionality, as they can be solved in time $n^{O(k^{1-1/d})}$ or
+$2^{n^{1-1/d}}$ in d dimensions, our work shows that widely-used clustering
+objectives have a lower bound of $n^{\Omega(k)}$, even in dimension four.
+We complete the picture by considering the two-dimensional case: we show that
+there is no algorithm that solves the penalized version in time less than
+$n^{o(\sqrt{k})}$, and provide a matching upper bound of $n^{O(\sqrt{k})}$.
+The main tool we use to establish these lower bounds is the placement of
+points on the moment curve, which takes its inspiration from constructions of
+point sets yielding Delaunay complexes of high complexity.
+",1,0,0,0,0,0
+6251,Blue-detuned magneto-optical trap,"  We present the properties and advantages of a new magneto-optical trap (MOT)
+where blue-detuned light drives `type-II' transitions that have dark ground
+states. Using $^{87}$Rb, we reach a radiation-pressure-limited density
+exceeding $10^{11}$cm$^{-3}$ and a temperature below 30$\mu$K. The phase-space
+density is higher than in normal atomic MOTs, and a million times higher than
+comparable red-detuned type-II MOTs, making it particularly attractive for
+molecular MOTs which rely on type-II transitions. The loss of atoms from the
+trap is dominated by ultracold collisions between Rb atoms. For typical
+trapping conditions, we measure a loss rate of
+$1.8(4)\times10^{-10}$cm$^{3}$s$^{-1}$.
+",0,1,0,0,0,0
+6252,Polishness of some topologies related to word or tree automata,"  We prove that the Büchi topology and the automatic topology are Polish. We
+also show that this cannot be fully extended to the case of a space of infinite
+labelled binary trees; in particular the Büchi and the Muller topologies are
+not Polish in this case.
+",1,0,1,0,0,0
+6253,Emulating satellite drag from large simulation experiments,"  Obtaining accurate estimates of satellite drag coefficients in low Earth
+orbit is a crucial component in positioning and collision avoidance. Simulators
+can produce accurate estimates, but their computational expense is much too
+large for real-time application. A pilot study showed that Gaussian process
+(GP) surrogate models could accurately emulate simulations. However, cubic
+runtime for training GPs means that they could only be applied to a narrow
+range of input configurations to achieve the desired level of accuracy. In this
+paper we show how extensions to the local approximate Gaussian Process (laGP)
+method allow accurate full-scale emulation. The new methodological
+contributions, which involve a multi-level global/local modeling approach, and
+a set-wise approach to local subset selection, are shown to perform well in
+benchmark and synthetic data settings. We conclude by demonstrating that our
+method achieves the desired level of accuracy, besting simpler viable (i.e.,
+computationally tractable) global and local modeling approaches, when trained
+on seventy thousand core hours of drag simulations for two real-world
+satellites: the Hubble space telescope (HST) and the gravity recovery and
+climate experiment (GRACE).
+",0,0,0,1,0,0
+6254,Rescaling and other forms of unsupervised preprocessing introduce bias into cross-validation,"  Cross-validation of predictive models is the de-facto standard for model
+selection and evaluation. In proper use, it provides an unbiased estimate of a
+model's predictive performance. However, data sets often undergo a preliminary
+data-dependent transformation, such as feature rescaling or dimensionality
+reduction, prior to cross-validation. It is widely believed that such a
+preprocessing stage, if done in an unsupervised manner that does not consider
+the class labels or response values, has no effect on the validity of
+cross-validation. In this paper, we show that this belief is not true.
+Preliminary preprocessing can introduce either a positive or negative bias into
+the estimates of model performance. Thus, it may lead to sub-optimal choices of
+model parameters and invalid inference. In light of this, the scientific
+community should re-examine the use of preliminary preprocessing prior to
+cross-validation across the various application domains. By default, all data
+transformations, including unsupervised preprocessing stages, should be learned
+only from the training samples, and then merely applied to the validation and
+testing samples.
+",1,0,0,1,0,0
+6255,Causal Inference on Discrete Data via Estimating Distance Correlations,"  In this paper, we deal with the problem of inferring causal directions when
+the data is on discrete domain. By considering the distribution of the cause
+$P(X)$ and the conditional distribution mapping cause to effect $P(Y|X)$ as
+independent random variables, we propose to infer the causal direction via
+comparing the distance correlation between $P(X)$ and $P(Y|X)$ with the
+distance correlation between $P(Y)$ and $P(X|Y)$. We infer ""$X$ causes $Y$"" if
+the dependence coefficient between $P(X)$ and $P(Y|X)$ is smaller. Experiments
+are performed to show the performance of the proposed method.
+",0,0,0,1,0,0
+6256,A Class of Exponential Sequences with Shift-Invariant Discriminators,"  The discriminator of an integer sequence s = (s(i))_{i>=0}, introduced by
+Arnold, Benkoski, and McCabe in 1985, is the function D_s(n) that sends n to
+the least integer m such that the numbers s(0), s(1), ..., s(n-1) are pairwise
+incongruent modulo m. In this note we present a class of exponential sequences
+that have the special property that their discriminators are shift-invariant,
+i.e., that the discriminator of the sequence is the same even if the sequence
+is shifted by any positive constant.
+",1,0,1,0,0,0
+6257,Contraction par Frobenius et modules de Steinberg,"  For a reductive group G defined over an algebraically closed field of
+positive characteristic, we show that the Frobenius contraction functor of
+G-modules is right adjoint to the Frobenius twist of the modules tensored with
+the Steinberg module twice. It follows that the Frobenius contraction functor
+preserves injectivity, good filtrations, but not semisiplicity.
+",0,0,1,0,0,0
+6258,SafeDrive: A Robust Lane Tracking System for Autonomous and Assisted Driving Under Limited Visibility,"  We present an approach towards robust lane tracking for assisted and
+autonomous driving, particularly under poor visibility. Autonomous detection of
+lane markers improves road safety, and purely visual tracking is desirable for
+widespread vehicle compatibility and reducing sensor intrusion, cost, and
+energy consumption. However, visual approaches are often ineffective because of
+a number of factors, including but not limited to occlusion, poor weather
+conditions, and paint wear-off. Our method, named SafeDrive, attempts to
+improve visual lane detection approaches in drastically degraded visual
+conditions without relying on additional active sensors. In scenarios where
+visual lane detection algorithms are unable to detect lane markers, the
+proposed approach uses location information of the vehicle to locate and access
+alternate imagery of the road and attempts detection on this secondary image.
+Subsequently, by using a combination of feature-based and pixel-based
+alignment, an estimated location of the lane marker is found in the current
+scene. We demonstrate the effectiveness of our system on actual driving data
+from locations in the United States with Google Street View as the source of
+alternate imagery.
+",1,0,0,0,0,0
+6259,On the analysis of personalized medication response and classification of case vs control patients in mobile health studies: the mPower case study,"  In this work we provide a couple of contributions to the analysis of
+longitudinal data collected by smartphones in mobile health applications.
+First, we propose a novel statistical approach to disentangle personalized
+treatment and ""time-of-the-day"" effects in observational studies. Under the
+assumption of no unmeasured confounders, we show how to use conditional
+independence relations in the data in order to determine if a difference in
+performance between activity tasks performed before and after the participant
+has taken medication, are potentially due to an effect of the medication or to
+a ""time-of-the-day"" effect (or still to both). Second, we show that smartphone
+data collected from a given study participant can represent a ""digital
+fingerprint"" of the participant, and that classifiers of case/control labels,
+constructed using longitudinal data, can show artificially improved performance
+when data from each participant is included in both training and test sets. We
+illustrate our contributions using data collected during the first 6 months of
+the mPower study.
+",0,0,0,1,0,0
+6260,Exponential Integrators in Time-Dependent Density Functional Calculations,"  The integrating factor and exponential time differencing methods are
+implemented and tested for solving the time-dependent Kohn--Sham equations.
+Popular time propagation methods used in physics, as well as other robust
+numerical approaches, are compared to these exponential integrator methods in
+order to judge the relative merit of the computational schemes. We determine an
+improvement in accuracy of multiple orders of magnitude when describing
+dynamics driven primarily by a nonlinear potential. For cases of dynamics
+driven by a time-dependent external potential, the accuracy of the exponential
+integrator methods are less enhanced but still match or outperform the best of
+the conventional methods tested.
+",0,1,0,0,0,0
+6261,A Distributed Scheduling Algorithm to Provide Quality-of-Service in Multihop Wireless Networks,"  Control of multihop Wireless networks in a distributed manner while providing
+end-to-end delay requirements for different flows, is a challenging problem.
+Using the notions of Draining Time and Discrete Review from the theory of fluid
+limits of queues, an algorithm that meets delay requirements to various flows
+in a network is constructed. The algorithm involves an optimization which is
+implemented in a cyclic distributed manner across nodes by using the technique
+of iterative gradient ascent, with minimal information exchange between nodes.
+The algorithm uses time varying weights to give priority to flows. The
+performance of the algorithm is studied in a network with interference modelled
+by independent sets.
+",1,0,0,0,0,0
+6262,A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams,"  In a world of global trading, maritime safety, security and efficiency are
+crucial issues. We propose a multi-task deep learning framework for vessel
+monitoring using Automatic Identification System (AIS) data streams. We combine
+recurrent neural networks with latent variable modeling and an embedding of AIS
+messages to a new representation space to jointly address key issues to be
+dealt with when considering AIS data streams: massive amount of streaming data,
+noisy data and irregular timesampling. We demonstrate the relevance of the
+proposed deep learning framework on real AIS datasets for a three-task setting,
+namely trajectory reconstruction, anomaly detection and vessel type
+identification.
+",0,0,0,1,0,0
+6263,The CCI30 Index,"  We describe the design of the CCI30 cryptocurrency index.
+",0,0,0,0,0,1
+6264,Training Probabilistic Spiking Neural Networks with First-to-spike Decoding,"  Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at
+harnessing the energy efficiency of spike-domain processing by building on
+computing elements that operate on, and exchange, spikes. In this paper, the
+problem of training a two-layer SNN is studied for the purpose of
+classification, under a Generalized Linear Model (GLM) probabilistic neural
+model that was previously considered within the computational neuroscience
+literature. Conventional classification rules for SNNs operate offline based on
+the number of output spikes at each output neuron. In contrast, a novel
+training method is proposed here for a first-to-spike decoding rule, whereby
+the SNN can perform an early classification decision once spike firing is
+detected at an output neuron. Numerical results bring insights into the optimal
+parameter selection for the GLM neuron and on the accuracy-complexity trade-off
+performance of conventional and first-to-spike decoding.
+",1,0,0,1,0,0
+6265,An Effective Training Method For Deep Convolutional Neural Network,"  In this paper, we propose the nonlinearity generation method to speed up and
+stabilize the training of deep convolutional neural networks. The proposed
+method modifies a family of activation functions as nonlinearity generators
+(NGs). NGs make the activation functions linear symmetric for their inputs to
+lower model capacity, and automatically introduce nonlinearity to enhance the
+capacity of the model during training. The proposed method can be considered an
+unusual form of regularization: the model parameters are obtained by training a
+relatively low-capacity model, that is relatively easy to optimize at the
+beginning, with only a few iterations, and these parameters are reused for the
+initialization of a higher-capacity model. We derive the upper and lower bounds
+of variance of the weight variation, and show that the initial symmetric
+structure of NGs helps stabilize training. We evaluate the proposed method on
+different frameworks of convolutional neural networks over two object
+recognition benchmark tasks (CIFAR-10 and CIFAR-100). Experimental results
+showed that the proposed method allows us to (1) speed up the convergence of
+training, (2) allow for less careful weight initialization, (3) improve or at
+least maintain the performance of the model at negligible extra computational
+cost, and (4) easily train a very deep model.
+",1,0,0,1,0,0
+6266,A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization,"  Data-target association is an important step in multi-target localization for
+the intelligent operation of un- manned systems in numerous applications such
+as search and rescue, traffic management and surveillance. The objective of
+this paper is to present an innovative data association learning approach named
+multi-layer K-means (MLKM) based on leveraging the advantages of some existing
+machine learning approaches, including K-means, K-means++, and deep neural
+networks. To enable the accurate data association from different sensors for
+efficient target localization, MLKM relies on the clustering capabilities of
+K-means++ structured in a multi-layer framework with the error correction
+feature that is motivated by the backpropogation that is well-known in deep
+learning research. To show the effectiveness of the MLKM method, numerous
+simulation examples are conducted to compare its performance with K-means,
+K-means++, and deep neural networks.
+",1,0,0,1,0,0
+6267,Investigation of Monaural Front-End Processing for Robust ASR without Retraining or Joint-Training,"  In recent years, monaural speech separation has been formulated as a
+supervised learning problem, which has been systematically researched and shown
+the dramatical improvement of speech intelligibility and quality for human
+listeners. However, it has not been well investigated whether the methods can
+be employed as the front-end processing and directly improve the performance of
+a machine listener, i.e., an automatic speech recognizer, without retraining or
+joint-training the acoustic model. In this paper, we explore the effectiveness
+of the independent front-end processing for the multi-conditional trained ASR
+on the CHiME-3 challenge. We find that directly feeding the enhanced features
+to ASR can make 36.40% and 11.78% relative WER reduction for the GMM-based and
+DNN-based ASR respectively. We also investigate the affect of noisy phase and
+generalization ability under unmatched noise condition.
+",1,0,0,0,0,0
+6268,Average treatment effects in the presence of unknown interference,"  We investigate large-sample properties of treatment effect estimators under
+unknown interference in randomized experiments. The inferential target is a
+generalization of the average treatment effect estimand that marginalizes over
+potential spillover effects. We show that estimators commonly used to estimate
+treatment effects under no-interference are consistent for the generalized
+estimand for several common experimental designs under limited but otherwise
+arbitrary and unknown interference. The rates of convergence depend on the rate
+at which the amount of interference grows and the degree to which it aligns
+with dependencies in treatment assignment. Importantly for practitioners, the
+results imply that if one erroneously assumes that units do not interfere in a
+setting with limited, or even moderate, interference, standard estimators are
+nevertheless likely to be close to an average treatment effect if the sample is
+sufficiently large.
+",0,0,1,1,0,0
+6269,Spatial localization for nonlinear dynamical stochastic models for excitable media,"  Nonlinear dynamical stochastic models are ubiquitous in different areas.
+Excitable media models are typical examples with large state dimensions. Their
+statistical properties are often of great interest but are also very
+challenging to compute. In this article, a theoretical framework to understand
+the spatial localization for a large class of stochastically coupled nonlinear
+systems in high dimensions is developed. Rigorous mathematical theories show
+the covariance decay behavior due to both local and nonlocal effects, which
+result from the diffusion and the mean field interaction, respectively. The
+analysis is based on a comparison with an appropriate linear surrogate model,
+of which the covariance propagation can be computed explicitly. Two important
+applications of these theoretical results are discussed. They are the spatial
+averaging strategy for efficiently sampling the covariance matrix and the
+localization technique in data assimilation. Test examples of a surrogate
+linear model and a stochastically coupled FitzHugh-Nagumo model for excitable
+media are adopted to validate the theoretical results. The latter is also used
+for a systematical study of the spatial averaging strategy in efficiently
+sampling the covariance matrix in different dynamical regimes.
+",0,0,1,1,0,0
+6270,Nonlinear photoionization of transparent solids: a nonperturbative theory obeying selection rules,"  We provide a nonperturbative theory for photoionization of transparent
+solids. By applying a particular steepest-descent method, we derive analytical
+expressions for the photoionization rate within the two-band structure model,
+which consistently account for the $selection$ $rules$ related to the parity of
+the number of absorbed photons ($odd$ or $even$). We demonstrate the crucial
+role of the interference of the transition amplitudes (saddle-points), which in
+the semi-classical limit, can be interpreted in terms of interfering quantum
+trajectories. Keldysh's foundational work of laser physics [Sov. Phys. JETP 20,
+1307 (1965)] disregarded this interference, resulting in the violation of
+$selection$ $rules$. We provide an improved Keldysh photoionization theory and
+show its excellent agreement with measurements for the frequency dependence of
+the two-photon absorption and nonlinear refractive index coefficients in
+dielectrics.
+",0,1,0,0,0,0
+6271,Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models,"  We focus on two particular aspects of model risk: the inability of a chosen
+model to fit observed market prices at a given point in time (calibration
+error) and the model risk due to recalibration of model parameters (in
+contradiction to the model assumptions). In this context, we follow the
+approach of Glasserman and Xu (2014) and use relative entropy as a pre-metric
+in order to quantify these two sources of model risk in a common framework, and
+consider the trade-offs between them when choosing a model and the frequency
+with which to recalibrate to the market. We illustrate this approach applied to
+the models of Black and Scholes (1973) and Heston (1993), using option data for
+Apple (AAPL) and Google (GOOG). We find that recalibrating a model more
+frequently simply shifts model risk from one type to another, without any
+substantial reduction of aggregate model risk. Furthermore, moving to a more
+complicated stochastic model is seen to be counterproductive if one requires a
+high degree of robustness, for example as quantified by a 99 percent quantile
+of aggregate model risk.
+",0,0,0,0,0,1
+6272,CN rings in full protoplanetary disks around young stars as probes of disk structure,"  Bright ring-like structure emission of the CN molecule has been observed in
+protoplanetary disks. We investigate whether such structures are due to the
+morphology of the disk itself or if they are instead an intrinsic feature of CN
+emission. With the intention of using CN as a diagnostic, we also address to
+which physical and chemical parameters CN is most sensitive. A set of disk
+models were run for different stellar spectra, masses, and physical structures
+via the 2D thermochemical code DALI. An updated chemical network that accounts
+for the most relevant CN reactions was adopted. Ring-shaped emission is found
+to be a common feature of all adopted models; the highest abundance is found in
+the upper outer regions of the disk, and the column density peaks at 30-100 AU
+for T Tauri stars with standard accretion rates. Higher mass disks generally
+show brighter CN. Higher UV fields, such as those appropriate for T Tauri stars
+with high accretion rates or for Herbig Ae stars or for higher disk flaring,
+generally result in brighter and larger rings. These trends are due to the main
+formation paths of CN, which all start with vibrationally excited H2*
+molecules, that are produced through far ultraviolet (FUV) pumping of H2. The
+model results compare well with observed disk-integrated CN fluxes and the
+observed location of the CN ring for the TW Hya disk. CN rings are produced
+naturally in protoplanetary disks and do not require a specific underlying disk
+structure such as a dust cavity or gap. The strong link between FUV flux and CN
+emission can provide critical information regarding the vertical structure of
+the disk and the distribution of dust grains which affects the UV penetration,
+and could help to break some degeneracies in the SED fitting. In contrast with
+C2H or c-C3H2, the CN flux is not very sensitive to carbon and oxygen
+depletion.
+",0,1,0,0,0,0
+6273,A Trio Neural Model for Dynamic Entity Relatedness Ranking,"  Measuring entity relatedness is a fundamental task for many natural language
+processing and information retrieval applications. Prior work often studies
+entity relatedness in static settings and an unsupervised manner. However,
+entities in real-world are often involved in many different relationships,
+consequently entity-relations are very dynamic over time. In this work, we
+propose a neural networkbased approach for dynamic entity relatedness,
+leveraging the collective attention as supervision. Our model is capable of
+learning rich and different entity representations in a joint framework.
+Through extensive experiments on large-scale datasets, we demonstrate that our
+method achieves better results than competitive baselines.
+",0,0,0,1,0,0
+6274,Dynamic k-Struve Sumudu Solutions for Fractional Kinetic Equations,"  In this present study, we investigate solutions for fractional kinetic
+equations, involving k-Struve functions using Sumudu transform. The methodology
+and results can be considered and applied to various related fractional
+problems in mathematical physics.
+",0,0,1,0,0,0
+6275,Does mitigating ML's impact disparity require treatment disparity?,"  Following related work in law and policy, two notions of disparity have come
+to shape the study of fairness in algorithmic decision-making. Algorithms
+exhibit treatment disparity if they formally treat members of protected
+subgroups differently; algorithms exhibit impact disparity when outcomes differ
+across subgroups, even if the correlation arises unintentionally. Naturally, we
+can achieve impact parity through purposeful treatment disparity. In one thread
+of technical work, papers aim to reconcile the two forms of parity proposing
+disparate learning processes (DLPs). Here, the learning algorithm can see group
+membership during training but produce a classifier that is group-blind at test
+time. In this paper, we show theoretically that: (i) When other features
+correlate to group membership, DLPs will (indirectly) implement treatment
+disparity, undermining the policy desiderata they are designed to address; (ii)
+When group membership is partly revealed by other features, DLPs induce
+within-class discrimination; and (iii) In general, DLPs provide a suboptimal
+trade-off between accuracy and impact parity. Based on our technical analysis,
+we argue that transparent treatment disparity is preferable to occluded methods
+for achieving impact parity. Experimental results on several real-world
+datasets highlight the practical consequences of applying DLPs vs. per-group
+thresholds.
+",1,0,0,1,0,0
+6276,Topological Terms and Phases of Sigma Models,"  We study boundary conditions of topological sigma models with the goal of
+generalizing the concepts of anomalous symmetry and symmetry protected
+topological order. We find a version of 't Hooft's anomaly matching conditions
+on the renormalization group flow of boundaries of invertible topological sigma
+models and discuss several examples of anomalous boundary theories. We also
+comment on bulk topological transitions in dynamical sigma models and argue
+that one can, with care, use topological data to draw sigma model phase
+diagrams.
+",0,1,0,0,0,0
+6277,A commuting-vector-field approach to some dispersive estimates,"  We prove the pointwise decay of solutions to three linear equations: (i) the
+transport equation in phase space generalizing the classical Vlasov equation,
+(ii) the linear Schrodinger equation, (iii) the Airy (linear KdV) equation. The
+usual proofs use explicit representation formulae, and either obtain
+$L^1$---$L^\infty$ decay through directly estimating the fundamental solution
+in physical space, or by studying oscillatory integrals coming from the
+representation in Fourier space. Our proof instead combines ""vector field""
+commutators that capture the inherent symmetries of the relevant equations with
+conservation laws for mass and energy to get space-time weighted energy
+estimates. Combined with a simple version of Sobolev's inequality this gives
+pointwise decay as desired. In the case of the Vlasov and Schrodinger equations
+we can recover sharp pointwise decay; in the Schrodinger case we also show how
+to obtain local energy decay as well as Strichartz-type estimates. For the Airy
+equation we obtain a local energy decay that is almost sharp from the scaling
+point of view, but nonetheless misses the classical estimates by a gap. This
+work is inspired by the work of Klainerman on $L^2$---$L^\infty$ decay of wave
+equations, as well as the recent work of Fajman, Joudioux, and Smulevici on
+decay of mass distributions for the relativistic Vlasov equation.
+",0,0,1,0,0,0
+6278,The connected countable spaces of Bing and Ritter are topologically homogeneous,"  Answering a problem posed by the second author on Mathoverflow, we prove that
+the connected countable Hausdorff spaces constructed by Bing and Ritter are
+topologically homogeneous.
+",0,0,1,0,0,0
+6279,Dynamic Graph Convolutional Networks,"  Many different classification tasks need to manage structured data, which are
+usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that
+the vertices/edges of each graph may change during time. Our goal is to jointly
+exploit structured data and temporal information through the use of a neural
+network model. To the best of our knowledge, this task has not been addressed
+using these kind of architectures. For this reason, we propose two novel
+approaches, which combine Long Short-Term Memory networks and Graph
+Convolutional Networks to learn long short-term dependencies together with
+graph structure. The quality of our methods is confirmed by the promising
+results achieved.
+",1,0,0,1,0,0
+6280,Robust and Efficient Parametric Spectral Estimation in Atomic Force Microscopy,"  An atomic force microscope (AFM) is capable of producing ultra-high
+resolution measurements of nanoscopic objects and forces. It is an
+indispensable tool for various scientific disciplines such as molecular
+engineering, solid-state physics, and cell biology. Prior to a given
+experiment, the AFM must be calibrated by fitting a spectral density model to
+baseline recordings. However, since AFM experiments typically collect large
+amounts of data, parameter estimation by maximum likelihood can be
+prohibitively expensive. Thus, practitioners routinely employ a much faster
+least-squares estimation method, at the cost of substantially reduced
+statistical efficiency. Additionally, AFM data is often contaminated by
+periodic electronic noise, to which parameter estimates are highly sensitive.
+This article proposes a two-stage estimator to address these issues.
+Preliminary parameter estimates are first obtained by a variance-stabilizing
+procedure, by which the simplicity of least-squares combines with the
+efficiency of maximum likelihood. A test for spectral periodicities then
+eliminates high-impact outliers, considerably and robustly protecting the
+second-stage estimator from the effects of electronic noise. Simulation and
+experimental results indicate that a two- to ten-fold reduction in mean squared
+error can be expected by applying our methodology.
+",0,0,0,1,0,0
+6281,A Robotic Auto-Focus System based on Deep Reinforcement Learning,"  Considering its advantages in dealing with high-dimensional visual input and
+learning control policies in discrete domain, Deep Q Network (DQN) could be an
+alternative method of traditional auto-focus means in the future. In this
+paper, based on Deep Reinforcement Learning, we propose an end-to-end approach
+that can learn auto-focus policies from visual input and finish at a clear spot
+automatically. We demonstrate that our method - discretizing the action space
+with coarse to fine steps and applying DQN is not only a solution to auto-focus
+but also a general approach towards vision-based control problems. Separate
+phases of training in virtual and real environments are applied to obtain an
+effective model. Virtual experiments, which are carried out after the virtual
+training phase, indicates that our method could achieve 100% accuracy on a
+certain view with different focus range. Further training on real robots could
+eliminate the deviation between the simulator and real scenario, leading to
+reliable performances in real applications.
+",1,0,0,0,0,0
+6282,Wall modeling via function enrichment: extension to detached-eddy simulation,"  We extend the approach of wall modeling via function enrichment to
+detached-eddy simulation. The wall model aims at using coarse cells in the
+near-wall region by modeling the velocity profile in the viscous sublayer and
+log-layer. However, unlike other wall models, the full Navier-Stokes equations
+are still discretely fulfilled, including the pressure gradient and convective
+term. This is achieved by enriching the elements of the high-order
+discontinuous Galerkin method with the law-of-the-wall. As a result, the
+Galerkin method can ""choose"" the optimal solution among the polynomial and
+enrichment shape functions. The detached-eddy simulation methodology provides a
+suitable turbulence model for the coarse near-wall cells. The approach is
+applied to wall-modeled LES of turbulent channel flow in a wide range of
+Reynolds numbers. Flow over periodic hills shows the superiority compared to an
+equilibrium wall model under separated flow conditions.
+",0,1,0,0,0,0
+6283,Existence and uniqueness of periodic solution of nth-order Equations with delay in Banach space having Fourier type,"  The aim of this work is to study the existence of a periodic solutions of
+nth-order differential equations with delay d dt x(t) + d 2 dt 2 x(t) + d 3 dt
+3 x(t) + ... + d n dt n x(t) = Ax(t) + L(xt) + f (t). Our approach is based on
+the M-boundedness of linear operators, Fourier type, B s p,q-multipliers and
+Besov spaces.
+",0,0,1,0,0,0
+6284,A proof of Hilbert's theorem on ternary quartic forms with the ladder technique,"  This paper proposes a totally constructive approach for the proof of
+Hilbert's theorem on ternary quartic forms. The main contribution is the ladder
+technique, with which the Hilbert's theorem is proved vividly.
+",1,0,1,0,0,0
+6285,Data-driven Job Search Engine Using Skills and Company Attribute Filters,"  According to a report online, more than 200 million unique users search for
+jobs online every month. This incredibly large and fast growing demand has
+enticed software giants such as Google and Facebook to enter this space, which
+was previously dominated by companies such as LinkedIn, Indeed and
+CareerBuilder. Recently, Google released their ""AI-powered Jobs Search Engine"",
+""Google For Jobs"" while Facebook released ""Facebook Jobs"" within their
+platform. These current job search engines and platforms allow users to search
+for jobs based on general narrow filters such as job title, date posted,
+experience level, company and salary. However, they have severely limited
+filters relating to skill sets such as C++, Python, and Java and company
+related attributes such as employee size, revenue, technographics and
+micro-industries. These specialized filters can help applicants and companies
+connect at a very personalized, relevant and deeper level. In this paper we
+present a framework that provides an end-to-end ""Data-driven Jobs Search
+Engine"". In addition, users can also receive potential contacts of recruiters
+and senior positions for connection and networking opportunities. The high
+level implementation of the framework is described as follows: 1) Collect job
+postings data in the United States, 2) Extract meaningful tokens from the
+postings data using ETL pipelines, 3) Normalize the data set to link company
+names to their specific company websites, 4) Extract and ranking the skill
+sets, 5) Link the company names and websites to their respective company level
+attributes with the EVERSTRING Company API, 6) Run user-specific search queries
+on the database to identify relevant job postings and 7) Rank the job search
+results. This framework offers a highly customizable and highly targeted search
+experience for end users.
+",1,0,0,0,0,0
+6286,"The universal DAHA of type $(C_1^\vee,C_1)$ and Leonard pairs of $q$-Racah type","  A Leonard pair is a pair of diagonalizable linear transformations of a
+finite-dimensional vector space, each of which acts in an irreducible
+tridiagonal fashion on an eigenbasis for the other one. Let $\mathbb F$ denote
+an algebraically closed field, and fix a nonzero $q \in \mathbb F$ that is not
+a root of unity. The universal double affine Hecke algebra (DAHA) $\hat{H}_q$
+of type $(C_1^\vee,C_1)$ is the associative $\mathbb F$-algebra defined by
+generators $\lbrace t_i^{\pm 1}\rbrace_{i=0}^3$ and relations (i)
+$t_it_i^{-1}=t_i^{-1}t_i=1$; (ii) $t_i+t_i^{-1}$ is central; (iii)
+$t_0t_1t_2t_3 = q^{-1}$. We consider the elements $X=t_3t_0$ and $Y=t_0t_1$ of
+$\hat{H}_q$. Let $\mathcal V$ denote a finite-dimensional irreducible
+$\hat{H}_q$-module on which each of $X$, $Y$ is diagonalizable and $t_0$ has
+two distinct eigenvalues. Then $\mathcal V$ is a direct sum of the two
+eigenspaces of $t_0$. We show that the pair $X+X^{-1}$, $Y+Y^{-1}$ acts on each
+eigenspace as a Leonard pair, and each of these Leonard pairs falls into a
+class said to have $q$-Racah type. Thus from $\mathcal V$ we obtain a pair of
+Leonard pairs of $q$-Racah type. It is known that a Leonard pair of $q$-Racah
+type is determined up to isomorphism by a parameter sequence $(a,b,c,d)$ called
+its Huang data. Given a pair of Leonard pairs of $q$-Racah type, we find
+necessary and sufficient conditions on their Huang data for that pair to come
+from the above construction.
+",0,0,1,0,0,0
+6287,Deep Learning for Computational Chemistry,"  The rise and fall of artificial neural networks is well documented in the
+scientific literature of both computer science and computational chemistry. Yet
+almost two decades later, we are now seeing a resurgence of interest in deep
+learning, a machine learning algorithm based on multilayer neural networks.
+Within the last few years, we have seen the transformative impact of deep
+learning in many domains, particularly in speech recognition and computer
+vision, to the extent that the majority of expert practitioners in those field
+are now regularly eschewing prior established models in favor of deep learning
+models. In this review, we provide an introductory overview into the theory of
+deep neural networks and their unique properties that distinguish them from
+traditional machine learning algorithms used in cheminformatics. By providing
+an overview of the variety of emerging applications of deep neural networks, we
+highlight its ubiquity and broad applicability to a wide range of challenges in
+the field, including QSAR, virtual screening, protein structure prediction,
+quantum chemistry, materials design and property prediction. In reviewing the
+performance of deep neural networks, we observed a consistent outperformance
+against non-neural networks state-of-the-art models across disparate research
+topics, and deep neural network based models often exceeded the ""glass ceiling""
+expectations of their respective tasks. Coupled with the maturity of
+GPU-accelerated computing for training deep neural networks and the exponential
+growth of chemical data on which to train these networks on, we anticipate that
+deep learning algorithms will be a valuable tool for computational chemistry.
+",1,0,0,1,0,0
+6288,Causal Interventions for Fairness,"  Most approaches in algorithmic fairness constrain machine learning methods so
+the resulting predictions satisfy one of several intuitive notions of fairness.
+While this may help private companies comply with non-discrimination laws or
+avoid negative publicity, we believe it is often too little, too late. By the
+time the training data is collected, individuals in disadvantaged groups have
+already suffered from discrimination and lost opportunities due to factors out
+of their control. In the present work we focus instead on interventions such as
+a new public policy, and in particular, how to maximize their positive effects
+while improving the fairness of the overall system. We use causal methods to
+model the effects of interventions, allowing for potential interference--each
+individual's outcome may depend on who else receives the intervention. We
+demonstrate this with an example of allocating a budget of teaching resources
+using a dataset of schools in New York City.
+",0,0,0,1,0,0
+6289,Analyzing Hypersensitive AI: Instability in Corporate-Scale Machine Learning,"  Predictive geometric models deliver excellent results for many Machine
+Learning use cases. Despite their undoubted performance, neural predictive
+algorithms can show unexpected degrees of instability and variance,
+particularly when applied to large datasets. We present an approach to measure
+changes in geometric models with respect to both output consistency and
+topological stability. Considering the example of a recommender system using
+word2vec, we analyze the influence of single data points, approximation methods
+and parameter settings. Our findings can help to stabilize models where needed
+and to detect differences in informational value of data points on a large
+scale.
+",0,0,0,1,0,0
+6290,Role of the orbital degree of freedom in iron-based superconductors,"  Almost a decade has passed since the serendipitous discovery of the
+iron-based high temperature superconductors (FeSCs) in 2008. The question of
+how much similarity the FeSCs have with the copper oxide high temperature
+superconductors emerged since the initial discovery of long-range
+antiferromagnetism in the FeSCs in proximity to superconductivity. Despite the
+great resemblance in their phase diagrams, there exist important disparities
+between FeSCs and cuprates that need to be considered in order to paint a full
+picture of these two families of high temperature superconductors. One of the
+key differences lies in the multi-orbital multi-band nature of FeSCs, in
+contrast to the effective single-band model for cuprates. Due to the complexity
+of multi-orbital band structures, the orbital degree of freedom is often
+neglected in formulating the theoretical models for FeSCs. On the experimental
+side, systematic studies of the orbital related phenomena in FeSCs have been
+largely lacking. In this review, we summarize angle-resolved photoemission
+spectroscopy (ARPES) measurements across various FeSC families in literature,
+focusing on the systematic trend of orbital dependent electron correlations and
+the role of different Fe 3d orbitals in driving the nematic transition, the
+spin-density-wave transition, and implications for superconductivity.
+",0,1,0,0,0,0
+6291,Perpetual points: New tool for localization of co-existing attractors in dynamical systems,"  Perpetual points (PPs) are special critical points for which the magnitude of
+acceleration describing dynamics drops to zero, while the motion is still
+possible (stationary points are excluded), e.g. considering the motion of the
+particle in the potential field, at perpetual point it has zero acceleration
+and non-zero velocity. We show that using PPs we can trace all the stable fixed
+points in the system, and that the structure of trajectories leading from
+former points to stable equilibria may be similar to orbits obtained from
+unstable stationary points. Moreover, we argue that the concept of perpetual
+points may be useful in tracing unexpected attractors (hidden or rare
+attractors with small basins of attraction). We show potential applicability of
+this approach by analysing several representative systems of physical
+significance, including the damped oscillator, pendula and the Henon map. We
+suggest that perpetual points may be a useful tool for localization of
+co-existing attractors in dynamical systems.
+",0,1,0,0,0,0
+6292,Loss Functions in Restricted Parameter Spaces and Their Bayesian Applications,"  A squared error loss remains the most commonly used loss function for
+constructing a Bayes estimator of the parameter of interest. It, however, can
+lead to sub-optimal solutions when a parameter is defined on a restricted
+space. It can also be an inappropriate choice in the context when an
+overestimation and/or underestimation results in severe consequences and a more
+conservative estimator is preferred. We advocate a class of loss functions for
+parameters defined on restricted spaces which infinitely penalize boundary
+decisions like the squared error loss does on the real line. We also recall
+several properties of loss functions such as symmetry, convexity and
+invariance. We propose generalizations of the squared error loss function for
+parameters defined on the positive real line and on an interval. We provide
+explicit solutions for corresponding Bayes estimators and discuss multivariate
+extensions. Three well-known Bayesian estimation problems are used to
+demonstrate inferential benefits the novel Bayes estimators can provide in the
+context of restricted estimation.
+",0,0,1,1,0,0
+6293,When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum,"  The study of complex systems benefits from graph models and their analysis.
+In particular, the eigendecomposition of the graph Laplacian lets emerge
+properties of global organization from local interactions; e.g., the Fiedler
+vector has the smallest non-zero eigenvalue and plays a key role for graph
+clustering. Graph signal processing focusses on the analysis of signals that
+are attributed to the graph nodes. The eigendecomposition of the graph
+Laplacian allows to define the graph Fourier transform and extend conventional
+signal-processing operations to graphs. Here, we introduce the design of
+Slepian graph signals, by maximizing energy concentration in a predefined
+subgraph for a graph spectral bandlimit. We establish a novel link with
+classical Laplacian embedding and graph clustering, which provides a meaning to
+localized graph frequencies.
+",1,0,0,0,0,0
+6294,Colored Image Encryption and Decryption Using Chaotic Lorenz System and DCT2,"  In this paper, a scheme for the encryption and decryption of colored images
+by using the Lorenz system and the discrete cosine transform in two dimensions
+(DCT2) is proposed. Although chaos is random, it has deterministic features
+that can be used for encryption; further, the same sequences can be produced at
+the transmitter and receiver under the same initial conditions. Another
+property of DCT2 is that the energy is concentrated in some elements of the
+coefficients. These two properties are used to efficiently encrypt and recover
+the image at the receiver by using three different keys with three different
+predefined number of shifts for each instance of key usage. Simulation results
+and statistical analysis show that the scheme high performance in weakening the
+correlation between the pixels of the image that resulted from the inverse of
+highest energy values of DCT2 that form 99.9 % of the energy as well as those
+of the difference image.
+",1,0,0,0,0,0
+6295,Self-Gluing formula of the monopole invariant and its application,"  Given a $4$-manifold $\hat{M}$ and two homeomorphic surfaces $\Sigma_1,
+\Sigma_2$ smoothly embedded in $\hat{M}$ with genus more than 1, we remove the
+neighborhoods of the surfaces and obtain a new $4$-manifold $M$ from gluing two
+boundaries $S^1 \times \Sigma_1$ and $S^1 \times \Sigma_1.$ In this artice, we
+prove a gluing formula which describes the relation of the Seiberg-Witten
+invariants of $M$ and $\hat{M}.$ Moreover, we show the application of the
+formula on the existence condition of the symplectic structure on a family of
+$4$-manfolds under some conditions.
+",0,0,1,0,0,0
+6296,An aptamer-biosensor for azole class antifungal drugs,"  This report describes the development of an aptamer for sensing azole
+antifungal drugs for therapeutic drug monitoring. Modified Synthetic Evolution
+of Ligands through Exponential Enrichment (SELEX) was used to discover a DNA
+aptamer recognizing azole class antifungal drugs. This aptamer undergoes a
+secondary structural change upon binding to its target molecule as shown
+through fluorescence anisotropy-based binding measurements. Experiments using
+circular dichroism spectroscopy, revealed a unique double G-quadruplex
+structure that was essential and specific for binding to the azole antifungal
+target. Aptamer-functionalized Graphene Field Effect Transistor (GFET) devices
+were created and used to measure the binding of strength of azole antifungals
+to this surface. In total this aptamer and the supporting sensing platform
+could provide a valuable tool for improving the treatment of patients with
+invasive fungal infections.
+",0,1,0,0,0,0
+6297,"Notes on ""Einstein metrics on compact simple Lie groups attached to standard triples""","  In the paper ""Einstein metrics on compact simple Lie groups attached to
+standard triples"", the authors introduced the definition of standard triples
+and proved that every compact simple Lie group $G$ attached to a standard
+triple $(G,K,H)$ admits a left-invariant Einstein metric which is not naturally
+reductive except the standard triple $(\Sp(4),2\Sp(2),4\Sp(1))$. For the triple
+$(\Sp(4),2\Sp(2),4\Sp(1))$, we find there exists an involution pair of $\sp(4)$
+such that $4\sp(1)$ is the fixed point of the pair, and then give the
+decomposition of $\sp(4)$ as a direct sum of irreducible
+$\ad(4\sp(1))$-modules. But $\Sp(4)/4\Sp(1)$ is not a generalized Wallach
+space. Furthermore we give left-invariant Einstein metrics on $\Sp(4)$ which
+are non-naturally reductive and $\Ad(4\Sp(1))$-invariant. For the general case
+$(\Sp(2n_1n_2),2\Sp(n_1n_2),2n_2\Sp(n_1))$, there exist $2n_2-1$ involutions of
+$\sp(2n_1n_2)$ such that $2n_2\sp(n_1))$ is the fixed point of these $2n_2-1$
+involutions, and it follows the decomposition of $\sp(2n_1n_2)$ as a direct sum
+of irreducible $\ad(2n_2\sp(n_1))$-modules. In order to give new non-naturally
+reductive and $\Ad(2n_2\Sp(n_1)))$-invariant Einstein metrics on
+$\Sp(2n_1n_2)$, we prove a general result, i.e. $\Sp(2k+l)$ admits at least two
+non-naturally reductive Einstein metrics which are
+$\Ad(\Sp(k)\times\Sp(k)\times\Sp(l))$-invariant if $k<l$. It implies that every
+compact simple Lie group $\Sp(n)$ for $n\geq 4$ admits at least
+$2[\frac{n-1}{3}]$ non-naturally reductive left-invariant Einstein metrics.
+",0,0,1,0,0,0
+6298,Harmonic quasi-isometric maps II : negatively curved manifolds,"  We prove that a quasi-isometric map, and more generally a coarse embedding,
+between pinched Hadamard manifolds is within bounded distance from a unique
+harmonic map.
+",0,0,1,0,0,0
+6299,Platform independent profiling of a QCD code,"  The supercomputing platforms available for high performance computing based
+research evolve at a great rate. However, this rapid development of novel
+technologies requires constant adaptations and optimizations of the existing
+codes for each new machine architecture. In such context, minimizing time of
+efficiently porting the code on a new platform is of crucial importance. A
+possible solution for this common challenge is to use simulations of the
+application that can assist in detecting performance bottlenecks. Due to
+prohibitive costs of classical cycle-accurate simulators, coarse-grain
+simulations are more suitable for large parallel and distributed systems. We
+present a procedure of implementing the profiling for openQCD code [1] through
+simulation, which will enable the global reduction of the cost of profiling and
+optimizing this code commonly used in the lattice QCD community. Our approach
+is based on well-known SimGrid simulator [2], which allows for fast and
+accurate performance predictions of HPC codes. Additionally, accurate
+estimations of the program behavior on some future machines, not yet accessible
+to us, are anticipated.
+",1,1,0,0,0,0
+6300,DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems,"  Deep learning (DL) defines a new data-driven programming paradigm that
+constructs the internal system logic of a crafted neuron network through a set
+of training data. We have seen wide adoption of DL in many safety-critical
+scenarios. However, a plethora of studies have shown that the state-of-the-art
+DL systems suffer from various vulnerabilities which can lead to severe
+consequences when applied to real-world applications. Currently, the testing
+adequacy of a DL system is usually measured by the accuracy of test data.
+Considering the limitation of accessible high quality test data, good accuracy
+performance on test data can hardly provide confidence to the testing adequacy
+and generality of DL systems. Unlike traditional software systems that have
+clear and controllable logic and functionality, the lack of interpretability in
+a DL system makes system analysis and defect detection difficult, which could
+potentially hinder its real-world deployment. In this paper, we propose
+DeepGauge, a set of multi-granularity testing criteria for DL systems, which
+aims at rendering a multi-faceted portrayal of the testbed. The in-depth
+evaluation of our proposed testing criteria is demonstrated on two well-known
+datasets, five DL systems, and with four state-of-the-art adversarial attack
+techniques against DL. The potential usefulness of DeepGauge sheds light on the
+construction of more generic and robust DL systems.
+",0,0,0,1,0,0
+6301,J0906+6930: a radio-loud quasar in the early Universe,"  Radio-loud high-redshift quasars (HRQs), although only a few of them are
+known to date, are crucial for the studies of the growth of supermassive black
+holes (SMBHs) and the evolution of active galactic nuclei (AGN) at early
+cosmological epochs. Radio jets offer direct evidence of SMBHs, and their radio
+structures can be studied with the highest angular resolution using Very Long
+Baseline Interferometry (VLBI). Here we report on the observations of three
+HRQs (J0131-0321, J0906+6930, J1026+2542) at z>5 using the Korean VLBI Network
+and VLBI Exploration of Radio Astrometry Arrays (together known as KaVA) with
+the purpose of studying their pc-scale jet properties. The observations were
+carried out at 22 and 43 GHz in 2016 January among the first-batch open-use
+experiments of KaVA. The quasar J0906+6930 was detected at 22 GHz but not at 43
+GHz. The other two sources were not detected and upper limits to their compact
+radio emission are given. Archival VLBI imaging data and single-dish 15-GHz
+monitoring light curve of J0906+6930 were also acquired as complementary
+information. J0906+6930 shows a moderate-level variability at 15 GHz. The radio
+image is characterized by a core-jet structure with a total detectable size of
+~5 pc in projection. The brightness temperature, 1.9x10^{11} K, indicates
+relativistic beaming of the jet. The radio properties of J0906+6930 are
+consistent with a blazar. Follow-up VLBI observations will be helpful for
+determining its structural variation.
+",0,1,0,0,0,0
+6302,Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields,"  This work investigates the training of conditional random fields (CRFs) via
+the stochastic dual coordinate ascent (SDCA) algorithm of Shalev-Shwartz and
+Zhang (2016). SDCA enjoys a linear convergence rate and a strong empirical
+performance for binary classification problems. However, it has never been used
+to train CRFs. Yet it benefits from an `exact' line search with a single
+marginalization oracle call, unlike previous approaches. In this paper, we
+adapt SDCA to train CRFs, and we enhance it with an adaptive non-uniform
+sampling strategy based on block duality gaps. We perform experiments on four
+standard sequence prediction tasks. SDCA demonstrates performances on par with
+the state of the art, and improves over it on three of the four datasets, which
+have in common the use of sparse features.
+",1,0,0,1,0,0
+6303,Accelerating Innovation Through Analogy Mining,"  The availability of large idea repositories (e.g., the U.S. patent database)
+could significantly accelerate innovation and discovery by providing people
+with inspiration from solutions to analogous problems. However, finding useful
+analogies in these large, messy, real-world repositories remains a persistent
+challenge for either human or automated methods. Previous approaches include
+costly hand-created databases that have high relational structure (e.g.,
+predicate calculus representations) but are very sparse. Simpler
+machine-learning/information-retrieval similarity metrics can scale to large,
+natural-language datasets, but struggle to account for structural similarity,
+which is central to analogy. In this paper we explore the viability and value
+of learning simpler structural representations, specifically, ""problem
+schemas"", which specify the purpose of a product and the mechanisms by which it
+achieves that purpose. Our approach combines crowdsourcing and recurrent neural
+networks to extract purpose and mechanism vector representations from product
+descriptions. We demonstrate that these learned vectors allow us to find
+analogies with higher precision and recall than traditional
+information-retrieval methods. In an ideation experiment, analogies retrieved
+by our models significantly increased people's likelihood of generating
+creative ideas compared to analogies retrieved by traditional methods. Our
+results suggest a promising approach to enabling computational analogy at scale
+is to learn and leverage weaker structural representations.
+",1,0,0,1,0,0
+6304,$η$-Ricci solitons in $(\varepsilon)$-almost paracontact metric manifolds,"  The object of this paper is to study $\eta$-Ricci solitons on
+$(\varepsilon)$-almost paracontact metric manifolds. We investigate
+$\eta$-Ricci solitons in the case when its potential vector field is exactly
+the characteristic vector field $\xi$ of the $(\varepsilon)$-almost paracontact
+metric manifold and when the potential vector field is torse-forming. We also
+study Einstein-like and $(\varepsilon)$-para Sasakian manifolds admitting
+$\eta$-Ricci solitons. Finally we obtain some results for $\eta$-Ricci solitons
+on $(\varepsilon)$-almost paracontact metric manifolds with a special view
+towards parallel symmetric (0,2)-tensor fields.
+",0,0,1,0,0,0
+6305,Entropy facilitated active transport,"  We show how active transport of ions can be interpreted as an entropy
+facilitated process. In this interpretation, the pore geometry through which
+substrates are transported can give rise to a driving force. This gives a
+direct link between the geometry and the changes in Gibbs energy required.
+Quantifying the size of this effect for several proteins we find that the
+entropic contribution from the pore geometry is significant and we discuss how
+the effect can be used to interpret variations in the affinity at the binding
+site.
+",0,1,0,0,0,0
+6306,Consistency of the Predicative Calculus of Cumulative Inductive Constructions (pCuIC),"  In order to avoid well-know paradoxes associated with self-referential
+definitions, higher-order dependent type theories stratify the theory using a
+countably infinite hierarchy of universes (also known as sorts), Type$_0$ :
+Type$_1$ : $\cdots$ . Such type systems are called cumulative if for any type
+$A$ we have that $A$ : Type$_{i}$ implies $A$ : Type$_{i+1}$. The predicative
+calculus of inductive constructions (pCIC) which forms the basis of the Coq
+proof assistant, is one such system.
+In this paper we present and establish the soundness of the predicative
+calculus of cumulative inductive constructions (pCuIC) which extends the
+cumulativity relation to inductive types.
+",1,0,0,0,0,0
+6307,Private Information Retrieval from MDS Coded Data with Colluding Servers: Settling a Conjecture by Freij-Hollanti et al.,"  A $(K, N, T, K_c)$ instance of the MDS-TPIR problem is comprised of $K$
+messages and $N$ distributed servers. Each message is separately encoded
+through a $(K_c, N)$ MDS storage code. A user wishes to retrieve one message,
+as efficiently as possible, while revealing no information about the desired
+message index to any colluding set of up to $T$ servers. The fundamental limit
+on the efficiency of retrieval, i.e., the capacity of MDS-TPIR is known only at
+the extremes where either $T$ or $K_c$ belongs to $\{1,N\}$. The focus of this
+work is a recent conjecture by Freij-Hollanti, Gnilke, Hollanti and Karpuk
+which offers a general capacity expression for MDS-TPIR. We prove that the
+conjecture is false by presenting as a counterexample a PIR scheme for the
+setting $(K, N, T, K_c) = (2,4,2,2)$, which achieves the rate $3/5$, exceeding
+the conjectured capacity, $4/7$. Insights from the counterexample lead us to
+capacity characterizations for various instances of MDS-TPIR including all
+cases with $(K, N, T, K_c) = (2,N,T,N-1)$, where $N$ and $T$ can be arbitrary.
+",1,0,0,0,0,0
+6308,ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation,"  Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are
+useful for many practical tasks in machine learning. Synaptic weights, as well
+as neuron activation functions within the deep network are typically stored
+with high-precision formats, e.g. 32 bit floating point. However, since storage
+capacity is limited and each memory access consumes power, both storage
+capacity and memory access are two crucial factors in these networks. Here we
+present a method and present the ADaPTION toolbox to extend the popular deep
+learning library Caffe to support training of deep CNNs with reduced numerical
+precision of weights and activations using fixed point notation. ADaPTION
+includes tools to measure the dynamic range of weights and activations. Using
+the ADaPTION tools, we quantized several CNNs including VGG16 down to 16-bit
+weights and activations with only 0.8% drop in Top-1 accuracy. The
+quantization, especially of the activations, leads to increase of up to 50% of
+sparsity especially in early and intermediate layers, which we exploit to skip
+multiplications with zero, thus performing faster and computationally cheaper
+inference.
+",1,0,0,0,0,0
+6309,Graph Attention Networks,"  We present graph attention networks (GATs), novel neural network
+architectures that operate on graph-structured data, leveraging masked
+self-attentional layers to address the shortcomings of prior methods based on
+graph convolutions or their approximations. By stacking layers in which nodes
+are able to attend over their neighborhoods' features, we enable (implicitly)
+specifying different weights to different nodes in a neighborhood, without
+requiring any kind of costly matrix operation (such as inversion) or depending
+on knowing the graph structure upfront. In this way, we address several key
+challenges of spectral-based graph neural networks simultaneously, and make our
+model readily applicable to inductive as well as transductive problems. Our GAT
+models have achieved or matched state-of-the-art results across four
+established transductive and inductive graph benchmarks: the Cora, Citeseer and
+Pubmed citation network datasets, as well as a protein-protein interaction
+dataset (wherein test graphs remain unseen during training).
+",1,0,0,1,0,0
+6310,Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data,"  The prevalence of online media has attracted researchers from various domains
+to explore human behavior and make interesting predictions. In this research,
+we leverage heterogeneous social media data collected from various online
+platforms to predict Taiwan's 2016 presidential election. In contrast to most
+existing research, we take a ""signal"" view of heterogeneous information and
+adopt the Kalman filter to fuse multiple signals into daily vote predictions
+for the candidates. We also consider events that influenced the election in a
+quantitative manner based on the so-called event study model that originated in
+the field of financial research. We obtained the following interesting
+findings. First, public opinions in online media dominate traditional polls in
+Taiwan election prediction in terms of both predictive power and timeliness.
+But offline polls can still function on alleviating the sample bias of online
+opinions. Second, although online signals converge as election day approaches,
+the simple Facebook ""Like"" is consistently the strongest indicator of the
+election result. Third, most influential events have a strong connection to
+cross-strait relations, and the Chou Tzu-yu flag incident followed by the
+apology video one day before the election increased the vote share of Tsai
+Ing-Wen by 3.66%. This research justifies the predictive power of online media
+in politics and the advantages of information fusion. The combined use of the
+Kalman filter and the event study method contributes to the data-driven
+political analytics paradigm for both prediction and attribution purposes.
+",1,0,0,1,0,0
+6311,The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments,"  Technological advancements in the field of mobile devices and wearable
+sensors have helped overcome obstacles in the delivery of care, making it
+possible to deliver behavioral treatments anytime and anywhere. Increasingly
+the delivery of these treatments is triggered by predictions of risk or
+engagement which may have been impacted by prior treatments. Furthermore the
+treatments are often designed to have an impact on individuals over a span of
+time during which subsequent treatments may be provided.
+Here we discuss our work on the design of a mobile health smoking cessation
+experimental study in which two challenges arose. First the randomizations to
+treatment should occur at times of stress and second the outcome of interest
+accrues over a period that may include subsequent treatment. To address these
+challenges we develop the ""stratified micro-randomized trial,"" in which each
+individual is randomized among treatments at times determined by predictions
+constructed from outcomes to prior treatment and with randomization
+probabilities depending on these outcomes. We define both conditional and
+marginal proximal treatment effects. Depending on the scientific goal these
+effects may be defined over a period of time during which subsequent treatments
+may be provided. We develop a primary analysis method and associated sample
+size formulae for testing these effects.
+",0,0,0,1,0,0
+6312,Multiplicative Convolution of Real Asymmetric and Real Antisymmetric Matrices,"  The singular values of products of standard complex Gaussian random matrices,
+or sub-blocks of Haar distributed unitary matrices, have the property that
+their probability distribution has an explicit, structured form referred to as
+a polynomial ensemble. It is furthermore the case that the corresponding
+bi-orthogonal system can be determined in terms of Meijer G-functions, and the
+correlation kernel given as an explicit double contour integral. It has
+recently been shown that the Hermitised product $X_M \cdots X_2 X_1A X_1^T
+X_2^T \cdots X_M^T$, where each $X_i$ is a standard real complex Gaussian
+matrix, and $A$ is real anti-symmetric shares exhibits analogous properties.
+Here we use the theory of spherical functions and transforms to present a
+theory which, for even dimensions, includes these properties of the latter
+product as a special case. As an example we show that the theory also allows
+for a treatment of this class of Hermitised product when the $X_i$ are chosen
+as sub-blocks of Haar distributed real orthogonal matrices.
+",0,0,1,0,0,0
+6313,An approach to Griffiths conjecture,"  The Griffiths conjecture asserts that every ample vector bundle $E$ over a
+compact complex manifold $S$ admits a hermitian metric with positive curvature
+in the sense of Griffiths. In this article we give a sufficient condition for a
+positive hermitian metric on $\mathcal{O}_{\mathbb{P}(E^*)}(1)$ to induce a
+Griffiths positive $L^2$-metric on the vector bundle $E$. This result suggests
+to study the relative Kähler-Ricci flow on $\mathcal{O}_{\mathbb{P}(E^*)}(1)$
+for the fibration $\mathbb{P}(E^*)\to S$. We define a flow and give arguments
+for the convergence.
+",0,0,1,0,0,0
+6314,On Detecting Adversarial Perturbations,"  Machine learning and deep learning in particular has advanced tremendously on
+perceptual tasks in recent years. However, it remains vulnerable against
+adversarial perturbations of the input that have been crafted specifically to
+fool the system while being quasi-imperceptible to a human. In this work, we
+propose to augment deep neural networks with a small ""detector"" subnetwork
+which is trained on the binary classification task of distinguishing genuine
+data from data containing adversarial perturbations. Our method is orthogonal
+to prior work on addressing adversarial perturbations, which has mostly focused
+on making the classification network itself more robust. We show empirically
+that adversarial perturbations can be detected surprisingly well even though
+they are quasi-imperceptible to humans. Moreover, while the detectors have been
+trained to detect only a specific adversary, they generalize to similar and
+weaker adversaries. In addition, we propose an adversarial attack that fools
+both the classifier and the detector and a novel training procedure for the
+detector that counteracts this attack.
+",1,0,0,1,0,0
+6315,"It's Time to Consider ""Time"" when Evaluating Recommender-System Algorithms [Proposal]","  In this position paper, we question the current practice of calculating
+evaluation metrics for recommender systems as single numbers (e.g. precision
+p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express
+only average effectiveness over a usually rather long period (e.g. a year or
+even longer), which provides only a vague and static view of the data. We
+propose that recommender-system researchers should instead calculate metrics
+for time-series such as weeks or months, and plot the results in e.g. a line
+chart. This way, results show how algorithms' effectiveness develops over time,
+and hence the results allow drawing more meaningful conclusions about how an
+algorithm will perform in the future. In this paper, we explain our reasoning,
+provide an example to illustrate our reasoning and present suggestions for what
+the community should do next.
+",1,0,0,0,0,0
+6316,Anomalous metals -- failed superconductors,"  The observation of metallic ground states in a variety of two-dimensional
+electronic systems poses a fundamental challenge for the theory of electron
+fluids. Here, we analyze evidence for the existence of a regime, which we call
+the ""anomalous metal regime,"" in diverse 2D superconducting systems driven
+through a quantum superconductor to metal transition (QSMT) by tuning physical
+parameters such as the magnetic field, the gate voltage in the case of systems
+with a MOSFET geometry, or the degree of disorder. The principal
+phenomenological observation is that in the anomalous metal, as a function of
+decreasing temperature, the resistivity first drops as if the system were
+approaching a superconducting ground state, but then saturates at low
+temperatures to a value that can be orders of magnitude smaller than the Drude
+value. The anomalous metal also shows a giant positive magneto-resistance.
+Thus, it behaves as if it were a ""failed superconductor."" This behavior is
+observed in a broad range of parameters. We moreover exhibit, by theoretical
+solution of a model of superconducting grains embedded in a metallic matrix,
+that as a matter of principle such anomalous metallic behavior can occur in the
+neighborhood of a QSMT. However, we also argue that the robustness and
+ubiquitous nature of the observed phenomena are difficult to reconcile with any
+existing theoretical treatment, and speculate about the character of a more
+fundamental theoretical framework.
+",0,1,0,0,0,0
+6317,Energy Optimization of Automatic Hybrid Sailboat,"  Autonomous Surface Vehicles (ASVs) provide an effective way to actualize
+applications such as environment monitoring, search and rescue, and scientific
+researches. However, the conventional ASVs depends overly on the stored energy.
+Hybrid Sailboat, mainly powered by the wind, can solve this problem by using an
+auxiliary propulsion system. The electric energy cost of Hybrid Sailboat needs
+to be optimized to achieve the ocean automatic cruise mission. Based on
+adjusted setting on sails and rudders, this paper seeks the optimal trajectory
+for autonomic cruising to reduce the energy cost by changing the heading angle
+of sailing upwind. The experiment results validate the heading angle accounts
+for energy cost and the trajectory with the best heading angle saves up to
+23.7% than other conditions. Furthermore, the energy-time line can be used to
+predict the energy cost for long-time sailing.
+",1,0,0,0,0,0
+6318,Estimating the Operating Characteristics of Ensemble Methods,"  In this paper we present a technique for using the bootstrap to estimate the
+operating characteristics and their variability for certain types of ensemble
+methods. Bootstrapping a model can require a huge amount of work if the
+training data set is large. Fortunately in many cases the technique lets us
+determine the effect of infinite resampling without actually refitting a single
+model. We apply the technique to the study of meta-parameter selection for
+random forests. We demonstrate that alternatives to bootstrap aggregation and
+to considering \sqrt{d} features to split each node, where d is the number of
+features, can produce improvements in predictive accuracy.
+",0,0,0,1,0,0
+6319,Plasma turbulence at ion scales: a comparison between PIC and Eulerian hybrid-kinetic approaches,"  Kinetic-range turbulence in magnetized plasmas and, in particular, in the
+context of solar-wind turbulence has been extensively investigated over the
+past decades via numerical simulations. Among others, one of the widely adopted
+reduced plasma model is the so-called hybrid-kinetic model, where the ions are
+fully kinetic and the electrons are treated as a neutralizing (inertial or
+massless) fluid. Within the same model, different numerical methods and/or
+approaches to turbulence development have been employed. In the present work,
+we present a comparison between two-dimensional hybrid-kinetic simulations of
+plasma turbulence obtained with two complementary approaches spanning about two
+decades in wavenumber - from MHD inertial range to scales well below the ion
+gyroradius - with a state-of-the-art accuracy. One approach employs hybrid
+particle-in-cell (HPIC) simulations of freely-decaying Alfvénic turbulence,
+whereas the other consists of Eulerian hybrid Vlasov-Maxwell (HVM) simulations
+of turbulence continuously driven with partially-compressible large-scale
+fluctuations. Despite the completely different initialization and
+injection/drive at large scales, the same properties of turbulent fluctuations
+at $k_\perp\rho_i\gtrsim1$ are observed. The system indeed self-consistently
+""reprocesses"" the turbulent fluctuations while they are cascading towards
+smaller and smaller scales, in a way which actually depends on the plasma beta
+parameter. Small-scale turbulence has been found to be mainly populated by
+kinetic Alfvén wave (KAW) fluctuations for $\beta\geq1$, whereas KAW
+fluctuations are only sub-dominant for low-$\beta$.
+",0,1,0,0,0,0
+6320,Computational determination of the largest lattice polytope diameter,"  A lattice (d, k)-polytope is the convex hull of a set of points in dimension
+d whose coordinates are integers between 0 and k. Let {\delta}(d, k) be the
+largest diameter over all lattice (d, k)-polytopes. We develop a computational
+framework to determine {\delta}(d, k) for small instances. We show that
+{\delta}(3, 4) = 7 and {\delta}(3, 5) = 9; that is, we verify for (d, k) = (3,
+4) and (3, 5) the conjecture whereby {\delta}(d, k) is at most (k + 1)d/2 and
+is achieved, up to translation, by a Minkowski sum of lattice vectors.
+",1,0,0,0,0,0
+6321,A high resolution ion microscope for cold atoms,"  We report on an ion-optical system that serves as a microscope for ultracold
+ground state and Rydberg atoms. The system is designed to achieve a
+magnification of up to 1000 and a spatial resolution in the 100 nm range,
+thereby surpassing many standard imaging techniques for cold atoms. The
+microscope consists of four electrostatic lenses and a microchannel plate in
+conjunction with a delay line detector in order to achieve single particle
+sensitivity with high temporal and spatial resolution. We describe the design
+process of the microscope including ion-optical simulations of the imaging
+system and characterize aberrations and the resolution limit. Furthermore, we
+present the experimental realization of the microscope in a cold atom setup and
+investigate its performance by patterned ionization with a structure size down
+to 2.7 {\mu}m. The microscope meets the requirements for studying various
+many-body effects, ranging from correlations in cold quantum gases up to
+Rydberg molecule formation.
+",0,1,0,0,0,0
+6322,Lock-Free Parallel Perceptron for Graph-based Dependency Parsing,"  Dependency parsing is an important NLP task. A popular approach for
+dependency parsing is structured perceptron. Still, graph-based dependency
+parsing has the time complexity of $O(n^3)$, and it suffers from slow training.
+To deal with this problem, we propose a parallel algorithm called parallel
+perceptron. The parallel algorithm can make full use of a multi-core computer
+which saves a lot of training time. Based on experiments we observe that
+dependency parsing with parallel perceptron can achieve 8-fold faster training
+speed than traditional structured perceptron methods when using 10 threads, and
+with no loss at all in accuracy.
+",1,0,0,0,0,0
+6323,Finite groups with systems of $K$-$\frak{F}$-subnormal subgroups,"  Let $\frak {F}$ be a class of group. A subgroup $A$ of a finite group $G$ is
+said to be $K$-$\mathfrak{F}$-subnormal in $G$ if there is a subgroup chain
+$$A=A_{0} \leq A_{1} \leq \cdots \leq A_{n}=G$$ such that either $A_{i-1}
+\trianglelefteq A_{i}$ or $A_{i}/(A_{i-1})_{A_{i}} \in \mathfrak{F}$ for all
+$i=1, \ldots , n$. A formation $\frak {F}$ is said to be $K$-lattice provided
+in every finite group $G$ the set of all its $K$-$\mathfrak{F}$-subnormal
+subgroups forms a sublattice of the lattice of all subgroups of $G$.
+In this paper we consider some new applications of the theory of $K$-lattice
+formations. In particular, we prove the following
+Theorem A. Let $\mathfrak{F}$ be a hereditary $K$-lattice saturated formation
+containing all nilpotent groups.
+(i) If every $\mathfrak{F}$-critical subgroup $H$ of $G$ is
+$K$-$\mathfrak{F}$-subnormal in $G$ with $H/F(H)\in {\mathfrak{F}}$, then
+$G/F(G)\in {\mathfrak{F}}$.
+(ii) If every Schmidt subgroup of $G$ is $K$-$\mathfrak{F}$-subnormal in $G$,
+then $G/G_{\mathfrak{F}}$ is abelian.
+",0,0,1,0,0,0
+6324,Actions Speak Louder Than Goals: Valuing Player Actions in Soccer,"  Assessing the impact of the individual actions performed by soccer players
+during games is a crucial aspect of the player recruitment process.
+Unfortunately, most traditional metrics fall short in addressing this task as
+they either focus on rare events like shots and goals alone or fail to account
+for the context in which the actions occurred. This paper introduces a novel
+advanced soccer metric for valuing any type of individual player action on the
+pitch, be it with or without the ball. Our metric values each player action
+based on its impact on the game outcome while accounting for the circumstances
+under which the action happened. When applied to on-the-ball actions like
+passes, dribbles, and shots alone, our metric identifies Argentine forward
+Lionel Messi, French teenage star Kylian Mbappé, and Belgian winger Eden
+Hazard as the most effective players during the 2016/2017 season.
+",0,0,0,1,0,0
+6325,Partitioning the Outburst Energy of a Low Eddington Accretion Rate AGN at the Center of an Elliptical Galaxy: the Recent 12 Myr History of the Supermassive Black Hole in M87,"  M87, the active galaxy at the center of the Virgo cluster, is ideal for
+studying the interaction of a supermassive black hole (SMBH) with a hot,
+gas-rich environment. A deep Chandra observation of M87 exhibits an
+approximately circular shock front (13 kpc radius, in projection) driven by the
+expansion of the central cavity (filled by the SMBH with relativistic
+radio-emitting plasma) with projected radius $\sim$1.9 kpc. We combine
+constraints from X-ray and radio observations of M87 with a shock model to
+derive the properties of the outburst that created the 13 kpc shock. Principal
+constraints for the model are 1) the measured Mach number ($M$$\sim$1.2), 2)
+the radius of the 13 kpc shock, and 3) the observed size of the central
+cavity/bubble (the radio-bright cocoon) that serves as the piston to drive the
+shock. We find an outburst of $\sim$5$\times$$10^{57}$ ergs that began about 12
+Myr ago and lasted $\sim$2 Myr matches all the constraints. In this model,
+$\sim$22% of the energy is carried by the shock as it expands. The remaining
+$\sim$80% of the outburst energy is available to heat the core gas. More than
+half the total outburst energy initially goes into the enthalpy of the central
+bubble, the radio cocoon. As the buoyant bubble rises, much of its energy is
+transferred to the ambient thermal gas. For an outburst repetition rate of
+about 12 Myrs (the age of the outburst), 80% of the outburst energy is
+sufficient to balance the radiative cooling.
+",0,1,0,0,0,0
+6326,"On the missing link between pressure drop, viscous dissipation, and the turbulent energy spectrum","  After decades of experimental, theoretical, and numerical research in fluid
+dynamics, many aspects of turbulence remain poorly understood. The main reason
+for this is often attributed to the multiscale nature of turbulent flows, which
+poses a formidable challenge. There are, however, properties of these flows
+whose roles and inter-connections have never been clarified fully. In this
+article, we present a new connection between the pressure drop, viscous
+dissipation, and the turbulent energy spectrum, which, to the best of our
+knowledge, has never been established prior to our work. We use this finding to
+show analytically that viscous dissipation in laminar pipe flows cannot
+increase the temperature of the fluid, and to also reproduce qualitatively
+Nikuradse's experimental results involving pressure drops in turbulent flows in
+rough pipes.
+",0,1,0,0,0,0
+6327,Discrete Local Induction Equation,"  The local induction equation, or the binormal flow on space curves is a
+well-known model of deformation of space curves as it describes the dynamics of
+vortex filaments, and the complex curvature is governed by the nonlinear
+Schrödinger equation. In this paper, we present its discrete analogue,
+namely, a model of deformation of discrete space curves by the discrete
+nonlinear Schrödinger equation. We also present explicit formulas for both
+smooth and discrete curves in terms of tau functions of the two-component KP
+hierarchy.
+",0,1,1,0,0,0
+6328,A sharp lower bound for the lifespan of small solutions to the Schrödinger equation with a subcritical power nonlinearity,"  Let $T_{\epsilon}$ be the lifespan for the solution to the Schrödinger
+equation on $\mathbb{R}^d$ with a power nonlinearity $\lambda |u|^{2\theta/d}u$
+($\lambda \in \mathbb{C}$, $0<\theta<1$) and the initial data in the form
+$\epsilon \varphi(x)$. We provide a sharp lower bound estimate for
+$T_{\epsilon}$ as $\epsilon \to +0$ which can be written explicitly by
+$\lambda$, $d$, $\theta$, $\varphi$ and $\epsilon$. This is an improvement of
+the previous result by H.Sasaki [Adv. Diff. Eq. 14 (2009), 1021--1039].
+",0,0,1,0,0,0
+6329,State Space Reduction for Reachability Graph of CSM Automata,"  Classical CTL temporal logics are built over systems with interleaving model
+concurrency. Many attempts are made to fight a state space explosion problem
+(for instance, compositional model checking). There are some methods of
+reduction of a state space based on independence of actions. However, in CSM
+model, which is based on coincidences rather than on interleaving, independence
+of actions cannot be defined. Therefore a state space reduction basing on
+identical temporal consequences rather than on independence of action is
+proposed. The new reduction is not as good as for interleaving systems, because
+all successors of a state (in depth of two levels) must be obtained before a
+reduction may be applied. This leads to reduction of space required for
+representation of a state space, but not in time of state space construction.
+Yet much savings may occur in regular state spaces for CSM systems.
+",1,0,0,0,0,0
+6330,Permission Inference for Array Programs,"  Information about the memory locations accessed by a program is, for
+instance, required for program parallelisation and program verification.
+Existing inference techniques for this information provide only partial
+solutions for the important class of array-manipulating programs. In this
+paper, we present a static analysis that infers the memory footprint of an
+array program in terms of permission pre- and postconditions as used, for
+example, in separation logic. This formulation allows our analysis to handle
+concurrent programs and produces specifications that can be used by
+verification tools. Our analysis expresses the permissions required by a loop
+via maximum expressions over the individual loop iterations. These maximum
+expressions are then solved by a novel maximum elimination algorithm, in the
+spirit of quantifier elimination. Our approach is sound and is implemented; an
+evaluation on existing benchmarks for memory safety of array programs
+demonstrates accurate results, even for programs with complex access patterns
+and nested loops.
+",1,0,0,0,0,0
+6331,Generating Query Suggestions to Support Task-Based Search,"  We address the problem of generating query suggestions to support users in
+completing their underlying tasks (which motivated them to search in the first
+place). Given an initial query, these query suggestions should provide a
+coverage of possible subtasks the user might be looking for. We propose a
+probabilistic modeling framework that obtains keyphrases from multiple sources
+and generates query suggestions from these keyphrases. Using the test suites of
+the TREC Tasks track, we evaluate and analyze each component of our model.
+",1,0,0,0,0,0
+6332,Application of Spin-Exchange Relaxation-Free Magnetometry to the Cosmic Axion Spin Precession Experiment,"  The Cosmic Axion Spin Precession Experiment (CASPEr) seeks to measure
+oscillating torques on nuclear spins caused by axion or axion-like-particle
+(ALP) dark matter via nuclear magnetic resonance (NMR) techniques. A sample
+spin-polarized along a leading magnetic field experiences a resonance when the
+Larmor frequency matches the axion/ALP Compton frequency, generating precessing
+transverse nuclear magnetization. Here we demonstrate a Spin-Exchange
+Relaxation-Free (SERF) magnetometer with sensitivity $\approx 1~{\rm
+fT/\sqrt{Hz}}$ and an effective sensing volume of 0.1 $\rm{cm^3}$ that may be
+useful for NMR detection in CASPEr. A potential drawback of
+SERF-magnetometer-based NMR detection is the SERF's limited dynamic range. Use
+of a magnetic flux transformer to suppress the leading magnetic field is
+considered as a potential method to expand the SERF's dynamic range in order to
+probe higher axion/ALP Compton frequencies.
+",0,1,0,0,0,0
+6333,Symmetry and the Geometric Phase in Ultracold Hydrogen-Exchange Reactions,"  Quantum reactive scattering calculations are reported for the ultracold
+hydrogen-exchange reaction and its non-reactive atom-exchange isotopic
+counterparts, proceeding from excited rotational states. It is shown that while
+the geometric phase (GP) does not necessarily control the reaction to all final
+states one can always find final states where it does. For the isotopic
+counterpart reactions these states can be used to make a measurement of the GP
+effect by separately measuring the even and odd symmetry contributions, which
+experimentally requires nuclear-spin final-state resolution. This follows from
+symmetry considerations that make the even and odd identical-particle exchange
+symmetry wavefunctions which include the GP locally equivalent to the opposite
+symmetry wavefunctions which do not. This equivalence reflects the important
+role discrete symmetries play in ultracold chemistry generally and highlights
+the key role ultracold reactions can play in understanding fundamental aspects
+of chemical reactivity.
+",0,1,0,0,0,0
+6334,Characterization and Photometric Performance of the Hyper Suprime-Cam Software Pipeline,"  The Subaru Strategic Program (SSP) is an ambitious multi-band survey using
+the Hyper Suprime-Cam (HSC) on the Subaru telescope. The Wide layer of the SSP
+is both wide and deep, reaching a detection limit of i~26.0 mag. At these
+depths, it is challenging to achieve accurate, unbiased, and consistent
+photometry across all five bands. The HSC data are reduced using a pipeline
+that builds on the prototype pipeline for the Large Synoptic Survey Telescope.
+We have developed a Python-based, flexible framework to inject synthetic
+galaxies into real HSC images called SynPipe. Here we explain the design and
+implementation of SynPipe and generate a sample of synthetic galaxies to
+examine the photometric performance of the HSC pipeline. For stars, we achieve
+1% photometric precision at i~19.0 mag and 6% precision at i~25.0 in the
+i-band. For synthetic galaxies with single-Sersic profiles, forced CModel
+photometry achieves 13% photometric precision at i~20.0 mag and 18% precision
+at i~25.0 in the i-band. We show that both forced PSF and CModel photometry
+yield unbiased color estimates that are robust to seeing conditions. We
+identify several caveats that apply to the version of HSC pipeline used for the
+first public HSC data release (DR1) that need to be taking into consideration.
+First, the degree to which an object is blended with other objects impacts the
+overall photometric performance. This is especially true for point sources.
+Highly blended objects tend to have larger photometric uncertainties,
+systematically underestimated fluxes and slightly biased colors. Second, >20%
+of stars at 22.5< i < 25.0 mag can be misclassified as extended objects. Third,
+the current CModel algorithm tends to strongly underestimate the half-light
+radius and ellipticity of galaxy with i>21.5 mag.
+",0,1,0,0,0,0
+6335,Information Geometry Approach to Parameter Estimation in Hidden Markov Models,"  We consider the estimation of hidden Markovian process by using information
+geometry with respect to transition matrices. We consider the case when we use
+only the histogram of $k$-memory data. Firstly, we focus on a partial
+observation model with Markovian process and we show that the asymptotic
+estimation error of this model is given as the inverse of projective Fisher
+information of transition matrices. Next, we apply this result to the
+estimation of hidden Markovian process. We carefully discuss the equivalence
+problem for hidden Markovian process on the tangent space. Then, we propose a
+novel method to estimate hidden Markovian process.
+",0,0,1,1,0,0
+6336,Parallel transport in principal 2-bundles,"  A nice differential-geometric framework for (non-abelian) higher gauge theory
+is provided by principal 2-bundles, i.e. categorified principal bundles. Their
+total spaces are Lie groupoids, local trivializations are kinds of Morita
+equivalences, and connections are Lie-2-algebra-valued 1-forms. In this
+article, we construct explicitly the parallel transport of a connection on a
+principal 2-bundle. Parallel transport along a path is a Morita equivalence
+between the fibres over the end points, and parallel transport along a surface
+is an intertwiner between Morita equivalences. We prove that our constructions
+fit into the general axiomatic framework for categorified parallel transport
+and surface holonomy.
+",0,0,1,0,0,0
+6337,Gotta Learn Fast: A New Benchmark for Generalization in RL,"  In this report, we present a new reinforcement learning (RL) benchmark based
+on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended
+to measure the performance of transfer learning and few-shot learning
+algorithms in the RL domain. We also present and evaluate some baseline
+algorithms on the new benchmark.
+",0,0,0,1,0,0
+6338,Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit,"  Observations of astrophysical objects such as galaxies are limited by various
+sources of random and systematic noise from the sky background, the optical
+system of the telescope and the detector used to record the data. Conventional
+deconvolution techniques are limited in their ability to recover features in
+imaging data by the Shannon-Nyquist sampling theorem. Here we train a
+generative adversarial network (GAN) on a sample of $4,550$ images of nearby
+galaxies at $0.01<z<0.02$ from the Sloan Digital Sky Survey and conduct
+$10\times$ cross validation to evaluate the results. We present a method using
+a GAN trained on galaxy images that can recover features from artificially
+degraded images with worse seeing and higher noise than the original with a
+performance which far exceeds simple deconvolution. The ability to better
+recover detailed features such as galaxy morphology from low-signal-to-noise
+and low angular resolution imaging data significantly increases our ability to
+study existing data sets of astrophysical objects as well as future
+observations with observatories such as the Large Synoptic Sky Telescope (LSST)
+and the Hubble and James Webb space telescopes.
+",0,1,0,1,0,0
+6339,Trends in European flood risk over the past 150 years,"  Flood risk changes in time and is influenced by both natural and
+socio-economic trends and interactions. In Europe, previous studies of
+historical flood losses corrected for demographic and economic growth
+(""normalized"") have been limited in temporal and spatial extent, leading to an
+incomplete representation in trends of losses over time. In this study we
+utilize a gridded reconstruction of flood exposure in 37 European countries and
+a new database of damaging floods since 1870. Our results indicate that since
+1870 there has been an increase in annually inundated area and number of
+persons affected, contrasted by a substantial decrease in flood fatalities,
+after correcting for change in flood exposure. For more recent decades we also
+found a considerable decline in financial losses per year. We estimate,
+however, that there is large underreporting of smaller floods beyond most
+recent years, and show that underreporting has a substantial impact on observed
+trends.
+",0,0,0,1,0,0
+6340,Kinetic Trans-assembly of DNA Nanostructures,"  The central dogma of molecular biology is the principal framework for
+understanding how nucleic acid information is propagated and used by living
+systems to create complex biomolecules. Here, by integrating the structural and
+dynamic paradigms of DNA nanotechnology, we present a rationally designed
+synthetic platform which functions in an analogous manner to create complex DNA
+nanostructures. Starting from one type of DNA nanostructure, DNA strand
+displacement circuits were designed to interact and pass along the information
+encoded in the initial structure to mediate the self-assembly of a different
+type of structure, the final output structure depending on the type of circuit
+triggered. Using this concept of a DNA structure ""trans-assembling"" a different
+DNA structure through non-local strand displacement circuitry, four different
+schemes were implemented. Specifically, 1D ladder and 2D double-crossover (DX)
+lattices were designed to kinetically trigger DNA circuits to activate
+polymerization of either ring structures or another type of DX lattice under
+enzyme-free, isothermal conditions. In each scheme, the desired multilayer
+reaction pathway was activated, among multiple possible pathways, ultimately
+leading to the downstream self-assembly of the correct output structure.
+",0,0,0,0,1,0
+6341,Synthesis and analysis in total variation regularization,"  We generalize the bridge between analysis and synthesis estimators by Elad,
+Milanfar and Rubinstein (2007) to rank deficient cases. This is a starting
+point for the study of the connection between analysis and synthesis for total
+variation regularized estimators. In particular, the case of first order total
+variation regularized estimators over general graphs and their synthesis form
+are studied.
+We give a definition of the discrete graph derivative operator based on the
+notion of line graph and provide examples of the synthesis form of
+$k^{\text{th}}$ order total variation regularized estimators over a range of
+graphs.
+",0,0,1,1,0,0
+6342,"The Leray transform: factorization, dual $CR$ structures and model hypersurfaces in $\mathbb{C}\mathbb{P}^2$","  We compute the exact norms of the Leray transforms for a family
+$\mathcal{S}_{\beta}$ of unbounded hypersurfaces in two complex dimensions. The
+$\mathcal{S}_{\beta}$ generalize the Heisenberg group, and provide local
+projective approximations to any smooth, strongly $\mathbb{C}$-convex
+hypersurface $\mathcal{S}_{\beta}$ to two orders of tangency. This work is then
+examined in the context of projective dual $CR$-structures and the
+corresponding pair of canonical dual Hardy spaces associated to
+$\mathcal{S}_{\beta}$, leading to a universal description of the Leray
+transform and a factorization of the transform through orthogonal projection
+onto the conjugate dual Hardy space.
+",0,0,1,0,0,0
+6343,A Fast Quantum-safe Asymmetric Cryptosystem Using Extra Superincreasing Sequences,"  This paper gives the definitions of an extra superincreasing sequence and an
+anomalous subset sum, and proposes a fast quantum-safe asymmetric cryptosystem
+called JUOAN2. The new cryptosystem is based on an additive multivariate
+permutation problem (AMPP) and an anomalous subset sum problem (ASSP) which
+parallel a multivariate polynomial problem and a shortest vector problem
+respectively, and composed of a key generator, an encryption algorithm, and a
+decryption algorithm. The authors analyze the security of the new cryptosystem
+against the Shamir minima accumulation point attack and the LLL lattice basis
+reduction attack, and prove it to be semantically secure (namely IND-CPA) on
+the assumption that AMPP and ASSP have no subexponential time solutions.
+Particularly, the analysis shows that the new cryptosystem has the potential to
+be resistant to quantum computing attack, and is especially suitable to the
+secret communication between two mobile terminals in maneuvering field
+operations under any weather. At last, an example explaining the correctness of
+the new cryptosystem is given.
+",1,0,0,0,0,0
+6344,Knowledge Transfer for Melanoma Screening with Deep Learning,"  Knowledge transfer impacts the performance of deep learning -- the state of
+the art for image classification tasks, including automated melanoma screening.
+Deep learning's greed for large amounts of training data poses a challenge for
+medical tasks, which we can alleviate by recycling knowledge from models
+trained on different tasks, in a scheme called transfer learning. Although much
+of the best art on automated melanoma screening employs some form of transfer
+learning, a systematic evaluation was missing. Here we investigate the presence
+of transfer, from which task the transfer is sourced, and the application of
+fine tuning (i.e., retraining of the deep learning model after transfer). We
+also test the impact of picking deeper (and more expensive) models. Our results
+favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an
+AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated.
+",1,0,0,0,0,0
+6345,Large odd order character sums and improvements of the Pólya-Vinogradov inequality,"  For a primitive Dirichlet character $\chi$ modulo $q$, we define
+$M(\chi)=\max_{t } |\sum_{n \leq t} \chi(n)|$. In this paper, we study this
+quantity for characters of a fixed odd order $g\geq 3$. Our main result
+provides a further improvement of the classical Pólya-Vinogradov inequality
+in this case. More specifically, we show that for any such character $\chi$ we
+have $$M(\chi)\ll_{\varepsilon} \sqrt{q}(\log q)^{1-\delta_g}(\log\log
+q)^{-1/4+\varepsilon},$$ where $\delta_g := 1-\frac{g}{\pi}\sin(\pi/g)$. This
+improves upon the works of Granville and Soundararajan and of Goldmakher.
+Furthermore, assuming the Generalized Riemann hypothesis (GRH) we prove that $$
+M(\chi) \ll \sqrt{q} \left(\log_2 q\right)^{1-\delta_g} \left(\log_3
+q\right)^{-\frac{1}{4}}\left(\log_4 q\right)^{O(1)}, $$ where $\log_j$ is the
+$j$-th iterated logarithm. We also show unconditionally that this bound is best
+possible (up to a power of $\log_4 q$). One of the key ingredients in the proof
+of the upper bounds is a new Halász-type inequality for logarithmic mean
+values of completely multiplicative functions, which might be of independent
+interest.
+",0,0,1,0,0,0
+6346,Estimation under group actions: recovering orbits from invariants,"  Motivated by geometric problems in signal processing, computer vision, and
+structural biology, we study a class of orbit recovery problems where we
+observe very noisy copies of an unknown signal, each acted upon by a random
+element of some group (such as Z/p or SO(3)). The goal is to recover the orbit
+of the signal under the group action in the high-noise regime. This generalizes
+problems of interest such as multi-reference alignment (MRA) and the
+reconstruction problem in cryo-electron microscopy (cryo-EM). We obtain
+matching lower and upper bounds on the sample complexity of these problems in
+high generality, showing that the statistical difficulty is intricately
+determined by the invariant theory of the underlying symmetry group.
+In particular, we determine that for cryo-EM with noise variance $\sigma^2$
+and uniform viewing directions, the number of samples required scales as
+$\sigma^6$. We match this bound with a novel algorithm for ab initio
+reconstruction in cryo-EM, based on invariant features of degree at most 3. We
+further discuss how to recover multiple molecular structures from heterogeneous
+cryo-EM samples.
+",1,0,1,0,0,0
+6347,Crystal field excitations from $\mathrm{Yb^{3+}}$ ions at defective sites in highly stuffed $\rm Yb_2Ti_2O_7$,"  The pyrochlore magnet $\rm Yb_2Ti_2O_7$ has been proposed as a quantum spin
+ice candidate, a spin liquid state expected to display emergent quantum
+electrodynamics with gauge photons among its elementary excitations. However,
+$\rm Yb_2Ti_2O_7$'s ground state is known to be very sensitive to its precise
+stoichiometry. Powder samples, produced by solid state synthesis at relatively
+low temperatures, tend to be stoichiometric, while single crystals grown from
+the melt tend to display weak ""stuffing"" wherein $\mathrm{\sim 2\%}$ of the
+$\mathrm{Yb^{3+}}$, normally at the $A$ site of the $A_2B_2O_7$ pyrochlore
+structure, reside as well at the $B$ site. In such samples $\mathrm{Yb^{3+}}$
+ions should exist in defective environments at low levels, and be subjected to
+crystalline electric fields (CEFs) very different from those at the
+stoichiometric $A$ sites. New neutron scattering measurements of
+$\mathrm{Yb^{3+}}$ in four compositions of $\rm Yb_{2+x}Ti_{2-x}O_{7-y}$, show
+the spectroscopic signatures for these defective $\mathrm{Yb^{3+}}$ ions and
+explicitly demonstrate that the spin anisotropy of the $\mathrm{Yb^{3+}}$
+moment changes from XY-like for stoichiometric $\mathrm{Yb^{3+}}$, to
+Ising-like for ""stuffed"" $B$ site $\mathrm{Yb^{3+}}$, or for $A$ site
+$\mathrm{Yb^{3+}}$ in the presence of an oxygen vacancy.
+",0,1,0,0,0,0
+6348,HOUDINI: Lifelong Learning as Program Synthesis,"  We present a neurosymbolic framework for the lifelong learning of algorithmic
+tasks that mix perception and procedural reasoning. Reusing high-level concepts
+across domains and learning complex procedures are key challenges in lifelong
+learning. We show that a program synthesis approach that combines gradient
+descent with combinatorial search over programs can be a more effective
+response to these challenges than purely neural methods. Our framework, called
+HOUDINI, represents neural networks as strongly typed, differentiable
+functional programs that use symbolic higher-order combinators to compose a
+library of neural functions. Our learning algorithm consists of: (1) a symbolic
+program synthesizer that performs a type-directed search over parameterized
+programs, and decides on the library functions to reuse, and the architectures
+to combine them, while learning a sequence of tasks; and (2) a neural module
+that trains these programs using stochastic gradient descent. We evaluate
+HOUDINI on three benchmarks that combine perception with the algorithmic tasks
+of counting, summing, and shortest-path computation. Our experiments show that
+HOUDINI transfers high-level concepts more effectively than traditional
+transfer learning and progressive neural networks, and that the typed
+representation of networks significantly accelerates the search.
+",1,0,0,1,0,0
+6349,Detecting Adversarial Examples via Key-based Network,"  Though deep neural networks have achieved state-of-the-art performance in
+visual classification, recent studies have shown that they are all vulnerable
+to the attack of adversarial examples. Small and often imperceptible
+perturbations to the input images are sufficient to fool the most powerful deep
+neural networks. Various defense methods have been proposed to address this
+issue. However, they either require knowledge on the process of generating
+adversarial examples, or are not robust against new attacks specifically
+designed to penetrate the existing defense. In this work, we introduce
+key-based network, a new detection-based defense mechanism to distinguish
+adversarial examples from normal ones based on error correcting output codes,
+using the binary code vectors produced by multiple binary classifiers applied
+to randomly chosen label-sets as signatures to match normal images and reject
+adversarial examples. In contrast to existing defense methods, the proposed
+method does not require knowledge of the process for generating adversarial
+examples and can be applied to defend against different types of attacks. For
+the practical black-box and gray-box scenarios, where the attacker does not
+know the encoding scheme, we show empirically that key-based network can
+effectively detect adversarial examples generated by several state-of-the-art
+attacks.
+",0,0,0,1,0,0
+6350,Guessing Attacks on Distributed-Storage Systems,"  The secrecy of a distributed-storage system for passwords is studied. The
+encoder, Alice, observes a length-n password and describes it using two hints,
+which she stores in different locations. The legitimate receiver, Bob, observes
+both hints. In one scenario the requirement is that the expected number of
+guesses it takes Bob to guess the password approach one as n tends to infinity,
+and in the other that the expected size of the shortest list that Bob must form
+to guarantee that it contain the password approach one. The eavesdropper, Eve,
+sees only one of the hints. Assuming that Alice cannot control which hints Eve
+observes, the largest normalized (by n) exponent that can be guaranteed for the
+expected number of guesses it takes Eve to guess the password is characterized
+for each scenario. Key to the proof are new results on Arikan's guessing and
+Bunte and Lapidoth's task-encoding problem; in particular, the paper
+establishes a close relation between the two problems. A rate-distortion
+version of the model is also discussed, as is a generalization that allows for
+Alice to produce {\delta} (not necessarily two) hints, for Bob to observe {\nu}
+(not necessarily two) of the hints, and for Eve to observe {\eta} (not
+necessarily one) of the hints. The generalized model is robust against {\delta}
+- {\nu} disk failures.
+",1,0,1,0,0,0
+6351,Numerical analysis of nonlocal fracture models in Hölder space,"  In this work, we calculate the convergence rate of the finite difference
+approximation for a class of nonlocal fracture models. We consider two point
+force interactions characterized by a double well potential. We show the
+existence of a evolving displacement field in Hölder space with Hölder
+exponent $\gamma \in (0,1]$. The rate of convergence of the finite difference
+approximation depends on the factor $C_s h^\gamma/\epsilon^2$ where $\epsilon$
+gives the length scale of nonlocal interaction, $h$ is the discretization
+length and $C_s$ is the maximum of Hölder norm of the solution and its second
+derivatives during the evolution. It is shown that the rate of convergence
+holds for both the forward Euler scheme as well as general single step implicit
+schemes. A stability result is established for the semi-discrete approximation.
+The Hölder continuous evolutions are seen to converge to a brittle fracture
+evolution in the limit of vanishing nonlocality.
+",0,0,1,0,0,0
+6352,Iterative Collaborative Filtering for Sparse Matrix Estimation,"  The sparse matrix estimation problem consists of estimating the distribution
+of an $n\times n$ matrix $Y$, from a sparsely observed single instance of this
+matrix where the entries of $Y$ are independent random variables. This captures
+a wide array of problems; special instances include matrix completion in the
+context of recommendation systems, graphon estimation, and community detection
+in (mixed membership) stochastic block models. Inspired by classical
+collaborative filtering for recommendation systems, we propose a novel
+iterative, collaborative filtering-style algorithm for matrix estimation in
+this generic setting. We show that the mean squared error (MSE) of our
+estimator converges to $0$ at the rate of $O(d^2 (pn)^{-2/5})$ as long as
+$\omega(d^5 n)$ random entries from a total of $n^2$ entries of $Y$ are
+observed (uniformly sampled), $\mathbb{E}[Y]$ has rank $d$, and the entries of
+$Y$ have bounded support. The maximum squared error across all entries
+converges to $0$ with high probability as long as we observe a little more,
+$\Omega(d^5 n \ln^2(n))$ entries. Our results are the best known sample
+complexity results in this generality.
+",0,0,1,1,0,0
+6353,Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering,"  In this short paper, we formulate parameter estimation for finite mixture
+models in the context of discrete optimal transportation with convex
+regularization. The proposed framework unifies hard and soft clustering methods
+for general mixture models. It also generalizes the celebrated
+$k$\nobreakdash-means and expectation-maximization algorithms in relation to
+associated Bregman divergences when applied to exponential family mixture
+models.
+",1,0,0,1,0,0
+6354,Robust parameter determination in epidemic models with analytical descriptions of uncertainties,"  Compartmental equations are primary tools in disease spreading studies. Their
+predictions are accurate for large populations but disagree with empirical and
+simulated data for finite populations, where uncertainties become a relevant
+factor. Starting from the agent-based approach, we investigate the role of
+uncertainties and autocorrelation functions in SIS epidemic model, including
+their relationship with epidemiological variables. We find new differential
+equations that take uncertainties into account. The findings provide improved
+predictions to the SIS model and it can offer new insights for emerging
+diseases.
+",0,0,0,0,1,0
+6355,Direct observation of domain wall surface tension by deflating or inflating a magnetic bubble,"  The surface energy of a magnetic Domain Wall (DW) strongly affects its static
+and dynamic behaviours. However, this effect was seldom directly observed and
+many related phenomena have not been well understood. Moreover, a reliable
+method to quantify the DW surface energy is still missing. Here, we report a
+series of experiments in which the DW surface energy becomes a dominant
+parameter. We observed that a semicircular magnetic domain bubble could
+spontaneously collapse under the Laplace pressure induced by DW surface energy.
+We further demonstrated that the surface energy could lead to a geometrically
+induced pinning when the DW propagates in a Hall cross or from a nanowire into
+a nucleation pad. Based on these observations, we developed two methods to
+quantify the DW surface energy, which could be very helpful to estimate
+intrinsic parameters such as Dzyaloshinskii-Moriya Interactions (DMI) or
+exchange stiffness in magnetic ultra-thin films.
+",0,1,0,0,0,0
+6356,Unified Halo-Independent Formalism From Convex Hulls for Direct Dark Matter Searches,"  Using the Fenchel-Eggleston theorem for convex hulls (an extension of the
+Caratheodory theorem), we prove that any likelihood can be maximized by either
+a dark matter 1- speed distribution $F(v)$ in Earth's frame or 2- Galactic
+velocity distribution $f^{\rm gal}(\vec{u})$, consisting of a sum of delta
+functions. The former case applies only to time-averaged rate measurements and
+the maximum number of delta functions is $({\mathcal N}-1)$, where ${\mathcal
+N}$ is the total number of data entries. The second case applies to any
+harmonic expansion coefficient of the time-dependent rate and the maximum
+number of terms is ${\mathcal N}$. Using time-averaged rates, the
+aforementioned form of $F(v)$ results in a piecewise constant unmodulated halo
+function $\tilde\eta^0_{BF}(v_{\rm min})$ (which is an integral of the speed
+distribution) with at most $({\mathcal N}-1)$ downward steps. The authors had
+previously proven this result for likelihoods comprised of at least one
+extended likelihood, and found the best-fit halo function to be unique. This
+uniqueness, however, cannot be guaranteed in the more general analysis applied
+to arbitrary likelihoods. Thus we introduce a method for determining whether
+there exists a unique best-fit halo function, and provide a procedure for
+constructing either a pointwise confidence band, if the best-fit halo function
+is unique, or a degeneracy band, if it is not. Using measurements of modulation
+amplitudes, the aforementioned form of $f^{\rm gal}(\vec{u})$, which is a sum
+of Galactic streams, yields a periodic time-dependent halo function
+$\tilde\eta_{BF}(v_{\rm min}, t)$ which at any fixed time is a piecewise
+constant function of $v_{\rm min}$ with at most ${\mathcal N}$ downward steps.
+In this case, we explain how to construct pointwise confidence and degeneracy
+bands from the time-averaged halo function. Finally, we show that requiring an
+isotropic ...
+",0,1,0,0,0,0
+6357,Finite size effects for spiking neural networks with spatially dependent coupling,"  We study finite-size fluctuations in a network of spiking deterministic
+neurons coupled with non-uniform synaptic coupling. We generalize a previously
+developed theory of finite size effects for uniform globally coupled neurons.
+In the uniform case, mean field theory is well defined by averaging over the
+network as the number of neurons in the network goes to infinity. However, for
+nonuniform coupling it is no longer possible to average over the entire network
+if we are interested in fluctuations at a particular location within the
+network. We show that if the coupling function approaches a continuous function
+in the infinite system size limit then an average over a local neighborhood can
+be defined such that mean field theory is well defined for a spatially
+dependent field. We then derive a perturbation expansion in the inverse system
+size around the mean field limit for the covariance of the input to a neuron
+(synaptic drive) and firing rate fluctuations due to dynamical deterministic
+finite-size effects.
+",0,0,0,0,1,0
+6358,Shape and fission instabilities of ferrofluids in non-uniform magnetic fields,"  We study static distributions of ferrofluid submitted to non-uniform magnetic
+fields. We show how the normal-field instability is modified in the presence of
+a weak magnetic field gradient. Then we consider a ferrofluid droplet and show
+how the gradient affects its shape. A rich phase transitions phenomenology is
+found. We also investigate the creation of droplets by successive splits when a
+magnet is vertically approached from below and derive theoretical expressions
+which are solved numerically to obtain the number of droplets and their aspect
+ratio as function of the field configuration. A quantitative comparison is
+performed with previous experimental results, as well as with our own
+experiments, and yields good agreement with the theoretical modeling.
+",0,1,0,0,0,0
+6359,Encrypted accelerated least squares regression,"  Information that is stored in an encrypted format is, by definition, usually
+not amenable to statistical analysis or machine learning methods. In this paper
+we present detailed analysis of coordinate and accelerated gradient descent
+algorithms which are capable of fitting least squares and penalised ridge
+regression models, using data encrypted under a fully homomorphic encryption
+scheme. Gradient descent is shown to dominate in terms of encrypted
+computational speed, and theoretical results are proven to give parameter
+bounds which ensure correctness of decryption. The characteristics of encrypted
+computation are empirically shown to favour a non-standard acceleration
+technique. This demonstrates the possibility of approximating conventional
+statistical regression methods using encrypted data without compromising
+privacy.
+",1,0,0,1,0,0
+6360,Unified Model of Chaotic Inflation and Dynamical Supersymmetry Breaking,"  The large hierarchy between the Planck scale and the weak scale can be
+explained by the dynamical breaking of supersymmetry in strongly coupled gauge
+theories. Similarly, the hierarchy between the Planck scale and the energy
+scale of inflation may also originate from strong dynamics, which dynamically
+generate the inflaton potential. We present a model of the hidden sector which
+unifies these two ideas, i.e., in which the scales of inflation and
+supersymmetry breaking are provided by the dynamics of the same gauge group.
+The resultant inflation model is chaotic inflation with a fractional power-law
+potential in accord with the upper bound on the tensor-to-scalar ratio. The
+supersymmetry breaking scale can be much smaller than the inflation scale, so
+that the solution to the large hierarchy problem of the weak scale remains
+intact. As an intrinsic feature of our model, we find that the sgoldstino,
+which might disturb the inflationary dynamics, is automatically stabilized
+during inflation by dynamically generated corrections in the strongly coupled
+sector. This renders our model a field-theoretical realization of what is
+sometimes referred to as sgoldstino-less inflation.
+",0,1,0,0,0,0
+6361,Tuplemax Loss for Language Identification,"  In many scenarios of a language identification task, the user will specify a
+small set of languages which he/she can speak instead of a large set of all
+possible languages. We want to model such prior knowledge into the way we train
+our neural networks, by replacing the commonly used softmax loss function with
+a novel loss function named tuplemax loss. As a matter of fact, a typical
+language identification system launched in North America has about 95% users
+who could speak no more than two languages. Using the tuplemax loss, our system
+achieved a 2.33% error rate, which is a relative 39.4% improvement over the
+3.85% error rate of standard softmax loss method.
+",1,0,0,0,0,0
+6362,Sparse Data Driven Mesh Deformation,"  Example-based mesh deformation methods are powerful tools for realistic shape
+editing. However, existing techniques typically combine all the example
+deformation modes, which can lead to overfitting, i.e. using a overly
+complicated model to explain the user-specified deformation. This leads to
+implausible or unstable deformation results, including unexpected global
+changes outside the region of interest. To address this fundamental limitation,
+we propose a sparse blending method that automatically selects a smaller number
+of deformation modes to compactly describe the desired deformation. This along
+with a suitably chosen deformation basis including spatially localized
+deformation modes leads to significant advantages, including more meaningful,
+reliable, and efficient deformations because fewer and localized deformation
+modes are applied. To cope with large rotations, we develop a simple but
+effective representation based on polar decomposition of deformation gradients,
+which resolves the ambiguity of large global rotations using an
+as-consistent-as-possible global optimization. This simple representation has a
+closed form solution for derivatives, making it efficient for sparse localized
+representation and thus ensuring interactive performance. Experimental results
+show that our method outperforms state-of-the-art data-driven mesh deformation
+methods, for both quality of results and efficiency.
+",1,0,0,0,0,0
+6363,Short Presburger arithmetic is hard,"  We study the computational complexity of short sentences in Presburger
+arithmetic (Short-PA). Here by ""short"" we mean sentences with a bounded number
+of variables, quantifiers, inequalities and Boolean operations; the input
+consists only of the integer coefficients involved in the linear inequalities.
+We prove that satisfiability of Short-PA sentences with $m+2$ alternating
+quantifiers is $\Sigma_{P}^m$-complete or $\Pi_{P}^m$-complete, when the first
+quantifier is $\exists$ or $\forall$, respectively. Counting versions and
+restricted systems are also analyzed. Further application are given to hardness
+of two natural problems in Integer Optimizations.
+",1,0,1,0,0,0
+6364,The Bias of the Log Power Spectrum for Discrete Surveys,"  A primary goal of galaxy surveys is to tighten constraints on cosmological
+parameters, and the power spectrum $P(k)$ is the standard means of doing so.
+However, at translinear scales $P(k)$ is blind to much of these surveys'
+information---information which the log density power spectrum recovers. For
+discrete fields (such as the galaxy density), $A^*$ denotes the statistic
+analogous to the log density: $A^*$ is a ""sufficient statistic"" in that its
+power spectrum (and mean) capture virtually all of a discrete survey's
+information. However, the power spectrum of $A^*$ is biased with respect to the
+corresponding log spectrum for continuous fields, and to use $P_{A^*}(k)$ to
+constrain the values of cosmological parameters, we require some means of
+predicting this bias. Here we present a prescription for doing so; for
+Euclid-like surveys (with cubical cells 16$h^{-1}$ Mpc across) our bias
+prescription's error is less than 3 per cent. This prediction will facilitate
+optimal utilization of the information in future galaxy surveys.
+",0,1,0,0,0,0
+6365,Consistent nonparametric change point detection combining CUSUM and marked empirical processes,"  A weakly dependent time series regression model with multivariate covariates
+and univariate observations is considered, for which we develop a procedure to
+detect whether the nonparametric conditional mean function is stable in time
+against change point alternatives. Our proposal is based on a modified CUSUM
+type test procedure, which uses a sequential marked empirical process of
+residuals. We show weak convergence of the considered process to a centered
+Gaussian process under the null hypothesis of no change in the mean function
+and a stationarity assumption. This requires some sophisticated arguments for
+sequential empirical processes of weakly dependent variables. As a consequence
+we obtain convergence of Kolmogorov-Smirnov and Cramér-von Mises type test
+statistics. The proposed procedure acquires a very simple limiting distribution
+and nice consistency properties, features from which related tests are lacking.
+We moreover suggest a bootstrap version of the procedure and discuss its
+applicability in the case of unstable variances.
+",0,0,1,1,0,0
+6366,Nonlinear electric field effect on perpendicular magnetic anisotropy in Fe/MgO interfaces,"  The electric field effect on magnetic anisotropy was studied in an ultrathin
+Fe(001) monocrystalline layer sandwiched between Cr buffer and MgO tunnel
+barrier layers, mainly through post-annealing temperature and measurement
+temperature dependences. A large coefficient of the electric field effect of
+more than 200 fJ/Vm was observed in the negative range of electric field, as
+well as an areal energy density of perpendicular magnetic anisotropy (PMA) of
+around 600 uJ/m2. More interestingly, nonlinear behavior, giving rise to a
+local minimum around +100 mV/nm, was observed in the electric field dependence
+of magnetic anisotropy, being independent of the post-annealing and measurement
+temperatures. The insensitivity to both the interface conditions and the
+temperature of the system suggests that the nonlinear behavior is attributed to
+an intrinsic origin such as an inherent electronic structure in the Fe/MgO
+interface. The present study can contribute to the progress in theoretical
+studies, such as ab initio calculations, on the mechanism of the electric field
+effect on PMA.
+",0,1,0,0,0,0
+6367,Local Symmetry and Global Structure in Adaptive Voter Models,"  ""Coevolving"" or ""adaptive"" voter models (AVMs) are natural systems for
+modeling the emergence of mesoscopic structure from local networked processes
+driven by conflict and homophily. Because of this, many methods for
+approximating the long-run behavior of AVMs have been proposed over the last
+decade. However, most such methods are either restricted in scope, expensive in
+computation, or inaccurate in predicting important statistics. In this work, we
+develop a novel, second-order moment closure approximation method for studying
+the equilibrium mesoscopic structure of AVMs and apply it to binary-state
+rewire-to-random and rewire-to-same model variants with random state-switching.
+This framework exploits an asymmetry in voting events that enables us to derive
+analytic approximations for the fast-timescale dynamics. The resulting
+numerical approximations enable the computation of key properties of the model
+behavior, such as the location of the fragmentation transition and the
+equilibrium active edge density, across the entire range of state densities.
+Numerically, they are nearly exact for the rewire-to-random model, and
+competitive with other current approaches for the rewire-to-same model. We
+conclude with suggestions for model refinement and extensions to more complex
+models.
+",1,0,0,0,0,0
+6368,A Weighted Model Confidence Set: Applications to Local and Mixture Model Confidence Sets,"  This article provides a weighted model confidence set, whenever underling
+model has been misspecified and some part of support of random variable $X$
+conveys some important information about underling true model. Application of
+such weighted model confidence set for local and mixture model confidence sets
+have been given. Two simulation studies have been conducted to show practical
+application of our findings.
+",0,0,0,1,0,0
+6369,"Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico","  Mapping the spatial distribution of poverty in developing countries remains
+an important and costly challenge. These ""poverty maps"" are key inputs for
+poverty targeting, public goods provision, political accountability, and impact
+evaluation, that are all the more important given the geographic dispersion of
+the remaining bottom billion severely poor individuals. In this paper we train
+Convolutional Neural Networks (CNNs) to estimate poverty directly from high and
+medium resolution satellite images. We use both Planet and Digital Globe
+imagery with spatial resolutions of 3-5 sq. m. and 50 sq. cm. respectively,
+covering all 2 million sq. km. of Mexico. Benchmark poverty estimates come from
+the 2014 MCS-ENIGH combined with the 2015 Intercensus and are used to estimate
+poverty rates for 2,456 Mexican municipalities. CNNs are trained using the 896
+municipalities in the 2014 MCS-ENIGH. We experiment with several architectures
+(GoogleNet, VGG) and use GoogleNet as a final architecture where weights are
+fine-tuned from ImageNet. We find that 1) the best models, which incorporate
+satellite-estimated land use as a predictor, explain approximately 57% of the
+variation in poverty in a validation sample of 10 percent of MCS-ENIGH
+municipalities; 2) Across all MCS-ENIGH municipalities explanatory power
+reduces to 44% in a CNN prediction and landcover model; 3) Predicted poverty
+from the CNN predictions alone explains 47% of the variation in poverty in the
+validation sample, and 37% over all MCS-ENIGH municipalities; 4) In urban areas
+we see slight improvements from using Digital Globe versus Planet imagery,
+which explain 61% and 54% of poverty variation respectively. We conclude that
+CNNs can be trained end-to-end on satellite imagery to estimate poverty,
+although there is much work to be done to understand how the training process
+influences out of sample validation.
+",1,0,0,1,0,0
+6370,Network of sensitive magnetometers for urban studies,"  The magnetic signature of an urban environment is investigated using a
+geographically distributed network of fluxgate magnetometers deployed in and
+around Berkeley, California. The system hardware and software are described and
+results from initial operation of the network are reported. The sensors sample
+the vector magnetic field with a 4 kHz resolution and are sensitive to
+fluctuations below 0.1 $\textrm{nT}/\sqrt{\textrm{Hz}}$. Data from separate
+stations are synchronized to around $\pm100$ $\mu{s}$ using GPS and computer
+system clocks. Data from all sensors are automatically uploaded to a central
+server. Anomalous events, such as lightning strikes, have been observed. A
+wavelet analysis is used to study observations over a wide range of temporal
+scales up to daily variations that show strong differences between weekend and
+weekdays. The Bay Area Rapid Transit (BART) is identified as the most dominant
+signal from these observations and a superposed epoch analysis is used to study
+and extract the BART signal. Initial results of the correlation between sensors
+are also presented.
+",0,1,0,0,0,0
+6371,Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization,"  The cooperative hierarchical structure is a common and significant data
+structure observed in, or adopted by, many research areas, such as: text mining
+(author-paper-word) and multi-label classification (label-instance-feature).
+Renowned Bayesian approaches for cooperative hierarchical structure modeling
+are mostly based on topic models. However, these approaches suffer from a
+serious issue in that the number of hidden topics/factors needs to be fixed in
+advance and an inappropriate number may lead to overfitting or underfitting.
+One elegant way to resolve this issue is Bayesian nonparametric learning, but
+existing work in this area still cannot be applied to cooperative hierarchical
+structure modeling.
+In this paper, we propose a cooperative hierarchical Dirichlet process (CHDP)
+to fill this gap. Each node in a cooperative hierarchical structure is assigned
+a Dirichlet process to model its weights on the infinite hidden factors/topics.
+Together with measure inheritance from hierarchical Dirichlet process, two
+kinds of measure cooperation, i.e., superposition and maximization, are defined
+to capture the many-to-many relationships in the cooperative hierarchical
+structure. Furthermore, two constructive representations for CHDP, i.e.,
+stick-breaking and international restaurant process, are designed to facilitate
+the model inference. Experiments on synthetic and real-world data with
+cooperative hierarchical structures demonstrate the properties and the ability
+of CHDP for cooperative hierarchical structure modeling and its potential for
+practical application scenarios.
+",1,0,0,1,0,0
+6372,The extended law of star formation: the combined role of gas and stars,"  We present a model for the origin of the extended law of star formation in
+which the surface density of star formation ($\Sigma_{\rm SFR}$) depends not
+only on the local surface density of the gas ($\Sigma_{g}$), but also on the
+stellar surface density ($\Sigma_{*}$), the velocity dispersion of the stars,
+and on the scaling laws of turbulence in the gas. We compare our model with the
+spiral, face-on galaxy NGC 628 and show that the dependence of the star
+formation rate on the entire set of physical quantities for both gas and stars
+can help explain both the observed general trends in the
+$\Sigma_{g}-\Sigma_{\rm SFR}$ and $\Sigma_{*}-\Sigma_{\rm SFR}$ relations, but
+also, and equally important, the scatter in these relations at any value of
+$\Sigma_{g}$ and $\Sigma_{*}$. Our results point out to the crucial role played
+by existing stars along with the gaseous component in setting the conditions
+for large scale gravitational instabilities and star formation in galactic
+disks.
+",0,1,0,0,0,0
+6373,Local and global similarity of holomorphic matrices,"  R. Guralnick (Linear Algebra Appl. 99, 85-96, 1988) proved that two
+holomorphic matrices on a noncompact connected Riemann surface, which are
+locally holomorphically similar, are globally holomorphically similar. We
+generalize this to (possibly, non-smooth) one-dimensional Stein spaces. For
+Stein spaces of arbitrary dimension, we prove that global $\mathcal C^\infty$
+similarity implies global holomorphic similarity, whereas global continuous
+similarity is not sufficient.
+",0,0,1,0,0,0
+6374,WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models,"  Learning sparse linear models with two-way interactions is desirable in many
+application domains such as genomics. l1-regularised linear models are popular
+to estimate sparse models, yet standard implementations fail to address
+specifically the quadratic explosion of candidate two-way interactions in high
+dimensions, and typically do not scale to genetic data with hundreds of
+thousands of features. Here we present WHInter, a working set algorithm to
+solve large l1-regularised problems with two-way interactions for binary design
+matrices. The novelty of WHInter stems from a new bound to efficiently identify
+working sets while avoiding to scan all features, and on fast computations
+inspired from solutions to the maximum inner product search problem. We apply
+WHInter to simulated and real genetic data and show that it is more scalable
+and two orders of magnitude faster than the state of the art.
+",0,0,0,1,1,0
+6375,On the Limitation of Convolutional Neural Networks in Recognizing Negative Images,"  Convolutional Neural Networks (CNNs) have achieved state-of-the-art
+performance on a variety of computer vision tasks, particularly visual
+classification problems, where new algorithms reported to achieve or even
+surpass the human performance. In this paper, we examine whether CNNs are
+capable of learning the semantics of training data. To this end, we evaluate
+CNNs on negative images, since they share the same structure and semantics as
+regular images and humans can classify them correctly. Our experimental results
+indicate that when training on regular images and testing on negative images,
+the model accuracy is significantly lower than when it is tested on regular
+images. This leads us to the conjecture that current training methods do not
+effectively train models to generalize the concepts. We then introduce the
+notion of semantic adversarial examples - transformed inputs that semantically
+represent the same objects, but the model does not classify them correctly -
+and present negative images as one class of such inputs.
+",1,0,0,1,0,0
+6376,$\aleph_1$ and the modal $μ$-calculus,"  For a regular cardinal $\kappa$, a formula of the modal $\mu$-calculus is
+$\kappa$-continuous in a variable x if, on every model, its interpretation as a
+unary function of x is monotone and preserves unions of $\kappa$-directed sets.
+We define the fragment $C_{\aleph_1}(x)$ of the modal $\mu$-calculus and prove
+that all the formulas in this fragment are $\aleph_1$-continuous. For each
+formula $\phi(x)$ of the modal $\mu$-calculus, we construct a formula $\psi(x)
+\in C_{\aleph_1 }(x)$ such that $\phi(x)$ is $\kappa$-continuous, for some
+$\kappa$, if and only if $\phi(x)$ is equivalent to $\psi(x)$. Consequently, we
+prove that (i) the problem whether a formula is $\kappa$-continuous for some
+$\kappa$ is decidable, (ii) up to equivalence, there are only two fragments
+determined by continuity at some regular cardinal: the fragment
+$C_{\aleph_0}(x)$ studied by Fontaine and the fragment $C_{\aleph_1}(x)$. We
+apply our considerations to the problem of characterizing closure ordinals of
+formulas of the modal $\mu$-calculus. An ordinal $\alpha$ is the closure
+ordinal of a formula $\phi(x)$ if its interpretation on every model converges
+to its least fixed-point in at most $\alpha$ steps and if there is a model
+where the convergence occurs exactly in $\alpha$ steps. We prove that
+$\omega_1$, the least uncountable ordinal, is such a closure ordinal. Moreover
+we prove that closure ordinals are closed under ordinal sum. Thus, any formal
+expression built from 0, 1, $\omega$, $\omega_1$ by using the binary operator
+symbol + gives rise to a closure ordinal.
+",1,0,1,0,0,0
+6377,Optimal Service Elasticity in Large-Scale Distributed Systems,"  A fundamental challenge in large-scale cloud networks and data centers is to
+achieve highly efficient server utilization and limit energy consumption, while
+providing excellent user-perceived performance in the presence of uncertain and
+time-varying demand patterns. Auto-scaling provides a popular paradigm for
+automatically adjusting service capacity in response to demand while meeting
+performance targets, and queue-driven auto-scaling techniques have been widely
+investigated in the literature. In typical data center architectures and cloud
+environments however, no centralized queue is maintained, and load balancing
+algorithms immediately distribute incoming tasks among parallel queues. In
+these distributed settings with vast numbers of servers, centralized
+queue-driven auto-scaling techniques involve a substantial communication
+overhead and major implementation burden, or may not even be viable at all.
+Motivated by the above issues, we propose a joint auto-scaling and load
+balancing scheme which does not require any global queue length information or
+explicit knowledge of system parameters, and yet provides provably near-optimal
+service elasticity. We establish the fluid-level dynamics for the proposed
+scheme in a regime where the total traffic volume and nominal service capacity
+grow large in proportion. The fluid-limit results show that the proposed scheme
+achieves asymptotic optimality in terms of user-perceived delay performance as
+well as energy consumption. Specifically, we prove that both the waiting time
+of tasks and the relative energy portion consumed by idle servers vanish in the
+limit. At the same time, the proposed scheme operates in a distributed fashion
+and involves only constant communication overhead per task, thus ensuring
+scalability in massive data center operations.
+",1,0,1,0,0,0
+6378,Variational Monte Carlo study of spin dynamics in underdoped cuprates,"  The hour-glass-like dispersion of spin excitations is a common feature of
+underdoped cuprates. It was qualitatively explained by the random phase
+approximation based on various ordered states with some phenomenological
+parameters; however, its origin remains elusive. Here, we present a numerical
+study of spin dynamics in the $t$-$J$ model using the variational Monte Carlo
+method. This parameter-free method satisfies the no double-occupancy constraint
+of the model and thus provides a better evaluation on the spin dynamics with
+respect to various mean-field trial states. We conclude that the lower branch
+of the hour-glass dispersion is a collective mode and the upper branch is more
+likely the consequence of the stripe state than the other candidates.
+",0,1,0,0,0,0
+6379,High brightness electron beam for radiation therapy: A new approach,"  I propose to use high brightness electron beam with 1 to 100 MeV energy as
+tool to combat tumor or cancerous tissues in deep part of body. The method is
+to directly deliver the electron beam to the tumor site via a small tube that
+connected to a high brightness electron beam accelerator that is commonly
+available around the world. Here I gave a basic scheme on the principle, I
+believe other issues people raises will be solved easily for those who are
+interested in solving the problems.
+",0,1,0,0,0,0
+6380,Parabolic equations with divergence-free drift in space $L_{t}^{l}L_{x}^{q}$,"  In this paper we study the fundamental solution $\varGamma(t,x;\tau,\xi)$ of
+the parabolic operator $L_{t}=\partial_{t}-\Delta+b(t,x)\cdot\nabla$, where for
+every $t$, $b(t,\cdot)$ is a divergence-free vector field, and we consider the
+case that $b$ belongs to the Lebesgue space
+$L^{l}\left(0,T;L^{q}\left(\mathbb{R}^{n}\right)\right)$. The regularity of
+weak solutions to the parabolic equation $L_{t}u=0$ depends critically on the
+value of the parabolic exponent $\gamma=\frac{2}{l}+\frac{n}{q}$. Without the
+divergence-free condition on $b$, the regularity of weak solutions has been
+established when $\gamma\leq1$, and the heat kernel estimate has been obtained
+as well, except for the case that $l=\infty,q=n$. The regularity of weak
+solutions was deemed not true for the critical case
+$L^{\infty}\left(0,T;L^{n}\left(\mathbb{R}^{n}\right)\right)$ for a general
+$b$, while it is true for the divergence-free case, and a written proof can be
+deduced from the results in [Semenov, 2006]. One of the results obtained in the
+present paper establishes the Aronson type estimate for critical and
+supercritical cases and for vector fields $b$ which are divergence-free. We
+will prove the best possible lower and upper bounds for the fundamental
+solution one can derive under the current approach. The significance of the
+divergence-free condition enters the study of parabolic equations rather
+recently, mainly due to the discovery of the compensated compactness. The
+interest for the study of such parabolic equations comes from its connections
+with Leray's weak solutions of the Navier-Stokes equations and the Taylor
+diffusion associated with a vector field where the heat operator $L_{t}$
+appears naturally.
+",0,0,1,0,0,0
+6381,Translating Terminological Expressions in Knowledge Bases with Neural Machine Translation,"  Our work presented in this paper focuses on the translation of terminological
+expressions represented in semantically structured resources, like ontologies
+or knowledge graphs. The challenge of translating ontology labels or
+terminological expressions represented in knowledge bases lies in the highly
+specific vocabulary and the lack of contextual information, which can guide a
+machine translation system to translate ambiguous words into the targeted
+domain. Due to these challenges, we evaluate the translation quality of
+domain-specific expressions in the medical and financial domain with
+statistical (SMT) as well as with neural machine translation (NMT) methods and
+experiment domain adaptation of the translation models with terminological
+expressions only. Furthermore, we perform experiments on the injection of
+external terminological expressions into the translation systems. Through these
+experiments, we observed a significant advantage in domain adaptation for the
+domain-specific resource in the medical and financial domain and the benefit of
+subword models over word-based NMT models for terminology translation.
+",1,0,0,0,0,0
+6382,Detecting Arbitrary Attacks Using Continuous Secured Side Information in Wireless Networks,"  This paper focuses on Byzantine attack detection for Gaussian two-hop one-way
+relay network, where an amplify-and-forward relay may conduct Byzantine attacks
+by forwarding altered symbols to the destination. For facilitating attack
+detection, we utilize the openness of wireless medium to make the destination
+observe some secured signals that are not attacked. Then, a detection scheme is
+developed for the destination by using its secured observations to
+statistically check other observations from the relay. On the other hand,
+notice the Gaussian channel is continuous, which allows the possible Byzantine
+attacks to be conducted within continuous alphabet(s). The existing work on
+discrete channel is not applicable for investigating the performance of the
+proposed scheme. The main contribution of this paper is to prove that if and
+only if the wireless relay network satisfies a non-manipulable channel
+condition, the proposed detection scheme achieves asymptotic errorless
+performance against arbitrary attacks that allow the stochastic distributions
+of altered symbols to vary arbitrarily and depend on each other. No pre-shared
+secret or secret transmission is needed for the detection. Furthermore, we also
+prove that the relay network is non-manipulable as long as all channel
+coefficients are non-zero, which is not essential restrict for many practical
+systems.
+",1,0,0,0,0,0
+6383,Phase Transitions in the Pooled Data Problem,"  In this paper, we study the pooled data problem of identifying the labels
+associated with a large collection of items, based on a sequence of pooled
+tests revealing the counts of each label within the pool. In the noiseless
+setting, we identify an exact asymptotic threshold on the required number of
+tests with optimal decoding, and prove a phase transition between complete
+success and complete failure. In addition, we present a novel noisy variation
+of the problem, and provide an information-theoretic framework for
+characterizing the required number of tests for general random noise models.
+Our results reveal that noise can make the problem considerably more difficult,
+with strict increases in the scaling laws even at low noise levels. Finally, we
+demonstrate similar behavior in an approximate recovery setting, where a given
+number of errors is allowed in the decoded labels.
+",1,0,0,1,0,0
+6384,Graph Convolutional Networks for Classification with a Structured Label Space,"  It is a usual practice to ignore any structural information underlying
+classes in multi-class classification. In this paper, we propose a graph
+convolutional network (GCN) augmented neural network classifier to exploit a
+known, underlying graph structure of labels. The proposed approach resembles an
+(approximate) inference procedure in, for instance, a conditional random field
+(CRF). We evaluate the proposed approach on document classification and object
+recognition and report both accuracies and graph-theoretic metrics that
+correspond to the consistency of the model's prediction. The experiment results
+reveal that the proposed model outperforms a baseline method which ignores the
+graph structures of a label space in terms of graph-theoretic metrics.
+",1,0,0,1,0,0
+6385,Decoupling of graphene from Ni(111) via oxygen intercalation,"  The combination of the surface science techniques (STM, XPS, ARPES) and
+density-functional theory calculations was used to study the decoupling of
+graphene from Ni(111) by oxygen intercalation. The formation of the
+antiferromagnetic (AFM) NiO layer at the interface between graphene and
+ferromagnetic (FM) Ni is found, where graphene protects the underlying AFM/FM
+sandwich system. It is found that graphene is fully decoupled in this system
+and strongly $p$-doped via charge transfer with a position of the Dirac point
+of $(0.69\pm0.02)$ eV above the Fermi level. Our theoretical analysis confirms
+all experimental findings, addressing also the interface properties between
+graphene and AFM NiO.
+",0,1,0,0,0,0
+6386,Model Risk Measurement under Wasserstein Distance,"  The paper proposes a new approach to model risk measurement based on the
+Wasserstein distance between two probability measures. It formulates the
+theoretical motivation resulting from the interpretation of fictitious
+adversary of robust risk management. The proposed approach accounts for all
+alternative models and incorporates the economic reality of the fictitious
+adversary. It provides practically feasible results that overcome the
+restriction and the integrability issue imposed by the nominal model. The
+Wasserstein approach suits for all types of model risk problems, ranging from
+the single-asset hedging risk problem to the multi-asset allocation problem.
+The robust capital allocation line, accounting for the correlation risk, is not
+achievable with other non-parametric approaches.
+",0,0,0,0,0,1
+6387,Fast kNN mode seeking clustering applied to active learning,"  A significantly faster algorithm is presented for the original kNN mode
+seeking procedure. It has the advantages over the well-known mean shift
+algorithm that it is feasible in high-dimensional vector spaces and results in
+uniquely, well defined modes. Moreover, without any additional computational
+effort it may yield a multi-scale hierarchy of clusterings. The time complexity
+is just O(n^1.5). resulting computing times range from seconds for 10^4 objects
+to minutes for 10^5 objects and to less than an hour for 10^6 objects. The
+space complexity is just O(n). The procedure is well suited for finding large
+sets of small clusters and is thereby a candidate to analyze thousands of
+clusters in millions of objects.
+The kNN mode seeking procedure can be used for active learning by assigning
+the clusters to the class of the modal objects of the clusters. Its feasibility
+is shown by some examples with up to 1.5 million handwritten digits. The
+obtained classification results based on the clusterings are compared with
+those obtained by the nearest neighbor rule and the support vector classifier
+based on the same labeled objects for training. It can be concluded that using
+the clustering structure for classification can be significantly better than
+using the trained classifiers. A drawback of using the clustering for
+classification, however, is that no classifier is obtained that may be used for
+out-of-sample objects.
+",1,0,0,1,0,0
+6388,Towards a Flow- and Path-Sensitive Information Flow Analysis: Technical Report,"  This paper investigates a flow- and path-sensitive static information flow
+analysis. Compared with security type systems with fixed labels, it has been
+shown that flow-sensitive type systems accept more secure programs. We show
+that an information flow analysis with fixed labels can be both flow- and
+path-sensitive. The novel analysis has two major components: 1) a
+general-purpose program transformation that removes false dataflow dependencies
+in a program that confuse a fixed-label type system, and 2) a fixed-label type
+system that allows security types to depend on path conditions. We formally
+prove that the proposed analysis enforces a rigorous security property:
+noninterference. Moreover, we show that the analysis is strictly more precise
+than a classic flow-sensitive type system, and it allows sound control of
+information flow in the presence of mutable variables without resorting to
+run-time mechanisms.
+",1,0,0,0,0,0
+6389,Topic supervised non-negative matrix factorization,"  Topic models have been extensively used to organize and interpret the
+contents of large, unstructured corpora of text documents. Although topic
+models often perform well on traditional training vs. test set evaluations, it
+is often the case that the results of a topic model do not align with human
+interpretation. This interpretability fallacy is largely due to the
+unsupervised nature of topic models, which prohibits any user guidance on the
+results of a model. In this paper, we introduce a semi-supervised method called
+topic supervised non-negative matrix factorization (TS-NMF) that enables the
+user to provide labeled example documents to promote the discovery of more
+meaningful semantic structure of a corpus. In this way, the results of TS-NMF
+better match the intuition and desired labeling of the user. The core of TS-NMF
+relies on solving a non-convex optimization problem for which we derive an
+iterative algorithm that is shown to be monotonic and convergent to a local
+optimum. We demonstrate the practical utility of TS-NMF on the Reuters and
+PubMed corpora, and find that TS-NMF is especially useful for conceptual or
+broad topics, where topic key terms are not well understood. Although
+identifying an optimal latent structure for the data is not a primary objective
+of the proposed approach, we find that TS-NMF achieves higher weighted Jaccard
+similarity scores than the contemporary methods, (unsupervised) NMF and latent
+Dirichlet allocation, at supervision rates as low as 10% to 20%.
+",1,0,0,1,0,0
+6390,Low-temperature lattice effects in the spin-liquid candidate $κ$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$,"  The quasi-two-dimensional organic charge-transfer salt
+$\kappa$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$ is one of the prime candidates for a
+quantum spin-liquid due the strong spin frustration of its anisotropic
+triangular lattice in combination with its proximity to the Mott transition.
+Despite intensive investigations of the material's low-temperature properties,
+several important questions remain to be answered. Particularly puzzling are
+the 6\,K anomaly and the enigmatic effects observed in magnetic fields. Here we
+report on low-temperature measurements of lattice effects which were shown to
+be particularly strongly pronounced in this material (R. S. Manna \emph{et
+al.}, Phys. Rev. Lett. \textbf{104}, 016403 (2010)). A special focus of our
+study lies on sample-to-sample variations of these effects and their
+implications on the interpretation of experimental data. By investigating
+overall nine single crystals from two different batches, we can state that
+there are considerable differences in the size of the second-order phase
+transition anomaly around 6\,K, varying within a factor of 3. In addition, we
+find field-induced anomalies giving rise to pronounced features in the sample
+length for two out of these nine crystals for temperatures $T <$ 9 K. We
+tentatively assign the latter effects to $B$-induced magnetic clusters
+suspected to nucleate around crystal imperfections. These $B$-induced effects
+are absent for the crystals where the 6\,K anomaly is most strongly pronounced.
+The large lattice effects observed at 6\,K are consistent with proposed pairing
+instabilities of fermionic excitations breaking the lattice symmetry. The
+strong sample-to-sample variation in the size of the phase transition anomaly
+suggests that the conversion of the fermions to bosons at the instability is
+only partial and to some extent influenced by not yet identified
+sample-specific parameters.
+",0,1,0,0,0,0
+6391,ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction,"  With advanced data analytical techniques, efforts for more accurate decision
+support systems for disease prediction are on rise. Surveys by World Health
+Organization (WHO) indicate a great increase in number of diabetic patients and
+related deaths each year. Early diagnosis of diabetes is a major concern among
+researchers and practitioners. The paper presents an application of
+\textit{Automatic Multilayer Perceptron }which\textit{ }is combined with an
+outlier detection method \textit{Enhanced Class Outlier Detection using
+distance based algorithm }to create a prediction framework named as Enhanced
+Class Outlier with Automatic Multi layer Perceptron (ECO-AMLP). A series of
+experiments are performed on publicly available Pima Indian Diabetes Dataset to
+compare ECO-AMLP with other individual classifiers as well as ensemble based
+methods. The outlier technique used in our framework gave better results as
+compared to other pre-processing and classification techniques. Finally, the
+results are compared with other state-of-the-art methods reported in literature
+for diabetes prediction on PIDD and achieved accuracy of 88.7\% bests all other
+reported studies.
+",1,0,0,0,0,0
+6392,Local approximation of non-holomorphic discs in almost complex manifolds,"  We provide a local approximation result of non-holomorphic discs with small
+d-bar by pseudoholomorphic ones. As an application, we provide a certain gluing
+construction.
+",0,0,1,0,0,0
+6393,A Tutorial on Deep Learning for Music Information Retrieval,"  Following their success in Computer Vision and other areas, deep learning
+techniques have recently become widely adopted in Music Information Retrieval
+(MIR) research. However, the majority of works aim to adopt and assess methods
+that have been shown to be effective in other domains, while there is still a
+great need for more original research focusing on music primarily and utilising
+musical knowledge and insight. The goal of this paper is to boost the interest
+of beginners by providing a comprehensive tutorial and reducing the barriers to
+entry into deep learning for MIR. We lay out the basic principles and review
+prominent works in this hard to navigate the field. We then outline the network
+structures that have been successful in MIR problems and facilitate the
+selection of building blocks for the problems at hand. Finally, guidelines for
+new tasks and some advanced topics in deep learning are discussed to stimulate
+new research in this fascinating field.
+",1,0,0,0,0,0
+6394,Modeling and control of modern wind turbine systems: An introduction,"  This chapter provides an introduction to the modeling and control of power
+generation from wind turbine systems. In modeling, the focus is on the
+electrical components: electrical machine (e.g. permanent-magnet synchronous
+generators), back-to-back converter (consisting of machine-side and grid-side
+converter sharing a common DC-link), mains filters and ideal (balanced) power
+grid. The aerodynamics and the torque generation of the wind turbine are
+explained in simplified terms using a so-called power coefficient. The overall
+control system is considered. In particular, the phase-locked loop system for
+grid-side voltage orientation, the nonlinear speed control system for the
+generator (and turbine), and the non-minimum phase DC-link voltage control
+system are discussed in detail; based on a brief derivation of the underlying
+machine-side and grid-side current control systems. With the help of the power
+balance of the wind turbine, the operation management and the control of the
+power flow are explained. Concluding simulation results illustrate the overall
+system behavior of a controlled wind turbine with a permanent-magnet
+synchronous generator.
+",1,0,0,0,0,0
+6395,Linear algebraic analogues of the graph isomorphism problem and the Erdős-Rényi model,"  A classical difficult isomorphism testing problem is to test isomorphism of
+p-groups of class 2 and exponent p in time polynomial in the group order. It is
+known that this problem can be reduced to solving the alternating matrix space
+isometry problem over a finite field in time polynomial in the underlying
+vector space size. We propose a venue of attack for the latter problem by
+viewing it as a linear algebraic analogue of the graph isomorphism problem.
+This viewpoint leads us to explore the possibility of transferring techniques
+for graph isomorphism to this long-believed bottleneck case of group
+isomorphism.
+In 1970's, Babai, Erdős, and Selkow presented the first average-case
+efficient graph isomorphism testing algorithm (SIAM J Computing, 1980).
+Inspired by that algorithm, we devise an average-case efficient algorithm for
+the alternating matrix space isometry problem over a key range of parameters,
+in a random model of alternating matrix spaces in vein of the Erdős-Rényi
+model of random graphs. For this, we develop a linear algebraic analogue of the
+classical individualisation technique, a technique belonging to a set of
+combinatorial techniques that has been critical for the progress on the
+worst-case time complexity for graph isomorphism, but was missing in the group
+isomorphism context. As a consequence of the main algorithm, we establish a
+weaker linear algebraic analogue of Erdős and Rényi's classical result
+that most graphs have the trivial automorphism group. We finally show that
+Luks' dynamic programming technique for graph isomorphism (STOC 1999) can be
+adapted to slightly improve the worst-case time complexity of the alternating
+matrix space isometry problem in a certain range of parameters.
+",1,0,1,0,0,0
+6396,A Family of Metrics for Clustering Algorithms,"  We give the motivation for scoring clustering algorithms and a metric $M : A
+\rightarrow \mathbb{N}$ from the set of clustering algorithms to the natural
+numbers which we realize as \begin{equation} M(A) = \sum_i \alpha_i |f_i -
+\beta_i|^{w_i} \end{equation} where $\alpha_i,\beta_i,w_i$ are parameters used
+for scoring the feature $f_i$, which is computed empirically.. We give a method
+by which one can score features such as stability, noise sensitivity, etc and
+derive the necessary parameters. We conclude by giving a sample set of scores.
+",1,0,0,0,0,0
+6397,A numerical scheme for an improved Green-Naghdi model in the Camassa-Holm regime for the propagation of internal waves,"  In this paper we introduce a new reformulation of the Green-Naghdi model in
+the Camassa-Holm regime for the propagation of internal waves over a flat
+topography derived by Duchêne, Israwi and Talhouk. These new Green-Naghdi
+systems are adapted to improve the frequency dispersion of the original model,
+they share the same order of precision as the standard one but have an
+appropriate structure which makes them much more suitable for the numerical
+resolution. We develop a second order splitting scheme where the hyperbolic
+part of the system is treated with a high-order finite volume scheme and the
+dispersive part is treated with a finite difference approach. Numerical
+simulations are then performed to validate the model and the numerical methods.
+",0,0,1,0,0,0
+6398,General Bayesian Updating and the Loss-Likelihood Bootstrap,"  In this paper we revisit the weighted likelihood bootstrap, a method that
+generates samples from an approximate Bayesian posterior of a parametric model.
+We show that the same method can be derived, without approximation, under a
+Bayesian nonparametric model with the parameter of interest defined as
+minimising an expected negative log-likelihood under an unknown sampling
+distribution. This interpretation enables us to extend the weighted likelihood
+bootstrap to posterior sampling for parameters minimizing an expected loss. We
+call this method the loss-likelihood bootstrap. We make a connection between
+this and general Bayesian updating, which is a way of updating prior belief
+distributions without needing to construct a global probability model, yet
+requires the calibration of two forms of loss function. The loss-likelihood
+bootstrap is used to calibrate the general Bayesian posterior by matching
+asymptotic Fisher information. We demonstrate the methodology on a number of
+examples.
+",0,0,0,1,0,0
+6399,Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles,"  For many applications, an ensemble of base classifiers is an effective
+solution. The tuning of its parameters(number of classes, amount of data on
+which each classifier is to be trained on, etc.) requires G, the generalization
+error of a given ensemble. The efficient estimation of G is the focus of this
+paper. The key idea is to approximate the variance of the class
+scores/probabilities of the base classifiers over the randomness imposed by the
+training subset by normal/beta distribution at each point x in the input
+feature space. We estimate the parameters of the distribution using a small set
+of randomly chosen base classifiers and use those parameters to give efficient
+estimation schemes for G. We give empirical evidence for the quality of the
+various estimators. We also demonstrate their usefulness in making design
+choices such as the number of classifiers in the ensemble and the size of a
+subset of data used for training that is needed to achieve a certain value of
+generalization error. Our approach also has great potential for designing
+distributed ensemble classifiers.
+",1,0,0,1,0,0
+6400,Self-consistent calculation of the flux-flow conductivity in diffusive superconductors,"  In the framework of Keldysh-Usadel kinetic theory, we study the temperature
+dependence of flux-flow conductivity (FFC) in diffusive superconductors. By
+using self-consistent vortex solutions we find the exact values of
+dimensionless parameters that determine the diffusion-controlled FFC both in
+the limit of the low temperatures and close to the critical one. Taking into
+account the electron-phonon scattering we study the transition between
+flux-flow regimes controlled either by the diffusion or the inelastic
+relaxation of non-equilibrium quasiparticles. We demonstrate that the inelastic
+electron-phonon relaxation leads to the strong suppression of FFC as compared
+to the previous estimates making it possible to obtain the numerical agreement
+with experimental results.
+",0,1,0,0,0,0
+6401,Generalized weighted Ostrowski and Ostrowski-Grüss type inequalities on time scales via a parameter function,"  We prove generalized weighted Ostrowski and Ostrowski--Grüss type
+inequalities on time scales via a parameter function. In particular, our result
+extends a result of Dragomir and Barnett. Furthermore, we apply our results to
+the continuous, discrete, and quantum cases, to obtain some interesting new
+inequalities.
+",0,0,1,0,0,0
+6402,"""Found in Translation"": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models","  There is an intuitive analogy of an organic chemist's understanding of a
+compound and a language speaker's understanding of a word. Consequently, it is
+possible to introduce the basic concepts and analyze potential impacts of
+linguistic analysis to the world of organic chemistry. In this work, we cast
+the reaction prediction task as a translation problem by introducing a
+template-free sequence-to-sequence model, trained end-to-end and fully
+data-driven. We propose a novel way of tokenization, which is arbitrarily
+extensible with reaction information. With this approach, we demonstrate
+results superior to the state-of-the-art solution by a significant margin on
+the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1%
+without relying on auxiliary knowledge such as reaction templates. Also, 66.4%
+accuracy is reached on a larger and noisier dataset.
+",1,0,0,1,0,0
+6403,Efficient Decomposition of High-Rank Tensors,"  Tensors are a natural way to express correlations among many physical
+variables, but storing tensors in a computer naively requires memory which
+scales exponentially in the rank of the tensor. This is not optimal, as the
+required memory is actually set not by the rank but by the mutual information
+amongst the variables in question. Representations such as the tensor tree
+perform near-optimally when the tree decomposition is chosen to reflect the
+correlation structure in question, but making such a choice is non-trivial and
+good heuristics remain highly context-specific. In this work I present two new
+algorithms for choosing efficient tree decompositions, independent of the
+physical context of the tensor. The first is a brute-force algorithm which
+produces optimal decompositions up to truncation error but is generally
+impractical for high-rank tensors, as the number of possible choices grows
+exponentially in rank. The second is a greedy algorithm, and while it is not
+optimal it performs extremely well in numerical experiments while having
+runtime which makes it practical even for tensors of very high rank.
+",1,1,0,0,0,0
+6404,Universality in Chaos: Lyapunov Spectrum and Random Matrix Theory,"  We propose the existence of a new universality in classical chaotic systems
+when the number of degrees of freedom is large: the statistical property of the
+Lyapunov spectrum is described by Random Matrix Theory. We demonstrate it by
+studying the finite-time Lyapunov exponents of the matrix model of a stringy
+black hole and the mass deformed models. The massless limit, which has a dual
+string theory interpretation, is special in that the universal behavior can be
+seen already at t=0, while in other cases it sets in at late time. The same
+pattern is demonstrated also in the product of random matrices.
+",0,1,0,0,0,0
+6405,Fixed points of competitive threshold-linear networks,"  Threshold-linear networks (TLNs) are models of neural networks that consist
+of simple, perceptron-like neurons and exhibit nonlinear dynamics that are
+determined by the network's connectivity. The fixed points of a TLN, including
+both stable and unstable equilibria, play a critical role in shaping its
+emergent dynamics. In this work, we provide two novel characterizations for the
+set of fixed points of a competitive TLN: the first is in terms of a simple
+sign condition, while the second relies on the concept of domination. We apply
+these results to a special family of TLNs, called combinatorial
+threshold-linear networks (CTLNs), whose connectivity matrices are defined from
+directed graphs. This leads us to prove a series of graph rules that enable one
+to determine fixed points of a CTLN by analyzing the underlying graph.
+Additionally, we study larger networks composed of smaller ""building block""
+subnetworks, and prove several theorems relating the fixed points of the full
+network to those of its components. Our results provide the foundation for a
+kind of ""graphical calculus"" to infer features of the dynamics from a network's
+connectivity.
+",0,0,0,0,1,0
+6406,A Decision Procedure for Herbrand Formulae without Skolemization,"  This paper describes a decision procedure for disjunctions of conjunctions of
+anti-prenex normal forms of pure first-order logic (FOLDNFs) that do not
+contain $\vee$ within the scope of quantifiers. The disjuncts of these FOLDNFs
+are equivalent to prenex normal forms whose quantifier-free parts are
+conjunctions of atomic and negated atomic formulae (= Herbrand formulae). In
+contrast to the usual algorithms for Herbrand formulae, neither skolemization
+nor unification algorithms with function symbols are applied. Instead, a
+procedure is described that rests on nothing but equivalence transformations
+within pure first-order logic (FOL). This procedure involves the application of
+a calculus for negative normal forms (the NNF-calculus) with $A \dashv\vdash A
+\wedge A$ (= $\wedge$I) as the sole rule that increases the complexity of given
+FOLDNFs. The described algorithm illustrates how, in the case of Herbrand
+formulae, decision problems can be solved through a systematic search for
+proofs that reduce the number of applications of the rule $\wedge$I to a
+minimum in the NNF-calculus. In the case of Herbrand formulae, it is even
+possible to entirely abstain from applying $\wedge$I. Finally, it is shown how
+the described procedure can be used within an optimized general search for
+proofs of contradiction and what kind of questions arise for a
+$\wedge$I-minimal proof strategy in the case of a general search for proofs of
+contradiction.
+",1,0,1,0,0,0
+6407,The role of relativistic many-body theory in probing new physics beyond the standard model via the electric dipole moments of diamagnetic atoms,"  The observation of electric dipole moments (EDMs) in atomic systems due to
+parity and time-reversal violating (P,T-odd) interactions can probe new physics
+beyond the standard model and also provide insights into the matter-antimatter
+asymmetry in the Universe. The EDMs of open-shell atomic systems are sensitive
+to the electron EDM and the P,T-odd scalar-pseudoscalar (S-PS) semi-leptonic
+interaction, but the dominant contributions to the EDMs of diamagnetic atoms
+come from the hadronic and tensor-pseudotensor (T-PT) semi-leptonic
+interactions. Several diamagnetic atoms like $^{129}$Xe, $^{171}$Yb,
+$^{199}$Hg, $^{223}$Rn, and $^{225}$Ra are candidates for the experimental
+search for the possible existence of EDMs, and among these $^{199}$Hg has
+yielded the lowest limit till date. The T or CP violating coupling constants of
+the aforementioned interactions can be extracted from these measurements by
+combining with atomic and nuclear calculations. In this work, we report the
+calculations of the EDMs of the above atoms by including both the
+electromagnetic and P,T-odd violating interactions simultaneously. These
+calculations are performed by employing relativistic many-body methods based on
+the random phase approximation (RPA) and the singles and doubles
+coupled-cluster (CCSD) method starting with the Dirac-Hartree-Fock (DHF) wave
+function in both cases. The differences in the results from both the methods
+shed light on the importance of the non-core-polarization electron correlation
+effects that are accounted for by the CCSD method. We also determine electric
+dipole polarizabilities of these atoms, which have computational similarities
+with EDMs and compare them with the available experimental and other
+theoretical results to assess the accuracy of our calculations.
+",0,1,0,0,0,0
+6408,Security Incident Recognition and Reporting (SIRR): An Industrial Perspective,"  Reports and press releases highlight that security incidents continue to
+plague organizations. While researchers and practitioners' alike endeavor to
+identify and implement realistic security solutions to prevent incidents from
+occurring, the ability to initially identify a security incident is paramount
+when researching a security incident lifecycle. Hence, this research
+investigates the ability of employees in a Global Fortune 500 financial
+organization, through internal electronic surveys, to recognize and report
+security incidents to pursue a more holistic security posture. The research
+contribution is an initial insight into security incident perceptions by
+employees in the financial sector as well as serving as an initial guide for
+future security incident recognition and reporting initiatives.
+",1,0,0,0,0,0
+6409,On attainability of optimal controls in coefficients for system of Hammerstein type with anisotropic p-Laplacia,"  In this paper we consider an optimal control problem for the coupled system
+of a nonlinear monotone Dirichlet problem with anisotropic p-Laplacian and
+matrix-valued nonsmooth controls in its coefficients and a nonlinear equation
+of Hammerstein type. Using the direct method in calculus of variations, we
+prove the existence of an optimal control in considered problem and provide
+sensitivity analysis for a specific case of considered problem with respect to
+two-parameter regularization.
+",0,0,1,0,0,0
+6410,Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models,"  The Tweedie Compound Poisson-Gamma model is routinely used for modeling
+non-negative continuous data with a discrete probability mass at zero. Mixed
+models with random effects account for the covariance structure related to the
+grouping hierarchy in the data. An important application of Tweedie mixed
+models is pricing the insurance policies, e.g. car insurance. However, the
+intractable likelihood function, the unknown variance function, and the
+hierarchical structure of mixed effects have presented considerable challenges
+for drawing inferences on Tweedie. In this study, we tackle the Bayesian
+Tweedie mixed-effects models via variational inference approaches. In
+particular, we empower the posterior approximation by implicit models trained
+in an adversarial setting. To reduce the variance of gradients, we
+reparameterize random effects, and integrate out one local latent variable of
+Tweedie. We also employ a flexible hyper prior to ensure the richness of the
+approximation. Our method is evaluated on both simulated and real-world data.
+Results show that the proposed method has smaller estimation bias on the random
+effects compared to traditional inference methods including MCMC; it also
+achieves a state-of-the-art predictive performance, meanwhile offering a richer
+estimation of the variance function.
+",0,0,0,1,0,0
+6411,Sampling of Temporal Networks: Methods and Biases,"  Temporal networks have been increasingly used to model a diversity of systems
+that evolve in time; for example human contact structures over which dynamic
+processes such as epidemics take place. A fundamental aspect of real-life
+networks is that they are sampled within temporal and spatial frames.
+Furthermore, one might wish to subsample networks to reduce their size for
+better visualization or to perform computationally intensive simulations. The
+sampling method may affect the network structure and thus caution is necessary
+to generalize results based on samples. In this paper, we study four sampling
+strategies applied to a variety of real-life temporal networks. We quantify the
+biases generated by each sampling strategy on a number of relevant statistics
+such as link activity, temporal paths and epidemic spread. We find that some
+biases are common in a variety of networks and statistics, but one strategy,
+uniform sampling of nodes, shows improved performance in most scenarios. Our
+results help researchers to better design network data collection protocols and
+to understand the limitations of sampled temporal network data.
+",1,0,0,0,0,0
+6412,Personalizing Path-Specific Effects,"  Unlike classical causal inference, which often has an average causal effect
+of a treatment within a population as a target, in settings such as
+personalized medicine, the goal is to map a given unit's characteristics to a
+treatment tailored to maximize the expected outcome for that unit. Obtaining
+high-quality mappings of this type is the goal of the dynamic regime literature
+(Chakraborty and Moodie 2013), with connections to reinforcement learning and
+experimental design. Aside from the average treatment effects, mechanisms
+behind causal relationships are also of interest. A well-studied approach to
+mechanism analysis is establishing average effects along with a particular set
+of causal pathways, in the simplest case the direct and indirect effects.
+Estimating such effects is the subject of the mediation analysis literature
+(Robins and Greenland 1992; Pearl 2001).
+In this paper, we consider how unit characteristics may be used to tailor a
+treatment assignment strategy that maximizes a particular path-specific effect.
+In healthcare applications, finding such a policy is of interest if, for
+instance, we are interested in maximizing the chemical effect of a drug on an
+outcome (corresponding to the direct effect), while assuming drug adherence
+(corresponding to the indirect effect) is set to some reference level. To solve
+our problem, we define counterfactuals associated with path-specific effects of
+a policy, give a general identification algorithm for these counterfactuals,
+give a proof of completeness, and show how classification algorithms in machine
+learning (Chen, Zeng, and Kosorok 2016) may be used to find a high-quality
+policy. We validate our approach via a simulation study.
+",0,0,0,1,0,0
+6413,Bridge Programs as an approach to improving diversity in physics,"  In most physical sciences, students from underrepresented minority (URM)
+groups constitute a small percentage of earned degrees at the undergraduate and
+graduate levels. Bridge programs can serve as an initiative to increase the
+number of URM students that gain access to graduate school and earn advanced
+degrees in physics. This talk discussed levels of representation in physical
+sciences as well as some results and best practices of current bridge programs
+in physics. The APS Bridge Program has enabled over 100 students to be placed
+into Bridge or graduate programs in physics, while retaining 88% of those
+placed.
+",0,1,0,0,0,0
+6414,NEURAL: quantitative features for newborn EEG using Matlab,"  Background: For newborn infants in critical care, continuous monitoring of
+brain function can help identify infants at-risk of brain injury. Quantitative
+features allow a consistent and reproducible approach to EEG analysis, but only
+when all implementation aspects are clearly defined.
+Methods: We detail quantitative features frequently used in neonatal EEG
+analysis and present a Matlab software package together with exact
+implementation details for all features. The feature set includes stationary
+features that capture amplitude and frequency characteristics and features of
+inter-hemispheric connectivity. The software, a Neonatal Eeg featURe set in
+mAtLab (NEURAL), is open source and freely available. The software also
+includes a pre-processing stage with a basic artefact removal procedure.
+Conclusions: NEURAL provides a common platform for quantitative analysis of
+neonatal EEG. This will support reproducible research and enable comparisons
+across independent studies. These features present summary measures of the EEG
+that can also be used in automated methods to determine brain development and
+health of the newborn in critical care.
+",0,1,0,1,0,0
+6415,False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs,"  Generating large quantities of quality labeled data in medical imaging is
+very time consuming and expensive. The performance of supervised algorithms for
+various tasks on imaging has improved drastically over the years, however the
+availability of data to train these algorithms have become one of the main
+bottlenecks for implementation. To address this, we propose a semi-supervised
+learning method where pseudo-negative labels from unlabeled data are used to
+further refine the performance of a pulmonary nodule detection network in chest
+radiographs. After training with the proposed network, the false positive rate
+was reduced to 0.1266 from 0.4864 while maintaining sensitivity at 0.89.
+",0,0,0,1,0,0
+6416,WASP-12b: A Mass-Losing Extremely Hot Jupiter,"  WASP-12b is an extreme hot Jupiter in a 1 day orbit, suffering profound
+irradiation from its F type host star. The planet is surrounded by a
+translucent exosphere which overfills the Roche lobe and produces
+line-blanketing absorption in the near-UV. The planet is losing mass. Another
+unusual property of the WASP-12 system is that observed chromospheric emission
+from the star is anomalously low: WASP-12 is an extreme outlier amongst
+thousands of stars when the log $R^{'}_{HK}$ chromospheric activity indicator
+is considered. Occam's razor suggests these two extremely rare properties
+coincide in this system because they are causally related. The absence of the
+expected chromospheric emission is attributable to absorption by a diffuse
+circumstellar gas shroud which surrounds the entire planetary system and fills
+our line of sight to the chromospherically active regions of the star. This
+circumstellar gas shroud is probably fed by mass loss from WASP-12b. The
+orbital eccentricity of WASP-12b is small but may be non-zero. The planet is
+part of a hierarchical quadruple system; its current orbit is consistent with
+prior secular dynamical evolution leading to a highly eccentric orbit followed
+by tidal circularization. When compared with the Galaxy's population of
+planets, WASP-12b lies on the upper boundary of the sub-Jovian desert in both
+the $(M_{\rm P}, P)$ and $(R_{\rm P}, P)$ planes. Determining the mass loss
+rate for WASP-12b will illuminate the mechanism(s) responsible for the
+sub-Jovian desert.
+",0,1,0,0,0,0
+6417,On the status of the Born-Oppenheimer expansion in molecular systems theory,"  It is shown that the adiabatic Born-Oppenheimer expansion does not satisfy
+the necessary condition for the applicability of perturbation theory. A simple
+example of an exact solution of a problem that can not be obtained from the
+Born-Oppenheimer expansion is given. A new version of perturbation theory for
+molecular systems is proposed.
+",0,1,0,0,0,0
+6418,Computing Tropical Prevarieties in Parallel,"  The computation of the tropical prevariety is the first step in the
+application of polyhedral methods to compute positive dimensional solution sets
+of polynomial systems. In particular, pretropisms are candidate leading
+exponents for the power series developments of the solutions. The computation
+of the power series may start as soon as one pretropism is available, so our
+parallel computation of the tropical prevariety has an application in a
+pipelined solver.
+We present a parallel implementation of dynamic enumeration. Our first
+distributed memory implementation with forked processes achieved good speedups,
+but quite often resulted in large variations in the execution times of the
+processes. The shared memory multithreaded version applies work stealing to
+reduce the variability of the run time. Our implementation applies the thread
+safe Parma Polyhedral Library (PPL), in exact arithmetic with the GNU
+Multiprecision Arithmetic Library (GMP), aided by the fast memory allocations
+of TCMalloc.
+Our parallel implementation is capable of computing the tropical prevariety
+of the cyclic 16-roots problem. We also report on computational experiments on
+the $n$-body and $n$-vortex problems; our computational results compare
+favorably with Gfan.
+",1,0,1,0,0,0
+6419,3k-4 theorem for ordered groups,"  Recently, G. A. Freiman, M. Herzog, P. Longobardi, M. Maj proved two
+`structure theorems' for ordered groups \cite{FHLM}. We give elementary proof
+of these two theorems.
+",0,0,1,0,0,0
+6420,The Extinction Properties of and Distance to the Highly Reddened Type Ia Supernova SN 2012cu,"  Correction of Type Ia Supernova brightnesses for extinction by dust has
+proven to be a vexing problem. Here we study the dust foreground to the highly
+reddened SN 2012cu, which is projected onto a dust lane in the galaxy NGC 4772.
+The analysis is based on multi-epoch, spectrophotometric observations spanning
+3,300 - 9,200 {\AA}, obtained by the Nearby Supernova Factory. Phase-matched
+comparison of the spectroscopically twinned SN 2012cu and SN 2011fe across 10
+epochs results in the best-fit color excess of (E(B-V), RMS) = (1.00, 0.03) and
+total-to-selective extinction ratio of (RV , RMS) = (2.95, 0.08) toward SN
+2012cu within its host galaxy. We further identify several diffuse interstellar
+bands, and compare the 5780 {\AA} band with the dust-to-band ratio for the
+Milky Way. Overall, we find the foreground dust-extinction properties for SN
+2012cu to be consistent with those of the Milky Way. Furthermore we find no
+evidence for significant time variation in any of these extinction tracers. We
+also compare the dust extinction curve models of Cardelli et al. (1989),
+O'Donnell (1994), and Fitzpatrick (1999), and find the predictions of
+Fitzpatrick (1999) fit SN 2012cu the best. Finally, the distance to NGC4772,
+the host of SN 2012cu, at a redshift of z = 0.0035, often assigned to the Virgo
+Southern Extension, is determined to be 16.6$\pm$1.1 Mpc. We compare this
+result with distance measurements in the literature.
+",0,1,0,0,0,0
+6421,On Whitham and related equations,"  The aim of this paper is to study, via theoretical analysis and numerical
+simulations, the dynamics of Whitham and related equations. In particular we
+establish rigorous bounds between solutions of the Whitham and KdV equations
+and provide some insights into the dynamics of the Whitham equation in
+different regimes, some of them being outside the range of validity of the
+Whitham equation as a water waves model.
+",0,0,1,0,0,0
+6422,Analytic Connectivity in General Hypergraphs,"  In this paper we extend the known results of analytic connectivity to
+non-uniform hypergraphs. We prove a modified Cheeger's inequality and also give
+a bound on analytic connectivity with respect to the degree sequence and
+diameter of a hypergraph.
+",1,0,0,0,0,0
+6423,Cosmological searches for a non-cold dark matter component,"  We explore an extended cosmological scenario where the dark matter is an
+admixture of cold and additional non-cold species. The mass and temperature of
+the non-cold dark matter particles are extracted from a number of cosmological
+measurements. Among others, we consider tomographic weak lensing data and Milky
+Way dwarf satellite galaxy counts. We also study the potential of these
+scenarios in alleviating the existing tensions between local measurements and
+Cosmic Microwave Background (CMB) estimates of the $S_8$ parameter, with
+$S_8=\sigma_8\sqrt{\Omega_m}$, and of the Hubble constant $H_0$. In principle,
+a sub-dominant, non-cold dark matter particle with a mass $m_X\sim$~keV, could
+achieve the goals above. However, the preferred ranges for its temperature and
+its mass are different when extracted from weak lensing observations and from
+Milky Way dwarf satellite galaxy counts, since these two measurements require
+suppressions of the matter power spectrum at different scales. Therefore,
+solving simultaneously the CMB-weak lensing tensions and the small scale crisis
+in the standard cold dark matter picture via only one non-cold dark matter
+component seems to be challenging.
+",0,1,0,0,0,0
+6424,F-pure threshold and height of quasi-homogeneous polynomials,"  We consider a quasi-homogeneous polynomial $f \in \mathbb{Z}[x_0, \ldots,
+x_N]$ of degree $w$ equal to the degree of $x_0 \cdots x_N$ and show that the
+$F$-pure threshold of the reduction $f_p \in \mathbb{F}_p[x_0, \ldots, x_N]$ is
+equal to the log canonical threshold if and only if the height of the
+Artin-Mazur formal group associated to $H^{N-1}\left( X, {\mathbb{G}}_{m,X}
+\right)$, where $X$ is the hypersurface given by $f$, is equal to 1. We also
+prove that a similar result holds for Fermat hypersurfaces of degree $>N+1$.
+Furthermore, we give examples of weighted Delsarte surfaces which show that
+other values of the $F$-pure threshold of a quasi-homogeneous polynomial of
+degree $w$ cannot be characterized by the height.
+",0,0,1,0,0,0
+6425,Optimal VWAP execution under transient price impact,"  We solve the problem of optimal liquidation with volume weighted average
+price (VWAP) benchmark when the market impact is linear and transient. Our
+setting is indeed more general as it considers the case when the trading
+interval is not necessarily coincident with the benchmark interval:
+Implementation Shortfall and Target Close execution are shown to be particular
+cases of our setting. We find explicit solutions in continuous and discrete
+time considering risk averse investors having a CARA utility function. Finally,
+we show that, contrary to what is observed for Implementation Shortfall, the
+optimal VWAP solution contains both buy and sell trades also when the decay
+kernel is convex.
+",0,0,0,0,0,1
+6426,Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching,"  Cellular Electron CryoTomography (CECT) is a 3D imaging technique that
+captures information about the structure and spatial organization of
+macromolecular complexes within single cells, in near-native state and at
+sub-molecular resolution. Although template matching is often used to locate
+macromolecules in a CECT image, it is insufficient as it only measures the
+relative structural similarity. Therefore, it is preferable to assess the
+statistical credibility of the decision through hypothesis testing, requiring
+many templates derived from a diverse population of macromolecular structures.
+Due to the very limited number of known structures, we need a generative model
+to efficiently and reliably sample pseudo-structures from the complex
+distribution of macromolecular structures. To address this challenge, we
+propose a novel image-derived approach for performing hypothesis testing for
+template matching by constructing generative models using the generative
+adversarial network. Finally, we conducted hypothesis testing experiments for
+template matching on both simulated and experimental subtomograms, allowing us
+to conclude the identity of subtomograms with high statistical credibility and
+significantly reducing false positives.
+",0,0,0,1,1,0
+6427,Bayesian Alignments of Warped Multi-Output Gaussian Processes,"  We propose a novel Bayesian approach to modelling nonlinear alignments of
+time series based on latent shared information. We apply the method to the
+real-world problem of finding common structure in the sensor data of wind
+turbines introduced by the underlying latent and turbulent wind field. The
+proposed model allows for both arbitrary alignments of the inputs and
+non-parametric output warpings to transform the observations. This gives rise
+to multiple deep Gaussian process models connected via latent generating
+processes. We present an efficient variational approximation based on nested
+variational compression and show how the model can be used to extract shared
+information between dependent time series, recovering an interpretable
+functional decomposition of the learning problem. We show results for an
+artificial data set and real-world data of two wind turbines.
+",1,0,0,1,0,0
+6428,Fast and unsupervised methods for multilingual cognate clustering,"  In this paper we explore the use of unsupervised methods for detecting
+cognates in multilingual word lists. We use online EM to train sound segment
+similarity weights for computing similarity between two words. We tested our
+online systems on geographically spread sixteen different language groups of
+the world and show that the Online PMI system (Pointwise Mutual Information)
+outperforms a HMM based system and two linguistically motivated systems:
+LexStat and ALINE. Our results suggest that a PMI system trained in an online
+fashion can be used by historical linguists for fast and accurate
+identification of cognates in not so well-studied language families.
+",1,0,0,0,0,0
+6429,"Structure, magnetic susceptibility and specific heat of the spin-orbital-liquid candidate FeSc2S4 : Influence of fe off-stoichiometry","  We report structural, susceptibility and specific heat studies of
+stoichiometric and off-stoichiometric poly- and single crystals of the A-site
+spinel compound FeSc2S4. In stoichiometric samples no long-range magnetic order
+is found down to 1.8 K. The magnetic susceptibility of these samples is field
+independent in the temperature range 10 - 400 K and does not show irreversible
+effects at low temperatures. In contrast, the magnetic susceptibility of
+samples with iron excess shows substantial field dependence at high
+temperatures and manifests a pronounced magnetic irreversibility at low
+temperatures with a difference between ZFC and FC susceptibilities and a
+maximum at 10 K reminiscent of a magnetic transition. Single crystal x-ray
+diffraction of the stoichiometric samples revealed a single phase spinel
+structure without site inversion. In single crystalline samples with Fe excess
+besides the main spinel phase a second ordered single-crystal phase was
+detected with the diffraction pattern of a vacancy-ordered superstructure of
+iron sulfide, close to the 5C polytype Fe9S10. Specific heat studies reveal a
+broad anomaly, which evolves below 20 K in both stoichiometric and
+off-stoichiometric crystals. We show that the low-temperature specific heat can
+be well described by considering the low-lying spin-orbital electronic levels
+of Fe2+ ions. Our results demonstrate significant influence of excess Fe ions
+on intrinsic magnetic behavior of FeSc2S4 and provide support for the
+spin-orbital liquid scenario proposed in earlier studies for the stoichiometric
+compound.
+",0,1,0,0,0,0
+6430,Directed Information as Privacy Measure in Cloud-based Control,"  We consider cloud-based control scenarios in which clients with local control
+tasks outsource their computational or physical duties to a cloud service
+provider. In order to address privacy concerns in such a control architecture,
+we first investigate the issue of finding an appropriate privacy measure for
+clients who desire to keep local state information as private as possible
+during the control operation. Specifically, we justify the use of Kramer's
+notion of causally conditioned directed information as a measure of privacy
+loss based on an axiomatic argument. Then we propose a methodology to design an
+optimal ""privacy filter"" that minimizes privacy loss while a given level of
+control performance is guaranteed. We show in particular that the optimal
+privacy filter for cloud-based Linear-Quadratic-Gaussian (LQG) control can be
+synthesized by a Linear-Matrix-Inequality (LMI) algorithm. The trade-off in the
+design is illustrated by a numerical example.
+",0,0,1,0,0,0
+6431,Classification of simple linearly compact Kantor triple systems over the complex numbers,"  Simple finite dimensional Kantor triple systems over the complex numbers are
+classified in terms of Satake diagrams. We prove that every simple and linearly
+compact Kantor triple system has finite dimension and give an explicit
+presentation of all the classical and exceptional systems.
+",0,0,1,0,0,0
+6432,Code-division multiplexed resistive pulse sensor networks for spatio-temporal detection of particles in microfluidic devices,"  Spatial separation of suspended particles based on contrast in their physical
+or chemical properties forms the basis of various biological assays performed
+on lab-on-achip devices. To electronically acquire this information, we have
+recently introduced a microfluidic sensing platform, called Microfluidic CODES,
+which combines the resistive pulse sensing with the code division multiple
+access in multiplexing a network of integrated electrical sensors. In this
+paper, we enhance the multiplexing capacity of the Microfluidic CODES by
+employing sensors that generate non-orthogonal code waveforms and a new
+decoding algorithm that combines machine learning techniques with minimum
+mean-squared error estimation. As a proof of principle, we fabricated a
+microfluidic device with a network of 10 code-multiplexed sensors and
+characterized it using cells suspended in phosphate buffer saline solution.
+",0,0,0,1,0,0
+6433,Navigability evaluation of complex networks by greedy routing efficiency,"  Network navigability is a key feature of complex networked systems. For a
+network embedded in a geometrical space, maximization of greedy routing (GR)
+measures based on the node geometrical coordinates can ensure efficient greedy
+navigability. In PNAS, Seguin et al. (PNAS 2018, vol. 115, no. 24) define a
+measure for quantifying the efficiency of brain network navigability in the
+Euclidean space, referred to as the efficiency ratio, whose formula exactly
+coincides with the GR-score (GR-efficiency) previously published by Muscoloni
+et al. (Nature Communications 2017, vol. 8, no. 1615). In this Letter, we point
+out potential flaws in the study of Seguin et al. regarding the discussion of
+the GR evaluation. In particular, we revise the concept of GR navigability,
+together with a careful discussion of the advantage offered by the new proposed
+GR-efficiency measure in comparison to the main measures previously adopted in
+literature. Finally, we clarify and standardize the GR-efficiency terminology
+in order to simplify and facilitate the discussion in future studies.
+",1,0,0,0,0,0
+6434,Fermion condensation and super pivotal categories,"  We study fermionic topological phases using the technique of fermion
+condensation. We give a prescription for performing fermion condensation in
+bosonic topological phases which contain a fermion. Our approach to fermion
+condensation can roughly be understood as coupling the parent bosonic
+topological phase to a phase of physical fermions, and condensing pairs of
+physical and emergent fermions. There are two distinct types of objects in
+fermionic theories, which we call ""m-type"" and ""q-type"" particles. The
+endomorphism algebras of q-type particles are complex Clifford algebras, and
+they have no analogues in bosonic theories. We construct a fermionic
+generalization of the tube category, which allows us to compute the
+quasiparticle excitations in fermionic topological phases. We then prove a
+series of results relating data in condensed theories to data in their parent
+theories; for example, if $\mathcal{C}$ is a modular tensor category containing
+a fermion, then the tube category of the condensed theory satisfies
+$\textbf{Tube}(\mathcal{C}/\psi) \cong \mathcal{C} \times (\mathcal{C}/\psi)$.
+We also study how modular transformations, fusion rules, and coherence
+relations are modified in the fermionic setting, prove a fermionic version of
+the Verlinde dimension formula, construct a commuting projector lattice
+Hamiltonian for fermionic theories, and write down a fermionic version of the
+Turaev-Viro-Barrett-Westbury state sum. A large portion of this work is devoted
+to three detailed examples of performing fermion condensation to produce
+fermionic topological phases: we condense fermions in the Ising theory, the
+$SO(3)_6$ theory, and the $\frac{1}{2}\text{E}_6$ theory, and compute the
+quasiparticle excitation spectrum in each of these examples.
+",0,1,1,0,0,0
+6435,PIMKL: Pathway Induced Multiple Kernel Learning,"  Reliable identification of molecular biomarkers is essential for accurate
+patient stratification. While state-of-the-art machine learning approaches for
+sample classification continue to push boundaries in terms of performance, most
+of these methods are not able to integrate different data types and lack
+generalization power, limiting their application in a clinical setting.
+Furthermore, many methods behave as black boxes, and we have very little
+understanding about the mechanisms that lead to the prediction. While
+opaqueness concerning machine behaviour might not be a problem in deterministic
+domains, in health care, providing explanations about the molecular factors and
+phenotypes that are driving the classification is crucial to build trust in the
+performance of the predictive system. We propose Pathway Induced Multiple
+Kernel Learning (PIMKL), a novel methodology to reliably classify samples that
+can also help gain insights into the molecular mechanisms that underlie the
+classification. PIMKL exploits prior knowledge in the form of a molecular
+interaction network and annotated gene sets, by optimizing a mixture of
+pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an
+approach that has demonstrated excellent performance in different machine
+learning applications. After optimizing the combination of kernels for
+prediction of a specific phenotype, the model provides a stable molecular
+signature that can be interpreted in the light of the ingested prior knowledge
+and that can be used in transfer learning tasks.
+",0,0,0,1,1,0
+6436,pyRecLab: A Software Library for Quick Prototyping of Recommender Systems,"  This paper introduces pyRecLab, a software library written in C++ with Python
+bindings which allows to quickly train, test and develop recommender systems.
+Although there are several software libraries for this purpose, only a few let
+developers to get quickly started with the most traditional methods, permitting
+them to try different parameters and approach several tasks without a
+significant loss of performance. Among the few libraries that have all these
+features, they are available in languages such as Java, Scala or C#, what is a
+disadvantage for less experienced programmers more used to the popular Python
+programming language. In this article we introduce details of pyRecLab, showing
+as well performance analysis in terms of error metrics (MAE and RMSE) and
+train/test time. We benchmark it against the popular Java-based library LibRec,
+showing similar results. We expect programmers with little experience and
+people interested in quickly prototyping recommender systems to be benefited
+from pyRecLab.
+",1,0,0,0,0,0
+6437,A unified theory of adaptive stochastic gradient descent as Bayesian filtering,"  We formulate stochastic gradient descent (SGD) as a Bayesian filtering
+problem. Inference in the Bayesian setting naturally gives rise to BRMSprop and
+BAdam: Bayesian variants of RMSprop and Adam. Remarkably, the Bayesian approach
+recovers many features of state-of-the-art adaptive SGD methods, including
+amoungst others root-mean-square normalization, Nesterov acceleration and
+AdamW. As such, the Bayesian approach provides one explanation for the
+empirical effectiveness of state-of-the-art adaptive SGD algorithms.
+Empirically comparing BRMSprop and BAdam with naive RMSprop and Adam on MNIST,
+we find that Bayesian methods have the potential to considerably reduce test
+loss and classification error.
+",0,0,0,1,0,0
+6438,Radiative effects during the assembly of direct collapse black holes,"  We perform a post-processing radiative feedback analysis on a 3D ab initio
+cosmological simulation of an atomic cooling halo under the direct collapse
+black hole (DCBH) scenario. We maintain the spatial resolution of the
+simulation by incorporating native ray-tracing on unstructured mesh data,
+including Monte Carlo Lyman-alpha (Ly{\alpha}) radiative transfer. DCBHs are
+born in gas-rich, metal-poor environments with the possibility of Compton-thick
+conditions, $N_H \gtrsim 10^{24} {\rm cm}^{-2}$. Therefore, the surrounding gas
+is capable of experiencing the full impact of the bottled-up radiation
+pressure. In particular, we find that multiple scattering of Ly{\alpha} photons
+provides an important source of mechanical feedback after the gas in the
+sub-parsec region becomes partially ionized, avoiding the bottleneck of
+destruction via the two-photon emission mechanism. We provide detailed
+discussion of the simulation environment, expansion of the ionization front,
+emission and escape of Ly{\alpha} radiation, and Compton scattering. A sink
+particle prescription allows us to extract approximate limits on the
+post-formation evolution of the radiative feedback. Fully coupled Ly{\alpha}
+radiation hydrodynamics will be crucial to consider in future DCBH simulations.
+",0,1,0,0,0,0
+6439,Spin-orbit interactions in optically active materials,"  We investigate the inherent influence of light polarization on the intensity
+distribution in anisotropic media undergoing a local inhomogeneous rotation of
+the principal axes. Whereas in general such configuration implies a complicated
+interaction between geometric and dynamic phase, we show that, in a medium
+showing an inhomogeneous circular birefringence, the geometric phase vanishes.
+Due to the spin-orbit interaction, the two circular polarizations perceive
+reversed spatial distribution of the dynamic phase. Based upon this effect,
+polarization-selective lens, waveguides and beam deflectors are proposed.
+",0,1,0,0,0,0
+6440,Automatic White-Box Testing of First-Order Logic Ontologies,"  Formal ontologies are axiomatizations in a logic-based formalism. The
+development of formal ontologies, and their important role in the Semantic Web
+area, is generating considerable research on the use of automated reasoning
+techniques and tools that help in ontology engineering. One of the main aims is
+to refine and to improve axiomatizations for enabling automated reasoning tools
+to efficiently infer reliable information. Defects in the axiomatization can
+not only cause wrong inferences, but can also hinder the inference of expected
+information, either by increasing the computational cost of, or even
+preventing, the inference. In this paper, we introduce a novel, fully automatic
+white-box testing framework for first-order logic ontologies. Our methodology
+is based on the detection of inference-based redundancies in the given
+axiomatization. The application of the proposed testing method is fully
+automatic since a) the automated generation of tests is guided only by the
+syntax of axioms and b) the evaluation of tests is performed by automated
+theorem provers. Our proposal enables the detection of defects and serves to
+certify the grade of suitability --for reasoning purposes-- of every axiom. We
+formally define the set of tests that are generated from any axiom and prove
+that every test is logically related to redundancies in the axiom from which
+the test has been generated. We have implemented our method and used this
+implementation to automatically detect several non-trivial defects that were
+hidden in various first-order logic ontologies. Throughout the paper we provide
+illustrative examples of these defects, explain how they were found, and how
+each proof --given by an automated theorem-prover-- provides useful hints on
+the nature of each defect. Additionally, by correcting all the detected
+defects, we have obtained an improved version of one of the tested ontologies:
+Adimen-SUMO.
+",1,0,0,0,0,0
+6441,Critical values in Bak-Sneppen type models,"  In the Bak-Sneppen model, the lowest fitness particle and its two nearest
+neighbors are renewed at each temporal step with a uniform (0,1) fitness
+distribution. The model presents a critical value that depends on the
+interaction criteria (two nearest neighbors) and on the update procedure
+(uniform). Here we calculate the critical value for models where one or both
+properties are changed. We study models with non-uniform updates, models with
+random neighbors and models with binary fitness and obtain exact results for
+the average fitness and for $p_c$.
+",0,1,0,0,0,0
+6442,On the correspondence of deviances and maximum likelihood and interval estimates from log-linear to logistic regression modelling,"  Consider a set of categorical variables $\mathcal{P}$ where at least one,
+denoted by $Y$, is binary. The log-linear model that describes the counts in
+the resulting contingency table implies a specific logistic regression model,
+with the binary variable as the outcome. Extending results in Christensen
+(1997), by also considering the case where factors present in the contingency
+table disappear from the logistic regression model, we prove that the Maximum
+Likelihood Estimate (MLE) for the parameters of the logistic regression equals
+the MLE for the corresponding parameters of the log-linear model. We prove
+that, asymptotically, standard errors for the two sets of parameters are also
+equal. Subsequently, Wald confidence intervals are asymptotically equal. These
+results demonstrate the extent to which inferences from the log-linear
+framework can be translated to inferences within the logistic regression
+framework, on the magnitude of main effects and interactions. Finally, we prove
+that the deviance of the log-linear model is equal to the deviance of the
+corresponding logistic regression, provided that the latter is fitted to a
+dataset where no cell observations are merged when one or more factors in
+$\mathcal{P} \setminus \{ Y \}$ become obsolete. We illustrate the derived
+results with the analysis of a real dataset.
+",0,0,0,1,0,0
+6443,ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters,"  We consider time-domain digital backpropagation with chromatic dispersion
+filters jointly optimized and quantized using machine-learning techniques.
+Compared to the baseline implementations, we show improved BER performance and
+>40% power dissipation reductions in 28-nm CMOS.
+",0,0,0,1,0,0
+6444,A formalization of convex polyhedra based on the simplex method,"  We present a formalization of convex polyhedra in the proof assistant Coq.
+The cornerstone of our work is a complete implementation of the simplex method,
+together with the proof of its correctness and termination. This allows us to
+define the basic predicates over polyhedra in an effective way (i.e., as
+programs), and relate them with the corresponding usual logical counterparts.
+To this end, we make an extensive use of the Boolean reflection methodology.
+The benefit of this approach is that we can easily derive the proof of several
+fundamental results on polyhedra, such as Farkas' Lemma, the duality theorem of
+linear programming, and Minkowski's Theorem.
+",1,0,1,0,0,0
+6445,Weight Spectrum of Quasi-Perfect Binary Codes with Distance 4,"  We consider the weight spectrum of a class of quasi-perfect binary linear
+codes with code distance 4. For example, extended Hamming code and Panchenko
+code are the known members of this class. Also, it is known that in many cases
+Panchenko code has the minimal number of weight 4 codewords. We give exact
+recursive formulas for the weight spectrum of quasi-perfect codes and their
+dual codes. As an example of application of the weight spectrum we derive a
+lower estimate for the conditional probability of correction of erasure
+patterns of high weights (equal to or greater than code distance).
+",1,0,0,0,0,0
+6446,Ultra-High Electro-Optic Activity Demonstrated in a Silicon-Organic Hybrid (SOH) Modulator,"  Efficient electro-optic (EO) modulators crucially rely on advanced materials
+that exhibit strong electro-optic activity and that can be integrated into
+high-speed and efficient phase shifter structures. In this paper, we
+demonstrate ultra-high in-device EO figures of merit of up to n3r33 = 2300 pm/V
+achieved in a silicon-organic hybrid (SOH) Mach-Zehnder Modulator (MZM) using
+the EO chromophore JRD1. This is the highest material-related in-device EO
+figure of merit hitherto achieved in a high-speed modulator at any operating
+wavelength. The {\pi}-voltage of the 1.5 mm-long device amounts to 210 mV,
+leading to a voltage-length product of U{\pi}L = 320 V{\mu}m - the lowest value
+reported for MZM that are based on low-loss dielectric waveguides. The
+viability of the devices is demonstrated by generating high-quality
+on-off-keying (OOK) signals at 40 Gbit/s with Q factors in excess of 8 at a
+drive voltage as low as 140 mVpp. We expect that efficient high-speed EO
+modulators will not only have major impact in the field of optical
+communications, but will also open new avenues towards ultra-fast
+photonic-electronic signal processing.
+",0,1,0,0,0,0
+6447,How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games,"  How should an AI-based explanation system explain an agent's complex behavior
+to ordinary end users who have no background in AI? Answering this question is
+an active research area, for if an AI-based explanation system could
+effectively explain intelligent agents' behavior, it could enable the end users
+to understand, assess, and appropriately trust (or distrust) the agents
+attempting to help them. To provide insights into this question, we turned to
+human expert explainers in the real-time strategy domain, ""shoutcaster"", to
+understand (1) how they foraged in an evolving strategy game in real time, (2)
+how they assessed the players' behaviors, and (3) how they constructed
+pertinent and timely explanations out of their insights and delivered them to
+their audience. The results provided insights into shoutcasters' foraging
+strategies for gleaning information necessary to assess and explain the
+players; a characterization of the types of implicit questions shoutcasters
+answered; and implications for creating explanations by using the patterns
+",1,0,0,0,0,0
+6448,Entanglement Entropy of Eigenstates of Quadratic Fermionic Hamiltonians,"  In a seminal paper [D. N. Page, Phys. Rev. Lett. 71, 1291 (1993)], Page
+proved that the average entanglement entropy of subsystems of random pure
+states is $S_{\rm ave}\simeq\ln{\cal D}_{\rm A} - (1/2) {\cal D}_{\rm
+A}^2/{\cal D}$ for $1\ll{\cal D}_{\rm A}\leq\sqrt{\cal D}$, where ${\cal
+D}_{\rm A}$ and ${\cal D}$ are the Hilbert space dimensions of the subsystem
+and the system, respectively. Hence, typical pure states are (nearly) maximally
+entangled. We develop tools to compute the average entanglement entropy
+$\langle S\rangle$ of all eigenstates of quadratic fermionic Hamiltonians. In
+particular, we derive exact bounds for the most general translationally
+invariant models $\ln{\cal D}_{\rm A} - (\ln{\cal D}_{\rm A})^2/\ln{\cal D}
+\leq \langle S \rangle \leq \ln{\cal D}_{\rm A} - [1/(2\ln2)] (\ln{\cal D}_{\rm
+A})^2/\ln{\cal D}$. Consequently we prove that: (i) if the subsystem size is a
+finite fraction of the system size then $\langle S\rangle<\ln{\cal D}_{\rm A}$
+in the thermodynamic limit, i.e., the average over eigenstates of the
+Hamiltonian departs from the result for typical pure states, and (ii) in the
+limit in which the subsystem size is a vanishing fraction of the system size,
+the average entanglement entropy is maximal, i.e., typical eigenstates of such
+Hamiltonians exhibit eigenstate thermalization.
+",0,1,0,0,0,0
+6449,Focusing light through dynamical samples using fast closed-loop wavefront optimization,"  We describe a fast closed-loop optimization wavefront shaping system able to
+focus light through dynamic scattering media. A MEMS-based spatial light
+modulator (SLM), a fast photodetector and FPGA electronics are combined to
+implement a closed-loop optimization of a wavefront with a single mode
+optimization rate of 4.1 kHz. The system performances are demonstrated by
+focusing light through colloidal solutions of TiO2 particles in glycerol with
+tunable temporal stability.
+",0,1,0,0,0,0
+6450,The curvature estimates for convex solutions of some fully nonlinear Hessian type equations,"  The curvature estimates of quotient curvature equation do not always exist
+even for convex setting \cite{GRW}. Thus it is natural question to find other
+type of elliptic equations possessing curvature estimates. In this paper, we
+discuss the existence of curvature estimate for fully nonlinear elliptic
+equations defined by symmetric polynomials, mainlly, the linear combination of
+elementary symmetric polynomials.
+",0,0,1,0,0,0
+6451,The Compressed Model of Residual CNDS,"  Convolutional neural networks have achieved a great success in the recent
+years. Although, the way to maximize the performance of the convolutional
+neural networks still in the beginning. Furthermore, the optimization of the
+size and the time that need to train the convolutional neural networks is very
+far away from reaching the researcher's ambition. In this paper, we proposed a
+new convolutional neural network that combined several techniques to boost the
+optimization of the convolutional neural network in the aspects of speed and
+size. As we used our previous model Residual-CNDS (ResCNDS), which solved the
+problems of slower convergence, overfitting, and degradation, and compressed
+it. The outcome model called Residual-Squeeze-CNDS (ResSquCNDS), which we
+demonstrated on our sold technique to add residual learning and our model of
+compressing the convolutional neural networks. Our model of compressing adapted
+from the SQUEEZENET model, but our model is more generalizable, which can be
+applied almost to any neural network model, and fully integrated into the
+residual learning, which addresses the problem of the degradation very
+successfully. Our proposed model trained on very large-scale MIT
+Places365-Standard scene datasets, which backing our hypothesis that the new
+compressed model inherited the best of the previous ResCNDS8 model, and almost
+get the same accuracy in the validation Top-1 and Top-5 with 87.64% smaller in
+size and 13.33% faster in the training time.
+",1,0,0,0,0,0
+6452,"Chow Rings of Mp_{0,2}(N,d) and Mbar_{0,2}(P^{N-1},d) and Gromov-Witten Invariants of Projective Hypersurfaces of Degree 1 and 2","  In this paper, we prove formulas that represent two-pointed Gromov-Witten
+invariant <O_{h^a}O_{h^b}>_{0,d} of projective hypersurfaces with d=1,2 in
+terms of Chow ring of Mbar_{0,2}(P^{N-1},d), the moduli spaces of stable maps
+from genus 0 stable curves to projective space P^{N-1}. Our formulas are based
+on representation of the intersection number w(O_{h^a}O_{h^b})_{0,d}, which was
+introduced by Jinzenji, in terms of Chow ring of Mp_{0,2}(N,d), the moduli
+space of quasi maps from P^1 to P^{N-1} with two marked points. In order to
+prove our formulas, we use the results on Chow ring of Mbar_{0,2}(P^{N-1},d),
+that were derived by A. Mustata and M. Mustata. We also present explicit toric
+data of Mp_{0,2}(N,d) and prove relations of Chow ring of Mp_{0,2}(N,d).
+",0,0,1,0,0,0
+6453,A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence,"  Due to increasing urban population and growing number of motor vehicles,
+traffic congestion is becoming a major problem of the 21st century. One of the
+main reasons behind traffic congestion is accidents which can not only result
+in casualties and losses for the participants, but also in wasted and lost time
+for the others that are stuck behind the wheels. Early detection of an accident
+can save lives, provides quicker road openings, hence decreases wasted time and
+resources, and increases efficiency. In this study, we propose a preliminary
+real-time autonomous accident-detection system based on computational
+intelligence techniques. Istanbul City traffic-flow data for the year 2015 from
+various sensor locations are populated using big data processing methodologies.
+The extracted features are then fed into a nearest neighbor model, a regression
+tree, and a feed-forward neural network model. For the output, the possibility
+of an occurrence of an accident is predicted. The results indicate that even
+though the number of false alarms dominates the real accident cases, the system
+can still provide useful information that can be used for status verification
+and early reaction to possible accidents.
+",1,0,0,1,0,0
+6454,Asymptotic Eigenfunctions for a class of Difference Operators,"  We analyze a general class of difference operators $H_\varepsilon =
+T_\varepsilon + V_\varepsilon$ on $\ell^2(\varepsilon \mathbb{Z}^d)$, where
+$V_\varepsilon$ is a one-well potential and $\varepsilon$ is a small parameter.
+We construct formal asymptotic expansions of WKB-type for eigenfunctions
+associated with the low lying eigenvalues of $H_\varepsilon$. These are
+obtained from eigenfunctions or quasimodes for the operator $H_\varepsilon$,
+acting on $L^2(\mathbb{R}^d)$, via restriction to the lattice
+$\varepsilon\mathbb{Z}^d$.
+",0,0,1,0,0,0
+6455,Common Glass-Forming Spin-Liquid State in the Pyrochlore Magnets Dy$_2$Ti$_2$O$_7$ and Ho$_2$Ti$_2$O$_7$,"  Despite a well-ordered pyrochlore crystal structure and strong magnetic
+interactions between the Dy$^{3+}$ or Ho$^{3+}$ ions, no long range magnetic
+order has been detected in the pyrochlore titanates Ho$_2$Ti$_2$O$_7$ and
+Dy$_2$Ti$_2$O$_7$. To explore the actual magnetic phase formed by cooling these
+materials, we measure their magnetization dynamics using toroidal,
+boundary-free magnetization transport techniques. We find that the dynamical
+magnetic susceptibility of both compounds has the same distinctive
+phenomenology, that is indistinguishable in form from that of the dielectric
+permittivity of dipolar glass-forming liquids. Moreover, Ho$_2$Ti$_2$O$_7$ and
+Dy$_2$Ti$_2$O$_7$ both exhibit microscopic magnetic relaxation times that
+increase along the super-Arrhenius trajectories analogous to those observed in
+glass-forming dipolar liquids. Thus, upon cooling below about 2K,
+Dy$_2$Ti$_2$O$_7$ and Ho$_2$Ti$_2$O$_7$ both appear to enter the same magnetic
+state exhibiting the characteristics of a glass-forming spin-liquid.
+",0,1,0,0,0,0
+6456,Britannia Rule the Waves,"  The students are introduced to navigation in general and the longitude
+problem in particular. A few videos provide insight into scientific and
+historical facts related to the issue. Then, the students learn in two steps
+how longitude can be derived from time measurements. They first build a
+Longitude Clock that visualises the math behind the concept. They use it to
+determine the longitudes corresponding to five time measurements. In the second
+step, they assume the position of James Cook's navigator and plot the location
+of seven destinations on Cook's second voyage between 1772 and 1775.
+",0,1,0,0,0,0
+6457,Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning,"  We introduce Error Forward-Propagation, a biologically plausible mechanism to
+propagate error feedback forward through the network. Architectural constraints
+on connectivity are virtually eliminated for error feedback in the brain;
+systematic backward connectivity is not used or needed to deliver error
+feedback. Feedback as a means of assigning credit to neurons earlier in the
+forward pathway for their contribution to the final output is thought to be
+used in learning in the brain. How the brain solves the credit assignment
+problem is unclear. In machine learning, error backpropagation is a highly
+successful mechanism for credit assignment in deep multilayered networks.
+Backpropagation requires symmetric reciprocal connectivity for every neuron.
+From a biological perspective, there is no evidence of such an architectural
+constraint, which makes backpropagation implausible for learning in the brain.
+This architectural constraint is reduced with the use of random feedback
+weights. Models using random feedback weights require backward connectivity
+patterns for every neuron, but avoid symmetric weights and reciprocal
+connections. In this paper, we practically remove this architectural
+constraint, requiring only a backward loop connection for effective error
+feedback. We propose reusing the forward connections to deliver the error
+feedback by feeding the outputs into the input receiving layer. This mechanism,
+Error Forward-Propagation, is a plausible basis for how error feedback occurs
+deep in the brain independent of and yet in support of the functionality
+underlying intricate network architectures. We show experimentally that
+recurrent neural networks with two and three hidden layers can be trained using
+Error Forward-Propagation on the MNIST and Fashion MNIST datasets, achieving
+$1.90\%$ and $11\%$ generalization errors respectively.
+",0,0,0,0,1,0
+6458,CANA: A python package for quantifying control and canalization in Boolean Networks,"  Logical models offer a simple but powerful means to understand the complex
+dynamics of biochemical regulation, without the need to estimate kinetic
+parameters. However, even simple automata components can lead to collective
+dynamics that are computationally intractable when aggregated into networks. In
+previous work we demonstrated that automata network models of biochemical
+regulation are highly canalizing, whereby many variable states and their
+groupings are redundant (Marques-Pita and Rocha, 2013). The precise charting
+and measurement of such canalization simplifies these models, making even very
+large networks amenable to analysis. Moreover, canalization plays an important
+role in the control, robustness, modularity and criticality of Boolean network
+dynamics, especially those used to model biochemical regulation (Gates and
+Rocha, 2016; Gates et al., 2016; Manicka, 2017). Here we describe a new
+publicly-available Python package that provides the necessary tools to extract,
+measure, and visualize canalizing redundancy present in Boolean network models.
+It extracts the pathways most effective in controlling dynamics in these
+models, including their effective graph and dynamics canalizing map, as well as
+other tools to uncover minimum sets of control variables.
+",1,0,0,0,1,0
+6459,$α$-Variational Inference with Statistical Guarantees,"  We propose a family of variational approximations to Bayesian posterior
+distributions, called $\alpha$-VB, with provable statistical guarantees. The
+standard variational approximation is a special case of $\alpha$-VB with
+$\alpha=1$. When $\alpha \in(0,1]$, a novel class of variational inequalities
+are developed for linking the Bayes risk under the variational approximation to
+the objective function in the variational optimization problem, implying that
+maximizing the evidence lower bound in variational inference has the effect of
+minimizing the Bayes risk within the variational density family. Operating in a
+frequentist setup, the variational inequalities imply that point estimates
+constructed from the $\alpha$-VB procedure converge at an optimal rate to the
+true parameter in a wide range of problems. We illustrate our general theory
+with a number of examples, including the mean-field variational approximation
+to (low)-high-dimensional Bayesian linear regression with spike and slab
+priors, mixture of Gaussian models, latent Dirichlet allocation, and (mixture
+of) Gaussian variational approximation in regular parametric models.
+",0,0,1,1,0,0
+6460,Stable monoenergetic ion acceleration by a two color laser tweezer,"  In the past decades, the phenomenal progress in the development of
+ultraintense lasers has opened up many exciting new frontiers in laser matter
+physics, including laser plasma ion acceleration. Currently a major challenge
+in this frontier is to find simple methods to stably produce monoenergetic ion
+beams with sufficient charge for real applications. Here, we propose a novel
+scheme using a two color laser tweezer to fulfill this goal. In this scheme,
+two circularly polarized lasers with different wavelengths collide right on a
+thin nano-foil target containing mixed ion species. The radiation pressure of
+this laser pair acts like a tweezer to pinch and fully drag the electrons out,
+forming a stable uniform accelerating field for the ions. Scaling laws and
+three-dimensional particle-in-cell simulations confirm that high energy
+(10-1000 MeV) high charge ($\sim 10^{10}$) proton beams with narrow energy
+spread ($\sim4\%-20\%$) can be obtained by commercially available lasers. Such
+a scheme may open up a new route for compact high quality ion sources for
+various applications.
+",0,1,0,0,0,0
+6461,Experimental demonstration of an ultra-compact on-chip polarization controlling structure,"  We demonstrated a novel on-chip polarization controlling structure,
+fabricated by standard 0.18-um foundry technology. It achieved polarization
+rotation with a size of 0.726 um * 5.27 um and can be easily extended into
+dynamic polarization controllers.
+",0,1,0,0,0,0
+6462,Letter-Based Speech Recognition with Gated ConvNets,"  In the recent literature, ""end-to-end"" speech systems often refer to
+letter-based acoustic models trained in a sequence-to-sequence manner, either
+via a recurrent model or via a structured output learning approach (such as
+CTC). In contrast to traditional phone (or senone)-based approaches, these
+""end-to-end'' approaches alleviate the need of word pronunciation modeling, and
+do not require a ""forced alignment"" step at training time. Phone-based
+approaches remain however state of the art on classical benchmarks. In this
+paper, we propose a letter-based speech recognition system, leveraging a
+ConvNet acoustic model. Key ingredients of the ConvNet are Gated Linear Units
+and high dropout. The ConvNet is trained to map audio sequences to their
+corresponding letter transcriptions, either via a classical CTC approach, or
+via a recent variant called ASG. Coupled with a simple decoder at inference
+time, our system matches the best existing letter-based systems on WSJ (in word
+error rate), and shows near state of the art performance on LibriSpeech.
+",1,0,0,0,0,0
+6463,The Frequent Network Neighborhood Mapping of the Human Hippocampus Shows Much More Frequent Neighbor Sets in Males Than in Females,"  In the study of the human connectome, the vertices and the edges of the
+network of the human brain are analyzed: the vertices of the graphs are the
+anatomically identified gray matter areas of the subjects; this set is exactly
+the same for all the subjects. The edges of the graphs correspond to the axonal
+fibers, connecting these areas. In the biological applications of graph theory,
+it happens very rarely that scientists examine numerous large graphs on the
+very same, labeled vertex set. Exactly this is the case in the study of the
+connectomes. Because of the particularity of these sets of graphs, novel,
+robust methods need to be developed for their analysis. Here we introduce the
+new method of the Frequent Network Neighborhood Mapping for the connectome,
+which serves as a robust identification of the neighborhoods of given vertices
+of special interest in the graph. We apply the novel method for mapping the
+neighborhoods of the human hippocampus and discover strong statistical
+asymmetries between the connectomes of the sexes, computed from the Human
+Connectome Project. We analyze 413 braingraphs, each with 463 nodes. We show
+that the hippocampi of men have much more significantly frequent neighbor sets
+than women; therefore, in a sense, the connections of the hippocampi are more
+regularly distributed in men and more varied in women. Our results are in
+contrast to the volumetric studies of the human hippocampus, where it was shown
+that the relative volume of the hippocampus is the same in men and women.
+",0,0,0,0,1,0
+6464,Recurrent Neural Networks as Weighted Language Recognizers,"  We investigate the computational complexity of various problems for simple
+recurrent neural networks (RNNs) as formal models for recognizing weighted
+languages. We focus on the single-layer, ReLU-activation, rational-weight RNNs
+with softmax, which are commonly used in natural language processing
+applications. We show that most problems for such RNNs are undecidable,
+including consistency, equivalence, minimization, and the determination of the
+highest-weighted string. However, for consistent RNNs the last problem becomes
+decidable, although the solution length can surpass all computable bounds. If
+additionally the string is limited to polynomial length, the problem becomes
+NP-complete and APX-hard. In summary, this shows that approximations and
+heuristic algorithms are necessary in practical applications of those RNNs.
+",1,0,0,0,0,0
+6465,Classifying and Qualifying GUI Defects,"  Graphical user interfaces (GUIs) are integral parts of software systems that
+require interactions from their users. Software testers have paid special
+attention to GUI testing in the last decade, and have devised techniques that
+are effective in finding several kinds of GUI errors. However, the introduction
+of new types of interactions in GUIs (e.g., direct manipulation) presents new
+kinds of errors that are not targeted by current testing techniques. We believe
+that to advance GUI testing, the community needs a comprehensive and high level
+GUI fault model, which incorporates all types of interactions. The work
+detailed in this paper establishes 4 contributions: 1) A GUI fault model
+designed to identify and classify GUI faults. 2) An empirical analysis for
+assessing the relevance of the proposed fault model against failures found in
+real GUIs. 3) An empirical assessment of two GUI testing tools (i.e. GUITAR and
+Jubula) against those failures. 4) GUI mutants we've developed according to our
+fault model. These mutants are freely available and can be reused by developers
+for benchmarking their GUI testing tools.
+",1,0,0,0,0,0
+6466,"A mechanistic model of connector hubs, modularity, and cognition","  The human brain network is modular--comprised of communities of tightly
+interconnected nodes. This network contains local hubs, which have many
+connections within their own communities, and connector hubs, which have
+connections diversely distributed across communities. A mechanistic
+understanding of these hubs and how they support cognition has not been
+demonstrated. Here, we leveraged individual differences in hub connectivity and
+cognition. We show that a model of hub connectivity accurately predicts the
+cognitive performance of 476 individuals in four distinct tasks. Moreover,
+there is a general optimal network structure for cognitive
+performance--individuals with diversely connected hubs and consequent modular
+brain networks exhibit increased cognitive performance, regardless of the task.
+Critically, we find evidence consistent with a mechanistic model in which
+connector hubs tune the connectivity of their neighbors to be more modular
+while allowing for task appropriate information integration across communities,
+which increases global modularity and cognitive performance.
+",0,0,0,0,1,0
+6467,The OGLE Collection of Variable Stars. Over 450 000 Eclipsing and Ellipsoidal Binary Systems Toward the Galactic Bulge,"  We present a collection of 450 598 eclipsing and ellipsoidal binary systems
+detected in the OGLE fields toward the Galactic bulge. The collection consists
+of binary systems of all types: detached, semi-detached, and contact eclipsing
+binaries, RS CVn stars, cataclysmic variables, HW Vir binaries, double periodic
+variables, and even planetary transits. For all stars we provide the I- and
+V-band time-series photometry obtained during the OGLE-II, OGLE-III, and
+OGLE-IV surveys. We discuss methods used to identify binary systems in the OGLE
+data and present several objects of particular interest.
+",0,1,0,0,0,0
+6468,The discrete moment problem with nonconvex shape constraints,"  The discrete moment problem is a foundational problem in distribution-free
+robust optimization, where the goal is to find a worst-case distribution that
+satisfies a given set of moments. This paper studies the discrete moment
+problems with additional ""shape constraints"" that guarantee the worst case
+distribution is either log-concave or has an increasing failure rate. These
+classes of shape constraints have not previously been studied in the
+literature, in part due to their inherent nonconvexities. Nonetheless, these
+classes of distributions are useful in practice. We characterize the structure
+of optimal extreme point distributions by developing new results in reverse
+convex optimization, a lesser-known tool previously employed in designing
+global optimization algorithms. We are able to show, for example, that an
+optimal extreme point solution to a moment problem with $m$ moments and
+log-concave shape constraints is piecewise geometric with at most $m$ pieces.
+Moreover, this structure allows us to design an exact algorithm for computing
+optimal solutions in a low-dimensional space of parameters. Moreover, We
+describe a computational approach to solving these low-dimensional problems,
+including numerical results for a representative set of instances.
+",0,0,1,1,0,0
+6469,Some studies using capillary for flow control in a closed loop gas recirculation system,"  A Pilot unit of a closed loop gas (CLS) mixing and distribution system for
+the INO project was designed and is being operated with (1.8 x 1.9) m^2 glass
+RPCs (Resistive Plate Chamber). The performance of an RPC depends on the
+quality and quantity of gas mixture being used, a number of studies on
+controlling the flow and optimization of the gas mixture is being carried out.
+In this paper the effect of capillary as a dynamic impedance element on the
+differential pressure across RPC detector in a closed loop gas system is being
+highlighted. The flow versus the pressure variation with different types of
+capillaries and also with different types of gasses that are being used in an
+RPC is presented. An attempt is also made to measure the transient time of the
+gas flow through the capillary.
+",0,1,0,0,0,0
+6470,Optimal Tuning of Two-Dimensional Keyboards,"  We give a new analysis of a tuning problem in music theory, pertaining
+specifically to the approximation of harmonics on a two-dimensional keyboard.
+We formulate the question as a linear programming problem on families of
+constraints and provide exact solutions for many new keyboard dimensions. We
+also show that an optimal tuning for harmonic approximation can be obtained for
+any keyboard of given width, provided sufficiently many rows of octaves.
+",1,0,0,0,0,0
+6471,Ultra Reliable Short Message Relaying with Wireless Power Transfer,"  We consider a dual-hop wireless network where an energy constrained relay
+node first harvests energy through the received radio-frequency signal from the
+source, and then uses the harvested energy to forward the source's information
+to the destination node. The throughput and delay metrics are investigated for
+a decode-and-forward relaying mechanism at finite blocklength regime and
+delay-limited transmission mode. We consider ultra-reliable communication
+scenarios under discussion for the next fifth-generation of wireless systems,
+with error and latency constraints. The impact on these metrics of the
+blocklength, information bits, and relay position is investigated.
+",1,0,0,1,0,0
+6472,Verifiable Light-Weight Monitoring for Certificate Transparency Logs,"  Trust in publicly verifiable Certificate Transparency (CT) logs is reduced
+through cryptography, gossip, auditing, and monitoring. The role of a monitor
+is to observe each and every log entry, looking for suspicious certificates
+that interest the entity running the monitor. While anyone can run a monitor,
+it requires continuous operation and copies of the logs to be inspected. This
+has lead to the emergence of monitoring-as-a-service: a trusted party runs the
+monitor and provides registered subjects with selective certificate
+notifications, e.g., ""notify me of all foo.com certificates"". We present a
+CT/bis extension for verifiable light-weight monitoring that enables subjects
+to verify the correctness of such notifications, reducing the trust that is
+placed in these monitors. Our extension supports verifiable monitoring of
+wild-card domains and piggybacks on CT's existing gossip-audit security model.
+",1,0,0,0,0,0
+6473,Chainspace: A Sharded Smart Contracts Platform,"  Chainspace is a decentralized infrastructure, known as a distributed ledger,
+that supports user defined smart contracts and executes user-supplied
+transactions on their objects. The correct execution of smart contract
+transactions is verifiable by all. The system is scalable, by sharding state
+and the execution of transactions, and using S-BAC, a distributed commit
+protocol, to guarantee consistency. Chainspace is secure against subsets of
+nodes trying to compromise its integrity or availability properties through
+Byzantine Fault Tolerance (BFT), and extremely high-auditability,
+non-repudiation and `blockchain' techniques. Even when BFT fails, auditing
+mechanisms are in place to trace malicious participants. We present the design,
+rationale, and details of Chainspace; we argue through evaluating an
+implementation of the system about its scaling and other features; we
+illustrate a number of privacy-friendly smart contracts for smart metering,
+polling and banking and measure their performance.
+",1,0,0,0,0,0
+6474,On Certain Properties of Convex Functions,"  This note deals with certain properties of convex functions. We provide
+results on the convexity of the set of minima of these functions, the behaviour
+of their subgradient set under restriction, and optimization of these functions
+over an affine subspace.
+",0,0,1,0,0,0
+6475,The detection of variable radio emission from the fast rotating magnetic hot B-star HR7355 and evidence for its X-ray aurorae,"  In this paper we investigate the multiwavelengths properties of the magnetic
+early B-type star HR7355. We present its radio light curves at several
+frequencies, taken with the Jansky Very Large Array, and X-ray spectra, taken
+with the XMM X-ray telescope. Modeling of the radio light curves for the Stokes
+I and V provides a quantitative analysis of the HR7355 magnetosphere. A
+comparison between HR7355 and a similar analysis for the Ap star CUVir, allows
+us to study how the different physical parameters of the two stars affect the
+structure of the respective magnetospheres where the non-thermal electrons
+originate. Our model includes a cold thermal plasma component that accumulates
+at high magnetic latitudes that influences the radio regime, but does not give
+rise to X-ray emission. Instead, the thermal X-ray emission arises from shocks
+generated by wind stream collisions close to the magnetic equatorial plane. The
+analysis of the X-ray spectrum of HR7355 also suggests the presence of a
+non-thermal radiation. Comparison between the spectral index of the power-law
+X-ray energy distribution with the non-thermal electron energy distribution
+indicates that the non-thermal X-ray component could be the auroral signature
+of the non-thermal electrons that impact the stellar surface, the same
+non-thermal electrons that are responsible for the observed radio emission. On
+the basis of our analysis, we suggest a novel model that simultaneously
+explains the X-ray and the radio features of HR7355 and is likely relevant for
+magnetospheres of other magnetic early type stars.
+",0,1,0,0,0,0
+6476,In silico optimization of critical currents in superconductors,"  For many technological applications of superconductors the performance of a
+material is determined by the highest current it can carry losslessly - the
+critical current. In turn, the critical current can be controlled by adding
+non-superconducting defects in the superconductor matrix. Here we report on
+systematic comparison of different local and global optimization strategies to
+predict optimal structures of pinning centers leading to the highest possible
+critical currents. We demonstrate performance of these methods for a
+superconductor with randomly placed spherical, elliptical, and columnar
+defects.
+",0,1,0,0,0,0
+6477,Attracting sequences of holomorphic automorphisms that agree to a certain order,"  The basin of attraction of a uniformly attracting sequence of holomorphic
+automorphisms that agree to a certain order in the common fixed point, is
+biholomorphic to $\mathbb{C}^n$. We also give sufficient estimates how large
+this order has to be.
+",0,0,1,0,0,0
+6478,Repulsive Fermi polarons with negative effective mass,"  Recent LENS experiment on a 3D Fermi gas has reported a negative effective
+mass ($m^*<0$) of Fermi polarons in the strongly repulsive regime. There
+naturally arise a question whether the negative $m^*$ is a precursor of the
+instability towards phase separation (or itinerant ferromagnetism). In this
+work, we make use of the exact solutions to study the ground state and
+excitation properties of repulsive Fermi polarons in 1D, which can also exhibit
+a negative $m^*$ in the super Tonks-Girardeau regime. By analyzing the total
+spin, quasi-momentum distribution and pair correlations, we conclude that the
+negative $m^*$ is irrelevant to the instability towards ferromagnetism or phase
+separation, but rather an intrinsic feature of collective excitations for
+fermions in the strongly repulsive regime. Surprisingly, for large and negative
+$m^*$, such excitation is accompanied with a spin density modulation when the
+majority fermions move closer to the impurity rather than being repelled far
+away, contrary to the picture of phase separation. These results suggest an
+alternative interpretation of negative $m^*$ as observed in recent LENS
+experiment.
+",0,1,0,0,0,0
+6479,Entropy Formula for Random $\mathbb{Z}^k$-actions,"  In this paper, entropies, including measure-theoretic entropy and topological
+entropy, are considered for random $\mathbb{Z}^k$-actions which are generated
+by random compositions of the generators of $\mathbb{Z}^k$-actions. Applying
+Pesin's theory for commutative diffeomorphisms we obtain a measure-theoretic
+entropy formula of $C^{2}$ random $\mathbb{Z}^k$-actions via the Lyapunov
+spectra of the generators. Some formulas and bounds of topological entropy for
+certain random $\mathbb{Z}^k$(or $\mathbb{Z}_+^k$ )-actions generated by more
+general maps, such as Lipschitz maps, continuous maps on finite graphs and
+$C^{1}$ expanding maps, are also obtained. Moreover, as an application, we give
+a formula of Friedland's entropy for certain $C^{2}$ $\mathbb{Z}^k$-actions.
+",0,0,1,0,0,0
+6480,Kohn anomalies in momentum dependence of magnetic susceptibility of some three-dimensional systems,"  We study a question of presence of Kohn points, yielding at low temperatures
+non-analytic momentum dependence of magnetic susceptibility near its maximum,
+in electronic spectum of some three-dimensional systems. In particular, we
+consider one-band model on face centered cubic lattice with hopping between
+nearest and next-nearest neighbors, which models some aspects of the dispersion
+of ZrZn$_2$, and the two-band model on body centered cubic lattice, modeling
+the dispersion of chromium. For the former model it is shown that Kohn points
+yielding maxima of susceptibility exist in a certain (sufficiently wide) region
+of electronic concentrations; the dependence of the wave vectors, corresponding
+to the maxima, on the chemical potential is investigated. For the two-band
+model we show existence of the lines of Kohn points, yielding maximum of the
+susceptibility, which position agrees with the results of band structure
+calculations and experimental data on the wave vector of antiferromagnetism of
+chromium.
+",0,1,0,0,0,0
+6481,"Stability conditions, $τ$-tilting Theory and Maximal Green Sequences","  Extending the notion of maximal green sequences to an abelian category, we
+characterize the stability functions, as defined by Rudakov, that induce a
+maximal green sequence in an abelian length category. Furthermore, we use
+$\tau$-tilting theory to give a description of the wall and chamber structure
+of any finite dimensional algebra. Finally we introduce the notion of green
+paths in the wall and chamber structure of an algebra and show that green paths
+serve as geometrical generalization of maximal green sequences in this context.
+",0,0,1,0,0,0
+6482,The thermal phase curve offset on tidally- and non-tidally-locked exoplanets: A shallow water model,"  Using a shallow water model with time-dependent forcing we show that the peak
+of an exoplanet thermal phase curve is, in general, offset from secondary
+eclipse when the planet is rotating. That is, the planetary hot-spot is offset
+from the point of maximal heating (the substellar point) and may lead or lag
+the forcing; the extent and sign of the offset is a function of both the
+rotation rate and orbital period of the planet. We also find that the system
+reaches a steady-state in the reference frame of the moving forcing. The model
+is an extension of the well studied Matsuno-Gill model into a full spherical
+geometry and with a planetary-scale translating forcing representing the
+insolation received on an exoplanet from a host star.
+The speed of the gravity waves in the model is shown to be a key metric in
+evaluating the phase curve offset. If the velocity of the substellar point
+(relative to the planet's surface) exceeds that of the gravity waves then the
+hotspot will lag the substellar point, as might be expected by consideration of
+forced gravity wave dynamics. However, when the substellar point is moving
+slower than the internal wavespeed of the system the hottest point can lead the
+passage of the forcing. We provide an interpretation of this result by
+consideration of the Rossby and Kelvin wave dynamics as well as, in the very
+slowly rotating case, a one-dimensional model that yields an analytic solution.
+Finally, we consider the inverse problem of constraining planetary rotation
+rate from an observed phase curve.
+",0,1,0,0,0,0
+6483,On Asymptotic Standard Normality of the Two Sample Pivot,"  The asymptotic solution to the problem of comparing the means of two
+heteroscedastic populations, based on two random samples from the populations,
+hinges on the pivot underpinning the construction of the confidence interval
+and the test statistic being asymptotically standard Normal, which is known to
+happen if the two samples are independent and the ratio of the sample sizes
+converges to a finite positive number. This restriction on the asymptotic
+behavior of the ratio of the sample sizes carries the risk of rendering the
+asymptotic justification of the finite sample approximation invalid. It turns
+out that neither the restriction on the asymptotic behavior of the ratio of the
+sample sizes nor the assumption of cross sample independence is necessary for
+the pivotal convergence in question to take place. If the joint distribution of
+the standardized sample means converges to a spherically symmetric
+distribution, then that distribution must be bivariate standard Normal (which
+can happen without the assumption of cross sample independence), and the
+aforesaid pivotal convergence holds.
+",0,0,1,1,0,0
+6484,Breakthrough revisited: investigating the requirements for growth of dust beyond the bouncing barrier,"  For grain growth to proceed effectively and lead to planet formation a number
+of barriers to growth must be overcome. One such barrier, relevant for compact
+grains in the inner regions of the disc, is the `bouncing barrier' in which
+large grains ($\sim$ mm size) tend to bounce off each other rather than
+sticking. However, by maintaining a population of small grains it has been
+suggested that cm-size particles may grow rapidly by sweeping up these small
+grains. We present the first numerically resolved investigation into the
+conditions under which grains may be lucky enough to grow beyond the bouncing
+barrier by a series of rare collisions leading to growth (so-called
+`breakthrough'). Our models support previous results, and show that in simple
+models breakthrough requires the mass ratio at which high velocity collisions
+transition to growth instead of causing fragmentation to be low, $\phi \lesssim
+50$. However, in models that take into account the dependence of the
+fragmentation threshold on mass-ratio, we find breakthrough occurs more
+readily, even if mass transfer is relatively inefficient. This suggests that
+bouncing may only slow down growth, rather than preventing growth beyond a
+threshold barrier. However, even when growth beyond the bouncing barrier is
+possible, radial drift will usually prevent growth to arbitrarily large sizes.
+",0,1,0,0,0,0
+6485,Extremely high magnetoresistance and conductivity in the type-II Weyl semimetals WP2 and MoP2,"  The peculiar band structure of semimetals exhibiting Dirac and Weyl crossings
+can lead to spectacular electronic properties such as large mobilities
+accompanied by extremely high magnetoresistance. In particular, two closely
+neighbouring Weyl points of the same chirality are protected from annihilation
+by structural distortions or defects, thereby significantly reducing the
+scattering probability between them. Here we present the electronic properties
+of the transition metal diphosphides, WP2 and MoP2, that are type-II Weyl
+semimetals with robust Weyl points. We present transport and angle resolved
+photoemission spectroscopy measurements, and first principles calculations. Our
+single crystals of WP2 display an extremely low residual low-temperature
+resistivity of 3 nohm-cm accompanied by an enormous and highly anisotropic
+magnetoresistance above 200 million % at 63 T and 2.5 K. These properties are
+likely a consequence of the novel Weyl fermions expressed in this compound. We
+observe a large suppression of charge carrier backscattering in WP2 from
+transport measurements.
+",0,1,0,0,0,0
+6486,Density and current profiles in $U_q(A^{(1)}_2)$ zero range process,"  The stochastic $R$ matrix for $U_q(A^{(1)}_n)$ introduced recently gives rise
+to an integrable zero range process of $n$ classes of particles in one
+dimension. For $n=2$ we investigate how finitely many first class particles
+fixed as defects influence the grand canonical ensemble of the second class
+particles. By using the matrix product stationary probabilities involving
+infinite products of $q$-bosons, exact formulas are derived for the local
+density and current of the second class particles in the large volume limit.
+",0,1,0,0,0,0
+6487,"Crystal structure, site selectivity, and electronic structure of layered chalcogenide LaOBiPbS3","  We have investigated the crystal structure of LaOBiPbS3 using neutron
+diffraction and synchrotron X-ray diffraction. From structural refinements, we
+found that the two metal sites, occupied by Bi and Pb, were differently
+surrounded by the sulfur atoms. Calculated bond valence sum suggested that one
+metal site was nearly trivalent and the other was nearly divalent. Neutron
+diffraction also revealed site selectivity of Bi and Pb in the LaOBiPbS3
+structure. These results suggested that the crystal structure of LaOBiPbS3 can
+be regarded as alternate stacks of the rock-salt-type Pb-rich sulfide layers
+and the LaOBiS2-type Bi-rich layers. From band calculations for an ideal
+(LaOBiS2)(PbS) system, we found that the S bands of the PbS layer were
+hybridized with the Bi bands of the BiS plane at around the Fermi energy, which
+resulted in the electronic characteristics different from that of LaOBiS2.
+Stacking the rock-salt type sulfide (chalcogenide) layers and the BiS2-based
+layered structure could be a new strategy to exploration of new BiS2-based
+layered compounds, exotic two-dimensional electronic states, or novel
+functionality.
+",0,1,0,0,0,0
+6488,"Responses of Pre-transitional Materials with Stress-Generating Defects to External Stimuli: Superelasticity, Supermagnetostriction, Invar and Elinvar Effects","  We considered a generic case of pre-transitional materials with static
+stress-generating defects, dislocations and coherent nano-precipitates, at
+temperatures close but above the starting temperature of martensitic
+transformation, Ms. Using the Phase Field Microelasticity theory and 3D
+simulation, we demonstrated that the local stress generated by these defects
+produces equilibrium nano-size martensitic embryos (MEs) in pre-transitional
+state, these embryos being orientation variants of martensite. This is a new
+type of equilibrium: the thermoelastic equilibrium between the MEs and parent
+phase in which the total volume of MEs and their size are equilibrium internal
+thermodynamic parameters. This thermoelastic equilibrium exists only in
+presence of the stress-generating defects. Cooling the pre-transitional state
+towards Ms or applying the external stimuli, stress or magnetic field, results
+in a shift of the thermoelastic equilibrium provided by a reversible
+anhysteretic growth of MEs that results in a giant ME-generated macroscopic
+strain. In particular, this effect can be associated with the diffuse phase
+transformations observed in some ferroelectrics above the Curie point. It is
+shown that the ME-generated strain is giant and describes a superelasticity if
+the applied field is stress. It describes a super magnetostriction if the
+martensite (or austenite) are ferromagnetic and the applied field is a magnetic
+field. In general, the material with defects can be a multiferroic with a giant
+multiferroic response if the parent and martensitic phase have different
+ferroic properties. Finally the ME-generated strain may explain or, at least,
+contribute to the Invar and Elinvar effects that are typically observed in
+pre-transitional austenite. The thermoelastic equilibrium and all these effects
+exist only if the interaction between the defects and MEs is infinite-range.
+",0,1,0,0,0,0
+6489,On The Robustness of Epsilon Skew Extension for Burr III Distribution on Real Line,"  The Burr III distribution is used in a wide variety of fields of lifetime
+data analysis, reliability theory, and financial literature, etc. It is defined
+on the positive axis and has two shape parameters, say $c$ and $k$. These shape
+parameters make the distribution quite flexible. They also control the tail
+behavior of the distribution. In this study, we extent the Burr III
+distribution to the real axis and also add a skewness parameter, say
+$\varepsilon$, with epsilon-skew extension approach. When the parameters $c$
+and $k$ have a relation such that $ck \approx 1 $ or $ck < 1 $, it is skewed
+unimodal. Otherwise, it is skewed bimodal with the same level of peaks on the
+negative and positive sides of real line. Thus, ESBIII distribution can capture
+fitting the various data sets even when the number of parameters are three.
+Location and scale form of this distribution are also given. Some
+distributional properties of the new distribution are investigated. The maximum
+likelihood (ML) estimation method for the parameters of ESBIII is considered.
+The robustness properties of ML estimators are studied and also tail behaviour
+of ESBIII distribution is examined. The applications on real data are
+considered to illustrate the modeling capacity of this distribution in the
+class of bimodal distributions.
+",0,0,1,1,0,0
+6490,Logics for Word Transductions with Synthesis,"  We introduce a logic, called LT, to express properties of transductions, i.e.
+binary relations from input to output (finite) words. In LT, the input/output
+dependencies are modelled via an origin function which associates to any
+position of the output word, the input position from which it originates. LT is
+well-suited to express relations (which are not necessarily functional), and
+can express all regular functional transductions, i.e. transductions definable
+for instance by deterministic two-way transducers. Despite its high expressive
+power, LT has decidable satisfiability and equivalence problems, with tight
+non-elementary and elementary complexities, depending on specific
+representation of LT-formulas. Our main contribution is a synthesis result:
+from any transduction R defined in LT , it is possible to synthesise a regular
+functional transduction f such that for all input words u in the domain of R, f
+is defined and (u,f(u)) belongs to R. As a consequence, we obtain that any
+functional transduction is regular iff it is LT-definable. We also investigate
+the algorithmic and expressiveness properties of several extensions of LT, and
+explicit a correspondence between transductions and data words. As a
+side-result, we obtain a new decidable logic for data words.
+",1,0,0,0,0,0
+6491,Topology and strong four fermion interactions in four dimensions,"  We study massless fermions interacting through a particular four fermion term
+in four dimensions. Exact symmetries prevent the generation of bilinear fermion
+mass terms. We determine the structure of the low energy effective action for
+the auxiliary field needed to generate the four fermion term and find it has an
+novel structure that admits topologically non-trivial defects with non-zero
+Hopf invariant. We show that fermions propagating in such a background pick up
+a mass without breaking symmetries. Furthermore pairs of such defects
+experience a logarithmic interaction. We argue that a phase transition
+separates a phase where these defects proliferate from a broken phase where
+they are bound tightly. We conjecture that by tuning one additional operator
+the broken phase can be eliminated with a single BKT-like phase transition
+separating the massless from massive phases.
+",0,1,0,0,0,0
+6492,Augmentor: An Image Augmentation Library for Machine Learning,"  The generation of artificial data based on existing observations, known as
+data augmentation, is a technique used in machine learning to improve model
+accuracy, generalisation, and to control overfitting. Augmentor is a software
+package, available in both Python and Julia versions, that provides a high
+level API for the expansion of image data using a stochastic, pipeline-based
+approach which effectively allows for images to be sampled from a distribution
+of augmented images at runtime. Augmentor provides methods for most standard
+augmentation practices as well as several advanced features such as
+label-preserving, randomised elastic distortions, and provides many helper
+functions for typical augmentation tasks used in machine learning.
+",1,0,0,1,0,0
+6493,Multilevel Sequential${}^2$ Monte Carlo for Bayesian Inverse Problems,"  The identification of parameters in mathematical models using noisy
+observations is a common task in uncertainty quantification. We employ the
+framework of Bayesian inversion: we combine monitoring and observational data
+with prior information to estimate the posterior distribution of a parameter.
+Specifically, we are interested in the distribution of a diffusion coefficient
+of an elliptic PDE. In this setting, the sample space is high-dimensional, and
+each sample of the PDE solution is expensive. To address these issues we
+propose and analyse a novel Sequential Monte Carlo (SMC) sampler for the
+approximation of the posterior distribution. Classical, single-level SMC
+constructs a sequence of measures, starting with the prior distribution, and
+finishing with the posterior distribution. The intermediate measures arise from
+a tempering of the likelihood, or, equivalently, a rescaling of the noise. The
+resolution of the PDE discretisation is fixed. In contrast, our estimator
+employs a hierarchy of PDE discretisations to decrease the computational cost.
+We construct a sequence of intermediate measures by decreasing the temperature
+or by increasing the discretisation level at the same time. This idea builds on
+and generalises the multi-resolution sampler proposed in [P.S. Koutsourelakis,
+J. Comput. Phys., 228 (2009), pp. 6184-6211] where a bridging scheme is used to
+transfer samples from coarse to fine discretisation levels. Importantly, our
+choice between tempering and bridging is fully adaptive. We present numerical
+experiments in 2D space, comparing our estimator to single-level SMC and the
+multi-resolution sampler.
+",0,0,0,1,0,0
+6494,Multiparameter actuation of a neutrally-stable shell: a flexible gear-less motor,"  We have designed and tested experimentally a morphing structure consisting of
+a neutrally stable thin cylindrical shell driven by a multiparameter
+piezoelectric actuation. The shell is obtained by plastically deforming an
+initially flat copper disk, so as to induce large isotropic and almost uniform
+inelastic curvatures. Following the plastic deformation, in a perfectly
+isotropic system, the shell is theoretically neutrally stable, owning a
+continuous manifold of stable cylindrical shapes corresponding to the rotation
+of the axis of maximal curvature. Small imperfections render the actual
+structure bistable, giving preferred orientations. A three-parameter
+piezoelectric actuation, exerted through micro-fiber-composite actuators,
+allows us to add a small perturbation to the plastic inelastic curvature and to
+control the direction of maximal curvature. This actuation law is designed
+through a geometrical analogy based on a fully non-linear inextensible
+uniform-curvature shell model. We report on the fabrication, identification,
+and experimental testing of a prototype and demonstrate the effectiveness of
+the piezoelectric actuators in controlling its shape. The resulting motion is
+an apparent rotation of the shell, controlled by the voltages as in a
+""gear-less motor"", which is, in reality, a precession of the axis of principal
+curvature.
+",0,1,0,0,0,0
+6495,An Evolutionary Game for User Access Mode Selection in Fog Radio Access Networks,"  The fog radio access network (F-RAN) is a promising paradigm for the fifth
+generation wireless communication systems to provide high spectral efficiency
+and energy efficiency. Characterizing users to select an appropriate
+communication mode among fog access point (F-AP), and device-to-device (D2D) in
+F-RANs is critical for performance optimization. Using evolutionary game
+theory, we investigate the dynamics of user access mode selection in F-RANs.
+Specifically, the competition among groups of potential users space is
+formulated as a dynamic evolutionary game, and the evolutionary equilibrium is
+the solution to this game. Stochastic geometry tool is used to derive the
+proposals' payoff expressions for both F-AP and D2D users by taking into
+account the different nodes locations, cache sizes as well as the delay cost.
+The analytical results obtained from the game model are evaluated via
+simulations, which show that the evolutionary game based access mode selection
+algorithm can reach a much higher payoff than the max rate based algorithm.
+",1,0,0,0,0,0
+6496,"Latent Estimation of GDP, GDP per capita, and Population from Historic and Contemporary Sources","  The concepts of Gross Domestic Product (GDP), GDP per capita, and population
+are central to the study of political science and economics. However, a growing
+literature suggests that existing measures of these concepts contain
+considerable error or are based on overly simplistic modeling choices. We
+address these problems by creating a dynamic, three-dimensional latent trait
+model, which uses observed information about GDP, GDP per capita, and
+population to estimate posterior prediction intervals for each of these
+important concepts. By combining historical and contemporary sources of
+information, we are able to extend the temporal and spatial coverage of
+existing datasets for country-year units back to 1500 A.D through 2015 A.D.
+and, because the model makes use of multiple indicators of the underlying
+concepts, we are able to estimate the relative precision of the different
+country-year estimates. Overall, our latent variable model offers a principled
+method for incorporating information from different historic and contemporary
+data sources. It can be expanded or refined as researchers discover new or
+alternative sources of information about these concepts.
+",0,0,0,1,0,0
+6497,Portfolio Optimization under Fast Mean-reverting and Rough Fractional Stochastic Environment,"  Fractional stochastic volatility models have been widely used to capture the
+non-Markovian structure revealed from financial time series of realized
+volatility. On the other hand, empirical studies have identified scales in
+stock price volatility: both fast-time scale on the order of days and
+slow-scale on the order of months. So, it is natural to study the portfolio
+optimization problem under the effects of dependence behavior which we will
+model by fractional Brownian motions with Hurst index $H$, and in the fast or
+slow regimes characterized by small parameters $\eps$ or $\delta$. For the
+slowly varying volatility with $H \in (0,1)$, it was shown that the first order
+correction to the problem value contains two terms of order $\delta^H$, one
+random component and one deterministic function of state processes, while for
+the fast varying case with $H > \half$, the same form holds at order
+$\eps^{1-H}$. This paper is dedicated to the remaining case of a fast-varying
+rough environment ($H < \half$) which exhibits a different behavior. We show
+that, in the expansion, only one deterministic term of order $\sqrt{\eps}$
+appears in the first order correction.
+",0,0,0,0,0,1
+6498,Triangle Generative Adversarial Networks,"  A Triangle Generative Adversarial Network ($\Delta$-GAN) is developed for
+semi-supervised cross-domain joint distribution matching, where the training
+data consists of samples from each domain, and supervision of domain
+correspondence is provided by only a few paired samples. $\Delta$-GAN consists
+of four neural networks, two generators and two discriminators. The generators
+are designed to learn the two-way conditional distributions between the two
+domains, while the discriminators implicitly define a ternary discriminative
+function, which is trained to distinguish real data pairs and two kinds of fake
+data pairs. The generators and discriminators are trained together using
+adversarial learning. Under mild assumptions, in theory the joint distributions
+characterized by the two generators concentrate to the data distribution. In
+experiments, three different kinds of domain pairs are considered, image-label,
+image-image and image-attribute pairs. Experiments on semi-supervised image
+classification, image-to-image translation and attribute-based image generation
+demonstrate the superiority of the proposed approach.
+",1,0,0,1,0,0
+6499,Learning Distributions of Meant Color,"  When a speaker says the name of a color, the color that they picture is not
+necessarily the same as the listener imagines. Color is a grounded semantic
+task, but that grounding is not a mapping of a single word (or phrase) to a
+single point in color-space. Proper understanding of color language requires
+the capacity to map a sequence of words to a probability distribution in
+color-space. A distribution is required as there is no clear agreement between
+people as to what a particular color describes -- different people have a
+different idea of what it means to be `very dark orange'. We propose a novel
+GRU-based model to handle this case. Learning how each word in a color name
+contributes to the color described, allows for knowledge sharing between uses
+of the words in different color names. This knowledge sharing significantly
+improves predicative capacity for color names with sparse training data. The
+extreme case of this challenge in data sparsity is for color names without any
+direct training data. Our model is able to predict reasonable distributions for
+these cases, as evaluated on a held-out dataset consisting only of such terms.
+",1,0,0,0,0,0
+6500,Bose - Einstein condensation of triplons with a weakly broken U(1) symmetry,"  The low-temperature properties of certain quantum magnets can be described in
+terms of a Bose-Einstein condensation (BEC) of magnetic quasiparticles
+(triplons). Some mean-field approaches (MFA) to describe these systems, based
+on the standard grand canonical ensemble, do not take the anomalous density
+into account and leads to an internal inconsistency, as it has been shown by
+Hohenberg and Martin, and may therefore produce unphysical results. Moreover,
+an explicit breaking of the U(1) symmetry as observed, for example, in TlCuCl3
+makes the application of MFA more complicated. In the present work, we develop
+a self-consistent MFA approach, similar to the Hartree-Fock-Bogolyubov
+approximation in the notion of representative statistical ensembles, including
+the effect of a weakly broken U(1) symmetry. We apply our results on
+experimental data of the quantum magnet TlCuCl3 and show that magnetization
+curves and the energy dispersion can be well described within this
+approximation assuming that the BEC scenario is still valid. We predict that
+the shift of the critical temperature Tc due to a finite exchange anisotropy is
+rather substantial even when the anisotropy parameter \gamma is small, e.g.,
+\Delta T_c \approx 10%$ of Tc in H = 6T and for \gamma\approx 4 \mu eV.
+",0,1,0,0,0,0
+6501,LPCNet: Improving Neural Speech Synthesis Through Linear Prediction,"  Neural speech synthesis models have recently demonstrated the ability to
+synthesize high quality speech for text-to-speech and compression applications.
+These new models often require powerful GPUs to achieve real-time operation, so
+being able to reduce their complexity would open the way for many new
+applications. We propose LPCNet, a WaveRNN variant that combines linear
+prediction with recurrent neural networks to significantly improve the
+efficiency of speech synthesis. We demonstrate that LPCNet can achieve
+significantly higher quality than WaveRNN for the same network size and that
+high quality LPCNet speech synthesis is achievable with a complexity under 3
+GFLOPS. This makes it easier to deploy neural synthesis applications on
+lower-power devices, such as embedded systems and mobile phones.
+",1,0,0,0,0,0
+6502,Coherent anti-Stokes Raman Scattering Lidar Using Slow Light: A Theoretical Study,"  We theoretically investigate a scheme in which backward coherent anti-Stokes
+Raman scattering (CARS) is significantly enhanced by using slow light.
+Specifically, we reduce the group velocity of the Stokes excitation pulse by
+introducing a coupling laser that causes electromagnetically induced
+transparency (EIT). When the Stokes pulse has a spatial length shorter than the
+CARS wavelength, the backward CARS emission is significantly enhanced. We also
+investigated the possibility of applying this scheme as a CARS lidar with O2 or
+N2 as the EIT medium. We found that if nanosecond laser with large pulse energy
+(>1 J) and a telescope with large aperture (~10 m) are equipped in the lidar
+system, a CARS lidar could become much more sensitive than a spontaneous Raman
+lidar.
+",0,1,0,0,0,0
+6503,"Tests based on characterizations, and their efficiencies: a survey","  A survey of goodness-of-fit and symmetry tests based on the characterization
+properties of distributions is presented. This approach became popular in
+recent years. In most cases the test statistics are functionals of
+$U$-empirical processes. The limiting distributions and large deviations of new
+statistics under the null hypothesis are described. Their local Bahadur
+efficiency for various parametric alternatives is calculated and compared with
+each other as well as with diverse previously known tests. We also describe new
+directions of possible research in this domain.
+",0,0,1,1,0,0
+6504,Hyperprior on symmetric Dirichlet distribution,"  In this article we introduce how to put vague hyperprior on Dirichlet
+distribution, and we update the parameter of it by adaptive rejection sampling
+(ARS). Finally we analyze this hyperprior in an over-fitted mixture model by
+some synthetic experiments.
+",1,0,0,0,0,0
+6505,On the interpretability and computational reliability of frequency-domain Granger causality,"  This is a comment to the paper 'A study of problems encountered in Granger
+causality analysis from a neuroscience perspective'. We agree that
+interpretation issues of Granger Causality in Neuroscience exist (partially due
+to the historical unfortunate use of the name 'causality', as nicely described
+in previous literature). On the other hand we think that the paper uses a
+formulation of Granger causality which is outdated (albeit still used), and in
+doing so it dismisses the measure based on a suboptimal use of it. Furthermore,
+since data from simulated systems are used, the pitfalls that are found with
+the used formulation are intended to be general, and not limited to
+neuroscience. It would be a pity if this paper, even written in good faith,
+became a wildcard against all possible applications of Granger Causality,
+regardless of the hard work of colleagues aiming to seriously address the
+methodological and interpretation pitfalls. In order to provide a balanced
+view, we replicated their simulations used the updated State Space
+implementation, proposed already some years ago, in which the pitfalls are
+mitigated or directly solved.
+",0,0,1,1,0,0
+6506,Artificial topological models based on a one-dimensional spin-dependent optical lattice,"  Topological matter is a popular topic in both condensed matter and cold atom
+research. In the past decades, a variety of models have been identified with
+fascinating topological features. Some, but not all, of the models can be found
+in materials. As a fully controllable system, cold atoms trapped in optical
+lattices provide an ideal platform to simulate and realize these topological
+models. Here we present a proposal for synthesizing topological models in cold
+atoms based on a one-dimensional (1D) spin-dependent optical lattice potential.
+In our system, features such as staggered tunneling, staggered Zeeman field,
+nearest-neighbor interaction, beyond-near-neighbor tunneling, etc. can be
+readily realized. They underlie the emergence of various topological phases.
+Our proposal can be realized with current technology and hence has potential
+applications in quantum simulation of topological matter.
+",0,1,0,0,0,0
+6507,Ramsey expansions of metrically homogeneous graphs,"  We discuss the Ramsey property, the existence of a stationary independence
+relation and the coherent extension property for partial isometries (coherent
+EPPA) for all classes of metrically homogeneous graphs from Cherlin's
+catalogue, which is conjectured to include all such structures. We show that,
+with the exception of tree-like graphs, all metric spaces in the catalogue have
+precompact Ramsey expansions (or lifts) with the expansion property. With two
+exceptions we can also characterise the existence of a stationary independence
+relation and the coherent EPPA.
+Our results can be seen as a new contribution to Nešetřil's
+classification programme of Ramsey classes and as empirical evidence of the
+recent convergence in techniques employed to establish the Ramsey property, the
+expansion (or lift or ordering) property, EPPA and the existence of a
+stationary independence relation. At the heart of our proof is a canonical way
+of completing edge-labelled graphs to metric spaces in Cherlin's classes. The
+existence of such a ""completion algorithm"" then allows us to apply several
+strong results in the areas that imply EPPA and respectively the Ramsey
+property.
+The main results have numerous corollaries on the automorphism groups of the
+Fraïssé limits of the classes, such as amenability, unique ergodicity,
+existence of universal minimal flows, ample generics, small index property,
+21-Bergman property and Serre's property (FA).
+",1,0,1,0,0,0
+6508,Rapidly star-forming galaxies adjacent to quasars at redshifts exceeding 6,"  The existence of massive ($10^{11}$ solar masses) elliptical galaxies by
+redshift z~4 (when the Universe was 1.5 billion years old) necessitates the
+presence of galaxies with star-formation rates exceeding 100 solar masses per
+year at z>6 (corresponding to an age of the Universe of less than 1 billion
+years). Surveys have discovered hundreds of galaxies at these early cosmic
+epochs, but their star-formation rates are more than an order of magnitude
+lower. The only known galaxies with very high star-formation rates at z>6 are,
+with only one exception, the host galaxies of quasars, but these galaxies also
+host accreting supermassive (more than $10^9$ solar masses) black holes, which
+probably affect the properties of the galaxies. Here we report observations of
+an emission line of singly ionized carbon ([CII] at a wavelength of 158
+micrometres) in four galaxies at z>6 that are companions of quasars, with
+velocity offsets of less than 600 kilometers per second and linear offsets of
+less than 600 kiloparsecs. The discovery of these four galaxies was
+serendipitous; they are close to their companion quasars and appear bright in
+the far-infrared. On the basis of the [CII] measurements, we estimate
+star-formation rates in the companions of more than 100 solar masses per year.
+These sources are similar to the host galaxies of the quasars in [CII]
+brightness, linewidth and implied dynamical masses, but do not show evidence
+for accreting supermassive black holes. Similar systems have previously been
+found at lower redshift. We find such close companions in four out of
+twenty-five z>6 quasars surveyed, a fraction that needs to be accounted for in
+simulations. If they are representative of the bright end of the [CII]
+luminosity function, then they can account for the population of massive
+elliptical galaxies at z~4 in terms of cosmic space density.
+",0,1,0,0,0,0
+6509,Regular irreducible characters of a hyperspecial compact group,"  A parametrization of irreducible unitary representations associated with the
+regular adjoint orbits of a hyperspecial compact subgroup of a reductive group
+over a non-dyadic non-archimedean local filed is presented. The parametrization
+is given by means of (a subset of) the character group of certain finite
+abelian groups arising from the reductive group. Our method is based upon
+Cliffod's theory and Weil representations over finite fields. It works under an
+assumption of the triviality of certain Schur multipliers defined for an
+algebraic group over a finite field. The assumption of the triviality has good
+evidences in the case of general linear groups and highly probable in general.
+",0,0,1,0,0,0
+6510,Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces,"  In this work we study the problem of exploring surfaces and building compact
+3D representations of the environment surrounding a robot through active
+perception. We propose an online probabilistic framework that merges visual and
+tactile measurements using Gaussian Random Field and Gaussian Process Implicit
+Surfaces. The system investigates incomplete point clouds in order to find a
+small set of regions of interest which are then physically explored with a
+robotic arm equipped with tactile sensors. We show experimental results
+obtained using a PrimeSense camera, a Kinova Jaco2 robotic arm and Optoforce
+sensors on different scenarios. We then demonstrate how to use the online
+framework for object detection and terrain classification.
+",1,0,0,0,0,0
+6511,Doing good vs. avoiding bad in prosocial choice: A refined test and extension of the morality preference hypothesis,"  Prosociality is fundamental to human social life, and, accordingly, much
+research has attempted to explain human prosocial behavior. Capraro and Rand
+(Judgment and Decision Making, 13, 99-111, 2018) recently provided experimental
+evidence that prosociality in anonymous, one-shot interactions (such as
+Prisoner's Dilemma and Dictator Game experiments) is not driven by
+outcome-based social preferences - as classically assumed - but by a
+generalized morality preference for ""doing the right thing"". Here we argue that
+the key experiments reported in Capraro and Rand (2018) comprise prominent
+methodological confounds and open questions that bear on influential
+psychological theory. Specifically, their design confounds: (i) preferences for
+efficiency with self-interest; and (ii) preferences for action with preferences
+for morality. Furthermore, their design fails to dissociate the preference to
+do ""good"" from the preference to avoid doing ""bad"". We thus designed and
+conducted a preregistered, refined and extended test of the morality preference
+hypothesis (N=801). Consistent with this hypothesis, our findings indicate that
+prosociality in the anonymous, one-shot Dictator Game is driven by preferences
+for doing the morally right thing. Inconsistent with influential psychological
+theory, however, our results suggest the preference to do ""good"" was as potent
+as the preference to avoid doing ""bad"" in this case.
+",0,0,0,0,1,0
+6512,Measuring Territorial Control in Civil Wars Using Hidden Markov Models: A Data Informatics-Based Approach,"  Territorial control is a key aspect shaping the dynamics of civil war.
+Despite its importance, we lack data on territorial control that are
+fine-grained enough to account for subnational spatio-temporal variation and
+that cover a large set of conflicts. To resolve this issue, we propose a
+theoretical model of the relationship between territorial control and tactical
+choice in civil war and outline how Hidden Markov Models (HMMs) are suitable to
+capture theoretical intuitions and estimate levels of territorial control. We
+discuss challenges of using HMMs in this application and mitigation strategies
+for future work.
+",1,0,0,1,0,0
+6513,Virtual quandle for links in lens spaces,"  We construct a virtual quandle for links in lens spaces $L(p,q)$, with $q=1$.
+This invariant has two valuable advantages over an ordinary fundamental quandle
+for links in lens spaces: the virtual quandle is an essential invariant and the
+presentation of the virtual quandle can be easily written from the band diagram
+of a link.
+",0,0,1,0,0,0
+6514,Adaptation to Easy Data in Prediction with Limited Advice,"  We derive an online learning algorithm with improved regret guarantees for
+`easy' loss sequences. We consider two types of `easiness': (a) stochastic loss
+sequences and (b) adversarial loss sequences with small effective range of the
+losses. While a number of algorithms have been proposed for exploiting small
+effective range in the full information setting, Gerchinovitz and Lattimore
+[2016] have shown the impossibility of regret scaling with the effective range
+of the losses in the bandit setting. We show that just one additional
+observation per round is sufficient to circumvent the impossibility result. The
+proposed Second Order Difference Adjustments (SODA) algorithm requires no prior
+knowledge of the effective range of the losses, $\varepsilon$, and achieves an
+$O(\varepsilon \sqrt{KT \ln K}) + \tilde{O}(\varepsilon K \sqrt[4]{T})$
+expected regret guarantee, where $T$ is the time horizon and $K$ is the number
+of actions. The scaling with the effective loss range is achieved under
+significantly weaker assumptions than those made by Cesa-Bianchi and Shamir
+[2018] in an earlier attempt to circumvent the impossibility result. We also
+provide a regret lower bound of $\Omega(\varepsilon\sqrt{T K})$, which almost
+matches the upper bound. In addition, we show that in the stochastic setting
+SODA achieves an $O\left(\sum_{a:\Delta_a>0}
+\frac{K\varepsilon^2}{\Delta_a}\right)$ pseudo-regret bound that holds
+simultaneously with the adversarial regret guarantee. In other words, SODA is
+safe against an unrestricted oblivious adversary and provides improved regret
+guarantees for at least two different types of `easiness' simultaneously.
+",0,0,0,1,0,0
+6515,Why Bohr was (Mostly) Right,"  After a discussion of the Frauchiger-Renner argument that no 'single- world'
+interpretation of quantum mechanics can be self-consistent, I propose a
+'Bohrian' alternative to many-worlds or QBism as the rational option.
+",0,1,0,0,0,0
+6516,Generalizing Distance Covariance to Measure and Test Multivariate Mutual Dependence,"  We propose three measures of mutual dependence between multiple random
+vectors. All the measures are zero if and only if the random vectors are
+mutually independent. The first measure generalizes distance covariance from
+pairwise dependence to mutual dependence, while the other two measures are sums
+of squared distance covariance. All the measures share similar properties and
+asymptotic distributions to distance covariance, and capture non-linear and
+non-monotone mutual dependence between the random vectors. Inspired by complete
+and incomplete V-statistics, we define the empirical measures and simplified
+empirical measures as a trade-off between the complexity and power when testing
+mutual independence. Implementation of the tests is demonstrated by both
+simulation results and real data examples.
+",0,0,1,1,0,0
+6517,Simultaneous Inference for High Dimensional Mean Vectors,"  Let $X_1, \ldots, X_n\in\mathbb{R}^p$ be i.i.d. random vectors. We aim to
+perform simultaneous inference for the mean vector $\mathbb{E} (X_i)$ with
+finite polynomial moments and an ultra high dimension. Our approach is based on
+the truncated sample mean vector. A Gaussian approximation result is derived
+for the latter under the very mild finite polynomial ($(2+\theta)$-th) moment
+condition and the dimension $p$ can be allowed to grow exponentially with the
+sample size $n$. Based on this result, we propose an innovative resampling
+method to construct simultaneous confidence intervals for mean vectors.
+",0,0,1,1,0,0
+6518,"Joint Routing, Scheduling and Power Control Providing Hard Deadline in Wireless Multihop Networks","  We consider optimal/efficient power allocation policies in a single/multihop
+wireless network in the presence of hard end-to-end deadline delay constraints
+on the transmitted packets. Such constraints can be useful for real time voice
+and video. Power is consumed in only transmission of the data. We consider the
+case when the power used in transmission is a convex function of the data
+transmitted. We develop a computationally efficient online algorithm, which
+minimizes the average power for the single hop. We model this problem as
+dynamic program (DP) and obtain the optimal solution. Next, we generalize it to
+the multiuser, multihop scenario when there are multiple real time streams with
+different hard deadline constraints.
+",1,0,0,0,0,0
+6519,Activation of Microwave Fields in a Spin-Torque Nano-Oscillator by Neuronal Action Potentials,"  Action potentials are the basic unit of information in the nervous system and
+their reliable detection and decoding holds the key to understanding how the
+brain generates complex thought and behavior. Transducing these signals into
+microwave field oscillations can enable wireless sensors that report on brain
+activity through magnetic induction. In the present work we demonstrate that
+action potentials from crayfish lateral giant neuron can trigger microwave
+oscillations in spin-torque nano-oscillators. These nanoscale devices take as
+input small currents and convert them to microwave current oscillations that
+can wirelessly broadcast neuronal activity, opening up the possibility for
+compact neuro-sensors. We show that action potentials activate microwave
+oscillations in spin-torque nano-oscillators with an amplitude that follows the
+action potential signal, demonstrating that the device has both the sensitivity
+and temporal resolution to respond to action potentials from a single neuron.
+The activation of magnetic oscillations by action potentials, together with the
+small footprint and the high frequency tunability, makes these devices
+promising candidates for high resolution sensing of bioelectric signals from
+neural tissues. These device attributes may be useful for design of
+high-throughput bi-directional brain-machine interfaces.
+",0,1,0,0,0,0
+6520,Responses in Large-Scale Structure,"  We introduce a rigorous definition of general power-spectrum responses as
+resummed vertices with two hard and $n$ soft momenta in cosmological
+perturbation theory. These responses measure the impact of long-wavelength
+perturbations on the local small-scale power spectrum. The kinematic structure
+of the responses (i.e., their angular dependence) can be decomposed
+unambiguously through a ""bias"" expansion of the local power spectrum, with a
+fixed number of physical response coefficients, which are only a function of
+the hard wavenumber $k$. Further, the responses up to $n$-th order completely
+describe the $(n+2)$-point function in the squeezed limit, i.e. with two hard
+and $n$ soft modes, which one can use to derive the response coefficients. This
+generalizes previous results, which relate the angle-averaged squeezed limit to
+isotropic response coefficients. We derive the complete expression of first-
+and second-order responses at leading order in perturbation theory, and present
+extrapolations to nonlinear scales based on simulation measurements of the
+isotropic response coefficients. As an application, we use these results to
+predict the non-Gaussian part of the angle-averaged matter power spectrum
+covariance ${\rm Cov}^{\rm NG}_{\ell = 0}(k_1,k_2)$, in the limit where one of
+the modes, say $k_2$, is much smaller than the other. Without any free
+parameters, our model results are in very good agreement with simulations for
+$k_2 \lesssim 0.06\ h/{\rm Mpc}$, and for any $k_1 \gtrsim 2 k_2$. The
+well-defined kinematic structure of the power spectrum response also permits a
+quick evaluation of the angular dependence of the covariance matrix. While we
+focus on the matter density field, the formalism presented here can be
+generalized to generic tracers such as galaxies.
+",0,1,0,0,0,0
+6521,Graph isomorphisms in quasi-polynomial time,"  Let us be given two graphs $\Gamma_1$, $\Gamma_2$ of $n$ vertices. Are they
+isomorphic? If they are, the set of isomorphisms from $\Gamma_1$ to $\Gamma_2$
+can be identified with a coset $H\cdot\pi$ inside the symmetric group on $n$
+elements. How do we find $\pi$ and a set of generators of $H$?
+The challenge of giving an always efficient algorithm answering these
+questions remained open for a long time. Babai has recently shown how to solve
+these problems -- and others linked to them -- in quasi-polynomial time, i.e.
+in time $\exp\left(O(\log n)^{O(1)}\right)$. His strategy is based in part on
+the algorithm by Luks (1980/82), who solved the case of graphs of bounded
+degree.
+",0,0,1,0,0,0
+6522,Hardware-Aware Machine Learning: Modeling and Optimization,"  Recent breakthroughs in Deep Learning (DL) applications have made DL models a
+key component in almost every modern computing system. The increased popularity
+of DL applications deployed on a wide-spectrum of platforms have resulted in a
+plethora of design challenges related to the constraints introduced by the
+hardware itself. What is the latency or energy cost for an inference made by a
+Deep Neural Network (DNN)? Is it possible to predict this latency or energy
+consumption before a model is trained? If yes, how can machine learners take
+advantage of these models to design the hardware-optimal DNN for deployment?
+From lengthening battery life of mobile devices to reducing the runtime
+requirements of DL models executing in the cloud, the answers to these
+questions have drawn significant attention.
+One cannot optimize what isn't properly modeled. Therefore, it is important
+to understand the hardware efficiency of DL models during serving for making an
+inference, before even training the model. This key observation has motivated
+the use of predictive models to capture the hardware performance or energy
+efficiency of DL applications. Furthermore, DL practitioners are challenged
+with the task of designing the DNN model, i.e., of tuning the hyper-parameters
+of the DNN architecture, while optimizing for both accuracy of the DL model and
+its hardware efficiency. Therefore, state-of-the-art methodologies have
+proposed hardware-aware hyper-parameter optimization techniques. In this paper,
+we provide a comprehensive assessment of state-of-the-art work and selected
+results on the hardware-aware modeling and optimization for DL applications. We
+also highlight several open questions that are poised to give rise to novel
+hardware-aware designs in the next few years, as DL applications continue to
+significantly impact associated hardware systems and platforms.
+",0,0,0,1,0,0
+6523,On the unit distance problem,"  The Erd\H os unit distance conjecture in the plane says that the number of
+pairs of points from a point set of size $n$ separated by a fixed (Euclidean)
+distance is $\leq C_{\epsilon} n^{1+\epsilon}$ for any $\epsilon>0$. The best
+known bound is $Cn^{\frac{4}{3}}$. We show that if the set under consideration
+is well-distributed and the fixed distance is much smaller than the diameter of
+the set, then the exponent $\frac{4}{3}$ is significantly improved.
+Corresponding results are also established in higher dimensions. The results
+are obtained by solving the corresponding continuous problem and using a
+continuous-to-discrete conversion mechanism. The degree of sharpness of results
+is tested using the known results on the distribution of lattice points dilates
+of convex domains.
+We also introduce the following variant of the Erd\H os unit distance
+problem: how many pairs of points from a set of size $n$ are separated by an
+integer distance? We obtain some results in this direction and formulate a
+conjecture.
+",0,0,1,0,0,0
+6524,Ordered states in the Kitaev-Heisenberg model: From 1D chains to 2D honeycomb,"  We study the ground state of the 1D Kitaev-Heisenberg (KH) model using the
+density-matrix renormalization group and Lanczos exact diagonalization methods.
+We obtain a rich ground-state phase diagram as a function of the ratio between
+Heisenberg ($J=\cos\phi)$ and Kitaev ($K=\sin\phi$) interactions. Depending on
+the ratio, the system exhibits four long-range ordered states:
+ferromagnetic-$z$ , ferromagnetic-$xy$, staggered-$xy$, Néel-$z$, and two
+liquid states: Tomonaga-Luttinger liquid and spiral-$xy$. The two Kitaev points
+$\phi=\frac{\pi}{2}$ and $\phi=\frac{3\pi}{2}$ are singular. The
+$\phi$-dependent phase diagram is similar to that for the 2D honeycomb-lattice
+KH model. Remarkably, all the ordered states of the honeycomb-lattice KH model
+can be interpreted in terms of the coupled KH chains. We also discuss the
+magnetic structure of the K-intercalated RuCl$_3$, a potential Kitaev material,
+in the framework of the 1D KH model. Furthermore, we demonstrate that the
+low-lying excitations of the 1D KH Hamiltonian can be explained within the
+combination of the known six-vertex model and spin-wave theory.
+",0,1,0,0,0,0
+6525,Quadratic forms and Sobolev spaces of fractional order,"  We study quadratic functionals on $L^2(\mathbb{R}^d)$ that generate seminorms
+in the fractional Sobolev space $H^s(\mathbb{R}^d)$ for $0 < s < 1$. The
+functionals under consideration appear in the study of Markov jump processes
+and, independently, in recent research on the Boltzmann equation. The
+functional measures differentiability of a function $f$ in a similar way as the
+seminorm of $H^s(\mathbb{R}^d)$. The major difference is that differences $f(y)
+- f(x)$ are taken into account only if $y$ lies in some double cone with apex
+at $x$ or vice versa. The configuration of double cones is allowed to be
+inhomogeneous without any assumption on the spatial regularity. We prove that
+the resulting seminorm is comparable to the standard one of
+$H^s(\mathbb{R}^d)$. The proof follows from a similar result on discrete
+quadratic forms in $\mathbb{Z}^d$, which is our second main result. We
+establish a general scheme for discrete approximations of nonlocal quadratic
+forms. Applications to Markov jump processes are discussed.
+",0,0,1,0,0,0
+6526,General Latent Feature Modeling for Data Exploration Tasks,"  This paper introduces a general Bayesian non- parametric latent feature model
+suitable to per- form automatic exploratory analysis of heterogeneous datasets,
+where the attributes describing each object can be either discrete, continuous
+or mixed variables. The proposed model presents several important properties.
+First, it accounts for heterogeneous data while can be inferred in linear time
+with respect to the number of objects and attributes. Second, its Bayesian
+nonparametric nature allows us to automatically infer the model complexity from
+the data, i.e., the number of features necessary to capture the latent
+structure in the data. Third, the latent features in the model are
+binary-valued variables, easing the interpretability of the obtained latent
+features in data exploration tasks.
+",1,0,0,1,0,0
+6527,The ALMA View of the OMC1 Explosion in Orion,"  Most massive stars form in dense clusters where gravitational interactions
+with other stars may be common. The two nearest forming massive stars, the BN
+object and Source I, located behind the Orion Nebula, were ejected with
+velocities of $\sim$29 and $\sim$13 km s$^{-1}$ about 500 years ago by such
+interactions. This event generated an explosion in the gas. New ALMA
+observations show in unprecedented detail, a roughly spherically symmetric
+distribution of over a hundred $^{12}$CO J=2$-$1 streamers with velocities
+extending from V$_{LSR}$ =$-$150 to +145 km s$^{-1}$. The streamer radial
+velocities increase (or decrease) linearly with projected distance from the
+explosion center, forming a `Hubble Flow' confined to within 50 arcseconds of
+the explosion center. They point toward the high proper-motion, shock-excited
+H$_2$ and [Fe ii ] `fingertips' and lower-velocity CO in the H$_2$ wakes
+comprising Orion's `fingers'. In some directions, the H$_2$ `fingers' extend
+more than a factor of two farther from the ejection center than the CO
+streamers. Such deviations from spherical symmetry may be caused by ejecta
+running into dense gas or the dynamics of the N-body interaction that ejected
+the stars and produced the explosion. This $\sim$10$^{48}$ erg event may have
+been powered by the release of gravitational potential energy associated with
+the formation of a compact binary or a protostellar merger. Orion may be the
+prototype for a new class of stellar explosion responsible for luminous
+infrared transients in nearby galaxies.
+",0,1,0,0,0,0
+6528,Neurology-as-a-Service for the Developing World,"  Electroencephalography (EEG) is an extensively-used and well-studied
+technique in the field of medical diagnostics and treatment for brain
+disorders, including epilepsy, migraines, and tumors. The analysis and
+interpretation of EEGs require physicians to have specialized training, which
+is not common even among most doctors in the developed world, let alone the
+developing world where physician shortages plague society. This problem can be
+addressed by teleEEG that uses remote EEG analysis by experts or by local
+computer processing of EEGs. However, both of these options are prohibitively
+expensive and the second option requires abundant computing resources and
+infrastructure, which is another concern in developing countries where there
+are resource constraints on capital and computing infrastructure. In this work,
+we present a cloud-based deep neural network approach to provide decision
+support for non-specialist physicians in EEG analysis and interpretation. Named
+`neurology-as-a-service,' the approach requires almost no manual intervention
+in feature engineering and in the selection of an optimal architecture and
+hyperparameters of the neural network. In this study, we deploy a pipeline that
+includes moving EEG data to the cloud and getting optimal models for various
+classification tasks. Our initial prototype has been tested only in developed
+world environments to-date, but our intention is to test it in developing world
+environments in future work. We demonstrate the performance of our proposed
+approach using the BCI2000 EEG MMI dataset, on which our service attains 63.4%
+accuracy for the task of classifying real vs. imaginary activity performed by
+the subject, which is significantly higher than what is obtained with a shallow
+approach such as support vector machines.
+",1,0,0,1,0,0
+6529,Estimation of Low-Rank Matrices via Approximate Message Passing,"  Consider the problem of estimating a low-rank symmetric matrix when its
+entries are perturbed by Gaussian noise, a setting that is known as `spiked
+model' or `deformed Wigner matrix'. If the empirical distribution of the
+entries of the spikes is known, optimal estimators that exploit this knowledge
+can substantially outperform spectral approaches. Recent work characterizes the
+accuracy of Bayes-optimal estimators in the high-dimensional limit. In this
+paper we present a practical algorithm that can achieve Bayes-optimal accuracy
+above the spectral threshold. A bold conjecture from statistical physics posits
+that no polynomial-time algorithm achieves optimal error below the same
+threshold (unless the best estimator is trivial). Our approach uses Approximate
+Message Passing (AMP) in conjunction with a spectral initialization. AMP has
+proven successful in a variety of statistical problem, and are amenable to
+exact asymptotic analysis via state evolution. Unfortunately, state evolution
+is uninformative when the algorithm is initialized near an unstable fixed
+point, as it often happens in matrix estimation. We develop a new analysis of
+AMP that allows for spectral initializations, and builds on a decoupling
+between the outlier eigenvectors and the bulk in the spiked random matrix
+model. Our main theorem is general and applies beyond matrix estimation.
+However, we use it to derive detailed predictions for the problem of estimating
+a rank-one matrix in noise. Special cases of these problem are closely related
+-via universality arguments- to the network community detection problem for two
+asymmetric communities. For general rank-one models, we show that AMP can be
+used to construct asymptotically valid confidence intervals. As a further
+illustration, we consider the example of a block-constant low-rank matrix with
+symmetric blocks, which we refer to as `Gaussian Block Model'.
+",0,0,1,1,0,0
+6530,Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing,"  Recent developments in quaternion-valued widely linear processing have
+established that the exploitation of complete second-order statistics requires
+consideration of both the standard covariance and the three complementary
+covariance matrices. Although such matrices have a tremendous amount of
+structure and their decomposition is a powerful tool in a variety of
+applications, the non-commutative nature of the quaternion product has been
+prohibitive to the development of quaternion uncorrelating transforms. To this
+end, we introduce novel techniques for a simultaneous decomposition of the
+covariance and complementary covariance matrices in the quaternion domain,
+whereby the quaternion version of the Takagi factorisation is explored to
+diagonalise symmetric quaternion-valued matrices. This gives new insights into
+the quaternion uncorrelating transform (QUT) and forms a basis for the proposed
+quaternion approximate uncorrelating transform (QAUT) which simultaneously
+diagonalises all four covariance matrices associated with improper quaternion
+signals. The effectiveness of the proposed uncorrelating transforms is
+validated by simulations on both synthetic and real-world quaternion-valued
+signals.
+",1,0,0,0,0,0
+6531,Arithmetic Siegel-Weil formula on $X_{0}(N)$,"  In this paper, we proved an arithmetic Siegel-Weil formula and the modularity
+of some arithmetic theta function on the modular curve $X_0(N)$ when $N$ is
+square free. In the process, we also construct some generalized Delta function
+for $\Gamma_0(N)$ and proved some explicit Kronecker limit formula for
+Eisenstein series on $X_0(N)$.
+",0,0,1,0,0,0
+6532,"Perturbation, Non-Gaussianity and Reheating in a GB-$α$-Attractor Model","  Motivated by $\alpha$-attractor models, in this paper we consider a
+Gauss-Bonnet inflation with E-model type of potential. We consider the
+Gauss-Bonnet coupling function to be the same as the E-model potential. In the
+small $\alpha$ limit we obtain an attractor at $r=0$ as expected, and in the
+large $\alpha$ limit we recover the Gauss-Bonnet model with potential and
+coupling function of the form $\phi^{2n}$. We study perturbations and
+non-Gaussianity in this setup and we find some constraints on the model's
+parameters in comparison with PLANCK datasets. We study also the reheating
+epoch after inflation in this setup. For this purpose, we seek the number of
+e-folds and temperature during reheating epoch. These quantities depend on the
+model's parameter and the effective equation of state of the dominating energy
+density in the reheating era. We find some observational constraints on these
+parameters.
+",0,1,0,0,0,0
+6533,Experimental determination of the frequency and field dependence of Specific Loss Power in Magnetic Fluid Hyperthermia,"  Magnetic nanoparticles are promising systems for biomedical applications and
+in particular for Magnetic Fluid Hyperthermia, a promising therapy that
+utilizes the heat released by such systems to damage tumor cells. We present an
+experimental study of the physical properties that influences the capability of
+heat release, i.e. the Specific Loss Power, SLP, of three biocompatible
+ferrofluid samples having a magnetic core of maghemite with different core
+diameter d= 10.2, 14.6 and 19.7 nm. The SLP was measured as a function of
+frequency f and intensity of the applied alternating magnetic field H, and it
+turned out to depend on the core diameter, as expected. The results allowed us
+to highlight experimentally that the physical mechanism responsible for the
+heating is size-dependent and to establish, at applied constant frequency, the
+phenomenological functional relationship SLP=cH^x, with 2<x<3 for all samples.
+The x-value depends on sample size and field frequency/ intensity, here chosen
+in the typical range of operating magnetic hyperthermia devices. For the
+smallest sample, the effective relaxation time Teff=19.5 ns obtained from SLP
+data is in agreement with the value estimated from magnetization data, thus
+confirming the validity of the Linear Response Theory model for this system at
+properly chosen field intensity and frequency.
+",0,1,0,0,0,0
+6534,Training wide residual networks for deployment using a single bit for each weight,"  For fast and energy-efficient deployment of trained deep neural networks on
+resource-constrained embedded hardware, each learned weight parameter should
+ideally be represented and stored using a single bit. Error-rates usually
+increase when this requirement is imposed. Here, we report large improvements
+in error rates on multiple datasets, for deep convolutional neural networks
+deployed with 1-bit-per-weight. Using wide residual networks as our main
+baseline, our approach simplifies existing methods that binarize weights by
+applying the sign function in training; we apply scaling factors for each layer
+with constant unlearned values equal to the layer-specific standard deviations
+used for initialization. For CIFAR-10, CIFAR-100 and ImageNet, and models with
+1-bit-per-weight requiring less than 10 MB of parameter memory, we achieve
+error rates of 3.9%, 18.5% and 26.0% / 8.5% (Top-1 / Top-5) respectively. We
+also considered MNIST, SVHN and ImageNet32, achieving 1-bit-per-weight test
+results of 0.27%, 1.9%, and 41.3% / 19.1% respectively. For CIFAR, our error
+rates halve previously reported values, and are within about 1% of our
+error-rates for the same network with full-precision weights. For networks that
+overfit, we also show significant improvements in error rate by not learning
+batch normalization scale and offset parameters. This applies to both full
+precision and 1-bit-per-weight networks. Using a warm-restart learning-rate
+schedule, we found that training for 1-bit-per-weight is just as fast as
+full-precision networks, with better accuracy than standard schedules, and
+achieved about 98%-99% of peak performance in just 62 training epochs for
+CIFAR-10/100. For full training code and trained models in MATLAB, Keras and
+PyTorch see this https URL .
+",0,0,0,1,0,0
+6535,IP Based Traffic Recovery: An Optimal Approach using SDN Application for Data Center Network,"  With the passage of time and indulgence in Information Technology, network
+management has proved its significance and has become one of the most important
+and challenging task in today's era of information flow. Communication networks
+implement a high level of sophistication in managing and flowing the data
+through secure channels, to make it almost impossible for data loss. That is
+why there are many proposed solution that are currently implemented in wide
+range of network-based applications like social networks and finance
+applications. The objective of this research paper is to propose a very
+reliable method of data flow: Choose best path for traffic using SDN
+application. This is an IP based method in which our SDN application implements
+provision of best possible path by filtering the requests on base of their IP
+origin. We'll distinguish among source and will provide the data flow with
+lowest traffic path, thus resulting in providing us minimum chances of data
+loss. A request to access our test application will be generated from host and
+request from each host will be distinguished by our SDN application that will
+get number of active users for all available servers and will redirect the
+request to server with minimum traffic load. It will also destroy sessions of
+inactive users, resulting in maintaining a best responsive channel for data
+flow.
+",1,0,0,0,0,0
+6536,Performance of Optimal Data Shaping Codes,"  Data shaping is a coding technique that has been proposed to increase the
+lifetime of flash memory devices. Several data shaping codes have been
+described in recent work, including endurance codes and direct shaping codes
+for structured data. In this paper, we study information-theoretic properties
+of a general class of data shaping codes and prove a separation theorem stating
+that optimal data shaping can be achieved by the concatenation of optimal
+lossless compression with optimal endurance coding. We also determine the
+expansion factor that minimizes the total wear cost. Finally, we analyze the
+performance of direct shaping codes and establish a condition for their
+optimality.
+",1,0,0,0,0,0
+6537,Finding influential nodes for integration in brain networks using optimal percolation theory,"  Global integration of information in the brain results from complex
+interactions of segregated brain networks. Identifying the most influential
+neuronal populations that efficiently bind these networks is a fundamental
+problem of systems neuroscience. Here we apply optimal percolation theory and
+pharmacogenetic interventions in-vivo to predict and subsequently target nodes
+that are essential for global integration of a memory network in rodents. The
+theory predicts that integration in the memory network is mediated by a set of
+low-degree nodes located in the nucleus accumbens. This result is confirmed
+with pharmacogenetic inactivation of the nucleus accumbens, which eliminates
+the formation of the memory network, while inactivations of other brain areas
+leave the network intact. Thus, optimal percolation theory predicts essential
+nodes in brain networks. This could be used to identify targets of
+interventions to modulate brain function.
+",0,0,0,0,1,0
+6538,Automatic Music Highlight Extraction using Convolutional Recurrent Attention Networks,"  Music highlights are valuable contents for music services. Most methods
+focused on low-level signal features. We propose a method for extracting
+highlights using high-level features from convolutional recurrent attention
+networks (CRAN). CRAN utilizes convolution and recurrent layers for sequential
+learning with an attention mechanism. The attention allows CRAN to capture
+significant snippets for distinguishing between genres, thus being used as a
+high-level feature. CRAN was evaluated on over 32,000 popular tracks in Korea
+for two months. Experimental results show our method outperforms three baseline
+methods through quantitative and qualitative evaluations. Also, we analyze the
+effects of attention and sequence information on performance.
+",1,0,0,1,0,0
+6539,On Oracle-Efficient PAC RL with Rich Observations,"  We study the computational tractability of PAC reinforcement learning with
+rich observations. We present new provably sample-efficient algorithms for
+environments with deterministic hidden state dynamics and stochastic rich
+observations. These methods operate in an oracle model of computation --
+accessing policy and value function classes exclusively through standard
+optimization primitives -- and therefore represent computationally efficient
+alternatives to prior algorithms that require enumeration. With stochastic
+hidden state dynamics, we prove that the only known sample-efficient algorithm,
+OLIVE, cannot be implemented in the oracle model. We also present several
+examples that illustrate fundamental challenges of tractable PAC reinforcement
+learning in such general settings.
+",0,0,0,1,0,0
+6540,Noise induced transition in Josephson junction with second harmonic,"  We show a noise-induced transition in Josephson junction with fundamental as
+well as second harmonic. A periodically modulated multiplicative colored noise
+can stabilize an unstable configuration in such a system. The stabilization of
+the unstable configuration has been captured in the effective potential of the
+system obtained by integrating out the high-frequency components of the noise.
+This is a classical approach to understand the stability of an unstable
+configuration due to the presence of such stochasticity in the system and our
+numerical analysis confirms the prediction from the analytical calculation.
+",0,1,0,0,0,0
+6541,Distributed Estimation of Principal Eigenspaces,"  Principal component analysis (PCA) is fundamental to statistical machine
+learning. It extracts latent principal factors that contribute to the most
+variation of the data. When data are stored across multiple machines, however,
+communication cost can prohibit the computation of PCA in a central location
+and distributed algorithms for PCA are thus needed. This paper proposes and
+studies a distributed PCA algorithm: each node machine computes the top $K$
+eigenvectors and transmits them to the central server; the central server then
+aggregates the information from all the node machines and conducts a PCA based
+on the aggregated information. We investigate the bias and variance for the
+resulting distributed estimator of the top $K$ eigenvectors. In particular, we
+show that for distributions with symmetric innovation, the empirical top
+eigenspaces are unbiased and hence the distributed PCA is ""unbiased"". We derive
+the rate of convergence for distributed PCA estimators, which depends
+explicitly on the effective rank of covariance, eigen-gap, and the number of
+machines. We show that when the number of machines is not unreasonably large,
+the distributed PCA performs as well as the whole sample PCA, even without full
+access of whole data. The theoretical results are verified by an extensive
+simulation study. We also extend our analysis to the heterogeneous case where
+the population covariance matrices are different across local machines but
+share similar top eigen-structures.
+",0,0,1,1,0,0
+6542,Invariant algebraic surfaces of the FitzHugh-Nagumo system,"  In this paper, we characterize all the irreducible Darboux polynomials and
+polynomial first integrals of FitzHugh-Nagumo (F-N) system. The method of the
+weight homogeneous polynomials and the characteristic curves is widely used to
+give a complete classification of Darboux polynomials of a system. However,
+this method does not work for F-N system. Here by considering the Darboux
+polynomials of an assistant system associated to F-N system, we classified the
+invariant algebraic surfaces of F-N system. Our results show that there is no
+invariant algebraic surface of F-N system in the biological parameters region.
+",0,0,1,0,0,0
+6543,Local and global boundary rigidity and the geodesic X-ray transform in the normal gauge,"  In this paper we analyze the local and global boundary rigidity problem for
+general Riemannian manifolds with boundary $(M,g)$ whose boundary is strictly
+convex. We show that the boundary distance function, i.e., $d_g|_{\partial
+M\times\partial M}$, known over suitable open sets of $\partial M$ determines
+$g$ in suitable corresponding open subsets of $M$, up to the natural
+diffeomorphism invariance of the problem. We also show that if there is a
+function on $M$ with suitable convexity properties relative to $g$ then
+$d_g|_{\partial M\times\partial M}$ determines $g$ globally in the sense that
+if $d_g|_{\partial M\times\partial M}=d_{\tilde g}|_{\partial M\times \partial
+M}$ then there is a diffeomorphism $\psi$ fixing $\partial M$ (pointwise) such
+that $g=\psi^*\tilde g$. This global assumption is satisfied, for instance, for
+the distance function from a given point if the manifold has no focal points
+(from that point).
+We also consider the lens rigidity problem. The lens relation measures the
+point of exit from $M$ and the direction of exit of geodesics issued from the
+boundary and the length of the geodesic. The lens rigidity problem is whether
+we can determine the metric up to isometry from the lens relation. We solve the
+lens rigidity problem under the same global assumption mentioned above. This
+shows, for instance, that manifolds with a strictly convex boundary and
+non-positive sectional curvature are lens rigid.
+The key tool is the analysis of the geodesic X-ray transform on 2-tensors,
+corresponding to a metric $g$, in the normal gauge, such as normal coordinates
+relative to a hypersurface, where one also needs to allow microlocal weights.
+This is handled by refining and extending our earlier results in the solenoidal
+gauge.
+",0,0,1,0,0,0
+6544,Adversarial Source Identification Game with Corrupted Training,"  We study a variant of the source identification game with training data in
+which part of the training data is corrupted by an attacker. In the addressed
+scenario, the defender aims at deciding whether a test sequence has been drawn
+according to a discrete memoryless source $X \sim P_X$, whose statistics are
+known to him through the observation of a training sequence generated by $X$.
+In order to undermine the correct decision under the alternative hypothesis
+that the test sequence has not been drawn from $X$, the attacker can modify a
+sequence produced by a source $Y \sim P_Y$ up to a certain distortion, and
+corrupt the training sequence either by adding some fake samples or by
+replacing some samples with fake ones. We derive the unique rationalizable
+equilibrium of the two versions of the game in the asymptotic regime and by
+assuming that the defender bases its decision by relying only on the first
+order statistics of the test and the training sequences. By mimicking Stein's
+lemma, we derive the best achievable performance for the defender when the
+first type error probability is required to tend to zero exponentially fast
+with an arbitrarily small, yet positive, error exponent. We then use such a
+result to analyze the ultimate distinguishability of any two sources as a
+function of the allowed distortion and the fraction of corrupted samples
+injected into the training sequence.
+",1,0,0,1,0,0
+6545,Toric actions and convexity in cosymplectic geometry,"  We prove a convexity theorem for Hamiltonian torus actions on compact
+cosymplectic manifolds. We show that compact toric cosymplectic manifolds are
+mapping tori of equivariant symplectomorphisms of toric symplectic manifolds.
+",0,0,1,0,0,0
+6546,Localization and Stationary Phase Approximation on Supermanifolds,"  Given an odd vector field $Q$ on a supermanifold $M$ and a $Q$-invariant
+density $\mu$ on $M$, under certain compactness conditions on $Q$, the value of
+the integral $\int_{M}\mu$ is determined by the value of $\mu$ on any
+neighborhood of the vanishing locus $N$ of $Q$. We present a formula for the
+integral in the case where $N$ is a subsupermanifold which is appropriately
+non-degenerate with respect to $Q$.
+In the process, we discuss the linear algebra necessary to express our result
+in a coordinate independent way. We also extend stationary phase approximation
+and the Morse-Bott Lemma to supermanifolds.
+",0,0,1,0,0,0
+6547,An Analytical Design Optimization Method for Electric Propulsion Systems of Multicopter UAVs with Desired Hovering Endurance,"  Multicopters are becoming increasingly important in both civil and military
+fields. Currently, most multicopter propulsion systems are designed by
+experience and trial-and-error experiments, which are costly and ineffective.
+This paper proposes a simple and practical method to help designers find the
+optimal propulsion system according to the given design requirements. First,
+the modeling methods for four basic components of the propulsion system
+including propellers, motors, electric speed controls, and batteries are
+studied respectively. Secondly, the whole optimization design problem is
+simplified and decoupled into several sub-problems. By solving these
+sub-problems, the optimal parameters of each component can be obtained
+respectively. Finally, based on the obtained optimal component parameters, the
+optimal product of each component can be quickly located and determined from
+the corresponding database. Experiments and statistical analyses demonstrate
+the effectiveness of the proposed method.
+",1,0,0,0,0,0
+6548,A Reassessment of Absolute Energies of the X-ray L Lines of Lanthanide Metals,"  We introduce a new technique for determining x-ray fluorescence line energies
+and widths, and we present measurements made with this technique of 22 x-ray L
+lines from lanthanide-series elements. The technique uses arrays of
+transition-edge sensors, microcalorimeters with high energy-resolving power
+that simultaneously observe both calibrated x-ray standards and the x-ray
+emission lines under study. The uncertainty in absolute line energies is
+generally less than 0.4 eV in the energy range of 4.5 keV to 7.5 keV. Of the
+seventeen line energies of neodymium, samarium, and holmium, thirteen are found
+to be consistent with the available x-ray reference data measured after 1990;
+only two of the four lines for which reference data predate 1980, however, are
+consistent with our results. Five lines of terbium are measured with
+uncertainties that improve on those of existing data by factors of two or more.
+These results eliminate a significant discrepancy between measured and
+calculated x-ray line energies for the terbium Ll line (5.551 keV). The line
+widths are also measured, with uncertainties of 0.6 eV or less on the
+full-width at half-maximum in most cases. These measurements were made with an
+array of approximately one hundred superconducting x- ray microcalorimeters,
+each sensitive to an energy band from 1 keV to 8 keV. No energy-dispersive
+spectrometer has previously been used for absolute-energy estimation at this
+level of accuracy. Future spectrometers, with superior linearity and energy
+resolution, will allow us to improve on these results and expand the
+measurements to more elements and a wider range of line energies.
+",0,1,0,0,0,0
+6549,Limitations of Source-Filter Coupling In Phonation,"  The coupling of vocal fold (source) and vocal tract (filter) is one of the
+most critical factors in source-filter articulation theory. The traditional
+linear source-filter theory has been challenged by current research which
+clearly shows the impact of acoustic loading on the dynamic behavior of the
+vocal fold vibration as well as the variations in the glottal flow pulses
+shape. This paper outlines the underlying mechanism of source-filter
+interactions; demonstrates the design and working principles of coupling for
+the various existing vocal cord and vocal tract biomechanical models. For our
+study, we have considered self-oscillating lumped-element models of the
+acoustic source and computational models of the vocal tract as articulators. To
+understand the limitations of source-filter interactions which are associated
+with each of those models, we compare them concerning their mechanical design,
+acoustic and physiological characteristics and aerodynamic simulation.
+",1,0,0,0,0,0
+6550,Correlation between clustering and degree in affiliation networks,"  We are interested in the probability that two randomly selected neighbors of
+a random vertex of degree (at least) $k$ are adjacent. We evaluate this
+probability for a power law random intersection graph, where each vertex is
+prescribed a collection of attributes and two vertices are adjacent whenever
+they share a common attribute. We show that the probability obeys the scaling
+$k^{-\delta}$ as $k\to+\infty$. Our results are mathematically rigorous. The
+parameter $0\le \delta\le 1$ is determined by the tail indices of power law
+random weights defining the links between vertices and attributes.
+",1,0,0,0,0,0
+6551,Automatic Disambiguation of French Discourse Connectives,"  Discourse connectives (e.g. however, because) are terms that can explicitly
+convey a discourse relation within a text. While discourse connectives have
+been shown to be an effective clue to automatically identify discourse
+relations, they are not always used to convey such relations, thus they should
+first be disambiguated between discourse-usage non-discourse-usage. In this
+paper, we investigate the applicability of features proposed for the
+disambiguation of English discourse connectives for French. Our results with
+the French Discourse Treebank (FDTB) show that syntactic and lexical features
+developed for English texts are as effective for French and allow the
+disambiguation of French discourse connectives with an accuracy of 94.2%.
+",1,0,0,0,0,0
+6552,Predicting Financial Crime: Augmenting the Predictive Policing Arsenal,"  Financial crime is a rampant but hidden threat. In spite of this, predictive
+policing systems disproportionately target ""street crime"" rather than white
+collar crime. This paper presents the White Collar Crime Early Warning System
+(WCCEWS), a white collar crime predictive model that uses random forest
+classifiers to identify high risk zones for incidents of financial crime.
+",1,0,0,0,0,0
+6553,Polar codes with a stepped boundary,"  We consider explicit polar constructions of blocklength $n\rightarrow\infty$
+for the two extreme cases of code rates $R\rightarrow1$ and $R\rightarrow0.$
+For code rates $R\rightarrow1,$ we design codes with complexity order of $n\log
+n$ in code construction, encoding, and decoding. These codes achieve the
+vanishing output bit error rates on the binary symmetric channels with any
+transition error probability $p\rightarrow 0$ and perform this task with a
+substantially smaller redundancy $(1-R)n$ than do other known high-rate codes,
+such as BCH codes or Reed-Muller (RM). We then extend our design to the
+low-rate codes that achieve the vanishing output error rates with the same
+complexity order of $n\log n$ and an asymptotically optimal code rate
+$R\rightarrow0$ for the case of $p\rightarrow1/2.$
+",1,0,0,0,0,0
+6554,Resonant Scattering Characteristics of Homogeneous Dielectric Sphere,"  In the present article the classical problem of electromagnetic scattering by
+a single homogeneous sphere is revisited. Main focus is the study of the
+scattering behavior as a function of the material contrast and the size
+parameters for all electric and magnetic resonances of a dielectric sphere.
+Specifically, the Padé approximants are introduced and utilized as an
+alternative system expansion of the Mie coefficients. Low order Padé
+approximants can give compact and physically insightful expressions for the
+scattering system and the enabled dynamic mechanisms. Higher order approximants
+are used for predicting accurately the resonant pole spectrum. These results
+are summarized into general pole formulae, covering up to fifth order magnetic
+and forth order electric resonances of a small dielectric sphere. Additionally,
+the connection between the radiative damping process and the resonant linewidth
+is investigated. The results obtained reveal the fundamental connection of the
+radiative damping mechanism with the maximum width occurring for each
+resonance. Finally, the suggested system ansatz is used for studying the
+resonant absorption maximum through a circuit-inspired perspective.
+",0,1,0,0,0,0
+6555,An efficient global optimization algorithm for maximizing the sum of two generalized Rayleigh quotients,"  Maximizing the sum of two generalized Rayleigh quotients (SRQ) can be
+reformulated as a one-dimensional optimization problem, where the function
+value evaluations are reduced to solving semi-definite programming (SDP)
+subproblems. In this paper, we first use the dual SDP subproblem to construct
+an explicit overestimation and then propose a branch-and-bound algorithm to
+globally solve (SRQ). Numerical results demonstrate that it is even more
+efficient than the recent SDP-based heuristic algorithm.
+",0,0,1,0,0,0
+6556,Location Dependent Dirichlet Processes,"  Dirichlet processes (DP) are widely applied in Bayesian nonparametric
+modeling. However, in their basic form they do not directly integrate
+dependency information among data arising from space and time. In this paper,
+we propose location dependent Dirichlet processes (LDDP) which incorporate
+nonparametric Gaussian processes in the DP modeling framework to model such
+dependencies. We develop the LDDP in the context of mixture modeling, and
+develop a mean field variational inference algorithm for this mixture model.
+The effectiveness of the proposed modeling framework is shown on an image
+segmentation task.
+",1,0,0,1,0,0
+6557,Poisson brackets symmetry from the pentagon-wheel cocycle in the graph complex,"  Kontsevich designed a scheme to generate infinitesimal symmetries
+$\dot{\mathcal{P}} = \mathcal{Q}(\mathcal{P})$ of Poisson brackets
+$\mathcal{P}$ on all affine manifolds $M^r$; every such deformation is encoded
+by oriented graphs on $n+2$ vertices and $2n$ edges. In particular, these
+symmetries can be obtained by orienting sums of non-oriented graphs $\gamma$ on
+$n$ vertices and $2n-2$ edges. The bi-vector flow $\dot{\mathcal{P}} =
+\text{Or}(\gamma)(\mathcal{P})$ preserves the space of Poisson structures if
+$\gamma$ is a cocycle with respect to the vertex-expanding differential in the
+graph complex.
+A class of such cocycles $\boldsymbol{\gamma}_{2\ell+1}$ is known to exist:
+marked by $\ell \in \mathbb{N}$, each of them contains a $(2\ell+1)$-gon wheel
+with a nonzero coefficient. At $\ell=1$ the tetrahedron $\boldsymbol{\gamma}_3$
+itself is a cocycle; at $\ell=2$ the Kontsevich--Willwacher pentagon-wheel
+cocycle $\boldsymbol{\gamma}_5$ consists of two graphs. We reconstruct the
+symmetry $\mathcal{Q}_5(\mathcal{P}) =
+\text{Or}(\boldsymbol{\gamma}_5)(\mathcal{P})$ and verify that $\mathcal{Q}_5$
+is a Poisson cocycle indeed:
+$[\![\mathcal{P},\mathcal{Q}_5(\mathcal{P})]\!]\doteq 0$ via
+$[\![\mathcal{P},\mathcal{P}]\!]=0$.
+",0,0,1,0,0,0
+6558,"Shadows of characteristic cycles, Verma modules, and positivity of Chern-Schwartz-MacPherson classes of Schubert cells","  Chern-Schwartz-MacPherson (CSM) classes generalize to singular and/or
+noncompact varieties the classical total homology Chern class of the tangent
+bundle of a smooth compact complex manifold. The theory of CSM classes has been
+extended to the equivariant setting by Ohmoto. We prove that for an arbitrary
+complex projective manifold $X$, the homogenized, torus equivariant CSM class
+of a constructible function $\varphi$ is the restriction of the characteristic
+cycle of $\varphi$ via the zero section of the cotangent bundle of $X$. This
+extends to the equivariant setting results of Ginzburg and Sabbah. We
+specialize $X$ to be a (generalized) flag manifold $G/B$. In this case CSM
+classes are determined by a Demazure-Lusztig (DL) operator. We prove a `Hecke
+orthogonality' of CSM classes, determined by the DL operator and its
+Poincar{é} adjoint. We further use the theory of holonomic
+$\mathcal{D}_X$-modules to show that the characteristic cycle of a Verma
+module, restricted to the zero section, gives the CSM class of the
+corresponding Schubert cell. Since the Verma characteristic cycles naturally
+identify with the Maulik and Okounkov's stable envelopes, we establish an
+equivalence between CSM classes and stable envelopes; this reproves results of
+Rim{á}nyi and Varchenko. As an application, we obtain a Segre type formula
+for CSM classes. In the non-equivariant case this formula is manifestly
+positive, showing that the expansion in the Schubert basis of the CSM class of
+a Schubert cell is effective. This proves a previous conjecture by Aluffi and
+Mihalcea, and it extends previous positivity results by J. Huh in the Grassmann
+manifold case. Finally, we generalize all of this to partial flag manifolds
+$G/P$.
+",0,0,1,0,0,0
+6559,An Iterative Scheme for Leverage-based Approximate Aggregation,"  The current data explosion poses great challenges to the approximate
+aggregation with an efficiency and accuracy. To address this problem, we
+propose a novel approach to calculate the aggregation answers with a high
+accuracy using only a small portion of the data. We introduce leverages to
+reflect individual differences in the samples from a statistical perspective.
+Two kinds of estimators, the leverage-based estimator, and the sketch estimator
+(a ""rough picture"" of the aggregation answer), are in constraint relations and
+iteratively improved according to the actual conditions until their difference
+is below a threshold. Due to the iteration mechanism and the leverages, our
+approach achieves a high accuracy. Moreover, some features, such as not
+requiring recording the sampled data and easy to extend to various execution
+modes (e.g., the online mode), make our approach well suited to deal with big
+data. Experiments show that our approach has an extraordinary performance, and
+when compared with the uniform sampling, our approach can achieve high-quality
+answers with only 1/3 of the same sample size.
+",1,0,0,0,0,0
+6560,A Game of Martingales,"  We consider a two player dynamic game played over $T \leq \infty$ periods. In
+each period each player chooses any probability distribution with support on
+$[0,1]$ with a given mean, where the mean is the realized value of the draw
+from the previous period. The player with the highest realization in the period
+achieves a payoff of $1$, and the other player, $0$; and each player seeks to
+maximize the discounted sum of his per-period payoffs over the whole time
+horizon. We solve for the unique subgame perfect equilibrium of this game, and
+establish properties of the equilibrium strategies and payoffs in the limit.
+The solution and comparative statics thereof provide insight about
+intertemporal choice with status concerns. In particular we find that patient
+players take fewer risks.
+",0,0,0,0,0,1
+6561,Effects of tunnelling and asymmetry for system-bath models of electron transfer,"  We apply the newly derived nonadiabatic golden-rule instanton theory to
+asymmetric models describing electron-transfer in solution. The models go
+beyond the usual spin-boson description and have anharmonic free-energy
+surfaces with different values for the reactant and product reorganization
+energies. The instanton method gives an excellent description of the behaviour
+of the rate constant with respect to asymmetry for the whole range studied. We
+derive a general formula for an asymmetric version of Marcus theory based on
+the classical limit of the instanton and find that this gives significant
+corrections to the standard Marcus theory. A scheme is given to compute this
+rate based only on equilibrium simulations. We also compare the rate constants
+obtained by the instanton method with its classical limit to study the effect
+of tunnelling and other quantum nuclear effects. These quantum effects can
+increase the rate constant by orders of magnitude.
+",0,1,0,0,0,0
+6562,Self-Modifying Morphology Experiments with DyRET: Dynamic Robot for Embodied Testing,"  If robots are to become ubiquitous, they will need to be able to adapt to
+complex and dynamic environments. Robots that can adapt their bodies while
+deployed might be flexible and robust enough to meet this challenge. Previous
+work on dynamic robot morphology has focused on simulation, combining simple
+modules, or switching between locomotion modes. Here, we present an alternative
+approach: a self-reconfigurable morphology that allows a single four-legged
+robot to actively adapt the length of its legs to different environments. We
+report the design of our robot, as well as the results of a study that verifies
+the performance impact of self-reconfiguration. This study compares three
+different control and morphology pairs under different levels of servo supply
+voltage in the lab. We also performed preliminary tests in different
+uncontrolled outdoor environments to see if changes to the external environment
+supports our findings in the lab. Our results show better performance with an
+adaptable body, lending evidence to the value of self-reconfiguration for
+quadruped robots.
+",1,0,0,0,0,0
+6563,Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning,"  Multi-layer neural networks have lead to remarkable performance on many kinds
+of benchmark tasks in text, speech and image processing. Nonlinear parameter
+estimation in hierarchical models is known to be subject to overfitting. One
+approach to this overfitting and related problems (local minima, colinearity,
+feature discovery etc.) is called dropout (Srivastava, et al 2014, Baldi et al
+2016). This method removes hidden units with a Bernoulli random variable with
+probability $p$ over updates. In this paper we will show that Dropout is a
+special case of a more general model published originally in 1990 called the
+stochastic delta rule ( SDR, Hanson, 1990). SDR parameterizes each weight in
+the network as a random variable with mean $\mu_{w_{ij}}$ and standard
+deviation $\sigma_{w_{ij}}$. These random variables are sampled on each forward
+activation, consequently creating an exponential number of potential networks
+with shared weights. Both parameters are updated according to prediction error,
+thus implementing weight noise injections that reflect a local history of
+prediction error and efficient model averaging. SDR therefore implements a
+local gradient-dependent simulated annealing per weight converging to a bayes
+optimal network. Tests on standard benchmarks (CIFAR) using a modified version
+of DenseNet shows the SDR outperforms standard dropout in error by over 50% and
+in loss by over 50%. Furthermore, the SDR implementation converges on a
+solution much faster, reaching a training error of 5 in just 15 epochs with
+DenseNet-40 compared to standard DenseNet-40's 94 epochs.
+",0,0,0,1,0,0
+6564,Kernel Regression with Sparse Metric Learning,"  Kernel regression is a popular non-parametric fitting technique. It aims at
+learning a function which estimates the targets for test inputs as precise as
+possible. Generally, the function value for a test input is estimated by a
+weighted average of the surrounding training examples. The weights are
+typically computed by a distance-based kernel function and they strongly depend
+on the distances between examples. In this paper, we first review the latest
+developments of sparse metric learning and kernel regression. Then a novel
+kernel regression method involving sparse metric learning, which is called
+kernel regression with sparse metric learning (KR$\_$SML), is proposed. The
+sparse kernel regression model is established by enforcing a mixed $(2,1)$-norm
+regularization over the metric matrix. It learns a Mahalanobis distance metric
+by a gradient descent procedure, which can simultaneously conduct
+dimensionality reduction and lead to good prediction results. Our work is the
+first to combine kernel regression with sparse metric learning. To verify the
+effectiveness of the proposed method, it is evaluated on 19 data sets for
+regression. Furthermore, the new method is also applied to solving practical
+problems of forecasting short-term traffic flows. In the end, we compare the
+proposed method with other three related kernel regression methods on all test
+data sets under two criterions. Experimental results show that the proposed
+method is much more competitive.
+",1,0,0,1,0,0
+6565,Learning MSO-definable hypotheses on string,"  We study the classification problems over string data for hypotheses
+specified by formulas of monadic second-order logic MSO. The goal is to design
+learning algorithms that run in time polynomial in the size of the training
+set, independently of or at least sublinear in the size of the whole data set.
+We prove negative as well as positive results. If the data set is an
+unprocessed string to which our algorithms have local access, then learning in
+sublinear time is impossible even for hypotheses definable in a small fragment
+of first-order logic. If we allow for a linear time pre-processing of the
+string data to build an index data structure, then learning of MSO-definable
+hypotheses is possible in time polynomial in the size of the training set,
+independently of the size of the whole data set.
+",1,0,0,0,0,0
+6566,From semimetal to chiral Fulde-Ferrell superfluids,"  The recent realization of two-dimensional (2D) synthetic spin-orbit (SO)
+coupling opens a broad avenue to study novel topological states for ultracold
+atoms. Here, we propose a new scheme to realize exotic chiral Fulde-Ferrell
+superfluid for ultracold fermions, with a generic theory being shown that the
+topology of superfluid pairing phases can be determined from the normal states.
+The main findings are two fold. First, a semimetal is driven by a new type of
+2D SO coupling whose realization is even simpler than the recent experiment,
+and can be tuned into massive Dirac fermion phases with or without inversion
+symmetry. Without inversion symmetry the superfluid phase with nonzero pairing
+momentum is favored under an attractive interaction. Furthermore, we show a
+fundamental theorem that the topology of a 2D chiral superfluid can be uniquely
+determined from the unpaired normal states, with which the topological chiral
+Fulde-Ferrell superfluid with a broad topological region is predicted for the
+present system. This generic theorem is also useful for condensed matter
+physics and material science in search for new topological superconductors.
+",0,1,0,0,0,0
+6567,TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process,"  Characterization of lung nodules as benign or malignant is one of the most
+important tasks in lung cancer diagnosis, staging and treatment planning. While
+the variation in the appearance of the nodules remains large, there is a need
+for a fast and robust computer aided system. In this work, we propose an
+end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for
+nodule characterization. First, we use median intensity projection to obtain a
+2D patch corresponding to each dimension. The three images are then
+concatenated to form a tensor, where the images serve as different channels of
+the input image. In order to increase the number of training samples, we
+perform data augmentation by scaling, rotating and adding noise to the input
+image. The trained network is used to extract features from the input image
+followed by a Gaussian Process (GP) regression to obtain the malignancy score.
+We also empirically establish the significance of different high level nodule
+attributes such as calcification, sphericity and others for malignancy
+determination. These attributes are found to be complementary to the deep
+multi-view CNN features and a significant improvement over other methods is
+obtained.
+",1,0,0,1,0,0
+6568,An apparatus architecture for femtosecond transmission electron microscopy,"  The motion of electrons in or near solids, liquids and gases can be tracked
+by forcing their ejection with attosecond x-ray pulses, derived from
+femtosecond lasers. The momentum of these emitted electrons carries the imprint
+of the electronic state. Aberration corrected transmission electron microscopes
+have observed individual atoms, and have sufficient energy sensitivity to
+quantify atom bonding and electronic configurations. Recent developments in
+ultrafast electron microscopy and diffraction indicate that spatial and
+temporal information can be collected simultaneously. In the present work, we
+push the capability of femtosecond transmission electron microscopy (fs-TEM)
+towards that of the state of the art in ultrafast lasers and electron
+microscopes. This is anticipated to facilitate unprecedented elucidation of
+physical, chemical and biological structural dynamics on electronic time and
+length scales. The fs-TEM numerically studied employs a nanotip source,
+electrostatic acceleration to 70 keV, magnetic lens beam transport and
+focusing, a condenser-objective around the sample and a terahertz temporal
+compressor, including space charge effects during propagation. With electron
+emission equivalent to a 20 fs laser pulse, we find a spatial resolution below
+10 nm and a temporal resolution of below 10 fs will be feasible for pulses
+comprised of on average 20 electrons. The influence of a transverse electric
+field at the sample is modelled, indicating that a field of 1 V/$\mu$m can be
+resolved.
+",0,1,0,0,0,0
+6569,HourGlass: Predictable Time-based Cache Coherence Protocol for Dual-Critical Multi-Core Systems,"  We present a hardware mechanism called HourGlass to predictably share data in
+a multi-core system where cores are explicitly designated as critical or
+non-critical. HourGlass is a time-based cache coherence protocol for
+dual-critical multi-core systems that ensures worst-case latency (WCL) bounds
+for memory requests originating from critical cores. Although HourGlass does
+not provide either WCL or bandwidth guarantees for memory requests from
+non-critical cores, it promotes the use of timers to improve its bandwidth
+utilization while still maintaining WCL bounds for critical cores. This
+encourages a trade-off between the WCL bounds for critical cores, and the
+improved memory bandwidth for non-critical cores via timer configurations. We
+evaluate HourGlass using gem5, and with multithreaded benchmark suites
+including SPLASH-2, and synthetic workloads. Our results show that the WCL for
+critical cores with HourGlass is always within the analytical WCL bounds, and
+provides a tighter WCL bound on critical cores compared to the state-of-the-art
+real-time cache coherence protocol. Further, we show that HourGlass enables a
+trade-off between provable WCL bounds for critical cores, and improved
+bandwidth utilization for non-critical cores. The average-case performance of
+HourGlass is comparable to the state-of-the-art real-time cache coherence
+protocol, and suffers a slowdown of 1.43x and 1.46x compared to the
+conventional MSI and MESI protocols.
+",1,0,0,0,0,0
+6570,Frictional Effects on RNA Folding: Speed Limit and Kramers Turnover,"  We investigated frictional effects on the folding rates of a human telomerase
+hairpin (hTR HP) and H-type pseudoknot from the Beet Western Yellow Virus (BWYV
+PK) using simulations of the Three Interaction Site (TIS) model for RNA. The
+heat capacity from TIS model simulations, calculated using temperature replica
+exchange simulations, reproduces nearly quantitatively the available
+experimental data for the hTR HP. The corresponding results for BWYV PK serve
+as predictions. We calculated the folding rates ($k_\mathrm{F}$) from more than
+100 folding trajectories for each value of the solvent viscosity ($\eta$) at a
+fixed salt concentration of 200 mM. By using the theoretical estimate
+($\propto$$\sqrt{N}$ where $N$ is the number of nucleotides) for folding free
+energy barrier, $k_\mathrm{F}$ data for both the RNAs are quantitatively fit
+using one-dimensional Kramers' theory with two parameters specifying the
+curvatures in the unfolded basin and the barrier top. In the high-friction
+regime ($\eta\gtrsim10^{-5}\,\textrm{Pa\ensuremath{\cdot}s}$), for both HP and
+PK, $k_\mathrm{F}$s decrease as $1/\eta$ whereas in the low friction regime,
+$k_\mathrm{F}$ values increase as $\eta$ increases, leading to a maximum
+folding rate at a moderate viscosity
+($\sim10^{-6}\,\textrm{Pa\ensuremath{\cdot}s}$), which is the Kramers turnover.
+From the fits, we find that the speed limit to RNA folding at water viscosity
+is between 1 and 4 $\mathrm{\mu s}$, which is in accord with our previous
+theoretical prediction as well as results from several single molecule
+experiments. Both the RNA constructs fold by parallel pathways. Surprisingly,
+we find that the flux through the pathways could be altered by changing solvent
+viscosity, a prediction that is more easily testable in RNA than in proteins.
+",0,0,0,0,1,0
+6571,Learning to Rank based on Analogical Reasoning,"  Object ranking or ""learning to rank"" is an important problem in the realm of
+preference learning. On the basis of training data in the form of a set of
+rankings of objects represented as feature vectors, the goal is to learn a
+ranking function that predicts a linear order of any new set of objects. In
+this paper, we propose a new approach to object ranking based on principles of
+analogical reasoning. More specifically, our inference pattern is formalized in
+terms of so-called analogical proportions and can be summarized as follows:
+Given objects $A,B,C,D$, if object $A$ is known to be preferred to $B$, and $C$
+relates to $D$ as $A$ relates to $B$, then $C$ is (supposedly) preferred to
+$D$. Our method applies this pattern as a main building block and combines it
+with ideas and techniques from instance-based learning and rank aggregation.
+Based on first experimental results for data sets from various domains (sports,
+education, tourism, etc.), we conclude that our approach is highly competitive.
+It appears to be specifically interesting in situations in which the objects
+are coming from different subdomains, and which hence require a kind of
+knowledge transfer.
+",1,0,0,1,0,0
+6572,Modeling Spatial Overdispersion with the Generalized Waring Process,"  Modeling spatial overdispersion requires point processes models with finite
+dimensional distributions that are overdisperse relative to the Poisson.
+Fitting such models usually heavily relies on the properties of stationarity,
+ergodicity, and orderliness. And, though processes based on negative binomial
+finite dimensional distributions have been widely considered, they typically
+fail to simultaneously satisfy the three required properties for fitting.
+Indeed, it has been conjectured by Diggle and Milne that no negative binomial
+model can satisfy all three properties. In light of this, we change
+perspective, and construct a new process based on a different overdisperse
+count model, the Generalized Waring Distribution. While comparably tractable
+and flexible to negative binomial processes, the Generalized Waring process is
+shown to possess all required properties, and additionally span the negative
+binomial and Poisson processes as limiting cases. In this sense, the GW process
+provides an approximate resolution to the conundrum highlighted by Diggle and
+Milne.
+",0,0,1,1,0,0
+6573,Adversarial examples for generative models,"  We explore methods of producing adversarial examples on deep generative
+models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning
+architectures are known to be vulnerable to adversarial examples, but previous
+work has focused on the application of adversarial examples to classification
+tasks. Deep generative models have recently become popular due to their ability
+to model input data distributions and generate realistic examples from those
+distributions. We present three classes of attacks on the VAE and VAE-GAN
+architectures and demonstrate them against networks trained on MNIST, SVHN and
+CelebA. Our first attack leverages classification-based adversaries by
+attaching a classifier to the trained encoder of the target generative model,
+which can then be used to indirectly manipulate the latent representation. Our
+second attack directly uses the VAE loss function to generate a target
+reconstruction image from the adversarial example. Our third attack moves
+beyond relying on classification or the standard loss for the gradient and
+directly optimizes against differences in source and target latent
+representations. We also motivate why an attacker might be interested in
+deploying such techniques against a target generative network.
+",0,0,0,1,0,0
+6574,Sparse covariance matrix estimation in high-dimensional deconvolution,"  We study the estimation of the covariance matrix $\Sigma$ of a
+$p$-dimensional normal random vector based on $n$ independent observations
+corrupted by additive noise. Only a general nonparametric assumption is imposed
+on the distribution of the noise without any sparsity constraint on its
+covariance matrix. In this high-dimensional semiparametric deconvolution
+problem, we propose spectral thresholding estimators that are adaptive to the
+sparsity of $\Sigma$. We establish an oracle inequality for these estimators
+under model miss-specification and derive non-asymptotic minimax convergence
+rates that are shown to be logarithmic in $n/\log p$. We also discuss the
+estimation of low-rank matrices based on indirect observations as well as the
+generalization to elliptical distributions. The finite sample performance of
+the threshold estimators is illustrated in a numerical example.
+",0,0,1,0,0,0
+6575,Dynamic controllers for column synchronization of rotation matrices: a QR-factorization approach,"  In the multi-agent systems setting, this paper addresses continuous-time
+distributed synchronization of columns of rotation matrices. More precisely, k
+specific columns shall be synchronized and only the corresponding k columns of
+the relative rotations between the agents are assumed to be available for the
+control design. When one specific column is considered, the problem is
+equivalent to synchronization on the (d-1)-dimensional unit sphere and when all
+the columns are considered, the problem is equivalent to synchronization on
+SO(d). We design dynamic control laws for these synchronization problems. The
+control laws are based on the introduction of auxiliary variables in
+combination with a QR-factorization approach. The benefit of this
+QR-factorization approach is that we can decouple the dynamics for the k
+columns from the remaining d-k ones. Under the control scheme, the closed loop
+system achieves almost global convergence to synchronization for quasi-strong
+interaction graph topologies.
+",1,0,1,0,0,0
+6576,The ELEGANT NMR Spectrometer,"  Compact and portable in-situ NMR spectrometers which can be dipped in the
+liquid to be measured, and are easily maintained, with affordable coil
+constructions and electronics, together with an apparatus to recover depleted
+magnets are presented, that provide a new real-time processing method for NMR
+spectrum acquisition, that remains stable despite magnetic field fluctuations.
+",0,1,0,0,0,0
+6577,What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization,"  Using different methods for laying out a graph can lead to very different
+visual appearances, with which the viewer perceives different information.
+Selecting a ""good"" layout method is thus important for visualizing a graph. The
+selection can be highly subjective and dependent on the given task. A common
+approach to selecting a good layout is to use aesthetic criteria and visual
+inspection. However, fully calculating various layouts and their associated
+aesthetic metrics is computationally expensive. In this paper, we present a
+machine learning approach to large graph visualization based on computing the
+topological similarity of graphs using graph kernels. For a given graph, our
+approach can show what the graph would look like in different layouts and
+estimate their corresponding aesthetic metrics. An important contribution of
+our work is the development of a new framework to design graph kernels. Our
+experimental study shows that our estimation calculation is considerably faster
+than computing the actual layouts and their aesthetic metrics. Also, our graph
+kernels outperform the state-of-the-art ones in both time and accuracy. In
+addition, we conducted a user study to demonstrate that the topological
+similarity computed with our graph kernel matches perceptual similarity
+assessed by human users.
+",1,0,0,1,0,0
+6578,A sure independence screening procedure for ultra-high dimensional partially linear additive models,"  We introduce a two-step procedure, in the context of ultra-high dimensional
+additive models, which aims to reduce the size of covariates vector and
+distinguish linear and nonlinear effects among nonzero components. Our proposed
+screening procedure, in the first step, is constructed based on the concept of
+cumulative distribution function and conditional expectation of response in the
+framework of marginal correlation. B-splines and empirical distribution
+functions are used to estimate the two above measures. The sure property of
+this procedure is also established. In the second step, a double penalization
+based procedure is applied to identify nonzero and linear components,
+simultaneously. The performance of the designed method is examined by several
+test functions to show its capabilities against competitor methods when errors
+distribution are varied. Simulation studies imply that the proposed screening
+procedure can be applied to the ultra-high dimensional data and well detect the
+in uential covariates. It is also demonstrate the superiority in comparison
+with the existing methods. This method is also applied to identify most in
+uential genes for overexpression of a G protein-coupled receptor in mice.
+",0,0,1,1,0,0
+6579,Unbiased Multi-index Monte Carlo,"  We introduce a new class of Monte Carlo based approximations of expectations
+of random variables such that their laws are only available via certain
+discretizations. Sampling from the discretized versions of these laws can
+typically introduce a bias. In this paper, we show how to remove that bias, by
+introducing a new version of multi-index Monte Carlo (MIMC) that has the added
+advantage of reducing the computational effort, relative to i.i.d. sampling
+from the most precise discretization, for a given level of error. We cover
+extensions of results regarding variance and optimality criteria for the new
+approach. We apply the methodology to the problem of computing an unbiased
+mollified version of the solution of a partial differential equation with
+random coefficients. A second application concerns the Bayesian inference (the
+smoothing problem) of an infinite dimensional signal modelled by the solution
+of a stochastic partial differential equation that is observed on a discrete
+space grid and at discrete times. Both applications are complemented by
+numerical simulations.
+",0,0,0,1,0,0
+6580,Hilbert Transformation and $r\mathrm{Spin}(n)+\mathbb{R}^n$ Group,"  In this paper we study symmetry properties of the Hilbert transformation of
+several real variables in the Clifford algebra setting. In order to describe
+the symmetry properties we introduce the group $r\mathrm{Spin}(n)+\mathbb{R}^n,
+r>0,$ which is essentially an extension of the ax+b group. The study concludes
+that the Hilbert transformation has certain characteristic symmetry properties
+in terms of $r\mathrm{Spin}(n)+\mathbb{R}^n.$ In the present paper, for $n=2$
+and $3$ we obtain, explicitly, the induced spinor representations of the
+$r\mathrm{Spin}(n)+\mathbb{R}^n$ group. Then we decompose the natural
+representation of $r\mathrm{Spin}(n)+\mathbb{R}^n$ into the direct sum of some
+two irreducible spinor representations, by which we characterize the Hilbert
+transformation in $\mathbb{R}^3$ and $\mathbb{R}^2.$ Precisely, we show that a
+nontrivial skew operator is the Hilbert transformation if and only if it is
+invariant under the action of the $r\mathrm{Spin}(n)+\mathbb{R}^n, n=2,3,$
+group.
+",0,0,1,0,0,0
+6581,Asymptotic limit and decay estimates for a class of dissipative linear hyperbolic systems in several dimensions,"  In this paper, we study the large-time behavior of solutions to a class of
+partially dissipative linear hyperbolic systems with applications in
+velocity-jump processes in several dimensions. Given integers $n,d\ge 1$, let
+$\mathbf A:=(A^1,\dots,A^d)\in (\mathbb R^{n\times n})^d$ be a matrix-vector,
+where $A^j\in\mathbb R^{n\times n}$, and let $B\in \mathbb R^{n\times n}$ be
+not required to be symmetric but have one single eigenvalue zero, we consider
+the Cauchy problem for linear $n\times n$ systems having the form
+\begin{equation*}
+\partial_{t}u+\mathbf A\cdot \nabla_{\mathbf x} u+Bu=0,\qquad (\mathbf
+x,t)\in \mathbb R^d\times \mathbb R_+. \end{equation*} Under appropriate
+assumptions, we show that the solution $u$ is decomposed into
+$u=u^{(1)}+u^{(2)}$, where $u^{(1)}$ has the asymptotic profile which is the
+solution, denoted by $U$, of a parabolic equation and $u^{(1)}-U$ decays at the
+rate $t^{-\frac d2(\frac 1q-\frac 1p)-\frac 12}$ as $t\to +\infty$ in any
+$L^p$-norm, and $u^{(2)}$ decays exponentially in $L^2$-norm, provided
+$u(\cdot,0)\in L^q(\mathbb R^d)\cap L^2(\mathbb R^d)$ for $1\le q\le p\le
+\infty$. Moreover, $u^{(1)}-U$ decays at the optimal rate $t^{-\frac d2(\frac
+1q-\frac 1p)-1}$ as $t\to +\infty$ if the system satisfies a symmetry property.
+The main proofs are based on asymptotic expansions of the solution $u$ in the
+frequency space and the Fourier analysis.
+",0,0,1,0,0,0
+6582,"(G, μ)-displays and Rapoport-Zink spaces","  Let (G, \mu) be a pair of a reductive group G over the p-adic integers and a
+minuscule cocharacter {\mu} of G defined over an unramified extension. We
+introduce and study ""(G, \mu)-displays"" which generalize Zink's Witt vector
+displays. We use these to define certain Rapoport-Zink formal schemes purely
+group theoretically, i.e. without p-divisible groups.
+",0,0,1,0,0,0
+6583,Selecting Representative Examples for Program Synthesis,"  Program synthesis is a class of regression problems where one seeks a
+solution, in the form of a source-code program, mapping the inputs to their
+corresponding outputs exactly. Due to its precise and combinatorial nature,
+program synthesis is commonly formulated as a constraint satisfaction problem,
+where input-output examples are encoded as constraints and solved with a
+constraint solver. A key challenge of this formulation is scalability: while
+constraint solvers work well with a few well-chosen examples, a large set of
+examples can incur significant overhead in both time and memory. We describe a
+method to discover a subset of examples that is both small and representative:
+the subset is constructed iteratively, using a neural network to predict the
+probability of unchosen examples conditioned on the chosen examples in the
+subset, and greedily adding the least probable example. We empirically evaluate
+the representativeness of the subsets constructed by our method, and
+demonstrate such subsets can significantly improve synthesis time and
+stability.
+",1,0,0,0,0,0
+6584,Semistable rank 2 sheaves with singularities of mixed dimension on $\mathbb{P}^3$,"  We describe new irreducible components of the Gieseker-Maruyama moduli scheme
+$\mathcal{M}(3)$ of semistable rank 2 coherent sheaves with Chern classes
+$c_1=0,\ c_2=3,\ c_3=0$ on $\mathbb{P}^3$, general points of which correspond
+to sheaves whose singular loci contain components of dimensions both 0 and 1.
+These sheaves are produced by elementary transformations of stable reflexive
+rank 2 sheaves with $c_1=0,\ c_2=2,\ c_3=2$ or 4 along a disjoint union of a
+projective line and a collection of points in $\mathbb{P}^3$. The constructed
+families of sheaves provide first examples of irreducible components of the
+Gieseker-Maruyama moduli scheme such that their general sheaves have
+singularities of mixed dimension.
+",0,0,1,0,0,0
+6585,From jamming to collective cell migration through a boundary induced transition,"  Cell monolayers provide an interesting example of active matter, exhibiting a
+phase transition from a flowing to jammed state as they age. Here we report
+experiments and numerical simulations illustrating how a jammed cellular layer
+rapidly reverts to a flowing state after a wound. Quantitative comparison
+between experiments and simulations shows that cells change their
+self-propulsion and alignement strength so that the system crosses a phase
+transition line, which we characterize by finite-size scaling in an active
+particle model. This wound-induced unjamming transition is found to occur
+generically in epithelial, endothelial and cancer cells.
+",0,0,0,0,1,0
+6586,An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection,"  Long Short-Term Memory networks trained with gradient descent and
+back-propagation have received great success in various applications. However,
+point estimation of the weights of the networks is prone to over-fitting
+problems and lacks important uncertainty information associated with the
+estimation. However, exact Bayesian neural network methods are intractable and
+non-applicable for real-world applications. In this study, we propose an
+approximate estimation of the weights uncertainty using Ensemble Kalman Filter,
+which is easily scalable to a large number of weights. Furthermore, we optimize
+the covariance of the noise distribution in the ensemble update step using
+maximum likelihood estimation. To assess the proposed algorithm, we apply it to
+outlier detection in five real-world events retrieved from the Twitter
+platform.
+",1,0,0,1,0,0
+6587,Estimates of covering type and the number of vertices of minimal triangulations,"  The covering type of a space $X$ is defined as the minimal cardinality of a
+good cover of a space that is homotopy equivalent to $X$. We derive estimates
+for the covering type of $X$ in terms of other invariants of $X$, namely the
+ranks of the homology groups, the multiplicative structure of the cohomology
+ring and the Lusternik-Schnirelmann category of $X$. By relating the covering
+type to the number of vertices of minimal triangulations of complexes and
+combinatorial manifolds, we obtain, within a unified framework, several
+estimates which are either new or extensions of results that have been
+previously obtained by ad hoc combinatorial arguments. Moreover, our methods
+give results that are valid for entire homotopy classes of spaces.
+",0,0,1,0,0,0
+6588,Principal Floquet subspaces and exponential separations of type II with applications to random delay differential equations,"  This paper deals with the study of principal Lyapunov exponents, principal
+Floquet subspaces, and exponential separation for positive random linear
+dynamical systems in ordered Banach spaces. The main contribution lies in the
+introduction of a new type of exponential separation, called of type II,
+important for its application to nonautonomous random differential equations
+with delay. Under weakened assumptions, the existence of an exponential
+separation of type II in an abstract general setting is shown, and an
+illustration of its application to dynamical systems generated by scalar linear
+random delay differential equations with finite delay is given.
+",0,0,1,0,0,0
+6589,DNA insertion mutations can be predicted by a periodic probability function,"  It is generally difficult to predict the positions of mutations in genomic
+DNA at the nucleotide level. Retroviral DNA insertion is one mode of mutation,
+resulting in host infections that are difficult to treat. This mutation process
+involves the integration of retroviral DNA into the host-infected cellular
+genomic DNA following the interaction between host DNA and a pre-integration
+complex consisting of retroviral DNA and integrase. Here, we report that
+retroviral insertion sites around a hotspot within the Zfp521 and N-myc genes
+can be predicted by a periodic function that is deduced using the diffraction
+lattice model. In conclusion, the mutagenesis process is described by a
+biophysical model for DNA-DNA interactions.
+",0,1,0,0,0,0
+6590,Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra,"  Machine learning has emerged as an invaluable tool in many research areas. In
+the present work, we harness this power to predict highly accurate molecular
+infrared spectra with unprecedented computational efficiency. To account for
+vibrational anharmonic and dynamical effects -- typically neglected by
+conventional quantum chemistry approaches -- we base our machine learning
+strategy on ab initio molecular dynamics simulations. While these simulations
+are usually extremely time consuming even for small molecules, we overcome
+these limitations by leveraging the power of a variety of machine learning
+techniques, not only accelerating simulations by several orders of magnitude,
+but also greatly extending the size of systems that can be treated. To this
+end, we develop a molecular dipole moment model based on environment dependent
+neural network charges and combine it with the neural network potentials of
+Behler and Parrinello. Contrary to the prevalent big data philosophy, we are
+able to obtain very accurate machine learning models for the prediction of
+infrared spectra based on only a few hundreds of electronic structure reference
+points. This is made possible through the introduction of a fully automated
+sampling scheme and the use of molecular forces during neural network potential
+training. We demonstrate the power of our machine learning approach by applying
+it to model the infrared spectra of a methanol molecule, n-alkanes containing
+up to 200 atoms and the protonated alanine tripeptide, which at the same time
+represents the first application of machine learning techniques to simulate the
+dynamics of a peptide. In all these case studies we find excellent agreement
+between the infrared spectra predicted via machine learning models and the
+respective theoretical and experimental spectra.
+",0,1,0,1,0,0
+6591,Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries,"  In this article, we develop methods for estimating a low rank tensor from
+noisy observations on a subset of its entries to achieve both statistical and
+computational efficiencies. There have been a lot of recent interests in this
+problem of noisy tensor completion. Much of the attention has been focused on
+the fundamental computational challenges often associated with problems
+involving higher order tensors, yet very little is known about their
+statistical performance. To fill in this void, in this article, we characterize
+the fundamental statistical limits of noisy tensor completion by establishing
+minimax optimal rates of convergence for estimating a $k$th order low rank
+tensor under the general $\ell_p$ ($1\le p\le 2$) norm which suggest
+significant room for improvement over the existing approaches. Furthermore, we
+propose a polynomial-time computable estimating procedure based upon power
+iteration and a second-order spectral initialization that achieves the optimal
+rates of convergence. Our method is fairly easy to implement and numerical
+experiments are presented to further demonstrate the practical merits of our
+estimator.
+",0,0,1,1,0,0
+6592,The cohomology of the full directed graph complex,"  In his seminal paper ""Formality conjecture"", M. Kontsevich introduced a graph
+complex $GC_{1ve}$ closely connected with the problem of constructing a
+formality quasi-isomorphism for Hochschild cochains. In this paper, we express
+the cohomology of the full directed graph complex explicitly in terms of the
+cohomology of $GC_{1ve}$. Applications of our results include a recent work by
+the first author which completely characterizes homotopy classes of formality
+quasi-isomorphisms for Hochschild cochains in the stable setting.
+",0,0,1,0,0,0
+6593,Model equations and structures formation for the media with memory,"  We propose new types of models of the appearance of small- and large scale
+structures in media with memory, including a hyperbolic modification of the
+Navier-Stokes equations and a class of dynamical low-dimensional models with
+memory effects. On the basis of computer modeling, the formation of the
+small-scale structures and collapses and the appearance of new chaotic
+solutions are demonstrated. Possibilities of the application of some proposed
+models to the description of the burst-type processes and collapses o nthe Sun
+are discussed.
+",0,1,0,0,0,0
+6594,On the Support Recovery of Jointly Sparse Gaussian Sources using Sparse Bayesian Learning,"  In this work, we provide non-asymptotic, probabilistic guarantees for
+successful sparse support recovery by the multiple sparse Bayesian learning
+(M-SBL) algorithm in the multiple measurement vector (MMV) framework. For joint
+sparse Gaussian sources, we show that M-SBL perfectly recovers their common
+nonzero support with arbitrarily high probability using only finitely many
+MMVs. In fact, the support error probability decays exponentially fast with the
+number of MMVs, with the decay rate depending on the restricted isometry
+property of the self Khatri-Rao product of the measurement matrix. Our analysis
+theoretically confirms that M-SBL is capable of recovering supports of size as
+high as $\mathcal{O}(m^2)$, where $m$ is the number of measurements per sparse
+vector. In contrast, popular MMV algorithms in compressed sensing such as
+simultaneous orthogonal matching pursuit and row-LASSO can recover only
+$\mathcal{O}(m)$ sized supports. In the special case of noiseless measurements,
+we show that a single MMV suffices for perfect recovery of the $k$-sparse
+support in M-SBL, provided any $k + 1$ columns of the measurement matrix are
+linearly independent. Unlike existing support recovery guarantees for M-SBL,
+our sufficient conditions are non-asymptotic in nature, and do not require the
+orthogonality of the nonzero rows of the joint sparse signals.
+",1,0,0,0,0,0
+6595,A Critical-like Collective State Leads to Long-range Cell Communication in Dictyostelium discoideum Aggregation,"  The transition from single-cell to multicellular behavior is important in
+early development but rarely studied. The starvation-induced aggregation of the
+social amoeba Dictyostelium discoideum into a multicellular slug is known to
+result from single-cell chemotaxis towards emitted pulses of cyclic adenosine
+monophosphate (cAMP). However, how exactly do transient short-range chemical
+gradients lead to coherent collective movement at a macroscopic scale? Here, we
+use a multiscale model verified by quantitative microscopy to describe
+wide-ranging behaviors from chemotaxis and excitability of individual cells to
+aggregation of thousands of cells. To better understand the mechanism of
+long-range cell-cell communication and hence aggregation, we analyze cell-cell
+correlations, showing evidence for self-organization at the onset of
+aggregation (as opposed to following a leader cell). Surprisingly, cell
+collectives, despite their finite size, show features of criticality known from
+phase transitions in physical systems. Application of external cAMP
+perturbations in our simulations near the sensitive critical point allows
+steering cells into early aggregation and towards certain locations but not
+once an aggregation center has been chosen.
+",0,0,0,0,1,0
+6596,Twistor theory at fifty: from contour integrals to twistor strings,"  We review aspects of twistor theory, its aims and achievements spanning
+thelast five decades. In the twistor approach, space--time is secondary with
+events being derived objects that correspond to compact holomorphic curves in a
+complex three--fold -- the twistor space. After giving an elementary
+construction of this space we demonstrate how solutions to linear and nonlinear
+equations of mathematical physics: anti-self-duality (ASD) equations on
+Yang--Mills, or conformal curvature can be encoded into twistor cohomology.
+These twistor correspondences yield explicit examples of Yang--Mills, and
+gravitational instantons which we review. They also underlie the twistor
+approach to integrability: the solitonic systems arise as symmetry reductions
+of ASD Yang--Mills equations, and Einstein--Weyl dispersionless systems are
+reductions of ASD conformal equations.
+We then review the holomorphic string theories in twistor and ambitwistor
+spaces, and explain how these theories give rise to remarkable new formulae for
+the computation of quantum scattering amplitudes. Finally we discuss the
+Newtonian limit of twistor theory, and its possible role in Penrose's proposal
+for a role of gravity in quantum collapse of a wave function.
+",0,1,1,0,0,0
+6597,Strong convergence rates of probabilistic integrators for ordinary differential equations,"  Probabilistic integration of a continuous dynamical system is a way of
+systematically introducing model error, at scales no larger than errors
+introduced by standard numerical discretisation, in order to enable thorough
+exploration of possible responses of the system to inputs. It is thus a
+potentially useful approach in a number of applications such as forward
+uncertainty quantification, inverse problems, and data assimilation. We extend
+the convergence analysis of probabilistic integrators for deterministic
+ordinary differential equations, as proposed by Conrad et al.\ (\textit{Stat.\
+Comput.}, 2016), to establish mean-square convergence in the uniform norm on
+discrete- or continuous-time solutions under relaxed regularity assumptions on
+the driving vector fields and their induced flows. Specifically, we show that
+randomised high-order integrators for globally Lipschitz flows and randomised
+Euler integrators for dissipative vector fields with polynomially-bounded local
+Lipschitz constants all have the same mean-square convergence rate as their
+deterministic counterparts, provided that the variance of the integration noise
+is not of higher order than the corresponding deterministic integrator. These
+and similar results are proven for probabilistic integrators where the random
+perturbations may be state-dependent, non-Gaussian, or non-centred random
+variables.
+",0,0,1,1,0,0
+6598,The G-centre and gradable derived equivalences,"  We propose a generalisation for the notion of the centre of an algebra in the
+setup of algebras graded by an arbitrary abelian group G.
+Our generalisation, which we call the G-centre, is designed to control the
+endomorphism category of the grading shift functors. We show that the G-centre
+is preserved by gradable derived equivalences given by tilting modules. We also
+discuss links with existing notions in superalgebra theory and apply our
+results to derived equivalences of superalgebras.
+",0,0,1,0,0,0
+6599,RFCDE: Random Forests for Conditional Density Estimation,"  Random forests is a common non-parametric regression technique which performs
+well for mixed-type data and irrelevant covariates, while being robust to
+monotonic variable transformations. Existing random forest implementations
+target regression or classification. We introduce the RFCDE package for fitting
+random forest models optimized for nonparametric conditional density
+estimation, including joint densities for multiple responses. This enables
+analysis of conditional probability distributions which is useful for
+propagating uncertainty and of joint distributions that describe relationships
+between multiple responses and covariates. RFCDE is released under the MIT
+open-source license and can be accessed at this https URL .
+Both R and Python versions, which call a common C++ library, are available.
+",0,0,0,1,0,0
+6600,The symplectic approach of gauged linear $σ$-model,"  Witten's Gauged Linear $\sigma$-Model (GLSM) unifies the Gromov-Witten theory
+and the Landau-Ginzburg theory, and provides a global perspective on mirror
+symmetry. In this article, we summarize a mathematically rigorous construction
+of the GLSM in the geometric phase using methods from symplectic geometry.
+",0,0,1,0,0,0
+6601,Analysis of nonsmooth stochastic approximation: the differential inclusion approach,"  In this paper we address the convergence of stochastic approximation when the
+functions to be minimized are not convex and nonsmooth. We show that the
+""mean-limit"" approach to the convergence which leads, for smooth problems, to
+the ODE approach can be adapted to the non-smooth case. The limiting dynamical
+system may be shown to be, under appropriate assumption, a differential
+inclusion. Our results expand earlier works in this direction by Benaim et al.
+(2005) and provide a general framework for proving convergence for
+unconstrained and constrained stochastic approximation problems, with either
+explicit or implicit updates. In particular, our results allow us to establish
+the convergence of stochastic subgradient and proximal stochastic gradient
+descent algorithms arising in a large class of deep learning and
+high-dimensional statistical inference with sparsity inducing penalties.
+",0,0,0,1,0,0
+6602,Clicks and Cliques. Exploring the Soul of the Community,"  In the paper we analyze 26 communities across the United States with the
+objective to understand what attaches people to their community and how this
+attachment differs among communities. How different are attached people from
+unattached? What attaches people to their community? How different are the
+communities? What are key drivers behind emotional attachment? To address these
+questions, graphical, supervised and unsupervised learning tools were used and
+information from the Census Bureau and the Knight Foundation were combined.
+Using the same pre-processed variables as Knight (2010) most likely will drive
+the results towards the same conclusions than the Knight foundation, so this
+paper does not use those variables.
+",0,0,0,1,0,0
+6603,Quickest Change Detection under Transient Dynamics: Theory and Asymptotic Analysis,"  The problem of quickest change detection (QCD) under transient dynamics is
+studied, where the change from the initial distribution to the final persistent
+distribution does not happen instantaneously, but after a series of transient
+phases. The observations within the different phases are generated by different
+distributions. The objective is to detect the change as quickly as possible,
+while controlling the average run length (ARL) to false alarm, when the
+durations of the transient phases are completely unknown. Two algorithms are
+considered, the dynamic Cumulative Sum (CuSum) algorithm, proposed in earlier
+work, and a newly constructed weighted dynamic CuSum algorithm. Both algorithms
+admit recursions that facilitate their practical implementation, and they are
+adaptive to the unknown transient durations. Specifically, their asymptotic
+optimality is established with respect to both Lorden's and Pollak's criteria
+as the ARL to false alarm and the durations of the transient phases go to
+infinity at any relative rate. Numerical results are provided to demonstrate
+the adaptivity of the proposed algorithms, and to validate the theoretical
+results.
+",0,0,1,1,0,0
+6604,The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks,"  Many application settings involve the analysis of timestamped relations or
+events between a set of entities, e.g. messages between users of an on-line
+social network. Static and discrete-time network models are typically used as
+analysis tools in these settings; however, they discard a significant amount of
+information by aggregating events over time to form network snapshots. In this
+paper, we introduce a block point process model (BPPM) for dynamic networks
+evolving in continuous time in the form of events at irregular time intervals.
+The BPPM is inspired by the well-known stochastic block model (SBM) for static
+networks and is a simpler version of the recently-proposed Hawkes infinite
+relational model (IRM). We show that networks generated by the BPPM follow an
+SBM in the limit of a growing number of nodes and leverage this property to
+develop an efficient inference procedure for the BPPM. We fit the BPPM to
+several real network data sets, including a Facebook network with over 3, 500
+nodes and 130, 000 events, several orders of magnitude larger than the Hawkes
+IRM and other existing point process network models.
+",1,0,0,1,0,0
+6605,Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks,"  This work demonstrates the potential of deep reinforcement learning
+techniques for transmit power control in emerging and future wireless networks.
+Various techniques have been proposed in the literature to find near-optimal
+power allocations, often by solving a challenging optimization problem. Most of
+these algorithms are not scalable to large networks in real-world scenarios
+because of their computational complexity and instantaneous cross-cell channel
+state information (CSI) requirement. In this paper, a model-free distributively
+executed dynamic power allocation scheme is developed based on deep
+reinforcement learning. Each transmitter collects CSI and quality of service
+(QoS) information from several neighbors and adapts its own transmit power
+accordingly. The objective is to maximize a weighted sum-rate utility function,
+which can be particularized to achieve maximum sum-rate or proportionally fair
+scheduling (with weights that are changing over time). Both random variations
+and delays in the CSI are inherently addressed using deep Q-learning. For a
+typical network architecture, the proposed algorithm is shown to achieve
+near-optimal power allocation in real time based on delayed CSI measurements
+available to the agents. This work indicates that deep reinforcement learning
+based radio resource management can be very fast and deliver highly competitive
+performance, especially in practical scenarios where the system model is
+inaccurate and CSI delay is non-negligible.
+",0,0,0,1,0,0
+6606,Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation,"  This paper provides a holistic study of how stock prices vary in their
+response to financial disclosures across different topics. Thereby, we
+specifically shed light into the extensive amount of filings for which no a
+priori categorization of their content exists. For this purpose, we utilize an
+approach from data mining - namely, latent Dirichlet allocation - as a means of
+topic modeling. This technique facilitates our task of automatically
+categorizing, ex ante, the content of more than 70,000 regulatory 8-K filings
+from U.S. companies. We then evaluate the subsequent stock market reaction. Our
+empirical evidence suggests a considerable discrepancy among various types of
+news stories in terms of their relevance and impact on financial markets. For
+instance, we find a statistically significant abnormal return in response to
+earnings results and credit rating, but also for disclosures regarding business
+strategy, the health sector, as well as mergers and acquisitions. Our results
+yield findings that benefit managers, investors and policy-makers by indicating
+how regulatory filings should be structured and the topics most likely to
+precede changes in stock valuations.
+",0,0,0,0,0,1
+6607,Truncated Variational EM for Semi-Supervised Neural Simpletrons,"  Inference and learning for probabilistic generative networks is often very
+challenging and typically prevents scalability to as large networks as used for
+deep discriminative approaches. To obtain efficiently trainable, large-scale
+and well performing generative networks for semi-supervised learning, we here
+combine two recent developments: a neural network reformulation of hierarchical
+Poisson mixtures (Neural Simpletrons), and a novel truncated variational EM
+approach (TV-EM). TV-EM provides theoretical guarantees for learning in
+generative networks, and its application to Neural Simpletrons results in
+particularly compact, yet approximately optimal, modifications of learning
+equations. If applied to standard benchmarks, we empirically find, that
+learning converges in fewer EM iterations, that the complexity per EM iteration
+is reduced, and that final likelihood values are higher on average. For the
+task of classification on data sets with few labels, learning improvements
+result in consistently lower error rates if compared to applications without
+truncation. Experiments on the MNIST data set herein allow for comparison to
+standard and state-of-the-art models in the semi-supervised setting. Further
+experiments on the NIST SD19 data set show the scalability of the approach when
+a manifold of additional unlabeled data is available.
+",0,0,0,1,0,0
+6608,The nature and origin of heavy tails in retweet activity,"  Modern social media platforms facilitate the rapid spread of information
+online. Modelling phenomena such as social contagion and information diffusion
+are contingent upon a detailed understanding of the information-sharing
+processes. In Twitter, an important aspect of this occurs with retweets, where
+users rebroadcast the tweets of other users. To improve our understanding of
+how these distributions arise, we analyse the distribution of retweet times. We
+show that a power law with exponential cutoff provides a better fit than the
+power laws previously suggested. We explain this fit through the burstiness of
+human behaviour and the priorities individuals place on different tasks.
+",1,1,0,1,0,0
+6609,Opinion Polarization by Learning from Social Feedback,"  We explore a new mechanism to explain polarization phenomena in opinion
+dynamics in which agents evaluate alternative views on the basis of the social
+feedback obtained on expressing them. High support of the favored opinion in
+the social environment, is treated as a positive feedback which reinforces the
+value associated to this opinion. In connected networks of sufficiently high
+modularity, different groups of agents can form strong convictions of competing
+opinions. Linking the social feedback process to standard equilibrium concepts
+we analytically characterize sufficient conditions for the stability of
+bi-polarization. While previous models have emphasized the polarization effects
+of deliberative argument-based communication, our model highlights an affective
+experience-based route to polarization, without assumptions about negative
+influence or bounded confidence.
+",1,1,0,0,0,0
+6610,Estimating Quality in Multi-Objective Bandits Optimization,"  Many real-world applications are characterized by a number of conflicting
+performance measures. As optimizing in a multi-objective setting leads to a set
+of non-dominated solutions, a preference function is required for selecting the
+solution with the appropriate trade-off between the objectives. The question
+is: how good do estimations of these objectives have to be in order for the
+solution maximizing the preference function to remain unchanged? In this paper,
+we introduce the concept of preference radius to characterize the robustness of
+the preference function and provide guidelines for controlling the quality of
+estimations in the multi-objective setting. More specifically, we provide a
+general formulation of multi-objective optimization under the bandits setting.
+We show how the preference radius relates to the optimal gap and we use this
+concept to provide a theoretical analysis of the Thompson sampling algorithm
+from multivariate normal priors. We finally present experiments to support the
+theoretical results and highlight the fact that one cannot simply scalarize
+multi-objective problems into single-objective problems.
+",1,0,0,1,0,0
+6611,Adaptive IGAFEM with optimal convergence rates: Hierarchical B-splines,"  We consider an adaptive algorithm for finite element methods for the
+isogeometric analysis (IGAFEM) of elliptic (possibly non-symmetric)
+second-order partial differential equations in arbitrary space dimension
+$d\ge2$. We employ hierarchical B-splines of arbitrary degree and different
+order of smoothness. We propose a refinement strategy to generate a sequence of
+locally refined meshes and corresponding discrete solutions. Adaptivity is
+driven by some weighted residual a posteriori error estimator. We prove linear
+convergence of the error estimator (resp. the sum of energy error plus data
+oscillations) with optimal algebraic rates. Numerical experiments underpin the
+theoretical findings.
+",0,0,1,0,0,0
+6612,Spin dynamics of quadrupole nuclei in InGaAs quantum dots,"  Photoluminescence polarization is experimentally studied for samples with
+(In,Ga)As/GaAs selfassembled quantum dots in transverse magnetic field (Hanle
+effect) under slow modulation of the excitation light polarization from
+fractions of Hz to tens of kHz. The polarization reflects the evolution of
+strongly coupled electron-nuclear spin system in the quantum dots. Strong
+modification of the Hanle curves under variation of the modulation period is
+attributed to the peculiarities of the spin dynamics of quadrupole nuclei,
+which states are split due to deformation of the crystal lattice in the quantum
+dots. Analysis of the Hanle curves is fulfilled in the framework of a
+phenomenological model considering a separate dynamics of a nuclear field BNd
+determined by the +/- 12 nuclear spin states and of a nuclear field BNq
+determined by the split-off states +/- 3/2, +/- 5/2, etc. It is found that the
+characteristic relaxation time for the nuclear field BNd is of order of 0.5 s,
+while the relaxation of the field BNq is faster by three orders of magnitude.
+",0,1,0,0,0,0
+6613,Calibration for Weak Variance-Alpha-Gamma Processes,"  The weak variance-alpha-gamma process is a multivariate Lévy process
+constructed by weakly subordinating Brownian motion, possibly with correlated
+components with an alpha-gamma subordinator. It generalises the
+variance-alpha-gamma process of Semeraro constructed by traditional
+subordination. We compare three calibration methods for the weak
+variance-alpha-gamma process, method of moments, maximum likelihood estimation
+(MLE) and digital moment estimation (DME). We derive a condition for Fourier
+invertibility needed to apply MLE and show in our simulations that MLE produces
+a better fit when this condition holds, while DME produces a better fit when it
+is violated. We also find that the weak variance-alpha-gamma process exhibits a
+wider range of dependence and produces a significantly better fit than the
+variance-alpha-gamma process on an S&P500-FTSE100 data set, and that DME
+produces the best fit in this situation.
+",0,0,0,0,0,1
+6614,Generalized Coordinated Transaction Scheduling: A Market Approach to Seamless Interfaces,"  A generalization of the coordinated transaction scheduling (CTS)---the
+state-of-the-art interchange scheduling---is proposed. Referred to as
+generalized coordinated transaction scheduling (GCTS), the proposed approach
+addresses major seams issues of CTS: the ad hoc use of proxy buses, the
+presence of loop flow as a result of proxy bus approximation, and difficulties
+in dealing with multiple interfaces. By allowing market participants to submit
+bids across market boundaries, GCTS also generalizes the joint economic
+dispatch that achieves seamless interchange without market participants. It is
+shown that GCTS asymptotically achieves seamless interface under certain
+conditions. GCTS is also shown to be revenue adequate in that each regional
+market has a non-negative net revenue that is equal to its congestion rent.
+Numerical examples are presented to illustrate the quantitative improvement of
+the proposed approach.
+",0,0,1,0,0,0
+6615,Boundaries as an Enhancement Technique for Physical Layer Security,"  In this paper, we study the receiver performance with physical layer security
+in a Poisson field of interferers. We compare the performance in two deployment
+scenarios: (i) the receiver is located at the corner of a quadrant, (ii) the
+receiver is located in the infinite plane. When the channel state information
+(CSI) of the eavesdropper is not available at the transmitter, we calculate the
+probability of secure connectivity using the Wyner coding scheme, and we show
+that hiding the receiver at the corner is beneficial at high rates of the
+transmitted codewords and detrimental at low transmission rates. When the CSI
+is available, we show that the average secrecy capacity is higher when the
+receiver is located at the corner, even if the intensity of interferers in this
+case is four times higher than the intensity of interferers in the bulk.
+Therefore boundaries can also be used as a secrecy enhancement technique for
+high data rate applications.
+",1,0,0,0,0,0
+6616,Magnetically induced Ferroelectricity in Bi$_2$CuO$_4$,"  The tetragonal copper oxide Bi$_2$CuO$_4$ has an unusual crystal structure
+with a three-dimensional network of well separated CuO$_4$ plaquettes. This
+material was recently predicted to host electronic excitations with an
+unconventional spectrum and the spin structure of its magnetically ordered
+state appearing at T$_N$ $\sim$43 K remains controversial. Here we present the
+results of detailed studies of specific heat, magnetic and dielectric
+properties of Bi$_2$CuO$_4$ single crystals grown by the floating zone
+technique, combined with the polarized neutron scattering and high-resolution
+X-ray measurements. Our polarized neutron scattering data show Cu spins are
+parallel to the $ab$ plane. Below the onset of the long range antiferromagnetic
+ordering we observe an electric polarization induced by an applied magnetic
+field, which indicates inversion symmetry breaking by the ordered state of Cu
+spins. For the magnetic field applied perpendicular to the tetragonal axis, the
+spin-induced ferroelectricity is explained in terms of the linear
+magnetoelectric effect that occurs in a metastable magnetic state. A relatively
+small electric polarization induced by the field parallel to the tetragonal
+axis may indicate a more complex magnetic ordering in Bi$_2$CuO$_4$.
+",0,1,0,0,0,0
+6617,Probabilistic Matrix Factorization for Automated Machine Learning,"  In order to achieve state-of-the-art performance, modern machine learning
+techniques require careful data pre-processing and hyperparameter tuning.
+Moreover, given the ever increasing number of machine learning models being
+developed, model selection is becoming increasingly important. Automating the
+selection and tuning of machine learning pipelines consisting of data
+pre-processing methods and machine learning models, has long been one of the
+goals of the machine learning community. In this paper, we tackle this
+meta-learning task by combining ideas from collaborative filtering and Bayesian
+optimization. Using probabilistic matrix factorization techniques and
+acquisition functions from Bayesian optimization, we exploit experiments
+performed in hundreds of different datasets to guide the exploration of the
+space of possible pipelines. In our experiments, we show that our approach
+quickly identifies high-performing pipelines across a wide range of datasets,
+significantly outperforming the current state-of-the-art.
+",0,0,0,1,0,0
+6618,Stochastic Multi-objective Optimization on a Budget: Application to multi-pass wire drawing with quantified uncertainties,"  Design optimization of engineering systems with multiple competing objectives
+is a painstakingly tedious process especially when the objective functions are
+expensive-to-evaluate computer codes with parametric uncertainties. The
+effectiveness of the state-of-the-art techniques is greatly diminished because
+they require a large number of objective evaluations, which makes them
+impractical for problems of the above kind. Bayesian global optimization (BGO),
+has managed to deal with these challenges in solving single-objective
+optimization problems and has recently been extended to multi-objective
+optimization (MOO). BGO models the objectives via probabilistic surrogates and
+uses the epistemic uncertainty to define an information acquisition function
+(IAF) that quantifies the merit of evaluating the objective at new designs.
+This iterative data acquisition process continues until a stopping criterion is
+met. The most commonly used IAF for MOO is the expected improvement over the
+dominated hypervolume (EIHV) which in its original form is unable to deal with
+parametric uncertainties or measurement noise. In this work, we provide a
+systematic reformulation of EIHV to deal with stochastic MOO problems. The
+primary contribution of this paper lies in being able to filter out the noise
+and reformulate the EIHV without having to observe or estimate the stochastic
+parameters. An addendum of the probabilistic nature of our methodology is that
+it enables us to characterize our confidence about the predicted Pareto front.
+We verify and validate the proposed methodology by applying it to synthetic
+test problems with known solutions. We demonstrate our approach on an
+industrial problem of die pass design for a steel wire drawing process.
+",0,0,1,0,0,0
+6619,Generation High resolution 3D model from natural language by Generative Adversarial Network,"  We present a method of generating high resolution 3D shapes from natural
+language descriptions. To achieve this goal, we propose two steps that
+generating low resolution shapes which roughly reflect texts and generating
+high resolution shapes which reflect the detail of texts. In a previous paper,
+the authors have shown a method of generating low resolution shapes. We improve
+it to generate 3D shapes more faithful to natural language and test the
+effectiveness of the method. To generate high resolution 3D shapes, we use the
+framework of Conditional Wasserstein GAN. We propose two roles of Critic
+separately, which calculate the Wasserstein distance between two probability
+distribution, so that we achieve generating high quality shapes or acceleration
+of learning speed of model. To evaluate our approach, we performed quantitive
+evaluation with several numerical metrics for Critic models. Our method is
+first to realize the generation of high quality model by propagating text
+embedding information to high resolution task when generating 3D model.
+",1,0,0,1,0,0
+6620,A survey on policy search algorithms for learning robot controllers in a handful of trials,"  Most policy search algorithms require thousands of training episodes to find
+an effective policy, which is often infeasible with a physical robot. This
+survey article focuses on the extreme other end of the spectrum: how can a
+robot adapt with only a handful of trials (a dozen) and a few minutes? By
+analogy with the word ""big-data"", we refer to this challenge as ""micro-data
+reinforcement learning"". We show that a first strategy is to leverage prior
+knowledge on the policy structure (e.g., dynamic movement primitives), on the
+policy parameters (e.g., demonstrations), or on the dynamics (e.g.,
+simulators). A second strategy is to create data-driven surrogate models of the
+expected reward (e.g., Bayesian optimization) or the dynamical model (e.g.,
+model-based policy search), so that the policy optimizer queries the model
+instead of the real system. Overall, all successful micro-data algorithms
+combine these two strategies by varying the kind of model and prior knowledge.
+The current scientific challenges essentially revolve around scaling up to
+complex robots (e.g., humanoids), designing generic priors, and optimizing the
+computing time.
+",1,0,0,1,0,0
+6621,Non-linear Cyclic Codes that Attain the Gilbert-Varshamov Bound,"  We prove that there exist non-linear binary cyclic codes that attain the
+Gilbert-Varshamov bound.
+",1,0,1,0,0,0
+6622,Consistency of Lipschitz learning with infinite unlabeled data and finite labeled data,"  We study the consistency of Lipschitz learning on graphs in the limit of
+infinite unlabeled data and finite labeled data. Previous work has conjectured
+that Lipschitz learning is well-posed in this limit, but is insensitive to the
+distribution of the unlabeled data, which is undesirable for semi-supervised
+learning. We first prove that this conjecture is true in the special case of a
+random geometric graph model with kernel-based weights. Then we go on to show
+that on a random geometric graph with self-tuning weights, Lipschitz learning
+is in fact highly sensitive to the distribution of the unlabeled data, and we
+show how the degree of sensitivity can be adjusted by tuning the weights. In
+both cases, our results follow from showing that the sequence of learned
+functions converges to the viscosity solution of an $\infty$-Laplace type
+equation, and studying the structure of the limiting equation.
+",1,0,0,0,0,0
+6623,On the Synthesis of Guaranteed-Quality Plans for Robot Fleets in Logistics Scenarios via Optimization Modulo Theories,"  In manufacturing, the increasing involvement of autonomous robots in
+production processes poses new challenges on the production management. In this
+paper we report on the usage of Optimization Modulo Theories (OMT) to solve
+certain multi-robot scheduling problems in this area. Whereas currently
+existing methods are heuristic, our approach guarantees optimality for the
+computed solution. We do not only present our final method but also its
+chronological development, and draw some general observations for the
+development of OMT-based approaches.
+",1,0,0,0,0,0
+6624,Geometrical morphology,"  We explore inflectional morphology as an example of the relationship of the
+discrete and the continuous in linguistics. The grammar requests a form of a
+lexeme by specifying a set of feature values, which corresponds to a corner M
+of a hypercube in feature value space. The morphology responds to that request
+by providing a morpheme, or a set of morphemes, whose vector sum is
+geometrically closest to the corner M. In short, the chosen morpheme $\mu$ is
+the morpheme (or set of morphemes) that maximizes the inner product of $\mu$
+and M.
+",1,0,0,0,0,0
+6625,A framework for cost-constrained genome rearrangement under Double Cut and Join,"  The study of genome rearrangement has many flavours, but they all are somehow
+tied to edit distances on variations of a multi-graph called the breakpoint
+graph. We study a weighted 2-break distance on Eulerian 2-edge-colored
+multi-graphs, which generalizes weighted versions of several Double Cut and
+Join problems, including those on genomes with unequal gene content. We affirm
+the connection between cycle decompositions and edit scenarios first discovered
+with the Sorting By Reversals problem. Using this we show that the problem of
+finding a parsimonious scenario of minimum cost on an Eulerian 2-edge-colored
+multi-graph - with a general cost function for 2-breaks - can be solved by
+decomposing the problem into independent instances on simple alternating
+cycles. For breakpoint graphs, and a more constrained cost function, based on
+coloring the vertices, we give a polynomial-time algorithm for finding a
+parsimonious 2-break scenario of minimum cost, while showing that finding a
+non-parsimonious 2-break scenario of minimum cost is NP-Hard.
+",0,0,0,0,1,0
+6626,Dual combination combination multi switching synchronization of eight chaotic systems,"  In this paper, a novel scheme for synchronizing four drive and four response
+systems is proposed by the authors. The idea of multi switching and dual
+combination synchronization is extended to dual combination-combination multi
+switching synchronization involving eight chaotic systems and is a first of its
+kind. Due to the multiple combination of chaotic systems and multi switching
+the resultant dynamic behaviour is so complex that, in communication theory,
+transmission and security of the resultant signal is more effective. Using
+Lyapunov stability theory, sufficient conditions are achieved and suitable
+controllers are designed to realise the desired synchronization. Corresponding
+theoretical analysis is presented and numerical simulations performed to
+demonstrate the effectiveness of the proposed scheme.
+",1,0,1,0,0,0
+6627,An Optimization Based Control Framework for Balancing and Walking: Implementation on the iCub Robot,"  A whole-body torque control framework adapted for balancing and walking tasks
+is presented in this paper. In the proposed approach, centroidal momentum terms
+are excluded in favor of a hierarchy of high-priority position and orientation
+tasks and a low-priority postural task. More specifically, the controller
+stabilizes the position of the center of mass, the orientation of the pelvis
+frame, as well as the position and orientation of the feet frames. The
+low-priority postural task provides reference positions for each joint of the
+robot. Joint torques and contact forces to stabilize tasks are obtained through
+quadratic programming optimization. Besides the exclusion of centroidal
+momentum terms, part of the novelty of the approach lies in the definition of
+control laws in SE(3) which do not require the use of Euler parameterization.
+Validation of the framework was achieved in a scenario where the robot kept
+balance while walking in place. Experiments have been conducted with the iCub
+robot, in simulation and in real-world experiments.
+",1,0,0,0,0,0
+6628,Self-similar groups of type FP_{n},"  We construct new classes of self-similar groups : S-aritmetic groups, affine
+groups and metabelian groups. Most of the soluble ones are finitely presented
+and of type FP_{n} for appropriate n.
+",0,0,1,0,0,0
+6629,Operator Fitting for Parameter Estimation of Stochastic Differential Equations,"  Estimation of parameters is a crucial part of model development. When models
+are deterministic, one can minimise the fitting error; for stochastic systems
+one must be more careful. Broadly parameterisation methods for stochastic
+dynamical systems fit into maximum likelihood estimation- and method of
+moment-inspired techniques. We propose a method where one matches a finite
+dimensional approximation of the Koopman operator with the implied Koopman
+operator as generated by an extended dynamic mode decomposition approximation.
+One advantage of this approach is that the objective evaluation cost can be
+independent the number of samples for some dynamical systems. We test our
+approach on two simple systems in the form of stochastic differential
+equations, compare to benchmark techniques, and consider limited
+eigen-expansions of the operators being approximated. Other small variations on
+the technique are also considered, and we discuss the advantages to our
+formulation.
+",0,0,1,1,0,0
+6630,Binomial transform of products,"  Given two infinite sequences with known binomial transforms, we compute the
+binomial transform of the product sequence. Various identities are obtained and
+numerous examples are given involving sequences of special numbers: Harmonic
+numbers, Bernoulli numbers, Fibonacci numbers, and also Laguerre polynomials.
+",0,0,1,0,0,0
+6631,Strength Factors: An Uncertainty System for a Quantified Modal Logic,"  We present a new system S for handling uncertainty in a quantified modal
+logic (first-order modal logic). The system is based on both probability theory
+and proof theory. The system is derived from Chisholm's epistemology. We
+concretize Chisholm's system by grounding his undefined and primitive (i.e.
+foundational) concept of reasonablenes in probability and proof theory. S can
+be useful in systems that have to interact with humans and provide
+justifications for their uncertainty. As a demonstration of the system, we
+apply the system to provide a solution to the lottery paradox. Another
+advantage of the system is that it can be used to provide uncertainty values
+for counterfactual statements. Counterfactuals are statements that an agent
+knows for sure are false. Among other cases, counterfactuals are useful when
+systems have to explain their actions to users. Uncertainties for
+counterfactuals fall out naturally from our system.
+Efficient reasoning in just simple first-order logic is a hard problem.
+Resolution-based first-order reasoning systems have made significant progress
+over the last several decades in building systems that have solved non-trivial
+tasks (even unsolved conjectures in mathematics). We present a sketch of a
+novel algorithm for reasoning that extends first-order resolution.
+Finally, while there have been many systems of uncertainty for propositional
+logics, first-order logics and propositional modal logics, there has been very
+little work in building systems of uncertainty for first-order modal logics.
+The work described below is in progress; and once finished will address this
+lack.
+",1,0,0,0,0,0
+6632,Binary companions of nearby supernova remnants found with Gaia,"  We search for runaway former companions of the progenitors of nearby Galactic
+core-collapse supernova remnants (SNRs) in the Tycho-Gaia astrometric solution
+(TGAS). We look for candidates for a sample of ten SNRs with distances less
+than $2\;\mathrm{kpc}$, taking astrometry and $G$ magnitude from TGAS and $B,V$
+magnitudes from the AAVSO Photometric All-Sky Survey (APASS). A simple method
+of tracking back stars and finding the closest point to the SNR centre is shown
+to have several failings when ranking candidates. In particular, it neglects
+our expectation that massive stars preferentially have massive companions. We
+evolve a grid of binary stars to exploit these covariances in the distribution
+of runaway star properties in colour - magnitude - ejection velocity space. We
+construct an analytic model which predicts the properties of a runaway star, in
+which the model parameters are the properties of the progenitor binary and the
+properties of the SNR. Using nested sampling we calculate the Bayesian evidence
+for each candidate to be the runaway and simultaneously constrain the
+properties of that runaway and of the SNR itself. We identify four likely
+runaway companions of the Cygnus Loop, HB 21, S147 and the Monoceros Loop. HD
+37424 has previously been suggested as the companion of S147, however the other
+three stars are new candidates. The favoured companion of HB 21 is the Be star
+BD+50 3188 whose emission-line features could be explained by pre-supernova
+mass transfer from the primary. There is a small probability that the
+$2\;\mathrm{M}_{\odot}$ candidate runaway TYC 2688-1556-1 associated with the
+Cygnus Loop is a hypervelocity star. If the Monoceros Loop is related to the
+on-going star formation in the Mon OB2 association, the progenitor of the
+Monoceros Loop is required to be more massive than $40\;\mathrm{M}_{\odot}$
+which is in tension with the posterior for HD 261393.
+",0,1,0,0,0,0
+6633,Some Distributions on Finite Rooted Binary Trees,"  We introduce some natural families of distributions on rooted binary ranked
+plane trees with a view toward unifying ideas from various fields, including
+macroevolution, epidemiology, computational group theory, search algorithms and
+other fields. In the process we introduce the notions of split-exchangeability
+and plane-invariance of a general Markov splitting model in order to readily
+obtain probabilities over various equivalence classes of trees that arise in
+statistics, phylogenetics, epidemiology and group theory.
+",0,0,1,0,0,0
+6634,Programmable DNA-mediated decision maker,"  DNA-mediated computing is a novel technology that seeks to capitalize on the
+enormous informational capacity of DNA and has tremendous computational ability
+to compete with the current silicon-mediated computing, due to massive
+parallelism and unique characteristics inherent in DNA interaction. In this
+paper, the methodology of DNA-mediated computing is utilized to enrich decision
+theory, by demonstrating how a novel programmable DNA-mediated normative
+decision-making apparatus is able to capture rational choice under uncertainty.
+",1,0,0,0,0,0
+6635,The Effects of Ram Pressure on the Cold Clouds in the Centers of Galaxy Clusters,"  We discuss the effect of ram pressure on the cold clouds in the centers of
+cool-core galaxy clusters, and in particular, how it reduces cloud velocity and
+sometimes causes an offset between the cold gas and young stars. The velocities
+of the molecular gas in both observations and our simulations fall in the range
+of $100-400$ km/s, much lower than expected if they fall from a few tens of kpc
+ballistically. If the intra-cluster medium (ICM) is at rest, the ram pressure
+of the ICM only slightly reduces the velocity of the clouds. When we assume
+that the clouds are actually ""fluffier"" because they are co-moving with a
+warm-hot layer, the velocity becomes smaller. If we also consider the AGN wind
+in the cluster center by adding a wind profile measured from the simulation,
+the clouds are further slowed down at small radii, and the resulting velocities
+are in general agreement with the observations and simulations. Because ram
+pressure only affects gas but not stars, it can cause a separation between a
+filament and young stars that formed in the filament as they move through the
+ICM together. This separation has been observed in Perseus and also exists in
+our simulations. We show that the star-filament offset combined with
+line-of-sight velocity measurements can help determine the true motion of the
+cold gas, and thus distinguish between inflows and outflows.
+",0,1,0,0,0,0
+6636,Time-delayed SIS epidemic model with population awareness,"  This paper analyses the dynamics of infectious disease with a concurrent
+spread of disease awareness. The model includes local awareness due to contacts
+with aware individuals, as well as global awareness due to reported cases of
+infection and awareness campaigns. We investigate the effects of time delay in
+response of unaware individuals to available information on the epidemic
+dynamics by establishing conditions for the Hopf bifurcation of the endemic
+steady state of the model. Analytical results are supported by numerical
+bifurcation analysis and simulations.
+",0,1,0,0,0,0
+6637,Determining Song Similarity via Machine Learning Techniques and Tagging Information,"  The task of determining item similarity is a crucial one in a recommender
+system. This constitutes the base upon which the recommender system will work
+to determine which items are more likely to be enjoyed by a user, resulting in
+more user engagement. In this paper we tackle the problem of determining song
+similarity based solely on song metadata (such as the performer, and song
+title) and on tags contributed by users. We evaluate our approach under a
+series of different machine learning algorithms. We conclude that tf-idf
+achieves better results than Word2Vec to model the dataset to feature vectors.
+We also conclude that k-NN models have better performance than SVMs and Linear
+Regression for this problem.
+",1,0,0,1,0,0
+6638,Uplink Performance Analysis in D2D-Enabled mmWave Cellular Networks,"  In this paper, we provide an analytical framework to analyze the uplink
+performance of device-to-device (D2D)-enabled millimeter wave (mmWave) cellular
+networks. Signal-to- interference-plus-noise ratio (SINR) outage probabilities
+are derived for both cellular and D2D links using tools from stochastic
+geometry. The distinguishing features of mmWave communications such as
+directional beamforming and having different path loss laws for line-of-sight
+(LOS) and non-line-of-sight (NLOS) links are incorporated into the outage
+analysis by employing a flexible mode selection scheme and Nakagami fading.
+Also, the effect of beamforming alignment errors on the outage probability is
+investigated to get insight on the performance in practical scenarios.
+",1,0,0,0,0,0
+6639,A critical topology for $L^p$-Carleman classes with $0<p<1$,"  In this paper, we explain a sharp phase transition phenomenon which occurs
+for $L^p$-Carleman classes with exponents $0<p<1$. In principle, these classes
+are defined as usual, only the traditional $L^\infty$-bounds are replaced by
+corresponding $L^p$-bounds. To mirror the classical definition, we add the
+feature of dilatation invariance as well, and consider a larger soft-topology
+space, the $L^p$-Carleman class. A particular degenerate instance is when we
+obtain the $L^p$-Sobolev spaces, analyzed previously by Peetre, following an
+initial insight by Douady. Peetre found that these $L^p$-Sobolev spaces are
+highly degenerate for $0<p<1$. Essentially, the contact is lost between the
+function and its derivatives. Here, we analyze this degeneracy for the more
+general $L^p$-Carleman classes defined by a weight sequence. Under some
+reasonable growth and regularity properties, and a condition on the collection
+of test functions, we find that there is a sharp boundary, defined in terms of
+the weight sequence: on the one side, we get Douady-Peetre's phenomenon of
+""disconnexion"" between the function and its derivatives, while on the other, we
+obtain a collection of highly smooth functions. We also look at the more
+standard second phase transition, between non-quasianalyticity and
+quasianalyticity, in the $L^p$ setting, with $0<p<1$.
+",0,0,1,0,0,0
+6640,"The GAN Landscape: Losses, Architectures, Regularization, and Normalization","  Generative adversarial networks (GANs) are a class of deep generative models
+which aim to learn a target distribution in an unsupervised fashion. While they
+were successfully applied to many problems, training a GAN is a notoriously
+challenging task and requires a significant amount of hyperparameter tuning,
+neural architecture engineering, and a non-trivial amount of ""tricks"". The
+success in many practical applications coupled with the lack of a measure to
+quantify the failure modes of GANs resulted in a plethora of proposed losses,
+regularization and normalization schemes, and neural architectures. In this
+work we take a sober view of the current state of GANs from a practical
+perspective. We reproduce the current state of the art and go beyond fairly
+exploring the GAN landscape. We discuss common pitfalls and reproducibility
+issues, open-source our code on Github, and provide pre-trained models on
+TensorFlow Hub.
+",0,0,0,1,0,0
+6641,An energy-based equilibrium contact angle boundary condition on jagged surfaces for phase-field methods,"  We consider an energy-based boundary condition to impose an equilibrium
+wetting angle for the Cahn-Hilliard-Navier-Stokes phase-field model on
+voxel-set-type computational domains. These domains typically stem from the
+micro-CT imaging of porous rock and approximate a (on {\mu}m scale) smooth
+domain with a certain resolution. Planar surfaces that are perpendicular to the
+main axes are naturally approximated by a layer of voxels. However, planar
+surfaces in any other directions and curved surfaces yield a jagged/rough
+surface approximation by voxels. For the standard Cahn-Hilliard formulation,
+where the contact angle between the diffuse interface and the domain boundary
+(fluid-solid interface/wall) is 90 degrees, jagged surfaces have no impact on
+the contact angle. However, a prescribed contact angle smaller or larger than
+90 degrees on jagged voxel surfaces is amplified in either direction. As a
+remedy, we propose the introduction of surface energy correction factors for
+each fluid-solid voxel face that counterbalance the difference of the voxel-set
+surface area with the underlying smooth one. The discretization of the model
+equations is performed with the discontinuous Galerkin method, however, the
+presented semi-analytical approach of correcting the surface energy is equally
+applicable to other direct numerical methods such as finite elements, finite
+volumes, or finite differences, since the correction factors appear in the
+strong formulation of the model.
+",0,1,0,0,0,0
+6642,"Period polynomials, derivatives of $L$-functions, and zeros of polynomials","  Period polynomials have long been fruitful tools for the study of values of
+$L$-functions in the context of major outstanding conjectures. In this paper,
+we survey some facets of this study from the perspective of Eichler cohomology.
+We discuss ways to incorporate non-cuspidal modular forms and values of
+derivatives of $L$-functions into the same framework. We further review
+investigations of the location of zeros of the period polynomial as well as of
+its analogue for $L$-derivatives.
+",0,0,1,0,0,0
+6643,Forming disc galaxies in major mergers II. The central mass concentration problem and a comparison of GADGET3 with GIZMO,"  Context: In a series of papers, we study the major merger of two disk
+galaxies in order to establish whether or not such a merger can produce a disc
+galaxy. Aims: Our aim here is to describe in detail the technical aspects of
+our numerical experiments. Methods: We discuss the initial conditions of our
+major merger, which consist of two protogalaxies on a collision orbit. We show
+that such merger simulations can produce a non-realistic central mass
+concentration, and we propose simple, parametric, AGN-like feedback as a
+solution to this problem. Our AGN-like feedback algorithm is very simple: at
+each time-step we take all particles whose local volume density is above a
+given threshold value and increase their temperature to a preset value. We also
+compare the GADGET3 and GIZMO codes, by applying both of them to the same
+initial conditions. Results: We show that the evolution of isolated
+protogalaxies resembles the evolution of disk galaxies, thus arguing that our
+protogalaxies are well suited for our merger simulations. We demonstrate that
+the problem with the unphysical central mass concentration in our merger
+simulations is further aggravated when we increase the resolution. We show that
+our AGN-like feedback removes this non-physical central mass concentration, and
+thus allows the formation of realistic bars. Note that our AGN-like feedback
+mainly affects the central region of a model, without significantly modifying
+the rest of the galaxy. We demonstrate that, in the context of our kind of
+simulation, GADGET3 gives results which are very similar to those obtained with
+the PSPH (density independent SPH) flavor of GIZMO. Moreover, in the examples
+we tried, the differences between the results of the two flavors of GIZMO,
+namely PSPH, and MFM (mesh-less algorithm) are similar to and, in some
+comparisons, larger than the differences between the results of GADGET3 and
+PSPH.
+",0,1,0,0,0,0
+6644,Measuring Systematic Risk with Neural Network Factor Model,"  In this paper, we measure systematic risk with a new nonparametric factor
+model, the neural network factor model. The suitable factors for systematic
+risk can be naturally found by inserting daily returns on a wide range of
+assets into the bottleneck network. The network-based model does not stick to a
+probabilistic structure unlike parametric factor models, and it does not need
+feature engineering because it selects notable features by itself. In addition,
+we compare performance between our model and the existing models using 20-year
+data of S&P 100 components. Although the new model can not outperform the best
+ones among the parametric factor models due to limitations of the variational
+inference, the estimation method used for this study, it is still noteworthy in
+that it achieves the performance as best the comparable models could without
+any prior knowledge.
+",0,0,0,0,0,1
+6645,Obstacle Avoidance Using Stereo Camera,"  In this paper we present a novel method for obstacle avoidance using the
+stereo camera. The conventional obstacle avoidance methods and their
+limitations are discussed. A new algorithm is developed for the real-time
+obstacle avoidance which responds faster to unexpected obstacles. In this
+approach the depth map is divided into optimized number of regions and the
+minimum depth at each section is assigned as the depth of that region. A fuzzy
+controller is designed to create the drive commands for the robot/quadcopter.
+The system was tested on multiple paths with different obstacles and the
+results demonstrated the high accuracy of the developed system.
+",1,0,0,0,0,0
+6646,Detecting Outliers in Data with Correlated Measures,"  Advances in sensor technology have enabled the collection of large-scale
+datasets. Such datasets can be extremely noisy and often contain a significant
+amount of outliers that result from sensor malfunction or human operation
+faults. In order to utilize such data for real-world applications, it is
+critical to detect outliers so that models built from these datasets will not
+be skewed by outliers.
+In this paper, we propose a new outlier detection method that utilizes the
+correlations in the data (e.g., taxi trip distance vs. trip time). Different
+from existing outlier detection methods, we build a robust regression model
+that explicitly models the outliers and detects outliers simultaneously with
+the model fitting.
+We validate our approach on real-world datasets against methods specifically
+designed for each dataset as well as the state of the art outlier detectors.
+Our outlier detection method achieves better performances, demonstrating the
+robustness and generality of our method. Last, we report interesting case
+studies on some outliers that result from atypical events.
+",0,0,0,1,0,0
+6647,Active bialkali photocathodes on free-standing graphene substrates,"  The hexagonal structure of graphene gives rise to the property of gas
+impermeability, motivating its investigation for a new application: protection
+of semiconductor photocathodes in electron accelerators. These materials are
+extremely susceptible to degradation in efficiency through multiple mechanisms
+related to contamination from the local imperfect vacuum environment of the
+host photoinjector. Few-layer graphene has been predicted to permit a modified
+photoemission response of protected photocathode surfaces, and recent
+experiments of single-layer graphene on copper have begun to confirm these
+predictions for single crystal metallic photocathodes. Unlike metallic
+photoemitters, the integration of an ultra-thin graphene barrier film with
+conventional semiconductor photocathode growth processes is not
+straightforward. A first step toward addressing this challenge is the growth
+and characterization of technologically relevant, high quantum efficiency
+bialkali photocathodes grown on ultra-thin free-standing graphene substrates.
+Photocathode growth on free-standing graphene provides the opportunity to
+integrate these two materials and study their interaction. Specifically,
+spectral response features and photoemission stability of cathodes grown on
+graphene substrates are compared to those deposited on established substrates.
+In addition we observed an increase of work function for the graphene
+encapsulated bialkali photocathode surfaces, which is predicted by our
+calculations. The results provide a unique demonstration of bialkali
+photocathodes on free-standing substrates, and indicate promise towards our
+goal of fabricating high-performance graphene encapsulated photocathodes with
+enhanced lifetime for accelerator applications.
+",0,1,0,0,0,0
+6648,Reconstruction of a compact Riemannian manifold from the scattering data of internal sources,"  Given a smooth non-trapping compact manifold with strictly con- vex boundary,
+we consider an inverse problem of reconstructing the manifold from the
+scattering data initiated from internal sources. This data consist of the exit
+directions of geodesics that are emaneted from interior points of the manifold.
+We show that under certain generic assumption of the metric, one can
+reconstruct an isometric copy of the manifold from such scattering data
+measured on the boundary.
+",0,0,1,0,0,0
+6649,Lensing and the Warm Hot Intergalactic Medium,"  The correlation of weak lensing and Cosmic Microwave Anisotropy (CMB) data
+traces the pressure distribution of the hot, ionized gas and the underlying
+matter density field. The measured correlation is dominated by baryons residing
+in halos. Detecting the contribution from unbound gas by measuring the residual
+cross-correlation after masking all known halos requires a theoretical
+understanding of this correlation and its dependence with model parameters. Our
+model assumes that the gas in filaments is well described by a log-normal
+probability distribution function, with temperatures $10^{5-7}$K and
+overdensities $\xi\le 100$. The lensing-comptonization cross-correlation is
+dominated by gas with overdensities in the range $\xi\approx[3-33]$; the signal
+is generated at redshifts $z\le 1$. If only 10\% of the measured
+cross-correlation is due to unbound gas, then the most recent measurements set
+an upper limit of $\bar{T}_e\lesssim 10^6$K on the mean temperature of Inter
+Galactic Medium. The amplitude is proportional to the baryon fraction stored in
+filaments. The lensing-comptonization power spectrum peaks at a different scale
+than the gas in halos making it possible to distinguish both contributions. To
+trace the distribution of the low density and low temperature plasma on
+cosmological scales, the effect of halos will have to be subtracted from the
+data, requiring observations with larger signal-to-noise ratio than currently
+available.
+",0,1,0,0,0,0
+6650,Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares,"  We consider the asymmetric orthogonal tensor decomposition problem, and
+present an orthogonalized alternating least square algorithm that converges to
+rank-$r$ of the true tensor factors simultaneously in
+$O(\log(\log(\frac{1}{\epsilon})))$ steps under our proposed Trace Based
+Initialization procedure. Trace Based Initialization requires $O(1/{\log
+(\frac{\lambda_{r}}{\lambda_{r+1}})})$ number of matrix subspace iterations to
+guarantee a ""good"" initialization for the simultaneous orthogonalized ALS
+method, where $\lambda_r$ is the $r$-th largest singular value of the tensor.
+We are the first to give a theoretical guarantee on orthogonal asymmetric
+tensor decomposition using Trace Based Initialization procedure and the
+orthogonalized alternating least squares. Our Trace Based Initialization also
+improves convergence for symmetric orthogonal tensor decomposition.
+",0,0,0,1,0,0
+6651,The average sizes of two-torsion subgroups in quotients of class groups of cubic fields,"  We prove a generalization of a result of Bhargava regarding the average size
+$\mathrm{Cl}(K)[2]$ as $K$ varies among cubic fields. For a fixed set of
+rational primes $S$, we obtain a formula for the average size of
+$\mathrm{Cl}(K)/\langle S \rangle[2]$ as $K$ varies among cubic fields with a
+fixed signature, where $\langle S \rangle$ is the subgroup of $\mathrm{Cl}(K)$
+generated by the classes of primes of $K$ above primes in $S$.
+As a consequence, we are able to calculate the average sizes of
+$K_{2n}(\mathcal{O}_K)[2]$ for $n > 0$ and for the relaxed Selmer group
+$\mathrm{Sel}_2^S(K)$ as $K$ varies in these same families.
+",0,0,1,0,0,0
+6652,A Strongly Consistent Finite Difference Scheme for Steady Stokes Flow and its Modified Equations,"  We construct and analyze a strongly consistent second-order finite difference
+scheme for the steady two-dimensional Stokes flow. The pressure Poisson
+equation is explicitly incorporated into the scheme. Our approach suggested by
+the first two authors is based on a combination of the finite volume method,
+difference elimination, and numerical integration. We make use of the
+techniques of the differential and difference Janet/Groebner bases. In order to
+prove strong consistency of the generated scheme we correlate the differential
+ideal generated by the polynomials in the Stokes equations with the difference
+ideal generated by the polynomials in the constructed difference scheme.
+Additionally, we compute the modified differential system of the obtained
+scheme and analyze the scheme's accuracy and strong consistency by considering
+this system. An evaluation of our scheme against the established
+marker-and-cell method is carried out.
+",1,0,0,0,0,0
+6653,"Classification of $δ(2,n-2)$-ideal Lagrangian submanifolds in $n$-dimensional complex space forms","  It was proven in [B.-Y. Chen, F. Dillen, J. Van der Veken and L. Vrancken,
+Curvature inequalities for Lagrangian submanifolds: the final solution, Differ.
+Geom. Appl. 31 (2013), 808-819] that every Lagrangian submanifold $M$ of a
+complex space form $\tilde M^{n}(4c)$ of constant holomorphic sectional
+curvature $4c$ satisfies the following optimal inequality: \begin{align*}
+\delta(2,n-2) \leq \frac{n^2(n-2)}{4(n-1)} H^2 + 2(n-2) c, \end{align*} where
+$H^2$ is the squared mean curvature and $\delta(2,n-2)$ is a $\delta$-invariant
+on $M$. In this paper we classify Lagrangian submanifolds of complex space
+forms $\tilde M^{n}(4c)$, $n \geq 5$, which satisfy the equality case of this
+inequality at every point.
+",0,0,1,0,0,0
+6654,Robust stability analysis of DC microgrids with constant power loads,"  This paper studies stability analysis of DC microgrids with uncertain
+constant power loads (CPLs). It is well known that CPLs have negative impedance
+effects, which may cause instability in a DC microgrid. Existing works often
+study the stability around a given equilibrium based on some nominal values of
+CPLs. However, in real applications, the equilibrium of a DC microgrid depends
+on the loading condition that often changes over time. Different from many
+previous results, this paper develops a framework that can analyze the DC
+microgrid stability for a given range of CPLs. The problem is formulated as a
+robust stability problem of a polytopic uncertain linear system. By exploiting
+the structure of the problem, we derive a set of sufficient conditions that can
+guarantee robust stability. The conditions can be efficiently checked by
+solving a convex optimization problem whose complexity does not grow with the
+number of buses in the microgrid. The effectiveness and non-conservativeness of
+the proposed framework are demonstrated using simulation examples.
+",0,0,1,0,0,0
+6655,Dimensional Analysis in Economics: A Study of the Neoclassical Economic Growth Model,"  The fundamental purpose of the present research article is to introduce the
+basic principles of Dimensional Analysis in the context of the neoclassical
+economic theory, in order to apply such principles to the fundamental relations
+that underlay most models of economic growth. In particular, basic instruments
+from Dimensional Analysis are used to evaluate the analytical consistency of
+the Neoclassical economic growth model. The analysis shows that an adjustment
+to the model is required in such a way that the principle of dimensional
+homogeneity is satisfied.
+",0,0,0,0,0,1
+6656,A high precision semi-analytic mass function,"  In this paper, extending past works of Del Popolo, we show how a high
+precision mass function (MF) can be obtained using the excursion set approach
+and an improved barrier taking implicitly into account a non-zero cosmological
+constant, the angular momentum acquired by tidal interaction of
+proto-structures and dynamical friction. In the case of the $\Lambda$CDM
+paradigm, we find that our MF is in agreement at the 3\% level to Klypin's
+Bolshoi simulation, in the mass range $M_{\rm vir} = 5 \times 10^9 h^{-1}
+M_{\odot} -- 5 \times 10^{14} h^{-1} M_{\odot}$ and redshift range $0 \lesssim
+z \lesssim 10$. For $z=0$ we also compared our MF to several fitting formulae,
+and found in particular agreement with Bhattacharya's within 3\% in the mass
+range $10^{12}-10^{16} h^{-1} M_{\odot}$. Moreover, we discuss our MF validity
+for different cosmologies.
+",0,1,0,0,0,0
+6657,Fast Compressed Self-Indexes with Deterministic Linear-Time Construction,"  We introduce a compressed suffix array representation that, on a text $T$ of
+length $n$ over an alphabet of size $\sigma$, can be built in $O(n)$
+deterministic time, within $O(n\log\sigma)$ bits of working space, and counts
+the number of occurrences of any pattern $P$ in $T$ in time $O(|P| + \log\log_w
+\sigma)$ on a RAM machine of $w=\Omega(\log n)$-bit words. This new index
+outperforms all the other compressed indexes that can be built in linear
+deterministic time, and some others. The only faster indexes can be built in
+linear time only in expectation, or require $\Theta(n\log n)$ bits. We also
+show that, by using $O(n\log\sigma)$ bits, we can build in linear time an index
+that counts in time $O(|P|/\log_\sigma n + \log n(\log\log n)^2)$, which is
+RAM-optimal for $w=\Theta(\log n)$ and sufficiently long patterns.
+",1,0,0,0,0,0
+6658,Solvability of the operator Riccati equation in the Feshbach case,"  We consider a bounded block operator matrix of the form $$
+L=\left(\begin{array}{cc} A & B \\ C & D \end{array} \right), $$ where the
+main-diagonal entries $A$ and $D$ are self-adjoint operators on Hilbert spaces
+$H_{_A}$ and $H_{_D}$, respectively; the coupling $B$ maps $H_{_D}$ to $H_{_A}$
+and $C$ is an operator from $H_{_A}$ to $H_{_D}$. It is assumed that the
+spectrum $\sigma_{_D}$ of $D$ is absolutely continuous and uniform, being
+presented by a single band $[\alpha,\beta]\subset\mathbb{R}$, $\alpha<\beta$,
+and the spectrum $\sigma_{_A}$ of $A$ is embedded into $\sigma_{_D}$, that is,
+$\sigma_{_A}\subset(\alpha,\beta)$. We formulate conditions under which there
+are bounded solutions to the operator Riccati equations associated with the
+complexly deformed block operator matrix $L$; in such a case the deformed
+operator matrix $L$ admits a block diagonalization. The same conditions also
+ensure the Markus-Matsaev-type factorization of the Schur complement
+$M_{_A}(z)=A-z-B(D-z)^{-1}C$ analytically continued onto the unphysical
+sheet(s) of the complex $z$ plane adjacent to the band $[\alpha,\beta]$. We
+prove that the operator roots of the continued Schur complement $M_{_A}$ are
+explicitly expressed through the respective solutions to the deformed Riccati
+equations.
+",0,0,1,0,0,0
+6659,Comparison results for first order linear operators with reflection and periodic boundary value conditions,"  This work is devoted to the study of the first order operator
+$x'(t)+m\,x(-t)$ coupled with periodic boundary value conditions. We describe
+the eigenvalues of the operator and obtain the expression of its related
+Green's function in the non resonant case. We also obtain the range of the
+values of the real parameter $m$ for which the integral kernel, which provides
+the unique solution, has constant sign. In this way, we automatically establish
+maximum and anti-maximum principles for the equation. Some applications to the
+existence of nonlinear periodic boundary value problems are showed.
+",0,0,1,0,0,0
+6660,A remark on oscillatory integrals associated with fewnomials,"  We prove that the $L^2$ bound of an oscillatory integral associated with a
+polynomial depends only on the number of monomials that this polynomial
+consists of.
+",0,0,1,0,0,0
+6661,Quantitative stochastic homogenization and regularity theory of parabolic equations,"  We develop a quantitative theory of stochastic homogenization for linear,
+uniformly parabolic equations with coefficients depending on space and time.
+Inspired by recent works in the elliptic setting, our analysis is focused on
+certain subadditive quantities derived from a variational interpretation of
+parabolic equations. These subadditive quantities are intimately connected to
+spatial averages of the fluxes and gradients of solutions. We implement a
+renormalization-type scheme to obtain an algebraic rate for their convergence,
+which is essentially a quantification of the weak convergence of the gradients
+and fluxes of solutions to their homogenized limits. As a consequence, we
+obtain estimates of the homogenization error for the Cauchy-Dirichlet problem
+which are optimal in stochastic integrability. We also develop a higher
+regularity theory for solutions of the heterogeneous equation, including a
+uniform $C^{0,1}$-type estimate and a Liouville theorem of every finite order.
+",0,0,1,0,0,0
+6662,On the periodicity problem of residual r-Fubini sequences,"  For any positive integer $r$, the $r$-Fubini number with parameter $n$,
+denoted by $F_{n,r}$, is equal to the number of ways that the elements of a set
+with $n+r$ elements can be weak ordered such that the $r$ least elements are in
+distinct orders. In this article we focus on the sequence of residues of the
+$r$-Fubini numbers modulo a positive integer $s$ and show that this sequence is
+periodic and then, exhibit how to calculate its period length. As an extra
+result, an explicit formula for the $r$-Stirling numbers is obtained which is
+frequently used in calculations.
+",0,0,1,0,0,0
+6663,Boundedness of the Bergman projection on generalized Fock-Sobolev spaces on ${\mathbb C}^n$,"  In this paper we solve a problem posed by H. Bommier-Hato, M. Engliš and
+E.H. Youssfi in [3] on the boundedness of the Bergman-type projections in
+generalized Fock spaces. It will be a consequence of two facts: a full
+description of the embeddings between generalized Fock-Sobolev spaces and a
+complete characterization of the boundedness of the above Bergman type
+projections between weighted $L^p$-spaces related to generalized Fock-Sobolev
+spaces.
+",0,0,1,0,0,0
+6664,Support Vector Machines and generalisation in HEP,"  We review the concept of Support Vector Machines (SVMs) and discuss examples
+of their use in a number of scenarios. Several SVM implementations have been
+used in HEP and we exemplify this algorithm using the Toolkit for Multivariate
+Analysis (TMVA) implementation. We discuss examples relevant to HEP including
+background suppression for $H\to\tau^+\tau^-$ at the LHC with several different
+kernel functions. Performance benchmarking leads to the issue of generalisation
+of hyper-parameter selection. The avoidance of fine tuning (over training or
+over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure
+generalised performance of an MVA that is independent of the training,
+validation and test samples, is of utmost importance. We discuss this issue and
+compare and contrast performance of hold-out and k-fold cross-validation. We
+have extended the SVM functionality and introduced tools to facilitate cross
+validation in TMVA and present results based on these improvements.
+",0,1,0,0,0,0
+6665,A sequent calculus for the Tamari order,"  We introduce a sequent calculus with a simple restriction of Lambek's product
+rules that precisely captures the classical Tamari order, i.e., the partial
+order on fully-bracketed words (equivalently, binary trees) induced by a
+semi-associative law (equivalently, tree rotation). We establish a focusing
+property for this sequent calculus (a strengthening of cut-elimination), which
+yields the following coherence theorem: every valid entailment in the Tamari
+order has exactly one focused derivation. One combinatorial application of this
+coherence theorem is a new proof of the Tutte-Chapoton formula for the number
+of intervals in the Tamari lattice $Y_n$. We also apply the sequent calculus
+and the coherence theorem to build a surprising bijection between intervals of
+the Tamari order and a certain fragment of lambda calculus, consisting of the
+$\beta$-normal planar lambda terms with no closed proper subterms.
+",1,0,1,0,0,0
+6666,A Measurement of CMB Cluster Lensing with SPT and DES Year 1 Data,"  Clusters of galaxies gravitationally lens the cosmic microwave background
+(CMB) radiation, resulting in a distinct imprint in the CMB on arcminute
+scales. Measurement of this effect offers a promising way to constrain the
+masses of galaxy clusters, particularly those at high redshift. We use CMB maps
+from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB
+lensing signal around galaxy clusters identified in optical imaging from first
+year observations of the Dark Energy Survey. The cluster catalog used in this
+analysis contains 3697 members with mean redshift of $\bar{z} = 0.45$. We
+detect lensing of the CMB by the galaxy clusters at $8.1\sigma$ significance.
+Using the measured lensing signal, we constrain the amplitude of the relation
+between cluster mass and optical richness to roughly $17\%$ precision, finding
+good agreement with recent constraints obtained with galaxy lensing. The error
+budget is dominated by statistical noise but includes significant contributions
+from systematic biases due to the thermal SZ effect and cluster miscentering.
+",0,1,0,0,0,0
+6667,Seebeck Effect in Nanoscale Ferromagnets,"  We present a theory of the Seebeck effect in nanoscale ferromagnets with
+dimensions smaller than the spin diffusion length. The spin accumulation
+generated by a temperature gradient strongly affects the thermopower. We also
+identify a correction arising from the transverse temperature gradient induced
+by the anomalous Ettingshausen effect. The effect of an induced spin-heat accu-
+mulation gradient is considered as well. The importance of these effects for
+nanoscale ferromagnets is illustrated by ab initio calculations for dilute
+ferromagnetic alloys.
+",0,1,0,0,0,0
+6668,Fast Asymmetric Fronts Propagation for Image Segmentation,"  In this paper, we introduce a generalized asymmetric fronts propagation model
+based on the geodesic distance maps and the Eikonal partial differential
+equations. One of the key ingredients for the computation of the geodesic
+distance map is the geodesic metric, which can govern the action of the
+geodesic distance level set propagation. We consider a Finsler metric with the
+Randers form, through which the asymmetry and anisotropy enhancements can be
+taken into account to prevent the fronts leaking problem during the fronts
+propagation. These enhancements can be derived from the image edge-dependent
+vector field such as the gradient vector flow. The numerical implementations
+are carried out by the Finsler variant of the fast marching method, leading to
+very efficient interactive segmentation schemes. We apply the proposed Finsler
+fronts propagation model to image segmentation applications. Specifically, the
+foreground and background segmentation is implemented by the Voronoi index map.
+In addition, for the application of tubularity segmentation, we exploit the
+level set lines of the geodesic distance map associated to the proposed Finsler
+metric providing that a thresholding value is given.
+",1,0,0,0,0,0
+6669,Efficient injection from large telescopes into single-mode fibres: Enabling the era of ultra-precision astronomy,"  Photonic technologies offer numerous advantages for astronomical instruments
+such as spectrographs and interferometers owing to their small footprints and
+diverse range of functionalities. Operating at the diffraction-limit, it is
+notoriously difficult to efficiently couple such devices directly with large
+telescopes. We demonstrate that with careful control of both the non-ideal
+pupil geometry of a telescope and residual wavefront errors, efficient coupling
+with single-mode devices can indeed be realised. A fibre injection was built
+within the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) instrument.
+Light was coupled into a single-mode fibre operating in the near-IR (J-H bands)
+which was downstream of the extreme adaptive optics system and the pupil
+apodising optics. A coupling efficiency of 86% of the theoretical maximum limit
+was achieved at 1550 nm for a diffraction-limited beam in the laboratory, and
+was linearly correlated with Strehl ratio. The coupling efficiency was constant
+to within <30% in the range 1250-1600 nm. Preliminary on-sky data with a Strehl
+ratio of 60% in the H-band produced a coupling efficiency into a single-mode
+fibre of ~50%, consistent with expectations. The coupling was >40% for 84% of
+the time and >50% for 41% of the time. The laboratory results allow us to
+forecast that extreme adaptive optics levels of correction (Strehl ratio >90%
+in H-band) would allow coupling of >67% (of the order of coupling to multimode
+fibres currently). For Strehl ratios <20%, few-port photonic lanterns become a
+superior choice but the signal-to-noise must be considered. These results
+illustrate a clear path to efficient on-sky coupling into a single-mode fibre,
+which could be used to realise modal-noise-free radial velocity machines,
+very-long-baseline optical/near-IR interferometers and/or simply exploit
+photonic technologies in future instrument design.
+",0,1,0,0,0,0
+6670,An upwind method for genuine weakly hyperbolic systems,"  In this article, we attempted to develop an upwind scheme based on Flux
+Difference Splitting using Jordan canonical forms to simulate genuine weakly
+hyperbolic systems. Theory of Jordan Canonical Forms is being used to complete
+defective set of linear independent eigenvectors. Proposed FDS-J scheme is
+capable of recognizing various shocks accurately.
+",0,0,1,0,0,0
+6671,Semi-tied Units for Efficient Gating in LSTM and Highway Networks,"  Gating is a key technique used for integrating information from multiple
+sources by long short-term memory (LSTM) models and has recently also been
+applied to other models such as the highway network. Although gating is
+powerful, it is rather expensive in terms of both computation and storage as
+each gating unit uses a separate full weight matrix. This issue can be severe
+since several gates can be used together in e.g. an LSTM cell. This paper
+proposes a semi-tied unit (STU) approach to solve this efficiency issue, which
+uses one shared weight matrix to replace those in all the units in the same
+layer. The approach is termed ""semi-tied"" since extra parameters are used to
+separately scale each of the shared output values. These extra scaling factors
+are associated with the network activation functions and result in the use of
+parameterised sigmoid, hyperbolic tangent, and rectified linear unit functions.
+Speech recognition experiments using British English multi-genre broadcast data
+showed that using STUs can reduce the calculation and storage cost by a factor
+of three for highway networks and four for LSTMs, while giving similar word
+error rates to the original models.
+",0,0,0,1,0,0
+6672,A step towards Twist Conjecture,"  Under the assumption that a defining graph of a Coxeter group admits only
+twists in $\mathbb{Z}_2$ and is of type FC, we prove Mühlherr's Twist
+Conjecture.
+",0,0,1,0,0,0
+6673,Achieveing reliable UDP transmission at 10 Gb/s using BSD socket for data acquisition systems,"  User Datagram Protocol (UDP) is a commonly used protocol for data
+transmission in small embedded systems. UDP as such is unreliable and packet
+losses can occur. The achievable data rates can suffer if optimal packet sizes
+are not used. The alternative, Transmission Control Protocol (TCP) guarantees
+the ordered delivery of data and automatically adjusts transmission to match
+the capability of the transmission link. Nevertheless UDP is often favored over
+TCP due to its simplicity, small memory and instruction footprints. Both UDP
+and TCP are implemented in all larger operating systems and commercial embedded
+frameworks. In addition UDP also supported on a variety of small hardware
+platforms such as Digital Signal Processors (DSP) Field Programmable Gate
+Arrays (FPGA). This is not so common for TCP. This paper describes how high
+speed UDP based data transmission with very low packet error ratios was
+achieved. The near-reliable communications link is used in a data acquisition
+(DAQ) system for the next generation of extremely intense neutron source,
+European Spallation Source. This paper presents measurements of UDP performance
+and reliability as achieved by employing several optimizations. The
+measurements were performed on Xeon E5 based CentOS (Linux) servers. The
+measured data rates are very close to the 10 Gb/s line rate, and zero packet
+loss was achieved. The performance was obtained utilizing a single processor
+core as transmitter and a single core as receiver. The results show that
+support for transmitting large data packets is a key parameter for good
+performance.
+Optimizations for throughput are: MTU, packet sizes, tuning Linux kernel
+parameters, thread affinity, core locality and efficient timers.
+",1,1,0,0,0,0
+6674,Epidemic Threshold in Continuous-Time Evolving Networks,"  Current understanding of the critical outbreak condition on temporal networks
+relies on approximations (time scale separation, discretization) that may bias
+the results. We propose a theoretical framework to compute the epidemic
+threshold in continuous time through the infection propagator approach. We
+introduce the {\em weak commutation} condition allowing the interpretation of
+annealed networks, activity-driven networks, and time scale separation into one
+formalism. Our work provides a coherent connection between discrete and
+continuous time representations applicable to realistic scenarios.
+",0,1,0,0,0,0
+6675,Demo Abstract: CDMA-based IoT Services with Shared Band Operation of LTE in 5G,"  With the vision of deployment of massive Internet-of-Things (IoTs) in 5G
+network, existing 4G network and protocols are inefficient to handle sporadic
+IoT traffic with requirements of low-latency, low control overhead and low
+power. To suffice these requirements, we propose a design of a PHY/MAC layer
+using Software Defined Radios (SDRs) that is backward compatible with existing
+OFDM based LTE protocols and supports CDMA based transmissions for low power
+IoT devices as well. This demo shows our implemented system based on that
+design and the viability of the proposal under different network scenarios.
+",1,0,0,0,0,0
+6676,Can Two-Way Direct Communication Protocols Be Considered Secure?,"  We consider attacks on two-way quantum key distribution protocols in which an
+undetectable eavesdropper copies all messages in the message mode. We show that
+under the attacks there is no disturbance in the message mode and that the
+mutual information between the sender and the receiver is always constant and
+equal to one. It follows that recent proofs of security for two-way protocols
+cannot be considered complete since they do not cover the considered attacks.
+",1,0,0,0,0,0
+6677,Using Deep Neural Network Approximate Bayesian Network,"  We present a new method to approximate posterior probabilities of Bayesian
+Network using Deep Neural Network. Experiment results on several public
+Bayesian Network datasets shows that Deep Neural Network is capable of learning
+joint probability distri- bution of Bayesian Network by learning from a few
+observation and posterior probability distribution pairs with high accuracy.
+Compared with traditional approximate method likelihood weighting sampling
+algorithm, our method is much faster and gains higher accuracy in medium sized
+Bayesian Network. Another advantage of our method is that our method can be
+parallelled much easier in GPU without extra effort. We also ex- plored the
+connection between the accuracy of our model and the number of training
+examples. The result shows that our model saturate as the number of training
+examples grow and we don't need many training examples to get reasonably good
+result. Another contribution of our work is that we have shown discriminative
+model like Deep Neural Network can approximate generative model like Bayesian
+Network.
+",0,0,0,1,0,0
+6678,Simple Classification using Binary Data,"  Binary, or one-bit, representations of data arise naturally in many
+applications, and are appealing in both hardware implementations and algorithm
+design. In this work, we study the problem of data classification from binary
+data and propose a framework with low computation and resource costs. We
+illustrate the utility of the proposed approach through stylized and realistic
+numerical experiments, and provide a theoretical analysis for a simple case. We
+hope that our framework and analysis will serve as a foundation for studying
+similar types of approaches.
+",1,0,0,1,0,0
+6679,Unstable normalized standing waves for the space periodic NLS,"  For the stationary nonlinear Schrödinger equation $-\Delta u+ V(x)u- f(u) =
+\lambda u$ with periodic potential $V$ we study the existence and stability
+properties of multibump solutions with prescribed $L^2$-norm. To this end we
+introduce a new nondegeneracy condition and develop new superposition
+techniques which allow to match the $L^2$-constraint. In this way we obtain the
+existence of infinitely many geometrically distinct solutions to the stationary
+problem. We then calculate the Morse index of these solutions with respect to
+the restriction of the underlying energy functional to the associated
+$L^2$-sphere, and we show their orbital instability with respect to the
+Schrödinger flow. Our results apply in both, the mass-subcritical and the
+mass-supercritical regime.
+",0,0,1,0,0,0
+6680,Inverse regression for ridge recovery: A data-driven approach for parameter reduction in computer experiments,"  Parameter reduction can enable otherwise infeasible design and uncertainty
+studies with modern computational science models that contain several input
+parameters. In statistical regression, techniques for sufficient dimension
+reduction (SDR) use data to reduce the predictor dimension of a regression
+problem. A computational scientist hoping to use SDR for parameter reduction
+encounters a problem: a computer prediction is best represented by a
+deterministic function of the inputs, so data comprised of computer simulation
+queries fail to satisfy the SDR assumptions. To address this problem, we
+interpret SDR methods sliced inverse regression (SIR) and sliced average
+variance estimation (SAVE) as estimating the directions of a ridge function,
+which is a composition of a low-dimensional linear transformation with a
+nonlinear function. Within this interpretation, SIR and SAVE estimate matrices
+of integrals whose column spaces are contained in the ridge directions' span;
+we analyze and numerically verify convergence of these column spaces as the
+number of computer model queries increases. Moreover, we show example functions
+that are not ridge functions but whose inverse conditional moment matrices are
+low-rank. Consequently, the computational scientist should beware when using
+SIR and SAVE for parameter reduction, since SIR and SAVE may mistakenly suggest
+that truly important directions are unimportant.
+",0,0,1,0,0,0
+6681,Thermodynamics of Higher Order Entropy Corrected Schwarzschild-Beltrami-de Sitter Black Hole,"  In this paper, we consider higher order correction of the entropy and study
+the thermodynamical properties of recently proposed Schwarzschild-Beltrami-de
+Sitter black hole, which is indeed an exact solution of Einstein equation with
+a positive cosmological constant. By using the corrected entropy and Hawking
+temperature we extract some thermodynamical quantities like Gibbs and Helmholtz
+free energies and heat capacity. We also investigate the first and second laws
+of thermodynamics. We find that presence of higher order corrections, which
+come from thermal fluctuations, may remove some instabilities of the black
+hole. Also unstable to stable phase transition is possible in presence of the
+first and second order corrections.
+",0,1,0,0,0,0
+6682,"Robust, high brightness, degenerate entangled photon source at room temperature","  We report on a compact, simple and robust high brightness entangled photon
+source at room temperature. Based on a 30 mm long periodically poled potassium
+titanyl phosphate (PPKTP), the source produces non-collinear, type0 phase
+matched, degenerate photons at 810 nm with pair production rate as high 39.13
+MHz per mW at room temperature. To the best of our knowledge, this is the
+highest photon pair rate generated using bulk crystals pump with
+continuous-wave laser. Combined with the inherently stable polarization Sagnac
+interferometer, the source produces entangled state violating the Bells
+inequality by nearly 10 standard deviations and a Bell state fidelity of 0.96.
+The compact footprint, simple and robust experimental design and room
+temperature operation, make our source ideal for various quantum communication
+experiments including long distance free space and satellite communications.
+",0,1,0,0,0,0
+6683,Emergence of superconductivity in the cuprates via a universal percolation process,"  A pivotal step toward understanding unconventional superconductors would be
+to decipher how superconductivity emerges from the unusual normal state upon
+cooling. In the cuprates, traces of superconducting pairing appear above the
+macroscopic transition temperature $T_c$, yet extensive investigation has led
+to disparate conclusions. The main difficulty has been the separation of
+superconducting contributions from complex normal state behaviour. Here we
+avoid this problem by measuring the nonlinear conductivity, an observable that
+is zero in the normal state. We uncover for several representative cuprates
+that the nonlinear conductivity vanishes exponentially above $T_c$, both with
+temperature and magnetic field, and exhibits temperature-scaling characterized
+by a nearly universal scale $T_0$. Attempts to model the response with the
+frequently evoked Ginzburg-Landau theory are unsuccessful. Instead, our
+findings are captured by a simple percolation model that can also explain other
+properties of the cuprates. We thus resolve a long-standing conundrum by
+showing that the emergence of superconductivity in the cuprates is dominated by
+their inherent inhomogeneity.
+",0,1,0,0,0,0
+6684,Exploiting Spatial Degrees of Freedom for High Data Rate Ultrasound Communication with Implantable Devices,"  We propose and demonstrate an ultrasonic communication link using spatial
+degrees of freedom to increase data rates for deeply implantable medical
+devices. Low attenuation and millimeter wavelengths make ultrasound an ideal
+communication medium for miniaturized low-power implants. While small spectral
+bandwidth has drastically limited achievable data rates in conventional
+ultrasonic implants, large spatial bandwidth can be exploited by using multiple
+transducers in a multiple-input/multiple-output system to provide spatial
+multiplexing gain without additional power, larger bandwidth, or complicated
+packaging. We experimentally verify the communication link in mineral oil with
+a transmitter and receiver 5 cm apart, each housing two custom-designed
+mm-sized piezoelectric transducers operating at the same frequency. Two streams
+of data modulated with quadrature phase-shift keying at 125 kbps are
+simultaneously transmitted and received on both channels, effectively doubling
+the data rate to 250 kbps with a measured bit error rate below 1e-4. We also
+evaluate the performance and robustness of the channel separation network by
+testing the communication link after introducing position offsets. These
+results demonstrate the potential of spatial multiplexing to enable more
+complex implant applications requiring higher data rates.
+",1,1,0,0,0,0
+6685,Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions,"  Global sensitivity analysis aims at determining which uncertain input
+parameters of a computational model primarily drives the variance of the output
+quantities of interest. Sobol' indices are now routinely applied in this
+context when the input parameters are modelled by classical probability theory
+using random variables. In many practical applications however, input
+parameters are affected by both aleatory and epistemic (so-called polymorphic)
+uncertainty, for which imprecise probability representations have become
+popular in the last decade. In this paper, we consider that the uncertain input
+parameters are modelled by parametric probability boxes (p-boxes). We propose
+interval-valued (so-called imprecise) Sobol' indices as an extension of their
+classical definition. An original algorithm based on the concepts of augmented
+space, isoprobabilistic transforms and sparse polynomial chaos expansions is
+devised to allow for the computation of these imprecise Sobol' indices at
+extremely low cost. In particular, phantoms points are introduced to build an
+experimental design in the augmented space (necessary for the calibration of
+the sparse PCE) which leads to a smart reuse of runs of the original
+computational model. The approach is illustrated on three analytical and
+engineering examples which allows one to validate the proposed algorithms
+against brute-force double-loop Monte Carlo simulation.
+",0,0,0,1,0,0
+6686,Unexpected Enhancement of Three-Dimensional Low-Energy Spin Correlations in Quasi-Two-Dimensional Fe$_{1+y}$Te$_{1-x}$Se$_{x}$ System at High Temperature,"  We report inelastic neutron scattering measurements of low energy ($\hbar
+\omega < 10$ meV) magnetic excitations in the ""11"" system
+Fe$_{1+y}$Te$_{1-x}$Se$_{x}$. The spin correlations are two-dimensional (2D) in
+the superconducting samples at low temperature, but appear much more
+three-dimensional when the temperature rises well above $T_c \sim 15$ K, with a
+clear increase of the (dynamic) spin correlation length perpendicular to the Fe
+planes. The spontaneous change of dynamic spin correlations from 2D to 3D on
+warming is unexpected and cannot be naturally explained when only the spin
+degree of freedom is considered. Our results suggest that the low temperature
+physics in the ""11"" system, in particular the evolution of low energy spin
+excitations towards %better satisfying the nesting condition for mediating
+superconducting pairing, is driven by changes in orbital correlations.
+",0,1,0,0,0,0
+6687,Agile Software Development Methods: Review and Analysis,"  Agile - denoting ""the quality of being agile, readiness for motion,
+nimbleness, activity, dexterity in motion"" - software development methods are
+attempting to offer an answer to the eager business community asking for
+lighter weight along with faster and nimbler software development processes.
+This is especially the case with the rapidly growing and volatile Internet
+software industry as well as for the emerging mobile application environment.
+The new agile methods have evoked substantial amount of literature and debates.
+However, academic research on the subject is still scarce, as most of existing
+publications are written by practitioners or consultants. The aim of this
+publication is to begin filling this gap by systematically reviewing the
+existing literature on agile software development methodologies. This
+publication has three purposes. First, it proposes a definition and a
+classification of agile software development approaches. Second, it analyses
+ten software development methods that can be characterized as being ""agile""
+against the defined criterion. Third, it compares these methods and highlights
+their similarities and differences. Based on this analysis, future research
+needs are identified and discussed.
+",1,0,0,0,0,0
+6688,Deep Learning: A Bayesian Perspective,"  Deep learning is a form of machine learning for nonlinear high dimensional
+pattern matching and prediction. By taking a Bayesian probabilistic
+perspective, we provide a number of insights into more efficient algorithms for
+optimisation and hyper-parameter tuning. Traditional high-dimensional data
+reduction techniques, such as principal component analysis (PCA), partial least
+squares (PLS), reduced rank regression (RRR), projection pursuit regression
+(PPR) are all shown to be shallow learners. Their deep learning counterparts
+exploit multiple deep layers of data reduction which provide predictive
+performance gains. Stochastic gradient descent (SGD) training optimisation and
+Dropout (DO) regularization provide estimation and variable selection. Bayesian
+regularization is central to finding weights and connections in networks to
+optimize the predictive bias-variance trade-off. To illustrate our methodology,
+we provide an analysis of international bookings on Airbnb. Finally, we
+conclude with directions for future research.
+",1,0,0,1,0,0
+6689,Tunnel-injected sub-260 nm ultraviolet light emitting diodes,"  We report on tunnel-injected deep ultraviolet light emitting diodes (UV LEDs)
+configured with a polarization engineered Al0.75Ga0.25N/ In0.2Ga0.8N tunnel
+junction structure. Tunnel-injected UV LED structure enables n-type contacts
+for both bottom and top contact layers. However, achieving Ohmic contact to
+wide bandgap n-AlGaN layers is challenging and typically requires high
+temperature contact metal annealing. In this work, we adopted a compositionally
+graded top contact layer for non-alloyed metal contact, and obtained a low
+contact resistance of Rc=4.8x10-5 Ohm cm2 on n-Al0.75Ga0.25N. We also observed
+a significant reduction in the forward operation voltage from 30.9 V to 19.2 V
+at 1 kA/cm2 by increasing the Mg doping concentration from 6.2x1018 cm-3 to
+1.5x1019 cm-3. Non-equilibrium hole injection into wide bandgap Al0.75Ga0.25N
+with Eg>5.2 eV was confirmed by light emission at 257 nm. This work
+demonstrates the feasibility of tunneling hole injection into deep UV LEDs, and
+provides a novel structural design towards high power deep-UV emitters.
+",0,1,0,0,0,0
+6690,Three-dimensional image reconstruction in J-PET using Filtered Back Projection method,"  We present a method and preliminary results of the image reconstruction in
+the Jagiellonian PET tomograph. Using GATE (Geant4 Application for Tomographic
+Emission), interactions of the 511 keV photons with a cylindrical detector were
+generated. Pairs of such photons, flying back-to-back, originate from e+e-
+annihilations inside a 1-mm spherical source. Spatial and temporal coordinates
+of hits were smeared using experimental resolutions of the detector. We
+incorporated the algorithm of the 3D Filtered Back Projection, implemented in
+the STIR and TomoPy software packages, which differ in approximation methods.
+Consistent results for the Point Spread Functions of ~5/7,mm and ~9/20, mm were
+obtained, using STIR, for transverse and longitudinal directions, respectively,
+with no time of flight information included.
+",0,1,0,0,0,0
+6691,Two-component domain decomposition scheme with overlapping subdomains for parabolic equations,"  An iteration-free method of domain decomposition is considered for
+approximate solving a boundary value problem for a second-order parabolic
+equation. A standard approach to constructing domain decomposition schemes is
+based on a partition of unity for the domain under the consideration. Here a
+new general approach is proposed for constructing domain decomposition schemes
+with overlapping subdomains based on indicator functions of subdomains. The
+basic peculiarity of this method is connected with a representation of the
+problem operator as the sum of two operators, which are constructed for two
+separate subdomains with the subtraction of the operator that is associated
+with the intersection of the subdomains. There is developed a two-component
+factorized scheme, which can be treated as a generalization of the standard
+Alternating Direction Implicit (ADI) schemes to the case of a special
+three-component splitting. There are obtained conditions for the unconditional
+stability of regionally additive schemes constructed using indicator functions
+of subdomains. Numerical results are presented for a model two-dimensional
+problem.
+",1,0,0,0,0,0
+6692,On the representation of finite convex geometries with convex sets,"  Very recently Richter and Rogers proved that any convex geometry can be
+represented by a family of convex polygons in the plane. We shall generalize
+their construction and obtain a wide variety of convex shapes for representing
+convex geometries. We present an Erdos-Szekeres type obstruction, which answers
+a question of Czedli negatively, that is general convex geometries cannot be
+represented with ellipses in the plane. Moreover, we shall prove that one
+cannot even bound the number of common supporting lines of the pairs of the
+representing convex sets. In higher dimensions we prove that all convex
+geometries can be represented with ellipsoids.
+",0,0,1,0,0,0
+6693,Resolving ultrafast exciton migration in organic solids at the nanoscale,"  The effectiveness of molecular-based light harvesting relies on transport of
+optical excitations, excitons, to charg-transfer sites. Measuring exciton
+migration has, however, been challenging because of the mismatch between
+nanoscale migration lengths and the diffraction limit. In organic
+semiconductors, common bulk methods employ a series of films terminated at
+quenching substrates, altering the spatioenergetic landscape for migration.
+Here we instead define quenching boundaries all-optically with sub-diffraction
+resolution, thus characterizing spatiotemporal exciton migration on its native
+nanometer and picosecond scales without disturbing morphology. By transforming
+stimulated emission depletion microscopy into a time-resolved ultrafast
+approach, we measure a 16-nm migration length in CN-PPV conjugated polymer
+films. Combining these experiments with Monte Carlo exciton hopping simulations
+shows that migration in CN-PPV films is essentially diffusive because intrinsic
+chromophore energetic disorder is comparable to inhomogeneous broadening among
+chromophores. This framework also illustrates general trends across materials.
+Our new approach's sub-diffraction resolution will enable previously
+unattainable correlations of local material structure to the nature of exciton
+migration, applicable not only to photovoltaic or display-destined organic
+semiconductors but also to explaining the quintessential exciton migration
+exhibited in photosynthesis.
+",0,1,0,0,0,0
+6694,"Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation","  We present a simple method to improve neural translation of a low-resource
+language pair using parallel data from a related, also low-resource, language
+pair. The method is based on the transfer method of Zoph et al., but whereas
+their method ignores any source vocabulary overlap, ours exploits it. First, we
+split words using Byte Pair Encoding (BPE) to increase vocabulary overlap.
+Then, we train a model on the first language pair and transfer its parameters,
+including its source word embeddings, to another model and continue training on
+the second language pair. Our experiments show that transfer learning helps
+word-based translation only slightly, but when used on top of a much stronger
+BPE baseline, it yields larger improvements of up to 4.3 BLEU.
+",1,0,0,0,0,0
+6695,"Dynamical correlations in the electronic structure of BiFeO$_{3}$, as revealed by dynamical mean field theory","  Using local density approximation plus dynamical mean-field theory
+(LDA+DMFT), we have computed the valence band photoelectron spectra of highly
+popular multiferroic BiFeO$_{3}$. Within DMFT, the local impurity problem is
+tackled by exact diagonalization (ED) solver. For comparison, we also present
+result from LDA+U approach, which is commonly used to compute physical
+properties of this compound. Our LDA+DMFT derived spectra match adequately with
+the experimental hard X-ray photoelectron spectroscopy (HAXPES) and resonant
+photoelectron spectroscopy (RPES) for Fe 3$d$ states, whereas the other
+theoretical method that we employed failed to capture the features of the
+measured spectra. Thus, our investigation shows the importance of accurately
+incorporating the dynamical aspects of electron-electron interaction among the
+Fe 3$d$ orbitals in calculations to produce the experimental excitation
+spectra, which establishes BiFeO$_{3}$ as a strongly correlated electron
+system. The LDA+DMFT derived density of states (DOSs) exhibit significant
+amount of Fe 3$d$ states at the energy of Bi lone-pairs, implying that the
+latter is not as alone as previously thought in the spectral scenario. Our
+study also demonstrates that the combination of orbital cross-sections for the
+constituent elements and broadening schemes for the calculated spectral
+function are pivotal to explain the detailed structures of the experimental
+spectra.
+",0,1,0,0,0,0
+6696,A Van-Der-Waals picture for metabolic networks from MaxEnt modeling: inherent bistability and elusive coexistence,"  In this work maximum entropy distributions in the space of steady states of
+metabolic networks are defined upon constraining the first and second moment of
+the growth rate. Inherent bistability of fast and slow phenotypes, akin to a
+Van-Der Waals picture, emerges upon considering control on the average growth
+(optimization/repression) and its fluctuations (heterogeneity). This is applied
+to the carbon catabolic core of E.coli where it agrees with some stylized facts
+on the persisters phenotype and it provides a quantitative map with metabolic
+fluxes, opening for the possibility to detect coexistence from flux data.
+Preliminary analysis on data for E.Coli cultures in standard conditions shows,
+on the other hand, degeneracy for the inferred parameters that extend in the
+coexistence region.
+",0,1,0,0,0,0
+6697,Robust Parameter Estimation of Regression Model with AR(p) Error Terms,"  In this paper, we consider a linear regression model with AR(p) error terms
+with the assumption that the error terms have a t distribution as a heavy
+tailed alternative to the normal distribution. We obtain the estimators for the
+model parameters by using the conditional maximum likelihood (CML) method. We
+conduct an iteratively reweighting algorithm (IRA) to find the estimates for
+the parameters of interest. We provide a simulation study and three real data
+examples to illustrate the performance of the proposed robust estimators based
+on t distribution.
+",0,0,0,1,0,0
+6698,"Measuring filament orientation: a new quantitative, local approach","  The relative orientation between filamentary structures in molecular clouds
+and the ambient magnetic field provides insight into filament formation and
+stability. To calculate the relative orientation, a measurement of filament
+orientation is first required. We propose a new method to calculate the
+orientation of the one pixel wide filament skeleton that is output by filament
+identification algorithms such as \textsc{filfinder}. We derive the local
+filament orientation from the direction of the intensity gradient in the
+skeleton image using the Sobel filter and a few simple post-processing steps.
+We call this the `Sobel-gradient method'. The resulting filament orientation
+map can be compared quantitatively on a local scale with the magnetic field
+orientation map to then find the relative orientation of the filament with
+respect to the magnetic field at each point along the filament. It can also be
+used in constructing radial profiles for filament width fitting. The proposed
+method facilitates automation in analysis of filament skeletons, which is
+imperative in this era of `big data'.
+",0,1,0,0,0,0
+6699,Controlled dynamic screening of excitonic complexes in 2D semiconductors,"  We report a combined theoretical/experimental study of dynamic screening of
+excitons in media with frequency-dependent dielectric functions. We develop an
+analytical model showing that interparticle interactions in an exciton are
+screened in the range of frequencies from zero to the characteristic binding
+energy depending on the symmetries and transition energies of that exciton. The
+problem of the dynamic screening is then reduced to simply solving the
+Schrodinger equation with an effectively frequency-independent potential.
+Quantitative predictions of the model are experimentally verified using a test
+system: neutral, charged and defect-bound excitons in two-dimensional monolayer
+WS2, screened by metallic, liquid, and semiconducting environments. The
+screening-induced shifts of the excitonic peaks in photoluminescence spectra
+are in good agreement with our model.
+",0,1,0,0,0,0
+6700,Motions about a fixed point by hypergeometric functions: new non-complex analytical solutions and integration of the herpolhode,"  We study four problems in the dynamics of a body moving about a fixed point,
+providing a non-complex, analytical solution for all of them. For the first
+two, we will work on the motion first integrals. For the symmetrical heavy
+body, that is the Lagrange-Poisson case, we compute the second and third Euler
+angles in explicit and real forms by means of multiple hypergeometric functions
+(Lauricella, functions). Releasing the weight load but adding the complication
+of the asymmetry, by means of elliptic integrals of third kind, we provide the
+precession angle completing some previous treatments of the Euler-Poinsot case.
+Integrating then the relevant differential equation, we reach the finite polar
+equation of a special trajectory named the {\it herpolhode}. In the last
+problem we keep the symmetry of the first problem, but without the weight, and
+take into account a viscous dissipation. The approach of first integrals is no
+longer practicable in this situation and the Euler equations are faced directly
+leading to dumped goniometric functions obtained as particular occurrences of
+Bessel functions of order $-1/2$.
+",0,0,1,0,0,0
+6701,Spatial solitons in thermo-optical media from the nonlinear Schrodinger-Poisson equation and dark matter analogues,"  We analyze theoretically the Schrodinger-Poisson equation in two transverse
+dimensions in the presence of a Kerr term. The model describes the nonlinear
+propagation of optical beams in thermooptical media and can be regarded as an
+analogue system for a self-gravitating self-interacting wave. We compute
+numerically the family of radially symmetric ground state bright stationary
+solutions for focusing and defocusing local nonlinearity, keeping in both cases
+a focusing nonlocal nonlinearity. We also analyze excited states and
+oscillations induced by fixing the temperature at the borders of the material.
+We provide simulations of soliton interactions, drawing analogies with the
+dynamics of galactic cores in the scalar field dark matter scenario.
+",0,1,0,0,0,0
+6702,Instrument-Armed Bandits,"  We extend the classic multi-armed bandit (MAB) model to the setting of
+noncompliance, where the arm pull is a mere instrument and the treatment
+applied may differ from it, which gives rise to the instrument-armed bandit
+(IAB) problem. The IAB setting is relevant whenever the experimental units are
+human since free will, ethics, and the law may prohibit unrestricted or forced
+application of treatment. In particular, the setting is relevant in bandit
+models of dynamic clinical trials and other controlled trials on human
+interventions. Nonetheless, the setting has not been fully investigate in the
+bandit literature. We show that there are various and divergent notions of
+regret in this setting, all of which coincide only in the classic MAB setting.
+We characterize the behavior of these regrets and analyze standard MAB
+algorithms. We argue for a particular kind of regret that captures the causal
+effect of treatments but show that standard MAB algorithms cannot achieve
+sublinear control on this regret. Instead, we develop new algorithms for the
+IAB problem, prove new regret bounds for them, and compare them to standard MAB
+algorithms in numerical examples.
+",1,0,0,1,0,0
+6703,Deep learning Inversion of Seismic Data,"  In this paper, we propose a new method to tackle the mapping challenge from
+time-series data to spatial image in the field of seismic exploration, i.e.,
+reconstructing the velocity model directly from seismic data by deep neural
+networks (DNNs). The conventional way to address this ill-posed seismic
+inversion problem is through iterative algorithms, which suffer from poor
+nonlinear mapping and strong non-uniqueness. Other attempts may either import
+human intervention errors or underuse seismic data. The challenge for DNNs
+mainly lies in the weak spatial correspondence, the uncertain
+reflection-reception relationship between seismic data and velocity model as
+well as the time-varying property of seismic data. To approach these
+challenges, we propose an end-to-end Seismic Inversion Networks (SeisInvNet for
+short) with novel components to make the best use of all seismic data.
+Specifically, we start with every seismic trace and enhance it with its
+neighborhood information, its observation setup and global context of its
+corresponding seismic profile. Then from enhanced seismic traces, the spatially
+aligned feature maps can be learned and further concatenated to reconstruct
+velocity model. In general, we let every seismic trace contribute to the
+reconstruction of the whole velocity model by finding spatial correspondence.
+The proposed SeisInvNet consistently produces improvements over the baselines
+and achieves promising performance on our proposed SeisInv dataset according to
+various evaluation metrics, and the inversion results are more consistent with
+the target from the aspects of velocity value, subsurface structure and
+geological interface. In addition to the superior performance, the mechanism is
+also carefully discussed, and some potential problems are identified for
+further study.
+",1,0,0,0,0,0
+6704,Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs,"  We propose NOPOL, an approach to automatic repair of buggy conditional
+statements (i.e., if-then-else statements). This approach takes a buggy program
+as well as a test suite as input and generates a patch with a conditional
+expression as output. The test suite is required to contain passing test cases
+to model the expected behavior of the program and at least one failing test
+case that reveals the bug to be repaired. The process of NOPOL consists of
+three major phases. First, NOPOL employs angelic fix localization to identify
+expected values of a condition during the test execution. Second, runtime trace
+collection is used to collect variables and their actual values, including
+primitive data types and objected-oriented features (e.g., nullness checks), to
+serve as building blocks for patch generation. Third, NOPOL encodes these
+collected data into an instance of a Satisfiability Modulo Theory (SMT)
+problem, then a feasible solution to the SMT instance is translated back into a
+code patch. We evaluate NOPOL on 22 real-world bugs (16 bugs with buggy IF
+conditions and 6 bugs with missing preconditions) on two large open-source
+projects, namely Apache Commons Math and Apache Commons Lang. Empirical
+analysis on these bugs shows that our approach can effectively fix bugs with
+buggy IF conditions and missing preconditions. We illustrate the capabilities
+and limitations of NOPOL using case studies of real bug fixes.
+",1,0,0,0,0,0
+6705,Parametric geometry of numbers in function fields,"  Parametric geometry of numbers is a new theory, recently created by Schmidt
+and Summerer, which unifies and simplifies many aspects of classical
+Diophantine approximations, providing a handle on problems which previously
+seemed out of reach. Our goal is to transpose this theory to fields of rational
+functions in one variable and to analyze in that context the problem of
+simultaneous approximation to exponential functions.
+",0,0,1,0,0,0
+6706,Refined open intersection numbers and the Kontsevich-Penner matrix model,"  A study of the intersection theory on the moduli space of Riemann surfaces
+with boundary was recently initiated in a work of R. Pandharipande, J. P.
+Solomon and the third author, where they introduced open intersection numbers
+in genus 0. Their construction was later generalized to all genera by J. P.
+Solomon and the third author. In this paper we consider a refinement of the
+open intersection numbers by distinguishing contributions from surfaces with
+different numbers of boundary components, and we calculate all these numbers.
+We then construct a matrix model for the generating series of the refined open
+intersection numbers and conjecture that it is equivalent to the
+Kontsevich-Penner matrix model. An evidence for the conjecture is presented.
+Another refinement of the open intersection numbers, which describes the
+distribution of the boundary marked points on the boundary components, is also
+discussed.
+",0,0,1,0,0,0
+6707,SEIRS epidemics in growing populations,"  An SEIRS epidemic with disease fatalities is introduced in a growing
+population (modelled as a super-critical linear birth and death process). The
+study of the initial phase of the epidemic is stochastic, while the analysis of
+the major outbreaks is deterministic. Depending on the values of the
+parameters, the following scenarios are possible. i) The disease dies out
+quickly, only infecting few; ii) the epidemic takes off, the \textit{number} of
+infected individuals grows exponentially, but the \textit{fraction} of infected
+individuals remains negligible; iii) the epidemic takes off, the
+\textit{number} of infected grows initially quicker than the population, the
+disease fatalities diminish the growth rate of the population, but it remains
+super critical, and the \emph{fraction} of infected go to an endemic
+equilibrium; iv) the epidemic takes off, the \textit{number} of infected
+individuals grows initially quicker than the population, the diseases
+fatalities turn the exponential growth of the population to an exponential
+decay.
+",0,1,0,0,0,0
+6708,A Multi-task Selected Learning Approach for Solving New Type 3D Bin Packing Problem,"  This paper studies a new type of 3D bin packing problem (BPP), in which a
+number of cuboid-shaped items must be put into a bin one by one orthogonally.
+The objective is to find a way to place these items that can minimize the
+surface area of the bin. This problem is based on the fact that there is no
+fixed-sized bin in many real business scenarios and the cost of a bin is
+proportional to its surface area. Based on previous research on 3D BPP, the
+surface area is determined by the sequence, spatial locations and orientations
+of items. It is a new NP-hard combinatorial optimization problem on
+unfixed-sized bin packing, for which we propose a multi-task framework based on
+Selected Learning, generating the sequence and orientations of items packed
+into the bin simultaneously. During training steps, Selected Learning chooses
+one of loss functions derived from Deep Reinforcement Learning and Supervised
+Learning corresponding to the training procedure. Numerical results show that
+the method proposed significantly outperforms Lego baselines by a substantial
+gain of 7.52%. Moreover, we produce large scale 3D Bin Packing order data set
+for studying bin packing problems and will release it to the research
+community.
+",0,0,0,1,0,0
+6709,Use of Docker for deployment and testing of astronomy software,"  We describe preliminary investigations of using Docker for the deployment and
+testing of astronomy software. Docker is a relatively new containerisation
+technology that is developing rapidly and being adopted across a range of
+domains. It is based upon virtualization at operating system level, which
+presents many advantages in comparison to the more traditional hardware
+virtualization that underpins most cloud computing infrastructure today. A
+particular strength of Docker is its simple format for describing and managing
+software containers, which has benefits for software developers, system
+administrators and end users.
+We report on our experiences from two projects -- a simple activity to
+demonstrate how Docker works, and a more elaborate set of services that
+demonstrates more of its capabilities and what they can achieve within an
+astronomical context -- and include an account of how we solved problems
+through interaction with Docker's very active open source development
+community, which is currently the key to the most effective use of this
+rapidly-changing technology.
+",1,1,0,0,0,0
+6710,Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks,"  We propose a method, called Label Embedding Network, which can learn label
+representation (label embedding) during the training process of deep networks.
+With the proposed method, the label embedding is adaptively and automatically
+learned through back propagation. The original one-hot represented loss
+function is converted into a new loss function with soft distributions, such
+that the originally unrelated labels have continuous interactions with each
+other during the training process. As a result, the trained model can achieve
+substantially higher accuracy and with faster convergence speed. Experimental
+results based on competitive tasks demonstrate the effectiveness of the
+proposed method, and the learned label embedding is reasonable and
+interpretable. The proposed method achieves comparable or even better results
+than the state-of-the-art systems. The source code is available at
+\url{this https URL}.
+",1,0,0,0,0,0
+6711,On the Performance of Multi-Instrument Solar Flare Observations During Solar Cycle 24,"  The current fleet of space-based solar observatories offers us a wealth of
+opportunities to study solar flares over a range of wavelengths. Significant
+advances in our understanding of flare physics often come from coordinated
+observations between multiple instruments. Consequently, considerable efforts
+have been, and continue to be made to coordinate observations among instruments
+(e.g. through the Max Millennium Program of Solar Flare Research). However,
+there has been no study to date that quantifies how many flares have been
+observed by combinations of various instruments. Here we describe a technique
+that retrospectively searches archival databases for flares jointly observed by
+RHESSI, SDO/EVE (MEGS-A and -B), Hinode/(EIS, SOT, and XRT), and IRIS. Out of
+the 6953 flares of GOES magnitude C1 or greater that we consider over the 6.5
+years after the launch of SDO, 40 have been observed by six or more instruments
+simultaneously. Using each instrument's individual rate of success in observing
+flares, we show that the numbers of flares co-observed by three or more
+instruments are higher than the number expected under the assumption that the
+instruments operated independently of one another. In particular, the number of
+flares observed by larger numbers of instruments is much higher than expected.
+Our study illustrates that these missions often acted in cooperation, or at
+least had aligned goals. We also provide details on an interactive widget now
+available in SSWIDL that allows a user to search for flaring events that have
+been observed by a chosen set of instruments. This provides access to a broader
+range of events in order to answer specific science questions. The difficulty
+in scheduling coordinated observations for solar-flare research is discussed
+with respect to instruments projected to begin operations during Solar Cycle
+25, such as DKIST, Solar Orbiter, and Parker Solar Probe.
+",0,1,0,0,0,0
+6712,A Feature Complete SPIKE Banded Algorithm and Solver,"  New features and enhancements for the SPIKE banded solver are presented.
+Among all the SPIKE algorithm versions, we focus our attention on the recursive
+SPIKE technique which provides the best trade-off between generality and
+parallel efficiency, but was known for its lack of flexibility. Its application
+was essentially limited to power of two number of cores/processors. This
+limitation is successfully addressed in this paper. In addition, we present a
+new transpose solve option, a standard feature of most numerical solver
+libraries which has never been addressed by the SPIKE algorithm so far. A
+pivoting recursive SPIKE strategy is finally presented as an alternative to
+non-pivoting scheme for systems with large condition numbers. All these new
+enhancements participate to create a feature complete SPIKE algorithm and a new
+black-box SPIKE-OpenMP package that significantly outperforms the performance
+and scalability obtained with other state-of-the-art banded solvers.
+",1,0,0,0,0,0
+6713,Dark Matter in the Local Group of Galaxies,"  We describe the neutrino flavor (e = electron, u = muon, t = tau) masses as
+m(i=e;u;t)= m + [Delta]mi with |[Delta]mij|/m < 1 and probably |[Delta]mij|/m
+<< 1. The quantity m is the degenerate neutrino mass. Because neutrino flavor
+is not a quantum number, this degenerate mass appears in the neutrino equation
+of state. We apply a Monte Carlo computational physics technique to the Local
+Group (LG) of galaxies to determine an approximate location for a Dark Matter
+embedding condensed neutrino object(CNO). The calculation is based on the
+rotational properties of the only spiral galaxies within the LG: M31, M33 and
+the Milky Way. CNOs could be the Dark Matter everyone is looking for and we
+estimate the CNO embedding the LG to have a mass 5.17x10^15 Mo and a radius
+1.316 Mpc, with the estimated value of m ~= 0.8 eV/c2. The up-coming KATRIN
+experiment will either be the definitive result or eliminate condensed
+neutrinos as a Dark Matter candidate.
+",0,1,0,0,0,0
+6714,Effective Extensible Programming: Unleashing Julia on GPUs,"  GPUs and other accelerators are popular devices for accelerating
+compute-intensive, parallelizable applications. However, programming these
+devices is a difficult task. Writing efficient device code is challenging, and
+is typically done in a low-level programming language. High-level languages are
+rarely supported, or do not integrate with the rest of the high-level language
+ecosystem. To overcome this, we propose compiler infrastructure to efficiently
+add support for new hardware or environments to an existing programming
+language.
+We evaluate our approach by adding support for NVIDIA GPUs to the Julia
+programming language. By integrating with the existing compiler, we
+significantly lower the cost to implement and maintain the new compiler, and
+facilitate reuse of existing application code. Moreover, use of the high-level
+Julia programming language enables new and dynamic approaches for GPU
+programming. This greatly improves programmer productivity, while maintaining
+application performance similar to that of the official NVIDIA CUDA toolkit.
+",1,0,0,0,0,0
+6715,Finite Time Adaptive Stabilization of LQ Systems,"  Stabilization of linear systems with unknown dynamics is a canonical problem
+in adaptive control. Since the lack of knowledge of system parameters can cause
+it to become destabilized, an adaptive stabilization procedure is needed prior
+to regulation. Therefore, the adaptive stabilization needs to be completed in
+finite time. In order to achieve this goal, asymptotic approaches are not very
+helpful. There are only a few existing non-asymptotic results and a full
+treatment of the problem is not currently available.
+In this work, leveraging the novel method of random linear feedbacks, we
+establish high probability guarantees for finite time stabilization. Our
+results hold for remarkably general settings because we carefully choose a
+minimal set of assumptions. These include stabilizability of the underlying
+system and restricting the degree of heaviness of the noise distribution. To
+derive our results, we also introduce a number of new concepts and technical
+tools to address regularity and instability of the closed-loop matrix.
+",1,0,0,1,0,0
+6716,On the composition of Berezin-Toeplitz operators on symplectic manifolds,"  We compute the second coefficient of the composition of two Berezin-Toeplitz
+operators associated with the $\text{spin}^c$ Dirac operator on a symplectic
+manifold, making use of the full-off diagonal expansion of the Bergman kernel.
+",0,0,1,0,0,0
+6717,Maximum and minimum operators of convex integrands,"  For given convex integrands $\gamma_{{}_{i}}: S^{n}\to \mathbb{R}_{+}$ (where
+$i=1, 2$), the functions $\gamma_{{}_{max}}$ and $\gamma_{{}_{min}}$ can be
+defined as natural way. In this paper, we show that the Wulff shape of
+$\gamma_{{}_{max}}$ (resp. the Wulff shape of $\gamma_{{}_{min}}$) is exactly
+the convex hull of $(\mathcal{W}_{\gamma_{{}_{1}}}\cup
+\mathcal{W}_{\gamma_{{}_{2}}})$ (resp. $\mathcal{W}_{\gamma_{{}_{1}}}\cap
+\mathcal{W}_{\gamma_{{}_{2}}}$).
+",0,0,1,0,0,0
+6718,Approximate Kernel PCA Using Random Features: Computational vs. Statistical Trade-off,"  Kernel methods are powerful learning methodologies that provide a simple way
+to construct nonlinear algorithms from linear ones. Despite their popularity,
+they suffer from poor scalability in big data scenarios. Various approximation
+methods, including random feature approximation have been proposed to alleviate
+the problem. However, the statistical consistency of most of these approximate
+kernel methods is not well understood except for kernel ridge regression
+wherein it has been shown that the random feature approximation is not only
+computationally efficient but also statistically consistent with a minimax
+optimal rate of convergence. In this paper, we investigate the efficacy of
+random feature approximation in the context of kernel principal component
+analysis (KPCA) by studying the trade-off between computational and statistical
+behaviors of approximate KPCA. We show that the approximate KPCA is both
+computationally and statistically efficient compared to KPCA in terms of the
+error associated with reconstructing a kernel function based on its projection
+onto the corresponding eigenspaces. Depending on the eigenvalue decay behavior
+of the covariance operator, we show that only $n^{2/3}$ features (polynomial
+decay) or $\sqrt{n}$ features (exponential decay) are needed to match the
+statistical performance of KPCA. We also investigate their statistical
+behaviors in terms of the convergence of corresponding eigenspaces wherein we
+show that only $\sqrt{n}$ features are required to match the performance of
+KPCA and if fewer than $\sqrt{n}$ features are used, then approximate KPCA has
+a worse statistical behavior than that of KPCA.
+",0,0,1,1,0,0
+6719,A Heuristic Search Algorithm Using the Stability of Learning Algorithms in Certain Scenarios as the Fitness Function: An Artificial General Intelligence Engineering Approach,"  This paper presents a non-manual design engineering method based on heuristic
+search algorithm to search for candidate agents in the solution space which
+formed by artificial intelligence agents modeled on the base of
+bionics.Compared with the artificial design method represented by meta-learning
+and the bionics method represented by the neural architecture chip,this method
+is more feasible for realizing artificial general intelligence,and it has a
+much better interaction with cognitive neuroscience;at the same time,the
+engineering method is based on the theoretical hypothesis that the final
+learning algorithm is stable in certain scenarios,and has generalization
+ability in various scenarios.The paper discusses the theory preliminarily and
+proposes the possible correlation between the theory and the fixed-point
+theorem in the field of mathematics.Limited by the author's knowledge
+level,this correlation is proposed only as a kind of conjecture.
+",1,0,0,0,0,0
+6720,"SEDIGISM: Structure, excitation, and dynamics of the inner Galactic interstellar medium","  The origin and life-cycle of molecular clouds are still poorly constrained,
+despite their importance for understanding the evolution of the interstellar
+medium. We have carried out a systematic, homogeneous, spectroscopic survey of
+the inner Galactic plane, in order to complement the many continuum Galactic
+surveys available with crucial distance and gas-kinematic information. Our aim
+is to combine this data set with recent infrared to sub-millimetre surveys at
+similar angular resolutions. The SEDIGISM survey covers 78 deg^2 of the inner
+Galaxy (-60 deg < l < +18 deg, |b| < 0.5 deg) in the J=2-1 rotational
+transition of 13CO. This isotopologue of CO is less abundant than 12CO by
+factors up to 100. Therefore, its emission has low to moderate optical depths,
+and higher critical density, making it an ideal tracer of the cold, dense
+interstellar medium. The data have been observed with the SHFI single-pixel
+instrument at APEX. The observational setup covers the 13CO(2-1) and C18O(2-1)
+lines, plus several transitions from other molecules. The observations have
+been completed. Data reduction is in progress, and the final data products will
+be made available in the near future. Here we give a detailed description of
+the survey and the dedicated data reduction pipeline. Preliminary results based
+on a science demonstration field covering -20 deg < l < -18.5 deg are
+presented. Analysis of the 13CO(2-1) data in this field reveals compact clumps,
+diffuse clouds, and filamentary structures at a range of heliocentric
+distances. By combining our data with data in the (1-0) transition of CO
+isotopologues from the ThrUMMS survey, we are able to compute a 3D realization
+of the excitation temperature and optical depth in the interstellar medium.
+Ultimately, this survey will provide a detailed, global view of the inner
+Galactic interstellar medium at an unprecedented angular resolution of ~30"".
+",0,1,0,0,0,0
+6721,Imitating Driver Behavior with Generative Adversarial Networks,"  The ability to accurately predict and simulate human driving behavior is
+critical for the development of intelligent transportation systems. Traditional
+modeling methods have employed simple parametric models and behavioral cloning.
+This paper adopts a method for overcoming the problem of cascading errors
+inherent in prior approaches, resulting in realistic behavior that is robust to
+trajectory perturbations. We extend Generative Adversarial Imitation Learning
+to the training of recurrent policies, and we demonstrate that our model
+outperforms rule-based controllers and maximum likelihood models in realistic
+highway simulations. Our model both reproduces emergent behavior of human
+drivers, such as lane change rate, while maintaining realistic control over
+long time horizons.
+",1,0,0,0,0,0
+6722,Topological semimetals with double-helix nodal link,"  Topological nodal line semimetals are characterized by the crossing of the
+conduction and valence bands along one or more closed loops in the Brillouin
+zone. Usually, these loops are either isolated or touch each other at some
+highly symmetric points. Here, we introduce a new kind of nodal line semimetal,
+that contains a pair of linked nodal loops. A concrete two-band model was
+constructed, which supports a pair of nodal lines with a double-helix
+structure, which can be further twisted into a Hopf link because of the
+periodicity of the Brillouin zone. The nodal lines are stabilized by the
+combined spatial inversion $\mathcal{P}$ and time reversal $\mathcal{T}$
+symmetry; the individual $\mathcal{P}$ and $\mathcal{T}$ symmetries must be
+broken. The band exhibits nontrivial topology that each nodal loop carries a
+$\pi$ Berry flux. Surface flat bands emerge at the open boundary and are
+exactly encircled by the projection of the nodal lines on the surface Brillouin
+zone. The experimental implementation of our model using cold atoms in optical
+lattices is discussed.
+",0,1,0,0,0,0
+6723,Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events,"  In this paper, an artificial intelligence based grid hardening model is
+proposed with the objective of improving power grid resilience in response to
+extreme weather events. At first, a machine learning model is proposed to
+predict the component states (either operational or outage) in response to the
+extreme event. Then, these predictions are fed into a hardening model, which
+determines strategic locations for placement of distributed generation (DG)
+units. In contrast to existing literature in hardening and resilience
+enhancement, this paper co-optimizes grid economic and resilience objectives by
+considering the intricate dependencies of the two. The numerical simulations on
+the standard IEEE 118-bus test system illustrate the merits and applicability
+of the proposed hardening model. The results indicate that the proposed
+hardening model through decentralized and distributed local energy resources
+can produce a more robust solution that can protect the system significantly
+against multiple component outages due to an extreme event.
+",1,0,0,0,0,0
+6724,Computing isomorphisms and embeddings of finite fields,"  Let $\mathbb{F}_q$ be a finite field. Given two irreducible polynomials $f,g$
+over $\mathbb{F}_q$, with $\mathrm{deg} f$ dividing $\mathrm{deg} g$, the
+finite field embedding problem asks to compute an explicit description of a
+field embedding of $\mathbb{F}_q[X]/f(X)$ into $\mathbb{F}_q[Y]/g(Y)$. When
+$\mathrm{deg} f = \mathrm{deg} g$, this is also known as the isomorphism
+problem.
+This problem, a special instance of polynomial factorization, plays a central
+role in computer algebra software. We review previous algorithms, due to
+Lenstra, Allombert, Rains, and Narayanan, and propose improvements and
+generalizations. Our detailed complexity analysis shows that our newly proposed
+variants are at least as efficient as previously known algorithms, and in many
+cases significantly better.
+We also implement most of the presented algorithms, compare them with the
+state of the art computer algebra software, and make the code available as open
+source. Our experiments show that our new variants consistently outperform
+available software.
+",1,0,1,0,0,0
+6725,Analysis of the current-driven domain wall motion in a ratchet ferromagnetic strip,"  The current-driven domain wall motion in a ratchet memory due to spin-orbit
+torques is studied from both full micromagnetic simulations and the one
+dimensional model. Within the framework of this model, the integration of the
+anisotropy energy contribution leads to a new term in the well known q-$\Phi$
+equations, being this contribution responsible for driving the domain wall to
+an equilibrium position. The comparison between the results drawn by the one
+dimensional model and full micromagnetic simulations proves the utility of such
+a model in order to predict the current-driven domain wall motion in the
+ratchet memory. Additionally, since current pulses are applied, the paper shows
+how the proper working of such a device requires the adequate balance of
+excitation and relaxation times, being the latter longer than the former.
+Finally, the current-driven regime of a ratchet memory is compared to the
+field-driven regime described elsewhere, then highlighting the advantages of
+this current-driven regime.
+",0,1,0,0,0,0
+6726,"On the Hilbert coefficients, depth of associated graded rings and reduction numbers","  Let $(R,\mathfrak{m})$ be a $d$-dimensional Cohen-Macaulay local ring, $I$ an
+$\mathfrak{m}$-primary ideal of $R$ and $J=(x_1,...,x_d)$ a minimal reduction
+of $I$. We show that if $J_{d-1}=(x_1,...,x_{d-1})$ and
+$\sum\limits_{n=1}^\infty\lambda{({I^{n+1}\cap J_{d-1}})/({J{I^n} \cap
+J_{d-1}})=i}$ where i=0,1, then depth $G(I)\geq{d-i-1}$. Moreover, we prove
+that if $e_2(I) = \sum_{n=2}^\infty (n-1) \lambda (I^n/JI^{n-1})-2;$ or if $I$
+is integrally closed and $e_2(I) = \sum_{n=2}^\infty
+(n-1)\lambda({I^{n}}/JI^{n-1})-i$ where $i=3,4$, then $e_1(I) =
+\sum_{n=1}^\infty \lambda(I^n / JI^{n-1})-1.$ In addition, we show that $r(I)$
+is independent. Furthermore, we study the independence of $r(I)$ with some
+other conditions.
+",0,0,1,0,0,0
+6727,End-to-End ASR-free Keyword Search from Speech,"  End-to-end (E2E) systems have achieved competitive results compared to
+conventional hybrid hidden Markov model (HMM)-deep neural network based
+automatic speech recognition (ASR) systems. Such E2E systems are attractive due
+to the lack of dependence on alignments between input acoustic and output
+grapheme or HMM state sequence during training. This paper explores the design
+of an ASR-free end-to-end system for text query-based keyword search (KWS) from
+speech trained with minimal supervision. Our E2E KWS system consists of three
+sub-systems. The first sub-system is a recurrent neural network (RNN)-based
+acoustic auto-encoder trained to reconstruct the audio through a
+finite-dimensional representation. The second sub-system is a character-level
+RNN language model using embeddings learned from a convolutional neural
+network. Since the acoustic and text query embeddings occupy different
+representation spaces, they are input to a third feed-forward neural network
+that predicts whether the query occurs in the acoustic utterance or not. This
+E2E ASR-free KWS system performs respectably despite lacking a conventional ASR
+system and trains much faster.
+",1,0,0,0,0,0
+6728,Tracking performance in high multiplicities environment at ALICE,"  In LHC Run 3, ALICE will increase the data taking rate significantly to
+50\,kHz continuous read out of minimum bias Pb-Pb events. This challenges the
+online and offline computing infrastructure, requiring to process 50 times as
+many events per second as in Run 2, and increasing the data compression ratio
+from 5 to 20. Such high data compression is impossible by lossless ZIP-like
+algorithms, but it must use results from online reconstruction, which in turn
+requires online calibration. These important online processing steps are the
+most computing-intense ones, and will use GPUs as hardware accelerators. The
+new online features are already under test during Run 2 in the High Level
+Trigger (HLT) online processing farm. The TPC (Time Projection Chamber)
+tracking algorithm for Run 3 is derived from the current HLT online tracking
+and is based on the Cellular Automaton and Kalman Filter. HLT has deployed
+online calibration for the TPC drift time, which needs to be extended to space
+charge distortions calibration. This requires online reconstruction for
+additional detectors like TRD (Transition Radiation Detector) and TOF (Time Of
+Flight). We present prototypes of these developments, in particular a data
+compression algorithm that achieves a compression factor of~9 on Run 2 TPC
+data, and the efficiency of online TRD tracking. We give an outlook to the
+challenges of TPC tracking with continuous read out.
+",0,1,0,0,0,0
+6729,Schatten class Hankel and $\overline{\partial}$-Neumann operators on pseudoconvex domains in $\mathbb{C}^n$,"  Let $\Omega$ be a $C^2$-smooth bounded pseudoconvex domain in $\mathbb{C}^n$
+for $n\geq 2$ and let $\varphi$ be a holomorphic function on $\Omega$ that is
+$C^2$-smooth on the closure of $\Omega$. We prove that if
+$H_{\overline{\varphi}}$ is in Schatten $p$-class for $p\leq 2n$ then $\varphi$
+is a constant function. As a corollary, we show that the
+$\overline{\partial}$-Neumann operator on $\Omega$ is not Hilbert-Schmidt.
+",0,0,1,0,0,0
+6730,"Cross-Sectional Variation of Intraday Liquidity, Cross-Impact, and their Effect on Portfolio Execution","  The composition of natural liquidity has been changing over time. An analysis
+of intraday volumes for the S&P500 constituent stocks illustrates that (i)
+volume surprises, i.e., deviations from their respective forecasts, are
+correlated across stocks, and (ii) this correlation increases during the last
+few hours of the trading session. These observations could be attributed, in
+part, to the prevalence of portfolio trading activity that is implicit in the
+growth of ETF, passive and systematic investment strategies; and, to the
+increased trading intensity of such strategies towards the end of the trading
+session, e.g., due to execution of mutual fund inflows/outflows that are
+benchmarked to the closing price on each day. In this paper, we investigate the
+consequences of such portfolio liquidity on price impact and portfolio
+execution. We derive a linear cross-asset market impact from a stylized model
+that explicitly captures the fact that a certain fraction of natural liquidity
+providers only trade portfolios of stocks whenever they choose to execute. We
+find that due to cross-impact and its intraday variation, it is optimal for a
+risk-neutral, cost minimizing liquidator to execute a portfolio of orders in a
+coupled manner, as opposed to a separable VWAP-like execution that is often
+assumed. The optimal schedule couples the execution of the various orders so as
+to be able to take advantage of increased portfolio liquidity towards the end
+of the day. A worst case analysis shows that the potential cost reduction from
+this optimized execution schedule over the separable approach can be as high as
+6% for plausible model parameters. Finally, we discuss how to estimate
+cross-sectional price impact if one had a dataset of realized portfolio
+transaction records that exploits the low-rank structure of its coefficient
+matrix suggested by our analysis.
+",0,0,0,0,0,1
+6731,An investigation of pulsar searching techniques with the Fast Folding Algorithm,"  Here we present an in-depth study of the behaviour of the Fast Folding
+Algorithm, an alternative pulsar searching technique to the Fast Fourier
+Transform. Weaknesses in the Fast Fourier Transform, including a susceptibility
+to red noise, leave it insensitive to pulsars with long rotational periods (P >
+1 s). This sensitivity gap has the potential to bias our understanding of the
+period distribution of the pulsar population. The Fast Folding Algorithm, a
+time-domain based pulsar searching technique, has the potential to overcome
+some of these biases. Modern distributed-computing frameworks now allow for the
+application of this algorithm to all-sky blind pulsar surveys for the first
+time. However, many aspects of the behaviour of this search technique remain
+poorly understood, including its responsiveness to variations in pulse shape
+and the presence of red noise. Using a custom CPU-based implementation of the
+Fast Folding Algorithm, ffancy, we have conducted an in-depth study into the
+behaviour of the Fast Folding Algorithm in both an ideal, white noise regime as
+well as a trial on observational data from the HTRU-S Low Latitude pulsar
+survey, including a comparison to the behaviour of the Fast Fourier Transform.
+We are able to both confirm and expand upon earlier studies that demonstrate
+the ability of the Fast Folding Algorithm to outperform the Fast Fourier
+Transform under ideal white noise conditions, and demonstrate a significant
+improvement in sensitivity to long-period pulsars in real observational data
+through the use of the Fast Folding Algorithm.
+",0,1,0,0,0,0
+6732,Symplectic stability on manifolds with cylindrical ends,"  A famous result of Jurgen Moser states that a symplectic form on a compact
+manifold cannot be deformed within its cohomology class to an inequivalent
+symplectic form. It is well known that this does not hold in general for
+noncompact symplectic manifolds. The notion of Eliashberg-Gromov convex ends
+provides a natural restricted setting for the study of analogs of Moser's
+symplectic stability result in the noncompact case, and this has been
+significantly developed in work of Cieliebak-Eliashberg. Retaining the end
+structure on the underlying smooth manifold, but dropping the convexity and
+completeness assumptions on the symplectic forms at infinity we show that
+symplectic stability holds under a natural growth condition on the path of
+symplectic forms. The result can be straightforwardly applied as we show
+through explicit examples.
+",0,0,1,0,0,0
+6733,Making the Dzyaloshinskii-Moriya interaction visible,"  Brillouin light spectroscopy is a powerful and robust technique for measuring
+the interfacial Dzyaloshinskii-Moriya interaction in thin films with broken
+inversion symmetry. Here we show that the magnon visibility, i.e. the intensity
+of the inelastically scattered light, strongly depends on the thickness of the
+dielectric seed material - SiO$_2$. By using both, analytical thin-film optics
+and numerical calculations, we reproduce the experimental data. We therefore
+provide a guideline for the maximization of the signal by adapting the
+substrate properties to the geometry of the measurement. Such a boost-up of the
+signal eases the magnon visualization in ultrathin magnetic films, speeds-up
+the measurement and increases the reliability of the data.
+",0,1,0,0,0,0
+6734,Approximate Collapsed Gibbs Clustering with Expectation Propagation,"  We develop a framework for approximating collapsed Gibbs sampling in
+generative latent variable cluster models. Collapsed Gibbs is a popular MCMC
+method, which integrates out variables in the posterior to improve mixing.
+Unfortunately for many complex models, integrating out these variables is
+either analytically or computationally intractable. We efficiently approximate
+the necessary collapsed Gibbs integrals by borrowing ideas from expectation
+propagation. We present two case studies where exact collapsed Gibbs sampling
+is intractable: mixtures of Student-t's and time series clustering. Our
+experiments on real and synthetic data show that our approximate sampler
+enables a runtime-accuracy tradeoff in sampling these types of models,
+providing results with competitive accuracy much more rapidly than the naive
+Gibbs samplers one would otherwise rely on in these scenarios.
+",0,0,0,1,0,0
+6735,Thermal graphene metamaterials and epsilon-near-zero high temperature plasmonics,"  The key feature of a thermophotovoltaic (TPV) emitter is the enhancement of
+thermal emission corresponding to energies just above the bandgap of the
+absorbing photovoltaic cell and simultaneous suppression of thermal emission
+below the bandgap. We show here that a single layer plasmonic coating can
+perform this task with high efficiency. Our key design principle involves
+tuning the epsilon-near-zero frequency (plasma frequency) of the metal acting
+as a thermal emitter to the electronic bandgap of the semiconducting cell. This
+approach utilizes the change in reflectivity of a metal near its plasma
+frequency (epsilon-near-zero frequency) to lead to spectrally selective thermal
+emission and can be adapted to large area coatings using high temperature
+plasmonic materials. We provide a detailed analysis of the spectral and angular
+performance of high temperature plasmonic coatings as TPV emitters. We show the
+potential of such high temperature plasmonic thermal emitter coatings (p-TECs)
+for narrowband near-field thermal emission. We also show the enhancement of
+near-surface energy density in graphene-multilayer thermal metamaterials due to
+a topological transition at an effective epsilon-near-zero frequency. This
+opens up spectrally selective thermal emission from graphene multilayers in the
+infrared frequency regime. Our design paves the way for the development of
+single layer p-TECs and graphene multilayers for spectrally selective radiative
+heat transfer applications.
+",0,1,0,0,0,0
+6736,Ferroionic states in ferroelectric thin films,"  The electric coupling between surface ions and bulk ferroelectricity gives
+rise to a continuum of mixed states in ferroelectric thin films, exquisitely
+sensitive to temperature and external factors, such as applied voltage and
+oxygen pressure. Here we develop the comprehensive analytical description of
+these coupled ferroelectric and ionic (""ferroionic"") states by combining the
+Ginzburg-Landau-Devonshire description of the ferroelectric properties of the
+film with Langmuir adsorption model for the electrochemical reaction at the
+film surface. We explore the thermodynamic and kinetic characteristics of the
+ferroionic states as a function of temperature, film thickness, and external
+electric potential. These studies provide a new insight into mesoscopic
+properties of ferroelectric thin films, whose surface is exposed to chemical
+environment as screening charges supplier.
+",0,1,0,0,0,0
+6737,Quantum Chebyshev's Inequality and Applications,"  In this paper we provide new quantum algorithms with polynomial speed-up for
+a range of problems for which no such results were known, or we improve
+previous algorithms. First, we consider the approximation of the frequency
+moments $F_k$ of order $k \geq 3$ in the multi-pass streaming model with
+updates (turnstile model). We design a $P$-pass quantum streaming algorithm
+with memory $M$ satisfying a tradeoff of $P^2 M = \tilde{O}(n^{1-2/k})$,
+whereas the best classical algorithm requires $P M = \Theta(n^{1-2/k})$. Then,
+we study the problem of estimating the number $m$ of edges and the number $t$
+of triangles given query access to an $n$-vertex graph. We describe optimal
+quantum algorithms that perform $\tilde{O}(\sqrt{n}/m^{1/4})$ and
+$\tilde{O}(\sqrt{n}/t^{1/6} + m^{3/4}/\sqrt{t})$ queries respectively. This is
+a quadratic speed-up compared to the classical complexity of these problems.
+For this purpose we develop a new quantum paradigm that we call Quantum
+Chebyshev's inequality. Namely we demonstrate that, in a certain model of
+quantum sampling, one can approximate with relative error the mean of any
+random variable with a number of quantum samples that is linear in the ratio of
+the square root of the variance to the mean. Classically the dependency is
+quadratic. Our algorithm subsumes a previous result of Montanaro [Mon15]. This
+new paradigm is based on a refinement of the Amplitude Estimation algorithm of
+Brassard et al. [BHMT02] and of previous quantum algorithms for the mean
+estimation problem. We show that this speed-up is optimal, and we identify
+another common model of quantum sampling where it cannot be obtained. For our
+applications, we also adapt the variable-time amplitude amplification technique
+of Ambainis [Amb10] into a variable-time amplitude estimation algorithm.
+",0,0,0,1,0,0
+6738,Learning Convex Regularizers for Optimal Bayesian Denoising,"  We propose a data-driven algorithm for the maximum a posteriori (MAP)
+estimation of stochastic processes from noisy observations. The primary
+statistical properties of the sought signal is specified by the penalty
+function (i.e., negative logarithm of the prior probability density function).
+Our alternating direction method of multipliers (ADMM)-based approach
+translates the estimation task into successive applications of the proximal
+mapping of the penalty function. Capitalizing on this direct link, we define
+the proximal operator as a parametric spline curve and optimize the spline
+coefficients by minimizing the average reconstruction error for a given
+training set. The key aspects of our learning method are that the associated
+penalty function is constrained to be convex and the convergence of the ADMM
+iterations is proven. As a result of these theoretical guarantees, adaptation
+of the proposed framework to different levels of measurement noise is extremely
+simple and does not require any retraining. We apply our method to estimation
+of both sparse and non-sparse models of Lévy processes for which the
+minimum mean square error (MMSE) estimators are available. We carry out a
+single training session and perform comparisons at various signal-to-noise
+ratio (SNR) values. Simulations illustrate that the performance of our
+algorithm is practically identical to the one of the MMSE estimator
+irrespective of the noise power.
+",1,0,0,1,0,0
+6739,On the $L^p$ boundedness of wave operators for two-dimensional Schrödinger operators with threshold obstructions,"  Let $H=-\Delta+V$ be a Schrödinger operator on $L^2(\mathbb R^2)$ with
+real-valued potential $V$, and let $H_0=-\Delta$. If $V$ has sufficient
+pointwise decay, the wave operators $W_{\pm}=s-\lim_{t\to \pm\infty}
+e^{itH}e^{-itH_0}$ are known to be bounded on $L^p(\mathbb R^2)$ for all $1< p<
+\infty$ if zero is not an eigenvalue or resonance. We show that if there is an
+s-wave resonance or an eigenvalue only at zero, then the wave operators are
+bounded on $L^p(\mathbb R^2)$ for $1 < p<\infty$. This result stands in
+contrast to results in higher dimensions, where the presence of zero energy
+obstructions is known to shrink the range of valid exponents $p$.
+",0,0,1,0,0,0
+6740,Data-driven Advice for Applying Machine Learning to Bioinformatics Problems,"  As the bioinformatics field grows, it must keep pace not only with new data
+but with new algorithms. Here we contribute a thorough analysis of 13
+state-of-the-art, commonly used machine learning algorithms on a set of 165
+publicly available classification problems in order to provide data-driven
+algorithm recommendations to current researchers. We present a number of
+statistical and visual comparisons of algorithm performance and quantify the
+effect of model selection and algorithm tuning for each algorithm and dataset.
+The analysis culminates in the recommendation of five algorithms with
+hyperparameters that maximize classifier performance across the tested
+problems, as well as general guidelines for applying machine learning to
+supervised classification problems.
+",1,0,0,1,0,0
+6741,Bivariate Causal Discovery and its Applications to Gene Expression and Imaging Data Analysis,"  The mainstream of research in genetics, epigenetics and imaging data analysis
+focuses on statistical association or exploring statistical dependence between
+variables. Despite their significant progresses in genetic research,
+understanding the etiology and mechanism of complex phenotypes remains elusive.
+Using association analysis as a major analytical platform for the complex data
+analysis is a key issue that hampers the theoretic development of genomic
+science and its application in practice. Causal inference is an essential
+component for the discovery of mechanical relationships among complex
+phenotypes. Many researchers suggest making the transition from association to
+causation. Despite its fundamental role in science, engineering and
+biomedicine, the traditional methods for causal inference require at least
+three variables. However, quantitative genetic analysis such as QTL, eQTL,
+mQTL, and genomic-imaging data analysis requires exploring the causal
+relationships between two variables. This paper will focus on bivariate causal
+discovery. We will introduce independence of cause and mechanism (ICM) as a
+basic principle for causal inference, algorithmic information theory and
+additive noise model (ANM) as major tools for bivariate causal discovery.
+Large-scale simulations will be performed to evaluate the feasibility of the
+ANM for bivariate causal discovery. To further evaluate their performance for
+causal inference, the ANM will be applied to the construction of gene
+regulatory networks. Also, the ANM will be applied to trait-imaging data
+analysis to illustrate three scenarios: presence of both causation and
+association, presence of association while absence of causation, and presence
+of causation, while lack of association between two variables.
+",0,0,0,0,1,0
+6742,Revisiting Distillation and Incremental Classifier Learning,"  One of the key differences between the learning mechanism of humans and
+Artificial Neural Networks (ANNs) is the ability of humans to learn one task at
+a time. ANNs, on the other hand, can only learn multiple tasks simultaneously.
+Any attempts at learning new tasks incrementally cause them to completely
+forget about previous tasks. This lack of ability to learn incrementally,
+called Catastrophic Forgetting, is considered a major hurdle in building a true
+AI system. In this paper, our goal is to isolate the truly effective existing
+ideas for incremental learning from those that only work under certain
+conditions. To this end, we first thoroughly analyze the current state of the
+art (iCaRL) method for incremental learning and demonstrate that the good
+performance of the system is not because of the reasons presented in the
+existing literature. We conclude that the success of iCaRL is primarily due to
+knowledge distillation and recognize a key limitation of knowledge
+distillation, i.e, it often leads to bias in classifiers. Finally, we propose a
+dynamic threshold moving algorithm that is able to successfully remove this
+bias. We demonstrate the effectiveness of our algorithm on CIFAR100 and MNIST
+datasets showing near-optimal results. Our implementation is available at
+this https URL.
+",0,0,0,1,0,0
+6743,Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace,"  Human-in-the-loop manipulation is useful in when autonomous grasping is not
+able to deal sufficiently well with corner cases or cannot operate fast enough.
+Using the teleoperator's hand as an input device can provide an intuitive
+control method but requires mapping between pose spaces which may not be
+similar. We propose a low-dimensional and continuous teleoperation subspace
+which can be used as an intermediary for mapping between different hand pose
+spaces. We present an algorithm to project between pose space and teleoperation
+subspace. We use a non-anthropomorphic robot to experimentally prove that it is
+possible for teleoperation subspaces to effectively and intuitively enable
+teleoperation. In experiments, novice users completed pick and place tasks
+significantly faster using teleoperation subspace mapping than they did using
+state of the art teleoperation methods.
+",1,0,0,0,0,0
+6744,A Hierarchical Bayes Approach to Adjust for Selection Bias in Before-After Analyses of Vision Zero Policies,"  American cities devote significant resources to the implementation of traffic
+safety countermeasures that prevent pedestrian fatalities. However, the
+before-after comparisons typically used to evaluate the success of these
+countermeasures often suffer from selection bias. This paper motivates the
+tendency for selection bias to overestimate the benefits of traffic safety
+policy, using New York City's Vision Zero strategy as an example. The NASS
+General Estimates System, Fatality Analysis Reporting System and other
+databases are combined into a Bayesian hierarchical model to calculate a more
+realistic before-after comparison. The results confirm the before-after
+analysis of New York City's Vision Zero policy did in fact overestimate the
+effect of the policy, and a more realistic estimate is roughly two-thirds the
+size.
+",0,0,0,1,0,0
+6745,Cavitation near the oscillating piezoelectric plate in water,"  It is known that gas bubbles on the surface bounding a fluid flow can change
+the coefficient of friction and affect the parameters of the boundary layer. In
+this paper, we propose a method that allows us to create, in the near-wall
+region, a thin layer of liquid filled with bubbles. It will be shown that if
+there is an oscillating piezoelectric plate on the surface bounding a liquid,
+then, under certain conditions, cavitation develops in the boundary layer. The
+relationship between the parameters of cavitation and the characteristics of
+the piezoelectric plate oscillations is obtained. Possible applications are
+discussed.
+",0,1,0,0,0,0
+6746,Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks,"  Inverse problems correspond to a certain type of optimization problems
+formulated over appropriate input distributions. Recently, there has been a
+growing interest in understanding the computational hardness of these
+optimization problems, not only in the worst case, but in an average-complexity
+sense under this same input distribution.
+In this revised note, we are interested in studying another aspect of
+hardness, related to the ability to learn how to solve a problem by simply
+observing a collection of previously solved instances. These 'planted
+solutions' are used to supervise the training of an appropriate predictive
+model that parametrizes a broad class of algorithms, with the hope that the
+resulting model will provide good accuracy-complexity tradeoffs in the average
+sense.
+We illustrate this setup on the Quadratic Assignment Problem, a fundamental
+problem in Network Science. We observe that data-driven models based on Graph
+Neural Networks offer intriguingly good performance, even in regimes where
+standard relaxation based techniques appear to suffer.
+",1,0,0,1,0,0
+6747,On Completeness Results of Hoare Logic Relative to the Standard Model,"  The general completeness problem of Hoare logic relative to the standard
+model $N$ of Peano arithmetic has been studied by Cook, and it allows for the
+use of arbitrary arithmetical formulas as assertions. In practice, the
+assertions would be simple arithmetical formulas, e.g. of a low level in the
+arithmetical hierarchy. In addition, we find that, by restricting inputs to
+$N$, the complexity of the minimal assertion theory for the completeness of
+Hoare logic to hold can be reduced. This paper further studies the completeness
+of Hoare Logic relative to $N$ by restricting assertions to subclasses of
+arithmetical formulas (and by restricting inputs to $N$). Our completeness
+results refine Cook's result by reducing the complexity of the assertion
+theory.
+",1,0,0,0,0,0
+6748,Shift-Coupling of Random Rooted Graphs and Networks,"  In this paper, we present a result similar to the shift-coupling result of
+Thorisson (1996) in the context of random graphs and networks. The result is
+that a given random rooted network can be obtained by changing the root of
+another given one if and only if the distributions of the two agree on the
+invariant sigma-field. Several applications of the result are presented for the
+case of unimodular networks. In particular, it is shown that the distribution
+of a unimodular network is uniquely determined by its restriction to the
+invariant sigma-filed. Also, the theorem is applied to the existence of an
+invariant transport kernel that balances between two given (discrete) measures
+on the vertices. An application is the existence of a so called extra head
+scheme for the Bernoulli process on an infinite unimodular graph. Moreover, a
+construction is presented for balancing transport kernels that is a
+generalization of the Gale-Shapley stable matching algorithm in bipartite
+graphs. Another application is on a general method that covers the situations
+where some vertices and edges are added to a unimodular network and then, to
+make it unimodular, the probability measure is biased and then a new root is
+selected. It is proved that this method provides all possible
+unimodularizations in these situations. Finally, analogous existing results for
+stationary point processes and unimodular networks are discussed in detail.
+",0,0,1,0,0,0
+6749,Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks,"  Quantifying image distortions caused by strong gravitational lensing and
+estimating the corresponding matter distribution in lensing galaxies has been
+primarily performed by maximum likelihood modeling of observations. This is
+typically a time and resource-consuming procedure, requiring sophisticated
+lensing codes, several data preparation steps, and finding the maximum
+likelihood model parameters in a computationally expensive process with
+downhill optimizers. Accurate analysis of a single lens can take up to a few
+weeks and requires the attention of dedicated experts. Tens of thousands of new
+lenses are expected to be discovered with the upcoming generation of ground and
+space surveys, the analysis of which can be a challenging task. Here we report
+the use of deep convolutional neural networks to accurately estimate lensing
+parameters in an extremely fast and automated way, circumventing the
+difficulties faced by maximum likelihood methods. We also show that lens
+removal can be made fast and automated using Independent Component Analysis of
+multi-filter imaging data. Our networks can recover the parameters of the
+Singular Isothermal Ellipsoid density profile, commonly used to model strong
+lensing systems, with an accuracy comparable to the uncertainties of
+sophisticated models, but about ten million times faster: 100 systems in
+approximately 1s on a single graphics processing unit. These networks can
+provide a way for non-experts to obtain lensing parameter estimates for large
+samples of data. Our results suggest that neural networks can be a powerful and
+fast alternative to maximum likelihood procedures commonly used in
+astrophysics, radically transforming the traditional methods of data reduction
+and analysis.
+",0,1,0,0,0,0
+6750,"Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks","  Deep neural networks (DNNs) have excellent representative power and are state
+of the art classifiers on many tasks. However, they often do not capture their
+own uncertainties well making them less robust in the real world as they
+overconfidently extrapolate and do not notice domain shift. Gaussian processes
+(GPs) with RBF kernels on the other hand have better calibrated uncertainties
+and do not overconfidently extrapolate far from data in their training set.
+However, GPs have poor representational power and do not perform as well as
+DNNs on complex domains. In this paper we show that GP hybrid deep networks,
+GPDNNs, (GPs on top of DNNs and trained end-to-end) inherit the nice properties
+of both GPs and DNNs and are much more robust to adversarial examples. When
+extrapolating to adversarial examples and testing in domain shift settings,
+GPDNNs frequently output high entropy class probabilities corresponding to
+essentially ""don't know"". GPDNNs are therefore promising as deep architectures
+that know when they don't know.
+",0,0,0,1,0,0
+6751,Elliptic regularization of the isometric immersion problem,"  We introduce an elliptic regularization of the PDE system representing the
+isometric immersion of a surface in $\mathbb R^{3}$. The regularization is
+geometric, and has a natural variational interpretation.
+",0,0,1,0,0,0
+6752,A Debris Backwards Flow Simulation System for Malaysia Airlines Flight 370,"  This paper presents a system based on a Two-Way Particle-Tracking Model to
+analyze possible crash positions of flight MH370. The particle simulator
+includes a simple flow simulation of the debris based on a Lagrangian approach
+and a module to extract appropriated ocean current data from netCDF files. The
+influence of wind, waves, immersion depth and hydrodynamic behavior are not
+considered in the simulation.
+",1,1,0,0,0,0
+6753,End-to-End Task-Completion Neural Dialogue Systems,"  One of the major drawbacks of modularized task-completion dialogue systems is
+that each module is trained individually, which presents several challenges.
+For example, downstream modules are affected by earlier modules, and the
+performance of the entire system is not robust to the accumulated errors. This
+paper presents a novel end-to-end learning framework for task-completion
+dialogue systems to tackle such issues. Our neural dialogue system can directly
+interact with a structured database to assist users in accessing information
+and accomplishing certain tasks. The reinforcement learning based dialogue
+manager offers robust capabilities to handle noises caused by other components
+of the dialogue system. Our experiments in a movie-ticket booking domain show
+that our end-to-end system not only outperforms modularized dialogue system
+baselines for both objective and subjective evaluation, but also is robust to
+noises as demonstrated by several systematic experiments with different error
+granularity and rates specific to the language understanding module.
+",1,0,0,0,0,0
+6754,Improving power of genetic association studies by extreme phenotype sampling: a review and some new results,"  Extreme phenotype sampling is a selective genotyping design for genetic
+association studies where only individuals with extreme values of a continuous
+trait are genotyped for a set of genetic variants. Under financial or other
+limitations, this design is assumed to improve the power to detect associations
+between genetic variants and the trait, compared to randomly selecting the same
+number of individuals for genotyping. Here we present extensions of likelihood
+models that can be used for inference when the data are sampled according to
+the extreme phenotype sampling design. Computational methods for parameter
+estimation and hypothesis testing are provided. We consider methods for common
+variant genetic effects and gene-environment interaction effects in linear
+regression models with a normally distributed trait. We use simulated and real
+data to show that extreme phenotype sampling can be powerful compared to random
+sampling, but that this does not hold for all extreme sampling methods and
+situations.
+",0,0,0,1,0,0
+6755,Energy network: towards an interconnected energy infrastructure for the future,"  The fundamental theory of energy networks in different energy forms is
+established following an in-depth analysis of the nature of energy for
+comprehensive energy utilization. The definition of an energy network is given.
+Combining the generalized balance equation of energy in space and the Pfaffian
+equation, the generalized transfer equations of energy in lines (pipes) are
+proposed. The energy variation laws in the transfer processes are investigated.
+To establish the equations of energy networks, the Kirchhoff's Law in electric
+networks is extended to energy networks, which is called the Generalized
+Kirchhoff""s Law. According to the linear phenomenological law, the generalized
+equivalent energy transfer equations with lumped parameters are derived in
+terms of the characteristic equations of energy transfer in lines(pipes).The
+equations are finally unified into a complete energy network equation system
+and its solvability is further discussed. Experiments are carried out on a
+combined cooling, heating and power(CCHP) system in engineering, the energy
+network theory proposed in this paper is used to model and analyze this system.
+By comparing the theoretical results obtained by our modeling approach and the
+data measured in experiments, the energy equations are validated.
+",0,1,0,0,0,0
+6756,Invitation to Alexandrov geometry: CAT[0] spaces,"  The idea is to demonstrate the beauty and power of Alexandrov geometry by
+reaching interesting applications with a minimum of preparation.
+The topics include
+1. Estimates on the number of collisions in billiards.
+2. Construction of exotic aspherical manifolds.
+3. The geometry of two-convex sets in Euclidean space.
+",0,0,1,0,0,0
+6757,Smoothing of transport plans with fixed marginals and rigorous semiclassical limit of the Hohenberg-Kohn functional,"  We prove rigorously that the exact N-electron Hohenberg-Kohn density
+functional converges in the strongly interacting limit to the strictly
+correlated electrons (SCE) functional, and that the absolute value squared of
+the associated constrained-search wavefunction tends weakly in the sense of
+probability measures to a minimizer of the multi-marginal optimal transport
+problem with Coulomb cost associated to the SCE functional. This extends our
+previous work for N=2 [CFK11]. The correct limit problem has been derived in
+the physics literature by Seidl [Se99] and Seidl, Gori-Giorgi and Savin
+[SGS07]; in these papers the lack of a rigorous proof was pointed out.
+We also give a mathematical counterexample to this type of result, by
+replacing the constraint of given one-body density -- an infinite-dimensional
+quadratic expression in the wavefunction -- by an infinite-dimensional
+quadratic expression in the wavefunction and its gradient. Connections with the
+Lawrentiev phenomenon in the calculus of variations are indicated.
+",0,0,1,0,0,0
+6758,The extension of some D(4)-pairs,"  In this paper we illustrate the use of the results from [1] proving that
+$D(4)$-triple $\{a, b, c\}$ with $a < b < a + 57\sqrt{a}$ has a unique
+extension to a quadruple with a larger element. This furthermore implies that
+$D(4)$-pair $\{a, b\}$ cannot be extended to a quintuple if $a < b < a +
+57\sqrt{a}$.
+",0,0,1,0,0,0
+6759,Standards for enabling heterogeneous IaaS cloud federations,"  Technology market is continuing a rapid growth phase where different resource
+providers and Cloud Management Frameworks are positioning to provide ad-hoc
+solutions -in terms of management interfaces, information discovery or billing-
+trying to differentiate from competitors but that as a result remain
+incompatible between them when addressing more complex scenarios like federated
+clouds. Grasping interoperability problems present in current infrastructures
+is then a must-do, tackled by studying how existing and emerging standards
+could enhance user experience in the cloud ecosystem. In this paper we will
+review the current open challenges in Infrastructure as a Service cloud
+interoperability and federation, as well as point to the potential standards
+that should alleviate these problems.
+",1,0,0,0,0,0
+6760,Grid-based Approaches for Distributed Data Mining Applications,"  The data mining field is an important source of large-scale applications and
+datasets which are getting more and more common. In this paper, we present
+grid-based approaches for two basic data mining applications, and a performance
+evaluation on an experimental grid environment that provides interesting
+monitoring capabilities and configuration tools. We propose a new distributed
+clustering approach and a distributed frequent itemsets generation well-adapted
+for grid environments. Performance evaluation is done using the Condor system
+and its workflow manager DAGMan. We also compare this performance analysis to a
+simple analytical model to evaluate the overheads related to the workflow
+engine and the underlying grid system. This will specifically show that
+realistic performance expectations are currently difficult to achieve on the
+grid.
+",1,0,0,0,0,0
+6761,Transforming acoustic characteristics to deceive playback spoofing countermeasures of speaker verification systems,"  Automatic speaker verification (ASV) systems use a playback detector to
+filter out playback attacks and ensure verification reliability. Since current
+playback detection models are almost always trained using genuine and
+played-back speech, it may be possible to degrade their performance by
+transforming the acoustic characteristics of the played-back speech close to
+that of the genuine speech. One way to do this is to enhance speech ""stolen""
+from the target speaker before playback. We tested the effectiveness of a
+playback attack using this method by using the speech enhancement generative
+adversarial network to transform acoustic characteristics. Experimental results
+showed that use of this ""enhanced stolen speech"" method significantly increases
+the equal error rates for the baseline used in the ASVspoof 2017 challenge and
+for a light convolutional neural network-based method. The results also showed
+that its use degrades the performance of a Gaussian mixture model-universal
+background model-based ASV system. This type of attack is thus an urgent
+problem needing to be solved.
+",1,0,0,0,0,0
+6762,LoopInvGen: A Loop Invariant Generator based on Precondition Inference,"  We describe the LoopInvGen tool for generating loop invariants that can
+provably guarantee correctness of a program with respect to a given
+specification. LoopInvGen is an efficient implementation of the inference
+technique originally proposed in our earlier work on PIE
+(this https URL).
+In contrast to existing techniques, LoopInvGen is not restricted to a fixed
+set of features -- atomic predicates that are composed together to build
+complex loop invariants. Instead, we start with no initial features, and use
+program synthesis techniques to grow the set on demand. This not only enables a
+less onerous and more expressive approach, but also appears to be significantly
+faster than the existing tools over the SyGuS-COMP 2017 benchmarks from the INV
+track.
+",1,0,0,0,0,0
+6763,Dimensional crossover of effective orbital dynamics in polar distorted 3He-A: Transitions to anti-spacetime,"  Topologically protected superfluid phases of $^3$He allow one to simulate
+many important aspects of relativistic quantum field theories and quantum
+gravity in condensed matter. Here we discuss a topological Lifshitz transition
+of the effective quantum vacuum in which the determinant of the tetrad field
+changes sign through a crossing to a vacuum state with a degenerate fermionic
+metric. Such a transition is realized in polar distorted superfluid $^3$He-A in
+terms of the effective tetrad fields emerging in the vicinity of the superfluid
+gap nodes: the tetrads of the Weyl points in the chiral A-phase of $^3$He and
+the degenerate tetrad in the vicinity of a Dirac nodal line in the polar phase
+of $^3$He. The continuous phase transition from the $A$-phase to the polar
+phase, i.e. in the transition from the Weyl nodes to the Dirac nodal line and
+back, allows one to follow the behavior of the fermionic and bosonic effective
+actions when the sign of the tetrad determinant changes, and the effective
+chiral space-time transforms to anti-chiral ""anti-spacetime"". This condensed
+matter realization demonstrates that while the original fermionic action is
+analytic across the transition, the effective action for the orbital degrees of
+freedom (pseudo-EM) fields and gravity have non-analytic behavior. In
+particular, the action for the pseudo-EM field in the vacuum with Weyl fermions
+(A-phase) contains the modulus of the tetrad determinant. In the vacuum with
+the degenerate metric (polar phase) the nodal line is effectively a family of
+$2+1$d Dirac fermion patches, which leads to a non-analytic $(B^2-E^2)^{3/4}$
+QED action in the vicinity of the Dirac line.
+",0,1,0,0,0,0
+6764,Low frequency spectral energy distributions of radio pulsars detected with the Murchison Widefield Array,"  We present low-frequency spectral energy distributions of 60 known radio
+pulsars observed with the Murchison Widefield Array (MWA) telescope. We
+searched the GaLactic and Extragalactic All-sky MWA (GLEAM) survey images for
+200-MHz continuum radio emission at the position of all pulsars in the ATNF
+pulsar catalogue. For the 60 confirmed detections we have measured flux
+densities in 20 x 8 MHz bands between 72 and 231 MHz. We compare our results to
+existing measurements and show that the MWA flux densities are in good
+agreement.
+",0,1,0,0,0,0
+6765,Scalable Spectrum Allocation and User Association in Networks with Many Small Cells,"  A scalable framework is developed to allocate radio resources across a large
+number of densely deployed small cells with given traffic statistics on a slow
+timescale. Joint user association and spectrum allocation is first formulated
+as a convex optimization problem by dividing the spectrum among all possible
+transmission patterns of active access points (APs). To improve scalability
+with the number of APs, the problem is reformulated using local patterns of
+interfering APs. To maintain global consistency among local patterns,
+inter-cluster interaction is characterized as hyper-edges in a hyper-graph with
+nodes corresponding to neighborhoods of APs. A scalable solution is obtained by
+iteratively solving a convex optimization problem for bandwidth allocation with
+reduced complexity and constructing a global spectrum allocation using
+hyper-graph coloring. Numerical results demonstrate the proposed solution for a
+network with 100 APs and several hundred user equipments. For a given quality
+of service (QoS), the proposed scheme can increase the network capacity several
+fold compared to assigning each user to the strongest AP with full-spectrum
+reuse.
+",1,0,1,0,0,0
+6766,On Geometry and Symmetry of Kepler Systems. I,"  We study the Kepler metrics on Kepler manifolds from the point of view of
+Sasakian geometry and Hessian geometry. This establishes a link between the
+problem of classical gravity and the modern geometric methods in the study of
+AdS/CFT correspondence in string theory.
+",0,0,1,0,0,0
+6767,The collisional frequency shift of a trapped-ion optical clock,"  Collisions with background gas can perturb the transition frequency of
+trapped ions in an optical atomic clock. We develop a non-perturbative
+framework based on a quantum channel description of the scattering process, and
+use it to derive a master equation which leads to a simple analytic expression
+for the collisional frequency shift. As a demonstration of our method, we
+calculate the frequency shift of the Sr$^+$ optical atomic clock transition due
+to elastic collisions with helium.
+",0,1,0,0,0,0
+6768,Continuity properties for Born-Jordan operators with symbols in Hörmander classes and modulation spaces,"  We show that the Weyl symbol of a Born-Jordan operator is in the same class
+as the Born-Jordan symbol, when Hörmander symbols and certain types of
+modulation spaces are used as symbol classes. We use these properties to carry
+over continuity and Schatten-von Neumann properties to the Born-Jordan
+calculus.
+",0,0,1,0,0,0
+6769,IoT Data Analytics Using Deep Learning,"  Deep learning is a popular machine learning approach which has achieved a lot
+of progress in all traditional machine learning areas. Internet of thing (IoT)
+and Smart City deployments are generating large amounts of time-series sensor
+data in need of analysis. Applying deep learning to these domains has been an
+important topic of research. The Long-Short Term Memory (LSTM) network has been
+proven to be well suited for dealing with and predicting important events with
+long intervals and delays in the time series. LTSM networks have the ability to
+maintain long-term memory. In an LTSM network, a stacked LSTM hidden layer also
+makes it possible to learn a high level temporal feature without the need of
+any fine tuning and preprocessing which would be required by other techniques.
+In this paper, we construct a long-short term memory (LSTM) recurrent neural
+network structure, use the normal time series training set to build the
+prediction model. And then we use the predicted error from the prediction model
+to construct a Gaussian naive Bayes model to detect whether the original sample
+is abnormal. This method is called LSTM-Gauss-NBayes for short. We use three
+real-world data sets, each of which involve long-term time-dependence or
+short-term time-dependence, even very weak time dependence. The experimental
+results show that LSTM-Gauss-NBayes is an effective and robust model.
+",1,0,0,0,0,0
+6770,Viscous Dissipation in One-Dimensional Quantum Liquids,"  We develop a theory of viscous dissipation in one-dimensional
+single-component quantum liquids at low temperatures. Such liquids are
+characterized by a single viscosity coefficient, the bulk viscosity. We show
+that for a generic interaction between the constituent particles this viscosity
+diverges in the zero-temperature limit. In the special case of integrable
+models, the viscosity is infinite at any temperature, which can be interpreted
+as a breakdown of the hydrodynamic description. Our consideration is applicable
+to all single-component Galilean-invariant one-dimensional quantum liquids,
+regardless of the statistics of the constituent particles and the interaction
+strength.
+",0,1,0,0,0,0
+6771,On the existence of homoclinic type solutions of inhomogenous Lagrangian systems,"  We study the existence of homoclinic type solutions for second order
+Lagrangian systems of the type $\ddot{q}(t)-q(t)+a(t)\nabla G(q(t))=f(t)$,
+where $t\in\mathbb{R}$, $q\in\mathbb{R}^n$, $a\colon\mathbb{R}\to\mathbb{R}$ is
+a continuous positive bounded function, $G\colon\mathbb{R}^n\to\mathbb{R}$ is a
+$C^1$-smooth potential satisfying the Ambrosetti-Rabinowitz superquadratic
+growth condition and $f\colon\mathbb{R}\to\mathbb{R}^n$ is a continuous bounded
+square integrable forcing term. A homoclinic type solution is obtained as limit
+of $2k$-periodic solutions of an approximative sequence of second order
+differential equations.
+",0,0,1,0,0,0
+6772,Correcting Two Deletions and Insertions in Racetrack Memory,"  Racetrack memory is a non-volatile memory engineered to provide both high
+density and low latency, that is subject to synchronization or shift errors.
+This paper describes a fast coding solution, in which delimiter bits assist in
+identifying the type of shift error, and easily implementable graph-based codes
+are used to correct the error, once identified. A code that is able to detect
+and correct double shift errors is described in detail.
+",1,0,0,0,0,0
+6773,Limits of Yang-Mills α-connections,"  In the spirit of recent work of Lamm, Malchiodi and Micallef in the setting
+of harmonic maps, we identify Yang-Mills connections obtained by approximations
+with respect to the Yang-Mills {\alpha}-energy. More specifically, we show that
+for the SU(2) Hopf fibration over the four sphere, for sufficiently small
+{\alpha} values the SO(4) invariant ADHM instanton is the unique
+{\alpha}-critical point which has Yang-Mills {\alpha}-energy lower than a
+specific threshold.
+",0,0,1,0,0,0
+6774,Online Robust Principal Component Analysis with Change Point Detection,"  Robust PCA methods are typically batch algorithms which requires loading all
+observations into memory before processing. This makes them inefficient to
+process big data. In this paper, we develop an efficient online robust
+principal component methods, namely online moving window robust principal
+component analysis (OMWRPCA). Unlike existing algorithms, OMWRPCA can
+successfully track not only slowly changing subspace but also abruptly changed
+subspace. By embedding hypothesis testing into the algorithm, OMWRPCA can
+detect change points of the underlying subspaces. Extensive simulation studies
+demonstrate the superior performance of OMWRPCA compared with other
+state-of-art approaches. We also apply the algorithm for real-time background
+subtraction of surveillance video.
+",1,0,0,1,0,0
+6775,Reactive User Behavior and Mobility Models,"  In this paper, we present a set of simulation models to more realistically
+mimic the behaviour of users reading messages. We propose a User Behaviour
+Model, where a simulated user reacts to a message by a flexible set of possible
+reactions (e.g. ignore, read, like, save, etc.) and a mobility-based reaction
+(visit a place, run away from danger, etc.). We describe our models and their
+implementation in OMNeT++. We strongly believe that these models will
+significantly contribute to the state of the art of simulating realistically
+opportunistic networks.
+",1,0,0,0,0,0
+6776,How production networks amplify economic growth,"  Technological improvement is the most important cause of long-term economic
+growth, but the factors that drive it are still not fully understood. In
+standard growth models technology is treated in the aggregate, and a main goal
+has been to understand how growth depends on factors such as knowledge
+production. But an economy can also be viewed as a network, in which producers
+purchase goods, convert them to new goods, and sell them to households or other
+producers. Here we develop a simple theory that shows how the network
+properties of an economy can amplify the effects of technological improvements
+as they propagate along chains of production. A key property of an industry is
+its output multiplier, which can be understood as the average number of
+production steps required to make a good. The model predicts that the output
+multiplier of an industry predicts future changes in prices, and that the
+average output multiplier of a country predicts future economic growth. We test
+these predictions using data from the World Input Output Database and find
+results in good agreement with the model. The results show how purely
+structural properties of an economy, that have nothing to do with innovation or
+human creativity, can exert an important influence on long-term growth.
+",0,0,0,0,0,1
+6777,The Minimal Resolution Conjecture on a general quartic surface in $\mathbb P^3$,"  Mustaţă has given a conjecture for the graded Betti numbers in the
+minimal free resolution of the ideal of a general set of points on an
+irreducible projective algebraic variety. For surfaces in $\mathbb P^3$ this
+conjecture has been proven for points on quadric surfaces and on general cubic
+surfaces. In the latter case, Gorenstein liaison was the main tool. Here we
+prove the conjecture for general quartic surfaces. Gorenstein liaison continues
+to be a central tool, but to prove the existence of our links we make use of
+certain dimension computations. We also discuss the higher degree case, but now
+the dimension count does not force the existence of our links.
+",0,0,1,0,0,0
+6778,Exact density functional obtained via the Levy constrained search,"  A stochastic minimization method for a real-space wavefunction, $\Psi({\bf
+r}_{1},{\bf r}_{2}\ldots{\bf r}_{n})$, constrained to a chosen density,
+$\rho({\bf r})$, is developed. It enables the explicit calculation of the Levy
+constrained search
+$F[\rho]=\min_{\Psi\rightarrow\rho}\langle\Psi|\hat{T}+\hat{V}_{ee}|\Psi\rangle$
+(Proc. Natl. Acad. Sci. 76 6062 (1979)), that gives the exact functional of
+density functional theory. This general method is illustrated in the evaluation
+of $F[\rho]$ for two-electron densities in one dimension with a soft-Coulomb
+interaction. Additionally, procedures are given to determine the first and
+second functional derivatives, $\frac{\delta F}{\delta\rho({\bf r})}$ and
+$\frac{\delta^{2}F}{\delta\rho({\bf r})\delta\rho({\bf r}')}$. For a chosen
+external potential, $v({\bf r})$, the functional and its derivatives are used
+in minimizations only over densities to give the exact energy, $E_{v}$ without
+needing to solve the Schrödinger equation.
+",0,1,0,0,0,0
+6779,Compile-Time Symbolic Differentiation Using C++ Expression Templates,"  Template metaprogramming is a popular technique for implementing compile time
+mechanisms for numerical computing. We demonstrate how expression templates can
+be used for compile time symbolic differentiation of algebraic expressions in
+C++ computer programs. Given a positive integer $N$ and an algebraic function
+of multiple variables, the compiler generates executable code for the $N$th
+partial derivatives of the function. Compile-time simplification of the
+derivative expressions is achieved using recursive templates. A detailed
+analysis indicates that current C++ compiler technology is already sufficient
+for practical use of our results, and highlights a number of issues where
+further improvements may be desirable.
+",1,0,0,0,0,0
+6780,On Multilingual Training of Neural Dependency Parsers,"  We show that a recently proposed neural dependency parser can be improved by
+joint training on multiple languages from the same family. The parser is
+implemented as a deep neural network whose only input is orthographic
+representations of words. In order to successfully parse, the network has to
+discover how linguistically relevant concepts can be inferred from word
+spellings. We analyze the representations of characters and words that are
+learned by the network to establish which properties of languages were
+accounted for. In particular we show that the parser has approximately learned
+to associate Latin characters with their Cyrillic counterparts and that it can
+group Polish and Russian words that have a similar grammatical function.
+Finally, we evaluate the parser on selected languages from the Universal
+Dependencies dataset and show that it is competitive with other recently
+proposed state-of-the art methods, while having a simple structure.
+",1,0,0,0,0,0
+6781,Autocommuting probability of a finite group,"  Let $G$ be a finite group and $\Aut(G)$ the automorphism group of $G$. The
+autocommuting probability of $G$, denoted by $\Pr(G, \Aut(G))$, is the
+probability that a randomly chosen automorphism of $G$ fixes a randomly chosen
+element of $G$. In this paper, we study $\Pr(G, \Aut(G))$ through a
+generalization. We obtain a computing formula, several bounds and
+characterizations of $G$ through $\Pr(G, \Aut(G))$. We conclude the paper by
+showing that the generalized autocommuting probability of $G$ remains unchanged
+under autoisoclinism.
+",0,0,1,0,0,0
+6782,Inside-Out Planet Formation. IV. Pebble Evolution and Planet Formation Timescales,"  Systems with tightly-packed inner planets (STIPs) are very common. Chatterjee
+& Tan proposed Inside-Out Planet Formation (IOPF), an in situ formation theory,
+to explain these planets. IOPF involves sequential planet formation from
+pebble-rich rings that are fed from the outer disk and trapped at the pressure
+maximum associated with the dead zone inner boundary (DZIB). Planet masses are
+set by their ability to open a gap and cause the DZIB to retreat outwards. We
+present models for the disk density and temperature structures that are
+relevant to the conditions of IOPF. For a wide range of DZIB conditions, we
+evaluate the gap opening masses of planets in these disks that are expected to
+lead to truncation of pebble accretion onto the forming planet. We then
+consider the evolution of dust and pebbles in the disk, estimating that pebbles
+typically grow to sizes of a few cm during their radial drift from several tens
+of AU to the inner, $\lesssim1\:$AU-scale disk. A large fraction of the
+accretion flux of solids is expected to be in such pebbles. This allows us to
+estimate the timescales for individual planet formation and entire planetary
+system formation in the IOPF scenario. We find that to produce realistic STIPs
+within reasonable timescales similar to disk lifetimes requires disk accretion
+rates of $\sim10^{-9}\:M_\odot\:{\rm yr}^{-1}$ and relatively low viscosity
+conditions in the DZIB region, i.e., Shakura-Sunyaev parameter of
+$\alpha\sim10^{-4}$.
+",0,1,0,0,0,0
+6783,SilhoNet: An RGB Method for 3D Object Pose Estimation and Grasp Planning,"  Autonomous robot manipulation often involves both estimating the pose of the
+object to be manipulated and selecting a viable grasp point. Methods using
+RGB-D data have shown great success in solving these problems. However, there
+are situations where cost constraints or the working environment may limit the
+use of RGB-D sensors. When limited to monocular camera data only, both the
+problem of object pose estimation and of grasp point selection are very
+challenging. In the past, research has focused on solving these problems
+separately. In this work, we introduce a novel method called SilhoNet that
+bridges the gap between these two tasks. We use a Convolutional Neural Network
+(CNN) pipeline that takes in ROI proposals to simultaneously predict an
+intermediate silhouette representation for objects with an associated occlusion
+mask. The 3D pose is then regressed from the predicted silhouettes. Grasp
+points from a precomputed database are filtered by back-projecting them onto
+the occlusion mask to find which points are visible in the scene. We show that
+our method achieves better overall performance than the state-of-the art
+PoseCNN network for 3D pose estimation on the YCB-video dataset.
+",1,0,0,0,0,0
+6784,Collapsed Tetragonal Phase Transition in LaRu$_2$P$_2$,"  The structural properties of LaRu$_2$P$_2$ under external pressure have been
+studied up to 14 GPa, employing high-energy x-ray diffraction in a
+diamond-anvil pressure cell. At ambient conditions, LaRu$_2$P$_2$ (I4/mmm) has
+a tetragonal structure with a bulk modulus of $B=105(2)$ GPa and exhibits
+superconductivity at $T_c= 4.1$ K. With the application of pressure,
+LaRu$_2$P$_2$ undergoes a phase transition to a collapsed tetragonal (cT) state
+with a bulk modulus of $B=175(5)$ GPa. At the transition, the c-lattice
+parameter exhibits a sharp decrease with a concurrent increase of the a-lattice
+parameter. The cT phase transition in LaRu$_2$P$_2$ is consistent with a second
+order transition, and was found to be temperature dependent, increasing from
+$P=3.9(3)$ GPa at 160 K to $P=4.6(3)$ GPa at 300 K. In total, our data are
+consistent with the cT transition being near, but slightly above 2 GPa at 5 K.
+Finally, we compare the effect of physical and chemical pressure in the
+RRu$_2$P$_2$ (R = Y, La-Er, Yb) isostructural series of compounds and find them
+to be analogous.
+",0,1,0,0,0,0
+6785,Bayesian Nonparametric Unmixing of Hyperspectral Images,"  Hyperspectral imaging is an important tool in remote sensing, allowing for
+accurate analysis of vast areas. Due to a low spatial resolution, a pixel of a
+hyperspectral image rarely represents a single material, but rather a mixture
+of different spectra. HSU aims at estimating the pure spectra present in the
+scene of interest, referred to as endmembers, and their fractions in each
+pixel, referred to as abundances. Today, many HSU algorithms have been
+proposed, based either on a geometrical or statistical model. While most
+methods assume that the number of endmembers present in the scene is known,
+there is only little work about estimating this number from the observed data.
+In this work, we propose a Bayesian nonparametric framework that jointly
+estimates the number of endmembers, the endmembers itself, and their
+abundances, by making use of the Indian Buffet Process as a prior for the
+endmembers. Simulation results and experiments on real data demonstrate the
+effectiveness of the proposed algorithm, yielding results comparable with
+state-of-the-art methods while being able to reliably infer the number of
+endmembers. In scenarios with strong noise, where other algorithms provide only
+poor results, the proposed approach tends to overestimate the number of
+endmembers slightly. The additional endmembers, however, often simply represent
+noisy replicas of present endmembers and could easily be merged in a
+post-processing step.
+",1,0,0,0,0,0
+6786,Dynamical control of electron-phonon interactions with high-frequency light,"  This work addresses the one-dimensional problem of Bloch electrons when they
+are rapidly driven by a homogeneous time-periodic light and linearly coupled to
+vibrational modes. Starting from a generic time-periodic electron-phonon
+Hamiltonian, we derive a time-independent effective Hamiltonian that describes
+the stroboscopic dynamics up to the third order in the high-frequency limit.
+This yields nonequilibrium corrections to the electron-phonon coupling that are
+controllable dynamically via the driving strength. This shows in particular
+that local Holstein interactions in equilibrium are corrected by nonlocal
+Peierls interactions out of equilibrium, as well as by phonon-assisted hopping
+processes that make the dynamical Wannier-Stark localization of Bloch electrons
+impossible. Subsequently, we revisit the Holstein polaron problem out of
+equilibrium in terms of effective Green functions, and specify explicitly how
+the binding energy and effective mass of the polaron can be controlled
+dynamically. These tunable properties are reported within the weak- and
+strong-coupling regimes since both can be visited within the same material when
+varying the driving strength. This work provides some insight into controllable
+microscopic mechanisms that may be involved during the multicycle laser
+irradiations of organic molecular crystals in ultrafast pump-probe experiments,
+although it should also be suitable for realizations in shaken optical lattices
+of ultracold atoms.
+",0,1,0,0,0,0
+6787,Controlling the thermoelectric effect by mechanical manipulation of the electron's quantum phase in atomic junctions,"  The thermoelectric voltage developed across an atomic metal junction (i.e., a
+nanostructure in which one or a few atoms connect two metal electrodes) in
+response to a temperature difference between the electrodes, results from the
+quantum interference of electrons that pass through the junction multiple times
+after being scattered by the surrounding defects. Here we report successfully
+tuning this quantum interference and thus controlling the magnitude and sign of
+the thermoelectric voltage by applying a mechanical force that deforms the
+junction. The observed switching of the thermoelectric voltage is reversible
+and can be cycled many times. Our ab initio and semi-empirical calculations
+elucidate the detailed mechanism by which the quantum interference is tuned. We
+show that the applied strain alters the quantum phases of electrons passing
+through the narrowest part of the junction and hence modifies the electronic
+quantum interference in the device. Tuning the quantum interference causes the
+energies of electronic transport resonances to shift, which affects the
+thermoelectric voltage. These experimental and theoretical studies reveal that
+Au atomic junctions can be made to exhibit both positive and negative
+thermoelectric voltages on demand, and demonstrate the importance and
+tunability of the quantum interference effect in the atomic-scale metal
+nanostructures.
+",0,1,0,0,0,0
+6788,The time geography of segregation during working hours,"  Understanding segregation is essential to develop planning tools for building
+more inclusive cities. Theoretically, segregation at the work place has been
+described as lower compared to residential segregation given the importance of
+skill complementarity among other productive factors shaping the economies of
+cities. This paper tackles segregation during working hours from a dynamical
+perspective by focusing on the movement of urbanites across the city. In
+contrast to measuring residential patterns of segregation, we used mobile phone
+data to infer home-work trajectory net- works and apply a community detection
+algorithm to the example city of Santiago, Chile. We then describe
+qualitatively and quantitatively outlined communities, in terms of their socio
+eco- nomic composition. We then evaluate segregation for each of these
+communities as the probability that a person from a specific community will
+interact with a co-worker from the same commu- nity. Finally, we compare these
+results with simulations where a new work location is set for each real user,
+following the empirical probability distributions of home-work distances and
+angles of direction for each community. Methodologically, this study shows that
+segregation during working hours for Santiago is unexpectedly high for most of
+the city with the exception of its central and business district. In fact, the
+only community that is not statistically segregated corresponds to the downtown
+area of Santiago, described as a zone of encounter and integration across the
+city.
+",1,0,0,1,0,0
+6789,Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks,"  Protein gamma-turn prediction is useful in protein function studies and
+experimental design. Several methods for gamma-turn prediction have been
+developed, but the results were unsatisfactory with Matthew correlation
+coefficients (MCC) around 0.2-0.4. One reason for the low prediction accuracy
+is the limited capacity of the methods; in particular, the traditional
+machine-learning methods like SVM may not extract high-level features well to
+distinguish between turn or non-turn. Hence, it is worthwhile exploring new
+machine-learning methods for the prediction. A cutting-edge deep neural
+network, named Capsule Network (CapsuleNet), provides a new opportunity for
+gamma-turn prediction. Even when the number of input samples is relatively
+small, the capsules from CapsuleNet are very effective to extract high-level
+features for classification tasks. Here, we propose a deep inception capsule
+network for gamma-turn prediction. Its performance on the gamma-turn benchmark
+GT320 achieved an MCC of 0.45, which significantly outperformed the previous
+best method with an MCC of 0.38. This is the first gamma-turn prediction method
+utilizing deep neural networks. Also, to our knowledge, it is the first
+published bioinformatics application utilizing capsule network, which will
+provides a useful example for the community.
+",0,0,0,0,1,0
+6790,Image transformations on locally compact spaces,"  An image is here defined to be a set which is either open or closed and an
+image transformation is structure preserving in the following sense: It
+corresponds to an algebra homomorphism for each singly generated algebra. The
+results extend parts of results of J.F. Aarnes on quasi-measures, -states,
+-homomorphisms, and image-transformations from the setting compact Hausdorff
+spaces to locally compact Hausdorff spaces.
+",0,0,1,1,0,0
+6791,From Half-metal to Semiconductor: Electron-correlation Effects in Zigzag SiC Nanoribbons From First Principles,"  We performed electronic structure calculations based on the first-principles
+many-body theory approach in order to study quasiparticle band gaps, and
+optical absorption spectra of hydrogen-passivated zigzag SiC nanoribbons.
+Self-energy corrections are included using the GW approximation, and excitonic
+effects are included using the Bethe-Salpeter equation. We have systematically
+studied nanoribbons that have widths between 0.6 $\text{nm}$ and 2.2
+$\text{nm}$. Quasiparticle corrections widened the Kohn-Sham band gaps because
+of enhanced interaction effects, caused by reduced dimensionality. Zigzag SiC
+nanoribbons with widths larger than 1 nm, exhibit half-metallicity at the
+mean-field level. The self-energy corrections increased band gaps
+substantially, thereby transforming the half-metallic zigzag SiC nanoribbons,
+to narrow gap spin-polarized semiconductors. Optical absorption spectra of
+these nanoribbons get dramatically modified upon inclusion of electron-hole
+interactions, and the narrowest ribbon exhibits strongly bound excitons, with
+binding energy of 2.1 eV. Thus, the narrowest zigzag SiC nanoribbon has the
+potential to be used in optoelectronic devices operating in the IR region of
+the spectrum, while the broader ones, exhibiting spin polarization, can be
+utilized in spintronic applications.
+",0,1,0,0,0,0
+6792,Non-exponential decoherence of radio-frequency resonance rotation of spin in storage rings,"  Precision experiments, such as the search for electric dipole moments of
+charged particles using radiofrequency spin rotators in storage rings, demand
+for maintaining the exact spin resonance condition for several thousand
+seconds. Synchrotron oscillations in the stored beam modulate the spin tune of
+off-central particles, moving it off the perfect resonance condition set for
+central particles on the reference orbit. Here we report an analytic
+description of how synchrotron oscillations lead to non-exponential decoherence
+of the radiofrequency resonance driven up-down spin rotations. This
+non-exponential decoherence is shown to be accompanied by a nontrivial walk of
+the spin phase. We also comment on sensitivity of the decoherence rate to the
+harmonics of the radiofreqency spin rotator and a possibility to check
+predictions of decoherence-free magic energies.
+",0,1,0,0,0,0
+6793,On the fundamental group of semi-Riemannian manifolds with positive curvature operator,"  This paper presents an investigation of the relation between some positivity
+of the curvature and the finiteness of fundamental groups in semi-Riemannian
+geometry. We consider semi-Riemannian submersions $\pi : (E, g) \rightarrow (B,
+-g_{B}) $ under the condition with $(B, g_{B})$ Riemannian, the fiber closed
+Riemannian, and the horizontal distribution integrable. Then we prove that, if
+the lightlike geodesically complete or timelike geodesically complete
+semi-Riemannian manifold $E$ has some positivity of curvature, then the
+fundamental group of the fiber is finite. Moreover we construct an example of
+semi-Riemannian submersions with some positivity of curvature, non-integrable
+horizontal distribution, and the finiteness of the fundamental group of the
+fiber.
+",0,0,1,0,0,0
+6794,Flow-Sensitive Composition of Thread-Modular Abstract Interpretation,"  We propose a constraint-based flow-sensitive static analysis for concurrent
+programs by iteratively composing thread-modular abstract interpreters via the
+use of a system of lightweight constraints. Our method is compositional in that
+it first applies sequential abstract interpreters to individual threads and
+then composes their results. It is flow-sensitive in that the causality
+ordering of interferences (flow of data from global writes to reads) is modeled
+by a system of constraints. These interference constraints are lightweight
+since they only refer to the execution order of program statements as opposed
+to their numerical properties: they can be decided efficiently using an
+off-the-shelf Datalog engine. Our new method has the advantage of being more
+accurate than existing, flow-insensitive, static analyzers while remaining
+scalable and providing the expected soundness and termination guarantees even
+for programs with unbounded data. We implemented our method and evaluated it on
+a large number of benchmarks, demonstrating its effectiveness at increasing the
+accuracy of thread-modular abstract interpretation.
+",1,0,0,0,0,0
+6795,Momentum Control of Humanoid Robots with Series Elastic Actuators,"  Humanoid robots may require a degree of compliance at the joint level for
+improving efficiency, shock tolerance, and safe interaction with humans. The
+presence of joint elasticity, however, complexifies the design of balancing and
+walking controllers. This paper proposes a control framework for extending
+momentum based controllers developed for stiff actuators to the case of series
+elastic actuators. The key point is to consider the motor velocities as an
+intermediate control input, and then apply high-gain control to stabilise the
+desired motor velocities achieving momentum control. Simulations carried out on
+a model of the robot iCub verify the soundness of the proposed approach.
+",1,0,1,0,0,0
+6796,SRN: Side-output Residual Network for Object Symmetry Detection in the Wild,"  In this paper, we establish a baseline for object symmetry detection in
+complex backgrounds by presenting a new benchmark and an end-to-end deep
+learning approach, opening up a promising direction for symmetry detection in
+the wild. The new benchmark, named Sym-PASCAL, spans challenges including
+object diversity, multi-objects, part-invisibility, and various complex
+backgrounds that are far beyond those in existing datasets. The proposed
+symmetry detection approach, named Side-output Residual Network (SRN),
+leverages output Residual Units (RUs) to fit the errors between the object
+symmetry groundtruth and the outputs of RUs. By stacking RUs in a
+deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales
+to ease the problems of fitting complex outputs with limited layers,
+suppressing the complex backgrounds, and effectively matching object symmetry
+of different scales. Experimental results validate both the benchmark and its
+challenging aspects related to realworld images, and the state-of-the-art
+performance of our symmetry detection approach. The benchmark and the code for
+SRN are publicly available at this https URL.
+",1,0,0,0,0,0
+6797,Time-Optimal Trajectories of Generic Control-Affine Systems Have at Worst Iterated Fuller Singularities,"  We consider in this paper the regularity problem for time-optimal
+trajectories of a single-input control-affine system on a n-dimensional
+manifold. We prove that, under generic conditions on the drift and the
+controlled vector field, any control u associated with an optimal trajectory is
+smooth out of a countable set of times. More precisely, there exists an integer
+K, only depending on the dimension n, such that the non-smoothness set of u is
+made of isolated points, accumulations of isolated points, and so on up to K-th
+order iterated accumulations.
+",0,0,1,0,0,0
+6798,Wikipedia for Smart Machines and Double Deep Machine Learning,"  Very important breakthroughs in data centric deep learning algorithms led to
+impressive performance in transactional point applications of Artificial
+Intelligence (AI) such as Face Recognition, or EKG classification. With all due
+appreciation, however, knowledge blind data only machine learning algorithms
+have severe limitations for non-transactional AI applications, such as medical
+diagnosis beyond the EKG results. Such applications require deeper and broader
+knowledge in their problem solving capabilities, e.g. integrating anatomy and
+physiology knowledge with EKG results and other patient findings. Following a
+review and illustrations of such limitations for several real life AI
+applications, we point at ways to overcome them. The proposed Wikipedia for
+Smart Machines initiative aims at building repositories of software structures
+that represent humanity science & technology knowledge in various parts of
+life; knowledge that we all learn in schools, universities and during our
+professional life. Target readers for these repositories are smart machines;
+not human. AI software developers will have these Reusable Knowledge structures
+readily available, hence, the proposed name ReKopedia. Big Data is by now a
+mature technology, it is time to focus on Big Knowledge. Some will be derived
+from data, some will be obtained from mankind gigantic repository of knowledge.
+Wikipedia for smart machines along with the new Double Deep Learning approach
+offer a paradigm for integrating datacentric deep learning algorithms with
+algorithms that leverage deep knowledge, e.g. evidential reasoning and
+causality reasoning. For illustration, a project is described to produce
+ReKopedia knowledge modules for medical diagnosis of about 1,000 disorders.
+Data is important, but knowledge deep, basic, and commonsense is equally
+important.
+",1,0,0,0,0,0
+6799,A binary main belt comet,"  The asteroids are primitive solar system bodies which evolve both
+collisionally and through disruptions due to rapid rotation [1]. These
+processes can lead to the formation of binary asteroids [2-4] and to the
+release of dust [5], both directly and, in some cases, through uncovering
+frozen volatiles. In a sub-set of the asteroids called main-belt comets (MBCs),
+the sublimation of excavated volatiles causes transient comet-like activity
+[6-8]. Torques exerted by sublimation measurably influence the spin rates of
+active comets [9] and might lead to the splitting of bilobate comet nuclei
+[10]. The kilometer-sized main-belt asteroid 288P (300163) showed activity for
+several months around its perihelion 2011 [11], suspected to be sustained by
+the sublimation of water ice [12] and supported by rapid rotation [13], while
+at least one component rotates slowly with a period of 16 hours [14]. 288P is
+part of a young family of at least 11 asteroids that formed from a ~10km
+diameter precursor during a shattering collision 7.5 million years ago [15].
+Here we report that 288P is a binary main-belt comet. It is different from the
+known asteroid binaries for its combination of wide separation, near-equal
+component size, high eccentricity, and comet-like activity. The observations
+also provide strong support for sublimation as the driver of activity in 288P
+and show that sublimation torques may play a significant role in binary orbit
+evolution.
+",0,1,0,0,0,0
+6800,Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models,"  Bayesian nonparametrics are a class of probabilistic models in which the
+model size is inferred from data. A recently developed methodology in this
+field is small-variance asymptotic analysis, a mathematical technique for
+deriving learning algorithms that capture much of the flexibility of Bayesian
+nonparametric inference algorithms, but are simpler to implement and less
+computationally expensive. Past work on small-variance analysis of Bayesian
+nonparametric inference algorithms has exclusively considered batch models
+trained on a single, static dataset, which are incapable of capturing time
+evolution in the latent structure of the data. This work presents a
+small-variance analysis of the maximum a posteriori filtering problem for a
+temporally varying mixture model with a Markov dependence structure, which
+captures temporally evolving clusters within a dataset. Two clustering
+algorithms result from the analysis: D-Means, an iterative clustering algorithm
+for linearly separable, spherical clusters; and SD-Means, a spectral clustering
+algorithm derived from a kernelized, relaxed version of the clustering problem.
+Empirical results from experiments demonstrate the advantages of using D-Means
+and SD-Means over contemporary clustering algorithms, in terms of both
+computational cost and clustering accuracy.
+",0,0,0,1,0,0
+6801,Deep Depth From Focus,"  Depth from focus (DFF) is one of the classical ill-posed inverse problems in
+computer vision. Most approaches recover the depth at each pixel based on the
+focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
+reliably estimate the sharpness level, particularly in low-textured areas. In
+this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end
+learning approach to this problem. One of the main challenges we face is the
+hunger for data of deep neural networks. In order to obtain a significant
+amount of focal stacks with corresponding groundtruth depth, we propose to
+leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us
+to digitally create focal stacks of varying sizes. Compared to existing
+benchmarks our dataset is 25 times larger, enabling the use of machine learning
+for this inverse problem. We compare our results with state-of-the-art DFF
+methods and we also analyze the effect of several key deep architectural
+components. These experiments show that our proposed method `DDFFNet' achieves
+state-of-the-art performance in all scenes, reducing depth error by more than
+75% compared to the classical DFF methods.
+",1,0,0,0,0,0
+6802,Deep Text Classification Can be Fooled,"  In this paper, we present an effective method to craft text adversarial
+samples, revealing one important yet underestimated fact that DNN-based text
+classifiers are also prone to adversarial sample attack. Specifically,
+confronted with different adversarial scenarios, the text items that are
+important for classification are identified by computing the cost gradients of
+the input (white-box attack) or generating a series of occluded test samples
+(black-box attack). Based on these items, we design three perturbation
+strategies, namely insertion, modification, and removal, to generate
+adversarial samples. The experiment results show that the adversarial samples
+generated by our method can successfully fool both state-of-the-art
+character-level and word-level DNN-based text classifiers. The adversarial
+samples can be perturbed to any desirable classes without compromising their
+utilities. At the same time, the introduced perturbation is difficult to be
+perceived.
+",1,0,0,0,0,0
+6803,Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition,"  It remains a challenge to efficiently extract spatialtemporal information
+from skeleton sequences for 3D human action recognition. Although most recent
+action recognition methods are based on Recurrent Neural Networks which present
+outstanding performance, one of the shortcomings of these methods is the
+tendency to overemphasize the temporal information. Since 3D convolutional
+neural network(3D CNN) is a powerful tool to simultaneously learn features from
+both spatial and temporal dimensions through capturing the correlations between
+three dimensional signals, this paper proposes a novel two-stream model using
+3D CNN. To our best knowledge, this is the first application of 3D CNN in
+skeleton-based action recognition. Our method consists of three stages. First,
+skeleton joints are mapped into a 3D coordinate space and then encoding the
+spatial and temporal information, respectively. Second, 3D CNN models are
+seperately adopted to extract deep features from two streams. Third, to enhance
+the ability of deep features to capture global relationships, we extend every
+stream into multitemporal version. Extensive experiments on the SmartHome
+dataset and the large-scale NTU RGB-D dataset demonstrate that our method
+outperforms most of RNN-based methods, which verify the complementary property
+between spatial and temporal information and the robustness to noise.
+",1,0,0,0,0,0
+6804,Strong Bayesian Evidence for the Normal Neutrino Hierarchy,"  The configuration of the three neutrino masses can take two forms, known as
+the normal and inverted hierarchies. We compute the Bayesian evidence
+associated with these two hierarchies. Previous studies found a mild preference
+for the normal hierarchy, and this was driven by the asymmetric manner in which
+cosmological data has confined the available parameter space. Here we identify
+the presence of a second asymmetry, which is imposed by data from neutrino
+oscillations. By combining constraints on the squared-mass splittings with the
+limit on the sum of neutrino masses of $\Sigma m_\nu < 0.13$ eV, and using a
+minimally informative prior on the masses, we infer odds of 42:1 in favour of
+the normal hierarchy, which is classified as ""strong"" in the Jeffreys' scale.
+We explore how these odds may evolve in light of higher precision cosmological
+data, and discuss the implications of this finding with regards to the nature
+of neutrinos. Finally the individual masses are inferred to be $m_1 =
+3.80^{+26.2}_{-3.73} \, \text{meV}, m_2 = 8.8^{+18}_{-1.2} \, \text{meV}, m_3 =
+50.4^{+5.8}_{-1.2} \, \text{meV}$ ($95\%$ credible intervals).
+",0,1,0,0,0,0
+6805,A fast reconstruction algorithm for geometric inverse problems using topological sensitivity analysis and Dirichlet-Neumann cost functional approach,"  This paper is concerned with the detection of objects immersed in anisotropic
+media from boundary measurements. We propose an accurate approach based on the
+Kohn-Vogelius formulation and the topological sensitivity analysis method. The
+inverse problem is formulated as a topology optimization one minimizing an
+energy like functional. A topological asymptotic expansion is derived for the
+anisotropic Laplace operator. The unknown object is reconstructed using a
+level-set curve of the topological gradient. The efficiency and accuracy of the
+proposed algorithm are illustrated by some numerical results. MOTS-CLÉS :
+Problème inverse géométrique, Laplace anisotrope, formulation de
+Kohn-Vogelius, analyse de sensibilité, optimisation topologique.
+",0,0,1,0,0,0
+6806,Tomographic X-ray data of carved cheese,"  This is the documentation of the tomographic X-ray data of a carved cheese
+slice. Data are available at www.fips.fi/dataset.php, and can be freely used
+for scientific purposes with appropriate references to them, and to this
+document in this http URL. The data set consists of (1) the X-ray sinogram
+of a single 2D slice of the cheese slice with three different resolutions and
+(2) the corresponding measurement matrices modeling the linear operation of the
+X-ray transform. Each of these sinograms was obtained from a measured
+360-projection fan-beam sinogram by down-sampling and taking logarithms. The
+original (measured) sinogram is also provided in its original form and
+resolution.
+",0,1,0,0,0,0
+6807,SMARTies: Sentiment Models for Arabic Target Entities,"  We consider entity-level sentiment analysis in Arabic, a morphologically rich
+language with increasing resources. We present a system that is applied to
+complex posts written in response to Arabic newspaper articles. Our goal is to
+identify important entity ""targets"" within the post along with the polarity
+expressed about each target. We achieve significant improvements over multiple
+baselines, demonstrating that the use of specific morphological representations
+improves the performance of identifying both important targets and their
+sentiment, and that the use of distributional semantic clusters further boosts
+performances for these representations, especially when richer linguistic
+resources are not available.
+",1,0,0,0,0,0
+6808,Quantile Markov Decision Process,"  In this paper, we consider the problem of optimizing the quantiles of the
+cumulative rewards of Markov Decision Processes (MDP), to which we refers as
+Quantile Markov Decision Processes (QMDP). Traditionally, the goal of a Markov
+Decision Process (MDP) is to maximize expected cumulative reward over a defined
+horizon (possibly to be infinite). In many applications, however, a decision
+maker may be interested in optimizing a specific quantile of the cumulative
+reward instead of its expectation. Our framework of QMDP provides analytical
+results characterizing the optimal QMDP solution and presents the algorithm for
+solving the QMDP. We provide analytical results characterizing the optimal QMDP
+solution and present the algorithms for solving the QMDP. We illustrate the
+model with two experiments: a grid game and a HIV optimal treatment experiment.
+",1,0,0,0,0,0
+6809,Explaining the elongated shape of 'Oumuamua by the Eikonal abrasion model,"  The photometry of the minor body with extrasolar origin (1I/2017 U1)
+'Oumuamua revealed an unprecedented shape: Meech et al. (2017) reported a shape
+elongation b/a close to 1/10, which calls for theoretical explanation. Here we
+show that the abrasion of a primordial asteroid by a huge number of tiny
+particles ultimately leads to such elongated shape. The model (called the
+Eikonal equation) predicting this outcome was already suggested in Domokos et
+al. (2009) to play an important role in the evolution of asteroid shapes.
+",0,1,0,0,0,0
+6810,Generation of $1/f$ noise motivated by a model for musical melodies,"  We present a model to generate power spectrum noise with intensity
+proportional to 1/f as a function of frequency f. The model arises from a
+broken-symmetry variable which corresponds to absolute pitch, where
+fluctuations occur in an attempt to restore that symmetry, influenced by
+interactions in the creation of musical melodies.
+",0,1,0,0,0,0
+6811,On the lattice of the $σ$-permutable subgroups of a finite group,"  Let $\sigma =\{\sigma_{i} | i\in I\}$ be some partition of the set of all
+primes $\Bbb{P}$, $G$ a finite group and $\sigma (G) =\{\sigma_{i}
+|\sigma_{i}\cap \pi (G)\ne \emptyset \}$. A set ${\cal H}$ of subgroups of $G$
+is said to be a complete Hall $\sigma $-set of $G$ if every member $\ne 1$ of
+${\cal H}$ is a Hall $\sigma_{i}$-subgroup of $G$ for some $\sigma_{i}\in
+\sigma $ and ${\cal H}$ contains exactly one Hall $\sigma_{i}$-subgroup of $G$
+for every $\sigma_{i}\in \sigma (G)$. A subgroup $A$ of $G$ is said to be
+${\sigma}$-permutable in $G$ if $G$ possesses a complete Hall $\sigma $-set and
+$A$ permutes with each Hall $\sigma_{i}$-subgroup $H$ of $G$, that is, $AH=HA$
+for all $i \in I$. We characterize finite groups with distributive lattice of
+the ${\sigma}$-permutable subgroups.
+",0,0,1,0,0,0
+6812,The geometry of the generalized algebraic Riccati equation and of the singular Hamiltonian system,"  This paper analyzes the properties of the solutions of the generalized
+continuous algebraic Riccati equation from a geometric perspective. This
+analysis reveals the presence of a subspace that may provide an appropriate
+degree of freedom to stabilize the system in the related optimal control
+problem even in cases where the Riccati equation does not admit a stabilizing
+solution.
+",0,0,1,0,0,0
+6813,High Order Numerical Integrators for Relativistic Charged Particle Tracking,"  In this paper, we extend several time reversible numerical integrators to
+solve the Lorentz force equations from second order accuracy to higher order
+accuracy for relativistic charged particle tracking in electromagnetic fields.
+A fourth order algorithm is given explicitly and tested with numerical
+examples. Such high order numerical integrators can significantly save the
+computational cost by using a larger step size in comparison to the second
+order integrators.
+",0,1,0,0,0,0
+6814,Astrophotonics: molding the flow of light in astronomical instruments,"  Since its emergence two decades ago, astrophotonics has found broad
+application in scientific instruments at many institutions worldwide. The case
+for astrophotonics becomes more compelling as telescopes push for AO-assisted,
+diffraction-limited performance, a mode of observing that is central to the
+next-generation of extremely large telescopes (ELTs). Even AO systems are
+beginning to incorporate advanced photonic principles as the community pushes
+for higher performance and more complex guide-star configurations. Photonic
+instruments like Gravity on the Very Large Telescope achieve milliarcsec
+resolution at 2000 nm which would be very difficult to achieve with
+conventional optics. While space photonics is not reviewed here, we foresee
+that remote sensing platforms will become a major beneficiary of astrophotonic
+components in the years ahead. The field has given back with the development of
+new technologies (e.g. photonic lantern, large area multi-core fibres) already
+finding widespread use in other fields; Google Scholar lists more than 400
+research papers making reference to this technology. This short review covers
+representative key developments since the 2009 Focus issue on Astrophotonics.
+",0,1,0,0,0,0
+6815,Towards quantitative methods to assess network generative models,"  Assessing generative models is not an easy task. Generative models should
+synthesize graphs which are not replicates of real networks but show
+topological features similar to real graphs. We introduce an approach for
+assessing graph generative models using graph classifiers. The inability of an
+established graph classifier for distinguishing real and synthesized graphs
+could be considered as a performance measurement for graph generators.
+",1,0,0,0,0,0
+6816,Soliton groups as the reason for extreme statistics of unidirectional sea waves,"  The results of the probabilistic analysis of the direct numerical simulations
+of irregular unidirectional deep-water waves are discussed. It is shown that an
+occurrence of large-amplitude soliton-like groups represents an extraordinary
+case, which is able to increase noticeably the probability of high waves even
+in moderately rough sea conditions. The ensemble of wave realizations should be
+large enough to take these rare events into account. Hence we provide a
+striking example when long-living coherent structures make the water wave
+statistics extreme.
+",0,1,0,0,0,0
+6817,Stack Overflow: A Code Laundering Platform?,"  Developers use Question and Answer (Q&A) websites to exchange knowledge and
+expertise. Stack Overflow is a popular Q&A website where developers discuss
+coding problems and share code examples. Although all Stack Overflow posts are
+free to access, code examples on Stack Overflow are governed by the Creative
+Commons Attribute-ShareAlike 3.0 Unported license that developers should obey
+when reusing code from Stack Overflow or posting code to Stack Overflow. In
+this paper, we conduct a case study with 399 Android apps, to investigate
+whether developers respect license terms when reusing code from Stack Overflow
+posts (and the other way around). We found 232 code snippets in 62 Android apps
+from our dataset that were potentially reused from Stack Overflow, and 1,226
+Stack Overflow posts containing code examples that are clones of code released
+in 68 Android apps, suggesting that developers may have copied the code of
+these apps to answer Stack Overflow questions. We investigated the licenses of
+these pieces of code and observed 1,279 cases of potential license violations
+(related to code posting to Stack overflow or code reuse from Stack overflow).
+This paper aims to raise the awareness of the software engineering community
+about potential unethical code reuse activities taking place on Q&A websites
+like Stack Overflow.
+",1,0,0,0,0,0
+6818,New Abilities and Limitations of Spectral Graph Bisection,"  Spectral based heuristics belong to well-known commonly used methods which
+determines provably minimal graph bisection or outputs ""fail"" when the
+optimality cannot be certified. In this paper we focus on Boppana's algorithm
+which belongs to one of the most prominent methods of this type. It is well
+known that the algorithm works well in the random \emph{planted bisection
+model} -- the standard class of graphs for analysis minimum bisection and
+relevant problems. In 2001 Feige and Kilian posed the question if Boppana's
+algorithm works well in the semirandom model by Blum and Spencer. In our paper
+we answer this question affirmatively. We show also that the algorithm achieves
+similar performance on graph classes which extend the semirandom model.
+Since the behavior of Boppana's algorithm on the semirandom graphs remained
+unknown, Feige and Kilian proposed a new semidefinite programming (SDP) based
+approach and proved that it works on this model. The relationship between the
+performance of the SDP based algorithm and Boppana's approach was left as an
+open problem. In this paper we solve the problem in a complete way by proving
+that the bisection algorithm of Feige and Kilian provides exactly the same
+results as Boppana's algorithm. As a consequence we get that Boppana's
+algorithm achieves the optimal threshold for exact cluster recovery in the
+\emph{stochastic block model}. On the other hand we prove some limitations of
+Boppana's approach: we show that if the density difference on the parameters of
+the planted bisection model is too small then the algorithm fails with high
+probability in the model.
+",1,0,0,0,0,0
+6819,Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks,"  Accelerated magnetic resonance (MR) scan acquisition with compressed sensing
+(CS) and parallel imaging is a powerful method to reduce MR imaging scan time.
+However, many reconstruction algorithms have high computational costs. To
+address this, we investigate deep residual learning networks to remove aliasing
+artifacts from artifact corrupted images. The proposed deep residual learning
+networks are composed of magnitude and phase networks that are separately
+trained. If both phase and magnitude information are available, the proposed
+algorithm can work as an iterative k-space interpolation algorithm using
+framelet representation. When only magnitude data is available, the proposed
+approach works as an image domain post-processing algorithm. Even with strong
+coherent aliasing artifacts, the proposed network successfully learned and
+removed the aliasing artifacts, whereas current parallel and CS reconstruction
+methods were unable to remove these artifacts. Comparisons using single and
+multiple coil show that the proposed residual network provides good
+reconstruction results with orders of magnitude faster computational time than
+existing compressed sensing methods. The proposed deep learning framework may
+have a great potential for accelerated MR reconstruction by generating accurate
+results immediately.
+",0,0,0,1,0,0
+6820,Quantification of market efficiency based on informational-entropy,"  Since the 1960s, the question whether markets are efficient or not is
+controversially discussed. One reason for the difficulty to overcome the
+controversy is the lack of a universal, but also precise, quantitative
+definition of efficiency that is able to graduate between different states of
+efficiency. The main purpose of this article is to fill this gap by developing
+a measure for the efficiency of markets that fulfill all the stated
+requirements. It is shown that the new definition of efficiency, based on
+informational-entropy, is equivalent to the two most used definitions of
+efficiency from Fama and Jensen. The new measure therefore enables steps to
+settle the dispute over the state of efficiency in markets. Moreover, it is
+shown that inefficiency in a market can either arise from the possibility to
+use information to predict an event with higher than chance level, or can
+emerge from wrong pricing/ quotes that do not reflect the right probabilities
+of possible events. Finally, the calculation of efficiency is demonstrated on a
+simple game (of coin tossing), to show how one could exactly quantify the
+efficiency in any market-like system, if all probabilities are known.
+",0,0,0,0,0,1
+6821,A critical analysis of resampling strategies for the regularized particle filter,"  We analyze the performance of different resampling strategies for the
+regularized particle filter regarding parameter estimation. We show in
+particular, building on analytical insight obtained in the linear Gaussian
+case, that resampling systematically can prevent the filtered density from
+converging towards the true posterior distribution. We discuss several means to
+overcome this limitation, including kernel bandwidth modulation, and provide
+evidence that the resulting particle filter clearly outperforms traditional
+bootstrap particle filters. Our results are supported by numerical simulations
+on a linear textbook example, the logistic map and a non-linear plant growth
+model.
+",0,0,1,1,0,0
+6822,Geometry of the free-sliding Bernoulli beam,"  If a variational problem comes with no boundary conditions prescribed
+beforehand, and yet these arise as a consequence of the variation process
+itself, we speak of a free boundary values variational problem. Such is, for
+instance, the problem of finding the shortest curve whose endpoints can slide
+along two prescribed curves. There exists a rigorous geometric way to formulate
+this sort of problems on smooth manifolds with boundary, which we review here
+in a friendly self-contained way. As an application, we study a particular free
+boundary values variational problem, the free-sliding Bernoulli beam.
+",0,0,1,0,0,0
+6823,Note on equivalences for degenerations of Calabi-Yau manifolds,"  This note studies the equivalencies among convergences of Ricci-flat
+Kähler-Einstein metrics on Calabi-Yau manifolds, cohomology classes and
+potential functions.
+",0,0,1,0,0,0
+6824,Improving DNN-based Music Source Separation using Phase Features,"  Music source separation with deep neural networks typically relies only on
+amplitude features. In this paper we show that additional phase features can
+improve the separation performance. Using the theoretical relationship between
+STFT phase and amplitude, we conjecture that derivatives of the phase are a
+good feature representation opposed to the raw phase. We verify this conjecture
+experimentally and propose a new DNN architecture which combines amplitude and
+phase. This joint approach achieves a better signal-to distortion ratio on the
+DSD100 dataset for all instruments compared to a network that uses only
+amplitude features. Especially, the bass instrument benefits from the phase
+information.
+",1,0,0,0,0,0
+6825,"Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing","  Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous
+mobile devices to facilitate largescale crowdsourcing tasks (e.g. urban sensing
+and collaborative computing). Many applications in this domain require
+Device-to-Device (D2D) communications between participating devices for
+interactive operations such as task collaborations and file transmissions.
+Considering the private participating devices and their opportunistic
+encountering behaviors, it is highly desired to establish secure and
+trustworthy D2D connections in a fast and autonomous way, which is vital for
+implementing practical Mobile Crowdsourcing Systems (MCSs). In this paper, we
+develop an efficient scheme, Trustworthy Device Pairing (TDP), which achieves
+user-transparent secure D2D connections and reliable peer device selections for
+trustworthy D2D communications. Through rigorous analysis, we demonstrate the
+effectiveness and security intensity of TDP in theory. The performance of TDP
+is evaluated based on both real-world prototype experiments and extensive
+trace-driven simulations. Evaluation results verify our theoretical analysis
+and show that TDP significantly outperforms existing approaches in terms of
+pairing speed, stability, and security.
+",1,0,0,0,0,0
+6826,liquidSVM: A Fast and Versatile SVM package,"  liquidSVM is a package written in C++ that provides SVM-type solvers for
+various classification and regression tasks. Because of a fully integrated
+hyper-parameter selection, very carefully implemented solvers, multi-threading
+and GPU support, and several built-in data decomposition strategies it provides
+unprecedented speed for small training sizes as well as for data sets of tens
+of millions of samples. Besides the C++ API and a command line interface,
+bindings to R, MATLAB, Java, Python, and Spark are available. We present a
+brief description of the package and report experimental comparisons to other
+SVM packages.
+",0,0,0,1,0,0
+6827,Thermalizing sterile neutrino dark matter,"  Sterile neutrinos produced through oscillations are a well motivated dark
+matter candidate, but recent constraints from observations have ruled out most
+of the parameter space. We analyze the impact of new interactions on the
+evolution of keV sterile neutrino dark matter in the early Universe. Based on
+general considerations we find a mechanism which thermalizes the sterile
+neutrinos after an initial production by oscillations. The thermalization of
+sterile neutrinos is accompanied by dark entropy production which increases the
+yield of dark matter and leads to a lower characteristic momentum. This
+resolves the growing tensions with structure formation and X-ray observations
+and even revives simple non-resonant production as a viable way to produce
+sterile neutrino dark matter. We investigate the parameters required for the
+realization of the thermalization mechanism in a representative model and find
+that a simple estimate based on energy- and entropy conservation describes the
+mechanism well.
+",0,1,0,0,0,0
+6828,Information Elicitation for Bayesian Auctions,"  In this paper we design information elicitation mechanisms for Bayesian
+auctions. While in Bayesian mechanism design the distributions of the players'
+private types are often assumed to be common knowledge, information elicitation
+considers the situation where the players know the distributions better than
+the decision maker. To weaken the information assumption in Bayesian auctions,
+we consider an information structure where the knowledge about the
+distributions is arbitrarily scattered among the players. In such an
+unstructured information setting, we design mechanisms for unit-demand auctions
+and additive auctions that aggregate the players' knowledge, generating revenue
+that are constant approximations to the optimal Bayesian mechanisms with a
+common prior. Our mechanisms are 2-step dominant-strategy truthful and the
+revenue increases gracefully with the amount of knowledge the players
+collectively have.
+",1,0,0,0,0,0
+6829,Partially chaotic orbits in a perturbed cubic force model,"  Three types of orbits are theoretically possible in autonomous Hamiltonian
+systems with three degrees of freedom: fully chaotic (they only obey the energy
+integral), partially chaotic (they obey an additional isolating integral
+besides energy) and regular (they obey two isolating integrals besides energy).
+The existence of partially chaotic orbits has been denied by several authors,
+however, arguing either that there is a sudden transition from regularity to
+full chaoticity, or that a long enough follow up of a supposedly partially
+chaotic orbit would reveal a fully chaotic nature. This situation needs
+clarification, because partially chaotic orbits might play a significant role
+in the process of chaotic diffusion. Here we use numerically computed Lyapunov
+exponents to explore the phase space of a perturbed three dimensional cubic
+force toy model, and a generalization of the Poincaré maps to show that
+partially chaotic orbits are actually present in that model. They turn out to
+be double orbits joined by a bifurcation zone, which is the most likely source
+of their chaos, and they are encapsulated in regions of phase space bounded by
+regular orbits similar to each one of the components of the double orbit.
+",0,1,0,0,0,0
+6830,Measurable process selection theorem and non-autonomous inclusions,"  A semi-process is an analog of the semi-flow for non-autonomous differential
+equations or inclusions. We prove an abstract result on the existence of
+measurable semi-processes in the situations where there is no uniqueness. Also,
+we allow solutions to blow up in finite time and then obtain local
+semi-processes.
+",0,1,1,0,0,0
+6831,Just-infinite C*-algebras and their invariants,"  Just-infinite C*-algebras, i.e., infinite dimensional C*-algebras, whose
+proper quotients are finite dimensional, were investigated in
+[Grigorchuk-Musat-Rordam, 2016]. One particular example of a just-infinite
+residually finite dimensional AF-algebras was constructed in that article. In
+this paper we extend that construction by showing that each infinite
+dimensional metrizable Choquet simplex is affinely homeomorphic to the trace
+simplex of a just-infinite residually finite dimensional C*-algebras. The trace
+simplex of any unital residually finite dimensional C*-algebra is hence
+realized by a just-infinite one. We determine the trace simplex of the
+particular residually finite dimensional AF-algebras constructed in the above
+mentioned article, and we show that it has precisely one extremal trace of type
+II_1.
+We give a complete description of the Bratteli diagrams corresponding to
+residually finite dimensional AF-algebras. We show that a modification of any
+such Bratteli diagram, similar to the modification that makes an arbitrary
+Bratteli diagram simple, will yield a just-infinite residually finite
+dimensional AF-algebra.
+",0,0,1,0,0,0
+6832,A Large-scale Dataset and Benchmark for Similar Trademark Retrieval,"  Trademark retrieval (TR) has become an important yet challenging problem due
+to an ever increasing trend in trademark applications and infringement
+incidents. There have been many promising attempts for the TR problem, which,
+however, fell impracticable since they were evaluated with limited and mostly
+trivial datasets. In this paper, we provide a large-scale dataset with
+benchmark queries with which different TR approaches can be evaluated
+systematically. Moreover, we provide a baseline on this benchmark using the
+widely-used methods applied to TR in the literature. Furthermore, we identify
+and correct two important issues in TR approaches that were not addressed
+before: reversal of contrast, and presence of irrelevant text in trademarks
+severely affect the TR methods. Lastly, we applied deep learning, namely,
+several popular Convolutional Neural Network models, to the TR problem. To the
+best of the authors, this is the first attempt to do so.
+",1,0,0,0,0,0
+6833,Tracing Networks of Knowledge in the Digital Age,"  The emergence of new digital technologies has allowed the study of human
+behaviour at a scale and at level of granularity that were unthinkable just a
+decade ago. In particular, by analysing the digital traces left by people
+interacting in the online and offline worlds, we are able to trace the
+spreading of knowledge and ideas at both local and global scales.
+In this article we will discuss how these digital traces can be used to map
+knowledge across the world, outlining both the limitations and the challenges
+in performing this type of analysis. We will focus on data collected from
+social media platforms, large-scale digital repositories and mobile data.
+Finally, we will provide an overview of the tools that are available to
+scholars and practitioners for understanding these processes using these
+emerging forms of data.
+",1,1,0,0,0,0
+6834,Observational evidence of galaxy assembly bias,"  We analyze the spectra of 300,000 luminous red galaxies (LRGs) with stellar
+masses $M_* \gtrsim 10^{11} M_{\odot}$ from the SDSS-III Baryon Oscillation
+Spectroscopic Survey (BOSS). By studying their star-formation histories, we
+find two main evolutionary paths converging into the same quiescent galaxy
+population at $z\sim0.55$. Fast-growing LRGs assemble $80\%$ of their stellar
+mass very early on ($z\sim5$), whereas slow-growing LRGs reach the same
+evolutionary state at $z\sim1.5$. Further investigation reveals that their
+clustering properties on scales of $\sim$1-30 Mpc are, at a high level of
+significance, also different. Fast-growing LRGs are found to be more strongly
+clustered and reside in overall denser large-scale structure environments than
+slow-growing systems, for a given stellar-mass threshold. Our results imply a
+dependence of clustering on stellar-mass assembly history (naturally connected
+to the mass-formation history of the corresponding halos) for a homogeneous
+population of similar mass and color, which constitutes a strong observational
+evidence of galaxy assembly bias.
+",0,1,0,0,0,0
+6835,Data-Driven Tree Transforms and Metrics,"  We consider the analysis of high dimensional data given in the form of a
+matrix with columns consisting of observations and rows consisting of features.
+Often the data is such that the observations do not reside on a regular grid,
+and the given order of the features is arbitrary and does not convey a notion
+of locality. Therefore, traditional transforms and metrics cannot be used for
+data organization and analysis. In this paper, our goal is to organize the data
+by defining an appropriate representation and metric such that they respect the
+smoothness and structure underlying the data. We also aim to generalize the
+joint clustering of observations and features in the case the data does not
+fall into clear disjoint groups. For this purpose, we propose multiscale
+data-driven transforms and metrics based on trees. Their construction is
+implemented in an iterative refinement procedure that exploits the
+co-dependencies between features and observations. Beyond the organization of a
+single dataset, our approach enables us to transfer the organization learned
+from one dataset to another and to integrate several datasets together. We
+present an application to breast cancer gene expression analysis: learning
+metrics on the genes to cluster the tumor samples into cancer sub-types and
+validating the joint organization of both the genes and the samples. We
+demonstrate that using our approach to combine information from multiple gene
+expression cohorts, acquired by different profiling technologies, improves the
+clustering of tumor samples.
+",1,0,0,1,0,0
+6836,NotiMind: Utilizing Responses to Smart Phone Notifications as Affective sensors,"  Today's mobile phone users are faced with large numbers of notifications on
+social media, ranging from new followers on Twitter and emails to messages
+received from WhatsApp and Facebook. These digital alerts continuously disrupt
+activities through instant calls for attention. This paper examines closely the
+way everyday users interact with notifications and their impact on users'
+emotion. Fifty users were recruited to download our application NotiMind and
+use it over a five-week period. Users' phones collected thousands of social and
+system notifications along with affect data collected via self-reported PANAS
+tests three times a day. Results showed a noticeable correlation between
+positive affective measures and keyboard activities. When large numbers of Post
+and Remove notifications occur, a corresponding increase in negative affective
+measures is detected. Our predictive model has achieved a good accuracy level
+using three different classifiers ""in the wild"" (F-measure 74-78%
+within-subject model, 72-76% global model). Our findings show that it is
+possible to automatically predict when people are experiencing positive,
+neutral or negative affective states based on interactions with notifications.
+We also show how our findings open the door to a wide range of applications in
+relation to emotion awareness on social and mobile communication.
+",1,0,0,0,0,0
+6837,Aktuelle Entwicklungen in der Automatischen Musikverfolgung,"  In this paper we present current trends in real-time music tracking (a.k.a.
+score following). Casually speaking, these algorithms ""listen"" to a live
+performance of music, compare the audio signal to an abstract representation of
+the score, and ""read"" along in the sheet music. In this way at any given time
+the exact position of the musician(s) in the sheet music is computed. Here, we
+focus on the aspects of flexibility and usability of these algorithms. This
+comprises work on automatic identification and flexible tracking of the piece
+being played as well as current approaches based on Deep Learning. The latter
+enables direct learning of correspondences between complex audio data and
+images of the sheet music, avoiding the complicated and time-consuming
+definition of a mid-level representation.
+-----
+Diese Arbeit befasst sich mit aktuellen Entwicklungen in der automatischen
+Musikverfolgung durch den Computer. Es handelt sich dabei um Algorithmen, die
+einer musikalischen Aufführung ""zuhören"", das aufgenommene Audiosignal mit
+einer (abstrakten) Repräsentation des Notentextes vergleichen und sozusagen
+in diesem mitlesen. Der Algorithmus kennt also zu jedem Zeitpunkt die Position
+der Musiker im Notentext. Neben der Vermittlung eines generellen Überblicks,
+liegt der Schwerpunkt dieser Arbeit auf der Beleuchtung des Aspekts der
+Flexibilität und der einfacheren Nutzbarkeit dieser Algorithmen. Es wird
+dargelegt, welche Schritte getätigt wurden (und aktuell getätigt werden) um
+den Prozess der automatischen Musikverfolgung einfacher zugänglich zu machen.
+Dies umfasst Arbeiten zur automatischen Identifikation von gespielten Stücken
+und deren flexible Verfolgung ebenso wie aktuelle Ansätze mithilfe von Deep
+Learning, die es erlauben Bild und Ton direkt zu verbinden, ohne Umwege über
+abstrakte und nur unter gro{\ss}em Zeitaufwand zu erstellende
+Zwischenrepräsentationen.
+",1,0,0,0,0,0
+6838,Blind Demixing and Deconvolution at Near-Optimal Rate,"  We consider simultaneous blind deconvolution of r source signals from their
+noisy superposition, a problem also referred to blind demixing and
+deconvolution. This signal processing problem occurs in the context of the
+Internet of Things where a massive number of sensors sporadically communicate
+only short messages over unknown channels. We show that robust recovery of
+message and channel vectors can be achieved via convex optimization when random
+linear encoding using i.i.d. complex Gaussian matrices is used at the devices
+and the number of required measurements at the receiver scales with the degrees
+of freedom of the overall estimation problem. Since the scaling is linear in r
+our result significantly improves over recent works.
+",1,0,0,0,0,0
+6839,A Visualization of the Classical Musical Tradition,"  A study of around 13,000 musical compositions from the Western classical
+tradition is carried out, spanning 33 major composers from the Baroque to the
+Romantic, with a focus on the usage of major/minor key signatures. A
+2-dimensional chromatic diagram is proposed to succinctly visualize the data.
+The diagram is found to be useful not only in distinguishing style and period,
+but also in tracking the career development of a particular composer.
+",0,0,0,1,0,0
+6840,Multivariate Locally Stationary Wavelet Process Analysis with the mvLSW R Package,"  This paper describes the R package mvLSW. The package contains a suite of
+tools for the analysis of multivariate locally stationary wavelet (LSW) time
+series. Key elements include: (i) the simulation of multivariate LSW time
+series for a given multivariate evolutionary wavelet spectrum (EWS); (ii)
+estimation of the time-dependent multivariate EWS for a given time series;
+(iii) estimation of the time-dependent coherence and partial coherence between
+time series channels; and, (iv) estimation of approximate confidence intervals
+for multivariate EWS estimates. A demonstration of the package is presented via
+both a simulated example and a case study with EuStockMarkets from the datasets
+package. This paper has been accepted by the Journal of Statistical Software.
+Presented code extracts demonstrating the mvLSW package is performed under
+version 1.2.1.
+",0,0,0,1,0,0
+6841,Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario,"  Item cold-start is a classical issue in recommender systems that affects
+anime and manga recommendations as well. This problem can be framed as follows:
+how to predict whether a user will like a manga that received few ratings from
+the community? Content-based techniques can alleviate this issue but require
+extra information, that is usually expensive to gather. In this paper, we use a
+deep learning technique, Illustration2Vec, to easily extract tag information
+from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
+(Blended Alternate Least Squares with Explanation), a new model for
+collaborative filtering, that benefits from this extra information to recommend
+mangas. We show, using real data from an online manga recommender system called
+Mangaki, that our model improves substantially the quality of recommendations,
+especially for less-known manga, and is able to provide an interpretation of
+the taste of the users.
+",1,0,0,1,0,0
+6842,Generalized least squares can overcome the critical threshold in respondent-driven sampling,"  In order to sample marginalized and/or hard-to-reach populations,
+respondent-driven sampling (RDS) and similar techniques reach their
+participants via peer referral. Under a Markov model for RDS, previous research
+has shown that if the typical participant refers too many contacts, then the
+variance of common estimators does not decay like $O(n^{-1})$, where $n$ is the
+sample size. This implies that confidence intervals will be far wider than
+under a typical sampling design. Here we show that generalized least squares
+(GLS) can effectively reduce the variance of RDS estimates. In particular, a
+theoretical analysis indicates that the variance of the GLS estimator is
+$O(n^{-1})$. We then derive two classes of feasible GLS estimators. The first
+class is based upon a Degree Corrected Stochastic Blockmodel for the underlying
+social network. The second class is based upon a rank-two model. It might be of
+independent interest that in both model classes, the theoretical results show
+that it is possible to estimate the spectral properties of the population
+network from the sampled observations. Simulations on empirical social networks
+show that the feasible GLS (fGLS) estimators can have drastically smaller error
+and rarely increase the error. A diagnostic plot helps to identify where fGLS
+will aid estimation. The fGLS estimators continue to outperform standard
+estimators even when they are built from a misspecified model and when there is
+preferential recruitment.
+",0,0,1,1,0,0
+6843,Electron-Hole Symmetry Breaking in Charge Transport in Nitrogen-Doped Graphene,"  Graphitic nitrogen-doped graphene is an excellent platform to study
+scattering processes of massless Dirac fermions by charged impurities, in which
+high mobility can be preserved due to the absence of lattice defects through
+direct substitution of carbon atoms in the graphene lattice by nitrogen atoms.
+In this work, we report on electrical and magnetotransport measurements of
+high-quality graphitic nitrogen-doped graphene. We show that the substitutional
+nitrogen dopants in graphene introduce atomically sharp scatters for electrons
+but long-range Coulomb scatters for holes and, thus, graphitic nitrogen-doped
+graphene exhibits clear electron-hole asymmetry in transport properties.
+Dominant scattering processes of charge carriers in graphitic nitrogen-doped
+graphene are analyzed. It is shown that the electron-hole asymmetry originates
+from a distinct difference in intervalley scattering of electrons and holes. We
+have also carried out the magnetotransport measurements of graphitic
+nitrogen-doped graphene at different temperatures and the temperature
+dependences of intervalley scattering, intravalley scattering and phase
+coherent scattering rates are extracted and discussed. Our results provide an
+evidence for the electron-hole asymmetry in the intervalley scattering induced
+by substitutional nitrogen dopants in graphene and shine a light on versatile
+and potential applications of graphitic nitrogen-doped graphene in electronic
+and valleytronic devices.
+",0,1,0,0,0,0
+6844,Micro-sized cold atmospheric plasma source for brain and breast cancer treatment,"  Micro-sized cold atmospheric plasma (uCAP) has been developed to expand the
+applications of CAP in cancer therapy. In this paper, uCAP devices with
+different nozzle lengths were applied to investigate effects on both brain
+(glioblastoma U87) and breast (MDA-MB-231) cancer cells. Various diagnostic
+techniques were employed to evaluate the parameters of uCAP devices with
+different lengths such as potential distribution, electron density, and optical
+emission spectroscopy. The generation of short- and long-lived species (such as
+hydroxyl radical (.OH), superoxide (O2-), hydrogen peroxide (H2O2), nitrite
+(NO2-), et al) were studied. These data revealed that uCAP treatment with a 20
+mm length tube has a stronger effect than that of the 60 mm tube due to the
+synergetic effects of reactive species and free radicals. Reactive species
+generated by uCAP enhanced tumor cell death in a dose-dependent fashion and was
+not specific with regards to tumor cell type.
+",0,0,0,0,1,0
+6845,How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments,"  Consistently checking the statistical significance of experimental results is
+one of the mandatory methodological steps to address the so-called
+""reproducibility crisis"" in deep reinforcement learning. In this tutorial
+paper, we explain how the number of random seeds relates to the probabilities
+of statistical errors. For both the t-test and the bootstrap confidence
+interval test, we recall theoretical guidelines to determine the number of
+random seeds one should use to provide a statistically significant comparison
+of the performance of two algorithms. Finally, we discuss the influence of
+deviations from the assumptions usually made by statistical tests. We show that
+they can lead to inaccurate evaluations of statistical errors and provide
+guidelines to counter these negative effects. We make our code available to
+perform the tests.
+",0,0,0,1,0,0
+6846,Training Quantized Nets: A Deeper Understanding,"  Currently, deep neural networks are deployed on low-power portable devices by
+first training a full-precision model using powerful hardware, and then
+deriving a corresponding low-precision model for efficient inference on such
+systems. However, training models directly with coarsely quantized weights is a
+key step towards learning on embedded platforms that have limited computing
+resources, memory capacity, and power consumption. Numerous recent publications
+have studied methods for training quantized networks, but these studies have
+mostly been empirical. In this work, we investigate training methods for
+quantized neural networks from a theoretical viewpoint. We first explore
+accuracy guarantees for training methods under convexity assumptions. We then
+look at the behavior of these algorithms for non-convex problems, and show that
+training algorithms that exploit high-precision representations have an
+important greedy search phase that purely quantized training methods lack,
+which explains the difficulty of training using low-precision arithmetic.
+",1,0,0,1,0,0
+6847,Some Sharpening and Generalizations of a result of T. J. Rivlin,"  Let $p(z)=a_0+a_1z+a_2z^2+a_3z^3+\cdots+a_nz^n$ be a polynomial of degree
+$n$. Rivlin \cite{Rivlin} proved that if $p(z)\neq 0$ in the unit disk, then
+for $0<r\leq 1$, $\displaystyle{\max_{|z| = r}|p(z)|} \geq
+\Big(\dfrac{r+1}{2}\Big)^n \displaystyle{\max_{|z|=1} |p(z)|}.$ ~In this paper,
+we prove a sharpening and generalization of this result, and show by means of
+examples that for some polynomials our result can significantly improve the
+bound obtained by the Rivlin's Theorem.
+",0,0,1,0,0,0
+6848,"Computational Aided Design for Generating a Modular, Lightweight Car Concept","  Developing an appropriate design process for a conceptual model is a stepping
+stone toward designing car bodies. This paper presents a methodology to design
+a lightweight and modular space frame chassis for a sedan electric car. The
+dual phase high strength steel with improved mechanical properties is employed
+to reduce the weight of the car body. Utilizing the finite element analysis
+yields two models in order to predict the performance of each component. The
+first model is a beam structure with a rapid response in structural stiffness
+simulation. This model is used for performing the static tests including modal
+frequency, bending stiffens and torsional stiffness evaluation. Whereas the
+second model, i.e., a shell model, is proposed to illustrate every module's
+mechanical behavior as well as its crashworthiness efficiency. In order to
+perform the crashworthiness analysis, the explicit nonlinear dynamic solver
+provided by ABAQUS, a commercial finite element software, is used. The results
+of finite element beam and shell models are in line with the concept design
+specifications. Implementation of this procedure leads to generate a
+lightweight and modular concept for an electric car.
+",1,0,0,0,0,0
+6849,Reaction-Diffusion Systems in Epidemiology,"  A key problem in modelling the evolution dynamics of infectious diseases is
+the mathematical representation of the mechanism of transmission of the
+contagion. Models with a finite number of subpopulations can be described via
+systems of ordinary differential equations. When dealing with populations with
+space structure the relevant quantities are spatial densities, whose evolution
+in time requires nonlinear partial differential equations, which are known as
+reaction-diffusion systems. Here we present an (historical) outline of
+mathematical epidemiology, with a particular attention to the role of spatial
+heterogeneity and dispersal in the population dynamics of infectious diseases.
+Two specific examples are discussed, which have been the subject of intensive
+research by the authors, i.e. man-environment-man epidemics, and malaria. In
+addition to the epidemiological relevance of these epidemics all over the
+world, their treatment requires a large amount of different sophisticate
+mathematical methods, and has even posed new non trivial mathematical problems,
+as one can realize from the list of references. One of the most relevant
+problems posed by the authors, i.e. regional control, has been emphasized here:
+the public health concern consists of eradicating the disease in the relevant
+population, as fast as possible. On the other hand, very often the entire
+domain of interest for the epidemic, is either unknown, or difficult to manage
+for an affordable implementation of suitable environmental programmes. For
+regional control instead it might be sufficient to implement such programmes
+only in a given subregion conveniently chosen so to lead to an effective
+(exponentially fast) eradication of the epidemic in the whole habitat; it is
+evident that this practice may have an enormous importance in real cases with
+respect to both financial and practical affordability.
+",0,0,1,0,0,0
+6850,Is charge order induced near an antiferromagnetic quantum critical point?,"  We investigate the interplay between charge order and superconductivity near
+an antiferromagnetic quantum critical point using sign-problem-free Quantum
+Monte Carlo simulations. We establish that, when the electronic dispersion is
+particle-hole symmetric, the system has an emergent SU(2) symmetry that implies
+a degeneracy between $d$-wave superconductivity and charge order with $d$-wave
+form factor. Deviations from particle-hole symmetry, however, rapidly lift this
+degeneracy, despite the fact that the SU(2) symmetry is preserved at low
+energies. As a result, we find a strong suppression of charge order caused by
+the competing, leading superconducting instability. Across the
+antiferromagnetic phase transition, we also observe a shift in the charge order
+wave-vector from diagonal to axial. We discuss the implications of our results
+to the universal phase diagram of antiferromagnetic quantum-critical metals and
+to the elucidation of the charge order experimentally observed in the cuprates.
+",0,1,0,0,0,0
+6851,Commissioning and performance results of the WFIRST/PISCES integral field spectrograph,"  The Prototype Imaging Spectrograph for Coronagraphic Exoplanet Studies
+(PISCES) is a high contrast integral field spectrograph (IFS) whose design was
+driven by WFIRST coronagraph instrument requirements. We present commissioning
+and operational results using PISCES as a camera on the High Contrast Imaging
+Testbed at JPL. PISCES has demonstrated ability to achieve high contrast
+spectral retrieval with flight-like data reduction and analysis techniques.
+",0,1,0,0,0,0
+6852,Metal nanospheres under intense continuous wave illumination - a unique case of non-perturbative nonlinear nanophotonics,"  We show that the standard perturbative (i.e., cubic) description of the
+thermal nonlinear response of small metal nanospheres to intense continuous
+wave illumination is insufficient already beyond temperature rises of a few
+tens of degrees. In some cases, a cubic-quintic nonlinear response is
+sufficient to describe accurately the intensity dependence of the temperature,
+permittivity and field, while in other cases, a full non-perturbative
+description is required. We further analyze the relative importance of the
+various contributions to the thermal nonlinearity, identify a qualitative
+difference between Au and Ag, and show that the thermo-optical nonlinearity of
+the host typically plays a minor role, but its thermal conductivity is
+important.
+",0,1,0,0,0,0
+6853,Revealing strong bias in common measures of galaxy properties using new inclination-independent structures,"  Accurate measurement of galaxy structures is a prerequisite for quantitative
+investigation of galaxy properties or evolution. Yet, the impact of galaxy
+inclination and dust on commonly used metrics of galaxy structure is poorly
+quantified. We use infrared data sets to select inclination-independent samples
+of disc and flattened elliptical galaxies. These samples show strong variation
+in Sérsic index, concentration, and half-light radii with inclination. We
+develop novel inclination-independent galaxy structures by collapsing the light
+distribution in the near-infrared on to the major axis, yielding
+inclination-independent `linear' measures of size and concentration. With these
+new metrics we select a sample of Milky Way analogue galaxies with similar
+stellar masses, star formation rates, sizes and concentrations. Optical
+luminosities, light distributions, and spectral properties are all found to
+vary strongly with inclination: When inclining to edge-on, $r$-band
+luminosities dim by $>$1 magnitude, sizes decrease by a factor of 2,
+`dust-corrected' estimates of star formation rate drop threefold, metallicities
+decrease by 0.1 dex, and edge-on galaxies are half as likely to be classified
+as star forming. These systematic effects should be accounted for in analyses
+of galaxy properties.
+",0,1,0,0,0,0
+6854,Behind Every Great Tree is a Great (Phylogenetic) Network,"  In Francis and Steel (2015), it was shown that there exists non-trivial
+networks on $4$ leaves upon which the distance metric affords a metric on a
+tree which is not the base tree of the network. In this paper we extend this
+result in two directions. We show that for any tree $T$ there exists a family
+of non-trivial HGT networks $N$ for which the distance metric $d_N$ affords a
+metric on $T$. We additionally provide a class of networks on any number of
+leaves upon which the distance metric affords a metric on a tree which is not
+the base tree of the network.
+The family of networks are all ""floating"" networks, a subclass of a novel
+family of networks introduced in this paper, and referred to as ""versatile""
+networks. Versatile networks are then characterised.
+Additionally, we find a lower bound for the number of `useful' HGT arcs in
+such networks, in a sense explained in the paper. This lower bound is equal to
+the number of HGT arcs required for each floating network in the main results,
+and thus our networks are minimal in this sense.
+",0,0,1,0,0,0
+6855,Cosmic-ray induced destruction of CO in star-forming galaxies,"  We explore the effects of the expected higher cosmic ray (CR) ionization
+rates $\zeta_{\rm CR}$ on the abundances of carbon monoxide (CO), atomic carbon
+(C), and ionized carbon (C$^+$) in the H$_2$ clouds of star-forming galaxies.
+The study of Bisbas et al. (2015) is expanded by: a) using realistic
+inhomogeneous Giant Molecular Cloud (GMC) structures, b) a detailed chemical
+analysis behind the CR-induced destruction of CO, and c) exploring the thermal
+state of CR-irradiated molecular gas. CRs permeating the interstellar medium
+with $\zeta_{\rm CR}$$\gtrsim 10\times$(Galactic) are found to significantly
+reduce the [CO]/[H$_2$] abundance ratios throughout the mass of a GMC. CO
+rotational line imaging will then show much clumpier structures than the actual
+ones. For $\zeta_{\rm CR}$$\gtrsim 100\times$(Galactic) this bias becomes
+severe, limiting the utility of CO lines for recovering structural and
+dynamical characteristics of H$_2$-rich galaxies throughout the Universe,
+including many of the so-called Main Sequence (MS) galaxies where the bulk of
+cosmic star formation occurs. Both C$^+$ and C abundances increase with rising
+$\zeta_{\rm CR}$, with C remaining the most abundant of the two throughout
+H$_2$ clouds, when $\zeta_{\rm CR}\sim (1-100)\times$(Galactic). C$^+$ starts
+to dominate for $\zeta_{\rm CR}$$\gtrsim 10^3\times$(Galactic). The thermal
+state of the gas in the inner and denser regions of GMCs is invariant with
+$T_{\rm gas}\sim 10\,{\rm K}$ for $\zeta_{\rm CR}\sim (1-10)\times$(Galactic).
+For $\zeta_{\rm CR}$$\sim 10^3\times$(Galactic) this is no longer the case and
+$T_{\rm gas}\sim 30-50\,{\rm K}$ are reached. Finally we identify OH as the key
+species whose $T_{\rm gas}-$sensitive abundance could mitigate the destruction
+of CO at high temperatures.
+",0,1,0,0,0,0
+6856,News Session-Based Recommendations using Deep Neural Networks,"  News recommender systems are aimed to personalize users experiences and help
+them to discover relevant articles from a large and dynamic search space.
+Therefore, news domain is a challenging scenario for recommendations, due to
+its sparse user profiling, fast growing number of items, accelerated item's
+value decay, and users preferences dynamic shift. Some promising results have
+been recently achieved by the usage of Deep Learning techniques on Recommender
+Systems, specially for item's feature extraction and for session-based
+recommendations with Recurrent Neural Networks. In this paper, it is proposed
+an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
+Recommender Systems. This architecture is composed of two modules, the first
+responsible to learn news articles representations, based on their text and
+metadata, and the second module aimed to provide session-based recommendations
+using Recurrent Neural Networks. The recommendation task addressed in this work
+is next-item prediction for users sessions: ""what is the next most likely
+article a user might read in a session?"" Users sessions context is leveraged by
+the architecture to provide additional information in such extreme cold-start
+scenario of news recommendation. Users' behavior and item features are both
+merged in an hybrid recommendation approach. A temporal offline evaluation
+method is also proposed as a complementary contribution, for a more realistic
+evaluation of such task, considering dynamic factors that affect global
+readership interests like popularity, recency, and seasonality. Experiments
+with an extensive number of session-based recommendation methods were performed
+and the proposed instantiation of CHAMELEON meta-architecture obtained a
+significant relative improvement in top-n accuracy and ranking metrics (10% on
+Hit Rate and 13% on MRR) over the best benchmark methods.
+",0,0,0,1,0,0
+6857,Online Nonparametric Anomaly Detection based on Geometric Entropy Minimization,"  We consider the online and nonparametric detection of abrupt and persistent
+anomalies, such as a change in the regular system dynamics at a time instance
+due to an anomalous event (e.g., a failure, a malicious activity). Combining
+the simplicity of the nonparametric Geometric Entropy Minimization (GEM) method
+with the timely detection capability of the Cumulative Sum (CUSUM) algorithm we
+propose a computationally efficient online anomaly detection method that is
+applicable to high-dimensional datasets, and at the same time achieve a
+near-optimum average detection delay performance for a given false alarm
+constraint. We provide new insights to both GEM and CUSUM, including new
+asymptotic analysis for GEM, which enables soft decisions for outlier
+detection, and a novel interpretation of CUSUM in terms of the discrepancy
+theory, which helps us generalize it to the nonparametric GEM statistic. We
+numerically show, using both simulated and real datasets, that the proposed
+nonparametric algorithm attains a close performance to the clairvoyant
+parametric CUSUM test.
+",0,0,0,1,0,0
+6858,Learning latent structure of large random graphs,"  In this paper, we estimate the distribution of hidden nodes weights in large
+random graphs from the observation of very few edges weights. In this very
+sparse setting, the first non-asymptotic risk bounds for maximum likelihood
+estimators (MLE) are established. The proof relies on the construction of a
+graphical model encoding conditional dependencies that is extremely efficient
+to study n-regular graphs obtained using a round-robin scheduling. This
+graphical model allows to prove geometric loss of memory properties and deduce
+the asymp-totic behavior of the likelihood function. Following a classical
+construction in learning theory, the asymptotic likelihood is used to define a
+measure of performance for the MLE. Risk bounds for the MLE are finally
+obtained by subgaussian deviation results derived from concentration
+inequalities for Markov chains applied to our graphical model.
+",0,0,1,1,0,0
+6859,Loop Tiling in Large-Scale Stencil Codes at Run-time with OPS,"  The key common bottleneck in most stencil codes is data movement, and prior
+research has shown that improving data locality through optimisations that
+schedule across loops do particularly well. However, in many large PDE
+applications it is not possible to apply such optimisations through compilers
+because there are many options, execution paths and data per grid point, many
+dependent on run-time parameters, and the code is distributed across different
+compilation units. In this paper, we adapt the data locality improving
+optimisation called iteration space slicing for use in large OPS applications
+both in shared-memory and distributed-memory systems, relying on run-time
+analysis and delayed execution. We evaluate our approach on a number of
+applications, observing speedups of 2$\times$ on the Cloverleaf 2D/3D proxy
+application, which contain 83/141 loops respectively, $3.5\times$ on the linear
+solver TeaLeaf, and $1.7\times$ on the compressible Navier-Stokes solver
+OpenSBLI. We demonstrate strong and weak scalability up to 4608 cores of
+CINECA's Marconi supercomputer. We also evaluate our algorithms on Intel's
+Knights Landing, demonstrating maintained throughput as the problem size grows
+beyond 16GB, and we do scaling studies up to 8704 cores. The approach is
+generally applicable to any stencil DSL that provides per loop data access
+information.
+",1,0,0,0,0,0
+6860,Second differentials in the Quillen spectral sequence,"  For an algebraic variety $X$ we introduce generalized first Chern classes,
+which are defined for coherent sheaves on $X$ with support in codimension $p$
+and take values in $CH^p(X)$. We use them to provide an explicit formula for
+the differentials ${d_2^p: E_2^{p,-p-1} \to E_2^{p+2, -p-2} \cong CH^{p+2}(X)}$
+in the Quillen spectral sequence.
+",0,0,1,0,0,0
+6861,General Dynamics of Spinors,"  In this paper, we consider a general twisted-curved space-time hosting Dirac
+spinors and we take into account the Lorentz covariant polar decomposition of
+the Dirac spinor field: the corresponding decomposition of the Dirac spinor
+field equation leads to a set of field equations that are real and where
+spinorial components have disappeared while still maintaining Lorentz
+covariance. We will see that the Dirac spinor will contain two real scalar
+degrees of freedom, the module and the so-called Yvon-Takabayashi angle, and we
+will display their field equations. This will permit us to study the coupling
+of curvature and torsion respectively to the module and the YT angle.
+",0,1,0,0,0,0
+6862,PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning,"  One of the most exciting advancements in AI over the last decade is the wide
+adoption of ANNs, such as DNN and CNN, in many real-world applications.
+However, the underlying massive amounts of computation and storage requirement
+greatly challenge their applicability in resource-limited platforms like the
+drone, mobile phone, and IoT devices etc. The third generation of neural
+network model--Spiking Neural Network (SNN), inspired by the working mechanism
+and efficiency of human brain, has emerged as a promising solution for
+achieving more impressive computing and power efficiency within light-weighted
+devices (e.g. single chip). However, the relevant research activities have been
+narrowly carried out on conventional rate-based spiking system designs for
+fulfilling the practical cognitive tasks, underestimating SNN's energy
+efficiency, throughput, and system flexibility. Although the time-based SNN can
+be more attractive conceptually, its potentials are not unleashed in realistic
+applications due to lack of efficient coding and practical learning schemes. In
+this work, a Precise-Time-Dependent Single Spike Neuromorphic Architecture,
+namely ""PT-Spike"", is developed to bridge this gap. Three constituent
+hardware-favorable techniques: precise single-spike temporal encoding,
+efficient supervised temporal learning, and fast asymmetric decoding are
+proposed accordingly to boost the energy efficiency and data processing
+capability of the time-based SNN at a more compact neural network model size
+when executing real cognitive tasks. Simulation results show that ""PT-Spike""
+demonstrates significant improvements in network size, processing efficiency
+and power consumption with marginal classification accuracy degradation when
+compared with the rate-based SNN and ANN under the similar network
+configuration.
+",0,0,0,0,1,0
+6863,Learning to Represent Edits,"  We introduce the problem of learning distributed representations of edits. By
+combining a ""neural editor"" with an ""edit encoder"", our models learn to
+represent the salient information of an edit and can be used to apply edits to
+new inputs. We experiment on natural language and source code edit data. Our
+evaluation yields promising results that suggest that our neural network models
+learn to capture the structure and semantics of edits. We hope that this
+interesting task and data source will inspire other researchers to work further
+on this problem.
+",1,0,0,0,0,0
+6864,Semiclassical Prediction of Large Spectral Fluctuations in Interacting Kicked Spin Chains,"  While plenty of results have been obtained for single-particle quantum
+systems with chaotic dynamics through a semiclassical theory, much less is
+known about quantum chaos in the many-body setting. We contribute to recent
+efforts to make a semiclassical analysis of many-body systems feasible. This is
+nontrivial due to both the enormous density of states and the exponential
+proliferation of periodic orbits with the number of particles. As a model
+system we study kicked interacting spin chains employing semiclassical methods
+supplemented by a newly developed duality approach. We show that for this model
+the line between integrability and chaos becomes blurred. Due to the
+interaction structure the system features (non-isolated) manifolds of periodic
+orbits possessing highly correlated, collective dynamics. As with the invariant
+tori in integrable systems, their presence lead to significantly enhanced
+spectral fluctuations, which by order of magnitude lie in-between integrable
+and chaotic cases.
+",0,1,0,0,0,0
+6865,"If it ain't broke, don't fix it: Sparse metric repair","  Many modern data-intensive computational problems either require, or benefit
+from distance or similarity data that adhere to a metric. The algorithms run
+faster or have better performance guarantees. Unfortunately, in real
+applications, the data are messy and values are noisy. The distances between
+the data points are far from satisfying a metric. Indeed, there are a number of
+different algorithms for finding the closest set of distances to the given ones
+that also satisfy a metric (sometimes with the extra condition of being
+Euclidean). These algorithms can have unintended consequences, they can change
+a large number of the original data points, and alter many other features of
+the data.
+The goal of sparse metric repair is to make as few changes as possible to the
+original data set or underlying distances so as to ensure the resulting
+distances satisfy the properties of a metric. In other words, we seek to
+minimize the sparsity (or the $\ell_0$ ""norm"") of the changes we make to the
+distances subject to the new distances satisfying a metric. We give three
+different combinatorial algorithms to repair a metric sparsely. In one setting
+the algorithm is guaranteed to return the sparsest solution and in the other
+settings, the algorithms repair the metric. Without prior information, the
+algorithms run in time proportional to the cube of the number of input data
+points and, with prior information we can reduce the running time considerably.
+",1,0,0,1,0,0
+6866,Optimal one-shot quantum algorithm for EQUALITY and AND,"  We study the computation complexity of Boolean functions in the quantum black
+box model. In this model our task is to compute a function
+$f:\{0,1\}\to\{0,1\}$ on an input $x\in\{0,1\}^n$ that can be accessed by
+querying the black box. Quantum algorithms are inherently probabilistic; we are
+interested in the lowest possible probability that the algorithm outputs
+incorrect answer (the error probability) for a fixed number of queries. We show
+that the lowest possible error probability for $AND_n$ and $EQUALITY_{n+1}$ is
+$1/2-n/(n^2+1)$.
+",1,0,0,0,0,0
+6867,Photon-gated spin transistor,"  Spin-polarized field-effect transistor (spin-FET), where a dielectric layer
+is generally employed for the electrical gating as the traditional FET, stands
+out as a seminal spintronic device under the miniaturization trend of
+electronics. It would be fundamentally transformative if optical gating was
+used for spin-FET. We report a new type of spin-polarized field-effect
+transistor (spin-FET) with optical gating, which is fabricated by partial
+exposure of the (La,Sr)MnO3 channel to light-emitting diode (LED) light. The
+manipulation of the channel conductivity is ascribed to the enhanced scattering
+of the spin-polarized current by photon-excited antiparallel aligned spins. And
+the photon-gated spin-FET shows strong light power dependence and reproducible
+enhancement of resistance under light illumination, indicting well-defined
+conductivity cycling features. Our finding would enrich the concept of spin-FET
+and promote the use of optical means in spintronics for low power consumption
+and ultrafast data processing.
+",0,1,0,0,0,0
+6868,Polarisation of submillimetre lines from interstellar medium,"  Magnetic fields play important roles in many astrophysical processes.
+However, there is no universal diagnostic for the magnetic fields in the
+interstellar medium (ISM) and each magnetic tracer has its limitation. Any new
+detection method is thus valuable. Theoretical studies have shown that
+submillimetre fine-structure lines are polarised due to atomic alignment by
+Ultraviolet (UV) photon-excitation, which opens up a new avenue to probe
+interstellar magnetic fields. We will, for the first time, perform synthetic
+observations on the simulated three-dimensional ISM to demonstrate the
+measurability of the polarisation of submillimetre atomic lines. The maximum
+polarisation for different absorption and emission lines expected from various
+sources, including Star-Forming Regions (SFRs) are provided. Our results
+demonstrate that the polarisation of submillimetre atomic lines is a powerful
+magnetic tracer and add great value to the observational studies of the
+submilimetre astronomy.
+",0,1,0,0,0,0
+6869,Two bosonic quantum walkers in one-dimensional optical lattices,"  Dynamical properties of two bosonic quantum walkers in a one-dimensional
+lattice are studied theoretically. Depending on the initial state,
+interactions, lattice tilting, and lattice disorder, whole plethora of
+different behaviors are observed. Particularly, it is shown that two bosons
+system manifests the many-body localization like behavior in the presence of a
+quenched disorder. The whole analysis is based on a specific decomposition of
+the temporal density profile into different contributions from singly and
+doubly occupied sites. In this way, the role of interactions is extracted.
+Since the contributions can be directly measured in experiments with ultra-cold
+atoms in optical lattices, the predictions presented may have some importance
+for upcoming experiment.
+",0,1,0,0,0,0
+6870,Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies,"  In this paper we present and characterize a nearest-neighbors color-matching
+photometric redshift estimator that features a direct relationship between the
+precision and accuracy of the input magnitudes and the output photometric
+redshifts. This aspect makes our estimator an ideal tool for evaluating the
+impact of changes to LSST survey parameters that affect the measurement errors
+of the photometry, which is the main motivation of our work (i.e., it is not
+intended to provide the ""best"" photometric redshifts for LSST data). We show
+how the photometric redshifts will improve with time over the 10-year LSST
+survey and confirm that the nominal distribution of visits per filter provides
+the most accurate photo-$z$ results. The LSST survey strategy naturally
+produces observations over a range of airmass, which offers the opportunity of
+using an SED- and $z$-dependent atmospheric affect on the observed photometry
+as a color-independent redshift indicator. We show that measuring this airmass
+effect and including it as a prior has the potential to improve the photometric
+redshifts and can ameliorate extreme outliers, but that it will only be
+adequately measured for the brightest galaxies, which limits its overall impact
+on LSST photometric redshifts. We furthermore demonstrate how this airmass
+effect can induce a bias in the photo-$z$ results, and caution against survey
+strategies that prioritize high-airmass observations for the purpose of
+improving this prior. Ultimately, we intend for this work to serve as a guide
+for the expectations and preparations of the LSST science community with
+regards to the minimum quality of photo-$z$ as the survey progresses.
+",0,1,0,0,0,0
+6871,"Modelling diverse sources of Clostridium difficile in the community: importance of animals, infants and asymptomatic carriers","  Clostridium difficile infections (CDIs) affect patients in hospitals and in
+the community, but the relative importance of transmission in each setting is
+unknown. We developed a mathematical model of C. difficile transmission in a
+hospital and surrounding community that included infants, adults, and
+transmission from animal reservoirs. We assessed the role of these transmission
+routes in maintaining disease and evaluated the recommended classification
+system for hospital and community-acquired CDIs. The reproduction number in the
+hospital was <1 (range: 0.16-0.46) for all scenarios. Outside the hospital, the
+reproduction number was >1 for nearly all scenarios without transmission from
+animal reservoirs (range: 1.0-1.34). However, the reproduction number for the
+human population was <1 if a minority (>3.5-26.0%) of human exposures
+originated from animal reservoirs. Symptomatic adults accounted for <10%
+transmission in the community. Under conservative assumptions, infants
+accounted for 17% of community transmission. An estimated 33-40% of
+community-acquired cases were reported but 28-39% of these reported cases were
+misclassified as hospital-acquired by recommended definitions. Transmission
+could be plausibly sustained by asymptomatically colonized adults and infants
+in the community or exposure to animal reservoirs, but not hospital
+transmission alone. Underreporting of community-onset cases and systematic
+misclassification underplays the role of community transmission.
+",0,0,0,0,1,0
+6872,Quivers with potentials for cluster varieties associated to braid semigroups,"  Let $C$ be a simply laced generalized Cartan matrix. Given an element $b$ of
+the generalized braid semigroup related to $C$, we construct a collection of
+mutation-equivalent quivers with potentials. A quiver with potential in such a
+collection corresponds to an expression of $b$ in terms of the standard
+generators. For two expressions that differ by a braid relation, the
+corresponding quivers with potentials are related by a mutation.
+The main application of this result is a construction of a family of $CY_3$
+$A_\infty$-categories associated to elements of the braid semigroup related to
+$C$. In particular, we construct a canonical up to equivalence $CY_3$
+$A_\infty$-category associated to quotient of any Double Bruhat cell
+$G^{u,v}/{\rm Ad} H$ in a simply laced reductive Lie group $G$.
+We describe the full set of parameters these categories depend on by defining
+a 2-dimensional CW-complex and proving that the set of parameters is identified
+with second cohomology group of this complex.
+",0,0,1,0,0,0
+6873,Effect of stellar flares on the upper atmospheres of HD 189733b and HD 209458b,"  Stellar flares are a frequent occurrence on young low-mass stars around which
+many detected exoplanets orbit. Flares are energetic, impulsive events, and
+their impact on exoplanetary atmospheres needs to be taken into account when
+interpreting transit observations. We have developed a model to describe the
+upper atmosphere of Extrasolar Giant Planets (EGPs) orbiting flaring stars. The
+model simulates thermal escape from the upper atmospheres of close-in EGPs.
+Ionisation by solar radiation and electron impact is included and photochemical
+and diffusive transport processes are simulated. This model is used to study
+the effect of stellar flares from the solar-like G star HD209458 and the young
+K star HD189733 on their respective planets. A hypothetical HD209458b-like
+planet orbiting the active M star AU Mic is also simulated. We find that the
+neutral upper atmosphere of EGPs is not significantly affected by typical
+flares. Therefore, stellar flares alone would not cause large enough changes in
+planetary mass loss to explain the variations in HD189733b transit depth seen
+in previous studies, although we show that it may be possible that an extreme
+stellar proton event could result in the required mass loss. Our simulations do
+however reveal an enhancement in electron number density in the ionosphere of
+these planets, the peak of which is located in the layer where stellar X-rays
+are absorbed. Electron densities are found to reach 2.2 to 3.5 times pre-flare
+levels and enhanced electron densities last from about 3 to 10 hours after the
+onset of the flare. The strength of the flare and the width of its spectral
+energy distribution affect the range of altitudes that see enhancements in
+ionisation. A large broadband continuum component in the XUV portion of the
+flaring spectrum in very young flare stars, such as AU Mic, results in a broad
+range of altitudes affected in planets orbiting this star.
+",0,1,0,0,0,0
+6874,Gross-Hopkins Duals of Higher Real K-theory Spectra,"  We determine the Gross-Hopkins duals of certain higher real K-theory spectra.
+More specifically, let p be an odd prime, and consider the Morava E-theory
+spectrum of height n=p-1. It is known, in the expert circles, that for certain
+finite subgroups G of the Morava stabilizer group, the homotopy fixed point
+spectra E_n^{hG} are Gross-Hopkins self-dual up to a shift. In this paper, we
+determine the shift for those finite subgroups G which contain p-torsion. This
+generalizes previous results for n=2 and p=3.
+",0,0,1,0,0,0
+6875,A Projection Method for Metric-Constrained Optimization,"  We outline a new approach for solving optimization problems which enforce
+triangle inequalities on output variables. We refer to this as
+metric-constrained optimization, and give several examples where problems of
+this form arise in machine learning applications and theoretical approximation
+algorithms for graph clustering. Although these problem are interesting from a
+theoretical perspective, they are challenging to solve in practice due to the
+high memory requirement of black-box solvers. In order to address this
+challenge we first prove that the metric-constrained linear program relaxation
+of correlation clustering is equivalent to a special case of the metric
+nearness problem. We then developed a general solver for metric-constrained
+linear and quadratic programs by generalizing and improving a simple projection
+algorithm originally developed for metric nearness. We give several novel
+approximation guarantees for using our framework to find lower bounds for
+optimal solutions to several challenging graph clustering problems. We also
+demonstrate the power of our framework by solving optimizing problems involving
+up to 10^{8} variables and 10^{11} constraints.
+",1,0,0,1,0,0
+6876,Calibrating the Planck Cluster Mass Scale with Cluster Velocity Dispersions,"  We measure the Planck cluster mass bias using dynamical mass measurements
+based on velocity dispersions of a subsample of 17 Planck-detected clusters.
+The velocity dispersions were calculated using redshifts determined from
+spectra obtained at Gemini observatory with the GMOS multi-object spectrograph.
+We correct our estimates for effects due to finite aperture, Eddington bias and
+correlated scatter between velocity dispersion and the Planck mass proxy. The
+result for the mass bias parameter, $(1-b)$, depends on the value of the galaxy
+velocity bias $b_v$ adopted from simulations: $(1-b)=(0.51\pm0.09) b_v^3$.
+Using a velocity bias of $b_v=1.08$ from Munari et al., we obtain
+$(1-b)=0.64\pm 0.11$, i.e, an error of 17% on the mass bias measurement with 17
+clusters. This mass bias value is consistent with most previous weak lensing
+determinations. It lies within $1\sigma$ of the value needed to reconcile the
+Planck cluster counts with the Planck primary CMB constraints. We emphasize
+that uncertainty in the velocity bias severely hampers precision measurements
+of the mass bias using velocity dispersions. On the other hand, when we fix the
+Planck mass bias using the constraints from Penna-Lima et al., based on weak
+lensing measurements, we obtain a positive velocity bias $b_v \gtrsim 0.9$ at
+$3\sigma$.
+",0,1,0,0,0,0
+6877,Curie: Policy-based Secure Data Exchange,"  Data sharing among partners---users, organizations, companies---is crucial
+for the advancement of data analytics in many domains. Sharing through secure
+computation and differential privacy allows these partners to perform private
+computations on their sensitive data in controlled ways. However, in reality,
+there exist complex relationships among members. Politics, regulations,
+interest, trust, data demands and needs are one of the many reasons. Thus,
+there is a need for a mechanism to meet these conflicting relationships on data
+sharing. This paper presents Curie, an approach to exchange data among members
+whose membership has complex relationships. The CPL policy language that allows
+members to define the specifications of data exchange requirements is
+introduced. Members (partners) assert who and what to exchange through their
+local policies and negotiate a global sharing agreement. The agreement is
+implemented in a multi-party computation that guarantees sharing among members
+will comply with the policy as negotiated. The use of Curie is validated
+through an example of a health care application built on recently introduced
+secure multi-party computation and differential privacy frameworks, and policy
+and performance trade-offs are explored.
+",1,0,0,0,0,0
+6878,"Boundedness in a fully parabolic chemotaxis system with nonlinear diffusion and sensitivity, and logistic source","  In this paper we study the zero-flux chemotaxis-system \begin{equation*}
+\begin{cases} u_{ t}=\nabla \cdot ((u+1)^{m-1} \nabla u-(u+1)^\alpha
+\chi(v)\nabla v) + ku-\mu u^2 & x\in \Omega, t>0, \\ v_{t} = \Delta v-vu & x\in
+\Omega, t>0,\\ \end{cases} \end{equation*} $\Omega$ being a bounded and smooth
+domain of $\mathbb{R}^n$, $n\geq 1$, and where $m,k \in \mathbb{R}$, $\mu>0$
+and $\alpha < \frac{m+1}{2}$. For any $v\geq 0$ the chemotactic sensitivity
+function is assumed to behave as the prototype $\chi(v) =
+\frac{\chi_0}{(1+av)^2}$, with $a\geq 0$ and $\chi_0>0$. We prove that for
+nonnegative and sufficiently regular initial data $u(x,0)$ and $v(x,0),$ the
+corresponding initial-boundary value problem admits a global bounded classical
+solution provided $\mu$ is large enough.
+",0,0,1,0,0,0
+6879,Gemini/GMOS Transmission Spectral Survey: Complete Optical Transmission Spectrum of the hot Jupiter WASP-4b,"  We present the complete optical transmission spectrum of the hot Jupiter
+WASP-4b from 440-940 nm at R ~ 400-1500 obtained with the Gemini Multi-Object
+Spectrometers (GMOS); this is the first result from a comparative
+exoplanetology survey program of close-in gas giants conducted with GMOS.
+WASP-4b has an equilibrium temperature of 1700 K and is favorable to study in
+transmission due to a large scale height (370 km). We derive the transmission
+spectrum of WASP-4b using 4 transits observed with the MOS technique. We
+demonstrate repeatable results across multiple epochs with GMOS, and derive a
+combined transmission spectrum at a precision about twice above photon noise,
+which is roughly equal to to one atmospheric scale height. The transmission
+spectrum is well fitted with a uniform opacity as a function of wavelength. The
+uniform opacity and absence of a Rayleigh slope from molecular hydrogen suggest
+that the atmosphere is dominated by clouds with condensate grain size of ~1 um.
+This result is consistent with previous observations of hot Jupiters since
+clouds have been seen in planets with similar equilibrium temperatures to
+WASP-4b. We describe a custom pipeline that we have written to reduce GMOS
+time-series data of exoplanet transits, and present a thorough analysis of the
+dominant noise sources in GMOS, which primarily consist of wavelength- and
+time- dependent displacements of the spectra on the detector, mainly due to a
+lack of atmospheric dispersion correction.
+",0,1,0,0,0,0
+6880,From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming,"  We consider linear programming (LP) problems in infinite dimensional spaces
+that are in general computationally intractable. Under suitable assumptions, we
+develop an approximation bridge from the infinite-dimensional LP to tractable
+finite convex programs in which the performance of the approximation is
+quantified explicitly. To this end, we adopt the recent developments in two
+areas of randomized optimization and first order methods, leading to a priori
+as well as a posterior performance guarantees. We illustrate the generality and
+implications of our theoretical results in the special case of the long-run
+average cost and discounted cost optimal control problems for Markov decision
+processes on Borel spaces. The applicability of the theoretical results is
+demonstrated through a constrained linear quadratic optimal control problem and
+a fisheries management problem.
+",1,0,1,0,0,0
+6881,An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting,"  The key component in forecasting demand and consumption of resources in a
+supply network is an accurate prediction of real-valued time series. Indeed,
+both service interruptions and resource waste can be reduced with the
+implementation of an effective forecasting system. Significant research has
+thus been devoted to the design and development of methodologies for short term
+load forecasting over the past decades. A class of mathematical models, called
+Recurrent Neural Networks, are nowadays gaining renewed interest among
+researchers and they are replacing many practical implementation of the
+forecasting systems, previously based on static methods. Despite the undeniable
+expressive power of these architectures, their recurrent nature complicates
+their understanding and poses challenges in the training procedures. Recently,
+new important families of recurrent architectures have emerged and their
+applicability in the context of load forecasting has not been investigated
+completely yet. In this paper we perform a comparative study on the problem of
+Short-Term Load Forecast, by using different classes of state-of-the-art
+Recurrent Neural Networks. We test the reviewed models first on controlled
+synthetic tasks and then on different real datasets, covering important
+practical cases of study. We provide a general overview of the most important
+architectures and we define guidelines for configuring the recurrent networks
+to predict real-valued time series.
+",1,0,0,0,0,0
+6882,Periods of abelian differentials and dynamics,"  Given a closed oriented surface S we describe those cohomology classes which
+appear as the period characters of abelian differentials for some choice of
+complex structure on S consistent with the orientation. The proof is based upon
+Ratner's solution of Raghunathan's conjecture.
+",0,0,1,0,0,0
+6883,Existence of regular solutions for a certain type of non-Newtonian Navier-Stokes equations,"  We are concerned with existence of regular solutions for non-Newtonian fluids
+in dimension three. For a certain type of non-Newtonian fluids we prove local
+existence of unique regular solutions, provided that the initial data are
+sufficiently smooth. Moreover, if the $H^3$-norm of initial data is
+sufficiently small, then the regular solution exists globally in time.
+",0,0,1,0,0,0
+6884,Multiferroic Quantum Criticality,"  The zero-temperature limit of a continuous phase transition is marked by a
+quantum critical point, which can generate exotic physics that extends to
+elevated temperatures. Magnetic quantum criticality is now well known, and has
+been explored in systems ranging from heavy fermion metals to quantum Ising
+materials. Ferroelectric quantum critical behaviour has also been recently
+established, motivating a flurry of research investigating its consequences.
+Here, we introduce the concept of multiferroic quantum criticality, in which
+both magnetic and ferroelectric quantum criticality occur in the same system.
+We develop the phenomenology of multiferroic quantum critical behaviour,
+describe the associated experimental signatures, and propose material systems
+and schemes to realize it.
+",0,1,0,0,0,0
+6885,Carbon Nanotube Wools Directly from CO2 By Molten Electrolysis Value Driven Pathways to Carbon Dioxide Greenhouse Gas Mitigation,"  A climate mitigation comprehensive solution is presented through the first
+high yield, low energy synthesis of macroscopic length carbon nanotubes (CNT)
+wool from CO2 by molten carbonate electrolysis, suitable for weaving into
+carbon composites and textiles. Growing CO2 concentrations, the concurrent
+climate change and species extinction can be addressed if CO2 becomes a sought
+resource rather than a greenhouse pollutant. Inexpensive carbon composites
+formed from carbon wool as a lighter metal, textiles and cement replacement
+comprise a major market sink to compactly store transformed anthropogenic CO2.
+100x-longer CNTs grow on Monel versus steel. Monel, electrolyte equilibration,
+and a mixed metal nucleation facilitate the synthesis. CO2, the sole reactant
+in this transformation, is directly extractable from dilute (atmospheric) or
+concentrated sources, and is cost constrained only by the (low) cost of
+electricity. Today's $100K per ton CNT valuation incentivizes CO2 removal.
+",0,1,0,0,0,0
+6886,Rock-Paper-Scissors Random Walks on Temporal Multilayer Networks,"  We study diffusion on a multilayer network where the contact dynamics between
+the nodes is governed by a random process and where the waiting time
+distribution differs for edges from different layers. We study the impact on a
+random walk of the competition that naturally emerges between the edges of the
+different layers. In opposition to previous studies which have imposed a priori
+inter-layer competition, the competition is here induced by the heterogeneity
+of the activity on the different layers. We first study the precedence relation
+between different edges and by extension between different layers, and show
+that it determines biased paths for the walker. We also discuss the emergence
+of cyclic, rock-paper-scissors random walks, when the precedence between layers
+is non-transitive. Finally, we numerically show the slowing-down effect due to
+the competition on a heterogeneous multilayer as the walker is likely to be
+trapped for a longer time either on a single layer, or on an oriented cycle .
+Keywords: random walks; multilayer networks; dynamical systems on networks;
+models of networks; simulations of networks; competition between layers.
+",1,0,0,0,0,0
+6887,Marginally compact fractal trees with semiflexibility,"  We study marginally compact macromolecular trees that are created by means of
+two different fractal generators. In doing so, we assume Gaussian statistics
+for the vectors connecting nodes of the trees. Moreover, we introduce bond-bond
+correlations that make the trees locally semiflexible. The symmetry of the
+structures allows an iterative construction of full sets of eigenmodes
+(notwithstanding the additional interactions that are present due to
+semiflexibility constraints), enabling us to get physical insights about the
+trees' behavior and to consider larger structures. Due to the local stiffness
+the self-contact density gets drastically reduced.
+",0,1,0,0,0,0
+6888,Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC,"  There has been considerable recent activity applying deep convolutional
+neural nets (CNNs) to data from particle physics experiments. Current
+approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter,
+and for identifying objects or particular particle types. We explore approaches
+that use the entire calorimeter, combined with track information, for directly
+conducting physics analyses: i.e. classifying events as known-physics
+background or new-physics signals.
+We use an existing RPV-Supersymmetry analysis as a case study and explore
+CNNs on multi-channel, high-resolution sparse images: applied on GPU and
+multi-node CPU architectures (including Knights Landing (KNL) Xeon Phi nodes)
+on the Cori supercomputer at NERSC.
+",1,0,0,0,0,0
+6889,Time-frequency analysis of ship wave patterns in shallow water: modelling and experiments,"  A spectrogram of a ship wake is a heat map that visualises the time-dependent
+frequency spectrum of surface height measurements taken at a single point as
+the ship travels by. Spectrograms are easy to compute and, if properly
+interpreted, have the potential to provide crucial information about various
+properties of the ship in question. Here we use geometrical arguments and
+analysis of an idealised mathematical model to identify features of
+spectrograms, concentrating on the effects of a finite-depth channel. Our
+results depend heavily on whether the flow regime is subcritical or
+supercritical. To support our theoretical predictions, we compare with data
+taken from experiments we conducted in a model test basin using a variety of
+realistic ship hulls. Finally, we note that vessels with a high aspect ratio
+appear to produce spectrogram data that contains periodic patterns. We can
+reproduce this behaviour in our mathematical model by using a so-called
+two-point wavemaker. These results highlight the role of wave interference
+effects in spectrograms of ship wakes.
+",0,1,0,0,0,0
+6890,HARE: Supporting efficient uplink multi-hop communications in self-organizing LPWANs,"  The emergence of low-power wide area networks (LPWANs) as a new agent in the
+Internet of Things (IoT) will result in the incorporation into the digital
+world of low-automated processes from a wide variety of sectors. The single-hop
+conception of typical LPWAN deployments, though simple and robust, overlooks
+the self-organization capabilities of network devices, suffers from lack of
+scalability in crowded scenarios, and pays little attention to energy
+consumption. Aimed to take the most out of devices' capabilities, the HARE
+protocol stack is proposed in this paper as a new LPWAN technology flexible
+enough to adopt uplink multi-hop communications when proving energetically more
+efficient. In this way, results from a real testbed show energy savings of up
+to 15% when using a multi-hop approach while keeping the same network
+reliability. System's self-organizing capability and resilience have been also
+validated after performing numerous iterations of the association mechanism and
+deliberately switching off network devices.
+",1,0,0,0,0,0
+6891,Tensor tomography in periodic slabs,"  The X-ray transform on the periodic slab $[0,1]\times\mathbb T^n$, $n\geq0$,
+has a non-trivial kernel due to the symmetry of the manifold and presence of
+trapped geodesics. For tensor fields gauge freedom increases the kernel
+further, and the X-ray transform is not solenoidally injective unless $n=0$. We
+characterize the kernel of the geodesic X-ray transform for $L^2$-regular
+$m$-tensors for any $m\geq0$. The characterization extends to more general
+manifolds, twisted slabs, including the Möbius strip as the simplest example.
+",0,0,1,0,0,0
+6892,Central limit theorem for linear spectral statistics of general separable sample covariance matrices with applications,"  In this paper, we consider the separable covariance model, which plays an
+important role in wireless communications and spatio-temporal statistics and
+describes a process where the time correlation does not depend on the spatial
+location and the spatial correlation does not depend on time. We established a
+central limit theorem for linear spectral statistics of general separable
+sample covariance matrices in the form of $\mathbf S_n=\frac1n\mathbf
+T_{1n}\mathbf X_n\mathbf T_{2n}\mathbf X_n^*\mathbf T_{1n}^*$ where $\mathbf
+X_n=(x_{jk})$ is of $m_1\times m_2$ dimension, the entries $\{x_{jk},
+j=1,...,m_1, k=1,...,m_2\}$ are independent and identically distributed complex
+variables with zero means and unit variances, $\mathbf T_{1n}$ is a $p\times
+m_1 $ complex matrix and $\mathbf T_{2n}$ is an $m_2\times m_2$ Hermitian
+matrix. We then apply this general central limit theorem to the problem of
+testing white noise in time series.
+",0,0,1,1,0,0
+6893,Malnormality and join-free subgroups in right-angled Coxeter groups,"  In this paper, we prove that all finitely generated malnormal subgroups of
+one-ended right-angled Coxeter groups are strongly quasiconvex and they are in
+particular quasiconvex when the ambient groups are hyperbolic. The key idea is
+to prove all infinite proper malnormal subgroups of one-ended right-angled
+Coxeter groups are join-free and then prove the strong quasiconvexity and the
+virtual freeness of these subgroups. We also study the subgroup divergence of
+join-free subgroups in right-angled Coxeter groups and compare them with the
+analogous subgroups in right-angled Artin groups. We characterize almost
+malnormal parabolic subgroups in terms of their defining graphs and also
+recognize them as strongly quasiconvex subgroups by the recent work of Genevois
+and Russell-Spriano-Tran. Finally, we discuss some results on hyperbolically
+embedded subgroups in right-angled Coxeter groups.
+",0,0,1,0,0,0
+6894,Mapping the Americanization of English in Space and Time,"  As global political preeminence gradually shifted from the United Kingdom to
+the United States, so did the capacity to culturally influence the rest of the
+world. In this work, we analyze how the world-wide varieties of written English
+are evolving. We study both the spatial and temporal variations of vocabulary
+and spelling of English using a large corpus of geolocated tweets and the
+Google Books datasets corresponding to books published in the US and the UK.
+The advantage of our approach is that we can address both standard written
+language (Google Books) and the more colloquial forms of microblogging messages
+(Twitter). We find that American English is the dominant form of English
+outside the UK and that its influence is felt even within the UK borders.
+Finally, we analyze how this trend has evolved over time and the impact that
+some cultural events have had in shaping it.
+",1,0,0,1,0,0
+6895,Robot Localisation and 3D Position Estimation Using a Free-Moving Camera and Cascaded Convolutional Neural Networks,"  Many works in collaborative robotics and human-robot interaction focuses on
+identifying and predicting human behaviour while considering the information
+about the robot itself as given. This can be the case when sensors and the
+robot are calibrated in relation to each other and often the reconfiguration of
+the system is not possible, or extra manual work is required. We present a deep
+learning based approach to remove the constraint of having the need for the
+robot and the vision sensor to be fixed and calibrated in relation to each
+other. The system learns the visual cues of the robot body and is able to
+localise it, as well as estimate the position of robot joints in 3D space by
+just using a 2D color image. The method uses a cascaded convolutional neural
+network, and we present the structure of the network, describe our own
+collected dataset, explain the network training and achieved results. A fully
+trained system shows promising results in providing an accurate mask of where
+the robot is located and a good estimate of its joints positions in 3D. The
+accuracy is not good enough for visual servoing applications yet, however, it
+can be sufficient for general safety and some collaborative tasks not requiring
+very high precision. The main benefit of our method is the possibility of the
+vision sensor to move freely. This allows it to be mounted on moving objects,
+for example, a body of the person or a mobile robot working in the same
+environment as the robots are operating in.
+",1,0,0,0,0,0
+6896,Meromorphic Jacobi Forms of Half-Integral Index and Umbral Moonshine Modules,"  In this work we consider an association of meromorphic Jacobi forms of
+half-integral index to the pure D-type cases of umbral moonshine, and solve the
+module problem for four of these cases by constructing vertex operator
+superalgebras that realise the corresponding meromorphic Jacobi forms as graded
+traces. We also present a general discussion of meromorphic Jacobi forms with
+half-integral index and their relationship to mock modular forms.
+",0,0,1,0,0,0
+6897,Active Hypothesis Testing: Beyond Chernoff-Stein,"  An active hypothesis testing problem is formulated. In this problem, the
+agent can perform a fixed number of experiments and then decide on one of the
+hypotheses. The agent is also allowed to declare its experiments inconclusive
+if needed. The objective is to minimize the probability of making an incorrect
+inference (misclassification probability) while ensuring that the true
+hypothesis is declared conclusively with moderately high probability. For this
+problem, lower and upper bounds on the optimal misclassification probability
+are derived and these bounds are shown to be asymptotically tight. In the
+analysis, a sub-problem, which can be viewed as a generalization of the
+Chernoff-Stein lemma, is formulated and analyzed. A heuristic approach to
+strategy design is proposed and its relationship with existing heuristic
+strategies is discussed.
+",1,0,1,1,0,0
+6898,Data-driven regularization of Wasserstein barycenters with an application to multivariate density registration,"  We present a framework to simultaneously align and smooth data in the form of
+multiple point clouds sampled from unknown densities with support in a
+$d$-dimensional Euclidean space. This work is motivated by applications in
+bioinformatics where researchers aim to automatically homogenize large datasets
+to compare and analyze characteristics within a same cell population.
+Inconveniently, the information acquired is most certainly noisy due to
+mis-alignment caused by technical variations of the environment. To overcome
+this problem, we propose to register multiple point clouds by using the notion
+of regularized barycenters (or Fréchet mean) of a set of probability
+measures with respect to the Wasserstein metric. A first approach consists in
+penalizing a Wasserstein barycenter with a convex functional as recently
+proposed in Bigot and al. (2018). A second strategy is to transform the
+Wasserstein metric itself into an entropy regularized transportation cost
+between probability measures as introduced in Cuturi (2013). The main
+contribution of this work is to propose data-driven choices for the
+regularization parameters involved in each approach using the
+Goldenshluger-Lepski's principle. Simulated data sampled from Gaussian mixtures
+are used to illustrate each method, and an application to the analysis of flow
+cytometry data is finally proposed.
+",0,0,0,1,0,0
+6899,BCS quantum critical phenomena,"  Theoretically, we recently showed that the scaling relation between the
+transition temperature T_c and the superfluid density at zero temperature n_s
+(0) might exhibit a parabolic pattern [Scientific Reports 6 (2016) 23863]. It
+is significantly different from the linear scaling described by Homes' law,
+which is well known as a mean-field result. More recently, Bozovic et al. have
+observed such a parabolic scaling in the overdoped copper oxides with a
+sufficiently low transition temperature T_c [Nature 536 (2016) 309-311]. They
+further point out that this experimental finding is incompatible with the
+standard Bardeen-Cooper-Schrieffer (BCS) description. Here we report that if
+T_c is sufficiently low, applying the renormalization group approach into the
+BCS action at zero temperature will naturally lead to the parabolic scaling.
+Our result indicates that when T_c sufficiently approaches zero, quantum
+fluctuations will be overwhelmingly amplified so that the mean-field
+approximation may break down at zero temperature.
+",0,1,0,0,0,0
+6900,Action preserving (weak) topologies on the category of presheaves,"  Let $\mathcal{C}$ be a finitely complete small category. In this paper, first
+we construct two weak (Lawvere-Tierney) topologies on the category of
+presheaves. One of them is established by means of a subfunctor of the Yoneda
+functor and the other one, is constructed by an admissible class on
+$\mathcal{C}$ and the internal existential quantifier in the presheaf topos
+$\widehat{\mathcal{C}}$. Moreover, by using an admissible class on
+$\mathcal{C},$ we are able to define an action on the subobject classifier
+$\Omega$ of $\widehat{\mathcal{C}}$. Then we find some necessary conditions for
+that the two weak topologies and also the double negation topology $\neg\neg$
+on $\widehat{\mathcal{C}}$ to be action preserving maps. Finally, among other
+things, we constitute an action preserving weak topology on
+$\widehat{\mathcal{C}}$.
+",0,0,1,0,0,0
+6901,Learning to detect chest radiographs containing lung nodules using visual attention networks,"  Machine learning approaches hold great potential for the automated detection
+of lung nodules in chest radiographs, but training the algorithms requires vary
+large amounts of manually annotated images, which are difficult to obtain. Weak
+labels indicating whether a radiograph is likely to contain pulmonary nodules
+are typically easier to obtain at scale by parsing historical free-text
+radiological reports associated to the radiographs. Using a repositotory of
+over 700,000 chest radiographs, in this study we demonstrate that promising
+nodule detection performance can be achieved using weak labels through
+convolutional neural networks for radiograph classification. We propose two
+network architectures for the classification of images likely to contain
+pulmonary nodules using both weak labels and manually-delineated bounding
+boxes, when these are available. Annotated nodules are used at training time to
+deliver a visual attention mechanism informing the model about its localisation
+performance. The first architecture extracts saliency maps from high-level
+convolutional layers and compares the estimated position of a nodule against
+the ground truth, when this is available. A corresponding localisation error is
+then back-propagated along with the softmax classification error. The second
+approach consists of a recurrent attention model that learns to observe a short
+sequence of smaller image portions through reinforcement learning. When a
+nodule annotation is available at training time, the reward function is
+modified accordingly so that exploring portions of the radiographs away from a
+nodule incurs a larger penalty. Our empirical results demonstrate the potential
+advantages of these architectures in comparison to competing methodologies.
+",0,0,0,1,0,0
+6902,New conformal map for the Sinc approximation for exponentially decaying functions over the semi-infinite interval,"  The Sinc approximation has shown high efficiency for numerical methods in
+many fields. Conformal maps play an important role in the success, i.e.,
+appropriate conformal map must be employed to elicit high performance of the
+Sinc approximation. Appropriate conformal maps have been proposed for typical
+cases; however, such maps may not be optimal. Thus, the performance of the Sinc
+approximation may be improved by using another conformal map rather than an
+existing map. In this paper, we propose a new conformal map for the case where
+functions are defined over the semi-infinite interval and decay exponentially.
+Then, we demonstrate in both theoretical and numerical ways that the
+convergence rate is improved by replacing the existing conformal map with the
+proposed map.
+",1,0,0,0,0,0
+6903,Evidence synthesis for stochastic epidemic models,"  In recent years the role of epidemic models in informing public health
+policies has progressively grown. Models have become increasingly realistic and
+more complex, requiring the use of multiple data sources to estimate all
+quantities of interest. This review summarises the different types of
+stochastic epidemic models that use evidence synthesis and highlights current
+challenges.
+",0,0,0,1,0,0
+6904,The multi-resonant Lugiato-Lefever model,"  We introduce a new model describing multiple resonances in Kerr optical
+cavities. It perfectly agrees quantitatively with the Ikeda map and predicts
+complex phenomena such as super cavity solitons and coexistence of multiple
+nonlinear states.
+",0,1,0,0,0,0
+6905,Scalable Bayesian shrinkage and uncertainty quantification in high-dimensional regression,"  Bayesian shrinkage methods have generated a lot of recent interest as tools
+for high-dimensional regression and model selection. These methods naturally
+facilitate tractable uncertainty quantification and incorporation of prior
+information. A common feature of these models, including the Bayesian lasso,
+global-local shrinkage priors, and spike-and-slab priors is that the
+corresponding priors on the regression coefficients can be expressed as scale
+mixture of normals. While the three-step Gibbs sampler used to sample from the
+often intractable associated posterior density has been shown to be
+geometrically ergodic for several of these models (Khare and Hobert, 2013; Pal
+and Khare, 2014), it has been demonstrated recently that convergence of this
+sampler can still be quite slow in modern high-dimensional settings despite
+this apparent theoretical safeguard. We propose a new method to draw from the
+same posterior via a tractable two-step blocked Gibbs sampler. We demonstrate
+that our proposed two-step blocked sampler exhibits vastly superior convergence
+behavior compared to the original three- step sampler in high-dimensional
+regimes on both real and simulated data. We also provide a detailed theoretical
+underpinning to the new method in the context of the Bayesian lasso. First, we
+derive explicit upper bounds for the (geometric) rate of convergence.
+Furthermore, we demonstrate theoretically that while the original Bayesian
+lasso chain is not Hilbert-Schmidt, the proposed chain is trace class (and
+hence Hilbert-Schmidt). The trace class property has useful theoretical and
+practical implications. It implies that the corresponding Markov operator is
+compact, and its eigenvalues are summable. It also facilitates a rigorous
+comparison of the two-step blocked chain with ""sandwich"" algorithms which aim
+to improve performance of the two-step chain by inserting an inexpensive extra
+step.
+",0,0,0,1,0,0
+6906,Inclusion and Majorization Properties of Certain Subclasses of Multivalent Analytic Functions Involving a Linear Operator,"  The object of the present paper is to study certain properties and
+characteristics of the operator $Q_{p,\beta}^{\alpha}$defined on p-valent
+analytic function by using technique of differential subordination.We also
+obtained result involving majorization problems by applying the operator to
+p-valent analytic function.Relevant connection of the the result are presented
+here with those obtained by earlier worker are pointed out.
+",0,0,1,0,0,0
+6907,Tameness in least fixed-point logic and McColm's conjecture,"  We investigate fundamental model-theoretic dividing lines (the order
+property, the independence property, the strict order property, and the tree
+property 2) in the context of least fixed-point (LFP) logic over families of
+finite structures. We show that, unlike the first-order (FO) case, the order
+property and the independence property are equivalent, but all of the other
+natural implications are strict. We identify the LFP strict order property with
+proficiency, a well-studied notion in finite model theory.
+Gregory McColm conjectured that FO and LFP definability coincide over a
+family C of finite structures exactly when C is non-proficient. McColm's
+conjecture is false in general, but as an application of our results, we show
+that it holds under standard FO tameness assumptions adapted to families of
+finite structures.
+",1,0,1,0,0,0
+6908,Enhanced activity of the Southern Taurids in 2005 and 2015,"  The paper presents an analysis of Polish Fireball Network (PFN) observations
+of enhanced activity of the Southern Taurid meteor shower in 2005 and 2015. In
+2005, between October 20 and November 10, seven stations of PFN determined 107
+accurate orbits with 37 of them belonging to the Southern Taurid shower. In the
+same period of 2015, 25 stations of PFN recorded 719 accurate orbits with 215
+orbits of the Southern Taurids. Both maxima were rich in fireballs which
+accounted to 17% of all observed Taurids. The whole sample of Taurid fireballs
+is quite uniform in the sense of starting and terminal heights of the
+trajectory. On the other hand a clear decreasing trend in geocentric velocity
+with increasing solar longitude was observed.
+Orbital parameters of observed Southern Taurids were compared to orbital
+elements of Near Earth Objects (NEO) from the NEODYS-2 database. Using the
+Drummond criterion $D'$ with threshold as low as 0.06, we found over 100
+fireballs strikingly similar to the orbit of asteroid 2015 TX24. Several dozens
+of Southern Taurids have orbits similar to three other asteroids, namely: 2005
+TF50, 2005 UR and 2010 TU149. All mentioned NEOs have orbital periods very
+close to the 7:2 resonance with Jupiter's orbit. It confirms a theory of a
+""resonant meteoroid swarm"" within the Taurid complex that predicts that in
+specific years, the Earth is hit by a greater number of meteoroids capable of
+producing fireballs.
+",0,1,0,0,0,0
+6909,Comparing anticyclotomic Selmer groups of positive coranks for congruent modular forms,"  We study the variation of Iwasawa invariants of the anticyclotomic Selmer
+groups of congruent modular forms under the Heegner hypothesis. In particular,
+we show that even if the Selmer groups we study may have positive coranks, the
+mu-invariant vanishes for one modular form if and only if it vanishes for the
+other, and that their lambda-invariants are related by an explicit formula.
+This generalizes results of Greenberg-Vatsal for the cyclotomic extension, as
+well as results of Pollack-Weston and Castella-Kim-Longo for the anticyclotomic
+extension when the Selmer groups in question are cotorsion.
+",0,0,1,0,0,0
+6910,Predicting the Results of LTL Model Checking using Multiple Machine Learning Algorithms,"  In this paper, we study how to predict the results of LTL model checking
+using some machine learning algorithms. Some Kripke structures and LTL formulas
+and their model checking results are made up data set. The approaches based on
+the Random Forest (RF), K-Nearest Neighbors (KNN), Decision tree (DT), and
+Logistic Regression (LR) are used to training and prediction. The experiment
+results show that the average computation efficiencies of the RF, LR, DT, and
+KNN-based approaches are 2066181, 2525333, 1894000 and 294 times than that of
+the existing approach, respectively.
+",1,0,0,0,0,0
+6911,Magnetoelectric properties of the layered room-temperature antiferromagnets BaMn2P2 and BaMn2As2,"  Properties of two ThCr2Si2-type materials are discussed within the context of
+their established structural and magnetic symmetries. Both materials develop
+collinear, G-type antiferromagnetic order above room temperature, and magnetic
+ions occupy acentric sites in centrosymmetric structures. We refute a previous
+conjecture that BaMn2As2 is an example of a magnetoelectric material with
+hexadecapole order by exposing flaws in supporting arguments, principally, an
+omission of discrete symmetries enforced by the symmetry of sites used by Mn
+ions and, also, improper classifications of the primary and secondary
+order-parameters. Implications for future experiments designed to improve our
+understanding of BaMn2P2 and BaMn2As2 magnetoelectric properties, using neutron
+and x-ray diffraction, are examined. Patterns of Bragg spots caused by
+conventional magnetic dipoles and magnetoelectric (Dirac) multipoles are
+predicted to be distinct, which raises the intriguing possibility of a unique
+and comprehensive examination of the magnetoelectric state by diffraction. A
+roto-inversion operation in Mn site symmetry is ultimately responsible for the
+distinguishing features.
+",0,1,0,0,0,0
+6912,Discrete Spectrum Reconstruction using Integral Approximation Algorithm,"  An inverse problem in spectroscopy is considered. The objective is to restore
+the discrete spectrum from observed spectrum data, taking into account the
+spectrometer's line spread function. The problem is reduced to solution of a
+system of linear-nonlinear equations (SLNE) with respect to intensities and
+frequencies of the discrete spectral lines. The SLNE is linear with respect to
+lines' intensities and nonlinear with respect to the lines' frequencies. The
+integral approximation algorithm is proposed for the solution of this SLNE. The
+algorithm combines solution of linear integral equations with solution of a
+system of linear algebraic equations and avoids nonlinear equations. Numerical
+examples of the application of the technique, both to synthetic and
+experimental spectra, demonstrate the efficacy of the proposed approach in
+enabling an effective enhancement of the spectrometer's resolution.
+",0,0,1,0,0,0
+6913,Tackling Over-pruning in Variational Autoencoders,"  Variational autoencoders (VAE) are directed generative models that learn
+factorial latent variables. As noted by Burda et al. (2015), these models
+exhibit the problem of factor over-pruning where a significant number of
+stochastic factors fail to learn anything and become inactive. This can limit
+their modeling power and their ability to learn diverse and meaningful latent
+representations. In this paper, we evaluate several methods to address this
+problem and propose a more effective model-based approach called the epitomic
+variational autoencoder (eVAE). The so-called epitomes of this model are groups
+of mutually exclusive latent factors that compete to explain the data. This
+approach helps prevent inactive units since each group is pressured to explain
+the data. We compare the approaches with qualitative and quantitative results
+on MNIST and TFD datasets. Our results show that eVAE makes efficient use of
+model capacity and generalizes better than VAE.
+",1,0,0,0,0,0
+6914,Elastic collision and molecule formation of spatiotemporal light bullets in a cubic-quintic nonlinear medium,"  We consider the statics and dynamics of a stable, mobile three-dimensional
+(3D) spatiotemporal light bullet in a cubic-quintic nonlinear medium with a
+focusing cubic nonlinearity above a critical value and any defocusing quintic
+nonlinearity. The 3D light bullet can propagate with a constant velocity in any
+direction. Stability of the light bullet under a small perturbation is
+established numerically.We consider frontal collision between two light bullets
+with different relative velocities. At large velocities the collision is
+elastic with the bullets emerge after collision with practically no distortion.
+At small velocities two bullets coalesce to form a bullet molecule. At a small
+range of intermediate velocities the localized bullets could form a single
+entity which expands indefinitely leading to a destruction of the bullets after
+collision. The present study is based on an analytic Lagrange variational
+approximation and a full numerical solution of the 3D nonlinear Schrödinger
+equation.
+",0,1,0,0,0,0
+6915,From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach,"  One key challenge in talent search is to translate complex criteria of a
+hiring position into a search query, while it is relatively easy for a searcher
+to list examples of suitable candidates for a given position. To improve search
+efficiency, we propose the next generation of talent search at LinkedIn, also
+referred to as Search By Ideal Candidates. In this system, a searcher provides
+one or several ideal candidates as the input to hire for a given position. The
+system then generates a query based on the ideal candidates and uses it to
+retrieve and rank results. Shifting from the traditional Query-By-Keyword to
+this new Query-By-Example system poses a number of challenges: How to generate
+a query that best describes the candidates? When moving to a completely
+different paradigm, how does one leverage previous product logs to learn
+ranking models and/or evaluate the new system with no existing usage logs?
+Finally, given the different nature between the two search paradigms, the
+ranking features typically used for Query-By-Keyword systems might not be
+optimal for Query-By-Example. This paper describes our approach to solving
+these challenges. We present experimental results confirming the effectiveness
+of the proposed solution, particularly on query building and search ranking
+tasks. As of writing this paper, the new system has been available to all
+LinkedIn members.
+",1,0,0,0,0,0
+6916,Cyber-Physical System for Energy-Efficient Stadium Operation: Methodology and Experimental Validation,"  The environmental impacts of medium to large scale buildings receive
+substantial attention in research, industry, and media. This paper studies the
+energy savings potential of a commercial soccer stadium during day-to-day
+operation. Buildings of this kind are characterized by special purpose system
+installations like grass heating systems and by event-driven usage patterns.
+This work presents a methodology to holistically analyze the stadiums
+characteristics and integrate its existing instrumentation into a
+Cyber-Physical System, enabling to deploy different control strategies
+flexibly. In total, seven different strategies for controlling the studied
+stadiums grass heating system are developed and tested in operation.
+Experiments in winter season 2014/2015 validated the strategies impacts within
+the real operational setup of the Commerzbank Arena, Frankfurt, Germany. With
+95% confidence, these experiments saved up to 66% of median daily
+weather-normalized energy consumption. Extrapolated to an average heating
+season, this corresponds to savings of 775 MWh and 148 t of CO2 emissions. In
+winter 2015/2016 an additional predictive nighttime heating experiment targeted
+lower temperatures, which increased the savings to up to 85%, equivalent to 1
+GWh (197 t CO2) in an average winter. Beyond achieving significant energy
+savings, the different control strategies also met the target temperature
+levels to the satisfaction of the stadiums operational staff. While the case
+study constitutes a significant part, the discussions dedicated to the
+transferability of this work to other stadiums and other building types show
+that the concepts and the approach are of general nature. Furthermore, this
+work demonstrates the first successful application of Deep Belief Networks to
+regress and predict the thermal evolution of building systems.
+",1,0,0,0,0,0
+6917,On Bayesian Exponentially Embedded Family for Model Order Selection,"  In this paper, we derive a Bayesian model order selection rule by using the
+exponentially embedded family method, termed Bayesian EEF. Unlike many other
+Bayesian model selection methods, the Bayesian EEF can use vague proper priors
+and improper noninformative priors to be objective in the elicitation of
+parameter priors. Moreover, the penalty term of the rule is shown to be the sum
+of half of the parameter dimension and the estimated mutual information between
+parameter and observed data. This helps to reveal the EEF mechanism in
+selecting model orders and may provide new insights into the open problems of
+choosing an optimal penalty term for model order selection and choosing a good
+prior from information theoretic viewpoints. The important example of linear
+model order selection is given to illustrate the algorithms and arguments.
+Lastly, the Bayesian EEF that uses Jeffreys prior coincides with the EEF rule
+derived by frequentist strategies. This shows another interesting relationship
+between the frequentist and Bayesian philosophies for model selection.
+",0,0,0,1,0,0
+6918,Estimating solar flux density at low radio frequencies using a sky brightness model,"  Sky models have been used in the past to calibrate individual low radio
+frequency telescopes. Here we generalize this approach from a single antenna to
+a two element interferometer and formulate the problem in a manner to allow us
+to estimate the flux density of the Sun using the normalized cross-correlations
+(visibilities) measured on a low resolution interferometric baseline. For wide
+field-of-view instruments, typically the case at low radio frequencies, this
+approach can provide robust absolute solar flux calibration for well
+characterized antennas and receiver systems. It can provide a reliable and
+computationally lean method for extracting parameters of physical interest
+using a small fraction of the voluminous interferometric data, which can be
+prohibitingly compute intensive to calibrate and image using conventional
+approaches. We demonstrate this technique by applying it to data from the
+Murchison Widefield Array and assess its reliability.
+",0,1,0,0,0,0
+6919,Revisiting Activation Regularization for Language RNNs,"  Recurrent neural networks (RNNs) serve as a fundamental building block for
+many sequence tasks across natural language processing. Recent research has
+focused on recurrent dropout techniques or custom RNN cells in order to improve
+performance. Both of these can require substantial modifications to the machine
+learning model or to the underlying RNN configurations. We revisit traditional
+regularization techniques, specifically L2 regularization on RNN activations
+and slowness regularization over successive hidden states, to improve the
+performance of RNNs on the task of language modeling. Both of these techniques
+require minimal modification to existing RNN architectures and result in
+performance improvements comparable or superior to more complicated
+regularization techniques or custom cell architectures. These regularization
+techniques can be used without any modification on optimized LSTM
+implementations such as the NVIDIA cuDNN LSTM.
+",1,0,0,0,0,0
+6920,Non-equilibrium time dynamics of genetic evolution,"  Biological systems are typically highly open, non-equilibrium systems that
+are very challenging to understand from a statistical mechanics perspective.
+While statistical treatments of evolutionary biological systems have a long and
+rich history, examination of the time-dependent non-equilibrium dynamics has
+been less studied. In this paper we first derive a generalized master equation
+in the genotype space for diploid organisms incorporating the processes of
+selection, mutation, recombination, and reproduction. The master equation is
+defined in terms of continuous time and can handle an arbitrary number of gene
+loci and alleles, and can be defined in terms of an absolute population or
+probabilities. We examine and analytically solve several prototypical cases
+which illustrate the interplay of the various processes and discuss the
+timescales of their evolution. The entropy production during the evolution
+towards steady state is calculated and we find that it agrees with predictions
+from non-equilibrium statistical mechanics where it is large when the
+population distribution evolves towards a more viable genotype. The stability
+of the non-equilibrium steady state is confirmed using the Glansdorff-Prigogine
+criterion.
+",0,0,0,0,1,0
+6921,On the scaling of entropy viscosity in high order methods,"  In this work, we outline the entropy viscosity method and discuss how the
+choice of scaling influences the size of viscosity for a simple shock problem.
+We present examples to illustrate the performance of the entropy viscosity
+method under two distinct scalings.
+",0,0,1,0,0,0
+6922,An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification,"  Convolutional neural networks (CNNs) are similar to ""ordinary"" neural
+networks in the sense that they are made up of hidden layers consisting of
+neurons with ""learnable"" parameters. These neurons receive inputs, performs a
+dot product, and then follows it with a non-linearity. The whole network
+expresses the mapping between raw image pixels and their class scores.
+Conventionally, the Softmax function is the classifier used at the last layer
+of this network. However, there have been studies (Alalshekmubarak and Smith,
+2013; Agarap, 2017; Tang, 2013) conducted to challenge this norm. The cited
+studies introduce the usage of linear support vector machine (SVM) in an
+artificial neural network architecture. This project is yet another take on the
+subject, and is inspired by (Tang, 2013). Empirical data has shown that the
+CNN-SVM model was able to achieve a test accuracy of ~99.04% using the MNIST
+dataset (LeCun, Cortes, and Burges, 2010). On the other hand, the CNN-Softmax
+was able to achieve a test accuracy of ~99.23% using the same dataset. Both
+models were also tested on the recently-published Fashion-MNIST dataset (Xiao,
+Rasul, and Vollgraf, 2017), which is suppose to be a more difficult image
+classification dataset than MNIST (Zalandoresearch, 2017). This proved to be
+the case as CNN-SVM reached a test accuracy of ~90.72%, while the CNN-Softmax
+reached a test accuracy of ~91.86%. The said results may be improved if data
+preprocessing techniques were employed on the datasets, and if the base CNN
+model was a relatively more sophisticated than the one used in this study.
+",1,0,0,1,0,0
+6923,Derivation of the cutoff length from the quantum quadratic enhancement of a mass in vacuum energy constant Lambda,"  Ultraviolet self-interaction energies in field theory sometimes contain
+meaningful physical quantities. The self-energies in such as classical
+electrodynamics are usually subtracted from the rest mass. For the consistent
+treatment of energies as sources of curvature in the Einstein field equations,
+this study includes these subtracted self-energies into vacuum energy expressed
+by the constant Lambda (used in such as Lambda-CDM). In this study, the
+self-energies in electrodynamics and macroscopic classical Einstein field
+equations are examined, using the formalisms with the ultraviolet cutoff
+scheme. One of the cutoff formalisms is the field theory in terms of the
+step-function-type basis functions, developed by the present authors. The other
+is a continuum theory of a fundamental particle with the same cutoff length.
+Based on the effectiveness of the continuum theory with the cutoff length shown
+in the examination, the dominant self-energy is the quadratic term of the Higgs
+field at a quantum level (classical self-energies are reduced to logarithmic
+forms by quantum corrections). The cutoff length is then determined to
+reproduce today's tiny value of Lambda for vacuum energy. Additionally, a field
+with nonperiodic vanishing boundary conditions is treated, showing that the
+field has no zero-point energy.
+",0,1,0,0,0,0
+6924,Exact MAP Inference by Avoiding Fractional Vertices,"  Given a graphical model, one essential problem is MAP inference, that is,
+finding the most likely configuration of states according to the model.
+Although this problem is NP-hard, large instances can be solved in practice. A
+major open question is to explain why this is true. We give a natural condition
+under which we can provably perform MAP inference in polynomial time. We
+require that the number of fractional vertices in the LP relaxation exceeding
+the optimal solution is bounded by a polynomial in the problem size. This
+resolves an open question by Dimakis, Gohari, and Wainwright. In contrast, for
+general LP relaxations of integer programs, known techniques can only handle a
+constant number of fractional vertices whose value exceeds the optimal
+solution. We experimentally verify this condition and demonstrate how efficient
+various integer programming methods are at removing fractional solutions.
+",1,0,0,1,0,0
+6925,Study on a Poisson's Equation Solver Based On Deep Learning Technique,"  In this work, we investigated the feasibility of applying deep learning
+techniques to solve Poisson's equation. A deep convolutional neural network is
+set up to predict the distribution of electric potential in 2D or 3D cases.
+With proper training data generated from a finite difference solver, the strong
+approximation capability of the deep convolutional neural network allows it to
+make correct prediction given information of the source and distribution of
+permittivity. With applications of L2 regularization, numerical experiments
+show that the predication error of 2D cases can reach below 1.5\% and the
+predication of 3D cases can reach below 3\%, with a significant reduction in
+CPU time compared with the traditional solver based on finite difference
+methods.
+",1,1,0,0,0,0
+6926,On the compressibility of the transition-metal carbides and nitrides alloys Zr_xNb_{1-x}C and Zr_xNb_{1-x}N,"  The 4d-transition-metals carbides (ZrC, NbC) and nitrides (ZrN, NbN) in the
+rocksalt structure, as well as their ternary alloys, have been recently studied
+by means of a first-principles full potential linearized augmented plane waves
+method within the local density approximation. These materials are important
+because of their interesting mechanical and physical properties, which make
+them suitable for many technological applications. Here, by using a simple
+theoretical model, we estimate the bulk moduli of their ternary alloys
+Zr$_x$Nb$_{1-x}$C and Zr$_x$Nb$_{1-x}$N in terms of the bulk moduli of the end
+members alone. The results are comparable to those deduced from the
+first-principles calculations.
+",0,1,0,0,0,0
+6927,On multifractals: a non-linear study of actigraphy data,"  This work aimed, to determine the characteristics of activity series from
+fractal geometry concepts application, in addition to evaluate the possibility
+of identifying individuals with fibromyalgia. Activity level data were
+collected from 27 healthy subjects and 27 fibromyalgia patients, with the use
+of clock-like devices equipped with accelerometers, for about four weeks, all
+day long. The activity series were evaluated through fractal and multifractal
+methods. Hurst exponent analysis exhibited values according to other studies
+($H>0.5$) for both groups ($H=0.98\pm0.04$ for healthy subjects and
+$H=0.97\pm0.03$ for fibromyalgia patients), however, it is not possible to
+distinguish between the two groups by such analysis. Activity time series also
+exhibited a multifractal pattern. A paired analysis of the spectra indices for
+the sleep and awake states revealed differences between healthy subjects and
+fibromyalgia patients. The individuals feature differences between awake and
+sleep states, having statistically significant differences for $\alpha_{q-} -
+\alpha_{0}$ in healthy subjects ($p = 0.014$) and $D_{0}$ for patients with
+fibromyalgia ($p = 0.013$). The approach has proven to be an option on the
+characterisation of such kind of signals and was able to differ between both
+healthy and fibromyalgia groups. This outcome suggests changes in the
+physiologic mechanisms of movement control.
+",0,1,0,1,0,0
+6928,Exact semi-separation of variables in waveguides with nonplanar boundaries,"  Series expansions of unknown fields $\Phi=\sum\varphi_n Z_n$ in elongated
+waveguides are commonly used in acoustics, optics, geophysics, water waves and
+other applications, in the context of coupled-mode theories (CMTs). The
+transverse functions $Z_n$ are determined by solving local Sturm-Liouville
+problems (reference waveguides). In most cases, the boundary conditions
+assigned to $Z_n$ cannot be compatible with the physical boundary conditions of
+$\Phi$, leading to slowly convergent series, and rendering CMTs mild-slope
+approximations. In the present paper, the heuristic approach introduced in
+(Athanassoulis & Belibassakis 1999, J. Fluid Mech. 389, 275-301) is generalized
+and justified. It is proved that an appropriately enhanced series expansion
+becomes an exact, rapidly-convergent representation of the field $\Phi$, valid
+for any smooth, nonplanar boundaries and any smooth enough $\Phi$. This series
+expansion can be differentiated termwise everywhere in the domain, including
+the boundaries, implementing an exact semi-separation of variables for
+non-separable domains. The efficiency of the method is illustrated by solving a
+boundary value problem for the Laplace equation, and computing the
+corresponding Dirichlet-to-Neumann operator, involved in Hamiltonian equations
+for nonlinear water waves. The present method provides accurate results with
+only a few modes for quite general domains. Extensions to general waveguides
+are also discussed.
+",0,0,1,0,0,0
+6929,Universality for eigenvalue algorithms on sample covariance matrices,"  We prove a universal limit theorem for the halting time, or iteration count,
+of the power/inverse power methods and the QR eigenvalue algorithm.
+Specifically, we analyze the required number of iterations to compute extreme
+eigenvalues of random, positive-definite sample covariance matrices to within a
+prescribed tolerance. The universality theorem provides a complexity estimate
+for the algorithms which, in this random setting, holds with high probability.
+The method of proof relies on recent results on the statistics of the
+eigenvalues and eigenvectors of random sample covariance matrices (i.e.,
+delocalization, rigidity and edge universality).
+",0,0,1,0,0,0
+6930,Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function,"  We demonstrate that in residual neural networks (ResNets) dynamical isometry
+is achievable irrespectively of the activation function used. We do that by
+deriving, with the help of Free Probability and Random Matrix Theories, a
+universal formula for the spectral density of the input-output Jacobian at
+initialization, in the large network width and depth limit. The resulting
+singular value spectrum depends on a single parameter, which we calculate for a
+variety of popular activation functions, by analyzing the signal propagation in
+the artificial neural network. We corroborate our results with numerical
+simulations of both random matrices and ResNets applied to the CIFAR-10
+classification problem. Moreover, we study the consequence of this universal
+behavior for the initial and late phases of the learning processes. We conclude
+by drawing attention to the simple fact, that initialization acts as a
+confounding factor between the choice of activation function and the rate of
+learning. We propose that in ResNets this can be resolved based on our results,
+by ensuring the same level of dynamical isometry at initialization.
+",0,0,0,1,0,0
+6931,Similarity-based Multi-label Learning,"  Multi-label classification is an important learning problem with many
+applications. In this work, we propose a principled similarity-based approach
+for multi-label learning called SML. We also introduce a similarity-based
+approach for predicting the label set size. The experimental results
+demonstrate the effectiveness of SML for multi-label classification where it is
+shown to compare favorably with a wide variety of existing algorithms across a
+range of evaluation criterion.
+",1,0,0,1,0,0
+6932,Burst Synchronization in A Scale-Free Neuronal Network with Inhibitory Spike-Timing-Dependent Plasticity,"  We are concerned about burst synchronization (BS), related to neural
+information processes in health and disease, in the Barabási-Albert
+scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose
+neurons. This inhibitory neuronal population has adaptive dynamic synaptic
+strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP).
+In previous works without considering iSTDP, BS was found to appear in a range
+of noise intensities for fixed synaptic inhibition strengths. In contrast, in
+our present work, we take into consideration iSTDP and investigate its effect
+on BS by varying the noise intensity. Our new main result is to find occurrence
+of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via
+LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory
+synaptic plasticity is in contrast to that in excitatory synaptic plasticity
+where good (bad) synchronization gets better (worse) via LTP (LTD). We note
+that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic
+plasticity are reversed in comparison with those in excitatory synaptic
+plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition
+strengths are intensively investigated via a microscopic method based on the
+distributions of time delays between the pre- and the post-synaptic burst onset
+times. Finally, in the presence of iSTDP we investigate the effects of network
+architecture on BS by varying the symmetric attachment degree $l^*$ and the
+asymmetry parameter $\Delta l$ in the SFN.
+",0,0,0,0,1,0
+6933,The Arrow of Time in the collapse of collisionless self-gravitating systems: non-validity of the Vlasov-Poisson equation during violent relaxation,"  The collapse of a collisionless self-gravitating system, with the fast
+achievement of a quasi-stationary state, is driven by violent relaxation, with
+a typical particle interacting with the time-changing collective potential. It
+is traditionally assumed that this evolution is governed by the Vlasov-Poisson
+equation, in which case entropy must be conserved. We run N-body simulations of
+isolated self-gravitating systems, using three simulation codes: NBODY-6
+(direct summation without softening), NBODY-2 (direct summation with softening)
+and GADGET-2 (tree code with softening), for different numbers of particles and
+initial conditions. At each snapshot, we estimate the Shannon entropy of the
+distribution function with three different techniques: Kernel, Nearest Neighbor
+and EnBiD. For all simulation codes and estimators, the entropy evolution
+converges to the same limit as N increases. During violent relaxation, the
+entropy has a fast increase followed by damping oscillations, indicating that
+violent relaxation must be described by a kinetic equation other than the
+Vlasov-Poisson, even for N as large as that of astronomical structures. This
+indicates that violent relaxation cannot be described by a time-reversible
+equation, shedding some light on the so-called ""fundamental paradox of stellar
+dynamics"". The long-term evolution is well described by the orbit-averaged
+Fokker-Planck model, with Coulomb logarithm values in the expected range 10-12.
+By means of NBODY-2, we also study the dependence of the 2-body relaxation
+time-scale on the softening length. The approach presented in the current work
+can potentially provide a general method for testing any kinetic equation
+intended to describe the macroscopic evolution of N-body systems.
+",0,1,0,0,0,0
+6934,Numerical Observation of Parafermion Zero Modes and their Stability in 2D Topological States,"  The possibility of realizing non-Abelian excitations (non-Abelions) in
+two-dimensional (2D) Abelian states of matter has generated a lot of interest
+recently. A well-known example of such non-Abelions are parafermion zeros modes
+(PFZMs) which can be realized at the endpoints of the so called genons in
+fractional quantum Hall (FQH) states or fractional Chern insulators (FCIs). In
+this letter, we discuss some known signatures of PFZMs and also introduce some
+novel ones. In particular, we show that the topological entanglement entropy
+(TEE) shifts by a quantized value after crossing PFZMs. Utilizing those
+signatures, we present the first large scale numerical study of PFZMs and their
+stability against perturbations in both FQH states and FCIs within the
+density-Matrix-Renormalization-Group (DMRG) framework. Our results can help
+build a closer connection with future experiments on FQH states with genons.
+",0,1,0,0,0,0
+6935,Stochastic Optimal Control of Epidemic Processes in Networks,"  We approach the development of models and control strategies of
+susceptible-infected-susceptible (SIS) epidemic processes from the perspective
+of marked temporal point processes and stochastic optimal control of stochastic
+differential equations (SDEs) with jumps. In contrast to previous work, this
+novel perspective is particularly well-suited to make use of fine-grained data
+about disease outbreaks and lets us overcome the shortcomings of current
+control strategies. Our control strategy resorts to treatment intensities to
+determine who to treat and when to do so to minimize the amount of infected
+individuals over time. Preliminary experiments with synthetic data show that
+our control strategy consistently outperforms several alternatives. Looking
+into the future, we believe our methodology provides a promising step towards
+the development of practical data-driven control strategies of epidemic
+processes.
+",1,0,0,0,0,0
+6936,Improved upper bounds in the moving sofa problem,"  The moving sofa problem, posed by L. Moser in 1966, asks for the planar shape
+of maximal area that can move around a right-angled corner in a hallway of unit
+width. It is known that a maximal area shape exists, and that its area is at
+least 2.2195... - the area of an explicit construction found by Gerver in 1992
+- and at most $2\sqrt{2}=2.82...$, with the lower bound being conjectured as
+the true value. We prove a new and improved upper bound of 2.37. The method
+involves a computer-assisted proof scheme that can be used to rigorously derive
+further improved upper bounds that converge to the correct value.
+",0,0,1,0,0,0
+6937,Neural correlates of episodic memory in the Memento cohort,"  IntroductionThe free and cued selective reminding test is used to identify
+memory deficits in mild cognitive impairment and demented patients. It allows
+assessing three processes: encoding, storage, and recollection of verbal
+episodic memory.MethodsWe investigated the neural correlates of these three
+memory processes in a large cohort study. The Memento cohort enrolled 2323
+outpatients presenting either with subjective cognitive decline or mild
+cognitive impairment who underwent cognitive, structural MRI and, for a subset,
+fluorodeoxyglucose--positron emission tomography evaluations.ResultsEncoding
+was associated with a network including parietal and temporal cortices; storage
+was mainly associated with entorhinal and parahippocampal regions, bilaterally;
+retrieval was associated with a widespread network encompassing frontal
+regions.DiscussionThe neural correlates of episodic memory processes can be
+assessed in large and standardized cohorts of patients at risk for Alzheimer's
+disease. Their relation to pathophysiological markers of Alzheimer's disease
+remains to be studied.
+",0,0,0,0,1,0
+6938,Neighborhood-Based Label Propagation in Large Protein Graphs,"  Understanding protein function is one of the keys to understanding life at
+the molecular level. It is also important in several scenarios including human
+disease and drug discovery. In this age of rapid and affordable biological
+sequencing, the number of sequences accumulating in databases is rising with an
+increasing rate. This presents many challenges for biologists and computer
+scientists alike. In order to make sense of this huge quantity of data, these
+sequences should be annotated with functional properties. UniProtKB consists of
+two components: i) the UniProtKB/Swiss-Prot database containing protein
+sequences with reliable information manually reviewed by expert bio-curators
+and ii) the UniProtKB/TrEMBL database that is used for storing and processing
+the unknown sequences. Hence, for all proteins we have available the sequence
+along with few more information such as the taxon and some structural domains.
+Pairwise similarity can be defined and computed on proteins based on such
+attributes. Other important attributes, while present for proteins in
+Swiss-Prot, are often missing for proteins in TrEMBL, such as their function
+and cellular localization. The enormous number of protein sequences now in
+TrEMBL calls for rapid procedures to annotate them automatically. In this work,
+we present DistNBLP, a novel Distributed Neighborhood-Based Label Propagation
+approach for large-scale annotation of proteins. To do this, the functional
+annotations of reviewed proteins are used to predict those of non-reviewed
+proteins using label propagation on a graph representation of the protein
+database. DistNBLP is built on top of the ""akka"" toolkit for building resilient
+distributed message-driven applications.
+",1,0,0,0,0,0
+6939,Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions,"  Compressive sensing is a powerful technique for recovering sparse solutions
+of underdetermined linear systems, which is often encountered in uncertainty
+quantification analysis of expensive and high-dimensional physical models. We
+perform numerical investigations employing several compressive sensing solvers
+that target the unconstrained LASSO formulation, with a focus on linear systems
+that arise in the construction of polynomial chaos expansions. With core
+solvers of l1_ls, SpaRSA, CGIST, FPC_AS, and ADMM, we develop techniques to
+mitigate overfitting through an automated selection of regularization constant
+based on cross-validation, and a heuristic strategy to guide the stop-sampling
+decision. Practical recommendations on parameter settings for these techniques
+are provided and discussed. The overall method is applied to a series of
+numerical examples of increasing complexity, including large eddy simulations
+of supersonic turbulent jet-in-crossflow involving a 24-dimensional input.
+Through empirical phase-transition diagrams and convergence plots, we
+illustrate sparse recovery performance under structures induced by polynomial
+chaos, accuracy and computational tradeoffs between polynomial bases of
+different degrees, and practicability of conducting compressive sensing for a
+realistic, high-dimensional physical application. Across test cases studied in
+this paper, we find ADMM to have demonstrated empirical advantages through
+consistent lower errors and faster computational times.
+",0,1,0,1,0,0
+6940,Closure Properties in the Class of Multiple Context Free Groups,"  We show that the class of groups with $k$-multiple context-free word problem
+is closed under graphs of groups with finite edge groups.
+",1,0,1,0,0,0
+6941,Random gauge models of the superconductor-insulator transition in two-dimensional disordered superconductors,"  We study numerically the superconductor-insulator transition in
+two-dimensional inhomogeneous superconductors with gauge disorder, described by
+four different quantum rotor models: a gauge glass, a flux glass, a binary
+phase glass and a Gaussian phase glass. The first two models, describe the
+combined effect of geometrical disorder in the array of local superconducting
+islands and a uniform external magnetic field while the last two describe the
+effects of random negative Josephson-junction couplings or $\pi$ junctions.
+Monte Carlo simulations in the path-integral representation of the models are
+used to determine the critical exponents and the universal conductivity at the
+quantum phase transition. The gauge and flux glass models display the same
+critical behavior, within the estimated numerical uncertainties. Similar
+agreement is found for the binary and Gaussian phase-glass models. Despite the
+different symmetries and disorder correlations, we find that the universal
+conductivity of these models is approximately the same. In particular, the
+ratio of this value to that of the pure model agrees with recent experiments on
+nanohole thin film superconductors in a magnetic field, in the large disorder
+limit.
+",0,1,0,0,0,0
+6942,"Extended quantum field theory, index theory and the parity anomaly","  We use techniques from functorial quantum field theory to provide a geometric
+description of the parity anomaly in fermionic systems coupled to background
+gauge and gravitational fields on odd-dimensional spacetimes. We give an
+explicit construction of a geometric cobordism bicategory which incorporates
+general background fields in a stack, and together with the theory of symmetric
+monoidal bicategories we use it to provide the concrete forms of invertible
+extended quantum field theories which capture anomalies in both the path
+integral and Hamiltonian frameworks. Specialising this situation by using the
+extension of the Atiyah-Patodi-Singer index theorem to manifolds with corners
+due to Loya and Melrose, we obtain a new Hamiltonian perspective on the parity
+anomaly. We compute explicitly the 2-cocycle of the projective representation
+of the gauge symmetry on the quantum state space, which is defined in a
+parity-symmetric way by suitably augmenting the standard chiral fermionic Fock
+spaces with Lagrangian subspaces of zero modes of the Dirac Hamiltonian that
+naturally appear in the index theorem. We describe the significance of our
+constructions for the bulk-boundary correspondence in a large class of
+time-reversal invariant gauge-gravity symmetry-protected topological phases of
+quantum matter with gapless charged boundary fermions, including the standard
+topological insulator in 3+1 dimensions.
+",0,1,1,0,0,0
+6943,Learning Graphical Models Using Multiplicative Weights,"  We give a simple, multiplicative-weight update algorithm for learning
+undirected graphical models or Markov random fields (MRFs). The approach is
+new, and for the well-studied case of Ising models or Boltzmann machines, we
+obtain an algorithm that uses a nearly optimal number of samples and has
+quadratic running time (up to logarithmic factors), subsuming and improving on
+all prior work. Additionally, we give the first efficient algorithm for
+learning Ising models over general alphabets.
+Our main application is an algorithm for learning the structure of t-wise
+MRFs with nearly-optimal sample complexity (up to polynomial losses in
+necessary terms that depend on the weights) and running time that is
+$n^{O(t)}$. In addition, given $n^{O(t)}$ samples, we can also learn the
+parameters of the model and generate a hypothesis that is close in statistical
+distance to the true MRF. All prior work runs in time $n^{\Omega(d)}$ for
+graphs of bounded degree d and does not generate a hypothesis close in
+statistical distance even for t=3. We observe that our runtime has the correct
+dependence on n and t assuming the hardness of learning sparse parities with
+noise.
+Our algorithm--the Sparsitron-- is easy to implement (has only one parameter)
+and holds in the on-line setting. Its analysis applies a regret bound from
+Freund and Schapire's classic Hedge algorithm. It also gives the first solution
+to the problem of learning sparse Generalized Linear Models (GLMs).
+",1,0,1,1,0,0
+6944,Neutron response of PARIS phoswich detector,"  We have studied neutron response of PARIS phoswich [LaBr$_3$(Ce)-NaI(Tl)]
+detector which is being developed for measuring the high energy (E$_{\gamma}$ =
+5 - 30 MeV) $\gamma$ rays emitted from the decay of highly collective states in
+atomic nuclei. The relative neutron detection efficiency of LaBr$_3$(Ce) and
+NaI(Tl) crystal of the phoswich detector has been measured using the
+time-of-flight (TOF) and pulse shape discrimination (PSD) technique in the
+energy range of E$_n$ = 1 - 9 MeV and compared with the GEANT4 based
+simulations. It has been found that for E$_n$ $>$ 3 MeV, $\sim$ 95 \% of
+neutrons have the primary interaction in the LaBr$_3$(Ce) crystal, indicating
+that a clear n-$\gamma$ separation can be achieved even at $\sim$15 cm flight
+path.
+",0,1,0,0,0,0
+6945,Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks,"  The mine detection in an unexplored area is an optimization problem where
+multiple mines, randomly distributed throughout an area, need to be discovered
+and disarmed in a minimum amount of time. We propose a strategy to explore an
+unknown area, using a stigmergy approach based on ants behavior, and a novel
+swarm based protocol to recruit and coordinate robots for disarming the mines
+cooperatively. Simulation tests are presented to show the effectiveness of our
+proposed Ant-based Task Robot Coordination (ATRC) with only the exploration
+task and with both exploration and recruiting strategies. Multiple minimization
+objectives have been considered: the robots' recruiting time and the overall
+area exploration time. We discuss, through simulation, different cases under
+different network and field conditions, performed by the robots. The results
+have shown that the proposed decentralized approaches enable the swarm of
+robots to perform cooperative tasks intelligently without any central control.
+",1,0,0,0,0,0
+6946,A polynomial time knot polynomial,"  We present the strongest known knot invariant that can be computed
+effectively (in polynomial time).
+",0,0,1,0,0,0
+6947,Corruption-free scheme of entering into contract: mathematical model,"  The main purpose of this paper is to formalize the modelling process,
+analysis and mathematical definition of corruption when entering into a
+contract between principal agent and producers. The formulation of the problem
+and the definition of concepts for the general case are considered. For
+definiteness, all calculations and formulas are given for the case of three
+producers, one principal agent and one intermediary. Economic analysis of
+corruption allowed building a mathematical model of interaction between agents.
+Financial resources distribution problem in a contract with a corrupted
+intermediary is considered.Then proposed conditions for corruption emergence
+and its possible consequences. Optimal non-corruption schemes of financial
+resources distribution in a contract are formed, when principal agent's choice
+is limited first only by asymmetrical information and then also by external
+influences.Numerical examples suggesting optimal corruption-free agents'
+behaviour are presented.
+",0,0,0,0,0,1
+6948,Geometric Biplane Graphs I: Maximal Graphs,"  We study biplane graphs drawn on a finite planar point set $S$ in general
+position. This is the family of geometric graphs whose vertex set is $S$ and
+can be decomposed into two plane graphs. We show that two maximal biplane
+graphs---in the sense that no edge can be added while staying biplane---may
+differ in the number of edges, and we provide an efficient algorithm for adding
+edges to a biplane graph to make it maximal. We also study extremal properties
+of maximal biplane graphs such as the maximum number of edges and the largest
+maximum connectivity over $n$-element point sets.
+",1,0,0,0,0,0
+6949,A Multiobjective Approach to Multimicrogrid System Design,"  The main goal of this paper is to design a market operator (MO) and a
+distribution network operator (DNO) for a network of microgrids in
+consideration of multiple objectives. This is a high-level design and only
+those microgrids with nondispatchable renewable energy sources are considered.
+For a power grid in the network, the net value derived from providing power to
+the network must be maximized. For a microgrid, it is desirable to maximize the
+net gain derived from consuming the received power. Finally, for an independent
+system operator, stored energy levels at microgrids must be maintained as close
+as possible to storage capacity to secure network emergency operation. To
+achieve these objectives, a multiobjective approach is proposed. The price
+signal generated by the MO and power distributed by the DNO are assigned based
+on a Pareto optimal solution of a multiobjective optimization problem. By using
+the proposed approach, a fair scheme that does not advantage one particular
+objective can be attained. Simulations are provided to validate the proposed
+methodology.
+",1,0,0,0,0,0
+6950,Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus,"  In Web search, entity-seeking queries often trigger a special Question
+Answering (QA) system. It may use a parser to interpret the question to a
+structured query, execute that on a knowledge graph (KG), and return direct
+entity responses. QA systems based on precise parsing tend to be brittle: minor
+syntax variations may dramatically change the response. Moreover, KG coverage
+is patchy. At the other extreme, a large corpus may provide broader coverage,
+but in an unstructured, unreliable form. We present AQQUCN, a QA system that
+gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of
+query syntax, between well-formed questions to short `telegraphic' keyword
+sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals
+from KGs and large corpora to directly rank KG entities, rather than commit to
+one semantic interpretation of the query. AQQUCN models the ideal
+interpretation as an unobservable or latent variable. Interpretations and
+candidate entity responses are scored as pairs, by combining signals from
+multiple convolutional networks that operate collectively on the query, KG and
+corpus. On four public query workloads, amounting to over 8,000 queries with
+diverse query syntax, we see 5--16% absolute improvement in mean average
+precision (MAP), compared to the entity ranking performance of recent systems.
+Our system is also competitive at entity set retrieval, almost doubling F1
+scores for challenging short queries.
+",1,0,0,0,0,0
+6951,"Calculating the closed ordinal Ramsey number $R^{cl}(ω\cdot 2,3)^2$","  We show that $R^{cl}(\omega\cdot 2,3)^2$ is equal to $\omega^3\cdot 2$.
+",0,0,1,0,0,0
+6952,A spatially explicit capture recapture model for partially identified individuals when trap detection rate is less than one,"  Spatially explicit capture recapture (SECR) models have gained enormous
+popularity to solve abundance estimation problems in ecology. In this study, we
+develop a novel Bayesian SECR model that disentangles the process of animal
+movement through a detector from the process of recording data by a detector in
+the face of imperfect detection. We integrate this complexity into an advanced
+version of a recent SECR model involving partially identified individuals
+(Royle, 2015). We assess the performance of our model over a range of realistic
+simulation scenarios and demonstrate that estimates of population size $N$
+improve when we utilize the proposed model relative to the model that does not
+explicitly estimate trap detection probability (Royle, 2015). We confront and
+investigate the proposed model with a spatial capture-recapture data set from a
+camera trapping survey on tigers (\textit{Panthera tigris}) in Nagarahole,
+southern India. Trap detection probability is estimated at 0.489 and therefore
+justifies the necessity to utilize our model in field situations. We discuss
+possible extensions, future work and relevance of our model to other
+statistical applications beyond ecology.
+",0,0,0,1,0,0
+6953,Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery,"  Obtaining models that capture imaging markers relevant for disease
+progression and treatment monitoring is challenging. Models are typically based
+on large amounts of data with annotated examples of known markers aiming at
+automating detection. High annotation effort and the limitation to a vocabulary
+of known markers limit the power of such approaches. Here, we perform
+unsupervised learning to identify anomalies in imaging data as candidates for
+markers. We propose AnoGAN, a deep convolutional generative adversarial network
+to learn a manifold of normal anatomical variability, accompanying a novel
+anomaly scoring scheme based on the mapping from image space to a latent space.
+Applied to new data, the model labels anomalies, and scores image patches
+indicating their fit into the learned distribution. Results on optical
+coherence tomography images of the retina demonstrate that the approach
+correctly identifies anomalous images, such as images containing retinal fluid
+or hyperreflective foci.
+",1,0,0,0,0,0
+6954,ZOOpt: Toolbox for Derivative-Free Optimization,"  Recent advances of derivative-free optimization allow efficient approximating
+the global optimal solutions of sophisticated functions, such as functions with
+many local optima, non-differentiable and non-continuous functions. This
+article describes the ZOOpt (this https URL) toolbox that
+provides efficient derivative-free solvers and are designed easy to use. ZOOpt
+provides a Python package for single-thread optimization, and a light-weighted
+distributed version with the help of the Julia language for Python described
+functions. ZOOpt toolbox particularly focuses on optimization problems in
+machine learning, addressing high-dimensional, noisy, and large-scale problems.
+The toolbox is being maintained toward ready-to-use tool in real-world machine
+learning tasks.
+",0,0,0,1,0,0
+6955,Non-Gaussian Stochastic Volatility Model with Jumps via Gibbs Sampler,"  In this work, we propose a model for estimating volatility from financial
+time series, extending the non-Gaussian family of space-state models with exact
+marginal likelihood proposed by Gamerman, Santos and Franco (2013). On the
+literature there are models focused on estimating financial assets risk,
+however, most of them rely on MCMC methods based on Metropolis algorithms,
+since full conditional posterior distributions are not known. We present an
+alternative model capable of estimating the volatility, in an automatic way,
+since all full conditional posterior distributions are known, and it is
+possible to obtain an exact sample of parameters via Gibbs Sampler. The
+incorporation of jumps in returns allows the model to capture speculative
+movements of the data, so that their influence does not propagate to
+volatility. We evaluate the performance of the algorithm using synthetic and
+real data time series.
+Keywords: Financial time series, Stochastic volatility, Gibbs Sampler,
+Dynamic linear models.
+",0,0,0,0,0,1
+6956,Smart Guiding Glasses for Visually Impaired People in Indoor Environment,"  To overcome the travelling difficulty for the visually impaired group, this
+paper presents a novel ETA (Electronic Travel Aids)-smart guiding device in the
+shape of a pair of eyeglasses for giving these people guidance efficiently and
+safely. Different from existing works, a novel multi sensor fusion based
+obstacle avoiding algorithm is proposed, which utilizes both the depth sensor
+and ultrasonic sensor to solve the problems of detecting small obstacles, and
+transparent obstacles, e.g. the French door. For totally blind people, three
+kinds of auditory cues were developed to inform the direction where they can go
+ahead. Whereas for weak sighted people, visual enhancement which leverages the
+AR (Augment Reality) technique and integrates the traversable direction is
+adopted. The prototype consisting of a pair of display glasses and several low
+cost sensors is developed, and its efficiency and accuracy were tested by a
+number of users. The experimental results show that the smart guiding glasses
+can effectively improve the user's travelling experience in complicated indoor
+environment. Thus it serves as a consumer device for helping the visually
+impaired people to travel safely.
+",1,0,0,0,0,0
+6957,Recurrent Poisson Factorization for Temporal Recommendation,"  Poisson factorization is a probabilistic model of users and items for
+recommendation systems, where the so-called implicit consumer data is modeled
+by a factorized Poisson distribution. There are many variants of Poisson
+factorization methods who show state-of-the-art performance on real-world
+recommendation tasks. However, most of them do not explicitly take into account
+the temporal behavior and the recurrent activities of users which is essential
+to recommend the right item to the right user at the right time. In this paper,
+we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
+the classical PF methods by utilizing a Poisson process for modeling the
+implicit feedback. RPF treats time as a natural constituent of the model and
+brings to the table a rich family of time-sensitive factorization models. To
+elaborate, we instantiate several variants of RPF who are capable of handling
+dynamic user preferences and item specification (DRPF), modeling the
+social-aspect of product adoption (SRPF), and capturing the consumption
+heterogeneity among users and items (HRPF). We also develop a variational
+algorithm for approximate posterior inference that scales up to massive data
+sets. Furthermore, we demonstrate RPF's superior performance over many
+state-of-the-art methods on synthetic dataset, and large scale real-world
+datasets on music streaming logs, and user-item interactions in M-Commerce
+platforms.
+",1,0,0,1,0,0
+6958,Categorification of sign-skew-symmetric cluster algebras and some conjectures on g-vectors,"  Using the unfolding method given in \cite{HL}, we prove the conjectures on
+sign-coherence and a recurrence formula respectively of ${\bf g}$-vectors for
+acyclic sign-skew-symmetric cluster algebras. As a following consequence, the
+conjecture is affirmed in the same case which states that the ${\bf g}$-vectors
+of any cluster form a basis of $\mathbb Z^n$. Also, the additive
+categorification of an acyclic sign-skew-symmetric cluster algebra $\mathcal
+A(\Sigma)$ is given, which is realized as $(\mathcal C^{\widetilde Q},\Gamma)$
+for a Frobenius $2$-Calabi-Yau category $\mathcal C^{\widetilde Q}$ constructed
+from an unfolding $(Q,\Gamma)$ of the acyclic exchange matrix $B$ of $\mathcal
+A(\Sigma)$.
+",0,0,1,0,0,0
+6959,Capacitated Bounded Cardinality Hub Routing Problem: Model and Solution Algorithm,"  In this paper, we address the Bounded Cardinality Hub Location Routing with
+Route Capacity wherein each hub acts as a transshipment node for one directed
+route. The number of hubs lies between a minimum and a maximum and the
+hub-level network is a complete subgraph. The transshipment operations take
+place at the hub nodes and flow transfer time from a hub-level transporter to a
+spoke-level vehicle influences spoke- to-hub allocations. We propose a
+mathematical model and a branch-and-cut algorithm based on Benders
+decomposition to solve the problem. To accelerate convergence, our solution
+framework embeds an efficient heuristic producing high-quality solutions in
+short computation times. In addition, we show how symmetry can be exploited to
+accelerate and improve the performance of our method.
+",0,0,1,0,0,0
+6960,Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network,"  One of recent trends [30, 31, 14] in network architec- ture design is
+stacking small filters (e.g., 1x1 or 3x3) in the entire network because the
+stacked small filters is more ef- ficient than a large kernel, given the same
+computational complexity. However, in the field of semantic segmenta- tion,
+where we need to perform dense per-pixel prediction, we find that the large
+kernel (and effective receptive field) plays an important role when we have to
+perform the clas- sification and localization tasks simultaneously. Following
+our design principle, we propose a Global Convolutional Network to address both
+the classification and localization issues for the semantic segmentation. We
+also suggest a residual-based boundary refinement to further refine the ob-
+ject boundaries. Our approach achieves state-of-art perfor- mance on two public
+benchmarks and significantly outper- forms previous results, 82.2% (vs 80.2%)
+on PASCAL VOC 2012 dataset and 76.9% (vs 71.8%) on Cityscapes dataset.
+",1,0,0,0,0,0
+6961,Is ram-pressure stripping an efficient mechanism to remove gas in galaxies?,"  We study how the gas in a sample of galaxies (M* > 10e9 Msun) in clusters,
+obtained in a cosmological simulation, is affected by the interaction with the
+intra-cluster medium (ICM). The dynamical state of each elemental parcel of gas
+is studied using the total energy. At z ~ 2, the galaxies in the simulation are
+evenly distributed within clusters, moving later on towards more central
+locations. In this process, gas from the ICM is accreted and mixed with the gas
+in the galactic halo. Simultaneously, the interaction with the environment
+removes part of the gas. A characteristic stellar mass around M* ~ 10e10 Msun
+appears as a threshold marking two differentiated behaviours. Below this mass,
+galaxies are located at the external part of clusters and have eccentric
+orbits. The effect of the interaction with the environment is marginal. Above,
+galaxies are mainly located at the inner part of clusters with mostly radial
+orbits with low velocities. In these massive systems, part of the gas, strongly
+correlated with the stellar mass of the galaxy, is removed. The amount of
+removed gas is sub-dominant compared with the quantity of retained gas which is
+continuously influenced by the hot gas coming from the ICM. The analysis of
+individual galaxies reveals the existence of a complex pattern of flows,
+turbulence and a constant fuelling of gas to the hot corona from the ICM that
+could make the global effect of the interaction of galaxies with their
+environment to be substantially less dramatic than previously expected.
+",0,1,0,0,0,0
+6962,Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data,"  We present hidden fluid mechanics (HFM), a physics informed deep learning
+framework capable of encoding an important class of physical laws governing
+fluid motions, namely the Navier-Stokes equations. In particular, we seek to
+leverage the underlying conservation laws (i.e., for mass, momentum, and
+energy) to infer hidden quantities of interest such as velocity and pressure
+fields merely from spatio-temporal visualizations of a passive scaler (e.g.,
+dye or smoke), transported in arbitrarily complex domains (e.g., in human
+arteries or brain aneurysms). Our approach towards solving the aforementioned
+data assimilation problem is unique as we design an algorithm that is agnostic
+to the geometry or the initial and boundary conditions. This makes HFM highly
+flexible in choosing the spatio-temporal domain of interest for data
+acquisition as well as subsequent training and predictions. Consequently, the
+predictions made by HFM are among those cases where a pure machine learning
+strategy or a mere scientific computing approach simply cannot reproduce. The
+proposed algorithm achieves accurate predictions of the pressure and velocity
+fields in both two and three dimensional flows for several benchmark problems
+motivated by real-world applications. Our results demonstrate that this
+relatively simple methodology can be used in physical and biomedical problems
+to extract valuable quantitative information (e.g., lift and drag forces or
+wall shear stresses in arteries) for which direct measurements may not be
+possible.
+",0,0,0,1,0,0
+6963,Testing Network Structure Using Relations Between Small Subgraph Probabilities,"  We study the problem of testing for structure in networks using relations
+between the observed frequencies of small subgraphs. We consider the statistics
+\begin{align*} T_3 & =(\text{edge frequency})^3 - \text{triangle frequency}\\
+T_2 & =3(\text{edge frequency})^2(1-\text{edge frequency}) - \text{V-shape
+frequency} \end{align*} and prove a central limit theorem for $(T_2, T_3)$
+under an Erdős-Rényi null model. We then analyze the power of the
+associated $\chi^2$ test statistic under a general class of alternative models.
+In particular, when the alternative is a $k$-community stochastic block model,
+with $k$ unknown, the power of the test approaches one. Moreover, the
+signal-to-noise ratio required is strictly weaker than that required for
+community detection. We also study the relation with other statistics over
+three-node subgraphs, and analyze the error under two natural algorithms for
+sampling small subgraphs. Together, our results show how global structural
+characteristics of networks can be inferred from local subgraph frequencies,
+without requiring the global community structure to be explicitly estimated.
+",1,0,1,1,0,0
+6964,A 3D MHD simulation of SN 1006: a polarized emission study for the turbulent case,"  Three dimensional magnetohydrodynamical simulations were carried out in order
+to perform a new polarization study of the radio emission of the supernova
+remnant SN 1006. These simulations consider that the remnant expands into a
+turbulent interstellar medium (including both magnetic field and density
+perturbations). Based on the referenced-polar angle technique, a statistical
+study was done on observational and numerical magnetic field position-angle
+distributions. Our results show that a turbulent medium with an adiabatic index
+of 1.3 can reproduce the polarization properties of the SN 1006 remnant. This
+statistical study reveals itself as a useful tool for obtaining the orientation
+of the ambient magnetic field, previous to be swept up by the main supernova
+remnant shock.
+",0,1,0,0,0,0
+6965,Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network,"  Automatic mesh-based shape generation is of great interest across a wide
+range of disciplines, from industrial design to gaming, computer graphics and
+various other forms of digital art. While most traditional methods focus on
+primitive based model generation, advances in deep learning made it possible to
+learn 3-dimensional geometric shape representations in an end-to-end manner.
+However, most current deep learning based frameworks focus on the
+representation and generation of voxel and point-cloud based shapes, making it
+not directly applicable to design and graphics communities. This study
+addresses the needs for automatic generation of mesh-based geometries, and
+propose a novel framework that utilizes signed distance function representation
+that generates detail preserving three-dimensional surface mesh by a deep
+learning based approach.
+",1,0,0,0,0,0
+6966,"Combined analysis of galaxy cluster number count, thermal Sunyaev-Zel'dovich power spectrum, and bispectrum","  The Sunyaev-Zel'dovich (SZ) effect is a powerful probe of the evolution of
+structures in the universe, and is thus highly sensitive to cosmological
+parameters $\sigma_8$ and $\Omega_m$, though its power is hampered by the
+current uncertainties on the cluster mass calibration. In this analysis we
+revisit constraints on these cosmological parameters as well as the hydrostatic
+mass bias, by performing (i) a robust estimation of the tSZ power-spectrum,
+(ii) a complete modeling and analysis of the tSZ bispectrum, and (iii) a
+combined analysis of galaxy clusters number count, tSZ power spectrum, and tSZ
+bispectrum. From this analysis, we derive as final constraints $\sigma_8 = 0.79
+\pm 0.02$, $\Omega_{\rm m} = 0.29 \pm 0.02$, and $(1-b) = 0.71 \pm 0.07$. These
+results favour a high value for the hydrostatic mass bias compared to numerical
+simulations and weak-lensing based estimations. They are furthermore consistent
+with both previous tSZ analyses, CMB derived cosmological parameters, and
+ancillary estimations of the hydrostatic mass bias.
+",0,1,0,0,0,0
+6967,On short cycle enumeration in biregular bipartite graphs,"  A number of recent works have used a variety of combinatorial constructions
+to derive Tanner graphs for LDPC codes and some of these have been shown to
+perform well in terms of their probability of error curves and error floors.
+Such graphs are bipartite and many of these constructions yield biregular
+graphs where the degree of left vertices is a constant $c+1$ and that of the
+right vertices is a constant $d+1$. Such graphs are termed $(c+1,d+1)$
+biregular bipartite graphs here. One property of interest in such work is the
+girth of the graph and the number of short cycles in the graph, cycles of
+length either the girth or slightly larger. Such numbers have been shown to be
+related to the error floor of the probability of error curve of the related
+LDPC code. Using known results of graph theory, it is shown how the girth and
+the number of cycles of length equal to the girth may be computed for these
+$(c+1,d+1)$ biregular bipartite graphs knowing only the parameters $c$ and $d$
+and the numbers of left and right vertices. While numerous algorithms to
+determine the number of short cycles in arbitrary graphs exist, the reduction
+of the problem from an algorithm to a computation for these biregular bipartite
+graphs is of interest.
+",1,0,1,0,0,0
+6968,Exhaled breath barbotage: a new method for pulmonary surfactant dysfunction assessment,"  Exhaled air contains aerosol of submicron droplets of the alveolar lining
+fluid (ALF), which are generated in the small airways of a human lung. Since
+the exhaled particles are micro-samples of the ALF, their trapping opens up an
+opportunity to collect non-invasively a native material from respiratory tract.
+Recent studies of the particle characteristics (such as size distribution,
+concentration and composition) in healthy and diseased subjects performed under
+various conditions have demonstrated a high potential of the analysis of
+exhaled aerosol droplets for identifying and monitoring pathological processes
+in the ALF. In this paper we present a new method for sampling of aerosol
+particles during the exhaled breath barbotage (EBB) through liquid. The
+barbotage procedure results in accumulation of the pulmonary surfactant, being
+the main component of ALF, on the liquid surface, which makes possible the
+study its surface properties. We also propose a data processing algorithm to
+evaluate the surface pressure ($\pi$) -- surface concentration ($\Gamma$)
+isotherm from the raw data measured in a Langmuir trough. Finally, we analyze
+the $(\pi-\Gamma)$ isotherms obtained for the samples collected in the groups
+of healthy volunteers and patients with pulmonary tuberculosis and compare them
+with the isotherm measured for the artificial pulmonary surfactant.
+",0,1,0,0,0,0
+6969,A Computational Study of the Role of Tonal Tension in Expressive Piano Performance,"  Expressive variations of tempo and dynamics are an important aspect of music
+performances, involving a variety of underlying factors. Previous work has
+showed a relation between such expressive variations (in particular expressive
+tempo) and perceptual characteristics derived from the musical score, such as
+musical expectations, and perceived tension. In this work we use a
+computational approach to study the role of three measures of tonal tension
+proposed by Herremans and Chew (2016) in the prediction of expressive
+performances of classical piano music. These features capture tonal
+relationships of the music represented in Chew's spiral array model, a three
+dimensional representation of pitch classes, chords and keys constructed in
+such a way that spatial proximity represents close tonal relationships. We use
+non-linear sequential models (recurrent neural networks) to assess the
+contribution of these features to the prediction of expressive dynamics and
+expressive tempo using a dataset of Mozart piano sonatas performed by a
+professional concert pianist. Experiments of models trained with and without
+tonal tension features show that tonal tension helps predict change of tempo
+and dynamics more than absolute tempo and dynamics values. Furthermore, the
+improvement is stronger for dynamics than for tempo.
+",1,0,0,0,0,0
+6970,On the least upper bound for the settling time of a class of fixed-time stable systems,"  This paper deals with the convergence time analysis of a class of fixed-time
+stable systems with the aim to provide a new non-conservative upper bound for
+its settling time. Our contribution is threefold. First, we revisit a
+well-known class of fixed-time stable systems showing the conservatism of the
+classical upper estimate of the settling time. Second, we provide the smallest
+constant that uniformly upper bounds the settling time of any trajectory of the
+system under consideration. Then, introducing a slight modification of the
+previous class of fixed-time systems, we propose a new predefined-time
+convergent algorithm where the least upper bound of the settling time is set a
+priori as a parameter of the system. This calculation is a valuable
+contribution toward online differentiators, observers, and controllers in
+applications with real-time constraints.
+",1,0,0,0,0,0
+6971,Self-organization and the Maximum Empower Principle in the Framework of max-plus Algebra,"  Self-organization is a process where order of a whole system arises out of
+local interactions between small components of a system.
+Emergy, defined as the amount of (solar) energy used to make a product or a
+service, is becoming an important ecological indicator. To explain observed
+self-organization of systems by emergy the Maximum Empower Principle (MEP) was
+proposed initially without a mathematical formulation.
+Emergy analysis is based on four rules called emergy algebra. Most of emergy
+computations in steady state are in fact approximate results, which rely on
+linear algebra. In such a context, a mathematical formulation of the MEP has
+been proposed by Giannantoni (2002).
+In 2012 Le Corre and the second author of this paper have proposed a rigorous
+mathematical framework for emergy analysis. They established that the exact
+computation of emergy is based on the so-called max-plus algebra and seven
+coherent axioms that replace the emergy algebra. In this paper the MEP in
+steady state is formalized in the context of the max-plus algebra and graph
+theory. The main concepts of the paper are (a) a particular graph called
+'emergy graph', (b) the notion of compatible paths of the emergy graph, and (c)
+sets of compatible paths, which are called 'emergy states'. The main results of
+the paper are as follows:
+(1) Emergy is mathematically expressed as a maximum over all possible emergy
+states. (2) The maximum is always reached by an emergy state. (3) Only prevail
+emergy states for which the maximum is reached.
+",1,1,0,0,0,0
+6972,Nonequilibrium Work and its Hamiltonian Connection for a Microstate in Nonequilibrium Statistical Thermodynamics: A Case of Mistaken Identity,"  Nonequilibrium work-Hamiltonian connection for a microstate plays a central
+role in diverse branches of statistical thermodynamics (fluctuation theorems,
+quantum thermodynamics, stochastic thermodynamics, etc.). We show that the
+change in the Hamiltonian for a microstate should be identified with the work
+done by it, and not the work done on it. This contradicts the current practice
+in the field. The difference represents a contribution whose average gives the
+work that is dissipated due to irreversibility. As the latter has been
+overlooked, the current identification does not properly account for
+irreversibilty. As an example, we show that the corrected version of
+Jarzynski's relation can be applied to free expansion, where the original
+relation fails. Thus, the correction has far-reaching consequences and requires
+reassessment of current applications.
+",0,1,0,0,0,0
+6973,Thick Subcategories of the stable category of modules over the exterior algebra I,"  We study thick subcategories defined by modules of complexity one in
+$\underline{\md}R$, where $R$ is the exterior algebra in $n+1$ indeterminates.
+",0,0,1,0,0,0
+6974,Planet-driven spiral arms in protoplanetary disks: II. Implications,"  We examine whether various characteristics of planet-driven spiral arms can
+be used to constrain the masses of unseen planets and their positions within
+their disks. By carrying out two-dimensional hydrodynamic simulations varying
+planet mass and disk gas temperature, we find that a larger number of spiral
+arms form with a smaller planet mass and a lower disk temperature. A planet
+excites two or more spiral arms interior to its orbit for a range of disk
+temperature characterized by the disk aspect ratio $0.04\leq(h/r)_p\leq0.15$,
+whereas exterior to a planet's orbit multiple spiral arms can form only in cold
+disks with $(h/r)_p \lesssim 0.06$. Constraining the planet mass with the pitch
+angle of spiral arms requires accurate disk temperature measurements that might
+be challenging even with ALMA. However, the property that the pitch angle of
+planet-driven spiral arms decreases away from the planet can be a powerful
+diagnostic to determine whether the planet is located interior or exterior to
+the observed spirals. The arm-to-arm separations increase as a function of
+planet mass, consistent with previous studies; however, the exact slope depends
+on disk temperature as well as the radial location where the arm-to-arm
+separations are measured. We apply these diagnostics to the spiral arms seen in
+MWC 758 and Elias 2-27. As shown in Bae et al. (2017), planet-driven spiral
+arms can create concentric rings and gaps, which can produce more dominant
+observable signature than spiral arms under certain circumstances. We discuss
+the observability of planet-driven spiral arms versus rings and gaps.
+",0,1,0,0,0,0
+6975,Ideal structure and pure infiniteness of ample groupoid $C^*$-algebras,"  In this paper, we study the ideal structure of reduced $C^*$-algebras
+$C^*_r(G)$ associated to étale groupoids $G$. In particular, we characterize
+when there is a one-to-one correspondence between the closed, two-sided ideals
+in $C_r^*(G)$ and the open invariant subsets of the unit space $G^{(0)}$ of
+$G$. As a consequence, we show that if $G$ is an inner exact, essentially
+principal, ample groupoid, then $C_r^*(G)$ is (strongly) purely infinite if and
+only if every non-zero projection in $C_0(G^{(0)})$ is properly infinite in
+$C_r^*(G)$. We also establish a sufficient condition on the ample groupoid $G$
+that ensures pure infiniteness of $C_r^*(G)$ in terms of paradoxicality of
+compact open subsets of the unit space $G^{(0)}$.
+Finally, we introduce the type semigroup for ample groupoids and also obtain
+a dichotomy result: Let $G$ be an ample groupoid with compact unit space which
+is minimal and topologically principal. If the type semigroup is almost
+unperforated, then $C_r^*(G)$ is a simple $C^*$-algebra which is either stably
+finite or strongly purely infinite.
+",0,0,1,0,0,0
+6976,Nonparametric mean curvature type flows of graphs with contact angle conditions,"  In this paper we study nonparametric mean curvature type flows in
+$M\times\mathbb{R}$ which are represented as graphs $(x,u(x,t))$ over a domain
+in a Riemannian manifold $M$ with prescribed contact angle. The speed of $u$ is
+the mean curvature speed minus an admissible function $\psi(x,u,Du)$. Long time
+existence and uniformly convergence are established if $\psi(x,u, Du)\equiv 0$
+with vertical contact angle and $\psi(x,u,Du)=h(x,u)\omega$ with $h_u(x,u)\geq
+h_0>0$ and $\omega=\sqrt{1+|Du|^2}$. Their applications include mean curvature
+type equations with prescribed contact angle boundary condition and the
+asymptotic behavior of nonparametric mean curvature flows of graphs over a
+convex domain in $M^2$ which is a surface with nonnegative Ricci curvature.
+",0,0,1,0,0,0
+6977,Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals,"  In this article we develop a new sequential Monte Carlo (SMC) method for
+multilevel (ML) Monte Carlo estimation. In particular, the method can be used
+to estimate expectations with respect to a target probability distribution over
+an infinite-dimensional and non-compact space as given, for example, by a
+Bayesian inverse problem with Gaussian random field prior. Under suitable
+assumptions the MLSMC method has the optimal $O(\epsilon^{-2})$ bound on the
+cost to obtain a mean-square error of $O(\epsilon^2)$. The algorithm is
+accelerated by dimension-independent likelihood-informed (DILI) proposals
+designed for Gaussian priors, leveraging a novel variation which uses empirical
+sample covariance information in lieu of Hessian information, hence eliminating
+the requirement for gradient evaluations. The efficiency of the algorithm is
+illustrated on two examples: inversion of noisy pressure measurements in a PDE
+model of Darcy flow to recover the posterior distribution of the permeability
+field, and inversion of noisy measurements of the solution of an SDE to recover
+the posterior path measure.
+",0,0,0,1,0,0
+6978,Spontaneous symmetry breaking as a triangular relation between pairs of Goldstone bosons and the degenerate vacuum: Interactions of D-branes,"  We formulate the Nambu-Goldstone theorem as a triangular relation between
+pairs of Goldstone bosons with the degenerate vacuum. The vacuum degeneracy is
+then a natural consequence of this relation. Inside the scenario of String
+Theory, we then find that there is a correspondence between the way how the
+$D$-branes interact and the properties of the Goldstone bosons.
+",0,1,0,0,0,0
+6979,Differentially Private Dropout,"  Large data collections required for the training of neural networks often
+contain sensitive information such as the medical histories of patients, and
+the privacy of the training data must be preserved. In this paper, we introduce
+a dropout technique that provides an elegant Bayesian interpretation to
+dropout, and show that the intrinsic noise added, with the primary goal of
+regularization, can be exploited to obtain a degree of differential privacy.
+The iterative nature of training neural networks presents a challenge for
+privacy-preserving estimation since multiple iterations increase the amount of
+noise added. We overcome this by using a relaxed notion of differential
+privacy, called concentrated differential privacy, which provides tighter
+estimates on the overall privacy loss. We demonstrate the accuracy of our
+privacy-preserving dropout algorithm on benchmark datasets.
+",1,0,0,1,0,0
+6980,On (in)stabilities of perturbations in mimetic models with higher derivatives,"  Usually when applying the mimetic model to the early universe, higher
+derivative terms are needed to promote the mimetic field to be dynamical.
+However such models suffer from the ghost and/or the gradient instabilities and
+simple extensions cannot cure this pathology. We point out in this paper that
+it is possible to overcome this difficulty by considering the direct couplings
+of the higher derivatives of the mimetic field to the curvature of the
+spacetime.
+",0,1,0,0,0,0
+6981,Quantitative Connection Between Ensemble Thermodynamics and Single-Molecule Kinetics: A Case Study Using Cryogenic Electron Microscopy and Single-Molecule Fluorescence Resonance Energy Transfer Investigations of the Ribosome,"  At equilibrium, thermodynamic and kinetic information can be extracted from
+biomolecular energy landscapes by many techniques. However, while static,
+ensemble techniques yield thermodynamic data, often only dynamic,
+single-molecule techniques can yield the kinetic data that describes
+transition-state energy barriers. Here we present a generalized framework based
+upon dwell-time distributions that can be used to connect such static, ensemble
+techniques with dynamic, single-molecule techniques, and thus characterize
+energy landscapes to greater resolutions. We demonstrate the utility of this
+framework by applying it to cryogenic electron microscopy (cryo-EM) and
+single-molecule fluorescence resonance energy transfer (smFRET) studies of the
+bacterial ribosomal pre-translocation complex. Among other benefits,
+application of this framework to these data explains why two transient,
+intermediate conformations of the pre-translocation complex, which are observed
+in a cryo-EM study, may not be observed in several smFRET studies.
+",0,1,0,0,0,0
+6982,Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training,"  Generative Adversarial Networks (GANs) have become a widely popular framework
+for generative modelling of high-dimensional datasets. However their training
+is well-known to be difficult. This work presents a rigorous statistical
+analysis of GANs providing straight-forward explanations for common training
+pathologies such as vanishing gradients. Furthermore, it proposes a new
+training objective, Kernel GANs, and demonstrates its practical effectiveness
+on large-scale real-world data sets. A key element in the analysis is the
+distinction between training with respect to the (unknown) data distribution,
+and its empirical counterpart. To overcome issues in GAN training, we pursue
+the idea of smoothing the Jensen-Shannon Divergence (JSD) by incorporating
+noise in the input distributions of the discriminator. As we show, this
+effectively leads to an empirical version of the JSD in which the true and the
+generator densities are replaced by kernel density estimates, which leads to
+Kernel GANs.
+",0,0,0,1,0,0
+6983,METAGUI 3: a graphical user interface for choosing the collective variables in molecular dynamics simulations,"  Molecular dynamics (MD) simulations allow the exploration of the phase space
+of biopolymers through the integration of equations of motion of their
+constituent atoms. The analysis of MD trajectories often relies on the choice
+of collective variables (CVs) along which the dynamics of the system is
+projected. We developed a graphical user interface (GUI) for facilitating the
+interactive choice of the appropriate CVs. The GUI allows: defining
+interactively new CVs; partitioning the configurations into microstates
+characterized by similar values of the CVs; calculating the free energies of
+the microstates for both unbiased and biased (metadynamics) simulations;
+clustering the microstates in kinetic basins; visualizing the free energy
+landscape as a function of a subset of the CVs used for the analysis. A simple
+mouse click allows one to quickly inspect structures corresponding to specific
+points in the landscape.
+",0,1,0,0,0,0
+6984,"Model compression as constrained optimization, with application to neural nets. Part II: quantization","  We consider the problem of deep neural net compression by quantization: given
+a large, reference net, we want to quantize its real-valued weights using a
+codebook with $K$ entries so that the training loss of the quantized net is
+minimal. The codebook can be optimally learned jointly with the net, or fixed,
+as for binarization or ternarization approaches. Previous work has quantized
+the weights of the reference net, or incorporated rounding operations in the
+backpropagation algorithm, but this has no guarantee of converging to a
+loss-optimal, quantized net. We describe a new approach based on the recently
+proposed framework of model compression as constrained optimization
+\citep{Carreir17a}. This results in a simple iterative ""learning-compression""
+algorithm, which alternates a step that learns a net of continuous weights with
+a step that quantizes (or binarizes/ternarizes) the weights, and is guaranteed
+to converge to local optimum of the loss for quantized nets. We develop
+algorithms for an adaptive codebook or a (partially) fixed codebook. The latter
+includes binarization, ternarization, powers-of-two and other important
+particular cases. We show experimentally that we can achieve much higher
+compression rates than previous quantization work (even using just 1 bit per
+weight) with negligible loss degradation.
+",1,0,1,1,0,0
+6985,Three Skewed Matrix Variate Distributions,"  Three-way data can be conveniently modelled by using matrix variate
+distributions. Although there has been a lot of work for the matrix variate
+normal distribution, there is little work in the area of matrix skew
+distributions. Three matrix variate distributions that incorporate skewness, as
+well as other flexible properties such as concentration, are discussed.
+Equivalences to multivariate analogues are presented, and moment generating
+functions are derived. Maximum likelihood parameter estimation is discussed,
+and simulated data is used for illustration.
+",0,0,1,1,0,0
+6986,Anticipating Persistent Infection,"  We explore the emergence of persistent infection in a closed region where the
+disease progression of the individuals is given by the SIRS model, with an
+individual becoming infected on contact with another infected individual within
+a given range. We focus on the role of synchronization in the persistence of
+contagion. Our key result is that higher degree of synchronization, both
+globally in the population and locally in the neighborhoods, hinders
+persistence of infection. Importantly, we find that early short-time asynchrony
+appears to be a consistent precursor to future persistence of infection, and
+can potentially provide valuable early warnings for sustained contagion in a
+population patch. Thus transient synchronization can help anticipate the
+long-term persistence of infection. Further we demonstrate that when the range
+of influence of an infected individual is wider, one obtains lower persistent
+infection. This counter-intuitive observation can also be understood through
+the relation of synchronization to infection burn-out.
+",0,0,0,0,1,0
+6987,The first global-scale 30 m resolution mangrove canopy height map using Shuttle Radar Topography Mission data,"  No high-resolution canopy height map exists for global mangroves. Here we
+present the first global mangrove height map at a consistent 30 m pixel
+resolution derived from digital elevation model data collected through shuttle
+radar topography mission. Additionally, we refined the current global mangrove
+area maps by discarding the non-mangrove areas that are included in current
+mangrove maps.
+",0,1,0,0,0,0
+6988,Direct Estimation of Regional Wall Thicknesses via Residual Recurrent Neural Network,"  Accurate estimation of regional wall thicknesses (RWT) of left ventricular
+(LV) myocardium from cardiac MR sequences is of significant importance for
+identification and diagnosis of cardiac disease. Existing RWT estimation still
+relies on segmentation of LV myocardium, which requires strong prior
+information and user interaction. No work has been devoted into direct
+estimation of RWT from cardiac MR images due to the diverse shapes and
+structures for various subjects and cardiac diseases, as well as the complex
+regional deformation of LV myocardium during the systole and diastole phases of
+the cardiac cycle. In this paper, we present a newly proposed Residual
+Recurrent Neural Network (ResRNN) that fully leverages the spatial and temporal
+dynamics of LV myocardium to achieve accurate frame-wise RWT estimation. Our
+ResRNN comprises two paths: 1) a feed forward convolution neural network (CNN)
+for effective and robust CNN embedding learning of various cardiac images and
+preliminary estimation of RWT from each frame itself independently, and 2) a
+recurrent neural network (RNN) for further improving the estimation by modeling
+spatial and temporal dynamics of LV myocardium. For the RNN path, we design for
+cardiac sequences a Circle-RNN to eliminate the effect of null hidden input for
+the first time-step. Our ResRNN is capable of obtaining accurate estimation of
+cardiac RWT with Mean Absolute Error of 1.44mm (less than 1-pixel error) when
+validated on cardiac MR sequences of 145 subjects, evidencing its great
+potential in clinical cardiac function assessment.
+",1,0,0,0,0,0
+6989,Non-dipole recollision-gated double ionization and observable effects,"  Using a three-dimensional semiclassical model, we study double ionization for
+strongly-driven He fully accounting for magnetic field effects. For linearly
+and slightly elliptically polarized laser fields, we show that recollisions and
+the magnetic field combined act as a gate. This gate favors more transverse -
+with respect to the electric field - initial momenta of the tunneling electron
+that are opposite to the propagation direction of the laser field. In the
+absence of non-dipole effects, the transverse initial momentum is symmetric
+with respect to zero. We find that this asymmetry in the transverse initial
+momentum gives rise to an asymmetry in a double ionization observable. Finally,
+we show that this asymmetry in the transverse initial momentum of the tunneling
+electron accounts for a recently-reported unexpectedly large average sum of the
+electron momenta parallel to the propagation direction of the laser field.
+",0,1,0,0,0,0
+6990,A Survey of Active Attacks on Wireless Sensor Networks and their Countermeasures,"  Lately, Wireless Sensor Networks (WSNs) have become an emerging technology
+and can be utilized in some crucial circumstances like battlegrounds,
+commercial applications, habitat observing, buildings, smart homes, traffic
+surveillance and other different places. One of the foremost difficulties that
+WSN faces nowadays is protection from serious attacks. While organizing the
+sensor nodes in an abandoned environment makes network systems helpless against
+an assortment of strong assaults, intrinsic memory and power restrictions of
+sensor nodes make the traditional security arrangements impractical. The
+sensing knowledge combined with the wireless communication and processing power
+makes it lucrative for being abused. The wireless sensor network technology
+also obtains a big variety of security intimidations. This paper describes four
+basic security threats and many active attacks on WSN with their possible
+countermeasures proposed by different research scholars.
+",1,0,0,0,0,0
+6991,A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees,"  We present a new Frank-Wolfe (FW) type algorithm that is applicable to
+minimization problems with a nonsmooth convex objective. We provide convergence
+bounds and show that the scheme yields so-called coreset results for various
+Machine Learning problems including 1-median, Balanced Development, Sparse PCA,
+Graph Cuts, and the $\ell_1$-norm-regularized Support Vector Machine (SVM)
+among others. This means that the algorithm provides approximate solutions to
+these problems in time complexity bounds that are not dependent on the size of
+the input problem. Our framework, motivated by a growing body of work on
+sublinear algorithms for various data analysis problems, is entirely
+deterministic and makes no use of smoothing or proximal operators. Apart from
+these theoretical results, we show experimentally that the algorithm is very
+practical and in some cases also offers significant computational advantages on
+large problem instances. We provide an open source implementation that can be
+adapted for other problems that fit the overall structure.
+",1,0,0,1,0,0
+6992,The problem of boundary conditions for the shallow water equations (Russian),"  The problem of choice of boundary conditions are discussed for the case of
+numerical integration of the shallow water equations on a substantially
+irregular relief. In modeling of unsteady surface water flows has a dynamic
+boundary partitioning liquid and dry bottom. The situation is complicated by
+the emergence of sub- and supercritical flow regimes for the problems of
+seasonal floodplain flooding, flash floods, tsunami landfalls. Analysis of the
+use of various methods of setting conditions for the physical quantities of
+liquid when the settlement of the boundary shows the advantages of using the
+waterfall type conditions in the presence of strong inhomogeneities landforms.
+When there is a waterfall on the border of the computational domain and
+heterogeneity of the relief in the vicinity of the boundary portion may occur,
+which is formed by the region of critical flow with the formation of a
+hydraulic jump, which greatly weakens the effect of the waterfall on the flow
+pattern upstream.
+",0,1,0,0,0,0
+6993,The exit time finite state projection scheme: bounding exit distributions and occupation measures of continuous-time Markov chains,"  We introduce the exit time finite state projection (ETFSP) scheme, a
+truncation-based method that yields approximations to the exit distribution and
+occupation measure associated with the time of exit from a domain (i.e., the
+time of first passage to the complement of the domain) of time-homogeneous
+continuous-time Markov chains. We prove that: (i) the computed approximations
+bound the measures from below; (ii) the total variation distances between the
+approximations and the measures decrease monotonically as states are added to
+the truncation; and (iii) the scheme converges, in the sense that, as the
+truncation tends to the entire state space, the total variation distances tend
+to zero. Furthermore, we give a computable bound on the total variation
+distance between the exit distribution and its approximation, and we delineate
+the cases in which the bound is sharp. We also revisit the related finite state
+projection scheme and give a comprehensive account of its theoretical
+properties. We demonstrate the use of the ETFSP scheme by applying it to two
+biological examples: the computation of the first passage time associated with
+the expression of a gene, and the fixation times of competing species subject
+to demographic noise.
+",0,0,0,0,1,0
+6994,Ontological Multidimensional Data Models and Contextual Data Qality,"  Data quality assessment and data cleaning are context-dependent activities.
+Motivated by this observation, we propose the Ontological Multidimensional Data
+Model (OMD model), which can be used to model and represent contexts as
+logic-based ontologies. The data under assessment is mapped into the context,
+for additional analysis, processing, and quality data extraction. The resulting
+contexts allow for the representation of dimensions, and multidimensional data
+quality assessment becomes possible. At the core of a multidimensional context
+we include a generalized multidimensional data model and a Datalog+/- ontology
+with provably good properties in terms of query answering. These main
+components are used to represent dimension hierarchies, dimensional
+constraints, dimensional rules, and define predicates for quality data
+specification. Query answering relies upon and triggers navigation through
+dimension hierarchies, and becomes the basic tool for the extraction of quality
+data. The OMD model is interesting per se, beyond applications to data quality.
+It allows for a logic-based, and computationally tractable representation of
+multidimensional data, extending previous multidimensional data models with
+additional expressive power and functionalities.
+",1,0,0,0,0,0
+6995,Time-Assisted Authentication Protocol,"  Authentication is the first step toward establishing a service provider and
+customer (C-P) association. In a mobile network environment, a lightweight and
+secure authentication protocol is one of the most significant factors to
+enhance the degree of service persistence. This work presents a secure and
+lightweight keying and authentication protocol suite termed TAP (Time-Assisted
+Authentication Protocol). TAP improves the security of protocols with the
+assistance of time-based encryption keys and scales down the authentication
+complexity by issuing a re-authentication ticket. While moving across the
+network, a mobile customer node sends a re-authentication ticket to establish
+new sessions with service-providing nodes. Consequently, this reduces the
+communication and computational complexity of the authentication process. In
+the keying protocol suite, a key distributor controls the key generation
+arguments and time factors, while other participants independently generate a
+keychain based on key generation arguments. We undertake a rigorous security
+analysis and prove the security strength of TAP using CSP and rank function
+analysis.
+",1,0,0,0,0,0
+6996,Improved Bounds for Online Dominating Sets of Trees,"  The online dominating set problem is an online variant of the minimum
+dominating set problem, which is one of the most important NP-hard problems on
+graphs. This problem is defined as follows: Given an undirected graph $G = (V,
+E)$, in which $V$ is a set of vertices and $E$ is a set of edges. We say that a
+set $D \subseteq V$ of vertices is a {\em dominating set} of $G$ if for each $v
+\in V \setminus D$, there exists a vertex $u \in D$ such that $\{ u, v \} \in
+E$. The vertices are revealed to an online algorithm one by one over time. When
+a vertex is revealed, edges between the vertex and vertices revealed in the
+past are also revealed. A revelaed subtree is connected at any time.
+Immediately after the revelation of each vertex, an online algorithm can choose
+vertices which were already revealed irrevocably and must maintain a dominating
+set of a graph revealed so far. The cost of an algorithm on a given tree is the
+number of vertices chosen by it, and its objective is to minimize the cost.
+Eidenbenz (Technical report, Institute of Theoretical Computer Science, ETH
+Zürich, 2002) and Boyar et al.\ (SWAT 2016) studied the case in which given
+graphs are trees. They designed a deterministic online algorithm whose
+competitive ratio is at most three, and proved that a lower bound on the
+competitive ratio of any deterministic algorithm is two. In this paper, we also
+focus on trees. We establish a matching lower bound for any deterministic
+algorithm. Moreover, we design a randomized online algorithm whose competitive
+ratio is at most $5/2 = 2.5$, and show that the competitive ratio of any
+randomized algorithm is at least $4/3 \approx 1.333$.
+",1,0,0,0,0,0
+6997,Systematical design and three-dimensional simulation of X-ray FEL oscillator for Shanghai Coherent Light Facility,"  Shanghai Coherent Light Facility (SCLF) is a quasi-CW hard X-ray free
+electron laser user facility which is recently proposed. Due to the high
+repetition rate, high quality electron beams, it is straightforward to consider
+an X-ray free electron laser oscillator (XFELO) operation for SCLF. The main
+processes for XFELO design, and parameters optimization of the undulator, X-ray
+cavity and electron beam are described. The first three-dimensional X-ray
+crystal Bragg diffraction code, named BRIGHT is built, which collaborates
+closely with GENESIS and OPC for numerical simulations of XFELO. The XFELO
+performances of SCLF is investigated and optimized by theoretical analysis and
+numerical simulation.
+",0,1,0,0,0,0
+6998,Who is the infector? Epidemic models with symptomatic and asymptomatic cases,"  What role do asymptomatically infected individuals play in the transmission
+dynamics? There are many diseases, such as norovirus and influenza, where some
+infected hosts show symptoms of the disease while others are asymptomatically
+infected, i.e. do not show any symptoms. The current paper considers a class of
+epidemic models following an SEIR (Susceptible $\to$ Exposed $\to$ Infectious
+$\to$ Recovered) structure that allows for both symptomatic and asymptomatic
+cases. The following question is addressed: what fraction $\rho$ of those
+individuals getting infected are infected by symptomatic (asymptomatic) cases?
+This is a more complicated question than the related question for the beginning
+of the epidemic: what fraction of the expected number of secondary cases of a
+typical newly infected individual, i.e. what fraction of the basic reproduction
+number $R_0$, is caused by symptomatic individuals? The latter fraction only
+depends on the type-specific reproduction numbers, while the former fraction
+$\rho$ also depends on timing and hence on the probabilistic distributions of
+latent and infectious periods of the two types (not only their means). Bounds
+on $\rho$ are derived for the situation where these distributions (and even
+their means) are unknown. Special attention is given to the class of Markov
+models and the class of continuous-time Reed-Frost models as two classes of
+distribution functions. We show how these two classes of models can exhibit
+very different behaviour.
+",0,0,0,0,1,0
+6999,Calibration of a two-state pitch-wise HMM method for note segmentation in Automatic Music Transcription systems,"  Many methods for automatic music transcription involves a multi-pitch
+estimation method that estimates an activity score for each pitch. A second
+processing step, called note segmentation, has to be performed for each pitch
+in order to identify the time intervals when the notes are played. In this
+study, a pitch-wise two-state on/off firstorder Hidden Markov Model (HMM) is
+developed for note segmentation. A complete parametrization of the HMM sigmoid
+function is proposed, based on its original regression formulation, including a
+parameter alpha of slope smoothing and beta? of thresholding contrast. A
+comparative evaluation of different note segmentation strategies was performed,
+differentiated according to whether they use a fixed threshold, called ""Hard
+Thresholding"" (HT), or a HMM-based thresholding method, called ""Soft
+Thresholding"" (ST). This evaluation was done following MIREX standards and
+using the MAPS dataset. Also, different transcription scenarios and recording
+natures were tested using three units of the Degradation toolbox. Results show
+that note segmentation through a HMM soft thresholding with a data-based
+optimization of the {alpha,beta} parameter couple significantly enhances
+transcription performance.
+",1,0,0,1,0,0
+7000,Action-depedent Control Variates for Policy Optimization via Stein's Identity,"  Policy gradient methods have achieved remarkable successes in solving
+challenging reinforcement learning problems. However, it still often suffers
+from the large variance issue on policy gradient estimation, which leads to
+poor sample efficiency during training. In this work, we propose a control
+variate method to effectively reduce variance for policy gradient methods.
+Motivated by the Stein's identity, our method extends the previous control
+variate methods used in REINFORCE and advantage actor-critic by introducing
+more general action-dependent baseline functions. Empirical studies show that
+our method significantly improves the sample efficiency of the state-of-the-art
+policy gradient approaches.
+",1,0,0,1,0,0
+7001,Even denominator fractional quantum Hall states at an isospin transition in monolayer graphene,"  Magnetic fields quench the kinetic energy of two dimensional electrons,
+confining them to highly degenerate Landau levels. In the absence of disorder,
+the ground state at partial Landau level filling is determined only by Coulomb
+interactions, leading to a variety of correlation-driven phenomena. Here, we
+realize a quantum Hall analog of the Neél-to-valence bond solid transition
+within the spin- and sublattice- degenerate monolayer graphene zero energy
+Landau level by experimentally controlling substrate-induced sublattice
+symmetry breaking. The transition is marked by unusual isospin transitions in
+odd denominator fractional quantum Hall states for filling factors $\nu$ near
+charge neutrality, and the unexpected appearance of incompressible even
+denominator fractional quantum Hall states at $\nu=\pm1/2$ and $\pm1/4$
+associated with pairing between composite fermions on different carbon
+sublattices.
+",0,1,0,0,0,0
+7002,Reverse approximation of gradient flows as Minimizing Movements: a conjecture by De Giorgi,"  We consider the Cauchy problem for the gradient flow \begin{equation}
+\label{eq:81}
+\tag{$\star$}
+u'(t)=-\nabla\phi(u(t)),\quad t\ge 0;\quad
+u(0)=u_0, \end{equation} generated by a continuously differentiable function
+$\phi:\mathbb H \to \mathbb R$ in a Hilbert space $\mathbb H$ and study the
+reverse approximation of solutions to ($\star$) by the De Giorgi Minimizing
+Movement approach. We prove that if $\mathbb H$ has finite dimension and $\phi$
+is quadratically bounded from below (in particular if $\phi$ is Lipschitz) then
+for every solution $u$ to ($\star$) (which may have an infinite number of
+solutions) there exist perturbations $\phi_\tau:\mathbb H \to \mathbb R \
+(\tau>0)$ converging to $\phi$ in the Lipschitz norm such that $u$ can be
+approximated by the Minimizing Movement scheme generated by the recursive
+minimization of $\Phi(\tau,U,V):=\frac 1{2\tau}|V-U|^2+ \phi_\tau(V)$:
+\begin{equation}
+\label{eq:abstract}
+\tag{$\star\star$}
+U_\tau^n\in \operatorname{argmin}_{V\in \mathbb H}
+\Phi(\tau,U_\tau^{n-1},V)\quad n\in\mathbb N, \quad U_\tau^0:=u_0.
+\end{equation} We show that the piecewise constant interpolations with time
+step $\tau > 0$ of all possible selections of solutions
+$(U_\tau^n)_{n\in\mathbb N}$ to ($\star\star$) will converge to $u$ as
+$\tau\downarrow 0$. This result solves a question raised by Ennio De Giorgi. We
+also show that even if $\mathbb H$ has infinite dimension the above
+approximation holds for the distinguished class of minimal solutions to
+($\star$), that generate all the other solutions to ($\star$) by time
+reparametrization.
+",0,0,1,0,0,0
+7003,Linking de novo assembly results with long DNA reads by dnaasm-link application,"  Currently, third-generation sequencing techniques, which allow to obtain much
+longer DNA reads compared to the next-generation sequencing technologies, are
+becoming more and more popular. There are many possibilities to combine data
+from next-generation and third-generation sequencing.
+Herein, we present a new application called dnaasm-link for linking contigs,
+a result of \textit{de novo} assembly of second-generation sequencing data,
+with long DNA reads. Our tool includes an integrated module to fill gaps with a
+suitable fragment of appropriate long DNA read, which improves the consistency
+of the resulting DNA sequences. This feature is very important, in particular
+for complex DNA regions, as presented in the paper. Finally, our implementation
+outperforms other state-of-the-art tools in terms of speed and memory
+requirements, which may enable the usage of the presented application for
+organisms with a large genome, which is not possible in~existing applications.
+The presented application has many advantages as (i) significant memory
+optimization and reduction of computation time (ii) filling the gaps through
+the appropriate fragment of a specified long DNA read (iii) reducing number of
+spanned and unspanned gaps in the existing genome drafts.
+The application is freely available to all users under GNU Library or Lesser
+General Public License version 3.0 (LGPLv3). The demo application, docker image
+and source code are available at this http URL.
+",0,0,0,0,1,0
+7004,ReBNet: Residual Binarized Neural Network,"  This paper proposes ReBNet, an end-to-end framework for training
+reconfigurable binary neural networks on software and developing efficient
+accelerators for execution on FPGA. Binary neural networks offer an intriguing
+opportunity for deploying large-scale deep learning models on
+resource-constrained devices. Binarization reduces the memory footprint and
+replaces the power-hungry matrix-multiplication with light-weight XnorPopcount
+operations. However, binary networks suffer from a degraded accuracy compared
+to their fixed-point counterparts. We show that the state-of-the-art methods
+for optimizing binary networks accuracy, significantly increase the
+implementation cost and complexity. To compensate for the degraded accuracy
+while adhering to the simplicity of binary networks, we devise the first
+reconfigurable scheme that can adjust the classification accuracy based on the
+application. Our proposition improves the classification accuracy by
+representing features with multiple levels of residual binarization. Unlike
+previous methods, our approach does not exacerbate the area cost of the
+hardware accelerator. Instead, it provides a tradeoff between throughput and
+accuracy while the area overhead of multi-level binarization is negligible.
+",1,0,0,0,0,0
+7005,Prime geodesic theorem for the modular surface,"  Under the generalized Lindelöf hypothesis, the exponent in the error term
+of the prime geodesic theorem for the modular surface is reduced to
+$\frac{5}{8}+\varepsilon $ outside a set of finite logarithmic measure.
+",0,0,1,0,0,0
+7006,Multiple regimes and coalescence timescales for massive black hole pairs ; the critical role of galaxy formation physics,"  We discuss the latest results of numerical simulations following the orbital
+decay of massive black hole pairs in galaxy mergers. We highlight important
+differences between gas-poor and gas-rich hosts, and between orbital evolution
+taking place at high redshift as opposed to low redshift. Two effects have a
+huge impact and are rather novel in the context of massive black hole binaries.
+The first is the increase in characteristic density of galactic nuclei of
+merger remnants as galaxies are more compact at high redshift due to the way
+dark halo collapse depends on redshift. This leads naturally to hardening
+timescales due to 3-body encounters that should decrease by two orders of
+magnitude up to $z=4$. It explains naturally the short binary coalescence
+timescale, $\sim 10$ Myr, found in novel cosmological simulations that follow
+binary evolution from galactic to milliparsec scales. The second one is the
+inhomogeneity of the interstellar medium in massive gas-rich disks at high
+redshift. In the latter star forming clumps 1-2 orders of magnitude more
+massive than local Giant Molecular Clouds (GMCs) can scatter massive black
+holes out of the disk plane via gravitational perturbations and direct
+encounters. This renders the character of orbital decay inherently stochastic,
+often increasing orbital decay timescales by as much as a Gyr. At low redshift
+a similar regime is present at scales of $1-10$ pc inside Circumnuclear Gas
+Disks (CNDs). In CNDs only massive black holes with masses below $10^7
+M_{\odot}$ can be significantly perturbed. They decay to sub-pc separations in
+up to $\sim 10^8$ yr rather than the in just a few million years as in a smooth
+CND. Finally implications for building robust forecasts of LISA event rates are
+discussed
+",0,1,0,0,0,0
+7007,The Molecular Gas Environment in the 20 km s$^{-1}$ Cloud in the Central Molecular Zone,"  We recently reported a population of protostellar candidates in the 20 km
+s$^{-1}$ cloud in the Central Molecular Zone of the Milky Way, traced by H$_2$O
+masers in gravitationally bound dense cores. In this paper, we report
+high-angular-resolution ($\sim$3'') molecular line studies of the environment
+of star formation in this cloud. Maps of various molecular line transitions as
+well as the continuum at 1.3 mm are obtained using the Submillimeter Array.
+Five NH$_3$ inversion lines and the 1.3 cm continuum are observed with the Karl
+G. Jansky Very Large Array. The interferometric observations are complemented
+with single-dish data. We find that the CH$_3$OH, SO, and HNCO lines, which are
+usually shock tracers, are better correlated spatially with the compact dust
+emission from dense cores among the detected lines. These lines also show
+enhancement in intensities with respect to SiO intensities toward the compact
+dust emission, suggesting the presence of slow shocks or hot cores in these
+regions. We find gas temperatures of $\gtrsim$100 K at 0.1-pc scales based on
+RADEX modelling of the H$_2$CO and NH$_3$ lines. Although no strong
+correlations between temperatures and linewidths/H$_2$O maser luminosities are
+found, in high-angular-resolution maps we notice several candidate shock heated
+regions offset from any dense cores, as well as signatures of localized heating
+by protostars in several dense cores. Our findings suggest that at 0.1-pc
+scales in this cloud star formation and strong turbulence may together affect
+the chemistry and temperature of the molecular gas.
+",0,1,0,0,0,0
+7008,Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks,"  Compared with artificial neural networks (ANNs), spiking neural networks
+(SNNs) are promising to explore the brain-like behaviors since the spikes could
+encode more spatio-temporal information. Although pre-training from ANN or
+direct training based on backpropagation (BP) makes the supervised training of
+SNNs possible, these methods only exploit the networks' spatial domain
+information which leads to the performance bottleneck and requires many
+complicated training skills. Another fundamental issue is that the spike
+activity is naturally non-differentiable which causes great difficulties in
+training SNNs. To this end, we build an iterative LIF model that is more
+friendly for gradient descent training. By simultaneously considering the
+layer-by-layer spatial domain (SD) and the timing-dependent temporal domain
+(TD) in the training phase, as well as an approximated derivative for the spike
+activity, we propose a spatio-temporal backpropagation (STBP) training
+framework without using any complicated technology. We achieve the best
+performance of multi-layered perceptron (MLP) compared with existing
+state-of-the-art algorithms over the static MNIST and the dynamic N-MNIST
+dataset as well as a custom object detection dataset. This work provides a new
+perspective to explore the high-performance SNNs for future brain-like
+computing paradigm with rich spatio-temporal dynamics.
+",1,0,0,1,0,0
+7009,Inverse of a Special Matrix and Application,"  The matrix inversion is an interesting topic in algebra mathematics. However,
+to determine an inverse matrix from a given matrix is required many computation
+tools and time resource if the size of matrix is huge. In this paper, we have
+shown an inverse closed form for an interesting matrix which has much
+applications in communication system. Base on this inverse closed form, the
+channel capacity closed form of a communication system can be determined via
+the error rate parameter alpha
+",1,0,0,0,0,0
+7010,Incompressible fluid problems on embedded surfaces: Modeling and variational formulations,"  Governing equations of motion for a viscous incompressible material surface
+are derived from the balance laws of continuum mechanics. The surface is
+treated as a time-dependent smooth orientable manifold of codimension one in an
+ambient Euclidian space. We use elementary tangential calculus to derive the
+governing equations in terms of exterior differential operators in Cartesian
+coordinates. The resulting equations can be seen as the Navier-Stokes equations
+posed on an evolving manifold. We consider a splitting of the surface
+Navier-Stokes system into coupled equations for the tangential and normal
+motions of the material surface. We then restrict ourselves to the case of a
+geometrically stationary manifold of codimension one embedded in $\Bbb{R}^n$.
+For this case, we present new well-posedness results for the simplified surface
+fluid model consisting of the surface Stokes equations. Finally, we propose and
+analyze several alternative variational formulations for this surface Stokes
+problem, including constrained and penalized formulations, which are convenient
+for Galerkin discretization methods.
+",0,1,1,0,0,0
+7011,Gaiotto's Lagrangian subvarieties via loop groups,"  The purpose of this note is to give a simple proof of the fact that a certain
+substack, defined in [2], of the moduli stack $T^{\ast}Bun_G(\Sigma)$ of Higgs
+bundles over a curve $\Sigma$, for a connected, simply connected semisimple
+group $G$, possesses a Lagrangian structure. The substack, roughly speaking,
+consists of images under the moment map of global sections of principal
+$G$-bundles over $\Sigma$ twisted by a smooth symplectic variety with a
+Hamiltonian $G$-action.
+",0,0,1,0,0,0
+7012,Geohyperbolic Routing and Addressing Schemes,"  The key requirement to routing in any telecommunication network, and
+especially in Internet-of-Things (IoT) networks, is scalability. Routing must
+route packets between any source and destination in the network without
+incurring unmanageable routing overhead that grows quickly with increasing
+network size and dynamics. Here we present an addressing scheme and a coupled
+network topology design scheme that guarantee essentially optimal routing
+scalability. The FIB sizes are as small as they can be, equal to the number of
+adjacencies a node has, while the routing control overhead is minimized as
+nearly zero routing control messages are exchanged even upon catastrophic
+failures in the network. The key new ingredient is the addressing scheme, which
+is purely local, based only on geographic coordinates of nodes and a centrality
+measure, and does not require any sophisticated non-local computations or
+global network topology knowledge for network embedding. The price paid for
+these benefits is that network topology cannot be arbitrary but should follow a
+specific design, resulting in Internet-like topologies. The proposed schemes
+can be most easily deployed in overlay networks, and also in other network
+deployments, where geolocation information is available, and where network
+topology can grow following the design specifications.
+",1,1,0,0,0,0
+7013,Conical: an extended module for computing a numerically satisfactory pair of solutions of the differential equation for conical functions,"  Conical functions appear in a large number of applications in physics and
+engineering. In this paper we describe an extension of our module CONICAL for
+the computation of conical functions. Specifically, the module includes now a
+routine for computing the function ${\rm R}^{m}_{-\frac{1}{2}+i\tau}(x)$, a
+real-valued numerically satisfactory companion of the function ${\rm
+P}^m_{-\tfrac12+i\tau}(x)$ for $x>1$. In this way, a natural basis for solving
+Dirichlet problems bounded by conical domains is provided.
+",1,0,1,0,0,0
+7014,"Online to Offline Conversions, Universality and Adaptive Minibatch Sizes","  We present an approach towards convex optimization that relies on a novel
+scheme which converts online adaptive algorithms into offline methods. In the
+offline optimization setting, our derived methods are shown to obtain
+favourable adaptive guarantees which depend on the harmonic sum of the queried
+gradients. We further show that our methods implicitly adapt to the objective's
+structure: in the smooth case fast convergence rates are ensured without any
+prior knowledge of the smoothness parameter, while still maintaining guarantees
+in the non-smooth setting. Our approach has a natural extension to the
+stochastic setting, resulting in a lazy version of SGD (stochastic GD), where
+minibathces are chosen \emph{adaptively} depending on the magnitude of the
+gradients. Thus providing a principled approach towards choosing minibatch
+sizes.
+",1,0,1,1,0,0
+7015,Kondo Length in Bosonic Lattices,"  Motivated by the fact that the low-energy properties of the Kondo model can
+be effectively simulated in spin chains, we study the realization of the effect
+with bond impurities in ultracold bosonic lattices at half-filling. After
+presenting a discussion of the effective theory and of the mapping of the
+bosonic chain onto a lattice spin Hamiltonian, we provide estimates for the
+Kondo length as a function of the parameters of the bosonic model. We point out
+that the Kondo length can be extracted from the integrated real space
+correlation functions, which are experimentally accessible quantities in
+experiments with cold atoms.
+",0,1,0,0,0,0
+7016,RVP-FLMS : A Robust Variable Power Fractional LMS Algorithm,"  In this paper, we propose an adaptive framework for the variable power of the
+fractional least mean square (FLMS) algorithm. The proposed algorithm named as
+robust variable power FLMS (RVP-FLMS) dynamically adapts the fractional power
+of the FLMS to achieve high convergence rate with low steady state error. For
+the evaluation purpose, the problems of system identification and channel
+equalization are considered. The experiments clearly show that the proposed
+approach achieves better convergence rate and lower steady-state error compared
+to the FLMS. The MATLAB code for the related simulation is available online at
+this https URL.
+",0,0,1,1,0,0
+7017,Dichotomy for Digraph Homomorphism Problems (two algorithms),"  Update : An issue has been found in the correctness of our algorithm, and we
+are working to resolve the issue. Until a resolution is found, we retract our
+main claim that our approach gives a combinatorial solution to the CSP
+conjecture. We remain hopeful that we can resolve the issues. We thank Ross
+Willard for carefully checking the algorithm and pointing out the mistake in
+the version of this manuscript. We briefly explain one issue at the beginning
+of the text, and leave the rest of the manuscript intact for the moment . Ross
+Willard is posting a more involved description of a counter-example to the
+algorithm in the present manuscript. We have an updated manuscript that
+corrects some issues while still not arriving at a full solution; we will keep
+this private as long as unresolved issues remain.
+Previous abstract : We consider the problem of finding a homomorphism from an
+input digraph G to a fixed digraph H. We show that if H admits a
+weak-near-unanimity polymorphism $\phi$ then deciding whether G admits a
+homomorphism to H (HOM(H)) is polynomial time solvable. This confirms the
+conjecture of Maroti and McKenzie, and consequently implies the validity of the
+celebrated dichotomy conjecture due to Feder and Vardi. We transform the
+problem into an instance of the list homomorphism problem where initially all
+the lists are full (contain all the vertices of H). Then we use the
+polymorphism $\phi$ as a guide to reduce the lists to singleton lists, which
+yields a homomorphism if one exists.
+",1,0,0,0,0,0
+7018,"Macro-molecular data storage with petabyte/cm^3 density, highly parallel read/write operations, and genuine 3D storage capability","  Digital information can be encoded in the building-block sequence of
+macro-molecules, such as RNA and single-stranded DNA. Methods of ""writing"" and
+""reading"" macromolecular strands are currently available, but they are slow and
+expensive. In an ideal molecular data storage system, routine operations such
+as write, read, erase, store, and transfer must be done reliably and at high
+speed within an integrated chip. As a first step toward demonstrating the
+feasibility of this concept, we report preliminary results of DNA readout
+experiments conducted in miniaturized chambers that are scalable to even
+smaller dimensions. We show that translocation of a single-stranded DNA
+molecule (consisting of 50 adenosine bases followed by 100 cytosine bases)
+through an ion-channel yields a characteristic signal that is attributable to
+the 2-segment structure of the molecule. We also examine the dependence of the
+rate and speed of molecular translocation on the adjustable parameters of the
+experiment.
+",1,1,0,0,0,0
+7019,Design and implementation of lighting control system using battery-less wireless human detection sensor networks,"  Artificial lighting is responsible for a large portion of total energy
+consumption and has great potential for energy saving. This paper designs an
+LED light control algorithm based on users' localization using multiple
+battery-less binary human detection sensors. The proposed lighting control
+system focuses on reducing office lighting energy consumption and satisfying
+users' illumination requirement. Most current lighting control systems use
+infrared human detection sensors, but the poor detection probability,
+especially for a static user, makes it difficult to realize comfortable and
+effective lighting control. To improve the detection probability of each
+sensor, we proposed to locate sensors as close to each user as possible by
+using a battery-less wireless sensor network, in which all sensors can be
+placed freely in the space with high energy stability. We also proposed to use
+a multi-sensor-based user localization algorithm to capture user's position
+more accurately and realize fine lighting control which works even with static
+users. The system is actually implemented in an indoor office environment in a
+pilot project. A verification experiment is conducted by measuring the
+practical illumination and power consumption. The performance agrees with
+design expectations. It shows that the proposed LED lighting control system
+reduces the energy consumption significantly, 57% compared to the batch control
+scheme, and satisfies user's illumination requirement with 100% probability.
+",1,0,0,0,0,0
+7020,Independence in generic incidence structures,"  We study the theory $T_{m,n}$ of existentially closed incidence structures
+omitting the complete incidence structure $K_{m,n}$, which can also be viewed
+as existentially closed $K_{m,n}$-free bipartite graphs. In the case $m = n =
+2$, this is the theory of existentially closed projective planes. We give an
+$\forall\exists$-axiomatization of $T_{m,n}$, show that $T_{m,n}$ does not have
+a countable saturated model when $m,n\geq 2$, and show that the existence of a
+prime model for $T_{2,2}$ is equivalent to a longstanding open question about
+finite projective planes. Finally, we analyze model theoretic notions of
+complexity for $T_{m,n}$. We show that $T_{m,n}$ is NSOP$_1$, but not simple
+when $m,n\geq 2$, and we show that $T_{m,n}$ has weak elimination of
+imaginaries but not full elimination of imaginaries. These results rely on
+combinatorial characterizations of various notions of independence, including
+algebraic independence, Kim independence, and forking independence.
+",0,0,1,0,0,0
+7021,Propagation of regularity for the MHD system in optimal Sobolev space,"  We study the problem of propagation of regularity of solutions to the
+incompressible viscous non-resistive magneto-hydrodynamics system. According to
+scaling, the Sobolev space $H^{\frac n2-1}(\mathbb R^n)\times H^{\frac
+n2}(\mathbb R^n)$ is critical for the system. We show that if a weak solution
+$(u(t),b(t))$ is in $H^{s}(\mathbb R^n)\times H^{s+1}(\mathbb R^n)$ with
+$s>\frac n2-1$ at a certain time $t_0$, then it will stay in the space for a
+short time, provided the initial velocity $u(0)\in H^s(\mathbb R^n)$. In the
+case that the uniqueness of weak solution in $H^{s}(\mathbb R^n)\times
+H^{s+1}(\mathbb R^n)$ is known, the assumption of $u(0)\in H^s(\mathbb R^n)$ is
+not necessary.
+",0,1,1,0,0,0
+7022,On the link between atmospheric cloud parameters and cosmic rays,"  We herewith attempt to investigate the cosmic rays behavior regarding the
+scaling features of their time series. Our analysis is based on cosmic ray
+observations made at four neutron monitor stations in Athens (Greece), Jung
+(Switzerland) and Oulu (Finland), for the period 2000 to early 2017. Each of
+these datasets was analyzed by using the Detrended Fluctuation Analysis (DFA)
+and Multifractal Detrended Fluctuation Analysis (MF-DFA) in order to
+investigate intrinsic properties, like self-similarity and the spectrum of
+singularities. The main result obtained is that the cosmic rays time series at
+all the neutron monitor stations exhibit positive long-range correlations (of
+1/f type) with multifractal behavior. On the other hand, we try to investigate
+the possible existence of similar scaling features in the time series of other
+meteorological parameters which are closely associated with the cosmic rays,
+such as parameters describing physical properties of clouds.
+",0,1,0,0,0,0
+7023,"To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference","  The recent advances in deep neural networks (DNNs) make them attractive for
+embedded systems. However, it can take a long time for DNNs to make an
+inference on resource-constrained computing devices. Model compression
+techniques can address the computation issue of deep inference on embedded
+devices. This technique is highly attractive, as it does not rely on
+specialized hardware, or computation-offloading that is often infeasible due to
+privacy concerns or high latency. However, it remains unclear how model
+compression techniques perform across a wide range of DNNs. To design efficient
+embedded deep learning solutions, we need to understand their behaviors. This
+work develops a quantitative approach to characterize model compression
+techniques on a representative embedded deep learning architecture, the NVIDIA
+Jetson Tx2. We perform extensive experiments by considering 11 influential
+neural network architectures from the image classification and the natural
+language processing domains. We experimentally show that how two mainstream
+compression techniques, data quantization and pruning, perform on these network
+architectures and the implications of compression techniques to the model
+storage size, inference time, energy consumption and performance metrics. We
+demonstrate that there are opportunities to achieve fast deep inference on
+embedded systems, but one must carefully choose the compression settings. Our
+results provide insights on when and how to apply model compression techniques
+and guidelines for designing efficient embedded deep learning systems.
+",1,0,0,0,0,0
+7024,Universal Scaling Laws for Correlation Spreading in Quantum Systems with Short- and Long-Range Interactions,"  We study the spreading of information in a wide class of quantum systems,
+with variable-range interactions. We show that, after a quench, it generally
+features a double structure, whose scaling laws are related to a set of
+universal microscopic exponents that we determine. When the system supports
+excitations with a finite maximum velocity, the spreading shows a twofold
+ballistic behavior. While the correlation edge spreads with a velocity equal to
+twice the maximum group velocity, the dominant correlation maxima propagate
+with a different velocity that we derive. When the maximum group velocity
+diverges, as realizable with long-range interactions, the correlation edge
+features a slower-than-ballistic motion. The motion of the maxima is, instead,
+either faster-than-ballistic, for gapless systems, or ballistic, for gapped
+systems. The phenomenology that we unveil here provides a unified framework,
+which encompasses existing experimental observations with ultracold atoms and
+ions. It also paves the way to simple extensions of those experiments to
+observe the structures we describe in their full generality.
+",0,1,0,0,0,0
+7025,Examples of plane rational curves with two Galois points in positive characteristic,"  We present four new examples of plane rational curves with two Galois points
+in positive characteristic, and determine the number of Galois points for three
+of them. Our results are related to a problem on projective linear groups.
+",0,0,1,0,0,0
+7026,Optimal Kullback-Leibler Aggregation in Mixture Density Estimation by Maximum Likelihood,"  We study the maximum likelihood estimator of density of $n$ independent
+observations, under the assumption that it is well approximated by a mixture
+with a large number of components. The main focus is on statistical properties
+with respect to the Kullback-Leibler loss. We establish risk bounds taking the
+form of sharp oracle inequalities both in deviation and in expectation. A
+simple consequence of these bounds is that the maximum likelihood estimator
+attains the optimal rate $((\log K)/n)^{1/2}$, up to a possible logarithmic
+correction, in the problem of convex aggregation when the number $K$ of
+components is larger than $n^{1/2}$. More importantly, under the additional
+assumption that the Gram matrix of the components satisfies the compatibility
+condition, the obtained oracle inequalities yield the optimal rate in the
+sparsity scenario. That is, if the weight vector is (nearly) $D$-sparse, we get
+the rate $(D\log K)/n$. As a natural complement to our oracle inequalities, we
+introduce the notion of nearly-$D$-sparse aggregation and establish matching
+lower bounds for this type of aggregation.
+",0,0,1,1,0,0
+7027,Non-asymptotic theory for nonparametric testing,"  We consider nonparametric testing in a non-asymptotic framework. Our
+statistical guarantees are exact in the sense that Type I and II errors are
+controlled for any finite sample size. Meanwhile, one proposed test is shown to
+achieve minimax optimality in the asymptotic sense. An important consequence of
+this non-asymptotic theory is a new and practically useful formula for
+selecting the optimal smoothing parameter in nonparametric testing. The leading
+example in this paper is smoothing spline models under Gaussian errors. The
+results obtained therein can be further generalized to the kernel ridge
+regression framework under possibly non-Gaussian errors. Simulations
+demonstrate that our proposed test improves over the conventional asymptotic
+test when sample size is small to moderate.
+",0,0,1,1,0,0
+7028,Classification of out-of-time-order correlators,"  The space of n-point correlation functions, for all possible time-orderings
+of operators, can be computed by a non-trivial path integral contour, which
+depends on how many time-ordering violations are present in the correlator.
+These contours, which have come to be known as timefolds, or out-of-time-order
+(OTO) contours, are a natural generalization of the Schwinger-Keldysh contour
+(which computes singly out-of-time-ordered correlation functions). We provide a
+detailed discussion of such higher OTO functional integrals, explaining their
+general structure, and the myriad ways in which a particular correlation
+function may be encoded in such contours. Our discussion may be seen as a
+natural generalization of the Schwinger-Keldysh formalism to higher OTO
+correlation functions. We provide explicit illustration for low point
+correlators (n=2,3,4) to exemplify the general statements.
+",0,1,0,0,0,0
+7029,Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies,"  In this work, we provide theoretical guarantees for reward decomposition in
+deterministic MDPs. Reward decomposition is a special case of Hierarchical
+Reinforcement Learning, that allows one to learn many policies in parallel and
+combine them into a composite solution. Our approach builds on mapping this
+problem into a Reward Discounted Traveling Salesman Problem, and then deriving
+approximate solutions for it. In particular, we focus on approximate solutions
+that are local, i.e., solutions that only observe information about the current
+state. Local policies are easy to implement and do not require substantial
+computational resources as they do not perform planning. While local
+deterministic policies, like Nearest Neighbor, are being used in practice for
+hierarchical reinforcement learning, we propose three stochastic policies that
+guarantee better performance than any deterministic policy.
+",0,0,0,1,0,0
+7030,End-to-End Musical Key Estimation Using a Convolutional Neural Network,"  We present an end-to-end system for musical key estimation, based on a
+convolutional neural network. The proposed system not only out-performs
+existing key estimation methods proposed in the academic literature; it is also
+capable of learning a unified model for diverse musical genres that performs
+comparably to existing systems specialised for specific genres. Our experiments
+confirm that different genres do differ in their interpretation of tonality,
+and thus a system tuned e.g. for pop music performs subpar on pieces of
+electronic music. They also reveal that such cross-genre setups evoke specific
+types of error (predicting the relative or parallel minor). However, using the
+data-driven approach proposed in this paper, we can train models that deal with
+multiple musical styles adequately, and without major losses in accuracy.
+",1,0,0,0,0,0
+7031,Approximate Optimal Designs for Multivariate Polynomial Regression,"  We introduce a new approach aiming at computing approximate optimal designs
+for multivariate polynomial regressions on compact (semi-algebraic) design
+spaces. We use the moment-sum-of-squares hierarchy of semidefinite programming
+problems to solve numerically the approximate optimal design problem. The
+geometry of the design is recovered via semidefinite programming duality
+theory. This article shows that the hierarchy converges to the approximate
+optimal design as the order of the hierarchy increases. Furthermore, we provide
+a dual certificate ensuring finite convergence of the hierarchy and showing
+that the approximate optimal design can be computed numerically with our
+method. As a byproduct, we revisit the equivalence theorem of the experimental
+design theory: it is linked to the Christoffel polynomial and it characterizes
+finite convergence of the moment-sum-of-square hierarchies.
+",0,0,1,1,0,0
+7032,Determinants of public cooperation in multiplex networks,"  Synergies between evolutionary game theory and statistical physics have
+significantly improved our understanding of public cooperation in structured
+populations. Multiplex networks, in particular, provide the theoretical
+framework within network science that allows us to mathematically describe the
+rich structure of interactions characterizing human societies. While research
+has shown that multiplex networks may enhance the resilience of cooperation,
+the interplay between the overlap in the structure of the layers and the
+control parameters of the corresponding games has not yet been investigated.
+With this aim, we consider here the public goods game on a multiplex network,
+and we unveil the role of the number of layers and the overlap of links, as
+well as the impact of different synergy factors in different layers, on the
+onset of cooperation. We show that enhanced public cooperation emerges only
+when a significant edge overlap is combined with at least one layer being able
+to sustain some cooperation by means of a sufficiently high synergy factor. In
+the absence of either of these conditions, the evolution of cooperation in
+multiplex networks is determined by the bounds of traditional network
+reciprocity with no enhanced resilience. These results caution against overly
+optimistic predictions that the presence of multiple social domains may in
+itself promote cooperation, and they help us better understand the complexity
+behind prosocial behavior in layered social systems.
+",1,1,0,0,0,0
+7033,Variational Encoding of Complex Dynamics,"  Often the analysis of time-dependent chemical and biophysical systems
+produces high-dimensional time-series data for which it can be difficult to
+interpret which individual features are most salient. While recent work from
+our group and others has demonstrated the utility of time-lagged co-variate
+models to study such systems, linearity assumptions can limit the compression
+of inherently nonlinear dynamics into just a few characteristic components.
+Recent work in the field of deep learning has led to the development of
+variational autoencoders (VAE), which are able to compress complex datasets
+into simpler manifolds. We present the use of a time-lagged VAE, or variational
+dynamics encoder (VDE), to reduce complex, nonlinear processes to a single
+embedding with high fidelity to the underlying dynamics. We demonstrate how the
+VDE is able to capture nontrivial dynamics in a variety of examples, including
+Brownian dynamics and atomistic protein folding. Additionally, we demonstrate a
+method for analyzing the VDE model, inspired by saliency mapping, to determine
+what features are selected by the VDE model to describe dynamics. The VDE
+presents an important step in applying techniques from deep learning to more
+accurately model and interpret complex biophysics.
+",0,1,0,1,0,0
+7034,Accelerating Imitation Learning with Predictive Models,"  Sample efficiency is critical in solving real-world reinforcement learning
+problems, where agent-environment interactions can be costly. Imitation
+learning from expert advice has proved to be an effective strategy for reducing
+the number of interactions required to train a policy. Online imitation
+learning, which interleaves policy evaluation and policy optimization, is a
+particularly effective technique with provable performance guarantees. In this
+work, we seek to further accelerate the convergence rate of online imitation
+learning, thereby making it more sample efficient. We propose two model-based
+algorithms inspired by Follow-the-Leader (FTL) with prediction: MoBIL-VI based
+on solving variational inequalities and MoBIL-Prox based on stochastic
+first-order updates. These two methods leverage a model to predict future
+gradients to speed up policy learning. When the model oracle is learned online,
+these algorithms can provably accelerate the best known convergence rate up to
+an order. Our algorithms can be viewed as a generalization of stochastic
+Mirror-Prox (Juditsky et al., 2011), and admit a simple constructive FTL-style
+analysis of performance.
+",0,0,0,1,0,0
+7035,Neural Networks for Predicting Algorithm Runtime Distributions,"  Many state-of-the-art algorithms for solving hard combinatorial problems in
+artificial intelligence (AI) include elements of stochasticity that lead to
+high variations in runtime, even for a fixed problem instance. Knowledge about
+the resulting runtime distributions (RTDs) of algorithms on given problem
+instances can be exploited in various meta-algorithmic procedures, such as
+algorithm selection, portfolios, and randomized restarts. Previous work has
+shown that machine learning can be used to individually predict mean, median
+and variance of RTDs. To establish a new state-of-the-art in predicting RTDs,
+we demonstrate that the parameters of an RTD should be learned jointly and that
+neural networks can do this well by directly optimizing the likelihood of an
+RTD given runtime observations. In an empirical study involving five algorithms
+for SAT solving and AI planning, we show that neural networks predict the true
+RTDs of unseen instances better than previous methods, and can even do so when
+only few runtime observations are available per training instance.
+",1,0,0,0,0,0
+7036,Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild,"  Face detection methods have relied on face datasets for training. However,
+existing face datasets tend to be in small scales for face learning in both
+constrained and unconstrained environments. In this paper, we first introduce
+our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy
+Large-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large
+number of unconstrained multi-view and partially occluded faces. The faces have
+many variations in color and grayscale, image quality, image resolution, image
+illumination, image background, image illusion, human face, cartoon face,
+facial expression, light and severe partial facial occlusion, make up, gender,
+age, and race. Many of these faces are partially occluded with accessories such
+as tattoos, hats, glasses, sunglasses, hands, hair, beards, scarves,
+microphones, or other objects or persons. The LSLF dataset is currently the
+largest labeled face image dataset in the literature in terms of the number of
+labeled images and the number of individuals compared to other existing labeled
+face image datasets. Second, we introduce our CrowedFaces and CrowedNonFaces
+image datasets. The crowedFaces and CrowedNonFaces datasets include faces and
+non-faces images from crowed scenes. These datasets essentially aim for
+researchers to provide a large number of training examples with many variations
+for large scale face learning and face recognition tasks.
+",1,0,0,0,0,0
+7037,Small Hankel operators on generalized Fock spaces,"  We consider Fock spaces $F^{p,\ell}_{\alpha}$ of entire functions on
+${\mathbb C}$ associated to the weights $e^{-\alpha |z|^{2\ell}}$, where
+$\alpha>0$ and $\ell$ is a positive integer. We compute explicitly the
+corresponding Bergman kernel associated to $F^{2,\ell}_{\alpha}$ and, using an
+adequate factorization of this kernel, we characterize the boundedness and the
+compactness of the small Hankel operator $\mathfrak{h}^{\ell}_{b,\alpha}$ on
+$F^{p,\ell}_{\alpha}$. Moreover, we also determine when
+$\mathfrak{h}^{\ell}_{b,\alpha}$ is a Hilbert-Schmidt operator on
+$F^{2,\ell}_{\alpha}$.
+",0,0,1,0,0,0
+7038,Efficient enumeration of solutions produced by closure operations,"  In this paper we address the problem of generating all elements obtained by
+the saturation of an initial set by some operations. More precisely, we prove
+that we can generate the closure of a boolean relation (a set of boolean
+vectors) by polymorphisms with a polynomial delay. Therefore we can compute
+with polynomial delay the closure of a family of sets by any set of ""set
+operations"": union, intersection, symmetric difference, subsets, supersets
+$\dots$). To do so, we study the $Membership_{\mathcal{F}}$ problem: for a set
+of operations $\mathcal{F}$, decide whether an element belongs to the closure
+by $\mathcal{F}$ of a family of elements. In the boolean case, we prove that
+$Membership_{\mathcal{F}}$ is in P for any set of boolean operations
+$\mathcal{F}$. When the input vectors are over a domain larger than two
+elements, we prove that the generic enumeration method fails, since
+$Membership_{\mathcal{F}}$ is NP-hard for some $\mathcal{F}$. We also study the
+problem of generating minimal or maximal elements of closures and prove that
+some of them are related to well known enumeration problems such as the
+enumeration of the circuits of a matroid or the enumeration of maximal
+independent sets of a hypergraph. This article improves on previous works of
+the same authors.
+",1,0,0,0,0,0
+7039,Compressive Sensing via Convolutional Factor Analysis,"  We solve the compressive sensing problem via convolutional factor analysis,
+where the convolutional dictionaries are learned {\em in situ} from the
+compressed measurements. An alternating direction method of multipliers (ADMM)
+paradigm for compressive sensing inversion based on convolutional factor
+analysis is developed. The proposed algorithm provides reconstructed images as
+well as features, which can be directly used for recognition ($e.g.$,
+classification) tasks. When a deep (multilayer) model is constructed, a
+stochastic unpooling process is employed to build a generative model. During
+reconstruction and testing, we project the upper layer dictionary to the data
+level and only a single layer deconvolution is required. We demonstrate that
+using $\sim30\%$ (relative to pixel numbers) compressed measurements, the
+proposed model achieves the classification accuracy comparable to the original
+data on MNIST. We also observe that when the compressed measurements are very
+limited ($e.g.$, $<10\%$), the upper layer dictionary can provide better
+reconstruction results than the bottom layer.
+",1,0,0,1,0,0
+7040,Exact Dimensionality Selection for Bayesian PCA,"  We present a Bayesian model selection approach to estimate the intrinsic
+dimensionality of a high-dimensional dataset. To this end, we introduce a novel
+formulation of the probabilisitic principal component analysis model based on a
+normal-gamma prior distribution. In this context, we exhibit a closed-form
+expression of the marginal likelihood which allows to infer an optimal number
+of components. We also propose a heuristic based on the expected shape of the
+marginal likelihood curve in order to choose the hyperparameters. In
+non-asymptotic frameworks, we show on simulated data that this exact
+dimensionality selection approach is competitive with both Bayesian and
+frequentist state-of-the-art methods.
+",0,0,1,1,0,0
+7041,Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model,"  Sequence-to-sequence models provide a simple and elegant solution for
+building speech recognition systems by folding separate components of a typical
+system, namely acoustic (AM), pronunciation (PM) and language (LM) models into
+a single neural network. In this work, we look at one such sequence-to-sequence
+model, namely listen, attend and spell (LAS), and explore the possibility of
+training a single model to serve different English dialects, which simplifies
+the process of training multi-dialect systems without the need for separate AM,
+PM and LMs for each dialect. We show that simply pooling the data from all
+dialects into one LAS model falls behind the performance of a model fine-tuned
+on each dialect. We then look at incorporating dialect-specific information
+into the model, both by modifying the training targets by inserting the dialect
+symbol at the end of the original grapheme sequence and also feeding a 1-hot
+representation of the dialect information into all layers of the model.
+Experimental results on seven English dialects show that our proposed system is
+effective in modeling dialect variations within a single LAS model,
+outperforming a LAS model trained individually on each of the seven dialects by
+3.1 ~ 16.5% relative.
+",1,0,0,0,0,0
+7042,Exponential convergence of testing error for stochastic gradient methods,"  We consider binary classification problems with positive definite kernels and
+square loss, and study the convergence rates of stochastic gradient methods. We
+show that while the excess testing loss (squared loss) converges slowly to zero
+as the number of observations (and thus iterations) goes to infinity, the
+testing error (classification error) converges exponentially fast if low-noise
+conditions are assumed.
+",1,0,0,1,0,0
+7043,What is a hierarchically hyperbolic space?,"  The first part of this survey is a heuristic, non-technical discussion of
+what an HHS is, and the aim is to provide a good mental picture both to those
+actively doing research on HHSs and to those who only seek a basic
+understanding out of pure curiosity. It can be read independently of the second
+part, which is a detailed technical discussion of the axioms and the main tools
+to deal with HHSs.
+",0,0,1,0,0,0
+7044,What makes a gesture a gesture? Neural signatures involved in gesture recognition,"  Previous work in the area of gesture production, has made the assumption that
+machines can replicate ""human-like"" gestures by connecting a bounded set of
+salient points in the motion trajectory. Those inflection points were
+hypothesized to also display cognitive saliency. The purpose of this paper is
+to validate that claim using electroencephalography (EEG). That is, this paper
+attempts to find neural signatures of gestures (also referred as placeholders)
+in human cognition, which facilitate the understanding, learning and repetition
+of gestures. Further, it is discussed whether there is a direct mapping between
+the placeholders and kinematic salient points in the gesture trajectories.
+These are expressed as relationships between inflection points in the gestures'
+trajectories with oscillatory mu rhythms (8-12 Hz) in the EEG. This is achieved
+by correlating fluctuations in mu power during gesture observation with salient
+motion points found for each gesture. Peaks in the EEG signal at central
+electrodes (motor cortex) and occipital electrodes (visual cortex) were used to
+isolate the salient events within each gesture. We found that a linear model
+predicting mu peaks from motion inflections fits the data well. Increases in
+EEG power were detected 380 and 500ms after inflection points at occipital and
+central electrodes, respectively. These results suggest that coordinated
+activity in visual and motor cortices is sensitive to motion trajectories
+during gesture observation, and it is consistent with the proposal that
+inflection points operate as placeholders in gesture recognition.
+",1,0,0,0,0,0
+7045,Neural networks and rational functions,"  Neural networks and rational functions efficiently approximate each other. In
+more detail, it is shown here that for any ReLU network, there exists a
+rational function of degree $O(\text{polylog}(1/\epsilon))$ which is
+$\epsilon$-close, and similarly for any rational function there exists a ReLU
+network of size $O(\text{polylog}(1/\epsilon))$ which is $\epsilon$-close. By
+contrast, polynomials need degree $\Omega(\text{poly}(1/\epsilon))$ to
+approximate even a single ReLU. When converting a ReLU network to a rational
+function as above, the hidden constants depend exponentially on the number of
+layers, which is shown to be tight; in other words, a compositional
+representation can be beneficial even for rational functions.
+",1,0,0,1,0,0
+7046,Local Structure Theorems for Erdos Renyi Graphs and their Algorithmic Application,"  We analyze some local properties of sparse Erdos-Renyi graphs, where $d(n)/n$
+is the edge probability. In particular we study the behavior of very short
+paths. For $d(n)=n^{o(1)}$ we show that $G(n,d(n)/n)$ has asymptotically almost
+surely (a.a.s.~) bounded local treewidth and therefore is a.a.s.~nowhere dense.
+We also discover a new and simpler proof that $G(n,d/n)$ has a.a.s.~bounded
+expansion for constant~$d$. The local structure of sparse Erdos-Renyi Gaphs is
+very special: The $r$-neighborhood of a vertex is a tree with some additional
+edges, where the probability that there are $m$ additional edges decreases
+with~$m$. This implies efficient algorithms for subgraph isomorphism, in
+particular for finding subgraphs with small diameter. Finally we note that
+experiments suggest that preferential attachment graphs might have similar
+properties after deleting a small number of vertices.
+",1,0,0,0,0,0
+7047,DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples,"  Recent studies have shown that deep neural networks (DNN) are vulnerable to
+adversarial samples: maliciously-perturbed samples crafted to yield incorrect
+model outputs. Such attacks can severely undermine DNN systems, particularly in
+security-sensitive settings. It was observed that an adversary could easily
+generate adversarial samples by making a small perturbation on irrelevant
+feature dimensions that are unnecessary for the current classification task. To
+overcome this problem, we introduce a defensive mechanism called DeepCloak. By
+identifying and removing unnecessary features in a DNN model, DeepCloak limits
+the capacity an attacker can use generating adversarial samples and therefore
+increase the robustness against such inputs. Comparing with other defensive
+approaches, DeepCloak is easy to implement and computationally efficient.
+Experimental results show that DeepCloak can increase the performance of
+state-of-the-art DNN models against adversarial samples.
+",1,0,0,0,0,0
+7048,Automatic Pill Reminder for Easy Supervision,"  In this paper we present a working model of an automatic pill reminder and
+dispenser setup that can alleviate irregularities in taking prescribed dosage
+of medicines at the right time dictated by the medical practitioner and switch
+from approaches predominantly dependent on human memory to automation with
+negligible supervision, thus relieving persons from error-prone tasks of giving
+wrong medicine at the wrong time in the wrong amount.
+",1,0,0,0,0,0
+7049,"Chaotic dynamics of movements stochastic instability and the hypothesis of N.A. Bernstein about ""repetition without repetition""","  The registration of tremor was performed in two groups of subjects (15 people
+in each group) with different physical fitness at rest and at a static loads of
+3N. Each subject has been tested 15 series (number of series N=15) in both
+states (with and without physical loads) and each series contained 15 samples
+(n=15) of tremorogramm measurements (500 elements in each sample, registered
+coordinates x1(t) of the finger position relative to eddy current sensor) of
+the finger. Using non-parametric Wilcoxon test of each series of experiment a
+pairwise comparison was made forming 15 tables in which the results of
+calculation of pairwise comparison was presented as a matrix (15x15) for
+tremorogramms are presented. The average number of hits random pairs of samples
+(<k>) and standard deviation {\sigma} were calculated for all 15 matrices
+without load and under the impact of physical load (3N), which showed an
+increase almost in twice in the number k of pairs of matching samples of
+tremorogramms at conditions of a static load. For all these samples it was
+calculated special quasi-attractor (this square was presented the distinguishes
+between physical load and without it. All samples present the stochastic
+unstable state.
+",0,0,0,0,1,0
+7050,Deep Learning for Real Time Crime Forecasting,"  Accurate real time crime prediction is a fundamental issue for public safety,
+but remains a challenging problem for the scientific community. Crime
+occurrences depend on many complex factors. Compared to many predictable
+events, crime is sparse. At different spatio-temporal scales, crime
+distributions display dramatically different patterns. These distributions are
+of very low regularity in both space and time. In this work, we adapt the
+state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et
+al, AAAI, 2017], to collectively predict crime distribution over the Los
+Angeles area. Our models are two staged. First, we preprocess the raw crime
+data. This includes regularization in both space and time to enhance
+predictable signals. Second, we adapt hierarchical structures of residual
+convolutional units to train multi-factor crime prediction models. Experiments
+over a half year period in Los Angeles reveal highly accurate predictive power
+of our models.
+",1,0,0,1,0,0
+7051,Unpredictable sequences and Poincaré chaos,"  To make research of chaos more friendly with discrete equations, we introduce
+the concept of an unpredictable sequence as a specific unpredictable function
+on the set of integers. It is convenient to be verified as a solution of a
+discrete equation. This is rigorously proved in this paper for quasilinear
+systems, and we demonstrate the result numerically for linear systems in the
+critical case with respect to the stability of the origin. The completed
+research contributes to the theory of chaos as well as to the theory of
+discrete equations, considering unpredictable solutions.
+",0,1,0,0,0,0
+7052,Scaling of the Detonation Product State with Reactant Kinetic Energy,"  This submissions has been withdrawn by arXiv administrators because the
+submitter did not have the right to agree to our license.
+",0,1,0,0,0,0
+7053,Lumping of Degree-Based Mean Field and Pair Approximation Equations for Multi-State Contact Processes,"  Contact processes form a large and highly interesting class of dynamic
+processes on networks, including epidemic and information spreading. While
+devising stochastic models of such processes is relatively easy, analyzing them
+is very challenging from a computational point of view, particularly for large
+networks appearing in real applications. One strategy to reduce the complexity
+of their analysis is to rely on approximations, often in terms of a set of
+differential equations capturing the evolution of a random node, distinguishing
+nodes with different topological contexts (i.e., different degrees of different
+neighborhoods), like degree-based mean field (DBMF), approximate master
+equation (AME), or pair approximation (PA). The number of differential
+equations so obtained is typically proportional to the maximum degree kmax of
+the network, which is much smaller than the size of the master equation of the
+underlying stochastic model, yet numerically solving these equations can still
+be problematic for large kmax. In this paper, we extend AME and PA, which has
+been proposed only for the binary state case, to a multi-state setting and
+provide an aggregation procedure that clusters together nodes having similar
+degrees, treating those in the same cluster as indistinguishable, thus reducing
+the number of equations while preserving an accurate description of global
+observables of interest. We also provide an automatic way to build such
+equations and to identify a small number of degree clusters that give accurate
+results. The method is tested on several case studies, where it shows a high
+level of compression and a reduction of computational time of several orders of
+magnitude for large networks, with minimal loss in accuracy.
+",1,1,0,0,0,0
+7054,Almost automorphic functions on the quantum time scale and applications,"  In this paper, we first propose two types of concepts of almost automorphic
+functions on the quantum time scale. Secondly, we study some basic properties
+of almost automorphic functions on the quantum time scale. Then, we introduce a
+transformation between functions defined on the quantum time scale and
+functions defined on the set of generalized integer numbers, by using this
+transformation we give equivalent definitions of almost automorphic functions
+on the quantum time scale. Finally, as an application of our results, we
+establish the existence of almost automorphic solutions of linear and
+semilinear dynamic equations on the quantum time scale.
+",0,0,1,0,0,0
+7055,Stochastic Game in Remote Estimation under DoS Attacks,"  This paper studies remote state estimation under denial-of-service (DoS)
+attacks. A sensor transmits its local estimate of an underlying physical
+process to a remote estimator via a wireless communication channel. A DoS
+attacker is capable to interfere the channel and degrades the remote estimation
+accuracy. Considering the tactical jamming strategies played by the attacker,
+the sensor adjusts its transmission power. This interactive process between the
+sensor and the attacker is studied in the framework of a zero-sum stochastic
+game. To derive their optimal power schemes, we first discuss the existence of
+stationary Nash equilibrium (SNE) for this game. We then present the monotone
+structure of the optimal strategies, which helps reduce the computational
+complexity of the stochastic game algorithm. Numerical examples are provided to
+illustrate the obtained results.
+",1,0,0,0,0,0
+7056,VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry,"  Semantic understanding and localization are fundamental enablers of robot
+autonomy that have for the most part been tackled as disjoint problems. While
+deep learning has enabled recent breakthroughs across a wide spectrum of scene
+understanding tasks, its applicability to state estimation tasks has been
+limited due to the direct formulation that renders it incapable of encoding
+scene-specific constrains. In this work, we propose the VLocNet++ architecture
+that employs a multitask learning approach to exploit the inter-task
+relationship between learning semantics, regressing 6-DoF global pose and
+odometry, for the mutual benefit of each of these tasks. Our network overcomes
+the aforementioned limitation by simultaneously embedding geometric and
+semantic knowledge of the world into the pose regression network. We propose a
+novel adaptive weighted fusion layer to aggregate motion-specific temporal
+information and to fuse semantic features into the localization stream based on
+region activations. Furthermore, we propose a self-supervised warping technique
+that uses the relative motion to warp intermediate network representations in
+the segmentation stream for learning consistent semantics. Finally, we
+introduce a first-of-a-kind urban outdoor localization dataset with pixel-level
+semantic labels and multiple loops for training deep networks. Extensive
+experiments on the challenging Microsoft 7-Scenes benchmark and our DeepLoc
+dataset demonstrate that our approach exceeds the state-of-the-art
+outperforming local feature-based methods while simultaneously performing
+multiple tasks and exhibiting substantial robustness in challenging scenarios.
+",1,0,0,0,0,0
+7057,On Decidability of the Ordered Structures of Numbers,"  The ordered structures of natural, integer, rational and real numbers are
+studied here. It is known that the theories of these numbers in the language of
+order are decidable and finitely axiomatizable. Also, their theories in the
+language of order and addition are decidable and infinitely axiomatizable. For
+the language of order and multiplication, it is known that the theories of
+$\mathbb{N}$ and $\mathbb{Z}$ are not decidable (and so not axiomatizable by
+any computably enumerable set of sentences). By Tarski's theorem, the
+multiplicative ordered structure of $\mathbb{R}$ is decidable also; here we
+prove this result directly and present an axiomatization. The structure of
+$\mathbb{Q}$ in the language of order and multiplication seems to be missing in
+the literature; here we show the decidability of its theory by the technique of
+quantifier elimination and after presenting an infinite axiomatization for this
+structure we prove that it is not finitely axiomatizable.
+",1,0,1,0,0,0
+7058,"Finite $p$-groups of conjugate type $\{ 1, p^3 \}$","  We classify finite $p$-groups, upto isoclinism, which have only two conjugacy
+class sizes $1$ and $p^3$. It turns out that the nilpotency class of such
+groups is $2$.
+",0,0,1,0,0,0
+7059,Betting on Quantum Objects,"  Dutch book arguments have been applied to beliefs about the outcomes of
+measurements of quantum systems, but not to beliefs about quantum objects prior
+to measurement. In this paper, we prove a quantum version of the probabilists'
+Dutch book theorem that applies to both sorts of beliefs: roughly, if ideal
+beliefs are given by vector states, all and only Born-rule probabilities avoid
+Dutch books. This theorem and associated results have implications for
+operational and realist interpretations of the logic of a Hilbert lattice. In
+the latter case, we show that the defenders of the eigenstate-value orthodoxy
+face a trilemma. Those who favor vague properties avoid the trilemma, admitting
+all and only those beliefs about quantum objects that avoid Dutch books.
+",0,1,0,0,0,0
+7060,Casimir-Polder size consistency -- a constraint violated by some dispersion theories,"  A key goal in quantum chemistry methods, whether ab initio or otherwise, is
+to achieve size consistency. In this manuscript we formulate the related idea
+of ""Casimir-Polder size consistency"" that manifests in long-range dispersion
+energetics. We show that local approximations in time-dependent density
+functional theory dispersion energy calculations violate the consistency
+condition because of incorrect treatment of highly non-local ""xc kernel""
+physics, by up to 10% in our tests on closed-shell atoms.
+",0,1,0,0,0,0
+7061,A combinatorial proof of Bass's determinant formula for the zeta function of regular graphs,"  We give an elementary combinatorial proof of Bass's determinant formula for
+the zeta function of a finite regular graph. This is done by expressing the
+number of non-backtracking cycles of a given length in terms of Chebychev
+polynomials in the eigenvalues of the adjacency operator of the graph.
+",1,0,0,0,0,0
+7062,Electron-Muon Ranger: hardware characterization,"  The Electron-Muon Ranger (EMR) is a fully-active tracking-calorimeter in
+charge of the electron background rejection downstream of the cooling channel
+at the international Muon Ionization Cooling Experiment. It consists of 2832
+plastic scintillator bars segmented in 48 planes in an X-Y arrangement and uses
+particle range as its main variable to tag muons and discriminate electrons. An
+array of analyses were conducted to characterize the hardware of the EMR and
+determine whether the detector performs to specifications. The clear fibres
+coming from the bars were shown to transmit the desired amount of light, and
+only four dead channels were identified in the electronics. Two channels had
+indubitably been mismatched during assembly and the DAQ channel map was
+subsequently corrected. The level of crosstalk is within acceptable values for
+the type of multi-anode photomultiplier used with an average of
+$0.20\pm0.03\,\%$ probability of occurrence in adjacent channels and a mean
+amplitude equivalent to $4.5\pm0.1\,\%$ of the primary signal intensity. The
+efficiency of the signal acquisition, defined as the probability of recording a
+signal in a plane when a particle goes through it in beam conditions, reached
+$99.73\pm0.02\,\%$.
+",0,1,0,0,0,0
+7063,Photometric Redshifts for Hyper Suprime-Cam Subaru Strategic Program Data Release 1,"  Photometric redshifts are a key component of many science objectives in the
+Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). In this paper, we
+describe and compare the codes used to compute photometric redshifts for
+HSC-SSP, how we calibrate them, and the typical accuracy we achieve with the
+HSC five-band photometry (grizy). We introduce a new point estimator based on
+an improved loss function and demonstrate that it works better than other
+commonly used estimators. We find that our photo-z's are most accurate at
+0.2<~zphot<~1.5, where we can straddle the 4000A break. We achieve
+sigma(d_zphot/(1+zphot))~0.05 and an outlier rate of about 15% for galaxies
+down to i=25 within this redshift range. If we limit to a brighter sample of
+i<24, we achieve sigma~0.04 and ~8% outliers. Our photo-z's should thus enable
+many science cases for HSC-SSP. We also characterize the accuracy of our
+redshift probability distribution function (PDF) and discover that some codes
+over/under-estimate the redshift uncertainties, which have implications for
+N(z) reconstruction. Our photo-z products for the entire area in the Public
+Data Release 1 are publicly available, and both our catalog products (such as
+point estimates) and full PDFs can be retrieved from the data release site,
+this https URL.
+",0,1,0,0,0,0
+7064,Shallow Updates for Deep Reinforcement Learning,"  Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN)
+have achieved state-of-the-art results in a variety of challenging,
+high-dimensional domains. This success is mainly attributed to the power of
+deep neural networks to learn rich domain representations for approximating the
+value function or policy. Batch reinforcement learning methods with linear
+representations, on the other hand, are more stable and require less hyper
+parameter tuning. Yet, substantial feature engineering is necessary to achieve
+good results. In this work we propose a hybrid approach -- the Least Squares
+Deep Q-Network (LS-DQN), which combines rich feature representations learned by
+a DRL algorithm with the stability of a linear least squares method. We do this
+by periodically re-training the last hidden layer of a DRL network with a batch
+least squares update. Key to our approach is a Bayesian regularization term for
+the least squares update, which prevents over-fitting to the more recent data.
+We tested LS-DQN on five Atari games and demonstrate significant improvement
+over vanilla DQN and Double-DQN. We also investigated the reasons for the
+superior performance of our method. Interestingly, we found that the
+performance improvement can be attributed to the large batch size used by the
+LS method when optimizing the last layer.
+",1,0,0,1,0,0
+7065,"Survey on Additive Manufacturing, Cloud 3D Printing and Services","  Cloud Manufacturing (CM) is the concept of using manufacturing resources in a
+service oriented way over the Internet. Recent developments in Additive
+Manufacturing (AM) are making it possible to utilise resources ad-hoc as
+replacement for traditional manufacturing resources in case of spontaneous
+problems in the established manufacturing processes. In order to be of use in
+these scenarios the AM resources must adhere to a strict principle of
+transparency and service composition in adherence to the Cloud Computing (CC)
+paradigm. With this review we provide an overview over CM, AM and relevant
+domains as well as present the historical development of scientific research in
+these fields, starting from 2002. Part of this work is also a meta-review on
+the domain to further detail its development and structure.
+",1,0,0,0,0,0
+7066,Barnacles and Gravity,"  Theories with more than one vacuum allow quantum transitions between them,
+which may proceed via bubble nucleation; theories with more than two vacua
+posses additional decay modes in which the wall of a bubble may further decay.
+The instantons which mediate such a process have $O(3)$ symmetry (in four
+dimensions, rather than the usual $O(4)$ symmetry of homogeneous vacuum decay),
+and have been called `barnacles'; previously they have been studied in flat
+space, in the thin wall limit, and this paper extends the analysis to include
+gravity. It is found that there are regions of parameter space in which, given
+an initial bubble, barnacles are the favoured subsequent decay process, and
+that the inclusion of gravity can enlarge this region. The relation to other
+heterogeneous vacuum decay scenarios, as well as some of the phenomenological
+implications of barnacles are briefly discussed.
+",0,1,0,0,0,0
+7067,A note on some constants related to the zeta-function and their relationship with the Gregory coefficients,"  In this paper new series for the first and second Stieltjes constants (also
+known as generalized Euler's constant), as well as for some closely related
+constants are obtained. These series contain rational terms only and involve
+the so-called Gregory coefficients, which are also known as (reciprocal)
+logarithmic numbers, Cauchy numbers of the first kind and Bernoulli numbers of
+the second kind. In addition, two interesting series with rational terms are
+given for Euler's constant and the constant ln(2*pi), and yet another
+generalization of Euler's constant is proposed and various formulas for the
+calculation of these constants are obtained. Finally, in the paper, we mention
+that almost all the constants considered in this work admit simple
+representations via the Ramanujan summation.
+",0,0,1,0,0,0
+7068,Thresholding Bandit for Dose-ranging: The Impact of Monotonicity,"  We analyze the sample complexity of the thresholding bandit problem, with and
+without the assumption that the mean values of the arms are increasing. In each
+case, we provide a lower bound valid for any risk $\delta$ and any
+$\delta$-correct algorithm; in addition, we propose an algorithm whose sample
+complexity is of the same order of magnitude for small risks. This work is
+motivated by phase 1 clinical trials, a practically important setting where the
+arm means are increasing by nature, and where no satisfactory solution is
+available so far.
+",0,0,1,1,0,0
+7069,Spin - Phonon Coupling in Nickel Oxide Determined from Ultraviolet Raman Spectroscopy,"  Nickel oxide (NiO) has been studied extensively for various applications
+ranging from electrochemistry to solar cells [1,2]. In recent years, NiO
+attracted much attention as an antiferromagnetic (AF) insulator material for
+spintronic devices [3-10]. Understanding the spin - phonon coupling in NiO is a
+key to its functionalization, and enabling AF spintronics' promise of
+ultra-high-speed and low-power dissipation [11,12]. However, despite its status
+as an exemplary AF insulator and a benchmark material for the study of
+correlated electron systems, little is known about the spin - phonon
+interaction, and the associated energy dissipation channel, in NiO. In
+addition, there is a long-standing controversy over the large discrepancies
+between the experimental and theoretical values for the electron, phonon, and
+magnon energies in NiO [13-23]. This gap in knowledge is explained by NiO
+optical selection rules, high Neel temperature and dominance of the magnon band
+in the visible Raman spectrum, which precludes a conventional approach for
+investigating such interaction. Here we show that by using ultraviolet (UV)
+Raman spectroscopy one can extract the spin - phonon coupling coefficients in
+NiO. We established that unlike in other materials, the spins of Ni atoms
+interact more strongly with the longitudinal optical (LO) phonons than with the
+transverse optical (TO) phonons, and produce opposite effects on the phonon
+energies. The peculiarities of the spin - phonon coupling are consistent with
+the trends given by density functional theory calculations. The obtained
+results shed light on the nature of the spin - phonon coupling in AF insulators
+and may help in developing innovative spintronic devices.
+",0,1,0,0,0,0
+7070,A Univariate Bound of Area Under ROC,"  Area under ROC (AUC) is an important metric for binary classification and
+bipartite ranking problems. However, it is difficult to directly optimizing AUC
+as a learning objective, so most existing algorithms are based on optimizing a
+surrogate loss to AUC. One significant drawback of these surrogate losses is
+that they require pairwise comparisons among training data, which leads to slow
+running time and increasing local storage for online learning. In this work, we
+describe a new surrogate loss based on a reformulation of the AUC risk, which
+does not require pairwise comparison but rankings of the predictions. We
+further show that the ranking operation can be avoided, and the learning
+objective obtained based on this surrogate enjoys linear complexity in time and
+storage. We perform experiments to demonstrate the effectiveness of the online
+and batch algorithms for AUC optimization based on the proposed surrogate loss.
+",0,0,0,1,0,0
+7071,On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis,"  Text preprocessing is often the first step in the pipeline of a Natural
+Language Processing (NLP) system, with potential impact in its final
+performance. Despite its importance, text preprocessing has not received much
+attention in the deep learning literature. In this paper we investigate the
+impact of simple text preprocessing decisions (particularly tokenizing,
+lemmatizing, lowercasing and multiword grouping) on the performance of a
+standard neural text classifier. We perform an extensive evaluation on standard
+benchmarks from text categorization and sentiment analysis. While our
+experiments show that a simple tokenization of input text is generally
+adequate, they also highlight significant degrees of variability across
+preprocessing techniques. This reveals the importance of paying attention to
+this usually-overlooked step in the pipeline, particularly when comparing
+different models. Finally, our evaluation provides insights into the best
+preprocessing practices for training word embeddings.
+",1,0,0,0,0,0
+7072,Evidences against cuspy dark matter halos in large galaxies,"  We develop and apply new techniques in order to uncover galaxy rotation
+curves (RC) systematics. Considering that an ideal dark matter (DM) profile
+should yield RCs that have no bias towards any particular radius, we find that
+the Burkert DM profile satisfies the test, while the Navarro-Frenk-While (NFW)
+profile has a tendency of better fitting the region between one and two disc
+scale lengths than the inner disc scale length region. Our sample indicates
+that this behaviour happens to more than 75% of the galaxies fitted with an NFW
+halo. Also, this tendency does not weaken by considering ""large"" galaxies, for
+instance those with $M_*\gtrsim 10^{10} M_\odot$. Besides the tests on the
+homogeneity of the fits, we also use a sample of 62 galaxies of diverse types
+to perform tests on the quality of the overall fit of each galaxy, and to
+search for correlations with stellar mass, gas mass and the disc scale length.
+In particular, we find that only 13 galaxies are better fitted by the NFW halo;
+and that even for the galaxies with $M_* \gtrsim 10^{10} M_\odot$ the Burkert
+profile either fits as good as, or better than, the NFW profile. This result is
+relevant since different baryonic effects important for the smaller galaxies,
+like supernova feedback and dynamical friction from baryonic clumps, indicate
+that at such large stellar masses the NFW profile should be preferred over the
+Burkert profile. Hence, our results either suggest a new baryonic effect or a
+change of the dark matter physics.
+",0,1,0,0,0,0
+7073,A Hybrid Framework for Multi-Vehicle Collision Avoidance,"  With the recent surge of interest in UAVs for civilian services, the
+importance of developing tractable multi-agent analysis techniques that provide
+safety and performance guarantees have drastically increased. Hamilton-Jacobi
+(HJ) reachability has successfully provided these guarantees to small-scale
+systems and is flexible in terms of system dynamics. However, the exponential
+complexity scaling of HJ reachability with respect to system dimension prevents
+its direct application to larger-scale problems where the number of vehicles is
+greater than two. In this paper, we propose a collision avoidance algorithm
+using a hybrid framework for N+1 vehicles through higher-level control logic
+given any N-vehicle collision avoidance algorithm. Our algorithm conservatively
+approximates a guaranteed-safe region in the joint state space of the N+1
+vehicles and produces a safety-preserving controller. In addition, our
+algorithm does not incur significant additional computation cost. We
+demonstrate our proposed method in simulation.
+",0,0,1,0,0,0
+7074,On the zeros of Riemann $Ξ(z)$ function,"  The Riemann $\Xi(z)$ function (even in $z$) admits a Fourier transform of an
+even kernel $\Phi(t)=4e^{9t/2}\theta''(e^{2t})+6e^{5t/2}\theta'(e^{2t})$. Here
+$\theta(x):=\theta_3(0,ix)$ and $\theta_3(0,z)$ is a Jacobi theta function, a
+modular form of weight $\frac{1}{2}$. (A) We discover a family of functions
+$\{\Phi_n(t)\}_{n\geqslant 2}$ whose Fourier transform on compact support
+$(-\frac{1}{2}\log n, \frac{1}{2}\log n)$, $\{F(n,z)\}_{n\geqslant2}$,
+converges to $\Xi(z)$ uniformly in the critical strip $S_{1/2}:=\{|\Im(z)|<
+\frac{1}{2}\}$. (B) Based on this we then construct another family of functions
+$\{H(14,n,z)\}_{n\geqslant 2}$ and show that it uniformly converges to $\Xi(z)$
+in the critical strip $S_{1/2}$. (C) Based on this we construct another family
+of functions $\{W(n,z)\}_{n\geqslant 8}:=\{H(14,n,2z/\log n)\}_{n\geqslant 8}$
+and show that if all the zeros of $\{W(n,z)\}_{n\geqslant 8}$ in the critical
+strip $S_{1/2}$ are real, then all the zeros of $\{H(14,n,z)\}_{n\geqslant 8}$
+in the critical strip $S_{1/2}$ are real. (D) We then show that
+$W(n,z)=U(n,z)-V(n,z)$ and $U(n,z^{1/2})$ and $V(n,z^{1/2})$ have only real,
+positive and simple zeros. And there exists a positive integer $N\geqslant 8$
+such that for all $n\geqslant N$, the zeros of $U(n,x^{1/2})$ are strictly
+left-interlacing with those of $V(n,x^{1/2})$. Using an entire function
+equivalent to Hermite-Kakeya Theorem for polynomials we show that $W(n\geqslant
+N,z^{1/2})$ has only real, positive and simple zeros. Thus $W(n\geqslant N,z)$
+have only real and imple zeros. (E) Using a corollary of Hurwitz's theorem in
+complex analysis we prove that $\Xi(z)$ has no zeros in
+$S_{1/2}\setminus\mathbb{R}$, i.e., $S_{1/2}\setminus \mathbb{R}$ is a
+zero-free region for $\Xi(z)$. Since all the zeros of $\Xi(z)$ are in
+$S_{1/2}$, all the zeros of $\Xi(z)$ are in $\mathbb{R}$, i.e., all the zeros
+of $\Xi(z)$ are real.
+",0,0,1,0,0,0
+7075,On Topologized Fundamental Groups with Small Loop Transfer Viewpoints,"  In this paper, by introducing some kind of small loop transfer spaces at a
+point, we study the behavior of topologized fundamental groups with the
+compact-open topology and the whisker topology, $\pi_{1}^{qtop}(X,x_{0})$ and
+$\pi_{1}^{wh}(X,x_{0})$, respectively. In particular, we give necessary or
+sufficient conditions for coincidence and being topological group of these two
+topologized fundamental groups. Finally, we give some examples to show that the
+reverse of some of these implications do not hold, in general.
+",0,0,1,0,0,0
+7076,The boundary value problem for Yang--Mills--Higgs fields,"  We show the existence of Yang--Mills--Higgs (YMH) fields over a Riemann
+surface with boundary where a free boundary condition is imposed on the section
+and a Neumann boundary condition on the connection. In technical terms, we
+study the convergence and blow-up behavior of a sequence of Sacks-Uhlenbeck
+type $\alpha$-YMH fields as $\alpha\to 1$. For $\alpha>1$, each $\alpha$-YMH
+field is shown to be smooth up to the boundary under some gauge transformation.
+This is achieved by showing a regularity theorem for more general coupled
+systems, which extends the classical results of Ladyzhenskaya-Ural'ceva and
+Morrey.
+",0,0,1,0,0,0
+7077,Polish Topologies for Graph Products of Groups,"  We give strong necessary conditions on the admissibility of a Polish group
+topology for an arbitrary graph product of groups $G(\Gamma, G_a)$, and use
+them to give a characterization modulo a finite set of nodes. As a corollary,
+we give a complete characterization in case all the factor groups $G_a$ are
+countable.
+",0,0,1,0,0,0
+7078,Iterated failure rate monotonicity and ordering relations within Gamma and Weibull distributions,"  Stochastic ordering of distributions of random variables may be defined by
+the relative convexity of the tail functions. This has been extended to higher
+order stochastic orderings, by iteratively reassigning tail-weights. The actual
+verification of those stochastic orderings is not simple, as this depends on
+inverting distribution functions for which there may be no explicit expression.
+The iterative definition of distributions, of course, contributes to make that
+verification even harder. We have a look at the stochastic ordering,
+introducing a method that allows for explicit usage, applying it to the Gamma
+and Weibull distributions, giving a complete description of the order relations
+within each of those families.
+",0,0,1,1,0,0
+7079,Quantum depletion of a homogeneous Bose-Einstein condensate,"  We have measured the quantum depletion of an interacting homogeneous
+Bose-Einstein condensate, and confirmed the 70-year old theory of N.N.
+Bogoliubov. The observed condensate depletion is reversibly tuneable by
+changing the strength of the interparticle interactions. Our atomic homogeneous
+condensate is produced in an optical-box trap, the interactions are tuned via a
+magnetic Feshbach resonance, and the condensed fraction probed by coherent
+two-photon Bragg scattering.
+",0,1,0,0,0,0
+7080,A Deep Generative Model for Graphs: Supervised Subset Selection to Create Diverse Realistic Graphs with Applications to Power Networks Synthesis,"  Creating and modeling real-world graphs is a crucial problem in various
+applications of engineering, biology, and social sciences; however, learning
+the distributions of nodes/edges and sampling from them to generate realistic
+graphs is still challenging. Moreover, generating a diverse set of synthetic
+graphs that all imitate a real network is not addressed. In this paper, the
+novel problem of creating diverse synthetic graphs is solved. First, we devise
+the deep supervised subset selection (DeepS3) algorithm; Given a ground-truth
+set of data points, DeepS3 selects a diverse subset of all items (i.e. data
+points) that best represent the items in the ground-truth set. Furthermore, we
+propose the deep graph representation recurrent network (GRRN) as a novel
+generative model that learns a probabilistic representation of a real weighted
+graph. Training the GRRN, we generate a large set of synthetic graphs that are
+likely to follow the same features and adjacency patterns as the original one.
+Incorporating GRRN with DeepS3, we select a diverse subset of generated graphs
+that best represent the behaviors of the real graph (i.e. our ground-truth). We
+apply our model to the novel problem of power grid synthesis, where a synthetic
+power network is created with the same physical/geometric properties as a real
+power system without revealing the real locations of the substations (nodes)
+and the lines (edges), since such data is confidential. Experiments on the
+Synthetic Power Grid Data Set show accurate synthetic networks that follow
+similar structural and spatial properties as the real power grid.
+",1,0,0,1,0,0
+7081,Analytic Combinatorics in Several Variables: Effective Asymptotics and Lattice Path Enumeration,"  The field of analytic combinatorics, which studies the asymptotic behaviour
+of sequences through analytic properties of their generating functions, has led
+to the development of deep and powerful tools with applications across
+mathematics and the natural sciences. In addition to the now classical
+univariate theory, recent work in the study of analytic combinatorics in
+several variables (ACSV) has shown how to derive asymptotics for the
+coefficients of certain D-finite functions represented by diagonals of
+multivariate rational functions. We give a pedagogical introduction to the
+methods of ACSV from a computer algebra viewpoint, developing rigorous
+algorithms and giving the first complexity results in this area under
+conditions which are broadly satisfied. Furthermore, we give several new
+applications of ACSV to the enumeration of lattice walks restricted to certain
+regions. In addition to proving several open conjectures on the asymptotics of
+such walks, a detailed study of lattice walk models with weighted steps is
+undertaken.
+",1,0,0,0,0,0
+7082,Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training,"  While strong progress has been made in image captioning over the last years,
+machine and human captions are still quite distinct. A closer look reveals that
+this is due to the deficiencies in the generated word distribution, vocabulary
+size, and strong bias in the generators towards frequent captions. Furthermore,
+humans -- rightfully so -- generate multiple, diverse captions, due to the
+inherent ambiguity in the captioning task which is not considered in today's
+systems.
+To address these challenges, we change the training objective of the caption
+generator from reproducing groundtruth captions to generating a set of captions
+that is indistinguishable from human generated captions. Instead of
+handcrafting such a learning target, we employ adversarial training in
+combination with an approximate Gumbel sampler to implicitly match the
+generated distribution to the human one. While our method achieves comparable
+performance to the state-of-the-art in terms of the correctness of the
+captions, we generate a set of diverse captions, that are significantly less
+biased and match the word statistics better in several aspects.
+",1,0,0,0,0,0
+7083,A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries,"  This paper presents a novel framework for accurate pedestrian intent
+prediction at intersections. Given some prior knowledge of the curbside
+geometry, the presented framework can accurately predict pedestrian
+trajectories, even in new intersections that it has not been trained on. This
+is achieved by making use of the contravariant components of trajectories in
+the curbside coordinate system, which ensures that the transformation of
+trajectories across intersections is affine, regardless of the curbside
+geometry. Our method is based on the Augmented Semi Nonnegative Sparse Coding
+(ASNSC) formulation and we use that as a baseline to show improvement in
+prediction performance on real pedestrian datasets collected at two
+intersections in Cambridge, with distinctly different curbside and crosswalk
+geometries. We demonstrate a 7.2% improvement in prediction accuracy in the
+case of same train and test intersections. Furthermore, we show a comparable
+prediction performance of TASNSC when trained and tested in different
+intersections with the baseline, trained and tested on the same intersection.
+",1,0,0,1,0,0
+7084,Generating GraphQL-Wrappers for REST(-like) APIs,"  GraphQL is a query language and thereupon-based paradigm for implementing web
+Application Programming Interfaces (APIs) for client-server interactions. Using
+GraphQL, clients define precise, nested data-requirements in typed queries,
+which are resolved by servers against (possibly multiple) backend systems, like
+databases, object storages, or other APIs. Clients receive only the data they
+care about, in a single request. However, providers of existing REST(-like)
+APIs need to implement additional GraphQL interfaces to enable these
+advantages. We here assess the feasibility of automatically generating GraphQL
+wrappers for existing REST(-like) APIs. A wrapper, upon receiving GraphQL
+queries, translates them to requests against the target API. We discuss the
+challenges for creating such wrappers, including dealing with data sanitation,
+authentication, or handling nested queries. We furthermore present a
+prototypical implementation of OASGraph. OASGraph takes as input an OpenAPI
+Specification (OAS) describing an existing REST(-like) web API and generates a
+GraphQL wrapper for it. We evaluate OASGraph by running it, as well as an
+existing open source alternative, against 959 publicly available OAS. This
+experiment shows that OASGraph outperforms the existing alternative and is able
+to create a GraphQL wrapper for 89.5% of the APIs -- however, with limitations
+in many cases. A subsequent analysis of errors and warnings produced by
+OASGraph shows that missing or ambiguous information in the assessed OAS
+hinders creating complete wrappers. Finally, we present a use case of the IBM
+Watson Language Translator API that shows that small changes to an OAS allow
+OASGraph to generate more idiomatic and more expressive GraphQL wrappers.
+",1,0,0,0,0,0
+7085,GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework,"  There is a pressing need to build an architecture that could subsume these
+networks under a unified framework that achieves both higher performance and
+less overhead. To this end, two fundamental issues are yet to be addressed. The
+first one is how to implement the back propagation when neuronal activations
+are discrete. The second one is how to remove the full-precision hidden weights
+in the training phase to break the bottlenecks of memory/computation
+consumption. To address the first issue, we present a multi-step neuronal
+activation discretization method and a derivative approximation technique that
+enable the implementing the back propagation algorithm on discrete DNNs. While
+for the second issue, we propose a discrete state transition (DST) methodology
+to constrain the weights in a discrete space without saving the hidden weights.
+Through this way, we build a unified framework that subsumes the binary or
+ternary networks as its special cases, and under which a heuristic algorithm is
+provided at the website this https URL. More
+particularly, we find that when both the weights and activations become ternary
+values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR
+networks (GXNOR-Nets) since only the event of non-zero weight and non-zero
+activation enables the control gate to start the XNOR logic operations in the
+original binary networks. This promises the event-driven hardware design for
+efficient mobile intelligence. We achieve advanced performance compared with
+state-of-the-art algorithms. Furthermore, the computational sparsity and the
+number of states in the discrete space can be flexibly modified to make it
+suitable for various hardware platforms.
+",1,0,0,1,0,0
+7086,The growth of carbon chains in IRC+10216 mapped with ALMA,"  Linear carbon chains are common in various types of astronomical molecular
+sources. Possible formation mechanisms involve both bottom-up and top-down
+routes. We have carried out a combined observational and modeling study of the
+formation of carbon chains in the C-star envelope IRC+10216, where the
+polymerization of acetylene and hydrogen cyanide induced by ultraviolet photons
+can drive the formation of linear carbon chains of increasing length. We have
+used ALMA to map the emission of 3 mm rotational lines of the hydrocarbon
+radicals C2H, C4H, and C6H, and the CN-containing species CN, C3N, HC3N, and
+HC5N with an angular resolution of 1"". The spatial distribution of all these
+species is a hollow, 5-10"" wide, spherical shell located at a radius of 10-20""
+from the star, with no appreciable emission close to the star. Our observations
+resolve the broad shell of carbon chains into thinner sub-shells which are 1-2""
+wide and not fully concentric, indicating that the mass loss process has been
+discontinuous and not fully isotropic. The radial distributions of the species
+mapped reveal subtle differences: while the hydrocarbon radicals have very
+similar radial distributions, the CN-containing species show more diverse
+distributions, with HC3N appearing earlier in the expansion and the radical CN
+extending later than the rest of the species. The observed morphology can be
+rationalized by a chemical model in which the growth of polyynes is mainly
+produced by rapid gas-phase chemical reactions of C2H and C4H radicals with
+unsaturated hydrocarbons, while cyanopolyynes are mainly formed from polyynes
+in gas-phase reactions with CN and C3N radicals.
+",0,1,0,0,0,0
+7087,Thermoelectric Transport Coefficients from Charged Solv and Nil Black Holes,"  In the present work we study charged black hole solutions of the
+Einstein-Maxwell action that have Thurston geometries on its near horizon
+region. In particular we find solutions with charged Solv and Nil geometry
+horizons. We also find Nil black holes with hyperscaling violation. For all our
+solutions we compute the thermoelectric DC transport coefficients of the
+corresponding dual field theory. We find that the Solv and Nil black holes
+without hyperscaling violation are dual to metals while those with hyperscaling
+violation are dual to insulators.
+",0,1,0,0,0,0
+7088,A unified method for maximal truncated Calderón-Zygmund operators in general function spaces by sparse domination,"  In this note we give simple proofs of several results involving maximal
+truncated Caldeón-Zygmund operators in the general setting of rearrangement
+invariant quasi-Banach function spaces by sparse domination. Our techniques
+allow us to track the dependence of the constants in weighted norm
+inequalities; additionally, our results hold in $\mathbb{R}^n$ as well as in
+many spaces of homogeneous type.
+",0,0,1,0,0,0
+7089,Evaluating Quality of Chatbots and Intelligent Conversational Agents,"  Chatbots are one class of intelligent, conversational software agents
+activated by natural language input (which can be in the form of text, voice,
+or both). They provide conversational output in response, and if commanded, can
+sometimes also execute tasks. Although chatbot technologies have existed since
+the 1960s and have influenced user interface development in games since the
+early 1980s, chatbots are now easier to train and implement. This is due to
+plentiful open source code, widely available development platforms, and
+implementation options via Software as a Service (SaaS). In addition to
+enhancing customer experiences and supporting learning, chatbots can also be
+used to engineer social harm - that is, to spread rumors and misinformation, or
+attack people for posting their thoughts and opinions online. This paper
+presents a literature review of quality issues and attributes as they relate to
+the contemporary issue of chatbot development and implementation. Finally,
+quality assessment approaches are reviewed, and a quality assessment method
+based on these attributes and the Analytic Hierarchy Process (AHP) is proposed
+and examined.
+",1,0,0,0,0,0
+7090,Fast and Lightweight Rate Control for Onboard Predictive Coding of Hyperspectral Images,"  Predictive coding is attractive for compression of hyperspecral images
+onboard of spacecrafts in light of the excellent rate-distortion performance
+and low complexity of recent schemes. In this letter we propose a rate control
+algorithm and integrate it in a lossy extension to the CCSDS-123 lossless
+compression recommendation. The proposed rate algorithm overhauls our previous
+scheme by being orders of magnitude faster and simpler to implement, while
+still providing the same accuracy in terms of output rate and comparable or
+better image quality.
+",1,0,0,0,0,0
+7091,Quantum-continuum calculation of the surface states and electrical response of silicon in solution,"  A wide range of electrochemical reactions of practical importance occur at
+the interface between a semiconductor and an electrolyte. We present an
+embedded density-functional theory method using the recently released
+self-consistent continuum solvation (SCCS) approach to study these interfaces.
+In this model, a quantum description of the surface is incorporated into a
+continuum representation of the bending of the bands within the electrode. The
+model is applied to understand the electrical response of silicon electrodes in
+solution, providing microscopic insights into the low-voltage region, where
+surface states determine the electrification of the semiconductor electrode.
+",0,1,0,0,0,0
+7092,Simulation study of signal formation in position sensitive planar p-on-n silicon detectors after short range charge injection,"  Segmented silicon detectors (micropixel and microstrip) are the main type of
+detectors used in the inner trackers of Large Hadron Collider (LHC) experiments
+at CERN. Due to the high luminosity and eventual high fluence, detectors with
+fast response to fit the short shaping time of 20 ns and sufficient radiation
+hardness are required.
+Measurements carried out at the Ioffe Institute have shown a reversal of the
+pulse polarity in the detector response to short-range charge injection. Since
+the measured negative signal is about 30-60% of the peak positive signal, the
+effect strongly reduces the CCE even in non-irradiated detectors. For further
+investigation of the phenomenon the measurements have been reproduced by TCAD
+simulations.
+As for the measurements, the simulation study was applied for the p-on-n
+strip detectors similar in geometry to those developed for the ATLAS experiment
+and for the Ioffe Institute designed p-on-n strip detectors with each strip
+having a window in the metallization covering the p$^+$ implant, allowing the
+generation of electron-hole pairs under the strip implant. Red laser scans
+across the strips and the interstrip gap with varying laser diameters and
+Si-SiO$_2$ interface charge densities were carried out. The results verify the
+experimentally observed negative response along the scan in the interstrip gap.
+When the laser spot is positioned on the strip p$^+$ implant the negative
+response vanishes and the collected charge at the active strip proportionally
+increases.
+The simulation results offer a further insight and understanding of the
+influence of the oxide charge density in the signal formation. The observed
+effects and details of the detector response for different charge injection
+positions are discussed in the context of Ramo's theorem.
+",0,1,0,0,0,0
+7093,Re-DPoctor: Real-time health data releasing with w-day differential privacy,"  Wearable devices enable users to collect health data and share them with
+healthcare providers for improved health service. Since health data contain
+privacy-sensitive information, unprotected data release system may result in
+privacy leakage problem. Most of the existing work use differential privacy for
+private data release. However, they have limitations in healthcare scenarios
+because they do not consider the unique features of health data being collected
+from wearables, such as continuous real-time collection and pattern
+preservation. In this paper, we propose Re-DPoctor, a real-time health data
+releasing scheme with $w$-day differential privacy where the privacy of health
+data collected from any consecutive $w$ days is preserved. We improve utility
+by using a specially-designed partition algorithm to protect the health data
+patterns. Meanwhile, we improve privacy preservation by applying newly proposed
+adaptive sampling technique and budget allocation method. We prove that
+Re-DPoctor satisfies $w$-day differential privacy. Experiments on real health
+data demonstrate that our method achieves better utility with strong privacy
+guarantee than existing state-of-the-art methods.
+",1,0,0,0,0,0
+7094,Efficient Computation of the Stochastic Behavior of Partial Sum Processes,"  In this paper the computational aspects of probability calculations for
+dynamical partial sum expressions are discussed. Such dynamical partial sum
+expressions have many important applications, and examples are provided in the
+fields of reliability, product quality assessment, and stochastic control.
+While these probability calculations are ostensibly of a high dimension, and
+consequently intractable in general, it is shown how a recursive integration
+methodology can be implemented to obtain exact calculations as a series of
+two-dimensional calculations. The computational aspects of the implementaion of
+this methodology, with the adoption of Fast Fourier Transforms, are discussed.
+",0,0,0,1,0,0
+7095,Laser electron acceleration on curved surfaces,"  Electron acceleration by relativistically intense laser beam propagating
+along a curved surface allows to split softly the accelerated electron bunch
+and the laser beam. The presence of a curved surface allows to switch an
+adiabatic invariant of electrons in the wave instantly leaving the gained
+energy to the particles. The efficient acceleration is provided by the presence
+of strong transient quasistationary fields in the interaction region and a long
+efficient acceleration length. The curvature of the surface allows to select
+the accelerated particles and provides their narrow angular distribution. The
+mechanism at work is explicitly demonstrated in theoretical models and
+experiments.
+",0,1,0,0,0,0
+7096,A Physicist's view on Chopin's Études,"  We propose the use of specific dynamical processes and more in general of
+ideas from Physics to model the evolution in time of musical structures. We
+apply this approach to two Études by F. Chopin, namely op.10 n.3 and op.25
+n.1, proposing some original description based on concepts of symmetry
+breaking/restoration and quantum coherence, which could be useful for
+interpretation. In this analysis, we take advantage of colored musical scores,
+obtained by implementing Scriabin's color code for sounds to musical notation.
+",0,1,0,0,0,0
+7097,Thermalization after holographic bilocal quench,"  We study thermalization in the holographic (1+1)-dimensional CFT after
+simultaneous generation of two high-energy excitations in the antipodal points
+on the circle. The holographic picture of such quantum quench is the creation
+of BTZ black hole from a collision of two massless particles. We perform
+holographic computation of entanglement entropy and mutual information in the
+boundary theory and analyze their evolution with time. We show that
+equilibration of the entanglement in the regions which contained one of the
+initial excitations is generally similar to that in other holographic quench
+models, but with some important distinctions. We observe that entanglement
+propagates along a sharp effective light cone from the points of initial
+excitations on the boundary. The characteristics of entanglement propagation in
+the global quench models such as entanglement velocity and the light cone
+velocity also have a meaning in the bilocal quench scenario. We also observe
+the loss of memory about the initial state during the equilibration process. We
+find that the memory loss reflects on the time behavior of the entanglement
+similarly to the global quench case, and it is related to the universal linear
+growth of entanglement, which comes from the interior of the forming black
+hole. We also analyze general two-point correlation functions in the framework
+of the geodesic approximation, focusing on the study of the late time behavior.
+",0,1,0,0,0,0
+7098,A mean-field approach to Kondo-attractive-Hubbard model,"  With the purpose of investigating coexistence between magnetic order and
+superconductivity, we consider a model in which conduction electrons interact
+with each other, via an attractive Hubbard on-site coupling $U$, and with local
+moments on every site, via a Kondo-like coupling, $J$. The model is solved on a
+simple cubic lattice through a Hartree-Fock approximation, within a
+`semi-classical' framework which allows spiral magnetic modes to be stabilized.
+For a fixed electronic density, $n_c$, the small $J$ region of the ground state
+($T=0$) phase diagram displays spiral antiferromagnetic (SAFM) states for small
+$U$. Upon increasing $U$, a state with coexistence between superconductivity
+(SC) and SAFM sets in; further increase in $U$ turns the spiral mode into a
+Néel antiferromagnet. The large $J$ region is a (singlet) Kondo phase. At
+finite temperatures, and in the region of coexistence, thermal fluctuations
+suppress the different ordered phases in succession: the SAFM phase at lower
+temperatures and SC at higher temperatures; also, reentrant behaviour is found
+to be induced by temperature. Our results provide a qualitative description of
+the competition between local moment magnetism and superconductivity in the
+borocarbides family.
+",0,1,0,0,0,0
+7099,Endpoint Sobolev and BV continuity for maximal operators,"  In this paper we investigate some questions related to the continuity of
+maximal operators in $W^{1,1}$ and $BV$ spaces, complementing some well-known
+boundedness results. Letting $\widetilde M$ be the one-dimensional uncentered
+Hardy-Littlewood maximal operator, we prove that the map $f \mapsto
+\big(\widetilde Mf\big)'$ is continuous from $W^{1,1}(\mathbb{R})$ to
+$L^1(\mathbb{R})$. In the discrete setting, we prove that $\widetilde M:
+BV(\mathbb{Z}) \to BV(\mathbb{Z})$ is also continuous. For the one-dimensional
+fractional Hardy-Littlewood maximal operator, we prove by means of
+counterexamples that the corresponding continuity statements do not hold, both
+in the continuous and discrete settings, and for the centered and uncentered
+versions.
+",0,0,1,0,0,0
+7100,Quantum models as classical cellular automata,"  A synopsis is offered of the properties of discrete and integer-valued, hence
+""natural"", cellular automata (CA). A particular class comprises the
+""Hamiltonian CA"" with discrete updating rules that resemble Hamilton's
+equations. The resulting dynamics is linear like the unitary evolution
+described by the Schrödinger equation. Employing Shannon's Sampling Theorem,
+we construct an invertible map between such CA and continuous quantum
+mechanical models which incorporate a fundamental discreteness scale $l$.
+Consequently, there is a one-to-one correspondence of quantum mechanical and CA
+conservation laws. We discuss the important issue of linearity, recalling that
+nonlinearities imply nonlocal effects in the continuous quantum mechanical
+description of intrinsically local discrete CA - requiring locality entails
+linearity. The admissible CA observables and the existence of solutions of the
+$l$-dependent dispersion relation for stationary states are mentioned, besides
+the construction of multipartite CA obeying the Superposition Principle. We
+point out problems when trying to match the deterministic CA here to those
+envisioned in 't Hooft's CA Interpretation of Quantum Mechanics.
+",0,1,1,0,0,0
+7101,K-closedness of weighted Hardy spaces on the two-dimensional torus,"  It is proved that, under certain restrictions on weights, a pair of weighted
+Hardy spaces on the two-dimensional torus is K-closed in the pair of the
+corresponding weighted Lebesgue spaces. By now, K-closedness of Hardy spaces on
+the two-dimensional torus was considered either in the case of no weights or in
+the case of weights that split into a product of two functions of one variable
+(the so-called ""split weights""). Here the case of certain nonsplit weights is
+studied.
+",0,0,1,0,0,0
+7102,Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis,"  Tissue characterization has long been an important component of Computer
+Aided Diagnosis (CAD) systems for automatic lesion detection and further
+clinical planning. Motivated by the superior performance of deep learning
+methods on various computer vision problems, there has been increasing work
+applying deep learning to medical image analysis. However, the development of a
+robust and reliable deep learning model for computer-aided diagnosis is still
+highly challenging due to the combination of the high heterogeneity in the
+medical images and the relative lack of training samples. Specifically,
+annotation and labeling of the medical images is much more expensive and
+time-consuming than other applications and often involves manual labor from
+multiple domain experts. In this work, we propose a multi-stage, self-paced
+learning framework utilizing a convolutional neural network (CNN) to classify
+Computed Tomography (CT) image patches. The key contribution of this approach
+is that we augment the size of training samples by refining the unlabeled
+instances with a self-paced learning CNN. By implementing the framework on high
+performance computing servers including the NVIDIA DGX1 machine, we obtained
+the experimental result, showing that the self-pace boosted network
+consistently outperformed the original network even with very scarce manual
+labels. The performance gain indicates that applications with limited training
+samples such as medical image analysis can benefit from using the proposed
+framework.
+",1,0,0,1,0,0
+7103,PS-DBSCAN: An Efficient Parallel DBSCAN Algorithm Based on Platform Of AI (PAI),"  We present PS-DBSCAN, a communication efficient parallel DBSCAN algorithm
+that combines the disjoint-set data structure and Parameter Server framework in
+Platform of AI (PAI). Since data points within the same cluster may be
+distributed over different workers which result in several disjoint-sets,
+merging them incurs large communication costs. In our algorithm, we employ a
+fast global union approach to union the disjoint-sets to alleviate the
+communication burden. Experiments over the datasets of different scales
+demonstrate that PS-DBSCAN outperforms the PDSDBSCAN with 2-10 times speedup on
+communication efficiency.
+We have released our PS-DBSCAN in an algorithm platform called Platform of AI
+(PAI - this https URL) in Alibaba Cloud. We have also
+demonstrated how to use the method in PAI.
+",1,0,0,0,0,0
+7104,Non-singular spacetimes with a negative cosmological constant: IV. Stationary black hole solutions with matter fields,"  We use an elliptic system of equations with complex coefficients for a set of
+complex-valued tensor fields as a tool to construct infinite-dimensional
+families of non-singular stationary black holes, real-valued Lorentzian
+solutions of the Einstein-Maxwell-dilaton-scalar
+fields-Yang-Mills-Higgs-Chern-Simons-$f(R)$ equations with a negative
+cosmological constant. The families include an infinite-dimensional family of
+solutions with the usual AdS conformal structure at conformal infinity.
+",0,0,1,0,0,0
+7105,Non-Negative Matrix Factorization Test Cases,"  Non-negative matrix factorization (NMF) is a prob- lem with many
+applications, ranging from facial recognition to document clustering. However,
+due to the variety of algorithms that solve NMF, the randomness involved in
+these algorithms, and the somewhat subjective nature of the problem, there is
+no clear ""correct answer"" to any particular NMF problem, and as a result, it
+can be hard to test new algorithms. This paper suggests some test cases for NMF
+algorithms derived from matrices with enumerable exact non-negative
+factorizations and perturbations of these matrices. Three algorithms using
+widely divergent approaches to NMF all give similar solutions over these test
+cases, suggesting that these test cases could be used as test cases for
+implementations of these existing NMF algorithms as well as potentially new NMF
+algorithms. This paper also describes how the proposed test cases could be used
+in practice.
+",1,0,1,0,0,0
+7106,Inequalities for the fundamental Robin eigenvalue of the Laplacian for box-shaped domains,"  This document consists of two papers, both submitted, and supplementary
+material. The submitted papers are here given as Parts I and II.
+Part I establishes results, used in Part II, 'on functions and inverses, both
+positive, decreasing and convex'.
+Part II uses results from Part I to extablish 'inequalities for the
+fundamental Robin eigenvalue for the Laplacian on N-dimensional boxes'
+",0,0,1,0,0,0
+7107,Non-Hamiltonian isotopic Lagrangians on the one-point blow-up of CP^2,"  We show that two Hamiltonian isotopic Lagrangians in
+(CP^2,\omega_\textup{FS}) induce two Lagrangian submanifolds in the one-point
+blow-up (\widetilde{CP}^2,\widetilde{\omega}_\rho) that are not Hamiltonian
+isotopic. Furthermore, we show that for any integer k>1 there are k Hamiltonian
+isotopic Lagrangians in (CP^2,\omega_\textup{FS}) that induce k Lagrangian
+submanifolds in the one-point blow-up such that no two of them are Hamiltonian
+isotopic.
+",0,0,1,0,0,0
+7108,Berezinskii-Kosterlitz-Thouless Type Scenario in Molecular Spin Liquid $A$Cr$_2$O$_4$,"  The spin relaxation in chromium spinel oxides $A$Cr$_{2}$O$_{4}$ ($A=$ Mg,
+Zn, Cd) is investigated in the paramagnetic regime by electron spin resonance
+(ESR). The temperature dependence of the ESR linewidth indicates an
+unconventional spin-relaxation behavior, similar to spin-spin relaxation in the
+two-dimensional (2D) chromium-oxide triangular lattice antiferromagnets. The
+data can be described in terms of a generalized Berezinskii-Kosterlitz-Thouless
+(BKT) type scenario for 2D systems with additional internal symmetries. Based
+on the characteristic exponents obtained from the evaluation of the ESR
+linewidth, short-range order with a hidden internal symmetry is suggested.
+",0,1,0,0,0,0
+7109,Continuum percolation theory of epimorphic regeneration,"  A biophysical model of epimorphic regeneration based on a continuum
+percolation process of fully penetrable disks in two dimensions is proposed.
+All cells within a randomly chosen disk of the regenerating organism are
+assumed to receive a signal in the form of a circular wave as a result of the
+action/reconfiguration of neoblasts and neoblast-derived mesenchymal cells in
+the blastema. These signals trigger the growth of the organism, whose cells
+read, on a faster time scale, the electric polarization state responsible for
+their differentiation and the resulting morphology. In the long time limit, the
+process leads to a morphological attractor that depends on experimentally
+accessible control parameters governing the blockage of cellular gap junctions
+and, therefore, the connectivity of the multicellular ensemble. When this
+connectivity is weakened, positional information is degraded leading to more
+symmetrical structures. This general theory is applied to the specifics of
+planaria regeneration. Computations and asymptotic analyses made with the model
+show that it correctly describes a significant subset of the most prominent
+experimental observations, notably anterior-posterior polarization (and its
+loss) or the formation of four-headed planaria.
+",0,1,0,0,0,0
+7110,Multiplicity of solutions for a nonhomogeneous quasilinear elliptic problem with critical growth,"  It is established some existence and multiplicity of solution results for a
+quasilinear elliptic problem driven by $\Phi$-Laplacian operator. One of these
+solutions is built as a ground state solution. In order to prove our main
+results we apply the Nehari method combined with the concentration compactness
+theorem in an Orlicz-Sobolev framework. One of the difficulties in dealing with
+this kind of operator is the lost of homogeneity properties.
+",0,0,1,0,0,0
+7111,Mahonian STAT on rearrangement class of words,"  In 2000, Babson and Steingrímsson generalized the notion of permutation
+patterns to the so-called vincular patterns, and they showed that many Mahonian
+statistics can be expressed as sums of vincular pattern occurrence statistics.
+STAT is one of such Mahonian statistics discoverd by them. In 2016, Kitaev and
+the third author introduced a words analogue of STAT and proved a joint
+equidistribution result involving two sextuple statistics on the whole set of
+words with fixed length and alphabet. Moreover, their computer experiments
+hinted at a finer involution on $R(w)$, the rearrangement class of a given word
+$w$. We construct such an involution in this paper, which yields a comparable
+joint equidistribution between two sextuple statistics over $R(w)$. Our
+involution builds on Burstein's involution and Foata-Schützenberger's
+involution that utilizes the celebrated RSK algorithm.
+",1,0,0,0,0,0
+7112,A bibliometric approach to Systematic Mapping Studies: The case of the evolution and perspectives of community detection in complex networks,"  Critical analysis of the state of the art is a necessary task when
+identifying new research lines worthwhile to pursue. To such an end, all the
+available work related to the field of interest must be taken into account. The
+key point is how to organize, analyze, and make sense of the huge amount of
+scientific literature available today on any topic. To tackle this problem, we
+present here a bibliometric approach to Systematic Mapping Studies (SMS). Thus,
+a modify SMS protocol is used relying on the scientific references metadata to
+extract, process and interpret the wealth of information contained in nowadays
+research literature. As a test case, the procedure is applied to determine the
+current state and perspectives of community detection in complex networks. Our
+results show that community detection is a still active, far from exhausted, in
+development, field. In addition, we find that, by far, the most exploited
+methods are those related to determining hierarchical community structures. On
+the other hand, the results show that fuzzy clustering techniques, despite
+their interest, are underdeveloped as well as the adaptation of existing
+algorithms to parallel or, more specifically, distributed, computational
+systems.
+",1,1,0,0,0,0
+7113,SPEW: Synthetic Populations and Ecosystems of the World,"  Agent-based models (ABMs) simulate interactions between autonomous agents in
+constrained environments over time. ABMs are often used for modeling the spread
+of infectious diseases. In order to simulate disease outbreaks or other
+phenomena, ABMs rely on ""synthetic ecosystems,"" or information about agents and
+their environments that is representative of the real world. Previous
+approaches for generating synthetic ecosystems have some limitations: they are
+not open-source, cannot be adapted to new or updated input data sources, and do
+not allow for alternative methods for sampling agent characteristics and
+locations. We introduce a general framework for generating Synthetic
+Populations and Ecosystems of the World (SPEW), implemented as an open-source R
+package. SPEW allows researchers to choose from a variety of sampling methods
+for agent characteristics and locations when generating synthetic ecosystems
+for any geographic region. SPEW can produce synthetic ecosystems for any agent
+(e.g. humans, mosquitoes, etc), provided that appropriate data is available. We
+analyze the accuracy and computational efficiency of SPEW given different
+sampling methods for agent characteristics and locations and provide a suite of
+diagnostics to screen our synthetic ecosystems. SPEW has generated over five
+billion human agents across approximately 100,000 geographic regions in about
+70 countries, available online.
+",0,1,0,1,0,0
+7114,Bayesian hierarchical weighting adjustment and survey inference,"  We combine Bayesian prediction and weighted inference as a unified approach
+to survey inference. The general principles of Bayesian analysis imply that
+models for survey outcomes should be conditional on all variables that affect
+the probability of inclusion. We incorporate the weighting variables under the
+framework of multilevel regression and poststratification, as a byproduct
+generating model-based weights after smoothing. We investigate deep
+interactions and introduce structured prior distributions for smoothing and
+stability of estimates. The computation is done via Stan and implemented in the
+open source R package ""rstanarm"" ready for public use. Simulation studies
+illustrate that model-based prediction and weighting inference outperform
+classical weighting. We apply the proposal to the New York Longitudinal Study
+of Wellbeing. The new approach generates robust weights and increases
+efficiency for finite population inference, especially for subsets of the
+population.
+",0,0,0,1,0,0
+7115,Comparison of the h-index for Different Fields of Research Using Bootstrap Methodology,"  An important disadvantage of the h-index is that typically it cannot take
+into account the specific field of research of a researcher. Usually sample
+point estimates of the average and median h-index values for the various fields
+are reported that are highly variable and dependent of the specific samples and
+it would be useful to provide confidence intervals of prediction accuracy. In
+this paper we apply the non-parametric bootstrap technique for constructing
+confidence intervals for the h-index for different fields of research. In this
+way no specific assumptions about the distribution of the empirical hindex are
+required as well as no large samples since that the methodology is based on
+resampling from the initial sample. The results of the analysis showed
+important differences between the various fields. The performance of the
+bootstrap intervals for the mean and median h-index for most fields seems to be
+rather satisfactory as revealed by the performed simulation.
+",1,0,0,1,0,0
+7116,Impact of Intervals on the Emotional Effect in Western Music,"  Every art form ultimately aims to invoke an emotional response over the
+audience, and music is no different. While the precise perception of music is a
+highly subjective topic, there is an agreement in the ""feeling"" of a piece of
+music in broad terms. Based on this observation, in this study, we aimed to
+determine the emotional feeling associated with short passages of music;
+specifically by analyzing the melodic aspects. We have used the dataset put
+together by Eerola et. al. which is comprised of labeled short passages of film
+music. Our initial survey of the dataset indicated that other than ""happy"" and
+""sad"" labels do not possess a melodic structure. We transcribed the main melody
+of the happy and sad tracks and used the intervals between the notes to
+classify them. Our experiments have shown that treating a melody as a
+bag-of-intervals do not possess any predictive power whatsoever, whereas
+counting intervals with respect to the key of the melody yielded a classifier
+with 85% accuracy.
+",1,0,0,0,1,0
+7117,Nonlocal Pertubations of Fractional Choquard Equation,"  We study the equation \begin{equation} (-\Delta)^{s}u+V(x)u=
+(I_{\alpha}*|u|^{p})|u|^{p-2}u+\lambda(I_{\beta}*|u|^{q})|u|^{q-2}u \quad\mbox{
+in } \R^{N}, \end{equation} where $I_\gamma(x)=|x|^{-\gamma}$ for any
+$\gamma\in (0,N)$, $p, q >0$, $\alpha,\beta\in (0,N)$, $N\geq 3$ and $ \lambda
+\in R$. First, the existence of a groundstate solutions using minimization
+method on the associated Nehari manifold is obtained. Next, the existence of
+least energy sign-changing solutions is investigated by considering the Nehari
+nodal set.
+",0,0,1,0,0,0
+7118,Clustering Patients with Tensor Decomposition,"  In this paper we present a method for the unsupervised clustering of
+high-dimensional binary data, with a special focus on electronic healthcare
+records. We present a robust and efficient heuristic to face this problem using
+tensor decomposition. We present the reasons why this approach is preferable
+for tasks such as clustering patient records, to more commonly used
+distance-based methods. We run the algorithm on two datasets of healthcare
+records, obtaining clinically meaningful results.
+",1,0,0,1,0,0
+7119,Posterior distribution existence and error control in Banach spaces in the Bayesian approach to UQ in inverse problems,"  We generalize the results of \cite{Capistran2016} on expected Bayes factors
+(BF) to control the numerical error in the posterior distribution to an
+infinite dimensional setting when considering Banach functional spaces and now
+in a prior setting. The main result is a bound on the absolute global error to
+be tolerated by the Forward Map numerical solver, to keep the BF of the
+numerical vs. the theoretical model near to 1, now in this more general
+setting, possibly including a truncated, finite dimensional approximate prior
+measure. In so doing we found a far more general setting to define and prove
+existence of the infinite dimensional posterior distribution than that depicted
+in, for example, \cite{Stuart2010}. Discretization consistency and rates of
+convergence are also investigated in this general setting for the Bayesian
+inverse problem.
+",0,0,1,1,0,0
+7120,High-$T_c$ mechanism through analysis of diverging effective mass for YaBa$_2$Cu$_3$O$_{6+x}$ and pairing symmetry in cuprate superconductors,"  In order to clarify the high-$T_c$ mechanism in inhomogeneous cuprate layer
+superconductors, we deduce and find the correlation strength not revealed
+before, contributing to the formation of the Cooper pair and the 2-D density of
+state, and demonstrate the pairing symmetry in the superconductors still
+controversial. To the open questions, the fitting and analysis of the diverging
+effective mass with decreasing doping, extracted from the acquired
+quantum-oscillation data in underdoped YBCOO$_{6+x}$ superconductors, can
+provide solutions. Here, the results of the fitting using the extended
+Brinkman-Rice(BR) picture reveal the nodal constant Fermi energy with the
+maximum carrier density, a constant Coulomb correlation strength
+$k_{BR}$=$U/U_c$>0.90, and a growing Fermi arc from the nodal Fermi point to
+the isotropic Fermi surface with an increasing $x$. The growing of the Fermi
+arc indicates that a superconducting gap develops with $x$ from the node to the
+anti-node. The large $k_{BR}$ results from the $d$-wave MIT for the pseudogap
+phase in lightly doped superconductors, which can be direct evidence for
+high-$T_c$ superconductivity. The quantum critical point is regarded as the
+nodal Fermi point satisfied with the BR picture. The experimentally-measured
+mass diverging behavior is an average effect and the true effective mass is
+constant. As an application of the nodal constant carrier density, to find a
+superconducting node gap, the ARPES data and tunneling data are analyzed. The
+superconducting node gap is a precursor of $s$-wave symmetry in underdoped
+cuprates. The half-flux quantum, induced by the circulation of $d$-wave
+supercurrent and observed by the phase sensitive Josephson-pi junction
+experiments, is not shown due to anisotropic or asymmetric effect appearing in
+superconductors with trapped flux. The absence of $d$-wave superconducting
+pairing symmetry is also revealed.
+",0,1,0,0,0,0
+7121,"The Long Term Fréchet distribution: Estimation, Properties and its Application","  In this paper a new long-term survival distribution is proposed. The so
+called long term Fréchet distribution allows us to fit data where a part of
+the population is not susceptible to the event of interest. This model may be
+used, for example, in clinical studies where a portion of the population can be
+cured during a treatment. It is shown an account of mathematical properties of
+the new distribution such as its moments and survival properties. As well is
+presented the maximum likelihood estimators (MLEs) for the parameters. A
+numerical simulation is carried out in order to verify the performance of the
+MLEs. Finally, an important application related to the leukemia free-survival
+times for transplant patients are discussed to illustrates our proposed
+distribution
+",0,0,1,1,0,0
+7122,Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space,"  Many engineering problems require identifying feasible domains under implicit
+constraints. One example is finding acceptable car body styling designs based
+on constraints like aesthetics and functionality. Current active-learning based
+methods learn feasible domains for bounded input spaces. However, we usually
+lack prior knowledge about how to set those input variable bounds. Bounds that
+are too small will fail to cover all feasible domains; while bounds that are
+too large will waste query budget. To avoid this problem, we introduce Active
+Expansion Sampling (AES), a method that identifies (possibly disconnected)
+feasible domains over an unbounded input space. AES progressively expands our
+knowledge of the input space, and uses successive exploitation and exploration
+stages to switch between learning the decision boundary and searching for new
+feasible domains. We show that AES has a misclassification loss guarantee
+within the explored region, independent of the number of iterations or labeled
+samples. Thus it can be used for real-time prediction of samples' feasibility
+within the explored region. We evaluate AES on three test examples and compare
+AES with two adaptive sampling methods -- the Neighborhood-Voronoi algorithm
+and the straddle heuristic -- that operate over fixed input variable bounds.
+",1,0,0,1,0,0
+7123,On diagrams of simplified trisections and mapping class groups,"  A simplified trisection is a trisection map on a 4-manifold such that, in its
+critical value set, there is no double point and cusps only appear in triples
+on innermost fold circles. We give a necessary and sufficient condition for a
+3-tuple of systems of simple closed curves in a surface to be a diagram of a
+simplified trisection in terms of mapping class groups. As an application of
+this criterion, we show that trisections of spun 4-manifolds due to Meier are
+diffeomorphic (as trisections) to simplified ones. Baykur and Saeki recently
+gave an algorithmic construction of a simplified trisection from a directed
+broken Lefschetz fibration. We also give an algorithm to obtain a diagram of a
+simplified trisection derived from their construction.
+",0,0,1,0,0,0
+7124,Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment,"  Many localization algorithms use a spatiotemporal window of sensory
+information in order to recognize spatial locations, and the length of this
+window is often a sensitive parameter that must be tuned to the specifics of
+the application. This letter presents a general method for environment-driven
+variation of the length of the spatiotemporal window based on searching for the
+most significant localization hypothesis, to use as much context as is
+appropriate but not more. We evaluate this approach on benchmark datasets using
+visual and Wi-Fi sensor modalities and a variety of sensory comparison
+front-ends under in-order and out-of-order traversals of the environment. Our
+results show that the system greatly reduces the maximum distance traveled
+without localization compared to a fixed-length approach while achieving
+competitive localization accuracy, and our proposed method achieves this
+performance without deployment-time tuning.
+",1,0,0,0,0,0
+7125,"The Gaia-ESO Survey: Dynamical models of flattened, rotating globular clusters","  We present a family of self-consistent axisymmetric rotating globular cluster
+models which are fitted to spectroscopic data for NGC 362, NGC 1851, NGC 2808,
+NGC 4372, NGC 5927 and NGC 6752 to provide constraints on their physical and
+kinematic properties, including their rotation signals. They are constructed by
+flattening Modified Plummer profiles, which have the same asymptotic behaviour
+as classical Plummer models, but can provide better fits to young clusters due
+to a slower turnover in the density profile. The models are in dynamical
+equilibrium as they depend solely on the action variables. We employ a fully
+Bayesian scheme to investigate the uncertainty in our model parameters
+(including mass-to-light ratios and inclination angles) and evaluate the
+Bayesian evidence ratio for rotating to non-rotating models. We find convincing
+levels of rotation only in NGC 2808. In the other clusters, there is just a
+hint of rotation (in particular, NGC 4372 and NGC 5927), as the data quality
+does not allow us to draw strong conclusions. Where rotation is present, we
+find that it is confined to the central regions, within radii of $R \leq 2
+r_h$. As part of this work, we have developed a novel q-Gaussian basis
+expansion of the line-of-sight velocity distributions, from which general
+models can be constructed via interpolation on the basis coefficients.
+",0,1,0,0,0,0
+7126,A two-dimensional data-driven model for traffic flow on highways,"  Based on experimental traffic data obtained from German and US highways, we
+propose a novel two-dimensional first-order macroscopic traffic flow model. The
+goal is to reproduce a detailed description of traffic dynamics for the real
+road geometry. In our approach both the dynamic along the road and across the
+lanes is continuous. The closure relations, being necessary to complete the
+hydrodynamic equation, are obtained by regression on fundamental diagram data.
+Comparison with prediction of one-dimensional models shows the improvement in
+performance of the novel model.
+",0,1,0,0,0,0
+7127,Cut Finite Element Methods for Elliptic Problems on Multipatch Parametric Surfaces,"  We develop a finite element method for the Laplace--Beltrami operator on a
+surface described by a set of patchwise parametrizations. The patches provide a
+partition of the surface and each patch is the image by a diffeomorphism of a
+subdomain of the unit square which is bounded by a number of smooth trim
+curves. A patchwise tensor product mesh is constructed by using a structured
+mesh in the reference domain. Since the patches are trimmed we obtain cut
+elements in the vicinity of the interfaces. We discretize the Laplace--Beltrami
+operator using a cut finite element method that utilizes Nitsche's method to
+enforce continuity at the interfaces and a consistent stabilization term to
+handle the cut elements. Several quantities in the method are conveniently
+computed in the reference domain where the mappings impose a Riemannian metric.
+We derive a priori estimates in the energy and $L^2$ norm and also present
+several numerical examples confirming our theoretical results.
+",0,0,1,0,0,0
+7128,"The rationality problem for forms of $\overline{M_{0, n}}$","  Let $X$ be a del Pezzo surface of degree $5$ defined over a field $F$. A
+theorem of Yu. I. Manin and P. Swinnerton-Dyer asserts that every Del Pezzo
+surface of degree $5$ is rational. In this paper we generalize this result as
+follows. Recall that del Pezzo surfaces of degree $5$ over a field $F$ are
+precisely the twisted $F$-forms of the moduli space $\overline{M_{0, 5}}$ of
+stable curves of genus $0$ with $5$ marked points. Suppose $n \geq 5$ is an
+integer, and $F$ is an infinite field of characteristic $\neq 2$. It is easy to
+see that every twisted $F$-form of $\overline{M_{0, n}}$ is unirational over
+$F$. We show that
+(a) if $n$ is odd, then every twisted $F$-form of $\overline{M_{0, n}}$ is
+rational over $F$.
+(b) If $n$ is even, there exists a field extension $F/k$ and a twisted
+$F$-form $X$ of $\overline{M_{0, n}}$ such that $X$ is not retract rational
+over $F$.
+",0,0,1,0,0,0
+7129,The p-adic Kummer-Leopoldt constant - Normalized p-adic regulator,"  The p-adic Kummer--Leopoldt constant kappa\_K of a number field K is
+(assuming the Leopoldt conjecture) the least integer c such that for all n
+\textgreater{}\textgreater{} 0, any global unit of K, which is locally a
+p^(n+c)th power at the p-places, is necessarily the p^nth power of a global
+unit of K. This constant has been computed by Assim \& Nguyen Quang Do using
+Iwasawa's techniques,after intricate studies and calculations by many authors.
+We give an elementary p-adic proof and an improvement of these results, then a
+class field theory interpretation of kappa\_K. We give some applications
+(including generalizations of Kummer's lemma on regular pth cyclotomic fields)
+and a natural definition of the normalized p-adic regulator for any K and any
+p$\ge$2.This is done without analytical computations, using only class field
+theoryand especially the properties of the so-called p-torsion group T\_K of
+Abelian p-ramification theory over K.
+",0,0,1,0,0,0
+7130,Regularization Learning Networks: Deep Learning for Tabular Datasets,"  Despite their impressive performance, Deep Neural Networks (DNNs) typically
+underperform Gradient Boosting Trees (GBTs) on many tabular-dataset learning
+tasks. We propose that applying a different regularization coefficient to each
+weight might boost the performance of DNNs by allowing them to make more use of
+the more relevant inputs. However, this will lead to an intractable number of
+hyperparameters. Here, we introduce Regularization Learning Networks (RLNs),
+which overcome this challenge by introducing an efficient hyperparameter tuning
+scheme which minimizes a new Counterfactual Loss. Our results show that RLNs
+significantly improve DNNs on tabular datasets, and achieve comparable results
+to GBTs, with the best performance achieved with an ensemble that combines GBTs
+and RLNs. RLNs produce extremely sparse networks, eliminating up to 99.8% of
+the network edges and 82% of the input features, thus providing more
+interpretable models and reveal the importance that the network assigns to
+different inputs. RLNs could efficiently learn a single network in datasets
+that comprise both tabular and unstructured data, such as in the setting of
+medical imaging accompanied by electronic health records. An open source
+implementation of RLN can be found at
+this https URL.
+",0,0,0,1,0,0
+7131,Numerical dimension and locally ample curves,"  In the paper \cite{Lau16}, it was shown that the restriction of a
+pseudoeffective divisor $D$ to a subvariety $Y$ with nef normal bundle is
+pseudoeffective. Assuming the normal bundle is ample and that $D|_Y$ is not
+big, we prove that the numerical dimension of $D$ is bounded above by that of
+its restriction, i.e. $\kappa_{\sigma}(D)\leq \kappa_{\sigma}(D|_Y)$. The main
+motivation is to study the cycle classes of ""positive"" curves: we show that the
+cycle class of a curve with ample normal bundle lies in the interior of the
+cone of curves, and the cycle class of an ample curve lies in the interior of
+the cone of movable curves. We do not impose any condition on the singularities
+on the curve or the ambient variety. For locally complete intersection curves
+in a smooth projective variety, this is the main result of Ottem \cite{Ott16}.
+The main tool in this paper is the theory of $q$-ample divisors.
+",0,0,1,0,0,0
+7132,Local-global principles in circle packings,"  We generalize work of Bourgain-Kontorovich and Zhang, proving an almost
+local-to-global property for the curvatures of certain circle packings, to a
+large class of Kleinian groups. Specifically, we associate in a natural way an
+infinite family of integral packings of circles to any Kleinian group $\mathcal
+A\leq\textrm{PSL}_2(K)$ satisfying certain conditions, where $K$ is an
+imaginary quadratic field, and show that the curvatures of the circles in any
+such packing satisfy an almost local-to-global principle. A key ingredient in
+the proof of this is that $\mathcal A$ possesses a spectral gap property, which
+we prove for any infinite-covolume, geometrically finite, Zariski dense
+Kleinian group in $\textrm{PSL}_2(\mathcal{O}_K)$ containing a Zariski dense
+subgroup of $\textrm{PSL}_2(\mathbb{Z})$.
+",0,0,1,0,0,0
+7133,Underscreening in concentrated electrolytes,"  Screening of a surface charge by electrolyte and the resulting interaction
+energy between charged objects is of fundamental importance in scenarios from
+bio-molecular interactions to energy storage. The conventional wisdom is that
+the interaction energy decays exponentially with object separation and the
+decay length is a decreasing function of ion concentration; the interaction is
+thus negligible in a concentrated electrolyte. Contrary to this conventional
+wisdom, we have shown by surface force measurements that the decay length is an
+increasing function of ion concentration and Bjerrum length for concentrated
+electrolytes. In this paper we report surface force measurements to test
+directly the scaling of the screening length with Bjerrum length. Furthermore,
+we identify a relationship between the concentration dependence of this
+screening length and empirical measurements of activity coefficient and
+differential capacitance. The dependence of the screening length on the ion
+concentration and the Bjerrum length can be explained by a simple scaling
+conjecture based on the physical intuition that solvent molecules, rather than
+ions, are charge carriers in a concentrated electrolyte.
+",0,1,0,0,0,0
+7134,Asymptotics for Small Nonlinear Price Impact: a PDE Approach to the Multidimensional Case,"  We provide an asymptotic expansion of the value function of a
+multidimensional utility maximization problem from consumption with small
+non-linear price impact. In our model cross-impacts between assets are allowed.
+In the limit for small price impact, we determine the asymptotic expansion of
+the value function around its frictionless version. The leading order
+correction is characterized by a nonlinear second order PDE related to an
+ergodic control problem and a linear parabolic PDE. We illustrate our result on
+a multivariate geometric Brownian motion price model.
+",0,0,0,0,0,1
+7135,Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation,"  Probabilistic atlases provide essential spatial contextual information for
+image interpretation, Bayesian modeling, and algorithmic processing. Such
+atlases are typically constructed by grouping subjects with similar demographic
+information. Importantly, use of the same scanner minimizes inter-group
+variability. However, generalizability and spatial specificity of such
+approaches is more limited than one might like. Inspired by Commowick
+""Frankenstein's creature paradigm"" which builds a personal specific anatomical
+atlas, we propose a data-driven framework to build a personal specific
+probabilistic atlas under the large-scale data scheme. The data-driven
+framework clusters regions with similar features using a point distribution
+model to learn different anatomical phenotypes. Regional structural atlases and
+corresponding regional probabilistic atlases are used as indices and targets in
+the dictionary. By indexing the dictionary, the whole brain probabilistic
+atlases adapt to each new subject quickly and can be used as spatial priors for
+visualization and processing. The novelties of this approach are (1) it
+provides a new perspective of generating personal specific whole brain
+probabilistic atlases (132 regions) under data-driven scheme across sites. (2)
+The framework employs the large amount of heterogeneous data (2349 images). (3)
+The proposed framework achieves low computational cost since only one affine
+registration and Pearson correlation operation are required for a new subject.
+Our method matches individual regions better with higher Dice similarity value
+when testing the probabilistic atlases. Importantly, the advantage the
+large-scale scheme is demonstrated by the better performance of using
+large-scale training data (1888 images) than smaller training set (720 images).
+",0,0,0,1,1,0
+7136,Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems,"  We present a machine learning framework for multi-agent systems to learn both
+the optimal policy for maximizing the rewards and the encoding of the high
+dimensional visual observation. The encoding is useful for sharing local visual
+observations with other agents under communication resource constraints. The
+actor-encoder encodes the raw images and chooses an action based on local
+observations and messages sent by the other agents. The machine learning agent
+generates not only an actuator command to the physical device, but also a
+communication message to the other agents. We formulate a reinforcement
+learning problem, which extends the action space to consider the communication
+action as well. The feasibility of the reinforcement learning framework is
+demonstrated using a 3D simulation environment with two collaborating agents.
+The environment provides realistic visual observations to be used and shared
+between the two agents.
+",1,0,0,0,0,0
+7137,Optimal investment-consumption problem post-retirement with a minimum guarantee,"  We study the optimal investment-consumption problem for a member of defined
+contribution plan during the decumulation phase. For a fixed annuitization
+time, to achieve higher final annuity, we consider a variable consumption rate.
+Moreover, to eliminate the ruin possibilities and having a minimum guarantee
+for the final annuity, we consider a safety level for the wealth process which
+consequently yields a Hamilton-Jacobi-Bellman (HJB) equation on a bounded
+domain. We apply the policy iteration method to find approximations of solution
+of the HJB equation. Finally, we give the simulation results for the optimal
+investment-consumption strategies, optimal wealth process and the final annuity
+for different ranges of admissible consumptions. Furthermore, by calculating
+the present market value of the future cash flows before and after the
+annuitization, we compare the results for different consumption policies.
+",0,0,0,0,0,1
+7138,Parametrices for the light ray transform on Minkowski spacetime,"  We consider restricted light ray transforms arising from an inverse problem
+of finding cosmic strings. We construct a relative left parametrix for the
+transform on two tensors, which recovers the space-like and some light-like
+singularities of the two tensor.
+",0,0,1,0,0,0
+7139,Derivative Principal Component Analysis for Representing the Time Dynamics of Longitudinal and Functional Data,"  We propose a nonparametric method to explicitly model and represent the
+derivatives of smooth underlying trajectories for longitudinal data. This
+representation is based on a direct Karhunen--Loève expansion of the
+unobserved derivatives and leads to the notion of derivative principal
+component analysis, which complements functional principal component analysis,
+one of the most popular tools of functional data analysis. The proposed
+derivative principal component scores can be obtained for irregularly spaced
+and sparsely observed longitudinal data, as typically encountered in biomedical
+studies, as well as for functional data which are densely measured. Novel
+consistency results and asymptotic convergence rates for the proposed estimates
+of the derivative principal component scores and other components of the model
+are derived under a unified scheme for sparse or dense observations and mild
+conditions. We compare the proposed representations for derivatives with
+alternative approaches in simulation settings and also in a wallaby growth
+curve application. It emerges that representations using the proposed
+derivative principal component analysis recover the underlying derivatives more
+accurately compared to principal component analysis-based approaches especially
+in settings where the functional data are represented with only a very small
+number of components or are densely sampled. In a second wheat spectra
+classification example, derivative principal component scores were found to be
+more predictive for the protein content of wheat than the conventional
+functional principal component scores.
+",0,0,1,1,0,0
+7140,Stokes phenomenon and confluence in non-autonomous Hamiltonian systems,"  This article studies a confluence of a pair of regular singular points to an
+irregular one in a generic family of time-dependent Hamiltonian systems in
+dimension 2. This is a general setting for the understanding of the
+degeneration of the sixth Painleve equation to the fifth one. The main result
+is a theorem of sectoral normalization of the family to an integrable formal
+normal form, through which is explained the relation between the local
+monodromy operators at the two regular singularities and the non-linear Stokes
+phenomenon at the irregular singularity of the limit system. The problem of
+analytic classification is also addressed.
+Key words: Non-autonomous Hamiltonian systems; irregular singularity;
+non-linear Stokes phenomenon; wild monodromy; confluence; local analytic
+classification; Painleve equations.
+",0,0,1,0,0,0
+7141,A General Sequential Delay-Doppler Estimation Scheme for Sub-Nyquist Pulse-Doppler Radar,"  Sequential estimation of the delay and Doppler parameters for sub-Nyquist
+radars by analog-to-information conversion (AIC) systems has received wide
+attention recently. However, the estimation methods reported are AIC-dependent
+and have poor performance for off-grid targets. This paper develops a general
+estimation scheme in the sense that it is applicable to all AICs regardless
+whether the targets are on or off the grids. The proposed scheme estimates the
+delay and Doppler parameters sequentially, in which the delay estimation is
+formulated into a beamspace direction-of- arrival problem and the Doppler
+estimation is translated into a line spectrum estimation problem. Then the
+well-known spatial and temporal spectrum estimation techniques are used to
+provide efficient and high-resolution estimates of the delay and Doppler
+parameters. In addition, sufficient conditions on the AIC to guarantee the
+successful estimation of off-grid targets are provided, while the existing
+conditions are mostly related to the on-grid targets. Theoretical analyses and
+numerical experiments show the effectiveness and the correctness of the
+proposed scheme.
+",1,0,1,0,0,0
+7142,"The independence number of the Birkhoff polytope graph, and applications to maximally recoverable codes","  Maximally recoverable codes are codes designed for distributed storage which
+combine quick recovery from single node failure and optimal recovery from
+catastrophic failure. Gopalan et al [SODA 2017] studied the alphabet size
+needed for such codes in grid topologies and gave a combinatorial
+characterization for it.
+Consider a labeling of the edges of the complete bipartite graph $K_{n,n}$
+with labels coming from $F_2^d$ , that satisfies the following condition: for
+any simple cycle, the sum of the labels over its edges is nonzero. The minimal
+d where this is possible controls the alphabet size needed for maximally
+recoverable codes in n x n grid topologies.
+Prior to the current work, it was known that d is between $(\log n)^2$ and
+$n\log n$. We improve both bounds and show that d is linear in n. The upper
+bound is a recursive construction which beats the random construction. The
+lower bound follows by first relating the problem to the independence number of
+the Birkhoff polytope graph, and then providing tight bounds for it using the
+representation theory of the symmetric group.
+",1,0,1,0,0,0
+7143,Spin Angular Momentum of Proton Spin Puzzle in Complex Octonion Spaces,"  The paper focuses on considering some special precessional motions as the
+spin motions, separating the octonion angular momentum of a proton into six
+components, elucidating the proton angular momentum in the proton spin puzzle,
+especially the proton spin, decomposition, quarks and gluons, and polarization
+and so forth. J. C. Maxwell was the first to use the quaternions to study the
+electromagnetic fields. Subsequently the complex octonions are utilized to
+depict the electromagnetic field, gravitational field, and quantum mechanics
+and so forth. In the complex octonion space, the precessional equilibrium
+equation infers the angular velocity of precession. The external
+electromagnetic strength may induce a new precessional motion, generating a new
+term of angular momentum, even if the orbital angular momentum is zero. This
+new term of angular momentum can be regarded as the spin angular momentum, and
+its angular velocity of precession is different from the angular velocity of
+revolution. The study reveals that the angular momentum of the proton must be
+separated into more components than ever before. In the proton spin puzzle, the
+orbital angular momentum and magnetic dipole moment are independent of each
+other, and they should be measured and calculated respectively.
+",0,1,0,0,0,0
+7144,Physics-Informed Regularization of Deep Neural Networks,"  This paper presents a novel physics-informed regularization method for
+training of deep neural networks (DNNs). In particular, we focus on the DNN
+representation for the response of a physical or biological system, for which a
+set of governing laws are known. These laws often appear in the form of
+differential equations, derived from first principles, empirically-validated
+laws, and/or domain expertise. We propose a DNN training approach that utilizes
+these known differential equations in addition to the measurement data, by
+introducing a penalty term to the training loss function to penalize divergence
+form the governing laws. Through three numerical examples, we will show that
+the proposed regularization produces surrogates that are physically
+interpretable with smaller generalization errors, when compared to other common
+regularization methods.
+",1,0,0,0,0,0
+7145,Gradient-based Filter Design for the Dual-tree Wavelet Transform,"  The wavelet transform has seen success when incorporated into neural network
+architectures, such as in wavelet scattering networks. More recently, it has
+been shown that the dual-tree complex wavelet transform can provide better
+representations than the standard transform. With this in mind, we extend our
+previous method for learning filters for the 1D and 2D wavelet transforms into
+the dual-tree domain. We show that with few modifications to our original
+model, we can learn directional filters that leverage the properties of the
+dual-tree wavelet transform.
+",0,0,0,1,0,0
+7146,Assimilated LVEF: A Bayesian technique combining human intuition with machine measurement for sharper estimates of left ventricular ejection fraction and stronger association with outcomes,"  The cardiologist's main tool for measuring systolic heart failure is left
+ventricular ejection fraction (LVEF). Trained cardiologist's report both a
+visual and machine-guided measurement of LVEF, but only use this machine-guided
+measurement in analysis. We use a Bayesian technique to combine visual and
+machine-guided estimates from the PARTNER-IIA Trial, a cohort of patients with
+aortic stenosis at moderate risk treated with bioprosthetic aortic valves, and
+find our combined estimate reduces measurement errors and improves the
+association between LVEF and a 1-year composite endpoint.
+",0,0,0,1,0,0
+7147,DBSCAN: Optimal Rates For Density Based Clustering,"  We study the problem of optimal estimation of the density cluster tree under
+various assumptions on the underlying density. Building up from the seminal
+work of Chaudhuri et al. [2014], we formulate a new notion of clustering
+consistency which is better suited to smooth densities, and derive minimax
+rates of consistency for cluster tree estimation for Holder smooth densities of
+arbitrary degree \alpha. We present a computationally efficient, rate optimal
+cluster tree estimator based on a straightforward extension of the popular
+density-based clustering algorithm DBSCAN by Ester et al. [1996]. The procedure
+relies on a kernel density estimator with an appropriate choice of the kernel
+and bandwidth to produce a sequence of nested random geometric graphs whose
+connected components form a hierarchy of clusters. The resulting optimal rates
+for cluster tree estimation depend on the degree of smoothness of the
+underlying density and, interestingly, match minimax rates for density
+estimation under the supremum norm. Our results complement and extend the
+analysis of the DBSCAN algorithm in Sriperumbudur and Steinwart [2012].
+Finally, we consider level set estimation and cluster consistency for densities
+with jump discontinuities, where the sizes of the jumps and the distance among
+clusters are allowed to vanish as the sample size increases. We demonstrate
+that our DBSCAN-based algorithm remains minimax rate optimal in this setting as
+well.
+",0,0,1,1,0,0
+7148,Steganographic Generative Adversarial Networks,"  Steganography is collection of methods to hide secret information (""payload"")
+within non-secret information (""container""). Its counterpart, Steganalysis, is
+the practice of determining if a message contains a hidden payload, and
+recovering it if possible. Presence of hidden payloads is typically detected by
+a binary classifier. In the present study, we propose a new model for
+generating image-like containers based on Deep Convolutional Generative
+Adversarial Networks (DCGAN). This approach allows to generate more
+setganalysis-secure message embedding using standard steganography algorithms.
+Experiment results demonstrate that the new model successfully deceives the
+steganography analyzer, and for this reason, can be used in steganographic
+applications.
+",1,0,0,1,0,0
+7149,Minimal Sum Labeling of Graphs,"  A graph $G$ is called a sum graph if there is a so-called sum labeling of
+$G$, i.e. an injective function $\ell: V(G) \rightarrow \mathbb{N}$ such that
+for every $u,v\in V(G)$ it holds that $uv\in E(G)$ if and only if there exists
+a vertex $w\in V(G)$ such that $\ell(u)+\ell(v) = \ell(w)$. We say that sum
+labeling $\ell$ is minimal if there is a vertex $u\in V(G)$ such that
+$\ell(u)=1$. In this paper, we show that if we relax the conditions (either
+allow non-injective labelings or consider graphs with loops) then there are sum
+graphs without a minimal labeling, which partially answers the question posed
+by Miller, Ryan and Smyth in 1998.
+",1,0,0,0,0,0
+7150,Provably efficient RL with Rich Observations via Latent State Decoding,"  We study the exploration problem in episodic MDPs with rich observations
+generated from a small number of latent states. Under certain identifiability
+assumptions, we demonstrate how to estimate a mapping from the observations to
+latent states inductively through a sequence of regression and clustering
+steps---where previously decoded latent states provide labels for later
+regression problems---and use it to construct good exploration policies. We
+provide finite-sample guarantees on the quality of the learned state decoding
+function and exploration policies, and complement our theory with an empirical
+evaluation on a class of hard exploration problems. Our method exponentially
+improves over $Q$-learning with naïve exploration, even when $Q$-learning has
+cheating access to latent states.
+",1,0,0,1,0,0
+7151,Markov Chain Lifting and Distributed ADMM,"  The time to converge to the steady state of a finite Markov chain can be
+greatly reduced by a lifting operation, which creates a new Markov chain on an
+expanded state space. For a class of quadratic objectives, we show an analogous
+behavior where a distributed ADMM algorithm can be seen as a lifting of
+Gradient Descent algorithm. This provides a deep insight for its faster
+convergence rate under optimal parameter tuning. We conjecture that this gain
+is always present, as opposed to the lifting of a Markov chain which sometimes
+only provides a marginal speedup.
+",1,0,1,1,0,0
+7152,Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis,"  Generating versatile and appropriate synthetic speech requires control over
+the output expression separate from the spoken text. Important non-textual
+speech variation is seldom annotated, in which case output control must be
+learned in an unsupervised fashion. In this paper, we perform an in-depth study
+of methods for unsupervised learning of control in statistical speech
+synthesis. For example, we show that popular unsupervised training heuristics
+can be interpreted as variational inference in certain autoencoder models. We
+additionally connect these models to VQ-VAEs, another, recently-proposed class
+of deep variational autoencoders, which we show can be derived from a very
+similar mathematical argument. The implications of these new probabilistic
+interpretations are discussed. We illustrate the utility of the various
+approaches with an application to acoustic modelling for emotional speech
+synthesis, where the unsupervised methods for learning expression control
+(without access to emotional labels) are found to give results that in many
+aspects match or surpass the previous best supervised approach.
+",1,0,0,1,0,0
+7153,Fast failover of multicast sessions in software-defined networks,"  With the rapid growth of services that stream to groups of users comes an
+increased importance of and demand for reliable multicast. In this paper, we
+turn to software-defined networking and develop a novel general-purpose
+multi-failure protection algorithm to provide quick failure recovery, via Fast
+Failover (FF) groups, for dynamic multicast groups. This extends previous
+research, which either could not realize fast failover, worked only for single
+link failures, or was only applicable to static multicast groups. However,
+while FF is know to be fast, it requires pre-installing back-up rules. These
+additional memory requirements, which in a multicast setting are even more
+pronounced than for unicast, are often mentioned as a big disadvantage of using
+FF.
+We develop an OpenFlow application for resilient multicast, with which we
+study FF resource usage, in an attempt to better understand the trade-off
+between recovery time and resource usage. Our tests on a realistic network
+suggest that using FF groups can reduce the recovery time of the network
+significantly compared to other methods, especially when the latency between
+the controller and the switches is relatively large.
+",1,0,0,0,0,0
+7154,AI Safety Gridworlds,"  We present a suite of reinforcement learning environments illustrating
+various safety properties of intelligent agents. These problems include safe
+interruptibility, avoiding side effects, absent supervisor, reward gaming, safe
+exploration, as well as robustness to self-modification, distributional shift,
+and adversaries. To measure compliance with the intended safe behavior, we
+equip each environment with a performance function that is hidden from the
+agent. This allows us to categorize AI safety problems into robustness and
+specification problems, depending on whether the performance function
+corresponds to the observed reward function. We evaluate A2C and Rainbow, two
+recent deep reinforcement learning agents, on our environments and show that
+they are not able to solve them satisfactorily.
+",1,0,0,0,0,0
+7155,Maximality of Galois actions for abelian varieties,"  Let $\{\rho_\ell\}_\ell$ be the system of $\ell$-adic representations arising
+from the $i$th $\ell$-adic cohomology of a complete smooth variety $X$ defined
+over a number field $K$. Denote the image of $\rho_\ell$ by $\Gamma_\ell$ and
+its Zariski closure, which is a linear algebraic group over $\mathbb{Q}_\ell$,
+by $\mathbf{G}_\ell$. We prove that $\mathbf{G}_\ell^{red}$, the quotient of
+$\mathbf{G}_\ell^\circ$ by its unipotent radical, is unramified over a totally
+ramified extension of $\mathbb{Q}_\ell$ for all sufficiently large $\ell$. We
+give a sufficient condition on $\{\rho_\ell\}_\ell$ such that for all
+sufficiently large $\ell$, $\Gamma_\ell$ is in some sense maximal compact in
+$\mathbf{G}_\ell(\mathbb{Q}_\ell)$. Since the condition is satisfied when $X$
+is an abelian variety by the Tate conjecture, we obtain maximality of Galois
+actions for abelian varieties.
+",0,0,1,0,0,0
+7156,An Application of Multi-band Forced Photometry to One Square Degree of SERVS: Accurate Photometric Redshifts and Implications for Future Science,"  We apply The Tractor image modeling code to improve upon existing multi-band
+photometry for the Spitzer Extragalactic Representative Volume Survey (SERVS).
+SERVS consists of post-cryogenic Spitzer observations at 3.6 and 4.5 micron
+over five well-studied deep fields spanning 18 square degrees. In concert with
+data from ground-based near-infrared (NIR) and optical surveys, SERVS aims to
+provide a census of the properties of massive galaxies out to z ~ 5. To
+accomplish this, we are using The Tractor to perform ""forced photometry."" This
+technique employs prior measurements of source positions and surface brightness
+profiles from a high-resolution fiducial band from the VISTA Deep Extragalactic
+Observations (VIDEO) survey to model and fit the fluxes at lower-resolution
+bands. We discuss our implementation of The Tractor over a square degree test
+region within the XMM-LSS field with deep imaging in 12 NIR/optical bands. Our
+new multi-band source catalogs offer a number of advantages over traditional
+position-matched catalogs, including 1) consistent source cross-identification
+between bands, 2) de-blending of sources that are clearly resolved in the
+fiducial band but blended in the lower-resolution SERVS data, 3) a higher
+source detection fraction in each band, 4) a larger number of candidate
+galaxies in the redshift range 5 < z < 6, and 5) a statistically significant
+improvement in the photometric redshift accuracy as evidenced by the
+significant decrease in the fraction of outliers compared to spectroscopic
+redshifts. Thus, forced photometry using The Tractor offers a means of
+improving the accuracy of multi-band extragalactic surveys designed for galaxy
+evolution studies. We will extend our application of this technique to the full
+SERVS footprint in the future.
+",0,1,0,0,0,0
+7157,Stacked Structure Learning for Lifted Relational Neural Networks,"  Lifted Relational Neural Networks (LRNNs) describe relational domains using
+weighted first-order rules which act as templates for constructing feed-forward
+neural networks. While previous work has shown that using LRNNs can lead to
+state-of-the-art results in various ILP tasks, these results depended on
+hand-crafted rules. In this paper, we extend the framework of LRNNs with
+structure learning, thus enabling a fully automated learning process. Similarly
+to many ILP methods, our structure learning algorithm proceeds in an iterative
+fashion by top-down searching through the hypothesis space of all possible Horn
+clauses, considering the predicates that occur in the training examples as well
+as invented soft concepts entailed by the best weighted rules found so far. In
+the experiments, we demonstrate the ability to automatically induce useful
+hierarchical soft concepts leading to deep LRNNs with a competitive predictive
+power.
+",1,0,0,1,0,0
+7158,Spec-QP: Speculative Query Planning for Joins over Knowledge Graphs,"  Organisations store huge amounts of data from multiple heterogeneous sources
+in the form of Knowledge Graphs (KGs). One of the ways to query these KGs is to
+use SPARQL queries over a database engine. Since SPARQL follows exact match
+semantics, the queries may return too few or no results. Recent works have
+proposed query relaxation where the query engine judiciously replaces a query
+predicate with similar predicates using weighted relaxation rules mined from
+the KG. The space of possible relaxations is potentially too large to fully
+explore and users are typically interested in only top-k results, so such query
+engines use top-k algorithms for query processing. However, they may still
+process all the relaxations, many of whose answers do not contribute towards
+top-k answers. This leads to computation overheads and delayed response times.
+We propose Spec-QP, a query planning framework that speculatively determines
+which relaxations will have their results in the top-k answers. Only these
+relaxations are processed using the top-k operators. We, therefore, reduce the
+computation overheads and achieve faster response times without adversely
+affecting the quality of results. We tested Spec-QP over two datasets - XKG and
+Twitter, to demonstrate the efficiency of our planning framework at reducing
+runtimes with reasonable accuracy for query engines supporting relaxations.
+",1,0,0,0,0,0
+7159,SurfClipse: Context-Aware Meta Search in the IDE,"  Despite various debugging supports of the existing IDEs for programming
+errors and exceptions, software developers often look at web for working
+solutions or any up-to-date information. Traditional web search does not
+consider the context of the problems that they search solutions for, and thus
+it often does not help much in problem solving. In this paper, we propose a
+context-aware meta search tool, SurfClipse, that analyzes an encountered
+exception and its context in the IDE, and recommends not only suitable search
+queries but also relevant web pages for the exception (and its context). The
+tool collects results from three popular search engines and a programming Q & A
+site against the exception in the IDE, refines the results for relevance
+against the context of the exception, and then ranks them before
+recommendation. It provides two working modes--interactive and proactive to
+meet the versatile needs of the developers, and one can browse the result pages
+using a customized embedded browser provided by the tool.
+Tool page: www.usask.ca/~masud.rahman/surfclipse
+",1,0,0,0,0,0
+7160,Chomp on numerical semigroups,"  We consider the two-player game chomp on posets associated to numerical
+semigroups and show that the analysis of strategies for chomp is strongly
+related to classical properties of semigroups. We characterize, which player
+has a winning-strategy for symmetric semigroups, semigroups of maximal
+embedding dimension and several families of numerical semigroups generated by
+arithmetic sequences. Furthermore, we show that which player wins on a given
+numerical semigroup is a decidable question. Finally, we extend several of our
+results to the more general setting of subsemigroups of $\mathbb{N} \times T$,
+where $T$ is a finite abelian group.
+",1,0,1,0,0,0
+7161,Single Index Latent Variable Models for Network Topology Inference,"  A semi-parametric, non-linear regression model in the presence of latent
+variables is applied towards learning network graph structure. These latent
+variables can correspond to unmodeled phenomena or unmeasured agents in a
+complex system of interacting entities. This formulation jointly estimates
+non-linearities in the underlying data generation, the direct interactions
+between measured entities, and the indirect effects of unmeasured processes on
+the observed data. The learning is posed as regularized empirical risk
+minimization. Details of the algorithm for learning the model are outlined.
+Experiments demonstrate the performance of the learned model on real data.
+",0,0,0,1,0,0
+7162,Quadratically-Regularized Optimal Transport on Graphs,"  Optimal transportation provides a means of lifting distances between points
+on a geometric domain to distances between signals over the domain, expressed
+as probability distributions. On a graph, transportation problems can be used
+to express challenging tasks involving matching supply to demand with minimal
+shipment expense; in discrete language, these become minimum-cost network flow
+problems. Regularization typically is needed to ensure uniqueness for the
+linear ground distance case and to improve optimization convergence;
+state-of-the-art techniques employ entropic regularization on the
+transportation matrix. In this paper, we explore a quadratic alternative to
+entropic regularization for transport over a graph. We theoretically analyze
+the behavior of quadratically-regularized graph transport, characterizing how
+regularization affects the structure of flows in the regime of small but
+nonzero regularization. We further exploit elegant second-order structure in
+the dual of this problem to derive an easily-implemented Newton-type
+optimization algorithm.
+",1,0,1,0,0,0
+7163,"Discussion on ""Random-projection ensemble classification"" by T. Cannings and R. Samworth","  Discussion on ""Random-projection ensemble classification"" by T. Cannings and
+R. Samworth. We believe that the proposed approach can find many applications
+in economics such as credit scoring (e.g. Altman (1968)) and can be extended to
+more general type of classifiers. In this discussion we would like to draw
+authors attention to the copula-based discriminant analysis (Han et al. (2013)
+and He et al. (2016)).
+",0,0,1,1,0,0
+7164,Deep Asymmetric Multi-task Feature Learning,"  We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can
+learn deep representations shared across multiple tasks while effectively
+preventing negative transfer that may happen in the feature sharing process.
+Specifically, we introduce an asymmetric autoencoder term that allows reliable
+predictors for the easy tasks to have high contribution to the feature learning
+while suppressing the influences of unreliable predictors for more difficult
+tasks. This allows the learning of less noisy representations, and enables
+unreliable predictors to exploit knowledge from the reliable predictors via the
+shared latent features. Such asymmetric knowledge transfer through shared
+features is also more scalable and efficient than inter-task asymmetric
+transfer. We validate our Deep-AMTFL model on multiple benchmark datasets for
+multitask learning and image classification, on which it significantly
+outperforms existing symmetric and asymmetric multitask learning models, by
+effectively preventing negative transfer in deep feature learning.
+",1,0,0,1,0,0
+7165,Sub-nanometre resolution of atomic motion during electronic excitation in phase-change materials,"  Phase-change materials based on Ge-Sb-Te alloys are widely used in industrial
+applications such as nonvolatile memories, but reaction pathways for
+crystalline-to-amorphous phase-change on picosecond timescales remain unknown.
+Femtosecond laser excitation and an ultrashort x-ray probe is used to show the
+temporal separation of electronic and thermal effects in a long-lived ($>$100
+ps) transient metastable state of Ge$_{2}$Sb$_{2}$Te$_{5}$ with muted
+interatomic interaction induced by a weakening of resonant bonding. Due to a
+specific electronic state, the lattice undergoes a reversible nondestructive
+modification over a nanoscale region, remaining cold for 4 ps. An independent
+time-resolved x-ray absorption fine structure experiment confirms the existence
+of an intermediate state with disordered bonds. This newly unveiled effect
+allows the utilization of non-thermal ultra-fast pathways enabling artificial
+manipulation of the switching process, ultimately leading to a redefined speed
+limit, and improved energy efficiency and reliability of phase-change memory
+technologies.
+",0,1,0,0,0,0
+7166,A Density Result for Real Hyperelliptic Curves,"  Let $\{\infty^+, \infty^-\}$ be the two points above $\infty$ on the real
+hyperelliptic curve $H: y^2 = (x^2 - 1) \prod_{i=1}^{2g} (x - a_i)$. We show
+that the divisor $([\infty^+] - [\infty^-])$ is torsion in $\operatorname{Jac}
+J$ for a dense set of $(a_1, a_2, \ldots, a_{2g}) \in (-1, 1)^{2g}$. In fact,
+we prove by degeneration to a nodal $\mathbb{P}^1$ that an associated period
+map has derivative generically of full rank.
+",0,0,1,0,0,0
+7167,On a class of integrable systems of Monge-Ampère type,"  We investigate a class of multi-dimensional two-component systems of
+Monge-Ampère type that can be viewed as generalisations of heavenly-type
+equations appearing in self-dual Ricci-flat geometry. Based on the
+Jordan-Kronecker theory of skew-symmetric matrix pencils, a classification of
+normal forms of such systems is obtained. All two-component systems of
+Monge-Ampère type turn out to be integrable, and can be represented as the
+commutativity conditions of parameter-dependent vector fields. Geometrically,
+systems of Monge-Ampère type are associated with linear sections of the
+Grassmannians. This leads to an invariant differential-geometric
+characterisation of the Monge-Ampère property.
+",0,1,1,0,0,0
+7168,When is the mode functional the Bayes classifier?,"  In classification problems, the mode of the conditional probability
+distribution, i.e., the most probable category, is the Bayes classifier under
+zero-one or misclassification loss. Under any other cost structure, the mode
+fails to persist.
+",0,0,1,1,0,0
+7169,Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data,"  One of the main benefits of a wrist-worn computer is its ability to collect a
+variety of physiological data in a minimally intrusive manner. Among these
+data, electrodermal activity (EDA) is readily collected and provides a window
+into a person's emotional and sympathetic responses. EDA data collected using a
+wearable wristband are easily influenced by motion artifacts (MAs) that may
+significantly distort the data and degrade the quality of analyses performed on
+the data if not identified and removed. Prior work has demonstrated that MAs
+can be successfully detected using supervised machine learning algorithms on a
+small data set collected in a lab setting. In this paper, we demonstrate that
+unsupervised learning algorithms perform competitively with supervised
+algorithms for detecting MAs on EDA data collected in both a lab-based setting
+and a real-world setting comprising about 23 hours of data. We also find,
+somewhat surprisingly, that incorporating accelerometer data as well as EDA
+improves detection accuracy only slightly for supervised algorithms and
+significantly degrades the accuracy of unsupervised algorithms.
+",1,0,0,0,0,0
+7170,CredSaT: Credibility Ranking of Users in Big Social Data incorporating Semantic Analysis and Temporal Factor,"  The widespread use of big social data has pointed the research community in
+several significant directions. In particular, the notion of social trust has
+attracted a great deal of attention from information processors | computer
+scientists and information consumers | formal organizations. This is evident in
+various applications such as recommendation systems, viral marketing and
+expertise retrieval. Hence, it is essential to have frameworks that can
+temporally measure users credibility in all domains categorised under big
+social data. This paper presents CredSaT (Credibility incorporating Semantic
+analysis and Temporal factor): a fine-grained users credibility analysis
+framework for big social data. A novel metric that includes both new and
+current features, as well as the temporal factor, is harnessed to establish the
+credibility ranking of users. Experiments on real-world dataset demonstrate the
+effectiveness and applicability of our model to indicate highly domain-based
+trustworthy users. Further, CredSaT shows the capacity in capturing spammers
+and other anomalous users.
+",1,0,0,0,0,0
+7171,On singular Finsler foliation,"  In this paper we introduce the concept of singular Finsler foliation, which
+generalizes the concepts of Finsler actions, Finsler submersions and (regular)
+Finsler foliations. We show that if $\mathcal{F}$ is a singular Finsler
+foliation on a Randers manifold $(M,Z)$ with Zermelo data $(\mathtt{h},W),$
+then $\mathcal{F}$ is a singular Riemannian foliation on the Riemannian
+manifold $(M,\mathtt{h} )$. As a direct consequence we infer that the regular
+leaves are equifocal submanifolds (a generalization of isoparametric
+submanifolds) when the wind $W$ is an infinitesimal homothety of $\mathtt{h}$
+(e.,g when $W$ is killing vector field or $M$ has constant Finsler curvature).
+We also present a slice theorem that relates local singular Finsler
+foliations on Finsler manifolds with singular Finsler foliations on Minkowski
+spaces.
+",0,0,1,0,0,0
+7172,Early Experiences with Crowdsourcing Airway Annotations in Chest CT,"  Measuring airways in chest computed tomography (CT) images is important for
+characterizing diseases such as cystic fibrosis, yet very time-consuming to
+perform manually. Machine learning algorithms offer an alternative, but need
+large sets of annotated data to perform well. We investigate whether
+crowdsourcing can be used to gather airway annotations which can serve directly
+for measuring the airways, or as training data for the algorithms. We generate
+image slices at known locations of airways and request untrained crowd workers
+to outline the airway lumen and airway wall. Our results show that the workers
+are able to interpret the images, but that the instructions are too complex,
+leading to many unusable annotations. After excluding unusable annotations,
+quantitative results show medium to high correlations with expert measurements
+of the airways. Based on this positive experience, we describe a number of
+further research directions and provide insight into the challenges of
+crowdsourcing in medical images from the perspective of first-time users.
+",1,0,0,0,0,0
+7173,Convergent Iteration in Sobolev Space for Time Dependent Closed Quantum Systems,"  Time dependent quantum systems have become indispensable in science and its
+applications, particularly at the atomic and molecular levels. Here, we discuss
+the approximation of closed time dependent quantum systems on bounded domains,
+via iterative methods in Sobolev space based upon evolution operators.
+Recently, existence and uniqueness of weak solutions were demonstrated by a
+contractive fixed point mapping defined by the evolution operators. Convergent
+successive approximation is then guaranteed. This article uses the same mapping
+to define quadratically convergent Newton and approximate Newton methods.
+Estimates for the constants used in the convergence estimates are provided. The
+evolution operators are ideally suited to serve as the framework for this
+operator approximation theory, since the Hamiltonian is time dependent. In
+addition, the hypotheses required to guarantee quadratic convergence of the
+Newton iteration build naturally upon the hypotheses used for the
+existence/uniqueness theory.
+",0,0,1,0,0,0
+7174,Prototyping and Experimentation of a Closed-Loop Wireless Power Transmission with Channel Acquisition and Waveform Optimization,"  A systematic design of adaptive waveform for Wireless Power Transfer (WPT)
+has recently been proposed and shown through simulations to lead to significant
+performance benefits compared to traditional non-adaptive and heuristic
+waveforms. In this study, we design the first prototype of a closed-loop
+wireless power transfer system with adaptive waveform optimization based on
+Channel State Information acquisition. The prototype consists of three
+important blocks, namely the channel estimator, the waveform optimizer, and the
+energy harvester. Software Defined Radio (SDR) prototyping tools are used to
+implement a wireless power transmitter and a channel estimator, and a voltage
+doubler rectenna is designed to work as an energy harvester. A channel adaptive
+waveform with 8 sinewaves is shown through experiments to improve the average
+harvested DC power at the rectenna output by 9.8% to 36.8% over a non-adaptive
+design with the same number of sinewaves.
+",1,0,0,0,0,0
+7175,Image-based immersed boundary model of the aortic root,"  Each year, approximately 300,000 heart valve repair or replacement procedures
+are performed worldwide, including approximately 70,000 aortic valve
+replacement surgeries in the United States alone. This paper describes progress
+in constructing anatomically and physiologically realistic immersed boundary
+(IB) models of the dynamics of the aortic root and ascending aorta. This work
+builds on earlier IB models of fluid-structure interaction (FSI) in the aortic
+root, which previously achieved realistic hemodynamics over multiple cardiac
+cycles, but which also were limited to simplified aortic geometries and
+idealized descriptions of the biomechanics of the aortic valve cusps. By
+contrast, the model described herein uses an anatomical geometry reconstructed
+from patient-specific computed tomography angiography (CTA) data, and employs a
+description of the elasticity of the aortic valve leaflets based on a
+fiber-reinforced constitutive model fit to experimental tensile test data.
+Numerical tests show that the model is able to resolve the leaflet biomechanics
+in diastole and early systole at practical grid spacings. The model is also
+used to examine differences in the mechanics and fluid dynamics yielded by
+fresh valve leaflets and glutaraldehyde-fixed leaflets similar to those used in
+bioprosthetic heart valves. Although there are large differences in the leaflet
+deformations during diastole, the differences in the open configurations of the
+valve models are relatively small, and nearly identical hemodynamics are
+obtained in all cases considered.
+",1,1,0,0,0,0
+7176,Large-Scale Cox Process Inference using Variational Fourier Features,"  Gaussian process modulated Poisson processes provide a flexible framework for
+modelling spatiotemporal point patterns. So far this had been restricted to one
+dimension, binning to a pre-determined grid, or small data sets of up to a few
+thousand data points. Here we introduce Cox process inference based on Fourier
+features. This sparse representation induces global rather than local
+constraints on the function space and is computationally efficient. This allows
+us to formulate a grid-free approximation that scales well with the number of
+data points and the size of the domain. We demonstrate that this allows MCMC
+approximations to the non-Gaussian posterior. We also find that, in practice,
+Fourier features have more consistent optimization behavior than previous
+approaches. Our approximate Bayesian method can fit over 100,000 events with
+complex spatiotemporal patterns in three dimensions on a single GPU.
+",0,0,0,1,0,0
+7177,Buildings-to-Grid Integration Framework,"  This paper puts forth a mathematical framework for Buildings-to-Grid (BtG)
+integration in smart cities. The framework explicitly couples power grid and
+building's control actions and operational decisions, and can be utilized by
+buildings and power grids operators to simultaneously optimize their
+performance. Simplified dynamics of building clusters and building-integrated
+power networks with algebraic equations are presented---both operating at
+different time-scales. A model predictive control (MPC)-based algorithm that
+formulates the BtG integration and accounts for the time-scale discrepancy is
+developed. The formulation captures dynamic and algebraic power flow
+constraints of power networks and is shown to be numerically advantageous. The
+paper analytically establishes that the BtG integration yields a reduced total
+system cost in comparison with decoupled designs where grid and building
+operators determine their controls separately. The developed framework is
+tested on standard power networks that include thousands of buildings modeled
+using industrial data. Case studies demonstrate building energy savings and
+significant frequency regulation, while these findings carry over in network
+simulations with nonlinear power flows and mismatch in building model
+parameters. Finally, simulations indicate that the performance does not
+significantly worsen when there is uncertainty in the forecasted weather and
+base load conditions.
+",1,0,1,0,0,0
+7178,Oxygen Partial Pressure during Pulsed Laser Deposition: Deterministic Role on Thermodynamic Stability of Atomic Termination Sequence at SrRuO3/BaTiO3 Interface,"  With recent trends on miniaturizing oxide-based devices, the need for
+atomic-scale control of surface/interface structures by pulsed laser deposition
+(PLD) has increased. In particular, realizing uniform atomic termination at the
+surface/interface is highly desirable. However, a lack of understanding on the
+surface formation mechanism in PLD has limited a deliberate control of
+surface/interface atomic stacking sequences. Here, taking the prototypical
+SrRuO3/BaTiO3/SrRuO3 (SRO/BTO/SRO) heterostructure as a model system, we
+investigated the formation of different interfacial termination sequences
+(BaO-RuO2 or TiO2-SrO) with oxygen partial pressure (PO2) during PLD. We found
+that a uniform SrO-TiO2 termination sequence at the SRO/BTO interface can be
+achieved by lowering the PO2 to 5 mTorr, regardless of the total background gas
+pressure (Ptotal), growth mode, or growth rate. Our results indicate that the
+thermodynamic stability of the BTO surface at the low-energy kinetics stage of
+PLD can play an important role in surface/interface termination formation. This
+work paves the way for realizing termination engineering in functional oxide
+heterostructures.
+",0,1,0,0,0,0
+7179,A Data-Driven Analysis of the Influence of Care Coordination on Trauma Outcome,"  OBJECTIVE: To test the hypothesis that variation in care coordination is
+related to LOS. DESIGN We applied a spectral co-clustering methodology to
+simultaneously infer groups of patients and care coordination patterns, in the
+form of interaction networks of health care professionals, from electronic
+medical record (EMR) utilization data. The care coordination pattern for each
+patient group was represented by standard social network characteristics and
+its relationship with hospital LOS was assessed via a negative binomial
+regression with a 95% confidence interval. SETTING AND PATIENTS This study
+focuses on 5,588 adult patients hospitalized for trauma at the Vanderbilt
+University Medical Center. The EMRs were accessed by healthcare professionals
+from 179 operational areas during 158,467 operational actions. MAIN OUTCOME
+MEASURES: Hospital LOS for trauma inpatients, as an indicator of care
+coordination efficiency. RESULTS: Three general types of care coordination
+patterns were discovered, each of which was affiliated with a specific patient
+group. The first patient group exhibited the shortest hospital LOS and was
+managed by a care coordination pattern that involved the smallest number of
+operational areas (102 areas, as opposed to 125 and 138 for the other patient
+groups), but exhibited the largest number of collaborations between operational
+areas (e.g., an average of 27.1 connections per operational area compared to
+22.5 and 23.3 for the other two groups). The hospital LOS for the second and
+third patient groups was 14 hours (P = 0.024) and 10 hours (P = 0.042) longer
+than the first patient group, respectively.
+",1,0,0,0,0,0
+7180,Construction of Non-asymptotic Confidence Sets in 2-Wasserstein Space,"  In this paper, we consider a probabilistic setting where the probability
+measures are considered to be random objects. We propose a procedure of
+construction non-asymptotic confidence sets for empirical barycenters in
+2-Wasserstein space and develop the idea further to construction of a
+non-parametric two-sample test that is then applied to the detection of
+structural breaks in data with complex geometry. Both procedures mainly rely on
+the idea of multiplier bootstrap (Spokoiny and Zhilova (2015), Chernozhukov et
+al. (2014)). The main focus lies on probability measures that have commuting
+covariance matrices and belong to the same scatter-location family: we proof
+the validity of a bootstrap procedure that allows to compute confidence sets
+and critical values for a Wasserstein-based two-sample test.
+",0,0,1,1,0,0
+7181,Limits on statistical anisotropy from BOSS DR12 galaxies using bipolar spherical harmonics,"  We measure statistically anisotropic signatures imprinted in
+three-dimensional galaxy clustering using bipolar spherical harmonics (BipoSHs)
+in both Fourier space and configuration space. We then constrain a well-known
+quadrupolar anisotropy parameter $g_{2M}$ in the primordial power spectrum,
+parametrized by $P(\vec{k}) = \bar{P}(k) [ 1 + \sum_{M} g_{2M} Y_{2M}(\hat{k})
+]$, with $M$ determining the direction of the anisotropy. Such an anisotropic
+signal is easily contaminated by artificial asymmetries due to specific survey
+geometry. We precisely estimate the contaminated signal and finally subtract it
+from the data. Using the galaxy samples obtained by the Baryon Oscillation
+Spectroscopic Survey Data Release 12, we find no evidence for violation of
+statistical isotropy, $g_{2M}$ for all $M$ to be of zero within the $2\sigma$
+level. The $g_{2M}$-type anisotropy can originate from the primordial curvature
+power spectrum involving a directional-dependent modulation $g_* (\hat{k} \cdot
+\hat{p})^2$. The bound on $g_{2M}$ is translated into $g_*$ as $-0.09 < g_* <
+0.08$ with a $95\%$ confidence level when $\hat{p}$ is marginalized over.
+",0,1,0,0,0,0
+7182,A Central Limit Theorem for Wasserstein type distances between two different laws,"  This article is dedicated to the estimation of Wasserstein distances and
+Wasserstein costs between two distinct continuous distributions $F$ and $G$ on
+$\mathbb R$. The estimator is based on the order statistics of (possibly
+dependent) samples of $F$ resp. $G$. We prove the consistency and the
+asymptotic normality of our estimators. \begin{it}Keywords:\end{it} Central
+Limit Theorems- Generelized Wasserstein distances- Empirical processes- Strong
+approximation- Dependent samples.
+",0,0,1,1,0,0
+7183,Confidence Intervals for Stochastic Arithmetic,"  Quantifying errors and losses due to the use of Floating-Point (FP)
+calculations in industrial scientific computing codes is an important part of
+the Verification, Validation and Uncertainty Quantification (VVUQ) process.
+Stochastic Arithmetic is one way to model and estimate FP losses of accuracy,
+which scales well to large, industrial codes. It exists in different flavors,
+such as CESTAC or MCA, implemented in various tools such as CADNA, Verificarlo
+or Verrou. These methodologies and tools are based on the idea that FP losses
+of accuracy can be modeled via randomness. Therefore, they share the same need
+to perform a statistical analysis of programs results in order to estimate the
+significance of the results. In this paper, we propose a framework to perform a
+solid statistical analysis of Stochastic Arithmetic. This framework unifies all
+existing definitions of the number of significant digits (CESTAC and MCA), and
+also proposes a new quantity of interest: the number of digits contributing to
+the accuracy of the results. Sound confidence intervals are provided for all
+estimators, both in the case of normally distributed results, and in the
+general case. The use of this framework is demonstrated by two case studies of
+large, industrial codes: Europlexus and code\_aster.
+",1,0,0,1,0,0
+7184,Contaminated speech training methods for robust DNN-HMM distant speech recognition,"  Despite the significant progress made in the last years, state-of-the-art
+speech recognition technologies provide a satisfactory performance only in the
+close-talking condition. Robustness of distant speech recognition in adverse
+acoustic conditions, on the other hand, remains a crucial open issue for future
+applications of human-machine interaction. To this end, several advances in
+speech enhancement, acoustic scene analysis as well as acoustic modeling, have
+recently contributed to improve the state-of-the-art in the field. One of the
+most effective approaches to derive a robust acoustic modeling is based on
+using contaminated speech, which proved helpful in reducing the acoustic
+mismatch between training and testing conditions.
+In this paper, we revise this classical approach in the context of modern
+DNN-HMM systems, and propose the adoption of three methods, namely, asymmetric
+context windowing, close-talk based supervision, and close-talk based
+pre-training. The experimental results, obtained using both real and simulated
+data, show a significant advantage in using these three methods, overall
+providing a 15% error rate reduction compared to the baseline systems. The same
+trend in performance is confirmed either using a high-quality training set of
+small size, and a large one.
+",1,0,0,0,0,0
+7185,Co-segmentation for Space-Time Co-located Collections,"  We present a co-segmentation technique for space-time co-located image
+collections. These prevalent collections capture various dynamic events,
+usually by multiple photographers, and may contain multiple co-occurring
+objects which are not necessarily part of the intended foreground object,
+resulting in ambiguities for traditional co-segmentation techniques. Thus, to
+disambiguate what the common foreground object is, we introduce a
+weakly-supervised technique, where we assume only a small seed, given in the
+form of a single segmented image. We take a distributed approach, where local
+belief models are propagated and reinforced with similar images. Our technique
+progressively expands the foreground and background belief models across the
+entire collection. The technique exploits the power of the entire set of image
+without building a global model, and thus successfully overcomes large
+variability in appearance of the common foreground object. We demonstrate that
+our method outperforms previous co-segmentation techniques on challenging
+space-time co-located collections, including dense benchmark datasets which
+were adapted for our novel problem setting.
+",1,0,0,0,0,0
+7186,Speaker Diarization with LSTM,"  For many years, i-vector based audio embedding techniques were the dominant
+approach for speaker verification and speaker diarization applications.
+However, mirroring the rise of deep learning in various domains, neural network
+based audio embeddings, also known as d-vectors, have consistently demonstrated
+superior speaker verification performance. In this paper, we build on the
+success of d-vector based speaker verification systems to develop a new
+d-vector based approach to speaker diarization. Specifically, we combine
+LSTM-based d-vector audio embeddings with recent work in non-parametric
+clustering to obtain a state-of-the-art speaker diarization system. Our system
+is evaluated on three standard public datasets, suggesting that d-vector based
+diarization systems offer significant advantages over traditional i-vector
+based systems. We achieved a 12.0% diarization error rate on NIST SRE 2000
+CALLHOME, while our model is trained with out-of-domain data from voice search
+logs.
+",1,0,0,1,0,0
+7187,Almost h-conformal semi-invariant submersions from almost quaternionic Hermitian manifolds,"  As a generalization of Riemannian submersions, horizontally conformal
+submersions, semi-invariant submersions, h-semi-invariant submersions, almost
+h-semi-invariant submersions, conformal semi-invariant submersions, we
+introduce h-conformal semi-invariant submersions and almost h-conformal
+semi-invariant submersions from almost quaternionic Hermitian manifolds onto
+Riemannian manifolds.
+We study their properties: the geometry of foliations, the conditions for
+total manifolds to be locally product manifolds, the conditions for such maps
+to be totally geodesic, etc. Finally, we give some examples of such maps.
+",0,0,1,0,0,0
+7188,Use of Source Code Similarity Metrics in Software Defect Prediction,"  In recent years, defect prediction has received a great deal of attention in
+the empirical software engineering world. Predicting software defects before
+the maintenance phase is very important not only to decrease the maintenance
+costs but also increase the overall quality of a software product. There are
+different types of product, process, and developer based software metrics
+proposed so far to measure the defectiveness of a software system. This paper
+suggests to use a novel set of software metrics which are based on the
+similarities detected among the source code files in a software project. To
+find source code similarities among different files of a software system,
+plagiarism and clone detection techniques are used. Two simple similarity
+metrics are calculated for each file, considering its overall similarity to the
+defective and non defective files in the project. Using these similarity
+metrics, we predict whether a specific file is defective or not. Our
+experiments on 10 open source data sets show that depending on the amount of
+detected similarity, proposed metrics could achieve significantly better
+performance compared to the existing static code metrics in terms of the area
+under the curve (AUC).
+",1,0,0,0,0,0
+7189,Connecting pairwise spheres by depth: DCOPS,"  We extend the classical notion of the spherical depth in \mathbb{R}^k, to the
+important setup of data on a Riemannian manifold. We show that this notion of
+depth satisfies a set of desirable properties. For the empirical version of
+this depth function both uniform consistency and the asymptotic distribution
+are studied. Consistency is also shown for functional data. The behaviour of
+the depth is illustrated through several examples for Riemannian manifold data.
+",0,0,1,1,0,0
+7190,Estimating the spectral gap of a trace-class Markov operator,"  The utility of a Markov chain Monte Carlo algorithm is, in large part,
+determined by the size of the spectral gap of the corresponding Markov
+operator. However, calculating (and even approximating) the spectral gaps of
+practical Monte Carlo Markov chains in statistics has proven to be an extremely
+difficult and often insurmountable task, especially when these chains move on
+continuous state spaces. In this paper, a method for accurate estimation of the
+spectral gap is developed for general state space Markov chains whose operators
+are non-negative and trace-class. The method is based on the fact that the
+second largest eigenvalue (and hence the spectral gap) of such operators can be
+bounded above and below by simple functions of the power sums of the
+eigenvalues. These power sums often have nice integral representations. A
+classical Monte Carlo method is proposed to estimate these integrals, and a
+simple sufficient condition for finite variance is provided. This leads to
+asymptotically valid confidence intervals for the second largest eigenvalue
+(and the spectral gap) of the Markov operator. The efficiency of the method is
+studied. For illustration, the method is applied to Albert and Chib's (1993)
+data augmentation (DA) algorithm for Bayesian probit regression, and also to a
+DA algorithm for Bayesian linear regression with non-Gaussian errors (Liu,
+1996).
+",0,0,1,1,0,0
+7191,Tidal synchronization of an anelastic multi-layered body: Titan's synchronous rotation,"  This paper presents one analytical tidal theory for a viscoelastic
+multi-layered body with an arbitrary number of homogeneous layers. Starting
+with the static equilibrium figure, modified to include tide and differential
+rotation, and using the Newtonian creep approach, we find the dynamical
+equilibrium figure of the deformed body, which allows us to calculate the tidal
+potential and the forces acting on the tide generating body, as well as the
+rotation and orbital elements variations. In the particular case of the
+two-layer model, we study the tidal synchronization when the gravitational
+coupling and the friction in the interface between the layers is added. For
+high relaxation factors (low viscosity), the stationary solution of each layer
+is synchronous with the orbital mean motion (n) when the orbit is circular, but
+the spin rates increase if the orbital eccentricity increases. For low
+relaxation factors (high viscosity), as in planetary satellites, if friction
+remains low, each layer can be trapped in different spin-orbit resonances with
+frequencies n/2,n,3n/2,... . We apply the theory to Titan. The main results
+are: i) the rotational constraint does not allow us confirm or reject the
+existence of a subsurface ocean in Titan; and ii) the crust-atmosphere exchange
+of angular momentum can be neglected. Using the rotation estimate based on
+Cassini's observation, we limit the possible value of the shell relaxation
+factor, when a subsurface ocean is assumed, to 10^-9 Hz, which correspond to a
+shell's viscosity 10^18 Pa s, depending on the ocean's thickness and viscosity
+values. In the case in which the ocean does not exist, the maximum shell
+relaxation factor is one order of magnitude smaller and the corresponding
+minimum shell's viscosity is one order higher.
+",0,1,0,0,0,0
+7192,On Lie algebras responsible for zero-curvature representations of multicomponent (1+1)-dimensional evolution PDEs,"  Zero-curvature representations (ZCRs) are one of the main tools in the theory
+of integrable $(1+1)$-dimensional PDEs. According to the preprint
+arXiv:1212.2199, for any given $(1+1)$-dimensional evolution PDE one can define
+a sequence of Lie algebras $F^p$, $p=0,1,2,3,\dots$, such that representations
+of these algebras classify all ZCRs of the PDE up to local gauge equivalence.
+ZCRs depending on derivatives of arbitrary finite order are allowed.
+Furthermore, these algebras provide necessary conditions for existence of
+Backlund transformations between two given PDEs. The algebras $F^p$ are defined
+in arXiv:1212.2199 in terms of generators and relations.
+In the present paper, we describe some methods to study the structure of the
+algebras $F^p$ for multicomponent $(1+1)$-dimensional evolution PDEs. Using
+these methods, we compute the explicit structure (up to non-essential nilpotent
+ideals) of the Lie algebras $F^p$ for the Landau-Lifshitz, nonlinear
+Schrodinger equations, and for the $n$-component Landau-Lifshitz system of
+Golubchik and Sokolov for any $n>3$. In particular, this means that for the
+$n$-component Landau-Lifshitz system we classify all ZCRs (depending on
+derivatives of arbitrary finite order), up to local gauge equivalence and up to
+killing nilpotent ideals in the corresponding Lie algebras.
+The presented methods to classify ZCRs can be applied also to other
+$(1+1)$-dimensional evolution PDEs. Furthermore, the obtained results can be
+used for proving non-existence of Backlund transformations between some PDEs,
+which will be described in forthcoming publications.
+",0,1,1,0,0,0
+7193,TRAMP: Tracking by a Real-time AMbisonic-based Particle filter,"  This article presents a multiple sound source localization and tracking
+system, fed by the Eigenmike array. The First Order Ambisonics (FOA) format is
+used to build a pseudointensity-based spherical histogram, from which the
+source position estimates are deduced. These instantaneous estimates are
+processed by a wellknown tracking system relying on a set of particle filters.
+While the novelty within localization and tracking is incremental, the
+fully-functional, complete and real-time running system based on these
+algorithms is proposed for the first time. As such, it could serve as an
+additional baseline method of the LOCATA challenge.
+",1,0,0,0,0,0
+7194,Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity,"  In this paper we present a scalable approach for robustly computing a 3D
+surface mesh from multi-scale multi-view stereo point clouds that can handle
+extreme jumps of point density (in our experiments three orders of magnitude).
+The backbone of our approach is a combination of octree data partitioning,
+local Delaunay tetrahedralization and graph cut optimization. Graph cut
+optimization is used twice, once to extract surface hypotheses from local
+Delaunay tetrahedralizations and once to merge overlapping surface hypotheses
+even when the local tetrahedralizations do not share the same topology.This
+formulation allows us to obtain a constant memory consumption per sub-problem
+while at the same time retaining the density independent interpolation
+properties of the Delaunay-based optimization. On multiple public datasets, we
+demonstrate that our approach is highly competitive with the state-of-the-art
+in terms of accuracy, completeness and outlier resilience. Further, we
+demonstrate the multi-scale potential of our approach by processing a newly
+recorded dataset with 2 billion points and a point density variation of more
+than four orders of magnitude - requiring less than 9GB of RAM per process.
+",1,0,0,0,0,0
+7195,General Bounds for Incremental Maximization,"  We propose a theoretical framework to capture incremental solutions to
+cardinality constrained maximization problems. The defining characteristic of
+our framework is that the cardinality/support of the solution is bounded by a
+value $k\in\mathbb{N}$ that grows over time, and we allow the solution to be
+extended one element at a time. We investigate the best-possible competitive
+ratio of such an incremental solution, i.e., the worst ratio over all $k$
+between the incremental solution after $k$ steps and an optimum solution of
+cardinality $k$. We define a large class of problems that contains many
+important cardinality constrained maximization problems like maximum matching,
+knapsack, and packing/covering problems. We provide a general
+$2.618$-competitive incremental algorithm for this class of problems, and show
+that no algorithm can have competitive ratio below $2.18$ in general.
+In the second part of the paper, we focus on the inherently incremental
+greedy algorithm that increases the objective value as much as possible in each
+step. This algorithm is known to be $1.58$-competitive for submodular objective
+functions, but it has unbounded competitive ratio for the class of incremental
+problems mentioned above. We define a relaxed submodularity condition for the
+objective function, capturing problems like maximum (weighted) ($b$-)matching
+and a variant of the maximum flow problem. We show that the greedy algorithm
+has competitive ratio (exactly) $2.313$ for the class of problems that satisfy
+this relaxed submodularity condition.
+Note that our upper bounds on the competitive ratios translate to
+approximation ratios for the underlying cardinality constrained problems.
+",1,0,1,0,0,0
+7196,Arrangements of pseudocircles on surfaces,"  A pseudocircle is a simple closed curve on some surface. Arrangements of
+pseudocircles were introduced by Grünbaum, who defined them as collections of
+pseudocircles that pairwise intersect in exactly two points, at which they
+cross. There are several variations on this notion in the literature, one of
+which requires that no three pseudocircles have a point in common. Working
+under this definition, Ortner proved that an arrangement of pseudocircles is
+embeddable into the sphere if and only if all of its subarrangements of size at
+most $4$ are embeddable into the sphere. Ortner asked if an analogous result
+held for embeddability into a compact orientable surface $\Sigma_g$ of genus
+$g>0$. In this paper we answer this question, under an even more general
+definition of an arrangement, in which the pseudocircles in the collection are
+not required to intersect each other, or that the intersections are crossings:
+it suffices to have one pseudocircle that intersects all other pseudocircles in
+the collection. We show that under this more general notion, an arrangement of
+pseudocircles is embeddable into $\Sigma_g$ if and only if all of its
+subarrangements of size at most $4g+5$ are embeddable into $\Sigma_g$, and that
+this can be improved to $4g+4$ under the concept of an arrangement used by
+Ortner. Our framework also allows us to generalize this result to arrangements
+of other objects, such as arcs.
+",1,0,1,0,0,0
+7197,Scalable methods for Bayesian selective inference,"  Modeled along the truncated approach in Panigrahi (2016), selection-adjusted
+inference in a Bayesian regime is based on a selective posterior. Such a
+posterior is determined together by a generative model imposed on data and the
+selection event that enforces a truncation on the assumed law. The effective
+difference between the selective posterior and the usual Bayesian framework is
+reflected in the use of a truncated likelihood. The normalizer of the truncated
+law in the adjusted framework is the probability of the selection event; this
+is typically intractable and it leads to the computational bottleneck in
+sampling from such a posterior. The current work lays out a primal-dual
+approach of solving an approximating optimization problem to provide valid
+post-selective Bayesian inference. The selection procedures are posed as
+data-queries that solve a randomized version of a convex learning program which
+have the advantage of preserving more left-over information for inference. We
+propose a randomization scheme under which the optimization has separable
+constraints that result in a partially separable objective in lower dimensions
+for many commonly used selective queries to approximate the otherwise
+intractable selective posterior. We show that the approximating optimization
+under a Gaussian randomization gives a valid exponential rate of decay for the
+selection probability on a large deviation scale. We offer a primal-dual method
+to solve the optimization problem leading to an approximate posterior; this
+allows us to exploit the usual merits of a Bayesian machinery in both low and
+high dimensional regimes where the underlying signal is effectively sparse. We
+show that the adjusted estimates empirically demonstrate better frequentist
+properties in comparison to the unadjusted estimates based on the usual
+posterior, when applied to a wide range of constrained, convex data queries.
+",0,0,0,1,0,0
+7198,Modified Recursive Cholesky (Rchol) Algorithm: An Explicit Estimation and Pseudo-inverse of Correlation Matrices,"  The Cholesky decomposition plays an important role in finding the inverse of
+the correlation matrices. As it is a fast and numerically stable for linear
+system solving, inversion, and factorization compared to singular valued
+decomposition (SVD), QR factorization and LU decomposition. As different
+methods exist to find the Cholesky decomposition of a given matrix. This paper
+presents the comparative study of a proposed RChol algorithm with the
+conventional methods. The RChol algorithm is an explicit way to estimate the
+modified Cholesky factors of a dynamic correlation matrix.
+",0,0,1,0,0,0
+7199,A Utility-Driven Multi-Queue Admission Control Solution for Network Slicing,"  The combination of recent emerging technologies such as network function
+virtualization (NFV) and network programmability (SDN) gave birth to the
+Network Slicing revolution. 5G networks consist of multi-tenant infrastructures
+capable of offering leased network ""slices"" to new customers (e.g., vertical
+industries) enabling a new telecom business model: Slice-as-aService (SlaaS).
+In this paper, we aim i ) to study the slicing admission control problem by
+means of a multi-queuing system for heterogeneous tenant requests, ii ) to
+derive its statistical behavior model, and iii ) to provide a utility-based
+admission control optimization. Our results analyze the capability of the
+proposed SlaaS system to be approximately Markovian and evaluate its
+performance as compared to legacy solutions.
+",1,0,0,0,0,0
+7200,Statistical inference for network samples using subgraph counts,"  We consider that a network is an observation, and a collection of observed
+networks forms a sample. In this setting, we provide methods to test whether
+all observations in a network sample are drawn from a specified model. We
+achieve this by deriving, under the null of the graphon model, the joint
+asymptotic properties of average subgraph counts as the number of observed
+networks increases but the number of nodes in each network remains finite. In
+doing so, we do not require that each observed network contains the same number
+of nodes, or is drawn from the same distribution. Our results yield joint
+confidence regions for subgraph counts, and therefore methods for testing
+whether the observations in a network sample are drawn from: a specified
+distribution, a specified model, or from the same model as another network
+sample. We present simulation experiments and an illustrative example on a
+sample of brain networks where we find that highly creative individuals' brains
+present significantly more short cycles.
+",1,0,0,1,0,0
+7201,Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking,"  This paper is concerned with the problem of top-$K$ ranking from pairwise
+comparisons. Given a collection of $n$ items and a few pairwise comparisons
+across them, one wishes to identify the set of $K$ items that receive the
+highest ranks. To tackle this problem, we adopt the logistic parametric model
+--- the Bradley-Terry-Luce model, where each item is assigned a latent
+preference score, and where the outcome of each pairwise comparison depends
+solely on the relative scores of the two items involved. Recent works have made
+significant progress towards characterizing the performance (e.g. the mean
+square error for estimating the scores) of several classical methods, including
+the spectral method and the maximum likelihood estimator (MLE). However, where
+they stand regarding top-$K$ ranking remains unsettled.
+We demonstrate that under a natural random sampling model, the spectral
+method alone, or the regularized MLE alone, is minimax optimal in terms of the
+sample complexity --- the number of paired comparisons needed to ensure exact
+top-$K$ identification, for the fixed dynamic range regime. This is
+accomplished via optimal control of the entrywise error of the score estimates.
+We complement our theoretical studies by numerical experiments, confirming that
+both methods yield low entrywise errors for estimating the underlying scores.
+Our theory is established via a novel leave-one-out trick, which proves
+effective for analyzing both iterative and non-iterative procedures. Along the
+way, we derive an elementary eigenvector perturbation bound for probability
+transition matrices, which parallels the Davis-Kahan $\sin\Theta$ theorem for
+symmetric matrices. This also allows us to close the gap between the $\ell_2$
+error upper bound for the spectral method and the minimax lower limit.
+",1,0,1,1,0,0
+7202,One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay,"  Recently, model-free reinforcement learning algorithms have been shown to
+solve challenging problems by learning from extensive interaction with the
+environment. A significant issue with transferring this success to the robotics
+domain is that interaction with the real world is costly, but training on
+limited experience is prone to overfitting. We present a method for learning to
+navigate, to a fixed goal and in a known environment, on a mobile robot. The
+robot leverages an interactive world model built from a single traversal of the
+environment, a pre-trained visual feature encoder, and stochastic environmental
+augmentation, to demonstrate successful zero-shot transfer under real-world
+environmental variations without fine-tuning.
+",1,0,0,0,0,0
+7203,Data Race Detection on Compressed Traces,"  We consider the problem of detecting data races in program traces that have
+been compressed using straight line programs (SLP), which are special
+context-free grammars that generate exactly one string, namely the trace that
+they represent. We consider two classical approaches to race detection ---
+using the happens-before relation and the lockset discipline. We present
+algorithms for both these methods that run in time that is linear in the size
+of the compressed, SLP representation. Typical program executions almost always
+exhibit patterns that lead to significant compression. Thus, our algorithms are
+expected to result in large speedups when compared with analyzing the
+uncompressed trace. Our experimental evaluation of these new algorithms on
+standard benchmarks confirms this observation.
+",1,0,0,0,0,0
+7204,Model and Integrate Medical Resource Availability into Verifiably Correct Executable Medical Guidelines - Technical Report,"  Improving effectiveness and safety of patient care is an ultimate objective
+for medical cyber-physical systems. A recent study shows that the patients'
+death rate can be reduced by computerizing medical guidelines. Most existing
+medical guideline models are validated and/or verified based on the assumption
+that all necessary medical resources needed for a patient care are always
+available. However, the reality is that some medical resources, such as special
+medical equipment or medical specialists, can be temporarily unavailable for an
+individual patient. In such cases, safety properties validated and/or verified
+in existing medical guideline models without considering medical resource
+availability may not hold any more. The paper argues that considering medical
+resource availability is essential in building verifiably correct executable
+medical guidelines. We present an approach to explicitly and separately model
+medical resource availability and automatically integrate resource availability
+models into an existing statechart-based computerized medical guideline model.
+This approach requires minimal change in existing medical guideline models to
+take into consideration of medical resource availability in validating and
+verifying medical guideline models. A simplified stroke scenario is used as a
+case study to investigate the effectiveness and validity of our approach.
+",1,0,0,0,0,0
+7205,Two-Party Function Computation on the Reconciled Data,"  In this paper, we initiate a study of a new problem termed function
+computation on the reconciled data, which generalizes a set reconciliation
+problem in the literature. Assume a distributed data storage system with two
+users $A$ and $B$. The users possess a collection of binary vectors $S_{A}$ and
+$S_{B}$, respectively. They are interested in computing a function $\phi$ of
+the reconciled data $S_{A} \cup S_{B}$.
+It is shown that any deterministic protocol, which computes a sum and a
+product of reconciled sets of binary vectors represented as nonnegative
+integers, has to communicate at least $2^n + n - 1$ and $2^n + n - 2$ bits in
+the worst-case scenario, respectively, where $n$ is the length of the binary
+vectors. Connections to other problems in computer science, such as set
+disjointness and finding the intersection, are established, yielding a variety
+of additional upper and lower bounds on the communication complexity. A
+protocol for computation of a sum function, which is based on use of a family
+of hash functions, is presented, and its characteristics are analyzed.
+",1,0,0,0,0,0
+7206,On Calabi-Yau compactifications of toric Landau-Ginzburg models for Fano complete intersections,"  Toric Landau--Ginzburg models of Givental's type for Fano complete
+intersections are known to have Calabi--Yau compactifications. We give an
+alternative proof of this fact. As an output of our proof we get a description
+of fibers over infinity for compactified toric Landau--Ginzburg models.
+",0,0,1,0,0,0
+7207,Halo assembly bias and the tidal anisotropy of the local halo environment,"  We study the role of the local tidal environment in determining the assembly
+bias of dark matter haloes. Previous results suggest that the anisotropy of a
+halo's environment (i.e, whether it lies in a filament or in a more isotropic
+region) can play a significant role in determining the eventual mass and age of
+the halo. We statistically isolate this effect using correlations between the
+large-scale and small-scale environments of simulated haloes at $z=0$ with
+masses between $10^{11.6}\lesssim (m/h^{-1}M_{\odot})\lesssim10^{14.9}$. We
+probe the large-scale environment using a novel halo-by-halo estimator of
+linear bias. For the small-scale environment, we identify a variable $\alpha_R$
+that captures the $\textit{tidal anisotropy}$ in a region of radius
+$R=4R_{\textrm{200b}}$ around the halo and correlates strongly with halo bias
+at fixed mass. Segregating haloes by $\alpha_R$ reveals two distinct
+populations. Haloes in highly isotropic local environments
+($\alpha_R\lesssim0.2$) behave as expected from the simplest, spherically
+averaged analytical models of structure formation, showing a
+$\textit{negative}$ correlation between their concentration and large-scale
+bias at $\textit{all}$ masses. In contrast, haloes in anisotropic,
+filament-like environments ($\alpha_R\gtrsim0.5$) tend to show a
+$\textit{positive}$ correlation between bias and concentration at any mass. Our
+multi-scale analysis cleanly demonstrates how the overall assembly bias trend
+across halo mass emerges as an average over these different halo populations,
+and provides valuable insights towards building analytical models that
+correctly incorporate assembly bias. We also discuss potential implications for
+the nature and detectability of galaxy assembly bias.
+",0,1,0,0,0,0
+7208,Towards a general theory for non-linear locally stationary processes,"  In this paper some general theory is presented for locally stationary
+processes based on the stationary approximation and the stationary derivative.
+Laws of large numbers, central limit theorems as well as deterministic and
+stochastic bias expansions are proved for processes obeying an expansion in
+terms of the stationary approximation and derivative. In addition it is shown
+that this applies to some general nonlinear non-stationary Markov-models. In
+addition the results are applied to derive the asymptotic properties of maximum
+likelihood estimates of parameter curves in such models.
+",0,0,1,1,0,0
+7209,Self-Learning Monte Carlo Method: Continuous-Time Algorithm,"  The recently-introduced self-learning Monte Carlo method is a general-purpose
+numerical method that speeds up Monte Carlo simulations by training an
+effective model to propose uncorrelated configurations in the Markov chain. We
+implement this method in the framework of continuous time Monte Carlo method
+with auxiliary field in quantum impurity models. We introduce and train a
+diagram generating function (DGF) to model the probability distribution of
+auxiliary field configurations in continuous imaginary time, at all orders of
+diagrammatic expansion. By using DGF to propose global moves in configuration
+space, we show that the self-learning continuous-time Monte Carlo method can
+significantly reduce the computational complexity of the simulation.
+",0,1,0,0,0,0
+7210,Single-cell diffraction tomography with optofluidic rotation about a tilted axis,"  Optical diffraction tomography (ODT) is a tomographic technique that can be
+used to measure the three-dimensional (3D) refractive index distribution within
+living cells without the requirement of any marker. In principle, ODT can be
+regarded as a generalization of optical projection tomography which is
+equivalent to computerized tomography (CT). Both optical tomographic techniques
+require projection-phase images of cells measured at multiple angles. However,
+the reconstruction of the 3D refractive index distribution post-measurement
+differs for the two techniques. It is known that ODT yields better results than
+projection tomography, because it takes into account diffraction of the imaging
+light due to the refractive index structure of the sample. Here, we apply ODT
+to biological cells in a microfluidic chip which combines optical trapping and
+microfluidic flow to achieve an optofluidic single-cell rotation. In
+particular, we address the problem that arises when the trapped cell is not
+rotating about an axis perpendicular to the imaging plane, but instead about an
+arbitrarily tilted axis. In this paper we show that the 3D reconstruction can
+be improved by taking into account such a tilted rotational axis in the
+reconstruction process.
+",0,0,0,0,1,0
+7211,Non-Linear Least-Squares Optimization of Rational Filters for the Solution of Interior Eigenvalue Problems,"  Rational filter functions can be used to improve convergence of contour-based
+eigensolvers, a popular family of algorithms for the solution of the interior
+eigenvalue problem. We present a framework for the optimization of rational
+filters based on a non-convex weighted Least-Squares scheme. When used in
+combination with the FEAST library, our filters out-perform existing ones on a
+large and representative set of benchmark problems. This work provides a
+detailed description of: (1) a set up of the optimization process that exploits
+symmetries of the filter function for Hermitian eigenproblems, (2) a
+formulation of the gradient descent and Levenberg-Marquardt algorithms that
+exploits the symmetries, (3) a method to select the starting position for the
+optimization algorithms that reliably produces effective filters, (4) a
+constrained optimization scheme that produces filter functions with specific
+properties that may be beneficial to the performance of the eigensolver that
+employs them.
+",1,0,0,0,0,0
+7212,Maps on statistical manifolds exactly reduced from the Perron-Frobenius equations for solvable chaotic maps,"  Maps on a parameter space for expressing distribution functions are exactly
+derived from the Perron-Frobenius equations for a generalized Boole transform
+family. Here the generalized Boole transform family is a one-parameter family
+of maps where it is defined on a subset of the real line and its probability
+distribution function is the Cauchy distribution with some parameters. With
+this reduction, some relations between the statistical picture and the orbital
+one are shown. From the viewpoint of information geometry, the parameter space
+can be identified with a statistical manifold, and then it is shown that the
+derived maps can be characterized. Also, with an induced symplectic structure
+from a statistical structure, symplectic and information geometric aspects of
+the derived maps are discussed.
+",0,1,0,0,0,0
+7213,Discontinuous classical ground state magnetic response as an even-odd effect in higher order rotationally invariant exchange interactions,"  The classical ground state magnetic response of the Heisenberg model when
+rotationally invariant exchange interactions of integer order q>1 are added is
+found to be discontinuous, even though the interactions lack magnetic
+anisotropy. This holds even in the case of bipartite lattices which are not
+frustrated, as well as for the frustrated triangular lattice. The total number
+of discontinuities is associated with even-odd effects as it depends on the
+parity of q via the relative strength of the bilinear and higher order exchange
+interactions, and increases with q. These results demonstrate that the precise
+form of the microscopic interactions is important for the ground state
+magnetization response.
+",0,1,0,0,0,0
+7214,Sparse Matrix Multiplication On An Associative Processor,"  Sparse matrix multiplication is an important component of linear algebra
+computations. Implementing sparse matrix multiplication on an associative
+processor (AP) enables high level of parallelism, where a row of one matrix is
+multiplied in parallel with the entire second matrix, and where the execution
+time of vector dot product does not depend on the vector size. Four sparse
+matrix multiplication algorithms are explored in this paper, combining AP and
+baseline CPU processing to various levels. They are evaluated by simulation on
+a large set of sparse matrices. The computational complexity of sparse matrix
+multiplication on AP is shown to be an O(nnz) where nnz is the number of
+nonzero elements. The AP is found to be especially efficient in binary sparse
+matrix multiplication. AP outperforms conventional solutions in power
+efficiency.
+",1,0,0,0,0,0
+7215,On the intersection of tame subgroups in groups acting on trees,"  Let $G$ be a group acting on a tree $T$ with finite edge stabilizers of
+bounded order. We provide, in some very interesting cases, upper bounds for the
+complexity of the intersection $H\cap K$ of two tame subgroups $H$ and $K$ of
+$G$ in terms of the complexities of $H$ and $K$. In particular, we obtain
+bounds for the Kurosh rank $Kr(H\cap K)$ of the intersection in terms of Kurosh
+ranks $Kr(H)$ and $Kr(K)$, in the case where $H$ and $K$ act freely on the
+edges of $T$.
+",0,0,1,0,0,0
+7216,A Lagrangian scheme for the solution of nonlinear diffusion equations using moving simplex meshes,"  A Lagrangian numerical scheme for solving nonlinear degenerate Fokker-Planck
+equations in space dimensions $d\ge2$ is presented. It applies to a large class
+of nonlinear diffusion equations, whose dynamics are driven by internal
+energies and given external potentials, e.g. the porous medium equation and the
+fast diffusion equation. The key ingredient in our approach is the gradient
+flow structure of the dynamics. For discretization of the Lagrangian map, we
+use a finite subspace of linear maps in space and a variational form of the
+implicit Euler method in time. Thanks to that time discretisation, the fully
+discrete solution inherits energy estimates from the original gradient flow,
+and these lead to weak compactness of the trajectories in the continuous limit.
+Consistency is analyzed in the planar situation, $d=2$. A variety of numerical
+experiments for the porous medium equation indicates that the scheme is
+well-adapted to track the growth of the solution's support.
+",0,0,1,0,0,0
+7217,Optimal Control for Multi-Mode Systems with Discrete Costs,"  This paper studies optimal time-bounded control in multi-mode systems with
+discrete costs. Multi-mode systems are an important subclass of linear hybrid
+systems, in which there are no guards on transitions and all invariants are
+global. Each state has a continuous cost attached to it, which is linear in the
+sojourn time, while a discrete cost is attached to each transition taken. We
+show that an optimal control for this model can be computed in NEXPTIME and
+approximated in PSPACE. We also show that the one-dimensional case is simpler:
+although the problem is NP-complete (and in LOGSPACE for an infinite time
+horizon), we develop an FPTAS for finding an approximate solution.
+",1,0,0,0,0,0
+7218,Digital Advertising Traffic Operation: Flow Management Analysis,"  In a Web Advertising Traffic Operation the Trafficking Routing Problem (TRP)
+consists in scheduling the management of Web Advertising (Adv) campaign between
+Trafficking campaigns in the most efficient way to oversee and manage
+relationship with partners and internal teams, managing expectations through
+integration and post-launch in order to ensure success for every stakeholders
+involved. For our own interest we did that independent research projects also
+through specific innovative tasks validate towards average working time
+declared on ""specification required"" by the main worldwide industry leading
+Advertising Agency. We present a Mixed Integer Linear Programming (MILP)
+formulation for end-to-end management of campaign workflow along a
+predetermined path and generalize it to include alternative path to oversee and
+manage detail-oriented relationship with partners and internal teams to achieve
+the goals above mentioned. To meet clients' KPIs, we consider an objective
+function that includes the punctuality indicators (the average waiting time and
+completion times) but also the main punctuality indicators (the average delay
+and the on time performance). Then we investigate their analytical
+relationships in the advertising domain through experiments based on real data
+from a Traffic Office. We show that the classic punctuality indicators are in
+contradiction with the task of reducing waiting times. We propose new
+indicators used for a synthesize analysis on projects or process changes for
+the wider team that are more sustainable, but also more relevant for
+stakeholders. We also show that the flow of a campaign (adv-ways) is the main
+bottleneck of a Traffic Office and that alternate paths cannot improve the
+performance indicators.
+",1,0,1,0,0,0
+7219,Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates,"  Randomizing the Fourier-transform (FT) phases of temporal-spatial data
+generates surrogates that approximate examples from the data-generating
+distribution. We propose such FT surrogates as a novel tool to augment and
+analyze training of neural networks and explore the approach in the example of
+sleep-stage classification. By computing FT surrogates of raw EEG, EOG, and EMG
+signals of under-represented sleep stages, we balanced the CAPSLPDB sleep
+database. We then trained and tested a convolutional neural network for sleep
+stage classification, and found that our surrogate-based augmentation improved
+the mean F1-score by 7%. As another application of FT surrogates, we formulated
+an approach to compute saliency maps for individual sleep epochs. The
+visualization is based on the response of inferred class probabilities under
+replacement of short data segments by partial surrogates. To quantify how well
+the distributions of the surrogates and the original data match, we evaluated a
+trained classifier on surrogates of correctly classified examples, and
+summarized these conditional predictions in a confusion matrix. We show how
+such conditional confusion matrices can qualitatively explain the performance
+of surrogates in class balancing. The FT-surrogate augmentation approach may
+improve classification on noisy signals if carefully adapted to the data
+distribution under analysis.
+",0,0,0,1,1,0
+7220,Generating global network structures by triad types,"  This paper addresses the question of whether it is possible to generate
+networks with a given global structure (defined by selected blockmodels, i.e.,
+cohesive, core-periphery, hierarchical and transitivity), considering only
+different types of triads. Two methods are used to generate networks: (i) the
+method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm
+implemented in the ""ergm"" package implemented in R. Although all types of
+triads can generate networks with the selected blockmodel types, the selection
+of only a subset of triads improves the generated networks' blockmodel
+structure. However, in the case of a hierarchical blockmodel without complete
+blocks on the diagonal, additional local structures are needed to achieve the
+desired global structure of generated networks. This shows that blockmodels can
+emerge based on only local processes that do not take attributes into account.
+",0,0,1,1,0,0
+7221,Simultaneous smoothness and simultaneous stability of a $C^\infty$ strictly convex integrand and its dual,"  In this paper, we investigate simultaneous properties of a convex integrand
+$\gamma$ and its dual $\delta$. The main results are the following three.
+(1) For a $C^\infty$ convex integrand $\gamma: S^n\to \mathbb{R}_+$, its dual
+convex integrand $\delta: S^n\to \mathbb{R}_+$ is of class $C^\infty$ if and
+only if $\gamma$ is a strictly convex integrand.
+(2) Let $\gamma: S^n\to \mathbb{R}_+$ be a $C^\infty$ strictly convex
+integrand. Then, $\gamma$ is stable if and only if its dual convex integrand
+$\delta: S^n\to \mathbb{R}_+$ is stable.
+(3) Let $\gamma: S^n\to \mathbb{R}_+$ be a $C^\infty$ strictly convex
+integrand. Suppose that $\gamma$ is stable. Then, for any $i$ $(0\le i\le n)$,
+a point $\theta_0\in S^n$ is a non-degenerate critical point of $\gamma$ with
+Morse index $i$ if and only if its antipodal point $-\theta_0\in S^n$ is a
+non-degenerate critical point of the dual convex integrand $\delta$ with Morse
+index $(n-i)$.
+",0,0,1,0,0,0
+7222,Adaptive quadrature by expansion for layer potential evaluation in two dimensions,"  When solving partial differential equations using boundary integral equation
+methods, accurate evaluation of singular and nearly singular integrals in layer
+potentials is crucial. A recent scheme for this is quadrature by expansion
+(QBX), which solves the problem by locally approximating the potential using a
+local expansion centered at some distance from the source boundary. In this
+paper we introduce an extension of the QBX scheme in 2D denoted AQBX - adaptive
+quadrature by expansion - which combines QBX with an algorithm for automated
+selection of parameters, based on a target error tolerance. A key component in
+this algorithm is the ability to accurately estimate the numerical errors in
+the coefficients of the expansion. Combining previous results for flat panels
+with a procedure for taking the panel shape into account, we derive such error
+estimates for arbitrarily shaped boundaries in 2D that are discretized using
+panel-based Gauss-Legendre quadrature. Applying our scheme to numerical
+solutions of Dirichlet problems for the Laplace and Helmholtz equations, and
+also for solving these equations, we find that the scheme is able to satisfy a
+given target tolerance to within an order of magnitude, making it useful for
+practical applications. This represents a significant simplification over the
+original QBX algorithm, in which choosing a good set of parameters can be hard.
+",0,0,1,0,0,0
+7223,Anomalous transport effects on switching currents of graphene-based Josephson junctions,"  We explore the effect of noise on the ballistic graphene-based small
+Josephson junctions in the framework of the resistively and capacitively
+shunted model. We use the non-sinusoidal current-phase relation specific for
+graphene layers partially covered by superconducting electrodes. The noise
+induced escapes from the metastable states, when the external bias current is
+ramped, give the switching current distribution, i.e. the probability
+distribution of the passages to finite voltage from the superconducting state
+as a function of the bias current, that is the information more promptly
+available in the experiments. We consider a noise source that is a mixture of
+two different types of processes: a Gaussian contribution to simulate an
+uncorrelated ordinary thermal bath, and non-Gaussian, $\alpha$-stable (or
+Lévy) term, generally associated to non-equilibrium transport phenomena. We
+find that the analysis of the switching current distribution makes it possible
+to efficiently detect a non-Gaussian noise component in a Gaussian background.
+",0,1,0,0,0,0
+7224,Fixed-Parameter Tractable Sampling for RNA Design with Multiple Target Structures,"  The design of multi-stable RNA molecules has important applications in
+biology, medicine, and biotechnology. Synthetic design approaches profit
+strongly from effective in-silico methods, which can tremendously impact their
+cost and feasibility. We revisit a central ingredient of most in-silico design
+methods: the sampling of sequences for the design of multi-target structures,
+possibly including pseudoknots. For this task, we present the efficient, tree
+decomposition-based algorithm. Our fixed parameter tractable approach is
+underpinned by establishing the P-hardness of uniform sampling. Modeling the
+problem as a constraint network, our program supports generic
+Boltzmann-weighted sampling for arbitrary additive RNA energy models; this
+enables the generation of RNA sequences meeting specific goals like expected
+free energies or \GCb-content. Finally, we empirically study general properties
+of the approach and generate biologically relevant multi-target
+Boltzmann-weighted designs for a common design benchmark. Generating seed
+sequences with our program, we demonstrate significant improvements over the
+previously best multi-target sampling strategy (uniform sampling).Our software
+is freely available at: this https URL .
+",0,0,0,0,1,0
+7225,On the relevance of generalized disclinations in defect mechanics,"  The utility of the notion of generalized disclinations in materials science
+is discussed within the physical context of modeling interfacial and bulk line
+defects like defected grain and phase boundaries, dislocations and
+disclinations. The Burgers vector of a disclination dipole in linear elasticity
+is derived, clearly demonstrating the equivalence of its stress field to that
+of an edge dislocation. We also prove that the inverse deformation/displacement
+jump of a defect line is independent of the cut-surface when its g.disclination
+strength vanishes. An explicit formula for the displacement jump of a single
+localized composite defect line in terms of given g.disclination and
+dislocation strengths is deduced based on the Weingarten theorem for
+g.disclination theory (Weingarten-gd theorem) at finite deformation. The
+Burgers vector of a g.disclination dipole at finite deformation is also
+derived.
+",0,1,0,0,0,0
+7226,PbTe(111) Sub-Thermionic Photocathode: A Route to High-Quality Electron Pulses,"  The emission properties of PbTe(111) single crystal have been extensively
+investigated to demonstrate that PbTe(111) is a promising low root mean square
+transverse momentum ({\Delta}p$_T$) and high brightness photocathode. The
+density functional theory (DFT) based photoemission analysis successfully
+elucidates that the 'hole-like' {\Lambda}$^+_6$ energy band in the $L$ valley
+with low effective mass $m^*$ results in low {\Delta}p$_T$. Especially, as a
+300K solid planar photocathode, Te-terminated PbTe(111) single crystal is
+expected to be a potential 50K electron source.
+",0,1,0,0,0,0
+7227,Envy-free Matchings with Lower Quotas,"  While every instance of the Hospitals/Residents problem admits a stable
+matching, the problem with lower quotas (HR-LQ) has instances with no stable
+matching. For such an instance, we expect the existence of an envy-free
+matching, which is a relaxation of a stable matching preserving a kind of
+fairness property. In this paper, we investigate the existence of an envy-free
+matching in several settings, in which hospitals have lower quotas and not all
+doctor-hospital pairs are acceptable. We first show that, for an HR-LQ
+instance, we can efficiently decide the existence of an envy-free matching.
+Then, we consider envy-freeness in the Classified Stable Matching model due to
+Huang (2010), i.e., each hospital has lower and upper quotas on subsets of
+doctors. We show that, for this model, deciding the existence of an envy-free
+matching is NP-hard in general, but solvable in polynomial time if quotas are
+paramodular.
+",1,0,0,0,0,0
+7228,Characterizing Feshbach resonances in ultracold scattering calculations,"  We describe procedures for converging on and characterizing zero-energy
+Feshbach resonances that appear in scattering lengths as a function of an
+external field. The elastic procedure is appropriate for purely elastic
+scattering, where the scattering length is real and displays a true pole. The
+regularized scattering length (RSL) procedure is appropriate when there is weak
+background inelasticity, so that the scattering length is complex and displays
+an oscillation rather than a pole, but the resonant scattering length $a_{\rm
+res}$ is close to real. The fully complex procedure is appropriate when there
+is substantial background inelasticity and the real and complex parts of
+$a_{\rm res}$ are required. We demonstrate these procedures for scattering of
+ultracold $^{85}$Rb in various initial states. All of them can converge on and
+provide full characterization of resonances, from initial guesses many
+thousands of widths away, using scattering calculations at only about 10 values
+of the external field.
+",0,1,0,0,0,0
+7229,Interacting Multi-particle Classical Szilard Engine,"  Szilard engine(SZE) is one of the best example of how information can be used
+to extract work from a system. Initially, the working substance of SZE was
+considered to be a single particle. Later on, researchers has extended the
+studies of SZE to multi-particle systems and even to quantum regime. Here we
+present a detailed study of classical SZE consisting of $N$ particles with
+inter-particle interactions, i.e., the working substance is a low density
+non-ideal gas and compare the work extraction with respect to SZE with
+non-interacting multi particle system as working substance. We have considered
+two cases of interactions namely: (i) hard core interactions and (ii) square
+well interaction. Our study reveals that work extraction is less when more
+particles are interacting through hard core interactions. More work is
+extracted when the particles are interacting via square well interaction.
+Another important result for the second case is that as we increase the
+particle number the work extraction becomes independent of the initial position
+of the partition, as opposed to the first case. Work extraction depends
+crucially on the initial position of the partition. More work can be extracted
+with larger number of particles when partition is inserted at positions near
+the boundary walls.
+",0,1,0,0,0,0
+7230,Speeding-up Object Detection Training for Robotics with FALKON,"  Latest deep learning methods for object detection provide remarkable
+performance, but have limits when used in robotic applications. One of the most
+relevant issues is the long training time, which is due to the large size and
+imbalance of the associated training sets, characterized by few positive and a
+large number of negative examples (i.e. background). Proposed approaches are
+based on end-to-end learning by back-propagation [22] or kernel methods trained
+with Hard Negatives Mining on top of deep features [8]. These solutions are
+effective, but prohibitively slow for on-line applications. In this paper we
+propose a novel pipeline for object detection that overcomes this problem and
+provides comparable performance, with a 60x training speedup. Our pipeline
+combines (i) the Region Proposal Network and the deep feature extractor from
+[22] to efficiently select candidate RoIs and encode them into powerful
+representations, with (ii) the FALKON [23] algorithm, a novel kernel-based
+method that allows fast training on large scale problems (millions of points).
+We address the size and imbalance of training data by exploiting the stochastic
+subsampling intrinsic into the method and a novel, fast, bootstrapping
+approach. We assess the effectiveness of the approach on a standard Computer
+Vision dataset (PASCAL VOC 2007 [5]) and demonstrate its applicability to a
+real robotic scenario with the iCubWorld Transformations [18] dataset.
+",1,0,0,0,0,0
+7231,Graph of Virtual Actors (GOVA): a Big Data Analytics Architecture for IoT,"  With the emergence of cloud computing and sensor technologies, Big Data
+analytics for the Internet of Things (IoT) has become the main force behind
+many innovative solutions for our society's problems. This paper provides
+practical explanations for the question ""why is the number of Big Data
+applications that succeed and have an effect on our daily life so limited,
+compared with all of the solutions proposed and tested in the literature?"",
+with examples taken from Smart Grids. We argue that ""noninvariants"" are the
+most challenging issues in IoT applications, which can be easily revealed if we
+use the term ""invariant"" to replace the more common terms such as
+""information"", ""knowledge"", or ""insight"" in any Big Data for IoT research. From
+our experience with developing Smart Grid applications, we produced a list of
+""noninvariants"", which we believe to be the main causes of the gaps between Big
+Data in a laboratory and in practice in IoT applications. This paper also
+proposes Graph of Virtual Actors (GOVA) as a Big Data analytics architecture
+for IoT applications, which not only can solve the noninvariants issues, but
+can also quickly scale horizontally in terms of computation, data storage,
+caching requirements, and programmability of the system.
+",1,0,0,0,0,0
+7232,A Variation of the $q$-Painlevé System with Affine Weyl Group Symmetry of Type $E_7^{(1)}$,"  Recently a certain $q$-Painlevé type system has been obtained from a
+reduction of the $q$-Garnier system. In this paper it is shown that the
+$q$-Painlevé type system is associated with another realization of the affine
+Weyl group symmetry of type $E_7^{(1)}$ and is different from the well-known
+$q$-Painlevé system of type $E_7^{(1)}$ from the point of view of evolution
+directions. We also study a connection between the $q$-Painlevé type system
+and the $q$-Painlevé system of type $E_7^{(1)}$. Furthermore determinant
+formulas of particular solutions for the $q$-Painlevé type system are
+constructed in terms of the terminating $q$-hypergeometric function.
+",0,1,1,0,0,0
+7233,Statics and dynamics of a self-bound dipolar matter-wave droplet,"  We study the statics and dynamics of a stable, mobile, self-bound
+three-dimensional dipolar matter-wave droplet created in the presence of a tiny
+repulsive three-body interaction. In frontal collision with an impact parameter
+and in angular collision at large velocities {along all directions} two
+droplets behave like quantum solitons. Such collision is found to be quasi
+elastic and the droplets emerge undeformed after collision without any change
+of velocity. However, in a collision at small velocities the axisymmeric
+dipolar interaction plays a significant role and the collision dynamics is
+sensitive to the direction of motion. For an encounter along the $z$ direction
+at small velocities, two droplets, polarized along the $z$ direction, coalesce
+to form a larger droplet $-$ a droplet molecule. For an encounter along the $x$
+direction at small velocities, the same droplets stay apart and never meet each
+other due to the dipolar repulsion. The present study is based on an analytic
+variational approximation and a numerical solution of the mean-field
+Gross-Pitaevskii equation using the parameters of $^{52}$Cr atoms.
+",0,1,0,0,0,0
+7234,Riemannian geometry in infinite dimensional spaces,"  We lay foundations of the subject in the title, on which we build in another
+paper devoted to isometries in spaces of Kähler metrics.
+",0,0,1,0,0,0
+7235,"Finite Blaschke products with prescribed critical points, Stieltjes polynomials, and moment problems","  The determination of a finite Blaschke product from its critical points is a
+well-known problem with interrelations to other topics. Though existence and
+uniqueness of solutions are established for long, we present several new
+aspects which have not yet been explored to their full extent. In particular,
+we show that the following three problems are equivalent: (i) determining a
+finite Blaschke product from its critical points, (ii) finding the equilibrium
+position of moveable point charges interacting with a special configuration of
+fixed charges, (iii) solving a moment problem for the canonical representation
+of power moments on the real axis. These equivalences are not only of
+theoretical interest, but also open up new perspectives for the design of
+algorithms. For instance, the second problem is closely linked to the
+determination of certain Stieltjes and Van Vleck polynomials for a second order
+ODE and allows the description of solutions as global minimizers of an energy
+functional.
+",0,0,1,0,0,0
+7236,The Stochastic Processes Generation in OpenModelica,"  Background: Component-based modeling language Modelica (OpenModelica is open
+source implementation) is used for the numerical simulation of complex
+processes of different nature represented by ODE system. However, in
+OpenModelica standard library there is no routines for pseudo-random numbers
+generation, which makes it impossible to use for stochastic modeling processes.
+Purpose: The goal of this article is a brief overview of a number of algorithms
+for generation a sequence of uniformly distributed pseudo random numbers and
+quality assessment of the sequence given by them, as well as the ways to
+implement some of these algorithms in OpenModelica system. Methods: All the
+algorithms are implemented in C language, and the results of their work tested
+using open source package DieHarder. For those algorithms that do not use bit
+operations, we describe there realisation using OpwnModelica. The other
+algorithms can be called in OpenModelica as C functions Results: We have
+implemented and tested about nine algorithms. DieHarder testing revealed the
+highest quality pseudo-random number generators. Also we have reviewed
+libraries Noise and AdvancedNoise, who claim to be adding to the Modelica
+Standard Library. Conclusions: In OpenModelica system can be implemented
+generators of uniformly distributed pseudo-random numbers, which is the first
+step towards to make OpenModelica suitable for simulation of stochastic
+processes.
+",1,1,0,0,0,0
+7237,MIT SuperCloud Portal Workspace: Enabling HPC Web Application Deployment,"  The MIT SuperCloud Portal Workspace enables the secure exposure of web
+services running on high performance computing (HPC) systems. The portal allows
+users to run any web application as an HPC job and access it from their
+workstation while providing authentication, encryption, and access control at
+the system level to prevent unintended access. This capability permits users to
+seamlessly utilize existing and emerging tools that present their user
+interface as a website on an HPC system creating a portal workspace.
+Performance measurements indicate that the MIT SuperCloud Portal Workspace
+incurs marginal overhead when compared to a direct connection of the same
+service.
+",1,0,0,0,0,0
+7238,Estimator of Prediction Error Based on Approximate Message Passing for Penalized Linear Regression,"  We propose an estimator of prediction error using an approximate message
+passing (AMP) algorithm that can be applied to a broad range of sparse
+penalties. Following Stein's lemma, the estimator of the generalized degrees of
+freedom, which is a key quantity for the construction of the estimator of the
+prediction error, is calculated at the AMP fixed point. The resulting form of
+the AMP-based estimator does not depend on the penalty function, and its value
+can be further improved by considering the correlation between predictors. The
+proposed estimator is asymptotically unbiased when the components of the
+predictors and response variables are independently generated according to a
+Gaussian distribution. We examine the behaviour of the estimator for real data
+under nonconvex sparse penalties, where Akaike's information criterion does not
+correspond to an unbiased estimator of the prediction error. The model selected
+by the proposed estimator is close to that which minimizes the true prediction
+error.
+",0,0,0,1,0,0
+7239,Faster Multiplication for Long Binary Polynomials,"  We set new speed records for multiplying long polynomials over finite fields
+of characteristic two. Our multiplication algorithm is based on an additive FFT
+(Fast Fourier Transform) by Lin, Chung, and Huang in 2014 comparing to
+previously best results based on multiplicative FFTs. Both methods have similar
+complexity for arithmetic operations on underlying finite field; however, our
+implementation shows that the additive FFT has less overhead. For further
+optimization, we employ a tower field construction because the multipliers in
+the additive FFT naturally fall into small subfields, which leads to speed-ups
+using table-lookup instructions in modern CPUs. Benchmarks show that our method
+saves about $40 \%$ computing time when multiplying polynomials of $2^{28}$ and
+$2^{29}$ bits comparing to previous multiplicative FFT implementations.
+",1,0,1,0,0,0
+7240,Full-angle Negative Reflection with An Ultrathin Acoustic Gradient Metasurface: Floquet-Bloch Modes Perspective and Experimental Verification,"  Metasurface with gradient phase response offers new alternative for steering
+the propagation of waves. Conventional Snell's law has been revised by taking
+the contribution of local phase gradient into account. However, the requirement
+of momentum matching along the metasurface sets its nontrivial beam
+manipulation functionality within a limited-angle incidence. In this work, we
+theoretically and experimentally demonstrate that the acoustic gradient
+metasurface supports the negative reflection for full-angle incidence. The mode
+expansion theory is developed to help understand how the gradient metasurface
+tailors the incident beams, and the full-angle negative reflection occurs when
+the first negative order Floquet-Bloch mode dominates. The coiling-up space
+structures are utilized to build desired acoustic gradient metasurface and the
+full-angle negative reflections have been perfectly verified by experimental
+measurements. Our work offers the Floquet-Bloch modes perspective for
+qualitatively understanding the reflection behaviors of the acoustic gradient
+metasurface and enables a new degree of the acoustic wave manipulating.
+",0,1,0,0,0,0
+7241,Semigroup C*-algebras and toric varieties,"  Let S be a finitely generated subsemigroup of Z^2. We derive a general
+formula for the K-theory of the left regular C*-algebra for S.
+",0,0,1,0,0,0
+7242,"Error analysis for small-sample, high-variance data: Cautions for bootstrapping and Bayesian bootstrapping","  Recent advances in molecular simulations allow the direct evaluation of
+kinetic parameters such as rate constants for protein folding or unfolding.
+However, these calculations are usually computationally expensive and even
+significant computing resources may result in a small number of independent
+rate estimates spread over many orders of magnitude. Such small, high-variance
+samples are not readily amenable to analysis using the standard uncertainty
+(""standard error of the mean"") because unphysical negative limits of confidence
+intervals result. Bootstrapping, a natural alternative guaranteed to yield a
+confidence interval within the minimum and maximum values, also exhibits a
+striking systematic bias of the lower confidence limit. As we show,
+bootstrapping artifactually assigns high probability to improbably low mean
+values. A second alternative, the Bayesian bootstrap strategy, does not suffer
+from the same deficit and is more logically consistent with the type of
+confidence interval desired, but must be used with caution nevertheless.
+Neither standard nor Bayesian bootstrapping can overcome the intrinsic
+challenge of under-estimating the mean from small, high-variance samples. Our
+report is based on extensive re-analysis of multiple estimates for rate
+constants obtained from independent atomistic simulations. Although we only
+analyze rate constants, similar considerations may apply to other types of
+high-variance calculations, such as may occur in highly non-linear averages
+like the Jarzynski relation.
+",0,0,0,1,0,0
+7243,Computing Influence of a Product through Uncertain Reverse Skyline,"  Understanding the influence of a product is crucially important for making
+informed business decisions. This paper introduces a new type of skyline
+queries, called uncertain reverse skyline, for measuring the influence of a
+probabilistic product in uncertain data settings. More specifically, given a
+dataset of probabilistic products P and a set of customers C, an uncertain
+reverse skyline of a probabilistic product q retrieves all customers c in C
+which include q as one of their preferred products. We present efficient
+pruning ideas and techniques for processing the uncertain reverse skyline query
+of a probabilistic product using R-Tree data index. We also present an
+efficient parallel approach to compute the uncertain reverse skyline and
+influence score of a probabilistic product. Our approach significantly
+outperforms the baseline approach derived from the existing literature. The
+efficiency of our approach is demonstrated by conducting extensive experiments
+with both real and synthetic datasets.
+",1,0,0,0,0,0
+7244,Effect of Heterogeneity in Models of El-Niño Southern Oscillations,"  The emergence of oscillations in models of the El-Niño effect is of utmost
+relevance. Here we investigate a coupled nonlinear delay differential system
+modeling theEl-Niño/ Southern Oscillation (ENSO) phenomenon, which arises
+through the strong coupling of the ocean-atmosphere system. In particular, we
+study the temporal patterns of the sea surface temperature anomaly of the two
+sub-regions. For identical sub-regions we typically observe a co-existence of
+amplitude and oscillator death behavior for low delays, and heterogeneous
+oscillations for high delays, when inter-region coupling is weak. For moderate
+inter-region coupling strengths one obtains homogeneous oscillations for
+sufficiently large delays and amplitude death for small delays. When the
+inter-region coupling strength is large, oscillations are suppressed
+altogether, implying that strongly coupled sub-regions do not exhibit ENSO-like
+oscillations. Further we observe that larger strengths of self-delay coupling
+favours oscillations, while oscillations die out when the delayed coupling is
+weak. This indicates again that delayed feedback, incorporating oceanic wave
+transit effects, is the principal cause of oscillatory behaviour. So the effect
+of trapped ocean waves propagating in a basin with closed boundaries is crucial
+for the emergence of ENSO. Further, we show how non-uniformity in delays, and
+difference in the strengths of the self-delay coupling of the sub-regions,
+affect the rise of oscillations. Interestingly we find that larger delays and
+self-delay coupling strengths lead to oscillations, while strong inter-region
+coupling kills oscillatory behaviour. Thus, we find that coupling sub-regions
+has a very significant effect on the emergence of oscillations, and strong
+coupling typically suppresses oscillations, while weak coupling of
+non-identical sub-regions can induce oscillations, thereby favouring ENSO.
+",0,1,0,0,0,0
+7245,A branch-and-price approach with MILP formulation to modularity density maximization on graphs,"  For clustering of an undirected graph, this paper presents an exact algorithm
+for the maximization of modularity density, a more complicated criterion to
+overcome drawbacks of the well-known modularity. The problem can be interpreted
+as the set-partitioning problem, which reminds us of its integer linear
+programming (ILP) formulation. We provide a branch-and-price framework for
+solving this ILP, or column generation combined with branch-and-bound. Above
+all, we formulate the column generation subproblem to be solved repeatedly as a
+simpler mixed integer linear programming (MILP) problem. Acceleration
+techniques called the set-packing relaxation and the
+multiple-cutting-planes-at-a-time combined with the MILP formulation enable us
+to optimize the modularity density for famous test instances including ones
+with over 100 vertices in around four minutes by a PC. Our solution method is
+deterministic and the computation time is not affected by any stochastic
+behavior. For one of them, column generation at the root node of the
+branch-and-bound tree provides a fractional upper bound solution and our
+algorithm finds an integral optimal solution after branching.
+",1,0,1,0,0,0
+7246,Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). I. Automatic search for galaxy-scale strong lenses,"  The Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) is an excellent
+survey for the search for strong lenses, thanks to its area, image quality and
+depth. We use three different methods to look for lenses among 43,000 luminous
+red galaxies from the Baryon Oscillation Spectroscopic Survey (BOSS) sample
+with photometry from the S16A internal data release of the HSC SSP. The first
+method is a newly developed algorithm, named YATTALENS, which looks for
+arc-like features around massive galaxies and then estimates the likelihood of
+an object being a lens by performing a lens model fit. The second method,
+CHITAH, is a modeling-based algorithm originally developed to look for lensed
+quasars. The third method makes use of spectroscopic data to look for emission
+lines from objects at a different redshift from that of the main galaxy. We
+find 15 definite lenses, 36 highly probable lenses and 282 possible lenses.
+Among the three methods, YATTALENS, which was developed specifically for this
+problem, performs best in terms of both completeness and purity. Nevertheless
+five highly probable lenses were missed by YATTALENS but found by the other two
+methods, indicating that the three methods are highly complementary. Based on
+these numbers we expect to find $\sim$300 definite or probable lenses by the
+end of the HSC SSP.
+",0,1,0,0,0,0
+7247,Learning Sparse Neural Networks through $L_0$ Regularization,"  We propose a practical method for $L_0$ norm regularization for neural
+networks: pruning the network during training by encouraging weights to become
+exactly zero. Such regularization is interesting since (1) it can greatly speed
+up training and inference, and (2) it can improve generalization. AIC and BIC,
+well-known model selection criteria, are special cases of $L_0$ regularization.
+However, since the $L_0$ norm of weights is non-differentiable, we cannot
+incorporate it directly as a regularization term in the objective function. We
+propose a solution through the inclusion of a collection of non-negative
+stochastic gates, which collectively determine which weights to set to zero. We
+show that, somewhat surprisingly, for certain distributions over the gates, the
+expected $L_0$ norm of the resulting gated weights is differentiable with
+respect to the distribution parameters. We further propose the \emph{hard
+concrete} distribution for the gates, which is obtained by ""stretching"" a
+binary concrete distribution and then transforming its samples with a
+hard-sigmoid. The parameters of the distribution over the gates can then be
+jointly optimized with the original network parameters. As a result our method
+allows for straightforward and efficient learning of model structures with
+stochastic gradient descent and allows for conditional computation in a
+principled way. We perform various experiments to demonstrate the effectiveness
+of the resulting approach and regularizer.
+",1,0,0,1,0,0
+7248,The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations,"  The interactions between PM2.5 and meteorological factors play a crucial role
+in air pollution analysis. However, previous studies that have researched the
+relationships between PM2.5 concentration and meteorological conditions have
+been mainly confined to a certain city or district, and the correlation over
+the whole of China remains unclear. Whether or not spatial and seasonal
+variations exit deserves further research. In this study, the relationships
+between PM2.5 concentration and meteorological factors were investigated in 74
+major cities in China for a continuous period of 22 months from February 2013
+to November 2014, at season, year, city, and regional scales, and the spatial
+and seasonal variations were analyzed. The meteorological factors were relative
+humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS).
+We found that spatial and seasonal variations of their relationships with PM2.5
+do exist. Spatially, RH is positively correlated with PM2.5 concentration in
+North China and Urumqi, but the relationship turns to negative in other areas
+of China. WS is negatively correlated with PM2.5 everywhere expect for Hainan
+Island. PS has a strong positive relationship with PM2.5 concentration in
+Northeast China and Mid-south China, and in other areas the correlation is
+weak. Seasonally, the positive correlation between PM2.5 concentration and RH
+is stronger in winter and spring. TEM has a negative relationship with PM2.5 in
+autumn and the opposite in winter. PS is more positively correlated with PM2.5
+in autumn than in other seasons. Our study investigated the relationships
+between PM2.5 and meteorological factors in terms of spatial and seasonal
+variations, and the conclusions about the relationships between PM2.5 and
+meteorological factors are more comprehensive and precise than before.
+",0,1,0,0,0,0
+7249,Rank-two Milnor idempotents for the multipullback quantum complex projective plane,"  The $K_0$-group of the C*-algebra of multipullback quantum complex projective
+plane is known to be $\mathbb{Z}^3$, with one generator given by the C*-algebra
+itself, one given by the section module of the noncommutative (dual)
+tautological line bundle, and one given by the Milnor module associated to a
+generator of the $K_1$-group of the C*-algebra of Calow-Matthes quantum
+3-sphere. Herein we prove that these Milnor modules are isomorphic either to
+the section module of a noncommutative vector bundle associated to the
+$SU_q(2)$-prolongation of the Heegaard quantum 5-sphere $S^5_H$ viewed as a
+$U(1)$-quantum principal bundle, or to a complement of this module in the
+rank-four free module. Finally, we demonstrate that one of the above Milnor
+modules always splits into the direct sum of the rank-one free module and a
+rank-one non-free projective module that is \emph{not} associated with $S^5_H$.
+",0,0,1,0,0,0
+7250,Lost Relatives of the Gumbel Trick,"  The Gumbel trick is a method to sample from a discrete probability
+distribution, or to estimate its normalizing partition function. The method
+relies on repeatedly applying a random perturbation to the distribution in a
+particular way, each time solving for the most likely configuration. We derive
+an entire family of related methods, of which the Gumbel trick is one member,
+and show that the new methods have superior properties in several settings with
+minimal additional computational cost. In particular, for the Gumbel trick to
+yield computational benefits for discrete graphical models, Gumbel
+perturbations on all configurations are typically replaced with so-called
+low-rank perturbations. We show how a subfamily of our new methods adapts to
+this setting, proving new upper and lower bounds on the log partition function
+and deriving a family of sequential samplers for the Gibbs distribution.
+Finally, we balance the discussion by showing how the simpler analytical form
+of the Gumbel trick enables additional theoretical results.
+",1,0,0,1,0,0
+7251,Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization,"  This paper presents a sequential randomized lowrank matrix factorization
+approach for incrementally predicting values of an unknown function at test
+points using the Gaussian Processes framework. It is well-known that in the
+Gaussian processes framework, the computational bottlenecks are the inversion
+of the (regularized) kernel matrix and the computation of the hyper-parameters
+defining the kernel. The main contributions of this paper are two-fold. First,
+we formalize an approach to compute the inverse of the kernel matrix using
+randomized matrix factorization algorithms in a streaming scenario, i.e., data
+is generated incrementally over time. The metrics of accuracy and computational
+efficiency of the proposed method are compared against a batch approach based
+on use of randomized matrix factorization and an existing streaming approach
+based on approximating the Gaussian process by a finite set of basis vectors.
+Second, we extend the sequential factorization approach to a class of kernel
+functions for which the hyperparameters can be efficiently optimized. All
+results are demonstrated on two publicly available datasets.
+",1,0,0,1,0,0
+7252,Large-scale dynamos in rapidly rotating plane layer convection,"  Context: Convectively-driven flows play a crucial role in the dynamo
+processes that are responsible for producing magnetic activity in stars and
+planets. It is still not fully understood why many astrophysical magnetic
+fields have a significant large-scale component. Aims: Our aim is to
+investigate the dynamo properties of compressible convection in a rapidly
+rotating Cartesian domain, focusing upon a parameter regime in which the
+underlying hydrodynamic flow is known to be unstable to a large-scale vortex
+instability. Methods: The governing equations of three-dimensional nonlinear
+magnetohydrodynamics (MHD) are solved numerically. Different numerical schemes
+are compared and we propose a possible benchmark case for other similar codes.
+Results: In keeping with previous related studies, we find that convection in
+this parameter regime can drive a large-scale dynamo. The components of the
+mean horizontal magnetic field oscillate, leading to a continuous overall
+rotation of the mean field. Whilst the large-scale vortex instability dominates
+the early evolution of the system, it is suppressed by the magnetic field and
+makes a negligible contribution to the mean electromotive force that is
+responsible for driving the large-scale dynamo. The cycle period of the dynamo
+is comparable to the ohmic decay time, with longer cycles for dynamos in
+convective systems that are closer to onset. In these particular simulations,
+large-scale dynamo action is found only when vertical magnetic field boundary
+conditions are adopted at the upper and lower boundaries. Strongly modulated
+large-scale dynamos are found at higher Rayleigh numbers, with periods of
+reduced activity (""grand minima""-like events) occurring during transient phases
+in which the large-scale vortex temporarily re-establishes itself, before being
+suppressed again by the magnetic field.
+",0,1,0,0,0,0
+7253,Statistical inference for misspecified ergodic Lévy driven stochastic differential equation models,"  This paper deals with the estimation problem of misspecified ergodic Lévy
+driven stochastic differential equation models based on high-frequency samples.
+We utilize the widely applicable and tractable Gaussian quasi-likelihood
+approach which focuses on (conditional) mean and variance structure. It is
+shown that the corresponding Gaussian quasi-likelihood estimators of drift and
+scale parameters satisfy tail probability estimates and asymptotic normality at
+the same rate as correctly specified case. In this process, extended Poisson
+equation for time-homogeneous Feller Markov processes plays an important role
+to handle misspecification effect. Our result confirms the practical usefulness
+of the Gaussian quasi-likelihood approach for SDE models, more firmly.
+",0,0,1,1,0,0
+7254,Bar formation in the Milky Way type galaxies,"  Many barred galaxies, possibly including the Milky Way, have cusps in the
+centres. There is a widespread belief, however, that usual bar instability
+taking place in bulgeless galaxy models is impossible for the cuspy models,
+because of the presence of the inner Lindblad resonance for any pattern speed.
+At the same time there are numerical evidences that the bar instability can
+form a bar. We analyse this discrepancy, by accurate and diverse N-body
+simulations and using the calculation of normal modes. We show that bar
+formation in cuspy galaxies can be explained by taking into account the disc
+thickness. The exponential growth time is moderate for typical current disc
+masses (about 250 Myr), but considerably increases (factor 2 or more) upon
+substitution of the live halo and bulge with a rigid halo/bulge potential;
+meanwhile pattern speeds remain almost the same. Normal mode analysis with
+different disc mass favours a young bar hypothesis, according to which the bar
+instability saturated only recently.
+",0,1,0,0,0,0
+7255,TLR: Transfer Latent Representation for Unsupervised Domain Adaptation,"  Domain adaptation refers to the process of learning prediction models in a
+target domain by making use of data from a source domain. Many classic methods
+solve the domain adaptation problem by establishing a common latent space,
+which may cause the loss of many important properties across both domains. In
+this manuscript, we develop a novel method, transfer latent representation
+(TLR), to learn a better latent space. Specifically, we design an objective
+function based on a simple linear autoencoder to derive the latent
+representations of both domains. The encoder in the autoencoder aims to project
+the data of both domains into a robust latent space. Besides, the decoder
+imposes an additional constraint to reconstruct the original data, which can
+preserve the common properties of both domains and reduce the noise that causes
+domain shift. Experiments on cross-domain tasks demonstrate the advantages of
+TLR over competing methods.
+",0,0,0,1,0,0
+7256,Automated and Robust Quantification of Colocalization in Dual-Color Fluorescence Microscopy: A Nonparametric Statistical Approach,"  Colocalization is a powerful tool to study the interactions between
+fluorescently labeled molecules in biological fluorescence microscopy. However,
+existing techniques for colocalization analysis have not undergone continued
+development especially in regards to robust statistical support. In this paper,
+we examine two of the most popular quantification techniques for colocalization
+and argue that they could be improved upon using ideas from nonparametric
+statistics and scan statistics. In particular, we propose a new colocalization
+metric that is robust, easily implementable, and optimal in a rigorous
+statistical testing framework. Application to several benchmark datasets, as
+well as biological examples, further demonstrates the usefulness of the
+proposed technique.
+",0,0,0,1,0,0
+7257,On the MISO Channel with Feedback: Can Infinitely Massive Antennas Achieve Infinite Capacity?,"  We consider communication over a multiple-input single-output (MISO) block
+fading channel in the presence of an independent noiseless feedback link. We
+assume that the transmitter and receiver have no prior knowledge of the channel
+state realizations, but the transmitter and receiver can acquire the channel
+state information (CSIT/CSIR) via downlink training and feedback. For this
+channel, we show that increasing the number of transmit antennas to infinity
+will not achieve an infinite capacity, for a finite channel coherence length
+and a finite input constraint on the second or fourth moment. This insight
+follows from our new capacity bounds that hold for any linear and nonlinear
+coding strategies, and any channel training schemes. In addition to the channel
+capacity bounds, we also provide a characterization on the beamforming gain
+that is also known as array gain or power gain, at the regime with a large
+number of antennas.
+",1,0,0,0,0,0
+7258,Structural Analysis and Optimal Design of Distributed System Throttlers,"  In this paper, we investigate the performance analysis and synthesis of
+distributed system throttlers (DST). A throttler is a mechanism that limits the
+flow rate of incoming metrics, e.g., byte per second, network bandwidth usage,
+capacity, traffic, etc. This can be used to protect a service's backend/clients
+from getting overloaded, or to reduce the effects of uncertainties in demand
+for shared services. We study performance deterioration of DSTs subject to
+demand uncertainty. We then consider network synthesis problems that aim to
+improve the performance of noisy DSTs via communication link modifications as
+well as server update cycle modifications.
+",1,0,1,0,0,0
+7259,Subadditivity and additivity of the Yang-Mills action functional in Noncommutative Geometry,"  We formulate notions of subadditivity and additivity of the Yang-Mills action
+functional in noncommutative geometry. We identify a suitable hypothesis on
+spectral triples which proves that the Yang-Mills functional is always
+subadditive, as per expectation. The additivity property is much stronger in
+the sense that it implies the subadditivity property. Under this hypothesis we
+obtain a necessary and sufficient condition for the additivity of the
+Yang-Mills functional. An instance of additivity is shown for the case of
+noncommutative $n$-tori. We also investigate the behaviour of critical points
+of the Yang-Mills functional under additivity. At the end we discuss few
+examples involving compact spin manifolds, matrix algebras, noncommutative
+$n$-torus and the quantum Heisenberg manifolds which validate our hypothesis.
+",0,0,1,0,0,0
+7260,Sensivity of the Hermite rank,"  The Hermite rank appears in limit theorems involving long memory. We show
+that an Hermite rank higher than one is unstable when the data is slightly
+perturbed by transformations such as shift and scaling. We carry out a ""near
+higher order rank analysis"" to illustrate how the limit theorems are affected
+by a shift perturbation that is decreasing in size. As a byproduct of our
+analysis, we also prove the coincidence of the Hermite rank and the power rank
+in the Gaussian context. The paper is a technical companion of
+\citet{bai:taqqu:2017:instability} which discusses the instability of the
+Hermite rank in the statistical context. (Older title ""Some properties of the
+Hermite rank"">)
+",0,0,1,1,0,0
+7261,Specifying a positive threshold function via extremal points,"  An extremal point of a positive threshold Boolean function $f$ is either a
+maximal zero or a minimal one. It is known that if $f$ depends on all its
+variables, then the set of its extremal points completely specifies $f$ within
+the universe of threshold functions. However, in some cases, $f$ can be
+specified by a smaller set. The minimum number of points in such a set is the
+specification number of $f$. It was shown in [S.-T. Hu. Threshold Logic, 1965]
+that the specification number of a threshold function of $n$ variables is at
+least $n+1$. In [M. Anthony, G. Brightwell, and J. Shawe-Taylor. On specifying
+Boolean functions by labelled examples. Discrete Applied Mathematics, 1995] it
+was proved that this bound is attained for nested functions and conjectured
+that for all other threshold functions the specification number is strictly
+greater than $n+1$. In the present paper, we resolve this conjecture negatively
+by exhibiting threshold Boolean functions of $n$ variables, which are
+non-nested and for which the specification number is $n+1$. On the other hand,
+we show that the set of extremal points satisfies the statement of the
+conjecture, i.e., a positive threshold Boolean function depending on all its
+$n$ variables has $n+1$ extremal points if and only if it is nested. To prove
+this, we reveal an underlying structure of the set of extremal points.
+",1,0,0,0,0,0
+7262,Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach,"  Virtual screening (VS) is widely used during computational drug discovery to
+reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to
+predict new compound-protein interactions (CPIs) from known CPI network data
+using several methods, including machine learning and data mining. Although
+CGBVS facilitates highly efficient and accurate CPI prediction, it has poor
+performance for prediction of new compounds for which CPIs are unknown. The
+pairwise kernel method (PKM) is a state-of-the-art CGBVS method and shows high
+accuracy for prediction of new compounds. In this study, on the basis of link
+mining, we improved the PKM by combining link indicator kernel (LIK) and
+chemical similarity and evaluated the accuracy of these methods. The proposed
+method obtained an average area under the precision-recall curve (AUPR) value
+of 0.562, which was higher than that achieved by the conventional Gaussian
+interaction profile (GIP) method (0.425), and the calculation time was only
+increased by a few percent.
+",1,0,0,1,0,0
+7263,Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals,"  The increasing availability of ""big"" (large volume) social media data has
+motivated a great deal of research in applying sentiment analysis to predict
+the movement of prices within financial markets. Previous work in this field
+investigates how the true sentiment of text (i.e. positive or negative
+opinions) can be used for financial predictions, based on the assumption that
+sentiments expressed online are representative of the true market sentiment.
+Here we consider the converse idea, that using the stock price as the
+ground-truth in the system may be a better indication of sentiment. Tweets are
+labelled as Buy or Sell dependent on whether the stock price discussed rose or
+fell over the following hour, and from this, stock-specific dictionaries are
+built for individual companies. A Bayesian classifier is used to generate stock
+predictions, which are input to an automated trading algorithm. Placing 468
+trades over a 1 month period yields a return rate of 5.18%, which annualises to
+approximately 83% per annum. This approach performs significantly better than
+random chance and outperforms two baseline sentiment analysis methods tested.
+",0,0,0,0,0,1
+7264,A novel approach to fractional calculus: utilizing fractional integrals and derivatives of the Dirac delta function,"  While the definition of a fractional integral may be codified by Riemann and
+Liouville, an agreed-upon fractional derivative has eluded discovery for many
+years. This is likely a result of integral definitions including numerous
+constants of integration in their results. An elimination of constants of
+integration opens the door to an operator that reconciles all known fractional
+derivatives and shows surprising results in areas unobserved before, including
+the appearance of the Riemann Zeta Function and fractional Laplace and Fourier
+Transforms. A new class of functions, known as Zero Functions and closely
+related to the Dirac Delta Function, are necessary for one to perform
+elementary operations of functions without using constants. The operator also
+allows for a generalization of the Volterra integral equation, and provides a
+method of solving for Riemann's ""complimentary"" function introduced during his
+research on fractional derivatives.
+",0,0,1,0,0,0
+7265,When does every definable nonempty set have a definable element?,"  The assertion that every definable set has a definable element is equivalent
+over ZF to the principle $V=\text{HOD}$, and indeed, we prove, so is the
+assertion merely that every $\Pi_2$-definable set has an ordinal-definable
+element. Meanwhile, every model of ZFC has a forcing extension satisfying
+$V\neq\text{HOD}$ in which every $\Sigma_2$-definable set has an
+ordinal-definable element. Similar results hold for $\text{HOD}(\mathbb{R})$
+and $\text{HOD}(\text{Ord}^\omega)$ and other natural instances of
+$\text{HOD}(X)$.
+",0,0,1,0,0,0
+7266,An exploration to visualize finite element data with a DSL,"  The scientific community use PDEs to model a range of problems. The people in
+this domain are interested in visualizing their results, but existing
+mechanisms for visualization can not handle the full richness of computations
+in the domain. We did an exploration to see how Diderot, a domain specific
+language for scientific visualization and image analysis, could be used to
+solve this problem.
+We demonstrate our first and modest approach of visualizing FE data with
+Diderot and provide examples. Using Diderot, we do a simple sampling and a
+volume rendering of a FE field. These examples showcase Diderot's ability to
+provide a visualization result for Firedrake. This paper describes the
+extension of the Diderot language to include FE data.
+",1,0,0,0,0,0
+7267,Measurement and Analysis of Quality of Service of Mobile Networks in Afghanistan End User Perspective,"  Enhanced Quality of Service (QoS) and satisfaction of mobile phone user are
+major concerns of a service provider. In order to manage network efficiently
+and to provide enhanced end to end Quality of Experience (QoE), operator is
+expected to measure and analyze QoS from various perspectives and at different
+relevant points of network. The scope of this paper is measurement and
+statistically analysis of QoS of mobile networks from end user perspective in
+Afghanistan. The study is based on primary data collected on random basis from
+1,515 mobile phone users of five cellular operators. The paper furthermore
+proposes adequate technical solutions to mobile operators in order to address
+existing challenges in the area of QoS and to remain competitive in the market.
+Based on the result of processed data, considering geographical locations,
+population and telecom regulations of the government, authors recommend
+deployment of small cells (SCs), increasing number of regular performance
+tests, optimal placement of base stations, increasing number of carriers, and
+high order sectorization as proposed technical solutions.
+",1,0,0,0,0,0
+7268,On the uncertainty of temperature estimation in a rapid compression machine,"  Rapid compression machines (RCMs) have been widely used in the combustion
+literature to study the low-to-intermediate temperature ignition of many fuels.
+In a typical RCM, the pressure during and after the compression stroke is
+measured. However, measurement of the temperature history in the RCM reaction
+chamber is challenging. Thus, the temperature is generally calculated by the
+isentropic relations between pressure and temperature, assuming that the
+adiabatic core hypothesis holds. To estimate the uncertainty in the calculated
+temperature, an uncertainty propagation analysis must be carried out. Our
+previous analyses assumed that the uncertainties of the parameters in the
+equation to calculate the temperature were normally distributed and
+independent, but these assumptions do not hold for typical RCM operating
+procedures. In this work, a Monte Carlo method is developed to estimate the
+uncertainty in the calculated temperature, while taking into account the
+correlation between parameters and the possibility of non-normal probability
+distributions. In addition, the Monte Carlo method is compared to an analysis
+that assumes normally distributed, independent parameters. Both analysis
+methods show that the magnitude of the initial pressure and the uncertainty of
+the initial temperature have strong influences on the magnitude of the
+uncertainty. Finally, the uncertainty estimation methods studied here provide a
+reference value for the uncertainty of the reference temperature in an RCM and
+can be generalized to other similar facilities.
+",0,1,0,0,0,0
+7269,Abelian varieties isogenous to a power of an elliptic curve over a Galois extension,"  Given an elliptic curve $E/k$ and a Galois extension $k'/k$, we construct an
+exact functor from torsion-free modules over the endomorphism ring ${\rm
+End}(E_{k'})$ with a semilinear ${\rm Gal}(k'/k)$ action to abelian varieties
+over $k$ that are $k'$-isogenous to a power of $E$. As an application, we show
+that every elliptic curve with complex multiplication geometrically is
+isogenous over the ground field to one with complex multiplication by a maximal
+order.
+",0,0,1,0,0,0
+7270,Radiative nonrecoil nuclear finite size corrections of order $α(Z α)^5$ to the Lamb shift in light muonic atoms,"  On the basis of quasipotential method in quantum electrodynamics we calculate
+nuclear finite size radiative corrections of order $\alpha(Z \alpha)^5$ to the
+Lamb shift in muonic hydrogen and helium. To construct the interaction
+potential of particles, which gives the necessary contributions to the energy
+spectrum, we use the method of projection operators to states with a definite
+spin. Separate analytic expressions for the contributions of the muon
+self-energy, the muon vertex operator and the amplitude with spanning photon
+are obtained. We present also numerical results for these contributions using
+modern experimental data on the electromagnetic form factors of light nuclei.
+",0,1,0,0,0,0
+7271,Improved Training of Wasserstein GANs,"  Generative Adversarial Networks (GANs) are powerful generative models, but
+suffer from training instability. The recently proposed Wasserstein GAN (WGAN)
+makes progress toward stable training of GANs, but sometimes can still generate
+only low-quality samples or fail to converge. We find that these problems are
+often due to the use of weight clipping in WGAN to enforce a Lipschitz
+constraint on the critic, which can lead to undesired behavior. We propose an
+alternative to clipping weights: penalize the norm of gradient of the critic
+with respect to its input. Our proposed method performs better than standard
+WGAN and enables stable training of a wide variety of GAN architectures with
+almost no hyperparameter tuning, including 101-layer ResNets and language
+models over discrete data. We also achieve high quality generations on CIFAR-10
+and LSUN bedrooms.
+",1,0,0,1,0,0
+7272,A cancellation theorem for Milnor-Witt correspondences,"  We show that finite Milnor-Witt correspondences satisfy a cancellation
+theorem with respect to the pointed multiplicative group scheme. This has
+several notable applications in the theory of Milnor-Witt motives and
+Milnor-Witt motivic cohomology.
+",0,0,1,0,0,0
+7273,Wind Riemannian spaceforms and Randers metrics of constant flag curvature,"  Recently, wind Riemannian structures (WRS) have been introduced as a
+generalization of Randers and Kropina metrics. They are constructed from the
+natural data for Zermelo navigation problem, namely, a Riemannian metric $g_R$
+and a vector field $W$ (the wind), where, now, the restriction of mild wind
+$g_R(W,W)<1$ is dropped.
+Here, the models of WRS spaceforms of constant flag curvature are determined.
+Indeed, the celebrated classification of Randers metrics of constant flag
+curvature by Bao, Robles and Shen, extended to the Kropina case in the works by
+Yoshikawa, Okubo and Sabau, can be used to obtain the local classification. For
+the global one, a suitable result on completeness for WRS yields the complete
+simply connected models. In particular, any of the local models in the Randers
+classification does admit an extension to a unique model of wind Riemannian
+structure, even if it cannot be extended as a complete Finslerian manifold.
+Thus, WRS's emerge as the natural framework for the analysis of Randers
+spaceforms and, prospectively, wind Finslerian structures would become
+important for other global problems too. For the sake of completeness, a brief
+overview about WRS (including a useful link with the conformal geometry of a
+class of relativistic spacetimes) is also provided.
+",0,0,1,0,0,0
+7274,Developing a Purely Visual Based Obstacle Detection using Inverse Perspective Mapping,"  Our solution is implemented in and for the frame of Duckietown. The goal of
+Duckietown is to provide a relatively simple platform to explore, tackle and
+solve many problems linked to autonomous driving. ""Duckietown"" is simple in the
+basics, but an infinitely expandable environment. From controlling single
+driving Duckiebots until complete fleet management, every scenario is possible
+and can be put into practice. So far, none of the existing modules was capable
+of reliably detecting obstacles and reacting to them in real time. We faced the
+general problem of detecting obstacles given images from a monocular RGB camera
+mounted at the front of our Duckiebot and reacting to them properly without
+crashing or erroneously stopping the Duckiebot. Both, the detection as well as
+the reaction have to be implemented and have to run on a Raspberry Pi in real
+time. Due to the strong hardware limitations, we decided to not use any
+learning algorithms for the obstacle detection part. As it later transpired, a
+working ""hard coded"" software needs thorough analysis and understanding of the
+given problem. In layman's terms, we simply seek to make Duckietown a safer
+place.
+",1,0,0,0,0,0
+7275,Weak Keys and Cryptanalysis of a Cold War Block Cipher,"  T-310 is a cipher that was used for encryption of governmental communications
+in East Germany during the final years of the Cold War. Due to its complexity
+and the encryption process,there was no published attack for a period of more
+than 40 years until 2018 by Nicolas T. Courtois et al. in [10]. In this thesis
+we study the so called 'long term keys' that were used in the cipher, in order
+to expose weaknesses which will assist the design of various attacks on T-310.
+",1,0,0,0,0,0
+7276,Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling,"  Deep neural networks (DNNs) are powerful machine learning models and have
+succeeded in various artificial intelligence tasks. Although various
+architectures and modules for the DNNs have been proposed, selecting and
+designing the appropriate network structure for a target problem is a
+challenging task. In this paper, we propose a method to simultaneously optimize
+the network structure and weight parameters during neural network training. We
+consider a probability distribution that generates network structures, and
+optimize the parameters of the distribution instead of directly optimizing the
+network structure. The proposed method can apply to the various network
+structure optimization problems under the same framework. We apply the proposed
+method to several structure optimization problems such as selection of layers,
+selection of unit types, and selection of connections using the MNIST,
+CIFAR-10, and CIFAR-100 datasets. The experimental results show that the
+proposed method can find the appropriate and competitive network structures.
+",0,0,0,1,0,0
+7277,Spectroscopy of Ultra-diffuse Galaxies in the Coma Cluster,"  We present spectra of 5 ultra-diffuse galaxies (UDGs) in the vicinity of the
+Coma Cluster obtained with the Multi-Object Double Spectrograph on the Large
+Binocular Telescope. We confirm 4 of these as members of the cluster,
+quintupling the number of spectroscopically confirmed systems. Like the
+previously confirmed large (projected half light radius $>$ 4.6 kpc) UDG, DF44,
+the systems we targeted all have projected half light radii $> 2.9$ kpc. As
+such, we spectroscopically confirm a population of physically large UDGs in the
+Coma cluster. The remaining UDG is located in the field, about $45$ Mpc behind
+the cluster. We observe Balmer and Ca II H \& K absorption lines in all of our
+UDG spectra. By comparing the stacked UDG spectrum against stellar population
+synthesis models, we conclude that, on average, these UDGs are composed of
+metal-poor stars ([Fe/H] $\lesssim -1.5$). We also discover the first UDG with
+[OII] and [OIII] emission lines within a clustered environment, demonstrating
+that not all cluster UDGs are devoid of gas and sources of ionizing radiation.
+",0,1,0,0,0,0
+7278,On Robust Tie-line Scheduling in Multi-Area Power Systems,"  The tie-line scheduling problem in a multi-area power system seeks to
+optimize tie-line power flows across areas that are independently operated by
+different system operators (SOs). In this paper, we leverage the theory of
+multi-parametric linear programming to propose algorithms for optimal tie-line
+scheduling within a deterministic and a robust optimization framework. Through
+a coordinator, the proposed algorithms are proved to converge to the optimal
+schedule within a finite number of iterations. A key feature of the proposed
+algorithms, besides their finite step convergence, is the privacy of the
+information exchanges; the SO in an area does not need to reveal its dispatch
+cost structure, network constraints, or the nature of the uncertainty set to
+the coordinator. The performance of the algorithms is evaluated using several
+power system examples.
+",0,0,1,0,0,0
+7279,Novel Phases of Semi-Conducting Silicon Nitride Bilayer: A First-Principle Study,"  In this paper, we have predicted the stabilities of several two-dimensional
+phases of silicon nitride, which we name as \alpha-phase, \beta-phase, and
+\gamma-phase, respectively. Both \alpha- and \beta-phases has formula
+Si$_{2}$N$_{2}$, and are consisted of two similar layer of buckled SiN sheet.
+Similarly, \gamma-phase is consisted of two puckered SiN sheets. For these
+phases, the two layers are connected with Si-Si covalent bonds. Transformation
+between \alpha- and \beta-phases is difficult because of the high energy
+barrier. Phonon spectra of both \alpha- and \beta-phase suggest their
+thermodynamic stabilities, because no phonon mode with imaginary frequency is
+present. By Contrast, \gamma-phase is unstable because phonon modes with
+imaginary frequencies are found along \Gamma-Y path in the Brilliouin zone.
+Both \alpha- and \beta-phase are semiconductor with narrow fundamental indirect
+band gap of 1.7eV and 1.9eV, respectively. As expected, only s and p orbitals
+in the outermost shells contribute the band structures. The p$_{z}$ orbitals
+have greater contribution near the Fermi level. These materials can easily
+exfoliate to form 2D structures, and may have potential electronic
+applications.
+",0,1,0,0,0,0
+7280,Schwarz-Christoffel: piliero en rivero (a pillar on a river),"  La transformoj de Schwarz-Christoffel mapas, konforme, la superan kompleksan
+duon-ebenon al regiono limigita per rektaj segmentoj. Cxi tie ni priskribas
+kiel konvene kunigi mapon de la suba duon-ebeno al mapo de la supera
+duon-ebeno. Ni emfazas la bezonon de klara difino de angulo de kompleksa
+nombro, por tiu kunigo. Ni diskutas kelkajn ekzemplojn kaj donas interesan
+aplikon pri movado de fluido.
+-------
+Schwarz-Christoffel transformations map, conformally, the complex upper half
+plane into a region bounded by right segments. Here we describe how to couple
+conveniently a map of the lower half plane to the map of the upper half plane.
+We emphasize the need of a clear definition of angle of a complex, to that
+coupling. We discuss some examples and give an interesting application for
+motion of fluid.
+",0,0,1,0,0,0
+7281,Towards Audio to Scene Image Synthesis using Generative Adversarial Network,"  Humans can imagine a scene from a sound. We want machines to do so by using
+conditional generative adversarial networks (GANs). By applying the techniques
+including spectral norm, projection discriminator and auxiliary classifier,
+compared with naive conditional GAN, the model can generate images with better
+quality in terms of both subjective and objective evaluations. Almost
+three-fourth of people agree that our model have the ability to generate images
+related to sounds. By inputting different volumes of the same sound, our model
+output different scales of changes based on the volumes, showing that our model
+truly knows the relationship between sounds and images to some extent.
+",1,0,0,0,0,0
+7282,Data-Mining Research in Education,"  As an interdisciplinary discipline, data mining (DM) is popular in education
+area especially when examining students' learning performances. It focuses on
+analyzing educational related data to develop models for improving learners'
+learning experiences and enhancing institutional effectiveness. Therefore, DM
+does help education institutions provide high-quality education for its
+learners. Applying data mining in education also known as educational data
+mining (EDM), which enables to better understand how students learn and
+identify how improve educational outcomes. Present paper is designed to justify
+the capabilities of data mining approaches in the filed of education. The
+latest trends on EDM research are introduced in this review. Several specific
+algorithms, methods, applications and gaps in the current literature and future
+insights are discussed here.
+",1,0,0,1,0,0
+7283,Stochastic Input Models in Online Computing,"  In this paper, we study twelve stochastic input models for online problems
+and reveal the relationships among the competitive ratios for the models. The
+competitive ratio is defined as the worst ratio between the expected optimal
+value and the expected profit of the solution obtained by the online algorithm
+where the input distribution is restricted according to the model. To handle a
+broad class of online problems, we use a framework called request-answer games
+that is introduced by Ben-David et al. The stochastic input models consist of
+two types: known distribution and unknown distribution. For each type, we
+consider six classes of distributions: dependent distributions, deterministic
+input, independent distributions, identical independent distribution, random
+order of a deterministic input, and random order of independent distributions.
+As an application of the models, we consider two basic online problems, which
+are variants of the secretary problem and the prophet inequality problem, under
+the twelve stochastic input models. We see the difference of the competitive
+ratios through these problems.
+",1,0,1,1,0,0
+7284,Generating Visual Representations for Zero-Shot Classification,"  This paper addresses the task of learning an image clas-sifier when some
+categories are defined by semantic descriptions only (e.g. visual attributes)
+while the others are defined by exemplar images as well. This task is often
+referred to as the Zero-Shot classification task (ZSC). Most of the previous
+methods rely on learning a common embedding space allowing to compare visual
+features of unknown categories with semantic descriptions. This paper argues
+that these approaches are limited as i) efficient discrimi-native classifiers
+can't be used ii) classification tasks with seen and unseen categories
+(Generalized Zero-Shot Classification or GZSC) can't be addressed efficiently.
+In contrast , this paper suggests to address ZSC and GZSC by i) learning a
+conditional generator using seen classes ii) generate artificial training
+examples for the categories without exemplars. ZSC is then turned into a
+standard supervised learning problem. Experiments with 4 generative models and
+5 datasets experimentally validate the approach, giving state-of-the-art
+results on both ZSC and GZSC.
+",1,0,0,0,0,0
+7285,Online Learning Rate Adaptation with Hypergradient Descent,"  We introduce a general method for improving the convergence rate of
+gradient-based optimizers that is easy to implement and works well in practice.
+We demonstrate the effectiveness of the method in a range of optimization
+problems by applying it to stochastic gradient descent, stochastic gradient
+descent with Nesterov momentum, and Adam, showing that it significantly reduces
+the need for the manual tuning of the initial learning rate for these commonly
+used algorithms. Our method works by dynamically updating the learning rate
+during optimization using the gradient with respect to the learning rate of the
+update rule itself. Computing this ""hypergradient"" needs little additional
+computation, requires only one extra copy of the original gradient to be stored
+in memory, and relies upon nothing more than what is provided by reverse-mode
+automatic differentiation.
+",1,0,0,1,0,0
+7286,Mitigating radiation damage of single photon detectors for space applications,"  Single-photon detectors in space must retain useful performance
+characteristics despite being bombarded with sub-atomic particles. Mitigating
+the effects of this space radiation is vital to enabling new space applications
+which require high-fidelity single-photon detection. To this end, we conducted
+proton radiation tests of various models of avalanche photodiodes (APDs) and
+one model of photomultiplier tube potentially suitable for satellite-based
+quantum communications. The samples were irradiated with 106 MeV protons at
+doses approximately equivalent to lifetimes of 0.6 , 6, 12 and 24 months in a
+low-Earth polar orbit. Although most detection properties were preserved,
+including efficiency, timing jitter and afterpulsing probability, all APD
+samples demonstrated significant increases in dark count rate (DCR) due to
+radiation-induced damage, many orders of magnitude higher than the 200 counts
+per second (cps) required for ground-to-satellite quantum communications. We
+then successfully demonstrated the mitigation of this DCR degradation through
+the use of deep cooling, to as low as -86 degrees C. This achieved DCR below
+the required 200 cps over the 24 months orbit duration. DCR was further reduced
+by thermal annealing at temperatures of +50 to +100 degrees C.
+",0,1,0,0,0,0
+7287,The Parameterized Complexity of Positional Games,"  We study the parameterized complexity of several positional games. Our main
+result is that Short Generalized Hex is W[1]-complete parameterized by the
+number of moves. This solves an open problem from Downey and Fellows'
+influential list of open problems from 1999. Previously, the problem was
+thought of as a natural candidate for AW[*]-completeness. Our main tool is a
+new fragment of first-order logic where universally quantified variables only
+occur in inequalities. We show that model-checking on arbitrary relational
+structures for a formula in this fragment is W[1]-complete when parameterized
+by formula size. We also consider a general framework where a positional game
+is represented as a hypergraph and two players alternately pick vertices. In a
+Maker-Maker game, the first player to have picked all the vertices of some
+hyperedge wins the game. In a Maker-Breaker game, the first player wins if she
+picks all the vertices of some hyperedge, and the second player wins otherwise.
+In an Enforcer-Avoider game, the first player wins if the second player picks
+all the vertices of some hyperedge, and the second player wins otherwise. Short
+Maker-Maker is AW[*]-complete, whereas Short Maker-Breaker is W[1]-complete and
+Short Enforcer-Avoider co-W[1]-complete parameterized by the number of moves.
+This suggests a rough parameterized complexity categorization into positional
+games that are complete for the first level of the W-hierarchy when the winning
+configurations only depend on which vertices one player has been able to pick,
+but AW[*]-completeness when the winning condition depends on which vertices
+both players have picked. However, some positional games where the board and
+the winning configurations are highly structured are fixed-parameter tractable.
+We give another example of such a game, Short k-Connect, which is
+fixed-parameter tractable when parameterized by the number of moves.
+",1,0,0,0,0,0
+7288,Lunar laser ranging in infrfared at hte Grasse laser station,"  For many years, lunar laser ranging (LLR) observations using a green
+wavelength have suffered an inhomogeneity problem both temporally and
+spatially. This paper reports on the implementation of a new infrared detection
+at the Grasse LLR station and describes how infrared telemetry improves this
+situation. Our first results show that infrared detection permits us to densify
+the observations and allows measurements during the new and the full Moon
+periods. The link budget improvement leads to homogeneous telemetric
+measurements on each lunar retro-reflector. Finally, a surprising result is
+obtained on the Lunokhod 2 array which attains the same efficiency as Lunokhod
+1 with an infrared laser link, although those two targets exhibit a
+differential efficiency of six with a green laser link.
+",0,1,0,0,0,0
+7289,An exact solution to a Stefan problem with variable thermal conductivity and a Robin boundary condition,"  In this article it is proved the existence of similarity solutions for a
+one-phase Stefan problem with temperature-dependent thermal conductivity and a
+Robin condition at the fixed face. The temperature distribution is obtained
+through a generalized modified error function which is defined as the solution
+to a nonlinear ordinary differential problem of second order. It is proved that
+the latter has a unique non-negative bounded analytic solution when the
+parameter on which it depends assumes small positive values. Moreover, it is
+shown that the generalized modified error function is concave and increasing,
+and explicit approximations are proposed for it. Relation between the Stefan
+problem considered in this article with those with either constant thermal
+conductivity or a temperature boundary condition is also analysed.
+",0,0,1,0,0,0
+7290,State-of-the-art Speech Recognition With Sequence-to-Sequence Models,"  Attention-based encoder-decoder architectures such as Listen, Attend, and
+Spell (LAS), subsume the acoustic, pronunciation and language model components
+of a traditional automatic speech recognition (ASR) system into a single neural
+network. In previous work, we have shown that such architectures are comparable
+to state-of-theart ASR systems on dictation tasks, but it was not clear if such
+architectures would be practical for more challenging tasks such as voice
+search. In this work, we explore a variety of structural and optimization
+improvements to our LAS model which significantly improve performance. On the
+structural side, we show that word piece models can be used instead of
+graphemes. We also introduce a multi-head attention architecture, which offers
+improvements over the commonly-used single-head attention. On the optimization
+side, we explore synchronous training, scheduled sampling, label smoothing, and
+minimum word error rate optimization, which are all shown to improve accuracy.
+We present results with a unidirectional LSTM encoder for streaming
+recognition. On a 12, 500 hour voice search task, we find that the proposed
+changes improve the WER from 9.2% to 5.6%, while the best conventional system
+achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to
+5% for the conventional system.
+",1,0,0,1,0,0
+7291,Bayesian inference for Stable Levy driven Stochastic Differential Equations with high-frequency data,"  In this article we consider parametric Bayesian inference for stochastic
+differential equations (SDE) driven by a pure-jump stable Levy process, which
+is observed at high frequency. In most cases of practical interest, the
+likelihood function is not available, so we use a quasi-likelihood and place an
+associated prior on the unknown parameters. It is shown under regularity
+conditions that there is a Bernstein-von Mises theorem associated to the
+posterior. We then develop a Markov chain Monte Carlo (MCMC) algorithm for
+Bayesian inference and assisted by our theoretical results, we show how to
+scale Metropolis-Hastings proposals when the frequency of the data grows, in
+order to prevent the acceptance ratio going to zero in the large data limit.
+Our algorithm is presented on numerical examples that help to verify our
+theoretical findings.
+",0,0,1,1,0,0
+7292,The Evolution of Reputation-Based Cooperation in Regular Networks,"  Despite recent advances in reputation technologies, it is not clear how
+reputation systems can affect human cooperation in social networks. Although it
+is known that two of the major mechanisms in the evolution of cooperation are
+spatial selection and reputation-based reciprocity, theoretical study of the
+interplay between both mechanisms remains almost uncharted. Here, we present a
+new individual-based model for the evolution of reciprocal cooperation between
+reputation and networks. We comparatively analyze four of the leading moral
+assessment rules---shunning, image scoring, stern judging, and simple
+standing---and base the model on the giving game in regular networks for
+Cooperators, Defectors, and Discriminators. Discriminators rely on a proper
+moral assessment rule. By using individual-based models, we show that the four
+assessment rules are differently characterized in terms of how cooperation
+evolves, depending on the benefit-to-cost ratio, the network-node degree, and
+the observation and error conditions. Our findings show that the most tolerant
+rule---simple standing---is the most robust among the four assessment rules in
+promoting cooperation in regular networks.
+",1,1,0,0,0,0
+7293,Cocycles of nilpotent quotients of free groups,"  We focus on the cohomology of the $k$-th nilpotent quotient of the free
+group, $F/F_k$. This paper describes all the group 2-, 3-cocycles in terms of
+Massey products, and gives expressions for some of the 3-cocycles. We also give
+simple proofs of some of the results on Milnor invariants and the
+Johnson-Morita homomorphisms.
+",0,0,1,0,0,0
+7294,The ABCD of topological recursion,"  Kontsevich and Soibelman reformulated and slightly generalised the
+topological recursion of math-ph/0702045, seeing it as a quantization of
+certain quadratic Lagrangians in $T^*V$ for some vector space $V$. KS
+topological recursion is a procedure which takes as initial data a quantum Airy
+structure -- a family of at most quadratic differential operators on $V$
+satisfying some axioms -- and gives as outcome a formal series of functions in
+$V$ (the partition function) simultaneously annihilated by these operators.
+Finding and classifying quantum Airy structures modulo gauge group action, is
+by itself an interesting problem which we study here. We provide some
+elementary, Lie-algebraic tools to address this problem, and give some elements
+of classification for ${\rm dim}\,V = 2$. We also describe four more
+interesting classes of quantum Airy structures, coming from respectively
+Frobenius algebras (here we retrieve the 2d TQFT partition function as a
+special case), non-commutative Frobenius algebras, loop spaces of Frobenius
+algebras and a $\mathbb{Z}_{2}$-invariant version of the latter. This
+$\mathbb{Z}_{2}$-invariant version in the case of a semi-simple Frobenius
+algebra corresponds to the topological recursion of math-ph/0702045.
+",0,0,1,0,0,0
+7295,Evaluating Compositionality in Sentence Embeddings,"  An important challenge for human-like AI is compositional semantics. Recent
+research has attempted to address this by using deep neural networks to learn
+vector space embeddings of sentences, which then serve as input to other tasks.
+We present a new dataset for one such task, `natural language inference' (NLI),
+that cannot be solved using only word-level knowledge and requires some
+compositionality. We find that the performance of state of the art sentence
+embeddings (InferSent; Conneau et al., 2017) on our new dataset is poor. We
+analyze the decision rules learned by InferSent and find that they are
+consistent with simple heuristics that are ecologically valid in its training
+dataset. Further, we find that augmenting training with our dataset improves
+test performance on our dataset without loss of performance on the original
+training dataset. This highlights the importance of structured datasets in
+better understanding and improving AI systems.
+",0,0,0,1,0,0
+7296,A rigourous demonstration of the validity of Boltzmann's scenario for the spatial homogenization of a freely expanding gas and the equilibration of the Kac ring,"  Boltzmann provided a scenario to explain why individual macroscopic systems
+composed of a large number $N$ of microscopic constituents are inevitably
+(i.e., with overwhelming probability) observed to approach a unique macroscopic
+state of thermodynamic equilibrium, and why after having done so, they are then
+observed to remain in that state, apparently forever. We provide here rigourous
+new results that mathematically prove the basic features of Boltzmann's
+scenario for two classical models: a simple boundary-free model for the spatial
+homogenization of a non-interacting gas of point particles, and the well-known
+Kac ring model. Our results, based on concentration inequalities that go back
+to Hoeffding, and which focus on the typical behavior of individual macroscopic
+systems, improve upon previous results by providing estimates, exponential in
+$N$, of probabilities and time scales involved.
+",0,1,1,0,0,0
+7297,Half-range lattice Boltzmann models for the simulation of Couette flow using the Shakhov collision term,"  The three-dimensional Couette flow between parallel plates is addressed using
+mixed lattice Boltzmann models which implement the half-range and the
+full-range Gauss-Hermite quadratures on the Cartesian axes perpendicular and
+parallel to the walls, respectively. The ability of our models to simulate
+rarefied flows are validated through comparison against previously reported
+results obtained using the linearized Boltzmann-BGK equation for values of the
+Knudsen number (Kn) up to $100$. We find that recovering the non-linear part of
+the velocity profile (i.e., its deviation from a linear function) at ${\rm Kn}
+\gtrsim 1$ requires high quadrature orders. We then employ the Shakhov model
+for the collision term to obtain macroscopic profiles for Maxwell molecules
+using the standard $\mu \sim T^\omega$ law, as well as for monatomic Helium and
+Argon gases, modeled through ab-initio potentials, where the viscosity is
+recovered using the Sutherland model. We validate our implementation by
+comparison with DSMC results and find excellent match for all macroscopic
+quantities for ${\rm Kn} \lesssim 0.1$. At ${\rm Kn} \gtrsim 0.1$, small
+deviations can be seen in the profiles of the diagonal components of the
+pressure tensor, the heat flux parallel to the plates, and the velocity
+profile, as well as in the values of the velocity gradient at the channel
+center. We attribute these deviations to the limited applicability of the
+Shakhov collision model for highly out of equilibrium flows.
+",0,1,0,0,0,0
+7298,A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics,"  Existing dimensionality reduction methods are adept at revealing hidden
+underlying manifolds arising from high-dimensional data and thereby producing a
+low-dimensional representation. However, the smoothness of the manifolds
+produced by classic techniques over sparse and noisy data is not guaranteed. In
+fact, the embedding generated using such data may distort the geometry of the
+manifold and thereby produce an unfaithful embedding. Herein, we propose a
+framework for nonlinear dimensionality reduction that generates a manifold in
+terms of smooth geodesics that is designed to treat problems in which manifold
+measurements are either sparse or corrupted by noise. Our method generates a
+network structure for given high-dimensional data using a nearest neighbors
+search and then produces piecewise linear shortest paths that are defined as
+geodesics. Then, we fit points in each geodesic by a smoothing spline to
+emphasize the smoothness. The robustness of this approach for sparse and noisy
+datasets is demonstrated by the implementation of the method on synthetic and
+real-world datasets.
+",1,0,0,1,0,0
+7299,Analyzing and improving maximal attainable accuracy in the communication hiding pipelined BiCGStab method,"  Pipelined Krylov subspace methods avoid communication latency by reducing the
+number of global synchronization bottlenecks and by hiding global communication
+behind useful computational work. In exact arithmetic pipelined Krylov subspace
+algorithms are equivalent to classic Krylov subspace methods and generate
+identical series of iterates. However, as a consequence of the reformulation of
+the algorithm to improve parallelism, pipelined methods may suffer from
+severely reduced attainable accuracy in a practical finite precision setting.
+This work presents a numerical stability analysis that describes and quantifies
+the impact of local rounding error propagation on the maximal attainable
+accuracy of the multi-term recurrences in the preconditioned pipelined BiCGStab
+method. Theoretical expressions for the gaps between the true and computed
+residual as well as other auxiliary variables used in the algorithm are
+derived, and the elementary dependencies between the gaps on the various
+recursively computed vector variables are analyzed. The norms of the
+corresponding propagation matrices and vectors provide insights in the possible
+amplification of local rounding errors throughout the algorithm. Stability of
+the pipelined BiCGStab method is compared numerically to that of pipelined CG
+on a symmetric benchmark problem. Furthermore, numerical evidence supporting
+the effectiveness of employing a residual replacement type strategy to improve
+the maximal attainable accuracy for the pipelined BiCGStab method is provided.
+",1,0,0,0,0,0
+7300,Associated Graded Rings and Connected Sums,"  In 2012, Ananthnarayan, Avramov and Moore gave a new construction of
+Gorenstein rings from two Gorenstein local rings, called their connected sum.
+In this article, we investigate conditions on the associated graded ring of a
+Gorenstein Artin local ring Q, which force it to be a connected sum over its
+residue field. In particular, we recover some results regarding short, and
+stretched, Gorenstein Artin rings. Finally, using these decompositions, we
+obtain results about the rationality of the Poincare series of Q.
+",0,0,1,0,0,0
+7301,Trigonometric integrators for quasilinear wave equations,"  Trigonometric time integrators are introduced as a class of explicit
+numerical methods for quasilinear wave equations. Second-order convergence for
+the semi-discretization in time with these integrators is shown for a
+sufficiently regular exact solution. The time integrators are also combined
+with a Fourier spectral method into a fully discrete scheme, for which error
+bounds are provided without requiring any CFL-type coupling of the
+discretization parameters. The proofs of the error bounds are based on energy
+techniques and on the semiclassical G\aa rding inequality.
+",0,0,1,0,0,0
+7302,La leggenda del quanto centenario,"  Around year 2000 the centenary of Planck's thermal radiation formula awakened
+interest in the origins of quantum theory, traditionally traced back to the
+Planck's conference on 14 December 1900 at the Berlin Academy of Sciences. A
+lot of more accurate historical reconstructions, conducted under the stimulus
+of that recurrence, placed the birth date of quantum theory in March 1905 when
+Einstein advanced his light quantum hypothesis. Both interpretations are yet
+controversial, but science historians agree on one point: the emergence of
+quantum theory from a presumed ""crisis"" of classical physics is a myth with
+scarce adherence to the historical truth. This article, written in Italian
+language, was originally presented in connection with the celebration of the
+World Year of Phyics 2005 with the aim of bringing these scholarly theses to a
+wider audience.
+---
+Tradizionalmente la nascita della teoria quantistica viene fatta risalire al
+14 dicembre 1900, quando Planck presentò all'Accademia delle Scienze di
+Berlino la dimostrazione della formula della radiazione termica. Numerose
+ricostruzioni storiche più accurate, effettuate nel periodo intorno al 2000
+sotto lo stimolo dell'interesse per il centenario di quell'avvenimento,
+collocano invece la nascita della teoria quantistica nel marzo del 1905, quando
+Einstein avanzò l'ipotesi dei quanti di luce. Entrambe le interpretazioni
+sono tuttora controverse, ma gli storici della scienza concordano su un punto:
+l'emergere della teoria quantistica da una presunta ""crisi"" della fisica
+classica è un mito con scarsa aderenza alla verità storica. Con questo
+articolo in italiano, presentato originariamente in occasione delle
+celebrazioni per il World Year of Phyics 2005, si è inteso portare a un più
+largo pubblico queste tesi già ben note agli specialisti.
+",0,1,0,0,0,0
+7303,A Highly Efficient Polarization-Independent Metamaterial-Based RF Energy-Harvesting Rectenna for Low-Power Applications,"  A highly-efficient multi-resonant RF energy-harvesting rectenna based on a
+metamaterial perfect absorber featuring closely-spaced polarization-independent
+absorption modes is presented. Its effective area is larger than its physical
+area, and so efficiencies of 230% and 130% are measured at power densities of
+10 uW/cm2 and 1 uW/cm2 respectively, for a linear absorption mode at 0.75 GHz.
+The rectenna exhibits a broad polarization-independent region between 1.4 GHz
+and 1.7 GHz with maximum efficiencies of 167% and 36% for those same power
+densities. Additionally, by adjustment of the distance between the rectenna and
+a reflecting ground plane, the absorption frequency can be adjusted to a
+limited extent within the polarization-independent region. Lastly, the rectenna
+should be capable of delivering 100 uW of power to a device located within 50 m
+of a cell-phone tower under ideal conditions.
+",0,1,0,0,0,0
+7304,Mixed Graphical Models for Causal Analysis of Multi-modal Variables,"  Graphical causal models are an important tool for knowledge discovery because
+they can represent both the causal relations between variables and the
+multivariate probability distributions over the data. Once learned, causal
+graphs can be used for classification, feature selection and hypothesis
+generation, while revealing the underlying causal network structure and thus
+allowing for arbitrary likelihood queries over the data. However, current
+algorithms for learning sparse directed graphs are generally designed to handle
+only one type of data (continuous-only or discrete-only), which limits their
+applicability to a large class of multi-modal biological datasets that include
+mixed type variables. To address this issue, we developed new methods that
+modify and combine existing methods for finding undirected graphs with methods
+for finding directed graphs. These hybrid methods are not only faster, but also
+perform better than the directed graph estimation methods alone for a variety
+of parameter settings and data set sizes. Here, we describe a new conditional
+independence test for learning directed graphs over mixed data types and we
+compare performances of different graph learning strategies on synthetic data.
+",1,0,0,1,0,0
+7305,Estimation of quantile oriented sensitivity indices,"  The paper concerns quantile oriented sensitivity analysis. We rewrite the
+corresponding indices using the Conditional Tail Expectation risk measure.
+Then, we use this new expression to built estimators.
+",0,0,1,1,0,0
+7306,Electromagnetically Induced Transparency (EIT) Amplitude Noise Spectroscopy,"  Intensity noise cross-correlation of the polarization eigenstates of light
+emerging from an atomic vapor cell in the Hanle configuration allows one to
+perform high resolution spectroscopy with free- running semiconductor lasers.
+Such an approach has shown promise as an inexpensive, simpler approach to
+magnetometry and timekeeping, and as a probe of dynamics of atomic coherence in
+warm vapor cells. We report that varying the post-cell polarization state basis
+yields intensity noise spectra which more completely probe the prepared atomic
+state. We advance and test the hypothesis that the observed intensity noise can
+be explained in terms of an underlying stochastic process in lightfield
+amplitudes themselves. Understanding this stochastic process in the light field
+amplitudes themselves provides a new test of the simple atomic quantum optics
+model of EIT noise.
+",0,1,0,0,0,0
+7307,Deep Robust Framework for Protein Function Prediction using Variable-Length Protein Sequences,"  Amino acid sequence portrays most intrinsic form of a protein and expresses
+primary structure of protein. The order of amino acids in a sequence enables a
+protein to acquire a particular stable conformation that is responsible for the
+functions of the protein. This relationship between a sequence and its function
+motivates the need to analyse the sequences for predicting protein functions.
+Early generation computational methods using BLAST, FASTA, etc. perform
+function transfer based on sequence similarity with existing databases and are
+computationally slow. Although machine learning based approaches are fast, they
+fail to perform well for long protein sequences (i.e., protein sequences with
+more than 300 amino acid residues). In this paper, we introduce a novel method
+for construction of two separate feature sets for protein sequences based on
+analysis of 1) single fixed-sized segments and 2) multi-sized segments, using
+bi-directional long short-term memory network. Further, model based on proposed
+feature set is combined with the state of the art Multi-lable Linear
+Discriminant Analysis (MLDA) features based model to improve the accuracy.
+Extensive evaluations using separate datasets for biological processes and
+molecular functions demonstrate promising results for both single-sized and
+multi-sized segments based feature sets. While former showed an improvement of
++3.37% and +5.48%, the latter produces an improvement of +5.38% and +8.00%
+respectively for two datasets over the state of the art MLDA based classifier.
+After combining two models, there is a significant improvement of +7.41% and
++9.21% respectively for two datasets compared to MLDA based classifier.
+Specifically, the proposed approach performed well for the long protein
+sequences and superior overall performance.
+",0,0,0,0,1,0
+7308,Helicity of convective flows from localized heat source in a rotating layer,"  Experimental and numerical study of the steady-state cyclonic vortex from
+isolated heat source in a rotating fluid layer is described. The structure of
+laboratory cyclonic vortex is similar to the typical structure of tropical
+cyclones from observational data and numerical modelling including secondary
+flows in the boundary layer. Differential characteristics of the flow were
+studied by numerical simulation using CFD software FlowVision. Helicity
+distribution in rotating fluid layer with localized heat source was analysed.
+Two mechanisms which play role in helicity generation are found. The first one
+is the strong correlation of cyclonic vortex and intensive upward motion in the
+central part of the vessel. The second one is due to large gradients of
+velocity on the periphery. The integral helicity in the considered case is
+substantial and its relative level is high.
+",0,1,0,0,0,0
+7309,Tunable $φ$-Josephson junction with a quantum anomalous Hall insulator,"  We theoretically study the Josephson current in a superconductor/quantum
+anomalous Hall insulator/superconductor junction by using the lattice Green
+function technique. When an in-plane external Zeeman field is applied to the
+quantum anomalous Hall insulator, the Josephson current $J$ flows without a
+phase difference across the junction $\theta$. The phase shift $\varphi$
+appealing in the current-phase relationship $J\propto \sin(\theta-\varphi$) is
+proportional to the amplitude of Zeeman fields and depends on the direction of
+Zeeman fields. A phenomenological analysis of the Andreev reflection processes
+explains the physical origin of $\varphi$. A quantum anomalous Hall insulator
+breaks time-reversal symmetry and mirror reflection symmetry simultaneously.
+However it preserves magnetic mirror reflection symmetry. Such characteristic
+symmetry property enable us to have a tunable $\varphi$-junction with a quantum
+Hall insulator.
+",0,1,0,0,0,0
+7310,What kind of content are you prone to tweet? Multi-topic Preference Model for Tweeters,"  According to tastes, a person could show preference for a given category of
+content to a greater or lesser extent. However, quantifying people's amount of
+interest in a certain topic is a challenging task, especially considering the
+massive digital information they are exposed to. For example, in the context of
+Twitter, aligned with his/her preferences a user may tweet and retweet more
+about technology than sports and do not share any music-related content. The
+problem we address in this paper is the identification of users' implicit topic
+preferences by analyzing the content categories they tend to post on Twitter.
+Our proposal is significant given that modeling their multi-topic profile may
+be useful to find patterns or association between preferences for categories,
+discover trending topics and cluster similar users to generate better group
+recommendations of content. In the present work, we propose a method based on
+the Mixed Gaussian Model to extract the multidimensional preference
+representation for 399 Ecuadorian tweeters concerning twenty-two different
+topics (or dimensions) which became known by manually categorizing 68.186
+tweets. Our experiment findings indicate that the proposed approach is
+effective at detecting the topic interests of users.
+",1,0,0,0,0,0
+7311,Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference,"  The purpose of this paper is to construct confidence intervals for the
+regression coefficients in the Fine-Gray model for competing risks data with
+random censoring, where the number of covariates can be larger than the sample
+size. Despite strong motivation from biostatistics applications,
+high-dimensional Fine-Gray model has attracted relatively little attention
+among the methodological or theoretical literatures. We fill in this blank by
+proposing first a consistent regularized estimator and then the confidence
+intervals based on the one-step bias-correcting estimator. We are able to
+generalize the partial likelihood approach for the Fine-Gray model under random
+censoring despite many technical difficulties. We lay down a methodological and
+theoretical framework for the one-step bias-correcting estimator with the
+partial likelihood, which does not have independent and identically distributed
+entries. We also handle for our theory the approximation error from the inverse
+probability weighting (IPW), proposing novel concentration results for time
+dependent processes. In addition to the theoretical results and algorithms, we
+present extensive numerical experiments and an application to a study of
+non-cancer mortality among prostate cancer patients using the linked
+Medicare-SEER data.
+",0,0,1,1,0,0
+7312,The Godunov Method for a 2-Phase Model,"  We consider the Godunov numerical method to the phase-transition traffic
+model, proposed in [6], by Colombo, Marcellini, and Rascle. Numerical tests are
+shown to prove the validity of the method. Moreover we highlight the
+differences between such model and the one proposed in [1], by Blandin, Work,
+Goatin, Piccoli, and Bayen.
+",0,0,1,0,0,0
+7313,Cartan's Conjecture for Moving Hypersurfaces,"  Let $f$ be a holomorphic curve in $\mathbb{P}^n({\mathbb{C}})$ and let
+$\mathcal{D}=\{D_1,\ldots,D_q\}$ be a family of moving hypersurfaces defined by
+a set of homogeneous polynomials $\mathcal{Q}=\{Q_1,\ldots,Q_q\}$. For
+$j=1,\ldots,q$, denote by
+$Q_j=\sum\limits_{i_0+\cdots+i_n=d_j}a_{j,I}(z)x_0^{i_0}\cdots x_n^{i_n}$,
+where $I=(i_0,\ldots,i_n)\in\mathbb{Z}_{\ge 0}^{n+1}$ and $a_{j,I}(z)$ are
+entire functions on ${\mathbb{C}}$ without common zeros. Let
+$\mathcal{K}_{\mathcal{Q}}$ be the smallest subfield of meromorphic function
+field $\mathcal{M}$ which contains ${\mathbb{C}}$ and all
+$\frac{a_{j,I'}(z)}{a_{j,I''}(z)}$ with $a_{j,I''}(z)\not\equiv 0$, $1\le j\le
+q$. In previous known second main theorems for $f$ and $\mathcal{D}$, $f$ is
+usually assumed to be algebraically nondegenerate over
+$\mathcal{K}_{\mathcal{Q}}$. In this paper, we prove a second main theorem in
+which $f$ is only assumed to be nonconstant. This result can be regarded as a
+generalization of Cartan's conjecture for moving hypersurfaces.
+",0,0,1,0,0,0
+7314,Safe Active Feature Selection for Sparse Learning,"  We present safe active incremental feature selection~(SAIF) to scale up the
+computation of LASSO solutions. SAIF does not require a solution from a heavier
+penalty parameter as in sequential screening or updating the full model for
+each iteration as in dynamic screening. Different from these existing screening
+methods, SAIF starts from a small number of features and incrementally recruits
+active features and updates the significantly reduced model. Hence, it is much
+more computationally efficient and scalable with the number of features. More
+critically, SAIF has the safe guarantee as it has the convergence guarantee to
+the optimal solution to the original full LASSO problem. Such an incremental
+procedure and theoretical convergence guarantee can be extended to fused LASSO
+problems. Compared with state-of-the-art screening methods as well as working
+set and homotopy methods, which may not always guarantee the optimal solution,
+SAIF can achieve superior or comparable efficiency and high scalability with
+the safe guarantee when facing extremely high dimensional data sets.
+Experiments with both synthetic and real-world data sets show that SAIF can be
+up to 50 times faster than dynamic screening, and hundreds of times faster than
+computing LASSO or fused LASSO solutions without screening.
+",0,0,0,1,0,0
+7315,Cobwebs from the Past and Present: Extracting Large Social Networks using Internet Archive Data,"  Social graph construction from various sources has been of interest to
+researchers due to its application potential and the broad range of technical
+challenges involved. The World Wide Web provides a huge amount of continuously
+updated data and information on a wide range of topics created by a variety of
+content providers, and makes the study of extracted people networks and their
+temporal evolution valuable for social as well as computer scientists. In this
+paper we present SocGraph - an extraction and exploration system for social
+relations from the content of around 2 billion web pages collected by the
+Internet Archive over the 17 years time period between 1996 and 2013. We
+describe methods for constructing large social graphs from extracted relations
+and introduce an interface to study their temporal evolution.
+",1,1,0,0,0,0
+7316,A Fluid-Flow Interpretation of SCED Scheduling,"  We show that a fluid-flow interpretation of Service Curve Earliest Deadline
+First (SCED) scheduling simplifies deadline derivations for this scheduler. By
+exploiting the recently reported isomorphism between min-plus and max-plus
+network calculus, and expressing deadlines in a max-plus algebra, deadline
+computations no longer require pseudo-inverse computations. SCED deadlines are
+provided for general convex or concave piecewise linear service curves.
+",1,0,0,0,0,0
+7317,"Emergence of Topological Nodal Lines and Type II Weyl Nodes in Strong Spin--Orbit Coupling System InNbX2(X=S,Se)","  Using first--principles density functional calculations, we systematically
+investigate electronic structures and topological properties of InNbX2 (X=S,
+Se). In the absence of spin--orbit coupling (SOC), both compounds show nodal
+lines protected by mirror symmetry. Including SOC, the Dirac rings in InNbS2
+split into two Weyl rings. This unique property is distinguished from other
+dicovered nodal line materials which normally requires the absence of SOC. On
+the other hand, SOC breaks the nodal lines in InNbSe2 and the compound becomes
+a type II Weyl semimetal with 12 Weyl points in the Brillouin Zone. Using a
+supercell slab calculation we study the dispersion of Fermi arcs surface states
+in InNbSe2, we also utilize a coherent potential approximation to probe their
+tolernace to the surface disorder effects. The quasi two--dimensionality and
+the absence of toxic elements makes these two compounds an ideal experimental
+platform for investigating novel properties of topological semimetals.
+",0,1,0,0,0,0
+7318,Number-conserving interacting fermion models with exact topological superconducting ground states,"  We present a method to construct number-conserving Hamiltonians whose ground
+states exactly reproduce an arbitrarily chosen BCS-type mean-field state. Such
+parent Hamiltonians can be constructed not only for the usual $s$-wave BCS
+state, but also for more exotic states of this form, including the ground
+states of Kitaev wires and 2D topological superconductors. This method leads to
+infinite families of locally-interacting fermion models with exact topological
+superconducting ground states. After explaining the general technique, we apply
+this method to construct two specific classes of models. The first one is a
+one-dimensional double wire lattice model with Majorana-like degenerate ground
+states. The second one is a two-dimensional $p_x+ip_y$ superconducting model,
+where we also obtain analytic expressions for topologically degenerate ground
+states in the presence of vortices. Our models may provide a deeper conceptual
+understanding of how Majorana zero modes could emerge in condensed matter
+systems, as well as inspire novel routes to realize them in experiment.
+",0,1,0,0,0,0
+7319,JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction,"  We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for
+developing and evaluating grammatical error correction (GEC). Unlike other
+corpora, it represents a broad range of language proficiency levels and uses
+holistic fluency edits to not only correct grammatical errors but also make the
+original text more native sounding. We describe the types of corrections made
+and benchmark four leading GEC systems on this corpus, identifying specific
+areas in which they do well and how they can improve. JFLEG fulfills the need
+for a new gold standard to properly assess the current state of GEC.
+",1,0,0,0,0,0
+7320,Scheduling with regular performance measures and optional job rejection on a single machine,"  We address single machine problems with optional jobs - rejection, studied
+recently in Zhang et al. [21] and Cao et al. [2]. In these papers, the authors
+focus on minimizing regular performance measures, i.e., functions that are
+non-decreasing in the jobs completion time, subject to the constraint that the
+total rejection cost cannot exceed a predefined upper bound. They also prove
+that the considered problems are ordinary NP-hard and provide
+pseudo-polynomial-time Dynamic Programming (DP) solutions. In this paper, we
+focus on three of these problems: makespan with release-dates; total completion
+times; and total weighted completion, and present enhanced DP solutions
+demonstrating both theoretical and practical improvements. Moreover, we provide
+extensive numerical studies verifying their efficiency.
+",1,0,0,0,0,0
+7321,Data-Driven Stochastic Robust Optimization: A General Computational Framework and Algorithm for Optimization under Uncertainty in the Big Data Era,"  A novel data-driven stochastic robust optimization (DDSRO) framework is
+proposed for optimization under uncertainty leveraging labeled multi-class
+uncertainty data. Uncertainty data in large datasets are often collected from
+various conditions, which are encoded by class labels. Machine learning methods
+including Dirichlet process mixture model and maximum likelihood estimation are
+employed for uncertainty modeling. A DDSRO framework is further proposed based
+on the data-driven uncertainty model through a bi-level optimization structure.
+The outer optimization problem follows a two-stage stochastic programming
+approach to optimize the expected objective across different data classes;
+adaptive robust optimization is nested as the inner problem to ensure the
+robustness of the solution while maintaining computational tractability. A
+decomposition-based algorithm is further developed to solve the resulting
+multi-level optimization problem efficiently. Case studies on process network
+design and planning are presented to demonstrate the applicability of the
+proposed framework and algorithm.
+",1,0,1,0,0,0
+7322,Algebraic multiscale method for flow in heterogeneous porous media with embedded discrete fractures (F-AMS),"  This paper introduces an Algebraic MultiScale method for simulation of flow
+in heterogeneous porous media with embedded discrete Fractures (F-AMS). First,
+multiscale coarse grids are independently constructed for both porous matrix
+and fracture networks. Then, a map between coarse- and fine-scale is obtained
+by algebraically computing basis functions with local support. In order to
+extend the localization assumption to the fractured media, four types of basis
+functions are investigated: (1) Decoupled-AMS, in which the two media are
+completely decoupled, (2) Frac-AMS and (3) Rock-AMS, which take into account
+only one-way transmissibilities, and (4) Coupled-AMS, in which the matrix and
+fracture interpolators are fully coupled. In order to ensure scalability, the
+F-AMS framework permits full flexibility in terms of the resolution of the
+fracture coarse grids. Numerical results are presented for two- and
+three-dimensional heterogeneous test cases. During these experiments, the
+performance of F-AMS, paired with ILU(0) as second-stage smoother in a
+convergent iterative procedure, is studied by monitoring CPU times and
+convergence rates. Finally, in order to investigate the scalability of the
+method, an extensive benchmark study is conducted, where a commercial algebraic
+multigrid solver is used as reference. The results show that, given an
+appropriate coarsening strategy, F-AMS is insensitive to severe fracture and
+matrix conductivity contrasts, as well as the length of the fracture networks.
+Its unique feature is that a fine-scale mass conservative flux field can be
+reconstructed after any iteration, providing efficient approximate solutions in
+time-dependent simulations.
+",1,1,0,0,0,0
+7323,From Pragmatic to Systematic Software Process Improvement: An Evaluated Approach,"  Software processes improvement (SPI) is a challenging task, as many different
+stakeholders, project settings, and contexts and goals need to be considered.
+SPI projects are often operated in a complex and volatile environment and,
+thus, require a sound management that is resource-intensive requiring many
+stakeholders to contribute to the process assessment, analysis, design,
+realisation, and deployment. Although there exist many valuable SPI approaches,
+none address the needs of both process engineers and project managers. This
+article presents an Artefact-based Software Process Improvement & Management
+approach (ArSPI) that closes this gap. ArSPI was developed and tested across
+several SPI projects in large organisations in Germany and Eastern Europe. The
+approach further encompasses a template for initiating, performing, and
+managing SPI projects by defining a set of 5 key artefacts and 24 support
+artefacts. We present ArSPI and discus results of its validation indicating
+ArSPI to be a helpful instrument to set up and steer SPI projects.
+",1,0,0,0,0,0
+7324,A Bayesian Mixture Model for Clustering on the Stiefel Manifold,"  Analysis of a Bayesian mixture model for the Matrix Langevin distribution on
+the Stiefel manifold is presented. The model exploits a particular
+parametrization of the Matrix Langevin distribution, various aspects of which
+are elaborated on. A general, and novel, family of conjugate priors, and an
+efficient Markov chain Monte Carlo (MCMC) sampling scheme for the corresponding
+posteriors is then developed for the mixture model. Theoretical properties of
+the prior and posterior distributions, including posterior consistency, are
+explored in detail. Extensive simulation experiments are presented to validate
+the efficacy of the framework. Real-world examples, including a large scale
+neuroimaging dataset, are analyzed to demonstrate the computational
+tractability of the approach.
+",0,0,0,1,0,0
+7325,Discrete Cycloids from Convex Symmetric Polygons,"  Cycloids, hipocycloids and epicycloids have an often forgotten common
+property: they are homothetic to their evolutes. But what if use convex
+symmetric polygons as unit balls, can we define evolutes and cycloids which are
+genuinely discrete? Indeed, we can! We define discrete cycloids as eigenvectors
+of a discrete double evolute transform which can be seen as a linear operator
+on a vector space we call curvature radius space. We are also able to classify
+such cycloids according to the eigenvalues of that transform, and show that the
+number of cusps of each cycloid is well determined by the ordering of those
+eigenvalues. As an elegant application, we easily establish a version of the
+four-vertex theorem for closed convex polygons. The whole theory is developed
+using only linear algebra, and concrete examples are given.
+",0,0,1,0,0,0
+7326,Multi-color image compression-encryption algorithm based on chaotic system and fuzzy transform,"  In this paper an algorithm for multi-color image compression-encryption is
+introduced. For compression step fuzzy transform based on exponential b-spline
+function is used. In encryption step, a novel combination chaotic system based
+on Sine and Tent systems is proposed. Also in the encryption algorithm, 3D
+shift based on chaotic system is introduced. The simulation results and
+security analysis show that the proposed algorithm is secure and efficient.
+",1,0,0,0,0,0
+7327,Gaussian Graphical Models: An Algebraic and Geometric Perspective,"  Gaussian graphical models are used throughout the natural sciences, social
+sciences, and economics to model the statistical relationships between
+variables of interest in the form of a graph. We here provide a pedagogic
+introduction to Gaussian graphical models and review recent results on maximum
+likelihood estimation for such models. Throughout, we highlight the rich
+algebraic and geometric properties of Gaussian graphical models and explain how
+these properties relate to convex optimization and ultimately result in
+insights on the existence of the maximum likelihood estimator (MLE) and
+algorithms for computing the MLE.
+",0,0,1,1,0,0
+7328,The Dynamical History of Chariklo and its Rings,"  Chariklo is the only small Solar system body confirmed to have rings. Given
+the instability of its orbit, the presence of rings is surprising, and their
+origin remains poorly understood. In this work, we study the dynamical history
+of the Chariklo system by integrating almost 36,000 Chariklo clones backwards
+in time for one Gyr under the influence of the Sun and the four giant planets.
+By recording all close encounters between the clones and planets, we
+investigate the likelihood that Chariklo's rings could have survived since its
+capture to the Centaur population. Our results reveal that Chariklo's orbit
+occupies a region of stable chaos, resulting in its orbit being marginally more
+stable than those of the other Centaurs. Despite this, we find that it was most
+likely captured to the Centaur population within the last 20 Myr, and that its
+orbital evolution has been continually punctuated by regular close encounters
+with the giant planets. The great majority (> 99%) of those encounters within
+one Hill radius of the planet have only a small effect on the rings. We
+conclude that close encounters with giant planets have not had a significant
+effect on the ring structure. Encounters within the Roche limit of the giant
+planets are rare, making ring creation through tidal disruption unlikely.
+",0,1,0,0,0,0
+7329,THAP: A Matlab Toolkit for Learning with Hawkes Processes,"  As a powerful tool of asynchronous event sequence analysis, point processes
+have been studied for a long time and achieved numerous successes in different
+fields. Among various point process models, Hawkes process and its variants
+attract many researchers in statistics and computer science these years because
+they capture the self- and mutually-triggering patterns between different
+events in complicated sequences explicitly and quantitatively and are broadly
+applicable to many practical problems. In this paper, we describe an
+open-source toolkit implementing many learning algorithms and analysis tools
+for Hawkes process model and its variants. Our toolkit systematically
+summarizes recent state-of-the-art algorithms as well as most classic
+algorithms of Hawkes processes, which is beneficial for both academical
+education and research. Source code can be downloaded from
+this https URL.
+",1,0,0,1,0,0
+7330,Studies of the Response of the SiD Silicon-Tungsten ECal,"  Studies of the response of the SiD silicon-tungsten electromagnetic
+calorimeter (ECal) are presented. Layers of highly granular (13 mm^2 pixels)
+silicon detectors embedded in thin gaps (~ 1 mm) between tungsten alloy plates
+give the SiD ECal the ability to separate electromagnetic showers in a crowded
+environment. A nine-layer prototype has been built and tested in a 12.1 GeV
+electron beam at the SLAC National Accelerator Laboratory. This data was
+simulated with a Geant4 model. Particular attention was given to the separation
+of nearby incident electrons, which demonstrated a high (98.5%) separation
+efficiency for two electrons at least 1 cm from each other. The beam test study
+will be compared to a full SiD detector simulation with a realistic geometry,
+where the ECal calibration constants must first be established. This work is
+continuing, as the geometry requires that the calibration constants depend upon
+energy, angle, and absorber depth. The derivation of these constants is being
+developed from first principles.
+",0,1,0,0,0,0
+7331,Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views,"  In this study, a multiple hypothesis tracking (MHT) algorithm for
+multi-target multi-camera tracking (MCT) with disjoint views is proposed. Our
+method forms track-hypothesis trees, and each branch of them represents a
+multi-camera track of a target that may move within a camera as well as move
+across cameras. Furthermore, multi-target tracking within a camera is performed
+simultaneously with the tree formation by manipulating a status of each track
+hypothesis. Each status represents three different stages of a multi-camera
+track: tracking, searching, and end-of-track. The tracking status means targets
+are tracked by a single camera tracker. In the searching status, the
+disappeared targets are examined if they reappear in other cameras. The
+end-of-track status does the target exited the camera network due to its
+lengthy invisibility. These three status assists MHT to form the
+track-hypothesis trees for multi-camera tracking. Furthermore, they present a
+gating technique for eliminating of unlikely observation-to-track association.
+In the experiments, they evaluate the proposed method using two datasets,
+DukeMTMC and NLPR-MCT, which demonstrates that the proposed method outperforms
+the state-of-the-art method in terms of improvement of the accuracy. In
+addition, they show that the proposed method can operate in real-time and
+online.
+",1,0,0,0,0,0
+7332,Smooth positon solutions of the focusing modified Korteweg-de Vries equation,"  The $n$-fold Darboux transformation $T_{n}$ of the focusing real mo\-di\-fied
+Kor\-te\-weg-de Vries (mKdV) equation is expressed in terms of the determinant
+representation. Using this representation, the $n$-soliton solutions of the
+mKdV equation are also expressed by determinants whose elements consist of the
+eigenvalues $\lambda_{j}$ and the corresponding eigenfunctions of the
+associated Lax equation. The nonsingular $n$-positon solutions of the focusing
+mKdV equation are obtained in the special limit
+$\lambda_{j}\rightarrow\lambda_{1}$, from the corresponding $n$-soliton
+solutions and by using the associated higher-order Taylor expansion.
+Furthermore, the decomposition method of the $n$-positon solution into $n$
+single-soliton solutions, the trajectories, and the corresponding ""phase
+shifts"" of the multi-positons are also investigated.
+",0,1,0,0,0,0
+7333,On discrimination between two close distribution tails,"  The goodness-of-fit test for discrimination of two tail distribution using
+higher order statistics is proposed. The consistency of proposed test is proved
+for two different alternatives. We do not assume belonging the corresponding
+distribution function to a maximum domain of attraction.
+",0,0,1,1,0,0
+7334,Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow,"  Belief Propagation algorithms are instruments used broadly to solve graphical
+model optimization and statistical inference problems. In the general case of a
+loopy Graphical Model, Belief Propagation is a heuristic which is quite
+successful in practice, even though its empirical success, typically, lacks
+theoretical guarantees. This paper extends the short list of special cases
+where correctness and/or convergence of a Belief Propagation algorithm is
+proven. We generalize formulation of Min-Sum Network Flow problem by relaxing
+the flow conservation (balance) constraints and then proving that the Belief
+Propagation algorithm converges to the exact result.
+",1,0,0,1,0,0
+7335,Sharper and Simpler Nonlinear Interpolants for Program Verification,"  Interpolation of jointly infeasible predicates plays important roles in
+various program verification techniques such as invariant synthesis and CEGAR.
+Intrigued by the recent result by Dai et al.\ that combines real algebraic
+geometry and SDP optimization in synthesis of polynomial interpolants, the
+current paper contributes its enhancement that yields sharper and simpler
+interpolants. The enhancement is made possible by: theoretical observations in
+real algebraic geometry; and our continued fraction-based algorithm that rounds
+off (potentially erroneous) numerical solutions of SDP solvers. Experiment
+results support our tool's effectiveness; we also demonstrate the benefit of
+sharp and simple interpolants in program verification examples.
+",1,0,0,0,0,0
+7336,"Quantized Laplacian growth, III: On conformal field theories of Laplacian growth","  A one-parametric stochastic dynamics of the interface in the quantized
+Laplacian growth with zero surface tension is introduced. The quantization
+procedure regularizes the growth by preventing the formation of cusps at the
+interface, and makes the interface dynamics chaotic. In a long time asymptotic,
+by coupling a conformal field theory to the stochastic growth process we
+introduce a set of observables (the martingales), whose expectation values are
+constant in time. The martingales are connected to degenerate representations
+of the Virasoro algebra, and can be written in terms of conformal correlation
+functions. A direct link between Laplacian growth and the conformal Liouville
+field theory with the central charge $c\geq25$ is proposed.
+",0,1,0,0,0,0
+7337,Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images,"  Lesion segmentation is the first step in most automatic melanoma recognition
+systems. Deficiencies and difficulties in dermoscopic images such as color
+inconstancy, hair occlusion, dark corners and color charts make lesion
+segmentation an intricate task. In order to detect the lesion in the presence
+of these problems, we propose a supervised saliency detection method tailored
+for dermoscopic images based on the discriminative regional feature integration
+(DRFI). DRFI method incorporates multi-level segmentation, regional contrast,
+property, background descriptors, and a random forest regressor to create
+saliency scores for each region in the image. In our improved saliency
+detection method, mDRFI, we have added some new features to regional property
+descriptors. Also, in order to achieve more robust regional background
+descriptors, a thresholding algorithm is proposed to obtain a new
+pseudo-background region. Findings reveal that mDRFI is superior to DRFI in
+detecting the lesion as the salient object in dermoscopic images. The proposed
+overall lesion segmentation framework uses detected saliency map to construct
+an initial mask of the lesion through thresholding and post-processing
+operations. The initial mask is then evolving in a level set framework to fit
+better on the lesion's boundaries. The results of evaluation tests on three
+public datasets show that our proposed segmentation method outperforms the
+other conventional state-of-the-art segmentation algorithms and its performance
+is comparable with most recent approaches that are based on deep convolutional
+neural networks.
+",1,0,0,0,0,0
+7338,The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description,"  Support vector data description (SVDD) is a popular technique for detecting
+anomalies. The SVDD classifier partitions the whole space into an inlier
+region, which consists of the region near the training data, and an outlier
+region, which consists of points away from the training data. The computation
+of the SVDD classifier requires a kernel function, and the Gaussian kernel is a
+common choice for the kernel function. The Gaussian kernel has a bandwidth
+parameter, whose value is important for good results. A small bandwidth leads
+to overfitting, and the resulting SVDD classifier overestimates the number of
+anomalies. A large bandwidth leads to underfitting, and the classifier fails to
+detect many anomalies. In this paper we present a new automatic, unsupervised
+method for selecting the Gaussian kernel bandwidth. The selected value can be
+computed quickly, and it is competitive with existing bandwidth selection
+methods.
+",1,0,0,1,0,0
+7339,Understanding the Impact of Label Granularity on CNN-based Image Classification,"  In recent years, supervised learning using Convolutional Neural Networks
+(CNNs) has achieved great success in image classification tasks, and large
+scale labeled datasets have contributed significantly to this achievement.
+However, the definition of a label is often application dependent. For example,
+an image of a cat can be labeled as ""cat"" or perhaps more specifically ""Persian
+cat."" We refer to this as label granularity. In this paper, we conduct
+extensive experiments using various datasets to demonstrate and analyze how and
+why training based on fine-grain labeling, such as ""Persian cat"" can improve
+CNN accuracy on classifying coarse-grain classes, in this case ""cat."" The
+experimental results show that training CNNs with fine-grain labels improves
+both network's optimization and generalization capabilities, as intuitively it
+encourages the network to learn more features, and hence increases
+classification accuracy on coarse-grain classes under all datasets considered.
+Moreover, fine-grain labels enhance data efficiency in CNN training. For
+example, a CNN trained with fine-grain labels and only 40% of the total
+training data can achieve higher accuracy than a CNN trained with the full
+training dataset and coarse-grain labels. These results point to two possible
+applications of this work: (i) with sufficient human resources, one can improve
+CNN performance by re-labeling the dataset with fine-grain labels, and (ii)
+with limited human resources, to improve CNN performance, rather than
+collecting more training data, one may instead use fine-grain labels for the
+dataset. We further propose a metric called Average Confusion Ratio to
+characterize the effectiveness of fine-grain labeling, and show its use through
+extensive experimentation. Code is available at
+this https URL.
+",1,0,0,0,0,0
+7340,Monte Carlo modified profile likelihood in models for clustered data,"  The main focus of the analysts who deal with clustered data is usually not on
+the clustering variables, and hence the group-specific parameters are treated
+as nuisance. If a fixed effects formulation is preferred and the total number
+of clusters is large relative to the single-group sizes, classical frequentist
+techniques relying on the profile likelihood are often misleading. The use of
+alternative tools, such as modifications to the profile likelihood or
+integrated likelihoods, for making accurate inference on a parameter of
+interest can be complicated by the presence of nonstandard modelling and/or
+sampling assumptions. We show here how to employ Monte Carlo simulation in
+order to approximate the modified profile likelihood in some of these
+unconventional frameworks. The proposed solution is widely applicable and is
+shown to retain the usual properties of the modified profile likelihood. The
+approach is examined in two instances particularly relevant in applications,
+i.e. missing-data models and survival models with unspecified censoring
+distribution. The effectiveness of the proposed solution is validated via
+simulation studies and two clinical trial applications.
+",0,0,0,1,0,0
+7341,Anisotropic spin-density distribution and magnetic anisotropy of strained La$_{1-x}$Sr$_x$MnO$_3$ thin films: Angle-dependent x-ray magnetic circular dichroism,"  Magnetic anisotropies of ferromagnetic thin films are induced by epitaxial
+strain from the substrate via strain-induced anisotropy in the orbital magnetic
+moment and that in the spatial distribution of spin-polarized electrons.
+However, the preferential orbital occupation in ferromagnetic metallic
+La$_{1-x}$Sr$_x$MnO$_3$ (LSMO) thin films studied by x-ray linear dichroism
+(XLD) has always been found out-of-plane for both tensile and compressive
+epitaxial strain and hence irrespective of the magnetic anisotropy. In order to
+resolve this mystery, we directly probed the preferential orbital occupation of
+spin-polarized electrons in LSMO thin films under strain by angle-dependent
+x-ray magnetic circular dichroism (XMCD). Anisotropy of the spin-density
+distribution was found to be in-plane for the tensile strain and out-of-plane
+for the compressive strain, consistent with the observed magnetic anisotropy.
+The ubiquitous out-of-plane preferential orbital occupation seen by XLD is
+attributed to the occupation of both spin-up and spin-down out-of-plane
+orbitals in the surface magnetic dead layer.
+",0,1,0,0,0,0
+7342,An Annotated Corpus of Relational Strategies in Customer Service,"  We create and release the first publicly available commercial customer
+service corpus with annotated relational segments. Human-computer data from
+three live customer service Intelligent Virtual Agents (IVAs) in the domains of
+travel and telecommunications were collected, and reviewers marked all text
+that was deemed unnecessary to the determination of user intention. After
+merging the selections of multiple reviewers to create highlighted texts, a
+second round of annotation was done to determine the classes of language
+present in the highlighted sections such as the presence of Greetings,
+Backstory, Justification, Gratitude, Rants, or Emotions. This resulting corpus
+is a valuable resource for improving the quality and relational abilities of
+IVAs. As well as discussing the corpus itself, we compare the usage of such
+language in human-human interactions on TripAdvisor forums. We show that
+removal of this language from task-based inputs has a positive effect on IVA
+understanding by both an increase in confidence and improvement in responses,
+demonstrating the need for automated methods of its discovery.
+",1,0,0,0,0,0
+7343,Putting gravity in control,"  The aim of the present manuscript is to present a novel proposal in Geometric
+Control Theory inspired in the principles of General Relativity and
+energy-shaping control.
+",0,0,1,0,0,0
+7344,Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples,"  Sometimes it is not enough for a DNN to produce an outcome. For example, in
+applications such as healthcare, users need to understand the rationale of the
+decisions. Therefore, it is imperative to develop algorithms to learn models
+with good interpretability (Doshi-Velez 2017). An important factor that leads
+to the lack of interpretability of DNNs is the ambiguity of neurons, where a
+neuron may fire for various unrelated concepts. This work aims to increase the
+interpretability of DNNs on the whole image space by reducing the ambiguity of
+neurons. In this paper, we make the following contributions:
+1) We propose a metric to evaluate the consistency level of neurons in a
+network quantitatively.
+2) We find that the learned features of neurons are ambiguous by leveraging
+adversarial examples.
+3) We propose to improve the consistency of neurons on adversarial example
+subset by an adversarial training algorithm with a consistent loss.
+",1,0,0,1,0,0
+7345,Learning a Hierarchical Latent-Variable Model of 3D Shapes,"  We propose the Variational Shape Learner (VSL), a generative model that
+learns the underlying structure of voxelized 3D shapes in an unsupervised
+fashion. Through the use of skip-connections, our model can successfully learn
+and infer a latent, hierarchical representation of objects. Furthermore,
+realistic 3D objects can be easily generated by sampling the VSL's latent
+probabilistic manifold. We show that our generative model can be trained
+end-to-end from 2D images to perform single image 3D model retrieval.
+Experiments show, both quantitatively and qualitatively, the improved
+generalization of our proposed model over a range of tasks, performing better
+or comparable to various state-of-the-art alternatives.
+",1,0,0,0,0,0
+7346,Maximally rotating waves in AdS and on spheres,"  We study the cubic wave equation in AdS_(d+1) (and a closely related cubic
+wave equation on S^3) in a weakly nonlinear regime. Via time-averaging, these
+systems are accurately described by simplified infinite-dimensional quartic
+Hamiltonian systems, whose structure is mandated by the fully resonant spectrum
+of linearized perturbations. The maximally rotating sector, comprising only the
+modes of maximal angular momentum at each frequency level, consistently
+decouples in the weakly nonlinear regime. The Hamiltonian systems obtained by
+this decoupling display remarkable periodic return behaviors closely analogous
+to what has been demonstrated in recent literature for a few other related
+equations (the cubic Szego equation, the conformal flow, the LLL equation).
+This suggests a powerful underlying analytic structure, such as integrability.
+We comment on the connection of our considerations to the Gross-Pitaevskii
+equation for harmonically trapped Bose-Einstein condensates.
+",0,1,1,0,0,0
+7347,Taming Wild High Dimensional Text Data with a Fuzzy Lash,"  The bag of words (BOW) represents a corpus in a matrix whose elements are the
+frequency of words. However, each row in the matrix is a very high-dimensional
+sparse vector. Dimension reduction (DR) is a popular method to address sparsity
+and high-dimensionality issues. Among different strategies to develop DR
+method, Unsupervised Feature Transformation (UFT) is a popular strategy to map
+all words on a new basis to represent BOW. The recent increase of text data and
+its challenges imply that DR area still needs new perspectives. Although a wide
+range of methods based on the UFT strategy has been developed, the fuzzy
+approach has not been considered for DR based on this strategy. This research
+investigates the application of fuzzy clustering as a DR method based on the
+UFT strategy to collapse BOW matrix to provide a lower-dimensional
+representation of documents instead of the words in a corpus. The quantitative
+evaluation shows that fuzzy clustering produces superior performance and
+features to Principal Components Analysis (PCA) and Singular Value
+Decomposition (SVD), two popular DR methods based on the UFT strategy.
+",1,0,0,1,0,0
+7348,Spatial structure of shock formation,"  The formation of a singularity in a compressible gas, as described by the
+Euler equation, is characterized by the steepening, and eventual overturning of
+a wave. Using a self-similar description in two space dimensions, we show that
+the spatial structure of this process, which starts at a point, is equivalent
+to the formation of a caustic, i.e. to a cusp catastrophe. The lines along
+which the profile has infinite slope correspond to the caustic lines, from
+which we construct the position of the shock. By solving the similarity
+equation, we obtain a complete local description of wave steepening and of the
+spreading of the shock from a point.
+",0,1,1,0,0,0
+7349,"How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)","  This paper investigates how far a very deep neural network is from attaining
+close to saturating performance on existing 2D and 3D face alignment datasets.
+To this end, we make the following 5 contributions: (a) we construct, for the
+first time, a very strong baseline by combining a state-of-the-art architecture
+for landmark localization with a state-of-the-art residual block, train it on a
+very large yet synthetically expanded 2D facial landmark dataset and finally
+evaluate it on all other 2D facial landmark datasets. (b) We create a guided by
+2D landmarks network which converts 2D landmark annotations to 3D and unifies
+all existing datasets, leading to the creation of LS3D-W, the largest and most
+challenging 3D facial landmark dataset to date ~230,000 images. (c) Following
+that, we train a neural network for 3D face alignment and evaluate it on the
+newly introduced LS3D-W. (d) We further look into the effect of all
+""traditional"" factors affecting face alignment performance like large pose,
+initialization and resolution, and introduce a ""new"" one, namely the size of
+the network. (e) We show that both 2D and 3D face alignment networks achieve
+performance of remarkable accuracy which is probably close to saturating the
+datasets used. Training and testing code as well as the dataset can be
+downloaded from this https URL
+",1,0,0,0,0,0
+7350,Inhabitants of interesting subsets of the Bousfield lattice,"  The set of Bousfield classes has some important subsets such as the
+distributive lattice $\mathbf{DL}$ of all classes $\langle E\rangle$ which are
+smash idempotent and the complete Boolean algebra $\mathbf{cBA}$ of closed
+classes. We provide examples of spectra that are in $\mathbf{DL}$, but not in
+$\mathbf{cBA}$; in particular, for every prime $p$, the Bousfield class of the
+Eilenberg-MacLane spectrum $\langle
+H\mathbb{F}_p\rangle\in\mathbf{DL}{\setminus}\mathbf{cBA}$.
+",0,0,1,0,0,0
+7351,A Framework for Implementing Machine Learning on Omics Data,"  The potential benefits of applying machine learning methods to -omics data
+are becoming increasingly apparent, especially in clinical settings. However,
+the unique characteristics of these data are not always well suited to machine
+learning techniques. These data are often generated across different
+technologies in different labs, and frequently with high dimensionality. In
+this paper we present a framework for combining -omics data sets, and for
+handling high dimensional data, making -omics research more accessible to
+machine learning applications. We demonstrate the success of this framework
+through integration and analysis of multi-analyte data for a set of 3,533
+breast cancers. We then use this data-set to predict breast cancer patient
+survival for individuals at risk of an impending event, with higher accuracy
+and lower variance than methods trained on individual data-sets. We hope that
+our pipelines for data-set generation and transformation will open up -omics
+data to machine learning researchers. We have made these freely available for
+noncommercial use at www.ccg.ai.
+",0,0,0,0,1,0
+7352,Actively Calibrated Line Mountable Capacitive Voltage Transducer For Power Systems Applications,"  A class of Actively Calibrated Line Mounted Capacitive Voltage Transducers
+(LMCVT) are introduced as a viable line mountable instrumentation option for
+deploying large numbers of voltage transducers onto the medium and high voltage
+systems. Active Calibration is shown to reduce the error of line mounted
+voltage measurements by an order of magnitude from previously published
+techniques. The instrument physics and sensing method is presented and the
+performance is evaluated in a laboratory setting. Finally, a roadmap to a fully
+deployable prototype is shown.
+",0,1,0,0,0,0
+7353,AirCode: Unobtrusive Physical Tags for Digital Fabrication,"  We present AirCode, a technique that allows the user to tag physically
+fabricated objects with given information. An AirCode tag consists of a group
+of carefully designed air pockets placed beneath the object surface. These air
+pockets are easily produced during the fabrication process of the object,
+without any additional material or postprocessing. Meanwhile, the air pockets
+affect only the scattering light transport under the surface, and thus are hard
+to notice to our naked eyes. But, by using a computational imaging method, the
+tags become detectable. We present a tool that automates the design of air
+pockets for the user to encode information. AirCode system also allows the user
+to retrieve the information from captured images via a robust decoding
+algorithm. We demonstrate our tagging technique with applications for metadata
+embedding, robotic grasping, as well as conveying object affordances.
+",1,0,0,0,0,0
+7354,Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features,"  This paper advances the state of the art in text understanding of medical
+guidelines by releasing two new annotated clinical guidelines datasets, and
+establishing baselines for using machine learning to extract condition-action
+pairs. In contrast to prior work that relies on manually created rules, we
+report experiment with several supervised machine learning techniques to
+classify sentences as to whether they express conditions and actions. We show
+the limitations and possible extensions of this work on text mining of medical
+guidelines.
+",1,0,0,0,0,0
+7355,Adversarial Learning for Neural Dialogue Generation,"  In this paper, drawing intuition from the Turing test, we propose using
+adversarial training for open-domain dialogue generation: the system is trained
+to produce sequences that are indistinguishable from human-generated dialogue
+utterances. We cast the task as a reinforcement learning (RL) problem where we
+jointly train two systems, a generative model to produce response sequences,
+and a discriminator---analagous to the human evaluator in the Turing test--- to
+distinguish between the human-generated dialogues and the machine-generated
+ones. The outputs from the discriminator are then used as rewards for the
+generative model, pushing the system to generate dialogues that mostly resemble
+human dialogues.
+In addition to adversarial training we describe a model for adversarial {\em
+evaluation} that uses success in fooling an adversary as a dialogue evaluation
+metric, while avoiding a number of potential pitfalls. Experimental results on
+several metrics, including adversarial evaluation, demonstrate that the
+adversarially-trained system generates higher-quality responses than previous
+baselines.
+",1,0,0,0,0,0
+7356,On the impact origin of Phobos and Deimos III: resulting composition from different impactors,"  The origin of Phobos and Deimos in a giant impact generated disk is gaining
+larger attention. Although this scenario has been the subject of many studies,
+an evaluation of the chemical composition of the Mars' moons in this framework
+is missing. The chemical composition of Phobos and Deimos is unconstrained. The
+large uncertainty about the origin of the mid-infrared features, the lack of
+absorption bands in the visible and near-infrared spectra, and the effects of
+secondary processes on the moons' surface make the determination of their
+composition very difficult from remote sensing data. Simulations suggest a
+formation of a disk made of gas and melt with their composition linked to the
+nature of the impactor and Mars. Using thermodynamic equilibrium we investigate
+the composition of dust (condensates from gas) and solids (from a cooling melt)
+that result from different types of Mars impactors (Mars-, CI-, CV-, EH-,
+comet-like). Our calculations show a wide range of possible chemical
+compositions and noticeable differences between dust and solids depending on
+the considered impactors. Assuming Phobos and Deimos as result of the accretion
+and mixing of dust and solids, we find that the derived assemblage (dust rich
+in metallic-iron, sulphides and/or carbon, and quenched solids rich in
+silicates) can be compatible with the observations. The JAXA's MMX (Martian
+Moons eXploration) mission will investigate the physical and chemical
+properties of the Maroons, especially sampling from Phobos, before returning to
+Earth. Our results could be then used to disentangle the origin and chemical
+composition of the pristine body that hit Mars and suggest guidelines for
+helping in the analysis of the returned samples.
+",0,1,0,0,0,0
+7357,Dealing with the Dimensionality Curse in Dynamic Pricing Competition: Using Frequent Repricing to Compensate Imperfect Market Anticipations,"  Most sales applications are characterized by competition and limited demand
+information. For successful pricing strategies, frequent price adjustments as
+well as anticipation of market dynamics are crucial. Both effects are
+challenging as competitive markets are complex and computations of optimized
+pricing adjustments can be time-consuming. We analyze stochastic dynamic
+pricing models under oligopoly competition for the sale of perishable goods. To
+circumvent the curse of dimensionality, we propose a heuristic approach to
+efficiently compute price adjustments. To demonstrate our strategy's
+applicability even if the number of competitors is large and their strategies
+are unknown, we consider different competitive settings in which competitors
+frequently and strategically adjust their prices. For all settings, we verify
+that our heuristic strategy yields promising results. We compare the
+performance of our heuristic against upper bounds, which are obtained by
+optimal strategies that take advantage of perfect price anticipations. We find
+that price adjustment frequencies can have a larger impact on expected profits
+than price anticipations. Finally, our approach has been applied on Amazon for
+the sale of used books. We have used a seller's historical market data to
+calibrate our model. Sales results show that our data-driven strategy
+outperforms the rule-based strategy of an experienced seller by a profit
+increase of more than 20%.
+",0,0,0,0,0,1
+7358,Interleaved Group Convolutions for Deep Neural Networks,"  In this paper, we present a simple and modularized neural network
+architecture, named interleaved group convolutional neural networks (IGCNets).
+The main point lies in a novel building block, a pair of two successive
+interleaved group convolutions: primary group convolution and secondary group
+convolution. The two group convolutions are complementary: (i) the convolution
+on each partition in primary group convolution is a spatial convolution, while
+on each partition in secondary group convolution, the convolution is a
+point-wise convolution; (ii) the channels in the same secondary partition come
+from different primary partitions. We discuss one representative advantage:
+Wider than a regular convolution with the number of parameters and the
+computation complexity preserved. We also show that regular convolutions, group
+convolution with summation fusion, and the Xception block are special cases of
+interleaved group convolutions. Empirical results over standard benchmarks,
+CIFAR-$10$, CIFAR-$100$, SVHN and ImageNet demonstrate that our networks are
+more efficient in using parameters and computation complexity with similar or
+higher accuracy.
+",1,0,0,0,0,0
+7359,Lower Bounding Diffusion Constant by the Curvature of Drude Weight,"  We establish a general connection between ballistic and diffusive transport
+in systems where the ballistic contribution in canonical ensemble vanishes. A
+lower bound on the Green-Kubo diffusion constant is derived in terms of the
+curvature of the ideal transport coefficient, the Drude weight, with respect to
+the filling parameter. As an application, we explicitly determine the lower
+bound on the high temperature diffusion constant in the anisotropic spin 1/2
+Heisenberg chain for anisotropy parameters $\Delta \geq 1$, thus settling the
+question whether the transport is sub-diffusive or not. Addi- tionally, the
+lower bound is shown to saturate the diffusion constant for a certain classical
+integrable model.
+",0,1,0,0,0,0
+7360,Face Detection using Deep Learning: An Improved Faster RCNN Approach,"  In this report, we present a new face detection scheme using deep learning
+and achieve the state-of-the-art detection performance on the well-known FDDB
+face detetion benchmark evaluation. In particular, we improve the
+state-of-the-art faster RCNN framework by combining a number of strategies,
+including feature concatenation, hard negative mining, multi-scale training,
+model pretraining, and proper calibration of key parameters. As a consequence,
+the proposed scheme obtained the state-of-the-art face detection performance,
+making it the best model in terms of ROC curves among all the published methods
+on the FDDB benchmark.
+",1,0,0,0,0,0
+7361,Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task,"  End-to-end control for robot manipulation and grasping is emerging as an
+attractive alternative to traditional pipelined approaches. However, end-to-end
+methods tend to either be slow to train, exhibit little or no generalisability,
+or lack the ability to accomplish long-horizon or multi-stage tasks. In this
+paper, we show how two simple techniques can lead to end-to-end (image to
+velocity) execution of a multi-stage task, which is analogous to a simple
+tidying routine, without having seen a single real image. This involves
+locating, reaching for, and grasping a cube, then locating a basket and
+dropping the cube inside. To achieve this, robot trajectories are computed in a
+simulator, to collect a series of control velocities which accomplish the task.
+Then, a CNN is trained to map observed images to velocities, using domain
+randomisation to enable generalisation to real world images. Results show that
+we are able to successfully accomplish the task in the real world with the
+ability to generalise to novel environments, including those with dynamic
+lighting conditions, distractor objects, and moving objects, including the
+basket itself. We believe our approach to be simple, highly scalable, and
+capable of learning long-horizon tasks that have until now not been shown with
+the state-of-the-art in end-to-end robot control.
+",1,0,0,0,0,0
+7362,"The cosmic spiderweb: equivalence of cosmic, architectural, and origami tessellations","  For over twenty years, the term 'cosmic web' has guided our understanding of
+the large-scale arrangement of matter in the cosmos, accurately evoking the
+concept of a network of galaxies linked by filaments. But the physical
+correspondence between the cosmic web and structural-engineering or textile
+'spiderwebs' is even deeper than previously known, and extends to origami
+tessellations as well. Here we explain that in a good structure-formation
+approximation known as the adhesion model, threads of the cosmic web form a
+spiderweb, i.e. can be strung up to be entirely in tension. The correspondence
+is exact if nodes sampling voids are included, and if structure is excluded
+within collapsed regions (walls, filaments and haloes), where dark-matter
+multistreaming and baryonic physics affect the structure. We also suggest how
+concepts arising from this link might be used to test cosmological models: for
+example, to test for large-scale anisotropy and rotational flows in the cosmos.
+",0,1,0,0,0,0
+7363,Data-driven polynomial chaos expansion for machine learning regression,"  We present a regression technique for data driven problems based on
+polynomial chaos expansion (PCE). PCE is a popular technique in the field of
+uncertainty quantification (UQ), where it is typically used to replace a
+runnable but expensive computational model subject to random inputs with an
+inexpensive-to-evaluate polynomial function. The metamodel obtained enables a
+reliable estimation of the statistics of the output, provided that a suitable
+probabilistic model of the input is available.
+In classical machine learning (ML) regression settings, however, the system
+is only known through observations of its inputs and output, and the interest
+lies in obtaining accurate pointwise predictions of the latter. Here, we show
+that a PCE metamodel purely trained on data can yield pointwise predictions
+whose accuracy is comparable to that of other ML regression models, such as
+neural networks and support vector machines. The comparisons are performed on
+benchmark datasets available from the literature. The methodology also enables
+the quantification of the output uncertainties and is robust to noise.
+Furthermore, it enjoys additional desirable properties, such as good
+performance for small training sets and simplicity of construction, with only
+little parameter tuning required. In the presence of statistically dependent
+inputs, we investigate two ways to build the PCE, and show through simulations
+that one approach is superior to the other in the stated settings.
+",0,0,0,1,0,0
+7364,Robust Implicit Backpropagation,"  Arguably the biggest challenge in applying neural networks is tuning the
+hyperparameters, in particular the learning rate. The sensitivity to the
+learning rate is due to the reliance on backpropagation to train the network.
+In this paper we present the first application of Implicit Stochastic Gradient
+Descent (ISGD) to train neural networks, a method known in convex optimization
+to be unconditionally stable and robust to the learning rate. Our key
+contribution is a novel layer-wise approximation of ISGD which makes its
+updates tractable for neural networks. Experiments show that our method is more
+robust to high learning rates and generally outperforms standard
+backpropagation on a variety of tasks.
+",0,0,0,1,0,0
+7365,Experimental and Theoretical Study of Magnetohydrodynamic Ship Models,"  Magnetohydrodynamic (MHD) ships represent a clear demonstration of the
+Lorentz force in fluids, which explains the number of students practicals or
+exercises described on the web. However, the related literature is rather
+specific and no complete comparison between theory and typical small scale
+experiments is currently available. This work provides, in a self-consistent
+framework, a detailed presentation of the relevant theoretical equations for
+small MHD ships and experimental measurements for future benchmarks.
+Theoretical results of the literature are adapted to these simple
+battery/magnets powered ships moving on salt water. Comparison between theory
+and experiments are performed to validate each theoretical step such as the
+Tafel and the Kohlrausch laws, or the predicted ship speed. A successful
+agreement is obtained without any adjustable parameter. Finally, based on these
+results, an optimal design is then deduced from the theory. Therefore this work
+provides a solid theoretical and experimental ground for small scale MHD ships,
+by presenting in detail several approximations and how they affect the boat
+efficiency. Moreover, the theory is general enough to be adapted to other
+contexts, such as large scale ships or industrial flow measurement techniques.
+",0,1,0,0,0,0
+7366,Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection,"  With a rapidly increasing number of devices connected to the internet, big
+data has been applied to various domains of human life. Nevertheless, it has
+also opened new venues for breaching users' privacy. Hence it is highly
+required to develop techniques that enable data owners to privatize their data
+while keeping it useful for intended applications. Existing methods, however,
+do not offer enough flexibility for controlling the utility-privacy trade-off
+and may incur unfavorable results when privacy requirements are high. To tackle
+these drawbacks, we propose a compressive-privacy based method, namely RUCA
+(Ratio Utility and Cost Analysis), which can not only maximize performance for
+a privacy-insensitive classification task but also minimize the ability of any
+classifier to infer private information from the data. Experimental results on
+Census and Human Activity Recognition data sets demonstrate that RUCA
+significantly outperforms existing privacy preserving data projection
+techniques for a wide range of privacy pricings.
+",0,0,0,1,0,0
+7367,Time-optimal control strategies in SIR epidemic models,"  We investigate the time-optimal control problem in SIR
+(Susceptible-Infected-Recovered) epidemic models, focusing on different control
+policies: vaccination, isolation, culling, and reduction of transmission.
+Applying the Pontryagin's Minimum Principle (PMP) to the unconstrained control
+problems (i.e. without costs of control or resource limitations), we prove
+that, for all the policies investigated, only bang-bang controls with at most
+one switch are admitted. When a switch occurs, the optimal strategy is to delay
+the control action some amount of time and then apply the control at the
+maximum rate for the remainder of the outbreak. This result is in contrast with
+previous findings on the unconstrained problems of minimizing the total
+infectious burden over an outbreak, where the optimal strategy is to use the
+maximal control for the entire epidemic. Then, the critical consequence of our
+results is that, in a wide range of epidemiological circumstances, it may be
+impossible to minimize the total infectious burden while minimizing the
+epidemic duration, and vice versa. Moreover, numerical simulations highlighted
+additional unexpected results, showing that the optimal control can be delayed
+also when the control reproduction number is lower than one and that the
+switching time from no control to maximum control can even occur after the peak
+of infection has been reached. Our results are especially important for
+livestock diseases where the minimization of outbreaks duration is a priority
+due to sanitary restrictions imposed to farms during ongoing epidemics, such as
+animal movements and export bans.
+",0,0,1,0,0,0
+7368,On the validity of the formal Edgeworth expansion for posterior densities,"  We consider a fundamental open problem in parametric Bayesian theory, namely
+the validity of the formal Edgeworth expansion of the posterior density. While
+the study of valid asymptotic expansions for posterior distributions
+constitutes a rich literature, the validity of the formal Edgeworth expansion
+has not been rigorously established. Several authors have claimed connections
+of various posterior expansions with the classical Edgeworth expansion, or have
+simply assumed its validity. Our main result settles this open problem. We also
+prove a lemma concerning the order of posterior cumulants which is of
+independent interest in Bayesian parametric theory. The most relevant
+literature is synthesized and compared to the newly-derived Edgeworth
+expansions. Numerical investigations illustrate that our expansion has the
+behavior expected of an Edgeworth expansion, and that it has better performance
+than the other existing expansion which was previously claimed to be of
+Edgeworth-type.
+",0,0,1,1,0,0
+7369,DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization,"  Adaptive gradient-based optimization methods such as ADAGRAD, RMSPROP, and
+ADAM are widely used in solving large-scale machine learning problems including
+deep learning. A number of schemes have been proposed in the literature aiming
+at parallelizing them, based on communications of peripheral nodes with a
+central node, but incur high communications cost. To address this issue, we
+develop a novel consensus-based distributed adaptive moment estimation method
+(DADAM) for online optimization over a decentralized network that enables data
+parallelization, as well as decentralized computation. The method is
+particularly useful, since it can accommodate settings where access to local
+data is allowed. Further, as established theoretically in this work, it can
+outperform centralized adaptive algorithms, for certain classes of loss
+functions used in applications. We analyze the convergence properties of the
+proposed algorithm and provide a dynamic regret bound on the convergence rate
+of adaptive moment estimation methods in both stochastic and deterministic
+settings. Empirical results demonstrate that DADAM works also well in practice
+and compares favorably to competing online optimization methods.
+",1,0,0,1,0,0
+7370,Paramagnetic Meissner effect in ZrB12 single crystal with non-monotonic vortex-vortex interactions,"  The magnetic response related to paramagnetic Meissner effect (PME) is
+studied in a high quality single crystal ZrB12 with non-monotonic vortex-vortex
+interactions. We observe the expulsion and penetration of magnetic flux in the
+form of vortex clusters with increasing temperature. A vortex phase diagram is
+constructed which shows that the PME can be explained by considering the
+interplay among the flux compression, the different temperature dependencies of
+the vortex-vortex and the vortex-pin interactions, and thermal fluctuations.
+Such a scenario is in good agreement with the results of the magnetic
+relaxation measurements.
+",0,1,0,0,0,0
+7371,Credal Networks under Epistemic Irrelevance,"  A credal network under epistemic irrelevance is a generalised type of
+Bayesian network that relaxes its two main building blocks. On the one hand,
+the local probabilities are allowed to be partially specified. On the other
+hand, the assessments of independence do not have to hold exactly.
+Conceptually, these two features turn credal networks under epistemic
+irrelevance into a powerful alternative to Bayesian networks, offering a more
+flexible approach to graph-based multivariate uncertainty modelling. However,
+in practice, they have long been perceived as very hard to work with, both
+theoretically and computationally.
+The aim of this paper is to demonstrate that this perception is no longer
+justified. We provide a general introduction to credal networks under epistemic
+irrelevance, give an overview of the state of the art, and present several new
+theoretical results. Most importantly, we explain how these results can be
+combined to allow for the design of recursive inference methods. We provide
+numerous concrete examples of how this can be achieved, and use these to
+demonstrate that computing with credal networks under epistemic irrelevance is
+most definitely feasible, and in some cases even highly efficient. We also
+discuss several philosophical aspects, including the lack of symmetry, how to
+deal with probability zero, the interpretation of lower expectations, the
+axiomatic status of graphoid properties, and the difference between updating
+and conditioning.
+",1,0,1,0,0,0
+7372,Resonance fluorescence in the resolvent operator formalism,"  The Mollow spectrum for the light scattered by a driven two-level atom is
+derived in the resolvent operator formalism. The derivation is based on the
+construction of a master equation from the resolvent operator of the atom-field
+system. We show that the natural linewidth of the excited atomic level remains
+essentially unmodified, to a very good level of approximation, even in the
+strong-field regime, where Rabi flopping becomes relevant inside the
+self-energy loop that yields the linewidth. This ensures that the obtained
+master equation and the spectrum derived matches that of Mollow.
+",0,1,0,0,0,0
+7373,On the the Berge Conjecture for tunnel number one knots,"  In this paper we use an approach based on dynamics to prove that if $K\subset
+S^3$ is a tunnel number one knot which admits a Dehn filling resulting in a
+lens space $L$ then $K$ is either a Berge knot, or $K\subset S^3$ is
+$(1,1)$-knot.
+",0,0,1,0,0,0
+7374,EMRIs and the relativistic loss-cone: The curious case of the fortunate coincidence,"  Extreme mass ratio inspiral (EMRI) events are vulnerable to perturbations by
+the stellar background, which can abort them prematurely by deflecting EMRI
+orbits to plunging ones that fall directly into the massive black hole (MBH),
+or to less eccentric ones that no longer interact strongly with the MBH. A
+coincidental hierarchy between the collective resonant Newtonian torques due to
+the stellar background, and the relative magnitudes of the leading-order
+post-Newtonian precessional and radiative terms of the general relativistic
+2-body problem, allows EMRIs to decouple from the background and produce
+semi-periodic gravitational wave signals. I review the recent theoretical
+developments that confirm this conjectured fortunate coincidence, and briefly
+discuss the implications for EMRI rates, and show how these dynamical effects
+can be probed locally by stars near the Galactic MBH.
+",0,1,0,0,0,0
+7375,Investigating the configurations in cross-shareholding: a joint copula-entropy approach,"  --- the companies populating a Stock market, along with their connections,
+can be effectively modeled through a directed network, where the nodes
+represent the companies, and the links indicate the ownership. This paper deals
+with this theme and discusses the concentration of a market. A
+cross-shareholding matrix is considered, along with two key factors: the node
+out-degree distribution which represents the diversification of investments in
+terms of the number of involved companies, and the node in-degree distribution
+which reports the integration of a company due to the sales of its own shares
+to other companies. While diversification is widely explored in the literature,
+integration is most present in literature on contagions. This paper captures
+such quantities of interest in the two frameworks and studies the stochastic
+dependence of diversification and integration through a copula approach. We
+adopt entropies as measures for assessing the concentration in the market. The
+main question is to assess the dependence structure leading to a better
+description of the data or to market polarization (minimal entropy) or market
+fairness (maximal entropy). In so doing, we derive information on the way in
+which the in- and out-degrees should be connected in order to shape the market.
+The question is of interest to regulators bodies, as witnessed by specific
+alert threshold published on the US mergers guidelines for limiting the
+possibility of acquisitions and the prevalence of a single company on the
+market. Indeed, all countries and the EU have also rules or guidelines in order
+to limit concentrations, in a country or across borders, respectively. The
+calibration of copulas and model parameters on the basis of real data serves as
+an illustrative application of the theoretical proposal.
+",0,0,0,0,0,1
+7376,The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering' Language Embeddings,"  Offensive or antagonistic language targeted at individuals and social groups
+based on their personal characteristics (also known as cyber hate speech or
+cyberhate) has been frequently posted and widely circulated viathe World Wide
+Web. This can be considered as a key risk factor for individual and societal
+tension linked toregional instability. Automated Web-based cyberhate detection
+is important for observing and understandingcommunity and regional societal
+tension - especially in online social networks where posts can be rapidlyand
+widely viewed and disseminated. While previous work has involved using
+lexicons, bags-of-words orprobabilistic language parsing approaches, they often
+suffer from a similar issue which is that cyberhate can besubtle and indirect -
+thus depending on the occurrence of individual words or phrases can lead to a
+significantnumber of false negatives, providing inaccurate representation of
+the trends in cyberhate. This problemmotivated us to challenge thinking around
+the representation of subtle language use, such as references toperceived
+threats from ""the other"" including immigration or job prosperity in a hateful
+context. We propose anovel framework that utilises language use around the
+concept of ""othering"" and intergroup threat theory toidentify these subtleties
+and we implement a novel classification method using embedding learning to
+computesemantic distances between parts of speech considered to be part of an
+""othering"" narrative. To validate ourapproach we conduct several experiments on
+different types of cyberhate, namely religion, disability, race andsexual
+orientation, with F-measure scores for classifying hateful instances obtained
+through applying ourmodel of 0.93, 0.86, 0.97 and 0.98 respectively, providing
+a significant improvement in classifier accuracy overthe state-of-the-art
+",1,0,0,0,0,0
+7377,Linear Programming Formulations of Deterministic Infinite Horizon Optimal Control Problems in Discrete Time,"  This paper is devoted to a study of infinite horizon optimal control problems
+with time discounting and time averaging criteria in discrete time. We
+establish that these problems are related to certain infinite-dimensional
+linear programming (IDLP) problems. We also establish asymptotic relationships
+between the optimal values of problems with time discounting and long-run
+average criteria.
+",0,0,1,0,0,0
+7378,Exact diagonalization of cubic lattice models in commensurate Abelian magnetic fluxes and translational invariant non-Abelian potentials,"  We present a general analytical formalism to determine the energy spectrum of
+a quantum particle in a cubic lattice subject to translationally invariant
+commensurate magnetic fluxes and in the presence of a general space-independent
+non-Abelian gauge potential. We first review and analyze the case of purely
+Abelian potentials, showing also that the so-called Hasegawa gauge yields a
+decomposition of the Hamiltonian into sub-matrices having minimal dimension.
+Explicit expressions for such matrices are derived, also for general
+anisotropic fluxes. Later on, we show that the introduction of a translational
+invariant non-Abelian coupling for multi-component spinors does not affect the
+dimension of the minimal Hamiltonian blocks, nor the dimension of the magnetic
+Brillouin zone. General formulas are presented for the U(2) case and explicit
+examples are investigated involving $\pi$ and $2\pi/3$ magnetic fluxes.
+Finally, we numerically study the effect of random flux perturbations.
+",0,1,0,0,0,0
+7379,"Non-normality, reactivity, and intrinsic stochasticity in neural dynamics: a non-equilibrium potential approach","  Intrinsic stochasticity can induce highly non-trivial effects on dynamical
+systems, including stochastic and coherence resonance, noise induced
+bistability, noise-induced oscillations, to name but a few. In this paper we
+revisit a mechanism first investigated in the context of neuroscience by which
+relatively small demographic (intrinsic) fluctuations can lead to the emergence
+of avalanching behavior in systems that are deterministically characterized by
+a single stable fixed point (up state). The anomalously large response of such
+systems to stochasticity stems (or is strongly associated with) the existence
+of a ""non-normal"" stability matrix at the deterministic fixed point, which may
+induce the system to be ""reactive"". Here, we further investigate this mechanism
+by exploring the interplay between non-normality and intrinsic (demographic)
+stochasticity, by employing a number of analytical and computational
+approaches. We establish, in particular, that the resulting dynamics in this
+type of systems cannot be simply derived from a scalar potential but,
+additionally, one needs to consider a curl flux which describes the essential
+non-equilibrium nature of this type of noisy non-normal systems. Moreover, we
+shed further light on the origin of the phenomenon, introduce the novel concept
+of ""non-linear reactivity"", and rationalize of the observed the value of the
+emerging avalanche exponents.
+",0,0,0,0,1,0
+7380,Latent Gaussian Mixture Models for Nationwide Kidney Transplant Center Evaluation,"  Five year post-transplant survival rate is an important indicator on quality
+of care delivered by kidney transplant centers in the United States. To provide
+a fair assessment of each transplant center, an effect that represents the
+center-specific care quality, along with patient level risk factors, is often
+included in the risk adjustment model. In the past, the center effects have
+been modeled as either fixed effects or Gaussian random effects, with various
+pros and cons. Our numerical analyses reveal that the distributional
+assumptions do impact the prediction of center effects especially when the
+effect is extreme. To bridge the gap between these two approaches, we propose
+to model the transplant center effect as a latent random variable with a finite
+Gaussian mixture distribution. Such latent Gaussian mixture models provide a
+convenient framework to study the heterogeneity among the transplant centers.
+To overcome the weak identifiability issues, we propose to estimate the latent
+Gaussian mixture model using a penalized likelihood approach, and develop
+sequential locally restricted likelihood ratio tests to determine the number of
+components in the Gaussian mixture distribution. The fitted mixture model
+provides a convenient means of controlling the false discovery rate when
+screening for underperforming or outperforming transplant centers. The
+performance of the methods is verified by simulations and by the analysis of
+the motivating data example.
+",0,0,0,1,0,0
+7381,Small nonlinearities in activation functions create bad local minima in neural networks,"  We investigate the loss surface of neural networks. We prove that even for
+one-hidden-layer networks with ""slightest"" nonlinearity, the empirical risks
+have spurious local minima in most cases. Our results thus indicate that in
+general ""no spurious local minima"" is a property limited to deep linear
+networks, and insights obtained from linear networks are not robust.
+Specifically, for ReLU(-like) networks we constructively prove that for almost
+all (in contrast to previous results) practical datasets there exist infinitely
+many local minima. We also present a counterexample for more general
+activations (sigmoid, tanh, arctan, ReLU, etc.), for which there exists a bad
+local minimum. Our results make the least restrictive assumptions relative to
+existing results on local optimality in neural networks. We complete our
+discussion by presenting a comprehensive characterization of global optimality
+for deep linear networks, which unifies other results on this topic.
+",0,0,0,1,0,0
+7382,Poisson brackets with prescribed family of functions in involution,"  It is well known that functions in involution with respect to Poisson
+brackets have a privileged role in the theory of completely integrable systems.
+Finding functionally independent functions in involution with a given function
+$h$ on a Poisson manifold is a fundamental problem of this theory and is very
+useful for the explicit integration of the equations of motion defined by $h$.
+In this paper, we present our results on the study of the inverse, so to speak,
+problem. By developing a technique analogous to that presented in P. Damianou
+and F. Petalidou, Poisson brackets with prescribed Casimirs, Canad. J. Math.,
+2012, vol. 64, 991-1018, for the establishment of Poisson brackets with
+prescribed Casimir invariants, we construct an algorithm which yields Poisson
+brackets having a given family of functions in involution. Our approach allows
+us to deal with bi-Hamiltonian structures constructively and therefore allows
+us to also deal with the completely integrable systems that arise in such a
+framework.
+",0,0,1,0,0,0
+7383,No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models,"  For decades, context-dependent phonemes have been the dominant sub-word unit
+for conventional acoustic modeling systems. This status quo has begun to be
+challenged recently by end-to-end models which seek to combine acoustic,
+pronunciation, and language model components into a single neural network. Such
+systems, which typically predict graphemes or words, simplify the recognition
+process since they remove the need for a separate expert-curated pronunciation
+lexicon to map from phoneme-based units to words. However, there has been
+little previous work comparing phoneme-based versus grapheme-based sub-word
+units in the end-to-end modeling framework, to determine whether the gains from
+such approaches are primarily due to the new probabilistic model, or from the
+joint learning of the various components with grapheme-based units.
+In this work, we conduct detailed experiments which are aimed at quantifying
+the value of phoneme-based pronunciation lexica in the context of end-to-end
+models. We examine phoneme-based end-to-end models, which are contrasted
+against grapheme-based ones on a large vocabulary English Voice-search task,
+where we find that graphemes do indeed outperform phonemes. We also compare
+grapheme and phoneme-based approaches on a multi-dialect English task, which
+once again confirm the superiority of graphemes, greatly simplifying the system
+for recognizing multiple dialects.
+",1,0,0,1,0,0
+7384,Stabilization Bounds for Linear Finite Dynamical Systems,"  A common problem to all applications of linear finite dynamical systems is
+analyzing the dynamics without enumerating every possible state transition. Of
+particular interest is the long term dynamical behaviour. In this paper, we
+study the number of iterations needed for a system to settle on a fixed set of
+elements. As our main result, we present two upper bounds on iterations needed,
+and each one may be readily applied to a fixed point system test. The bounds
+are based on submodule properties of iterated images and reduced systems modulo
+a prime. We also provide examples where our bounds are optimal.
+",0,0,1,0,0,0
+7385,Magneto-thermopower in the Weak Ferromagnetic Oxide CaRu0.8Sc0.2O3: An Experimental Test for the Kelvin Formula in a Magnetic Material,"  We have measured the resistivity, the thermopower, and the specific heat of
+the weak ferromagnetic oxide CaRu0.8Sc0.2O3 in external magnetic fields up to
+140 kOe below 80 K. We have observed that the thermopower Q is significantly
+suppressed by magnetic fields at around the ferromagnetic transition
+temperature of 30 K, and have further found that the magneto-thermopower
+{\Delta}Q(H, T) = Q(H, T) - Q(0, T) is roughly proportional to the
+magneto-entropy {\Delta}S(H, T) = S(H, T)-S(0, T).We discuss this relationship
+between the two quantities in terms of the Kelvin formula, and find that the
+observed {\Delta}Q is quantitatively consistent with the values expected from
+the Kelvin formula, a possible physical meaning of which is discussed.
+",0,1,0,0,0,0
+7386,Experimental Two-dimensional Quantum Walk on a Photonic Chip,"  Quantum walks, in virtue of the coherent superposition and quantum
+interference, possess exponential superiority over its classical counterpart in
+applications of quantum searching and quantum simulation. The quantum enhanced
+power is highly related to the state space of quantum walks, which can be
+expanded by enlarging the photon number and/or the dimensions of the evolution
+network, but the former is considerably challenging due to probabilistic
+generation of single photons and multiplicative loss. Here we demonstrate a
+two-dimensional continuous-time quantum walk by using the external geometry of
+photonic waveguide arrays, rather than the inner degree of freedoms of photons.
+Using femtosecond laser direct writing, we construct a large-scale
+three-dimensional structure which forms a two-dimensional lattice with up to
+49X49 nodes on a photonic chip. We demonstrate spatial two-dimensional quantum
+walks using heralded single photons and single-photon-level imaging. We analyze
+the quantum transport properties via observing the ballistic evolution pattern
+and the variance profile, which agree well with simulation results. We further
+reveal the transient nature that is the unique feature for quantum walks of
+beyond one dimension. An architecture that allows a walk to freely evolve in
+all directions and a large scale, combining with defect and disorder control,
+may bring up powerful and versatile quantum walk machines for classically
+intractable problems.
+",0,1,0,0,0,0
+7387,Dispersive Magnetic and Electronic Excitations in Iridate Perovskites Probed with Oxygen $K$-Edge Resonant Inelastic X-ray Scattering,"  Resonant inelastic X-ray scattering (RIXS) experiments performed at the
+oxygen-$K$ edge on the iridate perovskites {\SIOS} and {\SION} reveal a
+sequence of well-defined dispersive modes over the energy range up to $\sim
+0.8$ eV. The momentum dependence of these modes and their variation with the
+experimental geometry allows us to assign each of them to specific collective
+magnetic and/or electronic excitation processes, including single and
+bi-magnons, and spin-orbit and electron-hole excitons. We thus demonstrated
+that dispersive magnetic and electronic excitations are observable at the O-$K$
+edge in the presence of the strong spin-orbit coupling in the $5d$ shell of
+iridium and strong hybridization between Ir $5d$ and O $2p$ orbitals, which
+confirm and expand theoretical expectations. More generally, our results
+establish the utility of O-$K$ edge RIXS for studying the collective
+excitations in a range of $5d$ materials that are attracting increasing
+attention due to their novel magnetic and electronic properties. Especially,
+the strong RIXS response at O-$K$ edge opens up the opportunity for
+investigating collective excitations in thin films and heterostructures
+fabricated from these materials.
+",0,1,0,0,0,0
+7388,Reaction-Diffusion Models for Glioma Tumor Growth,"  Mathematical modelling of tumor growth is one of the most useful and
+inexpensive approaches to determine and predict the stage, size and progression
+of tumors in realistic geometries. Moreover, these models has been used to get
+an insight into cancer growth and invasion and in the analysis of tumor size
+and geometry for applications in cancer treatment and surgical planning. The
+present revision attempts to present a general perspective of the use of models
+based on reaction-diffusion equations not only for the description of tumor
+growth in gliomas, addressing for processes such as tumor heterogeneity,
+hypoxia, dormancy and necrosis, but also its potential use as a tool in
+designing optimized and patient specific therapies.
+",0,1,0,0,0,0
+7389,On reducing the communication cost of the diffusion LMS algorithm,"  The rise of digital and mobile communications has recently made the world
+more connected and networked, resulting in an unprecedented volume of data
+flowing between sources, data centers, or processes. While these data may be
+processed in a centralized manner, it is often more suitable to consider
+distributed strategies such as diffusion as they are scalable and can handle
+large amounts of data by distributing tasks over networked agents. Although it
+is relatively simple to implement diffusion strategies over a cluster, it
+appears to be challenging to deploy them in an ad-hoc network with limited
+energy budget for communication. In this paper, we introduce a diffusion LMS
+strategy that significantly reduces communication costs without compromising
+the performance. Then, we analyze the proposed algorithm in the mean and
+mean-square sense. Next, we conduct numerical experiments to confirm the
+theoretical findings. Finally, we perform large scale simulations to test the
+algorithm efficiency in a scenario where energy is limited.
+",1,0,0,1,0,0
+7390,Teaching computer code at school,"  In today's education systems, there is a deep concern about the importance of
+teaching code and computer programming in schools. Moving digital learning from
+a simple use of tools to understanding the processes of the internal
+functioning of these tools is an old / new debate originated with the digital
+laboratories of the 1960. Today, it is emerging again under impulse of the
+large - scale public sphere digitalization and the new constructivist education
+theories. Teachers and educators discuss not only the viability of code
+teaching in the classroom, but also the intellectual and cognitive advantages
+for students. The debate thus takes several orientations and is resourced in
+the entanglement of arguments and interpretations of any order, technical,
+educational, cultural, cognitive and psychological. However, that phenomenon
+which undoubtedly augurs for a profound transformation in the future models of
+learning and teaching , is predicting a new and almost congenital digital
+humanism
+",1,0,0,0,0,0
+7391,Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method,"  We propose statistical inferential procedures for panel data models with
+interactive fixed effects in a kernel ridge regression framework.Compared with
+traditional sieve methods, our method is automatic in the sense that it does
+not require the choice of basis functions and truncation parameters.Model
+complexity is controlled by a continuous regularization parameter which can be
+automatically selected by generalized cross validation. Based on empirical
+processes theory and functional analysis tools, we derive joint asymptotic
+distributions for the estimators in the heterogeneous setting. These joint
+asymptotic results are then used to construct confidence intervals for the
+regression means and prediction intervals for the future observations, both
+being the first provably valid intervals in literature. Marginal asymptotic
+normality of the functional estimators in homogeneous setting is also obtained.
+Simulation and real data analysis demonstrate the advantages of our method.
+",0,0,1,1,0,0
+7392,Nonconvex penalties with analytical solutions for one-bit compressive sensing,"  One-bit measurements widely exist in the real world, and they can be used to
+recover sparse signals. This task is known as the problem of learning
+halfspaces in learning theory and one-bit compressive sensing (1bit-CS) in
+signal processing. In this paper, we propose novel algorithms based on both
+convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We provide
+a sufficient condition to verify whether a solution is globally optimal or not.
+Then we show that the globally optimal solution for positive homogeneous
+penalties can be obtained in two steps: a proximal operator and a normalization
+step. For several nonconvex penalties, including minimax concave penalty (MCP),
+$\ell_0$ norm, and sorted $\ell_1$ penalty, we provide fast algorithms for
+finding the analytical solutions by solving the dual problem. Specifically, our
+algorithm is more than $200$ times faster than the existing algorithm for MCP.
+Its efficiency is comparable to the algorithm for the $\ell_1$ penalty in time,
+while its performance is much better. Among these penalties, the sorted
+$\ell_1$ penalty is most robust to noise in different settings.
+",1,0,0,1,0,0
+7393,How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets,"  The computational complexity of kernel methods has often been a major barrier
+for applying them to large-scale learning problems. We argue that this barrier
+can be effectively overcome. In particular, we develop methods to scale up
+kernel models to successfully tackle large-scale learning problems that are so
+far only approachable by deep learning architectures. Based on the seminal work
+by Rahimi and Recht on approximating kernel functions with features derived
+from random projections, we advance the state-of-the-art by proposing methods
+that can efficiently train models with hundreds of millions of parameters, and
+learn optimal representations from multiple kernels. We conduct extensive
+empirical studies on problems from image recognition and automatic speech
+recognition, and show that the performance of our kernel models matches that of
+well-engineered deep neural nets (DNNs). To the best of our knowledge, this is
+the first time that a direct comparison between these two methods on
+large-scale problems is reported. Our kernel methods have several appealing
+properties: training with convex optimization, cost for training a single model
+comparable to DNNs, and significantly reduced total cost due to fewer
+hyperparameters to tune for model selection. Our contrastive study between
+these two very different but equally competitive models sheds light on
+fundamental questions such as how to learn good representations.
+",1,0,0,0,0,0
+7394,Benchmarking Automatic Machine Learning Frameworks,"  AutoML serves as the bridge between varying levels of expertise when
+designing machine learning systems and expedites the data science process. A
+wide range of techniques is taken to address this, however there does not exist
+an objective comparison of these techniques. We present a benchmark of current
+open source AutoML solutions using open source datasets. We test auto-sklearn,
+TPOT, auto_ml, and H2O's AutoML solution against a compiled set of regression
+and classification datasets sourced from OpenML and find that auto-sklearn
+performs the best across classification datasets and TPOT performs the best
+across regression datasets.
+",0,0,0,1,0,0
+7395,Layered Based Augmented Complex Kalman Filter for Fast Forecasting-Aided State Estimation of Distribution Networks,"  In the presence of renewable resources, distribution networks have become
+extremely complex to monitor, operate and control. Furthermore, for the real
+time applications, active distribution networks require fast real time
+distribution state estimation (DSE). Forecasting aided state estimator (FASE),
+deploys measured data in consecutive time samples to refine the state estimate.
+Although most of the DSE algorithms deal with real and imaginary parts of
+distribution networks states independently, we propose a non iterative complex
+DSE algorithm based on augmented complex Kalman filter (ACKF) which considers
+the states as complex values. In case of real time DSE and in presence of a
+large number of customer loads in the system, employing DSEs in one single
+estimation layer is not computationally efficient. Consequently, our proposed
+method performs in several estimation layers hierarchically as a Multi layer
+DSE using ACKF (DSEMACKF). In the proposed method, a distribution network can
+be divided into one main area and several subareas. The aggregated loads in
+each subarea act like a big customer load in the main area. Load aggregation
+results in a lower variability and higher cross correlation. This increases the
+accuracy of the estimated states. Additionally, the proposed method is
+formulated to include unbalanced loads in low voltage (LV) distribution
+network.
+",0,0,0,1,0,0
+7396,Multiscale mixing patterns in networks,"  Assortative mixing in networks is the tendency for nodes with the same
+attributes, or metadata, to link to each other. It is a property often found in
+social networks manifesting as a higher tendency of links occurring between
+people with the same age, race, or political belief. Quantifying the level of
+assortativity or disassortativity (the preference of linking to nodes with
+different attributes) can shed light on the factors involved in the formation
+of links and contagion processes in complex networks. It is common practice to
+measure the level of assortativity according to the assortativity coefficient,
+or modularity in the case of discrete-valued metadata. This global value is the
+average level of assortativity across the network and may not be a
+representative statistic when mixing patterns are heterogeneous. For example, a
+social network spanning the globe may exhibit local differences in mixing
+patterns as a consequence of differences in cultural norms. Here, we introduce
+an approach to localise this global measure so that we can describe the
+assortativity, across multiple scales, at the node level. Consequently we are
+able to capture and qualitatively evaluate the distribution of mixing patterns
+in the network. We find that for many real-world networks the distribution of
+assortativity is skewed, overdispersed and multimodal. Our method provides a
+clearer lens through which we can more closely examine mixing patterns in
+networks.
+",1,0,0,0,0,0
+7397,Single-Queue Decoding for Neural Machine Translation,"  Neural machine translation models rely on the beam search algorithm for
+decoding. In practice, we found that the quality of hypotheses in the search
+space is negatively affected owing to the fixed beam size. To mitigate this
+problem, we store all hypotheses in a single priority queue and use a universal
+score function for hypothesis selection. The proposed algorithm is more
+flexible as the discarded hypotheses can be revisited in a later step. We
+further design a penalty function to punish the hypotheses that tend to produce
+a final translation that is much longer or shorter than expected. Despite its
+simplicity, we show that the proposed decoding algorithm is able to select
+hypotheses with better qualities and improve the translation performance.
+",1,0,0,0,0,0
+7398,Simulation of Parabolic Flow on an Eye-Shaped Domain with Moving Boundary,"  During the upstroke of a normal eye blink, the upper lid moves and paints a
+thin tear film over the exposed corneal and conjunctival surfaces. This thin
+tear film may be modeled by a nonlinear fourth-order PDE derived from
+lubrication theory. A challenge in the numerical simulation of this model is to
+include both the geometry of the eye and the movement of the eyelid. A pair of
+orthogonal and conformal maps transform a square into an approximate
+representation of the exposed ocular surface of a human eye. A spectral
+collocation method on the square produces relatively efficient solutions on the
+eye-shaped domain via these maps. The method is demonstrated on linear and
+nonlinear second-order diffusion equations and shown to have excellent accuracy
+as measured pointwise or by conservation checks. Future work will use the
+method for thin-film equations on the same type of domain.
+",0,0,1,0,0,0
+7399,Faster algorithms for 1-mappability of a sequence,"  In the k-mappability problem, we are given a string x of length n and
+integers m and k, and we are asked to count, for each length-m factor y of x,
+the number of other factors of length m of x that are at Hamming distance at
+most k from y. We focus here on the version of the problem where k = 1. The
+fastest known algorithm for k = 1 requires time O(mn log n/ log log n) and
+space O(n). We present two algorithms that require worst-case time O(mn) and
+O(n log^2 n), respectively, and space O(n), thus greatly improving the state of
+the art. Moreover, we present an algorithm that requires average-case time and
+space O(n) for integer alphabets if m = {\Omega}(log n/ log {\sigma}), where
+{\sigma} is the alphabet size.
+",1,0,0,0,0,0
+7400,PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network,"  Music creation is typically composed of two parts: composing the musical
+score, and then performing the score with instruments to make sounds. While
+recent work has made much progress in automatic music generation in the
+symbolic domain, few attempts have been made to build an AI model that can
+render realistic music audio from musical scores. Directly synthesizing audio
+with sound sample libraries often leads to mechanical and deadpan results,
+since musical scores do not contain performance-level information, such as
+subtle changes in timing and dynamics. Moreover, while the task may sound like
+a text-to-speech synthesis problem, there are fundamental differences since
+music audio has rich polyphonic sounds. To build such an AI performer, we
+propose in this paper a deep convolutional model that learns in an end-to-end
+manner the score-to-audio mapping between a symbolic representation of music
+called the piano rolls and an audio representation of music called the
+spectrograms. The model consists of two subnets: the ContourNet, which uses a
+U-Net structure to learn the correspondence between piano rolls and
+spectrograms and to give an initial result; and the TextureNet, which further
+uses a multi-band residual network to refine the result by adding the spectral
+texture of overtones and timbre. We train the model to generate music clips of
+the violin, cello, and flute, with a dataset of moderate size. We also present
+the result of a user study that shows our model achieves higher mean opinion
+score (MOS) in naturalness and emotional expressivity than a WaveNet-based
+model and two commercial sound libraries. We open our source code at
+this https URL
+",1,0,0,0,0,0
+7401,Simulation optimization: A review of algorithms and applications,"  Simulation Optimization (SO) refers to the optimization of an objective
+function subject to constraints, both of which can be evaluated through a
+stochastic simulation. To address specific features of a particular
+simulation---discrete or continuous decisions, expensive or cheap simulations,
+single or multiple outputs, homogeneous or heterogeneous noise---various
+algorithms have been proposed in the literature. As one can imagine, there
+exist several competing algorithms for each of these classes of problems. This
+document emphasizes the difficulties in simulation optimization as compared to
+mathematical programming, makes reference to state-of-the-art algorithms in the
+field, examines and contrasts the different approaches used, reviews some of
+the diverse applications that have been tackled by these methods, and
+speculates on future directions in the field.
+",1,0,1,0,0,0
+7402,Learning Disentangled Representations with Semi-Supervised Deep Generative Models,"  Variational autoencoders (VAEs) learn representations of data by jointly
+training a probabilistic encoder and decoder network. Typically these models
+encode all features of the data into a single variable. Here we are interested
+in learning disentangled representations that encode distinct aspects of the
+data into separate variables. We propose to learn such representations using
+model architectures that generalise from standard VAEs, employing a general
+graphical model structure in the encoder and decoder. This allows us to train
+partially-specified models that make relatively strong assumptions about a
+subset of interpretable variables and rely on the flexibility of neural
+networks to learn representations for the remaining variables. We further
+define a general objective for semi-supervised learning in this model class,
+which can be approximated using an importance sampling procedure. We evaluate
+our framework's ability to learn disentangled representations, both by
+qualitative exploration of its generative capacity, and quantitative evaluation
+of its discriminative ability on a variety of models and datasets.
+",1,0,0,1,0,0
+7403,Meteorites from Phobos and Deimos at Earth?,"  We examine the conditions under which material from the martian moons Phobos
+and Deimos could reach our planet in the form of meteorites. We find that the
+necessary ejection speeds from these moons (900 and 600 m/s for Phobos and
+Deimos respectively) are much smaller than from Mars' surface (5000 m/s). These
+speeds are below typical impact speeds for asteroids and comets (10-40 km/s) at
+Mars' orbit, and we conclude that the delivery of meteorites from Phobos and
+Deimos to the Earth can occur.
+",0,1,0,0,0,0
+7404,Computational Aspects of Optimal Strategic Network Diffusion,"  The diffusion of information has been widely modeled as stochastic diffusion
+processes on networks. Alshamsi et al. (2018) proposed a model of strategic
+diffusion in networks of related activities. In this work we investigate the
+computational aspects of finding the optimal strategy of strategic diffusion.
+We prove that finding an optimal solution to the problem is NP-complete in a
+general case. To overcome this computational difficulty, we present an
+algorithm to compute an optimal solution based on a dynamic programming
+technique. We also show that the problem is fixed parameter-tractable when
+parametrized by the product of the treewidth and maximum degree. We analyze the
+possibility of developing an efficient approximation algorithm and show that
+two heuristic algorithms proposed so far cannot have better than a logarithmic
+approximation guarantee. Finally, we prove that the problem does not admit
+better than a logarithmic approximation, unless P=NP.
+",1,0,0,0,0,0
+7405,Watermark Signal Detection and Its Application in Image Retrieval,"  We propose a few fundamental techniques to obtain effective watermark
+features of images in the image search index, and utilize the signals in a
+commercial search engine to improve the image search quality. We collect a
+diverse and large set (about 1M) of images with human labels indicating whether
+the image contains visible watermark. We train a few deep convolutional neural
+networks to extract watermark information from the raw images. We also analyze
+the images based on their domains to get watermark information from a
+domain-based watermark classifier. The deep CNN classifiers we trained can
+achieve high accuracy on the watermark data set. We demonstrate that using
+these signals in Bing image search ranker, powered by LambdaMART, can
+effectively reduce the watermark rate during the online image ranking.
+",1,0,0,0,0,0
+7406,Loop-augmented forests and a variant of the Foulkes' conjecture,"  A loop-augmented forest is a labeled rooted forest with loops on some of its
+roots. By exploiting an interplay between nilpotent partial functions and
+labeled rooted forests, we investigate the permutation action of the symmetric
+group on loop-augmented forests. Furthermore, we describe an extension of the
+Foulkes' conjecture and prove a special case. Among other important outcomes of
+our analysis are a complete description of the stabilizer subgroup of an
+idempotent in the semigroup of partial transformations and a generalization of
+the (Knuth-Sagan) hook length formula.
+",0,0,1,0,0,0
+7407,Replication issues in syntax-based aspect extraction for opinion mining,"  Reproducing experiments is an important instrument to validate previous work
+and build upon existing approaches. It has been tackled numerous times in
+different areas of science. In this paper, we introduce an empirical
+replicability study of three well-known algorithms for syntactic centric
+aspect-based opinion mining. We show that reproducing results continues to be a
+difficult endeavor, mainly due to the lack of details regarding preprocessing
+and parameter setting, as well as due to the absence of available
+implementations that clarify these details. We consider these are important
+threats to validity of the research on the field, specifically when compared to
+other problems in NLP where public datasets and code availability are critical
+validity components. We conclude by encouraging code-based research, which we
+think has a key role in helping researchers to understand the meaning of the
+state-of-the-art better and to generate continuous advances.
+",1,0,0,0,0,0
+7408,Manifold Adversarial Learning,"  The recently proposed adversarial training methods show the robustness to
+both adversarial and original examples and achieve state-of-the-art results in
+supervised and semi-supervised learning. All the existing adversarial training
+methods con- sider only how the worst perturbed examples (i.e., adversarial
+examples) could affect the model output. Despite their success, we argue that
+such setting may be in lack of generalization, since the output space (or label
+space) is apparently less informative. In this paper, we propose a novel
+method, called Manifold Adver- sarial Training (MAT). MAT manages to build an
+adversarial framework based on how the worst perturbation could affect the
+distributional manifold rather than the output space. Particularly, a latent
+data space with the Gaussian Mixture Model (GMM) will be first derived. On one
+hand, MAT tries to perturb the input samples in the way that would rough the
+distributional manifold the worst. On the other hand, the deep learning model
+is trained trying to promote in the latent space the manifold smoothness,
+measured by the variation of Gaussian mixtures (given the local perturbation
+around the data point). Importantly, since the latent space is more informative
+than the output space, the proposed MAT can learn better a ro- bust and compact
+data representation, leading to further performance improvemen- t. The proposed
+MAT is important in that it can be considered as a superset of one
+recently-proposed discriminative feature learning approach called center loss.
+We conducted a series of experiments in both supervised and semi-supervised
+learn- ing on three benchmark data sets, showing that the proposed MAT can
+achieve remarkable performance, much better than those of the state-of-the-art
+adversarial approaches.
+",0,0,0,1,0,0
+7409,An Extension of Proof Graphs for Disjunctive Parameterised Boolean Equation Systems,"  A parameterised Boolean equation system (PBES) is a set of equations that
+defines sets as the least and/or greatest fixed-points that satisfy the
+equations. This system is regarded as a declarative program defining functions
+that take a datum and returns a Boolean value. The membership problem of PBESs
+is a problem to decide whether a given element is in the defined set or not,
+which corresponds to an execution of the program. This paper introduces reduced
+proof graphs, and studies a technique to solve the membership problem of PBESs,
+which is undecidable in general, by transforming it into a reduced proof graph.
+A vertex X(v) in a proof graph represents that the data v is in the set X, if
+the graph satisfies conditions induced from a given PBES. Proof graphs are,
+however, infinite in general. Thus we introduce vertices each of which stands
+for a set of vertices of the original ones, which possibly results in a finite
+graph. For a subclass of disjunctive PBESs, we clarify some conditions which
+reduced proof graphs should satisfy. We also show some examples having no
+finite proof graph except for reduced one. We further propose a reduced
+dependency space, which contains reduced proof graphs as sub-graphs if a proof
+graph exists. We provide a procedure to construct finite reduced dependency
+spaces, and show the soundness and completeness of the procedure.
+",1,0,0,0,0,0
+7410,A direct measure of free electron gas via the Kinematic Sunyaev-Zel'dovich effect in Fourier-space analysis,"  We present the measurement of the kinematic Sunyaev-Zel'dovich (kSZ) effect
+in Fourier space, rather than in real space. We measure the density-weighted
+pairwise kSZ power spectrum, the first use of this promising approach, by
+cross-correlating a cleaned Cosmic Microwave Background (CMB) temperature map,
+which jointly uses both Planck Release 2 and Wilkinson Microwave Anisotropy
+Probe nine-year data, with the two galaxy samples, CMASS and LOWZ, derived fr
+om the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12. With the
+current data, we constrain the average optical depth $\tau$ multiplied by the
+ratio of the Hubble parameter at redshift $z$ and the present day, $E=H/H_0$;
+we find $\tau E = (3.95\pm1.62)\times10^{-5}$ for LOWZ and $\tau E = ( 1.25\pm
+1.06)\times10^{-5}$ for CMASS, with the optimal angular radius of an aperture
+photometry filter to estimate the CMB temperature distortion associ ated with
+each galaxy. By repeating the pairwise kSZ power analysis for various aperture
+radii, we measure the optical depth as a function of aperture ra dii. While
+this analysis results in the kSZ signals with only evidence for a detection,
+${\rm S/N}=2.54$ for LOWZ and $1.24$ for CMASS, the combination of future CMB
+and spectroscopic galaxy surveys should enable precision measurements. We
+estimate that the combination of CMB-S4 and data from DESI shoul d yield
+detections of the kSZ signal with ${\rm S/N}=70-100$, depending on the
+resolution of CMB-S4.
+",0,1,0,0,0,0
+7411,Scalable Structure Learning for Probabilistic Soft Logic,"  Statistical relational frameworks such as Markov logic networks and
+probabilistic soft logic (PSL) encode model structure with weighted first-order
+logical clauses. Learning these clauses from data is referred to as structure
+learning. Structure learning alleviates the manual cost of specifying models.
+However, this benefit comes with high computational costs; structure learning
+typically requires an expensive search over the space of clauses which involves
+repeated optimization of clause weights. In this paper, we propose the first
+two approaches to structure learning for PSL. We introduce a greedy
+search-based algorithm and a novel optimization method that trade-off
+scalability and approximations to the structure learning problem in varying
+ways. The highly scalable optimization method combines data-driven generation
+of clauses with a piecewise pseudolikelihood (PPLL) objective that learns model
+structure by optimizing clause weights only once. We compare both methods
+across five real-world tasks, showing that PPLL achieves an order of magnitude
+runtime speedup and AUC gains up to 15% over greedy search.
+",0,0,0,1,0,0
+7412,A mode-coupling theory analysis of the rotation driven translational motion of aqueous polyatomic ions,"  In contrast to simple monatomic alkali and halide ions, complex polyatomic
+ions like nitrate, acetate, nitrite, chlorate etc. have not been studied in any
+great detail. Experiments have shown that diffusion of polyatomic ions exhibits
+many remarkable anomalies, notable among them is the fact that polyatomic ions
+with similar size show large difference in their diffusivity values. This fact
+has drawn relatively little interest in scientific discussions. We show here
+that a mode-coupling theory (MCT) can provide a physically meaningful
+interpretation of the anomalous diffusivity of polyatomic ions in water, by
+including the contribution of rotational jumps on translational friction. The
+two systems discussed here, namely aqueous nitrate ion and aqueous acetate ion,
+although have similar ionic radii exhibit largely different diffusivity values
+due to the differences in the rate of their rotational jump motions. We have
+further verified the mode-coupling theory formalism by comparing it with
+experimental and simulation results that agrees well with the theoretical
+prediction.
+",0,1,0,0,0,0
+7413,Regulating Access to System Sensors in Cooperating Programs,"  Modern operating systems such as Android, iOS, Windows Phone, and Chrome OS
+support a cooperating program abstraction. Instead of placing all functionality
+into a single program, programs cooperate to complete tasks requested by users.
+However, untrusted programs may exploit interactions with other programs to
+obtain unauthorized access to system sensors either directly or through
+privileged services. Researchers have proposed that programs should only be
+authorized to access system sensors on a user-approved input event, but these
+methods do not account for possible delegation done by the program receiving
+the user input event. Furthermore, proposed delegation methods do not enable
+users to control the use of their input events accurately. In this paper, we
+propose ENTRUST, a system that enables users to authorize sensor operations
+that follow their input events, even if the sensor operation is performed by a
+program different from the program receiving the input event. ENTRUST tracks
+user input as well as delegation events and restricts the execution of such
+events to compute unambiguous delegation paths to enable accurate and reusable
+authorization of sensor operations. To demonstrate this approach, we implement
+the ENTRUST authorization system for Android. We find, via a laboratory user
+study, that attacks can be prevented at a much higher rate (54-64%
+improvement); and via a field user study, that ENTRUST requires no more than
+three additional authorizations per program with respect to the first-use
+approach, while incurring modest performance (<1%) and memory overheads (5.5 KB
+per program).
+",1,0,0,0,0,0
+7414,Identification of Treatment Effects under Conditional Partial Independence,"  Conditional independence of treatment assignment from potential outcomes is a
+commonly used but nonrefutable assumption. We derive identified sets for
+various treatment effect parameters under nonparametric deviations from this
+conditional independence assumption. These deviations are defined via a
+conditional treatment assignment probability, which makes it straightforward to
+interpret. Our results can be used to assess the robustness of empirical
+conclusions obtained under the baseline conditional independence assumption.
+",0,0,0,1,0,0
+7415,First demonstration of emulsion multi-stage shifter for accelerator neutrino experiment in J-PARC T60,"  We describe the first ever implementation of an emulsion multi-stage shifter
+in an accelerator neutrino experiment. The system was installed in the neutrino
+monitor building in J-PARC as a part of a test experiment T60 and stable
+operation was maintained for a total of 126.6 days. By applying time
+information to emulsion films, various results were obtained. Time resolutions
+of 5.3 to 14.7 s were evaluated in an operation spanning 46.9 days (time
+resolved numbers of 3.8--1.4$\times10^{5}$). By using timing and spatial
+information, a reconstruction of coincident events that consisted of high
+multiplicity events and vertex events, including neutrino events was performed.
+Emulsion events were matched to events observed by INGRID, one of near
+detectors of the T2K experiment, with high reliability (98.5\%) and hybrid
+analysis was established via use of the multi-stage shifter. The results
+demonstrate that the multi-stage shifter is feasible for use in neutrino
+experiments.
+",0,1,0,0,0,0
+7416,Deep Generative Adversarial Networks for Compressed Sensing Automates MRI,"  Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear
+inverse task demanding time and resource intensive computations that can
+substantially trade off {\it accuracy} for {\it speed} in real-time imaging. In
+addition, state-of-the-art compressed sensing (CS) analytics are not cognizant
+of the image {\it diagnostic quality}. To cope with these challenges we put
+forth a novel CS framework that permeates benefits from generative adversarial
+networks (GAN) to train a (low-dimensional) manifold of diagnostic-quality MR
+images from historical patients. Leveraging a mixture of least-squares (LS)
+GANs and pixel-wise $\ell_1$ cost, a deep residual network with skip
+connections is trained as the generator that learns to remove the {\it
+aliasing} artifacts by projecting onto the manifold. LSGAN learns the texture
+details, while $\ell_1$ controls the high-frequency noise. A multilayer
+convolutional neural network is then jointly trained based on diagnostic
+quality images to discriminate the projection quality. The test phase performs
+feed-forward propagation over the generator network that demands a very low
+computational overhead. Extensive evaluations are performed on a large
+contrast-enhanced MR dataset of pediatric patients. In particular, images rated
+based on expert radiologists corroborate that GANCS retrieves high contrast
+images with detailed texture relative to conventional CS, and pixel-wise
+schemes. In addition, it offers reconstruction under a few milliseconds, two
+orders of magnitude faster than state-of-the-art CS-MRI schemes.
+",1,0,0,1,0,0
+7417,On multiplicative independence of rational function iterates,"  We give lower bounds for the degree of multiplicative combinations of
+iterates of rational functions (with certain exceptions) over a general field,
+establishing the multiplicative independence of said iterates. This leads to a
+generalisation of Gao's method for constructing elements in the finite field
+$\mathbb{F}_{q^n}$ whose orders are larger than any polynomial in $n$ when $n$
+becomes large. Additionally, we discuss the finiteness of polynomials which
+translate a given finite set of polynomials to become multiplicatively
+dependent.
+",0,0,1,0,0,0
+7418,Haptic Assembly and Prototyping: An Expository Review,"  An important application of haptic technology to digital product development
+is in virtual prototyping (VP), part of which deals with interactive planning,
+simulation, and verification of assembly-related activities, collectively
+called virtual assembly (VA). In spite of numerous research and development
+efforts over the last two decades, the industrial adoption of haptic-assisted
+VP/VA has been slower than expected. Putting hardware limitations aside, the
+main roadblocks faced in software development can be traced to the lack of
+effective and efficient computational models of haptic feedback. Such models
+must 1) accommodate the inherent geometric complexities faced when assembling
+objects of arbitrary shape; and 2) conform to the computation time limitation
+imposed by the notorious frame rate requirements---namely, 1 kHz for haptic
+feedback compared to the more manageable 30-60 Hz for graphic rendering. The
+simultaneous fulfillment of these competing objectives is far from trivial.
+This survey presents some of the conceptual and computational challenges and
+opportunities as well as promising future directions in haptic-assisted VP/VA,
+with a focus on haptic assembly from a geometric modeling and spatial reasoning
+perspective. The main focus is on revisiting definitions and classifications of
+different methods used to handle the constrained multibody simulation in
+real-time, ranging from physics-based and geometry-based to hybrid and unified
+approaches using a variety of auxiliary computational devices to specify,
+impose, and solve assembly constraints. Particular attention is given to the
+newly developed 'analytic methods' inherited from motion planning and protein
+docking that have shown great promise as an alternative paradigm to the more
+popular combinatorial methods.
+",1,0,0,0,0,0
+7419,On links between horocyclic and geodesic orbits on geometrically infinite surfaces,"  We study the topological dynamics of the horocycle flow $h_\mathbb{R}$ on a
+geometrically infinite hyperbolic surface S. Let u be a non-periodic vector for
+$h_\mathbb{R}$ in T^1 S. Suppose that the half-geodesic $u(\mathbb{R}^+)$ is
+almost minimizing and that the injectivity radius along $u(\mathbb{R}^+)$ has a
+finite inferior limit $Inj(u(\mathbb{R}^+))$. We prove that the closure of
+$h_\mathbb{R} u$ meets the geodesic orbit along un unbounded sequence of points
+$g_{t_n} u$. Moreover, if $Inj(u(\mathbb{R}^+)) = 0$, the whole half-orbit
+$g_{\mathbb{R}^+} u$ is contained in $h_\mathbb{R} u$. When
+$Inj(u(\mathbb{R}^+)) > 0$, it is known that in general $g_{\mathbb{R}^+} u
+\subset h_\mathbb{R} u$. Yet, we give a construction where
+$Inj(u(\mathbb{R}^+)) > 0$ and $g_{\mathbb{R}^+} u \subset h_\mathbb{R} u$,
+which also constitutes a counterexample to Proposition 3 of [Led97].
+",0,0,1,0,0,0
+7420,Waist size for cusps in hyperbolic 3-manifolds II,"  The waist size of a cusp in an orientable hyperbolic 3-manifold is the length
+of the shortest nontrivial curve generated by a parabolic isometry in the
+maximal cusp boundary. Previously, it was shown that the smallest possible
+waist size, which is 1, is realized only by the cusp in the figure-eight knot
+complement. In this paper, it is proved that the next two smallest waist sizes
+are realized uniquely for the cusps in the $5_2$ knot complement and the
+manifold obtained by (2,1)-surgery on the Whitehead link. One application is an
+improvement on the universal upper bound for the length of an unknotting tunnel
+in a 2-cusped hyperbolic 3-manifold.
+",0,0,1,0,0,0
+7421,Landau levels from neutral Bogoliubov particles in two-dimensional nodal superconductors under strain and doping gradients,"  Motivated by recent work on strain-induced pseudo-magnetic fields in Dirac
+and Weyl semimetals, we analyze the possibility of analogous fields in
+two-dimensional nodal superconductors. We consider the prototypical case of a
+d-wave superconductor, a representative of the cuprate family, and find that
+the presence of weak strain leads to pseudo-magnetic fields and Landau
+quantization of Bogoliubov quasiparticles in the low-energy sector. A similar
+effect is induced by the presence of generic, weak doping gradients. In
+contrast to genuine magnetic fields in superconductors, the strain- and doping
+gradient-induced pseudo-magnetic fields couple in a way that preserves
+time-reversal symmetry and is not subject to the screening associated with the
+Meissner effect. These effects can be probed by tuning weak applied
+supercurrents which lead to shifts in the energies of the Landau levels and
+hence to quantum oscillations in thermodynamic and transport quantities.
+",0,1,0,0,0,0
+7422,Modular Labelled Sequent Calculi for Abstract Separation Logics,"  Abstract separation logics are a family of extensions of Hoare logic for
+reasoning about programs that manipulate resources such as memory locations.
+These logics are ""abstract"" because they are independent of any particular
+concrete resource model. Their assertion languages, called propositional
+abstract separation logics (PASLs), extend the logic of (Boolean) Bunched
+Implications (BBI) in various ways. In particular, these logics contain the
+connectives $*$ and $-\!*$, denoting the composition and extension of resources
+respectively.
+This added expressive power comes at a price since the resulting logics are
+all undecidable. Given their wide applicability, even a semi-decision procedure
+for these logics is desirable. Although several PASLs and their relationships
+with BBI are discussed in the literature, the proof theory and automated
+reasoning for these logics were open problems solved by the conference version
+of this paper, which developed a modular proof theory for various PASLs using
+cut-free labelled sequent calculi. This paper non-trivially improves upon this
+previous work by giving a general framework of calculi on which any new axiom
+in the logic satisfying a certain form corresponds to an inference rule in our
+framework, and the completeness proof is generalised to consider such axioms.
+Our base calculus handles Calcagno et al.'s original logic of separation
+algebras by adding sound rules for partial-determinism and cancellativity,
+while preserving cut-elimination. We then show that many important properties
+in separation logic, such as indivisible unit, disjointness, splittability, and
+cross-split, can be expressed in our general axiom form. Thus our framework
+offers inference rules and completeness for these properties for free. Finally,
+we show how our calculi reduce to calculi with global label substitutions,
+enabling more efficient implementation.
+",1,0,0,0,0,0
+7423,Affine maps between quadratic assignment polytopes and subgraph isomorphism polytopes,"  We consider two polytopes. The quadratic assignment polytope $QAP(n)$ is the
+convex hull of the set of tensors $x\otimes x$, $x \in P_n$, where $P_n$ is the
+set of $n\times n$ permutation matrices. The second polytope is defined as
+follows. For every permutation of vertices of the complete graph $K_n$ we
+consider appropriate $\binom{n}{2} \times \binom{n}{2}$ permutation matrix of
+the edges of $K_n$. The Young polytope $P((n-2,2))$ is the convex hull of all
+such matrices.
+In 2009, S. Onn showed that the subgraph isomorphism problem can be reduced
+to optimization both over $QAP(n)$ and over $P((n-2,2))$. He also posed the
+question whether $QAP(n)$ and $P((n-2,2))$, having $n!$ vertices each, are
+isomorphic. We show that $QAP(n)$ and $P((n-2,2))$ are not isomorphic. Also, we
+show that $QAP(n)$ is a face of $P((2n-2,2))$, but $P((n-2,2))$ is a projection
+of $QAP(n)$.
+",1,0,1,0,0,0
+7424,LinXGBoost: Extension of XGBoost to Generalized Local Linear Models,"  XGBoost is often presented as the algorithm that wins every ML competition.
+Surprisingly, this is true even though predictions are piecewise constant. This
+might be justified in high dimensional input spaces, but when the number of
+features is low, a piecewise linear model is likely to perform better. XGBoost
+was extended into LinXGBoost that stores at each leaf a linear model. This
+extension, equivalent to piecewise regularized least-squares, is particularly
+attractive for regression of functions that exhibits jumps or discontinuities.
+Those functions are notoriously hard to regress. Our extension is compared to
+the vanilla XGBoost and Random Forest in experiments on both synthetic and
+real-world data sets.
+",1,0,0,1,0,0
+7425,Superfluidity and relaxation dynamics of a laser-stirred 2D Bose gas,"  We investigate the superfluid behavior of a two-dimensional (2D) Bose gas of
+$^{87}$Rb atoms using classical field dynamics. In the experiment by R.
+Desbuquois \textit{et al.}, Nat. Phys. \textbf{8}, 645 (2012), a 2D
+quasicondensate in a trap is stirred by a blue-detuned laser beam along a
+circular path around the trap center. Here, we study this experiment from a
+theoretical perspective. The heating induced by stirring increases rapidly
+above a velocity $v_c$, which we define as the critical velocity. We identify
+the superfluid, the crossover, and the thermal regime by a finite, a sharply
+decreasing, and a vanishing critical velocity, respectively. We demonstrate
+that the onset of heating occurs due to the creation of vortex-antivortex
+pairs. A direct comparison of our numerical results to the experimental ones
+shows good agreement, if a systematic shift of the critical phase-space density
+is included. We relate this shift to the absence of thermal equilibrium between
+the condensate and the thermal wings, which were used in the experiment to
+extract the temperature. We expand on this observation by studying the full
+relaxation dynamics between the condensate and the thermal cloud.
+",0,1,0,0,0,0
+7426,Measuring Player Retention and Monetization using the Mean Cumulative Function,"  Game analytics supports game development by providing direct quantitative
+feedback about player experience. Player retention and monetization in
+particular have become central business statistics in free-to-play game
+development. Many metrics have been used for this purpose. However, game
+developers often want to perform analytics in a timely manner before all users
+have churned from the game. This causes data censoring which makes many metrics
+biased. In this work, we introduce how the Mean Cumulative Function (MCF) can
+be used to generalize many academic metrics to censored data. The MCF allows us
+to estimate the expected value of a metric over time, which for example may be
+the number of game sessions, number of purchases, total playtime and lifetime
+value. Furthermore, the popular retention rate metric is the derivative of this
+estimate applied to the expected number of distinct days played. Statistical
+tools based on the MCF allow game developers to determine whether a given
+change improves a game, or whether a game is yet good enough for public
+release. The advantages of this approach are demonstrated on a real
+in-development free-to-play mobile game, the Hipster Sheep.
+",0,0,0,1,0,0
+7427,Bohemian Upper Hessenberg Toeplitz Matrices,"  We look at Bohemian matrices, specifically those with entries from $\{-1, 0,
+{+1}\}$. More, we specialize the matrices to be upper Hessenberg, with
+subdiagonal entries $1$. Even more, we consider Toeplitz matrices of this kind.
+Many properties remain after these specializations, some of which surprised us.
+Focusing on only those matrices whose characteristic polynomials have maximal
+height allows us to explicitly identify these polynomials and give a lower
+bound on their height. This bound is exponential in the order of the matrix.
+",1,0,0,0,0,0
+7428,A Study of Energy Trading in a Low-Voltage Network: Centralised and Distributed Approaches,"  Over the past years, distributed energy resources (DER) have been the object
+of many studies, which recognise and establish their emerging role in the
+future of power systems. However, the implementation of many scenarios and
+mechanism are still challenging. This paper provides an overview of a local
+energy market and explores the approaches in which consumers and prosumers take
+part in this market. Therefore, the purpose of this paper is to review the
+benefits of local markets for users. This study assesses the performance of
+distributed and centralised trading mechanisms, comparing scenarios where the
+objective of the exchange may be based on individual or social welfare.
+Simulation results show the advantages of local markets and demonstrate the
+importance of advancing the understanding of local markets.
+",1,0,0,0,0,0
+7429,Evidence Logics with Relational Evidence,"  Dynamic evidence logics are logics for reasoning about the evidence and
+evidence-based beliefs of agents in a dynamic environment. In this paper, we
+introduce a family of logics for reasoning about relational evidence: evidence
+that involves an orderings of states in terms of their relative plausibility.
+We provide sound and complete axiomatizations for the logics. We also present
+several evidential actions and prove soundness and completeness for the
+associated dynamic logics.
+",1,0,0,0,0,0
+7430,Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data,"  Researchers are often interested in assessing the impact of an intervention
+on an outcome of interest in situations where the intervention is
+non-randomised, information is available at an aggregate level, the
+intervention is only applied to one or few units, the intervention is binary,
+and there are outcome measurements at multiple time points. In this paper, we
+review existing methods for causal inference in the setup just outlined. We
+detail the assumptions underlying each method, emphasise connections between
+the different approaches and provide guidelines regarding their practical
+implementation. Several open problems are identified thus highlighting the need
+for future research.
+",0,0,0,1,0,0
+7431,"On the Difference Between Closest, Furthest, and Orthogonal Pairs: Nearly-Linear vs Barely-Subquadratic Complexity in Computational Geometry","  Point location problems for $n$ points in $d$-dimensional Euclidean space
+(and $\ell_p$ spaces more generally) have typically had two kinds of
+running-time solutions:
+* (Nearly-Linear) less than $d^{poly(d)} \cdot n \log^{O(d)} n$ time, or
+* (Barely-Subquadratic) $f(d) \cdot n^{2-1/\Theta(d)}$ time, for various $f$.
+For small $d$ and large $n$, ""nearly-linear"" running times are generally
+feasible, while ""barely-subquadratic"" times are generally infeasible. For
+example, in the Euclidean metric, finding a Closest Pair among $n$ points in
+${\mathbb R}^d$ is nearly-linear, solvable in $2^{O(d)} \cdot n \log^{O(1)} n$
+time, while known algorithms for Furthest Pair (the diameter of the point set)
+are only barely-subquadratic, requiring $\Omega(n^{2-1/\Theta(d)})$ time. Why
+do these proximity problems have such different time complexities? Is there a
+barrier to obtaining nearly-linear algorithms for problems which are currently
+only barely-subquadratic?
+We give a novel exact and deterministic self-reduction for the Orthogonal
+Vectors problem on $n$ vectors in $\{0,1\}^d$ to $n$ vectors in ${\mathbb
+Z}^{\omega(\log d)}$ that runs in $2^{o(d)}$ time. As a consequence,
+barely-subquadratic problems such as Euclidean diameter, Euclidean bichromatic
+closest pair, ray shooting, and incidence detection do not have
+$O(n^{2-\epsilon})$ time algorithms (in Turing models of computation) for
+dimensionality $d = \omega(\log \log n)^2$, unless the popular Orthogonal
+Vectors Conjecture and the Strong Exponential Time Hypothesis are false. That
+is, while poly-log-log-dimensional Closest Pair is in $n^{1+o(1)}$ time, the
+analogous case of Furthest Pair can encode larger-dimensional problems
+conjectured to require $n^{2-o(1)}$ time. We also show that the All-Nearest
+Neighbors problem in $\omega(\log n)$ dimensions requires $n^{2-o(1)}$ time to
+solve, assuming either of the above conjectures.
+",1,0,0,0,0,0
+7432,Efficient Nonparametric Bayesian Inference For X-Ray Transforms,"  We consider the statistical inverse problem of recovering a function $f: M
+\to \mathbb R$, where $M$ is a smooth compact Riemannian manifold with
+boundary, from measurements of general $X$-ray transforms $I_a(f)$ of $f$,
+corrupted by additive Gaussian noise. For $M$ equal to the unit disk with
+`flat' geometry and $a=0$ this reduces to the standard Radon transform, but our
+general setting allows for anisotropic media $M$ and can further model local
+`attenuation' effects -- both highly relevant in practical imaging problems
+such as SPECT tomography. We propose a nonparametric Bayesian inference
+approach based on standard Gaussian process priors for $f$. The posterior
+reconstruction of $f$ corresponds to a Tikhonov regulariser with a reproducing
+kernel Hilbert space norm penalty that does not require the calculation of the
+singular value decomposition of the forward operator $I_a$. We prove
+Bernstein-von Mises theorems that entail that posterior-based inferences such
+as credible sets are valid and optimal from a frequentist point of view for a
+large family of semi-parametric aspects of $f$. In particular we derive the
+asymptotic distribution of smooth linear functionals of the Tikhonov
+regulariser, which is shown to attain the semi-parametric Cramér-Rao
+information bound. The proofs rely on an invertibility result for the `Fisher
+information' operator $I_a^*I_a$ between suitable function spaces, a result of
+independent interest that relies on techniques from microlocal analysis. We
+illustrate the performance of the proposed method via simulations in various
+settings.
+",0,0,1,1,0,0
+7433,Glass-Box Program Synthesis: A Machine Learning Approach,"  Recently proposed models which learn to write computer programs from data use
+either input/output examples or rich execution traces. Instead, we argue that a
+novel alternative is to use a glass-box loss function, given as a program
+itself that can be directly inspected. Glass-box optimization covers a wide
+range of problems, from computing the greatest common divisor of two integers,
+to learning-to-learn problems.
+In this paper, we present an intelligent search system which learns, given
+the partial program and the glass-box problem, the probabilities over the space
+of programs. We empirically demonstrate that our informed search procedure
+leads to significant improvements compared to brute-force program search, both
+in terms of accuracy and time. For our experiments we use rich context free
+grammars inspired by number theory, text processing, and algebra. Our results
+show that (i) performing 4 rounds of our framework typically solves about 70%
+of the target problems, (ii) our framework can improve itself even in domain
+agnostic scenarios, and (iii) it can solve problems that would be otherwise too
+slow to solve with brute-force search.
+",1,0,0,1,0,0
+7434,Cable-Driven Actuation for Highly Dynamic Robotic Systems,"  This paper presents design and experimental evaluations of an articulated
+robotic limb called Capler-Leg. The key element of Capler-Leg is its
+single-stage cable-pulley transmission combined with a high-gap radius motor.
+Our cable-pulley system is designed to be as light-weight as possible and to
+additionally serve as the primary cooling element, thus significantly
+increasing the power density and efficiency of the overall system. The total
+weight of active elements on the leg, i.e. the stators and the rotors,
+contribute more than 60% of the total leg weight, which is an order of
+magnitude higher than most existing robots. The resulting robotic leg has low
+inertia, high torque transparency, low manufacturing cost, no backlash, and a
+low number of parts. Capler-Leg system itself, serves as an experimental setup
+for evaluating the proposed cable- pulley design in terms of robustness and
+efficiency. A continuous jump experiment shows a remarkable 96.5 % recuperation
+rate, measured at the battery output. This means that almost all the mechanical
+energy output used during push-off returned back to the battery during
+touch-down.
+",1,0,0,0,0,0
+7435,Improved $A_1-A_\infty$ and related estimates for commutators of rough singular integrals,"  An $A_1-A_\infty$ estimate improving a previous result in arXiv:1607.06432 is
+obtained. Also new a result in terms of the ${A_\infty}$ constant and the one
+supremum $A_q-A_\infty^{\exp}$ constant, is proved, providing a counterpart for
+the result obained in arXiv:1705.08364. Both of the preceding results rely upon
+a sparse domination in terms of bilinear forms for $[b,T_\Omega]$ with
+$\Omega\in L^\infty(\mathbb{S}^{n-1})$ and $b\in BMO$ which is established
+relying upon techniques from arXiv:1705.07397.
+",0,0,1,0,0,0
+7436,Ramsey Classes with Closure Operations (Selected Combinatorial Applications),"  We state the Ramsey property of classes of ordered structures with closures
+and given local properties. This generalises many old and new results: the
+Nešetřil-Rödl Theorem, the author's Ramsey lift of bowtie-free
+graphs as well as the Ramsey Theorem for Finite Models (i.e. structures with
+both functions and relations) thus providing the ultimate generalisation of
+Structural Ramsey Theorem. We give here a more concise reformulation of recent
+authors paper ""All those Ramsey classes (Ramsey classes with closures and
+forbidden homomorphisms)"" and the main purpose of this paper is to show several
+applications. Particularly we prove the Ramsey property of ordered sets with
+equivalences on the power set, Ramsey theorem for Steiner systems, Ramsey
+theorem for resolvable designs and a partial Ramsey type results for
+$H$-factorizable graphs. All of these results are natural, easy to state, yet
+proofs involve most of the theory developed.
+",1,0,1,0,0,0
+7437,On Optimal Spectrum Access of Cognitive Relay With Finite Packet Buffer,"  We investigate a cognitive radio system where secondary user (SU) relays
+primary user (PU) packets using two-phase relaying. SU transmits its own
+packets with some access probability in relaying phase using time sharing. PU
+and SU have queues of finite capacity which results in packet loss when the
+queues are full. Utilizing knowledge of relay queue state, SU aims to maximize
+its packet throughput while keeping packet loss probability of PU below a
+threshold. By exploiting structure of the problem, we formulate it as a linear
+program and find optimal access policy of SU. We also propose low complexity
+sub-optimal access policies, namely constant probability transmission and step
+transmission. Numerical results are presented to compare performance of
+proposed methods and study effect of queue sizes on packet throughput.
+",1,0,0,0,0,0
+7438,Visibility of minorities in social networks,"  Homophily can put minority groups at a disadvantage by restricting their
+ability to establish links with people from a majority group. This can limit
+the overall visibility of minorities in the network. Building on a
+Barabási-Albert model variation with groups and homophily, we show how the
+visibility of minority groups in social networks is a function of (i) their
+relative group size and (ii) the presence or absence of homophilic behavior. We
+provide an analytical solution for this problem and demonstrate the existence
+of asymmetric behavior. Finally, we study the visibility of minority groups in
+examples of real-world social networks: sexual contacts, scientific
+collaboration, and scientific citation. Our work presents a foundation for
+assessing the visibility of minority groups in social networks in which
+homophilic or heterophilic behaviour is present.
+",1,1,0,0,0,0
+7439,Interpreted Formalisms for Configurations,"  Imprecise and incomplete specification of system \textit{configurations}
+threatens safety, security, functionality, and other critical system properties
+and uselessly enlarges the configuration spaces to be searched by configuration
+engineers and auto-tuners. To address these problems, this paper introduces
+\textit{interpreted formalisms based on real-world types for configurations}.
+Configuration values are lifted to values of real-world types, which we
+formalize as \textit{subset types} in Coq. Values of these types are dependent
+pairs whose components are values of underlying Coq types and proofs of
+additional properties about them. Real-world types both extend and further
+constrain \textit{machine-level} configurations, enabling richer, proof-based
+checking of their consistency with real-world constraints. Tactic-based proof
+scripts are written once to automate the construction of proofs, if proofs
+exist, for configuration fields and whole configurations. \textit{Failures to
+prove} reveal real-world type errors. Evaluation is based on a case study of
+combinatorial optimization of Hadoop performance by meta-heuristic search over
+Hadoop configurations spaces.
+",1,0,0,0,0,0
+7440,Solvability and microlocal analysis of the fractional Eringen wave equation,"  We discuss unique existence and microlocal regularity properties of Sobolev
+space solutions to the fractional Eringen wave equation, initially given in the
+form of a system of equations in which the classical non-local Eringen
+constitutive equation is generalized by employing space-fractional derivatives.
+Numerical examples illustrate the shape of solutions in dependence of the order
+of the space-fractional derivative.
+",0,0,1,0,0,0
+7441,High-Mobility OFDM Downlink Transmission with Large-Scale Antenna Array,"  In this correspondence, we propose a new receiver design for high-mobility
+orthogonal frequency division multiplexing (OFDM) downlink transmissions with a
+large-scale antenna array. The downlink signal experiences the challenging fast
+time-varying propagation channel. The time-varying nature originates from the
+multiple carrier frequency offsets (CFOs) due to the transceiver oscillator
+frequency offset (OFO) and multiple Doppler shifts. Let the received signal
+first go through a carefully designed beamforming network, which could separate
+multiple CFOs in the spatial domain with sufficient number of receive antennas.
+A joint estimation method for the Doppler shifts and the OFO is further
+developed. Then the conventional single-CFO compensation and channel estimation
+method can be carried out for each beamforming branch. The proposed receiver
+design avoids the complicated time-varying channel estimation, which differs a
+lot from the conventional methods. More importantly, the proposed scheme can be
+applied to the commonly used time-varying channel models, such as the Jakes'
+channel model.
+",1,0,1,0,0,0
+7442,Feature Enhancement in Visually Impaired Images,"  One of the major open problems in computer vision is detection of features in
+visually impaired images. In this paper, we describe a potential solution using
+Phase Stretch Transform, a new computational approach for image analysis, edge
+detection and resolution enhancement that is inspired by the physics of the
+photonic time stretch technique. We mathematically derive the intrinsic
+nonlinear transfer function and demonstrate how it leads to (1) superior
+performance at low contrast levels and (2) a reconfigurable operator for
+hyper-dimensional classification. We prove that the Phase Stretch Transform
+equalizes the input image brightness across the range of intensities resulting
+in a high dynamic range in visually impaired images. We also show further
+improvement in the dynamic range by combining our method with the conventional
+techniques. Finally, our results show a method for computation of mathematical
+derivatives via group delay dispersion operations.
+",1,1,0,0,0,0
+7443,Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks,"  Given two or more Deep Neural Networks (DNNs) with the same or similar
+architectures, and trained on the same dataset, but trained with different
+solvers, parameters, hyper-parameters, regularization, etc., can we predict
+which DNN will have the best test accuracy, and can we do so without peeking at
+the test data? In this paper, we show how to use a new Theory of Heavy-Tailed
+Self-Regularization (HT-SR) to answer this. HT-SR suggests, among other things,
+that modern DNNs exhibit what we call Heavy-Tailed Mechanistic Universality
+(HT-MU), meaning that the correlations in the layer weight matrices can be fit
+to a power law with exponents that lie in common Universality classes from
+Heavy-Tailed Random Matrix Theory (HT-RMT). From this, we develop a Universal
+capacity control metric that is a weighted average of these PL exponents.
+Rather than considering small toy NNs, we examine over 50 different,
+large-scale pre-trained DNNs, ranging over 15 different architectures, trained
+on ImagetNet, each of which has been reported to have different test
+accuracies. We show that this new capacity metric correlates very well with the
+reported test accuracies of these DNNs, looking across each architecture
+(VGG16/.../VGG19, ResNet10/.../ResNet152, etc.). We also show how to
+approximate the metric by the more familiar Product Norm capacity measure, as
+the average of the log Frobenius norm of the layer weight matrices. Our
+approach requires no changes to the underlying DNN or its loss function, it
+does not require us to train a model (although it could be used to monitor
+training), and it does not even require access to the ImageNet data.
+",1,0,0,1,0,0
+7444,Confidence Interval Estimators for MOS Values,"  For the quantification of QoE, subjects often provide individual rating
+scores on certain rating scales which are then aggregated into Mean Opinion
+Scores (MOS). From the observed sample data, the expected value is to be
+estimated. While the sample average only provides a point estimator, confidence
+intervals (CI) are an interval estimate which contains the desired expected
+value with a given confidence level. In subjective studies, the number of
+subjects performing the test is typically small, especially in lab
+environments. The used rating scales are bounded and often discrete like the
+5-point ACR rating scale. Therefore, we review statistical approaches in the
+literature for their applicability in the QoE domain for MOS interval
+estimation (instead of having only a point estimator, which is the MOS). We
+provide a conservative estimator based on the SOS hypothesis and binomial
+distributions and compare its performance (CI width, outlier ratio of CI
+violating the rating scale bounds) and coverage probability with well known CI
+estimators. We show that the provided CI estimator works very well in practice
+for MOS interval estimators, while the commonly used studentized CIs suffer
+from a positive outlier ratio, i.e., CIs beyond the bounds of the rating scale.
+As an alternative, bootstrapping, i.e., random sampling of the subjective
+ratings with replacement, is an efficient CI estimator leading to typically
+smaller CIs, but lower coverage than the proposed estimator.
+",1,0,0,0,0,0
+7445,Atmospheric Circulation and Cloud Evolution on the Highly Eccentric Extrasolar Planet HD 80606b,"  Observations of the highly-eccentric (e~0.9) hot-Jupiter HD 80606b with
+Spitzer have provided some of best probes of the physics at work in exoplanet
+atmospheres. By observing HD 80606b during its periapse passage, atmospheric
+radiative, advective, and chemical timescales can be directly measured and used
+to constrain fundamental planetary properties such as rotation period, tidal
+dissipation rate, and atmospheric composition (including aerosols). Here we
+present three-dimensional general circulation models for HD 80606b that aim to
+further explore the atmospheric physics shaping HD 80606b's observed Spitzer
+phase curves. We find that our models that assume a planetary rotation period
+twice that of the pseudo-synchronous rotation period best reproduce the phase
+variations observed for HD~80606b near periapse passage with Spitzer.
+Additionally, we find that the rapid formation/dissipation and vertical
+transport of clouds in HD 80606b's atmosphere near periapse passage likely
+shapes its observed phase variations. We predict that observations near
+periapse passage at visible wavelengths could constrain the composition and
+formation/advection timescales of the dominant cloud species in HD 80606b's
+atmosphere. The time-variable forcing experienced by exoplanets on eccentric
+orbits provides a unique and important window on radiative, dynamical, and
+chemical processes in planetary atmospheres and an important link between
+exoplanet observations and theory.
+",0,1,0,0,0,0
+7446,Removal of Batch Effects using Generative Adversarial Networks,"  Many biological data analysis processes like Cytometry or Next Generation
+Sequencing (NGS) produce massive amounts of data which needs to be processed in
+batches for down-stream analysis. Such datasets are prone to technical
+variations due to difference in handling the batches possibly at different
+times, by different experimenters or under other different conditions. This
+adds variation to the batches coming from the same source sample. These
+variations are known as Batch Effects. It is possible that these variations and
+natural variations due to biology confound but such situations can be avoided
+by performing experiments in a carefully planned manner. Batch effects can
+hamper down-stream analysis and may also cause results to be inconclusive.
+Thus, it is essential to correct for these effects. Some recent methods propose
+deep learning based solution to solve this problem. We demonstrate that this
+can be solved using a novel Generative Adversarial Networks (GANs) based
+framework. The advantage of using this framework over other prior approaches is
+that here we do not require to choose a reproducing kernel and define its
+parameters.We demonstrate results of our framework on a Mass Cytometry dataset.
+",1,0,0,1,0,0
+7447,The Current-Phase Relation of Ferromagnetic Josephson Junction Between Triplet Superconductors,"  We study the Josephson effect of a $\rm{T_1 F T_2}$ junction, consisting of
+spin-triplet superconductors (T), a weak ferromagnetic metal (F), and
+ferromagnetic insulating interfaces. Two types of the triplet order parameters
+are considered; $(k_x +ik_y)\hat{z}$ and $k_x \hat{x}+k_y\hat{y}$. We compute
+the current density in the ballistic limit by using the generalized
+quasiclassical formalism developed to take into account the interference effect
+of the multilayered ferromagnetic junction. We discuss in detail how the
+current-phase relation is affected by orientations of the d-vectors of
+superconductor and the magnetizations of the ferromagnetic tunneling barrier.
+General condition for the anomalous Josephson effect is also derived.
+",0,1,0,0,0,0
+7448,SpreadCluster: Recovering Versioned Spreadsheets through Similarity-Based Clustering,"  Version information plays an important role in spreadsheet understanding,
+maintaining and quality improving. However, end users rarely use version
+control tools to document spreadsheet version information. Thus, the
+spreadsheet version information is missing, and different versions of a
+spreadsheet coexist as individual and similar spreadsheets. Existing approaches
+try to recover spreadsheet version information through clustering these similar
+spreadsheets based on spreadsheet filenames or related email conversation.
+However, the applicability and accuracy of existing clustering approaches are
+limited due to the necessary information (e.g., filenames and email
+conversation) is usually missing. We inspected the versioned spreadsheets in
+VEnron, which is extracted from the Enron Corporation. In VEnron, the different
+versions of a spreadsheet are clustered into an evolution group. We observed
+that the versioned spreadsheets in each evolution group exhibit certain common
+features (e.g., similar table headers and worksheet names). Based on this
+observation, we proposed an automatic clustering algorithm, SpreadCluster.
+SpreadCluster learns the criteria of features from the versioned spreadsheets
+in VEnron, and then automatically clusters spreadsheets with the similar
+features into the same evolution group. We applied SpreadCluster on all
+spreadsheets in the Enron corpus. The evaluation result shows that
+SpreadCluster could cluster spreadsheets with higher precision and recall rate
+than the filename-based approach used by VEnron. Based on the clustering result
+by SpreadCluster, we further created a new versioned spreadsheet corpus
+VEnron2, which is much bigger than VEnron. We also applied SpreadCluster on the
+other two spreadsheet corpora FUSE and EUSES. The results show that
+SpreadCluster can cluster the versioned spreadsheets in these two corpora with
+high precision.
+",1,0,0,0,0,0
+7449,Field-induced coexistence of $s_{++}$ and $s_{\pm}$ superconducting states in dirty multiband superconductors,"  In multiband systems, such as iron-based superconductors, the superconducting
+states with locking and anti-locking of the interband phase differences, are
+usually considered as mutually exclusive. For example, a dirty two-band system
+with interband impurity scattering undergoes a sharp crossover between the
+$s_{\pm}$ state (which favors phase anti locking) and the $s_{++}$ state (which
+favors phase locking). We discuss here that the situation can be much more
+complex in the presence of an external field or superconducting currents. In an
+external applied magnetic field, dirty two-band superconductors do not feature
+a sharp $s_{\pm}\to s_{++}$ crossover but rather a washed-out crossover to a
+finite region in the parameter space where both $s_{\pm}$ and $s_{++}$ states
+can coexist for example as a lattice or a microemulsion of inclusions of
+different states. The current-carrying regions such as the regions near vortex
+cores can exhibit an $s_\pm$ state while it is the $s_{++}$ state that is
+favored in the bulk. This coexistence of both states can even be realized in
+the Meissner state at the domain's boundaries featuring Meissner currents. We
+demonstrate that there is a magnetic-field-driven crossover between the pure
+$s_{\pm}$ and the $s_{++}$ states.
+",0,1,0,0,0,0
+7450,Superregular grammars do not provide additional explanatory power but allow for a compact analysis of animal song,"  A pervasive belief with regard to the differences between human language and
+animal vocal sequences (song) is that they belong to different classes of
+computational complexity, with animal song belonging to regular languages,
+whereas human language is superregular. This argument, however, lacks empirical
+evidence since superregular analyses of animal song are understudied. The goal
+of this paper is to perform a superregular analysis of animal song, using data
+from gibbons as a case study, and demonstrate that a superregular analysis can
+be effectively used with non-human data. A key finding is that a superregular
+analysis does not increase explanatory power but rather provides for compact
+analysis. For instance, fewer grammatical rules are necessary once
+superregularity is allowed. This pattern is analogous to a previous
+computational analysis of human language, and accordingly, the null hypothesis,
+that human language and animal song are governed by the same type of
+grammatical systems, cannot be rejected.
+",0,0,0,0,1,0
+7451,Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce,"  In this paper, we present a unified end-to-end approach to build a large
+scale Visual Search and Recommendation system for e-commerce. Previous works
+have targeted these problems in isolation. We believe a more effective and
+elegant solution could be obtained by tackling them together. We propose a
+unified Deep Convolutional Neural Network architecture, called VisNet, to learn
+embeddings to capture the notion of visual similarity, across several semantic
+granularities. We demonstrate the superiority of our approach for the task of
+image retrieval, by comparing against the state-of-the-art on the Exact
+Street2Shop dataset. We then share the design decisions and trade-offs made
+while deploying the model to power Visual Recommendations across a catalog of
+50M products, supporting 2K queries a second at Flipkart, India's largest
+e-commerce company. The deployment of our solution has yielded a significant
+business impact, as measured by the conversion-rate.
+",1,0,0,0,0,0
+7452,The effect of inhomogeneous phase on the critical temperature of smart meta-superconductor MgB2,"  The critical temperature (TC) of MgB2, one of the key factors limiting its
+application, is highly desired to be improved. On the basis of the
+meta-material structure, we prepared a smart meta-superconductor structure
+consisting of MgB2 micro-particles and inhomogeneous phases by an ex situ
+process. The effect of inhomogeneous phase on the TC of smart
+meta-superconductor MgB2 was investigated. Results showed that the onset
+temperature (Ton C) of doping samples was lower than those of pure MgB2.
+However, the offset temperature (Toff C) of the sample doped with Y2O3:Eu3+
+nanosheets with a thickness of 2~3 nm which is much less than the coherence
+length of MgB2 is 1.2 K higher than that of pure MgB2. The effect of the
+applied electric field on the TC of sample was also studied. Results indicated
+that with the increase of current, Ton C is slightly increased in the samples
+doping with different inhomogeneous phases. When increasing current, the Toff C
+of the samples doped with nonluminous inhomogeneous phases was decreased.
+However, the Toff C of the luminescent inhomogeneous phase doping samples
+increased and then decreased as increasing current.
+",0,1,0,0,0,0
+7453,Journal of Open Source Software (JOSS): design and first-year review,"  This article describes the motivation, design, and progress of the Journal of
+Open Source Software (JOSS). JOSS is a free and open-access journal that
+publishes articles describing research software. It has the dual goals of
+improving the quality of the software submitted and providing a mechanism for
+research software developers to receive credit. While designed to work within
+the current merit system of science, JOSS addresses the dearth of rewards for
+key contributions to science made in the form of software. JOSS publishes
+articles that encapsulate scholarship contained in the software itself, and its
+rigorous peer review targets the software components: functionality,
+documentation, tests, continuous integration, and the license. A JOSS article
+contains an abstract describing the purpose and functionality of the software,
+references, and a link to the software archive. The article is the entry point
+of a JOSS submission, which encompasses the full set of software artifacts.
+Submission and review proceed in the open, on GitHub. Editors, reviewers, and
+authors work collaboratively and openly. Unlike other journals, JOSS does not
+reject articles requiring major revision; while not yet accepted, articles
+remain visible and under review until the authors make adequate changes (or
+withdraw, if unable to meet requirements). Once an article is accepted, JOSS
+gives it a DOI, deposits its metadata in Crossref, and the article can begin
+collecting citations on indexers like Google Scholar and other services.
+Authors retain copyright of their JOSS article, releasing it under a Creative
+Commons Attribution 4.0 International License. In its first year, starting in
+May 2016, JOSS published 111 articles, with more than 40 additional articles
+under review. JOSS is a sponsored project of the nonprofit organization
+NumFOCUS and is an affiliate of the Open Source Initiative.
+",1,0,0,0,0,0
+7454,Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems,"  Reinforcement learning is a promising approach to learning robot controllers.
+It has recently been shown that algorithms based on finite-difference estimates
+of the policy gradient are competitive with algorithms based on the policy
+gradient theorem. We propose a theoretical framework for understanding this
+phenomenon. Our key insight is that many dynamical systems (especially those of
+interest in robot control tasks) are \emph{nearly deterministic}---i.e., they
+can be modeled as a deterministic system with a small stochastic perturbation.
+We show that for such systems, finite-difference estimates of the policy
+gradient can have substantially lower variance than estimates based on the
+policy gradient theorem. We interpret these results in the context of
+counterfactual estimation. Finally, we empirically evaluate our insights in an
+experiment on the inverted pendulum.
+",1,0,0,1,0,0
+7455,NDSHA: robust and reliable seismic hazard assessment,"  The Neo-Deterministic Seismic Hazard Assessment (NDSHA) method reliably and
+realistically simulates the suite of earthquake ground motions that may impact
+civil populations as well as their heritage buildings. The modeling technique
+is developed from comprehensive physical knowledge of the seismic source
+process, the propagation of earthquake waves and their combined interactions
+with site effects. NDSHA effectively accounts for the tensor nature of
+earthquake ground motions formally described as the tensor product of the
+earthquake source functions and the Green Functions of the pathway. NDSHA uses
+all available information about the space distribution of large magnitude
+earthquake, including Maximum Credible Earthquake (MCE) and geological and
+geophysical data. It does not rely on scalar empirical ground motion
+attenuation models, as these are often both weakly constrained by available
+observations and unable to account for the tensor nature of earthquake ground
+motion. Standard NDSHA provides robust and safely conservative hazard estimates
+for engineering design and mitigation decision strategies without requiring
+(often faulty) assumptions about the probabilistic risk analysis model of
+earthquake occurrence. If specific applications may benefit from temporal
+information the definition of the Gutenberg-Richter (GR) relation is performed
+according to the multi-scale seismicity model and occurrence rate is associated
+to each modeled source. Observations from recent destructive earthquakes in
+Italy and Nepal have confirmed the validity of NDSHA approach and application,
+and suggest that more widespread application of NDSHA will enhance earthquake
+safety and resilience of civil populations in all earthquake-prone regions,
+especially in tectonically active areas where the historic earthquake record is
+too short.
+",0,1,0,0,0,0
+7456,Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones,"  Syllabification does not seem to improve word-level RNN language modeling
+quality when compared to character-based segmentation. However, our best
+syllable-aware language model, achieving performance comparable to the
+competitive character-aware model, has 18%-33% fewer parameters and is trained
+1.2-2.2 times faster.
+",1,0,0,1,0,0
+7457,Persistent Entropy for Separating Topological Features from Noise in Vietoris-Rips Complexes,"  Persistent homology studies the evolution of k-dimensional holes along a
+nested sequence of simplicial complexes (called a filtration). The set of bars
+(i.e. intervals) representing birth and death times of k-dimensional holes
+along such sequence is called the persistence barcode. k-Dimensional holes with
+short lifetimes are informally considered to be ""topological noise"", and those
+with long lifetimes are considered to be ""topological features"" associated to
+the filtration. Persistent entropy is defined as the Shannon entropy of the
+persistence barcode of a given filtration. In this paper we present new
+important properties of persistent entropy of Cech and Vietoris-Rips
+filtrations. Among the properties, we put a focus on the stability theorem that
+allows to use persistent entropy for comparing persistence barcodes. Later, we
+derive a simple method for separating topological noise from features in
+Vietoris-Rips filtrations.
+",1,0,0,0,0,0
+7458,Intrinsic alignment of redMaPPer clusters: cluster shape - matter density correlation,"  We measure the alignment of the shapes of galaxy clusters, as traced by their
+satellite distributions, with the matter density field using the public
+redMaPPer catalogue based on SDSS-DR8, which contains 26 111 clusters up to
+z~0.6. The clusters are split into nine redshift and richness samples; in each
+of them we detect a positive alignment, showing that clusters point towards
+density peaks. We interpret the measurements within the tidal alignment
+paradigm, allowing for a richness and redshift dependence. The intrinsic
+alignment (IA) amplitude at the pivot redshift z=0.3 and pivot richness
+\lambda=30 is A_{IA}^{gen}=12.6_{-1.2}^{+1.5}. We obtain tentative evidence
+that the signal increases towards higher richness and lower redshift. Our
+measurements agree well with results of maxBCG clusters and with
+dark-matter-only simulations. Comparing our results to IA measurements of
+luminous red galaxies, we find that the IA amplitude of galaxy clusters forms a
+smooth extension towards higher mass. This suggests that these systems share a
+common alignment mechanism, which can be exploited to improve our physical
+understanding of IA.
+",0,1,0,0,0,0
+7459,A note on degenerate stirling polynomials of the second kind,"  In this paper, we consider the degenerate Stirling polynomials of the second
+kind which are derived from the generating function. In addition, we give some
+new identities for these polynomials.
+",0,0,1,0,0,0
+7460,Active learning machine learns to create new quantum experiments,"  How useful can machine learning be in a quantum laboratory? Here we raise the
+question of the potential of intelligent machines in the context of scientific
+research. A major motivation for the present work is the unknown reachability
+of various entanglement classes in quantum experiments. We investigate this
+question by using the projective simulation model, a physics-oriented approach
+to artificial intelligence. In our approach, the projective simulation system
+is challenged to design complex photonic quantum experiments that produce
+high-dimensional entangled multiphoton states, which are of high interest in
+modern quantum experiments. The artificial intelligence system learns to create
+a variety of entangled states, and improves the efficiency of their
+realization. In the process, the system autonomously (re)discovers experimental
+techniques which are only now becoming standard in modern quantum optical
+experiments - a trait which was not explicitly demanded from the system but
+emerged through the process of learning. Such features highlight the
+possibility that machines could have a significantly more creative role in
+future research.
+",1,0,0,1,0,0
+7461,The Gaia-ESO Survey: radial distribution of abundances in the Galactic disc from open clusters and young field stars,"  The spatial distribution of elemental abundances in the disc of our Galaxy
+gives insights both on its assembly process and subsequent evolution, and on
+the stellar nucleogenesis of the different elements. Gradients can be traced
+using several types of objects as, for instance, (young and old) stars, open
+clusters, HII regions, planetary nebulae. We aim at tracing the radial
+distributions of abundances of elements produced through different
+nucleosynthetic channels -the alpha-elements O, Mg, Si, Ca and Ti, and the
+iron-peak elements Fe, Cr, Ni and Sc - by using the Gaia-ESO idr4 results of
+open clusters and young field stars. From the UVES spectra of member stars, we
+determine the average composition of clusters with ages >0.1 Gyr. We derive
+statistical ages and distances of field stars. We trace the abundance gradients
+using the cluster and field populations and we compare them with a
+chemo-dynamical Galactic evolutionary model. Results. The adopted
+chemo-dynamical model, with the new generation of metallicity-dependent stellar
+yields for massive stars, is able to reproduce the observed spatial
+distributions of abundance ratios, in particular the abundance ratios of [O/Fe]
+and [Mg/Fe] in the inner disc (5 kpc<RGC <7 kpc), with their differences, that
+were usually poorly explained by chemical evolution models. Often, oxygen and
+magnesium are considered as equivalent in tracing alpha-element abundances and
+in deducing, e.g., the formation time-scales of different Galactic stellar
+populations. In addition, often [alpha/Fe] is computed combining several
+alpha-elements. Our results indicate, as expected, a complex and diverse
+nucleosynthesis of the various alpha-elements, in particular in the high
+metallicity regimes, pointing towards a different origin of these elements and
+highlighting the risk of considering them as a single class with common
+features.
+",0,1,0,0,0,0
+7462,The deterioration of materials from air pollution as derived from satellite and ground based observations,"  Dose-Response Functions (DRFs) are widely used in estimating corrosion and/or
+soiling levels of materials used in constructions and cultural monuments. These
+functions quantify the effects of air pollution and environmental parameters on
+different materials through ground based measurements of specific air
+pollutants and climatic parameters. Here, we propose a new approach where
+available satellite observations are used instead of ground-based data. Through
+this approach, the usage of DRFs is expanded in cases/areas where there is no
+availability of in situ measurements, introducing also a totally new field
+where satellite data can be shown to be very helpful. In the present work
+satellite observations made by MODIS (MODerate resolution Imaging
+Spectroradiometer) on board Terra and Aqua, OMI (Ozone Monitoring Instrument)
+on board Aura and AIRS (Atmospheric Infrared Sounder) on board Aqua have been
+used.
+",0,1,0,0,0,0
+7463,In-situ Optical Characterization of Noble Metal Thin Film Deposition and Development of a High-performance Plasmonic Sensor,"  The present work addressed in this thesis introduces, for the first time, the
+use of tilted fiber Bragg grating (TFBG) sensors for accurate, real-time, and
+in-situ characterization of CVD and ALD processes for noble metals, but with a
+particular focus on gold due to its desirable optical and plasmonic properties.
+Through the use of orthogonally-polarized transverse electric (TE) and
+transverse magnetic (TM) resonance modes imposed by a boundary condition at the
+cladding-metal interface of the optical fiber, polarization-dependent
+resonances excited by the TFBG are easily decoupled. It was found that for
+ultrathin thicknesses of gold films from CVD (~6-65 nm), the anisotropic
+property of these films made it non-trivial to characterize their effective
+optical properties such as the real component of the permittivity.
+Nevertheless, the TFBG introduces a new sensing platform to the ALD and CVD
+community for extremely sensitive in-situ process monitoring. We later also
+demonstrate thin film growth at low (<10 cycle) numbers for the well-known
+Al2O3 thermal ALD process, as well as the plasma-enhanced gold ALD process.
+Finally, the use of ALD-grown gold coatings has been employed for the
+development of a plasmonic TFBG-based sensor with ultimate refractometric
+sensitivity (~550 nm/RIU).
+",0,1,0,0,0,0
+7464,Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots,"  This paper presents a solution for persistent monitoring of real-world
+stochastic phenomena, where the underlying covariance structure changes sharply
+across time, using a small number of mobile robot sensors. We propose an
+adaptive solution for the problem where stochastic real-world dynamics are
+modeled as a Gaussian Process (GP). The belief on the underlying covariance
+structure is learned from recently observed dynamics as a Gaussian Mixture (GM)
+in the low-dimensional hyper-parameters space of the GP and adapted across time
+using Sequential Monte Carlo methods. Each robot samples a belief point from
+the GM and locally optimizes a set of informative regions by greedy
+maximization of the submodular entropy function. The key contributions of this
+paper are threefold: adapting the belief on the covariance using Markov Chain
+Monte Carlo (MCMC) sampling such that particles survive even under sharp
+covariance changes across time; exploiting the belief to transform the problem
+of entropy maximization into a decentralized one; and developing an
+approximation algorithm to maximize entropy on a set of informative regions in
+the continuous space. We illustrate the application of the proposed solution
+through extensive simulations using an artificial dataset and multiple real
+datasets from fixed sensor deployments, and compare it to three competing
+state-of-the-art approaches.
+",1,0,0,1,0,0
+7465,Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent,"  In this paper, we propose a novel sufficient decrease technique for variance
+reduced stochastic gradient descent methods such as SAG, SVRG and SAGA. In
+order to make sufficient decrease for stochastic optimization, we design a new
+sufficient decrease criterion, which yields sufficient decrease versions of
+variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct. We
+introduce a coefficient to scale current iterate and satisfy the sufficient
+decrease property, which takes the decisions to shrink, expand or move in the
+opposite direction, and then give two specific update rules of the coefficient
+for Lasso and ridge regression. Moreover, we analyze the convergence properties
+of our algorithms for strongly convex problems, which show that both of our
+algorithms attain linear convergence rates. We also provide the convergence
+guarantees of our algorithms for non-strongly convex problems. Our experimental
+results further verify that our algorithms achieve significantly better
+performance than their counterparts.
+",1,0,1,1,0,0
+7466,Unified Gas-kinetic Scheme with Multigrid Convergence for Rarefied Flow Study,"  The unified gas kinetic scheme (UGKS) is a direct modeling method based on
+the gas dynamical model on the mesh size and time step scales. With the
+implementation of particle transport and collision in a time-dependent flux
+function, the UGKS can recover multiple flow physics from the kinetic particle
+transport to the hydrodynamic wave propagation. In comparison with direct
+simulation Monte Carlo (DSMC), the equations-based UGKS can use the implicit
+techniques in the updates of macroscopic conservative variables and microscopic
+distribution function. The implicit UGKS significantly increases the
+convergence speed for steady flow computations, especially in the highly
+rarefied and near continuum regime. In order to further improve the
+computational efficiency, for the first time a geometric multigrid technique is
+introduced into the implicit UGKS, where the prediction step for the
+equilibrium state and the evolution step for the distribution function are both
+treated with multigrid acceleration. The multigrid implicit UGKS (MIUGKS) is
+used in the non-equilibrium flow study, which includes microflow, such as
+lid-driven cavity flow and the flow passing through a finite-length flat plate,
+and high speed one, such as supersonic flow over a square cylinder. The MIUGKS
+shows 5 to 9 times efficiency increase over the previous implicit scheme. For
+the low speed microflow, the efficiency of MIUGKS is several orders of
+magnitude higher than the DSMC. Even for the hypersonic flow at Mach number 5
+and Knudsen number 0.1, the MIUGKS is still more than 100 times faster than the
+DSMC method for a convergent steady state solution.
+",0,1,0,0,0,0
+7467,Learning agile and dynamic motor skills for legged robots,"  Legged robots pose one of the greatest challenges in robotics. Dynamic and
+agile maneuvers of animals cannot be imitated by existing methods that are
+crafted by humans. A compelling alternative is reinforcement learning, which
+requires minimal craftsmanship and promotes the natural evolution of a control
+policy. However, so far, reinforcement learning research for legged robots is
+mainly limited to simulation, and only few and comparably simple examples have
+been deployed on real systems. The primary reason is that training with real
+robots, particularly with dynamically balancing systems, is complicated and
+expensive. In the present work, we introduce a method for training a neural
+network policy in simulation and transferring it to a state-of-the-art legged
+system, thereby leveraging fast, automated, and cost-effective data generation
+schemes. The approach is applied to the ANYmal robot, a sophisticated
+medium-dog-sized quadrupedal system. Using policies trained in simulation, the
+quadrupedal machine achieves locomotion skills that go beyond what had been
+achieved with prior methods: ANYmal is capable of precisely and
+energy-efficiently following high-level body velocity commands, running faster
+than before, and recovering from falling even in complex configurations.
+",1,0,0,1,0,0
+7468,Optimal partition problems for the fractional laplacian,"  In this work, we prove an existence result for an optimal partition problem
+of the form $$\min \{F_s(A_1,\dots,A_m)\colon A_i \in \mathcal{A}_s, \, A_i\cap
+A_j =\emptyset \mbox{ for } i\neq j\},$$ where $F_s$ is a cost functional with
+suitable assumptions of monotonicity and lowersemicontinuity, $\mathcal{A}_s$
+is the class of admissible domains and the condition $A_i\cap A_j =\emptyset$
+is understood in the sense of the Gagliardo $s$-capacity, where $0<s<1$.
+Examples of this type of problem are related to the fractional eigenvalues. In
+addition, we prove some type of convergence of the $s$-minimizers to the
+minimizer of the problem with $s=1$, studied in \cite{Bucur-Buttazzo-Henrot}.
+",0,0,1,0,0,0
+7469,Entire holomorphic curves into projective spaces intersecting a generic hypersurface of high degree,"  In this note, we establish the following Second Main Theorem type estimate
+for every entire non-algebraically degenerate holomorphic curve
+$f\colon\mathbb{C}\rightarrow\mathbb{P}^n(\mathbb{C})$, in present of a {\sl
+generic} hypersuface $D\subset\mathbb{P}^n(\mathbb{C})$ of sufficiently high
+degree $d\geq 15(5n+1)n^n$: \[ T_f(r) \leq \,N_f^{[1]}(r,D) + O\big(\log T_f(r)
++ \log r \big)\parallel, \] where $T_f(r)$ and $N_f^{[1]}(r,D)$ stand for the
+order function and the $1$-truncated counting function in Nevanlinna theory.
+This inequality quantifies recent results on the logarithmic Green--Griffiths
+conjecture.
+",0,0,1,0,0,0
+7470,New results on sum-product type growth over fields,"  We prove a range of new sum-product type growth estimates over a general
+field $\mathbb{F}$, in particular the special case $\mathbb{F}=\mathbb{F}_p$.
+They are unified by the theme of ""breaking the $3/2$ threshold"", epitomising
+the previous state of the art. These estimates stem from specially suited
+applications of incidence bounds over $\mathbb{F}$, which apply to higher
+moments of representation functions.
+We establish the estimate $|R[A]| \gtrsim |A|^{8/5}$ for cardinality of the
+set $R[A]$ of distinct cross-ratios defined by triples of elements of a
+(sufficiently small if $\mathbb{F}$ has positive characteristic, similarly for
+the rest of the estimates) set $A\subset \mathbb{F}$, pinned at infinity. The
+cross-ratio naturally arises in various sum-product type questions of
+projective nature and is the unifying concept underlying most of our results.
+It enables one to take advantage of its symmetry properties as an onset of
+growth of, for instance, products of difference sets. The geometric nature of
+the cross-ratio enables us to break the version of the above threshold for the
+minimum number of distinct triangle areas $Ouu'$, defined by points $u,u'$ of a
+non-collinear point set $P\subset \mathbb{F}^2$.
+Another instance of breaking the threshold is showing that if $A$ is
+sufficiently small and has additive doubling constant $M$, then $|AA|\gtrsim
+M^{-2}|A|^{14/9}$. This result has a second moment version, which allows for
+new upper bounds for the number of collinear point triples in the set $A\times
+A\subset \mathbb{F}^2$, the quantity often arising in applications of geometric
+incidence estimates.
+",0,0,1,0,0,0
+7471,Precision matrix expansion - efficient use of numerical simulations in estimating errors on cosmological parameters,"  Computing the inverse covariance matrix (or precision matrix) of large data
+vectors is crucial in weak lensing (and multi-probe) analyses of the large
+scale structure of the universe. Analytically computed covariances are
+noise-free and hence straightforward to invert, however the model
+approximations might be insufficient for the statistical precision of future
+cosmological data. Estimating covariances from numerical simulations improves
+on these approximations, but the sample covariance estimator is inherently
+noisy, which introduces uncertainties in the error bars on cosmological
+parameters and also additional scatter in their best fit values. For future
+surveys, reducing both effects to an acceptable level requires an unfeasibly
+large number of simulations.
+In this paper we describe a way to expand the true precision matrix around a
+covariance model and show how to estimate the leading order terms of this
+expansion from simulations. This is especially powerful if the covariance
+matrix is the sum of two contributions, $\smash{\mathbf{C} =
+\mathbf{A}+\mathbf{B}}$, where $\smash{\mathbf{A}}$ is well understood
+analytically and can be turned off in simulations (e.g. shape-noise for cosmic
+shear) to yield a direct estimate of $\smash{\mathbf{B}}$. We test our method
+in mock experiments resembling tomographic weak lensing data vectors from the
+Dark Energy Survey (DES) and the Large Synoptic Survey Telecope (LSST). For DES
+we find that $400$ N-body simulations are sufficient to achive negligible
+statistical uncertainties on parameter constraints. For LSST this is achieved
+with $2400$ simulations. The standard covariance estimator would require
+>$10^5$ simulations to reach a similar precision. We extend our analysis to a
+DES multi-probe case finding a similar performance.
+",0,1,0,0,0,0
+7472,Structural and electronic properties of germanene on MoS$_2$,"  To date, germanene has only been synthesized on metallic substrates. A
+metallic substrate is usually detrimental for the two-dimensional Dirac nature
+of germanene because the important electronic states near the Fermi level of
+germanene can hybridize with the electronic states of the metallic substrate.
+Here we report the successful synthesis of germanene on molybdenum disulfide
+(MoS$_2$), a band gap material. Pre-existing defects in the MoS$_2$ surface act
+as preferential nucleation sites for the germanene islands. The lattice
+constant of the germanene layer (3.8 $\pm$ 0.2 \AA) is about 20\% larger than
+the lattice constant of the MoS$_2$ substrate (3.16 \AA). Scanning tunneling
+spectroscopy measurements and density functional theory calculations reveal
+that there are, besides the linearly dispersing bands at the $K$ points, two
+parabolic bands that cross the Fermi level at the $\Gamma$ point.
+",0,1,0,0,0,0
+7473,Fractional quiver W-algebras,"  We introduce quiver gauge theory associated with the non-simply-laced type
+fractional quiver, and define fractional quiver W-algebras by using
+construction of arXiv:1512.08533 and arXiv:1608.04651 with representation of
+fractional quivers.
+",0,0,1,0,0,0
+7474,Vortex creep at very low temperatures in single crystals of the extreme type-II superconductor Rh$_9$In$_4$S$_4$,"  We image vortex creep at very low temperatures using Scanning Tunneling
+Microscopy (STM) in the superconductor Rh$_9$In$_4$S$_4$ ($T_c$=2.25 K). We
+measure the superconducting gap of Rh$_9$In$_4$S$_4$, finding $\Delta\approx
+0.33$meV and image a hexagonal vortex lattice up to close to H$_{c2}$,
+observing slow vortex creep at temperatures as low as 150 mK. We estimate
+thermal and quantum barriers for vortex motion and show that thermal
+fluctuations likely cause vortex creep, in spite of being at temperatures
+$T/T_c<0.1$. We study creeping vortex lattices by making images during long
+times and show that the vortex lattice remains hexagonal during creep with
+vortices moving along one of the high symmetry axis of the vortex lattice.
+Furthermore, the creep velocity changes with the scanning window suggesting
+that creep depends on the local arrangements of pinning centers. Vortices
+fluctuate on small scale erratic paths, indicating that the vortex lattice
+makes jumps trying different arrangements during its travel along the main
+direction for creep. The images provide a visual account of how vortex lattice
+motion maintains hexagonal order, while showing dynamic properties
+characteristic of a glass.
+",0,1,0,0,0,0
+7475,Intention Games,"  Strategic interactions between competitive entities are generally considered
+from the perspective of complete revelations of benefits achieved from those
+interactions, in the form of public payoff functions in the announced games. In
+this work, we propose a formal framework for a competitive ecosystem where each
+player is permitted to deviate from publicly optimal strategies under certain
+private payoffs greater than public payoffs, given that these deviations have
+certain acceptable bounds as agreed by all players. We call this game theoretic
+construction an Intention Game. We formally define an Intention Game, and
+notions of equilibria that exist in such deviant interactions. We give an
+example of a Cournot competition in a partially honest setting. We compare
+Intention Games with conventional strategic form games. Finally, we give a
+cryptographic use of Intention Games and a dual interpretation of this novel
+framework.
+",1,0,0,0,0,0
+7476,"Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT","  We investigate pivot-based translation between related languages in a low
+resource, phrase-based SMT setting. We show that a subword-level pivot-based
+SMT model using a related pivot language is substantially better than word and
+morpheme-level pivot models. It is also highly competitive with the best direct
+translation model, which is encouraging as no direct source-target training
+corpus is used. We also show that combining multiple related language pivot
+models can rival a direct translation model. Thus, the use of subwords as
+translation units coupled with multiple related pivot languages can compensate
+for the lack of a direct parallel corpus.
+",1,0,0,0,0,0
+7477,Multiband Electronic Structure of Magnetic Quantum Dots: Numerical Studies,"  Semiconductor quantum dots (QDs) doped with magnetic impurities have been a
+focus of continuous research for a couple of decades. A significant effort has
+been devoted to studies of magnetic polarons (MP) in these nanostructures.
+These collective states arise through exchange interaction between a carrier
+confined in a QD and localized spins of the magnetic impurities (typically:
+Mn). We discuss our theoretical description of various MP properties in
+self-assembled QDs. We present a self-consistent, temperature-dependent
+approach to MPs formed by a valence band hole. We use the Luttinger-Kohn k.p
+Hamiltonian to account for the important effects of spin-orbit interaction.
+",0,1,0,0,0,0
+7478,"Advances in Detection and Error Correction for Coherent Optical Communications: Regular, Irregular, and Spatially Coupled LDPC Code Designs","  In this chapter, we show how the use of differential coding and the presence
+of phase slips in the transmission channel affect the total achievable
+information rates and capacity of a system. By means of the commonly used QPSK
+modulation, we show that the use of differential coding does not decrease the
+total amount of reliably conveyable information over the channel. It is a
+common misconception that the use of differential coding introduces an
+unavoidable differential loss. This perceived differential loss is rather a
+consequence of simplified differential detection and decoding at the receiver.
+Afterwards, we show how capacity-approaching coding schemes based on LDPC and
+spatially coupled LDPC codes can be constructed by combining iterative
+demodulation and decoding. For this, we first show how to modify the
+differential decoder to account for phase slips and then how to use this
+modified differential decoder to construct good LDPC codes. This construction
+method can serve as a blueprint to construct good and practical LDPC codes for
+other applications with iterative detection, such as higher order modulation
+formats with non-square constellations, multi-dimensional optimized modulation
+formats, turbo equalization to mitigate ISI (e.g., due to nonlinearities) and
+many more. Finally, we introduce the class of spatially coupled (SC)-LDPC
+codes, which are a generalization of LDPC codes with some outstanding
+properties and which can be decoded with a very simple windowed decoder. We
+show that the universal behavior of spatially coupled codes makes them an ideal
+candidate for iterative differential demodulation/detection and decoding.
+",1,0,0,0,0,0
+7479,Risk-Averse Classification,"  We develop a new approach to solving classification problems, which is bases
+on the theory of coherent measures of risk and risk sharing ideas. The proposed
+approach aims at designing a risk-averse classifier. The new approach allows
+for associating distinct risk functional to each classes. The risk may be
+measured by different (non-linear in probability) measures,
+We analyze the structure of the new classifier design problem and establish
+its theoretical relation to known risk-neutral design problems. In particular,
+we show that the risk-sharing classification problem is equivalent to an
+implicitly defined optimization problem with unequal, implicitly defined but
+unknown, weights for each data point. We implement our methodology in a binary
+classification scenario on several different data sets and carry out numerical
+comparison with classifiers which are obtained using the Huber loss function
+and other loss functions known in the literature. We formulate specific
+risk-averse support vector machines in order to demonstrate the viability of
+our method.
+",0,0,0,1,0,0
+7480,Scattered Sentences have Few Separable Randomizations,"  In the paper ""Randomizations of Scattered Sentences"", Keisler showed that if
+Martin's axiom for aleph one holds, then every scattered sentence has few
+separable randomizations, and asked whether the conclusion could be proved in
+ZFC alone. We show here that the answer is ""yes"". It follows that the absolute
+Vaught conjecture holds if and only if every $L_{\omega_1\omega}$-sentence with
+few separable randomizations has countably many countable models.
+",0,0,1,0,0,0
+7481,Zero divisor and unit elements with support of size 4 in group algebras of torsion free groups,"  Kaplansky Zero Divisor Conjecture states that if $G $ is a torsion free group
+and $ \mathbb{F} $ is a field, then the group ring $\mathbb{F}[G]$ contains no
+zero divisor and Kaplansky Unit Conjecture states that if $G $ is a torsion
+free group and $ \mathbb{F} $ is a field, then $\mathbb{F}[G]$ contains no
+non-trivial units. The support of an element $ \alpha= \sum_{x\in G}\alpha_xx$
+in $\mathbb{F}[G] $, denoted by $supp(\alpha)$, is the set $ \{x \in
+G|\alpha_x\neq 0\} $. In this paper we study possible zero divisors and units
+with supports of size $ 4 $ in $\mathbb{F}[G]$. We prove that if
+$ \alpha, \beta $ are non-zero elements in $ \mathbb{F}[G] $ for a possible
+torsion free group $ G $ and an arbitrary field $ \mathbb{F} $ such that $
+|supp(\alpha)|=4 $ and $ \alpha\beta=0 $, then $|supp(\beta)|\geq 7 $. In [J.
+Group Theory, $16$ $ (2013),$ no. $5$, $667$-$693$], it is proved that if $
+\mathbb{F}=\mathbb{F}_2 $ is the field with two elements, $ G $ is a torsion
+free group and $ \alpha,\beta \in \mathbb{F}_2[G]\setminus \{0\}$ such that
+$|supp(\alpha)|=4 $ and $ \alpha\beta =0 $, then $|supp(\beta)|\geq 8$. We
+improve the latter result to $|supp(\beta)|\geq 9$. Also, concerning the Unit
+Conjecture, we prove that if $\mathsf{a}\mathsf{b}=1$ for some
+$\mathsf{a},\mathsf{b}\in \mathbb{F}[G]$ and $|supp(\mathsf{a})|=4$, then
+$|supp(\mathsf{b})|\geq 6$.
+",0,0,1,0,0,0
+7482,G2 instantons and the Seiberg-Witten monopoles,"  I describe a relation (mostly conjectural) between the Seiberg-Witten
+monopoles, Fueter sections, and G2 instantons. In the last part of this article
+I gathered some open questions connected with this relation.
+",0,0,1,0,0,0
+7483,Multimodal Word Distributions,"  Word embeddings provide point representations of words containing useful
+semantic information. We introduce multimodal word distributions formed from
+Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty
+information. To learn these distributions, we propose an energy-based
+max-margin objective. We show that the resulting approach captures uniquely
+expressive semantic information, and outperforms alternatives, such as word2vec
+skip-grams, and Gaussian embeddings, on benchmark datasets such as word
+similarity and entailment.
+",1,0,0,1,0,0
+7484,Efficient Policy Learning,"  In many areas, practitioners seek to use observational data to learn a
+treatment assignment policy that satisfies application-specific constraints,
+such as budget, fairness, simplicity, or other functional form constraints. For
+example, policies may be restricted to take the form of decision trees based on
+a limited set of easily observable individual characteristics. We propose a new
+approach to this problem motivated by the theory of semiparametrically
+efficient estimation. Our method can be used to optimize either binary
+treatments or infinitesimal nudges to continuous treatments, and can leverage
+observational data where causal effects are identified using a variety of
+strategies, including selection on observables and instrumental variables.
+Given a doubly robust estimator of the causal effect of assigning everyone to
+treatment, we develop an algorithm for choosing whom to treat, and establish
+strong guarantees for the asymptotic utilitarian regret of the resulting
+policy.
+",0,0,1,1,0,0
+7485,Optimization by a quantum reinforcement algorithm,"  A reinforcement algorithm solves a classical optimization problem by
+introducing a feedback to the system which slowly changes the energy landscape
+and converges the algorithm to an optimal solution in the configuration space.
+Here, we use this strategy to concentrate (localize) preferentially the wave
+function of a quantum particle, which explores the configuration space of the
+problem, on an optimal configuration. We examine the method by solving
+numerically the equations governing the evolution of the system, which are
+similar to the nonlinear Schrödinger equations, for small problem sizes. In
+particular, we observe that reinforcement increases the minimal energy gap of
+the system in a quantum annealing algorithm. Our numerical simulations and the
+latter observation show that such kind of quantum feedbacks might be helpful in
+solving a computationally hard optimization problem by a quantum reinforcement
+algorithm.
+",1,1,0,0,0,0
+7486,Fast Image Processing with Fully-Convolutional Networks,"  We present an approach to accelerating a wide variety of image processing
+operators. Our approach uses a fully-convolutional network that is trained on
+input-output pairs that demonstrate the operator's action. After training, the
+original operator need not be run at all. The trained network operates at full
+resolution and runs in constant time. We investigate the effect of network
+architecture on approximation accuracy, runtime, and memory footprint, and
+identify a specific architecture that balances these considerations. We
+evaluate the presented approach on ten advanced image processing operators,
+including multiple variational models, multiscale tone and detail manipulation,
+photographic style transfer, nonlocal dehazing, and nonphotorealistic
+stylization. All operators are approximated by the same model. Experiments
+demonstrate that the presented approach is significantly more accurate than
+prior approximation schemes. It increases approximation accuracy as measured by
+PSNR across the evaluated operators by 8.5 dB on the MIT-Adobe dataset (from
+27.5 to 36 dB) and reduces DSSIM by a multiplicative factor of 3 compared to
+the most accurate prior approximation scheme, while being the fastest. We show
+that our models generalize across datasets and across resolutions, and
+investigate a number of extensions of the presented approach. The results are
+shown in the supplementary video at this https URL
+",1,0,0,0,0,0
+7487,Revealing patterns in HIV viral load data and classifying patients via a novel machine learning cluster summarization method,"  HIV RNA viral load (VL) is an important outcome variable in studies of HIV
+infected persons. There exists only a handful of methods which classify
+patients by viral load patterns. Most methods place limits on the use of viral
+load measurements, are often specific to a particular study design, and do not
+account for complex, temporal variation. To address this issue, we propose a
+set of four unambiguous computable characteristics (features) of time-varying
+HIV viral load patterns, along with a novel centroid-based classification
+algorithm, which we use to classify a population of 1,576 HIV positive clinic
+patients into one of five different viral load patterns (clusters) often found
+in the literature: durably suppressed viral load (DSVL), sustained low viral
+load (SLVL), sustained high viral load (SHVL), high viral load suppression
+(HVLS), and rebounding viral load (RVL). The centroid algorithm summarizes
+these clusters in terms of their centroids and radii. We show that this allows
+new viral load patterns to be assigned pattern membership based on the distance
+from the centroid relative to its radius, which we term radial normalization
+classification. This method has the benefit of providing an objective and
+quantitative method to assign viral load pattern membership with a concise and
+interpretable model that aids clinical decision making. This method also
+facilitates meta-analyses by providing computably distinct HIV categories.
+Finally we propose that this novel centroid algorithm could also be useful in
+the areas of cluster comparison for outcomes research and data reduction in
+machine learning.
+",0,0,0,1,1,0
+7488,Superintegrable relativistic systems in spacetime-dependent background fields,"  We consider a relativistic charged particle in background electromagnetic
+fields depending on both space and time. We identify which symmetries of the
+fields automatically generate integrals (conserved quantities) of the charge
+motion, accounting fully for relativistic and gauge invariance. Using this we
+present new examples of superintegrable relativistic systems. This includes
+examples where the integrals of motion are quadratic or nonpolynomial in the
+canonical momenta.
+",0,1,1,0,0,0
+7489,Distributed Stochastic Model Predictive Control for Large-Scale Linear Systems with Private and Common Uncertainty Sources,"  This paper presents a distributed stochastic model predictive control (SMPC)
+approach for large-scale linear systems with private and common uncertainties
+in a plug-and-play framework. Using the so-called scenario approach, the
+centralized SMPC involves formulating a large-scale finite-horizon scenario
+optimization problem at each sampling time, which is in general computationally
+demanding, due to the large number of required scenarios. We present two novel
+ideas in this paper to address this issue. We first develop a technique to
+decompose the large-scale scenario program into distributed scenario programs
+that exchange a certain number of scenarios with each other in order to compute
+local decisions using the alternating direction method of multipliers (ADMM).
+We show the exactness of the decomposition with a-priori probabilistic
+guarantees for the desired level of constraint fulfillment for both uncertainty
+sources. As our second contribution, we develop an inter-agent soft
+communication scheme based on a set parametrization technique together with the
+notion of probabilistically reliable sets to reduce the required communication
+between the subproblems. We show how to incorporate the probabilistic
+reliability notion into existing results and provide new guarantees for the
+desired level of constraint violations. Two different simulation studies of two
+types of systems interactions, dynamically coupled and coupling constraints,
+are presented to illustrate the advantages of the proposed framework.
+",1,0,1,0,0,0
+7490,Formalization of Transform Methods using HOL Light,"  Transform methods, like Laplace and Fourier, are frequently used for
+analyzing the dynamical behaviour of engineering and physical systems, based on
+their transfer function, and frequency response or the solutions of their
+corresponding differential equations. In this paper, we present an ongoing
+project, which focuses on the higher-order logic formalization of transform
+methods using HOL Light theorem prover. In particular, we present the
+motivation of the formalization, which is followed by the related work. Next,
+we present the task completed so far while highlighting some of the challenges
+faced during the formalization. Finally, we present a roadmap to achieve our
+objectives, the current status and the future goals for this project.
+",1,0,0,0,0,0
+7491,The Velocity of the Decoding Wave for Spatially Coupled Codes on BMS Channels,"  We consider the dynamics of belief propagation decoding of spatially coupled
+Low-Density Parity-Check codes. It has been conjectured that after a short
+transient phase, the profile of ""error probabilities"" along the spatial
+direction of a spatially coupled code develops a uniquely-shaped wave-like
+solution that propagates with constant velocity v. Under this assumption, and
+for transmission over general Binary Memoryless Symmetric channels, we derive a
+formula for v. We also propose approximations that are simpler to compute and
+support our findings using numerical data.
+",1,0,1,0,0,0
+7492,In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling,"  Entity resolution (ER) presents unique challenges for evaluation methodology.
+While crowdsourcing platforms acquire ground truth, sound approaches to
+sampling must drive labelling efforts. In ER, extreme class imbalance between
+matching and non-matching records can lead to enormous labelling requirements
+when seeking statistically consistent estimates for rigorous evaluation. This
+paper addresses this important challenge with the OASIS algorithm: a sampler
+and F-measure estimator for ER evaluation. OASIS draws samples from a (biased)
+instrumental distribution, chosen to ensure estimators with optimal asymptotic
+variance. As new labels are collected OASIS updates this instrumental
+distribution via a Bayesian latent variable model of the annotator oracle, to
+quickly focus on unlabelled items providing more information. We prove that
+resulting estimates of F-measure, precision, recall converge to the true
+population values. Thorough comparisons of sampling methods on a variety of ER
+datasets demonstrate significant labelling reductions of up to 83% without loss
+to estimate accuracy.
+",1,0,0,1,0,0
+7493,Evidence of chaotic modes in the analysis of four delta Scuti stars,"  Since CoRoT observations unveiled the very low amplitude modes that form a
+flat plateau in the power spectrum structure of delta Scuti stars, the nature
+of this phenomenon, including the possibility of spurious signals due to the
+light curve analysis, has been a matter of long-standing scientific debate. We
+contribute to this debate by finding the structural parameters of a sample of
+four delta Scuti stars, CID 546, CID 3619, CID 8669, and KIC 5892969, and
+looking for a possible relation between these stars' structural parameters and
+their power spectrum structure. For the purposes of characterization, we
+developed a method of studying and analysing the power spectrum with high
+precision and have applied it to both CoRoT and Kepler light curves. We obtain
+the best estimates to date of these stars' structural parameters. Moreover, we
+observe that the power spectrum structure depends on the inclination,
+oblateness, and convective efficiency of each star. Our results suggest that
+the power spectrum structure is real and is possibly formed by 2-period island
+modes and chaotic modes.
+",0,1,0,0,0,0
+7494,Analytical Representations of Divisors of Integers,"  Certain analytical expressions which ""feel"" the divisors of natural numbers
+are investigated. We show that these expressions encode to some extent the
+well-known algorithm of the sieve of Eratosthenes. Most part of the text is
+written in pedagogical style, however some formulas are new.
+",0,0,1,0,0,0
+7495,Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze,"  This paper presents a problem of model learning for the purpose of learning
+how to navigate a ball to a goal state in a circular maze environment with two
+degrees of freedom. The motion of the ball in the maze environment is
+influenced by several non-linear effects such as dry friction and contacts,
+which are difficult to model physically. We propose a semiparametric model to
+estimate the motion dynamics of the ball based on Gaussian Process Regression
+equipped with basis functions obtained from physics first principles. The
+accuracy of this semiparametric model is shown not only in estimation but also
+in prediction at n-steps ahead and its compared with standard algorithms for
+model learning. The learned model is then used in a trajectory optimization
+algorithm to compute ball trajectories. We propose the system presented in the
+paper as a benchmark problem for reinforcement and robot learning, for its
+interesting and challenging dynamics and its relative ease of reproducibility.
+",1,0,0,1,0,0
+7496,Vestigial nematic order and superconductivity in the doped topological insulator Cu$_{x}$Bi$_{2}$Se$_{3}$,"  If the topological insulator Bi$_{2}$Se$_{3}$ is doped with electrons,
+superconductivity with $T_{\rm c}\approx3-4\:{\rm K}$ emerges for a low
+density of carriers ($n\approx10^{20}{\rm cm}^{-3}$) and with a small ratio of
+the superconducting coherence length and Fermi wave length:
+$\xi/\lambda_{F}\approx2\cdots4$. These values make fluctuations of the
+superconducting order parameter increasingly important, to the extend that the
+$T_{c}$-value is surprisingly large. Strong spin-orbit interaction led to the
+proposal of an odd-parity pairing state. This begs the question of the nature
+of the transition in an unconventional superconductor with strong pairing
+fluctuations. We show that for a multi-component order parameter, these
+fluctuations give rise to a nematic phase at $T_{\rm nem}>T_{c}$. Below
+$T_{c}$ several experiments demonstrated a rotational symmetry breaking where
+the Cooper pair wave function is locked to the lattice. Our theory shows that
+this rotational symmetry breaking, as vestige of the superconducting state,
+already occurs above $T_{c}$. The nematic phase is characterized by vanishing
+off-diagonal long range order, yet with anisotropic superconducting
+fluctuations. It can be identified through direction-dependent
+para-conductivity, lattice softening, and an enhanced Raman response in the
+$E_{g}$ symmetry channel. In addition, nematic order partially avoids the usual
+fluctuation suppression of $T_{c}$.
+",0,1,0,0,0,0
+7497,On the semi-continuity problem of normalized volumes of singularities,"  We show that in any $\mathbb{Q}$-Gorenstein flat family of klt singularities,
+normalized volumes can only jump down at countably many subvarieties. A quick
+consequence is that smooth points have the largest normalized volume among all
+klt singularities. Using an alternative characterization of K-semistability
+developed by Li, Xu and the author, we show that K-semistability is a very
+generic or empty condition in any $\mathbb{Q}$-Gorenstein flat family of log
+Fano pairs.
+",0,0,1,0,0,0
+7498,Non-Generic Unramified Representations in Metaplectic Covering Groups,"  Let $G^{(r)}$ denote the metaplectic covering group of the linear algebraic
+group $G$. In this paper we study conditions on unramified representations of
+the group $G^{(r)}$ not to have a nonzero Whittaker function. We state a
+general Conjecture about the possible unramified characters $\chi$ such that
+the unramified sub-representation of
+$Ind_{B^{(r)}}^{G^{(r)}}\chi\delta_B^{1/2}$ will have no nonzero Whittaker
+function. We prove this Conjecture for the groups $GL_n^{(r)}$ with $r\ge n-1$,
+and for the exceptional groups $G_2^{(r)}$ when $r\ne 2$.
+",0,0,1,0,0,0
+7499,A Generalization of Convolutional Neural Networks to Graph-Structured Data,"  This paper introduces a generalization of Convolutional Neural Networks
+(CNNs) from low-dimensional grid data, such as images, to graph-structured
+data. We propose a novel spatial convolution utilizing a random walk to uncover
+the relations within the input, analogous to the way the standard convolution
+uses the spatial neighborhood of a pixel on the grid. The convolution has an
+intuitive interpretation, is efficient and scalable and can also be used on
+data with varying graph structure. Furthermore, this generalization can be
+applied to many standard regression or classification problems, by learning the
+the underlying graph. We empirically demonstrate the performance of the
+proposed CNN on MNIST, and challenge the state-of-the-art on Merck molecular
+activity data set.
+",1,0,0,1,0,0
+7500,Implications of right-handed neutrinos in $B-L$ extended standard model with scalar dark matter,"  We investigate the Standard Model (SM) with a $U(1)_{B-L}$ gauge extension
+where a $B-L$ charged scalar is a viable dark matter (DM) candidate. The
+dominant annihilation process, for the DM particle is through the $B-L$
+symmetry breaking scalar to right-handed neutrino pair. We exploit the effect
+of decay and inverse decay of the right-handed neutrino in thermal relic
+abundance of the DM. Depending on the values of the decay rate, the DM relic
+density can be significantly different from what is obtained in the standard
+calculation assuming the right-handed neutrino is in thermal equilibrium and
+there appear different regions of the parameter space satisfying the observed
+DM relic density. For a DM mass less than $\mathcal{O}$(TeV), the direct
+detection experiments impose a competitive bound on the mass of the
+$U(1)_{B-L}$ gauge boson $Z^\prime$ with the collider experiments. Utilizing
+the non-observation of the displaced vertices arising from the right-handed
+neutrino decays, bound on the mass of $Z^\prime$ has been obtained at present
+and higher luminosities at the LHC with 14 TeV centre of mass energy where an
+integrated luminosity of 100fb$^{-1}$ is sufficient to probe $m_{Z'} \sim 5.5$
+TeV.
+",0,1,0,0,0,0
+7501,Robust Adversarial Reinforcement Learning,"  Deep neural networks coupled with fast simulation and improved computation
+have led to recent successes in the field of reinforcement learning (RL).
+However, most current RL-based approaches fail to generalize since: (a) the gap
+between simulation and real world is so large that policy-learning approaches
+fail to transfer; (b) even if policy learning is done in real world, the data
+scarcity leads to failed generalization from training to test scenarios (e.g.,
+due to different friction or object masses). Inspired from H-infinity control
+methods, we note that both modeling errors and differences in training and test
+scenarios can be viewed as extra forces/disturbances in the system. This paper
+proposes the idea of robust adversarial reinforcement learning (RARL), where we
+train an agent to operate in the presence of a destabilizing adversary that
+applies disturbance forces to the system. The jointly trained adversary is
+reinforced -- that is, it learns an optimal destabilization policy. We
+formulate the policy learning as a zero-sum, minimax objective function.
+Extensive experiments in multiple environments (InvertedPendulum, HalfCheetah,
+Swimmer, Hopper and Walker2d) conclusively demonstrate that our method (a)
+improves training stability; (b) is robust to differences in training/test
+conditions; and c) outperform the baseline even in the absence of the
+adversary.
+",1,0,0,0,0,0
+7502,Dynamic behaviour of Multilamellar Vesicles under Poiseuille flow,"  Surfactant solutions exhibit multilamellar surfactant vesicles (MLVs) under
+flow conditions and in concentration ranges which are found in a large number
+of industrial applications. MLVs are typically formed from a lamellar phase and
+play an important role in determining the rheological properties of surfactant
+solutions. Despite the wide literature on the collective dynamics of flowing
+MLVs, investigations on the flow behavior of single MLVs are scarce. In this
+work, we investigate a concentrated aqueous solution of linear alkylbenzene
+sulfonic acid (HLAS), characterized by MLVs dispersed in an isotropic micellar
+phase. Rheological tests show that the HLAS solution is a shear-thinning fluid
+with a power law index dependent on the shear rate. Pressure-driven shear flow
+of the HLAS solution in glass capillaries is investigated by high-speed video
+microscopy and image analysis. The so obtained velocity profiles provide
+evidence of a power-law fluid behaviour of the HLAS solution and images show a
+flow-focusing effect of the lamellar phase in the central core of the
+capillary. The flow behavior of individual MLVs shows analogies with that of
+unilamellar vesicles and emulsion droplets. Deformed MLVs exhibit typical
+shapes of unilamellar vesicles, such as parachute and bullet-like. Furthermore,
+MLV velocity follows the classical Hetsroni theory for droplets provided that
+the power law shear dependent viscosity of the HLAS solution is taken into
+account. The results of this work are relevant for the processing of
+surfactant-based systems in which the final properties depend on flow-induced
+morphology, such as cosmetic formulations and food products.
+",0,1,0,0,0,0
+7503,Approximate fixed points and B-amenable groups,"  A topological group $G$ is B-amenable if and only if every continuous affine
+action of $G$ on a bounded convex subset of a locally convex space has an
+approximate fixed point. Similar results hold more generally for slightly
+uniformly continuous semigroup actions.
+",0,0,1,0,0,0
+7504,Radiation hardness of small-pitch 3D pixel sensors up to HL-LHC fluences,"  A new generation of 3D silicon pixel detectors with a small pixel size of
+50$\times$50 and 25$\times$100 $\mu$m$^{2}$ is being developed for the HL-LHC
+tracker upgrades. The radiation hardness of such detectors was studied in beam
+tests after irradiation to HL-LHC fluences up to $1.4\times10^{16}$
+n$_{\mathrm{eq}}$/cm$^2$. At this fluence, an operation voltage of only 100 V
+is needed to achieve 97% hit efficiency, with a power dissipation of 13
+mW/cm$^2$ at -25$^{\circ}$C, considerably lower than for previous 3D sensor
+generations and planar sensors.
+",0,1,0,0,0,0
+7505,Simulating Cosmic Microwave Background anisotropy measurements for Microwave Kinetic Inductance Devices,"  Microwave Kinetic Inductance Devices (MKIDs) are poised to allow for
+massively and natively multiplexed photon detectors arrays and are a natural
+choice for the next-generation CMB-Stage 4 experiment which will require 105
+detectors. In this proceed- ing we discuss what noise performance of present
+generation MKIDs implies for CMB measurements. We consider MKID noise spectra
+and simulate a telescope scan strategy which projects the detector noise onto
+the CMB sky. We then analyze the simulated CMB + MKID noise to understand
+particularly low frequency noise affects the various features of the CMB, and
+thusly set up a framework connecting MKID characteristics with scan strategies,
+to the type of CMB signals we may probe with such detectors.
+",0,1,0,0,0,0
+7506,Optimized State Space Grids for Abstractions,"  The practical impact of abstraction-based controller synthesis methods is
+currently limited by the immense computational effort for obtaining
+abstractions. In this note we focus on a recently proposed method to compute
+abstractions whose state space is a cover of the state space of the plant by
+congruent hyper-intervals. The problem of how to choose the size of the
+hyper-intervals so as to obtain computable and useful abstractions is unsolved.
+This note provides a twofold contribution towards a solution. Firstly, we
+present a functional to predict the computational effort for the abstraction to
+be computed. Secondly, we propose a method for choosing the aspect ratio of the
+hyper-intervals when their volume is fixed. More precisely, we propose to
+choose the aspect ratio so as to minimize a predicted number of transitions of
+the abstraction to be computed, in order to reduce the computational effort. To
+this end, we derive a functional to predict the number of transitions in
+dependence of the aspect ratio. The functional is to be minimized subject to
+suitable constraints. We characterize the unique solvability of the respective
+optimization problem and prove that it transforms, under appropriate
+assumptions, into an equivalent convex problem with strictly convex objective.
+The latter problem can then be globally solved using standard numerical
+methods. We demonstrate our approach on an example.
+",1,0,0,0,0,0
+7507,Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis,"  We propose a novel denoising framework for task functional Magnetic Resonance
+Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the
+brain functional connectivity via dictionary learning and sparse coding (DLSC).
+In order to address the limitations of the unsupervised DLSC-based fMRI
+studies, we utilize the prior knowledge of task paradigm in the learning step
+to train a data-driven dictionary and to model the sparse representation. We
+apply the proposed DLSC-based method to Human Connectome Project (HCP) motor
+tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar
+circuits in somatomotor networks show that the DLSC-based denoising framework
+can significantly improve the prominent connectivity patterns, in comparison to
+the temporal non-local means (tNLM)-based denoising method as well as the case
+without denoising, which is consistent and neuroscientifically meaningful
+within motor area. The promising results show that the proposed method can
+provide an important foundation for the high-resolution functional connectivity
+analysis, and provide a better approach for fMRI preprocessing.
+",1,0,0,1,0,0
+7508,Optimal control of a Vlasov-Poisson plasma by an external magnetic field - The basics for variational calculus,"  We consider the three dimensional Vlasov-Poisson system that is equipped with
+an external magnetic field to describe a plasma. The aim of various concrete
+applications is to control a plasma in a desired fashion. This can be modeled
+by an optimal control problem. For that reason the basics for calculus of
+variations will be introduced in this paper. We have to find a suitable class
+of fields that are admissible for this procedure as they provide unique global
+solutions of the Vlasov-Poisson system. Then we can define a field-state
+operator that maps any admissible field onto its corresponding distribution
+function. We will show that this field-state operator is Lipschitz continuous
+and (weakly) compact. Last we will consider a model problem with a tracking
+type cost functional and we will show that this optimal control problem has at
+least one globally optimal solution.
+",0,0,1,0,0,0
+7509,Lensless Photography with only an image sensor,"  Photography usually requires optics in conjunction with a recording device
+(an image sensor). Eliminating the optics could lead to new form factors for
+cameras. Here, we report a simple demonstration of imaging using a bare CMOS
+sensor that utilizes computation. The technique relies on the space variant
+point-spread functions resulting from the interaction of a point source in the
+field of view with the image sensor. These space-variant point-spread functions
+are combined with a reconstruction algorithm in order to image simple objects
+displayed on a discrete LED array as well as on an LCD screen. We extended the
+approach to video imaging at the native frame rate of the sensor. Finally, we
+performed experiments to analyze the parametric impact of the object distance.
+Improving the sensor designs and reconstruction algorithms can lead to useful
+cameras without optics.
+",1,1,0,0,0,0
+7510,Truth-Telling Mechanism for Secure Two-Way Relay Communications with Energy-Harvesting Revenue,"  This paper brings the novel idea of paying the utility to the winning agents
+in terms of some physical entity in cooperative communications. Our setting is
+a secret two-way communication channel where two transmitters exchange
+information in the presence of an eavesdropper. The relays are selected from a
+set of interested parties such that the secrecy sum rate is maximized. In
+return, the selected relay nodes' energy harvesting requirements will be
+fulfilled up to a certain threshold through their own payoff so that they have
+the natural incentive to be selected and involved in the communication.
+However, relays may exaggerate their private information in order to improve
+their chance to be selected. Our objective is to develop a mechanism for relay
+selection that enforces them to reveal the truth since otherwise they may be
+penalized. We also propose a joint cooperative relay beamforming and transmit
+power optimization scheme based on an alternating optimization approach. Note
+that the problem is highly non-convex since the objective function appears as a
+product of three correlated Rayleigh quotients. While a common practice in the
+existing literature is to optimize the relay beamforming vector for given
+transmit power via rank relaxation, we propose a second-order cone programming
+(SOCP)-based approach in this paper which requires a significantly lower
+computational task. The performance of the incentive control mechanism and the
+optimization algorithm has been evaluated through numerical simulations.
+",1,0,0,0,0,0
+7511,Prediction of Individual Outcomes for Asthma Sufferers,"  We consider the problem of individual-specific medication level
+recommendation (initiation, removal, increase, or decrease) for asthma
+sufferers. Asthma is one of the most common chronic diseases in both adults and
+children, affecting 8% of the US population and costing $37-63 billion/year in
+the US. Asthma is a complex disease, whose symptoms may wax and wane, making it
+difficult for clinicians to predict outcomes and prognosis. Improved ability to
+predict prognosis can inform decision making and may promote conversations
+between clinician and provider around optimizing medication therapy. Data from
+the US Medical Expenditure Panel Survey (MEPS) years 2000-2010 were used to fit
+a longitudinal model for a multivariate response of adverse events (Emergency
+Department or In-patient visits, excessive rescue inhaler use, and oral steroid
+use). To reduce bias in the estimation of medication effects, medication level
+was treated as a latent process which was restricted to be consistent with
+prescription refill data. This approach is demonstrated to be effective in the
+MEPS cohort via predictions on a validation hold out set and a synthetic data
+simulation study. This framework can be easily generalized to medication
+decisions for other conditions as well.
+",0,0,0,1,0,0
+7512,Bayesian Probabilistic Numerical Methods,"  The emergent field of probabilistic numerics has thus far lacked clear
+statistical principals. This paper establishes Bayesian probabilistic numerical
+methods as those which can be cast as solutions to certain inverse problems
+within the Bayesian framework. This allows us to establish general conditions
+under which Bayesian probabilistic numerical methods are well-defined,
+encompassing both non-linear and non-Gaussian models. For general computation,
+a numerical approximation scheme is proposed and its asymptotic convergence
+established. The theoretical development is then extended to pipelines of
+computation, wherein probabilistic numerical methods are composed to solve more
+challenging numerical tasks. The contribution highlights an important research
+frontier at the interface of numerical analysis and uncertainty quantification,
+with a challenging industrial application presented.
+",1,0,1,1,0,0
+7513,The evolution of the temperature field during cavity collapse in liquid nitromethane. Part II: Reactive case,"  We study effect of cavity collapse in non-ideal explosives as a means of
+controlling their sensitivity. The main aim is to understand the origin of
+localised temperature peaks (hot spots) that play a leading order role at early
+ignition stages. Thus, we perform 2D and 3D numerical simulations of shock
+induced single gas-cavity collapse in nitromethane. Ignition is the result of a
+complex interplay between fluid dynamics and exothermic chemical reaction. In
+part I of this work we focused on the hydrodynamic effects in the collapse
+process by switching off the reaction terms in the mathematical model. Here, we
+reinstate the reactive terms and study the collapse of the cavity in the
+presence of chemical reactions. We use a multi-phase formulation which
+overcomes current challenges of cavity collapse modelling in reactive media to
+obtain oscillation-free temperature fields across material interfaces to allow
+the use of a temperature-based reaction rate law. The mathematical and physical
+models are validated against experimental and analytic data. We identify which
+of the previously-determined (in part I of this work) high-temperature regions
+lead to ignition and comment on their reactive strength and reaction growth
+rate. We quantify the sensitisation of nitromethane by the collapse of the
+cavity by comparing ignition times of neat and single-cavity material; the
+ignition occurs in less than half the ignition time of the neat material. We
+compare 2D and 3D simulations to examine the change in topology, temperature
+and reactive strength of the hot spots by the third dimension. It is apparent
+that belated ignition times can be avoided by the use of 3D simulations. The
+effect of the chemical reactions on the topology and strength of the hot spots
+in the timescales considered is studied by comparing inert and reactive
+simulations and examine maximum temperature fields and their growth rates.
+",0,1,0,0,0,0
+7514,Quantile Regression for Qualifying Match of GEFCom2017 Probabilistic Load Forecasting,"  We present a simple quantile regression-based forecasting method that was
+applied in a probabilistic load forecasting framework of the Global Energy
+Forecasting Competition 2017 (GEFCom2017). The hourly load data is log
+transformed and split into a long-term trend component and a remainder term.
+The key forecasting element is the quantile regression approach for the
+remainder term that takes into account weekly and annual seasonalities such as
+their interactions. Temperature information is only used to stabilize the
+forecast of the long-term trend component. Public holidays information is
+ignored. Still, the forecasting method placed second in the open data track and
+fourth in the definite data track with our forecasting method, which is
+remarkable given simplicity of the model. The method also outperforms the
+Vanilla benchmark consistently.
+",0,0,0,1,0,0
+7515,Strong Local Nondeterminism of Spherical Fractional Brownian Motion,"  Let $B = \left\{ B\left( x\right),\, x\in \mathbb{S}^{2}\right\} $ be the
+fractional Brownian motion indexed by the unit sphere $\mathbb{S}^{2}$ with
+index $0<H\leq \frac{1}{2}$, introduced by Istas \cite{IstasECP05}. We
+establish optimal estimates for its angular power spectrum $\{d_\ell, \ell = 0,
+1, 2, \ldots\}$, and then exploit its high-frequency behavior to establish the
+property of its strong local nondeterminism of $B$.
+",0,0,1,1,0,0
+7516,Sparse Named Entity Classification using Factorization Machines,"  Named entity classification is the task of classifying text-based elements
+into various categories, including places, names, dates, times, and monetary
+values. A bottleneck in named entity classification, however, is the data
+problem of sparseness, because new named entities continually emerge, making it
+rather difficult to maintain a dictionary for named entity classification.
+Thus, in this paper, we address the problem of named entity classification
+using matrix factorization to overcome the problem of feature sparsity.
+Experimental results show that our proposed model, with fewer features and a
+smaller size, achieves competitive accuracy to state-of-the-art models.
+",1,0,0,0,0,0
+7517,Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting,"  Latent space models are effective tools for statistical modeling and
+exploration of network data. These models can effectively model real world
+network characteristics such as degree heterogeneity, transitivity, homophily,
+etc. Due to their close connection to generalized linear models, it is also
+natural to incorporate covariate information in them. The current paper
+presents two universal fitting algorithms for networks with edge covariates:
+one based on nuclear norm penalization and the other based on projected
+gradient descent. Both algorithms are motivated by maximizing likelihood for a
+special class of inner-product models while working simultaneously for a wide
+range of different latent space models, such as distance models, which allow
+latent vectors to affect edge formation in flexible ways. These fitting
+methods, especially the one based on projected gradient descent, are fast and
+scalable to large networks. We obtain their rates of convergence for both
+inner-product models and beyond. The effectiveness of the modeling approach and
+fitting algorithms is demonstrated on five real world network datasets for
+different statistical tasks, including community detection with and without
+edge covariates, and network assisted learning.
+",1,0,0,1,0,0
+7518,Distant Supervision for Topic Classification of Tweets in Curated Streams,"  We tackle the challenge of topic classification of tweets in the context of
+analyzing a large collection of curated streams by news outlets and other
+organizations to deliver relevant content to users. Our approach is novel in
+applying distant supervision based on semi-automatically identifying curated
+streams that are topically focused (for example, on politics, entertainment, or
+sports). These streams provide a source of labeled data to train topic
+classifiers that can then be applied to categorize tweets from more
+topically-diffuse streams. Experiments on both noisy labels and human
+ground-truth judgments demonstrate that our approach yields good topic
+classifiers essentially ""for free"", and that topic classifiers trained in this
+manner are able to dynamically adjust for topic drift as news on Twitter
+evolves.
+",1,0,0,0,0,0
+7519,Relationship Maintenance in Software Language Repositories,"  The context of this research is testing and building software systems and,
+specifically, software language repositories (SLRs), i.e., repositories with
+components for language processing (interpreters, translators, analyzers,
+transformers, pretty printers, etc.). SLRs are typically set up for developing
+and using metaprogramming systems, language workbenches, language definition
+frameworks, executable semantic frameworks, and modeling frameworks. This work
+is an inquiry into testing and building SLRs in a manner that the repository is
+seen as a collection of language-typed artifacts being related by the
+applications of language-typed functions or relations which serve language
+processing. The notion of language is used in a broad sense to include text-,
+tree-, graph-based languages as well as representations based on interchange
+formats and also proprietary formats for serialization. The overall approach
+underlying this research is one of language design driven by a complex case
+study, i.e., a specific SLR with a significant number of processed languages
+and language processors as well as a noteworthy heterogeneity in terms of
+representation types and implementation languages. The knowledge gained by our
+research is best understood as a declarative language design for regression
+testing and build management, we introduce a corresponding language Ueber with
+an executable semantics which maintains relationships between language-typed
+artifacts in an SLR. The grounding of the reported research is based on the
+comprehensive, formal, executable (logic programming-based) definition of the
+Ueber language and its systematic application to the management of the SLR YAS
+which consists of hundreds of language definition and processing components
+(such as interpreters and transformations) for more than thirty languages (not
+counting different representation types) with Prolog, Haskell, Java, and Python
+being used as implementation languages. The importance of this work follows
+from the significant costs implied by regression testing and build management
+and also from the complexity of SLRs which calls for means to help with
+understanding.
+",1,0,0,0,0,0
+7520,The Emptiness Problem for Valence Automata over Graph Monoids,"  This work studies which storage mechanisms in automata permit decidability of
+the emptiness problem. The question is formalized using valence automata, an
+abstract model of automata in which the storage mechanism is given by a monoid.
+For each of a variety of storage mechanisms, one can choose a (typically
+infinite) monoid $M$ such that valence automata over $M$ are equivalent to
+(one-way) automata with this type of storage. In fact, many important storage
+mechanisms can be realized by monoids defined by finite graphs, called graph
+monoids. Examples include pushdown stacks, partially blind counters (which
+behave like Petri net places), blind counters (which may attain negative
+values), and combinations thereof.
+Hence, we study for which graph monoids the emptiness problem for valence
+automata is decidable. A particular model realized by graph monoids is that of
+Petri nets with a pushdown stack. For these, decidability is a long-standing
+open question and we do not answer it here.
+However, if one excludes subgraphs corresponding to this model, a
+characterization can be achieved. Moreover, we provide a description of those
+storage mechanisms for which decidability remains open. This leads to a model
+that naturally generalizes both pushdown Petri nets and the priority
+multicounter machines introduced by Reinhardt.
+The cases that are proven decidable constitute a natural and apparently new
+extension of Petri nets with decidable reachability. It is finally shown that
+this model can be combined with another such extension by Atig and Ganty: We
+present a further decidability result that subsumes both of these Petri net
+extensions.
+",1,0,1,0,0,0
+7521,On the Optimization Landscape of Tensor Decompositions,"  Non-convex optimization with local search heuristics has been widely used in
+machine learning, achieving many state-of-art results. It becomes increasingly
+important to understand why they can work for these NP-hard problems on typical
+data. The landscape of many objective functions in learning has been
+conjectured to have the geometric property that ""all local optima are
+(approximately) global optima"", and thus they can be solved efficiently by
+local search algorithms. However, establishing such property can be very
+difficult.
+In this paper, we analyze the optimization landscape of the random
+over-complete tensor decomposition problem, which has many applications in
+unsupervised learning, especially in learning latent variable models. In
+practice, it can be efficiently solved by gradient ascent on a non-convex
+objective. We show that for any small constant $\epsilon > 0$, among the set of
+points with function values $(1+\epsilon)$-factor larger than the expectation
+of the function, all the local maxima are approximate global maxima.
+Previously, the best-known result only characterizes the geometry in small
+neighborhoods around the true components. Our result implies that even with an
+initialization that is barely better than the random guess, the gradient ascent
+algorithm is guaranteed to solve this problem.
+Our main technique uses Kac-Rice formula and random matrix theory. To our
+best knowledge, this is the first time when Kac-Rice formula is successfully
+applied to counting the number of local minima of a highly-structured random
+polynomial with dependent coefficients.
+",1,0,1,1,0,0
+7522,Estimate of Joule Heating in a Flat Dechirper,"  We have performed Joule power loss calculations for a flat dechirper. We have
+considered the configurations of the beam on-axis between the two plates---for
+chirp control---and for the beam especially close to one plate---for use as a
+fast kicker. Our calculations use a surface impedance approach, one that is
+valid when corrugation parameters are small compared to aperture (the
+perturbative parameter regime). In our model we ignore effects of field
+reflections at the sides of the dechirper plates, and thus expect the results
+to underestimate the Joule losses. The analytical results were also tested by
+numerical, time-domain simulations. We find that most of the wake power lost by
+the beam is radiated out to the sides of the plates. For the case of the beam
+passing by a single plate, we derive an analytical expression for the
+broad-band impedance, and---in Appendix B---numerically confirm recently
+developed, analytical formulas for the short-range wakes. While our theory can
+be applied to the LCLS-II dechirper with large gaps, for the nominal apertures
+we are not in the perturbative regime and the reflection contribution to Joule
+losses is not negligible. With input from computer simulations, we estimate the
+Joule power loss (assuming bunch charge of 300 pC, repetition rate of 100 kHz)
+is 21~W/m for the case of two plates, and 24 W/m for the case of a single
+plate.
+",0,1,0,0,0,0
+7523,Accurate Kernel Learning for Linear Gaussian Markov Processes using a Scalable Likelihood Computation,"  We report an exact likelihood computation for Linear Gaussian Markov
+processes that is more scalable than existing algorithms for complex models and
+sparsely sampled signals. Better scaling is achieved through elimination of
+repeated computations in the Kalman likelihood, and by using the diagonalized
+form of the state transition equation. Using this efficient computation, we
+study the accuracy of kernel learning using maximum likelihood and the
+posterior mean in a simulation experiment. The posterior mean with a reference
+prior is more accurate for complex models and sparse sampling. Because of its
+lower computation load, the maximum likelihood estimator is an attractive
+option for more densely sampled signals and lower order models. We confirm
+estimator behavior in experimental data through their application to speleothem
+data.
+",0,0,0,1,0,0
+7524,Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach,"  The experimental design problem concerns the selection of k points from a
+potentially large design pool of p-dimensional vectors, so as to maximize the
+statistical efficiency regressed on the selected k design points. Statistical
+efficiency is measured by optimality criteria, including A(verage),
+D(eterminant), T(race), E(igen), V(ariance) and G-optimality. Except for the
+T-optimality, exact optimization is NP-hard.
+We propose a polynomial-time regret minimization framework to achieve a
+$(1+\varepsilon)$ approximation with only $O(p/\varepsilon^2)$ design points,
+for all the optimality criteria above.
+In contrast, to the best of our knowledge, before our work, no
+polynomial-time algorithm achieves $(1+\varepsilon)$ approximations for
+D/E/G-optimality, and the best poly-time algorithm achieving
+$(1+\varepsilon)$-approximation for A/V-optimality requires $k =
+\Omega(p^2/\varepsilon)$ design points.
+",1,0,0,1,0,0
+7525,Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions,"  In machine learning or statistics, it is often desirable to reduce the
+dimensionality of high dimensional data. We propose to obtain the low
+dimensional embedding coordinates as the eigenvectors of a positive
+semi-definite kernel matrix. This kernel matrix is the solution of a
+semi-definite program promoting a low rank solution and defined with the help
+of a diffusion kernel. Besides, we also discuss an infinite dimensional
+analogue of the same semi-definite program. From a practical perspective, a
+main feature of our approach is the existence of a non-linear out-of-sample
+extension formula of the embedding coordinates that we call a projected
+Nyström approximation. This extension formula yields an extension of the
+kernel matrix to a data-dependent Mercer kernel function. Although the
+semi-definite program may be solved directly, we propose another strategy based
+on a rank constrained formulation solved thanks to a projected power method
+algorithm followed by a singular value decomposition. This strategy allows for
+a reduced computational time.
+",1,0,0,1,0,0
+7526,Bounded Projective Functions and Hyperbolic Metrics with Isolated Singularities,"  We establish a correspondence on a Riemann surface between hyperbolic metrics
+with isolated singularities and bounded projective functions whose Schwarzian
+derivatives have at most double poles and whose monodromies lie in ${\rm
+PSU}(1,\,1)$. As an application, we construct explicitly a new class of
+hyperbolic metrics with countably many singularities on the unit disc.
+",0,0,1,0,0,0
+7527,A State-Space Approach to Dynamic Nonnegative Matrix Factorization,"  Nonnegative matrix factorization (NMF) has been actively investigated and
+used in a wide range of problems in the past decade. A significant amount of
+attention has been given to develop NMF algorithms that are suitable to model
+time series with strong temporal dependencies. In this paper, we propose a
+novel state-space approach to perform dynamic NMF (D-NMF). In the proposed
+probabilistic framework, the NMF coefficients act as the state variables and
+their dynamics are modeled using a multi-lag nonnegative vector autoregressive
+(N-VAR) model within the process equation. We use expectation maximization and
+propose a maximum-likelihood estimation framework to estimate the basis matrix
+and the N-VAR model parameters. Interestingly, the N-VAR model parameters are
+obtained by simply applying NMF. Moreover, we derive a maximum a posteriori
+estimate of the state variables (i.e., the NMF coefficients) that is based on a
+prediction step and an update step, similarly to the Kalman filter. We
+illustrate the benefits of the proposed approach using different numerical
+simulations where D-NMF significantly outperforms its static counterpart.
+Experimental results for three different applications show that the proposed
+approach outperforms two state-of-the-art NMF approaches that exploit temporal
+dependencies, namely a nonnegative hidden Markov model and a frame stacking
+approach, while it requires less memory and computational power.
+",1,0,0,1,0,0
+7528,Active Inductive Logic Programming for Code Search,"  Modern search techniques either cannot efficiently incorporate human feedback
+to refine search results or to express structural or semantic properties of
+desired code. The key insight of our interactive code search technique ALICE is
+that user feedback could be actively incorporated to allow users to easily
+express and refine search queries. We design a query language to model the
+structure and semantics of code as logic facts. Given a code example with user
+annotations, ALICE automatically extracts a logic query from features that are
+tagged as important. Users can refine the search query by labeling one or more
+examples as desired (positive) or irrelevant (negative). ALICE then infers a
+new logic query that separates the positives from negative examples via active
+inductive logic programming. Our comprehensive and systematic simulation
+experiment shows that ALICE removes a large number of false positives quickly
+by actively incorporating user feedback. Its search algorithm is also robust to
+noise and user labeling mistakes. Our choice of leveraging both positive and
+negative examples and the nested containment structure of selected code is
+effective in refining search queries. Compared with an existing technique,
+Critics, ALICE does not require a user to manually construct a search pattern
+and yet achieves comparable precision and recall with fewer search iterations
+on average. A case study with users shows that ALICE is easy to use and helps
+express complex code patterns.
+",1,0,0,0,0,0
+7529,"Dense families of modular curves, prime numbers and uniform symmetric tensor rank of multiplication in certain finite fields","  We obtain new uniform bounds for the symmetric tensor rank of multiplication
+in finite extensions of any finite field Fp or Fp2 where p denotes a prime
+number greater or equal than 5. In this aim, we use the symmetric
+Chudnovsky-type generalized algorithm applied on sufficiently dense families of
+modular curves defined over Fp2 attaining the Drinfeld-Vladuts bound and on the
+descent of these families to the definition field Fp. These families are
+obtained thanks to prime number density theorems of type Hoheisel, in
+particular a result due to Dudek (2016).
+",0,0,1,0,0,0
+7530,Continued fraction algorithms and Lagrange's theorem in ${\mathbb Q}_p$,"  We present several continued fraction algorithms, each of which gives an
+eventually periodic expansion for every quadratic element of ${\mathbb Q}_p$
+over ${\mathbb Q}$ and gives a finite expansion for every rational number. We
+also give, for each of our algorithms, the complete characterization of
+elements having purely periodic expansions.
+",0,0,1,0,0,0
+7531,Scaling and bias codes for modeling speaker-adaptive DNN-based speech synthesis systems,"  Most neural-network based speaker-adaptive acoustic models for speech
+synthesis can be categorized into either layer-based or input-code approaches.
+Although both approaches have their own pros and cons, most existing works on
+speaker adaptation focus on improving one or the other. In this paper, after we
+first systematically overview the common principles of neural-network based
+speaker-adaptive models, we show that these approaches can be represented in a
+unified framework and can be generalized further. More specifically, we
+introduce the use of scaling and bias codes as generalized means for
+speaker-adaptive transformation. By utilizing these codes, we can create a more
+efficient factorized speaker-adaptive model and capture advantages of both
+approaches while reducing their disadvantages. The experiments show that the
+proposed method can improve the performance of speaker adaptation compared with
+speaker adaptation based on the conventional input code.
+",1,0,0,1,0,0
+7532,Bayesian Network Regularized Regression for Modeling Urban Crime Occurrences,"  This paper considers the problem of statistical inference and prediction for
+processes defined on networks. We assume that the network is known and measures
+similarity, and our goal is to learn about an attribute associated with its
+vertices. Classical regression methods are not immediately applicable to this
+setting, as we would like our model to incorporate information from both
+network structure and pertinent covariates. Our proposed model consists of a
+generalized linear model with vertex indexed predictors and a basis expansion
+of their coefficients, allowing the coefficients to vary over the network. We
+employ a regularization procedure, cast as a prior distribution on the
+regression coefficients under a Bayesian setup, so that the predicted responses
+vary smoothly according to the topology of the network. We motivate the need
+for this model by examining occurrences of residential burglary in Boston,
+Massachusetts. Noting that crime rates are not spatially homogeneous, and that
+the rates appear to vary sharply across regions in the city, we construct a
+hierarchical model that addresses these issues and gives insight into spatial
+patterns of crime occurrences. Furthermore, we examine efficient
+expectation-maximization fitting algorithms and provide
+computationally-friendly methods for eliciting hyper-prior parameters.
+",0,0,0,1,0,0
+7533,Similarity forces and recurrent components in human face-to-face interaction networks,"  We show that the social dynamics responsible for the formation of connected
+components that appear recurrently in face-to-face interaction networks, find a
+natural explanation in the assumption that the agents of the temporal network
+reside in a hidden similarity space. Distances between the agents in this space
+act as similarity forces directing their motion towards other agents in the
+physical space and determining the duration of their interactions. By contrast,
+if such forces are ignored in the motion of the agents recurrent components do
+not form, although other main properties of such networks can still be
+reproduced.
+",1,0,0,0,0,0
+7534,CELLO-3D: Estimating the Covariance of ICP in the Real World,"  The fusion of Iterative Closest Point (ICP) reg- istrations in existing state
+estimation frameworks relies on an accurate estimation of their uncertainty. In
+this paper, we study the estimation of this uncertainty in the form of a
+covariance. First, we scrutinize the limitations of existing closed-form
+covariance estimation algorithms over 3D datasets. Then, we set out to estimate
+the covariance of ICP registrations through a data-driven approach, with over 5
+100 000 registrations on 1020 pairs from real 3D point clouds. We assess our
+solution upon a wide spectrum of environments, ranging from structured to
+unstructured and indoor to outdoor. The capacity of our algorithm to predict
+covariances is accurately assessed, as well as the usefulness of these
+estimations for uncertainty estimation over trajectories. The proposed method
+estimates covariances better than existing closed-form solutions, and makes
+predictions that are consistent with observed trajectories.
+",1,0,0,0,0,0
+7535,Projection Theorems Using Effective Dimension,"  In this paper we use the theory of computing to study fractal dimensions of
+projections in Euclidean spaces. A fundamental result in fractal geometry is
+Marstrand's projection theorem, which shows that for every analytic set E, for
+almost every line L, the Hausdorff dimension of the orthogonal projection of E
+onto L is maximal. We use Kolmogorov complexity to give two new results on the
+Hausdorff and packing dimensions of orthogonal projections onto lines. The
+first shows that the conclusion of Marstrand's theorem holds whenever the
+Hausdorff and packing dimensions agree on the set E, even if E is not analytic.
+Our second result gives a lower bound on the packing dimension of projections
+of arbitrary sets. Finally, we give a new proof of Marstrand's theorem using
+the theory of computing.
+",1,0,1,0,0,0
+7536,Exotic pairing symmetry of interacting Dirac fermions on a $π$ flux lattice,"  The pairing symmetry of interacting Dirac fermions on the $\pi$-flux lattice
+is studied with the determinant quantum Monte Carlo and numerical linked
+cluster expansion methods. The extended $s^*$- (i.e. extended $s$-) and d-wave
+pairing symmetries, which are distinct in the conventional square lattice, are
+degenerate under the Landau gauge. We demonstrate that the dominant pairing
+channel at strong interactions is an exotic $ds^*$-wave phase consisting of
+alternating stripes of $s^*$- and d-wave phases. A complementary mean-field
+analysis shows that while the $s^*$- and d-wave symmetries individually have
+nodes in the energy spectrum, the $ds^*$ channel is fully gapped. The results
+represent a new realization of pairing in Dirac systems, connected to the
+problem of chiral d-wave pairing on the honeycomb lattice, which might be more
+readily accessed by cold-atom experiments.
+",0,1,0,0,0,0
+7537,Learning Causal Structures Using Regression Invariance,"  We study causal inference in a multi-environment setting, in which the
+functional relations for producing the variables from their direct causes
+remain the same across environments, while the distribution of exogenous noises
+may vary. We introduce the idea of using the invariance of the functional
+relations of the variables to their causes across a set of environments. We
+define a notion of completeness for a causal inference algorithm in this
+setting and prove the existence of such algorithm by proposing the baseline
+algorithm. Additionally, we present an alternate algorithm that has
+significantly improved computational and sample complexity compared to the
+baseline algorithm. The experiment results show that the proposed algorithm
+outperforms the other existing algorithms.
+",1,0,0,1,0,0
+7538,Linear Exponential Comonads without Symmetry,"  The notion of linear exponential comonads on symmetric monoidal categories
+has been used for modelling the exponential modality of linear logic. In this
+paper we introduce linear exponential comonads on general (possibly
+non-symmetric) monoidal categories, and show some basic results on them.
+",1,0,1,0,0,0
+7539,Multi-speaker Recognition in Cocktail Party Problem,"  This paper proposes an original statistical decision theory to accomplish a
+multi-speaker recognition task in cocktail party problem. This theory relies on
+an assumption that the varied frequencies of speakers obey Gaussian
+distribution and the relationship of their voiceprints can be represented by
+Euclidean distance vectors. This paper uses Mel-Frequency Cepstral Coefficients
+to extract the feature of a voice in judging whether a speaker is included in a
+multi-speaker environment and distinguish who the speaker should be. Finally, a
+thirteen-dimension constellation drawing is established by mapping from
+Manhattan distances of speakers in order to take a thorough consideration about
+gross influential factors.
+",1,0,0,0,0,0
+7540,A DIRT-T Approach to Unsupervised Domain Adaptation,"  Domain adaptation refers to the problem of leveraging labeled data in a
+source domain to learn an accurate model in a target domain where labels are
+scarce or unavailable. A recent approach for finding a common representation of
+the two domains is via domain adversarial training (Ganin & Lempitsky, 2015),
+which attempts to induce a feature extractor that matches the source and target
+feature distributions in some feature space. However, domain adversarial
+training faces two critical limitations: 1) if the feature extraction function
+has high-capacity, then feature distribution matching is a weak constraint, 2)
+in non-conservative domain adaptation (where no single classifier can perform
+well in both the source and target domains), training the model to do well on
+the source domain hurts performance on the target domain. In this paper, we
+address these issues through the lens of the cluster assumption, i.e., decision
+boundaries should not cross high-density data regions. We propose two novel and
+related models: 1) the Virtual Adversarial Domain Adaptation (VADA) model,
+which combines domain adversarial training with a penalty term that punishes
+the violation the cluster assumption; 2) the Decision-boundary Iterative
+Refinement Training with a Teacher (DIRT-T) model, which takes the VADA model
+as initialization and employs natural gradient steps to further minimize the
+cluster assumption violation. Extensive empirical results demonstrate that the
+combination of these two models significantly improve the state-of-the-art
+performance on the digit, traffic sign, and Wi-Fi recognition domain adaptation
+benchmarks.
+",0,0,0,1,0,0
+7541,Robust valley polarization of helium ion modified atomically thin MoS$_{2}$,"  Atomically thin semiconductors have dimensions that are commensurate with
+critical feature sizes of future optoelectronic devices defined using
+electron/ion beam lithography. Robustness of their emergent optical and
+valleytronic properties is essential for typical exposure doses used during
+fabrication. Here, we explore how focused helium ion bombardment affects the
+intrinsic vibrational, luminescence and valleytronic properties of atomically
+thin MoS$_{2}$. By probing the disorder dependent vibrational response we
+deduce the interdefect distance by applying a phonon confinement model. We show
+that the increasing interdefect distance correlates with disorder-related
+luminescence arising 180 meV below the neutral exciton emission. We perform
+ab-initio density functional theory of a variety of defect related
+morphologies, which yield first indications on the origin of the observed
+additional luminescence. Remarkably, no significant reduction of free exciton
+valley polarization is observed until the interdefect distance approaches a few
+nanometers, namely the size of the free exciton Bohr radius. Our findings pave
+the way for direct writing of sub-10 nm nanoscale valleytronic devices and
+circuits using focused helium ions.
+",0,1,0,0,0,0
+7542,Sustained sensorimotor control as intermittent decisions about prediction errors: Computational framework and application to ground vehicle steering,"  A conceptual and computational framework is proposed for modelling of human
+sensorimotor control, and is exemplified for the sensorimotor task of steering
+a car. The framework emphasises control intermittency, and extends on existing
+models by suggesting that the nervous system implements intermittent control
+using a combination of (1) motor primitives, (2) prediction of sensory outcomes
+of motor actions, and (3) evidence accumulation of prediction errors. It is
+shown that approximate but useful sensory predictions in the intermittent
+control context can be constructed without detailed forward models, as a
+superposition of simple prediction primitives, resembling neurobiologically
+observed corollary discharges. The proposed mathematical framework allows
+straightforward extension to intermittent behaviour from existing
+one-dimensional continuous models in the linear control and ecological
+psychology traditions. Empirical observations from a driving simulator provide
+support for some of the framework assumptions: It is shown that human steering
+control, in routine lane-keeping and in a demanding near-limit task, is better
+described as a sequence of discrete stepwise steering adjustments, than as
+continuous control. Furthermore, the amplitudes of individual steering
+adjustments are well predicted by a compound visual cue signalling steering
+error, and even better so if also adjusting for predictions of how the same cue
+is affected by previous control. Finally, evidence accumulation is shown to
+explain observed covariability between inter-adjustment durations and
+adjustment amplitudes, seemingly better so than the type of threshold
+mechanisms that are typically assumed in existing models of intermittent
+control.
+",1,0,0,0,0,0
+7543,"COLOSSUS: A python toolkit for cosmology, large-scale structure, and dark matter halos","  This paper introduces Colossus, a public, open-source python package for
+calculations related to cosmology, the large-scale structure (LSS) of matter in
+the universe, and the properties of dark matter halos. The code is designed to
+be fast and easy to use, with a coherent, well-documented user interface. The
+cosmology module implements Friedman-Lemaitre-Robertson-Walker cosmologies
+including curvature, relativistic species, and different dark energy equations
+of state, and provides fast computations of the linear matter power spectrum,
+variance, and correlation function. The LSS module is concerned with the
+properties of peaks in Gaussian random fields and halos in a statistical sense,
+including their peak height, peak curvature, halo bias, and mass function. The
+halo module deals with spherical overdensity radii and masses, density
+profiles, concentration, and the splashback radius. To facilitate the rapid
+exploration of these quantities, Colossus implements more than 40 different
+fitting functions from the literature. I discuss the core routines in detail,
+with particular emphasis on their accuracy. Colossus is available at
+bitbucket.org/bdiemer/colossus.
+",0,1,0,0,0,0
+7544,Generalized Similarity U: A Non-parametric Test of Association Based on Similarity,"  Second generation sequencing technologies are being increasingly used for
+genetic association studies, where the main research interest is to identify
+sets of genetic variants that contribute to various phenotype. The phenotype
+can be univariate disease status, multivariate responses and even
+high-dimensional outcomes. Considering the genotype and phenotype as two
+complex objects, this also poses a general statistical problem of testing
+association between complex objects. We here proposed a similarity-based test,
+generalized similarity U (GSU), that can test the association between complex
+objects. We first studied the theoretical properties of the test in a general
+setting and then focused on the application of the test to sequencing
+association studies. Based on theoretical analysis, we proposed to use
+Laplacian kernel based similarity for GSU to boost power and enhance
+robustness. Through simulation, we found that GSU did have advantages over
+existing methods in terms of power and robustness. We further performed a whole
+genome sequencing (WGS) scan for Alzherimer Disease Neuroimaging Initiative
+(ADNI) data, identifying three genes, APOE, APOC1 and TOMM40, associated with
+imaging phenotype. We developed a C++ package for analysis of whole genome
+sequencing data using GSU. The source codes can be downloaded at
+this https URL.
+",0,0,0,1,1,0
+7545,Hydra: An Accelerator for Real-Time Edge-Aware Permeability Filtering in 65nm CMOS,"  Many modern video processing pipelines rely on edge-aware (EA) filtering
+methods. However, recent high-quality methods are challenging to run in
+real-time on embedded hardware due to their computational load. To this end, we
+propose an area-efficient and real-time capable hardware implementation of a
+high quality EA method. In particular, we focus on the recently proposed
+permeability filter (PF) that delivers promising quality and performance in the
+domains of HDR tone mapping, disparity and optical flow estimation. We present
+an efficient hardware accelerator that implements a tiled variant of the PF
+with low on-chip memory requirements and a significantly reduced external
+memory bandwidth (6.4x w.r.t. the non-tiled PF). The design has been taped out
+in 65 nm CMOS technology, is able to filter 720p grayscale video at 24.8 Hz and
+achieves a high compute density of 6.7 GFLOPS/mm2 (12x higher than embedded
+GPUs when scaled to the same technology node). The low area and bandwidth
+requirements make the accelerator highly suitable for integration into SoCs
+where silicon area budget is constrained and external memory is typically a
+heavily contended resource.
+",1,0,0,0,0,0
+7546,Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration,"  Since the matrix formed by nonlocal similar patches in a natural image is of
+low rank, the nuclear norm minimization (NNM) has been widely used in various
+image processing studies. Nonetheless, nuclear norm based convex surrogate of
+the rank function usually over-shrinks the rank components and makes different
+components equally, and thus may produce a result far from the optimum. To
+alleviate the above-mentioned limitations of the nuclear norm, in this paper we
+propose a new method for image restoration via the non-convex weighted Lp
+nuclear norm minimization (NCW-NNM), which is able to more accurately enforce
+the image structural sparsity and self-similarity simultaneously. To make the
+proposed model tractable and robust, the alternative direction multiplier
+method (ADMM) is adopted to solve the associated non-convex minimization
+problem. Experimental results on various types of image restoration problems,
+including image deblurring, image inpainting and image compressive sensing (CS)
+recovery, demonstrate that the proposed method outperforms many current
+state-of-the-art methods in both the objective and the perceptual qualities.
+",1,0,0,0,0,0
+7547,A Contextual Bandit Approach for Stream-Based Active Learning,"  Contextual bandit algorithms -- a class of multi-armed bandit algorithms that
+exploit the contextual information -- have been shown to be effective in
+solving sequential decision making problems under uncertainty. A common
+assumption adopted in the literature is that the realized (ground truth) reward
+by taking the selected action is observed by the learner at no cost, which,
+however, is not realistic in many practical scenarios. When observing the
+ground truth reward is costly, a key challenge for the learner is how to
+judiciously acquire the ground truth by assessing the benefits and costs in
+order to balance learning efficiency and learning cost. From the information
+theoretic perspective, a perhaps even more interesting question is how much
+efficiency might be lost due to this cost. In this paper, we design a novel
+contextual bandit-based learning algorithm and endow it with the active
+learning capability. The key feature of our algorithm is that in addition to
+sending a query to an annotator for the ground truth, prior information about
+the ground truth learned by the learner is sent together, thereby reducing the
+query cost. We prove that by carefully choosing the algorithm parameters, the
+learning regret of the proposed algorithm achieves the same order as that of
+conventional contextual bandit algorithms in cost-free scenarios, implying
+that, surprisingly, cost due to acquiring the ground truth does not increase
+the learning regret in the long-run. Our analysis shows that prior information
+about the ground truth plays a critical role in improving the system
+performance in scenarios where active learning is necessary.
+",1,0,0,0,0,0
+7548,"Localization of ions within one-, two- and three-dimensional Coulomb crystals by a standing wave optical potential","  We demonstrate light-induced localization of Coulomb-interacting particles in
+multi-dimensional structures. Subwavelength localization of ions within small
+multi-dimensional Coulomb crystals by an intracavity optical standing wave
+field is evidenced by measuring the difference in scattering inside
+symmetrically red- and blue-detuned optical lattices and is observed even for
+ions undergoing substantial radial micromotion. These results are promising
+steps towards the structural control of ion Coulomb crystals by optical fields
+as well as for complex many-body simulations with ion crystals or for the
+investigation of heat transfer at the nanoscale, and have potential
+applications for ion-based cavity quantum electrodynamics, cavity optomechanics
+and ultracold ion chemistry.
+",0,1,0,0,0,0
+7549,Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks,"  A variety of real-world processes (over networks) produce sequences of data
+whose complex temporal dynamics need to be studied. More especially, the event
+timestamps can carry important information about the underlying network
+dynamics, which otherwise are not available from the time-series evenly sampled
+from continuous signals. Moreover, in most complex processes, event sequences
+and evenly-sampled times series data can interact with each other, which
+renders joint modeling of those two sources of data necessary. To tackle the
+above problems, in this paper, we utilize the rich framework of (temporal)
+point processes to model event data and timely update its intensity function by
+the synergic twin Recurrent Neural Networks (RNNs). In the proposed
+architecture, the intensity function is synergistically modulated by one RNN
+with asynchronous events as input and another RNN with time series as input.
+Furthermore, to enhance the interpretability of the model, the attention
+mechanism for the neural point process is introduced. The whole model with
+event type and timestamp prediction output layers can be trained end-to-end and
+allows a black-box treatment for modeling the intensity. We substantiate the
+superiority of our model in synthetic data and three real-world benchmark
+datasets.
+",1,0,0,0,0,0
+7550,Low Auto-correlation Binary Sequences explored using Warning Propagation,"  The search of binary sequences with low auto-correlations (LABS) is a
+discrete combinatorial optimization problem contained in the NP-hard
+computational complexity class. We study this problem using Warning Propagation
+(WP) , a message passing algorithm, and compare the performance of the
+algorithm in the original problem and in two different disordered versions. We
+show that in all the cases Warning Propagation converges to low energy minima
+of the solution space. Our results highlight the importance of the local
+structure of the interaction graph of the variables for the convergence time of
+the algorithm and for the quality of the solutions obtained by WP. While in
+general the algorithm does not provide the optimal solutions in large systems
+it does provide, in polynomial time, solutions that are energetically similar
+to the optimal ones. Moreover, we designed hybrid models that interpolate
+between the standard LABS problem and the disordered versions of it, and
+exploit them to improved the convergence time of WP and the quality of the
+solutions.
+",0,1,0,0,0,0
+7551,Estimation of the marginal expected shortfall under asymptotic independence,"  We study the asymptotic behavior of the marginal expected shortfall when the
+two random variables are asymptotic independent but positive associated, which
+is modeled by the so-called tail dependent coefficient. We construct an
+estimator of the marginal expected shortfall which is shown to be
+asymptotically normal. The finite sample performance of the estimator is
+investigated in a small simulation study. The method is also applied to
+estimate the expected amount of rainfall at a weather station given that there
+is a once every 100 years rainfall at another weather station nearby.
+",0,0,1,1,0,0
+7552,Downgrade Attack on TrustZone,"  Security-critical tasks require proper isolation from untrusted software.
+Chip manufacturers design and include trusted execution environments (TEEs) in
+their processors to secure these tasks. The integrity and security of the
+software in the trusted environment depend on the verification process of the
+system.
+We find a form of attack that can be performed on the current implementations
+of the widely deployed ARM TrustZone technology. The attack exploits the fact
+that the trustlet (TA) or TrustZone OS loading verification procedure may use
+the same verification key and may lack proper rollback prevention across
+versions. If an exploit works on an out-of-date version, but the vulnerability
+is patched on the latest version, an attacker can still use the same exploit to
+compromise the latest system by downgrading the software to an older and
+exploitable version.
+We did experiments on popular devices on the market including those from
+Google, Samsung and Huawei, and found that all of them have the risk of being
+attacked. Also, we show a real-world example to exploit Qualcomm's QSEE.
+In addition, in order to find out which device images share the same
+verification key, pattern matching schemes for different vendors are analyzed
+and summarized.
+",1,0,0,0,0,0
+7553,Robust Inference under the Beta Regression Model with Application to Health Care Studies,"  Data on rates, percentages or proportions arise frequently in many different
+applied disciplines like medical biology, health care, psychology and several
+others. In this paper, we develop a robust inference procedure for the beta
+regression model which is used to describe such response variables taking
+values in $(0, 1)$ through some related explanatory variables. In relation to
+the beta regression model, the issue of robustness has been largely ignored in
+the literature so far. The existing maximum likelihood based inference has
+serious lack of robustness against outliers in data and generate drastically
+different (erroneous) inference in presence of data contamination. Here, we
+develop the robust minimum density power divergence estimator and a class of
+robust Wald-type tests for the beta regression model along with several
+applications. We derive their asymptotic properties and describe their
+robustness theoretically through the influence function analyses. Finite sample
+performances of the proposed estimators and tests are examined through suitable
+simulation studies and real data applications in the context of health care and
+psychology. Although we primarily focus on the beta regression models with a
+fixed dispersion parameter, some indications are also provided for extension to
+the variable dispersion beta regression models with an application.
+",0,0,0,1,0,0
+7554,PageRank in Undirected Random Graphs,"  PageRank has numerous applications in information retrieval, reputation
+systems, machine learning, and graph partitioning. In this paper, we study
+PageRank in undirected random graphs with an expansion property. The Chung-Lu
+random graph is an example of such a graph. We show that in the limit, as the
+size of the graph goes to infinity, PageR- ank can be approximated by a mixture
+of the restart distribution and the vertex degree distribution. We also extend
+the result to Stochastic Block Model (SBM) graphs, where we show that there is
+a correction term that depends on the community partitioning.
+",0,0,1,0,0,0
+7555,Room temperature line lists for CO\2 symmetric isotopologues with \textit{ab initio} computed intensities,"  Remote sensing experiments require high-accuracy, preferably sub-percent,
+line intensities and in response to this need we present computed room
+temperature line lists for six symmetric isotopologues of carbon dioxide:
+$^{13}$C$^{16}$O$_2$, $^{14}$C$^{16}$O$_2$, $^{12}$C$^{17}$O$_2$,
+$^{12}$C$^{18}$O$_2$, $^{13}$C$^{17}$O$_2$ and $^{13}$C$^{18}$O$_2$, covering
+the range 0-8000 \cm. Our calculation scheme is based on variational nuclear
+motion calculations and on a reliability analysis of the generated line
+intensities. Rotation-vibration wavefunctions and energy levels are computed
+using the DVR3D software suite and a high quality semi-empirical potential
+energy surface (PES), followed by computation of intensities using an
+\abinitio\ dipole moment surface (DMS). Four line lists are computed for each
+isotopologue to quantify sensitivity to minor distortions of the PES/DMS.
+Reliable lines are benchmarked against recent state-of-the-art measurements and
+against the HITRAN2012 database, supporting the claim that the majority of line
+intensities for strong bands are predicted with sub-percent accuracy. Accurate
+line positions are generated using an effective Hamiltonian. We recommend the
+use of these line lists for future remote sensing studies and their inclusion
+in databases.
+",0,1,0,0,0,0
+7556,The GIT moduli of semistable pairs consisting of a cubic curve and a line on ${\mathbb P}^{2}$,"  We discuss the GIT moduli of semistable pairs consisting of a cubic curve and
+a line on the projective plane. We study in some detail this moduli and compare
+it with another moduli suggested by Alexeev. It is the moduli of pairs (with no
+specified semi-abelian action) consisting of a cubic curve with at worst nodal
+singularities and a line which does not pass through singular points of the
+cubic curve. Meanwhile, we make a comparison between Nakamura's
+compactification of the moduli of level three elliptic curves and these two
+moduli spaces.
+",0,0,1,0,0,0
+7557,On the Genus of the Moonshine Module,"  We provide a novel and simple description of Schellekens' seventy-one affine
+Kac-Moody structures of self-dual vertex operator algebras of central charge 24
+by utilizing cyclic subgroups of the glue codes of the Niemeier lattices with
+roots. We also discuss a possible uniform construction procedure of the
+self-dual vertex operator algebras of central charge 24 starting from the Leech
+lattice. This also allows us to consider the uniqueness question for all
+non-trivial affine Kac-Moody structures. We finally discuss our description
+from a Lorentzian viewpoint.
+",0,0,1,0,0,0
+7558,A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers,"  We describe a method of reconstructing air showers induced by cosmic rays
+using deep learning techniques. We simulate an observatory consisting of
+ground-based particle detectors with fixed locations on a regular grid. The
+detector's responses to traversing shower particles are signal amplitudes as a
+function of time, which provide information on transverse and longitudinal
+shower properties. In order to take advantage of convolutional network
+techniques specialized in local pattern recognition, we convert all information
+to the image-like grid of the detectors. In this way, multiple features, such
+as arrival times of the first particles and optimized characterizations of time
+traces, are processed by the network. The reconstruction quality of the cosmic
+ray arrival direction turns out to be competitive with an analytic
+reconstruction algorithm. The reconstructed shower direction, energy and shower
+depth show the expected improvement in resolution for higher cosmic ray energy.
+",0,1,0,0,0,0
+7559,The unexpected resurgence of Weyl geometry in late 20-th century physics,"  Weyl's original scale geometry of 1918 (""purely infinitesimal geometry"") was
+withdrawn by its author from physical theorizing in the early 1920s. It had a
+comeback in the last third of the 20th century in different contexts: scalar
+tensor theories of gravity, foundations of gravity, foundations of quantum
+mechanics, elementary particle physics, and cosmology. It seems that Weyl
+geometry continues to offer an open research potential for the foundations of
+physics even after the turn to the new millennium.
+",0,1,1,0,0,0
+7560,Discrete Dynamic Causal Modeling and Its Relationship with Directed Information,"  This paper explores the discrete Dynamic Causal Modeling (DDCM) and its
+relationship with Directed Information (DI). We prove the conditional
+equivalence between DDCM and DI in characterizing the causal relationship
+between two brain regions. The theoretical results are demonstrated using fMRI
+data obtained under both resting state and stimulus based state. Our numerical
+analysis is consistent with that reported in previous study.
+",0,0,0,1,0,0
+7561,Two-step approach to scheduling quantum circuits,"  As the effort to scale up existing quantum hardware proceeds, it becomes
+necessary to schedule quantum gates in a way that minimizes the number of
+operations. There are three constraints that have to be satisfied: the order or
+dependency of the quantum gates in the specific algorithm, the fact that any
+qubit may be involved in at most one gate at a time, and the restriction that
+two-qubit gates are implementable only between connected qubits. The last
+aspect implies that the compilation depends not only on the algorithm, but also
+on hardware properties like connectivity. Here we suggest a two-step approach
+in which logical gates are initially scheduled neglecting connectivity
+considerations, while routing operations are added at a later step in a way
+that minimizes their overhead. We rephrase the subtasks of gate scheduling in
+terms of graph problems like edge-coloring and maximum subgraph isomorphism.
+While this approach is general, we specialize to a one dimensional array of
+qubits to propose a routing scheme that is minimal in the number of exchange
+operations. As a practical application, we schedule the Quantum Approximate
+Optimization Algorithm in a linear geometry and quantify the reduction in the
+number of gates and circuit depth that results from increasing the efficacy of
+the scheduling strategies.
+",1,0,0,0,0,0
+7562,A Study of the Allan Variance for Constant-Mean Non-Stationary Processes,"  The Allan Variance (AV) is a widely used quantity in areas focusing on error
+measurement as well as in the general analysis of variance for autocorrelated
+processes in domains such as engineering and, more specifically, metrology. The
+form of this quantity is widely used to detect noise patterns and indications
+of stability within signals. However, the properties of this quantity are not
+known for commonly occurring processes whose covariance structure is
+non-stationary and, in these cases, an erroneous interpretation of the AV could
+lead to misleading conclusions. This paper generalizes the theoretical form of
+the AV to some non-stationary processes while at the same time being valid also
+for weakly stationary processes. Some simulation examples show how this new
+form can help to understand the processes for which the AV is able to
+distinguish these from the stationary cases and hence allow for a better
+interpretation of this quantity in applied cases.
+",0,0,1,1,0,0
+7563,Foresight: Recommending Visual Insights,"  Current tools for exploratory data analysis (EDA) require users to manually
+select data attributes, statistical computations and visual encodings. This can
+be daunting for large-scale, complex data. We introduce Foresight, a system
+that helps the user rapidly discover visual insights from large
+high-dimensional datasets. Formally, an ""insight"" is a strong manifestation of
+a statistical property of the data, e.g., high correlation between two
+attributes, high skewness or concentration about the mean of a single
+attribute, a strong clustering of values, and so on. For each insight type,
+Foresight initially presents visualizations of the top k instances in the data,
+based on an appropriate ranking metric. The user can then look at ""nearby""
+insights by issuing ""insight queries"" containing constraints on insight
+strengths and data attributes. Thus the user can directly explore the space of
+insights, rather than the space of data dimensions and visual encodings as in
+other visual recommender systems. Foresight also provides ""global"" views of
+insight space to help orient the user and ensure a thorough exploration
+process. Furthermore, Foresight facilitates interactive exploration of large
+datasets through fast, approximate sketching.
+",1,0,0,0,0,0
+7564,Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization,"  We consider the stochastic composition optimization problem proposed in
+\cite{wang2017stochastic}, which has applications ranging from estimation to
+statistical and machine learning. We propose the first ADMM-based algorithm
+named com-SVR-ADMM, and show that com-SVR-ADMM converges linearly for strongly
+convex and Lipschitz smooth objectives, and has a convergence rate of $O( \log
+S/S)$, which improves upon the $O(S^{-4/9})$ rate in
+\cite{wang2016accelerating} when the objective is convex and Lipschitz smooth.
+Moreover, com-SVR-ADMM possesses a rate of $O(1/\sqrt{S})$ when the objective
+is convex but without Lipschitz smoothness. We also conduct experiments and
+show that it outperforms existing algorithms.
+",1,0,0,1,0,0
+7565,Hierarchical Behavioral Repertoires with Unsupervised Descriptors,"  Enabling artificial agents to automatically learn complex, versatile and
+high-performing behaviors is a long-lasting challenge. This paper presents a
+step in this direction with hierarchical behavioral repertoires that stack
+several behavioral repertoires to generate sophisticated behaviors. Each
+repertoire of this architecture uses the lower repertoires to create complex
+behaviors as sequences of simpler ones, while only the lowest repertoire
+directly controls the agent's movements. This paper also introduces a novel
+approach to automatically define behavioral descriptors thanks to an
+unsupervised neural network that organizes the produced high-level behaviors.
+The experiments show that the proposed architecture enables a robot to learn
+how to draw digits in an unsupervised manner after having learned to draw lines
+and arcs. Compared to traditional behavioral repertoires, the proposed
+architecture reduces the dimensionality of the optimization problems by orders
+of magnitude and provides behaviors with a twice better fitness. More
+importantly, it enables the transfer of knowledge between robots: a
+hierarchical repertoire evolved for a robotic arm to draw digits can be
+transferred to a humanoid robot by simply changing the lowest layer of the
+hierarchy. This enables the humanoid to draw digits although it has never been
+trained for this task.
+",1,0,0,0,0,0
+7566,Indirect Image Registration with Large Diffeomorphic Deformations,"  The paper adapts the large deformation diffeomorphic metric mapping framework
+for image registration to the indirect setting where a template is registered
+against a target that is given through indirect noisy observations. The
+registration uses diffeomorphisms that transform the template through a (group)
+action. These diffeomorphisms are generated by solving a flow equation that is
+defined by a velocity field with certain regularity. The theoretical analysis
+includes a proof that indirect image registration has solutions (existence)
+that are stable and that converge as the data error tends so zero, so it
+becomes a well-defined regularization method. The paper concludes with examples
+of indirect image registration in 2D tomography with very sparse and/or highly
+noisy data.
+",1,0,1,0,0,0
+7567,Morphological estimators on Sunyaev--Zel'dovich maps of MUSIC clusters of galaxies,"  The determination of the morphology of galaxy clusters has important
+repercussion on their cosmological and astrophysical studies. In this paper we
+address the morphological characterisation of synthetic maps of the
+Sunyaev--Zel'dovich (SZ) effect produced for a sample of 258 massive clusters
+($M_{vir}>5\times10^{14}h^{-1}$M$_\odot$ at $z=0$), extracted from the MUSIC
+hydrodynamical simulations. Specifically, we apply five known morphological
+parameters, already used in X-ray, two newly introduced ones, and we combine
+them together in a single parameter. We analyse two sets of simulations
+obtained with different prescriptions of the gas physics (non radiative and
+with cooling, star formation and stellar feedback) at four redshifts between
+0.43 and 0.82. For each parameter we test its stability and efficiency to
+discriminate the true cluster dynamical state, measured by theoretical
+indicators. The combined parameter discriminates more efficiently relaxed and
+disturbed clusters. This parameter had a mild correlation with the hydrostatic
+mass ($\sim 0.3$) and a strong correlation ($\sim 0.8$) with the offset between
+the SZ centroid and the cluster centre of mass. The latter quantity results as
+the most accessible and efficient indicator of the dynamical state for SZ
+studies.
+",0,1,0,0,0,0
+7568,Hierarchy of exchange interactions in the triangular-lattice spin-liquid YbMgGaO$_{4}$,"  The spin-1/2 triangular lattice antiferromagnet YbMgGaO$_{4}$ has attracted
+recent attention as a quantum spin-liquid candidate with the possible presence
+of off-diagonal anisotropic exchange interactions induced by spin-orbit
+coupling. Whether a quantum spin-liquid is stabilized or not depends on the
+interplay of various exchange interactions with chemical disorder that is
+inherent to the layered structure of the compound. We combine time-domain
+terahertz spectroscopy and inelastic neutron scattering measurements in the
+field polarized state of YbMgGaO$_{4}$ to obtain better microscopic insights on
+its exchange interactions. Terahertz spectroscopy in this fashion functions as
+high-field electron spin resonance and probes the spin-wave excitations at the
+Brillouin zone center, ideally complementing neutron scattering. A global
+spin-wave fit to all our spectroscopic data at fields over 4T, informed by the
+analysis of the terahertz spectroscopy linewidths, yields stringent constraints
+on $g$-factors and exchange interactions. Our results paint YbMgGaO$_{4}$ as an
+easy-plane XXZ antiferromagnet with the combined and necessary presence of
+sub-leading next-nearest neighbor and weak anisotropic off-diagonal
+nearest-neighbor interactions. Moreover, the obtained $g$-factors are
+substantially different from previous reports. This works establishes the
+hierarchy of exchange interactions in YbMgGaO$_{4}$ from high-field data alone
+and thus strongly constrains possible mechanisms responsible for the observed
+spin-liquid phenomenology.
+",0,1,0,0,0,0
+7569,Pulsar braking and the P-Pdot diagram,"  The location of radio pulsars in the period-period derivative (P-Pdot) plane
+has been a key diagnostic tool since the early days of pulsar astronomy. Of
+particular importance is how pulsars evolve through the P-Pdot diagram with
+time. Here we show that the decay of the inclination angle (alpha-dot) between
+the magnetic and rotation axes plays a critical role. In particular, alpha-dot
+strongly impacts on the braking torque, an effect which has been largely
+ignored in previous work. We carry out simulations which include a negative
+alpha-dot term, and show that it is possible to reproduce the observational
+P-Pdot diagram without the need for either pulsars with long birth periods or
+magnetic field decay. Our best model indicates a birth rate of 1 radio pulsar
+per century and a total Galactic population of ~20000 pulsars beaming towards
+Earth.
+",0,1,0,0,0,0
+7570,Uniqueness of stable capillary hypersurfaces in a ball,"  In this paper we prove that any immersed stable capillary hypersurfaces in a
+ball in space forms are totally umbilical. This solves completely a
+long-standing open problem. In the proof one of crucial ingredients is a new
+Minkowski type formula. We also prove a Heintze-Karcher-Ros type inequality for
+hypersurfaces in a ball, which, together with the new Minkowski formula, yields
+a new proof of Alexandrov's Theorem for embedded CMC hypersurfaces in a ball
+with free boundary.
+",0,0,1,0,0,0
+7571,A Potential Recoiling Supermassive Black Hole CXO J101527.2+625911,"  We have carried out a systematic search for recoiling supermassive black
+holes (rSMBH) using the Chandra Source and SDSS Cross Matched Catalog. From the
+survey, we have detected a potential rSMBH, 'CXO J101527.2+625911' at z=0.3504.
+The CXO J101527.2+625911 has a spatially offset (1.26$\pm$0.05 kpc) active SMBH
+and kinematically offset broad emission lines (175$\pm$25 km s$^{\rm -1}$
+relative to systemic velocity). The observed spatial and velocity offsets
+suggest this galaxy could be a rSMBH, but we also have considered a possibility
+of dual SMBH scenario. The column density towards the galaxy center was found
+to be Compton thin, but no X-ray source was detected. The non-detection of the
+X-ray source in the nucleus suggests either there is no obscured actively
+accreting SMBH, or there exists an SMBH but has a low accretion rate (i.e.
+low-luminosity AGN (LLAGN)). The possibility of the LLAGN was investigated and
+found to be unlikely based on the H$\alpha$ luminosity, radio power, and
+kinematic arguments. This, along with the null detection of X-ray source in the
+nucleus supports our hypothesis that the CXO J101527.2+625911 is a rSMBH. Our
+GALFIT analysis shows the host galaxy to be a bulge-dominated elliptical. The
+weak morphological disturbance and small spatial and velocity offsets suggest
+that CXO J101527.2+625911 could be in the final stage of merging process and
+about to turn into a normal elliptical galaxy.
+",0,1,0,0,0,0
+7572,WLAN Performance Analysis Ibrahim Group of industries Faisalabad Pakistan,"  Now a days several organizations are moving their LAN foundation towards
+remote LAN frame work. The purpose for this is extremely straight forward
+multinational organizations needs their clients surprise about their office
+surroundings and they additionally need to make wire free environment in their
+workplaces. Much IT equipment moved on Wireless for instance all in one Pc
+portable workstations Wireless IP telephones. Another thing is that step by
+step WLAN innovation moving towards extraordinary effectiveness. In this
+exploration work Wireless LAN innovation running in Ibrahim Group gathering of
+commercial enterprises Faisalabad has been investigated in term of their
+equipment, Wireless signal quality, data transmission, auto channel moving, and
+security in WLAN system. This examination work required physical proving
+ground, some WLAN system analyzer (TamoSof throughput) software, hardware point
+of interest, security testing programming. The investigation displayed in this
+examination has fill two key needs. One determination is to accept this kind of
+system interconnection could be broke down utilizing the exploratory models of
+the two system bits (wired and remote pieces. Second key factor is to determine
+the security issue in WLAN.
+",1,0,0,0,0,0
+7573,Archetypes for Representing Data about the Brazilian Public Hospital Information System and Outpatient High Complexity Procedures System,"  The Brazilian Ministry of Health has selected the openEHR model as a standard
+for electronic health record systems. This paper presents a set of archetypes
+to represent the main data from the Brazilian Public Hospital Information
+System and the High Complexity Procedures Module of the Brazilian public
+Outpatient Health Information System. The archetypes from the public openEHR
+Clinical Knowledge Manager (CKM), were examined in order to select archetypes
+that could be used to represent the data of the above mentioned systems. For
+several concepts, it was necessary to specialize the CKM archetypes, or design
+new ones. A total of 22 archetypes were used: 8 new, 5 specialized and 9 reused
+from CKM. This set of archetypes can be used not only for information exchange,
+but also for generating a big anonymized dataset for testing openEHR-based
+systems.
+",1,0,0,0,0,0
+7574,Global linear convergent algorithm to compute the minimum volume enclosing ellipsoid,"  The minimum volume enclosing ellipsoid (MVEE) problem is an optimization
+problem in the basis of many practical problems. This paper describes some new
+properties of this model and proposes a first-order oracle algorithm, the
+Adjusted Coordinate Descent (ACD) algorithm, to address the MVEE problem. The
+ACD algorithm is globally linear convergent and has an overwhelming advantage
+over the other algorithms in cases where the dimension of the data is large.
+Moreover, as a byproduct of the convergence property of the ACD algorithm, we
+prove the global linear convergence of the Frank-Wolfe type algorithm
+(illustrated by the case of Wolfe-Atwood's algorithm), which supports the
+conjecture of Todd. Furthermore, we provide a new interpretation for the means
+of choosing the coordinate axis of the Frank-Wolfe type algorithm from the
+perspective of the smoothness of the coordinate axis, i.e., the algorithm
+chooses the coordinate axis with the worst smoothness at each iteration. This
+finding connects the first-order oracle algorithm and the linear optimization
+oracle algorithm on the MVEE problem. The numerical tests support our
+theoretical results.
+",0,0,1,0,0,0
+7575,Asymptotic behavior of memristive circuits and combinatorial optimization,"  The interest in memristors has risen due to their possible application both
+as memory units and as computational devices in combination with CMOS. This is
+in part due to their nonlinear dynamics and a strong dependence on the circuit
+topology. We provide evidence that also purely memristive circuits can be
+employed for computational purposes. We show that a Lyapunov function,
+polynomial in the internal memory parameters, exists for the case of DC
+controlled memristors. Such Lyapunov function can be asymptotically mapped to
+quadratic combinatorial optimization problems. This shows a direct parallel
+between memristive circuits and the Hopfield-Little model. In the case of
+Erdos-Renyi random circuits, we provide numerical evidence that the
+distribution of the matrix elements of the couplings can be roughly
+approximated by a Gaussian distribution, and that they scale with the inverse
+square root of the number of elements. This provides an approximated but direct
+connection to the physics of disordered system and, in particular, of mean
+field spin glasses. Using this and the fact that the interaction is controlled
+by a projector operator on the loop space of the circuit, we estimate the
+number of stationary points of the Lyapunov function, and provide a scaling
+formula as an upper bound in terms of the circuit topology only. In order to
+put these ideas into practice, we provide an instance of optimization of the
+Nikkei 225 dataset in the Markowitz framework, and show that it is competitive
+compared to exponential annealing.
+",1,1,0,0,0,0
+7576,Contrasting information theoretic decompositions of modulatory and arithmetic interactions in neural information processing systems,"  Biological and artificial neural systems are composed of many local
+processors, and their capabilities depend upon the transfer function that
+relates each local processor's outputs to its inputs. This paper uses a recent
+advance in the foundations of information theory to study the properties of
+local processors that use contextual input to amplify or attenuate transmission
+of information about their driving inputs. This advance enables the information
+transmitted by processors with two distinct inputs to be decomposed into those
+components unique to each input, that shared between the two inputs, and that
+which depends on both though it is in neither, i.e. synergy. The decompositions
+that we report here show that contextual modulation has information processing
+properties that contrast with those of all four simple arithmetic operators,
+that it can take various forms, and that the form used in our previous studies
+of artificial neural nets composed of local processors with both driving and
+contextual inputs is particularly well-suited to provide the distinctive
+capabilities of contextual modulation under a wide range of conditions. We
+argue that the decompositions reported here could be compared with those
+obtained from empirical neurobiological and psychophysical data under
+conditions thought to reflect contextual modulation. That would then shed new
+light on the underlying processes involved. Finally, we suggest that such
+decompositions could aid the design of context-sensitive machine learning
+algorithms.
+",0,0,0,1,1,0
+7577,Recommendations for Marketing Campaigns in Telecommunication Business based on the footprint analysis,"  A major investment made by a telecom operator goes into the infrastructure
+and its maintenance, while business revenues are proportional to how big and
+good the customer base is. We present a data-driven analytic strategy based on
+combinatorial optimization and analysis of historical data. The data cover
+historical mobility of the users in one region of Sweden during a week.
+Applying the proposed method to the case study, we have identified the optimal
+proportion of geo-demographic segments in the customer base, developed a
+functionality to assess the potential of a planned marketing campaign, and
+explored the problem of an optimal number and types of the geo-demographic
+segments to target through marketing campaigns. With the help of fuzzy logic,
+the conclusions of data analysis are automatically translated into
+comprehensible recommendations in a natural language.
+",1,0,0,0,0,0
+7578,Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks,"  We consider the problem of detecting out-of-distribution images in neural
+networks. We propose ODIN, a simple and effective method that does not require
+any change to a pre-trained neural network. Our method is based on the
+observation that using temperature scaling and adding small perturbations to
+the input can separate the softmax score distributions between in- and
+out-of-distribution images, allowing for more effective detection. We show in a
+series of experiments that ODIN is compatible with diverse network
+architectures and datasets. It consistently outperforms the baseline approach
+by a large margin, establishing a new state-of-the-art performance on this
+task. For example, ODIN reduces the false positive rate from the baseline 34.7%
+to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is
+95%.
+",1,0,0,1,0,0
+7579,Two-fermion Bethe-Salpeter Equation in Minkowski Space: the Nakanishi Way,"  The possibility of solving the Bethe-Salpeter Equation in Minkowski space,
+even for fermionic systems, is becoming actual, through the applications of
+well-known tools: i) the Nakanishi integral representation of the
+Bethe-Salpeter amplitude and ii) the light-front projection onto the
+null-plane. The theoretical background and some preliminary calculations are
+illustrated, in order to show the potentiality and the wide range of
+application of the method.
+",0,1,0,0,0,0
+7580,"Fractional Laplacians on the sphere, the Minakshisundaram zeta function and semigroups","  In this paper we show novel underlying connections between fractional powers
+of the Laplacian on the unit sphere and functions from analytic number theory
+and differential geometry, like the Hurwitz zeta function and the
+Minakshisundaram zeta function. Inspired by Minakshisundaram's ideas, we find a
+precise pointwise description of $(-\Delta_{\mathbb{S}^{n-1}})^s u(x)$ in terms
+of fractional powers of the Dirichlet-to-Neumann map on the sphere. The Poisson
+kernel for the unit ball will be essential for this part of the analysis. On
+the other hand, by using the heat semigroup on the sphere, additional pointwise
+integro-differential formulas are obtained. Finally, we prove a
+characterization with a local extension problem and the interior Harnack
+inequality.
+",0,0,1,0,0,0
+7581,Nef partitions for codimension 2 weighted complete intersections,"  We prove that a smooth well formed Fano weighted complete intersection of
+codimension 2 has a nef partition. We discuss applications of this fact to
+Mirror Symmetry. In particular we list all nef partitions for smooth well
+formed Fano weighted complete intersections of dimensions 4 and 5 and present
+weak Landau--Ginzburg models for them.
+",0,0,1,0,0,0
+7582,Integrating sentiment and social structure to determine preference alignments: The Irish Marriage Referendum,"  We examine the relationship between social structure and sentiment through
+the analysis of a large collection of tweets about the Irish Marriage
+Referendum of 2015. We obtain the sentiment of every tweet with the hashtags
+#marref and #marriageref that was posted in the days leading to the referendum,
+and construct networks to aggregate sentiment and use it to study the
+interactions among users. Our results show that the sentiment of mention tweets
+posted by users is correlated with the sentiment of received mentions, and
+there are significantly more connections between users with similar sentiment
+scores than among users with opposite scores in the mention and follower
+networks. We combine the community structure of the two networks with the
+activity level of the users and sentiment scores to find groups of users who
+support voting `yes' or `no' in the referendum. There were numerous
+conversations between users on opposing sides of the debate in the absence of
+follower connections, which suggests that there were efforts by some users to
+establish dialogue and debate across ideological divisions. Our analysis shows
+that social structure can be integrated successfully with sentiment to analyse
+and understand the disposition of social media users. These results have
+potential applications in the integration of data and meta-data to study
+opinion dynamics, public opinion modelling, and polling.
+",1,1,0,0,0,0
+7583,Tailoring symmetric metallic and magnetic edge states of nanoribbon in semiconductive monolayer PtS2,"  Fabrication of atomic scale of metallic wire remains challenging. In present
+work, a nanoribbon with two parallel symmetric metallic and magnetic edges was
+designed from semiconductive monolayer PtS2 by employing first-principles
+calculations based on density functional theory. Edge energy, bonding charge
+density, band structure and simulated STM of possible edges states of PtS2 were
+systematically studied. It was found that Pt-terminated edge nanoribbons were
+the relatively stable metallic and magnetic edge tailored from a noble
+transition metal dichalcogenides PtS2. The nanoribbon with two atomic metallic
+wires may have promising application as nano power transmission lines, which at
+least two lines are needed.
+",0,1,0,0,0,0
+7584,The Ramsey theory of the universal homogeneous triangle-free graph,"  The universal homogeneous triangle-free graph, constructed by Henson and
+denoted $\mathcal{H}_3$, is the triangle-free analogue of the Rado graph. While
+the Ramsey theory of the Rado graph has been completely established, beginning
+with Erdős-Hajnal-Posá and culminating in work of Sauer and
+Laflamme-Sauer-Vuksanovic, the Ramsey theory of $\mathcal{H}_3$ had only
+progressed to bounds for vertex colorings (Komjáth-Rödl) and edge
+colorings (Sauer). This was due to a lack of broadscale techniques.
+We solve this problem in general: For each finite triangle-free graph $G$,
+there is a finite number $T(G)$ such that for any coloring of all copies of $G$
+in $\mathcal{H}_3$ into finitely many colors, there is a subgraph of
+$\mathcal{H}_3$ which is again universal homogeneous triangle-free in which the
+coloring takes no more than $T(G)$ colors. This is the first such result for a
+homogeneous structure omitting copies of some non-trivial finite structure. The
+proof entails developments of new broadscale techniques, including a flexible
+method for constructing trees which code $\mathcal{H}_3$ and the development of
+their Ramsey theory.
+",0,0,1,0,0,0
+7585,Restriction of representations of metaplectic $GL_{2}(F)$ to tori,"  Let $F$ be a non-Archimedean local field. We study the restriction of an
+irreducible admissible genuine representations of the two fold metaplectic
+cover $\widetilde{GL}_{2}(F)$ of $GL_{2}(F)$ to the inverse image in
+$\widetilde{GL}_{2}(F)$ of a maximal torus in $GL_{2}(F)$.
+",0,0,1,0,0,0
+7586,Five-dimensional Perfect Simplices,"  Let $Q_n=[0,1]^n$ be the unit cube in ${\mathbb R}^n$, $n \in {\mathbb N}$.
+For a nondegenerate simplex $S\subset{\mathbb R}^n$, consider the value
+$\xi(S)=\min \{\sigma>0: Q_n\subset \sigma S\}$. Here $\sigma S$ is a
+homothetic image of $S$ with homothety center at the center of gravity of $S$
+and coefficient of homothety $\sigma$. Let us introduce the value $\xi_n=\min
+\{\xi(S): S\subset Q_n\}$. We call $S$ a perfect simplex if $S\subset Q_n$ and
+$Q_n$ is inscribed into the simplex $\xi_n S$. It is known that such simplices
+exist for $n=1$ and $n=3$. The exact values of $\xi_n$ are known for $n=2$ and
+in the case when there exist an Hadamard matrix of order $n+1$, in the latter
+situation $\xi_n=n$. In this paper we show that $\xi_5=5$ and $\xi_9=9$. We
+also describe infinite families of simplices $S\subset Q_n$ such that
+$\xi(S)=\xi_n$ for $n=5,7,9$. The main result of the paper is the existence of
+perfect simplices in ${\mathbb R}^5$.
+Keywords: simplex, cube, homothety, axial diameter, Hadamard matrix
+",0,0,1,0,0,0
+7587,Quantum Fluctuations in Mesoscopic Systems,"  Recent experimental results point to the existence of coherent quantum
+phenomena in systems made of a large number of particles, despite the fact that
+for many-body systems the presence of decoherence is hardly negligible and
+emerging classicality is expected. This behaviour hinges on collective
+observables, named quantum fluctuations, that retain a quantum character even
+in the thermodynamic limit: they provide useful tools for studying properties
+of many-body systems at the mesoscopic level, in between the quantum
+microscopic scale and the classical macroscopic one. We hereby present the
+general theory of quantum fluctuations in mesoscopic systems and study their
+dynamics in a quantum open system setting, taking into account the unavoidable
+effects of dissipation and noise induced by the external environment. As in the
+case of microscopic systems, decoherence is not always the only dominating
+effect at the mesoscopic scale: certain type of environments can provide means
+for entangling collective fluctuations through a purely noisy mechanism.
+",0,1,0,0,0,0
+7588,Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks,"  Sensors are present in various forms all around the world such as mobile
+phones, surveillance cameras, smart televisions, intelligent refrigerators and
+blood pressure monitors. Usually, most of the sensors are a part of some other
+system with similar sensors that compose a network. One of such networks is
+composed of millions of sensors connect to the Internet which is called
+Internet of things (IoT). With the advances in wireless communication
+technologies, multimedia sensors and their networks are expected to be major
+components in IoT. Many studies have already been done on wireless multimedia
+sensor networks in diverse domains like fire detection, city surveillance,
+early warning systems, etc. All those applications position sensor nodes and
+collect their data for a long time period with real-time data flow, which is
+considered as big data. Big data may be structured or unstructured and needs to
+be stored for further processing and analyzing. Analyzing multimedia big data
+is a challenging task requiring a high-level modeling to efficiently extract
+valuable information/knowledge from data. In this study, we propose a big
+database model based on graph database model for handling data generated by
+wireless multimedia sensor networks. We introduce a simulator to generate
+synthetic data and store and query big data using graph model as a big
+database. For this purpose, we evaluate the well-known graph-based NoSQL
+databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a
+number of query experiments on our implemented simulator to show that which
+database system(s) for surveillance in wireless multimedia sensor networks is
+efficient and scalable.
+",1,0,0,0,0,0
+7589,EmbedJoin: Efficient Edit Similarity Joins via Embeddings,"  We study the problem of edit similarity joins, where given a set of strings
+and a threshold value $K$, we want to output all pairs of strings whose edit
+distances are at most $K$. Edit similarity join is a fundamental problem in
+data cleaning/integration, bioinformatics, collaborative filtering and natural
+language processing, and has been identified as a primitive operator for
+database systems. This problem has been studied extensively in the literature.
+However, we have observed that all the existing algorithms fall short on long
+strings and large distance thresholds.
+In this paper we propose an algorithm named EmbedJoin which scales very well
+with string length and distance threshold. Our algorithm is built on the recent
+advance of metric embeddings for edit distance, and is very different from all
+of the previous approaches. We demonstrate via an extensive set of experiments
+that EmbedJoin significantly outperforms the previous best algorithms on long
+strings and large distance thresholds.
+",1,0,0,0,0,0
+7590,Construction of curve pairs and their applications,"  In this study, we introduce a new approach to curve pairs by using integral
+curves. We consider the direction curve and donor curve to study curve couples
+such as involute-evolute curves, Mannheim partner curves and Bertrand partner
+curves. We obtain new methods to construct partner curves of a unit speed curve
+and give some applications related to helices, slant helices and plane curves.
+",0,0,1,0,0,0
+7591,Solution of linear ill-posed problems by model selection and aggregation,"  We consider a general statistical linear inverse problem, where the solution
+is represented via a known (possibly overcomplete) dictionary that allows its
+sparse representation. We propose two different approaches. A model selection
+estimator selects a single model by minimizing the penalized empirical risk
+over all possible models. By contrast with direct problems, the penalty depends
+on the model itself rather than on its size only as for complexity penalties. A
+Q-aggregate estimator averages over the entire collection of estimators with
+properly chosen weights. Under mild conditions on the dictionary, we establish
+oracle inequalities both with high probability and in expectation for the two
+estimators. Moreover, for the latter estimator these inequalities are sharp.
+The proposed procedures are implemented numerically and their performance is
+assessed by a simulation study.
+",0,0,1,0,0,0
+7592,Simulation studies for dielectric wakefield programme at CLARA facility,"  Short, high charge electron bunches can drive high magnitude electric fields
+in dielectric lined structures. The interaction of the electron bunch with this
+field has several applications including high gradient dielectric wakefield
+acceleration (DWA) and passive beam manipulation. The simulations presented
+provide a prelude to the commencement of an experimental DWA programme at the
+CLARA accelerator at Daresbury Laboratory. The key goals of this program are:
+tunable generation of THz radiation, understanding of the impact of transverse
+wakes, and design of a dechirper for the CLARA FEL. Computations of
+longitudinal and transverse phase space evolution were made with Impact-T and
+VSim to support both of these goals.
+",0,1,0,0,0,0
+7593,"Long-range dynamical magnetic order and spin tunneling in the cooperative paramagnetic states of the pyrochlore analogous spinel antiferromagnets CdYb2X4 (X = S, Se)","  Magnetic systems with spins sitting on a lattice of corner sharing regular
+tetrahedra have been particularly prolific for the discovery of new magnetic
+states for the last two decades. The pyrochlore compounds have offered the
+playground for these studies, while little attention has been comparatively
+devoted to other compounds where the rare earth R occupies the same
+sub-lattice, e.g. the spinel chalcogenides CdR2X4 (X = S, Se). Here we report
+measurements performed on powder samples of this series with R = Yb using
+specific heat, magnetic susceptibility, neutron diffraction and
+muon-spin-relaxation measurements. The two compounds are found to be
+magnetically similar. They long-range order into structures described by the
+\Gamma_5 irreducible representation. The magnitude of the magnetic moment at
+low temperature is 0.77 (1) and 0.62 (1) mu_B for X = S and Se, respectively.
+Persistent spin dynamics is present in the ordered states. The spontaneous
+field at the muon site is anomalously small, suggesting magnetic moment
+fragmentation. A double spin-flip tunneling relaxation mechanism is suggested
+in the cooperative paramagnetic state up to 10 K. The magnetic space groups
+into which magnetic moments of systems of corner-sharing regular tetrahedra
+order are provided for a number of insulating compounds characterized by null
+propagation wavevectors.
+",0,1,0,0,0,0
+7594,The Frobenius morphism in invariant theory,"  Let $R$ be the homogeneous coordinate ring of the Grassmannian
+$\mathbb{G}=\operatorname{Gr}(2,n)$ defined over an algebraically closed field
+of characteristic $p>0$. In this paper we give a completely characteristic free
+description of the decomposition of $R$, considered as a graded $R^p$-module,
+into indecomposables (""Frobenius summands""). As a corollary we obtain a similar
+decomposition for the Frobenius pushforward of the structure sheaf of
+$\mathbb{G}$ and we obtain in particular that this pushforward is almost never
+a tilting bundle. On the other hand we show that $R$ provides a ""noncommutative
+resolution"" for $R^p$ when $p\ge n-2$, generalizing a result known to be true
+for toric varieties.
+In both the invariant theory and the geometric setting we observe that if the
+characteristic is not too small the Frobenius summands do not depend on the
+characteristic in a suitable sense. In the geometric setting this is an
+explicit version of a general result by Bezrukavnikov and Mirković on
+Frobenius decompositions for partial flag varieities. We are hopeful that it is
+an instance of a more general ""$p$-uniformity"" principle.
+",0,0,1,0,0,0
+7595,A General Probabilistic Approach for Quantitative Assessment of LES Combustion Models,"  The Wasserstein metric is introduced as a probabilistic method to enable
+quantitative evaluations of LES combustion models. The Wasserstein metric can
+directly be evaluated from scatter data or statistical results using
+probabilistic reconstruction against experimental data. The method is derived
+and generalized for turbulent reacting flows, and applied to validation tests
+involving the Sydney piloted jet flame. It is shown that the Wasserstein metric
+is an effective validation tool that extends to multiple scalar quantities,
+providing an objective and quantitative evaluation of model deficiencies and
+boundary conditions on the simulation accuracy. Several test cases are
+considered, beginning with a comparison of mixture-fraction results, and the
+subsequent extension to reactive scalars, including temperature and species
+mass fractions of \ce{CO} and \ce{CO2}. To demonstrate the versatility of the
+proposed method in application to multiple datasets, the Wasserstein metric is
+applied to a series of different simulations that were contributed to the
+TNF-workshop. Analysis of the results allowed to identify competing
+contributions to model deviations, arising from uncertainties in the boundary
+conditions and model deficiencies. These applications demonstrate that the
+Wasserstein metric constitutes an easily applicable mathematical tool that
+reduce multiscalar combustion data and large datasets into a scalar-valued
+quantitative measure.
+",0,1,0,0,0,0
+7596,Sentiment Analysis by Joint Learning of Word Embeddings and Classifier,"  Word embeddings are representations of individual words of a text document in
+a vector space and they are often use- ful for performing natural language pro-
+cessing tasks. Current state of the art al- gorithms for learning word
+embeddings learn vector representations from large corpora of text documents in
+an unsu- pervised fashion. This paper introduces SWESA (Supervised Word
+Embeddings for Sentiment Analysis), an algorithm for sentiment analysis via
+word embeddings. SWESA leverages document label infor- mation to learn vector
+representations of words from a modest corpus of text doc- uments by solving an
+optimization prob- lem that minimizes a cost function with respect to both word
+embeddings as well as classification accuracy. Analysis re- veals that SWESA
+provides an efficient way of estimating the dimension of the word embeddings
+that are to be learned. Experiments on several real world data sets show that
+SWESA has superior per- formance when compared to previously suggested
+approaches to word embeddings and sentiment analysis tasks.
+",1,0,0,1,0,0
+7597,Normal-state Properties of a Unitary Bose-Fermi Mixture: A Combined Strong-coupling Approach with Universal Thermodynamics,"  We theoretically investigate normal-state properties of a unitary Bose-Fermi
+mixture. Including strong hetero-pairing fluctuations, we evaluate the Bose and
+Fermi chemical potential, internal energy, pressure, entropy, as well as
+specific heat at constant volume $C_V$, within the framework of a combined
+strong-coupling theory with exact thermodynamic identities. We show that
+hetero-pairing fluctuations at the unitarity cause non-monotonic temperature
+dependence of $C_V$, being qualitatively different from the monotonic behavior
+of this quantity in the weak- and strong-coupling limit. On the other hand,
+such an anomalous behavior is not seen in the other quantities. Our results
+indicate that the specific heat $C_V$, which has recently become observable in
+cold atom physics, is a useful quantity for understanding strong-coupling
+aspects of this quantum system.
+",0,1,0,0,0,0
+7598,A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop,"  The goal of Machine Learning to automatically learn from data, extract
+knowledge and to make decisions without any human intervention. Such automatic
+(aML) approaches show impressive success. Recent results even demonstrate
+intriguingly that deep learning applied for automatic classification of skin
+lesions is on par with the performance of dermatologists, yet outperforms the
+average. As human perception is inherently limited, such approaches can
+discover patterns, e.g. that two objects are similar, in arbitrarily
+high-dimensional spaces what no human is able to do. Humans can deal only with
+limited amounts of data, whilst big data is beneficial for aML; however, in
+health informatics, we are often confronted with a small number of data sets,
+where aML suffer of insufficient training samples and many problems are
+computationally hard. Here, interactive machine learning (iML) may be of help,
+where a human-in-the-loop contributes to reduce the complexity of NP-hard
+problems. A further motivation for iML is that standard black-box approaches
+lack transparency, hence do not foster trust and acceptance of ML among
+end-users. Rising legal and privacy aspects, e.g. with the new European General
+Data Protection Regulations, make black-box approaches difficult to use,
+because they often are not able to explain why a decision has been made. In
+this paper, we present some experiments to demonstrate the effectiveness of the
+human-in-the-loop approach, particularly in opening the black-box to a
+glass-box and thus enabling a human directly to interact with an learning
+algorithm. We selected the Ant Colony Optimization framework, and applied it on
+the Traveling Salesman Problem, which is a good example, due to its relevance
+for health informatics, e.g. for the study of protein folding. From studies of
+how humans extract so much from so little data, fundamental ML-research also
+may benefit.
+",1,0,0,1,0,0
+7599,Verification Studies for the Noh Problem using Non-ideal Equations of State and Finite Strength Shocks,"  The Noh verification test problem is extended beyond the commonly studied
+ideal gamma-law gas to more realistic equations of state (EOSs) including the
+stiff gas, the Noble-Abel gas, and the Carnahan-Starling EOS for hard-sphere
+fluids. Self-similarity methods are used to solve the Euler compressible flow
+equations, which in combination with the Rankine-Hugoniot jump conditions
+provide a tractable general solution. This solution can be applied to fluids
+with EOSs that meet criterion such as it being a convex function and having a
+corresponding bulk modulus. For the planar case the solution can be applied to
+shocks of arbitrary strength, but for cylindrical and spherical geometries it
+is required that the analysis be restricted to strong shocks. The exact
+solutions are used to perform a variety of quantitative code verification
+studies of the Los Alamos National Laboratory Lagrangian hydrocode FLAG.
+",1,1,0,0,0,0
+7600,Motivic zeta functions and infinite cyclic covers,"  We associate with an infinite cyclic cover of a punctured neighborhood of a
+simple normal crossing divisor on a complex quasi-projective manifold (assuming
+certain finiteness conditions are satisfied) a rational function in $K_0({\rm
+Var}^{\hat \mu}_{\mathbb{C}})[\mathbb{L}^{-1}]$, which we call {\it motivic
+infinite cyclic zeta function}, and show its birational invariance. Our
+construction is a natural extension of the notion of {\it motivic infinite
+cyclic covers} introduced by the authors, and as such, it generalizes the
+Denef-Loeser motivic Milnor zeta function of a complex hypersurface singularity
+germ.
+",0,0,1,0,0,0
+7601,"End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks","  Recent years have seen a sharp increase in the number of related yet distinct
+advances in semantic segmentation. Here, we tackle this problem by leveraging
+the respective strengths of these advances. That is, we formulate a conditional
+random field over a four-connected graph as end-to-end trainable convolutional
+and recurrent networks, and estimate them via an adversarial process.
+Importantly, our model learns not only unary potentials but also pairwise
+potentials, while aggregating multi-scale contexts and controlling higher-order
+inconsistencies. We evaluate our model on two standard benchmark datasets for
+semantic face segmentation, achieving state-of-the-art results on both of them.
+",1,0,0,0,0,0
+7602,Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent,"  Coherent uncertainty quantification is a key strength of Bayesian methods.
+But modern algorithms for approximate Bayesian posterior inference often
+sacrifice accurate posterior uncertainty estimation in the pursuit of
+scalability. This work shows that previous Bayesian coreset construction
+algorithms---which build a small, weighted subset of the data that approximates
+the full dataset---are no exception. We demonstrate that these algorithms scale
+the coreset log-likelihood suboptimally, resulting in underestimated posterior
+uncertainty. To address this shortcoming, we develop greedy iterative geodesic
+ascent (GIGA), a novel algorithm for Bayesian coreset construction that scales
+the coreset log-likelihood optimally. GIGA provides geometric decay in
+posterior approximation error as a function of coreset size, and maintains the
+fast running time of its predecessors. The paper concludes with validation of
+GIGA on both synthetic and real datasets, demonstrating that it reduces
+posterior approximation error by orders of magnitude compared with previous
+coreset constructions.
+",0,0,0,1,0,0
+7603,Practical Bayesian optimization in the presence of outliers,"  Inference in the presence of outliers is an important field of research as
+outliers are ubiquitous and may arise across a variety of problems and domains.
+Bayesian optimization is method that heavily relies on probabilistic inference.
+This allows outstanding sample efficiency because the probabilistic machinery
+provides a memory of the whole optimization process. However, that virtue
+becomes a disadvantage when the memory is populated with outliers, inducing
+bias in the estimation. In this paper, we present an empirical evaluation of
+Bayesian optimization methods in the presence of outliers. The empirical
+evidence shows that Bayesian optimization with robust regression often produces
+suboptimal results. We then propose a new algorithm which combines robust
+regression (a Gaussian process with Student-t likelihood) with outlier
+diagnostics to classify data points as outliers or inliers. By using an
+scheduler for the classification of outliers, our method is more efficient and
+has better convergence over the standard robust regression. Furthermore, we
+show that even in controlled situations with no expected outliers, our method
+is able to produce better results.
+",1,0,0,1,0,0
+7604,Is there any polynomial upper bound for the universal labeling of graphs?,"  A {\it universal labeling} of a graph $G$ is a labeling of the edge set in
+$G$ such that in every orientation $\ell$ of $G$ for every two adjacent
+vertices $v$ and $u$, the sum of incoming edges of $v$ and $u$ in the oriented
+graph are different from each other. The {\it universal labeling number} of a
+graph $G$ is the minimum number $k$ such that $G$ has {\it universal labeling}
+from $\{1,2,\ldots, k\}$ denoted it by $\overrightarrow{\chi_{u}}(G) $. We have
+$2\Delta(G)-2 \leq \overrightarrow{\chi_{u}} (G)\leq 2^{\Delta(G)}$, where
+$\Delta(G)$ denotes the maximum degree of $G$. In this work, we offer a
+provocative question that is:"" Is there any polynomial function $f$ such that
+for every graph $G$, $\overrightarrow{\chi_{u}} (G)\leq f(\Delta(G))$?"".
+Towards this question, we introduce some lower and upper bounds on their
+parameter of interest. Also, we prove that for every tree $T$,
+$\overrightarrow{\chi_{u}}(T)=\mathcal{O}(\Delta^3) $. Next, we show that for a
+given 3-regular graph $G$, the universal labeling number of $G$ is 4 if and
+only if $G$ belongs to Class 1. Therefore, for a given 3-regular graph $G$, it
+is an $ \mathbf{NP} $-complete to determine whether the universal labeling
+number of $G$ is 4. Finally, using probabilistic methods, we almost confirm a
+weaker version of the problem.
+",1,0,1,0,0,0
+7605,An Empirical Study on Team Formation in Online Games,"  Online games provide a rich recording of interactions that can contribute to
+our understanding of human behavior. One potential lesson is to understand what
+motivates people to choose their teammates and how their choices leadto
+performance. We examine several hypotheses about team formation using a large,
+longitudinal dataset from a team-based online gaming environment. Specifically,
+we test how positive familiarity, homophily, and competence determine team
+formationin Battlefield 4, a popular team-based game in which players choose
+one of two competing teams to play on. Our dataset covers over two months of
+in-game interactions between over 380,000 players. We show that familiarity is
+an important factorin team formation, while homophily is not. Competence
+affects team formation in more nuanced ways: players with similarly high
+competence team-up repeatedly, but large variations in competence discourage
+repeated interactions.
+",1,0,0,0,0,0
+7606,Nematic superconductivity in Cu$_{x}$Bi$_{2}$Se$_{3}$: The surface Andreev bound states,"  We study theoretically the topological surface states (TSSs) and the possible
+surface Andreev bound states (SABSs) of Cu$_{x}$Bi$_{2}$Se$_{3}$ which is known
+to be a topological insulator at $x=0$. The superconductivity (SC) pairing of
+this compound is assumed to have the broken spin-rotation symmetry, similar to
+that of the A-phase of $^{3}$He as suggested by recent nuclear-magnetic
+resonance experiments. For both spheroidal and corrugated cylindrical Fermi
+surfaces with the hexagonal warping terms, we show that the bulk SC gap is
+rather anisotropic; the minimum of the gap is negligibly small as comparing to
+the maximum of the gap. This would make the fully-gapped pairing effectively
+nodal. For a clean system, our results indicate the bulk of this compound to be
+a topological superconductor with the SABSs appearing inside the bulk SC gap.
+The zero-energy SABSs which are Majorana fermions, together with the TSSs not
+gapped by the pairing, produce a zero-energy peak in the surface density of
+states (SDOS). The SABSs are expected to be stable against short-range
+nonmagnetic impurities, and the local SDOS is calculated around a nonmagnetic
+impurity. The relevance of our results to experiments is discussed.
+",0,1,0,0,0,0
+7607,The structure of a minimal $n$-chart with two crossings II: Neighbourhoods of $Γ_1\cupΓ_{n-1}$,"  Given a 2-crossing minimal chart $\Gamma$, a minimal chart with two
+crossings, set $\alpha=\min\{~i~|~$there exists an edge of label $i$ containing
+a white vertex$\}$, and $\beta=\max\{~i~|~$there exists an edge of label $i$
+containing a white vertex$\}$. In this paper we study the structure of a
+neighbourhood of $\Gamma_\alpha\cup\Gamma_\beta$, and propose a normal form for
+2-crossing minimal $n$-charts, here $\Gamma_\alpha$ and $\Gamma_\beta$ mean the
+union of all the edges of label $\alpha$ and $\beta$ respectively.
+",0,0,1,0,0,0
+7608,Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations,"  Assistive robotics and particularly robot coaches may be very helpful for
+rehabilitation healthcare. In this context, we propose a method based on
+Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a
+physiotherapist, a robot coach and a patient. Our model is able to map visual
+human body features to robot data in order to facilitate the robot learning and
+imitation. In addition , we propose to extend the model to adapt robots'
+understanding to patient's physical limitations during the assessment of
+rehabilitation exercises. Experimental evaluation demonstrates promising
+results for both robot imitation and model adaptation according to the
+patients' limitations.
+",1,0,0,0,0,0
+7609,Superconvergence analysis of linear FEM based on the polynomial preserving recovery and Richardson extrapolation for Helmholtz equation with high wave number,"  We study superconvergence property of the linear finite element method with
+the polynomial preserving recovery (PPR) and Richardson extrapolation for the
+two dimensional Helmholtz equation. The $H^1$-error estimate with explicit
+dependence on the wave number $k$ {is} derived.
+First, we prove that under the assumption $k(kh)^2\leq C_0$ ($h$ is the mesh
+size) and certain mesh condition, the estimate between the finite element
+solution and the linear interpolation of the exact solution is superconvergent
+under the $H^1$-seminorm, although the pollution error still exists. Second, we
+prove a similar result for the recovered gradient by PPR and found that the PPR
+can only improve the interpolation error and has no effect on the pollution
+error. Furthermore, we estimate the error between the finite element gradient
+and recovered gradient and discovered that the pollution error is canceled
+between these two quantities. Finally, we apply the Richardson extrapolation to
+recovered gradient and demonstrate numerically that PPR combined with the
+Richardson extrapolation can reduce the interpolation and pollution errors
+simultaneously, and therefore, leads to an asymptotically exact {\it a
+posteriori} error estimator. All theoretical findings are verified by numerical
+tests.
+",0,0,1,0,0,0
+7610,Semi-blind source separation with multichannel variational autoencoder,"  This paper proposes a multichannel source separation technique called the
+multichannel variational autoencoder (MVAE) method, which uses a conditional
+VAE (CVAE) to model and estimate the power spectrograms of the sources in a
+mixture. By training the CVAE using the spectrograms of training examples with
+source-class labels, we can use the trained decoder distribution as a universal
+generative model capable of generating spectrograms conditioned on a specified
+class label. By treating the latent space variables and the class label as the
+unknown parameters of this generative model, we can develop a
+convergence-guaranteed semi-blind source separation algorithm that consists of
+iteratively estimating the power spectrograms of the underlying sources as well
+as the separation matrices. In experimental evaluations, our MVAE produced
+better separation performance than a baseline method.
+",0,0,0,1,0,0
+7611,Quantitative characterization of pore structure of several biochars with 3D imaging,"  Pore space characteristics of biochars may vary depending on the used raw
+material and processing technology. Pore structure has significant effects on
+the water retention properties of biochar amended soils. In this work, several
+biochars were characterized with three-dimensional imaging and image analysis.
+X-ray computed microtomography was used to image biochars at resolution of 1.14
+$\mu$m and the obtained images were analysed for porosity, pore-size
+distribution, specific surface area and structural anisotropy. In addition,
+random walk simulations were used to relate structural anisotropy to diffusive
+transport. Image analysis showed that considerable part of the biochar volume
+consist of pores in size range relevant to hydrological processes and storage
+of plant available water. Porosity and pore-size distribution were found to
+depend on the biochar type and the structural anisotopy analysis showed that
+used raw material considerably affects the pore characteristics at micrometre
+scale. Therefore attention should be paid to raw material selection and quality
+in applications requiring optimized pore structure.
+",0,1,0,0,0,0
+7612,First-principles-based method for electron localization: Application to monolayer hexagonal boron nitride,"  We present a first-principles-based many-body typical medium dynamical
+cluster approximation method for characterizing electron localization in
+disordered structures. This method applied to monolayer hexagonal boron nitride
+shows that the presence of a boron vacancies could turn this wide-gap insulator
+into a correlated metal. Depending on the strength of the electron
+interactions, these calculations suggest that conduction could be obtained at a
+boron vacancy concentration as low as $1.0\%$. We also explore the distribution
+of the local density of states, a fingerprint of spatial variations, which
+allows localized and delocalized states to be distinguished. The presented
+method enables the study of disorder-driven insulator-metal transitions not
+only in $h$-BN but also in other physical materials.
+",0,1,0,0,0,0
+7613,Compressed H$_3$S: inter-sublattice Coulomb coupling in a high-$\textit{T}$$_C$ superconductor,"  Upon thermal annealing at or above room temperature (RT) and high pressure
+$\it P$ $\sim$ 155 GPa, H$_3$S exhibits superconductivity at $\it T_C$ $\sim$
+200 K. Various theoretical frameworks with strong electron-phonon coupling and
+Coulomb repulsion have reproduced this record-level $\it T_C$. Of particular
+relevance is that observed H-D isotopic correlations among $\it T_C$, $\it P$,
+and annealed order indicate limitations on the H-D isotope effect, leaving open
+for consideration unconventional high-$\it T_C$ superconductivity with
+electronic-based enhancements. The present work examines Coulombic pairing
+arising from interactions between neighboring S and H species on separate
+interlaced sublattices constituting H$_3$S in the Im$\overline{3}$m structure.
+The optimal transition temperature is calculated from $\it{T}$$_{C0}$ =
+$\it{k}$$_B$$^{-1}$$\Lambda$$\it{e}$$^2$/$\ell$$\zeta$, with $\Lambda$ =
+0.007465 $\AA$, inter-sublattice S-H separation spacing $\zeta$ =
+$\it{a}$$_0$/$\sqrt{2}$, interaction charge linear spacing $\ell$ =
+$\it{a}$$_0$(3/$\sigma$)$^{1/2}$, average participating charge fraction
+$\sigma$ = 3.43 $\pm$ 0.10 estimated from theory, and lattice parameter
+$\it{a}$$_0$ = 3.0823 \AA. The result $\it{T}$$_{C0}$ = 198.5 $\pm$ 3.0 K is in
+excellent agreement with transition temperatures determined from resistivity
+and susceptibility data. Analysis of mid-infrared reflectivity confirms
+correlation between boson energy and $\zeta$$^{-1}$. Suppression of $\it T_C$
+with increasing residual resistance for $<$ RT annealing is treated by
+scattering-induced pair breaking. Correspondence with layered high-$\it T_C$
+superconductor structures are discussed. A model considering Compton scattering
+of virtual photons of energies $\leq$ $\it e$$^2$/$\zeta$ by inter-sublattice
+electrons is introduced, illustrating $\Lambda$ is proportional to the reduced
+electron Compton wavelength.
+",0,1,0,0,0,0
+7614,A physiology--based parametric imaging method for FDG--PET data,"  Parametric imaging is a compartmental approach that processes nuclear imaging
+data to estimate the spatial distribution of the kinetic parameters governing
+tracer flow. The present paper proposes a novel and efficient computational
+method for parametric imaging which is potentially applicable to several
+compartmental models of diverse complexity and which is effective in the
+determination of the parametric maps of all kinetic coefficients. We consider
+applications to [{18}F]-fluorodeoxyglucose Positron Emission Tomography
+(FDG-PET) data and analyze the two-compartment catenary model describing the
+standard FDG metabolization by an homogeneous tissue and the three-compartment
+non-catenary model representing the renal physiology. We show uniqueness
+theorems for both models. The proposed imaging method starts from the
+reconstructed FDG-PET images of tracer concentration and preliminarily applies
+image processing algorithms for noise reduction and image segmentation. The
+optimization procedure solves pixelwise the non-linear inverse problem of
+determining the kinetic parameters from dynamic concentration data through a
+regularized Gauss-Newton iterative algorithm. The reliability of the method is
+validated against synthetic data, for the two-compartment system, and
+experimental real data of murine models, for the renal three-compartment
+system.
+",0,1,1,0,0,0
+7615,Item Recommendation with Evolving User Preferences and Experience,"  Current recommender systems exploit user and item similarities by
+collaborative filtering. Some advanced methods also consider the temporal
+evolution of item ratings as a global background process. However, all prior
+methods disregard the individual evolution of a user's experience level and how
+this is expressed in the user's writing in a review community. In this paper,
+we model the joint evolution of user experience, interest in specific item
+facets, writing style, and rating behavior. This way we can generate individual
+recommendations that take into account the user's maturity level (e.g.,
+recommending art movies rather than blockbusters for a cinematography expert).
+As only item ratings and review texts are observables, we capture the user's
+experience and interests in a latent model learned from her reviews, vocabulary
+and writing style. We develop a generative HMM-LDA model to trace user
+evolution, where the Hidden Markov Model (HMM) traces her latent experience
+progressing over time -- with solely user reviews and ratings as observables
+over time. The facets of a user's interest are drawn from a Latent Dirichlet
+Allocation (LDA) model derived from her reviews, as a function of her (again
+latent) experience level. In experiments with five real-world datasets, we show
+that our model improves the rating prediction over state-of-the-art baselines,
+by a substantial margin. We also show, in a use-case study, that our model
+performs well in the assessment of user experience levels.
+",1,0,0,1,0,0
+7616,On some integrals of Hardy,"  We consider some properties of integrals considered by Hardy and Koshliakov,
+and which have also been further extended recently by Dixit. We establish a new
+general integral formula from some observations about the digamma function. We
+also obtain lower and upper bounds for Hardy's integral through properties of
+the digamma function.
+",0,0,1,0,0,0
+7617,Complexity of short generating functions,"  We give complexity analysis of the class of short generating functions (GF).
+Assuming $\#P \not\subseteq FP/poly$, we show that this class is not closed
+under taking many intersections, unions or projections of GFs, in the sense
+that these operations can increase the bitlength of coefficients of GFs by a
+super-polynomial factor. We also prove that truncated theta functions are hard
+in this class.
+",1,0,1,0,0,0
+7618,Finite-Size Effects in Non-Neutral Two-Dimensional Coulomb Fluids,"  Thermodynamic potential of a neutral two-dimensional (2D) Cou\-lomb fluid,
+confined to a large domain with a smooth boundary, exhibits at any (inverse)
+temperature $\beta$ a logarithmic finite-size correction term whose universal
+prefactor depends only on the Euler number of the domain and the conformal
+anomaly number $c=-1$. A minimal free boson conformal field theory, which is
+equivalent to the 2D symmetric two-component plasma of elementary $\pm e$
+charges at coupling constant $\Gamma=\beta e^2$, was studied in the past. It
+was shown that creating a non-neutrality by spreading out a charge $Q e$ at
+infinity modifies the anomaly number to $c(Q,\Gamma) = - 1 + 3\Gamma Q^2$.
+Here, we study the effect of non-neutrality on the finite-size expansion of the
+free energy for another Coulomb fluid, namely the 2D one-component plasma
+(jellium) composed of identical pointlike $e$-charges in a homogeneous
+background surface charge density. For the disk geometry of the confining
+domain we find that the non-neutrality induces the same change of the anomaly
+number in the finite-size expansion. We derive this result first at the
+free-fermion coupling $\Gamma\equiv\beta e^2=2$ and then, by using a mapping of
+the 2D one-component plasma onto an anticommuting field theory formulated on a
+chain, for an arbitrary coupling constant.
+",0,1,0,0,0,0
+7619,Type Safe Redis Queries: A Case Study of Type-Level Programming in Haskell,"  Redis is an in-memory data structure store, often used as a database, with a
+Haskell interface Hedis. Redis is dynamically typed --- a key can be discarded
+and re-associated to a value of a different type, and a command, when fetching
+a value of a type it does not expect, signals a runtime error. We develop a
+domain-specific language that, by exploiting Haskell type-level programming
+techniques including indexed monad, type-level literals and closed type
+families, keeps track of types of values in the database and statically
+guarantees that type errors cannot happen for a class of Redis programs.
+",1,0,0,0,0,0
+7620,Ground state solutions for a nonlinear Choquard equation,"  We discuss the existence of ground state solutions for the Choquard equation
+$$-\Delta u=(I_\alpha*F(u))F'(u)\quad\quad\quad\text{in }\mathbb R^N.$$ We
+prove the existence of solutions under general hypotheses, investigating in
+particular the case of a homogeneous nonlinearity $F(u)=\frac{|u|^p}p$. The
+cases $N=2$ and $N\ge3$ are treated differently in some steps. The solutions
+are found through a variational mountain pass strategy. The result presented
+are contained in the papers with arXiv ID 1212.2027 and 1604.03294
+",0,0,1,0,0,0
+7621,Ultrashort dark solitons interactions and nonlinear tunneling in the modified nonlinear Schrödinger equation with variable coefficients,"  We present the study of the dark soliton dynamics in an inhomogenous fiber by
+means of a variable coefficient modified nonlinear Schrödinger equation
+(Vc-MNLSE) with distributed dispersion, self-phase modulation, self-steepening
+and linear gain/loss. The ultrashort dark soliton pulse evolution and
+interaction is studied by using the Hirota bilinear (HB) method. In particular,
+we give much insight into the effect of self-steepening (SS) on the dark
+soliton dynamics. The study reveals a shock wave formation, as a major effect
+of SS. Numerically, we study the dark soliton propagation in the continuous
+wave background, and the stability of the soliton solution is tested in the
+presence of photon noise. The elastic collision behaviors of the dark solitons
+are discussed by the asymptotic analysis. On the other hand, considering the
+nonlinear tunneling of dark soliton through barrier/well, we find that the
+tunneling of the dark soliton depends on the height of the barrier and the
+amplitude of the soliton. The intensity of the tunneling soliton either forms a
+peak or valley and retains its shape after the tunneling. For the case of
+exponential background, the soliton tends to compress after tunneling through
+the barrier/well.
+",0,1,0,0,0,0
+7622,Long-range p-d exchange interaction in a ferromagnet-semiconductor Co/CdMgTe/CdTe quantum well hybrid structure,"  The exchange interaction between magnetic ions and charge carriers in
+semiconductors is considered as prime tool for spin control. Here, we solve a
+long-standing problem by uniquely determining the magnitude of the long-range
+$p-d$ exchange interaction in a ferromagnet-semiconductor (FM-SC) hybrid
+structure where a 10~nm thick CdTe quantum well is separated from the FM Co
+layer by a CdMgTe barrier with a thickness on the order of 10~nm. The exchange
+interaction is manifested by the spin splitting of acceptor bound holes in the
+effective magnetic field induced by the FM. The exchange splitting is directly
+evaluated using spin-flip Raman scattering by analyzing the dependence of the
+Stokes shift $\Delta_S$ on the external magnetic field $B$. We show that in
+strong magnetic field $\Delta_S$ is a linear function of $B$ with an offset of
+$\Delta_{pd} = 50-100~\mu$eV at zero field from the FM induced effective
+exchange field. On the other hand, the $s-d$ exchange interaction between
+conduction band electrons and FM, as well as the $p-d$ contribution for free
+valence band holes, are negligible. The results are well described by the model
+of indirect exchange interaction between acceptor bound holes in the CdTe
+quantum well and the FM layer mediated by elliptically polarized phonons in the
+hybrid structure.
+",0,1,0,0,0,0
+7623,Parsec-scale Faraday rotation and polarization of 20 active galactic nuclei jets,"  We perform polarimetry analysis of 20 active galactic nuclei (AGN) jets using
+the Very Long Baseline Array (VLBA) at 1.4, 1.6, 2.2, 2.4, 4.6, 5.0, 8.1, 8.4,
+and 15.4 GHz. The study allowed us to investigate linearly polarized properties
+of the jets at parsec-scales: distribution of the Faraday rotation measure (RM)
+and fractional polarization along the jets, Faraday effects and structure of
+Faraday-corrected polarization images. Wavelength-dependence of the fractional
+polarization and polarization angle is consistent with external Faraday
+rotation, while some sources show internal rotation. The RM changes along the
+jets, systematically increasing its value towards synchrotron self-absorbed
+cores at shorter wavelengths. The highest core RM reaches 16,900 rad/m^2 in the
+source rest frame for the quasar 0952+179, suggesting the presence of highly
+magnetized, dense media in these regions. The typical RM of transparent jet
+regions has values of an order of a hundred rad/m^2. Significant transverse
+rotation measure gradients are observed in seven sources. The magnetic field in
+the Faraday screen has no preferred orientation, and is observed to be random
+or regular from source to source. Half of the sources show evidence for the
+helical magnetic fields in their rotating magnetoionic media. At the same time
+jets themselves contain large-scale, ordered magnetic fields and tend to align
+its direction with the jet flow. The observed variety of polarized signatures
+can be explained by a model of spine-sheath jet structure.
+",0,1,0,0,0,0
+7624,Positive scalar curvature and connected sums,"  Let $N$ be a closed enlargeable manifold in the sense of Gromov-Lawson and
+$M$ a closed spin manifold of equal dimension, a famous theorem of
+Gromov-Lawson states that the connected sum $M\# N$ admits no metric of
+positive scalar curvature. We present a potential generalization of this result
+to the case where $M$ is nonspin. We use index theory for Dirac operators to
+prove our result.
+",0,0,1,0,0,0
+7625,Optical computing by injection-locked lasers,"  A programmable optical computer has remained an elusive concept. To construct
+a practical computing primitive equivalent to an electronic Boolean logic, one
+should find a nonlinear phenomenon that overcomes weaknesses present in many
+optical processing schemes. Ideally, the nonlinearity should provide a
+functionally complete set of logic operations, enable ultrafast all-optical
+programmability, and allow cascaded operations without a change in the
+operating wavelength or in the signal encoding format. Here we demonstrate a
+programmable logic gate using an injection-locked Vertical-Cavity
+Surface-Emitting Laser (VCSEL). The gate program is switched between the AND
+and the OR operations at the rate of 1 GHz with Bit Error Ratio (BER) of 10e-6
+without changes in the wavelength or in the signal encoding format. The scheme
+is based on nonlinearity of normalization operations, which can be used to
+construct any continuous complex function or operation, Boolean or otherwise.
+",1,0,0,0,0,0
+7626,On the joint asymptotic distribution of the restricted estimators in multivariate regression model,"  The main Theorem of Jain et al.[Jain, K., Singh, S., and Sharma, S. (2011),
+Re- stricted estimation in multivariate measurement error regression model;
+JMVA, 102, 2, 264-280] is established in its full generality. Namely, we derive
+the joint asymp- totic normality of the unrestricted estimator (UE) and the
+restricted estimators of the matrix of the regression coefficients. The derived
+result holds under the hypothesized restriction as well as under the sequence
+of alternative restrictions. In addition, we establish Asymptotic
+Distributional Risk for the estimators and compare their relative performance.
+It is established that near the restriction, the restricted estimators (REs)
+perform better than the UE. But the REs perform worse than the unrestricted
+estimator when one moves far away from the restriction.
+",0,0,1,1,0,0
+7627,"Rotational spectroscopy, tentative interstellar detection, and chemical modelling of N-methylformamide","  N-methylformamide, CH3NHCHO, may be an important molecule for interstellar
+pre-biotic chemistry because it contains a peptide bond. The rotational
+spectrum of the most stable trans conformer of CH3NHCHO is complicated by
+strong torsion-rotation interaction due to the low barrier of the methyl
+torsion. We use two absorption spectrometers in Kharkiv and Lille to measure
+the rotational spectra over 45--630 GHz. The analysis is carried out using the
+Rho-axis method and the RAM36 code. We search for N-methylformamide toward the
+hot molecular core Sgr B2(N2) using a spectral line survey carried out with
+ALMA. The astronomical results are put into a broader astrochemical context
+with the help of a gas-grain chemical kinetics model. The laboratory data set
+for the trans conformer of CH3NHCHO consists of 9469 line frequencies with J <=
+62, including the first assignment of the rotational spectra of the first and
+second excited torsional states. All these lines are fitted within experimental
+accuracy. We report the tentative detection of CH3NHCHO towards Sgr B2(N2). We
+find CH3NHCHO to be more than one order of magnitude less abundant than NH2CHO,
+a factor of two less abundant than CH3NCO, but only slightly less abundant than
+CH3CONH2. The chemical models indicate that the efficient formation of HNCO via
+NH + CO on grains is a necessary step in the achievement of the observed
+gas-phase abundance of CH3NCO. Production of CH3NHCHO may plausibly occur on
+grains either through the direct addition of functional-group radicals or
+through the hydrogenation of CH3NCO. Provided the detection of CH3NHCHO is
+confirmed, the only slight underabundance of this molecule compared to its more
+stable structural isomer acetamide and the sensitivity of the model abundances
+to the chemical kinetics parameters suggest that the formation of these two
+molecules is controlled by kinetics rather than thermal equilibrium.
+",0,1,0,0,0,0
+7628,Dynamic anisotropy in MHD turbulence induced by mean magnetic field,"  In this paper, we study the development of anisotropy in strong MHD
+turbulence in the presence of a large scale magnetic field B 0 by analyzing the
+results of direct numerical simulations. Our results show that the developed
+anisotropy among the different components of the velocity and magnetic field is
+a direct outcome of the inverse cascade of energy of the perpendicular velocity
+components u? and a forward cascade of the energy of the parallel component u k
+. The inverse cascade develops for a strong B0, where the flow exhibits a
+strong vortical structure by the suppression of fluctuations along the magnetic
+field. Both the inverse and the forward cascade are examined in detail by
+investigating the anisotropic energy spectra, the energy fluxes, and the shell
+to shell energy transfers among different scales.
+",0,1,0,0,0,0
+7629,Dealing with the exponential wall in electronic structure calculations,"  An alternative to Density Functional Theory are wavefunction based electronic
+structure calculations for solids. In order to perform them the Exponential
+Wall (EW) problem has to be resolved. It is caused by an exponential increase
+of the number of configurations with increasing electron number N. There are
+different routes one may follow. One is to characterize a many-electron
+wavefunction by a vector in Liouville space with a cumulant metric rather than
+in Hilbert space. This removes the EW problem. Another is to model the solid by
+an {\it impurity} or {\it fragment} embedded in a {\it bath} which is treated
+at a much lower level than the former. This is the case in Density Matrix
+Embedding Theory (DMET) or Density Embedding Theory (DET). The latter are
+closely related to a Schmidt decomposition of a system and to the determination
+of the associated entanglement. We show here the connection between the two
+approaches. It turns out that the DMET (or DET) has an identical active space
+as a previously used Local Ansatz, based on a projection and partitioning
+approach. Yet, the EW problem is resolved differently in the two cases. By
+studying a $H_{10}$ ring these differences are analyzed with the help of the
+method of increments.
+",0,1,0,0,0,0
+7630,Influence maximization on correlated networks through community identification,"  The identification of the minimal set of nodes that maximizes the propagation
+of information is one of the most important problems in network science. In
+this paper, we introduce a new method to find the set of initial spreaders to
+maximize the information propagation in complex networks. We evaluate this
+method in assortative networks and verify that degree-degree correlation plays
+a fundamental role on the spreading dynamics. Simulation results show that our
+algorithm is statistically similar, in terms of the average size of outbreaks,
+to the greedy approach. However, our method is much less time consuming than
+the greedy algorithm.
+",1,1,0,0,0,0
+7631,Conformal metrics with prescribed fractional scalar curvature on conformal infinities with positive fractional Yamabe constants,"  Let $(X, g^+)$ be an asymptotically hyperbolic manifold and $(M, [\hat{h}])$
+its conformal infinity. Our primary aim in this paper is to introduce the
+prescribed fractional scalar curvature problem on $M$ and provide solutions
+under various geometric conditions on $X$ and $M$. We also obtain the existence
+results for the fractional Yamabe problem in the endpoint case, e.g., $n = 3$,
+$\gamma = 1/2$ and $M$ is non-umbilic, etc. Every solution we find turns out to
+be smooth on $M$.
+",0,0,1,0,0,0
+7632,A Short Review of Ethical Challenges in Clinical Natural Language Processing,"  Clinical NLP has an immense potential in contributing to how clinical
+practice will be revolutionized by the advent of large scale processing of
+clinical records. However, this potential has remained largely untapped due to
+slow progress primarily caused by strict data access policies for researchers.
+In this paper, we discuss the concern for privacy and the measures it entails.
+We also suggest sources of less sensitive data. Finally, we draw attention to
+biases that can compromise the validity of empirical research and lead to
+socially harmful applications.
+",1,0,0,0,0,0
+7633,kd-switch: A Universal Online Predictor with an application to Sequential Two-Sample Testing,"  We propose a novel online predictor for discrete labels conditioned on
+multivariate features in $\mathbb{R}^d$. The predictor is pointwise universal:
+it achieves a normalized log loss performance asymptotically as good as the
+true conditional entropy of the labels given the features. The predictor is
+based on a feature space discretization induced by a full-fledged k-d tree with
+randomly picked directions and a switch distribution, requiring no
+hyperparameter setting and automatically selecting the most relevant scales in
+the feature space. Using recent results, a consistent sequential two-sample
+test is built from this predictor. In terms of discrimination power, on
+selected challenging datasets, it is comparable to or better than
+state-of-the-art non-sequential two-sample tests based on the train-test
+paradigm and, a recent sequential test requiring hyperparameters. The time
+complexity to process the $n$-th sample point is $O(\log n)$ in probability
+(with respect to the distribution generating the data points), in contrast to
+the linear complexity of the previous sequential approach.
+",1,0,0,1,0,0
+7634,A Heat Equation on some Adic Completions of Q and Ultrametric Analysis,"  This article deals with a Markov process related to the fundamental solution
+of a heat equation on the direct product ring Q_S, where Q_S is a finite direct
+product of p-adic fields. The techniques developed here are different from the
+well known ones: they are geometrical and very simple. As a result, the
+techniques developed here provides a general framework of these problems on
+other related ultrametric groups.
+",0,0,1,0,0,0
+7635,A simple alteration of the peridynamics correspondence principle to eliminate zero-energy deformation,"  We look for an enhancement of the correspondence model of peridynamics with a
+view to eliminating the zero-energy deformation modes. Since the non-local
+integral definition of the deformation gradient underlies the problem, we
+initially look for a remedy by introducing a class of localizing corrections to
+the integral. Since the strategy is found to afford only a reduction, and not
+complete elimination, of the oscillatory zero-energy deformation, we propose in
+the sequel an alternative approach based on the notion of sub-horizons. A most
+useful feature of the last proposal is that the setup, whilst providing the
+solution with the necessary stability, deviates only marginally from the
+original correspondence formulation. We also undertake a set of numerical
+simulations that attest to the remarkable efficacy of the sub-horizon based
+methodology.
+",0,1,0,0,0,0
+7636,Interpretable Active Learning,"  Active learning has long been a topic of study in machine learning. However,
+as increasingly complex and opaque models have become standard practice, the
+process of active learning, too, has become more opaque. There has been little
+investigation into interpreting what specific trends and patterns an active
+learning strategy may be exploring. This work expands on the Local
+Interpretable Model-agnostic Explanations framework (LIME) to provide
+explanations for active learning recommendations. We demonstrate how LIME can
+be used to generate locally faithful explanations for an active learning
+strategy, and how these explanations can be used to understand how different
+models and datasets explore a problem space over time. In order to quantify the
+per-subgroup differences in how an active learning strategy queries spatial
+regions, we introduce a notion of uncertainty bias (based on disparate impact)
+to measure the discrepancy in the confidence for a model's predictions between
+one subgroup and another. Using the uncertainty bias measure, we show that our
+query explanations accurately reflect the subgroup focus of the active learning
+queries, allowing for an interpretable explanation of what is being learned as
+points with similar sources of uncertainty have their uncertainty bias
+resolved. We demonstrate that this technique can be applied to track
+uncertainty bias over user-defined clusters or automatically generated clusters
+based on the source of uncertainty.
+",1,0,0,1,0,0
+7637,Orbital-Free Density-Functional Theory Simulations of Displacement Cascade in Aluminum,"  Here, we report orbital-free density-functional theory (OF DFT) molecular
+dynamics simulations of the displacement cascade in aluminum. The electronic
+effect is our main concern. The displacement threshold energies are calculated
+using OF DFT and classical molecular dynamics (MD) and the comparison reveals
+the role of charge bridge. Compared to MD simulation, the displacement spike
+from OF DFT has a lower peak and shorter duration time, which is attributed to
+the effect of electronic damping. The charge density profiles clearly display
+the existence of depleted zones, vacancy and interstitial clusters. And it is
+found that the energy exchanges between ions and electrons are mainly
+contributed by the kinetic energies.
+",0,1,0,0,0,0
+7638,Binary systems with an RR Lyrae component - progress in 2016,"  In this contribution, we summarize the progress made in the investigation of
+binary candidates with an RR Lyrae component in 2016. We also discuss the
+actual status of the RRLyrBinCan database.
+",0,1,0,0,0,0
+7639,Covering and tiling hypergraphs with tight cycles,"  Given $3 \leq k \leq s$, we say that a $k$-uniform hypergraph $C^k_s$ is a
+tight cycle on $s$ vertices if there is a cyclic ordering of the vertices of
+$C^k_s$ such that every $k$ consecutive vertices under this ordering form an
+edge. We prove that if $k \ge 3$ and $s \ge 2k^2$, then every $k$-uniform
+hypergraph on $n$ vertices with minimum codegree at least $(1/2 + o(1))n$ has
+the property that every vertex is covered by a copy of $C^k_s$. Our result is
+asymptotically best possible for infinitely many pairs of $s$ and $k$, e.g.
+when $s$ and $k$ are coprime.
+A perfect $C^k_s$-tiling is a spanning collection of vertex-disjoint copies
+of $C^k_s$. When $s$ is divisible by $k$, the problem of determining the
+minimum codegree that guarantees a perfect $C^k_s$-tiling was solved by a
+result of Mycroft. We prove that if $k \ge 3$ and $s \ge 5k^2$ is not divisible
+by $k$ and $s$ divides $n$, then every $k$-uniform hypergraph on $n$ vertices
+with minimum codegree at least $(1/2 + 1/(2s) + o(1))n$ has a perfect
+$C^k_s$-tiling. Again our result is asymptotically best possible for infinitely
+many pairs of $s$ and $k$, e.g. when $s$ and $k$ are coprime with $k$ even.
+",0,0,1,0,0,0
+7640,DNA methylation markers to assess biological age,"  Among the different biomarkers of aging based on omics and clinical data, DNA
+methylation clocks stand apart providing unmatched accuracy in assessing the
+biological age of both humans and animal models of aging. Here, we discuss
+robustness of DNA methylation clocks and bounds on their out-of-sample
+performance and review computational strategies for development of the clocks.
+",0,0,0,0,1,0
+7641,Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification,"  Effective and efficient mitigation of malware is a long-time endeavor in the
+information security community. The development of an anti-malware system that
+can counteract an unknown malware is a prolific activity that may benefit
+several sectors. We envision an intelligent anti-malware system that utilizes
+the power of deep learning (DL) models. Using such models would enable the
+detection of newly-released malware through mathematical generalization. That
+is, finding the relationship between a given malware $x$ and its corresponding
+malware family $y$, $f: x \mapsto y$. To accomplish this feat, we used the
+Malimg dataset (Nataraj et al., 2011) which consists of malware images that
+were processed from malware binaries, and then we trained the following DL
+models 1 to classify each malware family: CNN-SVM (Tang, 2013), GRU-SVM
+(Agarap, 2017), and MLP-SVM. Empirical evidence has shown that the GRU-SVM
+stands out among the DL models with a predictive accuracy of ~84.92%. This
+stands to reason for the mentioned model had the relatively most sophisticated
+architecture design among the presented models. The exploration of an even more
+optimal DL-SVM model is the next stage towards the engineering of an
+intelligent anti-malware system.
+",0,0,0,1,0,0
+7642,Exponential profiles from stellar scattering off interstellar clumps and holes in dwarf galaxy discs,"  Holes and clumps in the interstellar gas of dwarf irregular galaxies are
+gravitational scattering centers that heat field stars and change their radial
+and vertical distributions. Because the gas structures are extended and each
+stellar scattering is relatively weak, the stellar orbits remain nearly
+circular and the net effect accumulates slowly over time. We calculate the
+radial profile of scattered stars with an idealized model and find that it
+approaches an equilibrium shape that is exponential, similar to the observed
+shapes of galaxy discs. Our models treat only scattering and have no bars or
+spiral arms, so the results apply mostly to dwarf irregular galaxies where
+there are no other obvious scattering processes. Stellar scattering by gaseous
+perturbations slows down when the stellar population gets thicker than the gas
+layer. An accreting galaxy with a growing thin gas layer can form multiple
+stellar exponential profiles from the inside-out, preserving the remnants of
+each Gyr interval in a sequence of ever-lengthening and thinning stellar
+subdiscs.
+",0,1,0,0,0,0
+7643,Visualizations for an Explainable Planning Agent,"  In this paper, we report on the visualization capabilities of an Explainable
+AI Planning (XAIP) agent that can support human in the loop decision making.
+Imposing transparency and explainability requirements on such agents is
+especially important in order to establish trust and common ground with the
+end-to-end automated planning system. Visualizing the agent's internal
+decision-making processes is a crucial step towards achieving this. This may
+include externalizing the ""brain"" of the agent -- starting from its sensory
+inputs, to progressively higher order decisions made by it in order to drive
+its planning components. We also show how the planner can bootstrap on the
+latest techniques in explainable planning to cast plan visualization as a plan
+explanation problem, and thus provide concise model-based visualization of its
+plans. We demonstrate these functionalities in the context of the automated
+planning components of a smart assistant in an instrumented meeting space.
+",1,0,0,0,0,0
+7644,Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions,"  We present a neural network architecture based upon the Autoencoder (AE) and
+Generative Adversarial Network (GAN) that promotes a convex latent distribution
+by training adversarially on latent space interpolations. By using an AE as
+both the generator and discriminator of a GAN, we pass a pixel-wise error
+function across the discriminator, yielding an AE which produces non-blurry
+samples that match both high- and low-level features of the original images.
+Interpolations between images in this space remain within the latent-space
+distribution of real images as trained by the discriminator, and therfore
+preserve realistic resemblances to the network inputs.
+",0,0,0,1,0,0
+7645,Attribute-Guided Face Generation Using Conditional CycleGAN,"  We are interested in attribute-guided face generation: given a low-res face
+input image, an attribute vector that can be extracted from a high-res image
+(attribute image), our new method generates a high-res face image for the
+low-res input that satisfies the given attributes. To address this problem, we
+condition the CycleGAN and propose conditional CycleGAN, which is designed to
+1) handle unpaired training data because the training low/high-res and high-res
+attribute images may not necessarily align with each other, and to 2) allow
+easy control of the appearance of the generated face via the input attributes.
+We demonstrate impressive results on the attribute-guided conditional CycleGAN,
+which can synthesize realistic face images with appearance easily controlled by
+user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using
+the attribute image as identity to produce the corresponding conditional vector
+and by incorporating a face verification network, the attribute-guided network
+becomes the identity-guided conditional CycleGAN which produces impressive and
+interesting results on identity transfer. We demonstrate three applications on
+identity-guided conditional CycleGAN: identity-preserving face superresolution,
+face swapping, and frontal face generation, which consistently show the
+advantage of our new method.
+",1,0,0,1,0,0
+7646,Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models,"  In unsupervised data generation tasks, besides the generation of a sample
+based on previous observations, one would often like to give hints to the model
+in order to bias the generation towards desirable metrics. We propose a method
+that combines Generative Adversarial Networks (GANs) and reinforcement learning
+(RL) in order to accomplish exactly that. While RL biases the data generation
+process towards arbitrary metrics, the GAN component of the reward function
+ensures that the model still remembers information learned from data. We build
+upon previous results that incorporated GANs and RL in order to generate
+sequence data and test this model in several settings for the generation of
+molecules encoded as text sequences (SMILES) and in the context of music
+generation, showing for each case that we can effectively bias the generation
+process towards desired metrics.
+",1,0,0,1,0,0
+7647,Pluripotential theory on the support of closed positive currents and applications to dynamics in $\mathbb{C}^n$,"  We extend certain classical theorems in pluripotential theory to a class of
+functions defined on the support of a $(1,1)$-closed positive current $T$,
+analogous to plurisubharmonic functions, called $T$-plurisubharmonic functions.
+These functions are defined as limits, on the support of $T$, of sequences of
+plurisubharmonic functions decreasing on this support. In particular, we show
+that the poles of such functions are pluripolar sets. We also show that the
+maximum principle and the Hartogs's theorem remain valid in a weak sense. We
+study these functions by means of a class of measures, so-called ""pluri-Jensen
+measures"", about which we prove that they are numerous on the support of
+$(1,1)$-closed positive currents. We also obtain, for any fat compact set, an
+expression of its relative Green's function in terms of an infimum of an
+integral over a set of pluri-Jensen measures. We then deduce, by means of these
+measures, a characterization of the polynomially convex fat compact sets, as
+well as a characterization of pluripolar sets, and the fact that the support of
+a closed positive $(1,1)$-current is nowhere pluri-thin. In the second part of
+this article, these tools are used to study dynamics of a certain class of
+automorphisms of $\mathbb{C}^n$ which naturally generalize Hénon's
+automorphisms of $\mathbb{C}^2$. First we study the geometry of the support of
+canonical invariant currents. Then we obtain an equidistribution result for the
+convergence of pull-back of certain measures towards an ergodic invariant
+measure, with compact support.
+",0,0,1,0,0,0
+7648,Inconsistency of Measure-Theoretic Probability and Random Behavior of Microscopic Systems,"  We report an inconsistency found in probability theory (also referred to as
+measure-theoretic probability). For probability measures induced by real-valued
+random variables, we deduce an ""equality"" such that one side of the ""equality""
+is a probability, but the other side is not. For probability measures induced
+by extended random variables, we deduce an ""equality"" such that its two sides
+are unequal probabilities. The deduced expressions are erroneous only when it
+can be proved that measure-theoretic probability is a theory free from
+contradiction. However, such a proof does not exist. The inconsistency appears
+only in the theory rather than in the physical world, and will not affect
+practical applications as long as ideal events in the theory (which will not
+occur physically) are not mistaken for observable events in the real world.
+Nevertheless, unlike known paradoxes in mathematics, the inconsistency cannot
+be explained away and hence must be resolved. The assumption of infinite
+additivity in the theory is relevant to the inconsistency, and may cause
+confusion of ideal events and real events. As illustrated by an example in this
+article, since abstract properties of mathematical entities in theoretical
+thinking are not necessarily properties of physical quantities observed in the
+real world, mistaking the former for the latter may lead to misinterpreting
+random phenomena observed in experiments with microscopic systems. Actually the
+inconsistency is due to the notion of ""numbers"" adopted in conventional
+mathematics. A possible way to resolve the inconsistency is to treat ""numbers""
+from the viewpoint of constructive mathematics.
+",0,0,1,0,0,0
+7649,Robustness of Quasiparticle Interference Test for Sign-changing Gaps in Multiband Superconductors,"  Recently, a test for a sign-changing gap function in a candidate multiband
+unconventional superconductor involving quasiparticle interference data was
+proposed. The test was based on the antisymmetric, Fourier transformed
+conductance maps integrated over a range of momenta $\bf q$ corresponding to
+interband processes, which was argued to display a particular resonant form,
+provided the gaps changed sign between the Fermi surface sheets connected by
+$\bf q$. The calculation was performed for a single impurity, however, raising
+the question of how robust this measure is as a test of sign-changing pairing
+in a realistic system with many impurities. Here we reproduce the results of
+the previous work within a model with two distinct Fermi surface sheets, and
+show explicitly that the previous result, while exact for a single nonmagnetic
+scatterer and also in the limit of a dense set of random impurities, can be
+difficult to implement for a few dilute impurities. In this case, however,
+appropriate isolation of a single impurity is sufficient to recover the
+expected result, allowing a robust statement about the gap signs to be made.
+",0,1,0,0,0,0
+7650,Adaptive User Perspective Rendering for Handheld Augmented Reality,"  Handheld Augmented Reality commonly implements some variant of magic lens
+rendering, which turns only a fraction of the user's real environment into AR
+while the rest of the environment remains unaffected. Since handheld AR devices
+are commonly equipped with video see-through capabilities, AR magic lens
+applications often suffer from spatial distortions, because the AR environment
+is presented from the perspective of the camera of the mobile device. Recent
+approaches counteract this distortion based on estimations of the user's head
+position, rendering the scene from the user's perspective. To this end,
+approaches usually apply face-tracking algorithms on the front camera of the
+mobile device. However, this demands high computational resources and therefore
+commonly affects the performance of the application beyond the already high
+computational load of AR applications. In this paper, we present a method to
+reduce the computational demands for user perspective rendering by applying
+lightweight optical flow tracking and an estimation of the user's motion before
+head tracking is started. We demonstrate the suitability of our approach for
+computationally limited mobile devices and we compare it to device perspective
+rendering, to head tracked user perspective rendering, as well as to fixed
+point of view user perspective rendering.
+",1,0,0,0,0,0
+7651,A distributed primal-dual algorithm for computation of generalized Nash equilibria with shared affine coupling constraints via operator splitting methods,"  In this paper, we propose a distributed primal-dual algorithm for computation
+of a generalized Nash equilibrium (GNE) in noncooperative games over network
+systems. In the considered game, not only each player's local objective
+function depends on other players' decisions, but also the feasible decision
+sets of all the players are coupled together with a globally shared affine
+inequality constraint. Adopting the variational GNE, that is the solution of a
+variational inequality, as a refinement of GNE, we introduce a primal-dual
+algorithm that players can use to seek it in a distributed manner. Each player
+only needs to know its local objective function, local feasible set, and a
+local block of the affine constraint. Meanwhile, each player only needs to
+observe the decisions on which its local objective function explicitly depends
+through the interference graph and share information related to multipliers
+with its neighbors through a multiplier graph. Through a primal-dual analysis
+and an augmentation of variables, we reformulate the problem as finding the
+zeros of a sum of monotone operators. Our distributed primal-dual algorithm is
+based on forward-backward operator splitting methods. We prove its convergence
+to the variational GNE for fixed step-sizes under some mild assumptions. Then a
+distributed algorithm with inertia is also introduced and analyzed for
+variational GNE seeking. Finally, numerical simulations for network Cournot
+competition are given to illustrate the algorithm efficiency and performance.
+",1,0,1,0,0,0
+7652,Inverse dynamic and spectral problems for the one-dimensional Dirac system on a finite tree,"  We consider inverse dynamic and spectral problems for the one dimensional
+Dirac system on a finite tree. Our aim will be to recover the topology of a
+tree (lengths and connectivity of edges) as well as matrix potentials on each
+edge. As inverse data we use the Weyl-Titchmarsh matrix function or the dynamic
+response operator.
+",0,0,1,0,0,0
+7653,Extensions of isomorphisms of subvarieties in flexile varieties,"  Let $X$ be a quasi-affine algebraic variety isomorphic to the complement of a
+closed subvariety of dimension at most $n-3$ in $\C^n$. We find some conditions
+under which an isomorphism of two closed subvarieties of $X$ can be extended to
+an automorphism of $X$.
+",0,0,1,0,0,0
+7654,Frequentist coverage and sup-norm convergence rate in Gaussian process regression,"  Gaussian process (GP) regression is a powerful interpolation technique due to
+its flexibility in capturing non-linearity. In this paper, we provide a general
+framework for understanding the frequentist coverage of point-wise and
+simultaneous Bayesian credible sets in GP regression. As an intermediate
+result, we develop a Bernstein von-Mises type result under supremum norm in
+random design GP regression. Identifying both the mean and covariance function
+of the posterior distribution of the Gaussian process as regularized
+$M$-estimators, we show that the sampling distribution of the posterior mean
+function and the centered posterior distribution can be respectively
+approximated by two population level GPs. By developing a comparison inequality
+between two GPs, we provide exact characterization of frequentist coverage
+probabilities of Bayesian point-wise credible intervals and simultaneous
+credible bands of the regression function. Our results show that inference
+based on GP regression tends to be conservative; when the prior is
+under-smoothed, the resulting credible intervals and bands have minimax-optimal
+sizes, with their frequentist coverage converging to a non-degenerate value
+between their nominal level and one. As a byproduct of our theory, we show that
+the GP regression also yields minimax-optimal posterior contraction rate
+relative to the supremum norm, which provides a positive evidence to the long
+standing problem on optimal supremum norm contraction rate in GP regression.
+",0,0,1,1,0,0
+7655,Most Probable Evolution Trajectories in a Genetic Regulatory System Excited by Stable Lévy Noise,"  We study the most probable trajectories of the concentration evolution for
+the transcription factor activator in a genetic regulation system, with
+non-Gaussian stable Lévy noise in the synthesis reaction rate taking into
+account. We calculate the most probable trajectory by spatially maximizing the
+probability density of the system path, i.e., the solution of the associated
+nonlocal Fokker-Planck equation. We especially examine those most probable
+trajectories from low concentration state to high concentration state (i.e.,
+the likely transcription regime) for certain parameters, in order to gain
+insights into the transcription processes and the tipping time for the
+transcription likely to occur. This enables us: (i) to visualize the progress
+of concentration evolution (i.e., observe whether the system enters the
+transcription regime within a given time period); (ii) to predict or avoid
+certain transcriptions via selecting specific noise parameters in particular
+regions in the parameter space. Moreover, we have found some peculiar or
+counter-intuitive phenomena in this gene model system, including (a) a smaller
+noise intensity may trigger the transcription process, while a larger noise
+intensity can not, under the same asymmetric Lévy noise. This phenomenon does
+not occur in the case of symmetric Lévy noise; (b) the symmetric Lévy
+motion always induces transition to high concentration, but certain asymmetric
+Lévy motions do not trigger the switch to transcription. These findings
+provide insights for further experimental research, in order to achieve or to
+avoid specific gene transcriptions, with possible relevance for medical
+advances.
+",0,0,0,0,1,0
+7656,Simplified derivation of the collision probability of two objects in independent Keplerian orbits,"  Many topics in planetary studies demand an estimate of the collision
+probability of two objects moving on nearly Keplerian orbits. In the classic
+works of Öpik (1951) and Wetherill (1967), the collision probability was
+derived by linearizing the motion near the collision points, and there is now a
+vast literature using their method. We present here a simpler and more
+physically motivated derivation for non-tangential collisions in Keplerian
+orbits, as well as for tangential collisions that were not previously
+considered. Our formulas have the added advantage of being manifestly symmetric
+in the parameters of the two colliding bodies. In common with the
+Öpik-Wetherill treatments, we linearize the motion of the bodies in the
+vicinity of the point of orbit intersection (or near the points of minimum
+distance between the two orbits) and assume a uniform distribution of impact
+parameter within the collision radius. We point out that the linear
+approximation leads to singular results for the case of tangential encounters.
+We regularize this singularity by use of a parabolic approximation of the
+motion in the vicinity of a tangential encounter.
+",0,1,0,0,0,0
+7657,Optically Coupled Methods for Microwave Impedance Microscopy,"  Scanning Microwave Impedance Microscopy (MIM) measurement of
+photoconductivity with 50 nm resolution is demonstrated using a modulated
+optical source. The use of a modulated source allows for measurement of
+photoconductivity in a single scan without a reference region on the sample, as
+well as removing most topographical artifacts and enhancing signal to noise as
+compared with unmodulated measurement. A broadband light source with tunable
+monochrometer is then used to measure energy resolved photoconductivity with
+the same methodology. Finally, a pulsed optical source is used to measure local
+photo-carrier lifetimes via MIM, using the same 50 nm resolution tip.
+",0,1,0,0,0,0
+7658,Generalized feedback vertex set problems on bounded-treewidth graphs: chordality is the key to single-exponential parameterized algorithms,"  It has long been known that Feedback Vertex Set can be solved in time
+$2^{\mathcal{O}(w\log w)}n^{\mathcal{O}(1)}$ on $n$-vertex graphs of treewidth
+$w$, but it was only recently that this running time was improved to
+$2^{\mathcal{O}(w)}n^{\mathcal{O}(1)}$, that is, to single-exponential
+parameterized by treewidth. We investigate which generalizations of Feedback
+Vertex Set can be solved in a similar running time. Formally, for a class
+$\mathcal{P}$ of graphs, the Bounded $\mathcal{P}$-Block Vertex Deletion
+problem asks, given a graph~$G$ on $n$ vertices and positive integers~$k$
+and~$d$, whether $G$ contains a set~$S$ of at most $k$ vertices such that each
+block of $G-S$ has at most $d$ vertices and is in $\mathcal{P}$. Assuming that
+$\mathcal{P}$ is recognizable in polynomial time and satisfies a certain
+natural hereditary condition, we give a sharp characterization of when
+single-exponential parameterized algorithms are possible for fixed values of
+$d$: if $\mathcal{P}$ consists only of chordal graphs, then the problem can be
+solved in time $2^{\mathcal{O}(wd^2)} n^{\mathcal{O}(1)}$, and if $\mathcal{P}$
+contains a graph with an induced cycle of length $\ell\ge 4$, then the problem
+is not solvable in time $2^{o(w\log w)} n^{\mathcal{O}(1)}$ even for fixed
+$d=\ell$, unless the ETH fails. We also study a similar problem, called Bounded
+$\mathcal{P}$-Component Vertex Deletion, where the target graphs have connected
+components of small size rather than blocks of small size, and we present
+analogous results. For this problem, we also show that if $d$ is part of the
+input and $\mathcal{P}$ contains all chordal graphs, then it cannot be solved
+in time $f(w)n^{o(w)}$ for some function $f$, unless the ETH fails.
+",1,0,0,0,0,0
+7659,Electron-phonon coupling mechanisms for hydrogen-rich metals at high pressure,"  The mechanisms for strong electron-phonon coupling predicted for
+hydrogen-rich alloys with high superconducting critical temperature ($T_c$) are
+examined within the Migdal-Eliashberg theory. Analysis of the functional
+derivative of $T_c$ with respect to the electron-phonon spectral function shows
+that at low pressures, when the alloys often adopt layered structures, bending
+vibrations have the most dominant effect. At very high pressures, the H-H
+interactions in two-dimensional (2D) and three-dimensional (3D) extended
+structures are weakened, resulting in mixed bent (libration) and stretch
+vibrations, and the electron-phonon coupling process is distributed over a
+broad frequency range leading to very high $T_c$.
+",0,1,0,0,0,0
+7660,Competitive Equilibrium For almost All Incomes,"  Competitive equilibrium from equal incomes (CEEI) is a well-known rule for
+fair allocation of resources among agents with different preferences. It has
+many advantages, among them is the fact that a CEEI allocation is both Pareto
+efficient and envy-free. However, when the resources are indivisible, a CEEI
+allocation might not exist even when there are two agents and a single item.
+In contrast to this discouraging non-existence result, Babaioff, Nisan and
+Talgam-Cohen (2017) recently suggested a new and more encouraging approach to
+allocation of indivisible items: instead of insisting that the incomes be
+equal, they suggest to look at the entire space of possible incomes, and check
+whether there exists a competitive equilibrium for almost all income-vectors
+(CEFAI) --- all income-space except a subset of measure zero. They show that a
+CEFAI exists when there are at most 3 items, or when there are 4 items and two
+agents. They also show that when there are 5 items and two agents there might
+not exist a CEFAI. They leave open the cases of 4 items with three or four
+agents.
+This paper presents a new way to implement a CEFAI, as a subgame-perfect
+equilibrium of a sequential game. This new implementation allows us both to
+offer much simpler solutions to the known cases (at most 3 items, and 4 items
+with two agents), and to prove that a CEFAI exists even in the much more
+difficult case of 4 items and three agents. Moreover, we prove that a CEFAI
+might not exist with 4 items and four agents. When the items to be divided are
+bads (chores), CEFAI exists for two agents with at most 4 chores, but does not
+exist for two agents with 5 chores or with three agents with 3 or more chores.
+Thus, this paper completes the characterization of CEFAI existence for monotone
+preferences.
+",1,0,0,0,0,0
+7661,"Converting of algebraic Diophantine equations to a diagonal form with the help of an integer non-orthogonal transformation, maintaining the asymptotic behavior of the number of its integer solutions","  The author showed that any homogeneous algebraic Diophantine equation of the
+second order can be converted to a diagonal form using an integer
+non-orthogonal transformation maintaining asymptotic behavior of the number of
+its integer solutions. In this paper, we consider the transformation to the
+diagonal form of a wider class of algebraic second-order Diophantine equations,
+and also we consider the conditions for converting higher order algebraic
+Diophantine equations to this form. The author found an asymptotic estimate for
+the number of integer solutions of the diagonal Thue equation of odd degree
+with an amount of variables greater than two, and also he got and asymptotic
+estimates of the number of integer solutions of other types of diagonal
+algebraic Diophantine equations.
+",0,0,1,0,0,0
+7662,A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification,"  Automatic sleep staging is a challenging problem and state-of-the-art
+algorithms have not yet reached satisfactory performance to be used instead of
+manual scoring by a sleep technician. Much research has been done to find good
+feature representations that extract the useful information to correctly
+classify each epoch into the correct sleep stage. While many useful features
+have been discovered, the amount of features have grown to an extent that a
+feature reduction step is necessary in order to avoid the curse of
+dimensionality. One reason for the need of such a large feature set is that
+many features are good for discriminating only one of the sleep stages and are
+less informative during other stages. This paper explores how a second feature
+representation over a large set of pre-defined features can be learned using an
+auto-encoder with a selective attention for the current sleep stage in the
+training batch. This selective attention allows the model to learn feature
+representations that focuses on the more relevant inputs without having to
+perform any dimensionality reduction of the input data. The performance of the
+proposed algorithm is evaluated on a large data set of polysomnography (PSG)
+night recordings of patients with sleep-disordered breathing. The performance
+of the auto-encoder with selective attention is compared with a regular
+auto-encoder and previous works using a deep belief network (DBN).
+",0,0,0,0,1,0
+7663,Laser Wakefield Accelerators,"  The one-dimensional wakefield generation equations are solved for increasing
+levels of non-linearity, to demonstrate how they contribute to the overall
+behaviour of a non-linear wakefield in a plasma. The effect of laser guiding is
+also studied as a way to increase the interaction length of a laser wakefield
+accelerator.
+",0,1,0,0,0,0
+7664,Permutation complexity of images of Sturmian words by marked morphisms,"  We show that the permutation complexity of the image of a Sturmian word by a
+binary marked morphism is $n+k$ for some constant $k$ and all lengths $n$
+sufficiently large.
+",1,0,0,0,0,0
+7665,"Three principles of data science: predictability, computability, and stability (PCS)","  We propose the predictability, computability, and stability (PCS) framework
+to extract reproducible knowledge from data that can guide scientific
+hypothesis generation and experimental design. The PCS framework builds on key
+ideas in machine learning, using predictability as a reality check and
+evaluating computational considerations in data collection, data storage, and
+algorithm design. It augments PC with an overarching stability principle, which
+largely expands traditional statistical uncertainty considerations. In
+particular, stability assesses how results vary with respect to choices (or
+perturbations) made across the data science life cycle, including problem
+formulation, pre-processing, modeling (data and algorithm perturbations), and
+exploratory data analysis (EDA) before and after modeling.
+Furthermore, we develop PCS inference to investigate the stability of data
+results and identify when models are consistent with relatively simple
+phenomena. We compare PCS inference with existing methods, such as selective
+inference, in high-dimensional sparse linear model simulations to demonstrate
+that our methods consistently outperform others in terms of ROC curves over a
+wide range of simulation settings. Finally, we propose a PCS documentation
+based on Rmarkdown, iPython, or Jupyter Notebook, with publicly available,
+reproducible codes and narratives to back up human choices made throughout an
+analysis. The PCS workflow and documentation are demonstrated in a genomics
+case study available on Zenodo.
+",1,0,0,1,0,0
+7666,The Elasticity of Puiseux Monoids,"  Let $M$ be an atomic monoid and let $x$ be a non-unit element of $M$. The
+elasticity of $x$, denoted by $\rho(x)$, is the ratio of its largest
+factorization length to its shortest factorization length, and it measures how
+far is $x$ from having a unique factorization. The elasticity $\rho(M)$ of $M$
+is the supremum of the elasticities of all non-unit elements of $M$. The monoid
+$M$ has accepted elasticity if $\rho(M) = \rho(m)$ for some $m \in M$. In this
+paper, we study the elasticity of Puiseux monoids (i.e., additive submonoids of
+$\mathbb{Q}_{\ge 0}$). First, we characterize the Puiseux monoids $M$ having
+finite elasticity and find a formula for $\rho(M)$. Then we classify the
+Puiseux monoids having accepted elasticity in terms of their sets of atoms.
+When $M$ is a primary Puiseux monoid, we describe the topology of the set of
+elasticities of $M$, including a characterization of when $M$ is a bounded
+factorization monoid. Lastly, we give an example of a Puiseux monoid that is
+bifurcus (that is, every nonzero element has a factorization of length at most
+$2$).
+",0,0,1,0,0,0
+7667,Barcode Embeddings for Metric Graphs,"  Stable topological invariants are a cornerstone of persistence theory and
+applied topology, but their discriminative properties are often
+poorly-understood. In this paper we study a rich homology-based invariant first
+defined by Dey, Shi, and Wang, which we think of as embedding a metric graph in
+the barcode space. We prove that this invariant is locally injective on the
+space of metric graphs and globally injective on a GH-dense subset. Moreover,
+we define a new topology on the space of metric graphs, which we call the
+fibered topology, for which the barcode transform is injective on a generic
+(open and dense) subset.
+",0,0,1,0,0,0
+7668,On the extremal extensions of a non-negative Jacobi operator,"  We consider minimal non-negative Jacobi operator with $p\times p-$matrix
+entries. Using the technique of boundary triplets and the corresponding Weyl
+functions, we describe the Friedrichs and Krein extensions of the minimal
+Jacobi operator. Moreover, we parameterize the set of all non-negative
+self-adjoint extensions in terms of boundary conditions.
+",0,0,1,0,0,0
+7669,Note on the geodesic Monte Carlo,"  Geodesic Monte Carlo (gMC) is a powerful algorithm for Bayesian inference on
+non-Euclidean manifolds. The original gMC algorithm was cleverly derived in
+terms of its progenitor, the Riemannian manifold Hamiltonian Monte Carlo
+(RMHMC). Here, it is shown that alternative and theoretically simpler
+derivations are available in which the original algorithm is a special case of
+two general classes of algorithms characterized by non-trivial mass matrices.
+The proposed derivations work entirely in embedding coordinates and thus
+clarify the original algorithm as applied to manifolds embedded in Euclidean
+space.
+",0,0,0,1,0,0
+7670,Google Scholar and the gray literature: A reply to Bonato's review,"  Recently, a review concluded that Google Scholar (GS) is not a suitable
+source of information ""for identifying recent conference papers or other gray
+literature publications"". The goal of this letter is to demonstrate that GS can
+be an effective tool to search and find gray literature, as long as appropriate
+search strategies are used. To do this, we took as examples the same two case
+studies used by the original review, describing first how GS processes
+original's search strategies, then proposing alternative search strategies, and
+finally generalizing each case study to compose a general search procedure
+aimed at finding gray literature in Google Scholar for two wide selected case
+studies: a) all contributions belonging to a congress (the ASCO Annual
+Meeting); and b) indexed guidelines as well as gray literature within medical
+institutions (National Institutes of Health) and governmental agencies (U.S.
+Department of Health & Human Services). The results confirm that original
+search strategies were undertrained offering misleading results and erroneous
+conclusions. Google Scholar lacks many of the advanced search features
+available in other bibliographic databases (such as Pubmed), however, it is one
+thing to have a friendly search experience, and quite another to find gray
+literature. We finally conclude that Google Scholar is a powerful tool for
+searching gray literature, as long as the users are familiar with all the
+possibilities it offers as a search engine. Poorly formulated searches will
+undoubtedly return misleading results.
+",1,0,0,0,0,0
+7671,A Next-Best-Smell Approach for Remote Gas Detection with a Mobile Robot,"  The problem of gas detection is relevant to many real-world applications,
+such as leak detection in industrial settings and landfill monitoring. Using
+mobile robots for gas detection has several advantages and can reduce danger
+for humans. In our work, we address the problem of planning a path for a mobile
+robotic platform equipped with a remote gas sensor, which minimizes the time to
+detect all gas sources in a given environment. We cast this problem as a
+coverage planning problem by defining a basic sensing operation -- a scan with
+the remote gas sensor -- as the field of ""view"" of the sensor. Given the
+computing effort required by previously proposed offline approaches, in this
+paper we suggest a online coverage algorithm, called Next-Best-Smell, adapted
+from the Next-Best-View class of exploration algorithms. Our algorithm
+evaluates candidate locations with a global utility function, which combines
+utility values for travel distance, information gain, and sensing time, using
+Multi-Criteria Decision Making. In our experiments, conducted both in
+simulation and with a real robot, we found the performance of the
+Next-Best-Smell approach to be comparable with that of the state-of-the-art
+offline algorithm, at much lower computational cost.
+",1,0,0,0,0,0
+7672,KZ-Calogero correspondence revisited,"  We discuss the correspondence between the Knizhnik-Zamolodchikov equations
+associated with $GL(N)$ and the $n$-particle quantum Calogero model in the case
+when $n$ is not necessarily equal to $N$. This can be viewed as a natural
+""quantization"" of the quantum-classical correspondence between quantum Gaudin
+and classical Calogero models.
+",0,1,1,0,0,0
+7673,Spectral up- and downshifting of Akhmediev breathers under wind forcing,"  We experimentally and numerically investigate the effect of wind forcing on
+the spectral dynamics of Akhmediev breathers, a wave-type known to model the
+modulation instability. We develop the wind model to the same order in
+steepness as the higher order modifcation of the nonlinear Schroedinger
+equation, also referred to as the Dysthe equation. This results in an
+asymmetric wind term in the higher order, in addition to the leading order wind
+forcing term. The derived model is in good agreement with laboratory
+experiments within the range of the facility's length. We show that the leading
+order forcing term amplifies all frequencies equally and therefore induces only
+a broadening of the spectrum while the asymmetric higher order term in the
+model enhances higher frequencies more than lower ones. Thus, the latter term
+induces a permanent upshift of the spectral mean. On the other hand, in
+contrast to the direct effect of wind forcing, wind can indirectly lead to
+frequency downshifts, due to dissipative effects such as wave breaking, or
+through amplification of the intrinsic spectral asymmetry of the Dysthe
+equation. Furthermore, the definitions of the up- and downshift in terms of
+peak- and mean frequencies, that are critical to relate our work to previous
+results, are highlighted and discussed.
+",0,1,0,0,0,0
+7674,High Rate LDPC Codes from Difference Covering Arrays,"  This paper presents a combinatorial construction of low-density parity-check
+(LDPC) codes from difference covering arrays. While the original construction
+by Gallagher was by randomly allocating bits in a sparse parity-check matrix,
+over the past 20 years researchers have used a variety of more structured
+approaches to construct these codes, with the more recent constructions of
+well-structured LDPC coming from balanced incomplete block designs (BIBDs) and
+from Latin squares over finite fields. However these constructions have
+suffered from the limited orders for which these designs exist. Here we present
+a construction of LDPC codes of length $4n^2 - 2n$ for all $n$ using the cyclic
+group of order $2n$. These codes achieve high information rate (greater than
+0.8) for $n \geq 8$, have girth at least 6 and have minimum distance 6 for $n$
+odd.
+",1,0,1,0,0,0
+7675,Bootstrapping spectral statistics in high dimensions,"  Statistics derived from the eigenvalues of sample covariance matrices are
+called spectral statistics, and they play a central role in multivariate
+testing. Although bootstrap methods are an established approach to
+approximating the laws of spectral statistics in low-dimensional problems,
+these methods are relatively unexplored in the high-dimensional setting. The
+aim of this paper is to focus on linear spectral statistics as a class of
+prototypes for developing a new bootstrap in high-dimensions --- and we refer
+to this method as the Spectral Bootstrap. In essence, the method originates
+from the parametric bootstrap, and is motivated by the notion that, in high
+dimensions, it is difficult to obtain a non-parametric approximation to the
+full data-generating distribution. From a practical standpoint, the method is
+easy to use, and allows the user to circumvent the difficulties of complex
+asymptotic formulas for linear spectral statistics. In addition to proving the
+consistency of the proposed method, we provide encouraging empirical results in
+a variety of settings. Lastly, and perhaps most interestingly, we show through
+simulations that the method can be applied successfully to statistics outside
+the class of linear spectral statistics, such as the largest sample eigenvalue
+and others.
+",0,0,0,1,0,0
+7676,On parabolic subgroups of Artin-Tits groups of spherical type,"  We show that, in an Artin-Tits group of spherical type, the intersection of
+two parabolic subgroups is a parabolic subgroup. Moreover, we show that the set
+of parabolic subgroups forms a lattice with respect to inclusion. This extends
+to all Artin-Tits groups of spherical type a result that was previously known
+for braid groups.
+To obtain the above results, we show that every element in an Artin-Tits
+group of spherical type admits a unique minimal parabolic subgroup containing
+it. Also, the subgroup associated to an element coincides with the subgroup
+associated to any of its powers or roots. As a consequence, if an element
+belongs to a parabolic subgroup, all its roots belong to the same parabolic
+subgroup.
+We define the simplicial complex of irreducible parabolic subgroups, and we
+propose it as the analogue, in Artin-Tits groups of spherical type, of the
+celebrated complex of curves which is an important tool in braid groups, and
+more generally in mapping class groups. We conjecture that the complex of
+irreducible parabolic subgroups is $\delta$-hyperbolic.
+",0,0,1,0,0,0
+7677,A statistical test for the Zipf's law by deviations from the Heaps' law,"  We explore a probabilistic model of an artistic text: words of the text are
+chosen independently of each other in accordance with a discrete probability
+distribution on an infinite dictionary. The words are enumerated 1, 2,
+$\ldots$, and the probability of appearing the $i$'th word is asymptotically a
+power function. Bahadur proved that in this case the number of different words
+depends on the length of the text is asymptotically a power function, too. On
+the other hand, in the applied statistics community, there exist statements
+supported by empirical observations, the Zipf's and the Heaps' laws. We
+highlight the links between Bahadur results and Zipf's/Heaps' laws, and
+introduce and analyse a corresponding statistical test.
+",0,0,1,1,0,0
+7678,Weakly Supervised Classification in High Energy Physics,"  As machine learning algorithms become increasingly sophisticated to exploit
+subtle features of the data, they often become more dependent on simulations.
+This paper presents a new approach called weakly supervised classification in
+which class proportions are the only input into the machine learning algorithm.
+Using one of the most challenging binary classification tasks in high energy
+physics - quark versus gluon tagging - we show that weakly supervised
+classification can match the performance of fully supervised algorithms.
+Furthermore, by design, the new algorithm is insensitive to any mis-modeling of
+discriminating features in the data by the simulation. Weakly supervised
+classification is a general procedure that can be applied to a wide variety of
+learning problems to boost performance and robustness when detailed simulations
+are not reliable or not available.
+",0,1,0,1,0,0
+7679,Profit Driven Decision Trees for Churn Prediction,"  Customer retention campaigns increasingly rely on predictive models to detect
+potential churners in a vast customer base. From the perspective of machine
+learning, the task of predicting customer churn can be presented as a binary
+classification problem. Using data on historic behavior, classification
+algorithms are built with the purpose of accurately predicting the probability
+of a customer defecting. The predictive churn models are then commonly selected
+based on accuracy related performance measures such as the area under the ROC
+curve (AUC). However, these models are often not well aligned with the core
+business requirement of profit maximization, in the sense that, the models fail
+to take into account not only misclassification costs, but also the benefits
+originating from a correct classification. Therefore, the aim is to construct
+churn prediction models that are profitable and preferably interpretable too.
+The recently developed expected maximum profit measure for customer churn
+(EMPC) has been proposed in order to select the most profitable churn model. We
+present a new classifier that integrates the EMPC metric directly into the
+model construction. Our technique, called ProfTree, uses an evolutionary
+algorithm for learning profit driven decision trees. In a benchmark study with
+real-life data sets from various telecommunication service providers, we show
+that ProfTree achieves significant profit improvements compared to classic
+accuracy driven tree-based methods.
+",1,0,0,1,0,0
+7680,Carrier loss mechanisms in textured crystalline Si-based solar cells,"  A quite general device analysis method that allows the direct evaluation of
+optical and recombination losses in crystalline silicon (c-Si)-based solar
+cells has been developed. By applying this technique, the optical and physical
+limiting factors of the state-of-the-art solar cells with ~20% efficiencies
+have been revealed. In the established method, the carrier loss mechanisms are
+characterized from the external quantum efficiency (EQE) analysis with very low
+computational cost. In particular, the EQE analyses of textured c-Si solar
+cells are implemented by employing the experimental reflectance spectra
+obtained directly from the actual devices while using flat optical models
+without any fitting parameters. We find that the developed method provides
+almost perfect fitting to EQE spectra reported for various textured c-Si solar
+cells, including c-Si heterojunction solar cells, a dopant-free c-Si solar cell
+with a MoOx layer, and an n-type passivated emitter with rear locally diffused
+(PERL) solar cell. The modeling of the recombination loss further allows the
+extraction of the minority carrier diffusion length and surface recombination
+velocity from the EQE analysis. Based on the EQE analysis results, the carrier
+loss mechanisms in different types of c-Si solar cells are discussed.
+",0,1,0,0,0,0
+7681,The generating function for the Airy point process and a system of coupled Painlevé II equations,"  For a wide class of Hermitian random matrices, the limit distribution of the
+eigenvalues close to the largest one is governed by the Airy point process. In
+such ensembles, the limit distribution of the k-th largest eigenvalue is given
+in terms of the Airy kernel Fredholm determinant or in terms of Tracy-Widom
+formulas involving solutions of the Painlevé II equation. Limit distributions
+for quantities involving two or more near-extreme eigenvalues, such as the gap
+between the k-th and the \ell-th largest eigenvalue or the sum of the k largest
+eigenvalues, can be expressed in terms of Fredholm determinants of an Airy
+kernel with several discontinuities. We establish simple Tracy-Widom type
+expressions for these Fredholm determinants, which involve solutions to systems
+of coupled Painlevé II equations, and we investigate the asymptotic behavior
+of these solutions.
+",0,0,1,0,0,0
+7682,Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening,"  This paper proposes Power Slow Feature Analysis, a gradient-based method to
+extract temporally-slow features from a high-dimensional input stream that
+varies on a faster time-scale, and a variant of Slow Feature Analysis (SFA).
+While displaying performance comparable to hierarchical extensions to the SFA
+algorithm, such as Hierarchical Slow Feature Analysis, for a small number of
+output-features, our algorithm allows end-to-end training of arbitrary
+differentiable approximators (e.g., deep neural networks). We provide
+experimental evidence that PowerSFA is able to extract meaningful and
+informative low-dimensional features in the case of a) synthetic
+low-dimensional data, b) visual data, and also for c) a general dataset for
+which symmetric non-temporal relations between points can be defined.
+",0,0,0,1,0,0
+7683,Quantum Critical Behavior in the Asymptotic Limit of High Disorder: Entropy Stabilized NiCoCr0.8 Alloys,"  The behavior of matter near a quantum critical point (QCP) is one of the most
+exciting and challenging areas of physics research. Emergent phenomena such as
+high-temperature superconductivity are linked to the proximity to a QCP.
+Although significant progress has been made in understanding quantum critical
+behavior in some low dimensional magnetic insulators, the situation in metallic
+systems is much less clear. Here we demonstrate that NiCoCrx single crystal
+alloys are remarkable model systems for investigating QCP physics in a metallic
+environment. For NiCoCrx alloys with x = 0.8, the critical exponents associated
+with a ferromagnetic quantum critical point (FQCP) are experimentally
+determined from low temperature magnetization and heat capacity measurements.
+For the first time, all of the five critical exponents ( gamma-subT =1/2 ,
+beta-subT = 1, delta = 3/2, nuz-subm = 2, alpha-bar-subT = 0) are in remarkable
+agreement with predictions of Belitz-Kirkpatrick-Vojta (BKV) theory in the
+asymptotic limit of high disorder. Using these critical exponents, excellent
+scaling of the magnetization data is demonstrated with no adjustable
+parameters. We also find a divergence of the magnetic Gruneisen parameter,
+consistent with a FQCP. This work therefore demonstrates that entropy
+stabilized concentrated solid solutions represent a unique platform to study
+quantum critical behavior in a highly tunable class of materials.
+",0,1,0,0,0,0
+7684,Multilayer Network Modeling of Integrated Biological Systems,"  Biological systems, from a cell to the human brain, are inherently complex. A
+powerful representation of such systems, described by an intricate web of
+relationships across multiple scales, is provided by complex networks.
+Recently, several studies are highlighting how simple networks -- obtained by
+aggregating or neglecting temporal or categorical description of biological
+data -- are not able to account for the richness of information characterizing
+biological systems. More complex models, namely multilayer networks, are needed
+to account for interdependencies, often varying across time, of biological
+interacting units within a cell, a tissue or parts of an organism.
+",0,0,0,0,1,0
+7685,"Fast, Autonomous Flight in GPS-Denied and Cluttered Environments","  One of the most challenging tasks for a flying robot is to autonomously
+navigate between target locations quickly and reliably while avoiding obstacles
+in its path, and with little to no a-priori knowledge of the operating
+environment. This challenge is addressed in the present paper. We describe the
+system design and software architecture of our proposed solution, and showcase
+how all the distinct components can be integrated to enable smooth robot
+operation. We provide critical insight on hardware and software component
+selection and development, and present results from extensive experimental
+testing in real-world warehouse environments. Experimental testing reveals that
+our proposed solution can deliver fast and robust aerial robot autonomous
+navigation in cluttered, GPS-denied environments.
+",1,0,0,0,0,0
+7686,Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks,"  Efforts to reduce the numerical precision of computations in deep learning
+training have yielded systems that aggressively quantize weights and
+activations, yet employ wide high-precision accumulators for partial sums in
+inner-product operations to preserve the quality of convergence. The absence of
+any framework to analyze the precision requirements of partial sum
+accumulations results in conservative design choices. This imposes an
+upper-bound on the reduction of complexity of multiply-accumulate units. We
+present a statistical approach to analyze the impact of reduced accumulation
+precision on deep learning training. Observing that a bad choice for
+accumulation precision results in loss of information that manifests itself as
+a reduction in variance in an ensemble of partial sums, we derive a set of
+equations that relate this variance to the length of accumulation and the
+minimum number of bits needed for accumulation. We apply our analysis to three
+benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet
+AlexNet. In each case, with accumulation precision set in accordance with our
+proposed equations, the networks successfully converge to the single precision
+floating-point baseline. We also show that reducing accumulation precision
+further degrades the quality of the trained network, proving that our equations
+produce tight bounds. Overall this analysis enables precise tailoring of
+computation hardware to the application, yielding area- and power-optimal
+systems.
+",1,0,0,1,0,0
+7687,The Berkovich realization for rigid analytic motives,"  We prove that the functor associating to a rigid analytic variety the
+singular complex of the underlying Berkovich topological space is motivic, and
+defines the maximal Artin quotient of a motive. We use this to generalize
+Berkovich's results on the weight-zero part of the étale cohomology of a
+variety defined over a non-archimedean valued field.
+",0,0,1,0,0,0
+7688,Rates of estimation for determinantal point processes,"  Determinantal point processes (DPPs) have wide-ranging applications in
+machine learning, where they are used to enforce the notion of diversity in
+subset selection problems. Many estimators have been proposed, but surprisingly
+the basic properties of the maximum likelihood estimator (MLE) have received
+little attention. In this paper, we study the local geometry of the expected
+log-likelihood function to prove several rates of convergence for the MLE. We
+also give a complete characterization of the case where the MLE converges at a
+parametric rate. Even in the latter case, we also exhibit a potential curse of
+dimensionality where the asymptotic variance of the MLE is exponentially large
+in the dimension of the problem.
+",0,0,1,1,0,0
+7689,Negative refraction in Weyl semimetals,"  We theoretically propose that Weyl semimetals may exhibit negative refraction
+at some frequencies close to the plasmon frequency, allowing transverse
+magnetic (TM) electromagnetic waves with frequencies smaller than the plasmon
+frequency to propagate in the Weyl semimetals. The idea is justified by the
+calculation of reflection spectra, in which \textit{negative} refractive index
+at such frequencies gives physically correct spectra. In this case, a TM
+electromagnetic wave incident to the surface of the Weyl semimetal will be bent
+with a negative angle of refraction. We argue that the negative refractive
+index at the specified frequencies of the electromagnetic wave is required to
+conserve the energy of the wave, in which the incident energy should propagate
+away from the point of incidence.
+",0,1,0,0,0,0
+7690,Towards Object Life Cycle-Based Variant Generation of Business Process Models,"  Variability management of process models is a major challenge for
+Process-Aware Information Systems. Process model variants can be attributed to
+any of the following reasons: new technologies, governmental rules,
+organizational context or adoption of new standards. Current approaches to
+manage variants of process models address issues such as reducing the huge
+effort of modeling from scratch, preventing redundancy, and controlling
+inconsistency in process models. Although the effort to manage process model
+variants has been exerted, there are still limitations. Furthermore, existing
+approaches do not focus on variants that come from change in organizational
+perspective of process models. Organizational-driven variant management is an
+important area that still needs more study that we focus on in this paper.
+Object Life Cycle (OLC) is an important aspect that may change from an
+organization to another. This paper introduces an approach inspired by real
+life scenario to generate consistent process model variants that come from
+adaptations in the OLC.
+",1,0,0,0,0,0
+7691,Detection of sub-MeV Dark Matter with Three-Dimensional Dirac Materials,"  We propose the use of three-dimensional Dirac materials as targets for direct
+detection of sub-MeV dark matter. Dirac materials are characterized by a linear
+dispersion for low-energy electronic excitations, with a small band gap of
+O(meV) if lattice symmetries are broken. Dark matter at the keV scale carrying
+kinetic energy as small as a few meV can scatter and excite an electron across
+the gap. Alternatively, bosonic dark matter as light as a few meV can be
+absorbed by the electrons in the target. We develop the formalism for dark
+matter scattering and absorption in Dirac materials and calculate the
+experimental reach of these target materials. We find that Dirac materials can
+play a crucial role in detecting dark matter in the keV to MeV mass range that
+scatters with electrons via a kinetically mixed dark photon, as the dark photon
+does not develop an in-medium effective mass. The same target materials provide
+excellent sensitivity to absorption of light bosonic dark matter in the meV to
+hundreds of meV mass range, superior to all other existing proposals when the
+dark matter is a kinetically mixed dark photon.
+",0,1,0,0,0,0
+7692,Formation of Local Resonance Band Gaps in Finite Acoustic Metamaterials: A Closed-form Transfer Function Model,"  The objective of this paper is to use transfer functions to comprehend the
+formation of band gaps in locally resonant acoustic metamaterials. Identifying
+a recursive approach for any number of serially arranged locally resonant mass
+in mass cells, a closed form expression for the transfer function is derived.
+Analysis of the end-to-end transfer function helps identify the fundamental
+mechanism for the band gap formation in a finite metamaterial. This mechanism
+includes (a) repeated complex conjugate zeros located at the natural frequency
+of the individual local resonators, (b) the presence of two poles which flank
+the band gap, and (c) the absence of poles in the band-gap. Analysis of the
+finite cell dynamics are compared to the Bloch-wave analysis of infinitely long
+metamaterials to confirm the theoretical limits of the band gap estimated by
+the transfer function modeling. The analysis also explains how the band gap
+evolves as the number of cells in the metamaterial chain increases and
+highlights how the response varies depending on the chosen sensing location
+along the length of the metamaterial. The proposed transfer function approach
+to compute and evaluate band gaps in locally resonant structures provides a
+framework for the exploitation of control techniques to modify and tune band
+gaps in finite metamaterial realizations.
+",0,1,1,0,0,0
+7693,Online Multi-Label Classification: A Label Compression Method,"  Many modern applications deal with multi-label data, such as functional
+categorizations of genes, image labeling and text categorization.
+Classification of such data with a large number of labels and latent
+dependencies among them is a challenging task, and it becomes even more
+challenging when the data is received online and in chunks. Many of the current
+multi-label classification methods require a lot of time and memory, which make
+them infeasible for practical real-world applications. In this paper, we
+propose a fast linear label space dimension reduction method that transforms
+the labels into a reduced encoded space and trains models on the obtained
+pseudo labels. Additionally, it provides an analytical method to update the
+decoding matrix which maps the labels into the original space and is used
+during the test phase. Experimental results show the effectiveness of this
+approach in terms of running times and the prediction performance over
+different measures.
+",0,0,0,1,0,0
+7694,Mutual Alignment Transfer Learning,"  Training robots for operation in the real world is a complex, time consuming
+and potentially expensive task. Despite significant success of reinforcement
+learning in games and simulations, research in real robot applications has not
+been able to match similar progress. While sample complexity can be reduced by
+training policies in simulation, such policies can perform sub-optimally on the
+real platform given imperfect calibration of model dynamics. We present an
+approach -- supplemental to fine tuning on the real robot -- to further benefit
+from parallel access to a simulator during training and reduce sample
+requirements on the real robot. The developed approach harnesses auxiliary
+rewards to guide the exploration for the real world agent based on the
+proficiency of the agent in simulation and vice versa. In this context, we
+demonstrate empirically that the reciprocal alignment for both agents provides
+further benefit as the agent in simulation can adjust to optimize its behaviour
+for states commonly visited by the real-world agent.
+",1,0,0,0,0,0
+7695,Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming,"  The need to efficiently calculate first- and higher-order derivatives of
+increasingly complex models expressed in Python has stressed or exceeded the
+capabilities of available tools. In this work, we explore techniques from the
+field of automatic differentiation (AD) that can give researchers expressive
+power, performance and strong usability. These include source-code
+transformation (SCT), flexible gradient surgery, efficient in-place array
+operations, higher-order derivatives as well as mixing of forward and reverse
+mode AD. We implement and demonstrate these ideas in the Tangent software
+library for Python, the first AD framework for a dynamic language that uses
+SCT.
+",1,0,0,0,0,0
+7696,Scalar and Tensor Parameters for Importing Tensor Index Notation including Einstein Summation Notation,"  In this paper, we propose a method for importing tensor index notation,
+including Einstein summation notation, into functional programming. This method
+involves introducing two types of parameters, i.e, scalar and tensor
+parameters, and simplified tensor index rules that do not handle expressions
+that are valid only for the Cartesian coordinate system, in which the index can
+move up and down freely. An example of such an expression is ""c = A_i B_i"". As
+an ordinary function, when a tensor parameter obtains a tensor as an argument,
+the function treats the tensor argument as a whole. In contrast, when a scalar
+parameter obtains a tensor as an argument, the function is applied to each
+component of the tensor. In this paper, we show that introducing these two
+types of parameters and our simplified index rules enables us to apply
+arbitrary user-defined functions to tensor arguments using index notation
+including Einstein summation notation without requiring an additional
+description to enable each function to handle tensors.
+",1,0,0,0,0,0
+7697,Evidence for coherent spicule oscillations by correcting Hinode/SOT Ca II H in the southeast limb of the Sun,"  Wave theories of heating the chromosphere, corona, and solar wind due to
+photospheric fluctuations are strengthened by the existence of observed wave
+coherency up to the transition region (TR). The coherency of solar spicules'
+intensity oscillations was explored using the Solar Optical Telescope (SOT) on
+the Hinode spacecraft with a height increase above the solar limb in active
+region (AR). We used time sequences near the southeast region from the
+Hinode/SOT in Ca II H line obtained on April 3, 2015 and applied the
+de-convolution procedure to the spicule in order to illustrate how effectively
+our restoration method works on fine structures such as spicules. Moreover, the
+intensity oscillations at different heights above the solar limb were analysed
+through wavelet transforms. Afterwards, the phase difference was measured among
+oscillations at two certain heights in search of evidence for coherent
+oscillations. The results of wavelet transformations revealed dominant period
+peaks in 2, 4, 5.5, and 6.5 min at four separate heights. The dominant
+frequencies for coherency level higher than 75 percent was found to be around
+5.5 and 8.5 mHz. Mean phase speeds of 155-360 km s-1 were measured. We found
+that the mean phase speeds increased with height. The results suggest that the
+energy flux carried by coherent waves into the corona and heliosphere may be
+several times larger than previous estimates that were based solely on constant
+velocities. We provide compelling evidence for the existence of upwardly
+propagating coherent waves.
+",0,1,0,0,0,0
+7698,Dynamics and transverse relaxation of an unconventional spin-rotation mode in a two-dimensional strongly magnetized electron gas,"  An unconventional spin-rotation mode emerging in a quantum Hall ferromagnet
+due to excitation by a laser pulse is studied. This state, macroscopically
+representing a rotation of the entire electron spin-system to a certain angle,
+microscopically is not equivalent to a coherent turn of all spins as a
+single-whole and is presented in the form of a combination of eigen quantum
+states corresponding to all possible S_z spin numbers. Motion of the
+macroscopic quantum state is studied microscopically by solving a
+non-stationary Schroedinger equation and by means of a kinetic approach where
+damping of the spin-rotation mode is related to an elementary process -
+transformation of a 'Goldstone spin exciton' to a 'spin-wave exciton'. The
+system exhibits a spin stochastization mechanism (determined by spatial
+fluctuations of the g-factor) providing the damping - the transverse spin
+relaxation, but irrelevant to a decay of spin-wave excitons and thus not
+providing the longitudinal relaxation - recovery of the S_z number to its
+equilibrium value.
+",0,1,0,0,0,0
+7699,Opinion diversity and community formation in adaptive networks,"  It is interesting and of significant importance to investigate how network
+structures co-evolve with opinions. The existing models of such co-evolution
+typically lead to the final states where network nodes either reach a global
+consensus or break into separated communities, each of which holding its own
+community consensus. Such results, however, can hardly explain the richness of
+real-life observations that opinions are always diversified with no global or
+even community consensus, and people seldom, if not never, totally cut off
+themselves from dissenters. In this article, we show that, a simple model
+integrating consensus formation, link rewiring and opinion change allows
+complex system dynamics to emerge, driving the system into a dynamic
+equilibrium with co-existence of diversified opinions. Specifically, similar
+opinion holders may form into communities yet with no strict community
+consensus; and rather than being separated into disconnected communities,
+different communities remain to be interconnected by non-trivial proportion of
+inter-community links. More importantly, we show that the complex dynamics may
+lead to different numbers of communities at steady state with a given tolerance
+between different opinion holders. We construct a framework for theoretically
+analyzing the co-evolution process. Theoretical analysis and extensive
+simulation results reveal some useful insights into the complex co-evolution
+process, including the formation of dynamic equilibrium, the phase transition
+between different steady states with different numbers of communities, and the
+dynamics between opinion distribution and network modularity, etc.
+",1,1,0,0,0,0
+7700,On the local and global comparison of generalized Bajraktarević means,"  Given two continuous functions $f,g:I\to\mathbb{R}$ such that $g$ is positive
+and $f/g$ is strictly monotone, a measurable space $(T,A)$, a measurable family
+of $d$-variable means $m: I^d\times T\to I$, and a probability measure $\mu$ on
+the measurable sets $A$, the $d$-variable mean $M_{f,g,m;\mu}:I^d\to I$ is
+defined by $$
+M_{f,g,m;\mu}(\pmb{x})
+:=\left(\frac{f}{g}\right)^{-1}\left(
+\frac{\int_T f\big(m(x_1,\dots,x_d,t)\big) d\mu(t)}
+{\int_T g\big(m(x_1,\dots,x_d,t)\big) d\mu(t)}\right)
+\qquad(\pmb{x}=(x_1,\dots,x_d)\in I^d). $$ The aim of this paper is to study
+the local and global comparison problem of these means, i.e., to find
+conditions for the generating functions $(f,g)$ and $(h,k)$, for the families
+of means $m$ and $n$, and for the measures $\mu,\nu$ such that the comparison
+inequality $$
+M_{f,g,m;\mu}(\pmb{x})\leq M_{h,k,n;\nu}(\pmb{x}) \qquad(\pmb{x}\in I^d) $$
+be satisfied.
+",0,0,1,0,0,0
+7701,Torsion of elliptic curves and unlikely intersections,"  We study effective versions of unlikely intersections of images of torsion
+points of elliptic curves on the projective line.
+",0,0,1,0,0,0
+7702,BoostJet: Towards Combining Statistical Aggregates with Neural Embeddings for Recommendations,"  Recommenders have become widely popular in recent years because of their
+broader applicability in many e-commerce applications. These applications rely
+on recommenders for generating advertisements for various offers or providing
+content recommendations. However, the quality of the generated recommendations
+depends on user features (like demography, temporality), offer features (like
+popularity, price), and user-offer features (like implicit or explicit
+feedback). Current state-of-the-art recommenders do not explore such diverse
+features concurrently while generating the recommendations.
+In this paper, we first introduce the notion of Trackers which enables us to
+capture the above-mentioned features and thus incorporate users' online
+behaviour through statistical aggregates of different features (demography,
+temporality, popularity, price). We also show how to capture offer-to-offer
+relations, based on their consumption sequence, leveraging neural embeddings
+for offers in our Offer2Vec algorithm. We then introduce BoostJet, a novel
+recommender which integrates the Trackers along with the neural embeddings
+using MatrixNet, an efficient distributed implementation of gradient boosted
+decision tree, to improve the recommendation quality significantly. We provide
+an in-depth evaluation of BoostJet on Yandex's dataset, collecting online
+behaviour from tens of millions of online users, to demonstrate the
+practicality of BoostJet in terms of recommendation quality as well as
+scalability.
+",1,0,0,1,0,0
+7703,Fixed points of diffeomorphisms on nilmanifolds with a free nilpotent fundamental group,"  Let $M$ be a nilmanifold with a fundamental group which is free $2$-step
+nilpotent on at least 4 generators. We will show that for any nonnegative
+integer $n$ there exists a self-diffeomorphism $h_n$ of $M$ such that $h_n$ has
+exactly $n$ fixed points and any self-map $f$ of $M$ which is homotopic to
+$h_n$ has at least $n$ fixed points. We will also shed some light on the
+situation for less generators and also for higher nilpotency classes.
+",0,0,1,0,0,0
+7704,Offloading Execution from Edge to Cloud: a Dynamic Node-RED Based Approach,"  Fog computing enables use cases where data produced in end devices are
+stored, processed, and acted on directly at the edges of the network, yet
+computation can be offloaded to more powerful instances through the edge to
+cloud continuum. Such offloading mechanism is especially needed in case of
+modern multi-purpose IoT gateways, where both demand and operation conditions
+can vary largely between deployments. To facilitate the development and
+operations of gateways, we implement offloading directly as part of the IoT
+rapid prototyping process embedded in the software stack, based on Node-RED. We
+evaluate the implemented method using an image processing example, and compare
+various offloading strategies based on resource consumption and other system
+metrics, highlighting the differences in handling demand and service levels
+reached.
+",1,0,0,0,0,0
+7705,A nested sampling code for targeted searches for continuous gravitational waves from pulsars,"  This document describes a code to perform parameter estimation and model
+selection in targeted searches for continuous gravitational waves from known
+pulsars using data from ground-based gravitational wave detectors. We describe
+the general workings of the code and characterise it on simulated data
+containing both noise and simulated signals. We also show how it performs
+compared to a previous MCMC and grid-based approach to signal parameter
+estimation. Details how to run the code in a variety of cases are provided in
+Appendix A.
+",0,1,0,0,0,0
+7706,Using Big Data to Enhance the Bosch Production Line Performance: A Kaggle Challenge,"  This paper describes our approach to the Bosch production line performance
+challenge run by Kaggle.com. Maximizing the production yield is at the heart of
+the manufacturing industry. At the Bosch assembly line, data is recorded for
+products as they progress through each stage. Data science methods are applied
+to this huge data repository consisting records of tests and measurements made
+for each component along the assembly line to predict internal failures. We
+found that it is possible to train a model that predicts which parts are most
+likely to fail. Thus a smarter failure detection system can be built and the
+parts tagged likely to fail can be salvaged to decrease operating costs and
+increase the profit margins.
+",1,0,0,0,0,0
+7707,Golden Elliptical Orbits in Newtonian Gravitation,"  In spherical symmetry with radial coordinate $r$, classical Newtonian
+gravitation supports circular orbits and, for $-1/r$ and $r^2$ potentials only,
+closed elliptical orbits [1]. Various families of elliptical orbits can be
+thought of as arising from the action of perturbations on corresponding
+circular orbits. We show that one elliptical orbit in each family is singled
+out because its focal length is equal to the radius of the corresponding
+unperturbed circular orbit. The eccentricity of this special orbit is related
+to the famous irrational number known as the golden ratio. So inanimate
+Newtonian gravitation appears to exhibit (but not prefer) the golden ratio
+which has been previously identified mostly in settings within the animate
+world.
+",0,1,0,0,0,0
+7708,Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification,"  Audio events are quite often overlapping in nature, and more prone to noise
+than visual signals. There has been increasing evidence for the superior
+performance of representations learned using sparse dictionaries for
+applications like audio denoising and speech enhancement. This paper
+concentrates on modifying the traditional reconstructive dictionary learning
+algorithms, by incorporating a discriminative term into the objective function
+in order to learn class-specific adversarial dictionaries that are good at
+representing samples of their own class at the same time poor at representing
+samples belonging to any other class. We quantitatively demonstrate the
+effectiveness of our learned dictionaries as a stand-alone solution for both
+binary as well as multi-class audio classification problems.
+",1,0,0,0,0,0
+7709,Matter-wave solutions in the Bose-Einstein condensates with the harmonic and Gaussian potentials,"  We study exact solutions of the quasi-one-dimensional Gross-Pitaevskii (GP)
+equation with the (space, time)-modulated potential and nonlinearity and the
+time-dependent gain or loss term in Bose-Einstein condensates. In particular,
+based on the similarity transformation, we report several families of exact
+solutions of the GP equation in the combination of the harmonic and Gaussian
+potentials, in which some physically relevant solutions are described. The
+stability of the obtained matter-wave solutions is addressed numerically such
+that some stable solutions are found. Moreover, we also analyze the parameter
+regimes for the stable solutions. These results may raise the possibility of
+relative experiments and potential applications.
+",0,1,1,0,0,0
+7710,Histogram Transform-based Speaker Identification,"  A novel text-independent speaker identification (SI) method is proposed. This
+method uses the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic
+information among adjacent frames as feature sets to capture speaker's
+characteristics. In order to utilize dynamic information, we design super-MFCCs
+features by cascading three neighboring MFCCs frames together. The probability
+density function (PDF) of these super-MFCCs features is estimated by the
+recently proposed histogram transform~(HT) method, which generates more
+training data by random transforms to realize the histogram PDF estimation and
+recedes the commonly occurred discontinuity problem in multivariate histograms
+computing. Compared to the conventional PDF estimation methods, such as
+Gaussian mixture models, the HT model shows promising improvement in the SI
+performance.
+",1,0,0,1,0,0
+7711,Towards self-adaptable robots: from programming to training machines,"  We argue that hardware modularity plays a key role in the convergence of
+Robotics and Artificial Intelligence (AI). We introduce a new approach for
+building robots that leads to more adaptable and capable machines. We present
+the concept of a self-adaptable robot that makes use of hardware modularity and
+AI techniques to reduce the effort and time required to be built. We
+demonstrate in simulation and with a real robot how, rather than programming,
+training produces behaviors in the robot that generalize fast and produce
+robust outputs in the presence of noise. In particular, we advocate for
+mammals.
+",1,0,0,0,0,0
+7712,Incidence Results and Bounds of Trilinear and Quadrilinear Exponential Sums,"  We give a new bound on the number of collinear triples for two arbitrary
+subsets of a finite field. This improves on existing results which rely on the
+Cauchy inequality. We then us this to provide a new bound on trilinear and
+quadrilinear exponential sums.
+",0,0,1,0,0,0
+7713,"Comment on ""Kinetic decoupling of WIMPs: Analytic expressions""","  Visinelli and Gondolo (2015, hereafter VG15) derived analytic expressions for
+the evolution of the dark matter temperature in a generic cosmological model.
+They then calculated the dark matter kinetic decoupling temperature
+$T_{\mathrm{kd}}$ and compared their results to the Gelmini and Gondolo (2008,
+hereafter GG08) calculation of $T_{\mathrm{kd}}$ in an early matter-dominated
+era (EMDE), which occurs when the Universe is dominated by either a decaying
+oscillating scalar field or a semistable massive particle before Big Bang
+nucleosynthesis. VG15 found that dark matter decouples at a lower temperature
+in an EMDE than it would in a radiation-dominated era, while GG08 found that
+dark matter decouples at a higher temperature in an EMDE than it would in a
+radiation-dominated era. VG15 attributed this discrepancy to the presence of a
+matching constant that ensures that the dark matter temperature is continuous
+during the transition from the EMDE to the subsequent radiation-dominated era
+and concluded that the GG08 result is incorrect. We show that the disparity is
+due to the fact that VG15 compared $T_\mathrm{kd}$ in an EMDE to the decoupling
+temperature in a radiation-dominated universe that would result in the same
+dark matter temperature at late times. Since decoupling during an EMDE leaves
+the dark matter colder than it would be if it decoupled during radiation
+domination, this temperature is much higher than $T_\mathrm{kd}$ in a standard
+thermal history, which is indeed lower than $T_{\mathrm{kd}}$ in an EMDE, as
+stated by GG08.
+",0,1,0,0,0,0
+7714,Dimension preserving resolutions of singularities of Poisson structures,"  Some Poisson structures do admit resolutions by symplectic manifolds of the
+same dimension. We give examples and simple conditions under which such
+resolutions can not exist.
+",0,0,1,0,0,0
+7715,Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images,"  There is an interest to replace computed tomography (CT) images with magnetic
+resonance (MR) images for a number of diagnostic and therapeutic workflows. In
+this article, predicting CT images from a number of magnetic resonance imaging
+(MRI) sequences using regression approach is explored. Two principal areas of
+application for estimated CT images are dose calculations in MRI-based
+radiotherapy treatment planning and attenuation correction for positron
+emission tomography (PET)/MRI. The main purpose of this work is to investigate
+the performance of hidden Markov (chain) models (HMMs) in comparison to hidden
+Markov random field (HMRF) models when predicting CT images of head. Our study
+shows that HMMs have clear advantages over HMRF models in this particular
+application. Obtained results suggest that HMMs deserve a further study for
+investigating their potential in modelling applications where the most natural
+theoretical choice would be the class of HMRF models.
+",0,0,0,1,0,0
+7716,Spin-charge split pairing in underdoped cuprate superconductors: support from low-$T$ specific heat,"  We calculate the specific heat of a weakly interacting dilute system of
+bosons on a lattice and show that it is consistent with the measured electronic
+specific heat in the superconducting state of underdoped cuprates with boson
+concentration $\rho \sim x/2$, where $x$ is the hole (dopant) concentration. As
+usual, the $T^3$ term is due to Goldstone phonons. The zero-point energy,
+through its dependence on the condensate density $\rho_0(T)$, accounts for the
+anomalous $T$-linear term. These results support the split-pairing mechanism,
+in which spinons (pure spin) are paired at $T^*$ and holons (pure charge) form
+real-space pairs at $T_p < T^*$, creating a gauge-coupled physical pair of
+charge $+2e$ and concentration $x/2$ which Bose condenses below $T_c$,
+accounting for the observed phases.
+",0,1,0,0,0,0
+7717,Iterative Amortized Inference,"  Inference models are a key component in scaling variational inference to deep
+latent variable models, most notably as encoder networks in variational
+auto-encoders (VAEs). By replacing conventional optimization-based inference
+with a learned model, inference is amortized over data examples and therefore
+more computationally efficient. However, standard inference models are
+restricted to direct mappings from data to approximate posterior estimates. The
+failure of these models to reach fully optimized approximate posterior
+estimates results in an amortization gap. We aim toward closing this gap by
+proposing iterative inference models, which learn to perform inference
+optimization through repeatedly encoding gradients. Our approach generalizes
+standard inference models in VAEs and provides insight into several empirical
+findings, including top-down inference techniques. We demonstrate the inference
+optimization capabilities of iterative inference models and show that they
+outperform standard inference models on several benchmark data sets of images
+and text.
+",0,0,0,1,0,0
+7718,Rethinking probabilistic prediction in the wake of the 2016 U.S. presidential election,"  To many statisticians and citizens, the outcome of the most recent U.S.
+presidential election represents a failure of data-driven methods on the
+grandest scale. This impression has led to much debate and discussion about how
+the election predictions went awry -- Were the polls inaccurate? Were the
+models wrong? Did we misinterpret the probabilities? -- and how they went right
+-- Perhaps the analyses were correct even though the predictions were wrong,
+that's just the nature of probabilistic forecasting. With this in mind, we
+analyze the election outcome with respect to a core set of effectiveness
+principles. Regardless of whether and how the election predictions were right
+or wrong, we argue that they were ineffective in conveying the extent to which
+the data was informative of the outcome and the level of uncertainty in making
+these assessments. Among other things, our analysis sheds light on the
+shortcomings of the classical interpretations of probability and its
+communication to consumers in the form of predictions. We present here an
+alternative approach, based on a notion of validity, which offers two immediate
+insights for predictive inference. First, the predictions are more
+conservative, arguably more realistic, and come with certain guarantees on the
+probability of an erroneous prediction. Second, our approach easily and
+naturally reflects the (possibly substantial) uncertainty about the model by
+outputting plausibilities instead of probabilities. Had these simple steps been
+taken by the popular prediction outlets, the election outcome may not have been
+so shocking.
+",0,0,1,1,0,0
+7719,$L^1$ solutions to one-dimensional BSDEs with sublinear growth generators in $z$,"  This paper aims at solving a one-dimensional backward stochastic differential
+equation (BSDE for short) with only integrable parameters. We first establish
+the existence of a minimal $L^1$ solution for the BSDE when the generator $g$
+is stronger continuous in $(y,z)$ and monotonic in $y$ as well as it has a
+general growth in $y$ and a sublinear growth in $z$. Particularly, the $g$ may
+be not uniformly continuous in $z$. Then, we put forward and prove a comparison
+theorem and a Levi type theorem on the minimal $L^1$ solutions. A Lebesgue type
+theorem on $L^1$ solutions is also obtained. Furthermore, we investigate the
+same problem in the case that $g$ may be discontinuous in $y$. Finally, we
+prove a general comparison theorem on $L^1$ solutions when $g$ is weakly
+monotonic in $y$ and uniformly continuous in $z$ as well as it has a stronger
+sublinear growth in $z$. As a byproduct, we also obtain a general existence and
+unique theorem on $L^1$ solutions. Our results extend some known works.
+",0,0,1,0,0,0
+7720,Magnifying the early episodes of star formation: super star clusters at cosmological distances,"  We study the spectrophotometric properties of a highly magnified (\mu~40-70)
+pair of stellar systems identified at z=3.2222 behind the Hubble Frontier Field
+galaxy cluster MACS~J0416. Five multiple images (out of six) have been
+spectroscopically confirmed by means of VLT/MUSE and VLT/X-Shooter
+observations. Each image includes two faint (m_uv~30.6), young (<100 Myr),
+low-mass (<10^7 Msun), low-metallicity (12+Log(O/H)~7.7, or 1/10 solar) and
+compact (30 pc effective radius) stellar systems separated by ~300pc, after
+correcting for lensing amplification. We measured several rest-frame
+ultraviolet and optical narrow (\sigma_v <~ 25 km/s) high-ionization lines.
+These features may be the signature of very hot (T>50000 K) stars within dense
+stellar clusters, whose dynamical mass is likely dominated by the stellar
+component. Remarkably, the ultraviolet metal lines are not accompanied by Lya
+emission (e.g., CIV / Lya > 15), despite the fact that the Lya line flux is
+expected to be 150 times brighter (inferred from the Hbeta flux). A
+spatially-offset, strongly-magnified (\mu>50) Lya emission with a spatial
+extent <~7.6 kpc^2 is instead identified 2 kpc away from the system. The origin
+of such a faint emission can be the result of fluorescent Lya induced by a
+transverse leakage of ionizing radiation emerging from the stellar systems
+and/or can be associated to an underlying and barely detected object (with m_uv
+> 34 de-lensed). This is the first confirmed metal-line emitter at such
+low-luminosity and redshift without Lya emission, suggesting that, at least in
+some cases, a non-uniform covering factor of the neutral gas might hamper the
+Lya detection.
+",0,1,0,0,0,0
+7721,Ergodicity of a system of interacting random walks with asymmetric interaction,"  We study N interacting random walks on the positive integers. Each particle
+has drift {\delta} towards infinity, a reflection at the origin, and a drift
+towards particles with lower positions. This inhomogeneous mean field system is
+shown to be ergodic only when the interaction is strong enough. We focus on
+this latter regime, and point out the effect of piles of particles, a
+phenomenon absent in models of interacting diffusion in continuous space.
+",0,0,1,0,0,0
+7722,Extreme value statistics for censored data with heavy tails under competing risks,"  This paper addresses the problem of estimating, in the presence of random
+censoring as well as competing risks, the extreme value index of the
+(sub)-distribution function associated to one particular cause, in the
+heavy-tail case. Asymptotic normality of the proposed estimator (which has the
+form of an Aalen-Johansen integral, and is the first estimator proposed in this
+context) is established. A small simulation study exhibits its performances for
+finite samples. Estimation of extreme quantiles of the cumulative incidence
+function is also addressed.
+",0,0,1,1,0,0
+7723,"Response to ""Counterexample to global convergence of DSOS and SDSOS hierarchies""","  In a recent note [8], the author provides a counterexample to the global
+convergence of what his work refers to as ""the DSOS and SDSOS hierarchies"" for
+polynomial optimization problems (POPs) and purports that this refutes claims
+in our extended abstract [4] and slides in [3]. The goal of this paper is to
+clarify that neither [4], nor [3], and certainly not our full paper [5], ever
+defined DSOS or SDSOS hierarchies as it is done in [8]. It goes without saying
+that no claims about convergence properties of the hierarchies in [8] were ever
+made as a consequence. What was stated in [4,3] was completely different: we
+stated that there exist hierarchies based on DSOS and SDSOS optimization that
+converge. This is indeed true as we discuss in this response. We also emphasize
+that we were well aware that some (S)DSOS hierarchies do not converge even if
+their natural SOS counterparts do. This is readily implied by an example in our
+prior work [5], which makes the counterexample in [8] superfluous. Finally, we
+provide concrete counterarguments to claims made in [8] that aim to challenge
+the scalability improvements obtained by DSOS and SDSOS optimization as
+compared to sum of squares (SOS) optimization.
+[3] A. A. Ahmadi and A. Majumdar. DSOS and SDSOS: More tractable alternatives
+to SOS. Slides at the meeting on Geometry and Algebra of Linear Matrix
+Inequalities, CIRM, Marseille, 2013. [4] A. A. Ahmadi and A. Majumdar. DSOS and
+SDSOS optimization: LP and SOCP-based alternatives to sum of squares
+optimization. In proceedings of the 48th annual IEEE Conference on Information
+Sciences and Systems, 2014. [5] A. A. Ahmadi and A. Majumdar. DSOS and SDSOS
+optimization: more tractable alternatives to sum of squares and semidefinite
+optimization. arXiv:1706.02586, 2017. [8] C. Josz. Counterexample to global
+convergence of DSOS and SDSOS hierarchies. arXiv:1707.02964, 2017.
+",1,0,0,1,0,0
+7724,Thermal memristor and neuromorphic networks for manipulating heat flow,"  A memristor is one of four fundamental two-terminal solid elements in
+electronics. In addition with the resistor, the capacitor and the inductor,
+this passive element relates the electric charges to current in solid state
+elements. Here we report the existence of a thermal analog for this element
+made with metal-insulator transition materials. We demonstrate that these
+memristive systems can be used to create thermal neurons opening so the way to
+neuromophic networks for smart thermal management and information treatment.
+",0,1,0,0,0,0
+7725,Asymptotic Distribution and Simultaneous Confidence Bands for Ratios of Quantile Functions,"  Ratio of medians or other suitable quantiles of two distributions is widely
+used in medical research to compare treatment and control groups or in
+economics to compare various economic variables when repeated cross-sectional
+data are available. Inspired by the so-called growth incidence curves
+introduced in poverty research, we argue that the ratio of quantile functions
+is a more appropriate and informative tool to compare two distributions. We
+present an estimator for the ratio of quantile functions and develop
+corresponding simultaneous confidence bands, which allow to assess significance
+of certain features of the quantile functions ratio. Derived simultaneous
+confidence bands rely on the asymptotic distribution of the quantile functions
+ratio and do not require re-sampling techniques. The performance of the
+simultaneous confidence bands is demonstrated in simulations. Analysis of the
+expenditure data from Uganda in years 1999, 2002 and 2005 illustrates the
+relevance of our approach.
+",0,0,0,1,0,0
+7726,A Topological proof that $O_2$ is $2$-MCFL,"  We give a new proof of Salvati's theorem that the group language $O_2$ is $2$
+multiple context free. Unlike Salvati's proof, our arguments do not use any
+idea specific to two-dimensions. This raises the possibility that the argument
+might generalize to $O_n$.
+",1,0,1,0,0,0
+7727,Projected Variational Integrators for Degenerate Lagrangian Systems,"  We propose and compare several projection methods applied to variational
+integrators for degenerate Lagrangian systems, whose Lagrangian is of the form
+$L = \vartheta(q) \cdot \dot{q} - H(q)$ and thus linear in velocities. While
+previous methods for such systems only work reliably in the case of $\vartheta$
+being a linear function of $q$, our methods are long-time stable also for
+systems where $\vartheta$ is a nonlinear function of $q$. We analyse the
+properties of the resulting algorithms, in particular with respect to the
+conservation of energy, momentum maps and symplecticity. In numerical
+experiments, we verify the favourable properties of the projected integrators
+and demonstrate their excellent long-time fidelity. In particular, we consider
+a two-dimensional Lotka-Volterra system, planar point vortices with
+position-dependent circulation and guiding centre dynamics.
+",0,1,0,0,0,0
+7728,Boosted Generative Models,"  We propose a novel approach for using unsupervised boosting to create an
+ensemble of generative models, where models are trained in sequence to correct
+earlier mistakes. Our meta-algorithmic framework can leverage any existing base
+learner that permits likelihood evaluation, including recent deep expressive
+models. Further, our approach allows the ensemble to include discriminative
+models trained to distinguish real data from model-generated data. We show
+theoretical conditions under which incorporating a new model in the ensemble
+will improve the fit and empirically demonstrate the effectiveness of our
+black-box boosting algorithms on density estimation, classification, and sample
+generation on benchmark datasets for a wide range of generative models.
+",1,0,0,1,0,0
+7729,The Geometry of Strong Koszul Algebras,"  Koszul algebras with quadratic Groebner bases, called strong Koszul algebras,
+are studied. We introduce affine algebraic varieties whose points are in
+one-to-one correspondence with certain strong Koszul algebras and we
+investigate the connection between the varieties and the algebras.
+",0,0,1,0,0,0
+7730,Overlapping community detection using superior seed set selection in social networks,"  Community discovery in the social network is one of the tremendously
+expanding areas which earn interest among researchers for the past one decade.
+There are many already existing algorithms. However, new seed-based algorithms
+establish an emerging drift in this area. The basic idea behind these
+strategies is to identify exceptional nodes in the given network, called seeds,
+around which communities can be located. This paper proposes a blended strategy
+for locating suitable superior seed set by applying various centrality measures
+and using them to find overlapping communities. The examination of the
+algorithm has been performed regarding the goodness of the identified
+communities with the help of intra-cluster density and inter-cluster density.
+Finally, the runtime of the proposed algorithm has been compared with the
+existing community detection algorithms showing remarkable improvement.
+",1,0,0,0,0,0
+7731,Debugging Transactions and Tracking their Provenance with Reenactment,"  Debugging transactions and understanding their execution are of immense
+importance for developing OLAP applications, to trace causes of errors in
+production systems, and to audit the operations of a database. However,
+debugging transactions is hard for several reasons: 1) after the execution of a
+transaction, its input is no longer available for debugging, 2) internal states
+of a transaction are typically not accessible, and 3) the execution of a
+transaction may be affected by concurrently running transactions. We present a
+debugger for transactions that enables non-invasive, post-mortem debugging of
+transactions with provenance tracking and supports what-if scenarios (changes
+to transaction code or data). Using reenactment, a declarative replay technique
+we have developed, a transaction is replayed over the state of the DB seen by
+its original execution including all its interactions with concurrently
+executed transactions from the history. Importantly, our approach uses the
+temporal database and audit logging capabilities available in many DBMS and
+does not require any modifications to the underlying database system nor
+transactional workload.
+",1,0,0,0,0,0
+7732,How LinkedIn Economic Graph Bonds Information and Product: Applications in LinkedIn Salary,"  The LinkedIn Salary product was launched in late 2016 with the goal of
+providing insights on compensation distribution to job seekers, so that they
+can make more informed decisions when discovering and assessing career
+opportunities. The compensation insights are provided based on data collected
+from LinkedIn members and aggregated in a privacy-preserving manner. Given the
+simultaneous desire for computing robust, reliable insights and for having
+insights to satisfy as many job seekers as possible, a key challenge is to
+reliably infer the insights at the company level when there is limited or no
+data at all. We propose a two-step framework that utilizes a novel, semantic
+representation of companies (Company2vec) and a Bayesian statistical model to
+address this problem. Our approach makes use of the rich information present in
+the LinkedIn Economic Graph, and in particular, uses the intuition that two
+companies are likely to be similar if employees are very likely to transition
+from one company to the other and vice versa. We compute embeddings for
+companies by analyzing the LinkedIn members' company transition data using
+machine learning algorithms, then compute pairwise similarities between
+companies based on these embeddings, and finally incorporate company
+similarities in the form of peer company groups as part of the proposed
+Bayesian statistical model to predict insights at the company level. We perform
+extensive validation using several different evaluation techniques, and show
+that we can significantly increase the coverage of insights while, in fact,
+even improving the quality of the obtained insights. For example, we were able
+to compute salary insights for 35 times as many title-region-company
+combinations in the U.S. as compared to previous work, corresponding to 4.9
+times as many monthly active users. Finally, we highlight the lessons learned
+from deployment of our system.
+",1,0,0,0,0,0
+7733,Fast quantum logic gates with trapped-ion qubits,"  Quantum bits based on individual trapped atomic ions constitute a promising
+technology for building a quantum computer, with all the elementary operations
+having been achieved with the necessary precision for some error-correction
+schemes. However, the essential two-qubit logic gate used for generating
+quantum entanglement has hitherto always been performed in an adiabatic regime,
+where the gate is slow compared with the characteristic motional frequencies of
+ions in the trap, giving logic speeds of order 10kHz. There have been numerous
+proposals for performing gates faster than this natural ""speed limit"" of the
+trap. We implement the method of Steane et al., which uses tailored laser
+pulses: these are shaped on 10 ns timescales to drive the ions' motion along
+trajectories designed such that the gate operation is insensitive to optical
+phase fluctuations. This permits fast (MHz-rate) quantum logic which is robust
+to this important source of experimental error. We demonstrate entanglement
+generation for gate times as short as 480ns; this is less than a single
+oscillation period of an ion in the trap, and 8 orders of magnitude shorter
+than the memory coherence time measured in similar calcium-43 hyperfine qubits.
+The method's power is most evident at intermediate timescales, where it yields
+a gate error more than ten times lower than conventional techniques; for
+example, we achieve a 1.6 us gate with fidelity 99.8%. Still faster gates are
+possible at the price of higher laser intensity. The method requires only a
+single amplitude-shaped pulse and one pair of beams derived from a
+continuous-wave laser, and offers the prospect of combining the unrivalled
+coherence properties, operation fidelities and optical connectivity of
+trapped-ion qubits with the sub-microsecond logic speeds usually associated
+with solid state devices.
+",0,1,0,0,0,0
+7734,Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems,"  Fault detection problem for closed loop uncertain dynamical systems, is
+investigated in this paper, using different deep learning based methods.
+Traditional classifier based method does not perform well, because of the
+inherent difficulty of detecting system level faults for closed loop dynamical
+system. Specifically, acting controller in any closed loop dynamical system,
+works to reduce the effect of system level faults. A novel Generative
+Adversarial based deep Autoencoder is designed to classify datasets under
+normal and faulty operating conditions. This proposed network performs
+significantly well when compared to any available classifier based methods, and
+moreover, does not require labeled fault incorporated datasets for training
+purpose. Finally, this aforementioned network's performance is tested on a high
+complexity building energy system dataset.
+",0,0,0,1,0,0
+7735,"Non-Asymptotic Rates for Manifold, Tangent Space, and Curvature Estimation","  Given an $n$-sample drawn on a submanifold $M \subset \mathbb{R}^D$, we
+derive optimal rates for the estimation of tangent spaces $T\_X M$, the second
+fundamental form $II\_X^M$, and the submanifold $M$.After motivating their
+study, we introduce a quantitative class of $\mathcal{C}^k$-submanifolds in
+analogy with H{ö}lder classes.The proposed estimators are based on local
+polynomials and allow to deal simultaneously with the three problems at stake.
+Minimax lower bounds are derived using a conditional version of Assouad's lemma
+when the base point $X$ is random.
+",0,0,1,1,0,0
+7736,Learning to Optimize Neural Nets,"  Learning to Optimize is a recently proposed framework for learning
+optimization algorithms using reinforcement learning. In this paper, we explore
+learning an optimization algorithm for training shallow neural nets. Such
+high-dimensional stochastic optimization problems present interesting
+challenges for existing reinforcement learning algorithms. We develop an
+extension that is suited to learning optimization algorithms in this setting
+and demonstrate that the learned optimization algorithm consistently
+outperforms other known optimization algorithms even on unseen tasks and is
+robust to changes in stochasticity of gradients and the neural net
+architecture. More specifically, we show that an optimization algorithm trained
+with the proposed method on the problem of training a neural net on MNIST
+generalizes to the problems of training neural nets on the Toronto Faces
+Dataset, CIFAR-10 and CIFAR-100.
+",1,0,1,1,0,0
+7737,A space-time finite element method for neural field equations with transmission delays,"  We present and analyze a new space-time finite element method for the
+solution of neural field equations with transmission delays. The numerical
+treatment of these systems is rare in the literature and currently has several
+restrictions on the spatial domain and the functions involved, such as
+connectivity and delay functions. The use of a space-time discretization, with
+basis functions that are discontinuous in time and continuous in space
+(dGcG-FEM), is a natural way to deal with space-dependent delays, which is
+important for many neural field applications. In this article we provide a
+detailed description of a space-time dGcG-FEM algorithm for neural delay
+equations, including an a-priori error analysis. We demonstrate the application
+of the dGcG-FEM algorithm on several neural field models, including problems
+with an inhomogeneous kernel.
+",0,0,1,0,0,0
+7738,Notes on complexity of packing coloring,"  A packing $k$-coloring for some integer $k$ of a graph $G=(V,E)$ is a mapping
+$\varphi:V\to\{1,\ldots,k\}$ such that any two vertices $u, v$ of color
+$\varphi(u)=\varphi(v)$ are in distance at least $\varphi(u)+1$. This concept
+is motivated by frequency assignment problems. The \emph{packing chromatic
+number} of $G$ is the smallest $k$ such that there exists a packing
+$k$-coloring of $G$.
+Fiala and Golovach showed that determining the packing chromatic number for
+chordal graphs is \NP-complete for diameter exactly 5. While the problem is
+easy to solve for diameter 2, we show \NP-completeness for any diameter at
+least 3. Our reduction also shows that the packing chromatic number is hard to
+approximate within $n^{{1/2}-\varepsilon}$ for any $\varepsilon > 0$.
+In addition, we design an \FPT algorithm for interval graphs of bounded
+diameter. This leads us to exploring the problem of finding a partial coloring
+that maximizes the number of colored vertices.
+",1,0,0,0,0,0
+7739,Predicting Positive and Negative Links with Noisy Queries: Theory & Practice,"  Social networks involve both positive and negative relationships, which can
+be captured in signed graphs. The {\em edge sign prediction problem} aims to
+predict whether an interaction between a pair of nodes will be positive or
+negative. We provide theoretical results for this problem that motivate natural
+improvements to recent heuristics.
+The edge sign prediction problem is related to correlation clustering; a
+positive relationship means being in the same cluster. We consider the
+following model for two clusters: we are allowed to query any pair of nodes
+whether they belong to the same cluster or not, but the answer to the query is
+corrupted with some probability $0<q<\frac{1}{2}$. Let $\delta=1-2q$ be the
+bias. We provide an algorithm that recovers all signs correctly with high
+probability in the presence of noise with $O(\frac{n\log
+n}{\delta^2}+\frac{\log^2 n}{\delta^6})$ queries. This is the best known result
+for this problem for all but tiny $\delta$, improving on the recent work of
+Mazumdar and Saha \cite{mazumdar2017clustering}. We also provide an algorithm
+that performs $O(\frac{n\log n}{\delta^4})$ queries, and uses breadth first
+search as its main algorithmic primitive. While both the running time and the
+number of queries for this algorithm are sub-optimal, our result relies on
+novel theoretical techniques, and naturally suggests the use of edge-disjoint
+paths as a feature for predicting signs in online social networks.
+Correspondingly, we experiment with using edge disjoint $s-t$ paths of short
+length as a feature for predicting the sign of edge $(s,t)$ in real-world
+signed networks. Empirical findings suggest that the use of such paths improves
+the classification accuracy, especially for pairs of nodes with no common
+neighbors.
+",1,0,0,0,0,0
+7740,Simulating a Topological Transition in a Superconducting Phase Qubit by Fast Adiabatic Trajectories,"  The significance of topological phases has been widely recognized in the
+community of condensed matter physics. The well controllable quantum systems
+provide an artificial platform to probe and engineer various topological
+phases. The adiabatic trajectory of a quantum state describes the change of the
+bulk Bloch eigenstates with the momentum, and this adiabatic simulation method
+is however practically limited due to quantum dissipation. Here we apply the
+`shortcut to adiabaticity' (STA) protocol to realize fast adiabatic evolutions
+in the system of a superconducting phase qubit. The resulting fast adiabatic
+trajectories illustrate the change of the bulk Bloch eigenstates in the
+Su-Schrieffer-Heeger (SSH) model. A sharp transition is experimentally
+determined for the topological invariant of a winding number. Our experiment
+helps identify the topological Chern number of a two-dimensional toy model,
+suggesting the applicability of the fast adiabatic simulation method for
+topological systems.
+",0,1,0,0,0,0
+7741,Improved Absolute Frequency Measurement of the 171Yb Optical Lattice Clock at KRISS Relative to the SI Second,"  We measured the absolute frequency of the $^1S_0$ - $^3P_0$ transition of
+$^{171}$Yb atoms confined in a one-dimensional optical lattice relative to the
+SI second. The determined frequency was 518 295 836 590 863.38(57) Hz. The
+uncertainty was reduced by a factor of 14 compared with our previously reported
+value in 2013 due to the significant improvements in decreasing the systematic
+uncertainties. This result is expected to contribute to the determination of a
+new recommended value for the secondary representations of the second.
+",0,1,0,0,0,0
+7742,Theoretical properties of quasi-stationary Monte Carlo methods,"  This paper gives foundational results for the application of
+quasi-stationarity to Monte Carlo inference problems. We prove natural
+sufficient conditions for the quasi-limiting distribution of a killed diffusion
+to coincide with a target density of interest. We also quantify the rate of
+convergence to quasi-stationarity by relating the killed diffusion to an
+appropriate Langevin diffusion. As an example, we consider in detail a killed
+Ornstein--Uhlenbeck process with Gaussian quasi-stationary distribution.
+",0,0,1,1,0,0
+7743,A Hilbert Space of Stationary Ergodic Processes,"  Identifying meaningful signal buried in noise is a problem of interest
+arising in diverse scenarios of data-driven modeling. We present here a
+theoretical framework for exploiting intrinsic geometry in data that resists
+noise corruption, and might be identifiable under severe obfuscation. Our
+approach is based on uncovering a valid complete inner product on the space of
+ergodic stationary finite valued processes, providing the latter with the
+structure of a Hilbert space on the real field. This rigorous construction,
+based on non-standard generalizations of the notions of sum and scalar
+multiplication of finite dimensional probability vectors, allows us to
+meaningfully talk about ""angles"" between data streams and data sources, and,
+make precise the notion of orthogonal stochastic processes. In particular, the
+relative angles appear to be preserved, and identifiable, under severe noise,
+and will be developed in future as the underlying principle for robust
+classification, clustering and unsupervised featurization algorithms.
+",0,0,0,1,0,1
+7744,Total variation regularized non-negative matrix factorization for smooth hyperspectral unmixing,"  Hyperspectral analysis has gained popularity over recent years as a way to
+infer what materials are displayed on a picture whose pixels consist of a
+mixture of spectral signatures. Computing both signatures and mixture
+coefficients is known as unsupervised unmixing, a set of techniques usually
+based on non-negative matrix factorization. Unmixing is a difficult non-convex
+problem, and algorithms may converge to one out of many local minima, which may
+be far removed from the true global minimum. Computing this true minimum is
+NP-hard and seems therefore out of reach. Aiming for interesting local minima,
+we investigate the addition of total variation regularization terms. Advantages
+of these regularizers are two-fold. Their computation is typically rather
+light, and they are deemed to preserve sharp edges in pictures. This paper
+describes an algorithm for regularized hyperspectral unmixing based on the
+Alternating Direction Method of Multipliers.
+",0,0,1,0,0,0
+7745,Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning,"  Antihydrogen is at the forefront of antimatter research at the CERN
+Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry
+and antigravity effects require the efficient detection of antihydrogen
+annihilation events, which is performed using highly granular tracking
+detectors installed around an antimatter trap. Improving the efficiency of the
+antihydrogen annihilation detection plays a central role in the final
+sensitivity of the experiments. We propose deep learning as a novel technique
+to analyze antihydrogen annihilation data, and compare its performance with a
+traditional track and vertex reconstruction method. We report that the deep
+learning approach yields significant improvement, tripling event coverage while
+simultaneously improving performance by over 5% in terms of Area Under Curve
+(AUC).
+",1,1,0,0,0,0
+7746,Spin controlled atom-ion inelastic collisions,"  The control of the ultracold collisions between neutral atoms is an extensive
+and successful field of study. The tools developed allow for ultracold chemical
+reactions to be managed using magnetic fields, light fields and spin-state
+manipulation of the colliding particles among other methods. The control of
+chemical reactions in ultracold atom-ion collisions is a young and growing
+field of research. Recently, the collision energy and the ion electronic state
+were used to control atom-ion interactions. Here, we demonstrate
+spin-controlled atom-ion inelastic processes. In our experiment, both
+spin-exchange and charge-exchange reactions are controlled in an ultracold
+Rb-Sr$^+$ mixture by the atomic spin state. We prepare a cloud of atoms in a
+single hyperfine spin-state. Spin-exchange collisions between atoms and ion
+subsequently polarize the ion spin. Electron transfer is only allowed for
+(RbSr)$^+$ colliding in the singlet manifold. Initializing the atoms in various
+spin states affects the overlap of the collision wavefunction with the singlet
+molecular manifold and therefore also the reaction rate. We experimentally show
+that by preparing the atoms in different spin states one can vary the
+charge-exchange rate in agreement with theoretical predictions.
+",0,1,0,0,0,0
+7747,Nonparametric Cusum Charts for Angular Data with Applications in Health Science and Astrophysics,"  This paper develops non-parametric rotation invariant CUSUMs suited to the
+detection of changes in the mean direction as well as changes in the
+concentration parameter of angular data. The properties of the CUSUMs are
+illustrated by theoretical calculations, Monte Carlo simulation and application
+to sequentially observed angular data from health science and astrophysics.
+",0,0,0,1,0,0
+7748,Origin of Weak Turbulence in the Outer Regions of Protoplanetary Disks,"  The mechanism behind angular momentum transport in protoplanetary disks, and
+whether this transport is turbulent in nature, is a fundamental issue in planet
+formation studies. Recent ALMA observations have suggested that turbulent
+velocities in the outer regions of these disks are less than ~5-10% of the
+sound speed, contradicting theoretical predictions of turbulence driven by the
+magnetorotational instability (MRI). These observations have generally been
+interpreted to be consistent with a large-scale laminar magnetic wind driving
+accretion. Here, we carry out local, shearing box simulations with varying
+ionization levels and background magnetic field strengths in order to determine
+which parameters produce results consistent with observations. We find that
+even when the background magnetic field launches a strong largely laminar wind,
+significant turbulence persists and is driven by localized regions of vertical
+magnetic field (the result of zonal flows) that are unstable to the MRI. The
+only conditions for which we find turbulent velocities below the observational
+limits are weak background magnetic fields and ionization levels well below
+that usually assumed in theoretical studies. We interpret these findings within
+the context of a preliminary model in which a large scale magnetic field,
+confined to the inner disk, hinders ionizing sources from reaching large radial
+distances, e.g., through a sufficiently dense wind. Thus, in addition to such a
+wind, this model predicts that for disks with weakly turbulent outer regions,
+the outer disk will have significantly reduced ionization levels compared to
+standard models and will harbor only a weak vertical magnetic field.
+",0,1,0,0,0,0
+7749,Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments,"  In the context of robotic underwater operations, the visual degradations
+induced by the medium properties make difficult the exclusive use of cameras
+for localization purpose. Hence, most localization methods are based on
+expensive navigational sensors associated with acoustic positioning. On the
+other hand, visual odometry and visual SLAM have been exhaustively studied for
+aerial or terrestrial applications, but state-of-the-art algorithms fail
+underwater. In this paper we tackle the problem of using a simple low-cost
+camera for underwater localization and propose a new monocular visual odometry
+method dedicated to the underwater environment. We evaluate different tracking
+methods and show that optical flow based tracking is more suited to underwater
+images than classical approaches based on descriptors. We also propose a
+keyframe-based visual odometry approach highly relying on nonlinear
+optimization. The proposed algorithm has been assessed on both simulated and
+real underwater datasets and outperforms state-of-the-art visual SLAM methods
+under many of the most challenging conditions. The main application of this
+work is the localization of Remotely Operated Vehicles (ROVs) used for
+underwater archaeological missions but the developed system can be used in any
+other applications as long as visual information is available.
+",1,0,0,0,0,0
+7750,Casper the Friendly Finality Gadget,"  We introduce Casper, a proof of stake-based finality system which overlays an
+existing proof of work blockchain. Casper is a partial consensus mechanism
+combining proof of stake algorithm research and Byzantine fault tolerant
+consensus theory. We introduce our system, prove some desirable features, and
+show defenses against long range revisions and catastrophic crashes. The Casper
+overlay provides almost any proof of work chain with additional protections
+against block reversions.
+",1,0,0,0,0,0
+7751,Rating Protocol Design for Extortion and Cooperation in the Crowdsourcing Contest Dilemma,"  Crowdsourcing has emerged as a paradigm for leveraging human intelligence and
+activity to solve a wide range of tasks. However, strategic workers will find
+enticement in their self-interest to free-ride and attack in a crowdsourcing
+contest dilemma game. Hence, incentive mechanisms are of great importance to
+overcome the inefficiency of the socially undesirable equilibrium. Existing
+incentive mechanisms are not effective in providing incentives for cooperation
+in crowdsourcing competitions due to the following features: heterogeneous
+workers compete against each other in a crowdsourcing platform with imperfect
+monitoring. In this paper, we take these features into consideration, and
+develop a novel game-theoretic design of rating protocols, which integrates
+binary rating labels with differential pricing to maximize the requester's
+utility, by extorting selfish workers and enforcing cooperation among them. By
+quantifying necessary and sufficient conditions for the sustainable social
+norm, we formulate the problem of maximizing the revenue of the requester among
+all sustainable rating protocols, provide design guidelines for optimal rating
+protocols, and design a low-complexity algorithm to select optimal design
+parameters which are related to differential punishments and pricing schemes.
+Simulation results demonstrate how intrinsic parameters impact on design
+parameters, as well as the performance gain of the proposed rating protocol.
+",1,0,0,0,0,0
+7752,Form factors of local operators in supersymmetric quantum integrable models,"  We apply the nested algebraic Bethe ansatz to the models with gl(2|1) and
+gl}(1|2) supersymmetry. We show that form factors of local operators in these
+models can be expressed in terms of the universal form factors. Our derivation
+is based on the use of the RTT-algebra only. It does not refer to any specific
+representation of this algebra. We obtain thus determinant representations for
+form factors of local operators in the cases where an explicit solution of the
+quantum inverse scattering problem is not known.
+",0,0,1,0,0,0
+7753,On the $E$-polynomial of parabolic $\mathrm{Sp}_{2n}$-character varieties,"  We find the $E$-polynomials of a family of parabolic
+$\mathrm{Sp}_{2n}$-character varieties $\mathcal{M}^{\xi}_{n}$ of Riemann
+surfaces by constructing a stratification, proving that each stratum has
+polynomial count, applying a result of Katz regarding the counting functions,
+and finally adding up the resulting $E$-polynomials of the strata. To count the
+number of $\mathbb{F}_{q}$-points of the strata, we invoke a formula due to
+Frobenius. Our calculation make use of a formula for the evaluation of
+characters on semisimple elements coming from Deligne-Lusztig theory, applied
+to the character theory of $\mathrm{Sp}{\left(2n,\mathbb{F}_{q}\right)}$, and
+Möbius inversion on the poset of set-partitions. We compute the Euler
+characteristic of the $\mathcal{M}^{\xi}_{n}$ with these polynomials, and show
+they are connected.
+",0,0,1,0,0,0
+7754,Goldstone and Higgs Hydrodynamics in the BCS-BEC Crossover,"  We discuss the derivation of a low-energy effective field theory of phase
+(Goldstone) and amplitude (Higgs) modes of the pairing field from a microscopic
+theory of attractive fermions. The coupled equations for Goldstone and Higgs
+fields are critically analyzed in the Bardeen-Cooper-Schrieffer (BCS) to
+Bose-Einstein condensate (BEC) crossover both in three spatial dimensions and
+in two spatial dimensions. The crucial role of pair fluctuations is
+investigated, and the beyond-mean-field Gaussian theory of the BCS-BEC
+crossover is compared with available experimental data of the two-dimensional
+ultracold Fermi superfluid.
+",0,1,0,0,0,0
+7755,A note on knot concordance and involutive knot Floer homology,"  We prove that if two knots are concordant, their involutive knot Floer
+complexes satisfy a certain type of stable equivalence.
+",0,0,1,0,0,0
+7756,$M$-QAM Precoder Design for MIMO Directional Modulation Transceivers,"  Spectrally efficient multi-antenna wireless communication systems are a key
+challenge as service demands continue to increase. At the same time, powering
+up radio access networks is facing environmental and regulation limitations. In
+order to achieve more power efficiency, we design a directional modulation
+precoder by considering an $M$-QAM constellation, particularly with
+$M=4,8,16,32$. First, extended detection regions are defined for desired
+constellations using analytical geometry. Then, constellation points are placed
+in the optimal positions of these regions while the minimum Euclidean distance
+to adjacent constellation points and detection region boundaries is kept as in
+the conventional $M$-QAM modulation. For further power efficiency and symbol
+error rate similar to that of fixed design in high SNR, relaxed detection
+regions are modeled for inner points of $M=16,32$ constellations. The modeled
+extended and relaxed detection regions as well as the modulation
+characteristics are utilized to formulate symbol-level precoder design problems
+for directional modulation to minimize the transmission power while preserving
+the minimum required SNR at the destination. In addition, the extended and
+relaxed detection regions are used for precoder design to minimize the output
+of each power amplifier. We transform the design problems into convex ones and
+devise an interior point path-following iterative algorithm to solve the
+mentioned problems and provide details on finding the initial values of the
+parameters and the starting point. Results show that compared to the benchmark
+schemes, the proposed method performs better in terms of power and peak power
+reduction as well as symbol error rate reduction for a wide range of SNRs.
+",1,0,0,0,0,0
+7757,Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces,"  We study kernel least-squares estimation under a norm constraint. This form
+of regularisation is known as Ivanov regularisation and it provides better
+control of the norm of the estimator than the well-established Tikhonov
+regularisation. This choice of regularisation allows us to dispose of the
+standard assumption that the reproducing kernel Hilbert space (RKHS) has a
+Mercer kernel, which is restrictive as it usually requires compactness of the
+covariate set. Instead, we assume only that the RKHS is separable with a
+bounded and measurable kernel. We provide rates of convergence for the expected
+squared $L^2$ error of our estimator under the weak assumption that the
+variance of the response variables is bounded and the unknown regression
+function lies in an interpolation space between $L^2$ and the RKHS. We then
+obtain faster rates of convergence when the regression function is bounded by
+clipping the estimator. In fact, we attain the optimal rate of convergence.
+Furthermore, we provide a high-probability bound under the stronger assumption
+that the response variables have subgaussian errors and that the regression
+function lies in an interpolation space between $L^\infty$ and the RKHS.
+Finally, we derive adaptive results for the settings in which the regression
+function is bounded.
+",0,0,1,1,0,0
+7758,Using Ice and Dust Lines to Constrain the Surface Densities of Protoplanetary Disks,"  We present a novel method for determining the surface density of
+protoplanetary disks through consideration of disk 'dust lines' which indicate
+the observed disk radial scale at different observational wavelengths. This
+method relies on the assumption that the processes of particle growth and drift
+control the radial scale of the disk at late stages of disk evolution such that
+the lifetime of the disk is equal to both the drift timescale and growth
+timescale of the maximum particle size at a given dust line. We provide an
+initial proof of concept of our model through an application to the disk TW Hya
+and are able to estimate the disk dust-to-gas ratio, CO abundance, and
+accretion rate in addition to the total disk surface density. We find that our
+derived surface density profile and dust-to-gas ratio are consistent with the
+lower limits found through measurements of HD gas. The CO ice line also depends
+on surface density through grain adsorption rates and drift and we find that
+our theoretical CO ice line estimates have clear observational analogues. We
+further apply our model to a large parameter space of theoretical disks and
+find three observational diagnostics that may be used to test its validity.
+First we predict that the dust lines of disks other than TW Hya will be
+consistent with the normalized CO surface density profile shape for those
+disks. Second, surface density profiles that we derive from disk ice lines
+should match those derived from disk dust lines. Finally, we predict that disk
+dust and ice lines will scale oppositely, as a function of surface density,
+across a large sample of disks.
+",0,1,0,0,0,0
+7759,Quaternionic Projective Bundle Theorem and Gysin Triangle in MW-Motivic Cohomology,"  In this paper, we show that the motive $HP^n$ of the quaternionic
+Grassmannian (as defined by I. Panin and C. Walter) splits in the category of
+effective MW-motives (as defined by B. Calmès, F. Déglise and J. Fasel).
+Moreover, we extend this result to an arbitrary symplectic bundle, obtaining
+the so-called quaternionic projective bundle theorem. This enables us to define
+Pontryagin classes of symplectic bundles in the Chow-Witt ring.
+As an application, we prove that there is a Gysin triangle in MW-motivic
+cohomology in case the normal bundle is symplectic.
+",0,0,1,0,0,0
+7760,Green function for linearized Navier-Stokes around a boundary layer profile: near critical layers,"  This is a continuation and completion of the program (initiated in
+\cite{GrN1,GrN2}) to derive pointwise estimates on the Green function and sharp
+bounds on the semigroup of linearized Navier-Stokes around a generic stationary
+boundary layer profile. This is done via a spectral analysis approach and a
+careful study of the Orr-Sommerfeld equations, or equivalently the
+Navier-Stokes resolvent operator $(\lambda - L)^{-1}$. The earlier work
+(\cite{GrN1,GrN2}) treats the Orr-Sommerfeld equations away from critical
+layers: this is the case when the phase velocity is away from the range of the
+background profile or when $\lambda$ is away from the Euler continuous
+spectrum. In this paper, we study the critical case: the Orr-Sommerfeld
+equations near critical layers, providing pointwise estimates on the Green
+function as well as carefully studying the Dunford's contour integral near the
+critical layers.
+As an application, we obtain pointwise estimates on the Green function and
+sharp bounds on the semigroup of the linearized Navier-Stokes problem near
+monotonic boundary layers that are spectrally stable to the Euler equations,
+complementing \cite{GrN1,GrN2} where unstable profiles are considered.
+",0,0,1,0,0,0
+7761,Summary of a Literature Review in Scalability of QoS-aware Service Composition,"  This paper shows that authors have no consistent way to characterize the
+scalability of their solutions, and so consider only a limited number of
+scaling characteristics. This review aimed at establishing the evidence that
+the route for designing and evaluating the scalability of dynamic QoS-aware
+service composition mechanisms has been lacking systematic guidance, and has
+been informed by a very limited set of criteria. For such, we analyzed 47
+papers, from 2004 to 2018.
+",1,0,0,0,0,0
+7762,"Interacting superradiance samples: modified intensities and timescales, and frequency shifts","  We consider the interaction between distinct superradiance (SR) systems and
+use the dressed state formalism to solve the case of two interacting two-atom
+SR samples at resonance. We show that the ensuing entanglement modifies the
+transition rates and intensities of radiation, as well as introduces a
+potentially measurable frequency chirp in the SR cascade, the magnitude of
+which being a function of the separation between the samples. For the dominant
+SR cascade we find a significant reduction in the duration and an increase of
+the intensity of the SR pulse relative to the case of a single two-atom SR
+sample.
+",0,1,0,0,0,0
+7763,An optimal XP algorithm for Hamiltonian cycle on graphs of bounded clique-width,"  In this paper, we prove that, given a clique-width $k$-expression of an
+$n$-vertex graph, \textsc{Hamiltonian Cycle} can be solved in time
+$n^{\mathcal{O}(k)}$. This improves the naive algorithm that runs in time
+$n^{\mathcal{O}(k^2)}$ by Espelage et al. (WG 2001), and it also matches with
+the lower bound result by Fomin et al. that, unless the Exponential Time
+Hypothesis fails, there is no algorithm running in time $n^{o(k)}$ (SIAM. J.
+Computing 2014).
+We present a technique of representative sets using two-edge colored
+multigraphs on $k$ vertices. The essential idea is that, for a two-edge colored
+multigraph, the existence of an Eulerian trail that uses edges with different
+colors alternately can be determined by two information: the number of colored
+edges incident with each vertex, and the connectedness of the multigraph. With
+this idea, we avoid the bottleneck of the naive algorithm, which stores all the
+possible multigraphs on $k$ vertices with at most $n$ edges.
+",1,0,0,0,0,0
+7764,On the functional window of the avian compass,"  The functional window is an experimentally observed property of the avian
+compass that refers to its selectivity around the geomagnetic field strength.
+We show that the radical-pair model, using biologically feasible hyperfine
+parameters, can qualitatively explain the salient features of the avian compass
+as observed from behavioral experiments: its functional window, as well as
+disruption of the compass action by an RF field of specific frequencies.
+Further, we show that adjustment of the hyperfine parameters can tune the
+functional window, suggesting a possible mechanism for its observed
+adaptability to field variation. While these lend strong support to the
+radical-pair model, we find it impossible to explain quantitatively the
+observed width of the functional window within this model, or even with simple
+augmentations thereto. This suggests that a deeper generalization of this model
+may be called for; we conjecture that environmental coupling may be playing a
+subtle role here that has not been captured accurately. Lastly, we examine a
+possible biological purpose to the functional window; assuming evolutionary
+benefit from radical-pair magnetoreception, we conjecture that the functional
+window is simply a corollary thereof and brings no additional advantage.
+",0,1,0,0,0,0
+7765,Automated text summarisation and evidence-based medicine: A survey of two domains,"  The practice of evidence-based medicine (EBM) urges medical practitioners to
+utilise the latest research evidence when making clinical decisions. Because of
+the massive and growing volume of published research on various medical topics,
+practitioners often find themselves overloaded with information. As such,
+natural language processing research has recently commenced exploring
+techniques for performing medical domain-specific automated text summarisation
+(ATS) techniques-- targeted towards the task of condensing large medical texts.
+However, the development of effective summarisation techniques for this task
+requires cross-domain knowledge. We present a survey of EBM, the
+domain-specific needs for EBM, automated summarisation techniques, and how they
+have been applied hitherto. We envision that this survey will serve as a first
+resource for the development of future operational text summarisation
+techniques for EBM.
+",1,0,0,0,0,0
+7766,Generalized Moran sets Generated by Step-wise Adjustable Iterated Function Systems,"  In this article we provide a systematic way of creating generalized Moran
+sets using an analogous iterated function system (IFS) procedure. We use a
+step-wise adjustable IFS to introduce some variance (such as
+non-self-similarity) in the fractal limit sets. The process retains the
+computational simplicity of a standard IFS procedure. In our construction of
+the generalized Moran sets, we also weaken the fourth Moran Structure Condition
+that requires the same pattern of diameter ratios be used across a generation.
+Moreover, we provide upper and lower bounds for the Hausdorff dimension of the
+fractals created from this generalized process. Specific examples (Cantor-like
+sets, Sierpinski-like Triangles, etc) with the calculations of their
+corresponding dimensions are studied.
+",0,1,1,0,0,0
+7767,On the lateral instability analysis of MEMS comb-drive electrostatic transducers,"  This paper investigates the lateral pull-in effect of an in-plane
+overlap-varying transducer. The instability is induced by the translational and
+rotational displacements. Based on the principle of virtual work, the
+equilibrium conditions of force and moment in lateral directions are derived.
+The analytical solutions of the critical voltage, at which the pull-in
+phenomenon occurs, are developed when considering only the translational
+stiffness or only the rotational stiffness of the mechanical spring. The
+critical voltage in general case is numerically determined by using nonlinear
+optimization techniques, taking into account the combined effect of translation
+and rotation. The effects of possible translational offsets and angular
+deviations to the critical voltage are modeled and numerically analyzed. The
+investigation is then the first time expanded to anti-phase operation mode and
+Bennet's doubler configuration of the two transducers.
+",0,1,0,0,0,0
+7768,Isospectrality For Orbifold Lens Spaces,"  We answer Mark Kac's famous question, ""can one hear the shape of a drum?"" in
+the positive for orbifolds that are 3-dimensional and 4-dimensional lens
+spaces; we thus complete the answer to this question for orbifold lens spaces
+in all dimensions. We also show that the coefficients of the asymptotic
+expansion of the trace of the heat kernel are not sufficient to determine the
+above results.
+",0,0,1,0,0,0
+7769,How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models,"  Machine learning models are vulnerable to Adversarial Examples: minor
+perturbations to input samples intended to deliberately cause
+misclassification. Current defenses against adversarial examples, especially
+for Deep Neural Networks (DNN), are primarily derived from empirical
+developments, and their security guarantees are often only justified
+retroactively. Many defenses therefore rely on hidden assumptions that are
+subsequently subverted by increasingly elaborate attacks. This is not
+surprising: deep learning notoriously lacks a comprehensive mathematical
+framework to provide meaningful guarantees.
+In this paper, we leverage Gaussian Processes to investigate adversarial
+examples in the framework of Bayesian inference. Across different models and
+datasets, we find deviating levels of uncertainty reflect the perturbation
+introduced to benign samples by state-of-the-art attacks, including novel
+white-box attacks on Gaussian Processes. Our experiments demonstrate that even
+unoptimized uncertainty thresholds already reject adversarial examples in many
+scenarios.
+Comment: Thresholds can be broken in a modified attack, which was done in
+arXiv:1812.02606 (The limitations of model uncertainty in adversarial
+settings).
+",1,0,0,1,0,0
+7770,Energy Efficient Adaptive Network Coding Schemes for Satellite Communications,"  In this paper, we propose novel energy efficient adaptive network coding and
+modulation schemes for time variant channels. We evaluate such schemes under a
+realistic channel model for open area environments and Geostationary Earth
+Orbit (GEO) satellites. Compared to non-adaptive network coding and adaptive
+rate efficient network-coded schemes for time variant channels, we show that
+our proposed schemes, through physical layer awareness can be designed to
+transmit only if a target quality of service (QoS) is achieved. As a result,
+such schemes can provide remarkable energy savings.
+",1,0,0,0,0,0
+7771,DCCO: Towards Deformable Continuous Convolution Operators,"  Discriminative Correlation Filter (DCF) based methods have shown competitive
+performance on tracking benchmarks in recent years. Generally, DCF based
+trackers learn a rigid appearance model of the target. However, this reliance
+on a single rigid appearance model is insufficient in situations where the
+target undergoes non-rigid transformations. In this paper, we propose a unified
+formulation for learning a deformable convolution filter. In our framework, the
+deformable filter is represented as a linear combination of sub-filters. Both
+the sub-filter coefficients and their relative locations are inferred jointly
+in our formulation. Experiments are performed on three challenging tracking
+benchmarks: OTB- 2015, TempleColor and VOT2016. Our approach improves the
+baseline method, leading to performance comparable to state-of-the-art.
+",1,0,0,0,0,0
+7772,Multitarget search on complex networks: A logarithmic growth of global mean random cover time,"  We investigate multitarget search on complex networks and derive an exact
+expression for the mean random cover time that quantifies the expected time a
+walker needs to visit multiple targets. Based on this, we recover and extend
+some interesting results of multitarget search on networks. Specifically, we
+observe the logarithmic increase of the global mean random cover time with the
+target number for a broad range of random search processes, including generic
+random walks, biased random walks, and maximal entropy random walks. We show
+that the logarithmic growth pattern is a universal feature of multi-target
+search on networks by using the annealed network approach and the
+Sherman-Morrison formula. Moreover, we find that for biased random walks, the
+global mean random cover time can be minimized, and that the corresponding
+optimal parameter also minimizes the global mean first passage time, pointing
+towards its robustness. Our findings further confirm that the logarithmic
+growth pattern is a universal law governing multitarget search in confined
+media.
+",1,1,0,0,0,0
+7773,New X-ray bound on density of primordial black holes,"  We set a new upper limit on the abundance of primordial black holes (PBH)
+based on existing X-ray data. PBH interactions with interstellar medium should
+result in significant fluxes of X-ray photons, which would contribute to the
+observed number density of compact X-ray objects in galaxies. The data
+constrain PBH number density in the mass range from a few $M_\odot$ to $2\times
+10^7 M_\odot$. PBH density needed to account for the origin of black holes
+detected by LIGO is marginally allowed.
+",0,1,0,0,0,0
+7774,Hierarchical Clustering with Prior Knowledge,"  Hierarchical clustering is a class of algorithms that seeks to build a
+hierarchy of clusters. It has been the dominant approach to constructing
+embedded classification schemes since it outputs dendrograms, which capture the
+hierarchical relationship among members at all levels of granularity,
+simultaneously. Being greedy in the algorithmic sense, a hierarchical
+clustering partitions data at every step solely based on a similarity /
+dissimilarity measure. The clustering results oftentimes depend on not only the
+distribution of the underlying data, but also the choice of dissimilarity
+measure and the clustering algorithm. In this paper, we propose a method to
+incorporate prior domain knowledge about entity relationship into the
+hierarchical clustering. Specifically, we use a distance function in
+ultrametric space to encode the external ontological information. We show that
+popular linkage-based algorithms can faithfully recover the encoded structure.
+Similar to some regularized machine learning techniques, we add this distance
+as a penalty term to the original pairwise distance to regulate the final
+structure of the dendrogram. As a case study, we applied this method on real
+data in the building of a customer behavior based product taxonomy for an
+Amazon service, leveraging the information from a larger Amazon-wide browse
+structure. The method is useful when one wants to leverage the relational
+information from external sources, or the data used to generate the distance
+matrix is noisy and sparse. Our work falls in the category of semi-supervised
+or constrained clustering.
+",0,0,0,1,0,0
+7775,Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations,"  We introduce physics informed neural networks -- neural networks that are
+trained to solve supervised learning tasks while respecting any given law of
+physics described by general nonlinear partial differential equations. In this
+second part of our two-part treatise, we focus on the problem of data-driven
+discovery of partial differential equations. Depending on whether the available
+data is scattered in space-time or arranged in fixed temporal snapshots, we
+introduce two main classes of algorithms, namely continuous time and discrete
+time models. The effectiveness of our approach is demonstrated using a wide
+range of benchmark problems in mathematical physics, including conservation
+laws, incompressible fluid flow, and the propagation of nonlinear shallow-water
+waves.
+",1,0,0,1,0,0
+7776,The HoTT reals coincide with the Escardó-Simpson reals,"  Escardó and Simpson defined a notion of interval object by a universal
+property in any category with binary products. The Homotopy Type Theory book
+defines a higher-inductive notion of reals, and suggests that the interval may
+satisfy this universal property. We show that this is indeed the case in the
+category of sets of any universe. We also show that the type of HoTT reals is
+the least Cauchy complete subset of the Dedekind reals containing the
+rationals.
+",1,0,1,0,0,0
+7777,Noise2Noise: Learning Image Restoration without Clean Data,"  We apply basic statistical reasoning to signal reconstruction by machine
+learning -- learning to map corrupted observations to clean signals -- with a
+simple and powerful conclusion: it is possible to learn to restore images by
+only looking at corrupted examples, at performance at and sometimes exceeding
+training using clean data, without explicit image priors or likelihood models
+of the corruption. In practice, we show that a single model learns photographic
+noise removal, denoising synthetic Monte Carlo images, and reconstruction of
+undersampled MRI scans -- all corrupted by different processes -- based on
+noisy data only.
+",0,0,0,1,0,0
+7778,Simple Root Cause Analysis by Separable Likelihoods,"  Root Cause Analysis for Anomalies is challenging because of the trade-off
+between the accuracy and its explanatory friendliness, required for industrial
+applications. In this paper we propose a framework for simple and friendly RCA
+within the Bayesian regime under certain restrictions (that Hessian at the mode
+is diagonal, here referred to as \emph{separability}) imposed on the predictive
+posterior. We show that this assumption is satisfied for important base models,
+including Multinomal, Dirichlet-Multinomial and Naive Bayes. To demonstrate the
+usefulness of the framework, we embed it into the Bayesian Net and validate on
+web server error logs (real world data set).
+",0,0,0,1,0,0
+7779,Jeffrey's prior sampling of deep sigmoidal networks,"  Neural networks have been shown to have a remarkable ability to uncover low
+dimensional structure in data: the space of possible reconstructed images form
+a reduced model manifold in image space. We explore this idea directly by
+analyzing the manifold learned by Deep Belief Networks and Stacked Denoising
+Autoencoders using Monte Carlo sampling. The model manifold forms an only
+slightly elongated hyperball with actual reconstructed data appearing
+predominantly on the boundaries of the manifold. In connection with the results
+we present, we discuss problems of sampling high-dimensional manifolds as well
+as recent work [M. Transtrum, G. Hart, and P. Qiu, Submitted (2014)] discussing
+the relation between high dimensional geometry and model reduction.
+",1,1,0,0,0,0
+7780,Computing the quality of the Laplace approximation,"  Bayesian inference requires approximation methods to become computable, but
+for most of them it is impossible to quantify how close the approximation is to
+the true posterior. In this work, we present a theorem upper-bounding the KL
+divergence between a log-concave target density
+$f\left(\boldsymbol{\theta}\right)$ and its Laplace approximation
+$g\left(\boldsymbol{\theta}\right)$. The bound we present is computable: on the
+classical logistic regression model, we find our bound to be almost exact as
+long as the dimensionality of the parameter space is high.
+The approach we followed in this work can be extended to other Gaussian
+approximations, as we will do in an extended version of this work, to be
+submitted to the Annals of Statistics. It will then become a critical tool for
+characterizing whether, for a given problem, a given Gaussian approximation is
+suitable, or whether a more precise alternative method should be used instead.
+",0,0,1,1,0,0
+7781,Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades,"  Two node variables determine the evolution of cascades in random networks: a
+node's degree and threshold. Correlations between both fundamentally change the
+robustness of a network, yet, they are disregarded in standard analytic methods
+as local tree or heterogeneous mean field approximations because of the bad
+tractability of order statistics. We show how they become tractable in the
+thermodynamic limit of infinite network size. This enables the analytic
+description of node attacks that are characterized by threshold allocations
+based on node degree. Using two examples, we discuss possible implications of
+irregular phase transitions and different speeds of cascade evolution for the
+control of cascades.
+",0,1,0,0,0,0
+7782,Faster Learning by Reduction of Data Access Time,"  Nowadays, the major challenge in machine learning is the Big Data challenge.
+The big data problems due to large number of data points or large number of
+features in each data point, or both, the training of models have become very
+slow. The training time has two major components: Time to access the data and
+time to process (learn from) the data. So far, the research has focused only on
+the second part, i.e., learning from the data. In this paper, we have proposed
+one possible solution to handle the big data problems in machine learning. The
+idea is to reduce the training time through reducing data access time by
+proposing systematic sampling and cyclic/sequential sampling to select
+mini-batches from the dataset. To prove the effectiveness of proposed sampling
+techniques, we have used Empirical Risk Minimization, which is commonly used
+machine learning problem, for strongly convex and smooth case. The problem has
+been solved using SAG, SAGA, SVRG, SAAG-II and MBSGD (Mini-batched SGD), each
+using two step determination techniques, namely, constant step size and
+backtracking line search method. Theoretical results prove the same convergence
+for systematic sampling, cyclic sampling and the widely used random sampling
+technique, in expectation. Experimental results with bench marked datasets
+prove the efficacy of the proposed sampling techniques and show up to six times
+faster training.
+",0,0,0,1,0,0
+7783,Optimal Control for Constrained Coverage Path Planning,"  The problem of constrained coverage path planning involves a robot trying to
+cover maximum area of an environment under some constraints that appear as
+obstacles in the map. Out of the several coverage path planning methods, we
+consider augmenting the linear sweep-based coverage method to achieve minimum
+energy/ time optimality along with maximum area coverage. In addition, we also
+study the effects of variation of different parameters on the performance of
+the modified method.
+",1,0,0,0,0,0
+7784,ScaleSimulator: A Fast and Cycle-Accurate Parallel Simulator for Architectural Exploration,"  Design of next generation computer systems should be supported by simulation
+infrastructure that must achieve a few contradictory goals such as fast
+execution time, high accuracy, and enough flexibility to allow comparison
+between large numbers of possible design points. Most existing architecture
+level simulators are designed to be flexible and to execute the code in
+parallel for greater efficiency, but at the cost of scarified accuracy. This
+paper presents the ScaleSimulator simulation environment, which is based on a
+new design methodology whose goal is to achieve near cycle accuracy while still
+being flexible enough to simulate many different future system architectures
+and efficient enough to run meaningful workloads. We achieve these goals by
+making the parallelism a first-class citizen in our methodology. Thus, this
+paper focuses mainly on the ScaleSimulator design points that enable better
+parallel execution while maintaining the scalability and cycle accuracy of a
+simulated architecture. The paper indicates that the new proposed
+ScaleSimulator tool can (1) efficiently parallelize the execution of a
+cycle-accurate architecture simulator, (2) efficiently simulate complex
+architectures (e.g., out-of-order CPU pipeline, cache coherency protocol, and
+network) and massive parallel systems, and (3) use meaningful workloads, such
+as full simulation of OLTP benchmarks, to examine future architectural choices.
+",1,0,0,0,0,0
+7785,Annihilating wild kernels,"  Let $L/K$ be a finite Galois extension of number fields with Galois group
+$G$. Let $p$ be an odd prime and $r>1$ be an integer. Assuming a conjecture of
+Schneider, we formulate a conjecture that relates special values of equivariant
+Artin $L$-series at $s=r$ to the compact support cohomology of the étale
+$p$-adic sheaf $\mathbb Z_p(r)$. We show that our conjecture is essentially
+equivalent to the $p$-part of the equivariant Tamagawa number conjecture for
+the pair $(h^0(\mathrm{Spec}(L))(r), \mathbb Z[G])$. We derive from this
+explicit constraints on the Galois module structure of Banaszak's $p$-adic wild
+kernels.
+",0,0,1,0,0,0
+7786,Trading the Twitter Sentiment with Reinforcement Learning,"  This paper is to explore the possibility to use alternative data and
+artificial intelligence techniques to trade stocks. The efficacy of the daily
+Twitter sentiment on predicting the stock return is examined using machine
+learning methods. Reinforcement learning(Q-learning) is applied to generate the
+optimal trading policy based on the sentiment signal. The predicting power of
+the sentiment signal is more significant if the stock price is driven by the
+expectation of the company growth and when the company has a major event that
+draws the public attention. The optimal trading strategy based on reinforcement
+learning outperforms the trading strategy based on the machine learning
+prediction.
+",1,0,0,0,0,0
+7787,Kahler-Einstein metrics and algebraic geometry,"  This is a survey article, based on the author's lectures in the 2015 Current
+developments in Mathematics meeting; published in ""Current developments in
+Mathematics"". Version 2, references corrected and added.
+",0,0,1,0,0,0
+7788,Hemihelical local minimizers in prestrained elastic bi-strips,"  We consider a double layered prestrained elastic rod in the limit of
+vanishing cross section. For the resulting limit Kirchoff-rod model with
+intrinsic curvature we prove a supercritical bifurcation result, rigorously
+showing the emergence of a branch of hemihelical local minimizers from the
+straight configuration, at a critical force and under clamping at both ends. As
+a consequence we obtain the existence of nontrivial local minimizers of the
+$3$-d system.
+",0,0,1,0,0,0
+7789,AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks,"  Heterogeneous information networks (HINs) are ubiquitous in real-world
+applications. Due to the heterogeneity in HINs, the typed edges may not fully
+align with each other. In order to capture the semantic subtlety, we propose
+the concept of aspects with each aspect being a unit representing one
+underlying semantic facet. Meanwhile, network embedding has emerged as a
+powerful method for learning network representation, where the learned
+embedding can be used as features in various downstream applications.
+Therefore, we are motivated to propose a novel embedding learning
+framework---AspEm---to preserve the semantic information in HINs based on
+multiple aspects. Instead of preserving information of the network in one
+semantic space, AspEm encapsulates information regarding each aspect
+individually. In order to select aspects for embedding purpose, we further
+devise a solution for AspEm based on dataset-wide statistics. To corroborate
+the efficacy of AspEm, we conducted experiments on two real-words datasets with
+two types of applications---classification and link prediction. Experiment
+results demonstrate that AspEm can outperform baseline network embedding
+learning methods by considering multiple aspects, where the aspects can be
+selected from the given HIN in an unsupervised manner.
+",1,0,0,0,0,0
+7790,Nonlinear Information Bottleneck,"  Information bottleneck [IB] is a technique for extracting information in some
+`input' random variable that is relevant for predicting some different 'output'
+random variable. IB works by encoding the input in a compressed 'bottleneck
+variable' from which the output can then be accurately decoded. IB can be
+difficult to compute in practice, and has been mainly developed for two limited
+cases: (1) discrete random variables with small state spaces, and (2)
+continuous random variables that are jointly Gaussian distributed (in which
+case the encoding and decoding maps are linear). We propose a method to perform
+IB in more general domains. Our approach can be applied to discrete or
+continuous inputs and outputs, and allows for nonlinear encoding and decoding
+maps. The method uses a novel upper bound on the IB objective, derived using a
+non-parametric estimator of mutual information and a variational approximation.
+We show how to implement the method using neural networks and gradient-based
+optimization, and demonstrate its performance on the MNIST dataset.
+",1,0,0,1,0,0
+7791,Top-k Overlapping Densest Subgraphs: Approximation and Complexity,"  A central problem in graph mining is finding dense subgraphs, with several
+applications in different fields, a notable example being identifying
+communities. While a lot of effort has been put on the problem of finding a
+single dense subgraph, only recently the focus has been shifted to the problem
+of finding a set of dens- est subgraphs. Some approaches aim at finding
+disjoint subgraphs, while in many real-world networks communities are often
+overlapping. An approach introduced to find possible overlapping subgraphs is
+the Top-k Overlapping Densest Subgraphs problem. For a given integer k >= 1,
+the goal of this problem is to find a set of k densest subgraphs that may share
+some vertices. The objective function to be maximized takes into account both
+the density of the subgraphs and the distance between subgraphs in the
+solution.
+The Top-k Overlapping Densest Subgraphs problem has been shown to admit a
+1/10-factor approximation algorithm. Furthermore, the computational complexity
+of the problem has been left open. In this paper, we present contributions
+concerning the approximability and the computational complexity of the problem.
+For the approximability, we present approximation algorithms that improves the
+approximation factor to 1/2 , when k is bounded by the vertex set, and to 2/3
+when k is a constant. For the computational complexity, we show that the
+problem is NP-hard even when k = 3.
+",1,0,0,0,0,0
+7792,The energy-momentum tensor of electromagnetic fields in matter,"  We present a complete resolution of the Abraham-Minkowski controversy . This
+is done by considering several new aspects which invalidate previous
+discussions. We show that: 1)For polarized matter the center of mass theorem is
+no longer valid in its usual form. A contribution related to microscopic spin
+should be considered. 2)The electromagnetic dipolar energy density contributes
+to the inertia of matter and should be incorporated covariantly to the the
+energy-momentum tensor of matter. Then there is an electromagnetic component in
+matter's momentum density whose variation explains the results of the only
+experiment which supports Abraham's force. 3)Averaging the microscopic
+Lorentz's force results in the unambiguos expression for the force density
+exerted by the field. This force density is consistent with all the
+experimental evidence. 4)Momentum conservation determines the electromagnetic
+energy-momentum tensor. This tensor is different from Abraham's and Minkowski's
+tensors, but one recovers Minkowski's expression for the momentum density. The
+energy density is different from Poynting's expression but Poynting's vector
+remains the same. Our tensor is non-symmetric which allows the field to exert a
+distributed torque on matter. We use our results to discuss momentum and
+angular momentum exchange in various situations of physical interest. We find
+complete consistency of our equations in the description of the systems
+considered. We also show that several alternative expressions of the field
+energy-momentum tensor and force-density cannot be successfully used in all our
+examples. In particular we verify in two of these examples that the center of
+mass and spin introduced by us moves with constant velocity, but that the
+standard center of mass does not.
+",0,1,0,0,0,0
+7793,Pinhole induced efficiency variation in perovskite solar cells,"  Process induced efficiency variation is a major concern for all thin film
+solar cells, including the emerging perovskite based solar cells. In this
+manuscript, we address the effect of pinholes or process induced surface
+coverage aspects on the efficiency of such solar cells through detailed
+numerical simulations. Interestingly, we find the pinhole size distribution
+affects the short circuit current and open circuit voltage in contrasting
+manners. Specifically, while the Jsc is heavily dependent on the pinhole size
+distribution, surprisingly, the Voc seems to be only nominally affected by it.
+Further, our simulations also indicate that, with appropriate interface
+engineering, it is indeed possible to design a nanostructured device with
+efficiencies comparable to that of ideal planar structures. Additionally, we
+propose a simple technique based on terminal IV characteristics to estimate the
+surface coverage in perovskite solar cells.
+",0,1,0,0,0,0
+7794,An Overview of Robust Subspace Recovery,"  This paper will serve as an introduction to the body of work on robust
+subspace recovery. Robust subspace recovery involves finding an underlying
+low-dimensional subspace in a dataset that is possibly corrupted with outliers.
+While this problem is easy to state, it has been difficult to develop optimal
+algorithms due to its underlying nonconvexity. This work emphasizes advantages
+and disadvantages of proposed approaches and unsolved problems in the area.
+",0,0,0,1,0,0
+7795,Tuning Free Orthogonal Matching Pursuit,"  Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS)
+algorithm for recovering sparse signals in noisy linear regression models. The
+performance of OMP depends on its stopping criteria (SC). SC for OMP discussed
+in literature typically assumes knowledge of either the sparsity of the signal
+to be estimated $k_0$ or noise variance $\sigma^2$, both of which are
+unavailable in many practical applications. In this article we develop a
+modified version of OMP called tuning free OMP or TF-OMP which does not require
+a SC. TF-OMP is proved to accomplish successful sparse recovery under the usual
+assumptions on restricted isometry constants (RIC) and mutual coherence of
+design matrix. TF-OMP is numerically shown to deliver a highly competitive
+performance in comparison with OMP having \textit{a priori} knowledge of $k_0$
+or $\sigma^2$. Greedy algorithm for robust de-noising (GARD) is an OMP like
+algorithm proposed for efficient estimation in classical overdetermined linear
+regression models corrupted by sparse outliers. However, GARD requires the
+knowledge of inlier noise variance which is difficult to estimate. We also
+produce a tuning free algorithm (TF-GARD) for efficient estimation in the
+presence of sparse outliers by extending the operating principle of TF-OMP to
+GARD. TF-GARD is numerically shown to achieve a performance comparable to that
+of the existing implementation of GARD.
+",1,0,0,1,0,0
+7796,New constructions of MDS codes with complementary duals,"  Linear complementary-dual (LCD for short) codes are linear codes that
+intersect with their duals trivially. LCD codes have been used in certain
+communication systems. It is recently found that LCD codes can be applied in
+cryptography. This application of LCD codes renewed the interest in the
+construction of LCD codes having a large minimum distance. MDS codes are
+optimal in the sense that the minimum distance cannot be improved for given
+length and code size. Constructing LCD MDS codes is thus of significance in
+theory and practice. Recently, Jin (\cite{Jin}, IEEE Trans. Inf. Theory, 2016)
+constructed several classes of LCD MDS codes through generalized Reed-Solomon
+codes. In this paper, a different approach is proposed to obtain new LCD MDS
+codes from generalized Reed-Solomon codes. Consequently, new code constructions
+are provided and certain previously known results in \cite{Jin} are extended.
+",1,0,0,0,0,0
+7797,Porosity and regularity in metric measure spaces,"  This is a report of a joint work with E. Järvenpää, M. Järvenpää,
+T. Rajala, S. Rogovin, and V. Suomala. In [3], we characterized uniformly
+porous sets in $s$-regular metric spaces in terms of regular sets by verifying
+that a set $A$ is uniformly porous if and only if there is $t < s$ and a
+$t$-regular set $F \supset A$. Here we outline the main idea of the proof and
+also present an alternative proof for the crucial lemma needed in the proof of
+the result.
+",0,0,1,0,0,0
+7798,Strong-coupling superconductivity induced by calcium intercalation in bilayer transition-metal dichalcogenides,"  We theoretically investigate the possibility of achieving a superconducting
+state in transition-metal dichalcogenide bilayers through intercalation, a
+process previously and widely used to achieve metallization and superconducting
+states in novel superconductors. For the Ca-intercalated bilayers MoS$_2$ and
+WS$_2$, we find that the superconducting state is characterized by an
+electron-phonon coupling constant larger than $1.0$ and a superconducting
+critical temperature of $13.3$ and $9.3$ K, respectively. These results are
+superior to other predicted or experimentally observed two-dimensional
+conventional superconductors and suggest that the investigated materials may be
+good candidates for nanoscale superconductors. More interestingly, we proved
+that the obtained thermodynamic properties go beyond the predictions of the
+mean-field Bardeen--Cooper--Schrieffer approximation and that the calculations
+conducted within the framework of the strong-coupling Eliashberg theory should
+be treated as those that yield quantitative results.
+",0,1,0,0,0,0
+7799,Interval-type theorems concerning quasi-arithmetic means,"  Family of quasi-arithmetic means has a natural, partial order (point-wise
+order) $A^{[f]}\le A^{[g]}$ if and only if $A^{[f]}(v)\le A^{[g]}(v)$ for all
+admissible vectors $v$ ($f,\,g$ and, later, $h$ are continuous and monotone and
+defined on a common interval). Therefore one can introduce the notion of
+interval-type sets (sets $\mathcal{I}$ such that whenever $A^{[f]} \le A^{[h]}
+\le A^{[g]}$ for some $A^{[f]},\,A^{[g]} \in \mathcal{I}$ then $A^{[h]} \in
+\mathcal{I}$ too). Our aim is to give examples of interval-type sets involving
+vary smoothness assumptions of generating functions.
+",0,0,1,0,0,0
+7800,Integrable flows between exact CFTs,"  We explicitly construct families of integrable $\sigma$-model actions
+smoothly interpolating between exact CFTs. In the ultraviolet the theory is the
+direct product of two current algebras at levels $k_1$ and $k_2$. In the
+infrared and for the case of two deformation matrices the CFT involves a coset
+CFT, whereas for a single matrix deformation it is given by the ultraviolet
+direct product theories but at levels $k_1$ and $k_2-k_1$. For isotropic
+deformations we demonstrate integrability. In this case we also compute the
+exact beta-function for the deformation parameters using gravitational methods.
+This is shown to coincide with previous results obtained using perturbation
+theory and non-perturbative symmetries.
+",0,1,0,0,0,0
+7801,Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering,"  Existing deep multitask learning (MTL) approaches align layers shared between
+tasks in a parallel ordering. Such an organization significantly constricts the
+types of shared structure that can be learned. The necessity of parallel
+ordering for deep MTL is first tested by comparing it with permuted ordering of
+shared layers. The results indicate that a flexible ordering can enable more
+effective sharing, thus motivating the development of a soft ordering approach,
+which learns how shared layers are applied in different ways for different
+tasks. Deep MTL with soft ordering outperforms parallel ordering methods across
+a series of domains. These results suggest that the power of deep MTL comes
+from learning highly general building blocks that can be assembled to meet the
+demands of each task.
+",1,0,0,1,0,0
+7802,Relational Algebra for In-Database Process Mining,"  The execution logs that are used for process mining in practice are often
+obtained by querying an operational database and storing the result in a flat
+file. Consequently, the data processing power of the database system cannot be
+used anymore for this information, leading to constrained flexibility in the
+definition of mining patterns and limited execution performance in mining large
+logs. Enabling process mining directly on a database - instead of via
+intermediate storage in a flat file - therefore provides additional flexibility
+and efficiency. To help facilitate this ideal of in-database process mining,
+this paper formally defines a database operator that extracts the 'directly
+follows' relation from an operational database. This operator can both be used
+to do in-database process mining and to flexibly evaluate process mining
+related queries, such as: ""which employee most frequently changes the 'amount'
+attribute of a case from one task to the next"". We define the operator using
+the well-known relational algebra that forms the formal underpinning of
+relational databases. We formally prove equivalence properties of the operator
+that are useful for query optimization and present time-complexity properties
+of the operator. By doing so this paper formally defines the necessary
+relational algebraic elements of a 'directly follows' operator, which are
+required for implementation of such an operator in a DBMS.
+",1,0,0,0,0,0
+7803,Global existence for the nonlinear fractional Schrödinger equation with fractional dissipation,"  We consider the initial value problem for the fractional nonlinear
+Schrödinger equation with a fractional dissipation. Global existence and
+scattering are proved depending on the order of the fractional dissipation.
+",0,0,1,0,0,0
+7804,Statistical properties of an enstrophy conserving discretisation for the stochastic quasi-geostrophic equation,"  A framework of variational principles for stochastic fluid dynamics was
+presented by Holm (2015), and these stochastic equations were also derived by
+Cotter et al. (2017). We present a conforming finite element discretisation for
+the stochastic quasi-geostrophic equation that was derived from this framework.
+The discretisation preserves the first two moments of potential vorticity, i.e.
+the mean potential vorticity and the enstrophy. Following the work of Dubinkina
+and Frank (2007), who investigated the statistical mechanics of discretisations
+of the deterministic quasi-geostrophic equation, we investigate the statistical
+mechanics of our discretisation of the stochastic quasi-geostrophic equation.
+We compare the statistical properties of our discretisation with the Gibbs
+distribution under assumption of these conserved quantities, finding that there
+is agreement between the statistics under a wide range of set-ups.
+",0,1,0,0,0,0
+7805,Conditional Optimal Stopping: A Time-Inconsistent Optimization,"  Inspired by recent work of P.-L. Lions on conditional optimal control, we
+introduce a problem of optimal stopping under bounded rationality: the
+objective is the expected payoff at the time of stopping, conditioned on
+another event. For instance, an agent may care only about states where she is
+still alive at the time of stopping, or a company may condition on not being
+bankrupt. We observe that conditional optimization is time-inconsistent due to
+the dynamic change of the conditioning probability and develop an equilibrium
+approach in the spirit of R. H. Strotz' work for sophisticated agents in
+discrete time. Equilibria are found to be essentially unique in the case of a
+finite time horizon whereas an infinite horizon gives rise to non-uniqueness
+and other interesting phenomena. We also introduce a theory which generalizes
+the classical Snell envelope approach for optimal stopping by considering a
+pair of processes with Snell-type properties.
+",0,0,0,0,0,1
+7806,Principles for optimal cooperativity in allosteric materials,"  Allosteric proteins transmit a mechanical signal induced by binding a ligand.
+However, understanding the nature of the information transmitted and the
+architectures optimizing such transmission remains a challenge. Here we show
+using an {\it in-silico} evolution scheme and theoretical arguments that
+architectures optimized to be cooperative, which propagate efficiently energy,
+{qualitatively} differ from previously investigated materials optimized to
+propagate strain. Although we observe a large diversity of functioning
+cooperative architectures (including shear, hinge and twist designs), they all
+obey the same principle {of displaying a {\it mechanism}, i.e. an extended
+{soft} mode}. We show that its optimal frequency decreases with the spatial
+extension $L$ of the system as $L^{-d/2}$, where $d$ is the spatial dimension.
+For these optimal designs, cooperativity decays logarithmically with $L$ for
+$d=2$ and does not decay for $d=3$. Overall our approach leads to a natural
+explanation for several observations in allosteric proteins, and { indicates an
+experimental path to test if allosteric proteins lie close to optimality}.
+",0,1,0,0,0,0
+7807,Improved electronic structure and magnetic exchange interactions in transition metal oxides,"  We discuss the application of the Agapito Curtarolo and Buongiorno Nardelli
+(ACBN0) pseudo-hybrid Hubbard density functional to several transition metal
+oxides. ACBN0 is a fast, accurate and parameter-free alternative to traditional
+DFT+$U$ and hybrid exact exchange methods. In ACBN0, the Hubbard energy of
+DFT+$U$ is calculated via the direct evaluation of the local Coulomb and
+exchange integrals in which the screening of the bare Coulomb potential is
+accounted for by a renormalization of the density matrix. We demonstrate the
+success of the ACBN0 approach for the electronic properties of a series
+technologically relevant mono-oxides (MnO, CoO, NiO, FeO, both at equilibrium
+and under pressure). We also present results on two mixed valence compounds,
+Co$_3$O$_4$ and Mn$_3$O$_4$. Our results, obtained at the computational cost of
+a standard LDA/PBE calculation, are in excellent agreement with hybrid
+functionals, the GW approximation and experimental measurements.
+",0,1,0,0,0,0
+7808,Test of SensL SiPM coated with NOL-1 wavelength shifter in liquid xenon,"  A SensL MicroFC-SMT-60035 6x6 mm$^2$ silicon photo-multiplier coated with a
+NOL-1 wavelength shifter have been tested in the liquid xenon to detect the
+175-nm scintillation light. For comparison, a Hamamatsu vacuum ultraviolet
+sensitive MPPC VUV3 3x3 mm$^2$ was tested under the same conditions. The
+photodetection efficiency of $13.1 \pm 2.5$% and $6.0 \pm 1.0$%,
+correspondingly, is obtained.
+",0,1,0,0,0,0
+7809,Neon2: Finding Local Minima via First-Order Oracles,"  We propose a reduction for non-convex optimization that can (1) turn an
+stationary-point finding algorithm into an local-minimum finding one, and (2)
+replace the Hessian-vector product computations with only gradient
+computations. It works both in the stochastic and the deterministic settings,
+without hurting the algorithm's performance.
+As applications, our reduction turns Natasha2 into a first-order method
+without hurting its performance. It also converts SGD, GD, SCSG, and SVRG into
+algorithms finding approximate local minima, outperforming some best known
+results.
+",1,0,0,1,0,0
+7810,Geometrical Insights for Implicit Generative Modeling,"  Learning algorithms for implicit generative models can optimize a variety of
+criteria that measure how the data distribution differs from the implicit model
+distribution, including the Wasserstein distance, the Energy distance, and the
+Maximum Mean Discrepancy criterion. A careful look at the geometries induced by
+these distances on the space of probability measures reveals interesting
+differences. In particular, we can establish surprising approximate global
+convergence guarantees for the $1$-Wasserstein distance,even when the
+parametric generator has a nonconvex parametrization.
+",1,0,0,1,0,0
+7811,Simple Countermeasures to Mitigate the Effect of Pollution Attack in Network Coding Based Peer-to-Peer Live Streaming,"  Network coding based peer-to-peer streaming represents an effective solution
+to aggregate user capacities and to increase system throughput in live
+multimedia streaming. Nonetheless, such systems are vulnerable to pollution
+attacks where a handful of malicious peers can disrupt the communication by
+transmitting just a few bogus packets which are then recombined and relayed by
+unaware honest nodes, further spreading the pollution over the network. Whereas
+previous research focused on malicious nodes identification schemes and
+pollution-resilient coding, in this paper we show pollution countermeasures
+which make a standard network coding scheme resilient to pollution attacks.
+Thanks to a simple yet effective analytical model of a reference node
+collecting packets by malicious and honest neighbors, we demonstrate that i)
+packets received earlier are less likely to be polluted and ii) short
+generations increase the likelihood to recover a clean generation. Therefore,
+we propose a recombination scheme where nodes draw packets to be recombined
+according to their age in the input queue, paired with a decoding scheme able
+to detect the reception of polluted packets early in the decoding process and
+short generations. The effectiveness of our approach is experimentally
+evaluated in a real system we developed and deployed on hundreds to thousands
+peers. Experimental evidence shows that, thanks to our simple countermeasures,
+the effect of a pollution attack is almost canceled and the video quality
+experienced by the peers is comparable to pre-attack levels.
+",1,0,0,0,0,0
+7812,Small-scale structure and the Lyman-$α$ forest baryon acoustic oscillation feature,"  The baryon-acoustic oscillation (BAO) feature in the Lyman-$\alpha$ forest is
+one of the key probes of the cosmic expansion rate at redshifts z~2.5, well
+before dark energy is believed to have become dynamically significant. A key
+advantage of the BAO as a standard ruler is that it is a sharp feature and
+hence is more robust against broadband systematic effects than other
+cosmological probes. However, if the Lyman-$\alpha$ forest transmission is
+sensitive to the initial streaming velocity of the baryons relative to the dark
+matter, then the BAO peak position can be shifted. Here we investigate this
+sensitivity using a suite of hydrodynamic simulations of small regions of the
+intergalactic medium with a range of box sizes and physics assumptions; each
+simulation starts from initial conditions at the kinematic decoupling era
+(z~1059), undergoes a discrete change from neutral gas to ionized gas thermal
+evolution at reionization (z~8), and is finally processed into a Lyman-$\alpha$
+forest transmitted flux cube. Streaming velocities suppress small-scale
+structure, leading to less violent relaxation after reionization. The changes
+in the gas distribution and temperature-density relation at low redshift are
+more subtle, due to the convergent temperature evolution in the ionized phase.
+The change in the BAO scale is estimated to be of the order of 0.12% at z=2.5;
+some of the major uncertainties and avenues for future improvement are
+discussed. The predicted streaming velocity shift would be a subdominant but
+not negligible effect (of order $0.26\sigma$) for the upcoming DESI
+Lyman-$\alpha$ forest survey, and exceeds the cosmic variance floor. It is
+hoped that this study will motivate additional theoretical work on the
+magnitude of the BAO shift, both in the Lyman-$\alpha$ forest and in other
+tracers of large-scale structure.
+",0,1,0,0,0,0
+7813,Scale-dependent perturbations finally detectable by future galaxy surveys and their contribution to cosmological model selection,"  By means of the present geometrical and dynamical observational data, it is
+very hard to establish, from a statistical perspective, a clear preference
+among the vast majority of the proposed models for the dynamical dark energy
+and/or modified gravity theories alternative with respect to the $\Lambda$CDM
+scenario. On the other hand, on scales much smaller than present Hubble scale,
+there are possibly detectable differences in the growth of the matter
+perturbations for different modes of the perturbations, even in the context of
+the $\Lambda$CDM model. Here, we analyze the evolution of the dark matter
+perturbations in the context of $\Lambda$CDM and some dynamical dark energy
+models involving future cosmological singularities, such as the sudden future
+singularity and the finite scale factor singularity. We employ the Newtonian
+gauge formulation for the derivation of the perturbation equations for the
+growth function, and we abandon both the sub-Hubble approximation and the
+slowly varying potential assumption. We apply the Fisher Matrix approach to
+three future planned galaxy surveys e.g., DESI, Euclid, and WFirst-2.4. With
+the mentioned surveys on hand, only with the dynamical probes, we will achieve
+multiple goals: $1.$ the improvement in the accuracy of the determination of
+the $f\sigma_{8}$ will give the possibility to discriminate between the
+$\Lambda$CDM and the alternative dark energy models even in the
+scale-independent approach; $2.$ it will be possible to test the goodness of
+the scale-independence finally, and also to quantify the necessity of a scale
+dependent approach to the growth of the perturbations, in particular using
+surveys which encompass redshift bins with scales $k<0.005\,h$ Mpc$^{-1}$; $3.$
+the scale-dependence itself might add much more discriminating power in
+general, but further advanced surveys will be needed.
+",0,1,0,0,0,0
+7814,InfoCatVAE: Representation Learning with Categorical Variational Autoencoders,"  This paper describes InfoCatVAE, an extension of the variational autoencoder
+that enables unsupervised disentangled representation learning. InfoCatVAE uses
+multimodal distributions for the prior and the inference network and then
+maximizes the evidence lower bound objective (ELBO). We connect the new ELBO
+derived for our model with a natural soft clustering objective which explains
+the robustness of our approach. We then adapt the InfoGANs method to our
+setting in order to maximize the mutual information between the categorical
+code and the generated inputs and obtain an improved model.
+",0,0,0,1,0,0
+7815,Quadratic twists of abelian varieties and disparity in Selmer ranks,"  We study the parity of 2-Selmer ranks in the family of quadratic twists of a
+fixed principally polarised abelian variety over a number field. Specifically,
+we determine the proportion of twists having odd (resp. even) 2-Selmer rank.
+This generalises work of Klagsbrun--Mazur--Rubin for elliptic curves and Yu for
+Jacobians of hyperelliptic curves. Several differences in the statistics arise
+due to the possibility that the Shafarevich--Tate group (if finite) may have
+order twice a square. In particular, the statistics for parities of 2-Selmer
+ranks and 2-infinity Selmer ranks need no longer agree and we describe both.
+",0,0,1,0,0,0
+7816,From acquaintance to best friend forever: robust and fine-grained inference of social tie strengths,"  Social networks often provide only a binary perspective on social ties: two
+individuals are either connected or not. While sometimes external information
+can be used to infer the strength of social ties, access to such information
+may be restricted or impractical. Sintos and Tsaparas (KDD 2014) first
+suggested to infer the strength of social ties from the topology of the network
+alone, by leveraging the Strong Triadic Closure (STC) property. The STC
+property states that if person A has strong social ties with persons B and C, B
+and C must be connected to each other as well (whether with a weak or strong
+tie). Sintos and Tsaparas exploited this to formulate the inference of the
+strength of social ties as NP-hard optimization problem, and proposed two
+approximation algorithms. We refine and improve upon this landmark paper, by
+developing a sequence of linear relaxations of this problem that can be solved
+exactly in polynomial time. Usefully, these relaxations infer more fine-grained
+levels of tie strength (beyond strong and weak), which also allows to avoid
+making arbitrary strong/weak strength assignments when the network topology
+provides inconclusive evidence. One of the relaxations simultaneously infers
+the presence of a limited number of STC violations. An extensive theoretical
+analysis leads to two efficient algorithmic approaches. Finally, our
+experimental results elucidate the strengths of the proposed approach, and
+sheds new light on the validity of the STC property in practice.
+",1,0,0,0,0,0
+7817,Conditional bias robust estimation of the total of curve data by sampling in a finite population: an illustration on electricity load curves,"  For marketing or power grid management purposes, many studies based on the
+analysis of the total electricity consumption curves of groups of customers are
+now carried out by electricity companies. Aggregated total or mean load curves
+are estimated using individual curves measured at fine time grid and collected
+according to some sampling design. Due to the skewness of the distribution of
+electricity consumptions, these samples often contain outlying curves which may
+have an important impact on the usual estimation procedures. We introduce
+several robust estimators of the total consumption curve which are not
+sensitive to such outlying curves. These estimators are based on the
+conditional bias approach and robust functional methods. We also derive mean
+square error estimators of these robust estimators and finally, we evaluate and
+compare the performance of the suggested estimators on Irish electricity data.
+",0,0,0,1,0,0
+7818,Ulrich bundles on smooth projective varieties of minimal degree,"  We classify the Ulrich vector bundles of arbitrary rank on smooth projective
+varieties of minimal degree. In the process, we prove the stability of the
+sheaves of relative differentials on rational scrolls.
+",0,0,1,0,0,0
+7819,$k$-shellable simplicial complexes and graphs,"  In this paper we show that a $k$-shellable simplicial complex is the
+expansion of a shellable complex. We prove that the face ring of a pure
+$k$-shellable simplicial complex satisfies the Stanley conjecture. In this way,
+by applying expansion functor to the face ring of a given pure shellable
+complex, we construct a large class of rings satisfying the Stanley conjecture.
+Also, by presenting some characterizations of $k$-shellable graphs, we extend
+some results due to Castrillón-Cruz, Cruz-Estrada and Van Tuyl-Villareal.
+",0,0,1,0,0,0
+7820,The Effect of Phasor Measurement Units on the Accuracy of the Network Estimated Variables,"  The most commonly used weighted least square state estimator in power
+industry is nonlinear and formulated by using conventional measurements such as
+line flow and injection measurements. PMUs (Phasor Measurement Units) are
+gradually adding them to improve the state estimation process. In this paper
+the way of corporation the PMU data to the conventional measurements and a
+linear formulation of the state estimation using only PMU measured data are
+investigated. Six cases are tested while gradually increasing the number of
+PMUs which are added to the measurement set and the effect of PMUs on the
+accuracy of variables are illustrated and compared by applying them on IEEE 14,
+30 test systems.
+",1,0,1,0,0,0
+7821,$ε$-Regularity and Structure of 4-dimensional Shrinking Ricci Solitons,"  A closed four dimensional manifold cannot possess a non-flat Ricci soliton
+metric with arbitrarily small $L^2$-norm of the curvature. In this paper, we
+localize this fact in the case of shrinking Ricci solitons by proving an
+$\varepsilon$-regularity theorem, thus confirming a conjecture of Cheeger-Tian.
+As applications, we will also derive structural results concerning the
+degeneration of the metrics on a family of complete non-compact four
+dimensional shrinking Ricci solitons without a uniform entropy lower bound. In
+the appendix, we provide a detailed account of the equivariant good chopping
+theorem when collapsing with locally bounded curvature happens.
+",0,0,1,0,0,0
+7822,Cosmological model discrimination with Deep Learning,"  We demonstrate the potential of Deep Learning methods for measurements of
+cosmological parameters from density fields, focusing on the extraction of
+non-Gaussian information. We consider weak lensing mass maps as our dataset. We
+aim for our method to be able to distinguish between five models, which were
+chosen to lie along the $\sigma_8$ - $\Omega_m$ degeneracy, and have nearly the
+same two-point statistics. We design and implement a Deep Convolutional Neural
+Network (DCNN) which learns the relation between five cosmological models and
+the mass maps they generate. We develop a new training strategy which ensures
+the good performance of the network for high levels of noise. We compare the
+performance of this approach to commonly used non-Gaussian statistics, namely
+the skewness and kurtosis of the convergence maps. We find that our
+implementation of DCNN outperforms the skewness and kurtosis statistics,
+especially for high noise levels. The network maintains the mean discrimination
+efficiency greater than $85\%$ even for noise levels corresponding to ground
+based lensing observations, while the other statistics perform worse in this
+setting, achieving efficiency less than $70\%$. This demonstrates the ability
+of CNN-based methods to efficiently break the $\sigma_8$ - $\Omega_m$
+degeneracy with weak lensing mass maps alone. We discuss the potential of this
+method to be applied to the analysis of real weak lensing data and other
+datasets.
+",0,1,0,1,0,0
+7823,Deep Memory Networks for Attitude Identification,"  We consider the task of identifying attitudes towards a given set of entities
+from text. Conventionally, this task is decomposed into two separate subtasks:
+target detection that identifies whether each entity is mentioned in the text,
+either explicitly or implicitly, and polarity classification that classifies
+the exact sentiment towards an identified entity (the target) into positive,
+negative, or neutral.
+Instead, we show that attitude identification can be solved with an
+end-to-end machine learning architecture, in which the two subtasks are
+interleaved by a deep memory network. In this way, signals produced in target
+detection provide clues for polarity classification, and reversely, the
+predicted polarity provides feedback to the identification of targets.
+Moreover, the treatments for the set of targets also influence each other --
+the learned representations may share the same semantics for some targets but
+vary for others. The proposed deep memory network, the AttNet, outperforms
+methods that do not consider the interactions between the subtasks or those
+among the targets, including conventional machine learning methods and the
+state-of-the-art deep learning models.
+",1,0,0,0,0,0
+7824,Discrete flow posteriors for variational inference in discrete dynamical systems,"  Each training step for a variational autoencoder (VAE) requires us to sample
+from the approximate posterior, so we usually choose simple (e.g. factorised)
+approximate posteriors in which sampling is an efficient computation that fully
+exploits GPU parallelism. However, such simple approximate posteriors are often
+insufficient, as they eliminate statistical dependencies in the posterior.
+While it is possible to use normalizing flow approximate posteriors for
+continuous latents, some problems have discrete latents and strong statistical
+dependencies. The most natural approach to model these dependencies is an
+autoregressive distribution, but sampling from such distributions is inherently
+sequential and thus slow. We develop a fast, parallel sampling procedure for
+autoregressive distributions based on fixed-point iterations which enables
+efficient and accurate variational inference in discrete state-space latent
+variable dynamical systems. To optimize the variational bound, we considered
+two ways to evaluate probabilities: inserting the relaxed samples directly into
+the pmf for the discrete distribution, or converting to continuous logistic
+latent variables and interpreting the K-step fixed-point iterations as a
+normalizing flow. We found that converting to continuous latent variables gave
+considerable additional scope for mismatch between the true and approximate
+posteriors, which resulted in biased inferences, we thus used the former
+approach. Using our fast sampling procedure, we were able to realize the
+benefits of correlated posteriors, including accurate uncertainty estimates for
+one cell, and accurate connectivity estimates for multiple cells, in an order
+of magnitude less time.
+",0,0,0,1,1,0
+7825,Audio Super Resolution using Neural Networks,"  We introduce a new audio processing technique that increases the sampling
+rate of signals such as speech or music using deep convolutional neural
+networks. Our model is trained on pairs of low and high-quality audio examples;
+at test-time, it predicts missing samples within a low-resolution signal in an
+interpolation process similar to image super-resolution. Our method is simple
+and does not involve specialized audio processing techniques; in our
+experiments, it outperforms baselines on standard speech and music benchmarks
+at upscaling ratios of 2x, 4x, and 6x. The method has practical applications in
+telephony, compression, and text-to-speech generation; it demonstrates the
+effectiveness of feed-forward convolutional architectures on an audio
+generation task.
+",1,0,0,0,0,0
+7826,Thermoelectric power factor enhancement by spin-polarized currents - a nanowire case study,"  Thermoelectric (TE) measurements have been performed on the workhorses of
+today's data storage devices, exhibiting either the giant or the anisotropic
+magnetoresistance effect (GMR and AMR). The temperature-dependent (50-300 K)
+and magnetic field-dependent (up to 1 T) TE power factor (PF) has been
+determined for several Co-Ni alloy nanowires with varying Co:Ni ratios as well
+as for Co-Ni/Cu multilayered nanowires with various Cu layer thicknesses, which
+were all synthesized via a template-assisted electrodeposition process. A
+systematic investigation of the resistivity, as well as the Seebeck
+coefficient, is performed for Co-Ni alloy nanowires and Co-Ni/Cu multilayered
+nanowires. At room temperature, measured values of TE PFs up to 3.6 mWK-2m-1
+for AMR samples and 2.0 mWK-2m-1 for GMR nanowires are obtained. Furthermore,
+the TE PF is found to increase by up to 13.1 % for AMR Co-Ni alloy nanowires
+and by up to 52 % for GMR Co-Ni/Cu samples in an external applied magnetic
+field. The magnetic nanowires exhibit TE PFs that are of the same order of
+magnitude as TE PFs of Bi-Sb-Se-Te based thermoelectric materials and,
+additionally, give the opportunity to adjust the TE power output to changing
+loads and hotspots through external magnetic fields.
+",0,1,0,0,0,0
+7827,Risk-Sensitive Cooperative Games for Human-Machine Systems,"  Autonomous systems can substantially enhance a human's efficiency and
+effectiveness in complex environments. Machines, however, are often unable to
+observe the preferences of the humans that they serve. Despite the fact that
+the human's and machine's objectives are aligned, asymmetric information, along
+with heterogeneous sensitivities to risk by the human and machine, make their
+joint optimization process a game with strategic interactions. We propose a
+framework based on risk-sensitive dynamic games; the human seeks to optimize
+her risk-sensitive criterion according to her true preferences, while the
+machine seeks to adaptively learn the human's preferences and at the same time
+provide a good service to the human. We develop a class of performance measures
+for the proposed framework based on the concept of regret. We then evaluate
+their dependence on the risk-sensitivity and the degree of uncertainty. We
+present applications of our framework to self-driving taxis, and robo-financial
+advising.
+",1,0,0,1,0,0
+7828,A natural framework for isogeometric fluid-structure interaction based on BEM-shell coupling,"  The interaction between thin structures and incompressible Newtonian fluids
+is ubiquitous both in nature and in industrial applications. In this paper we
+present an isogeometric formulation of such problems which exploits a boundary
+integral formulation of Stokes equations to model the surrounding flow, and a
+non linear Kirchhoff-Love shell theory to model the elastic behaviour of the
+structure. We propose three different coupling strategies: a monolithic, fully
+implicit coupling, a staggered, elasticity driven coupling, and a novel
+semi-implicit coupling, where the effect of the surrounding flow is
+incorporated in the non-linear terms of the solid solver through its damping
+characteristics. The novel semi-implicit approach is then used to demonstrate
+the power and robustness of our method, which fits ideally in the isogeometric
+paradigm, by exploiting only the boundary representation (B-Rep) of the thin
+structure middle surface.
+",0,1,1,0,0,0
+7829,Inertial Effects on the Stress Generation of Active Fluids,"  Suspensions of self-propelled bodies generate a unique mechanical stress
+owing to their motility that impacts their large-scale collective behavior. For
+microswimmers suspended in a fluid with negligible particle inertia, we have
+shown that the virial `swim stress' is a useful quantity to understand the
+rheology and nonequilibrium behaviors of active soft matter systems. For larger
+self-propelled organisms like fish, it is unclear how particle inertia impacts
+their stress generation and collective movement. Here, we analyze the effects
+of finite particle inertia on the mechanical pressure (or stress) generated by
+a suspension of self-propelled bodies. We find that swimmers of all scales
+generate a unique `swim stress' and `Reynolds stress' that impacts their
+collective motion. We discover that particle inertia plays a similar role as
+confinement in overdamped active Brownian systems, where the reduced run length
+of the swimmers decreases the swim stress and affects the phase behavior.
+Although the swim and Reynolds stresses vary individually with the magnitude of
+particle inertia, the sum of the two contributions is independent of particle
+inertia. This points to an important concept when computing stresses in
+computer simulations of nonequilibrium systems---the Reynolds and the virial
+stresses must both be calculated to obtain the overall stress generated by a
+system.
+",0,1,0,0,0,0
+7830,On Gauge Invariance and Covariant Derivatives in Metric Spaces,"  In this manuscript, we will discuss the construction of covariant derivative
+operator in quantum gravity. We will find it is appropriate to use affine
+connections more general than metric compatible connections in quantum gravity.
+We will demonstrate this using the canonical quantization procedure. This is
+valid irrespective of the presence and nature of sources. The standard Palatini
+formalism, where metric and affine connections are the independent variables,
+is not sufficient to construct a source-free theory of gravity with affine
+connections more general than the metric compatible Levi-Civita connections.
+This is also valid for minimally coupled interacting theories where sources
+only couple with metric by using the metric compatible Levi-Civita connections
+exclusively. We will discuss a potential formalism and possible extensions of
+the action to introduce nonmetricity in these cases. This is also required to
+construct a general interacting quantum theory with dynamical general affine
+connections. We will have to use a modified Ricci tensor to state Einstein's
+equation in the Palatini formalism. General affine connections can be described
+by a third rank tensor with one contravariant and two covariant indices.
+Antisymmetric part of this tensor in the lower indices gives torsion with a
+half factor. In the Palatini formalism or its generalizations considered here,
+symmetric part of this tensor in the lower indices is finite when torsion is
+finite. This part can give a massless scalar field in a potential formalism. We
+will have to extend the local conservation laws when we use general affine
+connections. General affine connections can become significant to solve
+cosmological problems.
+",0,1,0,0,0,0
+7831,A Compressed Sensing Approach for Distribution Matching,"  In this work, we formulate the fixed-length distribution matching as a
+Bayesian inference problem. Our proposed solution is inspired from the
+compressed sensing paradigm and the sparse superposition (SS) codes. First, we
+introduce sparsity in the binary source via position modulation (PM). We then
+present a simple and exact matcher based on Gaussian signal quantization. At
+the receiver, the dematcher exploits the sparsity in the source and performs
+low-complexity dematching based on generalized approximate message-passing
+(GAMP). We show that GAMP dematcher and spatial coupling lead to asymptotically
+optimal performance, in the sense that the rate tends to the entropy of the
+target distribution with vanishing reconstruction error in a proper limit.
+Furthermore, we assess the performance of the dematcher on practical
+Hadamard-based operators. A remarkable feature of our proposed solution is the
+possibility to: i) perform matching at the symbol level (nonbinary); ii)
+perform joint channel coding and matching.
+",0,0,0,1,0,0
+7832,A simple descriptor and predictor for the stable structures of two-dimensional surface alloys,"  Predicting the ground state of alloy systems is challenging due to the large
+number of possible configurations. We identify an easily computed descriptor
+for the stability of binary surface alloys, the effective coordination number
+$\mathscr{E}$. We show that $\mathscr{E}(M)$ correlates well with the enthalpy
+of mixing, from density functional theory (DFT) calculations on
+$M_x$Au$_{1-x}$/Ru [$M$ = Mn or Fe]. At each $x$, the most favored structure
+has the highest [lowest] value of $\mathscr{E}(M)$ if the system is
+non-magnetic [ferromagnetic]. Importantly, little accuracy is lost upon
+replacing $\mathscr{E}(M)$ by $\mathscr{E}^*(M)$, which can be quickly computed
+without performing a DFT calculation, possibly offering a simple alternative to
+the frequently used cluster expansion method.
+",0,1,0,0,0,0
+7833,Fractional integrals and Fourier transforms,"  This paper gives a short survey of some basic results related to estimates of
+fractional integrals and Fourier transforms. It is closely adjoint to our
+previous survey papers \cite{K1998} and \cite{K2007}. The main methods used in
+the paper are based on nonincreasing rearrangements. We give alternative proofs
+of some results.
+We observe also that the paper represents the mini-course given by the author
+at Barcelona University in October, 2014.
+",0,0,1,0,0,0
+7834,Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation,"  As opposed to manual feature engineering which is tedious and difficult to
+scale, network representation learning has attracted a surge of research
+interests as it automates the process of feature learning on graphs. The
+learned low-dimensional node vector representation is generalizable and eases
+the knowledge discovery process on graphs by enabling various off-the-shelf
+machine learning tools to be directly applied. Recent research has shown that
+the past decade of network embedding approaches either explicitly factorize a
+carefully designed matrix to obtain the low-dimensional node vector
+representation or are closely related to implicit matrix factorization, with
+the fundamental assumption that the factorized node connectivity matrix is
+low-rank. Nonetheless, the global low-rank assumption does not necessarily hold
+especially when the factorized matrix encodes complex node interactions, and
+the resultant single low-rank embedding matrix is insufficient to capture all
+the observed connectivity patterns. In this regard, we propose a novel
+multi-level network embedding framework BoostNE, which can learn multiple
+network embedding representations of different granularity from coarse to fine
+without imposing the prevalent global low-rank assumption. The proposed BoostNE
+method is also in line with the successful gradient boosting method in ensemble
+learning as multiple weak embeddings lead to a stronger and more effective one.
+We assess the effectiveness of the proposed BoostNE framework by comparing it
+with existing state-of-the-art network embedding methods on various datasets,
+and the experimental results corroborate the superiority of the proposed
+BoostNE network embedding framework.
+",1,0,0,1,0,0
+7835,"Deviation from the dipole-ice model in the new spinel spin-ice candidate, MgEr$_2$Se$_4$","  In spin ice research, small variations in structure or interactions drive a
+multitude of different behaviors, yet the collection of known materials relies
+heavily on the `227' pyrochlore structure. Here, we present thermodynamic,
+structural and inelastic neutron scattering data on a new spin-ice material,
+MgEr$_2$Se$_4$, which contributes to the relatively underexplored family of
+rare-earth spinel chalcogenides. X-ray and neutron diffraction confirm a normal
+spinel structure, and places Er$^{3+}$ moments on an ideal pyrochlore
+sublattice. Measurement of crystal electric field excitations with neutron
+inelastic scattering confirms that the moments have perfect Ising character,
+and further identifies the ground state Kramers doublet as having
+dipole-octupolar form with a significant multipolar character. Heat capacity
+and magnetic neutron diffuse scattering have ice-like features, but are
+inconsistent with Monte Carlo simulations of the nearest-neighbor and
+next-nearest-neighbor dipolar spin-ice (DSI) models. A significant remnant
+entropy is observed as T$\rightarrow$0 K, but again falls short of the full
+Pauling expectation for DSI, unless significant disorder is added. We show that
+these observations are fully in-line with what is recently reported for
+CdEr$_2$Se$_4$, and point to the importance of quantum fluctuations in these
+materials.
+",0,1,0,0,0,0
+7836,Generating Nontrivial Melodies for Music as a Service,"  We present a hybrid neural network and rule-based system that generates pop
+music. Music produced by pure rule-based systems often sounds mechanical. Music
+produced by machine learning sounds better, but still lacks hierarchical
+temporal structure. We restore temporal hierarchy by augmenting machine
+learning with a temporal production grammar, which generates the music's
+overall structure and chord progressions. A compatible melody is then generated
+by a conditional variational recurrent autoencoder. The autoencoder is trained
+with eight-measure segments from a corpus of 10,000 MIDI files, each of which
+has had its melody track and chord progressions identified heuristically. The
+autoencoder maps melody into a multi-dimensional feature space, conditioned by
+the underlying chord progression. A melody is then generated by feeding a
+random sample from that space to the autoencoder's decoder, along with the
+chord progression generated by the grammar. The autoencoder can make musically
+plausible variations on an existing melody, suitable for recurring motifs. It
+can also reharmonize a melody to a new chord progression, keeping the rhythm
+and contour. The generated music compares favorably with that generated by
+other academic and commercial software designed for the music-as-a-service
+industry.
+",1,0,0,0,0,0
+7837,Vision and Challenges for Knowledge Centric Networking (KCN),"  In the creation of a smart future information society, Internet of Things
+(IoT) and Content Centric Networking (CCN) break two key barriers for both the
+front-end sensing and back-end networking. However, we still observe the
+missing piece of the research that dominates the current networking traffic
+control and system management, e.g., lacking of the knowledge penetrated into
+both sensing and networking to glue them holistically. In this paper, we
+envision to leverage emerging machine learning or deep learning techniques to
+create aspects of knowledge for facilitating the designs. In particular, we can
+extract knowledge from collected data to facilitate reduced data volume,
+enhanced system intelligence and interactivity, improved service quality,
+communication with better controllability and lower cost. We name such a
+knowledge-oriented traffic control and networking management paradigm as the
+Knowledge Centric Networking (KCN). This paper presents KCN rationale, KCN
+benefits, related works and research opportunities.
+",1,0,0,0,0,0
+7838,Extracting Geometry from Quantum Spacetime: Obstacles down the road,"  Any acceptable quantum gravity theory must allow us to recover the classical
+spacetime in the appropriate limit. Moreover, the spacetime geometrical notions
+should be intrinsically tied to the behavior of the matter that probes them. We
+consider some difficulties that would be confronted in attempting such an
+enterprise. The problems we uncover seem to go beyond the technical level to
+the point of questioning the overall feasibility of the project. The main issue
+is related to the fact that, in the quantum theory, it is impossible to assign
+a trajectory to a physical object, and, on the other hand, according to the
+basic tenets of the geometrization of gravity, it is precisely the trajectories
+of free localized objects that define the spacetime geometry. The insights
+gained in this analysis should be relevant to those interested in the quest for
+a quantum theory of gravity and might help refocus some of its goals.
+",0,1,0,0,0,0
+7839,Data-Driven Estimation of Travel Latency Cost Functions via Inverse Optimization in Multi-Class Transportation Networks,"  We develop a method to estimate from data travel latency cost functions in
+multi-class transportation networks, which accommodate different types of
+vehicles with very different characteristics (e.g., cars and trucks).
+Leveraging our earlier work on inverse variational inequalities, we develop a
+data-driven approach to estimate the travel latency cost functions. Extensive
+numerical experiments using benchmark networks, ranging from moderate-sized to
+large-sized, demonstrate the effectiveness and efficiency of our approach.
+",1,0,1,0,0,0
+7840,Autoencoder Based Sample Selection for Self-Taught Learning,"  Self-taught learning is a technique that uses a large number of unlabeled
+data as source samples to improve the task performance on target samples.
+Compared with other transfer learning techniques, self-taught learning can be
+applied to a broader set of scenarios due to the loose restrictions on source
+data. However, knowledge transferred from source samples that are not
+sufficiently related to the target domain may negatively influence the target
+learner, which is referred to as negative transfer. In this paper, we propose a
+metric for the relevance between a source sample and target samples. To be more
+specific, both source and target samples are reconstructed through a
+single-layer autoencoder with a linear relationship between source samples and
+target samples simultaneously enforced. An l_{2,1}-norm sparsity constraint is
+imposed on the transformation matrix to identify source samples relevant to the
+target domain. Source domain samples that are deemed relevant are assigned
+pseudo-labels reflecting their relevance to target domain samples, and are
+combined with target samples in order to provide an expanded training set for
+classifier training. Local data structures are also preserved during source
+sample selection through spectral graph analysis. Promising results in
+extensive experiments show the advantages of the proposed approach.
+",0,0,0,1,0,0
+7841,Guiding Chemical Synthesis: Computational Prediction of the Regioselectivity of CH Functionalization,"  We will develop a computational method (RegioSQM) for predicting the
+regioselectivity of CH functionalization reactions that can be used by
+synthetic chemists who are not experts in computational chemistry through a
+simple web interface (regiosqm.org). CH functionalization, i.e. replacing the
+hydrogen atom in a CH bond with another atom or molecule, is arguably single
+most promising technique for increasing the efficiency of chemical synthesis,
+but there are no generally applicable predictive tools that predict which CH
+bond is most reactive. RegioSQM uses semiempirical quantum chemistry methods to
+predict relative stabilities of reaction intermediates which correlates with
+reaction rate and our goal is to determine which quantum method and
+intermediate give the best result for each reaction type. Finally, we will
+experimentally demonstrate how RegioSQM can be used to make the chemical
+synthesis part of drug discovery more efficient.
+",0,1,0,0,0,0
+7842,Potential-Function Proofs for First-Order Methods,"  This note discusses proofs for convergence of first-order methods based on
+simple potential-function arguments. We cover methods like gradient descent
+(for both smooth and non-smooth settings), mirror descent, and some accelerated
+variants.
+",1,0,0,0,0,0
+7843,Multidimensional $p$-adic continued fraction algorithms,"  We give a new class of multidimensional $p$-adic continued fraction
+algorithms. We propose an algorithm in the class for which we can expect that
+multidimensional $p$-adic version of Lagrange's Theorem holds.
+",0,0,1,0,0,0
+7844,Shutting down or powering up a (U)LIRG? Merger components in distinctly different evolutionary states in IRAS 19115-2124 (The Bird),"  We present new SINFONI near-infrared integral field unit (IFU) spectroscopy
+and SALT optical long-slit spectroscopy characterising the history of a nearby
+merging luminous infrared galaxy, dubbed the Bird (IRAS19115-2114). The NIR
+line-ratio maps of the IFU data-cubes and stellar population fitting of the
+SALT spectra now allow dating of the star formation (SF) over the triple system
+uncovered from our previous adaptive optics data. The distinct components
+separate very clearly in a line-ratio diagnostic diagram. An off-nuclear pure
+starburst dominates the current SF of the Bird with 60-70% of the total, with a
+4-7 Myr age, and signs of a fairly constant long-term star formation of the
+underlying stellar population. The most massive nucleus, in contrast, is
+quenched with a starburst age of >40 Myr and shows hints of budding AGN
+activity. The secondary massive nucleus is at an intermediate stage. The two
+major components have a population of older stars, consistent with a starburst
+triggered 1 Gyr ago in a first encounter. The simplest explanation of the
+history is that of a triple merger, where the strongly star forming component
+has joined later. We detect multiple gas flows in different phases. The Bird
+offers an opportunity to witness multiple stages of galaxy evolution in the
+same system; triggering as well as quenching of SF, and the early appearance of
+AGN activity. It also serves as a cautionary note on interpretations of
+observations with lower spatial resolution and/or without infrared data. At
+high-redshift the system would look like a clumpy starburst with crucial pieces
+of its puzzle hidden, in danger of misinterpretations.
+",0,1,0,0,0,0
+7845,Asymptotics to all orders of the Hurwitz zeta function,"  We present several formulae for the large-$t$ asymptotics of the modified
+Hurwitz zeta function $\zeta_1(x,s),x>0,s=\sigma+it,0<\sigma\leq1,t>0,$ which
+are valid to all orders. In the case of $x=0$, these formulae reduce to the
+asymptotic expressions recently obtained for the Riemann zeta function, which
+include the classical results of Siegel as a particular case.
+",0,0,1,0,0,0
+7846,Distributed Stochastic Approximation with Local Projections,"  We propose a distributed version of a stochastic approximation scheme
+constrained to remain in the intersection of a finite family of convex sets.
+The projection to the intersection of these sets is also computed in a
+distributed manner and a `nonlinear gossip' mechanism is employed to blend the
+projection iterations with the stochastic approximation using multiple time
+scales
+",1,0,0,0,0,0
+7847,Expected Policy Gradients,"  We propose expected policy gradients (EPG), which unify stochastic policy
+gradients (SPG) and deterministic policy gradients (DPG) for reinforcement
+learning. Inspired by expected sarsa, EPG integrates across the action when
+estimating the gradient, instead of relying only on the action in the sampled
+trajectory. We establish a new general policy gradient theorem, of which the
+stochastic and deterministic policy gradient theorems are special cases. We
+also prove that EPG reduces the variance of the gradient estimates without
+requiring deterministic policies and, for the Gaussian case, with no
+computational overhead. Finally, we show that it is optimal in a certain sense
+to explore with a Gaussian policy such that the covariance is proportional to
+the exponential of the scaled Hessian of the critic with respect to the
+actions. We present empirical results confirming that this new form of
+exploration substantially outperforms DPG with the Ornstein-Uhlenbeck heuristic
+in four challenging MuJoCo domains.
+",1,0,0,1,0,0
+7848,A new Hysteretic Nonlinear Energy Sink (HNES),"  The behavior of a new Hysteretic Nonlinear Energy Sink (HNES) coupled to a
+linear primary oscillator is investigated in shock mitigation. Apart from a
+small mass and a nonlinear elastic spring of the Duffing oscillator, the HNES
+is also comprised of a purely hysteretic and a linear elastic spring of
+potentially negative stiffness, connected in parallel. The Bouc-Wen model is
+used to describe the force produced by both the purely hysteretic and linear
+elastic springs. Coupling the primary oscillator with the HNES three nonlinear
+equations of motion are derived, in terms of the two displacements and the
+dimensionless hysteretic variable, which are integrated numerically using the
+analog equation method. The performance of the HNES is examined by quantifying
+the percentage of the initially induced energy in the primary system that is
+passively transferred and dissipated by the HNES. Remarkable results are
+achieved for a wide range of initial input energies. The great performance of
+the HNES is mostly evidenced when the linear spring stiffness takes on negative
+values.
+",0,1,0,0,0,0
+7849,Ultra-Low Noise Amplifier Design for Magnetic Resonance Imaging systems,"  This paper demonstrates designing and developing of an Ultra-Low Noise
+Amplifier which should potentially increase the sensitivity of the existing
+Magnetic Resonance Imaging (MRI) systems. The Design of the LNA is fabricated
+and characterized including matching and input high power protection circuits.
+The estimate improvement of SNR of the LNA in comparison to room temperature
+operation is taken here. The Cascode amplifier topology is chosen to be
+investigated for high performance Low Noise amplifier design and for the
+fabrication. The fabricated PCB layout of the Cascode LNA is tested by using
+measurement instruments Spectrum Analyser and Vector Network analyzer. The
+measurements of fabricated PCB layout of the Cascode LNA at room temperature
+had the following performance, the operation frequency is 32 MHz, the noise
+figure is 0.45 dB at source impedance 50 {\Omega}, the gain is 11.6 dB, the
+output return loss is 21.1 dB, and the input return loss 0.12 dB and it is
+unconditionally stable for up to 6 GHz band. The goal of the research is
+achieved where the Cascode LNA had improvement of SNR.
+",0,1,0,0,0,0
+7850,Virtual Astronaut for Scientific Visualization - A Prototype for Santa Maria Crater on Mars,"  To support scientific visualization of multiple-mission data from Mars, the
+Virtual Astronaut (VA) creates an interactive virtual 3D environment built on
+the Unity3D Game Engine. A prototype study was conducted based on orbital and
+Opportunity Rover data covering Santa Maria Crater in Meridiani Planum on Mars.
+The VA at Santa Maria provides dynamic visual representations of the imaging,
+compositional, and mineralogical information. The VA lets one navigate through
+the scene and provides geomorphic and geologic contexts for the rover
+operations. User interactions include in-situ observations visualization,
+feature measurement, and an animation control of rover drives. This paper
+covers our approach and implementation of the VA system. A brief summary of the
+prototype system functions and user feedback is also covered. Based on external
+review and comments by the science community, the prototype at Santa Maria has
+proven the VA to be an effective tool for virtual geovisual analysis.
+",1,1,0,0,0,0
+7851,Self-Supervised Generalisation with Meta Auxiliary Learning,"  Learning with auxiliary tasks has been shown to improve the generalisation of
+a primary task. However, this comes at the cost of manually-labelling
+additional tasks which may, or may not, be useful for the primary task. We
+propose a new method which automatically learns labels for an auxiliary task,
+such that any supervised learning task can be improved without requiring access
+to additional data. The approach is to train two neural networks: a
+label-generation network to predict the auxiliary labels, and a multi-task
+network to train the primary task alongside the auxiliary task. The loss for
+the label-generation network incorporates the multi-task network's performance,
+and so this interaction between the two networks can be seen as a form of meta
+learning. We show that our proposed method, Meta AuXiliary Learning (MAXL),
+outperforms single-task learning on 7 image datasets by a significant margin,
+without requiring additional auxiliary labels. We also show that MAXL
+outperforms several other baselines for generating auxiliary labels, and is
+even competitive when compared with human-defined auxiliary labels. The
+self-supervised nature of our method leads to a promising new direction towards
+automated generalisation. The source code is available at
+\url{this https URL}.
+",1,0,0,1,0,0
+7852,Measuring High-Energy Spectra with HAWC,"  The High-Altitude Water-Cherenkov (HAWC) experiment is a TeV $\gamma$-ray
+observatory located \unit[4100]{m} above sea level on the Sierra Negra mountain
+in Puebla, Mexico. The detector consists of 300 water-filled tanks, each
+instrumented with 4 photomultiplier tubes that utilize the water-Cherenkov
+technique to detect atmospheric air showers produced by cosmic $\gamma$ rays.
+Construction of HAWC was completed in March of 2015. The experiment's wide
+instantaneous field of view (\unit[2]{sr}) and high duty cycle (> 95\%) make it
+a powerful survey instrument sensitive to pulsars, supernova remnants, and
+other $\gamma$-ray sources. The mechanisms of particle acceleration at these
+sources can be studied by analyzing their high-energy spectra. To this end, we
+have developed an event-by-event energy-reconstruction algorithm using an
+artificial neural network to estimate energies of primary $\gamma$ rays at
+HAWC. We will present the details of this technique and its performance as well
+as the current progress toward using it to measure energy spectra of
+$\gamma$-ray sources.
+",0,1,0,0,0,0
+7853,A Study on Arbitrarily Varying Channels with Causal Side Information at the Encoder,"  In this work, we study two models of arbitrarily varying channels, when
+causal side information is available at the encoder in a causal manner. First,
+we study the arbitrarily varying channel (AVC) with input and state
+constraints, when the encoder has state information in a causal manner. Lower
+and upper bounds on the random code capacity are developed. A lower bound on
+the deterministic code capacity is established in the case of a
+message-averaged input constraint. In the setting where a state constraint is
+imposed on the jammer, while the user is under no constraints, the random code
+bounds coincide, and the random code capacity is determined. Furthermore, for
+this scenario, a generalized non-symmetrizability condition is stated, under
+which the deterministic code capacity coincides with the random code capacity.
+A second model considered in our work is the arbitrarily varying degraded
+broadcast channel with causal side information at the encoder (without
+constraints). We establish inner and outer bounds on both the random code
+capacity region and the deterministic code capacity region. The capacity region
+is then determined for a class of channels satisfying a condition on the mutual
+informations between the strategy variables and the channel outputs. As an
+example, we show that the condition holds for the arbitrarily varying binary
+symmetric broadcast channel, and we find the corresponding capacity region.
+",1,0,1,0,0,0
+7854,On the Three Properties of Stationary Populations and knotting with Non-Stationary Populations,"  We propose three properties that are related to the stationary population
+identity (SPI) of population biology by connecting it with stationary
+populations and non-stationary populations which are approaching stationarity.
+These properties provide deeper insights into cohort formation in real-world
+populations and the length of the duration for which stationary and
+non-stationary conditions hold. The new concepts are based on the time gap
+between the occurrence of stationary and non-stationary populations within the
+SPI framework that we refer to as Oscillatory SPI and the Amplitude of SPI.
+",0,0,0,0,1,0
+7855,Generating and designing DNA with deep generative models,"  We propose generative neural network methods to generate DNA sequences and
+tune them to have desired properties. We present three approaches: creating
+synthetic DNA sequences using a generative adversarial network; a DNA-based
+variant of the activation maximization (""deep dream"") design method; and a
+joint procedure which combines these two approaches together. We show that
+these tools capture important structures of the data and, when applied to
+designing probes for protein binding microarrays, allow us to generate new
+sequences whose properties are estimated to be superior to those found in the
+training data. We believe that these results open the door for applying deep
+generative models to advance genomics research.
+",1,0,0,1,0,0
+7856,Radon background in liquid xenon detectors,"  The radioactive daughters isotope of 222Rn are one of the highest risk
+contaminants in liquid xenon detectors aiming for a small signal rate. The
+noble gas is permanently emanated from the detector surfaces and mixed with the
+xenon target. Because of its long half-life 222Rn is homogeneously distributed
+in the target and its subsequent decays can mimic signal events. Since no
+shielding is possible this background source can be the dominant one in future
+large scale experiments. This article provides an overview of strategies used
+to mitigate this source of background by means of material selection and
+on-line radon removal techniques.
+",0,1,0,0,0,0
+7857,Minimax Regret Bounds for Reinforcement Learning,"  We consider the problem of provably optimal exploration in reinforcement
+learning for finite horizon MDPs. We show that an optimistic modification to
+value iteration achieves a regret bound of $\tilde{O}( \sqrt{HSAT} +
+H^2S^2A+H\sqrt{T})$ where $H$ is the time horizon, $S$ the number of states,
+$A$ the number of actions and $T$ the number of time-steps. This result
+improves over the best previous known bound $\tilde{O}(HS \sqrt{AT})$ achieved
+by the UCRL2 algorithm of Jaksch et al., 2010. The key significance of our new
+results is that when $T\geq H^3S^3A$ and $SA\geq H$, it leads to a regret of
+$\tilde{O}(\sqrt{HSAT})$ that matches the established lower bound of
+$\Omega(\sqrt{HSAT})$ up to a logarithmic factor. Our analysis contains two key
+insights. We use careful application of concentration inequalities to the
+optimal value function as a whole, rather than to the transitions probabilities
+(to improve scaling in $S$), and we define Bernstein-based ""exploration
+bonuses"" that use the empirical variance of the estimated values at the next
+states (to improve scaling in $H$).
+",1,0,0,1,0,0
+7858,Asymptotic Theory for the Maximum of an Increasing Sequence of Parametric Functions,"  \cite{HillMotegi2017} present a new general asymptotic theory for the maximum
+of a random array $\{\mathcal{X}_{n}(i)$ $:$ $1$ $\leq $ $i$ $\leq $
+$\mathcal{L}\}_{n\geq 1}$, where each $\mathcal{X}_{n}(i)$ is assumed to
+converge in probability as $n$ $\rightarrow $ $\infty $. The array dimension
+$\mathcal{L}$ is allowed to increase with the sample size $n$. Existing extreme
+value theory arguments focus on observed data $\mathcal{X}_{n}(i)$, and require
+a well defined limit law for $\max_{1\leq i\leq
+\mathcal{L}}|\mathcal{X}_{n}(i)|$ by restricting dependence across $i$. The
+high dimensional central limit theory literature presumes approximability by a
+Gaussian law, and also restricts attention to observed data.
+\cite{HillMotegi2017} do not require $\max_{1\leq i\leq
+\mathcal{L}_{n}}|\mathcal{X}_{n}(i)|$ to have a well defined limit nor be
+approximable by a Gaussian random variable, and we do not make any assumptions
+about dependence across $i$. We apply the theory to filtered data when the
+variable of interest $\mathcal{X}_{n}(i,\theta _{0})$ is not observed, but its
+sample counterpart $\mathcal{X}_{n}(i,\hat{\theta}_{n})$ is observed where
+$\hat{\theta}_{n}$ estimates $\theta _{0}$. The main results are illustrated by
+looking at unit root tests for a high dimensional random variable, and a
+residuals white noise test.
+",0,0,1,1,0,0
+7859,Resilient Active Information Gathering with Mobile Robots,"  Applications of safety, security, and rescue in robotics, such as multi-robot
+target tracking, involve the execution of information acquisition tasks by
+teams of mobile robots. However, in failure-prone or adversarial environments,
+robots get attacked, their communication channels get jammed, and their sensors
+may fail, resulting in the withdrawal of robots from the collective task, and
+consequently the inability of the remaining active robots to coordinate with
+each other. As a result, traditional design paradigms become insufficient and,
+in contrast, resilient designs against system-wide failures and attacks become
+important. In general, resilient design problems are hard, and even though they
+often involve objective functions that are monotone or submodular, scalable
+approximation algorithms for their solution have been hitherto unknown. In this
+paper, we provide the first algorithm, enabling the following capabilities:
+minimal communication, i.e., the algorithm is executed by the robots based only
+on minimal communication between them; system-wide resiliency, i.e., the
+algorithm is valid for any number of denial-of-service attacks and failures;
+and provable approximation performance, i.e., the algorithm ensures for all
+monotone (and not necessarily submodular) objective functions a solution that
+is finitely close to the optimal. We quantify our algorithm's approximation
+performance using a notion of curvature for monotone set functions. We support
+our theoretical analyses with simulated and real-world experiments, by
+considering an active information gathering scenario, namely, multi-robot
+target tracking.
+",1,0,0,1,0,0
+7860,Optical properties of a four-layer waveguiding nanocomposite structure in near-IR regime,"  The theoretical study of the optical properties of TE- and TM- modes in a
+four-layer structure composed of the magneto-optical yttrium iron garnet
+guiding layer on a dielectric substrate covered by planar nanocomposite guiding
+multilayer is presented. The dispersion equation is obtained taking into
+account the bigyrotropic properties of yttrium-iron garnet, and an original
+algorithm for the guided modes identification is proposed. The dispersion
+spectra are analyzed and the energy flux distributions across the structure are
+calculated. The fourfold difference between the partial power fluxes within the
+waveguide layers is achieved in the wavelength range of 200 nm.
+",0,1,0,0,0,0
+7861,High Dimensional Structured Superposition Models,"  High dimensional superposition models characterize observations using
+parameters which can be written as a sum of multiple component parameters, each
+with its own structure, e.g., sum of low rank and sparse matrices, sum of
+sparse and rotated sparse vectors, etc. In this paper, we consider general
+superposition models which allow sum of any number of component parameters, and
+each component structure can be characterized by any norm. We present a simple
+estimator for such models, give a geometric condition under which the
+components can be accurately estimated, characterize sample complexity of the
+estimator, and give high probability non-asymptotic bounds on the componentwise
+estimation error. We use tools from empirical processes and generic chaining
+for the statistical analysis, and our results, which substantially generalize
+prior work on superposition models, are in terms of Gaussian widths of suitable
+sets.
+",1,0,0,1,0,0
+7862,Source Forager: A Search Engine for Similar Source Code,"  Developers spend a significant amount of time searching for code: e.g., to
+understand how to complete, correct, or adapt their own code for a new context.
+Unfortunately, the state of the art in code search has not evolved much beyond
+text search over tokenized source. Code has much richer structure and semantics
+than normal text, and this property can be exploited to specialize the
+code-search process for better querying, searching, and ranking of code-search
+results.
+We present a new code-search engine named Source Forager. Given a query in
+the form of a C/C++ function, Source Forager searches a pre-populated code
+database for similar C/C++ functions. Source Forager preprocesses the database
+to extract a variety of simple code features that capture different aspects of
+code. A search returns the $k$ functions in the database that are most similar
+to the query, based on the various extracted code features.
+We tested the usefulness of Source Forager using a variety of code-search
+queries from two domains. Our experiments show that the ranked results returned
+by Source Forager are accurate, and that query-relevant functions can be
+reliably retrieved even when searching through a large code database that
+contains very few query-relevant functions.
+We believe that Source Forager is a first step towards much-needed tools that
+provide a better code-search experience.
+",1,0,0,0,0,0
+7863,Crossmatching variable objects with the Gaia data,"  Tens of millions of new variable objects are expected to be identified in
+over a billion time series from the Gaia mission. Crossmatching known variable
+sources with those from Gaia is crucial to incorporate current knowledge,
+understand how these objects appear in the Gaia data, train supervised
+classifiers to recognise known classes, and validate the results of the
+Variability Processing and Analysis Coordination Unit (CU7) within the Gaia
+Data Analysis and Processing Consortium (DPAC). The method employed by CU7 to
+crossmatch variables for the first Gaia data release includes a binary
+classifier to take into account positional uncertainties, proper motion,
+targeted variability signals, and artefacts present in the early calibration of
+the Gaia data. Crossmatching with a classifier makes it possible to automate
+all those decisions which are typically made during visual inspection. The
+classifier can be trained with objects characterized by a variety of attributes
+to ensure similarity in multiple dimensions (astrometry, photometry,
+time-series features), with no need for a-priori transformations to compare
+different photometric bands, or of predictive models of the motion of objects
+to compare positions. Other advantages as well as some disadvantages of the
+method are discussed. Implementation steps from the training to the assessment
+of the crossmatch classifier and selection of results are described.
+",0,1,0,0,0,0
+7864,A New Test of Multivariate Nonlinear Causality,"  The multivariate nonlinear Granger causality developed by Bai et al. (2010)
+plays an important role in detecting the dynamic interrelationships between two
+groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by
+Hiemstra and Jones (1994), they attempt to establish a central limit theorem
+(CLT) of their test statistic by applying the asymptotical property of
+multivariate $U$-statistic. However, Bai et al. (2016) revisit the HJ test and
+find that the test statistic given by HJ is NOT a function of $U$-statistics
+which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor
+the one extended by Bai et al. (2010) is valid for statistical inference. In
+this paper, we re-estimate the probabilities and reestablish the CLT of the new
+test statistic. Numerical simulation shows that our new estimates are
+consistent and our new test performs decent size and power.
+",0,0,0,1,0,0
+7865,"Nonlinear dynamics of polar regions in paraelectric phase of (Ba1-x,Srx)TiO3 ceramics","  The dynamic dielectric nonlinearity of barium strontium titanate
+(Ba1-x,Srx)TiO3 ceramics is investigated in their paraelectric phase. With the
+goal to contribute to the identification of the mechanisms that govern the
+dielectric nonlinearity in this family, we analyze the amplitude and the phase
+angles of the first and the third harmonics of polarization. Our study shows
+that an interpretation of the field-dependent polarization in paraelectric
+(Ba1-x,Srx)TiO3 ceramics in terms of the Rayleigh-type dynamics is inadequate
+for our samples and that their nonlinear response rather resembles that
+observed in canonical relaxor Pb(Mg1/3Nb2/3)O3.
+",0,1,0,0,0,0
+7866,Nonlinear Modal Decoupling Based Power System Transient Stability Analysis,"  Nonlinear modal decoupling (NMD) was recently proposed to nonlinearly
+transform a multi-oscillator system into a number of decoupled oscillators
+which together behave the same as the original system in an extended
+neighborhood of the equilibrium. Each oscillator has just one degree of freedom
+and hence can easily be analyzed to infer the stability of the original system
+associated with one electromechanical mode. As the first attempt of applying
+the NMD methodology to realistic power system models, this paper proposes an
+NMD-based transient stability analysis approach. For a multi-machine power
+system, the approach first derives decoupled nonlinear oscillators by a
+coordinates transformation, and then applies Lyapunov stability analysis to
+oscillators to assess the stability of the original system. Nonlinear modal
+interaction is also considered. The approach can be efficiently applied to a
+large-scale power grid by conducting NMD regarding only selected modes. Case
+studies on a 3-machine 9-bus system and an NPCC 48-machine 140-bus system show
+the potentials of the approach in transient stability analysis for
+multi-machine systems.
+",1,0,0,0,0,0
+7867,"KELT-18b: Puffy Planet, Hot Host, Probably Perturbed","  We report the discovery of KELT-18b, a transiting hot Jupiter in a 2.87d
+orbit around the bright (V=10.1), hot, F4V star BD+60 1538 (TYC 3865-1173-1).
+We present follow-up photometry, spectroscopy, and adaptive optics imaging that
+allow a detailed characterization of the system. Our preferred model fits yield
+a host stellar temperature of 6670+/-120 K and a mass of 1.524+/-0.069 Msun,
+situating it as one of only a handful of known transiting planets with hosts
+that are as hot, massive, and bright. The planet has a mass of 1.18+/-0.11
+Mjup, a radius of 1.57+/-0.04 Rjup, and a density of 0.377+/-0.040 g/cm^3,
+making it one of the most inflated planets known around a hot star. We argue
+that KELT-18b's high temperature and low surface gravity, which yield an
+estimated ~600 km atmospheric scale height, combined with its hot, bright host
+make it an excellent candidate for observations aimed at atmospheric
+characterization. We also present evidence for a bound stellar companion at a
+projected separation of ~1100 AU, and speculate that it may have contributed to
+the strong misalignment we suspect between KELT-18's spin axis and its planet's
+orbital axis. The inferior conjunction time is 2457542.524998 +/-0.000416
+(BJD_TDB) and the orbital period is 2.8717510 +/- 0.0000029 days. We encourage
+Rossiter-McLaughlin measurements in the near future to confirm the suspected
+spin-orbit misalignment of this system.
+",0,1,0,0,0,0
+7868,BAMBI: An R package for Fitting Bivariate Angular Mixture Models,"  Statistical analyses of directional or angular data have applications in a
+variety of fields, such as geology, meteorology and bioinformatics. There is
+substantial literature on descriptive and inferential techniques for univariate
+angular data, with the bivariate (or more generally, multivariate) cases
+receiving more attention in recent years. However, there is a lack of software
+implementing inferential techniques in practice, especially in the bivariate
+situation. In this article, we introduce *BAMBI*, an R package for analyzing
+bivariate (and univariate) angular data. We implement random generation,
+density evaluation, and computation of summary measures for three bivariate
+(viz., bivariate wrapped normal, von Mises sine and von Mises cosine) and two
+univariate (viz., univariate wrapped normal and von Mises) angular
+distributions. The major contribution of BAMBI to statistical computing is in
+providing Bayesian methods for modeling angular data using finite mixtures of
+these distributions. We also provide functions for visual and numerical
+diagnostics and Bayesian inference for the fitted models. In this article, we
+first provide a brief review of the distributions and techniques used in BAMBI,
+then describe the capabilities of the package, and finally conclude with
+demonstrations of mixture model fitting using BAMBI on the two real datasets
+included in the package, one univariate and one bivariate.
+",0,0,0,1,0,0
+7869,Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning,"  Two-timescale Stochastic Approximation (SA) algorithms are widely used in
+Reinforcement Learning (RL). Their iterates have two parts that are updated
+using distinct stepsizes. In this work, we develop a novel recipe for their
+finite sample analysis. Using this, we provide a concentration bound, which is
+the first such result for a two-timescale SA. The type of bound we obtain is
+known as `lock-in probability'. We also introduce a new projection scheme, in
+which the time between successive projections increases exponentially. This
+scheme allows one to elegantly transform a lock-in probability into a
+convergence rate result for projected two-timescale SA. From this latter
+result, we then extract key insights on stepsize selection. As an application,
+we finally obtain convergence rates for the projected two-timescale RL
+algorithms GTD(0), GTD2, and TDC.
+",1,0,0,0,0,0
+7870,Existence and uniqueness of solutions to Y-systems and TBA equations,"  We consider Y-system functional equations of the form $$
+Y_n(x+i)Y_n(x-i)=\prod_{m=1}^N (1+Y_m(x))^{G_{nm}}$$ and the corresponding
+nonlinear integral equations of the Thermodynamic Bethe Ansatz. We prove an
+existence and uniqueness result for solutions of these equations, subject to
+appropriate conditions on the analytical properties of the $Y_n$, in particular
+the absence of zeros in a strip around the real axis. The matrix $G_{nm}$ must
+have non-negative real entries, and be irreducible and diagonalisable over
+$\mathbb{R}$ with spectral radius less than 2. This includes the adjacency
+matrices of finite Dynkin diagrams, but covers much more as we do not require
+$G_{nm}$ to be integers. Our results specialise to the constant Y-system,
+proving existence and uniqueness of a strictly positive solution in that case.
+",0,1,0,0,0,0
+7871,Normalization of Neural Networks using Analytic Variance Propagation,"  We address the problem of estimating statistics of hidden units in a neural
+network using a method of analytic moment propagation. These statistics are
+useful for approximate whitening of the inputs in front of saturating
+non-linearities such as a sigmoid function. This is important for
+initialization of training and for reducing the accumulated scale and bias
+dependencies (compensating covariate shift), which presumably eases the
+learning. In batch normalization, which is currently a very widely applied
+technique, sample estimates of statistics of hidden units over a batch are
+used. The proposed estimation uses an analytic propagation of mean and variance
+of the training set through the network. The result depends on the network
+structure and its current weights but not on the specific batch input. The
+estimates are suitable for initialization and normalization, efficient to
+compute and independent of the batch size. The experimental verification well
+supports these claims. However, the method does not share the generalization
+properties of BN, to which our experiments give some additional insight.
+",0,0,0,1,0,0
+7872,Ferrimagnetism in the Spin-1/2 Heisenberg Antiferromagnet on a Distorted Triangular Lattice,"  The ground state of the spin-$1/2$ Heisenberg antiferromagnet on a distorted
+triangular lattice is studied using a numerical-diagonalization method. The
+network of interactions is the $\sqrt{3}\times\sqrt{3}$ type; the interactions
+are continuously controlled between the undistorted triangular lattice and the
+dice lattice. We find new states between the nonmagnetic 120-degree-structured
+state of the undistorted triangular case and the up-up-down state of the dice
+case. The intermediate states show spontaneous magnetizations that are smaller
+than one third of the saturated magntization corresponding to the up-up-down
+state.
+",0,1,0,0,0,0
+7873,Delta sets for symmetric numerical semigroups with embedding dimension three,"  This work extends the results known for the Delta sets of non-symmetric
+numerical semigroups with embedding dimension three to the symmetric case.
+Thus, we have a fast algorithm to compute the Delta set of any embedding
+dimension three numerical semigroup. Also, as a consequence of these resutls,
+the sets that can be realized as Delta sets of numerical semigroups of
+embedding dimension three are fully characterized.
+",0,0,1,0,0,0
+7874,Riemann-Hilbert problems for the resolved conifold,"  We study the Riemann-Hilbert problems associated to the Donaldson-Thomas
+theory of the resolved conifold. We give explicit solutions in terms of the
+Barnes double and triple sine functions. We show that the corresponding tau
+function is a non-perturbative partition function, in the sense that its
+asymptotic expansion coincides with the topological string partition function.
+",0,0,1,0,0,0
+7875,On the Power of Over-parametrization in Neural Networks with Quadratic Activation,"  We provide new theoretical insights on why over-parametrization is effective
+in learning neural networks. For a $k$ hidden node shallow network with
+quadratic activation and $n$ training data points, we show as long as $ k \ge
+\sqrt{2n}$, over-parametrization enables local search algorithms to find a
+\emph{globally} optimal solution for general smooth and convex loss functions.
+Further, despite that the number of parameters may exceed the sample size,
+using theory of Rademacher complexity, we show with weight decay, the solution
+also generalizes well if the data is sampled from a regular distribution such
+as Gaussian. To prove when $k\ge \sqrt{2n}$, the loss function has benign
+landscape properties, we adopt an idea from smoothed analysis, which may have
+other applications in studying loss surfaces of neural networks.
+",0,0,0,1,0,0
+7876,Multi-Label Learning with Label Enhancement,"  The task of multi-label learning is to predict a set of relevant labels for
+the unseen instance. Traditional multi-label learning algorithms treat each
+class label as a logical indicator of whether the corresponding label is
+relevant or irrelevant to the instance, i.e., +1 represents relevant to the
+instance and -1 represents irrelevant to the instance. Such label represented
+by -1 or +1 is called logical label. Logical label cannot reflect different
+label importance. However, for real-world multi-label learning problems, the
+importance of each possible label is generally different. For the real
+applications, it is difficult to obtain the label importance information
+directly. Thus we need a method to reconstruct the essential label importance
+from the logical multilabel data. To solve this problem, we assume that each
+multi-label instance is described by a vector of latent real-valued labels,
+which can reflect the importance of the corresponding labels. Such label is
+called numerical label. The process of reconstructing the numerical labels from
+the logical multi-label data via utilizing the logical label information and
+the topological structure in the feature space is called Label Enhancement. In
+this paper, we propose a novel multi-label learning framework called LEMLL,
+i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the
+numerical labels and label enhancement into a unified framework. Extensive
+comparative studies validate that the performance of multi-label learning can
+be improved significantly with label enhancement and LEMLL can effectively
+reconstruct latent label importance information from logical multi-label data.
+",1,0,0,0,0,0
+7877,Unsure When to Stop? Ask Your Semantic Neighbors,"  In iterative supervised learning algorithms it is common to reach a point in
+the search where no further induction seems to be possible with the available
+data. If the search is continued beyond this point, the risk of overfitting
+increases significantly. Following the recent developments in inductive
+semantic stochastic methods, this paper studies the feasibility of using
+information gathered from the semantic neighborhood to decide when to stop the
+search. Two semantic stopping criteria are proposed and experimentally assessed
+in Geometric Semantic Genetic Programming (GSGP) and in the Semantic Learning
+Machine (SLM) algorithm (the equivalent algorithm for neural networks). The
+experiments are performed on real-world high-dimensional regression datasets.
+The results show that the proposed semantic stopping criteria are able to
+detect stopping points that result in a competitive generalization for both
+GSGP and SLM. This approach also yields computationally efficient algorithms as
+it allows the evolution of neural networks in less than 3 seconds on average,
+and of GP trees in at most 10 seconds. The usage of the proposed semantic
+stopping criteria in conjunction with the computation of optimal
+mutation/learning steps also results in small trees and neural networks.
+",1,0,0,1,0,0
+7878,Deep Generative Learning via Variational Gradient Flow,"  We propose a general framework to learn deep generative models via
+\textbf{V}ariational \textbf{Gr}adient Fl\textbf{ow} (VGrow) on probability
+spaces. The evolving distribution that asymptotically converges to the target
+distribution is governed by a vector field, which is the negative gradient of
+the first variation of the $f$-divergence between them. We prove that the
+evolving distribution coincides with the pushforward distribution through the
+infinitesimal time composition of residual maps that are perturbations of the
+identity map along the vector field. The vector field depends on the density
+ratio of the pushforward distribution and the target distribution, which can be
+consistently learned from a binary classification problem. Connections of our
+proposed VGrow method with other popular methods, such as VAE, GAN and
+flow-based methods, have been established in this framework, gaining new
+insights of deep generative learning. We also evaluated several commonly used
+divergences, including Kullback-Leibler, Jensen-Shannon, Jeffrey divergences as
+well as our newly discovered `logD' divergence which serves as the objective
+function of the logD-trick GAN. Experimental results on benchmark datasets
+demonstrate that VGrow can generate high-fidelity images in a stable and
+efficient manner, achieving competitive performance with state-of-the-art GANs.
+",1,0,0,1,0,0
+7879,Warming trend in cold season of the Yangtze River Delta and its correlation with Siberian high,"  Based on the meteorological data from 1960 to 2010, we investigated the
+temperature variation in the Yangtze River Delta (YRD) by using Mann-Kendall
+nonparametric test and explored the correlation between the temperature in the
+cold season and the Siberian high intensity (SHI) by using correlation analysis
+method. The main results are that (a) the temperature in YRD increased
+remarkably during the study period, (b) the warming trend in the cold season
+made the higher contribution to annual warming, and (c) there was a significant
+negative correlation between the temperature in the cold season and the SHI.
+",0,0,0,1,0,0
+7880,Modeling and Quantifying the Forces Driving Online Video Popularity Evolution,"  Video popularity is an essential reference for optimizing resource allocation
+and video recommendation in online video services. However, there is still no
+convincing model that can accurately depict a video's popularity evolution. In
+this paper, we propose a dynamic popularity model by modeling the video
+information diffusion process driven by various forms of recommendation.
+Through fitting the model with real traces collected from a practical system,
+we can quantify the strengths of the recommendation forces. Such quantification
+can lead to characterizing video popularity patterns, user behaviors and
+recommendation strategies, which is illustrated by a case study of TV episodes.
+",1,0,0,0,0,0
+7881,Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games,"  Many artificial intelligence (AI) applications often require multiple
+intelligent agents to work in a collaborative effort. Efficient learning for
+intra-agent communication and coordination is an indispensable step towards
+general AI. In this paper, we take StarCraft combat game as a case study, where
+the task is to coordinate multiple agents as a team to defeat their enemies. To
+maintain a scalable yet effective communication protocol, we introduce a
+Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a
+vectorised extension of actor-critic formulation. We show that BiCNet can
+handle different types of combats with arbitrary numbers of AI agents for both
+sides. Our analysis demonstrates that without any supervisions such as human
+demonstrations or labelled data, BiCNet could learn various types of advanced
+coordination strategies that have been commonly used by experienced game
+players. In our experiments, we evaluate our approach against multiple
+baselines under different scenarios; it shows state-of-the-art performance, and
+possesses potential values for large-scale real-world applications.
+",1,0,0,0,0,0
+7882,Measurement of the Lorentz-FitzGerald Body Contraction,"  A complete foundational discussion of acceleration in context of Special
+Relativity is presented. Acceleration allows the measurement of a
+Lorentz-FitzGerald body contraction created. It is argued that in the back
+scattering of a probing laser beam from a relativistic flying electron cloud
+mirror generated by an ultra-intense laser pulse, a first measurement of a
+Lorentz-FitzGerald body contraction is feasible.
+",0,1,0,0,0,0
+7883,Information Directed Sampling for Stochastic Bandits with Graph Feedback,"  We consider stochastic multi-armed bandit problems with graph feedback, where
+the decision maker is allowed to observe the neighboring actions of the chosen
+action. We allow the graph structure to vary with time and consider both
+deterministic and Erdős-Rényi random graph models. For such a graph
+feedback model, we first present a novel analysis of Thompson sampling that
+leads to tighter performance bound than existing work. Next, we propose new
+Information Directed Sampling based policies that are graph-aware in their
+decision making. Under the deterministic graph case, we establish a Bayesian
+regret bound for the proposed policies that scales with the clique cover number
+of the graph instead of the number of actions. Under the random graph case, we
+provide a Bayesian regret bound for the proposed policies that scales with the
+ratio of the number of actions over the expected number of observations per
+iteration. To the best of our knowledge, this is the first analytical result
+for stochastic bandits with random graph feedback. Finally, using numerical
+evaluations, we demonstrate that our proposed IDS policies outperform existing
+approaches, including adaptions of upper confidence bound, $\epsilon$-greedy
+and Exp3 algorithms.
+",1,0,0,1,0,0
+7884,"Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning","  Supervised object detection and semantic segmentation require object or even
+pixel level annotations. When there exist image level labels only, it is
+challenging for weakly supervised algorithms to achieve accurate predictions.
+The accuracy achieved by top weakly supervised algorithms is still
+significantly lower than their fully supervised counterparts. In this paper, we
+propose a novel weakly supervised curriculum learning pipeline for multi-label
+object recognition, detection and semantic segmentation. In this pipeline, we
+first obtain intermediate object localization and pixel labeling results for
+the training images, and then use such results to train task-specific deep
+networks in a fully supervised manner. The entire process consists of four
+stages, including object localization in the training images, filtering and
+fusing object instances, pixel labeling for the training images, and
+task-specific network training. To obtain clean object instances in the
+training images, we propose a novel algorithm for filtering, fusing and
+classifying object instances collected from multiple solution mechanisms. In
+this algorithm, we incorporate both metric learning and density-based
+clustering to filter detected object instances. Experiments show that our
+weakly supervised pipeline achieves state-of-the-art results in multi-label
+image classification as well as weakly supervised object detection and very
+competitive results in weakly supervised semantic segmentation on MS-COCO,
+PASCAL VOC 2007 and PASCAL VOC 2012.
+",0,0,0,1,0,0
+7885,Hausdorff operators on holomorphic Hardy spaces and applications,"  The aim of this paper is to characterize the nonnegative functions $\varphi$
+defined on $(0,\infty)$ for which the Hausdorff operator
+$$\mathscr H_\varphi f(z)= \int_0^\infty
+f\left(\frac{z}{t}\right)\frac{\varphi(t)}{t}dt$$ is bounded on the Hardy
+spaces of the upper half-plane $\mathcal H_a^p(\mathbb C_+)$, $p\in[1,\infty]$.
+The corresponding operator norms and their applications are also given.
+",0,0,1,0,0,0
+7886,Three-dimensional color code thresholds via statistical-mechanical mapping,"  Three-dimensional (3D) color codes have advantages for fault-tolerant quantum
+computing, such as protected quantum gates with relatively low overhead and
+robustness against imperfect measurement of error syndromes. Here we
+investigate the storage threshold error rates for bit-flip and phase-flip noise
+in the 3D color code on the body-centererd cubic lattice, assuming perfect
+syndrome measurements. In particular, by exploiting a connection between error
+correction and statistical mechanics, we estimate the threshold for 1D
+string-like and 2D sheet-like logical operators to be $p^{(1)}_\mathrm{3DCC}
+\simeq 1.9\%$ and $p^{(2)}_\mathrm{3DCC} \simeq 27.6\%$. We obtain these
+results by using parallel tempering Monte Carlo simulations to study the
+disorder-temperature phase diagrams of two new 3D statistical-mechanical
+models: the 4- and 6-body random coupling Ising models.
+",0,1,0,0,0,0
+7887,Does Your Phone Know Your Touch?,"  This paper explores supervised techniques for continuous anomaly detection
+from biometric touch screen data. A capacitive sensor array used to mimic a
+touch screen as used to collect touch and swipe gestures from participants. The
+gestures are recorded over fixed segments of time, with position and force
+measured for each gesture. Support Vector Machine, Logistic Regression, and
+Gaussian mixture models were tested to learn individual touch patterns. Test
+results showed true negative and true positive scores of over 95% accuracy for
+all gesture types, with logistic regression models far outperforming the other
+methods. A more expansive and varied data collection over longer periods of
+time is needed to determine pragmatic usage of these results.
+",0,0,0,1,0,0
+7888,Nucleus: A Pilot Project,"  Early in 2016, an environmental scan was conducted by the Research Library
+Data Working Group for three purposes:
+1.) Perform a survey of the data management landscape at Los Alamos National
+Laboratory in order to identify local gaps in data management services.
+2.) Conduct an environmental scan of external institutions to benchmark
+budgets, infrastructure, and personnel dedicated to data management.
+3.) Draft a research data infrastructure model that aligns with the current
+workflow and classification restrictions at Los Alamos National Laboratory.
+This report is a summary of those activities and the draft for a pilot data
+management project.
+",1,0,0,0,0,0
+7889,Non Volatile MoS$_{2}$ Field Effect Transistors Directly Gated By Single Crystalline Epitaxial Ferroelectric,"  We demonstrate non-volatile, n-type, back-gated, MoS$_{2}$ transistors,
+placed directly on an epitaxial grown, single crystalline,
+PbZr$_{0.2}$Ti$_{0.8}$O$_{3}$ (PZT) ferroelectric. The transistors show decent
+ON current (19 ${\mu}A/{\mu}$m), high on-off ratio (10$^{7}$), and a
+subthreshold swing of (SS ~ 92 mV/dec) with a 100 nm thick PZT layer as the
+back gate oxide. Importantly, the ferroelectric polarization can directly
+control the channel charge, showing a clear anti-clockwise hysteresis. We have
+selfconsistently confirmed the switching of the ferroelectric and corresponding
+change in channel current from a direct time-dependent measurement. Our results
+demonstrate that it is possible to obtain transistor operation directly on
+polar surfaces and therefore it should be possible to integrate 2D electronics
+with single crystalline functional oxides.
+",0,1,0,0,0,0
+7890,"Fast and Accurate Sparse Coding of Visual Stimuli with a Simple, Ultra-Low-Energy Spiking Architecture","  Memristive crossbars have become a popular means for realizing unsupervised
+and supervised learning techniques. In previous neuromorphic architectures with
+leaky integrate-and-fire neurons, the crossbar itself has been separated from
+the neuron capacitors to preserve mathematical rigor. In this work, we sought
+to simplify the design, creating a fast circuit that consumed significantly
+lower power at a minimal cost of accuracy. We also showed that connecting the
+neurons directly to the crossbar resulted in a more efficient sparse coding
+architecture, and alleviated the need to pre-normalize receptive fields. This
+work provides derivations for the design of such a network, named the Simple
+Spiking Locally Competitive Algorithm, or SSLCA, as well as CMOS designs and
+results on the CIFAR and MNIST datasets. Compared to a non-spiking model which
+scored 33% on CIFAR-10 with a single-layer classifier, this hardware scored 32%
+accuracy. When used with a state-of-the-art deep learning classifier, the
+non-spiking model achieved 82% and our simplified, spiking model achieved 80%,
+while compressing the input data by 92%. Compared to a previously proposed
+spiking model, our proposed hardware consumed 99% less energy to do the same
+work at 21x the throughput. Accuracy held out with online learning to a write
+variance of 3%, suitable for the often-reported 4-bit resolution required for
+neuromorphic algorithms; with offline learning to a write variance of 27%; and
+with read variance to 40%. The proposed architecture's excellent accuracy,
+throughput, and significantly lower energy usage demonstrate the utility of our
+innovations.
+",1,0,0,0,0,0
+7891,Astronomy of Cholanaikkan tribe of Kerala,"  Cholanaikkans are a diminishing tribe of India. With a population of less
+than 200 members, this tribe living in the reserved forests about 80 km from
+Kozhikode, it is one of the most isolated tribes. A programme of the Government
+of Kerala brings some of them to Kozhikode once a year. We studied various
+aspects of the tribe during such a visit in 2016. We report their science and
+technology.
+",0,1,0,0,0,0
+7892,Integral Equations and Machine Learning,"  As both light transport simulation and reinforcement learning are ruled by
+the same Fredholm integral equation of the second kind, reinforcement learning
+techniques may be used for photorealistic image synthesis: Efficiency may be
+dramatically improved by guiding light transport paths by an approximate
+solution of the integral equation that is learned during rendering. In the
+light of the recent advances in reinforcement learning for playing games, we
+investigate the representation of an approximate solution of an integral
+equation by artificial neural networks and derive a loss function for that
+purpose. The resulting Monte Carlo and quasi-Monte Carlo methods train neural
+networks with standard information instead of linear information and naturally
+are able to generate an arbitrary number of training samples. The methods are
+demonstrated for applications in light transport simulation.
+",1,0,0,0,0,0
+7893,Experiments on bright field and dark field high energy electron imaging with thick target material,"  Using a high energy electron beam for the imaging of high density matter with
+both high spatial-temporal and areal density resolution under extreme states of
+temperature and pressure is one of the critical challenges in high energy
+density physics . When a charged particle beam passes through an opaque target,
+the beam will be scattered with a distribution that depends on the thickness of
+the material. By collecting the scattered beam either near or off axis,
+so-called bright field or dark field images can be obtained. Here we report on
+an electron radiography experiment using 45 MeV electrons from an S-band
+photo-injector, where scattered electrons, after interacting with a sample, are
+collected and imaged by a quadrupole imaging system. We achieved a few
+micrometers (about 4 micrometers) spatial resolution and about 10 micrometers
+thickness resolution for a silicon target of 300-600 micron thickness. With
+addition of dark field images that are captured by selecting electrons with
+large scattering angle, we show that more useful information in determining
+external details such as outlines, boundaries and defects can be obtained.
+",0,1,0,0,0,0
+7894,A Non-linear Approach to Space Dimension Perception by a Naive Agent,"  Developmental Robotics offers a new approach to numerous AI features that are
+often taken as granted. Traditionally, perception is supposed to be an inherent
+capacity of the agent. Moreover, it largely relies on models built by the
+system's designer. A new approach is to consider perception as an
+experimentally acquired ability that is learned exclusively through the
+analysis of the agent's sensorimotor flow. Previous works, based on
+H.Poincaré's intuitions and the sensorimotor contingencies theory, allow a
+simulated agent to extract the dimension of geometrical space in which it is
+immersed without any a priori knowledge. Those results are limited to
+infinitesimal movement's amplitude of the system. In this paper, a non-linear
+dimension estimation method is proposed to push back this limitation.
+",1,0,0,0,0,0
+7895,Foolbox: A Python toolbox to benchmark the robustness of machine learning models,"  Even todays most advanced machine learning models are easily fooled by almost
+imperceptible perturbations of their inputs. Foolbox is a new Python package to
+generate such adversarial perturbations and to quantify and compare the
+robustness of machine learning models. It is build around the idea that the
+most comparable robustness measure is the minimum perturbation needed to craft
+an adversarial example. To this end, Foolbox provides reference implementations
+of most published adversarial attack methods alongside some new ones, all of
+which perform internal hyperparameter tuning to find the minimum adversarial
+perturbation. Additionally, Foolbox interfaces with most popular deep learning
+frameworks such as PyTorch, Keras, TensorFlow, Theano and MXNet and allows
+different adversarial criteria such as targeted misclassification and top-k
+misclassification as well as different distance measures. The code is licensed
+under the MIT license and is openly available at
+this https URL . The most up-to-date documentation can be
+found at this http URL .
+",1,0,0,1,0,0
+7896,Two-dimensional boron on Pb (110) surface,"  We simulate boron on Pb(110) surface by using ab initio evolutionary
+methodology. Interestingly, the two-dimensional (2D) Dirac Pmmn boron can be
+formed because of good lattice matching. Unexpectedly, by increasing the
+thickness of 2D boron, a three-bonded graphene-like structure (P2_1/c boron)
+was revealed to possess double anisotropic Dirac cones. It is 20 meV/atom lower
+in energy than the Pmmn structure, indicating the most stable 2D boron with
+particular Dirac cones. The puckered structure of P2_1/c boron results in the
+peculiar Dirac cones, as well as substantial mechanical anisotropy. The
+calculated Young's modulus is 320 GPa.nm along zigzag direction, which is
+comparable with graphene.
+",0,1,0,0,0,0
+7897,SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning,"  Model-based reinforcement learning (RL) methods can be broadly categorized as
+global model methods, which depend on learning models that provide sensible
+predictions in a wide range of states, or local model methods, which
+iteratively refit simple models that are used for policy improvement. While
+predicting future states that will result from the current actions is
+difficult, local model methods only attempt to understand system dynamics in
+the neighborhood of the current policy, making it possible to produce local
+improvements without ever learning to predict accurately far into the future.
+The main idea in this paper is that we can learn representations that make it
+easy to retrospectively infer simple dynamics given the data from the current
+policy, thus enabling local models to be used for policy learning in complex
+systems. To that end, we focus on learning representations with probabilistic
+graphical model (PGM) structure, which allows us to devise an efficient local
+model method that infers dynamics from real-world rollouts with the PGM as a
+global prior. We compare our method to other model-based and model-free RL
+methods on a suite of robotics tasks, including manipulation tasks on a real
+Sawyer robotic arm directly from camera images. Videos of our results are
+available at this https URL
+",1,0,0,1,0,0
+7898,Robust and Fast Decoding of High-Capacity Color QR Codes for Mobile Applications,"  The use of color in QR codes brings extra data capacity, but also inflicts
+tremendous challenges on the decoding process due to chromatic distortion,
+cross-channel color interference and illumination variation. Particularly, we
+further discover a new type of chromatic distortion in high-density color QR
+codes, cross-module color interference, caused by the high density which also
+makes the geometric distortion correction more challenging. To address these
+problems, we propose two approaches, namely, LSVM-CMI and QDA-CMI, which
+jointly model these different types of chromatic distortion. Extended from SVM
+and QDA, respectively, both LSVM-CMI and QDA-CMI optimize over a particular
+objective function to learn a color classifier. Furthermore, a robust geometric
+transformation method and several pipeline refinements are proposed to boost
+the decoding performance for mobile applications. We put forth and implement a
+framework for high-capacity color QR codes equipped with our methods, called
+HiQ. To evaluate the performance of HiQ, we collect a challenging large-scale
+color QR code dataset, CUHK-CQRC, which consists of 5390 high-density color QR
+code samples. The comparison with the baseline method [2] on CUHK-CQRC shows
+that HiQ at least outperforms [2] by 188% in decoding success rate and 60% in
+bit error rate. Our implementation of HiQ in iOS and Android also demonstrates
+the effectiveness of our framework in real-world applications.
+",1,0,0,0,0,0
+7899,The quest for H$_3^+$ at Neptune: deep burn observations with NASA IRTF iSHELL,"  Emission from the molecular ion H$_3^+$ is a powerful diagnostic of the upper
+atmosphere of Jupiter, Saturn, and Uranus, but it remains undetected at
+Neptune. In search of this emission, we present near-infrared spectral
+observations of Neptune between 3.93 and 4.00 $\mu$m taken with the newly
+commissioned iSHELL instrument on the NASA Infrared Telescope Facility in
+Hawaii, obtained 17-20 August 2017. We spent 15.4 h integrating across the disk
+of the planet, yet were unable to unambiguously identify any H$_3^+$ line
+emissions. Assuming a temperature of 550 K, we derive an upper limit on the
+column integrated density of $1.0^{+1.2}_{-0.8}\times10^{13}$ m$^{-2}$, which
+is an improvement of 30\% on the best previous observational constraint. This
+result means that models are over-estimating the density by at least a factor
+of 5, highlighting the need for renewed modelling efforts. A potential solution
+is strong vertical mixing of polyatomic neutral species from Neptune's upper
+stratosphere to the thermosphere, reacting with H$_3^+$, thus greatly reducing
+the column integrated H$_3^+$ densities. This upper limit also provide
+constraints on future attempts at detecting H$_3^+$ using the James Webb Space
+Telescope.
+",0,1,0,0,0,0
+7900,Unreasonable Effectivness of Deep Learning,"  We show how well known rules of back propagation arise from a weighted
+combination of finite automata. By redefining a finite automata as a predictor
+we combine the set of all $k$-state finite automata using a weighted majority
+algorithm. This aggregated prediction algorithm can be simplified using
+symmetry, and we prove the equivalence of an algorithm that does this. We
+demonstrate that this algorithm is equivalent to a form of a back propagation
+acting in a completely connected $k$-node neural network. Thus the use of the
+weighted majority algorithm allows a bound on the general performance of deep
+learning approaches to prediction via known results from online statistics. The
+presented framework opens more detailed questions about network topology; it is
+a bridge to the well studied techniques of semigroup theory and applying these
+techniques to answer what specific network topologies are capable of
+predicting. This informs both the design of artificial networks and the
+exploration of neuroscience models.
+",0,0,0,1,0,0
+7901,Collaborative Summarization of Topic-Related Videos,"  Large collections of videos are grouped into clusters by a topic keyword,
+such as Eiffel Tower or Surfing, with many important visual concepts repeating
+across them. Such a topically close set of videos have mutual influence on each
+other, which could be used to summarize one of them by exploiting information
+from others in the set. We build on this intuition to develop a novel approach
+to extract a summary that simultaneously captures both important
+particularities arising in the given video, as well as, generalities identified
+from the set of videos. The topic-related videos provide visual context to
+identify the important parts of the video being summarized. We achieve this by
+developing a collaborative sparse optimization method which can be efficiently
+solved by a half-quadratic minimization algorithm. Our work builds upon the
+idea of collaborative techniques from information retrieval and natural
+language processing, which typically use the attributes of other similar
+objects to predict the attribute of a given object. Experiments on two
+challenging and diverse datasets well demonstrate the efficacy of our approach
+over state-of-the-art methods.
+",1,0,0,0,0,0
+7902,ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information,"  Requirements elicitation requires extensive knowledge and deep understanding
+of the problem domain where the final system will be situated. However, in many
+software development projects, analysts are required to elicit the requirements
+from an unfamiliar domain, which often causes communication barriers between
+analysts and stakeholders. In this paper, we propose a requirements ELICitation
+Aid tool (ELICA) to help analysts better understand the target application
+domain by dynamic extraction and labeling of requirements-relevant knowledge.
+To extract the relevant terms, we leverage the flexibility and power of
+Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural
+language processing tasks. In addition to the information conveyed through
+text, ELICA captures and processes non-linguistic information about the
+intention of speakers such as their confidence level, analytical tone, and
+emotions. The extracted information is made available to the analysts as a set
+of labeled snippets with highlighted relevant terms which can also be exported
+as an artifact of the Requirements Engineering (RE) process. The application
+and usefulness of ELICA are demonstrated through a case study. This study shows
+how pre-existing relevant information about the application domain and the
+information captured during an elicitation meeting, such as the conversation
+and stakeholders' intentions, can be captured and used to support analysts
+achieving their tasks.
+",1,0,0,1,0,0
+7903,"Crystal field excitations and magnons: their roles in oxyselenides Pr2O2M2OSe2 (M = Mn, Fe)","  We present the results of neutron scattering experiments to study the crystal
+and magnetic structures of the Mott-insulating transition metal oxyselenides
+Pr2O2M2OSe2 (M = Mn, Fe). The structural role of the non-Kramers Pr3+ ion is
+investigated and analysis of Pr3+ crystal field excitations performed.
+Long-range order of Pr3+ moments in Pr2O2Fe2OSe2 can be induced by an applied
+magnetic field.
+",0,1,0,0,0,0
+7904,Redistributing Funds across Charitable Crowdfunding Campaigns,"  On Kickstarter only 36% of crowdfunding campaigns successfully raise
+sufficient funds for their projects. In this paper, we explore the possibility
+of redistribution of crowdfunding donations to increase the chances of success.
+We define several intuitive redistribution policies and, using data from a real
+crowdfunding platform, LaunchGood, we assess the potential improvement in
+campaign fundraising success rates. We find that an aggressive redistribution
+scheme can boost campaign success rates from 37% to 79%, but such
+choice-agnostic redistribution schemes come at the cost of disregarding donor
+preferences. Taking inspiration from offline giving societies and donor clubs,
+we build a case for choice preserving redistribution schemes that strike a
+balance between increasing the number of successful campaigns and respecting
+giving preference. We find that choice-preserving redistribution can easily
+achieve campaign success rates of 48%. Finally, we discuss the implications of
+these different redistribution schemes for the various stakeholders in the
+crowdfunding ecosystem.
+",1,0,0,0,0,0
+7905,Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning,"  In (Franceschi et al., 2018) we proposed a unified mathematical framework,
+grounded on bilevel programming, that encompasses gradient-based hyperparameter
+optimization and meta-learning. We formulated an approximate version of the
+problem where the inner objective is solved iteratively, and gave sufficient
+conditions ensuring convergence to the exact problem. In this work we show how
+to optimize learning rates, automatically weight the loss of single examples
+and learn hyper-representations with Far-HO, a software package based on the
+popular deep learning framework TensorFlow that allows to seamlessly tackle
+both HO and ML problems.
+",0,0,0,1,0,0
+7906,Analysis of Coupled Scalar Systems by Displacement Convexity,"  Potential functionals have been introduced recently as an important tool for
+the analysis of coupled scalar systems (e.g. density evolution equations). In
+this contribution, we investigate interesting properties of this potential.
+Using the tool of displacement convexity, we show that, under mild assumptions
+on the system, the potential functional is displacement convex. Furthermore, we
+give the conditions on the system such that the potential is strictly
+displacement convex, in which case the minimizer is unique.
+",1,0,1,0,0,0
+7907,Deterministic subgraph detection in broadcast CONGEST,"  We present simple deterministic algorithms for subgraph finding and
+enumeration in the broadcast CONGEST model of distributed computation:
+-- For any constant $k$, detecting $k$-paths and trees on $k$ nodes can be
+done in $O(1)$ rounds.
+-- For any constant $k$, detecting $k$-cycles and pseudotrees on $k$ nodes
+can be done in $O(n)$ rounds.
+-- On $d$-degenerate graphs, cliques and $4$-cycles can be enumerated in $O(d
++ \log n)$ rounds, and $5$-cycles in $O(d^2 + \log n)$ rounds.
+In many cases, these bounds are tight up to logarithmic factors. Moreover, we
+show that the algorithms for $d$-degenerate graphs can be improved to optimal
+complexity $O(d/\log n)$ and $O(d^2/\log n)$, respectively, in the supported
+CONGEST model, which can be seen as an intermediate model between CONGEST and
+the congested clique.
+",1,0,0,0,0,0
+7908,On Graded Lie Algebras of Characteristic Three With Classical Reductive Null Component,"  We consider finite-dimensional irreducible transitive graded Lie algebras $L
+= \sum_{i=-q}^rL_i$ over algebraically closed fields of characteristic three.
+We assume that the null component $L_0$ is classical and reductive. The adjoint
+representation of $L$ on itself induces a representation of the commutator
+subalgebra $L_0'$ of the null component on the minus-one component $L_{-1}.$ We
+show that if the depth $q$ of $L$ is greater than one, then this representation
+must be restricted.
+",0,0,1,0,0,0
+7909,Femtosecond mega-electron-volt electron microdiffraction,"  Instruments to visualize transient structural changes of inhomogeneous
+materials on the nanometer scale with atomic spatial and temporal resolution
+are demanded to advance materials science, bioscience, and fusion sciences. One
+such technique is femtosecond electron microdiffraction, in which a short pulse
+of electrons with femtosecond-scale duration is focused into a micron-scale
+spot and used to obtain diffraction images to resolve ultrafast structural
+dynamics over localized crystalline domain. In this letter, we report the
+experimental demonstration of time-resolved mega-electron-volt electron
+microdiffraction which achieves a 5 {\mu}m root-mean-square (rms) beam size on
+the sample and a 100 fs rms temporal resolution. Using pulses of 10k electrons
+at 4.2 MeV energy with a normalized emittance 3 nm-rad, we obtained high
+quality diffraction from a single 10 {\mu}m paraffin (C_44 H_90) crystal. The
+phonon softening mode in optical-pumped polycrystalline Bi was also
+time-resolved, demonstrating the temporal resolution limits of our instrument
+design. This new characterization capability will open many research
+opportunities in material and biological sciences.
+",0,1,0,0,0,0
+7910,Deep Recurrent Neural Network for Protein Function Prediction from Sequence,"  As high-throughput biological sequencing becomes faster and cheaper, the need
+to extract useful information from sequencing becomes ever more paramount,
+often limited by low-throughput experimental characterizations. For proteins,
+accurate prediction of their functions directly from their primary amino-acid
+sequences has been a long standing challenge. Here, machine learning using
+artificial recurrent neural networks (RNN) was applied towards classification
+of protein function directly from primary sequence without sequence alignment,
+heuristic scoring or feature engineering. The RNN models containing
+long-short-term-memory (LSTM) units trained on public, annotated datasets from
+UniProt achieved high performance for in-class prediction of four important
+protein functions tested, particularly compared to other machine learning
+algorithms using sequence-derived protein features. RNN models were used also
+for out-of-class predictions of phylogenetically distinct protein families with
+similar functions, including proteins of the CRISPR-associated nuclease,
+ferritin-like iron storage and cytochrome P450 families. Applying the trained
+RNN models on the partially unannotated UniRef100 database predicted not only
+candidates validated by existing annotations but also currently unannotated
+sequences. Some RNN predictions for the ferritin-like iron sequestering
+function were experimentally validated, even though their sequences differ
+significantly from known, characterized proteins and from each other and cannot
+be easily predicted using popular bioinformatics methods. As sequencing and
+experimental characterization data increases rapidly, the machine-learning
+approach based on RNN could be useful for discovery and prediction of
+homologues for a wide range of protein functions.
+",1,0,0,1,0,0
+7911,CubemapSLAM: A Piecewise-Pinhole Monocular Fisheye SLAM System,"  We present a real-time feature-based SLAM (Simultaneous Localization and
+Mapping) system for fisheye cameras featured by a large field-of-view (FoV).
+Large FoV cameras are beneficial for large-scale outdoor SLAM applications,
+because they increase visual overlap between consecutive frames and capture
+more pixels belonging to the static parts of the environment. However, current
+feature-based SLAM systems such as PTAM and ORB-SLAM limit their camera model
+to pinhole only. To compensate for the vacancy, we propose a novel SLAM system
+with the cubemap model that utilizes the full FoV without introducing
+distortion from the fisheye lens, which greatly benefits the feature matching
+pipeline. In the initialization and point triangulation stages, we adopt a
+unified vector-based representation to efficiently handle matches across
+multiple faces, and based on this representation we propose and analyze a novel
+inlier checking metric. In the optimization stage, we design and test a novel
+multi-pinhole reprojection error metric that outperforms other metrics by a
+large margin. We evaluate our system comprehensively on a public dataset as
+well as a self-collected dataset that contains real-world challenging
+sequences. The results suggest that our system is more robust and accurate than
+other feature-based fisheye SLAM approaches. The CubemapSLAM system has been
+released into the public domain.
+",1,0,0,0,0,0
+7912,$J$-holomorphic disks with pre-Lagrangian boundary conditions,"  The purpose of this paper is to carry out a classical construction of a
+non-constant holomorphic disk with boundary on (the suspension of) a Lagrangian
+submanifold in $\mathbb{R}^{2 n}$ in the case the Lagrangian is the lift of a
+coisotropic (a.k.a. pre-Lagrangian) submanifold in (a subset $U$ of)
+$\mathbb{R}^{2 n - 1}$. We show that the positive lower and finite upper bounds
+for the area of such a disk (which are due to M. Gromov and J.-C. Sikorav and
+F. Laudenbach-Sikorav for general Lagrangians) depend on the coisotropic
+submanifold only but not on its lift to the symplectization. The main
+application is to a $C^0$-characterization of contact embeddings in terms of
+coisotropic embeddings in another paper by the present author. Moreover, we
+prove a version of Gromov's non-existence of exact Lagrangian embeddings into
+standard $\mathbb{R}^{2 n}$ for coisotropic embeddings into $S^1 \times
+\mathbb{R}^{2 n}$. This allows us to distinguish different contact structures
+on the latter by means of the (modified) contact shape invariant. As in the
+general Lagrangian case, all of the existence results are based on Gromov's
+theory of $J$-holomorphic curves and his compactness theorem (or persistence
+principle). Analytical difficulties arise mainly at the ends of the cone
+$\mathbb{R}_+ \times U$.
+",0,0,1,0,0,0
+7913,Evolutionary sequences for hydrogen-deficient white dwarfs,"  We present a set of full evolutionary sequences for white dwarfs with
+hydrogen-deficient atmospheres. We take into account the evolutionary history
+of the progenitor stars, all the relevant energy sources involved in the
+cooling, element diffusion in the very outer layers, and outer boundary
+conditions provided by new and detailed non-gray white dwarf model atmospheres
+for pure helium composition. These model atmospheres are based on the most
+up-to-date physical inputs. Our calculations extend down to very low effective
+temperatures, of $\sim 2\,500$~K, provide a homogeneous set of evolutionary
+cooling tracks that are appropriate for mass and age determinations of old
+hydrogen-deficient white dwarfs, and represent a clear improvement over
+previous efforts, which were computed using gray atmospheres.
+",0,1,0,0,0,0
+7914,On the uniqueness of complete biconservative surfaces in $\mathbb{R}^3$,"  We study the uniqueness of complete biconservative surfaces in the Euclidean
+space $\mathbb{R}^3$, and prove that the only complete biconservative regular
+surfaces in $\mathbb{R}^3$ are either $CMC$ or certain surfaces of revolution.
+In particular, any compact biconservative regular surface in $\mathbb{R}^3$ is
+a round sphere.
+",0,0,1,0,0,0
+7915,Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management,"  We present the mapping of a class of simplified air traffic management (ATM)
+problems (strategic conflict resolution) to quadratic unconstrained boolean
+optimization (QUBO) problems. The mapping is performed through an original
+representation of the conflict-resolution problem in terms of a conflict graph,
+where nodes of the graph represent flights and edges represent a potential
+conflict between flights. The representation allows a natural decomposition of
+a real world instance related to wind- optimal trajectories over the Atlantic
+ocean into smaller subproblems, that can be discretized and are amenable to be
+programmed in quantum annealers. In the study, we tested the new programming
+techniques and we benchmark the hardness of the instances using both classical
+solvers and the D-Wave 2X and D-Wave 2000Q quantum chip. The preliminary
+results show that for reasonable modeling choices the most challenging
+subproblems which are programmable in the current devices are solved to
+optimality with 99% of probability within a second of annealing time.
+",1,0,0,0,0,0
+7916,On rumour propagation among sceptics,"  Junior, Machado and Zuluaga (2011) studied a model to understand the spread
+of a rumour. Their model consists of individuals situated at the integer points
+of the line $\N$. An individual at the origin $0$ starts a rumour and passes it
+to all individuals in the interval $[0,R_0]$, where $R_0$ is a non-negative
+random variable. An individual located at $i$ in this interval receives the
+rumour and transmits it further among individuals in $[i, i+R_i]$ where $R_0$
+and $R_i$ are i.i.d. random variables. The rumour spreads in this manner. An
+alternate model considers individuals seeking to find the rumour from
+individuals who have already heard it. For this s/he asks individuals to the
+left of her/him and lying in an interval of a random size. We study these two
+models, when the individuals are more sceptical and they transmit or accept the
+rumour only if they receive it from at least two different sources.
+In stochastic geometry the equivalent of this rumour process is the study of
+coverage of the space $\N^d$ by random sets. Our study here extends the study
+of coverage of space and considers the case when each vertex of $\N^d$ is
+covered by at least two distinct random sets.
+",0,0,1,1,0,0
+7917,Neutron activation and prompt gamma intensity in Ar/CO$_{2}$-filled neutron detectors at the European Spallation Source,"  Monte Carlo simulations using MCNP6.1 were performed to study the effect of
+neutron activation in Ar/CO$_{2}$ neutron detector counting gas. A general MCNP
+model was built and validated with simple analytical calculations. Simulations
+and calculations agree that only the $^{40}$Ar activation can have a
+considerable effect. It was shown that neither the prompt gamma intensity from
+the $^{40}$Ar neutron capture nor the produced $^{41}$Ar activity have an
+impact in terms of gamma dose rate around the detector and background level.
+",0,1,0,0,0,0
+7918,Solving 1ODEs with functions,"  Here we present a new approach to deal with first order ordinary differential
+equations (1ODEs), presenting functions. This method is an alternative to the
+one we have presented in [1]. In [2], we have establish the theoretical
+background to deal, in the extended Prelle-Singer approach context, with
+systems of 1ODEs. In this present paper, we will apply these results in order
+to produce a method that is more efficient in a great number of cases.
+Directly, the solving of 1ODEs is applicable to any problem presenting
+parameters to which the rate of change is related to the parameter itself.
+Apart from that, the solving of 1ODEs can be a part of larger mathematical
+processes vital to dealing with many problems.
+",0,1,1,0,0,0
+7919,System Level Framework for Assessing the Accuracy of Neonatal EEG Acquisition,"  Significant research has been conducted in recent years to design low-cost
+alternatives to the current EEG monitoring systems used in healthcare
+facilities. Testing such systems on a vulnerable population such as newborns is
+complicated due to ethical and regulatory considerations that slow down the
+technical development. This paper presents and validates a method for
+quantifying the accuracy of neonatal EEG acquisition systems and electrode
+technologies via clinical data simulations that do not require neonatal
+participants. The proposed method uses an extensive neonatal EEG database to
+simulate analogue signals, which are subsequently passed through electrical
+models of the skin-electrode interface, which are developed using wet and dry
+EEG electrode designs. The signal losses in the system are quantified at each
+stage of the acquisition process for electrode and acquisition board losses.
+SNR, correlation and noise values were calculated. The results verify that
+low-cost EEG acquisition systems are capable of obtaining clinical grade EEG.
+Although dry electrodes result in a significant increase in the skin-electrode
+impedance, accurate EEG recordings are still achievable.
+",0,0,0,1,0,0
+7920,Strong Consistency of Spectral Clustering for Stochastic Block Models,"  In this paper we prove the strong consistency of several methods based on the
+spectral clustering techniques that are widely used to study the community
+detection problem in stochastic block models (SBMs). We show that under some
+weak conditions on the minimal degree, the number of communities, and the
+eigenvalues of the probability block matrix, the K-means algorithm applied to
+the eigenvectors of the graph Laplacian associated with its first few largest
+eigenvalues can classify all individuals into the true community uniformly
+correctly almost surely. Extensions to both regularized spectral clustering and
+degree-corrected SBMs are also considered. We illustrate the performance of
+different methods on simulated networks.
+",0,0,0,1,0,0
+7921,Extremely fast simulations of heat transfer in fluidized beds,"  Besides their huge technological importance, fluidized beds have attracted a
+large amount of research because they are perfect playgrounds to investigate
+highly dynamic particulate flows. Their over-all behavior is determined by
+short-lasting particle collisions and the interaction between solid and gas
+phase. Modern simulation techniques that combine computational fluid dynamics
+(CFD) and discrete element methods (DEM) are capable of describing their
+evolution and provide detailed information on what is happening on the particle
+scale. However, these approaches are limited by small time steps and large
+numerical costs, which inhibits the investigation of slower long-term processes
+like heat transfer or chemical conversion.
+In a recent study (Lichtenegger and Pirker, 2016), we have introduced
+recurrence CFD (rCFD) as a way to decouple fast from slow degrees of freedom in
+systems with recurring patterns: A conventional simulation is carried out to
+capture such coherent structures. Their re-appearance is characterized with
+recurrence plots that allow us to extrapolate their evolution far beyond the
+simulated time. On top of these predicted flow fields, any passive or weakly
+coupled process can then be investigated at fractions of the original
+computational costs.
+Here, we present the application of rCFD to heat transfer in a lab-scale
+fluidized bed. Initially hot particles are fluidized with cool air and their
+temperature evolution is recorded. In comparison to conventional CFD-DEM, we
+observe speed-up factors of about two orders of magnitude at very good accuracy
+with regard to recent measurements.
+",0,1,0,0,0,0
+7922,Machine learning out-of-equilibrium phases of matter,"  Neural network based machine learning is emerging as a powerful tool for
+obtaining phase diagrams when traditional regression schemes using local
+equilibrium order parameters are not available, as in many-body localized or
+topological phases. Nevertheless, instances of machine learning offering new
+insights have been rare up to now. Here we show that a single feed-forward
+neural network can decode the defining structures of two distinct MBL phases
+and a thermalizing phase, using entanglement spectra obtained from individual
+eigenstates. For this, we introduce a simplicial geometry based method for
+extracting multi-partite phase boundaries. We find that this method outperforms
+conventional metrics (like the entanglement entropy) for identifying MBL phase
+transitions, revealing a sharper phase boundary and shedding new insight into
+the topology of the phase diagram. Furthermore, the phase diagram we acquire
+from a single disorder configuration confirms that the machine-learning based
+approach we establish here can enable speedy exploration of large phase spaces
+that can assist with the discovery of new MBL phases. To our knowledge this
+work represents the first example of a machine learning approach revealing new
+information beyond conventional knowledge.
+",0,1,0,0,0,0
+7923,Exact Tensor Completion from Sparsely Corrupted Observations via Convex Optimization,"  This paper conducts a rigorous analysis for provable estimation of
+multidimensional arrays, in particular third-order tensors, from a random
+subset of its corrupted entries. Our study rests heavily on a recently proposed
+tensor algebraic framework in which we can obtain tensor singular value
+decomposition (t-SVD) that is similar to the SVD for matrices, and define a new
+notion of tensor rank referred to as the tubal rank. We prove that by simply
+solving a convex program, which minimizes a weighted combination of tubal
+nuclear norm, a convex surrogate for the tubal rank, and the $\ell_1$-norm, one
+can recover an incoherent tensor exactly with overwhelming probability,
+provided that its tubal rank is not too large and that the corruptions are
+reasonably sparse. Interestingly, our result includes the recovery guarantees
+for the problems of tensor completion (TC) and tensor principal component
+analysis (TRPCA) under the same algebraic setup as special cases. An
+alternating direction method of multipliers (ADMM) algorithm is presented to
+solve this optimization problem. Numerical experiments verify our theory and
+real-world applications demonstrate the effectiveness of our algorithm.
+",1,0,0,1,0,0
+7924,On Triangle Inequality Based Approximation Error Estimation,"  The distance between the true and numerical solutions in some metric is
+considered as the discretization error magnitude. If error magnitude ranging is
+known, the triangle inequality enables the estimation of the vicinity of the
+approximate solution that contains the exact one (exact solution enclosure).
+The analysis of distances between the numerical solutions enables
+discretization error ranging, if solutions errors are significantly different.
+Numerical tests conducted using the steady supersonic flows, governed by the
+two dimensional Euler equations, demonstrate the properties of the exact
+solution enclosure. The set of solutions generated by solvers of different
+orders of approximation is used. The success of this approach depends on the
+choice of metric.
+",0,1,0,0,0,0
+7925,Maximal solutions for the Infinity-eigenvalue problem,"  In this article we prove that the first eigenvalue of the $\infty-$Laplacian
+$$ \left\{ \begin{array}{rclcl}
+\min\{ -\Delta_\infty v,\, |\nabla v|-\lambda_{1, \infty}(\Omega) v \} & = &
+0 & \text{in} & \Omega v & = & 0 & \text{on} & \partial \Omega, \end{array}
+\right. $$ has a unique (up to scalar multiplication) maximal solution. This
+maximal solution can be obtained as the limit as $\ell \nearrow 1$ of concave
+problems of the form $$ \left\{ \begin{array}{rclcl}
+\min\{ -\Delta_\infty v_{\ell},\, |\nabla v_{\ell}|-\lambda_{1,
+\infty}(\Omega) v_{\ell}^{\ell} \} & = & 0 & \text{in} & \Omega v_{\ell} & = &
+0 & \text{on} & \partial \Omega. \end{array} \right. $$ In this way we obtain
+that the maximal eigenfunction is the unique one that is the limit of the
+concave problems as happens for the usual eigenvalue problem for the
+$p-$Laplacian for a fixed $1<p<\infty$.
+",0,0,1,0,0,0
+7926,Algorithmic Decision Making in the Presence of Unmeasured Confounding,"  On a variety of complex decision-making tasks, from doctors prescribing
+treatment to judges setting bail, machine learning algorithms have been shown
+to outperform expert human judgments. One complication, however, is that it is
+often difficult to anticipate the effects of algorithmic policies prior to
+deployment, making the decision to adopt them risky. In particular, one
+generally cannot use historical data to directly observe what would have
+happened had the actions recommended by the algorithm been taken. One standard
+strategy is to model potential outcomes for alternative decisions assuming that
+there are no unmeasured confounders (i.e., to assume ignorability). But if this
+ignorability assumption is violated, the predicted and actual effects of an
+algorithmic policy can diverge sharply. In this paper we present a flexible,
+Bayesian approach to gauge the sensitivity of predicted policy outcomes to
+unmeasured confounders. We show that this policy evaluation problem is a
+generalization of estimating heterogeneous treatment effects in observational
+studies, and so our methods can immediately be applied to that setting.
+Finally, we show, both theoretically and empirically, that under certain
+conditions it is possible to construct near-optimal algorithmic policies even
+when ignorability is violated. We demonstrate the efficacy of our methods on a
+large dataset of judicial actions, in which one must decide whether defendants
+awaiting trial should be required to pay bail or can be released without
+payment.
+",0,0,0,1,0,0
+7927,BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder,"  Network embedding aims at projecting the network data into a low-dimensional
+feature space, where the nodes are represented as a unique feature vector and
+network structure can be effectively preserved. In recent years, more and more
+online application service sites can be represented as massive and complex
+networks, which are extremely challenging for traditional machine learning
+algorithms to deal with. Effective embedding of the complex network data into
+low-dimension feature representation can both save data storage space and
+enable traditional machine learning algorithms applicable to handle the network
+data. Network embedding performance will degrade greatly if the networks are of
+a sparse structure, like the emerging networks with few connections. In this
+paper, we propose to learn the embedding representation for a target emerging
+network based on the broad learning setting, where the emerging network is
+aligned with other external mature networks at the same time. To solve the
+problem, a new embedding framework, namely ""Deep alIgned autoencoder based
+eMbEdding"" (DIME), is introduced in this paper. DIME handles the diverse link
+and attribute in a unified analytic based on broad learning, and introduces the
+multiple aligned attributed heterogeneous social network concept to model the
+network structure. A set of meta paths are introduced in the paper, which
+define various kinds of connections among users via the heterogeneous link and
+attribute information. The closeness among users in the networks are defined as
+the meta proximity scores, which will be fed into DIME to learn the embedding
+vectors of users in the emerging network. Extensive experiments have been done
+on real-world aligned social networks, which have demonstrated the
+effectiveness of DIME in learning the emerging network embedding vectors.
+",1,0,0,0,0,0
+7928,Multi-channel discourse as an indicator for Bitcoin price and volume movements,"  This research aims to identify how Bitcoin-related news publications and
+online discourse are expressed in Bitcoin exchange movements of price and
+volume. Being inherently digital, all Bitcoin-related fundamental data (from
+exchanges, as well as transactional data directly from the blockchain) is
+available online, something that is not true for traditional businesses or
+currencies traded on exchanges. This makes Bitcoin an interesting subject for
+such research, as it enables the mapping of sentiment to fundamental events
+that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely
+takes place on online forums and chat channels. In stock trading, the value of
+sentiment data in trading decisions has been demonstrated numerous times [1]
+[2] [3], and this research aims to determine whether there is value in such
+data for Bitcoin trading models. To achieve this, data over the year 2015 has
+been collected from Bitcointalk.org, (the biggest Bitcoin forum in post
+volume), established news sources such as Bloomberg and the Wall Street
+Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and
+bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we
+find weak to moderate correlations between forum, news, and Reddit sentiment
+and movements in price and volume from 1 to 5 days after the sentiment was
+expressed. A Granger causality test confirms the predictive causality of the
+sentiment on the daily percentage price and volume movements, and at the same
+time underscores the predictive causality of market movements on sentiment
+expressions in online communities
+",0,0,0,0,0,1
+7929,Fano Resonances in a Photonic Crystal Covered with a Perforated Gold Film and its Application to Biosensing,"  Optical properties of the photonic crystal covered with a perforated metal
+film were investigated and the existence of the Fano-type resonances was shown.
+The Fano resonances originate from the interaction between the optical Tamm
+state and the waveguide modes of the photonic crystal. It manifests itself as a
+narrow dip in a broad peak in the transmission spectrum related to the optical
+Tamm state. The design of a sensor based on this Fano resonance that is
+sensitive to the change of the environment refractive index is suggested.
+",0,1,0,0,0,0
+7930,Non-Stationary Bandits with Habituation and Recovery Dynamics,"  Many settings involve sequential decision-making where a set of actions can
+be chosen at each time step, each action provides a stochastic reward, and the
+distribution for the reward of each action is initially unknown. However,
+frequent selection of a specific action may reduce its expected reward, while
+abstaining from choosing an action may cause its expected reward to increase.
+Such non-stationary phenomena are observed in many real world settings such as
+personalized healthcare-adherence improving interventions and targeted online
+advertising. Though finding an optimal policy for general models with
+non-stationarity is PSPACE-complete, we propose and analyze a new class of
+models called ROGUE (Reducing or Gaining Unknown Efficacy) bandits, which we
+show in this paper can capture these phenomena and are amenable to the design
+of effective policies. We first present a consistent maximum likelihood
+estimator for the parameters of these models. Next, we construct finite sample
+concentration bounds that lead to an upper confidence bound policy called the
+ROGUE Upper Confidence Bound (ROGUE-UCB) algorithm. We prove that under proper
+conditions the ROGUE-UCB algorithm achieves logarithmic in time regret, unlike
+existing algorithms which result in linear regret. We conclude with a numerical
+experiment using real data from a personalized healthcare-adherence improving
+intervention to increase physical activity. In this intervention, the goal is
+to optimize the selection of messages (e.g., confidence increasing vs.
+knowledge increasing) to send to each individual each day to increase adherence
+and physical activity. Our results show that ROGUE-UCB performs better in terms
+of regret and average reward as compared to state of the art algorithms, and
+the use of ROGUE-UCB increases daily step counts by roughly 1,000 steps a day
+(about a half-mile more of walking) as compared to other algorithms.
+",1,0,1,0,0,0
+7931,Exception-Based Knowledge Updates,"  Existing methods for dealing with knowledge updates differ greatly depending
+on the underlying knowledge representation formalism. When Classical Logic is
+used, updates are typically performed by manipulating the knowledge base on the
+model-theoretic level. On the opposite side of the spectrum stand the semantics
+for updating Answer-Set Programs that need to rely on rule syntax. Yet, a
+unifying perspective that could embrace both these branches of research is of
+great importance as it enables a deeper understanding of all involved methods
+and principles and creates room for their cross-fertilisation, ripening and
+further development.
+This paper bridges the seemingly irreconcilable approaches to updates. It
+introduces a novel monotonic characterisation of rules, dubbed RE-models, and
+shows it to be a more suitable semantic foundation for rule updates than
+SE-models. Then it proposes a generic scheme for specifying semantic rule
+update operators, based on the idea of viewing a program as the set of sets of
+RE-models of its rules; updates are performed by introducing additional
+interpretations - exceptions - to the sets of RE-models of rules in the
+original program. The introduced scheme is used to define rule update operators
+that are closely related to both classical update principles and traditional
+approaches to rules updates, and serve as a basis for a solution to the
+long-standing problem of state condensing, showing how they can be equivalently
+defined as binary operators on some class of logic programs.
+Finally, the essence of these ideas is extracted to define an abstract
+framework for exception-based update operators, viewing a knowledge base as the
+set of sets of models of its elements, which can capture a wide range of both
+model- and formula-based classical update operators, and thus serves as the
+first firm formal ground connecting classical and rule updates.
+",1,0,0,0,0,0
+7932,Dynamics of observables in rank-based models and performance of functionally generated portfolios,"  In the seminal work [9], several macroscopic market observables have been
+introduced, in an attempt to find characteristics capturing the diversity of a
+financial market. Despite the crucial importance of such observables for
+investment decisions, a concise mathematical description of their dynamics has
+been missing. We fill this gap in the setting of rank-based models and expect
+our ideas to extend to other models of large financial markets as well. The
+results are then used to study the performance of multiplicatively and
+additively functionally generated portfolios, in particular, over short-term
+and medium-term horizons.
+",0,0,0,0,0,1
+7933,An Approach to Controller Design Based on the Generalized Cloud Model,"  In this paper, an approach to controller design based on the cloud models,
+without using the analog plant model is presented.
+",1,0,0,0,0,0
+7934,A 3pi Search for Planet Nine at 3.4 microns with WISE and NEOWISE,"  The recent 'Planet Nine' hypothesis has led to many observational and
+archival searches for this giant planet proposed to orbit the Sun at hundreds
+of astronomical units. While trans-Neptunian object searches are typically
+conducted in the optical, models suggest Planet Nine could be self-luminous and
+potentially bright enough at ~3-5 microns to be detected by the Wide-field
+Infrared Survey Explorer (WISE). We have previously demonstrated a Planet Nine
+search methodology based on time-resolved WISE coadds, allowing us to detect
+moving objects much fainter than would be possible using single-frame
+extractions. In the present work, we extend our 3.4 micron (W1) search to cover
+more than three quarters of the sky and incorporate four years of WISE
+observations spanning a seven year time period. This represents the deepest and
+widest-area WISE search for Planet Nine to date. We characterize the spatial
+variation of our survey's sensitivity and rule out the presence of Planet Nine
+in the parameter space searched at W1 < 16.7 in high Galactic latitude regions
+(90% completeness).
+",0,1,0,0,0,0
+7935,Integrable 7-point discrete equations and evolution lattice equations of order 2,"  We consider differential-difference equations that determine the continuous
+symmetries of discrete equations on the triangular lattice. It is shown that a
+certain combination of continuous flows can be represented as a scalar
+evolution lattice equation of order 2. The general scheme is illustrated by a
+number of examples, including an analog of the elliptic Yamilov lattice
+equation.
+",0,1,0,0,0,0
+7936,Complete Analysis of a Random Forest Model,"  Random forests have become an important tool for improving accuracy in
+regression problems since their popularization by [Breiman, 2001] and others.
+In this paper, we revisit a random forest model originally proposed by
+[Breiman, 2004] and later studied by [Biau, 2012], where a feature is selected
+at random and the split occurs at the midpoint of the box containing the chosen
+feature. If the Lipschitz regression function is sparse and only depends on a
+small, unknown subset of $S$ out of $d$ features, we show that, given access to
+$n$ observations, this random forest model outputs a predictor that has a
+mean-squared prediction error $O((n(\sqrt{\log
+n})^{S-1})^{-\frac{1}{S\log2+1}})$. This positively answers an outstanding
+question of [Biau, 2012] about whether the rate of convergence therein could be
+improved. The second part of this article shows that the aforementioned
+prediction error cannot generally be improved, which we accomplish by
+characterizing the variance and by showing that the bias is tight for any
+linear model with nonzero parameter vector. As a striking consequence of our
+analysis, we show the variance of this forest is similar in form to the
+best-case variance lower bound of [Lin and Jeon, 2006] among all random forest
+models with nonadaptive splitting schemes (i.e., where the split protocol is
+independent of the training data).
+",0,0,0,1,0,0
+7937,Relative Chern character number and super-connection,"  For two complex vector bundles admitting a homomorphism, whose singularity
+locates in the disjoint union of some odd--dimensional spheres, we give a
+formula to compute the relative Chern characteristic number of these two
+complex vector bundles. In particular, for a spin manifold admitting some
+sphere bundle structure, we give a formula to express the index of a special
+twisted Dirac operator.
+",0,0,1,0,0,0
+7938,Mental Sampling in Multimodal Representations,"  Both resources in the natural environment and concepts in a semantic space
+are distributed ""patchily"", with large gaps in between the patches. To describe
+people's internal and external foraging behavior, various random walk models
+have been proposed. In particular, internal foraging has been modeled as
+sampling: in order to gather relevant information for making a decision, people
+draw samples from a mental representation using random-walk algorithms such as
+Markov chain Monte Carlo (MCMC). However, two common empirical observations
+argue against simple sampling algorithms such as MCMC. First, the spatial
+structure is often best described by a Lévy flight distribution: the
+probability of the distance between two successive locations follows a
+power-law on the distances. Second, the temporal structure of the sampling that
+humans and other animals produce have long-range, slowly decaying serial
+correlations characterized as $1/f$-like fluctuations. We propose that mental
+sampling is not done by simple MCMC, but is instead adapted to multimodal
+representations and is implemented by Metropolis-coupled Markov chain Monte
+Carlo (MC$^3$), one of the first algorithms developed for sampling from
+multimodal distributions. MC$^3$ involves running multiple Markov chains in
+parallel but with target distributions of different temperatures, and it swaps
+the states of the chains whenever a better location is found. Heated chains
+more readily traverse valleys in the probability landscape to propose moves to
+far-away peaks, while the colder chains make the local steps that explore the
+current peak or patch. We show that MC$^3$ generates distances between
+successive samples that follow a Lévy flight distribution and $1/f$-like
+serial correlations, providing a single mechanistic account of these two
+puzzling empirical phenomena.
+",1,0,0,0,0,0
+7939,The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime,"  We propose a novel technique for analyzing adaptive sampling called the {\em
+Simulator}. Our approach differs from the existing methods by considering not
+how much information could be gathered by any fixed sampling strategy, but how
+difficult it is to distinguish a good sampling strategy from a bad one given
+the limited amount of data collected up to any given time. This change of
+perspective allows us to match the strength of both Fano and change-of-measure
+techniques, without succumbing to the limitations of either method. For
+concreteness, we apply our techniques to a structured multi-arm bandit problem
+in the fixed-confidence pure exploration setting, where we show that the
+constraints on the means imply a substantial gap between the
+moderate-confidence sample complexity, and the asymptotic sample complexity as
+$\delta \to 0$ found in the literature. We also prove the first instance-based
+lower bounds for the top-k problem which incorporate the appropriate
+log-factors. Moreover, our lower bounds zero-in on the number of times each
+\emph{individual} arm needs to be pulled, uncovering new phenomena which are
+drowned out in the aggregate sample complexity. Our new analysis inspires a
+simple and near-optimal algorithm for the best-arm and top-k identification,
+the first {\em practical} algorithm of its kind for the latter problem which
+removes extraneous log factors, and outperforms the state-of-the-art in
+experiments.
+",1,0,0,1,0,0
+7940,A new method to suppress the bias in polarized intensity,"  Computing polarised intensities from noisy data in Stokes U and Q suffers
+from a positive bias that should be suppressed. To develop a correction method
+that, when applied to maps, should provide a distribution of polarised
+intensity that closely follows the signal from the source. We propose a new
+method to suppress the bias by estimating the polarisation angle of the source
+signal in a noisy environment with help of a modified median filter. We then
+determine the polarised intensity, including the noise, by projection of the
+observed values of Stokes U and Q onto the direction of this polarisation
+angle. We show that our new method represents the true signal very well. If the
+noise distribution in the maps of U and Q is Gaussian, then in the corrected
+map of polarised intensity it is also Gaussian. Smoothing to larger Gaussian
+beamsizes, to improve the signal-to-noise ratio, can be done directly with our
+method in the map of the polarised intensity. Our method also works in case of
+non-Gaussian noise distributions. The maps of the corrected polarised
+intensities and polarisation angles are reliable even in regions with weak
+signals and provide integrated flux densities and degrees of polarisation
+without the cumulative effect of the bias, which especially affects faint
+sources. Features at low intensity levels like 'depolarisation canals' are
+smoother than in the maps using the previous methods, which has broader
+implications, for example on the interpretation of interstellar turbulence.
+",0,1,0,0,0,0
+7941,Bootstrapping kernel intensity estimation for nonhomogeneous point processes depending on spatial covariates,"  In the spatial point process context, kernel intensity estimation has been
+mainly restricted to exploratory analysis due to its lack of consistency.
+Different methods have been analysed to overcome this problem, and the
+inclusion of covariates resulted to be one possible solution. In this paper we
+focus on de\-fi\-ning a theoretical framework to derive a consistent kernel
+intensity estimator using covariates, as well as a consistent smooth bootstrap
+procedure. We define two new data-driven bandwidth selectors specifically
+designed for our estimator: a rule-of-thumb and a plug-in bandwidth based on
+our consistent bootstrap method. A simulation study is accomplished to
+understand the performance of our proposals in finite samples. Finally, we
+describe an application to a real data set consisting of the wildfires in
+Canada during June 2015, using meteorological information as covariates.
+",0,0,0,1,0,0
+7942,"Levi-Kahler reduction of CR structures, products of spheres, and toric geometry","  We study CR geometry in arbitrary codimension, and introduce a process, which
+we call the Levi-Kahler quotient, for constructing Kahler metrics from CR
+structures with a transverse torus action. Most of the paper is devoted to the
+study of Levi-Kahler quotients of toric CR manifolds, and in particular,
+products of odd dimensional spheres. We obtain explicit descriptions and
+characterizations of such quotients, and find Levi-Kahler quotients of products
+of 3-spheres which are extremal in a weighted sense introduced by G. Maschler
+and the first author.
+",0,0,1,0,0,0
+7943,Network Representation Learning: A Survey,"  With the widespread use of information technologies, information networks are
+becoming increasingly popular to capture complex relationships across various
+disciplines, such as social networks, citation networks, telecommunication
+networks, and biological networks. Analyzing these networks sheds light on
+different aspects of social life such as the structure of societies,
+information diffusion, and communication patterns. In reality, however, the
+large scale of information networks often makes network analytic tasks
+computationally expensive or intractable. Network representation learning has
+been recently proposed as a new learning paradigm to embed network vertices
+into a low-dimensional vector space, by preserving network topology structure,
+vertex content, and other side information. This facilitates the original
+network to be easily handled in the new vector space for further analysis. In
+this survey, we perform a comprehensive review of the current literature on
+network representation learning in the data mining and machine learning field.
+We propose new taxonomies to categorize and summarize the state-of-the-art
+network representation learning techniques according to the underlying learning
+mechanisms, the network information intended to preserve, as well as the
+algorithmic designs and methodologies. We summarize evaluation protocols used
+for validating network representation learning including published benchmark
+datasets, evaluation methods, and open source algorithms. We also perform
+empirical studies to compare the performance of representative algorithms on
+common datasets, and analyze their computational complexity. Finally, we
+suggest promising research directions to facilitate future study.
+",1,0,0,1,0,0
+7944,Confidence intervals for the area under the receiver operating characteristic curve in the presence of ignorable missing data,"  Receiver operating characteristic (ROC) curves are widely used as a measure
+of accuracy of diagnostic tests and can be summarized using the area under the
+ROC curve (AUC). Often, it is useful to construct a confidence intervals for
+the AUC, however, since there are a number of different proposed methods to
+measure variance of the AUC, there are thus many different resulting methods
+for constructing these intervals. In this manuscript, we compare different
+methods of constructing Wald-type confidence interval in the presence of
+missing data where the missingness mechanism is ignorable. We find that
+constructing confidence intervals using multiple imputation (MI) based on
+logistic regression (LR) gives the most robust coverage probability and the
+choice of CI method is less important. However, when missingness rate is less
+severe (e.g. less than 70%), we recommend using Newcombe's Wald method for
+constructing confidence intervals along with multiple imputation using
+predictive mean matching (PMM).
+",0,0,0,1,0,0
+7945,Maximum Entropy Flow Networks,"  Maximum entropy modeling is a flexible and popular framework for formulating
+statistical models given partial knowledge. In this paper, rather than the
+traditional method of optimizing over the continuous density directly, we learn
+a smooth and invertible transformation that maps a simple distribution to the
+desired maximum entropy distribution. Doing so is nontrivial in that the
+objective being maximized (entropy) is a function of the density itself. By
+exploiting recent developments in normalizing flow networks, we cast the
+maximum entropy problem into a finite-dimensional constrained optimization, and
+solve the problem by combining stochastic optimization with the augmented
+Lagrangian method. Simulation results demonstrate the effectiveness of our
+method, and applications to finance and computer vision show the flexibility
+and accuracy of using maximum entropy flow networks.
+",0,0,0,1,0,0
+7946,Overcoming the Sign Problem at Finite Temperature: Quantum Tensor Network for the Orbital $e_g$ Model on an Infinite Square Lattice,"  The variational tensor network renormalization approach to two-dimensional
+(2D) quantum systems at finite temperature is applied for the first time to a
+model suffering the notorious quantum Monte Carlo sign problem --- the orbital
+$e_g$ model with spatially highly anisotropic orbital interactions.
+Coarse-graining of the tensor network along the inverse temperature $\beta$
+yields a numerically tractable 2D tensor network representing the Gibbs state.
+Its bond dimension $D$ --- limiting the amount of entanglement --- is a natural
+refinement parameter. Increasing $D$ we obtain a converged order parameter and
+its linear susceptibility close to the critical point. They confirm the
+existence of finite order parameter below the critical temperature $T_c$,
+provide a numerically exact estimate of~$T_c$, and give the critical exponents
+within $1\%$ of the 2D Ising universality class.
+",0,1,0,0,0,0
+7947,Nonasymptotic estimation and support recovery for high dimensional sparse covariance matrices,"  We propose a general framework for nonasymptotic covariance matrix estimation
+making use of concentration inequality-based confidence sets. We specify this
+framework for the estimation of large sparse covariance matrices through
+incorporation of past thresholding estimators with key emphasis on support
+recovery. This technique goes beyond past results for thresholding estimators
+by allowing for a wide range of distributional assumptions beyond merely
+sub-Gaussian tails. This methodology can furthermore be adapted to a wide range
+of other estimators and settings. The usage of nonasymptotic dimension-free
+confidence sets yields good theoretical performance. Through extensive
+simulations, it is demonstrated to have superior performance when compared with
+other such methods. In the context of support recovery, we are able to specify
+a false positive rate and optimize to maximize the true recoveries.
+",0,0,1,1,0,0
+7948,Most Complex Deterministic Union-Free Regular Languages,"  A regular language $L$ is union-free if it can be represented by a regular
+expression without the union operation. A union-free language is deterministic
+if it can be accepted by a deterministic one-cycle-free-path finite automaton;
+this is an automaton which has one final state and exactly one cycle-free path
+from any state to the final state. Jirásková and Masopust proved that the
+state complexities of the basic operations reversal, star, product, and boolean
+operations in deterministic union-free languages are exactly the same as those
+in the class of all regular languages. To prove that the bounds are met they
+used five types of automata, involving eight types of transformations of the
+set of states of the automata. We show that for each $n\ge 3$ there exists one
+ternary witness of state complexity $n$ that meets the bound for reversal and
+product. Moreover, the restrictions of this witness to binary alphabets meet
+the bounds for star and boolean operations. We also show that the tight upper
+bounds on the state complexity of binary operations that take arguments over
+different alphabets are the same as those for arbitrary regular languages.
+Furthermore, we prove that the maximal syntactic semigroup of a union-free
+language has $n^n$ elements, as in the case of regular languages, and that the
+maximal state complexities of atoms of union-free languages are the same as
+those for regular languages. Finally, we prove that there exists a most complex
+union-free language that meets the bounds for all these complexity measures.
+Altogether this proves that the complexity measures above cannot distinguish
+union-free languages from regular languages.
+",1,0,0,0,0,0
+7949,Piecewise Deterministic Markov Processes and their invariant measure,"  Piecewise Deterministic Markov Processes (PDMPs) are studied in a general
+framework. First, different constructions are proven to be equivalent. Second,
+we introduce a coupling between two PDMPs following the same differential flow
+which implies quantitative bounds on the total variation between the marginal
+distributions of the two processes. Finally two results are established
+regarding the invariant measures of PDMPs. A practical condition to show that a
+probability measure is invariant for the associated PDMP semi-group is
+presented. In a second time, a bound on the invariant probability measures in
+$V$-norm of two PDMPs following the same differential flow is established. This
+last result is then applied to study the asymptotic bias of some non-exact PDMP
+MCMC methods.
+",0,0,0,1,0,0
+7950,Visual Speech Language Models,"  Language models (LM) are very powerful in lipreading systems. Language models
+built upon the ground truth utterances of datasets learn grammar and structure
+rules of words and sentences (the latter in the case of continuous speech).
+However, visual co-articulation effects in visual speech signals damage the
+performance of visual speech LM's as visually, people do not utter what the
+language model expects. These models are commonplace but while higher-order
+N-gram LM's may improve classification rates, the cost of this model is
+disproportionate to the common goal of developing more accurate classifiers. So
+we compare which unit would best optimize a lipreading (visual speech) LM to
+observe their limitations. We compare three units; visemes (visual speech
+units) \cite{lan2010improving}, phonemes (audible speech units), and words.
+",1,0,0,0,0,0
+7951,"Millisecond Pulsars as Standards: Timing, positioning and communication","  Millisecond pulsars (MSPs) have a great potential to set standards in
+timekeeping, positioning and metadata communication.
+",0,1,0,0,0,0
+7952,Multipole resonances and directional scattering by hyperbolic-media antennas,"  We propose to use optical antennas made out of natural hyperbolic material
+hexagonal boron nitride (hBN), and we demonstrate that this medium is a
+promising alternative to plasmonic and all-dielectric materials for realizing
+efficient subwavelength scatterers and metasurfaces based on them. We
+theoretically show that particles out of hyperbolic medium possess different
+resonances enabled by the support of high-k waves and their reflection from the
+particle boundaries. Among those resonances, there are electric quadrupole
+excitations, which cause magnetic resonance of the particle similar to what
+occurs in high-refractive-index particles. Excitations of the particle
+resonances are accompanied by the drop in the reflection from nanoparticle
+array to near-zero value, which can be ascribed to resonant Kerker effect. If
+particles are arranged in the spacer array with period d, narrow lattice
+resonances are possible at wavelength d, d/2, d/3 etc. This provides an
+additional degree of control and possibility to excite resonances at the
+wavelength defined by the array spacing. For the hBN particle with hyperbolic
+dispersion, we show that the full range of the resonances, including magnetic
+resonance and a decrease of reflection, is possible.
+",0,1,0,0,0,0
+7953,Discerning Dark Energy Models with High-Redshift Standard Candles,"  Following the success of type Ia supernovae in constraining cosmologies at
+lower redshift $(z\lesssim2)$, effort has been spent determining if a similarly
+useful standardisable candle can be found at higher redshift. {In this work we
+determine the largest possible magnitude discrepancy between a constant dark
+energy $\Lambda$CDM cosmology and a cosmology in which the equation of state
+$w(z)$ of dark energy is a function of redshift for high redshift standard
+candles $(z\gtrsim2)$}. We discuss a number of popular parametrisations of
+$w(z)$ with two free parameters, $w_z$CDM cosmologies, including the
+Chevallier-Polarski-Linder and generalisation thereof, $n$CPL, as well as the
+Jassal-Bagla-Padmanabhan parametrisation. For each of these parametrisations we
+calculate and find extrema of $\Delta \mu$, the difference between the distance
+modulus of a $w_z$CDM cosmology and a fiducial $\Lambda$CDM cosmology as a
+function of redshift, given 68\% likelihood constraints on the parameters
+$P=(\Omega_{m,0}, w_0, w_a)$. The parameters are constrained using cosmic
+microwave background, baryon acoustic oscillations, and type Ia supernovae data
+using CosmoMC. We find that none of the tested cosmologies can deviate more
+than 0.05 mag from the fiducial $\Lambda$CDM cosmology at high redshift,
+implying that high redshift standard candles will not aid in discerning between
+a $w_z$CDM cosmology and the fiducial $\Lambda$CDM cosmology. Conversely, this
+implies that if high redshift standard candles are found to be in disagreement
+with $\Lambda$CDM at high redshift, then this is a problem not only for
+$\Lambda$CDM but for the entire family of $w_z$CDM cosmologies.
+",0,1,0,0,0,0
+7954,Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters,"  High-precision modeling of subatomic particle interactions is critical for
+many fields within the physical sciences, such as nuclear physics and high
+energy particle physics. Most simulation pipelines in the sciences are
+computationally intensive -- in a variety of scientific fields, Generative
+Adversarial Networks have been suggested as a solution to speed up the forward
+component of simulation, with promising results. An important component of any
+simulation system for the sciences is the ability to condition on any number of
+physically meaningful latent characteristics that can effect the forward
+generation procedure. We introduce an auxiliary task to the training of a
+Generative Adversarial Network on particle showers in a multi-layer
+electromagnetic calorimeter, which allows our model to learn an attribute-aware
+conditioning mechanism.
+",1,0,0,0,0,0
+7955,Max-value Entropy Search for Efficient Bayesian Optimization,"  Entropy Search (ES) and Predictive Entropy Search (PES) are popular and
+empirically successful Bayesian Optimization techniques. Both rely on a
+compelling information-theoretic motivation, and maximize the information
+gained about the $\arg\max$ of the unknown function; yet, both are plagued by
+the expensive computation for estimating entropies. We propose a new criterion,
+Max-value Entropy Search (MES), that instead uses the information about the
+maximum function value. We show relations of MES to other Bayesian optimization
+methods, and establish a regret bound. We observe that MES maintains or
+improves the good empirical performance of ES/PES, while tremendously
+lightening the computational burden. In particular, MES is much more robust to
+the number of samples used for computing the entropy, and hence more efficient
+for higher dimensional problems.
+",1,0,1,1,0,0
+7956,On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensions,"  In the recent article [Jentzen, A., Müller-Gronbach, T., and Yaroslavtseva,
+L., Commun. Math. Sci., 14(6), 1477--1500, 2016] it has been established that
+for every arbitrarily slow convergence speed and every natural number $d \in
+\{4,5,\ldots\}$ there exist $d$-dimensional stochastic differential equations
+(SDEs) with infinitely often differentiable and globally bounded coefficients
+such that no approximation method based on finitely many observations of the
+driving Brownian motion can converge in absolute mean to the solution faster
+than the given speed of convergence. In this paper we strengthen the above
+result by proving that this slow convergence phenomena also arises in two
+($d=2$) and three ($d=3$) space dimensions.
+",0,0,1,0,0,0
+7957,Energy Dissipation in Monolayer MoS$_2$ Electronics,"  The advancement of nanoscale electronics has been limited by energy
+dissipation challenges for over a decade. Such limitations could be
+particularly severe for two-dimensional (2D) semiconductors integrated with
+flexible substrates or multi-layered processors, both being critical thermal
+bottlenecks. To shed light into fundamental aspects of this problem, here we
+report the first direct measurement of spatially resolved temperature in
+functioning 2D monolayer MoS$_2$ transistors. Using Raman thermometry we
+simultaneously obtain temperature maps of the device channel and its substrate.
+This differential measurement reveals the thermal boundary conductance (TBC) of
+the MoS$_2$ interface (14 $\pm$ 4 MWm$^-$$^2$K$^-$$^1$) is an order magnitude
+larger than previously thought, yet near the low end of known solid-solid
+interfaces. Our study also reveals unexpected insight into non-uniformities of
+the MoS$_2$ transistors (small bilayer regions), which do not cause significant
+self-heating, suggesting that such semiconductors are less sensitive to
+inhomogeneity than expected. These results provide key insights into energy
+dissipation of 2D semiconductors and pave the way for the future design of
+energy-efficient 2D electronics.
+",0,1,0,0,0,0
+7958,Adaptive Estimation of Nonparametric Geometric Graphs,"  This article studies the recovery of graphons when they are convolution
+kernels on compact (symmetric) metric spaces. This case is of particular
+interest since it covers the situation where the probability of an edge depends
+only on some unknown nonparametric function of the distance between latent
+points, referred to as Nonparametric Geometric Graphs (NGG). In this setting,
+almost minimax adaptive estimation of NGG is possible using a spectral
+procedure combined with a Goldenshluger-Lepski adaptation method. The latent
+spaces covered by our framework encompasses (among others) compact symmetric
+spaces of rank one, namely real spheres and projective spaces. For these
+latter, explicit computations of the eigenbasis and of the model complexity can
+be achieved, leading to quantitative non-asymptotic results. The time
+complexity of our method scales cubicly in the size of the graph and
+exponentially in the regularity of the graphon. Hence, this paper offers an
+algorithmically and theoretically efficient procedure to estimate smooth NGG.
+As a by product, this paper shows a non-asymptotic concentration result on the
+spectrum of integral operators defined by symmetric kernels (not necessarily
+positive).
+",0,0,1,1,0,0
+7959,Angular and Temporal Correlation of V2X Channels Across Sub-6 GHz and mmWave Bands,"  5G millimeter wave (mmWave) technology is envisioned to be an integral part
+of next-generation vehicle-to-everything (V2X) networks and autonomous vehicles
+due to its broad bandwidth, wide field of view sensing, and precise
+localization capabilities. The reliability of mmWave links may be compromised
+due to difficulties in beam alignment for mobile channels and due to blocking
+effects between a mmWave transmitter and a receiver. To address such
+challenges, out-of-band information from sub-6 GHz channels can be utilized for
+predicting the temporal and angular channel characteristics in mmWave bands,
+which necessitates a good understanding of how propagation characteristics are
+coupled across different bands. In this paper, we use ray tracing simulations
+to characterize the angular and temporal correlation across a wide range of
+propagation frequencies for V2X channels ranging from 900 MHz up to 73 GHz, for
+a vehicle maintaining line-of-sight (LOS) and non-LOS (NLOS) beams with a
+transmitter in an urban environment. Our results shed light on increasing
+sparsity behavior of propagation channels with increasing frequency and
+highlight the strong temporal/angular correlation among 5.9 GHz and 28 GHz
+bands especially for LOS channels.
+",1,0,0,0,0,0
+7960,Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response,"  Plants monitor their surrounding environment and control their physiological
+functions by producing an electrical response. We recorded electrical signals
+from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3)
+and Sulfuric Acid (H2SO4) under laboratory conditions. After applying
+pre-processing techniques such as filtering and drift removal, we extracted few
+statistical features from the acquired plant electrical signals. Using these
+features, combined with different classification algorithms, we used a decision
+tree based multi-class classification strategy to identify the three different
+external chemical stimuli. We here present our exploration to obtain the
+optimum set of ranked feature and classifier combination that can separate a
+particular chemical stimulus from the incoming stream of plant electrical
+signals. The paper also reports an exhaustive comparison of similar feature
+based classification using the filtered and the raw plant signals, containing
+the high frequency stochastic part and also the low frequency trends present in
+it, as two different cases for feature extraction. The work, presented in this
+paper opens up new possibilities for using plant electrical signals to monitor
+and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.
+",1,1,0,1,0,0
+7961,Interactive Reinforcement Learning for Object Grounding via Self-Talking,"  Humans are able to identify a referred visual object in a complex scene via a
+few rounds of natural language communications. Success communication requires
+both parties to engage and learn to adapt for each other. In this paper, we
+introduce an interactive training method to improve the natural language
+conversation system for a visual grounding task. During interactive training,
+both agents are reinforced by the guidance from a common reward function. The
+parametrized reward function also cooperatively updates itself via
+interactions, and contribute to accomplishing the task. We evaluate the method
+on GuessWhat?! visual grounding task, and significantly improve the task
+success rate. However, we observe language drifting problem during training and
+propose to use reward engineering to improve the interpretability for the
+generated conversations. Our result also indicates evaluating goal-ended visual
+conversation tasks require semantic relevant metrics beyond task success rate.
+",1,0,0,0,0,0
+7962,Adaptive Bayesian Sampling with Monte Carlo EM,"  We present a novel technique for learning the mass matrices in samplers
+obtained from discretized dynamics that preserve some energy function. Existing
+adaptive samplers use Riemannian preconditioning techniques, where the mass
+matrices are functions of the parameters being sampled. This leads to
+significant complexities in the energy reformulations and resultant dynamics,
+often leading to implicit systems of equations and requiring inversion of
+high-dimensional matrices in the leapfrog steps. Our approach provides a
+simpler alternative, by using existing dynamics in the sampling step of a Monte
+Carlo EM framework, and learning the mass matrices in the M step with a novel
+online technique. We also propose a way to adaptively set the number of samples
+gathered in the E step, using sampling error estimates from the leapfrog
+dynamics. Along with a novel stochastic sampler based on Nosé-Poincaré
+dynamics, we use this framework with standard Hamiltonian Monte Carlo (HMC) as
+well as newer stochastic algorithms such as SGHMC and SGNHT, and show strong
+performance on synthetic and real high-dimensional sampling scenarios; we
+achieve sampling accuracies comparable to Riemannian samplers while being
+significantly faster.
+",1,0,0,1,0,0
+7963,Neutrino Fluxes from a Core-Collapse Supernova in a Model with Three Sterile Neutrinos,"  The characteristics of the gravitational collapse of a supernova and the
+fluxes of active and sterile neutrinos produced during the formation of its
+protoneutron core have been calculated numerically. The relative yields of
+active and sterile neutrinos in core matter with different degrees of
+neutronization have been calculated for various input parameters and various
+initial conditions. A significant increase in the fraction of sterile neutrinos
+produced in superdense core matter at the resonant degree of neutronization has
+been confirmed. The contributions of sterile neutrinos to the collapse dynamics
+and the total flux of neutrinos produced during collapse have been shown to be
+relatively small. The total luminosity of sterile neutrinos is considerably
+lower than the luminosity of electron neutrinos, but their spectrum is
+considerably harder at high energies.
+",0,1,0,0,0,0
+7964,Resource Allocation for Wireless Networks: A Distributed Optimization Approach,"  We consider the multi-cell joint power control and scheduling problem in
+cellular wireless networks as a weighted sum-rate maximization problem. This
+formulation is very general and applies to a wide range of applications and QoS
+requirements. The problem is inherently hard due to objective's non-convexity
+and the knapsack-like constraints. Moreover, practical system requires a
+distributed operation. We applied an existing algorithm proposed by Scutari et
+al. in distributed optimization literature to our problem. The algorithm
+performs local optimization followed by consensus update repeatedly. However,
+it is not fully applicable to our problem, as it requires all decision
+variables to be maintained at every base station (BS), which is impractical for
+large-scale networks; also, it relies on the Lipschitz continuity of the
+objective function's gradient, which does not hold here. We exploited the
+nature of our objective function, and proposed a localized version of the
+algorithm. Furthermore, we relaxed the requirements of Lipschitz continuity
+with the proximal approximation. Convergence to local optimal solutions was
+proved under some conditions. Future work includes proving the above results
+from a stochastic approximation perspective, and investigating non-linear
+consensus schemes to speed up the convergence.
+",0,0,1,0,0,0
+7965,Modular operads and Batalin-Vilkovisky geometry,"  This is a copy of the article published in IMRN (2007). I describe the
+noncommutative Batalin-Vilkovisky geometry associated naturally with arbitrary
+modular operad. The classical limit of this geometry is the noncommutative
+symplectic geometry of the corresponding tree-level cyclic operad. I show, in
+particular, that the algebras over the Feynman transform of a twisted modular
+operad P are in one-to-one correspondence with solutions to quantum master
+equation of Batalin-Vilkovisky geometry on the affine P-manifolds. As an
+application I give a construction of characteristic classes with values in the
+homology of the quotient of Deligne-Mumford moduli spaces. These classes are
+associated naturally with solutions to the quantum master equation on affine
+S[t]-manifolds, where S[t] is the twisted modular Det-operad constructed from
+symmetric groups, which generalizes the cyclic operad of associative algebras.
+",0,0,1,0,0,0
+7966,Response of QD to structured beams via convolution integrals,"  We propose a new expression for the response of a quadrant detector using
+convolution integrals. This expression is easier to evaluate by hand,
+exploiting the properties of the convolution. Computationally, it is also
+practicable to use since a large number of computer programs can right away
+evaluate convolutions. We use the new expression to obtain an analytical form
+of the response of a quadrant detector to a Gaussian beam and to
+Hermite-Gaussian beams in general. We compare this analytic expression for the
+response for the Gaussian beam with the approximations from previous studies
+and with a response obtained through simulations. From the response, we also
+obtained an analytical form for the sensitivity of the quadrant detector to a
+Gaussian beam. Lastly, we demonstrate the computational ease of using our new
+expression for the response calculating the sensitivity of the quadrant
+detector to the Bessel beam.
+",0,1,0,0,0,0
+7967,Regularized arrangements of cellular complexes,"  In this paper we propose a novel algorithm to combine two or more cellular
+complexes, providing a minimal fragmentation of the cells of the resulting
+complex. We introduce here the idea of arrangement generated by a collection of
+cellular complexes, producing a cellular decomposition of the embedding space.
+The algorithm that executes this computation is called \emph{Merge} of
+complexes. The arrangements of line segments in 2D and polygons in 3D are
+special cases, as well as the combination of closed triangulated surfaces or
+meshed models. This algorithm has several important applications, including
+Boolean and other set operations over large geometric models, the extraction of
+solid models of biomedical structures at the cellular scale, the detailed
+geometric modeling of buildings, the combination of 3D meshes, and the repair
+of graphical models. The algorithm is efficiently implemented using the Linear
+Algebraic Representation (LAR) of argument complexes, i.e., on sparse
+representation of binary characteristic matrices of $d$-cell bases, well-suited
+for implementation in last generation accelerators and GPGPU applications.
+",1,0,0,0,0,0
+7968,Duality of deconfined quantum critical point in two dimensional Dirac semimetals,"  In this paper we discuss the N$\acute{e}$el and Kekul$\acute{e}$ valence bond
+solids quantum criticality in graphene Dirac semimetal. Considering the quartic
+four-fermion interaction $g(\bar{\psi}_i\Gamma_{ij}\psi_j)^2$ that contains
+spin,valley, and sublattice degrees of freedom in the continuum field theory,
+we find the microscopic symmetry is spontaneously broken when the coupling $g$
+is greater than a critical value $g_c$. The symmetry breaking gaps out the
+fermion and leads to semimetal-insulator transition. All possible quartic
+fermion-bilinear interactions give rise to the uniform critical coupling, which
+exhibits the multicritical point for various orders and the Landau-forbidden
+quantum critical point. We also investigate the typical critical point between
+N$\acute{e}$el and Kekul$\acute{e}$ valence bond solid transition when the
+symmetry is broken. The quantum criticality is captured by the
+Wess-Zumino-Witten term and there exist a mutual-duality for
+N$\acute{e}$el-Kekul$\acute{e}$ VBS order. We show the emergent spinon in the
+N$\acute{e}$el-Kekul$\acute{e}$ VBS transition , from which we conclude the
+phase transition is a deconfined quantum critical point. Additionally, the
+connection between the index theorem and zero energy mode bounded by the
+topological defect in the Kekul$\acute{e}$ VBS phase is studied to reveal the
+N$\acute{e}$el-Kekul$\acute{e}$ VBS duality.
+",0,1,0,0,0,0
+7969,A Survey on Cloud Video Multicasting Over Mobile Networks,"  Since multimedia streaming has become very popular research topic in the
+recent years, this paper surveys the state of art techniques introduced for
+multimedia multicasting over mobile networks. In this paper, we give an
+overview of multimedia multicasting mechanisms in respect to cloud mobile
+communications, and we present some proposed solutions in perspective. We focus
+on the algorithms designed specifically for the video-on-demand applications.
+Our study on video-on-demand applications will eventually cover a wide range of
+applications such as cloud gaming without violating the limited scope of this
+survey.
+",1,0,0,0,0,0
+7970,The Brauer trees of unipotent blocks,"  In this paper we complete the determination of the Brauer trees of unipotent
+blocks (with cyclic defect groups) of finite groups of Lie type. These trees
+were conjectured by the first author. As a consequence, the Brauer trees of
+principal $\ell$-blocks of finite groups are known for $\ell>71$.
+",0,0,1,0,0,0
+7971,An Empirical Bayes Approach to Regularization Using Previously Published Models,"  This manuscript proposes a novel empirical Bayes technique for regularizing
+regression coefficients in predictive models. When predictions from a
+previously published model are available, this empirical Bayes method provides
+a natural mathematical framework for shrinking coefficients toward the
+estimates implied by the body of existing research rather than the shrinkage
+toward zero provided by traditional L1 and L2 penalization schemes. The method
+is applied to two different prediction problems. The first involves the
+construction of a model for predicting whether a single nucleotide polymorphism
+(SNP) of the KCNQ1 gene will result in dysfunction of the corresponding voltage
+gated ion channel. The second involves the prediction of preoperative serum
+creatinine change in patients undergoing cardiac surgery.
+",0,0,0,1,0,0
+7972,On minimum distance of locally repairable codes,"  Distributed and cloud storage systems are used to reliably store large-scale
+data. Erasure codes have been recently proposed and used in real-world
+distributed and cloud storage systems such as Google File System, Microsoft
+Azure Storage, and Facebook HDFS-RAID, to enhance the reliability. In order to
+decrease the repair bandwidth and disk I/O, a class of erasure codes called
+locally repairable codes (LRCs) have been proposed which have small locality
+compare to other erasure codes. Although LRCs have small locality, they have
+lower minimum distance compare to the Singleton bound. Hence, seeking the
+largest possible minimum distance for LRCs have been the topic of many recent
+studies. In this paper, we study the largest possible minimum distance of a
+class of LRCs and evaluate them in terms of achievability. Furthermore, we
+compare our results with the existence bounds in the literature.
+",1,0,0,0,0,0
+7973,The MoEDAL experiment at the LHC: status and results,"  The MoEDAL experiment at the LHC is optimised to detect highly ionising
+particles such as magnetic monopoles, dyons and (multiply) electrically charged
+stable massive particles predicted in a number of theoretical scenarios.
+MoEDAL, deployed in the LHCb cavern, combines passive nuclear track detectors
+with magnetic monopole trapping volumes (MMTs), while spallation-product
+backgrounds are being monitored with an array of MediPix pixel detectors. An
+introduction to the detector concept and its physics reach, complementary to
+that of the large general purpose LHC experiments ATLAS and CMS, will be given.
+Emphasis is given to the recent MoEDAL results at 13 TeV, where the null
+results from a search for magnetic monopoles in MMTs exposed in 2015 LHC
+collisions set the world-best limits on particles with magnetic charges more
+than 1.5 Dirac charge. The potential to search for heavy, long-lived
+supersymmetric electrically-charged particles is also discussed.
+",0,1,0,0,0,0
+7974,Towards a population synthesis model of self-gravitating disc fragmentation and tidal downsizing II: The effect of fragment-fragment interactions,"  It is likely that most protostellar systems undergo a brief phase where the
+protostellar disc is self-gravitating. If these discs are prone to
+fragmentation, then they are able to rapidly form objects that are initially of
+several Jupiter masses and larger. The fate of these disc fragments (and the
+fate of planetary bodies formed afterwards via core accretion) depends
+sensitively not only on the fragment's interaction with the disc, but with its
+neighbouring fragments.
+We return to and revise our population synthesis model of self-gravitating
+disc fragmentation and tidal downsizing. Amongst other improvements, the model
+now directly incorporates fragment-fragment interactions while the disc is
+still present. We find that fragment-fragment scattering dominates the orbital
+evolution, even when we enforce rapid migration and inefficient gap formation.
+Compared to our previous model, we see a small increase in the number of
+terrestrial-type objects being formed, although their survival under tidal
+evolution is at best unclear. We also see evidence for disrupted fragments with
+evolved grain populations - this is circumstantial evidence for the formation
+of planetesimal belts, a phenomenon not seen in runs where fragment-fragment
+interactions are ignored.
+In spite of intense dynamical evolution, our population is dominated by
+massive giant planets and brown dwarfs at large semimajor axis, which direct
+imaging surveys should, but only rarely, detect. Finally, disc fragmentation is
+shown to be an efficient manufacturer of free floating planetary mass objects,
+and the typical multiplicity of systems formed via gravitational instability
+will be low.
+",0,1,0,0,0,0
+7975,Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations,"  We review recent progress in modeling credit risk for correlated assets. We
+start from the Merton model which default events and losses are derived from
+the asset values at maturity. To estimate the time development of the asset
+values, the stock prices are used whose correlations have a strong impact on
+the loss distribution, particularly on its tails. These correlations are
+non-stationary which also influences the tails. We account for the asset
+fluctuations by averaging over an ensemble of random matrices that models the
+truly existing set of measured correlation matrices. As a most welcome side
+effect, this approach drastically reduces the parameter dependence of the loss
+distribution, allowing us to obtain very explicit results which show
+quantitatively that the heavy tails prevail over diversification benefits even
+for small correlations. We calibrate our random matrix model with market data
+and show how it is capable of grasping different market situations.
+Furthermore, we present numerical simulations for concurrent portfolio risks,
+i.e., for the joint probability densities of losses for two portfolios. For the
+convenience of the reader, we give an introduction to the Wishart random matrix
+model.
+",0,0,0,0,0,1
+7976,Peptide-Spectra Matching from Weak Supervision,"  As in many other scientific domains, we face a fundamental problem when using
+machine learning to identify proteins from mass spectrometry data: large ground
+truth datasets mapping inputs to correct outputs are extremely difficult to
+obtain. Instead, we have access to imperfect hand-coded models crafted by
+domain experts. In this paper, we apply deep neural networks to an important
+step of the protein identification problem, the pairing of mass spectra with
+short sequences of amino acids called peptides. We train our model to
+differentiate between top scoring results from a state-of-the art classical
+system and hard-negative second and third place results. Our resulting model is
+much better at identifying peptides with spectra than the model used to
+generate its training data. In particular, we achieve a 43% improvement over
+standard matching methods and a 10% improvement over a combination of the
+matching method and an industry standard cross-spectra reranking tool.
+Importantly, in a more difficult experimental regime that reflects current
+challenges facing biologists, our advantage over the previous state-of-the-art
+grows to 15% even after reranking. We believe this approach will generalize to
+other challenging scientific problems.
+",0,0,0,1,0,0
+7977,Overcoming data scarcity with transfer learning,"  Despite increasing focus on data publication and discovery in materials
+science and related fields, the global view of materials data is highly sparse.
+This sparsity encourages training models on the union of multiple datasets, but
+simple unions can prove problematic as (ostensibly) equivalent properties may
+be measured or computed differently depending on the data source. These hidden
+contextual differences introduce irreducible errors into analyses,
+fundamentally limiting their accuracy. Transfer learning, where information
+from one dataset is used to inform a model on another, can be an effective tool
+for bridging sparse data while preserving the contextual differences in the
+underlying measurements. Here, we describe and compare three techniques for
+transfer learning: multi-task, difference, and explicit latent variable
+architectures. We show that difference architectures are most accurate in the
+multi-fidelity case of mixed DFT and experimental band gaps, while multi-task
+most improves classification performance of color with band gaps. For
+activation energies of steps in NO reduction, the explicit latent variable
+method is not only the most accurate, but also enjoys cancellation of errors in
+functions that depend on multiple tasks. These results motivate the publication
+of high quality materials datasets that encode transferable information,
+independent of industrial or academic interest in the particular labels, and
+encourage further development and application of transfer learning methods to
+materials informatics problems.
+",1,0,0,1,0,0
+7978,Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle,"  A classic approach for learning Bayesian networks from data is to identify a
+maximum a posteriori (MAP) network structure. In the case of discrete Bayesian
+networks, MAP networks are selected by maximising one of several possible
+Bayesian Dirichlet (BD) scores; the most famous is the Bayesian Dirichlet
+equivalent uniform (BDeu) score from Heckerman et al (1995). The key properties
+of BDeu arise from its uniform prior over the parameters of each local
+distribution in the network, which makes structure learning computationally
+efficient; it does not require the elicitation of prior knowledge from experts;
+and it satisfies score equivalence.
+In this paper we will review the derivation and the properties of BD scores,
+and of BDeu in particular, and we will link them to the corresponding entropy
+estimates to study them from an information theoretic perspective. To this end,
+we will work in the context of the foundational work of Giffin and Caticha
+(2007), who showed that Bayesian inference can be framed as a particular case
+of the maximum relative entropy principle. We will use this connection to show
+that BDeu should not be used for structure learning from sparse data, since it
+violates the maximum relative entropy principle; and that it is also
+problematic from a more classic Bayesian model selection perspective, because
+it produces Bayes factors that are sensitive to the value of its only
+hyperparameter. Using a large simulation study, we found in our previous work
+(Scutari, 2016) that the Bayesian Dirichlet sparse (BDs) score seems to provide
+better accuracy in structure learning; in this paper we further show that BDs
+does not suffer from the issues above, and we recommend to use it for sparse
+data instead of BDeu. Finally, will show that these issues are in fact
+different aspects of the same problem and a consequence of the distributional
+assumptions of the prior.
+",0,0,1,1,0,0
+7979,Thermal lattice Boltzmann method for multiphase flows,"  New method to simulate heat transport in multiphase lattice Boltzmann (LB)
+method is proposed. The energy transport equation needs to be solved when phase
+boundaries are present. Internal energy is represented by an additional set of
+distribution functions, which evolve according to a LB-like equation simulating
+the transport of a passive scalar. Parasitic heat diffusion near boundaries
+with large density gradient is supressed by using the interparticle
+""pseudoforces"" which prevent the spreading of energy. The compression work and
+heat diffusion are calculated by finite differences. The latent heat of a phase
+transition is released or absorbed in the inner side of a thin transition layer
+between liquid and vapor. This allows one to avoide the interface tracking.
+Several tests were carried out concerning all aspects of the processes. It was
+shown that the Galilean invariance and the scaling of thermal conduction
+process hold as well as the correct dependence of sound speed on the heat
+capacity ratio. The method proposed has low scheme diffusion of the internal
+energy, and it can be applied to modeling a wide range of multiphase flows with
+heat and mass transfer.
+",0,1,0,0,0,0
+7980,Control for Schrödinger equation on hyperbolic surfaces,"  We show that the any nonempty open set on a hyperbolic surface provides
+observability and control for the time dependent Schrödinger equation. The
+only other manifolds for which this was previously known are flat tori. The
+proof is based on the main estimate in Dyatlov-Jin and standard arguments of
+control theory.
+",0,0,1,0,0,0
+7981,Grand Fujii-Fujii-Nakamoto operator inequality dealing with operator order and operator chaotic order,"  In this paper, we shall prove that a grand Fujii-Fujii-Nakamoto operator
+inequality implies operator order and operator chaotic order under different
+conditions.
+",0,0,1,0,0,0
+7982,Stability for gains from large investors' strategies in M1/J1 topologies,"  We prove continuity of a controlled SDE solution in Skorokhod's $M_1$ and
+$J_1$ topologies and also uniformly, in probability, as a non-linear functional
+of the control strategy. The functional comes from a finance problem to model
+price impact of a large investor in an illiquid market. We show that
+$M_1$-continuity is the key to ensure that proceeds and wealth processes from
+(self-financing) càdlàg trading strategies are determined as the
+continuous extensions for those from continuous strategies. We demonstrate by
+examples how continuity properties are useful to solve different stochastic
+control problems on optimal liquidation and to identify asymptotically
+realizable proceeds.
+",0,0,1,0,0,0
+7983,Apparent and Intrinsic Evolution of Active Region Upflows,"  We analyze the evolution of Fe XII coronal plasma upflows from the edges of
+ten active regions (ARs) as they cross the solar disk using the Hinode Extreme
+Ultraviolet Imaging Spectrometer (EIS). Confirming the results of Demoulin et
+al. (2013, Sol. Phys. 283, 341), we find that for each AR there is an observed
+long term evolution of the upflows which is largely due to the solar rotation
+progressively changing the viewpoint of dominantly stationary upflows. From
+this projection effect, we estimate the unprojected upflow velocity and its
+inclination to the local vertical. AR upflows typically fan away from the AR
+core by 40 deg. to near vertical for the following polarity. The span of
+inclination angles is more spread for the leading polarity with flows angled
+from -29 deg. (inclined towards the AR center) to 28 deg. (directed away from
+the AR). In addition to the limb-to-limb apparent evolution, we identify an
+intrinsic evolution of the upflows due to coronal activity which is AR
+dependent. Further, line widths are correlated with Doppler velocities only for
+the few ARs having the largest velocities. We conclude that for the line widths
+to be affected by the solar rotation, the spatial gradient of the upflow
+velocities must be large enough such that the line broadening exceeds the
+thermal line width of Fe XII. Finally, we find that upflows occurring in pairs
+or multiple pairs is a common feature of ARs observed by Hinode/EIS, with up to
+four pairs present in AR 11575. This is important for constraining the upflow
+driving mechanism as it implies that the mechanism is not a local one occurring
+over a single polarity. AR upflows originating from reconnection along
+quasi-separatrix layers (QSLs) between over-pressure AR loops and neighboring
+under-pressure loops is consistent with upflows occurring in pairs, unlike
+other proposed mechanisms acting locally in one polarity.
+",0,1,0,0,0,0
+7984,Size scaling of failure strength with fat-tailed disorder in a fiber bundle model,"  We investigate the size scaling of the macroscopic fracture strength of
+heterogeneous materials when microscopic disorder is controlled by fat-tailed
+distributions. We consider a fiber bundle model where the strength of single
+fibers is described by a power law distribution over a finite range. Tuning the
+amount of disorder by varying the power law exponent and the upper cutoff of
+fibers' strength, in the limit of equal load sharing an astonishing size effect
+is revealed: For small system sizes the bundle strength increases with the
+number of fibers and the usual decreasing size effect of heterogeneous
+materials is only restored beyond a characteristic size. We show analytically
+that the extreme order statistics of fibers' strength is responsible for this
+peculiar behavior. Analyzing the results of computer simulations we deduce a
+scaling form which describes the dependence of the macroscopic strength of
+fiber bundles on the parameters of microscopic disorder over the entire range
+of system sizes.
+",0,1,0,0,0,0
+7985,Time-dependent focusing Mean-Field Games: the sub-critical case,"  We consider time-dependent viscous Mean-Field Games systems in the case of
+local, decreasing and unbounded coupling. These systems arise in mean-field
+game theory, and describe Nash equilibria of games with a large number of
+agents aiming at aggregation. We prove the existence of weak solutions that are
+minimisers of an associated non-convex functional, by rephrasing the problem in
+a convex framework. Under additional assumptions involving the growth at
+infinity of the coupling, the Hamiltonian, and the space dimension, we show
+that such minimisers are indeed classical solutions by a blow-up argument and
+additional Sobolev regularity for the Fokker-Planck equation. We exhibit an
+example of non-uniqueness of solutions. Finally, by means of a contraction
+principle, we observe that classical solutions exist just by local regularity
+of the coupling if the time horizon is short.
+",0,0,1,0,0,0
+7986,Evolution of protoplanetary disks from their taxonomy in scattered light: Group I vs. Group II,"  High-resolution imaging reveals a large morphological variety of
+protoplanetary disks. To date, no constraints on their global evolution have
+been found from this census. An evolutionary classification of disks was
+proposed based on their IR spectral energy distribution, with the Group I
+sources showing a prominent cold component ascribed to an earlier stage of
+evolution than Group II. Disk evolution can be constrained from the comparison
+of disks with different properties. A first attempt of disk taxonomy is now
+possible thanks to the increasing number of high-resolution images of Herbig
+Ae/Be stars becoming available. Near-IR images of six Group II disks in
+scattered light were obtained with VLT/NACO in Polarimetric Differential
+Imaging, which is the most efficient technique to image the light scattered by
+the disk material close to the stars. We compare the stellar/disk properties of
+this sample with those of well-studied Group I sources available from the
+literature. Three Group II disks are detected. The brightness distribution in
+the disk of HD163296 indicates the presence of a persistent ring-like structure
+with a possible connection with the CO snowline. A rather compact (less than
+100 AU) disk is detected around HD142666 and AK Sco. A taxonomic analysis of 17
+Herbig Ae/Be sources reveals that the difference between Group I and Group II
+is due to the presence or absence of a large disk cavity (larger than 5 AU).
+There is no evidence supporting the evolution from Group I to Group II. Group
+II are not evolved version of the Group I. Within the Group II disks, very
+different geometries (both self-shadowed and compact) exist. HD163296 could be
+the primordial version of a typical Group I. Other Group II, like AK Sco and
+HD142666, could be smaller counterpart of Group I unable to open cavities as
+large as those of Group I.
+",0,1,0,0,0,0
+7987,Sandwich semigroups in locally small categories II: Transformations,"  Fix sets $X$ and $Y$, and write $\mathcal{PT}_{XY}$ for the set of all
+partial functions $X\to Y$. Fix a partial function $a:Y\to X$, and define the
+operation $\star_a$ on $\mathcal{PT}_{XY}$ by $f\star_ag=fag$ for
+$f,g\in\mathcal{PT}_{XY}$. The sandwich semigroup $(\mathcal{PT}_{XY},\star_a)$
+is denoted $\mathcal{PT}_{XY}^a$. We apply general results from Part I to
+thoroughly describe the structural and combinatorial properties of
+$\mathcal{PT}_{XY}^a$, as well as its regular and idempotent-generated
+subsemigroups, Reg$(\mathcal{PT}_{XY}^a)$ and $\mathbb E(\mathcal{PT}_{XY}^a)$.
+After describing regularity, stability and Green's relations and preorders, we
+exhibit Reg$(\mathcal{PT}_{XY}^a)$ as a pullback product of certain regular
+subsemigroups of the (non-sandwich) partial transformation semigroups
+$\mathcal{PT}_X$ and $\mathcal{PT}_Y$, and as a kind of ""inflation"" of
+$\mathcal{PT}_A$, where $A$ is the image of the sandwich element $a$. We also
+calculate the rank (minimal size of a generating set) and, where appropriate,
+the idempotent rank (minimal size of an idempotent generating set) of
+$\mathcal{PT}_{XY}^a$, Reg$(\mathcal{PT}_{XY}^a)$ and $\mathbb
+E(\mathcal{PT}_{XY}^a)$. The same program is also carried out for sandwich
+semigroups of totally defined functions and for injective partial functions.
+Several corollaries are obtained for various (non-sandwich) semigroups of
+(partial) transformations with restricted image, domain and/or kernel.
+",0,0,1,0,0,0
+7988,Environmental feedback drives cooperation in spatial social dilemmas,"  Exploiting others is beneficial individually but it could also be detrimental
+globally. The reverse is also true: a higher cooperation level may change the
+environment in a way that is beneficial for all competitors. To explore the
+possible consequence of this feedback we consider a coevolutionary model where
+the local cooperation level determines the payoff values of the applied
+prisoner's dilemma game. We observe that the coevolutionary rule provides a
+significantly higher cooperation level comparing to the traditional setup
+independently of the topology of the applied interaction graph. Interestingly,
+this cooperation supporting mechanism offers lonely defectors a high surviving
+chance for a long period hence the relaxation to the final cooperating state
+happens logarithmically slow. As a consequence, the extension of the
+traditional evolutionary game by considering interactions with the environment
+provides a good opportunity for cooperators, but their reward may arrive with
+some delay.
+",0,0,0,0,1,0
+7989,Finitely forcible graph limits are universal,"  The theory of graph limits represents large graphs by analytic objects called
+graphons. Graph limits determined by finitely many graph densities, which are
+represented by finitely forcible graphons, arise in various scenarios,
+particularly within extremal combinatorics. Lovasz and Szegedy conjectured that
+all such graphons possess a simple structure, e.g., the space of their typical
+vertices is always finite dimensional; this was disproved by several ad hoc
+constructions of complex finitely forcible graphons. We prove that any graphon
+is a subgraphon of a finitely forcible graphon. This dismisses any hope for a
+result showing that finitely forcible graphons possess a simple structure, and
+is surprising when contrasted with the fact that finitely forcible graphons
+form a meager set in the space of all graphons. In addition, since any finitely
+forcible graphon represents the unique minimizer of some linear combination of
+densities of subgraphs, our result also shows that such minimization problems,
+which conceptually are among the simplest kind within extremal graph theory,
+may in fact have unique optimal solutions with arbitrarily complex structure.
+",0,0,1,0,0,0
+7990,Sex-biased dispersal: a review of the theory,"  Dispersal is ubiquitous throughout the tree of life: factors selecting for
+dispersal include kin competition, inbreeding avoidance and spatiotemporal
+variation in resources or habitat suitability. These factors differ in whether
+they promote male and female dispersal equally strongly, and often selection on
+dispersal of one sex depends on how much the other disperses. For example, for
+inbreeding avoidance it can be sufficient that one sex disperses away from the
+natal site. Attempts to understand sex-specific dispersal evolution have
+created a rich body of theoretical literature, which we review here. We
+highlight an interesting gap between empirical and theoretical literature. The
+former associates different patterns of sex-biased dispersal with mating
+systems, such as female-biased dispersal in monogamous birds and male-biased
+dispersal in polygynous mammals. The predominant explanation is traceable back
+to Greenwood's (1980) ideas of how successful philopatric or dispersing
+individuals are at gaining mates or resources required to attract them. Theory,
+however, has developed surprisingly independently of these ideas: predominant
+ideas in theoretical work track how immigration and emigration change
+relatedness patterns and alleviate competition for limiting resources,
+typically considered sexually distinct, with breeding sites and fertilisable
+females limiting reproductive success for females and males, respectively. We
+show that the link between mating system and sex-biased dispersal is far from
+resolved: there are studies showing that mating systems matter, but the
+oft-stated association between polygyny and male-biased dispersal is not a
+straightforward theoretical expectation... (full abstract in the PDF)
+",0,0,0,0,1,0
+7991,Nonequilibrium quantum dynamics of partial symmetry breaking for ultracold bosons in an optical lattice ring trap,"  A vortex in a Bose-Einstein condensate on a ring undergoes quantum dynamics
+in response to a quantum quench in terms of partial symmetry breaking from a
+uniform lattice to a biperiodic one. Neither the current, a macroscopic
+measure, nor fidelity, a microscopic measure, exhibit critical behavior.
+Instead, the symmetry memory succeeds in identifying the point at which the
+system begins to forget its initial symmetry state. We further identify a
+symmetry gap in the low lying excited states which trends with the symmetry
+memory.
+",0,1,0,0,0,0
+7992,Learning Hidden Quantum Markov Models,"  Hidden Quantum Markov Models (HQMMs) can be thought of as quantum
+probabilistic graphical models that can model sequential data. We extend
+previous work on HQMMs with three contributions: (1) we show how classical
+hidden Markov models (HMMs) can be simulated on a quantum circuit, (2) we
+reformulate HQMMs by relaxing the constraints for modeling HMMs on quantum
+circuits, and (3) we present a learning algorithm to estimate the parameters of
+an HQMM from data. While our algorithm requires further optimization to handle
+larger datasets, we are able to evaluate our algorithm using several synthetic
+datasets. We show that on HQMM generated data, our algorithm learns HQMMs with
+the same number of hidden states and predictive accuracy as the true HQMMs,
+while HMMs learned with the Baum-Welch algorithm require more states to match
+the predictive accuracy.
+",0,0,0,1,0,0
+7993,Gradient Descent using Duality Structures,"  Gradient descent is commonly used to solve optimization problems arising in
+machine learning, such as training neural networks. Although it seems to be
+effective for many different neural network training problems, it is unclear if
+the effectiveness of gradient descent can be explained using existing
+performance guarantees for the algorithm. We argue that existing analyses of
+gradient descent rely on assumptions that are too strong to be applicable in
+the case of multi-layer neural networks. To address this, we propose an
+algorithm, duality structure gradient descent (DSGD), that is amenable to a
+non-asymptotic performance analysis, under mild assumptions on the training set
+and network architecture. The algorithm can be viewed as a form of layer-wise
+coordinate descent, where at each iteration the algorithm chooses one layer of
+the network to update. The decision of what layer to update is done in a greedy
+fashion, based on a rigorous lower bound of the function decrease for each
+possible choice of layer. In the analysis, we bound the time required to reach
+approximate stationary points, in both the deterministic and stochastic
+settings. The convergence is measured in terms of a Finsler geometry that is
+derived from the network architecture and designed to confirm a Lipschitz-like
+property on the gradient of the training objective function. Numerical
+experiments in both the full batch and mini-batch settings suggest that the
+algorithm is a promising step towards methods for training neural networks that
+are both rigorous and efficient.
+",1,0,0,0,0,0
+7994,On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks,"  Large-scale deep neural networks are both memory intensive and
+computation-intensive, thereby posing stringent requirements on the computing
+platforms. Hardware accelerations of deep neural networks have been extensively
+investigated in both industry and academia. Specific forms of binary neural
+networks (BNNs) and stochastic computing based neural networks (SCNNs) are
+particularly appealing to hardware implementations since they can be
+implemented almost entirely with binary operations. Despite the obvious
+advantages in hardware implementation, these approximate computing techniques
+are questioned by researchers in terms of accuracy and universal applicability.
+Also it is important to understand the relative pros and cons of SCNNs and BNNs
+in theory and in actual hardware implementations. In order to address these
+concerns, in this paper we prove that the ""ideal"" SCNNs and BNNs satisfy the
+universal approximation property with probability 1 (due to the stochastic
+behavior). The proof is conducted by first proving the property for SCNNs from
+the strong law of large numbers, and then using SCNNs as a ""bridge"" to prove
+for BNNs. Based on the universal approximation property, we further prove that
+SCNNs and BNNs exhibit the same energy complexity. In other words, they have
+the same asymptotic energy consumption with the growing of network size. We
+also provide a detailed analysis of the pros and cons of SCNNs and BNNs for
+hardware implementations and conclude that SCNNs are more suitable for
+hardware.
+",0,0,0,1,0,0
+7995,Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis,"  Stochastic variance reduction algorithms have recently become popular for
+minimizing the average of a large, but finite number of loss functions. The
+present paper proposes a Riemannian stochastic quasi-Newton algorithm with
+variance reduction (R-SQN-VR). The key challenges of averaging, adding, and
+subtracting multiple gradients are addressed with notions of retraction and
+vector transport. We present convergence analyses of R-SQN-VR on both
+non-convex and retraction-convex functions under retraction and vector
+transport operators. The proposed algorithm is evaluated on the Karcher mean
+computation on the symmetric positive-definite manifold and the low-rank matrix
+completion on the Grassmann manifold. In all cases, the proposed algorithm
+outperforms the state-of-the-art Riemannian batch and stochastic gradient
+algorithms.
+",1,0,1,1,0,0
+7996,Higher cohomology vanishing of line bundles on generalized Springer's resolution,"  We give a proof of a conjecture raised by Michael Finkelberg and Andrei
+Ionov. As a corollary, the coefficients of multivariable version of Kostka
+functions introduced by Finkelberg and Ionov are non-negative.
+",0,0,1,0,0,0
+7997,Strong instability of ground states to a fourth order Schrödinger equation,"  In this note we prove the instability by blow-up of the ground state
+solutions for a class of fourth order Schr\"" odinger equations. This extends
+the first rigorous results on blowing-up solutions for the biharmonic NLS due
+to Boulenger and Lenzmann \cite{BoLe} and confirm numerical conjectures from
+\cite{BaFi, BaFiMa1, BaFiMa, FiIlPa}.
+",0,0,1,0,0,0
+7998,Coalescing particle systems and applications to nonlinear Fokker-Planck equations,"  We study a stochastic particle system with a logarithmically-singular
+inter-particle interaction potential which allows for inelastic particle
+collisions. We relate the squared Bessel process to the evolution of localized
+clusters of particles, and develop a numerical method capable of detecting
+collisions of many point particles without the use of pairwise computations, or
+very refined adaptive timestepping. We show that when the system is in an
+appropriate parameter regime, the hydrodynamic limit of the empirical mass
+density of the system is a solution to a nonlinear Fokker-Planck equation, such
+as the Patlak-Keller-Segel (PKS) model, or its multispecies variant. We then
+show that the presented numerical method is well-suited for the simulation of
+the formation of finite-time singularities in the PKS, as well as PKS pre- and
+post-blow-up dynamics. Additionally, we present numerical evidence that blow-up
+with an increasing total second moment in the two species Keller-Segel system
+occurs with a linearly increasing second moment in one component, and a
+linearly decreasing second moment in the other component.
+",0,0,1,0,0,0
+7999,Periodic solution for strongly nonlinear oscillators by He's new amplitude-frequency relationship,"  This paper applies He's new amplitude-frequency relationship recently
+established by Ji-Huan He (Int J Appl Comput Math 3 1557-1560, 2017) to study
+periodic solutions of strongly nonlinear systems with odd nonlinearities. Some
+examples are given to illustrate the effectiveness, ease and convenience of the
+method. In general, the results are valid for small as well as large
+oscillation amplitude. The method can be easily extended to other nonlinear
+systems with odd nonlinearities and can therefore be found widely applicable in
+engineering and other science. The method used in this paper can be applied
+directly to highly nonlinear problems without any discretization, linearization
+or additional requirements.
+",0,1,0,0,0,0
+8000,"Singular Degenerations of Lie Supergroups of Type $D(2,1;a)$","  The complex Lie superalgebras $\mathfrak{g}$ of type $D(2,1;a)$ - also
+denoted by $\mathfrak{osp}(4,2;a) $ - are usually considered for ""non-singular""
+values of the parameter $a$, for which they are simple. In this paper we
+introduce five suitable integral forms of $\mathfrak{g}$, that are well-defined
+at singular values too, giving rise to ""singular specializations"" that are no
+longer simple: this extends the family of simple objects of type $D(2,1;a)$ in
+five different ways. The resulting five families coincide for general values of
+$a$, but are different at ""singular"" ones: here they provide non-simple Lie
+superalgebras, whose structure we describe explicitly. We also perform the
+parallel construction for complex Lie supergroups and describe their singular
+specializations (or ""degenerations"") at singular values of $a$. Although one
+may work with a single complex parameter $a$, in order to stress the overall
+$\mathfrak{S}_3$-symmetry of the whole situation, we shall work (following
+Kaplansky) with a two-dimensional parameter $\boldsymbol{\sigma} =
+(\sigma_1,\sigma_2,\sigma_3)$ ranging in the complex affine plane $\sigma_1 +
+\sigma_2 + \sigma_3 = 0$.
+",0,0,1,0,0,0
+8001,Logarithmic singularities and quantum oscillations in magnetically doped topological insulators,"  We report magnetotransport measurements on magnetically doped
+(Bi,Sb)$_2$Te$_3$ films grown by molecular beam epitaxy. In Hallbar devices,
+logarithmic dependence on temperature and bias voltage are obseved in both the
+longitudinal and anomalous Hall resistance. The interplay of disorder and
+electron-electron interactions is found to explain quantitatively the observed
+logarithmic singularities and is a dominant scattering mechanism in these
+samples. Submicron scale devices exhibit intriguing quantum oscillations at
+high magnetic fields with dependence on bias voltage. The observed quantum
+oscillations can be attributed to bulk and surface transport.
+",0,1,0,0,0,0
+8002,A General Algorithm to Calculate the Inverse Principal $p$-th Root of Symmetric Positive Definite Matrices,"  We address the general mathematical problem of computing the inverse $p$-th
+root of a given matrix in an efficient way. A new method to construct iteration
+functions that allow calculating arbitrary $p$-th roots and their inverses of
+symmetric positive definite matrices is presented. We show that the order of
+convergence is at least quadratic and that adaptively adjusting a parameter $q$
+always leads to an even faster convergence. In this way, a better performance
+than with previously known iteration schemes is achieved. The efficiency of the
+iterative functions is demonstrated for various matrices with different
+densities, condition numbers and spectral radii.
+",0,0,1,0,0,0
+8003,Latent Mixture Modeling for Clustered Data,"  This article proposes a mixture modeling approach to estimating cluster-wise
+conditional distributions in clustered (grouped) data. We adapt the
+mixture-of-experts model to the latent distributions, and propose a model in
+which each cluster-wise density is represented as a mixture of latent experts
+with cluster-wise mixing proportions distributed as Dirichlet distribution. The
+model parameters are estimated by maximizing the marginal likelihood function
+using a newly developed Monte Carlo Expectation-Maximization algorithm. We also
+extend the model such that the distribution of cluster-wise mixing proportions
+depends on some cluster-level covariates. The finite sample performance of the
+proposed model is compared with some existing mixture modeling approaches as
+well as linear mixed model through the simulation studies. The proposed model
+is also illustrated with the posted land price data in Japan.
+",0,0,0,1,0,0
+8004,"Fast Switching Dual Fabry-Perot-Cavity-based Optical Refractometry for Assessment of Gas Refractivity and Density - Estimates of Its Precision, Accuracy, and Temperature Dependence","  Dual Fabry-Perot-Cavity-based Optical Refractometry (DFCB-OR) have been shown
+to have excellent potential for characterization of gases, in particular their
+refractivity and density. However, its performance has in practice been found
+to be limited by drifts. To remedy this, drift-free DFPC-OR (DF-DFCB-OR) has
+recently been proposed. Suggested methodologies for realization of a specific
+type of DF-DFCB-OR, termed Fast Switching DFCB-OR (FS-DFCB-OR), have been
+presented in an accompanying work. This paper scrutinizes the performance and
+the limitations of both DF- and FS-DFCB-OR for assessments of refractivity and
+gas density, in particular their precision, accuracy, and temperature
+dependence. It is shown that both refractivity and gas density can be assessed
+by FS-DFCB-OR with a precision in the 10$^{-9}$ range under STP conditions. It
+is demonstrated that the absolute accuracy is mainly limited by the accuracy by
+which the instantaneous deformation of the cavity or the higher order virial
+coefficients can be assessed. It is also shown that the internal accuracy, i.e.
+the accuracy by which the system can be characterized with respect to an
+internal standard, can be several orders of magnitude better than the absolute.
+It is concluded that the temperature dependence of FS-DFCB-OR is exceptionally
+small, typically in the 10$^{-8}$ to 10$^{-7}$/C range, and primarily caused by
+thermal expansion of the FPC-spacer material. Finally, this paper discusses
+means on how to design a FS-DFCB-or system for optimal performance and
+epitomizes the conclusions of this and our accompanying works regarding both
+DF- and FS-DFCB-OR in terms of performance and provides an outlook for both
+techniques. Our works can serve as a basis for future realizations of
+instrumentation for assessments of gas refractivity and density that can fully
+benefit from the extraordinary potential of FPC-OR.
+",0,1,0,0,0,0
+8005,Dixmier traces and residues on weak operator ideals,"  We develop the theory of modulated operators in general principal ideals of
+compact operators. For Laplacian modulated operators we establish Connes' trace
+formula in its local Euclidean model and a global version thereof. It expresses
+Dixmier traces in terms of the vector-valued Wodzicki residue. We demonstrate
+the applicability of our main results in the context of log-classical
+pseudo-differential operators, studied by Lesch, and a class of operators
+naturally appearing in noncommutative geometry.
+",0,0,1,0,0,0
+8006,Investigating early-type galaxy evolution with a multiwavelength approach. II. The UV structure of 11 galaxies with Swift-UVOT,"  GALEX detected a significant fraction of early-type galaxies showing Far-UV
+bright structures. These features suggest the occurrence of recent star
+formation episodes. We aim at understanding their evolutionary path[s] and the
+mechanisms at the origin of their UV-bright structures. We investigate with a
+multi-lambda approach 11 early-types selected because of their nearly passive
+stage of evolution in the nuclear region. The paper, second of a series,
+focuses on the comparison between UV features detected by Swift-UVOT, tracing
+recent star formation, and the galaxy optical structure mapping older stellar
+populations. We performed their UV surface photometry and used BVRI photometry
+from other sources. Our integrated magnitudes have been analyzed and compared
+with corresponding values in the literature. We characterize the overall galaxy
+structure best fitting the UV and optical luminosity profiles using a single
+Sersic law. NGC 1366, NGC 1426, NGC 3818, NGC 3962 and NGC 7192 show
+featureless luminosity profiles. Excluding NGC 1366 which has a clear edge-on
+disk , n~1-2, and NGC 3818, the remaining three have Sersic's indices n~3-4 in
+optical and a lower index in the UV. Bright ring/arm-like structures are
+revealed by UV images and luminosity profiles of NGC 1415, NGC 1533, NGC 1543,
+NGC 2685, NGC 2974 and IC 2006. The ring/arm-like structures are different from
+galaxy to galaxy. Sersic indices of UV profiles for those galaxies are in the
+range n=1.5-3 both in S0s and in Es. In our sample optical Sersic indices are
+usually larger than the UV ones. (M2-V) color profiles are bluer in
+ring/arm-like structures with respect to the galaxy body. The lower values of
+Sersic's indices in the UV bands with respect to optical ones, suggesting the
+presence of a disk, point out that the role of the dissipation cannot be
+neglected in recent evolutionary phases of these early-type galaxies.
+",0,1,0,0,0,0
+8007,Lazy Automata Techniques for WS1S,"  We present a new decision procedure for the logic WS1S. It originates from
+the classical approach, which first builds an automaton accepting all models of
+a formula and then tests whether its language is empty. The main novelty is to
+test the emptiness on the fly, while constructing a symbolic, term-based
+representation of the automaton, and prune the constructed state space from
+parts irrelevant to the test. The pruning is done by a generalization of two
+techniques used in antichain-based language inclusion and universality checking
+of finite automata: subsumption and early termination. The richer structure of
+the WS1S decision problem allows us, however, to elaborate on these techniques
+in novel ways. Our experiments show that the proposed approach can in many
+cases significantly outperform the classical decision procedure (implemented in
+the MONA tool) as well as recently proposed alternatives.
+",1,0,0,0,0,0
+8008,Joint distribution of conjugate algebraic numbers: a random polynomial approach,"  Given a polynomial $q(z):=a_0+a_1z+\dots+a_nz^n$ and a vector of positive
+weights $\mathbf{w}=(w_0, w_1,\dots,w_n)$, define the $\mathbf{w}$-weighted
+$l_p$-norm of $q$ as $$ l_{p,\mathbf{w}}[q]:=\left(\sum_{k=0}^{n}|w_k
+a_k|^p\right)^{1/p},\quad p\in[1,\infty]. $$ Define the $\mathbf{w}$-weighted
+$l_p$-norm of an algebraic number to be the $\mathbf{w}$-weighted $l_p$-norm of
+its minimal polynomial. For non-negative integers $k,l$ such that $k+2l\leq n$
+and a Borel subset $B\subset \mathbb{R}^k\times\mathbb{C}_+^l$ denote by
+$\Phi_{p,\mathbf{w},k,l}(Q,B)$ the number of ordered $(k+l)$-tuples in $B$ of
+conjugate algebraic numbers of degree $n$ and $\mathbf{w}$-weighted $l_p$-norm
+at most $ Q$. We show that $$ \lim_{ Q\to\infty}\frac{\Phi_{p,\mathbf{w},k,l}(
+Q,B)}{
+Q^{n+1}}=\frac{\mathrm{Vol}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})}{2\zeta(n+1)}\int_B
+\rho_{p,\mathbf{w},k,l}(\mathbf{x},\mathbf{z})\rm d \mathbf{x}\rm d \mathbf{z},
+$$ where $\mathrm{Vol}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})$ is the volume of
+the unit $\mathbf{w}$-weighted $l_p$-ball and $\rho_{p,\mathbf{w},k,l}$ shall
+denote the correlation function of $k$ real and $l$ complex zeros of the random
+polynomial $\sum_{k=1}^n \frac{\eta_k}{w_k} z^k$ for i.i.d. random variables
+$\eta_k $ with density $c_p e^{|t|^p}$ for $p<\infty$ resp. with constant
+density on $[-1,1]$ for $p=\infty$. We give an explicit formula for
+$\rho_{p,\mathbf{w},k,l}$ which in the case $k+2l=n$ simplifies to $$
+\rho_{p,\mathbf{w},n-2l,l}=\frac{2}{(n+1)\mathrm{Vol}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})}\,\frac{\sqrt{|\mathrm{D}[q]|}\phantom{1^n}}{(l_{p,\mathbf{w}}[q])^{n+1}},
+$$ where $q$ is the monic polynomial whose zeros are the arguments of the
+correlation function $\rho_{p,\mathbf{w},n-2l,l}$ and $\mathrm{D}[q]$ denotes
+its discriminant.
+",0,0,1,0,0,0
+8009,On wrapping the Kalman filter and estimating with the SO(2) group,"  This paper analyzes directional tracking in 2D with the extended Kalman
+filter on Lie groups (LG-EKF). The study stems from the problem of tracking
+objects moving in 2D Euclidean space, with the observer measuring direction
+only, thus rendering the measurement space and object position on the
+circle---a non-Euclidean geometry. The problem is further inconvenienced if we
+need to include higher-order dynamics in the state space, like angular velocity
+which is a Euclidean variables. The LG-EKF offers a solution to this issue by
+modeling the state space as a Lie group or combination thereof, e.g., SO(2) or
+its combinations with Rn. In the present paper, we first derive the LG-EKF on
+SO(2) and subsequently show that this derivation, based on the mathematically
+grounded framework of filtering on Lie groups, yields the same result as
+heuristically wrapping the angular variable within the EKF framework. This
+result applies only to the SO(2) and SO(2)xRn LG-EKFs and is not intended to be
+extended to other Lie groups or combinations thereof. In the end, we showcase
+the SO(2)xR2 LG-EKF, as an example of a constant angular acceleration model, on
+the problem of speaker tracking with a microphone array for which real-world
+experiments are conducted and accuracy is evaluated with ground truth data
+obtained by a motion capture system.
+",1,0,0,0,0,0
+8010,Towards a Context-Aware IDE-Based Meta Search Engine for Recommendation about Programming Errors and Exceptions,"  Study shows that software developers spend about 19% of their time looking
+for information in the web during software development and maintenance.
+Traditional web search forces them to leave the working environment (e.g., IDE)
+and look for information in the web browser. It also does not consider the
+context of the problems that the developers search solutions for. The frequent
+switching between web browser and the IDE is both time-consuming and
+distracting, and the keyword-based traditional web search often does not help
+much in problem solving. In this paper, we propose an Eclipse IDE-based web
+search solution that exploits the APIs provided by three popular web search
+engines-- Google, Yahoo, Bing and a popular programming Q & A site, Stack
+Overflow, and captures the content-relevance, context-relevance, popularity and
+search engine confidence of each candidate result against the encountered
+programming problems. Experiments with 75 programming errors and exceptions
+using the proposed approach show that inclusion of different types of context
+information associated with a given exception can enhance the recommendation
+accuracy of a given exception. Experiments both with two existing approaches
+and existing web search engines confirm that our approach can perform better
+than them in terms of recall, mean precision and other performance measures
+with little computational cost.
+",1,0,0,0,0,0
+8011,Infrared Flares from M Dwarfs: a Hinderance to Future Transiting Exoplanet Studies,"  Many current and future exoplanet missions are pushing to infrared (IR)
+wavelengths where the flux contrast between the planet and star is more
+favorable (Deming et al. 2009), and the impact of stellar magnetic activity is
+decreased. Indeed, a recent analysis of starspots and faculae found these forms
+of stellar activity do not substantially impact the transit signatures or
+science potential for FGKM stars with JWST (Zellem et al. 2017). However, this
+is not true in the case of flares, which I demonstrate can be a hinderance to
+transit studies in this note.
+",0,1,0,0,0,0
+8012,Topic Identification for Speech without ASR,"  Modern topic identification (topic ID) systems for speech use automatic
+speech recognition (ASR) to produce speech transcripts, and perform supervised
+classification on such ASR outputs. However, under resource-limited conditions,
+the manually transcribed speech required to develop standard ASR systems can be
+severely limited or unavailable. In this paper, we investigate alternative
+unsupervised solutions to obtaining tokenizations of speech in terms of a
+vocabulary of automatically discovered word-like or phoneme-like units, without
+depending on the supervised training of ASR systems. Moreover, using automatic
+phoneme-like tokenizations, we demonstrate that a convolutional neural network
+based framework for learning spoken document representations provides
+competitive performance compared to a standard bag-of-words representation, as
+evidenced by comprehensive topic ID evaluations on both single-label and
+multi-label classification tasks.
+",1,0,0,0,0,0
+8013,Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals,"  Spiking neural networks (SNNs) enable power-efficient implementations due to
+their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN
+that uses unsupervised learning to extract discriminative features from speech
+signals, which can subsequently be used in a classifier. The architecture
+consists of a spiking convolutional/pooling layer followed by a fully connected
+spiking layer for feature discovery. The convolutional layer of leaky,
+integrate-and-fire (LIF) neurons represents primary acoustic features. The
+fully connected layer is equipped with a probabilistic spike-timing-dependent
+plasticity learning rule. This layer represents the discriminative features
+through probabilistic, LIF neurons. To assess the discriminative power of the
+learned features, they are used in a hidden Markov model (HMM) for spoken digit
+recognition. The experimental results show performance above 96% that compares
+favorably with popular statistical feature extraction methods. Our results
+provide a novel demonstration of unsupervised feature acquisition in an SNN.
+",1,0,0,0,0,0
+8014,Zonotope hit-and-run for efficient sampling from projection DPPs,"  Determinantal point processes (DPPs) are distributions over sets of items
+that model diversity using kernels. Their applications in machine learning
+include summary extraction and recommendation systems. Yet, the cost of
+sampling from a DPP is prohibitive in large-scale applications, which has
+triggered an effort towards efficient approximate samplers. We build a novel
+MCMC sampler that combines ideas from combinatorial geometry, linear
+programming, and Monte Carlo methods to sample from DPPs with a fixed sample
+cardinality, also called projection DPPs. Our sampler leverages the ability of
+the hit-and-run MCMC kernel to efficiently move across convex bodies. Previous
+theoretical results yield a fast mixing time of our chain when targeting a
+distribution that is close to a projection DPP, but not a DPP in general. Our
+empirical results demonstrate that this extends to sampling projection DPPs,
+i.e., our sampler is more sample-efficient than previous approaches which in
+turn translates to faster convergence when dealing with costly-to-evaluate
+functions, such as summary extraction in our experiments.
+",1,0,0,1,0,0
+8015,Brownian motion: from kinetics to hydrodynamics,"  Brownian motion has served as a pilot of studies in diffusion and other
+transport phenomena for over a century. The foundation of Brownian motion, laid
+by Einstein, has generally been accepted to be far from being complete since
+the late 1960s, because it fails to take important hydrodynamic effects into
+account. The hydrodynamic effects yield a time dependence of the diffusion
+coefficient, and this extends the ordinary hydrodynamics. However, the time
+profile of the diffusion coefficient across the kinetic and hydrodynamic
+regions is still absent, which prohibits a complete description of Brownian
+motion in the entire course of time. Here we close this gap. We manage to
+separate the diffusion process into two parts: a kinetic process governed by
+the kinetics based on molecular chaos approximation and a hydrodynamics process
+described by linear hydrodynamics. We find the analytical solution of vortex
+backflow of hydrodynamic modes triggered by a tagged particle. Coupling it to
+the kinetic process we obtain explicit expressions of the velocity
+autocorrelation function and the time profile of diffusion coefficient. This
+leads to an accurate account of both kinetic and hydrodynamic effects. Our
+theory is applicable for fluid and Brownian particles, even of irregular-shaped
+objects, in very general environments ranging from dilute gases to dense
+liquids. The analytical results are in excellent agreement with numerical
+experiments.
+",0,1,0,0,0,0
+8016,"Natural Time, Nowcasting and the Physics of Earthquakes: Estimation of Seismic Risk to Global Megacities","  This paper describes the use of the idea of natural time to propose a new
+method for characterizing the seismic risk to the world's major cities at risk
+of earthquakes. Rather than focus on forecasting, which is the computation of
+probabilities of future events, we define the term seismic nowcasting, which is
+the computation of the current state of seismic hazard in a defined geographic
+region.
+",0,1,0,0,0,0
+8017,Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile,"  Owing to their connection with generative adversarial networks (GANs),
+saddle-point problems have recently attracted considerable interest in machine
+learning and beyond. By necessity, most theoretical guarantees revolve around
+convex-concave (or even linear) problems; however, making theoretical inroads
+towards efficient GAN training depends crucially on moving beyond this classic
+framework. To make piecemeal progress along these lines, we analyze the
+behavior of mirror descent (MD) in a class of non-monotone problems whose
+solutions coincide with those of a naturally associated variational inequality
+- a property which we call coherence. We first show that ordinary, ""vanilla"" MD
+converges under a strict version of this condition, but not otherwise; in
+particular, it may fail to converge even in bilinear models with a unique
+solution. We then show that this deficiency is mitigated by optimism: by taking
+an ""extra-gradient"" step, optimistic mirror descent (OMD) converges in all
+coherent problems. Our analysis generalizes and extends the results of
+Daskalakis et al. (2018) for optimistic gradient descent (OGD) in bilinear
+problems, and makes concrete headway for establishing convergence beyond
+convex-concave games. We also provide stochastic analogues of these results,
+and we validate our analysis by numerical experiments in a wide array of GAN
+models (including Gaussian mixture models, as well as the CelebA and CIFAR-10
+datasets).
+",0,0,0,1,0,0
+8018,Design of an Autonomous Precision Pollination Robot,"  Precision robotic pollination systems can not only fill the gap of declining
+natural pollinators, but can also surpass them in efficiency and uniformity,
+helping to feed the fast-growing human population on Earth. This paper presents
+the design and ongoing development of an autonomous robot named ""BrambleBee"",
+which aims at pollinating bramble plants in a greenhouse environment. Partially
+inspired by the ecology and behavior of bees, BrambleBee employs
+state-of-the-art localization and mapping, visual perception, path planning,
+motion control, and manipulation techniques to create an efficient and robust
+autonomous pollination system.
+",1,0,0,0,0,0
+8019,A sufficiently complicated noded Schottky group of rank three,"  The theoretical existence of non-classical Schottky groups is due to Marden.
+Explicit examples of such kind of groups are only known in rank two, the first
+one by by Yamamoto in 1991 and later by Williams in 2009. In 2006, Maskit and
+the author provided a theoretical method to obtain examples of non-classical
+Schottky groups in any rank. The method assumes the knowledge of some algebraic
+limits of Schottky groups, called sufficiently complicated noded Schottky
+groups, whose existence was stated. In this paper we provide an explicit
+construction of a sufficiently complicated noded Schottky group of rank three
+and it is explained how to construct explicit non-classical Schottky groups of
+rank three.
+",0,0,1,0,0,0
+8020,DONUT: CTC-based Query-by-Example Keyword Spotting,"  Keyword spotting--or wakeword detection--is an essential feature for
+hands-free operation of modern voice-controlled devices. With such devices
+becoming ubiquitous, users might want to choose a personalized custom wakeword.
+In this work, we present DONUT, a CTC-based algorithm for online
+query-by-example keyword spotting that enables custom wakeword detection. The
+algorithm works by recording a small number of training examples from the user,
+generating a set of label sequence hypotheses from these training examples, and
+detecting the wakeword by aggregating the scores of all the hypotheses given a
+new audio recording. Our method combines the generalization and
+interpretability of CTC-based keyword spotting with the user-adaptation and
+convenience of a conventional query-by-example system. DONUT has low
+computational requirements and is well-suited for both learning and inference
+on embedded systems without requiring private user data to be uploaded to the
+cloud.
+",1,0,0,0,0,0
+8021,Emulation of the space radiation environment for materials testing and radiobiological experiments,"  Radiobiology studies on the effects of galactic cosmic ray radiation utilize
+mono-energetic single-ion particle beams, where the projected doses for
+exploration missions are given using highly-acute exposures. This methodology
+does not replicate the multi-ion species and energies found in the space
+radiation environment, nor does it reflect the low dose rate found in
+interplanetary space. In radiation biology studies, as well as in the
+assessment of health risk to astronaut crews, the differences in the biological
+effectiveness of different ions is primarily attributed to differences in the
+linear energy transfer of the radiation spectrum. Here we show that the linear
+energy transfer spectrum of the intravehicular environment of, e.g.,
+spaceflight vehicles can be accurately generated experimentally by perturbing
+the intrinsic properties of hydrogen-rich crystalline materials in order to
+instigate specific nuclear spallation and fragmentation processes when placed
+in an accelerated mono-energetic heavy ion beam. Modifications to the internal
+geometry and chemical composition of the materials allow for the shaping of the
+emerging field to specific spectra that closely resemble the intravehicular
+field. Our approach can also be utilized to emulate the external galactic
+cosmic ray field, the planetary surface spectrum (e.g., Mars), and the local
+radiation environment of orbiting satellites. This provides the first instance
+of a true ground-based analog for characterizing the effects of space
+radiation.
+",0,1,0,0,0,0
+8022,Convex Relaxations for Pose Graph Optimization with Outliers,"  Pose Graph Optimization involves the estimation of a set of poses from
+pairwise measurements and provides a formalization for many problems arising in
+mobile robotics and geometric computer vision. In this paper, we consider the
+case in which a subset of the measurements fed to pose graph optimization is
+spurious. Our first contribution is to develop robust estimators that can cope
+with heavy-tailed measurement noise, hence increasing robustness to the
+presence of outliers. Since the resulting estimators require solving nonconvex
+optimization problems, we further develop convex relaxations that approximately
+solve those problems via semidefinite programming. We then provide conditions
+under which the proposed relaxations are exact. Contrarily to existing
+approaches, our convex relaxations do not rely on the availability of an
+initial guess for the unknown poses, hence they are more suitable for setups in
+which such guess is not available (e.g., multi-robot localization, recovery
+after localization failure). We tested the proposed techniques in extensive
+simulations, and we show that some of the proposed relaxations are indeed tight
+(i.e., they solve the original nonconvex problem 10 exactly) and ensure
+accurate estimation in the face of a large number of outliers.
+",1,0,0,0,0,0
+8023,Quasi-Frobenius-splitting and lifting of Calabi-Yau varieties in characteristic $p$,"  Extending the notion of Frobenius-splitting, we prove that every finite
+height Calabi-Yau variety defined over an algebraically closed field of
+positive characteristic can be lifted to the ring of Witt vectors of length
+two.
+",0,0,1,0,0,0
+8024,Consistency and Asymptotic Normality of Latent Blocks Model Estimators,"  Latent Block Model (LBM) is a model-based method to cluster simultaneously
+the $d$ columns and $n$ rows of a data matrix. Parameter estimation in LBM is a
+difficult and multifaceted problem. Although various estimation strategies have
+been proposed and are now well understood empirically, theoretical guarantees
+about their asymptotic behavior is rather sparse. We show here that under some
+mild conditions on the parameter space, and in an asymptotic regime where
+$\log(d)/n$ and $\log(n)/d$ tend to $0$ when $n$ and $d$ tend to $+\infty$, (1)
+the maximum-likelihood estimate of the complete model (with known labels) is
+consistent and (2) the log-likelihood ratios are equivalent under the complete
+and observed (with unknown labels) models. This equivalence allows us to
+transfer the asymptotic consistency to the maximum likelihood estimate under
+the observed model. Moreover, the variational estimator is also consistent.
+",0,0,1,1,0,0
+8025,Linking Generative Adversarial Learning and Binary Classification,"  In this note, we point out a basic link between generative adversarial (GA)
+training and binary classification -- any powerful discriminator essentially
+computes an (f-)divergence between real and generated samples. The result,
+repeatedly re-derived in decision theory, has implications for GA Networks
+(GANs), providing an alternative perspective on training f-GANs by designing
+the discriminator loss function.
+",1,0,0,1,0,0
+8026,High Speed Elephant Flow Detection Under Partial Information,"  In this paper we introduce a new framework to detect elephant flows at very
+high speed rates and under uncertainty. The framework provides exact
+mathematical formulas to compute the detection likelihood and introduces a new
+flow reconstruction lemma under partial information. These theoretical results
+lead to the design of BubbleCache, a new elephant flow detection algorithm
+designed to operate near the optimal tradeoff between computational scalability
+and accuracy by dynamically tracking the traffic's natural cutoff sampling
+rate. We demonstrate on a real world 100 Gbps network that the BubbleCache
+algorithm helps reduce the computational cost by a factor of 1000 and the
+memory requirements by a factor of 100 while detecting the top flows on the
+network with very high probability.
+",1,0,0,0,0,0
+8027,Scalable k-Means Clustering via Lightweight Coresets,"  Coresets are compact representations of data sets such that models trained on
+a coreset are provably competitive with models trained on the full data set. As
+such, they have been successfully used to scale up clustering models to massive
+data sets. While existing approaches generally only allow for multiplicative
+approximation errors, we propose a novel notion of lightweight coresets that
+allows for both multiplicative and additive errors. We provide a single
+algorithm to construct lightweight coresets for k-means clustering as well as
+soft and hard Bregman clustering. The algorithm is substantially faster than
+existing constructions, embarrassingly parallel, and the resulting coresets are
+smaller. We further show that the proposed approach naturally generalizes to
+statistical k-means clustering and that, compared to existing results, it can
+be used to compute smaller summaries for empirical risk minimization. In
+extensive experiments, we demonstrate that the proposed algorithm outperforms
+existing data summarization strategies in practice.
+",1,0,0,1,0,0
+8028,Scaling relations in the diffusive infiltration in fractals,"  In a recent work on fluid infiltration in a Hele-Shaw cell with the
+pore-block geometry of Sierpinski carpets (SCs), the area filled by the
+invading fluid was shown to scale as F~t^n, with n<1/2, thus providing a
+macroscopic realization of anomalous diffusion [Filipovitch et al, Water
+Resour. Res. 52 5167 (2016)]. The results agree with simulations of a diffusion
+equation with constant pressure at one of the borders of those fractals, but
+the exponent n is very different from the anomalous exponent nu=1/D_W of single
+particle diffusion in the same fractals (D_W is the random walk dimension).
+Here we use a scaling approach to show that those exponents are related as
+n=nu(D_F-D_B), where D_F and D_B are the fractal dimensions of the bulk and of
+the border from which diffusing particles come, respectively. This relation is
+supported by accurate numerical estimates in two SCs and in two generalized
+Menger sponges (MSs), in which we performed simulations of single particle
+random walks (RWs) with a rigid impermeable border and of a diffusive
+infiltration model in which that border is permanently filled with diffusing
+particles. This study includes one MS whose external border is also fractal.
+The exponent relation is also consistent with the recent simulational and
+experimental results on fluid infiltration in SCs, and explains the approximate
+quadratic dependence of n on D_F in these fractals. We also show that the
+mean-square displacement of single particle RWs has log-periodic oscillations,
+whose periods are similar for fractals with the same scaling factor in the
+generator (even with different embedding dimensions), which is consistent with
+the discrete scale invariance scenario. The roughness of a diffusion front
+defined in the infiltration problem also shows this type of oscillation, which
+is enhanced in fractals with narrow channels between large lacunas.
+",0,1,0,0,0,0
+8029,Adaptive Sequential MCMC for Combined State and Parameter Estimation,"  In the case of a linear state space model, we implement an MCMC sampler with
+two phases. In the learning phase, a self-tuning sampler is used to learn the
+parameter mean and covariance structure. In the estimation phase, the parameter
+mean and covariance structure informs the proposed mechanism and is also used
+in a delayed-acceptance algorithm. Information on the resulting state of the
+system is given by a Gaussian mixture. In on-line mode, the algorithm is
+adaptive and uses a sliding window approach to accelerate sampling speed and to
+maintain appropriate acceptance rates. We apply the algorithm to joined state
+and parameter estimation in the case of irregularly sampled GPS time series
+data.
+",0,0,0,1,0,0
+8030,Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems,"  A new class of functions, called the `Information sensitivity functions'
+(ISFs), which quantify the information gain about the parameters through the
+measurements/observables of a dynamical system are presented. These functions
+can be easily computed through classical sensitivity functions alone and are
+based on Bayesian and information-theoretic approaches. While marginal
+information gain is quantified by decrease in differential entropy,
+correlations between arbitrary sets of parameters are assessed through mutual
+information. For individual parameters these information gains are also
+presented as marginal posterior variances, and, to assess the effect of
+correlations, as conditional variances when other parameters are given. The
+easy to interpret ISFs can be used to a) identify time-intervals or regions in
+dynamical system behaviour where information about the parameters is
+concentrated; b) assess the effect of measurement noise on the information gain
+for the parameters; c) assess whether sufficient information in an experimental
+protocol (input, measurements, and their frequency) is available to identify
+the parameters; d) assess correlation in the posterior distribution of the
+parameters to identify the sets of parameters that are likely to be
+indistinguishable; and e) assess identifiability problems for particular sets
+of parameters.
+",0,0,0,1,0,0
+8031,Combinatorial cost: a coarse setting,"  The main inspiration for this paper is a paper by Elek where he introduces
+combinatorial cost for graph sequences. We show that having cost equal to 1 and
+hyperfiniteness are coarse invariants. We also show `cost-1' for box spaces
+behaves multiplicatively when taking subgroups. We show that graph sequences
+coming from Farber sequences of a group have property A if and only if the
+group is amenable. The same is true for hyperfiniteness. This generalises a
+theorem by Elek. Furthermore we optimise this result when Farber sequences are
+replaced by sofic approximations. In doing so we introduce a new concept:
+property almost-A.
+",0,0,1,0,0,0
+8032,Uncharted Forest a Technique for Exploratory Data Analysis,"  Exploratory data analysis is crucial for developing and understanding
+classification models from high-dimensional datasets. We explore the utility of
+a new unsupervised tree ensemble called uncharted forest for visualizing class
+associations, sample-sample associations, class heterogeneity, and
+uninformative classes for provenance studies. The uncharted forest algorithm
+can be used to partition data using random selections of variables and metrics
+based on statistical spread. After each tree is grown, a tally of the samples
+that arrive at every terminal node is maintained. Those tallies are stored in
+single sample association matrix and a likelihood measure for each sample being
+partitioned with one another can be made. That matrix may be readily viewed as
+a heat map, and the probabilities can be quantified via new metrics that
+account for class or cluster membership. We display the advantages and
+limitations of using this technique by applying it to two classification
+datasets and three provenance study datasets. Two of the metrics presented in
+this paper are also compared with widely used metrics from two algorithms that
+have variance-based clustering mechanisms.
+",0,0,0,1,0,0
+8033,Surface thermophysical properties investigation of the potentially hazardous asteroid (99942) Apophis,"  In this work, we investigate the surface thermophysical properties (thermal
+emissivity, thermal inertia, roughness fraction and geometric albedo) of
+asteroid (99942) Apophis, using the currently available thermal infrared
+observations of CanariCam on Gran Telescopio CANARIAS and far-infrared data by
+PACS of Herschel, on the basis of the Advanced thermophysical model. We show
+that the thermal emissivity of Apophis should be wavelength dependent from
+$8.70~\mu m$ to $160~\mu m$, and the maximum emissivity may arise around
+$20~\mu m$ similar to that of Vesta. Moreover, we further derive the thermal
+inertia, roughness fraction, geometric albedo and effective diameter of Apophis
+within a possible 1$\sigma$ scale of
+$\Gamma=100^{+100}_{-52}\rm~Jm^{-2}s^{-0.5}K^{-1}$, $f_{\rm r}=0.78\sim1.0$,
+$p_{\rm v}=0.286^{+0.030}_{-0.026}$, $D_{\rm eff}=378^{+19}_{-25}\rm~m$, and
+3$\sigma$ scale of $\Gamma=100^{+240}_{-100}\rm~Jm^{-2}s^{-0.5}K^{-1}$, $f_{\rm
+r}=0.2\sim1.0$, $p_{\rm v}=0.286^{+0.039}_{-0.029}$, $D_{\rm
+eff}=378^{+27}_{-29}\rm~m$. The derived low thermal inertia but high roughness
+fraction may imply that Apophis could have regolith on its surface, and less
+regolith migration process has happened in comparison with asteroid Itokawa.
+Our results show that small-size asteroids could also have fine regolith on the
+surface, and further infer that Apophis may be delivered from the Main Belt by
+Yarkovsky effect.
+",0,1,0,0,0,0
+8034,Reconfigurable cluster state generation in specially poled nonlinear waveguide arrays,"  We present a new approach for generating cluster states on-chip, with the
+state encoded in the spatial component of the photonic wavefunction. We show
+that for spatial encoding, a change of measurement basis can improve the
+practicality of cluster state algorithm implementation, and demonstrate this by
+simulating Grover's search algorithm. Our state generation scheme involves
+shaping the wavefunction produced by spontaneous parametric down-conversion in
+on-chip waveguides using specially tailored nonlinear poling patterns.
+Furthermore the form of the cluster state can be reconfigured quickly by
+driving different waveguides in the array.
+",0,1,0,0,0,0
+8035,"Whole planet coupling between climate, mantle, and core: Implications for the evolution of rocky planets","  Earth's climate, mantle, and core interact over geologic timescales. Climate
+influences whether plate tectonics can take place on a planet, with cool
+climates being favorable for plate tectonics because they enhance stresses in
+the lithosphere, suppress plate boundary annealing, and promote hydration and
+weakening of the lithosphere. Plate tectonics plays a vital role in the
+long-term carbon cycle, which helps to maintain a temperate climate. Plate
+tectonics provides long-term cooling of the core, which is vital for generating
+a magnetic field, and the magnetic field is capable of shielding atmospheric
+volatiles from the solar wind. Coupling between climate, mantle, and core can
+potentially explain the divergent evolution of Earth and Venus. As Venus lies
+too close to the sun for liquid water to exist, there is no long-term carbon
+cycle and thus an extremely hot climate. Therefore plate tectonics cannot
+operate and a long-lived core dynamo cannot be sustained due to insufficient
+core cooling. On planets within the habitable zone where liquid water is
+possible, a wide range of evolutionary scenarios can take place depending on
+initial atmospheric composition, bulk volatile content, or the timing of when
+plate tectonics initiates, among other factors. Many of these evolutionary
+trajectories would render the planet uninhabitable. However, there is still
+significant uncertainty over the nature of the coupling between climate,
+mantle, and core. Future work is needed to constrain potential evolutionary
+scenarios and the likelihood of an Earth-like evolution.
+",0,1,0,0,0,0
+8036,Solutions of the Helmholtz equation given by solutions of the eikonal equation,"  We find the form of the refractive index such that a solution, $S$, of the
+eikonal equation yields an exact solution, $\exp ({\rm i} k_{0} S)$, of the
+corresponding Helmholtz equation.
+",0,1,0,0,0,0
+8037,SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation,"  Semantic Textual Similarity (STS) measures the meaning similarity of
+sentences. Applications include machine translation (MT), summarization,
+generation, question answering (QA), short answer grading, semantic search,
+dialog and conversational systems. The STS shared task is a venue for assessing
+the current state-of-the-art. The 2017 task focuses on multilingual and
+cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE)
+data. The task obtained strong participation from 31 teams, with 17
+participating in all language tracks. We summarize performance and review a
+selection of well performing methods. Analysis highlights common errors,
+providing insight into the limitations of existing models. To support ongoing
+work on semantic representations, the STS Benchmark is introduced as a new
+shared training and evaluation set carefully selected from the corpus of
+English STS shared task data (2012-2017).
+",1,0,0,0,0,0
+8038,Modelling Luminous-Blue-Variable Isolation,"  Observations show that luminous blue variables (LBVs) are far more dispersed
+than massive O-type stars, and Smith & Tombleson suggested that these large
+separations are inconsistent with a single-star evolution model of LBVs.
+Instead, they suggested that the large distances are most consistent with
+binary evolution scenarios. To test these suggestions, we modelled young
+stellar clusters and their passive dissolution, and we find that, indeed, the
+standard single-star evolution model is mostly inconsistent with the observed
+LBV environments. If LBVs are single stars, then the lifetimes inferred from
+their luminosity and mass are far too short to be consistent with their extreme
+isolation. This implies that there is either an inconsistency in the
+luminosity- to-mass mapping or the mass-to-age mapping. In this paper, we
+explore binary solutions that modify the mass-to-age mapping and are consistent
+with the isolation of LBVs. For the binary scenarios, our crude models suggest
+that LBVs are rejuvenated stars. They are either the result of mergers or they
+are mass gainers and received a kick when the primary star exploded. In the
+merger scenario, if the primary is about 19 solar masses, then the binary has
+enough time to wander far afield, merge and form a rejuvenated star. In the
+mass-gainer and kick scenario, we find that LBV isolation is consistent with a
+wide range of kick velocities, anywhere from 0 to ~ 105 km/s. In either
+scenario, binarity seems to play a major role in the isolation of LBVs.
+",0,1,0,0,0,0
+8039,Service adoption spreading in online social networks,"  The collective behaviour of people adopting an innovation, product or online
+service is commonly interpreted as a spreading phenomenon throughout the fabric
+of society. This process is arguably driven by social influence, social
+learning and by external effects like media. Observations of such processes
+date back to the seminal studies by Rogers and Bass, and their mathematical
+modelling has taken two directions: One paradigm, called simple contagion,
+identifies adoption spreading with an epidemic process. The other one, named
+complex contagion, is concerned with behavioural thresholds and successfully
+explains the emergence of large cascades of adoption resulting in a rapid
+spreading often seen in empirical data. The observation of real world adoption
+processes has become easier lately due to the availability of large digital
+social network and behavioural datasets. This has allowed simultaneous study of
+network structures and dynamics of online service adoption, shedding light on
+the mechanisms and external effects that influence the temporal evolution of
+behavioural or innovation adoption. These advancements have induced the
+development of more realistic models of social spreading phenomena, which in
+turn have provided remarkably good predictions of various empirical adoption
+processes. In this chapter we review recent data-driven studies addressing
+real-world service adoption processes. Our studies provide the first detailed
+empirical evidence of a heterogeneous threshold distribution in adoption. We
+also describe the modelling of such phenomena with formal methods and
+data-driven simulations. Our objective is to understand the effects of
+identified social mechanisms on service adoption spreading, and to provide
+potential new directions and open questions for future research.
+",1,1,0,0,0,0
+8040,The Amplitude-Phase Decomposition for the Magnetotelluric Impedance Tensor,"  The Phase Tensor (PT) marked a breakthrough in understanding and analysis of
+electric galvanic distortion but does not contain any impedance amplitude
+information and therefore cannot quantify resistivity without complementary
+data. We formulate a complete impedance tensor decomposition into the PT and a
+new Amplitude Tensor (AT) that is shown to be complementary and mathematically
+independent to the PT. We show that for the special cases of 1D and 2D models,
+the geometric AT parameters (strike and skew angles) converge to PT parameters
+and the singular values of the AT correspond to the impedance amplitudes of the
+transverse electric and transverse magnetic modes. In all cases, we show that
+the AT contains both galvanic and inductive amplitudes, the latter of which is
+argued to be physically related to the inductive information of the PT. The
+geometric parameters of the inductive AT and the PT represent the same geometry
+of the subsurface conductivity distribution that is affected by induction
+processes, and therefore we hypothesise that geometric PT parameters can be
+used to approximate the inductive AT. Then, this hypothesis leads to the
+estimation of the galvanic AT which is equal to the galvanic electric
+distortion tensor at the lowest measured period. This estimation of the
+galvanic distortion departs from the common assumption to consider 1D or 2D
+regional structures and can be applied for general 3D subsurfaces. We
+demonstrate exemplarily with an explicit formulation how our hypothesis can be
+used to recover the galvanic electric anisotropic distortion for 2D
+subsurfaces, which was, until now, believed to be indeterminable for 2D data.
+Moreover, we illustrate the AT as a mapping tool and we compare it to the PT
+with both synthetic and real data examples. Lastly, we argue that the AT can
+provide important non-redundant amplitude information to PT inversions.
+",0,1,0,0,0,0
+8041,Real-time Convolutional Neural Networks for Emotion and Gender Classification,"  In this paper we propose an implement a general convolutional neural network
+(CNN) building framework for designing real-time CNNs. We validate our models
+by creating a real-time vision system which accomplishes the tasks of face
+detection, gender classification and emotion classification simultaneously in
+one blended step using our proposed CNN architecture. After presenting the
+details of the training procedure setup we proceed to evaluate on standard
+benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66%
+in the FER-2013 emotion dataset. Along with this we also introduced the very
+recent real-time enabled guided back-propagation visualization technique.
+Guided back-propagation uncovers the dynamics of the weight changes and
+evaluates the learned features. We argue that the careful implementation of
+modern CNN architectures, the use of the current regularization methods and the
+visualization of previously hidden features are necessary in order to reduce
+the gap between slow performances and real-time architectures. Our system has
+been validated by its deployment on a Care-O-bot 3 robot used during
+RoboCup@Home competitions. All our code, demos and pre-trained architectures
+have been released under an open-source license in our public repository.
+",1,0,0,0,0,0
+8042,Compound-Specific Chlorine Isotope Analysis of Organochlorines Using Gas Chromatography-Double Focus Magnetic-Sector High Resolution Mass Spectrometry,"  Compound-specific chlorine isotope analysis (CSIA-Cl) is a practicable and
+high-performance approach for quantification of transformation processes and
+pollution source apportionment of chlorinated organic compounds. This study
+developed a CSIA-Cl method for perchlorethylene (PCE) and trichloroethylene
+(TCE) using gas chromatography-double focus magnetic-sector high resolution
+mass spectrometry (GC-DFS-HRMS) with a bracketing injection mode. The achieved
+highest precision for PCE was 0.021% (standard deviation of isotope ratios),
+and that for TCE was 0.025%. When one standard was used as the external
+isotopic standard for another of the same analyte, the lowest standard
+deviations of relative isotope-ratio variations ({\delta}37Cl') between the two
+corresponding standards were 0.064% and 0.080% for PCE and TCE, respectively.
+As a result, the critical {\delta}37Cl' for differentiating two isotope ratios
+are 0.26% and 0.32% for PCE and TCE, respectively, which are comparable with
+those in some reported studies using GC-quadrupole MS (GC-qMS). The lower limit
+of detection for CSIA-Cl of PCE was 0.1 ug/mL (0.1 ng on column), and that for
+TCE was determined to be 1.0 ug/mL (1.0 ng on column). Two isotope ratio
+calculation schemes, i.e., a scheme using complete molecular-ion isotopologues
+and another one using a pair of neighboring isotopologues, were evaluated in
+terms of precision and accuracy. The complete-isotopologue scheme showed
+evidently higher precision and was deduced to be more competent to reflect
+trueness in comparison with the isotopologue-pair scheme. The CSIA-Cl method
+developed in this study will be conducive to future studies concerning
+transformation processes and source apportionment of PCE and TCE, and light the
+ways to method development of CSIA-Cl for more organochlorines.
+",0,1,0,0,0,0
+8043,Realization of an atomically thin mirror using monolayer MoSe2,"  Advent of new materials such as van der Waals heterostructures, propels new
+research directions in condensed matter physics and enables development of
+novel devices with unique functionalities. Here, we show experimentally that a
+monolayer of MoSe2 embedded in a charge controlled heterostructure can be used
+to realize an electrically tunable atomically-thin mirror, that effects 90%
+extinction of an incident field that is resonant with its exciton transition.
+The corresponding maximum reflection coefficient of 45% is only limited by the
+ratio of the radiative decay rate to the linewidth of exciton transition and is
+independent of incident light intensity up to 400 Watts/cm2. We demonstrate
+that the reflectivity of the mirror can be drastically modified by applying a
+gate voltage that modifies the monolayer charge density. Our findings could
+find applications ranging from fast programmable spatial light modulators to
+suspended ultra-light mirrors for optomechanical devices.
+",0,1,0,0,0,0
+8044,Equivariant mirror symmetry for the weighted projective line,"  In this paper, we establish equivariant mirror symmetry for the weighted
+projective line. This extends the results by B. Fang, C.C. Liu and Z. Zong,
+where the projective line was considered [{\it Geometry \& Topology}
+24:2049-2092, 2017]. More precisely, we prove the equivalence of the
+$R$-matrices for A-model and B-model at large radius limit, and establish
+isomorphism for $R$-matrices for general radius. We further demonstrate that
+the graph sum of higher genus cases are the same for both models, hence
+establish equivariant mirror symmetry for the weighted projective line.
+",0,0,1,0,0,0
+8045,Precise Recovery of Latent Vectors from Generative Adversarial Networks,"  Generative adversarial networks (GANs) transform latent vectors into visually
+plausible images. It is generally thought that the original GAN formulation
+gives no out-of-the-box method to reverse the mapping, projecting images back
+into latent space. We introduce a simple, gradient-based technique called
+stochastic clipping. In experiments, for images generated by the GAN, we
+precisely recover their latent vector pre-images 100% of the time. Additional
+experiments demonstrate that this method is robust to noise. Finally, we show
+that even for unseen images, our method appears to recover unique encodings.
+",1,0,0,1,0,0
+8046,The dependence of protostar formation on the geometry and strength of the initial magnetic field,"  We report results from twelve simulations of the collapse of a molecular
+cloud core to form one or more protostars, comprising three field strengths
+(mass-to-flux ratios, {\mu}, of 5, 10, and 20) and four field geometries (with
+values of the angle between the field and rotation axes, {\theta}, of 0°,
+20°, 45°, and 90°), using a smoothed particle
+magnetohydrodynamics method. We find that the values of both parameters have a
+strong effect on the resultant protostellar system and outflows. This ranges
+from the formation of binary systems when {\mu} = 20 to strikingly differing
+outflow structures for differing values of {\theta}, in particular highly
+suppressed outflows when {\theta} = 90°. Misaligned magnetic fields can
+also produce warped pseudo-discs where the outer regions align perpendicular to
+the magnetic field but the innermost region re-orientates to be perpendicular
+to the rotation axis. We follow the collapse to sizes comparable to those of
+first cores and find that none of the outflow speeds exceed 8 km s$^{-1}$.
+These results may place constraints on both observed protostellar outflows, and
+also on which molecular cloud cores may eventually form either single stars and
+binaries: a sufficiently weak magnetic field may allow for disc fragmentation,
+whilst conversely the greater angular momentum transport of a strong field may
+inhibit disc fragmentation.
+",0,1,0,0,0,0
+8047,"Current-Voltage Characteristics of Weyl Semimetal Semiconducting Devices, Veselago Lenses and Hyperbolic Dirac Phase","  The current-voltage characteristics of a new range of devices built around
+Weyl semimetals has been predicted using the Landauer formalism. The potential
+step and barrier have been reconsidered for a three-dimensional Weyl
+semimetals, with analogies to the two-dimensional material graphene and to
+optics. With the use of our results we also show how a Veselago lens can be
+made from Weyl semimetals, e.g. from NbAs and NbP. Such a lens may have many
+practical applications and can be used as a probing tip in a scanning tunneling
+microscope (STM). The ballistic character of Weyl fermion transport inside the
+semimetal tip, combined with the ideal focusing of the Weyl fermions (by
+Veselago lens) on the surface of the tip may create a very narrow electron beam
+from the tip to the surface of the studied material. With a Weyl semimetal
+probing tip the resolution of the present STMs can be improved significantly,
+and one may image not only individual atoms but also individual electron
+orbitals or chemical bonding and therewith to resolve the long-term issue of
+chemical and hydrogen bond formation. We show that applying a pressure to the
+Weyl semimental, having no centre of spacial inversion one may model matter at
+extreme conditions such as those arising in the vicinity of a black hole. As
+the materials Cd3As2 and Na3Bi show an asymmetry in their Dirac cones, a
+scaling factor was used to model this asymmetry. The scaling factor created
+additional regions of no propagation and condensed the appearance of
+resonances. We argue that under an external pressure there may arise a
+topological phase transition in Weyl semimetals, where the electron transport
+changes character and becomes anisotropic. There a hyperbolic Dirac phases
+occurs where there is a strong light absorption and photo-current generation.
+",0,1,0,0,0,0
+8048,Safer Classification by Synthesis,"  The discriminative approach to classification using deep neural networks has
+become the de-facto standard in various fields. Complementing recent
+reservations about safety against adversarial examples, we show that
+conventional discriminative methods can easily be fooled to provide incorrect
+labels with very high confidence to out of distribution examples. We posit that
+a generative approach is the natural remedy for this problem, and propose a
+method for classification using generative models. At training time, we learn a
+generative model for each class, while at test time, given an example to
+classify, we query each generator for its most similar generation, and select
+the class corresponding to the most similar one. Our approach is general and
+can be used with expressive models such as GANs and VAEs. At test time, our
+method accurately ""knows when it does not know,"" and provides resilience to out
+of distribution examples while maintaining competitive performance for standard
+examples.
+",1,0,0,1,0,0
+8049,A geometrical analysis of global stability in trained feedback networks,"  Recurrent neural networks have been extensively studied in the context of
+neuroscience and machine learning due to their ability to implement complex
+computations. While substantial progress in designing effective learning
+algorithms has been achieved in the last years, a full understanding of trained
+recurrent networks is still lacking. Specifically, the mechanisms that allow
+computations to emerge from the underlying recurrent dynamics are largely
+unknown. Here we focus on a simple, yet underexplored computational setup: a
+feedback architecture trained to associate a stationary output to a stationary
+input. As a starting point, we derive an approximate analytical description of
+global dynamics in trained networks which assumes uncorrelated connectivity
+weights in the feedback and in the random bulk. The resulting mean-field theory
+suggests that the task admits several classes of solutions, which imply
+different stability properties. Different classes are characterized in terms of
+the geometrical arrangement of the readout with respect to the input vectors,
+defined in the high-dimensional space spanned by the network population. We
+find that such approximate theoretical approach can be used to understand how
+standard training techniques implement the input-output task in finite-size
+feedback networks. In particular, our simplified description captures the local
+and the global stability properties of the target solution, and thus predicts
+training performance.
+",0,0,0,0,1,0
+8050,Submolecular-resolution non-invasive imaging of interfacial water with atomic force microscopy,"  Scanning probe microscopy (SPM) has been extensively applied to probe
+interfacial water in many interdisciplinary fields but the disturbance of the
+probes on the hydrogen-bonding structure of water has remained an intractable
+problem. Here we report submolecular-resolution imaging of the water clusters
+on a NaCl(001) surface within the nearly non-invasive region by a qPlus-based
+noncontact atomic force microscopy. Comparison with theoretical simulations
+reveals that the key lies in probing the weak high-order electrostatic force
+between the quadrupole-like CO-terminated tip and the polar water molecules at
+large tip-water distances. This interaction allows the imaging and structural
+determination of the weakly bonded water clusters and even of their metastable
+states without inducing any disturbance. This work may open up new possibility
+of studying the intrinsic structure and electrostatics of ice or water on bulk
+insulating surfaces, ion hydration and biological water with atomic precision.
+",0,1,0,0,0,0
+8051,A Novel Stochastic Stratified Average Gradient Method: Convergence Rate and Its Complexity,"  SGD (Stochastic Gradient Descent) is a popular algorithm for large scale
+optimization problems due to its low iterative cost. However, SGD can not
+achieve linear convergence rate as FGD (Full Gradient Descent) because of the
+inherent gradient variance. To attack the problem, mini-batch SGD was proposed
+to get a trade-off in terms of convergence rate and iteration cost. In this
+paper, a general CVI (Convergence-Variance Inequality) equation is presented to
+state formally the interaction of convergence rate and gradient variance. Then
+a novel algorithm named SSAG (Stochastic Stratified Average Gradient) is
+introduced to reduce gradient variance based on two techniques, stratified
+sampling and averaging over iterations that is a key idea in SAG (Stochastic
+Average Gradient). Furthermore, SSAG can achieve linear convergence rate of
+$\mathcal {O}((1-\frac{\mu}{8CL})^k)$ at smaller storage and iterative costs,
+where $C\geq 2$ is the category number of training data. This convergence rate
+depends mainly on the variance between classes, but not on the variance within
+the classes. In the case of $C\ll N$ ($N$ is the training data size), SSAG's
+convergence rate is much better than SAG's convergence rate of $\mathcal
+{O}((1-\frac{\mu}{8NL})^k)$. Our experimental results show SSAG outperforms SAG
+and many other algorithms.
+",1,0,0,1,0,0
+8052,First observation of Ce volume collapse in CeN,"  On the occasion of the 80th anniversary of the first observation of Ce volume
+collapse in CeN a remembrance of the implications of that transcendent event is
+presented, along with a review of the knowledge of Ce physical properties
+available at that time. Coincident anniversary corresponds to the first
+proposal for Ce as a mix valence element, motivating to briefly review how the
+valence instability of Ce was investigated since that time.
+",0,1,0,0,0,0
+8053,Experimentation with MANETs of Smartphones,"  Mobile AdHoc NETworks (MANETs) have been identified as a key emerging
+technology for scenarios in which IEEE 802.11 or cellular communications are
+either infeasible, inefficient, or cost-ineffective. Smartphones are the most
+adequate network nodes in many of these scenarios, but it is not
+straightforward to build a network with them. We extensively survey existing
+possibilities to build applications on top of ad-hoc smartphone networks for
+experimentation purposes, and introduce a taxonomy to classify them. We present
+AdHocDroid, an Android package that creates an IP-level MANET of (rooted)
+Android smartphones, and make it publicly available to the community.
+AdHocDroid supports standard TCP/IP applications, providing real smartphone
+IEEE 802.11 MANET and the capability to easily change the routing protocol. We
+tested our framework on several smartphones and a laptop. We validate the MANET
+running off-the-shelf applications, and reporting on experimental performance
+evaluation, including network metrics and battery discharge rate.
+",1,0,0,0,0,0
+8054,Interaction between cluster synchronization and epidemic spread in community networks,"  In real world, there is a significant relation between human behaviors and
+epidemic spread. Especially, the reactions among individuals in different
+communities to epidemics may be different, which lead to cluster
+synchronization of human behaviors. So, a mathematical model that embeds
+community structures, behavioral evolution and epidemic transmission is
+constructed to study the interaction between cluster synchronization and
+epidemic spread. The epidemic threshold of the model is obtained by using
+Gersgorin Lemma and dynamical system theory. By applying the Lyapunov stability
+method, the stability analysis of cluster synchronization and spreading
+dynamics are presented. Then, some numerical simulations are performed to
+illustrate and complement our theoretical results. As far as we know, this work
+is the first one to address the interplay between cluster synchronization and
+epidemic transmission in community networks, so it may deepen the understanding
+of the impact of cluster behaviors on infectious disease dynamics.
+",0,1,0,0,0,0
+8055,Focusing on a Probability Element: Parameter Selection of Message Importance Measure in Big Data,"  Message importance measure (MIM) is applicable to characterize the importance
+of information in the scenario of big data, similar to entropy in information
+theory. In fact, MIM with a variable parameter can make an effect on the
+characterization of distribution. Furthermore, by choosing an appropriate
+parameter of MIM, it is possible to emphasize the message importance of a
+certain probability element in a distribution. Therefore, parametric MIM can
+play a vital role in anomaly detection of big data by focusing on probability
+of an anomalous event. In this paper, we propose a parameter selection method
+of MIM focusing on a probability element and then present its major properties.
+In addition, we discuss the parameter selection with prior probability, and
+investigate the availability in a statistical processing model of big data for
+anomaly detection problem.
+",1,0,1,0,0,0
+8056,Deep Multi-camera People Detection,"  This paper addresses the problem of multi-view people occupancy map
+estimation. Existing solutions for this problem either operate per-view, or
+rely on a background subtraction pre-processing. Both approaches lessen the
+detection performance as scenes become more crowded. The former does not
+exploit joint information, whereas the latter deals with ambiguous input due to
+the foreground blobs becoming more and more interconnected as the number of
+targets increases.
+Although deep learning algorithms have proven to excel on remarkably numerous
+computer vision tasks, such a method has not been applied yet to this problem.
+In large part this is due to the lack of large-scale multi-camera data-set.
+The core of our method is an architecture which makes use of monocular
+pedestrian data-set, available at larger scale then the multi-view ones,
+applies parallel processing to the multiple video streams, and jointly utilises
+it. Our end-to-end deep learning method outperforms existing methods by large
+margins on the commonly used PETS 2009 data-set. Furthermore, we make publicly
+available a new three-camera HD data-set. Our source code and trained models
+will be made available under an open-source license.
+",1,0,0,0,0,0
+8057,SMAGEXP: a galaxy tool suite for transcriptomics data meta-analysis,"  Bakground: With the proliferation of available microarray and high throughput
+sequencing experiments in the public domain, the use of meta-analysis methods
+increases. In these experiments, where the sample size is often limited,
+meta-analysis offers the possibility to considerably enhance the statistical
+power and give more accurate results. For those purposes, it combines either
+effect sizes or results of single studies in a appropriate manner. R packages
+metaMA and metaRNASeq perform meta-analysis on microarray and NGS data,
+respectively. They are not interchangeable as they rely on statistical modeling
+specific to each technology.
+Results: SMAGEXP (Statistical Meta-Analysis for Gene EXPression) integrates
+metaMA and metaRNAseq packages into Galaxy. We aim to propose a unified way to
+carry out meta-analysis of gene expression data, while taking care of their
+specificities. We have developed this tool suite to analyse microarray data
+from Gene Expression Omnibus (GEO) database or custom data from affymetrix
+microarrays. These data are then combined to carry out meta-analysis using
+metaMA package. SMAGEXP also offers to combine raw read counts from Next
+Generation Sequencing (NGS) experiments using DESeq2 and metaRNASeq package. In
+both cases, key values, independent from the technology type, are reported to
+judge the quality of the meta-analysis. These tools are available on the Galaxy
+main tool shed. Source code, help and installation instructions are available
+on github.
+Conclusion: The use of Galaxy offers an easy-to-use gene expression
+meta-analysis tool suite based on the metaMA and metaRNASeq packages.
+",0,0,0,1,1,0
+8058,Big Data Fusion to Estimate Fuel Consumption: A Case Study of Riyadh,"  Falling oil revenues and rapid urbanization are putting a strain on the
+budgets of oil producing nations which often subsidize domestic fuel
+consumption. A direct way to decrease the impact of subsidies is to reduce fuel
+consumption by reducing congestion and car trips. While fuel consumption models
+have started to incorporate data sources from ubiquitous sensing devices, the
+opportunity is to develop comprehensive models at urban scale leveraging
+sources such as Global Positioning System (GPS) data and Call Detail Records.
+We combine these big data sets in a novel method to model fuel consumption
+within a city and estimate how it may change due to different scenarios. To do
+so we calibrate a fuel consumption model for use on any car fleet fuel economy
+distribution and apply it in Riyadh, Saudi Arabia. The model proposed, based on
+speed profiles, is then used to test the effects on fuel consumption of
+reducing flow, both randomly and by targeting the most fuel inefficient trips
+in the city. The estimates considerably improve baseline methods based on
+average speeds, showing the benefits of the information added by the GPS data
+fusion. The presented method can be adapted to also measure emissions. The
+results constitute a clear application of data analysis tools to help decision
+makers compare policies aimed at achieving economic and environmental goals.
+",1,0,0,0,0,0
+8059,Person Following by Autonomous Robots: A Categorical Overview,"  A wide range of human-robot collaborative applications in industry, search
+and rescue operations, healthcare, and social interactions require an
+autonomous robot to follow its human companion. Different operating mediums and
+applications pose diverse challenges by adding constraints on the choice of
+sensors, the degree of autonomy, and dynamics of the person following robot.
+Researchers have addressed these challenges in many ways and contributed to the
+development of a large body of literature. This paper provides a comprehensive
+overview of the literature by categorizing different aspects of
+person-following by autonomous robots. Also, the corresponding operational
+challenges are identified based on various design choices for ground,
+underwater, and aerial scenarios. In addition, state-of-the-art methods for
+perception, planning, control, and interaction are elaborately discussed, and
+their feasibilities are evaluated in terms of standard operational and
+performance metrics. Furthermore, several prospective application areas are
+identified, and open problems are highlighted for future research.
+",1,0,0,0,0,0
+8060,"Structures, phase transitions, and magnetic properties of Co3Si from first-principles calculations","  Co3Si was recently reported to exhibit remarkable magnetic properties in the
+nanoparticle form [Appl. Phys. Lett. 108, 152406 (2016)], yet better
+understanding of this material is to be promoted. Here we report a study on the
+crystal structures of Co3Si using adaptive genetic algorithm, and discuss its
+electronic and magnetic properties from first-principles calculations. Several
+competing phases of Co3Si have been revealed from our calculations. We show
+that the hexagonal Co3Si structure reported in experiments has lower energy in
+non-magnetic state than ferromagnetic state at zero temperature. The
+ferromagnetic state of the hexagonal structure is dynamically unstable with
+imaginary phonon modes and transforms to a new orthorhombic structure, which is
+confirmed by our structure searches to have the lowest energy for both Co3Si
+and Co3Ge. Magnetic properties of the experimental hexagonal structure and the
+lowest-energy structures obtained from our structure searches are investigated
+in detail.
+",0,1,0,0,0,0
+8061,Rigid realizations of modular forms in Calabi--Yau threefolds,"  We construct examples of modular rigid Calabi--Yau threefolds, which give a
+realization of some new weight 4 cusp forms.
+",0,0,1,0,0,0
+8062,Steady-state analysis of single exponential vacation in a $PH/MSP/1/\infty$ queue using roots,"  We consider an infinite-buffer single-server queue where inter-arrival times
+are phase-type ($PH$), the service is provided according to Markovian service
+process $(MSP)$, and the server may take single, exponentially distributed
+vacations when the queue is empty. The proposed analysis is based on roots of
+the associated characteristic equation of the vector-generating function (VGF)
+of system-length distribution at a pre-arrival epoch. Also, we obtain the
+steady-state system-length distribution at an arbitrary epoch along with some
+important performance measures such as the mean number of customers in the
+system and the mean system sojourn time of a customer. Later, we have
+established heavy- and light-traffic approximations as well as an approximation
+for the tail probabilities at pre-arrival epoch based on one root of the
+characteristic equation. At the end, we present numerical results in the form
+of tables to show the effect of model parameters on the performance measures.
+",1,0,1,0,0,0
+8063,The agreement distance of rooted phylogenetic networks,"  The minimal number of rooted subtree prune and regraft (rSPR) operations
+needed to transform one phylogenetic tree into another one induces a metric on
+phylogenetic trees - the rSPR-distance. The rSPR-distance between two
+phylogenetic trees $T$ and $T'$ can be characterised by a maximum agreement
+forest; a forest with a minimal number of components that covers both $T$ and
+$T'$. The rSPR operation has recently been generalised to phylogenetic networks
+with, among others, the subnetwork prune and regraft (SNPR) operation. Here, we
+introduce maximum agreement graphs as an explicit representations of
+differences of two phylogenetic networks, thus generalising maximum agreement
+forests. We show that maximum agreement graphs induce a metric on phylogenetic
+networks - the agreement distance. While this metric does not characterise the
+distances induced by SNPR and other generalisations of rSPR, we prove that it
+still bounds these distances with constant factors.
+",0,0,0,0,1,0
+8064,Optimization of the Waiting Time for H-R Coordination,"  An analytical model of Human-Robot (H-R) coordination is presented for a
+Human-Robot system executing a collaborative task in which a high level of
+synchronization among the agents is desired. The influencing parameters and
+decision variables that affect the waiting time of the collaborating agents
+were analyzed. The performance of the model was evaluated based on the costs of
+the waiting times of each of the agents at the pre-defined spatial point of
+handover. The model was tested for two cases of dynamic H-R coordination
+scenarios. Results indicate that this analytical model can be used as a tool
+for designing an H-R system that optimizes the agent waiting time thereby
+increasing the joint-efficiency of the system and making coordination fluent
+and natural.
+",1,0,0,0,0,0
+8065,Introducing AIC model averaging in ecological niche modeling: a single-algorithm multi-model strategy to account for uncertainty in suitability predictions,"  Aim: The Akaike information Criterion (AIC) is widely used science to make
+predictions about complex phenomena based on an entire set of models weighted
+by Akaike weights. This approach (AIC model averaging; hereafter AvgAICc) is
+often preferable than alternatives based on the selection of a single model.
+Surprisingly, AvgAICc has not yet been introduced in ecological niche modeling
+(ENM). We aimed to introduce AvgAICc in the context of ENM to serve both as an
+optimality criterion in analyses that tune-up model parameters and as a
+multi-model prediction strategy.
+Innovation: Results from the AvgAICc approach differed from those of
+alternative approaches with respect to model complexity, contribution of
+environmental variables, and predicted amount and geographic location of
+suitable conditions for the focal species. Two theoretical properties of the
+AvgAICc approach might justify that future studies will prefer its use over
+alternative approaches: (1) it is not limited to make predictions based on a
+single model, but it also uses secondary models that might have important
+predictive power absent in a given single model favored by alternative
+optimality criteria; (2) it balances goodness of fit and model accuracy, this
+being of critical importance in applications of ENM that require model
+transference.
+Main conclusions: Our introduction of the AvgAICc approach in ENM; its
+theoretical properties, which are expected to confer advantages over
+alternatives approaches; and the differences we found when comparing the
+AvgAICc approach with alternative ones; should eventually lead to a wider use
+of the AvgAICc approach. Our work should also promote further methodological
+research comparing properties of the AvgAICc approach with respect to those of
+alternative procedures.
+",0,0,0,0,1,0
+8066,Extrasolar Planets and Their Host Stars,"  In order to understand the exoplanet, you need to understand its parent star.
+Astrophysical parameters of extrasolar planets are directly and indirectly
+dependent on the properties of their respective host stars. These host stars
+are very frequently the only visible component in the systems. This book
+describes our work in the field of characterization of exoplanet host stars
+using interferometry to determine angular diameters, trigonometric parallax to
+determine physical radii, and SED fitting to determine effective temperatures
+and luminosities. The interferometry data are based on our decade-long survey
+using the CHARA Array. We describe our methods and give an update on the status
+of the field, including a table with the astrophysical properties of all stars
+with high-precision interferometric diameters out to 150 pc (status Nov 2016).
+In addition, we elaborate in more detail on a number of particularly
+significant or important exoplanet systems, particularly with respect to (1)
+insights gained from transiting exoplanets, (2) the determination of system
+habitable zones, and (3) the discrepancy between directly determined and
+model-based stellar radii. Finally, we discuss current and future work
+including the calibration of semi-empirical methods based on interferometric
+data.
+",0,1,0,0,0,0
+8067,Modeling polypharmacy side effects with graph convolutional networks,"  The use of drug combinations, termed polypharmacy, is common to treat
+patients with complex diseases and co-existing conditions. However, a major
+consequence of polypharmacy is a much higher risk of adverse side effects for
+the patient. Polypharmacy side effects emerge because of drug-drug
+interactions, in which activity of one drug may change if taken with another
+drug. The knowledge of drug interactions is limited because these complex
+relationships are rare, and are usually not observed in relatively small
+clinical testing. Discovering polypharmacy side effects thus remains an
+important challenge with significant implications for patient mortality. Here,
+we present Decagon, an approach for modeling polypharmacy side effects. The
+approach constructs a multimodal graph of protein-protein interactions,
+drug-protein target interactions, and the polypharmacy side effects, which are
+represented as drug-drug interactions, where each side effect is an edge of a
+different type. Decagon is developed specifically to handle such multimodal
+graphs with a large number of edge types. Our approach develops a new graph
+convolutional neural network for multirelational link prediction in multimodal
+networks. Decagon predicts the exact side effect, if any, through which a given
+drug combination manifests clinically. Decagon accurately predicts polypharmacy
+side effects, outperforming baselines by up to 69%. We find that it
+automatically learns representations of side effects indicative of
+co-occurrence of polypharmacy in patients. Furthermore, Decagon models
+particularly well side effects with a strong molecular basis, while on
+predominantly non-molecular side effects, it achieves good performance because
+of effective sharing of model parameters across edge types. Decagon creates
+opportunities to use large pharmacogenomic and patient data to flag and
+prioritize side effects for follow-up analysis.
+",0,0,0,1,1,0
+8068,Musical intervals under 12-note equal temperament: a geometrical interpretation,"  Musical intervals in multiple of semitones under 12-note equal temperament,
+or more specifically pitch-class subsets of assigned cardinality ($n$-chords)
+are conceived as positive integer points within an Euclidean $n$-space. The
+number of distinct $n$-chords is inferred from combinatorics with the extension
+to $n=0$, involving an Euclidean 0-space. The number of repeating $n$-chords,
+or points which are turned into themselves during a circular permutation,
+$T_n$, of their coordinates, is inferred from algebraic considerations.
+Finally, the total number of $n$-chords and the number of $T_n$ set classes are
+determined. Palindrome and pseudo palindrome $n$-chords are defined and
+included among repeating $n$-chords, with regard to an equivalence relation,
+$T_n/T_nI$, where reflection is added to circular permutation. To this respect,
+the number of $T_n$ set classes is inferred concerning palindrome and pseudo
+palindrome $n$-chords and the remaining $n$-chords. The above results are
+reproduced within the framework of a geometrical interpretation, where positive
+integer points related to $n$-chords of cardinality, $n$, belong to a regular
+inclined $n$-hedron, $\Psi_{12}^n$, the vertexes lying on the coordinate axes
+of a Cartesian orthogonal reference frame at a distance, $x_i=12$, $1\le i\le
+n$, from the origin. Considering $\Psi_{12}^n$ as special cases of lattice
+polytopes, the number of related nonnegative integer points is also determined
+for completeness. A comparison is performed with the results inferred from
+group theory.
+",0,0,1,0,0,0
+8069,Determining rough first order perturbations of the polyharmonic operator,"  We show that the knowledge of Dirichlet to Neumann map for rough $A$ and $q$
+in $(-\Delta)^m +A\cdot D +q$ for $m \geq 2$ for a bounded domain in
+$\mathbb{R}^n$, $n \geq 3$ determines $A$ and $q$ uniquely. The unique
+identifiability is proved using property of products of functions in Sobolev
+spaces and constructing complex geometrical optics solutions with sufficient
+decay of remainder terms.
+",0,0,1,0,0,0
+8070,Data Science: A Three Ring Circus or a Big Tent?,"  This is part of a collection of discussion pieces on David Donoho's paper 50
+Years of Data Science, appearing in Volume 26, Issue 4 of the Journal of
+Computational and Graphical Statistics (2017).
+",0,0,0,1,0,0
+8071,Optimal Control of Partially Observable Piecewise Deterministic Markov Processes,"  In this paper we consider a control problem for a Partially Observable
+Piecewise Deterministic Markov Process of the following type: After the jump of
+the process the controller receives a noisy signal about the state and the aim
+is to control the process continuously in time in such a way that the expected
+discounted cost of the system is minimized. We solve this optimization problem
+by reducing it to a discrete-time Markov Decision Process. This includes the
+derivation of a filter for the unobservable state. Imposing sufficient
+continuity and compactness assumptions we are able to prove the existence of
+optimal policies and show that the value function satisfies a fixed point
+equation. A generic application is given to illustrate the results.
+",0,0,1,0,0,0
+8072,Moment-based parameter estimation in binomial random intersection graph models,"  Binomial random intersection graphs can be used as parsimonious statistical
+models of large and sparse networks, with one parameter for the average degree
+and another for transitivity, the tendency of neighbours of a node to be
+connected. This paper discusses the estimation of these parameters from a
+single observed instance of the graph, using moment estimators based on
+observed degrees and frequencies of 2-stars and triangles. The observed data
+set is assumed to be a subgraph induced by a set of $n_0$ nodes sampled from
+the full set of $n$ nodes. We prove the consistency of the proposed estimators
+by showing that the relative estimation error is small with high probability
+for $n_0 \gg n^{2/3} \gg 1$. As a byproduct, our analysis confirms that the
+empirical transitivity coefficient of the graph is with high probability close
+to the theoretical clustering coefficient of the model.
+",1,0,1,1,0,0
+8073,Efficient Compression and Indexing of Trajectories,"  We present a new compressed representation of free trajectories of moving
+objects. It combines a partial-sums-based structure that retrieves in constant
+time the position of the object at any instant, with a hierarchical
+minimum-bounding-boxes representation that allows determining if the object is
+seen in a certain rectangular area during a time period. Combined with spatial
+snapshots at regular intervals, the representation is shown to outperform
+classical ones by orders of magnitude in space, and also to outperform previous
+compressed representations in time performance, when using the same amount of
+space.
+",1,0,0,0,0,0
+8074,Denoising Linear Models with Permuted Data,"  The multivariate linear regression model with shuffled data and additive
+Gaussian noise arises in various correspondence estimation and matching
+problems. Focusing on the denoising aspect of this problem, we provide a
+characterization the minimax error rate that is sharp up to logarithmic
+factors. We also analyze the performance of two versions of a computationally
+efficient estimator, and establish their consistency for a large range of input
+parameters. Finally, we provide an exact algorithm for the noiseless problem
+and demonstrate its performance on an image point-cloud matching task. Our
+analysis also extends to datasets with outliers.
+",0,0,1,1,0,0
+8075,Collisions in shape memory alloys,"  We present here a model for instantaneous collisions in a solid made of shape
+memory alloys (SMA) by means of a predictive theory which is based on the
+introduction not only of macroscopic velocities and temperature, but also of
+microscopic velocities responsible of the austenite-martensites phase changes.
+Assuming time discontinuities for velocities, volume fractions and temperature,
+and applying the principles of thermodynamics for non-smooth evolutions
+together with constitutive laws typical of SMA, we end up with a system of
+nonlinearly coupled elliptic equations for which we prove an existence and
+uniqueness result in the 2 and 3 D cases. Finally, we also present numerical
+results for a SMA 2D solid subject to an external percussion by an hammer
+stroke.
+",0,0,1,0,0,0
+8076,Big Data Meets HPC Log Analytics: Scalable Approach to Understanding Systems at Extreme Scale,"  Today's high-performance computing (HPC) systems are heavily instrumented,
+generating logs containing information about abnormal events, such as critical
+conditions, faults, errors and failures, system resource utilization, and about
+the resource usage of user applications. These logs, once fully analyzed and
+correlated, can produce detailed information about the system health, root
+causes of failures, and analyze an application's interactions with the system,
+providing valuable insights to domain scientists and system administrators.
+However, processing HPC logs requires a deep understanding of hardware and
+software components at multiple layers of the system stack. Moreover, most log
+data is unstructured and voluminous, making it more difficult for system users
+and administrators to manually inspect the data. With rapid increases in the
+scale and complexity of HPC systems, log data processing is becoming a big data
+challenge. This paper introduces a HPC log data analytics framework that is
+based on a distributed NoSQL database technology, which provides scalability
+and high availability, and the Apache Spark framework for rapid in-memory
+processing of the log data. The analytics framework enables the extraction of a
+range of information about the system so that system administrators and end
+users alike can obtain necessary insights for their specific needs. We describe
+our experience with using this framework to glean insights from the log data
+about system behavior from the Titan supercomputer at the Oak Ridge National
+Laboratory.
+",1,0,0,0,0,0
+8077,Clustering Spectrum of scale-free networks,"  Real-world networks often have power-law degrees and scale-free properties
+such as ultra-small distances and ultra-fast information spreading. In this
+paper, we study a third universal property: three-point correlations that
+suppress the creation of triangles and signal the presence of hierarchy. We
+quantify this property in terms of $\bar c(k)$, the probability that two
+neighbors of a degree-$k$ node are neighbors themselves. We investigate how the
+clustering spectrum $k\mapsto\bar c(k)$ scales with $k$ in the hidden variable
+model and show that $c(k)$ follows a {\it universal curve} that consists of
+three $k$-ranges where $\bar c(k)$ remains flat, starts declining, and
+eventually settles on a power law $\bar c(k)\sim k^{-\alpha}$ with $\alpha$
+depending on the power law of the degree distribution. We test these results
+against ten contemporary real-world networks and explain analytically why the
+universal curve properties only reveal themselves in large networks.
+",1,1,0,0,0,0
+8078,Multiplex model of mental lexicon reveals explosive learning in humans,"  Word similarities affect language acquisition and use in a multi-relational
+way barely accounted for in the literature. We propose a multiplex network
+representation of this mental lexicon of word similarities as a natural
+framework for investigating large-scale cognitive patterns. Our representation
+accounts for semantic, taxonomic, and phonological interactions and it
+identifies a cluster of words which are used with greater frequency, are
+identified, memorised, and learned more easily, and have more meanings than
+expected at random. This cluster emerges around age 7 through an explosive
+transition not reproduced by null models. We relate this explosive emergence to
+polysemy -- redundancy in word meanings. Results indicate that the word cluster
+acts as a core for the lexicon, increasing both lexical navigability and
+robustness to linguistic degradation. Our findings provide quantitative
+confirmation of existing conjectures about core structure in the mental lexicon
+and the importance of integrating multi-relational word-word interactions in
+psycholinguistic frameworks.
+",1,1,0,0,0,0
+8079,"Weighted $L_{p,q}$-estimates for higher order elliptic and parabolic systems with BMO coefficients on Reifenberg flat domains","  We prove weighted $L_{p,q}$-estimates for divergence type higher order
+elliptic and parabolic systems with irregular coefficients on Reifenberg flat
+domains. In particular, in the parabolic case the coefficients do not have any
+regularity assumptions in the time variable. As functions of the spatial
+variables, the leading coefficients are permitted to have small mean
+oscillations. The weights are in the class of Muckenhoupt weights $A_p$. We
+also prove the solvability in weighted Sobolev spaces for the systems in the
+whole space, on a half space, and on bounded Reifenberg flat domains.
+",0,0,1,0,0,0
+8080,Various sharp estimates for semi-discrete Riesz transforms of the second order,"  We give several sharp estimates for a class of combinations of second order
+Riesz transforms on Lie groups ${G}={G}_{x} \times {G}_{y}$ that are multiply
+connected, composed of a discrete abelian component ${G}_{x}$ and a connected
+component ${G}_{y}$ endowed with a biinvariant measure. These estimates include
+new sharp $L^p$ estimates via Choi type constants, depending upon the
+multipliers of the operator. They also include weak-type, logarithmic and
+exponential estimates. We give an optimal $L^q \to L^p$ estimate as well.
+It was shown recently by Arcozzi, Domelevo and Petermichl that such second
+order Riesz transforms applied to a function may be written as conditional
+expectation of a simple transformation of a stochastic integral associated with
+the function.
+The proofs of our theorems combine this stochastic integral representation
+with a number of deep estimates for pairs of martingales under strong
+differential subordination by Choi, Banuelos and Osekowski.
+When two continuous directions are available, sharpness is shown via the
+laminates technique. We show that sharpness is preserved in the discrete case
+using Lax-Richtmyer theorem.
+",0,0,1,0,0,0
+8081,Linear Spectral Estimators and an Application to Phase Retrieval,"  Phase retrieval refers to the problem of recovering real- or complex-valued
+vectors from magnitude measurements. The best-known algorithms for this problem
+are iterative in nature and rely on so-called spectral initializers that
+provide accurate initialization vectors. We propose a novel class of estimators
+suitable for general nonlinear measurement systems, called linear spectral
+estimators (LSPEs), which can be used to compute accurate initialization
+vectors for phase retrieval problems. The proposed LSPEs not only provide
+accurate initialization vectors for noisy phase retrieval systems with
+structured or random measurement matrices, but also enable the derivation of
+sharp and nonasymptotic mean-squared error bounds. We demonstrate the efficacy
+of LSPEs on synthetic and real-world phase retrieval problems, and show that
+our estimators significantly outperform existing methods for structured
+measurement systems that arise in practice.
+",0,0,0,1,0,0
+8082,A geometric perspective on the method of descent,"  We derive a representation formula for the tensorial wave equation $\Box_\bg
+\phi^I=F^I$ in globally hyperbolic Lorentzian spacetimes $(\M^{2+1}, \bg)$ by
+giving a geometric formulation of the method of descent which is applicable for
+any dimension.
+",0,0,1,0,0,0
+8083,Detecting Changes in Hidden Markov Models,"  We consider the problem of sequential detection of a change in the
+statistical behavior of a hidden Markov model. By adopting a worst-case
+analysis with respect to the time of change and by taking into account the data
+that can be accessed by the change-imposing mechanism we offer alternative
+formulations of the problem. For each formulation we derive the optimum
+Shewhart test that maximizes the worst-case detection probability while
+guaranteeing infrequent false alarms.
+",0,0,1,1,0,0
+8084,Towards an Empirical Study of Affine Types for Isolated Actors in Scala,"  LaCasa is a type system and programming model to enforce the object
+capability discipline in Scala, and to provide affine types. One important
+application of LaCasa's type system is software isolation of concurrent
+processes. Isolation is important for several reasons including security and
+data-race freedom. Moreover, LaCasa's affine references enable efficient,
+by-reference message passing while guaranteeing a ""deep-copy"" semantics. This
+deep-copy semantics enables programmers to seamlessly port concurrent programs
+running on a single machine to distributed programs running on large-scale
+clusters of machines.
+This paper presents an integration of LaCasa with actors in Scala,
+specifically, the Akka actor-based middleware, one of the most widely-used
+actor systems in industry. The goal of this integration is to statically ensure
+the isolation of Akka actors. Importantly, we present the results of an
+empirical study investigating the effort required to use LaCasa's type system
+in existing open-source Akka-based systems and applications.
+",1,0,0,0,0,0
+8085,FPGA Architecture for Deep Learning and its application to Planetary Robotics,"  Autonomous control systems onboard planetary rovers and spacecraft benefit
+from having cognitive capabilities like learning so that they can adapt to
+unexpected situations in-situ. Q-learning is a form of reinforcement learning
+and it has been efficient in solving certain class of learning problems.
+However, embedded systems onboard planetary rovers and spacecraft rarely
+implement learning algorithms due to the constraints faced in the field, like
+processing power, chip size, convergence rate and costs due to the need for
+radiation hardening. These challenges present a compelling need for a portable,
+low-power, area efficient hardware accelerator to make learning algorithms
+practical onboard space hardware. This paper presents a FPGA implementation of
+Q-learning with Artificial Neural Networks (ANN). This method matches the
+massive parallelism inherent in neural network software with the fine-grain
+parallelism of an FPGA hardware thereby dramatically reducing processing time.
+Mars Science Laboratory currently uses Xilinx-Space-grade Virtex FPGA devices
+for image processing, pyrotechnic operation control and obstacle avoidance. We
+simulate and program our architecture on a Xilinx Virtex 7 FPGA. The
+architectural implementation for a single neuron Q-learning and a more complex
+Multilayer Perception (MLP) Q-learning accelerator has been demonstrated. The
+results show up to a 43-fold speed up by Virtex 7 FPGAs compared to a
+conventional Intel i5 2.3 GHz CPU. Finally, we simulate the proposed
+architecture using the Symphony simulator and compiler from Xilinx, and
+evaluate the performance and power consumption.
+",1,1,0,0,0,0
+8086,Boundary Layer Problems in the Viscosity-Diffusion Vanishing Limits for the Incompressible MHD Systems,"  In this paper, we we study boundary layer problems for the incompressible MHD
+systems in the presence of physical boundaries with the standard Dirichlet
+oundary conditions with small generic viscosity and diffusion coefficients. We
+identify a non-trivial class of initial data for which we can establish the
+uniform stability of the Prandtl's type boundary layers and prove rigorously
+that the solutions to the viscous and diffusive incompressible MHD systems
+converges strongly to the superposition of the solution to the ideal MHD
+systems with a Prandtl's type boundary layer corrector. One of the main
+difficulties is to deal with the effect of the difference between viscosity and
+diffusion coefficients and to control the singular boundary layers resulting
+from the Dirichlet boundary conditions for both the viscosity and the magnetic
+fields. One key derivation here is that for the class of initial data we
+identify here, there exist cancelations between the boundary layers of the
+velocity field and that of the magnetic fields so that one can use an elaborate
+energy method to take advantage this special structure. In addition, in the
+case of fixed positive viscosity, we also establish the stability of diffusive
+boundary layer for the magnetic field and convergence of solutions in the limit
+of zero magnetic diffusion for general initial data.
+",0,0,1,0,0,0
+8087,Why Adaptively Collected Data Have Negative Bias and How to Correct for It,"  From scientific experiments to online A/B testing, the previously observed
+data often affects how future experiments are performed, which in turn affects
+which data will be collected. Such adaptivity introduces complex correlations
+between the data and the collection procedure. In this paper, we prove that
+when the data collection procedure satisfies natural conditions, then sample
+means of the data have systematic \emph{negative} biases. As an example,
+consider an adaptive clinical trial where additional data points are more
+likely to be tested for treatments that show initial promise. Our surprising
+result implies that the average observed treatment effects would underestimate
+the true effects of each treatment. We quantitatively analyze the magnitude and
+behavior of this negative bias in a variety of settings. We also propose a
+novel debiasing algorithm based on selective inference techniques. In
+experiments, our method can effectively reduce bias and estimation error.
+",1,0,0,1,0,0
+8088,Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis,"  INTRODUCTION: Advanced machine learning methods might help to identify
+dementia risk from neuroimaging, but their accuracy to date is unclear.
+METHODS: We systematically reviewed the literature, 2006 to late 2016, for
+machine learning studies differentiating healthy ageing through to dementia of
+various types, assessing study quality, and comparing accuracy at different
+disease boundaries.
+RESULTS: Of 111 relevant studies, most assessed Alzheimer's disease (AD) vs
+healthy controls, used ADNI data, support vector machines and only T1-weighted
+sequences. Accuracy was highest for differentiating AD from healthy controls,
+and poor for differentiating healthy controls vs MCI vs AD, or MCI converters
+vs non-converters. Accuracy increased using combined data types, but not by
+data source, sample size or machine learning method.
+DISCUSSION: Machine learning does not differentiate clinically-relevant
+disease categories yet. More diverse datasets, combinations of different types
+of data, and close clinical integration of machine learning would help to
+advance the field.
+",0,0,0,0,1,0
+8089,Exact Diffusion for Distributed Optimization and Learning --- Part II: Convergence Analysis,"  Part I of this work [2] developed the exact diffusion algorithm to remove the
+bias that is characteristic of distributed solutions for deterministic
+optimization problems. The algorithm was shown to be applicable to a larger set
+of combination policies than earlier approaches in the literature. In
+particular, the combination matrices are not required to be doubly stochastic,
+which impose stringent conditions on the graph topology and communications
+protocol. In this Part II, we examine the convergence and stability properties
+of exact diffusion in some detail and establish its linear convergence rate. We
+also show that it has a wider stability range than the EXTRA consensus
+solution, meaning that it is stable for a wider range of step-sizes and can,
+therefore, attain faster convergence rates. Analytical examples and numerical
+simulations illustrate the theoretical findings.
+",0,0,1,0,0,0
+8090,Approximating meta-heuristics with homotopic recurrent neural networks,"  Much combinatorial optimisation problems constitute a non-polynomial (NP)
+hard optimisation problem, i.e., they can not be solved in polynomial time. One
+such problem is finding the shortest route between two nodes on a graph.
+Meta-heuristic algorithms such as $A^{*}$ along with mixed-integer programming
+(MIP) methods are often employed for these problems. Our work demonstrates that
+it is possible to approximate solutions generated by a meta-heuristic algorithm
+using a deep recurrent neural network. We compare different methodologies based
+on reinforcement learning (RL) and recurrent neural networks (RNN) to gauge
+their respective quality of approximation. We show the viability of recurrent
+neural network solutions on a graph that has over 300 nodes and argue that a
+sequence-to-sequence network rather than other recurrent networks has improved
+approximation quality. Additionally, we argue that homotopy continuation --
+that increases chances of hitting an extremum -- further improves the estimate
+generated by a vanilla RNN.
+",1,0,0,1,0,0
+8091,Real embedding and equivariant eta forms,"  In 1993, Bismut and Zhang establish a mod Z embedding formula of
+Atiyah-Patodi-Singer reduced eta invariants. In this paper, we explain the
+hidden mod Z term as a spectral flow and extend this embedding formula to the
+equivariant family case. In this case, the spectral flow is generalized to the
+equivariant chern character of some equivariant Dai-Zhang higher spectral flow.
+",0,0,1,0,0,0
+8092,Hierarchical Block Sparse Neural Networks,"  Sparse deep neural networks(DNNs) are efficient in both memory and compute
+when compared to dense DNNs. But due to irregularity in computation of sparse
+DNNs, their efficiencies are much lower than that of dense DNNs on regular
+parallel hardware such as TPU. This inefficiency leads to poor/no performance
+benefits for sparse DNNs. Performance issue for sparse DNNs can be alleviated
+by bringing structure to the sparsity and leveraging it for improving runtime
+efficiency. But such structural constraints often lead to suboptimal
+accuracies. In this work, we jointly address both accuracy and performance of
+sparse DNNs using our proposed class of sparse neural networks called HBsNN
+(Hierarchical Block sparse Neural Networks). For a given sparsity, HBsNN models
+achieve better runtime performance than unstructured sparse models and better
+accuracy than highly structured sparse models.
+",0,0,0,1,0,0
+8093,Are crossing dependencies really scarce?,"  The syntactic structure of a sentence can be modelled as a tree, where
+vertices correspond to words and edges indicate syntactic dependencies. It has
+been claimed recurrently that the number of edge crossings in real sentences is
+small. However, a baseline or null hypothesis has been lacking. Here we
+quantify the amount of crossings of real sentences and compare it to the
+predictions of a series of baselines. We conclude that crossings are really
+scarce in real sentences. Their scarcity is unexpected by the hubiness of the
+trees. Indeed, real sentences are close to linear trees, where the potential
+number of crossings is maximized.
+",1,1,0,0,0,0
+8094,Theory of Compact Hausdorff Shape,"  In this paper, we aim to establish a new shape theory, compact Hausdorff
+shape (CH-shape) for general Hausdorff spaces. We use the ""internal"" method and
+direct system approach on the homotopy category of compact Hausdorff spaces.
+Such a construction can preserve most good properties of H-shape given by Rubin
+and Sanders. Most importantly, we can moreover develop the entire homology
+theory for CH-shape, including the exactness, dual to the consequence of
+Mardešić and Segal.
+",0,0,1,0,0,0
+8095,A Proof of the Herschel-Maxwell Theorem Using the Strong Law of Large Numbers,"  In this article, we use the strong law of large numbers to give a proof of
+the Herschel-Maxwell theorem, which characterizes the normal distribution as
+the distribution of the components of a spherically symmetric random vector,
+provided they are independent. We present shorter proofs under additional
+moment assumptions, and include a remark, which leads to another strikingly
+short proof of Maxwell's characterization using the central limit theorem.
+",0,0,1,0,0,0
+8096,Heating and cooling of coronal loops with turbulent suppression of parallel heat conduction,"  Using the ""enthalpy-based thermal evolution of loops"" (EBTEL) model, we
+investigate the hydrodynamics of the plasma in a flaring coronal loop in which
+heat conduction is limited by turbulent scattering of the electrons that
+transport the thermal heat flux. The EBTEL equations are solved analytically in
+each of the two (conduction-dominated and radiation-dominated) cooling phases.
+Comparison of the results with typical observed cooling times in solar flares
+shows that the turbulent mean free-path $\lambda_T$ lies in a range
+corresponding to a regime in which classical (collision-dominated) conduction
+plays at most a limited role. We also consider the magnitude and duration of
+the heat input that is necessary to account for the enhanced values of
+temperature and density at the beginning of the cooling phase and for the
+observed cooling times. We find through numerical modeling that in order to
+produce a peak temperature $\simeq 1.5 \times 10^7$~K and a 200~s cooling time
+consistent with observations, the flare heating profile must extend over a
+significant period of time; in particular, its lingering role must be taken
+into consideration in any description of the cooling phase. Comparison with
+observationally-inferred values of post-flare loop temperatures, densities, and
+cooling times thus leads to useful constraints on both the magnitude and
+duration of the magnetic energy release in the loop, as well as on the value of
+the turbulent mean free-path $\lambda_T$.
+",0,1,0,0,0,0
+8097,How to avoid the curse of dimensionality: scalability of particle filters with and without importance weights,"  Particle filters are a popular and flexible class of numerical algorithms to
+solve a large class of nonlinear filtering problems. However, standard particle
+filters with importance weights have been shown to require a sample size that
+increases exponentially with the dimension D of the state space in order to
+achieve a certain performance, which precludes their use in very
+high-dimensional filtering problems. Here, we focus on the dynamic aspect of
+this curse of dimensionality (COD) in continuous time filtering, which is
+caused by the degeneracy of importance weights over time. We show that the
+degeneracy occurs on a time-scale that decreases with increasing D. In order to
+soften the effects of weight degeneracy, most particle filters use particle
+resampling and improved proposal functions for the particle motion. We explain
+why neither of the two can prevent the COD in general. In order to address this
+fundamental problem, we investigate an existing filtering algorithm based on
+optimal feedback control that sidesteps the use of importance weights. We use
+numerical experiments to show that this Feedback Particle Filter (FPF) by Yang
+et al. (2013) does not exhibit a COD.
+",0,0,1,1,0,0
+8098,Motion and Cooperative Transportation Planning for Multi-Agent Systems under Temporal Logic Formulas,"  This paper presents a hybrid control framework for the motion planning of a
+multi-agent system including N robotic agents and M objects, under high level
+goals expressed as Linear Temporal Logic (LTL) formulas. In particular, we
+design control protocols that allow the transition of the agents as well as the
+cooperative transportation of the objects by the agents, among predefined
+regions of interest in the workspace. This allows to abstract the coupled
+behavior of the agents and the objects as a finite transition system and to
+design a high-level multi-agent plan that satisfies the agents' and the
+objects' specifications, given as temporal logic formulas. Simulation results
+verify the proposed framework.
+",1,0,0,0,0,0
+8099,Realistic finite temperature simulations of magnetic systems using quantum statistics,"  We have performed realistic atomistic simulations at finite temperatures
+using Monte Carlo and atomistic spin dynamics simulations incorporating quantum
+(Bose-Einstein) statistics. The description is much improved at low
+temperatures compared to classical (Boltzmann) statistics normally used in
+these kind of simulations, while at higher temperatures the classical
+statistics are recovered. This corrected low-temperature description is
+reflected in both magnetization and the magnetic specific heat, the latter
+allowing for improved modeling of the magnetic contribution to free energies. A
+central property in the method is the magnon density of states at finite
+temperatures and we have compared several different implementations for
+obtaining it. The method has no restrictions regarding chemical and magnetic
+order of the considered materials. This is demonstrated by applying the method
+to elemental ferromagnetic systems, including Fe and Ni, as well as Fe-Co
+random alloys and the ferrimagnetic system GdFe$_3$ .
+",0,1,0,0,0,0
+8100,Effective Tensor Sketching via Sparsification,"  In this paper, we investigate effective sketching schemes via sparsification
+for high dimensional multilinear arrays or tensors. More specifically, we
+propose a novel tensor sparsification algorithm that retains a subset of the
+entries of a tensor in a judicious way, and prove that it can attain a given
+level of approximation accuracy in terms of tensor spectral norm with a much
+smaller sample complexity when compared with existing approaches. In
+particular, we show that for a $k$th order $d\times\cdots\times d$ cubic tensor
+of {\it stable rank} $r_s$, the sample size requirement for achieving a
+relative error $\varepsilon$ is, up to a logarithmic factor, of the order
+$r_s^{1/2} d^{k/2} /\varepsilon$ when $\varepsilon$ is relatively large, and
+$r_s d /\varepsilon^2$ and essentially optimal when $\varepsilon$ is
+sufficiently small. It is especially noteworthy that the sample size
+requirement for achieving a high accuracy is of an order independent of $k$. To
+further demonstrate the utility of our techniques, we also study how higher
+order singular value decomposition (HOSVD) of large tensors can be efficiently
+approximated via sparsification.
+",1,0,0,1,0,0
+8101,Recommendation with k-anonymized Ratings,"  Recommender systems are widely used to predict personalized preferences of
+goods or services using users' past activities, such as item ratings or
+purchase histories. If collections of such personal activities were made
+publicly available, they could be used to personalize a diverse range of
+services, including targeted advertisement or recommendations. However, there
+would be an accompanying risk of privacy violations. The pioneering work of
+Narayanan et al.\ demonstrated that even if the identifiers are eliminated, the
+public release of user ratings can allow for the identification of users by
+those who have only a small amount of data on the users' past ratings.
+In this paper, we assume the following setting. A collector collects user
+ratings, then anonymizes and distributes them. A recommender constructs a
+recommender system based on the anonymized ratings provided by the collector.
+Based on this setting, we exhaustively list the models of recommender systems
+that use anonymized ratings. For each model, we then present an item-based
+collaborative filtering algorithm for making recommendations based on
+anonymized ratings. Our experimental results show that an item-based
+collaborative filtering based on anonymized ratings can perform better than
+collaborative filterings based on 5--10 non-anonymized ratings. This surprising
+result indicates that, in some settings, privacy protection does not
+necessarily reduce the usefulness of recommendations. From the experimental
+analysis of this counterintuitive result, we observed that the sparsity of the
+ratings can be reduced by anonymization and the variance of the prediction can
+be reduced if $k$, the anonymization parameter, is appropriately tuned. In this
+way, the predictive performance of recommendations based on anonymized ratings
+can be improved in some settings.
+",1,0,0,1,0,0
+8102,Ro-vibrational states of H$_2^+$. Variational calculations,"  The nonrelativistic variational calculation of a complete set of
+ro-vibrational states in the H$_2^+$ molecular ion supported by the ground
+$1s\sigma$ adiabatic potential is presented. It includes both bound states and
+resonances located above the $n=1$ threshold. In the latter case we also
+evaluate a predissociation width of a state wherever it is significant.
+Relativistic and radiative corrections are discussed and effective adiabatic
+potentials of these corrections are included as supplementary files.
+",0,1,0,0,0,0
+8103,Control of automated guided vehicles without collision by quantum annealer and digital devices,"  We formulate an optimization problem to control a large number of automated
+guided vehicles in a plant without collision. The formulation consists of
+binary variables. A quadratic cost function over these variables enables us to
+utilize certain solvers on digital computers and recently developed
+purpose-specific hardware such as D-Wave 2000Q and the Fujitsu digital
+annealer. In the present study, we consider an actual plant in Japan, in which
+vehicles run, and assess efficiency of our formulation for optimizing the
+vehicles via several solvers. We confirm that our formulation can be a powerful
+approach for performing smooth control while avoiding collisions between
+vehicles, as compared to a conventional method. In addition, comparative
+experiments performed using several solvers reveal that D-Wave 2000Q can be
+useful as a rapid solver for generating a plan for controlling the vehicles in
+a short time although it deals only with a small number of vehicles, while a
+digital computer can rapidly solve the corresponding optimization problem even
+with a large number of binary variables.
+",1,0,0,0,0,0
+8104,Meta-learning: searching in the model space,"  There is no free lunch, no single learning algorithm that will outperform
+other algorithms on all data. In practice different approaches are tried and
+the best algorithm selected. An alternative solution is to build new algorithms
+on demand by creating a framework that accommodates many algorithms. The best
+combination of parameters and procedures is searched here in the space of all
+possible models belonging to the framework of Similarity-Based Methods (SBMs).
+Such meta-learning approach gives a chance to find the best method in all
+cases. Issues related to the meta-learning and first tests of this approach are
+presented.
+",0,0,0,1,0,0
+8105,GIFT: Guided and Interpretable Factorization for Tensors - An Application to Large-Scale Multi-platform Cancer Analysis,"  Given multi-platform genome data with prior knowledge of functional gene
+sets, how can we extract interpretable latent relationships between patients
+and genes? More specifically, how can we devise a tensor factorization method
+which produces an interpretable gene factor matrix based on gene set
+information while maintaining the decomposition quality and speed? We propose
+GIFT, a Guided and Interpretable Factorization for Tensors. GIFT provides
+interpretable factor matrices by encoding prior knowledge as a regularization
+term in its objective function. Experiment results demonstrate that GIFT
+produces interpretable factorizations with high scalability and accuracy, while
+other methods lack interpretability. We apply GIFT to the PanCan12 dataset, and
+GIFT reveals significant relations between cancers, gene sets, and genes, such
+as influential gene sets for specific cancer (e.g., interferon-gamma response
+gene set for ovarian cancer) or relations between cancers and genes (e.g., BRCA
+cancer - APOA1 gene and OV, UCEC cancers - BST2 gene).
+",1,0,0,0,1,0
+8106,Synchronization of spin torque oscillators through spin Hall magnetoresistance,"  Spin torque oscillators placed onto a nonmagnetic heavy metal show
+synchronized auto-oscillations due to the coupling originating from spin Hall
+magnetoresistance effect. Here, we study a system having two spin torque
+oscillators under the effect of the spin Hall torque, and show that switching
+the external current direction enables us to control the phase difference of
+the synchronization between in-phase and antiphase.
+",0,1,0,0,0,0
+8107,Long quasi-polycyclic $t-$CIS codes,"  We study complementary information set codes of length $tn$ and dimension $n$
+of order $t$ called ($t-$CIS code for short). Quasi-cyclic and quasi-twisted
+$t$-CIS codes are enumerated by using their concatenated structure. Asymptotic
+existence results are derived for one-generator and have co-index $n$ by
+Artin's conjecture for quasi cyclic and special case for quasi twisted. This
+shows that there are infinite families of long QC and QT $t$-CIS codes with
+relative distance satisfying a modified Varshamov-Gilbert bound for rate $1/t$
+codes.
+Similar results are defined for the new and more general class of
+quasi-polycyclic codes introduced recently by Berger and Amrani.
+",1,0,0,0,0,0
+8108,Fairness in Criminal Justice Risk Assessments: The State of the Art,"  Objectives: Discussions of fairness in criminal justice risk assessments
+typically lack conceptual precision. Rhetoric too often substitutes for careful
+analysis. In this paper, we seek to clarify the tradeoffs between different
+kinds of fairness and between fairness and accuracy.
+Methods: We draw on the existing literatures in criminology, computer science
+and statistics to provide an integrated examination of fairness and accuracy in
+criminal justice risk assessments. We also provide an empirical illustration
+using data from arraignments.
+Results: We show that there are at least six kinds of fairness, some of which
+are incompatible with one another and with accuracy.
+Conclusions: Except in trivial cases, it is impossible to maximize accuracy
+and fairness at the same time, and impossible simultaneously to satisfy all
+kinds of fairness. In practice, a major complication is different base rates
+across different legally protected groups. There is a need to consider
+challenging tradeoffs.
+",0,0,0,1,0,0
+8109,Remarks to the article: New Light on the Invention of the Achromatic Telescope Objective,"  The article analysis was carried out within the confines of the replication
+project of the telescope, which was used by Mikhail Lomonosov at observation
+the transit of Venus in 1761. At that time he discovered the Venusian
+atmosphere. It is known that Lomonosov used Dollond 4.5 feet long achromatic
+telescope. The investigation revealed significant faults in the description of
+the approximation method, which most likely was used by J. Dollond & Son during
+manufacturing of the early achromatic lenses.
+",0,1,0,0,0,0
+8110,The spin-Brauer diagram algebra,"  We investigate the spin-Brauer diagram algebra, denoted ${\bf SB}_n(\delta)$,
+that arises from studying an analogous form of Schur-Weyl duality for the
+action of the pin group on ${\bf V}^{\otimes n} \otimes \Delta$. Here ${\bf V}$
+is the standard $N$-dimensional complex representation of ${\bf Pin}(N)$ and
+$\Delta$ is the spin representation. When $\delta = N$ is a positive integer,
+we define a surjective map ${\bf SB}_n(N) \twoheadrightarrow {\rm End}_{{\bf
+Pin}(N)}({\bf V}^{\otimes n} \otimes \Delta)$ and show it is an isomorphism for
+$N \geq 2n$. We show ${\bf SB}_n(\delta)$ is a cellular algebra and use
+cellularity to characterize its irreducible representations.
+",0,0,1,0,0,0
+8111,Superheating in coated niobium,"  Using muon spin rotation it is shown that the field of first flux penetration
+H_entry in Nb is enhanced by about 30% if coated with an overlayer of Nb_3Sn or
+MgB_2. This is consistent with an increase from the lower critical magnetic
+field H_c1 up to the superheating field H_sh of the Nb substrate. In the
+experiments presented here coatings of Nb_3Sn and MgB_2 with a thickness
+between 50 and 2000nm have been tested. H_entry does not depend on material or
+thickness. This suggests that the energy barrier at the boundary between the
+two materials prevents flux entry up to H_sh of the substrate. A mechanism
+consistent with these findings is that the proximity effect recovers the
+stability of the energy barrier for flux penetration, which is suppressed by
+defects for uncoated samples. Additionally, a low temperature baked Nb sample
+has been tested. Here a 6% increase of H_entry was found, also pushing H_entry
+beyond H_c1.
+",0,1,0,0,0,0
+8112,Contraction Analysis of Nonlinear DAE Systems,"  This paper studies the contraction properties of nonlinear
+differential-algebraic equation (DAE) systems. Specifically we develop scalable
+techniques for constructing the attraction regions associated with a particular
+stable equilibrium, by establishing the relation between the contraction rates
+of the original systems and the corresponding virtual extended systems. We show
+that for a contracting DAE system, the reduced system always contracts faster
+than the extended ones; furthermore, there always exists an extension with
+contraction rate arbitrarily close to that of the original system. The proposed
+construction technique is illustrated with a power system example in the
+context of transient stability assessment.
+",0,0,1,0,0,0
+8113,New Braided $T$-Categories over Hopf (co)quasigroups,"  Let $H$ be a Hopf quasigroup with bijective antipode and let $Aut_{HQG}(H)$
+be the set of all Hopf quasigroup automorphisms of $H$. We introduce a category
+${_{H}\mathcal{YDQ}^{H}}(\alpha,\beta)$ with $\alpha,\beta\in Aut_{HQG}(H)$ and
+construct a braided $T$-category $\mathcal{YDQ}(H)$ having all the categories
+${_{H}\mathcal{YDQ}^{H}}(\alpha,\beta)$ as components.
+",0,0,1,0,0,0
+8114,On the Reconstruction Risk of Convolutional Sparse Dictionary Learning,"  Sparse dictionary learning (SDL) has become a popular method for adaptively
+identifying parsimonious representations of a dataset, a fundamental problem in
+machine learning and signal processing. While most work on SDL assumes a
+training dataset of independent and identically distributed samples, a variant
+known as convolutional sparse dictionary learning (CSDL) relaxes this
+assumption, allowing more general sequential data sources, such as time series
+or other dependent data. Although recent work has explored the statistical
+properties of classical SDL, the statistical properties of CSDL remain
+unstudied. This paper begins to study this by identifying the minimax
+convergence rate of CSDL in terms of reconstruction risk, by both upper
+bounding the risk of an established CSDL estimator and proving a matching
+information-theoretic lower bound. Our results indicate that consistency in
+reconstruction risk is possible precisely in the `ultra-sparse' setting, in
+which the sparsity (i.e., the number of feature occurrences) is in $o(N)$ in
+terms of the length N of the training sequence. Notably, our results make very
+weak assumptions, allowing arbitrary dictionaries and dependent measurement
+noise. Finally, we verify our theoretical results with numerical experiments on
+synthetic data.
+",1,0,1,1,0,0
+8115,Adaptive Questionnaires for Direct Identification of Optimal Product Design,"  We consider the problem of identifying the most profitable product design
+from a finite set of candidates under unknown consumer preference. A standard
+approach to this problem follows a two-step strategy: First, estimate the
+preference of the consumer population, represented as a point in part-worth
+space, using an adaptive discrete-choice questionnaire. Second, integrate the
+estimated part-worth vector with engineering feasibility and cost models to
+determine the optimal design. In this work, we (1) demonstrate that accurate
+preference estimation is neither necessary nor sufficient for identifying the
+optimal design, (2) introduce a novel adaptive questionnaire that leverages
+knowledge about engineering feasibility and manufacturing costs to directly
+determine the optimal design, and (3) interpret product design in terms of a
+nonlinear segmentation of part-worth space, and use this interpretation to
+illuminate the intrinsic difficulty of optimal design in the presence of noisy
+questionnaire responses. We establish the superiority of the proposed approach
+using a well-documented optimal product design task. This study demonstrates
+how the identification of optimal product design can be accelerated by
+integrating marketing and manufacturing knowledge into the adaptive
+questionnaire.
+",1,0,0,1,0,0
+8116,Identities involving Bernoulli and Euler polynomials,"  We present various identities involving the classical Bernoulli and Euler
+polynomials. Among others, we prove that $$ \sum_{k=0}^{[n/4]}(-1)^k {n\choose
+4k}\frac{B_{n-4k}(z) }{2^{6k}} =\frac{1}{2^{n+1}}\sum_{k=0}^{n} (-1)^k
+\frac{1+i^k}{(1+i)^k} {n\choose k}{B_{n-k}(2z)} $$ and $$ \sum_{k=1}^{n}
+2^{2k-1} {2n\choose 2k-1} B_{2k-1}(z) = \sum_{k=1}^n k \, 2^{2k} {2n\choose 2k}
+E_{2k-1}(z). $$ Applications of our results lead to formulas for Bernoulli and
+Euler numbers, like, for instance, $$ n E_{n-1} =\sum_{k=1}^{[n/2]}
+\frac{2^{2k}-1}{k} (2^{2k}-2^n){n\choose 2k-1} B_{2k}B_{n-2k}. $$
+",0,0,1,0,0,0
+8117,Gate Tunable Magneto-resistance of Ultra-Thin WTe2 Devices,"  In this work, the magneto-resistance (MR) of ultra-thin WTe2/BN
+heterostructures far away from electron-hole equilibrium is measured. The
+change of MR of such devices is found to be determined largely by a single
+tunable parameter, i.e. the amount of imbalance between electrons and holes. We
+also found that the magnetoresistive behavior of ultra-thin WTe2 devices is
+well-captured by a two-fluid model. According to the model, the change of MR
+could be as large as 400,000%, the largest potential change of MR among all
+materials known, if the ultra-thin samples are tuned to neutrality when
+preserving the mobility of 167,000 cm2V-1s-1 observed in bulk samples. Our
+findings show the prospects of ultra-thin WTe2 as a variable magnetoresistance
+material in future applications such as magnetic field sensors, information
+storage and extraction devices, and galvanic isolators. The results also
+provide important insight into the electronic structure and the origin of the
+large MR in ultra-thin WTe2 samples.
+",0,1,0,0,0,0
+8118,Morpheo: Traceable Machine Learning on Hidden data,"  Morpheo is a transparent and secure machine learning platform collecting and
+analysing large datasets. It aims at building state-of-the art prediction
+models in various fields where data are sensitive. Indeed, it offers strong
+privacy of data and algorithm, by preventing anyone to read the data, apart
+from the owner and the chosen algorithms. Computations in Morpheo are
+orchestrated by a blockchain infrastructure, thus offering total traceability
+of operations. Morpheo aims at building an attractive economic ecosystem around
+data prediction by channelling crypto-money from prediction requests to useful
+data and algorithms providers. Morpheo is designed to handle multiple data
+sources in a transfer learning approach in order to mutualize knowledge
+acquired from large datasets for applications with smaller but similar
+datasets.
+",1,0,0,1,0,0
+8119,Hölder regularity of the 2D dual semigeostrophic equations via analysis of linearized Monge-Ampère equations,"  We obtain the Hölder regularity of time derivative of solutions to the dual
+semigeostrophic equations in two dimensions when the initial potential density
+is bounded away from zero and infinity. Our main tool is an interior Hölder
+estimate in two dimensions for an inhomogeneous linearized Monge-Ampère
+equation with right hand side being the divergence of a bounded vector field.
+As a further application of our Hölder estimate, we prove the Hölder
+regularity of the polar factorization for time-dependent maps in two dimensions
+with densities bounded away from zero and infinity. Our applications improve
+previous work by G. Loeper who considered the cases of densities sufficiently
+close to a positive constant.
+",0,0,1,0,0,0
+8120,"New nanostructures of carbon: Quasifullerenes Cn-q (n=20,42,48,60)","  Based on the third allotropic form of carbon (Fullerenes) through theoretical
+study have been predicted structures described as non-classical fullerenes. We
+have studied novel allotropic carbon structures with a closed cage
+configuration that have been predicted for the first time, by using DFT at the
+B3LYP level. Such carbon Cn-q structures (where, n=20, 42, 48 and 60), combine
+states of hybridization sp1 and sp2, for the formation of bonds. A comparative
+analysis of quasi-fullerenes with respect to their isomers of greater stability
+was also performed. Chemical stability was evaluated with the criteria of
+aromaticity through the different rings that build the systems. The results
+show new isomerism of carbon nanostructures with interesting chemical
+properties such as hardness, chemical potential and HOMO-LUMO gaps. We also
+studied thermal stability with Lagrangian molecular dynamics method using Atom-
+Center Density propagation (ADMP) method.
+",0,1,0,0,0,0
+8121,The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study,"  Which topics of machine learning are most commonly addressed in research?
+This question was initially answered in 2007 by doing a qualitative survey
+among distinguished researchers. In our study, we revisit this question from a
+quantitative perspective. Concretely, we collect 54K abstracts of papers
+published between 2007 and 2016 in leading machine learning journals and
+conferences. We then use machine learning in order to determine the top 10
+topics in machine learning. We not only include models, but provide a holistic
+view across optimization, data, features, etc. This quantitative approach
+allows reducing the bias of surveys. It reveals new and up-to-date insights
+into what the 10 most prolific topics in machine learning research are. This
+allows researchers to identify popular topics as well as new and rising topics
+for their research.
+",1,0,0,0,0,0
+8122,Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions,"  This study proposes a mixed logit model with multivariate nonparametric
+finite mixture distributions. The support of the distribution is specified as a
+high-dimensional grid over the coefficient space, with equal or unequal
+intervals between successive points along the same dimension; the location of
+each point on the grid and the probability mass at that point are model
+parameters that need to be estimated. The framework does not require the
+analyst to specify the shape of the distribution prior to model estimation, but
+can approximate any multivariate probability distribution function to any
+arbitrary degree of accuracy. The grid with unequal intervals, in particular,
+offers greater flexibility than existing multivariate nonparametric
+specifications, while requiring the estimation of a small number of additional
+parameters. An expectation maximization algorithm is developed for the
+estimation of these models. Multiple synthetic datasets and a case study on
+travel mode choice behavior are used to demonstrate the value of the model
+framework and estimation algorithm. Compared to extant models that incorporate
+random taste heterogeneity through continuous mixture distributions, the
+proposed model provides better out-of-sample predictive ability. Findings
+reveal significant differences in willingness to pay measures between the
+proposed model and extant specifications. The case study further demonstrates
+the ability of the proposed model to endogenously recover patterns of attribute
+non-attendance and choice set formation.
+",0,0,0,1,0,0
+8123,Microscopic mechanism of tunable band gap in potassium doped few-layer black phosphorus,"  Tuning band gaps in two-dimensional (2D) materials is of great interest in
+the fundamental and practical aspects of contemporary material sciences.
+Recently, black phosphorus (BP) consisting of stacked layers of phosphorene was
+experimentally observed to show a widely tunable band gap by means of the
+deposition of potassium (K) atoms on the surface, thereby allowing great
+flexibility in design and optimization of electronic and optoelectronic
+devices. Here, based on the density-functional theory calculations, we
+demonstrates that the donated electrons from K dopants are mostly localized at
+the topmost BP layer and such a surface charging efficiently screens the K ion
+potential. It is found that, as the K doping increases, the extreme surface
+charging and its screening of K atoms shift the conduction bands down in
+energy, i.e., towards higher binding energy, because they have more charge near
+the surface, while it has little influence on the valence bands having more
+charge in the deeper layers. This result provides a different explanation for
+the observed tunable band gap compared to the previously proposed giant Stark
+effect where a vertical electric field from the positively ionized K overlayer
+to the negatively charged BP layers shifts the conduction band minimum
+${\Gamma}_{\rm 1c}$ (valence band minimum ${\Gamma}_{\rm 8v}$) downwards
+(upwards). The present prediction of ${\Gamma}_{\rm 1c}$ and ${\Gamma}_{\rm
+8v}$ as a function of the K doping reproduces well the widely tunable band gap,
+anisotropic Dirac semimetal state, and band-inverted semimetal state, as
+observed by angle-resolved photoemission spectroscopy experiment. Our findings
+shed new light on a route for tunable band gap engineering of 2D materials
+through the surface doping of alkali metals.
+",0,1,0,0,0,0
+8124,Quasi-steady state reduction for the Michaelis-Menten reaction-diffusion system,"  The Michaelis-Menten mechanism is probably the best known model for an
+enzyme-catalyzed reaction. For spatially homogeneous concentrations, QSS
+reductions are well known, but this is not the case when chemical species are
+allowed to diffuse. We will discuss QSS reductions for both the irreversible
+and reversible Michaelis-Menten reaction in the latter case, given small
+initial enzyme concentration and slow diffusion. Our work is based on a
+heuristic method to obtain an ordinary differential equation which admits
+reduction by Tikhonov-Fenichel theory. We will not give convergence proofs but
+we provide numerical results that support the accuracy of the reductions.
+",0,0,1,0,0,0
+8125,All the people around me: face discovery in egocentric photo-streams,"  Given an unconstrained stream of images captured by a wearable photo-camera
+(2fpm), we propose an unsupervised bottom-up approach for automatic clustering
+appearing faces into the individual identities present in these data. The
+problem is challenging since images are acquired under real world conditions;
+hence the visible appearance of the people in the images undergoes intensive
+variations. Our proposed pipeline consists of first arranging the photo-stream
+into events, later, localizing the appearance of multiple people in them, and
+finally, grouping various appearances of the same person across different
+events. Experimental results performed on a dataset acquired by wearing a
+photo-camera during one month, demonstrate the effectiveness of the proposed
+approach for the considered purpose.
+",1,0,0,0,0,0
+8126,"Graphene oxide nanosheets disrupt lipid composition, Ca2+ homeostasis and synaptic transmission in primary cortical neurons","  Graphene has the potential to make a very significant impact on society, with
+important applications in the biomedical field. The possibility to engineer
+graphene-based medical devices at the neuronal interface is of particular
+interest, making it imperative to determine the biocompatibility of graphene
+materials with neuronal cells. Here we conducted a comprehensive analysis of
+the effects of chronic and acute exposure of rat primary cortical neurons to
+few-layers pristine graphene (GR) and monolayer graphene oxide (GO) flakes. By
+combining a range of cell biology, microscopy, electrophysiology and omics
+approaches we characterized the graphene neuron interaction from the first
+steps of membrane contact and internalization to the long-term effects on cell
+viability, synaptic transmission and cell metabolism. GR/GO flakes are found in
+contact with the neuronal membrane, free in the cytoplasm and internalized
+through the endolysosomal pathway, with no significant impact on neuron
+viability. However, GO exposure selectively caused the inhibition of excitatory
+transmission, paralleled by a reduction in the number of excitatory synaptic
+contacts, and a concomitant enhancement of the inhibitory activity. This was
+accompanied by induction of autophagy, altered Ca2+ dynamics and by a
+downregulation of some of the main players in the regulation of Ca2+
+homeostasis in both excitatory and inhibitory neurons. Our results show that,
+although graphene exposure does not impact on neuron viability, it does
+nevertheless have important effects on neuronal transmission and network
+functionality, thus warranting caution when planning to employ this material
+for neuro-biological applications.
+",0,0,0,0,1,0
+8127,Physical insight into the thermodynamic uncertainty relation using Brownian motion in tilted periodic potentials,"  Using Brownian motion in periodic potentials $V(x)$ tilted by a force $f$, we
+provide physical insight into the thermodynamic uncertainty relation, a
+recently conjectured principle for statistical errors and irreversible heat
+dissipation in nonequilibrium steady states. According to the relation,
+nonequilibrium output generated from dissipative processes necessarily incurs
+an energetic cost or heat dissipation $q$, and in order to limit the output
+fluctuation within a relative uncertainty $\epsilon$, at least
+$2k_BT/\epsilon^2$ of heat must be dissipated. Our model shows that this bound
+is attained not only at near-equilibrium ($f\ll V'(x)$) but also at
+far-from-equilibrium $(f\gg V'(x))$, more generally when the dissipated heat is
+normally distributed. Furthermore, the energetic cost is maximized near the
+critical force when the barrier separating the potential wells is about to
+vanish and the fluctuation of Brownian particle is maximized. These findings
+indicate that the deviation of heat distribution from Gaussianity gives rise to
+the inequality of the uncertainty relation, further clarifying the meaning of
+the uncertainty relation. Our derivation of the uncertainty relation also
+recognizes a new bound of nonequilibrium fluctuations that the variance of
+dissipated heat ($\sigma_q^2$) increases with its mean ($\mu_q$) and cannot be
+smaller than $2k_BT\mu_q$.
+",0,1,0,0,0,0
+8128,Observation of a Modulational Instability in Bose-Einstein condensates,"  We observe the breakup dynamics of an elongated cloud of condensed $^{85}$Rb
+atoms placed in an optical waveguide. The number of localized spatial
+components observed in the breakup is compared with the number of solitons
+predicted by a plane-wave stability analysis of the nonpolynomial nonlinear
+Schrödinger equation, an effective one-dimensional approximation of the
+Gross-Pitaevskii equation for cigar-shaped condensates. It is shown that the
+numbers predicted from the fastest growing sidebands are consistent with the
+experimental data, suggesting that modulational instability is the key
+underlying physical mechanism driving the breakup.
+",0,1,0,0,0,0
+8129,Dynamics of the scenery flow and conical density theorems,"  Conical density theorems are used in the geometric measure theory to derive
+geometric information from given metric information. The idea is to examine how
+a measure is distributed in small balls. Finding conditions that guarantee the
+measure to be effectively spread out in different directions is a classical
+question going back to Besicovitch (1938) and Marstrand (1954). Classically,
+conical density theorems deal with the distribution of the Hausdorff measure.
+The process of taking blow-ups of a measure around a point induces a natural
+dynamical system called the scenery flow. Relying on this dynamics makes it
+possible to apply ergodic-theoretical methods to understand the statistical
+behavior of tangent measures. This approach was initiated by Furstenberg (1970,
+2008) and greatly developed by Hochman (2010). The scenery flow is a
+well-suited tool to address problems concerning conical densities.
+In this survey, we demonstrate how to develop the ergodic-theoretical
+machinery around the scenery flow and use it to study conical density theorems.
+",0,0,1,0,0,0
+8130,Model-independent analyses of non-Gaussianity in Planck CMB maps using Minkowski Functionals,"  Despite the wealth of $Planck$ results, there are difficulties in
+disentangling the primordial non-Gaussianity of the Cosmic Microwave Background
+(CMB) from the secondary and the foreground non-Gaussianity (NG). For each of
+these forms of NG the lack of complete data introduces model-dependencies.
+Aiming at detecting the NGs of the CMB temperature anisotropy $\delta T$, while
+paying particular attention to a model-independent quantification of NGs, our
+analysis is based upon statistical and morphological univariate descriptors,
+respectively: the probability density function $P(\delta T)$, related to
+${\mathrm v}_{0}$, the first Minkowski Functional (MF), and the two other MFs,
+${\mathrm v}_{1}$ and ${\mathrm v}_{2}$. From their analytical Gaussian
+predictions we build the discrepancy functions $\Delta_{k}$ ($k=P,0,1,2$) which
+are applied to an ensemble of $10^{5}$ CMB realization maps of the $\Lambda$CDM
+model and to the $Planck$ CMB maps. In our analysis we use general Hermite
+expansions of the $\Delta_{k}$ up to the $12^{th}$ order, where the
+coefficients are explicitly given in terms of cumulants. Assuming hierarchical
+ordering of the cumulants, we obtain the perturbative expansions generalizing
+the $2^{nd}$ order expansions of Matsubara to arbitrary order in the standard
+deviation $\sigma_0$ for $P(\delta T)$ and ${\mathrm v}_0$, where the
+perturbative expansion coefficients are explicitly given in terms of complete
+Bell polynomials. The comparison of the Hermite expansions and the perturbative
+expansions is performed for the $\Lambda$CDM map sample and the $Planck$ data.
+We confirm the weak level of non-Gaussianity ($1$-$2$)$\sigma$ of the
+foreground corrected masked $Planck$ $2015$ maps.
+",0,1,0,0,0,0
+8131,Enhanced mixing in giant impact simulations with a new Lagrangian method,"  Giant impacts (GIs) are common in the late stage of planet formation. The
+Smoothed Particle Hydrodynamics (SPH) method is widely used for simulating the
+outcome of such violent collisions, one prominent example being the formation
+of the Moon. However, a decade of numerical studies in various areas of
+computational astrophysics has shown that the standard formulation of SPH
+suffers from several shortcomings such as artificial surface tension and its
+tendency to promptly damp turbulent motions on scales much larger than the
+physical dissipation scale, both resulting in the suppression of mixing. In
+order to quantify how severe these limitations are when modeling GIs we carried
+out a comparison of simulations with identical initial conditions performed
+with the standard SPH as well as with the novel Lagrangian Meshless Finite Mass
+(MFM) method in the GIZMO code. We confirm the lack of mixing between the
+impactor and target when SPH is employed, while MFM is capable of driving
+vigorous sub-sonic turbulence and leads to significant mixing between the two
+bodies. Modern SPH variants with artificial conductivity, a different
+formulation of the hydro force or reduced artificial viscosity, do not improve
+mixing as significantly. Angular momentum is conserved similarly well in both
+methods, but MFM does not suffer from spurious transport induced by artificial
+viscosity, resulting in a slightly higher angular momentum of the proto-lunar
+disk. Furthermore, SPH initial conditions exhibit an unphysical density
+discontinuity at the core-mantle boundary which is easily removed in MFM.
+",0,1,0,0,0,0
+8132,"New Reinforcement Learning Using a Chaotic Neural Network for Emergence of ""Thinking"" - ""Exploration"" Grows into ""Thinking"" through Learning -","  Expectation for the emergence of higher functions is getting larger in the
+framework of end-to-end reinforcement learning using a recurrent neural
+network. However, the emergence of ""thinking"" that is a typical higher function
+is difficult to realize because ""thinking"" needs non fixed-point, flow-type
+attractors with both convergence and transition dynamics. Furthermore, in order
+to introduce ""inspiration"" or ""discovery"" in ""thinking"", not completely random
+but unexpected transition should be also required.
+By analogy to ""chaotic itinerancy"", we have hypothesized that ""exploration""
+grows into ""thinking"" through learning by forming flow-type attractors on
+chaotic random-like dynamics. It is expected that if rational dynamics are
+learned in a chaotic neural network (ChNN), coexistence of rational state
+transition, inspiration-like state transition and also random-like exploration
+for unknown situation can be realized.
+Based on the above idea, we have proposed new reinforcement learning using a
+ChNN as an actor. The positioning of exploration is completely different from
+the conventional one. The chaotic dynamics inside the ChNN produces exploration
+factors by itself. Since external random numbers for stochastic action
+selection are not used, exploration factors cannot be isolated from the output.
+Therefore, the learning method is also completely different from the
+conventional one.
+At each non-feedback connection, one variable named causality trace takes in
+and maintains the input through the connection according to the change in its
+output. Using the trace and TD error, the weight is updated.
+In this paper, as the result of a recent simple task to see whether the new
+learning works or not, it is shown that a robot with two wheels and two visual
+sensors reaches a target while avoiding an obstacle after learning though there
+are still many rooms for improvement.
+",1,0,0,0,0,0
+8133,Tensorizing Generative Adversarial Nets,"  Generative Adversarial Network (GAN) and its variants exhibit
+state-of-the-art performance in the class of generative models. To capture
+higher-dimensional distributions, the common learning procedure requires high
+computational complexity and a large number of parameters. The problem of
+employing such massive framework arises when deploying it on a platform with
+limited computational power such as mobile phones. In this paper, we present a
+new generative adversarial framework by representing each layer as a tensor
+structure connected by multilinear operations, aiming to reduce the number of
+model parameters by a large factor while preserving the generative performance
+and sample quality. To learn the model, we employ an efficient algorithm which
+alternatively optimizes both discriminator and generator. Experimental outcomes
+demonstrate that our model can achieve high compression rate for model
+parameters up to $35$ times when compared to the original GAN for MNIST
+dataset.
+",1,0,0,1,0,0
+8134,Pretending Fair Decisions via Stealthily Biased Sampling,"  Fairness by decision-makers is believed to be auditable by third parties. In
+this study, we show that this is not always true.
+We consider the following scenario. Imagine a decision-maker who discloses a
+subset of his dataset with decisions to make his decisions auditable. If he is
+corrupt, and he deliberately selects a subset that looks fair even though the
+overall decision is unfair, can we identify this decision-maker's fraud?
+We answer this question negatively. We first propose a sampling method that
+produces a subset whose distribution is biased from the original (to pretend to
+be fair); however, its differentiation from uniform sampling is difficult. We
+call such a sampling method as stealthily biased sampling, which is formulated
+as a Wasserstein distance minimization problem, and is solved through a
+minimum-cost flow computation. We proved that the stealthily biased sampling
+minimizes an upper-bound of the indistinguishability. We conducted experiments
+to see that the stealthily biased sampling is, in fact, difficult to detect.
+",1,0,0,1,0,0
+8135,Design of a Time Delay Reservoir Using Stochastic Logic: A Feasibility Study,"  This paper presents a stochastic logic time delay reservoir design. The
+reservoir is analyzed using a number of metrics, such as kernel quality,
+generalization rank, performance on simple benchmarks, and is also compared to
+a deterministic design. A novel re-seeding method is introduced to reduce the
+adverse effects of stochastic noise, which may also be implemented in other
+stochastic logic reservoir computing designs, such as echo state networks.
+Benchmark results indicate that the proposed design performs well on
+noise-tolerant classification problems, but more work needs to be done to
+improve the stochastic logic time delay reservoir's robustness for regression
+problems.
+",1,0,0,1,0,0
+8136,Polaritons in Living Systems: Modifying Energy Landscapes in Photosynthetic Organisms Using a Photonic Structure,"  Photosynthetic organisms rely on a series of self-assembled nanostructures
+with tuned electronic energy levels in order to transport energy from where it
+is collected by photon absorption, to reaction centers where the energy is used
+to drive chemical reactions. In the photosynthetic bacteria Chlorobaculum
+tepidum (Cba. tepidum), a member of the green sulphur bacteria (GSB) family,
+light is absorbed by large antenna complexes called chlorosomes. The exciton
+generated is transferred to a protein baseplate attached to the chlorosome,
+before traveling through the Fenna-Matthews-Olson (FMO) complex to the reaction
+center. The energy levels of these systems are generally defined by their
+chemical structure. Here we show that by placing bacteria within a photonic
+microcavity, we can access the strong exciton-photon coupling regime between a
+confined cavity mode and exciton states of the chlorosome, whereby a coherent
+exchange of energy between the bacteria and cavity mode results in the
+formation of polariton states. The polaritons have an energy distinct from that
+of the exciton and photon, and can be tuned in situ via the microcavity length.
+This results in real-time, non-invasive control over the relative energy levels
+within the bacteria. This demonstrates the ability to strongly influence living
+biological systems with photonic structures such as microcavities. We believe
+that by creating polariton states, that are in this case a superposition of a
+photon and excitons within a living bacteria, we can modify energy transfer
+pathways and therefore study the importance of energy level alignment on the
+efficiency of photosynthetic systems.
+",0,1,0,0,0,0
+8137,A Matrix Expander Chernoff Bound,"  We prove a Chernoff-type bound for sums of matrix-valued random variables
+sampled via a random walk on an expander, confirming a conjecture due to
+Wigderson and Xiao. Our proof is based on a new multi-matrix extension of the
+Golden-Thompson inequality which improves in some ways the inequality of
+Sutter, Berta, and Tomamichel, and may be of independent interest, as well as
+an adaptation of an argument for the scalar case due to Healy. Secondarily, we
+also provide a generic reduction showing that any concentration inequality for
+vector-valued martingales implies a concentration inequality for the
+corresponding expander walk, with a weakening of parameters proportional to the
+squared mixing time.
+",1,0,0,0,0,0
+8138,Learning Neural Representations of Human Cognition across Many fMRI Studies,"  Cognitive neuroscience is enjoying rapid increase in extensive public
+brain-imaging datasets. It opens the door to large-scale statistical models.
+Finding a unified perspective for all available data calls for scalable and
+automated solutions to an old challenge: how to aggregate heterogeneous
+information on brain function into a universal cognitive system that relates
+mental operations/cognitive processes/psychological tasks to brain networks? We
+cast this challenge in a machine-learning approach to predict conditions from
+statistical brain maps across different studies. For this, we leverage
+multi-task learning and multi-scale dimension reduction to learn
+low-dimensional representations of brain images that carry cognitive
+information and can be robustly associated with psychological stimuli. Our
+multi-dataset classification model achieves the best prediction performance on
+several large reference datasets, compared to models without cognitive-aware
+low-dimension representations, it brings a substantial performance boost to the
+analysis of small datasets, and can be introspected to identify universal
+template cognitive concepts.
+",1,0,0,1,0,0
+8139,Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks,"  Automatic body part recognition for CT slices can benefit various medical
+image applications. Recent deep learning methods demonstrate promising
+performance, with the requirement of large amounts of labeled images for
+training. The intrinsic structural or superior-inferior slice ordering
+information in CT volumes is not fully exploited. In this paper, we propose a
+convolutional neural network (CNN) based Unsupervised Body part Regression
+(UBR) algorithm to address this problem. A novel unsupervised learning method
+and two inter-sample CNN loss functions are presented. Distinct from previous
+work, UBR builds a coordinate system for the human body and outputs a
+continuous score for each axial slice, representing the normalized position of
+the body part in the slice. The training process of UBR resembles a
+self-organization process: slice scores are learned from inter-slice
+relationships. The training samples are unlabeled CT volumes that are abundant,
+thus no extra annotation effort is needed. UBR is simple, fast, and accurate.
+Quantitative and qualitative experiments validate its effectiveness. In
+addition, we show two applications of UBR in network initialization and anomaly
+detection.
+",1,0,0,0,0,0
+8140,Investigating the Application of Common-Sense Knowledge-Base for Identifying Term Obfuscation in Adversarial Communication,"  Word obfuscation or substitution means replacing one word with another word
+in a sentence to conceal the textual content or communication. Word obfuscation
+is used in adversarial communication by terrorist or criminals for conveying
+their messages without getting red-flagged by security and intelligence
+agencies intercepting or scanning messages (such as emails and telephone
+conversations). ConceptNet is a freely available semantic network represented
+as a directed graph consisting of nodes as concepts and edges as assertions of
+common sense about these concepts. We present a solution approach exploiting
+vast amount of semantic knowledge in ConceptNet for addressing the technically
+challenging problem of word substitution in adversarial communication. We frame
+the given problem as a textual reasoning and context inference task and utilize
+ConceptNet's natural-language-processing tool-kit for determining word
+substitution. We use ConceptNet to compute the conceptual similarity between
+any two given terms and define a Mean Average Conceptual Similarity (MACS)
+metric to identify out-of-context terms. The test-bed to evaluate our proposed
+approach consists of Enron email dataset (having over 600000 emails generated
+by 158 employees of Enron Corporation) and Brown corpus (totaling about a
+million words drawn from a wide variety of sources). We implement word
+substitution techniques used by previous researches to generate a test dataset.
+We conduct a series of experiments consisting of word substitution methods used
+in the past to evaluate our approach. Experimental results reveal that the
+proposed approach is effective.
+",1,0,0,0,0,0
+8141,Titanium dioxide hole-blocking layer in ultra-thin-film crystalline silicon solar cells,"  One of the remaining obstacles to approaching the theoretical efficiency
+limit of crystalline silicon (c-Si) solar cells is the exceedingly high
+interface recombination loss for minority carriers at the Ohmic contacts. In
+ultra-thin-film c-Si solar cells, this contact recombination loss is far more
+severe than for traditional thick cells due to the smaller volume and higher
+minority carrier concentration of the former. This paper presents a novel
+design of an electron passing (Ohmic) contact to n-type Si that is
+hole-blocking with significantly reduced hole recombination. This contact is
+formed by depositing a thin titanium dioxide (TiO2) layer to form a silicon
+metal-insulator-semiconductor (MIS) contact. A 2 {\mu}m thick Si cell with this
+TiO2 MIS contact achieved an open circuit voltage (Voc) of 645 mV, which is 10
+mV higher than that of an ultra-thin cell with a metal contact. This MIS
+contact demonstrates a new path for ultra-thin-film c-Si solar cells to achieve
+high efficiencies as high as traditional thick cells, and enables the
+fabrication of high-efficiency c-Si solar cells at a lower cost.
+",0,1,0,0,0,0
+8142,Phase Space Sketching for Crystal Image Analysis based on Synchrosqueezed Transforms,"  Recent developments of imaging techniques enable researchers to visualize
+materials at the atomic resolution to better understand the microscopic
+structures of materials. This paper aims at automatic and quantitative
+characterization of potentially complicated microscopic crystal images,
+providing feedback to tweak theories and improve synthesis in materials
+science. As such, an efficient phase-space sketching method is proposed to
+encode microscopic crystal images in a translation, rotation, illumination, and
+scale invariant representation, which is also stable with respect to small
+deformations. Based on the phase-space sketching, we generalize our previous
+analysis framework for crystal images with simple structures to those with
+complicated geometry.
+",0,1,0,0,0,0
+8143,Specification tests in semiparametric transformation models - a multiplier bootstrap approach,"  We consider semiparametric transformation models, where after pre-estimation
+of a parametric transformation of the response the data are modeled by means of
+nonparametric regression. We suggest subsequent procedures for testing
+lack-of-fit of the regression function and for significance of covariables,
+which - in contrast to procedures from the literature - are asymptotically not
+influenced by the pre-estimation of the transformation. The test statistics are
+asymptotically pivotal and have the same asymptotic distribution as in
+regression models without transformation. We show validity of a multiplier
+bootstrap procedure which is easier to implement and much less computationally
+demanding than bootstrap procedures based on the transformation model. In a
+simulation study we demonstrate the superior performance of the procedure in
+comparison with the competitors from the literature.
+",0,0,1,1,0,0
+8144,Collusions in Teichmüller expansions,"  If $\mathfrak{p} \subseteq \mathbb{Z}[\zeta]$ is a prime ideal over $p$ in
+the $(p^d - 1)$th cyclotomic extension of $\mathbb{Z}$, then every element
+$\alpha$ of the completion $\mathbb{Z}[\zeta]_\mathfrak{p}$ has a unique
+expansion as a power series in $p$ with coefficients in $\mu_{p^d -1} \cup
+\{0\}$ called the Teichmüller expansion of $\alpha$ at $\mathfrak{p}$. We
+observe three peculiar and seemingly unrelated patterns that frequently appear
+in the computation of Teichmüller expansions, then develop a unifying theory
+to explain these patterns in terms of the dynamics of an affine group action on
+$\mathbb{Z}[\zeta]$.
+",0,0,1,0,0,0
+8145,Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR),"  One big challenge that hinders the transition of brain-computer interfaces
+(BCIs) from laboratory settings to real-life applications is the availability
+of high-performance and robust learning algorithms that can effectively handle
+individual differences, i.e., algorithms that can be applied to a new subject
+with zero or very little subject-specific calibration data. Transfer learning
+and domain adaptation have been extensively used for this purpose. However,
+most previous works focused on classification problems. This paper considers an
+important regression problem in BCI, namely, online driver drowsiness
+estimation from EEG signals. By integrating fuzzy sets with domain adaptation,
+we propose a novel online weighted adaptation regularization for regression
+(OwARR) algorithm to reduce the amount of subject-specific calibration data,
+and also a source domain selection (SDS) approach to save about half of the
+computational cost of OwARR. Using a simulated driving dataset with 15
+subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller
+estimation errors than several other approaches. We also provide comprehensive
+analyses on the robustness of OwARR and OwARR-SDS.
+",1,0,0,0,0,0
+8146,The complex social network of surnames: A comparison between Brazil and Portugal,"  We present a study of social networks based on the analysis of Brazilian and
+Portuguese family names (surnames). We construct networks whose nodes are names
+of families and whose edges represent parental relations between two families.
+From these networks we extract the connectivity distribution, clustering
+coefficient, shortest path and centrality. We find that the connectivity
+distribution follows an approximate power law. We associate the number of hubs,
+centrality and entropy to the degree of miscegenation in the societies in both
+countries. Our results show that Portuguese society has a higher miscegenation
+degree than Brazilian society. All networks analyzed lead to approximate
+inverse square power laws in the degree distribution. We conclude that the
+thermodynamic limit is reached for small networks (3 or 4 thousand nodes). The
+assortative mixing of all networks is negative, showing that the more connected
+vertices are connected to vertices with lower connectivity. Finally, the
+network of surnames presents some small world characteristics.
+",1,1,0,0,0,0
+8147,Electrical characterization of structured platinum diselenide devices,"  Platinum diselenide (PtSe2) is an exciting new member of the two-dimensional
+(2D) transition metal dichalcogenide (TMD) family. it has a semimetal to
+semiconductor transition when approaching monolayer thickness and has already
+shown significant potential for use in device applications. Notably, PtSe2 can
+be grown at low temperature making it potentially suitable for industrial
+usage. Here, we address thickness dependent transport properties and
+investigate electrical contacts to PtSe2, a crucial and universal element of
+TMD-based electronic devices. PtSe2 films have been synthesized at various
+thicknesses and structured to allow contact engineering and the accurate
+extraction of electrical properties. Contact resistivity and sheet resistance
+extracted from transmission line method (TLM) measurements are compared for
+different contact metals and different PtSe2 film thicknesses. Furthermore, the
+transition from semimetal to semiconductor in PtSe2 has been indirectly
+verified by electrical characterization of field-effect devices. Finally, the
+influence of edge contacts at the metal - PtSe2 interface has been studied by
+nanostructuring the contact area using electron beam lithography. By increasing
+the edge contact length, the contact resistivity was improved by up to 70%
+compared to devices with conventional top contacts. The results presented here
+represent crucial steps towards realizing high-performance nanoelectronic
+devices based on group-10 TMDs.
+",0,1,0,0,0,0
+8148,Topological $\mathbb{Z}_2$ Resonating-Valence-Bond Spin Liquid on the Square Lattice,"  A one-parameter family of long-range resonating valence bond (RVB) state on
+the square lattice was previously proposed to describe a critical spin liquid
+(SL) phase of the spin-$1/2$ frustrated Heisenberg model. We provide evidence
+that this RVB state in fact also realises a topological (long-range entangled)
+$\mathbb{Z}_2$ SL, limited by two transitions to critical SL phases. The
+topological phase is naturally connected to the $\mathbb{Z}_2$ gauge symmetry
+of the local tensor. This work shows that, on one hand, spin-$1/2$ topological
+SL with $C_{4v}$ point group symmetry and $SU(2)$ spin rotation symmetry exists
+on the square lattice and, on the other hand, criticality and nonbipartiteness
+are compatible. We also point out that, strong similarities between our phase
+diagram and the ones of classical interacting dimer models suggest both can be
+described by similar Kosterlitz-Thouless transitions. This scenario is further
+supported by the analysis of the one-dimensional boundary state.
+",0,1,0,0,0,0
+8149,Scalable Inference for Space-Time Gaussian Cox Processes,"  The log-Gaussian Cox process is a flexible and popular class of point pattern
+models for capturing spatial and space-time dependence for point patterns.
+Model fitting requires approximation of stochastic integrals which is
+implemented through discretization over the domain of interest. With fine scale
+discretization, inference based on Markov chain Monte Carlo is computationally
+burdensome because of the cost of matrix decompositions and storage, such as
+the Cholesky, for high dimensional covariance matrices associated with latent
+Gaussian variables. This article addresses these computational bottlenecks by
+combining two recent developments: (i) a data augmentation strategy that has
+been proposed for space-time Gaussian Cox processes that is based on exact
+Bayesian inference and does not require fine grid approximations for infinite
+dimensional integrals, and (ii) a recently developed family of
+sparsity-inducing Gaussian processes, called nearest-neighbor Gaussian
+processes, to avoid expensive matrix computations. Our inference is delivered
+within the fully model-based Bayesian paradigm and does not sacrifice the
+richness of traditional log-Gaussian Cox processes. We apply our method to
+crime event data in San Francisco and investigate the recovery of the intensity
+surface.
+",0,0,0,1,0,0
+8150,The MISRA C Coding Standard and its Role in the Development and Analysis of Safety- and Security-Critical Embedded Software,"  The MISRA project started in 1990 with the mission of providing world-leading
+best practice guidelines for the safe and secure application of both embedded
+control systems and standalone software. MISRA C is a coding standard defining
+a subset of the C language, initially targeted at the automotive sector, but
+now adopted across all industry sectors that develop C software in safety-
+and/or security-critical contexts. In this paper, we introduce MISRA C, its
+role in the development of critical software, especially in embedded systems,
+its relevance to industry safety standards, as well as the challenges of
+working with a general-purpose programming language standard that is written in
+natural language with a slow evolution over the last 40+ years. We also outline
+the role of static analysis in the automatic checking of compliance with
+respect to MISRA C, and the role of the MISRA C language subset in enabling a
+wider application of formal methods to industrial software written in C.
+",1,0,0,0,0,0
+8151,Finger Grip Force Estimation from Video using Two Stream Approach,"  Estimation of a hand grip force is essential for the understanding of force
+pattern during the execution of assembly or disassembly operations. Human
+demonstration of a correct way of doing an operation is a powerful source of
+information which can be used for guided robot teaching. Typically to assess
+this problem instrumented approach is used, which requires hand or object
+mounted devices and poses an inconvenience for an operator or limits the scope
+of addressable objects. The work demonstrates that contact force may be
+estimated using a noninvasive contactless method with the help of vision system
+alone. We propose a two-stream approach for video processing, which utilizes
+both spatial information of each frame and dynamic information of frame change.
+In this work, image processing and machine learning techniques are used along
+with dense optical flow for frame change tracking and Kalman filter is used for
+stream fusion. Our studies show that the proposed method can successfully
+estimate contact grip force with RMSE < 10% of sensor range (RMSE $\approx 0.2$
+N), the performances of each stream and overall method performance are
+reported. The proposed method has a wide range of applications, including robot
+teaching through demonstration, haptic force feedback, and validation of human-
+performed operations.
+",1,0,0,0,0,0
+8152,Markov Properties for Graphical Models with Cycles and Latent Variables,"  We investigate probabilistic graphical models that allow for both cycles and
+latent variables. For this we introduce directed graphs with hyperedges
+(HEDGes), generalizing and combining both marginalized directed acyclic graphs
+(mDAGs) that can model latent (dependent) variables, and directed mixed graphs
+(DMGs) that can model cycles. We define and analyse several different Markov
+properties that relate the graphical structure of a HEDG with a probability
+distribution on a corresponding product space over the set of nodes, for
+example factorization properties, structural equations properties,
+ordered/local/global Markov properties, and marginal versions of these. The
+various Markov properties for HEDGes are in general not equivalent to each
+other when cycles or hyperedges are present, in contrast with the simpler case
+of directed acyclic graphical (DAG) models (also known as Bayesian networks).
+We show how the Markov properties for HEDGes - and thus the corresponding
+graphical Markov models - are logically related to each other.
+",0,0,1,1,0,0
+8153,A Fast Algorithm for Solving Henderson's Mixed Model Equation,"  This article investigates a fast and stable method to solve Henderson's mixed
+model equation. The proposed algorithm is stable in that it avoids inverting a
+matrix of a large dimension and hence is free from the curse of dimensionality.
+This tactic is enabled through row operations performed on the design matrix.
+",0,0,0,1,0,0
+8154,Regularity and stability results for the level set flow via the mean curvature flow with surgery,"  In this article we us the mean curvature flow with surgery to derive
+regularity estimates going past Brakke regularity for the level set flow. We
+also show a stability result for the plane under the level set flow.
+",0,0,1,0,0,0
+8155,Non-canonical Conformal Attractors for Single Field Inflation,"  We extend the idea of conformal attractors in inflation to non-canonical
+sectors by developing a non-canonical conformally invariant theory from two
+different approaches. In the first approach, namely, ${\cal N}=1$ supergravity,
+the construction is more or less phenomenological, where the non-canonical
+kinetic sector is derived from a particular form of the K$\ddot{a}$hler
+potential respecting shift symmetry. In the second approach i.e.,
+superconformal theory, we derive the form of the Lagrangian from a
+superconformal action and it turns out to be exactly of the same form as in the
+first approach. Conformal breaking of these theories results in a new class of
+non-canonical models which can govern inflation with modulated shape of the
+T-models. We further employ this framework to explore inflationary
+phenomenology with a representative example and show how the form of the
+K$\ddot{a}$hler potential can possibly be constrained in non-canonical models
+using the latest confidence contour in the $n_s-r$ plane given by Planck.
+",0,1,0,0,0,0
+8156,Learning model-based planning from scratch,"  Conventional wisdom holds that model-based planning is a powerful approach to
+sequential decision-making. It is often very challenging in practice, however,
+because while a model can be used to evaluate a plan, it does not prescribe how
+to construct a plan. Here we introduce the ""Imagination-based Planner"", the
+first model-based, sequential decision-making agent that can learn to
+construct, evaluate, and execute plans. Before any action, it can perform a
+variable number of imagination steps, which involve proposing an imagined
+action and evaluating it with its model-based imagination. All imagined actions
+and outcomes are aggregated, iteratively, into a ""plan context"" which
+conditions future real and imagined actions. The agent can even decide how to
+imagine: testing out alternative imagined actions, chaining sequences of
+actions together, or building a more complex ""imagination tree"" by navigating
+flexibly among the previously imagined states using a learned policy. And our
+agent can learn to plan economically, jointly optimizing for external rewards
+and computational costs associated with using its imagination. We show that our
+architecture can learn to solve a challenging continuous control problem, and
+also learn elaborate planning strategies in a discrete maze-solving task. Our
+work opens a new direction toward learning the components of a model-based
+planning system and how to use them.
+",1,0,0,1,0,0
+8157,A Simple Reservoir Model of Working Memory with Real Values,"  The prefrontal cortex is known to be involved in many high-level cognitive
+functions, in particular, working memory. Here, we study to what extent a group
+of randomly connected units (namely an Echo State Network, ESN) can store and
+maintain (as output) an arbitrary real value from a streamed input, i.e. can
+act as a sustained working memory unit. Furthermore, we explore to what extent
+such an architecture can take advantage of the stored value in order to produce
+non-linear computations. Comparison between different architectures (with and
+without feedback, with and without a working memory unit) shows that an
+explicit memory improves the performances.
+",0,0,0,0,1,0
+8158,Tailoring spin defects in diamond,"  Atomic-size spin defects in solids are unique quantum systems. Most
+applications require nanometer positioning accuracy, which is typically
+achieved by low energy ion implantation. So far, a drawback of this technique
+is the significant residual implantation-induced damage to the lattice, which
+strongly degrades the performance of spins in quantum applications. In this
+letter we show that the charge state of implantation-induced defects
+drastically influences the formation of lattice defects during thermal
+annealing. We demonstrate that charging of vacancies localized at e.g.
+individual nitrogen implantation sites suppresses the formation of vacancy
+complexes, resulting in a tenfold-improved spin coherence time of single
+nitrogen-vacancy (NV) centers in diamond. This has been achieved by confining
+implantation defects into the space charge layer of free carriers generated by
+a nanometer-thin boron-doped diamond structure. Besides, a twofold-improved
+yield of formation of NV centers is observed. By combining these results with
+numerical calculations, we arrive at a quantitative understanding of the
+formation and dynamics of the implanted spin defects. The presented results
+pave the way for improved engineering of diamond spin defect quantum devices
+and other solid-state quantum systems.
+",0,1,0,0,0,0
+8159,Relaxed Oracles for Semi-Supervised Clustering,"  Pairwise ""same-cluster"" queries are one of the most widely used forms of
+supervision in semi-supervised clustering. However, it is impractical to ask
+human oracles to answer every query correctly. In this paper, we study the
+influence of allowing ""not-sure"" answers from a weak oracle and propose an
+effective algorithm to handle such uncertainties in query responses. Two
+realistic weak oracle models are considered where ambiguity in answering
+depends on the distance between two points. We show that a small query
+complexity is adequate for effective clustering with high probability by
+providing better pairs to the weak oracle. Experimental results on synthetic
+and real data show the effectiveness of our approach in overcoming supervision
+uncertainties and yielding high quality clusters.
+",1,0,0,1,0,0
+8160,Searching for axion stars and Q-balls with a terrestrial magnetometer network,"  Light (pseudo-)scalar fields are promising candidates to be the dark matter
+in the Universe. Under certain initial conditions in the early Universe and/or
+with certain types of self-interactions, they can form compact dark-matter
+objects such as axion stars or Q-balls. Direct encounters with such objects can
+be searched for by using a global network of atomic magnetometers. It is shown
+that for a range of masses and radii not ruled out by existing observations,
+the terrestrial encounter rate with axion stars or Q-balls can be sufficiently
+high (at least once per year) for a detection. Furthermore, it is shown that a
+global network of atomic magnetometers is sufficiently sensitive to
+pseudoscalar couplings to atomic spins so that a transit through an axion star
+or Q-ball could be detected over a broad range of unexplored parameter space.
+",0,1,0,0,0,0
+8161,Measuring Information Leakage in Website Fingerprinting Attacks,"  Tor is a low-latency anonymity system intended to provide low-latency
+anonymous and uncensored network access against a local or network adversary.
+Because of the design choice to minimize traffic overhead (and increase the
+pool of potential users) Tor allows some information about the client's
+connections to leak in the form of packet timing. Attacks that use (features
+extracted from) this information to infer the website a user visits are
+referred to as Website Fingerprinting (WF) attacks. We develop a methodology
+and tools to directly measure the amount of information about a website leaked
+by a given set of features. We apply this tool to a comprehensive set of
+features extracted from a large set of websites and WF defense mechanisms,
+allowing us to make more fine-grained observations about WF attack and defense
+mechanisms.
+",1,0,0,0,0,0
+8162,Improving galaxy morphology with machine learning,"  This paper presents machine learning experiments performed over results of
+galaxy classification into elliptical (E) and spiral (S) with morphological
+parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3),
+entropy (H) and gradient pattern analysis parameter (GA). Except concentration,
+all parameters performed a image segmentation pre-processing. For supervision
+and to compute confusion matrices, we used as true label the galaxy
+classification from GalaxyZoo. With a 48145 objects dataset after preprocessing
+(44760 galaxies labeled as S and 3385 as E), we performed experiments with
+Support Vector Machine (SVM) and Decision Tree (DT). Whit a 1962 objects
+balanced dataset, we applied K- means and Agglomerative Hierarchical
+Clustering. All experiments with supervision reached an Overall Accuracy OA >=
+97%.
+",0,1,0,0,0,0
+8163,"Magnetized strange quark model with Big Rip singularity in $f(R,T)$ gravity","  LRS (Locally Rotationally symmetric) Bianchi type-I magnetized strange quark
+matter cosmological model have been studied based on $f(R,T)$ gravity. The
+exact solutions of the field equations are derived with linearly time varying
+deceleration parameter which is consistent with observational data (from SNIa,
+BAO and CMB) of standard cosmology. It is observed that the model start with
+big bang and ends with a Big Rip. The transition of deceleration parameter from
+decelerating phase to accelerating phase with respect to redshift obtained in
+our model fits with the recent observational data obtained by Farook et al. in
+2017. The well known Hubble parameter $H(z)$ and distance modulus $\mu(z)$ are
+discussed with redshift.
+",0,1,0,0,0,0
+8164,NPC: Neighbors Progressive Competition Algorithm for Classification of Imbalanced Data Sets,"  Learning from many real-world datasets is limited by a problem called the
+class imbalance problem. A dataset is imbalanced when one class (the majority
+class) has significantly more samples than the other class (the minority
+class). Such datasets cause typical machine learning algorithms to perform
+poorly on the classification task. To overcome this issue, this paper proposes
+a new approach Neighbors Progressive Competition (NPC) for classification of
+imbalanced datasets. Whilst the proposed algorithm is inspired by weighted
+k-Nearest Neighbor (k-NN) algorithms, it has major differences from them.
+Unlike k- NN, NPC does not limit its decision criteria to a preset number of
+nearest neighbors. In contrast, NPC considers progressively more neighbors of
+the query sample in its decision making until the sum of grades for one class
+is much higher than the other classes. Furthermore, NPC uses a novel method for
+grading the training samples to compensate for the imbalance issue. The grades
+are calculated using both local and global information. In brief, the
+contribution of this paper is an entirely new classifier for handling the
+imbalance issue effectively without any manually-set parameters or any need for
+expert knowledge. Experimental results compare the proposed approach with five
+representative algorithms applied to fifteen imbalanced datasets and illustrate
+this algorithms effectiveness.
+",1,0,0,1,0,0
+8165,On symmetric intersecting families,"  A family of sets is said to be \emph{symmetric} if its automorphism group is
+transitive, and \emph{intersecting} if any two sets in the family have nonempty
+intersection. Our purpose here is to study the following question: for $n, k\in
+\mathbb{N}$ with $k \le n/2$, how large can a symmetric intersecting family of
+$k$-element subsets of $\{1,2,\ldots,n\}$ be? As a first step towards a
+complete answer, we prove that such a family has size at most
+\[\exp\left(-\frac{c(n-2k)\log n}{k( \log n - \log k)} \right) \binom{n}{k},\]
+where $c > 0$ is a universal constant. We also describe various combinatorial
+and algebraic approaches to constructing such families.
+",0,0,1,0,0,0
+8166,The Geometry of Limit State Function Graphs and Subset Simulation,"  In the last fifteen the subset sampling method has often been used in
+reliability problems as a tool for calculating small probabilities. This method
+is extrapolating from an initial Monte Carlo estimate for the probability
+content of a failure domain found by a suitable higher level of the original
+limit state function. Then iteratively conditional probabilities are estimated
+for failures domains decreasing to the original failure domain.
+But there are assumptions not immediately obvious about the structure of the
+failure domains which must be fulfilled that the method works properly. Here
+examples are studied that show that at least in some cases if these premises
+are not fulfilled, inaccurate results may be obtained. For the further
+development of the subset sampling method it is certainly desirable to find
+approaches where it is possible to check that these implicit assumptions are
+not violated. Also it would be probably important to develop further
+improvements of the concept to get rid of these limitations.
+",0,0,0,1,0,0
+8167,Decay of Solutions to the Maxwell Equations on Schwarzschild-de Sitter Spacetimes,"  In this work, we consider solutions of the Maxwell equations on the
+Schwarzschild-de Sitter family of black hole spacetimes. We prove that, in the
+static region bounded by black hole and cosmological horizons, solutions of the
+Maxwell equations decay to stationary Coulomb solutions at a super-polynomial
+rate, with decay measured according to ingoing and outgoing null coordinates.
+Our method employs a differential transformation of Maxwell tensor components
+to obtain higher-order quantities satisfying a Fackerell-Ipser equation, in the
+style of Chandrasekhar and the more recent work of Pasqualotto. The analysis of
+the Fackerell-Ipser equation is accomplished by means of the vector field
+method, with decay estimates for the higher-order quantities leading to decay
+estimates for components of the Maxwell tensor.
+",0,0,1,0,0,0
+8168,Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry,"  We consider a problem of diagnostic pattern recognition/classification from
+neuroimaging data. We propose a common data analysis pipeline for
+neuroimaging-based diagnostic classification problems using various ML
+algorithms and processing toolboxes for brain imaging. We illustrate the
+pipeline application by discovering new biomarkers for diagnostics of epilepsy
+and depression based on clinical and MRI/fMRI data for patients and healthy
+volunteers.
+",0,0,0,1,0,0
+8169,Covering and separation of Chebyshev points for non-integrable Riesz potentials,"  For Riesz $s$-potentials $K(x,y)=|x-y|^{-s}$, $s>0$, we investigate
+separation and covering properties of $N$-point configurations
+$\omega^*_N=\{x_1, \ldots, x_N\}$ on a $d$-dimensional compact set $A\subset
+\mathbb{R}^\ell$ for which the minimum of $\sum_{j=1}^N K(x, x_j)$ is maximal.
+Such configurations are called $N$-point optimal Riesz $s$-polarization (or
+Chebyshev) configurations. For a large class of $d$-dimensional sets $A$ we
+show that for $s>d$ the configurations $\omega^*_N$ have the optimal order of
+covering. Furthermore, for these sets we investigate the asymptotics as $N\to
+\infty$ of the best covering constant. For these purposes we compare
+best-covering configurations with optimal Riesz $s$-polarization configurations
+and determine the $s$-th root asymptotic behavior (as $s\to \infty$) of the
+maximal $s$-polarization constants. In addition, we introduce the notion of
+""weak separation"" for point configurations and prove this property for optimal
+Riesz $s$-polarization configurations on $A$ for $s>\text{dim}(A)$, and for
+$d-1\leqslant s < d$ on the sphere $\mathbb{S}^d$.
+",0,0,1,0,0,0
+8170,The Strong Small Index Property for Free Homogeneous Structures,"  We show that in algebraically locally finite countable homogeneous structures
+with a free stationary independence relation the small index property implies
+the strong small index property. We use this and the main result of [15] to
+deduce that countable free homogeneous structures in a locally finite
+relational language have the strong small index property. We also exhibit new
+continuum sized classes of $\aleph_0$-categorical structures with the strong
+small index property whose automorphism groups are pairwise non-isomorphic.
+",0,0,1,0,0,0
+8171,The Unheralded Value of the Multiway Rendezvous: Illustration with the Production Cell Benchmark,"  The multiway rendezvous introduced in Theoretical CSP is a powerful paradigm
+to achieve synchronization and communication among a group of (possibly more
+than two) processes. We illustrate the advantages of this paradigm on the
+production cell benchmark, a model of a real metal processing plant, for which
+we propose a compositional software controller, which is written in LNT and
+LOTOS, and makes intensive use of the multiway rendezvous.
+",1,0,0,0,0,0
+8172,Sequential Multiple Testing,"  We study an online multiple testing problem where the hypotheses arrive
+sequentially in a stream. The test statistics are independent and assumed to
+have the same distribution under their respective null hypotheses. We
+investigate two procedures LORD and LOND, proposed by (Javanmard and Montanari,
+2015), which are proved to control the FDR in an online manner. In some
+(static) model, we show that LORD is optimal in some asymptotic sense, in
+particular as powerful as the (static) Benjamini-Hochberg procedure to first
+asymptotic order. We also quantify the performance of LOND. Some numerical
+experiments complement our theory.
+",0,0,1,1,0,0
+8173,On the selection of polynomials for the DLP algorithm,"  In this paper we characterize the set of polynomials $f\in\mathbb F_q[X]$
+satisfying the following property: there exists a positive integer $d$ such
+that for any positive integer $\ell$ less or equal than the degree of $f$,
+there exists $t_0$ in $\mathbb F_{q^d}$ such that the polynomial $f-t_0$ has an
+irreducible factor of degree $\ell$ over $\mathbb F_{q^d}[X]$. This result is
+then used to progress in the last step which is needed to remove the heuristic
+from one of the quasi-polynomial time algorithms for discrete logarithm
+problems (DLP) in small characteristic. Our characterization allows a
+construction of polynomials satisfying the wanted property.
+",1,0,1,0,0,0
+8174,Existence of global weak solutions to the kinetic Peterlin model,"  We consider a class of kinetic models for polymeric fluids motivated by the
+Peterlin dumbbell theories for dilute polymer solutions with a nonlinear spring
+law for an infinitely extensible spring. The polymer molecules are suspended in
+an incompressible viscous Newtonian fluid confined to a bounded domain in two
+or three space dimensions. The unsteady motion of the solvent is described by
+the incompressible Navier-Stokes equations with the elastic extra stress tensor
+appearing as a forcing term in the momentum equation. The elastic stress tensor
+is defined by the Kramers expression through the probability density function
+that satisfies the corresponding Fokker-Planck equation. In this case, a
+coefficient depending on the average length of polymer molecules appears in the
+latter equation. Following the recent work of Barrett and Süli we prove the
+existence of global-in-time weak solutions to the kinetic Peterlin model in two
+space dimensions.
+",0,0,1,0,0,0
+8175,Passivation and Cooperative Control of Equilibrium-Independent Passivity-Short Systems,"  Maximal equilibrium-independent passivity (MEIP) is a recently introduced
+system property which has acquired special attention in the study of networked
+dynamical systems. MEIP requires a system to be passive with respect to any
+forced equilibrium configuration and the associated steady-state input-output
+map must be maximally monotone. In practice, however, most of the systems are
+not well behaved and possess shortage of passivity or non-passiveness in their
+operation. In this paper, we consider a class of passivity-short systems,
+namely equilibrium-independent passivity-short (EIPS) systems, and presents an
+input-output transformation based generalized passivation approach to ensure
+their MEIP properties. We characterize the steady-state input-output relations
+of the EIPS systems and establish their connection with that of the transformed
+MEIP systems. We further study the diffusively-coupled networked interactions
+of such EIPS systems and explore their connection to a pair of dual network
+optimization problems, under the proposed matrix transformation. A simulation
+example is given to illustrate the theoretical results.
+",1,0,0,0,0,0
+8176,OpenCluster: A Flexible Distributed Computing Framework for Astronomical Data Processing,"  The volume of data generated by modern astronomical telescopes is extremely
+large and rapidly growing. However, current high-performance data processing
+architectures/frameworks are not well suited for astronomers because of their
+limitations and programming difficulties. In this paper, we therefore present
+OpenCluster, an open-source distributed computing framework to support rapidly
+developing high-performance processing pipelines of astronomical big data. We
+first detail the OpenCluster design principles and implementations and present
+the APIs facilitated by the framework. We then demonstrate a case in which
+OpenCluster is used to resolve complex data processing problems for developing
+a pipeline for the Mingantu Ultrawide Spectral Radioheliograph. Finally, we
+present our OpenCluster performance evaluation. Overall, OpenCluster provides
+not only high fault tolerance and simple programming interfaces, but also a
+flexible means of scaling up the number of interacting entities. OpenCluster
+thereby provides an easily integrated distributed computing framework for
+quickly developing a high-performance data processing system of astronomical
+telescopes and for significantly reducing software development expenses.
+",1,1,0,0,0,0
+8177,Magnetic order and spin dynamics across a ferromagnetic quantum critical point: $μ$SR investigations of YbNi$_4$(P$_{1-x}$As$_x$)$_2$,"  In the quasi-1D heavy-fermion system YbNi$_4$(P$_{1-x}$As$_x$)$_2$ the
+presence of a ferromagnetic (FM) quantum critical point (QCP) at $x_c$ $\approx
+0.1$ with unconventional quantum critical exponents in the thermodynamic
+properties has been recently reported. Here, we present muon-spin relaxation
+($\mu$SR) experiments on polycrystals of this series to study the magnetic
+order and the low energy 4$f$-electronic spin dynamics across the FM QCP. The
+zero field $\mu$SR measurements on pure YbNi$_4$(P$_{2}$ proved static long
+range magnetic order and suggested a strongly reduced ordered Yb moment of
+about 0.04$\mu_B$. With increasing As substitution the ordered moment is
+reduced by half at $x = 0.04$ and to less than 0.005 $\mu_B$ at $x=0.08$. The
+dynamic behavior in the $\mu$SR response show that magnetism remains
+homogeneous upon As substitution, without evidence for disorder effect. In the
+paramagnetic state across the FM QCP the dynamic muon-spin relaxation rate
+follows 1/$T_{1}T\propto T^{-n}$ with $1.01 \pm 0.04 \leq n \leq 1.13 \pm
+0.06$. The critical fluctuations are very slow and are even becoming slower
+when approaching the QCP.
+",0,1,0,0,0,0
+8178,Pixel-Level Statistical Analyses of Prescribed Fire Spread,"  Wildland fire dynamics is a complex turbulent dimensional process. Cellular
+automata (CA) is an efficient tool to predict fire dynamics, but the main
+parameters of the method are challenging to estimate. To overcome this
+challenge, we compute statistical distributions of the key parameters of a CA
+model using infrared images from controlled burns. Moreover, we apply this
+analysis to different spatial scales and compare the experimental results to a
+simple statistical model. By performing this analysis and making this
+comparison, several capabilities and limitations of CA are revealed.
+",0,1,0,0,0,0
+8179,Deep Reinforcement Learning for Swarm Systems,"  Recently, deep reinforcement learning (RL) methods have been applied
+successfully to multi-agent scenarios. Typically, these methods rely on a
+concatenation of agent states to represent the information content required for
+decentralized decision making. However, concatenation scales poorly to swarm
+systems with a large number of homogeneous agents as it does not exploit the
+fundamental properties inherent to these systems: (i) the agents in the swarm
+are interchangeable and (ii) the exact number of agents in the swarm is
+irrelevant. Therefore, we propose a new state representation for deep
+multi-agent RL based on mean embeddings of distributions. We treat the agents
+as samples of a distribution and use the empirical mean embedding as input for
+a decentralized policy. We define different feature spaces of the mean
+embedding using histograms, radial basis functions and a neural network learned
+end-to-end. We evaluate the representation on two well known problems from the
+swarm literature (rendezvous and pursuit evasion), in a globally and locally
+observable setup. For the local setup we furthermore introduce simple
+communication protocols. Of all approaches, the mean embedding representation
+using neural network features enables the richest information exchange between
+neighboring agents facilitating the development of more complex collective
+strategies.
+",1,0,0,1,0,0
+8180,Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering,"  Evaluating the computational reproducibility of data analysis pipelines has
+become a critical issue. It is, however, a cumbersome process for analyses that
+involve data from large populations of subjects, due to their computational and
+storage requirements. We present a method to predict the computational
+reproducibility of data analysis pipelines in large population studies. We
+formulate the problem as a collaborative filtering process, with constraints on
+the construction of the training set. We propose 6 different strategies to
+build the training set, which we evaluate on 2 datasets, a synthetic one
+modeling a population with a growing number of subject types, and a real one
+obtained with neuroinformatics pipelines. Results show that one sampling
+method, ""Random File Numbers (Uniform)"" is able to predict computational
+reproducibility with a good accuracy. We also analyze the relevance of
+including file and subject biases in the collaborative filtering model. We
+conclude that the proposed method is able to speedup reproducibility
+evaluations substantially, with a reduced accuracy loss.
+",0,0,0,1,0,0
+8181,Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks,"  We propose a deep learning model for identifying structure within experiment
+narratives in scientific literature. We take a sequence labeling approach to
+this problem, and label clauses within experiment narratives to identify the
+different parts of the experiment. Our dataset consists of paragraphs taken
+from open access PubMed papers labeled with rhetorical information as a result
+of our pilot annotation. Our model is a Recurrent Neural Network (RNN) with
+Long Short-Term Memory (LSTM) cells that labels clauses. The clause
+representations are computed by combining word representations using a novel
+attention mechanism that involves a separate RNN. We compare this model against
+LSTMs where the input layer has simple or no attention and a feature rich CRF
+model. Furthermore, we describe how our work could be useful for information
+extraction from scientific literature.
+",1,0,0,0,0,0
+8182,Information-Theoretic Analysis of Refractory Effects in the P300 Speller,"  The P300 speller is a brain-computer interface that enables people with
+neuromuscular disorders to communicate based on eliciting event-related
+potentials (ERP) in electroencephalography (EEG) measurements. One challenge to
+reliable communication is the presence of refractory effects in the P300 ERP
+that induces temporal dependence in the user's EEG responses. We propose a
+model for the P300 speller as a communication channel with memory. By studying
+the maximum information rate on this channel, we gain insight into the
+fundamental constraints imposed by refractory effects. We construct codebooks
+based on the optimal input distribution, and compare them to existing codebooks
+in literature.
+",1,0,1,0,0,0
+8183,"Growth, Industrial Externality, Prospect Dynamics and Well-being on Markets","  Functions or 'functionnings' enable to give a structure to any economic
+activity whether they are used to describe a good or a service that is
+exchanged on a market or they constitute the capability of an agent to provide
+the labor market with specific work and skills. That structure encompasses the
+basic law of supply and demand and the conditions of growth within a
+transaction and of the inflation control. Functional requirements can be
+followed from the design of a product to the delivery of a solution to a
+customer needs with different levels of externalities while value is created
+integrating organizational and technical constraints whereas a budget is
+allocated to the various entities of the firm involved in the production.
+Entering the market through that structure leads to designing basic equations
+of its dynamics and to finding canonical solutions out of particular
+equilibria. This approach enables to tackle behavioral foundations of Prospect
+Theory within a generalization of its probability weighting function turned
+into an operator which applies to Western, Educated, Industrialized, Rich, and
+Democratic societies as well as to the poorest ones. The nature of reality and
+well-being appears then as closely related to the relative satisfaction reached
+on the market, as it can be conceived by an agent, according to business
+cycles. This reality being the result of the complementary systems that govern
+human mind as structured by rational psychologists.
+",0,0,0,0,0,1
+8184,Structure Preserving Model Reduction of Parametric Hamiltonian Systems,"  While reduced-order models (ROMs) have been popular for efficiently solving
+large systems of differential equations, the stability of reduced models over
+long-time integration is of present challenges. We present a greedy approach
+for ROM generation of parametric Hamiltonian systems that captures the
+symplectic structure of Hamiltonian systems to ensure stability of the reduced
+model. Through the greedy selection of basis vectors, two new vectors are added
+at each iteration to the linear vector space to increase the accuracy of the
+reduced basis. We use the error in the Hamiltonian due to model reduction as an
+error indicator to search the parameter space and identify the next best basis
+vectors. Under natural assumptions on the set of all solutions of the
+Hamiltonian system under variation of the parameters, we show that the greedy
+algorithm converges with exponential rate. Moreover, we demonstrate that
+combining the greedy basis with the discrete empirical interpolation method
+also preserves the symplectic structure. This enables the reduction of the
+computational cost for nonlinear Hamiltonian systems. The efficiency, accuracy,
+and stability of this model reduction technique is illustrated through
+simulations of the parametric wave equation and the parametric Schrodinger
+equation.
+",0,0,1,0,0,0
+8185,Consensus measure of rankings,"  A ranking is an ordered sequence of items, in which an item with higher
+ranking score is more preferred than the items with lower ranking scores. In
+many information systems, rankings are widely used to represent the preferences
+over a set of items or candidates. The consensus measure of rankings is the
+problem of how to evaluate the degree to which the rankings agree. The
+consensus measure can be used to evaluate rankings in many information systems,
+as quite often there is not ground truth available for evaluation.
+This paper introduces a novel approach for consensus measure of rankings by
+using graph representation, in which the vertices or nodes are the items and
+the edges are the relationship of items in the rankings. Such representation
+leads to various algorithms for consensus measure in terms of different aspects
+of rankings, including the number of common patterns, the number of common
+patterns with fixed length and the length of the longest common patterns. The
+proposed measure can be adopted for various types of rankings, such as full
+rankings, partial rankings and rankings with ties. This paper demonstrates how
+the proposed approaches can be used to evaluate the quality of rank aggregation
+and the quality of top-$k$ rankings from Google and Bing search engines.
+",1,0,0,0,0,0
+8186,Possible resonance effect of dark matter axions in SNS Josephson junctions,"  Dark matter axions can generate peculiar effects in special types of
+Josephson junctions, so-called SNS junctions. One can show that the axion field
+equations in a Josephson environment allow for very small oscillating
+supercurrents, which manifest themselves as a tiny wiggle in the I-V curve, a
+so-called Shapiro step, which occurs at a frequency given by the axion mass.
+The effect is very small but perfectly measurable in modern nanotechnological
+devices. In this paper I will summarize the theory and then present evidence
+that candidate Shapiro steps of this type have indeed been seen in several
+independent condensed matter experiments. Assuming the observed tiny Shapiro
+steps are due to axion flow then these data point to an axion mass of $(106 \pm
+6)\mu$eV, consistent with what is expected for the QCD axion. In addition to
+the above small Shapiro resonance effects at frequencies in the GHz region one
+also expects to see broad-band noise effects at much lower frequencies. Overall
+this approach provides a novel pathway for the future design of new types of
+axionic dark matter detectors. The resonant Josephson data summarized in this
+paper are consistent with a 'vanilla' axion with a coupling constant
+$f_a=\sqrt{v_{EW}m_{Pl}}=5.48 \cdot 10^{10}$GeV given by the geometric average
+of the electroweak symmetry breaking scale $v_{EW}$ and the Planck mass
+$m_{Pl}$.
+",0,1,0,0,0,0
+8187,A Composition Theorem for Randomized Query Complexity,"  Let the randomized query complexity of a relation for error probability
+$\epsilon$ be denoted by $R_\epsilon(\cdot)$. We prove that for any relation $f
+\subseteq \{0,1\}^n \times \mathcal{R}$ and Boolean function $g:\{0,1\}^m
+\rightarrow \{0,1\}$, $R_{1/3}(f\circ g^n) = \Omega(R_{4/9}(f)\cdot
+R_{1/2-1/n^4}(g))$, where $f \circ g^n$ is the relation obtained by composing
+$f$ and $g$. We also show that $R_{1/3}\left(f \circ \left(g^\oplus_{O(\log
+n)}\right)^n\right)=\Omega(\log n \cdot R_{4/9}(f) \cdot R_{1/3}(g))$, where
+$g^\oplus_{O(\log n)}$ is the function obtained by composing the xor function
+on $O(\log n)$ bits and $g^t$.
+",1,0,0,0,0,0
+8188,Soliton solutions for the elastic metric on spaces of curves,"  In this article we investigate a first order reparametrization-invariant
+Sobolev metric on the space of immersed curves. Motivated by applications in
+shape analysis where discretizations of this infinite-dimensional space are
+needed, we extend this metric to the space of Lipschitz curves, establish the
+wellposedness of the geodesic equation thereon, and show that the space of
+piecewise linear curves is a totally geodesic submanifold. Thus, piecewise
+linear curves are natural finite elements for the discretization of the
+geodesic equation. Interestingly, geodesics in this space can be seen as
+soliton solutions of the geodesic equation, which were not known to exist for
+reparametrization-invariant Sobolev metrics on spaces of curves.
+",0,0,1,0,0,0
+8189,Influence of Personal Preferences on Link Dynamics in Social Networks,"  We study a unique network dataset including periodic surveys and electronic
+logs of dyadic contacts via smartphones. The participants were a sample of
+freshmen entering university in the Fall 2011. Their opinions on a variety of
+political and social issues and lists of activities on campus were regularly
+recorded at the beginning and end of each semester for the first three years of
+study. We identify a behavioral network defined by call and text data, and a
+cognitive network based on friendship nominations in ego-network surveys. Both
+networks are limited to study participants. Since a wide range of attributes on
+each node were collected in self-reports, we refer to these networks as
+attribute-rich networks. We study whether student preferences for certain
+attributes of friends can predict formation and dissolution of edges in both
+networks. We introduce a method for computing student preferences for different
+attributes which we use to predict link formation and dissolution. We then rank
+these attributes according to their importance for making predictions. We find
+that personal preferences, in particular political views, and preferences for
+common activities help predict link formation and dissolution in both the
+behavioral and cognitive networks.
+",1,0,0,0,0,0
+8190,PBW bases and marginally large tableaux in types B and C,"  We explicitly describe the isomorphism between two combinatorial realizations
+of Kashiwara's infinity crystal in types B and C. The first realization is in
+terms of marginally large tableaux and the other is in terms of Kostant
+partitions coming from PBW bases. We also discuss a stack notation for Kostant
+partitions which simplifies that realization.
+",0,0,1,0,0,0
+8191,Ensemble Clustering for Graphs,"  We propose an ensemble clustering algorithm for graphs (ECG), which is based
+on the Louvain algorithm and the concept of consensus clustering. We validate
+our approach by replicating a recently published study comparing graph
+clustering algorithms over artificial networks, showing that ECG outperforms
+the leading algorithms from that study. We also illustrate how the ensemble
+obtained with ECG can be used to quantify the presence of community structure
+in the graph.
+",1,0,0,1,0,0
+8192,Testing for Feature Relevance: The HARVEST Algorithm,"  Feature selection with high-dimensional data and a very small proportion of
+relevant features poses a severe challenge to standard statistical methods. We
+have developed a new approach (HARVEST) that is straightforward to apply,
+albeit somewhat computer-intensive. This algorithm can be used to pre-screen a
+large number of features to identify those that are potentially useful. The
+basic idea is to evaluate each feature in the context of many random subsets of
+other features. HARVEST is predicated on the assumption that an irrelevant
+feature can add no real predictive value, regardless of which other features
+are included in the subset. Motivated by this idea, we have derived a simple
+statistical test for feature relevance. Empirical analyses and simulations
+produced so far indicate that the HARVEST algorithm is highly effective in
+predictive analytics, both in science and business.
+",0,0,0,1,0,0
+8193,Energy Harvesting Enabled MIMO Relaying through PS,"  This paper considers a multiple-input multiple-output (MIMO) relay system
+with an energy harvesting relay node. All nodes are equipped with multiple
+antennas, and the relay node depends on the harvested energy from the received
+signal to support information forwarding. In particular, the relay node deploys
+power splitting based energy harvesting scheme. The capacity maximization
+problem subject to power constraints at both the source and relay nodes is
+considered for both fixed source covariance matrix and optimal source
+covariance matrix cases. Instead of using existing software solvers, iterative
+approaches using dual decomposition technique are developed based on the
+structures of the optimal relay precoding and source covariance matrices.
+Simulation results demonstrate the performance gain of the joint optimization
+against the fixed source covariance matrix case.
+",1,0,0,0,0,0
+8194,X-ray diagnostics of massive star winds,"  Observations with powerful X-ray telescopes, such as XMM-Newton and Chandra,
+significantly advance our understanding of massive stars. Nearly all early-type
+stars are X-ray sources. Studies of their X-ray emission provide important
+diagnostics of stellar winds. High-resolution X-ray spectra of O-type stars are
+well explained when stellar wind clumping is taking into account, providing
+further support to a modern picture of stellar winds as non-stationary,
+inhomogeneous outflows. X-ray variability is detected from such winds, on time
+scales likely associated with stellar rotation. High-resolution X-ray
+spectroscopy indicates that the winds of late O-type stars are predominantly in
+a hot phase. Consequently, X-rays provide the best observational window to
+study these winds. X-ray spectroscopy of evolved, Wolf-Rayet type, stars allows
+to probe their powerful metal enhanced winds, while the mechanisms responsible
+for the X-ray emission of these stars are not yet understood.
+",0,1,0,0,0,0
+8195,Local asymptotic properties for Cox-Ingersoll-Ross process with discrete observations,"  In this paper, we consider a one-dimensional Cox-Ingersoll-Ross (CIR) process
+whose drift coefficient depends on unknown parameters. Considering the process
+discretely observed at high frequency, we prove the local asymptotic normality
+property in the subcritical case, the local asymptotic quadraticity in the
+critical case, and the local asymptotic mixed normality property in the
+supercritical case. To obtain these results, we use the Malliavin calculus
+techniques developed recently for CIR process together with the $L^p$-norm
+estimation for positive and negative moments of the CIR process. In this study,
+we require the same conditions of high frequency $\Delta_n\rightarrow 0$ and
+infinite horizon $n\Delta_n\rightarrow\infty$ as in the case of ergodic
+diffusions with globally Lipschitz coefficients studied earlier by Gobet
+\cite{G02}. However, in the non-ergodic cases, additional assumptions on the
+decreasing rate of $\Delta_n$ are required due to the fact that the square root
+diffusion coefficient of the CIR process is not regular enough. Indeed, we
+assume $\frac{n\Delta_n^{\frac{3}{2}}}{\log(n\Delta_n)}\to 0$ for the critical
+case and $n\Delta_n^2\to 0$ for the supercritical case.
+",0,0,1,1,0,0
+8196,JHelioviewer - Time-dependent 3D visualisation of solar and heliospheric data,"  Context. Solar observatories are providing the world-wide community with a
+wealth of data, covering large time ranges, multiple viewpoints, and returning
+large amounts of data. In particular, the large volume of SDO data presents
+challenges: it is available only from a few repositories, and full-disk,
+full-cadence data for reasonable durations of scientific interest are difficult
+to download practically due to their size and download data rates available to
+most users. From a scientist's perspective this poses three problems:
+accessing, browsing and finding interesting data as efficiently as possible.
+Aims. To address these challenges, we have developed JHelioviewer, a
+visualisation tool for solar data based on the JPEG2000 compression standard
+and part of the open source ESA/NASA Helioviewer Project. Since the first
+release of JHelioviewer, the scientific functionality of the software has been
+extended significantly, and the objective of this paper is to highlight these
+improvements.
+Methods. The JPEG2000 standard offers useful new features that facilitate the
+dissemination and analysis of high-resolution image data and offers a solution
+to the challenge of efficiently browsing petabyte-scale image archives. The
+JHelioviewer software is open source, platform independent and extendable via a
+plug-in architecture.
+Results. With JHelioviewer, users can visualise the Sun for any time period
+between September 1991 and today. They can perform basic image processing in
+real time, track features on the Sun and interactively overlay magnetic field
+extrapolations. The software integrates solar event data and a time line
+display. As a first step towards supporting science planning of the upcoming
+Solar Orbiter mission, JHelioviewer offers a virtual camera model that enables
+users to set the vantage point to the location of a spacecraft or celestial
+body at any given time.
+",0,1,0,0,0,0
+8197,Topological Dirac Nodal-net Fermions in AlB$_2$-type TiB$_2$ and ZrB$_2$,"  Based on first-principles calculations and effective model analysis, a Dirac
+nodal-net semimetal state is recognized in AlB$_2$-type TiB$_2$ and ZrB$_2$
+when spin-orbit coupling (SOC) is ignored. Taking TiB$_2$ as an example, there
+are several topological excitations in this nodal-net structure including
+triple point, nexus, and nodal link, which are protected by coexistence of
+spatial-inversion symmetry and time reversal symmetry. This nodal-net state is
+remarkably different from that of IrF$_4$, which requires sublattice chiral
+symmetry. In addition, linearly and quadratically dispersed two-dimensional
+surface Dirac points are identified as having emerged on the B-terminated and
+Ti-terminated (001) surfaces of TiB$_2$ respectively, which are analogous to
+those of monolayer and bilayer graphene.
+",0,1,0,0,0,0
+8198,"Model-based Design Evaluation of a Compact, High-Efficiency Neutron Scatter Camera","  This paper presents the model-based design and evaluation of an instrument
+that estimates incident neutron direction using the kinematics of neutron
+scattering by hydrogen-1 nuclei in an organic scintillator. The instrument
+design uses a single, nearly contiguous volume of organic scintillator that is
+internally subdivided only as necessary to create optically isolated pillars.
+Scintillation light emitted in a given pillar is confined to that pillar by a
+combination of total internal reflection and a specular reflector applied to
+the four sides of the pillar transverse to its long axis. The scintillation
+light is collected at each end of the pillar using a photodetector. In this
+optically segmented design, the (x, y) position of scintillation light emission
+(where the x and y coordinates are transverse to the long axis of the pillars)
+is estimated as the pillar's (x, y) position in the scintillator ""block"", and
+the z-position (the position along the pillar's long axis) is estimated from
+the amplitude and relative timing of the signals produced by the photodetectors
+at each end of the pillar. For proton recoils greater than 1 MeV, we show that
+the (x, y, z)-position of neutron-proton scattering can be estimated with < 1
+cm root-mean-squared [RMS] error and the proton recoil energy can be estimated
+with < 50 keV RMS error by fitting the photodetectors' response time history to
+models of optical photon transport within the scintillator pillars. Finally, we
+evaluate several alternative designs of this proposed single-volume scatter
+camera made of pillars of plastic scintillator (SVSC-PiPS), studying the effect
+of pillar dimensions, scintillator material, and photodetector response vs.
+time. Specifically, we conclude that an SVSC-PiPS constructed using EJ-204 and
+an MCP-PM will produce the most precise estimates of incident neutron direction
+and energy.
+",0,1,0,0,0,0
+8199,Galactic Pal-eontology: Abundance Analysis of the Disrupting Globular Cluster Palomar 5,"  We present a chemical abundance analysis of the tidally disrupted globular
+cluster (GC) Palomar 5. By co-adding high-resolution spectra of 15 member stars
+from the cluster's main body, taken at low signal-to-noise with the Keck/HIRES
+spectrograph, we were able to measure integrated abundance ratios of 24 species
+of 20 elements including all major nucleosynthetic channels (namely the light
+element Na; $\alpha$-elements Mg, Si, Ca, Ti; Fe-peak and heavy elements Sc, V,
+Cr, Mn, Co, Ni, Cu, Zn; and the neutron-capture elements Y, Zr, Ba, La, Nd, Sm,
+Eu). The mean metallicity of $-1.56\pm0.02\pm0.06$ dex (statistical and
+systematic errors) agrees well with the values from individual, low-resolution
+measurements of individual stars, but it is lower than previous high-resolution
+results of a small number of stars in the literature. Comparison with Galactic
+halo stars and other disrupted and unperturbed GCs renders Pal~5 a typical
+representative of the Milky Way halo population, as has been noted before,
+emphasizing that the early chemical evolution of such clusters is decoupled
+from their later dynamical history. We also performed a test as to the
+detectability of light element variations in this co-added abundance analysis
+technique and found that this approach is not sensitive even in the presence of
+a broad range in sodium of $\sim$0.6 dex, a value typically found in the old
+halo GCs. Thus, while methods of determining the global abundance patterns of
+such objects are well suited to study their overall enrichment histories,
+chemical distinctions of their multiple stellar populations is still best
+obtained from measurements of individual stars.
+",0,1,0,0,0,0
+8200,Anisotropic mechanical and optical response and negative Poissons ratio in Mo2C nanomembranes revealed by first-principles simulations,"  Transition metal carbides include a wide variety of materials with attractive
+properties that are suitable for numerous and diverse applications. Most recent
+experimental advance could provide a path toward successful synthesis of
+large-area and high-quality ultrathin Mo2C membranes with superconducting
+properties. In the present study, we used first-principles density functional
+theory calculations to explore the mechanical and optical response of
+single-layer and free-standing Mo2C. Uniaxial tensile simulations along the
+armchair and zigzag directions were conducted and we found that while the
+elastic properties are close along various loading directions, nonlinear
+regimes in stress-strain curves are considerably different. We found that Mo2C
+sheets present negative Poisson's ratio and thus can be categorized as an
+auxetic material. Our simulations also reveal that Mo2C films retain their
+metallic electronic characteristic upon the uniaxial loading. We found that for
+Mo2C nanomembranes the dielectric function becomes anisotropic along in-plane
+and out-of plane directions. Our findings can be useful for the practical
+application of Mo2C sheets in nanodevices.
+",0,1,0,0,0,0
+8201,A critical analysis of string APIs: The case of Pharo,"  Most programming languages, besides C, provide a native abstraction for
+character strings, but string APIs vary widely in size, expressiveness, and
+subjective convenience across languages. In Pharo, while at first glance the
+API of the String class seems rich, it often feels cumbersome in practice; to
+improve its usability, we faced the challenge of assessing its design. However,
+we found hardly any guideline about design forces and how they structure the
+design space, and no comprehensive analysis of the expected string operations
+and their different variations. In this article, we first analyse the Pharo 4
+String library, then contrast it with its Haskell, Java, Python, Ruby, and Rust
+counterparts. We harvest criteria to describe a string API, and reflect on
+features and design tensions. This analysis should help language designers in
+understanding the design space of strings, and will serve as a basis for a
+future redesign of the string library in Pharo.
+",1,0,0,0,0,0
+8202,"Power Maxwell distribution: Statistical Properties, Estimation and Application","  In this article, we proposed a new probability distribution named as power
+Maxwell distribution (PMaD). It is another extension of Maxwell distribution
+(MaD) which would lead more flexibility to analyze the data with non-monotone
+failure rate. Different statistical properties such as reliability
+characteristics, moments, quantiles, mean deviation, generating function,
+conditional moments, stochastic ordering, residual lifetime function and
+various entropy measures have been derived. The estimation of the parameters
+for the proposed probability distribution has been addressed by maximum
+likelihood estimation method and Bayes estimation method. The Bayes estimates
+are obtained under gamma prior using squared error loss function. Lastly,
+real-life application for the proposed distribution has been illustrated
+through different lifetime data.
+",0,0,0,1,0,0
+8203,Deep Graphs,"  We propose an algorithm for deep learning on networks and graphs. It relies
+on the notion that many graph algorithms, such as PageRank, Weisfeiler-Lehman,
+or Message Passing can be expressed as iterative vertex updates. Unlike
+previous methods which rely on the ingenuity of the designer, Deep Graphs are
+adaptive to the estimation problem. Training and deployment are both efficient,
+since the cost is $O(|E| + |V|)$, where $E$ and $V$ are the sets of edges and
+vertices respectively. In short, we learn the recurrent update functions rather
+than positing their specific functional form. This yields an algorithm that
+achieves excellent accuracy on both graph labeling and regression tasks.
+",0,0,0,1,0,0
+8204,On the optimal investment-consumption and life insurance selection problem with an external stochastic factor,"  In this paper, we study a stochastic optimal control problem with stochastic
+volatility. We prove the sufficient and necessary maximum principle for the
+proposed problem. Then we apply the results to solve an investment, consumption
+and life insurance problem with stochastic volatility, that is, we consider a
+wage earner investing in one risk-free asset and one risky asset described by a
+jump-diffusion process and has to decide concerning consumption and life
+insurance purchase. We assume that the life insurance for the wage earner is
+bought from a market composed of $M>1$ life insurance companies offering
+pairwise distinct life insurance contracts. The goal is to maximize the
+expected utilities derived from the consumption, the legacy in the case of a
+premature death and the investor's terminal wealth.
+",0,0,0,0,0,1
+8205,An Introduction to Adjoints and Output Error Estimation in Computational Fluid Dynamics,"  In recent years, the use of adjoint vectors in Computational Fluid Dynamics
+(CFD) has seen a dramatic rise. Their utility in numerous applications,
+including design optimization, data assimilation, and mesh adaptation has
+sparked the interest of both researchers and practitioners alike. In many of
+these fields, the concept of an adjoint is explained differently, with various
+notations and motivations employed. Further complicating matters is the
+existence of two seemingly different types of adjoints -- ""continuous"" and
+""discrete"" -- as well as the more formal definition of adjoint operators
+employed in linear algebra and functional analysis. These issues can make the
+fundamental concept of an adjoint difficult to pin down. In these notes, we
+hope to clarify some of the ideas surrounding adjoint vectors and to provide a
+useful reference for both continuous and discrete adjoints alike. In
+particular, we focus on the use of adjoints within the context of output-based
+mesh adaptation, where the goal is to achieve accuracy in a particular quantity
+(or ""output"") of interest by performing targeted adaptation of the
+computational mesh. While this is our application of interest, the ideas
+discussed here apply directly to design optimization, data assimilation, and
+many other fields where adjoints are employed.
+",1,1,0,0,0,0
+8206,Incremental Sharpe and other performance ratios,"  We present a new methodology of computing incremental contribution for
+performance ratios for portfolio like Sharpe, Treynor, Calmar or Sterling
+ratios. Using Euler's homogeneous function theorem, we are able to decompose
+these performance ratios as a linear combination of individual modified
+performance ratios. This allows understanding the drivers of these performance
+ratios as well as deriving a condition for a new asset to provide incremental
+performance for the portfolio. We provide various numerical examples of this
+performance ratio decomposition.
+",0,0,0,0,0,1
+8207,Saliency Detection by Forward and Backward Cues in Deep-CNNs,"  As prior knowledge of objects or object features helps us make relations for
+similar objects on attentional tasks, pre-trained deep convolutional neural
+networks (CNNs) can be used to detect salient objects on images regardless of
+the object class is in the network knowledge or not. In this paper, we propose
+a top-down saliency model using CNN, a weakly supervised CNN model trained for
+1000 object labelling task from RGB images. The model detects attentive regions
+based on their objectness scores predicted by selected features from CNNs. To
+estimate the salient objects effectively, we combine both forward and backward
+features, while demonstrating that partially-guided backpropagation will
+provide sufficient information for selecting the features from forward run of
+CNN model. Finally, these top-down cues are enhanced with a state-of-the-art
+bottom-up model as complementing the overall saliency. As the proposed model is
+an effective integration of forward and backward cues through objectness
+without any supervision or regression to ground truth data, it gives promising
+results compared to state-of-the-art models in two different datasets.
+",1,0,0,0,0,0
+8208,Active sorting of orbital angular momentum states of light with cascaded tunable resonators,"  Light carrying orbital angular momentum (OAM) has been shown to be of use in
+a disparate range of fields ranging from astronomy to optical trapping, and as
+a promising new dimension for multiplexing signals in optical communications
+and data storage. A challenge to many of these applications is a reliable and
+dynamic method that sorts incident OAM states without altering them. Here we
+report a wavelength-independent technique capable of dynamically filtering
+individual OAM states based on the resonant transmission of a tunable optical
+cavity. The cavity length is piezo-controlled to facilitate dynamic
+reconfiguration, and the sorting process leaves both the transmitted and
+reflected signals in their original states for subsequent processing. As a
+result, we also show that a reconfigurable sorting network can be constructed
+by cascading such optical resonators to handle multiple OAM states
+simultaneously. This approach to sorting OAM states is amenable to integration
+into optical communication networks and has implications in quantum optics,
+astronomy, optical data storage and optical trapping.
+",0,1,0,0,0,0
+8209,Factorization tests and algorithms arising from counting modular forms and automorphic representations,"  A theorem of Gekeler compares the number of non-isomorphic automorphic
+representations associated with the space of cusp forms of weight $k$ on
+$\Gamma_0(N)$ to a simpler function of $k$ and $N$, showing that the two are
+equal whenever $N$ is squarefree. We prove the converse of this theorem (with
+one small exception), thus providing a characterization of squarefree integers.
+We also establish a similar characterization of prime numbers in terms of the
+number of Hecke newforms of weight $k$ on $\Gamma_0(N)$.
+It follows that a hypothetical fast algorithm for computing the number of
+such automorphic representations for even a single weight $k$ would yield a
+fast test for whether $N$ is squarefree. We also show how to obtain bounds on
+the possible square divisors of a number $N$ that has been found to not be
+squarefree via this test, and we show how to probabilistically obtain the
+complete factorization of the squarefull part of $N$ from the number of such
+automorphic representations for two different weights. If in addition we have
+the number of such Hecke newforms for even a single weight $k$, then we show
+how to probabilistically factor $N$ entirely. All of these computations could
+be performed quickly in practice, given the number(s) of automorphic
+representations and modular forms as input.
+",0,0,1,0,0,0
+8210,ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems,"  Model-free policy learning has enabled robust performance of complex tasks
+with relatively simple algorithms. However, this simplicity comes at the cost
+of requiring an Oracle and arguably very poor sample complexity. This renders
+such methods unsuitable for physical systems. Variants of model-based methods
+address this problem through the use of simulators, however, this gives rise to
+the problem of policy transfer from simulated to the physical system. Model
+mismatch due to systematic parameter shift and unmodelled dynamics error may
+cause sub-optimal or unsafe behavior upon direct transfer. We introduce the
+Adaptive Policy Transfer for Stochastic Dynamics (ADAPT) algorithm that
+achieves provably safe and robust, dynamically-feasible zero-shot transfer of
+RL-policies to new domains with dynamics error. ADAPT combines the strengths of
+offline policy learning in a black-box source simulator with online tube-based
+MPC to attenuate bounded model mismatch between the source and target dynamics.
+ADAPT allows online transfer of policy, trained solely in a simulation offline,
+to a family of unknown targets without fine-tuning. We also formally show that
+(i) ADAPT guarantees state and control safety through state-action tubes under
+the assumption of Lipschitz continuity of the divergence in dynamics and, (ii)
+ADAPT results in a bounded loss of reward accumulation relative to a policy
+trained and evaluated in the source environment. We evaluate ADAPT on 2
+continuous, non-holonomic simulated dynamical systems with 4 different
+disturbance models, and find that ADAPT performs between 50%-300% better on
+mean reward accrual than direct policy transfer.
+",1,0,0,0,0,0
+8211,Learning the Kernel for Classification and Regression,"  We investigate a series of learning kernel problems with polynomial
+combinations of base kernels, which will help us solve regression and
+classification problems. We also perform some numerical experiments of
+polynomial kernels with regression and classification tasks on different
+datasets.
+",1,0,0,0,0,0
+8212,Critical system involving fractional Laplacian,"  In this paper, we study the following critical system with fractional
+Laplacian: \begin{equation*} \begin{cases}
+(-\Delta)^{s}u=
+\mu_{1}|u|^{2^{\ast}-2}u+\frac{\alpha\gamma}{2^{\ast}}|u|^{\alpha-2}u|v|^{\beta}
+\ \ \ \text{in} \ \ \mathbb{R}^{n},
+(-\Delta)^{s}v=
+\mu_{2}|v|^{2^{\ast}-2}v+\frac{\beta\gamma}{2^{\ast}}|u|^{\alpha}|v|^{\beta-2}v\
+\ \ \ \text{in} \ \ \mathbb{R}^{n}, u,v\in D_{s}(\mathbb{R}^{n}). \end{cases}
+\end{equation*}
+By using the Nehari\ manifold,\ under proper conditions, we establish the
+existence and nonexistence of positive least energy solution of the system.
+",0,0,1,0,0,0
+8213,Skin Lesion Classification Using Hybrid Deep Neural Networks,"  Skin cancer is one of the major types of cancers and its incidence has been
+increasing over the past decades. Skin lesions can arise from various
+dermatologic disorders and can be classified to various types according to
+their texture, structure, color and other morphological features. The accuracy
+of diagnosis of skin lesions, specifically the discrimination of benign and
+malignant lesions, is paramount to ensure appropriate patient treatment.
+Machine learning-based classification approaches are among popular automatic
+methods for skin lesion classification. While there are many existing methods,
+convolutional neural networks (CNN) have shown to be superior over other
+classical machine learning methods for object detection and classification
+tasks. In this work, a fully automatic computerized method is proposed, which
+employs well established pre-trained convolutional neural networks and
+ensembles learning to classify skin lesions. We trained the networks using 2000
+skin lesion images available from the ISIC 2017 challenge, which has three main
+categories and includes 374 melanoma, 254 seborrheic keratosis and 1372 benign
+nevi images. The trained classifier was then tested on 150 unlabeled images.
+The results, evaluated by the challenge organizer and based on the area under
+the receiver operating characteristic curve (AUC), were 84.8% and 93.6% for
+Melanoma and seborrheic keratosis binary classification problem, respectively.
+The proposed method achieved competitive results to experienced
+dermatologist. Further improvement and optimization of the proposed method with
+a larger training dataset could lead to a more precise, reliable and robust
+method for skin lesion classification.
+",1,0,0,0,0,0
+8214,Nice derivations over principal ideal domains,"  In this paper we investigate to what extent the results of Z. Wang and D.
+Daigle on nice derivations of the polynomial ring in three variables over a
+field k of characteristic zero extend to the polynomial ring over a PID R,
+containing the field of rational numbers. One of our results shows that the
+kernel of a nice derivation on the polynomial ring in four variables over k of
+rank at most three is a polynomial ring over k.
+",0,0,1,0,0,0
+8215,Cooperative Estimation via Altruism,"  A novel approach, based on the notion of altruism, is presented to
+cooperative estimation in a system comprising two information-sharing
+estimators. The underlying assumption is that the system's global mission can
+be accomplished even if only one of the estimators achieves satisfactory
+performance. The notion of altruism motivates a new definition of cooperative
+estimation optimality that generalizes the common definition of minimum mean
+square error optimality. Fundamental equations are derived for two types of
+altruistic cooperative estimation problems, corresponding to heterarchical and
+hierarchical setups. Although these equations are hard to solve in the general
+case, their solution in the Gaussian case is straightforward and only entails
+the largest eigenvalue of the conditional covariance matrix and its
+corresponding eigenvector. Moreover, in that case the performance improvement
+of the two altruistic cooperative estimation techniques over the conventional
+(egoistic) estimation approach is shown to depend on the problem's
+dimensionality and statistical distribution. In particular, the performance
+improvement grows with the dispersion of the spectrum of the conditional
+covariance matrix, rendering the new estimation approach especially appealing
+in ill-conditioned problems. The performance of the new approach is
+demonstrated using a numerical simulation study.
+",1,0,1,0,0,0
+8216,Multi-GPU maximum entropy image synthesis for radio astronomy,"  The maximum entropy method (MEM) is a well known deconvolution technique in
+radio-interferometry. This method solves a non-linear optimization problem with
+an entropy regularization term. Other heuristics such as CLEAN are faster but
+highly user dependent. Nevertheless, MEM has the following advantages: it is
+unsupervised, it has a statistical basis, it has a better resolution and better
+image quality under certain conditions. This work presents a high performance
+GPU version of non-gridding MEM, which is tested using real and simulated data.
+We propose a single-GPU and a multi-GPU implementation for single and
+multi-spectral data, respectively. We also make use of the Peer-to-Peer and
+Unified Virtual Addressing features of newer GPUs which allows to exploit
+transparently and efficiently multiple GPUs. Several ALMA data sets are used to
+demonstrate the effectiveness in imaging and to evaluate GPU performance. The
+results show that a speedup from 1000 to 5000 times faster than a sequential
+version can be achieved, depending on data and image size. This allows to
+reconstruct the HD142527 CO(6-5) short baseline data set in 2.1 minutes,
+instead of 2.5 days that takes a sequential version on CPU.
+",1,1,0,0,0,0
+8217,Green function for linearized Navier-Stokes around a boundary layer profile: away from critical layers,"  In this paper, we construct the Green function for the classical
+Orr-Sommerfeld equations, which are the linearized Navier-Stokes equations
+around a boundary layer profile. As an immediate application, we derive uniform
+sharp bounds on the semigroup of the linearized Navier-Stokes problem around
+unstable profiles in the vanishing viscosity limit.
+",0,0,1,0,0,0
+8218,SETI in vivo: testing the we-are-them hypothesis,"  After it was proposed that life on Earth might descend from seeding by an
+earlier civilization, some authors noted that this alternative offers a
+testable aspect: the seeds could be supplied with a signature that might be
+found in extant organisms. In particular, it was suggested that the optimal
+location for such an artifact is the genetic code, as the least evolving part
+of cells. However, as the mainstream view goes, this scenario is too
+speculative and cannot be meaningfully tested because encoding/decoding a
+signature within the genetic code is ill-defined, so any retrieval attempt is
+doomed to guesswork. Here we refresh the seeded-Earth hypothesis and discuss
+the motivation for inserting a signature. We then show that ""biological SETI""
+involves even weaker assumptions than traditional SETI and admits a
+well-defined methodological framework. After assessing the possibility in terms
+of molecular and evolutionary biology, we formalize the approach and, adopting
+the guideline of SETI that encoding/decoding should follow from first
+principles and be convention-free, develop a retrieval strategy. Applied to the
+canonical code, it reveals a nontrivial precision structure of interlocked
+systematic attributes. To assess this result in view of the initial assumption,
+we perform statistical, comparison, interdependence, and semiotic analyses.
+Statistical analysis reveals no causal connection to evolutionary models of the
+code, interdependence analysis precludes overinterpretation, and comparison
+analysis shows that known code variations lack any precision-logic structures,
+in agreement with these variations being post-seeding deviations from the
+canonical code. Finally, semiotic analysis shows that not only the found
+attributes are consistent with the initial assumption, but that they make
+perfect sense from SETI perspective, as they maintain some of the most
+universal codes of culture.
+",0,1,0,0,0,0
+8219,Explicit Time Integration of Transient Eddy Current Problems,"  For time integration of transient eddy current problems commonly implicit
+time integration methods are used, where in every time step one or several
+nonlinear systems of equations have to be linearized with the Newton-Raphson
+method due to ferromagnetic materials involved. In this paper, a generalized
+Schur-complement is applied to the magnetic vector potential formulation, which
+converts a differential-algebraic equation system of index 1 into a system of
+ordinary differential equations (ODE) with reduced stiffness. For the time
+integration of this ODE system of equations, the explicit Euler method is
+applied. The Courant-Friedrich-Levy (CFL) stability criterion of explicit time
+integration methods may result in small time steps. Applying a pseudo-inverse
+of the discrete curl-curl operator in nonconducting regions of the problem is
+required in every time step. For the computation of the pseudo-inverse, the
+preconditioned conjugate gradient (PCG) method is used. The cascaded Subspace
+Extrapolation method (CSPE) is presented to produce suitable start vectors for
+these PCG iterations. The resulting scheme is validated using the TEAM 10
+benchmark problem.
+",1,1,1,0,0,0
+8220,Transversality for local Morse homology with symmetries and applications,"  We prove the transversality result necessary for defining local Morse chain
+complexes with finite cyclic group symmetry. Our arguments use special
+regularized distance functions constructed using classical covering lemmas, and
+an inductive perturbation process indexed by the strata of the isotropy set. A
+global existence theorem for symmetric Morse-Smale pairs is also proved.
+Regarding applications, we focus on Hamiltonian dynamics and rigorously
+establish a local contact homology package based on discrete action
+functionals. We prove a persistence theorem, analogous to the classical
+shifting lemma for geodesics, asserting that the iteration map is an
+isomorphism for good and admissible iterations. We also consider a
+Chas-Sullivan product on non-invariant local Morse homology, which plays the
+role of pair-of-pants product, and study its relationship to symplectically
+degenerate maxima. Finally, we explore how our invariants can be used to study
+bifurcation of critical points (and periodic points) under additional
+symmetries.
+",0,0,1,0,0,0
+8221,On the maximum principle for the Riesz transform,"  Let $\mu$ be a measure in $\mathbb R^d$ with compact support and continuous
+density, and let $$ R^s\mu(x)=\int\frac{y-x}{|y-x|^{s+1}}\,d\mu(y),\ \
+x,y\in\mathbb R^d,\ \ 0<s<d. $$ We consider the following conjecture: $$
+\sup_{x\in\mathbb R^d}|R^s\mu(x)|\le
+C\sup_{x\in\text{supp}\,\mu}|R^s\mu(x)|,\quad C=C(d,s). $$ This relation was
+known for $d-1\le s<d$, and is still an open problem in the general case. We
+prove the maximum principle for $0< s<1$, and also for $0<s<d$ in the case of
+radial measure. Moreover, we show that this conjecture is incorrect for
+non-positive measures.
+",0,0,1,0,0,0
+8222,The complex case of Schmidt's going-down Theorem,"  In 1967, Schmidt wrote a seminal paper [10] on heights of subspaces of R n or
+C n defined over a number field K, and diophantine approximation problems. The
+going-down Theorem -- one of the main theorems he proved in his paper --
+remains valid in two cases depending on whether the embedding of K in the
+complex field C is a real or a complex non-real embedding. For the latter, and
+more generally as soon as K is not totally real, at some point of the proof,
+the arguments in [10] do not exactly work as announced. In this note, Schmidt's
+ideas are worked out in details and his proof of the complex case is presented,
+solving the aforementioned problem. Some definitions of Schmidt are
+reformulated in terms of multilinear algebra and wedge product, following the
+approaches of Laurent [5], Bugeaud and Laurent [1] and Roy [7], [8]. In [5]
+Laurent introduces in the case K = Q a family of exponents and he gives a
+series of inequalities relating them. In Section 5 these exponents are defined
+for an arbitrary number field K. Using the going-up and the going-down Theorems
+Laurent's inequalities are generalized to this setting.
+",0,0,1,0,0,0
+8223,Optimal rates of estimation for multi-reference alignment,"  In this paper, we establish optimal rates of adaptive estimation of a vector
+in the multi-reference alignment model, a problem with important applications
+in fields such as signal processing, image processing, and computer vision,
+among others. We describe how this model can be viewed as a multivariate
+Gaussian mixture model under the constraint that the centers belong to the
+orbit of a group. This enables us to derive matching upper and lower bounds
+that feature an interesting dependence on the signal-to-noise ratio of the
+model. Both upper and lower bounds are articulated around a tight local control
+of Kullback-Leibler divergences that showcases the central role of moment
+tensors in this problem.
+",0,0,1,1,0,0
+8224,Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints,"  Voltage control plays an important role in the operation of electricity
+distribution networks, especially with high penetration of distributed energy
+resources. These resources introduce significant and fast varying
+uncertainties. In this paper, we focus on reactive power compensation to
+control voltage in the presence of uncertainties. We adopt a chance constraint
+approach that accounts for arbitrary correlations between renewable resources
+at each of the buses. We show how the problem can be solved efficiently using
+historical samples via a stochastic quasi gradient method. We also show that
+this optimization problem is convex for a wide variety of probabilistic
+distributions. Compared to conventional per-bus chance constraints, our
+formulation is more robust to uncertainty and more computationally tractable.
+We illustrate the results using standard IEEE distribution test feeders.
+",1,0,1,1,0,0
+8225,Overcomplete Frame Thresholding for Acoustic Scene Analysis,"  In this work, we derive a generic overcomplete frame thresholding scheme
+based on risk minimization. Overcomplete frames being favored for analysis
+tasks such as classification, regression or anomaly detection, we provide a way
+to leverage those optimal representations in real-world applications through
+the use of thresholding. We validate the method on a large scale bird activity
+detection task via the scattering network architecture performed by means of
+continuous wavelets, known for being an adequate dictionary in audio
+environments.
+",1,0,0,1,0,0
+8226,A Hybrid Approach for Trajectory Control Design,"  This work presents a methodology to design trajectory tracking feedback
+control laws, which embed non-parametric statistical models, such as Gaussian
+Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired
+slippages. The proposed approach has the benefit of avoiding complex
+terramechanics analysis to directly estimate from data the robot dynamics on a
+wide class of trajectories. Experiments in both real and simulated environments
+prove that the proposed methodology is promising.
+",1,0,0,0,0,0
+8227,"Achieving and Managing Availability SLAs with ITIL Driven Processes, DevOps, and Workflow Tools","  System and application availability continues to be a fundamental
+characteristic of IT services. In recent years the IT Operations team at
+Wolters Kluwer CT Corporation has placed special focus on this area. Using a
+combination of goals, metrics, processes, organizational models, communication
+methods, corrective maintenance, root cause analysis, preventative engineering,
+automated alerting, and workflow automation significant progress has been made
+in meeting availability SLAs or Service Level Agreements. This paper presents
+the background of this work, approach, details of its implementation, and
+results. A special focus is provided on the use of a classical ITIL view as
+operationalized in an Agile and DevOps environment.
+Keywords: System Availability, Software Reliability, ITIL, Workflow
+Automation, Process Engineering, Production Support, Customer Support, Product
+Support, Change Management, Release Management, Incident Management, Problem
+Management, Organizational Design, Scrum, Agile, DevOps, Service Level
+Agreements, Software Measurement, Microsoft SharePoint.
+",1,0,0,0,0,0
+8228,Foresight: Rapid Data Exploration Through Guideposts,"  Current tools for exploratory data analysis (EDA) require users to manually
+select data attributes, statistical computations and visual encodings. This can
+be daunting for large-scale, complex data. We introduce Foresight, a
+visualization recommender system that helps the user rapidly explore large
+high-dimensional datasets through ""guideposts."" A guidepost is a visualization
+corresponding to a pronounced instance of a statistical descriptor of the
+underlying data, such as a strong linear correlation between two attributes,
+high skewness or concentration about the mean of a single attribute, or a
+strong clustering of values. For each descriptor, Foresight initially presents
+visualizations of the ""strongest"" instances, based on an appropriate ranking
+metric. Given these initial guideposts, the user can then look at ""nearby""
+guideposts by issuing ""guidepost queries"" containing constraints on metric
+type, metric strength, data attributes, and data values. Thus, the user can
+directly explore the network of guideposts, rather than the overwhelming space
+of data attributes and visual encodings. Foresight also provides for each
+descriptor a global visualization of ranking-metric values to both help orient
+the user and ensure a thorough exploration process. Foresight facilitates
+interactive exploration of large datasets using fast, approximate sketching to
+compute ranking metrics. We also contribute insights on EDA practices of data
+scientists, summarizing results from an interview study we conducted to inform
+the design of Foresight.
+",1,0,0,0,0,0
+8229,Transport by Lagrangian Vortices in the Eastern Pacific,"  Rotationally coherent Lagrangian vortices (RCLVs) are identified from
+satellite-derived surface geostrophic velocities in the Eastern Pacific
+(180$^\circ$-130$^\circ$ W) using the objective (frame-invariant) finite-time
+Lagrangian-coherent-structure detection method of Haller et al. (2016) based on
+the Lagrangian-averaged vorticity deviation. RCLVs are identified for 30, 90,
+and 270 day intervals over the entire satellite dataset, beginning in 1993. In
+contrast to structures identified using Eulerian eddy-tracking methods, the
+RCLVs maintain material coherence over the specified time intervals, making
+them suitable for material transport estimates. Statistics of RCLVs are
+compared to statistics of eddies identified from sea-surface height (SSH) by
+Chelton et al. 2011. RCLVs and SSH eddies are found to propagate westward at
+similar speeds at each latitude, consistent with the Rossby wave dispersion
+relation. However, RCLVs are uniformly smaller and shorter-lived than SSH
+eddies. A coherent eddy diffusivity is derived to quantify the contribution of
+RCLVs to meridional transport; it is found that RCLVs contribute less than 1%
+to net meridional dispersion and diffusion in this sector, implying that eddy
+transport of tracers is mostly due to incoherent motions, such as swirling and
+filamentation outside of the eddy cores, rather than coherent meridional
+translation of eddies themselves. These findings call into question prior
+estimates of coherent eddy transport based on Eulerian eddy identification
+methods.
+",0,1,0,0,0,0
+8230,"Online Adaptive Methods, Universality and Acceleration","  We present a novel method for convex unconstrained optimization that, without
+any modifications, ensures: (i) accelerated convergence rate for smooth
+objectives, (ii) standard convergence rate in the general (non-smooth) setting,
+and (iii) standard convergence rate in the stochastic optimization setting. To
+the best of our knowledge, this is the first method that simultaneously applies
+to all of the above settings. At the heart of our method is an adaptive
+learning rate rule that employs importance weights, in the spirit of adaptive
+online learning algorithms (Duchi et al., 2011; Levy, 2017), combined with an
+update that linearly couples two sequences, in the spirit of (Allen-Zhu and
+Orecchia, 2017). An empirical examination of our method demonstrates its
+applicability to the above mentioned scenarios and corroborates our theoretical
+findings.
+",0,0,0,1,0,0
+8231,A Note on a Quantitative Form of the Solovay-Kitaev Theorem,"  The problem of finding good approximations of arbitrary 1-qubit gates is
+identical to that of finding a dense group generated by a universal subset of
+$SU(2)$ to approximate an arbitrary element of $SU(2)$. The Solovay-Kitaev
+Theorem is a well-known theorem that guarantees the existence of a finite
+sequence of 1-qubit quantum gates approximating an arbitrary unitary matrix in
+$SU(2)$ within specified accuracy $\varepsilon > 0$. In this note we study a
+quantitative description of this theorem in the following sense. We will work
+with a universal gate set $T$, a subset of $SU(2)$ such that the group
+generated by the elements of $T$ is dense in $SU(2)$. For $\varepsilon > 0$
+small enough, we define $t_{\varepsilon}$ as the minimum reduced word length
+such that every point of $SU(2)$ lies within a ball of radius $\varepsilon$
+centered at the points in the dense subgroup generated by $T$. For a measure of
+efficiency on T, which we denote $K(T)$, we prove the following theorem: Fix a
+$\delta$ in $[0, \frac{2}{3}]$. Choose $f: (0, \infty) \rightarrow (1, \infty)$
+satisfying $\lim_{\varepsilon\to
+0+}\dfrac{\log(f(t_{\varepsilon}))}{t_{\varepsilon}}$ exists with value $0$.
+Assume that the inequality $\varepsilon \leqslant f(t_{\varepsilon})\cdot
+5^{\frac{-t_{\varepsilon}}{6-3\delta}}$ holds. Then $K(T) \leqslant 2-\delta$.
+Our conjecture implies the following: Let $\nu(5^{t_{\varepsilon}})$ denote the
+set of integer solutions of the quadratic form:
+$x_1^2+x_2^2+x_3^2+x_4^2=5^{t_{\varepsilon}}$. Let $M\equiv
+M_{S^3}(\mathcal{N})$ denote the covering radius of the points
+$\mathcal{N}=\nu(5^{t_{\varepsilon}})\cup\nu(5^{t_{\varepsilon}-1})$ on the
+sphere $S^{3}$ in $\mathbb{R}^{4}$. Then $M \sim f(\log
+N)N^{\frac{-1}{6-3\delta}}$. Here $N\equiv
+N(\varepsilon)=6\cdot5^{t_{\varepsilon}}-2$.
+",0,0,1,0,0,0
+8232,Learning Steerable Filters for Rotation Equivariant CNNs,"  In many machine learning tasks it is desirable that a model's prediction
+transforms in an equivariant way under transformations of its input.
+Convolutional neural networks (CNNs) implement translational equivariance by
+construction; for other transformations, however, they are compelled to learn
+the proper mapping. In this work, we develop Steerable Filter CNNs (SFCNNs)
+which achieve joint equivariance under translations and rotations by design.
+The proposed architecture employs steerable filters to efficiently compute
+orientation dependent responses for many orientations without suffering
+interpolation artifacts from filter rotation. We utilize group convolutions
+which guarantee an equivariant mapping. In addition, we generalize He's weight
+initialization scheme to filters which are defined as a linear combination of a
+system of atomic filters. Numerical experiments show a substantial enhancement
+of the sample complexity with a growing number of sampled filter orientations
+and confirm that the network generalizes learned patterns over orientations.
+The proposed approach achieves state-of-the-art on the rotated MNIST benchmark
+and on the ISBI 2012 2D EM segmentation challenge.
+",1,0,0,0,0,0
+8233,Stochastic Multi-armed Bandits in Constant Space,"  We consider the stochastic bandit problem in the sublinear space setting,
+where one cannot record the win-loss record for all $K$ arms. We give an
+algorithm using $O(1)$ words of space with regret \[
+\sum_{i=1}^{K}\frac{1}{\Delta_i}\log \frac{\Delta_i}{\Delta}\log T \] where
+$\Delta_i$ is the gap between the best arm and arm $i$ and $\Delta$ is the gap
+between the best and the second-best arms. If the rewards are bounded away from
+$0$ and $1$, this is within an $O(\log 1/\Delta)$ factor of the optimum regret
+possible without space constraints.
+",1,0,0,1,0,0
+8234,Max K-armed bandit: On the ExtremeHunter algorithm and beyond,"  This paper is devoted to the study of the max K-armed bandit problem, which
+consists in sequentially allocating resources in order to detect extreme
+values. Our contribution is twofold. We first significantly refine the analysis
+of the ExtremeHunter algorithm carried out in Carpentier and Valko (2014), and
+next propose an alternative approach, showing that, remarkably, Extreme Bandits
+can be reduced to a classical version of the bandit problem to a certain
+extent. Beyond the formal analysis, these two approaches are compared through
+numerical experiments.
+",1,0,0,1,0,0
+8235,Comparing Different Models for Investigating Cascading Failures in Power Systems,"  This paper centers on the comparison of three different models that describe
+cascading failures of power systems. Specifically, these models are different
+in characterizing the physical properties of power networks and computing the
+branch power flow. Optimal control approach is applied on these models to
+identify the critical disturbances that result in the worst-case cascading
+failures of power networks. Then we compare these models by analyzing the
+critical disturbances and cascading processes. Significantly, comparison
+results on IEEE 9 bus system demonstrate that physical and electrical
+properties of power networks play a crucial role in the evolution of cascading
+failures, and it is necessary to take into account these properties
+appropriately while applying the model in the analysis of cascading blackout.
+",0,0,1,0,0,0
+8236,Initial-boundary value problems in a rectangle for two-dimensional Zakharov-Kuznetsov equation,"  Initial-boundary value problems in a bounded rectangle with different types
+of boundary conditions for two-dimensional Zakharov-Kuznetsov equation are
+considered. Results on global well-posedness in the classes of weak and regular
+solution are established. As applications of the developed technique results on
+boundary controllability and long-time decay of weak solutions are also
+obtained.
+",0,0,1,0,0,0
+8237,Two-dimensional Fermi gases near a p-wave resonance: effect of quantum fluctuations,"  We study the stability of p-wave superfluidity against quantum fluctuations
+in two-dimensional Fermi gases near a p-wave Feshbach resonance . An analysis
+is carried out in the limit when the interchannel coupling is strong. By
+investigating the effective potential for the pairing field via the standard
+loop expansion, we show that a homogeneous p-wave pairing state becomes
+unstable when two-loop quantum fluctuations are taken into account. This is in
+contrast to the previously predicted $p + ip$ supefluid in the weak-coupling
+limit [V. Gurarie et al., Phys. Rev. Lett. 94, 230403 (2005)]. It implies a
+possible onset of instability at certain intermediate interchannel coupling
+strength. Alternatively, the instability can also be driven by lowering the
+particle density. We also discuss the validity of our analysis.
+",0,1,0,0,0,0
+8238,Optimal Strong Rates of Convergence for a Space-Time Discretization of the Stochastic Allen-Cahn Equation with multiplicative noise,"  The stochastic Allen-Cahn equation with multiplicative noise involves the
+nonlinear drift operator ${\mathscr A}(x) = \Delta x - \bigl(\vert x\vert^2
+-1\bigr)x$. We use the fact that ${\mathscr A}(x) = -{\mathcal J}^{\prime}(x)$
+satisfies a weak monotonicity property to deduce uniform bounds in strong norms
+for solutions of the temporal, as well as of the spatio-temporal discretization
+of the problem. This weak monotonicity property then allows for the estimate $
+\underset{1 \leq j \leq J}\sup {\mathbb E}\bigl[ \Vert X_{t_j} -
+Y^j\Vert_{{\mathbb L}^2}^2\bigr] \leq C_{\delta}(k^{1-\delta} + h^2)$ for all
+small $\delta>0$, where $X$ is the strong variational solution of the
+stochastic Allen-Cahn equation, while $\big\{Y^j:0\le j\le J\big\}$ solves a
+structure preserving finite element based space-time discretization of the
+problem on a temporal mesh $\{ t_j;\, 1 \leq j \leq J\}$ of size $k>0$ which
+covers $[0,T]$.
+",0,0,1,0,0,0
+8239,A generalized quantum Slepian-Wolf,"  In this work we consider a quantum generalization of the task considered by
+Slepian and Wolf [1973] regarding distributed source compression. In our task
+Alice, Bob, Charlie and Reference share a joint pure state. Alice and Bob wish
+to send a part of their respective systems to Charlie without collaborating
+with each other. We give achievability bounds for this task in the one-shot
+setting and provide the asymptotic and i.i.d. analysis in the case when there
+is no side information with Charlie.
+Our result implies the result of Abeyesinghe, Devetak, Hayden and Winter
+[2009] who studied a special case of this problem. As another special case
+wherein Bob holds trivial registers, we recover the result of Devetak and Yard
+[2008] regarding quantum state redistribution.
+",1,0,0,0,0,0
+8240,The rational SPDE approach for Gaussian random fields with general smoothness,"  A popular approach for modeling and inference in spatial statistics is to
+represent Gaussian random fields as solutions to stochastic partial
+differential equations (SPDEs) of the form $L^{\beta}u = \mathcal{W}$, where
+$\mathcal{W}$ is Gaussian white noise, $L$ is a second-order differential
+operator, and $\beta>0$ is a parameter that determines the smoothness of $u$.
+However, this approach has been limited to the case $2\beta\in\mathbb{N}$,
+which excludes several important covariance models and makes it necessary to
+keep $\beta$ fixed during inference.
+We introduce a new method, the rational SPDE approach, which is applicable
+for any $\beta>0$ and therefore remedies the mentioned limitation. The
+presented scheme combines a finite element discretization in space with a
+rational approximation of the function $x^{-\beta}$ to approximate $u$. For the
+resulting approximation, an explicit rate of strong convergence to $u$ is
+derived and we show that the method has the same computational benefits as in
+the restricted case $2\beta\in\mathbb{N}$ when used for statistical inference
+and prediction. Several numerical experiments are performed to illustrate the
+accuracy of the method, and to show how it can be used for likelihood-based
+inference for all model parameters including $\beta$.
+",0,0,0,1,0,0
+8241,"An Analog of the Neumann Problem for the $1$-Laplace Equation in the Metric Setting: Existence, Boundary Regularity, and Stability","  We study an inhomogeneous Neumann boundary value problem for functions of
+least gradient on bounded domains in metric spaces that are equipped with a
+doubling measure and support a Poincaré inequality. We show that solutions
+exist under certain regularity assumptions on the domain, but are generally
+nonunique. We also show that solutions can be taken to be differences of two
+characteristic functions, and that they are regular up to the boundary when the
+boundary is of positive mean curvature. By regular up to the boundary we mean
+that if the boundary data is $1$ in a neighborhood of a point on the boundary
+of the domain, then the solution is $-1$ in the intersection of the domain with
+a possibly smaller neighborhood of that point. Finally, we consider the
+stability of solutions with respect to boundary data.
+",0,0,1,0,0,0
+8242,Activating spin-forbidden transitions in molecules by the highly localized plasmonic field,"  Optical spectroscopy has been the primary tool to study the electronic
+structure of molecules. However the strict spin selection rule has severely
+limited its ability to access states of different spin multiplicities. Here we
+propose a new strategy to activate spin-forbidden transitions in molecules by
+introducing spatially highly inhomogeneous plasmonic field. The giant
+enhancement of the magnetic field strength resulted from the curl of the
+inhomogeneous vector potential makes the transition between states of different
+spin multiplicities naturally feasible. The dramatic effect of the
+inhomogeneity of the plasmonic field on the spin and symmetry selection rules
+is well illustrated by first principles calculations of C60. Remarkably, the
+intensity of singlet-triplet transitions can even be stronger than that of
+singlet-singlet transitions when the plasmon spatial distribution is comparable
+with the molecular size. This approach offers a powerful means to completely
+map out all excited states of molecules and to actively control their
+photochemical processes. The same concept can also be applied to study nano and
+biological systems.
+",0,1,0,0,0,0
+8243,On the Essential Spectrum of Schrödinger Operators on Trees,"  It is known that the essential spectrum of a Schrödinger operator $H$ on
+$\ell^{2}\left(\mathbb{N}\right)$ is equal to the union of the spectra of right
+limits of $H$. The natural generalization of this relation to $\mathbb{Z}^{n}$
+is known to hold as well. In this paper we generalize the notion of right
+limits to general infinite connected graphs and construct examples of graphs
+for which the essential spectrum of the Laplacian is strictly bigger than the
+union of the spectra of its right limits. As these right limits are trees, this
+result is complemented by the fact that the equality still holds for general
+bounded operators on regular trees. We prove this and characterize the
+essential spectrum in the spherically symmetric case.
+",0,0,1,0,0,0
+8244,The equilibrium of over-pressurised polytropes,"  We investigate the impact of an external pressure on the structure of
+self-gravitating polytropes for axially symmetric ellipsoids and rings. The
+confinement of the fluid by photons is accounted for through a boundary
+condition on the enthalpy $H$. Equilibrium configurations are determined
+numerically from a generalised ""Self-Consistent-Field""-method. The new
+algorithm incorporates an intra-loop re-scaling operator ${\cal R}(H)$, which
+is essential for both convergence and getting self-normalised solutions. The
+main control parameter is the external-to-core enthalpy ratio. In the case of
+uniform rotation rate and uniform surrounding pressure, we compute the mass,
+the volume, the rotation rate and the maximum enthalpy. This is repeated for a
+few polytropic indices $n$. For a given axis ratio, over-pressurization
+globally increases all output quantities, and this is more pronounced for large
+$n$. Density profiles are flatter than in the absence of an external pressure.
+When the control parameter asymptotically tends to unity, the fluid converges
+toward the incompressible solution, whatever the index, but becomes
+geometrically singular. Equilibrium sequences, obtained by varying the axis
+ratio, are built. States of critical rotation are greatly exceeded or even
+disappear. The same trends are observed with differential rotation. Finally,
+the typical response to a photon point source is presented. Strong irradiation
+favours sharp edges. Applications concern star forming regions and matter
+orbiting young stars and black holes.
+",0,1,0,0,0,0
+8245,Charge and pairing dynamics in the attractive Hubbard model: mode coupling and the validity of linear-response theory,"  Pump-probe experiments have turned out as a powerful tool in order to study
+the dynamics of competing orders in a large variety of materials. The
+corresponding analysis of the data often relies on standard linear-response
+theory generalized to non-equilibrium situations. Here we examine the validity
+of such an approach within the attractive Hubbard model for which the dynamics
+of pairing and charge-density wave orders is computed using the time-dependent
+Hartree-Fock approximation (TDHF). Our calculations reveal that the
+`linear-response assumption' is justified for small to moderate non-equilibrium
+situations (i.e., pump pulses) when the symmetry of the pump-induced state
+differs from that of the external field. This is the case, when we consider the
+pairing response in a charge-ordered state or the charge-order response in a
+superconducting state. The situation is very different when the non-equilibrium
+state and the external probe field have the same symmetry. In this case, we
+observe significant changes of the response in magnitude but also due to mode
+coupling when moving away from an equilibrium state, indicating the failure of
+the linear-response assumption.
+",0,1,0,0,0,0
+8246,From voids to filaments: environmental transformations of galaxies in the SDSS,"  We investigate the impact of filament and void environments on galaxies,
+looking for residual effects beyond the known relations with environment
+density. We quantified the host environment of galaxies as the distance to the
+spine of the nearest filament, and compared various galaxy properties within 12
+bins of this distance. We considered galaxies up to 10 $h^{-1}$Mpc from
+filaments, i.e. deep inside voids. The filaments were defined by a point
+process (the Bisous model) from the Sloan Digital Sky Survey data release 10.
+In order to remove the dependence of galaxy properties on the environment
+density and redshift, we applied weighting to normalise the corresponding
+distributions of galaxy populations in each bin. After the normalisation with
+respect to environment density and redshift, several residual dependencies of
+galaxy properties still remain. Most notable is the trend of morphology
+transformations, resulting in a higher elliptical-to-spiral ratio while moving
+from voids towards filament spines, bringing along a corresponding increase in
+the $g-i$ colour index and a decrease in star formation rate. After separating
+elliptical and spiral subsamples, some of the colour index and star formation
+rate evolution still remains. The mentioned trends are characteristic only for
+galaxies brighter than about $M_{r} = -20$ mag. Unlike some other recent
+studies, we do not witness an increase in the galaxy stellar mass while
+approaching filaments. The detected transformations can be explained by an
+increase in the galaxy-galaxy merger rate and/or the cut-off of extragalactic
+gas supplies (starvation) near and inside filaments. Unlike voids, large-scale
+galaxy filaments are not a mere density enhancement, but have their own
+specific impact on the constituent galaxies, reducing the star formation rate
+and raising the chances of elliptical morphology also at a fixed environment
+density level.
+",0,1,0,0,0,0
+8247,Distributed Algorithms Made Secure: A Graph Theoretic Approach,"  In the area of distributed graph algorithms a number of network's entities
+with local views solve some computational task by exchanging messages with
+their neighbors. Quite unfortunately, an inherent property of most existing
+distributed algorithms is that throughout the course of their execution, the
+nodes get to learn not only their own output but rather learn quite a lot on
+the inputs or outputs of many other entities. This leakage of information might
+be a major obstacle in settings where the output (or input) of network's
+individual is a private information. In this paper, we introduce a new
+framework for \emph{secure distributed graph algorithms} and provide the first
+\emph{general compiler} that takes any ""natural"" non-secure distributed
+algorithm that runs in $r$ rounds, and turns it into a secure algorithm that
+runs in $\widetilde{O}(r \cdot D \cdot poly(\Delta))$ rounds where $\Delta$ is
+the maximum degree in the graph and $D$ is its diameter. The security of the
+compiled algorithm is information-theoretic but holds only against a
+semi-honest adversary that controls a single node in the network.
+This compiler is made possible due to a new combinatorial structure called
+\emph{private neighborhood trees}: a collection of $n$ trees
+$T(u_1),\ldots,T(u_n)$, one for each vertex $u_i \in V(G)$, such that each tree
+$T(u_i)$ spans the neighbors of $u_i$ {\em without going through $u_i$}.
+Intuitively, each tree $T(u_i)$ allows all neighbors of $u_i$ to exchange a
+\emph{secret} that is hidden from $u_i$, which is the basic graph
+infrastructure of the compiler. In a $(d,c)$-private neighborhood trees each
+tree $T(u_i)$ has depth at most $d$ and each edge $e \in G$ appears in at most
+$c$ different trees. We show a construction of private neighborhood trees with
+$d=\widetilde{O}(\Delta \cdot D)$ and $c=\widetilde{O}(D)$.
+",1,0,0,0,0,0
+8248,Interpretation of Neural Networks is Fragile,"  In order for machine learning to be deployed and trusted in many
+applications, it is crucial to be able to reliably explain why the machine
+learning algorithm makes certain predictions. For example, if an algorithm
+classifies a given pathology image to be a malignant tumor, then the doctor may
+need to know which parts of the image led the algorithm to this classification.
+How to interpret black-box predictors is thus an important and active area of
+research. A fundamental question is: how much can we trust the interpretation
+itself? In this paper, we show that interpretation of deep learning predictions
+is extremely fragile in the following sense: two perceptively indistinguishable
+inputs with the same predicted label can be assigned very different
+interpretations. We systematically characterize the fragility of several
+widely-used feature-importance interpretation methods (saliency maps, relevance
+propagation, and DeepLIFT) on ImageNet and CIFAR-10. Our experiments show that
+even small random perturbation can change the feature importance and new
+systematic perturbations can lead to dramatically different interpretations
+without changing the label. We extend these results to show that
+interpretations based on exemplars (e.g. influence functions) are similarly
+fragile. Our analysis of the geometry of the Hessian matrix gives insight on
+why fragility could be a fundamental challenge to the current interpretation
+approaches.
+",1,0,0,1,0,0
+8249,Dipolar phonons and electronic screening in monolayer FeSe on SrTiO$_3$,"  Monolayer films of FeSe grown on SrTiO$_3$ substrates exhibit significantly
+higher superconducting transition temperatures than those of bulk FeSe.
+Interaction of electrons in the FeSe layer with dipolar SrTiO$_3$ phonons has
+been suggested as the cause of the enhanced transition temperature. In this
+paper we systematically study the coupling of SrTiO$_3$ longitudinal optical
+phonons to the FeSe electron, including also electron-electron Coulomb
+interactions at the random phase approximation level. We find that the
+electron-phonon interaction between FeSe and SrTiO$_3$ substrate is almost
+entirely screened by the electronic fluctuations in the FeSe monolayer, so that
+the net electron-phonon interaction is very weak and unlikely to lead to
+superconductivity.
+",0,1,0,0,0,0
+8250,A Supervised Approach to Extractive Summarisation of Scientific Papers,"  Automatic summarisation is a popular approach to reduce a document to its
+main arguments. Recent research in the area has focused on neural approaches to
+summarisation, which can be very data-hungry. However, few large datasets exist
+and none for the traditionally popular domain of scientific publications, which
+opens up challenging research avenues centered on encoding large, complex
+documents. In this paper, we introduce a new dataset for summarisation of
+computer science publications by exploiting a large resource of author provided
+summaries and show straightforward ways of extending it further. We develop
+models on the dataset making use of both neural sentence encoding and
+traditionally used summarisation features and show that models which encode
+sentences as well as their local and global context perform best, significantly
+outperforming well-established baseline methods.
+",1,0,0,1,0,0
+8251,Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems,"  Channel feedback is essential in frequency division duplexing (FDD) massive
+multiple-input multiple-output (MIMO) systems. Unfortunately, previous work on
+multiuser MIMO has shown that the codebook size for channel feedback should
+scale exponentially with the number of base station (BS) antennas, which is
+greatly increased in massive MIMO systems. To reduce the codebook size and
+feedback overhead, we propose an angle-of-departure (AoD)-adaptive subspace
+codebook for channel feedback in FDD massive MIMO systems. Our key insight is
+to leverage the observation that path AoDs vary more slowly than the path
+gains. Within the angle coherence time, by utilizing the constant AoD
+information, the proposed AoD-adaptive subspace codebook is able to quantize
+the channel vector in a more accurate way. We also provide performance analysis
+of the proposed codebook in the large-dimensional regime, where we prove that
+to limit the capacity degradation within an acceptable level, the required
+number of feedback bits only scales linearly with the number of resolvable
+(path) AoDs, which is much smaller than the number of BS antennas. Moreover, we
+compare quantized channel feedback using the proposed AoD-adaptive subspace
+codebook with analog channel feedback. Extensive simulations that verify the
+analytical results are provided.
+",1,0,0,0,0,0
+8252,Thermalization in simple metals: The role of electron-phonon and phonon-phonon scatterings,"  We study the electron and phonon thermalization in simple metals excited by a
+laser pulse. The thermalization is investigated numerically by solving the
+Boltzmann transport equation taking into account all the relevant scattering
+mechanism: the electron-electron, electron-phonon (e-ph), phonon-electron
+(ph-e), and phonon-phonon (ph-ph) scatterings. In the initial stage of the
+relaxation, most of the excitation energy is transferred from the electrons to
+phonons through the e-ph scattering. This creates hot high-frequency phonons
+due to the ph-e scatterings, followed by an energy redistribution between
+phonon subsystems through the ph-ph scatterings. This yields an overshoot of
+the total longitudinal-acoustic phonon energy at a time, across which a
+crossover occurs from a nonequilibrium state, where the e-ph and ph-e
+scatterings frequently occur, to a state, where the ph-ph scattering occurs to
+reach a thermal equilibrium. This picture is quite different from the scenario
+of the well-known two-temperature model (2TM). The behavior of the relaxation
+dynamics is compared with those calculated by several models, including the
+2TM, the four-temperature model, and nonequilibrium electron or phonon models.
+The relationship between the relaxation time and the initial distribution
+function is also discussed.
+",0,1,0,0,0,0
+8253,Convergence of the free Boltzmann quadrangulation with simple boundary to the Brownian disk,"  We prove that the free Boltzmann quadrangulation with simple boundary and
+fixed perimeter, equipped with its graph metric, natural area measure, and the
+path which traces its boundary converges in the scaling limit to the free
+Boltzmann Brownian disk. The topology of convergence is the so-called
+Gromov-Hausdorff-Prokhorov-uniform (GHPU) topology, the natural analog of the
+Gromov-Hausdorff topology for curve-decorated metric measure spaces. From this
+we deduce that a random quadrangulation of the sphere decorated by a $2l$-step
+self-avoiding loop converges in law in the GHPU topology to the random
+curve-decorated metric measure space obtained by gluing together two
+independent Brownian disks along their boundaries.
+",0,0,1,0,0,0
+8254,"Canonical quantization of nonlinear sigma models with theta term, with applications to symmetry-protected topological phases","  We canonically quantize $O(D+2)$ nonlinear sigma models (NLSMs) with theta
+term on arbitrary smooth, closed, connected, oriented $D$-dimensional spatial
+manifolds $\mathcal{M}$, with the goal of proving the suitability of these
+models for describing symmetry-protected topological (SPT) phases of bosons in
+$D$ spatial dimensions. We show that in the disordered phase of the NLSM, and
+when the coefficient $\theta$ of the theta term is an integer multiple of
+$2\pi$, the theory on $\mathcal{M}$ has a unique ground state and a finite
+energy gap to all excitations. We also construct the ground state wave
+functional of the NLSM in this parameter regime, and we show that it is
+independent of the metric on $\mathcal{M}$ and given by the exponential of a
+Wess-Zumino term for the NLSM field, in agreement with previous results on flat
+space. Our results show that the NLSM in the disordered phase and at
+$\theta=2\pi k$, $k\in\mathbb{Z}$, has a symmetry-preserving ground state but
+no topological order (i.e., no topology-dependent ground state degeneracy),
+making it an ideal model for describing SPT phases of bosons. Thus, our work
+places previous results on SPT phases derived using NLSMs on solid theoretical
+ground. To canonically quantize the NLSM on $\mathcal{M}$ we use Dirac's method
+for the quantization of systems with second class constraints, suitably
+modified to account for the curvature of space. In a series of four appendices
+we provide the technical background needed to follow the discussion in the main
+sections of the paper.
+",0,1,0,0,0,0
+8255,A Nonparametric Bayesian Approach to Copula Estimation,"  We propose a novel Dirichlet-based Pólya tree (D-P tree) prior on the
+copula and based on the D-P tree prior, a nonparametric Bayesian inference
+procedure. Through theoretical analysis and simulations, we are able to show
+that the flexibility of the D-P tree prior ensures its consistency in copula
+estimation, thus able to detect more subtle and complex copula structures than
+earlier nonparametric Bayesian models, such as a Gaussian copula mixture.
+Further, the continuity of the imposed D-P tree prior leads to a more favorable
+smoothing effect in copula estimation over classic frequentist methods,
+especially with small sets of observations. We also apply our method to the
+copula prediction between the S\&P 500 index and the IBM stock prices during
+the 2007-08 financial crisis, finding that D-P tree-based methods enjoy strong
+robustness and flexibility over classic methods under such irregular market
+behaviors.
+",0,0,0,1,0,0
+8256,Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone,"  In this paper, we present a Model Predictive Control (MPC) framework based on
+path velocity decomposition paradigm for autonomous driving. The optimization
+underlying the MPC has a two layer structure wherein first, an appropriate path
+is computed for the vehicle followed by the computation of optimal forward
+velocity along it. The very nature of the proposed path velocity decomposition
+allows for seamless compatibility between the two layers of the optimization. A
+key feature of the proposed work is that it offloads most of the responsibility
+of collision avoidance to velocity optimization layer for which computationally
+efficient formulations can be derived. In particular, we extend our previously
+developed concept of time scaled collision cone (TSCC) constraints and
+formulate the forward velocity optimization layer as a convex quadratic
+programming problem. We perform validation on autonomous driving scenarios
+wherein proposed MPC repeatedly solves both the optimization layers in receding
+horizon manner to compute lane change, overtaking and merging maneuvers among
+multiple dynamic obstacles.
+",1,0,0,0,0,0
+8257,A Grouping Genetic Algorithm for Joint Stratification and Sample Allocation Designs,"  Predicting the cheapest sample size for the optimal stratification in
+multivariate survey design is a problem in cases where the population frame is
+large. A solution exists that iteratively searches for the minimum sample size
+necessary to meet accuracy constraints in partitions of atomic strata created
+by the Cartesian product of auxiliary variables into larger strata. The optimal
+stratification can be found by testing all possible partitions. However the
+number of possible partitions grows exponentially with the number of initial
+strata. There are alternative ways of modelling this problem, one of the most
+natural is using Genetic Algorithms (GA). These evolutionary algorithms use
+recombination, mutation and selection to search for optimal solutions. They
+often converge on optimal or near-optimal solution more quickly than exact
+methods. We propose a new GA approach to this problem using grouping genetic
+operators instead of traditional operators. The results show a significant
+improvement in solution quality for similar computational effort, corresponding
+to large monetary savings.
+",0,0,0,1,0,0
+8258,Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks,"  In this article, we propose a novel technique for classification of the
+Murmurs in heart sound. We introduce a novel deep neural network architecture
+using parallel combination of the Recurrent Neural Network (RNN) based
+Bidirectional Long Short-Term Memory (BiLSTM) & Convolutional Neural Network
+(CNN) to learn visual and time-dependent characteristics of Murmur in PCG
+waveform. Set of acoustic features are presented to our proposed deep neural
+network to discriminate between Normal and Murmur class. The proposed method
+was evaluated on a large dataset using 5-fold cross-validation, resulting in a
+sensitivity and specificity of 96 +- 0.6 % , 100 +- 0 % respectively and F1
+Score of 98 +- 0.3 %.
+",1,0,0,1,0,0
+8259,Time-of-Flight Electron Energy Loss Spectroscopy by Longitudinal Phase Space Manipulation with Microwave Cavities,"  The possibility to perform high-resolution time-resolved electron energy loss
+spectroscopy has the potential to impact a broad range of research fields.
+Resolving small energy losses with ultrashort electron pulses, however, is an
+enormous challenge due to the low average brightness of a pulsed beam. In this
+letter, we propose to use time-of-flight measurements combined with
+longitudinal phase space manipulation using resonant microwave cavities. This
+allows for both an accurate detection of energy losses with a high current
+throughput, and efficient monochromation. First, a proof-of-principle
+experiment is presented, showing that with the incorporation of a compression
+cavity the flight time resolution can be improved significantly. Then, it is
+shown through simulations that by adding a cavity-based monochromation
+technique, a full-width-at-half-maximum energy resolution of 22 meV can be
+achieved with 3.1 ps pulses at a beam energy of 30 keV with currently available
+technology. By combining state-of-the-art energy resolutions with a pulsed
+electron beam, the technique proposed here opens up the way to detecting
+short-lived excitations within the regime of highly collective physics.
+",0,1,0,0,0,0
+8260,Learning across scales - A multiscale method for Convolution Neural Networks,"  In this work we establish the relation between optimal control and training
+deep Convolution Neural Networks (CNNs). We show that the forward propagation
+in CNNs can be interpreted as a time-dependent nonlinear differential equation
+and learning as controlling the parameters of the differential equation such
+that the network approximates the data-label relation for given training data.
+Using this continuous interpretation we derive two new methods to scale CNNs
+with respect to two different dimensions. The first class of multiscale methods
+connects low-resolution and high-resolution data through prolongation and
+restriction of CNN parameters. We demonstrate that this enables classifying
+high-resolution images using CNNs trained with low-resolution images and vice
+versa and warm-starting the learning process. The second class of multiscale
+methods connects shallow and deep networks and leads to new training strategies
+that gradually increase the depths of the CNN while re-using parameters for
+initializations.
+",1,0,0,0,0,0
+8261,Capacitated Covering Problems in Geometric Spaces,"  In this article, we consider the following capacitated covering problem. We
+are given a set $P$ of $n$ points and a set $\mathcal{B}$ of balls from some
+metric space, and a positive integer $U$ that represents the capacity of each
+of the balls in $\mathcal{B}$. We would like to compute a subset $\mathcal{B}'
+\subseteq \mathcal{B}$ of balls and assign each point in $P$ to some ball in
+$\mathcal{B}$ that contains it, such that the number of points assigned to any
+ball is at most $U$. The objective function that we would like to minimize is
+the cardinality of $\mathcal{B}$.
+We consider this problem in arbitrary metric spaces as well as Euclidean
+spaces of constant dimension. In the metric setting, even the uncapacitated
+version of the problem is hard to approximate to within a logarithmic factor.
+In the Euclidean setting, the best known approximation guarantee in dimensions
+$3$ and higher is logarithmic in the number of points. Thus we focus on
+obtaining ""bi-criteria"" approximations. In particular, we are allowed to expand
+the balls in our solution by some factor, but optimal solutions do not have
+that flexibility. Our main result is that allowing constant factor expansion of
+the input balls suffices to obtain constant approximations for these problems.
+In fact, in the Euclidean setting, only $(1+\epsilon)$ factor expansion is
+sufficient for any $\epsilon > 0$, with the approximation factor being a
+polynomial in $1/\epsilon$. We obtain these results using a unified scheme for
+rounding the natural LP relaxation; this scheme may be useful for other
+capacitated covering problems. We also complement these bi-criteria
+approximations by obtaining hardness of approximation results that shed light
+on our understanding of these problems.
+",1,0,0,0,0,0
+8262,Irregular Oscillatory-Patterns in the Early-Time Region of Coherent Phonon Generation in Silicon,"  Coherent phonon (CP) generation in an undoped Si crystal is theoretically
+investigated to shed light on unexplored quantum-mechanical effects in the
+early-time region immediately after the irradiation of ultrashort laser pulse.
+One examines time signals attributed to an induced charge density of an ionic
+core, placing the focus on the effects of the Rabi frequency $\Omega_{0cv}$ on
+the signals; this frequency corresponds to the peak electric-field of the
+pulse. It is found that at specific $\Omega_{0cv}$'s where the energy of
+plasmon caused by photoexcited carriers coincides with the longitudinal-optical
+phonon energy, the energetically {\it resonant } interaction between these two
+modes leads to striking anticrossings, revealing irregular oscillations with
+anomalously enhanced amplitudes in the observed time signals. Also, the
+oscillatory pattern is subject to the Rabi flopping of the excited carrier
+density that is controlled by $\Omega_{0cv}$. These findings show that the
+early-time region is enriched with quantum-mechanical effects inherent in the
+CP generation, though experimental signals are more or less masked by the
+so-called coherent artifact due to nonlinear optical effects.
+",0,1,0,0,0,0
+8263,Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing,"  Recent advances in neural networks (NNs) exhibit unprecedented success at
+transforming large, unstructured data streams into compact higher-level
+semantic information for tasks such as handwriting recognition, image
+classification, and speech recognition. Ideally, systems would employ
+near-sensor computation to execute these tasks at sensor endpoints to maximize
+data reduction and minimize data movement. However, near- sensor computing
+presents its own set of challenges such as operating power constraints, energy
+budgets, and communication bandwidth capacities. In this paper, we propose a
+stochastic- binary hybrid design which splits the computation between the
+stochastic and binary domains for near-sensor NN applications. In addition, our
+design uses a new stochastic adder and multiplier that are significantly more
+accurate than existing adders and multipliers. We also show that retraining the
+binary portion of the NN computation can compensate for precision losses
+introduced by shorter stochastic bit-streams, allowing faster run times at
+minimal accuracy losses. Our evaluation shows that our hybrid stochastic-binary
+design can achieve 9.8x energy efficiency savings, and application-level
+accuracies within 0.05% compared to conventional all-binary designs.
+",1,0,0,0,0,0
+8264,Position-based coding and convex splitting for private communication over quantum channels,"  The classical-input quantum-output (cq) wiretap channel is a communication
+model involving a classical sender $X$, a legitimate quantum receiver $B$, and
+a quantum eavesdropper $E$. The goal of a private communication protocol that
+uses such a channel is for the sender $X$ to transmit a message in such a way
+that the legitimate receiver $B$ can decode it reliably, while the eavesdropper
+$E$ learns essentially nothing about which message was transmitted. The
+$\varepsilon $-one-shot private capacity of a cq wiretap channel is equal to
+the maximum number of bits that can be transmitted over the channel, such that
+the privacy error is no larger than $\varepsilon\in(0,1)$. The present paper
+provides a lower bound on the $\varepsilon$-one-shot private classical
+capacity, by exploiting the recently developed techniques of Anshu,
+Devabathini, Jain, and Warsi, called position-based coding and convex
+splitting. The lower bound is equal to a difference of the hypothesis testing
+mutual information between $X$ and $B$ and the ""alternate"" smooth
+max-information between $X$ and $E$. The one-shot lower bound then leads to a
+non-trivial lower bound on the second-order coding rate for private classical
+communication over a memoryless cq wiretap channel.
+",1,0,0,0,0,0
+8265,Geometric vulnerability of democratic institutions against lobbying: a sociophysics approach,"  An alternative voting scheme is proposed to fill the democratic gap between a
+president elected democratically via universal suffrage (deterministic outcome,
+the actual majority decides), and a president elected by one person randomly
+selected from the population (probabilistic outcome depending on respective
+supports). Moving from one voting agent to a group of r randomly selected
+voting agents reduces the probabilistic character of the outcome. Building r
+such groups, each one electing its president, to constitute a group of the
+groups with the r local presidents electing a higher-level president, does
+reduce further the outcome probabilistic aspect. Repeating the process n times
+leads to a n-level bottom-up pyramidal structure. The hierarchy top president
+is still elected with a probability but the distance from a deterministic
+outcome reduces quickly with increasing n. At a critical value n_{c,r} the
+outcome turns deterministic recovering the same result a universal suffrage
+would yield. The scheme yields several social advantages like the distribution
+of local power to the competing minority making the structure more resilient,
+yet preserving the presidency allocation to the actual majority. An area is
+produced around fifty percent for which the president is elected with an almost
+equiprobability slightly biased in favor of the actual majority. However, our
+results reveal the existence of a severe geometric vulnerability to lobbying. A
+tiny lobbying group is able to kill the democratic balance by seizing the
+presidency democratically. It is sufficient to complete a correlated
+distribution of a few agents at the hierarchy bottom. Moreover, at the present
+stage, identifying an actual killing distribution is not feasible, which sheds
+a disturbing light on the devastating effect geometric lobbying can have on
+democratic hierarchical institutions.
+",0,1,0,0,0,0
+8266,Strongly correlated double Dirac fermions,"  Double Dirac fermions have recently been identified as possible
+quasiparticles hosted by three-dimensional crystals with particular
+non-symmorphic point group symmetries. Applying a combined approach of
+ab-initio methods and dynamical mean field theory, we investigate how
+interactions and double Dirac band topology conspire to form the electronic
+quantum state of Bi$_2$CuO$_4$. We derive a downfolded eight-band model of the
+pristine material at low energies around the Fermi level. By tuning the model
+parameters from the free band structure to the realistic strongly correlated
+regime, we find a persistence of the double Dirac dispersion until its
+constituting time reveral symmetry is broken due to the onset of magnetic
+ordering at the Mott transition. We analyze pressure as a promising route to
+realize a double-Dirac metal in Bi$_2$CuO$_4$.
+",0,1,0,0,0,0
+8267,A Phase Variable Approach for Improved Volitional and Rhythmic Control of a Powered Knee-Ankle Prosthesis,"  Although there has been recent progress in control of multi-joint prosthetic
+legs for periodic tasks such as walking, volitional control of these systems
+for non-periodic maneuvers is still an open problem. In this paper, we develop
+a new controller that is capable of both periodic walking and common volitional
+leg motions based on a piecewise holonomic phase variable through a finite
+state machine. The phase variable is constructed by measuring the thigh angle,
+and the transitions in the finite state machine are formulated through sensing
+foot contact along with attributes of a nominal reference gait trajectory. The
+controller was implemented on a powered knee-ankle prosthesis and tested with a
+transfemoral amputee subject, who successfully performed a wide range of
+periodic and non-periodic tasks, including low- and high-speed walking, quick
+start and stop, backward walking, walking over obstacles, and kicking a soccer
+ball. Use of the powered leg resulted in significant reductions in amputee
+compensations including vaulting and hip circumduction when compared to use of
+the take-home passive leg. The proposed approach is expected to provide better
+understanding of volitional motions and lead to more reliable control of
+multi-joint prostheses for a wider range of tasks.
+",1,0,0,0,0,0
+8268,New estimates for the $n$th prime number,"  In this paper we establish a new explicit upper and lower bound for the
+$n$-th prime number, which improve the currently best estimates given by Dusart
+in 2010. As the main tool we use some recently obtained explicit estimates for
+the prime counting function. A further main tool is the usage of estimates
+concerning the reciprocal of $\log p_n$. As an application we derive refined
+estimates for $\vartheta(p_n)$ in terms of $n$, where $\vartheta(x)$ is
+Chebyshev's $\vartheta$-function.
+",0,0,1,0,0,0
+8269,Unbiased Markov chain Monte Carlo for intractable target distributions,"  Performing numerical integration when the integrand itself cannot be
+evaluated point-wise is a challenging task that arises in statistical analysis,
+notably in Bayesian inference for models with intractable likelihood functions.
+Markov chain Monte Carlo (MCMC) algorithms have been proposed for this setting,
+such as the pseudo-marginal method for latent variable models and the exchange
+algorithm for a class of undirected graphical models. As with any MCMC
+algorithm, the resulting estimators are justified asymptotically in the limit
+of the number of iterations, but exhibit a bias for any fixed number of
+iterations due to the Markov chains starting outside of stationarity. This
+""burn-in"" bias is known to complicate the use of parallel processors for MCMC
+computations. We show how to use coupling techniques to generate unbiased
+estimators in finite time, building on recent advances for generic MCMC
+algorithms. We establish the theoretical validity of some of these procedures
+by extending existing results to cover the case of polynomially ergodic Markov
+chains. The efficiency of the proposed estimators is compared with that of
+standard MCMC estimators, with theoretical arguments and numerical experiments
+including state space models and Ising models.
+",0,0,0,1,0,0
+8270,An Observer for an Occluded Reaction-Diffusion System With Spatially Varying Parameters,"  Spatially dependent parameters of a two-component chaotic reaction-diffusion
+PDE model describing ocean ecology are observed by sampling a single species.
+We estimate model parameters and the other species in the system by
+autosynchronization, where quantities of interest are evolved according to
+misfit between model and observations, to only partially observed data. Our
+motivating example comes from oceanic ecology as viewed by remote sensing data,
+but where noisy occluded data are realized in the form of cloud cover. We
+demonstrate a method to learn a large-scale coupled synchronizing system that
+represents spatio-temporal dynamics and apply a network approach to analyze
+manifold stability.
+",0,0,1,0,0,0
+8271,D4M 3.0,"  The D4M tool is used by hundreds of researchers to perform complex analytics
+on unstructured data. Over the past few years, the D4M toolbox has evolved to
+support connectivity with a variety of database engines, graph analytics in the
+Apache Accumulo database, and an implementation using the Julia programming
+language. In this article, we describe some of our latest additions to the D4M
+toolbox and our upcoming D4M 3.0 release.
+",1,0,0,0,0,0
+8272,Human-in-the-Loop SLAM,"  Building large-scale, globally consistent maps is a challenging problem, made
+more difficult in environments with limited access, sparse features, or when
+using data collected by novice users. For such scenarios, where
+state-of-the-art mapping algorithms produce globally inconsistent maps, we
+introduce a systematic approach to incorporating sparse human corrections,
+which we term Human-in-the-Loop Simultaneous Localization and Mapping
+(HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM
+accepts approximate, potentially erroneous, and rank-deficient human input,
+infers the intended correction via expectation maximization (EM),
+back-propagates the extracted corrections over the pose graph, and finally
+jointly optimizes the factor graph including the human inputs as human
+correction factor terms, to yield globally consistent large-scale maps. We thus
+contribute an EM formulation for inferring potentially rank-deficient human
+corrections to mapping, and human correction factor extensions to the factor
+graphs for pose graph SLAM that result in a principled approach to joint
+optimization of the pose graph while simultaneously accounting for multiple
+forms of human correction. We present empirical results showing the
+effectiveness of HitL-SLAM at generating globally accurate and consistent maps
+even when given poor initial estimates of the map.
+",1,0,0,0,0,0
+8273,What drives gravitational instability in nearby star-forming spirals? The impact of CO and HI velocity dispersions,"  The velocity dispersion of cold interstellar gas, sigma, is one of the
+quantities that most radically affect the onset of gravitational instabilities
+in galaxy discs, and the quantity that is most drastically approximated in
+stability analyses. Here we analyse the stability of a large sample of nearby
+star-forming spirals treating molecular gas, atomic gas and stars as three
+distinct components, and using radial profiles of sigma_CO and sigma_HI derived
+from HERACLES and THINGS observations. We show that the radial variations of
+sigma_CO and sigma_HI have a weak effect on the local stability level of galaxy
+discs, which remains remarkably flat and well above unity, but is low enough to
+ensure (marginal) instability against non-axisymmetric perturbations and gas
+dissipation. More importantly, the radial variation of sigma_CO has a strong
+impact on the size of the regions over which gravitational instabilities
+develop, and results in a characteristic instability scale that is one order of
+magnitude larger than the Toomre length of molecular gas. Disc instabilities
+are driven, in fact, by the self-gravity of stars at kpc scales. This is true
+across the entire optical disc of every galaxy in the sample, with few
+exceptions. In the linear phase of the disc instability process, stars and
+molecular gas are strongly coupled, and it is such a coupling that ultimately
+triggers local gravitational collapse/fragmentation in the molecular gas.
+",0,1,0,0,0,0
+8274,An improved belief propagation algorithm for detecting meso-scale structure in complex networks,"  The framework of statistical inference has been successfully used to detect
+the meso-scale structures in complex networks, such as community structure,
+core-periphery (CP) structure. The main principle is that the stochastic block
+model (SBM) is used to fit the observed network and the learnt parameters
+indicate the group assignment, in which the parameters of model are often
+calculated via an expectation-maximization (EM) algorithm and a belief
+propagation (BP) algorithm is implemented to calculate the decomposition
+itself. In the derivation process of the BP algorithm, some approximations were
+made by omitting the effects of node's neighbors, the approximations do not
+hold if networks are dense or some nodes holding large degrees. As a result,
+for example, the BP algorithm cannot well detect CP structure in networks and
+even yields wrong detection because the nodal degrees in core group are very
+large. In doing so, we propose an improved BP algorithm to solve the problem in
+the original BP algorithm without increasing any computational complexity. By
+comparing the improved BP algorithm with the original BP algorithm on community
+detection and CP detection, we find that the two algorithms yield the same
+performance on the community detection when the network is sparse, for the
+community structure in dense networks or CP structure in networks, our improved
+BP algorithm is much better and more stable. The improved BP algorithm may help
+us correctly partition different types of meso-scale structures in networks.
+",1,0,0,0,0,0
+8275,On Least Squares Linear Regression Without Second Moment,"  If X and Y are real valued random variables such that the first moments of X,
+Y, and XY exist and the conditional expectation of Y given X is an affine
+function of X, then the intercept and slope of the conditional expectation
+equal the intercept and slope of the least squares linear regression function,
+even though Y may not have a finite second moment. As a consequence, the affine
+in X form of the conditional expectation and zero covariance imply mean
+independence.
+",0,0,1,1,0,0
+8276,Combinatorics of involutive divisions,"  The classical involutive division theory by Janet decomposes in the same way
+both the ideal and the escalier. The aim of this paper, following Janet's
+approach, is to discuss the combinatorial properties of involutive divisions,
+when defined on the set of all terms in a fixed degree D, postponing the
+discussion of ideal membership and related test. We adapt the theory by Gerdt
+and Blinkov, introducing relative involutive divisions and then, given a
+complete description of the combinatorial structure of a relative involutive
+division, we turn our attention to the problem of membership. In order to deal
+with this problem, we introduce two graphs as tools, one is strictly related to
+Seiler's L-graph, whereas the second generalizes it, to cover the case of
+""non-continuous"" (in the sense of Gerdt-Blinkov) relative involutive divisions.
+Indeed, given an element in the ideal (resp. escalier), walking backwards
+(resp. forward) in the graph, we can identify all the other generators of the
+ideal (resp. elements of degree D in the escalier).
+",0,0,1,0,0,0
+8277,Reducing asynchrony to synchronized rounds,"  Synchronous computation models simplify the design and the verification of
+fault-tolerant distributed systems. For efficiency reasons such systems are
+designed and implemented using an asynchronous semantics. In this paper, we
+bridge the gap between these two worlds. We introduce a (synchronous)
+round-based computational model and we prove a reduction for a class of
+asynchronous protocols to our new model. The reduction is based on properties
+of the code that can be checked with sequential methods. We apply the reduction
+to state machine replication systems, such as, Paxos, Zab, and Viewstamped
+Replication.
+",1,0,0,0,0,0
+8278,A wearable general-purpose solution for Human-Swarm Interaction,"  Swarms of robots will revolutionize many industrial applications, from
+targeted material delivery to precision farming. Controlling the motion and
+behavior of these swarms presents unique challenges for human operators, who
+cannot yet effectively convey their high-level intentions to a group of robots
+in application. This work proposes a new human-swarm interface based on novel
+wearable gesture-control and haptic-feedback devices. This work seeks to
+combine a wearable gesture recognition device that can detect high-level
+intentions, a portable device that can detect Cartesian information and finger
+movements, and a wearable advanced haptic device that can provide real-time
+feedback. This project is the first to envisage a wearable Human-Swarm
+Interaction (HSI) interface that separates the input and feedback components of
+the classical control loop (input, output, feedback), as well as being the
+first of its kind suitable for both indoor and outdoor environments.
+",1,0,0,0,0,0
+8279,Numerical simulation of oxidation processes in a cross-flow around tube bundles,"  An oxidation process is simulated for a bundle of metal tubes in a
+cross-flow. A fluid flow is governed by the incompressible Navier-Stokes
+equations. To describe the transport of oxygen, the corresponding
+convection-diffusion equation is applied. The key point of the model is related
+to the description of oxidation processes taking into account the growth of a
+thin oxide film in the quasi-stationary approximation. Mathematical modeling of
+oxidant transport in a tube bundle is carried out in the 2D approximation. The
+numerical algorithm employed in the work is based on the finite-element
+discretization in space and the fully implicit discretization in time. The tube
+rows of a bundle can be either in-line or staggered in the direction of the
+fluid flow velocity. The growth of the oxide film on tube walls is predicted
+for various bundle structures using the developed oxidation model.
+",1,1,0,0,0,0
+8280,Design and optimization of a portable LQCD Monte Carlo code using OpenACC,"  The present panorama of HPC architectures is extremely heterogeneous, ranging
+from traditional multi-core CPU processors, supporting a wide class of
+applications but delivering moderate computing performance, to many-core GPUs,
+exploiting aggressive data-parallelism and delivering higher performances for
+streaming computing applications. In this scenario, code portability (and
+performance portability) become necessary for easy maintainability of
+applications; this is very relevant in scientific computing where code changes
+are very frequent, making it tedious and prone to error to keep different code
+versions aligned. In this work we present the design and optimization of a
+state-of-the-art production-level LQCD Monte Carlo application, using the
+directive-based OpenACC programming model. OpenACC abstracts parallel
+programming to a descriptive level, relieving programmers from specifying how
+codes should be mapped onto the target architecture. We describe the
+implementation of a code fully written in OpenACC, and show that we are able to
+target several different architectures, including state-of-the-art traditional
+CPUs and GPUs, with the same code. We also measure performance, evaluating the
+computing efficiency of our OpenACC code on several architectures, comparing
+with GPU-specific implementations and showing that a good level of
+performance-portability can be reached.
+",0,1,0,0,0,0
+8281,NodeTrix Planarity Testing with Small Clusters,"  We study the NodeTrix planarity testing problem for flat clustered graphs
+when the maximum size of each cluster is bounded by a constant $k$. We consider
+both the case when the sides of the matrices to which the edges are incident
+are fixed and the case when they can be arbitrarily chosen. We show that
+NodeTrix planarity testing with fixed sides can be solved in
+$O(k^{3k+\frac{3}{2}} n^3)$ time for every flat clustered graph that can be
+reduced to a partial 2-tree by collapsing its clusters into single vertices. In
+the general case, NodeTrix planarity testing with fixed sides can be solved in
+$O(n^3)$ time for $k = 2$, but it is NP-complete for any $k \geq 3$. NodeTrix
+planarity testing remains NP-complete also in the free side model when $k > 4$.
+",1,0,0,0,0,0
+8282,Exact results for directed random networks that grow by node duplication,"  We present exact analytical results for the degree distribution and for the
+distribution of shortest path lengths (DSPL) in a directed network model that
+grows by node duplication. Such models are useful in the study of the structure
+and growth dynamics of gene regulatory and scientific citation networks.
+Starting from an initial seed network, at each time step a random node,
+referred to as a mother node, is selected for duplication. Its daughter node is
+added to the network and duplicates, with probability p, each one of the
+outgoing links of the mother node. In addition, the daughter node forms a
+directed link to the mother node itself. Thus, the model is referred to as the
+corded directed-node-duplication (DND) model. We obtain analytical results for
+the in-degree distribution, $P_t(K_{in})$, and for the out-degree distribution,
+$P_t(K_{out})$, of the network at time t. It is found that the in-degrees
+follow a shifted power-law distribution, so the network is asymptotically scale
+free. In contrast, the out-degree distribution is a narrow distribution, that
+converges to a Poisson distribution in the sparse limit and to a Gaussian
+distribution in the dense limit. Using these distributions we calculate the
+mean degree, $\langle K_{in} \rangle_t = \langle K_{out} \rangle_t$. To
+calculate the DSPL we derive a master equation for the time evolution of the
+probability $P_t(L=\ell)$, $\ell=1,2,\dots$, that for two nodes, i and j,
+selected randomly at time t, the shortest path from i to j is of length $\ell$.
+Solving the master equation, we obtain a closed form expression for
+$P_t(L=\ell)$. It is found that the DSPL at time t consists of a convolution of
+the initial DSPL, $P_0(L=\ell)$, with a Poisson distribution and a sum of
+Poisson distributions. The mean distance, $<L>_t$, is found to depend
+logarithmically on the network size, $N_t$, namely the corded DND network is a
+small-world network.
+",1,0,0,0,0,0
+8283,"Investigating the past history of EXors: the cases of V1118 Ori, V1143 Ori, and NY Ori","  EXor objects are young variables that show episodic variations of brightness
+commonly associated to enhanced accretion outbursts. With the aim of
+investigating the long-term photometric behaviour of a few EXor sources, we
+present here data from the archival plates of the Asiago Observatory, showing
+the Orion field where the three EXors V1118, V1143, and NY are located. A total
+of 484 plates were investigated, providing a total of more than 1000 magnitudes
+for the three stars, which cover a period of about 35 yrs between 1959 to 1993.
+We then compared our data with literature data. Apart from a newly discovered
+flare-up of V1118, we identify the same outbursts already known, but we provide
+two added values: (i) a long-term sampling of the quiescence phase; and (ii)
+repeated multi-colour observations (BVRI bands). The former allows us to give a
+reliable characterisation of the quiescence, which represents a unique
+reference for studies that will analyze future outbursts and the physical
+changes induced by these events. The latter is useful for confirming whether
+the intermittent increases of brightness are accretion-driven (as in the case
+of V1118), or extinction-driven (as in the case of V1143). Accordingly, doubts
+arise about the V1143 classification as a pure EXor object. Finally, although
+our plates do not separate NY Ori and the star very close to it, they indicate
+that this EXor did not undergo any major outbursts during our 40 yrs of
+monitoring.
+",0,1,0,0,0,0
+8284,Some estimates for $θ$-type Calderón-Zygmund operators and linear commutators on certain weighted amalgam spaces,"  In this paper, we first introduce some new kinds of weighted amalgam spaces.
+Then we discuss the strong type and weak type estimates for a class of
+Calderón--Zygmund type operators $T_\theta$ in these new weighted spaces.
+Furthermore, the strong type estimate and endpoint estimate of linear
+commutators $[b,T_{\theta}]$ formed by $b$ and $T_{\theta}$ are established.
+Also we study related problems about two-weight, weak type inequalities for
+$T_{\theta}$ and $[b,T_{\theta}]$ in the weighted amalgam spaces and give some
+results.
+",0,0,1,0,0,0
+8285,A Cluster Elastic Net for Multivariate Regression,"  We propose a method for estimating coefficients in multivariate regression
+when there is a clustering structure to the response variables. The proposed
+method includes a fusion penalty, to shrink the difference in fitted values
+from responses in the same cluster, and an L1 penalty for simultaneous variable
+selection and estimation. The method can be used when the grouping structure of
+the response variables is known or unknown. When the clustering structure is
+unknown the method will simultaneously estimate the clusters of the response
+and the regression coefficients. Theoretical results are presented for the
+penalized least squares case, including asymptotic results allowing for p >> n.
+We extend our method to the setting where the responses are binomial variables.
+We propose a coordinate descent algorithm for both the normal and binomial
+likelihood, which can easily be extended to other generalized linear model
+(GLM) settings. Simulations and data examples from business operations and
+genomics are presented to show the merits of both the least squares and
+binomial methods.
+",0,0,0,1,0,0
+8286,Achieving robust and high-fidelity quantum control via spectral phase optimization,"  Achieving high-fidelity control of quantum systems is of fundamental
+importance in physics, chemistry and quantum information sciences. However, the
+successful implementation of a high-fidelity quantum control scheme also
+requires robustness against control field fluctuations. Here, we demonstrate a
+robust optimization method for control of quantum systems by optimizing the
+spectral phase of an ultrafast laser pulse, which is accomplished in the
+framework of frequency domain quantum optimal control theory. By incorporating
+a filtering function of frequency into the optimization algorithm, our
+numerical simulations in an abstract two-level quantum system as well as in a
+three-level atomic rubidium show that the optimization procedure can be
+enforced to search optimal solutions while achieving remarkable robustness
+against the control field fluctuations, providing an efficient approach to
+optimize the spectral phase of the ultrafast laser pulse to achieve a desired
+final quantum state of the system.
+",0,1,0,0,0,0
+8287,Help Me Find a Job: A Graph-based Approach for Job Recommendation at Scale,"  Online job boards are one of the central components of modern recruitment
+industry. With millions of candidates browsing through job postings everyday,
+the need for accurate, effective, meaningful, and transparent job
+recommendations is apparent more than ever. While recommendation systems are
+successfully advancing in variety of online domains by creating social and
+commercial value, the job recommendation domain is less explored. Existing
+systems are mostly focused on content analysis of resumes and job descriptions,
+relying heavily on the accuracy and coverage of the semantic analysis and
+modeling of the content in which case, they end up usually suffering from
+rigidity and the lack of implicit semantic relations that are uncovered from
+users' behavior and could be captured by Collaborative Filtering (CF) methods.
+Few works which utilize CF do not address the scalability challenges of
+real-world systems and the problem of cold-start. In this paper, we propose a
+scalable item-based recommendation system for online job recommendations. Our
+approach overcomes the major challenges of sparsity and scalability by
+leveraging a directed graph of jobs connected by multi-edges representing
+various behavioral and contextual similarity signals. The short lived nature of
+the items (jobs) in the system and the rapid rate in which new users and jobs
+enter the system make the cold-start a serious problem hindering CF methods. We
+address this problem by harnessing the power of deep learning in addition to
+user behavior to serve hybrid recommendations. Our technique has been leveraged
+by CareerBuilder.com which is one of the largest job boards in the world to
+generate high-quality recommendations for millions of users.
+",1,0,0,0,0,0
+8288,Bundle Optimization for Multi-aspect Embedding,"  Understanding semantic similarity among images is the core of a wide range of
+computer vision applications. An important step towards this goal is to collect
+and learn human perceptions. Interestingly, the semantic context of images is
+often ambiguous as images can be perceived with emphasis on different aspects,
+which may be contradictory to each other.
+In this paper, we present a method for learning the semantic similarity among
+images, inferring their latent aspects and embedding them into multi-spaces
+corresponding to their semantic aspects.
+We consider the multi-embedding problem as an optimization function that
+evaluates the embedded distances with respect to the qualitative clustering
+queries. The key idea of our approach is to collect and embed qualitative
+measures that share the same aspects in bundles. To ensure similarity aspect
+sharing among multiple measures, image classification queries are presented to,
+and solved by users. The collected image clusters are then converted into
+bundles of tuples, which are fed into our bundle optimization algorithm that
+jointly infers the aspect similarity and multi-aspect embedding. Extensive
+experimental results show that our approach significantly outperforms
+state-of-the-art multi-embedding approaches on various datasets, and scales
+well for large multi-aspect similarity measures.
+",1,0,0,0,0,0
+8289,Glitch Classification and Clustering for LIGO with Deep Transfer Learning,"  The detection of gravitational waves with LIGO and Virgo requires a detailed
+understanding of the response of these instruments in the presence of
+environmental and instrumental noise. Of particular interest is the study of
+anomalous non-Gaussian noise transients known as glitches, since their high
+occurrence rate in LIGO/Virgo data can obscure or even mimic true gravitational
+wave signals. Therefore, successfully identifying and excising glitches is of
+utmost importance to detect and characterize gravitational waves. In this
+article, we present the first application of Deep Learning combined with
+Transfer Learning for glitch classification, using real data from LIGO's first
+discovery campaign labeled by Gravity Spy, showing that knowledge from
+pre-trained models for real-world object recognition can be transferred for
+classifying spectrograms of glitches. We demonstrate that this method enables
+the optimal use of very deep convolutional neural networks for glitch
+classification given small unbalanced training datasets, significantly reduces
+the training time, and achieves state-of-the-art accuracy above 98.8%. Once
+trained via transfer learning, we show that the networks can be truncated and
+used as feature extractors for unsupervised clustering to automatically group
+together new classes of glitches and anomalies. This novel capability is of
+critical importance to identify and remove new types of glitches which will
+occur as the LIGO/Virgo detectors gradually attain design sensitivity.
+",1,1,0,1,0,0
+8290,Provable Smoothness Guarantees for Black-Box Variational Inference,"  Black-box variational inference tries to approximate a complex target
+distribution though a gradient-based optimization of the parameters of a
+simpler distribution. Provable convergence guarantees require structural
+properties of the objective. This paper shows that for location-scale family
+approximations, if the target is M-Lipschitz smooth, then so is the objective,
+if the entropy is excluded. The key proof idea is to describe gradients in a
+certain inner-product space, thus permitting use of Bessel's inequality. This
+result gives insight into how to parameterize distributions, gives bounds the
+location of the optimal parameters, and is a key ingredient for convergence
+guarantees.
+",1,0,0,1,0,0
+8291,A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data,"  This paper proposes a clustering procedure for samples of multivariate
+functions in $(L^2(I))^{J}$, with $J\geq1$. This method is based on a k-means
+algorithm in which the distance between the curves is measured with a metrics
+that generalizes the Mahalanobis distance in Hilbert spaces, considering the
+correlation and the variability along all the components of the functional
+data. The proposed procedure has been studied in simulation and compared with
+the k-means based on other distances typically adopted for clustering
+multivariate functional data. In these simulations, it is shown that the
+k-means algorithm with the generalized Mahalanobis distance provides the best
+clustering performances, both in terms of mean and standard deviation of the
+number of misclassified curves. Finally, the proposed method has been applied
+to two real cases studies, concerning ECG signals and growth curves, where the
+results obtained in simulation are confirmed and strengthened.
+",0,0,0,1,0,0
+8292,Tidal Dissipation in WASP-12,"  WASP-12 is a hot Jupiter system with an orbital period of $P= 1.1\textrm{
+day}$, making it one of the shortest-period giant planets known. Recent transit
+timing observations by Maciejewski et al. (2016) and Patra et al. (2017) find a
+decreasing period with $P/|\dot{P}| = 3.2\textrm{ Myr}$. This has been
+interpreted as evidence of either orbital decay due to tidal dissipation or a
+long term oscillation of the apparent period due to apsidal precession. Here we
+consider the possibility that it is orbital decay. We show that the parameters
+of the host star are consistent with either a $M_\ast \simeq 1.3 M_\odot$ main
+sequence star or a $M_\ast \simeq 1.2 M_\odot$ subgiant. We find that if the
+star is on the main sequence, the tidal dissipation is too inefficient to
+explain the observed $\dot{P}$. However, if it is a subgiant, the tidal
+dissipation is significantly enhanced due to nonlinear wave breaking of the
+dynamical tide near the star's center. The subgiant models have a tidal quality
+factor $Q_\ast'\simeq 2\times10^5$ and an orbital decay rate that agrees well
+with the observed $\dot{P}$. It would also explain why the planet survived for
+$\simeq 3\textrm{ Gyr}$ while the star was on the main sequence and yet is now
+inspiraling on a 3 Myr timescale. Although this suggests that we are witnessing
+the last $\sim 0.1\%$ of the planet's life, the probability of such a detection
+is a few percent given the observed sample of $\simeq 30$ hot Jupiters in
+$P<3\textrm{ day}$ orbits around $M_\ast>1.2 M_\odot$ hosts.
+",0,1,0,0,0,0
+8293,Label Sanitization against Label Flipping Poisoning Attacks,"  Many machine learning systems rely on data collected in the wild from
+untrusted sources, exposing the learning algorithms to data poisoning.
+Attackers can inject malicious data in the training dataset to subvert the
+learning process, compromising the performance of the algorithm producing
+errors in a targeted or an indiscriminate way. Label flipping attacks are a
+special case of data poisoning, where the attacker can control the labels
+assigned to a fraction of the training points. Even if the capabilities of the
+attacker are constrained, these attacks have been shown to be effective to
+significantly degrade the performance of the system. In this paper we propose
+an efficient algorithm to perform optimal label flipping poisoning attacks and
+a mechanism to detect and relabel suspicious data points, mitigating the effect
+of such poisoning attacks.
+",0,0,0,1,0,0
+8294,DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction,"  Chemical-chemical interaction (CCI) plays a key role in predicting candidate
+drugs, toxicity, therapeutic effects, and biological functions. In various
+types of chemical analyses, computational approaches are often required due to
+the amount of data that needs to be handled. The recent remarkable growth and
+outstanding performance of deep learning have attracted considerable research
+attention. However,even in state-of-the-art drug analysis methods, deep
+learning continues to be used only as a classifier, although deep learning is
+capable of not only simple classification but also automated feature
+extraction. In this paper, we propose the first end-to-end learning method for
+CCI, named DeepCCI. Hidden features are derived from a simplified molecular
+input line entry system (SMILES), which is a string notation representing the
+chemical structure, instead of learning from crafted features. To discover
+hidden representations for the SMILES strings, we use convolutional neural
+networks (CNNs). To guarantee the commutative property for homogeneous
+interaction, we apply model sharing and hidden representation merging
+techniques. The performance of DeepCCI was compared with a plain deep
+classifier and conventional machine learning methods. The proposed DeepCCI
+showed the best performance in all seven evaluation metrics used. In addition,
+the commutative property was experimentally validated. The automatically
+extracted features through end-to-end SMILES learning alleviates the
+significant efforts required for manual feature engineering. It is expected to
+improve prediction performance, in drug analyses.
+",1,0,0,0,0,0
+8295,Intervals between numbers that are sums of two squares,"  In this paper, we improve the moment estimates for the gaps between numbers
+that can be represented as a sum of two squares of integers. We consider
+certain sum of Bessel functions and prove the upper bound for its weighted mean
+value. This bound provides estimates for the $\gamma$-th moments of gaps for
+all $\gamma\leq 2$.
+",0,0,1,0,0,0
+8296,On a family of Caldero-Chapoton algebras that have the Laurent phenomenon,"  We realize a family of generalized cluster algebras as Caldero-Chapoton
+algebras of quivers with relations. Each member of this family arises from an
+unpunctured polygon with one orbifold point of order 3, and is realized as a
+Caldero-Chapoton algebra of a quiver with relations naturally associated to any
+triangulation of the alluded polygon. The realization is done by defining for
+every arc $j$ on the polygon with orbifold point a representation $M(j)$ of the
+referred quiver with relations, and by proving that for every triangulation
+$\tau$ and every arc $j\in\tau$, the product of the Caldero-Chapoton functions
+of $M(j)$ and $M(j')$, where $j'$ is the arc that replaces $j$ when we flip $j$
+in $\tau$, equals the corresponding exchange polynomial of Chekhov-Shapiro in
+the generalized cluster algebra. Furthermore, we show that there is a bijection
+between the set of generalized cluster variables and the isomorphism classes of
+$E$-rigid indecomposable decorated representations of $\Lambda$.
+",0,0,1,0,0,0
+8297,Ricean K-factor Estimation based on Channel Quality Indicator in OFDM Systems using Neural Network,"  Ricean channel model is widely used in wireless communications to
+characterize the channels with a line-of-sight path. The Ricean K factor,
+defined as the ratio of direct path and scattered paths, provides a good
+indication of the link quality. Most existing works estimate K factor based on
+either maximum-likelihood criterion or higher-order moments, and the existing
+works are targeted at K-factor estimation at receiver side. In this work, a
+novel approach is proposed. Cast as a classification problem, the estimation of
+K factor by neural network provides high accuracy. Moreover, the proposed
+K-factor estimation is done at transmitter side for transmit processing, thus
+saving the limited feedback bandwidth.
+",0,0,0,1,0,0
+8298,Augmented lagrangian two-stage algorithm for LP and SCQP,"  In this paper, we consider a framework of projected gradient iterations for
+linear programming (LP) and an augmented lagrangian two-stage algorithm for
+strongly convex quadratic programming (SCQP). Based on the framework of
+projected gradient, LP problem is transformed to a finite number of SCQP
+problems. Furthermore, we give an estimate of the number of the SCQP problems.
+We use augmented lagrangian method (ALM) to solve SCQP and each augmented
+lagrangian subproblem is solved by a two-stage algorithm exactly, which ensures
+the superlinear convergence of ALM for SCQP. The two-stage algorithm consists
+of the accelerated proximal gradient algorithm as the first stage algorithm
+which provides an approximate solution, and a simplified parametric active-set
+method as the second stage algorithm which gives an exact solution. Moreover,
+we improve the parametric active-set method by introducing a sorting technique
+to update the cholesky factorization. Finally, the numerical experiments on
+randomly generated and real-world test problems indicate that our algorithm is
+effective, especially for random problems.
+",0,0,1,0,0,0
+8299,Exact relations between homoclinic and periodic orbit actions in chaotic systems,"  Homoclinic and unstable periodic orbits in chaotic systems play central roles
+in various semiclassical sum rules. The interferences between terms are
+governed by the action functions and Maslov indices. In this article, we
+identify geometric relations between homoclinic and unstable periodic orbits,
+and derive exact formulae expressing the periodic orbit classical actions in
+terms of corresponding homoclinic orbit actions plus certain phase space areas.
+The exact relations provide a basis for approximations of the periodic orbit
+actions as action differences between homoclinic orbits with well-estimated
+errors. This make possible the explicit study of relations between periodic
+orbits, which results in an analytic expression for the action differences
+between long periodic orbits and their shadowing decomposed orbits in the cycle
+expansion.
+",0,1,0,0,0,0
+8300,Subexponentially growing Hilbert space and nonconcentrating distributions in a constrained spin model,"  Motivated by recent experiments with two-component Bose-Einstein condensates,
+we study fully-connected spin models subject to an additional constraint. The
+constraint is responsible for the Hilbert space dimension to scale only
+linearly with the system size. We discuss the unconventional statistical
+physical and thermodynamic properties of such a system, in particular the
+absence of concentration of the underlying probability distributions. As a
+consequence, expectation values are less suitable to characterize such systems,
+and full distribution functions are required instead. Sharp signatures of phase
+transitions do not occur in such a setting, but transitions from singly peaked
+to doubly peaked distribution functions of an ""order parameter"" may be present.
+",0,1,0,0,0,0
+8301,A spectral approach to transit timing variations,"  The high planetary multiplicity revealed by Kepler implies that Transit Time
+Variations (TTVs) are intrinsically common. The usual procedure for detecting
+these TTVs is biased to long-period, deep transit planets whereas most
+transiting planets have short periods and shallow transits. Here we introduce
+the Spectral Approach to TTVs technique that allows expanding the TTVs catalog
+towards lower TTV amplitude, shorter orbital period, and shallower transit
+depth. In the Spectral Approach we assume that a sinusoidal TTV exists in the
+data and then calculate the improvement to $\chi^2$ this model allows over that
+of linear ephemeris model. This enables detection of TTVs even in cases where
+the transits are too shallow so individual transits cannot be timed. The
+Spectral Approach is more sensitive due to the reduced number of free
+parameters in its model. Using the Spectral Approach, we: (a) detect 131 new
+periodic TTVs in Kepler data (an increase of ~2/3 over a previous TTV catalog);
+(b) Constrain the TTV periods of 34 long-period TTVs and reduce amplitude
+errors of known TTVs; (c) Identify cases of multi-periodic TTVs, for which
+absolute planetary mass determination may be possible. We further extend our
+analysis by using perturbation theory assuming small TTV amplitude at the
+detection stage, which greatly speeds up our detection (to a level of few
+seconds per star). Our extended TTVs sample shows no deficit of short period or
+low amplitude transits, in contrast to previous surveys in which the detection
+schemes were significantly biased against such systems.
+",0,1,0,0,0,0
+8302,Distinct dynamical behavior in random and all-to-all neuronal networks,"  Neuronal network dynamics depends on network structure. It is often assumed
+that neurons are connected at random when their actual connectivity structure
+is unknown. Such models are then often approximated by replacing the random
+network by an all-to-all network, where every neuron is connected to all other
+neurons. This mean-field approximation is a common approach in statistical
+physics. In this paper we show that such approximation can be invalid. We solve
+analytically a neuronal network model with binary-state neurons in both random
+and all-to-all networks. We find strikingly different phase diagrams
+corresponding to each network structure. Neuronal network dynamics is not only
+different within certain parameter ranges, but it also undergoes different
+bifurcations. Our results therefore suggest cautiousness when using mean-field
+models based on all-to-all network topologies to represent random networks.
+",0,0,0,0,1,0
+8303,A Note on Band-limited Minorants of an Euclidean Ball,"  We study the Beurling-Selberg problem of finding band-limited $L^1$-functions
+that lie below the indicator function of an Euclidean ball. We compute the
+critical radius of the support of the Fourier transform for which such
+construction can have a positive integral.
+",0,0,1,0,0,0
+8304,From Distance Correlation to Multiscale Graph Correlation,"  Understanding and developing a correlation measure that can detect general
+dependencies is not only imperative to statistics and machine learning, but
+also crucial to general scientific discovery in the big data age. In this
+paper, we establish a new framework that generalizes distance correlation --- a
+correlation measure that was recently proposed and shown to be universally
+consistent for dependence testing against all joint distributions of finite
+moments --- to the Multiscale Graph Correlation (MGC). By utilizing the
+characteristic functions and incorporating the nearest neighbor machinery, we
+formalize the population version of local distance correlations, define the
+optimal scale in a given dependency, and name the optimal local correlation as
+MGC. The new theoretical framework motivates a theoretically sound Sample MGC
+and allows a number of desirable properties to be proved, including the
+universal consistency, convergence and almost unbiasedness of the sample
+version. The advantages of MGC are illustrated via a comprehensive set of
+simulations with linear, nonlinear, univariate, multivariate, and noisy
+dependencies, where it loses almost no power in monotone dependencies while
+achieving better performance in general dependencies, compared to distance
+correlation and other popular methods.
+",0,0,0,1,0,0
+8305,Out-of-time-order Operators and the Butterfly Effect,"  Out-of-time-order (OTO) operators have recently become popular diagnostics of
+quantum chaos in many-body systems. The usual way they are introduced is via a
+quantization of classical Lyapunov growth, which measures the divergence of
+classical trajectories in phase space due to the butterfly effect. However, it
+is not obvious how exactly they capture the sensitivity of a quantum system to
+its initial conditions beyond the classical limit. In this paper, we analyze
+sensitivity to initial conditions in the quantum regime by recasting OTO
+operators for many-body systems using various formulations of quantum
+mechanics. Notably, we utilize the Wigner phase space formulation to derive an
+$\hbar$-expansion of the OTO operator for spatial degrees of freedom, and a
+large spin $1/s$-expansion for spin degrees of freedom. We find in each case
+that the leading term is the Lyapunov growth for the classical limit of the
+system and argue that quantum corrections become dominant at around the
+scrambling time, which is also when we expect the OTO operator to saturate. We
+also express the OTO operator in terms of propagators and see from a different
+point of view how it is a quantum generalization of the divergence of classical
+trajectories.
+",0,1,0,0,0,0
+8306,Science and Facebook: the same popularity law!,"  The distribution of scientific citations for publications selected with
+different rules (author, topic, institution, country, journal, etc.) collapse
+on a single curve if one plots the citations relative to their mean value. We
+find that the distribution of shares for the Facebook posts re-scale in the
+same manner to the very same curve with scientific citations. This finding
+suggests that citations are subjected to the same growth mechanism with
+Facebook popularity measures, being influenced by a statistically similar
+social environment and selection mechanism. In a simple master-equation
+approach the exponential growth of the number of publications and a
+preferential selection mechanism leads to a Tsallis-Pareto distribution
+offering an excellent description for the observed statistics. Based on our
+model and on the data derived from PubMed we predict that according to the
+present trend the average citations per scientific publications exponentially
+relaxes to about 4.
+",1,1,0,0,0,0
+8307,Monocular Vision-based Vehicle Localization Aided by Fine-grained Classification,"  Monocular camera systems are prevailing in intelligent transportation
+systems, but by far they have rarely been used for dimensional purposes such as
+to accurately estimate the localization information of a vehicle. In this
+paper, we show that this capability can be realized. By integrating a series of
+advanced computer vision techniques including foreground extraction, edge and
+line detection, etc., and by utilizing deep learning networks for fine-grained
+vehicle model classification, we developed an algorithm which can estimate
+vehicles location (position, orientation and boundaries) within the environment
+down to 3.79 percent position accuracy and 2.5 degrees orientation accuracy.
+With this enhancement, current massive surveillance camera systems can
+potentially play the role of e-traffic police and trigger many new intelligent
+transportation applications, for example, to guide vehicles for parking or even
+for autonomous driving.
+",1,0,0,0,0,0
+8308,Verifying Patterns of Dynamic Architectures using Model Checking,"  Architecture patterns capture architectural design experience and provide
+abstract solutions to recurring architectural design problems. They consist of
+a description of component types and restrict component connection and
+activation. Therefore, they guarantee some desired properties for architectures
+employing the pattern. Unfortunately, most documented patterns do not provide a
+formal guarantee of whether their specification indeed leads to the desired
+guarantee. Failure in doing so, however, might lead to wrong architectures,
+i.e., architectures wrongly supposed to show certain desired properties. Since
+architectures, in general, have a high impact on the quality of the resulting
+system and architectural flaws are only difficult, if not to say impossible, to
+repair, this may lead to badly reparable quality issues in the resulting
+system. To address this problem, we propose an approach based on model checking
+to verify pattern specifications w.r.t. their guarantees. In the following we
+apply the approach to three well-known patterns for dynamic architectures: the
+Singleton, the Model-View-Controller, and the Broker pattern. Thereby, we
+discovered ambiguities and missing constraints for all three specifications.
+Thus, we conclude that verifying patterns of dynamic architectures using model
+checking is feasible and useful to discover ambiguities and flaws in pattern
+specifications.
+",1,0,0,0,0,0
+8309,Some integrals of hypergeometric functions,"  We consider a certain definite integral involving the product of two
+classical hypergeometric functions having complicated arguments. We show in
+this paper the surprising fact that this integral does not depend on the
+parameters of the hypergeometric functions.
+",0,0,1,0,0,0
+8310,On the second Feng-Rao distance of Algebraic Geometry codes related to Arf semigroups,"  We describe the second (generalized) Feng-Rao distance for elements in an Arf
+numerical semigroup that are greater than or equal to the conductor of the
+semigroup. This provides a lower bound for the second Hamming weight for one
+point AG codes. In particular, we can obtain the second Feng-Rao distance for
+the codes defined by asymptotically good towers of function fields whose
+Weierstrass semigroups are inductive. In addition, we compute the second
+Feng-Rao number, and provide some examples and comparisons with previous
+results on this topic. These calculations rely on Apéry sets, and thus
+several results concerning Apéry sets of Arf semigroups are presented.
+",1,0,0,0,0,0
+8311,Posterior contraction rates for support boundary recovery,"  Given a sample of a Poisson point process with intensity $\lambda_f(x,y) = n
+\mathbf{1}(f(x) \leq y),$ we study recovery of the boundary function $f$ from a
+nonparametric Bayes perspective. Because of the irregularity of this model, the
+analysis is non-standard. We establish a general result for the posterior
+contraction rate with respect to the $L^1$-norm based on entropy and one-sided
+small probability bounds. From this, specific posterior contraction results are
+derived for Gaussian process priors and priors based on random wavelet series.
+",0,0,1,1,0,0
+8312,A Fast Image Simulation Algorithm for Scanning Transmission Electron Microscopy,"  Image simulation for scanning transmission electron microscopy at atomic
+resolution for samples with realistic dimensions can require very large
+computation times using existing simulation algorithms. We present a new
+algorithm named PRISM that combines features of the two most commonly used
+algorithms, the Bloch wave and multislice methods. PRISM uses a Fourier
+interpolation factor $f$ that has typical values of 4-20 for atomic resolution
+simulations. We show that in many cases PRISM can provide a speedup that scales
+with $f^4$ compared to multislice simulations, with a negligible loss of
+accuracy. We demonstrate the usefulness of this method with large-scale
+scanning transmission electron microscopy image simulations of a crystalline
+nanoparticle on an amorphous carbon substrate.
+",0,1,0,0,0,0
+8313,X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM,"  The study on point sources in astronomical images is of special importance,
+since most energetic celestial objects in the Universe exhibit a point-like
+appearance. An approach to recognize the point sources (PS) in the X-ray
+astronomical images using our newly designed granular binary-tree support
+vector machine (GBT-SVM) classifier is proposed. First, all potential point
+sources are located by peak detection on the image. The image and spectral
+features of these potential point sources are then extracted. Finally, a
+classifier to recognize the true point sources is build through the extracted
+features. Experiments and applications of our approach on real X-ray
+astronomical images are demonstrated. comparisons between our approach and
+other SVM-based classifiers are also carried out by evaluating the precision
+and recall rates, which prove that our approach is better and achieves a higher
+accuracy of around 89%.
+",1,0,0,0,0,0
+8314,Sample-Efficient Learning of Mixtures,"  We consider PAC learning of probability distributions (a.k.a. density
+estimation), where we are given an i.i.d. sample generated from an unknown
+target distribution, and want to output a distribution that is close to the
+target in total variation distance. Let $\mathcal F$ be an arbitrary class of
+probability distributions, and let $\mathcal{F}^k$ denote the class of
+$k$-mixtures of elements of $\mathcal F$. Assuming the existence of a method
+for learning $\mathcal F$ with sample complexity $m_{\mathcal{F}}(\epsilon)$,
+we provide a method for learning $\mathcal F^k$ with sample complexity
+$O({k\log k \cdot m_{\mathcal F}(\epsilon) }/{\epsilon^{2}})$. Our mixture
+learning algorithm has the property that, if the $\mathcal F$-learner is
+proper/agnostic, then the $\mathcal F^k$-learner would be proper/agnostic as
+well.
+This general result enables us to improve the best known sample complexity
+upper bounds for a variety of important mixture classes. First, we show that
+the class of mixtures of $k$ axis-aligned Gaussians in $\mathbb{R}^d$ is
+PAC-learnable in the agnostic setting with $\widetilde{O}({kd}/{\epsilon ^ 4})$
+samples, which is tight in $k$ and $d$ up to logarithmic factors. Second, we
+show that the class of mixtures of $k$ Gaussians in $\mathbb{R}^d$ is
+PAC-learnable in the agnostic setting with sample complexity
+$\widetilde{O}({kd^2}/{\epsilon ^ 4})$, which improves the previous known
+bounds of $\widetilde{O}({k^3d^2}/{\epsilon ^ 4})$ and
+$\widetilde{O}(k^4d^4/\epsilon ^ 2)$ in its dependence on $k$ and $d$. Finally,
+we show that the class of mixtures of $k$ log-concave distributions over
+$\mathbb{R}^d$ is PAC-learnable using
+$\widetilde{O}(d^{(d+5)/2}\epsilon^{-(d+9)/2}k)$ samples.
+",1,0,0,0,0,0
+8315,The Bag Semantics of Ontology-Based Data Access,"  Ontology-based data access (OBDA) is a popular approach for integrating and
+querying multiple data sources by means of a shared ontology. The ontology is
+linked to the sources using mappings, which assign views over the data to
+ontology predicates. Motivated by the need for OBDA systems supporting
+database-style aggregate queries, we propose a bag semantics for OBDA, where
+duplicate tuples in the views defined by the mappings are retained, as is the
+case in standard databases. We show that bag semantics makes conjunctive query
+answering in OBDA coNP-hard in data complexity. To regain tractability, we
+consider a rather general class of queries and show its rewritability to a
+generalisation of the relational calculus to bags.
+",1,0,0,0,0,0
+8316,Multilink Communities of Multiplex Networks,"  Multiplex networks describe a large number of complex social, biological and
+transportation networks where a set of nodes is connected by links of different
+nature and connotation. Here we uncover the rich community structure of
+multiplex networks by associating a community to each multilink where the
+multilinks characterize the connections existing between any two nodes of the
+multiplex network. Our community detection method reveals the rich interplay
+between the mesoscale structure of the multiplex networks and their
+multiplexity. For instance some nodes can belong to many layers and few
+communities while others can belong to few layers but many communities.
+Moreover the multilink communities can be formed by a different number of
+relevant layers. These results point out that mesoscopically there can be large
+differences in the compressibility of multiplex networks.
+",1,1,0,0,0,0
+8317,P-wave superfluidity of atomic lattice fermions,"  We discuss the emergence of p-wave superfluidity of identical atomic fermions
+in a two-dimensional optical lattice. The optical lattice potential manifests
+itself in an interplay between an increase in the density of states on the
+Fermi surface and the modification of the fermion-fermion interaction
+(scattering) amplitude. The density of states is enhanced due to an increase of
+the effective mass of atoms. In deep lattices the scattering amplitude is
+strongly reduced compared to free space due to a small overlap of wavefunctions
+of fermion sitting in the neighboring lattice sites, which suppresses the
+p-wave superfluidity. However, for moderate lattice depths the enhancement of
+the density of states can compensate the decrease of the scattering amplitude.
+Moreover, the lattice setup significantly reduces inelastic collisional losses,
+which allows one to get closer to a p-wave Feshbach resonance. This opens
+possibilities to obtain the topological $p_x+ip_y$ superfluid phase, especially
+in the recently proposed subwavelength lattices. We demonstrate this for the
+two-dimensional version of the Kronig-Penney model allowing a transparent
+physical analysis.
+",0,1,0,0,0,0
+8318,Deep generative models of genetic variation capture mutation effects,"  The functions of proteins and RNAs are determined by a myriad of interactions
+between their constituent residues, but most quantitative models of how
+molecular phenotype depends on genotype must approximate this by simple
+additive effects. While recent models have relaxed this constraint to also
+account for pairwise interactions, these approaches do not provide a tractable
+path towards modeling higher-order dependencies. Here, we show how latent
+variable models with nonlinear dependencies can be applied to capture
+beyond-pairwise constraints in biomolecules. We present a new probabilistic
+model for sequence families, DeepSequence, that can predict the effects of
+mutations across a variety of deep mutational scanning experiments
+significantly better than site independent or pairwise models that are based on
+the same evolutionary data. The model, learned in an unsupervised manner solely
+from sequence information, is grounded with biologically motivated priors,
+reveals latent organization of sequence families, and can be used to
+extrapolate to new parts of sequence space
+",0,1,0,1,0,0
+8319,Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks,"  While extraordinary progress has been made towards developing neural network
+architectures for classification tasks, commonly used loss functions such as
+the multi-category cross entropy loss are inadequate for ranking and ordinal
+regression problems. To address this issue, approaches have been developed that
+transform ordinal target variables series of binary classification tasks,
+resulting in robust ranking algorithms with good generalization performance.
+However, to model ordinal information appropriately, ideally, a rank-monotonic
+prediction function is required such that confidence scores are ordered and
+consistent. We propose a new framework (Consistent Rank Logits, CORAL) with
+theoretical guarantees for rank-monotonicity and consistent confidence scores.
+Through parameter sharing, our framework benefits from low training complexity
+and can easily be implemented to extend common convolutional neural network
+classifiers for ordinal regression tasks. Furthermore, our empirical results
+support the proposed theory and show a substantial improvement compared to the
+current state-of-the-art ordinal regression method for age prediction from face
+images.
+",1,0,0,1,0,0
+8320,High-Pressure Synthesis and Characterization of $β$-GeSe - A Semiconductor with Six-Rings in an Uncommon Boat Conformation,"  Two-dimensional materials have significant potential for the development of
+new devices. Here we report the electronic and structural properties of
+$\beta$-GeSe, a previously unreported polymorph of GeSe, with a unique crystal
+structure that displays strong two-dimensional structural features.
+$\beta$-GeSe is made at high pressure and temperature and is stable under
+ambient conditions. We compare it to its structural and electronic relatives
+$\alpha$-GeSe and black phosphorus. The $\beta$ form of GeSe displays a boat
+conformation for its Ge-Se six-ring, while the previously known $\alpha$ form,
+and black phosphorus, display the more common chair conformation for their
+six-rings. Electronic structure calculations indicate that $\beta$-GeSe is a
+semiconductor, with an approximate bulk band gap of $\Delta~\approx$ 0.5 eV,
+and, in its monolayer form, $\Delta~\approx$ 0.9 eV. These values fall between
+those of $\alpha$-GeSe and black phosphorus, making $\beta$-GeSe a promising
+candidate for future applications. The resistivity of our $\beta$-GeSe crystals
+measured in-plane is on the order of $\rho \approx$ 1 $\Omega$cm, while being
+essentially temperature independent.
+",0,1,0,0,0,0
+8321,Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach,"  With the rapid growth of social media, massive misinformation is also
+spreading widely on social media, such as microblog, and bring negative effects
+to human life. Nowadays, automatic misinformation identification has drawn
+attention from academic and industrial communities. For an event on social
+media usually consists of multiple microblogs, current methods are mainly based
+on global statistical features. However, information on social media is full of
+noisy and outliers, which should be alleviated. Moreover, most of microblogs
+about an event have little contribution to the identification of
+misinformation, where useful information can be easily overwhelmed by useless
+information. Thus, it is important to mine significant microblogs for a
+reliable misinformation identification method. In this paper, we propose an
+Attention-based approach for Identification of Misinformation (AIM). Based on
+the attention mechanism, AIM can select microblogs with largest attention
+values for misinformation identification. The attention mechanism in AIM
+contains two parts: content attention and dynamic attention. Content attention
+is calculated based textual features of each microblog. Dynamic attention is
+related to the time interval between the posting time of a microblog and the
+beginning of the event. To evaluate AIM, we conduct a series of experiments on
+the Weibo dataset and the Twitter dataset, and the experimental results show
+that the proposed AIM model outperforms the state-of-the-art methods.
+",1,0,0,0,0,0
+8322,Machine Learning for Set-Identified Linear Models,"  Set-identified models often restrict the number of covariates leading to wide
+identified sets in practice. This paper provides estimation and inference
+methods for set-identified linear models with high-dimensional covariates where
+the model selection is based on modern machine learning tools. I characterize
+the boundary (i.e, support function) of the identified set using a
+semiparametric moment condition. Combining Neyman-orthogonality and sample
+splitting ideas, I construct a root-N consistent, the uniformly asymptotically
+Gaussian estimator of the support function. I also prove the validity of the
+Bayesian bootstrap procedure to conduct inference about the identified set. I
+provide a general method to construct a Neyman-orthogonal moment condition for
+the support function. I apply this result to estimate sharp nonparametric
+bounds on the average treatment effect in Lee (2008)'s model of endogenous
+selection and substantially tighten the bounds on this parameter in Angrist et
+al. (2006)'s empirical setting. I also apply this result to estimate sharp
+identified sets for two other parameters - a new parameter, called a partially
+linear predictor, and the average partial derivative when the outcome variable
+is recorded in intervals.
+",1,0,0,1,0,0
+8323,An efficient algorithm to decide periodicity of b-recognisable sets using MSDF convention,"  Given an integer base $b>1$, a set of integers is represented in base $b$ by
+a language over $\{0,1,...,b-1\}$. The set is said to be $b$-recognisable if
+its representation is a regular language. It is known that eventually periodic
+sets are $b$-recognisable in every base $b$, and Cobham's theorem implies the
+converse: no other set is $b$-recognisable in every base $b$.
+We are interested in deciding whether a $b$-recognisable set of integers
+(given as a finite automaton) is eventually periodic. Honkala showed that this
+problem decidable in 1986 and recent developments give efficient decision
+algorithms. However, they only work when the integers are written with the
+least significant digit first.
+In this work, we consider the natural order of digits (Most Significant Digit
+First) and give a quasi-linear algorithm to solve the problem in this case.
+",1,0,0,0,0,0
+8324,Electrical Tuning of Polarizaion-state Using Graphene-Integrated Metasurfaces,"  Plasmonic metasurfaces have been employed for tuning and controlling light
+enabling various novel applications. Their appeal is enhanced with the
+incorporation of an active element with the metasurfaces paving the way for
+dynamic control. In this letter, we realize a dynamic polarization state
+generator using graphene-integrated anisotropic metasurface (GIAM), where a
+linear incidence polarization is controllably converted into an elliptical one.
+The anisotropic metasurface leads to an intrinsic polarization conversion when
+illuminated with non-orthogonal incident polarization. Additionally, the
+single-layer graphene allows us to tune the phase and intensity of the
+reflected light on the application of a gate voltage, enabling dynamic
+polarization control. The stokes polarization parameters of the reflected light
+are measured using rotating polarizer method and it is demonstrated that a
+large change in the ellipticity as well as orientation angle can be induced by
+this device. We also provide experimental evidence that the titl angle can
+change independent of the ellipticity going from positive values to nearly zero
+to negative values while ellipticity is constant.
+",0,1,0,0,0,0
+8325,An efficient distribution method for nonlinear two-phase flow in highly heterogeneous multidimensional stochastic porous media,"  In the context of stochastic two-phase flow in porous media, we introduce a
+novel and efficient method to estimate the probability distribution of the
+wetting saturation field under uncertain rock properties in highly
+heterogeneous porous systems, where streamline patterns are dominated by
+permeability heterogeneity, and for slow displacement processes (viscosity
+ratio close to unity). Our method, referred to as the frozen streamline
+distribution method (FROST), is based on a physical understanding of the
+stochastic problem. Indeed, we identify key random fields that guide the
+wetting saturation variability, namely fluid particle times of flight and
+injection times. By comparing saturation statistics against full-physics Monte
+Carlo simulations, we illustrate how this simple, yet accurate FROST method
+performs under the preliminary approximation of frozen streamlines. Further, we
+inspect the performance of an accelerated FROST variant that relies on a
+simplification about injection time statistics. Finally, we introduce how
+quantiles of saturation can be efficiently computed within the FROST framework,
+hence leading to robust uncertainty assessment.
+",0,1,0,0,0,0
+8326,Multi-Kernel LS-SVM Based Bio-Clinical Data Integration: Applications to Ovarian Cancer,"  The medical research facilitates to acquire a diverse type of data from the
+same individual for particular cancer. Recent studies show that utilizing such
+diverse data results in more accurate predictions. The major challenge faced is
+how to utilize such diverse data sets in an effective way. In this paper, we
+introduce a multiple kernel based pipeline for integrative analysis of
+high-throughput molecular data (somatic mutation, copy number alteration, DNA
+methylation and mRNA) and clinical data. We apply the pipeline on Ovarian
+cancer data from TCGA. After multiple kernels have been generated from the
+weighted sum of individual kernels, it is used to stratify patients and predict
+clinical outcomes. We examine the survival time, vital status, and neoplasm
+cancer status of each subtype to verify how well they cluster. We have also
+examined the power of molecular and clinical data in predicting dichotomized
+overall survival data and to classify the tumor grade for the cancer samples.
+It was observed that the integration of various data types yields higher
+log-rank statistics value. We were also able to predict clinical status with
+higher accuracy as compared to using individual data types.
+",0,0,0,1,0,0
+8327,The universal property of derived geometry,"  Derived geometry can be defined as the universal way to adjoin finite
+homotopical limits to a given category of manifolds compatibly with products
+and glueing. The point of this paper is to show that a construction closely
+resembling existing approaches to derived geometry in fact produces a geometry
+with this universal property.
+I also investigate consequences of this definition in particular in the
+differentiable setting, and compare the theory so obtained to D. Spivak's
+axioms for derived C-infinity geometry.
+",0,0,1,0,0,0
+8328,Feature Model-to-Ontology for SPL Application Realisation,"  Feature model are widely used to capture commonalities and variabilities of
+artefacts in Software Product Line (SPL). Several studies have discussed the
+formal representation of feature diagram using ontologies with different styles
+of mapping. However, they still focused on the ontology approach for problem
+space and keep the solution space aside. In this paper, we present the
+modelling of feature model using OWL ontology and produce an application based
+on the ontology. Firstly, we map the features in a running example feature
+diagram to OWL classes and properties. Secondly, we verify the consistency of
+the OWL ontology by using reasoning engines. Finally, we use the ontology as an
+input of Zotonic framework for application realisation.
+",1,0,0,0,0,0
+8329,Rethinking Reprojection: Closing the Loop for Pose-aware ShapeReconstruction from a Single Image,"  An emerging problem in computer vision is the reconstruction of 3D shape and
+pose of an object from a single image. Hitherto, the problem has been addressed
+through the application of canonical deep learning methods to regress from the
+image directly to the 3D shape and pose labels. These approaches, however, are
+problematic from two perspectives. First, they are minimizing the error between
+3D shapes and pose labels - with little thought about the nature of this label
+error when reprojecting the shape back onto the image. Second, they rely on the
+onerous and ill-posed task of hand labeling natural images with respect to 3D
+shape and pose. In this paper we define the new task of pose-aware shape
+reconstruction from a single image, and we advocate that cheaper 2D annotations
+of objects silhouettes in natural images can be utilized. We design
+architectures of pose-aware shape reconstruction which re-project the predicted
+shape back on to the image using the predicted pose. Our evaluation on several
+object categories demonstrates the superiority of our method for predicting
+pose-aware 3D shapes from natural images.
+",1,0,0,0,0,0
+8330,Rigid local systems and alternating groups,"  In earlier work, Katz exhibited some very simple one parameter families of
+exponential sums which gave rigid local systems on the affine line in
+characteristic p whose geometric (and usually, arithmetic) monodromy groups
+were SL(2,q), and he exhibited other such very simple families giving SU(3,q).
+[Here q is a power of the characteristic p with p odd]. In this paper, we
+exhibit equally simple families whose geometric monodromy groups are the
+alternating groups Alt(2q). $. We also determine their arithmetic monodromy
+groups. By Raynaud's solution of the Abhyankar Conjecture, any finite simple
+group whose order is divisible by p will occur as the geometric monodromy group
+of some local system on the affine line in characteristic p; the interest here
+is that it occurs in our particularly simple local systems.
+In the earlier work of Katz, he used a theorem to Kubert to know that the
+monodromy groups in question were finite, then work of Gross to determine which
+finite groups they were. Here we do not have, at present, any direct way of
+showing this finiteness. Rather, the situation is more complicated and more
+interesting. Using some basic information about these local systems, a
+fundamental dichotomy is proved:
+The geometric monodromy group is either Alt(2q) or it is the special
+orthogonal group SO(2q-1). An elementary polynomial identity is used to show
+that the third moment is 1. This rules out the SO(2q-1) case. This roundabout
+method establishes the theorem. It would be interesting to find a ""direct""
+proof that these local systems have integer (rather than rational) traces; this
+integrality is in fact equivalent to their monodromy groups being finite, Even
+if one had such a direct proof, it would still require serious group theory to
+show that their geometric monodromy groups are the alternating groups.
+",0,0,1,0,0,0
+8331,Interference effects of deleterious and beneficial mutations in large asexual populations,"  Linked beneficial and deleterious mutations are known to decrease the
+fixation probability of a favorable mutation in large asexual populations.
+While the hindering effect of strongly deleterious mutations on adaptive
+evolution has been well studied, how weak deleterious mutations, either in
+isolation or with superior beneficial mutations, influence the fixation of a
+beneficial mutation has not been fully explored. Here, using a multitype
+branching process, we obtain an accurate analytical expression for the fixation
+probability when deleterious effects are weak, and exploit this result along
+with the clonal interference theory to investigate the joint effect of linked
+beneficial and deleterious mutations on the rate of adaptation. We find that
+when the mutation rate is increased beyond the beneficial fitness effect, the
+fixation probability of the beneficial mutant decreases from Haldane's
+classical result towards zero. This has the consequence that above a critical
+mutation rate that may depend on the population size, the adaptation rate
+decreases exponentially with the mutation rate and is independent of the
+population size. In addition, we find that for a range of mutation rates, both
+beneficial and deleterious mutations interfere and impede the adaptation
+process in large populations. We also study the evolution of mutation rates in
+adapting asexual populations, and conclude that linked beneficial mutations
+have a stronger influence on mutator fixation than the deleterious mutations.
+",0,0,0,0,1,0
+8332,Grassmanians and Pseudosphere Arrangements,"  We extend vector configurations to more general objects that have nicer
+combinatorial and topological properties, called weighted pseudosphere
+arrangements. These are defined as a weighted variant of arrangements of
+pseudospheres, as in the Topological Representation Theorem for oriented
+matroids. We show that in rank 3, the real Stiefel manifold, Grassmanian, and
+oriented Grassmanian are homotopy equivalent to the analagously defined spaces
+of weighted pseudosphere arrangements. We also show for all rank 3 oriented
+matroids, that the space of realizations by weighted pseudosphere arrangements
+is contractible. This is a sharp contrast with vector configurations, where the
+space of realizations can have the homotopy type of any primary semialgebraic
+set.
+",0,0,1,0,0,0
+8333,Rational homotopy theory via Sullivan models: a survey,"  This survey contains the main results in rational homotopy, from the
+beginning to the most recent ones. It makes the status of the art, gives a
+short presentation of some areas where rational homotopy has been used, and
+contains a lot of important open problems
+",0,0,1,0,0,0
+8334,A Spectroscopic Orbit for the late-type Be star $β$ CMi,"  The late-type Be star $\beta$ CMi is remarkably stable compared to other Be
+stars that have been studied. This has led to a realistic model of the
+outflowing Be disk by Klement et al. These results showed that the disk is
+likely truncated at a finite radius from the star, which Klement et al.~suggest
+is evidence for an unseen binary companion in orbit. Here we report on an
+analysis of the Ritter Observatory spectroscopic archive of $\beta$ CMi to
+search for evidence of the elusive companion. We detect periodic Doppler shifts
+in the wings of the H$\alpha$ line with a period of 170 d and an amplitude of
+2.25 km s$^{-1}$, consistent with a low-mass binary companion ($M\approx 0.42
+M_\odot$). We then compared the small changes in the violet-to-red peak height
+changes ($V/R$) with the orbital motion. We find weak evidence that it does
+follow the orbital motion, as suggested by recent Be binary models by Panoglou
+et al. Our results, which are similar to those for several other Be stars,
+suggest that $\beta$ CMi may be a product of binary evolution where Roche lobe
+overflow has spun up the current Be star, likely leaving a hot subdwarf or
+white dwarf in orbit around the star. Unfortunately, no direct sign of this
+companion star is found in the very limited archive of {\it International
+Ultraviolet Explorer} spectra.
+",0,1,0,0,0,0
+8335,Variations on known and recent cardinality bounds,"  Sapirovskii [18] proved that $|X|\leq\pi\chi(X)^{c(X)\psi(X)}$, for a regular
+space $X$. We introduce the $\theta$-pseudocharacter of a Urysohn space $X$,
+denoted by $\psi_\theta (X)$, and prove that the previous inequality holds for
+Urysohn spaces replacing the bounds on celluarity $c(X)\leq\kappa$ and on
+pseudocharacter $\psi(X)\leq\kappa$ with a bound on Urysohn cellularity
+$Uc(X)\leq\kappa$ (which is a weaker conditon because $Uc(X)\leq c(X)$) and on
+$\theta$-pseudocharacter $\psi_\theta (X)\leq\kappa$ respectivly (note that in
+general $\psi(\cdot)\leq\psi_\theta (\cdot)$ and in the class of regular spaces
+$\psi(\cdot)=\psi_\theta(\cdot)$). Further, in [6] the authors generalized the
+Dissanayake and Willard's inequality: $|X|\leq 2^{aL_{c}(X)\chi(X)}$, for
+Hausdorff spaces $X$ [25], in the class of $n$-Hausdorff spaces and de Groot's
+result: $|X|\leq 2^{hL(X)}$, for Hausdorff spaces [11], in the class of $T_1$
+spaces (see Theorems 2.22 and 2.23 in [6]). In this paper we restate Theorem
+2.22 in [6] in the class of $n$-Urysohn spaces and give a variation of Theorem
+2.23 in [6] using new cardinal functions, denoted by $UW(X)$, $\psi
+w_\theta(X)$, $\theta\hbox{-}aL(X)$, $h\theta\hbox{-}aL(X)$,
+$\theta\hbox{-}aL_c(X)$ and $\theta\hbox{-}aL_{\theta}(X)$. In [5] the authors
+introduced the Hausdorff point separating weight of a space $X$ denoted by
+$Hpsw(X)$ and proved a Hausdorff version of Charlesworth's inequality $|X|\leq
+psw(X)^{L(X)\psi(X)}$ [7]. In this paper, we introduce the Urysohn point
+separating weight of a space $X$, denoted by $Upsw(X)$, and prove that $|X|\leq
+Upsw(X)^{\theta\hbox{-}aL_{c}(X)\psi(X)}$, for a Urysohn space $X$.
+",0,0,1,0,0,0
+8336,Spatio-temporal intermittency of the turbulent energy cascade,"  In incompressible and periodic statistically stationary turbulence, exchanges
+of turbulent energy across scales and space are characterised by very intense
+and intermittent spatio-temporal fluctuations around zero of the
+time-derivative term, the spatial turbulent transport of fluctuating energy,
+and the pressure-velocity term. These fluctuations are correlated with each
+other and with the intense intermittent fluctuations of the interscale energy
+transfer rate. These correlations are caused by the sweeping effect, the link
+between non-linearity and non-locality, and also relate to geometrical
+alignments between the two-point fluctuating pressure force difference and the
+two-point fluctuating velocity difference in the case of the correlation
+between the interscale transfer rate and the pressure-velocity term. All these
+processes are absent from the spatio-temporal average picture of the turbulence
+cascade in statistically stationary and homogeneous turbulence.
+",0,1,0,0,0,0
+8337,Voltage Analytics for Power Distribution Network Topology Verification,"  Distribution grids constitute complex networks of lines often times
+reconfigured to minimize losses, balance loads, alleviate faults, or for
+maintenance purposes. Topology monitoring becomes a critical task for optimal
+grid scheduling. While synchrophasor installations are limited in low-voltage
+grids, utilities have an abundance of smart meter data at their disposal. In
+this context, a statistical learning framework is put forth for verifying
+single-phase grid structures using non-synchronized voltage data. The related
+maximum likelihood task boils down to minimizing a non-convex function over a
+non-convex set. The function involves the sample voltage covariance matrix and
+the feasible set is relaxed to its convex hull. Asymptotically in the number of
+data, the actual topology yields the global minimizer of the original and the
+relaxed problems. Under a simplified data model, the function turns out to be
+convex, thus offering optimality guarantees. Prior information on line statuses
+is also incorporated via a maximum a-posteriori approach. The formulated tasks
+are tackled using solvers having complementary strengths. Numerical tests using
+real data on benchmark feeders demonstrate that reliable topology estimates can
+be acquired even with a few smart meter data, while the non-convex schemes
+exhibit superior line verification performance at the expense of additional
+computational time.
+",0,0,1,0,0,0
+8338,The comprehension construction,"  In this paper we construct an analogue of Lurie's ""unstraightening""
+construction that we refer to as the ""comprehension construction"". Its input is
+a cocartesian fibration $p \colon E \to B$ between $\infty$-categories together
+with a third $\infty$-category $A$. The comprehension construction then defines
+a map from the quasi-category of functors from $A$ to $B$ to the large
+quasi-category of cocartesian fibrations over $A$ that acts on $f \colon A \to
+B$ by forming the pullback of $p$ along $f$. To illustrate the versatility of
+this construction, we define the covariant and contravariant Yoneda embeddings
+as special cases of the comprehension functor. We then prove that the hom-wise
+action of the comprehension functor coincides with an ""external action"" of the
+hom-spaces of $B$ on the fibres of $p$ and use this to prove that the Yoneda
+embedding is fully faithful, providing an explicit equivalence between a
+quasi-category and the homotopy coherent nerve of a Kan-complex enriched
+category.
+",0,0,1,0,0,0
+8339,Approximate String Matching: Theory and Applications (La Recherche Approchée de Motifs : Théorie et Applications),"  The approximate string matching is a fundamental and recurrent problem that
+arises in most computer science fields. This problem can be defined as follows:
+Let $D=\{x_1,x_2,\ldots x_d\}$ be a set of $d$ words defined on an alphabet
+$\Sigma$, let $q$ be a query defined also on $\Sigma$, and let $k$ be a
+positive integer. We want to build a data structure on $D$ capable of answering
+the following query: find all words in $D$ that are at most different from the
+query word $q$ with $k$ errors.
+In this thesis, we study the approximate string matching methods in
+dictionaries, texts, and indexes, to propose practical methods that solve this
+problem efficiently. We explore this problem in three complementary directions:
+1) The approximate string matching in the dictionary. We propose two
+solutions to this problem, the first one uses hash tables for $k \geq 2$, the
+second uses the Trie and reverse Trie, and it is restricted to (k = 1). The two
+solutions are adaptable, without loss of performance, to the approximate string
+matching in a text.
+2) The approximate string matching for \textit{autocompletion}, which is,
+find all suffixes of a given prefix that may contain errors. We give a new
+solution better in practice than all the previous proposed solutions.
+3) The problem of the alignment of biological sequences can be interpreted as
+an approximate string matching problem. We propose a solution for peers and
+multiple sequences alignment.
+\medskip All the results obtained showed that our algorithms, give the best
+performance on sets of practical data (benchmark from the real world). All our
+methods are proposed as libraries, and they are published online.
+",1,0,0,0,0,0
+8340,"Imaging anomalous nematic order and strain in optimally doped BaFe$_2$(As,P)$_2$","  We present the strain and temperature dependence of an anomalous nematic
+phase in optimally doped BaFe$_2$(As,P)$_2$. Polarized ultrafast optical
+measurements reveal broken 4-fold rotational symmetry in a temperature range
+above $T_c$ in which bulk probes do not detect a phase transition. Using
+ultrafast microscopy, we find that the magnitude and sign of this nematicity
+vary on a ${50{-}100}~\mu$m length scale, and the temperature at which it
+onsets ranges from 40 K near a domain boundary to 60 K deep within a domain.
+Scanning Laue microdiffraction maps of local strain at room temperature
+indicate that the nematic order appears most strongly in regions of weak,
+isotropic strain. These results indicate that nematic order arises in a genuine
+phase transition rather than by enhancement of local anisotropy by a strong
+nematic susceptibility. We interpret our results in the context of a proposed
+surface nematic phase.
+",0,1,0,0,0,0
+8341,Deception Detection in Videos,"  We present a system for covert automated deception detection in real-life
+courtroom trial videos. We study the importance of different modalities like
+vision, audio and text for this task. On the vision side, our system uses
+classifiers trained on low level video features which predict human
+micro-expressions. We show that predictions of high-level micro-expressions can
+be used as features for deception prediction. Surprisingly, IDT (Improved Dense
+Trajectory) features which have been widely used for action recognition, are
+also very good at predicting deception in videos. We fuse the score of
+classifiers trained on IDT features and high-level micro-expressions to improve
+performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio
+domain also provide a significant boost in performance, while information from
+transcripts is not very beneficial for our system. Using various classifiers,
+our automated system obtains an AUC of 0.877 (10-fold cross-validation) when
+evaluated on subjects which were not part of the training set. Even though
+state-of-the-art methods use human annotations of micro-expressions for
+deception detection, our fully automated approach outperforms them by 5%. When
+combined with human annotations of micro-expressions, our AUC improves to
+0.922. We also present results of a user-study to analyze how well do average
+humans perform on this task, what modalities they use for deception detection
+and how they perform if only one modality is accessible. Our project page can
+be found at \url{this https URL}.
+",1,0,0,0,0,0
+8342,Extended degenerate Stirling numbers of the second kind and extended degenerate Bell polynomials,"  In a recent work, the degenerate Stirling polynomials of the second kind were
+studied by T. Kim. In this paper, we investigate the extended degenerate
+Stirling numbers of the second kind and the extended degenerate Bell
+polynomials associated with them. As results, we give some expressions,
+identities and properties about the extended degener- ate Stirling numbers of
+the second kind and the extended degenerate Bell polynomials.
+",0,0,1,0,0,0
+8343,On time and consistency in multi-level agent-based simulations,"  The integration of multiple viewpoints became an increasingly popular
+approach to deal with agent-based simulations. Despite their disparities,
+recent approaches successfully manage to run such multi-level simulations. Yet,
+are they doing it appropriately?
+This paper tries to answer that question, with an analysis based on a generic
+model of the temporal dynamics of multi-level simulations. This generic model
+is then used to build an orthogonal approach to multi-level simulation called
+SIMILAR. In this approach, most time-related issues are explicitly modeled,
+owing to an implementation-oriented approach based on the influence/reaction
+principle.
+",1,0,0,0,0,0
+8344,On The Inductive Bias of Words in Acoustics-to-Word Models,"  Acoustics-to-word models are end-to-end speech recognizers that use words as
+targets without relying on pronunciation dictionaries or graphemes. These
+models are notoriously difficult to train due to the lack of linguistic
+knowledge. It is also unclear how the amount of training data impacts the
+optimization and generalization of such models. In this work, we study the
+optimization and generalization of acoustics-to-word models under different
+amounts of training data. In addition, we study three types of inductive bias,
+leveraging a pronunciation dictionary, word boundary annotations, and
+constraints on word durations. We find that constraining word durations leads
+to the most improvement. Finally, we analyze the word embedding space learned
+by the model, and find that the space has a structure dominated by the
+pronunciation of words. This suggests that the contexts of words, instead of
+their phonetic structure, should be the future focus of inductive bias in
+acoustics-to-word models.
+",1,0,0,0,0,0
+8345,A multi-layered energy consumption model for smart wireless acoustic sensor networks,"  Smart sensing is expected to become a pervasive technology in smart cities
+and environments of the near future. These services are improving their
+capabilities due to integrated devices shrinking in size while maintaining
+their computational power, which can run diverse Machine Learning algorithms
+and achieve high performance in various data-processing tasks. One attractive
+sensor modality to be used for smart sensing are acoustic sensors, which can
+convey highly informative data while keeping a moderate energy consumption.
+Unfortunately, the energy budget of current wireless sensor networks is usually
+not enough to support the requirements of standard microphones. Therefore,
+energy efficiency needs to be increased at all layers --- sensing, signal
+processing and communication --- in order to bring wireless smart acoustic
+sensors into the market. To help to attain this goal, this paper introduces
+WASN-EM: an energy consumption model for wireless acoustic sensors networks
+(WASN), whose aim is to aid in the development of novel techniques to increase
+the energy-efficient of smart wireless acoustic sensors. This model provides a
+first step of exploration prior to custom design of a smart wireless acoustic
+sensor, and also can be used to compare the energy consumption of different
+protocols.
+",1,0,0,0,0,0
+8346,BRAVO - Biased Locking for Reader-Writer Locks,"  Designers of modern reader-writer locks confront a difficult trade-off
+related to reader scalability. Locks that have a compact memory representation
+for active readers will typically suffer under high intensity read-dominated
+workloads when the ""reader indicator""' state is updated frequently by a diverse
+set of threads, causing cache invalidation and coherence traffic. Other
+designs, such as cohort reader-writer locks, use distributed reader indicators,
+one per NUMA node. This improves reader-reader scalability, but also increases
+the size of each lock instance.
+We propose a simple transformation BRAVO, that augments any existing
+reader-writer lock, adding just two integer fields to the lock instance.
+Readers make their presence known to writers by hashing their thread's identity
+with the lock address, forming an index into a visible readers table. Readers
+attempt to install the lock address into that element in the table, making
+their existence known to potential writers. All locks and threads in an address
+space can share the visible readers table. Updates by readers tend to be
+diffused over the table, resulting in a NUMA-friendly design. Crucially,
+readers of the same lock tend to write to different locations in the array,
+reducing coherence traffic.
+Specifically, BRAVO allows a simple compact lock to be augmented so as to
+provide scalable concurrent reading but with only a modest increase in
+footprint.
+",1,0,0,0,0,0
+8347,Internal DLA on Sierpinski gasket graphs,"  Internal diffusion-limited aggregation (IDLA) is a stochastic growth model on
+a graph $G$ which describes the formation of a random set of vertices growing
+from the origin (some fixed vertex) of $G$. Particles start at the origin and
+perform simple random walks; each particle moves until it lands on a site which
+was not previously visited by other particles. This random set of occupied
+sites in $G$ is called the IDLA cluster.
+In this paper we consider IDLA on Sierpinski gasket graphs, and show that the
+IDLA cluster fills balls (in the graph metric) with probability 1.
+",0,1,1,0,0,0
+8348,A PCA-based approach for subtracting thermal background emission in high-contrast imaging data,"  Ground-based observations at thermal infrared wavelengths suffer from large
+background radiation due to the sky, telescope and warm surfaces in the
+instrument. This significantly limits the sensitivity of ground-based
+observations at wavelengths longer than 3 microns. We analyzed this background
+emission in infrared high contrast imaging data, show how it can be modelled
+and subtracted and demonstrate that it can improve the detection of faint
+sources, such as exoplanets. We applied principal component analysis to model
+and subtract the thermal background emission in three archival high contrast
+angular differential imaging datasets in the M and L filter. We describe how
+the algorithm works and explain how it can be applied. The results of the
+background subtraction are compared to the results from a conventional mean
+background subtraction scheme. Finally, both methods for background subtraction
+are also compared by performing complete data reductions. We analyze the
+results from the M dataset of HD100546 qualitatively. For the M band dataset of
+beta Pic and the L band dataset of HD169142, which was obtained with an annular
+groove phase mask vortex vector coronagraph, we also calculate and analyze the
+achieved signal to noise (S/N). We show that applying PCA is an effective way
+to remove spatially and temporarily varying thermal background emission down to
+close to the background limit. The procedure also proves to be very successful
+at reconstructing the background that is hidden behind the PSF. In the complete
+data reductions, we find at least qualitative improvements for HD100546 and
+HD169142, however, we fail to find a significant increase in S/N of beta Pic b.
+We discuss these findings and argue that in particular datasets with strongly
+varying observing conditions or infrequently sampled sky background will
+benefit from the new approach.
+",0,1,0,0,0,0
+8349,A weak law of large numbers for estimating the correlation in bivariate Brownian semistationary processes,"  This article presents various weak laws of large numbers for the so-called
+realised covariation of a bivariate stationary stochastic process which is not
+a semimartingale. More precisely, we consider two cases: Bivariate moving
+average processes with stochastic correlation and bivariate Brownian
+semistationary processes with stochastic correlation. In both cases, we can
+show that the (possibly scaled) realised covariation converges to the
+integrated (possibly volatility modulated) stochastic correlation process.
+",0,0,1,1,0,0
+8350,Synthesis of Spatial Charging/Discharging Patterns of In-Vehicle Batteries for Provision of Ancillary Service and Mitigation of Voltage Impact,"  We develop an algorithm for synthesizing a spatial pattern of
+charging/discharging operations of in-vehicle batteries for provision of
+Ancillary Service (AS) in power distribution grids. The algorithm is based on
+the ODE (Ordinary Differential Equation) model of distribution voltage that has
+been recently introduced. In this paper, firstly, we derive analytical
+solutions of the ODE model for a single straight-line feeder through a partial
+linearization, thereby providing a physical insight to the impact of spatial EV
+charging/discharging to the distribution voltage. Second, based on the
+analytical solutions, we propose an algorithm for determining the values of
+charging/discharging power (active and reactive) by in-vehicle batteries in the
+single feeder grid, so that the power demanded as AS (e.g. a regulation signal
+to distribution system operator for primary frequency control reserve) is
+provided by EVs, and the deviation of distribution voltage from a nominal value
+is reduced in the grid. Effectiveness of the algorithm is established with
+numerical simulations on the single feeder grid and on a realistic feeder grid
+with multiple bifurcations.
+",1,0,0,0,0,0
+8351,Matter fields interacting with photons,"  We have extended the biquaternionic Dirac's equation to include interactions
+with photons. The electric field is found to be perpendicular to the matter
+magnetic field, and the magnetic field is perpendicular to the matter inertial
+field. Inertial and magnetic masses are found to be conserved separately. The
+magnetic mass density is a consequence of the coupling between the vector
+potential and the matter inertial field. The presence of the vector and scalar
+potentials, and the matter inertial and magnetic fields are found to modify the
+standard form of the derived Maxwell's equations. The resulting interacting
+electrodynamics equations are found to generalize those of axion-like fields of
+Frank Wilczek or Chern-Simons equations. The axion field satisfies massive
+Klein-Gordon equation if Lorenz gauge condition is violated. Therefore, axion
+could be our massive photon. The electromagnetic field vector,
+$\vec{F}=\vec{E}+ic\vec{B}$, is found to satisfy massive Dirac's equation in
+addition to the fact that $\vec{\nabla}\cdot\vec{F}=0$, where $\vec{E}$ and
+$\vec{B}$ are the electric and magnetic fields, respectively.
+",0,1,0,0,0,0
+8352,A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization,"  We describe a framework for deriving and analyzing online optimization
+algorithms that incorporate adaptive, data-dependent regularization, also
+termed preconditioning. Such algorithms have been proven useful in stochastic
+optimization by reshaping the gradients according to the geometry of the data.
+Our framework captures and unifies much of the existing literature on adaptive
+online methods, including the AdaGrad and Online Newton Step algorithms as well
+as their diagonal versions. As a result, we obtain new convergence proofs for
+these algorithms that are substantially simpler than previous analyses. Our
+framework also exposes the rationale for the different preconditioned updates
+used in common stochastic optimization methods.
+",1,0,1,1,0,0
+8353,Discretization error cancellation in electronic structure calculation: a quantitative study,"  It is often claimed that error cancellation plays an essential role in
+quantum chemistry and first-principle simulation for condensed matter physics
+and materials science. Indeed, while the energy of a large, or even
+medium-size, molecular system cannot be estimated numerically within chemical
+accuracy (typically 1 kcal/mol or 1 mHa), it is considered that the energy
+difference between two configurations of the same system can be computed in
+practice within the desired accuracy.
+The purpose of this paper is to provide a quantitative study of
+discretization error cancellation. The latter is the error component due to the
+fact that the model used in the calculation (e.g. Kohn-Sham LDA) must be
+discretized in a finite basis set to be solved by a computer. We first report
+comprehensive numerical simulations performed with Abinit on two simple
+chemical systems, the hydrogen molecule on the one hand, and a system
+consisting of two oxygen atoms and four hydrogen atoms on the other hand. We
+observe that errors on energy differences are indeed significantly smaller than
+errors on energies, but that these two quantities asymptotically converge at
+the same rate when the energy cut-off goes to infinity. We then analyze a
+simple one-dimensional periodic Schrödinger equation with Dirac potentials,
+for which analytic solutions are available. This allows us to explain the
+discretization error cancellation phenomenon on this test case with
+quantitative mathematical arguments.
+",0,1,1,0,0,0
+8354,Analysis of spectral clustering algorithms for community detection: the general bipartite setting,"  We consider spectral clustering algorithms for community detection under a
+general bipartite stochastic block model (SBM). A modern spectral clustering
+algorithm consists of three steps: (1) regularization of an appropriate
+adjacency or Laplacian matrix (2) a form of spectral truncation and (3) a
+k-means type algorithm in the reduced spectral domain. We focus on the
+adjacency-based spectral clustering and for the first step, propose a new
+data-driven regularization that can restore the concentration of the adjacency
+matrix even for the sparse networks. This result is based on recent work on
+regularization of random binary matrices, but avoids using unknown population
+level parameters, and instead estimates the necessary quantities from the data.
+We also propose and study a novel variation of the spectral truncation step and
+show how this variation changes the nature of the misclassification rate in a
+general SBM. We then show how the consistency results can be extended to models
+beyond SBMs, such as inhomogeneous random graph models with approximate
+clusters, including a graphon clustering problem, as well as general
+sub-Gaussian biclustering. A theme of the paper is providing a better
+understanding of the analysis of spectral methods for community detection and
+establishing consistency results, under fairly general clustering models and
+for a wide regime of degree growths, including sparse cases where the average
+expected degree grows arbitrarily slowly.
+",1,0,0,1,0,0
+8355,A computational approach to calculate the heat of transport of aqueous solutions,"  Thermal gradients induce concentration gradients in alkali halide solutions,
+and the salt migrates towards hot or cold regions depending on the average
+temperature of the solution. This effect has been interpreted using the heat of
+transport, which provides a route to rationalize thermophoretic phenomena.
+Early theories provide estimates of the heat of transport at infinite dilution.
+These values are used to interpret thermodiffusion (Soret) and thermoelectric
+(Seebeck) effects. However, accessing heats of transport of individual ions at
+finite concentration remains an outstanding question both theoretically and
+experimentally. Here we discuss a computational approach to calculate heats of
+transport of aqueous solutions at finite concentrations, and apply our method
+to study lithium chloride solutions at concentrations $>0.5$~M. The heats of
+transport are significantly different for Li$^+$ and Cl$^-$ ions, unlike what
+is expected at infinite dilution. We find theoretical evidence for the
+existence of minima in the Soret coefficient of LiCl, where the magnitude of
+the heat of transport is maximized. The Seebeck coefficient obtained from the
+ionic heats of transport varies significantly with temperature and
+concentration. We identify thermodynamic conditions leading to a maximization
+of the thermoelectric response of aqueous solutions.
+",0,1,0,0,0,0
+8356,Dark Matter Annihilation in the Circumgalactic Medium at High Redshifts,"  Annihilating dark matter (DM) models offer promising avenues for future DM
+detection, in particular via modification of astrophysical signals. However
+when modelling such potential signals at high redshift the emergence of both
+dark matter and baryonic structure, as well as the complexities of the energy
+transfer process, need to be taken into account. In the following paper we
+present a detailed energy deposition code and use this to examine the energy
+transfer efficiency of annihilating dark matter at high redshift, including the
+effects on baryonic structure. We employ the PYTHIA code to model
+neutralino-like DM candidates and their subsequent annihilation products for a
+range of masses and annihilation channels. We also compare different density
+profiles and mass-concentration relations for 10^5-10^7 M_sun haloes at
+redshifts 20 and 40. For these DM halo and particle models, we show radially
+dependent ionisation and heating curves and compare the deposited energy to the
+haloes' gravitational binding energy. We use the ""filtered"" annihilation
+spectra escaping the halo to calculate the heating of the circumgalactic medium
+and show that the mass of the minimal star forming object is increased by a
+factor of 2-3 at redshift 20 and 4-5 at redshift 40 for some DM models.
+",0,1,0,0,0,0
+8357,The p-convolution forest: a method for solving graphical models with additive probabilistic equations,"  Convolution trees, loopy belief propagation, and fast numerical p-convolution
+are combined for the first time to efficiently solve networks with several
+additive constraints between random variables. An implementation of this
+""convolution forest"" approach is constructed from scratch, including an
+improved trimmed convolution tree algorithm and engineering details that permit
+fast inference in practice, and improve the ability of scientists to prototype
+models with additive relationships between discrete variables. The utility of
+this approach is demonstrated using several examples: these include
+illustrations on special cases of some classic NP-complete problems (subset sum
+and knapsack), identification of GC-rich genomic regions with a large hidden
+Markov model, inference of molecular composition from summary statistics of the
+intact molecule, and estimation of elemental abundance in the presence of
+overlapping isotope peaks.
+",0,0,0,1,0,0
+8358,Statistical Timing Analysis for Latch-Controlled Circuits with Reduced Iterations and Graph Transformations,"  Level-sensitive latches are widely used in high- performance designs. For
+such circuits efficient statistical timing analysis algorithms are needed to
+take increasing process vari- ations into account. But existing methods solving
+this problem are still computationally expensive and can only provide the yield
+at a given clock period. In this paper we propose a method combining reduced
+iterations and graph transformations. The reduced iterations extract setup time
+constraints and identify a subgraph for the following graph transformations
+handling the constraints from nonpositive loops. The combined algorithms are
+very efficient, more than 10 times faster than other existing methods, and
+result in a parametric minimum clock period, which together with the hold time
+constraints can be used to compute the yield at any given clock period very
+easily.
+",1,0,0,0,0,0
+8359,A moment-angle manifold whose cohomology is not torsion free,"  In this paper we give a method to construct moment-angle manifolds whose
+cohomologies are not torsion free. We also give method to describe the
+corresponding simplicial sphere by its non-faces.
+",0,0,1,0,0,0
+8360,The committee machine: Computational to statistical gaps in learning a two-layers neural network,"  Heuristic tools from statistical physics have been used in the past to locate
+the phase transitions and compute the optimal learning and generalization
+errors in the teacher-student scenario in multi-layer neural networks. In this
+contribution, we provide a rigorous justification of these approaches for a
+two-layers neural network model called the committee machine. We also introduce
+a version of the approximate message passing (AMP) algorithm for the committee
+machine that allows to perform optimal learning in polynomial time for a large
+set of parameters. We find that there are regimes in which a low generalization
+error is information-theoretically achievable while the AMP algorithm fails to
+deliver it, strongly suggesting that no efficient algorithm exists for those
+cases, and unveiling a large computational gap.
+",0,0,0,1,0,0
+8361,On the anomalous {changes of seismicity and} geomagnetic field prior to the 2011 $M_w$ 9.0 Tohoku earthquake,"  Xu et al. [J. Asian Earth Sci. {\bf 77}, 59-65 (2013)] It has just been
+reported that approximately 2 months prior to the $M_w$9.0 Tohoku earthquake
+that occurred in Japan on 11 March 2011 anomalous variations of the geomagnetic
+field have been observed in the vertical component at a measuring station about
+135 km from the epicenter for about 10 days (4 to 14 January 2011). Here, we
+show that this observation is in striking agreement with independent recent
+results obtained from natural time analysis of seismicity in Japan. In
+particular, this analysis has revealed that an unprecedented minimum of the
+order parameter fluctuations of seismicity was observed around 5 January 2011,
+thus pointing to the initiation at that date of a strong precursory Seismic
+Electric Signals activity accompanied by the anomalous geomagnetic field
+variations. Starting from this date, natural time analysis of the subsequent
+seismicity indicates that a strong mainshock was expected in a few days to one
+week after 08:40 LT on 10 March 2011.
+",0,1,0,0,0,0
+8362,Exponentially small splitting of separatrices near a period-doubling bifurcation in area-preserving maps,"  We consider the conservative Hénon family at the period-doubling
+bifurcation of its fixed point and demonstrate that the separatrices of the
+fixed saddle point nearing the bifurcation split exponentially: given that
+$\lambda_+$ is the smaller of the eigenvalues of the saddle point, the angle
+between the separatrices along the homoclinic orbit satisfies $$\sin \alpha =
+O(e^{-{\pi^2 \over \log |\lambda_+|}})+ O\left( e^{-2 (1-\kappa) {\pi^2 \over
+\log |\lambda_+|}} \right),$$ for any positive $\kappa<1$.
+",0,0,1,0,0,0
+8363,Learning Dynamics and the Co-Evolution of Competing Sexual Species,"  We analyze a stylized model of co-evolution between any two purely competing
+species (e.g., host and parasite), both sexually reproducing. Similarly to a
+recent model of Livnat \etal~\cite{evolfocs14} the fitness of an individual
+depends on whether the truth assignments on $n$ variables that reproduce
+through recombination satisfy a particular Boolean function. Whereas in the
+original model a satisfying assignment always confers a small evolutionary
+advantage, in our model the two species are in an evolutionary race with the
+parasite enjoying the advantage if the value of its Boolean function matches
+its host, and the host wishing to mismatch its parasite. Surprisingly, this
+model makes a simple and robust behavioral prediction. The typical system
+behavior is \textit{periodic}. These cycles stay bounded away from the boundary
+and thus, \textit{learning-dynamics competition between sexual species can
+provide an explanation for genetic diversity.} This explanation is due solely
+to the natural selection process. No mutations, environmental changes, etc.,
+need be invoked.
+The game played at the gene level may have many Nash equilibria with widely
+diverse fitness levels. Nevertheless, sexual evolution leads to gene
+coordination that implements an optimal strategy, i.e., an optimal population
+mixture, at the species level. Namely, the play of the many ""selfish genes""
+implements a time-averaged correlated equilibrium where the average fitness of
+each species is exactly equal to its value in the two species zero-sum
+competition.
+Our analysis combines tools from game theory, dynamical systems and Boolean
+functions to establish a novel class of conservative dynamical systems.
+",1,0,0,0,0,0
+8364,Deep Neural Networks for Multiple Speaker Detection and Localization,"  We propose to use neural networks for simultaneous detection and localization
+of multiple sound sources in human-robot interaction. In contrast to
+conventional signal processing techniques, neural network-based sound source
+localization methods require fewer strong assumptions about the environment.
+Previous neural network-based methods have been focusing on localizing a single
+sound source, which do not extend to multiple sources in terms of detection and
+localization. In this paper, we thus propose a likelihood-based encoding of the
+network output, which naturally allows the detection of an arbitrary number of
+sources. In addition, we investigate the use of sub-band cross-correlation
+information as features for better localization in sound mixtures, as well as
+three different network architectures based on different motivations.
+Experiments on real data recorded from a robot show that our proposed methods
+significantly outperform the popular spatial spectrum-based approaches.
+",1,0,0,0,0,0
+8365,Active Tolerant Testing,"  In this work, we give the first algorithms for tolerant testing of nontrivial
+classes in the active model: estimating the distance of a target function to a
+hypothesis class C with respect to some arbitrary distribution D, using only a
+small number of label queries to a polynomial-sized pool of unlabeled examples
+drawn from D. Specifically, we show that for the class D of unions of d
+intervals on the line, we can estimate the error rate of the best hypothesis in
+the class to an additive error epsilon from only $O(\frac{1}{\epsilon^6}\log
+\frac{1}{\epsilon})$ label queries to an unlabeled pool of size
+$O(\frac{d}{\epsilon^2}\log \frac{1}{\epsilon})$. The key point here is the
+number of labels needed is independent of the VC-dimension of the class. This
+extends the work of Balcan et al. [2012] who solved the non-tolerant testing
+problem for this class (distinguishing the zero-error case from the case that
+the best hypothesis in the class has error greater than epsilon).
+We also consider the related problem of estimating the performance of a given
+learning algorithm A in this setting. That is, given a large pool of unlabeled
+examples drawn from distribution D, can we, from only a few label queries,
+estimate how well A would perform if the entire dataset were labeled? We focus
+on k-Nearest Neighbor style algorithms, and also show how our results can be
+applied to the problem of hyperparameter tuning (selecting the best value of k
+for the given learning problem).
+",1,0,0,1,0,0
+8366,Temperature inside tumor as time function in RF hyperthermia,"  A simplified 2-D model which is an example of regional RF hyperthermia is
+presented. Human body is inside the wire with exciting current and the
+electromagnetic energy is concentrated within the tumor. The analyzed model is
+therefore a coupling of the electromagnetic field and the temperature field.
+Exciting current density in human body has been calculated using the finite
+element method, and then bioheat equation in timedepended nonstationary case
+has been resolved. At the and obtained results are presented.
+",0,1,0,0,0,0
+8367,"Dirac Line-nodes and Effect of Spin-orbit Coupling in Non-symmorphic Critical Semimetal MSiS (M=Hf, Zr)","  Topological Dirac semimetals (TDSs) represent a new state of quantum matter
+recently discovered that offers a platform for realizing many exotic physical
+phenomena. A TDS is characterized by the linear touching of bulk (conduction
+and valance) bands at discrete points in the momentum space (i.e. 3D Dirac
+points), such as in Na3Bi and Cd3As2. More recently, new types of Dirac
+semimetals with robust Dirac line-nodes (with non-trivial topology or near the
+critical point between topological phase transitions) have been proposed that
+extends the bulk linear touching from discrete points to 1D lines. In this
+work, using angle-resolved photoemission spectroscopy (ARPES), we explored the
+electronic structure of the non-symmorphic crystals MSiS (M=Hf, Zr).
+Remarkably, by mapping out the band structure in the full 3D Brillouin Zone
+(BZ), we observed two sets of Dirac line-nodes in parallel with the kz-axis and
+their dispersions. Interestingly, along directions other than the line-nodes in
+the 3D BZ, the bulk degeneracy is lifted by spin-orbit coupling (SOC) in both
+compounds with larger magnitude in HfSiS. Our work not only experimentally
+confirms a new Dirac line-node semimetal family protected by non-symmorphic
+symmetry, but also helps understanding and further exploring the exotic
+properties as well as practical applications of the MSiS family of compounds.
+",0,1,0,0,0,0
+8368,An Arcsine Law for Markov Random Walks,"  The classic arcsine law for the number
+$N_{n}^{>}:=n^{-1}\sum_{k=1}^{n}\mathbf{1}_{\{S_{k}>0\}}$ of positive terms, as
+$n\to\infty$, in an ordinary random walk $(S_{n})_{n\ge 0}$ is extended to the
+case when this random walk is governed by a positive recurrent Markov chain
+$(M_{n})_{n\ge 0}$ on a countable state space $\mathcal{S}$, that is, for a
+Markov random walk $(M_{n},S_{n})_{n\ge 0}$ with positive recurrent discrete
+driving chain. More precisely, it is shown that $n^{-1}N_{n}^{>}$ converges in
+distribution to a generalized arcsine law with parameter $\rho\in [0,1]$ (the
+classic arcsine law if $\rho=1/2$) iff the Spitzer condition $$
+\lim_{n\to\infty}\frac{1}{n}\sum_{k=1}^{n}\mathbb{P}_{i}(S_{n}>0)\ =\ \rho $$
+holds true for some and then all $i\in\mathcal{S}$, where
+$\mathbb{P}_{i}:=\mathbb{P}(\cdot|M_{0}=i)$ for $i\in\mathcal{S}$. It is also
+proved, under an extra assumption on the driving chain if $0<\rho<1$, that this
+condition is equivalent to the stronger variant $$
+\lim_{n\to\infty}\mathbb{P}_{i}(S_{n}>0)\ =\ \rho. $$ For an ordinary random
+walk, this was shown by Doney for $0<\rho<1$ and by Bertoin and Doney for
+$\rho\in\{0,1\}$.
+",0,0,1,0,0,0
+8369,Linear Estimation of Treatment Effects in Demand Response: An Experimental Design Approach,"  Demand response aims to stimulate electricity consumers to modify their loads
+at critical time periods. In this paper, we consider signals in demand response
+programs as a binary treatment to the customers and estimate the average
+treatment effect, which is the average change in consumption under the demand
+response signals. More specifically, we propose to estimate this effect by
+linear regression models and derive several estimators based on the different
+models. From both synthetic and real data, we show that including more
+information about the customers does not always improve estimation accuracy:
+the interaction between the side information and the demand response signal
+must be carefully modeled. In addition, we compare the traditional linear
+regression model with the modified covariate method which models the
+interaction between treatment effect and covariates. We analyze the variances
+of these estimators and discuss different cases where each respective estimator
+works the best. The purpose of these comparisons is not to claim the
+superiority of the different methods, rather we aim to provide practical
+guidance on the most suitable estimator to use under different settings. Our
+results are validated using data collected by Pecan Street and EnergyPlus.
+",1,0,1,0,0,0
+8370,Detection principle of gravitational wave detectors,"  With the first two detections in late 2015, astrophysics has officially
+entered into the new era of gravitational wave observations. Since then, much
+has been going on in the field with a lot of work focussing on the observations
+and implications for astrophysics and tests of general relativity in the strong
+regime. However much less is understood about how gravitational detectors
+really work at their fundamental level. For decades, the response to incoming
+signals has been customarily calculated using the very same physical principle,
+which has proved so successful in the first detections. In this paper we review
+the physical principle that is behind such a detection at the very fundamental
+level, and we try to highlight the peculiar subtleties that make it so hard in
+practice. We will then mention how detectors are built starting from this
+fundamental measurement element.
+",0,1,0,0,0,0
+8371,Private Learning on Networks: Part II,"  This paper considers a distributed multi-agent optimization problem, with the
+global objective consisting of the sum of local objective functions of the
+agents. The agents solve the optimization problem using local computation and
+communication between adjacent agents in the network. We present two randomized
+iterative algorithms for distributed optimization. To improve privacy, our
+algorithms add ""structured"" randomization to the information exchanged between
+the agents. We prove deterministic correctness (in every execution) of the
+proposed algorithms despite the information being perturbed by noise with
+non-zero mean. We prove that a special case of a proposed algorithm (called
+function sharing) preserves privacy of individual polynomial objective
+functions under a suitable connectivity condition on the network topology.
+",1,0,1,0,0,0
+8372,auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks,"  auDeep is a Python toolkit for deep unsupervised representation learning from
+acoustic data. It is based on a recurrent sequence to sequence autoencoder
+approach which can learn representations of time series data by taking into
+account their temporal dynamics. We provide an extensive command line interface
+in addition to a Python API for users and developers, both of which are
+comprehensively documented and publicly available at
+this https URL. Experimental results indicate that auDeep
+features are competitive with state-of-the art audio classification.
+",1,0,0,0,0,0
+8373,The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification,"  Annotation of training data is the major bottleneck in the creation of text
+classification systems. Active learning is a commonly used technique to reduce
+the amount of training data one needs to label. A crucial aspect of active
+learning is determining when to stop labeling data. Three potential sources for
+informing when to stop active learning are an additional labeled set of data,
+an unlabeled set of data, and the training data that is labeled during the
+process of active learning. To date, no one has compared and contrasted the
+advantages and disadvantages of stopping methods based on these three
+information sources. We find that stopping methods that use unlabeled data are
+more effective than methods that use labeled data.
+",1,0,0,1,0,0
+8374,RAFP-Pred: Robust Prediction of Antifreeze Proteins using Localized Analysis of n-Peptide Compositions,"  In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs)
+to counter the otherwise lethal intracellular formation of ice. Structures and
+sequences of various AFPs exhibit a high degree of heterogeneity, consequently
+the prediction of the AFPs is considered to be a challenging task. In this
+research, we propose to handle this arduous manifold learning task using the
+notion of localized processing. In particular an AFP sequence is segmented into
+two sub-segments each of which is analyzed for amino acid and di-peptide
+compositions. We propose to use only the most significant features using the
+concept of information gain (IG) followed by a random forest classification
+approach. The proposed RAFP-Pred achieved an excellent performance on a number
+of standard datasets. We report a high Youden's index
+(sensitivity+specificity-1) value of 0.75 on the standard independent test data
+set outperforming the AFP-PseAAC, AFP\_PSSM, AFP-Pred and iAFP by a margin of
+0.05, 0.06, 0.14 and 0.68 respectively. The verification rate on the UniProKB
+dataset is found to be 83.19\% which is substantially superior to the 57.18\%
+reported for the iAFP method.
+",0,0,0,0,1,0
+8375,Second descent and rational points on Kummer varieties,"  A powerful method pioneered by Swinnerton-Dyer allows one to study rational
+points on pencils of curves of genus 1 by combining the fibration method with a
+sophisticated form of descent. A variant of this method, first used by
+Skorobogatov and Swinnerton-Dyer in 2005, can be applied to study rational
+points on Kummer varieties. In this paper we extend the method to include an
+additional step of second descent. Assuming finiteness of the relevant
+Tate-Shafarevich groups, we use the extended method to show that the
+Brauer-Manin obstruction is the only obstruction to the Hasse principle on
+Kummer varieties associated to abelian varieties with all rational 2-torsion,
+under mild additional hypotheses.
+",0,0,1,0,0,0
+8376,When Can Neural Networks Learn Connected Decision Regions?,"  Previous work has questioned the conditions under which the decision regions
+of a neural network are connected and further showed the implications of the
+corresponding theory to the problem of adversarial manipulation of classifiers.
+It has been proven that for a class of activation functions including leaky
+ReLU, neural networks having a pyramidal structure, that is no layer has more
+hidden units than the input dimension, produce necessarily connected decision
+regions. In this paper, we advance this important result by further developing
+the sufficient and necessary conditions under which the decision regions of a
+neural network are connected. We then apply our framework to overcome the
+limits of existing work and further study the capacity to learn connected
+regions of neural networks for a much wider class of activation functions
+including those widely used, namely ReLU, sigmoid, tanh, softlus, and
+exponential linear function.
+",1,0,0,1,0,0
+8377,Note on the backwards uniqueness of mean curvature flow,"  In this note, we will show a backwards uniqueness theorem of the mean
+curvature flow with bounded second fundamental form in arbitrary codimension.
+",0,0,1,0,0,0
+8378,A Volcanic Hydrogen Habitable Zone,"  The classical habitable zone is the circular region around a star in which
+liquid water could exist on the surface of a rocky planet. The outer edge of
+the traditional N2-CO2-H2O habitable zone (HZ) extends out to nearly 1.7 AU in
+our Solar System, beyond which condensation and scattering by CO2 outstrips its
+greenhouse capacity. Here, we show that volcanic outgassing of atmospheric H2
+on a planet near the outer edge can extend the habitable zone out to ~2.4 AU in
+our solar system. This wider volcanic hydrogen habitable zone (N2-CO2-H2O-H2)
+can be sustained as long as volcanic H2 output offsets its escape from the top
+of the atmosphere. We use a single-column radiative-convective climate model to
+compute the HZ limits of this volcanic hydrogen habitable zone for hydrogen
+concentrations between 1% and 50%, assuming diffusion-limited atmospheric
+escape. At a hydrogen concentration of 50%, the effective stellar flux required
+to support the outer edge decreases by ~35% to 60% for M to A stars. The
+corresponding orbital distances increase by ~30% to 60%. The inner edge of this
+HZ only moves out by ~0.1 to 4% relative to the classical HZ because H2 warming
+is reduced in dense H2O atmospheres. The atmospheric scale heights of such
+volcanic H2 atmospheres near the outer edge of the HZ also increase,
+facilitating remote detection of atmospheric signatures.
+",0,1,0,0,0,0
+8379,A Penrose type inequaltiy for graphs over Reissner-Nordström-anti-deSitter manifold,"  In this paper, we use the inverse mean curvature flow to establish an optimal
+Minkowski type inquality, weighted Alexandrov-Fenchel inequality for the mean
+convex star shaped hypersurfaces in Reissner-Nordström-anti-deSitter manifold
+and Penrose type inequality for asymptotically locally hyperbolic manifolds in
+which can be realized as graphs over Reissner-Nordström-anti-deSitter
+manifold.
+",0,0,1,0,0,0
+8380,A Novel Approach for Fast and Accurate Mean Error Distance Computation in Approximate Adders,"  In error-tolerant applications, approximate adders have been exploited
+extensively to achieve energy efficient system designs. Mean error distance is
+one of the important error metrics used as a performance measure of approximate
+adders. In this work, a fast and efficient methodology is proposed to determine
+the exact mean error distance in approximate lower significant bit adders. A
+detailed description of the proposed algorithm along with an example has been
+demonstrated in this paper. Experimental analysis shows that the proposed
+method performs better than existing Monte Carlo simulation approach both in
+terms of accuracy and execution time.
+",1,0,0,0,0,0
+8381,Fluorescent Troffer-powered Internet of Things: An Experimental Study of Electric-field Energy Harvesting,"  A totally new energy harvesting architecture that exploits ambient
+electric-field (E-field) emitting from fluorescent light fixtures is presented.
+A copper plate, 50 x 50 cm in size, is placed in between the ambient field to
+extract energy by capacitive coupling. A low voltage prototype is designed,
+structured and tested on a conventional ceiling-type 4-light fluorescent
+troffer operating in 50 Hz 220 V AC power grid. It is examined that the
+harvester is able to collect roughly 1.25 J of energy in 25 min when a 0.1 F of
+super-capacitor is employed. The equivalent circuit and the physical model of
+the proposed harvesting paradigm are provided, and the attainable power is
+evaluated in both theoretical and experimental manner. The scavenged energy is
+planned to be utilized for building battery-less Internet of Things (IoT)
+networks that are obliged to sense environmental parameters, analyze the
+gathered data, and remotely inform a higher authority within predefined
+periods.
+",1,1,0,0,0,0
+8382,Parallel Simultaneous Perturbation Optimization,"  Stochastic computer simulations enable users to gain new insights into
+complex physical systems. Optimization is a common problem in this context:
+users seek to find model inputs that maximize the expected value of an
+objective function. The objective function, however, is time-intensive to
+evaluate, and cannot be directly measured. Instead, the stochastic nature of
+the model means that individual realizations are corrupted by noise. More
+formally, we consider the problem of optimizing the expected value of an
+expensive black-box function with continuously-differentiable mean, from which
+observations are corrupted by Gaussian noise. We present Parallel Simultaneous
+Perturbation Optimization (PSPO), which extends a well-known stochastic
+optimization algorithm, simultaneous perturbation stochastic approximation, in
+several important ways. Our modifications allow the algorithm to fully take
+advantage of parallel computing resources, like high-performance cloud
+computing. The resulting PSPO algorithm takes fewer time-consuming iterations
+to converge, automatically chooses the step size, and can vary the error
+tolerance by step. Theoretical results are supported by a numerical example. To
+demonstrate the performance of the algorithm, we implemented the algorithm to
+maximize the pseudo-likelihood of a stochastic epidemiological model to data of
+a measles outbreak.
+",0,0,1,0,0,0
+8383,Achievable Rate Region of Non-Orthogonal Multiple Access Systems with Wireless Powered Decoder,"  Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme
+in 5G systems for the simultaneous access of tremendous number of wireless
+nodes. On the other hand, RF-enabled wireless energy harvesting is a promising
+technology for self-sustainable wireless nodes. In this paper, we consider a
+NOMA system where the near user harvests energy from the strong radio signal to
+power-on the information decoder. A generalized energy harvesting scheme is
+proposed by combining the conventional time switching and power splitting
+scheme. The achievable rate region of the proposed scheme is characterized
+under both constant and dynamic decoding power consumption models. If the
+decoding power is constant, the achievable rate region can be found by solving
+two convex optimization subproblems, and the regions for two special cases:
+time switching and power splitting, are characterized in closed-form. If the
+decoding power is proportional to data rate, the achievable rate region can be
+found by exhaustive search algorithm. Numerical results show that the
+achievable rate region of the proposed generalized scheme is larger than those
+of time switching scheme and power splitting scheme, and rate-dependent decoder
+design helps to enlarge the achievable rate region.
+",1,0,0,0,0,0
+8384,Agent-based model for the origins of scaling in human language,"  Background/Introduction: The Zipf's law establishes that if the words of a
+(large) text are ordered by decreasing frequency, the frequency versus the rank
+decreases as a power law with exponent close to -1. Previous work has stressed
+that this pattern arises from a conflict of interests of the participants of
+communication: speakers and hearers. Methods: The challenge here is to define a
+computational language game on a population of agents, playing games mainly
+based on a parameter that measures the relative participant's interests.
+Results: Numerical simulations suggest that at critical values of the parameter
+a human-like vocabulary, exhibiting scaling properties, seems to appear.
+Conclusions: The appearance of an intermediate distribution of frequencies at
+some critical values of the parameter suggests that on a population of
+artificial agents the emergence of scaling partly arises as a self-organized
+process only from local interactions between agents.
+",1,1,0,0,0,0
+8385,Credible Review Detection with Limited Information using Consistency Analysis,"  Online reviews provide viewpoints on the strengths and shortcomings of
+products/services, influencing potential customers' purchasing decisions.
+However, the proliferation of non-credible reviews -- either fake (promoting/
+demoting an item), incompetent (involving irrelevant aspects), or biased --
+entails the problem of identifying credible reviews. Prior works involve
+classifiers harnessing rich information about items/users -- which might not be
+readily available in several domains -- that provide only limited
+interpretability as to why a review is deemed non-credible. This paper presents
+a novel approach to address the above issues. We utilize latent topic models
+leveraging review texts, item ratings, and timestamps to derive consistency
+features without relying on item/user histories, unavailable for ""long-tail""
+items/users. We develop models, for computing review credibility scores to
+provide interpretable evidence for non-credible reviews, that are also
+transferable to other domains -- addressing the scarcity of labeled data.
+Experiments on real-world datasets demonstrate improvements over
+state-of-the-art baselines.
+",1,0,0,1,0,0
+8386,The Asymptotically Self-Similar Regime for the Einstein Vacuum Equations,"  We develop a local theory for the construction of singular spacetimes in all
+spacetime dimensions which become asymptotically self-similar as the
+singularity is approached. The techniques developed also allow us to construct
+and classify exact self-similar solutions which correspond to the formal
+asymptotic expansions of Fefferman and Graham's ambient metric.
+",0,0,1,0,0,0
+8387,Kinematics and dynamics of an egg-shaped robot with a gyro driven inertia actuator,"  The manuscript discusses still preliminary considerations with regard to the
+dynamics and kinematics of an egg shaped robot with an gyro driven inertia
+actuator. The method of calculation follows the idea that we would like to
+express the entire dynamic equations in terms of moments instead of forces.
+Also we avoid to derive the equations from a Lagrange function with
+constraints. The result of the calculations is meant to be applicable to two
+robot prototypes that have been build at the AES\&R Laboratory at the National
+Chung Cheng University in Taiwan.
+",1,0,0,0,0,0
+8388,Cloaking and anamorphism for light and mass diffusion,"  We first review classical results on cloaking and mirage effects for
+electromagnetic waves. We then show that transformation optics allows the
+masking of objects or produces mirages in diffusive regimes. In order to
+achieve this, we consider the equation for diffusive photon density in
+transformed coordinates, which is valid for diffusive light in scattering
+media. More precisely, generalizing transformations for star domains introduced
+in [Diatta and Guenneau, J. Opt. 13, 024012, 2011] for matter waves, we
+numerically demonstrate that infinite conducting objects of different shapes
+scatter diffusive light in exactly the same way. We also propose a design of
+external light-diffusion cloak with spatially varying sign-shifting parameters
+that hides a finite size scatterer outside the cloak. We next analyse
+non-physical parameter in the transformed Fick's equation derived in [Guenneau
+and Puvirajesinghe, R. Soc. Interface 10, 20130106, 2013], and propose to use a
+non-linear transform that overcomes this problem. We finally investigate other
+form invariant transformed diffusion-like equations in the time domain, and
+touch upon conformal mappings and non-Euclidean cloaking applied to diffusion
+processes.
+",0,1,1,0,0,0
+8389,Super Rogers-Szegö polynomials associated with $BC_N$ type of Polychronakos spin chains,"  As is well known, multivariate Rogers-Szegö polynomials are closely
+connected with the partition functions of the $A_{N-1}$ type of Polychronakos
+spin chains having long-range interactions. Applying the `freezing trick', here
+we derive the partition functions for a class of $BC_N$ type of Polychronakos
+spin chains containing supersymmetric analogues of polarized spin reversal
+operators and subsequently use those partition functions to obtain novel
+multivariate super Rogers-Szegö (SRS) polynomials depending on four types of
+variables. We construct the generating functions for such SRS polynomials and
+show that these polynomials can be written as some bilinear combinations of the
+$A_{N-1}$ type of SRS polynomials. We also use the above mentioned generating
+functions to derive a set of recursion relations for the partition functions of
+the $BC_N$ type of Polychronakos spin chains involving different numbers of
+lattice sites and internal degrees of freedom.
+",0,1,0,0,0,0
+8390,A statistical physics approach to learning curves for the Inverse Ising problem,"  Using methods of statistical physics, we analyse the error of learning
+couplings in large Ising models from independent data (the inverse Ising
+problem). We concentrate on learning based on local cost functions, such as the
+pseudo-likelihood method for which the couplings are inferred independently for
+each spin. Assuming that the data are generated from a true Ising model, we
+compute the reconstruction error of the couplings using a combination of the
+replica method with the cavity approach for densely connected systems. We show
+that an explicit estimator based on a quadratic cost function achieves minimal
+reconstruction error, but requires the length of the true coupling vector as
+prior knowledge. A simple mean field estimator of the couplings which does not
+need such knowledge is asymptotically optimal, i.e. when the number of
+observations is much large than the number of spins. Comparison of the theory
+with numerical simulations shows excellent agreement for data generated from
+two models with random couplings in the high temperature region: a model with
+independent couplings (Sherrington-Kirkpatrick model), and a model where the
+matrix of couplings has a Wishart distribution.
+",0,1,0,1,0,0
+8391,Fast Kinetic Scheme : efficient MPI parallelization strategy for 3D Boltzmann equation,"  In this paper we present a parallelization strategy on distributed memory
+systems for the Fast Kinetic Scheme --- a semi-Lagrangian scheme developed in
+[J. Comput. Phys., Vol. 255, 2013, pp 680-698] for solving kinetic equations.
+The original algorithm was proposed for the BGK approximation of the collision
+kernel. In this work we deal with its extension to the full Boltzmann equation
+in six dimensions, where the collision operator is resolved by means of fast
+spectral method. We present close to ideal scalability of the proposed
+algorithm on tera- and peta-scale systems.
+",0,1,1,0,0,0
+8392,Finding a Feasible Initial Solution for Flatness-Based Multi-Link Manipulator Motion Planning under State and Control Constraints,"  In this paper, we present a method to initialize at a feasible point and
+unfailingly solve a non-convex optimization problem in which a set-point motion
+is planned for a multi-link manipulator under state and control constraints. We
+construct an initial feasible solution by analyzing the final time effect for
+feasibility problems of flatness based motion planning problems. More
+specifically, we first find a feasible time-optimal trajectory under state
+constraints without a control constraint by solving a linear programming
+problem. Then, we find a feasible trajectory under control constraints by
+scaling the trajectory. To evaluate the practical applicability of the proposed
+method, we did numerical experiments to solve a multi-link manipulator motion
+planning problem by combining the method with recursive inverse dynamics
+algorithms.
+",1,0,1,0,0,0
+8393,Techniques for visualizing LSTMs applied to electrocardiograms,"  This paper explores four different visualization techniques for long
+short-term memory (LSTM) networks applied to continuous-valued time series. On
+the datasets analysed, we find that the best visualization technique is to
+learn an input deletion mask that optimally reduces the true class score. With
+a specific focus on single-lead electrocardiograms from the MIT-BIH arrhythmia
+dataset, we show that salient input features for the LSTM classifier align well
+with medical theory.
+",1,0,0,1,0,0
+8394,Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration,"  Discovery of an accurate causal Bayesian network structure from observational
+data can be useful in many areas of science. Often the discoveries are made
+under uncertainty, which can be expressed as probabilities. To guide the use of
+such discoveries, including directing further investigation, it is important
+that those probabilities be well-calibrated. In this paper, we introduce a
+novel framework to derive calibrated probabilities of causal relationships from
+observational data. The framework consists of three components: (1) an
+approximate method for generating initial probability estimates of the edge
+types for each pair of variables, (2) the availability of a relatively small
+number of the causal relationships in the network for which the truth status is
+known, which we call a calibration training set, and (3) a calibration method
+for using the approximate probability estimates and the calibration training
+set to generate calibrated probabilities for the many remaining pairs of
+variables. We also introduce a new calibration method based on a shallow neural
+network. Our experiments on simulated data support that the proposed approach
+improves the calibration of causal edge predictions. The results also support
+that the approach often improves the precision and recall of predictions.
+",1,0,0,1,0,0
+8395,Faster Bounding Box Annotation for Object Detection in Indoor Scenes,"  This paper proposes an approach for rapid bounding box annotation for object
+detection datasets. The procedure consists of two stages: The first step is to
+annotate a part of the dataset manually, and the second step proposes
+annotations for the remaining samples using a model trained with the first
+stage annotations. We experimentally study which first/second stage split
+minimizes to total workload. In addition, we introduce a new fully labeled
+object detection dataset collected from indoor scenes. Compared to other indoor
+datasets, our collection has more class categories, different backgrounds,
+lighting conditions, occlusion and high intra-class differences. We train deep
+learning based object detectors with a number of state-of-the-art models and
+compare them in terms of speed and accuracy. The fully annotated dataset is
+released freely available for the research community.
+",0,0,0,1,0,0
+8396,Junk News on Military Affairs and National Security: Social Media Disinformation Campaigns Against US Military Personnel and Veterans,"  Social media provides political news and information for both active duty
+military personnel and veterans. We analyze the subgroups of Twitter and
+Facebook users who spend time consuming junk news from websites that target US
+military personnel and veterans with conspiracy theories, misinformation, and
+other forms of junk news about military affairs and national security issues.
+(1) Over Twitter we find that there are significant and persistent interactions
+between current and former military personnel and a broad network of extremist,
+Russia-focused, and international conspiracy subgroups. (2) Over Facebook, we
+find significant and persistent interactions between public pages for military
+and veterans and subgroups dedicated to political conspiracy, and both sides of
+the political spectrum. (3) Over Facebook, the users who are most interested in
+conspiracy theories and the political right seem to be distributing the most
+junk news, whereas users who are either in the military or are veterans are
+among the most sophisticated news consumers, and share very little junk news
+through the network.
+",1,0,0,0,0,0
+8397,The SeaQuest Spectrometer at Fermilab,"  The SeaQuest spectrometer at Fermilab was designed to detect
+oppositely-charged pairs of muons (dimuons) produced by interactions between a
+120 GeV proton beam and liquid hydrogen, liquid deuterium and solid nuclear
+targets. The primary physics program uses the Drell-Yan process to probe
+antiquark distributions in the target nucleon. The spectrometer consists of a
+target system, two dipole magnets and four detector stations. The upstream
+magnet is a closed-aperture solid iron magnet which also serves as the beam
+dump, while the second magnet is an open aperture magnet. Each of the detector
+stations consists of scintillator hodoscopes and a high-resolution tracking
+device. The FPGA-based trigger compares the hodoscope signals to a set of
+pre-programmed roads to determine if the event contains oppositely-signed,
+high-mass muon pairs.
+",0,1,0,0,0,0
+8398,Direct-Manipulation Visualization of Deep Networks,"  The recent successes of deep learning have led to a wave of interest from
+non-experts. Gaining an understanding of this technology, however, is
+difficult. While the theory is important, it is also helpful for novices to
+develop an intuitive feel for the effect of different hyperparameters and
+structural variations. We describe TensorFlow Playground, an interactive, open
+sourced visualization that allows users to experiment via direct manipulation
+rather than coding, enabling them to quickly build an intuition about neural
+nets.
+",1,0,0,1,0,0
+8399,How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices?,"  How many samples are sufficient to guarantee that the eigenvectors and
+eigenvalues of the sample covariance matrix are close to those of the actual
+covariance matrix? For a wide family of distributions, including distributions
+with finite second moment and distributions supported in a centered Euclidean
+ball, we prove that the inner product between eigenvectors of the sample and
+actual covariance matrices decreases proportionally to the respective
+eigenvalue distance. Our findings imply non-asymptotic concentration bounds for
+eigenvectors, eigenspaces, and eigenvalues. They also provide conditions for
+distinguishing principal components based on a constant number of samples.
+",0,0,1,1,0,0
+8400,Wavefronts for a nonlinear nonlocal bistable reaction-diffusion equation in population dynamics,"  The wavefronts of a nonlinear nonlocal bistable reaction-diffusion equation,
+\begin{align*} \frac{\partial u}{\partial t}=\frac{\partial^2u}{\partial
+x^2}+u^2(1-J_\sigma*u)-du,\quad(t,x)\in(0,\infty)\times\mathbb R, \end{align*}
+with $J_\sigma(x)=(1/\sigma)= J(x/\sigma)$ and $ \int_{\mathbb R} J(x)dx=1 $
+are investigated in this article. It is proven that there exists a
+$c_*(\sigma)$ such that for all $c\geq c_*(\sigma)$, a monotone wavefront
+$(c,\omega)$ can be connected by the two positive equilibrium points. On the
+other hand, there exists a $c^*(\sigma)$ such that the model admits a
+semi-wavefront $(c^*(\sigma),\omega)$ with $\omega(-\infty)=0$. Furthermore, it
+is shown that for sufficiently small $\sigma$, the semi-wavefronts are in fact
+wavefronts connecting $0$ to the largest equilibrium. In addition, the
+wavefronts converge to those of the local problem as $\sigma\to0$.
+",0,0,1,0,0,0
+8401,Kafnets: kernel-based non-parametric activation functions for neural networks,"  Neural networks are generally built by interleaving (adaptable) linear layers
+with (fixed) nonlinear activation functions. To increase their flexibility,
+several authors have proposed methods for adapting the activation functions
+themselves, endowing them with varying degrees of flexibility. None of these
+approaches, however, have gained wide acceptance in practice, and research in
+this topic remains open. In this paper, we introduce a novel family of flexible
+activation functions that are based on an inexpensive kernel expansion at every
+neuron. Leveraging over several properties of kernel-based models, we propose
+multiple variations for designing and initializing these kernel activation
+functions (KAFs), including a multidimensional scheme allowing to nonlinearly
+combine information from different paths in the network. The resulting KAFs can
+approximate any mapping defined over a subset of the real line, either convex
+or nonconvex. Furthermore, they are smooth over their entire domain, linear in
+their parameters, and they can be regularized using any known scheme, including
+the use of $\ell_1$ penalties to enforce sparseness. To the best of our
+knowledge, no other known model satisfies all these properties simultaneously.
+In addition, we provide a relatively complete overview on alternative
+techniques for adapting the activation functions, which is currently lacking in
+the literature. A large set of experiments validates our proposal.
+",1,0,0,1,0,0
+8402,Unsteady Propulsion by an Intermittent Swimming Gait,"  Inviscid computational results are presented on a self-propelled swimmer
+modeled as a virtual body combined with a two-dimensional hydrofoil pitching
+intermittently about its leading edge. Lighthill (1971) originally proposed
+that this burst-and-coast behavior can save fish energy during swimming by
+taking advantage of the viscous Bone-Lighthill boundary layer thinning
+mechanism. Here, an additional inviscid Garrick mechanism is discovered that
+allows swimmers to control the ratio of their added mass thrust-producing
+forces to their circulatory drag-inducing forces by decreasing their duty
+cycle, DC, of locomotion. This mechanism can save intermittent swimmers as much
+as 60% of the energy it takes to swim continuously at the same speed. The
+inviscid energy savings are shown to increase with increasing amplitude of
+motion, increase with decreasing Lighthill number, Li, and switch to an
+energetic cost above continuous swimming for sufficiently low DC. Intermittent
+swimmers are observed to shed four vortices per cycle that form into groups
+that are self-similar with the DC. In addition, previous thrust and power
+scaling laws of continuous self-propelled swimming are further generalized to
+include intermittent swimming. The key is that by averaging the thrust and
+power coefficients over only the bursting period then the intermittent problem
+can be transformed into a continuous one. Furthermore, the intermittent thrust
+and power scaling relations are extended to predict the mean speed and cost of
+transport of swimmers. By tuning a few coefficients with a handful of
+simulations these self-propelled relations can become predictive. In the
+current study, the mean speed and cost of transport are predicted to within 3%
+and 18% of their full-scale values by using these relations.
+",0,1,0,0,0,0
+8403,Modeling rooted in-trees by finite p-groups,"  The aim of this chapter is to provide an adequate graph theoretic framework
+for the description of periodic bifurcations which have recently been
+discovered in descendant trees of finite p-groups. The graph theoretic concepts
+of rooted in-trees with weighted vertices and edges perfectly admit an abstract
+formulation of the group theoretic notions of successive extensions, nuclear
+rank, multifurcation, and step size. Since all graphs in this chapter are
+infinite and dense, we use methods of pattern recognition and independent
+component analysis to reduce the complex structure to periodically repeating
+finite patterns. The method of group cohomology yields subgraph isomorphisms
+required for proving the periodicity of branches along mainlines. Finally the
+mainlines are glued together with the aid of infinite limit groups whose finite
+quotients form the vertices of mainlines. The skeleton of the infinite graph is
+a countable union of infinite mainlines, connected by periodic bifurcations.
+Each mainline is the backbone of a minimal subtree consisting of a periodically
+repeating finite pattern of branches with bounded depth. A second periodicity
+is caused by isomorphisms between all minimal subtrees which make up the
+complete infinite graph. Only the members of the first minimal tree are
+metabelian and the bifurcations, which were unknown up to now, open the long
+desired door to non-metabelian extensions whose second derived quotients are
+isomorphic to the metabelian groups. An application of this key result to
+algebraic number theory solves the problem of p-class field towers of exact
+length three.
+",0,0,1,0,0,0
+8404,Towards Algorithmic Typing for DOT,"  The Dependent Object Types (DOT) calculus formalizes key features of Scala.
+The D$_{<: }$ calculus is the core of DOT. To date, presentations of D$_{<: }$
+have used declarative typing and subtyping rules, as opposed to algorithmic.
+Unfortunately, algorithmic typing for full D$_{<: }$ is known to be an
+undecidable problem.
+We explore the design space for a restricted version of D$_{<: }$ that has
+decidable typechecking. Even in this simplified D$_{<: }$ , algorithmic typing
+and subtyping are tricky, due to the ""bad bounds"" problem. The Scala compiler
+bypasses bad bounds at the cost of a loss in expressiveness in its type system.
+Based on the approach taken in the Scala compiler, we present the Step Typing
+and Step Subtyping relations for D$_{<: }$. We prove these relations sound and
+decidable. They are not complete with respect to the original D$_{<: }$ rules.
+",1,0,0,0,0,0
+8405,Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue,"  More and more of the information on the web is dialogic, from Facebook
+newsfeeds, to forum conversations, to comment threads on news articles. In
+contrast to traditional, monologic Natural Language Processing resources such
+as news, highly social dialogue is frequent in social media, making it a
+challenging context for NLP. This paper tests a bootstrapping method,
+originally proposed in a monologic domain, to train classifiers to identify two
+different types of subjective language in dialogue: sarcasm and nastiness. We
+explore two methods of developing linguistic indicators to be used in a first
+level classifier aimed at maximizing precision at the expense of recall. The
+best performing classifier for the first phase achieves 54% precision and 38%
+recall for sarcastic utterances. We then use general syntactic patterns from
+previous work to create more general sarcasm indicators, improving precision to
+62% and recall to 52%. To further test the generality of the method, we then
+apply it to bootstrapping a classifier for nastiness dialogic acts. Our first
+phase, using crowdsourced nasty indicators, achieves 58% precision and 49%
+recall, which increases to 75% precision and 62% recall when we bootstrap over
+the first level with generalized syntactic patterns.
+",1,0,0,0,0,0
+8406,A new statistical method for characterizing the atmospheres of extrasolar planets,"  By detecting light from extrasolar planets,we can measure their compositions
+and bulk physical properties. The technologies used to make these measurements
+are still in their infancy, and a lack of self-consistency suggests that
+previous observations have underestimated their systemic errors.We demonstrate
+a statistical method, newly applied to exoplanet characterization, which uses a
+Bayesian formalism to account for underestimated errorbars. We use this method
+to compare photometry of a substellar companion, GJ 758b, with custom
+atmospheric models. Our method produces a probability distribution of
+atmospheric model parameters including temperature, gravity, cloud model
+(fsed), and chemical abundance for GJ 758b. This distribution is less sensitive
+to highly variant data, and appropriately reflects a greater uncertainty on
+parameter fits.
+",0,1,0,0,0,0
+8407,Prioritizing network communities,"  Uncovering modular structure in networks is fundamental for systems in
+biology, physics, and engineering. Community detection identifies candidate
+modules as hypotheses, which then need to be validated through experiments,
+such as mutagenesis in a biological laboratory. Only a few communities can
+typically be validated, and it is thus important to prioritize which
+communities to select for downstream experimentation. Here we develop CRank, a
+mathematically principled approach for prioritizing network communities. CRank
+efficiently evaluates robustness and magnitude of structural features of each
+community and then combines these features into the community prioritization.
+CRank can be used with any community detection method. It needs only
+information provided by the network structure and does not require any
+additional metadata or labels. However, when available, CRank can incorporate
+domain-specific information to further boost performance. Experiments on many
+large networks show that CRank effectively prioritizes communities, yielding a
+nearly 50-fold improvement in community prioritization.
+",0,0,0,1,1,0
+8408,New ALMA constraints on the star-forming ISM at low metallicity: A 50 pc view of the blue compact dwarf galaxy SBS0335-052,"  Properties of the cold interstellar medium of low-metallicity galaxies are
+not well-known due to the faintness and extremely small scale on which emission
+is expected. We present deep ALMA band 6 (230GHz) observations of the nearby,
+low-metallicity (12 + log(O/H) = 7.25) blue compact dwarf galaxy SBS0335-052 at
+an unprecedented resolution of 0.2 arcsec (52 pc). The 12CO J=2-1 line is not
+detected and we report a 3-sigma upper limit of LCO(2-1) = 3.6x10^4 K km/s
+pc^2. Assuming that molecular gas is converted into stars with a given
+depletion time, ranging from 0.02 to 2 Gyr, we find lower limits on the
+CO-to-H2 conversion factor alpha_CO in the range 10^2-10^4 Msun pc^-2 (K
+km/s)^-1. The continuum emission is detected and resolved over the two main
+super star clusters. Re-analysis of the IR-radio spectral energy distribution
+suggests that the mm-fluxes are not only free-free emission but are most likely
+also associated with a cold dust component coincident with the position of the
+brightest cluster. With standard dust properties, we estimate its mass to be as
+large as 10^5 Msun. Both line and continuum results suggest the presence of a
+large cold gas reservoir unseen in CO even with ALMA.
+",0,1,0,0,0,0
+8409,On Structured Prediction Theory with Calibrated Convex Surrogate Losses,"  We provide novel theoretical insights on structured prediction in the context
+of efficient convex surrogate loss minimization with consistency guarantees.
+For any task loss, we construct a convex surrogate that can be optimized via
+stochastic gradient descent and we prove tight bounds on the so-called
+""calibration function"" relating the excess surrogate risk to the actual risk.
+In contrast to prior related work, we carefully monitor the effect of the
+exponential number of classes in the learning guarantees as well as on the
+optimization complexity. As an interesting consequence, we formalize the
+intuition that some task losses make learning harder than others, and that the
+classical 0-1 loss is ill-suited for general structured prediction.
+",1,0,0,1,0,0
+8410,Improving Sharir and Welzl's bound on crossing-free matchings through solving a stronger recurrence,"  Sharir and Welzl [1] derived a bound on crossing-free matchings primarily
+based on solving a recurrence based on the size of the matchings. We show that
+the recurrence given in Lemma 2.3 in Sharir and Welzl can be improve to
+$(2n-6s)\textbf{Ma}_{m}(P)\leq\frac{68}{3}(s+2)\textbf{Ma}_{m-1}(P)$ and
+$(3n-7s)\textbf{Ma}_{m}(P)\leq44.5(s+2)\textbf{Ma}_{m-1}(P)$, thereby improving
+the upper bound for crossing-free matchings.
+",0,0,1,0,0,0
+8411,Computing eigenfunctions and eigenvalues of boundary value problems with the orthogonal spectral renormalization method,"  The spectral renormalization method was introduced in 2005 as an effective
+way to compute ground states of nonlinear Schrödinger and Gross-Pitaevskii
+type equations. In this paper, we introduce an orthogonal spectral
+renormalization (OSR) method to compute ground and excited states (and their
+respective eigenvalues) of linear and nonlinear eigenvalue problems. The
+implementation of the algorithm follows four simple steps: (i) reformulate the
+underlying eigenvalue problem as a fixed point equation, (ii) introduce a
+renormalization factor that controls the convergence properties of the
+iteration, (iii) perform a Gram-Schmidt orthogonalization process in order to
+prevent the iteration from converging to an unwanted mode; and (iv) compute the
+solution sought using a fixed-point iteration. The advantages of the OSR scheme
+over other known methods (such as Newton's and self-consistency) are: (i) it
+allows the flexibility to choose large varieties of initial guesses without
+diverging, (ii) easy to implement especially at higher dimensions and (iii) it
+can easily handle problems with complex and random potentials. The OSR method
+is implemented on benchmark Hermitian linear and nonlinear eigenvalue problems
+as well as linear and nonlinear non-Hermitian $\mathcal{PT}$-symmetric models.
+",0,1,0,0,0,0
+8412,A Discontinuity Adjustment for Subdistribution Function Confidence Bands Applied to Right-Censored Competing Risks Data,"  The wild bootstrap is the resampling method of choice in survival analytic
+applications. Theoretic justifications rely on the assumption of existing
+intensity functions which is equivalent to an exclusion of ties among the event
+times. However, such ties are omnipresent in practical studies. It turns out
+that the wild bootstrap should only be applied in a modified manner that
+corrects for altered limit variances and emerging dependencies. This again
+ensures the asymptotic exactness of inferential procedures. An analogous
+necessity is the use of the Greenwood-type variance estimator for Nelson-Aalen
+estimators which is particularly preferred in tied data regimes. All theoretic
+arguments are transferred to bootstrapping Aalen-Johansen estimators for
+cumulative incidence functions in competing risks. An extensive simulation
+study as well as an application to real competing risks data of male intensive
+care unit patients suffering from pneumonia illustrate the practicability of
+the proposed technique.
+",0,0,1,1,0,0
+8413,Estimation of block sparsity in compressive sensing,"  In this paper, we consider a soft measure of block sparsity,
+$k_\alpha(\mathbf{x})=\left(\lVert\mathbf{x}\rVert_{2,\alpha}/\lVert\mathbf{x}\rVert_{2,1}\right)^{\frac{\alpha}{1-\alpha}},\alpha\in[0,\infty]$
+and propose a procedure to estimate it by using multivariate isotropic
+symmetric $\alpha$-stable random projections without sparsity or block sparsity
+assumptions. The limiting distribution of the estimator is given. Some
+simulations are conducted to illustrate our theoretical results.
+",1,0,0,1,0,0
+8414,Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP,"  A fundamental question in reinforcement learning is whether model-free
+algorithms are sample efficient. Recently, Jin et al. \cite{jin2018q} proposed
+a Q-learning algorithm with UCB exploration policy, and proved it has nearly
+optimal regret bound for finite-horizon episodic MDP. In this paper, we adapt
+Q-learning with UCB-exploration bonus to infinite-horizon MDP with discounted
+rewards \emph{without} accessing a generative model. We show that the
+\textit{sample complexity of exploration} of our algorithm is bounded by
+$\tilde{O}({\frac{SA}{\epsilon^2(1-\gamma)^7}})$. This improves the previously
+best known result of $\tilde{O}({\frac{SA}{\epsilon^4(1-\gamma)^8}})$ in this
+setting achieved by delayed Q-learning \cite{strehl2006pac}, and matches the
+lower bound in terms of $\epsilon$ as well as $S$ and $A$ except for
+logarithmic factors.
+",1,0,0,1,0,0
+8415,You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems,"  Properly benchmarking Automated Program Repair (APR) systems should
+contribute to the development and adoption of the research outputs by
+practitioners. To that end, the research community must ensure that it reaches
+significant milestones by reliably comparing state-of-the-art tools for a
+better understanding of their strengths and weaknesses. In this work, we
+identify and investigate a practical bias caused by the fault localization (FL)
+step in a repair pipeline. We propose to highlight the different fault
+localization configurations used in the literature, and their impact on APR
+systems when applied to the Defects4J benchmark. Then, we explore the
+performance variations that can be achieved by `tweaking' the FL step.
+Eventually, we expect to create a new momentum for (1) full disclosure of APR
+experimental procedures with respect to FL, (2) realistic expectations of
+repairing bugs in Defects4J, as well as (3) reliable performance comparison
+among the state-of-the-art APR systems, and against the baseline performance
+results of our thoroughly assessed kPAR repair tool. Our main findings include:
+(a) only a subset of Defects4J bugs can be currently localized by commonly-used
+FL techniques; (b) current practice of comparing state-of-the-art APR systems
+(i.e., counting the number of fixed bugs) is potentially misleading due to the
+bias of FL configurations; and (c) APR authors do not properly qualify their
+performance achievement with respect to the different tuning parameters
+implemented in APR systems.
+",1,0,0,0,0,0
+8416,Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates,"  Spatial understanding is a fundamental problem with wide-reaching real-world
+applications. The representation of spatial knowledge is often modeled with
+spatial templates, i.e., regions of acceptability of two objects under an
+explicit spatial relationship (e.g., ""on"", ""below"", etc.). In contrast with
+prior work that restricts spatial templates to explicit spatial prepositions
+(e.g., ""glass on table""), here we extend this concept to implicit spatial
+language, i.e., those relationships (generally actions) for which the spatial
+arrangement of the objects is only implicitly implied (e.g., ""man riding
+horse""). In contrast with explicit relationships, predicting spatial
+arrangements from implicit spatial language requires significant common sense
+spatial understanding. Here, we introduce the task of predicting spatial
+templates for two objects under a relationship, which can be seen as a spatial
+question-answering task with a (2D) continuous output (""where is the man w.r.t.
+a horse when the man is walking the horse?""). We present two simple
+neural-based models that leverage annotated images and structured text to learn
+this task. The good performance of these models reveals that spatial locations
+are to a large extent predictable from implicit spatial language. Crucially,
+the models attain similar performance in a challenging generalized setting,
+where the object-relation-object combinations (e.g.,""man walking dog"") have
+never been seen before. Next, we go one step further by presenting the models
+with unseen objects (e.g., ""dog""). In this scenario, we show that leveraging
+word embeddings enables the models to output accurate spatial predictions,
+proving that the models acquire solid common sense spatial knowledge allowing
+for such generalization.
+",1,0,0,1,0,0
+8417,Design of Capacity Approaching Ensembles of LDPC Codes for Correlated Sources using EXIT Charts,"  This paper is concerned with the design of capacity approaching ensembles of
+Low-Densiy Parity-Check (LDPC) codes for correlated sources. We consider
+correlated binary sources where the data is encoded independently at each
+source through a systematic LDPC encoder and sent over two independent
+channels. At the receiver, a iterative joint decoder consisting of two
+component LDPC decoders is considered where the encoded bits at the output of
+each component decoder are used at the other decoder as the a priori
+information. We first provide asymptotic performance analysis using the concept
+of extrinsic information transfer (EXIT) charts. Compared to the conventional
+EXIT charts devised to analyze LDPC codes for point to point communication, the
+proposed EXIT charts have been completely modified to able to accommodate the
+systematic nature of the codes as well as the iterative behavior between the
+two component decoders. Then the developed modified EXIT charts are deployed to
+design ensembles for different levels of correlation. Our results show that as
+the average degree of the designed ensembles grow, the thresholds corresponding
+to the designed ensembles approach the capacity. In particular, for ensembles
+with average degree of around 9, the gap to capacity is reduced to about 0.2dB.
+Finite block length performance evaluation is also provided for the designed
+ensembles to verify the asymptotic results.
+",1,0,0,0,0,0
+8418,Evolution of Morphological and Physical Properties of Laboratory Interstellar Organic Residues with Ultraviolet Irradiation,"  Refractory organic compounds formed in molecular clouds are among the
+building blocks of the solar system objects and could be the precursors of
+organic matter found in primitive meteorites and cometary materials. However,
+little is known about the evolutionary pathways of molecular cloud organics
+from dense molecular clouds to planetary systems. In this study, we focus on
+the evolution of the morphological and viscoelastic properties of molecular
+cloud refractory organic matter. We found that the organic residue,
+experimentally synthesized at about 10 K from UV-irradiated H2O-CH3OH-NH3 ice,
+changed significantly in terms of its nanometer- to micrometer-scale morphology
+and viscoelastic properties after UV irradiation at room temperature. The dose
+of this irradiation was equivalent to that experienced after short residence in
+diffuse clouds (equal or less than 10,000 years) or irradiation in outer
+protoplanetary disks. The irradiated organic residues became highly porous and
+more rigid and formed amorphous nanospherules. These nanospherules are
+morphologically similar to organic nanoglobules observed in the least-altered
+chondrites, chondritic porous interplanetary dust particles, and cometary
+samples, suggesting that irradiation of refractory organics could be a possible
+formation pathway for such nanoglobules. The storage modulus (elasticity) of
+photo-irradiated organic residues is about 100 MPa irrespective of vibrational
+frequency, a value that is lower than the storage moduli of minerals and ice.
+Dust grains coated with such irradiated organics would therefore stick together
+efficiently, but growth to larger grains might be suppressed due to an increase
+in aggregate brittleness caused by the strong connections between grains.
+",0,1,0,0,0,0
+8419,"Evaporating pure, binary and ternary droplets: thermal effects and axial symmetry breaking","  The Greek aperitif Ouzo is not only famous for its specific anise-flavored
+taste, but also for its ability to turn from a transparent miscible liquid to a
+milky-white colored emulsion when water is added. Recently, it has been shown
+that this so-called Ouzo effect, i.e. the spontaneous emulsification of oil
+microdroplets, can also be triggered by the preferential evaporation of ethanol
+in an evaporating sessile Ouzo drop, leading to an amazingly rich drying
+process with multiple phase transitions [H. Tan et al., Proc. Natl. Acad. Sci.
+USA 113(31) (2016) 8642]. Due to the enhanced evaporation near the contact
+line, the nucleation of oil droplets starts at the rim which results in an oil
+ring encircling the drop. Furthermore, the oil droplets are advected through
+the Ouzo drop by a fast solutal Marangoni flow. In this article, we investigate
+the evaporation of mixture droplets in more detail, by successively increasing
+the mixture complexity from pure water over a binary water-ethanol mixture to
+the ternary Ouzo mixture (water, ethanol and anise oil). In particular,
+axisymmetric and full three-dimensional finite element method simulations have
+been performed on these droplets to discuss thermal effects and the complicated
+flow in the droplet driven by an interplay of preferential evaporation,
+evaporative cooling and solutal and thermal Marangoni flow. By using image
+analysis techniques and micro-PIV measurements, we are able to compare the
+numerically predicted volume evolutions and velocity fields with experimental
+data. The Ouzo droplet is furthermore investigated by confocal microscopy. It
+is shown that the oil ring predominantly emerges due to coalescence.
+",0,1,0,0,0,0
+8420,A semianalytical approach for determining the nonclassical mechanical properties of materials,"  In this article, a semianalytical approach for demonstrating elastic waves
+propagation in nanostructures has been presented based on the modified
+couple-stress theory including acceleration gradients. Using the experimental
+results and atomic simulations, the static and dynamic length scales were
+calculated for several materials, zinc oxide (ZnO), silicon (Si), silicon
+carbide (SiC), indium antimonide (InSb), and diamond. To evaluate the predicted
+static and dynamic length scales as well as the presented model, the natural
+frequencies of a beam in addition to the phase velocity and group velocity of
+Si were studied and compared with the available static length scales, estimated
+using strain-gradient theory without considering acceleration gradients. These
+three criteria, natural frequency, phase velocity, and group velocity, show
+that the presented model is dynamically stable even for larger wavevector
+values. Furthermore, it is explained why the previous works, which all are
+based on the strain-gradient theory without acceleration gradients, predicted
+very small values for the static length scale in the longitudinal direction
+rather than the static length scale in the transverse directions.
+",0,1,0,0,0,0
+8421,Gaussian Processes for Demand Unconstraining,"  One of the key challenges in revenue management is unconstraining demand
+data. Existing state of the art single-class unconstraining methods make
+restrictive assumptions about the form of the underlying demand and can perform
+poorly when applied to data which breaks these assumptions. In this paper, we
+propose a novel unconstraining method that uses Gaussian process (GP)
+regression. We develop a novel GP model by constructing and implementing a new
+non-stationary covariance function for the GP which enables it to learn and
+extrapolate the underlying demand trend. We show that this method can cope with
+important features of realistic demand data, including nonlinear demand trends,
+variations in total demand, lengthy periods of constraining, non-exponential
+inter-arrival times, and discontinuities/changepoints in demand data. In all
+such circumstances, our results indicate that GPs outperform existing
+single-class unconstraining methods.
+",0,0,0,1,0,0
+8422,Generalization of two Bonnet's Theorems to the relative Differential Geometry of the 3-dimensional Euclidean space,"  This paper is devoted to the 3-dimensional relative differential geometry of
+surfaces. In the Euclidean space $\R{E} ^3 $ we consider a surface $\varPhi
+%\colon \vect{x} = \vect{x}(u^1,u^2) $ with position vector field $\vect{x}$,
+which is relatively normalized by a relative normalization $\vect{y}% (u^1,u^2)
+$. A surface $\varPhi^*% \colon \vect{x}^* = \vect{x}^*(u^1,u^2) $ with
+position vector field $\vect{x}^* = \vect{x} + \mu \, \vect{y}$, where $\mu$ is
+a real constant, is called a relatively parallel surface to $\varPhi$. Then
+$\vect{y}$ is also a relative normalization of $\varPhi^*$. The aim of this
+paper is to formulate and prove the relative analogues of two well known
+theorems of O.~Bonnet which concern the parallel surfaces (see~\cite{oB1853}).
+",0,0,1,0,0,0
+8423,Some Aspects of Uniqueness Theory of Entire and Meromorphic Functions (Ph.D. thesis),"  The subject of our thesis is the uniqueness theory of meromorphic functions
+and it is devoted to problems concerning Bruck conjecture, set sharing and
+related topics. The tool, we used in our discussions is classical Nevanlinna
+theory of meromorphic functions. In 1996, in order to find the relation between
+an entire function with its derivative, counterpart sharing one value CM, a
+famous conjecture was proposed by R. Bruck. Since then the conjecture and its
+analogous results have been investigated by many researchers and continuous
+efforts have been put on by them. In our thesis, we have obtained similar types
+of conclusions as that of Bruck for two differential polynomials which in turn
+improve several existing results under different sharing environment. A number
+of examples have been exhibited to justify the necessity or sharpness of some
+conditions, hypothesis used in the thesis. As a variation of value sharing, F.
+Gross first introduced the idea of set sharing, by proposing a problem, which
+has later became popular as Gross Problem. Inspired by the Gross' Problem, the
+set sharing problems were started which was later shifted towards the
+characterization of the polynomial backbone of different unique range sets. In
+our study, we introduced some new type of unique range sets and at the same
+time, we further explored the anatomy of these unique range sets generating
+polynomials as well as connected Bruck conjecture with Gross' Problem.
+",0,0,1,0,0,0
+8424,Transfer Regression via Pairwise Similarity Regularization,"  Transfer learning methods address the situation where little labeled training
+data from the ""target"" problem exists, but much training data from a related
+""source"" domain is available. However, the overwhelming majority of transfer
+learning methods are designed for simple settings where the source and target
+predictive functions are almost identical, limiting the applicability of
+transfer learning methods to real world data. We propose a novel, weaker,
+property of the source domain that can be transferred even when the source and
+target predictive functions diverge. Our method assumes the source and target
+functions share a Pairwise Similarity property, where if the source function
+makes similar predictions on a pair of instances, then so will the target
+function. We propose Pairwise Similarity Regularization Transfer, a flexible
+graph-based regularization framework which can incorporate this modeling
+assumption into standard supervised learning algorithms. We show how users can
+encode domain knowledge into our regularizer in the form of spatial continuity,
+pairwise ""similarity constraints"" and how our method can be scaled to large
+data sets using the Nystrom approximation. Finally, we present positive and
+negative results on real and synthetic data sets and discuss when our Pairwise
+Similarity transfer assumption seems to hold in practice.
+",1,0,0,0,0,0
+8425,Star formation in a galactic outflow,"  Recent observations have revealed massive galactic molecular outflows that
+may have physical conditions (high gas densities) required to form stars.
+Indeed, several recent models predict that such massive galactic outflows may
+ignite star formation within the outflow itself. This star-formation mode, in
+which stars form with high radial velocities, could contribute to the
+morphological evolution of galaxies, to the evolution in size and velocity
+dispersion of the spheroidal component of galaxies, and would contribute to the
+population of high-velocity stars, which could even escape the galaxy. Such
+star formation could provide in-situ chemical enrichment of the circumgalactic
+and intergalactic medium (through supernova explosions of young stars on large
+orbits), and some models also predict that it may contribute substantially to
+the global star formation rate observed in distant galaxies. Although there
+exists observational evidence for star formation triggered by outflows or jets
+into their host galaxy, as a consequence of gas compression, evidence for star
+formation occurring within galactic outflows is still missing. Here we report
+new spectroscopic observations that unambiguously reveal star formation
+occurring in a galactic outflow at a redshift of 0.0448. The inferred star
+formation rate in the outflow is larger than 15 Msun/yr. Star formation may
+also be occurring in other galactic outflows, but may have been missed by
+previous observations owing to the lack of adequate diagnostics.
+",0,1,0,0,0,0
+8426,Artificial intelligence in peer review: How can evolutionary computation support journal editors?,"  With the volume of manuscripts submitted for publication growing every year,
+the deficiencies of peer review (e.g. long review times) are becoming more
+apparent. Editorial strategies, sets of guidelines designed to speed up the
+process and reduce editors workloads, are treated as trade secrets by
+publishing houses and are not shared publicly. To improve the effectiveness of
+their strategies, editors in small publishing groups are faced with undertaking
+an iterative trial-and-error approach. We show that Cartesian Genetic
+Programming, a nature-inspired evolutionary algorithm, can dramatically improve
+editorial strategies. The artificially evolved strategy reduced the duration of
+the peer review process by 30%, without increasing the pool of reviewers (in
+comparison to a typical human-developed strategy). Evolutionary computation has
+typically been used in technological processes or biological ecosystems. Our
+results demonstrate that genetic programs can improve real-world social systems
+that are usually much harder to understand and control than physical systems.
+",1,0,0,0,0,0
+8427,"Invariant Bianchi type I models in $f\left(R,T\right)$ Gravity","  In this paper, we search the existence of invariant solutions of Bianchi type
+I space-time in the context of $f\left(R,T\right)$ gravity. The exact solution
+of the Einstein's field equations are derived by using Lie point symmetry
+analysis method that yield two models of invariant universe for symmetries
+$X^{(1)}$ and $X^{(3)}$. The model with symmetries $X^{(1)}$ begins with big
+bang singularity while the model with symmetries $X^{(3)}$ does not favour the
+big bang singularity. Under this specification, we find out at set of singular
+and non singular solution of Bianchi type I model which present several other
+physically valid features within the framework of $f\left(R,T\right)$.
+",0,1,0,0,0,0
+8428,Representation theoretic realization of non-symmetric Macdonald polynomials at infinity,"  We study the nonsymmetric Macdonald polynomials specialized at infinity from
+various points of view. First, we define a family of modules of the Iwahori
+algebra whose characters are equal to the nonsymmetric Macdonald polynomials
+specialized at infinity. Second, we show that these modules are isomorphic to
+the dual spaces of sections of certain sheaves on the semi-infinite Schubert
+varieties. Third, we prove that the global versions of these modules are
+homologically dual to the level one affine Demazure modules.
+",0,0,1,0,0,0
+8429,Complex spectrogram enhancement by convolutional neural network with multi-metrics learning,"  This paper aims to address two issues existing in the current speech
+enhancement methods: 1) the difficulty of phase estimations; 2) a single
+objective function cannot consider multiple metrics simultaneously. To solve
+the first problem, we propose a novel convolutional neural network (CNN) model
+for complex spectrogram enhancement, namely estimating clean real and imaginary
+(RI) spectrograms from noisy ones. The reconstructed RI spectrograms are
+directly used to synthesize enhanced speech waveforms. In addition, since
+log-power spectrogram (LPS) can be represented as a function of RI
+spectrograms, its reconstruction is also considered as another target. Thus a
+unified objective function, which combines these two targets (reconstruction of
+RI spectrograms and LPS), is equivalent to simultaneously optimizing two
+commonly used objective metrics: segmental signal-to-noise ratio (SSNR) and
+logspectral distortion (LSD). Therefore, the learning process is called
+multi-metrics learning (MML). Experimental results confirm the effectiveness of
+the proposed CNN with RI spectrograms and MML in terms of improved standardized
+evaluation metrics on a speech enhancement task.
+",1,0,0,1,0,0
+8430,Optimal Threshold Design for Quanta Image Sensor,"  Quanta Image Sensor (QIS) is a binary imaging device envisioned to be the
+next generation image sensor after CCD and CMOS. Equipped with a massive number
+of single photon detectors, the sensor has a threshold $q$ above which the
+number of arriving photons will trigger a binary response ""1"", or ""0""
+otherwise. Existing methods in the device literature typically assume that
+$q=1$ uniformly. We argue that a spatially varying threshold can significantly
+improve the signal-to-noise ratio of the reconstructed image. In this paper, we
+present an optimal threshold design framework. We make two contributions.
+First, we derive a set of oracle results to theoretically inform the maximally
+achievable performance. We show that the oracle threshold should match exactly
+with the underlying pixel intensity. Second, we show that around the oracle
+threshold there exists a set of thresholds that give asymptotically unbiased
+reconstructions. The asymptotic unbiasedness has a phase transition behavior
+which allows us to develop a practical threshold update scheme using a
+bisection method. Experimentally, the new threshold design method achieves
+better rate of convergence than existing methods.
+",1,0,0,0,0,0
+8431,Statistics of $K$-groups modulo $p$ for the ring of integers of a varying quadratic number field,"  For each odd prime $p$, we conjecture the distribution of the $p$-torsion
+subgroup of $K_{2n}(\mathcal{O}_F)$ as $F$ ranges over real quadratic fields,
+or over imaginary quadratic fields. We then prove that the average size of the
+$3$-torsion subgroup of $K_{2n}(\mathcal{O}_F)$ is as predicted by this
+conjecture.
+",0,0,1,0,0,0
+8432,Learning Models for Shared Control of Human-Machine Systems with Unknown Dynamics,"  We present a novel approach to shared control of human-machine systems. Our
+method assumes no a priori knowledge of the system dynamics. Instead, we learn
+both the dynamics and information about the user's interaction from observation
+through the use of the Koopman operator. Using the learned model, we define an
+optimization problem to compute the optimal policy for a given task, and
+compare the user input to the optimal input. We demonstrate the efficacy of our
+approach with a user study. We also analyze the individual nature of the
+learned models by comparing the effectiveness of our approach when the
+demonstration data comes from a user's own interactions, from the interactions
+of a group of users and from a domain expert. Positive results include
+statistically significant improvements on task metrics when comparing a
+user-only control paradigm with our shared control paradigm. Surprising results
+include findings that suggest that individualizing the model based on a user's
+own data does not effect the ability to learn a useful dynamic system. We
+explore this tension as it relates to developing human-in-the-loop systems
+further in the discussion.
+",1,0,0,0,0,0
+8433,A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets,"  Following the rapidly growing digital image usage, automatic image
+categorization has become preeminent research area. It has broaden and adopted
+many algorithms from time to time, whereby multi-feature (generally,
+hand-engineered features) based image characterization comes handy to improve
+accuracy. Recently, in machine learning, pre-trained deep convolutional neural
+networks (DCNNs or ConvNets) have been that the features extracted through such
+DCNN can improve classification accuracy. Thence, in this paper, we further
+investigate a feature embedding strategy to exploit cues from multiple DCNNs.
+We derive a generalized feature space by embedding three different DCNN
+bottleneck features with weights respect to their Softmax cross-entropy loss.
+Test outcomes on six different object classification data-sets and an action
+classification data-set show that regardless of variation in image statistics
+and tasks the proposed multi-DCNN bottleneck feature fusion is well suited to
+image classification tasks and an effective complement of DCNN. The comparisons
+to existing fusion-based image classification approaches prove that the
+proposed method surmounts the state-of-the-art methods and produces competitive
+results with fully trained DCNNs as well.
+",1,0,0,0,0,0
+8434,Look-Ahead in the Two-Sided Reduction to Compact Band Forms for Symmetric Eigenvalue Problems and the SVD,"  We address the reduction to compact band forms, via unitary similarity
+transformations, for the solution of symmetric eigenvalue problems and the
+computation of the singular value decomposition (SVD). Concretely, in the first
+case we revisit the reduction to symmetric band form while, for the second
+case, we propose a similar alternative, which transforms the original matrix to
+(unsymmetric) band form, replacing the conventional reduction method that
+produces a triangular--band output. In both cases, we describe algorithmic
+variants of the standard Level-3 BLAS-based procedures, enhanced with
+look-ahead, to overcome the performance bottleneck imposed by the panel
+factorization. Furthermore, our solutions employ an algorithmic block size that
+differs from the target bandwidth, illustrating the important performance
+benefits of this decision. Finally, we show that our alternative compact band
+form for the SVD is key to introduce an effective look-ahead strategy into the
+corresponding reduction procedure.
+",1,0,0,0,0,0
+8435,Non-existence of a Wente's $L^\infty$ estimate for the Neumann problem,"  We provide a counterexample of Wente's inequality in the context of Neumann
+boundary conditions. We will also show that Wente's estimates fails for general
+boundary conditions of Robin type.
+",0,0,1,0,0,0
+8436,Regular characters of classical groups over complete discrete valuation rings,"  Let $\mathfrak{o}$ be a complete discrete valuation ring with finide residue
+field $\mathsf{k}$ of odd characteristic, and let $\mathbf{G}$ be a symplectic
+or special orthogonal group scheme over $\mathfrak{o}$. For any
+$\ell\in\mathbb{N}$ let $G^\ell$ denote the $\ell$-th principal congruence
+subgroup of $\mathbf{G}(\mathfrak{o})$. An irreducible character of the group
+$\mathbf{G}(\mathfrak{o})$ is said to be regular if it is trivial on a subgroup
+$G^{\ell+1}$ for some $\ell$, and if its restriction to
+$G^\ell/G^{\ell+1}\simeq \mathrm{Lie}(\mathbf{G})(\mathsf{k})$ consists of
+characters of minimal $\mathbf{G}(\mathsf{k}^{\rm alg})$ stabilizer dimension.
+In the present paper we consider the regular characters of such classical
+groups over $\mathfrak{o}$, and construct and enumerate all regular characters
+of $\mathbf{G}(\mathfrak{o})$, when the characteristic of $\mathsf{k}$ is
+greater than two. As a result, we compute the regular part of their
+representation zeta function.
+",0,0,1,0,0,0
+8437,Quantitative analysis of nonadiabatic effects in dense H$_3$S and PH$_3$ superconductors,"  The comparison study of high pressure superconducting state of recently
+synthesized H$_3$S and PH$_3$ compounds are conducted within the framework of
+the strong-coupling theory. By generalization of the standard Eliashberg
+equations to include the lowest-order vertex correction, we have investigated
+the influence of the nonadiabatic effects on the Coulomb pseudopotential,
+electron effective mass, energy gap function and on the $2\Delta(0)/T_C$ ratio.
+We found that, for a fixed value of critical temperature ($178$ K for H$_3$S
+and $81$ K for PH$_3$), the nonadiabatic corrections reduce the Coulomb
+pseudopotential for H$_3$S from $0.204$ to $0.185$ and for PH$_3$ from $0.088$
+to $0.083$, however, the electron effective mass and ratio $2\Delta(0)/T_C$
+remain unaffected. Independently of the assumed method of analysis, the
+thermodynamic parameters of superconducting H$_3$S and PH$_3$ strongly deviate
+from the prediction of BCS theory due to the strong-coupling and retardation
+effects.
+",0,1,0,0,0,0
+8438,Radio-flaring Ultracool Dwarf Population Synthesis,"  Over a dozen ultracool dwarfs (UCDs), low-mass objects of spectral types
+$\geq$M7, are known to be sources of radio flares. These typically
+several-minutes-long radio bursts can be up to 100\% circularly polarized and
+have high brightness temperatures, consistent with coherent emission via the
+electron cyclotron maser operating in $\sim$kG magnetic fields. Recently, the
+statistical properties of the bulk physical parameters that describe these UCDs
+have become adequately described to permit synthesis of the population of
+radio-flaring objects. For the first time, I construct a Monte Carlo simulator
+to model the population of these radio-flaring UCDs. This simulator is powered
+by Intel Secure Key (ISK)- a new processor technology that uses a local entropy
+source to improve random number generation that has heretofore been used to
+improve cryptography. The results from this simulator indicate that only
+$\sim$5% of radio-flaring UCDs within the local interstellar neighborhood
+($<$25 pc away) have been discovered. I discuss a number of scenarios which may
+explain this radio-flaring fraction, and suggest that the observed behavior is
+likely a result of several factors. The performance of ISK as compared to other
+pseudorandom number generators is also evaluated, and its potential utility for
+other astrophysical codes briefly described.
+",0,1,0,0,0,0
+8439,Janus: An Uncertain Cache Architecture to Cope with Side Channel Attacks,"  Side channel attacks are a major class of attacks to crypto-systems.
+Attackers collect and analyze timing behavior, I/O data, or power consumption
+in these systems to undermine their effectiveness in protecting sensitive
+information. In this work, we propose a new cache architecture, called Janus,
+to enable crypto-systems to introduce randomization and uncertainty in their
+runtime timing behavior and power utilization profile. In the proposed cache
+architecture, each data block is equipped with an on-off flag to enable/disable
+the data block. The Janus architecture has two special instructions in its
+instruction set to support the on-off flag. Beside the analytical evaluation of
+the proposed cache architecture, we deploy it in an ARM-7 processor core to
+study its feasibility and practicality. Results show a significant variation in
+the timing behavior across all the benchmarks. The new secure processor
+architecture has minimal hardware overhead and significant improvement in
+protecting against power analysis and timing behavior attacks.
+",1,0,0,0,0,0
+8440,Predicting Adversarial Examples with High Confidence,"  It has been suggested that adversarial examples cause deep learning models to
+make incorrect predictions with high confidence. In this work, we take the
+opposite stance: an overly confident model is more likely to be vulnerable to
+adversarial examples. This work is one of the most proactive approaches taken
+to date, as we link robustness with non-calibrated model confidence on noisy
+images, providing a data-augmentation-free path forward. The adversarial
+examples phenomenon is most easily explained by the trend of increasing
+non-regularized model capacity, while the diversity and number of samples in
+common datasets has remained flat. Test accuracy has incorrectly been
+associated with true generalization performance, ignoring that training and
+test splits are often extremely similar in terms of the overall representation
+space. The transferability property of adversarial examples was previously used
+as evidence against overfitting arguments, a perceived random effect, but
+overfitting is not always random.
+",0,0,0,1,0,0
+8441,A New Taxonomy for Symbiotic EM Sensors,"  It is clear that the EM spectrum is now rapidly reaching saturation,
+especially for frequencies below 10~GHz. Governments, who influence the
+regulatory authorities around the world, have resorted to auctioning the use of
+spectrum, in a sense to gauge the importance of a particular user. Billions of
+USD are being paid for modest bandwidths.
+The earth observation, astronomy and similar science driven communities
+cannot compete financially with such a pressure system, so this is where
+governments have to step in and assess /regulate the situation.
+It has been a pleasure to see a situation where the communications and
+broadcast communities have come together to formulate sharing of an important
+part of the spectrum (roughly, 50 MHz to 800 MHz) in an IEEE standard,
+IEEE802.22. This standard (known as the ""TV White Space Network"" (built on
+lower level standards) shows a way that fixed and mobile users can collaborate
+in geographically widespread regions, using cognitive radio and geographic
+databases of users. This White Space (WS) standard is well described in the
+literature and is not the major topic of this short paper.
+We wish to extend the idea of the WS concept to include the idea of EM
+sensors (such as Radar) adopting this approach to spectrum sharing, providing a
+quantum leap in access to spectrum. We postulate that networks of sensors,
+using the tools developed by the WS community, can replace and enhance our
+present set of EM sensors.
+We first define what Networks of Sensors entail (with some history), and then
+go on to define, based on a Taxonomy of Symbiosis defined by de
+Bary\cite{symb}, how these sensors and other users (especially communications)
+can co-exist. This new taxonomy is important for understanding, and should
+replace somewhat outdated terminologies from the radar world.
+",1,0,0,0,0,0
+8442,Local-ring network automata and the impact of hyperbolic geometry in complex network link-prediction,"  Topological link-prediction can exploit the entire network topology (global
+methods) or only the neighbourhood (local methods) of the link to predict.
+Global methods are believed the best. Is this common belief well-founded?
+Stochastic-Block-Model (SBM) is a global method believed as one of the best
+link-predictors, therefore it is considered a reference for comparison. But,
+our results suggest that SBM, whose computational time is high, cannot in
+general overcome the Cannistraci-Hebb (CH) network automaton model that is a
+simple local-learning-rule of topological self-organization proved as the
+current best local-based and parameter-free deterministic rule for
+link-prediction. To elucidate the reasons of this unexpected result, we
+formally introduce the notion of local-ring network automata models and their
+relation with the nature of common-neighbours' definition in complex network
+theory. After extensive tests, we recommend Structural-Perturbation-Method
+(SPM) as the new best global method baseline. However, even SPM overall does
+not outperform CH and in several evaluation frameworks we astonishingly found
+the opposite. In particular, CH was the best predictor for synthetic networks
+generated by the Popularity-Similarity-Optimization (PSO) model, and its
+performance in PSO networks with community structure was even better than using
+the original internode-hyperbolic-distance as link-predictor. Interestingly,
+when tested on non-hyperbolic synthetic networks the performance of CH
+significantly dropped down indicating that this rule of network
+self-organization could be strongly associated to the rise of hyperbolic
+geometry in complex networks. The superiority of global methods seems a
+""misleading belief"" caused by a latent geometry bias of the few small networks
+used as benchmark in previous studies. We propose to found a latent geometry
+theory of link-prediction in complex networks.
+",1,0,0,0,0,0
+8443,Emission of Circularly Polarized Terahertz Wave from Inhomogeneous Intrinsic Josephson Junctions,"  We have theoretically demonstrated the emission of circularly-polarized
+terahertz (THz) waves from intrinsic Josephson junctions (IJJs) which is
+locally heated by an external heat source such as the laser irradiation. We
+focus on a mesa-structured IJJ whose geometry is slightly deviate from a square
+and find that the local heating make it possible to emit circularly-polarized
+THz waves. In this mesa, the inhomogeneity of critical current density induced
+by the local heating excites the electromagnetic cavity modes TM (1,0) and TM
+(0,1), whose polarizations are orthogonal to each other. The mixture of these
+modes results in the generation of circularly-polarized THz waves. We also show
+that the circular polarization dramatically changes with the applied voltage.
+The emitter based on IJJs can emit circularly-polarized and continuum THz waves
+by the local heating, and will be useful for various technological application.
+",0,1,0,0,0,0
+8444,Asymmetry-Induced Synchronization in Oscillator Networks,"  A scenario has recently been reported in which in order to stabilize complete
+synchronization of an oscillator network---a symmetric state---the symmetry of
+the system itself has to be broken by making the oscillators nonidentical. But
+how often does such behavior---which we term asymmetry-induced synchronization
+(AISync)---occur in oscillator networks? Here we present the first general
+scheme for constructing AISync systems and demonstrate that this behavior is
+the norm rather than the exception in a wide class of physical systems that can
+be seen as multilayer networks. Since a symmetric network in complete synchrony
+is the basic building block of cluster synchronization in more general
+networks, AISync should be common also in facilitating cluster synchronization
+by breaking the symmetry of the cluster subnetworks.
+",0,1,0,0,0,0
+8445,Opportunistic Downlink Interference Alignment for Multi-Cell MIMO Networks,"  In this paper, we propose an opportunistic downlink interference alignment
+(ODIA) for interference-limited cellular downlink, which intelligently combines
+user scheduling and downlink IA techniques. The proposed ODIA not only
+efficiently reduces the effect of inter-cell interference from other-cell base
+stations (BSs) but also eliminates intra-cell interference among spatial
+streams in the same cell. We show that the minimum number of users required to
+achieve a target degrees-of-freedom (DoF) can be fundamentally reduced, i.e.,
+the fundamental user scaling law can be improved by using the ODIA, compared
+with the existing downlink IA schemes. In addition, we adopt a limited feedback
+strategy in the ODIA framework, and then analyze the number of feedback bits
+required for the system with limited feedback to achieve the same user scaling
+law of the ODIA as the system with perfect CSI. We also modify the original
+ODIA in order to further improve sum-rate, which achieves the optimal multiuser
+diversity gain, i.e., $\log\log N$, per spatial stream even in the presence of
+downlink inter-cell interference, where $N$ denotes the number of users in a
+cell. Simulation results show that the ODIA significantly outperforms existing
+interference management techniques in terms of sum-rate in realistic cellular
+environments. Note that the ODIA operates in a non-collaborative and decoupled
+manner, i.e., it requires no information exchange among BSs and no iterative
+beamformer optimization between BSs and users, thus leading to an easier
+implementation.
+",1,0,1,0,0,0
+8446,The Repeated Divisor Function and Possible Correlation with Highly Composite Numbers,"  Let n be a non-null positive integer and $d(n)$ is the number of positive
+divisors of n, called the divisor function. Of course, $d(n) \leq n$. $d(n) =
+1$ if and only if $n = 1$. For $n > 2$ we have $d(n) \geq 2$ and in this paper
+we try to find the smallest $k$ such that $d(d(...d(n)...)) = 2$ where the
+divisor function is applied $k$ times. At the end of the paper we make a
+conjecture based on some observations.
+",0,0,1,0,0,0
+8447,A Language Hierarchy and Kitchens-Type Theorem for Self-Similar Groups,"  We generalize the notion of self-similar groups of infinite tree
+automorphisms to allow for groups which are defined on a tree but do not act
+faithfully on it. The elements of such a group correspond to labeled trees
+which may be recognized by a tree automaton (e.g. Rabin, Büchi, etc.), or
+considered as elements of a tree shift (e.g. of finite type, sofic) as in
+symbolic dynamics. We give examples to show that the various classes of
+self-similar groups defined in this way do not coincide. As the main result,
+extending the classical result of Kitchens on one-dimensional group shifts, we
+provide a sufficient condition for a self-similar group whose elements form a
+sofic tree shift to be a tree shift of finite type. As an application, we show
+that the closure of certain self-similar groups of tree automorphisms are not
+Rabin-recognizable. \end{abstract}
+",0,0,1,0,0,0
+8448,Distance Covariance in Metric Spaces: Non-Parametric Independence Testing in Metric Spaces (Master's thesis),"  The aim of this thesis is to find a solution to the non-parametric
+independence problem in separable metric spaces. Suppose we are given finite
+collection of samples from an i.i.d. sequence of paired random elements, where
+each marginal has values in some separable metric space. The non-parametric
+independence problem raises the question on how one can use these samples to
+reasonably draw inference on whether the marginal random elements are
+independent or not. We will try to answer this question by utilizing the
+so-called distance covariance functional in metric spaces developed by Russell
+Lyons. We show that, if the marginal spaces are so-called metric spaces of
+strong negative type (e.g. seperable Hilbert spaces), then the distance
+covariance functional becomes a direct indicator of independence. That is, one
+can directly determine whether the marginals are independent or not based
+solely on the value of this functional. As the functional formally takes the
+simultaneous distribution as argument, its value is not known in the posed
+non-parametric independence problem. Hence, we construct estimators of the
+distance covariance functional, and show that they exhibit asymptotic
+properties which can be used to construct asymptotically consistent statistical
+tests of independence. Finally, as the rejection thresholds of these
+statistical tests are non-traceable we argue that they can be reasonably
+bootstrapped.
+",0,0,1,1,0,0
+8449,Sum-Product-Quotient Networks,"  We present a novel tractable generative model that extends Sum-Product
+Networks (SPNs) and significantly boosts their power. We call it
+Sum-Product-Quotient Networks (SPQNs), whose core concept is to incorporate
+conditional distributions into the model by direct computation using quotient
+nodes, e.g. $P(A|B) = \frac{P(A,B)}{P(B)}$. We provide sufficient conditions
+for the tractability of SPQNs that generalize and relax the decomposable and
+complete tractability conditions of SPNs. These relaxed conditions give rise to
+an exponential boost to the expressive efficiency of our model, i.e. we prove
+that there are distributions which SPQNs can compute efficiently but require
+SPNs to be of exponential size. Thus, we narrow the gap in expressivity between
+tractable graphical models and other Neural Network-based generative models.
+",1,0,0,1,0,0
+8450,When Simpler Data Does Not Imply Less Information: A Study of User Profiling Scenarios with Constrained View of Mobile HTTP(S) Traffic,"  The exponential growth in smartphone adoption is contributing to the
+availability of vast amounts of human behavioral data. This data enables the
+development of increasingly accurate data-driven user models that facilitate
+the delivery of personalized services which are often free in exchange for the
+use of its customers' data. Although such usage conventions have raised many
+privacy concerns, the increasing value of personal data is motivating diverse
+entities to aggressively collect and exploit the data. In this paper, we unfold
+profiling scenarios around mobile HTTP(S) traffic, focusing on those that have
+limited but meaningful segments of the data. The capability of the scenarios to
+profile personal information is examined with real user data, collected
+in-the-wild from 61 mobile phone users for a minimum of 30 days. Our study
+attempts to model heterogeneous user traits and interests, including
+personality, boredom proneness, demographics, and shopping interests. Based on
+our modeling results, we discuss various implications to personalization,
+privacy, and personal data rights.
+",1,0,0,0,0,0
+8451,Algebraic Description of Shape Invariance Revisited,"  We revisit the algebraic description of shape invariance method in
+one-dimensional quantum mechanics. In this note we focus on four particular
+examples: the Kepler problem in flat space, the Kepler problem in spherical
+space, the Kepler problem in hyperbolic space, and the Rosen-Morse potential
+problem. Following the prescription given by Gangopadhyaya et al., we first
+introduce certain nonlinear algebraic systems. We then show that, if the model
+parameters are appropriately quantized, the bound-state problems can be solved
+solely by means of representation theory.
+",0,1,0,0,0,0
+8452,"Joint Smoothing, Tracking, and Forecasting Based on Continuous-Time Target Trajectory Fitting","  We present a continuous time state estimation framework that unifies
+traditionally individual tasks of smoothing, tracking, and forecasting (STF),
+for a class of targets subject to smooth motion processes, e.g., the target
+moves with nearly constant acceleration or affected by insignificant noises.
+Fundamentally different from the conventional Markov transition formulation,
+the state process is modeled by a continuous trajectory function of time (FoT)
+and the STF problem is formulated as an online data fitting problem with the
+goal of finding the trajectory FoT that best fits the observations in a sliding
+time-window. Then, the state of the target, whether the past (namely,
+smoothing), the current (filtering) or the near-future (forecasting), can be
+inferred from the FoT. Our framework releases stringent statistical modeling of
+the target motion in real time, and is applicable to a broad range of real
+world targets of significance such as passenger aircraft and ships which move
+on scheduled, (segmented) smooth paths but little statistical knowledge is
+given about their real time movement and even about the sensors. In addition,
+the proposed STF framework inherits the advantages of data fitting for
+accommodating arbitrary sensor revisit time, target maneuvering and missed
+detection. The proposed method is compared with state of the art estimators in
+scenarios of either maneuvering or non-maneuvering target.
+",1,0,0,1,0,0
+8453,Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions,"  We search for digital biomarkers from Parkinson's Disease by observing
+approximate repetitive patterns matching hypothesized step and stride periodic
+cycles. These observations were modeled as a cycle of hidden states with
+randomness allowing deviation from a canonical pattern of transitions and
+emissions, under the hypothesis that the averaged features of hidden states
+would serve to informatively characterize classes of patients/controls. We
+propose a Hidden Semi-Markov Model (HSMM), a latent-state model, emitting
+3D-acceleration vectors. Transitions and emissions are inferred from data. We
+fit separate models per unique device and training label. Hidden Markov Models
+(HMM) force geometric distributions of the duration spent at each state before
+transition to a new state. Instead, our HSMM allows us to specify the
+distribution of state duration. This modified version is more effective because
+we are interested more in each state's duration than the sequence of distinct
+states, allowing inclusion of these durations the feature vector.
+",1,0,0,1,0,0
+8454,Bifurcation to locked fronts in two component reaction-diffusion systems,"  We study invasion fronts and spreading speeds in two component
+reaction-diffusion systems. Using a variation of Lin's method, we construct
+traveling front solutions and show the existence of a bifurcation to locked
+fronts where both components invade at the same speed. Expansions of the wave
+speed as a function of the diffusion constant of one species are obtained. The
+bifurcation can be sub or super-critical depending on whether the locked fronts
+exist for parameter values above or below the bifurcation value. Interestingly,
+in the sub-critical case numerical simulations reveal that the spreading speed
+of the PDE system does not depend continuously on the coefficient of diffusion.
+",0,1,1,0,0,0
+8455,Contrastive Hebbian Learning with Random Feedback Weights,"  Neural networks are commonly trained to make predictions through learning
+algorithms. Contrastive Hebbian learning, which is a powerful rule inspired by
+gradient backpropagation, is based on Hebb's rule and the contrastive
+divergence algorithm. It operates in two phases, the forward (or free) phase,
+where the data are fed to the network, and a backward (or clamped) phase, where
+the target signals are clamped to the output layer of the network and the
+feedback signals are transformed through the transpose synaptic weight
+matrices. This implies symmetries at the synaptic level, for which there is no
+evidence in the brain. In this work, we propose a new variant of the algorithm,
+called random contrastive Hebbian learning, which does not rely on any synaptic
+weights symmetries. Instead, it uses random matrices to transform the feedback
+signals during the clamped phase, and the neural dynamics are described by
+first order non-linear differential equations. The algorithm is experimentally
+verified by solving a Boolean logic task, classification tasks (handwritten
+digits and letters), and an autoencoding task. This article also shows how the
+parameters affect learning, especially the random matrices. We use the
+pseudospectra analysis to investigate further how random matrices impact the
+learning process. Finally, we discuss the biological plausibility of the
+proposed algorithm, and how it can give rise to better computational models for
+learning.
+",0,0,0,1,1,0
+8456,A model of electrical impedance tomography on peripheral nerves for a neural-prosthetic control interface,"  Objective: A model is presented to evaluate the viability of using electrical
+impedance tomography (EIT) with a nerve cuff to record neural activity in
+peripheral nerves. Approach: Established modelling approaches in neural-EIT are
+expanded on to be used, for the first time, on myelinated fibres which are
+abundant in mammalian peripheral nerves and transmit motor commands. Main
+results: Fibre impedance models indicate activity in unmyelinated fibres can be
+screened out using operating frequencies above 100 Hz. At 1 kHz and 10 mm
+electrode spacing, impedance magnitude of inactive intra-fascicle tissue and
+the fraction changes during neural activity are estimated to be 1,142
+{\Omega}.cm and -8.8x10-4, respectively, with a transverse current, and 328
+{\Omega}.cm & -0.30, respectively with a longitudinal current. We show that a
+novel EIT drive and measurement electrode pattern which utilises longitudinal
+current and longitudinal differential boundary voltage measurements could
+distinguish activity in different fascicles of a three-fascicle mammalian nerve
+using pseudo-experimental data synthesised to replicate real operating
+conditions. Significance: The results of this study provide an estimate of the
+transient change in impedance of intra-fascicle tissue during neural activity
+in mammalian nerve, and present a viable EIT electrode pattern, both of which
+are critical steps towards implementing EIT in a nerve cuff for neural
+prosthetics interfaces.
+",0,1,0,0,0,0
+8457,Above threshold scattering about a Feshbach resonance for ultracold atoms in an optical collider,"  Ultracold atomic gases have realised numerous paradigms of condensed matter
+physics where control over interactions has crucially been afforded by tunable
+Feshbach resonances. So far, the characterisation of these Feshbach resonances
+has almost exclusively relied on experiments in the threshold regime near zero
+energy. Here we use a laser-based collider to probe a narrow magnetic Feshbach
+resonance of rubidium above threshold. By measuring the overall atomic loss
+from colliding clouds as a function of magnetic field, we track the
+energy-dependent resonance position. At higher energy, our collider scheme
+broadens the loss feature, making the identification of the narrow resonance
+challenging. However, we observe that the collisions give rise to shifts in the
+centre-of-mass positions of outgoing clouds. The shifts cross zero at the
+resonance and this allows us to accurately determine its location well above
+threshold. Our inferred resonance positions are in excellent agreement with
+theory.
+",0,1,0,0,0,0
+8458,Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data,"  Identifying anomalous patterns in real-world data is essential for
+understanding where, when, and how systems deviate from their expected
+dynamics. Yet methods that separately consider the anomalousness of each
+individual data point have low detection power for subtle, emerging
+irregularities. Additionally, recent detection techniques based on subset
+scanning make strong independence assumptions and suffer degraded performance
+in correlated data. We introduce methods for identifying anomalous patterns in
+non-iid data by combining Gaussian processes with novel log-likelihood ratio
+statistic and subset scanning techniques. Our approaches are powerful,
+interpretable, and can integrate information across multiple data streams. We
+illustrate their performance on numeric simulations and three open source
+spatiotemporal datasets of opioid overdose deaths, 311 calls, and storm
+reports.
+",0,0,0,1,0,0
+8459,Isolated resonances and nonlinear damping,"  We analyze isolated resonance curves (IRCs) in a single-degree-of-freedom
+system with nonlinear damping. The adopted procedure exploits singularity
+theory in conjunction with the harmonic balance method. The analysis unveils a
+geometrical connection between the topology of the damping force and IRCs.
+Specifically, we demonstrate that extremas and zeros of the damping force
+correspond to the appearance and merging of IRCs.
+",0,1,0,0,0,0
+8460,"UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies and Implementation","  As air pollution is becoming the largest environmental health risk, the
+monitoring of air quality has drawn much attention in both theoretical studies
+and practical implementations. In this article, we present a real-time,
+fine-grained and power-efficient air quality monitoring system based on aerial
+and ground sensing. The architecture of this system consists of four layers:
+the sensing layer to collect data, the transmission layer to enable
+bidirectional communications, the processing layer to analyze and process the
+data, and the presentation layer to provide graphic interface for users. Three
+major techniques are investigated in our implementation, given by the data
+processing, the deployment strategy and the power control. For data processing,
+spacial fitting and short-term prediction are performed to eliminate the
+influences of the incomplete measurement and the latency of data uploading. The
+deployment strategies of ground sensing and aerial sensing are investigated to
+improve the quality of the collected data. The power control is further
+considered to balance between power consumption and data accuracy. Our
+implementation has been deployed in Peking University and Xidian University
+since February 2018, and has collected about 100 thousand effective data
+samples by June 2018.
+",1,0,0,0,0,0
+8461,Photoinduced vibronic coupling in two-level dissipative systems,"  Interaction of an electron system with a strong electromagnetic wave leads to
+rearrangement both the electron and vibrational energy spectra of a dissipative
+system. For instance, the optically coupled electron levels become split in the
+conditions of the ac Stark effect that gives rise to appearance of the
+nonadiabatic coupling between the electron and vibrational motions. The
+nonadiabatic coupling exerts a substantial impact on the electron and phonon
+dynamics and must be taken into account to determine the system wave functions.
+In this paper, the vibronic coupling induced by the ac Stark effect is
+considered. It is shown that the interaction between the electron states
+dressed by an electromagnetic field and the forced vibrations of reservoir
+oscillators under the action of rapid changing of the electron density with the
+Rabi frequency is responsible for establishment of the photoinduced vibronic
+coupling. However, if the resonance conditions for the optical phonon frequency
+and the transition frequency of electrons in the dressed state basis are
+satisfied, the vibronic coupling is due to the electron-phonon interaction.
+Additionally, photoinduced vibronic coupling results in appearance of the
+doubly dressed states which are formed by both the electron-photon and
+electron-vibrational interactions.
+",0,1,0,0,0,0
+8462,"Crowd Science: Measurements, Models, and Methods","  The increasing practice of engaging crowds, where organizations use IT to
+connect with dispersed individuals for explicit resource creation purposes, has
+precipitated the need to measure the precise processes and benefits of these
+activities over myriad different implementations. In this work, we seek to
+address these salient and non-trivial considerations by laying a foundation of
+theory, measures, and research methods that allow us to test crowd-engagement
+efficacy across organizations, industries, technologies, and geographies. To do
+so, we anchor ourselves in the Theory of Crowd Capital, a generalizable
+framework for studying IT-mediated crowd-engagement phenomena, and put forth an
+empirical apparatus of testable measures and generalizable methods to begin to
+unify the field of crowd science.
+",1,0,0,0,0,0
+8463,Procedural Content Generation via Machine Learning (PCGML),"  This survey explores Procedural Content Generation via Machine Learning
+(PCGML), defined as the generation of game content using machine learning
+models trained on existing content. As the importance of PCG for game
+development increases, researchers explore new avenues for generating
+high-quality content with or without human involvement; this paper addresses
+the relatively new paradigm of using machine learning (in contrast with
+search-based, solver-based, and constructive methods). We focus on what is most
+often considered functional game content such as platformer levels, game maps,
+interactive fiction stories, and cards in collectible card games, as opposed to
+cosmetic content such as sprites and sound effects. In addition to using PCG
+for autonomous generation, co-creativity, mixed-initiative design, and
+compression, PCGML is suited for repair, critique, and content analysis because
+of its focus on modeling existing content. We discuss various data sources and
+representations that affect the resulting generated content. Multiple PCGML
+methods are covered, including neural networks, long short-term memory (LSTM)
+networks, autoencoders, and deep convolutional networks; Markov models,
+$n$-grams, and multi-dimensional Markov chains; clustering; and matrix
+factorization. Finally, we discuss open problems in the application of PCGML,
+including learning from small datasets, lack of training data, multi-layered
+learning, style-transfer, parameter tuning, and PCG as a game mechanic.
+",1,0,0,0,0,0
+8464,Automated Vulnerability Detection in Source Code Using Deep Representation Learning,"  Increasing numbers of software vulnerabilities are discovered every year
+whether they are reported publicly or discovered internally in proprietary
+code. These vulnerabilities can pose serious risk of exploit and result in
+system compromise, information leaks, or denial of service. We leveraged the
+wealth of C and C++ open-source code available to develop a large-scale
+function-level vulnerability detection system using machine learning. To
+supplement existing labeled vulnerability datasets, we compiled a vast dataset
+of millions of open-source functions and labeled it with carefully-selected
+findings from three different static analyzers that indicate potential
+exploits. The labeled dataset is available at: this https URL. Using
+these datasets, we developed a fast and scalable vulnerability detection tool
+based on deep feature representation learning that directly interprets lexed
+source code. We evaluated our tool on code from both real software packages and
+the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature
+representation learning on source code is a promising approach for automated
+software vulnerability detection.
+",1,0,0,1,0,0
+8465,X-Shooter study of accretion in Chamaeleon I: II. A steeper increase of accretion with stellar mass for very low mass stars?,"  The dependence of the mass accretion rate on the stellar properties is a key
+constraint for star formation and disk evolution studies. Here we present a
+study of a sample of stars in the Chamaeleon I star forming region carried out
+using the VLT/X-Shooter spectrograph. The sample is nearly complete down to
+M~0.1Msun for the young stars still harboring a disk in this region. We derive
+the stellar and accretion parameters using a self-consistent method to fit the
+broad-band flux-calibrated medium resolution spectrum. The correlation between
+the accretion luminosity to the stellar luminosity, and of the mass accretion
+rate to the stellar mass in the logarithmic plane yields slopes of 1.9 and 2.3,
+respectively. These slopes and the accretion rates are consistent with previous
+results in various star forming regions and with different theoretical
+frameworks. However, we find that a broken power-law fit, with a steeper slope
+for stellar luminosity smaller than ~0.45 Lsun and for stellar masses smaller
+than ~ 0.3 Msun, is slightly preferred according to different statistical
+tests, but the single power-law model is not excluded. The steeper relation for
+lower mass stars can be interpreted as a faster evolution in the past for
+accretion in disks around these objects, or as different accretion regimes in
+different stellar mass ranges. Finally, we find two regions on the mass
+accretion versus stellar mass plane empty of objects. One at high mass
+accretion rates and low stellar masses, which is related to the steeper
+dependence of the two parameters we derived. The second one is just above the
+observational limits imposed by chromospheric emission. This empty region is
+located at M~0.3-0.4Msun, typical masses where photoevaporation is known to be
+effective, and at mass accretion rates ~10^-10 Msun/yr, a value compatible with
+the one expected for photoevaporation to rapidly dissipate the inner disk.
+",0,1,0,0,0,0
+8466,A General Model for Robust Tensor Factorization with Unknown Noise,"  Because of the limitations of matrix factorization, such as losing spatial
+structure information, the concept of low-rank tensor factorization (LRTF) has
+been applied for the recovery of a low dimensional subspace from high
+dimensional visual data. The low-rank tensor recovery is generally achieved by
+minimizing the loss function between the observed data and the factorization
+representation. The loss function is designed in various forms under different
+noise distribution assumptions, like $L_1$ norm for Laplacian distribution and
+$L_2$ norm for Gaussian distribution. However, they often fail to tackle the
+real data which are corrupted by the noise with unknown distribution. In this
+paper, we propose a generalized weighted low-rank tensor factorization method
+(GWLRTF) integrated with the idea of noise modelling. This procedure treats the
+target data as high-order tensor directly and models the noise by a Mixture of
+Gaussians, which is called MoG GWLRTF. The parameters in the model are
+estimated under the EM framework and through a new developed algorithm of
+weighted low-rank tensor factorization. We provide two versions of the
+algorithm with different tensor factorization operations, i.e., CP
+factorization and Tucker factorization. Extensive experiments indicate the
+respective advantages of this two versions in different applications and also
+demonstrate the effectiveness of MoG GWLRTF compared with other competing
+methods.
+",1,0,0,0,0,0
+8467,Phase locking the spin precession in a storage ring,"  This letter reports the successful use of feedback from a spin polarization
+measurement to the revolution frequency of a 0.97 GeV/$c$ bunched and polarized
+deuteron beam in the Cooler Synchrotron (COSY) storage ring in order to control
+both the precession rate ($\approx 121$ kHz) and the phase of the horizontal
+polarization component. Real time synchronization with a radio frequency (rf)
+solenoid made possible the rotation of the polarization out of the horizontal
+plane, yielding a demonstration of the feedback method to manipulate the
+polarization. In particular, the rotation rate shows a sinusoidal function of
+the horizontal polarization phase (relative to the rf solenoid), which was
+controlled to within a one standard deviation range of $\sigma = 0.21$ rad. The
+minimum possible adjustment was 3.7 mHz out of a revolution frequency of 753
+kHz, which changes the precession rate by 26 mrad/s. Such a capability meets a
+requirement for the use of storage rings to look for an intrinsic electric
+dipole moment of charged particles.
+",0,1,0,0,0,0
+8468,Learning Vertex Representations for Bipartite Networks,"  Recent years have witnessed a widespread increase of interest in network
+representation learning (NRL). By far most research efforts have focused on NRL
+for homogeneous networks like social networks where vertices are of the same
+type, or heterogeneous networks like knowledge graphs where vertices (and/or
+edges) are of different types. There has been relatively little research
+dedicated to NRL for bipartite networks. Arguably, generic network embedding
+methods like node2vec and LINE can also be applied to learn vertex embeddings
+for bipartite networks by ignoring the vertex type information. However, these
+methods are suboptimal in doing so, since real-world bipartite networks concern
+the relationship between two types of entities, which usually exhibit different
+properties and patterns from other types of network data. For example,
+E-Commerce recommender systems need to capture the collaborative filtering
+patterns between customers and products, and search engines need to consider
+the matching signals between queries and webpages. This work addresses the
+research gap of learning vertex representations for bipartite networks. We
+present a new solution BiNE, short for Bipartite Network Embedding}, which
+accounts for two special properties of bipartite networks: long-tail
+distribution of vertex degrees and implicit connectivity relations between
+vertices of the same type. Technically speaking, we make three contributions:
+(1) We design a biased random walk generator to generate vertex sequences that
+preserve the long-tail distribution of vertices; (2) We propose a new
+optimization framework by simultaneously modeling the explicit relations (i.e.,
+observed links) and implicit relations (i.e., unobserved but transitive links);
+(3) We explore the theoretical foundations of BiNE to shed light on how it
+works, proving that BiNE can be interpreted as factorizing multiple matrices.
+",1,0,0,1,0,0
+8469,Liouville integrability of conservative peakons for a modified CH equation,"  The modified Camassa-Holm equation (also called FORQ) is one of numerous
+$cousins$ of the Camassa-Holm equation possessing non-smoth solitons
+($peakons$) as special solutions. The peakon sector of solutions is not
+uniquely defined: in one peakon sector (dissapative) the Sobolev $H^1$ norm is
+not preserved, in the other sector (conservative), introduced in [2], the time
+evolution of peakons leaves the $H^1$ norm invariant. In this Letter, it is
+shown that the conservative peakon equations of the modified Camassa-Holm can
+be given an appropriate Poisson structure relative to which the equations are
+Hamiltonian and, in fact, Liouville integrable. The latter is proved directly
+by exploiting the inverse spectral techniques, especially asymptotic analysis
+of solutions, developed elsewhere (in [3]).
+",0,1,1,0,0,0
+8470,State Representation Learning for Control: An Overview,"  Representation learning algorithms are designed to learn abstract features
+that characterize data. State representation learning (SRL) focuses on a
+particular kind of representation learning where learned features are in low
+dimension, evolve through time, and are influenced by actions of an agent. The
+representation is learned to capture the variation in the environment generated
+by the agent's actions; this kind of representation is particularly suitable
+for robotics and control scenarios. In particular, the low dimension
+characteristic of the representation helps to overcome the curse of
+dimensionality, provides easier interpretation and utilization by humans and
+can help improve performance and speed in policy learning algorithms such as
+reinforcement learning.
+This survey aims at covering the state-of-the-art on state representation
+learning in the most recent years. It reviews different SRL methods that
+involve interaction with the environment, their implementations and their
+applications in robotics control tasks (simulated or real). In particular, it
+highlights how generic learning objectives are differently exploited in the
+reviewed algorithms. Finally, it discusses evaluation methods to assess the
+representation learned and summarizes current and future lines of research.
+",0,0,0,1,0,0
+8471,Tracking Emerges by Colorizing Videos,"  We use large amounts of unlabeled video to learn models for visual tracking
+without manual human supervision. We leverage the natural temporal coherency of
+color to create a model that learns to colorize gray-scale videos by copying
+colors from a reference frame. Quantitative and qualitative experiments suggest
+that this task causes the model to automatically learn to track visual regions.
+Although the model is trained without any ground-truth labels, our method
+learns to track well enough to outperform the latest methods based on optical
+flow. Moreover, our results suggest that failures to track are correlated with
+failures to colorize, indicating that advancing video colorization may further
+improve self-supervised visual tracking.
+",1,0,0,0,0,0
+8472,Observation of topological valley transport of sound in sonic crystals,"  Valley pseudospin, labeling quantum states of energy extrema in momentum
+space, is attracting tremendous attention1-13 because of its potential in
+constructing new carrier of information. Compared with the non-topological bulk
+valley transport realized soon after predictions1-5, the topological valley
+transport in domain walls6-13 is extremely challenging owing to the
+inter-valley scattering inevitably induced by atomic scale imperfectness, until
+the recent electronic signature observed in bilayer graphene12,13. Here we
+report the first experimental observation of topological valley transport of
+sound in sonic crystals. The macroscopic nature of sonic crystals permits the
+flexible and accurate design of domain walls. In addition to a direct
+visualization of the valley-selective edge modes through spatial scanning of
+sound field, reflection immunity is observed in sharply curved interfaces. The
+topologically protected interface transport of sound, strikingly different from
+that in traditional sound waveguides14,15, may serve as the basis of designing
+devices with unconventional functions.
+",0,1,0,0,0,0
+8473,Equilibrium distributions and discrete Schur-constant models,"  This paper introduces Schur-constant equilibrium distribution models of
+dimension n for arithmetic non-negative random variables. Such a model is
+defined through the (several orders) equilibrium distributions of a univariate
+survival function. First, the bivariate case is considered and analyzed in
+depth, stressing the main characteristics of the Poisson case. The analysis is
+then extended to the multivariate case. Several properties are derived,
+including the implicit correlation and the distribution of the sum.
+",0,0,0,1,0,0
+8474,Tempered homogeneous spaces,"  Let $G$ be a semisimple real Lie group with finite center and $H$ a connected
+closed subgroup.
+We establish a geometric criterion which detects whether the representation
+of $G$ in $L^2(G/H)$ is tempered.
+",0,0,1,0,0,0
+8475,Consistency of Dirichlet Partitions,"  A Dirichlet $k$-partition of a domain $U \subseteq \mathbb{R}^d$ is a
+collection of $k$ pairwise disjoint open subsets such that the sum of their
+first Laplace-Dirichlet eigenvalues is minimal. A discrete version of Dirichlet
+partitions has been posed on graphs with applications in data analysis. Both
+versions admit variational formulations: solutions are characterized by
+minimizers of the Dirichlet energy of mappings from $U$ into a singular space
+$\Sigma_k \subseteq \mathbb{R}^k$. In this paper, we extend results of N.\
+García Trillos and D.\ Slepčev to show that there exist solutions of the
+continuum problem arising as limits to solutions of a sequence of discrete
+problems. Specifically, a sequence of points $\{x_i\}_{i \in \mathbb{N}}$ from
+$U$ is sampled i.i.d.\ with respect to a given probability measure $\nu$ on $U$
+and for all $n \in \mathbb{N}$, a geometric graph $G_n$ is constructed from the
+first $n$ points $x_1, x_2, \ldots, x_n$ and the pairwise distances between the
+points. With probability one with respect to the choice of points $\{x_i\}_{i
+\in \mathbb{N}}$, we show that as $n \to \infty$ the discrete Dirichlet
+energies for functions $G_n \to \Sigma_k$ $\Gamma$-converge to (a scalar
+multiple of) the continuum Dirichlet energy for functions $U \to \Sigma_k$ with
+respect to a metric coming from the theory of optimal transport. This, along
+with a compactness property for the aforementioned energies that we prove,
+implies the convergence of minimizers. When $\nu$ is the uniform distribution,
+our results also imply the statistical consistency statement that Dirichlet
+partitions of geometric graphs converge to partitions of the sampled space in
+the Hausdorff sense.
+",0,0,1,1,0,0
+8476,Experimental investigation of the wake behind a rotating sphere,"  The wake behind a sphere, rotating about an axis aligned with the streamwise
+direction, has been experimentally investigated in a low-velocity water tunnel
+using LIF visualizations and PIV measurements. The measurements focused on the
+evolution of the flow regimes that appear depending on two control parameters,
+namely the Reynolds number $Re$ and the dimensionless rotation or swirl rate
+$\Omega$, which is the ratio of the maximum azimuthal velocity of the body to
+the free stream velocity. In the present investigation, we cover the range of
+$Re$ smaller than 400 and $\Omega$ from 0 and 4. Different wakes regimes such
+as an axisymmetric flow, a low helical state and a high helical mode are
+represented in the ($Re$, $\Omega$) parameter plane.
+",0,1,0,0,0,0
+8477,Test results of a prototype device to calibrate the Large Size Telescope camera proposed for the Cherenkov Telescope Array,"  A Large Size air Cherenkov Telescope (LST) prototype, proposed for the
+Cherenkov Telescope Array (CTA), is under construction at the Canary Island of
+La Palma (Spain) this year. The LST camera, which comprises an array of about
+500 photomultipliers (PMTs), requires a precise and regular calibration over a
+large dynamic range, up to $10^3$ photo-electrons (pe's), for each PMT. We
+present a system built to provide the optical calibration of the camera
+consisting of a pulsed laser (355 nm wavelength, 400 ps pulse width), a set of
+filters to guarantee a large dynamic range of photons on the sensors, and a
+diffusing sphere to uniformly spread the laser light, with flat fielding within
+3%, over the camera focal plane 28 m away. The prototype of the system
+developed at INFN is hermetically closed and filled with dry air to make the
+system completely isolated from the external environment. In the paper we
+present the results of the tests for the evaluation of the photon density at
+the camera plane, the system isolation from the environment, and the shape of
+the signal as detected by the PMTs. The description of the communication of the
+system with the rest of detector is also given.
+",0,1,0,0,0,0
+8478,Extended superalgebras from twistor and Killing spinors,"  The basic first-order differential operators of spin geometry that are Dirac
+operator and twistor operator are considered. Special types of spinors defined
+from these operators such as twistor spinors and Killing spinors are discussed.
+Symmetry operators of massless and massive Dirac equations are introduced and
+relevant symmetry operators of twistor spinors and Killing spinors are
+constructed from Killing-Yano (KY) and conformal Killing-Yano (CKY) forms in
+constant curvature and Einstein manifolds. The squaring map of spinors gives KY
+and CKY forms for Killing and twistor spinors respectively. They constitute a
+graded Lie algebra structure in some special cases. By using the graded Lie
+algebra structure of KY and CKY forms, extended Killing and conformal
+superalgebras are constructed in constant curvature and Einstein manifolds.
+",0,0,1,0,0,0
+8479,The Physics of Eccentric Binary Black Hole Mergers. A Numerical Relativity Perspective,"  Gravitational wave observations of eccentric binary black hole mergers will
+provide unequivocal evidence for the formation of these systems through
+dynamical assembly in dense stellar environments. The study of these
+astrophysically motivated sources is timely in view of electromagnetic
+observations, consistent with the existence of stellar mass black holes in the
+globular cluster M22 and in the Galactic center, and the proven detection
+capabilities of ground-based gravitational wave detectors. In order to get
+insights into the physics of these objects in the dynamical, strong-field
+gravity regime, we present a catalog of 89 numerical relativity waveforms that
+describe binary systems of non-spinning black holes with mass-ratios $1\leq q
+\leq 10$, and initial eccentricities as high as $e_0=0.18$ fifteen cycles
+before merger. We use this catalog to provide landmark results regarding the
+loss of energy through gravitational radiation, both for quadrupole and
+higher-order waveform multipoles, and the astrophysical properties, final mass
+and spin, of the post-merger black hole as a function of eccentricity and
+mass-ratio. We discuss the implications of these results for gravitational wave
+source modeling, and the design of algorithms to search for and identify the
+complex signatures of these events in realistic detection scenarios.
+",1,0,0,0,0,0
+8480,Joint Computation and Communication Cooperation for Mobile Edge Computing,"  This paper proposes a novel joint computation and communication cooperation
+approach in mobile edge computing (MEC) systems, which enables user cooperation
+in both computation and communication for improving the MEC performance. In
+particular, we consider a basic three-node MEC system that consists of a user
+node, a helper node, and an access point (AP) node attached with an MEC server.
+We focus on the user's latency-constrained computation over a finite block, and
+develop a four-slot protocol for implementing the joint computation and
+communication cooperation. Under this setup, we jointly optimize the
+computation and communication resource allocation at both the user and the
+helper, so as to minimize their total energy consumption subject to the user's
+computation latency constraint. We provide the optimal solution to this
+problem. Numerical results show that the proposed joint cooperation approach
+significantly improves the computation capacity and the energy efficiency at
+the user and helper nodes, as compared to other benchmark schemes without such
+a joint design.
+",1,0,0,0,0,0
+8481,Propagating wave correlations in complex systems,"  We describe a novel approach for computing wave correlation functions inside
+finite spatial domains driven by complex and statistical sources. By exploiting
+semiclassical approximations, we provide explicit algorithms to calculate the
+local mean of these correlation functions in terms of the underlying classical
+dynamics. By defining appropriate ensemble averages, we show that fluctuations
+about the mean can be characterised in terms of classical correlations. We give
+in particular an explicit expression relating fluctuations of diagonal
+contributions to those of the full wave correlation function. The methods have
+a wide range of applications both in quantum mechanics and for classical wave
+problems such as in vibro-acoustics and electromagnetism. We apply the methods
+here to simple quantum systems, so-called quantum maps, which model the
+behaviour of generic problems on Poincaré sections. Although low-dimensional,
+these models exhibit a chaotic classical limit and share common characteristics
+with wave propagation in complex structures.
+",0,1,0,0,0,0
+8482,Cost-Effective Cache Deployment in Mobile Heterogeneous Networks,"  This paper investigates one of the fundamental issues in cache-enabled
+heterogeneous networks (HetNets): how many cache instances should be deployed
+at different base stations, in order to provide guaranteed service in a
+cost-effective manner. Specifically, we consider two-tier HetNets with
+hierarchical caching, where the most popular files are cached at small cell
+base stations (SBSs) while the less popular ones are cached at macro base
+stations (MBSs). For a given network cache deployment budget, the cache sizes
+for MBSs and SBSs are optimized to maximize network capacity while satisfying
+the file transmission rate requirements. As cache sizes of MBSs and SBSs affect
+the traffic load distribution, inter-tier traffic steering is also employed for
+load balancing. Based on stochastic geometry analysis, the optimal cache sizes
+for MBSs and SBSs are obtained, which are threshold-based with respect to cache
+budget in the networks constrained by SBS backhauls. Simulation results are
+provided to evaluate the proposed schemes and demonstrate the applications in
+cost-effective network deployment.
+",1,0,0,0,0,0
+8483,Experimental demonstration of a Josephson magnetic memory cell with a programmable π-junction,"  We experimentally demonstrate the operation of a Josephson magnetic random
+access memory unit cell, built with a Ni_80Fe_20/Cu/Ni pseudo spin-valve
+Josephson junction with Nb electrodes and an integrated readout SQUID in a
+fully planarized Nb fabrication process. We show that the parallel and
+anti-parallel memory states of the spin-valve can be mapped onto a junction
+equilibrium phase of either zero or pi by appropriate choice of the ferromagnet
+thicknesses, and that the magnetic Josephson junction can be written to either
+a zero-junction or pi-junction state by application of write fields of
+approximately 5 mT. This work represents a first step towards a scalable,
+dense, and power-efficient cryogenic memory for superconducting
+high-performance digital computing.
+",0,1,0,0,0,0
+8484,"On the Dynamics of Supermassive Black Holes in Gas-Rich, Star-Forming Galaxies: the Case for Nuclear Star Cluster Coevolution","  We introduce a new model for the formation and evolution of supermassive
+black holes (SMBHs) in the RAMSES code using sink particles, improving over
+previous work the treatment of gas accretion and dynamical evolution. This new
+model is tested against a suite of high-resolution simulations of an isolated,
+gas-rich, cooling halo. We study the effect of various feedback models on the
+SMBH growth and its dynamics within the galaxy.
+In runs without any feedback, the SMBH is trapped within a massive bulge and
+is therefore able to grow quickly, but only if the seed mass is chosen larger
+than the minimum Jeans mass resolved by the simulation. We demonstrate that, in
+the absence of supernovae (SN) feedback, the maximum SMBH mass is reached when
+Active Galactic Nucleus (AGN) heating balances gas cooling in the nuclear
+region.
+When our efficient SN feedback is included, it completely prevents bulge
+formation, so that massive gas clumps can perturb the SMBH orbit, and reduce
+the accretion rate significantly. To overcome this issue, we propose an
+observationally motivated model for the joint evolution of the SMBH and a
+parent nuclear star cluster (NSC), which allows the SMBH to remain in the
+nuclear region, grow fast and resist external perturbations. In this scenario,
+however, SN feedback controls the gas supply and the maximum SMBH mass now
+depends on the balance between AGN heating and gravity. We conclude that
+SMBH/NSC co-evolution is crucial for the growth of SMBH in high-z galaxies, the
+progenitors of massive elliptical today.
+",0,1,0,0,0,0
+8485,A Concave Optimization Algorithm for Matching Partially Overlapping Point Sets,"  Point matching refers to the process of finding spatial transformation and
+correspondences between two sets of points. In this paper, we focus on the case
+that there is only partial overlap between two point sets. Following the
+approach of the robust point matching method, we model point matching as a
+mixed linear assignment-least square problem and show that after eliminating
+the transformation variable, the resulting problem of minimization with respect
+to point correspondence is a concave optimization problem. Furthermore, this
+problem has the property that the objective function can be converted into a
+form with few nonlinear terms via a linear transformation. Based on these
+properties, we employ the branch-and-bound (BnB) algorithm to optimize the
+resulting problem where the dimension of the search space is small. To further
+improve efficiency of the BnB algorithm where computation of the lower bound is
+the bottleneck, we propose a new lower bounding scheme which has a
+k-cardinality linear assignment formulation and can be efficiently solved.
+Experimental results show that the proposed algorithm outperforms
+state-of-the-art methods in terms of robustness to disturbances and point
+matching accuracy.
+",1,0,0,0,0,0
+8486,Adaptive Mesh Refinement in Analog Mesh Computers,"  The call for efficient computer architectures has introduced a variety of
+application-specific compute engines to the heterogeneous computing landscape.
+One particular engine, the analog mesh computer, has been well received due to
+its ability to efficiently solve partial differential equations by eliminating
+the iterative stages common to numerical solvers. This article introduces an
+implementation of refinement for analog mesh computers.
+",1,0,0,0,0,0
+8487,On the local view of atmospheric available potential energy,"  The possibility of constructing Lorenz's concept of available potential
+energy (APE) from a local principle has been known for some time, but has
+received very little attention so far. Yet, the local APE framework offers the
+advantage of providing a positive definite local form of potential energy,
+which like kinetic energy can be transported, converted, and created/dissipated
+locally. In contrast to Lorenz's definition, which relies on the exact from of
+potential energy, the local APE theory uses the particular form of potential
+energy appropriate to the approximations considered. In this paper, this idea
+is illustrated for the dry hydrostatic primitive equations, whose relevant form
+of potential energy is the specific enthalpy. The local APE density is
+non-quadratic in general, but can nevertheless be partitioned exactly into mean
+and eddy components regardless of the Reynolds averaging operator used.
+This paper introduces a new form of the local APE that is easily computable
+from atmospheric datasets. The advantages of using the local APE over the
+classical Lorenz APE are highlighted. The paper also presents the first
+calculation of the three-dimensional local APE in observation-based atmospheric
+data. Finally, it illustrates how the eddy and mean components of the local APE
+can be used to study regional and temporal variability in the large-scale
+circulation. It is revealed that advection from high latitudes is necessary to
+supply APE into the storm track regions, and that Greenland and Ross Sea, which
+have suffered from rapid land ice and sea ice loss in recent decades, are
+particularly susceptible to APE variability.
+",0,1,0,0,0,0
+8488,GdRh$_2$Si$_2$: An exemplary tetragonal system for antiferromagnetic order with weak in-plane anisotropy,"  The anisotropy of magnetic properties commonly is introduced in textbooks
+using the case of an antiferromagnetic system with Ising type anisotropy. This
+model presents huge anisotropic magnetization and a pronounced metamagnetic
+transition and is well-known and well-documented both, in experiments and
+theory. In contrast, the case of an antiferromagnetic $X$-$Y$ system with weak
+in-plane anisotropy is only poorly documented. We studied the anisotropic
+magnetization of the compound GdRh$_2$Si$_2$ and found that it is a perfect
+model system for such a weak-anisotropy setting because the Gd$^{3+}$ ions in
+GdRh$_2$Si$_2$ have a pure spin moment of S=7/2 which orders in a simple AFM
+structure with ${\bf Q} = (001)$. We observed experimentally in $M(B)$ a
+continuous spin-flop transition and domain effects for field applied along the
+$[100]$- and the $[110]$-direction, respectively. We applied a mean field model
+for the free energy to describe our data and combine it with an Ising chain
+model to account for domain effects. Our calculations reproduce the
+experimental data very well. In addition, we performed magnetic X-ray
+scattering and X-ray magnetic circular dichroism measurements, which confirm
+the AFM propagation vector to be ${\bf Q} = (001)$ and indicate the absence of
+polarization on the rhodium atoms.
+",0,1,0,0,0,0
+8489,A Kullback-Leibler Divergence-based Distributionally Robust Optimization Model for Heat Pump Day-ahead Operational Schedule in Distribution Networks,"  For its high coefficient of performance and zero local emissions, the heat
+pump (HP) has recently become popular in North Europe and China. However, the
+integration of HPs may aggravate the daily peak-valley gap in distribution
+networks significantly.
+",1,0,0,0,0,0
+8490,Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning,"  Developing a safe and efficient collision avoidance policy for multiple
+robots is challenging in the decentralized scenarios where each robot generate
+its paths without observing other robots' states and intents. While other
+distributed multi-robot collision avoidance systems exist, they often require
+extracting agent-level features to plan a local collision-free action, which
+can be computationally prohibitive and not robust. More importantly, in
+practice the performance of these methods are much lower than their centralized
+counterparts.
+We present a decentralized sensor-level collision avoidance policy for
+multi-robot systems, which directly maps raw sensor measurements to an agent's
+steering commands in terms of movement velocity. As a first step toward
+reducing the performance gap between decentralized and centralized methods, we
+present a multi-scenario multi-stage training framework to find an optimal
+policy which is trained over a large number of robots on rich, complex
+environments simultaneously using a policy gradient based reinforcement
+learning algorithm. We validate the learned sensor-level collision avoidance
+policy in a variety of simulated scenarios with thorough performance
+evaluations and show that the final learned policy is able to find time
+efficient, collision-free paths for a large-scale robot system. We also
+demonstrate that the learned policy can be well generalized to new scenarios
+that do not appear in the entire training period, including navigating a
+heterogeneous group of robots and a large-scale scenario with 100 robots.
+Videos are available at this https URL
+",1,0,0,0,0,0
+8491,Metropolis-Hastings Algorithms for Estimating Betweenness Centrality in Large Networks,"  Betweenness centrality is an important index widely used in different domains
+such as social networks, traffic networks and the world wide web. However, even
+for mid-size networks that have only a few hundreds thousands vertices, it is
+computationally expensive to compute exact betweenness scores. Therefore in
+recent years, several approximate algorithms have been developed. In this
+paper, first given a network $G$ and a vertex $r \in V(G)$, we propose a
+Metropolis-Hastings MCMC algorithm that samples from the space $V(G)$ and
+estimates betweenness score of $r$. The stationary distribution of our MCMC
+sampler is the optimal sampling proposed for betweenness centrality estimation.
+We show that our MCMC sampler provides an $(\epsilon,\delta)$-approximation,
+where the number of required samples depends on the position of $r$ in $G$ and
+in many cases, it is a constant. Then, given a network $G$ and a set $R \subset
+V(G)$, we present a Metropolis-Hastings MCMC sampler that samples from the
+joint space $R$ and $V(G)$ and estimates relative betweenness scores of the
+vertices in $R$. We show that for any pair $r_i, r_j \in R$, the ratio of the
+expected values of the estimated relative betweenness scores of $r_i$ and $r_j$
+respect to each other is equal to the ratio of their betweenness scores. We
+also show that our joint-space MCMC sampler provides an
+$(\epsilon,\delta)$-approximation of the relative betweenness score of $r_i$
+respect to $r_j$, where the number of required samples depends on the position
+of $r_j$ in $G$ and in many cases, it is a constant.
+",1,0,0,0,0,0
+8492,Learning Flexible and Reusable Locomotion Primitives for a Microrobot,"  The design of gaits for robot locomotion can be a daunting process which
+requires significant expert knowledge and engineering. This process is even
+more challenging for robots that do not have an accurate physical model, such
+as compliant or micro-scale robots. Data-driven gait optimization provides an
+automated alternative to analytical gait design. In this paper, we propose a
+novel approach to efficiently learn a wide range of locomotion tasks with
+walking robots. This approach formalizes locomotion as a contextual policy
+search task to collect data, and subsequently uses that data to learn
+multi-objective locomotion primitives that can be used for planning. As a
+proof-of-concept we consider a simulated hexapod modeled after a recently
+developed microrobot, and we thoroughly evaluate the performance of this
+microrobot on different tasks and gaits. Our results validate the proposed
+controller and learning scheme on single and multi-objective locomotion tasks.
+Moreover, the experimental simulations show that without any prior knowledge
+about the robot used (e.g., dynamics model), our approach is capable of
+learning locomotion primitives within 250 trials and subsequently using them to
+successfully navigate through a maze.
+",1,0,0,1,0,0
+8493,Statistical comparison of (brain) networks,"  The study of random networks in a neuroscientific context has developed
+extensively over the last couple of decades. By contrast, techniques for the
+statistical analysis of these networks are less developed. In this paper, we
+focus on the statistical comparison of brain networks in a nonparametric
+framework and discuss the associated detection and identification problems. We
+tested network differences between groups with an analysis of variance (ANOVA)
+test we developed specifically for networks. We also propose and analyse the
+behaviour of a new statistical procedure designed to identify different
+subnetworks. As an example, we show the application of this tool in
+resting-state fMRI data obtained from the Human Connectome Project. Finally, we
+discuss the potential bias in neuroimaging findings that is generated by some
+behavioural and brain structure variables. Our method can also be applied to
+other kind of networks such as protein interaction networks, gene networks or
+social networks.
+",0,1,0,1,0,0
+8494,Sensitivity analysis using perturbed-law based indices for quantiles and application to an industrial case,"  In this paper, we present perturbed law-based sensitivity indices and how to
+adapt them for quantile-oriented sensitivity analysis. We exhibit a simple way
+to compute these indices in practice using an importance sampling estimator for
+quantiles. Some useful asymptotic results about this estimator are also
+provided. Finally, we apply this method to the study of a numerical model which
+simulates the behaviour of a component in a hydraulic system in case of severe
+transient solicitations. The sensitivity analysis is used to assess the impact
+of epistemic uncertainties about some physical parameters on the output of the
+model.
+",0,0,1,1,0,0
+8495,"The Robustness of LWPP and WPP, with an Application to Graph Reconstruction","  We show that the counting class LWPP [FFK94] remains unchanged even if one
+allows a polynomial number of gap values rather than one. On the other hand, we
+show that it is impossible to improve this from polynomially many gap values to
+a superpolynomial number of gap values by relativizable proof techniques.
+The first of these results implies that the Legitimate Deck Problem (from the
+study of graph reconstruction) is in LWPP (and thus low for PP, i.e., $\rm
+PP^{\mbox{Legitimate Deck}} = PP$) if the weakened version of the
+Reconstruction Conjecture holds in which the number of nonisomorphic preimages
+is assumed merely to be polynomially bounded. This strengthens the 1992 result
+of Köbler, Schöning, and Torán [KST92] that the Legitimate Deck
+Problem is in LWPP if the Reconstruction Conjecture holds, and provides
+strengthened evidence that the Legitimate Deck Problem is not NP-hard.
+We additionally show on the one hand that our main LWPP robustness result
+also holds for WPP, and also holds even when one allows both the rejection- and
+acceptance- gap-value targets to simultaneously be polynomial-sized lists; yet
+on the other hand, we show that for the #P-based analog of LWPP the behavior
+much differs in that, in some relativized worlds, even two target values
+already yield a richer class than one value does. Despite that nonrobustness
+result for a #P-based class, we show that the #P-based ""exact counting"" class
+$\rm C_{=}P$ remains unchanged even if one allows a polynomial number of target
+values for the number of accepting paths of the machine.
+",1,0,0,0,0,0
+8496,Time- and spatially-resolved magnetization dynamics driven by spin-orbit torques,"  Current-induced spin-orbit torques (SOTs) represent one of the most effective
+ways to manipulate the magnetization in spintronic devices. The orthogonal
+torque-magnetization geometry, the strong damping, and the large domain wall
+velocities inherent to materials with strong spin-orbit coupling make SOTs
+especially appealing for fast switching applications in nonvolatile memory and
+logic units. So far, however, the timescale and evolution of the magnetization
+during the switching process have remained undetected. Here, we report the
+direct observation of SOT-driven magnetization dynamics in Pt/Co/AlO$_x$ dots
+during current pulse injection. Time-resolved x-ray images with 25 nm spatial
+and 100 ps temporal resolution reveal that switching is achieved within the
+duration of a sub-ns current pulse by the fast nucleation of an inverted domain
+at the edge of the dot and propagation of a tilted domain wall across the dot.
+The nucleation point is deterministic and alternates between the four dot
+quadrants depending on the sign of the magnetization, current, and external
+field. Our measurements reveal how the magnetic symmetry is broken by the
+concerted action of both damping-like and field-like SOT and show that
+reproducible switching events can be obtained for over $10^{12}$ reversal
+cycles.
+",0,1,0,0,0,0
+8497,Giant Planets Can Act As Stabilizing Agents on Debris Disks,"  We have explored the evolution of a cold debris disk under the gravitational
+influence of dwarf planet sized objects (DPs), both in the presence and absence
+of an interior giant planet. Through detailed long-term numerical simulations,
+we demonstrate that, when the giant planet is not present, DPs can stir the
+eccentricities and inclinations of disk particles, in linear proportion to the
+total mass of the DPs; on the other hand, when the giant planet is included in
+the simulations, the stirring is approximately proportional to the mass
+squared. This creates two regimes: below a disk mass threshold (defined by the
+total mass of DPs), the giant planet acts as a stabilizing agent of the orbits
+of cometary nucleii, diminishing the effect of the scatterers; above the
+threshold, the giant contributes to the dispersion of the particles.
+",0,1,0,0,0,0
+8498,Definition of geometric space around analytic fractal trees using derivative coordinate funtions,"  The concept of derivative coordinate functions proved useful in the
+formulation of analytic fractal functions to represent smooth symmetric binary
+fractal trees [1]. In this paper we introduce a new geometry that defines the
+fractal space around these fractal trees. We present the canonical and
+degenerate form of this fractal space and extend the fractal geometrical space
+to R3 specifically and Rn by a recurrence relation. We also discuss the usage
+of such fractal geometry.
+",1,0,0,0,0,0
+8499,To Pool or Not To Pool? Revisiting an Old Pattern,"  We revisit the well-known object-pool design pattern in Java. In the last
+decade, the pattern has attracted a lot of criticism regarding its validity
+when used for light-weight objects that are only meant to hold memory rather
+than any other resources (database connections, sockets etc.) and in fact,
+common opinion holds that is an anti-pattern in such cases. Nevertheless, we
+show through several experiments in different systems that the use of this
+pattern for extremely short-lived and light-weight memory objects can in fact
+significantly reduce the response time of high-performance multi-threaded
+applications, especially in memory-constrained environments. In certain
+multi-threaded applications where high performance is a requirement and/or
+memory constraints exist, we recommend therefore that the object pool pattern
+be given consideration and tested for possible run-time as well as memory
+footprint improvements.
+",1,0,0,0,0,0
+8500,Quenching of supermassive black hole growth around the apparent maximum mass,"  Recent quasar surveys have revealed that supermassive black holes (SMBHs)
+rarely exceed a mass of $M_{\rm BH} \sim {\rm a~few}\times10^{10}~M_{\odot}$
+during the entire cosmic history. It has been argued that quenching of the BH
+growth is caused by a transition of a nuclear accretion disk into an advection
+dominated accretion flow, with which strong outflows and/or jets are likely to
+be associated. We investigate a relation between the maximum mass of SMBHs and
+the radio-loudness of quasars with a well-defined sample of $\sim 10^5$ quasars
+at a redshift range of $0<z<2$, obtained from the Sloan Digital Sky Surveys DR7
+catalog. We find that the number fraction of the radio-loud (RL) quasars
+increases above a threshold of $M_{\rm BH} \simeq 10^{9.5}~M_{\odot}$,
+independent of their redshifts. Moreover, the number fraction of RL quasars
+with lower Eddington ratios (out of the whole RL quasars), indicating lower
+accretion rates, increases above the critical BH mass. These observational
+trends can be natural consequences of the proposed scenario of suppressing BH
+growth around the apparent maximum mass of $\sim 10^{10}~M_{\odot}$. The
+ongoing VLA Sky Survey in radio will allow us to estimate of the exact number
+fraction of RL quasars more precisely, which gives further insights to
+understand quenching processes for BH growth.
+",0,1,0,0,0,0
+8501,Self-Organizing Maps as a Storage and Transfer Mechanism in Reinforcement Learning,"  The idea of reusing information from previously learned tasks (source tasks)
+for the learning of new tasks (target tasks) has the potential to significantly
+improve the sample efficiency reinforcement learning agents. In this work, we
+describe an approach to concisely store and represent learned task knowledge,
+and reuse it by allowing it to guide the exploration of an agent while it
+learns new tasks. In order to do so, we use a measure of similarity that is
+defined directly in the space of parameterized representations of the value
+functions. This similarity measure is also used as a basis for a variant of the
+growing self-organizing map algorithm, which is simultaneously used to enable
+the storage of previously acquired task knowledge in an adaptive and scalable
+manner.We empirically validate our approach in a simulated navigation
+environment and discuss possible extensions to this approach along with
+potential applications where it could be particularly useful.
+",0,0,0,1,0,0
+8502,Distributed model predictive control for continuous-time nonlinear systems based on suboptimal ADMM,"  The paper presents a distributed model predictive control (DMPC) scheme for
+continuous-time nonlinear systems based on the alternating direction method of
+multipliers (ADMM). A stopping criterion in the ADMM algorithm limits the
+iterations and therefore the required communication effort during the
+distributed MPC solution at the expense of a suboptimal solution. Stability
+results are presented for the suboptimal DMPC scheme under two different ADMM
+convergence assumptions. In particular, it is shown that the required
+iterations in each ADMM step are bounded, which is also confirmed in simulation
+studies.
+",0,0,1,0,0,0
+8503,Implementing focal-plane phase masks optimized for real telescope apertures with SLM-based digital adaptive coronagraphy,"  Direct imaging of exoplanets or circumstellar disk material requires extreme
+contrast at the 10-6 to 10-12 levels at < 100 mas angular separation from the
+star. Focal-plane mask (FPM) coronagraphic imaging has played a key role in
+this field, taking advantage of progress in Adaptive Optics on ground-based 8+m
+class telescopes. However, large telescope entrance pupils usually consist of
+complex, sometimes segmented, non-ideal apertures, which include a central
+obstruction for the secondary mirror and its support structure. In practice,
+this negatively impacts wavefront quality and coronagraphic performance, in
+terms of achievable contrast and inner working angle. Recent theoretical works
+on structured darkness have shown that solutions for FPM phase profiles,
+optimized for non-ideal apertures, can be numerically derived. Here we present
+and discuss a first experimental validation of this concept, using reflective
+liquid crystal spatial light modulators as adaptive FPM coronagraphs.
+",0,1,0,0,0,0
+8504,Verifying Asynchronous Interactions via Communicating Session Automata,"  The relationship between communicating automata and session types is the
+cornerstone of many diverse theories and tools, including type checking, code
+generation, and runtime verification. A serious limitation of session types is
+that, while endpoint programs interact asynchronously, the underlying property
+which guarantees safety of session types is too synchronous: it requires a
+one-to-one synchronisation between send and receive actions. This paper
+proposes a sound procedure to verify properties of communicating session
+automata (CSA), i.e., communicating automata that correspond to multiparty
+session types. We introduce a new asynchronous compatibility property for CSA,
+called k-multiparty compatibility (k-MC), which is a strict superset of the
+synchronous multiparty compatibility proposed in the literature. It is
+decomposed into two bounded properties: (i) a condition called k-safety which
+guarantees that, within the bound, all sent messages can be received and each
+automaton can make a move; and (ii) a condition called k-exhaustivity which
+guarantees that all k-reachable send actions can be fired within the bound. We
+show that k-exhaustive systems soundly and completely characterise systems
+where each automaton behaves uniformly for any bound greater or equal to k. We
+show that checking k-MC is PSPACE-complete, but can be done efficiently over
+large systems by using partial order reduction techniques. We demonstrate that
+several examples from the literature are k-MC, but not synchronous compatible.
+",1,0,0,0,0,0
+8505,Phase induced transparency mediated structured beam generation in a closed-loop tripod configuration,"  We present a phase induced transparency based scheme to generate structured
+beam patterns in a closed four level atomic system. We employ phase structured
+probe beam and a transverse magnetic field (TMF) to create phase dependent
+medium susceptibility. We show that such phase dependent modulation of
+absorption holds the key to formation of a structured beam. We use a full
+density matrix formalism to explain the experiments of Radwell et al. [Phys.
+Rev. Lett. 114,123603 (2015)] at weak probe limits. Our numerical results on
+beam propagation confirms that the phase information present in the absorption
+profile gets encoded on the spatial probe envelope which creates petal-like
+structures even in the strong field limit. The contrast of the formed
+structured beam can be enhanced by changing the strength of TMF as well as of
+the probe intensity. In weak field limits an absorption profile is solely
+responsible for creating a structured beam, whereas in the strong probe regime,
+both dispersion and absorption profiles facilitate the generation of high
+contrast structured beam. Furthermore we find the rotation of structured beams
+owing to strong field induced nonlinear magneto-optical rotation (NMOR).
+",0,1,0,0,0,0
+8506,Additional cases of positive twisted torus knots,"  A twisted torus knot is a knot obtained from a torus knot by twisting
+adjacent strands by full twists. The twisted torus knots lie in $F$, the genus
+2 Heegaard surface for $S^3$. Primitive/primitive and primitive/Seifert knots
+lie in $F$ in a particular way. Dean gives sufficient conditions for the
+parameters of the twisted torus knots to ensure they are primitive/primitive or
+primitive/Seifert. Using Dean's conditions, Doleshal shows that there are
+infinitely many twisted torus knots that are fibered and that there are twisted
+torus knots with distinct primitive/Seifert representatives with the same slope
+in $F$. In this paper, we extend Doleshal's results to show there is a four
+parameter family of positive twisted torus knots. Additionally, we provide new
+examples of twisted torus knots with distinct representatives with the same
+surface slope in $F$.
+",0,0,1,0,0,0
+8507,Action-conditional Sequence Modeling for Recommendation,"  In many online applications interactions between a user and a web-service are
+organized in a sequential way, e.g., user browsing an e-commerce website. In
+this setting, recommendation system acts throughout user navigation by showing
+items. Previous works have addressed this recommendation setup through the task
+of predicting the next item user will interact with. In particular, Recurrent
+Neural Networks (RNNs) has been shown to achieve substantial improvements over
+collaborative filtering baselines. In this paper, we consider interactions
+triggered by the recommendations of deployed recommender system in addition to
+browsing behavior. Indeed, it is reported that in online services interactions
+with recommendations represent up to 30\% of total interactions. Moreover, in
+practice, recommender system can greatly influence user behavior by promoting
+specific items. In this paper, we extend the RNN modeling framework by taking
+into account user interaction with recommended items. We propose and evaluate
+RNN architectures that consist of the recommendation action module and the
+state-action fusion module. Using real-world large-scale datasets we
+demonstrate improved performance on the next item prediction task compared to
+the baselines.
+",0,0,0,1,0,0
+8508,Limits of End-to-End Learning,"  End-to-end learning refers to training a possibly complex learning system by
+applying gradient-based learning to the system as a whole. End-to-end learning
+system is specifically designed so that all modules are differentiable. In
+effect, not only a central learning machine, but also all ""peripheral"" modules
+like representation learning and memory formation are covered by a holistic
+learning process. The power of end-to-end learning has been demonstrated on
+many tasks, like playing a whole array of Atari video games with a single
+architecture. While pushing for solutions to more challenging tasks, network
+architectures keep growing more and more complex.
+In this paper we ask the question whether and to what extent end-to-end
+learning is a future-proof technique in the sense of scaling to complex and
+diverse data processing architectures. We point out potential inefficiencies,
+and we argue in particular that end-to-end learning does not make optimal use
+of the modular design of present neural networks. Our surprisingly simple
+experiments demonstrate these inefficiencies, up to the complete breakdown of
+learning.
+",1,0,0,1,0,0
+8509,Quantum Graphs: $ \mathcal{PT}$-symmetry and reflection symmetry of the spectrum,"  Not necessarily self-adjoint quantum graphs -- differential operators on
+metric graphs -- are considered. Assume in addition that the underlying metric
+graph possesses an automorphism (symmetry) $ \mathcal P $. If the differential
+operator is $ \mathcal P \mathcal T$-symmetric, then its spectrum has
+reflection symmetry with respect to the real line. Our goal is to understand
+whether the opposite statement holds, namely whether the reflection symmetry of
+the spectrum of a quantum graph implies that the underlying metric graph
+possesses a non-trivial automorphism and the differential operator is $
+\mathcal P \mathcal T$-symmetric. We give partial answer to this question by
+considering equilateral star-graphs. The corresponding Laplace operator with
+Robin vertex conditions possesses reflection-symmetric spectrum if and only if
+the operator is $ \mathcal P \mathcal T$-symmetric with $ \mathcal P $ being an
+automorphism of the metric graph.
+",0,0,1,0,0,0
+8510,On Diophantine equations involving sums of Fibonacci numbers and powers of $2$,"  In this paper, we completely solve the Diophantine equations $F_{n_1} +
+F_{n_2} = 2^{a_1} + 2^{a_2} + 2^{a_3}$ and $ F_{m_1} + F_{m_2} + F_{m_3}
+=2^{t_1} + 2^{t_2} $, where $F_k$ denotes the $k$-th Fibonacci number. In
+particular, we prove that $\max \{n_1, n_2, a_1, a_2, a_3 \}\leq 18$ and $\max
+\{ m_1, m_2, m_3, t_1, t_2 \}\leq 16$.
+",0,0,1,0,0,0
+8511,T-duality in rational homotopy theory via $L_\infty$-algebras,"  We combine Sullivan models from rational homotopy theory with Stasheff's
+$L_\infty$-algebras to describe a duality in string theory. Namely, what in
+string theory is known as topological T-duality between $K^0$-cocycles in type
+IIA string theory and $K^1$-cocycles in type IIB string theory, or as Hori's
+formula, can be recognized as a Fourier-Mukai transform between twisted
+cohomologies when looked through the lenses of rational homotopy theory. We
+show this as an example of topological T-duality in rational homotopy theory,
+which in turn can be completely formulated in terms of morphisms of
+$L_\infty$-algebras.
+",0,0,1,0,0,0
+8512,Phasebook and Friends: Leveraging Discrete Representations for Source Separation,"  Deep learning based speech enhancement and source separation systems have
+recently reached unprecedented levels of quality, to the point that performance
+is reaching a new ceiling. Most systems rely on estimating the magnitude of a
+target source by estimating a real-valued mask to be applied to a
+time-frequency representation of the mixture signal. A limiting factor in such
+approaches is a lack of phase estimation: the phase of the mixture is most
+often used when reconstructing the estimated time-domain signal. Here, we
+propose `MagBook', `phasebook', and `Combook', three new types of layers based
+on discrete representations that can be used to estimate complex time-frequency
+masks. MagBook layers extend classical sigmoidal units and a recently
+introduced convex softmax activation for mask-based magnitude estimation.
+Phasebook layers use a similar structure to give an estimate of the phase mask
+without suffering from phase wrapping issues. Combook layers are an alternative
+to the MagBook-Phasebook combination that directly estimate complex masks. We
+present various training and inference regimes involving these representations,
+and explain in particular how to include them in an end-to-end learning
+framework. We also present an oracle study to assess upper bounds on
+performance for various types of masks using discrete phase representations. We
+evaluate the proposed methods on the wsj0-2mix dataset, a well-studied corpus
+for single-channel speaker-independent speaker separation, matching the
+performance of state-of-the-art mask-based approaches without requiring
+additional phase reconstruction steps.
+",1,0,0,0,0,0
+8513,RankDCG: Rank-Ordering Evaluation Measure,"  Ranking is used for a wide array of problems, most notably information
+retrieval (search). There are a number of popular approaches to the evaluation
+of ranking such as Kendall's $\tau$, Average Precision, and nDCG. When dealing
+with problems such as user ranking or recommendation systems, all these
+measures suffer from various problems, including an inability to deal with
+elements of the same rank, inconsistent and ambiguous lower bound scores, and
+an inappropriate cost function. We propose a new measure, rankDCG, that
+addresses these problems. This is a modification of the popular nDCG algorithm.
+We provide a number of criteria for any effective ranking algorithm and show
+that only rankDCG satisfies all of them. Results are presented on constructed
+and real data sets. We release a publicly available rankDCG evaluation package.
+",1,0,0,0,0,0
+8514,FUV Spectral Signatures of Molecules and the Evolution of the Gaseous Coma of Comet 67P/Churyumov-Gerasimenko,"  The Alice far-ultraviolet imaging spectrograph onboard Rosetta observed
+emissions from atomic and molecular species from within the coma of comet
+67P/Churyumov-Gerasimenko during the entire escort phase of the mission from
+2014 August to 2016 September. The initial observations showed that emissions
+of atomic hydrogen and oxygen close to the surface were produced by energetic
+electron impact dissociation of H2O. Following delivery of the lander, Philae,
+on 2014 November 12, the trajectory of Rosetta shifted to near-terminator
+orbits that allowed for these emissions to be observed against the shadowed
+nucleus that, together with the compositional heterogeneity, enabled us to
+identify unique spectral signatures of dissociative electron impact excitation
+of H2O, CO2, and O2. CO emissions were found to be due to both electron and
+photoexcitation processes. Thus we are able, from far-ultraviolet spectroscopy,
+to qualitatively study the evolution of the primary molecular constituents of
+the gaseous coma from start to finish of the escort phase. Our results show
+asymmetric outgassing of H2O and CO2 about perihelion, H2O dominant before and
+CO2 dominant after, consistent with the results from both the in situ and other
+remote sensing instruments on Rosetta.
+",0,1,0,0,0,0
+8515,Adsorption and desorption of hydrogen at nonpolar GaN(1-100) surfaces: Kinetics and impact on surface vibrational and electronic properties,"  The adsorption of hydrogen at nonpolar GaN(1-100) surfaces and its impact on
+the electronic and vibrational properties is investigated using surface
+electron spectroscopy in combination with density functional theory (DFT)
+calculations. For the surface mediated dissociation of H2 and the subsequent
+adsorption of H, an energy barrier of 0.55 eV has to be overcome. The
+calculated kinetic surface phase diagram indicates that the reaction is
+kinetically hindered at low pressures and low temperatures. At higher
+temperatures ab-initio thermodynamics show, that the H-free surface is
+energetically favored. To validate these theoretical predictions experiments at
+room temperature and under ultrahigh vacuum conditions were performed. They
+reveal that molecular hydrogen does not dissociatively adsorb at the GaN(1-100)
+surface. Only activated atomic hydrogen atoms attach to the surface. At
+temperatures above 820 K, the attached hydrogen gets desorbed. The adsorbed
+hydrogen atoms saturate the dangling bonds of the gallium and nitrogen surface
+atoms and result in an inversion of the Ga-N surface dimer buckling. The
+signatures of the Ga-H and N-H vibrational modes on the H-covered surface have
+experimentally been identified and are in good agreement with the DFT
+calculations of the surface phonon modes. Both theory and experiment show that
+H adsorption results in a removal of occupied and unoccupied intragap electron
+states of the clean GaN(1-100) surface and a reduction of the surface upward
+band bending by 0.4 eV. The latter mechanism largely reduces surface electron
+depletion.
+",0,1,0,0,0,0
+8516,Application of the Fast Multipole Fully Coupled Poroelastic Displacement Discontinuity Method to Hydraulic Fracturing Problems,"  In this study, a fast multipole method (FMM) is used to decrease the
+computational time of a fully-coupled poroelastic hydraulic fracture model with
+a controllable effect on its accuracy. The hydraulic fracture model is based on
+the poroelastic formulation of the displacement discontinuity method (DDM)
+which is a special formulation of the boundary element method (BEM). DDM is a
+powerful and efficient method for problems involving fractures. However, this
+method becomes slow as the number of temporal, or spatial elements increases,
+or necessary details such as poroelasticity, that makes the solution
+history-dependent, are added to the model. FMM is a technique to expedite
+matrix-vector multiplications within a controllable error without forming the
+matrix explicitly. Fully-coupled poroelastic formulation of DDM involves the
+multiplication of a dense matrix with a vector in several places. A crucial
+modification to DDM is suggested in two places in the algorithm to leverage the
+speed efficiency of FMM for carrying out these multiplications. The first
+modification is in the time-marching scheme, which accounts for the solution of
+previous time steps to compute the current time step. The second modification
+is in the generalized minimal residual method (GMRES) to iteratively solve for
+the problem unknowns. Several examples are provided to show the efficiency of
+the proposed approach in problems with large degrees of freedom (in time and
+space). Examples include hydraulic fracturing of a horizontal well and randomly
+distributed pressurized fractures at different orientations with respect to
+horizontal stresses. The results are compared to the conventional DDM in terms
+of computational processing time and accuracy. Accordingly, the proposed
+algorithm may be used for fracture propagation studies while substantially
+reducing the processing time with a controllable error.
+",1,0,0,0,0,0
+8517,Conduction Channels of an InAs-Al nanowire Josephson weak link,"  We present a quantitative characterization of an electrically tunable
+Josephson junction defined in an InAs nanowire proximitized by an
+epitax-ially-grown superconducting Al shell. The gate-dependence of the number
+of conduction channels and of the set of transmission coefficients are
+extracted from the highly nonlinear current-voltage characteristics. Although
+the transmissions evolve non-monotonically, the number of independent channels
+can be tuned, and configurations with a single quasi-ballistic channel
+achieved.
+",0,1,0,0,0,0
+8518,Two-level Chebyshev filter based complementary subspace method: pushing the envelope of large-scale electronic structure calculations,"  We describe a novel iterative strategy for Kohn-Sham density functional
+theory calculations aimed at large systems (> 1000 electrons), applicable to
+metals and insulators alike. In lieu of explicit diagonalization of the
+Kohn-Sham Hamiltonian on every self-consistent field (SCF) iteration, we employ
+a two-level Chebyshev polynomial filter based complementary subspace strategy
+to: 1) compute a set of vectors that span the occupied subspace of the
+Hamiltonian; 2) reduce subspace diagonalization to just partially occupied
+states; and 3) obtain those states in an efficient, scalable manner via an
+inner Chebyshev-filter iteration. By reducing the necessary computation to just
+partially occupied states, and obtaining these through an inner Chebyshev
+iteration, our approach reduces the cost of large metallic calculations
+significantly, while eliminating subspace diagonalization for insulating
+systems altogether. We describe the implementation of the method within the
+framework of the Discontinuous Galerkin (DG) electronic structure method and
+show that this results in a computational scheme that can effectively tackle
+bulk and nano systems containing tens of thousands of electrons, with chemical
+accuracy, within a few minutes or less of wall clock time per SCF iteration on
+large-scale computing platforms. We anticipate that our method will be
+instrumental in pushing the envelope of large-scale ab initio molecular
+dynamics. As a demonstration of this, we simulate a bulk silicon system
+containing 8,000 atoms at finite temperature, and obtain an average SCF step
+wall time of 51 seconds on 34,560 processors; thus allowing us to carry out 1.0
+ps of ab initio molecular dynamics in approximately 28 hours (of wall time).
+",0,1,0,0,0,0
+8519,Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications,"  Medical applications challenge today's text categorization techniques by
+demanding both high accuracy and ease-of-interpretation. Although deep learning
+has provided a leap ahead in accuracy, this leap comes at the sacrifice of
+interpretability. To address this accuracy-interpretability challenge, we here
+introduce, for the first time, a text categorization approach that leverages
+the recently introduced Tsetlin Machine. In all brevity, we represent the terms
+of a text as propositional variables. From these, we capture categories using
+simple propositional formulae, such as: if ""rash"" and ""reaction"" and
+""penicillin"" then Allergy. The Tsetlin Machine learns these formulae from a
+labelled text, utilizing conjunctive clauses to represent the particular facets
+of each category. Indeed, even the absence of terms (negated features) can be
+used for categorization purposes. Our empirical comparison with Naïve Bayes,
+decision trees, linear support vector machines (SVMs), random forest, long
+short-term memory (LSTM) neural networks, and other techniques, is quite
+conclusive. The Tsetlin Machine either performs on par with or outperforms all
+of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well
+as on a non-public clinical dataset. On average, the Tsetlin Machine delivers
+the best recall and precision scores across the datasets. Finally, our GPU
+implementation of the Tsetlin Machine executes 5 to 15 times faster than the
+CPU implementation, depending on the dataset. We thus believe that our novel
+approach can have a significant impact on a wide range of text analysis
+applications, forming a promising starting point for deeper natural language
+understanding with the Tsetlin Machine.
+",0,0,0,1,0,0
+8520,Deep Learning with Experience Ranking Convolutional Neural Network for Robot Manipulator,"  Supervised learning, more specifically Convolutional Neural Networks (CNN),
+has surpassed human ability in some visual recognition tasks such as detection
+of traffic signs, faces and handwritten numbers. On the other hand, even
+state-of-the-art reinforcement learning (RL) methods have difficulties in
+environments with sparse and binary rewards. They requires manually shaping
+reward functions, which might be challenging to come up with. These tasks,
+however, are trivial to human. One of the reasons that human are better
+learners in these tasks is that we are embedded with much prior knowledge of
+the world. These knowledge might be either embedded in our genes or learned
+from imitation - a type of supervised learning. For that reason, the best way
+to narrow the gap between machine and human learning ability should be to mimic
+how we learn so well in various tasks by a combination of RL and supervised
+learning. Our method, which integrates Deep Deterministic Policy Gradients and
+Hindsight Experience Replay (RL method specifically dealing with sparse
+rewards) with an experience ranking CNN, provides a significant speedup over
+the learning curve on simulated robotics tasks. Experience ranking allows
+high-reward transitions to be replayed more frequently, and therefore help
+learn more efficiently. Our proposed approach can also speed up learning in any
+other tasks that provide additional information for experience ranking.
+",1,0,0,0,0,0
+8521,Isotope Shifts in the 7s$\rightarrow$8s Transition of Francium: Measurements and Comparison to \textit{ab initio} Theory,"  We observe the electric-dipole forbidden $7s\rightarrow8s$ transition in the
+francium isotopes $^{208-211}$Fr and $^{213}$Fr using a two-photon excitation
+scheme. We collect the atoms online from an accelerator and confine them in a
+magneto optical trap for the measurements. In combination with previous
+measurements of the $7s\rightarrow7p_{1/2}$ transition we perform a King Plot
+analysis. We compare the thus determined ratio of the field shift constants
+(1.230 $\pm$ 0.019) to results obtained from new ab initio calculations (1.234
+$\pm$ 0.010) and find excellent agreement.
+",0,1,0,0,0,0
+8522,SRM: An Efficient Framework for Autonomous Robotic Exploration in Indoor Environments,"  In this paper, we propose an integrated framework for the autonomous robotic
+exploration in indoor environments. Specially, we present a hybrid map, named
+Semantic Road Map (SRM), to represent the topological structure of the explored
+environment and facilitate decision-making in the exploration. The SRM is built
+incrementally along with the exploration process. It is a graph structure with
+collision-free nodes and edges that are generated within the sensor coverage.
+Moreover, each node has a semantic label and the expected information gain at
+that location. Based on the concise SRM, we present a novel and effective
+decision-making model to determine the next-best-target (NBT) during the
+exploration. The model concerns the semantic information, the information gain,
+and the path cost to the target location. We use the nodes of SRM to represent
+the candidate targets, which enables the target evaluation to be performed
+directly on the SRM. With the SRM, both the information gain of a node and the
+path cost to the node can be obtained efficiently. Besides, we adopt the
+cross-entropy method to optimize the path to make it more informative. We
+conduct experimental studies in both simulated and real-world environments,
+which demonstrate the effectiveness of the proposed method.
+",1,0,0,0,0,0
+8523,Mechanisms for bacterial gliding motility on soft substrates,"  The motility mechanism of certain rod-shaped bacteria has long been a
+mystery, since no external appendages are involved in their motion which is
+known as gliding. However, the physical principles behind gliding motility
+still remain poorly understood. Using myxobacteria as a canonical example of
+such organisms, we identify here the physical principles behind gliding
+motility, and develop a theoretical model that predicts a two-regime behavior
+of the gliding speed as a function of the substrate stiffness. Our theory
+describes the elastic, viscous, and capillary interactions between the
+bacterial membrane carrying a traveling wave, the secreted slime acting as a
+lubricating film, and the substrate which we model as a soft solid. Defining
+the myxobacterial gliding as the horizontal motion on the substrate under zero
+net force, we find the two-regime behavior is due to two different mechanisms
+of motility thrust. On stiff substrates, the thrust arises from the bacterial
+shape deformations creating a flow of slime that exerts a pressure along the
+bacterial length. This pressure in conjunction with the bacterial shape
+provides the necessary thrust for propulsion. However, we show that such a
+mechanism cannot lead to gliding on very soft substrates. Instead, we show that
+capillary effects lead to the formation of a ridge at the slime-substrate-air
+interface, which creates a thrust in the form of a localized pressure gradient
+at the tip of the bacteria. To test our theory, we perform experiments with
+isolated cells on agar substrates of varying stiffness and find the measured
+gliding speeds to be in good agreement with the predictions from our
+elasto-capillary-hydrodynamic model. The physical mechanisms reported here
+serve as an important step towards an accurate theory of friction and
+substrate-mediated interaction between bacteria in a swarm of cells
+proliferating in soft media.
+",0,0,0,0,1,0
+8524,Random Walk in a N-cube Without Hamiltonian Cycle to Chaotic Pseudorandom Number Generation: Theoretical and Practical Considerations,"  Designing a pseudorandom number generator (PRNG) is a difficult and complex
+task. Many recent works have considered chaotic functions as the basis of built
+PRNGs: the quality of the output would indeed be an obvious consequence of some
+chaos properties. However, there is no direct reasoning that goes from chaotic
+functions to uniform distribution of the output. Moreover, embedding such kind
+of functions into a PRNG does not necessarily allow to get a chaotic output,
+which could be required for simulating some chaotic behaviors.
+In a previous work, some of the authors have proposed the idea of walking
+into a $\mathsf{N}$-cube where a balanced Hamiltonian cycle has been removed as
+the basis of a chaotic PRNG. In this article, all the difficult issues observed
+in the previous work have been tackled. The chaotic behavior of the whole PRNG
+is proven. The construction of the balanced Hamiltonian cycle is theoretically
+and practically solved. An upper bound of the expected length of the walk to
+obtain a uniform distribution is calculated. Finally practical experiments show
+that the generators successfully pass the classical statistical tests.
+",1,1,0,0,0,0
+8525,A Hardware Platform for Efficient Multi-Modal Sensing with Adaptive Approximation,"  We present Warp, a hardware platform to support research in approximate
+computing, sensor energy optimization, and energy-scavenged systems. Warp
+incorporates 11 state-of-the-art sensor integrated circuits, computation, and
+an energy-scavenged power supply, all within a miniature system that is just
+3.6 cm x 3.3 cm x 0.5 cm. Warp's sensor integrated circuits together contain a
+total of 21 sensors with a range of precisions and accuracies for measuring
+eight sensing modalities of acceleration, angular rate, magnetic flux density
+(compass heading), humidity, atmospheric pressure (elevation), infrared
+radiation, ambient temperature, and color. Warp uses a combination of analog
+circuits and digital control to facilitate further tradeoffs between sensor and
+communication accuracy, energy efficiency, and performance. This article
+presents the design of Warp and presents an evaluation of our hardware
+implementation. The results show how Warp's design enables performance and
+energy efficiency versus ac- curacy tradeoffs.
+",1,0,0,0,0,0
+8526,"Electrocaloric effects in the lead-free Ba(Zr,Ti)O$_{3}$ relaxor ferroelectric from atomistic simulations","  Atomistic effective Hamiltonian simulations are used to investigate
+electrocaloric (EC) effects in the lead-free Ba(Zr$_{0.5}$Ti$_{0.5}$)O$_{3}$
+(BZT) relaxor ferroelectric. We find that the EC coefficient varies
+non-monotonically with the field at any temperature, presenting a maximum that
+can be traced back to the behavior of BZT's polar nanoregions. We also
+introduce a simple Landau-based model that reproduces the EC behavior of BZT as
+a function of field and temperature, and which is directly applicable to other
+compounds. Finally, we confirm that, for low temperatures (i.e., in non-ergodic
+conditions), the usual indirect approach to measure the EC response provides an
+estimate that differs quantitatively from a direct evaluation of the
+field-induced temperature change.
+",0,1,0,0,0,0
+8527,Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM,"  Permutation testing is a non-parametric method for obtaining the max null
+distribution used to compute corrected $p$-values that provide strong control
+of false positives. In neuroimaging, however, the computational burden of
+running such an algorithm can be significant. We find that by viewing the
+permutation testing procedure as the construction of a very large permutation
+testing matrix, $T$, one can exploit structural properties derived from the
+data and the test statistics to reduce the runtime under certain conditions. In
+particular, we see that $T$ is low-rank plus a low-variance residual. This
+makes $T$ a good candidate for low-rank matrix completion, where only a very
+small number of entries of $T$ ($\sim0.35\%$ of all entries in our experiments)
+have to be computed to obtain a good estimate. Based on this observation, we
+present RapidPT, an algorithm that efficiently recovers the max null
+distribution commonly obtained through regular permutation testing in
+voxel-wise analysis. We present an extensive validation on a synthetic dataset
+and four varying sized datasets against two baselines: Statistical
+NonParametric Mapping (SnPM13) and a standard permutation testing
+implementation (referred as NaivePT). We find that RapidPT achieves its best
+runtime performance on medium sized datasets ($50 \leq n \leq 200$), with
+speedups of 1.5x - 38x (vs. SnPM13) and 20x-1000x (vs. NaivePT). For larger
+datasets ($n \geq 200$) RapidPT outperforms NaivePT (6x - 200x) on all
+datasets, and provides large speedups over SnPM13 when more than 10000
+permutations (2x - 15x) are needed. The implementation is a standalone toolbox
+and also integrated within SnPM13, able to leverage multi-core architectures
+when available.
+",1,0,0,1,0,0
+8528,Image restoration of solar spectra,"  When recording spectra from the ground, atmospheric turbulence causes
+degradation of the spatial resolution. We present a data reduction method that
+restores the spatial resolution of the spectra to their undegraded state. By
+assuming that the point spread function (PSF) estimated from a strictly
+synchronized, broadband slit-jaw camera is the same as the PSF that spatially
+degraded the spectra, we can quantify what linear combination of undegraded
+spectra is present in each degraded data point. The set of equations obtained
+in this way is found to be generally well-conditioned and sufficiently diagonal
+to be solved using an iterative linear solver. The resulting solution has
+regained a spatial resolution comparable to that of the restored slit-jaw
+images.
+",0,1,0,0,0,0
+8529,Who Said What: Modeling Individual Labelers Improves Classification,"  Data are often labeled by many different experts with each expert only
+labeling a small fraction of the data and each data point being labeled by
+several experts. This reduces the workload on individual experts and also gives
+a better estimate of the unobserved ground truth. When experts disagree, the
+standard approaches are to treat the majority opinion as the correct label or
+to model the correct label as a distribution. These approaches, however, do not
+make any use of potentially valuable information about which expert produced
+which label. To make use of this extra information, we propose modeling the
+experts individually and then learning averaging weights for combining them,
+possibly in sample-specific ways. This allows us to give more weight to more
+reliable experts and take advantage of the unique strengths of individual
+experts at classifying certain types of data. Here we show that our approach
+leads to improvements in computer-aided diagnosis of diabetic retinopathy. We
+also show that our method performs better than competing algorithms by Welinder
+and Perona (2010), and by Mnih and Hinton (2012). Our work offers an innovative
+approach for dealing with the myriad real-world settings that use expert
+opinions to define labels for training.
+",1,0,0,0,0,0
+8530,Automated Tiling of Unstructured Mesh Computations with Application to Seismological Modelling,"  Sparse tiling is a technique to fuse loops that access common data, thus
+increasing data locality. Unlike traditional loop fusion or blocking, the loops
+may have different iteration spaces and access shared datasets through indirect
+memory accesses, such as A[map[i]] -- hence the name ""sparse"". One notable
+example of such loops arises in discontinuous-Galerkin finite element methods,
+because of the computation of numerical integrals over different domains (e.g.,
+cells, facets). The major challenge with sparse tiling is implementation -- not
+only is it cumbersome to understand and synthesize, but it is also onerous to
+maintain and generalize, as it requires a complete rewrite of the bulk of the
+numerical computation. In this article, we propose an approach to extend the
+applicability of sparse tiling based on raising the level of abstraction.
+Through a sequence of compiler passes, the mathematical specification of a
+problem is progressively lowered, and eventually sparse-tiled C for-loops are
+generated. Besides automation, we advance the state-of-the-art by introducing:
+a revisited, more efficient sparse tiling algorithm; support for
+distributed-memory parallelism; a range of fine-grained optimizations for
+increased run-time performance; implementation in a publicly-available library,
+SLOPE; and an in-depth study of the performance impact in Seigen, a real-world
+elastic wave equation solver for seismological problems, which shows speed-ups
+up to 1.28x on a platform consisting of 896 Intel Broadwell cores.
+",1,1,0,0,0,0
+8531,A copula approach for dependence modeling in multivariate nonparametric time series,"  This paper is concerned with modeling the dependence structure of two (or
+more) time-series in the presence of a (possible multivariate) covariate which
+may include past values of the time series. We assume that the covariate
+influences only the conditional mean and the conditional variance of each of
+the time series but the distribution of the standardized innovations is not
+influenced by the covariate and is stable in time. The joint distribution of
+the time series is then determined by the conditional means, the conditional
+variances and the marginal distributions of the innovations, which we estimate
+nonparametrically, and the copula of the innovations, which represents the
+dependency structure. We consider a nonparametric as well as a semiparametric
+estimator based on the estimated residuals. We show that under suitable
+assumptions these copula estimators are asymptotically equivalent to estimators
+that would be based on the unobserved innovations. The theoretical results are
+illustrated by simulations and a real data example.
+",0,0,1,1,0,0
+8532,A geometric second-order-rectifiable stratification for closed subsets of Euclidean space,"  Defining the $m$-th stratum of a closed subset of an $n$ dimensional
+Euclidean space to consist of those points, where it can be touched by a ball
+from at least $n-m$ linearly independent directions, we establish that the
+$m$-th stratum is second-order rectifiable of dimension $m$ and a Borel set.
+This was known for convex sets, but is new even for sets of positive reach. The
+result is based on a new criterion for second-order rectifiability.
+",0,0,1,0,0,0
+8533,Probabilistic Formulations of Regression with Mixed Guidance,"  Regression problems assume every instance is annotated (labeled) with a real
+value, a form of annotation we call \emph{strong guidance}. In order for these
+annotations to be accurate, they must be the result of a precise experiment or
+measurement. However, in some cases additional \emph{weak guidance} might be
+given by imprecise measurements, a domain expert or even crowd sourcing.
+Current formulations of regression are unable to use both types of guidance. We
+propose a regression framework that can also incorporate weak guidance based on
+relative orderings, bounds, neighboring and similarity relations. Consider
+learning to predict ages from portrait images, these new types of guidance
+allow weaker forms of guidance such as stating a person is in their 20s or two
+people are similar in age. These types of annotations can be easier to generate
+than strong guidance. We introduce a probabilistic formulation for these forms
+of weak guidance and show that the resulting optimization problems are convex.
+Our experimental results show the benefits of these formulations on several
+data sets.
+",0,0,0,1,0,0
+8534,DCN+: Mixed Objective and Deep Residual Coattention for Question Answering,"  Traditional models for question answering optimize using cross entropy loss,
+which encourages exact answers at the cost of penalizing nearby or overlapping
+answers that are sometimes equally accurate. We propose a mixed objective that
+combines cross entropy loss with self-critical policy learning. The objective
+uses rewards derived from word overlap to solve the misalignment between
+evaluation metric and optimization objective. In addition to the mixed
+objective, we improve dynamic coattention networks (DCN) with a deep residual
+coattention encoder that is inspired by recent work in deep self-attention and
+residual networks. Our proposals improve model performance across question
+types and input lengths, especially for long questions that requires the
+ability to capture long-term dependencies. On the Stanford Question Answering
+Dataset, our model achieves state-of-the-art results with 75.1% exact match
+accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy
+and 86.0% F1.
+",1,0,0,0,0,0
+8535,An optimal unrestricted learning procedure,"  We study learning problems involving arbitrary classes of functions $F$,
+distributions $X$ and targets $Y$. Because proper learning procedures, i.e.,
+procedures that are only allowed to select functions in $F$, tend to perform
+poorly unless the problem satisfies some additional structural property (e.g.,
+that $F$ is convex), we consider unrestricted learning procedures that are free
+to choose functions outside the given class.
+We present a new unrestricted procedure that is optimal in a very strong
+sense: the required sample complexity is essentially the best one can hope for,
+and the estimate holds for (almost) any problem, including heavy-tailed
+situations. Moreover, the sample complexity coincides with the what one would
+expect if $F$ were convex, even when $F$ is not. And if $F$ is convex, the
+procedure turns out to be proper. Thus, the unrestricted procedure is actually
+optimal in both realms, for convex classes as a proper procedure and for
+arbitrary classes as an unrestricted procedure.
+",0,0,0,1,0,0
+8536,Means of infinite sets I,"  We open a new field on how one can define means on infinite sets. We
+investigate many different ways on how such means can be constructed. One
+method is based on sequences of ideals, other deals with accumulation points,
+one uses isolated points, other deals with average using integral, other with
+limit of average on surroundings and one deals with evenly distributed samples.
+We study various properties of such means and their relations to each other.
+",0,0,1,0,0,0
+8537,On tamed almost complex four manifolds,"  This paper proves that on any tamed closed almost complex four-manifold
+$(M,J)$ whose dimension of $J$-anti-invariant cohomology is equal to self-dual
+second Betti number minus one, there exists a new symplectic form compatible
+with the given almost complex structure $J$. In particular, if the self-dual
+second Betti number is one, we give an affirmative answer to Donaldson question
+for tamed closed almost complex four-manifolds that is a conjecture in joint
+paper of Tosatti, Weinkove and Yau. Our approach is along the lines used by
+Buchdahl to give a unified proof of the Kodaira conjecture. Thus, our main
+result gives an affirmative answer to the Kodaira conjecture in symplectic
+version.
+",0,0,1,0,0,0
+8538,Evaluation and Prediction of Polygon Approximations of Planar Contours for Shape Analysis,"  Contours may be viewed as the 2D outline of the image of an object. This type
+of data arises in medical imaging as well as in computer vision and can be
+modeled as data on a manifold and can be studied using statistical shape
+analysis. Practically speaking, each observed contour, while theoretically
+infinite dimensional, must be discretized for computations. As such, the
+coordinates for each contour as obtained at k sampling times, resulting in the
+contour being represented as a k-dimensional complex vector. While choosing
+large values of k will result in closer approximations to the original contour,
+this will also result in higher computational costs in the subsequent analysis.
+The goal of this study is to determine reasonable values for k so as to keep
+the computational cost low while maintaining accuracy. To do this, we consider
+two methods for selecting sample points and determine lower bounds for k for
+obtaining a desired level of approximation error using two different criteria.
+Because this process is computationally inefficient to perform on a large
+scale, we then develop models for predicting the lower bounds for k based on
+simple characteristics of the contours.
+",0,0,0,1,0,0
+8539,Perturbative Expansion of Irreversible Work in Fokker-Planck Equation a la Quantum Mechanics,"  We discuss the systematic expansion of the solution of the Fokker-Planck
+equation with the help of the eigenfunctions of the time-dependent
+Fokker-Planck operator. The expansion parameter is the time derivative of the
+external parameter which controls the form of an external potential. Our
+expansion corresponds to the perturbative calculation of the adiabatic motion
+in quantum mechanics. With this method, we derive a new formula to calculate
+the irreversible work order by order, which is expressed as the expectation
+value with a pseudo density matrix. Applying this method to the case of the
+harmonic potential, we show that the first order term of the expansion gives
+the exact result. Because we do not need to solve the coupled differential
+equations of moments, our method simplifies the calculations of various
+functions such as the fluctuation of the irreversible work per unit time. We
+further investigate the exact optimized protocol to minimize the irreversible
+work by calculating its variation with respect to the control parameter itself.
+",0,1,1,0,0,0
+8540,Operational Semantics of Process Monitors,"  CSPe is a specification language for runtime monitors that can directly
+express concurrency in a bottom-up manner that composes the system from
+simpler, interacting components. It includes constructs to explicitly flag
+failures to the monitor, which unlike deadlocks and livelocks in conventional
+process algebras, propagate globally and aborts the whole system's execution.
+Although CSPe has a trace semantics along with an implementation demonstrating
+acceptable performance, it lacks an operational semantics. An operational
+semantics is not only more accessible than trace semantics but also
+indispensable for ensuring the correctness of the implementation. Furthermore,
+a process algebra like CSPe admits multiple denotational semantics appropriate
+for different purposes, and an operational semantics is the basis for
+justifying such semantics' integrity and relevance. In this paper, we develop
+an SOS-style operational semantics for CSPe, which properly accounts for
+explicit failures and will serve as a basis for further study of its
+properties, its optimization, and its use in runtime verification.
+",1,0,0,0,0,0
+8541,Anyonic Entanglement and Topological Entanglement Entropy,"  We study the properties of entanglement in two-dimensional topologically
+ordered phases of matter. Such phases support anyons, quasiparticles with
+exotic exchange statistics. The emergent nonlocal state spaces of anyonic
+systems admit a particular form of entanglement that does not exist in
+conventional quantum mechanical systems. We study this entanglement by adapting
+standard notions of entropy to anyonic systems. We use the algebraic theory of
+anyon models (modular tensor categories) to illustrate the nonlocal
+entanglement structure of anyonic systems. Using this formalism, we present a
+general method of deriving the universal topological contributions to the
+entanglement entropy for general system configurations of a topological phase,
+including surfaces of arbitrary genus, punctures, and quasiparticle content. We
+analyze a number of examples in detail. Our results recover and extend prior
+results for anyonic entanglement and the topological entanglement entropy.
+",0,1,0,0,0,0
+8542,Water-based and Biocompatible 2D Crystal Inks: from Ink Formulation to All- Inkjet Printed Heterostructures,"  Fully exploiting the properties of 2D crystals requires a mass production
+method able to produce heterostructures of arbitrary complexity on any
+substrate, including plastic. Solution processing of graphene allows simple and
+low-cost techniques such as inkjet printing to be used for device fabrication.
+However, available inkjet printable formulations are still far from ideal as
+they are either based on toxic solvents, have low concentration, or require
+time-consuming and expensive formulation processing. In addition, none of those
+formulations are suitable for thin-film heterostructure fabrication due to the
+re-mixing of different 2D crystals, giving rise to uncontrolled interfaces,
+which results in poor device performance and lack of reproducibility. In this
+work we show a general formulation engineering approach to achieve highly
+concentrated, and inkjet printable water-based 2D crystal formulations, which
+also provides optimal film formation for multi-stack fabrication. We show
+examples of all-inkjet printed heterostructures, such as large area arrays of
+photosensors on plastic and paper and programmable logic memory devices, fully
+exploiting the design flexibility of inkjet printing. Finally, dose-escalation
+cytotoxicity assays in vitro also confirm the inks biocompatible character,
+revealing the possibility of extending use of such 2D crystal formulations to
+drug delivery and biomedical applications.
+",0,1,0,0,0,0
+8543,Clustering to Reduce Spatial Data Set Size,"  Traditionally it had been a problem that researchers did not have access to
+enough spatial data to answer pressing research questions or build compelling
+visualizations. Today, however, the problem is often that we have too much
+data. Spatially redundant or approximately redundant points may refer to a
+single feature (plus noise) rather than many distinct spatial features. We use
+a machine learning approach with density-based clustering to compress such
+spatial data into a set of representative features.
+",0,0,0,1,0,0
+8544,Thermal diffusivity and chaos in metals without quasiparticles,"  We study the thermal diffusivity $D_T$ in models of metals without
+quasiparticle excitations (`strange metals'). The many-body quantum chaos and
+transport properties of such metals can be efficiently described by a
+holographic representation in a gravitational theory in an emergent curved
+spacetime with an additional spatial dimension. We find that at generic
+infra-red fixed points $D_T$ is always related to parameters characterizing
+many-body quantum chaos: the butterfly velocity $v_B$, and Lyapunov time
+$\tau_L$ through $D_T \sim v_B^2 \tau_L$. The relationship holds independently
+of the charge density, periodic potential strength or magnetic field at the
+fixed point. The generality of this result follows from the observation that
+the thermal conductivity of strange metals depends only on the metric near the
+horizon of a black hole in the emergent spacetime, and is otherwise insensitive
+to the profile of any matter fields.
+",0,1,0,0,0,0
+8545,The Quest for Scalability and Accuracy in the Simulation of the Internet of Things: an Approach based on Multi-Level Simulation,"  This paper presents a methodology for simulating the Internet of Things (IoT)
+using multi-level simulation models. With respect to conventional simulators,
+this approach allows us to tune the level of detail of different parts of the
+model without compromising the scalability of the simulation. As a use case, we
+have developed a two-level simulator to study the deployment of smart services
+over rural territories. The higher level is base on a coarse grained,
+agent-based adaptive parallel and distributed simulator. When needed, this
+simulator spawns OMNeT++ model instances to evaluate in more detail the issues
+concerned with wireless communications in restricted areas of the simulated
+world. The performance evaluation confirms the viability of multi-level
+simulations for IoT environments.
+",1,0,0,0,0,0
+8546,Harmonic spinors from twistors and potential forms,"  Symmetry operators of twistor spinors and harmonic spinors can be constructed
+from conformal Killing-Yano forms. Transformation operators relating twistors
+to harmonic spinors are found in terms of potential forms. These constructions
+are generalized to gauged twistor spinors and gauged harmonic spinors. The
+operators that transform gauged twistor spinors to gauged harmonic spinors are
+found. Symmetry operators of gauged harmonic spinors in terms of conformal
+Killing-Yano forms are obtained. Algebraic conditions to obtain solutions of
+the Seiberg-Witten equations are discussed.
+",0,0,1,0,0,0
+8547,Goodness-of-fit tests for the functional linear model based on randomly projected empirical processes,"  We consider marked empirical processes indexed by a randomly projected
+functional covariate to construct goodness-of-fit tests for the functional
+linear model with scalar response. The test statistics are built from
+continuous functionals over the projected process, resulting in computationally
+efficient tests that exhibit root-n convergence rates and circumvent the curse
+of dimensionality. The weak convergence of the empirical process is obtained
+conditionally on a random direction, whilst the almost surely equivalence
+between the testing for significance expressed on the original and on the
+projected functional covariate is proved. The computation of the test in
+practice involves calibration by wild bootstrap resampling and the combination
+of several p-values, arising from different projections, by means of the false
+discovery rate method. The finite sample properties of the tests are
+illustrated in a simulation study for a variety of linear models, underlying
+processes, and alternatives. The software provided implements the tests and
+allows the replication of simulations and data applications.
+",0,0,0,1,0,0
+8548,Efficient method for estimating the number of communities in a network,"  While there exist a wide range of effective methods for community detection
+in networks, most of them require one to know in advance how many communities
+one is looking for. Here we present a method for estimating the number of
+communities in a network using a combination of Bayesian inference with a novel
+prior and an efficient Monte Carlo sampling scheme. We test the method
+extensively on both real and computer-generated networks, showing that it
+performs accurately and consistently, even in cases where groups are widely
+varying in size or structure.
+",1,1,0,0,0,0
+8549,The Pluto System After New Horizons,"  The discovery of Pluto in 1930 presaged the discoveries of both the Kuiper
+Belt and ice dwarf planets, which are the third class of planets in our solar
+system. From the 1970s to the 19990s numerous fascinating attributes of the
+Pluto system were discovered, including multiple surface volatile species,
+Pluto's large satellite Charon, and its atmosphere. These attributes, and the
+1990s discovery of the Kuiper Belt and Pluto's cohort of small Kuiper Belt
+planets, motivated the exploration of Pluto. That mission, called New Horizons
+(NH), revolutionized knowledge of Pluto and its system of satellites in 2015.
+Beyond providing rich geological, compositional, and atmospheric data sets, New
+Horizons demonstrated that Pluto itself has been surprisingly geologically
+active throughout the past 4 billion years, and that the planet exhibits a
+surprisingly complex range of atmospheric phenomenology and geologic expression
+that rival Mars in their richness.
+",0,1,0,0,0,0
+8550,Global-in-time Strichartz estimates and cubic Schrodinger equation on metric cone,"  We study the Strichartz estimates for Schrödinger equation on a metric cone
+$X$, where the metric cone $X=C(Y)=(0,\infty)_r\times Y$ and the cross section
+$Y$ is a $(n-1)$-dimensional closed Riemannian manifold $(Y,h)$. The equipped
+metric on $X$ is given by $g=dr^2+r^2h$, and let $\Delta_g$ be the Friedrich
+extension positive Laplacian on $X$ and $V=V_0 r^{-2}$ where
+$V_0\in\CC^\infty(Y)$ is a real function such that the operator
+$\Delta_h+V_0+(n-2)^2/4$ is a strictly positive operator on $L^2(Y)$. We
+establish the full range of the global-in-time Strichartz estimate without loss
+for the Schödinger equation associated with the operator $\LL_V=\Delta_g+V_0
+r^{-2}$ including the endpoint estimate both in homogeneous and inhomogeneous
+cases. As an application, we study the well-posed theory and scattering theory
+for the Schödinger equation with cubic nonlinearity on this setting.
+",0,0,1,0,0,0
+8551,Compressive Embedding and Visualization using Graphs,"  Visualizing high-dimensional data has been a focus in data analysis
+communities for decades, which has led to the design of many algorithms, some
+of which are now considered references (such as t-SNE for example). In our era
+of overwhelming data volumes, the scalability of such methods have become more
+and more important. In this work, we present a method which allows to apply any
+visualization or embedding algorithm on very large datasets by considering only
+a fraction of the data as input and then extending the information to all data
+points using a graph encoding its global similarity. We show that in most
+cases, using only $\mathcal{O}(\log(N))$ samples is sufficient to diffuse the
+information to all $N$ data points. In addition, we propose quantitative
+methods to measure the quality of embeddings and demonstrate the validity of
+our technique on both synthetic and real-world datasets.
+",1,0,0,1,0,0
+8552,Electrical control of metallic heavy-metal/ferromagnet interfacial states,"  Voltage control effects provide an energy-efficient means of tailoring
+material properties, especially in highly integrated nanoscale devices.
+However, only insulating and semiconducting systems can be controlled so far.
+In metallic systems, there is no electric field due to electron screening
+effects and thus no such control effect exists. Here we demonstrate that
+metallic systems can also be controlled electrically through ionic not
+electronic effects. In a Pt/Co structure, the control of the metallic Pt/Co
+interface can lead to unprecedented control effects on the magnetic properties
+of the entire structure. Consequently, the magnetization and perpendicular
+magnetic anisotropy of the Co layer can be independently manipulated to any
+desired state, the efficient spin toques can be enhanced about 3.5 times, and
+the switching current can be reduced about one order of magnitude. This ability
+to control a metallic system may be extended to control other physical
+phenomena.
+",0,1,0,0,0,0
+8553,An Expectation-Maximization Algorithm for the Fractal Inverse Problem,"  We present an Expectation-Maximization algorithm for the fractal inverse
+problem: the problem of fitting a fractal model to data. In our setting the
+fractals are Iterated Function Systems (IFS), with similitudes as the family of
+transformations. The data is a point cloud in ${\mathbb R}^H$ with arbitrary
+dimension $H$. Each IFS defines a probability distribution on ${\mathbb R}^H$,
+so that the fractal inverse problem can be cast as a problem of parameter
+estimation. We show that the algorithm reconstructs well-known fractals from
+data, with the model converging to high precision parameters. We also show the
+utility of the model as an approximation for datasources outside the IFS model
+class.
+",1,0,0,1,0,0
+8554,An Improved SCFlip Decoder for Polar Codes,"  This paper focuses on the recently introduced Successive Cancellation Flip
+(SCFlip) decoder of polar codes. Our contribution is twofold. First, we propose
+the use of an optimized metric to determine the flipping positions within the
+SCFlip decoder, which improves its ability to find the first error that
+occurred during the initial SC decoding attempt. We also show that the proposed
+metric allows closely approaching the performance of an ideal SCFlip decoder.
+Second, we introduce a generalisation of the SCFlip decoder to a number of
+$\omega$ nested flips, denoted by SCFlip-$\omega$, using a similar optimized
+metric to determine the positions of the nested flips. We show that the
+SCFlip-2 decoder yields significant gains in terms of decoding performance and
+competes with the performance of the CRC-aided SC-List decoder with list size
+L=4, while having an average decoding complexity similar to that of the
+standard SC decoding, at medium to high signal to noise ratio.
+",1,0,0,0,0,0
+8555,Generation of controllable plasma wakefield noise in particle-in-cell simulations,"  Numerical simulations of beam-plasma instabilities may produce quantitatively
+incorrect results because of unrealistically high initial noise from which the
+instabilities develop. Of particular importance is the wakefield noise, the
+potential perturbations that have a phase velocity which is equal to the beam
+velocity. Controlling the noise level in simulations may offer the possibility
+of extrapolating simulation results to the more realistic low-noise case. We
+propose a novel method for generating wakefield noise with a controllable
+amplitude by randomly located charged rods propagating ahead of the beam. We
+also illustrate the method with particle-in-cell simulations. The generation of
+this noise is not accompanied by parasitic Cherenkov radiation waves.
+",0,1,0,0,0,0
+8556,Variable Exponent Fock Spaces,"  In this article we introduce Variable exponent Fock spaces and study some of
+their basic properties such as the boundedness of evaluation functionals,
+density of polynomials, boundedness of a Bergman-type projection and duality.
+",0,0,1,0,0,0
+8557,Pattern representation and recognition with accelerated analog neuromorphic systems,"  Despite being originally inspired by the central nervous system, artificial
+neural networks have diverged from their biological archetypes as they have
+been remodeled to fit particular tasks. In this paper, we review several
+possibilites to reverse map these architectures to biologically more realistic
+spiking networks with the aim of emulating them on fast, low-power neuromorphic
+hardware. Since many of these devices employ analog components, which cannot be
+perfectly controlled, finding ways to compensate for the resulting effects
+represents a key challenge. Here, we discuss three different strategies to
+address this problem: the addition of auxiliary network components for
+stabilizing activity, the utilization of inherently robust architectures and a
+training method for hardware-emulated networks that functions without perfect
+knowledge of the system's dynamics and parameters. For all three scenarios, we
+corroborate our theoretical considerations with experimental results on
+accelerated analog neuromorphic platforms.
+",1,0,0,1,0,0
+8558,Dynamic Policies for Cooperative Networked Systems,"  A set of economic entities embedded in a network graph collaborate by
+opportunistically exchanging their resources to satisfy their dynamically
+generated needs. Under what conditions their collaboration leads to a
+sustainable economy? Which online policy can ensure a feasible resource
+exchange point will be attained, and what information is needed to implement
+it? Furthermore, assuming there are different resources and the entities have
+diverse production capabilities, which production policy each entity should
+employ in order to maximize the economy's sustainability? Importantly, can we
+design such policies that are also incentive compatible even when there is no a
+priori information about the entities' needs? We introduce a dynamic production
+scheduling and resource exchange model to capture this fundamental problem and
+provide answers to the above questions. Applications range from infrastructure
+sharing, trade and organisation management, to social networks and sharing
+economy services.
+",1,0,0,0,0,0
+8559,On certain families of planar patterns and fractals,"  This survey article is dedicated to some families of fractals that were
+introduced and studied during the last decade, more precisely, families of
+Sierpiński carpets: limit net sets, generalised Sierpiński carpets and
+labyrinth fractals. We give a unifying approach of these fractals and several
+of their topological and geometrical properties, by using the framework of
+planar patterns.
+",0,0,1,0,0,0
+8560,Static Dalvik VM bytecode instrumentation,"  This work proposes a novel approach to restricting the access for blacklisted
+Android system API calls. Main feature of the suggested method introduced in
+this paper is that it requires only rootless or (user-mode) access to the
+system unlike previous works. For that reason this method is valuable for
+end-users due to the possibility of project distribution via Play Market and it
+does not require any phone system modifications and/or updates. This paper
+explains the required background of Android OS necessary for understanding and
+describes the method for modification Android application. In this paper the
+proof-of-concept implementation. That is able to block the application's IMEI
+requests is introduced. Also this paper lists unsuccessful methods that tried
+to provide the user security. Obviously with those restrictions application may
+lack some of features that can only be granted in unsecured environment.
+",1,0,0,0,0,0
+8561,PMU-Based Estimation of Dynamic State Jacobian Matrix,"  In this paper, a hybrid measurement- and model-based method is proposed which
+can estimate the dynamic state Jacobian matrix in near real-time. The proposed
+method is computationally efficient and robust to the variation of network
+topology. A numerical example is given to show that the proposed method is able
+to provide good estimation for the dynamic state Jacobian matrix and is
+superior to the model-based method under undetectable network topology change.
+The proposed method may also help identify big discrepancy in the assumed
+network model.
+",1,0,0,0,0,0
+8562,DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecast,"  Running high-resolution physical models is computationally expensive and
+essential for many disciplines. Agriculture, transportation, and energy are
+sectors that depend on high-resolution weather models, which typically consume
+many hours of large High Performance Computing (HPC) systems to deliver timely
+results. Many users cannot afford to run the desired resolution and are forced
+to use low resolution output. One simple solution is to interpolate results for
+visualization. It is also possible to combine an ensemble of low resolution
+models to obtain a better prediction. However, these approaches fail to capture
+the redundant information and patterns in the low-resolution input that could
+help improve the quality of prediction. In this paper, we propose and evaluate
+a strategy based on a deep neural network to learn a high-resolution
+representation from low-resolution predictions using weather forecast as a
+practical use case. We take a supervised learning approach, since obtaining
+labeled data can be done automatically. Our results show significant
+improvement when compared with standard practices and the strategy is still
+lightweight enough to run on modest computer systems.
+",0,0,0,1,0,0
+8563,"Open quantum random walks on the half-line: the Karlin-McGregor formula, path counting and Foster's Theorem","  In this work we consider open quantum random walks on the non-negative
+integers. By considering orthogonal matrix polynomials we are able to describe
+transition probability expressions for classes of walks via a matrix version of
+the Karlin-McGregor formula. We focus on absorbing boundary conditions and, for
+simpler classes of examples, we consider path counting and the corresponding
+combinatorial tools. A non-commutative version of the gambler's ruin is studied
+by obtaining the probability of reaching a certain fortune and the mean time to
+reach a fortune or ruin in terms of generating functions. In the case of the
+Hadamard coin, a counting technique for boundary restricted paths in a lattice
+is also presented. We discuss an open quantum version of Foster's Theorem for
+the expected return time together with applications.
+",0,0,1,0,0,0
+8564,Nevanlinna classes associated to a closed set on $\partial$D,"  We introduce Nevanlinna classes of holomorphic functions associated to a
+closed set on the boundary of the unit disc in the complex plane and we get
+Blaschke type theorems relative to these classes by use of several complex
+variables methods. This gives alternative proofs of some results of Favorov \&
+Golinskii, useful, in particular, for the study of eigenvalues of non self
+adjoint Schr{ö}dinger operators.
+",0,0,1,0,0,0
+8565,"Exploring the nuances in the relationship ""culture-strategy"" for the business world","  The current article explores interesting, significant and recently identified
+nuances in the relationship ""culture-strategy"". The shared views of leading
+scholars at the University of National and World Economy in relation with the
+essence, direction, structure, role and hierarchy of ""culture-strategy""
+relation are defined as a starting point of the analysis. The research emphasis
+is directed on recent developments in interpreting the observed realizations of
+the aforementioned link among the community of international scholars and
+consultants, publishing in selected electronic scientific databases. In this
+way a contemporary notion of the nature of ""culture-strategy"" relationship for
+the entities from the world of business is outlined.
+",0,0,0,0,0,1
+8566,Audio style transfer,"  'Style transfer' among images has recently emerged as a very active research
+topic, fuelled by the power of convolution neural networks (CNNs), and has
+become fast a very popular technology in social media. This paper investigates
+the analogous problem in the audio domain: How to transfer the style of a
+reference audio signal to a target audio content? We propose a flexible
+framework for the task, which uses a sound texture model to extract statistics
+characterizing the reference audio style, followed by an optimization-based
+audio texture synthesis to modify the target content. In contrast to mainstream
+optimization-based visual transfer method, the proposed process is initialized
+by the target content instead of random noise and the optimized loss is only
+about texture, not structure. These differences proved key for audio style
+transfer in our experiments. In order to extract features of interest, we
+investigate different architectures, whether pre-trained on other tasks, as
+done in image style transfer, or engineered based on the human auditory system.
+Experimental results on different types of audio signal confirm the potential
+of the proposed approach.
+",1,1,0,0,0,0
+8567,Hyperbolic pseudoinverses for kinematics in the Euclidean group,"  The kinematics of a robot manipulator are described in terms of the mapping
+connecting its joint space and the 6-dimensional Euclidean group of motions
+$SE(3)$. The associated Jacobian matrices map into its Lie algebra
+$\mathfrak{se}(3)$, the space of twists describing infinitesimal motion of a
+rigid body. Control methods generally require knowledge of an inverse for the
+Jacobian. However for an arm with fewer or greater than six actuated joints or
+at singularities of the kinematic mapping this breaks down. The Moore-Penrose
+pseudoinverse has frequently been used as a surrogate but is not invariant
+under change of coordinates. Since the Euclidean Lie algebra carries a pencil
+of invariant bilinear forms that are indefinite, a family of alternative
+hyperbolic pseudoinverses is available. Generalised Gram matrices and the
+classification of screw systems are used to determine conditions for their
+existence. The existence or otherwise of these pseudoinverses also relates to a
+classical problem addressed by Sylvester concerning the conditions for a system
+of lines to be in involution or, equivalently, the corresponding system of
+generalised forces to be in equilibrium.
+",0,0,1,0,0,0
+8568,Nonlinear Unknown Input and State Estimation Algorithm in Mobile Robots,"  This technical report provides the description and the derivation of a novel
+nonlinear unknown input and state estimation algorithm (NUISE) for mobile
+robots. The algorithm is designed for real-world robots with nonlinear dynamic
+models and subject to stochastic noises on sensing and actuation. Leveraging
+sensor readings and planned control commands, the algorithm detects and
+quantifies anomalies on both sensors and actuators. Later, we elaborate the
+dynamic models of two distinctive mobile robots for the purpose of
+demonstrating the application of NUISE. This report serves as a supplementary
+document for [1].
+",1,0,0,0,0,0
+8569,Acute sets,"  A set of points in $\mathbb{R}^d$ is acute, if any three points from this set
+form an acute triangle. In this note we construct an acute set in
+$\mathbb{R}^d$ of size at least $1.618^d$. Also, we present a simple example of
+an acute set of size at least $2^{\frac{d}{2}}$.
+",0,0,1,0,0,0
+8570,Session Analysis using Plan Recognition,"  This paper presents preliminary results of our work with a major financial
+company, where we try to use methods of plan recognition in order to
+investigate the interactions of a costumer with the company's online interface.
+In this paper, we present the first steps of integrating a plan recognition
+algorithm in a real-world application for detecting and analyzing the
+interactions of a costumer. It uses a novel approach for plan recognition from
+bare-bone UI data, which reasons about the plan library at the lowest
+recognition level in order to define the relevancy of actions in our domain,
+and then uses it to perform plan recognition.
+We present preliminary results of inference on three different use-cases
+modeled by domain experts from the company, and show that this approach manages
+to decrease the overload of information required from an analyst to evaluate a
+costumer's session - whether this is a malicious or benign session, whether the
+intended tasks were completed, and if not - what actions are expected next.
+",1,0,0,0,0,0
+8571,Personalized Driver Stress Detection with Multi-task Neural Networks using Physiological Signals,"  Stress can be seen as a physiological response to everyday emotional, mental
+and physical challenges. A long-term exposure to stressful situations can have
+negative health consequences, such as increased risk of cardiovascular diseases
+and immune system disorder. Therefore, a timely stress detection can lead to
+systems for better management and prevention in future circumstances. In this
+paper, we suggest a multi-task learning based neural network approach (with
+hard parameter sharing of mutual representation and task-specific layers) for
+personalized stress recognition using skin conductance and heart rate from
+wearable devices. The proposed method is tested on multi-modal physiological
+responses collected during real-world and simulator driving tasks.
+",1,0,0,0,0,0
+8572,Budgeted Experiment Design for Causal Structure Learning,"  We study the problem of causal structure learning when the experimenter is
+limited to perform at most $k$ non-adaptive experiments of size $1$. We
+formulate the problem of finding the best intervention target set as an
+optimization problem, which aims to maximize the average number of edges whose
+directions are resolved. We prove that the corresponding objective function is
+submodular and a greedy algorithm suffices to achieve
+$(1-\frac{1}{e})$-approximation of the optimal value. We further present an
+accelerated variant of the greedy algorithm, which can lead to orders of
+magnitude performance speedup. We validate our proposed approach on synthetic
+and real graphs. The results show that compared to the purely observational
+setting, our algorithm orients the majority of the edges through a considerably
+small number of interventions.
+",1,0,0,1,0,0
+8573,Evaluating the Robustness of Rogue Waves Under Perturbations,"  Rogue waves, and their periodic counterparts, have been shown to exist in a
+number of integrable models. However, relatively little is known about the
+existence of these objects in models where an exact formula is unattainable. In
+this work, we develop a novel numerical perspective towards identifying such
+states as localized solutions in space-time. Importantly, we illustrate that
+this methodology in addition to benchmarking known solutions (and confirming
+their numerical propagation under controllable error) enables the continuation
+of such solutions over parametric variations to non-integrable models. As a
+result, we can answer in the positive the question about the parametric
+robustness of Peregrine-like waveforms and even of generalizations thereof on a
+cnoidal wave background.
+",0,1,0,0,0,0
+8574,Feeble fish in time-dependent waters and homogenization of the G-equation,"  We study the following control problem. A fish with bounded aquatic
+locomotion speed swims in fast waters. Can this fish, under reasonable
+assumptions, get to a desired destination? It can, even if the flow is
+time-dependent. Moreover, given a prescribed sufficiently large time $t$, it
+can be there at exactly the time $t$. The major difference from our previous
+work is the time-dependence of the flow. We also give an application to
+homogenization of the G-equation.
+",0,0,1,0,0,0
+8575,Betti tables for indecomposable matrix factorizations of $XY(X-Y)(X-λY)$,"  We classify the Betti tables of indecomposable graded matrix factorizations
+over the simple elliptic singularity $f_\lambda = XY(X-Y)(X-\lambda Y)$ by
+making use of an associated weighted projective line of genus one.
+",0,0,1,0,0,0
+8576,"Discrete Integrable Systems, Supersymmetric Quantum Mechanics, and Framed BPS States - I","  It is possible to understand whether a given BPS spectrum is generated by a
+relevant deformation of a 4D N=2 SCFT or of an asymptotically free theory from
+the periodicity properties of the corresponding quantum monodromy. With the aim
+of giving a better understanding of the above conjecture, in this paper we
+revisit the description of framed BPS states of four-dimensional relativistic
+quantum field theories with eight conserved supercharges in terms of
+supersymmetric quantum mechanics. We unveil aspects of the deep
+interrelationship in between the Seiberg-dualities of the latter, the discrete
+symmetries of the theory in the bulk, and quantum discrete integrable systems.
+",0,1,1,0,0,0
+8577,Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression,"  In order to scale standard Gaussian process (GP) regression to large-scale
+datasets, aggregation models employ factorized training process and then
+combine predictions from distributed experts. The state-of-the-art aggregation
+models, however, either provide inconsistent predictions or require
+time-consuming aggregation process. We first prove the inconsistency of typical
+aggregations using disjoint or random data partition, and then present a
+consistent yet efficient aggregation model for large-scale GP. The proposed
+model inherits the advantages of aggregations, e.g., closed-form inference and
+aggregation, parallelization and distributed computing. Furthermore,
+theoretical and empirical analyses reveal that the new aggregation model
+performs better due to the consistent predictions that converge to the true
+underlying function when the training size approaches infinity.
+",0,0,0,1,0,0
+8578,Network Inference from a Link-Traced Sample using Approximate Bayesian Computation,"  We present a new inference method based on approximate Bayesian computation
+for estimating parameters governing an entire network based on link-traced
+samples of that network. To do this, we first take summary statistics from an
+observed link-traced network sample, such as a recruitment network of subjects
+in a hard-to-reach population. Then we assume prior distributions, such as
+multivariate uniform, for the distribution of some parameters governing the
+structure of the network and behaviour of its nodes. Then, we draw many
+independent and identically distributed values for these parameters. For each
+set of values, we simulate a population network, take a link-traced sample from
+that network, and find the summary statistics for that sample. The statistics
+from the sample, and the parameters that eventually led to that sample, are
+collectively treated as a single point. We take a Kernel Density estimate of
+the points from many simulations, and observe the density across the hyperplane
+coinciding with the statistic values of the originally observed sample. This
+density function is treat as a posterior estimate of the paramaters of the
+network that provided the observed sample.
+We also apply this method to a network of precedence citations between legal
+documents, centered around cases overseen by the Supreme Court of Canada, is
+observed. The features of certain cases that lead to their frequent citation
+are inferred, and their effects estimated by ABC. Future work and extensions
+are also briefly discussed.
+",1,1,0,1,0,0
+8579,Variational Dropout Sparsifies Deep Neural Networks,"  We explore a recently proposed Variational Dropout technique that provided an
+elegant Bayesian interpretation to Gaussian Dropout. We extend Variational
+Dropout to the case when dropout rates are unbounded, propose a way to reduce
+the variance of the gradient estimator and report first experimental results
+with individual dropout rates per weight. Interestingly, it leads to extremely
+sparse solutions both in fully-connected and convolutional layers. This effect
+is similar to automatic relevance determination effect in empirical Bayes but
+has a number of advantages. We reduce the number of parameters up to 280 times
+on LeNet architectures and up to 68 times on VGG-like networks with a
+negligible decrease of accuracy.
+",1,0,0,1,0,0
+8580,A note on clustered cells,"  This note contains additions to the paper 'Clustered cell decomposition in
+P-minimal structures' (arXiv:1612.02683). We discuss a question which was
+raised in that paper, on the order of clustered cells. We also consider a
+notion of cells of minimal order, which is a slight optimalisation of the
+theorem from the original paper.
+",0,0,1,0,0,0
+8581,Record statistics of a strongly correlated time series: random walks and Lévy flights,"  We review recent advances on the record statistics of strongly correlated
+time series, whose entries denote the positions of a random walk or a Lévy
+flight on a line. After a brief survey of the theory of records for independent
+and identically distributed random variables, we focus on random walks. During
+the last few years, it was indeed realized that random walks are a very useful
+""laboratory"" to test the effects of correlations on the record statistics. We
+start with the simple one-dimensional random walk with symmetric jumps (both
+continuous and discrete) and discuss in detail the statistics of the number of
+records, as well as of the ages of the records, i.e., the lapses of time
+between two successive record breaking events. Then we review the results that
+were obtained for a wide variety of random walk models, including random walks
+with a linear drift, continuous time random walks, constrained random walks
+(like the random walk bridge) and the case of multiple independent random
+walkers. Finally, we discuss further observables related to records, like the
+record increments, as well as some questions raised by physical applications of
+record statistics, like the effects of measurement error and noise.
+",0,1,1,0,0,0
+8582,On nonlinear instability of Prandtl's boundary layers: the case of Rayleigh's stable shear flows,"  In this paper, we study Prandtl's boundary layer asymptotic expansion for
+incompressible fluids on the half-space in the inviscid limit. In \cite{Gr1},
+E. Grenier proved that Prandtl's Ansatz is false for data with Sobolev
+regularity near Rayleigh's unstable shear flows. In this paper, we show that
+this Ansatz is also false for Rayleigh's stable shear flows. Namely we
+construct unstable solutions near arbitrary stable monotonic boundary layer
+profiles. Such shear flows are stable for Euler equations, but not for
+Navier-Stokes equations: adding a small viscosity destabilizes the flow.
+",0,0,1,0,0,0
+8583,Subspace Clustering with Missing and Corrupted Data,"  Given full or partial information about a collection of points that lie close
+to a union of several subspaces, subspace clustering refers to the process of
+clustering the points according to their subspace and identifying the
+subspaces. One popular approach, sparse subspace clustering (SSC), represents
+each sample as a weighted combination of the other samples, with weights of
+minimal $\ell_1$ norm, and then uses those learned weights to cluster the
+samples. SSC is stable in settings where each sample is contaminated by a
+relatively small amount of noise. However, when there is a significant amount
+of additive noise, or a considerable number of entries are missing, theoretical
+guarantees are scarce. In this paper, we study a robust variant of SSC and
+establish clustering guarantees in the presence of corrupted or missing data.
+We give explicit bounds on amount of noise and missing data that the algorithm
+can tolerate, both in deterministic settings and in a random generative model.
+Notably, our approach provides guarantees for higher tolerance to noise and
+missing data than existing analyses for this method. By design, the results
+hold even when we do not know the locations of the missing data; e.g., as in
+presence-only data.
+",0,0,0,1,0,0
+8584,Monte Carlo Tensor Network Renormalization,"  Techniques for approximately contracting tensor networks are limited in how
+efficiently they can make use of parallel computing resources. In this work we
+demonstrate and characterize a Monte Carlo approach to the tensor network
+renormalization group method which can be used straightforwardly on modern
+computing architectures. We demonstrate the efficiency of the technique and
+show that Monte Carlo tensor network renormalization provides an attractive
+path to improving the accuracy of a wide class of challenging computations
+while also providing useful estimates of uncertainty and a statistical
+guarantee of unbiased results.
+",0,1,0,0,0,0
+8585,Experimental study of electron and phonon dynamics in nanoscale materials by ultrafast laser time-domain spectroscopy,"  With the rapid advances in the development of nanotechnology, nowadays, the
+sizes of elementary unit, i.e. transistor, of micro- and nanoelectronic devices
+are well deep into nanoscale. For the pursuit of cheaper and faster nanoscale
+electronic devices, the size of transistors keeps scaling down. As the
+miniaturization of the nanoelectronic devices, the electrical resistivity
+increases dramatically, resulting rapid growth in the heat generation. The heat
+generation and limited thermal dissipation in nanoscale materials have become a
+critical problem in the development of the next generation nanoelectronic
+devices. Copper (Cu) is widely used conducting material in nanoelectronic
+devices, and the electron-phonon scattering is the dominant contributor to the
+resistivity in Cu nanowires at room temperature. Meanwhile, phonons are the
+main carriers of heat in insulators, intrinsic and lightly doped
+semiconductors. The thermal transport is an ensemble of phonon transport, which
+strongly depends on the phonon frequency. In addition, the phonon transport in
+nanoscale materials can behave fundamentally different than in bulk materials,
+because of the spatial confinement. However, the size effect on electron-phonon
+scattering and frequency dependent phonon transport in nanoscale materials
+remain largely unexplored, due to the lack of suitable experimental techniques.
+This thesis is mainly focusing on the study of carrier dynamics and acoustic
+phonon transport in nanoscale materials.
+",0,1,0,0,0,0
+8586,SSGP topologies on abelian groups of positive finite divisible rank,"  Let G be an abelian group. For a subset A of G, Cyc(A) denotes the set of all
+elements x of G such that the cyclic subgroup generated by x is contained in A,
+and G is said to have the small subgroup generating property (abbreviated to
+SSGP) if the smallest subgroup of G generated by Cyc(U) is dense in G for every
+neighbourhood U of zero of G. SSGP groups form a proper subclass of the class
+of minimally almost periodic groups. Comfort and Gould asked for a
+characterization of abelian groups G which admit an SSGP group topology, and
+they solved this problem for bounded torsion groups (which have divisible rank
+zero). Dikranjan and the first author proved that an abelian group of infinite
+divisible rank admits an SSGP group topology. In the remaining case of positive
+finite divisible rank, the same authors found a necessary condition on G in
+order to admit an SSGP group topology and asked if this condition is also
+sufficient. We answer this question positively, thereby completing the
+characterization of abelian groups which admit an SSGP group topology.
+",0,0,1,0,0,0
+8587,Intelligent Pothole Detection and Road Condition Assessment,"  Poor road conditions are a public nuisance, causing passenger discomfort,
+damage to vehicles, and accidents. In the U.S., road-related conditions are a
+factor in 22,000 of the 42,000 traffic fatalities each year. Although we often
+complain about bad roads, we have no way to detect or report them at scale. To
+address this issue, we developed a system to detect potholes and assess road
+conditions in real-time. Our solution is a mobile application that captures
+data on a car's movement from gyroscope and accelerometer sensors in the phone.
+To assess roads using this sensor data, we trained SVM models to classify road
+conditions with 93% accuracy and potholes with 92% accuracy, beating the base
+rate for both problems. As the user drives, the models use the sensor data to
+classify whether the road is good or bad, and whether it contains potholes.
+Then, the classification results are used to create data-rich maps that
+illustrate road conditions across the city. Our system will empower civic
+officials to identify and repair damaged roads which inconvenience passengers
+and cause accidents. This paper details our data science process for collecting
+training data on real roads, transforming noisy sensor data into useful
+signals, training and evaluating machine learning models, and deploying those
+models to production through a real-time classification app. It also highlights
+how cities can use our system to crowdsource data and deliver road repair
+resources to areas in need.
+",1,0,0,0,0,0
+8588,Concrete Autoencoders for Differentiable Feature Selection and Reconstruction,"  We introduce the concrete autoencoder, an end-to-end differentiable method
+for global feature selection, which efficiently identifies a subset of the most
+informative features and simultaneously learns a neural network to reconstruct
+the input data from the selected features. Our method is unsupervised, and is
+based on using a concrete selector layer as the encoder and using a standard
+neural network as the decoder. During the training phase, the temperature of
+the concrete selector layer is gradually decreased, which encourages a
+user-specified number of discrete features to be learned. During test time, the
+selected features can be used with the decoder network to reconstruct the
+remaining input features. We evaluate concrete autoencoders on a variety of
+datasets, where they significantly outperform state-of-the-art methods for
+feature selection and data reconstruction. In particular, on a large-scale gene
+expression dataset, the concrete autoencoder selects a small subset of genes
+whose expression levels can be use to impute the expression levels of the
+remaining genes. In doing so, it improves on the current widely-used
+expert-curated L1000 landmark genes, potentially reducing measurement costs by
+20%. The concrete autoencoder can be implemented by adding just a few lines of
+code to a standard autoencoder.
+",1,0,0,1,0,0
+8589,Emergence and complexity in theoretical models of self-organized criticality,"  In this thesis we present few theoretical studies of the models of
+self-organized criticality. Following a brief introduction of self-organized
+criticality, we discuss three main problems. The first problem is about growing
+patterns formed in the abelian sandpile model (ASM). The patterns exhibit
+proportionate growth where different parts of the pattern grow in same rate,
+keeping the overall shape unchanged. This non-trivial property, often found in
+biological growth, has received increasing attention in recent years. In this
+thesis, we present a mathematical characterization of a large class of such
+patterns in terms of discrete holomorphic functions. In the second problem, we
+discuss a well known model of self-organized criticality introduced by Zhang in
+1989. We present an exact analysis of the model and quantitatively explain an
+intriguing property known as the emergence of quasi-units. In the third
+problem, we introduce an operator algebra to determine the steady state of a
+class of stochastic sandpile models.
+",0,1,1,0,0,0
+8590,An Extension of Averaged-Operator-Based Algorithms,"  Many of the algorithms used to solve minimization problems with
+sparsity-inducing regularizers are generic in the sense that they do not take
+into account the sparsity of the solution in any particular way. However,
+algorithms known as semismooth Newton are able to take advantage of this
+sparsity to accelerate their convergence. We show how to extend these
+algorithms in different directions, and study the convergence of the resulting
+algorithms by showing that they are a particular case of an extension of the
+well-known Krasnosel'ski\u{\i}--Mann scheme.
+",0,0,0,1,0,0
+8591,Along the sun-drenched roadside: On the interplay between urban street orientation entropy and the buildings' solar potential,"  We explore the relation between urban road network characteristics
+particularly circuitry, street orientation entropy and the city's topography on
+the one hand and the building's orientation entropy on the other in order to
+quantify their effect on the city's solar potential. These statistical measures
+of the road network reveal the interplay between the built environment's design
+and its sustainability.
+",0,1,0,0,0,0
+8592,$HD(M\setminus L)>0.353$,"  The complement $M\setminus L$ of the Lagrange spectrum $L$ in the Markov
+spectrum $M$ was studied by many authors (including Freiman, Berstein, Cusick
+and Flahive). After their works, we disposed of a countable collection of
+points in $M\setminus L$.
+In this article, we describe the structure of $M\setminus L$ near a
+non-isolated point $\alpha_{\infty}$ found by Freiman in 1973, and we use this
+description to exhibit a concrete Cantor set $X$ whose Hausdorff dimension
+coincides with the Hausdorff dimension of $M\setminus L$ near
+$\alpha_{\infty}$.
+A consequence of our results is the lower bound $HD(M\setminus L)>0.353$ on
+the Hausdorff dimension $HD(M\setminus L)$ of $M\setminus L$. Another
+by-product of our analysis is the explicit construction of new elements of
+$M\setminus L$, including its largest known member $c\in M\setminus L$
+(surpassing the former largest known number $\alpha_4\in M\setminus L$ obtained
+by Cusick and Flahive in 1989).
+",0,0,1,0,0,0
+8593,"Comparing the dark matter models, modified Newtonian dynamics and modified gravity in accounting for the galaxy rotation curves","  We compare six models (including the baryonic model, two dark matter models,
+two modified Newtonian dynamics models and one modified gravity model) in
+accounting for the galaxy rotation curves. For the dark matter models, we
+assume NFW profile and core-modified profile for the dark halo, respectively.
+For the modified Newtonian dynamics models, we discuss Milgrom's MOND theory
+with two different interpolation functions, i.e. the standard and the simple
+interpolation functions. As for the modified gravity, we focus on Moffat's MSTG
+theory. We fit these models to the observed rotation curves of 9 high-surface
+brightness and 9 low-surface brightness galaxies. We apply the Bayesian
+Information Criterion and the Akaike Information Criterion to test the
+goodness-of-fit of each model. It is found that non of the six models can well
+fit all the galaxy rotation curves. Two galaxies can be best fitted by the
+baryonic model without involving the nonluminous dark matter. MOND can fit the
+largest number of galaxies, and only one galaxy can be best fitted by MSTG
+model. Core-modified model can well fit about one half LSB galaxies but no HSB
+galaxy, while NFW model can fit only a small fraction of HSB galaxies but no
+LSB galaxy. This may imply that the oversimplified NFW and Core-modified
+profiles couldn't well mimic the postulated dark matter halo.
+",0,1,0,0,0,0
+8594,Good Clusterings Have Large Volume,"  The clustering of a data set is one of the core tasks in data analytics. Many
+clustering algorithms exhibit a strong contrast between a favorable performance
+in practice and bad theoretical worst-cases. Prime examples are least-squares
+assignments and the popular $k$-means algorithm. We are interested in this
+contrast and study it through polyhedral theory. Several popular clustering
+algorithms can be connected to finding a vertex of the so-called bounded-shape
+partition polytopes. The vertices correspond to clusterings with extraordinary
+separation properties, in particular allowing the construction of a separating
+power diagram, defined by its so-called sites, such that each cluster has its
+own cell.
+First, we quantitatively measure the space of all sites that allow
+construction of a separating power diagram for a clustering by the volume of
+the normal cone at the corresponding vertex. This gives rise to a new quality
+criterion for clusterings, and explains why good clusterings are also the most
+likely to be found by some classical algorithms. Second, we characterize the
+edges of the bounded-shape partition polytopes. Through this, we obtain an
+explicit description of the normal cones. This allows us to compute measures
+with respect to the new quality criterion, and even compute ""most stable""
+sites, and thereby ""most stable"" power diagrams, for the separation of
+clusters. The hardness of these computations depends on the number of edges
+incident to a vertex, which may be exponential. However, the computational
+effort is rewarded with a wealth of information that can be gained from the
+results, which we highlight through some proof-of-concept computations.
+",0,0,1,0,0,0
+8595,Regularity of Lie Groups,"  We solve the regularity problem for Milnor's infinite dimensional Lie groups
+in the $C^0$-topological context, and provide necessary and sufficient
+regularity conditions for the standard setting ($C^k$-topology). We prove that
+the evolution map is $C^0$-continuous on its domain $\textit{iff}\hspace{1pt}$
+the Lie group $G$ is locally $\mu$-convex. We furthermore show that if the
+evolution map is defined on all smooth curves, then $G$ is Mackey complete -
+This is a completeness condition formulated in terms of the Lie group
+operations that generalizes Mackey completeness as defined for locally convex
+vector spaces. Then, under the presumption that $G$ is locally $\mu$-convex, we
+show that each $C^k$-curve, for $k\in \mathbb{N}_{\geq
+1}\sqcup\{\mathrm{lip},\infty\}$, is integrable (contained in the domain of the
+evolution map) $\textit{iff}\hspace{1pt}$ $G$ is Mackey complete and
+$\mathrm{k}$-confined. The latter condition states that each $C^k$-curve in the
+Lie algebra $\mathfrak{g}$ of $G$ can be uniformly approximated by a special
+type of sequence consisting of piecewise integrable curves - A similar result
+is proven for the case $k\equiv 0$; and we provide several mild conditions that
+ensure that $G$ is $\mathrm{k}$-confined for each $k\in
+\mathbb{N}\sqcup\{\mathrm{lip},\infty\}$. We finally discuss the
+differentiation of parameter-dependent integrals in the standard topological
+context. In particular, we show that if the evolution map is well defined and
+continuous on $C^k([0,1],\mathfrak{g})$ for $k\in \mathbb{N}\sqcup\{\infty\}$,
+then it is smooth thereon $\textit{iff}\hspace{1pt}$ $\mathfrak{g}$ is
+$\hspace{0.2pt}$ Mackey complete for $k\in \mathbb{N}_{\geq 1}\sqcup\{\infty\}$
+$\hspace{1pt}/\hspace{1pt}$ integral complete for $k\equiv 0$. This result is
+obtained by calculating the directional derivatives explicitly - recovering the
+standard formulas that hold in the Banach case.
+",0,0,1,0,0,0
+8596,A study on text-score disagreement in online reviews,"  In this paper, we focus on online reviews and employ artificial intelligence
+tools, taken from the cognitive computing field, to help understanding the
+relationships between the textual part of the review and the assigned numerical
+score. We move from the intuitions that 1) a set of textual reviews expressing
+different sentiments may feature the same score (and vice-versa); and 2)
+detecting and analyzing the mismatches between the review content and the
+actual score may benefit both service providers and consumers, by highlighting
+specific factors of satisfaction (and dissatisfaction) in texts.
+To prove the intuitions, we adopt sentiment analysis techniques and we
+concentrate on hotel reviews, to find polarity mismatches therein. In
+particular, we first train a text classifier with a set of annotated hotel
+reviews, taken from the Booking website. Then, we analyze a large dataset, with
+around 160k hotel reviews collected from Tripadvisor, with the aim of detecting
+a polarity mismatch, indicating if the textual content of the review is in
+line, or not, with the associated score.
+Using well established artificial intelligence techniques and analyzing in
+depth the reviews featuring a mismatch between the text polarity and the score,
+we find that -on a scale of five stars- those reviews ranked with middle scores
+include a mixture of positive and negative aspects.
+The approach proposed here, beside acting as a polarity detector, provides an
+effective selection of reviews -on an initial very large dataset- that may
+allow both consumers and providers to focus directly on the review subset
+featuring a text/score disagreement, which conveniently convey to the user a
+summary of positive and negative features of the review target.
+",1,0,0,0,0,0
+8597,Convergence rates for nonequilibrium Langevin dynamics,"  We study the exponential convergence to the stationary state for
+nonequilibrium Langevin dynamics, by a perturbative approach based on
+hypocoercive techniques developed for equilibrium Langevin dynamics. The
+Hamiltonian and overdamped limits (corresponding respectively to frictions
+going to zero or infinity) are carefully investigated. In particular, the
+maximal magnitude of admissible perturbations are quantified as a function of
+the friction. Numerical results based on a Galerkin discretization of the
+generator of the dynamics confirm the theoretical lower bounds on the spectral
+gap.
+",0,1,1,0,0,0
+8598,Natural Extension of Hartree-Fock through extremal $1$-fermion information: Overview and application to the lithium atom,"  Fermionic natural occupation numbers do not only obey Pauli's exclusion
+principle but are even stronger restricted by so-called generalized Pauli
+constraints. Whenever given natural occupation numbers lie on the boundary of
+the allowed region the corresponding $N$-fermion quantum state has a
+significantly simpler structure. We recall the recently proposed natural
+extension of the Hartree-Fock ansatz based on this structural simplification.
+This variational ansatz is tested for the lithium atom. Intriguingly, the
+underlying mathematical structure yields universal geometrical bounds on the
+correlation energy reconstructed by this ansatz.
+",0,1,0,0,0,0
+8599,Homotopy Parametric Simplex Method for Sparse Learning,"  High dimensional sparse learning has imposed a great computational challenge
+to large scale data analysis. In this paper, we are interested in a broad class
+of sparse learning approaches formulated as linear programs parametrized by a
+{\em regularization factor}, and solve them by the parametric simplex method
+(PSM). Our parametric simplex method offers significant advantages over other
+competing methods: (1) PSM naturally obtains the complete solution path for all
+values of the regularization parameter; (2) PSM provides a high precision dual
+certificate stopping criterion; (3) PSM yields sparse solutions through very
+few iterations, and the solution sparsity significantly reduces the
+computational cost per iteration. Particularly, we demonstrate the superiority
+of PSM over various sparse learning approaches, including Dantzig selector for
+sparse linear regression, LAD-Lasso for sparse robust linear regression, CLIME
+for sparse precision matrix estimation, sparse differential network estimation,
+and sparse Linear Programming Discriminant (LPD) analysis. We then provide
+sufficient conditions under which PSM always outputs sparse solutions such that
+its computational performance can be significantly boosted. Thorough numerical
+experiments are provided to demonstrate the outstanding performance of the PSM
+method.
+",1,0,1,1,0,0
+8600,"Fractional Operators with Inhomogeneous Boundary Conditions: Analysis, Control, and Discretization","  In this paper we introduce new characterizations of spectral fractional
+Laplacian to incorporate nonhomogeneous Dirichlet and Neumann boundary
+conditions. The classical cases with homogeneous boundary conditions arise as a
+special case. We apply our definition to fractional elliptic equations of order
+$s \in (0,1)$ with nonzero Dirichlet and Neumann boundary condition. Here the
+domain $\Omega$ is assumed to be a bounded, quasi-convex Lipschitz domain. To
+impose the nonzero boundary conditions, we construct fractional harmonic
+extensions of the boundary data. It is shown that solving for the fractional
+harmonic extension is equivalent to solving for the standard harmonic extension
+in the very-weak form. The latter result is of independent interest as well.
+The remaining fractional elliptic problem (with homogeneous boundary data) can
+be realized using the existing techniques. We introduce finite element
+discretizations and derive discretization error estimates in natural norms,
+which are confirmed by numerical experiments. We also apply our
+characterizations to Dirichlet and Neumann boundary optimal control problems
+with fractional elliptic equation as constraints.
+",0,0,1,0,0,0
+8601,An alternative definition of cobordism map of ECH,"  In this article, we reformulate the cobordism map of embedded contact
+homology, which is induced by exact symplectic cobordism and defined as direct
+limit of homomorphisms called filtered ECH cobordism map. The filtered ECH
+cobordism map is defined by counting embedded holomorphic curves with zero ECH
+index and we prove that it is independent on almost complex structure by
+Seiberg Witten theory. Moreover, our definition in fact is equivalent to the
+existing definition.
+",0,0,1,0,0,0
+8602,Healthy imperfect dark matter from effective theory of mimetic cosmological perturbations,"  We study the stability of a recently proposed model of scalar-field matter
+called mimetic dark matter or imperfect dark matter. It has been known that
+mimetic matter with higher derivative terms suffers from gradient instabilities
+in scalar perturbations. To seek for an instability-free extension of imperfect
+dark matter, we develop an effective theory of cosmological perturbations
+subject to the constraint on the scalar field's kinetic term. This is done by
+using the unifying framework of general scalar-tensor theories based on the ADM
+formalism. We demonstrate that it is indeed possible to construct a model of
+imperfect dark matter which is free from ghost and gradient instabilities. As a
+side remark, we also show that mimetic $F({\cal R})$ theory is plagued with the
+Ostrogradsky instability.
+",0,1,0,0,0,0
+8603,Disorder-protected topological entropy after a quantum quench,"  Topological phases of matter are considered the bedrock of novel quantum
+materials as well as ideal candidates for quantum computers that possess
+robustness at the physical level. The robustness of the topological phase at
+finite temperature or away from equilibrium is therefore a very desirable
+feature. Disorder can improve the lifetime of the encoded topological qubits.
+Here we tackle the problem of the survival of the topological phase as detected
+by topological entropy, after a sudden quantum quench. We introduce a method to
+study analytically the time evolution of the system after a quantum quench and
+show that disorder in the couplings of the Hamiltonian of the toric code and
+the resulting Anderson localization can make the topological entropy resilient.
+",0,1,0,0,0,0
+8604,The evolution of gravitons in accelerating cosmologies: the case of extended gravity,"  We discuss the production and evolution of cosmological gravitons showing how
+the cosmological background affects their dynamics. Besides, the detection of
+cosmological gravitons could constitute an extremely important signature to
+discriminate among different cosmological models. Here we consider the cases of
+scalar-tensor gravity and $f(R)$ gravity where it is demonstrated the
+amplification of graviton amplitude changes if compared with General
+Relativity. Possible observational constraints are discussed.
+",0,1,0,0,0,0
+8605,CO2 packing polymorphism under confinement in cylindrical nanopores,"  We investigate the effect of cylindrical nano-confinement on the phase
+behaviour of a rigid model of carbon dioxide using both molecular dynamics and
+well tempered metadynamics. To this aim we study a simplified pore model across
+a parameter space comprising pore diameter, CO2-pore wall potential and CO2
+density. In order to systematically identify ordering events within the pore
+model we devise a generally applicable approach based on the analysis of the
+distribution of intermolecular orientations. Our simulations suggest that,
+while confinement in nano-pores inhibits the formation of known crystal
+structures, it induces a remarkable variety of ordered packings unrelated to
+their bulk counterparts, and favours the establishment of short range order in
+the fluid phase. We summarise our findings by proposing a qualitative phase
+diagram for this model.
+",0,1,0,0,0,0
+8606,CLIC: Curriculum Learning and Imitation for feature Control in non-rewarding environments,"  In this paper, we propose an unsupervised reinforcement learning agent called
+CLIC for Curriculum Learning and Imitation for Control. This agent learns to
+control features in its environment without external rewards, and observes the
+actions of a third party agent, Bob, who does not necessarily provide explicit
+guidance. CLIC selects which feature to train on and what to imitate from Bob's
+behavior by maximizing its learning progress. We show that CLIC can effectively
+identify helpful behaviors in Bob's actions, and imitate them to control the
+environment faster. CLIC can also follow Bob when he acts as a mentor and
+provides ordered demonstrations. Finally, when Bob controls features than the
+agent cannot, or in presence of a hierarchy between aspects of the environment,
+we show that CLIC ignores non-reproducible and already mastered behaviors,
+resulting in a greater benefit from imitation.
+",1,0,0,1,0,0
+8607,On the structure of continua with finite length and Golab's semicontinuity theorem,"  The main results in this note concern the characterization of the length of
+continua 1 (Theorems 2.5) and the parametrization of continua with finite
+length (Theorem 4.4). Using these results we give two independent and
+relatively elementary proofs of Golab's semicontinuity theorem.
+",0,0,1,0,0,0
+8608,Identifying combinatorially symmetric Hidden Markov Models,"  We provide a sufficient criterion for the unique parameter identification of
+combinatorially symmetric Hidden Markov Models based on the structure of their
+transition matrix. If the observed states of the chain form a zero forcing set
+of the graph of the Markov model then it is uniquely identifiable and an
+explicit reconstruction method is given.
+",0,0,1,1,0,0
+8609,Systems of small linear forms and Diophantine approximation on manifolds,"  We develop the theory of Diophantine approximation for systems of
+simultaneously small linear forms, which coefficients are drawn from any given
+analytic non-degenerate manifolds. This setup originates from a problem of
+Sprindžuk from the 1970s on approximations to several real numbers by
+conjugate algebraic numbers. Our main result is a Khintchine type theorem,
+which convergence case is established without usual monotonicity constrains and
+the divergence case is proved for Hausdorff measures. The result encompasses
+several previous findings and, within the setup considered, gives the best
+possible improvement of a recent theorem of Aka, Breuillard, Rosenzweig and
+Saxcé on extremality.
+",0,0,1,0,0,0
+8610,Generalized Zero-Shot Learning via Synthesized Examples,"  We present a generative framework for generalized zero-shot learning where
+the training and test classes are not necessarily disjoint. Built upon a
+variational autoencoder based architecture, consisting of a probabilistic
+encoder and a probabilistic conditional decoder, our model can generate novel
+exemplars from seen/unseen classes, given their respective class attributes.
+These exemplars can subsequently be used to train any off-the-shelf
+classification model. One of the key aspects of our encoder-decoder
+architecture is a feedback-driven mechanism in which a discriminator (a
+multivariate regressor) learns to map the generated exemplars to the
+corresponding class attribute vectors, leading to an improved generator. Our
+model's ability to generate and leverage examples from unseen classes to train
+the classification model naturally helps to mitigate the bias towards
+predicting seen classes in generalized zero-shot learning settings. Through a
+comprehensive set of experiments, we show that our model outperforms several
+state-of-the-art methods, on several benchmark datasets, for both standard as
+well as generalized zero-shot learning.
+",1,0,0,1,0,0
+8611,Dependence and dependence structures: estimation and visualization using distance multivariance,"  Distance multivariance is a multivariate dependence measure, which can detect
+dependencies between an arbitrary number of random vectors each of which can
+have a distinct dimension. Here we discuss several new aspects and present a
+concise overview. We relax the required moment conditions considerably and show
+that distance multivariance unifies (and extends) distance covariance and the
+Hilbert-Schmidt independence criterion HSIC, moreover also the classical linear
+dependence measures: covariance, Pearson's correlation and the RV coefficient
+appear as limiting cases. For measures based on distance multivariance the
+corresponding resampling tests are introduced, and several related measures are
+defined: a new multicorrelation which satisfies a natural set of multivariate
+dependence measure axioms and $m$-multivariance which is a new dependence
+measure yielding tests for pairwise independence and independence of higher
+order. These tests are computationally feasible and under very mild moment
+conditions they are consistent against all alternatives. Moreover, a general
+visualization scheme for higher order dependencies is proposed.
+Many illustrative examples are included. All functions for the use of
+distance multivariance in applications are published in the R-package
+'multivariance'.
+",0,0,1,1,0,0
+8612,Hybrid simulation scheme for volatility modulated moving average fields,"  We develop a simulation scheme for a class of spatial stochastic processes
+called volatility modulated moving averages. A characteristic feature of this
+model is that the behaviour of the moving average kernel at zero governs the
+roughness of realisations, whereas its behaviour away from zero determines the
+global properties of the process, such as long range dependence. Our simulation
+scheme takes this into account and approximates the moving average kernel by a
+power function around zero and by a step function elsewhere. For this type of
+approach the authors of [8], who considered an analogous model in one
+dimension, coined the expression hybrid simulation scheme. We derive the
+asymptotic mean square error of the simulation scheme and compare it in a
+simulation study with several other simulation techniques and exemplify its
+favourable performance in a simulation study.
+",0,0,0,1,0,0
+8613,Thermodynamic dislocation theory for non-uniform plastic deformations,"  The present paper extends the thermodynamic dislocation theory developed by
+Langer, Bouchbinder, and Lookmann to non-uniform plastic deformations. The free
+energy density as well as the positive definite dissipation function are
+proposed. The governing equations are derived from the variational equation. As
+illustration, the problem of plane strain constrained shear of single crystal
+deforming in single slip is solved within the proposed theory.
+",0,1,0,0,0,0
+8614,Integrated Fabry-Perot cavities as a mechanism for enhancing micro-ring resonator performance,"  We propose and experimentally demonstrate the enhancement in the filtering
+quality (Q) factor of an integrated micro-ring resonator (MRR) by embedding it
+in an integrated Fabry-Perot (FP) cavity formed by cascaded Sagnac loop
+reflectors (SLRs). By utilizing coherent interference within the FP cavity to
+reshape the transmission spectrum of the MRR, both the Q factor and the
+extinction ratio (ER) can be significantly improved. The device is
+theoretically analyzed, and practically fabricated on a silicon-on-insulator
+(SOI) wafer. Experimental results show that up to 11-times improvement in Q
+factor, together with an 8-dB increase in ER, can be achieved via our proposed
+method. The impact of varying structural parameters on the device performance
+is also investigated and verified by the measured spectra of the fabricated
+devices with different structural parameters.
+",0,1,0,0,0,0
+8615,Dark matter spin determination with directional direct detection experiments,"  If the dark matter particle has spin 0, only two types of WIMP-nucleon
+interaction can arise from the non-relativistic reduction of renormalisable
+single-mediator models for dark matter-quark interactions. Based on this
+crucial observation, we show that about 100 signal events at next generation
+directional detection experiments can be enough to enable a $2\sigma$ rejection
+of the spin 0 dark matter hypothesis in favour of alternative hypotheses where
+the dark matter particle has spin 1/2 or 1. In this context directional
+sensitivity is crucial, since anisotropy patterns in the sphere of nuclear
+recoil directions depend on the spin of the dark matter particle. For
+comparison, about 100 signal events are expected in a CF$_4$ detector operating
+at a pressure of 30 torr with an exposure of approximately 26,000
+cubic-meter-detector days for WIMPs of 100 GeV mass and a WIMP-Fluorine
+scattering cross-section of 0.25 pb. Comparable exposures are within reach of
+an array of cubic meter time projection chamber detectors.
+",0,1,0,0,0,0
+8616,Layers and Matroids for the Traveling Salesman's Paths,"  Gottschalk and Vygen proved that every solution of the subtour elimination
+linear program for traveling salesman paths is a convex combination of more and
+more restrictive ""generalized Gao-trees"". We give a short proof of this fact,
+as a layered convex combination of bases of a sequence of increasingly
+restrictive matroids. A strongly polynomial, combinatorial algorithm follows
+for finding this convex combination, which is a new tool offering polyhedral
+insight, already instrumental in recent results for the $s-t$ path TSP.
+",1,0,0,0,0,0
+8617,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,"  Recently, deep neural networks have demonstrated excellent performances in
+recognizing the age and gender on human face images. However, these models were
+applied in a black-box manner with no information provided about which facial
+features are actually used for prediction and how these features depend on
+image preprocessing, model initialization and architecture choice. We present a
+study investigating these different effects.
+In detail, our work compares four popular neural network architectures,
+studies the effect of pretraining, evaluates the robustness of the considered
+alignment preprocessings via cross-method test set swapping and intuitively
+visualizes the model's prediction strategies in given preprocessing conditions
+using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our
+evaluations on the challenging Adience benchmark show that suitable parameter
+initialization leads to a holistic perception of the input, compensating
+artefactual data representations. With a combination of simple preprocessing
+steps, we reach state of the art performance in gender recognition.
+",1,0,0,1,0,0
+8618,Variational discretization of a control-constrained parabolic bang-bang optimal control problem,"  We consider a control-constrained parabolic optimal control problem without
+Tikhonov term in the tracking functional. For the numerical treatment, we use
+variational discretization of its Tikhonov regularization: For the state and
+the adjoint equation, we apply Petrov-Galerkin schemes from [Daniels et al
+2015] in time and usual conforming finite elements in space. We prove a-priori
+estimates for the error between the discretized regularized problem and the
+limit problem. Since these estimates are not robust if the regularization
+parameter tends to zero, we establish robust estimates, which --- depending on
+the problem's regularity --- enhance the previous ones. In the special case of
+bang-bang solutions, these estimates are further improved. A numerical example
+confirms our analytical findings.
+",0,0,1,0,0,0
+8619,Determining r-Robustness of Arbitrary Digraphs Using Zero-One Linear Integer Programming,"  There has been an increase in the use of resilient control algorithms based
+on the graph theoretic properties of $r$- and $(r,s)$-robustness. These
+algorithms guarantee consensus of normally behaving agents in the presence of a
+bounded number of arbitrarily misbehaving agents if the values of the integers
+$r$ and $s$ are sufficiently high. However, determining the largest integer $r$
+for which an arbitrary digraph is $r$-robust is highly nontrivial. This paper
+introduces a novel method for calculating this value using zero-one integer
+programming. The method only requires knowledge of the graph Laplacian matrix,
+and can be formulated with affine objective and constraints, except for the
+integer constraint. Integer programming methods such as branch-and-bound can
+allow both lower and upper bounds on $r$ to be iteratively tightened.
+Simulations suggest the proposed method demonstrates greater efficiency than
+prior algorithms.
+",1,0,0,0,0,0
+8620,Uncertainty quantification in graph-based classification of high dimensional data,"  Classification of high dimensional data finds wide-ranging applications. In
+many of these applications equipping the resulting classification with a
+measure of uncertainty may be as important as the classification itself. In
+this paper we introduce, develop algorithms for, and investigate the properties
+of, a variety of Bayesian models for the task of binary classification; via the
+posterior distribution on the classification labels, these methods
+automatically give measures of uncertainty. The methods are all based around
+the graph formulation of semi-supervised learning.
+We provide a unified framework which brings together a variety of methods
+which have been introduced in different communities within the mathematical
+sciences. We study probit classification in the graph-based setting, generalize
+the level-set method for Bayesian inverse problems to the classification
+setting, and generalize the Ginzburg-Landau optimization-based classifier to a
+Bayesian setting; we also show that the probit and level set approaches are
+natural relaxations of the harmonic function approach introduced in [Zhu et al
+2003].
+We introduce efficient numerical methods, suited to large data-sets, for both
+MCMC-based sampling as well as gradient-based MAP estimation. Through numerical
+experiments we study classification accuracy and uncertainty quantification for
+our models; these experiments showcase a suite of datasets commonly used to
+evaluate graph-based semi-supervised learning algorithms.
+",1,0,0,1,0,0
+8621,Defect-induced large spin-orbit splitting in the monolayer of PtSe$_2$,"  The effect of spin-orbit coupling (SOC) on the electronic properties of
+monolayer (ML) PtSe$_2$ is dictated by the presence of the crystal inversion
+symmetry to exhibit spin polarized band without characteristic of spin
+splitting. Through fully-relativistic density-functional theory calculations,
+we show that large spin-orbit splitting can be induced by introducing point
+defects. We calculate stability of native point defects such as a Se vacancy
+(V$_{\texttt{Se}}$), a Se interstitial (Se$_{i}$), a Pt vacancy
+(V$_{\texttt{Pt}}$), and a Pt interstitial (Pt$_{i}$), and find that both the
+V$_{\texttt{Se}}$ and Se$_{i}$ have the lowest formation energy. We also find
+that in contrast to the Se$_{i}$ case exhibiting spin degeneracy in the defect
+states, the large spin-orbit splitting up to 152 meV is observed in the defect
+states of the V$_{\texttt{Se}}$. Our analyses of orbital contributions to the
+defect states show that the large spin splitting is originated from the strong
+hybridization between Pt-$d_{x{^2}+y{^2}}+d_{xy}$ and Se-$p_{x}+p_{y}$
+orbitals. Our study clarifies that the defects play an important role in the
+spin splitting properties of the PtSe$_2$ ML, which is important for designing
+future spintronic devices.
+",0,1,0,0,0,0
+8622,Clusters of Integers with Equal Total Stopping Times in the 3x + 1 Problem,"  The clustering of integers with equal total stopping times has long been
+observed in the 3x + 1 Problem, and a number of elementary results about it
+have been used repeatedly in the literature. In this paper we introduce a
+simple recursively defined function C(n), and we use it to give a necessary and
+sufficient condition for pairs of consecutive even and odd integers to have
+trajectories which coincide after a specific pair-dependent number of steps.
+Then we derive a number of standard total stopping time equalities, including
+the ones in Garner (1985), as well as several novel results.
+",0,0,1,0,0,0
+8623,Modelling the Milky Way's globular cluster system,"  We construct a model for the Galactic globular cluster system based on a
+realistic gravitational potential and a distribution function (DF) analytic in
+the action integrals. The DF comprises disc and halo components whose
+functional forms resemble those recently used to describe the stellar discs and
+stellar halo. We determine the posterior distribution of our model parameters
+using a Bayesian approach. This gives us an understanding of how well the
+globular cluster data constrain our model. The favoured parameter values of the
+disc and halo DFs are similar to values previously obtained from fits to the
+stellar disc and halo, although the cluster halo system shows clearer rotation
+than does the stellar halo. Our model reproduces the generic features of the
+globular cluster system, namely the density profile, the mean rotation
+velocity. The fraction of disc clusters coincides with the observed fraction of
+metal-rich clusters. However, the data indicate either incompatibility between
+catalogued cluster distances and current estimates of distance to the Galactic
+Centre, or failure to identify clusters behind the bulge. As the data for our
+Galaxy's components increase in volume and precision over the next few years,
+it will be rewarding to revisit the present analysis.
+",0,1,0,0,0,0
+8624,Diversity-aware Multi-Video Summarization,"  Most video summarization approaches have focused on extracting a summary from
+a single video; we propose an unsupervised framework for summarizing a
+collection of videos. We observe that each video in the collection may contain
+some information that other videos do not have, and thus exploring the
+underlying complementarity could be beneficial in creating a diverse
+informative summary. We develop a novel diversity-aware sparse optimization
+method for multi-video summarization by exploring the complementarity within
+the videos. Our approach extracts a multi-video summary which is both
+interesting and representative in describing the whole video collection. To
+efficiently solve our optimization problem, we develop an alternating
+minimization algorithm that minimizes the overall objective function with
+respect to one video at a time while fixing the other videos. Moreover, we
+introduce a new benchmark dataset, Tour20, that contains 140 videos with
+multiple human created summaries, which were acquired in a controlled
+experiment. Finally, by extensive experiments on the new Tour20 dataset and
+several other multi-view datasets, we show that the proposed approach clearly
+outperforms the state-of-the-art methods on the two problems-topic-oriented
+video summarization and multi-view video summarization in a camera network.
+",1,0,0,0,0,0
+8625,Derivation relations and duality for the sum of multiple zeta values,"  We show that the duality relation for the sum of multiple zeta values with
+fixed weight, depth and $k_1$ is deduced from the derivation relations, which
+was first conjectured by N. Kawasaki and T. Tanaka.
+",0,0,1,0,0,0
+8626,Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field,"  Statistical pattern recognition methods have provided competitive solutions
+for variable star classification at a relatively low computational cost. In
+order to perform supervised classification, a set of features is proposed and
+used to train an automatic classification system. Quantities related to the
+magnitude density of the light curves and their Fourier coefficients have been
+chosen as features in previous studies. However, some of these features are not
+robust to the presence of outliers and the calculation of Fourier coefficients
+is computationally expensive for large data sets. We propose and evaluate the
+performance of a new robust set of features using supervised classifiers in
+order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic
+pole field. We calculated the proposed set of features on six types of variable
+stars and on a set of Be star candidates reported in the literature. We
+evaluated the performance of these features using classification trees and
+random forests along with K-nearest neighbours, support vector machines, and
+gradient boosted trees methods. We tuned the classifiers with a 10-fold
+cross-validation and grid search. We validated the performance of the best
+classifier on a set of OGLE-IV light curves and applied this to find new Be
+star candidates. The random forest classifier outperformed the others. By using
+the random forest classifier and colour criteria we found 50 Be star candidates
+in the direction of the Gaia south ecliptic pole field, four of which have
+infrared colours consistent with Herbig Ae/Be stars. Supervised methods are
+very useful in order to obtain preliminary samples of variable stars extracted
+from large databases. As usual, the stars classified as Be stars candidates
+must be checked for the colours and spectroscopic characteristics expected for
+them.
+",0,1,0,0,0,0
+8627,Infinite-Dimensionality in Quantum Foundations: W*-algebras as Presheaves over Matrix Algebras,"  In this paper, W*-algebras are presented as canonical colimits of diagrams of
+matrix algebras and completely positive maps. In other words, matrix algebras
+are dense in W*-algebras.
+",1,0,1,0,0,0
+8628,MSE estimates for multitaper spectral estimation and off-grid compressive sensing,"  We obtain estimates for the Mean Squared Error (MSE) for the multitaper
+spectral estimator and certain compressive acquisition methods for multi-band
+signals. We confirm a fact discovered by Thomson [Spectrum estimation and
+harmonic analysis, Proc. IEEE, 1982]: assuming bandwidth $W$ and $N$ time
+domain observations, the average of the square of the first $K=2NW$ Slepian
+functions approaches, as $K$ grows, an ideal band-pass kernel for the interval
+$[-W,W]$. We provide an analytic proof of this fact and measure the
+corresponding rate of convergence in the $L^{1}$ norm. This validates a
+heuristic approximation used to control the MSE of the multitaper estimator.
+The estimates have also consequences for the method of compressive acquisition
+of multi-band signals introduced by Davenport and Wakin, giving MSE
+approximation bounds for the dictionary formed by modulation of the critical
+number of prolates.
+",1,0,1,1,0,0
+8629,Communication-Free Parallel Supervised Topic Models,"  Embarrassingly (communication-free) parallel Markov chain Monte Carlo (MCMC)
+methods are commonly used in learning graphical models. However, MCMC cannot be
+directly applied in learning topic models because of the quasi-ergodicity
+problem caused by multimodal distribution of topics. In this paper, we develop
+an embarrassingly parallel MCMC algorithm for sLDA. Our algorithm works by
+switching the order of sampled topics combination and labeling variable
+prediction in sLDA, in which it overcomes the quasi-ergodicity problem because
+high-dimension topics that follow a multimodal distribution are projected into
+one-dimension document labels that follow a unimodal distribution. Our
+empirical experiments confirm that the out-of-sample prediction performance
+using our embarrassingly parallel algorithm is comparable to non-parallel sLDA
+while the computation time is significantly reduced.
+",1,0,0,1,0,0
+8630,Multilayer Network Model of Movie Script,"  Network models have been increasingly used in the past years to support
+summarization and analysis of narratives, such as famous TV series, books and
+news. Inspired by social network analysis, most of these models focus on the
+characters at play. The network model well captures all characters
+interactions, giving a broad picture of the narration's content. A few works
+went beyond by introducing additional semantic elements, always captured in a
+single layer network. In contrast, we introduce in this work a multilayer
+network model to capture more elements of the narration of a movie from its
+script: people, locations, and other semantic elements. This model enables new
+measures and insights on movies. We demonstrate this model on two very popular
+movies.
+",1,0,0,0,0,0
+8631,On the Performance of Millimeter Wave-based RF-FSO Multi-hop and Mesh Networks,"  This paper studies the performance of multi-hop and mesh networks composed of
+millimeter wave (MMW)-based radio frequency (RF) and free-space optical (FSO)
+links. The results are obtained in cases with and without hybrid automatic
+repeat request (HARQ). Taking the MMW characteristics of the RF links into
+account, we derive closed-form expressions for the networks' outage probability
+and ergodic achievable rates. We also evaluate the effect of various parameters
+such as power amplifiers efficiency, number of antennas as well as different
+coherence times of the RF and the FSO links on the system performance. Finally,
+we determine the minimum number of the transmit antennas in the RF link such
+that the same rate is supported in the RF- and the FSO-based hops. The results
+show the efficiency of the RF-FSO setups in different conditions. Moreover,
+HARQ can effectively improve the outage probability/energy efficiency, and
+compensate for the effect of hardware impairments in RF-FSO networks. For
+common parameter settings of the RF-FSO dual-hop networks, outage probability
+of 10^{-4} and code rate of 3 nats-per-channel-use, the implementation of HARQ
+with a maximum of 2 and 3 retransmissions reduces the required power, compared
+to cases with open-loop communication, by 13 and 17 dB, respectively.
+",1,0,0,0,0,0
+8632,Pi Visits Manhattan,"  Is it possible to draw a circle in Manhattan, using only its discrete network
+of streets and boulevards? In this study, we will explore the construction and
+properties of circular paths on an integer lattice, a discrete space where the
+distance between two points is not governed by the familiar Euclidean metric,
+but the Manhattan or taxicab distance, a metric linear in its coordinates. In
+order to achieve consistency with the continuous ideal, we need to abandon
+Euclid's very original definition of the circle in favour of a parametric
+construction. Somewhat unexpectedly, we find that the Euclidean circle's
+defining constant $\pi$ can be recovered in such a discrete setting.
+",0,0,1,0,0,0
+8633,A Bernstein Inequality For Exponentially Growing Graphs,"  In this article we present a Bernstein inequality for sums of random
+variables which are defined on a graphical network whose nodes grow at an
+exponential rate. The inequality can be used to derive concentration
+inequalities in highly-connected networks. It can be useful to obtain
+consistency properties for nonparametric estimators of conditional expectation
+functions which are derived from such networks.
+",0,0,1,1,0,0
+8634,Monolithic InGaAs nanowire array lasers on silicon-on-insulator operating at room temperature,"  Chip-scale integrated light sources are a crucial component in a broad range
+of photonics applications. III-V semiconductor nanowire emitters have gained
+attention as a fascinating approach due to their superior material properties,
+extremely compact size, and the capability to grow directly on
+lattice-mismatched silicon substrates. Although there have been remarkable
+advances in nanowire-based emitters, their practical applications are still in
+the early stages due to the difficulties in integrating nanowire emitters with
+photonic integrated circuits (PICs). Here, we demonstrate for the first time
+optically pumped III-V nanowire array lasers monolithically integrated on
+silicon-on-insulator (SOI) platform. Selective-area growth of purely
+single-crystalline InGaAs/InGaP core/shell nanowires on an SOI substrate
+enables the nanowire array to form a photonic crystal nanobeam cavity with
+superior optical and structural properties, resulting in the laser to operate
+at room temperature. We also show that the nanowire array lasers are
+effectively coupled with SOI waveguides by employing nanoepitaxy on a
+pre-patterned SOI platform. These results represent a new platform for
+ultra-compact and energy-efficient optical links, and unambiguously point the
+way toward practical and functional nanowire lasers.
+",0,1,0,0,0,0
+8635,DMFT study on the electron-hole asymmetry of the electron correlation strength in the high Tc cuprates,"  Recent experiments revealed a striking asymmetry in the phase diagram of the
+high temperature cuprate superconductors. The correlation effect seems strong
+in the hole-doped systems and weak in the electron-doped systems. On the other
+hand, a recent theoretical study shows that the interaction strengths (the
+Hubbard U) are comparable in these systems. Therefore, it is difficult to
+explain this asymmetry by their interaction strengths. Given this background,
+we analyze the one-particle spectrum of a single band model of a cuprate
+superconductor near the Fermi level using the dynamical mean field theory. We
+find the difference in the ""visibility"" of the strong correlation effect
+between the hole- and electron-doped systems. This can explain the
+electron-hole asymmetry of the correlation strength without introducing the
+difference in the interaction strength.
+",0,1,0,0,0,0
+8636,On the Information Theoretic Distance Measures and Bidirectional Helmholtz Machines,"  By establishing a connection between bi-directional Helmholtz machines and
+information theory, we propose a generalized Helmholtz machine. Theoretical and
+experimental results show that given \textit{shallow} architectures, the
+generalized model outperforms the previous ones substantially.
+",0,0,0,1,0,0
+8637,KMS states on the C*-algebras of Fell bundles over groupoids,"  We consider fiberwise singly generated Fell-bundles over etale groupoids.
+Given a continuous real-valued 1-cocycle on the groupoid, there is a natural
+dynamics on the cross-sectional algebra of the Fell bundle. We study the
+Kubo-Martin-Schwinger equilibrium states for this dynamics. Following work of
+Neshveyev on equilibrium states on groupoid C*-algebras, we describe the
+equilibrium states of the cross-sectional algebra in terms of measurable fields
+of traces on the C*-algebras of the restrictions of the Fell bundle to the
+isotropy subgroups of the groupoid. As a special case, we obtain a description
+of the trace space of the cross-sectional algebra. We apply our result to
+generalise Neshveyev's main theorem to twisted groupoid C*-algebras, and then
+apply this to twisted C*-algebras of strongly connected finite k-graphs.
+",0,0,1,0,0,0
+8638,Object Detection Using Deep CNNs Trained on Synthetic Images,"  The need for large annotated image datasets for training Convolutional Neural
+Networks (CNNs) has been a significant impediment for their adoption in
+computer vision applications. We show that with transfer learning an effective
+object detector can be trained almost entirely on synthetically rendered
+datasets. We apply this strategy for detecting pack- aged food products
+clustered in refrigerator scenes. Our CNN trained only with 4000 synthetic
+images achieves mean average precision (mAP) of 24 on a test set with 55
+distinct products as objects of interest and 17 distractor objects. A further
+increase of 12% in the mAP is obtained by adding only 400 real images to these
+4000 synthetic images in the training set. A high degree of photorealism in the
+synthetic images was not essential in achieving this performance. We analyze
+factors like training data set size and 3D model dictionary size for their
+influence on detection performance. Additionally, training strategies like
+fine-tuning with selected layers and early stopping which affect transfer
+learning from synthetic scenes to real scenes are explored. Training CNNs with
+synthetic datasets is a novel application of high-performance computing and a
+promising approach for object detection applications in domains where there is
+a dearth of large annotated image data.
+",1,0,0,0,0,0
+8639,Breaking the 3/2 barrier for unit distances in three dimensions,"  We prove that every set of $n$ points in $\mathbb{R}^3$ spans
+$O(n^{295/197+\epsilon})$ unit distances. This is an improvement over the
+previous bound of $O(n^{3/2})$. A key ingredient in the proof is a new result
+for cutting circles in $\mathbb{R}^3$ into pseudo-segments.
+",1,0,1,0,0,0
+8640,Periodic orbits of planets in binary systems,"  Periodic solutions of the three body problem are very important for
+understanding its dynamics either in a theoretical framework or in various
+applications in celestial mechanics. In this paper we discuss the computation
+and continuation of periodic orbits for planetary systems. The study is
+restricted to coplanar motion. Staring from known results of two-planet systems
+around single stars, we perform continuation of solutions with respect to the
+mass and approach periodic orbits of single planets in two-star systems. Also,
+families of periodic solutions can be computed for fixed masses of the
+primaries. When they are linearly stable, we can conclude about the existence
+of phase space domains of long-term orbital stability.
+",0,1,0,0,0,0
+8641,Review of image quality measures for solar imaging,"  The observations of solar photosphere from the ground encounter significant
+problems due to the presence of Earth's turbulent atmosphere. Prior to applying
+image reconstruction techniques, the frames obtained in most favorable
+atmospheric conditions (so-called lucky frames) have to be carefully selected.
+However, the estimation of the quality of images containing complex
+photospheric structures is not a trivial task and the standard routines applied
+in night-time Lucky Imaging observations are not applicable. In this paper we
+evaluate 36 methods dedicated for the assessment of image quality which were
+presented in the rich literature over last 40 years. We compare their
+effectiveness on simulated solar observations of both active regions and
+granulation patches, using reference data obtained by the Solar Optical
+Telescope on the Hindoe satellite. To create the images affected by a known
+degree of atmospheric degradation, we employ the Random Wave Vector method
+which faithfully models all the seeing characteristics. The results provide
+useful information about the methods performance depending on the average
+seeing conditions expressed by the ratio of the telescope's aperture to the
+Fried parameter, $D/r_0$. The comparison identifies three methods for
+consideration by observers: Helmli and Scherer's Mean, Median Filter Gradient
+Similarity, and Discrete Cosine Transform Energy Ratio. While the first one
+requires less computational effort and can be used effectively virtually in any
+atmospherics conditions, the second one shows its superiority at good seeing
+($D/r_0<4$). The last one should be considered mainly for the post-processing
+of strongly blurred images.
+",0,1,0,0,0,0
+8642,Joint Maximum Likelihood Estimation for High-dimensional Exploratory Item Response Analysis,"  Multidimensional item response theory is widely used in education and
+psychology for measuring multiple latent traits. However, exploratory analysis
+of large-scale item response data with many items, respondents, and latent
+traits is still a challenge. In this paper, we consider a high-dimensional
+setting that both the number of items and the number of respondents grow to
+infinity. A constrained joint maximum likelihood estimator is proposed for
+estimating both item and person parameters, which yields good theoretical
+properties and computational advantage. Specifically, we derive error bounds
+for parameter estimation and develop an efficient algorithm that can scale to
+very large datasets. The proposed method is applied to a large scale
+personality assessment data set from the Synthetic Aperture Personality
+Assessment (SAPA) project. Simulation studies are conducted to evaluate the
+proposed method.
+",0,0,0,1,0,0
+8643,"Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss, and Simpson's Paradox","  Statistical learning using imprecise probabilities is gaining more attention
+because it presents an alternative strategy for reducing irreplicable findings
+by freeing the user from the task of making up unwarranted high-resolution
+assumptions. However, model updating as a mathematical operation is inherently
+exact, hence updating imprecise models requires the user's judgment in choosing
+among competing updating rules. These rules often lead to incompatible
+inferences, and can exhibit unsettling phenomena like dilation, contraction and
+sure loss, which cannot occur with the Bayes rule and precise probabilities. We
+revisit a number of famous ""paradoxes"", including the three prisoners/Monty
+Hall problem, revealing that a logical fallacy arises from a set of marginally
+plausible yet jointly incommensurable assumptions when updating the underlying
+imprecise model. We establish an equivalence between Simpson's paradox and an
+implicit adoption of a pair of aggregation rules that induce sure loss. We also
+explore behavioral discrepancies between the generalized Bayes rule, Dempster's
+rule and the Geometric rule as alternative posterior updating rules for Choquet
+capacities of order 2. We show that both the generalized Bayes rule and
+Geometric rule are incapable of updating without prior information regardless
+of how strong the information in our data is, and that Dempster's rule and the
+Geometric rule can mathematically contradict each other with respect to
+dilation and contraction. Our findings show that unsettling updates reflect a
+collision between the rules' assumptions and the inexactness allowed by the
+model itself, highlighting the invaluable role of judicious judgment in
+handling low-resolution information, and the care we must take when applying
+learning rules to update imprecise probabilities.
+",0,0,1,1,0,0
+8644,"Planar segment processes with reference mark distributions, modeling and estimation","  The paper deals with planar segment processes given by a density with respect
+to the Poisson process. Parametric models involve reference distributions of
+directions and/or lengths of segments. These distributions generally do not
+coincide with the corresponding observed distributions. Statistical methods are
+presented which first estimate scalar parameters by known approaches and then
+the reference distribution is estimated non-parametrically. Besides a general
+theory we offer two models, first a Gibbs type segment process with reference
+directional distribution and secondly an inhomogeneous process with reference
+length distribution. The estimation is demonstrated in simulation studies where
+the variability of estimators is presented graphically.
+",0,0,1,1,0,0
+8645,A Note on Bayesian Model Selection for Discrete Data Using Proper Scoring Rules,"  We consider the problem of choosing between parametric models for a discrete
+observable, taking a Bayesian approach in which the within-model prior
+distributions are allowed to be improper. In order to avoid the ambiguity in
+the marginal likelihood function in such a case, we apply a homogeneous scoring
+rule. For the particular case of distinguishing between Poisson and Negative
+Binomial models, we conduct simulations that indicate that, applied
+prequentially, the method will consistently select the true model.
+",0,0,1,1,0,0
+8646,Quantum criticality in many-body parafermion chains,"  We construct local generalizations of 3-state Potts models with exotic
+critical points. We analytically show that these are described by non-diagonal
+modular invariant partition functions of products of $Z_3$ parafermion or
+$u(1)_6$ conformal field theories (CFTs). These correspond either to
+non-trivial permutation invariants or block diagonal invariants, that one can
+understand in terms of anyon condensation. In terms of lattice parafermion
+operators, the constructed models correspond to parafermion chains with
+many-body terms. Our construction is based on how the partition function of a
+CFT depends on symmetry sectors and boundary conditions. This enables to write
+the partition function corresponding to one modular invariant as a linear
+combination of another over different sectors and boundary conditions, which
+translates to a general recipe how to write down a microscopic model, tuned to
+criticality. We show that the scheme can also be extended to construct critical
+generalizations of $k$-state Potts models.
+",0,1,0,0,0,0
+8647,Simulation-based reachability analysis for nonlinear systems using componentwise contraction properties,"  A shortcoming of existing reachability approaches for nonlinear systems is
+the poor scalability with the number of continuous state variables. To mitigate
+this problem we present a simulation-based approach where we first sample a
+number of trajectories of the system and next establish bounds on the
+convergence or divergence between the samples and neighboring trajectories. We
+compute these bounds using contraction theory and reduce the conservatism by
+partitioning the state vector into several components and analyzing contraction
+properties separately in each direction. Among other benefits this allows us to
+analyze the effect of constant but uncertain parameters by treating them as
+state variables and partitioning them into a separate direction. We next
+present a numerical procedure to search for weighted norms that yield a
+prescribed contraction rate, which can be incorporated in the reachability
+algorithm to adjust the weights to minimize the growth of the reachable set.
+",1,0,0,0,0,0
+8648,User Donations in a Crowdsourced Video System,"  Crowdsourced video systems like YouTube and Twitch.tv have been a major
+internet phenomenon and are nowadays entertaining over a billion users. In
+addition to video sharing and viewing, over the years they have developed new
+features to boost the community engagement and some managed to attract users to
+donate, to the community as well as to other users. User donation directly
+reflects and influences user engagement in the community, and has a great
+impact on the success of such systems. Nevertheless, user donations in
+crowdsourced video systems remain trade secrets for most companies and to date
+are still unexplored. In this work, we attempt to fill this gap, and we obtain
+and provide a publicly available dataset on user donations in one crowdsourced
+video system named BiliBili. Based on information on nearly 40 thousand
+donators, we examine the dynamics of user donations and their social
+relationships, we quantitively reveal the factors that potentially impact user
+donation, and we adopt machine-learned classifiers and network representation
+learning models to timely and accurately predict the destinations of the
+majority and the individual donations.
+",1,0,0,0,0,0
+8649,Dynein catch bond as a mediator of codependent bidirectional cellular transport,"  Intracellular bidirectional transport of cargo on Microtubule filaments is
+achieved by the collective action of oppositely directed dynein and kinesin
+motors. Experimental investigations probing the nature of bidirectional
+transport have found that in certain cases, inhibiting the activity of one type
+of motor results in an overall decline in the motility of the cellular cargo in
+both directions. This somewhat counter-intuitive observation, referred to as
+paradox of codependence is inconsistent with the existing paradigm of a
+mechanistic tug-of-war between oppositely directed motors. Existing theoretical
+models do not take into account a key difference in the functionality of
+kinesin and dynein. Unlike kinesin, dynein motors exhibit catchbonding, wherein
+the unbinding rates of these motors from the filaments are seen to decrease
+with increasing force on them. Incorporating this catchbonding behavior of
+dynein in a theoretical model and using experimentally relevant measures
+characterizing cargo transport, we show that the functional divergence of the
+two motors species manifests itself as an internal regulatory mechanism for
+bidirectional transport and resolves the paradox of codependence. Our model
+reproduces the key experimental features in appropriate parameter regimes and
+provides an unifying framework for bidirectional cargo transport.
+",0,0,0,0,1,0
+8650,Limiting Laws for Divergent Spiked Eigenvalues and Largest Non-spiked Eigenvalue of Sample Covariance Matrices,"  We study the asymptotic distributions of the spiked eigenvalues and the
+largest nonspiked eigenvalue of the sample covariance matrix under a general
+covariance matrix model with divergent spiked eigenvalues, while the other
+eigenvalues are bounded but otherwise arbitrary. The limiting normal
+distribution for the spiked sample eigenvalues is established. It has distinct
+features that the asymptotic mean relies on not only the population spikes but
+also the nonspikes and that the asymptotic variance in general depends on the
+population eigenvectors. In addition, the limiting Tracy-Widom law for the
+largest nonspiked sample eigenvalue is obtained.
+Estimation of the number of spikes and the convergence of the leading
+eigenvectors are also considered. The results hold even when the number of the
+spikes diverges. As a key technical tool, we develop a Central Limit Theorem
+for a type of random quadratic forms where the random vectors and random
+matrices involved are dependent. This result can be of independent interest.
+",0,0,1,1,0,0
+8651,Nonparametric Poisson regression from independent and weakly dependent observations by model selection,"  We consider the non-parametric Poisson regression problem where the integer
+valued response $Y$ is the realization of a Poisson random variable with
+parameter $\lambda(X)$. The aim is to estimate the functional parameter
+$\lambda$ from independent or weakly dependent observations
+$(X_1,Y_1),\ldots,(X_n,Y_n)$ in a random design framework.
+First we determine upper risk bounds for projection estimators on finite
+dimensional subspaces under mild conditions. In the case of Sobolev ellipsoids
+the obtained rates of convergence turn out to be optimal.
+The main part of the paper is devoted to the construction of adaptive
+projection estimators of $\lambda$ via model selection. We proceed in two
+steps: first, we assume that an upper bound for $\Vert \lambda \Vert_\infty$ is
+known. Under this assumption, we construct an adaptive estimator whose
+dimension parameter is defined as the minimizer of a penalized contrast
+criterion. Second, we replace the known upper bound on $\Vert \lambda
+\Vert_\infty$ by an appropriate plug-in estimator of $\Vert \lambda
+\Vert_\infty$. The resulting adaptive estimator is shown to attain the minimax
+optimal rate up to an additional logarithmic factor both in the independent and
+the weakly dependent setup. Appropriate concentration inequalities for Poisson
+point processes turn out to be an important ingredient of the proofs.
+We illustrate our theoretical findings by a short simulation study and
+conclude by indicating directions of future research.
+",0,0,1,1,0,0
+8652,Ontology-Aware Token Embeddings for Prepositional Phrase Attachment,"  Type-level word embeddings use the same set of parameters to represent all
+instances of a word regardless of its context, ignoring the inherent lexical
+ambiguity in language. Instead, we embed semantic concepts (or synsets) as
+defined in WordNet and represent a word token in a particular context by
+estimating a distribution over relevant semantic concepts. We use the new,
+context-sensitive embeddings in a model for predicting prepositional phrase(PP)
+attachments and jointly learn the concept embeddings and model parameters. We
+show that using context-sensitive embeddings improves the accuracy of the PP
+attachment model by 5.4% absolute points, which amounts to a 34.4% relative
+reduction in errors.
+",1,0,0,0,0,0
+8653,Bayesian Detection of Abnormal ADS in Mutant Caenorhabditis elegans Embryos,"  Cell division timing is critical for cell fate specification and
+morphogenesis during embryogenesis. How division timings are regulated among
+cells during development is poorly understood. Here we focus on the comparison
+of asynchrony of division between sister cells (ADS) between wild-type and
+mutant individuals of Caenorhabditis elegans. Since the replicate number of
+mutant individuals of each mutated gene, usually one, is far smaller than that
+of wild-type, direct comparison of two distributions of ADS between wild-type
+and mutant type, such as Kolmogorov- Smirnov test, is not feasible. On the
+other hand, we find that sometimes ADS is correlated with the life span of
+corresponding mother cell in wild-type. Hence, we apply a semiparametric
+Bayesian quantile regression method to estimate the 95% confidence interval
+curve of ADS with respect to life span of mother cell of wild-type individuals.
+Then, mutant-type ADSs outside the corresponding confidence interval are
+selected out as abnormal one with a significance level of 0.05. Simulation
+study demonstrates the accuracy of our method and Gene Enrichment Analysis
+validates the results of real data sets.
+",0,0,0,1,1,0
+8654,Towards a constraint solver for proving confluence with invariant and equivalence of realistic CHR programs,"  Confluence of a nondeterministic program ensures a functional input-output
+relation, freeing the programmer from considering the actual scheduling
+strategy, and allowing optimized and perhaps parallel implementations. The more
+general property of confluence modulo equivalence ensures that equivalent
+inputs are related to equivalent outputs, that need not be identical.
+Confluence under invariants is also considered. Constraint Handling Rules (CHR)
+is an important example of a rewrite based logic programming language, and we
+aim at a mechanizable method for proving confluence modulo equivalence of
+terminating programs. While earlier approaches to confluence for CHR programs
+concern an idealized logic subset, we refer to a semantics compatible with
+standard Prolog-based implementations. We specify a meta-level constraint
+language in which invariants and equivalences can be expressed and manipulated,
+extending our previous theoretical results towards a practical implementation.
+",1,0,0,0,0,0
+8655,cGANs with Projection Discriminator,"  We propose a novel, projection based way to incorporate the conditional
+information into the discriminator of GANs that respects the role of the
+conditional information in the underlining probabilistic model. This approach
+is in contrast with most frameworks of conditional GANs used in application
+today, which use the conditional information by concatenating the (embedded)
+conditional vector to the feature vectors. With this modification, we were able
+to significantly improve the quality of the class conditional image generation
+on ILSVRC2012 (ImageNet) 1000-class image dataset from the current
+state-of-the-art result, and we achieved this with a single pair of a
+discriminator and a generator. We were also able to extend the application to
+super-resolution and succeeded in producing highly discriminative
+super-resolution images. This new structure also enabled high quality category
+transformation based on parametric functional transformation of conditional
+batch normalization layers in the generator.
+",0,0,0,1,0,0
+8656,"The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios","  The QLBS model is a discrete-time option hedging and pricing model that is
+based on Dynamic Programming (DP) and Reinforcement Learning (RL). It combines
+the famous Q-Learning method for RL with the Black-Scholes (-Merton) model's
+idea of reducing the problem of option pricing and hedging to the problem of
+optimal rebalancing of a dynamic replicating portfolio for the option, which is
+made of a stock and cash. Here we expand on several NuQLear (Numerical
+Q-Learning) topics with the QLBS model. First, we investigate the performance
+of Fitted Q Iteration for a RL (data-driven) solution to the model, and
+benchmark it versus a DP (model-based) solution, as well as versus the BSM
+model. Second, we develop an Inverse Reinforcement Learning (IRL) setting for
+the model, where we only observe prices and actions (re-hedges) taken by a
+trader, but not rewards. Third, we outline how the QLBS model can be used for
+pricing portfolios of options, rather than a single option in isolation, thus
+providing its own, data-driven and model independent solution to the (in)famous
+volatility smile problem of the Black-Scholes model.
+",0,0,0,0,0,1
+8657,InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity,"  We demonstrate an approach to face attribute detection that retains or
+improves attribute detection accuracy across gender and race subgroups by
+learning demographic information prior to learning the attribute detection
+task. The system, which we call InclusiveFaceNet, detects face attributes by
+transferring race and gender representations learned from a held-out dataset of
+public race and gender identities. Leveraging learned demographic
+representations while withholding demographic inference from the downstream
+face attribute detection task preserves potential users' demographic privacy
+while resulting in some of the best reported numbers to date on attribute
+detection in the Faces of the World and CelebA datasets.
+",1,0,0,0,0,0
+8658,Reputation is required for cooperation to emerge in dynamic networks,"  Melamed, Harrell, and Simpson have recently reported on an experiment which
+appears to show that cooperation can arise in a dynamic network without
+reputational knowledge, i.e., purely via dynamics [1]. We believe that their
+experimental design is actually not testing this, in so far as players do know
+the last action of their current partners before making a choice on their own
+next action and subsequently deciding which link to cut. Had the authors given
+no information at all, the result would be a decline in cooperation as shown in
+[2].
+",1,0,0,0,0,0
+8659,Active Anomaly Detection via Ensembles,"  In critical applications of anomaly detection including computer security and
+fraud prevention, the anomaly detector must be configurable by the analyst to
+minimize the effort on false positives. One important way to configure the
+anomaly detector is by providing true labels for a few instances. We study the
+problem of label-efficient active learning to automatically tune anomaly
+detection ensembles and make four main contributions. First, we present an
+important insight into how anomaly detector ensembles are naturally suited for
+active learning. This insight allows us to relate the greedy querying strategy
+to uncertainty sampling, with implications for label-efficiency. Second, we
+present a novel formalism called compact description to describe the discovered
+anomalies and show that it can also be employed to improve the diversity of the
+instances presented to the analyst without loss in the anomaly discovery rate.
+Third, we present a novel data drift detection algorithm that not only detects
+the drift robustly, but also allows us to take corrective actions to adapt the
+detector in a principled manner. Fourth, we present extensive experiments to
+evaluate our insights and algorithms in both batch and streaming settings. Our
+results show that in addition to discovering significantly more anomalies than
+state-of-the-art unsupervised baselines, our active learning algorithms under
+the streaming-data setup are competitive with the batch setup.
+",0,0,0,1,0,0
+8660,Effect of Decreasing Cobalt Content on the Electrochemical Properties and Structural Stability of Li_(1-x)Ni_(y)Co_(z)Al_(0.05)O_(2) Type Cathode Materials,"  In Lithium ion batteries (LIBs), proper design of cathode materials
+influences its intercalation behavior, overall cost, structural stability, and
+its impact on environment. At present, the most common type of cathode
+materials, NCA , has very high cobalt concentration. Since cobalt is toxic and
+expensive, the existing design of cathode materials is not cost-effective, and
+environmentally benign. However, these immensely important issues have not yet
+been properly addressed. Therefore, we have performed density functional theory
+(DFT) calculations to investigate three types of NCA cathode materials
+NCA_(Co=0.15), NCA_(Co=0.10), NCA_(Co=0.05). Our results show that even if the
+cobalt concentration is significantly decreased from NCA_(Co=0.15) to
+NCA_(Co=0.05), variation in intercalation potential and specific capacity is
+negligible. For example, in case of 50% Li concentration, voltage drop is
+~0.12V while change in specific capacity is negligible. Moreover, decrease in
+cobalt concentration doesn't influence the structural stability. We have also
+explored the influence of sodium doping on the electrochemical and structural
+properties of these three structures. Our results provide insight into the
+design of cathode materials with reduced cobalt concentration, environmentally
+benign, low-cost cathode materials.
+",0,1,0,0,0,0
+8661,Dispersive estimates for massive Dirac operators in dimension two,"  We study the massive two dimensional Dirac operator with an electric
+potential. In particular, we show that the $t^{-1}$ decay rate holds in the
+$L^1\to L^\infty$ setting if the threshold energies are regular. We also show
+these bounds hold in the presence of s-wave resonances at the threshold. We
+further show that, if the threshold energies are regular that a faster decay
+rate of $t^{-1}(\log t)^{-2}$ is attained for large $t$, at the cost of
+logarithmic spatial weights. The free Dirac equation does not satisfy this
+bound due to the s-wave resonances at the threshold energies.
+",0,0,1,0,0,0
+8662,A novel agent-based simulation framework for sensing in complex adaptive environments,"  In this paper we present a novel Formal Agent-Based Simulation framework
+(FABS). FABS uses formal specification as a means of clear description of
+wireless sensor networks (WSN) sensing a Complex Adaptive Environment. This
+specification model is then used to develop an agent-based model of both the
+wireless sensor network as well as the environment. As proof of concept, we
+demonstrate the application of FABS to a boids model of self-organized flocking
+of animals monitored by a random deployment of proximity sensors.
+",1,1,0,0,0,0
+8663,"Transversal fluctuations of the ASEP, stochastic six vertex model, and Hall-Littlewood Gibbsian line ensembles","  We consider the ASEP and the stochastic six vertex model started with step
+initial data. After a long time, $T$, it is known that the one-point height
+function fluctuations for these systems are of order $T^{1/3}$. We prove the
+KPZ prediction of $T^{2/3}$ scaling in space. Namely, we prove tightness (and
+Brownian absolute continuity of all subsequential limits) as $T$ goes to
+infinity of the height function with spatial coordinate scaled by $T^{2/3}$ and
+fluctuations scaled by $T^{1/3}$. The starting point for proving these results
+is a connection discovered recently by Borodin-Bufetov-Wheeler between the
+stochastic six vertex height function and the Hall-Littlewood process (a
+certain measure on plane partitions). Interpreting this process as a line
+ensemble with a Gibbsian resampling invariance, we show that the one-point
+tightness of the top curve can be propagated to the tightness of the entire
+curve.
+",0,0,1,0,0,0
+8664,Instantons in self-organizing logic gates,"  Self-organizing logic is a recently-suggested framework that allows the
+solution of Boolean truth tables ""in reverse,"" i.e., it is able to satisfy the
+logical proposition of gates regardless to which terminal(s) the truth value is
+assigned (""terminal-agnostic logic""). It can be realized if time non-locality
+(memory) is present. A practical realization of self-organizing logic gates
+(SOLGs) can be done by combining circuit elements with and without memory. By
+employing one such realization, we show, numerically, that SOLGs exploit
+elementary instantons to reach equilibrium points. Instantons are classical
+trajectories of the non-linear equations of motion describing SOLGs, and
+connect topologically distinct critical points in the phase space. By linear
+analysis at those points, we show that these instantons connect the initial
+critical point of the dynamics, with at least one unstable direction, directly
+to the final fixed point. We also show that the memory content of these gates
+only affects the relaxation time to reach the logically consistent solution.
+Finally, we demonstrate, by solving the corresponding stochastic differential
+equations, that since instantons connect critical points, noise and
+perturbations may change the instanton trajectory in the phase space, but not
+the initial and final critical points. Therefore, even for extremely large
+noise levels, the gates self-organize to the correct solution. Our work
+provides a physical understanding of, and can serve as an inspiration for, new
+models of bi-directional logic gates that are emerging as important tools in
+physics-inspired, unconventional computing.
+",1,0,0,0,0,0
+8665,Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments,"  A robot that can carry out a natural-language instruction has been a dream
+since before the Jetsons cartoon series imagined a life of leisure mediated by
+a fleet of attentive robot helpers. It is a dream that remains stubbornly
+distant. However, recent advances in vision and language methods have made
+incredible progress in closely related areas. This is significant because a
+robot interpreting a natural-language navigation instruction on the basis of
+what it sees is carrying out a vision and language process that is similar to
+Visual Question Answering. Both tasks can be interpreted as visually grounded
+sequence-to-sequence translation problems, and many of the same methods are
+applicable. To enable and encourage the application of vision and language
+methods to the problem of interpreting visually-grounded navigation
+instructions, we present the Matterport3D Simulator -- a large-scale
+reinforcement learning environment based on real imagery. Using this simulator,
+which can in future support a range of embodied vision and language tasks, we
+provide the first benchmark dataset for visually-grounded natural language
+navigation in real buildings -- the Room-to-Room (R2R) dataset.
+",1,0,0,0,0,0
+8666,Dissecting spin-phonon equilibration in ferrimagnetic insulators by ultrafast lattice excitation,"  To gain control over magnetic order on ultrafast time scales, a fundamental
+understanding of the way electron spins interact with the surrounding crystal
+lattice is required. However, measurement and analysis even of basic collective
+processes such as spin-phonon equilibration have remained challenging. Here, we
+directly probe the flow of energy and angular momentum in the model insulating
+ferrimagnet yttrium iron garnet. Following ultrafast resonant lattice
+excitation, we observe that magnetic order reduces on distinct time scales of 1
+ps and 100 ns. Temperature-dependent measurements, a spin-coupling analysis and
+simulations show that the two dynamics directly reflect two stages of
+spin-lattice equilibration. On the 1-ps scale, spins and phonons reach
+quasi-equilibrium in terms of energy through phonon-induced modulation of the
+exchange interaction. This mechanism leads to identical demagnetization of the
+ferrimagnet's two spin-sublattices and a novel ferrimagnetic state of increased
+temperature yet unchanged total magnetization. Finally, on the much slower,
+100-ns scale, the excess of spin angular momentum is released to the crystal
+lattice, resulting in full equilibrium. Our findings are relevant for all
+insulating ferrimagnets and indicate that spin manipulation by phonons,
+including the spin Seebeck effect, can be extended to antiferromagnets and into
+the terahertz frequency range.
+",0,1,0,0,0,0
+8667,Inferring health conditions from fMRI-graph data,"  Automated classification methods for disease diagnosis are currently in the
+limelight, especially for imaging data. Classification does not fully meet a
+clinician's needs, however: in order to combine the results of multiple tests
+and decide on a course of treatment, a clinician needs the likelihood of a
+given health condition rather than binary classification yielded by such
+methods. We illustrate how likelihoods can be derived step by step from first
+principles and approximations, and how they can be assessed and selected,
+illustrating our approach using fMRI data from a publicly available data set
+containing schizophrenic and healthy control subjects. We start from the basic
+assumption of partial exchangeability, and then the notion of sufficient
+statistics and the ""method of translation"" (Edgeworth, 1898) combined with
+conjugate priors. This method can be used to construct a likelihood that can be
+used to compare different data-reduction algorithms. Despite the
+simplifications and possibly unrealistic assumptions used to illustrate the
+method, we obtain classification results comparable to previous, more realistic
+studies about schizophrenia, whilst yielding likelihoods that can naturally be
+combined with the results of other diagnostic tests.
+",0,0,0,1,1,0
+8668,Rich-clubness test: how to determine whether a complex network has or doesn't have a rich-club?,"  The rich-club concept has been introduced in order to characterize the
+presence of a cohort of nodes with a large number of links (rich nodes) that
+tend to be well connected between each other, creating a tight group (club).
+Rich-clubness defines the extent to which a network displays a topological
+organization characterized by the presence of a node rich-club. It is crucial
+for the investigation of internal organization and function of networks arising
+in systems of disparate fields such as transportation, social, communication
+and neuroscience. Different methods have been proposed for assessing the
+rich-clubness and various null-models have been adopted for performing
+statistical tests. However, a procedure that assigns a unique value of
+rich-clubness significance to a given network is still missing. Our solution to
+this problem grows on the basis of three new pillars. We introduce: i) a
+null-model characterized by a lower rich-club coefficient; ii) a fair strategy
+to normalize the level of rich-clubness of a network in respect to the
+null-model; iii) a statistical test that, exploiting the maximum deviation of
+the normalized rich-club coefficient attributes a unique p-value of
+rich-clubness to a given network. In conclusion, this study proposes the first
+attempt to quantify, using a unique measure, whether a network presents a
+significant rich-club topological organization. The general impact of our study
+on engineering and science is that simulations investigating how the functional
+performance of a network is changing in relation to rich-clubness might be more
+easily tuned controlling one unique value: the proposed rich-clubness measure.
+",1,1,0,0,0,0
+8669,Structured Variational Inference for Coupled Gaussian Processes,"  Sparse variational approximations allow for principled and scalable inference
+in Gaussian Process (GP) models. In settings where several GPs are part of the
+generative model, theses GPs are a posteriori coupled. For many applications
+such as regression where predictive accuracy is the quantity of interest, this
+coupling is not crucial. Howewer if one is interested in posterior uncertainty,
+it cannot be ignored. A key element of variational inference schemes is the
+choice of the approximate posterior parameterization. When the number of latent
+variables is large, mean field (MF) methods provide fast and accurate posterior
+means while more structured posterior lead to inference algorithm of greater
+computational complexity. Here, we extend previous sparse GP approximations and
+propose a novel parameterization of variational posteriors in the multi-GP
+setting allowing for fast and scalable inference capturing posterior
+dependencies.
+",1,0,0,1,0,0
+8670,Imaging anyons with scanning tunneling microscopy,"  Anyons are exotic quasi-particles with fractional charge that can emerge as
+fundamental excitations of strongly interacting topological quantum phases of
+matter. Unlike ordinary fermions and bosons, they may obey non-abelian
+statistics--a property that would help realize fault tolerant quantum
+computation. Non-abelian anyons have long been predicted to occur in the
+fractional quantum Hall (FQH) phases that form in two-dimensional electron
+gases (2DEG) in the presence of a large magnetic field, su ch as the
+$\nu=\tfrac{5}{2}$ FQH state. However, direct experimental evidence of anyons
+and tests that can distinguish between abelian and non-abelian quantum ground
+states with such excitations have remained elusive. Here we propose a new
+experimental approach to directly visualize the structure of interacting
+electronic states of FQH states with the scanning tunneling microscope (STM).
+Our theoretical calculations show how spectroscopy mapping with the STM near
+individual impurity defects can be used to image fractional statistics in FQH
+states, identifying unique signatures in such measurements that can distinguish
+different proposed ground states. The presence of locally trapped anyons should
+leave distinct signatures in STM spectroscopic maps, and enables a new approach
+to directly detect - and perhaps ultimately manipulate - these exotic
+quasi-particles.
+",0,1,0,0,0,0
+8671,Proof of Correspondence between Keys and Encoding Maps in an Authentication Code,"  In a former paper the authors introduced two new systematic authentication
+codes based on the Gray map over a Galois ring. In this paper, it is proved the
+one-to-one onto correspondence between keys and encoding maps for the second
+introduced authentication code.
+",1,0,1,0,0,0
+8672,Following the density perturbations through a bounce with AdS/CFT Correspondence,"  A bounce universe model, known as the coupled-scalar-tachyon bounce (CSTB)
+universe, has been shown to solve the Horizon, Flatness and Homogeneity
+problems as well as the Big Bang Singularity problem. Furthermore a scale
+invariant spectrum of primordial density perturbations generated from the phase
+of pre-bounce contraction is shown to be stable against time evolution. In this
+work we study the detailed dynamics of the bounce and its imprints on the scale
+invariance of the spectrum. The dynamics of the gravitational interactions near
+the bounce point may be strongly coupled as the spatial curvature becomes big.
+There is no a prior reason to expect the spectral index of the primordial
+perturbations of matter density can be preserved. By encoding the bounce
+dynamics holographically onto the dynamics of dual Yang-Mills system while the
+latter is weakly coupled, via the AdS/CFT correspondence, we can safely evolve
+the spectrum of the cosmic perturbations with full control. In this way we can
+compare the post-bounce spectrum with the pre-bounce one: in the CSTB model we
+explicitly show that the spectrum of primordial density perturbations generated
+in the contraction phase preserves its stability as well as scale invariance
+throughout the bounce process.
+",0,1,0,0,0,0
+8673,Privacy in Information-Rich Intelligent Infrastructure,"  Intelligent infrastructure will critically rely on the dense instrumentation
+of sensors and actuators that constantly transmit streaming data to cloud-based
+analytics for real-time monitoring. For example, driverless cars communicate
+real-time location and other data to companies like Google, which aggregate
+regional data in order to provide real-time traffic maps. Such traffic maps can
+be extremely useful to the driver (for optimal travel routing), as well as to
+city transportation administrators for real-time accident response that can
+have an impact on traffic capacity. Intelligent infrastructure monitoring
+compromises the privacy of drivers who continuously share their location to
+cloud aggregators, with unpredictable consequences.
+Without a framework for protecting the privacy of the driver's data, drivers
+may be very conservative about sharing their data with cloud-based analytics
+that will be responsible for adding the intelligence to intelligent
+infrastructure. In the energy sector, the Smart Grid revolution relies
+critically on real-time metering of energy supply and demand with very high
+granularity. This is turn enables real-time demand response and creates a new
+energy market that can incorporate unpredictable renewable energy sources while
+ensuring grid stability and reliability. However, real-time streaming data
+captured by smart meters contain a lot of private information, such as our home
+activities or lack of, which can be easily inferred by anyone that has access
+to the smart meter data, resulting not only in loss of privacy but potentially
+also putting us at risk.
+",1,0,0,0,0,0
+8674,Towards equation of state for a market: A thermodynamical paradigm of economics,"  Foundations of equilibrium thermodynamics are the equation of state (EoS) and
+four postulated laws of thermodynamics. We use equilibrium thermodynamics
+paradigms in constructing the EoS for microeconomics system that is a market.
+This speculation is hoped to be first step towards whole pictures of
+thermodynamical paradigm of economics.
+",0,0,0,0,0,1
+8675,On the sub-Gaussianity of the Beta and Dirichlet distributions,"  We obtain the optimal proxy variance for the sub-Gaussianity of Beta
+distribution, thus proving upper bounds recently conjectured by Elder (2016).
+We provide different proof techniques for the symmetrical (around its mean)
+case and the non-symmetrical case. The technique in the latter case relies on
+studying the ordinary differential equation satisfied by the Beta
+moment-generating function known as the confluent hypergeometric function. As a
+consequence, we derive the optimal proxy variance for the Dirichlet
+distribution, which is apparently a novel result. We also provide a new proof
+of the optimal proxy variance for the Bernoulli distribution, and discuss in
+this context the proxy variance relation to log-Sobolev inequalities and
+transport inequalities.
+",0,0,1,1,0,0
+8676,Chaos in three coupled rotators: From Anosov dynamics to hyperbolic attractors,"  Starting from Anosov chaotic dynamics of geodesic flow on a surface of
+negative curvature, we develop and consider a number of self-oscillatory
+systems including those with hinged mechanical coupling of three rotators and a
+system of rotators interacting through a potential function. These results are
+used to design an electronic circuit for generation of rough (structurally
+stable) chaos. Results of numerical integration of the model equations of
+different degree of accuracy are presented and discussed. Also, circuit
+simulation of the electronic generator is provided using the NI Multisim
+environment. Portraits of attractors, waveforms of generated oscillations,
+Lyapunov exponents, and spectra are considered and found to be in good
+correspondence for the dynamics on the attractive sets of the self-oscillatory
+systems and for the original Anosov geodesic flow. The hyperbolic nature of the
+dynamics is tested numerically using a criterion based on statistics of angles
+of intersection of stable and unstable subspaces of the perturbation vectors at
+a reference phase trajectory on the attractor.
+",0,1,0,0,0,0
+8677,A novel analytical method for analysis of electromagnetic scattering from inhomogeneous spherical structures using duality principles,"  In this article, a novel analytical approach is presented for the analysis of
+electromagnetic (EM) scattering from radially inhomogeneous spherical
+structures (RISSs) based on the duality principle. According to the spherical
+symmetry, similar angular dependencies in all the regions are considered using
+spherical harmonics. To extract the radial dependency, the system of
+differential equations of wave propagation toward the inhomogeneity direction
+is equated with the dual planar ones. A general duality between electromagnetic
+fields and parameters and scattering parameters of the two structures is
+introduced. The validity of the proposed approach is verified through a
+comprehensive example. The presented approach substitutes a complicated problem
+in spherical coordinate to an easy, well posed, and a previously solved problem
+in planar geometry. This approach is valid for all continuously varying
+inhomogeneity profiles. One of the major advantages of the proposed method is
+the capability of studying two general and applicable types of RISSs. As an
+interesting application, a new class of lens antenna based on the physical
+concept of the gradient refractive index material is introduced. The approach
+is used to analyze the EM scattering from the structure and validate strong
+performance of the lens.
+",0,1,0,0,0,0
+8678,Causal Inference with Two Versions of Treatment,"  Causal effects are commonly defined as comparisons of the potential outcomes
+under treatment and control, but this definition is threatened by the
+possibility that the treatment or control condition is not well-defined,
+existing instead in more than one version. A simple, widely applicable analysis
+is proposed to address the possibility that the treatment or control condition
+exists in two versions with two different treatment effects. This analysis
+loses no power in the main comparison of treatment and control, provides
+additional information about version effects, and controls the family-wise
+error rate in several comparisons. The method is motivated and illustrated
+using an on-going study of the possibility that repeated head trauma in high
+school football causes an increase in risk of early on-set dementia.
+",0,0,0,1,0,0
+8679,Characterization of Multi-scale Invariant Random Fields,"  Applying certain flexible geometric sampling of a multi-scale invariant (MSI)
+field we provide a multi-dimensional multi-selfsimilar field which has a one to
+one correspondence with such sampled MSI field. This sampling enables us to
+characterize harmonic-like representation and spectral density function of the
+sampled MSI field. Imposing Markov property for the MSI field, we find that the
+covariance function and spectral density matrix of such sampled Markov MSI
+field are characterized by the covariance functions of samples of the first
+scale rectangle. We present an example of MSI field as two-dimensional simple
+fractional Brownian motion. We consider a real data example of the
+precipitation in some area of Brisbane in Australia for some special period. We
+show that precipitation on this area has MSI property and estimate time
+dependent scale and Hurst parameters of this MSI field in three dimension as
+latitude, longitude and time. Our method enables one to predict precipitation
+in time and place.
+",0,0,0,1,0,0
+8680,Interpretable 3D Human Action Analysis with Temporal Convolutional Networks,"  The discriminative power of modern deep learning models for 3D human action
+recognition is growing ever so potent. In conjunction with the recent
+resurgence of 3D human action representation with 3D skeletons, the quality and
+the pace of recent progress have been significant. However, the inner workings
+of state-of-the-art learning based methods in 3D human action recognition still
+remain mostly black-box. In this work, we propose to use a new class of models
+known as Temporal Convolutional Neural Networks (TCN) for 3D human action
+recognition. Compared to popular LSTM-based Recurrent Neural Network models,
+given interpretable input such as 3D skeletons, TCN provides us a way to
+explicitly learn readily interpretable spatio-temporal representations for 3D
+human action recognition. We provide our strategy in re-designing the TCN with
+interpretability in mind and how such characteristics of the model is leveraged
+to construct a powerful 3D activity recognition method. Through this work, we
+wish to take a step towards a spatio-temporal model that is easier to
+understand, explain and interpret. The resulting model, Res-TCN, achieves
+state-of-the-art results on the largest 3D human action recognition dataset,
+NTU-RGBD.
+",1,0,0,0,0,0
+8681,Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding,"  t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for
+dimensionality reduction and visualization that has become widely popular in
+recent years. Efficient implementations of t-SNE are available, but they scale
+poorly to datasets with hundreds of thousands to millions of high dimensional
+data-points. We present Fast Fourier Transform-accelerated Interpolation-based
+t-SNE (FIt-SNE), which dramatically accelerates the computation of t-SNE. The
+most time-consuming step of t-SNE is a convolution that we accelerate by
+interpolating onto an equispaced grid and subsequently using the fast Fourier
+transform to perform the convolution. We also optimize the computation of input
+similarities in high dimensions using multi-threaded approximate nearest
+neighbors. We further present a modification to t-SNE called ""late
+exaggeration,"" which allows for easier identification of clusters in t-SNE
+embeddings. Finally, for datasets that cannot be loaded into the memory, we
+present out-of-core randomized principal component analysis (oocPCA), so that
+the top principal components of a dataset can be computed without ever fully
+loading the matrix, hence allowing for t-SNE of large datasets to be computed
+on resource-limited machines.
+",1,0,0,1,0,0
+8682,An arithmetic site of Connes-Consani type for imaginary quadratic fields with class number 1,"  We construct, for imaginary quadratic number fields with class number 1, an
+arithmetic site of Connes-Consani type. The main difficulty here is that the
+constructions of Connes and Consani and part of their results strongly rely on
+the natural order existing on real numbers which is compatible with basic
+arithmetic operations. Of course nothing of this sort exists in the case of
+imaginary quadratic number fields with class number 1. We first define what we
+call arithmetic site for such number fields, we then calculate the points of
+those arithmetic sites and we express them in terms of the adèles class space
+considered by Connes to give a spectral interpretation of zeroes of Hecke L
+functions of number fields. We get therefore that for a fixed imaginary
+quadratic number field with class number 1, that the points of our arithmetic
+site are related to the zeroes of the Dedekind zeta function of the number
+field considered and to the zeroes of some Hecke L functions. We then study the
+relation between the spectrum of the ring of integers of the number field and
+the arithmetic site. Finally we construct the square of the arithmetic site.
+",0,0,1,0,0,0
+8683,Linear Matrix Inequalities for Physically-Consistent Inertial Parameter Identification: A Statistical Perspective on the Mass Distribution,"  With the increased application of model-based whole-body control in legged
+robots, there has been a resurgence of research interest into methods for
+accurate system identification. An important class of methods focuses on the
+inertial parameters of rigid-body systems. These parameters consist of the
+mass, first mass moment (related to center of mass location), and rotational
+inertia matrix of each link. The main contribution of this paper is to
+formulate physical-consistency constraints on these parameters as Linear Matrix
+Inequalities (LMIs). The use of these constraints in identification can
+accelerate convergence and increase robustness to noisy data. It is critically
+observed that the proposed LMIs are expressed in terms of the covariance of the
+mass distribution, rather than its rotational moments of inertia. With this
+perspective, connections to the classical problem of moments in mathematics are
+shown to yield new bounding-volume constraints on the mass distribution of each
+link. While previous work ensured physical plausibility or used convex
+optimization in identification, the LMIs here uniquely enable both advantages.
+Constraints are applied to identification of a leg for the MIT Cheetah 3 robot.
+Detailed properties of transmission components are identified alongside link
+inertias, with parameter optimization carried out to global optimality through
+semidefinite programming.
+",1,0,0,0,0,0
+8684,Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning,"  In this paper, we consider the block-sparse signals recovery problem in the
+context of multiple measurement vectors (MMV) with common row sparsity
+patterns. We develop a new method for recovery of common row sparsity MMV
+signals, where a pattern-coupled hierarchical Gaussian prior model is
+introduced to characterize both the block-sparsity of the coefficients and the
+statistical dependency between neighboring coefficients of the common row
+sparsity MMV signals. Unlike many other methods, the proposed method is able to
+automatically capture the block sparse structure of the unknown signal. Our
+method is developed using an expectation-maximization (EM) framework.
+Simulation results show that our proposed method offers competitive performance
+in recovering block-sparse common row sparsity pattern MMV signals.
+",1,0,0,1,0,0
+8685,A temperate rocky super-Earth transiting a nearby cool star,"  M dwarf stars, which have masses less than 60 per cent that of the Sun, make
+up 75 per cent of the population of the stars in the Galaxy [1]. The
+atmospheres of orbiting Earth-sized planets are observationally accessible via
+transmission spectroscopy when the planets pass in front of these stars [2,3].
+Statistical results suggest that the nearest transiting Earth-sized planet in
+the liquid-water, habitable zone of an M dwarf star is probably around 10.5
+parsecs away [4]. A temperate planet has been discovered orbiting Proxima
+Centauri, the closest M dwarf [5], but it probably does not transit and its
+true mass is unknown. Seven Earth-sized planets transit the very low-mass star
+TRAPPIST-1, which is 12 parsecs away [6,7], but their masses and, particularly,
+their densities are poorly constrained. Here we report observations of LHS
+1140b, a planet with a radius of 1.4 Earth radii transiting a small, cool star
+(LHS 1140) 12 parsecs away. We measure the mass of the planet to be 6.6 times
+that of Earth, consistent with a rocky bulk composition. LHS 1140b receives an
+insolation of 0.46 times that of Earth, placing it within the liquid-water,
+habitable zone [8]. With 90 per cent confidence, we place an upper limit on the
+orbital eccentricity of 0.29. The circular orbit is unlikely to be the result
+of tides and therefore was probably present at formation. Given its large
+surface gravity and cool insolation, the planet may have retained its
+atmosphere despite the greater luminosity (compared to the present-day) of its
+host star in its youth [9,10]. Because LHS 1140 is nearby, telescopes currently
+under construction might be able to search for specific atmospheric gases in
+the future [2,3].
+",0,1,0,0,0,0
+8686,Boundary driven Brownian gas,"  We consider a gas of independent Brownian particles on a bounded interval in
+contact with two particle reservoirs at the endpoints. Due to the Brownian
+nature of the particles, infinitely many particles enter and leave the system
+in each time interval. Nonetheless, the dynamics can be constructed as a Markov
+process with continuous paths on a suitable space. If $\lambda_0$ and
+$\lambda_1$ are the chemical potentials of the boundary reservoirs, the
+stationary distribution (reversible if and only if $\lambda_0=\lambda_1$) is a
+Poisson point process with intensity given by the linear interpolation between
+$\lambda_0$ and $\lambda_1$. We then analyze the empirical flow that it is
+defined by counting, in a time interval $[0,t]$, the net number of particles
+crossing a given point $x$. In the stationary regime we identify its statistics
+and show that it is given, apart an $x$ dependent correction that is bounded
+for large $t$, by the difference of two independent Poisson processes with
+parameters $\lambda_0$ and $\lambda_1$.
+",0,0,1,0,0,0
+8687,"Fully Resolved Numerical Simulations of Fused Deposition Modeling. Part II-Solidification, Residual Stresses, and Modeling of the Nozzle","  Purpose - This paper continues the development of a comprehensive methodology
+for fully resolved numerical simulations of fusion deposition modeling.
+Design/methodology/approach - A front-tracking/finite volume method introduced
+in Part I to simulate the heat transfer and fluid dynamics of the deposition of
+a polymer filament on a fixed bed is extended by adding an improved model for
+the injection nozzle, including the shrinkage of the polymer as it cools down,
+and accounting for stresses in the solid. Findings - The accuracy and
+convergence properties of the new method are tested by grid refinement and the
+method is shown to produce convergent solutions for the shape of the filament,
+the temperature distribution, the shrinkage and the solid stresses. Research
+limitations/implications - The method presented in the paper focuses on
+modeling the fluid flow, the cooling and solidification, as well as volume
+changes and residual stresses, using a relatively simple viscoelastic
+constitutive model. More complex material models, depending, for example, on
+the evolution of the configuration tensor, are not included. Practical
+implications - The ability to carry out fully resolved numerical simulations of
+the fusion deposition process is expected to be critical for the validation of
+mathematical models for the material behavior, to help explore new deposition
+strategies, and to provide the ""ground truth"" for the development of reduced
+order models. Originality/value - The paper completes the development of the
+first numerical method for fully resolved simulation of fusion filament
+modeling.
+",0,1,0,0,0,0
+8688,High Dimensional Cluster Analysis Using Path Lengths,"  A hierarchical scheme for clustering data is presented which applies to
+spaces with a high number of dimension ($N_{_{D}}>3$). The data set is first
+reduced to a smaller set of partitions (multi-dimensional bins). Multiple
+clustering techniques are used, including spectral clustering, however, new
+techniques are also introduced based on the path length between partitions that
+are connected to one another. A Line-Of-Sight algorithm is also developed for
+clustering. A test bank of 12 data sets with varying properties is used to
+expose the strengths and weaknesses of each technique. Finally, a robust
+clustering technique is discussed based on reaching a consensus among the
+multiple approaches, overcoming the weaknesses found individually.
+",1,0,0,0,0,0
+8689,Forecasting elections using compartmental models of infections,"  To forecast political elections, popular pollsters gather polls and combine
+information from them with fundamental data such as historical trends, the
+national economy, and incumbency. This process is complicated, and it includes
+many subjective choices (e.g., when identifying likely voters, estimating
+turnout, and quantifying other sources of uncertainty), leading to forecasts
+that differ between sources even when they use the same underlying polling
+data. With the goal of shedding light on election forecasts (using the United
+States as an example), we develop a framework for forecasting elections from
+the perspective of dynamical systems. Through a simple approach that borrows
+ideas from epidemiology, we show how to combine a compartmental model of
+disease spreading with public polling data to forecast gubernatorial,
+senatorial, and presidential elections at the state level. Our results for the
+2012 and 2016 U.S. races are largely in agreement with those of popular
+pollsters, and we use our new model to explore how subjective choices about
+uncertainty impact results. Our goals are to open up new avenues for improving
+how elections are forecast, to increase understanding of the results that are
+reported by popular news sources, and to illustrate a fascinating example of
+data-driven forecasting using dynamical systems. We conclude by forecasting the
+senatorial and gubernatorial races in the 2018 U.S. midterm elections of 6
+November.
+",1,0,0,0,0,0
+8690,Online Learning Without Prior Information,"  The vast majority of optimization and online learning algorithms today
+require some prior information about the data (often in the form of bounds on
+gradients or on the optimal parameter value). When this information is not
+available, these algorithms require laborious manual tuning of various
+hyperparameters, motivating the search for algorithms that can adapt to the
+data with no prior information. We describe a frontier of new lower bounds on
+the performance of such algorithms, reflecting a tradeoff between a term that
+depends on the optimal parameter value and a term that depends on the
+gradients' rate of growth. Further, we construct a family of algorithms whose
+performance matches any desired point on this frontier, which no previous
+algorithm reaches.
+",1,0,0,1,0,0
+8691,Sparse Identification for Nonlinear Optical Communication Systems: SINO Method,"  We introduce low complexity machine learning based approach for mitigating
+nonlinear impairments in optical fiber communications systems. The immense
+intricacy of the problem calls for the development of ""smart"" methodology,
+simplifying the analysis without losing the key features that are important for
+recovery of transmitted data. The proposed sparse identification method for
+optical systems (SINO) allows to determine the minimal (optimal) number of
+degrees of freedom required for adaptive mitigation of detrimental nonlinear
+effects. We demonstrate successful application of the SINO method both for
+standard fiber communication links and for few-mode
+spatial-division-multiplexing systems.
+",0,1,0,0,0,0
+8692,Serial Correlations in Single-Subject fMRI with Sub-Second TR,"  When performing statistical analysis of single-subject fMRI data, serial
+correlations need to be taken into account to allow for valid inference.
+Otherwise, the variability in the parameter estimates might be under-estimated
+resulting in increased false-positive rates. Serial correlations in fMRI data
+are commonly characterized in terms of a first-order autoregressive (AR)
+process and then removed via pre-whitening. The required noise model for the
+pre-whitening depends on a number of parameters, particularly the repetition
+time (TR). Here we investigate how the sub-second temporal resolution provided
+by simultaneous multislice (SMS) imaging changes the noise structure in fMRI
+time series. We fit a higher-order AR model and then estimate the optimal AR
+model order for a sequence with a TR of less than 600 ms providing whole brain
+coverage. We show that physiological noise modelling successfully reduces the
+required AR model order, but remaining serial correlations necessitate an
+advanced noise model. We conclude that commonly used noise models, such as the
+AR(1) model, are inadequate for modelling serial correlations in fMRI using
+sub-second TRs. Rather, physiological noise modelling in combination with
+advanced pre-whitening schemes enable valid inference in single-subject
+analysis using fast fMRI sequences.
+",0,0,0,1,0,0
+8693,Randomization-based Inference for Bernoulli-Trial Experiments and Implications for Observational Studies,"  We present a randomization-based inferential framework for experiments
+characterized by a strongly ignorable assignment mechanism where units have
+independent probabilities of receiving treatment. Previous works on
+randomization tests often assume these probabilities are equal within blocks of
+units. We consider the general case where they differ across units and show how
+to perform randomization tests and obtain point estimates and confidence
+intervals. Furthermore, we develop a rejection-sampling algorithm to conduct
+randomization-based inference conditional on ancillary statistics, covariate
+balance, or other statistics of interest. Through simulation we demonstrate how
+our algorithm can yield powerful randomization tests and thus precise
+inference. Our work also has implications for observational studies, which
+commonly assume a strongly ignorable assignment mechanism. Most methodologies
+for observational studies make additional modeling or asymptotic assumptions,
+while our framework only assumes the strongly ignorable assignment mechanism,
+and thus can be considered a minimal-assumption approach.
+",0,0,0,1,0,0
+8694,Cloaking using complementary media for electromagnetic waves,"  Negative index materials are artificial structures whose refractive index has
+negative value over some frequency range. The study of these materials has
+attracted a lot of attention in the scientific community not only because of
+their many potential interesting applications but also because of challenges in
+understanding their intriguing properties due to the sign-changing coefficients
+in equations describing their properties. In this paper, we establish cloaking
+using complementary media for electromagnetic waves. This confirms and extends
+the suggestions in two dimensions of Lai et al. for the full Maxwell equations.
+The analysis is based on the reflecting and removing localized singularity
+techniques, three-sphere inequalities, and the fact that the Maxwell equations
+can be reduced to a weakly coupled second order elliptic equations.
+",0,0,1,0,0,0
+8695,Deterministic parallel analysis: An improved method for selecting the number of factors and principal components,"  Factor analysis and principal component analysis (PCA) are used in many
+application areas. The first step, choosing the number of components, remains a
+serious challenge. Our work proposes improved methods for this important
+problem. One of the most popular state-of-the-art methods is Parallel Analysis
+(PA), which compares the observed factor strengths to simulated ones under a
+noise-only model. This paper proposes improvements to PA. We first de-randomize
+it, proposing Deterministic Parallel Analysis (DPA), which is faster and more
+reproducible than PA. Both PA and DPA are prone to a shadowing phenomenon in
+which a strong factor makes it hard to detect smaller but more interesting
+factors. We propose deflation to counter shadowing. We also propose to raise
+the decision threshold to improve estimation accuracy. We prove several
+consistency results for our methods, and test them in simulations. We also
+illustrate our methods on data from the Human Genome Diversity Project, where
+they significantly improve the accuracy.
+",0,0,0,1,0,0
+8696,Laplacian Spectrum of non-commuting graphs of finite groups,"  In this paper, we compute the Laplacian spectrum of non-commuting graphs of
+some classes of finite non-abelian groups. Our computations reveal that the
+non-commuting graphs of all the groups considered in this paper are L-integral.
+We also obtain some conditions on a group $G$ so that its non-commuting graph
+is L-integral.
+",0,0,1,0,0,0
+8697,Application of Coulomb energy density functional for atomic nuclei: Case studies of local density approximation and generalized gradient approximation,"  We test the Coulomb exchange and correlation energy density functionals of
+electron systems for atomic nuclei in the local density approximation (LDA) and
+the generalized gradient approximation (GGA). For the exchange Coulomb
+energies, it is found that the deviation between the LDA and GGA ranges from
+around $ 11 \, \% $ in $ {}^{4} \mathrm{He} $ to around $ 2.2 \, \% $ in $
+{}^{208} \mathrm{Pb} $, by taking the Perdew-Burke-Ernzerhof (PBE) functional
+as an example of the GGA\@. For the correlation Coulomb energies, it is shown
+that those functionals of electron systems are not suitable for atomic nuclei.
+",0,1,0,0,0,0
+8698,Transient frequency control with regional cooperation for power networks,"  This paper proposes a centralized and a distributed sub-optimal control
+strategy to maintain in safe regions the real-time transient frequencies of a
+given collection of buses, and simultaneously preserve asymptotic stability of
+the entire network. In a receding horizon fashion, the centralized control
+input is obtained by iteratively solving an open-loop optimization aiming to
+minimize the aggregate control effort over controllers regulated on individual
+buses with transient frequency and stability constraints. Due to the
+non-convexity of the optimization, we propose a convexification technique by
+identifying a reference control input trajectory. We then extend the
+centralized control to a distributed scheme, where each subcontroller can only
+access the state information within a local region. Simulations on a IEEE-39
+network illustrate our results.
+",1,0,0,0,0,0
+8699,Unsupervised prototype learning in an associative-memory network,"  Unsupervised learning in a generalized Hopfield associative-memory network is
+investigated in this work. First, we prove that the (generalized) Hopfield
+model is equivalent to a semi-restricted Boltzmann machine with a layer of
+visible neurons and another layer of hidden binary neurons, so it could serve
+as the building block for a multilayered deep-learning system. We then
+demonstrate that the Hopfield network can learn to form a faithful internal
+representation of the observed samples, with the learned memory patterns being
+prototypes of the input data. Furthermore, we propose a spectral method to
+extract a small set of concepts (idealized prototypes) as the most concise
+summary or abstraction of the empirical data.
+",1,1,0,0,0,0
+8700,One-Step Fabrication of pH-Responsive Membranes and Microcapsules through Interfacial H-Bond Polymer Complexation,"  Biocompatible microencapsulation is of widespread interest for the targeted
+delivery of active species in fields such as pharmaceuticals, cosmetics and
+agro-chemistry. Capsules obtained by the self-assembly of polymers at
+interfaces enable the combination of responsiveness to stimuli,
+biocompatibility and scaled up production. Here, we present a one-step method
+to produce in situ membranes at oil-water interfaces, based on the hydrogen
+bond complexation of polymers between H-bond acceptor and donor in the oil and
+aqueous phases, respectively. This robust process is realized through different
+methods, to obtain capsules of various sizes, from the micrometer scale using
+microfluidics or rotor-stator emulsification up to the centimeter scale using
+drop dripping. The polymer layer exhibits unique self-healing and pH-responsive
+properties. The membrane is viscoelastic at pH = 3, softens as pH is
+progressively raised, and eventually dissolves above pH = 6 to release the oil
+phase. This one-step method of preparation paves the way to the production of
+large quantities of functional capsules.
+",0,1,0,0,0,0
+8701,FA*IR: A Fair Top-k Ranking Algorithm,"  In this work, we define and solve the Fair Top-k Ranking problem, in which we
+want to determine a subset of k candidates from a large pool of n >> k
+candidates, maximizing utility (i.e., select the ""best"" candidates) subject to
+group fairness criteria. Our ranked group fairness definition extends group
+fairness using the standard notion of protected groups and is based on ensuring
+that the proportion of protected candidates in every prefix of the top-k
+ranking remains statistically above or indistinguishable from a given minimum.
+Utility is operationalized in two ways: (i) every candidate included in the
+top-$k$ should be more qualified than every candidate not included; and (ii)
+for every pair of candidates in the top-k, the more qualified candidate should
+be ranked above. An efficient algorithm is presented for producing the Fair
+Top-k Ranking, and tested experimentally on existing datasets as well as new
+datasets released with this paper, showing that our approach yields small
+distortions with respect to rankings that maximize utility without considering
+fairness criteria.
+To the best of our knowledge, this is the first algorithm grounded in
+statistical tests that can mitigate biases in the representation of an
+under-represented group along a ranked list.
+",1,0,0,0,0,0
+8702,A novel delayed-choice experimental proposal testing local decisions,"  Entangled states are notoriously non-separable, their sub-ensembles being
+only statistical mixtures yielding no coherences and no quantum interference
+phenomena. The interesting features of entangled states can be revealed only by
+coincidence counts over the (typically) two sub-ensembles of the system. In
+this paper we show that this feature extends to properties thought to be local,
+for example the transmissivity coefficient of a beam splitter. We discuss a
+well-known experimental setup and propose modifications, so that delayed-choice
+can be added and this new feature of entanglement tested.
+",0,1,0,0,0,0
+8703,Better accuracy with quantified privacy: representations learned via reconstructive adversarial network,"  The remarkable success of machine learning, especially deep learning, has
+produced a variety of cloud-based services for mobile users. Such services
+require an end user to send data to the service provider, which presents a
+serious challenge to end-user privacy. To address this concern, prior works
+either add noise to the data or send features extracted from the raw data. They
+struggle to balance between the utility and privacy because added noise reduces
+utility and raw data can be reconstructed from extracted features. This work
+represents a methodical departure from prior works: we balance between a
+measure of privacy and another of utility by leveraging adversarial learning to
+find a sweeter tradeoff. We design an encoder that optimizes against the
+reconstruction error (a measure of privacy), adversarially by a Decoder, and
+the inference accuracy (a measure of utility) by a Classifier. The result is
+RAN, a novel deep model with a new training algorithm that automatically
+extracts features for classification that are both private and useful. It turns
+out that adversarially forcing the extracted features to only conveys the
+intended information required by classification leads to an implicit
+regularization leading to better classification accuracy than the original
+model which completely ignores privacy. Thus, we achieve better privacy with
+better utility, a surprising possibility in machine learning! We conducted
+extensive experiments on five popular datasets over four training schemes, and
+demonstrate the superiority of RAN compared with existing alternatives.
+",1,0,0,1,0,0
+8704,Compositional descriptor-based recommender system accelerating the materials discovery,"  Structures and properties of many inorganic compounds have been collected
+historically. However, it only covers a very small portion of possible
+inorganic crystals, which implies the presence of numerous currently unknown
+compounds. A powerful machine-learning strategy is mandatory to discover new
+inorganic compounds from all chemical combinations. Herein we propose a
+descriptor-based recommender-system approach to estimate the relevance of
+chemical compositions where stable crystals can be formed [i.e., chemically
+relevant compositions (CRCs)]. As well as data-driven compositional similarity
+used in the literature, the use of compositional descriptors as a prior
+knowledge can accelerate the discovery of new compounds. We validate our
+recommender systems in two ways. Firstly, one database is used to construct a
+model, while another is used for the validation. Secondly, we estimate the
+phase stability for compounds at expected CRCs using density functional theory
+calculations.
+",0,1,0,0,0,0
+8705,Structure Formation and Microlensing with Axion Miniclusters,"  If the symmetry breaking responsible for axion dark matter production occurs
+during the radiation-dominated epoch in the early Universe, then this produces
+large amplitude perturbations that collapse into dense objects known as axion
+miniclusters. The characteristic minicluster mass, $M_0$, is set by the mass
+inside the horizon when axion oscillations begin. For the QCD axion $M_0\sim
+10^{-10}M_\odot$, however for an axion-like particle $M_0$ can approach
+$M_\odot$ or higher. Using the Press-Schechter formalism we compute the mass
+function of halos formed by hierarchical structure formation from these seeds.
+We compute the concentrations and collapse times of these halos and show that
+they can grow to be as massive as $10^6M_0$. Within the halos, miniclusters
+likely remain tightly bound, and we compute their gravitational microlensing
+signal taking the fraction of axion dark matter collapsed into miniclusters,
+$f_{\rm MC}$, as a free parameter. A large value of $f_{\rm MC}$ severely
+weakens constraints on axion scenarios from direct detection experiments. We
+take into account the non-Gaussian distribution of sizes of miniclusters and
+determine how this effects the number of microlensing events. We develop the
+tools to consider microlensing by an extended mass function of non-point-like
+objects, and use microlensing data to place the first observational constraints
+on $f_{\rm MC}$. This opens a new window for the potential discovery of the
+axion.
+",0,1,0,0,0,0
+8706,Clustering Residential Electricity Load Curves via Community Detection in Network,"  Performing analytic of household load curves (LCs) has significant value in
+predicting individual electricity consumption patterns, and hence facilitate
+developing demand-response strategy, and finally achieve energy efficiency
+improvement and emission reduction. LC clustering is a widely used analytic
+technology, which discovers electricity consumption patterns by grouping
+similar LCs into same sub-groups. However, previous clustering methods treat
+each LC in the data set as an individual time series, ignoring the inherent
+relationship among different LCs, restraining the performance of the clustering
+methods. What's more, due to the significant volatility and uncertainty of LCs,
+the previous LC clustering approaches inevitably result in either lager number
+of clusters or huge variances within a cluster, which is unacceptable for
+actual application needs. In this paper, we proposed an integrated approach to
+address this issue. First, we converted the LC clustering task into the
+community detection task in network. Second, we proposed a clustering approach
+incorporated with community detection to improve the performance of LC
+clustering, which includes network construction, community detection and
+typical load profile extraction. The method convert the data set into a network
+in which the inherent relationship among LCs is represented by the edges of the
+network. Third, we construct a multi-layer typical load profile directory to
+make the trade-off between variances within a cluster and the number of the
+clusters, enabling the researchers to assess the LCs and the customers in
+different layers according to practical application requirements. The
+experiments demonstrate that our integrated approach outperform the
+state-of-the-art methods.
+",1,0,0,0,0,0
+8707,Lie and Noether point Symmetries for a Class of Nonautonomous Dynamical Systems,"  We prove two general theorems which determine the Lie and the Noether point
+symmetries for the equations of motion of a dynamical system which moves in a
+general Riemannian space under the action of a time dependent potential
+$W(t,x)=\omega(t)V(x)$. We apply the theorems to the case of a time dependent
+central potential and the harmonic oscillator and determine all Lie and Noether
+point symmetries. Finally we prove that these theorems also apply to the case
+of a dynamical system with linear dumping and study two examples.
+",0,0,1,0,0,0
+8708,Thermal Inflation with a Thermal Waterfall Scalar Field Coupled to a Light Spectator Scalar Field,"  A new model of thermal inflation is introduced, in which the mass of the
+thermal waterfall field is dependent on a light spectator scalar field. Using
+the $\delta N$ formalism, the ""end of inflation"" scenario is investigated in
+order to ascertain whether this model is able to produce the dominant
+contribution to the primordial curvature perturbation. A multitude of
+constrains are considered so as to explore the parameter space, with particular
+emphasis to key observational signatures. For natural values of the parameters,
+the model is found to yield a sharp prediction for the scalar spectral index
+and its running, well within the current observational bounds.
+",0,1,0,0,0,0
+8709,DeepMoTIon: Learning to Navigate Like Humans,"  We present a novel human-aware navigation approach, where the robot learns to
+mimic humans to navigate safely in crowds. The presented model referred to as
+DeepMoTIon, is trained with pedestrian surveillance data to predict human
+velocity. The robot processes LiDAR scans via the trained network to navigate
+to the target location. We conduct extensive experiments to assess the
+different components of our network and prove the necessity of each to imitate
+humans. Our experiments show that DeepMoTIon outperforms state-of-the-art in
+terms of human imitation and reaches the target on 100% of the test cases
+without breaching humans' safe distance.
+",1,0,0,1,0,0
+8710,Multidimensional Free Poisson Limits on Free Stochastic Integral Algebras,"  In this paper, we prove four-moment theorems for multidimensional free
+Poisson limits on free Wigner chaos or the free Poisson algebra. We prove that,
+under mild technical conditions, a bi-indexed sequence of free stochastic
+integrals in free Wigner algebra or free Poisson algebra converges to a free
+sequence of free Poisson random variables if and only if the moments with order
+not greater than four of the sequence converge to the corresponding moments of
+the limit sequence of random variables. Similar four-moment theorems hold when
+the limit sequence is not free, but has a multidimensional free Poisson
+distribution with parameters $\lambda>0$ and $\alpha=\{\alpha_i: 0\ne
+\alpha_i\in \mathbb{R}, i=1, 2, \cdots\}$.
+",0,0,1,0,0,0
+8711,On the finite $W$-algebra for the Lie superalgebra Q(N) in the non-regular case,"  In this paper we study the finite W-algebra for the queer Lie superalgebra
+Q(n) associated with the non-regular even nilpotent coadjoint orbits in the
+case when the corresponding nilpotent element has Jordan blocks each of size l.
+We prove that this finite W-algebra is isomorphic to a quotient of the
+super-Yangian of Q({n/l})
+",0,0,1,0,0,0
+8712,SNeCT: Scalable network constrained Tucker decomposition for integrative multi-platform data analysis,"  Motivation: How do we integratively analyze large-scale multi-platform
+genomic data that are high dimensional and sparse? Furthermore, how can we
+incorporate prior knowledge, such as the association between genes, in the
+analysis systematically? Method: To solve this problem, we propose a Scalable
+Network Constrained Tucker decomposition method we call SNeCT. SNeCT adopts
+parallel stochastic gradient descent approach on the proposed parallelizable
+network constrained optimization function. SNeCT decomposition is applied to
+tensor constructed from large scale multi-platform multi-cohort cancer data,
+PanCan12, constrained on a network built from PathwayCommons database. Results:
+The decomposed factor matrices are applied to stratify cancers, to search for
+top-k similar patients, and to illustrate how the matrices can be used for
+personalized interpretation. In the stratification test, combined twelve-cohort
+data is clustered to form thirteen subclasses. The thirteen subclasses have a
+high correlation to tissue of origin in addition to other interesting
+observations, such as clear separation of OV cancers to two groups, and high
+clinical correlation within subclusters formed in cohorts BRCA and UCEC. In the
+top-k search, a new patient's genomic profile is generated and searched against
+existing patients based on the factor matrices. The similarity of the top-k
+patient to the query is high for 23 clinical features, including
+estrogen/progesterone receptor statuses of BRCA patients with average precision
+value ranges from 0.72 to 0.86 and from 0.68 to 0.86, respectively. We also
+provide an illustration of how the factor matrices can be used for
+interpretable personalized analysis of each patient.
+",1,0,0,1,0,0
+8713,CANAL: A Cache Timing Analysis Framework via LLVM Transformation,"  A unified modeling framework for non-functional properties of a program is
+essential for research in software analysis and verification, since it reduces
+burdens on individual researchers to implement new approaches and compare
+existing approaches. We present CANAL, a framework that models the cache
+behaviors of a program by transforming its intermediate representation in the
+LLVM compiler. CANAL inserts auxiliary variables and instructions over these
+variables, to allow standard verification tools to handle a new class of cache
+related properties, e.g., for computing the worst-case execution time and
+detecting side-channel leaks. We demonstrate the effectiveness of CANAL using
+three verification tools: KLEE, SMACK and Crab-llvm. We confirm the accuracy of
+our cache model by comparing with CPU cycle-accurate simulation results of
+GEM5. CANAL is available on GitHub and YouTube.
+",1,0,0,0,0,0
+8714,Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging,"  Obtaining magnetic resonance images (MRI) with high resolution and generating
+quantitative image-based biomarkers for assessing tissue biochemistry is
+crucial in clinical and research applications. How- ever, acquiring
+quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at
+odds with high-resolution in MRI, especially in a single rapid sequence. In
+this paper, we demonstrate how super-resolution can be utilized to maintain
+adequate SNR for accurate quantification of the T2 relaxation time biomarker,
+while simultaneously generating high- resolution images. We compare the
+efficacy of resolution enhancement using metrics such as peak SNR and
+structural similarity. We assess accuracy of cartilage T2 relaxation times by
+comparing against a standard reference method. Our evaluation suggests that SR
+can successfully maintain high-resolution and generate accurate biomarkers for
+accelerating MRI scans and enhancing the value of clinical and research MRI.
+",0,0,0,1,0,0
+8715,Inverse problems in models of resource distribution,"  We continue to study the problem of modeling of substitution of production
+factors motivated by the need for computable mathematical models of economics
+that could be used as a basis in applied developments. This problem has been
+studied for several decades, and several connections to complex analysis and
+geometry has been established. We describe several models of resource
+distribution and discuss the inverse problems for the generalized Radon
+transform arising is these models. We give a simple explicit range
+characterization for a particular case of the generalized Radon transform, and
+we apply it to show that the most popular production functions are compatible
+with these models. Besides, we give a necessary condition and a sufficient
+condition for solvability of the model identification problem in the form of an
+appropriate moment problem. These conditions are formulated in terms of rhombic
+tilings.
+",0,0,1,0,0,0
+8716,"Critical behaviour in one dimension: unconventional pairing, phase separation, BEC-BCS crossover and magnetic Lifshitz transition","  We study the superconducting properties of population-imbalanced ultracold
+Fermi mixtures in one-dimensional (1D) optical lattices that can be effectively
+described by the spin-imbalanced attractive Hubbard model (AHM) in the presence
+of a Zeeman magnetic field. We use the mean-field theory approach to obtain the
+ground state phase diagrams including some unconventional superconducting
+phases such as the Fulde--Ferrell--Larkin--Ovchinnikov (FFLO) phase, and the
+$\eta$ phase (an extremal case of the FFLO phase), both for the case of a fixed
+chemical potential and for a fixed number of particles. It allows to determine
+optimal regimes for the FFLO phase as well as $\eta$-pairing stability. We also
+investigate the evolution from the weak coupling (BCS-like limit) to the strong
+coupling limit of tightly bound local pairs (BEC) with increasing attraction,
+at $T=0$. Finally, the obtained results show that despite of the occurrence of
+the Lifshitz transition induced by an external magnetic field, the
+superconducting state can still exist in the system, at higher magnetic field
+values.
+",0,1,0,0,0,0
+8717,Andreev reflections and the quantum physics of black holes,"  We establish an analogy between superconductor-metal interfaces and the
+quantum physics of a black hole, using the proximity effect. We show that the
+metal-superconductor interface can be thought of as an event horizon and
+Andreev reflection from the interface is analogous to the Hawking radiation in
+black holes. We describe quantum information transfer in Andreev reflection
+with a final state projection model similar to the Horowitz-Maldacena model for
+black hole evaporation. We also propose the Andreev reflection-analogue of
+Hayden and Preskill's description of a black hole final state, where the black
+hole is described as an information mirror. The analogy between Crossed Andreev
+Reflections and Einstein-Rosen bridges is discussed: our proposal gives a
+precise mechanism for the apparent loss of quantum information in a black hole
+by the process of nonlocal Andreev reflection, transferring the quantum
+information through a wormhole and into another universe. Given these
+established connections, we conjecture that the final quantum state of a black
+hole is exactly the same as the ground state wavefunction of the
+superconductor/superfluid in the Bardeen-Cooper-Schrieffer (BCS) theory of
+superconductivity; in particular, the infalling matter and the infalling
+Hawking quanta, described in the Horowitz-Maldacena model, forms a Cooper
+pair-like singlet state inside the black hole. A black hole evaporating and
+shrinking in size can be thought of as the analogue of Andreev reflection by a
+hole where the superconductor loses a Cooper pair. Our model does not suffer
+from the black hole information problem since Andreev reflection is unitary. We
+also relate the thermodynamic properties of a black hole to that of a
+superconductor, and propose an experiment which can demonstrate the negative
+specific heat feature of black holes in a growing/evaporating condensate.
+",0,1,0,0,0,0
+8718,"Wind, Sand and Water. The Orientation of the Late Roman Forts in the Kharga Oasis (Egyptian Western Desert)","  The chain of late Roman fortified settlements built in the Kharga Oasis, in
+Egypt Western Desert, represents an interesting case study to analyse how the
+ancient Roman town planners interacted with the landscape. A peculiar feature
+of the site is the existence of a prevailing, north westerly wind, and it is
+possible to identify the average azimuth of the wind by measuring the central
+axes of the halfmoon shaped sand dunes which characterize the landscape. Using
+the methods of Archaeoastronomy, we compared these azimuths with the orthogonal
+layout of both the settlements and the agricultural installations and showed
+that these are oriented on the prevailing wind. A description and the possible
+implications of this <<weathervane orientation>> are discussed in this article.
+",0,1,0,0,0,0
+8719,On generalized Dold manifolds,"  Let $X$ be a smooth manifold with a (smooth) involution $\sigma:X\to X$ such
+that $Fix(\sigma)\ne \emptyset$. We call the space $P(m,X):=\mathbb{S}^m\times
+X/\!\sim$ where $(v,x)\sim (-v,\sigma(x))$ a generalized Dold manifold. When
+$X$ is an almost complex manifold and the differential $T\sigma: TX\to TX$ is
+conjugate complex linear on each fibre, we obtain a formula for the
+Stiefel-Whitney polynomial of $P(m,X)$ when $H^1(X;\mathbb{Z}_2)=0$. We obtain
+results on stable parallelizability of $P(m,X)$ and a very general criterion
+for the (non) vanishing of the unoriented cobordism class $[P(m,X)]$ in terms
+of the corresponding properties for $X$. These results are applied to the case
+when $X$ is a complex flag manifold.
+",0,0,1,0,0,0
+8720,Pilot system development in metre-scale laboratory discharge,"  The pilot system development in metre-scale negative laboratory discharges is
+studied with ns-fast photography. The systems appear as bipolar structures in
+the vicinity of the negative high-voltage electrode. They appear as a result of
+a single negative streamer propagation and determine further discharge
+development. Such systems possess features like glowing beads, bipolarity,
+different brightness of the top and bottom parts, and mutual reconnection. A 1D
+model of the ionization evolution in the spark gap is proposed. In the process
+of the nonlinear development of ionization growth, the model shows features
+similar to those observed. The visual similarities between high-altitude
+sprites and laboratory pilots are striking and may indicate that they are two
+manifestations of the same natural phenomenon.
+",0,1,0,0,0,0
+8721,Abstract Interpretation of Binary Code with Memory Accesses using Polyhedra,"  In this paper we propose a novel methodology for static analysis of binary
+code using abstract interpretation. We use an abstract domain based on
+polyhedra and two mapping functions that associate polyhedra variables with
+registers and memory. We demonstrate our methodology to the problem of
+computing upper bounds to loop iterations in the code. This problem is
+particularly important in the domain of Worst-Case Execution Time (WCET)
+analysis of safety-critical real-time code. However, our approach is general
+and it can applied to other static analysis problems.
+",1,0,0,0,0,0
+8722,Optimal learning via local entropies and sample compression,"  The aim of this paper is to provide several novel upper bounds on the excess
+risk with a primal focus on classification problems. We suggest two approaches
+and the obtained bounds are represented via the distribution dependent local
+entropies of the classes or the sizes of specific sample com- pression schemes.
+We show that in some cases, our guarantees are optimal up to constant factors
+and outperform previously known results. As an application of our results, we
+provide a new tight PAC bound for the hard-margin SVM, an extended analysis of
+certain empirical risk minimizers under log-concave distributions, a new
+variant of an online to batch conversion, and distribution dependent localized
+bounds in the aggregation framework. We also develop techniques that allow to
+replace empirical covering number or covering numbers with bracketing by the
+coverings with respect to the distribution of the data. The proofs for the
+sample compression schemes are based on the moment method combined with the
+analysis of voting algorithms.
+",0,0,1,1,0,0
+8723,Domain Specific Semantic Validation of Schema.org Annotations,"  Since its unveiling in 2011, schema.org has become the de facto standard for
+publishing semantically described structured data on the web, typically in the
+form of web page annotations. The increasing adoption of schema.org facilitates
+the growth of the web of data, as well as the development of automated agents
+that operate on this data. Schema.org is a large heterogeneous vocabulary that
+covers many domains. This is obviously not a bug, but a feature, since
+schema.org aims to describe almost everything on the web, and the web is huge.
+However, the heterogeneity of schema.org may cause a side effect, which is the
+challenge of picking the right classes and properties for an annotation in a
+certain domain, as well as keeping the annotation semantically consistent. In
+this work, we introduce our rule based approach and an implementation of it for
+validating schema.org annotations from two aspects: (a) the completeness of the
+annotations in terms of a specified domain, (b) the semantic consistency of the
+values based on pre-defined rules. We demonstrate our approach in the tourism
+domain.
+",1,0,0,0,0,0
+8724,Vector-valued extensions of operators through multilinear limited range extrapolation,"  We give an extension of Rubio de Francia's extrapolation theorem for
+functions taking values in UMD Banach function spaces to the multilinear
+limited range setting. In particular we show how boundedness of an
+$m$-(sub)linear operator \[T:L^{p_1}(w_1^{p_1})\times\cdots\times
+L^{p_m}(w_m^{p_m})\to L^p(w^p) \] for a certain class of Muckenhoupt weights
+yields an extension of the operator to Bochner spaces $L^{p}(w^p;X)$ for a wide
+class of Banach function spaces $X$, which includes certain Lebesgue, Lorentz
+and Orlicz spaces.
+We apply the extrapolation result to various operators, which yields new
+vector-valued bounds. Our examples include the bilinear Hilbert transform,
+certain Fourier multipliers and various operators satisfying sparse domination
+results.
+",0,0,1,0,0,0
+8725,Phase Diagram of Carbon Nickel Tungsten: Superatom Model,"  Carbon solubility in face-centered cubic Ni-W alloys and the phase diagram of
+C-Ni-W are investigated by means of first principle calculations and semi-grand
+canonical Monte Carlo simulations. With density functional theory (DFT) total
+energies as fitting data, we build a superatom model for efficient simulation.
+Multi-histogram analysis is utilized to predict free energies for different
+compositions and temperatures. By comparing free energies of competing phases,
+we are able to predict carbon solubility and phase diagrams of C-Ni-W at
+different temperatures. A simple ideal mixing approximation gives qualitatively
+similar predictions.
+",0,1,0,0,0,0
+8726,Classical solution for the linear sigma model,"  In this paper, the linear sigma model is studied using a method for finding
+analytical solutions based on Padé approximants. Using the solutions of two
+and three traveling waves in 1+3 dimensions we found, we are able to show a
+solution that is valid for an arbitrary number of bosons and traveling waves.
+",0,1,0,0,0,0
+8727,The Importance of System-Level Information in Multiagent Systems Design: Cardinality and Covering Problems,"  A fundamental challenge in multiagent systems is to design local control
+algorithms to ensure a desirable collective behaviour. The information
+available to the agents, gathered either through communication or sensing,
+naturally restricts the achievable performance. Hence, it is fundamental to
+identify what piece of information is valuable and can be exploited to design
+control laws with enhanced performance guarantees. This paper studies the case
+when such information is uncertain or inaccessible for a class of submodular
+resource allocation problems termed covering problems. In the first part of
+this work we pinpoint a fundamental risk-reward tradeoff faced by the system
+operator when conditioning the control design on a valuable but uncertain piece
+of information, which we refer to as the cardinality, that represents the
+maximum number of agents that can simultaneously select any given resource.
+Building on this analysis, we propose a distributed algorithm that allows
+agents to learn the cardinality while adjusting their behaviour over time. This
+algorithm is proved to perform on par or better to the optimal design obtained
+when the exact cardinality is known a priori.
+",1,0,0,0,0,0
+8728,Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings,"  Foreign policy analysis has been struggling to find ways to measure policy
+preferences and paradigm shifts in international political systems. This paper
+presents a novel, potential solution to this challenge, through the application
+of a neural word embedding (Word2vec) model on a dataset featuring speeches by
+heads of state or government in the United Nations General Debate. The paper
+provides three key contributions based on the output of the Word2vec model.
+First, it presents a set of policy attention indices, synthesizing the semantic
+proximity of political speeches to specific policy themes. Second, it
+introduces country-specific semantic centrality indices, based on topological
+analyses of countries' semantic positions with respect to each other. Third, it
+tests the hypothesis that there exists a statistical relation between the
+semantic content of political speeches and UN voting behavior, falsifying it
+and suggesting that political speeches contain information of different nature
+then the one behind voting outcomes. The paper concludes with a discussion of
+the practical use of its results and consequences for foreign policy analysis,
+public accountability, and transparency.
+",1,0,0,1,0,0
+8729,FLASH: A Faster Optimizer for SBSE Tasks,"  Most problems in search-based software engineering involve balancing
+conflicting objectives. Prior approaches to this task have required a large
+number of evaluations- making them very slow to execute and very hard to
+comprehend. To solve these problems, this paper introduces FLASH, a decision
+tree based optimizer that incrementally grows one decision tree per objective.
+These trees are then used to select the next best sample. This paper compares
+FLASH to state-of-the-art algorithms from search-based SE and machine learning.
+This comparison uses multiple SBSE case studies for release planning,
+configuration control, process modeling, and sprint planning for agile
+development. FLASH was found to be the fastest optimizer (sometimes requiring
+less than 1% of the evaluations used by evolutionary algorithms). Also,
+measured in terms of model size, FLASH's reasoning was far more succinct and
+comprehensible. Further, measured in terms of finding effective optimization,
+FLASH's recommendations were highly competitive with other approaches. Finally,
+FLASH scaled to more complex models since it always terminated (while
+state-of-the-art algorithm did not).
+",1,0,0,0,0,0
+8730,On the complexity of generalized chromatic polynomials,"  J. Makowsky and B. Zilber (2004) showed that many variations of graph
+colorings, called CP-colorings in the sequel, give rise to graph polynomials.
+This is true in particular for harmonious colorings, convex colorings,
+mcc_t-colorings, and rainbow colorings, and many more. N. Linial (1986) showed
+that the chromatic polynomial $\chi(G;X)$ is #P-hard to evaluate for all but
+three values X=0,1,2, where evaluation is in P. This dichotomy includes
+evaluation at real or complex values, and has the further property that the set
+of points for which evaluation is in P is finite. We investigate how the
+complexity of evaluating univariate graph polynomials that arise from
+CP-colorings varies for different evaluation points. We show that for some
+CP-colorings (harmonious, convex) the complexity of evaluation follows a
+similar pattern to the chromatic polynomial. However, in other cases (proper
+edge colorings, mcc_t-colorings, H-free colorings) we could only obtain a
+dichotomy for evaluations at non-negative integer points. We also discuss some
+CP-colorings where we only have very partial results.
+",1,0,1,0,0,0
+8731,Hybrid SGP4 orbit propagator,"  Two-Line Elements (TLEs) continue to be the sole public source of orbiter
+observations. The accuracy of TLE propagations through the Simplified General
+Perturbations-4 (SGP4) software decreases dramatically as the propagation
+horizon increases, and thus the period of validity of TLEs is very limited. As
+a result, TLEs are gradually becoming insufficient for the growing demands of
+Space Situational Awareness (SSA). We propose a technique, based on the hybrid
+propagation methodology, aimed at extending TLE validity with minimal changes
+to the current TLE-SGP4 system in a non-intrusive way. It requires that the
+institution in possession of the osculating elements distributes hybrid TLEs,
+HTLEs, which encapsulate the standard TLE and the model of its propagation
+error. The validity extension can be accomplished when the end user processes
+HTLEs through the hybrid SGP4 propagator, HSGP4, which comprises the standard
+SGP4 and an error corrector.
+",0,1,0,0,0,0
+8732,PDE-Net: Learning PDEs from Data,"  In this paper, we present an initial attempt to learn evolution PDEs from
+data. Inspired by the latest development of neural network designs in deep
+learning, we propose a new feed-forward deep network, called PDE-Net, to
+fulfill two objectives at the same time: to accurately predict dynamics of
+complex systems and to uncover the underlying hidden PDE models. The basic idea
+of the proposed PDE-Net is to learn differential operators by learning
+convolution kernels (filters), and apply neural networks or other machine
+learning methods to approximate the unknown nonlinear responses. Comparing with
+existing approaches, which either assume the form of the nonlinear response is
+known or fix certain finite difference approximations of differential
+operators, our approach has the most flexibility by learning both differential
+operators and the nonlinear responses. A special feature of the proposed
+PDE-Net is that all filters are properly constrained, which enables us to
+easily identify the governing PDE models while still maintaining the expressive
+and predictive power of the network. These constrains are carefully designed by
+fully exploiting the relation between the orders of differential operators and
+the orders of sum rules of filters (an important concept originated from
+wavelet theory). We also discuss relations of the PDE-Net with some existing
+networks in computer vision such as Network-In-Network (NIN) and Residual
+Neural Network (ResNet). Numerical experiments show that the PDE-Net has the
+potential to uncover the hidden PDE of the observed dynamics, and predict the
+dynamical behavior for a relatively long time, even in a noisy environment.
+",1,0,0,1,0,0
+8733,Stable components in the parameter plane of meromorphic functions of finite type,"  We study the parameter planes of certain one-dimensional, dynamically-defined
+slices of holomorphic families of meromorphic transcendental maps of finite
+type for which infinity is not an asymptotic value. Our planes are defined by
+constraining the orbits of all but one of the asymptotic values. We study the
+structure of the regions in the parameter plane where the dynamics have a free
+asymptotic value that is attracted to an attracting periodic orbit. The tangent
+family is an example that has been studied in detail, \cite{KK}, and our goal
+is to understand in the general context of meromorphic functions to what extent
+the structure one sees in parameter plane of the tangent family is generic.
+For the analogous dynamically defined slices of parameter spaces for rational
+maps, there are Mandelbrot-like components that have a unique {\em center} that
+can be used to give a combinatorial descripton of the components. No such
+center exists for our slices, no matter how carefully they are chosen. We can
+show, however, that there is an analogous concept, a ""virtual"" center, that
+plays essentially the same role as a center would have. It lies on the boundary
+of the component and corresponds to a map for which there is a ""virtual cycle"",
+namely, an iterate of the free asymptotic value that lands at a pole. These
+virtual centers are dense in the bifurcation locus of our one-dimensional
+families.
+",0,0,1,0,0,0
+8734,Optimal Regulation Response of Batteries Under Cycle Aging Mechanisms,"  When providing frequency regulation in a pay-for-performance market,
+batteries need to carefully balance the trade-off between following regulation
+signals and their degradation costs in real-time. Existing battery control
+strategies either do not consider mismatch penalties in pay-for-performance
+markets, or cannot accurately account for battery cycle aging mechanism during
+operation. This paper derives an online control policy that minimizes a battery
+owner's operating cost for providing frequency regulation in a
+pay-for-performance market. The proposed policy considers an accurate
+electrochemical battery cycle aging model, and is applicable to most types of
+battery cells. It has a threshold structure, and achieves near-optimal
+performance with respect to an offline controller that has complete future
+information. We explicitly characterize this gap and show it is independent of
+the duration of operation. Simulation results with both synthetic and real
+regulation traces are conducted to illustrate the theoretical results.
+",0,0,1,0,0,0
+8735,Spin-structures on real Bott manifolds with Kähler structures,"  Let M be a real Bott manifold with Kähler structure. Using Ishida
+characterization we give necessary and sufficient condition for the existence
+of the Spin-structure on M. In proof we use the technic developed in Popko,
+Szczepański ""Cohomological rigity of oriented Hantzsche-Wendt manifolds""
+(Adv. Math. 302 (2016), 1044 - 1068) and characteristic classes.
+",0,0,1,0,0,0
+8736,No difference in orbital parameters of RV-detected giant planets between 0.1 and 5 au in single vs multi-stellar systems,"  Our Keck/NIRC2 imaging survey searches for stellar companions around 144
+systems with radial velocity (RV) detected giant planets to determine whether
+stellar binaries influence the planets' orbital parameters. This survey, the
+largest of its kind to date, finds eight confirmed binary systems and three
+confirmed triple systems. These include three new multi-stellar systems (HD
+30856, HD 86081, and HD 207832) and three multi-stellar systems with newly
+confirmed common proper motion (HD 43691, HD 116029, and HD 164509). We combine
+these systems with seven RV planet-hosting multi-stellar systems from the
+literature in order to test for differences in the properties of planets with
+semimajor axes ranging between 0.1-5 au in single vs multi-stellar systems. We
+find no evidence that the presence or absence of stellar companions alters the
+distribution of planet properties in these systems. Although the observed
+stellar companions might influence the orbits of more distant planetary
+companions in these systems, our RV observations currently provide only weak
+constraints on the masses and orbital properties of planets beyond 5 au. In
+order to aid future efforts to characterize long period RV companions in these
+systems, we publish our contrast curves for all 144 targets. Using four years
+of astrometry for six hierarchical triple star systems hosting giant planets,
+we fit the orbits of the stellar companions in order to characterize the
+orbital architecture in these systems. We find that the orbital plane of the
+secondary and tertiary companions are inconsistent with an edge-on orbit in
+four out of six cases.
+",0,1,0,0,0,0
+8737,On overfitting and post-selection uncertainty assessments,"  In a regression context, when the relevant subset of explanatory variables is
+uncertain, it is common to use a data-driven model selection procedure.
+Classical linear model theory, applied naively to the selected sub-model, may
+not be valid because it ignores the selected sub-model's dependence on the
+data. We provide an explanation of this phenomenon, in terms of overfitting,
+for a class of model selection criteria.
+",0,0,1,1,0,0
+8738,Concept of multiple-cell cavity for axion dark matter search,"  In cavity-based axion dark matter search experiments exploring high mass
+regions, multiple-cavity design is considered to increase the detection volume
+within a given magnet bore. We introduce a new idea, referred to as
+multiple-cell cavity, which provides various benefits including a larger
+detection volume, simpler experimental setup, and easier phase-matching
+mechanism. We present the characteristics of this concept and demonstrate the
+experimental feasibility with an example of a double-cell cavity.
+",0,1,0,0,0,0
+8739,Detecting Recycled Commodity SoCs: Exploiting Aging-Induced SRAM PUF Unreliability,"  A physical unclonable function (PUF), analogous to a human fingerprint, has
+gained an enormous amount of attention from both academia and industry. SRAM
+PUF is among one of the popular silicon PUF constructions that exploits random
+initial power-up states from SRAM cells to extract hardware intrinsic secrets
+for identification and key generation applications. The advantage of SRAM PUFs
+is that they are widely embedded into commodity devices, thus such a PUF is
+obtained without a custom design and virtually free of implementation costs. A
+phenomenon known as `aging' alters the consistent
+reproducibility---reliability---of responses that can be extracted from a
+readout of a set of SRAM PUF cells. Similar to how a PUF exploits undesirable
+manufacturing randomness for generating a hardware intrinsic fingerprint, SRAM
+PUF unreliability induced by aging can be exploited to detect recycled
+commodity devices requiring no additional cost to the device. In this context,
+the SRAM PUF itself acts as an aging sensor by exploiting responses sensitive
+to aging. We use SRAMs available in pervasively deployed commercial
+off-the-shelf micro-controllers for experimental validations, which complements
+recent work demonstrated in FPGA platforms, and we present a simplified
+detection methodology along experimental results. We show that less than 1,000
+SRAM responses are adequate to guarantee that both false acceptance rate and
+false rejection rate are no more than 0.001.
+",1,0,0,0,0,0
+8740,Ginzburg-Landau-type theory of non-polarized spin superconductivity,"  Since the concept of spin superconductor was proposed, all the related
+studies concentrate on spin-polarized case. Here, we generalize the study to
+spin-non-polarized case. The free energy of non-polarized spin superconductor
+is obtained, and the Ginzburg-Landau-type equations are derived by using the
+variational method. These Ginzburg-Landau-type equations can be reduced to the
+spin-polarized case when the spin direction is fixed. Moreover, the expressions
+of super linear and angular spin currents inside the superconductor are
+derived. We demonstrate that the electric field induced by super spin current
+is equal to the one induced by equivalent charge obtained from the second
+Ginzburg-Landau-type equation, which shows self-consistency of our theory. By
+applying these Ginzburg-Landau-type equations, the effect of electric field on
+the superconductor is also studied. These results will help us get a better
+understanding of the spin superconductor and the related topics such as
+Bose-Einstein condensate of magnons and spin superfluidity.
+",0,1,0,0,0,0
+8741,"Evolution of Anisotropic Displacement Parameters and Superconductivity with Chemical Pressure in BiS2-Based REO0.5F0.5BiS2 (RE = La, Ce, Pr, and Nd)","  In order to understand the mechanisms behind the emergence of
+superconductivity by the chemical pressure effect in REO0.5F0.5BiS2 (RE = La,
+Ce, Pr, and Nd), where bulk superconductivity is induced by the substitutions
+with a smaller-radius RE, we performed synchrotron powder X-ray diffraction,
+and analyzed the crystal structure and anisotropic displacement parameters.
+With the decrease of the RE3+ ionic radius, the in-plane disorder of the S1
+sites significantly decreased, very similar to the trend observed in the
+Se-substituted systems: LaO0.5F0.5BiS2-xSex and Eu0.5La0.5FBiS2-xSex.
+Therefore, the emergence of bulk superconductivity upon the suppression of the
+in-plane disorder at the chalcogen sites is a universal scenario for the
+BiCh2-based superconductors. In addition, we indicated that the amplitude of
+vibration along the c-axis of the in-plane chalcogen sites may be related to
+the Tc in the BiCh2-based superconductors.
+",0,1,0,0,0,0
+8742,A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training,"  Training deep neural networks (DNNs) efficiently is a challenge due to the
+associated highly nonconvex optimization. The backpropagation (backprop)
+algorithm has long been the most widely used algorithm for gradient computation
+of parameters of DNNs and is used along with gradient descent-type algorithms
+for this optimization task. Recent work have shown the efficiency of block
+coordinate descent (BCD) type methods empirically for training DNNs. In view of
+this, we propose a novel algorithm based on the BCD method for training DNNs
+and provide its global convergence results built upon the powerful framework of
+the Kurdyka-Lojasiewicz (KL) property. Numerical experiments on standard
+datasets demonstrate its competitive efficiency against standard optimizers
+with backprop.
+",0,0,0,1,0,0
+8743,Surface zeta potential and diamond seeding on gallium nitride films,"  Measurement of zeta potential of Ga and N-face gallium nitride has been
+carried out as function of pH. Both the faces show negative zeta potential in
+the pH range 5.5-9. The Ga face has an isoelectric point at pH 5.5. The N-face
+shows higher negative zeta potential due to larger concentration of adsorbed
+oxygen. Zeta potential data clearly showed that H-terminated diamond seed
+solution at pH 8 will be optimal for the self assembly of a monolayer of
+diamond nanoparticles on the GaN surface. Subsequent growth of thin diamond
+films on GaN seeded with H-terminated diamond seeds produced fully coalesced
+films confirming a seeding density in excess of 10$^{12}$ cm$^{-2}$. This
+technique removes the requirement for a low thermal conduction seeding layer
+like silicon nitride on GaN.
+",0,1,0,0,0,0
+8744,Analysing Temporal Evolution of Interlingual Wikipedia Article Pairs,"  Wikipedia articles representing an entity or a topic in different language
+editions evolve independently within the scope of the language-specific user
+communities. This can lead to different points of views reflected in the
+articles, as well as complementary and inconsistent information. An analysis of
+how the information is propagated across the Wikipedia language editions can
+provide important insights in the article evolution along the temporal and
+cultural dimensions and support quality control. To facilitate such analysis,
+we present MultiWiki - a novel web-based user interface that provides an
+overview of the similarities and differences across the article pairs
+originating from different language editions on a timeline. MultiWiki enables
+users to observe the changes in the interlingual article similarity over time
+and to perform a detailed visual comparison of the article snapshots at a
+particular time point.
+",1,0,0,0,0,0
+8745,Smith Ideals of Operadic Algebras in Monoidal Model Categories,"  Building upon Hovey's work on Smith ideals for monoids, we develop a homotopy
+theory of Smith ideals for general operads in a symmetric monoidal category.
+For a sufficiently nice stable monoidal model category and an operad satisfying
+a cofibrancy condition, we show that there is a Quillen equivalence between a
+model structure on Smith ideals and a model structure on algebra maps induced
+by the cokernel and the kernel. For symmetric spectra this applies to the
+commutative operad and all Sigma-cofibrant operads. For chain complexes over a
+field of characteristic zero and the stable module category, this Quillen
+equivalence holds for all operads.
+",0,0,1,0,0,0
+8746,Phase and Power Control in the RF Magnetron Power Stations of Superconducting Accelerators,"  Phase and power control methods that satisfy the requirements of
+superconducting accelerators to magnetron RF sources were considered by a
+simplified kinetic model of a magnetron driven by a resonant injected signal.
+The model predicting and explaining stable, low noise operation of the tube
+below the threshold of self-excitation (the Hatrree voltage in free run mode)
+at a highest efficiency, a wide range of power control and a wide-band phase
+control was well verified in experiments demonstrating capabilities of the
+magnetron transmitters for powering of state of the art superconducting
+accelerators. Descriptions of the kinetic model, the experimental verification
+and a conceptual scheme of the highly-efficient magnetron RF transmitter for
+the accelerators are presented and discussed.
+",0,1,0,0,0,0
+8747,Cohomologies of locally conformally symplectic manifolds and solvmanifolds,"  We study the Morse-Novikov cohomology and its almost-symplectic counterpart
+on manifolds admitting locally conformally symplectic structures. More
+precisely, we introduce lcs cohomologies and we study elliptic Hodge theory,
+dualities, Hard Lefschetz Condition. We consider solvmanifolds and
+Oeljeklaus-Toma manifolds. In particular, we prove that Oeljeklaus-Toma
+manifolds with precisely one complex place, and under an additional arithmetic
+condition, satisfy the Mostow property. This holds in particular for the Inoue
+surface of type $S^0$.
+",0,0,1,0,0,0
+8748,Frequency Offset Estimation for OFDM Systems with a Novel Frequency Domain Training Sequence,"  A novel frequency domain training sequence and the corresponding carrier
+frequency offset (CFO) estimator are proposed for orthogonal frequency division
+multiplexing (OFDM) systems over frequency-selective fading channels. The
+proposed frequency domain training sequence comprises two types of pilot tones,
+namely distinctively spaced pilot tones with high energies and uniformly spaced
+ones with low energies. Based on the distinctively spaced pilot tones, integer
+CFO estimation is accomplished. After the subcarriers occupied by the
+distinctively spaced pilot tones and their adjacent subcarriers are nulled for
+the sake of interference cancellation, fractional CFO estimation is executed
+according to the uniformly spaced pilot tones. By exploiting a predefined
+lookup table making the best of the structure of the distinctively spaced pilot
+tones, computational complexity of the proposed CFO estimator can be decreased
+considerably. With the aid of the uniformly spaced pilot tones generated from
+Chu sequence with cyclically orthogonal property, the ability of the proposed
+estimator to combat multipath effect is enhanced to a great extent. Simulation
+results illustrate the good performance of the proposed CFO estimator.
+",1,0,0,0,0,0
+8749,Cusp shape and tunnel number,"  We show that the set of cusp shapes of hyperbolic tunnel number one manifolds
+is dense in the Teichmuller space of the torus. A similar result holds for
+tunnel number n manifolds. As a consequence, for fixed n, there are infinitely
+many hyperbolic tunnel number n manifolds with at most one exceptional Dehn
+filling. This is in contrast to large volume Berge knots, which are tunnel
+number one manifolds, but with cusp shapes converging to a single point in
+Teichmuller space.
+",0,0,1,0,0,0
+8750,Divergence-free positive symmetric tensors and fluid dynamics,"  We consider $d\times d$ tensors $A(x)$ that are symmetric, positive
+semi-definite, and whose row-divergence vanishes identically. We establish
+sharp inequalities for the integral of $(\det A)^{\frac1{d-1}}$. We apply them
+to models of compressible inviscid fluids: Euler equations, Euler--Fourier,
+relativistic Euler, Boltzman, BGK, etc... We deduce an {\em a priori} estimate
+for a new quantity, namely the space-time integral of $\rho^{\frac1n}p$, where
+$\rho$ is the mass density, $p$ the pressure and $n$ the space dimension. For
+kinetic models, the corresponding quantity generalizes Bony's functional.
+",0,1,1,0,0,0
+8751,Admire vs. Safire: Objective comparison of CT reconstruction algorithms and their noise properties,"  Purpose: Siemens has developed several iterative reconstruction (IR)
+algorithms on their CT scanners. SAFIRE is available on most of their CT
+scanners. The latest algorithm, ADMIRE, is available on their newest high-end
+CT scanners. The aim of our study was to compare the noise reduction properties
+of the two IR algorithms using objective methods. Methods and Materials: The
+homogeneous module of the Catphan phantom was scanned on a Siemens AS+ and a
+Siemens Flash CT scanner using an axial abdomen protocol with fixed tube
+current at two dose levels. The images were reconstructed with an abdomen
+filter (B30) using filtered back projection (FBP) and a low, medium, and high
+level of SAFIRE or ADMIRE. Noise Power Spectrum (NPS) curves were calculated
+using these images. Then, an anthropomorphic abdomen phantom (Kyoto Kagaku
+PH-5) was scanned using the same setup and exposure parameters. Fifty axial
+images at the same slice location were used to calculate inter-image standard
+deviation maps. Results: At full dose, the median values of the NPS curves were
+similar for both scanners at all IR levels. At low dose the median values of
+the NPS curves were generally shifted towards lower spatial frequencies,
+usually resulting in a more blotchy image texture. This shift was more
+prominent for ADMIRE compared to SAFIRE for all IR levels. Based on the
+inter-image standard deviation maps of the anthropomorphic phantom, ADMIRE
+removed noise near edges more efficiently than SAFIRE. Conclusion: No
+significant improvement in maintaining noise structure were found for the
+ADMIRE algorithm. Based on the inter-image standard deviation maps, ADMIRE
+removed noise near edges more efficiently than SAFIRE.
+",0,1,0,0,0,0
+8752,Occupants in simplicial complexes,"  Let $M$ be a smooth manifold and $K\subset M$ be a simplicial complex of
+codimension at least 3. Functor calculus methods lead to a homotopical formula
+of $M\setminus K$ in terms of spaces $M\setminus T$ where $T$ is a finite
+subset of $K$. This is a generalization of the author's previous work with
+Michael Weiss where the subset $K$ is assumed to be a smooth submanifold of $M$
+and uses his generalization of manifold calculus adapted for simplicial
+complexes.
+",0,0,1,0,0,0
+8753,Temporal Stability in Predictive Process Monitoring,"  Predictive process monitoring is concerned with the analysis of events
+produced during the execution of a business process in order to predict as
+early as possible the final outcome of an ongoing case. Traditionally,
+predictive process monitoring methods are optimized with respect to accuracy.
+However, in environments where users make decisions and take actions in
+response to the predictions they receive, it is equally important to optimize
+the stability of the successive predictions made for each case. To this end,
+this paper defines a notion of temporal stability for binary classification
+tasks in predictive process monitoring and evaluates existing methods with
+respect to both temporal stability and accuracy. We find that methods based on
+XGBoost and LSTM neural networks exhibit the highest temporal stability. We
+then show that temporal stability can be enhanced by hyperparameter-optimizing
+random forests and XGBoost classifiers with respect to inter-run stability.
+Finally, we show that time series smoothing techniques can further enhance
+temporal stability at the expense of slightly lower accuracy.
+",1,0,0,1,0,0
+8754,Discrete Gradient Line Fields on Surfaces,"  A line field on a manifold is a smooth map which assigns a tangent line to
+all but a finite number of points of the manifold. As such, it can be seen as a
+generalization of vector fields. They model a number of geometric and physical
+properties, e.g. the principal curvature directions dynamics on surfaces or the
+stress flux in elasticity.
+We propose a discretization of a Morse-Smale line field on surfaces,
+extending Forman's construction for discrete vector fields. More general
+critical elements and their indices are defined from local matchings, for which
+Euler theorem and the characterization of homotopy type in terms of critical
+cells still hold.
+",1,0,1,0,0,0
+8755,New Directions In Cellular Automata,"  We Propose A Novel Automaton Model which uses Arithmetic Operations as the
+Evolving Rules, each cell has the states of the Natural Numbers k = (N), a
+radius of r = 1/2 and operates on an arbitrary input size. The Automaton reads
+an Arithmetic Expression as an input and outputs another Arithmetic Expression.
+In Addition, we simulate a variety of One Dimensional Cellular Automata
+Structures with different Dynamics including Elementary Cellular Automata.
+",1,1,0,0,0,0
+8756,Smart Fog: Fog Computing Framework for Unsupervised Clustering Analytics in Wearable Internet of Things,"  The increasing use of wearables in smart telehealth generates heterogeneous
+medical big data. Cloud and fog services process these data for assisting
+clinical procedures. IoT based ehealthcare have greatly benefited from
+efficient data processing. This paper proposed and evaluated use of low
+resource machine learning on Fog devices kept close to the wearables for smart
+healthcare. In state of the art telecare systems, the signal processing and
+machine learning modules are deployed in the cloud for processing physiological
+data. We developed a prototype of Fog-based unsupervised machine learning big
+data analysis for discovering patterns in physiological data. We employed Intel
+Edison and Raspberry Pi as Fog computer in proposed architecture. We performed
+validation studies on real-world pathological speech data from in home
+monitoring of patients with Parkinson's disease (PD). Proposed architecture
+employed machine learning for analysis of pathological speech data obtained
+from smartwatches worn by the patients with PD. Results showed that proposed
+architecture is promising for low-resource clinical machine learning. It could
+be useful for other applications within wearable IoT for smart telehealth
+scenarios by translating machine learning approaches from the cloud backend to
+edge computing devices such as Fog.
+",1,0,0,0,0,0
+8757,Subspace Clustering via Optimal Direction Search,"  This letter presents a new spectral-clustering-based approach to the subspace
+clustering problem. Underpinning the proposed method is a convex program for
+optimal direction search, which for each data point d finds an optimal
+direction in the span of the data that has minimum projection on the other data
+points and non-vanishing projection on d. The obtained directions are
+subsequently leveraged to identify a neighborhood set for each data point. An
+alternating direction method of multipliers framework is provided to
+efficiently solve for the optimal directions. The proposed method is shown to
+notably outperform the existing subspace clustering methods, particularly for
+unwieldy scenarios involving high levels of noise and close subspaces, and
+yields the state-of-the-art results for the problem of face clustering using
+subspace segmentation.
+",1,0,0,1,0,0
+8758,The Covering Principle: A New Approach to Address Multiplicity in Hypotheses Testing,"  The closure and the partitioning principles have been used to build various
+multiple testing procedures in the past three decades. The essence of these two
+principles is based on parameter space partitioning. In this article, we
+propose a novel approach coined the covering principle from the perspective of
+rejection region coverage in the sample space. The covering principle divides
+the whole family of null hypotheses into a few overlapped sub-families when
+there is a priority of making decisions for hypothesis testing. We have proven
+that the multiple testing procedure constructed by the covering principle
+strongly controls the familywise error rate as long as the multiple tests for
+each sub-familiy strongly control the type I error. We have illustrated the
+covering principle can be applied to solve the general gate-keeping problems.
+",0,0,0,1,0,0
+8759,Mechanisms of Lagrangian analyticity in fluids,"  Certain systems of inviscid fluid dynamics have the property that for
+solutions that are only slightly better than differentiable in Eulerian
+variables, the corresponding Lagrangian trajectories are analytic in time. We
+elucidate the mechanisms in fluid dynamics systems that give rise to this
+automatic Lagrangian analyticity, as well as mechanisms in some particular
+fluids systems which prevent it from occurring.
+We give a conceptual argument for a general fluids model which shows that the
+fulfillment of a basic set of criteria results in the analyticity of the
+trajectory maps in time. We then apply this to the incompressible Euler
+equations to prove analyticity of trajectories for vortex patch solutions. We
+also use the method to prove the Lagrangian trajectories are analytic for
+solutions to the pressureless Euler-Poisson equations, for initial data with
+moderate regularity.
+We then examine the compressible Euler equations, and find that the finite
+speed of propagation in the system is incompatible with the Lagrangian
+analyticity property. By taking advantage of this finite speed we are able to
+construct smooth initial data with the property that some corresponding
+Lagrangian trajectory is not analytic in time. We also study the Vlasov-Poisson
+system, uncovering another mechanism that deters the analyticity of
+trajectories. In this instance, we find that a key nonlocal operator does not
+preserve analytic dependence in time. For this system we can also construct
+smooth initial data for which the corresponding solution has some non-analytic
+Lagrangian trajectory. This provides a counterexample to Lagrangian analyticity
+for a system in which there is an infinite speed of propagation, unlike the
+compressible Euler equations.
+",0,0,1,0,0,0
+8760,How to Differentiate Collective Variables in Free Energy Codes: Computer-Algebra Code Generation and Automatic Differentiation,"  The proper choice of collective variables (CVs) is central to biased-sampling
+free energy reconstruction methods in molecular dynamics simulations. The
+PLUMED 2 library, for instance, provides several sophisticated CV choices,
+implemented in a C++ framework; however, developing new CVs is still time
+consuming due to the need to provide code for the analytical derivatives of all
+functions with respect to atomic coordinates. We present two solutions to this
+problem, namely (a) symbolic differentiation and code generation, and (b)
+automatic code differentiation, in both cases leveraging open-source libraries
+(SymPy and Stan Math respectively). The two approaches are demonstrated and
+discussed in detail implementing a realistic example CV, the local radius of
+curvature of a polymer. Users may use the code as a template to streamline the
+implementation of their own CVs using high-level constructs and automatic
+gradient computation.
+",0,1,0,0,0,0
+8761,"Blocks of the category of smooth $\ell$-modular representations of $GL(n,F)$ and its inner forms: reduction to level-$0$","  Let $G$ be an inner form of a general linear group over a non-archimedean
+locally compact field of residue characteristic $p$, let $R$ be an
+algebraically closed field of characteristic different from $p$ and let
+$\mathscr{R}_R(G)$ be the category of smooth representations of $G$ over $R$.
+In this paper, we prove that a block (indecomposable summand) of
+$\mathscr{R}_R(G)$ is equivalent to a level-$0$ block (a block in which every
+object has non-zero invariant vectors for the pro-$p$-radical of a maximal
+compact open subgroup) of $\mathscr{R}_R(G')$, where $G'$ is a direct product
+of groups of the same type of $G$.
+",0,0,1,0,0,0
+8762,An Investigation of Newton-Sketch and Subsampled Newton Methods,"  The concepts of sketching and subsampling have recently received much
+attention by the optimization and statistics communities. In this paper, we
+study Newton-Sketch and Subsampled Newton (SSN) methods for the finite-sum
+optimization problem. We consider practical versions of the two methods in
+which the Newton equations are solved approximately using the conjugate
+gradient (CG) method or a stochastic gradient iteration. We establish new
+complexity results for the SSN-CG method that exploit the spectral properties
+of CG. Controlled numerical experiments compare the relative strengths of
+Newton-Sketch and SSN methods and show that for many finite-sum problems, they
+are far more efficient than SVRG, a popular first-order method.
+",1,0,1,1,0,0
+8763,On angled bounce-off impact of a drop impinging on a flowing soap film,"  Small drops impinging angularly on thin flowing soap films frequently
+demonstrate the rare emergence of bulk elastic effects working in-tandem with
+the more common-place hydrodynamic interactions. Three collision regimes are
+observable: (a) drop piercing through the film, (b) it coalescing with the
+flow, and (c) it bouncing off the film surface. During impact, the drop deforms
+along with a bulk elastic deformation of the film. For impacts that are
+close-to-tangential, the bounce-off regime predominates. We outline a reduced
+order analytical framework assuming a deformable drop and a deformable
+three-dimensional film, and the idealization invokes a phase-based parametric
+study. Angular inclination of the film and the ratio of post and pre impact
+drop sizes entail the phase parameters. We also perform experiments with
+vertically descending droplets impacting against an inclined soap film, flowing
+under constant pressure head. Model predicted phase domain for bounce-off
+compares well to our experimental findings. Additionally, the experiments
+exhibit momentum transfer to the film in the form of shed vortex dipole, along
+with propagation of free surface waves. On consulting prior published work, we
+note that for locomotion of water-walking insects using an impulsive action,
+the momentum distribution to the shed vortices and waves are both significant,
+taking up respectively 2/3-rd and 1/3-rd of the imparted streamwise momentum.
+In view of the potentially similar impulse actions, this theory is applied to
+the bounce-off examples in our experiments, and the resultant shed vortex
+dipole momenta are compared to the momenta computed from particle imaging
+velocimetry data. The magnitudes reveal identical order ($10^{-7}$ N$\cdot$s),
+suggesting that the bounce-off regime can be tapped as a simple analogue for
+interfacial bio-locomotion relying on impulse reactions.
+",0,1,0,0,0,0
+8764,Fast evaluation of solid harmonic Gaussian integrals for local resolution-of-the-identity methods and range-separated hybrid functionals,"  An integral scheme for the efficient evaluation of two-center integrals over
+contracted solid harmonic Gaussian functions is presented. Integral expressions
+are derived for local operators that depend on the position vector of one of
+the two Gaussian centers. These expressions are then used to derive the formula
+for three-index overlap integrals where two of the three Gaussians are located
+at the same center. The efficient evaluation of the latter is essential for
+local resolution-of-the-identity techniques that employ an overlap metric. We
+compare the performance of our integral scheme to the widely used Cartesian
+Gaussian-based method of Obara and Saika (OS). Non-local interaction potentials
+such as standard Coulomb, modified Coulomb and Gaussian-type operators, that
+occur in range-separated hybrid functionals, are also included in the
+performance tests. The speed-up with respect to the OS scheme is up to three
+orders of magnitude for both, integrals and their derivatives. In particular,
+our method is increasingly efficient for large angular momenta and highly
+contracted basis sets.
+",0,1,0,0,0,0
+8765,A note on integrating products of linear forms over the unit simplex,"  Integrating a product of linear forms over the unit simplex can be done in
+polynomial time if the number of variables n is fixed (V. Baldoni et al.,
+2011). In this note, we highlight that this problem is equivalent to obtaining
+the normalizing constant of state probabilities for a popular class of Markov
+processes used in queueing network theory. In light of this equivalence, we
+survey existing computational algorithms developed in queueing theory that can
+be used for exact integration. For example, under some regularity conditions,
+queueing theory algorithms can exactly integrate a product of linear forms of
+total degree N by solving N systems of linear equations.
+",1,0,1,0,0,0
+8766,Linearly constrained Gaussian processes,"  We consider a modification of the covariance function in Gaussian processes
+to correctly account for known linear constraints. By modelling the target
+function as a transformation of an underlying function, the constraints are
+explicitly incorporated in the model such that they are guaranteed to be
+fulfilled by any sample drawn or prediction made. We also propose a
+constructive procedure for designing the transformation operator and illustrate
+the result on both simulated and real-data examples.
+",0,0,0,1,0,0
+8767,Racks as multiplicative graphs,"  We interpret augmented racks as a certain kind of multiplicative graphs and
+show that this point of view is natural for defining rack homology. We also
+define the analogue of the group algebra for these objects; in particular, we
+see how discrete racks give rise to Hopf algebras and Lie algebras in the
+Loday-Pirashvili category $\mathcal{LM}$. Finally, we discuss the integration
+of Lie algebras in $\mathcal{LM}$ in the context of multiplicative graphs and
+augmented racks.
+",0,0,1,0,0,0
+8768,Bielliptic intermediate modular curves,"  We determine which of the modular curves $X_\Delta(N)$, that is, curves lying
+between $X_0(N)$ and $X_1(N)$, are bielliptic. Somewhat surprisingly, we find
+that one of these curves has exceptional automorphisms. Finally we find all
+$X_\Delta(N)$ that have infinitely many quadratic points over $\mathbb{Q}$.
+",0,0,1,0,0,0
+8769,Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment,"  Many households in developing countries lack formal financial histories,
+making it difficult for banks to extend loans, and for potential borrowers to
+receive them. However, many of these households have mobile phones, which
+generate rich data about behavior. This paper shows that behavioral signatures
+in mobile phone data predict loan default, using call records matched to loan
+outcomes. In a middle income South American country, individuals in the highest
+quintile of risk by our measure are 2.8 times more likely to default than those
+in the lowest quintile. On our sample of individuals with (thin) financial
+histories, our method outperforms models using credit bureau information, both
+within time and when tested on a different time period. The method forms the
+basis for new forms of lending that reach the unbanked.
+",1,0,0,0,0,0
+8770,Robust and structural ergodicity analysis of stochastic biomolecular networks involving synthetic antithetic integral controllers,"  Ergodicity and output controllability have been shown to be fundamental
+concepts for the analysis and synthetic design of closed-loop stochastic
+reaction networks, as exemplified by the use of antithetic integral feedback
+controllers. In [Gupta, Briat & Khammash, PLoS Comput. Biol., 2014], some
+ergodicity and output controllability conditions for unimolecular and certain
+classes of bimolecular reaction networks were obtained and formulated through
+linear programs. To account for context dependence, these conditions were later
+extended in [Briat & Khammash, CDC, 2016] to reaction networks with uncertain
+rate parameters using simple and tractable, yet potentially conservative,
+methods. Here we develop some exact theoretical methods for verifying, in a
+robust setting, the original ergodicity and output controllability conditions
+based on algebraic and polynomial techniques. Some examples are given for
+illustration.
+",1,0,1,0,0,0
+8771,A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering,"  We propose an effective method to solve the event sequence clustering
+problems based on a novel Dirichlet mixture model of a special but significant
+type of point processes --- Hawkes process. In this model, each event sequence
+belonging to a cluster is generated via the same Hawkes process with specific
+parameters, and different clusters correspond to different Hawkes processes.
+The prior distribution of the Hawkes processes is controlled via a Dirichlet
+distribution. We learn the model via a maximum likelihood estimator (MLE) and
+propose an effective variational Bayesian inference algorithm. We specifically
+analyze the resulting EM-type algorithm in the context of inner-outer
+iterations and discuss several inner iteration allocation strategies. The
+identifiability of our model, the convergence of our learning method, and its
+sample complexity are analyzed in both theoretical and empirical ways, which
+demonstrate the superiority of our method to other competitors. The proposed
+method learns the number of clusters automatically and is robust to model
+misspecification. Experiments on both synthetic and real-world data show that
+our method can learn diverse triggering patterns hidden in asynchronous event
+sequences and achieve encouraging performance on clustering purity and
+consistency.
+",1,0,0,1,0,0
+8772,On the number of cyclic subgroups of a finite group,"  Let $G$ be a finite group and let $c(G)$ be the number of cyclic subgroups of
+$G$. We study the function $\alpha(G) = c(G)/|G|$. We explore its basic
+properties and we point out a connection with the probability of commutation.
+For many families $\mathscr{F}$ of groups we characterize the groups $G \in
+\mathscr{F}$ for which $\alpha(G)$ is maximal and we classify the groups $G$
+for which $\alpha(G) > 3/4$. We also study the number of cyclic subgroups of a
+direct power of a given group deducing an asymptotic result and we characterize
+the equality $\alpha(G) = \alpha(G/N)$ when $G/N$ is a symmetric group.
+",0,0,1,0,0,0
+8773,Accelerated Extra-Gradient Descent: A Novel Accelerated First-Order Method,"  We provide a novel accelerated first-order method that achieves the
+asymptotically optimal convergence rate for smooth functions in the first-order
+oracle model. To this day, Nesterov's Accelerated Gradient Descent (AGD) and
+variations thereof were the only methods achieving acceleration in this
+standard blackbox model. In contrast, our algorithm is significantly different
+from AGD, as it relies on a predictor-corrector approach similar to that used
+by Mirror-Prox [Nemirovski, 2004] and Extra-Gradient Descent [Korpelevich,
+1977] in the solution of convex-concave saddle point problems. For this reason,
+we dub our algorithm Accelerated Extra-Gradient Descent (AXGD). Its
+construction is motivated by the discretization of an accelerated
+continuous-time dynamics [Krichene et al., 2015] using the classical method of
+implicit Euler discretization. Our analysis explicitly shows the effects of
+discretization through a conceptually novel primal-dual viewpoint. Moreover, we
+show that the method is quite general: it attains optimal convergence rates for
+other classes of objectives (e.g., those with generalized smoothness properties
+or that are non-smooth and Lipschitz-continuous) using the appropriate choices
+of step lengths. Finally, we present experiments showing that our algorithm
+matches the performance of Nesterov's method, while appearing more robust to
+noise in some cases.
+",1,0,1,0,0,0
+8774,Disruption of Alfvénic turbulence by magnetic reconnection in a collisionless plasma,"  We calculate the disruption scale $\lambda_{\rm D}$ at which sheet-like
+structures in dynamically aligned Alfvénic turbulence are destroyed by the
+onset of magnetic reconnection in a low-$\beta$ collisionless plasma. The
+scaling of $\lambda_{\rm D}$ depends on the order of the statistics being
+considered, with more intense structures being disrupted at larger scales. The
+disruption scale for the structures that dominate the energy spectrum is
+$\lambda_{\rm D}\sim L_\perp^{1/9}(d_e\rho_s)^{4/9}$, where $d_e$ is the
+electron inertial scale, $\rho_s$ is the ion sound scale, and $L_\perp$ is the
+outer scale of the turbulence. When $\beta_e$ and $\rho_s/L_\perp$ are
+sufficiently small, the scale $\lambda_{\rm D}$ is larger than $\rho_s$ and
+there is a break in the energy spectrum at $\lambda_{\rm D}$, rather than at
+$\rho_s$. We propose that the fluctuations produced by the disruption are
+circularised flux ropes, which may have already been observed in the solar
+wind. We predict the relationship between the amplitude and radius of these
+structures and quantify the importance of the disruption process to the cascade
+in terms of the filling fraction of undisrupted structures and the fractional
+reduction of the energy contained in them at the ion sound scale $\rho_s$. Both
+of these fractions depend strongly on $\beta_e$, with the disrupted structures
+becoming more important at lower $\beta_e$. Finally, we predict that the energy
+spectrum between $\lambda_{\rm D}$ and $\rho_s$ is steeper than $k_\perp^{-3}$,
+when this range exists. Such a steep ""transition range"" is sometimes observed
+in short intervals of solar-wind turbulence. The onset of collisionless
+magnetic reconnection may therefore significantly affect the nature of plasma
+turbulence around the ion gyroscale.
+",0,1,0,0,0,0
+8775,New neutrino physics and the altered shapes of solar neutrino spectra,"  Neutrinos coming from the Sun's core are now measured with a high precision,
+and fundamental neutrino oscillations parameters are determined with a good
+accuracy. In this work, we estimate the impact that a new neutrino physics
+model, the so-called generalized Mikheyev-Smirnov-Wolfenstein (MSW) oscillation
+mechanism, has on the shape of some of leading solar neutrino spectra, some of
+which will be partially tested by the next generation of solar neutrino
+experiments. In these calculations, we use a high-precision standard solar
+model in good agreement with helioseismology data. We found that the neutrino
+spectra of the different solar nuclear reactions of the proton-proton chains
+and carbon-nitrogen-oxygen cycle have quite distinct sensitivities to the new
+neutrino physics. The $HeP$ and $^8B$ neutrino spectra are the ones for which
+their shapes are more affected when neutrinos interact with quarks in addition
+to electrons. The shape of the $^{15}O$ and $^{17}F$ neutrino spectra are also
+modified, although in these cases the impact is much smaller. Finally, the
+impact in the shape of the $PP$ and $^{13}N$ neutrino spectra is practically
+negligible.
+",0,1,0,0,0,0
+8776,Searching for a Single Community in a Graph,"  In standard graph clustering/community detection, one is interested in
+partitioning the graph into more densely connected subsets of nodes. In
+contrast, the ""search"" problem of this paper aims to only find the nodes in a
+""single"" such community, the target, out of the many communities that may
+exist. To do so , we are given suitable side information about the target; for
+example, a very small number of nodes from the target are labeled as such.
+We consider a general yet simple notion of side information: all nodes are
+assumed to have random weights, with nodes in the target having higher weights
+on average. Given these weights and the graph, we develop a variant of the
+method of moments that identifies nodes in the target more reliably, and with
+lower computation, than generic community detection methods that do not use
+side information and partition the entire graph. Our empirical results show
+significant gains in runtime, and also gains in accuracy over other graph
+clustering algorithms.
+",1,0,0,1,0,0
+8777,Superinjective Simplicial Maps of the Two-sided Curve Complexes on Nonorientable Surfaces,"  Let $N$ be a compact, connected, nonorientable surface of genus $g$ with $n$
+boundary components with $g \geq 5$, $n \geq 0$. Let $\mathcal{T}(N)$ be the
+two-sided curve complex of $N$. If $\lambda :\mathcal{T}(N) \rightarrow
+\mathcal{T}(N)$ is a superinjective simplicial map, then there exists a
+homeomorphism $h : N \rightarrow N$ unique up to isotopy such that $H(\alpha) =
+\lambda(\alpha)$ for every vertex $\alpha$ in $\mathcal{T}(N)$ where $H=[h]$.
+",0,0,1,0,0,0
+8778,Geometry-Oblivious FMM for Compressing Dense SPD Matrices,"  We present GOFMM (geometry-oblivious FMM), a novel method that creates a
+hierarchical low-rank approximation, ""compression,"" of an arbitrary dense
+symmetric positive definite (SPD) matrix. For many applications, GOFMM enables
+an approximate matrix-vector multiplication in $N \log N$ or even $N$ time,
+where $N$ is the matrix size. Compression requires $N \log N$ storage and work.
+In general, our scheme belongs to the family of hierarchical matrix
+approximation methods. In particular, it generalizes the fast multipole method
+(FMM) to a purely algebraic setting by only requiring the ability to sample
+matrix entries. Neither geometric information (i.e., point coordinates) nor
+knowledge of how the matrix entries have been generated is required, thus the
+term ""geometry-oblivious."" Also, we introduce a shared-memory parallel scheme
+for hierarchical matrix computations that reduces synchronization barriers. We
+present results on the Intel Knights Landing and Haswell architectures, and on
+the NVIDIA Pascal architecture for a variety of matrices.
+",1,0,0,0,0,0
+8779,Deep learning for universal linear embeddings of nonlinear dynamics,"  Identifying coordinate transformations that make strongly nonlinear dynamics
+approximately linear is a central challenge in modern dynamical systems. These
+transformations have the potential to enable prediction, estimation, and
+control of nonlinear systems using standard linear theory. The Koopman operator
+has emerged as a leading data-driven embedding, as eigenfunctions of this
+operator provide intrinsic coordinates that globally linearize the dynamics.
+However, identifying and representing these eigenfunctions has proven to be
+mathematically and computationally challenging. This work leverages the power
+of deep learning to discover representations of Koopman eigenfunctions from
+trajectory data of dynamical systems. Our network is parsimonious and
+interpretable by construction, embedding the dynamics on a low-dimensional
+manifold that is of the intrinsic rank of the dynamics and parameterized by the
+Koopman eigenfunctions. In particular, we identify nonlinear coordinates on
+which the dynamics are globally linear using a modified auto-encoder. We also
+generalize Koopman representations to include a ubiquitous class of systems
+that exhibit continuous spectra, ranging from the simple pendulum to nonlinear
+optics and broadband turbulence. Our framework parametrizes the continuous
+frequency using an auxiliary network, enabling a compact and efficient
+embedding at the intrinsic rank, while connecting our models to half a century
+of asymptotics. In this way, we benefit from the power and generality of deep
+learning, while retaining the physical interpretability of Koopman embeddings.
+",1,0,0,1,0,0
+8780,Mean-field modeling of the basal ganglia-thalamocortical system. II. Dynamics of parkinsonian oscillations,"  Neuronal correlates of Parkinson's disease (PD) include a slowing of the
+electroencephalogram (EEG) and enhanced synchrony at 3-7 and 7-30 Hz in the
+basal ganglia, thalamus, and cortex. This study describes the dynamics of a
+physiologically based mean-field model of the basal ganglia-thalamocortical
+system, and shows how it accounts for key electrophysiological correlates of
+PD. Its connectivity comprises partially segregated direct and indirect
+pathways through the striatum, a hyperdirect pathway involving a
+corticosubthalamic projection, thalamostriatal feedback, and local inhibition
+in striatum and external pallidum (GPe). In a companion paper, realistic
+steady-state firing rates were obtained for the healthy state, and after
+dopamine loss modeled by weaker direct and stronger indirect pathways, reduced
+intrapallidal inhibition, lower firing thresholds of the GPe and subthalamic
+nucleus (STN), a stronger striato-GPe projection, and weaker cortical
+interactions. Here we show that oscillations around 5 and 20 Hz can arise with
+a strong indirect pathway, which also increases synchrony throughout the basal
+ganglia. Further, increased theta power with nigrostriatal degeneration
+correlates with reduced alpha power and peak frequency, matching experiments.
+Unlike the hyperdirect pathway, the indirect pathway sustains oscillations with
+realistic phase relationships. Changes in basal ganglia responses to transient
+stimuli accord with experimental data. Reduced cortical gains due to both
+nigrostriatal and mesocortical dopamine loss lead to slower cortical activity
+changes and may be related to bradykinesia. Finally, increased EEG power found
+in some studies may be partly explained by a lower effective GPe firing
+threshold, reduced GPe-GPe inhibition, and/or weaker intracortical connections
+in PD. Strict separation of the direct and indirect pathways is not necessary
+for these results.
+",0,0,0,0,1,0
+8781,Applying Machine Learning To Maize Traits Prediction,"  Heterosis is the improved or increased function of any biological quality in
+a hybrid offspring. We have studied yet the largest maize SNP dataset for
+traits prediction. We develop linear and non-linear models which consider
+relationships between different hybrids as well as other effect. Specially
+designed model proved to be efficient and robust in prediction maize's traits.
+",0,0,0,1,0,0
+8782,Perceptual Compressive Sensing based on Contrast Sensitivity Function: Can we avoid non-visible redundancies acquisition?,"  In this paper, we propose a novel CS approach in which the acquisition of
+non-visible information is also avoided.
+",1,0,0,0,0,0
+8783,Poincaré surfaces of section around a 3-D irregular body: The case of asteroid 4179 Toutatis,"  In general, small bodies of the solar system, e.g., asteroids and comets,
+have a very irregular shape. This feature affects significantly the
+gravitational potential around these irregular bodies, which hinders dynamical
+studies. The Poincaré surface of sec- tion technique is often used to look
+for stable and chaotic regions in two-dimensional dynamic cases. In this work,
+we show that this tool can be useful for exploring the surroundings of
+irregular bodies such as the asteroid 4179 Toutatis. Considering a rotating
+system with a particle, under the effect of the gravitational field computed
+three-dimensionally, we define a plane in the phase space to build the
+Poincaré surface of sections. Despite the extra dimension, the sections
+created allow us to find trajec- tories and classify their stabilities. Thus,
+we have also been able to map stable and chaotic regions, as well as to find
+correlations between those regions and the contri- bution of the third
+dimension of the system to the trajectory dynamics as well. As examples, we
+show details of periodic(resonant or not) and quasi-periodic trajectories.
+",0,1,0,0,0,0
+8784,On the second Dirichlet eigenvalue of some nonlinear anisotropic elliptic operators,"  Let $\Omega$ be a bounded open set of $\mathbb R^{n}$, $n\ge 2$. In this
+paper we mainly study some properties of the second Dirichlet eigenvalue
+$\lambda_{2}(p,\Omega)$ of the anisotropic $p$-Laplacian \[ -\mathcal
+Q_{p}u:=-\textrm{div} \left(F^{p-1}(\nabla u)F_\xi (\nabla u)\right), \] where
+$F$ is a suitable smooth norm of $\mathbb R^{n}$ and $p\in]1,+\infty[$. We
+provide a lower bound of $\lambda_{2}(p,\Omega)$ among bounded open sets of
+given measure, showing the validity of a Hong-Krahn-Szego type inequality.
+Furthermore, we investigate the limit problem as $p\to+\infty$.
+",0,0,1,0,0,0
+8785,A parametric level-set method for partially discrete tomography,"  This paper introduces a parametric level-set method for tomographic
+reconstruction of partially discrete images. Such images consist of a
+continuously varying background and an anomaly with a constant (known)
+grey-value. We represent the geometry of the anomaly using a level-set
+function, which we represent using radial basis functions. We pose the
+reconstruction problem as a bi-level optimization problem in terms of the
+background and coefficients for the level-set function. To constrain the
+background reconstruction we impose smoothness through Tikhonov regularization.
+The bi-level optimization problem is solved in an alternating fashion; in each
+iteration we first reconstruct the background and consequently update the
+level-set function. We test our method on numerical phantoms and show that we
+can successfully reconstruct the geometry of the anomaly, even from limited
+data. On these phantoms, our method outperforms Total Variation reconstruction,
+DART and P-DART.
+",1,0,0,0,0,0
+8786,Using PCA and Factor Analysis for Dimensionality Reduction of Bio-informatics Data,"  Large volume of Genomics data is produced on daily basis due to the
+advancement in sequencing technology. This data is of no value if it is not
+properly analysed. Different kinds of analytics are required to extract useful
+information from this raw data. Classification, Prediction, Clustering and
+Pattern Extraction are useful techniques of data mining. These techniques
+require appropriate selection of attributes of data for getting accurate
+results. However, Bioinformatics data is high dimensional, usually having
+hundreds of attributes. Such large a number of attributes affect the
+performance of machine learning algorithms used for classification/prediction.
+So, dimensionality reduction techniques are required to reduce the number of
+attributes that can be further used for analysis. In this paper, Principal
+Component Analysis and Factor Analysis are used for dimensionality reduction of
+Bioinformatics data. These techniques were applied on Leukaemia data set and
+the number of attributes was reduced from to.
+",1,0,0,0,0,0
+8787,Three-dimensional vortex structures and dynamics in hexagonal manganites,"  Hexagonal manganites REMnO3 (RE, rare earths) have attracted significant
+attention due to their potential applications as multiferroic materials and the
+intriguing physics associated with the topological defects. The two-dimensional
+(2D) and 3D domain and vortex structure evolution of REMnO3 is predicted using
+the phase-field method based on a thermodynamic potential constructed from
+first-principles calculations. In 3D spaces, vortex lines show three types of
+topological changes, i.e. shrinking, coalescence, and splitting, with the
+latter two caused by the interaction and exchange of vortex loops. Compared to
+the coarsening rate of the isotropic XY model, the six-fold degeneracy gives
+rise to negligible differences with the vortex-antivortex annihilation
+controlling the scaling dynamics, whereas the anisotropy of interfacial energy
+results in a deviation. The temporal evolution of domain and vortex structures
+serves as a platform to fully explore the mesoscale mechanisms for the 0-D and
+1-D topological defects.
+",0,1,0,0,0,0
+8788,A spectral-Galerkin turbulent channel flow solver for large-scale simulations,"  A fully (pseudo-)spectral solver for direct numerical simulations of
+large-scale turbulent channel flows is described. The solver utilizes the
+Chebyshev base functions suggested by J. Shen [SIAM J. Sci. Comput., 16, 1,
+1995], that lead to stable and robust numerical schemes, even at very large
+scale. New and fast algorithms for the direct solution of the linear systems
+are devised, and algorithms and matrices for all required scalar products and
+transforms are provided. We validate the solver for very high Reynolds numbers.
+Specifically, the solver is shown to reproduce the first order statistics of
+Hoyas and Jiménez [Phys. Fluids, 18(1), 2006], for a channel flow at
+$Re_{\tau}=2000$. The solver is available through the open source project
+spectralDNS [this https URL].
+",1,1,1,0,0,0
+8789,Bi-Lagrangian structures and Teichmüller theory,"  This paper has two purposes: the first is to study several structures on
+manifolds in the general setting of real and complex differential geometry; the
+second is to apply this study to Teichmüller theory. We primarily focus on
+bi-Lagrangian structures, which are the data of a symplectic structure and a
+pair of transverse Lagrangian foliations, and are equivalent to para-Kähler
+structures. First we carefully study real and complex bi-Lagrangian structures
+and discuss other closely related structures and their interrelationships. Next
+we prove the existence of a canonical complex bi-Lagrangian structure in the
+complexification of any real-analytic Kähler manifold and showcase its
+properties. We later use this bi-Lagrangian structure to construct a natural
+almost hyper-Hermitian structure. We then specialize our study to moduli spaces
+of geometric structures on closed surfaces, which tend to have a rich
+symplectic structure. We show that some of the recognized geometric features of
+these moduli spaces are formal consequences of the general theory, while
+revealing other new geometric features. We also gain clarity on several
+well-known results of Teichmüller theory by deriving them from pure
+differential geometric machinery.
+",0,0,1,0,0,0
+8790,Proof of a conjecture of Abdollahi-Akbari-Maimani concerning the non-commutative graph of finite groups,"  The non--commuting graph $\Gamma(G)$ of a non--abelian group $G$ is defined
+as follows. The vertex set $V(\Gamma(G))$ of $\Gamma(G)$ is $G\setminus Z(G)$
+where $Z(G)$ denotes the center of $G$ and two vertices $x$ and $y$ are
+adjacent if and only if $xy\neq yx$. For non--abelian finite groups $G$ and $H$
+it is conjectured that if $\Gamma(G) \cong \Gamma(H)$, then $|G|=|H|$. We prove
+the conjecture.
+",0,0,1,0,0,0
+8791,LQG Control and Sensing Co-design,"  Linear-Quadratic-Gaussian (LQG) control is concerned with the design of an
+optimal controller and estimator for linear Gaussian systems with imperfect
+state information. Standard LQG control assumes the set of sensor measurements
+to be fed to the estimator to be given. However, in many problems in networked
+systems and robotics, one is interested in designing a suitable set of sensors
+for LQG control. In this paper, we introduce the LQG control and sensing
+co-design problem, where one has to jointly design a suitable sensing,
+estimation, and control policy. We consider two dual instances of the co-design
+problem: the sensing-constrained LQG control problem, where the design
+maximizes the control performance subject to sensing constraints, and the
+minimum-sensing LQG control, where the design minimizes the amount of sensing
+subject to performance constraints. We focus on the case in which the sensing
+design has to be selected among a finite set of possible sensing modalities,
+where each modality is associated with a different cost. While we observe that
+the computation of the optimal sensing design is intractable in general, we
+present the first scalable LQG co-design algorithms to compute near-optimal
+policies with provable sub-optimality guarantees. To this end, (i) we show that
+a separation principle holds, which partially decouples the design of sensing,
+estimation, and control; (ii) we frame LQG co-design as the optimization of
+(approximately) supermodular set functions; (iii) we develop novel algorithms
+to solve the resulting optimization problems; (iv) we prove original results on
+the performance of these algorithms and establish connections between their
+sub-optimality gap and control-theoretic quantities. We conclude the paper by
+discussing two practical applications of the co-design problem, namely,
+sensing-constrained formation control and resource-constrained robot
+navigation.
+",1,0,0,0,0,0
+8792,Subspace Learning in The Presence of Sparse Structured Outliers and Noise,"  Subspace learning is an important problem, which has many applications in
+image and video processing. It can be used to find a low-dimensional
+representation of signals and images. But in many applications, the desired
+signal is heavily distorted by outliers and noise, which negatively affect the
+learned subspace. In this work, we present a novel algorithm for learning a
+subspace for signal representation, in the presence of structured outliers and
+noise. The proposed algorithm tries to jointly detect the outliers and learn
+the subspace for images. We present an alternating optimization algorithm for
+solving this problem, which iterates between learning the subspace and finding
+the outliers. This algorithm has been trained on a large number of image
+patches, and the learned subspace is used for image segmentation, and is shown
+to achieve better segmentation results than prior methods, including least
+absolute deviation fitting, k-means clustering based segmentation in DjVu, and
+shape primitive extraction and coding algorithm.
+",1,0,0,0,0,0
+8793,Trends in scientific research in Online Information Review. Part 2. Mapping the scientific knowledge through bibliometric and social network analyses,"  Objective. The purpose of this work is to analyse the knowledge structure and
+trends in scientific research in the Online Information Reviews journal by
+bibliometric analysis of key words and social network analysis of co-words.
+Methods. Key words included in a set of 758 papers included in the Web of
+Science database from 2000 to 2014 were analysed. We conducted a subject
+analysis considering the key words assigned to papers. A social network
+analysis was also conducted to identify the number of co-occurrences between
+key words (co-words). The Pajek software was used to create and graphically
+visualize the networks. Results. Internet is the most frequent key word (n=219)
+and the most central in the network of co-words, strongly associated with
+Information retrieval, search engines, the World Wide Web, libraries and users
+Conclusions. Information science, as represented by Online Information Review
+in the present study, is an evolving discipline that draws on literature from a
+relatively wide range of subjects. Although Online Information Review appears
+to have well-defined and established research topics, the journal also changes
+rapidly to embrace new lines of research.
+",1,0,0,0,0,0
+8794,Balance between quantum Markov semigroups,"  The concept of balance between two state preserving quantum Markov semigroups
+on von Neumann algebras is introduced and studied as an extension of conditions
+appearing in the theory of quantum detailed balance. This is partly motivated
+by the theory of joinings. Balance is defined in terms of certain correlated
+states (couplings), with entangled states as a specific case. Basic properties
+of balance are derived and the connection to correspondences in the sense of
+Connes is discussed. Some applications and possible applications, including to
+non-equilibrium statistical mechanics, are briefly explored.
+",0,0,1,0,0,0
+8795,Deep Stochastic Configuration Networks with Universal Approximation Property,"  This paper develops a randomized approach for incrementally building deep
+neural networks, where a supervisory mechanism is proposed to constrain the
+random assignment of the weights and biases, and all the hidden layers have
+direct links to the output layer. A fundamental result on the universal
+approximation property is established for such a class of randomized leaner
+models, namely deep stochastic configuration networks (DeepSCNs). A learning
+algorithm is presented to implement DeepSCNs with either specific architecture
+or self-organization. The read-out weights attached with all direct links from
+each hidden layer to the output layer are evaluated by the least squares
+method. Given a set of training examples, DeepSCNs can speedily produce a
+learning representation, that is, a collection of random basis functions with
+the cascaded inputs together with the read-out weights. An empirical study on a
+function approximation is carried out to demonstrate some properties of the
+proposed deep learner model.
+",1,0,0,0,0,0
+8796,On convergence for graphexes,"  We study four different notions of convergence for graphexes, recently
+introduced by Borgs, Chayes, Cohn and Holden, and by Veitch and Roy. We give
+some properties of them and some relations between them. We also extend results
+by Veitch and Roy on convergence of empirical graphons.
+",0,0,1,0,0,0
+8797,Improving text classification with vectors of reduced precision,"  This paper presents the analysis of the impact of a floating-point number
+precision reduction on the quality of text classification. The precision
+reduction of the vectors representing the data (e.g. TF-IDF representation in
+our case) allows for a decrease of computing time and memory footprint on
+dedicated hardware platforms. The impact of precision reduction on the
+classification quality was performed on 5 corpora, using 4 different
+classifiers. Also, dimensionality reduction was taken into account. Results
+indicate that the precision reduction improves classification accuracy for most
+cases (up to 25% of error reduction). In general, the reduction from 64 to 4
+bits gives the best scores and ensures that the results will not be worse than
+with the full floating-point representation.
+",1,0,0,0,0,0
+8798,On ramification in transcendental extensions of local fields,"  Let $L/K$ be an extension of complete discrete valuation fields, and assume
+that the residue field of $K$ is perfect and of positive characteristic. The
+residue field of $L$ is not assumed to be perfect.
+In this paper, we prove a formula for the Swan conductor of the image of a
+character $\chi \in H^1(K, \mathbb{Q}/\mathbb{Z})$ in $H^1(L,
+\mathbb{Q}/\mathbb{Z})$ for $\chi$ sufficiently ramified. Further, we define
+generalizations $\psi_{L/K}^{\mathrm{ab}}$ and $\psi_{L/K}^{\mathrm{AS}}$ of
+the classical Hasse-Herbrand $\psi$-function and prove a formula for
+$\psi_{L/K}^{\mathrm{ab}}(t)$ for sufficiently large $t\in \mathbb{R}$.
+",0,0,1,0,0,0
+8799,Local Formulas for Ehrhart Coefficients from Lattice Tiles,"  As shown by McMullen in 1983, the coefficients of the Ehrhart polynomial of a
+lattice polytope can be written as a weighted sum of facial volumes. The
+weights in such a local formula depend only on the outer normal cones of faces,
+but are far from being unique. In this paper, we develop an infinite class of
+such local formulas. These are based on choices of fundamental domains in
+sublattices and obtained by polyhedral volume computations. We hereby also give
+a kind of geometric interpretation for the Ehrhart coefficients. Since our
+construction gives us a great variety of possible local formulas, these can,
+for instance, be chosen to fit well with a given polyhedral symmetry group. In
+contrast to other constructions of local formulas, ours does not rely on
+triangulations of rational cones into simplicial or even unimodular ones.
+",0,0,1,0,0,0
+8800,Atypicality for Heart Rate Variability Using a Pattern-Tree Weighting Method,"  Heart rate variability (HRV) is a vital measure of the autonomic nervous
+system functionality and a key indicator of cardiovascular condition. This
+paper proposes a novel method, called pattern tree which is an extension of
+Willem's context tree to real-valued data, to investigate HRV via an
+atypicality framework. In a previous paper atypicality was developed as method
+for mining and discovery in ""Big Data,"" which requires a universal approach.
+Using the proposed pattern tree as a universal source coder in this framework
+led to discovery of arrhythmias and unknown patterns in HRV Holter Monitoring.
+",1,0,0,0,0,0
+8801,Rectangular Photonic Crystal Nanobeam Cavities in Bulk Diamond,"  We demonstrate the fabrication of photonic crystal nanobeam cavities with
+rectangular cross section into bulk diamond. In simulation, these cavities have
+an unloaded quality factor (Q) of over 1 million. Measured cavity resonances
+show fundamental modes with spectrometer-limited quality factors larger than
+14,000 within 1nm of the NV center's zero phonon line at 637nm. We find high
+cavity yield across the full diamond chip with deterministic resonance trends
+across the fabricated parameter sweeps.
+",0,1,0,0,0,0
+8802,Accelerated Primal-Dual Proximal Block Coordinate Updating Methods for Constrained Convex Optimization,"  Block Coordinate Update (BCU) methods enjoy low per-update computational
+complexity because every time only one or a few block variables would need to
+be updated among possibly a large number of blocks. They are also easily
+parallelized and thus have been particularly popular for solving problems
+involving large-scale dataset and/or variables. In this paper, we propose a
+primal-dual BCU method for solving linearly constrained convex program in
+multi-block variables. The method is an accelerated version of a primal-dual
+algorithm proposed by the authors, which applies randomization in selecting
+block variables to update and establishes an $O(1/t)$ convergence rate under
+weak convexity assumption. We show that the rate can be accelerated to
+$O(1/t^2)$ if the objective is strongly convex. In addition, if one block
+variable is independent of the others in the objective, we then show that the
+algorithm can be modified to achieve a linear rate of convergence. The
+numerical experiments show that the accelerated method performs stably with a
+single set of parameters while the original method needs to tune the parameters
+for different datasets in order to achieve a comparable level of performance.
+",1,0,1,1,0,0
+8803,Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields,"  Overhead depth map measurements capture sufficient amount of information to
+enable human experts to track pedestrians accurately. However, fully automating
+this process using image analysis algorithms can be challenging. Even though
+hand-crafted image analysis algorithms are successful in many common cases,
+they fail frequently when there are complex interactions of multiple objects in
+the image. Many of the assumptions underpinning the hand-crafted solutions do
+not hold in these cases and the multitude of exceptions are hard to model
+precisely. Deep Learning (DL) algorithms, on the other hand, do not require
+hand crafted solutions and are the current state-of-the-art in object
+localization in images. However, they require exceeding amount of annotations
+to produce successful models. In the case of object localization these
+annotations are difficult and time consuming to produce. In this work we
+present an approach for developing pedestrian localization models using DL
+algorithms with efficient weak supervision from an expert. We circumvent the
+need for annotation of large corpus of data by annotating only small amount of
+patches and relying on synthetic data augmentation as a vehicle for injecting
+expert knowledge in the model training. This approach of weak supervision
+through expert selection of representative patches, suitable transformations
+and synthetic data augmentations enables us to successfully develop DL models
+for pedestrian localization efficiently.
+",1,1,0,0,0,0
+8804,Absence of magnetic long range order in Ba$_3$ZnRu$_2$O$_9$: A spin-liquid candidate in the $S=3/2$ dimer lattice,"  We have discovered a novel candidate for a spin liquid state in a ruthenium
+oxide composed of dimers of $S = $ 3/2 spins of Ru$^{5+}$,Ba$_3$ZnRu$_2$O$_9$.
+This compound lacks a long range order down to 37 mK, which is a temperature
+5000-times lower than the magnetic interaction scale of around 200 K. Partial
+substitution for Zn can continuously vary the magnetic ground state from an
+antiferromagnetic order to a spin-gapped state through the liquid state. This
+indicates that the spin-liquid state emerges from a delicate balance of inter-
+and intra-dimer interactions, and the spin state of the dimer plays a vital
+role. This unique feature should realize a new type of quantum magnetism.
+",0,1,0,0,0,0
+8805,"Deforming Representations of SL(2,R)","  The spherical principal series representations $\pi(\nu)$ of SL(2,$\mathbb
+R$) is a family of infinite dimensional representations parametrized by
+$\nu\in\mathbb C$. The representation $\pi(\nu)$ is irreducible unless $\nu$ is
+an odd integer, in which case it is indecomposable. We find a new continuous
+family of representations $\Pi(\nu)$ such that $\pi(\nu)$ and $\Pi(\nu)$ have
+the same composition factors, and $\Pi(\nu)$ is completely reducible, for all
+$\nu$. We also describe a connection between this construction and families of
+invariant Hermitian forms on the representations.
+",0,0,1,0,0,0
+8806,3D Reconstruction & Assessment Framework based on affordable 2D Lidar,"  Lidar is extensively used in the industry and mass-market. Due to its
+measurement accuracy and insensitivity to illumination compared to cameras, It
+is applied onto a broad range of applications, like geodetic engineering, self
+driving cars or virtual reality. But the 3D Lidar with multi-beam is very
+expensive, and the massive measurements data can not be fully leveraged on some
+constrained platforms. The purpose of this paper is to explore the possibility
+of using cheap 2D Lidar off-the-shelf, to preform complex 3D Reconstruction,
+moreover, the generated 3D map quality is evaluated by our proposed metrics at
+the end. The 3D map is constructed in two ways, one way in which the scan is
+performed at known positions with an external rotary axis at another plane. The
+other way, in which the 2D Lidar for mapping and another 2D Lidar for
+localization are placed on a trolley, the trolley is pushed on the ground
+arbitrarily. The generated maps by different approaches are converted to
+octomaps uniformly before the evaluation. The similarity and difference between
+two maps will be evaluated by the proposed metrics thoroughly. The whole
+mapping system is composed of several modular components. A 3D bracket was made
+for assembling of the Lidar with a long range, the driver and the motor
+together. A cover platform made for the IMU and 2D Lidar with a shorter range
+but high accuracy. The software is stacked up in different ROS packages.
+",1,0,0,0,0,0
+8807,Probabilistic Relational Reasoning via Metrics,"  The Fuzz programming language [Reed and Pierce, 2010] uses an elegant linear
+type system combined with a monad-like type to express and reason about
+probabilistic sensitivity properties, most notably $\epsilon$-differential
+privacy. We show how to extend Fuzz to capture more general relational
+properties of probabilistic programs, with approximate, or $(\epsilon,
+\delta)$-differential privacy serving as a leading example. Our technical
+contributions are threefold. First, we introduce the categorical notion of
+comonadic lifting of a monad to model composition properties of probabilistic
+divergences. Then, we show how to express relational properties in terms of
+sensitivity properties via an adjunction we call the path construction.
+Finally, we instantiate our semantics to model the terminating fragment of Fuzz
+extended with types carrying information about other divergences between
+distributions.
+",1,0,0,0,0,0
+8808,Upper bounds for constant slope $p$-adic families of modular forms,"  We study $p$-adic families of eigenforms for which the $p$-th Hecke
+eigenvalue $a_p$ has constant $p$-adic valuation (""constant slope families"").
+We prove two separate upper bounds for the size of such families. The first is
+in terms of the logarithmic derivative of $a_p$ while the second depends only
+on the slope of the family. We also investigate the numerical relationship
+between our results and the former Gouvêa--Mazur conjecture.
+",0,0,1,0,0,0
+8809,Size dependence of the surface tension of a free surface of an isotropic fluid,"  We report on the size dependence of the surface tension of a free surface of
+an isotropic fluid. The size dependence of the surface tension is evaluated
+based on the Gibbs-Tolman-Koenig-Buff equation for positive and negative values
+of curvatures and the Tolman lengths. For all combinations of positive and
+negative signs of curvature and the Tolman length, we succeed to have a
+continuous function, avoiding the existing discontinuity at zero curvature
+(flat interfaces). As an example, a water droplet in the thermodynamical
+equilibrium with the vapor is analyzed in detail. The size dependence of the
+surface tension and the Tolman length are evaluated with the use of
+experimental data of the International Association for the Properties of Water
+and Steam. The evaluated Tolman length of our approach is in good agreement
+with molecular dynamics and experimental data
+",0,1,0,0,0,0
+8810,Bayesian Belief Updating of Spatiotemporal Seizure Dynamics,"  Epileptic seizure activity shows complicated dynamics in both space and time.
+To understand the evolution and propagation of seizures spatially extended sets
+of data need to be analysed. We have previously described an efficient
+filtering scheme using variational Laplace that can be used in the Dynamic
+Causal Modelling (DCM) framework [Friston, 2003] to estimate the temporal
+dynamics of seizures recorded using either invasive or non-invasive electrical
+recordings (EEG/ECoG). Spatiotemporal dynamics are modelled using a partial
+differential equation -- in contrast to the ordinary differential equation used
+in our previous work on temporal estimation of seizure dynamics [Cooray, 2016].
+We provide the requisite theoretical background for the method and test the
+ensuing scheme on simulated seizure activity data and empirical invasive ECoG
+data. The method provides a framework to assimilate the spatial and temporal
+dynamics of seizure activity, an aspect of great physiological and clinical
+importance.
+",1,0,0,1,0,0
+8811,"Split, Send, Reassemble: A Formal Specification of a CAN Bus Protocol Stack","  We present a formal model for a fragmentation and a reassembly protocol
+running on top of the standardised CAN bus, which is widely used in automotive
+and aerospace applications. Although the CAN bus comes with an in-built
+mechanism for prioritisation, we argue that this is not sufficient and provide
+another protocol to overcome this shortcoming.
+",1,0,0,0,0,0
+8812,An extinction free AGN selection by 18-band SED fitting in mid-infrared in the AKARI NEP deep field,"  We have developed an efficient Active Galactic Nucleus (AGN) selection method
+using 18-band Spectral Energy Distribution (SED) fitting in mid-infrared
+(mid-IR). AGNs are often obscured by gas and dust, and those obscured AGNs tend
+to be missed in optical, UV and soft X-ray observations. Mid-IR light can help
+us to recover them in an obscuration free way using their thermal emission. On
+the other hand, Star-Forming Galaxies (SFG) also have strong PAH emission
+features in mid-IR. Hence, establishing an accurate method to separate
+populations of AGN and SFG is important. However, in previous mid-IR surveys,
+only 3 or 4 filters were available, and thus the selection was limited. We
+combined AKARI's continuous 9 mid-IR bands with WISE and Spitzer data to create
+18 mid-IR bands for AGN selection. Among 4682 galaxies in the AKARI NEP deep
+field, 1388 are selected to be AGN hosts, which implies an AGN fraction of
+29.6$\pm$0.8$\%$ (among them 47$\%$ are Seyfert 1.8 and 2). Comparing the
+result from SED fitting into WISE and Spitzer colour-colour diagram reveals
+that Seyferts are often missed by previous studies. Our result has been tested
+by stacking median magnitude for each sample. Using X-ray data from Chandra, we
+compared the result of our SED fitting with WISE's colour box selection. We
+recovered more X-ray detected AGN than previous methods by 20$\%$.
+",0,1,0,0,0,0
+8813,Spatial point processes intensity estimation with a diverging number of covariates,"  Feature selection procedures for spatial point processes parametric intensity
+estimation have been recently developed since more and more applications
+involve a large number of covariates. In this paper, we investigate the setting
+where the number of covariates diverges as the domain of observation increases.
+In particular, we consider estimating equations based on Campbell theorems
+derived from Poisson and logistic regression likelihoods regularized by a
+general penalty function. We prove that, under some conditions, the
+consistency, the sparsity, and the asymptotic normality are valid for such a
+setting. We support the theoretical results by numerical ones obtained from
+simulation experiments and an application to forestry datasets.
+",0,0,1,1,0,0
+8814,A novel methodology on distributed representations of proteins using their interacting ligands,"  The effective representation of proteins is a crucial task that directly
+affects the performance of many bioinformatics problems. Related proteins
+usually bind to similar ligands. Chemical characteristics of ligands are known
+to capture the functional and mechanistic properties of proteins suggesting
+that a ligand based approach can be utilized in protein representation. In this
+study, we propose SMILESVec, a SMILES-based method to represent ligands and a
+novel method to compute similarity of proteins by describing them based on
+their ligands. The proteins are defined utilizing the word-embeddings of the
+SMILES strings of their ligands. The performance of the proposed protein
+description method is evaluated in protein clustering task using TransClust and
+MCL algorithms. Two other protein representation methods that utilize protein
+sequence, BLAST and ProtVec, and two compound fingerprint based protein
+representation methods are compared. We showed that ligand-based protein
+representation, which uses only SMILES strings of the ligands that proteins
+bind to, performs as well as protein-sequence based representation methods in
+protein clustering. The results suggest that ligand-based protein description
+can be an alternative to the traditional sequence or structure based
+representation of proteins and this novel approach can be applied to different
+bioinformatics problems such as prediction of new protein-ligand interactions
+and protein function annotation.
+",0,0,0,1,1,0
+8815,Distributed Robust Set-Invariance for Interconnected Linear Systems,"  We introduce a class of distributed control policies for networks of
+discrete-time linear systems with polytopic additive disturbances. The
+objective is to restrict the network-level state and controls to user-specified
+polyhedral sets for all times. This problem arises in many safety-critical
+applications. We consider two problems. First, given a communication graph
+characterizing the structure of the information flow in the network, we find
+the optimal distributed control policy by solving a single linear program.
+Second, we find the sparsest communication graph required for the existence of
+a distributed invariance-inducing control policy. Illustrative examples,
+including one on platooning, are presented.
+",1,0,0,0,0,0
+8816,Liouville's theorem and comparison results for solutions of degenerate elliptic equations in exterior domains,"  A version of Liouville's theorem is proved for solutions of some degenerate
+elliptic equations defined in $\mathbb{R}^n\backslash K$, where $K$ is a
+compact set, provided the structure of this equation and the dimension $n$ are
+related. This result is a correction of a previous one established by Serrin,
+since some additional hypotheses are necessary. Theoretical and numerical
+examples are given. Furthermore, a comparison result and the uniqueness of
+solution are obtained for such equations in exterior domains.
+",0,0,1,0,0,0
+8817,An Optimal Control Problem for the Steady Nonhomogeneous Asymmetric Fluids,"  We study an optimal boundary control problem for the two-dimensional
+stationary micropolar fluids system with variable density. We control the
+system by considering boundary controls, for the velocity vector and angular
+velocity of rotation of particles, on parts of the boundary of the flow domain.
+On the remaining part of the boundary, we consider mixed boundary conditions
+for the vector velocity (Dirichlet and Navier conditions) and Dirichlet
+boundary conditions for the angular velocity. We analyze the existence of a
+weak solution obtaining the fluid density as a scalar function of the stream
+function. We prove the existence of an optimal solution and, by using the
+Lagrange multipliers theorem, we state first-order optimality conditions. We
+also derive, through a penalty method, some optimality conditions satisfied by
+the optimal controls.
+",0,0,1,0,0,0
+8818,A thermodynamic view of dusty protoplanetary disks,"  Small solids embedded in gaseous protoplanetary disks are subject to strong
+dust-gas friction. Consequently, tightly-coupled dust particles almost follow
+the gas flow. This near conservation of dust-to-gas ratio along streamlines is
+analogous to the near conservation of entropy along flows of (dust-free) gas
+with weak heating and cooling. We develop this thermodynamic analogy into a
+framework to study dusty gas dynamics in protoplanetary disks. We show that an
+isothermal dusty gas behaves like an adiabatic pure gas; and that finite
+dust-gas coupling may be regarded as an effective heating/cooling. We exploit
+this correspondence to deduce that 1) perfectly coupled, thin dust layers
+cannot cause axisymmetric instabilities; 2) radial dust edges are unstable if
+the dust is vertically well-mixed; 3) the streaming instability necessarily
+involves a gas pressure response that lags behind dust density; 4) dust-loading
+introduces buoyancy forces that generally stabilizes the vertical shear
+instability associated with global radial temperature gradients. We also
+discuss dusty analogs of other hydrodynamic processes (e.g. Rossby wave
+instability, convective overstability, and zombie vortices), and how to
+simulate dusty protoplanetary disks with minor tweaks to existing codes for
+pure gas dynamics.
+",0,1,0,0,0,0
+8819,Stable determination of a Lamé coefficient by one internal measurement of displacement,"  In this paper we show that the shear modulus $\mu$ of an isotropic elastic
+body can be stably recovered by the knowledge of one single displacement field
+whose boundary data can be assigned independently of the unknown elasticity
+tensor.
+",0,0,1,0,0,0
+8820,Mordell-Weil Groups of Linear Systems and the Hitchin Fibration,"  In this paper, we study rational sections of the relative Picard scheme of a
+linear system on a smooth projective variety. We prove that if the linear
+system is basepoint-free and the locus of non-integral divisors has codimension
+at least two, then all rational sections of the relative Picard scheme come
+from restrictions of line bundles on the variety. As a consequence, we describe
+the group of sections of the Hitchin fibration for moduli spaces of Higgs
+bundles on curves.
+",0,0,1,0,0,0
+8821,Subgradients of Minimal Time Functions without Calmness,"  In recent years there has been great interest in variational analysis of a
+class of nonsmooth functions called the minimal time function. In this paper we
+continue this line of research by providing new results on generalized
+differentiation of this class of functions, relaxing assumptions imposed on the
+functions and sets involved for the results. In particular, we focus on the
+singular subdifferential and the limiting subdifferential of this class of
+functions.
+",0,0,1,0,0,0
+8822,Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data,"  Alzheimer's disease is a major cause of dementia. Its diagnosis requires
+accurate biomarkers that are sensitive to disease stages. In this respect, we
+regard probabilistic classification as a method of designing a probabilistic
+biomarker for disease staging. Probabilistic biomarkers naturally support the
+interpretation of decisions and evaluation of uncertainty associated with them.
+In this paper, we obtain probabilistic biomarkers via Gaussian Processes.
+Gaussian Processes enable probabilistic kernel machines that offer flexible
+means to accomplish Multiple Kernel Learning. Exploiting this flexibility, we
+propose a new variation of Automatic Relevance Determination and tackle the
+challenges of high dimensionality through multiple kernels. Our research
+results demonstrate that the Gaussian Process models are competitive with or
+better than the well-known Support Vector Machine in terms of classification
+performance even in the cases of single kernel learning. Extending the basic
+scheme towards the Multiple Kernel Learning, we improve the efficacy of the
+Gaussian Process models and their interpretability in terms of the known
+anatomical correlates of the disease. For instance, the disease pathology
+starts in and around the hippocampus and entorhinal cortex. Through the use of
+Gaussian Processes and Multiple Kernel Learning, we have automatically and
+efficiently determined those portions of neuroimaging data. In addition to
+their interpretability, our Gaussian Process models are competitive with recent
+deep learning solutions under similar settings.
+",1,0,0,1,0,0
+8823,Optimal bounds and extremal trajectories for time averages in nonlinear dynamical systems,"  For any quantity of interest in a system governed by ordinary differential
+equations, it is natural to seek the largest (or smallest) long-time average
+among solution trajectories, as well as the extremal trajectories themselves.
+Upper bounds on time averages can be proved a priori using auxiliary functions,
+the optimal choice of which is a convex optimization problem. We prove that the
+problems of finding maximal trajectories and minimal auxiliary functions are
+strongly dual. Thus, auxiliary functions provide arbitrarily sharp upper bounds
+on time averages. Moreover, any nearly minimal auxiliary function provides
+phase space volumes in which all nearly maximal trajectories are guaranteed to
+lie. For polynomial equations, auxiliary functions can be constructed by
+semidefinite programming, which we illustrate using the Lorenz system.
+",0,1,1,0,0,0
+8824,Unsupervised Learning with Stein's Unbiased Risk Estimator,"  Learning from unlabeled and noisy data is one of the grand challenges of
+machine learning. As such, it has seen a flurry of research with new ideas
+proposed continuously. In this work, we revisit a classical idea: Stein's
+Unbiased Risk Estimator (SURE). We show that, in the context of image recovery,
+SURE and its generalizations can be used to train convolutional neural networks
+(CNNs) for a range of image denoising and recovery problems {\em without any
+ground truth data.}
+Specifically, our goal is to reconstruct an image $x$ from a {\em noisy}
+linear transformation (measurement) of the image. We consider two scenarios:
+one where no additional data is available and one where we have measurements of
+other images that are drawn from the same noisy distribution as $x$, but have
+no access to the clean images. Such is the case, for instance, in the context
+of medical imaging, microscopy, and astronomy, where noise-less ground truth
+data is rarely available.
+We show that in this situation, SURE can be used to estimate the
+mean-squared-error loss associated with an estimate of $x$. Using this estimate
+of the loss, we train networks to perform denoising and compressed sensing
+recovery. In addition, we also use the SURE framework to partially explain and
+improve upon an intriguing results presented by Ulyanov et al. in ""Deep Image
+Prior"": that a network initialized with random weights and fit to a single
+noisy image can effectively denoise that image.
+",0,0,0,1,0,0
+8825,Cellular function given parametric variation: excitability in the Hodgkin-Huxley model,"  How is reliable physiological function maintained in cells despite
+considerable variability in the values of key parameters of multiple
+interacting processes that govern that function? Here we use the classic
+Hodgkin-Huxley formulation of the squid giant axon action potential to propose
+a possible approach to this problem. Although the full Hodgkin-Huxley model is
+very sensitive to fluctuations that independently occur in its many parameters,
+the outcome is in fact determined by simple combinations of these parameters
+along two physiological dimensions: Structural and Kinetic (denoted $S$ and
+$K$). Structural parameters describe the properties of the cell, including its
+capacitance and the densities of its ion channels. Kinetic parameters are those
+that describe the opening and closing of the voltage-dependent conductances.
+The impacts of parametric fluctuations on the dynamics of the system, seemingly
+complex in the high dimensional representation of the Hodgkin-Huxley model, are
+tractable when examined within the $S-K$ plane. We demonstrate that slow
+inactivation, a ubiquitous activity-dependent feature of ionic channels, is a
+powerful local homeostatic control mechanism that stabilizes excitability amid
+changes in structural and kinetic parameters.
+",0,0,0,0,1,0
+8826,P4K: A Formal Semantics of P4 and Applications,"  Programmable packet processors and P4 as a programming language for such
+devices have gained significant interest, because their flexibility enables
+rapid development of a diverse set of applications that work at line rate.
+However, this flexibility, combined with the complexity of devices and
+networks, increases the chance of introducing subtle bugs that are hard to
+discover manually. Worse, this is a domain where bugs can have catastrophic
+consequences, yet formal analysis tools for P4 programs / networks are missing.
+We argue that formal analysis tools must be based on a formal semantics of
+the target language, rather than on its informal specification. To this end, we
+provide an executable formal semantics of the P4 language in the K framework.
+Based on this semantics, K provides an interpreter and various analysis tools
+including a symbolic model checker and a deductive program verifier for P4.
+This paper overviews our formal K semantics of P4, as well as several P4
+language design issues that we found during our formalization process. We also
+discuss some applications resulting from the tools provided by K for P4
+programmers and network administrators as well as language designers and
+compiler developers, such as detection of unportable code, state space
+exploration of P4 programs and of networks, bug finding using symbolic
+execution, data plane verification, program verification, and translation
+validation.
+",1,0,0,0,0,0
+8827,Global aspects of polarization optics and coset space geometry,"  We use group theoretic ideas and coset space methods to deal with problems in
+polarization optics of a global nature. These include the possibility of a
+globally smooth phase convention for electric fields for all points on the
+Poincaré sphere, and a similar possibility of real or complex bases of
+transverse electric vectors for all possible propagation directions. It is
+shown that these methods help in understanding some known results in an
+effective manner, and in answering new questions as well. We find that apart
+from the groups $SU(2)$ and $SO(3)$ which occur naturally in these problems,
+the group $SU(3)$ also plays an important role.
+",0,1,0,0,0,0
+8828,Nonlinear Large Deviations: Beyond the Hypercube,"  We present a framework to calculate large deviations for nonlinear functions
+of independent random variables supported on compact sets in Banach spaces, by
+extending the result in Chatterjee and Dembo [6]. Previous research on
+nonlinear large deviations has only focused on random variables supported on
+$\{-1,+1\}^{n}$, a small subset of random objects people usually study, thus it
+is of natural interest and need to research the corresponding theory for random
+variables with general distributions. Since our results put fewer constraints
+on the random variables, it has considerable flexibility in application. To
+show this, we provide examples with continuous and high dimensional random
+variables. Our framework could also be used to verify the mathematical rigor of
+the mean field approximation method; to demonstrate, we verify the mean field
+approximation for a class of spin vector models.
+",0,0,1,0,0,0
+8829,Force and torque of a string on a pulley,"  Every university introductory physics course considers the problem of
+Atwood's machine taking into account the mass of the pulley. In the usual
+treatment the tensions at the two ends of the string are offhandedly taken to
+act on the pulley and be responsible for its rotation. However such a free-body
+diagram of the forces on the pulley is not {\it a priori} justified, inducing
+students to construct wrong hypotheses such as that the string transfers its
+tension to the pulley or that some symmetry is in operation. We reexamine this
+problem by integrating the contact forces between each element of the string
+and the pulley and show that although the pulley does behave as if the tensions
+were acting on it, this comes only as the end result of a detailed analysis. We
+also address the question of how much friction is needed to prevent the string
+from slipping over the pulley. Finally, we deal with the case in which the
+string is on the verge of sliding and show that this will never happen unless
+certain conditions are met by the coefficient of friction and the masses
+involved.
+",0,1,0,0,0,0
+8830,Imagining Probabilistic Belief Change as Imaging (Technical Report),"  Imaging is a form of probabilistic belief change which could be employed for
+both revision and update. In this paper, we propose a new framework for
+probabilistic belief change based on imaging, called Expected Distance Imaging
+(EDI). EDI is sufficiently general to define Bayesian conditioning and other
+forms of imaging previously defined in the literature. We argue that, and
+investigate how, EDI can be used for both revision and update. EDI's definition
+depends crucially on a weight function whose properties are studied and whose
+effect on belief change operations is analysed. Finally, four EDI
+instantiations are proposed, two for revision and two for update, and
+probabilistic rationality postulates are suggested for their analysis.
+",1,0,0,0,0,0
+8831,Sparse Rational Function Interpolation with Finitely Many Values for the Coefficients,"  In this paper, we give new sparse interpolation algorithms for black box
+univariate and multivariate rational functions h=f/g whose coefficients are
+integers with an upper bound. The main idea is as follows: choose a proper
+integer beta and let h(beta) = a/b with gcd(a,b)=1. Then f and g can be
+computed by solving the polynomial interpolation problems f(beta)=ka and
+g(beta)=ka for some integer k. It is shown that the univariate interpolation
+algorithm is almost optimal and multivariate interpolation algorithm has low
+complexity in T but the data size is exponential in n.
+",1,0,0,0,0,0
+8832,Research Opportunities and Visions for Smart and Pervasive Health,"  Improving the health of the nation's population and increasing the
+capabilities of the US healthcare system to support diagnosis, treatment, and
+prevention of disease is a critical national and societal priority. In the past
+decade, tremendous advances in expanding computing capabilities--sensors, data
+analytics, networks, advanced imaging, and cyber-physical systems--have, and
+will continue to, enhance healthcare and health research, with resulting
+improvements in health and wellness. However, the cost and complexity of
+healthcare continues to rise alongside the impact of poor health on
+productivity and quality of life. What is lacking are transformative
+capabilities that address significant health and healthcare trends: the growing
+demands and costs of chronic disease, the greater responsibility placed on
+patients and informal caregivers, and the increasing complexity of health
+challenges in the US, including mental health, that are deeply rooted in a
+person's social and environmental context.
+",1,0,0,0,0,0
+8833,Training L1-Regularized Models with Orthant-Wise Passive Descent Algorithms,"  The $L_1$-regularized models are widely used for sparse regression or
+classification tasks. In this paper, we propose the orthant-wise passive
+descent algorithm (OPDA) for optimizing $L_1$-regularized models, as an
+improved substitute of proximal algorithms, which are the standard tools for
+optimizing the models nowadays. OPDA uses a stochastic variance-reduced
+gradient (SVRG) to initialize the descent direction, then apply a novel
+alignment operator to encourage each element keeping the same sign after one
+iteration of update, so the parameter remains in the same orthant as before. It
+also explicitly suppresses the magnitude of each element to impose sparsity.
+The quasi-Newton update can be utilized to incorporate curvature information
+and accelerate the speed. We prove a linear convergence rate for OPDA on
+general smooth and strongly-convex loss functions. By conducting experiments on
+$L_1$-regularized logistic regression and convolutional neural networks, we
+show that OPDA outperforms state-of-the-art stochastic proximal algorithms,
+implying a wide range of applications in training sparse models.
+",1,0,0,1,0,0
+8834,Zero-Delay Source-Channel Coding with a One-Bit ADC Front End and Correlated Side Information at the Receiver,"  Zero-delay transmission of a Gaussian source over an additive white Gaussian
+noise (AWGN) channel is considered with a one-bit analog-to-digital converter
+(ADC) front end and a correlated side information at the receiver. The design
+of the optimal encoder and decoder is studied for two performance criteria,
+namely, the mean squared error (MSE) distortion and the distortion outage
+probability (DOP), under an average power constraint on the channel input. For
+both criteria, necessary optimality conditions for the encoder and the decoder
+are derived. Using these conditions, it is observed that the numerically
+optimized encoder (NOE) under the MSE distortion criterion is periodic, and its
+period increases with the correlation between the source and the receiver side
+information. For the DOP, it is instead seen that the NOE mappings periodically
+acquire positive and negative values, which decay to zero with increasing
+source magnitude, and the interval over which the mapping takes non-zero
+values, becomes wider with the correlation between the source and the side
+information.
+",1,0,0,0,0,0
+8835,Non-Oscillatory Pattern Learning for Non-Stationary Signals,"  This paper proposes a novel non-oscillatory pattern (NOP) learning scheme for
+several oscillatory data analysis problems including signal decomposition,
+super-resolution, and signal sub-sampling. To the best of our knowledge, the
+proposed NOP is the first algorithm for these problems with fully
+non-stationary oscillatory data with close and crossover frequencies, and
+general oscillatory patterns. NOP is capable of handling complicated situations
+while existing algorithms fail; even in simple cases, e.g., stationary cases
+with trigonometric patterns, numerical examples show that NOP admits
+competitive or better performance in terms of accuracy and robustness than
+several state-of-the-art algorithms.
+",0,0,0,1,0,0
+8836,Analysis of Set-Valued Stochastic Approximations: Applications to Noisy Approximate Value and Fixed point Iterations,"  The main aim of this paper is the development of Lyapunov function based
+sufficient conditions for stability (almost sure boundedness) and convergence
+of stochastic approximation algorithms (SAAs) with set-valued mean-fields, a
+class of model-free algorithms that have become important in recent times. In
+this paper we provide a complete analysis of such algorithms under three
+different, yet related set of sufficient conditions, based on the existence of
+an associated global/local Lyapunov function. Unlike previous Lyapunov function
+based approaches, we provide a simple recipe for explicitly constructing the
+Lyapunov function needed for analysis. Our work builds on the works of
+Abounadi, Bertsekas and Borkar (2002), Munos (2005) and Ramaswamy and Bhatnagar
+(2016). An important motivation to the flavor of our assumptions comes from the
+need to understand approximate dynamic programming and reinforcement learning
+algorithms, that use deep neural networks (DNNs) for function approximations
+and parameterizations. These algorithms are popularly known as deep
+reinforcement learning algorithms. As an important application of our theory we
+provide a complete analysis of the stochastic approximation counterpart of
+approximate value iteration (AVI), an important dynamic programming method
+designed to tackle Bellman's curse of dimensionality. Although motivated by the
+need to understand deep reinforcement learning algorithms our theory is more
+generally applicable. It is further used to develop the first SAA for finding
+fixed points of contractive set-valued maps and provide a comprehensive
+analysis of the same.
+",1,0,0,1,0,0
+8837,Predicting Surgery Duration with Neural Heteroscedastic Regression,"  Scheduling surgeries is a challenging task due to the fundamental uncertainty
+of the clinical environment, as well as the risks and costs associated with
+under- and over-booking. We investigate neural regression algorithms to
+estimate the parameters of surgery case durations, focusing on the issue of
+heteroscedasticity. We seek to simultaneously estimate the duration of each
+surgery, as well as a surgery-specific notion of our uncertainty about its
+duration. Estimating this uncertainty can lead to more nuanced and effective
+scheduling strategies, as we are able to schedule surgeries more efficiently
+while allowing an informed and case-specific margin of error. Using surgery
+records %from the UC San Diego Health System, from a large United States health
+system we demonstrate potential improvements on the order of 20% (in terms of
+minutes overbooked) compared to current scheduling techniques. Moreover, we
+demonstrate that surgery durations are indeed heteroscedastic. We show that
+models that estimate case-specific uncertainty better fit the data (log
+likelihood). Additionally, we show that the heteroscedastic predictions can
+more optimally trade off between over and under-booking minutes, especially
+when idle minutes and scheduling collisions confer disparate costs.
+",1,0,0,1,0,0
+8838,Multichannel End-to-end Speech Recognition,"  The field of speech recognition is in the midst of a paradigm shift:
+end-to-end neural networks are challenging the dominance of hidden Markov
+models as a core technology. Using an attention mechanism in a recurrent
+encoder-decoder architecture solves the dynamic time alignment problem,
+allowing joint end-to-end training of the acoustic and language modeling
+components. In this paper we extend the end-to-end framework to encompass
+microphone array signal processing for noise suppression and speech enhancement
+within the acoustic encoding network. This allows the beamforming components to
+be optimized jointly within the recognition architecture to improve the
+end-to-end speech recognition objective. Experiments on the noisy speech
+benchmarks (CHiME-4 and AMI) show that our multichannel end-to-end system
+outperformed the attention-based baseline with input from a conventional
+adaptive beamformer.
+",1,0,0,0,0,0
+8839,Universal Rules for Fooling Deep Neural Networks based Text Classification,"  Recently, deep learning based natural language processing techniques are
+being extensively used to deal with spam mail, censorship evaluation in social
+networks, among others. However, there is only a couple of works evaluating the
+vulnerabilities of such deep neural networks. Here, we go beyond attacks to
+investigate, for the first time, universal rules, i.e., rules that are sample
+agnostic and therefore could turn any text sample in an adversarial one. In
+fact, the universal rules do not use any information from the method itself (no
+information from the method, gradient information or training dataset
+information is used), making them black-box universal attacks. In other words,
+the universal rules are sample and method agnostic. By proposing a
+coevolutionary optimization algorithm we show that it is possible to create
+universal rules that can automatically craft imperceptible adversarial samples
+(only less than five perturbations which are close to misspelling are inserted
+in the text sample). A comparison with a random search algorithm further
+justifies the strength of the method. Thus, universal rules for fooling
+networks are here shown to exist. Hopefully, the results from this work will
+impact the development of yet more sample and model agnostic attacks as well as
+their defenses, culminating in perhaps a new age for artificial intelligence.
+",1,0,0,1,0,0
+8840,Synthetic Observations of 21cm HI Line Profiles from Inhomogeneous Turbulent Interstellar HI Gas with Magnetic Field,"  We carried out synthetic observations of interstellar atomic hydrogen at 21cm
+wavelength by utilizing the magneto-hydrodynamical numerical simulations of the
+inhomogeneous turbulent interstellar medium (ISM) Inoue and Inutsuka (2012).
+The cold neutral medium (CNM) shows significantly clumpy distribution with a
+small volume filling factor of 3.5%, whereas the warm neutral medium (WNM)
+distinctly different smooth distribution with a large filling factor of 96.5%.
+In projection on the sky, the CNM exhibits highly filamentary distribution with
+a sub-pc width, whereas the WNM shows smooth extended distribution. In the HI
+optical depth the CNM is dominant and the contribution of the WNM is negligibly
+small. The CNM has an area covering factor of 30% in projection, while the WNM
+has a covering factor of 70%. This causes that the emission-absorption
+measurements toward radio continuum compact sources tend to sample the WNM with
+a probability of 70%, yielding smaller HI optical depth and smaller HI column
+density than those of the bulk HI gas. The emission-absorption measurements,
+which are significantly affected by the small-scale large fluctuations of the
+CNM properties, are not suitable to characterize the bulk HI gas. Larger-beam
+emission measurements which are able to fully sample the HI gas will provide a
+better tool for that purpose, if a reliable proxy for hydrogen column density,
+possibly dust optical depth and gamma rays, is available.
+",0,1,0,0,0,0
+8841,Clarifying Trust in Social Internet of Things,"  A social approach can be exploited for the Internet of Things (IoT) to manage
+a large number of connected objects. These objects operate as autonomous agents
+to request and provide information and services to users. Establishing
+trustworthy relationships among the objects greatly improves the effectiveness
+of node interaction in the social IoT and helps nodes overcome perceptions of
+uncertainty and risk. However, there are limitations in the existing trust
+models. In this paper, a comprehensive model of trust is proposed that is
+tailored to the social IoT. The model includes ingredients such as trustor,
+trustee, goal, trustworthiness evaluation, decision, action, result, and
+context. Building on this trust model, we clarify the concept of trust in the
+social IoT in five aspects such as (1) mutuality of trustor and trustee, (2)
+inferential transfer of trust, (3) transitivity of trust, (4) trustworthiness
+update, and (5) trustworthiness affected by dynamic environment. With network
+connectivities that are from real-world social networks, a series of
+simulations are conducted to evaluate the performance of the social IoT
+operated with the proposed trust model. An experimental IoT network is used to
+further validate the proposed trust model.
+",1,0,0,0,0,0
+8842,The Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis,"  In this paper, we introduce a powerful technique, Leave-One-Out, to the
+analysis of low-rank matrix completion problems. Using this technique, we
+develop a general approach for obtaining fine-grained, entry-wise bounds on
+iterative stochastic procedures. We demonstrate the power of this approach in
+analyzing two of the most important algorithms for matrix completion: the
+non-convex approach based on Singular Value Projection (SVP), and the convex
+relaxation approach based on nuclear norm minimization (NNM). In particular, we
+prove for the first time that the original form of SVP, without re-sampling or
+sample splitting, converges linearly in the infinity norm. We further apply our
+leave-one-out approach to an iterative procedure that arises in the analysis of
+the dual solutions of NNM. Our results show that NNM recovers the true $ d
+$-by-$ d $ rank-$ r $ matrix with $\mathcal{O}(\mu^2 r^3d \log d )$ observed
+entries, which has optimal dependence on the dimension and is independent of
+the condition number of the matrix. To the best of our knowledge, this is the
+first sample complexity result for a tractable matrix completion algorithm that
+satisfies these two properties simultaneously.
+",0,0,0,1,0,0
+8843,Weyl calculus with respect to the Gaussian measure and restricted $L^p$-$L^q$ boundedness of the Ornstein-Uhlenbeck semigroup in complex time,"  In this paper, we introduce a Weyl functional calculus $a \mapsto a(Q,P)$ for
+the position and momentum operators $Q$ and $P$ associated with the
+Ornstein-Uhlenbeck operator $ L = -\Delta + x\cdot \nabla$, and give a simple
+criterion for restricted $L^p$-$L^q$ boundedness of operators in this
+functional calculus. The analysis of this non-commutative functional calculus
+is simpler than the analysis of the functional calculus of $L$. It allows us to
+recover, unify, and extend, old and new results concerning the boundedness of
+$\exp(-zL)$ as an operator from $L^p(\mathbb{R}^d,\gamma_{\alpha})$ to
+$L^q(\mathbb{R}^d,\gamma_{\beta})$ for suitable values of $z\in \mathbb{C}$
+with $\Re z>0$, $p,q\in [1,\infty)$, and $\alpha,\beta>0$. Here, $\gamma_\tau$
+denotes the centred Gaussian measure on $\mathbb{R}^d$ with density
+$(2\pi\tau)^{-d/2}\exp(-|x|^2/2\tau)$.
+",0,0,1,0,0,0
+8844,Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning,"  Recent model-free reinforcement learning algorithms have proposed
+incorporating learned dynamics models as a source of additional data with the
+intention of reducing sample complexity. Such methods hold the promise of
+incorporating imagined data coupled with a notion of model uncertainty to
+accelerate the learning of continuous control tasks. Unfortunately, they rely
+on heuristics that limit usage of the dynamics model. We present model-based
+value expansion, which controls for uncertainty in the model by only allowing
+imagination to fixed depth. By enabling wider use of learned dynamics models
+within a model-free reinforcement learning algorithm, we improve value
+estimation, which, in turn, reduces the sample complexity of learning.
+",0,0,0,1,0,0
+8845,A new method of joint nonparametric estimation of probability density and its support,"  In this paper we propose a new method of joint nonparametric estimation of
+probability density and its support. As is well known, nonparametric kernel
+density estimator has ""boundary bias problem"" when the support of the
+population density is not the whole real line. To avoid the unknown boundary
+effects, our estimator detects the boundary, and eliminates the boundary-bias
+of the estimator simultaneously. Moreover, we refer an extension to a simple
+multivariate case, and propose an improved estimator free from the unknown
+boundary bias.
+",0,0,1,1,0,0
+8846,An OpenCL(TM) Deep Learning Accelerator on Arria 10,"  Convolutional neural nets (CNNs) have become a practical means to perform
+vision tasks, particularly in the area of image classification. FPGAs are well
+known to be able to perform convolutions efficiently, however, most recent
+efforts to run CNNs on FPGAs have shown limited advantages over other devices
+such as GPUs. Previous approaches on FPGAs have often been memory bound due to
+the limited external memory bandwidth on the FPGA device. We show a novel
+architecture written in OpenCL(TM), which we refer to as a Deep Learning
+Accelerator (DLA), that maximizes data reuse and minimizes external memory
+bandwidth. Furthermore, we show how we can use the Winograd transform to
+significantly boost the performance of the FPGA. As a result, when running our
+DLA on Intel's Arria 10 device we can achieve a performance of 1020 img/s, or
+23 img/s/W when running the AlexNet CNN benchmark. This comes to 1382 GFLOPs
+and is 10x faster with 8.4x more GFLOPS and 5.8x better efficiency than the
+state-of-the-art on FPGAs. Additionally, 23 img/s/W is competitive against the
+best publicly known implementation of AlexNet on nVidia's TitanX GPU.
+",1,0,0,0,0,0
+8847,Two-channel conduction in YbPtBi,"  We investigated transport, magnetotransport, and broadband optical properties
+of the half-Heusler compound YbPtBi. Hall measurements evidence two types of
+charge carriers: highly mobile electrons with a temperature-dependent
+concentration and low-mobile holes; their concentration stays almost constant
+within the investigated temperature range from 2.5 to 300 K. The optical
+spectra (10 meV - 2.7 eV) can be naturally decomposed into contributions from
+intra- and interband absorption processes, the former manifesting themselves as
+two Drude bands with very different scattering rates, corresponding to the
+charges with different mobilities. These results of the optical measurements
+allow us to separate the contributions from electrons and holes to the total
+conductivity and to implement a two-channel-conduction model for description of
+the magnetotransport data. In this approach, the electron and hole mobilities
+are found to be around 50000 and 10 cm$^{2}$/Vs at the lowest temperatures (2.5
+K), respectively.
+",0,1,0,0,0,0
+8848,Cosmological constraints on scalar-tensor gravity and the variation of the gravitational constant,"  We present cosmological constraints on the scalar-tensor theory of gravity by
+analyzing the angular power spectrum data of the cosmic microwave background
+obtained from the Planck 2015 results together with the baryon acoustic
+oscillations (BAO) data. We find that the inclusion of the BAO data improves
+the constraints on the time variation of the effective gravitational constant
+by more than $10\%$, that is, the time variation of the effective gravitational
+constant between the recombination and the present epochs is constrained as
+$G_{\rm rec}/G_0-1 <1.9\times 10^{-3}\ (95.45\%\ {\rm C.L.})$ and $G_{\rm
+rec}/G_0-1 <5.5\times 10^{-3}\ (99.99 \%\ {\rm C.L.})$. We also discuss the
+dependence of the constraints on the choice of the prior.
+",0,1,0,0,0,0
+8849,Extending Bayesian structural time-series estimates of causal impact to many-household conservation initiatives,"  Government agencies offer economic incentives to citizens for conservation
+actions, such as rebates for installing efficient appliances and compensation
+for modifications to homes. The intention of these conservation actions is
+frequently to reduce the consumption of a utility. Measuring the conservation
+impact of incentives is important for guiding policy, but doing so is
+technically difficult. However, the methods for estimating the impact of public
+outreach efforts have seen substantial developments in marketing to consumers
+in recent years as marketers seek to substantiate the value of their services.
+One such method uses Bayesian Stuctural Time Series (BSTS) to compare a market
+exposed to an advertising campaign with control markets identified through a
+matching procedure. This paper introduces an extension of the matching/BSTS
+method for impact estimation to make it applicable for general conservation
+program impact estimation when multi-household data is available. This is
+accomplished by household matching/BSTS steps to obtain conservation estimates
+and then aggregating the results using a meta-regression step to aggregate the
+findings. A case study examining the impact of rebates for household turf
+removal on water consumption in multiple Californian water districts is
+conducted to illustrate the work flow of this method.
+",0,0,0,1,0,0
+8850,"Absorption probabilities for Gaussian polytopes, and regular spherical simplices","  The Gaussian polytope $\mathcal P_{n,d}$ is the convex hull of $n$
+independent standard normally distributed points in $\mathbb R^d$. We derive
+explicit expressions for the probability that $\mathcal P_{n,d}$ contains a
+fixed point $x\in\mathbb R^d$ as a function of the Euclidean norm of $x$, and
+the probability that $\mathcal P_{n,d}$ contains the point $\sigma X$, where
+$\sigma\geq 0$ is constant and $X$ is a standard normal vector independent of
+$\mathcal P_{n,d}$. As a by-product, we also compute the expected number of
+$k$-faces and the expected volume of $\mathcal P_{n,d}$, thus recovering the
+results of Affentranger and Schneider [Discr. and Comput. Geometry, 1992] and
+Efron [Biometrika, 1965], respectively. All formulas are in terms of the
+volumes of regular spherical simplices, which, in turn, can be expressed
+through the standard normal distribution function $\Phi(z)$ and its complex
+version $\Phi(iz)$. The main tool used in the proofs is the conic version of
+the Crofton formula.
+",0,0,1,0,0,0
+8851,Modeling news spread as an SIR process over temporal networks,"  News spread in internet media outlets can be seen as a contagious process
+generating temporal networks representing the influence between published
+articles. In this article we propose a methodology based on the application of
+natural language analysis of the articles to reconstruct the spread network.
+From the reconstructed network, we show that the dynamics of the news spread
+can be approximated by a classical SIR epidemiological dynamics upon the
+network. From the results obtained we argue that the methodology proposed can
+be used to make predictions about media repercussion, and also to detect viral
+memes in news streams.
+",1,1,0,0,0,0
+8852,Price dynamics on a risk-averse market with asymmetric information,"  A market with asymmetric information can be viewed as a repeated exchange
+game between the informed sector and the uninformed one. In a market with
+risk-neutral agents, De Meyer [2010] proves that the price process should be a
+particular kind of Brownian martingale called CMMV. This type of dynamics is
+due to the strategic use of their private information by the informed agents.
+In the current paper, we consider the more realistic case where agents on the
+market are risk-averse. This case is much more complex to analyze as it leads
+to a non-zero-sum game. Our main result is that the price process is still a
+CMMV under a martingale equivalent measure. This paper provides thus a
+theoretical justification for the use of the CMMV class of dynamics in
+financial analysis. This class contains as a particular case the Black and
+Scholes dynamics.
+",1,0,1,0,0,0
+8853,Learning to Generalize: Meta-Learning for Domain Generalization,"  Domain shift refers to the well known problem that a model trained in one
+source domain performs poorly when applied to a target domain with different
+statistics. {Domain Generalization} (DG) techniques attempt to alleviate this
+issue by producing models which by design generalize well to novel testing
+domains. We propose a novel {meta-learning} method for domain generalization.
+Rather than designing a specific model that is robust to domain shift as in
+most previous DG work, we propose a model agnostic training procedure for DG.
+Our algorithm simulates train/test domain shift during training by synthesizing
+virtual testing domains within each mini-batch. The meta-optimization objective
+requires that steps to improve training domain performance should also improve
+testing domain performance. This meta-learning procedure trains models with
+good generalization ability to novel domains. We evaluate our method and
+achieve state of the art results on a recent cross-domain image classification
+benchmark, as well demonstrating its potential on two classic reinforcement
+learning tasks.
+",1,0,0,0,0,0
+8854,Divide and Conquer: Recovering Contextual Information of Behaviors in Android Apps around Limited-quantity Audit Logs,"  Android users are now suffering serious threats from various unwanted apps.
+The analysis of apps' audit logs is one of the critical methods for some device
+manufactures to unveil the underlying malice of apps. We propose and implement
+DroidHolmes, a novel system that recovers contextual information around
+limited-quantity audit logs. It also can help improving the performance of
+existing analysis tools, such as FlowDroid and IccTA. The key module of
+DroidHolmes is finding a path matched with the logs on the app's control-flow
+graph. The challenge, however, is that the limited-quantity logs may incur high
+computational complexity in log matching, where there are a large amount of
+candidates caused by the coupling relation of successive logs. To address the
+challenge, we propose a divide and conquer algorithm for effectively
+positioning each log record individually. In our evaluation, DroidHolmes helps
+existing tools to achieve 94.87% and 100% in precision and recall respectively
+on 132 apps from open-source test suites. Based on the result of DroidHolmes,
+the contextual information in the behaviors of 500 real-world apps is also
+recovered. Meanwhile, DroidHolmes incurs negligible performance overhead on the
+smartphone.
+",1,0,0,0,0,0
+8855,Homotopy Theoretic Classification of Symmetry Protected Phases,"  We classify a number of symmetry protected phases using Freed-Hopkins'
+homotopy theoretic classification. Along the way we compute the low-dimensional
+homotopy groups of a number of novel cobordism spectra.
+",0,1,1,0,0,0
+8856,Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks,"  Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI)
+images could help in diagnosis and treatment of kidney diseases of children.
+Automatic segmentation of renal parenchyma is an important step in this
+process. In this paper, we propose a time and memory efficient fully automated
+segmentation method which achieves high segmentation accuracy with running time
+in the order of seconds in both normal kidneys and kidneys with hydronephrosis.
+The proposed method is based on a cascaded application of two 3D convolutional
+neural networks that employs spatial and temporal information at the same time
+in order to learn the tasks of localization and segmentation of kidneys,
+respectively. Segmentation performance is evaluated on both normal and abnormal
+kidneys with varying levels of hydronephrosis. We achieved a mean dice
+coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric
+patients, respectively.
+",0,0,0,1,0,0
+8857,Self-adjoint approximations of degenerate Schrodinger operator,"  The problem of construction a quantum mechanical evolution for the
+Schrodinger equation with a degenerate Hamiltonian which is a symmetric
+operator that does not have self-adjoint extensions is considered. Self-adjoint
+regularization of the Hamiltonian does not lead to a preserving probability
+limiting evolution for vectors from the Hilbert space but it is used to
+construct a limiting evolution of states on a C*-algebra of compact operators
+and on an abelian subalgebra of operators in the Hilbert space. The limiting
+evolution of the states on the abelian algebra can be presented by the Kraus
+decomposition with two terms. Both of this terms are corresponded to the
+unitary and shift components of Wold's decomposition of isometric semigroup
+generated by the degenerate Hamiltonian. Properties of the limiting evolution
+of the states on the C*-algebras are investigated and it is shown that pure
+states could evolve into mixed states.
+",0,0,1,0,0,0
+8858,Inelastic deformation during sill and laccolith emplacement: Insights from an analytic elastoplastic model,"  Numerous geological observations evidence that inelastic deformation occurs
+during sills and laccoliths emplacement. However, most models of sill and
+laccolith emplacement neglect inelastic processes by assuming purely elastic
+deformation of the host rock. This assumption has never been tested, so that
+the role of inelastic deformation on the growth dynamics of magma intrusions
+remains poorly understood. In this paper, we introduce the first analytical
+model of shallow sill and laccolith emplacement that accounts for
+elasto-plastic deformation of the host rock. It considers the intrusion's
+overburden as a thin elastic bending plate attached to an
+elastic-perfectly-plastic foundation. We find that, for geologically realistic
+values of the model parameters, the horizontal extent of the plastic zone lp is
+much smaller than the radius of the intrusion a. By modeling the quasi-static
+growth of a sill, we find that the ratio lp/a decreases during propagation, as
+1/ $\sqrt$ a 4 $\Delta$P , with $\Delta$P the magma overpressure. The model
+also shows that the extent of the plastic zone decreases with the intrusion's
+depth, while it increases if the host rock is weaker. Comparison between our
+elasto-plastic model and existing purely elastic models shows that plasticity
+can have a significant effect on intrusion propagation dynamics, with e.g. up
+to a doubling of the overpressure necessary for the sill to grow. Our results
+suggest that plasticity effects might be small for large sills, but conversely
+that they might be substantial for early sill propagation. 2
+",0,1,0,0,0,0
+8859,On the magnetic shield for a Vlasov-Poisson plasma,"  We study the screening of a bounded body $\Gamma$ against the effect of a
+wind of charged particles, by means of a shield produced by a magnetic field
+which becomes infinite on the border of $\Gamma$. The charged wind is modeled
+by a Vlasov-Poisson plasma, the bounded body by a torus, and the external
+magnetic field is taken close to the border of $\Gamma$. We study two models: a
+plasma composed by different species with positive or negative charges, and
+finite total mass of each species, and another made of many species of the same
+sign, each having infinite mass. We investigate the time evolution of both
+systems, showing in particular that the plasma particles cannot reach the body.
+Finally we discuss possible extensions to more general initial data. We show
+also that when the magnetic lines are straight lines, (that imposes an
+unbounded body), the previous results can be improved.
+",0,0,1,0,0,0
+8860,Judicious partitions of uniform hypergraphs,"  The vertices of any graph with $m$ edges may be partitioned into two parts so
+that each part meets at least $\frac{2m}{3}$ edges. Bollobás and Thomason
+conjectured that the vertices of any $r$-uniform hypergraph with $m$ edges may
+likewise be partitioned into $r$ classes such that each part meets at least
+$\frac{r}{2r-1}m$ edges. In this paper we prove the weaker statement that, for
+each $r\ge 4$, a partition into $r$ classes may be found in which each class
+meets at least $\frac{r}{3r-4}m$ edges, a substantial improvement on previous
+bounds.
+",0,0,1,0,0,0
+8861,Full and maximal squashed flat antichains of minimum weight,"  A full squashed flat antichain (FSFA) in the Boolean lattice $B_n$ is a
+family $\mathcal{A}\cup\mathcal{B}$ of subsets of $[n]=\{1,2,\dots,n\}$ such
+that, for some $k\in [n]$ and $0\le m\le \binom n k$, $\mathcal{A}$ is the
+family of the first $m$ $k$-sets in squashed (reverse-lexicographic) order and
+$\mathcal{B}$ contains exactly those $(k-1)$-subsets of $[n]$ that are not
+contained in some $A\in\mathcal{A}$. If, in addition, every $k$-subset of $[n]$
+which is not in $\mathcal{A}$ contains some $B\in\mathcal{B}$, then
+$\mathcal{A}\cup\mathcal{B}$ is a maximal squashed flat antichain (MSFA). For
+any $n,k$ and positive real numbers $\alpha,\beta$, we determine all FSFA and
+all MSFA of minimum weight $\alpha\cdot|\mathcal{A}|+\beta\cdot|\mathcal{B}|$.
+Based on this, asymptotic results on MSFA with minimum size and minimum BLYM
+value, respectively, are derived.
+",1,0,0,0,0,0
+8862,On the phantom barrier crossing and the bounds on the speed of sound in non-minimal derivative coupling theories,"  In this paper we investigate the so called ""phantom barrier crossing"" issue
+in a cosmological model based in the scalar-tensor theory with non-minimal
+derivative coupling to the Einstein's tensor. Special attention will be paid to
+the physical bounds on the squared sound speed. The numeric results are
+geometrically illustrated by means of a qualitative procedure of analysis that
+is based on the mapping of the orbits in the phase plane onto the surfaces that
+represent physical quantities in the extended phase space, that is: the phase
+plane complemented with an additional dimension relative to the given physical
+parameter. We find that the cosmological model based in the non-minimal
+derivative coupling theory -- this includes both the quintessence and the pure
+derivative coupling cases -- has serious causality problems related with
+superluminal propagation of the scalar and tensor perturbations. Even more
+disturbing is the finding that, despite that the underlying theory is free of
+the Ostrogradsky instability, the corresponding cosmological model is plagued
+by the Laplacian (classical) instability related with negative squared sound
+speed. This instability leads to an uncontrollable growth of the energy density
+of the perturbations that is inversely proportional to their wavelength. We
+show that independent of the self-interaction potential, for the positive
+coupling the tensor perturbations propagate superluminally, while for the
+negative coupling a Laplacian instability arises. This latter instability
+invalidates the possibility for the model to describe the primordial inflation.
+",0,1,0,0,0,0
+8863,Detecting tropical defects of polynomial equations,"  We introduce the notion of tropical defects, certificates that a system of
+polynomial equations is not a tropical basis, and provide algorithms for
+finding them around affine spaces of complementary dimension to the zero set.
+We use these techniques to solve open problems regarding del Pezzo surfaces of
+degree 3 and realizability of valuated gaussoids of rank 4.
+",1,0,0,0,0,0
+8864,Wasserstein Variational Inference,"  This paper introduces Wasserstein variational inference, a new form of
+approximate Bayesian inference based on optimal transport theory. Wasserstein
+variational inference uses a new family of divergences that includes both
+f-divergences and the Wasserstein distance as special cases. The gradients of
+the Wasserstein variational loss are obtained by backpropagating through the
+Sinkhorn iterations. This technique results in a very stable likelihood-free
+training method that can be used with implicit distributions and probabilistic
+programs. Using the Wasserstein variational inference framework, we introduce
+several new forms of autoencoders and test their robustness and performance
+against existing variational autoencoding techniques.
+",0,0,0,1,0,0
+8865,Comptage probabiliste sur la frontière de Furstenberg,"  Let $G$ be a real linear semisimple algebraic group without compact factors
+and $\Gamma$ a Zariski dense subgroup of $G$. In this paper, we use a
+probabilistic counting in order to study the asymptotic properties of $\Gamma$
+acting on the Furstenberg boundary of $G$. First, we show that the $K$
+components of the elements of $\Gamma$ in the KAK decomposition of $G$ become
+asymptotically independent. This result is an analog of a result of Gorodnik-Oh
+in the context of the Archimedean counting. Then, we give a new proof of a
+result of Guivarc'h concerning the positivity of the Hausdorff dimension of the
+unique stationary probability measure on the Furstenberg Boundary of $G$.
+Finally, we show how these results can be combined to give a probabilistic
+proof of the Tit's alternative; namely that two independent random walks on
+$\Gamma$ will eventually generate a free subgroup. This result answered a
+question of Guivarc'h and was published earlier by the author. Since we're
+working with the field of real numbers, we give here a more direct proof and a
+more general statement.
+",0,0,1,0,0,0
+8866,Bures-Hall Ensemble: Spectral Densities and Average Entropies,"  We consider an ensemble of random density matrices distributed according to
+the Bures measure. The corresponding joint probability density of eigenvalues
+is described by the fixed trace Bures-Hall ensemble of random matrices which,
+in turn, is related to its unrestricted trace counterpart via a Laplace
+transform. We investigate the spectral statistics of both these ensembles and,
+in particular, focus on the level density, for which we obtain exact
+closed-form results involving Pfaffians. In the fixed trace case, the level
+density expression is used to obtain an exact result for the average
+Havrda-Charvát-Tsallis (HCT) entropy as a finite sum. Averages of von Neumann
+entropy, linear entropy and purity follow by considering appropriate limits in
+the average HCT expression. Based on exact evaluations of the average von
+Neumann entropy and the average purity, we also conjecture very simple formulae
+for these, which are similar to those in the Hilbert-Schmidt ensemble.
+",0,0,0,1,0,0
+8867,Electron and Nucleon Localization Functions of Oganesson: Approaching the Thomas-Fermi Limit,"  Fermion localization functions are used to discuss electronic and nucleonic
+shell structure effects in the superheavy element oganesson, the heaviest
+element discovered to date. Spin-orbit splitting in the $7p$ electronic shell
+becomes so large ($\sim$ 10 eV) that Og is expected to show uniform-gas-like
+behavior in the valence region with a rather large dipole polarizability
+compared to the lighter rare gas elements. The nucleon localization in Og is
+also predicted to undergo a transition to the Thomas-Fermi gas behavior in the
+valence region. This effect, particularly strong for neutrons, is due to the
+high density of single-particle orbitals.
+",0,1,0,0,0,0
+8868,Constrained Bayesian Optimization with Noisy Experiments,"  Randomized experiments are the gold standard for evaluating the effects of
+changes to real-world systems. Data in these tests may be difficult to collect
+and outcomes may have high variance, resulting in potentially large measurement
+error. Bayesian optimization is a promising technique for efficiently
+optimizing multiple continuous parameters, but existing approaches degrade in
+performance when the noise level is high, limiting its applicability to many
+randomized experiments. We derive an expression for expected improvement under
+greedy batch optimization with noisy observations and noisy constraints, and
+develop a quasi-Monte Carlo approximation that allows it to be efficiently
+optimized. Simulations with synthetic functions show that optimization
+performance on noisy, constrained problems outperforms existing methods. We
+further demonstrate the effectiveness of the method with two real-world
+experiments conducted at Facebook: optimizing a ranking system, and optimizing
+server compiler flags.
+",1,0,0,1,0,0
+8869,"On synthetic data with predetermined subject partitioning and cluster profiling, and pre-specified categorical variable marginal dependence structure","  A standard approach for assessing the performance of partition or mixture
+models is to create synthetic data sets with a pre-specified clustering
+structure, and assess how well the model reveals this structure. A common
+format is that subjects are assigned to different clusters, with variable
+observations simulated so that subjects within the same cluster have similar
+profiles, allowing for some variability. In this manuscript, we consider
+observations from nominal, ordinal and interval categorical variables.
+Theoretical and empirical results are utilized to explore the dependence
+structure between the variables, in relation to the clustering structure for
+the subjects. A novel approach is proposed that allows to control the marginal
+association or correlation structure of the variables, and to specify exact
+correlation values. Practical examples are shown and additional theoretical
+results are derived for interval data, commonly observed in cohort studies,
+including observations that emulate Single Nucleotide Polymorphisms. We compare
+a synthetic dataset to a real one, to demonstrate similarities and differences.
+",0,0,0,1,0,0
+8870,Multi-Agent Diverse Generative Adversarial Networks,"  We propose MAD-GAN, an intuitive generalization to the Generative Adversarial
+Networks (GANs) and its conditional variants to address the well known problem
+of mode collapse. First, MAD-GAN is a multi-agent GAN architecture
+incorporating multiple generators and one discriminator. Second, to enforce
+that different generators capture diverse high probability modes, the
+discriminator of MAD-GAN is designed such that along with finding the real and
+fake samples, it is also required to identify the generator that generated the
+given fake sample. Intuitively, to succeed in this task, the discriminator must
+learn to push different generators towards different identifiable modes. We
+perform extensive experiments on synthetic and real datasets and compare
+MAD-GAN with different variants of GAN. We show high quality diverse sample
+generations for challenging tasks such as image-to-image translation and face
+generation. In addition, we also show that MAD-GAN is able to disentangle
+different modalities when trained using highly challenging diverse-class
+dataset (e.g. dataset with images of forests, icebergs, and bedrooms). In the
+end, we show its efficacy on the unsupervised feature representation task. In
+Appendix, we introduce a similarity based competing objective (MAD-GAN-Sim)
+which encourages different generators to generate diverse samples based on a
+user defined similarity metric. We show its performance on the image-to-image
+translation, and also show its effectiveness on the unsupervised feature
+representation task.
+",1,0,0,1,0,0
+8871,Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data,"  The aetiology of polygenic obesity is multifactorial, which indicates that
+life-style and environmental factors may influence multiples genes to aggravate
+this disorder. Several low-risk single nucleotide polymorphisms (SNPs) have
+been associated with BMI. However, identified loci only explain a small
+proportion of the variation ob-served for this phenotype. The linear nature of
+genome wide association studies (GWAS) used to identify associations between
+genetic variants and the phenotype have had limited success in explaining the
+heritability variation of BMI and shown low predictive capacity in
+classification studies. GWAS ignores the epistatic interactions that less
+significant variants have on the phenotypic outcome. In this paper we utilise a
+novel deep learning-based methodology to reduce the high dimensional space in
+GWAS and find epistatic interactions between SNPs for classification purposes.
+SNPs were filtered based on the effects associations have with BMI. Since
+Bonferroni adjustment for multiple testing is highly conservative, an important
+proportion of SNPs involved in SNP-SNP interactions are ignored. Therefore,
+only SNPs with p-values < 1x10-2 were considered for subsequent epistasis
+analysis using stacked auto encoders (SAE). This allows the nonlinearity
+present in SNP-SNP interactions to be discovered through progressively smaller
+hidden layer units and to initialise a multi-layer feedforward artificial
+neural network (ANN) classifier. The classifier is fine-tuned to classify
+extremely obese and non-obese individuals. The best results were obtained with
+2000 compressed units (SE=0.949153, SP=0.933014, Gini=0.949936,
+Lo-gloss=0.1956, AUC=0.97497 and MSE=0.054057). Using 50 compressed units it
+was possible to achieve (SE=0.785311, SP=0.799043, Gini=0.703566,
+Logloss=0.476864, AUC=0.85178 and MSE=0.156315).
+",0,0,0,0,1,0
+8872,Packet Throughput Analysis of Static and Dynamic TDD in Small Cell Networks,"  We develop an analytical framework for the perfor- mance comparison of small
+cell networks operating under static time division duplexing (S-TDD) and
+dynamic TDD (D-TDD). While in S-TDD downlink/uplink (DL/UL) cell transmissions
+are synchronized, in D-TDD each cell dynamically allocates resources to the
+most demanding direction. By leveraging stochastic geom- etry and queuing
+theory, we derive closed-form expressions for the UL and DL packet throughput,
+also capturing the impact of random traffic arrivals and packet
+retransmissions. Through our analysis, which is validated via simulations, we
+confirm that D-TDD outperforms S-TDD in DL, with the vice versa occurring in
+UL, since asymmetric transmissions reduce DL interference at the expense of an
+increased UL interference. We also find that in asymmetric scenarios, where
+most of the traffic is in DL, D-TDD provides a DL packet throughput gain by
+better controlling the queuing delay, and that such gain vanishes in the
+light-traffic regime.
+",1,0,0,0,0,0
+8873,False Discovery Rate Control via Debiased Lasso,"  We consider the problem of variable selection in high-dimensional statistical
+models where the goal is to report a set of variables, out of many predictors
+$X_1, \dotsc, X_p$, that are relevant to a response of interest. For linear
+high-dimensional model, where the number of parameters exceeds the number of
+samples $(p>n)$, we propose a procedure for variables selection and prove that
+it controls the \emph{directional} false discovery rate (FDR) below a
+pre-assigned significance level $q\in [0,1]$. We further analyze the
+statistical power of our framework and show that for designs with subgaussian
+rows and a common precision matrix $\Omega\in\mathbb{R}^{p\times p}$, if the
+minimum nonzero parameter $\theta_{\min}$ satisfies $$\sqrt{n} \theta_{\min} -
+\sigma \sqrt{2(\max_{i\in [p]}\Omega_{ii})\log\left(\frac{2p}{qs_0}\right)} \to
+\infty\,,$$ then this procedure achieves asymptotic power one.
+Our framework is built upon the debiasing approach and assumes the standard
+condition $s_0 = o(\sqrt{n}/(\log p)^2)$, where $s_0$ indicates the number of
+true positives among the $p$ features. Notably, this framework achieves exact
+directional FDR control without any assumption on the amplitude of unknown
+regression parameters, and does not require any knowledge of the distribution
+of covariates or the noise level. We test our method in synthetic and real data
+experiments to asses its performance and to corroborate our theoretical
+results.
+",0,0,0,1,0,0
+8874,Relativistic Astronomy,"  The ""Breakthrough Starshot"" aims at sending near-speed-of-light cameras to
+nearby stellar systems in the future. Due to the relativistic effects, a
+trans-relativistic camera naturally serves as a spectrograph, a lens, and a
+wide-field camera. We demonstrate this through a simulation of the optical-band
+image of the nearby galaxy M51 in the rest frame of the trans-relativistic
+camera. We suggest that observing celestial objects using a trans-relativistic
+camera may allow one to study the astronomical objects in a special way, and to
+perform unique tests on the principles of special relativity. We outline
+several examples that trans-relativistic cameras may make important
+contributions to astrophysics and suggest that the Breakthrough Starshot
+cameras may be launched in any direction to serve as a unique astronomical
+observatory.
+",0,1,0,0,0,0
+8875,Connection between Fermi contours of zero-field electrons and $ν=\frac12$ composite fermions in two-dimensional systems,"  We investigate the relation between the Fermi sea (FS) of zero-field carriers
+in two-dimensional systems and the FS of the corresponding composite fermions
+which emerge in a high magnetic field at filling $\nu = \frac{1}{2}$, as the
+kinetic energy dispersion is varied. We study cases both with and without
+rotational symmetry, and find that there is generally no straightforward
+relation between the geometric shapes and topologies of the two FSs. In
+particular, we show analytically that the composite Fermi liquid (CFL) is
+completely insensitive to a wide range of changes to the zero-field dispersion
+which preserve rotational symmetry, including ones that break the zero-field FS
+into multiple disconnected pieces. In the absence of rotational symmetry, we
+show that the notion of `valley pseudospin' in many-valley systems is
+generically not transferred to the CFL, in agreement with experimental
+observations. We also discuss how a rotationally symmetric band structure can
+induce a reordering of the Landau levels, opening interesting possibilities of
+observing higher-Landau-level physics in the high-field regime.
+",0,1,0,0,0,0
+8876,An instrumental intelligibility metric based on information theory,"  We propose a monaural intrusive instrumental intelligibility metric called
+speech intelligibility in bits (SIIB). SIIB is an estimate of the amount of
+information shared between a talker and a listener in bits per second. Unlike
+existing information theoretic intelligibility metrics, SIIB accounts for
+talker variability and statistical dependencies between time-frequency units.
+Our evaluation shows that relative to state-of-the-art intelligibility metrics,
+SIIB is highly correlated with the intelligibility of speech that has been
+degraded by noise and processed by speech enhancement algorithms.
+",1,0,0,0,0,0
+8877,Scattering of kinks in a non-polynomial model,"  We study a model described by a single real scalar field in the
+two-dimensional space-time. The model is specified by a potential which is
+non-polynomial and supports analytical kink-like solutions that are similar to
+the standard kink-like solutions that appear in the $\varphi^4$ model when it
+develops spontaneous symmetry breaking. We investigate the kink-antikink
+scattering problem in the non-polynomial model numerically and highlight some
+specific features, which are not present in the standard case.
+",0,1,0,0,0,0
+8878,On the Specification of Constraints for Dynamic Architectures,"  In dynamic architectures, component activation and connections between
+components may vary over time. With the emergence of mobile computing such
+architectures became increasingly important and several techniques emerged to
+support in their specification. These techniques usually allow for the
+specification of concrete architecture instances. Sometimes, however, it is
+desired to focus on the specification of constraints, rather than concrete
+architectures. Especially specifications of architecture patterns usually focus
+on a few, important constraints, leaving out the details of the concrete
+architecture implementing the pattern. With this article we introduce an
+approach to specify such constraints for dynamic architectures. To this end, we
+introduce the notion of configuration traces as an abstract model for dynamic
+architectures. Then, we introduce the notion of configuration trace assertions
+as a formal language based on linear temporal logic to specify constraints for
+such architectures. In addition, we also introduce the notion of configuration
+diagrams to specify interfaces and certain common activation and connection
+constraints in one single, graphical notation. The approach is well-suited to
+specify patterns for dynamic architectures and verify them by means of formal
+analyses. This is demonstrated by applying the approach to specify and verify
+the Blackboard pattern for dynamic architectures.
+",1,0,0,0,0,0
+8879,Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional Neural Networks,"  Forward-looking sonar can capture high resolution images of underwater
+scenes, but their interpretation is complex. Generic object detection in such
+images has not been solved, specially in cases of small and unknown objects. In
+comparison, detection proposal algorithms have produced top performing object
+detectors in real-world color images. In this work we develop a Convolutional
+Neural Network that can reliably score objectness of image windows in
+forward-looking sonar images and by thresholding objectness, we generate
+detection proposals. In our dataset of marine garbage objects, we obtain 94%
+recall, generating around 60 proposals per image. The biggest strength of our
+method is that it can generalize to previously unseen objects. We show this by
+detecting chain links, walls and a wrench without previous training in such
+objects. We strongly believe our method can be used for class-independent
+object detection, with many real-world applications such as chain following and
+mine detection.
+",1,0,0,0,0,0
+8880,Rgtsvm: Support Vector Machines on a GPU in R,"  Rgtsvm provides a fast and flexible support vector machine (SVM)
+implementation for the R language. The distinguishing feature of Rgtsvm is that
+support vector classification and support vector regression tasks are
+implemented on a graphical processing unit (GPU), allowing the libraries to
+scale to millions of examples with >100-fold improvement in performance over
+existing implementations. Nevertheless, Rgtsvm retains feature parity and has
+an interface that is compatible with the popular e1071 SVM package in R.
+Altogether, Rgtsvm enables large SVM models to be created by both experienced
+and novice practitioners.
+",1,0,0,1,0,0
+8881,"Geometric Bijections for Regular Matroids, Zonotopes, and Ehrhart Theory","  Let $M$ be a regular matroid. The Jacobian group ${\rm Jac}(M)$ of $M$ is a
+finite abelian group whose cardinality is equal to the number of bases of $M$.
+This group generalizes the definition of the Jacobian group (also known as the
+critical group or sandpile group) ${\rm Jac}(G)$ of a graph $G$ (in which case
+bases of the corresponding regular matroid are spanning trees of $G$).
+There are many explicit combinatorial bijections in the literature between
+the Jacobian group of a graph ${\rm Jac}(G)$ and spanning trees. However, most
+of the known bijections use vertices of $G$ in some essential way and are
+inherently ""non-matroidal"". In this paper, we construct a family of explicit
+and easy-to-describe bijections between the Jacobian group of a regular matroid
+$M$ and bases of $M$, many instances of which are new even in the case of
+graphs. We first describe our family of bijections in a purely combinatorial
+way in terms of orientations; more specifically, we prove that the Jacobian
+group of $M$ admits a canonical simply transitive action on the set ${\mathcal
+G}(M)$ of circuit-cocircuit reversal classes of $M$, and then define a family
+of combinatorial bijections $\beta_{\sigma,\sigma^*}$ between ${\mathcal G}(M)$
+and bases of $M$. (Here $\sigma$ (resp. $\sigma^*$) is an acyclic signature of
+the set of circuits (resp. cocircuits) of $M$.) We then give a geometric
+interpretation of each such map $\beta=\beta_{\sigma,\sigma^*}$ in terms of
+zonotopal subdivisions which is used to verify that $\beta$ is indeed a
+bijection.
+Finally, we give a combinatorial interpretation of lattice points in the
+zonotope $Z$; by passing to dilations we obtain a new derivation of Stanley's
+formula linking the Ehrhart polynomial of $Z$ to the Tutte polynomial of $M$.
+",0,0,1,0,0,0
+8882,Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping,"  Collaborative Filtering (CF) is a widely adopted technique in recommender
+systems. Traditional CF models mainly focus on predicting a user's preference
+to the items in a single domain such as the movie domain or the music domain. A
+major challenge for such models is the data sparsity problem, and especially,
+CF cannot make accurate predictions for the cold-start users who have no
+ratings at all. Although Cross-Domain Collaborative Filtering (CDCF) is
+proposed for effectively transferring users' rating preference across different
+domains, it is still difficult for existing CDCF models to tackle the
+cold-start users in the target domain due to the extreme data sparsity. In this
+paper, we propose a Cross-Domain Latent Feature Mapping (CDLFM) model for
+cold-start users in the target domain. Firstly, in order to better characterize
+users in sparse domains, we take the users' similarity relationship on rating
+behaviors into consideration and propose the Matrix Factorization by
+incorporating User Similarities (MFUS) in which three similarity measures are
+proposed. Next, to perform knowledge transfer across domains, we propose a
+neighborhood based gradient boosting trees method to learn the cross-domain
+user latent feature mapping function. For each cold-start user, we learn
+his/her feature mapping function based on the latent feature pairs of those
+linked users who have similar rating behaviors with the cold-start user in the
+auxiliary domain. And the preference of the cold-start user in the target
+domain can be predicted based on the mapping function and his/her latent
+features in the auxiliary domain. Experimental results on two real data sets
+extracted from Amazon transaction data demonstrate the superiority of our
+proposed model against other state-of-the-art methods.
+",1,0,0,0,0,0
+8883,Deep Interest Network for Click-Through Rate Prediction,"  Click-through rate prediction is an essential task in industrial
+applications, such as online advertising. Recently deep learning based models
+have been proposed, which follow a similar Embedding\&MLP paradigm. In these
+methods large scale sparse input features are first mapped into low dimensional
+embedding vectors, and then transformed into fixed-length vectors in a
+group-wise manner, finally concatenated together to fed into a multilayer
+perceptron (MLP) to learn the nonlinear relations among features. In this way,
+user features are compressed into a fixed-length representation vector, in
+regardless of what candidate ads are. The use of fixed-length vector will be a
+bottleneck, which brings difficulty for Embedding\&MLP methods to capture
+user's diverse interests effectively from rich historical behaviors. In this
+paper, we propose a novel model: Deep Interest Network (DIN) which tackles this
+challenge by designing a local activation unit to adaptively learn the
+representation of user interests from historical behaviors with respect to a
+certain ad. This representation vector varies over different ads, improving the
+expressive ability of model greatly. Besides, we develop two techniques:
+mini-batch aware regularization and data adaptive activation function which can
+help training industrial deep networks with hundreds of millions of parameters.
+Experiments on two public datasets as well as an Alibaba real production
+dataset with over 2 billion samples demonstrate the effectiveness of proposed
+approaches, which achieve superior performance compared with state-of-the-art
+methods. DIN now has been successfully deployed in the online display
+advertising system in Alibaba, serving the main traffic.
+",1,0,0,1,0,0
+8884,Nonlinear Dirac Cones,"  Physics arising from two-dimensional~(2D) Dirac cones has been a topic of
+great theoretical and experimental interest to studies of gapless topological
+phases and to simulations of relativistic systems. Such $2$D Dirac cones are
+often characterized by a $\pi$ Berry phase and are destroyed by a perturbative
+mass term. By considering mean-field nonlinearity in a minimal two-band Chern
+insulator model, we obtain a novel type of Dirac cones that are robust to local
+perturbations without symmetry restrictions. Due to a different pseudo-spin
+texture, the Berry phase of the Dirac cone is no longer quantized in $\pi$, and
+can be continuously tuned as an order parameter. Furthermore, in an
+Aharonov-Bohm~(AB) interference setup to detect such Dirac cones, the adiabatic
+AB phase is found to be $\pi$ both theoretically and computationally, offering
+an observable topological invariant and a fascinating example where the Berry
+phase and AB phase are fundamentally different. We hence discover a
+nonlinearity-induced quantum phase transition from a known topological
+insulating phase to an unusual gapless topological phase.
+",0,1,0,0,0,0
+8885,Portfolio Optimization in Fractional and Rough Heston Models,"  We consider a fractional version of the Heston volatility model which is
+inspired by [16]. Within this model we treat portfolio optimization problems
+for power utility functions. Using a suitable representation of the fractional
+part, followed by a reasonable approximation we show that it is possible to
+cast the problem into the classical stochastic control framework. This approach
+is generic for fractional processes. We derive explicit solutions and obtain as
+a by-product the Laplace transform of the integrated volatility. In order to
+get rid of some undesirable features we introduce a new model for the rough
+path scenario which is based on the Marchaud fractional derivative. We provide
+a numerical study to underline our results.
+",0,0,0,0,0,1
+8886,Vanishing in stable motivic homotopy sheaves,"  We determine systematic regions in which the bigraded homotopy sheaves of the
+motivic sphere spectrum vanish.
+",0,0,1,0,0,0
+8887,Classifying Character Degree Graphs With 6 Vertices,"  We investigate prime character degree graphs of solvable groups that have six
+vertices. There are one hundred twelve non-isomorphic connected graphs with six
+vertices, of which all except nine are classified in this paper. We also
+completely classify the disconnected graphs with six vertices.
+",0,0,1,0,0,0
+8888,On the Table of Marks of a Direct Product of Finite Groups,"  We present a method for computing the table of marks of a direct product of
+finite groups. In contrast to the character table of a direct product of two
+finite groups, its table of marks is not simply the Kronecker product of the
+tables of marks of the two groups. Based on a decomposition of the inclusion
+order on the subgroup lattice of a direct product as a relation product of
+three smaller partial orders, we describe the table of marks of the direct
+product essentially as a matrix product of three class incidence matrices. Each
+of these matrices is in turn described as a sparse block diagonal matrix. As an
+application, we use a variant of this matrix product to construct a ghost ring
+and a mark homomorphism for the rational double Burnside algebra of the
+symmetric group~$S_3$.
+",0,0,1,0,0,0
+8889,An Adiabatic Decomposition of the Hodge Cohomology of Manifolds Fibred over Graphs,"  In this article we use the combinatorial and geometric structure of manifolds
+with embedded cylinders in order to develop an adiabatic decomposition of the
+Hodge cohomology of these manifolds. We will on the one hand describe the
+adiabatic behaviour of spaces of harmonic forms by means of a certain
+Čech-de Rham complex and on the other hand generalise the
+Cappell-Lee-Miller splicing map to the case of a finite number of edges, thus
+combining the topological and the analytic viewpoint. In parts, this work is a
+generalisation of works of Cappell, Lee and Miller in which a single-edged
+graph is considered, but it is more specific since only the Gauss-Bonnet
+operator is studied.
+",0,0,1,0,0,0
+8890,Search Intelligence: Deep Learning For Dominant Category Prediction,"  Deep Neural Networks, and specifically fully-connected convolutional neural
+networks are achieving remarkable results across a wide variety of domains.
+They have been trained to achieve state-of-the-art performance when applied to
+problems such as speech recognition, image classification, natural language
+processing and bioinformatics. Most of these deep learning models when applied
+to classification employ the softmax activation function for prediction and aim
+to minimize cross-entropy loss. In this paper, we have proposed a supervised
+model for dominant category prediction to improve search recall across all eBay
+classifieds platforms. The dominant category label for each query in the last
+90 days is first calculated by summing the total number of collaborative clicks
+among all categories. The category having the highest number of collaborative
+clicks for the given query will be considered its dominant category. Second,
+each query is transformed to a numeric vector by mapping each unique word in
+the query document to a unique integer value; all padded to equal length based
+on the maximum document length within the pre-defined vocabulary size. A
+fully-connected deep convolutional neural network (CNN) is then applied for
+classification. The proposed model achieves very high classification accuracy
+compared to other state-of-the-art machine learning techniques.
+",1,0,0,1,0,0
+8891,Grouped Convolutional Neural Networks for Multivariate Time Series,"  Analyzing multivariate time series data is important for many applications
+such as automated control, fault diagnosis and anomaly detection. One of the
+key challenges is to learn latent features automatically from dynamically
+changing multivariate input. In visual recognition tasks, convolutional neural
+networks (CNNs) have been successful to learn generalized feature extractors
+with shared parameters over the spatial domain. However, when high-dimensional
+multivariate time series is given, designing an appropriate CNN model structure
+becomes challenging because the kernels may need to be extended through the
+full dimension of the input volume. To address this issue, we present two
+structure learning algorithms for deep CNN models. Our algorithms exploit the
+covariance structure over multiple time series to partition input volume into
+groups. The first algorithm learns the group CNN structures explicitly by
+clustering individual input sequences. The second algorithm learns the group
+CNN structures implicitly from the error backpropagation. In experiments with
+two real-world datasets, we demonstrate that our group CNNs outperform existing
+CNN based regression methods.
+",1,0,0,0,0,0
+8892,A mathematical bridge between discretized gauge theories in quantum physics and approximate reasoning in pairwise comparisons,"  We describe a mathematical link between aspects of information theory, called
+pairwise comparisons, and discretized gauge theories. The link is made by the
+notion of holonomy along the edges of a simplex. This correspondance leads to
+open questions in both field.
+",0,0,1,0,0,0
+8893,Ten Simple Rules for Reproducible Research in Jupyter Notebooks,"  Reproducibility of computational studies is a hallmark of scientific
+methodology. It enables researchers to build with confidence on the methods and
+findings of others, reuse and extend computational pipelines, and thereby drive
+scientific progress. Since many experimental studies rely on computational
+analyses, biologists need guidance on how to set up and document reproducible
+data analyses or simulations.
+In this paper, we address several questions about reproducibility. For
+example, what are the technical and non-technical barriers to reproducible
+computational studies? What opportunities and challenges do computational
+notebooks offer to overcome some of these barriers? What tools are available
+and how can they be used effectively?
+We have developed a set of rules to serve as a guide to scientists with a
+specific focus on computational notebook systems, such as Jupyter Notebooks,
+which have become a tool of choice for many applications. Notebooks combine
+detailed workflows with narrative text and visualization of results. Combined
+with software repositories and open source licensing, notebooks are powerful
+tools for transparent, collaborative, reproducible, and reusable data analyses.
+",1,0,0,0,0,0
+8894,No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search Results,"  Users are rarely familiar with the content of a data source they are
+querying, and therefore cannot avoid using keywords that do not exist in the
+data source. Traditional systems may respond with an empty result, causing
+dissatisfaction, while the data source in effect holds semantically related
+content. In this paper we study this no-but-semantic-match problem on XML
+keyword search and propose a solution which enables us to present the top-k
+semantically related results to the user. Our solution involves two steps: (a)
+extracting semantically related candidate queries from the original query and
+(b) processing candidate queries and retrieving the top-k semantically related
+results. Candidate queries are generated by replacement of non-mapped keywords
+with candidate keywords obtained from an ontological knowledge base. Candidate
+results are scored using their cohesiveness and their similarity to the
+original query. Since the number of queries to process can be large, with each
+result having to be analyzed, we propose pruning techniques to retrieve the
+top-$k$ results efficiently. We develop two query processing algorithms based
+on our pruning techniques. Further, we exploit a property of the candidate
+queries to propose a technique for processing multiple queries in batch, which
+improves the performance substantially. Extensive experiments on two real
+datasets verify the effectiveness and efficiency of the proposed approaches.
+",1,0,0,0,0,0
+8895,Avalanches and Plastic Flow in Crystal Plasticity: An Overview,"  Crystal plasticity is mediated through dislocations, which form knotted
+configurations in a complex energy landscape. Once they disentangle and move,
+they may also be impeded by permanent obstacles with finite energy barriers or
+frustrating long-range interactions. The outcome of such complexity is the
+emergence of dislocation avalanches as the basic mechanism of plastic flow in
+solids at the nanoscale. While the deformation behavior of bulk materials
+appears smooth, a predictive model should clearly be based upon the character
+of these dislocation avalanches and their associated strain bursts. We provide
+here a comprehensive overview of experimental observations, theoretical models
+and computational approaches that have been developed to unravel the multiple
+aspects of dislocation avalanche physics and the phenomena leading to strain
+bursts in crystal plasticity.
+",0,1,0,0,0,0
+8896,Explicit expression for the stationary distribution of reflected brownian motion in a wedge,"  For Brownian motion in a (two-dimensional) wedge with negative drift and
+oblique reflection on the axes, we derive an explicit formula for the Laplace
+transform of its stationary distribution (when it exists), in terms of Cauchy
+integrals and generalized Chebyshev polyno-mials. To that purpose we solve a
+Carleman-type boundary value problem on a hyperbola, satisfied by the Laplace
+transforms of the boundary stationary distribution.
+",0,0,1,0,0,0
+8897,A Data-Driven Framework for Assessing Cold Load Pick-up Demand in Service Restoration,"  Cold load pick-up (CLPU) has been a critical concern to utilities.
+Researchers and industry practitioners have underlined the impact of CLPU on
+distribution system design and service restoration. The recent large-scale
+deployment of smart meters has provided the industry with a huge amount of data
+that is highly granular, both temporally and spatially. In this paper, a
+data-driven framework is proposed for assessing CLPU demand of residential
+customers using smart meter data. The proposed framework consists of two
+interconnected layers: 1) At the feeder level, a nonlinear auto-regression
+model is applied to estimate the diversified demand during the system
+restoration and calculate the CLPU demand ratio. 2) At the customer level,
+Gaussian Mixture Models (GMM) and probabilistic reasoning are used to quantify
+the CLPU demand increase. The proposed methodology has been verified using real
+smart meter data and outage cases.
+",1,0,0,0,0,0
+8898,On the topological complexity of aspherical spaces,"  The well-known theorem of Eilenberg and Ganea expresses the Lusternik -
+Schnirelmann category of an aspherical space as the cohomological dimension of
+its fundamental group. In this paper we study a similar problem of determining
+algebraically the topological complexity of the Eilenberg-MacLane spaces. One
+of our main results states that in the case when the fundamental group is
+hyperbolic in the sense of Gromov the topological complexity of an aspherical
+space $K(\pi, 1)$ either equals or is by one larger than the cohomological
+dimension of $\pi\times \pi$. We approach the problem by studying essential
+cohomology classes, i.e. classes which can be obtained from the powers of the
+canonical class via coefficient homomorphisms. We describe a spectral sequence
+which allows to specify a full set of obstructions for a cohomology class to be
+essential. In the case of a hyperbolic group we establish a vanishing property
+of this spectral sequence which leads to the main result.
+",0,0,1,0,0,0
+8899,Computable Isomorphisms for Certain Classes of Infinite Graphs,"  We investigate (2,1):1 structures, which consist of a countable set $A$
+together with a function $f: A \to A$ such that for every element $x$ in $A$,
+$f$ maps either exactly one element or exactly two elements of $A$ to $x$.
+These structures extend the notions of injection structures, 2:1 structures,
+and (2,0):1 structures studied by Cenzer, Harizanov, and Remmel, all of which
+can be thought of as infinite directed graphs. We look at various
+computability-theoretic properties of (2,1):1 structures, most notably that of
+computable categoricity. We say that a structure $\mathcal{A}$ is computably
+categorical if there exists a computable isomorphism between any two computable
+copies of $\mathcal{A}$. We give a sufficient condition under which a (2,1):1
+structure is computably categorical, and present some examples of (2,1):1
+structures with different computability-theoretic properties.
+",0,0,1,0,0,0
+8900,Tug-of-War: Observations on Unified Content Handling,"  Modern applications and Operating Systems vary greatly with respect to how
+they register and identify different types of content. These discrepancies lead
+to exploits and inconsistencies in user experience. In this paper, we highlight
+the issues arising in the modern content handling ecosystem, and examine how
+the operating system can be used to achieve unified and consistent content
+identification.
+",1,0,0,0,0,0
+8901,An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization,"  We consider maximum likelihood estimation for Gaussian Mixture Models (Gmms).
+This task is almost invariably solved (in theory and practice) via the
+Expectation Maximization (EM) algorithm. EM owes its success to various
+factors, of which is its ability to fulfill positive definiteness constraints
+in closed form is of key importance. We propose an alternative to EM by
+appealing to the rich Riemannian geometry of positive definite matrices, using
+which we cast Gmm parameter estimation as a Riemannian optimization problem.
+Surprisingly, such an out-of-the-box Riemannian formulation completely fails
+and proves much inferior to EM. This motivates us to take a closer look at the
+problem geometry, and derive a better formulation that is much more amenable to
+Riemannian optimization. We then develop (Riemannian) batch and stochastic
+gradient algorithms that outperform EM, often substantially. We provide a
+non-asymptotic convergence analysis for our stochastic method, which is also
+the first (to our knowledge) such global analysis for Riemannian stochastic
+gradient. Numerous empirical results are included to demonstrate the
+effectiveness of our methods.
+",1,0,0,1,0,0
+8902,The Coprime Quantum Chain,"  In this paper we introduce and study the coprime quantum chain, i.e. a
+strongly correlated quantum system defined in terms of the integer eigenvalues
+$n_i$ of the occupation number operators at each site of a chain of length $M$.
+The $n_i$'s take value in the interval $[2,q]$ and may be regarded as $S_z$
+eigenvalues in the spin representation $j = (q-2)/2$. The distinctive
+interaction of the model is based on the coprimality matrix $\bf \Phi$: for the
+ferromagnetic case, this matrix assigns lower energy to configurations where
+occupation numbers $n_i$ and $n_{i+1}$ of neighbouring sites share a common
+divisor, while for the anti-ferromagnetic case it assigns lower energy to
+configurations where $n_i$ and $n_{i+1}$ are coprime. The coprime chain, both
+in the ferro and anti-ferromagnetic cases, may present an exponential number of
+ground states whose values can be exactly computed by means of graph
+theoretical tools. In the ferromagnetic case there are generally also
+frustration phenomena. A fine tuning of local operators may lift the
+exponential ground state degeneracy and, according to which operators are
+switched on, the system may be driven into different classes of universality,
+among which the Ising or Potts universality class. The paper also contains an
+appendix by Don Zagier on the exact eigenvalues and eigenvectors of the
+coprimality matrix in the limit $q \rightarrow \infty$.
+",0,1,1,0,0,0
+8903,Inverse cascades and resonant triads in rotating and stratified turbulence,"  Kraichnan seminal ideas on inverse cascades yielded new tools to study common
+phenomena in geophysical turbulent flows. In the atmosphere and the oceans,
+rotation and stratification result in a flow that can be approximated as
+two-dimensional at very large scales, but which requires considering
+three-dimensional effects to fully describe turbulent transport processes and
+non-linear phenomena. Motions can thus be classified into two classes: fast
+modes consisting of inertia-gravity waves, and slow quasi-geostrophic modes for
+which the Coriolis force and horizontal pressure gradients are close to
+balance. In this paper we review previous results on the strength of the
+inverse cascade in rotating and stratified flows, and then present new results
+on the effect of varying the strength of rotation and stratification (measured
+by the ratio $N/f$ of the Brunt-Väisäla frequency to the Coriolis
+frequency) on the amplitude of the waves and on the flow quasi-geostrophic
+behavior. We show that the inverse cascade is more efficient in the range of
+$N/f$ for which resonant triads do not exist, $1/2 \le N/f \le 2$. We then use
+the spatio-temporal spectrum, and characterization of the flow temporal and
+spatial scales, to show that in this range slow modes dominate the dynamics,
+while the strength of the waves (and their relevance in the flow dynamics) is
+weaker.
+",0,1,0,0,0,0
+8904,Towards a Rigorous Methodology for Measuring Adoption of RPKI Route Validation and Filtering,"  A proposal to improve routing security---Route Origin Authorization
+(ROA)---has been standardized. A ROA specifies which network is allowed to
+announce a set of Internet destinations. While some networks now specify ROAs,
+little is known about whether other networks check routes they receive against
+these ROAs, a process known as Route Origin Validation (ROV). Which networks
+blindly accept invalid routes? Which reject them outright? Which de-preference
+them if alternatives exist?
+Recent analysis attempts to use uncontrolled experiments to characterize ROV
+adoption by comparing valid routes and invalid routes. However, we argue that
+gaining a solid understanding of ROV adoption is impossible using currently
+available data sets and techniques. Our measurements suggest that, although
+some ISPs are not observed using invalid routes in uncontrolled experiments,
+they are actually using different routes for (non-security) traffic engineering
+purposes, without performing ROV. We conclude with a description of a
+controlled, verifiable methodology for measuring ROV and present three ASes
+that do implement ROV, confirmed by operators.
+",1,0,0,0,0,0
+8905,Half-quadratic transportation problems,"  We present a primal--dual memory efficient algorithm for solving a relaxed
+version of the general transportation problem. Our approach approximates the
+original cost function with a differentiable one that is solved as a sequence
+of weighted quadratic transportation problems. The new formulation allows us to
+solve differentiable, non-- convex transportation problems.
+",1,0,1,0,0,0
+8906,Entanglement induced interactions in binary mixtures,"  We establish a conceptual framework for the identification and the
+characterization of induced interactions in binary mixtures and reveal their
+intricate relation to entanglement between the components or species of the
+mixture. Exploiting an expansion in terms of the strength of the entanglement
+among the two species, enables us to deduce an effective single-species
+description. In this way, we naturally incorporate the mutual feedback of the
+species and obtain induced interactions for both species which are effectively
+present among the particles of same type. Importantly, our approach
+incorporates few-body and inhomogeneous systems extending the scope of induced
+interactions where two particles interact via a bosonic bath-type environment.
+Employing the example of a one-dimensional spin-polarized ultracold Bose-Fermi
+mixture, we obtain induced Bose-Bose and Fermi-Fermi interactions with
+short-range attraction and long-range repulsion. With this, we show how beyond
+species mean-field physics visible in the two-body correlation functions can be
+understood via the induced interactions.
+",0,1,0,0,0,0
+8907,Conditional fiducial models,"  The fiducial is not unique in general, but we prove that in a restricted
+class of models it is uniquely determined by the sampling distribution of the
+data. It depends in particular not on the choice of a data generating model.
+The arguments lead to a generalization of the classical formula found by Fisher
+(1930). The restricted class includes cases with discrete distributions, the
+case of the shape parameter in the Gamma distribution, and also the case of the
+correlation coefficient in a bivariate Gaussian model. One of the examples can
+also be used in a pedagogical context to demonstrate possible difficulties with
+likelihood-, Bayesian-, and bootstrap-inference. Examples that demonstrate
+non-uniqueness are also presented. It is explained that they can be seen as
+cases with restrictions on the parameter space. Motivated by this the concept
+of a conditional fiducial model is introduced. This class of models includes
+the common case of iid samples from a one-parameter model investigated by
+Hannig (2013), the structural group models investigated by Fraser (1968), and
+also certain models discussed by Fisher (1973) in his final writing on the
+subject.
+",0,0,1,1,0,0
+8908,Statistics students' identification of inferential model elements within contexts of their own invention,"  Statistical thinking partially depends upon an iterative process by which
+essential features of a problem setting are identified and mapped onto an
+abstract model or archetype, and then translated back into the context of the
+original problem setting (Wild and Pfannkuch 1999). Assessment in introductory
+statistics often relies on tasks that present students with data in context and
+expects them to choose and describe an appropriate model. This study explores
+post-secondary student responses to an alternative task that prompts students
+to clearly identify a sample, population, statistic, and parameter using a
+context of their own invention. The data include free text narrative responses
+of a random sample of 500 students from a sample of more than 1600 introductory
+statistics students. Results suggest that students' responses often portrayed
+sample and population accurately. Portrayals of statistic and parameter were
+less reliable and were associated with descriptions of a wide variety of other
+concepts. Responses frequently attributed a variable of some kind to the
+statistic, or a study design detail to the parameter. Implications for
+instruction and research are discussed, including a call for emphasis on a
+modeling paradigm in introductory statistics.
+",0,0,0,1,0,0
+8909,Mask R-CNN,"  We present a conceptually simple, flexible, and general framework for object
+instance segmentation. Our approach efficiently detects objects in an image
+while simultaneously generating a high-quality segmentation mask for each
+instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a
+branch for predicting an object mask in parallel with the existing branch for
+bounding box recognition. Mask R-CNN is simple to train and adds only a small
+overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to
+generalize to other tasks, e.g., allowing us to estimate human poses in the
+same framework. We show top results in all three tracks of the COCO suite of
+challenges, including instance segmentation, bounding-box object detection, and
+person keypoint detection. Without bells and whistles, Mask R-CNN outperforms
+all existing, single-model entries on every task, including the COCO 2016
+challenge winners. We hope our simple and effective approach will serve as a
+solid baseline and help ease future research in instance-level recognition.
+Code has been made available at: this https URL
+",1,0,0,0,0,0
+8910,An overview of the marine food web in Icelandic waters using Ecopath with Ecosim,"  Fishing activities have broad impacts that affect, although not exclusively,
+the targeted stocks. These impacts affect predators and prey of the harvested
+species, as well as the whole ecosystem it inhabits. Ecosystem models can be
+used to study the interactions that occur within a system, including those
+between different organisms and those between fisheries and targeted species.
+Trophic web models like Ecopath with Ecosim (EwE) can handle fishing fleets as
+a top predator, with top-down impact on harvested organisms. The aim of this
+study was to better understand the Icelandic marine ecosystem and the
+interactions within. This was done by constructing an EwE model of Icelandic
+waters. The model was run from 1984 to 2013 and was fitted to time series of
+biomass estimates, landings data and mean annual temperature. The final model
+was chosen by selecting the model with the lowest Akaike information criterion.
+A skill assessment was performed using the Pearson's correlation coefficient,
+the coefficient of determination, the modelling efficiency and the reliability
+index to evaluate the model performance. The model performed satisfactorily
+when simulating previously estimated biomass and known landings. Most of the
+groups with time series were estimated to have top-down control over their
+prey. These are harvested species with direct and/or indirect links to lower
+trophic levels and future fishing policies should take this into account. This
+model could be used as a tool to investigate how such policies could impact the
+marine ecosystem in Icelandic waters.
+",0,0,0,0,1,0
+8911,Character tables and the problem of existence of finite projective planes,"  Recently, the authors of the present work (together with M. N. Kolountzakis)
+introduced a new version of the non-commutative Delsarte scheme and applied it
+to the problem of mutually unbiased bases. Here we use this method to
+investigate the existence of a finite projective plane of a given order d. In
+particular, a short new proof is obtained for the nonexistence of a projective
+plane of order 6. For higher orders like 10 and 12, the method is non decisive
+but could turn out to give important supplementary informations.
+",0,0,1,0,0,0
+8912,A bilevel approach for optimal contract pricing of independent dispatchable DG units in distribution networks,"  Distributed Generation (DG) units are increasingly installed in the power
+systems. Distribution Companies (DisCo) can opt to purchase the electricity
+from DG in an energy purchase contract to supply the customer demand and reduce
+energy loss. This paper proposes a framework for optimal contract pricing of
+independent dispatchable DG units considering competition among them. While DG
+units tend to increase their profit from the energy purchase contract, DisCo
+minimizes the demand supply cost. Multi-leader follower game theory concept is
+used to analyze the situation in which competing DG units offer the energy
+price to DisCo and DisCo determines the DG generation. A bi-level approach is
+used to formulate the competition in which each DG problem is the upper-level
+problem and the DisCo problem is considered as the lower-level one. Combining
+the optimality conditions ofall upper-level problems with the lower level
+problem results in a multi-DG equilibrium problem formulated as an equilibrium
+problem with equilibrium constraints (EPEC). Using a nonlinear approach, the
+EPEC problem is reformulated as a single nonlinear optimization model which is
+simultaneously solved for all independent DG units. The proposed framework was
+applied to the Modified IEEE 34-Bus Distribution Test System. Performance and
+robustness of the proposed framework in determining econo-technically fare DG
+contract price has been demonstrated through a series of analyses.
+",1,0,0,0,0,0
+8913,Human-in-the-loop Artificial Intelligence,"  Little by little, newspapers are revealing the bright future that Artificial
+Intelligence (AI) is building. Intelligent machines will help everywhere.
+However, this bright future has a dark side: a dramatic job market contraction
+before its unpredictable transformation. Hence, in a near future, large numbers
+of job seekers will need financial support while catching up with these novel
+unpredictable jobs. This possible job market crisis has an antidote inside. In
+fact, the rise of AI is sustained by the biggest knowledge theft of the recent
+years. Learning AI machines are extracting knowledge from unaware skilled or
+unskilled workers by analyzing their interactions. By passionately doing their
+jobs, these workers are digging their own graves.
+In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI)
+as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward
+aware and unaware knowledge producers with a different scheme: decisions of AI
+systems generating revenues will repay the legitimate owners of the knowledge
+used for taking those decisions. As modern Robin Hoods, HIT-AI researchers
+should fight for a fairer Artificial Intelligence that gives back what it
+steals.
+",1,0,0,0,0,0
+8914,Increased adaptability to rapid environmental change can more than make up for the two-fold cost of males,"  The famous ""two-fold cost of sex"" is really the cost of anisogamy -- why
+should females mate with males who do not contribute resources to offspring,
+rather than isogamous partners who contribute equally? In typical anisogamous
+populations, a single very fit male can have an enormous number of offspring,
+far larger than is possible for any female or isogamous individual. If the
+sexual selection on males aligns with the natural selection on females,
+anisogamy thus allows much more rapid adaptation via super-successful males. We
+show via simulations that this effect can be sufficient to overcome the
+two-fold cost and maintain anisogamy against isogamy in populations adapting to
+environmental change. The key quantity is the variance in male fitness -- if
+this exceeds what is possible in an isogamous population, anisogamous
+populations can win out in direct competition by adapting faster.
+",0,0,0,0,1,0
+8915,Searching edges in the overlap of two plane graphs,"  Consider a pair of plane straight-line graphs, whose edges are colored red
+and blue, respectively, and let n be the total complexity of both graphs. We
+present a O(n log n)-time O(n)-space technique to preprocess such pair of
+graphs, that enables efficient searches among the red-blue intersections along
+edges of one of the graphs. Our technique has a number of applications to
+geometric problems. This includes: (1) a solution to the batched red-blue
+search problem [Dehne et al. 2006] in O(n log n) queries to the oracle; (2) an
+algorithm to compute the maximum vertical distance between a pair of 3D
+polyhedral terrains one of which is convex in O(n log n) time, where n is the
+total complexity of both terrains; (3) an algorithm to construct the Hausdorff
+Voronoi diagram of a family of point clusters in the plane in O((n+m) log^3 n)
+time and O(n+m) space, where n is the total number of points in all clusters
+and m is the number of crossings between all clusters; (4) an algorithm to
+construct the farthest-color Voronoi diagram of the corners of n axis-aligned
+rectangles in O(n log^2 n) time; (5) an algorithm to solve the stabbing circle
+problem for n parallel line segments in the plane in optimal O(n log n) time.
+All these results are new or improve on the best known algorithms.
+",1,0,0,0,0,0
+8916,Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigation,"  As more industries integrate machine learning into socially sensitive
+decision processes like hiring, loan-approval, and parole-granting, we are at
+risk of perpetuating historical and contemporary socioeconomic disparities.
+This is a critical problem because on the one hand, organizations who use but
+do not understand the discriminatory potential of such systems will facilitate
+the widening of social disparities under the assumption that algorithms are
+categorically objective. On the other hand, the responsible use of machine
+learning can help us measure, understand, and mitigate the implicit historical
+biases in socially sensitive data by expressing implicit decision-making mental
+models in terms of explicit statistical models. In this paper we specify,
+implement, and evaluate a ""fairness-aware"" machine learning interface called
+themis-ml, which is intended for use by individual data scientists and
+engineers, academic research teams, or larger product teams who use machine
+learning in production systems.
+",1,0,0,0,0,0
+8917,Supersymmetric field theories and geometric Langlands: The other side of the coin,"  This note announces results on the relations between the approach of
+Beilinson and Drinfeld to the geometric Langlands correspondence based on
+conformal field theory, the approach of Kapustin and Witten based on $N=4$ SYM,
+and the AGT-correspondence. The geometric Langlands correspondence is described
+as the Nekrasov-Shatashvili limit of a generalisation of the AGT-correspondence
+in the presence of surface operators. Following the approaches of Kapustin -
+Witten and Nekrasov - Witten we interpret some aspects of the resulting picture
+using an effective description in terms of two-dimensional sigma models having
+Hitchin's moduli spaces as target-manifold.
+",0,0,1,0,0,0
+8918,Non-dispersive conservative regularisation of nonlinear shallow water (and isothermal Euler) equations,"  A new regularisation of the shallow water (and isentropic Euler) equations is
+proposed. The regularised equations are non-dissipative, non-dispersive and
+possess a variational structure. Thus, the mass, the momentum and the energy
+are conserved. Hence, for instance, regularised hydraulic jumps are smooth and
+non-oscillatory. Another particularly interesting feature of this
+regularisation is that smoothed `shocks' propagates at exactly the same speed
+as the original discontinuous ones. The performance of the new model is
+illustrated numerically on some dam-break test cases, which are classical in
+the hyperbolic realm.
+",0,1,0,0,0,0
+8919,Some Identities associated with mock theta functions $ω(q)$ and $ν(q)$,"  Recently, Andrews, Dixit and Yee defined two partition functions
+$p_{\omega}(n)$ and $p_{\nu}(n)$ that are related with Ramanujan's mock theta
+functions $\omega(q)$ and $\nu(q)$, respectively. In this paper, we present two
+variable generalizations of their results. As an application, we reprove their
+results on $p_{\omega}(n)$ and $p_{\nu}(n)$ that are analogous to Euler's
+pentagonal number theorem.
+",0,0,1,0,0,0
+8920,Model order reduction for random nonlinear dynamical systems and low-dimensional representations for their quantities of interest,"  We examine nonlinear dynamical systems of ordinary differential equations or
+differential algebraic equations. In an uncertainty quantification, physical
+parameters are replaced by random variables. The inner variables as well as a
+quantity of interest are expanded into series with orthogonal basis functions
+like the polynomial chaos expansions, for example. On the one hand, the
+stochastic Galerkin method yields a large coupled dynamical system. On the
+other hand, a stochastic collocation method, which uses a quadrature rule or a
+sampling scheme, can be written in the form of a large weakly coupled dynamical
+system. We apply projection-based methods of nonlinear model order reduction to
+the large systems. A reduced-order model implies a low-dimensional
+representation of the quantity of interest. We focus on model order reduction
+by proper orthogonal decomposition. The error of a best approximation located
+in a low-dimensional subspace is analysed. We illustrate results of numerical
+computations for test examples.
+",0,0,1,0,0,0
+8921,Monomial generators of complete planar ideals,"  We provide an algorithm that computes a set of generators for any complete
+ideal in a smooth complex surface. More interestingly, these generators admit a
+presentation as monomials in a set of maximal contact elements associated to
+the minimal log-resolution of the ideal. Furthermore, the monomial expression
+given by our method is an equisingularity invariant of the ideal. As an
+outcome, we provide a geometric method to compute the integral closure of a
+planar ideal and we apply our algorithm to some families of complete ideals.
+",0,0,1,0,0,0
+8922,Optimization of a SSP's Header Bidding Strategy using Thompson Sampling,"  Over the last decade, digital media (web or app publishers) generalized the
+use of real time ad auctions to sell their ad spaces. Multiple auction
+platforms, also called Supply-Side Platforms (SSP), were created. Because of
+this multiplicity, publishers started to create competition between SSPs. In
+this setting, there are two successive auctions: a second price auction in each
+SSP and a secondary, first price auction, called header bidding auction,
+between SSPs.In this paper, we consider an SSP competing with other SSPs for ad
+spaces. The SSP acts as an intermediary between an advertiser wanting to buy ad
+spaces and a web publisher wanting to sell its ad spaces, and needs to define a
+bidding strategy to be able to deliver to the advertisers as many ads as
+possible while spending as little as possible. The revenue optimization of this
+SSP can be written as a contextual bandit problem, where the context consists
+of the information available about the ad opportunity, such as properties of
+the internet user or of the ad placement.Using classical multi-armed bandit
+strategies (such as the original versions of UCB and EXP3) is inefficient in
+this setting and yields a low convergence speed, as the arms are very
+correlated. In this paper we design and experiment a version of the Thompson
+Sampling algorithm that easily takes this correlation into account. We combine
+this bayesian algorithm with a particle filter, which permits to handle
+non-stationarity by sequentially estimating the distribution of the highest bid
+to beat in order to win an auction. We apply this methodology on two real
+auction datasets, and show that it significantly outperforms more classical
+approaches.The strategy defined in this paper is being developed to be deployed
+on thousands of publishers worldwide.
+",0,0,0,1,0,0
+8923,Mixed Rademacher and BPS Black Holes,"  Dyonic 1/4-BPS states in Type IIB string theory compactified on $\mathrm{K}3
+\times T^2$ are counted by meromorphic Jacobi forms. The finite parts of these
+functions, which are mixed mock Jacobi forms, account for the degeneracy of
+states stable throughout the moduli space of the compactification. In this
+paper, we obtain an exact asymptotic expansion for their Fourier coefficients,
+refining the Hardy-Ramanujan-Littlewood circle method to deal with their
+mixed-mock character. The result is compared to a low-energy supergravity
+computation of the exact entropy of extremal dyonic 1/4-BPS single-centered
+black holes, obtained by applying supersymmetric localization techniques to the
+quantum entropy function.
+",0,0,1,0,0,0
+8924,A generalization of the injectivity condition for Projected Entangled Pair States,"  We introduce a family of tensor network states that we term semi-injective
+Projected Entangled-Pair States (PEPS). They extend the class of injective PEPS
+and include other states, like the ground states of the AKLT and the CZX models
+in square lattices. We construct parent Hamiltonians for which semi-injective
+PEPS are unique ground states. We also determine the necessary and sufficient
+conditions for two tensors to generate the same family of such states in two
+spatial dimensions. Using this result, we show that the third cohomology
+labeling of Symmetry Protected Topological phases extends to semi-injective
+PEPS.
+",0,1,0,0,0,0
+8925,Limits to single photon transduction by a single atom: Non-Markov theory,"  Single atoms form a model system for understanding the limits of single
+photon detection. Here, we develop a non-Markov theory of single-photon
+absorption by a two-level atom to place limits on the absorption (transduction)
+time. We show the existence of a finite rise time in the probability of
+excitation of the atom during the absorption event which is infinitely fast in
+previous Markov theories. This rise time is governed by the bandwidth of the
+atom-field interaction spectrum and leads to a fundamental jitter in
+time-stamping the absorption event. Our theoretical framework captures both the
+weak and strong atom-field coupling regimes and sheds light on the spectral
+matching between the interaction bandwidth and single photon Fock state pulse
+spectrum. Our work opens questions whether such jitter in the absorption event
+can be observed in a multi-mode realistic single photon detector. Finally, we
+also shed light on the fundamental differences between linear and nonlinear
+detector outputs for single photon Fock state vs. coherent state pulses.
+",0,1,0,0,0,0
+8926,Quantifying the Contributions of Training Data and Algorithm Logic to the Performance of Automated Cause-assignment Algorithms for Verbal Autopsy,"  A verbal autopsy (VA) consists of a survey with a relative or close contact
+of a person who has recently died. VA surveys are commonly used to infer likely
+causes of death for individuals when deaths happen outside of hospitals or
+healthcare facilities. Several statistical and algorithmic methods are
+available to assign cause of death using VA surveys. Each of these methods
+require as inputs some information about the joint distribution of symptoms and
+causes. In this note, we examine the generalizability of this symptom-cause
+information by comparing different automated coding methods using various
+combinations of inputs and evaluation data. VA algorithm performance is
+affected by both the specific SCI themselves and the logic of a given
+algorithm. Using a variety of performance metrics for all existing VA
+algorithms, we demonstrate that in general the adequacy of the information
+about the joint distribution between symptoms and cause affects performance at
+least as much or more than algorithm logic.
+",0,0,0,1,0,0
+8927,Rydberg excitation of cold atoms inside a hollow core fiber,"  We report on a versatile, highly controllable hybrid cold Rydberg atom fiber
+interface, based on laser cooled atoms transported into a hollow core
+Kagomé crystal fiber. Our experiments are the first to demonstrate the
+feasibility of exciting cold Rydberg atoms inside a hollow core fiber and we
+study the influence of the fiber on Rydberg electromagnetically induced
+transparency (EIT) signals. Using a temporally resolved detection method to
+distinguish between excitation and loss, we observe two different regimes of
+the Rydberg excitations: one EIT regime and one regime dominated by atom loss.
+These results are a substantial advancement towards future use of our system
+for quantum simulation or information.
+",0,1,0,0,0,0
+8928,Dynamics and evolution of planets in mean-motion resonances,"  In some planetary systems the orbital periods of two of its members present a
+commensurability, usually known by mean-motion resonance. These resonances
+greatly enhance the mutual gravitational influence of the planets. As a
+consequence, these systems present uncommon behaviours and their motions need
+to be studied with specific methods. Some features are unique and allow us a
+better understanding and characterisation of these systems. Moreover,
+mean-motion resonances are a result of an early migration of the orbits in an
+accretion disk, so it is possible to derive constraints on their formation.
+Here we review the dynamics of a pair of resonant planets and explain how their
+orbits evolve in time. We apply our results to the HD45365 planetary system
+",0,1,1,0,0,0
+8929,The Gain in the Field of Two Electromagnetic Waves,"  We consider the motion of a nonrelativistic electron in the field of two
+strong monochromatic light waves propagating counter to each other. The matrix
+elements of emission and absorption are found. An expression is obtained for
+the gain of a weak test wave by using such matrix elements.
+",0,1,0,0,0,0
+8930,Periods and factors of weak model sets,"  There is a renewed interest in weak model sets due to their connection to
+$\mathcal B$-free systems, which emerged from Sarnak's program on the Möbius
+disjointness conjecture. Here we continue our recent investigation
+[arXiv:1511.06137] of the extended hull ${\mathcal M}^{\scriptscriptstyle
+G}_{\scriptscriptstyle W}$, a dynamical system naturally associated to a weak
+model set in an abelian group $G$ with relatively compact window $W$. For
+windows having a nowhere dense boundary (this includes compact windows), we
+identify the maximal equicontinuous factor of ${\mathcal M}^{\scriptscriptstyle
+G}_{\scriptscriptstyle W}$ and give a sufficient condition when ${\mathcal
+M}^{\scriptscriptstyle G}_{\scriptscriptstyle W}$ is an almost 1:1 extension of
+its maximal equicontinuous factor. If the window is measurable with positive
+Haar measure and is almost compact, then the system ${\mathcal
+M}^{\scriptscriptstyle G}_{\scriptscriptstyle W}$ equipped with its Mirsky
+measure is isomorphic to its Kronecker factor. For general nontrivial ergodic
+probability measures on ${\mathcal M}^{\scriptscriptstyle
+G}_{\scriptscriptstyle W}$, we provide a kind of lower bound for the Kronecker
+factor. All relevant factor systems are natural $G$-actions on quotient
+subgroups of the torus underlying the weak model set. These are obtained by
+factoring out suitable window periods. Our results are specialised to the usual
+hull of the weak model set, and they are also interpreted for ${\mathcal
+B}$-free systems.
+",0,0,1,0,0,0
+8931,The Individual Impact Index ($i^3$) Statistic: A Novel Article-Level Citation Metric,"  Citation metrics are analytic measures used to evaluate the usage, impact and
+dissemination of scientific research. Traditionally, citation metrics have been
+independently measured at each level of the publication pyramid, namely at the
+article-level, at the author-level, and at the journal-level. The most commonly
+used metrics have been focused on journal-level measurements, such as the
+Impact Factor and the Eigenfactor, as well as on researcher-level metrics like
+the Hirsch index (h-index) and i10 index. On the other hand, reliable
+article-level metrics are less widespread, and are often reserved to
+non-standardized and non-scientific characteristics of individual articles,
+such as views, citations, downloads, and mentions in social and news media.
+These characteristics are known as 'altmetrics'. However, when the number of
+views and citations are similar between two articles, no discriminating measure
+currently exists with which to assess and compare each articles' individual
+impact. Given the modern, exponentially growing scientific literature,
+scientists and readers of Science need optimized, reliable, objective methods
+for managing, measuring and comparing research outputs and individual
+publications. To this end, I hereby describe and propose a new standardized
+article-level metric henceforth known as the 'Individual Impact Index
+Statistic', or $i^3$ for short. The $i^3$ is a weighted algorithm that takes
+advantage of the peer-review process, and considers a number of characteristics
+of individual scientific publications in order to yield a standardized and
+readily comparable measure of impact and dissemination. The strengths,
+limitations, and potential uses of this novel metric are also discussed.
+",1,0,0,0,0,0
+8932,Time-of-Flight Three Dimensional Neutron Diffraction in Transmission Mode for Mapping Crystal Grain Structures,"  The physical properties of polycrystalline materials depend on their
+microstructure, which is the nano-to-centimeter-scale arrangement of phases and
+defects in their interior. Such microstructure depends on the shape,
+crystallographic phase and orientation, and interfacing of the grains
+constituting the material. This article presents a new non-destructive 3D
+technique to study bulk samples with sizes in the cm range with a resolution of
+hundred micrometers: time-of-flight three-dimensional neutron diffraction (ToF
+3DND). Compared to existing analogous X-ray diffraction techniques, ToF 3DND
+enables studies of samples that can be both larger in size and made of heavier
+elements. Moreover, ToF 3DND facilitates the use of complicated sample
+environments. The basic ToF 3DND setup, utilizing an imaging detector with high
+spatial and temporal resolution, can easily be implemented at a time-of-flight
+neutron beamline. The technique was developed and tested with data collected at
+the Materials and Life Science Experimental Facility of the Japan Proton
+Accelerator Complex (J-PARC) for an iron sample. We successfully reconstructed
+the shape of 108 grains and developed an indexing procedure. The reconstruction
+algorithms have been validated by reconstructing two stacked Co-Ni-Ga single
+crystals and by comparison with a grain map obtained by post-mortem electron
+backscatter diffraction (EBSD).
+",0,1,0,0,0,0
+8933,Integrable $sl(\infty)$-modules and Category $\mathcal O$ for $\mathfrak{gl}(m|n)$,"  We introduce and study new categories T(g,k)of integrable sl(\infty)-modules
+which depend on the choice of a certain reductive subalgebra k in g=sl(\infty).
+The simple objects of these categories are tensor modules as in the previously
+studied category, however, the choice of k provides more flexibility of
+nonsimple modules. We then choose k to have two infinite-dimensional diagonal
+blocks, and show that a certain injective object K(m|n) in T(g,k) realizes a
+categorical sl(\infty)-action on the integral category O(m|n) of the Lie
+superalgebra gl(m|n). We show that the socle of K(m|n) is generated by the
+projective modules in O(m|n), and compute the socle filtration of K(m|n)
+explicitly. We conjecture that the socle filtration of K(m|n) reflects a
+""degree of atypicality filtration"" on the category O(m|n). We also conjecture
+that a natural tensor filtration on K(m|n) arises via the Duflo--Serganova
+functor sending the category O(m|n) to O(m-1|n-1). We prove this latter
+conjecture for a direct summand of K(m|n) corresponding to the
+finite-dimensional gl(m|n)-modules.
+",0,0,1,0,0,0
+8934,Island dynamics and anisotropy during vapor phase epitaxy of m-plane GaN,"  Using in situ grazing-incidence x-ray scattering, we have measured the
+diffuse scattering from islands that form during layer-by-layer growth of GaN
+by metal-organic vapor phase epitaxy on the (1010) m-plane surface. The diffuse
+scattering is extended in the (0001) in-plane direction in reciprocal space,
+indicating a strong anisotropy with islands elongated along [1 $\overline{2}$
+10] and closely spaced along [0001]. This is confirmed by atomic force
+microscopy of a quenched sample. Islands were characterized as a function of
+growth rate G and temperature. The island spacing along [0001] observed during
+the growth of the first monolayer obeys a power-law dependence on growth rate
+G$^{-n}$, with an exponent $n = 0.25 \pm 0.02$. Results are in agreement with
+recent kinetic Monte Carlo simulations, indicating that elongated islands
+result from the dominant anisotropy in step edge energy and not from surface
+diffusion anisotropy. The observed power-law exponent can be explained using a
+simple steady-state model, which gives n = 1/4.
+",0,1,0,0,0,0
+8935,Komlós-Major-Tusnády approximations to increments of uniform empirical processes,"  The well-known Komlós-Major-Tusnády inequalities [Z. Wahrsch. Verw.
+Gebiete 32 (1975) 111-131; Z. Wahrsch. Verw. Gebiete 34 (1976) 33-58] provide
+sharp inequalities to partial sums of iid standard exponential random variables
+by a sequence of standard Brownian motions. In this paper, we employ these
+results to establish Gaussian approximations to weighted increments of uniform
+empirical and quantile processes. This approach provides rates to the
+approximations which, among others, have direct applications to statistics of
+extreme values for randomly censored data.
+",0,0,1,1,0,0
+8936,Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation,"  We consider the problem of learning the level set for which a noisy black-box
+function exceeds a given threshold.
+To efficiently reconstruct the level set, we investigate Gaussian process
+(GP) metamodels. Our focus is on strongly stochastic samplers, in particular
+with heavy-tailed simulation noise and low signal-to-noise ratio.
+To guard against noise misspecification, we assess the performance of three
+variants: (i) GPs with Student-$t$ observations; (ii) Student-$t$ processes
+(TPs); and (iii) classification GPs modeling the sign of the response. As a
+fourth extension, we study GP surrogates with monotonicity constraints that are
+relevant when the level set is known to be connected. In conjunction with these
+metamodels, we analyze several acquisition functions for guiding the sequential
+experimental designs, extending existing stepwise uncertainty reduction
+criteria to the stochastic contour-finding context. This also motivates our
+development of (approximate) updating formulas to efficiently compute such
+acquisition functions. Our schemes are benchmarked by using a variety of
+synthetic experiments in 1--6 dimensions. We also consider an application of
+level set estimation for determining the optimal exercise policy and valuation
+of Bermudan options in finance.
+",0,0,0,1,0,0
+8937,MTBase: Optimizing Cross-Tenant Database Queries,"  In the last decade, many business applications have moved into the cloud. In
+particular, the ""database-as-a-service"" paradigm has become mainstream. While
+existing multi-tenant data management systems focus on single-tenant query
+processing, we believe that it is time to rethink how queries can be processed
+across multiple tenants in such a way that we do not only gain more valuable
+insights, but also at minimal cost. As we will argue in this paper, standard
+SQL semantics are insufficient to process cross-tenant queries in an
+unambiguous way, which is why existing systems use other, expensive means like
+ETL or data integration. We first propose MTSQL, a set of extensions to
+standard SQL, which fixes the ambiguity problem. Next, we present MTBase, a
+query processing middleware that efficiently processes MTSQL on top of SQL. As
+we will see, there is a canonical, provably correct, rewrite algorithm from
+MTSQL to SQL, which may however result in poor query execution performance,
+even on high-performance database products. We further show that with
+carefully-designed optimizations, execution times can be reduced in such ways
+that the difference to single-tenant queries becomes marginal.
+",1,0,0,0,0,0
+8938,Mechanism Deduction from Noisy Chemical Reaction Networks,"  We introduce KiNetX, a fully automated meta-algorithm for the kinetic
+analysis of complex chemical reaction networks derived from semi-accurate but
+efficient electronic structure calculations. It is designed to (i) accelerate
+the automated exploration of such networks, and (ii) cope with model-inherent
+errors in electronic structure calculations on elementary reaction steps. We
+developed and implemented KiNetX to possess three features. First, KiNetX
+evaluates the kinetic relevance of every species in a (yet incomplete) reaction
+network to confine the search for new elementary reaction steps only to those
+species that are considered possibly relevant. Second, KiNetX identifies and
+eliminates all kinetically irrelevant species and elementary reactions to
+reduce a complex network graph to a comprehensible mechanism. Third, KiNetX
+estimates the sensitivity of species concentrations toward changes in
+individual rate constants (derived from relative free energies), which allows
+us to systematically select the most efficient electronic structure model for
+each elementary reaction given a predefined accuracy. The novelty of KiNetX
+consists in the rigorous propagation of correlated free-energy uncertainty
+through all steps of our kinetic analyis. To examine the performance of KiNetX,
+we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction
+networks by encoding chemical logic into their underlying graph structure.
+AutoNetGen allows us to consider a vast number of distinct chemistry-like
+scenarios and, hence, to discuss assess the importance of rigorous uncertainty
+propagation in a statistical context. Our results reveal that KiNetX reliably
+supports the deduction of product ratios, dominant reaction pathways, and
+possibly other network properties from semi-accurate electronic structure data.
+",0,0,0,0,1,0
+8939,Structural scale $q-$derivative and the LLG-Equation in a scenario with fractionality,"  In the present contribution, we study the Landau-Lifshitz-Gilbert equation
+with two versions of structural derivatives recently proposed: the scale
+$q-$derivative in the non-extensive statistical mechanics and the axiomatic
+metric derivative, which presents Mittag-Leffler functions as eigenfunctions.
+The use of structural derivatives aims to take into account long-range forces,
+possible non-manifest or hidden interactions and the dimensionality of space.
+Having this purpose in mind, we build up an evolution operator and a deformed
+version of the LLG equation. Damping in the oscillations naturally show up
+without an explicit Gilbert damping term.
+",0,1,1,0,0,0
+8940,Analog Optical Computing by Half-Wavelength Slabs,"  A new approach to perform analog optical differentiation is presented using
+half-wavelength slabs. First, a half-wavelength dielectric slab is used to
+design a first order differentiator. The latter works properly for both major
+polarizations, in contrast to designs based on Brewster effect [Opt. Lett. 41,
+3467 (2016)]. Inspired by the proposed dielectric differentiator, and by
+exploiting the unique features of graphene, we further design and demonstrate a
+reconfigurable and highly miniaturized differentiator using a half-wavelength
+plasmonic graphene film. To the best of our knowledge, our proposed
+graphene-based differentiator is even smaller than the most compact
+differentiator presented so far [Opt. Lett. 40, 5239 (2015)].
+",0,1,0,0,0,0
+8941,A Separation Principle for Control in the Age of Deep Learning,"  We review the problem of defining and inferring a ""state"" for a control
+system based on complex, high-dimensional, highly uncertain measurement streams
+such as videos. Such a state, or representation, should contain all and only
+the information needed for control, and discount nuisance variability in the
+data. It should also have finite complexity, ideally modulated depending on
+available resources. This representation is what we want to store in memory in
+lieu of the data, as it ""separates"" the control task from the measurement
+process. For the trivial case with no dynamics, a representation can be
+inferred by minimizing the Information Bottleneck Lagrangian in a function
+class realized by deep neural networks. The resulting representation has much
+higher dimension than the data, already in the millions, but it is smaller in
+the sense of information content, retaining only what is needed for the task.
+This process also yields representations that are invariant to nuisance factors
+and having maximally independent components. We extend these ideas to the
+dynamic case, where the representation is the posterior density of the task
+variable given the measurements up to the current time, which is in general
+much simpler than the prediction density maintained by the classical Bayesian
+filter. Again this can be finitely-parametrized using a deep neural network,
+and already some applications are beginning to emerge. No explicit assumption
+of Markovianity is needed; instead, complexity trades off approximation of an
+optimal representation, including the degree of Markovianity.
+",1,0,0,1,0,0
+8942,Improving Neural Network Quantization using Outlier Channel Splitting,"  Quantization can improve the execution latency and energy efficiency of
+neural networks on both commodity GPUs and specialized accelerators. The
+majority of existing literature focuses on training quantized DNNs, while this
+work examines the less-studied topic of quantizing a floating-point model
+without (re)training. DNN weights and activations follow a bell-shaped
+distribution post-training, while practical hardware uses a linear quantization
+grid. This leads to challenges in dealing with outliers in the distribution.
+Prior work has addressed this by clipping the outliers or using specialized
+hardware. In this work, we propose outlier channel splitting (OCS), which
+duplicates channels containing outliers, then halves the channel values. The
+network remains functionally identical, but affected outliers are moved toward
+the center of the distribution. OCS requires no additional training and works
+on commodity hardware. Experimental evaluation on ImageNet classification and
+language modeling shows that OCS can outperform state-of-the-art clipping
+techniques with only minor overhead.
+",1,0,0,1,0,0
+8943,On some further properties and application of Weibull-R family of distributions,"  In this paper, we provide some new results for the Weibull-R family of
+distributions (Alzaghal, Ghosh and Alzaatreh (2016)). We derive some new
+structural properties of the Weibull-R family of distributions. We provide
+various characterizations of the family via conditional moments, some functions
+of order statistics and via record values.
+",0,0,1,1,0,0
+8944,Rejection of the principle of material frame indifference,"  The principle of material frame indifference is shown to be incompatible with
+the basic balance laws of continuum mechanics. In its role of providing
+constraints on possible constitutive prescriptions it must be replaced by the
+classical principle of Galilean invariance.
+",0,1,0,0,0,0
+8945,Construction of flows of finite-dimensional algebras,"  Recently, we introduced the notion of flow (depending on time) of
+finite-dimensional algebras. A flow of algebras (FA) is a particular case of a
+continuous-time dynamical system whose states are finite-dimensional algebras
+with (cubic) matrices of structural constants satisfying an analogue of the
+Kolmogorov-Chapman equation (KCE). Since there are several kinds of
+multiplications between cubic matrices one has fix a multiplication first and
+then consider the KCE with respect to the fixed multiplication. The existence
+of a solution for the KCE provides the existence of an FA. In this paper our
+aim is to find sufficient conditions on the multiplications under which the
+corresponding KCE has a solution. Mainly our conditions are given on the
+algebra of cubic matrices (ACM) considered with respect to a fixed
+multiplication of cubic matrices. Under some assumptions on the ACM (e.g. power
+associative, unital, associative, commutative) we describe a wide class of FAs,
+which contain algebras of arbitrary finite dimension. In particular, adapting
+the theory of continuous-time Markov processes, we construct a class of FAs
+given by the matrix exponent of cubic matrices. Moreover, we remarkably extend
+the set of FAs given with respect to the Maksimov's multiplications of our
+previous paper (J. Algebra 470 (2017) 263--288). For several FAs we study the
+time-dependent behavior (dynamics) of the algebras. We derive a system of
+differential equations for FAs.
+",0,0,1,0,0,0
+8946,PdBI U/LIRG Survey (PULS): Dense Molecular Gas in Arp 220 and NGC 6240,"  Aims. We present new IRAM Plateau de Bure Interferometer observations of Arp
+220 in HCN, HCO$^{+}$, HN$^{13}$C J=1-0, C$_{2}$H N=1-0, SiO J = 2-1, HNCO
+J$_{k,k'}$ = 5$_{0,4}$ - 4$_{0,4}$, CH$_{3}$CN(6-5), CS J=2-1 and 5-4 and
+$^{13}$CO J=1-0 and 2-1 and of NGC 6240 in HCN, HCO$^{+}$ J = 1-0 and C$_{2}$H
+N = 1-0. In addition, we present Atacama Large Millimeter/submillmeter Array
+science verification observations of Arp 220 in CS J = 4-3 and
+CH$_{3}$CN(10-9). Various lines are used to analyse the physical conditions of
+the molecular gas including the [$^{12}$CO]/[$^{13}$CO] and
+[$^{12}$CO]/[C$^{18}$O] abundance ratios. These observations will be made
+available to the public. Methods. We create brightness temperature line ratio
+maps to present the different physical conditions across Arp 220 and NGC 6240.
+In addition, we use the radiative transfer code RADEX and a Monte Carlo Markov
+Chain likelihood code to model the $^{12}$CO, $^{13}$CO and C$^{18}$O lines of
+Arp 220 at ~2"" (~700 pc) scales, where the $^{12}$CO and C$^{18}$O measurements
+were obtained from literature. Results. Line ratios of optically thick lines
+such as $^{12}$CO show smoothly varying ratios while the line ratios of
+optically thin lines such as $^{13}$CO show a east-west gradient across Arp
+220. The HCN/HCO$^{+}$ line ratio differs between Arp 220 and NGC 6240, where
+Arp 220 has line ratios above 2 and NGC 6240 below 1. The radiative transfer
+analysis solution is consistent with a warm (~40 K), moderately dense
+(~10$^{3.4}$ cm$^{-3}$) molecular gas component averaged over the two nuclei.
+We find [$^{12}$CO]/[$^{13}$CO] and [$^{12}$CO]/[C$^{18}$O] abundance ratios of
+~90 for both. The abundance enhancement of C$^{18}$O can be explained by
+stellar nucleosynthesis enrichment of the interstellar medium.
+",0,1,0,0,0,0
+8947,The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis,"  In recent years, mobile devices (e.g., smartphones and tablets) have met an
+increasing commercial success and have become a fundamental element of the
+everyday life for billions of people all around the world. Mobile devices are
+used not only for traditional communication activities (e.g., voice calls and
+messages) but also for more advanced tasks made possible by an enormous amount
+of multi-purpose applications (e.g., finance, gaming, and shopping). As a
+result, those devices generate a significant network traffic (a consistent part
+of the overall Internet traffic). For this reason, the research community has
+been investigating security and privacy issues that are related to the network
+traffic generated by mobile devices, which could be analyzed to obtain
+information useful for a variety of goals (ranging from device security and
+network optimization, to fine-grained user profiling).
+In this paper, we review the works that contributed to the state of the art
+of network traffic analysis targeting mobile devices. In particular, we present
+a systematic classification of the works in the literature according to three
+criteria: (i) the goal of the analysis; (ii) the point where the network
+traffic is captured; and (iii) the targeted mobile platforms. In this survey,
+we consider points of capturing such as Wi-Fi Access Points, software
+simulation, and inside real mobile devices or emulators. For the surveyed
+works, we review and compare analysis techniques, validation methods, and
+achieved results. We also discuss possible countermeasures, challenges and
+possible directions for future research on mobile traffic analysis and other
+emerging domains (e.g., Internet of Things). We believe our survey will be a
+reference work for researchers and practitioners in this research field.
+",1,0,0,0,0,0
+8948,Non-Ergodic Delocalization in the Rosenzweig-Porter Model,"  We consider the Rosenzweig-Porter model $H = V + \sqrt{T}\, \Phi$, where $V$
+is a $N \times N$ diagonal matrix, $\Phi$ is drawn from the $N \times N$
+Gaussian Orthogonal Ensemble, and $N^{-1} \ll T \ll 1$. We prove that the
+eigenfunctions of $H$ are typically supported in a set of approximately $NT$
+sites, thereby confirming the existence of a previously conjectured non-ergodic
+delocalized phase. Our proof is based on martingale estimates along the
+characteristic curves of the stochastic advection equation satisfied by the
+local resolvent of the Brownian motion representation of $H$.
+",0,1,0,0,0,0
+8949,MUDA: A Truthful Multi-Unit Double-Auction Mechanism,"  In a seminal paper, McAfee (1992) presented a truthful mechanism for double
+auctions, attaining asymptotically-optimal gain-from-trade without any prior
+information on the valuations of the traders. McAfee's mechanism handles
+single-parametric agents, allowing each seller to sell a single unit and each
+buyer to buy a single unit. This paper presents a double-auction mechanism that
+handles multi-parametric agents and allows multiple units per trader, as long
+as the valuation functions of all traders have decreasing marginal returns. The
+mechanism is prior-free, ex-post individually-rational, dominant-strategy
+truthful and strongly-budget-balanced. Its gain-from-trade approaches the
+optimum when the market size is sufficiently large.
+",1,0,0,0,0,0
+8950,Multi-variable LSTM neural network for autoregressive exogenous model,"  In this paper, we propose multi-variable LSTM capable of accurate forecasting
+and variable importance interpretation for time series with exogenous
+variables. Current attention mechanism in recurrent neural networks mostly
+focuses on the temporal aspect of data and falls short of characterizing
+variable importance. To this end, the multi-variable LSTM equipped with
+tensorized hidden states is developed to learn hidden states for individual
+variables, which give rise to our mixture temporal and variable attention.
+Based on such attention mechanism, we infer and quantify variable importance.
+Extensive experiments using real datasets with Granger-causality test and the
+synthetic dataset with ground truth demonstrate the prediction performance and
+interpretability of multi-variable LSTM in comparison to a variety of
+baselines. It exhibits the prospect of multi-variable LSTM as an end-to-end
+framework for both forecasting and knowledge discovery.
+",0,0,0,1,0,0
+8951,Spectral Image Visualization Using Generative Adversarial Networks,"  Spectral images captured by satellites and radio-telescopes are analyzed to
+obtain information about geological compositions distributions, distant asters
+as well as undersea terrain. Spectral images usually contain tens to hundreds
+of continuous narrow spectral bands and are widely used in various fields. But
+the vast majority of those image signals are beyond the visible range, which
+calls for special visualization technique. The visualizations of spectral
+images shall convey as much information as possible from the original signal
+and facilitate image interpretation. However, most of the existing visualizatio
+methods display spectral images in false colors, which contradict with human's
+experience and expectation. In this paper, we present a novel visualization
+generative adversarial network (GAN) to display spectral images in natural
+colors. To achieve our goal, we propose a loss function which consists of an
+adversarial loss and a structure loss. The adversarial loss pushes our solution
+to the natural image distribution using a discriminator network that is trained
+to differentiate between false-color images and natural-color images. We also
+use a cycle loss as the structure constraint to guarantee structure
+consistency. Experimental results show that our method is able to generate
+structure-preserved and natural-looking visualizations.
+",0,0,0,1,0,0
+8952,Simulations for 21 cm radiation lensing at EoR redshifts,"  We introduce simulations aimed at assessing how well weak gravitational
+lensing of 21cm radiation from the Epoch of Reionization ($z \sim 8$) can be
+measured by an SKA-like radio telescope. A simulation pipeline has been
+implemented to study the performance of lensing reconstruction techniques. We
+show how well the lensing signal can be reconstructed using the
+three-dimensional quadratic lensing estimator in Fourier space assuming
+different survey strategies. The numerical code introduced in this work is
+capable of dealing with issues that can not be treated analytically such as the
+discreteness of visibility measurements and the inclusion of a realistic model
+for the antennae distribution. This paves the way for future numerical studies
+implementing more realistic reionization models, foreground subtraction
+schemes, and testing the performance of lensing estimators that take into
+account the non-Gaussian distribution of HI after reionization. If multiple
+frequency channels covering $z \sim 7-11.6$ are combined, Phase 1 of SKA-Low
+should be able to obtain good quality images of the lensing potential with a
+total resolution of $\sim 1.6$ arcmin. The SKA-Low Phase 2 should be capable of
+providing images with high-fidelity even using data from $z\sim 7.7 - 8.3$. We
+perform tests aimed at evaluating the numerical implementation of the mapping
+reconstruction. We also discuss the possibility of measuring an accurate
+lensing power spectrum. Combining data from $z \sim 7-11.6$ using the SKA2-Low
+telescope model, we find constraints comparable to sample variance in the range
+$L<1000$, even for survey areas as small as $25\mbox{ deg}^2$.
+",0,1,0,0,0,0
+8953,Coverage characteristics of self-repelling random walks in mobile ad-hoc networks,"  A self-repelling random walk of a token on a graph is one in which at each
+step, the token moves to a neighbor that has been visited least often (with
+ties broken randomly). The properties of self-repelling random walks have been
+analyzed for two dimensional lattices and these walks have been shown to
+exhibit a remarkable uniformity with which they visit nodes in a graph. In this
+paper, we extend this analysis to self-repelling random walks on mobile
+networks in which the underlying graph itself is temporally evolving. Using
+network simulations in ns-3, we characterize the number of times each node is
+visited from the start until all nodes have been visited at least once. We
+evaluate under different mobility models and on networks ranging from 100 to
+1000 nodes. Our results show that until about 85% coverage, duplicate visits
+are very rare highlighting the efficiency with which a majority of nodes in the
+network can be visited. Even at 100% coverage, the exploration overhead (the
+ratio of number of steps to number of unique visited nodes) remains low and
+under 2. Our analysis shows that self-repelling random walks are effective,
+structure-free tools for data aggregation in mobile ad-hoc networks.
+",1,0,0,0,0,0
+8954,Model Agnostic Time Series Analysis via Matrix Estimation,"  We propose an algorithm to impute and forecast a time series by transforming
+the observed time series into a matrix, utilizing matrix estimation to recover
+missing values and de-noise observed entries, and performing linear regression
+to make predictions. At the core of our analysis is a representation result,
+which states that for a large model class, the transformed time series matrix
+is (approximately) low-rank. In effect, this generalizes the widely used
+Singular Spectrum Analysis (SSA) in time series literature, and allows us to
+establish a rigorous link between time series analysis and matrix estimation.
+The key to establishing this link is constructing a Page matrix with
+non-overlapping entries rather than a Hankel matrix as is commonly done in the
+literature (e.g., SSA). This particular matrix structure allows us to provide
+finite sample analysis for imputation and prediction, and prove the asymptotic
+consistency of our method. Another salient feature of our algorithm is that it
+is model agnostic with respect to both the underlying time dynamics and the
+noise distribution in the observations. The noise agnostic property of our
+approach allows us to recover the latent states when only given access to noisy
+and partial observations a la a Hidden Markov Model; e.g., recovering the
+time-varying parameter of a Poisson process without knowing that the underlying
+process is Poisson. Furthermore, since our forecasting algorithm requires
+regression with noisy features, our approach suggests a matrix estimation based
+method - coupled with a novel, non-standard matrix estimation error metric - to
+solve the error-in-variable regression problem, which could be of interest in
+its own right. Through synthetic and real-world datasets, we demonstrate that
+our algorithm outperforms standard software packages (including R libraries) in
+the presence of missing data as well as high levels of noise.
+",0,0,0,1,0,0
+8955,Efficient motion planning for problems lacking optimal substructure,"  We consider the motion-planning problem of planning a collision-free path of
+a robot in the presence of risk zones. The robot is allowed to travel in these
+zones but is penalized in a super-linear fashion for consecutive accumulative
+time spent there. We suggest a natural cost function that balances path length
+and risk-exposure time. Specifically, we consider the discrete setting where we
+are given a graph, or a roadmap, and we wish to compute the minimal-cost path
+under this cost function. Interestingly, paths defined using our cost function
+do not have an optimal substructure. Namely, subpaths of an optimal path are
+not necessarily optimal. Thus, the Bellman condition is not satisfied and
+standard graph-search algorithms such as Dijkstra cannot be used. We present a
+path-finding algorithm, which can be seen as a natural generalization of
+Dijkstra's algorithm. Our algorithm runs in $O\left((n_B\cdot n) \log( n_B\cdot
+n) + n_B\cdot m\right)$ time, where~$n$ and $m$ are the number of vertices and
+edges of the graph, respectively, and $n_B$ is the number of intersections
+between edges and the boundary of the risk zone. We present simulations on
+robotic platforms demonstrating both the natural paths produced by our cost
+function and the computational efficiency of our algorithm.
+",1,0,0,0,0,0
+8956,Bayesian Optimal Data Detector for mmWave OFDM System with Low-Resolution ADC,"  Orthogonal frequency division multiplexing (OFDM) has been widely used in
+communication systems operating in the millimeter wave (mmWave) band to combat
+frequency-selective fading and achieve multi-Gbps transmissions, such as IEEE
+802.15.3c and IEEE 802.11ad. For mmWave systems with ultra high sampling rate
+requirements, the use of low-resolution analog-to-digital converters (ADCs)
+(i.e., 1-3 bits) ensures an acceptable level of power consumption and system
+costs. However, orthogonality among sub-channels in the OFDM system cannot be
+maintained because of the severe non-linearity caused by low-resolution ADC,
+which renders the design of data detector challenging. In this study, we
+develop an efficient algorithm for optimal data detection in the mmWave OFDM
+system with low-resolution ADCs. The analytical performance of the proposed
+detector is derived and verified to achieve the fundamental limit of the
+Bayesian optimal design. On the basis of the derived analytical expression, we
+further propose a power allocation (PA) scheme that seeks to minimize the
+average symbol error rate. In addition to the optimal data detector, we also
+develop a feasible channel estimation method, which can provide high-quality
+channel state information without significant pilot overhead. Simulation
+results confirm the accuracy of our analysis and illustrate that the
+performance of the proposed detector in conjunction with the proposed PA scheme
+is close to the optimal performance of the OFDM system with infinite-resolution
+ADC.
+",1,0,0,0,0,0
+8957,A second-order stochastic maximum principle for generalized mean-field control problem,"  In this paper, we study the generalized mean-field stochastic control problem
+when the usual stochastic maximum principle (SMP) is not applicable due to the
+singularity of the Hamiltonian function. In this case, we derive a second order
+SMP. We introduce the adjoint process by the generalized mean-field backward
+stochastic differential equation. The keys in the proofs are the expansion of
+the cost functional in terms of a perturbation parameter, and the use of the
+range theorem for vector-valued measures.
+",0,0,1,0,0,0
+8958,Representation of I(1) and I(2) autoregressive Hilbertian processes,"  We extend the Granger-Johansen representation theorems for I(1) and I(2)
+vector autoregressive processes to accommodate processes that take values in an
+arbitrary complex separable Hilbert space. This more general setting is of
+central relevance for statistical applications involving functional time
+series. We first obtain a range of necessary and sufficient conditions for a
+pole in the inverse of a holomorphic index-zero Fredholm operator pencil to be
+of first or second order. Those conditions form the basis for our development
+of I(1) and I(2) representations of autoregressive Hilbertian processes.
+Cointegrating and attractor subspaces are characterized in terms of the
+behavior of the autoregressive operator pencil in a neighborhood of one.
+",0,0,1,1,0,0
+8959,Constraining cosmology with the velocity function of low-mass galaxies,"  The number density of field galaxies per rotation velocity, referred to as
+the velocity function, is an intriguing statistical measure probing the
+smallest scales of structure formation. In this paper we point out that the
+velocity function is sensitive to small shifts in key cosmological parameters
+such as the amplitude of primordial perturbations ($\sigma_8$) or the total
+matter density ($\Omega_{\rm m}$). Using current data and applying conservative
+assumptions about baryonic effects, we show that the observed velocity function
+of the Local Volume favours cosmologies in tension with the measurements from
+Planck but in agreement with the latest findings from weak lensing surveys.
+While the current systematics regarding the relation between observed and true
+rotation velocities are potentially important, upcoming data from HI surveys as
+well as new insights from hydrodynamical simulations will dramatically improve
+the situation in the near future.
+",0,1,0,0,0,0
+8960,On the coefficients of symmetric power $L$-functions,"  We study the signs of the Fourier coefficients of a newform. Let $f$ be a
+normalized newform of weight $k$ for $\Gamma_0(N)$. Let $a_f(n)$ be the $n$th
+Fourier coefficient of $f$. For any fixed positive integer $m$, we study the
+distribution of the signs of $\{a_f(p^m)\}_p$, where $p$ runs over all prime
+numbers. We also find out the abscissas of absolute convergence of two
+Dirichlet series with coefficients involving the Fourier coefficients of cusp
+forms and the coefficients of symmetric power $L$-functions.
+",0,0,1,0,0,0
+8961,Spin-resolved electronic structure of ferroelectric α-GeTe and multiferroic Ge1-xMnxTe,"  Germanium telluride features special spin-electric effects originating from
+spin-orbit coupling and symmetry breaking by the ferroelectric lattice
+polarization, which opens up many prospectives for electrically tunable and
+switchable spin electronic devices. By Mn doping of the {\alpha}-GeTe host
+lattice, the system becomes a multiferroic semiconductor possessing
+magnetoelectric properties in which the electric polarization, magnetization
+and spin texture are coupled to each other. Employing spin- and angle-resolved
+photoemission spectroscopy in bulk- and surface-sensitive energy ranges and by
+varying dipole transition matrix elements, we disentangle the bulk, surface and
+surface-resonance states of the electronic structure and determine the spin
+textures for selected parameters. From our results, we derive a comprehensive
+model of the {\alpha}-GeTe surface electronic structure which fits experimental
+data and first principle theoretical predictions and we discuss the
+unconventional evolution of the Rashba-type spin splitting upon manipulation by
+external B- and E-fields.
+",0,1,0,0,0,0
+8962,Recurrent Scene Parsing with Perspective Understanding in the Loop,"  Objects may appear at arbitrary scales in perspective images of a scene,
+posing a challenge for recognition systems that process images at a fixed
+resolution. We propose a depth-aware gating module that adaptively selects the
+pooling field size in a convolutional network architecture according to the
+object scale (inversely proportional to the depth) so that small details are
+preserved for distant objects while larger receptive fields are used for those
+nearby. The depth gating signal is provided by stereo disparity or estimated
+directly from monocular input. We integrate this depth-aware gating into a
+recurrent convolutional neural network to perform semantic segmentation. Our
+recurrent module iteratively refines the segmentation results, leveraging the
+depth and semantic predictions from the previous iterations.
+Through extensive experiments on four popular large-scale RGB-D datasets, we
+demonstrate this approach achieves competitive semantic segmentation
+performance with a model which is substantially more compact. We carry out
+extensive analysis of this architecture including variants that operate on
+monocular RGB but use depth as side-information during training, unsupervised
+gating as a generic attentional mechanism, and multi-resolution gating. We find
+that gated pooling for joint semantic segmentation and depth yields
+state-of-the-art results for quantitative monocular depth estimation.
+",1,0,0,0,0,0
+8963,Child-sized 3D Printed igus Humanoid Open Platform,"  The use of standard platforms in the field of humanoid robotics can
+accelerate research, and lower the entry barrier for new research groups. While
+many affordable humanoid standard platforms exist in the lower size ranges of
+up to 60cm, beyond this the few available standard platforms quickly become
+significantly more expensive, and difficult to operate and maintain. In this
+paper, the igus Humanoid Open Platform is presented---a new, affordable,
+versatile and easily customisable standard platform for humanoid robots in the
+child-sized range. At 90cm, the robot is large enough to interact with a
+human-scale environment in a meaningful way, and is equipped with enough torque
+and computing power to foster research in many possible directions. The
+structure of the robot is entirely 3D printed, allowing for a lightweight and
+appealing design. The electrical and mechanical designs of the robot are
+presented, and the main features of the corresponding open-source ROS software
+are discussed. The 3D CAD files for all of the robot parts have been released
+open-source in conjunction with this paper.
+",1,0,0,0,0,0
+8964,Lower Bounds on the Complexity of Solving Two Classes of Non-cooperative Games,"  This paper studies the complexity of solving two classes of non-cooperative
+games in a distributed manner in which the players communicate with a set of
+system nodes over noisy communication channels. The complexity of solving each
+game class is defined as the minimum number of iterations required to find a
+Nash equilibrium (NE) of any game in that class with $\epsilon$ accuracy.
+First, we consider the class $\mathcal{G}$ of all $N$-player non-cooperative
+games with a continuous action space that admit at least one NE. Using
+information-theoretic inequalities, we derive a lower bound on the complexity
+of solving $\mathcal{G}$ that depends on the Kolmogorov $2\epsilon$-capacity of
+the constraint set and the total capacity of the communication channels. We
+also derive a lower bound on the complexity of solving games in $\mathcal{G}$
+which depends on the volume and surface area of the constraint set. We next
+consider the class of all $N$-player non-cooperative games with at least one NE
+such that the players' utility functions satisfy a certain (differential)
+constraint. We derive lower bounds on the complexity of solving this game class
+under both Gaussian and non-Gaussian noise models. Our result in the
+non-Gaussian case is derived by establishing a connection between the
+Kullback-Leibler distance and Fisher information.
+",1,0,0,0,0,0
+8965,Orbital misalignment of the Neptune-mass exoplanet GJ 436b with the spin of its cool star,"  The angle between the spin of a star and its planets' orbital planes traces
+the history of the planetary system. Exoplanets orbiting close to cool stars
+are expected to be on circular, aligned orbits because of strong tidal
+interactions with the stellar convective envelope. Spin-orbit alignment can be
+measured when the planet transits its star, but such ground-based spectroscopic
+measurements are challenging for cool, slowly-rotating stars. Here we report
+the characterization of a planet three-dimensional trajectory around an M dwarf
+star, derived by mapping the spectrum of the stellar photosphere along the
+chord transited by the planet. We find that the eccentric orbit of the
+Neptune-mass exoplanet GJ 436b is nearly perpendicular to the stellar equator.
+Both eccentricity and misalignment, surprising around a cool star, can result
+from dynamical interactions (via Kozai migration) with a yet-undetected outer
+companion. This inward migration of GJ 436b could have triggered the
+atmospheric escape that now sustains its giant exosphere. Eccentric, misaligned
+exoplanets orbiting close to cool stars might thus hint at the presence of
+unseen perturbers and illustrate the diversity of orbital architectures seen in
+exoplanetary systems.
+",0,1,0,0,0,0
+8966,Flux-flow and vortex-glass phase in iron pnictide BaFe$_{2-x}$Ni$_x$As$_2$ single crystals with $T_c$ $\sim$ 20 K,"  We analysed the flux-flow region of isofield magneto resistivity data
+obtained on three crystals of BaFe$_{2-x}$Ni$_x$As$_2$ with $T_c$$\sim$20 K for
+three different geometries relative to the angle formed between the applied
+magnetic field and the c-axis of the crystals. The field dependent activation
+energy, $U_0$, was obtained from the TAFF and modified vortex-glass models,
+which were compared with the values of $U_0$ obtained from flux-creep available
+in the literature. We observed that the $U_0$ obtained from the TAFF model show
+deviations among the different crystals, while the correspondent glass lines
+obtained from the vortex glass model are virtually coincident. It is shown that
+the data is well explained by the modified vortex glass model, allowing to
+extract values of $T_g$, the glass transition temperature, and $T^*$, a
+temperature which scales with the mean field critical temperature $T_c(H)$. The
+resulting glass lines obey the anisotropic Ginzburg-Landau theory and are well
+fitted by a theory developed in the literature by considering the effect of
+disorder.
+",0,1,0,0,0,0
+8967,Word problems in Elliott monoids,"  Algorithmic issues concerning Elliott local semigroups are seldom considered
+in the literature, although these combinatorial structures completely classify
+AF algebras. In general, the addition operation of an Elliott local semigroup
+is {\it partial}, but for every AF algebra $\mathfrak B$ whose Murray-von
+Neumann order of projections is a lattice, this operation is uniquely
+extendible to the addition of an involutive monoid $E(\mathfrak B)$. Let
+$\mathfrak M_1$ be the Farey AF algebra introduced by the present author in
+1988 and rediscovered by F. Boca in 2008. The freeness properties of the
+involutive monoid $E(\mathfrak M_1)$ yield a natural word problem for every AF
+algebra $\mathfrak B$ with singly generated $E(\mathfrak B)$, because
+$\mathfrak B$ is automatically a quotient of $\mathfrak M_1$. Given two
+formulas $\phi$ and $\psi$ in the language of involutive monoids, the problem
+asks to decide whether $\phi$ and $\psi$ code the same equivalence of
+projections of $\mathfrak B$. This mimics the classical definition of the word
+problem of a group presented by generators and relations. We show that the word
+problem of $\mathfrak M_1$ is solvable in polynomial time, and so is the word
+problem of the Behnke-Leptin algebras $\mathcal A_{n,k}$, and of the
+Effros-Shen algebras $\mathfrak F_{\theta}$, for $\theta\in [0,1]\setminus
+\mathbb Q$ a real algebraic number, or $\theta = 1/e$. We construct a quotient
+of $\mathfrak M_1$ having a Gödel incomplete word problem, and show that no
+primitive quotient of $\mathfrak M_1$ is Gödel incomplete.
+",1,0,1,0,0,0
+8968,Improved lower bounds for the Mahler measure of the Fekete polynomials,"  We show that there is an absolute constant $c > 1/2$ such that the Mahler
+measure of the Fekete polynomials $f_p$ of the form $$f_p(z) :=
+\sum_{k=1}^{p-1}{\left( \frac kp \right)z^k}\,,$$ (where the coefficients are
+the usual Legendre symbols) is at least $c\sqrt{p}$ for all sufficiently large
+primes $p$. This improves the lower bound $\left(\frac 12 -
+\varepsilon\right)\sqrt{p}$ known before for the Mahler measure of the Fekete
+polynomials $f_p$ for all sufficiently large primes $p \geq c_{\varepsilon}$.
+Our approach is based on the study of the zeros of the Fekete polynomials on
+the unit circle.
+",0,0,1,0,0,0
+8969,Supervised Hashing based on Energy Minimization,"  Recently, supervised hashing methods have attracted much attention since they
+can optimize retrieval speed and storage cost while preserving semantic
+information. Because hashing codes learning is NP-hard, many methods resort to
+some form of relaxation technique. But the performance of these methods can
+easily deteriorate due to the relaxation. Luckily, many supervised hashing
+formulations can be viewed as energy functions, hence solving hashing codes is
+equivalent to learning marginals in the corresponding conditional random field
+(CRF). By minimizing the KL divergence between a fully factorized distribution
+and the Gibbs distribution of this CRF, a set of consistency equations can be
+obtained, but updating them in parallel may not yield a local optimum since the
+variational lower bound is not guaranteed to increase. In this paper, we use a
+linear approximation of the sigmoid function to convert these consistency
+equations to linear systems, which have a closed-form solution. By applying
+this novel technique to two classical hashing formulations KSH and SPLH, we
+obtain two new methods called EM (energy minimizing based)-KSH and EM-SPLH.
+Experimental results on three datasets show the superiority of our methods.
+",1,0,0,1,0,0
+8970,Sampled-Data Boundary Feedback Control of 1-D Hyperbolic PDEs with Non-Local Terms,"  The paper provides results for the application of boundary feedback control
+with Zero-Order-Hold (ZOH) to 1-D linear, first-order, hyperbolic systems with
+non-local terms on bounded domains. It is shown that the emulation design based
+on the recently proposed continuous-time, boundary feedback, designed by means
+of backstepping, guarantees closed-loop exponential stability, provided that
+the sampling period is sufficiently small. It is also shown that, contrary to
+the parabolic case, a smaller sampling period implies a faster convergence rate
+with no upper bound for the achieved convergence rate. The obtained results
+provide stability estimates for the sup-norm of the state and robustness with
+respect to perturbations of the sampling schedule is guaranteed.
+",1,0,1,0,0,0
+8971,Scalable Entity Resolution Using Probabilistic Signatures on Parallel Databases,"  Accurate and efficient entity resolution is an open challenge of particular
+relevance to intelligence organisations that collect large datasets from
+disparate sources with differing levels of quality and standard. Starting from
+a first-principles formulation of entity resolution, this paper presents a
+novel Entity Resolution algorithm that introduces a data-driven blocking and
+record-linkage technique based on the probabilistic identification of entity
+signatures in data. The scalability and accuracy of the proposed algorithm are
+evaluated using benchmark datasets and shown to achieve state-of-the-art
+results. The proposed algorithm can be implemented simply on modern parallel
+databases, which allows it to be deployed with relative ease in large
+industrial applications.
+",1,0,0,0,0,0
+8972,ac properties of short Josephson weak links,"  The admittance of two types of Josephson weak links is calculated, i.e., of a
+one-dimensional superconducting wire with a local suppression of the order
+parameter, and the second is a short S-c-S structure, where S denotes a
+superconductor and c---a constriction. The systems of the first type are
+analyzed on the basis of time-dependent Ginzburg-Landau equations. We show that
+the impedance $Z(\Omega)$ has a maximum as a function of the frequency
+$\Omega$, and the electric field $E_{\Omega}$ is determined by two
+gauge-invariant quantities---the condensate momentum $Q_{\Omega}$ and the
+potential $\mu$ related to charge imbalance. The structures of the second type
+are studied on the basis of microscopic equations for quasiclassical Green's
+functions in the Keldysh technique. For short S-c-S contacts (the Thouless
+energy ${E_{\text{Th}} = D/L^{2} \gg \Delta}$) we present a formula for
+admittance $Y$ valid at frequencies $\Omega$ and temperatures $T$ less than the
+Thouless energy but arbitrary with respect to the energy gap $\Delta$. It is
+shown that, at low temperatures, the absorption is absent [${\mathrm{Re}(Y) =
+0}$] if the frequency does not exceed the energy gap in the center of the
+constriction (${\Omega < \Delta \cos \varphi_{0}}$, where $2 \varphi_{0}$ is
+the phase difference between the S reservoirs). The absorption gradually
+increases with increasing the difference ${(\Omega - \Delta \cos \varphi_{0})}$
+if $2 \varphi_{0}$ is less than the phase difference $2 \varphi_{\text{c}}$
+corresponding to the critical Josephson current. In the interval ${2
+\varphi_{\text{c}} < 2 \varphi_{0} < \pi}$, the absorption has a maximum. This
+interval of the phase difference is achievable in phase-biased Josephson
+junctions. Close to $T_{\text{c}}$ the admittance has a maximum at low $\Omega$
+which is described by an analytical formula.
+",0,1,0,0,0,0
+8973,ProtoDash: Fast Interpretable Prototype Selection,"  In this paper we propose an efficient algorithm ProtoDash for selecting
+prototypical examples from complex datasets. Our generalizes the learn to
+criticize (L2C) work by Kim et al. (2016) to not only select prototypes for a
+given sparsity level $m$ but also to associate non-negative (for
+interpretability) weights with each of them indicative of the importance of
+each prototype. This extension provides a single coherent framework under which
+both prototypes and criticisms can be found. Furthermore, our framework works
+for any symmetric positive definite kernel thus addressing one of the key open
+questions laid out in Kim et al. (2016). Our additional requirement of learning
+non-negative weights no longer maintains submodularity of the objective as in
+the previous work, however, we show that the problem is weakly submodular and
+derive approximation guarantees for our fast ProtoDash algorithm. We
+demonstrate the efficacy of our method on diverse domains such as retail, digit
+recognition (MNIST) and on publicly available 40 health questionnaires obtained
+from the Center for Disease Control (CDC) website maintained by the US Dept. of
+Health. We validate the results quantitatively as well as qualitatively based
+on expert feedback and recently published scientific studies on public health,
+thus showcasing the power of our method in providing actionability (for
+retail), utility (for MNIST) and insight (on CDC datasets), which presumably
+are the hallmark of an effective interpretable method.
+",1,0,0,1,0,0
+8974,Regional Multi-Armed Bandits,"  We consider a variant of the classic multi-armed bandit problem where the
+expected reward of each arm is a function of an unknown parameter. The arms are
+divided into different groups, each of which has a common parameter. Therefore,
+when the player selects an arm at each time slot, information of other arms in
+the same group is also revealed. This regional bandit model naturally bridges
+the non-informative bandit setting where the player can only learn the chosen
+arm, and the global bandit model where sampling one arms reveals information of
+all arms. We propose an efficient algorithm, UCB-g, that solves the regional
+bandit problem by combining the Upper Confidence Bound (UCB) and greedy
+principles. Both parameter-dependent and parameter-free regret upper bounds are
+derived. We also establish a matching lower bound, which proves the
+order-optimality of UCB-g. Moreover, we propose SW-UCB-g, which is an extension
+of UCB-g for a non-stationary environment where the parameters slowly vary over
+time.
+",0,0,0,1,0,0
+8975,Determining stellar parameters of asteroseismic targets: going beyond the use of scaling relations,"  Asteroseismic parameters allow us to measure the basic stellar properties of
+field giants observed far across the Galaxy. Most of such determinations are,
+up to now, based on simple scaling relations involving the large frequency
+separation, \Delta\nu, and the frequency of maximum power, \nu$_{max}$. In this
+work, we implement \Delta\nu\ and the period spacing, {\Delta}P, computed along
+detailed grids of stellar evolutionary tracks, into stellar isochrones and
+hence in a Bayesian method of parameter estimation. Tests with synthetic data
+reveal that masses and ages can be determined with typical precision of 5 and
+19 per cent, respectively, provided precise seismic parameters are available.
+Adding independent information on the stellar luminosity, these values can
+decrease down to 3 and 10 per cent respectively. The application of these
+methods to NGC 6819 giants produces a mean age in agreement with those derived
+from isochrone fitting, and no evidence of systematic differences between RGB
+and RC stars. The age dispersion of NGC 6819 stars, however, is larger than
+expected, with at least part of the spread ascribable to stars that underwent
+mass-transfer events.
+",0,1,0,0,0,0
+8976,"Polarised target for Drell-Yan experiment in COMPASS at CERN, part I","  In the polarised Drell-Yan experiment at the COMPASS facility in CERN pion
+beam with momentum of 190 GeV/c and intensity about $10^8$ pions/s interacted
+with transversely polarised NH$_3$ target. Muon pairs produced in Drel-Yan
+process were detected. The measurement was done in 2015 as the 1st ever
+polarised Drell-Yan fixed target experiment. The hydrogen nuclei in the
+solid-state NH$_3$ were polarised by dynamic nuclear polarisation in 2.5 T
+field of large-acceptance superconducting magnet. Large helium dilution
+cryostat was used to cool the target down below 100 mK. Polarisation of
+hydrogen nuclei reached during the data taking was about 80 %. Two oppositely
+polarised target cells, each 55 cm long and 4 cm in diameter were used.
+Overview of COMPASS facility and the polarised target with emphasis on the
+dilution cryostat and magnet is given. Results of the polarisation measurement
+in the Drell-Yan run and overviews of the target material, cell and dynamic
+nuclear polarisation system are given in the part II.
+",0,1,0,0,0,0
+8977,Spectral Mixture Kernels for Multi-Output Gaussian Processes,"  Early approaches to multiple-output Gaussian processes (MOGPs) relied on
+linear combinations of independent, latent, single-output Gaussian processes
+(GPs). This resulted in cross-covariance functions with limited parametric
+interpretation, thus conflicting with the ability of single-output GPs to
+understand lengthscales, frequencies and magnitudes to name a few. On the
+contrary, current approaches to MOGP are able to better interpret the
+relationship between different channels by directly modelling the
+cross-covariances as a spectral mixture kernel with a phase shift. We extend
+this rationale and propose a parametric family of complex-valued cross-spectral
+densities and then build on Cramér's Theorem (the multivariate version of
+Bochner's Theorem) to provide a principled approach to design multivariate
+covariance functions. The so-constructed kernels are able to model delays among
+channels in addition to phase differences and are thus more expressive than
+previous methods, while also providing full parametric interpretation of the
+relationship across channels. The proposed method is first validated on
+synthetic data and then compared to existing MOGP methods on two real-world
+examples.
+",0,0,0,1,0,0
+8978,Supergravity and its Legacy,"  A personal recollection of events that preceded the construction of
+Supergravity and of some subsequent developments.
+",0,1,0,0,0,0
+8979,Manton's five vortex equations from self-duality,"  We demonstrate that the five vortex equations recently introduced by Manton
+ariseas symmetry reductions of the anti-self-dual Yang--Mills equations in four
+dimensions. In particular the Jackiw--Pi vortex and the Ambj\o rn--Olesen
+vortex correspond to the gauge group $SU(1, 1)$, and respectively the Euclidean
+or the $SU(2)$ symmetry groups acting with two-dimensional orbits. We show how
+to obtain vortices with higher vortex numbers, by superposing vortex equations
+of different types. Finally we use the kinetic energy of the Yang--Mills theory
+in 4+1 dimensions to construct a metric on vortex moduli spaces. This metric is
+not positive-definite in cases of non-compact gauge groups.
+",0,1,1,0,0,0
+8980,Deep Style Match for Complementary Recommendation,"  Humans develop a common sense of style compatibility between items based on
+their attributes. We seek to automatically answer questions like ""Does this
+shirt go well with that pair of jeans?"" In order to answer these kinds of
+questions, we attempt to model human sense of style compatibility in this
+paper. The basic assumption of our approach is that most of the important
+attributes for a product in an online store are included in its title
+description. Therefore it is feasible to learn style compatibility from these
+descriptions. We design a Siamese Convolutional Neural Network architecture and
+feed it with title pairs of items, which are either compatible or incompatible.
+Those pairs will be mapped from the original space of symbolic words into some
+embedded style space. Our approach takes only words as the input with few
+preprocessing and there is no laborious and expensive feature engineering.
+",1,0,0,0,0,0
+8981,SpectralLeader: Online Spectral Learning for Single Topic Models,"  We study the problem of learning a latent variable model from a stream of
+data. Latent variable models are popular in practice because they can explain
+observed data in terms of unobserved concepts. These models have been
+traditionally studied in the offline setting. In the online setting, on the
+other hand, the online EM is arguably the most popular algorithm for learning
+latent variable models. Although the online EM is computationally efficient, it
+typically converges to a local optimum. In this work, we develop a new online
+learning algorithm for latent variable models, which we call SpectralLeader.
+SpectralLeader always converges to the global optimum, and we derive a
+sublinear upper bound on its $n$-step regret in the bag-of-words model. In both
+synthetic and real-world experiments, we show that SpectralLeader performs
+similarly to or better than the online EM with tuned hyper-parameters.
+",1,0,0,1,0,0
+8982,Fractional Calculus and certain integrals of Generalized multiindex Bessel function,"  We aim to introduce the generalized multiindex Bessel function $J_{\left(
+\beta _{j}\right) _{m},\kappa ,b}^{\left( \alpha _{j}\right)_{m},\gamma
+,c}\left[ z\right] $ and to present some formulas of the Riemann-Liouville
+fractional integration and differentiation operators. Further, we also derive
+certain integral formulas involving the newly defined generalized multiindex
+Bessel function $J_{\left( \beta _{j}\right) _{m},\kappa ,b}^{\left( \alpha
+_{j}\right)_{m},\gamma ,c}\left[ z\right] $. We prove that such integrals are
+expressed in terms of the Fox-Wright function $_{p}\Psi_{q}(z)$. The results
+presented here are of general in nature and easily reducible to new and known
+results.
+",0,0,1,0,0,0
+8983,Tameness from two successive good frames,"  We show, assuming a mild set-theoretic hypothesis, that if an abstract
+elementary class (AEC) has a superstable-like forking notion for models of
+cardinality $\lambda$ and a superstable-like forking notion for models of
+cardinality $\lambda^+$, then orbital types over models of cardinality
+$\lambda^+$ are determined by their restrictions to submodels of cardinality
+$\lambda$. By a superstable-like forking notion, we mean here a good frame, a
+central concept of Shelah's book on AECs.
+It is known that locality of orbital types together with the existence of a
+superstable-like notion for models of cardinality $\lambda$ implies the
+existence of a superstable-like notion for models of cardinality $\lambda^+$,
+but here we prove the converse. An immediate consequence is that forking in
+$\lambda^+$ can be described in terms of forking in $\lambda$.
+",0,0,1,0,0,0
+8984,On the $\mathbf{\rmΨ}-$fractional integral and applications,"  Motivated by the ${\rm \Psi}$-Riemann-Liouville $({\rm \Psi-RL})$ fractional
+derivative and by the ${\rm \Psi}$-Hilfer $({\rm \Psi-H})$ fractional
+derivative, we introduced a new fractional operator the so-called
+$\rm\Psi-$fractional integral. We present some important results by means of
+theorems and in particular, that the $\rm\Psi-$fractional integration operator
+is limited. In this sense, we discuss some examples, in particular, involving
+the Mittag-Leffler $({\rm M-L})$ function, of paramount importance in the
+solution of population growth problem, as approached. On the other hand, we
+realize a brief discussion on the uniqueness of nonlinear $\Psi$-fractional
+Volterra integral equation (${\rm VIE}$) using $\beta-$distance functions.
+",0,0,1,0,0,0
+8985,Detection and Characterization of Illegal Marketing and Promotion of Prescription Drugs on Twitter,"  Illicit online pharmacies allow the purchase of prescription drugs online
+without a prescription. Such pharmacies leverage social media platforms such as
+Twit- ter as a promotion and marketing tool with the intent of reaching out to
+a larger, potentially younger demographics of the population. Given the serious
+negative health effects that arise from abusing such drugs, it is important to
+identify the relevant content on social media and exterminate their presence as
+quickly as pos- sible. In response, we collected all the tweets that contained
+the names of certain preselected controlled substances over a period of 5
+months. We found that an unsupervised topic modeling based methodology is able
+to identify tweets that promote and market controlled substances with high
+precision. We also study the meta-data characteristics of such tweets and the
+users who post them and find that they have several distinguishing
+characteristics that sets them apart. We were able to train supervised methods
+and achieve high performance in detecting such content and the users who post
+them.
+",1,0,0,0,0,0
+8986,First Hochschild cohomology group and stable equivalence classification of Morita type of some tame symmetric algebras,"  We use the dimension and the Lie algebra structure of the first Hochschild
+cohomology group to distinguish some algebras of dihedral, semi-dihedral and
+quaternion type up to stable equivalence of Morita type. In particular, we
+complete the classification of algebras of dihedral type that was mostly
+determined by Zhou and Zimmermann.
+",0,0,1,0,0,0
+8987,When is a Convolutional Filter Easy To Learn?,"  We analyze the convergence of (stochastic) gradient descent algorithm for
+learning a convolutional filter with Rectified Linear Unit (ReLU) activation
+function. Our analysis does not rely on any specific form of the input
+distribution and our proofs only use the definition of ReLU, in contrast with
+previous works that are restricted to standard Gaussian input. We show that
+(stochastic) gradient descent with random initialization can learn the
+convolutional filter in polynomial time and the convergence rate depends on the
+smoothness of the input distribution and the closeness of patches. To the best
+of our knowledge, this is the first recovery guarantee of gradient-based
+algorithms for convolutional filter on non-Gaussian input distributions. Our
+theory also justifies the two-stage learning rate strategy in deep neural
+networks. While our focus is theoretical, we also present experiments that
+illustrate our theoretical findings.
+",1,0,0,1,0,0
+8988,Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up,"  A large body of compelling evidence has been accumulated demonstrating that
+embodiment - the agent's physical setup, including its shape, materials,
+sensors and actuators - is constitutive for any form of cognition and as a
+consequence, models of cognition need to be embodied. In contrast to methods
+from empirical sciences to study cognition, robots can be freely manipulated
+and virtually all key variables of their embodiment and control programs can be
+systematically varied. As such, they provide an extremely powerful tool of
+investigation. We present a robotic bottom-up or developmental approach,
+focusing on three stages: (a) low-level behaviors like walking and reflexes,
+(b) learning regularities in sensorimotor spaces, and (c) human-like cognition.
+We also show that robotic based research is not only a productive path to
+deepening our understanding of cognition, but that robots can strongly benefit
+from human-like cognition in order to become more autonomous, robust,
+resilient, and safe.
+",1,0,0,0,1,0
+8989,Chiral magnetic textures in Ir/Fe/Co/Pt multilayers: Evolution and topological Hall signature,"  Skyrmions are topologically protected, two-dimensional, localized hedgehogs
+and whorls of spin. Originally invented as a concept in field theory for
+nuclear interactions, skyrmions are central to a wide range of phenomena in
+condensed matter. Their realization at room temperature (RT) in magnetic
+multilayers has generated considerable interest, fueled by technological
+prospects and the access granted to fundamental questions. The interaction of
+skyrmions with charge carriers gives rise to exotic electrodynamics, such as
+the topological Hall effect (THE), the Hall response to an emergent magnetic
+field, a manifestation of the skyrmion Berry-phase. The proposal that THE can
+be used to detect skyrmions needs to be tested quantitatively. For that it is
+imperative to develop comprehensive understanding of skyrmions and other chiral
+textures, and their electrical fingerprint. Here, using Hall transport and
+magnetic imaging, we track the evolution of magnetic textures and their THE
+signature in a technologically viable multilayer film as a function of
+temperature ($T$) and out-of-plane applied magnetic field ($H$). We show that
+topological Hall resistivity ($\rho_\mathrm{TH}$) scales with the density of
+isolated skyrmions ($n_\mathrm{sk}$) over a wide range of $T$, confirming the
+impact of the skyrmion Berry-phase on electronic transport. We find that at
+higher $n_\mathrm{sk}$ skyrmions cluster into worms which carry considerable
+topological charge, unlike topologically-trivial spin spirals. While we
+establish a qualitative agreement between $\rho_\mathrm{TH}(H,T)$ and areal
+density of topological charge $n_\mathrm{T}(H,T)$, our detailed quantitative
+analysis shows a much larger $\rho_\mathrm{TH}$ than the prevailing theory
+predicts for observed $n_\mathrm{T}$.
+",0,1,0,0,0,0
+8990,Conductance distribution in the magnetic field,"  Using a modification of the Shapiro scaling approach, we derive the
+distribution of conductance in the magnetic field applicable in the vicinity of
+the Anderson transition. This distribution is described by the same equations
+as in the absence of a field. Variation of the magnetic field does not lead to
+any qualitative effects in the conductance distribution and only changes its
+quantitative characteristics, moving a position of the system in the
+three-parameter space. In contrast to the original Shapiro approach, the
+evolution equation for quasi-1D systems is established from the generalized
+DMPK equation, and not by a simple analogy with one-dimensional systems; as a
+result, the whole approach became more rigorous and accurate.
+",0,1,0,0,0,0
+8991,Linear Convergence of An Iterative Phase Retrieval Algorithm with Data Reuse,"  Phase retrieval has been an attractive but difficult problem rising from
+physical science, and there has been a gap between state-of-the-art theoretical
+convergence analyses and the corresponding efficient retrieval methods.
+Firstly, these analyses all assume that the sensing vectors and the iterative
+updates are independent, which only fits the ideal model with infinite
+measurements but not the reality, where data are limited and have to be reused.
+Secondly, the empirical results of some efficient methods, such as the
+randomized Kaczmarz method, show linear convergence, which is beyond existing
+theoretical explanations considering its randomness and reuse of data. In this
+work, we study for the first time, without the independence assumption, the
+convergence behavior of the randomized Kaczmarz method for phase retrieval.
+Specifically, beginning from taking expectation of the squared estimation error
+with respect to the index of measurement by fixing the sensing vector and the
+error in the previous step, we discard the independence assumption, rigorously
+derive the upper and lower bounds of the reduction of the mean squared error,
+and prove the linear convergence. This work fills the gap between a fast
+converging algorithm and its theoretical understanding. The proposed
+methodology may contribute to the study of other iterative algorithms for phase
+retrieval and other problems in the broad area of signal processing and machine
+learning.
+",1,0,0,0,0,0
+8992,Representing smooth functions as compositions of near-identity functions with implications for deep network optimization,"  We show that any smooth bi-Lipschitz $h$ can be represented exactly as a
+composition $h_m \circ ... \circ h_1$ of functions $h_1,...,h_m$ that are close
+to the identity in the sense that each $\left(h_i-\mathrm{Id}\right)$ is
+Lipschitz, and the Lipschitz constant decreases inversely with the number $m$
+of functions composed. This implies that $h$ can be represented to any accuracy
+by a deep residual network whose nonlinear layers compute functions with a
+small Lipschitz constant. Next, we consider nonlinear regression with a
+composition of near-identity nonlinear maps. We show that, regarding Fréchet
+derivatives with respect to the $h_1,...,h_m$, any critical point of a
+quadratic criterion in this near-identity region must be a global minimizer. In
+contrast, if we consider derivatives with respect to parameters of a fixed-size
+residual network with sigmoid activation functions, we show that there are
+near-identity critical points that are suboptimal, even in the realizable case.
+Informally, this means that functional gradient methods for residual networks
+cannot get stuck at suboptimal critical points corresponding to near-identity
+layers, whereas parametric gradient methods for sigmoidal residual networks
+suffer from suboptimal critical points in the near-identity region.
+",0,0,0,1,0,0
+8993,Bosonizing three-dimensional quiver gauge theories,"  We start with the recently conjectured 3d bosonization dualities and gauge
+global symmetries to generate an infinite sequence of new dualities. These
+equate theories with non-Abelian product gauge groups and bifundamental matter.
+We uncover examples of Bose/Bose and Fermi/Fermi dualities, as well as a
+sequence of dualities between theories with scalar matter in two-index
+representations. Our conjectures are consistent with level/rank duality in
+massive phases.
+",0,1,0,0,0,0
+8994,Deep Learning for Sentiment Analysis : A Survey,"  Deep learning has emerged as a powerful machine learning technique that
+learns multiple layers of representations or features of the data and produces
+state-of-the-art prediction results. Along with the success of deep learning in
+many other application domains, deep learning is also popularly used in
+sentiment analysis in recent years. This paper first gives an overview of deep
+learning and then provides a comprehensive survey of its current applications
+in sentiment analysis.
+",0,0,0,1,0,0
+8995,Utility Preserving Secure Private Data Release,"  Differential privacy mechanisms that also make reconstruction of the data
+impossible come at a cost - a decrease in utility. In this paper, we tackle
+this problem by designing a private data release mechanism that makes
+reconstruction of the original data impossible and also preserves utility for a
+wide range of machine learning algorithms. We do so by combining the
+Johnson-Lindenstrauss (JL) transform with noise generated from a Laplace
+distribution. While the JL transform can itself provide privacy guarantees
+\cite{blocki2012johnson} and make reconstruction impossible, we do not rely on
+its differential privacy properties and only utilize its ability to make
+reconstruction impossible. We present novel proofs to show that our mechanism
+is differentially private under single element changes as well as single row
+changes to any database. In order to show utility, we prove that our mechanism
+maintains pairwise distances between points in expectation and also show that
+its variance is proportional to the the dimensionality of the subspace we
+project the data into. Finally, we experimentally show the utility of our
+mechanism by deploying it on the task of clustering.
+",1,0,0,0,0,0
+8996,Towards colloidal spintronics through Rashba spin-orbit interaction in lead sulphide nanosheets,"  Employing the spin degree of freedom of charge carriers offers the
+possibility to extend the functionality of conventional electronic devices,
+while colloidal chemistry can be used to synthesize inexpensive and tuneable
+nanomaterials. In order to benefit from both concepts, Rashba spin-orbit
+interaction has been investigated in colloidal lead sulphide nanosheets by
+electrical measurements on the circular photo-galvanic effect. Lead sulphide
+nanosheets possess rock salt crystal structure, which is centrosymmetric. The
+symmetry can be broken by quantum confinement, asymmetric vertical interfaces
+and a gate electric field leading to Rashba-type band splitting in momentum
+space at the M points, which results in an unconventional selection mechanism
+for the excitation of the carriers. The effect, which is supported by
+simulations of the band structure using density functional theory, can be tuned
+by the gate electric field and by the thickness of the sheets. Spin-related
+electrical transport phenomena in colloidal materials open a promising pathway
+towards future inexpensive spintronic devices.
+",0,1,0,0,0,0
+8997,On the use and abuse of Price equation concepts in ecology,"  In biodiversity and ecosystem functioning (BEF) research, the Loreau-Hector
+(LH) statistical scheme is widely-used to partition the effect of biodiversity
+on ecosystem properties into a ""complementarity effect"" and a ""selection
+effect"". This selection effect was originally considered analogous to the
+selection term in the Price equation from evolutionary biology. However, a key
+paper published over thirteen years ago challenged this interpretation by
+devising a new tripartite partitioning scheme that purportedly quantified the
+role of selection in biodiversity experiments more accurately. This tripartite
+method, as well as its recent spatiotemporal extension, were both developed as
+an attempt to apply the Price equation in a BEF context. Here, we demonstrate
+that the derivation of this tripartite method, as well as its spatiotemporal
+extension, involve a set of incoherent and nonsensical mathematical arguments
+driven largely by naïve visual analogies with the original Price equation,
+that result in neither partitioning scheme quantifying any real property in the
+natural world. Furthermore, we show that Loreau and Hector's original selection
+effect always represented a true analog of the original Price selection term,
+making the tripartite partitioning scheme a nonsensical solution to a
+non-existent problem [...]
+",0,0,0,0,1,0
+8998,A Data-Driven MHD Model of the Global Solar Corona within Multi-Scale Fluid-Kinetic Simulation Suite (MS-FLUKSS),"  We have developed a data-driven magnetohydrodynamic (MHD) model of the global
+solar corona which uses characteristically-consistent boundary conditions (BCs)
+at the inner boundary. Our global solar corona model can be driven by different
+observational data including Solar Dynamics Observatory/Helioseismic and
+Magnetic Imager (SDO/HMI) synoptic vector magnetograms together with the
+horizontal velocity data in the photosphere obtained by the time-distance
+helioseismology method, and the line-of-sight (LOS) magnetogram data obtained
+by HMI, Solar and Heliospheric Observatory/Michelson Doppler Imager (SOHO/MDI),
+National Solar Observatory/Global Oscillation Network Group (NSO/GONG) and
+Wilcox Solar Observatory (WSO). We implemented our model in the Multi-Scale
+Fluid-Kinetic Simulation Suite (MS-FLUKSS) - a suite of adaptive mesh
+refinement (AMR) codes built upon the Chombo AMR framework developed at the
+Lawrence Berkeley National Laboratory. We present an overview of our model,
+characteristic BCs, and two results we obtained using our model: A benchmark
+test of relaxation of a dipole field using characteristic BCs, and relaxation
+of an initial PFSS field driven by HMI LOS magnetogram data, and horizontal
+velocity data obtained by the time-distance helioseismology method using a set
+of non-characteristic BCs.
+",0,1,0,0,0,0
+8999,AXNet: ApproXimate computing using an end-to-end trainable neural network,"  Neural network based approximate computing is a universal architecture
+promising to gain tremendous energy-efficiency for many error resilient
+applications. To guarantee the approximation quality, existing works deploy two
+neural networks (NNs), e.g., an approximator and a predictor. The approximator
+provides the approximate results, while the predictor predicts whether the
+input data is safe to approximate with the given quality requirement. However,
+it is non-trivial and time-consuming to make these two neural network
+coordinate---they have different optimization objectives---by training them
+separately. This paper proposes a novel neural network structure---AXNet---to
+fuse two NNs to a holistic end-to-end trainable NN. Leveraging the philosophy
+of multi-task learning, AXNet can tremendously improve the invocation
+(proportion of safe-to-approximate samples) and reduce the approximation error.
+The training effort also decrease significantly. Experiment results show 50.7%
+more invocation and substantial cuts of training time when compared to existing
+neural network based approximate computing framework.
+",0,0,0,1,0,0
+9000,500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow),"  Deep learning methods are useful for high-dimensional data and are becoming
+widely used in many areas of software engineering. Deep learners utilizes
+extensive computational power and can take a long time to train-- making it
+difficult to widely validate and repeat and improve their results. Further,
+they are not the best solution in all domains. For example, recent results show
+that for finding related Stack Overflow posts, a tuned SVM performs similarly
+to a deep learner, but is significantly faster to train. This paper extends
+that recent result by clustering the dataset, then tuning very learners within
+each cluster. This approach is over 500 times faster than deep learning (and
+over 900 times faster if we use all the cores on a standard laptop computer).
+Significantly, this faster approach generates classifiers nearly as good
+(within 2\% F1 Score) as the much slower deep learning method. Hence we
+recommend this faster methods since it is much easier to reproduce and utilizes
+far fewer CPU resources. More generally, we recommend that before researchers
+release research results, that they compare their supposedly sophisticated
+methods against simpler alternatives (e.g applying simpler learners to build
+local models).
+",0,0,0,1,0,0
+9001,A two-dimensional hexagonal sheet of TiO$_2$,"  We report on the ab initio discovery of a novel putative ground state for
+quasi two-dimensional TiO$_2$ through a structural search using the minima
+hopping method with an artificial neural network potential. The structure is
+based on a honeycomb lattice and is energetically lower than the experimentally
+reported lepidocrocite sheet by 7~meV/atom, and merely 13~meV/atom higher in
+energy than the ground state rutile bulk structure. According to our
+calculations, the hexagonal sheet is stable against mechanical stress, it is
+chemically inert and can be deposited on various substrates without disrupting
+the structure. Its properties differ significantly from all known TiO$_2$ bulk
+phases with a large gap of 5.05~eV that can be tuned through strain
+engineering.
+",0,1,0,0,0,0
+9002,Fast Markov Chain Monte Carlo Algorithms via Lie Groups,"  From basic considerations of the Lie group that preserves a target
+probability measure, we derive the Barker, Metropolis, and ensemble Markov
+chain Monte Carlo (MCMC) algorithms, as well as two new MCMC algorithms. The
+convergence properties of these new algorithms successively improve on the
+state of the art. We illustrate the new algorithms with explicit numerical
+computations, and we empirically demonstrate the improved convergence on a spin
+glass.
+",0,0,1,1,0,0
+9003,"On the (parameterized) complexity of recognizing well-covered (r,l)-graphs","  An $(r, \ell)$-partition of a graph $G$ is a partition of its vertex set into
+$r$ independent sets and $\ell$ cliques. A graph is $(r, \ell)$ if it admits an
+$(r, \ell)$-partition. A graph is well-covered if every maximal independent set
+is also maximum. A graph is $(r,\ell)$-well-covered if it is both $(r,\ell)$
+and well-covered. In this paper we consider two different decision problems. In
+the $(r,\ell)$-Well-Covered Graph problem ($(r,\ell)$WCG for short), we are
+given a graph $G$, and the question is whether $G$ is an
+$(r,\ell)$-well-covered graph. In the Well-Covered $(r,\ell)$-Graph problem
+(WC$(r,\ell)$G for short), we are given an $(r,\ell)$-graph $G$ together with
+an $(r,\ell)$-partition of $V(G)$ into $r$ independent sets and $\ell$ cliques,
+and the question is whether $G$ is well-covered. We classify most of these
+problems into P, coNP-complete, NP-complete, NP-hard, or coNP-hard. Only the
+cases WC$(r,0)$G for $r\geq 3$ remain open. In addition, we consider the
+parameterized complexity of these problems for several choices of parameters,
+such as the size $\alpha$ of a maximum independent set of the input graph, its
+neighborhood diversity, its clique-width, or the number $\ell$ of cliques in an
+$(r, \ell)$-partition. In particular, we show that the parameterized problem of
+deciding whether a general graph is well-covered parameterized by $\alpha$ can
+be reduced to the WC$(0,\ell)$G problem parameterized by $\ell$. In addition,
+we prove that both problems are coW[2]-hard but can be solved in XP-time.
+",1,0,0,0,0,0
+9004,Comments on avalanche flow models based on the concept of random kinetic energy,"  In a series of papers, Bartelt and co-workers developed novel snow-avalanche
+models in which \emph{random kinetic energy} $R_K$ (a.k.a.\ granular
+temperature) is a key concept. The earliest models were for a single, constant
+density layer, using a Voellmy model but with $R_K$-dependent friction
+parameters. This was then extended to variable density, and finally a
+suspension layer (powder-snow cloud) was added. The physical basis and
+mathematical formulation of these models is critically reviewed here, with the
+following main findings: (i) Key assumptions in the original RKE model differ
+substantially from established results on dense granular flows; in particular,
+the effective friction coefficient decreases to zero with velocity in the RKE
+model. (ii) In the variable-density model, non-canonical interpretation of the
+energy balance leads to a third-order evolution equation for the flow depth or
+density, whereas the stated assumptions imply a first-order equation. (iii) The
+model for the suspension layer neglects gravity and disregards well established
+theoretical and experimental results on particulate gravity currents. Some
+options for improving these aspects are discussed.
+",0,1,0,0,0,0
+9005,Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System,"  As mobile devices have become indispensable in modern life, mobile security
+is becoming much more important. Traditional password or PIN-like
+point-of-entry security measures score low on usability and are vulnerable to
+brute force and other types of attacks. In order to improve mobile security, an
+adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication
+system is proposed in this paper to provide authentication in a continuous and
+transparent manner.To illustrate the applicability and capability of ANFIS in
+our implicit authentication system, experiments were conducted on behavioural
+data collected for up to 12 weeks from different Android users. The ability of
+the ANFIS-based system to detect an adversary is also tested with scenarios
+involving an attacker with varying levels of knowledge. The results demonstrate
+that ANFIS is a feasible and efficient approach for implicit authentication
+with an average of 95% user recognition rate. Moreover, the use of ANFIS-based
+system for implicit authentication significantly reduces manual tuning and
+configuration tasks due to its selflearning capability.
+",1,0,0,0,0,0
+9006,Ultracold heteronuclear three-body systems: How diabaticity limits the universality of recombination into shallow dimers,"  The mass-imbalanced three-body recombination process that forms a shallow
+dimer is shown to possess a rich Efimov-Stückelberg landscape, with
+corresponding spectra that differ fundamentally from the homonuclear case. A
+semi-analytical treatment of the three-body recombination predicts an unusual
+spectra with intertwined resonance peaks and minima, and yields in-depth
+insight into the behavior of the corresponding Efimov spectra. In particular,
+the patterns of the Efimov-Stückelberg landscape are shown to depend
+inherently on the degree of diabaticity of the three-body collisions, which
+strongly affects the universality of the heteronuclear Efimov states.
+",0,1,0,0,0,0
+9007,Variational Inference for Data-Efficient Model Learning in POMDPs,"  Partially observable Markov decision processes (POMDPs) are a powerful
+abstraction for tasks that require decision making under uncertainty, and
+capture a wide range of real world tasks. Today, effective planning approaches
+exist that generate effective strategies given black-box models of a POMDP
+task. Yet, an open question is how to acquire accurate models for complex
+domains. In this paper we propose DELIP, an approach to model learning for
+POMDPs that utilizes amortized structured variational inference. We empirically
+show that our model leads to effective control strategies when coupled with
+state-of-the-art planners. Intuitively, model-based approaches should be
+particularly beneficial in environments with changing reward structures, or
+where rewards are initially unknown. Our experiments confirm that DELIP is
+particularly effective in this setting.
+",0,0,0,1,0,0
+9008,The ANAIS-112 experiment at the Canfranc Underground Laboratory,"  The ANAIS experiment aims at the confirmation of the DAMA/LIBRA signal at the
+Canfranc Underground Laboratory (LSC). Several 12.5 kg NaI(Tl) modules produced
+by Alpha Spectra Inc. have been operated there during the last years in various
+set-ups; an outstanding light collection at the level of 15 photoelectrons per
+keV, which allows triggering at 1 keV of visible energy, has been measured for
+all of them and a complete characterization of their background has been
+achieved. In the first months of 2017, the full ANAIS-112 set-up consisting of
+nine Alpha Spectra detectors with a total mass of 112.5 kg was commissioned at
+LSC and the first dark matter run started in August, 2017. Here, the latest
+results on the detectors performance and measured background from the
+commissioning run will be presented and the sensitivity prospects of the
+ANAIS-112 experiment will be discussed.
+",0,1,0,0,0,0
+9009,"A non-ellipticity result, or the impossible taming of the logarithmic strain measure","  The logarithmic strain measures $\lVert\log U\rVert^2$, where $\log U$ is the
+principal matrix logarithm of the stretch tensor $U=\sqrt{F^TF}$ corresponding
+to the deformation gradient $F$ and $\lVert\,.\,\rVert$ denotes the Frobenius
+matrix norm, arises naturally via the geodesic distance of $F$ to the special
+orthogonal group $\operatorname{SO}(n)$. This purely geometric characterization
+of this strain measure suggests that a viable constitutive law of nonlinear
+elasticity may be derived from an elastic energy potential which depends solely
+on this intrinsic property of the deformation, i.e. that an energy function
+$W\colon\operatorname{GL^+}(n)\to\mathbb{R}$ of the form \begin{equation}
+W(F)=\Psi(\lVert\log U\rVert^2) \tag{1} \end{equation} with a suitable
+function $\Psi\colon[0,\infty)\to\mathbb{R}$ should be used to describe finite
+elastic deformations.
+However, while such energy functions enjoy a number of favorable properties,
+we show that it is not possible to find a strictly monotone function $\Psi$
+such that $W$ of the form (1) is Legendre-Hadamard elliptic.
+Similarly, we consider the related isochoric strain measure
+$\lVert\operatorname{dev}_n\log U\rVert^2$, where $\operatorname{dev}_n \log U$
+is the deviatoric part of $\log U$. Although a polyconvex energy function in
+terms of this strain measure has recently been constructed in the planar case
+$n=2$, we show that for $n\geq3$, no strictly monotone function
+$\Psi\colon[0,\infty)\to\mathbb{R}$ exists such that $F\mapsto
+\Psi(\lVert\operatorname{dev}_n\log U\rVert^2)$ is polyconvex or even rank-one
+convex. Moreover, a volumetric-isochorically decoupled energy of the form
+$F\mapsto \Psi(\lVert\operatorname{dev}_n\log U\rVert^2) +
+W_{\mathrm{vol}}(\det F)$ cannot be rank-one convex for any function
+$W_{\mathrm{vol}}\colon(0,\infty)\to\mathbb{R}$ if $\Psi$ is strictly monotone.
+",0,0,1,0,0,0
+9010,Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records,"  We present an interpretable neural network for predicting an important
+clinical outcome (1-year mortality) from multi-modal Electronic Health Record
+(EHR) data. Our approach builds on prior multi-modal machine learning models by
+now enabling visualization of how individual factors contribute to the overall
+outcome risk, assuming other factors remain constant, which was previously
+impossible.
+We demonstrate the value of this approach using a large multi-modal clinical
+dataset including both EHR data and 31,278 echocardiographic videos of the
+heart from 26,793 patients. We generated separate models for (i) clinical data
+only (CD) (e.g. age, sex, diagnoses and laboratory values), (ii) numeric
+variables derived from the videos, which we call echocardiography-derived
+measures (EDM), and (iii) CD+EDM+raw videos (pixel data). The interpretable
+multi-modal model maintained performance compared to non-interpretable models
+(Random Forest, XGBoost), and also performed significantly better than a model
+using a single modality (average AUC=0.82). Clinically relevant insights and
+multi-modal variable importance rankings were also facilitated by the new
+model, which have previously been impossible.
+",1,0,0,1,0,0
+9011,Automatic Temperature Setpoint Tuning of a Thermoforming Machine using Fuzzy Terminal Iterative Learning Control,"  This paper presents a new way to design a Fuzzy Terminal Iterative Learning
+Control (TILC) to control the heater temperature setpoints of a thermoforming
+machine. This fuzzy TILC is based on the inverse of a fuzzy model of this
+machine, and is built from experimental (or simulation) data with kriging
+interpolation. The Fuzzy Inference System usually used for a fuzzy model is the
+zero order Takagi Sugeno Kwan system (constant consequents). In this paper, the
+1st order Takagi Sugeno Kwan system is used, with the fuzzy model rules
+expressed using matrices. This makes the inversion of the fuzzy model much
+easier than the inversion of the fuzzy model based on the TSK of order 0. Based
+on simulation results, the proposed fuzzy TILC seems able to give a very good
+initial guess as to the heater temperature setpoints, making it possible to
+have almost no wastage of plastic sheets. Simulation results show the
+effectiveness of the fuzzy TILC compared to a crisp TILC, even though the fuzzy
+controller is based on a fuzzy model built from noisy data.
+",1,0,0,0,0,0
+9012,A New Class of Integrals Involving Extended Hypergeometric Function,"  Our purpose in this present paper is to investigate generalized integration
+formulas containing the extended generalized hypergeometric function and
+obtained results are expressed in terms of extended hypergeometric function.
+Certain special cases of the main results presented here are also pointed out
+for the extended Gauss' hypergeometric and confluent hypergeometric functions.
+",0,0,1,0,0,0
+9013,The Special Theory of Relativity as Applied to the Born-Oppenheimer-Huang Approach,"  In two recent publications ( Int. J. Quant. Chem. 114, 1645 (2014) and Molec.
+Phys. 114, 227 (2016)) it was shown that the Born -Hwang (BH) treatment of a
+molecular system perturbed by an external field yields a set of decoupled
+vectorial Wave Equations, just like in Electromagnetism. This finding led us to
+declare on the existence of a new type of Fields, which were termed Molecular
+Fields. The fact that such fields exist implies that at the vicinity of conical
+intersections exist a mechanism that transforms a passing-by electric beam into
+a field which differs from the original electric field. This situation is
+reminiscent of what is encountered in astronomy where Black Holes formed by
+massive stars may affect the nature of a near-by beam of light. Thus if the
+NonAdiabatic-Coupling-Terms (NACT) with their singular points may affect the
+nature of such a beam (see the above two publications) then it would be
+interesting to know to what extend NACTs (and consequently also the BH
+equation) will be affected by the special theory of relativity as introduced by
+Dirac. Indeed while applying the Dirac approach we derived the relativistic
+affected NACTs as well as the corresponding BH equation.
+",0,1,0,0,0,0
+9014,An efficient algorithm for finding all possible input nodes for controlling complex networks,"  Understanding structural controllability of a complex network requires to
+identify a Minimum Input nodes Set (MIS) of the network. It has been suggested
+that finding an MIS is equivalent to computing a maximum matching of the
+network, where the unmatched nodes constitute an MIS. However, maximum matching
+of a network is often not unique, and finding all MISs may provide deep
+insights to the controllability of the network. Finding all possible input
+nodes, which form the union of all MISs, is computationally challenging for
+large networks. Here we present an efficient enumerative algorithm for the
+problem. The main idea is to modify a maximum matching algorithm to make it
+efficient for finding all possible input nodes by computing only one MIS. We
+rigorously proved the correctness of the new algorithm and evaluated its
+performance on synthetic and large real networks. The experimental results
+showed that the new algorithm ran several orders of magnitude faster than the
+existing method on large real networks.
+",1,1,0,0,0,0
+9015,"Transformation thermal convection: Cloaking, concentrating, and camouflage","  Heat can generally transfer via thermal conduction, thermal radiation, and
+thermal convection. All the existing theories of transformation thermotics and
+optics can treat thermal conduction and thermal radiation, respectively.
+Unfortunately, thermal convection has never been touched in transformation
+theories due to the lack of a suitable theory, thus limiting applications
+associated with heat transfer through fluids (liquid or gas). Here, we develop,
+for the first time, a general theory of transformation thermal convection by
+considering the convection-diffusion equation, the Navier-Stokes equation, and
+the Darcy law. By introducing porous media, we get a set of coupled equations
+keeping their forms under coordinate transformation. As model applications, the
+theory helps to show the effects of cloaking, concentrating, and camouflage.
+Our finite element simulations confirm the theoretical findings. This work
+offers a general transformation theory for thermal convection, thus revealing
+some novel behaviors of thermal convection; it not only provides new hints on
+how to control heat transfer by combining thermal conduction, thermal
+radiation, and thermal convection, but also benefits the study of mass
+diffusion and other related fields that contain a set of equations and need to
+transform velocities at the same time.
+",0,1,0,0,0,0
+9016,Tunable terahertz reflection of graphene via ionic liquid gating,"  We report a highly efficient tunable THz reflector in graphene. By applying a
+small gate voltage (up to 3 V), the reflectance of graphene is modulated from a
+minimum of 0.79% to a maximum of 33.4% using graphene/ionic liquid structures
+at room temperature, and the reflection tuning is uniform within a wide
+spectral range (0.1 - 1.5 THz). Our observation is explained by the Drude
+model, which describes the THz wave-induced intraband transition in graphene.
+This tunable reflectance of graphene may contribute to broadband THz mirrors,
+deformable THz mirrors, variable THz beam splitters and other optical
+components.
+",0,1,0,0,0,0
+9017,Second-Order Kernel Online Convex Optimization with Adaptive Sketching,"  Kernel online convex optimization (KOCO) is a framework combining the
+expressiveness of non-parametric kernel models with the regret guarantees of
+online learning. First-order KOCO methods such as functional gradient descent
+require only $\mathcal{O}(t)$ time and space per iteration, and, when the only
+information on the losses is their convexity, achieve a minimax optimal
+$\mathcal{O}(\sqrt{T})$ regret. Nonetheless, many common losses in kernel
+problems, such as squared loss, logistic loss, and squared hinge loss posses
+stronger curvature that can be exploited. In this case, second-order KOCO
+methods achieve $\mathcal{O}(\log(\text{Det}(\boldsymbol{K})))$ regret, which
+we show scales as $\mathcal{O}(d_{\text{eff}}\log T)$, where $d_{\text{eff}}$
+is the effective dimension of the problem and is usually much smaller than
+$\mathcal{O}(\sqrt{T})$. The main drawback of second-order methods is their
+much higher $\mathcal{O}(t^2)$ space and time complexity. In this paper, we
+introduce kernel online Newton step (KONS), a new second-order KOCO method that
+also achieves $\mathcal{O}(d_{\text{eff}}\log T)$ regret. To address the
+computational complexity of second-order methods, we introduce a new matrix
+sketching algorithm for the kernel matrix $\boldsymbol{K}_t$, and show that for
+a chosen parameter $\gamma \leq 1$ our Sketched-KONS reduces the space and time
+complexity by a factor of $\gamma^2$ to $\mathcal{O}(t^2\gamma^2)$ space and
+time per iteration, while incurring only $1/\gamma$ times more regret.
+",1,0,0,1,0,0
+9018,On Corecursive Algebras for Functors Preserving Coproducts,"  For an endofunctor $H$ on a hyper-extensive category preserving countable
+coproducts we describe the free corecursive algebra on $Y$ as the coproduct of
+the final coalgebra for $H$ and the free $H$-algebra on $Y$. As a consequence,
+we derive that $H$ is a cia functor, i.e., its corecursive algebras are
+precisely the cias (completely iterative algebras). Also all functors $H(-) +
+Y$ are then cia functors. For finitary set functors we prove that, conversely,
+if $H$ is a cia functor, then it has the form $H = W \times (-) + Y$ for some
+sets $W$ and $Y$.
+",1,0,0,0,0,0
+9019,Semisimple characters for inner froms I: GL_n(D),"  The article is about the representation theory of an inner form~$G$ of a
+general linear group over a non-archimedean local field. We introduce
+semisimple characters for~$G$ whose intertwining classes describe conjecturally
+via Local Langlands correspondence the behavior on wild inertia. These
+characters also play a potential role to understand the classification of
+irreducible smooth representations of inner forms of classical groups. We prove
+the intertwining formula for semisimple characters and an intertwining implies
+conjugacy like theorem. Further we show that endo-parameters for~$G$, i.e.
+invariants consisting of simple endo-classes and a numerical part, classify the
+intertwining classes of semisimple characters for~$G$. They should be the
+counter part for restrictions of Langlands-parameters to wild inertia under
+Local Langlands correspondence.
+",0,0,1,0,0,0
+9020,Quantum Memristors in Quantum Photonics,"  We propose a method to build quantum memristors in quantum photonic
+platforms. We firstly design an effective beam splitter, which is tunable in
+real-time, by means of a Mach-Zehnder-type array with two equal 50:50 beam
+splitters and a tunable retarder, which allows us to control its reflectivity.
+Then, we show that this tunable beam splitter, when equipped with weak
+measurements and classical feedback, behaves as a quantum memristor. Indeed, in
+order to prove its quantumness, we show how to codify quantum information in
+the coherent beams. Moreover, we estimate the memory capability of the quantum
+memristor. Finally, we show the feasibility of the proposed setup in integrated
+quantum photonics.
+",1,0,0,1,0,0
+9021,Spin dynamics and magnetic-field-induced polarization of excitons in ultrathin GaAs/AlAs quantum wells with indirect band gap and type-II band alignment,"  The exciton spin dynamics are investigated both experimentally and
+theoretically in two-monolayer-thick GaAs/AlAs quantum wells with an indirect
+band gap and a type-II band alignment. The magnetic-field-induced circular
+polarization of photoluminescence, $P_c$, is studied as function of the
+magnetic field strength and direction as well as sample temperature. The
+observed nonmonotonic behaviour of these functions is provided by the interplay
+of bright and dark exciton states contributing to the emission. To interpret
+the experiment, we have developed a kinetic master equation model which
+accounts for the dynamics of the spin states in this exciton quartet, radiative
+and nonradiative recombination processes, and redistribution of excitons
+between these states as result of spin relaxation. The model offers
+quantitative agreement with experiment and allows us to evaluate, for the
+studied structure, the heavy-hole $g$ factor, $g_{hh}=+3.5$, and the spin
+relaxation times of electron, $\tau_{se} = 33~\mu$s, and hole, $\tau_{sh} =
+3~\mu$s, bound in the exciton.
+",0,1,0,0,0,0
+9022,Resonant inelastic x-ray scattering probes the electron-phonon coupling in the spin-liquid kappa-(BEDT-TTF)2Cu2(CN)3,"  Resonant inelastic x-ray scattering at the N K edge reveals clearly resolved
+harmonics of the anion plane vibrations in the kappa-(BEDT-TTF)2Cu2(CN)3
+spin-liquid insulator. Tuning the incoming light energy at the K edge of two
+distinct N sites permits to excite different sets of phonon modes. Cyanide CN
+stretching mode is selected at the edge of the ordered N sites which are more
+strongly connected to the BEDT-TTF molecules, while positionally disordered N
+sites show multi-mode excitation. Combining measurements with calculations on
+an anion plane cluster permits to estimate the sitedependent electron-phonon
+coupling of the modes related to nitrogen excitation.
+",0,1,0,0,0,0
+9023,Thermodynamic and kinetic fragility of Freon113: the most fragile plastic crystal,"  We present a dynamic and thermodynamic study of the orientational glass
+former Freon113 (CCl2F-CClF2) in order to analyze its kinetic and thermodynamic
+fragilities. Freon113 displays internal molecular degrees of freedom which
+promote a complex energy landscape. Experimental specific heat and its
+microscopic origin, the vibrational density of states from inelastic neutron
+scattering, together with the orientational dynamics obtained by means of
+dielectric spectroscopy have revealed the highest fragility value, both
+thermodynamic and kinetic, found for this orientational glass former. The
+excess in both Debye-reduced specific heat and density of states (boson peak)
+evidences the existence of glassy low-energy excitations. We demonstrate that
+early proposed correlations between the boson peak and the Debye specific heat
+value are elusive as revealed by the clear counterexample of the studied case.
+",0,1,0,0,0,0
+9024,Semi-supervised Feature Learning For Improving Writer Identification,"  Data augmentation is usually used by supervised learning approaches for
+offline writer identification, but such approaches require extra training data
+and potentially lead to overfitting errors. In this study, a semi-supervised
+feature learning pipeline was proposed to improve the performance of writer
+identification by training with extra unlabeled data and the original labeled
+data simultaneously. Specifically, we proposed a weighted label smoothing
+regularization (WLSR) method for data augmentation, which assigned the weighted
+uniform label distribution to the extra unlabeled data. The WLSR method could
+regularize the convolutional neural network (CNN) baseline to allow more
+discriminative features to be learned to represent the properties of different
+writing styles. The experimental results on well-known benchmark datasets
+(ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning
+approach could significantly improve the baseline measurement and perform
+competitively with existing writer identification approaches. Our findings
+provide new insights into offline write identification.
+",0,0,0,1,0,0
+9025,Error Bounds for Piecewise Smooth and Switching Regression,"  The paper deals with regression problems, in which the nonsmooth target is
+assumed to switch between different operating modes. Specifically, piecewise
+smooth (PWS) regression considers target functions switching deterministically
+via a partition of the input space, while switching regression considers
+arbitrary switching laws. The paper derives generalization error bounds in
+these two settings by following the approach based on Rademacher complexities.
+For PWS regression, our derivation involves a chaining argument and a
+decomposition of the covering numbers of PWS classes in terms of the ones of
+their component functions and the capacity of the classifier partitioning the
+input space. This yields error bounds with a radical dependency on the number
+of modes. For switching regression, the decomposition can be performed directly
+at the level of the Rademacher complexities, which yields bounds with a linear
+dependency on the number of modes. By using once more chaining and a
+decomposition at the level of covering numbers, we show how to recover a
+radical dependency. Examples of applications are given in particular for PWS
+and swichting regression with linear and kernel-based component functions.
+",1,0,0,1,0,0
+9026,Civil Asset Forfeiture: A Judicial Perspective,"  Civil Asset Forfeiture (CAF) is a longstanding and controversial legal
+process viewed on the one hand as a powerful tool for combating drug crimes and
+on the other hand as a violation of the rights of US citizens. Data used to
+support both sides of the controversy to date has come from government sources
+representing records of the events at the time of occurrence. Court dockets
+represent litigation events initiated following the forfeiture, however, and
+can thus provide a new perspective on the CAF legal process. This paper will
+show new evidence supporting existing claims about the growth of the practice
+and bias in its application based on the quantitative analysis of data derived
+from these court cases.
+",1,0,0,0,0,0
+9027,Covariance-Insured Screening,"  Modern bio-technologies have produced a vast amount of high-throughput data
+with the number of predictors far greater than the sample size. In order to
+identify more novel biomarkers and understand biological mechanisms, it is
+vital to detect signals weakly associated with outcomes among
+ultrahigh-dimensional predictors. However, existing screening methods, which
+typically ignore correlation information, are likely to miss these weak
+signals. By incorporating the inter-feature dependence, we propose a
+covariance-insured screening methodology to identify predictors that are
+jointly informative but only marginally weakly associated with outcomes. The
+validity of the method is examined via extensive simulations and real data
+studies for selecting potential genetic factors related to the onset of cancer.
+",0,0,0,1,0,0
+9028,Amari Functors and Dynamics in Gauge Structures,"  We deal with finite dimensional differentiable manifolds. All items are
+concerned with are differentiable as well. The class of differentiability is
+$C^\infty$. A metric structure in a vector bundle $E$ is a constant rank
+symmetric bilinear vector bundle homomorphism of $E\times E$ in the trivial
+bundle line bundle. We address the question whether a given gauge structure in
+$E$ is metric. That is the main concerns. We use generalized Amari functors of
+the information geometry for introducing two index functions defined in the
+moduli space of gauge structures in $E$. Beside we introduce a differential
+equation whose analysis allows to link the new index functions just mentioned
+with the main concerns. We sketch applications in the differential geometry
+theory of statistics. Reader interested in a former forum on the question
+whether a giving connection is metric are referred to appendix.
+",0,0,1,0,0,0
+9029,An Experimental Analysis of the Power Consumption of Convolutional Neural Networks for Keyword Spotting,"  Nearly all previous work on small-footprint keyword spotting with neural
+networks quantify model footprint in terms of the number of parameters and
+multiply operations for a feedforward inference pass. These values are,
+however, proxy measures since empirical performance in actual deployments is
+determined by many factors. In this paper, we study the power consumption of a
+family of convolutional neural networks for keyword spotting on a Raspberry Pi.
+We find that both proxies are good predictors of energy usage, although the
+number of multiplies is more predictive than the number of model parameters. We
+also confirm that models with the highest accuracies are, unsurprisingly, the
+most power hungry.
+",1,0,0,0,0,0
+9030,"Formal Geometric Quantization III, Functoriality in the spin-c setting","  In this paper, we prove a functorial aspect of the formal geometric
+quantization procedure of non-compact spin-c manifolds.
+",0,0,1,0,0,0
+9031,Infinitesimal perturbation analysis for risk measures based on the Smith max-stable random field,"  When using risk or dependence measures based on a given underlying model, it
+is essential to be able to quantify the sensitivity or robustness of these
+measures with respect to the model parameters. In this paper, we consider an
+underlying model which is very popular in spatial extremes, the Smith
+max-stable random field. We study the sensitivity properties of risk or
+dependence measures based on the values of this field at a finite number of
+locations. Max-stable fields play a key role, e.g., in the modelling of natural
+disasters. As their multivariate density is generally not available for more
+than three locations, the Likelihood Ratio Method cannot be used to estimate
+the derivatives of the risk measures with respect to the model parameters.
+Thus, we focus on a pathwise method, the Infinitesimal Perturbation Analysis
+(IPA). We provide a convenient and tractable sufficient condition for
+performing IPA, which is intricate to obtain because of the very structure of
+max-stable fields involving pointwise maxima over an infinite number of random
+functions. IPA enables the consistent estimation of the considered measures'
+derivatives with respect to the parameters characterizing the spatial
+dependence. We carry out a simulation study which shows that the approach
+performs well in various configurations.
+",0,0,0,0,0,1
+9032,Modeling and Analysis of Two-Way Relay Non-Orthogonal Multiple Access Systems,"  A two-way relay non-orthogonal multiple access (TWR-NOMA) system is
+investigated, where two groups of NOMA users exchange messages with the aid of
+one half-duplex (HD) decode-and-forward (DF) relay. Since the
+signal-plus-interference-to-noise ratios (SINRs) of NOMA signals mainly depend
+on effective successive interference cancellation (SIC) schemes, imperfect SIC
+(ipSIC) and perfect SIC (pSIC) are taken into account. In order to characterize
+the performance of TWR-NOMA systems, we first derive closed-form expressions
+for both exact and asymptotic outage probabilities of NOMA users' signals with
+ipSIC/pSIC. Based on the derived results, the diversity order and throughput of
+the system are examined. Then we study the ergodic rates of users' signals by
+providing the asymptotic analysis in high SNR regimes. Lastly, numerical
+simulations are provided to verify the analytical results and show that: 1)
+TWR-NOMA is superior to TWR-OMA in terms of outage probability in low SNR
+regimes; 2) Due to the impact of interference signal (IS) at the relay, error
+floors and throughput ceilings exist in outage probabilities and ergodic rates
+for TWR-NOMA, respectively; and 3) In delay-limited transmission mode, TWR-NOMA
+with ipSIC and pSIC have almost the same energy efficiency. However, in
+delay-tolerant transmission mode, TWR-NOMA with pSIC is capable of achieving
+larger energy efficiency compared to TWR-NOMA with ipSIC.
+",1,0,0,0,0,0
+9033,Totally positive matrices and dilogarithm identities,"  We show that two involutions on the variety $N_n^+$ of upper triangular
+totally positive matrices are related, on the one hand, to the tetrahedron
+equation and, on the other hand, to the action of the symmetric group $S_3$ on
+some subvariety of $N_n^+$ and on the set of certain functions on $N_n^+$.
+Using these involutions, we obtain a family of dilogarithm identities involving
+minors of totally positive matrices. These identities admit a form manifestly
+invariant under the action of the symmetric group $S_3$.
+",0,0,1,0,0,0
+9034,On the zeros of random harmonic polynomials: the Weyl model,"  Li and Wei (2009) studied the density of zeros of Gaussian harmonic
+polynomials with independent Gaussian coefficients. They derived a formula for
+the expected number of zeros of random harmonic polynomials as well as
+asymptotics for the case that the polynomials are drawn from the Kostlan
+ensemble. In this paper we extend their work to cover the case that the
+polynomials are drawn from the Weyl ensemble by deriving asymptotics for this
+class of harmonic polynomials.
+",0,0,1,0,0,0
+9035,Online deforestation detection,"  Deforestation detection using satellite images can make an important
+contribution to forest management. Current approaches can be broadly divided
+into those that compare two images taken at similar periods of the year and
+those that monitor changes by using multiple images taken during the growing
+season. The CMFDA algorithm described in Zhu et al. (2012) is an algorithm that
+builds on the latter category by implementing a year-long, continuous,
+time-series based approach to monitoring images. This algorithm was developed
+for 30m resolution, 16-day frequency reflectance data from the Landsat
+satellite. In this work we adapt the algorithm to 1km, 16-day frequency
+reflectance data from the modis sensor aboard the Terra satellite. The CMFDA
+algorithm is composed of two submodels which are fitted on a pixel-by-pixel
+basis. The first estimates the amount of surface reflectance as a function of
+the day of the year. The second estimates the occurrence of a deforestation
+event by comparing the last few predicted and real reflectance values. For this
+comparison, the reflectance observations for six different bands are first
+combined into a forest index. Real and predicted values of the forest index are
+then compared and high absolute differences for consecutive observation dates
+are flagged as deforestation events. Our adapted algorithm also uses the two
+model framework. However, since the modis 13A2 dataset used, includes
+reflectance data for different spectral bands than those included in the
+Landsat dataset, we cannot construct the forest index. Instead we propose two
+contrasting approaches: a multivariate and an index approach similar to that of
+CMFDA.
+",1,0,0,1,0,0
+9036,Analysis of Different Approaches of Parallel Block Processing for K-Means Clustering Algorithm,"  Distributed Computation has been a recent trend in engineering research.
+Parallel Computation is widely used in different areas of Data Mining, Image
+Processing, Simulating Models, Aerodynamics and so forth. One of the major
+usage of Parallel Processing is widely implemented for clustering the satellite
+images of size more than dimension of 1000x1000 in a legacy system. This paper
+mainly focuses on the different approaches of parallel block processing such as
+row-shaped, column-shaped and square-shaped. These approaches are applied for
+classification problem. These approaches is applied to the K-Means clustering
+algorithm as this is widely used for the detection of features for high
+resolution orthoimagery satellite images. The different approaches are
+analyzed, which lead to reduction in execution time and resulted the influence
+of improvement in performance measurement compared to sequential K-Means
+Clustering algorithm.
+",1,0,0,0,0,0
+9037,Preserving Order of Data When Validating Defect Prediction Models,"  [Context] The use of defect prediction models, such as classifiers, can
+support testing resource allocations by using data of the previous releases of
+the same project for predicting which software components are likely to be
+defective. A validation technique, hereinafter technique defines a specific way
+to split available data in training and test sets to measure a classifier
+accuracy. Time-series techniques have the unique ability to preserve the
+temporal order of data; i.e., preventing the testing set to have data
+antecedent to the training set. [Aim] The aim of this paper is twofold: first
+we check if there is a difference in the classifiers accuracy measured by
+time-series versus non-time-series techniques. Afterward, we check for a
+possible reason for this difference, i.e., if defect rates change across
+releases of a project. [Method] Our method consists of measuring the accuracy,
+i.e., AUC, of 10 classifiers on 13 open and two closed projects by using three
+validation techniques, namely cross validation, bootstrap, and walk-forward,
+where only the latter is a time-series technique. [Results] We find that the
+AUC of the same classifier used on the same project and measured by 10-fold
+varies compared to when measured by walk-forward in the range [-0.20, 0.22],
+and it is statistically different in 45% of the cases. Similarly, the AUC
+measured by bootstrap varies compared to when measured by walk-forward in the
+range [-0.17, 0.43], and it is statistically different in 56% of the cases.
+[Conclusions] We recommend choosing the technique to be used by carefully
+considering the conclusions to draw, the property of the available datasets,
+and the level of realism with the classifier usage scenario.
+",1,0,0,0,0,0
+9038,Graphettes: Constant-time determination of graphlet and orbit identity including (possibly disconnected) graphlets up to size 8,"  Graphlets are small connected induced subgraphs of a larger graph $G$.
+Graphlets are now commonly used to quantify local and global topology of
+networks in the field. Methods exist to exhaustively enumerate all graphlets
+(and their orbits) in large networks as efficiently as possible using orbit
+counting equations. However, the number of graphlets in $G$ is exponential in
+both the number of nodes and edges in $G$. Enumerating them all is already
+unacceptably expensive on existing large networks, and the problem will only
+get worse as networks continue to grow in size and density. Here we introduce
+an efficient method designed to aid statistical sampling of graphlets up to
+size $k=8$ from a large network. We define graphettes as the generalization of
+graphlets allowing for disconnected graphlets. Given a particular (undirected)
+graphette $g$, we introduce the idea of the canonical graphette $\mathcal K(g)$
+as a representative member of the isomorphism group $Iso(g)$ of $g$. We compute
+the mapping $\mathcal K$, in the form of a lookup table, from all
+$2^{k(k-1)/2}$ undirected graphettes $g$ of size $k\le 8$ to their canonical
+representatives $\mathcal K(g)$, as well as the permutation that transforms $g$
+to $\mathcal K(g)$. We also compute all automorphism orbits for each canonical
+graphette. Thus, given any $k\le 8$ nodes in a graph $G$, we can in constant
+time infer which graphette it is, as well as which orbit each of the $k$ nodes
+belongs to. Sampling a large number $N$ of such $k$-sets of nodes provides an
+approximation of both the distribution of graphlets and orbits across $G$, and
+the orbit degree vector at each node.
+",1,0,0,0,0,0
+9039,The toric sections: a simple introduction,"  We review, from a didactic point of view, the definition of a toric section
+and the different shapes it can take. We'll then discuss some properties of
+this curve, investigate its analogies and differences with the most renowned
+conic section and show how to build its general quartic equation. A curious and
+unexpected result was to find that, with some algebraic manipulation, a toric
+section can also be obtained as the intersection of a cylinder with a cone.
+Finally we'll show how it is possible to construct and represent toric sections
+in the 3D Graphics view of Geogebra. In the article only elementary algebra is
+used, and the requirements to follow it are just some notion of goniometry and
+of tridimensional analytic geometry.
+",0,0,1,0,0,0
+9040,"Quantum Field Theory, Quantum Geometry, and Quantum Algebras","  We demonstrate how one can see quantization of geometry, and quantum
+algebraic structure in supersymmetric gauge theory.
+",0,0,1,0,0,0
+9041,Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning,"  Embedded, continual learning for autonomous and adaptive behavior is a key
+application of neuromorphic hardware. However, neuromorphic implementations of
+embedded learning at large scales that are both flexible and efficient have
+been hindered by a lack of a suitable algorithmic framework. As a result, the
+most neuromorphic hardware is trained off-line on large clusters of dedicated
+processors or GPUs and transferred post hoc to the device. We address this by
+introducing the neural and synaptic array transceiver (NSAT), a neuromorphic
+computational framework facilitating flexible and efficient embedded learning
+by matching algorithmic requirements and neural and synaptic dynamics. NSAT
+supports event-driven supervised, unsupervised and reinforcement learning
+algorithms including deep learning. We demonstrate the NSAT in a wide range of
+tasks, including the simulation of Mihalas-Niebur neuron, dynamic neural
+fields, event-driven random back-propagation for event-based deep learning,
+event-based contrastive divergence for unsupervised learning, and voltage-based
+learning rules for sequence learning. We anticipate that this contribution will
+establish the foundation for a new generation of devices enabling adaptive
+mobile systems, wearable devices, and robots with data-driven autonomy.
+",1,0,0,0,0,0
+9042,Discrete Wavelet Transform Based Algorithm for Recognition of QRS Complexes,"  This paper proposes the application of Discrete Wavelet Transform (DWT) to
+detect the QRS (ECG is characterized by a recurrent wave sequence of P, QRS and
+T-wave) of an electrocardiogram (ECG) signal. Wavelet Transform provides
+localization in both time and frequency. In preprocessing stage, DWT is used to
+remove the baseline wander in the ECG signal. The performance of the algorithm
+of QRS detection is evaluated against the standard MIT BIH (Massachusetts
+Institute of Technology, Beth Israel Hospital) Arrhythmia database. The average
+QRS complexes detection rate of 98.1 % is achieved.
+",1,0,0,0,0,0
+9043,Multipolar moments of weak lensing signal around clusters. Weighing filaments in harmonic space,"  Context. Upcoming weak lensing surveys such as Euclid will provide an
+unprecedented opportunity to quantify the geometry and topology of the cosmic
+web, in particular in the vicinity of lensing clusters. Aims. Understanding the
+connectivity of the cosmic web with unbiased mass tracers, such as weak
+lensing, is of prime importance to probe the underlying cosmology, seek
+dynamical signatures of dark matter, and quantify environmental effects on
+galaxy formation. Methods. Mock catalogues of galaxy clusters are extracted
+from the N-body PLUS simulation. For each cluster, the aperture multipolar
+moments of the convergence are calculated in two annuli (inside and outside the
+virial radius). By stacking their modulus, a statistical estimator is built to
+characterise the angular mass distribution around clusters. The moments are
+compared to predictions from perturbation theory and spherical collapse.
+Results. The main weakly chromatic excess of multipolar power on large scales
+is understood as arising from the contraction of the primordial cosmic web
+driven by the growing potential well of the cluster. Besides this boost, the
+quadrupole prevails in the cluster (ellipsoidal) core, while at the outskirts,
+harmonic distortions are spread on small angular modes, and trace the
+non-linear sharpening of the filamentary structures. Predictions for the signal
+amplitude as a function of the cluster-centric distance, mass, and redshift are
+presented. The prospects of measuring this signal are estimated for current and
+future lensing data sets. Conclusions. The Euclid mission should provide all
+the necessary information for studying the cosmic evolution of the connectivity
+of the cosmic web around lensing clusters using multipolar moments and probing
+unique signatures of, for example, baryons and warm dark matter.
+",0,1,0,0,0,0
+9044,Charge and spin transport on graphene grain boundaries in a quantizing magnetic field,"  We study charge and spin transport along grain boundaries in single layer
+graphene in the presence of a quantizing magnetic field. Transport states in a
+grain boundary are produced by hybridization of Landau zero modes with
+interfacial states. In selected energy regimes quantum Hall edge states can be
+deflected either fully or partially into grain boundary states. The degree of
+edge state deflection is studied in the nonlocal conductance and in the shot
+noise. We also consider the possibility of grain boundaries as gate-switchable
+spin filters, a functionality enabled by counterpropagating transport channels
+laterally confined in the grain boundary.
+",0,1,0,0,0,0
+9045,Point Cloud Movement For Fully Lagrangian Meshfree Methods,"  In Lagrangian meshfree methods, the underlying spatial discretization,
+referred to as a point cloud or a particle cloud, moves with the flow velocity.
+In this paper, we consider different numerical methods of performing this
+movement of points or particles. The movement is most commonly done by a first
+order method, which assumes the velocity to be constant within a time step. We
+show that this method is very inaccurate and that it introduces volume and mass
+conservation errors. We further propose new methods for the same which
+prescribe an additional ODE system that describes the characteristic velocity.
+Movement is then performed along this characteristic velocity. The first new
+way of moving points is an extension of mesh-based streamline tracing ideas to
+meshfree methods. In the second way, the movement is done based on the
+difference in approximated streamlines between two time levels, which
+approximates the pathlines in unsteady flow. Numerical comparisons show these
+method to be vastly superior to the conventionally used first order method.
+",0,1,1,0,0,0
+9046,A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks,"  We consider the problem of optimally designing a body wireless sensor
+network, while taking into account the uncertainty of data generation of
+biosensors. Since the related min-max robustness Integer Linear Programming
+(ILP) problem can be difficult to solve even for state-of-the-art commercial
+optimization solvers, we propose an original heuristic for its solution. The
+heuristic combines deterministic and probabilistic variable fixing strategies,
+guided by the information coming from strengthened linear relaxations of the
+ILP robust model, and includes a very large neighborhood search for reparation
+and improvement of generated solutions, formulated as an ILP problem solved
+exactly. Computational tests on realistic instances show that our heuristic
+finds solutions of much higher quality than a state-of-the-art solver and than
+an effective benchmark heuristic.
+",1,0,1,0,0,0
+9047,Strain broadening of the 1042-nm zero-phonon line of the NV- center in diamond: a promising spectroscopic tool for defect tomography,"  The negatively charged nitrogen-vacancy (NV-) center in diamond is a
+promising candidate for many quantum applications. Here, we examine the
+splitting and broadening of the center's infrared (IR) zero-phonon line (ZPL).
+We develop a model for these effects that accounts for the strain induced by
+photo-dependent microscopic distributions of defects. We apply this model to
+interpret observed variations of the IR ZPL shape with temperature and
+photoexcitation conditions. We identify an anomalous temperature dependent
+broadening mechanism and that defects other than the substitutional nitrogen
+center significantly contribute to strain broadening. The former conclusion
+suggests the presence of a strong Jahn-Teller effect in the center's singlet
+levels and the latter indicates that major sources of broadening are yet to be
+identified. These conclusions have important implications for the understanding
+of the center and the engineering of diamond quantum devices. Finally, we
+propose that the IR ZPL can be used as a sensitive spectroscopic tool for
+probing microscopic strain fields and performing defect tomography.
+",0,1,0,0,0,0
+9048,Learning Depthwise Separable Graph Convolution from Data Manifold,"  Convolution Neural Network (CNN) has gained tremendous success in computer
+vision tasks with its outstanding ability to capture the local latent features.
+Recently, there has been an increasing interest in extending convolution
+operations to the non-Euclidean geometry. Although various types of convolution
+operations have been proposed for graphs or manifolds, their connections with
+traditional convolution over grid-structured data are not well-understood. In
+this paper, we show that depthwise separable convolution can be successfully
+generalized for the unification of both graph-based and grid-based convolution
+methods. Based on this insight we propose a novel Depthwise Separable Graph
+Convolution (DSGC) approach which is compatible with the tradition convolution
+network and subsumes existing convolution methods as special cases. It is
+equipped with the combined strengths in model expressiveness, compatibility
+(relatively small number of parameters), modularity and computational
+efficiency in training. Extensive experiments show the outstanding performance
+of DSGC in comparison with strong baselines on multi-domain benchmark datasets.
+",1,0,0,1,0,0
+9049,Candidate Hα emission and absorption line sources in the Galactic Bulge Survey,"  We present a catalogue of candidate H{\alpha} emission and absorption line
+sources and blue objects in the Galactic Bulge Survey (GBS) region. We use a
+point source catalogue of the GBS fields (two strips of (l x b) = (6 x 1)
+degrees centred at b = 1.5 above and below the Galactic centre), covering the
+magnitude range 16 < r' < 22.5. We utilize (r'-i', r'-H{\alpha}) colour-colour
+diagrams to select H{\alpha} emission and absorption line candidates, and also
+identify blue objects (compared to field stars) using the r'-i' colour index.
+We identify 1337 H{\alpha} emission line candidates and 336 H{\alpha}
+absorption line candidates. These catalogues likely contain a plethora of
+sources, ranging from active (binary) stars, early-type emission line objects,
+cataclysmic variables (CVs) and low-mass X-ray binaries (LMXBs) to background
+active galactic nuclei (AGN). The 389 blue objects we identify are likely
+systems containing a compact object, such as CVs, planetary nebulae and LMXBs.
+Hot subluminous dwarfs (sdO/B stars) are also expected to be found as blue
+outliers. Crossmatching our outliers with the GBS X-ray catalogue yields
+sixteen sources, including seven (magnetic) CVs and one qLMXB candidate among
+the emission line candidates, and one background AGN for the absorption line
+candidates. One of the blue outliers is a high state AM CVn system.
+Spectroscopic observations combined with the multi-wavelength coverage of this
+area, including X-ray, ultraviolet and (time-resolved) optical and infrared
+observations, can be used to further constrain the nature of individual
+sources.
+",0,1,0,0,0,0
+9050,Timely Updates over an Erasure Channel,"  Using an age of information (AoI) metric, we examine the transmission of
+coded updates through a binary erasure channel to a monitor/receiver. We start
+by deriving the average status update age of an infinite incremental redundancy
+(IIR) system in which the transmission of a k-symbol update continuesuntil k
+symbols are received. This system is then compared to a fixed redundancy (FR)
+system in which each update is transmitted as an n symbol packet and the packet
+is successfully received if and only if at least k symbols are received. If
+fewer than k symbols are received, the update is discarded. Unlike the IIR
+system, the FR system requires no feedback from the receiver. For a single
+monitor system, we show that tuning the redundancy to the symbol erasure rate
+enables the FR system to perform as well as the IIR system. As the number of
+monitors is increased, the FR system outperforms the IIR system that guarantees
+delivery of all updates to all monitors.
+",1,0,0,0,0,0
+9051,"Stellar Absorption Line Analysis of Local Star-Forming Galaxies: The Relation Between Stellar Mass, Metallicity, Dust Attenuation and Star Formation Rate","  We analyze the optical continuum of star-forming galaxies in SDSS by fitting
+stacked spectra with stellar population synthesis models to investigate the
+relation between stellar mass, stellar metallicity, dust attenuation and star
+formation rate. We fit models calculated with star formation and chemical
+evolution histories that are derived empirically from multi-epoch observations
+of the stellar mass---star formation rate and the stellar mass---gas-phase
+metallicity relations, respectively. We also fit linear combinations of single
+burst models with a range of metallicities and ages. Star formation and
+chemical evolution histories are unconstrained for these models. The stellar
+mass---stellar metallicity relations obtained from the two methods agree with
+the relation measured from individual supergiant stars in nearby galaxies.
+These relations are also consistent with the relation obtained from emission
+line analysis of gas-phase metallicity after accounting for systematic offsets
+in the gas-phase-metallicity. We measure dust attenuation of the stellar
+continuum and show that its dependence on stellar mass and star formation rate
+is consistent with previously reported results derived from nebular emission
+lines. However, stellar continuum attenuation is smaller than nebular emission
+line attenuation. The continuum-to-nebular attenuation ratio depends on stellar
+mass and is smaller in more massive galaxies. Our consistent analysis of
+stellar continuum and nebular emission lines paves the way for a comprehensive
+investigation of stellar metallicities of star-forming and quiescent galaxies.
+",0,1,0,0,0,0
+9052,Stratification as a general variance reduction method for Markov chain Monte Carlo,"  The Eigenvector Method for Umbrella Sampling (EMUS) belongs to a popular
+class of methods in statistical mechanics which adapt the principle of
+stratified survey sampling to the computation of free energies. By theoretical
+analysis and numerical experiments, we demonstrate that EMUS is an efficient
+general method for computing averages with respect to arbitrary target
+distributions. We show that EMUS can be dramatically more efficient than direct
+MCMC when the target distribution is multimodal or when the goal is to compute
+tail probabilities.
+",0,1,0,1,0,0
+9053,Adaptive MCMC via Combining Local Samplers,"  Markov chain Monte Carlo (MCMC) methods are widely used in machine learning.
+One of the major problems with MCMC is the question of how to design chains
+that mix fast over the whole space; in particular, how to select the parameters
+of an MCMC algorithm. Here we take a different approach and, similarly to
+parallel MCMC methods, instead of trying to find a single chain to sample from
+the whole distribution, we combine samples from several chains run in parallel,
+each exploring only a few modes. The chains are prioritized based on the kernel
+Stein discrepancy, which provides a good measure of performance locally. The
+samples from the independent chains are combined using a novel technique for
+estimating the probability of different regions of the sample space.
+Experimental results demonstrate that the resulting algorithm may provide
+significant speedups in different sampling problems. Most importantly, when
+combined with the state-of-the-art NUTS algorithm as the base MCMC sampler, our
+algorithm remained competitive with the basic version of NUTS on sampling from
+unimodal distributions, while significantly outperformed state-of-the-art
+competitors on synthetic multimodal problems as well as on a challenging sensor
+localization task.
+",1,0,0,1,0,0
+9054,GAMBIT: The Global and Modular Beyond-the-Standard-Model Inference Tool,"  We describe the open-source global fitting package GAMBIT: the Global And
+Modular Beyond-the-Standard-Model Inference Tool. GAMBIT combines extensive
+calculations of observables and likelihoods in particle and astroparticle
+physics with a hierarchical model database, advanced tools for automatically
+building analyses of essentially any model, a flexible and powerful system for
+interfacing to external codes, a suite of different statistical methods and
+parameter scanning algorithms, and a host of other utilities designed to make
+scans faster, safer and more easily-extendible than in the past. Here we give a
+detailed description of the framework, its design and motivation, and the
+current models and other specific components presently implemented in GAMBIT.
+Accompanying papers deal with individual modules and present first GAMBIT
+results. GAMBIT can be downloaded from gambit.hepforge.org.
+",0,1,0,0,0,0
+9055,Odd holes in bull-free graphs,"  The complexity of testing whether a graph contains an induced odd cycle of
+length at least five is currently unknown. In this paper we show that this can
+be done in polynomial time if the input graph has no induced subgraph
+isomorphic to the bull (a triangle with two disjoint pendant edges).
+",1,0,0,0,0,0
+9056,Magnetic-Field-Induced Superconductivity in Ultrathin Pb Films with Magnetic Impurities,"  It is well known that external magnetic fields and magnetic moments of
+impurities both suppress superconductivity. Here, we demonstrate that their
+combined effect enhances the superconductivity of a few atomic layer thick Pb
+films grown on a cleaved GaAs(110) surface. A Ce-doped film, where
+superconductivity is totally suppressed at zero-field, actually turns
+superconducting when an external magnetic field is applied parallel to the
+conducting plane. For films with Mn adatoms, the screening of the magnetic
+moment by conduction electrons, i.e., the Kondo singlet formation, becomes
+important. We found that the degree of screening can be reduced by capping the
+Pb film with a Au layer, and observed the positive magnetic field dependence of
+the superconducting transition temperature.
+",0,1,0,0,0,0
+9057,Unimodal Category and the Monotonicity Conjecture,"  We completely characterize the unimodal category for functions $f:\mathbb
+R\to[0,\infty)$ using a decomposition theorem obtained by generalizing the
+sweeping algorithm of Baryshnikov and Ghrist. We also give a characterization
+of the unimodal category for functions $f:S^1\to[0,\infty)$ and provide an
+algorithm to compute the unimodal category of such a function in the case of
+finitely many critical points.
+We then turn to the monotonicity conjecture of Baryshnikov and Ghrist. We
+show that this conjecture is true for functions on $\mathbb R$ and $S^1$ using
+the above characterizations and that it is false on certain graphs and on the
+Euclidean plane by providing explicit counterexamples. We also show that it
+holds for functions on the Euclidean plane whose Morse-Smale graph is a tree
+using a result of Hickok, Villatoro and Wang.
+",0,0,1,0,0,0
+9058,Holomorphic primary fields in free CFT4 and Calabi-Yau orbifolds,"  Counting formulae for general primary fields in free four dimensional
+conformal field theories of scalars, vectors and matrices are derived. These
+are specialised to count primaries which obey extremality conditions defined in
+terms of the dimensions and left or right spins (i.e. in terms of relations
+between the charges under the Cartan subgroup of $SO(4,2)$). The construction
+of primary fields for scalar field theory is mapped to a problem of determining
+multi-variable polynomials subject to a system of symmetry and differential
+constraints. For the extremal primaries, we give a construction in terms of
+holomorphic polynomial functions on permutation orbifolds, which are shown to
+be Calabi-Yau spaces.
+",0,0,1,0,0,0
+9059,A Deep Generative Framework for Paraphrase Generation,"  Paraphrase generation is an important problem in NLP, especially in question
+answering, information retrieval, information extraction, conversation systems,
+to name a few. In this paper, we address the problem of generating paraphrases
+automatically. Our proposed method is based on a combination of deep generative
+models (VAE) with sequence-to-sequence models (LSTM) to generate paraphrases,
+given an input sentence. Traditional VAEs when combined with recurrent neural
+networks can generate free text but they are not suitable for paraphrase
+generation for a given sentence. We address this problem by conditioning the
+both, encoder and decoder sides of VAE, on the original sentence, so that it
+can generate the given sentence's paraphrases. Unlike most existing models, our
+model is simple, modular and can generate multiple paraphrases, for a given
+sentence. Quantitative evaluation of the proposed method on a benchmark
+paraphrase dataset demonstrates its efficacy, and its performance improvement
+over the state-of-the-art methods by a significant margin, whereas qualitative
+human evaluation indicate that the generated paraphrases are well-formed,
+grammatically correct, and are relevant to the input sentence. Furthermore, we
+evaluate our method on a newly released question paraphrase dataset, and
+establish a new baseline for future research.
+",1,0,0,0,0,0
+9060,Stiff-response-induced instability for chemotactic bacteria and flux-limited Keller-Segel equation,"  Collective motion of chemotactic bacteria as E. Coli relies, at the
+individual level, on a continuous reorientation by runs and tumbles. It has
+been established that the length of run is decided by a stiff response to a
+temporal sensingof chemical cues along the pathway.We describe a novel
+mechanism for pattern formation stemming from the stiffness of chemotactic
+response relying on a kinetic chemotaxis model which includes a recently
+discovered formalism for the bacterial chemotaxis. We prove instability both
+for amicroscopic description in the space-velocity space and for the
+macroscopic equation, a flux-limited Keller-Segel equation, which has attracted
+much attention recently.A remarkable property is that the unstable frequencies
+remain bounded, as it is the case in Turing instability. Numerical
+illustrations based on a powerful Monte Carlo method show that the stationary
+homogeneous state of population density isdestabilized and periodic patterns
+are generated in realistic ranges of parameters. These theoretical developments
+are in accordance with several biological observations.
+",0,1,1,0,0,0
+9061,The Pfaffian state in an electron gas with small Landau level gaps,"  Landau level mixing plays an important role in the Pfaffian (or
+anti-Pfaffian) states. In ZnO the Landau level gap is essentially an order of
+magnitude smaller than that in a GaAs quantum well. We introduce the screened
+Coulomb interaction in a single Landau level to tackle that situation. Here we
+study the overlap of the ground state and the Pfaffian (or anti-Pfaffian) state
+at evendenominator fractional quantum Hall (FQH) states present in ZnO. The
+overlap is strongly system size-dependent which suggests a newly proposed
+particle-hole symmetry Pfaffian ground state in the extreme Landau level mixing
+limit. When the ratio of Coulomb interaction to the Landau level gap \k{appa}
+varies, we find a possible topological phase transition in the range 2 <
+\k{appa} < 3, which was actually observed in an experiment. We then study how
+the width of quantum well combined with screening influences the overlap.
+",0,1,0,0,0,0
+9062,Three-Dimensional Numerical Modeling of Shear Stimulation of Naturally Fractured Reservoirs,"  Shear dilation based hydraulic stimulations enable exploitation of geothermal
+energy from reservoirs with inadequate initial permeability. While contributing
+to enhancing the reservoir's permeability, hydraulic stimulation processes may
+lead to undesired seismic activity. Here, we present a three dimensional
+numerical model aiming to increase understanding of this mechanism and its
+consequences. The fractured reservoir is modeled as a network of explicitly
+represented large scale fractures immersed in a permeable rock matrix. The
+numerical formulation is constructed by coupling three physical processes:
+fluid flow, fracture deformation, and rock matrix deformation. For flow
+simulations, the discrete fracture matrix model is used, which allows the fluid
+transport from high permeable conductive fractures to the rock matrix and vice
+versa. The mechanical behavior of the fractures is modeled using a hyperbolic
+model with reversible and irreversible deformations. Linear elasticity is
+assumed for the mechanical deformation and stress alteration of the rock
+matrix. Fractures are modeled as lower dimensional surfaces embodied in the
+domain, subjected to specific governing equations for their deformation along
+the tangential and normal directions. Both the fluid flow and momentum balance
+equations are approximated by finite volume discretizations. The new numerical
+model is demonstrated considering a three dimensional fractured formation with
+a network of 20 explicitly represented fractures. The effects of fluid exchange
+between fractures and rock matrix on the permeability evolution and the
+generated seismicity are examined for test cases resembling realistic reservoir
+conditions.
+",0,1,0,0,0,0
+9063,Decentralized DC MicroGrid Monitoring and Optimization via Primary Control Perturbations,"  We treat the emerging power systems with direct current (DC) MicroGrids,
+characterized with high penetration of power electronic converters. We rely on
+the power electronics to propose a decentralized solution for autonomous
+learning of and adaptation to the operating conditions of the DC Mirogrids; the
+goal is to eliminate the need to rely on an external communication system for
+such purpose. The solution works within the primary droop control loops and
+uses only local bus voltage measurements. Each controller is able to estimate
+(i) the generation capacities of power sources, (ii) the load demands, and
+(iii) the conductances of the distribution lines. To define a well-conditioned
+estimation problem, we employ decentralized strategy where the primary droop
+controllers temporarily switch between operating points in a coordinated
+manner, following amplitude-modulated training sequences. We study the use of
+the estimator in a decentralized solution of the Optimal Economic Dispatch
+problem. The evaluations confirm the usefulness of the proposed solution for
+autonomous MicroGrid operation.
+",1,0,0,0,0,0
+9064,AI4AI: Quantitative Methods for Classifying Host Species from Avian Influenza DNA Sequence,"  Avian Influenza breakouts cause millions of dollars in damage each year
+globally, especially in Asian countries such as China and South Korea. The
+impact magnitude of a breakout directly correlates to time required to fully
+understand the influenza virus, particularly the interspecies pathogenicity.
+The procedure requires laboratory tests that require resources typically
+lacking in a breakout emergency. In this study, we propose new quantitative
+methods utilizing machine learning and deep learning to correctly classify host
+species given raw DNA sequence data of the influenza virus, and provide
+probabilities for each classification. The best deep learning models achieve
+top-1 classification accuracy of 47%, and top-3 classification accuracy of 82%,
+on a dataset of 11 host species classes.
+",0,0,0,1,1,0
+9065,Constructing tame supercuspidal representations,"  A new approach to Jiu-Kang Yu's construction of tame supercuspidal
+representations of $p$-adic reductive groups is presented. Connections with the
+theory of cuspidal Deligne-Lusztig representations of finite groups of Lie type
+are also discussed.
+",0,0,1,0,0,0
+9066,Cohomologies on hypercomplex manifolds,"  We review some cohomological aspects of complex and hypercomplex manifolds
+and underline the differences between both realms. Furthermore, we try to
+highlight the similarities between compact complex surfaces on one hand and
+compact hypercomplex manifolds of real dimension 8 with holonomy of the Obata
+connection in SL(2,H) on the other hand.
+",0,0,1,0,0,0
+9067,Automorphism groups of quandles and related groups,"  In this paper we study different questions concerning automorphisms of
+quandles. For a conjugation quandle $Q={\rm Conj}(G)$ of a group $G$ we
+determine several subgroups of ${\rm Aut}(Q)$ and find necessary and sufficient
+conditions when these subgroups coincide with the whole group ${\rm Aut}(Q)$.
+In particular, we prove that ${\rm Aut}({\rm Conj}(G))={\rm Z}(G)\rtimes {\rm
+Aut}(G)$ if and only if either ${\rm Z}(G)=1$ or $G$ is one of the groups
+$\mathbb{Z}_2$, $\mathbb{Z}_2^2$ or $\mathbb{Z}_3$. For a big list of Takasaki
+quandles $T(G)$ of an abelian group $G$ with $2$-torsion we prove that the
+group of inner automorphisms ${\rm Inn}(T(G))$ is a Coxeter group. We study
+automorphisms of certain extensions of quandles and determine some interesting
+subgroups of the automorphism groups of these quandles. Also we classify finite
+quandles $Q$ with $3\leq k$-transitive action of ${\rm Aut}(Q)$.
+",0,0,1,0,0,0
+9068,Forward Amortized Inference for Likelihood-Free Variational Marginalization,"  In this paper, we introduce a new form of amortized variational inference by
+using the forward KL divergence in a joint-contrastive variational loss. The
+resulting forward amortized variational inference is a likelihood-free method
+as its gradient can be sampled without bias and without requiring any
+evaluation of either the model joint distribution or its derivatives. We prove
+that our new variational loss is optimized by the exact posterior marginals in
+the fully factorized mean-field approximation, a property that is not shared
+with the more conventional reverse KL inference. Furthermore, we show that
+forward amortized inference can be easily marginalized over large families of
+latent variables in order to obtain a marginalized variational posterior. We
+consider two examples of variational marginalization. In our first example we
+train a Bayesian forecaster for predicting a simplified chaotic model of
+atmospheric convection. In the second example we train an amortized variational
+approximation of a Bayesian optimal classifier by marginalizing over the model
+space. The result is a powerful meta-classification network that can solve
+arbitrary classification problems without further training.
+",0,0,0,1,0,0
+9069,Beyond-CMOS Device Benchmarking for Boolean and Non-Boolean Logic Applications,"  The latest results of benchmarking research are presented for a variety of
+beyond-CMOS charge- and spin-based devices. In addition to improving the
+device-level models, several new device proposals and a few majorly modified
+devices are investigated. Deep pipelining circuits are employed to boost the
+throughput of low-power devices. Furthermore, the benchmarking methodology is
+extended to interconnect-centric analyses and non-Boolean logic applications.
+In contrast to Boolean circuits, non-Boolean circuits based on the cellular
+neural network demonstrate that spintronic devices can potentially outperform
+conventional CMOS devices.
+",1,0,0,0,0,0
+9070,Cosmology and Convention,"  I argue that some important elements of the current cosmological model are
+""conventionalist"" in the sense defined by Karl Popper. These elements include
+dark matter and dark energy; both are auxiliary hypotheses that were invoked in
+response to observations that falsified the standard model as it existed at the
+time. The use of conventionalist stratagems in response to unexpected
+observations implies that the field of cosmology is in a state of ""degenerating
+problemshift"" in the language of Imre Lakatos. I show that the ""concordance""
+argument, often put forward by cosmologists in support of the current paradigm,
+is weaker than the convergence arguments that were made in the past in support
+of the atomic theory of matter or the quantization of energy.
+",0,1,0,0,0,0
+9071,Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering,"  Standard clustering algorithms usually find regular-structured clusters such
+as ellipsoidally- or spherically-dispersed groups, but are more challenged with
+groups lacking formal structure or definition. Syncytial clustering is the name
+that we introduce for methods that merge groups obtained from standard
+clustering algorithms in order to reveal complex group structure in the data.
+Here, we develop a distribution-free fully-automated syncytial clustering
+algorithm that can be used with $k$-means and other algorithms. Our approach
+computes the cumulative distribution function of the normed residuals from an
+appropriately fit $k$-groups model and calculates the nonparametric overlap
+between each pair of groups. Groups with high pairwise overlaps are merged as
+long as the generalized overlap decreases. Our methodology is always a top
+performer in identifying groups with regular and irregular structures in
+several datasets. The approach is also used to identify the distinct kinds of
+gamma ray bursts in the Burst and Transient Source Experiment 4Br catalog and
+also the distinct kinds of activation in a functional Magnetic Resonance
+Imaging study.
+",0,0,0,1,0,0
+9072,Goal-oriented Trajectories for Efficient Exploration,"  Exploration is a difficult challenge in reinforcement learning and even
+recent state-of-the art curiosity-based methods rely on the simple
+epsilon-greedy strategy to generate novelty. We argue that pure random walks do
+not succeed to properly expand the exploration area in most environments and
+propose to replace single random action choices by random goals selection
+followed by several steps in their direction. This approach is compatible with
+any curiosity-based exploration and off-policy reinforcement learning agents
+and generates longer and safer trajectories than individual random actions. To
+illustrate this, we present a task-independent agent that learns to reach
+coordinates in screen frames and demonstrate its ability to explore with the
+game Super Mario Bros. improving significantly the score of a baseline DQN
+agent.
+",0,0,0,1,0,0
+9073,Adaptive Sampling Strategies for Stochastic Optimization,"  In this paper, we propose a stochastic optimization method that adaptively
+controls the sample size used in the computation of gradient approximations.
+Unlike other variance reduction techniques that either require additional
+storage or the regular computation of full gradients, the proposed method
+reduces variance by increasing the sample size as needed. The decision to
+increase the sample size is governed by an inner product test that ensures that
+search directions are descent directions with high probability. We show that
+the inner product test improves upon the well known norm test, and can be used
+as a basis for an algorithm that is globally convergent on nonconvex functions
+and enjoys a global linear rate of convergence on strongly convex functions.
+Numerical experiments on logistic regression problems illustrate the
+performance of the algorithm.
+",0,0,0,1,0,0
+9074,Explicit estimates for the distribution of numbers free of large prime factors,"  There is a large literature on the asymptotic distribution of numbers free of
+large prime factors, so-called $\textit{smooth}$ or $\textit{friable}$ numbers.
+But there is very little known about this distribution that is numerically
+explicit. In this paper we follow the general plan for the saddle point
+argument of Hildebrand and Tenenbaum, giving explicit and fairly tight
+intervals in which the true count lies. We give two numerical examples of our
+method, and with the larger one, our interval is so tight we can exclude the
+famous Dickman-de Bruijn asymptotic estimate as too small and the
+Hildebrand-Tenenbaum main term as too large.
+",0,0,1,0,0,0
+9075,Completely integrally closed Prufer $v$-multiplication domains,"  We study the effects on $D$ of assuming that the power series ring $D[[X]]$
+is a $v$-domain or a PVMD. We show that a PVMD $D$ is completely integrally
+closed if and only if $\bigcap_{n=1}^{\infty }(I^{n})_{v}=(0)$ for every proper
+$t$-invertible $t$-ideal $I$ of $D$. Using this, we show that if $D$ is an AGCD
+domain, then $D[[X]]$ is integrally closed if and only if $D$ is a completely
+integrally closed PVMD with torsion $t$-class group. We also determine several
+classes of PVMDs for which being Archimedean is equivalent to being completely
+integrally closed and give some new characterizations of integral domains
+related to Krull domains.
+",0,0,1,0,0,0
+9076,Masked Autoregressive Flow for Density Estimation,"  Autoregressive models are among the best performing neural density
+estimators. We describe an approach for increasing the flexibility of an
+autoregressive model, based on modelling the random numbers that the model uses
+internally when generating data. By constructing a stack of autoregressive
+models, each modelling the random numbers of the next model in the stack, we
+obtain a type of normalizing flow suitable for density estimation, which we
+call Masked Autoregressive Flow. This type of flow is closely related to
+Inverse Autoregressive Flow and is a generalization of Real NVP. Masked
+Autoregressive Flow achieves state-of-the-art performance in a range of
+general-purpose density estimation tasks.
+",1,0,0,1,0,0
+9077,Temperature-dependent optical properties of plasmonic titanium nitride thin films,"  Due to their exceptional plasmonic properties, noble metals such as gold and
+silver have been the materials of choice for the demonstration of various
+plasmonic and nanophotonic phenomena. However, noble metals' softness, lack of
+tailorability and low melting point along with challenges in thin film
+fabrication and device integration have prevented the realization of real-life
+plasmonic devices.In the recent years, titanium nitride (TiN) has emerged as a
+promising plasmonic material with good metallic and refractory (high
+temperature stable) properties. The refractory nature of TiN could enable
+practical plasmonic devices operating at elevated temperatures for energy
+conversion and harsh-environment industries such as gas and oil. Here we report
+on the temperature dependent dielectric functions of TiN thin films of varying
+thicknesses in the technologically relevant visible and near-infrared
+wavelength range from 330 nm to 2000 nm for temperatures up to 900 0C using
+in-situ high temperature ellipsometry. Our findings show that the complex
+dielectric function of TiN at elevated temperatures deviates from the optical
+parameters at room temperature, indicating degradation in plasmonic properties
+both in the real and imaginary parts of the dielectric constant. However, quite
+strikingly, the relative changes of the optical properties of TiN are
+significantly smaller compared to its noble metal counterparts. Using
+simulations, we demonstrate that incorporating the temperature-induced
+deviations into the numerical models leads to significant differences in the
+optical responses of high temperature nanophotonic systems. These studies hold
+the key for accurate modeling of high temperature TiN based optical elements
+and nanophotonic systems for energy conversion, harsh-environment sensors and
+heat-assisted applications.
+",0,1,0,0,0,0
+9078,Succinct Partial Sums and Fenwick Trees,"  We consider the well-studied partial sums problem in succint space where one
+is to maintain an array of n k-bit integers subject to updates such that
+partial sums queries can be efficiently answered. We present two succint
+versions of the Fenwick Tree - which is known for its simplicity and
+practicality. Our results hold in the encoding model where one is allowed to
+reuse the space from the input data. Our main result is the first that only
+requires nk + o(n) bits of space while still supporting sum/update in O(log_b
+n) / O(b log_b n) time where 2 <= b <= log^O(1) n. The second result shows how
+optimal time for sum/update can be achieved while only slightly increasing the
+space usage to nk + o(nk) bits. Beyond Fenwick Trees, the results are primarily
+based on bit-packing and sampling - making them very practical - and they also
+allow for simple optimal parallelization.
+",1,0,0,0,0,0
+9079,Deep Morphing: Detecting bone structures in fluoroscopic X-ray images with prior knowledge,"  We propose approaches based on deep learning to localize objects in images
+when only a small training dataset is available and the images have low
+quality. That applies to many problems in medical image processing, and in
+particular to the analysis of fluoroscopic (low-dose) X-ray images, where the
+images have low contrast. We solve the problem by incorporating high-level
+information about the objects, which could be a simple geometrical model, like
+a circular outline, or a more complex statistical model. A simple geometrical
+representation can sufficiently describe some objects and only requires minimal
+labeling. Statistical shape models can be used to represent more complex
+objects. We propose computationally efficient two-stage approaches, which we
+call deep morphing, for both representations by fitting the representation to
+the output of a deep segmentation network.
+",0,0,0,1,0,0
+9080,Persistence Flamelets: multiscale Persistent Homology for kernel density exploration,"  In recent years there has been noticeable interest in the study of the ""shape
+of data"". Among the many ways a ""shape"" could be defined, topology is the most
+general one, as it describes an object in terms of its connectivity structure:
+connected components (topological features of dimension 0), cycles (features of
+dimension 1) and so on. There is a growing number of techniques, generally
+denoted as Topological Data Analysis, aimed at estimating topological
+invariants of a fixed object; when we allow this object to change, however,
+little has been done to investigate the evolution in its topology. In this work
+we define the Persistence Flamelets, a multiscale version of one of the most
+popular tool in TDA, the Persistence Landscape. We examine its theoretical
+properties and we show how it could be used to gain insights on KDEs bandwidth
+parameter.
+",0,0,1,1,0,0
+9081,"Effective modeling of ground penetrating radar in fractured media using analytic solutions for propagation, thin-bed interaction and dipolar scattering","  We propose a new approach to model ground penetrating radar signals that
+propagate through a homogeneous and isotropic medium, and are scattered at thin
+planar fractures of arbitrary dip, azimuth, thickness and material filling. We
+use analytical expressions for the Maxwell equations in a homogeneous space to
+describe the propagation of the signal in the rock matrix, and account for
+frequency-dependent dispersion and attenuation through the empirical Jonscher
+formulation. We discretize fractures into elements that are linearly polarized
+by the incoming electric field that arrives from the source to each element,
+locally, as a plane wave. To model the effective source wavelet we use a
+generalized Gamma distribution to define the antenna dipole moment. We combine
+microscopic and macroscopic Maxwell's equations to derive an analytic
+expression for the response of each element, which describes the full electric
+dipole radiation patterns along with effective reflection coefficients of thin
+layers. Our results compare favorably with finite-difference time-domain
+modeling in the case of constant electrical parameters of the rock-matrix and
+fracture filling. Compared with traditional finite-difference time-domain
+modeling, the proposed approach is faster and more flexible in terms of
+fracture orientations. A comparison with published laboratory results suggests
+that the modeling approach can reproduce the main characteristics of the
+reflected wavelet.
+",0,1,0,0,0,0
+9082,Crystallites in Color Glass Beads of the 19th Century and Their Influence on Fatal Deterioration of Glass,"  Glass corrosion is a crucial problem in keeping and conservation of beadworks
+in museums. All kinds of glass beads undergo deterioration but blue-green
+lead-potassium glass beads of the 19th century are subjected to the destruction
+to the greatest extent. Blue-green lead-potassium glass beads of the 19th
+century obtained from exhibits kept in Russian museums were studied with the
+purpose to determine the causes of the observed phenomenon. For the comparison,
+yellow lead beads of the 19th century were also explored. Both kinds of beads
+contain Sb but yellow ones are stable. Using scanning electron microscopy,
+energy dispersive X-ray microspectrometry, electron backscatter diffraction,
+transmission electron microscopy and X-ray powder analysis, we have registered
+the presence of crystallites of orthorhombic KSbOSiO$_4$ and cubic
+Pb$_2$Sb$_{1.5}$Fe$_{0.5}$O$_{6.5}$ in glass matrix of blue-green and yellow
+beads, respectively. Both compounds form at rather high temperatures obviously
+during glass melting and/or melt cooling. We suppose that the crystallites
+generate internal tensile strain in glass during its cooling which causes
+formation of multiple microcracks in inner domains of blue-green beads. We
+suggest that the deterioration degree depends on quantity of the precipitates,
+their sizes and their temperature coefficients of linear expansion. In
+blue-green beads, the crystallites are distributed in their sizes from
+$\sim\,$200 nm to several tens of $\mu$m and tend to gather in large colonies.
+The sizes of crystallites in yellow beads are several hundreds of nm and their
+clusters contain few crystallites. This explains the difference in corrosion of
+these kinds of beads containing crystals of Sb compounds.
+",0,1,0,0,0,0
+9083,CO2 infrared emission as a diagnostic of planet-forming regions of disks,"  [Abridged] The infrared ro-vibrational emission lines from organic molecules
+in the inner regions of protoplanetary disks are unique probes of the physical
+and chemical structure of planet forming regions and the processes that shape
+them. The non-LTE excitation effects of carbon dioxide (CO2) are studied in a
+full disk model to evaluate: (i) what the emitting regions of the different CO2
+ro-vibrational bands are; (ii) how the CO2 abundance can be best traced using
+CO2 ro-vibrational lines using future JWST data and; (iii) what the excitation
+and abundances tell us about the inner disk physics and chemistry. CO2 is a
+major ice component and its abundance can potentially test models with
+migrating icy pebbles across the iceline. A full non-LTE CO2 excitation model
+has been built. The characteristics of the model are tested using non-LTE slab
+models. Subsequently the CO2 line formation has been modelled using a
+two-dimensional disk model representative of T-Tauri disks. The CO2 gas that
+emits in the 15 $\mu$m and 4.5 $\mu$m regions of the spectrum is not in LTE and
+arises in the upper layers of disks, pumped by infrared radiation. The v$_2$ 15
+$\mu$m feature is dominated by optically thick emission for most of the models
+that fit the observations and increases linearly with source luminosity. Its
+narrowness compared with that of other molecules stems from a combination of
+the low rotational excitation temperature (~250 K) and the inherently narrower
+feature for CO2. The inferred CO2 abundances derived for observed disks are
+more than two orders of magnitude lower than those in interstellar ices
+(~10$^5$), similar to earlier LTE disk estimates. Line-to-continuum ratios are
+low, of order a few %, thus high signal-to-noise (S/N > 300) observations are
+needed for individual line detections. Prospects of accurate abundance
+retreival with JWST-MIRI and JWST-NIRSpec are discussed.
+",0,1,0,0,0,0
+9084,Public Evidence from Secret Ballots,"  Elections seem simple---aren't they just counting? But they have a unique,
+challenging combination of security and privacy requirements. The stakes are
+high; the context is adversarial; the electorate needs to be convinced that the
+results are correct; and the secrecy of the ballot must be ensured. And they
+have practical constraints: time is of the essence, and voting systems need to
+be affordable and maintainable, and usable by voters, election officials, and
+pollworkers. It is thus not surprising that voting is a rich research area
+spanning theory, applied cryptography, practical systems analysis, usable
+security, and statistics. Election integrity involves two key concepts:
+convincing evidence that outcomes are correct and privacy, which amounts to
+convincing assurance that there is no evidence about how any given person
+voted. These are obviously in tension. We examine how current systems walk this
+tightrope.
+",1,0,0,0,0,0
+9085,Imprints of Zero-Age Velocity Dispersions and Dynamical Heating on the Age-Velocity dispersion Relation,"  Observations of stars in the the solar vicinity show a clear tendency for old
+stars to have larger velocity dispersions. This relation is called the
+age-velocity dispersion relation (AVR) and it is believed to provide insight
+into the heating history of the Milky Way galaxy. Here, in order to investigate
+the origin of the AVR, we performed smoothed particle hydrodynamic simulations
+of the self-gravitating multiphase gas disks in the static disk-halo
+potentials. Star formation from cold and dense gas is taken into account, and
+we analyze the evolution of these star particles. We find that exponents of
+simulated AVR and the ratio of the radial to vertical velocity dispersion are
+close to the observed values. We also find that the simulated AVR is not a
+simple consequence of dynamical heating. The evolution tracks of stars with
+different epochs evolve gradually in the age-velocity dispersion plane as a
+result of: (1) the decrease in velocity dispersion in star forming regions, and
+(2) the decrease in the number of cold/dense/gas as scattering sources. These
+results suggest that the AVR involves not only the heating history of a stellar
+disk, but also the historical evolution of the ISM in a galaxy.
+",0,1,0,0,0,0
+9086,Spatial Random Sampling: A Structure-Preserving Data Sketching Tool,"  Random column sampling is not guaranteed to yield data sketches that preserve
+the underlying structures of the data and may not sample sufficiently from
+less-populated data clusters. Also, adaptive sampling can often provide
+accurate low rank approximations, yet may fall short of producing descriptive
+data sketches, especially when the cluster centers are linearly dependent.
+Motivated by that, this paper introduces a novel randomized column sampling
+tool dubbed Spatial Random Sampling (SRS), in which data points are sampled
+based on their proximity to randomly sampled points on the unit sphere. The
+most compelling feature of SRS is that the corresponding probability of
+sampling from a given data cluster is proportional to the surface area the
+cluster occupies on the unit sphere, independently from the size of the cluster
+population. Although it is fully randomized, SRS is shown to provide
+descriptive and balanced data representations. The proposed idea addresses a
+pressing need in data science and holds potential to inspire many novel
+approaches for analysis of big data.
+",1,0,0,1,0,0
+9087,Adiponitrile-LiTFSI solution as alkylcarbonate free electrolyte for LTO/NMC Li-ion batteries,"  Recently, dinitriles (NC(CH2)nCN) and especially adiponitrile (ADN, n=4) have
+attracted the attention as secure electrolyte solvents due to their chemical
+stability, high boiling points, high flash points and low vapor pressure. The
+good solvating properties of ADN toward lithium salts and its high
+electrochemical stability (~ 6V vs. Li/Li+) make it suitable for safer Li-ions
+cells without performances loss. In this study, ADN is used as a single
+electrolyte solvent with lithium bis(trimethylsulfonyl)imide (LiTFSI). This
+electrolyte allows the use of aluminum collectors as almost no corrosion occurs
+at voltages up to 4.2 V. Physico-chemical properties of ADN-LiTFSI electrolyte
+such as salt dissolution, conductivity and viscosity were determined. The
+cycling performances of batteries using Li4Ti5O12 (LTO) as anode and
+LiNi1/3Co1/3Mn1/3O2 (NMC) as cathode were determined. The results indicate that
+LTO/NMC batteries exhibit excellent rate capabilities with a columbic
+efficiency close to 100%. As an example, cells were able to reach a capacity of
+165 mAh.g-1 at 0.1C and a capacity retention of more than 98% after 200 cycles
+at 0.5C. In addition, electrodes analyses by SEM, XPS and electrochemical
+impedance spectroscopy after cycling confirming minimal surface changes of the
+electrodes in the studied battery system
+",0,1,0,0,0,0
+9088,Sea of Lights: Practical Device-to-Device Security Bootstrapping in the Dark,"  Practical solutions to bootstrap security in today's information and
+communication systems critically depend on centralized services for
+authentication as well as key and trust management. This is particularly true
+for mobile users. Identity providers such as Google or Facebook have active
+user bases of two billion each, and the subscriber number of mobile operators
+exceeds five billion unique users as of early 2018. If these centralized
+services go completely `dark' due to natural or man made disasters, large scale
+blackouts, or country-wide censorship, the users are left without practical
+solutions to bootstrap security on their mobile devices. Existing distributed
+solutions, for instance, the so-called web-of-trust are not sufficiently
+lightweight. Furthermore, they support neither cross-application on mobile
+devices nor strong protection of key material using hardware security modules.
+We propose Sea of Lights(SoL), a practical lightweight scheme for bootstrapping
+device-to-device security wirelessly, thus, enabling secure distributed
+self-organized networks. It is tailored to operate `in the dark' and provides
+strong protection of key material as well as an intuitive means to build a
+lightweight web-of-trust. SoL is particularly well suited for local or urban
+operation in scenarios such as the coordination of emergency response, where it
+helps containing/limiting the spreading of misinformation. As a proof of
+concept, we implement SoL in the Android platform and hence test its
+feasibility on real mobile devices. We further evaluate its key performance
+aspects using simulation.
+",1,0,0,0,0,0
+9089,On the correlation between a level of structure order and properties of composites. In Memory of Yu.L. Klimontovich,"  Proposed the computerized method for calculating the relative level of order
+composites. Correlation between a level of structure order and properties of
+solids is shown. Discussed the possibility of clarifying the terminology used
+in describing the structure.
+",1,0,0,0,0,0
+9090,Schmidt's subspace theorem for moving hypersurface targets,"  It was discovered that there is a formal analogy between Nevanlinna theory
+and Diophantine approximation. Via Vojta's dictionary, the Second Main Theorem
+in Nevanlinna theory corresponds to Schmidt's Subspace Theorem in Diophantine
+approximation. Recently, Cherry, Dethloff, and Tan (arXiv:1503.08801v2
+[math.CV]) obtained a Second Main Theorem for moving hypersurfaces intersecting
+projective varieites. In this paper, we shall give the counterpart of their
+Second Main Theorem in Diophantine approximation.
+",0,0,1,0,0,0
+9091,HoNVis: Visualizing and Exploring Higher-Order Networks,"  Unlike the conventional first-order network (FoN), the higher-order network
+(HoN) provides a more accurate description of transitions by creating
+additional nodes to encode higher-order dependencies. However, there exists no
+visualization and exploration tool for the HoN. For applications such as the
+development of strategies to control species invasion through global shipping
+which is known to exhibit higher-order dependencies, the existing FoN
+visualization tools are limited. In this paper, we present HoNVis, a novel
+visual analytics framework for exploring higher-order dependencies of the
+global ocean shipping network. Our framework leverages coordinated multiple
+views to reveal the network structure at three levels of detail (i.e., the
+global, local, and individual port levels). Users can quickly identify ports of
+interest at the global level and specify a port to investigate its higher-order
+nodes at the individual port level. Investigating a larger-scale impact is
+enabled through the exploration of HoN at the local level. Using the global
+ocean shipping network data, we demonstrate the effectiveness of our approach
+with a real-world use case conducted by domain experts specializing in species
+invasion. Finally, we discuss the generalizability of this framework to other
+real-world applications such as information diffusion in social networks and
+epidemic spreading through air transportation.
+",1,1,0,0,0,0
+9092,Direct observation of coupled geochemical and geomechanical impacts on chalk microstructural evolution under elevated CO2 pressure. Part I,"  The dissolution of porous media in a geologic formation induced by the
+injection of massive amounts of CO2 can undermine the mechanical stability of
+the formation structure before carbon mineralization takes place. The
+geomechanical impact of geologic carbon storage is therefore closely related to
+the structural sustainability of the chosen reservoir as well as the
+probability of buoyancy driven CO2 leakage through caprocks. Here we show, with
+a combination of ex situ nanotomography and in situ microtomography, that the
+presence of dissolved CO2 in water produces a homogeneous dissolution pattern
+in natural chalk microstructure. This pattern stems from a greater apparent
+solubility of chalk and therefore a greater reactive subvolume in a sample.
+When a porous medium dissolves homogeneously in an imposed flow field, three
+geomechanical effects were observed: material compaction, fracturing and grain
+relocation. These phenomena demonstrated distinct feedbacks to the migration of
+the dissolution front and severely complicated the infiltration instability
+problem. We conclude that the presence of dissolved CO2 makes the dissolution
+front less susceptible to spatial and temporal perturbations in the strongly
+coupled geochemical and geomechanical processes.
+",0,1,0,0,0,0
+9093,Symplectic resolutions for Higgs moduli spaces,"  In this paper, we study the algebraic symplectic geometry of the singular
+moduli spaces of Higgs bundles of degree $0$ and rank $n$ on a compact Riemann
+surface $X$ of genus $g$. In particular, we prove that such moduli spaces are
+symplectic singularities, in the sense of Beauville [Bea00], and admit a
+projective symplectic resolution if and only if $g=1$ or $(g, n)=(2,2)$. These
+results are an application of a recent paper by Bellamy and Schedler [BS16] via
+the so-called Isosingularity Theorem.
+",0,0,1,0,0,0
+9094,Investigation of Defect Modes of Chiral Photonic Crystals,"  Some properties of defect modes of cholesteric liquid crystals (CLC) are
+presented. It is shown that when the CLC layer is thin the density of states
+and emission intensity are maximum for the defect mode, whereas when the CLC
+layer is thick, these peaks are observed at the edges of the photonic band gap.
+Similarly, when the gain is low, the density of states and emission intensity
+are maximum for the defect mode, whereas at high gains these peaks are also
+observed at the edges of the photonic band gap. The possibilities of
+low-threshold lasing and obtaining high-Q microcavities have been investigated.
+",0,1,0,0,0,0
+9095,"Crystal Growth of Cu6(Ge,Si)6O18.6H2O and Assignment of UV-VIS Spectra in Comparison to Dehydrated Dioptase and Selected Cu(II) Oxo-Compounds Including Cuprates","  It is reported on growth of mm-sized single-crystals of the low-dimensional S
+= 1/2 spin compound Cu6(Ge,Si)6O18.6H2O by a diffusion technique in aqueous
+solution. A route to form Si-rich crystals down to possibly dioptase, the pure
+silicate, is discussed. Further, the assignment of dd excitations from UV-VIS
+spectra of the hexahydrate and the fully dehydrated compound is proposed in
+comparison to dioptase and selected Cu(II) oxo-compounds using bond strength
+considerations. Non-doped cuprates as layer compounds show higher excitation
+energies than the title compound. However, when the antiferromagnetic
+interaction energy as Jzln(2) is taken into account for cuprates, a single
+linear relationship between the Dqe excitation energy and equatorial Cu(II)-O
+bond strength is confirmed for all compounds. A linear representation is also
+confirmed between 2A1g energies and a function of axial and equatorial Cu-O
+bond distances, when auxiliary axial bonds are used for four-coordinated
+compounds. The quotient Dt/Ds of experimental orbital energies deviating from
+the general trend to smaller values indicates the existence of H2O respectively
+Cl1- axial ligands in comparison to oxo-ligands, whereas larger Dt/Dqe values
+indicate missing axial bonds. The quotient of the excitation energy 2A1g by
+2x2Eg-2B2g allows to check for correctness of the assignment and to distinguish
+between axial oxo-ligands and others like H2O or Cl1-. Some assignments
+previously reported were corrected.
+",0,1,0,0,0,0
+9096,Optical reconfiguration and polarization control in semi-continuous gold films close to the percolation threshold,"  Controlling and confining light by exciting plasmons in resonant metallic
+nanostructures is an essential aspect of many new emerging optical
+technologies. Here we explore the possibility of controllably reconfiguring the
+intrinsic optical properties of semi-continuous gold films, by inducing
+permanent morphological changes with a femtosecond (fs)-pulsed laser above a
+critical power. Optical transmission spectroscopy measurements show a
+correlation between the spectra of the morphologically modified films and the
+wavelength, polarization, and the intensity of the laser used for alteration.
+In order to understand the modifications induced by the laser writing, we
+explore the near-field properties of these films with electron energy-loss
+spectroscopy (EELS). A comparison between our experimental data and full-wave
+simulations on the exact film morphologies hints toward a restructuring of the
+intrinsic plasmonic eigenmodes of the metallic film by photothermal effects. We
+explain these optical changes with a simple model and demonstrate
+experimentally that laser writing can be used to controllably modify the
+optical properties of these semi-continuous films. These metal films offer an
+easy-to-fabricate and scalable platform for technological applications such as
+molecular sensing and ultra-dense data storage.
+",0,1,0,0,0,0
+9097,Some Repeated-Root Constacyclic Codes over Galois Rings,"  Codes over Galois rings have been studied extensively during the last three
+decades. Negacyclic codes over $GR(2^a,m)$ of length $2^s$ have been
+characterized: the ring $\mathcal{R}_2(a,m,-1)= \frac{GR(2^a,m)[x]}{\langle
+x^{2^s}+1\rangle}$ is a chain ring. Furthermore, these results have been
+generalized to $\lambda$-constacyclic codes for any unit $\lambda$ of the form
+$4z-1$, $z\in GR(2^a, m)$. In this paper, we study more general cases and
+investigate all cases where $\mathcal{R}_p(a,m,\gamma)=
+\frac{GR(p^a,m)[x]}{\langle x^{p^s}-\gamma \rangle}$ is a chain ring. In
+particular, necessary and sufficient conditions for the ring
+$\mathcal{R}_p(a,m,\gamma)$ to be a chain ring are obtained. In addition, by
+using this structure we investigate all $\gamma$-constacyclic codes over
+$GR(p^a,m)$ when $\mathcal{R}_p(a,m,\gamma)$ is a chain ring. Necessary and
+sufficient conditions for the existence of self-orthogonal and self-dual
+$\gamma$-constacyclic codes are also provided. Among others, for any prime $p$,
+the structure of $\mathcal{R}_p(a,m,\gamma)=\frac{GR(p^a,m)[x]}{\langle
+x^{p^s}-\gamma\rangle}$ is used to establish the Hamming and homogeneous
+distances of $\gamma$-constacyclic codes.
+",1,0,1,0,0,0
+9098,Statistical solutions and Onsager's conjecture,"  We prove a version of Onsager's conjecture on the conservation of energy for
+the incompressible Euler equations in the context of statistical solutions, as
+introduced recently by Fjordholm et al. As a byproduct, we also obtain a new
+proof for the conservative direction of Onsager's conjecture for weak
+solutions. Dedicated to Edriss S. Titi on the occasion of his 60th birthday.
+",0,1,1,0,0,0
+9099,The sum of log-normal variates in geometric Brownian motion,"  Geometric Brownian motion (GBM) is a key model for representing
+self-reproducing entities. Self-reproduction may be considered the definition
+of life [5], and the dynamics it induces are of interest to those concerned
+with living systems from biology to economics. Trajectories of GBM are
+distributed according to the well-known log-normal density, broadening with
+time. However, in many applications, what's of interest is not a single
+trajectory but the sum, or average, of several trajectories. The distribution
+of these objects is more complicated. Here we show two different ways of
+finding their typical trajectories. We make use of an intriguing connection to
+spin glasses: the expected free energy of the random energy model is an average
+of log-normal variates. We make the mapping to GBM explicit and find that the
+free energy result gives qualitatively correct behavior for GBM trajectories.
+We then also compute the typical sum of lognormal variates using Ito calculus.
+This alternative route is in close quantitative agreement with numerical work.
+",0,0,0,0,0,1
+9100,Slow Spin Dynamics and Self-Sustained Clusters in Sparsely Connected Systems,"  To identify emerging microscopic structures in low temperature spin glasses,
+we study self-sustained clusters (SSC) in spin models defined on sparse random
+graphs. A message-passing algorithm is developed to determine the probability
+of individual spins to belong to SSC. Results for specific instances, which
+compare the predicted SSC associations with the dynamical properties of spins
+obtained from numerical simulations, show that SSC association identifies
+individual slow-evolving spins. This insight gives rise to a powerful approach
+for predicting individual spin dynamics from a single snapshot of an
+equilibrium spin configuration, namely from limited static information, which
+can be used to devise generic prediction tools applicable to a wide range of
+areas.
+",0,1,0,0,0,0
+9101,Continuous Authentication of Smartphones Based on Application Usage,"  An empirical investigation of active/continuous authentication for
+smartphones is presented in this paper by exploiting users' unique application
+usage data, i.e., distinct patterns of use, modeled by a Markovian process.
+Variations of Hidden Markov Models (HMMs) are evaluated for continuous user
+verification, and challenges due to the sparsity of session-wise data, an
+explosion of states, and handling unforeseen events in the test data are
+tackled. Unlike traditional approaches, the proposed formulation does not
+depend on the top N-apps, rather uses the complete app-usage information to
+achieve low latency. Through experimentation, empirical assessment of the
+impact of unforeseen events, i.e., unknown applications and unforeseen
+observations, on user verification is done via a modified edit-distance
+algorithm for simple sequence matching. It is found that for enhanced
+verification performance, unforeseen events should be incorporated in the
+models by adopting smoothing techniques with HMMs. For validation, extensive
+experiments on two distinct datasets are performed. The marginal smoothing
+technique is the most effective for user verification in terms of equal error
+rate (EER) and with a sampling rate of 1/30s^{-1} and 30 minutes of historical
+data, and the method is capable of detecting an intrusion within ~2.5 minutes
+of application use.
+",0,0,0,1,0,0
+9102,On the Universality of Invariant Networks,"  Constraining linear layers in neural networks to respect symmetry
+transformations from a group $G$ is a common design principle for invariant
+networks that has found many applications in machine learning.
+In this paper, we consider a fundamental question that has received little
+attention to date: Can these networks approximate any (continuous) invariant
+function?
+We tackle the rather general case where $G\leq S_n$ (an arbitrary subgroup of
+the symmetric group) that acts on $\mathbb{R}^n$ by permuting coordinates. This
+setting includes several recent popular invariant networks. We present two main
+results: First, $G$-invariant networks are universal if high-order tensors are
+allowed. Second, there are groups $G$ for which higher-order tensors are
+unavoidable for obtaining universality.
+$G$-invariant networks consisting of only first-order tensors are of special
+interest due to their practical value. We conclude the paper by proving a
+necessary condition for the universality of $G$-invariant networks that
+incorporate only first-order tensors. Lastly, we propose a conjecture stating
+that this condition is also sufficient.
+",1,0,0,1,0,0
+9103,Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?,"  We study how the behavior of deep policy gradient algorithms reflects the
+conceptual framework motivating their development. We propose a fine-grained
+analysis of state-of-the-art methods based on key aspects of this framework:
+gradient estimation, value prediction, optimization landscapes, and trust
+region enforcement. We find that from this perspective, the behavior of deep
+policy gradient algorithms often deviates from what their motivating framework
+would predict. Our analysis suggests first steps towards solidifying the
+foundations of these algorithms, and in particular indicates that we may need
+to move beyond the current benchmark-centric evaluation methodology.
+",1,0,0,0,0,0
+9104,Achieving Privacy in the Adversarial Multi-Armed Bandit,"  In this paper, we improve the previously best known regret bound to achieve
+$\epsilon$-differential privacy in oblivious adversarial bandits from
+$\mathcal{O}{(T^{2/3}/\epsilon)}$ to $\mathcal{O}{(\sqrt{T} \ln T /\epsilon)}$.
+This is achieved by combining a Laplace Mechanism with EXP3. We show that
+though EXP3 is already differentially private, it leaks a linear amount of
+information in $T$. However, we can improve this privacy by relying on its
+intrinsic exponential mechanism for selecting actions. This allows us to reach
+$\mathcal{O}{(\sqrt{\ln T})}$-DP, with a regret of $\mathcal{O}{(T^{2/3})}$
+that holds against an adaptive adversary, an improvement from the best known of
+$\mathcal{O}{(T^{3/4})}$. This is done by using an algorithm that run EXP3 in a
+mini-batch loop. Finally, we run experiments that clearly demonstrate the
+validity of our theoretical analysis.
+",1,0,0,0,0,0
+9105,Proximity Variational Inference,"  Variational inference is a powerful approach for approximate posterior
+inference. However, it is sensitive to initialization and can be subject to
+poor local optima. In this paper, we develop proximity variational inference
+(PVI). PVI is a new method for optimizing the variational objective that
+constrains subsequent iterates of the variational parameters to robustify the
+optimization path. Consequently, PVI is less sensitive to initialization and
+optimization quirks and finds better local optima. We demonstrate our method on
+three proximity statistics. We study PVI on a Bernoulli factor model and
+sigmoid belief network with both real and synthetic data and compare to
+deterministic annealing (Katahira et al., 2008). We highlight the flexibility
+of PVI by designing a proximity statistic for Bayesian deep learning models
+such as the variational autoencoder (Kingma and Welling, 2014; Rezende et al.,
+2014). Empirically, we show that PVI consistently finds better local optima and
+gives better predictive performance.
+",1,0,0,1,0,0
+9106,Mandarin tone modeling using recurrent neural networks,"  We propose an Encoder-Classifier framework to model the Mandarin tones using
+recurrent neural networks (RNN). In this framework, extracted frames of
+features for tone classification are fed in to the RNN and casted into a fixed
+dimensional vector (tone embedding) and then classified into tone types using a
+softmax layer along with other auxiliary inputs. We investigate various
+configurations that help to improve the model, including pooling, feature
+splicing and utilization of syllable-level tone embeddings. Besides, tone
+embeddings and durations of the contextual syllables are exploited to
+facilitate tone classification. Experimental results on Mandarin tone
+classification show the proposed network setups improve tone classification
+accuracy. The results indicate that the RNN encoder-classifier based tone model
+flexibly accommodates heterogeneous inputs (sequential and segmental) and hence
+has the advantages from both the sequential classification tone models and
+segmental classification tone models.
+",1,0,0,0,0,0
+9107,Shallow water models with constant vorticity,"  We modify the nonlinear shallow water equations, the Korteweg-de Vries
+equation, and the Whitham equation, to permit constant vorticity, and examine
+wave breaking, or the lack thereof. By wave breaking, we mean that the solution
+remains bounded but its slope becomes unbounded in finite time. We show that a
+solution of the vorticity-modified shallow water equations breaks if it carries
+an increase of elevation; the breaking time decreases to zero as the size of
+vorticity increases. We propose a full-dispersion shallow water model, which
+combines the dispersion relation of water waves and the nonlinear shallow water
+equations in the constant vorticity setting, and which extends the Whitham
+equation to permit bidirectional propagation. We show that its small amplitude
+and periodic traveling wave is unstable to long wavelength perturbations if the
+wave number is greater than a critical value, and stable otherwise, similarly
+to the Benjamin-Feir instability in the irrotational setting; the critical wave
+number grows unboundedly large with the size of vorticity. The result agrees
+with that from a multiple scale expansion of the physical problem. We show that
+vorticity considerably alters the modulational stability and instability in the
+presence of the effects of surface tension.
+",0,1,1,0,0,0
+9108,Tensor Balancing on Statistical Manifold,"  We solve tensor balancing, rescaling an Nth order nonnegative tensor by
+multiplying N tensors of order N - 1 so that every fiber sums to one. This
+generalizes a fundamental process of matrix balancing used to compare matrices
+in a wide range of applications from biology to economics. We present an
+efficient balancing algorithm with quadratic convergence using Newton's method
+and show in numerical experiments that the proposed algorithm is several orders
+of magnitude faster than existing ones. To theoretically prove the correctness
+of the algorithm, we model tensors as probability distributions in a
+statistical manifold and realize tensor balancing as projection onto a
+submanifold. The key to our algorithm is that the gradient of the manifold,
+used as a Jacobian matrix in Newton's method, can be analytically obtained
+using the Moebius inversion formula, the essential of combinatorial
+mathematics. Our model is not limited to tensor balancing, but has a wide
+applicability as it includes various statistical and machine learning models
+such as weighted DAGs and Boltzmann machines.
+",1,0,0,1,0,0
+9109,Learning Unknown Markov Decision Processes: A Thompson Sampling Approach,"  We consider the problem of learning an unknown Markov Decision Process (MDP)
+that is weakly communicating in the infinite horizon setting. We propose a
+Thompson Sampling-based reinforcement learning algorithm with dynamic episodes
+(TSDE). At the beginning of each episode, the algorithm generates a sample from
+the posterior distribution over the unknown model parameters. It then follows
+the optimal stationary policy for the sampled model for the rest of the
+episode. The duration of each episode is dynamically determined by two stopping
+criteria. The first stopping criterion controls the growth rate of episode
+length. The second stopping criterion happens when the number of visits to any
+state-action pair is doubled. We establish $\tilde O(HS\sqrt{AT})$ bounds on
+expected regret under a Bayesian setting, where $S$ and $A$ are the sizes of
+the state and action spaces, $T$ is time, and $H$ is the bound of the span.
+This regret bound matches the best available bound for weakly communicating
+MDPs. Numerical results show it to perform better than existing algorithms for
+infinite horizon MDPs.
+",1,0,0,0,0,0
+9110,Models of Random Knots,"  The study of knots and links from a probabilistic viewpoint provides insight
+into the behavior of ""typical"" knots, and opens avenues for new constructions
+of knots and other topological objects with interesting properties. The
+knotting of random curves arises also in applications to the natural sciences,
+such as in the context of the structure of polymers. We present here several
+known and new randomized models of knots and links. We review the main known
+results on the knot distribution in each model. We discuss the nature of these
+models and the properties of the knots they produce. Of particular interest to
+us are finite type invariants of random knots, and the recently studied
+Petaluma model. We report on rigorous results and numerical experiments
+concerning the asymptotic distribution of such knot invariants. Our approach
+raises questions of universality and classification of the various random knot
+models.
+",0,0,1,0,0,0
+9111,Revival structures of coherent states for Xm exceptional orthogonal polynomials of the Scarf I potential within position-dependent effective mass,"  The revival structures for the X_m exceptional orthogonal polynomials of the
+Scarf I potential endowed with position-dependent effective mass is studied in
+the context of the generalized Gazeau-Klauder coherent states. It is shown that
+in the case of the constant mass, the deduced coherent states mimic full and
+fractional revivals phenomena. However in the case of position-dependent
+effective mass, although full revivals take place during their time evolution,
+there is no fractional revivals as defined in the common sense. These
+properties are illustrated numerically by means of some specific profile mass
+functions, with and without singularities. We have also observed a close
+connection between the coherence time {\tau}_coh^m? and the mass parameter ?.
+",0,0,1,0,0,0
+9112,"Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming","  Autonomous aerial cinematography has the potential to enable automatic
+capture of aesthetically pleasing videos without requiring human intervention,
+empowering individuals with the capability of high-end film studios. Current
+approaches either only handle off-line trajectory generation, or offer
+strategies that reason over short time horizons and simplistic representations
+for obstacles, which result in jerky movement and low real-life applicability.
+In this work we develop a method for aerial filming that is able to trade off
+shot smoothness, occlusion, and cinematography guidelines in a principled
+manner, even under noisy actor predictions. We present a novel algorithm for
+real-time covariant gradient descent that we use to efficiently find the
+desired trajectories by optimizing a set of cost functions. Experimental
+results show that our approach creates attractive shots, avoiding obstacles and
+occlusion 65 times over 1.25 hours of flight time, re-planning at 5 Hz with a
+10 s time horizon. We robustly film human actors, cars and bicycles performing
+different motion among obstacles, using various shot types.
+",1,0,0,0,0,0
+9113,Fifth order finite volume WENO in general orthogonally-curvilinear coordinates,"  High order reconstruction in the finite volume (FV) approach is achieved by a
+more fundamental form of the fifth order WENO reconstruction in the framework
+of orthogonally-curvilinear coordinates, for solving the hyperbolic
+conservation equations. The derivation employs a piecewise parabolic polynomial
+approximation to the zone averaged values to reconstruct the right, middle, and
+left interface values. The grid dependent linear weights of the WENO are
+recovered by inverting a Vandermode-like linear system of equations with
+spatially varying coefficients. A scheme for calculating the linear weights,
+optimal weights, and smoothness indicator on a regularly- and
+irregularly-spaced grid in orthogonally-curvilinear coordinates is proposed. A
+grid independent relation for evaluating the smoothness indicator is derived
+from the basic definition. Finally, the procedures for the source term
+integration and extension to multi-dimensions are proposed. Analytical values
+of the linear and optimal weights, and also the weights required for the source
+term integration and flux averaging, are provided for a regularly-spaced grid
+in Cartesian, cylindrical, and spherical coordinates. Conventional fifth order
+WENO reconstruction for the regularly-spaced grids in the Cartesian coordinates
+can be fully recovered in the case of limiting curvature. The fifth order
+finite volume WENO-C (orthogonally-curvilinear version of WENO) reconstruction
+scheme is tested for several 1D and 2D benchmark test cases involving smooth
+and discontinuous flows in cylindrical and spherical coordinates.
+",0,1,0,0,0,0
+9114,Traffic Minimizing Caching and Latent Variable Distributions of Order Statistics,"  Given a statistical model for the request frequencies and sizes of data
+objects in a caching system, we derive the probability density of the size of
+the file that accounts for the largest amount of data traffic. This is
+equivalent to finding the required size of the cache for a caching placement
+that maximizes the expected byte hit ratio for given file size and popularity
+distributions. The file that maximizes the expected byte hit ratio is the file
+for which the product of its size and popularity is the highest -- thus, it is
+the file that incurs the greatest load on the network. We generalize this
+theoretical problem to cover factors and addends of arbitrary order statistics
+for given parent distributions. Further, we study the asymptotic behavior of
+these distributions. We give several factor and addend densities of widely-used
+distributions, and verify our results by extensive computer simulations.
+",1,0,0,0,0,0
+9115,Maximizing the information learned from finite data selects a simple model,"  We use the language of uninformative Bayesian prior choice to study the
+selection of appropriately simple effective models. We advocate for the prior
+which maximizes the mutual information between parameters and predictions,
+learning as much as possible from limited data. When many parameters are poorly
+constrained by the available data, we find that this prior puts weight only on
+boundaries of the parameter manifold. Thus it selects a lower-dimensional
+effective theory in a principled way, ignoring irrelevant parameter directions.
+In the limit where there is sufficient data to tightly constrain any number of
+parameters, this reduces to Jeffreys prior. But we argue that this limit is
+pathological when applied to the hyper-ribbon parameter manifolds generic in
+science, because it leads to dramatic dependence on effects invisible to
+experiment.
+",0,0,1,1,0,0
+9116,Aging Feynman-Kac Equation,"  Aging, the process of growing old or maturing, is one of the most widely seen
+natural phenomena in the world. For the stochastic processes, sometimes the
+influence of aging can not be ignored. For example, in this paper, by analyzing
+the functional distribution of the trajectories of aging particles performing
+anomalous diffusion, we reveal that for the fraction of the occupation time
+$T_+/t$ of strong aging particles, $\langle (T^+(t)^2)\rangle=\frac{1}{2}t^2$
+with coefficient $\frac{1}{2}$, having no relation with the aging time $t_a$
+and $\alpha$ and being completely different from the case of weak (none) aging.
+In fact, we first build the models governing the corresponding functional
+distributions, i.e., the aging forward and backward Feynman-Kac equations; the
+above result is one of the applications of the models. Another application of
+the models is to solve the asymptotic behaviors of the distribution of the
+first passage time, $g(t_a,t)$. The striking discovery is that for weakly aging
+systems, $g(t_a,t)\sim t_a^{\frac{\alpha}{2}}t^{-1-\frac{\alpha}{2}}$, while
+for strongly aging systems, $g(t_a,t)$ behaves as $ t_a^{\alpha-1}t^{-\alpha}$.
+",0,1,0,0,0,0
+9117,Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods,"  We present and apply a general-purpose, multi-start algorithm for improving
+the performance of low-energy samplers used for solving optimization problems.
+The algorithm iteratively fixes the value of a large portion of the variables
+to values that have a high probability of being optimal. The resulting problems
+are smaller and less connected, and samplers tend to give better low-energy
+samples for these problems. The algorithm is trivially parallelizable, since
+each start in the multi-start algorithm is independent, and could be applied to
+any heuristic solver that can be run multiple times to give a sample. We
+present results for several classes of hard problems solved using simulated
+annealing, path-integral quantum Monte Carlo, parallel tempering with
+isoenergetic cluster moves, and a quantum annealer, and show that the success
+metrics as well as the scaling are improved substantially. When combined with
+this algorithm, the quantum annealer's scaling was substantially improved for
+native Chimera graph problems. In addition, with this algorithm the scaling of
+the time to solution of the quantum annealer is comparable to the Hamze--de
+Freitas--Selby algorithm on the weak-strong cluster problems introduced by
+Boixo et al. Parallel tempering with isoenergetic cluster moves was able to
+consistently solve 3D spin glass problems with 8000 variables when combined
+with our method, whereas without our method it could not solve any.
+",1,1,0,0,0,0
+9118,BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning,"  We introduce a novel method to train agents of reinforcement learning (RL) by
+sharing knowledge in a way similar to the concept of using a book. The recorded
+information in the form of a book is the main means by which humans learn
+knowledge. Nevertheless, the conventional deep RL methods have mainly focused
+either on experiential learning where the agent learns through interactions
+with the environment from the start or on imitation learning that tries to
+mimic the teacher. Contrary to these, our proposed book learning shares key
+information among different agents in a book-like manner by delving into the
+following two characteristic features: (1) By defining the linguistic function,
+input states can be clustered semantically into a relatively small number of
+core clusters, which are forwarded to other RL agents in a prescribed manner.
+(2) By defining state priorities and the contents for recording, core
+experiences can be selected and stored in a small container. We call this
+container as `BOOK'. Our method learns hundreds to thousand times faster than
+the conventional methods by learning only a handful of core cluster
+information, which shows that deep RL agents can effectively learn through the
+shared knowledge from other agents.
+",1,0,0,0,0,0
+9119,A global scavenging and circulation ocean model of thorium-230 and protactinium-231 with realistic particle dynamics (NEMO-ProThorP 0.1),"  In this paper, we set forth a 3-D ocean model of the radioactive trace
+isotopes Th-230 and Pa-231. The interest arises from the fact that these
+isotopes are extensively used for investigating particle transport in the ocean
+and reconstructing past ocean circulation. The tracers are reversibly scavenged
+by biogenic and lithogenic particles.
+Our simulations of Th-230 and Pa-231 are based on the NEMO-PISCES ocean
+biogeochemistry general circulation model, which includes biogenic particles,
+namely small and big particulate organic carbon, calcium carbonate and biogenic
+silica. Small and big lithogenic particles from dust deposition are included in
+our model as well. Their distributions generally compare well with the small
+and big lithogenic particle concentrations from recent observations from the
+GEOTRACES programme, except for boundary nepheloid layers for which, as up to
+today, there are no non-trivial, prognostic models available on a global scale.
+Our simulations reproduce Th-230 and Pa-231 dissolved concentrations: they
+compare well with recent GEOTRACES observations in many parts of the ocean.
+Particulate Th-230 and Pa-231 concentrations are significantly improved
+compared to previous studies, but they are still too low because of missing
+particles from nepheloid layers. Our simulation reproduces the main
+characteristics of the Pa-231/Th-230 ratio observed in the sediments, and
+supports a moderate affinity of Pa-231 to biogenic silica as suggested by
+recent observations, relative to Th-230.
+Future model development may further improve understanding, especially when
+this will include a more complete representation of all particles, including
+different size classes, manganese hydroxides and nepheloid layers. This can be
+done based on our model, as its source code is readily available.
+",1,1,0,0,0,0
+9120,The Dynamic Geometry of Interaction Machine: A Call-by-need Graph Rewriter,"  Girard's Geometry of Interaction (GoI), a semantics designed for linear logic
+proofs, has been also successfully applied to programming language semantics.
+One way is to use abstract machines that pass a token on a fixed graph along a
+path indicated by the GoI. These token-passing abstract machines are space
+efficient, because they handle duplicated computation by repeating the same
+moves of a token on the fixed graph. Although they can be adapted to obtain
+sound models with regard to the equational theories of various evaluation
+strategies for the lambda calculus, it can be at the expense of significant
+time costs. In this paper we show a token-passing abstract machine that can
+implement evaluation strategies for the lambda calculus, with certified time
+efficiency. Our abstract machine, called the Dynamic GoI Machine (DGoIM),
+rewrites the graph to avoid replicating computation, using the token to find
+the redexes. The flexibility of interleaving token transitions and graph
+rewriting allows the DGoIM to balance the trade-off of space and time costs.
+This paper shows that the DGoIM can implement call-by-need evaluation for the
+lambda calculus by using a strategy of interleaving token passing with as much
+graph rewriting as possible. Our quantitative analysis confirms that the DGoIM
+with this strategy of interleaving the two kinds of possible operations on
+graphs can be classified as ""efficient"" following Accattoli's taxonomy of
+abstract machines.
+",1,0,0,0,0,0
+9121,Classification of finite W-groups,"  We determine the structure of the W-group $\mathcal{G}_F$, the small Galois
+quotient of the absolute Galois group $G_F$ of the Pythagorean formally real
+field $F$ when the space of orderings $X_F$ has finite order. Based on
+Marshall's work (1979), we reduce the structure of $\mathcal{G}_F$ to that of
+$\mathcal{G}_{\bar{F}}$, the W-group of the residue field $\bar{F}$ when $X_F$
+is a connected space. In the disconnected case, the structure of
+$\mathcal{G}_F$ is the free product of the W-groups $\mathcal{G}_{F_i}$
+corresponding to the connected components $X_i$ of $X_F$. We also give a
+completely Galois theoretic proof for Marshall's Basic Lemma.
+",0,0,1,0,0,0
+9122,A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices,"  Principal component pursuit (PCP) is a state-of-the-art approach for
+background estimation problems. Due to their higher computational cost, PCP
+algorithms, such as robust principal component analysis (RPCA) and its
+variants, are not feasible in processing high definition videos. To avoid the
+curse of dimensionality in those algorithms, several methods have been proposed
+to solve the background estimation problem in an incremental manner. We propose
+a batch-incremental background estimation model using a special weighted
+low-rank approximation of matrices. Through experiments with real and synthetic
+video sequences, we demonstrate that our method is superior to the
+state-of-the-art background estimation algorithms such as GRASTA, ReProCS,
+incPCP, and GFL.
+",1,0,1,0,0,0
+9123,Chromospheric Activity of HAT-P-11: an Unusually Active Planet-Hosting K Star,"  Kepler photometry of the hot Neptune host star HAT-P-11 suggests that its
+spot latitude distribution is comparable to the Sun's near solar maximum. We
+search for evidence of an activity cycle in the CaII H & K chromospheric
+emission $S$-index with archival Keck/HIRES spectra and observations from the
+echelle spectrograph on the ARC 3.5 m Telescope at APO. The chromospheric
+emission of HAT-P-11 is consistent with a $\gtrsim 10$ year activity cycle,
+which plateaued near maximum during the Kepler mission. In the cycle that we
+observed, the star seemed to spend more time near active maximum than minimum.
+We compare the $\log R^\prime_{HK}$ normalized chromospheric emission index of
+HAT-P-11 with other stars. HAT-P-11 has unusually strong chromospheric emission
+compared to planet-hosting stars of similar effective temperature and rotation
+period, perhaps due to tides raised by its planet.
+",0,1,0,0,0,0
+9124,Automated Detection of Serializability Violations under Weak Consistency,"  While a number of weak consistency mechanisms have been developed in recent
+years to improve performance and ensure availability in distributed, replicated
+systems, ensuring correctness of transactional applications running on top of
+such systems remains a difficult and important problem. Serializability is a
+well-understood correctness criterion for transactional programs; understanding
+whether applications are serializable when executed in a weakly-consistent
+environment, however remains a challenging exercise. In this work, we combine
+the dependency graph-based characterization of serializability and the
+framework of abstract executions to develop a fully automated approach for
+statically finding bounded serializability violations under \emph{any} weak
+consistency model. We reduce the problem of serializability to satisfiability
+of a formula in First-Order Logic, which allows us to harness the power of
+existing SMT solvers. We provide rules to automatically construct the FOL
+encoding from programs written in SQL (allowing loops and conditionals) and the
+consistency specification written as a formula in FOL. In addition to detecting
+bounded serializability violations, we also provide two orthogonal schemes to
+reason about unbounded executions by providing sufficient conditions (in the
+form of FOL formulae) whose satisfiability would imply the absence of anomalies
+in any arbitrary execution. We have applied the proposed technique on TPC-C, a
+real world database program with complex application logic, and were able to
+discover anomalies under Parallel Snapshot Isolation, and verify
+serializability for unbounded executions under Snapshot Isolation, two
+consistency mechanisms substantially weaker than serializability.
+",1,0,0,0,0,0
+9125,Andreev reflection in 2D relativistic materials with realistic tunneling transparency in normal-metal-superconductor junctions,"  The Andreev conductance across 2d normal metal (N)/superconductor (SC)
+junctions with relativistic Dirac spectrum is investigated theoretically in the
+Blonder-Tinkham-Klapwijk formalism. It is shown that for relativistic
+materials, due to the Klein tunneling instead of impurity potentials, the local
+strain in the junction is the key factor that determines the transparency of
+the junction. The local strain is shown to generate an effective Dirac
+$\delta$-gauge field. A remarkable suppression of the conductance are observed
+as the strength of the gauge field increases. The behaviors of the conductance
+are in well agreement with the results obtained in the case of 1d N/SC
+junction. We also study the Andreev reflection in a topological material near
+the chiral-to-helical phase transition in the presence of a local strain. The N
+side of the N/SC junction is modeled by the doped Kane-Mele (KM) model. The SC
+region is a doped correlated KM t-J (KMtJ) model, which has been shown to
+feature d+id'-wave spin-singlet pairing. With increasing intrinsic spin-orbit
+(SO) coupling, the doped KMtJ system undergoes a topological phase transition
+from the chiral d-wave superconductivity to the spin-Chern superconducting
+phase with helical Majorana fermions at edges. We explore the Andreev
+conductance at the two inequivalent Dirac points, respectively and predict the
+distinctive behaviors for the Andreev conductance across the topological phase
+transition. Relevance of our results for the adatom-doped graphene is
+discussed.
+",0,1,0,0,0,0
+9126,Multi-Task Learning for Contextual Bandits,"  Contextual bandits are a form of multi-armed bandit in which the agent has
+access to predictive side information (known as the context) for each arm at
+each time step, and have been used to model personalized news recommendation,
+ad placement, and other applications. In this work, we propose a multi-task
+learning framework for contextual bandit problems. Like multi-task learning in
+the batch setting, the goal is to leverage similarities in contexts for
+different arms so as to improve the agent's ability to predict rewards from
+contexts. We propose an upper confidence bound-based multi-task learning
+algorithm for contextual bandits, establish a corresponding regret bound, and
+interpret this bound to quantify the advantages of learning in the presence of
+high task (arm) similarity. We also describe an effective scheme for estimating
+task similarity from data, and demonstrate our algorithm's performance on
+several data sets.
+",1,0,0,1,0,0
+9127,"Admissible topologies on $C(Y,Z)$ and ${\cal O}_Z(Y)$","  Let $Y$ and $Z$ be two given topological spaces, ${\cal O}(Y)$ (respectively,
+${\cal O}(Z)$) the set of all open subsets of $Y$ (respectively, $Z$), and
+$C(Y,Z)$ the set of all continuous maps from $Y$ to $Z$. We study Scott type
+topologies on ${\mathcal O}(Y)$ and we construct admissible topologies on
+$C(Y,Z)$ and ${\mathcal O}_Z(Y)=\{f^{-1}(U)\in {\mathcal O}(Y): f\in C(Y,Z)\
+{\rm and}\ U\in {\mathcal O}(Z)\}$, introducing new problems in the field.
+",0,0,1,0,0,0
+9128,Weighted spherical means generated by generalized translation and general Euler-Poisson-Darboux equation,"  We consider the spherical mean generated by a multidimensional generalized
+translation and general Euler-Poisson-Darboux equation corresponding to this
+mean. The Asgeirsson property of solutions of the ultrahyperbolic equation that
+includes singular differential Bessel operators acting by each variable is
+provided.
+",0,0,1,0,0,0
+9129,K-Means Clustering using Tabu Search with Quantized Means,"  The Tabu Search (TS) metaheuristic has been proposed for K-Means clustering
+as an alternative to Lloyd's algorithm, which for all its ease of
+implementation and fast runtime, has the major drawback of being trapped at
+local optima. While the TS approach can yield superior performance, it involves
+a high computational complexity. Moreover, the difficulty in parameter
+selection in the existing TS approach does not make it any more attractive.
+This paper presents an alternative, low-complexity formulation of the TS
+optimization procedure for K-Means clustering. This approach does not require
+many parameter settings. We initially constrain the centers to points in the
+dataset. We then aim at evolving these centers using a unique neighborhood
+structure that makes use of gradient information of the objective function.
+This results in an efficient exploration of the search space, after which the
+means are refined. The proposed scheme is implemented in MATLAB and tested on
+four real-world datasets, and it achieves a significant improvement over the
+existing TS approach in terms of the intra cluster sum of squares and
+computational time.
+",1,0,0,0,0,0
+9130,In-silico Feedback Control of a MIMO Synthetic Toggle Switch via Pulse-Width Modulation,"  The synthetic toggle switch, first proposed by Gardner & Collins [1] is a
+MIMO control system that can be controlled by varying the concentrations of two
+inducer molecules, aTc and IPTG, to achieve a desired level of expression of
+the two genes it comprises. It has been shown [2] that this can be accomplished
+through an open-loop external control strategy where the two inputs are
+selected as mutually exclusive periodic pulse waves of appropriate amplitude
+and duty-cycle. In this paper, we use a recently derived average model of the
+genetic toggle switch subject to these inputs to synthesize new feedback
+control approaches that adjust the inputs duty-cycle in real-time via two
+different possible strategies, a model based hybrid PI-PWM approach and a
+so-called Zero-Average dynamics (ZAD) controller. The controllers are validated
+in-silico via both deterministic and stochastic simulations (SSA) illustrating
+the advantages and limitations of each strategy
+",1,0,0,0,0,0
+9131,Holomorphic foliations tangent to Levi-flat subsets,"  We study Segre varieties associated to Levi-flat subsets in complex manifolds
+and apply them to establish local and global results on the integration of
+tangent holomorphic foliations.
+",0,0,1,0,0,0
+9132,Real-Time Panoramic Tracking for Event Cameras,"  Event cameras are a paradigm shift in camera technology. Instead of full
+frames, the sensor captures a sparse set of events caused by intensity changes.
+Since only the changes are transferred, those cameras are able to capture quick
+movements of objects in the scene or of the camera itself. In this work we
+propose a novel method to perform camera tracking of event cameras in a
+panoramic setting with three degrees of freedom. We propose a direct camera
+tracking formulation, similar to state-of-the-art in visual odometry. We show
+that the minimal information needed for simultaneous tracking and mapping is
+the spatial position of events, without using the appearance of the imaged
+scene point. We verify the robustness to fast camera movements and dynamic
+objects in the scene on a recently proposed dataset and self-recorded
+sequences.
+",1,0,0,0,0,0
+9133,Halo nonlinear reconstruction,"  We apply the nonlinear reconstruction method to simulated halo fields. For
+halo number density $2.77\times 10^{-2}$ $(h^{-1} {\rm Mpc})^{-3}$ at $z=0$,
+corresponding to the SDSS main sample density, we find the scale where the
+noise saturates the linear signal is improved to $k\gtrsim0.36\ h {\rm
+Mpc}^{-1}$, a factor of $2.29$ improvement in scale, or $12$ in number of
+linear modes. The improvement is less for higher redshift or lower halo
+density. We expect this to substantially improve the BAO accuracy of dense, low
+redshift surveys, including the SDSS main sample, 6dFGS and 21cm intensity
+mapping initiatives.
+",0,1,0,0,0,0
+9134,Molecular semimetallic hydrogen,"  Establishing metallic hydrogen is a goal of intensive theoretical and
+experimental work since 1935 when Wigner and Hungtinton [1] predicted that
+insulating molecular hydrogen will dissociate at high pressures and transform
+to a metal. This metal is predicted to be a superconductor with very high
+critical temperature [2]. In another scenario, the metallization can be
+realized through overlapping of electronic bands in molecular hydrogen in the
+similar 400 - 500 GPa pressure range [3-5]. The calculations are not accurate
+enough to predict which option will be realized. Our data are consistent with
+transforms of hydrogen to semimetal by closing the indirect band gap in the
+molecular phase III at pressure ~ 360 GPa. Above this pressure, the metallic
+behaviour in the electrical conductivity appears, the reflection significantly
+increases. With pressure, the electrical conductivity strongly increases as
+measured up to 440 GPa. The Raman measurements evidence that hydrogen is in the
+molecular phase III at pressures at least up to 440 GPa. At higher pressures
+measured up to 480 GPa, the Raman signal gradually disappears indicating
+further transformation to a good molecular metal or to an atomic state.
+",0,1,0,0,0,0
+9135,Order-Sensitivity and Equivariance of Scoring Functions,"  The relative performance of competing point forecasts is usually measured in
+terms of loss or scoring functions. It is widely accepted that these scoring
+function should be strictly consistent in the sense that the expected score is
+minimized by the correctly specified forecast for a certain statistical
+functional such as the mean, median, or a certain risk measure. Thus, strict
+consistency opens the way to meaningful forecast comparison, but is also
+important in regression and M-estimation. Usually strictly consistent scoring
+functions for an elicitable functional are not unique. To give guidance on the
+choice of a scoring function, this paper introduces two additional quality
+criteria. Order-sensitivity opens the possibility to compare two deliberately
+misspecified forecasts given that the forecasts are ordered in a certain sense.
+On the other hand, equivariant scoring functions obey similar equivariance
+properties as the functional at hand - such as translation invariance or
+positive homogeneity. In our study, we consider scoring functions for popular
+functionals, putting special emphasis on vector-valued functionals, e.g. the
+pair (mean, variance) or (Value at Risk, Expected Shortfall).
+",0,0,1,1,0,0
+9136,Light in Power: A General and Parameter-free Algorithm for Caustic Design,"  We present in this paper a generic and parameter-free algorithm to
+efficiently build a wide variety of optical components, such as mirrors or
+lenses, that satisfy some light energy constraints. In all of our problems, one
+is given a collimated or point light source and a desired illumination after
+reflection or refraction and the goal is to design the geometry of a mirror or
+lens which transports exactly the light emitted by the source onto the target.
+We first propose a general framework and show that eight different optical
+component design problems amount to solving a light energy conservation
+equation that involves the computation of visibility diagrams. We then show
+that these diagrams all have the same structure and can be obtained by
+intersecting a 3D Power diagram with a planar or spherical domain. This allows
+us to propose an efficient and fully generic algorithm capable to solve these
+eight optical component design problems. The support of the prescribed target
+illumination can be a set of directions or a set of points located at a finite
+distance. Our solutions satisfy design constraints such as convexity or
+concavity. We show the effectiveness of our algorithm on simulated and
+fabricated examples.
+",1,0,0,0,0,0
+9137,Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data,"  In this paper, we investigate the potential of estimating the soil-moisture
+content based on VNIR hyperspectral data combined with LWIR data. Measurements
+from a multi-sensor field campaign represent the benchmark dataset which
+contains measured hyperspectral, LWIR, and soil-moisture data conducted on
+grassland site. We introduce a regression framework with three steps consisting
+of feature selection, preprocessing, and well-chosen regression models. The
+latter are mainly supervised machine learning models. An exception are the
+self-organizing maps which combine unsupervised and supervised learning. We
+analyze the impact of the distinct preprocessing methods on the regression
+results. Of all regression models, the extremely randomized trees model without
+preprocessing provides the best estimation performance. Our results reveal the
+potential of the respective regression framework combined with the VNIR
+hyperspectral data to estimate soil moisture measured under real-world
+conditions. In conclusion, the results of this paper provide a basis for
+further improvements in different research directions.
+",0,0,0,1,0,0
+9138,Distilling a Neural Network Into a Soft Decision Tree,"  Deep neural networks have proved to be a very effective way to perform
+classification tasks. They excel when the input data is high dimensional, the
+relationship between the input and the output is complicated, and the number of
+labeled training examples is large. But it is hard to explain why a learned
+network makes a particular classification decision on a particular test case.
+This is due to their reliance on distributed hierarchical representations. If
+we could take the knowledge acquired by the neural net and express the same
+knowledge in a model that relies on hierarchical decisions instead, explaining
+a particular decision would be much easier. We describe a way of using a
+trained neural net to create a type of soft decision tree that generalizes
+better than one learned directly from the training data.
+",1,0,0,1,0,0
+9139,A General Scheme Implicit Force Control for a Flexible-Link Manipulator,"  In this paper we propose an implicit force control scheme for a one-link
+flexible manipulator that interact with a compliant environment. The controller
+was based in the mathematical model of the manipulator, considering the
+dynamics of the beam flexible and the gravitational force. With this method,
+the controller parameters are obtained from the structural parameters of the
+beam (link) of the manipulator. This controller ensure the stability based in
+the Lyapunov Theory. The controller proposed has two closed loops: the inner
+loop is a tracking control with gravitational force and vibration frequencies
+compensation and the outer loop is a implicit force control. To evaluate the
+performance of the controller, we have considered to three different
+manipulators (the length, the diameter were modified) and three environments
+with compliance modified. The results obtained from simulations verify the
+asymptotic tracking and regulated in position and force respectively and the
+vibrations suppression of the beam in a finite time.
+",1,1,0,0,0,0
+9140,CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments,"  Casual conversations involving multiple speakers and noises from surrounding
+devices are part of everyday environments and pose challenges for automatic
+speech recognition systems. These challenges in speech recognition are target
+for the CHiME-5 challenge. In the present study, an attempt is made to overcome
+these challenges by employing a convolutional neural network (CNN)-based
+multichannel end-to-end speech recognition system. The system comprises an
+attention-based encoder-decoder neural network that directly generates a text
+as an output from a sound input. The mulitchannel CNN encoder, which uses
+residual connections and batch renormalization, is trained with augmented data,
+including white noise injection. The experimental results show that the word
+error rate (WER) was reduced by 11.9% absolute from the end-to-end baseline.
+",1,0,0,0,0,0
+9141,Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision,"  Tool manipulation is vital for facilitating robots to complete challenging
+task goals. It requires reasoning about the desired effect of the task and thus
+properly grasping and manipulating the tool to achieve the task. Task-agnostic
+grasping optimizes for grasp robustness while ignoring crucial task-specific
+constraints. In this paper, we propose the Task-Oriented Grasping Network
+(TOG-Net) to jointly optimize both task-oriented grasping of a tool and the
+manipulation policy for that tool. The training process of the model is based
+on large-scale simulated self-supervision with procedurally generated tool
+objects. We perform both simulated and real-world experiments on two tool-based
+manipulation tasks: sweeping and hammering. Our model achieves overall 71.1%
+task success rate for sweeping and 80.0% task success rate for hammering.
+Supplementary material is available at: bit.ly/task-oriented-grasp
+",1,0,0,1,0,0
+9142,Classification of Minimal Separating Sets in Low Genus Surfaces,"  Consider a surface $S$ and let $M\subset S$. If $S\setminus M$ is not
+connected, then we say $M$ \emph{separates} $S$, and we refer to $M$ as a
+\emph{separating set} of $S$. If $M$ separates $S$, and no proper subset of $M$
+separates $S$, then we say $M$ is a \emph{minimal separating set} of $S$. In
+this paper we use methods of computational combinatorial topology to classify
+the minimal separating sets of the orientable surfaces of genus $g=2$ and
+$g=3$. The classification for genus 0 and 1 was done in earlier work, using
+methods of algebraic topology.
+",0,0,1,0,0,0
+9143,A feasibility study on SSVEP-based interaction with motivating and immersive virtual and augmented reality,"  Non-invasive steady-state visual evoked potential (SSVEP) based
+brain-computer interface (BCI) systems offer high bandwidth compared to other
+BCI types and require only minimal calibration and training. Virtual reality
+(VR) has been already validated as effective, safe, affordable and motivating
+feedback modality for BCI experiments. Augmented reality (AR) enhances the
+physical world by superimposing informative, context sensitive, computer
+generated content. In the context of BCI, AR can be used as a friendlier and
+more intuitive real-world user interface, thereby facilitating a more seamless
+and goal directed interaction. This can improve practicality and usability of
+BCI systems and may help to compensate for their low bandwidth. In this
+feasibility study, three healthy participants had to finish a complex
+navigation task in immersive VR and AR conditions using an online SSVEP BCI.
+Two out of three subjects were successful in all conditions. To our knowledge,
+this is the first work to present an SSVEP BCI that operates using target
+stimuli integrated in immersive VR and AR (head-mounted display and camera).
+This research direction can benefit patients by introducing more intuitive and
+effective real-world interaction (e.g. smart home control). It may also be
+relevant for user groups that require or benefit from hands free operation
+(e.g. due to temporary situational disability).
+",1,0,0,0,0,0
+9144,Intelligent User Interfaces - A Tutorial,"  IUIs aim to incorporate intelligent automated capabilities in human computer
+interaction, where the net impact is a human-computer interaction that improves
+performance or usability in critical ways. It also involves designing and
+implementing an artificial intelligence (AI) component that effectively
+leverages human skills and capabilities, so that human performance with an
+application excels. IUIs embody capabilities that have traditionally been
+associated more strongly with humans than with computers: how to perceive,
+interpret, learn, use language, reason, plan, and decide.
+",1,0,0,0,0,0
+9145,Graph Partitioning with Acyclicity Constraints,"  Graphs are widely used to model execution dependencies in applications. In
+particular, the NP-complete problem of partitioning a graph under constraints
+receives enormous attention by researchers because of its applicability in
+multiprocessor scheduling. We identified the additional constraint of acyclic
+dependencies between blocks when mapping computer vision and imaging
+applications to a heterogeneous embedded multiprocessor. Existing algorithms
+and heuristics do not address this requirement and deliver results that are not
+applicable for our use-case. In this work, we show that this more constrained
+version of the graph partitioning problem is NP-complete and present heuristics
+that achieve a close approximation of the optimal solution found by an
+exhaustive search for small problem instances and much better scalability for
+larger instances. In addition, we can show a positive impact on the schedule of
+a real imaging application that improves communication volume and execution
+time.
+",1,0,0,0,0,0
+9146,Learning Sublinear-Time Indexing for Nearest Neighbor Search,"  Most of the efficient sublinear-time indexing algorithms for the
+high-dimensional nearest neighbor search problem (NNS) are based on space
+partitions of the ambient space $\mathbb{R}^d$. Inspired by recent theoretical
+work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn,
+Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing
+such partitions that reduces the problem to balanced graph partitioning
+followed by supervised classification. We instantiate this general approach
+with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and neural
+networks, respectively, to obtain a new partitioning procedure called Neural
+Locality-Sensitive Hashing (Neural LSH). On several standard benchmarks for
+NNS, our experiments show that the partitions found by Neural LSH consistently
+outperform partitions found by quantization- and tree-based methods.
+",1,0,0,1,0,0
+9147,"Lempel-Ziv Jaccard Distance, an Effective Alternative to Ssdeep and Sdhash","  Recent work has proposed the Lempel-Ziv Jaccard Distance (LZJD) as a method
+to measure the similarity between binary byte sequences for malware
+classification. We propose and test LZJD's effectiveness as a similarity digest
+hash for digital forensics. To do so we develop a high performance Java
+implementation with the same command-line arguments as sdhash, making it easy
+to integrate into existing workflows. Our testing shows that LZJD is effective
+for this task, and significantly outperforms sdhash and ssdeep in its ability
+to match related file fragments and files corrupted with random noise. In
+addition, LZJD is up to 60x faster than sdhash at comparison time.
+",1,0,0,0,0,0
+9148,Graded Steinberg algebras and their representations,"  We study the category of left unital graded modules over the Steinberg
+algebra of a graded ample Hausdorff groupoid. In the first part of the paper,
+we show that this category is isomorphic to the category of unital left modules
+over the Steinberg algebra of the skew-product groupoid arising from the
+grading. To do this, we show that the Steinberg algebra of the skew product is
+graded isomorphic to a natural generalisation of the the Cohen-Montgomery smash
+product of the Steinberg algebra of the underlying groupoid with the grading
+group. In the second part of the paper, we study the minimal (that is,
+irreducible) representations in the category of graded modules of a Steinberg
+algebra, and establish a connection between the annihilator ideals of these
+minimal representations, and effectiveness of the groupoid.
+Specialising our results, we produce a representation of the monoid of graded
+finitely generated projective modules over a Leavitt path algebra. We deduce
+that the lattice of order-ideals in the $K_0$-group of the Leavitt path algebra
+is isomorphic to the lattice of graded ideals of the algebra. We also
+investigate the graded monoid for Kumjian--Pask algebras of row-finite
+$k$-graphs with no sources. We prove that these algebras are graded von Neumann
+regular rings, and record some structural consequences of this.
+",0,0,1,0,0,0
+9149,Movement of time-delayed hot spots in Euclidean space for special initial states,"  We consider the Cauchy problem for the damped wave equation under the initial
+state that the sum of an initial position and an initial velocity vanishes.
+When the initial position is non-zero, non-negative and compactly supported, we
+study the large time behavior of the spatial null, critical, maximum and
+minimum sets of the solution. The spatial null set becomes a smooth
+hyper-surface homeomorphic to a sphere after a large enough time. The spatial
+critical set has at least three points after a large enough time. The set of
+spatial maximum points escapes from the convex hull of the support of the
+initial position. The set of spatial minimum points consists of one point after
+a large time, and the unique spatial minimum point converges to the centroid of
+the initial position at time infinity.
+",0,0,1,0,0,0
+9150,Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction,"  Time series prediction has been studied in a variety of domains. However, it
+is still challenging to predict future series given historical observations and
+past exogenous data. Existing methods either fail to consider the interactions
+among different components of exogenous variables which may affect the
+prediction accuracy, or cannot model the correlations between exogenous data
+and target data. Besides, the inherent temporal dynamics of exogenous data are
+also related to the target series prediction, and thus should be considered as
+well. To address these issues, we propose an end-to-end deep learning model,
+i.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which
+incorporates spatio-temporal feature extraction of exogenous variables and
+temporal dynamics modeling of target variables into a single framework.
+Moreover, by introducing the hierarchical attention mechanism, HRHN can
+adaptively select the relevant exogenous features in different semantic levels.
+We carry out comprehensive empirical evaluations with various methods over
+several datasets, and show that HRHN outperforms the state of the arts in time
+series prediction, especially in capturing sudden changes and sudden
+oscillations of time series.
+",0,0,0,1,0,0
+9151,Mass distribution and skewness for passive scalar transport in pipes with polygonal and smooth cross-sections,"  We extend our previous results characterizing the loading properties of a
+diffusing passive scalar advected by a laminar shear flow in ducts and channels
+to more general cross-sectional shapes, including regular polygons and smoothed
+corner ducts originating from deformations of ellipses. For the case of the
+triangle, short time skewness is calculated exactly to be positive, while
+long-time asymptotics shows it to be negative. Monte-Carlo simulations confirm
+these predictions, and document the time scale for sign change. Interestingly,
+the equilateral triangle is the only regular polygon with this property, all
+other polygons possess positive skewness at all times, although this cannot
+cannot be proved on finite times due to the lack of closed form flow solutions
+for such geometries. Alternatively, closed form flow solutions can be
+constructed for smooth deformations of ellipses, and illustrate how the
+possibility of multiple sign switching in time is unrelated to domain corners.
+Exact conditions relating the median and the skewness to the mean are developed
+which guarantee when the sign for the skewness implies front (back) loading
+properties of the evolving tracer distribution along the pipe. Short and long
+time asymptotics confirm this condition, and Monte-Carlo simulations verify
+this at all times.
+",0,1,0,0,0,0
+9152,An Optics-Based Approach to Thermal Management of Photovoltaics: Selective-Spectral and Radiative Cooling,"  For commercial one-sun solar modules, up to 80% of the incoming sunlight may
+be dissipated as heat, potentially raising the temperature 20 C - 30 C higher
+than the ambient. In the long term, extreme self-heating erodes efficiency and
+shortens lifetime, thereby dramatically reducing the total energy output.
+Therefore, it is critically important to develop effective and practical (and
+preferably passive) cooling methods to reduce operating temperature of PV
+modules. In this paper, we explore two fundamental (but often overlooked)
+origins of PV self-heating, namely, sub-bandgap absorption and imperfect
+thermal radiation. The analysis suggests that we redesign the optical
+properties of the solar module to eliminate parasitic absorption
+(selective-spectral cooling) and enhance thermal emission (radiative cooling).
+Our Comprehensive opto-electro-thermal simulation shows that the proposed
+techniques would cool the one-sun and low-concentrated terrestrial solar
+modules up to 10 C and 20 C, respectively. This self-cooling would
+substantially extend the lifetime for solar modules, with The corresponding
+increase in energy yields and reduced LCOE.
+",0,1,0,0,0,0
+9153,GOLDRUSH. II. Clustering of Galaxies at $z\sim 4-6$ Revealed with the Half-Million Dropouts Over the 100 deg$^2$ Area Corresponding to 1 Gpc$^3$,"  We present clustering properties from 579,492 Lyman break galaxies (LBGs) at
+z~4-6 over the 100 deg^2 sky (corresponding to a 1.4 Gpc^3 volume) identified
+in early data of the Hyper Suprime-Cam (HSC) Subaru strategic program survey.
+We derive angular correlation functions (ACFs) of the HSC LBGs with
+unprecedentedly high statistical accuracies at z~4-6, and compare them with the
+halo occupation distribution (HOD) models. We clearly identify significant ACF
+excesses in 10""<$\theta$<90"", the transition scale between 1- and 2-halo terms,
+suggestive of the existence of the non-linear halo bias effect. Combining the
+HOD models and previous clustering measurements of faint LBGs at z~4-7, we
+investigate dark-matter halo mass (Mh) of the z~4-7 LBGs and its correlation
+with various physical properties including the star-formation rate (SFR), the
+stellar-to-halo mass ratio (SHMR), and the dark matter accretion rate (dotMh)
+over a wide-mass range of Mh/M$_\odot$=4x10^10-4x10^12. We find that the SHMR
+increases from z~4 to 7 by a factor of ~4 at Mh~1x10^11 M$_\odot$, while the
+SHMR shows no strong evolution in the similar redshift range at Mh~1x10^12
+M$_\odot$. Interestingly, we identify a tight relation of SFR/dotMh-Mh showing
+no significant evolution beyond 0.15 dex in this wide-mass range over z~4-7.
+This weak evolution suggests that the SFR/dotMh-Mh relation is a fundamental
+relation in high-redshift galaxy formation whose star formation activities are
+regulated by the dark matter mass assembly. Assuming this fundamental relation,
+we calculate the cosmic SFR densities (SFRDs) over z=0-10 (a.k.a. Madau-Lilly
+plot). The cosmic SFRD evolution based on the fundamental relation agrees with
+the one obtained by observations, suggesting that the cosmic SFRD increase from
+z~10 to 4-2 (decrease from z~4-2 to 0) is mainly driven by the increase of the
+halo abundance (the decrease of the accretion rate).
+",0,1,0,0,0,0
+9154,The Least-Area Tetrahedral Tile of Space,"  We prove the least-area, unit-volume, tetrahedral tile of Euclidean space,
+without the assumption of Gallagher et al. that the tiling uses only
+orientation-preserving images of the tile. The winner remains Sommerville's
+type 4v.
+",0,0,1,0,0,0
+9155,Renormalization group theory for percolation in time-varying networks,"  Motivated by multi-hop communication in unreliable wireless networks, we
+present a percolation theory for time-varying networks. We develop a
+renormalization group theory for a prototypical network on a regular grid,
+where individual links switch stochastically between active and inactive
+states. The question whether a given source node can communicate with a
+destination node along paths of active links is equivalent to a percolation
+problem. Our theory maps the temporal existence of multi-hop paths on an
+effective two-state Markov process. We show analytically how this Markov
+process converges towards a memory-less Bernoulli process as the hop distance
+between source and destination node increases. Our work extends classical
+percolation theory to the dynamic case and elucidates temporal correlations of
+message losses. Quantification of temporal correlations has implications for
+the design of wireless communication and control protocols, e.g. in
+cyber-physical systems such as self-organized swarms of drones or smart traffic
+networks.
+",1,1,0,0,0,0
+9156,Spectral properties and breathing dynamics of a few-body Bose-Bose mixture in a 1D harmonic trap,"  We investigate a few-body mixture of two bosonic components, each consisting
+of two particles confined in a quasi one-dimensional harmonic trap. By means of
+exact diagonalization with a correlated basis approach we obtain the low-energy
+spectrum and eigenstates for the whole range of repulsive intra- and
+inter-component interaction strengths. We analyse the eigenvalues as a function
+of the inter-component coupling, covering hereby all the limiting regimes, and
+characterize the behaviour in-between these regimes by exploiting the
+symmetries of the Hamiltonian. Provided with this knowledge we study the
+breathing dynamics in the linear-response regime by slightly quenching the trap
+frequency symmetrically for both components. Depending on the choice of
+interactions strengths, we identify 1 to 3 monopole modes besides the breathing
+mode of the center of mass coordinate. For the uncoupled mixture each monopole
+mode corresponds to the breathing oscillation of a specific relative
+coordinate. Increasing the inter-component coupling first leads to multi-mode
+oscillations in each relative coordinate, which turn into single-mode
+oscillations of the same frequency in the composite-fermionization regime.
+",0,1,0,0,0,0
+9157,"Study of secondary neutron interactions with $^{232}$Th, $^{129}$I, and $^{127}$I nuclei with the uranium assembly ""QUINTA"" at 2, 4, and 8 GeV deuteron beams of the JINR Nuclotron accelerator","  The natural uranium assembly, ""QUINTA"", was irradiated with 2, 4, and 8 GeV
+deuterons. The $^{232}$Th, $^{127}$I, and $^{129}$I samples have been exposed
+to secondary neutrons produced in the assembly at a 20-cm radial distance from
+the deuteron beam axis. The spectra of gamma rays emitted by the activated
+$^{232}$Th, $^{127}$I, and $^{129}$I samples have been analyzed and several
+tens of product nuclei have been identified. For each of those products,
+neutron-induced reaction rates have been determined. The transmutation power
+for the $^{129}$I samples is estimated. Experimental results were compared to
+those calculated with well-known stochastic and deterministic codes.
+",0,1,0,0,0,0
+9158,Short Proofs for Slow Consistency,"  Let $\operatorname{Con}(\mathbf T)\!\restriction\!x$ denote the finite
+consistency statement ""there are no proofs of contradiction in $\mathbf T$ with
+$\leq x$ symbols"". For a large class of natural theories $\mathbf T$, Pudlák
+has shown that the lengths of the shortest proofs of
+$\operatorname{Con}(\mathbf T)\!\restriction\!n$ in the theory $\mathbf T$
+itself are bounded by a polynomial in $n$. At the same time he conjectures that
+$\mathbf T$ does not have polynomial proofs of the finite consistency
+statements $\operatorname{Con}(\mathbf T+\operatorname{Con}(\mathbf
+T))\!\restriction\!n$. In contrast we show that Peano arithmetic
+($\mathbf{PA}$) has polynomial proofs of
+$\operatorname{Con}(\mathbf{PA}+\operatorname{Con}^*(\mathbf{PA}))\!\restriction\!n$,
+where $\operatorname{Con}^*(\mathbf{PA})$ is the slow consistency statement for
+Peano arithmetic, introduced by S.-D. Friedman, Rathjen and Weiermann. We also
+obtain a new proof of the result that the usual consistency statement
+$\operatorname{Con}(\mathbf{PA})$ is equivalent to $\varepsilon_0$ iterations
+of slow consistency. Our argument is proof-theoretic, while previous
+investigations of slow consistency relied on non-standard models of arithmetic.
+",0,0,1,0,0,0
+9159,"Response to Comment on ""Cell nuclei have lower refractive index and mass density than cytoplasm""","  In a recent study entitled ""Cell nuclei have lower refractive index and mass
+density than cytoplasm"", we provided strong evidence indicating that the
+nuclear refractive index (RI) is lower than the RI of the cytoplasm for several
+cell lines. In a complementary study in 2017, entitled ""Is the nuclear
+refractive index lower than cytoplasm? Validation of phase measurements and
+implications for light scattering technologies"", Steelman et al. observed a
+lower nuclear RI also for other cell lines and ruled out methodological error
+sources such as phase wrapping and scattering effects. Recently, Yurkin
+composed a comment on these 2 publications, entitled ""How a phase image of a
+cell with nucleus refractive index smaller than that of the cytoplasm should
+look like?"", putting into question the methods used for measuring the cellular
+and nuclear RI in the aforementioned publications by suggesting that a lower
+nuclear RI would produce a characteristic dip in the measured phase profile in
+situ. We point out the difficulty of identifying this dip in the presence of
+other cell organelles, noise, or blurring due to the imaging point spread
+function. Furthermore, we mitigate Yurkin's concerns regarding the ability of
+the simple-transmission approximation to compare cellular and nuclear RI by
+analyzing a set of phase images with a novel, scattering-based approach. We
+conclude that the absence of a characteristic dip in the measured phase
+profiles does not contradict the usage of the simple-transmission approximation
+for the determination of the average cellular or nuclear RI. Our response can
+be regarded as an addition to the response by Steelman, Eldridge and Wax. We
+kindly ask the reader to attend to their thorough ascertainment prior to
+reading our response.
+",0,0,0,0,1,0
+9160,On the composition of an arbitrary collection of $SU(2)$ spins: An Enumerative Combinatoric Approach,"  The whole enterprise of spin compositions can be recast as simple enumerative
+combinatoric problems. We show here that enumerative combinatorics
+(EC)\citep{book:Stanley-2011} is a natural setting for spin composition, and
+easily leads to very general analytic formulae -- many of which hitherto not
+present in the literature. Based on it, we propose three general methods for
+computing spin multiplicities; namely, 1) the multi-restricted composition, 2)
+the generalized binomial and 3) the generating function methods. Symmetric and
+anti-symmetric compositions of $SU(2)$ spins are also discussed, using
+generating functions. Of particular importance is the observation that while
+the common Clebsch-Gordan decomposition (CGD) -- which considers the spins as
+distinguishable -- is related to integer compositions, the symmetric and
+anti-symmetric compositions (where one considers the spins as
+indistinguishable) are obtained considering integer partitions. The integers in
+question here are none other but the occupation numbers of the
+Holstein-Primakoff bosons.
+\par The pervasiveness of $q-$analogues in our approach is a testament to the
+fundamental role they play in spin compositions. In the appendix, some new
+results in the power series representation of Gaussian polynomials (or
+$q-$binomial coefficients) -- relevant to symmetric and antisymmetric
+compositions -- are presented.
+",1,0,1,0,0,0
+9161,Perceived Performance of Webpages In the Wild: Insights from Large-scale Crowdsourcing of Above-the-Fold QoE,"  Clearly, no one likes webpages with poor quality of experience (QoE). Being
+perceived as slow or fast is a key element in the overall perceived QoE of web
+applications. While extensive effort has been put into optimizing web
+applications (both in industry and academia), not a lot of work exists in
+characterizing what aspects of webpage loading process truly influence human
+end-user's perception of the ""Speed"" of a page. In this paper we present
+""SpeedPerception"", a large-scale web performance crowdsourcing framework
+focused on understanding the perceived loading performance of above-the-fold
+(ATF) webpage content. Our end goal is to create free open-source benchmarking
+datasets to advance the systematic analysis of how humans perceive webpage
+loading process. In Phase-1 of our ""SpeedPerception"" study using Internet
+Retailer Top 500 (IR 500) websites
+(this https URL), we found that commonly used
+navigation metrics such as ""onLoad"" and ""Time To First Byte (TTFB)"" fail (less
+than 60% match) to represent majority human perception when comparing the speed
+of two webpages. We present a simple 3-variable-based machine learning model
+that explains the majority end-user choices better (with $87 \pm 2\%$
+accuracy). In addition, our results suggest that the time needed by end-users
+to evaluate relative perceived speed of webpage is far less than the time of
+its ""visualComplete"" event.
+",1,0,0,1,0,0
+9162,Entanglement and exotic superfluidity in spin-imbalanced lattices,"  We investigate the properties of entanglement in one-dimensional fermionic
+lattices at the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) superfluid regime. By
+analyzing occupation probabilities, which are concepts closely related to FFLO
+and entanglement, we obtain approximate analytical expressions for the
+spin-flip processes at the FFLO regime. We also apply density matrix
+renormalization group calculations to obtain the exact ground-state
+entanglement of the system in superfluid and non-superfluid regimes. Our
+results reveal a breaking pairs avalanche appearing precisely at the
+FFLO-normal phase transition. We find that entanglement is non-monotonic in
+superfluid regimes, feature that could be used as a signature of exotic
+superfluidity.
+",0,1,0,0,0,0
+9163,Informed Non-convex Robust Principal Component Analysis with Features,"  We revisit the problem of robust principal component analysis with features
+acting as prior side information. To this aim, a novel, elegant, non-convex
+optimization approach is proposed to decompose a given observation matrix into
+a low-rank core and the corresponding sparse residual. Rigorous theoretical
+analysis of the proposed algorithm results in exact recovery guarantees with
+low computational complexity. Aptly designed synthetic experiments demonstrate
+that our method is the first to wholly harness the power of non-convexity over
+convexity in terms of both recoverability and speed. That is, the proposed
+non-convex approach is more accurate and faster compared to the best available
+algorithms for the problem under study. Two real-world applications, namely
+image classification and face denoising further exemplify the practical
+superiority of the proposed method.
+",0,0,0,1,0,0
+9164,A note on self orbit equivalences of Anosov flows and bundles with fiberwise Anosov flows,"  We show that a self orbit equivalence of a transitive Anosov flow on a
+$3$-manifold which is homotopic to identity has to either preserve every orbit
+or the Anosov flow is $\mathbb{R}$-covered and the orbit equivalence has to be
+of a specific type. This result shows that one can remove a relatively
+unnatural assumption in a result of Farrell and Gogolev about the topological
+rigidity of bundles supporting a fiberwise Anosov flow when the fiber is
+$3$-dimensional.
+",0,0,1,0,0,0
+9165,The Metallicity of the Intracluster Medium Over Cosmic Time: Further Evidence for Early Enrichment,"  We use Chandra X-ray data to measure the metallicity of the intracluster
+medium (ICM) in 245 massive galaxy clusters selected from X-ray and
+Sunyaev-Zel'dovich (SZ) effect surveys, spanning redshifts $0<z<1.2$.
+Metallicities were measured in three different radial ranges, spanning cluster
+cores through their outskirts. We explore trends in these measurements as a
+function of cluster redshift, temperature, and surface brightness ""peakiness""
+(a proxy for gas cooling efficiency in cluster centers). The data at large
+radii (0.5--1 $r_{500}$) are consistent with a constant metallicity, while at
+intermediate radii (0.1-0.5 $r_{500}$) we see a late-time increase in
+enrichment, consistent with the expected production and mixing of metals in
+cluster cores. In cluster centers, there are strong trends of metallicity with
+temperature and peakiness, reflecting enhanced metal production in the
+lowest-entropy gas. Within the cool-core/sharply peaked cluster population,
+there is a large intrinsic scatter in central metallicity and no overall
+evolution, indicating significant astrophysical variations in the efficiency of
+enrichment. The central metallicity in clusters with flat surface brightness
+profiles is lower, with a smaller intrinsic scatter, but increases towards
+lower redshifts. Our results are consistent with other recent measurements of
+ICM metallicity as a function of redshift. They reinforce the picture implied
+by observations of uniform metal distributions in the outskirts of nearby
+clusters, in which most of the enrichment of the ICM takes place before cluster
+formation, with significant later enrichment taking place only in cluster
+centers, as the stellar populations of the central galaxies evolve.
+",0,1,0,0,0,0
+9166,"Graphical Models: An Extension to Random Graphs, Trees, and Other Objects","  In this work, we consider an extension of graphical models to random graphs,
+trees, and other objects. To do this, many fundamental concepts for
+multivariate random variables (e.g., marginal variables, Gibbs distribution,
+Markov properties) must be extended to other mathematical objects; it turns out
+that this extension is possible, as we will discuss, if we have a consistent,
+complete system of projections on a given object. Each projection defines a
+marginal random variable, allowing one to specify independence assumptions
+between them. Furthermore, these independencies can be specified in terms of a
+small subset of these marginal variables (which we call the atomic variables),
+allowing the compact representation of independencies by a directed graph.
+Projections also define factors, functions on the projected object space, and
+hence a projection family defines a set of possible factorizations for a
+distribution; these can be compactly represented by an undirected graph.
+The invariances used in graphical models are essential for learning
+distributions, not just on multivariate random variables, but also on other
+objects. When they are applied to random graphs and random trees, the result is
+a general class of models that is applicable to a broad range of problems,
+including those in which the graphs and trees have complicated edge structures.
+These models need not be conditioned on a fixed number of vertices, as is often
+the case in the literature for random graphs, and can be used for problems in
+which attributes are associated with vertices and edges. For graphs,
+applications include the modeling of molecules, neural networks, and relational
+real-world scenes; for trees, applications include the modeling of infectious
+diseases, cell fusion, the structure of language, and the structure of objects
+in visual scenes. Many classic models are particular instances of this
+framework.
+",1,0,0,1,0,0
+9167,"Boundary Control method and De Branges spaces. Schrödinger equation, Dirac system and Discrete Schrödinger operator","  In the framework of the application of the Boundary Control method to solving
+the inverse dynamical problems for the one-dimensional Schrödinger and Dirac
+operators on the half-line and semi-infinite discrete Schrödinger operator,
+we establish the connections with the method of De Branges: for each of the
+system we construct the De Branges space and give a natural dynamical
+interpretation of all its ingredients: the set of function the De Brange space
+consists of, the scalar product, the reproducing kernel.
+",0,0,1,0,0,0
+9168,On the evolution of galaxy spin in a cosmological hydrodynamic simulation of galaxy clusters,"  The traditional view of the morphology-spin connection is being challenged by
+recent integral-field-unit observations, as the majority of early-type galaxies
+are found to have a rotational component that is often as large as a dispersion
+component. Mergers are often suspected to be critical in galaxy spin evolution,
+yet the details of their roles are still unclear. We present the first results
+on the spin evolution of galaxies in cluster environments through a
+cosmological hydrodynamic simulation. Galaxies spin down globally with cosmic
+evolution. Major (mass ratios > 1/4) and minor (1/4 $\geq$ mass ratios > 1/50)
+mergers are important contributors to the spin down in particular in massive
+galaxies. Minor mergers appear to have stronger cumulative effects than major
+mergers. Surprisingly, the dominant driver of galaxy spin down seems to be
+environmental effects rather than mergers. However, since multiple processes
+act in combination, it is difficult to separate their individual roles. We
+briefly discuss the caveats and future studies that are called for.
+",0,1,0,0,0,0
+9169,Diagrammatic Approach to Multiphoton Scattering,"  We present a method to systematically study multi-photon transmission in one
+dimensional systems comprised of correlated quantum emitters coupled to input
+and output waveguides. Within the Green's function approach of the scattering
+matrix (S-matrix), we develop a diagrammatic technique to analytically obtain
+the system's scattering amplitudes while at the same time visualise all the
+possible absorption and emission processes. Our method helps to reduce the
+significant effort in finding the general response of a many-body bosonic
+system, particularly the nonlinear response embedded in the Green's functions.
+We demonstrate our proposal through physically relevant examples involving
+scattering of multi-photon states from two-level emitters as well as from
+arrays of correlated Kerr nonlinear resonators in the Bose-Hubbard model.
+",0,1,0,0,0,0
+9170,Efficient Computation of Feedback Control for Constrained Systems,"  A method is presented for solving the discrete-time finite-horizon Linear
+Quadratic Regulator (LQR) problem subject to auxiliary linear equality
+constraints, such as fixed end-point constraints. The method explicitly
+determines an affine relationship between the control and state variables, as
+in standard Riccati recursion, giving rise to feedback control policies that
+account for constraints. Since the linearly-constrained LQR problem arises
+commonly in robotic trajectory optimization, having a method that can
+efficiently compute these solutions is important. We demonstrate some of the
+useful properties and interpretations of said control policies, and we compare
+the computation time of our method against existing methods.
+",1,0,0,0,0,0
+9171,A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations,"  As the size of modern data sets exceeds the disk and memory capacities of a
+single computer, machine learning practitioners have resorted to parallel and
+distributed computing. Given that optimization is one of the pillars of machine
+learning and predictive modeling, distributed optimization methods have
+recently garnered ample attention, in particular when either observations or
+features are distributed, but not both. We propose a general stochastic
+algorithm where observations, features, and gradient components can be sampled
+in a double distributed setting, i.e., with both features and observations
+distributed. Very technical analyses establish convergence properties of the
+algorithm under different conditions on the learning rate (diminishing to zero
+or constant). Computational experiments in Spark demonstrate a superior
+performance of our algorithm versus a benchmark in early iterations of the
+algorithm, which is due to the stochastic components of the algorithm.
+",0,0,0,1,0,0
+9172,Unified Model of D-Term Inflation,"  Hybrid inflation, driven by a Fayet-Iliopoulos (FI) D term, is an intriguing
+inflationary model. In its usual formulation, it however suffers from several
+shortcomings. These pertain to the origin of the FI mass scale, the stability
+of scalar fields during inflation, gravitational corrections in supergravity,
+as well as to the latest constraints from the cosmic microwave background. We
+demonstrate that these issues can be remedied if D-term inflation is realized
+in the context of strongly coupled supersymmetric gauge theories. We suppose
+that the D term is generated in consequence of dynamical supersymmetry
+breaking. Moreover, we assume canonical kinetic terms in the Jordan frame as
+well as an approximate shift symmetry along the inflaton direction. This
+provides us with a unified picture of D-term inflation and high-scale
+supersymmetry breaking. The D term may be associated with a gauged U(1)_B-L, so
+that the end of inflation spontaneously breaks B-L in the visible sector.
+",0,1,0,0,0,0
+9173,Signal Recovery from Unlabeled Samples,"  In this paper, we study the recovery of a signal from a set of noisy linear
+projections (measurements), when such projections are unlabeled, that is, the
+correspondence between the measurements and the set of projection vectors
+(i.e., the rows of the measurement matrix) is not known a priori. We consider a
+special case of unlabeled sensing referred to as Unlabeled Ordered Sampling
+(UOS) where the ordering of the measurements is preserved. We identify a
+natural duality between this problem and classical Compressed Sensing (CS),
+where we show that the unknown support (location of nonzero elements) of a
+sparse signal in CS corresponds to the unknown indices of the measurements in
+UOS. While in CS it is possible to recover a sparse signal from an
+under-determined set of linear equations (less equations than the signal
+dimension), successful recovery in UOS requires taking more samples than the
+dimension of the signal. Motivated by this duality, we develop a Restricted
+Isometry Property (RIP) similar to that in CS. We also design a low-complexity
+Alternating Minimization algorithm that achieves a stable signal recovery under
+the established RIP. We analyze our proposed algorithm for different signal
+dimensions and number of measurements theoretically and investigate its
+performance empirically via simulations. The results are reminiscent of
+phase-transition similar to that occurring in CS.
+",1,0,0,1,0,0
+9174,Piecewise linear generalized Alexander's theorem in dimension at most 5,"  We study piecewise linear co-dimension two embeddings of closed oriented
+manifolds in Euclidean space, and show that any such embedding can always be
+isotoped to be a closed braid as long as the ambient dimension is at most five,
+extending results of Alexander (in ambient dimension three), and Viro and
+independently Kamada (in ambient dimension four). We also show an analogous
+result for higher co-dimension embeddings.
+",0,0,1,0,0,0
+9175,Sample complexity of population recovery,"  The problem of population recovery refers to estimating a distribution based
+on incomplete or corrupted samples. Consider a random poll of sample size $n$
+conducted on a population of individuals, where each pollee is asked to answer
+$d$ binary questions. We consider one of the two polling impediments: (a) in
+lossy population recovery, a pollee may skip each question with probability
+$\epsilon$, (b) in noisy population recovery, a pollee may lie on each question
+with probability $\epsilon$. Given $n$ lossy or noisy samples, the goal is to
+estimate the probabilities of all $2^d$ binary vectors simultaneously within
+accuracy $\delta$ with high probability.
+This paper settles the sample complexity of population recovery. For lossy
+model, the optimal sample complexity is
+$\tilde\Theta(\delta^{-2\max\{\frac{\epsilon}{1-\epsilon},1\}})$, improving the
+state of the art by Moitra and Saks in several ways: a lower bound is
+established, the upper bound is improved and the result depends at most on the
+logarithm of the dimension. Surprisingly, the sample complexity undergoes a
+phase transition from parametric to nonparametric rate when $\epsilon$ exceeds
+$1/2$. For noisy population recovery, the sharp sample complexity turns out to
+be more sensitive to dimension and scales as $\exp(\Theta(d^{1/3}
+\log^{2/3}(1/\delta)))$ except for the trivial cases of $\epsilon=0,1/2$ or
+$1$.
+For both models, our estimators simply compute the empirical mean of a
+certain function, which is found by pre-solving a linear program (LP).
+Curiously, the dual LP can be understood as Le Cam's method for lower-bounding
+the minimax risk, thus establishing the statistical optimality of the proposed
+estimators. The value of the LP is determined by complex-analytic methods.
+",1,0,1,1,0,0
+9176,Asymptotic Exponentiality of the First Exit Time of the Shiryaev-Roberts Diffusion with Constant Positive Drift,"  We consider the first exit time of a Shiryaev-Roberts diffusion with constant
+positive drift from the interval $[0,A]$ where $A>0$. We show that the moment
+generating function (Laplace transform) of a suitably standardized version of
+the first exit time converges to that of the unit-mean exponential distribution
+as $A\to+\infty$. The proof is explicit in that the moment generating function
+of the first exit time is first expressed analytically and in a closed form,
+and then the desired limit as $A\to+\infty$ is evaluated directly. The result
+is of importance in the area of quickest change-point detection, and its
+discrete-time counterpart has been previously established - although in a
+different manner - by Pollak and Tartakovsky (2009).
+",0,0,1,1,0,0
+9177,Learning to Teach Reinforcement Learning Agents,"  In this article we study the transfer learning model of action advice under a
+budget. We focus on reinforcement learning teachers providing action advice to
+heterogeneous students playing the game of Pac-Man under a limited advice
+budget. First, we examine several critical factors affecting advice quality in
+this setting, such as the average performance of the teacher, its variance and
+the importance of reward discounting in advising. The experiments show the
+non-trivial importance of the coefficient of variation (CV) as a statistic for
+choosing policies that generate advice. The CV statistic relates variance to
+the corresponding mean. Second, the article studies policy learning for
+distributing advice under a budget. Whereas most methods in the relevant
+literature rely on heuristics for advice distribution we formulate the problem
+as a learning one and propose a novel RL algorithm capable of learning when to
+advise, adapting to the student and the task at hand. Furthermore, we argue
+that learning to advise under a budget is an instance of a more generic
+learning problem: Constrained Exploitation Reinforcement Learning.
+",1,0,0,0,0,0
+9178,Giant room-temperature barocaloric effects in PDMS rubber at low pressures,"  The barocaloric effect is still an incipient scientific topic, but it has
+been attracting an increasing attention in the last years due to the promising
+perspectives for its application in alternative cooling devices. Here, we
+present giant values of barocaloric entropy change and temperature change
+induced by low pressures in PDMS elastomer around room temperature. Adiabatic
+temperature changes of 12.0 K and 28.5 K were directly measured for pressure
+changes of 173 MPa and 390 MPa, respectively, associated with large normalized
+temperature changes (~70 K GPa-1). From adiabatic temperature change data, we
+obtained entropy change values larger than 140 J kg-1 K-1. We found barocaloric
+effect values that exceed those previously reported for any promising
+barocaloric materials from direct measurements of temperature change around
+room temperature. Our results stimulate the study of the barocaloric effect in
+elastomeric polymers and broaden the pathway to use this effect in solid-state
+cooling technologies.
+",0,1,0,0,0,0
+9179,"$({\mathfrak{gl}}_M, {\mathfrak{gl}}_N)$-Dualities in Gaudin Models with Irregular Singularities","  We establish $({\mathfrak{gl}}_M, {\mathfrak{gl}}_N)$-dualities between
+quantum Gaudin models with irregular singularities. Specifically, for any $M, N
+\in {\mathbb Z}_{\geq 1}$ we consider two Gaudin models: the one associated
+with the Lie algebra ${\mathfrak{gl}}_M$ which has a double pole at infinity
+and $N$ poles, counting multiplicities, in the complex plane, and the same
+model but with the roles of $M$ and $N$ interchanged. Both models can be
+realized in terms of Weyl algebras, i.e., free bosons; we establish that, in
+this realization, the algebras of integrals of motion of the two models
+coincide. At the classical level we establish two further generalizations of
+the duality. First, we show that there is also a duality for realizations in
+terms of free fermions. Second, in the bosonic realization we consider the
+classical cyclotomic Gaudin model associated with the Lie algebra
+${\mathfrak{gl}}_M$ and its diagram automorphism, with a double pole at
+infinity and $2N$ poles, counting multiplicities, in the complex plane. We
+prove that it is dual to a non-cyclotomic Gaudin model associated with the Lie
+algebra ${\mathfrak{sp}}_{2N}$, with a double pole at infinity and $M$ simple
+poles in the complex plane. In the special case $N=1$ we recover the well-known
+self-duality in the Neumann model.
+",0,0,1,0,0,0
+9180,Dynamic coupling of a finite element solver to large-scale atomistic simulations,"  We propose a method for efficiently coupling the finite element method with
+atomistic simulations, while using molecular dynamics or kinetic Monte Carlo
+techniques. Our method can dynamically build an optimized unstructured mesh
+that follows the geometry defined by atomistic data. On this mesh, different
+multiphysics problems can be solved to obtain distributions of physical
+quantities of interest, which can be fed back to the atomistic system. The
+simulation flow is optimized to maximize computational efficiency while
+maintaining good accuracy. This is achieved by providing the modules for a)
+optimization of the density of the generated mesh according to requirements of
+a specific geometry and b) efficient extension of the finite element domain
+without a need to extend the atomistic one. Our method is organized as an
+open-source C++ code. In the current implementation, an efficient Laplace
+equation solver for calculation of electric field distribution near rough
+atomistic surface demonstrates the capability of the suggested approach.
+",1,1,0,0,0,0
+9181,A study of periodograms standardized using training data sets and application to exoplanet detection,"  When the noise affecting time series is colored with unknown statistics, a
+difficulty for sinusoid detection is to control the true significance level of
+the test outcome. This paper investigates the possibility of using training
+data sets of the noise to improve this control. Specifically, we analyze the
+performances of various detectors {applied to} periodograms standardized using
+training data sets. Emphasis is put on sparse detection in the Fourier domain
+and on the limitation posed by the necessarily finite size of the training sets
+available in practice. We study the resulting false alarm and detection rates
+and show that standardization leads in some cases to powerful constant false
+alarm rate tests. The study is both analytical and numerical. Although
+analytical results are derived in an asymptotic regime, numerical results show
+that theory accurately describes the tests' behaviour for moderately large
+sample sizes. Throughout the paper, an application of the considered
+periodogram standardization is presented for exoplanet detection in radial
+velocity data.
+",0,1,1,1,0,0
+9182,An Efficient Deep Learning Technique for the Navier-Stokes Equations: Application to Unsteady Wake Flow Dynamics,"  We present an efficient deep learning technique for the model reduction of
+the Navier-Stokes equations for unsteady flow problems. The proposed technique
+relies on the Convolutional Neural Network (CNN) and the stochastic gradient
+descent method. Of particular interest is to predict the unsteady fluid forces
+for different bluff body shapes at low Reynolds number. The discrete
+convolution process with a nonlinear rectification is employed to approximate
+the mapping between the bluff-body shape and the fluid forces. The deep neural
+network is fed by the Euclidean distance function as the input and the target
+data generated by the full-order Navier-Stokes computations for primitive bluff
+body shapes. The convolutional networks are iteratively trained using the
+stochastic gradient descent method with the momentum term to predict the fluid
+force coefficients of different geometries and the results are compared with
+the full-order computations. We attempt to provide a physical analogy of the
+stochastic gradient method with the momentum term with the simplified form of
+the incompressible Navier-Stokes momentum equation. We also construct a direct
+relationship between the CNN-based deep learning and the Mori-Zwanzig formalism
+for the model reduction of a fluid dynamical system. A systematic convergence
+and sensitivity study is performed to identify the effective dimensions of the
+deep-learned CNN process such as the convolution kernel size, the number of
+kernels and the convolution layers. Within the error threshold, the prediction
+based on our deep convolutional network has a speed-up nearly four orders of
+magnitude compared to the full-order results and consumes an insignificant
+fraction of computational resources. The proposed CNN-based approximation
+procedure has a profound impact on the parametric design of bluff bodies and
+the feedback control of separated flows.
+",0,1,0,0,0,0
+9183,Accurate fast computation of steady two-dimensional surface gravity waves in arbitrary depth,"  This paper describes an efficient algorithm for computing steady
+two-dimensional surface gravity wave in irrotational motion. The algorithm
+complexity is O(N log N), N being the number of Fourier modes. The algorithm
+allows the arbitrary precision computation of waves in arbitrary depth, i.e.,
+it works efficiently for Stokes, cnoidal and solitary waves, even for quite
+large steepnesses. The method is based on conformal mapping, Babenko equation
+rewritten in a suitable way, pseudo-spectral method and Petviashvili's
+iterations. The efficiency of the algorithm is illustrated via some relevant
+numerical examples. The code is open source, so interested readers can easily
+check the claims, use and modify the algorithm.
+",0,1,1,0,0,0
+9184,AutoWIG: Automatic Generation of Python Bindings for C++ Libraries,"  Most of Python and R scientific packages incorporate compiled scientific
+libraries to speed up the code and reuse legacy libraries. While several
+semi-automatic solutions exist to wrap these compiled libraries, the process of
+wrapping a large library is cumbersome and time consuming. In this paper, we
+introduce AutoWIG, a Python package that wraps automatically compiled libraries
+into high-level languages using LLVM/Clang technologies and the Mako templating
+engine. Our approach is automatic, extensible, and applies to complex C++
+libraries, composed of thousands of classes or incorporating modern
+meta-programming constructs.
+",1,0,0,0,0,0
+9185,Fate of Weyl semimetals in the presence of incommensurate potentials,"  We investigate the effect of the incommensurate potential on Weyl semimetal,
+which is proposed to be realized in ultracold atomic systems trapped in
+three-dimensional optical lattices. For the system without the Fermi arc, we
+find that the Weyl points are robust against the incommensurate potential and
+the system enters into a metallic phase only when the incommensurate potential
+strength exceeds a critical value. We unveil the trastition by analysing the
+properties of wave functions and the density of states as a function of the
+incommensurate potential strength. We also study the system with Fermi arcs and
+find the Fermi arcs are sensitive against the incommensurate potential and can
+be destoryed by a weak incommensurate potential.
+",0,1,0,0,0,0
+9186,Word equations in linear space,"  Word equations are an important problem on the intersection of formal
+languages and algebra. Given two sequences consisting of letters and variables
+we are to decide whether there is a substitution for the variables that turns
+this equation into true equality of strings. The computational complexity of
+this problem remains unknown, with the best lower and upper bounds being NP and
+PSPACE. Recently, a novel technique of recompression was applied to this
+problem, simplifying the known proofs and lowering the space complexity to
+(nondeterministic) O(n log n). In this paper we show that word equations are in
+nondeterministic linear space, thus the language of satisfiable word equations
+is context-sensitive. We use the known recompression-based algorithm and
+additionally employ Huffman coding for letters. The proof, however, uses
+analysis of how the fragments of the equation depend on each other as well as a
+new strategy for nondeterministic choices of the algorithm, which uses several
+new ideas to limit the space occupied by the letters.
+",1,0,0,0,0,0
+9187,Selective Strong Structural Minimum Cost Resilient Co-Design for Regular Descriptor Linear Systems,"  This paper addresses the problem of minimum cost resilient
+actuation-sensing-communication co-design for regular descriptor systems while
+ensuring selective strong structural system's properties. More specifically,
+the problem consists of determining the minimum cost deployment of actuation
+and sensing technology, as well as communication between the these, such that
+decentralized control approaches are viable for an arbitrary realization of
+regular descriptor systems satisfying a pre-specified selective structure,
+i.e., some entries can be zero, nonzero, or either zero/nonzero. Towards this
+goal, we rely on strong structural systems theory and extend it to cope with
+the selective structure that casts resiliency/robustness properties and
+uncertainty properties of system's model. Upon such framework, we introduce the
+notion of selective strong structural fixed modes as a characterization of the
+feasibility of decentralized control laws. Also, we provide necessary and
+sufficient conditions for this property to hold, and show how these conditions
+can be leveraged to determine the minimum cost resilient placement of
+actuation-sensing-communication technology ensuring feasible solutions. In
+particular, we study the minimum cost resilient actuation and sensing
+placement, upon which we construct the solution to our problem. Finally, we
+illustrate the applicability the main results of this paper on an electric
+power grid example.
+",0,0,1,0,0,0
+9188,Maximum a Posteriori Policy Optimisation,"  We introduce a new algorithm for reinforcement learning called Maximum
+aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relative
+entropy objective. We show that several existing methods can directly be
+related to our derivation. We develop two off-policy algorithms and demonstrate
+that they are competitive with the state-of-the-art in deep reinforcement
+learning. In particular, for continuous control, our method outperforms
+existing methods with respect to sample efficiency, premature convergence and
+robustness to hyperparameter settings while achieving similar or better final
+performance.
+",1,0,0,1,0,0
+9189,On Geodesic Completeness for Riemannian Metrics on Smooth Probability Densities,"  The geometric approach to optimal transport and information theory has
+triggered the interpretation of probability densities as an
+infinite-dimensional Riemannian manifold. The most studied Riemannian
+structures are Otto's metric, yielding the $L^2$-Wasserstein distance of
+optimal mass transport, and the Fisher--Rao metric, predominant in the theory
+of information geometry. On the space of smooth probability densities, none of
+these Riemannian metrics are geodesically complete---a property desirable for
+example in imaging applications. That is, the existence interval for solutions
+to the geodesic flow equations cannot be extended to the whole real line. Here
+we study a class of Hamilton--Jacobi-like partial differential equations
+arising as geodesic flow equations for higher-order Sobolev type metrics on the
+space of smooth probability densities. We give order conditions for global
+existence and uniqueness, thereby providing geodesic completeness. The system
+we study is an interesting example of a flow equation with loss of derivatives,
+which is well-posed in the smooth category, yet non-parabolic and fully
+non-linear. On a more general note, the paper establishes a link between
+geometric analysis on the space of probability densities and analysis of
+Euler-Arnold equations in topological hydrodynamics.
+",0,0,1,0,0,0
+9190,Hacker Combat: A Competitive Sport from Programmatic Dueling & Cyberwarfare,"  The history of humanhood has included competitive activities of many
+different forms. Sports have offered many benefits beyond that of
+entertainment. At the time of this article, there exists not a competitive
+ecosystem for cyber security beyond that of conventional capture the flag
+competitions, and the like. This paper introduces a competitive framework with
+a foundation on computer science, and hacking. This proposed competitive
+landscape encompasses the ideas underlying information security, software
+engineering, and cyber warfare. We also demonstrate the opportunity to rank,
+score, & categorize actionable skill levels into tiers of capability.
+Physiological metrics are analyzed from participants during gameplay. These
+analyses provide support regarding the intricacies required for competitive
+play, and analysis of play. We use these intricacies to build a case for an
+organized competitive ecosystem. Using previous player behavior from gameplay,
+we also demonstrate the generation of an artificial agent purposed with
+gameplay at a competitive level.
+",1,0,0,0,0,0
+9191,Providing Effective Real-time Feedback in Simulation-based Surgical Training,"  Virtual reality simulation is becoming popular as a training platform in
+surgical education. However, one important aspect of simulation-based surgical
+training that has not received much attention is the provision of automated
+real-time performance feedback to support the learning process. Performance
+feedback is actionable advice that improves novice behaviour. In simulation,
+automated feedback is typically extracted from prediction models trained using
+data mining techniques. Existing techniques suffer from either low
+effectiveness or low efficiency resulting in their inability to be used in
+real-time. In this paper, we propose a random forest based method that finds a
+balance between effectiveness and efficiency. Experimental results in a
+temporal bone surgery simulation show that the proposed method is able to
+extract highly effective feedback at a high level of efficiency.
+",1,0,0,0,0,0
+9192,Large-Scale Classification of Structured Objects using a CRF with Deep Class Embedding,"  This paper presents a novel deep learning architecture to classify structured
+objects in datasets with a large number of visually similar categories. We
+model sequences of images as linear-chain CRFs, and jointly learn the
+parameters from both local-visual features and neighboring classes. The visual
+features are computed by convolutional layers, and the class embeddings are
+learned by factorizing the CRF pairwise potential matrix. This forms a highly
+nonlinear objective function which is trained by optimizing a local likelihood
+approximation with batch-normalization. This model overcomes the difficulties
+of existing CRF methods to learn the contextual relationships thoroughly when
+there is a large number of classes and the data is sparse. The performance of
+the proposed method is illustrated on a huge dataset that contains images of
+retail-store product displays, taken in varying settings and viewpoints, and
+shows significantly improved results compared to linear CRF modeling and
+unnormalized likelihood optimization.
+",1,0,0,0,0,0
+9193,code2seq: Generating Sequences from Structured Representations of Code,"  The ability to generate natural language sequences from source code snippets
+has a variety of applications such as code summarization, documentation, and
+retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine
+translation (NMT), have achieved state-of-the-art performance on these tasks by
+treating source code as a sequence of tokens. We present ${\rm {\scriptsize
+CODE2SEQ}}$: an alternative approach that leverages the syntactic structure of
+programming languages to better encode source code. Our model represents a code
+snippet as the set of compositional paths in its abstract syntax tree (AST) and
+uses attention to select the relevant paths while decoding. We demonstrate the
+effectiveness of our approach for two tasks, two programming languages, and
+four datasets of up to $16$M examples. Our model significantly outperforms
+previous models that were specifically designed for programming languages, as
+well as state-of-the-art NMT models. An interactive online demo of our model is
+available at this http URL. Our code, data and trained models are
+available at this http URL.
+",1,0,0,1,0,0
+9194,Learning Neural Parsers with Deterministic Differentiable Imitation Learning,"  We explore the problem of learning to decompose spatial tasks into segments,
+as exemplified by the problem of a painting robot covering a large object.
+Inspired by the ability of classical decision tree algorithms to construct
+structured partitions of their input spaces, we formulate the problem of
+decomposing objects into segments as a parsing approach. We make the insight
+that the derivation of a parse-tree that decomposes the object into segments
+closely resembles a decision tree constructed by ID3, which can be done when
+the ground-truth available. We learn to imitate an expert parsing oracle, such
+that our neural parser can generalize to parse natural images without ground
+truth. We introduce a novel deterministic policy gradient update, DRAG (i.e.,
+DeteRministically AGgrevate) in the form of a deterministic actor-critic
+variant of AggreVaTeD, to train our neural parser. From another perspective,
+our approach is a variant of the Deterministic Policy Gradient suitable for the
+imitation learning setting. The deterministic policy representation offered by
+training our neural parser with DRAG allows it to outperform state of the art
+imitation and reinforcement learning approaches.
+",1,0,0,1,0,0
+9195,Dimension of the minimum set for the real and complex Monge-Ampère equations in critical Sobolev spaces,"  We prove that the zero set of a nonnegative plurisubharmonic function that
+solves $\det (\partial \overline{\partial} u) \geq 1$ in $\mathbb{C}^n$ and is
+in $W^{2, \frac{n(n-k)}{k}}$ contains no analytic sub-variety of dimension $k$
+or larger. Along the way we prove an analogous result for the real
+Monge-Ampère equation, which is also new. These results are sharp in view of
+well-known examples of Pogorelov and B{\l}ocki. As an application, in the real
+case we extend interior regularity results to the case that $u$ lies in a
+critical Sobolev space (or more generally, certain Sobolev-Orlicz spaces).
+",0,0,1,0,0,0
+9196,Road Detection Technique Using Filters with Application to Autonomous Driving System,"  Autonomous driving systems are broadly used equipment in the industries and
+in our daily lives, they assist in production, but are majorly used for
+exploration in dangerous or unfamiliar locations. Thus, for a successful
+exploration, navigation plays a significant role. Road detection is an
+essential factor that assists autonomous robots achieved perfect navigation.
+Various techniques using camera sensors have been proposed by numerous scholars
+with inspiring results, but their techniques are still vulnerable to these
+environmental noises: rain, snow, light intensity and shadow. In addressing
+these problems, this paper proposed to enhance the road detection system with
+filtering algorithm to overcome these limitations. Normalized Differences Index
+(NDI) and morphological operation are the filtering algorithms used to address
+the effect of shadow and guidance and re-guidance image filtering algorithms
+are used to address the effect of rain and/or snow, while dark channel image
+and specular-to-diffuse are the filters used to address light intensity
+effects. The experimental performance of the road detection system with
+filtering algorithms was tested qualitatively and quantitatively using the
+following evaluation schemes: False Negative Rate (FNR) and False Positive Rate
+(FPR). Comparison results of the road detection system with and without
+filtering algorithm shows the filtering algorithm's capability to suppress the
+effect of environmental noises because better road/non-road classification is
+achieved by the road detection system. with filtering algorithm. This
+achievement has further improved path planning/region classification for
+autonomous driving system
+",1,0,0,0,0,0
+9197,Relativistic Spacecraft Propelled by Directed Energy,"  Achieving relativistic flight to enable extrasolar exploration is one of the
+dreams of humanity and the long term goal of our NASA Starlight program. We
+derive a fully relativistic solution for the motion of a spacecraft propelled
+by radiation pressure from a directed energy system. Depending on the system
+parameters, low mass spacecraft can achieve relativistic speeds; thereby
+enabling interstellar exploration. The diffraction of the directed energy
+system plays an important role and limits the maximum speed of the spacecraft.
+We consider 'photon recycling' as a possible method to achieving higher speeds.
+We also discuss recent claims that our previous work on this topic is incorrect
+and show that these claims arise from an improper treatment of causality.
+",0,1,0,0,0,0
+9198,Outward Influence and Cascade Size Estimation in Billion-scale Networks,"  Estimating cascade size and nodes' influence is a fundamental task in social,
+technological, and biological networks. Yet this task is extremely challenging
+due to the sheer size and the structural heterogeneity of networks. We
+investigate a new influence measure, termed outward influence (OI), defined as
+the (expected) number of nodes that a subset of nodes $S$ will activate,
+excluding the nodes in S. Thus, OI equals, the de facto standard measure,
+influence spread of S minus |S|. OI is not only more informative for nodes with
+small influence, but also, critical in designing new effective sampling and
+statistical estimation methods.
+Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence
+spread/outward influence at scale and with rigorous theoretical guarantees. The
+proposed methods are built on two novel components 1) IICP an important
+sampling method for outward influence, and 2) RSA, a robust mean estimation
+method that minimize the number of samples through analyzing variance and range
+of random variables. Compared to the state-of-the art for influence estimation,
+SIEA is $\Omega(\log^4 n)$ times faster in theory and up to several orders of
+magnitude faster in practice. For the first time, influence of nodes in the
+networks of billions of edges can be estimated with high accuracy within a few
+minutes. Our comprehensive experiments on real-world networks also give
+evidence against the popular practice of using a fixed number, e.g. 10K or 20K,
+of samples to compute the ""ground truth"" for influence spread.
+",1,0,0,0,0,0
+9199,Estimation of a noisy subordinated Brownian Motion via two-scales power variations,"  High frequency based estimation methods for a semiparametric pure-jump
+subordinated Brownian motion exposed to a small additive microstructure noise
+are developed building on the two-scales realized variations approach
+originally developed by Zhang et. al. (2005) for the estimation of the
+integrated variance of a continuous Ito process. The proposed estimators are
+shown to be robust against the noise and, surprisingly, to attain better rates
+of convergence than their precursors, method of moment estimators, even in the
+absence of microstructure noise. Our main results give approximate optimal
+values for the number K of regular sparse subsamples to be used, which is an
+important tune-up parameter of the method. Finally, a data-driven plug-in
+procedure is devised to implement the proposed estimators with the optimal
+K-value. The developed estimators exhibit superior performance as illustrated
+by Monte Carlo simulations and a real high-frequency data application.
+",0,0,1,1,0,0
+9200,Mirror actuation design for the interferometer control of the KAGRA gravitational wave telescope,"  KAGRA is a 3-km cryogenic interferometric gravitational wave telescope
+located at an underground site in Japan. In order to achieve its target
+sensitivity, the relative positions of the mirrors of the interferometer must
+be finely adjusted with attached actuators. We have developed a model to
+simulate the length control loops of the KAGRA interferometer with realistic
+suspension responses and various noises for mirror actuation. Using our model,
+we have designed the actuation parameters to have sufficient force range to
+acquire lock as well as to control all the length degrees of freedom without
+introducing excess noise.
+",0,1,0,0,0,0
+9201,ZFIRE: The Evolution of the Stellar Mass Tully-Fisher Relation to Redshift 2.0 < Z < 2.5 with MOSFIRE,"  Using observations made with MOSFIRE on Keck I as part of the ZFIRE survey,
+we present the stellar mass Tully-Fisher relation at 2.0 < z < 2.5. The sample
+was drawn from a stellar mass limited, Ks-band selected catalog from ZFOURGE
+over the CANDELS area in the COSMOS field. We model the shear of the Halpha
+emission line to derive rotational velocities at 2.2X the scale radius of an
+exponential disk (V2.2). We correct for the blurring effect of a
+two-dimensional PSF and the fact that the MOSFIRE PSF is better approximated by
+a Moffat than a Gaussian, which is more typically assumed for natural seeing.
+We find for the Tully-Fisher relation at 2.0 < z < 2.5 that logV2.2 =(2.18 +/-
+0.051)+(0.193 +/- 0.108)(logM/Msun - 10) and infer an evolution of the
+zeropoint of Delta M/Msun = -0.25 +/- 0.16 dex or Delta M/Msun = -0.39 +/- 0.21
+dex compared to z = 0 when adopting a fixed slope of 0.29 or 1/4.5,
+respectively. We also derive the alternative kinematic estimator S0.5, with a
+best-fit relation logS0.5 =(2.06 +/- 0.032)+(0.211 +/- 0.086)(logM/Msun - 10),
+and infer an evolution of Delta M/Msun= -0.45 +/- 0.13 dex compared to z < 1.2
+if we adopt a fixed slope. We investigate and review various systematics,
+ranging from PSF effects, projection effects, systematics related to stellar
+mass derivation, selection biases and slope. We find that discrepancies between
+the various literature values are reduced when taking these into account. Our
+observations correspond well with the gradual evolution predicted by
+semi-analytic models.
+",0,1,0,0,0,0
+9202,A new computational method for a model of C. elegans biomechanics: Insights into elasticity and locomotion performance,"  An organism's ability to move freely is a fundamental behaviour in the animal
+kingdom. To understand animal locomotion requires a characterisation of the
+material properties, as well as the biomechanics and physiology. We present a
+biomechanical model of C. elegans locomotion together with a novel finite
+element method. We formulate our model as a nonlinear initial-boundary value
+problem which allows the study of the dynamics of arbitrary body shapes,
+undulation gaits and the link between the animal's material properties and its
+performance across a range of environments. Our model replicates behaviours
+across a wide range of environments. It makes strong predictions on the viable
+range of the worm's Young's modulus and suggests that animals can control speed
+via the known mechanism of gait modulation that is observed across different
+media.
+",0,1,0,0,0,0
+9203,Word forms - not just their lengths- are optimized for efficient communication,"  The inverse relationship between the length of a word and the frequency of
+its use, first identified by G.K. Zipf in 1935, is a classic empirical law that
+holds across a wide range of human languages. We demonstrate that length is one
+aspect of a much more general property of words: how distinctive they are with
+respect to other words in a language. Distinctiveness plays a critical role in
+recognizing words in fluent speech, in that it reflects the strength of
+potential competitors when selecting the best candidate for an ambiguous
+signal. Phonological information content, a measure of a word's string
+probability under a statistical model of a language's sound or character
+sequences, concisely captures distinctiveness. Examining large-scale corpora
+from 13 languages, we find that distinctiveness significantly outperforms word
+length as a predictor of frequency. This finding provides evidence that
+listeners' processing constraints shape fine-grained aspects of word forms
+across languages.
+",1,0,0,0,0,0
+9204,Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization,"  In recent years, a number of artificial intelligent services have been
+developed such as defect detection system or diagnosis system for customer
+services. Unfortunately, the core in these services is a black-box in which
+human cannot understand the underlying decision making logic, even though the
+inspection of the logic is crucial before launching a commercial service. Our
+goal in this paper is to propose an analytic method of a model explanation that
+is applicable to general classification models. To this end, we introduce the
+concept of a contribution matrix and an explanation embedding in a constraint
+space by using a matrix factorization. We extract a rule-like model explanation
+from the contribution matrix with the help of the nonnegative matrix
+factorization. To validate our method, the experiment results provide with open
+datasets as well as an industry dataset of a LTE network diagnosis and the
+results show our method extracts reasonable explanations.
+",1,0,0,1,0,0
+9205,Generalization Bounds for Unsupervised Cross-Domain Mapping with WGANs,"  The recent empirical success of cross-domain mapping algorithms, between two
+domains that share common characteristics, is not well-supported by theoretical
+justifications. This lacuna is especially troubling, given the clear ambiguity
+in such mappings. We work with the adversarial training method called the
+Wasserstein GAN. We derive a novel generalization bound, which limits the risk
+between the learned mapping $h$ and the target mapping $y$, by a sum of two
+terms: (i) the risk between $h$ and the most distant alternative mapping that
+was learned by the same cross-domain mapping algorithm, and (ii) the minimal
+Wasserstein GAN divergence between the target domain and the domain obtained by
+applying a hypothesis $h^*$ on the samples of the source domain, where $h^*$ is
+a hypothesis selected by the same algorithm. The bound is directly related to
+Occam's razor and it encourages the selection of the minimal architecture that
+supports a small Wasserstein GAN divergence. From the bound, we derive
+algorithms for hyperparameter selection and early stopping in cross-domain
+mapping GANs. We also demonstrate a novel capability of estimating confidence
+in the mapping of every specific sample. Lastly, we show how non-minimal
+architectures can be effectively trained by an inverted knowledge distillation
+in which a minimal architecture is used to train a larger one, leading to
+higher quality outputs.
+",0,0,0,1,0,0
+9206,Quantum chaos in an electron-phonon bad metal,"  We calculate the scrambling rate $\lambda_L$ and the butterfly velocity $v_B$
+associated with the growth of quantum chaos for a solvable large-$N$
+electron-phonon system. We study a temperature regime in which the electrical
+resistivity of this system exceeds the Mott-Ioffe-Regel limit and increases
+linearly with temperature - a sign that there are no long-lived charged
+quasiparticles - although the phonons remain well-defined quasiparticles. The
+long-lived phonons determine $\lambda_L$, rendering it parametrically smaller
+than the theoretical upper-bound $\lambda_L \ll \lambda_{max}=2\pi T/\hbar$.
+Significantly, the chaos properties seem to be intrinsic - $\lambda_L$ and
+$v_B$ are the same for electronic and phononic operators. We consider two
+models - one in which the phonons are dispersive, and one in which they are
+dispersionless. In either case, we find that $\lambda_L$ is proportional to the
+inverse phonon lifetime, and $v_B$ is proportional to the effective phonon
+velocity. The thermal and chaos diffusion constants, $D_E$ and $D_L\equiv
+v_B^2/\lambda_L$, are always comparable, $D_E \sim D_L$. In the dispersive
+phonon case, the charge diffusion constant $D_C$ satisfies $D_L\gg D_C$, while
+in the dispersionless case $D_L \ll D_C$.
+",0,1,0,0,0,0
+9207,Data-driven Optimal Transport Cost Selection for Distributionally Robust Optimizatio,"  Recently, (Blanchet, Kang, and Murhy 2016) showed that several machine
+learning algorithms, such as square-root Lasso, Support Vector Machines, and
+regularized logistic regression, among many others, can be represented exactly
+as distributionally robust optimization (DRO) problems. The distributional
+uncertainty is defined as a neighborhood centered at the empirical
+distribution. We propose a methodology which learns such neighborhood in a
+natural data-driven way. We show rigorously that our framework encompasses
+adaptive regularization as a particular case. Moreover, we demonstrate
+empirically that our proposed methodology is able to improve upon a wide range
+of popular machine learning estimators.
+",0,0,0,1,0,0
+9208,Formally Secure Compilation of Unsafe Low-Level Components (Extended Abstract),"  We propose a new formal criterion for secure compilation, providing strong
+security guarantees for components written in unsafe, low-level languages with
+C-style undefined behavior. Our criterion goes beyond recent proposals, which
+protect the trace properties of a single component against an adversarial
+context, to model dynamic compromise in a system of mutually distrustful
+components. Each component is protected from all the others until it receives
+an input that triggers an undefined behavior, causing it to become compromised
+and attack the remaining uncompromised components. To illustrate this model, we
+demonstrate a secure compilation chain for an unsafe language with buffers,
+procedures, and components, compiled to a simple RISC abstract machine with
+built-in compartmentalization. The protection guarantees offered by this
+abstract machine can be achieved at the machine-code level using either
+software fault isolation or tag-based reference monitoring. We are working on
+machine-checked proofs showing that this compiler satisfies our secure
+compilation criterion.
+",1,0,0,0,0,0
+9209,Detecting Potential Local Adversarial Examples for Human-Interpretable Defense,"  Machine learning models are increasingly used in the industry to make
+decisions such as credit insurance approval. Some people may be tempted to
+manipulate specific variables, such as the age or the salary, in order to get
+better chances of approval. In this ongoing work, we propose to discuss, with a
+first proposition, the issue of detecting a potential local adversarial example
+on classical tabular data by providing to a human expert the locally critical
+features for the classifier's decision, in order to control the provided
+information and avoid a fraud.
+",0,0,0,1,0,0
+9210,The formation of magnetic depletions and flux annihilation due to reconnection in the heliosheath,"  The misalignment of the solar rotation axis and the magnetic axis of the Sun
+produces a periodic reversal of the Parker spiral magnetic field and the
+sectored solar wind. The compression of the sectors is expected to lead to
+reconnection in the heliosheath (HS). We present particle-in-cell simulations
+of the sectored HS that reflect the plasma environment along the Voyager 1 and
+2 trajectories, specifically including unequal positive and negative azimuthal
+magnetic flux as seen in the Voyager data \citep{Burlaga03}. Reconnection
+proceeds on individual current sheets until islands on adjacent current layers
+merge. At late time bands of the dominant flux survive, separated by bands of
+deep magnetic field depletion. The ambient plasma pressure supports the strong
+magnetic pressure variation so that pressure is anti-correlated with magnetic
+field strength. There is little variation in the magnetic field direction
+across the boundaries of the magnetic depressions. At irregular intervals
+within the magnetic depressions are long-lived pairs of magnetic islands where
+the magnetic field direction reverses so that spacecraft data would reveal
+sharp magnetic field depressions with only occasional crossings with jumps in
+magnetic field direction. This is typical of the magnetic field data from the
+Voyager spacecraft \citep{Burlaga11,Burlaga16}. Voyager 2 data reveals that
+fluctuations in the density and magnetic field strength are anti-correlated in
+the sector zone as expected from reconnection but not in unipolar regions. The
+consequence of the annihilation of subdominant flux is a sharp reduction in the
+""number of sectors"" and a loss in magnetic flux as documented from the Voyager
+1 magnetic field and flow data \citep{Richardson13}.
+",0,1,0,0,0,0
+9211,Optimization Based Methods for Partially Observed Chaotic Systems,"  In this paper we consider filtering and smoothing of partially observed
+chaotic dynamical systems that are discretely observed, with an additive
+Gaussian noise in the observation. These models are found in a wide variety of
+real applications and include the Lorenz 96' model. In the context of a fixed
+observation interval $T$, observation time step $h$ and Gaussian observation
+variance $\sigma_Z^2$, we show under assumptions that the filter and smoother
+are well approximated by a Gaussian with high probability when $h$ and
+$\sigma^2_Z h$ are sufficiently small. Based on this result we show that the
+Maximum-a-posteriori (MAP) estimators are asymptotically optimal in mean square
+error as $\sigma^2_Z h$ tends to $0$. Given these results, we provide a batch
+algorithm for the smoother and filter, based on Newton's method, to obtain the
+MAP. In particular, we show that if the initial point is close enough to the
+MAP, then Newton's method converges to it at a fast rate. We also provide a
+method for computing such an initial point. These results contribute to the
+theoretical understanding of widely used 4D-Var data assimilation method. Our
+approach is illustrated numerically on the Lorenz 96' model with state vector
+up to 1 million dimensions, with code running in the order of minutes. To our
+knowledge the results in this paper are the first of their type for this class
+of models.
+",0,0,1,1,0,0
+9212,LongHCPulse: Long Pulse Heat Capacity on a Quantum Design PPMS,"  This paper presents LongHCPulse: software which enables heat capacity to be
+collected on a Quantum Design PPMS using a long-pulse method. This method,
+wherein heat capacity is computed from the time derivative of sample
+temperature over long (30 min) measurement times, is necessary for probing
+first order transitions and shortens the measurement time by a factor of five.
+LongHCPulse also includes plotting utilities based on the Matplotlib library. I
+illustrate the use of LongHCPulse with the example of data taken on ${\rm
+Yb_{2}Ti_{2}O_{7}}$, and compare the results to the standard semi-adiabatic
+method.
+",0,1,0,0,0,0
+9213,Calculation of hyperfine structure constants of small molecules using Z-vector method in the relativistic coupled-cluster framework,"  The Z-vector method in the relativistic coupled-cluster framework is employed
+to calculate the parallel and perpendicular components of the magnetic
+hyperfine structure constant of a few small alkaline earth hydrides (BeH, MgH,
+and CaH) and fluorides (MgF and CaF). We have compared our Z-vector results
+with the values calculated by the extended coupled-cluster (ECC) method
+reported in Phys. Rev. A 91 022512 (2015). All these results are compared with
+the available experimental values. The Z-vector results are found to be in
+better agreement with the experimental values than those of the ECC values.
+",0,1,0,0,0,0
+9214,Autoregressive Point-Processes as Latent State-Space Models: a Moment-Closure Approach to Fluctuations and Autocorrelations,"  Modeling and interpreting spike train data is a task of central importance in
+computational neuroscience, with significant translational implications. Two
+popular classes of data-driven models for this task are autoregressive Point
+Process Generalized Linear models (PPGLM) and latent State-Space models (SSM)
+with point-process observations. In this letter, we derive a mathematical
+connection between these two classes of models. By introducing an auxiliary
+history process, we represent exactly a PPGLM in terms of a latent, infinite
+dimensional dynamical system, which can then be mapped onto an SSM by basis
+function projections and moment closure. This representation provides a new
+perspective on widely used methods for modeling spike data, and also suggests
+novel algorithmic approaches to fitting such models. We illustrate our results
+on a phasic bursting neuron model, showing that our proposed approach provides
+an accurate and efficient way to capture neural dynamics.
+",0,0,0,0,1,0
+9215,Phase transitions of the dimerized Kane-Mele model with/without the strong interaction,"  The dimerized Kane-Mele model with/without the strong interaction is studied
+using analytical methods. The boundary of the topological phase transition of
+the model without strong interaction is obtained. Our results show that the
+occurrence of the transition only depends on dimerized parameter . From the
+one-particle spectrum, we obtain the completed phase diagram including the
+quantum spin Hall (QSH) state and the topologically trivial insulator. Then,
+using different mean-field methods, we investigate the Mott transition and the
+magnetic transition of the strongly correlated dimerized Kane-Mele model. In
+the region between the two transitions, the topological Mott insulator (TMI)
+with characters of Mott insulators and topological phases may be the most
+interesting phase. In this work, effects of the hopping anisotropy and Hubbard
+interaction U on boundaries of the two transitions are observed in detail. The
+completed phase diagram of the dimerized Kane-Mele-Hubbard model is also
+obtained in this work. Quantum fluctuations have extremely important influences
+on a quantum system. However, investigations are under the framework of the
+mean field treatment in this work and the effects of fluctuations in this model
+will be discussed in the future.
+",0,1,0,0,0,0
+9216,A Unified Approach to Interpreting Model Predictions,"  Understanding why a model makes a certain prediction can be as crucial as the
+prediction's accuracy in many applications. However, the highest accuracy for
+large modern datasets is often achieved by complex models that even experts
+struggle to interpret, such as ensemble or deep learning models, creating a
+tension between accuracy and interpretability. In response, various methods
+have recently been proposed to help users interpret the predictions of complex
+models, but it is often unclear how these methods are related and when one
+method is preferable over another. To address this problem, we present a
+unified framework for interpreting predictions, SHAP (SHapley Additive
+exPlanations). SHAP assigns each feature an importance value for a particular
+prediction. Its novel components include: (1) the identification of a new class
+of additive feature importance measures, and (2) theoretical results showing
+there is a unique solution in this class with a set of desirable properties.
+The new class unifies six existing methods, notable because several recent
+methods in the class lack the proposed desirable properties. Based on insights
+from this unification, we present new methods that show improved computational
+performance and/or better consistency with human intuition than previous
+approaches.
+",1,0,0,1,0,0
+9217,Approximate Value Iteration for Risk-aware Markov Decision Processes,"  We consider large-scale Markov decision processes (MDPs) with a risk measure
+of variability in cost, under the risk-aware MDPs paradigm. Previous studies
+showed that risk-aware MDPs, based on a minimax approach to handling risk, can
+be solved using dynamic programming for small to medium sized problems.
+However, due to the ""curse of dimensionality"", MDPs that model real-life
+problems are typically prohibitively large for such approaches. In this paper,
+we employ an approximate dynamic programming approach, and develop a family of
+simulation-based algorithms to approximately solve large-scale risk-aware MDPs.
+In parallel, we develop a unified convergence analysis technique to derive
+sample complexity bounds for this new family of algorithms.
+",1,0,1,0,0,0
+9218,Dataflow Matrix Machines as a Model of Computations with Linear Streams,"  We overview dataflow matrix machines as a Turing complete generalization of
+recurrent neural networks and as a programming platform. We describe vector
+space of finite prefix trees with numerical leaves which allows us to combine
+expressive power of dataflow matrix machines with simplicity of traditional
+recurrent neural networks.
+",1,0,0,0,0,0
+9219,Attention-based Vocabulary Selection for NMT Decoding,"  Neural Machine Translation (NMT) models usually use large target vocabulary
+sizes to capture most of the words in the target language. The vocabulary size
+is a big factor when decoding new sentences as the final softmax layer
+normalizes over all possible target words. To address this problem, it is
+widely common to restrict the target vocabulary with candidate lists based on
+the source sentence. Usually, the candidate lists are a combination of external
+word-to-word aligner, phrase table entries or most frequent words. In this
+work, we propose a simple and yet novel approach to learn candidate lists
+directly from the attention layer during NMT training. The candidate lists are
+highly optimized for the current NMT model and do not need any external
+computation of the candidate pool. We show significant decoding speedup
+compared with using the entire vocabulary, without losing any translation
+quality for two language pairs.
+",1,0,0,0,0,0
+9220,Reconstruction by Calibration over Tensors for Multi-Coil Multi-Acquisition Balanced SSFP Imaging,"  Purpose: To develop a rapid imaging framework for balanced steady-state free
+precession (bSSFP) that jointly reconstructs undersampled data (by a factor of
+R) across multiple coils (D) and multiple acquisitions (N). To devise a
+multi-acquisition coil compression technique for improved computational
+efficiency.
+Methods: The bSSFP image for a given coil and acquisition is modeled to be
+modulated by a coil sensitivity and a bSSFP profile. The proposed
+reconstruction by calibration over tensors (ReCat) recovers missing data by
+tensor interpolation over the coil and acquisition dimensions. Coil compression
+is achieved using a new method based on multilinear singular value
+decomposition (MLCC). ReCat is compared with iterative self-consistent parallel
+imaging (SPIRiT) and profile encoding (PE-SSFP) reconstructions.
+Results: Compared to parallel imaging or profile-encoding methods, ReCat
+attains sensitive depiction of high-spatial-frequency information even at
+higher R. In the brain, ReCat improves peak SNR (PSNR) by 1.1$\pm$1.0 dB over
+SPIRiT and by 0.9$\pm$0.3 dB over PE-SSFP (mean$\pm$std across subjects;
+average for N=2-8, R=8-16). Furthermore, reconstructions based on MLCC achieve
+0.8$\pm$0.6 dB higher PSNR compared to those based on geometric coil
+compression (GCC) (average for N=2-8, R=4-16).
+Conclusion: ReCat is a promising acceleration framework for
+banding-artifact-free bSSFP imaging with high image quality; and MLCC offers
+improved computational efficiency for tensor-based reconstructions.
+",0,1,0,0,0,0
+9221,Spectral Sparsification of Simplicial Complexes for Clustering and Label Propagation,"  As a generalization of the use of graphs to describe pairwise interactions,
+simplicial complexes can be used to model higher-order interactions between
+three or more objects in complex systems. There has been a recent surge in
+activity for the development of data analysis methods applicable to simplicial
+complexes, including techniques based on computational topology, higher-order
+random processes, generalized Cheeger inequalities, isoperimetric inequalities,
+and spectral methods. In particular, spectral learning methods (e.g. label
+propagation and clustering) that directly operate on simplicial complexes
+represent a new direction for analyzing such complex datasets.
+To apply spectral learning methods to massive datasets modeled as simplicial
+complexes, we develop a method for sparsifying simplicial complexes that
+preserves the spectrum of the associated Laplacian matrices. We show that the
+theory of Spielman and Srivastava for the sparsification of graphs extends to
+simplicial complexes via the up Laplacian. In particular, we introduce a
+generalized effective resistance for simplices, provide an algorithm for
+sparsifying simplicial complexes at a fixed dimension, and give a specific
+version of the generalized Cheeger inequality for weighted simplicial
+complexes. Finally, we introduce higher-order generalizations of spectral
+clustering and label propagation for simplicial complexes and demonstrate via
+experiments the utility of the proposed spectral sparsification method for
+these applications.
+",1,0,0,0,0,0
+9222,BHK mirror symmetry for K3 surfaces with non-symplectic automorphism,"  In this paper we consider the class of K3 surfaces defined as hypersurfaces
+in weighted projective space, and admitting a non-symplectic automorphism of
+non-prime order, excluding the orders 4, 8, and 12. We show that on these
+surfaces the Berglund-Hübsch-Krawitz mirror construction and mirror symmetry
+for lattice polarized K3 surfaces constructed by Dolgachev agree; that is, both
+versions of mirror symmetry define the same mirror K3 surface.
+",0,0,1,0,0,0
+9223,Automatic Rule Extraction from Long Short Term Memory Networks,"  Although deep learning models have proven effective at solving problems in
+natural language processing, the mechanism by which they come to their
+conclusions is often unclear. As a result, these models are generally treated
+as black boxes, yielding no insight of the underlying learned patterns. In this
+paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new
+approach for tracking the importance of a given input to the LSTM for a given
+output. By identifying consistently important patterns of words, we are able to
+distill state of the art LSTMs on sentiment analysis and question answering
+into a set of representative phrases. This representation is then
+quantitatively validated by using the extracted phrases to construct a simple,
+rule-based classifier which approximates the output of the LSTM.
+",1,0,0,1,0,0
+9224,On the validity of parametric block correlation matrices with constant within and between group correlations,"  We consider the set Bp of parametric block correlation matrices with p blocks
+of various (and possibly different) sizes, whose diagonal blocks are compound
+symmetry (CS) correlation matrices and off-diagonal blocks are constant
+matrices. Such matrices appear in probabilistic models on categorical data,
+when the levels are partitioned in p groups, assuming a constant correlation
+within a group and a constant correlation for each pair of groups. We obtain
+two necessary and sufficient conditions for positive definiteness of elements
+of Bp. Firstly we consider the block average map $\phi$, consisting in
+replacing a block by its mean value. We prove that for any A $\in$ Bp , A is
+positive definite if and only if $\phi$(A) is positive definite. Hence it is
+equivalent to check the validity of the covariance matrix of group means, which
+only depends on the number of groups and not on their sizes. This theorem can
+be extended to a wider set of block matrices. Secondly, we consider the subset
+of Bp for which the between group correlation is the same for all pairs of
+groups. Positive definiteness then comes down to find the positive definite
+interval of a matrix pencil on Sp. We obtain a simple characterization by
+localizing the roots of the determinant with within group correlation values.
+",0,0,1,1,0,0
+9225,Two-dimensional off-lattice Boltzmann model for van der Waals fluids with variable temperature,"  We develop a two-dimensional Lattice Boltzmann model for liquid-vapour
+systems with variable temperature. Our model is based on a single particle
+distribution function expanded with respect to the full-range Hermite
+polynomials. In order to ensure the recovery of the hydrodynamic equations for
+thermal flows, we use a fourth order expansion together with a set of momentum
+vectors with 25 elements whose Cartesian projections are the roots of the
+Hermite polynomial of order Q = 5. Since these vectors are off-lattice, a
+fifth-order projection scheme is used to evolve the corresponding set of
+distribution functions. A fourth order scheme employing a 49 point stencil is
+used to compute the gradient operators in the force term that ensures the
+liquid-vapour phase separation and diffuse reflection boundary conditions are
+used on the walls. We demonstrate at least fourth order convergence with
+respect to the lattice spacing in the contexts of shear and longitudinal wave
+propagation through the van der Waals fluid. For the planar interface, fourth
+order convergence can be seen at small enough lattice spacings, while the
+effect of the spurious velocity on the temperature profile is found to be
+smaller than 1.0%, even when T w ' 0.7 T c . We further validate our scheme by
+considering the Laplace pressure test. Galilean invariance is shown to be
+preserved up to second order with respect to the background velocity. We
+further investigate the liquid-vapour phase separation between two parallel
+walls kept at a constant temperature T w smaller than the critical temperature
+T c and discuss the main features of this process.
+",0,1,0,0,0,0
+9226,Minimal coloring number on minimal diagrams for $\mathbb{Z}$-colorable links,"  It was shown that any $\mathbb{Z}$-colorable link has a diagram which admits
+a non-trivial $\mathbb{Z}$-coloring with at most four colors. In this paper, we
+consider minimal numbers of colors for non-trivial $\mathbb{Z}$-colorings on
+minimal diagrams of $\mathbb{Z}$-colorable links. We show, for any positive
+integer $N$, there exists a minimal diagram of a $\mathbb{Z}$-colorable link
+such that any $\mathbb{Z}$-coloring on the diagram has at least $N$ colors. On
+the other hand, it is shown that certain $\mathbb{Z}$-colorable torus links
+have minimal diagrams admitting $\mathbb{Z}$-colorings with only four colors.
+",0,0,1,0,0,0
+9227,Automatic Gradient Boosting,"  Automatic machine learning performs predictive modeling with high performing
+machine learning tools without human interference. This is achieved by making
+machine learning applications parameter-free, i.e. only a dataset is provided
+while the complete model selection and model building process is handled
+internally through (often meta) optimization. Projects like Auto-WEKA and
+auto-sklearn aim to solve the Combined Algorithm Selection and Hyperparameter
+optimization (CASH) problem resulting in huge configuration spaces. However,
+for most real-world applications, the optimization over only a few different
+key learning algorithms can not only be sufficient, but also potentially
+beneficial. The latter becomes apparent when one considers that models have to
+be validated, explained, deployed and maintained. Here, less complex model are
+often preferred, for validation or efficiency reasons, or even a strict
+requirement. Automatic gradient boosting simplifies this idea one step further,
+using only gradient boosting as a single learning algorithm in combination with
+model-based hyperparameter tuning, threshold optimization and encoding of
+categorical features. We introduce this general framework as well as a concrete
+implementation called autoxgboost. It is compared to current AutoML projects on
+16 datasets and despite its simplicity is able to achieve comparable results on
+about half of the datasets as well as performing best on two.
+",0,0,0,1,0,0
+9228,Speeding up Memory-based Collaborative Filtering with Landmarks,"  Recommender systems play an important role in many scenarios where users are
+overwhelmed with too many choices to make. In this context, Collaborative
+Filtering (CF) arises by providing a simple and widely used approach for
+personalized recommendation. Memory-based CF algorithms mostly rely on
+similarities between pairs of users or items, which are posteriorly employed in
+classifiers like k-Nearest Neighbor (kNN) to generalize for unknown ratings. A
+major issue regarding this approach is to build the similarity matrix.
+Depending on the dimensionality of the rating matrix, the similarity
+computations may become computationally intractable. To overcome this issue, we
+propose to represent users by their distances to preselected users, namely
+landmarks. This procedure allows to drastically reduce the computational cost
+associated with the similarity matrix. We evaluated our proposal on two
+distinct distinguishing databases, and the results showed our method has
+consistently and considerably outperformed eight CF algorithms (including both
+memory-based and model-based) in terms of computational performance.
+",1,0,0,0,0,0
+9229,Spatio-temporal variations in the urban rhythm: the travelling waves of crime,"  In the last decades, the notion that cities are in a state of equilibrium
+with a centralised organisation has given place to the viewpoint of cities in
+disequilibrium and organised from bottom to up. In this perspective, cities are
+evolving systems that exhibit emergent phenomena built from local decisions.
+While urban evolution promotes the emergence of positive social phenomena such
+as the formation of innovation hubs and the increase in cultural diversity, it
+also yields negative phenomena such as increases in criminal activity. Yet, we
+are still far from understanding the driving mechanisms of these phenomena. In
+particular, approaches to analyse urban phenomena are limited in scope by
+neglecting both temporal non-stationarity and spatial heterogeneity. In the
+case of criminal activity, we know for more than one century that crime peaks
+during specific times of the year, but the literature still fails to
+characterise the mobility of crime. Here we develop an approach to describe the
+spatial, temporal, and periodic variations in urban quantities. With crime data
+from 12 cities, we characterise how the periodicity of crime varies spatially
+across the city over time. We confirm one-year criminal cycles and show that
+this periodicity occurs unevenly across the city. These `waves of crime' keep
+travelling across the city: while cities have a stable number of regions with a
+circannual period, the regions exhibit non-stationary series. Our findings
+support the concept of cities in a constant change, influencing urban
+phenomena---in agreement with the notion of cities not in equilibrium.
+",1,0,0,0,0,0
+9230,Smith-Purcell Radiation,"  The simplest model of the magnetized infinitely thin electron beam is
+considered. The basic equations that describe the periodic solutions for a
+self-consistent system of a couple of Maxwell equations and equations for the
+medium are obtained.
+",0,1,0,0,0,0
+9231,Dynamics of Bose-Einstein condensate with account of pair correlations,"  The system of dynamic equations for Bose-Einstein condensate at zero
+temperature with account of pair correlations is obtained. The spectrum of
+small oscillations of the condensate in a spatially homogeneous state is
+explored. It is shown that this spectrum has two branches: the sound wave
+branch and branch with an energy gap.
+",0,1,0,0,0,0
+9232,On the generalization of Erdős-Vincze's theorem about the approximation of closed convex plane curves by polyellipses,"  A polyellipse is a curve in the Euclidean plane all of whose points have the
+same sum of distances from finitely many given points (focuses). The classical
+version of Erdős-Vincze's theorem states that regular triangles can not be
+presented as the Hausdorff limit of polyellipses even if the number of the
+focuses can be arbitrary large. In other words the topological closure of the
+set of polyellipses with respect to the Hausdorff distance does not contain any
+regular triangle and we have a negative answer to the problem posed by E.
+Vázsonyi (Weissfeld) about the approximation of closed convex plane curves by
+polyellipses. It is the additive version of the approximation of simple closed
+plane curves by polynomial lemniscates all of whose points have the same
+product of distances from finitely many given points (focuses). Here we are
+going to generalize the classical version of Erdős-Vincze's theorem for
+regular polygons in the plane. We will conclude that the error of the
+approximation tends to zero as the number of the vertices of the regular
+polygon tends to the infinity. The decreasing tendency of the approximation
+error gives the idea to construct curves in the topological closure of the set
+of polyellipses. If we use integration to compute the average distance of a
+point from a given (focal) set in the plane then the curves all of whose points
+have the same average distance from the focal set can be given as the Hausdorff
+limit of polyellipses corresponding to partial sums.
+",0,0,1,0,0,0
+9233,Improved self-energy correction method for accurate and efficient band structure calculation,"  The LDA-1/2 method for self-energy correction is a powerful tool for
+calculating accurate band structures of semiconductors, while keeping the
+computational load as low as standard LDA. Nevertheless, controversies remain
+regarding the arbitrariness of choice between (1/2)e and (1/4)e charge
+stripping from the atoms in group IV semiconductors, the incorrect direct band
+gap predicted for Ge, and inaccurate band structures for III-V semiconductors.
+Here we propose an improved method named shell-LDA-1/2 (shLDA-1/2 for short),
+which is based on a shell-like trimming function for the self-energy potential.
+With the new approach, we obtained accurate band structures for group IV, and
+for III-V and II-VI compound semiconductors. In particular, we reproduced the
+complete band structure of Ge in good agreement with experimental data.
+Moreover, we have defined clear rules for choosing when (1/2)e or (1/4)e charge
+ought to be stripped in covalent semiconductors, and for identifying materials
+for which shLDA-1/2 is expected to fail.
+",0,1,0,0,0,0
+9234,Magnetic domains in thin ferromagnetic films with strong perpendicular anisotropy,"  We investigate the scaling of the ground state energy and optimal domain
+patterns in thin ferromagnetic films with strong uniaxial anisotropy and the
+easy axis perpendicular to the film plane. Starting from the full
+three-dimensional micromagnetic model, we identify the critical scaling where
+the transition from single domain to multidomain ground states such as bubble
+or maze patterns occurs. Furthermore, we analyze the asymptotic behavior of the
+energy in two regimes separated by a transition. In the single domain regime,
+the energy $\Gamma$-converges towards a much simpler two-dimensional and local
+model. In the second regime, we derive the scaling of the minimal energy and
+deduce a scaling law for the typical domain size.
+",0,1,1,0,0,0
+9235,Learning a Robust Society of Tracking Parts,"  Object tracking is an essential task in computer vision that has been studied
+since the early days of the field. Being able to follow objects that undergo
+different transformations in the video sequence, including changes in scale,
+illumination, shape and occlusions, makes the problem extremely difficult. One
+of the real challenges is to keep track of the changes in objects appearance
+and not drift towards the background clutter. Different from previous
+approaches, we obtain robustness against background with a tracker model that
+is composed of many different parts. They are classifiers that respond at
+different scales and locations. The tracker system functions as a society of
+parts, each having its own role and level of credibility. Reliable classifiers
+decide the tracker's next move, while newcomers are first monitored before
+gaining the necessary level of reliability to participate in the decision
+process. Some parts that loose their consistency are rejected, while others
+that show consistency for a sufficiently long time are promoted to permanent
+roles. The tracker system, as a whole, could also go through different phases,
+from the usual, normal functioning to states of weak agreement and even crisis.
+The tracker system has different governing rules in each state. What truly
+distinguishes our work from others is not necessarily the strength of
+individual tracking parts, but the way in which they work together and build a
+strong and robust organization. We also propose an efficient way to learn
+simultaneously many tracking parts, with a single closed-form formulation. We
+obtain a fast and robust tracker with state of the art performance on the
+challenging OTB50 dataset.
+",1,0,0,0,0,0
+9236,A direct proof of dimerization in a family of SU(n)-invariant quantum spin chains,"  We study the family of spin-S quantum spin chains with a nearest neighbor
+interaction given by the negative of the singlet projection operator. Using a
+random loop representation of the partition function in the limit of zero
+temperature and standard techniques of classical statistical mechanics, we
+prove dimerization for all sufficiently large values of S.
+",0,1,1,0,0,0
+9237,An accelerated splitting algorithm for radio-interferometric imaging: when natural and uniform weighting meet,"  Next generation radio-interferometers, like the Square Kilometre Array, will
+acquire tremendous amounts of data with the goal of improving the size and
+sensitivity of the reconstructed images by orders of magnitude. The efficient
+processing of large-scale data sets is of great importance. We propose an
+acceleration strategy for a recently proposed primal-dual distributed
+algorithm. A preconditioning approach can incorporate into the algorithmic
+structure both the sampling density of the measured visibilities and the noise
+statistics. Using the sampling density information greatly accelerates the
+convergence speed, especially for highly non-uniform sampling patterns, while
+relying on the correct noise statistics optimises the sensitivity of the
+reconstruction. In connection to CLEAN, our approach can be seen as including
+in the same algorithmic structure both natural and uniform weighting, thereby
+simultaneously optimising both the resolution and the sensitivity. The method
+relies on a new non-Euclidean proximity operator for the data fidelity term,
+that generalises the projection onto the $\ell_2$ ball where the noise lives
+for naturally weighted data, to the projection onto a generalised ellipsoid
+incorporating sampling density information through uniform weighting.
+Importantly, this non-Euclidean modification is only an acceleration strategy
+to solve the convex imaging problem with data fidelity dictated only by noise
+statistics. We showcase through simulations with realistic sampling patterns
+the acceleration obtained using the preconditioning. We also investigate the
+algorithm performance for the reconstruction of the 3C129 radio galaxy from
+real visibilities and compare with multi-scale CLEAN, showing better
+sensitivity and resolution. Our MATLAB code is available online on GitHub.
+",0,1,0,0,0,0
+9238,BaFe2(As1-xPx)2 (x = 0.22-0.42) thin films grown on practical metal-tape substrates and their critical current densities,"  We optimized the substrate temperature (Ts) and phosphorus concentration (x)
+of BaFe2(As1-xPx)2 films on practical metal-tape substrates for pulsed laser
+deposition from the viewpoints of crystallinity, superconductor critical
+temperature (Tc), and critical current density (Jc). It was found that the
+optimum Ts and x values are 1050 degree C and x = 0.28, respectively. The
+optimized film exhibits Tc_onset = 26.6 and Tc_zero = 22.4 K along with a high
+self-field Jc at 4 K (~1 MA/cm2) and relatively isotropic Jc under magnetic
+fields up to 9 T. Unexpectedly, we found that lower crystallinity samples,
+which were grown at a higher Ts of 1250 degree C than the optimized Ts = 1050
+degree C, exhibit higher Jc along the ab plane under high magnetic fields than
+the optimized samples. The presence of horizontal defects that act as strong
+vortex pinning centers, such as stacking faults, are a possible origin of the
+high Jc values in the poor crystallinity samples.
+",0,1,0,0,0,0
+9239,How does the accuracy of interatomic force constants affect the prediction of lattice thermal conductivity,"  Solving Peierls-Boltzmann transport equation with interatomic force constants
+(IFCs) from first-principles calculations has been a widely used method for
+predicting lattice thermal conductivity of three-dimensional materials. With
+the increasing research interests in two-dimensional materials, this method is
+directly applied to them but different works show quite different results. In
+this work, classical potential was used to investigate the effect of the
+accuracy of IFCs on the predicted thermal conductivity. Inaccuracies were
+introduced to the third-order IFCs by generating errors in the input forces.
+When the force error lies in the typical value from first-principles
+calculations, the calculated thermal conductivity would be quite different from
+the benchmark value. It is found that imposing translational invariance
+conditions cannot always guarantee a better thermal conductivity result. It is
+also shown that Grüneisen parameters cannot be used as a necessary and
+sufficient criterion for the accuracy of third-order IFCs in the aspect of
+predicting thermal conductivity.
+",0,1,0,0,0,0
+9240,Mesh-to-raster based non-rigid registration of multi-modal images,"  Region of interest (ROI) alignment in medical images plays a crucial role in
+diagnostics, procedure planning, treatment, and follow-up. Frequently, a model
+is represented as triangulated mesh while the patient data is provided from CAT
+scanners as pixel or voxel data. Previously, we presented a 2D method for
+curve-to-pixel registration. This paper contributes (i) a general
+mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a
+3D surface-to-voxel application, and (iii) a comprehensive quantitative
+evaluation in 2D using ground truth provided by the simultaneous truth and
+performance level estimation (STAPLE) method. The registration is formulated as
+a minimization problem where the objective consists of a data term, which
+involves the signed distance function of the ROI from the reference image, and
+a higher order elastic regularizer for the deformation. The evaluation is based
+on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of
+decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each
+showing one corresponding tooth in both modalities. The ROI in each image is
+manually marked by three experts (900 curves in total). In the QLF-DP setting,
+our approach significantly outperforms the mutual information-based
+registration algorithm implemented with the Insight Segmentation and
+Registration Toolkit (ITK) and Elastix.
+",1,0,0,0,0,0
+9241,Vision-based Obstacle Removal System for Autonomous Ground Vehicles Using a Robotic Arm,"  Over the past few years, the use of camera-equipped robotic platforms for
+data collection and visually monitoring applications has exponentially grown.
+Cluttered construction sites with many objects (e.g., bricks, pipes, etc.) on
+the ground are challenging environments for a mobile unmanned ground vehicle
+(UGV) to navigate. To address this issue, this study presents a mobile UGV
+equipped with a stereo camera and a robotic arm that can remove obstacles along
+the UGV's path. To achieve this objective, the surrounding environment is
+captured by the stereo camera and obstacles are detected. The obstacle's
+relative location to the UGV is sent to the robotic arm module through Robot
+Operating System (ROS). Then, the robotic arm picks up and removes the
+obstacle. The proposed method will greatly enhance the degree of automation and
+the frequency of data collection for construction monitoring. The proposed
+system is validated through two case studies. The results successfully
+demonstrate the detection and removal of obstacles, serving as one of the
+enabling factors for developing an autonomous UGV with various construction
+operating applications.
+",1,0,0,0,0,0
+9242,Polyatomic trilobite Rydberg molecules in a dense random gas,"  Trilobites are exotic giant dimers with enormous dipole moments. They consist
+of a Rydberg atom and a distant ground-state atom bound together by short-range
+electron-neutral attraction. We show that highly polar, polyatomic trilobite
+states unexpectedly persist and thrive in a dense ultracold gas of randomly
+positioned atoms. This is caused by perturbation-induced quantum scarring and
+the localization of electron density on randomly occurring atom clusters. At
+certain densities these states also mix with a s-state, overcoming selection
+rules that hinder the photoassociation of ordinary trilobites.
+",0,1,0,0,0,0
+9243,Does warm debris dust stem from asteroid belts?,"  Many debris discs reveal a two-component structure, with a cold outer and a
+warm inner component. While the former are likely massive analogues of the
+Kuiper belt, the origin of the latter is still a matter of debate. In this work
+we investigate whether the warm dust may be a signature of asteroid belt
+analogues. In the scenario tested here the current two-belt architecture stems
+from an originally extended protoplanetary disc, in which planets have opened a
+gap separating it into the outer and inner discs which, after the gas
+dispersal, experience a steady-state collisional decay. This idea is explored
+with an analytic collisional evolution model for a sample of 225 debris discs
+from a Spitzer/IRS catalogue that are likely to possess a two-component
+structure. We find that the vast majority of systems (220 out of 225, or 98%)
+are compatible with this scenario. For their progenitors, original
+protoplanetary discs, we find an average surface density slope of
+$-0.93\pm0.06$ and an average initial mass of
+$\left(3.3^{+0.4}_{-0.3}\right)\times 10^{-3}$ solar masses, both of which are
+in agreement with the values inferred from submillimetre surveys. However, dust
+production by short-period comets and - more rarely - inward transport from the
+outer belts may be viable, and not mutually excluding, alternatives to the
+asteroid belt scenario. The remaining five discs (2% of the sample: HIP 11486,
+HIP 23497, HIP 57971, HIP 85790, HIP 89770) harbour inner components that
+appear inconsistent with dust production in an ""asteroid belt."" Warm dust in
+these systems must either be replenished from cometary sources or represent an
+aftermath of a recent rare event, such as a major collision or planetary system
+instability.
+",0,1,0,0,0,0
+9244,The Multiple Roots Phenomenon in Maximum Likelihood Estimation for Factor Analysis,"  Multiple root estimation problems in statistical inference arise in many
+contexts in the literature. In the context of maximum likelihood estimation,
+the existence of multiple roots causes uncertainty in the computation of
+maximum likelihood estimators using hill-climbing algorithms, and consequent
+difficulties in the resulting statistical inference.
+In this paper, we study the multiple roots phenomenon in maximum likelihood
+estimation for factor analysis. We prove that the corresponding likelihood
+equations have uncountably many feasible solutions even in the simplest cases.
+For the case in which the observed data are two-dimensional and the unobserved
+factor scores are one-dimensional, we prove that the solutions to the
+likelihood equations form a one-dimensional real curve.
+",0,0,1,1,0,0
+9245,Group-like projections for locally compact quantum groups,"  Let $\mathbb{G}$ be a locally compact quantum group. We give a 1-1
+correspondence between group-like projections in $L^\infty(\mathbb{G})$
+preserved by the scaling group and idempotent states on the dual quantum group
+$\widehat{\mathbb{G}}$. As a byproduct we give a simple proof that normal
+integrable coideals in $L^\infty(\mathbb{G})$ which are preserved by the
+scaling group are in 1-1 correspondence with compact quantum subgroups of
+$\mathbb{G}$.
+",0,0,1,0,0,0
+9246,Exciton-phonon interaction in the strong coupling regime in hexagonal boron nitride,"  The temperature-dependent optical response of excitons in semiconductors is
+controlled by the exciton-phonon interaction. When the exciton-lattice coupling
+is weak, the excitonic line has a Lorentzian profile resulting from motional
+narrowing, with a width increasing linearly with the lattice temperature $T$.
+In contrast, when the exciton-lattice coupling is strong, the lineshape is
+Gaussian with a width increasing sublinearly with the lattice temperature,
+proportional to $\sqrt{T}$. While the former case is commonly reported in the
+literature, here the latter is reported for the first time, for hexagonal boron
+nitride. Thus the theoretical predictions of Toyozawa [Progr. Theor. Phys. 20,
+53 (1958)] are supported by demonstrating that the exciton-phonon interaction
+is in the strong coupling regime in this Van der Waals crystal.
+",0,1,0,0,0,0
+9247,Exact lowest-Landau-level solutions for vortex precession in Bose-Einstein condensates,"  The Lowest Landau Level (LLL) equation emerges as an accurate approximation
+for a class of dynamical regimes of Bose-Einstein Condensates (BEC) in
+two-dimensional isotropic harmonic traps in the limit of weak interactions.
+Building on recent developments in the field of spatially confined extended
+Hamiltonian systems, we find a fully nonlinear solution of this equation
+representing periodically modulated precession of a single vortex. Motions of
+this type have been previously seen in numerical simulations and experiments at
+moderately weak coupling. Our work provides the first controlled analytic
+prediction for trajectories of a single vortex, suggests new targets for
+experiments, and opens up the prospect of finding analytic multi-vortex
+solutions.
+",0,1,1,0,0,0
+9248,Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation,"  In this work we present a novel framework that uses deep learning to predict
+object feature points that are out-of-view in the input image. This system was
+developed with the application of model-based tracking in mind, particularly in
+the case of autonomous inspection robots, where only partial views of the
+object are available. Out-of-view prediction is enabled by applying scaling to
+the feature point labels during network training. This is combined with a
+recurrent neural network architecture designed to provide the final prediction
+layers with rich feature information from across the spatial extent of the
+input image. To show the versatility of these out-of-view predictions, we
+describe how to integrate them in both a particle filter tracker and an
+optimisation based tracker. To evaluate our work we compared our framework with
+one that predicts only points inside the image. We show that as the amount of
+the object in view decreases, being able to predict outside the image bounds
+adds robustness to the final pose estimation.
+",1,0,0,0,0,0
+9249,A Simple PTAS for the Dual Bin Packing Problem and Advice Complexity of Its Online Version,"  Recently, Renault (2016) studied the dual bin packing problem in the
+per-request advice model of online algorithms. He showed that given
+$O(1/\epsilon)$ advice bits for each input item allows approximating the dual
+bin packing problem online to within a factor of $1+\epsilon$. Renault asked
+about the advice complexity of dual bin packing in the tape-advice model of
+online algorithms. We make progress on this question. Let $s$ be the maximum
+bit size of an input item weight. We present a conceptually simple online
+algorithm that with total advice $O\left(\frac{s + \log n}{\epsilon^2}\right)$
+approximates the dual bin packing to within a $1+\epsilon$ factor. To this end,
+we describe and analyze a simple offline PTAS for the dual bin packing problem.
+Although a PTAS for a more general problem was known prior to our work
+(Kellerer 1999, Chekuri and Khanna 2006), our PTAS is arguably simpler to state
+and analyze. As a result, we could easily adapt our PTAS to obtain the
+advice-complexity result.
+We also consider whether the dependence on $s$ is necessary in our algorithm.
+We show that if $s$ is unrestricted then for small enough $\epsilon > 0$
+obtaining a $1+\epsilon$ approximation to the dual bin packing requires
+$\Omega_\epsilon(n)$ bits of advice. To establish this lower bound we analyze
+an online reduction that preserves the advice complexity and approximation
+ratio from the binary separation problem due to Boyar et al. (2016). We define
+two natural advice complexity classes that capture the distinction similar to
+the Turing machine world distinction between pseudo polynomial time algorithms
+and polynomial time algorithms. Our results on the dual bin packing problem
+imply the separation of the two classes in the advice complexity world.
+",1,0,0,0,0,0
+9250,Constructive Néron Desingularization of algebras with big smooth locus,"  An algorithmic proof of the General Néron Desingularization theorem and its
+uniform version is given for morphisms with big smooth locus. This generalizes
+the results for the one-dimensional case.
+",0,0,1,0,0,0
+9251,List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussians,"  We study the problem of list-decodable Gaussian mean estimation and the
+related problem of learning mixtures of separated spherical Gaussians. We
+develop a set of techniques that yield new efficient algorithms with
+significantly improved guarantees for these problems.
+{\bf List-Decodable Mean Estimation.} Fix any $d \in \mathbb{Z}_+$ and $0<
+\alpha <1/2$. We design an algorithm with runtime $O
+(\mathrm{poly}(n/\alpha)^{d})$ that outputs a list of $O(1/\alpha)$ many
+candidate vectors such that with high probability one of the candidates is
+within $\ell_2$-distance $O(\alpha^{-1/(2d)})$ from the true mean. The only
+previous algorithm for this problem achieved error $\tilde O(\alpha^{-1/2})$
+under second moment conditions. For $d = O(1/\epsilon)$, our algorithm runs in
+polynomial time and achieves error $O(\alpha^{\epsilon})$. We also give a
+Statistical Query lower bound suggesting that the complexity of our algorithm
+is qualitatively close to best possible.
+{\bf Learning Mixtures of Spherical Gaussians.} We give a learning algorithm
+for mixtures of spherical Gaussians that succeeds under significantly weaker
+separation assumptions compared to prior work. For the prototypical case of a
+uniform mixture of $k$ identity covariance Gaussians we obtain: For any
+$\epsilon>0$, if the pairwise separation between the means is at least
+$\Omega(k^{\epsilon}+\sqrt{\log(1/\delta)})$, our algorithm learns the unknown
+parameters within accuracy $\delta$ with sample complexity and running time
+$\mathrm{poly} (n, 1/\delta, (k/\epsilon)^{1/\epsilon})$. The previously best
+known polynomial time algorithm required separation at least $k^{1/4}
+\mathrm{polylog}(k/\delta)$.
+Our main technical contribution is a new technique, using degree-$d$
+multivariate polynomials, to remove outliers from high-dimensional datasets
+where the majority of the points are corrupted.
+",1,0,1,1,0,0
+9252,Complexity Dichotomies for the Minimum F-Overlay Problem,"  For a (possibly infinite) fixed family of graphs F, we say that a graph G
+overlays F on a hypergraph H if V(H) is equal to V(G) and the subgraph of G
+induced by every hyperedge of H contains some member of F as a spanning
+subgraph.While it is easy to see that the complete graph on |V(H)| overlays F
+on a hypergraph H whenever the problem admits a solution, the Minimum F-Overlay
+problem asks for such a graph with the minimum number of edges.This problem
+allows to generalize some natural problems which may arise in practice. For
+instance, if the family F contains all connected graphs, then Minimum F-Overlay
+corresponds to the Minimum Connectivity Inference problem (also known as Subset
+Interconnection Design problem) introduced for the low-resolution
+reconstruction of macro-molecular assembly in structural biology, or for the
+design of networks.Our main contribution is a strong dichotomy result regarding
+the polynomial vs. NP-hard status with respect to the considered family F.
+Roughly speaking, we show that the easy cases one can think of (e.g. when
+edgeless graphs of the right sizes are in F, or if F contains only cliques) are
+the only families giving rise to a polynomial problem: all others are
+NP-complete.We then investigate the parameterized complexity of the problem and
+give similar sufficient conditions on F that give rise to W[1]-hard, W[2]-hard
+or FPT problems when the parameter is the size of the solution.This yields an
+FPT/W[1]-hard dichotomy for a relaxed problem, where every hyperedge of H must
+contain some member of F as a (non necessarily spanning) subgraph.
+",1,0,0,0,0,0
+9253,Asymptotic behavior of 3-D stochastic primitive equations of large-scale moist atmosphere with additive noise,"  Using a new and general method, we prove the existence of random attractor
+for the three dimensional stochastic primitive equations defined on a manifold
+$\D\subset\R^3$ improving the existence of weak attractor for the deterministic
+model. Furthermore, we show the existence of the invariant measure.
+",0,0,1,0,0,0
+9254,Analytical Approach for Calculating Chemotaxis Sensitivity Function,"  We consider the chemotaxis problem for a one-dimensional system. To analyze
+the interaction of bacteria and attractant we use a modified Keller-Segel model
+which accounts attractant absorption. To describe the system we use the
+chemotaxis sensitivity function, which characterizes nonuniformity of bacteria
+distribution. In particular, we investigate how the chemotaxis sensitivity
+function depends on the concentration of attractant at the boundary of the
+system. It is known that in the system without absorption the chemotaxis
+sensitivity function has a bell shape maximum. Here we show that attractant
+absorption and special boundary conditions for bacteria can cause the
+appearance of an additional maximum in the chemotaxis sensitivity function. The
+value of this maximum is determined by the intensity of absorption.
+",0,1,0,0,0,0
+9255,Copy the dynamics using a learning machine,"  Is it possible to generally construct a dynamical system to simulate a black
+system without recovering the equations of motion of the latter? Here we show
+that this goal can be approached by a learning machine. Trained by a set of
+input-output responses or a segment of time series of a black system, a
+learning machine can be served as a copy system to mimic the dynamics of
+various black systems. It can not only behave as the black system at the
+parameter set that the training data are made, but also recur the evolution
+history of the black system. As a result, the learning machine provides an
+effective way for prediction, and enables one to probe the global dynamics of a
+black system. These findings have significance for practical systems whose
+equations of motion cannot be approached accurately. Examples of copying the
+dynamics of an artificial neural network, the Lorenz system, and a variable
+star are given. Our idea paves a possible way towards copy a living brain.
+",0,1,1,1,0,0
+9256,Algebraic Bethe ansatz for the XXZ Heisenberg spin chain with triangular boundaries and the corresponding Gaudin model,"  The implementation of the algebraic Bethe ansatz for the XXZ Heisenberg spin
+chain, of arbitrary spin-$s$, in the case, when both reflection matrices have
+the upper-triangular form is analyzed. The general form of the Bethe vectors is
+studied. In the particular form, Bethe vectors admit the recurrent procedure,
+with an appropriate modification, used previously in the case of the XXX
+Heisenberg chain. As expected, these Bethe vectors yield the strikingly simple
+expression for the off-shell action of the transfer matrix of the chain as well
+as the spectrum of the transfer matrix and the corresponding Bethe equations.
+As in the XXX case, the so-called quasi-classical limit gives the off-shell
+action of the generating function of the corresponding trigonometric Gaudin
+Hamiltonians with boundary terms.
+",0,1,0,0,0,0
+9257,Using Programmable Graphene Channels as Weights in Spin-Diffusive Neuromorphic Computing,"  A graphene-based spin-diffusive (GrSD) neural network is presented in this
+work that takes advantage of the locally tunable spin transport of graphene and
+the non-volatility of nanomagnets. By using electrostatically gated graphene as
+spintronic synapses, a weighted summation operation can be performed in the
+spin domain while the weights can be programmed using circuits in the charge
+domain. Four-component spin/charge circuit simulations coupled to magnetic
+dynamics are used to show the feasibility of the neuron-synapse functionality
+and quantify the analog weighting capability of the graphene under different
+spin relaxation mechanisms. By realizing transistor-free weight implementation,
+the graphene spin-diffusive neural network reduces the energy consumption to
+0.08-0.32 fJ per cell-synapse and achieves significantly better scalability
+compared to its digital counterparts, particularly as the number and bit
+accuracy of the synapses increases.
+",0,1,0,0,0,0
+9258,Construction and Encoding of QC-LDPC Codes Using Group Rings,"  Quasi-cyclic (QC) low-density parity-check (LDPC) codes which are known as
+QC-LDPC codes, have many applications due to their simple encoding
+implementation by means of cyclic shift registers. In this paper, we construct
+QC-LDPC codes from group rings. A group ring is a free module (at the same time
+a ring) constructed in a natural way from any given ring and any given group.
+We present a structure based on the elements of a group ring for constructing
+QC-LDPC codes. Some of the previously addressed methods for constructing
+QC-LDPC codes based on finite fields are special cases of the proposed
+construction method. The constructed QC-LDPC codes perform very well over the
+additive white Gaussian noise (AWGN) channel with iterative decoding in terms
+of bit-error probability and block-error probability. Simulation results
+demonstrate that the proposed codes have competitive performance in comparison
+with the similar existing LDPC codes. Finally, we propose a new encoding method
+for the proposed group ring based QC-LDPC codes that can be implemented faster
+than the current encoding methods. The encoding complexity of the proposed
+method is analyzed mathematically, and indicates a significate reduction in the
+required number of operations, even when compared to the available efficient
+encoding methods that have linear time and space complexities.
+",1,0,1,0,0,0
+9259,"Imaginary time, shredded propagator method for large-scale GW calculations","  The GW method is a many-body approach capable of providing quasiparticle
+bands for realistic systems spanning physics, chemistry, and materials science.
+Despite its power, GW is not routinely applied to large complex materials due
+to its computational expense. We perform an exact recasting of the GW
+polarizability and the self-energy as Laplace integrals over imaginary time
+propagators. We then ""shred"" the propagators (via energy windowing) and
+approximate them in a controlled manner by using Gauss-Laguerre quadrature and
+discrete variable methods to treat the imaginary time propagators in real
+space. The resulting cubic scaling GW method has a sufficiently small prefactor
+to outperform standard quartic scaling methods on small systems (>=10 atoms)
+and also represents a substantial improvement over several other cubic methods
+tested. This approach is useful for evaluating quantum mechanical response
+function involving large sums containing energy (difference) denominators.
+",0,1,0,0,0,0
+9260,A time change strategy to model reporting delay dynamics in claims reserving,"  This paper considers the problem of predicting the number of claims that have
+already incurred in past exposure years, but which have not yet been reported
+to the insurer. This is an important building block in the risk management
+strategy of an insurer since the company should be able to fulfill its
+liabilities with respect to such claims. Our approach puts emphasis on modeling
+the time between the occurrence and reporting of claims, the so-called
+reporting delay. Using data at a daily level we propose a micro-level model for
+the heterogeneity in reporting delay caused by calendar day effects in the
+reporting process, such as the weekday pattern and holidays. A simulation study
+identifies the strengths and weaknesses of our approach in several scenarios
+compared to traditional methods to predict the number of incurred but not
+reported claims from aggregated data (i.e. the chain ladder method). We also
+illustrate our model on a European general liability insurance data set and
+conclude that the granular approach compared to the chain ladder method is more
+robust with respect to volatility in the occurrence process. Our framework can
+be extended to other predictive problems where interest goes to events that
+incurred in the past but which are subject to an observation delay (e.g. the
+number of infections during an epidemic).
+",0,0,0,0,0,1
+9261,Solving Parameter Estimation Problems with Discrete Adjoint Exponential Integrators,"  The solution of inverse problems in a variational setting finds best
+estimates of the model parameters by minimizing a cost function that penalizes
+the mismatch between model outputs and observations. The gradients required by
+the numerical optimization process are computed using adjoint models.
+Exponential integrators are a promising family of time discretizations for
+evolutionary partial differential equations. In order to allow the use of these
+discretizations in the context of inverse problems adjoints of exponential
+integrators are required. This work derives the discrete adjoint formulae for a
+W-type exponential propagation iterative methods of Runge-Kutta type (EPIRK-W).
+These methods allow arbitrary approximations of the Jacobian while maintaining
+the overall accuracy of the forward integration. The use of Jacobian
+approximation matrices that do not depend on the model state avoids the complex
+calculation of Hessians in the discrete adjoint formulae, and allows efficient
+adjoint code generation via algorithmic differentiation. We use the discrete
+EPIRK-W adjoints to solve inverse problems with the Lorenz-96 model and a
+computational magnetics benchmark test. Numerical results validate our
+theoretical derivations.
+",1,0,1,0,0,0
+9262,Induced and intrinsic Hashiguchi connections on Finsler submanifolds,"  We study the geometry of Finsler submanifolds using the pulled-back approach.
+We define the Finsler normal pulled-back bundle and obtain the induced
+geometric objects, namely, induced pullback Finsler connection, normal pullback
+Finsler connection, second fundamental form and shape operator. Under a certain
+condition, we prove that induced and intrinsic Hashiguchi connections coincide
+on the pulled-back bundle of Finsler submanifold.
+",0,0,1,0,0,0
+9263,Iterative PET Image Reconstruction Using Convolutional Neural Network Representation,"  PET image reconstruction is challenging due to the ill-poseness of the
+inverse problem and limited number of detected photons. Recently deep neural
+networks have been widely and successfully used in computer vision tasks and
+attracted growing interests in medical imaging. In this work, we trained a deep
+residual convolutional neural network to improve PET image quality by using the
+existing inter-patient information. An innovative feature of the proposed
+method is that we embed the neural network in the iterative reconstruction
+framework for image representation, rather than using it as a post-processing
+tool. We formulate the objective function as a constraint optimization problem
+and solve it using the alternating direction method of multipliers (ADMM)
+algorithm. Both simulation data and hybrid real data are used to evaluate the
+proposed method. Quantification results show that our proposed iterative neural
+network method can outperform the neural network denoising and conventional
+penalized maximum likelihood methods.
+",0,1,0,1,0,0
+9264,Wave-Shaped Round Functions and Primitive Groups,"  Round functions used as building blocks for iterated block ciphers, both in
+the case of Substitution-Permutation Networks and Feistel Networks, are often
+obtained as the composition of different layers which provide confusion and
+diffusion, and key additions. The bijectivity of any encryption function,
+crucial in order to make the decryption possible, is guaranteed by the use of
+invertible layers or by the Feistel structure. In this work a new family of
+ciphers, called wave ciphers, is introduced. In wave ciphers, round functions
+feature wave functions, which are vectorial Boolean functions obtained as the
+composition of non-invertible layers, where the confusion layer enlarges the
+message which returns to its original size after the diffusion layer is
+applied. This is motivated by the fact that relaxing the requirement that all
+the layers are invertible allows to consider more functions which are optimal
+with regard to non-linearity. In particular it allows to consider injective APN
+S-boxes. In order to guarantee efficient decryption we propose to use wave
+functions in Feistel Networks. With regard to security, the immunity from some
+group-theoretical attacks is investigated. In particular, it is shown how to
+avoid that the group generated by the round functions acts imprimitively, which
+represent a serious flaw for the cipher.
+",1,0,1,0,0,0
+9265,Clean Floquet Time Crystals: Models and Realizations in Cold Atoms,"  Time crystals, a phase showing spontaneous breaking of time-translation
+symmetry, has been an intriguing subject for systems far away from equilibrium.
+Recent experiments found such a phase both in the presence and absence of
+localization, while in theories localization by disorder is usually assumed a
+priori. In this work, we point out that time crystals can generally exist in
+systems without disorder. A series of clean quasi-one-dimensional models under
+Floquet driving are proposed to demonstrate this unexpected result in
+principle. Robust time crystalline orders are found in the strongly interacting
+regime along with the emergent integrals of motion in the dynamical system,
+which can be characterized by level statistics and the out-of-time-ordered
+correlators. We propose two cold atom experimental schemes to realize the clean
+Floquet time crystals, one by making use of dipolar gases and another by
+synthetic dimensions.
+",0,1,0,0,0,0
+9266,Fractional Derivatives of Convex Lyapunov Functions and Control Problems in Fractional Order Systems,"  The paper is devoted to the development of control procedures with a guide
+for conflict-controlled dynamical systems described by ordinary fractional
+differential equations with the Caputo derivative of an order $\alpha \in (0,
+1).$ For the case when the guide is in a certain sense a copy of the system, a
+mutual aiming procedure between the initial system and the guide is elaborated.
+The proof of proximity between motions of the systems is based on the estimate
+of the fractional derivative of the superposition of a convex Lyapunov function
+and a function represented by the fractional integral of an essentially bounded
+measurable function. This estimate can be considered as a generalization of the
+known estimates of such type. An example is considered which illustrates the
+workability of the proposed control procedures.
+",0,0,1,0,0,0
+9267,The role of tachysterol in vitamin D photosynthesis - A non-adiabatic molecular dynamics study,"  To investigate the role of tachysterol in the photophysical/chemical
+regulation of vitamin D photosynthesis, we studied its electronic absorption
+properties and excited state dynamics using time-dependent density functional
+theory (TDDFT), coupled cluster theory (CC2), and non-adiabatic molecular
+dynamics. In excellent agreement with experiments, the simulated electronic
+spectrum shows a broad absorption band covering the spectra of the other
+vitamin D photoisomers. The broad band stems from the spectral overlap of four
+different ground state rotamers. After photoexcitation, the first excited
+singlet state (S1) decays within 882 fs. The S1 dynamics is characterized by a
+strong twisting of the central double bond. 96% of all trajectories relax
+without chemical transformation to the ground state. In 2.3 % of the
+trajectories we observed [1,5]-sigmatropic hydrogen shift forming the partly
+deconjugated toxisterol D1. 1.4 % previtamin D formation is observed via
+hula-twist double bond isomerization. We find a strong dependence between
+photoreactivity and dihedral angle conformation: hydrogen shift only occurs in
+cEc and cEt rotamers and double bond isomerization occurs mainly in cEc
+rotamers. Our study confirms the hypothesis that cEc rotamers are more prone to
+previtamin D formation than other isomers. We also observe the formation of a
+cyclobutene-toxisterol in the hot ground state (0.7 %). Due to its strong
+absorption and unreactive behavior, tachysterol acts mainly as a sun shield
+suppressing previtamin D formation. Tachysterol shows stronger toxisterol
+formation than previtamin D. Absorption of low energy UV light by the cEc
+rotamer can lead to previtamin D formation. Our study reinforces a recent
+hypothesis that tachysterol can act as a previtamin D source when only low
+energy ultraviolet light is available, as it is the case in winter or in the
+morning and evening hours of the day.
+",0,1,0,0,0,0
+9268,A Web of Hate: Tackling Hateful Speech in Online Social Spaces,"  Online social platforms are beset with hateful speech - content that
+expresses hatred for a person or group of people. Such content can frighten,
+intimidate, or silence platform users, and some of it can inspire other users
+to commit violence. Despite widespread recognition of the problems posed by
+such content, reliable solutions even for detecting hateful speech are lacking.
+In the present work, we establish why keyword-based methods are insufficient
+for detection. We then propose an approach to detecting hateful speech that
+uses content produced by self-identifying hateful communities as training data.
+Our approach bypasses the expensive annotation process often required to train
+keyword systems and performs well across several established platforms, making
+substantial improvements over current state-of-the-art approaches.
+",1,0,0,0,0,0
+9269,Lacunary Eta-quotients Modulo Powers of Primes,"  An integral power series is called lacunary modulo $M$ if almost all of its
+coefficients are divisible by $M$. Motivated by the parity problem for the
+partition function, $p(n)$, Gordon and Ono studied the generating functions for
+$t$-regular partitions, and determined conditions for when these functions are
+lacunary modulo powers of primes. We generalize their results in a number of
+ways by studying infinite products called Dedekind eta-quotients and
+generalized Dedekind eta-quotients. We then apply our results to the generating
+functions for the partition functions considered by Nekrasov, Okounkov, and
+Han.
+",0,0,1,0,0,0
+9270,An elementary approach to sofic groupoids,"  We describe sofic groupoids in elementary terms and prove several permanence
+properties for sofcity. We show that sofcity can be determined in terms of the
+full group alone, answering a question by Conley, Kechris and Tucker-Drob.
+",0,0,1,0,0,0
+9271,A New Combination of Message Passing Techniques for Receiver Design in MIMO-OFDM Systems,"  In this paper, we propose a new combined message passing algorithm which
+allows belief propagation (BP) and mean filed (MF) applied on a same factor
+node, so that MF can be applied to hard constraint factors. Based on the
+proposed message passing algorithm, a iterative receiver is designed for
+MIMO-OFDM systems. Both BP and MF are exploited to deal with the hard
+constraint factor nodes involving the multiplication of channel coefficients
+and data symbols to reduce the complexity of the only BP used. The numerical
+results show that the BER performance of the proposed low complexity receiver
+closely approach that of the state-of-the-art receiver, where only BP is used
+to handled the hard constraint factors, in the high SNRs.
+",1,0,0,0,0,0
+9272,Modern Python at the Large Synoptic Survey Telescope,"  The LSST software systems make extensive use of Python, with almost all of it
+initially being developed solely in Python 2. Since LSST will be commissioned
+when Python 2 is end-of-lifed it is critical that we have all our code support
+Python 3 before commissioning begins. Over the past year we have made
+significant progress in migrating the bulk of the code from the Data Management
+system onto Python 3. This paper presents our migration methodology, and the
+current status of the port, with our eventual aim to be running completely on
+Python 3 by early 2018. We also discuss recent modernizations to our Python
+codebase.
+",0,1,0,0,0,0
+9273,Matrix Completion Methods for Causal Panel Data Models,"  In this paper we study methods for estimating causal effects in settings with
+panel data, where a subset of units are exposed to a treatment during a subset
+of periods, and the goal is estimating counterfactual (untreated) outcomes for
+the treated unit/period combinations. We develop a class of matrix completion
+estimators that uses the observed elements of the matrix of control outcomes
+corresponding to untreated unit/periods to predict the ""missing"" elements of
+the matrix, corresponding to treated units/periods. The approach estimates a
+matrix that well-approximates the original (incomplete) matrix, but has lower
+complexity according to the nuclear norm for matrices. From a technical
+perspective, we generalize results from the matrix completion literature by
+allowing the patterns of missing data to have a time series dependency
+structure. We also present novel insights concerning the connections between
+the matrix completion literature, the literature on interactive fixed effects
+models and the literatures on program evaluation under unconfoundedness and
+synthetic control methods.
+",0,0,1,0,0,0
+9274,Singular surfaces of revolution with prescribed unbounded mean curvature,"  We give an explicit formula for singular surfaces of revolution with
+prescribed unbounded mean curvature. Using it, we give conditions for
+singularities of that surfaces. Periodicity of that surface is also discussed.
+",0,0,1,0,0,0
+9275,A Possible Mechanism for Driving Oscillations in Hot Giant Planets,"  The $\kappa$-mechanism has been successful in explaining the origin of
+observed oscillations of many types of ""classical"" pulsating variable stars.
+Here we examine quantitatively if that same process is prominent enough to
+excite the potential global oscillations within Jupiter, whose energy flux is
+powered by gravitational collapse rather than nuclear fusion. Additionally, we
+examine whether external radiative forcing, i.e. starlight, could be a driver
+for global oscillations in hot Jupiters orbiting various main-sequence stars at
+defined orbital semimajor axes. Using planetary models generated by the Modules
+for Experiments in Stellar Astrophysics (MESA) and nonadiabatic oscillation
+calculations, we confirm that Jovian oscillations cannot be driven via the
+$\kappa$-mechanism. However, we do show that in hot Jupiters oscillations can
+likely be excited via the suppression of radiative cooling due to external
+radiation given a large enough stellar flux and the absence of a significant
+oscillatory damping zone within the planet. This trend seems to not be
+dependent on the planetary mass. In future observations we can thus expect that
+such planets may be pulsating, thereby giving greater insight into the internal
+structure of these bodies.
+",0,1,0,0,0,0
+9276,Cubic Fields: A Primer,"  We classify all cubic extensions of any field of arbitrary characteristic, up
+to isomorphism, via an explicit construction involving three fundamental types
+of cubic forms. We deduce a classification of any Galois cubic extension of a
+field. The splitting and ramification of places in a separable cubic extension
+of any global function field are completely determined, and precise
+Riemann-Hurwitz formulae are given. In doing so, we determine the decomposition
+of any cubic polynomial over a finite field.
+",0,0,1,0,0,0
+9277,The GBT Beam Shape at 109 GHz,"  With the installation of the Argus 16-pixel receiver covering 75-115 GHz on
+the Green Bank Telescope (GBT), it is now possible to characterize the antenna
+beam at very high frequencies, where the use of the active surface and
+out-of-focus holography are critical to the telescope's performance. A recent
+measurement in good weather conditions (low atmospheric opacity, low winds, and
+stable night-time thermal conditions) at 109.4 GHz yielded a FWHM beam of
+6.7""x6.4"" in azimuth and elevation, respectively. This corresponds to
+1.16+/-0.03 Lambda/D at 109.4 GHz. The derived ratio agrees well with the
+low-frequency value of 1.18+/-0.03 Lambda/D measured at 9.0 GHz. There are no
+detectable side-lobes at either frequency. In good weather conditions and after
+applying the standard antenna corrections (pointing, focus, and the active
+surface corrections for gravity and thermal effects), there is no measurable
+degradation of the beam of the GBT at its highest operational frequencies.
+",0,1,0,0,0,0
+9278,Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC,"  It is challenging to develop stochastic gradient based scalable inference for
+deep discrete latent variable models (LVMs), due to the difficulties in not
+only computing the gradients, but also adapting the step sizes to different
+latent factors and hidden layers. For the Poisson gamma belief network (PGBN),
+a recently proposed deep discrete LVM, we derive an alternative representation
+that is referred to as deep latent Dirichlet allocation (DLDA). Exploiting data
+augmentation and marginalization techniques, we derive a block-diagonal Fisher
+information matrix and its inverse for the simplex-constrained global model
+parameters of DLDA. Exploiting that Fisher information matrix with stochastic
+gradient MCMC, we present topic-layer-adaptive stochastic gradient Riemannian
+(TLASGR) MCMC that jointly learns simplex-constrained global parameters across
+all layers and topics, with topic and layer specific learning rates.
+State-of-the-art results are demonstrated on big data sets.
+",1,0,0,1,0,0
+9279,Bridging Finite and Super Population Causal Inference,"  There are two general views in causal analysis of experimental data: the
+super population view that the units are an independent sample from some
+hypothetical infinite populations, and the finite population view that the
+potential outcomes of the experimental units are fixed and the randomness comes
+solely from the physical randomization of the treatment assignment. These two
+views differs conceptually and mathematically, resulting in different sampling
+variances of the usual difference-in-means estimator of the average causal
+effect. Practically, however, these two views result in identical variance
+estimators. By recalling a variance decomposition and exploiting a
+completeness-type argument, we establish a connection between these two views
+in completely randomized experiments. This alternative formulation could serve
+as a template for bridging finite and super population causal inference in
+other scenarios.
+",0,0,1,1,0,0
+9280,Deep Reinforcement Learning that Matters,"  In recent years, significant progress has been made in solving challenging
+problems across various domains using deep reinforcement learning (RL).
+Reproducing existing work and accurately judging the improvements offered by
+novel methods is vital to sustaining this progress. Unfortunately, reproducing
+results for state-of-the-art deep RL methods is seldom straightforward. In
+particular, non-determinism in standard benchmark environments, combined with
+variance intrinsic to the methods, can make reported results tough to
+interpret. Without significance metrics and tighter standardization of
+experimental reporting, it is difficult to determine whether improvements over
+the prior state-of-the-art are meaningful. In this paper, we investigate
+challenges posed by reproducibility, proper experimental techniques, and
+reporting procedures. We illustrate the variability in reported metrics and
+results when comparing against common baselines and suggest guidelines to make
+future results in deep RL more reproducible. We aim to spur discussion about
+how to ensure continued progress in the field by minimizing wasted effort
+stemming from results that are non-reproducible and easily misinterpreted.
+",1,0,0,1,0,0
+9281,Testing Docker Performance for HPC Applications,"  The main goal for this article is to compare performance penalties when using
+KVM virtualization and Docker containers for creating isolated environments for
+HPC applications. The article provides both data obtained using commonly
+accepted synthetic tests (High Performance Linpack) and real life applications
+(OpenFOAM). The article highlights the influence on resulting application
+performance of major infrastructure configuration options: CPU type presented
+to VM, networking connection type used.
+",1,0,0,0,0,0
+9282,Can Neural Machine Translation be Improved with User Feedback?,"  We present the first real-world application of methods for improving neural
+machine translation (NMT) with human reinforcement, based on explicit and
+implicit user feedback collected on the eBay e-commerce platform. Previous work
+has been confined to simulation experiments, whereas in this paper we work with
+real logged feedback for offline bandit learning of NMT parameters. We conduct
+a thorough analysis of the available explicit user judgments---five-star
+ratings of translation quality---and show that they are not reliable enough to
+yield significant improvements in bandit learning. In contrast, we successfully
+utilize implicit task-based feedback collected in a cross-lingual search task
+to improve task-specific and machine translation quality metrics.
+",0,0,0,1,0,0
+9283,A new concept multi-stage Zeeman decelerator: experimental implementation,"  We demonstrate the successful experimental implementation of a multi-stage
+Zeeman decelerator utilizing the new concept described in the accompanying
+paper. The decelerator consists of an array of 25 hexapoles and 24 solenoids.
+The performance of the decelerator in acceleration, deceleration and guiding
+modes is characterized using beams of metastable Helium ($^3S$) atoms. Up to
+60% of the kinetic energy was removed for He atoms that have an initial
+velocity of 520 m/s. The hexapoles consist of permanent magnets, whereas the
+solenoids are produced from a single hollow copper capillary through which
+cooling liquid is passed. The solenoid design allows for excellent thermal
+properties, and enables the use of readily available and cheap electronics
+components to pulse high currents through the solenoids. The Zeeman decelerator
+demonstrated here is mechanically easy to build, can be operated with
+cost-effective electronics, and can run at repetition rates up to 10 Hz.
+",0,1,0,0,0,0
+9284,Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training,"  Simulation-based training (SBT) is gaining popularity as a low-cost and
+convenient training technique in a vast range of applications. However, for a
+SBT platform to be fully utilized as an effective training tool, it is
+essential that feedback on performance is provided automatically in real-time
+during training. It is the aim of this paper to develop an efficient and
+effective feedback generation method for the provision of real-time feedback in
+SBT. Existing methods either have low effectiveness in improving novice skills
+or suffer from low efficiency, resulting in their inability to be used in
+real-time. In this paper, we propose a neural network based method to generate
+feedback using the adversarial technique. The proposed method utilizes a
+bounded adversarial update to minimize a L1 regularized loss via
+back-propagation. We empirically show that the proposed method can be used to
+generate simple, yet effective feedback. Also, it was observed to have high
+effectiveness and efficiency when compared to existing methods, thus making it
+a promising option for real-time feedback generation in SBT.
+",1,0,0,1,0,0
+9285,Models of the strongly lensed quasar DES J0408-5354,"  We present gravitational lens models of the multiply imaged quasar DES
+J0408-5354, recently discovered in the Dark Energy Survey (DES) footprint, with
+the aim of interpreting its remarkable quad-like configuration. We first model
+the DES single-epoch $grizY$ images as a superposition of a lens galaxy and
+four point-like objects, obtaining spectral energy distributions (SEDs) and
+relative positions for the objects. Three of the point sources (A,B,D) have
+SEDs compatible with the discovery quasar spectra, while the faintest
+point-like image (G2/C) shows significant reddening and a `grey' dimming of
+$\approx0.8$mag. In order to understand the lens configuration, we fit
+different models to the relative positions of A,B,D. Models with just a single
+deflector predict a fourth image at the location of G2/C but considerably
+brighter and bluer. The addition of a small satellite galaxy ($R_{\rm
+E}\approx0.2$"") in the lens plane near the position of G2/C suppresses the flux
+of the fourth image and can explain both the reddening and grey dimming. All
+models predict a main deflector with Einstein radius between $1.7""$ and $2.0"",$
+velocity dispersion $267-280$km/s and enclosed mass $\approx
+6\times10^{11}M_{\odot},$ even though higher resolution imaging data are needed
+to break residual degeneracies in model parameters. The longest time-delay
+(B-A) is estimated as $\approx 85$ (resp. $\approx125$) days by models with
+(resp. without) a perturber near G2/C. The configuration and predicted
+time-delays of J0408-5354 make it an excellent target for follow-up aimed at
+understanding the source quasar host galaxy and substructure in the lens, and
+measuring cosmological parameters. We also discuss some lessons learnt from
+J0408-5354 on lensed quasar finding strategies, due to its chromaticity and
+morphology.
+",0,1,0,0,0,0
+9286,Efficient Test-based Variable Selection for High-dimensional Linear Models,"  Variable selection plays a fundamental role in high-dimensional data
+analysis. Various methods have been developed for variable selection in recent
+years. Well-known examples are forward stepwise regression (FSR) and least
+angle regression (LARS), among others. These methods typically add variables
+into the model one by one. For such selection procedures, it is crucial to find
+a stopping criterion that controls model complexity. One of the most commonly
+used techniques to this end is cross-validation (CV) which, in spite of its
+popularity, has two major drawbacks: expensive computational cost and lack of
+statistical interpretation. To overcome these drawbacks, we introduce a
+flexible and efficient test-based variable selection approach that can be
+incorporated into any sequential selection procedure. The test, which is on the
+overall signal in the remaining inactive variables, is based on the maximal
+absolute partial correlation between the inactive variables and the response
+given active variables. We develop the asymptotic null distribution of the
+proposed test statistic as the dimension tends to infinity uniformly in the
+sample size. We also show that the test is consistent. With this test, at each
+step of the selection, a new variable is included if and only if the $p$-value
+is below some pre-defined level. Numerical studies show that the proposed
+method delivers very competitive performance in terms of variable selection
+accuracy and computational complexity compared to CV.
+",0,0,0,1,0,0
+9287,Star Cluster Formation from Turbulent Clumps. I. The Fast Formation Limit,"  We investigate the formation and early evolution of star clusters assuming
+that they form from a turbulent starless clump of given mass bounded inside a
+parent self-gravitating molecular cloud characterized by a particular mass
+surface density. As a first step we assume instantaneous star cluster formation
+and gas expulsion. We draw our initial conditions from observed properties of
+starless clumps. We follow the early evolution of the clusters up to 20 Myr,
+investigating effects of different star formation efficiencies, primordial
+binary fractions and eccentricities and primordial mass segregation levels. We
+investigate clumps with initial masses of $M_{\rm cl}=3000\:{\rm M}_\odot$
+embedded in ambient cloud environments with mass surface densities,
+$\Sigma_{\rm cloud}=0.1$ and $1\:{\rm g\:cm^{-2}}$. We show that these models
+of fast star cluster formation result, in the fiducial case, in clusters that
+expand rapidly, even considering only the bound members. Clusters formed from
+higher $\Sigma_{\rm cloud}$ environments tend to expand more quickly, so are
+soon larger than clusters born from lower $\Sigma_{\rm cloud}$ conditions. To
+form a young cluster of a given age, stellar mass and mass surface density,
+these models need to assume a parent molecular clump that is many times denser,
+which is unrealistic compared to observed systems. We also show that in these
+models the initial binary properties are only slightly modified by
+interactions, meaning that binary properties, e.g., at 20 Myr, are very similar
+to those at birth. With this study we set up the basis of future work where we
+will investigate more realistic models of star formation compared to this
+instantaneous, baseline case.
+",0,1,0,0,0,0
+9288,A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series,"  Sleep stage classification constitutes an important preliminary exam in the
+diagnosis of sleep disorders. It is traditionally performed by a sleep expert
+who assigns to each 30s of signal a sleep stage, based on the visual inspection
+of signals such as electroencephalograms (EEG), electrooculograms (EOG),
+electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first
+deep learning approach for sleep stage classification that learns end-to-end
+without computing spectrograms or extracting hand-crafted features, that
+exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG,
+EMG and EOG), and that can exploit the temporal context of each 30s window of
+data. For each modality the first layer learns linear spatial filters that
+exploit the array of sensors to increase the signal-to-noise ratio, and the
+last layer feeds the learnt representation to a softmax classifier. Our model
+is compared to alternative automatic approaches based on convolutional networks
+or decisions trees. Results obtained on 61 publicly available PSG records with
+up to 20 EEG channels demonstrate that our network architecture yields
+state-of-the-art performance. Our study reveals a number of insights on the
+spatio-temporal distribution of the signal of interest: a good trade-off for
+optimal classification performance measured with balanced accuracy is to use 6
+EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting one
+minute of data before and after each data segment offers the strongest
+improvement when a limited number of channels is available. As sleep experts,
+our system exploits the multivariate and multimodal nature of PSG signals in
+order to deliver state-of-the-art classification performance with a small
+computational cost.
+",0,0,0,1,0,0
+9289,An anti-incursion algorithm for unknown probabilistic adversaries on connected graphs,"  A gambler moves on the vertices $1, \ldots, n$ of a graph using the
+probability distribution $p_{1}, \ldots, p_{n}$. A cop pursues the gambler on
+the graph, only being able to move between adjacent vertices. What is the
+expected number of moves that the gambler can make until the cop catches them?
+Komarov and Winkler proved an upper bound of approximately $1.97n$ for the
+expected capture time on any connected $n$-vertex graph when the cop does not
+know the gambler's distribution. We improve this upper bound to approximately
+$1.95n$ by modifying the cop's pursuit algorithm.
+",1,0,1,0,0,0
+9290,Virtual Breakpoints for x86/64,"  Efficient, reliable trapping of execution in a program at the desired
+location is a hot area of research for security professionals. The progression
+of debuggers and malware is akin to a game of cat and mouse - each are
+constantly in a state of trying to thwart one another. At the core of most
+efficient debuggers today is a combination of virtual machines and traditional
+binary modification breakpoints (int3). In this paper, we present a design for
+Virtual Breakpoints, a modification to the x86 MMU which brings breakpoint
+management into hardware alongside page tables. We demonstrate the fundamental
+abstraction failures of current trapping methods, and rebuild the mechanism
+from the ground up. Our design delivers fast, reliable trapping without the
+pitfalls of binary modification.
+",1,0,0,0,0,0
+9291,Accurate Multi-physics Numerical Analysis of Particle Preconcentration Based on Ion Concentration Polarization,"  This paper studies mechanism of preconcentration of charged particles in a
+straight micro-channel embedded with permselective membranes, by numerically
+solving coupled transport equations of ions, charged particles and solvent
+fluid without any simplifying assumptions. It is demonstrated that trapping and
+preconcentration of charged particles are determined by the interplay between
+drag force from the electroosmotic fluid flow and the electrophoretic force
+applied trough the electric field. Several insightful characteristics are
+revealed, including the diverse dynamics of co-ions and counter ions,
+replacement of co-ions by focused particles, lowered ion concentrations in
+particle enriched zone, and enhanced electroosmotic pumping effect etc.
+Conditions for particles that may be concentrated are identified in terms of
+charges, sizes and electrophoretic mobilities of particles and co-ions.
+Dependences of enrichment factor on cross-membrane voltage, initial particle
+concentration and buffer ion concentrations are analyzed and the underlying
+reasons are elaborated. Finally, post priori a condition for validity of
+decoupled simulation model is given based on charges carried by focused charge
+particles and that by buffer co-ions. These results provide important guidance
+in the design and optimization of nanofluidic preconcentration and other
+related devices.
+",0,1,0,0,0,0
+9292,Learning to Embed Words in Context for Syntactic Tasks,"  We present models for embedding words in the context of surrounding words.
+Such models, which we refer to as token embeddings, represent the
+characteristics of a word that are specific to a given context, such as word
+sense, syntactic category, and semantic role. We explore simple, efficient
+token embedding models based on standard neural network architectures. We learn
+token embeddings on a large amount of unannotated text and evaluate them as
+features for part-of-speech taggers and dependency parsers trained on much
+smaller amounts of annotated data. We find that predictors endowed with token
+embeddings consistently outperform baseline predictors across a range of
+context window and training set sizes.
+",1,0,0,0,0,0
+9293,Modeling Impact of Human Errors on the Data Unavailability and Data Loss of Storage Systems,"  Data storage systems and their availability play a crucial role in
+contemporary datacenters. Despite using mechanisms such as automatic fail-over
+in datacenters, the role of human agents and consequently their destructive
+errors is inevitable. Due to very large number of disk drives used in exascale
+datacenters and their high failure rates, the disk subsystem in storage systems
+has become a major source of Data Unavailability (DU) and Data Loss (DL)
+initiated by human errors. In this paper, we investigate the effect of
+Incorrect Disk Replacement Service (IDRS) on the availability and reliability
+of data storage systems. To this end, we analyze the consequences of IDRS in a
+disk array, and conduct Monte Carlo simulations to evaluate DU and DL during
+mission time. The proposed modeling framework can cope with a) different
+storage array configurations and b) Data Object Survivability (DOS),
+representing the effect of system level redundancies such as remote backups and
+mirrors. In the proposed framework, the model parameters are obtained from
+industrial and scientific reports alongside field data which have been
+extracted from a datacenter operating with 70 storage racks. The results show
+that ignoring the impact of IDRS leads to unavailability underestimation by up
+to three orders of magnitude. Moreover, our study suggests that by considering
+the effect of human errors, the conventional beliefs about the dependability of
+different Redundant Array of Independent Disks (RAID) mechanisms should be
+revised. The results show that RAID1 can result in lower availability compared
+to RAID5 in the presence of human errors. The results also show that employing
+automatic fail-over policy (using hot spare disks) can reduce the drastic
+impacts of human errors by two orders of magnitude.
+",1,0,0,0,0,0
+9294,Gaussian and Sparse Processes Are Limits of Generalized Poisson Processes,"  The theory of sparse stochastic processes offers a broad class of statistical
+models to study signals. In this framework, signals are represented as
+realizations of random processes that are solution of linear stochastic
+differential equations driven by white Lévy noises. Among these processes,
+generalized Poisson processes based on compound-Poisson noises admit an
+interpretation as random L-splines with random knots and weights. We
+demonstrate that every generalized Lévy process-from Gaussian to sparse-can
+be understood as the limit in law of a sequence of generalized Poisson
+processes. This enables a new conceptual understanding of sparse processes and
+suggests simple algorithms for the numerical generation of such objects.
+",1,0,1,0,0,0
+9295,Survival time of Princess Kaguya in an air-tight bamboo chamber,"  Princess Kaguya is a heroine of a famous folk tale, as every Japanese knows.
+She was assumed to be confined in a bamboo cavity with cylindrical shape, and
+then fortuitously discovered by an elderly man in the forest. Here, we pose a
+question as to how long she could have survived in an enclosed space such as
+the bamboo chamber, which had no external oxygen supply at all. We demonstrate
+that the survival time should be determined by three geometric quantities: the
+inner volume of the bamboo chamber, the volumetric size of her body, and her
+body's total surface area that governs the rate of oxygen consumption in the
+body. We also emphasize that this geometric problem shed light on an
+interesting scaling relation between biological quantities for living
+organisms.
+",0,1,0,0,0,0
+9296,Descriptor System Tools (DSTOOLS) User's Guide,"  The Descriptor System Tools (DSTOOLS) is a collection of MATLAB functions for
+the operation on and manipulation of rational transfer function matrices via
+their descriptor system realizations. The DSTOOLS collection relies on the
+Control System Toolbox and several mex-functions based on the Systems and
+Control Library SLICOT. Many of the implemented functions are based on the
+computational procedures described in Chapter 10 of the book: ""A. Varga,
+Solving Fault Diagnosis Problems - Linear Synthesis Techniques, Springer,
+2017"". This document is the User's Guide for the version V0.71 of DSTOOLS.
+First, we present the mathematical background on rational matrices and
+descriptor systems. Then, we give in-depth information on the command syntax of
+the main computational functions. Several examples illustrate the use of the
+main functions of DSTOOLS.
+",1,0,0,0,0,0
+9297,Laplacian Prior Variational Automatic Relevance Determination for Transmission Tomography,"  In the classic sparsity-driven problems, the fundamental L-1 penalty method
+has been shown to have good performance in reconstructing signals for a wide
+range of problems. However this performance relies on a good choice of penalty
+weight which is often found from empirical experiments. We propose an algorithm
+called the Laplacian variational automatic relevance determination (Lap-VARD)
+that takes this penalty weight as a parameter of a prior Laplace distribution.
+Optimization of this parameter using an automatic relevance determination
+framework results in a balance between the sparsity and accuracy of signal
+reconstruction. Our algorithm is implemented in a transmission tomography model
+with sparsity constraint in wavelet domain.
+",0,0,0,1,0,0
+9298,Mitigating Confirmation Bias on Twitter by Recommending Opposing Views,"  In this work, we propose a content-based recommendation approach to increase
+exposure to opposing beliefs and opinions. Our aim is to help provide users
+with more diverse viewpoints on issues, which are discussed in partisan groups
+from different perspectives. Since due to the backfire effect, people's
+original beliefs tend to strengthen when challenged with counter evidence, we
+need to expose them to opposing viewpoints at the right time. The preliminary
+work presented here describes our first step into this direction. As
+illustrative showcase, we take the political debate on Twitter around the
+presidency of Donald Trump.
+",1,0,0,0,0,0
+9299,First-principles based Landau-Devonshire potential for BiFeO$_3$,"  The work describes a first-principles-based computational strategy for
+studying structural phase transitions, and in particular, for determination of
+the so-called Landau-Devonshire potential - the classical zero-temperature
+limit of the Gibbs energy, expanded in terms of order parameters. It exploits
+the configuration space attached to the eigenvectors of the modes frozen in the
+ground state, rather than the space spanned by the unstable modes of the
+high-symmetry phase, as done usually. This allows us to carefully probe the
+part of the energy surface in the vicinity of the ground state, which is most
+relevant for the properties of the ordered phase. We apply this procedure to
+BiFeO$_3$ and perform ab-initio calculations in order to determine potential
+energy contributions associated with strain, polarization and oxygen octahedra
+tilt degrees of freedom, compatible with its two-formula unit cell periodic
+boundary conditions.
+",0,1,0,0,0,0
+9300,A topological characterization of the omega-limit sets of analytic vector fields on open subsets of the sphere,"  In [15], V. Jimenez and J. Llibre characterized, up to homeomorphism, the
+omega limit sets of analytic vector fields on the sphere and the projective
+plane. The authors also studied the same problem for open subsets of these
+surfaces.
+Unfortunately, an essential lemma in their programme for general surfaces has
+a gap. Although the proof of this lemma can be amended in the case of the
+sphere, the plane, the projective plane and the projective plane minus one
+point (and therefore the characterizations for these surfaces in [8] are
+correct), the lemma is not generally true, see [15].
+Consequently, the topological characterization for analytic vector fields on
+open subsets of the sphere and the projective plane is still pending. In this
+paper, we close this problem in the case of open subsets of the sphere.
+",0,0,1,0,0,0
+9301,A Decision Tree Based Approach Towards Adaptive Profiling of Distributed Applications,"  The adoption of the distributed paradigm has allowed applications to increase
+their scalability, robustness and fault tolerance, but it has also complicated
+their structure, leading to an exponential growth of the applications'
+configuration space and increased difficulty in predicting their performance.
+In this work, we describe a novel, automated profiling methodology that makes
+no assumptions on application structure. Our approach utilizes oblique Decision
+Trees in order to recursively partition an application's configuration space in
+disjoint regions, choose a set of representative samples from each subregion
+according to a defined policy and return a model for the entire space as a
+composition of linear models over each subregion. An extensive evaluation over
+real-life applications and synthetic performance functions showcases that our
+scheme outperforms other state-of-the-art profiling methodologies. It
+particularly excels at reflecting abnormalities and discontinuities of the
+performance function, allowing the user to influence the sampling policy based
+on the modeling accuracy and the space coverage.
+",1,0,0,0,0,0
+9302,A Tractable Approach to Dynamic Network Dimensioning Based on the Best-cell Configuration,"  Spatial distributions of other cell interference (OCIF) and interference to
+own-cell power ratio (IOPR) with reference to the distance between a mobile and
+its serving base station (BS) are modeled for the down-link reception of
+cellular systems based on the best-cell configuration instead of the
+nearest-cell configuration. This enables a more realistic evaluation of two
+competing objectives in network dimensioning: coverage and rate capacity. More
+outcomes useful for dynamic network dimensioning are also derived, including
+maximum BS transmission power per cell size and the cell density required for
+an adequate coverage of a given traffic density.
+",1,0,0,0,0,0
+9303,Parametric uncertainty in complex environmental models: a cheap emulation approach for models with high-dimensional output,"  In order to understand underlying processes governing environmental and
+physical processes, and predict future outcomes, a complex computer model is
+frequently required to simulate these dynamics. However there is inevitably
+uncertainty related to the exact parametric form or the values of such
+parameters to be used when developing these simulators, with \emph{ranges} of
+plausible values prevalent in the literature. Systematic errors introduced by
+failing to account for these uncertainties have the potential to have a large
+effect on resulting estimates in unknown quantities of interest. Due to the
+complexity of these types of models, it is often unfeasible to run large
+numbers of training runs that are usually required for full statistical
+emulators of the environmental processes. We therefore present a method for
+accounting for uncertainties in complex environmental simulators without the
+need for very large numbers of training runs and illustrate the method through
+an application to the Met Office's atmospheric transport model NAME. We
+conclude that there are two principle parameters that are linked with
+variability in NAME outputs, namely the free tropospheric turbulence parameter
+and particle release height. Our results suggest the former should be
+significantly larger than is currently implemented as a default in NAME, whilst
+changes in the latter most likely stem from inconsistencies between the model
+specified ground height at the observation locations and the true height at
+this location. Estimated discrepancies from independent data are consistent
+with the discrepancy between modelled and true ground height.
+",0,0,0,1,0,0
+9304,Prediction and Control with Temporal Segment Models,"  We introduce a method for learning the dynamics of complex nonlinear systems
+based on deep generative models over temporal segments of states and actions.
+Unlike dynamics models that operate over individual discrete timesteps, we
+learn the distribution over future state trajectories conditioned on past
+state, past action, and planned future action trajectories, as well as a latent
+prior over action trajectories. Our approach is based on convolutional
+autoregressive models and variational autoencoders. It makes stable and
+accurate predictions over long horizons for complex, stochastic systems,
+effectively expressing uncertainty and modeling the effects of collisions,
+sensory noise, and action delays. The learned dynamics model and action prior
+can be used for end-to-end, fully differentiable trajectory optimization and
+model-based policy optimization, which we use to evaluate the performance and
+sample-efficiency of our method.
+",1,0,0,1,0,0
+9305,Artificial Intelligence and Statistics,"  Artificial intelligence (AI) is intrinsically data-driven. It calls for the
+application of statistical concepts through human-machine collaboration during
+generation of data, development of algorithms, and evaluation of results. This
+paper discusses how such human-machine collaboration can be approached through
+the statistical concepts of population, question of interest,
+representativeness of training data, and scrutiny of results (PQRS). The PQRS
+workflow provides a conceptual framework for integrating statistical ideas with
+human input into AI products and research. These ideas include experimental
+design principles of randomization and local control as well as the principle
+of stability to gain reproducibility and interpretability of algorithms and
+data results. We discuss the use of these principles in the contexts of
+self-driving cars, automated medical diagnoses, and examples from the authors'
+collaborative research.
+",1,0,0,1,0,0
+9306,Shortcut Sequence Tagging,"  Deep stacked RNNs are usually hard to train. Adding shortcut connections
+across different layers is a common way to ease the training of stacked
+networks. However, extra shortcuts make the recurrent step more complicated. To
+simply the stacked architecture, we propose a framework called shortcut block,
+which is a marriage of the gating mechanism and shortcuts, while discarding the
+self-connected part in LSTM cell. We present extensive empirical experiments
+showing that this design makes training easy and improves generalization. We
+propose various shortcut block topologies and compositions to explore its
+effectiveness. Based on this architecture, we obtain a 6% relatively
+improvement over the state-of-the-art on CCGbank supertagging dataset. We also
+get comparable results on POS tagging task.
+",1,0,0,0,0,0
+9307,Distortions of the Cosmic Microwave Background through cooling lines during the epoch of Reionization,"  By using N-body hydrodynamical cosmological simulations in which the
+chemistry of major metals and molecules is consistently solved for, we study
+the interaction of metallic fine-structure lines with the CMB. Our analysis
+shows that the collisional induced emissions in the OI 145 $\mu$m and CII 158
+$\mu$m lines during reionization introduce a distortion of the CMB spectrum at
+low frequencies ($\nu < 300$ GHz) with amplitudes up to $\Delta
+I_{\nu}/B_{\nu}(T_{\rm CMB})\sim 10^{-8}$-$10^{-7}$, i.e., at the $\sim 0.1$
+percent level of FIRAS upper limits. Shorter wavelength fine-structure
+transitions (OI 63 $\mu$m, FeII 26 $\mu$m, and SiII 35 $\mu$m) typically sample
+the reionization epoch at higher observing frequencies ($\nu > 400$ GHz). This
+corresponds to the Wien tail of the CMB spectrum and the distortion level
+induced by those lines may be as high as $\Delta I_{\nu}/B_{\nu}(T_{\rm
+CMB})\sim 10^{-4}$. The angular anisotropy produced by these lines should be
+more relevant at higher frequencies: while practically negligible at $\nu=145
+$GHz, signatures from CII 158 $\mu$m and OI 145 $\mu$m should amount to 1%-5%
+of the anisotropy power measured at $l \sim 5000$ and $\nu=220 $GHz by the ACT
+and SPT collaborations (after assuming $\Delta \nu_{\rm obs}/\nu_{\rm
+obs}\simeq 0.005$ for the line observations). Our simulations show that
+anisotropy maps from different lines (e.g., OI 145 $\mu$m and CII 158 $\mu$m)
+at the same redshift show a very high degree ($>0.8$) of spatial correlation,
+allowing for the use of observations at different frequencies to unveil the
+same snapshot of the reionization epoch. Finally, our simulations demonstrate
+that line-emission anisotropies extracted in narrow frequency/redshift shells
+are practically uncorrelated in frequency space, thus enabling standard methods
+for removal of foregrounds that vary smoothly in frequency, just as in HI 21 cm
+studies.
+",0,1,0,0,0,0
+9308,Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks,"  One of the big restrictions in brain computer interface field is the very
+limited training samples, it is difficult to build a reliable and usable system
+with such limited data. Inspired by generative adversarial networks, we propose
+a conditional Deep Convolutional Generative Adversarial (cDCGAN) Networks
+method to generate more artificial EEG signal automatically for data
+augmentation to improve the performance of convolutional neural networks in
+brain computer interface field and overcome the small training dataset
+problems. We evaluate the proposed cDCGAN method on BCI competition dataset of
+motor imagery. The results show that the generated artificial EEG data from
+Gaussian noise can learn the features from raw EEG data and has no less than
+the classification accuracy of raw EEG data in the testing dataset. Also by
+using generated artificial data can effectively improve classification accuracy
+at the same model with limited training data.
+",0,0,0,1,1,0
+9309,From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood,"  Our goal is to learn a semantic parser that maps natural language utterances
+into executable programs when only indirect supervision is available: examples
+are labeled with the correct execution result, but not the program itself.
+Consequently, we must search the space of programs for those that output the
+correct result, while not being misled by spurious programs: incorrect programs
+that coincidentally output the correct result. We connect two common learning
+paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML),
+and then present a new learning algorithm that combines the strengths of both.
+The new algorithm guards against spurious programs by combining the systematic
+search traditionally employed in MML with the randomized exploration of RL, and
+by updating parameters such that probability is spread more evenly across
+consistent programs. We apply our learning algorithm to a new neural semantic
+parser and show significant gains over existing state-of-the-art results on a
+recent context-dependent semantic parsing task.
+",1,0,0,1,0,0
+9310,GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data,"  Object Transfiguration replaces an object in an image with another object
+from a second image. For example it can perform tasks like ""putting exactly
+those eyeglasses from image A on the nose of the person in image B"". Usage of
+exemplar images allows more precise specification of desired modifications and
+improves the diversity of conditional image generation. However, previous
+methods that rely on feature space operations, require paired data and/or
+appearance models for training or disentangling objects from background. In
+this work, we propose a model that can learn object transfiguration from two
+unpaired sets of images: one set containing images that ""have"" that kind of
+object, and the other set being the opposite, with the mild constraint that the
+objects be located approximately at the same place. For example, the training
+data can be one set of reference face images that have eyeglasses, and another
+set of images that have not, both of which spatially aligned by face landmarks.
+Despite the weak 0/1 labels, our model can learn an ""eyeglasses"" subspace that
+contain multiple representatives of different types of glasses. Consequently,
+we can perform fine-grained control of generated images, like swapping the
+glasses in two images by swapping the projected components in the ""eyeglasses""
+subspace, to create novel images of people wearing eyeglasses.
+Overall, our deterministic generative model learns disentangled attribute
+subspaces from weakly labeled data by adversarial training. Experiments on
+CelebA and Multi-PIE datasets validate the effectiveness of the proposed model
+on real world data, in generating images with specified eyeglasses, smiling,
+hair styles, and lighting conditions etc. The code is available online.
+",1,0,0,0,0,0
+9311,Flow-free Video Object Segmentation,"  Segmenting foreground object from a video is a challenging task because of
+the large deformations of the objects, occlusions, and background clutter. In
+this paper, we propose a frame-by-frame but computationally efficient approach
+for video object segmentation by clustering visually similar generic object
+segments throughout the video. Our algorithm segments various object instances
+appearing in the video and then perform clustering in order to group visually
+similar segments into one cluster. Since the object that needs to be segmented
+appears in most part of the video, we can retrieve the foreground segments from
+the cluster having maximum number of segments, thus filtering out noisy
+segments that do not represent any object. We then apply a track and fill
+approach in order to localize the objects in the frames where the object
+segmentation framework fails to segment any object. Our algorithm performs
+comparably to the recent automatic methods for video object segmentation when
+benchmarked on DAVIS dataset while being computationally much faster.
+",1,0,0,0,0,0
+9312,Finding Modes by Probabilistic Hypergraphs Shifting,"  In this paper, we develop a novel paradigm, namely hypergraph shift, to find
+robust graph modes by probabilistic voting strategy, which are semantically
+sound besides the self-cohesiveness requirement in forming graph modes. Unlike
+the existing techniques to seek graph modes by shifting vertices based on
+pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting
+high-order edges (hyperedges) to deliver graph modes. Specifically, we convert
+the problem of seeking graph modes as the problem of seeking maximizers of a
+novel objective function with the aim to generate good graph modes based on
+sifting edges in hypergraphs. As a result, the generated graph modes based on
+dense subhypergraphs may more accurately capture the object semantics besides
+the self-cohesiveness requirement. We also formally prove that our technique is
+always convergent. Extensive empirical studies on synthetic and real world data
+sets are conducted on clustering and graph matching. They demonstrate that our
+techniques significantly outperform the existing techniques.
+",1,0,0,0,0,0
+9313,Bounding the size of an almost-equidistant set in Euclidean space,"  A set of points in d-dimensional Euclidean space is almost equidistant if
+among any three points of the set, some two are at distance 1. We show that an
+almost-equidistant set in $\mathbb{R}^d$ has cardinality $O(d^{4/3})$.
+",1,0,1,0,0,0
+9314,Equipping weak equivalences with algebraic structure,"  We investigate the extent to which the weak equivalences in a model category
+can be equipped with algebraic structure. We prove, for instance, that there
+exists a monad T such that a morphism of topological spaces admits T-algebra
+structure if and only it is a weak homotopy equivalence. Likewise for
+quasi-isomorphisms and many other examples. The basic trick is to consider
+injectivity in arrow categories. Using algebraic injectivity and cone
+injectivity we obtain general results about the extent to which the weak
+equivalences in a combinatorial model category can be equipped with algebraic
+structure.
+",0,0,1,0,0,0
+9315,An Experimental Study of the Treewidth of Real-World Graph Data (Extended Version),"  Treewidth is a parameter that measures how tree-like a relational instance
+is, and whether it can reasonably be decomposed into a tree. Many computation
+tasks are known to be tractable on databases of small treewidth, but computing
+the treewidth of a given instance is intractable. This article is the first
+large-scale experimental study of treewidth and tree decompositions of
+real-world database instances (25 datasets from 8 different domains, with sizes
+ranging from a few thousand to a few million vertices). The goal is to
+determine which data, if any, can benefit of the wealth of algorithms for
+databases of small treewidth. For each dataset, we obtain upper and lower bound
+estimations of their treewidth, and study the properties of their tree
+decompositions. We show in particular that, even when treewidth is high, using
+partial tree decompositions can result in data structures that can assist
+algorithms.
+",1,0,0,0,0,0
+9316,A yield-cost tradeoff governs Escherichia coli's decision between fermentation and respiration in carbon-limited growth,"  Many microbial systems are known to actively reshape their proteomes in
+response to changes in growth conditions induced e.g. by nutritional stress or
+antibiotics. Part of the re-allocation accounts for the fact that, as the
+growth rate is limited by targeting specific metabolic activities, cells simply
+respond by fine-tuning their proteome to invest more resources into the
+limiting activity (i.e. by synthesizing more proteins devoted to it). However,
+this is often accompanied by an overall re-organization of metabolism, aimed at
+improving the growth yield under limitation by re-wiring resource through
+different pathways. While both effects impact proteome composition, the latter
+underlies a more complex systemic response to stress. By focusing on E. coli's
+`acetate switch', we use mathematical modeling and a re-analysis of empirical
+data to show that the transition from a predominantly fermentative to a
+predominantly respirative metabolism in carbon-limited growth results from the
+trade-off between maximizing the growth yield and minimizing its costs in terms
+of required the proteome share. In particular, E. coli's metabolic phenotypes
+appear to be Pareto-optimal for these objective functions over a broad range of
+dilutions.
+",0,1,0,0,0,0
+9317,Towards a Unified Taxonomy of Biclustering Methods,"  Being an unsupervised machine learning and data mining technique,
+biclustering and its multimodal extensions are becoming popular tools for
+analysing object-attribute data in different domains. Apart from conventional
+clustering techniques, biclustering is searching for homogeneous groups of
+objects while keeping their common description, e.g., in binary setting, their
+shared attributes. In bioinformatics, biclustering is used to find genes, which
+are active in a subset of situations, thus being candidates for biomarkers.
+However, the authors of those biclustering techniques that are popular in gene
+expression analysis, may overlook the existing methods. For instance, BiMax
+algorithm is aimed at finding biclusters, which are well-known for decades as
+formal concepts. Moreover, even if bioinformatics classify the biclustering
+methods according to reasonable domain-driven criteria, their classification
+taxonomies may be different from survey to survey and not full as well. So, in
+this paper we propose to use concept lattices as a tool for taxonomy building
+(in the biclustering domain) and attribute exploration as means for
+cross-domain taxonomy completion.
+",1,0,0,1,0,0
+9318,Global center stable manifold for the defocusing energy critical wave equation with potential,"  In this paper we consider the defocusing energy critical wave equation with a
+trapping potential in dimension $3$. We prove that the set of initial data for
+which solutions scatter to an unstable excited state $(\phi, 0)$ forms a finite
+co-dimensional path connected $C^1$ manifold in the energy space. This manifold
+is a global and unique center-stable manifold associated with $(\phi,0)$. It is
+constructed in a first step locally around any solution scattering to $\phi$,
+which might be very far away from $\phi$ in the $\dot{H}^1\times
+L^2(\mathbb{R}^3)$ norm. In a second crucial step a no-return property is
+proved for any solution which starts near, but not on the local manifolds. This
+ensures that the local manifolds form a global one. Scattering to an unstable
+steady state is therefore a non-generic behavior, in a strong topological sense
+in the energy space. This extends our previous result [18] to the nonradial
+case. The new ingredients here are (i) application of the reversed Strichartz
+estimate from [3] to construct a local center stable manifold near any solution
+that scatters to $(\phi, 0)$. This is needed since the endpoint of the standard
+Strichartz estimates fails nonradially. (ii) The nonradial channel of energy
+estimate introduced by Duyckaerts-Kenig-Merle [14], which is used to show that
+solutions that start off but near the local manifolds associated with $\phi$
+emit some amount of energy into the far field in excess of the amount of energy
+beyond that of the steady state $\phi$.
+",0,0,1,0,0,0
+9319,Real-time convolutional networks for sonar image classification in low-power embedded systems,"  Deep Neural Networks have impressive classification performance, but this
+comes at the expense of significant computational resources at inference time.
+Autonomous Underwater Vehicles use low-power embedded systems for sonar image
+perception, and cannot execute large neural networks in real-time. We propose
+the use of max-pooling aggressively, and we demonstrate it with a Fire-based
+module and a new Tiny module that includes max-pooling in each module. By
+stacking them we build networks that achieve the same accuracy as bigger ones,
+while reducing the number of parameters and considerably increasing
+computational performance. Our networks can classify a 96x96 sonar image with
+98.8 - 99.7 accuracy on only 41 to 61 milliseconds on a Raspberry Pi 2, which
+corresponds to speedups of 28.6 - 19.7.
+",1,0,0,0,0,0
+9320,Mathematics in Caging of Robotics,"  It is a crucial problem in robotics field to cage an object using robots like
+multifingered hand. However the problem what is the caging for general
+geometrical objects and robots has not been well-described in mathematics
+though there were many rigorous studies on the methods how to cage an object by
+certain robots. In this article, we investigate the caging problem more
+mathematically and describe the problem in terms of recursion of the simple
+euclidean moves. Using the description, we show that the caging has the degree
+of difficulty which is closely related to a combinatorial problem and a wire
+puzzle. It implies that in order to capture an object by caging, from a
+practical viewpoint the difficulty plays an important role.
+",1,0,1,0,0,0
+9321,Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces,"  Graph models are relevant in many fields, such as distributed computing,
+intelligent tutoring systems or social network analysis. In many cases, such
+models need to take changes in the graph structure into account, i.e. a varying
+number of nodes or edges. Predicting such changes within graphs can be expected
+to yield important insight with respect to the underlying dynamics, e.g. with
+respect to user behaviour. However, predictive techniques in the past have
+almost exclusively focused on single edges or nodes. In this contribution, we
+attempt to predict the future state of a graph as a whole. We propose to phrase
+time series prediction as a regression problem and apply dissimilarity- or
+kernel-based regression techniques, such as 1-nearest neighbor, kernel
+regression and Gaussian process regression, which can be applied to graphs via
+graph kernels. The output of the regression is a point embedded in a
+pseudo-Euclidean space, which can be analyzed using subsequent dissimilarity-
+or kernel-based processing methods. We discuss strategies to speed up Gaussian
+Processes regression from cubic to linear time and evaluate our approach on two
+well-established theoretical models of graph evolution as well as two real data
+sets from the domain of intelligent tutoring systems. We find that simple
+regression methods, such as kernel regression, are sufficient to capture the
+dynamics in the theoretical models, but that Gaussian process regression
+significantly improves the prediction error for real-world data.
+",1,0,0,0,0,0
+9322,Geometric comparison of phylogenetic trees with different leaf sets,"  The metric space of phylogenetic trees defined by Billera, Holmes, and
+Vogtmann, which we refer to as BHV space, provides a natural geometric setting
+for describing collections of trees on the same set of taxa. However, it is
+sometimes necessary to analyze collections of trees on non-identical taxa sets
+(i.e., with different numbers of leaves), and in this context it is not evident
+how to apply BHV space. Davidson et al. recently approached this problem by
+describing a combinatorial algorithm extending tree topologies to regions in
+higher dimensional tree spaces, so that one can quickly compute which
+topologies contain a given tree as partial data. In this paper, we refine and
+adapt their algorithm to work for metric trees to give a full characterization
+of the subspace of extensions of a subtree. We describe how to apply our
+algorithm to define and search a space of possible supertrees and, for a
+collection of tree fragments with different leaf sets, to measure their
+compatibility.
+",0,0,0,0,1,0
+9323,Automatic Detection of Cyberbullying in Social Media Text,"  While social media offer great communication opportunities, they also
+increase the vulnerability of young people to threatening situations online.
+Recent studies report that cyberbullying constitutes a growing problem among
+youngsters. Successful prevention depends on the adequate detection of
+potentially harmful messages and the information overload on the Web requires
+intelligent systems to identify potential risks automatically. The focus of
+this paper is on automatic cyberbullying detection in social media text by
+modelling posts written by bullies, victims, and bystanders of online bullying.
+We describe the collection and fine-grained annotation of a training corpus for
+English and Dutch and perform a series of binary classification experiments to
+determine the feasibility of automatic cyberbullying detection. We make use of
+linear support vector machines exploiting a rich feature set and investigate
+which information sources contribute the most for this particular task.
+Experiments on a holdout test set reveal promising results for the detection of
+cyberbullying-related posts. After optimisation of the hyperparameters, the
+classifier yields an F1-score of 64% and 61% for English and Dutch
+respectively, and considerably outperforms baseline systems based on keywords
+and word unigrams.
+",1,0,0,0,0,0
+9324,Holographic Entanglement Entropy in Cyclic Cosmology,"  We discuss a cyclic cosmology in which the visible universe, or introverse,
+is all that is accessible to an observer while the extroverse represents the
+total spacetime originating from the time when the dark energy began to
+dominate. It is argued that entanglement entropy of the introverse is the more
+appropriate quantity to render infinitely cyclic, rather than the entropy of
+the total universe. Since vanishing entanglement entropy implies disconnected
+spacetimes, at the turnaround when the introverse entropy is zero the
+disconnected extroverse can be jettisoned with impunity.
+",0,1,0,0,0,0
+9325,Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data,"  Electronic health records (EHRs) have contributed to the computerization of
+patient records and can thus be used not only for efficient and systematic
+medical services, but also for research on biomedical data science. However,
+there are many missing values in EHRs when provided in matrix form, which is an
+important issue in many biomedical EHR applications. In this paper, we propose
+a two-stage framework that includes missing data imputation and disease
+prediction to address the missing data problem in EHRs. We compared the disease
+prediction performance of generative adversarial networks (GANs) and
+conventional learning algorithms in combination with missing data prediction
+methods. As a result, we obtained a level of accuracy of 0.9777, sensitivity of
+0.9521, specificity of 0.9925, area under the receiver operating characteristic
+curve (AUC-ROC) of 0.9889, and F-score of 0.9688 with a stacked autoencoder as
+the missing data prediction method and an auxiliary classifier GAN (AC-GAN) as
+the disease prediction method. The comparison results show that a combination
+of a stacked autoencoder and an AC-GAN significantly outperforms other existing
+approaches. Our results suggest that the proposed framework is more robust for
+disease prediction from EHRs with missing data.
+",1,0,0,1,0,0
+9326,Hydrodynamic stability in the presence of a stochastic forcing:a case study in convection,"  We investigate the stability of a statistically stationary conductive state
+for Rayleigh-Bénard convection between stress-free plates that arises due to
+a bulk stochastic internal heating. This setup may be seen as a generalization
+to a stochastic setting of the seminal 1916 study of Lord Rayleigh. Our results
+indicate that stochastic forcing at small magnitude has a stabilizing effect,
+while strong stochastic forcing has a destabilizing effect. The methodology put
+forth in this article, which combines rigorous analysis with careful
+computation, also provides an approach to hydrodynamic stability for a variety
+of systems subject to a large scale stochastic forcing.
+",0,1,1,0,0,0
+9327,Asset Price Bubbles: An Option-based Indicator,"  We construct a statistical indicator for the detection of short-term asset
+price bubbles based on the information content of bid and ask market quotes for
+plain vanilla put and call options. Our construction makes use of the
+martingale theory of asset price bubbles and the fact that such scenarios where
+the price for an asset exceeds its fundamental value can in principle be
+detected by analysis of the asymptotic behavior of the implied volatility
+surface. For extrapolating this implied volatility, we choose the SABR model,
+mainly because of its decent fit to real option market quotes for a broad range
+of maturities and its ease of calibration. As main theoretical result, we show
+that under lognormal SABR dynamics, we can compute a simple yet powerful
+closed-form martingale defect indicator by solving an ill-posed inverse
+calibration problem. In order to cope with the ill-posedness and to quantify
+the uncertainty which is inherent to such an indicator, we adopt a Bayesian
+statistical parameter estimation perspective. We probe the resulting posterior
+densities with a combination of optimization and adaptive Markov chain Monte
+Carlo methods, thus providing a full-blown uncertainty estimation of all the
+underlying parameters and the martingale defect indicator. Finally, we provide
+real-market tests of the proposed option-based indicator with focus on tech
+stocks due to increasing concerns about a tech bubble 2.0.
+",0,0,0,0,0,1
+9328,Interleaving and Gromov-Hausdorff distance,"  One of the central notions to emerge from the study of persistent homology is
+that of interleaving distance. It has found recent applications in symplectic
+and contact geometry, sheaf theory, computational geometry, and phylogenetics.
+Here we present a general study of this topic. We define interleaving of
+functors with common codomain as solutions to an extension problem. In order to
+define interleaving distance in this setting we are led to categorical
+generalizations of Hausdorff distance, Gromov-Hausdorff distance, and the space
+of metric spaces. We obtain comparisons with previous notions of interleaving
+via the study of future equivalences. As an application we recover a definition
+of shift equivalences of discrete dynamical systems.
+",0,0,1,0,0,0
+9329,Attainable Knowledge,"  The article investigates an evidence-based semantics for epistemic logics in
+which pieces of evidence are interpreted as equivalence relations on the
+epistemic worlds. It is shown that the properties of knowledge obtained from
+potentially infinitely many pieces of evidence are described by modal logic S5.
+At the same time, the properties of knowledge obtained from only a finite
+number of pieces of evidence are described by modal logic S4. The main
+technical result is a sound and complete bi-modal logical system that describes
+properties of these two modalities and their interplay.
+",1,0,0,0,0,0
+9330,Linear complementarity problems on extended second order cones,"  In this paper, we study the linear complementarity problems on extended
+second order cones. We convert a linear complementarity problem on an extended
+second order cone into a mixed complementarity problem on the non-negative
+orthant. We state necessary and sufficient conditions for a point to be a
+solution of the converted problem. We also present solution strategies for this
+problem, such as the Newton method and Levenberg-Marquardt algorithm. Finally,
+we present some numerical examples.
+",0,0,1,0,0,0
+9331,On the Bernstein-Von Mises Theorem for High Dimensional Nonlinear Bayesian Inverse Problems,"  We prove a Bernstein-von Mises theorem for a general class of high
+dimensional nonlinear Bayesian inverse problems in the vanishing noise limit.
+We propose a sufficient condition on the growth rate of the number of unknown
+parameters under which the posterior distribution is asymptotically normal.
+This growth condition is expressed explicitly in terms of the model dimension,
+the degree of ill-posedness of the inverse problem and the noise parameter. The
+theoretical results are applied to a Bayesian estimation of the medium
+parameter in an elliptic problem.
+",0,0,1,1,0,0
+9332,Dynamic Word Embeddings for Evolving Semantic Discovery,"  Word evolution refers to the changing meanings and associations of words
+throughout time, as a byproduct of human language evolution. By studying word
+evolution, we can infer social trends and language constructs over different
+periods of human history. However, traditional techniques such as word
+representation learning do not adequately capture the evolving language
+structure and vocabulary. In this paper, we develop a dynamic statistical model
+to learn time-aware word vector representation. We propose a model that
+simultaneously learns time-aware embeddings and solves the resulting ""alignment
+problem"". This model is trained on a crawled NYTimes dataset. Additionally, we
+develop multiple intuitive evaluation strategies of temporal word embeddings.
+Our qualitative and quantitative tests indicate that our method not only
+reliably captures this evolution over time, but also consistently outperforms
+state-of-the-art temporal embedding approaches on both semantic accuracy and
+alignment quality.
+",1,0,0,1,0,0
+9333,Constructing Words with High Distinct Square Densities,"  Fraenkel and Simpson showed that the number of distinct squares in a word of
+length n is bounded from above by 2n, since at most two distinct squares have
+their rightmost, or last, occurrence begin at each position. Improvements by
+Ilie to $2n-\Theta(\log n)$ and by Deza et al. to 11n/6 rely on the study of
+combinatorics of FS-double-squares, when the maximum number of two last
+occurrences of squares begin. In this paper, we first study how to maximize
+runs of FS-double-squares in the prefix of a word. We show that for a given
+positive integer m, the minimum length of a word beginning with m
+FS-double-squares, whose lengths are equal, is 7m+3. We construct such a word
+and analyze its distinct-square-sequence as well as its
+distinct-square-density. We then generalize our construction. We also construct
+words with high distinct-square-densities that approach 5/6.
+",1,0,0,0,0,0
+9334,Annihilators of Koszul Homologies and Almost Complete Intersections,"  In this article, we propound a question on the annihilator of Koszul
+homologies of a system of parameters of an almost complete intersection $R$.
+The question can be stated in terms of the acyclicity of certain (finite)
+residual approximation complexes whose $0$-th homologies are the residue field
+of $R$. We show that our question has an affirmative answer for certain almost
+complete intersection rings with small multiplicities, as well as for the
+$1$-th Koszul homology of any almost complete intersection. The statement about
+the $1$-th Koszul homology is shown to be equivalent to the Monomial Conjecture
+and thus follows from its validity.
+",0,0,1,0,0,0
+9335,Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections,"  We analyze how the knowledge to autonomously handle one type of intersection,
+represented as a Deep Q-Network, translates to other types of intersections
+(tasks). We view intersection handling as a deep reinforcement learning
+problem, which approximates the state action Q function as a deep neural
+network. Using a traffic simulator, we show that directly copying a network
+trained for one type of intersection to another type of intersection decreases
+the success rate. We also show that when a network that is pre-trained on Task
+A and then is fine-tuned on a Task B, the resulting network not only performs
+better on the Task B than an network exclusively trained on Task A, but also
+retained knowledge on the Task A. Finally, we examine a lifelong learning
+setting, where we train a single network on five different types of
+intersections sequentially and show that the resulting network exhibited
+catastrophic forgetting of knowledge on previous tasks. This result suggests a
+need for a long-term memory component to preserve knowledge.
+",1,0,0,0,0,0
+9336,From modelling of systems with constraints to generalized geometry and back to numerics,"  In this note we describe how some objects from generalized geometry appear in
+the qualitative analysis and numerical simulation of mechanical systems. In
+particular we discuss double vector bundles and Dirac structures. It turns out
+that those objects can be naturally associated to systems with constraints --
+we recall the mathematical construction in the context of so called implicit
+Lagrangian systems. We explain how they can be used to produce new numerical
+methods, that we call Dirac integrators.
+On a test example of a simple pendulum in a gravity field we compare the
+Dirac integrators with classical explicit and implicit methods, we pay special
+attention to conservation of constrains. Then, on a more advanced example of
+the Ziegler column we show that the choice of numerical methods can indeed
+affect the conclusions of qualitative analysis of the dynamics of mechanical
+systems. We also tell why we think that Dirac integrators are appropriate for
+this kind of systems by explaining the relation with the notions of geometric
+degree of non-conservativity and kinematic structural stability.
+",1,0,0,0,0,0
+9337,A Deeper Look at Experience Replay,"  Recently experience replay is widely used in various deep reinforcement
+learning (RL) algorithms, in this paper we rethink the utility of experience
+replay. It introduces a new hyper-parameter, the memory buffer size, which
+needs carefully tuning. However unfortunately the importance of this new
+hyper-parameter has been underestimated in the community for a long time. In
+this paper we did a systematic empirical study of experience replay under
+various function representations. We showcase that a large replay buffer can
+significantly hurt the performance. Moreover, we propose a simple O(1) method
+to remedy the negative influence of a large replay buffer. We showcase its
+utility in both simple grid world and challenging domains like Atari games.
+",1,0,0,0,0,0
+9338,"Dirac operators with $W^{1,\infty}$-potential under codimension one collapse","  We study the behavior of the spectrum of the Dirac operator together with a
+symmetric $W^{1, \infty}$-potential on spin manifolds under a collapse of
+codimension one with bounded sectional curvature and diameter. If there is an
+induced spin structure on the limit space $N$ then there are convergent
+eigenvalues which converge to the spectrum of a first order differential
+operator $D$ on $N$ together with a symmetric $W^{1,\infty}$-potential. If $N$
+is orientable and the dimension of the limit space is even then $D$ is the
+Dirac operator $D^N$ on $N$ and if the dimension of the limit space is odd,
+then $D = D^N \oplus -D^N$.
+",0,0,1,0,0,0
+9339,Tunable Superconducting Qubits with Flux-Independent Coherence,"  We have studied the impact of low-frequency magnetic flux noise upon
+superconducting transmon qubits with various levels of tunability. We find that
+qubits with weaker tunability exhibit dephasing that is less sensitive to flux
+noise. This insight was used to fabricate qubits where dephasing due to flux
+noise was suppressed below other dephasing sources, leading to flux-independent
+dephasing times T2* ~ 15 us over a tunable range of ~340 MHz. Such tunable
+qubits have the potential to create high-fidelity, fault-tolerant qubit gates
+and fundamentally improve scalability for a quantum processor.
+",0,1,0,0,0,0
+9340,Kulish-Sklyanin type models: integrability and reductions,"  We start with a Riemann-Hilbert problem (RHP) related to a BD.I-type
+symmetric spaces $SO(2r+1)/S(O(2r-2s +1)\otimes O(2s))$, $s\geq 1$. We consider
+two Riemann-Hilbert problems: the first formulated on the real axis
+$\mathbb{R}$ in the complex $\lambda$-plane; the second one is formulated on
+$\mathbb{R} \oplus i\mathbb{R}$. The first RHP for $s=1$ allows one to solve
+the Kulish-Sklyanin (KS) model; the second RHP is relevant for a new type of KS
+model. An important example for nontrivial deep reductions of KS model is
+given. Its effect on the scattering matrix is formulated. In particular we
+obtain new 2-component NLS equations. Finally, using the Wronskian relations we
+demonstrate that the inverse scattering method for KS models may be understood
+as a generalized Fourier transforms. Thus we have a tool to derive all their
+fundamental properties, including the hierarchy of equations and the hierarchy
+of their Hamiltonian structures.
+",0,1,0,0,0,0
+9341,Takiff algebras with polynomial rings of symmetric invariants,"  Extending results of Rais-Tauvel, Macedo-Savage, and Arakawa-Premet, we prove
+that under mild restrictions on the Lie algebra $\mathfrak q$ having the
+polynomial ring of symmetric invariants, the m-th Takiff algebra of $\mathfrak
+q$, $\mathfrak q\langle m\rangle$, also has a polynomial ring of symmetric
+invariants.
+",0,0,1,0,0,0
+9342,Can We Prove Time Protection?,"  Timing channels are a significant and growing security threat in computer
+systems, with no established solution. We have recently argued that the OS must
+provide time protection, in analogy to the established memory protection, to
+protect applications from information leakage through timing channels. Based on
+a recently-proposed implementation of time protection in the seL4 microkernel,
+we investigate how such an implementation could be formally proved to prevent
+timing channels. We postulate that this should be possible by reasoning about a
+highly abstracted representation of the shared hardware resources that cause
+timing channels.
+",1,0,0,0,0,0
+9343,On some Graphs with a Unique Perfect Matching,"  We show that deciding whether a given graph $G$ of size $m$ has a unique
+perfect matching as well as finding that matching, if it exists, can be done in
+time $O(m)$ if $G$ is either a cograph, or a split graph, or an interval graph,
+or claw-free. Furthermore, we provide a constructive characterization of the
+claw-free graphs with a unique perfect matching.
+",1,0,0,0,0,0
+9344,Trade-Offs in Stochastic Event-Triggered Control,"  This paper studies the optimal output-feedback control of a linear
+time-invariant system where a stochastic event-based scheduler triggers the
+communication between the sensor and the controller. The primary goal of the
+use of this type of scheduling strategy is to provide significant reductions in
+the usage of the sensor-to-controller communication and, in turn, improve
+energy expenditure in the network. In this paper, we aim to design an
+admissible control policy, which is a function of the observed output, to
+minimize a quadratic cost function while employing a stochastic event-triggered
+scheduler that preserves the Gaussian property of the plant state and the
+estimation error. For the infinite horizon case, we present analytical
+expressions that quantify the trade-off between the communication cost and
+control performance of such event-triggered control systems. This trade-off is
+confirmed quantitatively via numerical examples.
+",1,0,0,0,0,0
+9345,Third Harmonic THz Generation from Graphene in a Parallel-Plate Waveguide,"  Graphene as a zero-bandgap two-dimensional semiconductor with a linear
+electron band dispersion near the Dirac points has the potential to exhibit
+very interesting nonlinear optical properties. In particular, third harmonic
+generation of terahertz radiation should occur due to the nonlinear
+relationship between the crystal momentum and the current density. In this
+work, we investigate the terahertz nonlinear response of graphene inside a
+parallel-plate waveguide. We optimize the plate separation and Fermi energy of
+the graphene to maximize third harmonic generation, by maximizing the nonlinear
+interaction while minimizing the loss and phase mismatch. The results obtained
+show an increase by more than a factor of 100 in the power efficiency relative
+to a normal-incidence configuration for a 2 terahertz incident field.
+",0,1,0,0,0,0
+9346,Taming the Signal-to-Noise Problem in Lattice QCD by Phase Reweighting,"  Path integrals describing quantum many-body systems can be calculated with
+Monte Carlo sampling techniques, but average quantities are often subject to
+signal-to-noise ratios that degrade exponentially with time. A
+phase-reweighting technique inspired by recent observations of random walk
+statistics in correlation functions is proposed that allows energy levels to be
+extracted from late-time correlation functions with time-independent
+signal-to-noise ratios. Phase reweighting effectively includes dynamical
+refinement of source magnitudes but introduces a bias associated with the
+phase. This bias can be removed by performing an extrapolation, but at the
+expense of re-introducing a signal-to-noise problem. Lattice Quantum
+Chromodynamics calculations of the $\rho$ and nucleon masses and of the
+$\Xi\Xi$ binding energy show consistency between standard results obtained
+using earlier-time correlation functions and phase-reweighted results using
+late-time correlation functions inaccessible to standard statistical analysis
+methods.
+",0,1,0,0,0,0
+9347,Description of radiation damage in diamond sensors using an effective defect model,"  The BCML system is a beam monitoring device in the CMS experiment at the LHC.
+As detectors poly-crystalline diamond sensors are used. Here high particle
+rates occur from the colliding beams scattering particles outside the beam
+pipe. These particles cause defects, which act as traps for the ionization,
+thus reducing the CCE. However, the loss in CCE was much more severe than
+expected. The reason why in real experiments the CCE is so much worse than in
+laboratory experiments is related to the rate of incident particles. At high
+particle rates the trapping rate of the ionization is so high compared with the
+detrapping rate, that space charge builds up. This space charge reduces locally
+the internal electric field, which in turn increases the trapping rate and
+hence reduces the CCE even further. In order to connect these macroscopic
+measurements with the microscopic defects acting as traps for the ionization
+charge the TCAD simulation program SILVACO was used. Two effective acceptor and
+donor levels were needed to fit the data. Using this effective defect model the
+highly non- linear rate dependent diamond polarization as function of the
+particle rate environment and the resulting signal loss could be simulated.
+",0,1,0,0,0,0
+9348,Design and Analysis of Time-Invariant SC-LDPC Convolutional Codes With Small Constraint Length,"  In this paper, we deal with time-invariant spatially coupled low-density
+parity-check convolutional codes (SC-LDPC-CCs). Classic design approaches
+usually start from quasi-cyclic low-density parity-check (QC-LDPC) block codes
+and exploit suitable unwrapping procedures to obtain SC-LDPC-CCs. We show that
+the direct design of the SC-LDPC-CCs syndrome former matrix or, equivalently,
+the symbolic parity-check matrix, leads to codes with smaller syndrome former
+constraint lengths with respect to the best solutions available in the
+literature. We provide theoretical lower bounds on the syndrome former
+constraint length for the most relevant families of SC-LDPC-CCs, under
+constraints on the minimum length of cycles in their Tanner graphs. We also
+propose new code design techniques that approach or achieve such theoretical
+limits.
+",1,0,0,0,0,0
+9349,Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network,"  The prediction of organic reaction outcomes is a fundamental problem in
+computational chemistry. Since a reaction may involve hundreds of atoms, fully
+exploring the space of possible transformations is intractable. The current
+solution utilizes reaction templates to limit the space, but it suffers from
+coverage and efficiency issues. In this paper, we propose a template-free
+approach to efficiently explore the space of product molecules by first
+pinpointing the reaction center -- the set of nodes and edges where graph edits
+occur. Since only a small number of atoms contribute to reaction center, we can
+directly enumerate candidate products. The generated candidates are scored by a
+Weisfeiler-Lehman Difference Network that models high-order interactions
+between changes occurring at nodes across the molecule. Our framework
+outperforms the top-performing template-based approach with a 10\% margin,
+while running orders of magnitude faster. Finally, we demonstrate that the
+model accuracy rivals the performance of domain experts.
+",1,0,0,1,0,0
+9350,Legendre curves and singularities of a ruled surface according to rotation minimizing frame,"  In this paper, Legendre curves on unit tangent bundle are given using
+rotation minimizing (RM) vector fields. Ruled surfaces corresponding to these
+curves are represented. Singularities of these ruled surfaces are also analyzed
+and classifed.
+",0,0,1,0,0,0
+9351,Field-free nucleation of antivortices and giant vortices in non-superconducting materials,"  Giant vortices with higher phase-winding than $2\pi$ are usually
+energetically unfavorable, but geometric symmetry constraints on a
+superconductor in a magnetic field are known to stabilize such objects. Here,
+we show via microscopic calculations that giant vortices can appear in
+intrinsically non-superconducting materials, even without any applied magnetic
+field. The enabling mechanism is the proximity effect to a host superconductor
+where a current flows, and we also demonstrate that antivortices can appear in
+this setup. Our results open the possibility to study electrically controllable
+topological defects in unusual environments, which do not have to be exposed to
+magnetic fields or intrinsically superconducting, but instead display other
+types of order.
+",0,1,0,0,0,0
+9352,Quantum criticality in photorefractive optics: vortices in laser beams and antiferromagnets,"  We study vortex patterns in a prototype nonlinear optical system:
+counterpropagating laser beams in a photorefractive crystal, with or without
+the background photonic lattice. The vortices are effectively planar and
+described by the winding number and the ""flavor"" index, stemming from the fact
+that we have two parallel beams propagating in opposite directions. The problem
+is amenable to the methods of statistical field theory and generalizes the
+Berezinsky-Kosterlitz-Thouless transition of the XY model to the ""two-flavor""
+case. In addition to the familiar conductor and insulator phases, we also have
+the perfect conductor (vortex proliferation in both beams/""flavors"") and the
+frustrated insulator (energy costs of vortex proliferation and vortex
+annihilation balance each other). In the presence of disorder in the background
+lattice, a novel phase appears which shows long-range correlations and absence
+of long-range order, thus being analogous to spin glasses. An important benefit
+of this approach is that qualitative behavior of patterns can be known without
+intensive numerical work over large areas of the parameter space. More
+generally, we would like to draw attention to connections between the
+(classical) pattern-forming systems in photorefractive optics and the methods
+of (quantum) condensed matter and field theory: on one hand, we use the
+field-theoretical methods (renormalization group, replica formalism) to analyze
+the patterns; on the other hand, the observed phases are analogous to those
+seen in magnetic systems, and make photorefractive optics a fruitful testing
+ground for condensed matter systems. As an example, we map our system to a
+doped $O(3)$ antiferromagnet with $\mathbb{Z}_2$ defects, which has the same
+structure of the phase diagram.
+",0,1,0,0,0,0
+9353,Burn-In Demonstrations for Multi-Modal Imitation Learning,"  Recent work on imitation learning has generated policies that reproduce
+expert behavior from multi-modal data. However, past approaches have focused
+only on recreating a small number of distinct, expert maneuvers, or have relied
+on supervised learning techniques that produce unstable policies. This work
+extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce
+behavior over an extended period of time. Our approach involves reformulating
+the typical imitation learning setting to include ""burn-in demonstrations"" upon
+which policies are conditioned at test time. We demonstrate that our approach
+outperforms standard InfoGAIL in maximizing the mutual information between
+predicted and unseen style labels in road scene simulations, and we show that
+our method leads to policies that imitate expert autonomous driving systems
+over long time horizons.
+",1,0,0,1,0,0
+9354,Elliptic Determinantal Processes and Elliptic Dyson Models,"  We introduce seven families of stochastic systems of interacting particles in
+one-dimension corresponding to the seven families of irreducible reduced affine
+root systems. We prove that they are determinantal in the sense that all
+spatio-temporal correlation functions are given by determinants controlled by a
+single function called the spatio-temporal correlation kernel. For the four
+families ${A}_{N-1}$, ${B}_N$, ${C}_N$ and ${D}_N$, we identify the systems of
+stochastic differential equations solved by these determinantal processes,
+which will be regarded as the elliptic extensions of the Dyson model. Here we
+use the notion of martingales in probability theory and the elliptic
+determinant evaluations of the Macdonald denominators of irreducible reduced
+affine root systems given by Rosengren and Schlosser.
+",0,1,1,0,0,0
+9355,Searching for chemical signatures of brown dwarf formation,"  Recent studies have shown that close-in brown dwarfs in the mass range 35-55
+M$_{\rm Jup}$ are almost depleted as companions to stars, suggesting that
+objects with masses above and below this gap might have different formation
+mechanisms. We determine the fundamental stellar parameters, as well as
+individual abundances for a large sample of stars known to have a substellar
+companion in the brown dwarf regime. The sample is divided into stars hosting
+""massive"" and ""low-mass"" brown dwarfs. Following previous works a threshold of
+42.5 M$_{\rm Jup}$ was considered. Our results confirm that stars with brown
+dwarf companions do not follow the well-established gas-giant planet
+metallicity correlation seen in main-sequence planet hosts. Stars harbouring
+""massive"" brown dwarfs show similar metallicity and abundance distribution as
+stars without known planets or with low-mass planets. We find a tendency of
+stars harbouring ""less-massive"" brown dwarfs of having slightly larger
+metallicity, [X$_{\rm Fe}$/Fe] values, and abundances of Sc II, Mn I, and Ni I
+in comparison with the stars having the massive brown dwarfs. The data suggest,
+as previously reported, that massive and low-mass brown dwarfs might present
+differences in period and eccentricity. We find evidence of a non-metallicity
+dependent mechanism for the formation of massive brown dwarfs. Our results
+agree with a scenario in which massive brown dwarfs are formed as stars. At
+high-metallicities, the core-accretion mechanism might become efficient in the
+formation of low-mass brown dwarfs while at lower metallicities low-mass brown
+dwarfs could form by gravitational instability in turbulent protostellar discs.
+",0,1,0,0,0,0
+9356,The Klein Paradox: A New Treatment,"  The Dirac equation requires a treatment of the step potential that differs
+fundamentally from the traditional treatment, because the Dirac plane waves,
+besides momentum and spin, are characterized by a quantum number with the
+physical meaning of sign of charge. Since the Hermitean operator corresponding
+to this quantum number does not commute with the step potential, the time
+displacement parameter used in the ansatz of the stationary state does not have
+the physical meaning of energy. Therefore there are no paradoxal values of the
+energy. The new solution of the Dirac equation with a step potential is
+obtained. This solution, again, allows for phenomena of the Klein paradox type,
+but in addition it contains a positron amplitude localized at the threshold
+point of the step potential.
+",0,1,0,0,0,0
+9357,What are the most important factors that influence the changes in London Real Estate Prices? How to quantify them?,"  In recent years, real estate industry has captured government and public
+attention around the world. The factors influencing the prices of real estate
+are diversified and complex. However, due to the limitations and one-sidedness
+of their respective views, they did not provide enough theoretical basis for
+the fluctuation of house price and its influential factors. The purpose of this
+paper is to build a housing price model to make the scientific and objective
+analysis of London's real estate market trends from the year 1996 to 2016 and
+proposes some countermeasures to reasonably control house prices. Specifically,
+the paper analyzes eight factors which affect the house prices from two
+aspects: housing supply and demand and find out the factor which is of vital
+importance to the increase of housing price per square meter. The problem of a
+high level of multicollinearity between them is solved by using principal
+components analysis.
+",0,0,0,1,0,1
+9358,A Multi-Wavelength Analysis of Dust and Gas in the SR 24S Transition Disk,"  We present new Atacama Large Millimeter/sub-millimeter Array (ALMA) 1.3 mm
+continuum observations of the SR 24S transition disk with an angular resolution
+$\lesssim0.18""$ (12 au radius). We perform a multi-wavelength investigation by
+combining new data with previous ALMA data at 0.45 mm. The visibilities and
+images of the continuum emission at the two wavelengths are well characterized
+by a ring-like emission. Visibility modeling finds that the ring-like emission
+is narrower at longer wavelengths, in good agreement with models of dust
+trapping in pressure bumps, although there are complex residuals that suggest
+potentially asymmetric structures. The 0.45 mm emission has a shallower profile
+inside the central cavity than the 1.3 mm emission. In addition, we find that
+the $^{13}$CO and C$^{18}$O (J=2-1) emission peaks at the center of the
+continuum cavity. We do not detect either continuum or gas emission from the
+northern companion to this system (SR 24N), which is itself a binary system.
+The upper limit for the dust disk mass of SR 24N is $\lesssim
+0.12\,M_{\bigoplus}$, which gives a disk mass ratio in dust between the two
+components of $M_{\mathrm{dust, SR\,24S}}/M_{\mathrm{dust,
+SR\,24N}}\gtrsim840$. The current ALMA observations may imply that either
+planets have already formed in the SR 24N disk or that dust growth to mm-sizes
+is inhibited there and that only warm gas, as seen by ro-vibrational CO
+emission inside the truncation radii of the binary, is present.
+",0,1,0,0,0,0
+9359,Step bunching with both directions of the current: Vicinal W(110) surfaces versus atomistic scale model,"  We report for the first time the observation of bunching of monoatomic steps
+on vicinal W(110) surfaces induced by step up or step down currents across the
+steps. Measurements reveal that the size scaling exponent {\gamma}, connecting
+the maximal slope of a bunch with its height, differs depending on the current
+direction. We provide a numerical perspective by using an atomistic scale model
+with a conserved surface flux to mimic experimental conditions, and also for
+the first time show that there is an interval of parameters in which the
+vicinal surface is unstable against step bunching for both directions of the
+adatom drift.
+",0,1,0,0,0,0
+9360,Causal Bandits with Propagating Inference,"  Bandit is a framework for designing sequential experiments. In each
+experiment, a learner selects an arm $A \in \mathcal{A}$ and obtains an
+observation corresponding to $A$. Theoretically, the tight regret lower-bound
+for the general bandit is polynomial with respect to the number of arms
+$|\mathcal{A}|$. This makes bandit incapable of handling an exponentially large
+number of arms, hence the bandit problem with side-information is often
+considered to overcome this lower bound. Recently, a bandit framework over a
+causal graph was introduced, where the structure of the causal graph is
+available as side-information. A causal graph is a fundamental model that is
+frequently used with a variety of real problems. In this setting, the arms are
+identified with interventions on a given causal graph, and the effect of an
+intervention propagates throughout all over the causal graph. The task is to
+find the best intervention that maximizes the expected value on a target node.
+Existing algorithms for causal bandit overcame the
+$\Omega(\sqrt{|\mathcal{A}|/T})$ simple-regret lower-bound; however, their
+algorithms work only when the interventions $\mathcal{A}$ are localized around
+a single node (i.e., an intervention propagates only to its neighbors).
+We propose a novel causal bandit algorithm for an arbitrary set of
+interventions, which can propagate throughout the causal graph. We also show
+that it achieves $O(\sqrt{ \gamma^*\log(|\mathcal{A}|T) / T})$ regret bound,
+where $\gamma^*$ is determined by using a causal graph structure. In
+particular, if the in-degree of the causal graph is bounded, then $\gamma^* =
+O(N^2)$, where $N$ is the number $N$ of nodes.
+",0,0,0,1,0,0
+9361,The 2D Tree Sliding Window Discrete Fourier Transform,"  We present a new algorithm for the 2D Sliding Window Discrete Fourier
+Transform (SWDFT). Our algorithm avoids repeating calculations in overlapping
+windows by storing them in a tree data-structure based on the ideas of the
+Cooley- Tukey Fast Fourier Transform (FFT). For an $N_0 \times N_1$ array and
+$n_0 \times n_1$ windows, our algorithm takes $O(N_0 N_1 n_0 n_1)$ operations.
+We provide a C implementation of our algorithm for the Radix-2 case, compare
+ours with existing algorithms, and show how our algorithm easily extends to
+higher dimensions.
+",1,0,0,1,0,0
+9362,Towards security defect prediction with AI,"  In this study, we investigate the limits of the current state of the art AI
+system for detecting buffer overflows and compare it with current static
+analysis tools. To do so, we developed a code generator, s-bAbI, capable of
+producing an arbitrarily large number of code samples of controlled complexity.
+We found that the static analysis engines we examined have good precision, but
+poor recall on this dataset, except for a sound static analyzer that has good
+precision and recall. We found that the state of the art AI system, a memory
+network modeled after Choi et al. [1], can achieve similar performance to the
+static analysis engines, but requires an exhaustive amount of training data in
+order to do so. Our work points towards future approaches that may solve these
+problems; namely, using representations of code that can capture appropriate
+scope information and using deep learning methods that are able to perform
+arithmetic operations.
+",0,0,0,1,0,0
+9363,Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks,"  In general, neural networks are not currently capable of learning tasks in a
+sequential fashion. When a novel, unrelated task is learnt by a neural network,
+it substantially forgets how to solve previously learnt tasks. One of the
+original solutions to this problem is pseudo-rehearsal, which involves learning
+the new task while rehearsing generated items representative of the previous
+task/s. This is very effective for simple tasks. However, pseudo-rehearsal has
+not yet been successfully applied to very complex tasks because in these tasks
+it is difficult to generate representative items. We accomplish
+pseudo-rehearsal by using a Generative Adversarial Network to generate items so
+that our deep network can learn to sequentially classify the CIFAR-10, SVHN and
+MNIST datasets. After training on all tasks, our network loses only 1.67%
+absolute accuracy on CIFAR-10 and gains 0.24% absolute accuracy on SVHN. Our
+model's performance is a substantial improvement compared to the current state
+of the art solution.
+",0,0,0,1,0,0
+9364,Multivariate Hadamard self-similarity: testing fractal connectivity,"  While scale invariance is commonly observed in each component of real world
+multivariate signals, it is also often the case that the inter-component
+correlation structure is not fractally connected, i.e., its scaling behavior is
+not determined by that of the individual components. To model this situation in
+a versatile manner, we introduce a class of multivariate Gaussian stochastic
+processes called Hadamard fractional Brownian motion (HfBm). Its theoretical
+study sheds light on the issues raised by the joint requirement of entry-wise
+scaling and departures from fractal connectivity. An asymptotically normal
+wavelet-based estimator for its scaling parameter, called the Hurst matrix, is
+proposed, as well as asymptotically valid confidence intervals. The latter are
+accompanied by original finite sample procedures for computing confidence
+intervals and testing fractal connectivity from one single and finite size
+observation. Monte Carlo simulation studies are used to assess the estimation
+performance as a function of the (finite) sample size, and to quantify the
+impact of omitting wavelet cross-correlation terms. The simulation studies are
+shown to validate the use of approximate confidence intervals, together with
+the significance level and power of the fractal connectivity test. The test
+performance and properties are further studied as functions of the HfBm
+parameters.
+",0,0,1,1,0,0
+9365,Efficient barycentric point sampling on meshes,"  We present an easy-to-implement and efficient analytical inversion algorithm
+for the unbiased random sampling of a set of points on a triangle mesh whose
+surface density is specified by barycentric interpolation of non-negative
+per-vertex weights. The correctness of the inversion algorithm is verified via
+statistical tests, and we show that it is faster on average than rejection
+sampling.
+",1,0,0,0,0,0
+9366,Femtosecond Optical Superregular Breathers,"  Superregular (SR) breathers are nonlinear wave structures formed by a unique
+nonlinear superposition of pairs of quasi-Akhmediev breathers. They describe a
+complete scenario of modulation instability that develops from localized small
+perturbations as well as an unusual quasiannihilation of breather collision.
+Here, we demonstrate that femtosecond optical SR breathers in optical fibers
+exhibit intriguing half-transition and full-suppression states, which are
+absent in the picosecond regime governed by the standard nonlinear
+Schrödinger equation. In particular, the full-suppression mode, which is
+strictly associated with the regime of vanishing growth rate of modulation
+instability, reveals a crucial \textit{non-amplifying} nonlinear dynamics of
+localized small perturbations. We numerically confirm the robustness of such
+different SR modes excited from ideal and nonideal initial states in both
+integrable and nonintegrable cases.
+",0,1,0,0,0,0
+9367,"Diversity, Topology, and the Risk of Node Re-identification in Labeled Social Graphs","  Real network datasets provide significant benefits for understanding
+phenomena such as information diffusion or network evolution. Yet the privacy
+risks raised from sharing real graph datasets, even when stripped of user
+identity information, are significant. When nodes have associated attributes,
+the privacy risks increase. In this paper we quantitatively study the impact of
+binary node attributes on node privacy by employing machine-learning-based
+re-identification attacks and exploring the interplay between graph topology
+and attribute placement. Our experiments show that the population's diversity
+on the binary attribute consistently degrades anonymity.
+",1,0,0,0,0,0
+9368,Braid group action and root vectors for the $q$-Onsager algebra,"  We define two algebra automorphisms $T_0$ and $T_1$ of the $q$-Onsager
+algebra $B_c$, which provide an analog of G. Lusztig's braid group action for
+quantum groups. These automorphisms are used to define root vectors which give
+rise to a PBW basis for $B_c$. We show that the root vectors satisfy
+$q$-analogs of Onsager's original commutation relations. The paper is much
+inspired by I. Damiani's construction and investigation of root vectors for the
+quantized enveloping algebra of $\widehat{\mathfrak{sl}}_2$.
+",0,0,1,0,0,0
+9369,Underapproximation of Reach-Avoid Sets for Discrete-Time Stochastic Systems via Lagrangian Methods,"  We examine Lagrangian techniques for computing underapproximations of
+finite-time horizon, stochastic reach-avoid level-sets for discrete-time,
+nonlinear systems. We use the concept of reachability of a target tube in the
+control literature to define robust reach-avoid sets which are parameterized by
+the target set, safe set, and the set in which the disturbance is drawn from.
+We unify two existing Lagrangian approaches to compute these sets and establish
+that there exists an optimal control policy of the robust reach-avoid sets
+which is a Markov policy. Based on these results, we characterize the subset of
+the disturbance space whose corresponding robust reach-avoid set for the given
+target and safe set is a guaranteed underapproximation of the stochastic
+reach-avoid level-set of interest. The proposed approach dramatically improves
+the computational efficiency for obtaining an underapproximation of stochastic
+reach-avoid level-sets when compared to the traditional approaches based on
+gridding. Our method, while conservative, does not rely on a grid, implying
+scalability as permitted by the known computational geometry constraints. We
+demonstrate the method on two examples: a simple two-dimensional integrator,
+and a space vehicle rendezvous-docking problem.
+",1,0,1,0,0,0
+9370,A Domain-Specific Language and Editor for Parallel Particle Methods,"  Domain-specific languages (DSLs) are of increasing importance in scientific
+high-performance computing to reduce development costs, raise the level of
+abstraction and, thus, ease scientific programming. However, designing and
+implementing DSLs is not an easy task, as it requires knowledge of the
+application domain and experience in language engineering and compilers.
+Consequently, many DSLs follow a weak approach using macros or text generators,
+which lack many of the features that make a DSL a comfortable for programmers.
+Some of these features---e.g., syntax highlighting, type inference, error
+reporting, and code completion---are easily provided by language workbenches,
+which combine language engineering techniques and tools in a common ecosystem.
+In this paper, we present the Parallel Particle-Mesh Environment (PPME), a DSL
+and development environment for numerical simulations based on particle methods
+and hybrid particle-mesh methods. PPME uses the meta programming system (MPS),
+a projectional language workbench. PPME is the successor of the Parallel
+Particle-Mesh Language (PPML), a Fortran-based DSL that used conventional
+implementation strategies. We analyze and compare both languages and
+demonstrate how the programmer's experience can be improved using static
+analyses and projectional editing. Furthermore, we present an explicit domain
+model for particle abstractions and the first formal type system for particle
+methods.
+",1,0,0,0,0,0
+9371,Manifold regularization based on Nystr{ö}m type subsampling,"  In this paper, we study the Nystr{ö}m type subsampling for large scale
+kernel methods to reduce the computational complexities of big data. We discuss
+the multi-penalty regularization scheme based on Nystr{ö}m type subsampling
+which is motivated from well-studied manifold regularization schemes. We
+develop a theoretical analysis of multi-penalty least-square regularization
+scheme under the general source condition in vector-valued function setting,
+therefore the results can also be applied to multi-task learning problems. We
+achieve the optimal minimax convergence rates of multi-penalty regularization
+using the concept of effective dimension for the appropriate subsampling size.
+We discuss an aggregation approach based on linear function strategy to combine
+various Nystr{ö}m approximants. Finally, we demonstrate the performance of
+multi-penalty regularization based on Nystr{ö}m type subsampling on
+Caltech-101 data set for multi-class image classification and NSL-KDD benchmark
+data set for intrusion detection problem.
+",1,0,0,1,0,0
+9372,Path integral molecular dynamics with surface hopping for thermal equilibrium sampling of nonadiabatic systems,"  In this work, a novel ring polymer representation for multi-level quantum
+system is proposed for thermal average calculations. The proposed presentation
+keeps the discreteness of the electronic states: besides position and momentum,
+each bead in the ring polymer is also characterized by a surface index
+indicating the electronic energy surface. A path integral molecular dynamics
+with surface hopping (PIMD-SH) dynamics is also developed to sample the
+equilibrium distribution of ring polymer configurational space. The PIMD-SH
+sampling method is validated theoretically and by numerical examples.
+",0,1,1,0,0,0
+9373,The interplay of the collisionless nonlinear thin-shell instability with the ion acoustic instability,"  The nonlinear thin-shell instability (NTSI) may explain some of the turbulent
+hydrodynamic structures that are observed close to the collision boundary of
+energetic astrophysical outflows. It develops in nonplanar shells that are
+bounded on either side by a hydrodynamic shock, provided that the amplitude of
+the seed oscillations is sufficiently large. The hydrodynamic NTSI has a
+microscopic counterpart in collisionless plasma. A sinusoidal displacement of a
+thin shell, which is formed by the collision of two clouds of unmagnetized
+electrons and protons, grows and saturates on timescales of the order of the
+inverse proton plasma frequency. Here we increase the wavelength of the seed
+perturbation by a factor 4 compared to that in a previous study. Like in the
+case of the hydrodynamic NTSI, the increase in the wavelength reduces the
+growth rate of the microscopic NTSI. The prolonged growth time of the
+microscopic NTSI allows the waves, which are driven by the competing ion
+acoustic instability, to grow to a large amplitude before the NTSI saturates
+and they disrupt the latter. The ion acoustic instability thus imposes a limit
+on the largest wavelength that can be destabilized by the NTSI in collisionless
+plasma. The limit can be overcome by binary collisions. We bring forward
+evidence for an overstability of the collisionless NTSI.
+",0,1,0,0,0,0
+9374,Multiple Scaled Contaminated Normal Distribution and Its Application in Clustering,"  The multivariate contaminated normal (MCN) distribution represents a simple
+heavy-tailed generalization of the multivariate normal (MN) distribution to
+model elliptical contoured scatters in the presence of mild outliers, referred
+to as ""bad"" points. The MCN can also automatically detect bad points. The price
+of these advantages is two additional parameters, both with specific and useful
+interpretations: proportion of good observations and degree of contamination.
+However, points may be bad in some dimensions but good in others. The use of an
+overall proportion of good observations and of an overall degree of
+contamination is limiting. To overcome this limitation, we propose a multiple
+scaled contaminated normal (MSCN) distribution with a proportion of good
+observations and a degree of contamination for each dimension. Once the model
+is fitted, each observation has a posterior probability of being good with
+respect to each dimension. Thanks to this probability, we have a method for
+simultaneous directional robust estimation of the parameters of the MN
+distribution based on down-weighting and for the automatic directional
+detection of bad points by means of maximum a posteriori probabilities. The
+term ""directional"" is added to specify that the method works separately for
+each dimension. Mixtures of MSCN distributions are also proposed as an
+application of the proposed model for robust clustering. An extension of the EM
+algorithm is used for parameter estimation based on the maximum likelihood
+approach. Real and simulated data are used to show the usefulness of our
+mixture with respect to well-established mixtures of symmetric distributions
+with heavy tails.
+",0,0,0,1,0,0
+9375,The Galaxy-Halo Connection Over The Last 13.3 Gyrs,"  We present new determinations of the stellar-to-halo mass relation (SHMR) at
+$z=0-10$ that match the evolution of the galaxy stellar mass function, the
+SFR$-M_*$ relation,and the cosmic star formation rate. We utilize a compilation
+of 40 observational studies from the literature and correct them for potential
+biases. Using our robust determinations of halo mass assembly and the SHMR, we
+infer star formation histories, merger rates, and structural properties for
+average galaxies, combining star-forming and quenched galaxies. Our main
+findings: (1) The halo mass $M_{50}$ above which 50\% of galaxies are quenched
+coincides with sSFR/sMAR$\sim1$, where sMAR is the specific halo mass accretion
+rate. (2) $M_{50}$ increases with redshift, presumably due to cold streams
+being more efficient at high redshift while virial shocks and AGN feedback
+become more relevant at lower redshifts. (3) The ratio sSFR/sMAR has a peak
+value, which occurs around $M_{\rm vir}\sim2\times10^{11}M_{\odot}$. (4) The
+stellar mass density within 1 kpc, $\Sigma_1$, is a good indicator of the
+galactic global sSFR. (5) Galaxies are statistically quenched after they reach
+a maximum in $\Sigma_1$, consistent with theoretical expectations of the gas
+compaction model; this maximum depends on redshift. (6) In-situ star formation
+is responsible for most galactic stellar mass growth, especially for lower-mass
+galaxies. (7) Galaxies grow inside out. The marked change in the slope of the
+size--mass relation when galaxies became quenched, from $d\log R_{\rm
+eff}/d\log M_*\sim0.35$ to $\sim2.5$, could be the result of dry minor mergers.
+",0,1,0,0,0,0
+9376,State of the art of Trust and Reputation Systems in E-Commerce Context,"  This article proposes in depth comparative study of the most popular, used
+and analyzed Trust and Reputation System (TRS) according to the trust and
+reputation literature and in terms of specific trustworthiness criteria. This
+survey is realized relying on a selection of trustworthiness criteria that
+analyze and evaluate the maturity and effectiveness of TRS. These criteria
+describe the utility, the usability, the performance and the effectiveness of
+the TRS. We also provide a summary table of the compared TRS within a detailed
+and granular selection of trust and reputation aspects.
+",1,0,0,0,0,0
+9377,Placing your Coins on a Shelf,"  We consider the problem of packing a family of disks ""on a shelf"", that is,
+such that each disk touches the $x$-axis from above and such that no two disks
+overlap. We prove that the problem of minimizing the distance between the
+leftmost point and the rightmost point of any disk is NP-hard. On the positive
+side, we show how to approximate this problem within a factor of 4/3 in $O(n
+\log n)$ time, and provide an $O(n \log n)$-time exact algorithm for a special
+case, in particular when the ratio between the largest and smallest radius is
+at most four.
+",1,0,1,0,0,0
+9378,Randomized Rumor Spreading in Ad Hoc Networks with Buffers,"  The randomized rumor spreading problem generates a big interest in the area
+of distributed algorithms due to its simplicity, robustness and wide range of
+applications. The two most popular communication paradigms used for spreading
+the rumor are Push and Pull algorithms. The former protocol allows nodes to
+send the rumor to a randomly selected neighbor at each step, while the latter
+is based on sending a request and downloading the rumor from a randomly
+selected neighbor, provided the neighbor has it. Previous analysis of these
+protocols assumed that every node could process all such push/pull operations
+within a single step, which could be unrealistic in practical situations.
+Therefore we propose a new framework for analysis rumor spreading accommodating
+buffers, in which a node can process only one push/pull message or push request
+at a time. We develop upper and lower bounds for randomized rumor spreading
+time in the new framework, and compare the results with analogous in the old
+framework without buffers.
+",1,0,0,0,0,0
+9379,A simple and efficient feedback control strategy for wastewater denitrification,"  Due to severe mathematical modeling and calibration difficulties open-loop
+feedforward control is mainly employed today for wastewater denitrification,
+which is a key ecological issue. In order to improve the resulting poor
+performances a new model-free control setting and its corresponding
+""intelligent"" controller are introduced. The pitfall of regulating two output
+variables via a single input variable is overcome by introducing also an
+open-loop knowledge-based control deduced from the plant behavior. Several
+convincing computer simulations are presented and discussed.
+",1,0,1,0,0,0
+9380,The interaction of Airy waves and solitons in the three-wave system,"  We employ the generic three-wave system, with the $\chi ^{(2)}$ interaction
+between two components of the fundamental-frequency (FF) wave and
+second-harmonic (SH) one, to consider collisions of truncated Airy waves (TAWs)
+and three-wave solitons in a setting which is not available in other nonlinear
+systems. The advantage is that the single-wave TAWs, carried by either one of
+the FF component, are not distorted by the nonlinearity and are stable,
+three-wave solitons being stable too in the same system. The collision between
+mutually symmetric TAWs, carried by the different FF components, transforms
+them into a set of solitons, the number of which decreases with the increase of
+the total power. The TAW absorbs an incident small-power soliton, and a
+high-power soliton absorbs the TAW. Between these limits, the collision with an
+incident soliton converts the TAW into two solitons, with a remnant of the TAW
+attached to one of them, or leads to formation of a complex TAW-soliton bound
+state. At large velocities, the collisions become quasi-elastic.
+",0,1,0,0,0,0
+9381,A penalty criterion for score forecasting in soccer,"  This note proposes a penalty criterion for assessing correct score
+forecasting in a soccer match. The penalty is based on hierarchical priorities
+for such a forecast i.e., i) Win, Draw and Loss exact prediction and ii)
+normalized Euclidian distance between actual and forecast scores. The procedure
+is illustrated on typical scores, and different alternatives on the penalty
+components are discussed.
+",0,0,0,1,0,0
+9382,A robust RUV-testing procedure via gamma-divergence,"  Identification of differentially expressed genes (DE-genes) is commonly
+conducted in modern biomedical researches. However, unwanted variation
+inevitably arises during the data collection process, which could make the
+detection results heavily biased. It is suggested to remove the unwanted
+variation while keeping the biological variation to ensure a reliable analysis
+result. Removing Unwanted Variation (RUV) is recently proposed for this purpose
+by the virtue of negative control genes. On the other hand, outliers are
+frequently appear in modern high-throughput genetic data that can heavily
+affect the performances of RUV and its downstream analysis. In this work, we
+propose a robust RUV-testing procedure via gamma-divergence. The advantages of
+our method are twofold: (1) it does not involve any modeling for the outlier
+distribution, which is applicable to various situations, (2) it is easy to
+implement in the sense that its robustness is controlled by a single tuning
+parameter gamma of gamma-divergence, and a data-driven criterion is developed
+to select $\gamma$. In the Gender Study, our method can successfully remove
+unwanted variation, and is able to identify more DE-genes than conventional
+methods.
+",0,0,0,1,0,0
+9383,"On the Distribution, Model Selection Properties and Uniqueness of the Lasso Estimator in Low and High Dimensions","  We derive expressions for the finite-sample distribution of the Lasso
+estimator in the context of a linear regression model with normally distributed
+errors in low as well as in high dimensions by exploiting the structure of the
+optimization problem defining the estimator. In low dimensions we assume full
+rank of the regressor matrix and present expressions for the cumulative
+distribution function as well as the densities of the absolutely continuous
+parts of the estimator. Additionally, we establish an explicit formula for the
+correspondence between the Lasso and the least-squares estimator. We derive
+analogous results for the distribution in less explicit form in high dimensions
+where we make no assumptions on the regressor matrix at all. In this setting,
+we also investigate the model selection properties of the Lasso and show that
+possibly only a subset of models might be selected by the estimator, completely
+independently of the observed response vector. Finally, we present a condition
+for uniqueness of the estimator that is necessary as well as sufficient.
+",0,0,1,1,0,0
+9384,Transductive Boltzmann Machines,"  We present transductive Boltzmann machines (TBMs), which firstly achieve
+transductive learning of the Gibbs distribution. While exact learning of the
+Gibbs distribution is impossible by the family of existing Boltzmann machines
+due to combinatorial explosion of the sample space, TBMs overcome the problem
+by adaptively constructing the minimum required sample space from data to avoid
+unnecessary generalization. We theoretically provide bias-variance
+decomposition of the KL divergence in TBMs to analyze its learnability, and
+empirically demonstrate that TBMs are superior to the fully visible Boltzmann
+machines and popularly used restricted Boltzmann machines in terms of
+efficiency and effectiveness.
+",0,0,0,1,0,0
+9385,Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems,"  This paper presents a human-robot trust integrated task allocation and motion
+planning framework for multi-robot systems (MRS) in performing a set of tasks
+concurrently. A set of task specifications in parallel are conjuncted with MRS
+to synthesize a task allocation automaton. Each transition of the task
+allocation automaton is associated with the total trust value of human in
+corresponding robots. Here, the human-robot trust model is constructed with a
+dynamic Bayesian network (DBN) by considering individual robot performance,
+safety coefficient, human cognitive workload and overall evaluation of task
+allocation. Hence, a task allocation path with maximum encoded human-robot
+trust can be searched based on the current trust value of each robot in the
+task allocation automaton. Symbolic motion planning (SMP) is implemented for
+each robot after they obtain the sequence of actions. The task allocation path
+can be intermittently updated with this DBN based trust model. The overall
+strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask
+automata.
+",1,0,0,0,0,0
+9386,A five-decision testing procedure to infer on unidimensional parameter,"  A statistical test can be seen as a procedure to produce a decision based on
+observed data, where some decisions consist of rejecting a hypothesis (yielding
+a significant result) and some do not, and where one controls the probability
+to make a wrong rejection at some pre-specified significance level. Whereas
+traditional hypothesis testing involves only two possible decisions (to reject
+or not a null hypothesis), Kaiser's directional two-sided test as well as the
+more recently introduced Jones and Tukey's testing procedure involve three
+possible decisions to infer on unidimensional parameter. The latter procedure
+assumes that a point null hypothesis is impossible (e.g. that two treatments
+cannot have exactly the same effect), allowing a gain of statistical power.
+There are however situations where a point hypothesis is indeed plausible, for
+example when considering hypotheses derived from Einstein's theories. In this
+article, we introduce a five-decision rule testing procedure, which combines
+the advantages of the testing procedures of Kaiser (no assumption on a point
+hypothesis being impossible) and of Jones and Tukey (higher power), allowing
+for a non-negligible (typically 20%) reduction of the sample size needed to
+reach a given statistical power to get a significant result, compared to the
+traditional approach.
+",0,0,1,1,0,0
+9387,PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts,"  We present PubMed 200k RCT, a new dataset based on PubMed for sequential
+sentence classification. The dataset consists of approximately 200,000
+abstracts of randomized controlled trials, totaling 2.3 million sentences. Each
+sentence of each abstract is labeled with their role in the abstract using one
+of the following classes: background, objective, method, result, or conclusion.
+The purpose of releasing this dataset is twofold. First, the majority of
+datasets for sequential short-text classification (i.e., classification of
+short texts that appear in sequences) are small: we hope that releasing a new
+large dataset will help develop more accurate algorithms for this task. Second,
+from an application perspective, researchers need better tools to efficiently
+skim through the literature. Automatically classifying each sentence in an
+abstract would help researchers read abstracts more efficiently, especially in
+fields where abstracts may be long, such as the medical field.
+",1,0,0,1,0,0
+9388,Convergence radius of perturbative Lindblad driven non-equilibrium steady states,"  We address the problem of analyzing the radius of convergence of perturbative
+expansion of non-equilibrium steady states of Lindblad driven spin chains. A
+simple formal approach is developed for systematically computing the
+perturbative expansion of small driven systems. We consider the paradigmatic
+model of an open $XXZ$ spin 1/2 chain with boundary supported ultralocal
+Lindblad dissipators and treat two different perturbative cases: (i) expansion
+in system-bath coupling parameter and (ii) expansion in driving (bias)
+parameter. In the first case (i) we find that the radius of convergence quickly
+shrinks with increasing the system size, while in the second case (ii) we find
+that the convergence radius is always larger than $1$, and in particular it
+approaches $1$ from above as we change the anisotropy from easy plane ($XY$) to
+easy axis (Ising) regime.
+",0,1,0,0,0,0
+9389,A 2D metamaterial with auxetic out-of-plane behavior and non-auxetic in-plane behavior,"  Customarily, in-plane auxeticity and synclastic bending behavior (i.e.
+out-of-plane auxeticity) are not independent, being the latter a manifestation
+of the former. Basically, this is a feature of three-dimensional bodies. At
+variance, two-dimensional bodies have more freedom to deform than
+three-dimensional ones. Here, we exploit this peculiarity and propose a
+two-dimensional honeycomb structure with out-of-plane auxetic behavior opposite
+to the in-plane one. With a suitable choice of the lattice constitutive
+parameters, in its continuum description such a structure can achieve the whole
+range of values for the bending Poisson coefficient, while retaining a
+membranal Poisson coefficient equal to 1. In particular, this structure can
+reach the extreme values, $-1$ and $+1$, of the bending Poisson coefficient.
+Analytical calculations are supported by numerical simulations, showing the
+accuracy of the continuum formulas in predicting the response of the discrete
+structure.
+",0,1,0,0,0,0
+9390,Isolated Loops in Quantum Feedback Networks,"  A scheme making use of an isolated feedback loop was recently proposed in
+\cite{GP_} for creating an arbitrary bilinear Hamiltonian interaction between
+two multi-mode Linear Quantum Stochastic Systems (LQSSs). In this work we
+examine the presence of an isolated feedback loop in a general SLH network, and
+derive the modified Hamiltonian of the network due to the presence of the loop.
+In the case of a bipartite network with an isolated loop running through both
+parts, this results in modified Hamiltonians for each subnetwork, as well as a
+Hamiltonian interaction between them. As in the LQSS case, by engineering
+appropriate ports in each subnetwork, we may create desired interactions
+between them. Examples are provided that illustrate the general theory.
+",0,0,1,0,0,0
+9391,Distance-based Depths for Directional Data,"  Directional data are constrained to lie on the unit sphere of~$\mathbb{R}^q$
+for some~$q\geq 2$. To address the lack of a natural ordering for such data,
+depth functions have been defined on spheres. However, the depths available
+either lack flexibility or are so computationally expensive that they can only
+be used for very small dimensions~$q$. In this work, we improve on this by
+introducing a class of distance-based depths for directional data. Irrespective
+of the distance adopted, these depths can easily be computed in high dimensions
+too. We derive the main structural properties of the proposed depths and study
+how they depend on the distance used. We discuss the asymptotic and robustness
+properties of the corresponding deepest points. We show the practical relevance
+of the proposed depths in two applications, related to (i) spherical location
+estimation and (ii) supervised classification. For both problems, we show
+through simulation studies that distance-based depths have strong advantages
+over their competitors.
+",0,0,1,1,0,0
+9392,Two-dimensional Schrödinger symmetry and three-dimensional breathers and Kelvin-ripple complexes as quasi-massive-Nambu-Goldstone modes,"  Bose-Einstein condensates (BECs) confined in a two-dimensional (2D) harmonic
+trap are known to possess a hidden 2D Schrödinger symmetry, that is, the
+Schrödinger symmetry modified by a trapping potential. Spontaneous breaking
+of this symmetry gives rise to a breathing motion of the BEC, whose oscillation
+frequency is robustly determined by the strength of the harmonic trap. In this
+paper, we demonstrate that the concept of the 2D Schrödinger symmetry can be
+applied to predict the nature of three dimensional (3D) collective modes
+propagating along a condensate confined in an elongated trap. We find three
+kinds of collective modes whose existence is robustly ensured by the
+Schrödinger symmetry, which are physically interpreted as one breather mode
+and two Kelvin-ripple complex modes, i.e., composite modes in which the vortex
+core and the condensate surface oscillate interactively. We provide analytical
+expressions for the dispersion relations (energy-momentum relation) of these
+modes using the Bogoliubov theory [D. A. Takahashi and M. Nitta, Ann. Phys.
+354, 101 (2015)]. Furthermore, we point out that these modes can be interpreted
+as ""quasi-massive-Nambu-Goldstone (NG) modes"", that is, they have the
+properties of both quasi-NG and massive NG modes: quasi-NG modes appear when a
+symmetry of a part of a Lagrangian, which is not a symmetry of full a
+Lagrangian, is spontaneously broken, while massive NG modes appear when a
+modified symmetry is spontaneously broken.
+",0,1,0,0,0,0
+9393,Groupoid of morphisms of groupoids,"  In this paper we construct two groupoids from morphisms of groupoids, with
+one from a categorical viewpoint and the other from a geometric viewpoint. We
+show that for each pair of groupoids, the two kinds of groupoids of morphisms
+are equivalent. Then we study the automorphism groupoid of a groupoid.
+",0,0,1,0,0,0
+9394,Spin alignment of stars in old open clusters,"  Stellar clusters form by gravitational collapse of turbulent molecular
+clouds, with up to several thousand stars per cluster. They are thought to be
+the birthplace of most stars and therefore play an important role in our
+understanding of star formation, a fundamental problem in astrophysics. The
+initial conditions of the molecular cloud establish its dynamical history until
+the stellar cluster is born. However, the evolution of the cloud's angular
+momentum during cluster formation is not well understood. Current observations
+have suggested that turbulence scrambles the angular momentum of the
+cluster-forming cloud, preventing spin alignment amongst stars within a
+cluster. Here we use asteroseismology to measure the inclination angles of spin
+axes in 48 stars from the two old open clusters NGC~6791 and NGC~6819. The
+stars within each cluster show strong alignment. Three-dimensional
+hydrodynamical simulations of proto-cluster formation show that at least 50 %
+of the initial proto-cluster kinetic energy has to be rotational in order to
+obtain strong stellar-spin alignment within a cluster. Our result indicates
+that the global angular momentum of the cluster-forming clouds was efficiently
+transferred to each star and that its imprint has survived after several
+gigayears since the clusters formed.
+",0,1,0,0,0,0
+9395,Global well-posedness of critical surface quasigeostrophic equation on the sphere,"  In this paper we prove global well-posedness of the critical surface
+quasigeostrophic equation on the two dimensional sphere building on some
+earlier work of the authors. The proof relies on an improving of the previously
+known pointwise inequality for fractional laplacians as in the work of
+Constantin and Vicol for the euclidean setting.
+",0,0,1,0,0,0
+9396,On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples,"  Understanding and characterizing the subspaces of adversarial examples aid in
+studying the robustness of deep neural networks (DNNs) to adversarial
+perturbations. Very recently, Ma et al. (ICLR 2018) proposed to use local
+intrinsic dimensionality (LID) in layer-wise hidden representations of DNNs to
+study adversarial subspaces. It was demonstrated that LID can be used to
+characterize the adversarial subspaces associated with different attack
+methods, e.g., the Carlini and Wagner's (C&W) attack and the fast gradient sign
+attack.
+In this paper, we use MNIST and CIFAR-10 to conduct two new sets of
+experiments that are absent in existing LID analysis and report the limitation
+of LID in characterizing the corresponding adversarial subspaces, which are (i)
+oblivious attacks and LID analysis using adversarial examples with different
+confidence levels; and (ii) black-box transfer attacks. For (i), we find that
+the performance of LID is very sensitive to the confidence parameter deployed
+by an attack, and the LID learned from ensembles of adversarial examples with
+varying confidence levels surprisingly gives poor performance. For (ii), we
+find that when adversarial examples are crafted from another DNN model, LID is
+ineffective in characterizing their adversarial subspaces. These two findings
+together suggest the limited capability of LID in characterizing the subspaces
+of adversarial examples.
+",0,0,0,1,0,0
+9397,A properly embedded holomorphic disc in the ball with finite area and dense boundary curve,"  In this paper we construct a properly embedded holomorphic disc in the unit
+ball $\mathbb{B}^2$ of $\mathbb{C}^2$ having a surprising combination of
+properties: on the one hand, it has finite area and hence is the zero set of a
+bounded holomorphic function on $\mathbb{B}^2$; on the other hand, its boundary
+curve is everywhere dense in the sphere $b\mathbb{B}^2$.
+",0,0,1,0,0,0
+9398,Kähler differential algebras for 0-dimensional schemes,"  Given a 0-dimensional scheme in a projective space $\mathbb{P}^n$ over a
+field $K$, we study the Kähler differential algebra $\Omega_{R/K}$ of its
+homogeneous coordinate ring $R$. Using explicit presentations of the modules
+$\Omega^m_{R/K}$ of Kähler differential $m$-forms, we determine many values
+of their Hilbert functions explicitly and bound their Hilbert polynomials and
+regularity indices. Detailed results are obtained for subschemes of
+$\mathbb{P}^1$, fat point schemes, and subschemes of $\mathbb{P}^2$ supported
+on a conic.
+",0,0,1,0,0,0
+9399,Benchmarks and reliable DFT results for spin-crossover complexes,"  DFT is used throughout nanoscience, especially when modeling spin-dependent
+properties that are important in spintronics. But standard quantum chemical
+methods (both CCSD(T) and self-consistent semilocal density functional
+calculations) fail badly for the spin adiabatic energy difference in Fe(II)
+spin-crossover complexes. We show that all-electron fixed-node diffusion Monte
+Carlo can be converged at significant computational cost, and that the B3LYP
+single-determinant has sufficiently accurate nodes, providing benchmarks for
+these systems. We also find that density-corrected DFT, using Hartree-Fock
+densities (HF-DFT), greatly improves accuracy and reduces dependence on
+approximations for these calculations. The small gap in the self-consistent DFT
+calculations for the high-spin state is consistent with this. For the spin
+adiabatic energy differences in these complexes, HF-DFT is both accurate and
+reliable, and we make a strong prediction for the Fe-Porphyrin complex. The
+""parameter-dilemma"" of needing different amounts of mixing for different
+properties is eliminated by HF-DFT.
+",0,1,0,0,0,0
+9400,The Road to Success: Assessing the Fate of Linguistic Innovations in Online Communities,"  We investigate the birth and diffusion of lexical innovations in a large
+dataset of online social communities. We build on sociolinguistic theories and
+focus on the relation between the spread of a novel term and the social role of
+the individuals who use it, uncovering characteristics of innovators and
+adopters. Finally, we perform a prediction task that allows us to anticipate
+whether an innovation will successfully spread within a community.
+",1,0,0,0,0,0
+9401,Enumeration of Graphs and the Characteristic Polynomial of the Hyperplane Arrangements $\mathcal{J}_n$,"  We give a complete formula for the characteristic polynomial of hyperplane
+arrangements $\mathcal J_n$ consisting of the hyperplanes $x_i+x_j=1$, $x_k=0$,
+$x_l=1$, $ 1\leq i, j, k, l\leq n$. The formula is obtained by associating
+hyperplane arrangements with graphs, and then enumerating central graphs via
+generating functions for the number of bipartite graphs of given order, size
+and number of connected components.
+",0,0,1,0,0,0
+9402,On the Scientific Value of Large-scale Testbeds for Wireless Multi-hop Networks,"  Large-scale wireless testbeds have been setup in the last years with the goal
+to study wireless multi-hop networks in more realistic environments. Since the
+setup and operation of such a testbed is expensive in terms of money, time, and
+labor, the crucial question rises whether this effort is justified with the
+scientific results the testbed generates.
+In this paper, we give an answer to this question based on our experience
+with the DES-Testbed, a large-scale wireless sensor network and wireless mesh
+network testbed. The DES-Testbed has been operated for almost 5 years. Our
+analysis comprises more than 1000 experiments that have been run on the testbed
+in the years 2010 and 2011. We discuss the scientific value in respect to the
+effort of experimentation.
+",1,0,0,0,0,0
+9403,Brain Damage and Motor Cortex Impairment in Chronic Obstructive Pulmonary Disease: Implication of Nonrapid Eye Movement Sleep Desaturation,"  Nonrapid eye movement (NREM) sleep desaturation may cause neuronal damage due
+to the withdrawal of cerebrovascular reactivity. The current study (1) assessed
+the prevalence of NREM sleep desaturation in nonhypoxemic patients with chronic
+obstructive pulmonary disease (COPD) and (2) compared a biological marker of
+cerebral lesion and neuromuscular function in patients with and without NREM
+sleep desaturation.
+",0,1,0,0,0,0
+9404,FPT-algorithms for The Shortest Lattice Vector and Integer Linear Programming Problems,"  In this paper, we present FPT-algorithms for special cases of the shortest
+vector problem (SVP) and the integer linear programming problem (ILP), when
+matrices included to the problems' formulations are near square. The main
+parameter is the maximal absolute value of rank minors of matrices included to
+the problem formulation. Additionally, we present FPT-algorithms with respect
+to the same main parameter for the problems, when the matrices have no singular
+rank sub-matrices.
+",1,0,0,0,0,0
+9405,A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images,"  Purpose: To improve kidney segmentation in clinical ultrasound (US) images,
+we develop a new graph cuts based method to segment kidney US images by
+integrating original image intensity information and texture feature maps
+extracted using Gabor filters. Methods: To handle large appearance variation
+within kidney images and improve computational efficiency, we build a graph of
+image pixels close to kidney boundary instead of building a graph of the whole
+image. To make the kidney segmentation robust to weak boundaries, we adopt
+localized regional information to measure similarity between image pixels for
+computing edge weights to build the graph of image pixels. The localized graph
+is dynamically updated and the GC based segmentation iteratively progresses
+until convergence. The proposed method has been evaluated and compared with
+state of the art image segmentation methods based on clinical kidney US images
+of 85 subjects. We randomly selected US images of 20 subjects as training data
+for tuning the parameters, and validated the methods based on US images of the
+remaining 65 subjects. The segmentation results have been quantitatively
+analyzed using 3 metrics, including Dice Index, Jaccard Index, and Mean
+Distance. Results: Experiment results demonstrated that the proposed method
+obtained segmentation results for bilateral kidneys of 65 subjects with average
+Dice index of 0.9581, Jaccard index of 0.9204, and Mean Distance of 1.7166,
+better than other methods under comparison (p<10-19, paired Wilcoxon rank sum
+tests). Conclusions: The proposed method achieved promising performance for
+segmenting kidneys in US images, better than segmentation methods that built on
+any single channel of image information. This method will facilitate extraction
+of kidney characteristics that may predict important clinical outcomes such
+progression chronic kidney disease.
+",1,0,0,0,0,0
+9406,Natural Scales in Geographical Patterns,"  Human mobility is known to be distributed across several orders of magnitude
+of physical distances , which makes it generally difficult to endogenously find
+or define typical and meaningful scales. Relevant analyses, from movements to
+geographical partitions, seem to be relative to some ad-hoc scale, or no scale
+at all. Relying on geotagged data collected from photo-sharing social media, we
+apply community detection to movement networks constrained by increasing
+percentiles of the distance distribution. Using a simple parameter-free
+discontinuity detection algorithm, we discover clear phase transitions in the
+community partition space. The detection of these phases constitutes the first
+objective method of characterising endogenous, natural scales of human
+movement. Our study covers nine regions, ranging from cities to countries of
+various sizes and a transnational area. For all regions, the number of natural
+scales is remarkably low (2 or 3). Further, our results hint at scale-related
+behaviours rather than scale-related users. The partitions of the natural
+scales allow us to draw discrete multi-scale geographical boundaries,
+potentially capable of providing key insights in fields such as epidemiology or
+cultural contagion where the introduction of spatial boundaries is pivotal.
+",1,1,0,0,0,0
+9407,Drawing materials studied by THz spectroscopy,"  THz time-domain spectroscopy in transmission mode was applied to study dry
+and wet drawing inks. In specific, cochineal-, indigo- and iron-gall based inks
+have been investigated; some prepared following ancient recipes and others by
+using synthetic materials. The THz investigations have been realized on both
+pellet samples, made by dried inks blended with polyethylene powder, and
+layered inks, made by liquid deposition on polyethylene pellicles. We
+implemented an improved THz spectroscopic technique that enabled the
+measurement of the material optical parameters and thicknesses of the layered
+ink samples on absolute scale. This experimental investigation shows that the
+THz techniques have the potentiality to recognize drawing inks by their
+spectroscopic features.
+",0,1,0,0,0,0
+9408,A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning,"  With the advancement of treatment modalities in radiation therapy for cancer
+patients, outcomes have improved, but at the cost of increased treatment plan
+complexity and planning time. The accurate prediction of dose distributions
+would alleviate this issue by guiding clinical plan optimization to save time
+and maintain high quality plans. We have modified a convolutional deep network
+model, U-net (originally designed for segmentation purposes), for predicting
+dose from patient image contours of the planning target volume (PTV) and organs
+at risk (OAR). We show that, as an example, we are able to accurately predict
+the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer
+patients, where the average Dice similarity coefficient is 0.91 when comparing
+the predicted vs. true isodose volumes between 0% and 100% of the prescription
+dose. The average value of the absolute differences in [max, mean] dose is
+found to be under 5% of the prescription dose, specifically for each structure
+is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%,
+1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%,
+1.62%](Rectum) of the prescription dose. We thus managed to map a desired
+radiation dose distribution from a patient's PTV and OAR contours. As an
+additional advantage, relatively little data was used in the techniques and
+models described in this paper.
+",1,1,0,0,0,0
+9409,Single-molecule imaging of DNA gyrase activity in living Escherichia coli,"  Bacterial DNA gyrase introduces negative supercoils into chromosomal DNA and
+relaxes positive supercoils introduced by replication and transiently by
+transcription. Removal of these positive supercoils is essential for
+replication fork progression and for the overall unlinking of the two duplex
+DNA strands, as well as for ongoing transcription. To address how gyrase copes
+with these topological challenges, we used high-speed single-molecule
+fluorescence imaging in live Escherichia coli cells. We demonstrate that at
+least 300 gyrase molecules are stably bound to the chromosome at any time, with
+~12 enzymes enriched near each replication fork. Trapping of reaction
+intermediates with ciprofloxacin revealed complexes undergoing catalysis. Dwell
+times of ~2 s were observed for the dispersed gyrase molecules, which we
+propose maintain steady-state levels of negative supercoiling of the
+chromosome. In contrast, the dwell time of replisome-proximal molecules was ~8
+s, consistent with these catalyzing processive positive supercoil relaxation in
+front of the progressing replisome.
+",0,0,0,0,1,0
+9410,Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions,"  From self-driving vehicles and back-flipping robots to virtual assistants who
+book our next appointment at the hair salon or at that restaurant for dinner -
+machine learning systems are becoming increasingly ubiquitous. The main reason
+for this is that these methods boast remarkable predictive capabilities.
+However, most of these models remain black boxes, meaning that it is very
+challenging for humans to follow and understand their intricate inner workings.
+Consequently, interpretability has suffered under this ever-increasing
+complexity of machine learning models. Especially with regards to new
+regulations, such as the General Data Protection Regulation (GDPR), the
+necessity for plausibility and verifiability of predictions made by these black
+boxes is indispensable. Driven by the needs of industry and practice, the
+research community has recognised this interpretability problem and focussed on
+developing a growing number of so-called explanation methods over the past few
+years. These methods explain individual predictions made by black box machine
+learning models and help to recover some of the lost interpretability. With the
+proliferation of these explanation methods, it is, however, often unclear,
+which explanation method offers a higher explanation quality, or is generally
+better-suited for the situation at hand. In this thesis, we thus propose an
+axiomatic framework, which allows comparing the quality of different
+explanation methods amongst each other. Through experimental validation, we
+find that the developed framework is useful to assess the explanation quality
+of different explanation methods and reach conclusions that are consistent with
+independent research.
+",0,0,0,1,0,0
+9411,Click-based porous cationic polymers for enhanced carbon dioxide capture,"  Imidazolium based porous cationic polymers were synthesized using an
+innovative and facile approach, which takes advantage of the Debus Radziszewski
+reaction to obtain meso- and microporous polymers following click chemistry
+principles. In the obtained set of materials, click based porous cationic
+polymers have the same cationic backbone, whereas they bear the commonly used
+anions of imidazolium poly(ionic liquid)s. These materials show hierarchical
+porosity and a good specific surface area. Furthermore, their chemical
+structure was extensively characterized using ATR FTIR and SS NMR
+spectroscopies, and HR MS. These polymers show good performance towards carbon
+dioxide sorption, especially those possessing the acetate anion. This polymer
+has an uptake of 2 mmol per g of CO2 at 1 bar and 273 K, a value which is among
+the highest recorded for imidazolium poly(ionic liquid)s. These polymers were
+also modified in order to introduce N-heterocyclic carbenes along the backbone.
+Carbon dioxide loading in the carbene-containing polymer is in the same range
+as that of the non-modified versions, but the nature of the interaction is
+substantially different. The combined use of in situ FTIR spectroscopy and
+microcalorimetry evidenced a chemisorption phenomenon that brings about the
+formation of an imidazolium carboxylate zwitterion.
+",0,1,0,0,0,0
+9412,Oscillons in the presence of external potential,"  We discuss similarity between oscillons and oscillational mode in perturbed
+$\phi^4$. For small depths of the perturbing potential it is difficult to
+distinguish between oscillons and the mode in moderately long time evolution,
+moreover one can transform one into the other by adiabatically switching on and
+off the potential. Basins of attraction are presented in the parameter space
+describing the potential and initial conditions.
+",0,1,0,0,0,0
+9413,"Dynamical Tides in Highly Eccentric Binaries: Chaos, Dissipation and Quasi-Steady State","  Highly eccentric binary systems appear in many astrophysical contexts,
+ranging from tidal capture in dense star clusters, precursors of stellar
+disruption by massive black holes, to high-eccentricity migration of giant
+planets. In a highly eccentric binary, the tidal potential of one body can
+excite oscillatory modes in the other during a pericenter passage, resulting in
+energy exchange between the modes and the binary orbit. These modes exhibit one
+of three behaviors over multiple passages: low-amplitude oscillations, large
+amplitude oscillations corresponding to a resonance between the orbital
+frequency and the mode frequency, and chaotic growth. We study these phenomena
+with an iterative map, fully exploring how the mode evolution depends on the
+pericenter distance and other parameters. In addition, we show that the
+dissipation of mode energy results in a quasi-steady state, with gradual
+orbital decay punctuated by resonances, even in systems where the mode
+amplitude would initially grow stochastically. A newly captured star around a
+black hole can experience significant orbital decay and heating due to the
+chaotic growth of the mode amplitude and dissipation. A giant planet pushed
+into a high-eccentricity orbit may experience a similar effect and become a hot
+or warm Jupiter.
+",0,1,0,0,0,0
+9414,Calabi-Yau threefolds fibred by high rank lattice polarized K3 surfaces,"  We study threefolds fibred by K3 surfaces admitting a lattice polarization by
+a certain class of rank 19 lattices. We begin by showing that any family of
+such K3 surfaces is completely determined by a map from the base of the family
+to the appropriate K3 moduli space, which we call the generalized functional
+invariant. Then we show that if the threefold total space is a smooth
+Calabi-Yau, there are only finitely many possibilities for the polarizing
+lattice and the form of the generalized functional invariant. Finally, we
+construct explicit examples of Calabi-Yau threefolds realizing each case and
+compute their Hodge numbers.
+",0,0,1,0,0,0
+9415,Lipschitz perturbations of Morse-Smale semigroups,"  In this paper we will deal with Lipschitz continuous perturbations of
+Morse-Smale semigroups with only equilibrium points as critical elements. We
+study the behavior of the structure of equilibrium points and their connections
+when subjected to non-differentiable perturbations. To this end we define more
+general notions of \emph{hyperbolicity} and \emph{transversality}, which do not
+require differentiability.
+",0,0,1,0,0,0
+9416,Semi-Supervised Learning for Detecting Human Trafficking,"  Human trafficking is one of the most atrocious crimes and among the
+challenging problems facing law enforcement which demands attention of global
+magnitude. In this study, we leverage textual data from the website ""Backpage""-
+used for classified advertisement- to discern potential patterns of human
+trafficking activities which manifest online and identify advertisements of
+high interest to law enforcement. Due to the lack of ground truth, we rely on a
+human analyst from law enforcement, for hand-labeling a small portion of the
+crawled data. We extend the existing Laplacian SVM and present S3VM-R, by
+adding a regularization term to exploit exogenous information embedded in our
+feature space in favor of the task at hand. We train the proposed method using
+labeled and unlabeled data and evaluate it on a fraction of the unlabeled data,
+herein referred to as unseen data, with our expert's further verification.
+Results from comparisons between our method and other semi-supervised and
+supervised approaches on the labeled data demonstrate that our learner is
+effective in identifying advertisements of high interest to law enforcement
+",1,0,0,0,0,0
+9417,Einstein's 1935 papers: EPR=ER?,"  In May of 1935, Einstein published with two co-authors the famous EPR-paper
+about entangled particles, which questioned the completeness of Quantum
+Mechanics by means of a gedankenexperiment. Only one month later, he published
+a work that seems unconnected to the EPR-paper at first, the so called
+Einstein-Rosen-paper, that presented a solution of the field equations for
+particles in the framework of general relativity. Both papers ask for the
+conception of completeness in a theory and, from a modern perspective, it is
+easy to believe that there is a connection between these topics. We question
+whether Einstein might have considered that a correlation between nonlocal
+features of Quantum Mechanics and the Einstein-Rosen bridge can be used to
+explain entanglement. We analyse this question by discussing the used
+conceptions of ""completeness,"" ""atomistic structure of matter,"" and ""quantum
+phenomena."" We discuss the historical embedding of the two works and the
+context to modern research. Recent approaches are presented that formulate an
+EPR=ER principle and claim an equivalence of the basic principles of these two
+papers.
+",0,1,0,0,0,0
+9418,An overview of process model quality literature - The Comprehensive Process Model Quality Framework,"  The rising interest in the construction and the quality of (business) process
+models resulted in an abundancy of emerged research studies and different
+findings about process model quality. The lack of overview and the lack of
+consensus hinder the development of the research field. The research objective
+is to collect, analyse, structure, and integrate the existing knowledge in a
+comprehensive framework that strives to find a balance between completeness and
+relevance without hindering the overview. The Systematic Literature Review
+methodology was applied to collect the relevant studies. Because several
+studies exist that each partially addresses this research objective, the review
+was performed at a tertiary level. Based on a critical analysis of the
+collected papers, a comprehensive, but structured overview of the state of the
+art in the field was composed. The existing academic knowledge about process
+model quality was carefully integrated and structured into the Comprehensive
+Process Model Quality Framework (CPMQF). The framework summarizes 39 quality
+dimensions, 21 quality metrics, 28 quality (sub)drivers, 44 (sub)driver
+metrics, 64 realization initiatives and 15 concrete process model purposes
+related to 4 types of organizational benefits, as well as the relations between
+all of these. This overview is thus considered to form a valuable instrument
+for both researchers and practitioners that are concerned about process model
+quality. The framework is the first to address the concept of process model
+quality in such a comprehensive way.
+",1,0,0,0,0,0
+9419,Convergence rates of least squares regression estimators with heavy-tailed errors,"  We study the performance of the Least Squares Estimator (LSE) in a general
+nonparametric regression model, when the errors are independent of the
+covariates but may only have a $p$-th moment ($p\geq 1$). In such a
+heavy-tailed regression setting, we show that if the model satisfies a standard
+`entropy condition' with exponent $\alpha \in (0,2)$, then the $L_2$ loss of
+the LSE converges at a rate \begin{align*}
+\mathcal{O}_{\mathbf{P}}\big(n^{-\frac{1}{2+\alpha}} \vee
+n^{-\frac{1}{2}+\frac{1}{2p}}\big). \end{align*} Such a rate cannot be improved
+under the entropy condition alone.
+This rate quantifies both some positive and negative aspects of the LSE in a
+heavy-tailed regression setting. On the positive side, as long as the errors
+have $p\geq 1+2/\alpha$ moments, the $L_2$ loss of the LSE converges at the
+same rate as if the errors are Gaussian. On the negative side, if
+$p<1+2/\alpha$, there are (many) hard models at any entropy level $\alpha$ for
+which the $L_2$ loss of the LSE converges at a strictly slower rate than other
+robust estimators.
+The validity of the above rate relies crucially on the independence of the
+covariates and the errors. In fact, the $L_2$ loss of the LSE can converge
+arbitrarily slowly when the independence fails.
+The key technical ingredient is a new multiplier inequality that gives sharp
+bounds for the `multiplier empirical process' associated with the LSE. We
+further give an application to the sparse linear regression model with
+heavy-tailed covariates and errors to demonstrate the scope of this new
+inequality.
+",0,0,1,1,0,0
+9420,Some properties of nested Kriging predictors,"  Kriging is a widely employed technique, in particular for computer
+experiments, in machine learning or in geostatistics. An important challenge
+for Kriging is the computational burden when the data set is large. We focus on
+a class of methods aiming at decreasing this computational cost, consisting in
+aggregating Kriging predictors based on smaller data subsets. We prove that
+aggregations based solely on the conditional variances provided by the
+different Kriging predictors can yield an inconsistent final Kriging
+prediction. In contrasts, we study theoretically the recent proposal by
+[Rulli{è}re et al., 2017] and obtain additional attractive properties for it.
+We prove that this predictor is consistent, we show that it can be interpreted
+as an exact conditional distribution for a modified process and we provide
+error bounds for it.
+",0,0,1,1,0,0
+9421,Optimal Transport on Discrete Domains,"  Inspired by the matching of supply to demand in logistical problems, the
+optimal transport (or Monge--Kantorovich) problem involves the matching of
+probability distributions defined over a geometric domain such as a surface or
+manifold. In its most obvious discretization, optimal transport becomes a
+large-scale linear program, which typically is infeasible to solve efficiently
+on triangle meshes, graphs, point clouds, and other domains encountered in
+graphics and machine learning. Recent breakthroughs in numerical optimal
+transport, however, enable scalability to orders-of-magnitude larger problems,
+solvable in a fraction of a second. Here, we discuss advances in numerical
+optimal transport that leverage understanding of both discrete and smooth
+aspects of the problem. State-of-the-art techniques in discrete optimal
+transport combine insight from partial differential equations (PDE) with convex
+analysis to reformulate, discretize, and optimize transportation problems. The
+end result is a set of theoretically-justified models suitable for domains with
+thousands or millions of vertices. Since numerical optimal transport is a
+relatively new discipline, special emphasis is placed on identifying and
+explaining open problems in need of mathematical insight and additional
+research.
+",1,0,0,0,0,0
+9422,High-Resolution Altitude Profiles of the Atmospheric Turbulence with PML at the Sutherland Observatory,"  With the prospect of the next generation of ground-based telescopes, the
+extremely large telescopes (ELTs), increasingly complex and demanding adaptive
+optics (AO) systems are needed. This is to compensate for image distortion
+caused by atmospheric turbulence and fully take advantage of mirrors with
+diameters of 30 to 40 m. This requires a more precise characterization of the
+turbulence. The PML (Profiler of Moon Limb) was developed within this context.
+The PML aims to provide high-resolution altitude profiles of the turbulence
+using differential measurements of the Moon limb position to calculate the
+transverse spatio-angular covariance of the Angle of Arrival fluctuations. The
+covariance of differential image motion for different separation angles is
+sensitive to the altitude distribution of the seeing. The use of the continuous
+Moon limb provides a large number of separation angles allowing for the
+high-resolution altitude of the profiles. The method is presented and tested
+with simulated data. Moreover a PML instrument was deployed at the Sutherland
+Observatory in South Africa in August 2011. We present here the results of this
+measurement campaign.
+",0,1,0,0,0,0
+9423,Spice up Your Chat: The Intentions and Sentiment Effects of Using Emoji,"  Emojis, as a new way of conveying nonverbal cues, are widely adopted in
+computer-mediated communications. In this paper, first from a message sender
+perspective, we focus on people's motives in using four types of emojis --
+positive, neutral, negative, and non-facial. We compare the willingness levels
+of using these emoji types for seven typical intentions that people usually
+apply nonverbal cues for in communication. The results of extensive statistical
+hypothesis tests not only report the popularities of the intentions, but also
+uncover the subtle differences between emoji types in terms of intended uses.
+Second, from a perspective of message recipients, we further study the
+sentiment effects of emojis, as well as their duplications, on verbal messages.
+Different from previous studies in emoji sentiment, we study the sentiments of
+emojis and their contexts as a whole. The experiment results indicate that the
+powers of conveying sentiment are different between four emoji types, and the
+sentiment effects of emojis vary in the contexts of different valences.
+",1,0,0,0,0,0
+9424,Self-shielding of hydrogen in the IGM during the epoch of reionization,"  We investigate self-shielding of intergalactic hydrogen against ionizing
+radiation in radiative transfer simulations of cosmic reionization carefully
+calibrated with Lyman alpha forest data. While self-shielded regions manifest
+as Lyman-limit systems in the post-reionization Universe, here we focus on
+their evolution during reionization (redshifts z=6-10). At these redshifts, the
+spatial distribution of hydrogen-ionizing radiation is highly inhomogeneous,
+and some regions of the Universe are still neutral. After masking the neutral
+regions and ionizing sources in the simulation, we find that the hydrogen
+photoionization rate depends on the local hydrogen density in a manner very
+similar to that in the post-reionization Universe. The characteristic physical
+hydrogen density above which self-shielding becomes important at these
+redshifts is about $\mathrm{n_H \sim 3 \times 10^{-3} cm^{-3}}$, or $\sim$ 20
+times the mean hydrogen density, reflecting the fact that during reionization
+photoionization rates are typically low enough that the filaments in the cosmic
+web are often self-shielded. The value of the typical self-shielding density
+decreases by a factor of 3 between redshifts z=3 and 10, and follows the
+evolution of the average photoionization rate in ionized regions in a simple
+fashion. We provide a simple parameterization of the photoionization rate as a
+function of density in self-shielded regions during the epoch of reionization.
+",0,1,0,0,0,0
+9425,Universal Conditional Machine,"  We propose a single neural probabilistic model based on variational
+autoencoder that can be conditioned on an arbitrary subset of observed features
+and then sample the remaining features in ""one shot"". The features may be both
+real-valued and categorical. Training of the model is performed by stochastic
+variational Bayes. The experimental evaluation on synthetic data, as well as
+feature imputation and image inpainting problems, shows the effectiveness of
+the proposed approach and diversity of the generated samples.
+",0,0,0,1,0,0
+9426,A hybrid isogeometric approach on multi-patches with applications to Kirchhoff plates and eigenvalue problems,"  We present a systematic study on higher-order penalty techniques for
+isogeometric mortar methods. In addition to the weak-continuity enforced by a
+mortar method, normal derivatives across the interface are penalized. The
+considered applications are fourth order problems as well as eigenvalue
+problems for second and fourth order equations. The hybrid coupling enables the
+discretization of fourth order problems in a multi-patch setting as well as a
+convenient implementation of natural boundary conditions. For second order
+eigenvalue problems, the pollution of the discrete spectrum - typically
+referred to as 'outliers' - can be avoided.
+Numerical results illustrate the good behaviour of the proposed method in
+simple systematic studies as well as more complex multi-patch mapped geometries
+for linear elasticity and Kirchhoff plates.
+",0,0,1,0,0,0
+9427,Circumstellar discs: What will be next?,"  This prospective chapter gives our view on the evolution of the study of
+circumstellar discs within the next 20 years from both observational and
+theoretical sides. We first present the expected improvements in our knowledge
+of protoplanetary discs as for their masses, sizes, chemistry, the presence of
+planets as well as the evolutionary processes shaping these discs. We then
+explore the older debris disc stage and explain what will be learnt concerning
+their birth, the intrinsic links between these discs and planets, the hot dust
+and the gas detected around main sequence stars as well as discs around white
+dwarfs.
+",0,1,0,0,0,0
+9428,Algorithmic Verification of Linearizability for Ordinary Differential Equations,"  For a nonlinear ordinary differential equation solved with respect to the
+highest order derivative and rational in the other derivatives and in the
+independent variable, we devise two algorithms to check if the equation can be
+reduced to a linear one by a point transformation of the dependent and
+independent variables. The first algorithm is based on a construction of the
+Lie point symmetry algebra and on the computation of its derived algebra. The
+second algorithm exploits the differential Thomas decomposition and allows not
+only to test the linearizability, but also to generate a system of nonlinear
+partial differential equations that determines the point transformation and the
+coefficients of the linearized equation. Both algorithms have been implemented
+in Maple and their application is illustrated using several examples.
+",1,0,1,0,0,0
+9429,Space weather challenges of the polar cap ionosphere,"  This paper presents research on polar cap ionosphere space weather phenomena
+conducted during the European Cooperation in Science and Technology (COST)
+action ES0803 from 2008 to 2012. The main part of the work has been directed
+toward the study of plasma instabilities and scintillations in association with
+cusp flow channels and polar cap electron density structures/patches,which is
+considered as critical knowledge in order to develop forecast models for
+scintillations in the polar cap. We have approached this problem by
+multi-instrument techniques that comprise the EISCAT Svalbard Radar, SuperDARN
+radars, in-situ rocket, and GPS scintillation measurements. The Discussion
+section aims to unify the bits and pieces of highly specialized information
+from several papers into a generalized picture. The cusp ionosphere appears as
+a hot region in GPS scintillation climatology maps. Our results are consistent
+with the existing view that scintillations in the cusp and the polar cap
+ionosphere are mainly due to multi-scale structures generated by instability
+processes associated with the cross-polar transport of polar cap patches. We
+have demonstrated that the SuperDARN convection model can be used to track
+these patches backward and forward in time. Hence, once a patch has been
+detected in the cusp inflow region, SuperDARN can be used to forecast its
+destination in the future. However, the high-density gradient of polar cap
+patches is not the only prerequisite for high-latitude scintillations.
+Unprecedented high resolution rocket measurements reveal that the cusp
+ionosphere is associated with filamentary precipitation giving rise to
+kilometer scale gradients onto which the gradient drift instability can operate
+very efficiently... (continued)
+",0,1,0,0,0,0
+9430,Unconventional Large Linear Magnetoresistance in Cu$_{2-x}$Te,"  We report a large linear magnetoresistance in Cu$_{2-x}$Te, reaching
+$\Delta\rho/\rho(0)$ = 250\% at 2 K in a 9 T field. This is observed for
+samples with $x$ in the range 0.13 to 0.22, and the results are comparable to
+the effects observed in Ag$_2 X$ materials, although in this case the results
+appear for a much wider range of bulk carrier density. Examining the magnitude
+vs. crossover field from low-field quadratic to high-field linear behavior, we
+show that models based on classical transport behavior best explain the
+observed results. The effects are traced to misdirected currents due to
+topologically inverted behavior in this system, such that stable surface states
+provide the high mobility transport channels. The resistivity also crosses over
+to a $T^2$ dependence in the temperature range where the large linear MR
+appears, an indicator of electron-electron interaction effects within the
+surface states. Thus this is an example of a system in which these interactions
+dominate the low-temperature behavior of the surface states.
+",0,1,0,0,0,0
+9431,Which Distribution Distances are Sublinearly Testable?,"  Given samples from an unknown distribution $p$ and a description of a
+distribution $q$, are $p$ and $q$ close or far? This question of ""identity
+testing"" has received significant attention in the case of testing whether $p$
+and $q$ are equal or far in total variation distance. However, in recent work,
+the following questions have been been critical to solving problems at the
+frontiers of distribution testing:
+-Alternative Distances: Can we test whether $p$ and $q$ are far in other
+distances, say Hellinger?
+-Tolerance: Can we test when $p$ and $q$ are close, rather than equal? And if
+so, close in which distances?
+Motivated by these questions, we characterize the complexity of distribution
+testing under a variety of distances, including total variation, $\ell_2$,
+Hellinger, Kullback-Leibler, and $\chi^2$. For each pair of distances $d_1$ and
+$d_2$, we study the complexity of testing if $p$ and $q$ are close in $d_1$
+versus far in $d_2$, with a focus on identifying which problems allow strongly
+sublinear testers (i.e., those with complexity $O(n^{1 - \gamma})$ for some
+$\gamma > 0$ where $n$ is the size of the support of the distributions $p$ and
+$q$). We provide matching upper and lower bounds for each case. We also study
+these questions in the case where we only have samples from $q$ (equivalence
+testing), showing qualitative differences from identity testing in terms of
+when tolerance can be achieved. Our algorithms fall into the classical paradigm
+of $\chi^2$-statistics, but require crucial changes to handle the challenges
+introduced by each distance we consider. Finally, we survey other recent
+results in an attempt to serve as a reference for the complexity of various
+distribution testing problems.
+",1,0,1,1,0,0
+9432,Hund's coupling driven photo-carrier relaxation in the two-band Mott insulator,"  We study the relaxation dynamics of photo-carriers in the paramagnetic Mott
+insulating phase of the half-filled two-band Hubbard model. Using
+nonequilibrium dynamical mean field theory, we excite charge carriers across
+the Mott gap by a short hopping modulation, and simulate the evolution of the
+photo-doped population within the Hubbard bands. We observe an ultrafast
+charge-carrier relaxation driven by emission of local spin excitations with an
+inverse relaxation time proportional to the Hund's coupling. The photo-doping
+generates additional side-bands in the spectral function, and for strong Hund's
+coupling, the photo-doped population also splits into several resonances. The
+dynamics of the local many-body states reveals two effects, thermal blocking
+and kinetic freezing, which manifest themselves when the Hund's coupling
+becomes of the order of the temperature or the bandwidth, respectively. These
+effects, which are absent in the single-band Hubbard model, should be relevant
+for the interpretation of experiments on correlated materials with multiple
+active orbitals. In particular, the features revealed in the non-equilibrium
+energy distribution of the photo-carriers are experimentally accessible, and
+provide information on the role of the Hund's coupling in these materials.
+",0,1,0,0,0,0
+9433,A Connection between Feed-Forward Neural Networks and Probabilistic Graphical Models,"  Two of the most popular modelling paradigms in computer vision are
+feed-forward neural networks (FFNs) and probabilistic graphical models (GMs).
+Various connections between the two have been studied in recent works, such as
+e.g. expressing mean-field based inference in a GM as an FFN. This paper
+establishes a new connection between FFNs and GMs. Our key observation is that
+any FFN implements a certain approximation of a corresponding Bayesian network
+(BN). We characterize various benefits of having this connection. In
+particular, it results in a new learning algorithm for BNs. We validate the
+proposed methods for a classification problem on CIFAR-10 dataset and for
+binary image segmentation on Weizmann Horse dataset. We show that statistically
+learned BNs improve performance, having at the same time essentially better
+generalization capability, than their FFN counterparts.
+",1,0,0,1,0,0
+9434,On the digital representation of smooth numbers,"  Let $b \ge 2$ be an integer. Among other results, we establish, in a
+quantitative form, that any sufficiently large integer which is not a multiple
+of $b$ cannot have simultaneously only few distinct prime factors and only few
+nonzero digits in its representation in base $b$.
+",0,0,1,0,0,0
+9435,On Constraint Qualifications of a Nonconvex Inequality,"  In this paper, we study constraint qualifications for the nonconvex
+inequality defined by a proper lower semicontinuous function. These constraint
+qualifications involve the generalized construction of normal cones and
+subdifferentials. Several conditions for these constraint qualifications are
+also provided therein. When restricted to the convex inequality, these
+constraint qualifications reduce to basic constraint qualification (BCQ) and
+strong BCQ studied in [SIAM J. Optim., 14(2004), 757-772] and [Math. Oper.
+Res., 30 (2005), 956-965].
+",0,0,1,0,0,0
+9436,A Framework for Dynamic Stability Analysis of Power Systems with Volatile Wind Power,"  We propose a framework employing stochastic differential equations to
+facilitate the long-term stability analysis of power grids with intermittent
+wind power generations. This framework takes into account the discrete dynamics
+which play a critical role in the long-term stability analysis, incorporates
+the model of wind speed with different probability distributions, and also
+develops an approximation methodology (by a deterministic hybrid model) for the
+stochastic hybrid model to reduce the computational burden brought about by the
+uncertainty of wind power. The theoretical and numerical studies show that a
+deterministic hybrid model can provide an accurate trajectory approximation and
+stability assessments for the stochastic hybrid model under mild conditions. In
+addition, we discuss the critical cases that the deterministic hybrid model
+fails and discover that these cases are caused by a violation of the proposed
+sufficient conditions. Such discussion complements the proposed framework and
+methodology and also reaffirms the importance of the stochastic hybrid model
+when the system operates close to its stability limit.
+",1,0,0,0,0,0
+9437,Confidence interval for correlation estimator between latent processes,"  Kimura and Yoshida treated a model in which the finite variation part of a
+two-dimensional semimartingale is expressed by time-integration of latent
+processes. They proposed a correlation estimator between the latent processes
+and proved its consistency and asymptotic mixed normality. In this paper, we
+discuss the confidence interval of the correlation estimator to detect the
+correlation. %between latent processes. We propose two types of estimators for
+asymptotic variance of the correlation estimator and prove their consistency in
+a high frequency setting. Our model includes doubly stochastic Poisson
+processes whose intensity processes are correlated Itô processes. We compare
+our estimators based on the simulation of the doubly stochastic Poisson
+processes.
+",0,0,1,1,0,0
+9438,Novel event classification based on spectral analysis of scintillation waveforms in Double Chooz,"  Liquid scintillators are a common choice for neutrino physics experiments,
+but their capabilities to perform background rejection by scintillation pulse
+shape discrimination is generally limited in large detectors. This paper
+describes a novel approach for a pulse shape based event classification
+developed in the context of the Double Chooz reactor antineutrino experiment.
+Unlike previous implementations, this method uses the Fourier power spectra of
+the scintillation pulse shapes to obtain event-wise information. A
+classification variable built from spectral information was able to achieve an
+unprecedented performance, despite the lack of optimization at the detector
+design level. Several examples of event classification are provided, ranging
+from differentiation between the detector volumes and an efficient rejection of
+instrumental light noise, to some sensitivity to the particle type, such as
+stopping muons, ortho-positronium formation, alpha particles as well as
+electrons and positrons. In combination with other techniques the method is
+expected to allow for a versatile and more efficient background rejection in
+the future, especially if detector optimization is taken into account at the
+design level.
+",0,1,0,0,0,0
+9439,Symmetric Riemannian problem on the group of proper isometries of hyperbolic plane,"  We consider the Lie group PSL(2) (the group of orientation preserving
+isometries of the hyperbolic plane) and a left-invariant Riemannian metric on
+this group with two equal eigenvalues that correspond to space-like
+eigenvectors (with respect to the Killing form). For such metrics we find a
+parametrization of geodesics, the conjugate time, the cut time and the cut
+locus. The injectivity radius is computed. We show that the cut time and the
+cut locus in such Riemannian problem converge to the cut time and the cut locus
+in the corresponding sub-Riemannian problem as the third eigenvalue of the
+metric tends to infinity. Similar results are also obtained for SL(2).
+",0,0,1,0,0,0
+9440,Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach,"  Relation extraction is a fundamental task in information extraction. Most
+existing methods have heavy reliance on annotations labeled by human experts,
+which are costly and time-consuming. To overcome this drawback, we propose a
+novel framework, REHession, to conduct relation extractor learning using
+annotations from heterogeneous information source, e.g., knowledge base and
+domain heuristics. These annotations, referred as heterogeneous supervision,
+often conflict with each other, which brings a new challenge to the original
+relation extraction task: how to infer the true label from noisy labels for a
+given instance. Identifying context information as the backbone of both
+relation extraction and true label discovery, we adopt embedding techniques to
+learn the distributed representations of context, which bridges all components
+with mutual enhancement in an iterative fashion. Extensive experimental results
+demonstrate the superiority of REHession over the state-of-the-art.
+",1,0,0,0,0,0
+9441,Variational integrators for anelastic and pseudo-incompressible flows,"  The anelastic and pseudo-incompressible equations are two well-known
+soundproof approximations of compressible flows useful for both theoretical and
+numerical analysis in meteorology, atmospheric science, and ocean studies. In
+this paper, we derive and test structure-preserving numerical schemes for these
+two systems. The derivations are based on a discrete version of the
+Euler-Poincaré variational method. This approach relies on a finite
+dimensional approximation of the (Lie) group of diffeomorphisms that preserve
+weighted-volume forms. These weights describe the background stratification of
+the fluid and correspond to the weighed velocity fields for anelastic and
+pseudo-incompressible approximations. In particular, we identify to these
+discrete Lie group configurations the associated Lie algebras such that
+elements of the latter correspond to weighted velocity fields that satisfy the
+divergence-free conditions for both systems. Defining discrete Lagrangians in
+terms of these Lie algebras, the discrete equations follow by means of
+variational principles. Descending from variational principles, the schemes
+exhibit further a discrete version of Kelvin circulation theorem, are
+applicable to irregular meshes, and show excellent long term energy behavior.
+We illustrate the properties of the schemes by performing preliminary test
+cases.
+",0,1,1,0,0,0
+9442,The reparameterization trick for acquisition functions,"  Bayesian optimization is a sample-efficient approach to solving global
+optimization problems. Along with a surrogate model, this approach relies on
+theoretically motivated value heuristics (acquisition functions) to guide the
+search process. Maximizing acquisition functions yields the best performance;
+unfortunately, this ideal is difficult to achieve since optimizing acquisition
+functions per se is frequently non-trivial. This statement is especially true
+in the parallel setting, where acquisition functions are routinely non-convex,
+high-dimensional, and intractable. Here, we demonstrate how many popular
+acquisition functions can be formulated as Gaussian integrals amenable to the
+reparameterization trick and, ensuingly, gradient-based optimization. Further,
+we use this reparameterized representation to derive an efficient Monte Carlo
+estimator for the upper confidence bound acquisition function in the context of
+parallel selection.
+",1,0,0,1,0,0
+9443,Multi-model ensembles for ecosystem prediction,"  When making predictions about ecosystems, we often have available a number of
+different ecosystem models that attempt to represent their dynamics in a
+detailed mechanistic way. Each of these can be used as simulators of
+large-scale experiments and make forecasts about the fate of ecosystems under
+different scenarios in order to support the development of appropriate
+management strategies. However, structural differences, systematic
+discrepancies and uncertainties lead to different models giving different
+predictions under these scenarios. This is further complicated by the fact that
+the models may not be run with the same species or functional groups, spatial
+structure or time scale. Rather than simply trying to select a 'best' model, or
+taking some weighted average, it is important to exploit the strengths of each
+of the available models, while learning from the differences between them. To
+achieve this, we construct a flexible statistical model of the relationships
+between a collection or 'ensemble' of mechanistic models and their biases,
+allowing for structural and parameter uncertainty and for different ways of
+representing reality. Using this statistical meta-model, we can combine prior
+beliefs, model estimates and direct observations using Bayesian methods, and
+make coherent predictions of future outcomes under different scenarios with
+robust measures of uncertainty. In this paper we present the modelling
+framework and discuss results obtained using a diverse ensemble of models in
+scenarios involving future changes in fishing levels. These examples illustrate
+the value of our approach in predicting outcomes for possible strategies
+pertaining to climate and fisheries policy aimed at improving food security and
+maintaining ecosystem integrity.
+",0,0,0,1,0,0
+9444,Nonlinear atomic vibrations and structural phase transitions in strained carbon chains,"  We consider longitudinal nonlinear atomic vibrations in uniformly strained
+carbon chains with the cumulene structure ($=C=C=)_{n}$. With the aid of ab
+initio simulations, based on the density functional theory, we have revealed
+the phenomenon of the $\pi$-mode softening in a certain range of its amplitude
+for the strain above the critical value $\eta_{c}\approx 11\,{\%}$.
+Condensation of this soft mode induces the structural transformation of the
+carbon chain with doubling of its unit cell. This is the Peierls phase
+transition in the strained cumulene, which was previously revealed in [Nano
+Lett. 14, 4224 (2014)]. The Peierls transition leads to appearance of the
+energy gap in the electron spectrum of the strained carbyne, and this material
+transforms from the conducting state to semiconducting or insulating states.
+The authors of the above paper emphasize that such phenomenon can be used for
+construction of various nanodevices. The $\pi$-mode softening occurs because
+the old equilibrium positions (EQPs), around which carbon atoms vibrate at
+small strains, lose their stability and these atoms begin to vibrate in the new
+potential wells located near old EQPs. We study the stability of the new EQPs,
+as well as stability of vibrations in their vicinity. In previous paper
+[Physica D 203, 121(2005)], we proved that only three symmetry-determined
+Rosenberg nonlinear normal modes can exist in monoatomic chains with arbitrary
+interparticle interactions. They are the above-discussed $\pi$-mode and two
+other modes, which we call $\sigma$-mode and $\tau$-mode. These modes
+correspond to the multiplication of the unit cell of the vibrational state by
+two, three or four times compared to that of the equilibrium state. We study
+properties of these modes in the chain model with arbitrary pair potential of
+interparticle interactions.
+",0,1,0,0,0,0
+9445,Homotopy classes of gauge fields and the lattice,"  For a smooth manifold $M$, possibly with boundary and corners, and a Lie
+group $G$, we consider a suitable description of gauge fields in terms of
+parallel transport, as groupoid homomorphisms from a certain path groupoid in
+$M$ to $G$. Using a cotriangulation $\mathscr{C}$ of $M$, and collections of
+finite-dimensional families of paths relative to $\mathscr{C}$, we define a
+homotopical equivalence relation of parallel transport maps, leading to the
+concept of an extended lattice gauge (ELG) field. A lattice gauge field, as
+used in Lattice Gauge Theory, is part of the data contained in an ELG field,
+but the latter contains further local topological information sufficient to
+reconstruct a principal $G$-bundle on $M$ up to equivalence. The space of ELG
+fields of a given pair $(M,\mathscr{C})$ is a covering for the space of fields
+in Lattice Gauge Theory, whose connected components parametrize equivalence
+classes of principal $G$-bundles on $M$. We give a criterion to determine when
+ELG fields over different cotriangulations define equivalent bundles.
+",0,0,1,0,0,0
+9446,Classical System of Martin-Lof's Inductive Definitions is not Equivalent to Cyclic Proofs,"  A cyclic proof system, called CLKID-omega, gives us another way of
+representing inductive definitions and effcient proof search. The 2011 paper by
+Brotherston and Simpson showed that the provability of CLKID-omega includes the
+provability of Martin-Lof's system of inductive definitions, called LKID, and
+conjectured the equivalence. Since then, the equivalence has been left an open
+question. This paper shows that CLKID-omega and LKID are indeed not equivalent.
+This paper considers a statement called 2-Hydra in these two systems with the
+first-order language formed by 0, the successor, the natural number predicate,
+and a binary predicate symbol used to express 2-Hydra. This paper shows that
+the 2-Hydra statement is provable in CLKID-omega, but the statement is not
+provable in LKID, by constructing some Henkin model where the statement is
+false.
+",1,0,0,0,0,0
+9447,Factorization and non-factorization theorems for pseudocontinuable functions,"  Let $\theta$ be an inner function on the unit disk, and let
+$K^p_\theta:=H^p\cap\theta\overline{H^p_0}$ be the associated star-invariant
+subspace of the Hardy space $H^p$, with $p\ge1$. While a nontrivial function
+$f\in K^p_\theta$ is never divisible by $\theta$, it may have a factor $h$
+which is ""not too different"" from $\theta$ in the sense that the ratio
+$h/\theta$ (or just the anti-analytic part thereof) is smooth on the circle. In
+this case, $f$ is shown to have additional integrability and/or smoothness
+properties, much in the spirit of the Hardy--Littlewood--Sobolev embedding
+theorem. The appropriate norm estimates are established, and their sharpness is
+discussed.
+",0,0,1,0,0,0
+9448,Adaptively Detecting Malicious Queries in Web Attacks,"  Web request query strings (queries), which pass parameters to the referenced
+resource, are always manipulated by attackers to retrieve sensitive data and
+even take full control of victim web servers and web applications. However,
+existing malicious query detection approaches in the current literature cannot
+cope with changing web attacks with constant detection models. In this paper,
+we propose AMODS, an adaptive system that periodically updates the detection
+model to detect the latest unknown attacks. We also propose an adaptive
+learning strategy, called SVM HYBRID, leveraged by our system to minimize
+manual work. In the evaluation, an up-to-date detection model is trained on a
+ten-day query dataset collected from an academic institute's web server logs.
+Our system outperforms existing web attack detection methods, with an F-value
+of 94.79% and FP rate of 0.09%. The total number of malicious queries obtained
+by SVM HYBRID is 2.78 times that by the popular Support Vector Machine Adaptive
+Learning (SVM AL) method. The malicious queries obtained can be used to update
+the Web Application Firewall (WAF) signature library.
+",1,0,0,0,0,0
+9449,Derivatives pricing using signature payoffs,"  We introduce signature payoffs, a family of path-dependent derivatives that
+are given in terms of the signature of the price path of the underlying asset.
+We show that these derivatives are dense in the space of continuous payoffs, a
+result that is exploited to quickly price arbitrary continuous payoffs. This
+approach to pricing derivatives is then tested with European options, American
+options, Asian options, lookback options and variance swaps. As we show,
+signature payoffs can be used to price these derivatives with very high
+accuracy.
+",0,0,0,0,0,1
+9450,ALMA constraints on star-forming gas in a prototypical z=1.5 clumpy galaxy: the dearth of CO(5-4) emission from UV-bright clumps,"  We present deep ALMA CO(5-4) observations of a main sequence, clumpy galaxy
+at z=1.5 in the HUDF. Thanks to the ~0.5"" resolution of the ALMA data, we can
+link stellar population properties to the CO(5-4) emission on scales of a few
+kpc. We detect strong CO(5-4) emission from the nuclear region of the galaxy,
+consistent with the observed $L_{\rm IR}$-$L^{\prime}_{\rm CO(5-4)}$
+correlation and indicating on-going nuclear star formation. The CO(5-4) gas
+component appears more concentrated than other star formation tracers or the
+dust distribution in this galaxy. We discuss possible implications of this
+difference in terms of star formation efficiency and mass build-up at the
+galaxy centre. Conversely, we do not detect any CO(5-4) emission from the
+UV-bright clumps. This might imply that clumps have a high star formation
+efficiency (although they do not display unusually high specific star formation
+rates) and are not entirely gas dominated, with gas fractions no larger than
+that of their host galaxy (~50%). Stellar feedback and disk instability torques
+funnelling gas towards the galaxy centre could contribute to the relatively low
+gas content. Alternatively, clumps could fall in a more standard star formation
+efficiency regime if their actual star-formation rates are lower than generally
+assumed. We find that clump star-formation rates derived with several
+different, plausible methods can vary by up to an order of magnitude. The
+lowest estimates would be compatible with a CO(5-4) non-detection even for
+main-sequence like values of star formation efficiency and gas content.
+",0,1,0,0,0,0
+9451,Data-driven framework for real-time thermospheric density estimation,"  In this paper, we demonstrate a new data-driven framework for real-time
+neutral density estimation via model-data fusion in quasi-physical
+ionosphere-thermosphere models. The framework has two main components: (i) the
+development of a quasi-physical dynamic reduced order model (ROM) that uses a
+linear approximation of the underlying dynamics and effect of the drivers, and
+(ii) dynamic calibration of the ROM through estimation of the ROM coefficients
+that represent the model parameters. We have previously demonstrated the
+development of a quasi-physical ROM using simulation output from a physical
+model and assimilation of non-operational density estimates derived from
+accelerometer measurements along a single orbit. In this paper, we demonstrate
+the potential of the framework for use with operational measurements. We use
+simulated GPS-derived orbit ephemerides with 5 minute resolution as
+measurements. The framework is a first of its kind, simple yet robust and
+accurate method with high potential for providing real-time operational updates
+to the state of the upper atmosphere using quasi-physical models with inherent
+forecasting/predictive capabilities.
+",1,0,0,0,0,0
+9452,Synergies between Asteroseismology and Three-dimensional Simulations of Stellar Turbulence,"  Turbulent mixing of chemical elements by convection has fundamental effects
+on the evolution of stars. The standard algorithm at present, mixing-length
+theory (MLT), is intrinsically local, and must be supplemented by extensions
+with adjustable parameters. As a step toward reducing this arbitrariness, we
+compare asteroseismically inferred internal structures of two Kepler slowly
+pulsating B stars (SPB's; $M\sim 3.25 M_\odot$) to predictions of 321D
+turbulence theory, based upon well-resolved, truly turbulent three-dimensional
+simulations (Arnett , et al. 2015, Christini, et al. 2016) which include
+boundary physics absent from MLT. We find promising agreement between the
+steepness and shapes of the theoretically-predicted composition profile outside
+the convective region in 3D simulations and in asteroseismically constrained
+composition profiles in the best 1D models of the two SPBs. The structure and
+motion of the boundary layer, and the generation of waves, are discussed.
+",0,1,0,0,0,0
+9453,A polyharmonic Maass form of depth 3/2 for SL_2(Z),"  Duke, Imamoglu, and Toth constructed a polyharmonic Maass form of level 4
+whose Fourier coefficients encode real quadratic class numbers. A more general
+construction of such forms was subsequently given by Bruinier, Funke, and
+Imamoglu. Here we give a direct construction of such a form for the full
+modular group and study the properties of its coefficients. We give
+interpretations of the coefficients of the holomorphic parts of each of these
+polyharmonic Maass forms as inner products of certain weakly holomorphic
+modular forms and harmonic Maass forms. The coefficients of square index are
+particularly intractable; in order to address these, we develop various
+extensions of the usual normalized Peterson inner product using a strategy of
+Bringmann, Ehlen and Diamantis.
+",0,0,1,0,0,0
+9454,Systems of cubic forms in many variables,"  We consider a system of $R$ cubic forms in $n$ variables, with integer
+coefficients, which define a smooth complete intersection in projective space.
+Provided $n\geq 25R$, we prove an asymptotic formula for the number of integer
+points in an expanding box at which these forms simultaneously vanish. In
+particular we can handle systems of forms in $O(R)$ variables, previous work
+having required that $n \gg R^2$. One conjectures that $n \geq 6R+1$ should be
+sufficient. We reduce the problem to an upper bound for the number of solutions
+to a certain auxiliary inequality. To prove this bound we adapt a method of
+Davenport.
+",0,0,1,0,0,0
+9455,Nitrogen-doped Nanoporous Carbon Membranes Functionalized with Co/CoP Janus-type nanocrystals as Hydrogen Evolution Electrode in Both Acid and Alkaline Environment,"  Self-supported electrocatalysts being generated and employed directly as
+electrode for energy conversion has been intensively pursued in the fields of
+materials chemistry and energy. Herein, we report a synthetic strategy to
+prepare freestanding hierarchically structured, nitrogen-doped nanoporous
+graphitic carbon membranes functionalized with Janus-type Co/CoP nanocrystals
+(termed as HNDCM-Co/CoP), which were successfully applied as a
+highly-efficient, binder-free electrode in hydrogen evolution reaction (HER).
+Benefited from multiple structural merits, such as high degree of
+graphitization, three-dimensionally interconnected micro-/meso-/macropores,
+uniform nitrogen-doping, well-dispersed Co/CoP nanocrystals as well as the
+confinement effect of the thin carbon layer on the nanocrystals, HNDCM-Co/CoP
+exhibited superior electrocatalytic activity and long-term operation stability
+for HER under both acid and alkaline conditions. As a proof-of-concept of
+practical usage, a macroscopic piece of HNDCM-Co/CoP of 5.6 cm x 4 cm x 60 um
+in size was prepared in our laboratory. Driven by a solar cell,
+electroreduction of water in alkaline condition (pH 14) was performed, and H2
+has been produced at a rate of 16 ml/min, demonstrating its potential as
+real-life energy conversion systems.
+",0,1,0,0,0,0
+9456,Cyber-Physical Systems Security -- A Survey,"  With the exponential growth of cyber-physical systems (CPS), new security
+challenges have emerged. Various vulnerabilities, threats, attacks, and
+controls have been introduced for the new generation of CPS. However, there
+lack a systematic study of CPS security issues. In particular, the
+heterogeneity of CPS components and the diversity of CPS systems have made it
+very difficult to study the problem with one generalized model.
+In this paper, we capture and systematize existing research on CPS security
+under a unified framework. The framework consists of three orthogonal
+coordinates: (1) from the \emph{security} perspective, we follow the well-known
+taxonomy of threats, vulnerabilities, attacks and controls; (2)from the
+\emph{CPS components} perspective, we focus on cyber, physical, and
+cyber-physical components; and (3) from the \emph{CPS systems} perspective, we
+explore general CPS features as well as representative systems (e.g., smart
+grids, medical CPS and smart cars). The model can be both abstract to show
+general interactions of a CPS application and specific to capture any details
+when needed. By doing so, we aim to build a model that is abstract enough to be
+applicable to various heterogeneous CPS applications; and to gain a modular
+view of the tightly coupled CPS components. Such abstract decoupling makes it
+possible to gain a systematic understanding of CPS security, and to highlight
+the potential sources of attacks and ways of protection.
+",1,0,0,0,0,0
+9457,WebPol: Fine-grained Information Flow Policies for Web Browsers,"  In the standard web browser programming model, third-party scripts included
+in an application execute with the same privilege as the application's own
+code. This leaves the application's confidential data vulnerable to theft and
+leakage by malicious code and inadvertent bugs in the third-party scripts.
+Security mechanisms in modern browsers (the same-origin policy, cross-origin
+resource sharing and content security policies) are too coarse to suit this
+programming model. All these mechanisms (and their extensions) describe whether
+or not a script can access certain data, whereas the meaningful requirement is
+to allow untrusted scripts access to confidential data that they need and to
+prevent the scripts from leaking data on the side. Motivated by this gap, we
+propose WebPol, a policy mechanism that allows a website developer to include
+fine-grained policies on confidential application data in the familiar syntax
+of the JavaScript programming language. The policies can be associated with any
+webpage element, and specify what aspects of the element can be accessed by
+which third-party domains. A script can access data that the policy allows it
+to, but it cannot pass the data (or data derived from it) to other scripts or
+remote hosts in contravention of the policy. To specify the policies, we expose
+a small set of new native APIs in JavaScript. Our policies can be enforced
+using any of the numerous existing proposals for information flow tracking in
+web browsers. We have integrated our policies into one such proposal that we
+use to evaluate performance overheads and to test our examples.
+",1,0,0,0,0,0
+9458,Large Sample Asymptotics of the Pseudo-Marginal Method,"  The pseudo-marginal algorithm is a variant of the Metropolis-Hastings
+algorithm which samples asymptotically from a probability distribution when it
+is only possible to estimate unbiasedly an unnormalized version of its density.
+Practically, one has to trade-off the computational resources used to obtain
+this estimator against the asymptotic variances of the ergodic averages
+obtained by the pseudo-marginal algorithm. Recent works optimizing this
+trade-off rely on some strong assumptions which can cast doubts over their
+practical relevance. In particular, they all assume that the distribution of
+the additive error in the log-likelihood estimator is independent of the
+parameter value at which it is evaluated. Under weak regularity conditions we
+show here that, as the number of data points tends to infinity, a
+space-rescaled version of the pseudo-marginal chain converges weakly towards
+another pseudo-marginal chain for which this assumption indeed holds. A study
+of this limiting chain allows us to provide parameter dimension-dependent
+guidelines on how to optimally scale a normal random walk proposal and the
+number of Monte Carlo samples for the pseudo-marginal method in the large
+sample regime. This complements and validates currently available results.
+",0,0,0,1,0,0
+9459,Large-time behavior of solutions to Vlasov-Poisson-Fokker-Planck equations: from evanescent collisions to diffusive limit,"  The present contribution investigates the dynamics generated by the
+two-dimensional Vlasov-Poisson-Fokker-Planck equation for charged particles in
+a steady inhomogeneous background of opposite charges. We provide global in
+time estimates that are uniform with respect to initial data taken in a bounded
+set of a weighted $L^2$ space, and where dependencies on the mean-free path
+$\tau$ and the Debye length $\delta$ are made explicit. In our analysis the
+mean free path covers the full range of possible values: from the regime of
+evanescent collisions $\tau\to\infty$ to the strongly collisional regime
+$\tau\to0$. As a counterpart, the largeness of the Debye length, that enforces
+a weakly nonlinear regime, is used to close our nonlinear estimates.
+Accordingly we pay a special attention to relax as much as possible the
+$\tau$-dependent constraint on $\delta$ ensuring exponential decay with
+explicit $\tau$-dependent rates towards the stationary solution. In the
+strongly collisional limit $\tau\to0$, we also examine all possible asymptotic
+regimes selected by a choice of observation time scale. Here also, our emphasis
+is on strong convergence, uniformity with respect to time and to initial data
+in bounded sets of a $L^2$ space. Our proofs rely on a detailed study of the
+nonlinear elliptic equation defining stationary solutions and a careful
+tracking and optimization of parameter dependencies of
+hypocoercive/hypoelliptic estimates.
+",0,0,1,0,0,0
+9460,Towards Adaptive Resilience in High Performance Computing,"  Failure rates in high performance computers rapidly increase due to the
+growth in system size and complexity. Hence, failures became the norm rather
+than the exception. Different approaches on high performance computing (HPC)
+systems have been introduced, to prevent failures (e. g., redundancy) or at
+least minimize their impacts (e. g., checkpoint and restart). In most cases,
+when these approaches are employed to increase the resilience of certain parts
+of a system, energy consumption rapidly increases, or performance significantly
+degrades. To address this challenge, we propose on-demand resilience as an
+approach to achieve adaptive resilience in HPC systems. In this work, the HPC
+system is considered in its entirety and resilience mechanisms such as
+checkpointing, isolation, and migration, are activated on-demand. Using the
+proposed approach, the unavoidable increase in total energy consumption and
+system performance degradation is decreased compared to the typical
+checkpoint/restart and redundant resilience mechanisms. Our work aims to
+mitigate a large number of failures occurring at various layers in the system,
+to prevent their propagation, and to minimize their impact, all of this in an
+energy-saving manner. In the case of failures that are estimated to occur but
+cannot be mitigated using the proposed on-demand resilience approach, the
+system administrators will be notified in view of performing further
+investigations into the causes of these failures and their impacts.
+",1,0,0,0,0,0
+9461,Discrete Sequential Prediction of Continuous Actions for Deep RL,"  It has long been assumed that high dimensional continuous control problems
+cannot be solved effectively by discretizing individual dimensions of the
+action space due to the exponentially large number of bins over which policies
+would have to be learned. In this paper, we draw inspiration from the recent
+success of sequence-to-sequence models for structured prediction problems to
+develop policies over discretized spaces. Central to this method is the
+realization that complex functions over high dimensional spaces can be modeled
+by neural networks that predict one dimension at a time. Specifically, we show
+how Q-values and policies over continuous spaces can be modeled using a next
+step prediction model over discretized dimensions. With this parameterization,
+it is possible to both leverage the compositional structure of action spaces
+during learning, as well as compute maxima over action spaces (approximately).
+On a simple example task we demonstrate empirically that our method can perform
+global search, which effectively gets around the local optimization issues that
+plague DDPG. We apply the technique to off-policy (Q-learning) methods and show
+that our method can achieve the state-of-the-art for off-policy methods on
+several continuous control tasks.
+",1,0,0,1,0,0
+9462,Sampling Errors in Nested Sampling Parameter Estimation,"  Sampling errors in nested sampling parameter estimation differ from those in
+Bayesian evidence calculation, but have been little studied in the literature.
+This paper provides the first explanation of the two main sources of sampling
+errors in nested sampling parameter estimation, and presents a new diagrammatic
+representation for the process. We find no current method can accurately
+measure the parameter estimation errors of a single nested sampling run, and
+propose a method for doing so using a new algorithm for dividing nested
+sampling runs. We empirically verify our conclusions and the accuracy of our
+new method.
+",0,1,0,1,0,0
+9463,Towards a Generic Diver-Following Algorithm: Balancing Robustness and Efficiency in Deep Visual Detection,"  This paper explores the design and development of a class of robust
+diver-following algorithms for autonomous underwater robots. By considering the
+operational challenges for underwater visual tracking in diverse real-world
+settings, we formulate a set of desired features of a generic diver following
+algorithm. We attempt to accommodate these features and maximize general
+tracking performance by exploiting the state-of-the-art deep object detection
+models. We fine-tune the building blocks of these models with a goal of
+balancing the trade-off between robustness and efficiency in an onboard setting
+under real-time constraints. Subsequently, we design an architecturally simple
+Convolutional Neural Network (CNN)-based diver-detection model that is much
+faster than the state-of-the-art deep models yet provides comparable detection
+performances. In addition, we validate the performance and effectiveness of the
+proposed diver-following modules through a number of field experiments in
+closed-water and open-water environments.
+",1,0,0,0,0,0
+9464,Blockchain: A Graph Primer,"  Bitcoin and its underlying technology Blockchain have become popular in
+recent years. Designed to facilitate a secure distributed platform without
+central authorities, Blockchain is heralded as a paradigm that will be as
+powerful as Big Data, Cloud Computing and Machine learning. Blockchain
+incorporates novel ideas from various fields such as public key encryption and
+distributed systems. As such, a reader often comes across resources that
+explain the Blockchain technology from a certain perspective only, leaving the
+reader with more questions than before. We will offer a holistic view on
+Blockchain. Starting with a brief history, we will give the building blocks of
+Blockchain, and explain their interactions. As graph mining has become a major
+part its analysis, we will elaborate on graph theoretical aspects of the
+Blockchain technology. We also devote a section to the future of Blockchain and
+explain how extensions like Smart Contracts and De-centralized Autonomous
+Organizations will function. Without assuming any reader expertise, our aim is
+to provide a concise but complete description of the Blockchain technology.
+",1,0,0,0,0,0
+9465,Comparison of forcing functions in magnetohydrodynamic turbulence,"  Results are presented of direct numerical simulations of incompressible,
+homogeneous magnetohydrodynamic turbulence without a mean magnetic field,
+subject to different mechanical forcing functions commonly used in the
+literature. Specifically, the forces are negative damping (which uses the
+large-scale velocity field as a forcing function), a nonhelical random force,
+and a nonhelical static sinusoidal force (analogous to helical ABC forcing).
+The time evolution of the three ideal invariants (energy, magnetic helicity and
+cross helicity), the time-averaged energy spectra, the energy ratios and the
+dissipation ratios are examined. All three forcing functions produce
+qualitatively similar steady states with regards to the time evolution of the
+energy and magnetic helicity. However, differences in the cross helicity
+evolution are observed, particularly in the case of the static sinusoidal
+method of energy injection. Indeed, an ensemble of sinusoidally-forced
+simulations with identical parameters shows significant variations in the cross
+helicity over long time periods, casting some doubt on the validity of the
+principle of ergodicity in systems in which the injection of helicity cannot be
+controlled. Cross helicity can unexpectedly enter the system through the
+forcing function and must be carefully monitored.
+",0,1,0,0,0,0
+9466,Chance-Constrained AC Optimal Power Flow Integrating HVDC Lines and Controllability,"  The integration of large-scale renewable generation has major implications on
+the operation of power systems, two of which we address in this paper. First,
+system operators have to deal with higher degrees of uncertainty. Second, with
+abundant potential of renewable generation in remote locations, they need to
+incorporate the operation of High Voltage Direct Current lines (HVDC). This
+paper introduces an optimization tool that addresses both challenges by
+incorporating; the full AC power flow equations and chance constraints to
+address the uncertainty of renewable infeed, HVDC modeling for point-to-point
+lines, and optimizing generator and HVDC corrective control policies in
+reaction to uncertainty. The main contributions are twofold. First, we
+introduce a HVDC line model and the corresponding HVDC participation factors in
+a chance-constrained AC-OPF framework. Second, we modify an existing algorithm
+for solving the chance-constrained AC optimal power flow to allow for
+optimization of the generation and HVDC participation factors. Using realistic
+wind forecast data, and a 10 bus system with one HVDC line and two wind farms,
+we demonstrate the performance of our algorithm and show the benefit of
+controllability.
+",1,0,0,0,0,0
+9467,"A Scalable, Linear-Time Dynamic Cutoff Algorithm for Molecular Dynamics","  Recent results on supercomputers show that beyond 65K cores, the efficiency
+of molecular dynamics simulations of interfacial systems decreases
+significantly. In this paper, we introduce a dynamic cutoff method (DCM) for
+interfacial systems of arbitrarily large size. The idea consists in adopting a
+cutoff-based method in which the cutoff is cho- sen on a particle-by-particle
+basis, according to the distance from the interface. Computationally, the
+challenge is shifted from the long-range solvers to the detection of the
+interfaces and to the computation of the particle-interface distances. For
+these tasks, we present linear-time algorithms that do not rely on global
+communication patterns. As a result, the DCM algorithm is suited for large
+systems of particles and mas- sively parallel computers. To demonstrate its
+potential, we integrated DCM into the LAMMPS open-source molecular dynamics
+package, and simulated large liquid/vapor systems on two supercomputers:
+SuperMuc and JUQUEEN. In all cases, the accuracy of DCM is comparable to the
+traditional particle-particle particle-mesh (PPPM) algorithm, while the
+performance is considerably superior for large numbers of particles. For
+JUQUEEN, we provide timings for simulations running on the full system (458,
+752 cores), and show nearly perfect strong and weak scaling.
+",1,1,0,0,0,0
+9468,Spiral arms and disc stability in the Andromeda galaxy,"  Aims: Density waves are often considered as the triggering mechanism of star
+formation in spiral galaxies. Our aim is to study relations between different
+star formation tracers (stellar UV and near-IR radiation and emission from HI,
+CO and cold dust) in the spiral arms of M31, to calculate stability conditions
+in the galaxy disc and to draw conclusions about possible star formation
+triggering mechanisms.
+Methods: We select fourteen spiral arm segments from the de-projected data
+maps and compare emission distributions along the cross sections of the
+segments in different datasets to each other, in order to detect spatial
+offsets between young stellar populations and the star forming medium. By using
+the disc stability condition as a function of perturbation wavelength and
+distance from the galaxy centre we calculate the effective disc stability
+parameters and the least stable wavelengths at different distances. For this we
+utilise a mass distribution model of M31 with four disc components (old and
+young stellar discs, cold and warm gaseous discs) embedded within the external
+potential of the bulge, the stellar halo and the dark matter halo. Each
+component is considered to have a realistic finite thickness.
+Results: No systematic offsets between the observed UV and CO/far-IR emission
+across the spiral segments are detected. The calculated effective stability
+parameter has a minimal value Q_{eff} ~ 1.8 at galactocentric distances 12 - 13
+kpc. The least stable wavelengths are rather long, with the minimal values
+starting from ~ 3 kpc at distances R > 11 kpc.
+Conclusions: The classical density wave theory is not a realistic explanation
+for the spiral structure of M31. Instead, external causes should be considered,
+e.g. interactions with massive gas clouds or dwarf companions of M31.
+",0,1,0,0,0,0
+9469,Anomalous Brownian motion via linear Fokker-Planck equations,"  According to a traditional point of view Boltzmann entropy is intimately
+related to linear Fokker-Planck equations (Smoluchowski, Klein-Kramers, and
+Rayleigh equations) that describe a well-known nonequilibrium phenomenon:
+(normal) Brownian motion of a particle immersed in a thermal bath.
+Nevertheless, current researches have claimed that non-Boltzmann entropies
+(Tsallis and Renyi entropies, for instance) may give rise to anomalous Brownian
+motion through nonlinear Fokker-Planck equations. The novelty of the present
+article is to show that anomalous diffusion could be investigated within the
+framework of non-Markovian linear Fokker-Planck equations. So on the ground of
+this non-Markovian approach to Brownian motion, we find out anomalous diffusion
+characterized by the mean square displacement of a free particle and a harmonic
+oscillator in absence of inertial force as well as the mean square momentum of
+a free particle in presence of inertial force.
+",0,1,0,0,0,0
+9470,An RKHS model for variable selection in functional regression,"  A mathematical model for variable selection in functional regression models
+with scalar response is proposed. By ""variable selection"" we mean a procedure
+to replace the whole trajectories of the functional explanatory variables with
+their values at a finite number of carefully selected instants (or ""impact
+points""). The basic idea of our approach is to use the Reproducing Kernel
+Hilbert Space (RKHS) associated with the underlying process, instead of the
+more usual L2[0,1] space, in the definition of the linear model. This turns out
+to be especially suitable for variable selection purposes, since the
+finite-dimensional linear model based on the selected ""impact points"" can be
+seen as a particular case of the RKHS-based linear functional model. In this
+framework, we address the consistent estimation of the optimal design of impact
+points and we check, via simulations and real data examples, the performance of
+the proposed method.
+",0,0,0,1,0,0
+9471,"Dual-LED-based multichannel microscopy for whole-slide multiplane, multispectral, and phase imaging","  We report the development of a multichannel microscopy for whole-slide
+multiplane, multispectral, and phase imaging. We use trinocular heads to split
+the beam path into 6 independent channels and employ a camera array for
+parallel data acquisition, achieving a maximum data throughput of ~1 gigapixel
+per second. To perform single-frame rapid autofocusing, we place two
+near-infrared LEDs at the back focal plane of the condenser lens to illuminate
+the sample from two different incident angles. A hot mirror is used to direct
+the near-infrared light to an autofocusing camera. For multiplane whole slide
+imaging (WSI), we acquire 6 different focal planes of a thick specimen
+simultaneously. For multispectral WSI, we relay the 6 independent image planes
+to the same focal position and simultaneously acquire information at 6 spectral
+bands. For whole-slide phase imaging, we acquire images at 3 focal positions
+simultaneously and use the transport-of-intensity equation to recover the phase
+information. We also provide an open-source design to further increase the
+number of channels from 6 to 15. The reported platform provides a simple
+solution for multiplexed fluorescence imaging and multimodal WSI. Acquiring an
+instant focal stack without z-scanning may also enable fast 3D dynamic tracking
+of various biological samples.
+",0,1,0,0,0,0
+9472,"Revisiting the quest for a universal log-law and the role of pressure gradient in ""canonical"" wall-bounded turbulent flows","  The trinity of so-called ""canonical"" wall-bounded turbulent flows, comprising
+the zero pressure gradient turbulent boundary layer, abbreviated ZPG TBL,
+turbulent pipe flow and channel/duct flows has continued to receive intense
+attention as new and more reliable experimental data have become available.
+Nevertheless, the debate on whether the logarithmic part of the mean velocity
+profile, in particular the Kármán constant $\kappa$, is identical for these
+three canonical flows or flow-dependent is still ongoing. In this paper, which
+expands upon Monkewitz and Nagib (24th ICTAM Conf., Montreal, 2016), the
+asymptotic matching requirement of equal $\kappa$ in the log-law and in the
+expression for the centerline/free-stream velocity is reiterated and shown to
+preclude a single universal log-law in the three canonical flows or at least
+make it very unlikely. The current re-analysis of high quality mean velocity
+profiles in ZPG TBL's, the Princeton ""Superpipe"" and in channels and ducts
+leads to a coherent description of (almost) all seemingly contradictory data
+interpretations in terms of TWO logarithmic regions in pipes and channels: A
+universal interior, near-wall logarithmic region with the same parameters as in
+the ZPG TBL, in particular $\kappa_{\mathrm{wall}} \cong 0.384$, but only
+extending from around $150$ to around $10^3$ wall units, and shrinking with
+increasing pressure gradient, followed by an exterior logarithmic region with a
+flow specific $\kappa$ matching the logarithmic slope of the respective
+free-stream or centerline velocity. The log-law parameters of the exterior
+logarithmic region in channels and pipes are shown to depend monotonically on
+the pressure gradient.
+",0,1,0,0,0,0
+9473,Teaching robots to imitate a human with no on-teacher sensors. What are the key challenges?,"  In this paper, we consider the problem of learning object manipulation tasks
+from human demonstration using RGB or RGB-D cameras. We highlight the key
+challenges in capturing sufficiently good data with no tracking devices -
+starting from sensor selection and accurate 6DoF pose estimation to natural
+language processing. In particular, we focus on two showcases: gluing task with
+a glue gun and simple block-stacking with variable blocks. Furthermore, we
+discuss how a linguistic description of the task could help to improve the
+accuracy of task description. We also present the whole architecture of our
+transfer of the imitated task to the simulated and real robot environment.
+",1,0,0,0,0,0
+9474,Control and Observability Aspects of Phase Synchronization,"  This paper addresses important control and observability aspects of the phase
+synchronization of two oscillators. To this aim a feedback control framework is
+proposed based on which issues related to master-slave synchronization are
+analyzed. Comparing results using Cartesian and cylindrical coordinates in the
+context of the proposed framework it is argued that: i)~observability does not
+play a significant role in phase synchronization, although it is granted that
+it might be relevant for complete synchronization; and ii)~a practical
+difficulty is faced when phase synchronization is aimed at but the control
+action is not a direct function of the phase error. A procedure for overcoming
+such a problem is proposed. The only assumption made is that the phase can be
+estimated using the arctangent function. The main aspects of the paper are
+illustrated using the Poincaré equations, van der Pol and Rössler
+oscillators in dynamical regimes for which the phase is well defined.
+",0,1,0,0,0,0
+9475,Dynamic density structure factor of a unitary Fermi gas at finite temperature,"  We present a theoretical investigation of the dynamic density structure
+factor of a strongly interacting Fermi gas near a Feshbach resonance at finite
+temperature. The study is based on a gauge invariant linear response theory.
+The theory is consistent with a diagrammatic approach for the equilibrium state
+taking into account the pair fluctuation effects and respects some important
+restrictions like the $f$-sum rule. Our numerical results show that the dynamic
+density structure factor at large incoming momentum and at half recoil
+frequency has a qualitatively similar behavior as the order parameter, which
+can signify the appearance of the condensate. This qualitatively agrees with
+the recent Bragg spectroscopy experiment results. We also present the results
+at small incoming momentum.
+",0,1,0,0,0,0
+9476,Surjunctivity and topological rigidity of algebraic dynamical systems,"  Let $X$ be a compact metrizable group and $\Gamma$ a countable group acting
+on $X$ by continuous group automorphisms. We give sufficient conditions under
+which the dynamical system $(X,\Gamma)$ is surjunctive, i.e., every injective
+continuous map $\tau \colon X \to X$ commuting with the action of $\Gamma$ is
+surjective.
+",0,0,1,0,0,0
+9477,High Resilience Diverse Domain Multilevel Audio Watermarking with Adaptive Threshold,"  A novel diverse domain (DCT-SVD & DWT-SVD) watermarking scheme is proposed in
+this paper. Here, the watermark is embedded simultaneously onto the two
+domains. It is shown that an audio signal watermarked using this scheme has
+better subjective and objective quality when compared with other watermarking
+schemes. Also proposed are two novel watermark detection algorithms viz., AOT
+(Adaptively Optimised Threshold) and AOTx (AOT eXtended). The fundamental idea
+behind both is finding an optimum threshold for detecting a known character
+embedded along with the actual watermarks in a known location, with the
+constraint that the Bit Error Rate (BER) is minimum. This optimum threshold is
+used for detecting the other characters in the watermarks. This approach is
+shown to make the watermarking scheme less susceptible to various signal
+processing attacks, thus making the watermarks more robust.
+",1,0,0,0,0,0
+9478,Multiprocessor Approximate Message Passing with Column-Wise Partitioning,"  Solving a large-scale regularized linear inverse problem using multiple
+processors is important in various real-world applications due to the
+limitations of individual processors and constraints on data sharing policies.
+This paper focuses on the setting where the matrix is partitioned column-wise.
+We extend the algorithmic framework and the theoretical analysis of approximate
+message passing (AMP), an iterative algorithm for solving linear inverse
+problems, whose asymptotic dynamics are characterized by state evolution (SE).
+In particular, we show that column-wise multiprocessor AMP (C-MP-AMP) obeys an
+SE under the same assumptions when the SE for AMP holds. The SE results imply
+that (i) the SE of C-MP-AMP converges to a state that is no worse than that of
+AMP and (ii) the asymptotic dynamics of C-MP-AMP and AMP can be identical.
+Moreover, for a setting that is not covered by SE, numerical results show that
+damping can improve the convergence performance of C-MP-AMP.
+",1,0,1,0,0,0
+9479,Qualitative robustness for bootstrap approximations,"  An important property of statistical estimators is qualitative robustness,
+that is small changes in the distribution of the data only result in small
+chances of the distribution of the estimator. Moreover, in practice, the
+distribution of the data is commonly unknown, therefore bootstrap
+approximations can be used to approximate the distribution of the estimator.
+Hence qualitative robustness of the statistical estimator under the bootstrap
+approximation is a desirable property. Currently most theoretical
+investigations on qualitative robustness assume independent and identically
+distributed pairs of random variables. However, in practice this assumption is
+not fulfilled. Therefore, we examine the qualitative robustness of bootstrap
+approximations for non-i.i.d. random variables, for example $\alpha$-mixing and
+weakly dependent processes. In the i.i.d. case qualitative robustness is
+ensured via the continuity of the statistical operator, representing the
+estimator, see Hampel (1971) and Cuevas and Romo (1993). We show, that
+qualitative robustness of the bootstrap approximation is still ensured under
+the assumption that the statistical operator is continuous and under an
+additional assumption on the stochastic process. In particular, we require a
+convergence condition of the empirical measure of the underlying process, the
+so called Varadarajan property.
+",0,0,1,1,0,0
+9480,On Asymptotic Properties of Hyperparameter Estimators for Kernel-based Regularization Methods,"  The kernel-based regularization method has two core issues: kernel design and
+hyperparameter estimation. In this paper, we focus on the second issue and
+study the properties of several hyperparameter estimators including the
+empirical Bayes (EB) estimator, two Stein's unbiased risk estimators (SURE) and
+their corresponding Oracle counterparts, with an emphasis on the asymptotic
+properties of these hyperparameter estimators. To this goal, we first derive
+and then rewrite the first order optimality conditions of these hyperparameter
+estimators, leading to several insights on these hyperparameter estimators.
+Then we show that as the number of data goes to infinity, the two SUREs
+converge to the best hyperparameter minimizing the corresponding mean square
+error, respectively, while the more widely used EB estimator converges to
+another best hyperparameter minimizing the expectation of the EB estimation
+criterion. This indicates that the two SUREs are asymptotically optimal but the
+EB estimator is not. Surprisingly, the convergence rate of two SUREs is slower
+than that of the EB estimator, and moreover, unlike the two SUREs, the EB
+estimator is independent of the convergence rate of $\Phi^T\Phi/N$ to its
+limit, where $\Phi$ is the regression matrix and $N$ is the number of data. A
+Monte Carlo simulation is provided to demonstrate the theoretical results.
+",1,0,0,0,0,0
+9481,SECS: Efficient Deep Stream Processing via Class Skew Dichotomy,"  Despite that accelerating convolutional neural network (CNN) receives an
+increasing research focus, the save on resource consumption always comes with a
+decrease in accuracy. To both increase accuracy and decrease resource
+consumption, we explore an environment information, called class skew, which is
+easily available and exists widely in daily life. Since the class skew may
+switch as time goes, we bring up probability layer to utilize class skew
+without any overhead during the runtime. Further, we observe class skew
+dichotomy that some class skew may appear frequently in the future, called hot
+class skew, and others will never appear again or appear seldom, called cold
+class skew. Inspired by techniques from source code optimization, two modes,
+i.e., interpretation and compilation, are proposed. The interpretation mode
+pursues efficient adaption during runtime for cold class skew and the
+compilation mode aggressively optimize on hot ones for more efficient
+deployment in the future. Aggressive optimization is processed by
+class-specific pruning and provides extra benefit. Finally, we design a
+systematic framework, SECS, to dynamically detect class skew, processing
+interpretation and compilation, as well as select the most accurate
+architectures under the runtime resource budget. Extensive evaluations show
+that SECS can realize end-to-end classification speedups by a factor of 3x to
+11x relative to state-of-the-art convolutional neural networks, at a higher
+accuracy.
+",1,0,0,0,0,0
+9482,Implementation of infinite-range exterior complex scaling to the time-dependent complete-active-space self-consistent-field method,"  We present a numerical implementation of the infinite-range exterior complex
+scaling (irECS) [Phys. Rev. A 81, 053845 (2010)] as an efficient absorbing
+boundary to the time-dependent complete-active-space self-consistent field
+(TD-CASSCF) method [Phys. Rev. A 94, 023405 (2016)] for multielectron atoms
+subject to an intense laser pulse. We introduce Gauss-Laguerre-Radau quadrature
+points to construct discrete variable representation basis functions in the
+last radial finite element extending to infinity. This implementation is
+applied to strong-field ionization and high-harmonic generation in He, Be, and
+Ne atoms. It efficiently prevents unphysical reflection of photoelectron wave
+packets at the simulation boundary, enabling accurate simulations with
+substantially reduced computational cost, even under significant (~ 50%) double
+ionization. For the case of a simulation of high-harmonic generation from Ne,
+for example, 80% cost reduction is achieved, compared to a mask-function
+absorption boundary.
+",0,1,0,0,0,0
+9483,A consistent measure of the merger histories of massive galaxies using close-pair statistics I: Major mergers at $z < 3.5$,"  We use a large sample of $\sim 350,000$ galaxies constructed by combining the
+UKIDSS UDS, VIDEO/CFHT-LS, UltraVISTA/COSMOS and GAMA survey regions to probe
+the major merging histories of massive galaxies ($>10^{10}\ \mathrm{M}_\odot$)
+at $0.005 < z < 3.5$. We use a method adapted from that presented in
+Lopez-Sanjuan et al. (2014) using the full photometric redshift probability
+distributions, to measure pair $\textit{fractions}$ of flux-limited, stellar
+mass selected galaxy samples using close-pair statistics. The pair fraction is
+found to weakly evolve as $\propto (1+z)^{0.8}$ with no dependence on stellar
+mass. We subsequently derive major merger $\textit{rates}$ for galaxies at $>
+10^{10}\ \mathrm{M}_\odot$ and at a constant number density of $n > 10^{-4}$
+Mpc$^{-3}$, and find rates a factor of 2-3 smaller than previous works,
+although this depends strongly on the assumed merger timescale and likelihood
+of a close-pair merging. Galaxies undergo approximately 0.5 major mergers at $z
+< 3.5$, accruing an additional 1-4 $\times 10^{10}\ \mathrm{M}_\odot$ in the
+process. Major merger accretion rate densities of $\sim 2 \times 10^{-4}$
+$\mathrm{M}_\odot$ yr$^{-1}$ Mpc$^{-3}$ are found for number density selected
+samples, indicating that direct progenitors of local massive
+($>10^{11}\mathrm{M}_\odot$) galaxies have experienced a steady supply of
+stellar mass via major mergers throughout their evolution. While pair fractions
+are found to agree with those predicted by the Henriques et al. (2014)
+semi-analytic model, the Illustris hydrodynamical simulation fails to
+quantitatively reproduce derived merger rates. Furthermore, we find major
+mergers become a comparable source of stellar mass growth compared to
+star-formation at $z < 1$, but is 10-100 times smaller than the SFR density at
+higher redshifts.
+",0,1,0,0,0,0
+9484,EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD,"  We present a generic framework for trading off fidelity and cost in computing
+stochastic gradients when the costs of acquiring stochastic gradients of
+different quality are not known a priori. We consider a mini-batch oracle that
+distributes a limited query budget over a number of stochastic gradients and
+aggregates them to estimate the true gradient. Since the optimal mini-batch
+size depends on the unknown cost-fidelity function, we propose an algorithm,
+{\it EE-Grad}, that sequentially explores the performance of mini-batch oracles
+and exploits the accumulated knowledge to estimate the one achieving the best
+performance in terms of cost-efficiency. We provide performance guarantees for
+EE-Grad with respect to the optimal mini-batch oracle, and illustrate these
+results in the case of strongly convex objectives. We also provide a simple
+numerical example that corroborates our theoretical findings.
+",1,0,0,1,0,0
+9485,Test of special relativity using a fiber network of optical clocks,"  Phase compensated optical fiber links enable high accuracy atomic clocks
+separated by thousands of kilometers to be compared with unprecedented
+statistical resolution. By searching for a daily variation of the frequency
+difference between four strontium optical lattice clocks in different locations
+throughout Europe connected by such links, we improve upon previous tests of
+time dilation predicted by special relativity. We obtain a constraint on the
+Robertson--Mansouri--Sexl parameter $|\alpha|\lesssim 1.1 \times10^{-8}$
+quantifying a violation of time dilation, thus improving by a factor of around
+two the best known constraint obtained with Ives--Stilwell type experiments,
+and by two orders of magnitude the best constraint obtained by comparing atomic
+clocks. This work is the first of a new generation of tests of fundamental
+physics using optical clocks and fiber links. As clocks improve, and as fiber
+links are routinely operated, we expect that the tests initiated in this paper
+will improve by orders of magnitude in the near future.
+",0,1,0,0,0,0
+9486,Magnetic phases of spin-1 lattice gases with random interactions,"  A spin-1 atomic gas in an optical lattice, in the unit-filling Mott Insulator
+(MI) phase and in the presence of disordered spin-dependent interaction, is
+considered. In this regime, at zero temperature, the system is well described
+by a disordered rotationally-invariant spin-1 bilinear-biquadratic model. We
+study, via the density matrix renormalization group algorithm, a bounded
+disorder model such that the spin interactions can be locally either
+ferromagnetic or antiferromagnetic. Random interactions induce the appearance
+of a disordered ferromagnetic phase characterized by a non-vanishing value of
+spin-glass order parameter across the boundary between a ferromagnetic phase
+and a dimer phase exhibiting random singlet order. The study of the
+distribution of the block entanglement entropy reveals that in this region
+there is no random singlet order.
+",0,1,0,0,0,0
+9487,On the second boundary value problem for Monge-Ampere type equations and geometric optics,"  In this paper, we prove the existence of classical solutions to second
+boundary value prob- lems for generated prescribed Jacobian equations, as
+recently developed by the second author, thereby obtaining extensions of
+classical solvability of optimal transportation problems to problems arising in
+near field geometric optics. Our results depend in particular on a priori
+second derivative estimates recently established by the authors under weak
+co-dimension one convexity hypotheses on the associated matrix functions with
+respect to the gradient variables, (A3w). We also avoid domain deformations by
+using the convexity theory of generating functions to construct unique initial
+solutions for our homotopy family, thereby enabling application of the degree
+theory for nonlinear oblique boundary value problems.
+",0,0,1,0,0,0
+9488,Is the kinetic equation for turbulent gas-particle flows ill-posed?,"  This paper is about well-posedness and realizability of the kinetic equation
+for gas-particle flows and its relationship to the Generalized Langevin Model
+(GLM) PDF equation. Previous analyses claim that this kinetic equation is
+ill-posed, that in particular it has the properties of a backward heat equation
+and as a consequence, its solutions will in the course of time exhibit
+finite-time singularities. We show that the analysis leading to this conclusion
+is fundamentally incorrect because it ignores the coupling between the phase
+space variables in the kinetic equation and the time and particle inertia
+dependence of the phase space diffusion tensor. This contributes an extra $+ve$
+diffusion that always outweighs the contribution from the$-ve$ diffusion
+associated with the dispersion along one of the principal axes of the phase
+space diffusion tensor. This is confirmed by a numerical evaluation of analytic
+solutions of these $+ve$ and $-ve$ contributions to the particle diffusion
+coefficient along this principal axis. We also examine other erroneous claims
+and assumptions made in previous studies that demonstrate the apparent
+superiority of the GLM PDF approach over the kinetic approach. In so doing we
+have drawn attention to the limitations of the GLM approach which these studies
+have ignored or not properly considered, to give a more balanced appraisal of
+the benefits of both PDF approaches.
+",0,1,0,0,0,0
+9489,On the construction of small subsets containing special elements in a finite field,"  In this note we construct a series of small subsets containing a non-d-th
+power element in a finite field by applying certain bounds on incomplete
+character sums.
+Precisely, let $h=\lfloor q^{\delta}\rfloor>1$ and $d\mid q^h-1$. Let $r$ be
+a prime divisor of $q-1$ such that the largest prime power part of $q-1$ has
+the form $r^s$. Then there is a constant $0<\epsilon<1$ such that for a ratio
+at least $ {q^{-\epsilon h}}$ of $\alpha\in \mathbb{F}_{q^{h}}
+\backslash\mathbb{F}_{q}$, the set $S=\{ \alpha-x^t, x\in\mathbb{F}_{q}\}$ of
+cardinality $1+\frac {q-1} {M(h)}$ contains a non-d-th power in
+$\mathbb{F}_{q^{\lfloor q^\delta\rfloor}}$, where $t$ is the largest power of
+$r$ such that $t<\sqrt{q}/h$ and $M(h)$ is defined as $$M(h)=\max_{r \mid
+(q-1)} r^{\min\{v_r(q-1), \lfloor\log_r{q}/2-\log_r h\rfloor\}}.$$ Here $r$
+runs thourgh prime divisors and $v_r(x)$ is the $r$-adic oder of $x$. For odd
+$q$, the choice of $\delta=\frac 12-d, d=o(1)>0$ shows that there exists an
+explicit subset of cardinality $q^{1-d}=O(\log^{2+\epsilon'}(q^h))$ containing
+a non-quadratic element in the field $\mathbb{F}_{q^h}$. On the other hand, the
+choice of $h=2$ shows that for any odd prime power $q$, there is an explicit
+subset of cardinality $1+\frac {q-1}{M(2)}$ containing a non-quadratic element
+in $\mathbb{F}_{q^2}$. This improves a $q-1$ construction by Coulter and Kosick
+\cite{CK} since $\lfloor \log_2{(q-1)}\rfloor\leq M(2) < \sqrt{q}$.
+In addition, we obtain a similar construction for small sets containing a
+primitive element. The construction works well provided $\phi(q^h-1)$ is very
+small, where $\phi$ is the Euler's totient function.
+",1,0,1,0,0,0
+9490,Information Potential Auto-Encoders,"  In this paper, we suggest a framework to make use of mutual information as a
+regularization criterion to train Auto-Encoders (AEs). In the proposed
+framework, AEs are regularized by minimization of the mutual information
+between input and encoding variables of AEs during the training phase. In order
+to estimate the entropy of the encoding variables and the mutual information,
+we propose a non-parametric method. We also give an information theoretic view
+of Variational AEs (VAEs), which suggests that VAEs can be considered as
+parametric methods that estimate entropy. Experimental results show that the
+proposed non-parametric models have more degree of freedom in terms of
+representation learning of features drawn from complex distributions such as
+Mixture of Gaussians, compared to methods which estimate entropy using
+parametric approaches, such as Variational AEs.
+",1,0,0,1,0,0
+9491,Irreducible compositions of degree two polynomials over finite fields have regular structure,"  Let $q$ be an odd prime power and $D$ be the set of monic irreducible
+polynomials in $\mathbb F_q[x]$ which can be written as a composition of monic
+degree two polynomials. In this paper we prove that $D$ has a natural regular
+structure by showing that there exists a finite automaton having $D$ as
+accepted language. Our method is constructive.
+",1,0,1,0,0,0
+9492,Quantum interferometry in multi-mode systems,"  We consider the situation when the signal propagating through each arm of an
+interferometer has a complicated multi-mode structure. We find the relation
+between the particle-entanglement and the possibility to surpass the shot-noise
+limit of the phase estimation. Our results are general---they apply to pure and
+mixed states of identical and distinguishable particles (or combinations of
+both), for a fixed and fluctuating number of particles. We also show that the
+method for detecting the entanglement often used in two-mode system can give
+misleading results when applied to the multi-mode case.
+",0,1,0,0,0,0
+9493,Glider representations of chains of semisimple Lie algebras,"  We start the study of glider representations in the setting of semisimple Lie
+algebras. A glider representation is defined for some positively filtered ring
+$FR$ and here we consider the right bounded algebra filtration
+$FU(\mathfrak{g})$ on the universal enveloping algebra $U(\mathfrak{g})$ of
+some semisimple Lie algebra $\mathfrak{g}$ given by a fixed chain of semisimple
+sub Lie algebras $\mathfrak{g}_1 \subset \mathfrak{g}_2 \subset \ldots \subset
+\mathfrak{g}_n = \mathfrak{g}$. Inspired by the classical representation
+theory, we introduce so-called Verma glider representations. Their existence is
+related to the relations between the root systems of the appearing Lie algebras
+$\mathfrak{g}_i$. In particular, we consider chains of simple Lie algebras of
+the same type $A,B,C$ and $D$.
+",0,0,1,0,0,0
+9494,Equilibrium configurations of large nanostructures using the embedded saturated-fragments stochastic density functional theory,"  An \emph{ab initio} Langevin dynamics approach is developed based on
+stochastic density functional theory (sDFT) within a new \emph{embedded
+saturated } \emph{fragment }formalism, applicable to covalently bonded systems.
+The forces on the nuclei generated by sDFT contain a random component natural
+to Langevin dynamics and its standard deviation is used to estimate the
+friction term on each atom by satisfying the fluctuation\textendash dissipation
+relation. The overall approach scales linearly with system size even if the
+density matrix is not local and is thus applicable to ordered as well as
+disordered extended systems. We implement the approach for a series of silicon
+nanocrystals (NCs) of varying size with a diameter of up to $3$nm corresponding
+to $N_{e}=3000$ electrons and generate a set of configurations that are
+distributed canonically at a fixed temperature, ranging from cryogenic to room
+temperature. We also analyze the structure properties of the NCs and discuss
+the reconstruction of the surface geometry.
+",0,1,0,0,0,0
+9495,"Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks","  Biological plastic neural networks are systems of extraordinary computational
+capabilities shaped by evolution, development, and lifetime learning. The
+interplay of these elements leads to the emergence of adaptive behavior and
+intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
+Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
+plastic neural networks with a large variety of dynamics, architectures, and
+plasticity rules: these artificial systems are composed of inputs, outputs, and
+plastic components that change in response to experiences in an environment.
+These systems may autonomously discover novel adaptive algorithms, and lead to
+hypotheses on the emergence of biological adaptation. EPANNs have seen
+considerable progress over the last two decades. Current scientific and
+technological advances in artificial neural networks are now setting the
+conditions for radically new approaches and results. In particular, the
+limitations of hand-designed networks could be overcome by more flexible and
+innovative solutions. This paper brings together a variety of inspiring ideas
+that define the field of EPANNs. The main methods and results are reviewed.
+Finally, new opportunities and developments are presented.
+",1,0,0,0,0,0
+9496,Hypergraph Convolution and Hypergraph Attention,"  Recently, graph neural networks have attracted great attention and achieved
+prominent performance in various research fields. Most of those algorithms have
+assumed pairwise relationships of objects of interest. However, in many real
+applications, the relationships between objects are in higher-order, beyond a
+pairwise formulation. To efficiently learn deep embeddings on the high-order
+graph-structured data, we introduce two end-to-end trainable operators to the
+family of graph neural networks, i.e., hypergraph convolution and hypergraph
+attention. Whilst hypergraph convolution defines the basic formulation of
+performing convolution on a hypergraph, hypergraph attention further enhances
+the capacity of representation learning by leveraging an attention module. With
+the two operators, a graph neural network is readily extended to a more
+flexible model and applied to diverse applications where non-pairwise
+relationships are observed. Extensive experimental results with semi-supervised
+node classification demonstrate the effectiveness of hypergraph convolution and
+hypergraph attention.
+",1,0,0,1,0,0
+9497,Network Capacity Bound for Personalized PageRank in Multimodal Networks,"  In a former paper the concept of Bipartite PageRank was introduced and a
+theorem on the limit of authority flowing between nodes for personalized
+PageRank has been generalized. In this paper we want to extend those results to
+multimodal networks. In particular we introduce a hypergraph type that may be
+used for describing multimodal network where a hyperlink connects nodes from
+each of the modalities. We introduce a generalisation of PageRank for such
+graphs and define the respective random walk model that can be used for
+computations. we finally state and prove theorems on the limit of outflow of
+authority for cases where individual modalities have identical and distinct
+damping factors.
+",1,1,0,0,0,0
+9498,Hessian corrections to Hybrid Monte Carlo,"  A method for the introduction of second-order derivatives of the log
+likelihood into HMC algorithms is introduced, which does not require the
+Hessian to be evaluated at each leapfrog step but only at the start and end of
+trajectories.
+",0,0,0,1,0,0
+9499,Are Over-massive Haloes of Ultra Diffuse Galaxies Consistent with Extended MOND?,"  A sample of Coma cluster ultra-diffuse galaxies (UDGs) are modelled in the
+context of Extended Modified Newtonian Dynamics (EMOND) with the aim to explain
+the large dark matter-like effect observed in these cluster galaxies.
+We first build a model of the Coma cluster in the context of EMOND using gas
+and galaxy mass profiles from the literature. Then assuming the dynamical mass
+of the UDGs satisfies the fundamental manifold of other ellipticals, and that
+the UDG stellar mass-to-light matches their colour, we can verify the EMOND
+formulation by comparing two predictions of the baryonic mass of UDGs.
+We find that EMOND can explain the UDG mass, within the expected modelling
+errors, if they lie on the fundamental manifold of ellipsoids, however, given
+that measurements show one UDG lying off the fundamental manifold, observations
+of more UDGs are needed to confirm this assumption.
+",0,1,0,0,0,0
+9500,Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots,"  Reliable and real-time 3D reconstruction and localization functionality is a
+crucial prerequisite for the navigation of actively controlled capsule
+endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic
+technology for use in the gastrointestinal (GI) tract. In this study, we
+propose a fully dense, non-rigidly deformable, strictly real-time,
+intraoperative map fusion approach for actively controlled endoscopic capsule
+robot applications which combines magnetic and vision-based localization, with
+non-rigid deformations based frame-to-model map fusion. The performance of the
+proposed method is demonstrated using four different ex-vivo porcine stomach
+models. Across different trajectories of varying speed and complexity, and four
+different endoscopic cameras, the root mean square surface reconstruction
+errors 1.58 to 2.17 cm.
+",1,0,0,0,0,0
+9501,Algebraic laminations for free products and arational trees,"  This work is the first step towards a description of the Gromov boundary of
+the free factor graph of a free product, with applications to subgroup
+classification for outer automorphisms. We extend the theory of algebraic
+laminations dual to trees, as developed by Coulbois, Hilion, Lustig and
+Reynolds, to the context of free products; this also gives us an opportunity to
+give a unified account of this theory. We first show that any $\mathbb{R}$-tree
+with dense orbits in the boundary of the corresponding outer space can be
+reconstructed as a quotient of the boundary of the group by its dual
+lamination. We then describe the dual lamination in terms of a band complex on
+compact $\mathbb{R}$-trees (generalizing Coulbois-Hilion-Lustig's compact
+heart), and we analyze this band complex using versions of the Rips machine and
+of the Rauzy-Veech induction. An important output of the theory is that the
+above map from the boundary of the group to the $\mathbb{R}$-tree is 2-to-1
+almost everywhere.
+A key point for our intended application is a unique duality result for
+arational trees. It says that if two trees have a leaf in common in their dual
+laminations, and if one of the trees is arational and relatively free, then
+they are equivariantly homeomorphic.
+This statement is an analogue of a result in the free group saying that if
+two trees are dual to a common current and one of the trees is free arational,
+then the two trees are equivariantly homeomorphic. However, we notice that in
+the setting of free products, the continuity of the pairing between trees and
+currents fails. For this reason, in all this paper, we work with laminations
+rather than with currents.
+",0,0,1,0,0,0
+9502,Real-Time Recovery Efficiencies and Performance of the Palomar Transient Factory's Transient Discovery Pipeline,"  We present the transient source detection efficiencies of the Palomar
+Transient Factory (PTF), parameterizing the number of transients that PTF
+found, versus the number of similar transients that occurred over the same
+period in the survey search area but that were missed. PTF was an optical sky
+survey carried out with the Palomar 48-inch telescope over 2009-2012, observing
+more than 8000 square degrees of sky with cadences of between 1 and 5 days,
+locating around 50,000 non-moving transient sources, and spectroscopically
+confirming around 1900 supernovae. We assess the effectiveness with which PTF
+detected transient sources, by inserting ~7 million artificial point sources
+into real PTF data. We then study the efficiency with which the PTF real-time
+pipeline recovered these sources as a function of the source magnitude, host
+galaxy surface brightness, and various observing conditions (using proxies for
+seeing, sky brightness, and transparency). The product of this study is a
+multi-dimensional recovery efficiency grid appropriate for the range of
+observing conditions that PTF experienced, and that can then be used for
+studies of the rates, environments, and luminosity functions of different
+transient types using detailed Monte Carlo simulations. We illustrate the
+technique using the observationally well-understood class of type Ia
+supernovae.
+",0,1,0,0,0,0
+9503,Spatial Risk Measure for Max-Stable and Max-Mixture Processes,"  In this paper, we consider isotropic and stationary max-stable, inverse
+max-stable and max-mixture processes $X=(X(s))\_{s\in\bR^2}$ and the damage
+function $\cD\_X^{\nu}= |X|^\nu$ with $0<\nu<1/2$. We study the quantitative
+behavior of a risk measure which is the variance of the average of
+$\cD\_X^{\nu}$ over a region $\mathcal{A}\subset \bR^2$.} This kind of risk
+measure has already been introduced and studied for \vero{some} max-stable
+processes in \cite{koch2015spatial}. %\textcolor{red}{In this study, we
+generalised this risk measure to be applicable for several models: asymptotic
+dependence represented by max-stable, asymptotic independence represented by
+inverse max-stable and mixing between of them.} We evaluated the proposed risk
+measure by a simulation study.
+",0,0,1,1,0,0
+9504,Scale invariant transfer matrices and Hamiltionians,"  Given a direct system of Hilbert spaces $s\mapsto \mathcal H_s$ (with
+isometric inclusion maps $\iota_s^t:\mathcal H_s\rightarrow \mathcal H_t$ for
+$s\leq t$) corresponding to quantum systems on scales $s$, we define notions of
+scale invariant and weakly scale invariant operators. Is some cases of quantum
+spin chains we find conditions for transfer matrices and nearest neighbour
+Hamiltonians to be scale invariant or weakly so. Scale invariance forces
+spatial inhomogeneity of the spectral parameter. But weakly scale invariant
+transfer matrices may be spatially homogeneous in which case the change of
+spectral parameter from one scale to another is governed by a classical
+dynamical system exhibiting fractal behaviour.
+",0,0,1,0,0,0
+9505,Visual-Based Analysis of Classification Measures with Applications to Imbalanced Data,"  With a plethora of available classification performance measures, choosing
+the right metric for the right task requires careful thought. To make this
+decision in an informed manner, one should study and compare general properties
+of candidate measures. However, analysing measures with respect to complete
+ranges of their domain values is a difficult and challenging task. In this
+study, we attempt to support such analyses with a specialized visualization
+technique, which operates in a barycentric coordinate system using a 3D
+tetrahedron. Additionally, we adapt this technique to the context of imbalanced
+data and put forward a set of properties which should be taken into account
+when selecting a classification performance measure. As a result, we compare 22
+popular measures and show important differences in their behaviour. Moreover,
+for parametric measures such as the F$_{\beta}$ and IBA$_\alpha$(G-mean), we
+analytically derive parameter thresholds that change measure properties.
+Finally, we provide an online visualization tool that can aid the analysis of
+complete domain ranges of performance measures.
+",1,0,0,0,0,0
+9506,Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation,"  Modern large displacement optical flow algorithms usually use an
+initialization by either sparse descriptor matching techniques or dense
+approximate nearest neighbor fields. While the latter have the advantage of
+being dense, they have the major disadvantage of being very outlier-prone as
+they are not designed to find the optical flow, but the visually most similar
+correspondence. In this article we present a dense correspondence field
+approach that is much less outlier-prone and thus much better suited for
+optical flow estimation than approximate nearest neighbor fields. Our approach
+does not require explicit regularization, smoothing (like median filtering) or
+a new data term. Instead we solely rely on patch matching techniques and a
+novel multi-scale matching strategy. We also present enhancements for outlier
+filtering. We show that our approach is better suited for large displacement
+optical flow estimation than modern descriptor matching techniques. We do so by
+initializing EpicFlow with our approach instead of their originally used
+state-of-the-art descriptor matching technique. We significantly outperform the
+original EpicFlow on MPI-Sintel, KITTI 2012, KITTI 2015 and Middlebury. In this
+extended article of our former conference publication we further improve our
+approach in matching accuracy as well as runtime and present more experiments
+and insights.
+",1,0,0,0,0,0
+9507,Critical current density and vortex pinning mechanism of Lix(NH3)yFe2Te1.2Se0.8 single crystals,"  We grew Lix(NH3)yFe2Te1.2Se0.8 single crystals successfully using the
+low-temperature ammonothermal method and the onset superconducting transition
+temperature Tc,onset is increased to 21 K when compared to 14 K in the parent
+compound FeTe0.6Se0.4. The derived critical current density Jc increases
+remarkably to 2.6*10^5 A/cm^2 at 2 K. Further analysis indicates that the
+dominant pinning mechanism in Lix(NH3)yFe2Te1.2Se0.8 single crystal is the
+interaction between vortex and surface-like defects with normal core, possibly
+originating from the stacking faults along the c axis, by variations in the
+charge-carrier mean free path l near the defects (delta l pinning). Moreover,
+the flux creep is important to the vortex dynamics of this material.
+",0,1,0,0,0,0
+9508,Pseudopotential for Many-Electron Atoms and Ions,"  Electron-electron correlation forms the basis of difficulties encountered in
+many-body problems. Accurate treatment of the correlation problem is likely to
+unravel some nice physical properties of matter embedded in this correlation.
+In an effort to tackle this many-body problem, two complementary parameter-free
+pseudopotentials for $n$-electron atoms and ions are suggested in this study.
+Using one of the pseudopotentials, near-exact values of the groundstate
+ionization potentials of helium, lithium, and berrylium atoms have been
+calculated. The other pseudopotential also proves to be capable of yielding
+reasonable and reliable quantum physical observables within the
+non-relativistic quantum mechanics.
+",0,1,0,0,0,0
+9509,Secular Orbit Evolution in Systems with a Strong External Perturber - A Simple and Accurate Model,"  We present a semi-analytical correction to the seminal solution for the
+secular motion of a planet's orbit under gravitational influence of an external
+perturber derived by Heppenheimer (1978). A comparison between analytical
+predictions and numerical simulations allows us to determine corrective factors
+for the secular frequency and forced eccentricity in the co-planar restricted
+three-body problem. The correction is given in the form of a polynomial
+function of the system's parameters that can be applied to first-order forced
+eccentricity and secular frequency estimates. The resulting secular equations
+are simple, straight forward to use and improve the fidelity of Heppenheimer's
+solution well beyond higher-order models. The quality and convergence of the
+corrected secular equations are tested for a wide range of parameters and
+limits of its applicability are given.
+",0,1,0,0,0,0
+9510,Reliable counting of weakly labeled concepts by a single spiking neuron model,"  Making an informed, correct and quick decision can be life-saving. It's
+crucial for animals during an escape behaviour or for autonomous cars during
+driving. The decision can be complex and may involve an assessment of the
+amount of threats present and the nature of each threat. Thus, we should expect
+early sensory processing to supply classification information fast and
+accurately, even before relying the information to higher brain areas or more
+complex system components downstream. Today, advanced convolutional artificial
+neural networks can successfully solve visual detection and classification
+tasks and are commonly used to build complex decision making systems. However,
+in order to perform well on these tasks they require increasingly complex,
+""very deep"" model structure, which is costly in inference run-time, energy
+consumption and number of training samples, only trainable on cloud-computing
+clusters. A single spiking neuron has been shown to be able to solve
+recognition tasks for homogeneous Poisson input statistics, a commonly used
+model for spiking activity in the neocortex. When modeled as leaky integrate
+and fire with gradient decent learning algorithm it was shown to posses a
+variety of complex computational capabilities. Here we improve its
+implementation. We also account for more natural stimulus generated inputs that
+deviate from this homogeneous Poisson spiking. The improved gradient-based
+local learning rule allows for significantly better and stable generalization.
+We also show that with its improved capabilities it can count weakly labeled
+concepts by applying our model to a problem of multiple instance learning (MIL)
+with counting where labels are only available for collections of concepts. In
+this counting MNIST task the neuron exploits the improved implementation and
+outperforms conventional ConvNet architecture under similar condtions.
+",0,0,0,0,1,0
+9511,On the stability and applications of distance-based flexible formations,"  This paper investigates the stability of distance-based \textit{flexible}
+undirected formations in the plane. Without rigidity, there exists a set of
+connected shapes for given distance constraints, which is called the ambit. We
+show that a flexible formation can lose its flexibility, or equivalently may
+reduce the degrees of freedom of its ambit, if a small disturbance is
+introduced in the range sensor of the agents. The stability of the disturbed
+equilibrium can be characterized by analyzing the eigenvalues of the linearized
+augmented error system. Unlike infinitesimally rigid formations, the disturbed
+desired equilibrium can be turned unstable regardless of how small the
+disturbance is. We finally present two examples of how to exploit these
+disturbances as design parameters. The first example shows how to combine rigid
+and flexible formations such that some of the agents can move freely in the
+desired and locally stable ambit. The second example shows how to achieve a
+specific shape with fewer edges than the necessary for the standard controller
+in rigid formations.
+",1,0,0,0,0,0
+9512,Space-time domain solutions of the wave equation by a non-singular boundary integral method and Fourier transform,"  The general space-time evolution of the scattering of an incident acoustic
+plane wave pulse by an arbitrary configuration of targets is treated by
+employing a recently developed non-singular boundary integral method to solve
+the Helmholtz equation in the frequency domain from which the fast Fourier
+transform is used to obtain the full space-time solution of the wave equation.
+The non-singular boundary integral solution can enforce the radiation boundary
+condition at infinity exactly and can account for multiple scattering effects
+at all spacings between scatterers without adverse effects on the numerical
+precision. More generally, the absence of singular kernels in the non-singular
+integral equation confers high numerical stability and precision for smaller
+numbers of degrees of freedom. The use of fast Fourier transform to obtain the
+time dependence is not constrained to discrete time steps and is particularly
+efficient for studying the response to different incident pulses by the same
+configuration of scatterers. The precision that can be attained using a smaller
+number of Fourier components is also quantified.
+",0,1,0,0,0,0
+9513,Constrained Least Squares for Extended Complex Factor Analysis,"  For subspace estimation with an unknown colored noise, Factor Analysis (FA)
+is a good candidate for replacing the popular eigenvalue decomposition (EVD).
+Finding the unknowns in factor analysis can be done by solving a non-linear
+least square problem. For this type of optimization problems, the Gauss-Newton
+(GN) algorithm is a powerful and simple method. The most expensive part of the
+GN algorithm is finding the direction of descent by solving a system of
+equations at each iteration. In this paper we show that for FA, the matrices
+involved in solving these systems of equations can be diagonalized in a closed
+form fashion and the solution can be found in a computationally efficient way.
+We show how the unknown parameters can be updated without actually constructing
+these matrices. The convergence performance of the algorithm is studied via
+numerical simulations.
+",0,0,0,1,0,0
+9514,Generalized $k$-core pruning process on directed networks,"  The resilience of a complex interconnected system concerns the size of the
+macroscopic functioning node clusters after external perturbations based on a
+random or designed scheme. For a representation of the interconnected systems
+with directional or asymmetrical interactions among constituents, the directed
+network is a convenient choice. Yet how the interaction directions affect the
+network resilience still lacks thorough exploration. Here, we study the
+resilience of directed networks with a generalized $k$-core pruning process as
+a simple failure procedure based on both the in- and out-degrees of nodes, in
+which any node with an in-degree $< k_{in}$ or an out-degree $< k_{ou}$ is
+removed iteratively. With an explicitly analytical framework, we can predict
+the relative sizes of residual node clusters on uncorrelated directed random
+graphs. We show that the discontinuous transitions rise for cases with $k_{in}
+\geq 2$ or $k_{ou} \geq 2$, and the unidirectional interactions among nodes
+drive the networks more vulnerable against perturbations based on in- and
+out-degrees separately.
+",0,1,0,0,0,0
+9515,Benchmarks for single-phase flow in fractured porous media,"  This paper presents several test cases intended to be benchmarks for
+numerical schemes for single-phase fluid flow in fractured porous media. A
+number of solution strategies are compared, including a vertex and a
+cell-centered finite volume method, a non-conforming embedded discrete fracture
+model, a primal and a dual extended finite element formulation, and a mortar
+discrete fracture model. The proposed benchmarks test the schemes by increasing
+the difficulties in terms of network geometry, e.g. intersecting fractures, and
+physical parameters, e.g. low and high fracture-matrix permeability ratio as
+well as heterogeneous fracture permeabilities. For each problem, the results
+presented by the participants are the number of unknowns, the approximation
+errors in the porous matrix and in the fractures with respect to a reference
+solution, and the sparsity and condition number of the discretized linear
+system. All data and meshes used in this study are publicly available for
+further comparisons.
+",1,0,1,0,0,0
+9516,"On the ""Calligraphy"" of Books","  Authorship attribution is a natural language processing task that has been
+widely studied, often by considering small order statistics. In this paper, we
+explore a complex network approach to assign the authorship of texts based on
+their mesoscopic representation, in an attempt to capture the flow of the
+narrative. Indeed, as reported in this work, such an approach allowed the
+identification of the dominant narrative structure of the studied authors. This
+has been achieved due to the ability of the mesoscopic approach to take into
+account relationships between different, not necessarily adjacent, parts of the
+text, which is able to capture the story flow. The potential of the proposed
+approach has been illustrated through principal component analysis, a
+comparison with the chance baseline method, and network visualization. Such
+visualizations reveal individual characteristics of the authors, which can be
+understood as a kind of calligraphy.
+",1,0,0,0,0,0
+9517,Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks,"  Building a voice conversion (VC) system from non-parallel speech corpora is
+challenging but highly valuable in real application scenarios. In most
+situations, the source and the target speakers do not repeat the same texts or
+they may even speak different languages. In this case, one possible, although
+indirect, solution is to build a generative model for speech. Generative models
+focus on explaining the observations with latent variables instead of learning
+a pairwise transformation function, thereby bypassing the requirement of speech
+frame alignment. In this paper, we propose a non-parallel VC framework with a
+variational autoencoding Wasserstein generative adversarial network (VAW-GAN)
+that explicitly considers a VC objective when building the speech model.
+Experimental results corroborate the capability of our framework for building a
+VC system from unaligned data, and demonstrate improved conversion quality.
+",1,0,0,0,0,0
+9518,"Calidad en repositorios digitales en Argentina, estudio comparativo y cualitativo","  Numerous institutions and organizations need not only to preserve the
+material and publications they produce, but also have as their task (although
+it would be desirable it was an obligation) to publish, disseminate and make
+publicly available all the results of the research and any other
+scientific/academic material. The Open Archives Initiative (OAI) and the
+introduction of Open Archives Initiative Protocol for Metadata Harvesting
+(OAI-PMH), make this task much easier. The main objective of this work is to
+make a comparative and qualitative study of the data -metadata specifically-
+contained in the whole set of Argentine repositories listed in the ROAR portal,
+focusing on the functional perspective of the quality of this metadata. Another
+objective is to offer an overview of the status of these repositories, in an
+attempt to detect common failures and errors institutions incur when storing
+the metadata of the resources contained in these repositories, and thus be able
+to suggest measures to be able to improve the load and further retrieval
+processes. It was found that the eight most used Dublin Core fields are:
+identifier, type, title, date, subject, creator, language and description. Not
+all repositories fill all the fields, and the lack of normalization, or the
+excessive use of fields like language, type, format and subject is somewhat
+striking, and in some cases even alarming
+",1,0,0,0,0,0
+9519,Time-triggering versus event-triggering control over communication channels,"  Time-triggered and event-triggered control strategies for stabilization of an
+unstable plant over a rate-limited communication channel subject to unknown,
+bounded delay are studied and compared. Event triggering carries implicit
+information, revealing the state of the plant. However, the delay in the
+communication channel causes information loss, as it makes the state
+information out of date. There is a critical delay value, when the loss of
+information due to the communication delay perfectly compensates the implicit
+information carried by the triggering events. This occurs when the maximum
+delay equals the inverse of the entropy rate of the plant. In this context,
+extensions of our previous results for event triggering strategies are
+presented for vector systems and are compared with the data-rate theorem for
+time-triggered control, that is extended here to a setting with unknown delay.
+",1,0,1,0,0,0
+9520,Lower spectral radius and spectral mapping theorem for suprema preserving mappings,"  We study Lipschitz, positively homogeneous and finite suprema preserving
+mappings defined on a max-cone of positive elements in a normed vector lattice.
+We prove that the lower spectral radius of such a mapping is always a minimum
+value of its approximate point spectrum. We apply this result to show that the
+spectral mapping theorem holds for the approximate point spectrum of such a
+mapping. By applying this spectral mapping theorem we obtain new inequalites
+for the Bonsall cone spectral radius of max type kernel operators.
+",0,0,1,0,0,0
+9521,SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction,"  In online social networks people often express attitudes towards others,
+which forms massive sentiment links among users. Predicting the sign of
+sentiment links is a fundamental task in many areas such as personal
+advertising and public opinion analysis. Previous works mainly focus on textual
+sentiment classification, however, text information can only disclose the ""tip
+of the iceberg"" about users' true opinions, of which the most are unobserved
+but implied by other sources of information such as social relation and users'
+profile. To address this problem, in this paper we investigate how to predict
+possibly existing sentiment links in the presence of heterogeneous information.
+First, due to the lack of explicit sentiment links in mainstream social
+networks, we establish a labeled heterogeneous sentiment dataset which consists
+of users' sentiment relation, social relation and profile knowledge by
+entity-level sentiment extraction method. Then we propose a novel and flexible
+end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework
+to extract users' latent representations from heterogeneous networks and
+predict the sign of unobserved sentiment links. SHINE utilizes multiple deep
+autoencoders to map each user into a low-dimension feature space while
+preserving the network structure. We demonstrate the superiority of SHINE over
+state-of-the-art baselines on link prediction and node recommendation in two
+real-world datasets. The experimental results also prove the efficacy of SHINE
+in cold start scenario.
+",1,0,0,1,0,0
+9522,Eliminating Field Quantifiers in Strongly Dependent Henselian Fields,"  We prove elimination of field quantifiers for strongly dependent henselian
+fields in the Denef-Pas language. This is achieved by proving the result for a
+class of fields generalizing algebraically maximal Kaplansky fields. We deduce
+that if $(K,v)$ is strongly dependent then so is its henselization.
+",0,0,1,0,0,0
+9523,A study of existing Ontologies in the IoT-domain,"  Several domains have adopted the increasing use of IoT-based devices to
+collect sensor data for generating abstractions and perceptions of the real
+world. This sensor data is multi-modal and heterogeneous in nature. This
+heterogeneity induces interoperability issues while developing cross-domain
+applications, thereby restricting the possibility of reusing sensor data to
+develop new applications. As a solution to this, semantic approaches have been
+proposed in the literature to tackle problems related to interoperability of
+sensor data. Several ontologies have been proposed to handle different aspects
+of IoT-based sensor data collection, ranging from discovering the IoT sensors
+for data collection to applying reasoning on the collected sensor data for
+drawing inferences. In this paper, we survey these existing semantic ontologies
+to provide an overview of the recent developments in this field. We highlight
+the fundamental ontological concepts (e.g., sensor-capabilities and
+context-awareness) required for an IoT-based application, and survey the
+existing ontologies which include these concepts. Based on our study, we also
+identify the shortcomings of currently available ontologies, which serves as a
+stepping stone to state the need for a common unified ontology for the IoT
+domain.
+",1,0,0,0,0,0
+9524,Dynamic Advisor-Based Ensemble (dynABE): Case Study in Stock Trend Prediction of Critical Metal Companies,"  The demand for metals by modern technology has been shifting from common base
+metals to a variety of minor metals, such as cobalt or indium. The industrial
+importance and limited geological availability of some minor metals have led to
+them being considered more ""critical,"" and there is a growing investment
+interest in such critical metals and their producing companies. In this
+research, we create a novel framework, Dynamic Advisor-Based Ensemble (dynABE),
+for stock prediction and use critical metal companies as case study. dynABE
+uses domain knowledge to diversify the feature set by dividing them into
+different ""advisors."" creates high-level ensembles with complex base models for
+each advisor, and combines the advisors together dynamically during validation
+with a novel and effective online update strategy. We test dynABE on three
+cobalt-related companies, and it achieves the best-case misclassification error
+of 31.12% and excess return of 477% compared to the stock itself in a year and
+a half. In addition to presenting an effective stock prediction model with
+decent profitabilities, this research further analyzes dynABE to visualize how
+it works in practice, which also yields discoveries of its interesting
+behaviors when processing time-series data.
+",0,0,0,0,0,1
+9525,High-Speed Demodulation of weak FBGs Based on Microwave Photonics and Chromatic Dispersion,"  A high speed quasi-distributed demodulation method based on the microwave
+photonics and the chromatic dispersion effect is designed and implemented for
+weak fiber Bragg gratings (FBGs). Due to the effect of dispersion compensation
+fiber (DCF), FBG wavelength shift leads to the change of the difference
+frequency signal at the mixer. With the way of crossing microwave sweep cycle,
+all wavelengths of cascade FBGs can be high speed obtained by measuring the
+frequencies change. Moreover, through the introduction of Chirp-Z and Hanning
+window algorithm, the analysis of difference frequency signal is achieved very
+well. By adopting the single-peak filter as a reference, the length disturbance
+of DCF caused by temperature can be also eliminated. Therefore, the accuracy of
+this novel method is greatly improved, and high speed demodulation of FBGs can
+easily realize. The feasibility and performance are experimentally demonstrated
+using 105 FBGs with 0.1% reflectivity, 1 m spatial interval. Results show that
+each grating can be distinguished well, and the demodulation rate is as high as
+40 kHz, the accuracy is about 8 pm.
+",0,1,0,0,0,0
+9526,Destructive Impact of Molecular Noise on Nanoscale Electrochemical Oscillators,"  We study the loss of coherence of electrochemical oscillations on meso- and
+nanosized electrodes with numeric simulations of the electrochemical master
+equation for a prototypical electrochemical oscillator, the hydrogen peroxide
+reduction on Pt electrodes in the presence of halides. On nanoelectrodes, the
+electrode potential changes whenever a stochastic electron-transfer event takes
+place. Electrochemical reaction rate coefficients depend exponentially on the
+electrode potential and become thus fluctuating quantities as well. Therefore,
+also the transition rates between system states become time-dependent which
+constitutes a fundamental difference to purely chemical nanoscale oscillators.
+Three implications are demonstrated: (a) oscillations and steady states shift
+in phase space with decreasing system size, thereby also decreasing
+considerably the oscillating parameter regions; (b) the minimal number of
+molecules necessary to support correlated oscillations is more than 10 times as
+large as for nanoscale chemical oscillators; (c) the relation between
+correlation time and variance of the period of the oscillations predicted for
+chemical oscillators in the weak noise limit is only fulfilled in a very
+restricted parameter range for the electrochemical nano-oscillator.
+",0,1,0,0,0,0
+9527,A new method for recognising Suzuki groups,"  We present a new algorithm for constructive recognition of the Suzuki groups
+in their natural representations. The algorithm runs in Las Vegas polynomial
+time given a discrete logarithm oracle. An implementation is available in the
+Magma computer algebra system.
+",1,0,1,0,0,0
+9528,Pure $Σ_2$-Elementarity beyond the Core,"  We display the entire structure ${\cal R}_2$ coding $\Sigma_1$- and
+$\Sigma_2$-elementarity on the ordinals. This leads to the first steps for
+analyzing pure $\Sigma_3$-elementary substructures.
+",0,0,1,0,0,0
+9529,Parsimonious Inference on Convolutional Neural Networks: Learning and applying on-line kernel activation rules,"  A new, radical CNN design approach is presented in this paper, considering
+the reduction of the total computational load during inference. This is
+achieved by a new holistic intervention on both the CNN architecture and the
+training procedure, which targets to the parsimonious inference by learning to
+exploit or remove the redundant capacity of a CNN architecture. This is
+accomplished, by the introduction of a new structural element that can be
+inserted as an add-on to any contemporary CNN architecture, whilst preserving
+or even improving its recognition accuracy. Our approach formulates a
+systematic and data-driven method for developing CNNs that are trained to
+eventually change size and form in real-time during inference, targeting to the
+smaller possible computational footprint. Results are provided for the optimal
+implementation on a few modern, high-end mobile computing platforms indicating
+a significant speed-up of up to x3 times.
+",1,0,0,0,0,0
+9530,Betweenness and Diversity in Journal Citation Networks as Measures of Interdisciplinarity -- A Tribute to Eugene Garfield --,"  Journals were central to Eugene Garfield's research interests. Among other
+things, journals are considered as units of analysis for bibliographic
+databases such as the Web of Science (WoS) and Scopus. In addition to
+disciplinary classifications of journals, journal citation patterns span
+networks across boundaries to variable extents. Using betweenness centrality
+(BC) and diversity, we elaborate on the question of how to distinguish and rank
+journals in terms of interdisciplinarity. Interdisciplinarity, however, is
+difficult to operationalize in the absence of an operational definition of
+disciplines, the diversity of a unit of analysis is sample-dependent. BC can be
+considered as a measure of multi-disciplinarity. Diversity of co-citation in a
+citing document has been considered as an indicator of knowledge integration,
+but an author can also generate trans-disciplinary--that is,
+non-disciplined--variation by citing sources from other disciplines. Diversity
+in the bibliographic coupling among citing documents can analogously be
+considered as diffusion of knowledge across disciplines. Because the citation
+networks in the cited direction reflect both structure and variation, diversity
+in this direction is perhaps the best available measure of interdisciplinarity
+at the journal level. Furthermore, diversity is based on a summation and can
+therefore be decomposed, differences among (sub)sets can be tested for
+statistical significance. In an appendix, a general-purpose routine for
+measuring diversity in networks is provided.
+",1,0,0,0,0,0
+9531,Non-Markovian Control with Gated End-to-End Memory Policy Networks,"  Partially observable environments present an important open challenge in the
+domain of sequential control learning with delayed rewards. Despite numerous
+attempts during the two last decades, the majority of reinforcement learning
+algorithms and associated approximate models, applied to this context, still
+assume Markovian state transitions. In this paper, we explore the use of a
+recently proposed attention-based model, the Gated End-to-End Memory Network,
+for sequential control. We call the resulting model the Gated End-to-End Memory
+Policy Network. More precisely, we use a model-free value-based algorithm to
+learn policies for partially observed domains using this memory-enhanced neural
+network. This model is end-to-end learnable and it features unbounded memory.
+Indeed, because of its attention mechanism and associated non-parametric
+memory, the proposed model allows us to define an attention mechanism over the
+observation stream unlike recurrent models. We show encouraging results that
+illustrate the capability of our attention-based model in the context of the
+continuous-state non-stationary control problem of stock trading. We also
+present an OpenAI Gym environment for simulated stock exchange and explain its
+relevance as a benchmark for the field of non-Markovian decision process
+learning.
+",1,0,0,1,0,0
+9532,A likely detection of a local interplanetary dust cloud passing near the Earth in the AKARI mid-infrared all-sky map,"  Context. We are creating the AKARI mid-infrared all-sky diffuse maps. Through
+a foreground removal of the zodiacal emission, we serendipitously detected a
+bright residual component whose angular size is about 50 x 20 deg. at a
+wavelength of 9 micron. Aims. We investigate the origin and the physical
+properties of the residual component. Methods. We measured the surface
+brightness of the residual component in the AKARI mid-infrared all-sky maps.
+Results. The residual component was significantly detected only in 2007
+January, even though the same region was observed in 2006 July and 2007 July,
+which shows that it is not due to the Galactic emission. We suggest that this
+may be a small cloud passing near the Earth. By comparing the observed
+intensity ratio of I_9um/I_18um with the expected intensity ratio assuming
+thermal equilibrium of dust grains at 1 AU for various dust compositions and
+sizes, we find that dust grains in the moving cloud are likely to be much
+smaller than typical grains that produce the bulk of the zodiacal light.
+Conclusions. Considering the observed date and position, it is likely that it
+originates in the solar coronal mass ejection (CME) which took place on 2007
+January 25.
+",0,1,0,0,0,0
+9533,Kepler red-clump stars in the field and in open clusters: constraints on core mixing,"  Convective mixing in Helium-core-burning (HeCB) stars is one of the
+outstanding issues in stellar modelling. The precise asteroseismic measurements
+of gravity-modes period spacing ($\Delta\Pi_1$) has opened the door to detailed
+studies of the near-core structure of such stars, which had not been possible
+before. Here we provide stringent tests of various core-mixing scenarios
+against the largely unbiased population of red-clump stars belonging to the old
+open clusters monitored by Kepler, and by coupling the updated precise
+inference on $\Delta\Pi_1$ in thousands field stars with spectroscopic
+constraints. We find that models with moderate overshooting successfully
+reproduce the range observed of $\Delta\Pi_1$ in clusters. In particular we
+show that there is no evidence for the need to extend the size of the
+adiabatically stratified core, at least at the beginning of the HeCB phase.
+This conclusion is based primarily on ensemble studies of $\Delta\Pi_1$ as a
+function of mass and metallicity. While $\Delta\Pi_1$ shows no appreciable
+dependence on the mass, we have found a clear dependence of $\Delta\Pi_1$ on
+metallicity, which is also supported by predictions from models.
+",0,1,0,0,0,0
+9534,"Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity","  Neurons and networks in the cerebral cortex must operate reliably despite
+multiple sources of noise. To evaluate the impact of both input and output
+noise, we determine the robustness of single-neuron stimulus selective
+responses, as well as the robustness of attractor states of networks of neurons
+performing memory tasks. We find that robustness to output noise requires
+synaptic connections to be in a balanced regime in which excitation and
+inhibition are strong and largely cancel each other. We evaluate the conditions
+required for this regime to exist and determine the properties of networks
+operating within it. A plausible synaptic plasticity rule for learning that
+balances weight configurations is presented. Our theory predicts an optimal
+ratio of the number of excitatory and inhibitory synapses for maximizing the
+encoding capacity of balanced networks for a given statistics of afferent
+activations. Previous work has shown that balanced networks amplify
+spatio-temporal variability and account for observed asynchronous irregular
+states. Here we present a novel type of balanced network that amplifies small
+changes in the impinging signals, and emerges automatically from learning to
+perform neuronal and network functions robustly.
+",1,1,0,1,0,0
+9535,On the coherent emission of radio frequency radiation from high energy particle showers,"  Extended Air Showers produced by cosmic rays impinging on the earth
+atmosphere irradiate radio frequency radiation through different mechanisms.
+Upon certain conditions, the emission has a coherent nature, with the
+consequence that the emitted power is not proportional to the energy of the
+primary cosmic rays, but to the energy squared. The effect was predicted in
+1962 by Askaryan and it is nowadays experimentally well established and
+exploited for the detection of ultra high energy cosmic rays.
+In this paper we discuss in details the conditions for coherence, which in
+literature have been too often taken for granted, and calculate them
+analytically, finding a formulation which comprehends both the coherent and the
+incoherent emissions. We apply the result to the Cherenkov effect, obtaining
+the same conclusions derived by Askaryan, and to the geosynchrotron radiation.
+",0,1,0,0,0,0
+9536,La falacia del empate técnico electoral,"  It is argued that the concept of ""technical tie"" in electoral polls and quick
+counts has no probabilistic basis, and that instead the uncertainty associated
+with these statistical exercises should be expressed in terms of a probability
+of victory of the leading candidate.
+-----
+Se argumenta que el concepto de ""empate técnico"" en encuestas y conteos
+rápidos electorales no tiene fundamento probabilístico, y que en su lugar
+la incertidumbre asociada a dichos ejercicios estadísticos debiera expresarse
+en términos de una probabilidad de triunfo del candidato puntero.
+",0,0,0,1,0,0
+9537,"Unexpected 3+ valence of iron in FeO$_2$, a geologically important material lying ""in between"" oxides and peroxides","  Recent discovery of pyrite FeO$_2$, which can be an important ingredient of
+the Earth's lower mantle and which in particular may serve as an extra source
+of water in the Earth's interior, opens new perspectives for geophysics and
+geochemistry, but this is also an extremely interesting material from physical
+point of view. We found that in contrast to naive expectations Fe is nearly 3+
+in this material, which strongly affects its magnetic properties and makes it
+qualitatively different from well known sulfide analogue - FeS$_2$. Doping,
+which is most likely to occur in the Earth's mantle, makes FeO$_2$ much more
+magnetic. In addition we show that unique electronic structure places FeO$_2$
+""in between"" the usual dioxides and peroxides making this system interesting
+both for physics and solid state chemistry.
+",0,1,0,0,0,0
+9538,Towards Smart Proof Search for Isabelle,"  Despite the recent progress in automatic theorem provers, proof engineers are
+still suffering from the lack of powerful proof automation. In this position
+paper we first report our proof strategy language based on a meta-tool
+approach. Then, we propose an AI-based approach to drastically improve proof
+automation for Isabelle, while identifying three major challenges we plan to
+address for this objective.
+",1,0,0,0,0,0
+9539,A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model,"  We study the active learning problem of top-$k$ ranking from multi-wise
+comparisons under the popular multinomial logit model. Our goal is to identify
+the top-$k$ items with high probability by adaptively querying sets for
+comparisons and observing the noisy output of the most preferred item from each
+comparison. To achieve this goal, we design a new active ranking algorithm
+without using any information about the underlying items' preference scores. We
+also establish a matching lower bound on the sample complexity even when the
+set of preference scores is given to the algorithm. These two results together
+show that the proposed algorithm is nearly instance optimal (similar to
+instance optimal [FLN03], but up to polylog factors). Our work extends the
+existing literature on rank aggregation in three directions. First, instead of
+studying a static problem with fixed data, we investigate the top-$k$ ranking
+problem in an active learning setting. Second, we show our algorithm is nearly
+instance optimal, which is a much stronger theoretical guarantee. Finally, we
+extend the pairwise comparison to the multi-wise comparison, which has not been
+fully explored in ranking literature.
+",1,0,0,1,0,0
+9540,Inverse problem on conservation laws,"  The first concise formulation of the inverse problem on conservation laws is
+presented. In this problem one aims to derive the general form of systems of
+differential equations that admit a prescribed set of conservation laws. The
+particular cases of the inverse problem on first integrals of ordinary
+differential equations and on conservation laws for evolution equations are
+considered. We also solve the inverse problem on conservation laws for
+differential equations admitting an infinite dimensional space of zero-order
+characteristics. This particular case is further studied in the context of
+conservative parameterization schemes for the two-dimensional incompressible
+Euler equations. We exhaustively classify conservative parameterization schemes
+for the eddy-vorticity flux that lead to a class of closed, averaged Euler
+equations possessing generalized circulation, generalized momentum and energy
+conservation.
+",0,1,1,0,0,0
+9541,Slow to fast infinitely extended reservoirs for the symmetric exclusion process with long jumps,"  We consider an exclusion process with long jumps in the box $\Lambda\_N=\{1,
+\ldots,N-1\}$, for $N \ge 2$, in contact with infinitely extended reservoirs on
+its left and on its right. The jump rate is described by a transition
+probability $p(\cdot)$ which is symmetric, with infinite support but with
+finite variance. The reservoirs add or remove particles with rate proportional
+to $\kappa N^{-\theta}$, where $\kappa>0$ and $\theta \in\mathbb R$. If
+$\theta>0$ (resp. $\theta<0$) the reservoirs add and fastly remove (resp.
+slowly remove) particles in the bulk. According to the value of $\theta$ we
+prove that the time evolution of the spatial density of particles is described
+by some reaction-diffusion equations with various boundary conditions.
+",0,1,1,0,0,0
+9542,Multi-Dimensional Conservation Laws and Integrable Systems,"  In this paper we introduce a new property of two-dimensional integrable
+systems -- existence of infinitely many local three-dimensional conservation
+laws for pairs of integrable two-dimensional commuting flows. Infinitely many
+three-dimensional local conservation laws for the Korteweg de Vries pair of
+commuting flows and for the Benney commuting hydrodynamic chains are
+constructed. As a by-product we established a new method for computation of
+local conservation laws for three-dimensional integrable systems. The Mikhalev
+equation and the dispersionless limit of the Kadomtsev--Petviashvili equation
+are investigated. All known local and infinitely many new quasi-local
+three-dimensional conservation laws are presented. Also four-dimensional
+conservation laws are considered for couples of three-dimensional integrable
+quasilinear systems and for triples of corresponding hydrodynamic chains.
+",0,1,0,0,0,0
+9543,Fibers in the NGC1333 proto-cluster,"  Are the initial conditions for clustered star formation the same as for
+non-clustered star formation? To investigate the initial gas properties in
+young proto-clusters we carried out a comprehensive and high-sensitivity study
+of the internal structure, density, temperature, and kinematics of the dense
+gas content of the NGC1333 region in Perseus, one of the nearest and best
+studied embedded clusters. The analysis of the gas velocities in the
+Position-Position-Velocity space reveals an intricate underlying gas
+organization both in space and velocity. We identified a total of 14
+velocity-coherent, (tran-)sonic structures within NGC1333, with similar
+physical and kinematic properties than those quiescent, star-forming (aka
+fertile) fibers previously identified in low-mass star-forming clouds. These
+fibers are arranged in a complex spatial network, build-up the observed total
+column density, and contain the dense cores and protostars in this cloud. Our
+results demonstrate that the presence of fibers is not restricted to low-mass
+clouds but can be extended to regions of increasing mass and complexity. We
+propose that the observational dichotomy between clustered and non-clustered
+star-forming regions might be naturally explained by the distinct spatial
+density of fertile fibers in these environments.
+",0,1,0,0,0,0
+9544,Exploiting Friction in Torque Controlled Humanoid Robots,"  A common architecture for torque controlled humanoid robots consists in two
+nested loops. The outer loop generates desired joint/motor torques, and the
+inner loop stabilises these desired values. In doing so, the inner loop usually
+compensates for joint friction phenomena, thus removing their inherent
+stabilising property that may be also beneficial for high level control
+objectives. This paper shows how to exploit friction for joint and task space
+control of humanoid robots. Experiments are carried out using the humanoid
+robot iCub.
+",1,0,0,0,0,0
+9545,What caused what? A quantitative account of actual causation using dynamical causal networks,"  Actual causation is concerned with the question ""what caused what?"" Consider
+a transition between two states within a system of interacting elements, such
+as an artificial neural network, or a biological brain circuit. Which
+combination of synapses caused the neuron to fire? Which image features caused
+the classifier to misinterpret the picture? Even detailed knowledge of the
+system's causal network, its elements, their states, connectivity, and dynamics
+does not automatically provide a straightforward answer to the ""what caused
+what?"" question. Counterfactual accounts of actual causation based on graphical
+models, paired with system interventions, have demonstrated initial success in
+addressing specific problem cases in line with intuitive causal judgments.
+Here, we start from a set of basic requirements for causation (realization,
+composition, information, integration, and exclusion) and develop a rigorous,
+quantitative account of actual causation that is generally applicable to
+discrete dynamical systems. We present a formal framework to evaluate these
+causal requirements that is based on system interventions and partitions, and
+considers all counterfactuals of a state transition. This framework is used to
+provide a complete causal account of the transition by identifying and
+quantifying the strength of all actual causes and effects linking the two
+consecutive system states. Finally, we examine several exemplary cases and
+paradoxes of causation and show that they can be illuminated by the proposed
+framework for quantifying actual causation.
+",1,0,1,1,0,0
+9546,Gaussian One-Armed Bandit and Optimization of Batch Data Processing,"  We consider the minimax setup for Gaussian one-armed bandit problem, i.e. the
+two-armed bandit problem with Gaussian distributions of incomes and known
+distribution corresponding to the first arm. This setup naturally arises when
+the optimization of batch data processing is considered and there are two
+alternative processing methods available with a priori known efficiency of the
+first method. One should estimate the efficiency of the second method and
+provide predominant usage of the most efficient of both them. According to the
+main theorem of the theory of games minimax strategy and minimax risk are
+searched for as Bayesian ones corresponding to the worst-case prior
+distribution. As a result, we obtain the recursive integro-difference equation
+and the second order partial differential equation in the limiting case as the
+number of batches goes to infinity. This makes it possible to determine minimax
+risk and minimax strategy by numerical methods. If the number of batches is
+large enough we show that batch data processing almost does not influence the
+control performance, i.e. the value of the minimax risk. Moreover, in case of
+Bernoulli incomes and large number of batches, batch data processing provides
+almost the same minimax risk as the optimal one-by-one data processing.
+",0,0,1,1,0,0
+9547,Machine Learning with World Knowledge: The Position and Survey,"  Machine learning has become pervasive in multiple domains, impacting a wide
+variety of applications, such as knowledge discovery and data mining, natural
+language processing, information retrieval, computer vision, social and health
+informatics, ubiquitous computing, etc. Two essential problems of machine
+learning are how to generate features and how to acquire labels for machines to
+learn. Particularly, labeling large amount of data for each domain-specific
+problem can be very time consuming and costly. It has become a key obstacle in
+making learning protocols realistic in applications. In this paper, we will
+discuss how to use the existing general-purpose world knowledge to enhance
+machine learning processes, by enriching the features or reducing the labeling
+work. We start from the comparison of world knowledge with domain-specific
+knowledge, and then introduce three key problems in using world knowledge in
+learning processes, i.e., explicit and implicit feature representation,
+inference for knowledge linking and disambiguation, and learning with direct or
+indirect supervision. Finally we discuss the future directions of this research
+topic.
+",1,0,0,1,0,0
+9548,The Case for Learned Index Structures,"  Indexes are models: a B-Tree-Index can be seen as a model to map a key to the
+position of a record within a sorted array, a Hash-Index as a model to map a
+key to a position of a record within an unsorted array, and a BitMap-Index as a
+model to indicate if a data record exists or not. In this exploratory research
+paper, we start from this premise and posit that all existing index structures
+can be replaced with other types of models, including deep-learning models,
+which we term learned indexes. The key idea is that a model can learn the sort
+order or structure of lookup keys and use this signal to effectively predict
+the position or existence of records. We theoretically analyze under which
+conditions learned indexes outperform traditional index structures and describe
+the main challenges in designing learned index structures. Our initial results
+show, that by using neural nets we are able to outperform cache-optimized
+B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over
+several real-world data sets. More importantly though, we believe that the idea
+of replacing core components of a data management system through learned models
+has far reaching implications for future systems designs and that this work
+just provides a glimpse of what might be possible.
+",1,0,0,0,0,0
+9549,Radiation reaction for spinning bodies in effective field theory II: Spin-spin effects,"  We compute the leading Post-Newtonian (PN) contributions at quadratic order
+in the spins to the radiation-reaction acceleration and spin evolution for
+binary systems, entering at four-and-a-half PN order. Our calculation includes
+the back-reaction from finite-size spin effects, which is presented for the
+first time. The computation is carried out, from first principles, using the
+effective field theory framework for spinning extended objects. At this order,
+nonconservative effects in the spin-spin sector are independent of the spin
+supplementary conditions. A non-trivial consistency check is performed by
+showing that the energy loss induced by the resulting radiation-reaction force
+is equivalent to the total emitted power in the far zone. We find that, in
+contrast to the spin-orbit contributions (reported in a companion paper), the
+radiation reaction affects the evolution of the spin vectors once spin-spin
+effects are incorporated.
+",0,1,0,0,0,0
+9550,Incremental control and guidance of hybrid aircraft applied to the Cyclone tailsitter UAV,"  Hybrid unmanned aircraft, that combine hover capability with a wing for fast
+and efficient forward flight, have attracted a lot of attention in recent
+years. Many different designs are proposed, but one of the most promising is
+the tailsitter concept. However, tailsitters are difficult to control across
+the entire flight envelope, which often includes stalled flight. Additionally,
+their wing surface makes them susceptible to wind gusts. In this paper, we
+propose incremental nonlinear dynamic inversion control for the attitude and
+position control. The result is a single, continuous controller, that is able
+to track the acceleration of the vehicle across the flight envelope. The
+proposed controller is implemented on the Cyclone hybrid UAV. Multiple outdoor
+experiments are performed, showing that unmodeled forces and moments are
+effectively compensated by the incremental control structure, and that
+accelerations can be tracked across the flight envelope. Finally, we provide a
+comprehensive procedure for the implementation of the controller on other types
+of hybrid UAVs.
+",1,0,0,0,0,0
+9551,Micromechanics based framework with second-order damage tensors,"  The harmonic product of tensors---leading to the concept of harmonic
+factorization---has been defined in a previous work (Olive et al, 2017). In the
+practical case of 3D crack density measurements on thin or thick walled
+structures, this mathematical tool allows us to factorize the harmonic
+(irreducible) part of the fourth-order damage tensor as an harmonic square: an
+exact harmonic square in 2D, an harmonic square over the set of so-called
+mechanically accessible directions for measurements in the 3D case. The
+corresponding micro-mechanics framework based on second---instead of
+fourth---order damage tensors is derived. An illustrating example is provided
+showing how the proposed framework allows for the modeling of the so-called
+hydrostatic sensitivity up to high damage levels.
+",0,1,0,0,0,0
+9552,Towards formal models and languages for verifiable Multi-Robot Systems,"  Incorrect operations of a Multi-Robot System (MRS) may not only lead to
+unsatisfactory results, but can also cause economic losses and threats to
+safety. These threats may not always be apparent, since they may arise as
+unforeseen consequences of the interactions between elements of the system.
+This call for tools and techniques that can help in providing guarantees about
+MRSs behaviour. We think that, whenever possible, these guarantees should be
+backed up by formal proofs to complement traditional approaches based on
+testing and simulation.
+We believe that tailored linguistic support to specify MRSs is a major step
+towards this goal. In particular, reducing the gap between typical features of
+an MRS and the level of abstraction of the linguistic primitives would simplify
+both the specification of these systems and the verification of their
+properties. In this work, we review different agent-oriented languages and
+their features; we then consider a selection of case studies of interest and
+implement them useing the surveyed languages. We also evaluate and compare
+effectiveness of the proposed solution, considering, in particular, easiness of
+expressing non-trivial behaviour.
+",1,0,0,0,0,0
+9553,On the Complexity of Simple and Optimal Deterministic Mechanisms for an Additive Buyer,"  We show that the Revenue-Optimal Deterministic Mechanism Design problem for a
+single additive buyer is #P-hard, even when the distributions have support size
+2 for each item and, more importantly, even when the optimal solution is
+guaranteed to be of a very simple kind: the seller picks a price for each
+individual item and a price for the grand bundle of all the items; the buyer
+can purchase either the grand bundle at its given price or any subset of items
+at their total individual prices. The following problems are also #P-hard, as
+immediate corollaries of the proof:
+1. determining if individual item pricing is optimal for a given instance,
+2. determining if grand bundle pricing is optimal, and
+3. computing the optimal (deterministic) revenue.
+On the positive side, we show that when the distributions are i.i.d. with
+support size 2, the optimal revenue obtainable by any mechanism, even a
+randomized one, can be achieved by a simple solution of the above kind
+(individual item pricing with a discounted price for the grand bundle) and
+furthermore, it can be computed in polynomial time. The problem can be solved
+in polynomial time too when the number of items is constant.
+",1,0,0,0,0,0
+9554,Further constraints on variations in the IMF from LMXB populations,"  We present constraints on variations in the initial mass function (IMF) of
+nine local early-type galaxies based on their low mass X-ray binary (LMXB)
+populations. Comprised of accreting black holes and neutron stars, these LMXBs
+can be used to constrain the important high mass end of the IMF. We consider
+the LMXB populations beyond the cores of the galaxies ($>0.2R_{e}$; covering
+$75-90\%$ of their stellar light) and find no evidence for systematic
+variations of the IMF with velocity dispersion ($\sigma$). We reject IMFs which
+become increasingly bottom heavy with $\sigma$, up to steep power-laws
+(exponent, $\alpha>2.8$) in massive galaxies ($\sigma>300$km/s), for
+galactocentric radii $>1/4\ R_{e}$. Previously proposed IMFs that become
+increasingly bottom heavy with $\sigma$ are consistent with these data if only
+the number of low mass stars $(<0.5M_{\odot}$) varies. We note that our results
+are consistent with some recent work which proposes that extreme IMFs are only
+present in the central regions of these galaxies. We also consider IMFs that
+become increasingly top-heavy with $\sigma$, resulting in significantly more
+LMXBs. Such a model is consistent with these observations, but additional data
+are required to significantly distinguish between this and an invariant IMF.
+For six of these galaxies, we directly compare with published IMF mismatch
+parameters from the Atlas3D survey, $\alpha_{dyn}$. We find good agreement with
+the LMXB population if galaxies with higher $\alpha_{dyn}$ have more top-heavy
+IMFs -- although we caution that our sample is quite small. Future LMXB
+observations can provide further insights into the origin of $\alpha_{dyn}$
+variations.
+",0,1,0,0,0,0
+9555,Black holes in vector-tensor theories,"  We study static and spherically symmetric black hole (BH) solutions in
+second-order generalized Proca theories with nonminimal vector field derivative
+couplings to the Ricci scalar, the Einstein tensor, and the double dual Riemann
+tensor. We find concrete Lagrangians which give rise to exact BH solutions by
+imposing two conditions of the two identical metric components and the constant
+norm of the vector field. These exact solutions are described by either
+Reissner-Nordström (RN), stealth Schwarzschild, or extremal RN solutions
+with a non-trivial longitudinal mode of the vector field. We then numerically
+construct BH solutions without imposing these conditions. For cubic and quartic
+Lagrangians with power-law couplings which encompass vector Galileons as the
+specific cases, we show the existence of BH solutions with the difference
+between two non-trivial metric components. The quintic-order power-law
+couplings do not give rise to non-trivial BH solutions regular throughout the
+horizon exterior. The sixth-order and intrinsic vector-mode couplings can lead
+to BH solutions with a secondary hair. For all the solutions, the vector field
+is regular at least at the future or past horizon. The deviation from General
+Relativity induced by the Proca hair can be potentially tested by future
+measurements of gravitational waves in the nonlinear regime of gravity.
+",0,1,0,0,0,0
+9556,Proving the existence of loops in robot trajectories,"  This paper presents a reliable method to verify the existence of loops along
+the uncertain trajectory of a robot, based on proprioceptive measurements only,
+within a bounded-error context. The loop closure detection is one of the key
+points in SLAM methods, especially in homogeneous environments with difficult
+scenes recognitions. The proposed approach is generic and could be coupled with
+conventional SLAM algorithms to reliably reduce their computing burden, thus
+improving the localization and mapping processes in the most challenging
+environments such as unexplored underwater extents.
+To prove that a robot performed a loop whatever the uncertainties in its
+evolution, we employ the notion of topological degree that originates in the
+field of differential topology. We show that a verification tool based on the
+topological degree is an optimal method for proving robot loops. This is
+demonstrated both on datasets from real missions involving autonomous
+underwater vehicles, and by a mathematical discussion.
+",1,0,0,0,0,0
+9557,The clock of chemical evolution,"  Chemical evolution is essential in understanding the origins of life. We
+present a theory for the evolution of molecule masses and show that small
+molecules grow by random diffusion and large molecules by a preferential
+attachment process leading eventually to life's molecules. It reproduces
+correctly the distribution of molecules found via mass spectroscopy for the
+Murchison meteorite and estimates the start of chemical evolution back to 12.8
+billion years following the birth of stars and supernovae. From the Frontier
+mass between the random and preferential attachment dynamics the birth time of
+molecule families can be estimated. Amino acids emerge about 165 million years
+after the start of evolution. Using the scaling of reaction rates with the
+distance of the molecules in space we recover correctly the few days emergence
+time of amino acids in the Miller-Urey experiment. The distribution of
+interstellar and extragalactic molecules are both consistent with the
+evolutionary mass distribution, and their age is estimated to 108 and 65
+million years after the start of evolution. From the model, we can determine
+the number of different molecule compositions at the time of the creation of
+Earth to be 1.6 million and the number of molecule compositions in interstellar
+space to a mere 719.
+",0,0,0,0,1,0
+9558,Fast readout algorithm for cylindrical beam position monitors providing good accuracy for particle bunches with large offsets,"  A simple, analytically correct algorithm is developed for calculating pencil
+beam coordinates using the signals from an ideal cylindrical particle beam
+position monitor (BPM) with four pickup electrodes (PUEs) of infinitesimal
+widths. The algorithm is then applied to simulations of realistic BPMs with
+finite width PUEs. Surprisingly small deviations are found. Simple empirically
+determined correction terms reduce the deviations even further. The algorithm
+is then used to study the impact of beam-size upon the precision of BPMs in the
+non-linear region. As an example of the data acquisition speed advantage, a
+FPGA-based BPM readout implementation of the new algorithm has been developed
+and characterized. Finally,the algorithm is tested with BPM data from the
+Cornell Preinjector.
+",0,1,0,0,0,0
+9559,Approximation Algorithms for Independence and Domination on B$_1$-VPG and B$_1$-EPG Graphs,"  A graph $G$ is called B$_k$-VPG (resp., B$_k$-EPG), for some constant $k\geq
+0$, if it has a string representation on a grid such that each vertex is an
+orthogonal path with at most $k$ bends and two vertices are adjacent in $G$ if
+and only if the corresponding strings intersect (resp., the corresponding
+strings share at least one grid edge). If two adjacent strings of a B$_k$-VPG
+graph intersect exactly once, then the graph is called a one-string B$_k$-VPG
+graph.
+In this paper, we study the Maximum Independent Set and Minimum Dominating
+Set problems on B$_1$-VPG and B$_1$-EPG graphs. We first give a simple $O(\log
+n)$-approximation algorithm for the Maximum Independent Set problem on
+B$_1$-VPG graphs, improving the previous $O((\log n)^2)$-approximation
+algorithm of Lahiri et al. (COCOA 2015). Then, we consider the Minimum
+Dominating Set problem. We give an $O(1)$-approximation algorithm for this
+problem on one-string B$_1$-VPG graphs, providing the first constant-factor
+approximation algorithm for this problem. Moreover, we show that the Minimum
+Dominating Set problem is APX-hard on B$_1$-EPG graphs, ruling out the
+possibility of a PTAS unless P=NP. Finally, we give constant-factor
+approximation algorithms for this problem on two non-trivial subclasses of
+B$_1$-EPG graphs. To our knowledge, these are the first results for the Minimum
+Dominating Set problem on B$_1$-EPG graphs, partially answering a question
+posed by Epstein et al. (WADS 2013).
+",1,0,0,0,0,0
+9560,"Phonon-mediated repulsion, sharp transitions and (quasi)self-trapping in the extended Peierls-Hubbard model","  We study two identical fermions, or two hard-core bosons, in an infinite
+chain and coupled to phonons by interactions that modulate their hopping as
+described by the Peierls/Su-Schrieffer-Heeger (SSH) model. We show that
+exchange of phonons generates effective nearest-neighbor repulsion between
+particles and also gives rise to interactions that move the pair as a whole.
+The two-polaron phase diagram exhibits two sharp transitions, leading to light
+dimers at strong coupling and the flattening of the dimer dispersion at some
+critical values of the parameters. This dimer (quasi)self-trapping occurs at
+coupling strengths where single polarons are mobile. This illustrates that,
+depending on the strength of the phonon-mediated interactions, the coupling to
+phonons may completely suppress or strongly enhance quantum transport of
+correlated particles.
+",0,1,0,0,0,0
+9561,Group Synchronization on Grids,"  Group synchronization requires to estimate unknown elements
+$({\theta}_v)_{v\in V}$ of a compact group ${\mathfrak G}$ associated to the
+vertices of a graph $G=(V,E)$, using noisy observations of the group
+differences associated to the edges. This model is relevant to a variety of
+applications ranging from structure from motion in computer vision to graph
+localization and positioning, to certain families of community detection
+problems.
+We focus on the case in which the graph $G$ is the $d$-dimensional grid.
+Since the unknowns ${\boldsymbol \theta}_v$ are only determined up to a global
+action of the group, we consider the following weak recovery question. Can we
+determine the group difference ${\theta}_u^{-1}{\theta}_v$ between far apart
+vertices $u, v$ better than by random guessing? We prove that weak recovery is
+possible (provided the noise is small enough) for $d\ge 3$ and, for certain
+finite groups, for $d\ge 2$. Viceversa, for some continuous groups, we prove
+that weak recovery is impossible for $d=2$. Finally, for strong enough noise,
+weak recovery is always impossible.
+",0,0,1,1,0,0
+9562,An energy-based analysis of reduced-order models of (networked) synchronous machines,"  Stability of power networks is an increasingly important topic because of the
+high penetration of renewable distributed generation units. This requires the
+development of advanced (typically model-based) techniques for the analysis and
+controller design of power networks. Although there are widely accepted
+reduced-order models to describe the dynamic behavior of power networks, they
+are commonly presented without details about the reduction procedure, hampering
+the understanding of the physical phenomena behind them. The present paper aims
+to provide a modular model derivation of multi-machine power networks. Starting
+from first-principle fundamental physics, we present detailed dynamical models
+of synchronous machines and clearly state the underlying assumptions which lead
+to some of the standard reduced-order multi-machine models, including the
+classical second-order swing equations. In addition, the energy functions for
+the reduced-order multi-machine models are derived, which allows to represent
+the multi-machine systems as port-Hamiltonian systems. Moreover, the systems
+are proven to be passive with respect to its steady states, which permits for a
+power-preserving interconnection with other passive components, including
+passive controllers. As a result, the corresponding energy function or
+Hamiltonian can be used to provide a rigorous stability analysis of advanced
+models for the power network without having to linearize the system.
+",1,0,0,0,0,0
+9563,Binary Evolution and the Progenitor of SN 1987A,"  Since the majority of massive stars are members of binary systems, an
+understanding of the intricacies of binary interactions is essential for
+understanding the large variety of supernova types and sub-types. I therefore
+briefly review the basic elements of binary evolution theory and discuss how
+binary interactions affect the presupernova structure of massive stars and the
+resulting supernovae.
+SN 1987A was a highly anomalous supernova, almost certainly because of a
+previous binary interaction. The most likely scenario at present is that the
+progenitor was a member of a massive close binary that experienced dynamical
+mass transfer during its second red-supergiant phase and merged completely with
+its companion as a consequence. This can naturally explain the three main
+anomalies of SN 1987A: the blue color of the progenitor, the chemical anomalies
+and the complex triple-ring nebula.
+",0,1,0,0,0,0
+9564,A Generative Model for Dynamic Networks with Applications,"  Networks observed in real world like social networks, collaboration networks
+etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear
+over time. In this paper, we propose a generative, latent space based,
+statistical model for such networks (called dynamic networks). We consider the
+case where the number of nodes is fixed, but the presence of edges can vary
+over time. Our model allows the number of communities in the network to be
+different at different time steps. We use a neural network based methodology to
+perform approximate inference in the proposed model and its simplified version.
+Experiments done on synthetic and real world networks for the task of community
+detection and link prediction demonstrate the utility and effectiveness of our
+model as compared to other similar existing approaches.
+",1,0,0,1,0,0
+9565,Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning,"  We investigate contextual online learning with nonparametric (Lipschitz)
+comparison classes under different assumptions on losses and feedback
+information. For full information feedback and Lipschitz losses, we design the
+first explicit algorithm achieving the minimax regret rate (up to log factors).
+In a partial feedback model motivated by second-price auctions, we obtain
+algorithms for Lipschitz and semi-Lipschitz losses with regret bounds improving
+on the known bounds for standard bandit feedback. Our analysis combines novel
+results for contextual second-price auctions with a novel algorithmic approach
+based on chaining. When the context space is Euclidean, our chaining approach
+is efficient and delivers an even better regret bound.
+",0,0,1,1,0,0
+9566,A hybrid primal heuristic for Robust Multiperiod Network Design,"  We investigate the Robust Multiperiod Network Design Problem, a
+generalization of the classical Capacitated Network Design Problem that
+additionally considers multiple design periods and provides solutions protected
+against traffic uncertainty. Given the intrinsic difficulty of the problem,
+which proves challenging even for state-of-the art commercial solvers, we
+propose a hybrid primal heuristic based on the combination of ant colony
+optimization and an exact large neighborhood search. Computational experiments
+on a set of realistic instances from the SNDlib show that our heuristic can
+find solutions of extremely good quality with low optimality gap.
+",1,0,1,0,0,0
+9567,"Real-world Multi-object, Multi-grasp Detection","  A deep learning architecture is proposed to predict graspable locations for
+robotic manipulation. It considers situations where no, one, or multiple
+object(s) are seen. By defining the learning problem to be classification with
+null hypothesis competition instead of regression, the deep neural network with
+RGB-D image input predicts multiple grasp candidates for a single object or
+multiple objects, in a single shot. The method outperforms state-of-the-art
+approaches on the Cornell dataset with 96.0% and 96.1% accuracy on image-wise
+and object- wise splits, respectively. Evaluation on a multi-object dataset
+illustrates the generalization capability of the architecture. Grasping
+experiments achieve 96.0% grasp localization and 88.0% grasping success rates
+on a test set of household objects. The real-time process takes less than .25 s
+from image to plan.
+",1,0,0,0,0,0
+9568,Fault Tolerance of Random Graphs with respect to Connectivity: Phase Transition in Logarithmic Average Degree,"  The fault tolerance of random graphs with unbounded degrees with respect to
+connectivity is investigated. It is related to the reliability of wireless
+sensor networks with unreliable relay nodes. The model evaluates the network
+breakdown probability that a graph is disconnected after stochastic node
+removal. To establish a mean-field approximation for the model, the cavity
+method for finite systems is proposed. Then the asymptotic analysis is applied.
+As a result, the former enables us to obtain an approximation formula for any
+number of nodes and an arbitrary and degree distribution. In addition, the
+latter reveals that the phase transition occurs on random graphs with
+logarithmic average degrees. Those results, which are supported by numerical
+simulations, coincide with the mathematical results, indicating successful
+predictions by mean-field approximation for unbounded but not dense random
+graphs.
+",0,1,0,0,0,0
+9569,"Belief Propagation, Bethe Approximation and Polynomials","  Factor graphs are important models for succinctly representing probability
+distributions in machine learning, coding theory, and statistical physics.
+Several computational problems, such as computing marginals and partition
+functions, arise naturally when working with factor graphs. Belief propagation
+is a widely deployed iterative method for solving these problems. However,
+despite its significant empirical success, not much is known about the
+correctness and efficiency of belief propagation.
+Bethe approximation is an optimization-based framework for approximating
+partition functions. While it is known that the stationary points of the Bethe
+approximation coincide with the fixed points of belief propagation, in general,
+the relation between the Bethe approximation and the partition function is not
+well understood. It has been observed that for a few classes of factor graphs,
+the Bethe approximation always gives a lower bound to the partition function,
+which distinguishes them from the general case, where neither a lower bound,
+nor an upper bound holds universally. This has been rigorously proved for
+permanents and for attractive graphical models.
+Here we consider bipartite normal factor graphs and show that if the local
+constraints satisfy a certain analytic property, the Bethe approximation is a
+lower bound to the partition function. We arrive at this result by viewing
+factor graphs through the lens of polynomials. In this process, we reformulate
+the Bethe approximation as a polynomial optimization problem. Our sufficient
+condition for the lower bound property to hold is inspired by recent
+developments in the theory of real stable polynomials. We believe that this way
+of viewing factor graphs and its connection to real stability might lead to a
+better understanding of belief propagation and factor graphs in general.
+",1,0,0,1,0,0
+9570,Combining the Ensemble and Franck-Condon Approaches for Spectral Shapes of Molecules in Solution,"  The correct treatment of vibronic effects is vital for the modeling of
+absorption spectra of solvated dyes, as many prominent spectral features can
+often be ascribed to vibronic transitions. Vibronic spectra can be computed
+within the Franck-Condon approximation for small dyes in solution using an
+implicit solvent model. However, implicit solvent models neglect specific
+solute-solvent interactions and provide only an approximate treatment of
+solvent polarization effects. Furthermore, temperature-dependent solvent
+broadening effects are often only accounted for using a broadening parameter
+chosen to match experimental spectra. On the other hand, ensemble approaches
+provide a straightforward way of accounting for solute-solvent interactions and
+temperature-dependent broadening effects by computing vertical excitation
+energies obtained from an ensemble of solute-solvent conformations. However,
+ensemble approaches do not explicitly account for vibronic effects and often
+produce spectral shapes in poor agreement with experiment. We address these
+shortcomings by combining the vibronic fine structure of an excitation obtained
+in the Franck-Condon picture at zero temperature with vertical excitations
+computed for a room-temperature ensemble of solute-solvent configurations. In
+this combined approach, all temperature-dependent broadening is therefore
+treated classically through the sampling of configurations, with vibronic
+contributions included as a zero-temperature correction to each vertical
+transition. We test the proposed method on Nile Red and the green fluorescent
+protein chromophore in polar and non-polar solvents. For systems with strong
+solute-solvent interaction, the approach yields a significant improvement over
+the ensemble approach, whereas for systems with weaker interactions, both the
+shape and the width of the spectra are in excellent agreement with experiment.
+",0,1,0,0,0,0
+9571,Personalized Dialogue Generation with Diversified Traits,"  Endowing a dialogue system with particular personality traits is essential to
+deliver more human-like conversations. However, due to the challenge of
+embodying personality via language expression and the lack of large-scale
+persona-labeled dialogue data, this research problem is still far from
+well-studied. In this paper, we investigate the problem of incorporating
+explicit personality traits in dialogue generation to deliver personalized
+dialogues.
+To this end, firstly, we construct PersonalDialog, a large-scale multi-turn
+dialogue dataset containing various traits from a large number of speakers. The
+dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers.
+Each utterance is associated with a speaker who is marked with traits like Age,
+Gender, Location, Interest Tags, etc. Several anonymization schemes are
+designed to protect the privacy of each speaker. This large-scale dataset will
+facilitate not only the study of personalized dialogue generation, but also
+other researches on sociolinguistics or social science.
+Secondly, to study how personality traits can be captured and addressed in
+dialogue generation, we propose persona-aware dialogue generation models within
+the sequence to sequence learning framework. Explicit personality traits
+(structured by key-value pairs) are embedded using a trait fusion module.
+During the decoding process, two techniques, namely persona-aware attention and
+persona-aware bias, are devised to capture and address trait-related
+information. Experiments demonstrate that our model is able to address proper
+traits in different contexts. Case studies also show interesting results for
+this challenging research problem.
+",1,0,0,0,0,0
+9572,Hyperelliptic Jacobians and isogenies,"  Motivated by results of Mestre and Voisin, in this note we mainly consider
+abelian varieties isogenous to hyperelliptic Jacobians
+In the first part we prove that a very general hyperelliptic Jacobian of
+genus $g\ge 4$ is not isogenous to a non-hyperelliptic Jacobian. As a
+consequence we obtain that the Intermediate Jacobian of a very general cubic
+threefold is not isogenous to a Jacobian. Another corollary tells that the
+Jacobian of a very general $d$-gonal curve of genus $g \ge 4$ is not isogenous
+to a different Jacobian.
+In the second part we consider a closed subvariety $\mathcal Y \subset
+\mathcal A_g$ of the moduli space of principally polarized varieties of
+dimension $g\ge 3$. We show that if a very general element of $\mathcal Y$ is
+dominated by a hyperelliptic Jacobian, then $\dim \mathcal Y\ge 2g$. In
+particular, if the general element in $\mathcal Y$ is simple, its Kummer
+variety does not contain rational curves. Finally we show that a closed
+subvariety $\mathcal Y\subset \mathcal M_g$ of dimension $2g-1$ such that the
+Jacobian of a very general element of $\mathcal Y$ is dominated by a
+hyperelliptic Jacobian is contained either in the hyperelliptic or in the
+trigonal locus.
+",0,0,1,0,0,0
+9573,Signal and Noise Statistics Oblivious Sparse Reconstruction using OMP/OLS,"  Orthogonal matching pursuit (OMP) and orthogonal least squares (OLS) are
+widely used for sparse signal reconstruction in under-determined linear
+regression problems. The performance of these compressed sensing (CS)
+algorithms depends crucially on the \textit{a priori} knowledge of either the
+sparsity of the signal ($k_0$) or noise variance ($\sigma^2$). Both $k_0$ and
+$\sigma^2$ are unknown in general and extremely difficult to estimate in under
+determined models. This limits the application of OMP and OLS in many practical
+situations. In this article, we develop two computationally efficient
+frameworks namely TF-IGP and RRT-IGP for using OMP and OLS even when $k_0$ and
+$\sigma^2$ are unavailable. Both TF-IGP and RRT-IGP are analytically shown to
+accomplish successful sparse recovery under the same set of restricted isometry
+conditions on the design matrix required for OMP/OLS with \textit{a priori}
+knowledge of $k_0$ and $\sigma^2$. Numerical simulations also indicate a highly
+competitive performance of TF-IGP and RRT-IGP in comparison to OMP/OLS with
+\textit{a priori} knowledge of $k_0$ and $\sigma^2$.
+",0,0,0,1,0,0
+9574,Experimenting with the p4est library for AMR simulations of two-phase flows,"  Many physical problems involve spatial and temporal inhomogeneities that
+require a very fine discretization in order to be accurately simulated. Using
+an adaptive mesh, a high level of resolution is used in the appropriate areas
+while keeping a coarse mesh elsewhere. This idea allows to save time and
+computations, but represents a challenge for distributed-memory environments.
+The MARS project (for Multiphase Adaptative Refinement Solver) intends to
+assess the parallel library p4est for adaptive mesh, in a case of a finite
+volume scheme applied to two-phase flows. Besides testing the library's
+performances, particularly for load balancing, its user-friendliness in use and
+implementation are also exhibited here. First promising 3D simulations are even
+presented.
+",1,1,0,0,0,0
+9575,Lifting CDCL to Template-based Abstract Domains for Program Verification,"  The success of Conflict Driven Clause Learning (CDCL) for Boolean
+satisfiability has inspired adoption in other domains. We present a novel
+lifting of CDCL to program analysis called Abstract Conflict Driven Learning
+for Programs (ACDLP). ACDLP alternates between model search, which performs
+over-approximate deduction with constraint propagation, and conflict analysis,
+which performs under-approximate abduction with heuristic choice. We
+instantiate the model search and conflict analysis algorithms to an abstract
+domain of template polyhedra, strictly generalizing CDCL from the Boolean
+lattice to a richer lattice structure. Our template polyhedra can express
+intervals, octagons and restricted polyhedral constraints over program
+variables. We have imple- mented ACDLP for automatic bounded safety
+verification of C programs. We evaluate the performance of our analyser by
+comparing with CBMC, which uses CDCL, and Astree, a commercial abstract
+interpretation tool. We observe two orders of magnitude reduction in the number
+of decisions, propagations, and conflicts as well as a 1.5x speedup in runtime
+compared to CBMC. Compared to Astree, ACDLP solves twice as many benchmarks and
+has much higher precision. This is the first instantiation of CDCL with a
+template polyhedra abstract domain.
+",1,0,0,0,0,0
+9576,Binary Voting with Delegable Proxy: An Analysis of Liquid Democracy,"  The paper provides an analysis of the voting method known as delegable proxy
+voting, or liquid democracy. The analysis first positions liquid democracy
+within the theory of binary aggregation. It then focuses on two issues of the
+system: the occurrence of delegation cycles; and the effect of delegations on
+individual rationality when voting on logically interdependent propositions. It
+finally points to proposals on how the system may be modified in order to
+address the above issues.
+",1,0,0,0,0,0
+9577,Exact description of coalescing eigenstates in open quantum systems in terms of microscopic Hamiltonian dynamics,"  At the exceptional point where two eigenstates coalesce in open quantum
+systems, the usual diagonalization scheme breaks down and the Hamiltonian can
+only be reduced to Jordan block form. Most of the studies on the exceptional
+point appearing in the literature introduce a phenomenological effective
+Hamiltonian that essentially reduces the problem to that of a finite
+non-Hermitian matrix for which it is straightforward to obtain the Jordan form.
+In this paper, we demonstrate how the Hamiltonian of an open quantum system
+reduces to Jordan block form at an exceptional point in an exact manner that
+treats the continuum without any approximation. Our method relies on the
+Brillouin-Wigner-Feshbach projection method according to which we can obtain a
+finite dimensional effective Hamiltonian that shares the discrete sector of the
+spectrum with the original Hamiltonian. While owing to its eigenvalue
+dependence this effective Hamiltonian cannot be used to write the Jordan block
+directly, we show that by formally extending the problem to include eigenstates
+with complex eigenvalues that reside outside the usual Hilbert space, we can
+obtain the Jordan block form at the exceptional point without introducing any
+approximation. We also introduce an extended Jordan form basis away from the
+exceptional point, which provides an alternative way to obtain the Jordan block
+at an exceptional point. The extended Jordan block connects continuously to the
+Jordan block exactly at the exceptional point implying that the observable
+quantities are continuous at the exceptional point.
+",0,1,0,0,0,0
+9578,The maximum number of cycles in a graph with fixed number of edges,"  The main topic considered is maximizing the number of cycles in a graph with
+given number of edges. In 2009, Király conjectured that there is constant $c$
+such that any graph with $m$ edges has at most $(1.4)^m$ cycles. In this paper,
+it is shown that for sufficiently large $m$, a graph with $m$ edges has at most
+$(1.443)^m$ cycles. For sufficiently large $m$, examples of a graph with $m$
+edges and $(1.37)^m$ cycles are presented. For a graph with given number of
+vertices and edges an upper bound on the maximal number of cycles is given.
+Also, exponentially tight bounds are proved for the maximum number of cycles in
+a multigraph with given number of edges, as well as in a multigraph with given
+number of vertices and edges.
+",0,0,1,0,0,0
+9579,Structural Nonrealism and Quantum Information,"  The article introduces a new concept of structure, defined, echoing J. A.
+Wheeler's concept of ""law without law,"" as a ""structure without law,"" and a new
+philosophical viewpoint, that of structural nnnrealism, and considers how this
+concept and this viewpoint work in quantum theory in general and quantum
+information theory in particular. It takes as its historical point of departure
+W. Heisenberg's discovery of quantum mechanics, which, the article argues,
+could, in retrospect, be considered in quantum-informational terms, while,
+conversely, quantum information theory could be seen in Heisenbergian terms.
+The article takes advantage of the circumstance that any instance of quantum
+information is a ""structure""--an organization of elements, ultimately bits, of
+classical information, manifested in measuring instruments. While, however,
+this organization can, along with the observed behavior of measuring
+instruments, be described by means of classical physics, it cannot be predicted
+by means of classical physics, but only, probabilistically or statistically, by
+means of quantum mechanics, or in high-energy physics, by means of quantum
+field theory (or possibly some alternative theories within each scope). By
+contrast, the emergences of this information and of this structure cannot, in
+the present view, be described by either classical or quantum theory, or
+possibly by any other means, which leads to the concept of ""structure without
+law"" and the viewpoint of structural nnnrealism. The article also considers,
+from this perspective, some recent work in quantum information theory.
+",0,1,0,0,0,0
+9580,"Robotics CTF (RCTF), a playground for robot hacking","  Robots state of insecurity is onstage. There is an emerging concern about
+major robot vulnerabilities and their adverse consequences. However, there is
+still a considerable gap between robotics and cybersecurity domains. For the
+purpose of filling that gap, the present technical report presents the Robotics
+CTF (RCTF), an online playground to challenge robot security from any browser.
+We describe the architecture of the RCTF and provide 9 scenarios where hackers
+can challenge the security of different robotic setups. Our work empowers
+security researchers to a) reproduce virtual robotic scenarios locally and b)
+change the networking setup to mimic real robot targets. We advocate for hacker
+powered security in robotics and contribute by open sourcing our scenarios.
+",1,0,0,0,0,0
+9581,Effects of transmutation elements in tungsten as a plasma facing material,"  Tungsten (W) is widely considered as the most promising plasma facing
+material, which is used in nuclear fusion devices. During the operation of the
+nuclear fusion devices, transmutation elements, such as Re, Os, and Ta, are
+generated in W due to the transmutation reaction under fusion neutron
+irradiation. In this paper, we investigated the effects of the transmutation
+elements on the mechanical properties of W and the behavior of hydrogen/helium
+(H/He) atom in W by using the rst principles calculation method. The results
+are that the generation of the transmutation elements can enhance the ductility
+of W without considering the dislocation and other defects, and this phenomenon
+is called as solution toughen. However, there is not a strict linear
+relationship between the change of the mechanical properties and the
+transmutation elements concentration. Compared with the H/He atom in pure W,
+the formation energy of the H/He in W are decreased by the transmutation
+elements, but the transmutation elements does not change the most favorable
+sites for H/He in W. An attractive interaction exists between the transmutation
+elements and H/He in W, while a repulsive interaction exists between Ta and He
+in W. The best diffusion path H/He in W is changed due to the interaction
+between the transmutation elements and H/He. All of the above results provide
+important information for application of W as the plasma facing material in the
+nuclear fusion devices.
+",0,1,0,0,0,0
+9582,A playful note on spanning and surplus edges,"  Consider a (not necessarily near-critical) random graph running in continuous
+time. A recent breadth-first-walk construction is extended in order to account
+for the surplus edge data in addition to the spanning edge data. Two different
+graph representations of the multiplicative coalescent, with different
+advantages and drawbacks, are discussed in detail. A canonical multi-graph of
+Bhamidi, Budhiraja and Wang (2014) naturally emerges. The presented framework
+should facilitate understanding of scaling limits with surplus edges for
+near-critical random graphs in the domain of attraction of general (not
+necessarily standard) eternal multiplicative coalescent.
+",0,0,1,0,0,0
+9583,Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks,"  Designing a logo for a new brand is a lengthy and tedious back-and-forth
+process between a designer and a client. In this paper we explore to what
+extent machine learning can solve the creative task of the designer. For this,
+we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web.
+Training Generative Adversarial Networks (GANs) for logo synthesis on such
+multi-modal data is not straightforward and results in mode collapse for some
+state-of-the-art methods. We propose the use of synthetic labels obtained
+through clustering to disentangle and stabilize GAN training. We are able to
+generate a high diversity of plausible logos and we demonstrate latent space
+exploration techniques to ease the logo design task in an interactive manner.
+Moreover, we validate the proposed clustered GAN training on CIFAR 10,
+achieving state-of-the-art Inception scores when using synthetic labels
+obtained via clustering the features of an ImageNet classifier. GANs can cope
+with multi-modal data by means of synthetic labels achieved through clustering,
+and our results show the creative potential of such techniques for logo
+synthesis and manipulation. Our dataset and models will be made publicly
+available at this https URL.
+",1,0,0,1,0,0
+9584,Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency,"  We study the notion of consistency between a 3D shape and a 2D observation
+and propose a differentiable formulation which allows computing gradients of
+the 3D shape given an observation from an arbitrary view. We do so by
+reformulating view consistency using a differentiable ray consistency (DRC)
+term. We show that this formulation can be incorporated in a learning framework
+to leverage different types of multi-view observations e.g. foreground masks,
+depth, color images, semantics etc. as supervision for learning single-view 3D
+prediction. We present empirical analysis of our technique in a controlled
+setting. We also show that this approach allows us to improve over existing
+techniques for single-view reconstruction of objects from the PASCAL VOC
+dataset.
+",1,0,0,0,0,0
+9585,Efficient Rank Minimization via Solving Non-convexPenalties by Iterative Shrinkage-Thresholding Algorithm,"  Rank minimization (RM) is a wildly investigated task of finding solutions by
+exploiting low-rank structure of parameter matrices. Recently, solving RM
+problem by leveraging non-convex relaxations has received significant
+attention. It has been demonstrated by some theoretical and experimental work
+that non-convex relaxation, e.g. Truncated Nuclear Norm Regularization (TNNR)
+and Reweighted Nuclear Norm Regularization (RNNR), can provide a better
+approximation of original problems than convex relaxations. However, designing
+an efficient algorithm with theoretical guarantee remains a challenging
+problem. In this paper, we propose a simple but efficient proximal-type method,
+namely Iterative Shrinkage-Thresholding Algorithm(ISTA), with concrete analysis
+to solve rank minimization problems with both non-convex weighted and
+reweighted nuclear norm as low-rank regularizers. Theoretically, the proposed
+method could converge to the critical point under very mild assumptions with
+the rate in the order of $O(1/T)$. Moreover, the experimental results on both
+synthetic data and real world data sets show that proposed algorithm
+outperforms state-of-arts in both efficiency and accuracy.
+",0,0,0,1,0,0
+9586,Almost Buchsbaumness of some rings arising from complexes with isolated singularities,"  We study properties of the Stanley-Reisner rings of simplicial complexes with
+isolated singularities modulo two generic linear forms. Miller, Novik, and
+Swartz proved that if a complex has homologically isolated singularities, then
+its Stanley-Reisner ring modulo one generic linear form is Buchsbaum. Here we
+examine the case of non-homologically isolated singularities, providing many
+examples in which the Stanley-Reisner ring modulo two generic linear forms is a
+quasi-Buchsbaum but not Buchsbaum ring.
+",0,0,1,0,0,0
+9587,Dynamically controlled plasmonic nano-antenna phased array utilizing vanadium dioxide,"  We propose and analyze theoretically an approach for realizing a tunable
+optical phased-array antenna utilizing the properties of VO2 for electronic
+beam steering applications in the near-IR spectral range. The device is based
+on a 1D array of slot nano-antennas engraved in a thin Au film grown over VO2
+layer. The tuning is obtained by inducing a temperature gradient over the
+device, which changes the refractive index of the VO2, and hence modifies the
+phase response of the elements comprising the array, by producing a thermal
+gradient within the underlying PCM layer. Using a 10-element array, we show
+that an incident beam can be steered up to with respect to the normal, by
+applying a gradient of less than 10°C.
+",0,1,0,0,0,0
+9588,Comparing deep neural networks against humans: object recognition when the signal gets weaker,"  Human visual object recognition is typically rapid and seemingly effortless,
+as well as largely independent of viewpoint and object orientation. Until very
+recently, animate visual systems were the only ones capable of this remarkable
+computational feat. This has changed with the rise of a class of computer
+vision algorithms called deep neural networks (DNNs) that achieve human-level
+classification performance on object recognition tasks. Furthermore, a growing
+number of studies report similarities in the way DNNs and the human visual
+system process objects, suggesting that current DNNs may be good models of
+human visual object recognition. Yet there clearly exist important
+architectural and processing differences between state-of-the-art DNNs and the
+primate visual system. The potential behavioural consequences of these
+differences are not well understood. We aim to address this issue by comparing
+human and DNN generalisation abilities towards image degradations. We find the
+human visual system to be more robust to image manipulations like contrast
+reduction, additive noise or novel eidolon-distortions. In addition, we find
+progressively diverging classification error-patterns between humans and DNNs
+when the signal gets weaker, indicating that there may still be marked
+differences in the way humans and current DNNs perform visual object
+recognition. We envision that our findings as well as our carefully measured
+and freely available behavioural datasets provide a new useful benchmark for
+the computer vision community to improve the robustness of DNNs and a
+motivation for neuroscientists to search for mechanisms in the brain that could
+facilitate this robustness.
+",1,0,0,1,0,0
+9589,Quantum critical response: from conformal perturbation theory to holography,"  We discuss dynamical response functions near quantum critical points,
+allowing for both a finite temperature and detuning by a relevant operator.
+When the quantum critical point is described by a conformal field theory (CFT),
+conformal perturbation theory and the operator product expansion can be used to
+fix the first few leading terms at high frequencies. Knowledge of the high
+frequency response allows us then to derive non-perturbative sum rules. We
+show, via explicit computations, how holography recovers the general results of
+CFT, and the associated sum rules, for any holographic field theory with a
+conformal UV completion -- regardless of any possible new ordering and/or
+scaling physics in the IR. We numerically obtain holographic response functions
+at all frequencies, allowing us to probe the breakdown of the asymptotic
+high-frequency regime. Finally, we show that high frequency response functions
+in holographic Lifshitz theories are quite similar to their conformal
+counterparts, even though they are not strongly constrained by symmetry.
+",0,1,0,0,0,0
+9590,Testing High-dimensional Covariance Matrices under the Elliptical Distribution and Beyond,"  We study testing high-dimensional covariance matrices under a generalized
+elliptical model. The model accommodates several stylized facts of real data
+including heteroskedasticity, heavy-tailedness, asymmetry, etc. We consider the
+high-dimensional setting where the dimension $p$ and the sample size $n$ grow
+to infinity proportionally, and establish a central limit theorem for the
+{linear spectral statistic} of the sample covariance matrix based on
+self-normalized observations. The central limit theorem is different from the
+existing ones for the linear spectral statistic of the usual sample covariance
+matrix. Our tests based on the new central limit theorem neither assume a
+specific parametric distribution nor involve the kurtosis of data. Simulation
+studies show that our tests work well even when the fourth moment does not
+exist. Empirically, we analyze the idiosyncratic returns under the Fama-French
+three-factor model for S\&P 500 Financials sector stocks, and our tests reject
+the hypothesis that the idiosyncratic returns are uncorrelated.
+",0,0,1,1,0,0
+9591,Work Analysis with Resource-Aware Session Types,"  While there exist several successful techniques for supporting programmers in
+deriving static resource bounds for sequential code, analyzing the resource
+usage of message-passing concurrent processes poses additional challenges. To
+meet these challenges, this article presents an analysis for statically
+deriving worst-case bounds on the total work performed by message-passing
+processes. To decompose interacting processes into components that can be
+analyzed in isolation, the analysis is based on novel resource-aware session
+types, which describe protocols and resource contracts for inter-process
+communication. A key innovation is that both messages and processes carry
+potential to share and amortize cost while communicating. To symbolically
+express resource usage in a setting without static data structures and
+intrinsic sizes, resource contracts describe bounds that are functions of
+interactions between processes. Resource-aware session types combine standard
+binary session types and type-based amortized resource analysis in a linear
+type system. This type system is formulated for a core session-type calculus of
+the language SILL and proved sound with respect to a multiset-based operational
+cost semantics that tracks the total number of messages that are exchanged in a
+system. The effectiveness of the analysis is demonstrated by analyzing standard
+examples from amortized analysis and the literature on session types and by a
+comparative performance analysis of different concurrent programs implementing
+the same interface.
+",1,0,0,0,0,0
+9592,Seven dimensional cohomogeneity one manifolds with nonnegative curvature,"  We show that a certain family of cohomogeneity one manifolds does not admit
+an invariant metric of nonnegative sectional curvature, unless it admits one
+with positive curvature. As a consequence, the classification of nonnegatively
+curved cohomogeneity one manifolds in dimension 7 is reduced to only one
+further family of candidates
+",0,0,1,0,0,0
+9593,Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces,"  We investigate regularized algorithms combining with projection for
+least-squares regression problem over a Hilbert space, covering nonparametric
+regression over a reproducing kernel Hilbert space. We prove convergence
+results with respect to variants of norms, under a capacity assumption on the
+hypothesis space and a regularity condition on the target function. As a
+result, we obtain optimal rates for regularized algorithms with randomized
+sketches, provided that the sketch dimension is proportional to the effective
+dimension up to a logarithmic factor. As a byproduct, we obtain similar results
+for Nyström regularized algorithms. Our results are the first ones with
+optimal, distribution-dependent rates that do not have any saturation effect
+for sketched/Nyström regularized algorithms, considering both the
+attainable and non-attainable cases.
+",0,0,0,1,0,0
+9594,The scaling properties and the multiple derivative of Legendre polynomials,"  In this paper, we study the scaling properties of Legendre polynomials Pn(x).
+We show that Pn(ax), where a is a constant, can be expanded as a sum of either
+Legendre polynomials Pn(x) or their multiple derivatives dkPn(x)/dxk, and we
+derive a general expression for the expansion coefficients. In addition, we
+demonstrate that the multiple derivative dkPn(x)/dxk can also be expressed as a
+sum of Legendre polynomials and we obtain a recurrence relation for the
+coefficients.
+",0,0,1,0,0,0
+9595,Knockoffs for the mass: new feature importance statistics with false discovery guarantees,"  An important problem in machine learning and statistics is to identify
+features that causally affect the outcome. This is often impossible to do from
+purely observational data, and a natural relaxation is to identify features
+that are correlated with the outcome even conditioned on all other observed
+features. For example, we want to identify that smoking really is correlated
+with cancer conditioned on demographics. The knockoff procedure is a recent
+breakthrough in statistics that, in theory, can identify truly correlated
+features while guaranteeing that the false discovery is limited. The idea is to
+create synthetic data -knockoffs- that captures correlations amongst the
+features. However there are substantial computational and practical challenges
+to generating and using knockoffs. This paper makes several key advances that
+enable knockoff application to be more efficient and powerful. We develop an
+efficient algorithm to generate valid knockoffs from Bayesian Networks. Then we
+systematically evaluate knockoff test statistics and develop new statistics
+with improved power. The paper combines new mathematical guarantees with
+systematic experiments on real and synthetic data.
+",0,0,0,1,0,0
+9596,Phase boundaries in alternating field quantum XY model with Dzyaloshinskii-Moriya interaction: Sustainable entanglement in dynamics,"  We report all phases and corresponding critical lines of the quantum
+anisotropic transverse XY model with Dzyaloshinskii-Moriya (DM) interaction
+along with uniform and alternating transverse magnetic fields (ATXY) by using
+appropriately chosen order parameters. We prove that when DM interaction is
+weaker than the anisotropy parameter, it has no effect at all on the
+zero-temperature states of the XY model with uniform transverse magnetic field
+which is not the case for the ATXY model. However, when DM interaction is
+stronger than the anisotropy parameter, we show appearance of a new gapless
+phase - a chiral phase - in the XY model with uniform as well as alternating
+field. We further report that first derivatives of nearest neighbor two-site
+entanglement with respect to magnetic fields can detect all the critical lines
+present in the system. We also observe that the factorization surface at
+zero-temperature present in this model without DM interaction becomes a volume
+on the introduction of the later. We find that DM interaction can generate
+bipartite entanglement sustainable at large times, leading to a proof of
+ergodic nature of bipartite entanglement in this system, and can induce a
+transition from non-monotonicity of entanglement with temperature to a
+monotonic one.
+",0,1,0,0,0,0
+9597,Descent of equivalences and character bijections,"  Categorical equivalences between block algebras of finite groups - such as
+Morita and derived equivalences - are well-known to induce character bijections
+which commute with the Galois groups of field extensions. This is the
+motivation for attempting to realise known Morita and derived equivalences over
+non splitting fields. This article presents various result on the theme of
+descent. We start with the observation that perfect isometries induced by a
+virtual Morita equivalence induce isomorphisms of centers in non-split
+situations, and explain connections with Navarro's generalisation of the
+Alperin-McKay conjecture. We show that Rouquier's splendid Rickard complex for
+blocks with cyclic defect groups descends to the non-split case. We also prove
+a descent theorem for Morita equivalences with endopermutation source.
+",0,0,1,0,0,0
+9598,A function field analogue of the Rasmussen-Tamagawa conjecture: The Drinfeld module case,"  In the arithmetic of function fields, Drinfeld modules play the role that
+elliptic curves play in the arithmetic of number fields. The aim of this paper
+is to study a non-existence problem of Drinfeld modules with constrains on
+torsion points at places with large degree. This is motivated by a conjecture
+of Christopher Rasmussen and Akio Tamagawa on the non-existence of abelian
+varieties over number fields with some arithmetic constraints. We prove the
+non-existence of Drinfeld modules satisfying Rasmussen-Tamagawa type conditions
+in the case where the inseparable degree of base fields is not divisible by the
+rank of Drinfeld modules. Conversely if the rank divides the inseparable
+degree, then we give an example of Drinfeld modules satisfying
+Rasmussen-Tamagawa-type conditions.
+",0,0,1,0,0,0
+9599,Malware Detection Using Dynamic Birthmarks,"  In this paper, we explore the effectiveness of dynamic analysis techniques
+for identifying malware, using Hidden Markov Models (HMMs) and Profile Hidden
+Markov Models (PHMMs), both trained on sequences of API calls. We contrast our
+results to static analysis using HMMs trained on sequences of opcodes, and show
+that dynamic analysis achieves significantly stronger results in many cases.
+Furthermore, in contrasting our two dynamic analysis techniques, we find that
+using PHMMs consistently outperforms our analysis based on HMMs.
+",1,0,0,1,0,0
+9600,Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variables,"  This note is concerned with accurate and computationally efficient
+approximations of moments of Gaussian random variables passed through sigmoid
+or softmax mappings. These approximations are semi-analytical (i.e. they
+involve the numerical adjustment of parametric forms) and highly accurate (they
+yield 5% error at most). We also highlight a few niche applications of these
+approximations, which arise in the context of, e.g., drift-diffusion models of
+decision making or non-parametric data clustering approaches. We provide these
+as examples of efficient alternatives to more tedious derivations that would be
+needed if one was to approach the underlying mathematical issues in a more
+formal way. We hope that this technical note will be helpful to modellers
+facing similar mathematical issues, although maybe stemming from different
+academic prospects.
+",0,0,0,1,0,0
+9601,Morphology of PbTe crystal surface sputtered by argon plasma under Secondary Neutral Mass Spectrometry conditions,"  We have investigated morphology of the lateral surfaces of PbTe crystal
+samples grown from melt by the Bridgman method sputtered by Ar+ plasma with ion
+energy of 50-550 eV for 5-50 minutes under Secondary Neutral Mass Spectrometry
+(SNMS) conditions. The sputtered PbTe crystal surface was found to be
+simultaneously both the source of sputtered material and the efficient
+substrate for re-deposition of the sputtered material during the depth
+profiling. During sputtering PbTe crystal surface is forming the dimple relief.
+To be redeposited the sputtered Pb and Te form arrays of the microscopic
+surface structures in the shapes of hillocks, pyramids, cones and others on the
+PbTe crystal sputtered surface. Correlation between the density of re-deposited
+microscopic surface structures, their shape, and average size, on the one hand,
+and the energy and duration of sputtering, on the other, is revealed.
+",0,1,0,0,0,0
+9602,Indefinite Integrals of Spherical Bessel Functions,"  Highly oscillatory integrals, such as those involving Bessel functions, are
+best evaluated analytically as much as possible, as numerical errors can be
+difficult to control. We investigate indefinite integrals involving monomials
+in $x$ multiplying one or two spherical Bessel functions of the first kind
+$j_l(x)$ with integer order $l$. Closed-form solutions are presented where
+possible, and recursion relations are developed that are guaranteed to reduce
+all integrals in this class to closed-form solutions. These results allow for
+definite integrals over spherical Bessel functions to be computed quickly and
+accurately. For completeness, we also present our results in terms of ordinary
+Bessel functions, but in general, the recursion relations do not terminate.
+",0,0,1,0,0,0
+9603,Proceedings 14th International Workshop on the ACL2 Theorem Prover and its Applications,"  This volume contains the proceedings of the Fourteenth International Workshop
+on the ACL2 Theorem Prover and Its Applications, ACL2 2017, a two-day workshop
+held in Austin, Texas, USA, on May 22-23, 2017. ACL2 workshops occur at
+approximately 18-month intervals, and they provide a technical forum for
+researchers to present and discuss improvements and extensions to the theorem
+prover, comparisons of ACL2 with other systems, and applications of ACL2 in
+formal verification.
+ACL2 is a state-of-the-art automated reasoning system that has been
+successfully applied in academia, government, and industry for specification
+and verification of computing systems and in teaching computer science courses.
+Boyer, Kaufmann, and Moore were awarded the 2005 ACM Software System Award for
+their work on ACL2 and the other theorem provers in the Boyer-Moore
+theorem-prover family.
+The proceedings of ACL2 2017 include the seven technical papers and two
+extended abstracts that were presented at the workshop. Each submission
+received two or three reviews. The workshop also included three invited talks:
+""Using Mechanized Mathematics in an Organization with a Simulation-Based
+Mentality"", by Glenn Henry of Centaur Technology, Inc.; ""Formal Verification of
+Financial Algorithms, Progress and Prospects"", by Grant Passmore of Aesthetic
+Integration; and ""Verifying Oracle's SPARC Processors with ACL2"" by Greg
+Grohoski of Oracle. The workshop also included several rump sessions discussing
+ongoing research and the use of ACL2 within industry.
+",1,0,0,0,0,0
+9604,Mean Field Stochastic Games with Binary Action Spaces and Monotone Costs,"  This paper considers mean field games in a multi-agent Markov decision
+process (MDP) framework. Each player has a continuum state and binary action.
+By active control, a player can bring its state to a resetting point. All
+players are coupled through their cost functions. The structural property of
+the individual strategies is characterized in terms of threshold policies when
+the mean field game admits a solution. We further introduce a stationary
+equation system of the mean field game and analyze uniqueness of its solution
+under positive externalities.
+",0,0,1,0,0,0
+9605,Heterogeneous Cellular Networks with LoS and NLoS Transmissions--The Role of Massive MIMO and Small Cells,"  We develop a framework for downlink heterogeneous cellular networks with
+line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Using
+stochastic geometry, we derive tight approximation of achievable downlink rate
+that enables us to compare the performance between densifying small cells and
+expanding BS antenna arrays. Interestingly, we find that adding small cells
+into the network improves the achievable rate much faster than expanding
+antenna arrays at the macro BS. However, when the small cell density exceeds a
+critical threshold, the spacial densification will lose its benefits and
+further impair the network capacity. To this end, we present the optimal small
+cell density that maximizes the rate as practical deployment guidance. In
+contrast, expanding macro BS antenna array can always benefit the capacity
+until an upper bound caused by pilot contamination, and this bound also
+surpasses the peak rate obtained from deployment of small cells. Furthermore,
+we find that allocating part of antennas to distributed small cell BSs works
+better than centralizing all antennas at the macro BS, and the optimal
+allocation proportion is also given for practical configuration reference. In
+summary, this work provides a further understanding on how to leverage small
+cells and massive MIMO in future heterogeneous cellular networks deployment.
+",1,0,1,0,0,0
+9606,Gould's Belt: Local Large Scale Structure in the Milky Way,"  Gould's Belt is a flat local system composed of young OB stars, molecular
+clouds and neutral hydrogen within 500 pc from the Sun. It is inclined about 20
+degrees to the galactic plane and its velocity field significantly deviates
+from rotation around the distant center of the Milky Way. We discuss possible
+models of its origin: free expansion from a point or from a ring, expansion of
+a shell, or a collision of a high velocity cloud with the plane of the Milky
+Way. Currently, no convincing model exists. Similar structures are identified
+in HI and CO distribution in our and other nearby galaxies.
+",0,1,0,0,0,0
+9607,Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise,"  In recent years, correntropy and its applications in machine learning have
+been drawing continuous attention owing to its merits in dealing with
+non-Gaussian noise and outliers. However, theoretical understanding of
+correntropy, especially in the statistical learning context, is still limited.
+In this study, within the statistical learning framework, we investigate
+correntropy based regression in the presence of non-Gaussian noise or outliers.
+Motivated by the practical way of generating non-Gaussian noise or outliers, we
+introduce mixture of symmetric stable noise, which include Gaussian noise,
+Cauchy noise, and their mixture as special cases, to model non-Gaussian noise
+or outliers. We demonstrate that under the mixture of symmetric stable noise
+assumption, correntropy based regression can learn the conditional mean
+function or the conditional median function well without resorting to the
+finite-variance or even the finite first-order moment condition on the noise.
+In particular, for the above two cases, we establish asymptotic optimal
+learning rates for correntropy based regression estimators that are
+asymptotically of type $\mathcal{O}(n^{-1})$. These results justify the
+effectiveness of the correntropy based regression estimators in dealing with
+outliers as well as non-Gaussian noise. We believe that the present study
+completes our understanding towards correntropy based regression from a
+statistical learning viewpoint, and may also shed some light on robust
+statistical learning for regression.
+",0,0,0,1,0,0
+9608,The Suppression and Promotion of Magnetic Flux Emergence in Fully Convective Stars,"  Evidence of surface magnetism is now observed on an increasing number of cool
+stars. The detailed manner by which dynamo-generated magnetic fields giving
+rise to starspots traverse the convection zone still remains unclear. Some
+insight into this flux emergence mechanism has been gained by assuming bundles
+of magnetic field can be represented by idealized thin flux tubes (TFTs). Weber
+& Browning (2016) have recently investigated how individual flux tubes might
+evolve in a 0.3 solar-mass M dwarf by effectively embedding TFTs in
+time-dependent flows representative of a fully convective star. We expand upon
+this work by initiating flux tubes at various depths in the upper 50-75% of the
+star in order to sample the differing convective flow pattern and differential
+rotation across this region. Specifically, we comment on the role of
+differential rotation and time-varying flows in both the suppression and
+promotion of the magnetic flux emergence process.
+",0,1,0,0,0,0
+9609,Opportunistic Content Delivery in Fading Broadcast Channels,"  We consider content delivery over fading broadcast channels. A server wants
+to transmit K files to K users, each equipped with a cache of finite size.
+Using the coded caching scheme of Maddah-Ali and Niesen, we design an
+opportunistic delivery scheme where the long-term sum content delivery rate
+scales with K the number of users in the system. The proposed delivery scheme
+combines superposition coding together with appropriate power allocation across
+sub-files intended to different subsets of users. We analyze the long-term
+average sum content delivery rate achieved by two special cases of our scheme:
+a) a selection scheme that chooses the subset of users with the largest
+weighted rate, and b) a baseline scheme that transmits to K users using the
+scheme of Maddah-Ali and Niesen. We prove that coded caching with appropriate
+user selection is scalable since it yields a linear increase of the average sum
+content delivery rate.
+",1,0,0,0,0,0
+9610,Conservation Laws With Random and Deterministic Data,"  The dynamics of nonlinear conservation laws have long posed fascinating
+problems. With the introduction of some nonlinearity, e.g. Burgers' equation,
+discontinuous behavior in the solutions is exhibited, even for smooth initial
+data. The introduction of randomness in any of several forms into the initial
+condition makes the problem even more interesting. We present a broad spectrum
+of results from a number of works, both deterministic and random, to provide a
+diverse introduction to some of the methods of analysis for conservation laws.
+Some of the deep theorems are applied to discrete examples and illuminated
+using diagrams.
+",0,0,1,0,0,0
+9611,"Theoretical study of HfF$^+$ cation to search for the T,P-odd interactions","  The combined all-electron and two-step approach is applied to calculate the
+molecular parameters which are required to interpret the ongoing experiment to
+search for the effects of manifestation of the T,P-odd fundamental interactions
+in the HfF$^+$ cation by Cornell/Ye group [Science 342, 1220 (2013); J. Mol.
+Spectrosc. 300, 12 (2014)]. The effective electric field that is required to
+interpret the experiment in terms of the electron electric dipole moment is
+found to be 22.5 GV/cm. In Ref. [Phys. Rev. D 89, 056006 (2014)] it was shown
+that another source of T,P-odd interaction, the scalar-pseudoscalar
+nucleus-electron interaction with the dimensionless strength constant $k_{T,P}$
+can dominate over the direct contribution from the electron EDM within the
+standard model and some of its extensions. Therefore, for the comprehensive and
+correct interpretation of the HfF$^+$ experiment one should also know the
+molecular parameter $W_{T,P}$ the value of which is reported here to be 20.1
+kHz.
+",0,1,0,0,0,0
+9612,Adaptive Information Gathering via Imitation Learning,"  In the adaptive information gathering problem, a policy is required to select
+an informative sensing location using the history of measurements acquired thus
+far. While there is an extensive amount of prior work investigating effective
+practical approximations using variants of Shannon's entropy, the efficacy of
+such policies heavily depends on the geometric distribution of objects in the
+world. On the other hand, the principled approach of employing online POMDP
+solvers is rendered impractical by the need to explicitly sample online from a
+posterior distribution of world maps.
+We present a novel data-driven imitation learning framework to efficiently
+train information gathering policies. The policy imitates a clairvoyant oracle
+- an oracle that at train time has full knowledge about the world map and can
+compute maximally informative sensing locations. We analyze the learnt policy
+by showing that offline imitation of a clairvoyant oracle is implicitly
+equivalent to online oracle execution in conjunction with posterior sampling.
+This observation allows us to obtain powerful near-optimality guarantees for
+information gathering problems possessing an adaptive sub-modularity property.
+As demonstrated on a spectrum of 2D and 3D exploration problems, the trained
+policies enjoy the best of both worlds - they adapt to different world map
+distributions while being computationally inexpensive to evaluate.
+",1,0,0,0,0,0
+9613,Demonstration of dispersive rarefaction shocks in hollow elliptical cylinder chains,"  We report an experimental and numerical demonstration of dispersive
+rarefaction shocks (DRS) in a 3D-printed soft chain of hollow elliptical
+cylinders. We find that, in contrast to conventional nonlinear waves, these DRS
+have their lower amplitude components travel faster, while the higher amplitude
+ones propagate slower. This results in the backward-tilted shape of the front
+of the wave (the rarefaction segment) and the breakage of wave tails into a
+modulated waveform (the dispersive shock segment). Examining the DRS under
+various impact conditions, we find the counter-intuitive feature that the
+higher striker velocity causes the slower propagation of the DRS. These unique
+features can be useful for mitigating impact controllably and efficiently
+without relying on material damping or plasticity effects.
+",0,1,0,0,0,0
+9614,Gaussian Process Regression for Arctic Coastal Erosion Forecasting,"  Arctic coastal morphology is governed by multiple factors, many of which are
+affected by climatological changes. As the season length for shorefast ice
+decreases and temperatures warm permafrost soils, coastlines are more
+susceptible to erosion from storm waves. Such coastal erosion is a concern,
+since the majority of the population centers and infrastructure in the Arctic
+are located near the coasts. Stakeholders and decision makers increasingly need
+models capable of scenario-based predictions to assess and mitigate the effects
+of coastal morphology on infrastructure and land use. Our research uses
+Gaussian process models to forecast Arctic coastal erosion along the Beaufort
+Sea near Drew Point, AK. Gaussian process regression is a data-driven modeling
+methodology capable of extracting patterns and trends from data-sparse
+environments such as remote Arctic coastlines. To train our model, we use
+annual coastline positions and near-shore summer temperature averages from
+existing datasets and extend these data by extracting additional coastlines
+from satellite imagery. We combine our calibrated models with future climate
+models to generate a range of plausible future erosion scenarios. Our results
+show that the Gaussian process methodology substantially improves yearly
+predictions compared to linear and nonlinear least squares methods, and is
+capable of generating detailed forecasts suitable for use by decision makers.
+",0,1,0,1,0,0
+9615,Calibration for Stratified Classification Models,"  In classification problems, sampling bias between training data and testing
+data is critical to the ranking performance of classification scores. Such bias
+can be both unintentionally introduced by data collection and intentionally
+introduced by the algorithm, such as under-sampling or weighting techniques
+applied to imbalanced data. When such sampling bias exists, using the raw
+classification score to rank observations in the testing data can lead to
+suboptimal results. In this paper, I investigate the optimal calibration
+strategy in general settings, and develop a practical solution for one specific
+sampling bias case, where the sampling bias is introduced by stratified
+sampling. The optimal solution is developed by analytically solving the problem
+of optimizing the ROC curve. For practical data, I propose a ranking algorithm
+for general classification models with stratified data. Numerical experiments
+demonstrate that the proposed algorithm effectively addresses the stratified
+sampling bias issue. Interestingly, the proposed method shows its potential
+applicability in two other machine learning areas: unsupervised learning and
+model ensembling, which can be future research topics.
+",1,0,0,1,0,0
+9616,Two classes of fast-declining type Ia supernovae,"  Fast-declining Type Ia supernovae (SN Ia) separate into two categories based
+on their bolometric and near-infrared (NIR) properties. The peak bolometric
+luminosity ($\mathrm{L_{max}}$), the phase of the first maximum relative to the
+optical, the NIR peak luminosity and the occurrence of a second maximum in the
+NIR distinguish a group of very faint SN Ia. Fast-declining supernovae show a
+large range of peak bolometric luminosities ($\mathrm{L_{max}}$ differing by up
+to a factor of $\sim$ 8). All fast-declining SN Ia with $\mathrm{L_{max}} < 0.3
+\cdot$ 10$^{43}\mathrm{erg s}^{-1}$ are spectroscopically classified as
+91bg-like and show only a single NIR peak. SNe with $\mathrm{L_{max}} > 0.5
+\cdot$ 10$^{43}\mathrm{erg s}^{-1}$ appear to smoothly connect to normal SN Ia.
+The total ejecta mass (M$_{ej}$) values for SNe with enough late time data are
+$\lesssim$1 $M_{\odot}$, indicating a sub-Chandrasekhar mass progenitor for
+these SNe.
+",0,1,0,0,0,0
+9617,What Sets the Radial Locations of Warm Debris Disks?,"  The architectures of debris disks encode the history of planet formation in
+these systems. Studies of debris disks via their spectral energy distributions
+(SEDs) have found infrared excesses arising from cold dust, warm dust, or a
+combination of the two. The cold outer belts of many systems have been imaged,
+facilitating their study in great detail. Far less is known about the warm
+components, including the origin of the dust. The regularity of the disk
+temperatures indicates an underlying structure that may be linked to the water
+snow line. If the dust is generated from collisions in an exo-asteroid belt,
+the dust will likely trace the location of the water snow line in the
+primordial protoplanetary disk where planetesimal growth was enhanced. If
+instead the warm dust arises from the inward transport from a reservoir of icy
+material farther out in the system, the dust location is expected to be set by
+the current snow line. We analyze the SEDs of a large sample of debris disks
+with warm components. We find that warm components in single-component systems
+(those without detectable cold components) follow the primordial snow line
+rather than the current snow line, so they likely arise from exo-asteroid
+belts. While the locations of many warm components in two-component systems are
+also consistent with the primordial snow line, there is more diversity among
+these systems, suggesting additional effects play a role.
+",0,1,0,0,0,0
+9618,Nonlinear Modulational Instability of Dispersive PDE Models,"  We prove nonlinear modulational instability for both periodic and localized
+perturbations of periodic traveling waves for several dispersive PDEs,
+including the KDV type equations (e.g. the Whitham equation, the generalized
+KDV equation, the Benjamin-Ono equation), the nonlinear Schrödinger equation
+and the BBM equation. First, the semigroup estimates required for the nonlinear
+proof are obtained by using the Hamiltonian structures of the linearized PDEs;
+Second, for KDV type equations the loss of derivative in the nonlinear term is
+overcome in two complementary cases: (1) for smooth nonlinear terms and general
+dispersive operators, we construct higher order approximation solutions and
+then use energy type estimates; (2) for nonlinear terms of low regularity, with
+some additional assumption on the dispersive operator, we use a bootstrap
+argument to overcome the loss of derivative.
+",0,0,1,0,0,0
+9619,Unsupervised Learning of Disentangled Representations from Video,"  We present a new model DrNET that learns disentangled image representations
+from video. Our approach leverages the temporal coherence of video and a novel
+adversarial loss to learn a representation that factorizes each frame into a
+stationary part and a temporally varying component. The disentangled
+representation can be used for a range of tasks. For example, applying a
+standard LSTM to the time-vary components enables prediction of future frames.
+We evaluate our approach on a range of synthetic and real videos, demonstrating
+the ability to coherently generate hundreds of steps into the future.
+",1,0,0,1,0,0
+9620,Control of Ultracold Photodissociation with Magnetic Fields,"  Photodissociation of a molecule produces a spatial distribution of
+photofragments determined by the molecular structure and the characteristics of
+the dissociating light. Performing this basic chemical reaction at ultracold
+temperatures allows its quantum mechanical features to dominate. In this
+regime, weak applied fields can be used to control the reaction. Here, we
+photodissociate ultracold diatomic strontium in magnetic fields below 10 G and
+observe striking changes in photofragment angular distributions. The
+observations are in excellent qualitative agreement with a multichannel quantum
+chemistry model that includes nonadiabatic effects and predicts strong mixing
+of partial waves in the photofragment energy continuum. The experiment is
+enabled by precise quantum-state control of the molecules.
+",0,1,0,0,0,0
+9621,JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets,"  A new generative adversarial network is developed for joint distribution
+matching. Distinct from most existing approaches, that only learn conditional
+distributions, the proposed model aims to learn a joint distribution of
+multiple random variables (domains). This is achieved by learning to sample
+from conditional distributions between the domains, while simultaneously
+learning to sample from the marginals of each individual domain. The proposed
+framework consists of multiple generators and a single softmax-based critic,
+all jointly trained via adversarial learning. From a simple noise source, the
+proposed framework allows synthesis of draws from the marginals, conditional
+draws given observations from a subset of random variables, or complete draws
+from the full joint distribution. Most examples considered are for joint
+analysis of two domains, with examples for three domains also presented.
+",0,0,0,1,0,0
+9622,A lightweight MapReduce framework for secure processing with SGX,"  MapReduce is a programming model used extensively for parallel data
+processing in distributed environments. A wide range of algorithms were
+implemented using MapReduce, from simple tasks like sorting and searching up to
+complex clustering and machine learning operations. Many of these
+implementations are part of services externalized to cloud infrastructures.
+Over the past years, however, many concerns have been raised regarding the
+security guarantees offered in such environments. Some solutions relying on
+cryptography were proposed for countering threats but these typically imply a
+high computational overhead. Intel, the largest manufacturer of commodity CPUs,
+recently introduced SGX (software guard extensions), a set of hardware
+instructions that support execution of code in an isolated secure environment.
+In this paper, we explore the use of Intel SGX for providing privacy guarantees
+for MapReduce operations, and based on our evaluation we conclude that it
+represents a viable alternative to a cryptographic mechanism. We present
+results based on the widely used k-means clustering algorithm, but our
+implementation can be generalized to other applications that can be expressed
+using MapReduce model.
+",1,0,0,0,0,0
+9623,Formation of wide-orbit gas giants near the stability limit in multi-stellar systems,"  We have investigated the formation of a circumstellar wide-orbit gas giant
+planet in a multiple stellar system. We consider a model of orbital
+circularization for the core of a giant planet after it is scattered from an
+inner disk region by a more massive planet, which was proposed by Kikuchi et al
+(2014). We extend their model for single star systems to binary (multiple) star
+systems, by taking into account tidal truncation of the protoplanetary gas disk
+by a binary companion. As an example, we consider a wide-orbit gas giant in a
+hierarchical triple system, HD131399Ab. The best-fit orbit of the planet is
+that with semimajor axis $\sim 80$ au and eccentricity $\sim 0.35$. As the
+binary separation is $\sim 350$ au, it is very close to the stability limit,
+which is puzzling. With the original core location $\sim 20$-30 au, the core
+(planet) mass $\sim 50 M_{\rm E}$ and the disk truncation radius $\sim 150$ au,
+our model reproduces the best-fit orbit of HD131399Ab. We find that the orbit
+after the circularization is usually close to the stability limit against the
+perturbations from the binary companion, because the scattered core accretes
+gas from the truncated disk. Our conclusion can also be applied to wider or
+more compact binary systems if the separation is not too large and another
+planet with $> \sim$ 20-30 Earth masses that scattered the core existed in
+inner region of the system.
+",0,1,0,0,0,0
+9624,Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint,"  Symmetric nonnegative matrix factorization has found abundant applications in
+various domains by providing a symmetric low-rank decomposition of nonnegative
+matrices. In this paper we propose a Frank-Wolfe (FW) solver to optimize the
+symmetric nonnegative matrix factorization problem under a simplicial
+constraint, which has recently been proposed for probabilistic clustering.
+Compared with existing solutions, this algorithm is simple to implement, and
+has no hyperparameters to be tuned. Building on the recent advances of FW
+algorithms in nonconvex optimization, we prove an $O(1/\varepsilon^2)$
+convergence rate to $\varepsilon$-approximate KKT points, via a tight bound
+$\Theta(n^2)$ on the curvature constant, which matches the best known result in
+unconstrained nonconvex setting using gradient methods. Numerical results
+demonstrate the effectiveness of our algorithm. As a side contribution, we
+construct a simple nonsmooth convex problem where the FW algorithm fails to
+converge to the optimum. This result raises an interesting question about
+necessary conditions of the success of the FW algorithm on convex problems.
+",1,0,1,1,0,0
+9625,A Fourier Disparity Layer representation for Light Fields,"  In this paper, we present a new Light Field representation for efficient
+Light Field processing and rendering called Fourier Disparity Layers (FDL). The
+proposed FDL representation samples the Light Field in the depth (or
+equivalently the disparity) dimension by decomposing the scene as a discrete
+sum of layers. The layers can be constructed from various types of Light Field
+inputs including a set of sub-aperture images, a focal stack, or even a
+combination of both. From our derivations in the Fourier domain, the layers are
+simply obtained by a regularized least square regression performed
+independently at each spatial frequency, which is efficiently parallelized in a
+GPU implementation. Our model is also used to derive a gradient descent based
+calibration step that estimates the input view positions and an optimal set of
+disparity values required for the layer construction. Once the layers are
+known, they can be simply shifted and filtered to produce different viewpoints
+of the scene while controlling the focus and simulating a camera aperture of
+arbitrary shape and size. Our implementation in the Fourier domain allows real
+time Light Field rendering. Finally, direct applications such as view
+interpolation or extrapolation and denoising are presented and evaluated.
+",1,0,0,0,0,0
+9626,Narrating Networks,"  Networks have become the de facto diagram of the Big Data age (try searching
+Google Images for [big data AND visualisation] and see). The concept of
+networks has become central to many fields of human inquiry and is said to
+revolutionise everything from medicine to markets to military intelligence.
+While the mathematical and analytical capabilities of networks have been
+extensively studied over the years, in this article we argue that the
+storytelling affordances of networks have been comparatively neglected. In
+order to address this we use multimodal analysis to examine the stories that
+networks evoke in a series of journalism articles. We develop a protocol by
+means of which narrative meanings can be construed from network imagery and the
+context in which it is embedded, and discuss five different kinds of narrative
+readings of networks, illustrated with analyses of examples from journalism.
+Finally, to support further research in this area, we discuss methodological
+issues that we encountered and suggest directions for future study to advance
+and broaden research around this defining aspect of visual culture after the
+digital turn.
+",1,0,0,0,0,0
+9627,Policy Evaluation and Optimization with Continuous Treatments,"  We study the problem of policy evaluation and learning from batched
+contextual bandit data when treatments are continuous, going beyond previous
+work on discrete treatments. Previous work for discrete treatment/action spaces
+focuses on inverse probability weighting (IPW) and doubly robust (DR) methods
+that use a rejection sampling approach for evaluation and the equivalent
+weighted classification problem for learning. In the continuous setting, this
+reduction fails as we would almost surely reject all observations. To tackle
+the case of continuous treatments, we extend the IPW and DR approaches to the
+continuous setting using a kernel function that leverages treatment proximity
+to attenuate discrete rejection. Our policy estimator is consistent and we
+characterize the optimal bandwidth. The resulting continuous policy optimizer
+(CPO) approach using our estimator achieves convergent regret and approaches
+the best-in-class policy for learnable policy classes. We demonstrate that the
+estimator performs well and, in particular, outperforms a discretization-based
+benchmark. We further study the performance of our policy optimizer in a case
+study on personalized dosing based on a dataset of Warfarin patients, their
+covariates, and final therapeutic doses. Our learned policy outperforms
+benchmarks and nears the oracle-best linear policy.
+",0,0,0,1,0,0
+9628,Geometry of Factored Nuclear Norm Regularization,"  This work investigates the geometry of a nonconvex reformulation of
+minimizing a general convex loss function $f(X)$ regularized by the matrix
+nuclear norm $\|X\|_*$. Nuclear-norm regularized matrix inverse problems are at
+the heart of many applications in machine learning, signal processing, and
+control. The statistical performance of nuclear norm regularization has been
+studied extensively in literature using convex analysis techniques. Despite its
+optimal performance, the resulting optimization has high computational
+complexity when solved using standard or even tailored fast convex solvers. To
+develop faster and more scalable algorithms, we follow the proposal of
+Burer-Monteiro to factor the matrix variable $X$ into the product of two
+smaller rectangular matrices $X=UV^T$ and also replace the nuclear norm
+$\|X\|_*$ with $(\|U\|_F^2+\|V\|_F^2)/2$. In spite of the nonconvexity of the
+factored formulation, we prove that when the convex loss function $f(X)$ is
+$(2r,4r)$-restricted well-conditioned, each critical point of the factored
+problem either corresponds to the optimal solution $X^\star$ of the original
+convex optimization or is a strict saddle point where the Hessian matrix has a
+strictly negative eigenvalue. Such a geometric structure of the factored
+formulation allows many local search algorithms to converge to the global
+optimum with random initializations.
+",1,0,1,0,0,0
+9629,Effect of Scrape-Off-Layer Current on Reconstructed Tokamak Equilibrium,"  Methods are described that extend fields from reconstructed equilibria to
+include scrape-off-layer current through extrapolated parametrized and
+experimental fits. The extrapolation includes both the effects of the
+toroidal-field and pressure gradients which produce scrape-off-layer current
+after recomputation of the Grad-Shafranov solution. To quantify the degree that
+inclusion of scrape-off-layer current modifies the equilibrium, the
+$\chi$-squared goodness-of-fit parameter is calculated for cases with and
+without scrape-off-layer current. The change in $\chi$-squared is found to be
+minor when scrape-off-layer current is included however flux surfaces are
+shifted by up to 3 cm. The impact on edge modes of these scrape-off-layer
+modifications is also found to be small and the importance of these methods to
+nonlinear computation is discussed.
+",0,1,0,0,0,0
+9630,Holomorphy of Osborn loops,"  Let $(L,\cdot)$ be any loop and let $A(L)$ be a group of automorphisms of
+$(L,\cdot)$ such that $\alpha$ and $\phi$ are elements of $A(L)$. It is shown
+that, for all $x,y,z\in L$, the $A(L)$-holomorph $(H,\circ)=H(L)$ of
+$(L,\cdot)$ is an Osborn loop if and only if $x\alpha (yz\cdot x\phi^{-1})=
+x\alpha (yx^\lambda\cdot x) \cdot zx\phi^{-1}$. Furthermore, it is shown that
+for all $x\in L$, $H(L)$ is an Osborn loop if and only if $(L,\cdot)$ is an
+Osborn loop, $(x\alpha\cdot x^{\rho})x=x\alpha$, $x(x^{\lambda}\cdot
+x\phi^{-1})=x\phi^{-1}$ and every pair of automorphisms in $A(L)$ is nuclear
+(i.e. $x\alpha\cdot x^{\rho},x^{\lambda}\cdot x\phi\in N(L,\cdot )$). It is
+shown that if $H(L)$ is an Osborn loop, then $A(L,\cdot)=
+\mathcal{P}(L,\cdot)\cap\Lambda(L,\cdot)\cap\Phi(L,\cdot)\cap\Psi(L,\cdot)$ and
+for any $\alpha\in A(L)$, $\alpha= L_{e\pi}=R^{-1}_{e\varrho}$ for some $\pi\in
+\Phi(L,\cdot)$ and some $\varrho\in \Psi(L,\cdot)$. Some commutative diagrams
+are deduced by considering isomorphisms among the various groups of regular
+bijections (whose intersection is $A(L)$) and the nucleus of $(L,\cdot)$.
+",0,0,1,0,0,0
+9631,A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders,"  Multi-objective recommender systems address the difficult task of
+recommending items that are relevant to multiple, possibly conflicting,
+criteria. However these systems are most often designed to address the
+objective of one single stakeholder, typically, in online commerce, the
+consumers whose input and purchasing decisions ultimately determine the success
+of the recommendation systems. In this work, we address the multi-objective,
+multi-stakeholder, recommendation problem involving one or more objective(s)
+per stakeholder. In addition to the consumer stakeholder, we also consider two
+other stakeholders; the suppliers who provide the goods and services for sale
+and the intermediary who is responsible for helping connect consumers to
+suppliers via its recommendation algorithms. We analyze the multi-objective,
+multi-stakeholder, problem from the point of view of the online marketplace
+intermediary whose objective is to maximize its commission through its
+recommender system. We define a multi-objective problem relating all our three
+stakeholders which we solve with a novel learning-to-re-rank approach that
+makes use of a novel regularization function based on the Kendall tau
+correlation metric and its kernel version; given an initial ranking of item
+recommendations built for the consumer, we aim to re-rank it such that the new
+ranking is also optimized for the secondary objectives while staying close to
+the initial ranking. We evaluate our approach on a real-world dataset of hotel
+recommendations provided by Expedia where we show the effectiveness of our
+approach against a business-rules oriented baseline model.
+",1,0,0,0,0,0
+9632,Interactive Discovery System for Direct Democracy,"  Decide Madrid is the civic technology of Madrid City Council which allows
+users to create and support online petitions. Despite the initial success, the
+platform is encountering problems with the growth of petition signing because
+petitions are far from the minimum number of supporting votes they must gather.
+Previous analyses have suggested that this problem is produced by the
+interface: a paginated list of petitions which applies a non-optimal ranking
+algorithm. For this reason, we present an interactive system for the discovery
+of topics and petitions. This approach leads us to reflect on the usefulness of
+data visualization techniques to address relevant societal challenges.
+",1,0,0,0,0,0
+9633,Deep Learning to Attend to Risk in ICU,"  Modeling physiological time-series in ICU is of high clinical importance.
+However, data collected within ICU are irregular in time and often contain
+missing measurements. Since absence of a measure would signify its lack of
+importance, the missingness is indeed informative and might reflect the
+decision making by the clinician. Here we propose a deep learning architecture
+that can effectively handle these challenges for predicting ICU mortality
+outcomes. The model is based on Long Short-Term Memory, and has layered
+attention mechanisms. At the sensing layer, the model decides whether to
+observe and incorporate parts of the current measurements. At the reasoning
+layer, evidences across time steps are weighted and combined. The model is
+evaluated on the PhysioNet 2012 dataset showing competitive and interpretable
+results.
+",1,0,0,1,0,0
+9634,Steklov problem on differential forms,"  In this paper we study spectral properties of Dirichlet-to-Neumann map on
+differential forms obtained by a slight modification of the definition due to
+Belishev and Sharafutdinov. The resulting operator $\Lambda$ is shown to be
+self-adjoint on the subspace of coclosed forms and to have purely discrete
+spectrum there.We investigate properies of eigenvalues of $\Lambda$ and prove a
+Hersch-Payne-Schiffer type inequality relating products of those eigenvalues to
+eigenvalues of Hodge Laplacian on the boundary. Moreover, non-trivial
+eigenvalues of $\Lambda$ are always at least as large as eigenvalues of
+Dirichlet-to-Neumann map defined by Raulot and Savo. Finally, we remark that a
+particular case of $p$-forms on the boundary of $2p+2$-dimensional manifold
+shares a lot of important properties with the classical Steklov eigenvalue
+problem on surfaces.
+",0,0,1,0,0,0
+9635,Constraining a dark matter and dark energy interaction scenario with a dynamical equation of state,"  In this work we have used the recent cosmic chronometers data along with the
+latest estimation of the local Hubble parameter value, $H_0$ at 2.4\% precision
+as well as the standard dark energy probes, such as the Supernovae Type Ia,
+baryon acoustic oscillation distance measurements, and cosmic microwave
+background measurements (PlanckTT $+$ lowP) to constrain a dark energy model
+where the dark energy is allowed to interact with the dark matter. A general
+equation of state of dark energy parametrized by a dimensionless parameter
+`$\beta$' is utilized. From our analysis, we find that the interaction is
+compatible with zero within the 1$\sigma$ confidence limit. We also show that
+the same evolution history can be reproduced by a small pressure of the dark
+matter.
+",0,1,0,0,0,0
+9636,Comprehensive evaluation of statistical speech waveform synthesis,"  Statistical TTS systems that directly predict the speech waveform have
+recently reported improvements in synthesis quality. This investigation
+evaluates Amazon's statistical speech waveform synthesis (SSWS) system. An
+in-depth evaluation of SSWS is conducted across a number of domains to better
+understand the consistency in quality. The results of this evaluation are
+validated by repeating the procedure on a separate group of testers. Finally,
+an analysis of the nature of speech errors of SSWS compared to hybrid unit
+selection synthesis is conducted to identify the strengths and weaknesses of
+SSWS. Having a deeper insight into SSWS allows us to better define the focus of
+future work to improve this new technology.
+",1,0,0,0,0,0
+9637,Component response rate variation drives stability in large complex systems,"  The stability of a complex system generally decreases with increasing system
+size and interconnectivity, a counterintuitive result of widespread importance
+across the physical, life, and social sciences. Despite recent interest in the
+relationship between system properties and stability, the effect of variation
+in the response rate of individual system components remains unconsidered. Here
+I vary the component response rates ($\boldsymbol{\gamma}$) of randomly
+generated complex systems. I show that when component response rates vary, the
+potential for system stability is markedly increased. Variation in
+$\boldsymbol{\gamma}$ becomes increasingly important as system size increases,
+such that the largest stable complex systems would be unstable if not for
+$\boldsymbol{Var(\gamma)}$. My results reveal a previously unconsidered driver
+of system stability that is likely to be pervasive across all complex systems.
+",0,0,0,0,1,0
+9638,Time-resolved ultrafast x-ray scattering from an incoherent electronic mixture,"  Time-resolved ultrafast x-ray scattering from photo-excited matter is an
+emerging method to image ultrafast dynamics in matter with atomic-scale spatial
+and temporal resolutions. For a correct and rigorous understanding of current
+and upcoming imaging experiments, we present the theory of time-resolved x-ray
+scattering from an incoherent electronic mixture using quantum electrodynamical
+theory of light-matter interaction. We show that the total scattering signal is
+an incoherent sum of the individual scattering signals arising from different
+electronic states and therefore heterodyning of the individual signals is not
+possible for an ensemble of gas-phase photo-excited molecules. We scrutinize
+the information encoded in the total signal for the experimentally important
+situation when pulse duration and coherence time of the x-ray pulse are short
+in comparison to the timescale of the vibrational motion and long in comparison
+to the timescale of the electronic motion, respectively. Finally, we show that
+in the case of an electronically excited crystal the total scattering signal
+imprints the interference of the individual scattering amplitudes associated
+with different electronic states and heterodyning is possible.
+",0,1,0,0,0,0
+9639,Calculation of the critical overdensity in the spherical-collapse approximation,"  Critical overdensity $\delta_c$ is a key concept in estimating the number
+count of halos for different redshift and halo-mass bins, and therefore, it is
+a powerful tool to compare cosmological models to observations. There are
+currently two different prescriptions in the literature for its calculation,
+namely, the differential-radius and the constant-infinity methods. In this work
+we show that the latter yields precise results {\it only} if we are careful in
+the definition of the so-called numerical infinities. Although the subtleties
+we point out are crucial ingredients for an accurate determination of
+$\delta_c$ both in general relativity and in any other gravity theory, we focus
+on $f(R)$ modified-gravity models in the metric approach; in particular, we use
+the so-called large ($F=1/3$) and small-field ($F=0$) limits. For both of them,
+we calculate the relative errors (between our method and the others) in the
+critical density $\delta_c$, in the comoving number density of halos per
+logarithmic mass interval $n_{\ln M}$ and in the number of clusters at a given
+redshift in a given mass bin $N_{\rm bin}$, as functions of the redshift. We
+have also derived an analytical expression for the density contrast in the
+linear regime as a function of the collapse redshift $z_c$ and $\Omega_{m0}$
+for any $F$.
+",0,1,0,0,0,0
+9640,"Point distributions in compact metric spaces, II","  We consider finite point subsets (distributions) in compact metric spaces. In
+the case of general rectifiable metric spaces, non-trivial bounds for sums of
+distances between points of distributions and for discrepancies of
+distributions in metric balls are given (Theorem 1.1). We generalize
+Stolarsky's invariance principle to distance-invariant spaces (Theorem 2.1).
+For arbitrary metric spaces, we prove a probabilistic invariance principle
+(Theorem 3.1). Furthermore, we construct equal-measure partitions of general
+rectifiable compact metric spaces into parts of small average diameter (Theorem
+4.1). This version of the paper will be published in Mathematika
+",0,0,1,0,0,0
+9641,A Variational Projection Scheme for Nonmatching Surface-to-Line Coupling between 3D Flexible Multibody System and Incompressible Turbulent Flow,"  This paper is concerned with the partitioned iterative formulation to
+simulate the fluid-structure interaction of a nonlinear multibody system in an
+incompressible turbulent flow. The proposed formulation relies on a
+three-dimensional (3D) incompressible turbulent flow solver, a nonlinear
+monolithic elastic structural solver for constrained flexible multibody system
+and the nonlinear iterative force correction scheme for coupling of the
+turbulent fluid-flexible multibody system with nonmatching interface meshes.
+While the fluid equations are discretized using a stabilized Petrov-Galerkin
+formulation in space and the generalized-$\alpha$ updates in time, the
+multibody system utilizes a discontinuous space-time Galerkin finite element
+method. We address two key challenges in the present formulation. Firstly, the
+coupling of the incompressible turbulent flow with a system of nonlinear
+elastic bodies described in a co-rotated frame. Secondly, the projection of the
+tractions and displacements across the nonmatching 3D fluid surface elements
+and the one-dimensional line elements for the flexible multibody system in a
+conservative manner. Through the nonlinear iterative correction and the
+conservative projection, the developed fluid-flexible multibody interaction
+solver is stable for problems involving strong inertial effects between the
+fluid-flexible multibody system and the coupled interactions among each
+multibody component. The accuracy of the proposed coupled finite element
+framework is validated against the available experimental data for a long
+flexible cylinder undergoing vortex-induced vibration in a uniform current flow
+condition. Finally, a practical application of the proposed framework is
+demonstrated by simulating the flow-induced vibration of a realistic offshore
+floating platform connected to a long riser and an elastic mooring system.
+",0,1,0,0,0,0
+9642,The Hubble Catalog of Variables,"  The Hubble Catalog of Variables (HCV) is a 3 year ESA funded project that
+aims to develop a set of algorithms to identify variables among the sources
+included in the Hubble Source Catalog (HSC) and produce the HCV. We will
+process all HSC sources with more than a predefined number of measurements in a
+single filter/instrument combination and compute a range of lightcurve features
+to determine the variability status of each source. At the end of the project,
+the first release of the Hubble Catalog of Variables will be made available at
+the Mikulski Archive for Space Telescopes (MAST) and the ESA Science Archives.
+The variability detection pipeline will be implemented at the Space Telescope
+Science Institute (STScI) so that updated versions of the HCV may be created
+following the future releases of the HSC.
+",0,1,0,0,0,0
+9643,A Loop-Based Methodology for Reducing Computational Redundancy in Workload Sets,"  The design of general purpose processors relies heavily on a workload
+gathering step in which representative programs are collected from various
+application domains. Processor performance, when running the workload set, is
+profiled using simulators that model the targeted processor architecture.
+However, simulating the entire workload set is prohibitively time-consuming,
+which precludes considering a large number of programs. To reduce simulation
+time, several techniques in the literature have exploited the internal program
+repetitiveness to extract and execute only representative code segments.
+Existing so- lutions are based on reducing cross-program computational
+redundancy or on eliminating internal-program redundancy to decrease execution
+time. In this work, we propose an orthogonal and complementary loop- centric
+methodology that targets loop-dominant programs by exploiting internal-program
+characteristics to reduce cross-program computational redundancy. The approach
+employs a newly developed framework that extracts and analyzes core loops
+within workloads. The collected characteristics model memory behavior,
+computational complexity, and data structures of a program, and are used to
+construct a signature vector for each program. From these vectors,
+cross-workload similarity metrics are extracted, which are processed by a novel
+heuristic to exclude similar programs and reduce redundancy within the set.
+Finally, a reverse engineering approach that synthesizes executable
+micro-benchmarks having the same instruction mix as the loops in the original
+workload is introduced. A tool that automates the flow steps of the proposed
+methodology is developed. Simulation results demonstrate that applying the
+proposed methodology to a set of workloads reduces the set size by half, while
+preserving the main characterizations of the initial workloads.
+",1,0,0,0,0,0
+9644,Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization,"  Most traditional video summarization methods are designed to generate
+effective summaries for single-view videos, and thus they cannot fully exploit
+the complicated intra and inter-view correlations in summarizing multi-view
+videos in a camera network. In this paper, with the aim of summarizing
+multi-view videos, we introduce a novel unsupervised framework via joint
+embedding and sparse representative selection. The objective function is
+two-fold. The first is to capture the multi-view correlations via an embedding,
+which helps in extracting a diverse set of representatives. The second is to
+use a `2;1- norm to model the sparsity while selecting representative shots for
+the summary. We propose to jointly optimize both of the objectives, such that
+embedding can not only characterize the correlations, but also indicate the
+requirements of sparse representative selection. We present an efficient
+alternating algorithm based on half-quadratic minimization to solve the
+proposed non-smooth and non-convex objective with convergence analysis. A key
+advantage of the proposed approach with respect to the state-of-the-art is that
+it can summarize multi-view videos without assuming any prior
+correspondences/alignment between them, e.g., uncalibrated camera networks.
+Rigorous experiments on several multi-view datasets demonstrate that our
+approach clearly outperforms the state-of-the-art methods.
+",1,0,0,0,0,0
+9645,A Logical Approach to Cloud Federation,"  Federated clouds raise a variety of challenges for managing identity,
+resource access, naming, connectivity, and object access control. This paper
+shows how to address these challenges in a comprehensive and uniform way using
+a data-centric approach. The foundation of our approach is a trust logic in
+which participants issue authenticated statements about principals, objects,
+attributes, and relationships in a logic language, with reasoning based on
+declarative policy rules. We show how to use the logic to implement a trust
+infrastructure for cloud federation that extends the model of NSF GENI, a
+federated IaaS testbed. It captures shared identity management, GENI authority
+services, cross-site interconnection using L2 circuits, and a naming and access
+control system similar to AWS Identity and Access Management (IAM), but
+extended to a federated system without central control.
+",1,0,0,0,0,0
+9646,Deep Affordance-grounded Sensorimotor Object Recognition,"  It is well-established by cognitive neuroscience that human perception of
+objects constitutes a complex process, where object appearance information is
+combined with evidence about the so-called object ""affordances"", namely the
+types of actions that humans typically perform when interacting with them. This
+fact has recently motivated the ""sensorimotor"" approach to the challenging task
+of automatic object recognition, where both information sources are fused to
+improve robustness. In this work, the aforementioned paradigm is adopted,
+surpassing current limitations of sensorimotor object recognition research.
+Specifically, the deep learning paradigm is introduced to the problem for the
+first time, developing a number of novel neuro-biologically and
+neuro-physiologically inspired architectures that utilize state-of-the-art
+neural networks for fusing the available information sources in multiple ways.
+The proposed methods are evaluated using a large RGB-D corpus, which is
+specifically collected for the task of sensorimotor object recognition and is
+made publicly available. Experimental results demonstrate the utility of
+affordance information to object recognition, achieving an up to 29% relative
+error reduction by its inclusion.
+",1,0,0,0,0,0
+9647,Liquid crystal induced elasto-capillary suppression of crack formation in thin colloidal films,"  Drying of colloidal droplets on solid, rigid substrates is associated with a
+capillary pressure developing within the droplet. In due course of time, the
+capillary pressure builds up due to droplet evaporation resulting in the
+formation of a colloidal thin film that is prone to crack formation. In this
+study, we show that introducing a minimal amount of nematic liquid crystal
+(NLC) can completely suppress the crack formation. The mechanism behind the
+curbing of the crack formation may be attributed to the capillary
+stress-absorbing cushion provided by the elastic arrangements of the liquid
+crystal at the substrate-droplet interface. Cracks and allied surface
+instabilities are detrimental to the quality of the final product like surface
+coatings, and therefore, its suppression by an external inert additive is a
+promising technique that will be of immense importance for several industrial
+applications. We believe this fundamental investigation of crack suppression
+will open up an entire avenue of applications for the NLCs in the field of
+coatings, broadening its already existing wide range of benefits.
+",0,1,0,0,0,0
+9648,Autonomous Reactive Mission Scheduling and Task-Path Planning Architecture for Autonomous Underwater Vehicle,"  An Autonomous Underwater Vehicle (AUV) should carry out complex tasks in a
+limited time interval. Since existing AUVs have limited battery capacity and
+restricted endurance, they should autonomously manage mission time and the
+resources to perform effective persistent deployment in longer missions. Task
+assignment requires making decisions subject to resource constraints, while
+tasks are assigned with costs and/or values that are budgeted in advance. Tasks
+are distributed in a particular operation zone and mapped by a waypoint covered
+network. Thus, design an efficient routing-task priority assign framework
+considering vehicle's availabilities and properties is essential for increasing
+mission productivity and on-time mission completion. This depends strongly on
+the order and priority of the tasks that are located between node-like
+waypoints in an operation network. On the other hand, autonomous operation of
+AUVs in an unfamiliar dynamic underwater and performing quick response to
+sudden environmental changes is a complicated process. Water current
+instabilities can deflect the vehicle to an undesired direction and perturb
+AUVs safety. The vehicle's robustness to strong environmental variations is
+extremely crucial for its safe and optimum operations in an uncertain and
+dynamic environment. To this end, the AUV needs to have a general overview of
+the environment in top level to perform an autonomous action selection (task
+selection) and a lower level local motion planner to operate successfully in
+dealing with continuously changing situations. This research deals with
+developing a novel reactive control architecture to provide a higher level of
+decision autonomy for the AUV operation that enables a single vehicle to
+accomplish multiple tasks in a single mission in the face of periodic
+disturbances in a turbulent and highly uncertain environment.
+",1,0,0,0,0,0
+9649,Stealth Attacks on the Smart Grid,"  Random attacks that jointly minimize the amount of information acquired by
+the operator about the state of the grid and the probability of attack
+detection are presented. The attacks minimize the information acquired by the
+operator by minimizing the mutual information between the observations and the
+state variables describing the grid. Simultaneously, the attacker aims to
+minimize the probability of attack detection by minimizing the Kullback-Leibler
+(KL) divergence between the distribution when the attack is present and the
+distribution under normal operation. The resulting cost function is the
+weighted sum of the mutual information and the KL divergence mentioned above.
+The tradeoff between the probability of attack detection and the reduction of
+mutual information is governed by the weighting parameter on the KL divergence
+term in the cost function. The probability of attack detection is evaluated as
+a function of the weighting parameter. A sufficient condition on the weighting
+parameter is given for achieving an arbitrarily small probability of attack
+detection. The attack performance is numerically assessed on the IEEE 30-Bus
+and 118-Bus test systems.
+",1,0,0,0,0,0
+9650,Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network,"  In this paper we present an alternative strategy for fine-tuning the
+parameters of a network. We named the technique Gradual Tuning. Once trained on
+a first task, the network is fine-tuned on a second task by modifying a
+progressively larger set of the network's parameters. We test Gradual Tuning on
+different transfer learning tasks, using networks of different sizes trained
+with different regularization techniques. The result shows that compared to the
+usual fine tuning, our approach significantly reduces catastrophic forgetting
+of the initial task, while still retaining comparable if not better performance
+on the new task.
+",1,0,0,0,0,0
+9651,Bayesian power-spectrum inference with foreground and target contamination treatment,"  This work presents a joint and self-consistent Bayesian treatment of various
+foreground and target contaminations when inferring cosmological power-spectra
+and three dimensional density fields from galaxy redshift surveys. This is
+achieved by introducing additional block sampling procedures for unknown
+coefficients of foreground and target contamination templates to the previously
+presented ARES framework for Bayesian large scale structure analyses. As a
+result the method infers jointly and fully self-consistently three dimensional
+density fields, cosmological power-spectra, luminosity dependent galaxy biases,
+noise levels of respective galaxy distributions and coefficients for a set of a
+priori specified foreground templates. In addition this fully Bayesian approach
+permits detailed quantification of correlated uncertainties amongst all
+inferred quantities and correctly marginalizes over observational systematic
+effects. We demonstrate the validity and efficiency of our approach in
+obtaining unbiased estimates of power-spectra via applications to realistic
+mock galaxy observations subject to stellar contamination and dust extinction.
+While simultaneously accounting for galaxy biases and unknown noise levels our
+method reliably and robustly infers three dimensional density fields and
+corresponding cosmological power-spectra from deep galaxy surveys. Further our
+approach correctly accounts for joint and correlated uncertainties between
+unknown coefficients of foreground templates and the amplitudes of the
+power-spectrum. An effect amounting up to $10$ percent correlations and
+anti-correlations across large ranges in Fourier space.
+",0,1,0,0,0,0
+9652,3-Lie bialgebras and 3-Lie classical Yang-Baxter equations in low dimensions,"  In this paper, we give some low-dimensional examples of local cocycle 3-Lie
+bialgebras and double construction 3-Lie bialgebras which were introduced in
+the study of the classical Yang-Baxter equation and Manin triples for 3-Lie
+algebras. We give an explicit and practical formula to compute the
+skew-symmetric solutions of the 3-Lie classical Yang-Baxter equation (CYBE). As
+an illustration, we obtain all skew-symmetric solutions of the 3-Lie CYBE in
+complex 3-Lie algebras of dimension 3 and 4 and then the induced local cocycle
+3-Lie bialgebras. On the other hand, we classify the double construction 3-Lie
+bialgebras for complex 3-Lie algebras in dimensions 3 and 4 and then give the
+corresponding 8-dimensional pseudo-metric 3-Lie algebras.
+",0,0,1,0,0,0
+9653,Adaptive pixel-super-resolved lensfree holography for wide-field on-chip microscopy,"  High-resolution wide field-of-view (FOV) microscopic imaging plays an
+essential role in various fields of biomedicine, engineering, and physical
+sciences. As an alternative to conventional lens-based scanning techniques,
+lensfree holography provides a new way to effectively bypass the intrinsical
+trade-off between the spatial resolution and FOV of conventional microscopes.
+Unfortunately, due to the limited sensor pixel-size, unpredictable disturbance
+during image acquisition, and sub-optimum solution to the phase retrieval
+problem, typical lensfree microscopes only produce compromised imaging quality
+in terms of lateral resolution and signal-to-noise ratio (SNR). Here, we
+propose an adaptive pixel-super-resolved lensfree imaging (APLI) method which
+can solve, or at least partially alleviate these limitations. Our approach
+addresses the pixel aliasing problem by Z-scanning only, without resorting to
+subpixel shifting or beam-angle manipulation. Automatic positional error
+correction algorithm and adaptive relaxation strategy are introduced to enhance
+the robustness and SNR of reconstruction significantly. Based on APLI, we
+perform full-FOV reconstruction of a USAF resolution target ($\sim$29.85
+$m{m^2}$) and achieve half-pitch lateral resolution of 770 $nm$, surpassing
+2.17 times of the theoretical Nyquist-Shannon sampling resolution limit imposed
+by the sensor pixel-size (1.67 $\mu m$). Full-FOV imaging result of a typical
+dicot root is also provided to demonstrate its promising potential applications
+in biologic imaging.
+",0,1,0,0,0,0
+9654,Attend to You: Personalized Image Captioning with Context Sequence Memory Networks,"  We address personalization issues of image captioning, which have not been
+discussed yet in previous research. For a query image, we aim to generate a
+descriptive sentence, accounting for prior knowledge such as the user's active
+vocabularies in previous documents. As applications of personalized image
+captioning, we tackle two post automation tasks: hashtag prediction and post
+generation, on our newly collected Instagram dataset, consisting of 1.1M posts
+from 6.3K users. We propose a novel captioning model named Context Sequence
+Memory Network (CSMN). Its unique updates over previous memory network models
+include (i) exploiting memory as a repository for multiple types of context
+information, (ii) appending previously generated words into memory to capture
+long-term information without suffering from the vanishing gradient problem,
+and (iii) adopting CNN memory structure to jointly represent nearby ordered
+memory slots for better context understanding. With quantitative evaluation and
+user studies via Amazon Mechanical Turk, we show the effectiveness of the three
+novel features of CSMN and its performance enhancement for personalized image
+captioning over state-of-the-art captioning models.
+",1,0,0,0,0,0
+9655,Hardness of almost embedding simplicial complexes in $\mathbb R^d$,"  A map $f\colon K\to \mathbb R^d$ of a simplicial complex is an almost
+embedding if $f(\sigma)\cap f(\tau)=\emptyset$ whenever $\sigma,\tau$ are
+disjoint simplices of $K$.
+Theorem. Fix integers $d,k\ge2$ such that $d=\frac{3k}2+1$.
+(a) Assume that $P\ne NP$. Then there exists a finite $k$-dimensional complex
+$K$ that does not admit an almost embedding in $\mathbb R^d$ but for which
+there exists an equivariant map $\tilde K\to S^{d-1}$.
+(b) The algorithmic problem of recognition almost embeddability of finite
+$k$-dimensional complexes in $\mathbb R^d$ is NP hard.
+The proof is based on the technique from the Matoušek-Tancer-Wagner paper
+(proving an analogous result for embeddings), and on singular versions of the
+higher-dimensional Borromean rings lemma and a generalized van Kampen--Flores
+theorem.
+",1,0,1,0,0,0
+9656,Asymmetry of short-term control of spatio-temporal gait parameters during treadmill walking,"  Optimization of energy cost determines average values of spatio-temporal gait
+parameters such as step duration, step length or step speed. However, during
+walking, humans need to adapt these parameters at every step to respond to
+exogenous and/or endogenic perturbations. While some neurological mechanisms
+that trigger these responses are known, our understanding of the fundamental
+principles governing step-by-step adaptation remains elusive. We determined the
+gait parameters of 20 healthy subjects with right-foot preference during
+treadmill walking at speeds of 1.1, 1.4 and 1.7 m/s. We found that when the
+value of the gait parameter was conspicuously greater (smaller) than the mean
+value, it was either followed immediately by a smaller (greater) value of the
+contralateral leg (interleg control), or the deviation from the mean value
+decreased during the next movement of ipsilateral leg (intraleg control). The
+selection of step duration and the selection of step length during such
+transient control events were performed in unique ways. We quantified the
+symmetry of short-term control of gait parameters and observed the significant
+dominance of the right leg in short-term control of all three parameters at
+higher speeds (1.4 and 1.7 m/s).
+",0,1,0,0,0,0
+9657,Edge N-Level Sparse Visibility Graphs: Fast Optimal Any-Angle Pathfinding Using Hierarchical Taut Paths,"  In the Any-Angle Pathfinding problem, the goal is to find the shortest path
+between a pair of vertices on a uniform square grid, that is not constrained to
+any fixed number of possible directions over the grid. Visibility Graphs are a
+known optimal algorithm for solving the problem with the use of pre-processing.
+However, Visibility Graphs are known to perform poorly in terms of running
+time, especially on large, complex maps. In this paper, we introduce two
+improvements over the Visibility Graph Algorithm to compute optimal paths.
+Sparse Visibility Graphs (SVGs) are constructed by pruning unnecessary edges
+from the original Visibility Graph. Edge N-Level Sparse Visibility Graphs
+(ENLSVGs) is a hierarchical SVG built by iteratively pruning non-taut paths. We
+also introduce Line-of-Sight Scans, a faster algorithm for building Visibility
+Graphs over a grid. SVGs run much faster than Visibility Graphs by reducing the
+average vertex degree. ENLSVGs, a hierarchical algorithm, improves this
+further, especially on larger maps. On large maps, with the use of
+pre-processing, these algorithms are orders of magnitude faster than existing
+algorithms like Visibility Graphs and Theta*.
+",1,0,0,0,0,0
+9658,Model-Free Renewable Scenario Generation Using Generative Adversarial Networks,"  Scenario generation is an important step in the operation and planning of
+power systems with high renewable penetrations. In this work, we proposed a
+data-driven approach for scenario generation using generative adversarial
+networks, which is based on two interconnected deep neural networks. Compared
+with existing methods based on probabilistic models that are often hard to
+scale or sample from, our method is data-driven, and captures renewable energy
+production patterns in both temporal and spatial dimensions for a large number
+of correlated resources. For validation, we use wind and solar times-series
+data from NREL integration data sets. We demonstrate that the proposed method
+is able to generate realistic wind and photovoltaic power profiles with full
+diversity of behaviors. We also illustrate how to generate scenarios based on
+different conditions of interest by using labeled data during training. For
+example, scenarios can be conditioned on weather events~(e.g. high wind day) or
+time of the year~(e,g. solar generation for a day in July). Because of the
+feedforward nature of the neural networks, scenarios can be generated extremely
+efficiently without sophisticated sampling techniques.
+",1,0,1,0,0,0
+9659,Higher-genus quasimap wall-crossing via localization,"  We give a new proof of Ciocan-Fontanine and Kim's wall-crossing formula
+relating the virtual classes of the moduli spaces of $\epsilon$-stable
+quasimaps for different $\epsilon$ in any genus, whenever the target is a
+complete intersection in projective space and there is at least one marked
+point.
+Our techniques involve a twisted graph space, which we expect to generalize
+to yield wall-crossing formulas for general gauged linear sigma models.
+",0,0,1,0,0,0
+9660,Uncertainty in Cyber Security Investments,"  When undertaking cyber security risk assessments, we must assign numeric
+values to metrics to compute the final expected loss that represents the risk
+that an organization is exposed to due to cyber threats. Even if risk
+assessment is motivated from real-world observations and data, there is always
+a high chance of assigning inaccurate values due to different uncertainties
+involved (e.g., evolving threat landscape, human errors) and the natural
+difficulty of quantifying risk per se. Our previous work has proposed a model
+and a software tool that empowers organizations to compute optimal cyber
+security strategies given their financial constraints, i.e., available cyber
+security budget. We have also introduced a general game-theoretic model with
+uncertain payoffs (probability-distribution-valued payoffs) showing that such
+uncertainty can be incorporated in the game-theoretic model by allowing payoffs
+to be random. In this paper, we combine our aforesaid works and we conclude
+that although uncertainties in cyber security risk assessment lead, on average,
+to different cyber security strategies, they do not play significant role into
+the final expected loss of the organization when using our model and
+methodology to derive this strategies. We show that our tool is capable of
+providing effective decision support. To the best of our knowledge this is the
+first paper that investigates how uncertainties on various parameters affect
+cyber security investments.
+",1,0,0,0,0,0
+9661,ELT Linear Algebra II,"  This paper is a continuation of [arXiv:1603.02204]. Exploded layered tropical
+(ELT) algebra is an extension of tropical algebra with a structure of layers.
+These layers allow us to use classical algebraic results in order to easily
+prove analogous tropical results. Specifically we prove and use an ELT version
+of the transfer principal presented in [2]. In this paper we use the transfer
+principle to prove an ELT version of Cayley-Hamilton Theorem, and study the
+multiplicity of the ELT determinant, ELT adjoint matrices and quasi-invertible
+matrices. We also define a new notion of trace -- the essential trace -- and
+study its properties.
+",0,0,1,0,0,0
+9662,Inner-Scene Similarities as a Contextual Cue for Object Detection,"  Using image context is an effective approach for improving object detection.
+Previously proposed methods used contextual cues that rely on semantic or
+spatial information. In this work, we explore a different kind of contextual
+information: inner-scene similarity. We present the CISS (Context by Inner
+Scene Similarity) algorithm, which is based on the observation that two
+visually similar sub-image patches are likely to share semantic identities,
+especially when both appear in the same image. CISS uses base-scores provided
+by a base detector and performs as a post-detection stage. For each candidate
+sub-image (denoted anchor), the CISS algorithm finds a few similar sub-images
+(denoted supporters), and, using them, calculates a new enhanced score for the
+anchor. This is done by utilizing the base-scores of the supporters and a
+pre-trained dependency model. The new scores are modeled as a linear function
+of the base scores of the anchor and the supporters and is estimated using a
+minimum mean square error optimization. This approach results in: (a) improved
+detection of partly occluded objects (when there are similar non-occluded
+objects in the scene), and (b) fewer false alarms (when the base detector
+mistakenly classifies a background patch as an object). This work relates to
+Duncan and Humphreys' ""similarity theory,"" a psychophysical study. which
+suggested that the human visual system perceptually groups similar image
+regions and that the classification of one region is affected by the estimated
+identity of the other. Experimental results demonstrate the enhancement of a
+base detector's scores on the PASCAL VOC dataset.
+",1,0,0,0,0,0
+9663,Time-dependent spectral renormalization method,"  The spectral renormalization method was introduced by Ablowitz and Musslimani
+in 2005, [Opt. Lett. 30, pp. 2140-2142] as an effective way to numerically
+compute (time-independent) bound states for certain nonlinear boundary value
+problems. % of the nonlinear Schrödinger (NLS), Gross-Pitaevskii and water
+wave type equations to mention a few. In this paper, we extend those ideas to
+the time domain and introduce a time-dependent spectral renormalization method
+as a numerical means to simulate linear and nonlinear evolution equations. The
+essence of the method is to convert the underlying evolution equation from its
+partial or ordinary differential form (using Duhamel's principle) into an
+integral equation. The solution sought is then viewed as a fixed point in both
+space and time. The resulting integral equation is then numerically solved
+using a simple renormalized fixed-point iteration method. Convergence is
+achieved by introducing a time-dependent renormalization factor which is
+numerically computed from the physical properties of the governing evolution
+equation. The proposed method has the ability to incorporate physics into the
+simulations in the form of conservation laws or dissipation rates. This novel
+scheme is implemented on benchmark evolution equations: the classical nonlinear
+Schrödinger (NLS), integrable $PT$ symmetric nonlocal NLS and the viscous
+Burgers' equations, each of which being a prototypical example of a
+conservative and dissipative dynamical system. Numerical implementation and
+algorithm performance are also discussed.
+",0,1,0,0,0,0
+9664,Cloth Manipulation Using Random-Forest-Based Imitation Learning,"  We present a novel approach for robust manipulation of high-DOF deformable
+objects such as cloth. Our approach uses a random forest-based controller that
+maps the observed visual features of the cloth to an optimal control action of
+the manipulator. The topological structure of this random forest-based
+controller is determined automatically based on the training data consisting
+visual features and optimal control actions. This enables us to integrate the
+overall process of training data classification and controller optimization
+into an imitation learning (IL) approach. Our approach enables learning of
+robust control policy for cloth manipulation with guarantees on convergence.We
+have evaluated our approach on different multi-task cloth manipulation
+benchmarks such as flattening, folding and twisting. In practice, our approach
+works well with different deformable features learned based on the specific
+task or deep learning. Moreover, our controller outperforms a simple or
+piecewise linear controller in terms of robustness to noise. In addition, our
+approach is easy to implement and does not require much parameter tuning.
+",1,0,0,0,0,0
+9665,Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations,"  We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the
+coefficients (e.g. material parameters) exhibit random, fine scale variability.
+The direct solution to such problems requires grids that are small enough to
+resolve this fine scale variability which unavoidably requires the repeated
+solution of very large systems of algebraic equations. We establish a
+physically inspired, data-driven coarse-grained model which learns a low-
+dimensional set of microstructural features that are predictive of the
+fine-grained model (FG) response. Once learned, those features provide a sharp
+distribution over the coarse scale effec- tive coefficients of the PDE that are
+most suitable for prediction of the fine scale model output. This ultimately
+allows to replace the computationally expensive FG by a generative proba-
+bilistic model based on evaluating the much cheaper CG several times. Sparsity
+enforcing pri- ors further increase predictive efficiency and reveal
+microstructural features that are important in predicting the FG response.
+Moreover, the model yields probabilistic rather than single-point predictions,
+which enables the quantification of the unavoidable epistemic uncertainty that
+is present due to the information loss that occurs during the coarse-graining
+process.
+",0,0,0,1,0,0
+9666,A practical guide to the simultaneous determination of protein structure and dynamics using metainference,"  Accurate protein structural ensembles can be determined with metainference, a
+Bayesian inference method that integrates experimental information with prior
+knowledge of the system and deals with all sources of uncertainty and errors as
+well as with system heterogeneity. Furthermore, metainference can be
+implemented using the metadynamics approach, which enables the computational
+study of complex biological systems requiring extensive conformational
+sampling. In this chapter, we provide a step-by-step guide to perform and
+analyse metadynamic metainference simulations using the ISDB module of the
+open-source PLUMED library, as well as a series of practical tips to avoid
+common mistakes. Specifically, we will guide the reader in the process of
+learning how to model the structural ensemble of a small disordered peptide by
+combining state-of-the-art molecular mechanics force fields with nuclear
+magnetic resonance data, including chemical shifts, scalar couplings and
+residual dipolar couplings.
+",0,0,0,0,1,0
+9667,Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods,"  The optimal learner for prediction modeling varies depending on the
+underlying data-generating distribution. Super Learner (SL) is a generic
+ensemble learning algorithm that uses cross-validation to select among a
+""library"" of candidate prediction models. The SL is not restricted to a single
+prediction model, but uses the strengths of a variety of learning algorithms to
+adapt to different databases. While the SL has been shown to perform well in a
+number of settings, it has not been thoroughly evaluated in large electronic
+healthcare databases that are common in pharmacoepidemiology and comparative
+effectiveness research. In this study, we applied and evaluated the performance
+of the SL in its ability to predict treatment assignment using three electronic
+healthcare databases. We considered a library of algorithms that consisted of
+both nonparametric and parametric models. We also considered a novel strategy
+for prediction modeling that combines the SL with the high-dimensional
+propensity score (hdPS) variable selection algorithm. Predictive performance
+was assessed using three metrics: the negative log-likelihood, area under the
+curve (AUC), and time complexity. Results showed that the best individual
+algorithm, in terms of predictive performance, varied across datasets. The SL
+was able to adapt to the given dataset and optimize predictive performance
+relative to any individual learner. Combining the SL with the hdPS was the most
+consistent prediction method and may be promising for PS estimation and
+prediction modeling in electronic healthcare databases.
+",0,0,0,1,0,0
+9668,Transmission spectroscopy of the hot Jupiter TrES-3 b: Disproof of an overly large Rayleigh-like feature,"  Context. Transit events of extrasolar planets offer the opportunity to study
+the composition of their atmospheres. Previous work on transmission
+spectroscopy of the close-in gas giant TrES-3 b revealed an increase in
+absorption towards blue wavelengths of very large amplitude in terms of
+atmospheric pressure scale heights, too large to be explained by
+Rayleigh-scattering in the planetary atmosphere. Aims. We present a follow-up
+study of the optical transmission spectrum of the hot Jupiter TrES-3 b to
+investigate the strong increase in opacity towards short wavelengths found by a
+previous study. Furthermore, we aim to estimate the effect of stellar spots on
+the transmission spectrum. Methods. This work uses previously published long
+slit spectroscopy transit data of the Gran Telescopio Canarias (GTC) and
+published broad band observations as well as new observations in different
+bands from the near-UV to the near-IR, for a homogeneous transit light curve
+analysis. Additionally, a long-term photometric monitoring of the TrES-3 host
+star was performed. Results. Our newly analysed GTC spectroscopic transit
+observations show a slope of much lower amplitude than previous studies. We
+conclude from our results the previously reported increasing signal towards
+short wavelengths is not intrinsic to the TrES-3 system. Furthermore, the broad
+band spectrum favours a flat spectrum. Long-term photometric monitoring rules
+out a significant modification of the transmission spectrum by unocculted star
+spots.
+",0,1,0,0,0,0
+9669,Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters,"  Markov decision processes (MDPs) are a popular model for performance analysis
+and optimization of stochastic systems. The parameters of stochastic behavior
+of MDPs are estimates from empirical observations of a system; their values are
+not known precisely. Different types of MDPs with uncertain, imprecise or
+bounded transition rates or probabilities and rewards exist in the literature.
+Commonly, analysis of models with uncertainties amounts to searching for the
+most robust policy which means that the goal is to generate a policy with the
+greatest lower bound on performance (or, symmetrically, the lowest upper bound
+on costs). However, hedging against an unlikely worst case may lead to losses
+in other situations. In general, one is interested in policies that behave well
+in all situations which results in a multi-objective view on decision making.
+In this paper, we consider policies for the expected discounted reward
+measure of MDPs with uncertain parameters. In particular, the approach is
+defined for bounded-parameter MDPs (BMDPs) [8]. In this setting the worst, best
+and average case performances of a policy are analyzed simultaneously, which
+yields a multi-scenario multi-objective optimization problem. The paper
+presents and evaluates approaches to compute the pure Pareto optimal policies
+in the value vector space.
+",1,0,0,0,0,0
+9670,Certificates for triangular equivalence and rank profiles,"  In this paper, we give novel certificates for triangular equivalence and rank
+profiles. These certificates enable to verify the row or column rank profiles
+or the whole rank profile matrix faster than recomputing them, with a
+negligible overall overhead. We first provide quadratic time and space
+non-interactive certificates saving the logarithmic factors of previously known
+ones. Then we propose interactive certificates for the same problems whose
+Monte Carlo verification complexity requires a small constant number of
+matrix-vector multiplications, a linear space, and a linear number of extra
+field operations. As an application we also give an interactive protocol ,
+certifying the determinant of dense matrices, faster than the best previously
+known one.
+",1,0,0,0,0,0
+9671,TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting,"  TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed
+training of gradient boosted trees. It is based on TensorFlow, and its
+distinguishing features include a novel architecture, automatic loss
+differentiation, layer-by-layer boosting that results in smaller ensembles and
+faster prediction, principled multi-class handling, and a number of
+regularization techniques to prevent overfitting.
+",1,0,0,1,0,0
+9672,"Circuit Treewidth, Sentential Decision, and Query Compilation","  The evaluation of a query over a probabilistic database boils down to
+computing the probability of a suitable Boolean function, the lineage of the
+query over the database. The method of query compilation approaches the task in
+two stages: first, the query lineage is implemented (compiled) in a circuit
+form where probability computation is tractable; and second, the desired
+probability is computed over the compiled circuit. A basic theoretical quest in
+query compilation is that of identifying pertinent classes of queries whose
+lineages admit compact representations over increasingly succinct, tractable
+circuit classes. Fostering previous work by Jha and Suciu (2012) and Petke and
+Razgon (2013), we focus on queries whose lineages admit circuit implementations
+with small treewidth, and investigate their compilability within tame classes
+of decision diagrams. In perfect analogy with the characterization of bounded
+circuit pathwidth by bounded OBDD width, we show that a class of Boolean
+functions has bounded circuit treewidth if and only if it has bounded SDD
+width. Sentential decision diagrams (SDDs) are central in knowledge
+compilation, being essentially as tractable as OBDDs but exponentially more
+succinct. By incorporating constant width SDDs and polynomial size SDDs, we
+refine the panorama of query compilation for unions of conjunctive queries with
+and without inequalities.
+",1,0,0,0,0,0
+9673,Improvements in the Small Sample Efficiency of the Minimum $S$-Divergence Estimators under Discrete Models,"  This paper considers the problem of inliers and empty cells and the resulting
+issue of relative inefficiency in estimation under pure samples from a discrete
+population when the sample size is small. Many minimum divergence estimators in
+the $S$-divergence family, although possessing very strong outlier stability
+properties, often have very poor small sample efficiency in the presence of
+inliers and some are not even defined in the presence of a single empty cell;
+this limits the practical applicability of these estimators, in spite of their
+otherwise sound robustness properties and high asymptotic efficiency. Here, we
+will study a penalized version of the $S$-divergences such that the resulting
+minimum divergence estimators are free from these issues without altering their
+robustness properties and asymptotic efficiencies. We will give a general proof
+for the asymptotic properties of these minimum penalized $S$-divergence
+estimators. This provides a significant addition to the literature as the
+asymptotics of penalized divergences which are not finitely defined are
+currently unavailable in the literature. The small sample advantages of the
+minimum penalized $S$-divergence estimators are examined through an extensive
+simulation study and some empirical suggestions regarding the choice of the
+relevant underlying tuning parameters are also provided.
+",0,0,1,1,0,0
+9674,Homogeneous Kobayashi-hyperbolic manifolds with automorphism group of subcritical dimension,"  We determine all connected homogeneous Kobayashi-hyperbolic manifolds of
+dimension $n\ge 2$ whose holomorphic automorphism group has dimension $n^2-3$.
+This result complements existing classifications for automorphism group
+dimension $n^2-2$ (which is in some sense critical) and greater.
+",0,0,1,0,0,0
+9675,Centroidal localization game,"  One important problem in a network is to locate an (invisible) moving entity
+by using distance-detectors placed at strategical locations. For instance, the
+metric dimension of a graph $G$ is the minimum number $k$ of detectors placed
+in some vertices $\{v_1,\cdots,v_k\}$ such that the vector $(d_1,\cdots,d_k)$
+of the distances $d(v_i,r)$ between the detectors and the entity's location $r$
+allows to uniquely determine $r \in V(G)$. In a more realistic setting, instead
+of getting the exact distance information, given devices placed in
+$\{v_1,\cdots,v_k\}$, we get only relative distances between the entity's
+location $r$ and the devices (for every $1\leq i,j\leq k$, it is provided
+whether $d(v_i,r) >$, $<$, or $=$ to $d(v_j,r)$). The centroidal dimension of a
+graph $G$ is the minimum number of devices required to locate the entity in
+this setting.
+We consider the natural generalization of the latter problem, where vertices
+may be probed sequentially until the moving entity is located. At every turn, a
+set $\{v_1,\cdots,v_k\}$ of vertices is probed and then the relative distances
+between the vertices $v_i$ and the current location $r$ of the entity are
+given. If not located, the moving entity may move along one edge. Let $\zeta^*
+(G)$ be the minimum $k$ such that the entity is eventually located, whatever it
+does, in the graph $G$.
+We prove that $\zeta^* (T)\leq 2$ for every tree $T$ and give an upper bound
+on $\zeta^*(G\square H)$ in cartesian product of graphs $G$ and $H$. Our main
+result is that $\zeta^* (G)\leq 3$ for any outerplanar graph $G$. We then prove
+that $\zeta^* (G)$ is bounded by the pathwidth of $G$ plus 1 and that the
+optimization problem of determining $\zeta^* (G)$ is NP-hard in general graphs.
+Finally, we show that approximating (up to any constant distance) the entity's
+location in the Euclidean plane requires at most two vertices per turn.
+",1,0,0,0,0,0
+9676,Improving the Burgess bound via Polya-Vinogradov,"  We show that even mild improvements of the Polya-Vinogradov inequality would
+imply significant improvements of Burgess' bound on character sums. Our main
+ingredients are a lower bound on certain types of character sums (coming from
+works of the second author joint with J. Bober and Y. Lamzouri) and a
+quantitative relationship between the mean and the logarithmic mean of a
+completely multiplicative function.
+",0,0,1,0,0,0
+9677,Processes accompanying stimulated recombination of atoms,"  The phenomenon of polarization of nuclei in the process of stimulated
+recombination of atoms in the field of circularly polarized laser radiation is
+considered. This effect is considered for the case of the proton-electron beams
+used in the method of electron cooling. An estimate is obtained for the maximum
+degree of polarization of the protons on components of the hyperfine structure
+of the 2s state of the hydrogen atom.
+",0,1,0,0,0,0
+9678,The Complexity of Factors of Multivariate Polynomials,"  The existence of string functions, which are not polynomial time computable,
+but whose graph is checkable in polynomial time, is a basic assumption in
+cryptography. We prove that in the framework of algebraic complexity, there are
+no such families of polynomial functions of polynomially bounded degree over
+fields of characteristic zero. The proof relies on a polynomial upper bound on
+the approximative complexity of a factor g of a polynomial f in terms of the
+(approximative) complexity of f and the degree of the factor g. This extends a
+result by Kaltofen (STOC 1986). The concept of approximative complexity allows
+to cope with the case that a factor has an exponential multiplicity, by using a
+perturbation argument. Our result extends to randomized (two-sided error)
+decision complexity.
+",1,0,0,0,0,0
+9679,Chatbots as Conversational Recommender Systems in Urban Contexts,"  In this paper, we outline the vision of chatbots that facilitate the
+interaction between citizens and policy-makers at the city scale. We report the
+results of a co-design session attended by more than 60 participants. We give
+an outlook of how some challenges associated with such chatbot systems could be
+addressed in the future.
+",1,0,0,0,0,0
+9680,An estimate of the first non-zero eigenvalue of the Laplacian by the Ricci curvature on edges of graphs,"  We define the distance between edges of graphs and study the coarse Ricci
+curvature on edges. We consider the Laplacian on edges based on the
+Jost-Horak's definition of the Laplacian on simplicial complexes. As one of our
+main results, we obtain an estimate of the first non-zero eigenvalue of the
+Laplacian by the Ricci curvature for a regular graph.
+",0,0,1,0,0,0
+9681,A Deep Reinforcement Learning Chatbot,"  We present MILABOT: a deep reinforcement learning chatbot developed by the
+Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize
+competition. MILABOT is capable of conversing with humans on popular small talk
+topics through both speech and text. The system consists of an ensemble of
+natural language generation and retrieval models, including template-based
+models, bag-of-words models, sequence-to-sequence neural network and latent
+variable neural network models. By applying reinforcement learning to
+crowdsourced data and real-world user interactions, the system has been trained
+to select an appropriate response from the models in its ensemble. The system
+has been evaluated through A/B testing with real-world users, where it
+performed significantly better than many competing systems. Due to its machine
+learning architecture, the system is likely to improve with additional data.
+",1,0,0,1,0,0
+9682,Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization,"  We propose a data-driven method to solve a stochastic optimal power flow
+(OPF) problem based on limited information about forecast error distributions.
+The objective is to determine power schedules for controllable devices in a
+power network to balance operation cost and conditional value-at-risk (CVaR) of
+device and network constraint violations. These decisions include scheduled
+power output adjustments and reserve policies, which specify planned reactions
+to forecast errors in order to accommodate fluctuating renewable energy
+sources. Instead of assuming the uncertainties across the networks follow
+prescribed probability distributions, we assume the distributions are only
+observable through a finite training dataset. By utilizing the Wasserstein
+metric to quantify differences between the empirical data-based distribution
+and the real data-generating distribution, we formulate a distributionally
+robust optimization OPF problem to search for power schedules and reserve
+policies that are robust to sampling errors inherent in the dataset. A simple
+numerical example illustrates inherent tradeoffs between operation cost and
+risk of constraint violation, and we show how our proposed method offers a
+data-driven framework to balance these objectives.
+",1,0,1,0,0,0
+9683,Deep Learning for Real-Time Crime Forecasting and its Ternarization,"  Real-time crime forecasting is important. However, accurate prediction of
+when and where the next crime will happen is difficult. No known physical model
+provides a reasonable approximation to such a complex system. Historical crime
+data are sparse in both space and time and the signal of interests is weak. In
+this work, we first present a proper representation of crime data. We then
+adapt the spatial temporal residual network on the well represented data to
+predict the distribution of crime in Los Angeles at the scale of hours in
+neighborhood-sized parcels. These experiments as well as comparisons with
+several existing approaches to prediction demonstrate the superiority of the
+proposed model in terms of accuracy. Finally, we present a ternarization
+technique to address the resource consumption issue for its deployment in real
+world. This work is an extension of our short conference proceeding paper [Wang
+et al, Arxiv 1707.03340].
+",1,0,0,1,0,0
+9684,Thermal Modeling of Comet-Like Objects from AKARI Observation,"  We investigated the physical properties of the comet-like objects 107P/(4015)
+Wilson--Harrington (4015WH) and P/2006 HR30 (Siding Spring; HR30) by applying a
+simple thermophysical model (TPM) to the near-infrared spectroscopy and
+broadband observation data obtained by AKARI satellite of JAXA when they showed
+no detectable comet-like activity. We selected these two targets since the
+tendency of thermal inertia to decrease with the size of an asteroid, which has
+been demonstrated in recent studies, has not been confirmed for comet-like
+objects. It was found that 4015WH, which was originally discovered as a comet
+but has not shown comet-like activity since its discovery, has effective size $
+D= $ 3.74--4.39 km and geometric albedo $ p_V \approx $ 0.040--0.055 with
+thermal inertia $ \Gamma = $ 100--250 J m$ ^{-2} $ K$ ^{-1} $ s$ ^{-1/2}$. The
+corresponding grain size is estimated to 1--3 mm. We also found that HR30,
+which was observed as a bare cometary nucleus at the time of our observation,
+have $ D= $ 23.9--27.1 km and $ p_V= $0.035--0.045 with $ \Gamma= $ 250--1,000
+J m$ ^{-2} $ K$ ^{-1} $ s$ ^{-1/2}$. We conjecture the pole latitude $ -
+20^{\circ} \lesssim \beta_s \lesssim +60^{\circ}$. The results for both targets
+are consistent with previous studies. Based on the results, we propose that
+comet-like objects are not clearly distinguishable from asteroidal counterpart
+on the $ D $--$ \Gamma $ plane.
+",0,1,0,0,0,0
+9685,Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model,"  Availability of a validated, realistic fuel cost model is a prerequisite to
+the development and validation of new optimization methods and control tools.
+This paper uses an autoregressive integrated moving average (ARIMA) model with
+historical fuel cost data in development of a three-step-ahead fuel cost
+distribution prediction. First, the data features of Form EIA-923 are explored
+and the natural gas fuel costs of Texas generating facilities are used to
+develop and validate the forecasting algorithm for the Texas example.
+Furthermore, the spot price associated with the natural gas hub in Texas is
+utilized to enhance the fuel cost prediction. The forecasted data is fit to a
+normal distribution and the Kullback-Leibler divergence is employed to evaluate
+the difference between the real fuel cost distributions and the estimated
+distributions. The comparative evaluation suggests the proposed forecasting
+algorithm is effective in general and is worth pursuing further.
+",0,0,0,1,0,0
+9686,Timed Discrete-Event Systems are Synchronous Product Structures,"  In this work, we show that the model of timed discrete-event systems (TDES)
+proposed by Brandin and Wonham is essentially a synchronous product structure.
+This resolves an open problem that has remained unaddressed for the past 25
+years and has its application in developing a more efficient timed state-tree
+structures (TSTS) framework. The proof is constructive in the sense that an
+explicit synchronous production rule is provided to generate a TDES from the
+activity automaton and the timer automata after a suitable transformation of
+the model.
+",1,0,0,0,0,0
+9687,Maturation Trajectories of Cortical Resting-State Networks Depend on the Mediating Frequency Band,"  The functional significance of resting state networks and their abnormal
+manifestations in psychiatric disorders are firmly established, as is the
+importance of the cortical rhythms in mediating these networks. Resting state
+networks are known to undergo substantial reorganization from childhood to
+adulthood, but whether distinct cortical rhythms, which are generated by
+separable neural mechanisms and are often manifested abnormally in psychiatric
+conditions, mediate maturation differentially, remains unknown. Using
+magnetoencephalography (MEG) to map frequency band specific maturation of
+resting state networks from age 7 to 29 in 162 participants (31 independent),
+we found significant changes with age in networks mediated by the beta
+(13-30Hz) and gamma (31-80Hz) bands. More specifically, gamma band mediated
+networks followed an expected asymptotic trajectory, but beta band mediated
+networks followed a linear trajectory. Network integration increased with age
+in gamma band mediated networks, while local segregation increased with age in
+beta band mediated networks. Spatially, the hubs that changed in importance
+with age in the beta band mediated networks had relatively little overlap with
+those that showed the greatest changes in the gamma band mediated networks.
+These findings are relevant for our understanding of the neural mechanisms of
+cortical maturation, in both typical and atypical development.
+",0,0,0,1,1,0
+9688,High-power closed-cycle $^4$He cryostat with top-loading sample exchange,"  We report on the development of a versatile cryogen-free laboratory cryostat
+based upon a commercial pulse tube cryocooler. It provides enough cooling power
+for continuous recondensation of circulating $^4$He gas at a condensation
+pressure of approximately 250~mbar. Moreover, the cryostat allows for exchange
+of different cryostat-inserts as well as fast and easy ""wet"" top-loading of
+samples directly into the 1 K pot with a turn-over time of less than 75~min.
+Starting from room temperature and using a $^4$He cryostat-insert, a base
+temperature of 1.0~K is reached within approximately seven hours and a cooling
+power of 250~mW is established at 1.24~K.
+",0,1,0,0,0,0
+9689,A new proof of Kirchberg's $\mathcal O_2$-stable classification,"  I present a new proof of Kirchberg's $\mathcal O_2$-stable classification
+theorem: two separable, nuclear, stable/unital, $\mathcal O_2$-stable
+$C^\ast$-algebras are isomorphic if and only if their ideal lattices are order
+isomorphic, or equivalently, their primitive ideal spaces are homeomorphic.
+Many intermediate results do not depend on pure infiniteness of any sort.
+",0,0,1,0,0,0
+9690,Scaling evidence of the homothetic nature of cities,"  In this paper we analyse the profile of land use and population density with
+respect to the distance to the city centre for the European city. In addition
+to providing the radial population density and soil-sealing profiles for a
+large set of cities, we demonstrate a remarkable constancy of the profiles
+across city size.
+Our analysis combines the GMES/Copernicus Urban Atlas 2006 land use database
+at 5m resolution for 300 European cities with more than 100.000 inhabitants and
+the Geostat population grid at 1km resolution. Population is allocated
+proportionally to surface and weighted by soil sealing and density classes of
+the Urban Atlas. We analyse the profile of each artificial land use and
+population with distance to the town hall.
+In line with earlier literature, we confirm the strong monocentricity of the
+European city and the negative exponential curve for population density.
+Moreover, we find that land use curves, in particular the share of housing and
+roads, scale along the two horizontal dimensions with the square root of city
+population, while population curves scale in three dimensions with the cubic
+root of city population. In short, European cities of different sizes are
+homothetic in terms of land use and population density. While earlier
+literature documented the scaling of average densities (total surface and
+population) with city size, we document the scaling of the whole radial
+distance profile with city size, thus liaising intra-urban radial analysis and
+systems of cities. In addition to providing a new empirical view of the
+European city, our scaling offers a set of practical and coherent definitions
+of a city, independent of its population, from which we can re-question urban
+scaling laws and Zipf's law for cities.
+",0,1,0,0,0,0
+9691,Subconvex bounds for Hecke-Maass forms on compact arithmetic quotients of semisimple Lie groups,"  Let $H$ be a semisimple algebraic group, $K$ a maximal compact subgroup of
+$G:=H(\mathbb{R})$, and $\Gamma\subset H(\mathbb{Q})$ a congruence arithmetic
+subgroup. In this paper, we generalize existing subconvex bounds for
+Hecke-Maass forms on the locally symmetric space $\Gamma \backslash G/K$ to
+corresponding bounds on the arithmetic quotient $\Gamma \backslash G$ for
+cocompact lattices using the spectral function of an elliptic operator. The
+bounds obtained extend known subconvex bounds for automorphic forms to
+non-trivial $K$-types, yielding subconvex bounds for new classes of automorphic
+representations, and constitute subconvex bounds for eigenfunctions on compact
+manifolds with both positive and negative sectional curvature. We also obtain
+new subconvex bounds for holomorphic modular forms in the weight aspect.
+",0,0,1,0,0,0
+9692,Studying Positive Speech on Twitter,"  We present results of empirical studies on positive speech on Twitter. By
+positive speech we understand speech that works for the betterment of a given
+situation, in this case relations between different communities in a
+conflict-prone country. We worked with four Twitter data sets. Through
+semi-manual opinion mining, we found that positive speech accounted for < 1% of
+the data . In fully automated studies, we tested two approaches: unsupervised
+statistical analysis, and supervised text classification based on distributed
+word representation. We discuss benefits and challenges of those approaches and
+report empirical evidence obtained in the study.
+",1,0,0,0,0,0
+9693,Geostatistical inference in the presence of geomasking: a composite-likelihood approach,"  In almost any geostatistical analysis, one of the underlying, often implicit,
+modelling assump- tions is that the spatial locations, where measurements are
+taken, are recorded without error. In this study we develop geostatistical
+inference when this assumption is not valid. This is often the case when, for
+example, individual address information is randomly altered to provide pri-
+vacy protection or imprecisions are induced by geocoding processes and
+measurement devices. Our objective is to develop a method of inference based on
+the composite likelihood that over- comes the inherent computational limits of
+the full likelihood method as set out in Fanshawe and Diggle (2011). Through a
+simulation study, we then compare the performance of our proposed approach with
+an N-weighted least squares estimation procedure, based on a corrected version
+of the empirical variogram. Our results indicate that the composite-likelihood
+approach outper- forms the latter, leading to smaller root-mean-square-errors
+in the parameter estimates. Finally, we illustrate an application of our method
+to analyse data on malnutrition from a Demographic and Health Survey conducted
+in Senegal in 2011, where locations were randomly perturbed to protect the
+privacy of respondents.
+",0,0,0,1,0,0
+9694,Large sample analysis of the median heuristic,"  In kernel methods, the median heuristic has been widely used as a way of
+setting the bandwidth of RBF kernels. While its empirical performances make it
+a safe choice under many circumstances, there is little theoretical
+understanding of why this is the case. Our aim in this paper is to advance our
+understanding of the median heuristic by focusing on the setting of kernel
+two-sample test. We collect new findings that may be of interest for both
+theoreticians and practitioners. In theory, we provide a convergence analysis
+that shows the asymptotic normality of the bandwidth chosen by the median
+heuristic in the setting of kernel two-sample test. Systematic empirical
+investigations are also conducted in simple settings, comparing the
+performances based on the bandwidths chosen by the median heuristic and those
+by the maximization of test power.
+",0,0,1,1,0,0
+9695,Divergence Framework for EEG based Multiclass Motor Imagery Brain Computer Interface,"  Similar to most of the real world data, the ubiquitous presence of
+non-stationarities in the EEG signals significantly perturb the feature
+distribution thus deteriorating the performance of Brain Computer Interface. In
+this letter, a novel method is proposed based on Joint Approximate
+Diagonalization (JAD) to optimize stationarity for multiclass motor imagery
+Brain Computer Interface (BCI) in an information theoretic framework.
+Specifically, in the proposed method, we estimate the subspace which optimizes
+the discriminability between the classes and simultaneously preserve
+stationarity within the motor imagery classes. We determine the subspace for
+the proposed approach through optimization using gradient descent on an
+orthogonal manifold. The performance of the proposed stationarity enforcing
+algorithm is compared to that of baseline One-Versus-Rest (OVR)-CSP and JAD on
+publicly available BCI competition IV dataset IIa. Results show that an
+improvement in average classification accuracies across the subjects over the
+baseline algorithms and thus essence of alleviating within session
+non-stationarities.
+",1,0,0,0,1,0
+9696,Accelerated Stochastic Power Iteration,"  Principal component analysis (PCA) is one of the most powerful tools in
+machine learning. The simplest method for PCA, the power iteration, requires
+$\mathcal O(1/\Delta)$ full-data passes to recover the principal component of a
+matrix with eigen-gap $\Delta$. Lanczos, a significantly more complex method,
+achieves an accelerated rate of $\mathcal O(1/\sqrt{\Delta})$ passes. Modern
+applications, however, motivate methods that only ingest a subset of available
+data, known as the stochastic setting. In the online stochastic setting, simple
+algorithms like Oja's iteration achieve the optimal sample complexity $\mathcal
+O(\sigma^2/\Delta^2)$. Unfortunately, they are fully sequential, and also
+require $\mathcal O(\sigma^2/\Delta^2)$ iterations, far from the $\mathcal
+O(1/\sqrt{\Delta})$ rate of Lanczos. We propose a simple variant of the power
+iteration with an added momentum term, that achieves both the optimal sample
+and iteration complexity. In the full-pass setting, standard analysis shows
+that momentum achieves the accelerated rate, $\mathcal O(1/\sqrt{\Delta})$. We
+demonstrate empirically that naively applying momentum to a stochastic method,
+does not result in acceleration. We perform a novel, tight variance analysis
+that reveals the ""breaking-point variance"" beyond which this acceleration does
+not occur. By combining this insight with modern variance reduction techniques,
+we construct stochastic PCA algorithms, for the online and offline setting,
+that achieve an accelerated iteration complexity $\mathcal O(1/\sqrt{\Delta})$.
+Due to the embarassingly parallel nature of our methods, this acceleration
+translates directly to wall-clock time if deployed in a parallel environment.
+Our approach is very general, and applies to many non-convex optimization
+problems that can now be accelerated using the same technique.
+",1,0,1,1,0,0
+9697,Generalized phase mixing: Turbulence-like behaviour from unidirectionally propagating MHD waves,"  We present the results of three-dimensional (3D) ideal magnetohydrodynamics
+(MHD) simulations on the dynamics of a perpendicularly inhomogeneous plasma
+disturbed by propagating Alfvénic waves. Simpler versions of this scenario
+have been extensively studied as the phenomenon of phase mixing. We show that,
+by generalizing the textbook version of phase mixing, interesting phenomena are
+obtained, such as turbulence-like behavior and complex current-sheet structure,
+a novelty in longitudinally homogeneous plasma excited by unidirectionally
+propagating waves. This constitutes an important finding for turbulence-related
+phenomena in astrophysics in general, relaxing the conditions that have to be
+fulfilled in order to generate turbulent behavior.
+",0,1,0,0,0,0
+9698,Discovering Bayesian Market Views for Intelligent Asset Allocation,"  Along with the advance of opinion mining techniques, public mood has been
+found to be a key element for stock market prediction. However, how market
+participants' behavior is affected by public mood has been rarely discussed.
+Consequently, there has been little progress in leveraging public mood for the
+asset allocation problem, which is preferred in a trusted and interpretable
+way. In order to address the issue of incorporating public mood analyzed from
+social media, we propose to formalize public mood into market views, because
+market views can be integrated into the modern portfolio theory. In our
+framework, the optimal market views will maximize returns in each period with a
+Bayesian asset allocation model. We train two neural models to generate the
+market views, and benchmark the model performance on other popular asset
+allocation strategies. Our experimental results suggest that the formalization
+of market views significantly increases the profitability (5% to 10% annually)
+of the simulated portfolio at a given risk level.
+",0,0,0,0,0,1
+9699,Bayesian Static Parameter Estimation for Partially Observed Diffusions via Multilevel Monte Carlo,"  In this article we consider static Bayesian parameter estimation for
+partially observed diffusions that are discretely observed. We work under the
+assumption that one must resort to discretizing the underlying diffusion
+process, for instance using the Euler-Maruyama method. Given this assumption,
+we show how one can use Markov chain Monte Carlo (MCMC) and particularly
+particle MCMC [Andrieu, C., Doucet, A. and Holenstein, R. (2010). Particle
+Markov chain Monte Carlo methods (with discussion). J. R. Statist. Soc. Ser. B,
+72, 269--342] to implement a new approximation of the multilevel (ML) Monte
+Carlo (MC) collapsing sum identity. Our approach comprises constructing an
+approximate coupling of the posterior density of the joint distribution over
+parameter and hidden variables at two different discretization levels and then
+correcting by an importance sampling method. The variance of the weights are
+independent of the length of the observed data set. The utility of such a
+method is that, for a prescribed level of mean square error, the cost of this
+MLMC method is provably less than i.i.d. sampling from the posterior associated
+to the most precise discretization. However the method here comprises using
+only known and efficient simulation methodologies. The theoretical results are
+illustrated by inference of the parameters of two prototypical processes given
+noisy partial observations of the process: the first is an Ornstein Uhlenbeck
+process and the second is a more general Langevin equation.
+",0,0,1,1,0,0
+9700,EigenNetworks,"  In many applications, the interdependencies among a set of $N$ time series
+$\{ x_{nk}, k>0 \}_{n=1}^{N}$ are well captured by a graph or network $G$. The
+network itself may change over time as well (i.e., as $G_k$). We expect the
+network changes to be at a much slower rate than that of the time series. This
+paper introduces eigennetworks, networks that are building blocks to compose
+the actual networks $G_k$ capturing the dependencies among the time series.
+These eigennetworks can be estimated by first learning the time series of
+graphs $G_k$ from the data, followed by a Principal Network Analysis procedure.
+Algorithms for learning both the original time series of graphs and the
+eigennetworks are presented and discussed. Experiments on simulated and real
+time series data demonstrate the performance of the learning and the
+interpretation of the eigennetworks.
+",1,0,0,1,0,0
+9701,Alliance formation with exclusion in the spatial public goods game,"  Detecting defection and alarming partners about the possible danger could be
+essential to avoid being exploited. This act, however, may require a huge
+individual effort from those who take this job, hence such a strategy seems to
+be unfavorable. But structured populations can provide an opportunity where a
+largely unselfish excluder strategy can form an effective alliance with other
+cooperative strategies, hence they can sweep out defection. Interestingly, this
+alliance is functioning even at the extremely high cost of exclusion where the
+sole application of an exclusion strategy would be harmful otherwise. These
+results may explain why the emergence of extreme selfless behavior is not
+necessarily against individual selection but could be the result of an
+evolutionary process.
+",1,1,0,0,0,0
+9702,Fast and In Sync: Periodic Swarm Patterns for Quadrotors,"  This paper aims to design quadrotor swarm performances, where the swarm acts
+as an integrated, coordinated unit embodying moving and deforming objects. We
+divide the task of creating a choreography into three basic steps: designing
+swarm motion primitives, transitioning between those movements, and
+synchronizing the motion of the drones. The result is a flexible framework for
+designing choreographies comprised of a wide variety of motions. The motion
+primitives can be intuitively designed using few parameters, providing a rich
+library for choreography design. Moreover, we combine and adapt existing goal
+assignment and trajectory generation algorithms to maximize the smoothness of
+the transitions between motion primitives. Finally, we propose a correction
+algorithm to compensate for motion delays and synchronize the motion of the
+drones to a desired periodic motion pattern. The proposed methodology was
+validated experimentally by generating and executing choreographies on a swarm
+of 25 quadrotors.
+",1,0,0,0,0,0
+9703,Nonparametric estimation of locally stationary Hawkes processe,"  In this paper we consider multivariate Hawkes processes with baseline hazard
+and kernel functions that depend on time. This defines a class of locally
+stationary processes. We discuss estimation of the time-dependent baseline
+hazard and kernel functions based on a localized criterion. Theory on
+stationary Hawkes processes is extended to develop asymptotic theory for the
+estimator in the locally stationary model.
+",0,0,1,1,0,0
+9704,Comparison of Flow Scheduling Policies for Mix of Regular and Deadline Traffic in Datacenter Environments,"  Datacenters are the main infrastructure on top of which cloud computing
+services are offered. Such infrastructure may be shared by a large number of
+tenants and applications generating a spectrum of datacenter traffic. Delay
+sensitive applications and applications with specific Service Level Agreements
+(SLAs), generate deadline constrained flows, while other applications initiate
+flows that are desired to be delivered as early as possible. As a result,
+datacenter traffic is a mix of two types of flows: deadline and regular. There
+are several scheduling policies for either traffic type with focus on
+minimizing completion times or deadline miss rate. In this report, we apply
+several scheduling policies to mix traffic scenario while varying the ratio of
+regular to deadline traffic. We consider FCFS (First Come First Serve), SRPT
+(Shortest Remaining Processing Time) and Fair Sharing as deadline agnostic
+approaches and a combination of Earliest Deadline First (EDF) with either FCFS
+or SRPT as deadline-aware schemes. In addition, for the latter, we consider
+both cases of prioritizing deadline traffic (Deadline First) and prioritizing
+regular traffic (Deadline Last). We study both light-tailed and heavy-tailed
+flow size distributions and measure mean, median and tail flow completion times
+(FCT) for regular flows along with Deadline Miss Rate (DMR) and average
+lateness for deadline flows. We also consider two operation regimes of
+lightly-loaded (low utilization) and heavily-loaded (high utilization). We find
+that performance of deadline-aware schemes is highly dependent on fraction of
+deadline traffic. With light-tailed flow sizes, we find that FCFS performs
+better in terms of tail times and average lateness while SRPT performs better
+in average times and deadline miss rate. For heavy-tailed flow sizes, except
+for tail times, SRPT performs better in all other metrics.
+",1,0,0,0,0,0
+9705,Getting around the Halting Problem,"  The Halting Theorem establishes that there is no program (or Turing machine)
+H that can decide in all cases if an arbitrary program n halts on input m. The
+conjecture of this paper is that nevertheless there exists a sound program H
+such that if it halts it answers either yes or no, and can also in a certain
+sense identify all the cases it is unable to decide. The Halting Theorem can be
+proved by constructing a counterexample, i.e. a program that attempts to assert
+that it itself does not halt. The thesis is that there exists a program that
+proves about itself that its own attempt to prove, that the counterexample does
+not halt, does not halt. This outcome can be interpreted as it is NOT TRUE that
+the counterexample does not halt as opposed to it is FALSE that the
+counterexample does not halt. This becomes possible when the Recursion Theorem
+is reinterpreted as mutual necessitation rather than equivalence.
+",1,0,0,0,0,0
+9706,Molecular Beam Epitaxy Growth of [CrGe/MnGe/FeGe] Superlattices: Toward Artificial B20 Skyrmion Materials with Tunable Interactions,"  Skyrmions are localized magnetic spin textures whose stability has been shown
+theoretically to depend on material parameters including bulk Dresselhaus spin
+orbit coupling (SOC), interfacial Rashba SOC, and magnetic anisotropy. Here, we
+establish the growth of a new class of artificial skyrmion materials, namely
+B20 superlattices, where these parameters could be systematically tuned.
+Specifically, we report the successful growth of B20 superlattices comprised of
+single crystal thin films of FeGe, MnGe, and CrGe on Si(111) substrates. Thin
+films and superlattices are grown by molecular beam epitaxy and are
+characterized through a combination of reflection high energy electron
+diffraction, x-ray diffraction, and cross-sectional scanning transmission
+electron microscopy (STEM). X-ray energy dispersive spectroscopy (XEDS)
+distinguishes layers by elemental mapping and indicates good interface quality
+with relatively low levels of intermixing in the [CrGe/MnGe/FeGe] superlattice.
+This demonstration of epitaxial, single-crystalline B20 superlattices is a
+significant advance toward tunable skyrmion systems for fundamental scientific
+studies and applications in magnetic storage and logic.
+",0,1,0,0,0,0
+9707,"A comparative study of different exchange-correlation functionals in understanding structural, electronic and thermoelectric properties of Fe$_{2}$VAl and Fe$_{2}$TiSn compounds","  Fe$_{2}$VAl and Fe$_{2}$TiSn are full Heusler compounds with non-magnetic
+ground state. The two compouds are good thermoelectric materials. PBE and
+LDA(PW92) are the two most commonly used density functionals to study the
+Heusler compounds. Along with these two well studied exchange-correlation
+functionals, recently developed PBEsol, mBJ and SCAN functionals are employed
+to study the two compounds. Using the five functionals equilibrium lattice
+parameter and bulk modulus are calculated. Obtained values are compared with
+experimental reports wherever available. Electronic structure properties are
+studied by calculating dispersion curves, total and partial density of states.
+For Fe$_{2}$VAl, band gap of 0.22 eV is obtained from the mBJ potential which
+is in reasonable agreement with experimental value while, for Fe$_{2}$TiSn band
+gap of 0.68 eV is obtained. Fe$_{2}$VAl is predicted to be semimetallic with
+different values of negative gaps from LDA,PBEsol,PBE and SCAN functionals.
+Whereas, Fe$_{2}$TiSn is found to be semimetallic(semiconducting) from
+LDA,PBEsol(PBE,SCAN) functionals employed calculations. From the dispersion
+curve effective mass values are also computed to see the contribution to the
+Seebeck coefficient. In Fe$_{2}$TiSn, a flat band is present along the
+$\Gamma$-X direction with calculated value of effective mass $\sim$36 more than
+the mass of electron. The improvements or inadequacies among the functionals in
+explaining the properties of full Heusler alloys for thermoelectric application
+are thus observed through this study.
+",0,1,0,0,0,0
+9708,On a problem of Pillai with Fibonacci numbers and powers of 2,"  In this paper, we find all integers c having at least two representations as
+a difference between a Fibonacci number and a power of 2.
+",0,0,1,0,0,0
+9709,Evidence from web-based dietary search patterns to the role of B12 deficiency in chronic pain,"  Profound vitamin B12 deficiency is a known cause of disease, but the role of
+low or intermediate levels of B12 in the development of neuropathy and other
+neuropsychiatric symptoms as well as the relationship of eating meat and B12
+levels is unclear. Here we use food-related internet search patterns from a
+sample of 8.5 million US-based people as a proxy to B12 intake and correlate
+these searches with internet searches related to possible effects of B12
+deficiency. Food-related search patterns are highly correlated with known
+consumption and food-related searches (Spearman 0.69). Awareness of B12
+deficiency was associated with a higher consumption of B12-rich foods and with
+queries for B12 supplements. Searches for terms related to neurological
+disorders were correlated with searches for B12-poor foods, in contrast with
+control terms. Popular medicines, those having fewer indications, and those
+which are predominantly used to treat pain are more strongly correlated with
+the ability to predict neuropathic pain queries using the B12 contents of food.
+Our findings provide evidence for the utility of using Internet search patterns
+to investigate health questions in large populations and suggest that low B12
+intake may be associated with a broader spectrum of neurological disorders than
+currently appreciated.
+",1,0,0,0,0,0
+9710,Non-Uniform Attacks Against Pseudoentropy,"  De, Trevisan and Tulsiani [CRYPTO 2010] show that every distribution over
+$n$-bit strings which has constant statistical distance to uniform (e.g., the
+output of a pseudorandom generator mapping $n-1$ to $n$ bit strings), can be
+distinguished from the uniform distribution with advantage $\epsilon$ by a
+circuit of size $O( 2^n\epsilon^2)$.
+We generalize this result, showing that a distribution which has less than
+$k$ bits of min-entropy, can be distinguished from any distribution with $k$
+bits of $\delta$-smooth min-entropy with advantage $\epsilon$ by a circuit of
+size $O(2^k\epsilon^2/\delta^2)$. As a special case, this implies that any
+distribution with support at most $2^k$ (e.g., the output of a pseudoentropy
+generator mapping $k$ to $n$ bit strings) can be distinguished from any given
+distribution with min-entropy $k+1$ with advantage $\epsilon$ by a circuit of
+size $O(2^k\epsilon^2)$.
+Our result thus shows that pseudoentropy distributions face basically the
+same non-uniform attacks as pseudorandom distributions.
+",1,0,0,0,0,0
+9711,The Swift/BAT AGN Spectroscopic Survey (BASS) -- VI. The Gamma_X - L/L_Edd relation,"  We study the observed relation between accretion rate (in terms of L/L_Edd)
+and shape of the hard X-ray spectral energy distribution (namely the photon
+index Gamma_X) for a large sample of 228 hard X-ray selected, low-redshift
+active galactic nuclei (AGN), drawn from the Swift/BAT AGN Spectroscopic Survey
+(BASS). This includes 30 AGN for which black hole mass (and therefore L/L_Edd)
+is measured directly through masers, spatially resolved gas or stellar
+dynamics, or reverberation mapping. The high quality and broad energy coverage
+of the data provided through BASS allow us to examine several alternative
+determinations of both Gamma_X and L/L_Edd. For the BASS sample as a whole, we
+find a statistically significant, albeit very weak correlation between Gamma_X
+and L/L_Edd. The best-fitting relations we find, Gamma_X=0.15
+log(L/L_Edd)+const., are considerably shallower than those reported in previous
+studies. Moreover, we find no corresponding correlations among the subsets of
+AGN with different M_BH determination methodology. In particular, we find no
+robust evidence for a correlation when considering only those AGN with direct
+or single-epoch M_BH estimates. This latter finding is in contrast to several
+previous studies which focused on z>0.5 broad-line AGN. We discuss this tension
+and conclude that it can be partially accounted for if one adopts a simplified,
+power-law X-ray spectral model, combined with L/L_Edd estimates that are based
+on the continuum emission and on single-epoch broad line spectroscopy in the
+optical regime. We finally highlight the limitations on using Gamma_X as a
+probe of supermassive black hole evolution in deep extragalactic X-ray surveys.
+",0,1,0,0,0,0
+9712,Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning,"  In deep reinforcement learning (RL) tasks, an efficient exploration mechanism
+should be able to encourage an agent to take actions that lead to less frequent
+states which may yield higher accumulative future return. However, both knowing
+about the future and evaluating the frequentness of states are non-trivial
+tasks, especially for deep RL domains, where a state is represented by
+high-dimensional image frames. In this paper, we propose a novel informed
+exploration framework for deep RL, where we build the capability for an RL
+agent to predict over the future transitions and evaluate the frequentness for
+the predicted future frames in a meaningful manner. To this end, we train a
+deep prediction model to predict future frames given a state-action pair, and a
+convolutional autoencoder model to hash over the seen frames. In addition, to
+utilize the counts derived from the seen frames to evaluate the frequentness
+for the predicted frames, we tackle the challenge of matching the predicted
+future frames and their corresponding seen frames at the latent feature level.
+In this way, we derive a reliable metric for evaluating the novelty of the
+future direction pointed by each action, and hence inform the agent to explore
+the least frequent one.
+",1,0,0,1,0,0
+9713,The infrared to X-ray correlation spectra of unobscured type 1 active galactic nuclei,"  We use new X-ray data obtained with the Nuclear Spectroscopic Telescope Array
+(NuSTAR), near-infrared (NIR) fluxes, and mid-infrared (MIR) spectra of a
+sample of 24 unobscured type 1 active galactic nuclei (AGN) to study the
+correlation between various hard X-ray bands between 3 and 80 keV and the
+infrared (IR) emission. The IR to X-ray correlation spectrum (IRXCS) shows a
+maximum at ~15-20 micron, coincident with the peak of the AGN contribution to
+the MIR spectra of the majority of the sample. There is also a NIR correlation
+peak at ~2 micron, which we associate with the NIR bump observed in some type 1
+AGN at ~1-5 micron and is likely produced by nuclear hot dust emission. The
+IRXCS shows practically the same behaviour in all the X-ray bands considered,
+indicating a common origin for all of them. We finally evaluated correlations
+between the X-ray luminosities and various MIR emission lines. All the lines
+show a good correlation with the hard X-rays (rho>0.7), but we do not find the
+expected correlation between their ionization potentials and the strength of
+the IRXCS.
+",0,1,0,0,0,0
+9714,Quantum ensembles of quantum classifiers,"  Quantum machine learning witnesses an increasing amount of quantum algorithms
+for data-driven decision making, a problem with potential applications ranging
+from automated image recognition to medical diagnosis. Many of those algorithms
+are implementations of quantum classifiers, or models for the classification of
+data inputs with a quantum computer. Following the success of collective
+decision making with ensembles in classical machine learning, this paper
+introduces the concept of quantum ensembles of quantum classifiers. Creating
+the ensemble corresponds to a state preparation routine, after which the
+quantum classifiers are evaluated in parallel and their combined decision is
+accessed by a single-qubit measurement. This framework naturally allows for
+exponentially large ensembles in which -- similar to Bayesian learning -- the
+individual classifiers do not have to be trained. As an example, we analyse an
+exponentially large quantum ensemble in which each classifier is weighed
+according to its performance in classifying the training data, leading to new
+results for quantum as well as classical machine learning.
+",0,0,1,1,0,0
+9715,Recurrent Additive Networks,"  We introduce recurrent additive networks (RANs), a new gated RNN which is
+distinguished by the use of purely additive latent state updates. At every time
+step, the new state is computed as a gated component-wise sum of the input and
+the previous state, without any of the non-linearities commonly used in RNN
+transition dynamics. We formally show that RAN states are weighted sums of the
+input vectors, and that the gates only contribute to computing the weights of
+these sums. Despite this relatively simple functional form, experiments
+demonstrate that RANs perform on par with LSTMs on benchmark language modeling
+problems. This result shows that many of the non-linear computations in LSTMs
+and related networks are not essential, at least for the problems we consider,
+and suggests that the gates are doing more of the computational work than
+previously understood.
+",1,0,0,0,0,0
+9716,Nematic phase with colossal magnetoresistance and orbital polarons in manganite La$_{1-x}$Sr$_x$MnO$_3$,"  The origin of colossal magnetoresistance (CMR) is still controversial. The
+spin dynamics of La$_{1-x}$Sr$_x$MnO$_3$ is revisited along the Mn-O-Mn
+direction at $x\leq 0.5$, $T\leq T_C$ with a new study at $x$=0.4. A new
+lattice dynamics study is also reported at $x_0$=0.2,representative of the
+optimal doping for CMR. In large-$q$ wavevector range, typical of the scale of
+polarons, spin dynamics exhibits a discrete spectrum, $E^n_{\rm mag}$ with $n$
+equal to the degeneracy of orbital-pseudospin transitions and energy values in
+coincidence with the phonon ones. It corresponds to the spin-orbital excitation
+spectrum of short life-time polarons, in which the orbital pseudospin
+degeneracy is lift by phonons. For $x\neq x_0$, its q-range reveals a $\ell
+\approx 1.7a$ size of polarons with a dimension $2d$ at $x=1/8$ partly
+increasing to $\approx$ $3d$ at $x=0.3$. At $x_0=0.2$ ($T<T_C$) two distinct
+$q$ and energy ranges appear separated by $\Delta E(q_0\approx 0.35)=3meV$. The
+same $\Delta E(q_0)$ value separates two unusual transverse acoustic branches
+($T>T_C$). Both characterize a nematic-phase defined by chains of ""orbital
+polarons"" of $2a$ size, distant from $3a$, typical of $x_0=1/6$. It could
+explain CMR.
+",0,1,0,0,0,0
+9717,The generalized optical memory effect,"  The optical memory effect is a well-known type of wave correlation that is
+observed in coherent fields that scatter through thin and diffusive materials,
+like biological tissue. It is a fundamental physical property of scattering
+media that can be harnessed for deep-tissue microscopy or 'through-the-wall'
+imaging applications. Here we show that the optical memory effect is a special
+case of a far more general class of wave correlation. Our new theoretical
+framework explains how waves remain correlated over both space and angle when
+they are jointly shifted and tilted inside scattering media of arbitrary
+geometry. We experimentally demonstrate the existence of such coupled
+correlations and describe how they can be used to optimize the scanning range
+in adaptive optics microscopes.
+",0,1,0,0,0,0
+9718,Geometry of Policy Improvement,"  We investigate the geometry of optimal memoryless time independent decision
+making in relation to the amount of information that the acting agent has about
+the state of the system. We show that the expected long term reward, discounted
+or per time step, is maximized by policies that randomize among at most $k$
+actions whenever at most $k$ world states are consistent with the agent's
+observation. Moreover, we show that the expected reward per time step can be
+studied in terms of the expected discounted reward. Our main tool is a
+geometric version of the policy improvement lemma, which identifies a
+polyhedral cone of policy changes in which the state value function increases
+for all states.
+",1,0,1,0,0,0
+9719,Modelling and characterization of a pneumatically actuated peristaltic micropump,"  There is an emerging class of microfluidic bioreactors which possess
+long-term, closed circuit perfusion under sterile conditions with in vivo-like
+flow parameters. Integrated into microfluidics, peristaltic-like pneumatically
+actuated displacement micropumps are able to meet these requirements. We
+present both a theoretical and experimental characterization of such pumps. In
+order to examine volume flow rate, we have developed a mathemati- cal model
+describing membrane motion under external pressure. The viscoelasticity of the
+membrane and hydrodynamic resistance of the microfluidic channel have been
+taken into account. Unlike other models, the developed model includes only the
+physical parameters of the pump and allows the estimation of their impact on
+the resulting flow. The model has been validated experimentally.
+",0,1,0,0,0,0
+9720,Semi-algebraic triangulation over p-adically closed fields,"  We prove a triangulation theorem for semi-algebraic sets over a p-adically
+closed field, quite similar to its real counterpart. We derive from it several
+applications like the existence of flexible retractions and splitting for
+semi-algebraic sets.
+",0,0,1,0,0,0
+9721,Improving the Performance of OTDOA based Positioning in NB-IoT Systems,"  In this paper, we consider positioning with
+observed-time-difference-of-arrival (OTDOA) for a device deployed in
+long-term-evolution (LTE) based narrow-band Internet-of-things (NB-IoT)
+systems. We propose an iterative expectation-maximization based successive
+interference cancellation (EM-SIC) algorithm to jointly consider estimations of
+residual frequency-offset (FO), fading-channel taps and time-of-arrival (ToA)
+of the first arrival-path for each of the detected cells. In order to design a
+low complexity ToA detector and also due to the limits of low-cost analog
+circuits, we assume an NB-IoT device working at a low-sampling rate such as
+1.92 MHz or lower. The proposed EM-SIC algorithm comprises two stages to detect
+ToA, based on which OTDOA can be calculated. In a first stage, after running
+the EM-SIC block a predefined number of iterations, a coarse ToA is estimated
+for each of the detected cells. Then in a second stage, to improve the ToA
+resolution, a low-pass filter is utilized to interpolate the correlations of
+time-domain PRS signal evaluated at a low sampling-rate to a high sampling-rate
+such as 30.72 MHz. To keep low-complexity, only the correlations inside a small
+search window centered at the coarse ToA estimates are upsampled. Then, the
+refined ToAs are estimated based on upsampled correlations. If at least three
+cells are detected, with OTDOA and the locations of detected cell sites, the
+position of the NB-IoT device can be estimated. We show through numerical
+simulations that, the proposed EM-SIC based ToA detector is robust against
+impairments introduced by inter-cell interference, fading-channel and residual
+FO. Thus significant signal-to-noise (SNR) gains are obtained over traditional
+ToA detectors that do not consider these impairments when positioning a device.
+",1,0,0,0,0,0
+9722,OGLE-2015-BLG-1459L: The Challenges of Exo-Moon Microlensing,"  We show that dense OGLE and KMTNet $I$-band survey data require four bodies
+(sources plus lenses) to explain the microlensing light curve of
+OGLE-2015-BLG-1459. However, these can equally well consist of three lenses and
+one source (3L1S), two lenses and two sources (2L2S) or one lens and three
+sources (1L3S). In the 3L1S and 2L2S interpretations, the host is a brown dwarf
+and the dominant companion is a Neptune-class planet, with the third body (in
+the 3L1S case) being a Mars-class object that could have been a moon of the
+planet. In the 1L3S solution, the light curve anomalies are explained by a
+tight (five stellar radii) low-luminosity binary source that is offset from the
+principal source of the event by $\sim 0.17\,\au$. These degeneracies are
+resolved in favor of the 1L3S solution by color effects derived from comparison
+to MOA data, which are taken in a slightly different ($R/I$) passband. To
+enable current and future ($WFIRST$) surveys to routinely characterize exomoons
+and distinguish among such exotic systems requires an observing strategy that
+includes both a cadence faster than 9 min$^{-1}$ and observations in a second
+band on a similar timescale.
+",0,1,0,0,0,0
+9723,Particle-flow reconstruction and global event description with the CMS detector,"  The CMS apparatus was identified, a few years before the start of the LHC
+operation at CERN, to feature properties well suited to particle-flow (PF)
+reconstruction: a highly-segmented tracker, a fine-grained electromagnetic
+calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an
+excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned
+to the CMS detector was therefore developed and has been consistently used in
+physics analyses for the first time at a hadron collider. For each collision,
+the comprehensive list of final-state particles identified and reconstructed by
+the algorithm provides a global event description that leads to unprecedented
+CMS performance for jet and hadronic tau decay reconstruction, missing
+transverse momentum determination, and electron and muon identification. This
+approach also allows particles from pileup interactions to be identified and
+enables efficient pileup mitigation methods. The data collected by CMS at a
+centre-of-mass energy of 8 TeV show excellent agreement with the simulation and
+confirm the superior PF performance at least up to an average of 20 pileup
+interactions.
+",0,1,0,0,0,0
+9724,Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts,"  This paper proposes a discontinuity-sensitive approach to learn the solutions
+of parametric optimal control problems with high accuracy. Many tasks, ranging
+from model predictive control to reinforcement learning, may be solved by
+learning optimal solutions as a function of problem parameters. However,
+nonconvexity, discrete homotopy classes, and control switching cause
+discontinuity in the parameter-solution mapping, thus making learning difficult
+for traditional continuous function approximators. A mixture of experts (MoE)
+model composed of a classifier and several regressors is proposed to address
+such an issue. The optimal trajectories of different parameters are clustered
+such that in each cluster the trajectories are continuous function of problem
+parameters. Numerical examples on benchmark problems show that training the
+classifier and regressors individually outperforms joint training of MoE. With
+suitably chosen clusters, this approach not only achieves lower prediction
+error with less training data and fewer model parameters, but also leads to
+dramatic improvements in the reliability of trajectory tracking compared to
+traditional universal function approximation models (e.g., neural networks).
+",1,0,0,0,0,0
+9725,Ergodicity analysis and antithetic integral control of a class of stochastic reaction networks with delays,"  Delays are an important phenomenon arising in a wide variety of real world
+systems. They occur in biological models because of diffusion effects or as
+simplifying modeling elements. We propose here to consider delayed stochastic
+reaction networks. The difficulty here lies in the fact that the state-space of
+a delayed reaction network is infinite-dimensional, which makes their analysis
+more involved. We demonstrate here that a particular class of stochastic
+time-varying delays, namely those that follow a phase-type distribution, can be
+exactly implemented in terms of a chemical reaction network. Hence, any
+delay-free network can be augmented to incorporate those delays through the
+addition of delay-species and delay-reactions. Hence, for this class of
+stochastic delays, which can be used to approximate any delay distribution
+arbitrarily accurately, the state-space remains finite-dimensional and,
+therefore, standard tools developed for standard reaction network still apply.
+In particular, we demonstrate that for unimolecular mass-action reaction
+networks that the delayed stochastic reaction network is ergodic if and only if
+the non-delayed network is ergodic as well. Bimolecular reactions are more
+difficult to consider but an analogous result is also obtained. These results
+tell us that delays that are phase-type distributed, regardless of their
+distribution, are not harmful to the ergodicity property of reaction networks.
+We also prove that the presence of those delays adds convolution terms in the
+moment equation but does not change the value of the stationary means compared
+to the delay-free case. Finally, the control of a certain class of delayed
+stochastic reaction network using a delayed antithetic integral controller is
+considered. It is proven that this controller achieves its goal provided that
+the delay-free network satisfy the conditions of ergodicity and
+output-controllability.
+",0,0,0,0,1,0
+9726,The three-dimensional standard solution to the Ricci flow is modeled by the Bryant soliton,"  It came to my attention after posting this paper that Yu Ding has proved the
+same result before. I would like to apologize to Yu Ding for the appearance of
+this paper.
+",0,0,1,0,0,0
+9727,Dynamic classifier chains for multi-label learning,"  In this paper, we deal with the task of building a dynamic ensemble of chain
+classifiers for multi-label classification. To do so, we proposed two concepts
+of classifier chains algorithms that are able to change label order of the
+chain without rebuilding the entire model. Such modes allows anticipating the
+instance-specific chain order without a significant increase in computational
+burden. The proposed chain models are built using the Naive Bayes classifier
+and nearest neighbour approach as a base single-label classifiers. To take the
+benefits of the proposed algorithms, we developed a simple heuristic that
+allows the system to find relatively good label order. The heuristic sort
+labels according to the label-specific classification quality gained during the
+validation phase. The heuristic tries to minimise the phenomenon of error
+propagation in the chain. The experimental results showed that the proposed
+model based on Naive Bayes classifier the above-mentioned heuristic is an
+efficient tool for building dynamic chain classifiers.
+",1,0,0,1,0,0
+9728,Big Data Regression Using Tree Based Segmentation,"  Scaling regression to large datasets is a common problem in many application
+areas. We propose a two step approach to scaling regression to large datasets.
+Using a regression tree (CART) to segment the large dataset constitutes the
+first step of this approach. The second step of this approach is to develop a
+suitable regression model for each segment. Since segment sizes are not very
+large, we have the ability to apply sophisticated regression techniques if
+required. A nice feature of this two step approach is that it can yield models
+that have good explanatory power as well as good predictive performance.
+Ensemble methods like Gradient Boosted Trees can offer excellent predictive
+performance but may not provide interpretable models. In the experiments
+reported in this study, we found that the predictive performance of the
+proposed approach matched the predictive performance of Gradient Boosted Trees.
+",1,0,0,1,0,0
+9729,Real-time Road Traffic Information Detection Through Social Media,"  In current study, a mechanism to extract traffic related information such as
+congestion and incidents from textual data from the internet is proposed. The
+current source of data is Twitter. As the data being considered is extremely
+large in size automated models are developed to stream, download, and mine the
+data in real-time. Furthermore, if any tweet has traffic related information
+then the models should be able to infer and extract this data.
+Currently, the data is collected only for United States and a total of
+120,000 geo-tagged traffic related tweets are extracted, while six million
+geo-tagged non-traffic related tweets are retrieved and classification models
+are trained. Furthermore, this data is used for various kinds of spatial and
+temporal analysis. A mechanism to calculate level of traffic congestion,
+safety, and traffic perception for cities in U.S. is proposed. Traffic
+congestion and safety rankings for the various urban areas are obtained and
+then they are statistically validated with existing widely adopted rankings.
+Traffic perception depicts the attitude and perception of people towards the
+traffic.
+It is also seen that traffic related data when visualized spatially and
+temporally provides the same pattern as the actual traffic flows for various
+urban areas. When visualized at the city level, it is clearly visible that the
+flow of tweets is similar to flow of vehicles and that the traffic related
+tweets are representative of traffic within the cities. With all the findings
+in current study, it is shown that significant amount of traffic related
+information can be extracted from Twitter and other sources on internet.
+Furthermore, Twitter and these data sources are freely available and are not
+bound by spatial and temporal limitations. That is, wherever there is a user
+there is a potential for data.
+",1,0,0,0,0,0
+9730,The stable Picard group of $\mathcal{A}(2)$,"  Using a form of descent in the stable category of $\mathcal{A}(2)$-modules,
+we show that there are no exotic elements in the stable Picard group of
+$\mathcal{A}(2)$, \textit{i.e.} that the stable Picard group of
+$\mathcal{A}(2)$ is free on $2$ generators.
+",0,0,1,0,0,0
+9731,Understanding kernel size in blind deconvolution,"  Most blind deconvolution methods usually pre-define a large kernel size to
+guarantee the support domain. Blur kernel estimation error is likely to be
+introduced, and is proportional to kernel size. In this paper, we
+experimentally and theoretically show the reason of noises introduction in
+oversized kernel by demonstrating that sizeable kernels lead to lower
+optimization cost. To eliminate this adverse effect, we propose a low
+rank-based regularization on blur kernel by analyzing the structural
+information in degraded kernels. Compared with the sparsity prior, e.g.,
+$\ell_\alpha$-norm, our regularization term can effectively suppress random
+noises in oversized kernels. On benchmark test dataset, the proposed method is
+compared with several state-of-the-art methods, and can achieve better
+quantitative score. Especially, the improvement margin is much more significant
+for oversized blur kernels. We also validate the proposed method on real-world
+blurry images.
+",1,0,0,0,0,0
+9732,Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions,"  We provide new results for noise-tolerant and sample-efficient learning
+algorithms under $s$-concave distributions. The new class of $s$-concave
+distributions is a broad and natural generalization of log-concavity, and
+includes many important additional distributions, e.g., the Pareto distribution
+and $t$-distribution. This class has been studied in the context of efficient
+sampling, integration, and optimization, but much remains unknown about the
+geometry of this class of distributions and their applications in the context
+of learning. The challenge is that unlike the commonly used distributions in
+learning (uniform or more generally log-concave distributions), this broader
+class is not closed under the marginalization operator and many such
+distributions are fat-tailed. In this work, we introduce new convex geometry
+tools to study the properties of $s$-concave distributions and use these
+properties to provide bounds on quantities of interest to learning including
+the probability of disagreement between two halfspaces, disagreement outside a
+band, and the disagreement coefficient. We use these results to significantly
+generalize prior results for margin-based active learning, disagreement-based
+active learning, and passive learning of intersections of halfspaces. Our
+analysis of geometric properties of $s$-concave distributions might be of
+independent interest to optimization more broadly.
+",1,0,0,1,0,0
+9733,Connectivity Properties of Factorization Posets in Generated Groups,"  We consider three notions of connectivity and their interactions in partially
+ordered sets coming from reduced factorizations of an element in a generated
+group. While one form of connectivity essentially reflects the connectivity of
+the poset diagram, the other two are a bit more involved: Hurwitz-connectivity
+has its origins in algebraic geometry, and shellability in topology. We propose
+a framework to study these connectivity properties in a uniform way. Our main
+tool is a certain total order of the generators that is compatible with the
+chosen element.
+",0,0,1,0,0,0
+9734,Boundary-sum irreducible finite order corks,"  We prove for any positive integer $n$ there exist boundary-sum irreducible
+${\mathbb Z}_n$-corks with Stein structure. Here `boundary-sum irreducible'
+means the manifold is indecomposable with respect to boundary-sum. We also
+verify that some of the finite order corks admit hyperbolic boundary by HIKMOT.
+",0,0,1,0,0,0
+9735,Deep Learning as a Mixed Convex-Combinatorial Optimization Problem,"  As neural networks grow deeper and wider, learning networks with
+hard-threshold activations is becoming increasingly important, both for network
+quantization, which can drastically reduce time and energy requirements, and
+for creating large integrated systems of deep networks, which may have
+non-differentiable components and must avoid vanishing and exploding gradients
+for effective learning. However, since gradient descent is not applicable to
+hard-threshold functions, it is not clear how to learn networks of them in a
+principled way. We address this problem by observing that setting targets for
+hard-threshold hidden units in order to minimize loss is a discrete
+optimization problem, and can be solved as such. The discrete optimization goal
+is to find a set of targets such that each unit, including the output, has a
+linearly separable problem to solve. Given these targets, the network
+decomposes into individual perceptrons, which can then be learned with standard
+convex approaches. Based on this, we develop a recursive mini-batch algorithm
+for learning deep hard-threshold networks that includes the popular but poorly
+justified straight-through estimator as a special case. Empirically, we show
+that our algorithm improves classification accuracy in a number of settings,
+including for AlexNet and ResNet-18 on ImageNet, when compared to the
+straight-through estimator.
+",1,0,0,0,0,0
+9736,A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems,"  This paper presents a novel data-driven approach for predicting the number of
+vegetation-related outages that occur in power distribution systems on a
+monthly basis. In order to develop an approach that is able to successfully
+fulfill this objective, there are two main challenges that ought to be
+addressed. The first challenge is to define the extent of the target area. An
+unsupervised machine learning approach is proposed to overcome this difficulty.
+The second challenge is to correctly identify the main causes of
+vegetation-related outages and to thoroughly investigate their nature. In this
+paper, these outages are categorized into two main groups: growth-related and
+weather-related outages, and two types of models, namely time series and
+non-linear machine learning regression models are proposed to conduct the
+prediction tasks, respectively. Moreover, various features that can explain the
+variability in vegetation-related outages are engineered and employed. Actual
+outage data, obtained from a major utility in the U.S., in addition to
+different types of weather and geographical data are utilized to build the
+proposed approach. Finally, a comprehensive case study is carried out to
+demonstrate how the proposed approach can be used to successfully predict the
+number of vegetation-related outages and to help decision-makers to detect
+vulnerable zones in their systems.
+",0,0,0,1,0,0
+9737,"Structural subnetwork evolution across the life-span: rich-club, feeder, seeder","  The impact of developmental and aging processes on brain connectivity and the
+connectome has been widely studied. Network theoretical measures and certain
+topological principles are computed from the entire brain, however there is a
+need to separate and understand the underlying subnetworks which contribute
+towards these observed holistic connectomic alterations. One organizational
+principle is the rich-club - a core subnetwork of brain regions that are
+strongly connected, forming a high-cost, high-capacity backbone that is
+critical for effective communication in the network. Investigations primarily
+focus on its alterations with disease and age. Here, we present a systematic
+analysis of not only the rich-club, but also other subnetworks derived from
+this backbone - namely feeder and seeder subnetworks. Our analysis is applied
+to structural connectomes in a normal cohort from a large, publicly available
+lifespan study. We demonstrate changes in rich-club membership with age
+alongside a shift in importance from 'peripheral' seeder to feeder subnetworks.
+Our results show a refinement within the rich-club structure (increase in
+transitivity and betweenness centrality), as well as increased efficiency in
+the feeder subnetwork and decreased measures of network integration and
+segregation in the seeder subnetwork. These results demonstrate the different
+developmental patterns when analyzing the connectome stratified according to
+its rich-club and the potential of utilizing this subnetwork analysis to reveal
+the evolution of brain architectural alterations across the life-span.
+",0,0,0,0,1,0
+9738,No Silk Road for Online Gamers!: Using Social Network Analysis to Unveil Black Markets in Online Games,"  Online game involves a very large number of users who are interconnected and
+interact with each other via the Internet. We studied the characteristics of
+exchanging virtual goods with real money through processes called ""real money
+trading (RMT)."" This exchange might influence online game user behaviors and
+cause damage to the reputation of game companies. We examined in-game
+transactions to reveal RMT by constructing a social graph of virtual goods
+exchanges in an online game and identifying network communities of users.
+We analyzed approximately 6,000,000 transactions in a popular online game and
+inferred RMT transactions by comparing the RMT transactions crawled from an
+out-game market. Our findings are summarized as follows: (1) the size of the
+RMT market could be approximately estimated; (2) professional RMT providers
+typically form a specific network structure (either star-shape or chain) in the
+trading network, which can be used as a clue for tracing RMT transactions; and
+(3) the observed RMT market has evolved over time into a monopolized market
+with a small number of large-sized virtual goods providers.
+",1,0,0,0,0,0
+9739,Parameter-dependent Stochastic Optimal Control in Finite Discrete Time,"  We prove a general existence result in stochastic optimal control in discrete
+time where controls take values in conditional metric spaces, and depend on the
+current state and the information of past decisions through the evolution of a
+recursively defined forward process. The generality of the problem lies beyond
+the scope of standard techniques in stochastic control theory such as random
+sets, normal integrands and measurable selection theory. The main novelty is a
+formalization in conditional metric space and the use of techniques in
+conditional analysis. We illustrate the existence result by several examples
+including wealth-dependent utility maximization under risk constraints with
+bounded and unbounded wealth-dependent control sets, utility maximization with
+a measurable dimension, and dynamic risk sharing. Finally, we discuss how
+conditional analysis relates to random set theory.
+",0,0,1,0,0,0
+9740,VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator,"  A monocular visual-inertial system (VINS), consisting of a camera and a
+low-cost inertial measurement unit (IMU), forms the minimum sensor suite for
+metric six degrees-of-freedom (DOF) state estimation. However, the lack of
+direct distance measurement poses significant challenges in terms of IMU
+processing, estimator initialization, extrinsic calibration, and nonlinear
+optimization. In this work, we present VINS-Mono: a robust and versatile
+monocular visual-inertial state estimator.Our approach starts with a robust
+procedure for estimator initialization and failure recovery. A tightly-coupled,
+nonlinear optimization-based method is used to obtain high accuracy
+visual-inertial odometry by fusing pre-integrated IMU measurements and feature
+observations. A loop detection module, in combination with our tightly-coupled
+formulation, enables relocalization with minimum computation overhead.We
+additionally perform four degrees-of-freedom pose graph optimization to enforce
+global consistency. We validate the performance of our system on public
+datasets and real-world experiments and compare against other state-of-the-art
+algorithms. We also perform onboard closed-loop autonomous flight on the MAV
+platform and port the algorithm to an iOS-based demonstration. We highlight
+that the proposed work is a reliable, complete, and versatile system that is
+applicable for different applications that require high accuracy localization.
+We open source our implementations for both PCs and iOS mobile devices.
+",1,0,0,0,0,0
+9741,Equation of State Effects on Gravitational Waves from Rotating Core Collapse,"  Gravitational waves (GWs) generated by axisymmetric rotating collapse,
+bounce, and early postbounce phases of a galactic core-collapse supernova will
+be detectable by current-generation gravitational wave observatories. Since
+these GWs are emitted from the quadrupole-deformed nuclear-density core, they
+may encode information on the uncertain nuclear equation of state (EOS). We
+examine the effects of the nuclear EOS on GWs from rotating core collapse and
+carry out 1824 axisymmetric general-relativistic hydrodynamic simulations that
+cover a parameter space of 98 different rotation profiles and 18 different EOS.
+We show that the bounce GW signal is largely independent of the EOS and
+sensitive primarily to the ratio of rotational to gravitational energy, and at
+high rotation rates, to the degree of differential rotation. The GW frequency
+of postbounce core oscillations shows stronger EOS dependence that can be
+parameterized by the core's EOS-dependent dynamical frequency
+$\sqrt{G\bar{\rho}_c}$. We find that the ratio of the peak frequency to the
+dynamical frequency follows a universal trend that is obeyed by all EOS and
+rotation profiles and that indicates that the nature of the core oscillations
+changes when the rotation rate exceeds the dynamical frequency. We find that
+differences in the treatments of low-density nonuniform nuclear matter, of the
+transition from nonuniform to uniform nuclear matter, and in the description of
+nuclear matter up to around twice saturation density can mildly affect the GW
+signal. We find that approximations and uncertainties in electron capture rates
+can lead to variations in the GW signal that are of comparable magnitude to
+those due to different nuclear EOS. This emphasizes the need for reliable
+nuclear electron capture rates and for self-consistent multi-dimensional
+neutrino radiation-hydrodynamic simulations of rotating core collapse.
+",0,1,0,0,0,0
+9742,Quantum and thermal fluctuations in a Raman spin-orbit coupled Bose gas,"  We theoretically study a three-dimensional weakly-interacting Bose gas with
+Raman-induced spin-orbit coupling at finite temperature. By employing a
+generalized Hartree-Fock-Bogoliubov theory with Popov approximation, we
+determine a complete finite-temperature phase diagram of three exotic
+condensation phases (i.e., the stripe, plane-wave and zero-momentum phases),
+against both quantum and thermal fluctuations. We find that the plane-wave
+phase is significantly broadened by thermal fluctuations. The phonon mode and
+sound velocity at the transition from the plane-wave phase to the zero-momentum
+phase are thoughtfully analyzed. At zero temperature, we find that quantum
+fluctuations open an unexpected gap in sound velocity at the phase transition,
+in stark contrast to the previous theoretical prediction of a vanishing sound
+velocity. At finite temperature, thermal fluctuations continue to significantly
+enlarge the gap, and simultaneously shift the critical minimum. For a Bose gas
+of $^{87}$Rb atoms at the typical experimental temperature, $T=0.3T_{0}$, where
+$T_{0}$ is the critical temperature of an ideal Bose gas without spin-orbit
+coupling, our results of gap opening and critical minimum shifting in the sound
+velocity, are qualitatively consistent with the recent experimental observation
+{[}S.-C. Ji \textit{et al.}, Phys. Rev. Lett. \textbf{114}, 105301 (2015){]}.
+",0,1,0,0,0,0
+9743,San Pedro Meeting on Wide Field Variability Surveys: Some Concluding Comments,"  This is a written version of the closing talk at the 22nd Los Alamos Stellar
+pulsation conference on wide field variability surveys. It comments on some of
+the issues which arise from the meeting. These include the need for attention
+to photometric standardization (especially in the infrared) and the somewhat
+controversial problem of statistical bias in the use of parallaxes (and other
+methods of distance determination). Some major advances in the use of pulsating
+variables to study Galactic structure are mentioned. The paper includes a
+clarification of apparently conflicting results from classical Cepheids and RR
+Lyrae stars in the inner Galaxy and bulge. The importance of understanding
+non-periodic phenomena in variable stars,particularly AGB variables and RCB
+stars is stressed, especially for its relevance to mass-loss, in which
+pulsation may only play a minor role.
+",0,1,0,0,0,0
+9744,Weighted Community Detection and Data Clustering Using Message Passing,"  Grouping objects into clusters based on similarities or weights between them
+is one of the most important problems in science and engineering. In this work,
+by extending message passing algorithms and spectral algorithms proposed for
+unweighted community detection problem, we develop a non-parametric method
+based on statistical physics, by mapping the problem to Potts model at the
+critical temperature of spin glass transition and applying belief propagation
+to solve the marginals corresponding to the Boltzmann distribution. Our
+algorithm is robust to over-fitting and gives a principled way to determine
+whether there are significant clusters in the data and how many clusters there
+are. We apply our method to different clustering tasks and use extensive
+numerical experiments to illustrate the advantage of our method over existing
+algorithms. In the community detection problem in weighted and directed
+networks, we show that our algorithm significantly outperforms existing
+algorithms. In the clustering problem when the data was generated by mixture
+models in the sparse regime we show that our method works to the theoretical
+limit of detectability and gives accuracy very close to that of the optimal
+Bayesian inference. In the semi-supervised clustering problem, our method only
+needs several labels to work perfectly in classic datasets. Finally, we further
+develop Thouless-Anderson-Palmer equations which reduce heavily the computation
+complexity in dense-networks but gives almost the same performance as belief
+propagation.
+",1,0,0,1,0,0
+9745,Edge Control of Graphene Domains Grown on Hexagonal Boron Nitride,"  Edge structure of graphene has a significant influence on its electronic
+properties. However, control over the edge structure of graphene domains on
+insulating substrates is still challenging. Here we demonstrate edge control of
+graphene domains on hexagonal boron nitride (h-BN) by modifying ratio of
+working-gases. Edge directions were determined with the help of both moiré
+pattern and atomic-resolution image obtained via atomic force microscopy
+measurement. It is believed that the variation on graphene edges mainly
+attributes to different growth rates of armchair and zigzag edges. This work
+demonstrated here points out a potential approach to fabricate graphene ribbons
+on h-BN.
+",0,1,0,0,0,0
+9746,On MASAs in $q$-deformed von Neumann algebras,"  We study certain $q$-deformed analogues of the maximal abelian subalgebras of
+the group von Neumann algebras of free groups. The radial subalgebra is defined
+for Hecke deformed von Neumann algebras of the Coxeter group
+$(\mathbb{Z}/{2\mathbb{Z}})^{\star k}$ and shown to be a maximal abelian
+subalgebra which is singular and with Pukánszky invariant $\{\infty\}$.
+Further all non-equal generator masas in the $q$-deformed Gaussian von Neumann
+algebras are shown to be mutually non-unitarily conjugate.
+",0,0,1,0,0,0
+9747,A note on computing range space bases of rational matrices,"  We discuss computational procedures based on descriptor state-space
+realizations to compute proper range space bases of rational matrices. The main
+computation is the orthogonal reduction of the system matrix pencil to a
+special Kronecker-like form, which allows to extract a full column rank factor,
+whose columns form a proper rational basis of the range space. The computation
+of several types of bases can be easily accommodated, such as minimum-degree
+bases, stable inner minimum-degree bases, etc. Several straightforward
+applications of the range space basis computation are discussed, such as, the
+computation of full rank factorizations, normalized coprime factorizations,
+pseudo-inverses, and inner-outer factorizations.
+",1,0,0,0,0,0
+9748,Directed-Loop Quantum Monte Carlo Method for Retarded Interactions,"  The directed-loop quantum Monte Carlo method is generalized to the case of
+retarded interactions. Using the path integral, fermion-boson or spin-boson
+models are mapped to actions with retarded interactions by analytically
+integrating out the bosons. This yields an exact algorithm that combines the
+highly-efficient loop updates available in the stochastic series expansion
+representation with the advantages of avoiding a direct sampling of the bosons.
+The application to electron-phonon models reveals that the method overcomes the
+previously detrimental issues of long autocorrelation times and exponentially
+decreasing acceptance rates. For example, the resulting dramatic speedup allows
+us to investigate the Peierls quantum phase transition on chains of up to
+$1282$ sites.
+",0,1,0,0,0,0
+9749,Towards CNN map representation and compression for camera relocalisation,"  This paper presents a study on the use of Convolutional Neural Networks for
+camera relocalisation and its application to map compression. We follow state
+of the art visual relocalisation results and evaluate the response to different
+data inputs. We use a CNN map representation and introduce the notion of map
+compression under this paradigm by using smaller CNN architectures without
+sacrificing relocalisation performance. We evaluate this approach in a series
+of publicly available datasets over a number of CNN architectures with
+different sizes, both in complexity and number of layers. This formulation
+allows us to improve relocalisation accuracy by increasing the number of
+training trajectories while maintaining a constant-size CNN.
+",1,0,0,0,0,0
+9750,Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning,"  In this paper, we unravel a fundamental connection between weighted finite
+automata~(WFAs) and second-order recurrent neural networks~(2-RNNs): in the
+case of sequences of discrete symbols, WFAs and 2-RNNs with linear activation
+functions are expressively equivalent. Motivated by this result, we build upon
+a recent extension of the spectral learning algorithm to vector-valued WFAs and
+propose the first provable learning algorithm for linear 2-RNNs defined over
+sequences of continuous input vectors. This algorithm relies on estimating low
+rank sub-blocks of the so-called Hankel tensor, from which the parameters of a
+linear 2-RNN can be provably recovered. The performances of the proposed method
+are assessed in a simulation study.
+",0,0,0,1,0,0
+9751,Evaluating Predictive Models of Student Success: Closing the Methodological Gap,"  Model evaluation -- the process of making inferences about the performance of
+predictive models -- is a critical component of predictive modeling research in
+learning analytics. We survey the state of the practice with respect to model
+evaluation in learning analytics, which overwhelmingly uses only naive methods
+for model evaluation or statistical tests which are not appropriate for
+predictive model evaluation. We conduct a critical comparison of both null
+hypothesis significance testing (NHST) and a preferred Bayesian method for
+model evaluation. Finally, we apply three methods -- the na{ï}ve average
+commonly used in learning analytics, NHST, and Bayesian -- to a predictive
+modeling experiment on a large set of MOOC data. We compare 96 different
+predictive models, including different feature sets, statistical modeling
+algorithms, and tuning hyperparameters for each, using this case study to
+demonstrate the different experimental conclusions these evaluation techniques
+provide.
+",0,0,0,1,0,0
+9752,A vertex and edge deletion game on graphs,"  Starting with a graph, two players take turns in either deleting an edge or
+deleting a vertex and all incident edges. The player removing the last vertex
+wins. We review the known results for this game and extend the computation of
+nim-values to new families of graphs. A conjecture of Khandhawit and Ye on the
+nim-values of graphs with one odd cycle is proved. We also see that, for wheels
+and their subgraphs, this game exhibits a surprising amount of unexplained
+regularity.
+",1,0,0,0,0,0
+9753,Determinants of cyclization-decyclization kinetics of short DNA with sticky ends,"  Cyclization of DNA with sticky ends is commonly used to construct DNA
+minicircles and to measure DNA bendability. The cyclization probability of
+short DNA (< 150 bp) has a strong length dependence, but how it depends on the
+rotational positioning of the sticky ends around the helical axis is less
+clear. To shed light upon the determinants of the cyclization probability of
+short DNA, we measured cyclization and decyclization rates of ~100-bp DNA with
+sticky ends over two helical periods using single-molecule Fluorescence
+Resonance Energy Transfer (FRET). The cyclization rate increases monotonically
+with length, indicating no excess twisting, while the decyclization rate
+oscillates with length, higher at half-integer helical turns and lower at
+integer helical turns. The oscillation profile is kinetically and
+thermodynamically consistent with a three-state cyclization model in which
+sticky-ended short DNA first bends into a torsionally-relaxed teardrop, and
+subsequently transitions to a more stable loop upon terminal base stacking. We
+also show that the looping probability density (the J factor) extracted from
+this study is in good agreement with the worm-like chain model near 100 bp. For
+shorter DNA, we discuss various experimental factors that prevent an accurate
+measurement of the J factor.
+",0,0,0,0,1,0
+9754,Remark on a theorem of H. Hauser on textile maps,"  We give a counter example to the new theorem that appeared in the survey
+\cite{H} on Artin approximation. We then provide a correct statement and a
+proof of it.
+",0,0,1,0,0,0
+9755,Faster and Simpler Distributed Algorithms for Testing and Correcting Graph Properties in the CONGEST-Model,"  In this paper we present distributed testing algorithms of graph properties
+in the CONGEST-model [Censor-Hillel et al. 2016]. We present one-sided error
+testing algorithms in the general graph model.
+We first describe a general procedure for converting $\epsilon$-testers with
+a number of rounds $f(D)$, where $D$ denotes the diameter of the graph, to
+$O((\log n)/\epsilon)+f((\log n)/\epsilon)$ rounds, where $n$ is the number of
+processors of the network. We then apply this procedure to obtain an optimal
+tester, in terms of $n$, for testing bipartiteness, whose round complexity is
+$O(\epsilon^{-1}\log n)$, which improves over the $poly(\epsilon^{-1} \log
+n)$-round algorithm by Censor-Hillel et al. (DISC 2016). Moreover, for
+cycle-freeness, we obtain a \emph{corrector} of the graph that locally corrects
+the graph so that the corrected graph is acyclic. Note that, unlike a tester, a
+corrector needs to mend the graph in many places in the case that the graph is
+far from having the property.
+In the second part of the paper we design algorithms for testing whether the
+network is $H$-free for any connected $H$ of size up to four with round
+complexity of $O(\epsilon^{-1})$. This improves over the
+$O(\epsilon^{-2})$-round algorithms for testing triangle freeness by
+Censor-Hillel et al. (DISC 2016) and for testing excluded graphs of size $4$ by
+Fraigniaud et al. (DISC 2016).
+In the last part we generalize the global tester by Iwama and Yoshida (ITCS
+2014) of testing $k$-path freeness to testing the exclusion of any tree of
+order $k$. We then show how to simulate this algorithm in the CONGEST-model in
+$O(k^{k^2+1}\cdot\epsilon^{-k})$ rounds.
+",1,0,0,0,0,0
+9756,Reflexive Regular Equivalence for Bipartite Data,"  Bipartite data is common in data engineering and brings unique challenges,
+particularly when it comes to clustering tasks that impose on strong structural
+assumptions. This work presents an unsupervised method for assessing similarity
+in bipartite data. Similar to some co-clustering methods, the method is based
+on regular equivalence in graphs. The algorithm uses spectral properties of a
+bipartite adjacency matrix to estimate similarity in both dimensions. The
+method is reflexive in that similarity in one dimension is used to inform
+similarity in the other. Reflexive regular equivalence can also use the
+structure of transitivities -- in a network sense -- the contribution of which
+is controlled by the algorithm's only free-parameter, $\alpha$. The method is
+completely unsupervised and can be used to validate assumptions of
+co-similarity, which are required but often untested, in co-clustering
+analyses. Three variants of the method with different normalizations are tested
+on synthetic data. The method is found to be robust to noise and well-suited to
+asymmetric co-similar structure, making it particularly informative for cluster
+analysis and recommendation in bipartite data of unknown structure. In
+experiments, the convergence and speed of the algorithm are found to be stable
+for different levels of noise. Real-world data from a network of malaria genes
+are analyzed, where the similarity produced by the reflexive method is shown to
+out-perform other measures' ability to correctly classify genes.
+",1,0,0,1,0,0
+9757,Structural and magnetic properties of core-shell Au/Fe3O4 nanoparticles,"  We present a systematic study of core-shell Au/Fe_3O_4 nanoparticles produced
+by thermal decomposition under mild conditions. The morphology and crystal
+structure of the nanoparticles revealed the presence of Au core of <d> =
+(6.9\pm 1.0) nm surrounded by Fe_3O_4 shell with a thickness of ~3.5 nm,
+epitaxially grown onto the Au core surface. The Au/Fe_3O_4 core-shell structure
+was demonstrated by high angle annular dark field scanning transmission
+electron microscopy analysis. The magnetite shell grown on top of the Au
+nanoparticle displayed a thermal blocking state at temperatures below T_B = 59
+K and a relaxed state well above T_B. Remarkably, an exchange bias effect was
+observed when cooling down the samples below room temperature under an external
+magnetic field. Moreover, the exchange bias field (H_{EX}) started to appear at
+T~40 K and its value increased by decreasing the temperature. This effect has
+been assigned to the interaction of spins located in the magnetically
+disordered regions (in the inner and outer surface of the Fe_3O_4 shell) and
+spins located in the ordered region of the Fe_3O_4 shell.
+",0,1,0,0,0,0
+9758,Boron-doped diamond,"  Boron-doped diamond undergoes an insulator-metal transition at some critical
+value (around 2.21 at %) of the dopand concentration. Here, we report a simple
+method for the calculation of its bulk modulus, based on the thermodynamical
+model, by Varotsos and Alexopoulos, that has been originally suggested for the
+interconnection between the defect formation parameters in solids and bulk
+properties. The results obtained at the doping level of 2.6 at %, which was
+later improved at the level 0.5 at %, are in agreement with the experimental
+values.
+",0,1,0,0,0,0
+9759,The kinematics of the white dwarf population from the SDSS DR12,"  We use the Sloan Digital Sky Survey Data Release 12, which is the largest
+available white dwarf catalog to date, to study the evolution of the
+kinematical properties of the population of white dwarfs in the Galactic disc.
+We derive masses, ages, photometric distances and radial velocities for all
+white dwarfs with hydrogen-rich atmospheres. For those stars for which proper
+motions from the USNO-B1 catalog are available the true three-dimensional
+components of the stellar space velocity are obtained. This subset of the
+original sample comprises 20,247 objects, making it the largest sample of white
+dwarfs with measured three-dimensional velocities. Furthermore, the volume
+probed by our sample is large, allowing us to obtain relevant kinematical
+information. In particular, our sample extends from a Galactocentric radial
+distance $R_{\rm G}=7.8$~kpc to 9.3~kpc, and vertical distances from the
+Galactic plane ranging from $Z=-0.5$~kpc to 0.5~kpc. We examine the mean
+components of the stellar three-dimensional velocities, as well as their
+dispersions with respect to the Galactocentric and vertical distances. We
+confirm the existence of a mean Galactocentric radial velocity gradient,
+$\partial\langle V_{\rm R}\rangle/\partial R_{\rm
+G}=-3\pm5$~km~s$^{-1}$~kpc$^{-1}$. We also confirm North-South differences in
+$\langle V_{\rm z}\rangle$. Specifically, we find that white dwarfs with $Z>0$
+(in the North Galactic hemisphere) have $\langle V_{\rm z}\rangle<0$, while the
+reverse is true for white dwarfs with $Z<0$. The age-velocity dispersion
+relation derived from the present sample indicates that the Galactic population
+of white dwarfs may have experienced an additional source of heating, which
+adds to the secular evolution of the Galactic disc.
+",0,1,0,0,0,0
+9760,Geometric tracking control of thrust vectoring UAVs,"  In this paper a geometric approach to the trajectory tracking control of
+Unmanned Aerial Vehicles with thrust vectoring capabilities is proposed. The
+control design is suitable for aerial systems that allow to effectively
+decouple position and orientation tracking tasks. The control problem is
+developed within the framework of geometric control theory on the group of
+rigid displacements SE(3), yielding a control law that is independent of any
+parametrization of the configuration space. The proposed design works seamlessy
+when the thrust vectoring capability is limited, by prioritizing position over
+orientation tracking. A characterization of the region of attraction and of the
+convergence properties is explicitly derived. Finally, a numerical example is
+presented to test the proposed control law. The generality of the control
+scheme can be exploited for a broad class of aerial vehicles.
+",1,0,1,0,0,0
+9761,Two scenarios of advective washing-out of localized convective patterns under frozen parametric disorder,"  The effect of spatial localization of states in distributed parameter systems
+under frozen parametric disorder is well known as the Anderson localization and
+thoroughly studied for the Schrödinger equation and linear dissipation-free
+wave equations. Some similar (or mimicking) phenomena can occur in dissipative
+systems such as the thermal convection ones. Specifically, many of these
+dissipative systems are governed by a modified Kuramoto-Sivashinsky equation,
+where the frozen spatial disorder of parameters has been reported to lead to
+excitation of localized patterns. Imposed advection in the modified
+Kuramoto-Sivashinsky equation can affect the localized patterns in a nontrivial
+way; it changes the localization properties and suppresses the pattern. The
+latter effect is considered in this paper by means of both numerical simulation
+and model reduction, which turns out to be useful for a comprehensive
+understanding of the bifurcation scenarios in the system. Two possible
+bifurcation scenarios of advective suppression (""washing-out"") of localized
+patterns are revealed and characterised.
+",0,1,0,0,0,0
+9762,Neural Models for Key Phrase Detection and Question Generation,"  We propose a two-stage neural model to tackle question generation from
+documents. First, our model estimates the probability that word sequences in a
+document are ones that a human would pick when selecting candidate answers by
+training a neural key-phrase extractor on the answers in a question-answering
+corpus. Predicted key phrases then act as target answers and condition a
+sequence-to-sequence question-generation model with a copy mechanism.
+Empirically, our key-phrase extraction model significantly outperforms an
+entity-tagging baseline and existing rule-based approaches. We further
+demonstrate that our question generation system formulates fluent, answerable
+questions from key phrases. This two-stage system could be used to augment or
+generate reading comprehension datasets, which may be leveraged to improve
+machine reading systems or in educational settings.
+",1,0,0,0,0,0
+9763,Radio Tomography for Roadside Surveillance,"  Radio tomographic imaging (RTI) has recently been proposed for tracking
+object location via radio waves without requiring the objects to transmit or
+receive radio signals. The position is extracted by inferring which voxels are
+obstructing a subset of radio links in a dense wireless sensor network. This
+paper proposes a variety of modeling and algorithmic improvements to RTI for
+the scenario of roadside surveillance. These include the use of a more
+physically motivated weight matrix, a method for mitigating negative
+(aphysical) data due to noisy observations, and a method for combining frames
+of a moving vehicle into a single image. The proposed approaches are used to
+show improvement in both imaging (useful for human-in-the-loop target
+recognition) and automatic target recognition in a measured data set.
+",1,0,0,0,0,0
+9764,But How Does It Work in Theory? Linear SVM with Random Features,"  We prove that, under low noise assumptions, the support vector machine with
+$N\ll m$ random features (RFSVM) can achieve the learning rate faster than
+$O(1/\sqrt{m})$ on a training set with $m$ samples when an optimized feature
+map is used. Our work extends the previous fast rate analysis of random
+features method from least square loss to 0-1 loss. We also show that the
+reweighted feature selection method, which approximates the optimized feature
+map, helps improve the performance of RFSVM in experiments on a synthetic data
+set.
+",0,0,0,1,0,0
+9765,Adaptive Bayesian nonparametric regression using kernel mixture of polynomials with application to partial linear model,"  We propose a kernel mixture of polynomials prior for Bayesian nonparametric
+regression. The regression function is modeled by local averages of polynomials
+with kernel mixture weights. We obtain the minimax-optimal rate of contraction
+of the full posterior distribution up to a logarithmic factor that adapts to
+the smoothness level of the true function by estimating metric entropies of
+certain function classes. We also provide a frequentist sieve maximum
+likelihood estimator with a near-optimal convergence rate. We further
+investigate the application of the kernel mixture of polynomials to the partial
+linear model and obtain both the near-optimal rate of contraction for the
+nonparametric component and the Bernstein-von Mises limit (i.e., asymptotic
+normality) of the parametric component. The proposed method is illustrated with
+numerical examples and shows superior performance in terms of computational
+efficiency, accuracy, and uncertainty quantification compared to the local
+polynomial regression, DiceKriging, and the robust Gaussian stochastic process.
+",0,0,1,1,0,0
+9766,Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing,"  Mobile edge computing is a new computing paradigm, which pushes cloud
+computing capabilities away from the centralized cloud to the network edge.
+However, with the sinking of computing capabilities, the new challenge incurred
+by user mobility arises: since end-users typically move erratically, the
+services should be dynamically migrated among multiple edges to maintain the
+service performance, i.e., user-perceived latency. Tackling this problem is
+non-trivial since frequent service migration would greatly increase the
+operational cost. To address this challenge in terms of the performance-cost
+trade-off, in this paper we study the mobile edge service performance
+optimization problem under long-term cost budget constraint. To address user
+mobility which is typically unpredictable, we apply Lyapunov optimization to
+decompose the long-term optimization problem into a series of real-time
+optimization problems which do not require a priori knowledge such as user
+mobility. As the decomposed problem is NP-hard, we first design an
+approximation algorithm based on Markov approximation to seek a near-optimal
+solution. To make our solution scalable and amenable to future 5G application
+scenario with large-scale user devices, we further propose a distributed
+approximation scheme with greatly reduced time complexity, based on the
+technique of best response update. Rigorous theoretical analysis and extensive
+evaluations demonstrate the efficacy of the proposed centralized and
+distributed schemes.
+",1,0,0,0,0,0
+9767,Assessing student's achievement gap between ethnic groups in Brazil,"  Achievement gaps refer to the difference in the performance on examinations
+of students belonging to different social groups. Achievement gaps between
+ethnic groups have been observed in several countries with heterogeneous
+populations. In this paper, we analyze achievement gaps between ethnic
+populations in Brazil by studying the performance of a large cohort of senior
+high-school students in a standardized national exam. We separate ethnic groups
+into the Brazilian states to remove potential biases associated to
+infrastructure and financial resources, cultural background and ethnic
+clustering. We focus on the disciplines of mathematics and writing that involve
+different cognitive functions. We estimate the gaps and their statistical
+significance through the Welch's t-test and study key socio-economic variables
+that may explain the existence or absence of gaps. We identify that gaps
+between ethnic groups are either statistically insignificant (p<.01) or small
+(2%-6%) if statistically significant, for students living in households with
+low income. Increasing gaps however may be observed for higher income. On the
+other hand, while higher parental education is associated to higher
+performance, it may either increase, decrease or maintain the gaps between
+White and Black, and between White and Pardo students. Our results support that
+socio-economic variables have major impact on student's performance in both
+mathematics and writing examinations irrespectively of ethnic backgrounds,
+giving evidence that genetic factors have little or no effect on ethnic group
+performance when students are exposed to similar cultural and financial
+contexts.
+",0,0,0,1,0,0
+9768,SONS: The JCMT legacy survey of debris discs in the submillimetre,"  Debris discs are evidence of the ongoing destructive collisions between
+planetesimals, and their presence around stars also suggests that planets exist
+in these systems. In this paper, we present submillimetre images of the thermal
+emission from debris discs that formed the SCUBA-2 Observations of Nearby Stars
+(SONS) survey, one of seven legacy surveys undertaken on the James Clerk
+Maxwell telescope between 2012 and 2015. The overall results of the survey are
+presented in the form of 850 microns (and 450 microns, where possible) images
+and fluxes for the observed fields. Excess thermal emission, over that expected
+from the stellar photosphere, is detected around 49 stars out of the 100
+observed fields. The discs are characterised in terms of their flux density,
+size (radial distribution of the dust) and derived dust properties from their
+spectral energy distributions. The results show discs over a range of sizes,
+typically 1-10 times the diameter of the Edgeworth-Kuiper Belt in our Solar
+System. The mass of a disc, for particles up to a few millimetres in size, is
+uniquely obtainable with submillimetre observations and this quantity is
+presented as a function of the host stars' age, showing a tentative decline in
+mass with age. Having doubled the number of imaged discs at submillimetre
+wavelengths from ground-based, single dish telescope observations, one of the
+key legacy products from the SONS survey is to provide a comprehensive target
+list to observe at high angular resolution using submillimetre/millimetre
+interferometers (e.g., ALMA, SMA).
+",0,1,0,0,0,0
+9769,Visual Search at eBay,"  In this paper, we propose a novel end-to-end approach for scalable visual
+search infrastructure. We discuss the challenges we faced for a massive
+volatile inventory like at eBay and present our solution to overcome those. We
+harness the availability of large image collection of eBay listings and
+state-of-the-art deep learning techniques to perform visual search at scale.
+Supervised approach for optimized search limited to top predicted categories
+and also for compact binary signature are key to scale up without compromising
+accuracy and precision. Both use a common deep neural network requiring only a
+single forward inference. The system architecture is presented with in-depth
+discussions of its basic components and optimizations for a trade-off between
+search relevance and latency. This solution is currently deployed in a
+distributed cloud infrastructure and fuels visual search in eBay ShopBot and
+Close5. We show benchmark on ImageNet dataset on which our approach is faster
+and more accurate than several unsupervised baselines. We share our learnings
+with the hope that visual search becomes a first class citizen for all large
+scale search engines rather than an afterthought.
+",1,0,0,0,0,0
+9770,Isomorphism and classification for countable structures,"  We introduce a topology on the space of all isomorphism types represented in
+a given class of countable models, and use this topology as an aid in
+classifying the isomorphism types. This mixes ideas from effective descriptive
+set theory and computable structure theory, extending concepts from the latter
+beyond computable structures to examine the isomorphism problem on arbitrary
+countable structures. We give examples using specific classes of fields and of
+trees, illustrating how the new concepts can yield classifications that reveal
+differences between seemingly similar classes. Finally, we use a computable
+homeomorphism to define a measure on the space of isomorphism types of
+algebraic fields, and examine the prevalence of relative computable
+categoricity under this measure.
+",0,0,1,0,0,0
+9771,Complex Networks Unveiling Spatial Patterns in Turbulence,"  Numerical and experimental turbulence simulations are nowadays reaching the
+size of the so-called big data, thus requiring refined investigative tools for
+appropriate statistical analyses and data mining. We present a new approach
+based on the complex network theory, offering a powerful framework to explore
+complex systems with a huge number of interacting elements. Although interest
+on complex networks has been increasing in the last years, few recent studies
+have been applied to turbulence. We propose an investigation starting from a
+two-point correlation for the kinetic energy of a forced isotropic field
+numerically solved. Among all the metrics analyzed, the degree centrality is
+the most significant, suggesting the formation of spatial patterns which
+coherently move with similar vorticity over the large eddy turnover time scale.
+Pattern size can be quantified through a newly-introduced parameter (i.e.,
+average physical distance) and varies from small to intermediate scales. The
+network analysis allows a systematic identification of different spatial
+regions, providing new insights into the spatial characterization of turbulent
+flows. Based on present findings, the application to highly inhomogeneous flows
+seems promising and deserves additional future investigation.
+",0,1,0,0,0,0
+9772,Convexity in scientific collaboration networks,"  Convexity in a network (graph) has been recently defined as a property of
+each of its subgraphs to include all shortest paths between the nodes of that
+subgraph. It can be measured on the scale [0, 1] with 1 being assigned to fully
+convex networks. The largest convex component of a graph that emerges after the
+removal of the least number of edges is called a convex skeleton. It is
+basically a tree of cliques, which has been shown to have many interesting
+features. In this article the notions of convexity and convex skeletons in the
+context of scientific collaboration networks are discussed. More specifically,
+we analyze the co-authorship networks of Slovenian researchers in computer
+science, physics, sociology, mathematics, and economics and extract convex
+skeletons from them. We then compare these convex skeletons with the residual
+graphs (remainders) in terms of collaboration frequency distributions by
+various parameters such as the publication year and type, co-authors' birth
+year, status, gender, discipline, etc. We also show the top-ranked scientists
+by four basic centrality measures as calculated on the original networks and
+their skeletons and conclude that convex skeletons may help detect influential
+scholars that are hardly identifiable in the original collaboration network. As
+their inherent feature, convex skeletons retain the properties of collaboration
+networks. These include high-level structural properties but also the fact that
+the same authors are highlighted by centrality measures. Moreover, the most
+important ties and thus the most important collaborations are retained in the
+skeletons.
+",1,0,0,0,0,0
+9773,Contact Localization through Spatially Overlapping Piezoresistive Signals,"  Achieving high spatial resolution in contact sensing for robotic manipulation
+often comes at the price of increased complexity in fabrication and
+integration. One traditional approach is to fabricate a large number of taxels,
+each delivering an individual, isolated response to a stimulus. In contrast, we
+propose a method where the sensor simply consists of a continuous volume of
+piezoresistive elastomer with a number of electrodes embedded inside. We
+measure piezoresistive effects between all pairs of electrodes in the set, and
+count on this rich signal set containing the information needed to pinpoint
+contact location with high accuracy using regression algorithms. In our
+validation experiments, we demonstrate submillimeter median accuracy in
+locating contact on a 10mm by 16mm sensor using only four electrodes (creating
+six unique pairs). In addition to extracting more information from fewer wires,
+this approach lends itself to simple fabrication methods and makes no
+assumptions about the underlying geometry, simplifying future integration on
+robot fingers.
+",1,0,0,0,0,0
+9774,On the Parallel Parameterized Complexity of the Graph Isomorphism Problem,"  In this paper, we study the parallel and the space complexity of the graph
+isomorphism problem (\GI{}) for several parameterizations. Let
+$\mathcal{H}=\{H_1,H_2,\cdots,H_l\}$ be a finite set of graphs where
+$|V(H_i)|\leq d$ for all $i$ and for some constant $d$. Let $\mathcal{G}$ be an
+$\mathcal{H}$-free graph class i.e., none of the graphs $G\in \mathcal{G}$
+contain any $H \in \mathcal{H}$ as an induced subgraph. We show that \GI{}
+parameterized by vertex deletion distance to $\mathcal{G}$ is in a
+parameterized version of $\AC^1$, denoted $\PL$-$\AC^1$, provided the colored
+graph isomorphism problem for graphs in $\mathcal{G}$ is in $\AC^1$. From this,
+we deduce that \GI{} parameterized by the vertex deletion distance to cographs
+is in $\PL$-$\AC^1$.
+The parallel parameterized complexity of \GI{} parameterized by the size of a
+feedback vertex set remains an open problem. Towards this direction we show
+that the graph isomorphism problem is in $\PL$-$\TC^0$ when parameterized by
+vertex cover or by twin-cover.
+Let $\mathcal{G}'$ be a graph class such that recognizing graphs from
+$\mathcal{G}'$ and the colored version of \GI{} for $\mathcal{G}'$ is in
+logspace ($\L$). We show that \GI{} for bounded vertex deletion distance to
+$\mathcal{G}'$ is in $\L$. From this, we obtain logspace algorithms for \GI{}
+for graphs with bounded vertex deletion distance to interval graphs and graphs
+with bounded vertex deletion distance to cographs.
+",1,0,0,0,0,0
+9775,Direct measurement of superdiffusive and subdiffusive energy transport in disordered granular chains,"  The study of energy transport properties in heterogeneous materials has
+attracted scientific interest for more than a century, and it continues to
+offer fundamental and rich questions. One of the unanswered challenges is to
+extend Anderson theory for uncorrelated and fully disordered lattices in
+condensed-matter systems to physical settings in which additional effects
+compete with disorder. Specifically, the effect of strong nonlinearity has been
+largely unexplored experimentally, partly due to the paucity of testbeds that
+can combine the effect of disorder and nonlinearity in a controllable manner.
+Here we present the first systematic experimental study of energy transport and
+localization properties in simultaneously disordered and nonlinear granular
+crystals. We demonstrate experimentally that disorder and nonlinearity ---
+which are known from decades of studies to individually favor energy
+localization --- can in some sense ""cancel each other out"", resulting in the
+destruction of wave localization. We also report that the combined effect of
+disorder and nonlinearity can enable the manipulation of energy transport speed
+in granular crystals from subdiffusive to superdiffusive ranges.
+",0,1,0,0,0,0
+9776,Reading the Sky and The Spiral of Teaching and Learning in Astronomy,"  This theoretical paper introduces a new way to view and characterize teaching
+and learning astronomy. It describes a framework, based on results from
+empirical data, analyzed through standard qualitative research methodology, in
+which a theoretical model for vital competencies of learning astronomy is
+proposed: Reading the Sky. This model takes into account not only disciplinary
+knowledge but also disciplinary discernment and extrapolating
+three-dimensionality. Together, these constitute the foundation for the
+competency referred to as Reading the Sky. In this paper, I describe these
+concepts and how I see them being connected and intertwined to form a new
+competency model for learning astronomy and how this can be used to inform
+astronomy education to better match the challenges students face when entering
+the discipline of astronomy: The Spiral of Teaching and Learning. Two examples
+are presented to highlight how this model can be used in teaching situations.
+",0,1,0,0,0,0
+9777,Emergence of grid-like representations by training recurrent neural networks to perform spatial localization,"  Decades of research on the neural code underlying spatial navigation have
+revealed a diverse set of neural response properties. The Entorhinal Cortex
+(EC) of the mammalian brain contains a rich set of spatial correlates,
+including grid cells which encode space using tessellating patterns. However,
+the mechanisms and functional significance of these spatial representations
+remain largely mysterious. As a new way to understand these neural
+representations, we trained recurrent neural networks (RNNs) to perform
+navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find
+that grid-like spatial response patterns emerge in trained networks, along with
+units that exhibit other spatial correlates, including border cells and
+band-like cells. All these different functional types of neurons have been
+observed experimentally. The order of the emergence of grid-like and border
+cells is also consistent with observations from developmental studies.
+Together, our results suggest that grid cells, border cells and others as
+observed in EC may be a natural solution for representing space efficiently
+given the predominant recurrent connections in the neural circuits.
+",0,0,0,1,1,0
+9778,Random walks on the discrete affine group,"  We introduce the discrete affine group of a regular tree as a finitely
+generated subgroup of the affine group. We describe the Poisson boundary of
+random walks on it as a space of configurations. We compute isoperimetric
+profile and Hilbert compression exponent of the group. We also discuss metric
+relationship with some lamplighter groups and lamplighter graphs.
+",0,0,1,0,0,0
+9779,Spectroscopic evidence of odd frequency superconducting order,"  Spin filter superconducting S/I/N tunnel junctions (NbN/GdN/TiN) show a
+robust and pronounced zero bias conductance peak at low temperatures, the
+magnitude of which is several times the normal state conductance of the
+junction. Such a conductance anomaly is representative of unconventional
+superconductivity and is interpreted as a direct signature of an odd frequency
+superconducting order.
+",0,1,0,0,0,0
+9780,Double spend races,"  We correct the double spend race analysis given in Nakamoto's foundational
+Bitcoin article and give a closed-form formula for the probability of success
+of a double spend attack using the Regularized Incomplete Beta Function. We
+give a proof of the exponential decay on the number of confirmations, often
+cited in the literature, and find an asymptotic formula. Larger number of
+confirmations are necessary compared to those given by Nakamoto. We also
+compute the probability conditional to the known validation time of the blocks.
+This provides a finer risk analysis than the classical one.
+",1,0,1,0,0,0
+9781,An Asymptotic Analysis of Queues with Delayed Information and Time Varying Arrival Rates,"  Understanding how delayed information impacts queueing systems is an
+important area of research. However, much of the current literature neglects
+one important feature of many queueing systems, namely non-stationary arrivals.
+Non-stationary arrivals model the fact that customers tend to access services
+during certain times of the day and not at a constant rate. In this paper, we
+analyze two two-dimensional deterministic fluid models that incorporate
+customer choice behavior based on delayed queue length information with time
+varying arrivals. In the first model, customers receive queue length
+information that is delayed by a constant Delta. In the second model, customers
+receive information about the queue length through a moving average of the
+queue length where the moving average window is Delta. We analyze the impact of
+the time varying arrival rate and show using asymptotic analysis that the time
+varying arrival rate does not impact the critical delay unless the frequency of
+the time varying arrival rate is twice that of the critical delay. When the
+frequency of the arrival rate is twice that of the critical delay, then the
+stability is enlarged by a wedge that is determined by the model parameters. As
+a result, this problem allows us to combine the theory of nonlinear dynamics,
+parametric excitation, delays, and time varying queues together to provide
+insight on the impact of information in queueing systems.
+",0,0,1,0,0,0
+9782,Effect of mixed pinning landscapes produced by 6 MeV Oxygen irradiation on the resulting critical current densities J$_c$ in 1.3 $μ$m thick GdBa$_2$Cu$_3$O$_{7-d}$ coated conductors grown by co-evaporation,"  We report the influence of crystalline defects introduced by 6 MeV
+$^{16}$O$^{3+}$ irradiation on the critical current densities J$_c$ and flux
+creep rates in 1.3 $\mu$m thick GdBa$_2$Cu$_3$O$_{7-d}$ coated conductor
+produced by co-evaporation. Pristine films with pinning produced mainly by
+random nanoparticles with diameter close to 50 nm were irradiated with doses
+between 2x10$^{13}$ cm$^{-2}$ and 4x10$^{14}$ cm$^{-2}$. At temperatures below
+40 K with the magnetic field applied parallel (H//c) and at 45°
+(H//45°) to the c-axis, the in-field J$_c$ dependences can be
+significantly improved by irradiation. For doses of 1x10$^{14}$ cm$^{-2}$ the
+J$_c$ values at $\mu$$_0$H = 5 T are doubled without affecting significantly
+the J$_c$ at small fields. Analyzing the flux creep rates as function of the
+temperature in both magnetic field configurations, it can be observed that the
+irradiation suppresses the peak associated with double-kink relaxation and
+increases the flux creep rates at intermediate and high temperatures. Under 0.5
+T, the flux relaxation for H//c and H//45° in pristine films presents
+characteristic glassy exponents $\mu$ = 1.63 and $\mu$ = 1.45, respectively.
+For samples irradiated with 1x10$^{14}$ cm$^{-2}$, these values drop to $\mu$ =
+1.45 and $\mu$ =1.24, respectively.
+",0,1,0,0,0,0
+9783,Merge or Not? Learning to Group Faces via Imitation Learning,"  Given a large number of unlabeled face images, face grouping aims at
+clustering the images into individual identities present in the data. This task
+remains a challenging problem despite the remarkable capability of deep
+learning approaches in learning face representation. In particular, grouping
+results can still be egregious given profile faces and a large number of
+uninteresting faces and noisy detections. Often, a user needs to correct the
+erroneous grouping manually. In this study, we formulate a novel face grouping
+framework that learns clustering strategy from ground-truth simulated behavior.
+This is achieved through imitation learning (a.k.a apprenticeship learning or
+learning by watching) via inverse reinforcement learning (IRL). In contrast to
+existing clustering approaches that group instances by similarity, our
+framework makes sequential decision to dynamically decide when to merge two
+face instances/groups driven by short- and long-term rewards. Extensive
+experiments on three benchmark datasets show that our framework outperforms
+unsupervised and supervised baselines.
+",1,0,0,0,0,0
+9784,Entropic Causality and Greedy Minimum Entropy Coupling,"  We study the problem of identifying the causal relationship between two
+discrete random variables from observational data. We recently proposed a novel
+framework called entropic causality that works in a very general functional
+model but makes the assumption that the unobserved exogenous variable has small
+entropy in the true causal direction.
+This framework requires the solution of a minimum entropy coupling problem:
+Given marginal distributions of m discrete random variables, each on n states,
+find the joint distribution with minimum entropy, that respects the given
+marginals. This corresponds to minimizing a concave function of nm variables
+over a convex polytope defined by nm linear constraints, called a
+transportation polytope. Unfortunately, it was recently shown that this minimum
+entropy coupling problem is NP-hard, even for 2 variables with n states. Even
+representing points (joint distributions) over this space can require
+exponential complexity (in n, m) if done naively.
+In our recent work we introduced an efficient greedy algorithm to find an
+approximate solution for this problem. In this paper we analyze this algorithm
+and establish two results: that our algorithm always finds a local minimum and
+also is within an additive approximation error from the unknown global optimum.
+",1,0,0,1,0,0
+9785,When few survive to tell the tale: thymus and gonad as auditioning organs: historical overview,"  Unlike other organs, the thymus and gonads generate non-uniform cell
+populations, many members of which perish, and a few survive. While it is
+recognized that thymic cells are 'audited' to optimize an organism's immune
+repertoire, whether gametogenesis could be orchestrated similarly to favour
+high quality gametes is uncertain. Ideally, such quality would be affirmed at
+early stages before the commitment of extensive parental resources. A case is
+here made that, along the lines of a previously proposed lymphocyte quality
+control mechanism, gamete quality can be registered indirectly through
+detection of incompatibilities between proteins encoded by the grandparental
+DNA sequences within the parent from which haploid gametes are meiotically
+derived. This 'stress test' is achieved in the same way that thymic screening
+for potential immunological incompatibilities is achieved - by 'promiscuous'
+expression, under the influence of the AIRE protein, of the products of genes
+that are not normally specific for that organ. Consistent with this, the Aire
+gene is expressed in both thymus and gonads, and AIRE deficiency impedes
+function in both organs. While not excluding the subsequent emergence of hybrid
+incompatibilities due to the intermixing of genomic sequences from parents
+(rather than grandparents), many observations, such as the number of proteins
+that are aberrantly expressed during gametogenesis, can be explained on this
+basis. Indeed, promiscuous expression could have first evolved in
+gamete-forming cells where incompatible proteins would be manifest as aberrant
+protein aggregates that cause apoptosis. This mechanism would later have been
+co-opted by thymic epithelial cells which display peptides from aggregates to
+remove potentially autoreactive T cells.
+",0,0,0,0,1,0
+9786,SfMLearner++: Learning Monocular Depth & Ego-Motion using Meaningful Geometric Constraints,"  Most geometric approaches to monocular Visual Odometry (VO) provide robust
+pose estimates, but sparse or semi-dense depth estimates. Off late, deep
+methods have shown good performance in generating dense depths and VO from
+monocular images by optimizing the photometric consistency between images.
+Despite being intuitive, a naive photometric loss does not ensure proper pixel
+correspondences between two views, which is the key factor for accurate depth
+and relative pose estimations. It is a well known fact that simply minimizing
+such an error is prone to failures.
+We propose a method using Epipolar constraints to make the learning more
+geometrically sound. We use the Essential matrix, obtained using Nister's Five
+Point Algorithm, for enforcing meaningful geometric constraints on the loss,
+rather than using it as labels for training. Our method, although simplistic
+but more geometrically meaningful, using lesser number of parameters, gives a
+comparable performance to state-of-the-art methods which use complex losses and
+large networks showing the effectiveness of using epipolar constraints. Such a
+geometrically constrained learning method performs successfully even in cases
+where simply minimizing the photometric error would fail.
+",1,0,0,0,0,0
+9787,Approximate Bayesian inference as a gauge theory,"  In a published paper [Sengupta, 2016], we have proposed that the brain (and
+other self-organized biological and artificial systems) can be characterized
+via the mathematical apparatus of a gauge theory. The picture that emerges from
+this approach suggests that any biological system (from a neuron to an
+organism) can be cast as resolving uncertainty about its external milieu,
+either by changing its internal states or its relationship to the environment.
+Using formal arguments, we have shown that a gauge theory for neuronal dynamics
+-- based on approximate Bayesian inference -- has the potential to shed new
+light on phenomena that have thus far eluded a formal description, such as
+attention and the link between action and perception. Here, we describe the
+technical apparatus that enables such a variational inference on manifolds.
+Particularly, the novel contribution of this paper is an algorithm that utlizes
+a Schild's ladder for parallel transport of sufficient statistics (means,
+covariances, etc.) on a statistical manifold.
+",1,0,0,0,0,0
+9788,DROPWAT: an Invisible Network Flow Watermark for Data Exfiltration Traceback,"  Watermarking techniques have been proposed during the last 10 years as an
+approach to trace network flows for intrusion detection purposes. These
+techniques aim to impress a hidden signature on a traffic flow. A central
+property of network flow watermarking is invisibility, i.e., the ability to go
+unidentified by an unauthorized third party. Although widely sought after, the
+development of an invisible watermark is a challenging task that has not yet
+been accomplished.
+In this paper we take a step forward in addressing the invisibility problem
+with DROPWAT, an active network flow watermarking technique developed for
+tracing Internet flows directed to the staging server that is the final
+destination in a data exfiltration attack, even in the presence of several
+intermediate stepping stones or an anonymous network. DROPWAT is a timing-based
+technique that indirectly modifies interpacket delays by exploiting network
+reaction to packet loss. We empirically demonstrate that the watermark embedded
+by means of DROPWAT is invisible to a third party observing the watermarked
+traffic. We also validate DROPWAT and analyze its performance in a controlled
+experimental framework involving the execution of a series of experiments on
+the Internet, using Web proxy servers as stepping stones executed on several
+instances in Amazon Web Services, as well as the TOR anonymous network in the
+place of the stepping stones. Our results show that the detection algorithm is
+able to identify an embedded watermark achieving over 95% accuracy while being
+invisible.
+",1,0,0,0,0,0
+9789,Outage Analysis of Offloading in Heterogeneous Networks: Composite Fading Channels,"  Small cells deployment is one of the most significant long-term strategic
+policies of the mobile network operators. In heterogeneous networks (HetNets),
+small cells serve as offloading spots in the radio access network to offload
+macro users (MUs) and their associated traffic from congested macrocells. In
+this paper, we perform analytical analysis and investigate how the radio
+propagation effects such as multipath and shadowing and small cell base station
+density affect MUs' offloading to small cell network (SCN). In particular, we
+exploit composite fading channels in our evaluation when an MU is offloaded to
+SCN with varying small and macro cell densities in the stochastic HetNets
+framework. We derive the expressions for outage probability (equivalently
+success probability) of the MU in macro network and SCN for two different
+cases, viz.: i) Nakagami-lognormal channel fading; ii) time-shared (combined)
+shadowed/unshadowed channel fading. We propose efficient approximations for the
+probability density functions of the channel fading (power) for the
+above-mentioned fading distributions that do not have closed-form expressions
+employing Gauss-Hermite integration and finite exponential series,
+respectively. Finally, the outage probability performance of MU with and
+without offloading options/services is analyzed for various settings of fading
+channels.
+",1,0,0,0,0,0
+9790,StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks,"  Although Generative Adversarial Networks (GANs) have shown remarkable success
+in various tasks, they still face challenges in generating high quality images.
+In this paper, we propose Stacked Generative Adversarial Networks (StackGAN)
+aiming at generating high-resolution photo-realistic images. First, we propose
+a two-stage generative adversarial network architecture, StackGAN-v1, for
+text-to-image synthesis. The Stage-I GAN sketches the primitive shape and
+colors of the object based on given text description, yielding low-resolution
+images. The Stage-II GAN takes Stage-I results and text descriptions as inputs,
+and generates high-resolution images with photo-realistic details. Second, an
+advanced multi-stage generative adversarial network architecture, StackGAN-v2,
+is proposed for both conditional and unconditional generative tasks. Our
+StackGAN-v2 consists of multiple generators and discriminators in a tree-like
+structure; images at multiple scales corresponding to the same scene are
+generated from different branches of the tree. StackGAN-v2 shows more stable
+training behavior than StackGAN-v1 by jointly approximating multiple
+distributions. Extensive experiments demonstrate that the proposed stacked
+generative adversarial networks significantly outperform other state-of-the-art
+methods in generating photo-realistic images.
+",1,0,0,1,0,0
+9791,Towards Secure and Safe Appified Automated Vehicles,"  The advancement in Autonomous Vehicles (AVs) has created an enormous market
+for the development of self-driving functionalities,raising the question of how
+it will transform the traditional vehicle development process. One adventurous
+proposal is to open the AV platform to third-party developers, so that AV
+functionalities can be developed in a crowd-sourcing way, which could provide
+tangible benefits to both automakers and end users. Some pioneering companies
+in the automotive industry have made the move to open the platform so that
+developers are allowed to test their code on the road. Such openness, however,
+brings serious security and safety issues by allowing untrusted code to run on
+the vehicle. In this paper, we introduce the concept of an Appified AV platform
+that opens the development framework to third-party developers. To further
+address the safety challenges, we propose an enhanced appified AV design schema
+called AVGuard, which focuses primarily on mitigating the threats brought about
+by untrusted code, leveraging theory in the vehicle evaluation field, and
+conducting program analysis techniques in the cybersecurity area. Our study
+provides guidelines and suggested practice for the future design of open AV
+platforms.
+",1,0,0,0,0,0
+9792,Equidimensional adic eigenvarieties for groups with discrete series,"  We extend Urban's construction of eigenvarieties for reductive groups $G$
+such that $G(\mathbb{R})$ has discrete series to include characteristic $p$
+points at the boundary of weight space. In order to perform this construction,
+we define a notion of ""locally analytic"" functions and distributions on a
+locally $\mathbb{Q}_p$-analytic manifold taking values in a complete Tate
+$\mathbb{Z}_p$-algebra in which $p$ is not necessarily invertible. Our
+definition agrees with the definition of locally analytic distributions on
+$p$-adic Lie groups given by Johansson and Newton.
+",0,0,1,0,0,0
+9793,Latent Association Mining in Binary Data,"  We consider the problem of identifying groups of mutually associated
+variables in moderate or high dimensional data. In many cases, ordinary Pearson
+correlation provides useful information concerning the linear relationship
+between variables. However, for binary data, ordinary correlation may lose
+power and may lack interpretability. In this paper, we develop and investigate
+a new method called Latent Association Mining in Binary Data (LAMB). The LAMB
+method is built on the assumption that the binary observations represent a
+random thresholding of a latent continuous variable that may have a complex
+correlation structure. We consider a new measure of association, latent
+correlation, that is designed to assess association in the underlying
+continuous variable, without bias due to the mediating effects of the
+thresholding procedure. The full LAMB procedure makes use of iterative
+hypothesis testing to identify groups of latently correlated variables. LAMB is
+shown to improve power over existing methods in simulated settings, to be
+computationally efficient for large datasets, and to uncover new meaningful
+results from common real data types.
+",0,0,0,1,0,0
+9794,The Supernova -- Supernova Remnant Connection,"  Many aspects of the progenitor systems, environments, and explosion dynamics
+of the various subtypes of supernovae are difficult to investigate at
+extragalactic distances where they are observed as unresolved sources.
+Alternatively, young supernova remnants in our own galaxy and in the Large and
+Small Magellanic Clouds offer opportunities to resolve, measure, and track
+expanding stellar ejecta in fine detail, but the handful that are known exhibit
+widely different properties that reflect the diversity of their parent
+explosions and local circumstellar and interstellar environments. A way of
+complementing both supernova and supernova remnant research is to establish
+strong empirical links between the two separate stages of stellar explosions.
+Here we briefly review recent progress in the development of
+supernova---supernova remnant connections, paying special attention to
+connections made through the study of ""middle-aged"" (10-100 yr) supernovae and
+young (< 1000 yr) supernova remnants. We highlight how this approach can
+uniquely inform several key areas of supernova research, including the origins
+of explosive mixing, high-velocity jets, and the formation of dust in the
+ejecta.
+",0,1,0,0,0,0
+9795,DSVO: Direct Stereo Visual Odometry,"  This paper proposes a novel approach to stereo visual odometry without stereo
+matching. It is particularly robust in scenes of repetitive high-frequency
+textures. Referred to as DSVO (Direct Stereo Visual Odometry), it operates
+directly on pixel intensities, without any explicit feature matching, and is
+thus efficient and more accurate than the state-of-the-art
+stereo-matching-based methods. It applies a semi-direct monocular visual
+odometry running on one camera of the stereo pair, tracking the camera pose and
+mapping the environment simultaneously; the other camera is used to optimize
+the scale of monocular visual odometry. We evaluate DSVO in a number of
+challenging scenes to evaluate its performance and present comparisons with the
+state-of-the-art stereo visual odometry algorithms.
+",1,0,0,0,0,0
+9796,Urban Analytics: Multiplexed and Dynamic Community Networks,"  In the past decade, cities have experienced rapid growth, expansion, and
+changes in their community structure. Many aspects of critical urban
+infrastructure are closely coupled with the human communities that they serve.
+Urban communities are composed of a multiplex of overlapping factors which can
+be distinguished into cultural, religious, social-economic, political, and
+geographical layers. In this paper, we review how increasingly available
+heterogeneous mobile big data sets can be leveraged to detect the community
+interaction structure using natural language processing and machine learning
+techniques. A number of community layer and interaction detection algorithms
+are then reviewed, with a particular focus on robustness, stability, and
+causality of evolving communities. The better understanding of the structural
+dynamics and multiplexed relationships can provide useful information to inform
+both urban planning policies and shape the design of socially coupled urban
+infrastructure systems.
+",1,1,0,0,0,0
+9797,A quantum phase transition induced by a microscopic boundary condition,"  Quantum phase transitions are sudden changes in the ground-state wavefunction
+of a many-body system that can occur as a control parameter such as a
+concentration or a field strength is varied. They are driven purely by the
+competition between quantum fluctuations and mutual interactions among
+constituents of the system, not by thermal fluctuations; hence they can occur
+even at zero temperature. Examples of quantum phase transitions in many-body
+physics may be found in systems ranging from high-temperature superconductors
+to topological insulators. A quantum phase transition usually can be
+characterized by nonanalyticity/discontinuity in certain order parameters or
+divergence of the ground state energy eigenvalue and/or its derivatives with
+respect to certain physical quantities. Here in a circular one-dimensional spin
+model with Heisenberg XY interaction and no magnetic field, we observe critical
+phenomena for the $n_0=1/N\rightarrow0$ Mott insulator caused by a qualitative
+change of the boundary condition. We demonstrate in the vicinity of the
+transition point a sudden change in ground-state properties accompanied by an
+avoided level-crossing between the ground and the first excited states.
+Notably, our result links conventional quantum phase transitions to microscopic
+boundary conditions, with significant implications for quantum information,
+quantum control, and quantum computing.
+",0,1,0,0,0,0
+9798,Asymptotic Goodness-of-Fit Tests for Point Processes Based on Scaled Empirical K-Functions,"  We study sequences of scaled edge-corrected empirical (generalized)
+K-functions (modifying Ripley's K-function) each of them constructed from a
+single observation of a $d$-dimensional fourth-order stationary point process
+in a sampling window W_n which grows together with some scaling rate
+unboundedly as n --> infty. Under some natural assumptions it is shown that the
+normalized difference between scaled empirical and scaled theoretical
+K-function converges weakly to a mean zero Gaussian process with simple
+covariance function. This result suggests discrepancy measures between
+empirical and theoretical K-function with known limit distribution which allow
+to perform goodness-of-fit tests for checking a hypothesized point process
+based only on its intensity and (generalized) K-function. Similar test
+statistics are derived for testing the hypothesis that two independent point
+processes in W_n have the same distribution without explicit knowledge of their
+intensities and K-functions.
+",0,0,1,1,0,0
+9799,Effects of Arrival Type and Degree of Saturation on Queue Length Estimation at Signalized Intersections,"  Purpose of this study is evaluation of the relationship between different
+arrival types and degree of saturation (X) with overestimations of HCM 2010
+procedure for estimating the back of queue within a study area. Further
+analysis is performed to establish the relationship between queue length and
+delay and also between each of them individually and X in cases with
+overestimation. The analyses are based on the 50th percentile queue lengths for
+data collected at four signalized intersections along a corridor in 4 time
+periods (off peak period and AM, Noon and PM peak periods). Based on the
+statistical test results, arrival type did not play a role in overestimations.
+However, there is a significant relationship between the overestimations on
+minor and major street and different ranges of X. On minor streets, about 59%
+of the overestimations are at X values less than half; while near 23% of the
+overestimations are at oversaturation condition with X values greater than 1.
+The relationship between amount of overestimations and degree of saturation
+should be established based on the numerical amount of overestimations versus X
+values rather than the relative amounts; since the statistical comparison
+between the relative amount of overestimations and X values, resulted in a
+wrong idea of the real world condition. There was a significant correlation
+between field queue and delay data of the cases with overestimated queue length
+in all cases on major and minor streets. Also, field queue is correlated to X,
+in all cases on minor and major streets.
+",0,0,0,1,0,0
+9800,Sourcerer's Apprentice and the study of code snippet migration,"  On the worldwide web, not only are webpages connected but source code is too.
+Software development is becoming more accessible to everyone and the licensing
+for software remains complicated. We need to know if software licenses are
+being maintained properly throughout their reuse and evolution. This motivated
+the development of the Sourcerer's Apprentice, a webservice that helps track
+clone relicensing, because software typically employ software licenses to
+describe how their software may be used and adapted. But most developers do not
+have the legal expertise to sort out license conflicts. In this paper we put
+the Apprentice to work on empirical studies that demonstrate there is much
+sharing between StackOverflow code and Python modules and Python documentation
+that violates the licensing of the original Python modules and documentation:
+software snippets shared through StackOverflow are often being relicensed
+improperly to CC-BY-SA 3.0 without maintaining the appropriate attribution. We
+show that many snippets on StackOverflow are inappropriately relicensed by
+StackOverflow users, jeopardizing the status of the software built by companies
+and developers who reuse StackOverflow snippets.
+",1,0,0,0,0,0
+9801,"""I can assure you [$\ldots$] that it's going to be all right"" -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships","  As technology become more advanced, those who design, use and are otherwise
+affected by it want to know that it will perform correctly, and understand why
+it does what it does, and how to use it appropriately. In essence they want to
+be able to trust the systems that are being designed. In this survey we present
+assurances that are the method by which users can understand how to trust this
+technology. Trust between humans and autonomy is reviewed, and the implications
+for the design of assurances are highlighted. A survey of research that has
+been performed with respect to assurances is presented, and several key ideas
+are extracted in order to refine the definition of assurances. Several
+directions for future research are identified and discussed.
+",1,0,0,1,0,0
+9802,Study of Electro-Caloric Effect in Ca and Sn co-Doped BaTiO3 Ceramics,"  The present work deals with the study of structural, ferroelectric,
+dielectric and electro-caloric effects in lead free ferroelectric
+polycrystalline Ba1-xCaxTi0.95Sn0.05O3 (x= 2, 5 and 10 %) i.e., Ca, Sn co-doped
+BaTiO3 (BTO). Phase purity of the samples is confirmed from X-ray data by using
+Rietveld refinement. 119Sn Mössbauer reveals homogenous phase as well as
+iso-valent substitution of Sn at Ti site. Enhancements in ferroelectric and
+dielectric properties have been observed. Indirect method which is based on
+Maxwell equation has been used to determine the electro-caloric (EC) effect in
+the studied ferroelectric ceramics and maximum EC coefficient is observed for
+Ba0.95Ca0.05Ti0.95Sn0.05O3.
+",0,1,0,0,0,0
+9803,Motivic Measures through Waldhausen K-Theories,"  In this paper we introduce the notion of a $cdp$-functor to a Waldhausen
+category. We show that such functors admit extensions that satisfy the excision
+property, to which we associate Euler-Poincaré characteristics that send the
+class of a proper scheme to the class of its image. As an application, we show
+that the Yoneda embedding gives rise to a monoidal proper-fibred Waldhausen
+category over Noetherian schemes of finite Krull dimensions, with canonical
+$cdp$-functors to its fibres.
+",0,0,1,0,0,0
+9804,A Brief Introduction to Machine Learning for Engineers,"  This monograph aims at providing an introduction to key concepts, algorithms,
+and theoretical results in machine learning. The treatment concentrates on
+probabilistic models for supervised and unsupervised learning problems. It
+introduces fundamental concepts and algorithms by building on first principles,
+while also exposing the reader to more advanced topics with extensive pointers
+to the literature, within a unified notation and mathematical framework. The
+material is organized according to clearly defined categories, such as
+discriminative and generative models, frequentist and Bayesian approaches,
+exact and approximate inference, as well as directed and undirected models.
+This monograph is meant as an entry point for researchers with a background in
+probability and linear algebra.
+",1,0,0,1,0,0
+9805,The Development of Microfluidic Systems within the Harrison Research Team,"  D. Jed Harrison is a full professor at the Department of Chemistry at the
+University of Alberta. Here he describes the development of microfluidic
+techniques in his lab from the initial demonstration of an integrated
+separation system for samples in liquids to the recent development of methods
+to fabricate crystalline packed beds with very low defect density.
+",0,0,0,0,1,0
+9806,Landau phonon-roton theory revisited for superfluid helium 4 and Fermi gases,"  Liquid helium and spin-1/2 cold-atom Fermi gases both exhibit in their
+superfluid phase two distinct types of excitations, gapless phonons and gapped
+rotons or fermionic pair-breaking excitations. In the long wavelength limit,
+revising and extending Landau and Khalatnikov's theory initially developed for
+helium [ZhETF 19, 637 (1949)], we obtain universal expressions for three- and
+four-body couplings among these two types of excitations. We calculate the
+corresponding phonon damping rates at low temperature and compare them to those
+of a pure phononic origin in high-pressure liquid helium and in strongly
+interacting Fermi gases, paving the way to experimental observations.
+",0,1,0,0,0,0
+9807,Counting points on hyperelliptic curves with explicit real multiplication in arbitrary genus,"  We present a probabilistic Las Vegas algorithm for computing the local zeta
+function of a genus-$g$ hyperelliptic curve defined over $\mathbb F_q$ with
+explicit real multiplication (RM) by an order $\Z[\eta]$ in a degree-$g$
+totally real number field.
+It is based on the approaches by Schoof and Pila in a more favorable case
+where we can split the $\ell$-torsion into $g$ kernels of endomorphisms, as
+introduced by Gaudry, Kohel, and Smith in genus 2. To deal with these kernels
+in any genus, we adapt a technique that the author, Gaudry, and Spaenlehauer
+introduced to model the $\ell$-torsion by structured polynomial systems.
+Applying this technique to the kernels, the systems we obtain are much smaller
+and so is the complexity of solving them.
+Our main result is that there exists a constant $c>0$ such that, for any
+fixed $g$, this algorithm has expected time and space complexity $O((\log
+q)^{c})$ as $q$ grows and the characteristic is large enough. We prove that
+$c\le 8$ and we also conjecture that the result still holds for $c=6$.
+",1,0,0,0,0,0
+9808,Minimal surfaces and Schwarz lemma,"  We prove a sharp Schwarz type inequality for the Weierstrass-Enneper
+representation of the minimal surfaces.
+",0,0,1,0,0,0
+9809,Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend,"  Period estimation is one of the central topics in astronomical time series
+analysis, where data is often unevenly sampled. Especially challenging are
+studies of stellar magnetic cycles, as there the periods looked for are of the
+order of the same length than the datasets themselves. The datasets often
+contain trends, the origin of which is either a real long-term cycle or an
+instrumental effect, but these effects cannot be reliably separated, while they
+can lead to erroneous period determinations if not properly handled. In this
+study we aim at developing a method that can handle the trends properly, and by
+performing extensive set of testing, we show that this is the optimal procedure
+when contrasted with methods that do not include the trend directly to the
+model. The effect of the form of the noise (whether constant or
+heteroscedastic) on the results is also investigated. We introduce a Bayesian
+Generalised Lomb-Scargle Periodogram with Trend (BGLST), which is a
+probabilistic linear regression model using Gaussian priors for the
+coefficients and uniform prior for the frequency parameter. We show, using
+synthetic data, that when there is no prior information on whether and to what
+extent the true model of the data contains a linear trend, the introduced BGLST
+method is preferable to the methods which either detrend the data or leave the
+data untrended before fitting the periodic model. Whether to use noise with
+different than constant variance in the model depends on the density of the
+data sampling as well as on the true noise type of the process.
+",0,1,0,1,0,0
+9810,Mean Actor Critic,"  We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action
+continuous-state reinforcement learning. MAC is a policy gradient algorithm
+that uses the agent's explicit representation of all action values to estimate
+the gradient of the policy, rather than using only the actions that were
+actually executed. We prove that this approach reduces variance in the policy
+gradient estimate relative to traditional actor-critic methods. We show
+empirical results on two control domains and on six Atari games, where MAC is
+competitive with state-of-the-art policy search algorithms.
+",1,0,0,1,0,0
+9811,HAWC Observations Strongly Favor Pulsar Interpretations of the Cosmic-Ray Positron Excess,"  Recent measurements of the Geminga and B0656+14 pulsars by the gamma-ray
+telescope HAWC (along with earlier measurements by Milagro) indicate that these
+objects generate significant fluxes of very high-energy electrons. In this
+paper, we use the very high-energy gamma-ray intensity and spectrum of these
+pulsars to calculate and constrain their expected contributions to the local
+cosmic-ray positron spectrum. Among models that are capable of reproducing the
+observed characteristics of the gamma-ray emission, we find that pulsars
+invariably produce a flux of high-energy positrons that is similar in spectrum
+and magnitude to the positron fraction measured by PAMELA and AMS-02. In light
+of this result, we conclude that it is very likely that pulsars provide the
+dominant contribution to the long perplexing cosmic-ray positron excess.
+",0,1,0,0,0,0
+9812,Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning,"  Deep neural networks require a large amount of labeled training data during
+supervised learning. However, collecting and labeling so much data might be
+infeasible in many cases. In this paper, we introduce a source-target selective
+joint fine-tuning scheme for improving the performance of deep learning tasks
+with insufficient training data. In this scheme, a target learning task with
+insufficient training data is carried out simultaneously with another source
+learning task with abundant training data. However, the source learning task
+does not use all existing training data. Our core idea is to identify and use a
+subset of training images from the original source learning task whose
+low-level characteristics are similar to those from the target learning task,
+and jointly fine-tune shared convolutional layers for both tasks. Specifically,
+we compute descriptors from linear or nonlinear filter bank responses on
+training images from both tasks, and use such descriptors to search for a
+desired subset of training samples for the source learning task.
+Experiments demonstrate that our selective joint fine-tuning scheme achieves
+state-of-the-art performance on multiple visual classification tasks with
+insufficient training data for deep learning. Such tasks include Caltech 256,
+MIT Indoor 67, Oxford Flowers 102 and Stanford Dogs 120. In comparison to
+fine-tuning without a source domain, the proposed method can improve the
+classification accuracy by 2% - 10% using a single model.
+",1,0,0,1,0,0
+9813,The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning,"  With the development of robotics, there are growing needs for real time
+motion planning. However, due to obstacles in the environment, the planning
+problem is highly non-convex, which makes it difficult to achieve real time
+computation using existing non-convex optimization algorithms. This paper
+introduces the convex feasible set algorithm (CFS) which is a fast algorithm
+for non-convex optimization problems that have convex costs and non-convex
+constraints. The idea is to find a convex feasible set for the original problem
+and iteratively solve a sequence of subproblems using the convex constraints.
+The feasibility and the convergence of the proposed algorithm are proved in the
+paper. The application of this method on motion planning for mobile robots is
+discussed. The simulations demonstrate the effectiveness of the proposed
+algorithm.
+",1,0,0,0,0,0
+9814,Continuous-Time Visual-Inertial Odometry for Event Cameras,"  Event cameras are bio-inspired vision sensors that output pixel-level
+brightness changes instead of standard intensity frames. They offer significant
+advantages over standard cameras, namely a very high dynamic range, no motion
+blur, and a latency in the order of microseconds. However, due to the
+fundamentally different structure of the sensor's output, new algorithms that
+exploit the high temporal resolution and the asynchronous nature of the sensor
+are required. Recent work has shown that a continuous-time representation of
+the event camera pose can deal with the high temporal resolution and
+asynchronous nature of this sensor in a principled way. In this paper, we
+leverage such a continuous-time representation to perform visual-inertial
+odometry with an event camera. This representation allows direct integration of
+the asynchronous events with micro-second accuracy and the inertial
+measurements at high frequency. The event camera trajectory is approximated by
+a smooth curve in the space of rigid-body motions using cubic splines. This
+formulation significantly reduces the number of variables in trajectory
+estimation problems. We evaluate our method on real data from several scenes
+and compare the results against ground truth from a motion-capture system. We
+show that our method provides improved accuracy over the result of a
+state-of-the-art visual odometry method for event cameras. We also show that
+both the map orientation and scale can be recovered accurately by fusing events
+and inertial data. To the best of our knowledge, this is the first work on
+visual-inertial fusion with event cameras using a continuous-time framework.
+",1,0,0,0,0,0
+9815,Reconstructing a $f(R)$ theory from the $α$-Attractors,"  We show an analogy at high curvature between a $f(R) = R + aR^{n - 1} + bR^2$
+theory and the $\alpha$-Attractors. We calculate the expressions of the
+parameters $a$, $b$ and $n$ as functions of $\alpha$ and the predictions of the
+model $f(R) = R + aR^{n - 1} + bR^2$ on the scalar spectral index $n_{\rm s}$
+and the tensor-to-scalar ratio $r$. We find that the power law correction $R^{n
+- 1}$ allows for a production of gravitational waves enhanced with respect to
+the one in the Starobinsky model, while maintaining a viable prediction on
+$n_{\rm s}$. We numerically reconstruct the full $\alpha$-Attractors class of
+models testing the goodness of our high-energy approximation $f(R) = R + aR^{n
+- 1} + bR^2$. Moreover, we also investigate the case of a single power law
+$f(R) = \gamma R^{2 - \delta}$ theory, with $\gamma$ and $\delta$ free
+parameters. We calculate analytically the predictions of this model on the
+scalar spectral index $n_{\rm s}$ and the tensor-to-scalar ratio $r$ and the
+values of $\delta$ which are allowed from the current observational results. We
+find that $-0.015 < \delta < 0.016$, confirming once again the excellent
+agreement between the Starobinsky model and observation.
+",0,1,0,0,0,0
+9816,Systems of ergodic BSDE arising in regime switching forward performance processes,"  We introduce and solve a new type of quadratic backward stochastic
+differential equation systems defined in an infinite time horizon, called
+\emph{ergodic BSDE systems}. Such systems arise naturally as candidate
+solutions to characterize forward performance processes and their associated
+optimal trading strategies in a regime switching market. In addition, we
+develop a connection between the solution of the ergodic BSDE system and the
+long-term growth rate of classical utility maximization problems, and use the
+ergodic BSDE system to study the large time behavior of PDE systems with
+quadratic growth Hamiltonians.
+",0,0,0,0,0,1
+9817,Extraction and Classification of Diving Clips from Continuous Video Footage,"  Due to recent advances in technology, the recording and analysis of video
+data has become an increasingly common component of athlete training
+programmes. Today it is incredibly easy and affordable to set up a fixed camera
+and record athletes in a wide range of sports, such as diving, gymnastics,
+golf, tennis, etc. However, the manual analysis of the obtained footage is a
+time-consuming task which involves isolating actions of interest and
+categorizing them using domain-specific knowledge. In order to automate this
+kind of task, three challenging sub-problems are often encountered: 1)
+temporally cropping events/actions of interest from continuous video; 2)
+tracking the object of interest; and 3) classifying the events/actions of
+interest.
+Most previous work has focused on solving just one of the above sub-problems
+in isolation. In contrast, this paper provides a complete solution to the
+overall action monitoring task in the context of a challenging real-world
+exemplar. Specifically, we address the problem of diving classification. This
+is a challenging problem since the person (diver) of interest typically
+occupies fewer than 1% of the pixels in each frame. The model is required to
+learn the temporal boundaries of a dive, even though other divers and
+bystanders may be in view. Finally, the model must be sensitive to subtle
+changes in body pose over a large number of frames to determine the
+classification code. We provide effective solutions to each of the sub-problems
+which combine to provide a highly functional solution to the task as a whole.
+The techniques proposed can be easily generalized to video footage recorded
+from other sports.
+",1,0,0,0,0,0
+9818,The inertial Jacquet-Langlands correspondence,"  We give a parametrization of the simple Bernstein components of inner forms
+of a general linear group over a local field by invariants constructed from
+type theory, and explicitly describe its behaviour under the Jacquet-Langlands
+correspondence. Along the way, we prove a conjecture of Broussous, Sécherre
+and Stevens on preservation of endo-classes.
+",0,0,1,0,0,0
+9819,Universal geometric constraints during epithelial jamming,"  As an injury heals, an embryo develops, or a carcinoma spreads, epithelial
+cells systematically change their shape. In each of these processes cell shape
+is studied extensively, whereas variation of shape from cell-to-cell is
+dismissed most often as biological noise. But where do cell shape and variation
+of cell shape come from? Here we report that cell shape and shape variation are
+mutually constrained through a relationship that is purely geometrical. That
+relationship is shown to govern maturation of the pseudostratified bronchial
+epithelial layer cultured from both non-asthmatic and asthmatic donors as well
+as formation of the ventral furrow in the epithelial monolayer of the
+Drosophila embryo in vivo. Across these and other vastly different epithelial
+systems, cell shape variation collapses to a family of distributions that is
+common to all and potentially universal. That distribution, in turn, is
+accounted for quantitatively by a mechanistic theory of cell-cell interaction
+showing that cell shape becomes progressively less elongated and less variable
+as the layer becomes progressively more jammed. These findings thus uncover a
+connection between jamming and geometry that is generic -spanning jammed living
+and inert systems alike- and demonstrate that proximity of the cell layer to
+the jammed state is the principal determinant of the most primitive features of
+epithelial cell shape and shape variation.
+",0,1,0,0,0,0
+9820,Semi-independent resampling for particle filtering,"  Among Sequential Monte Carlo (SMC) methods,Sampling Importance Resampling
+(SIR) algorithms are based on Importance Sampling (IS) and on some
+resampling-based)rejuvenation algorithm which aims at fighting against weight
+degeneracy. However %whichever the resampling technique used this mechanism
+tends to be insufficient when applied to informative or high-dimensional
+models. In this paper we revisit the rejuvenation mechanism and propose a class
+of parameterized SIR-based solutions which enable to adjust the tradeoff
+between computational cost and statistical performances.
+",0,0,0,1,0,0
+9821,Free Information Flow Benefits Truth Seeking,"  How can we approach the truth in a society? It may depend on various factors.
+In this paper, using a well-established truth seeking model, we show that the
+persistent free information flow will bring us to the truth. Here the free
+information flow is modeled as the environmental random noise that could alter
+one's cognition. Without the random noise, the model predicts that the truth
+can only be captured by the truth seekers who own active perceptive ability of
+the truth and their believers, while the other individuals may stick to
+falsehood. But under the influence of the random noise, we strictly prove that
+even there is only one truth seeker in the group, all individuals will finally
+approach the truth.
+",1,1,1,0,0,0
+9822,Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions,"  Increasing evidence has shown that theory-based health behavior change
+interventions are more effective than non-theory-based ones. However, only a
+few segments of relevant studies were theory-based, especially the studies
+conducted by non-psychology researchers. On the other hand, many mobile health
+interventions, even those based on the behavioral theories, may still fail in
+the absence of a user-centered design process. The gap between behavioral
+theories and user-centered design increases the difficulty of designing and
+implementing mobile health interventions. To bridge this gap, we propose a
+holistic approach to designing theory-based mobile health interventions built
+on the existing theories and frameworks of three categories: (1) behavioral
+theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior,
+and the Health Action Process Approach), (2) the technological models and
+frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design
+and Behavior Change Support System, and the Just-in-Time Adaptive
+Interventions), and (3) the user-centered systematic approaches (e.g., the
+CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic
+approach provides researchers a lens to see the whole picture for developing
+mobile health interventions.
+",1,0,0,0,0,0
+9823,Connections between transport of intensity equation and two-dimensional phase unwrapping,"  In a recent publication [Appl. Opt. 55, 2418 (2016)], a method for
+two-dimensional phase unwrapping based on the transport of intensity equation
+(TIE) was studied. We wish to show that this approach is associated with the
+standard least squares phase unwrapping algorithm, but with additional
+numerical errors.
+",0,1,0,0,0,0
+9824,On approximations of Value at Risk and Expected Shortfall involving kurtosis,"  We derive new approximations for the Value at Risk and the Expected Shortfall
+at high levels of loss distributions with positive skewness and excess
+kurtosis, and we describe their precisions for notable ones such as for
+exponential, Pareto type I, lognormal and compound (Poisson) distributions. Our
+approximations are motivated by extensions of the so-called Normal Power
+Approximation, used for approximating the cumulative distribution function of a
+random variable, incorporating not only the skewness but the kurtosis of the
+random variable in question as well. We show the performance of our
+approximations in numerical examples and we also give comparisons with some
+known ones in the literature.
+",0,0,0,0,0,1
+9825,DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications,"  Computational methods that predict differential gene expression from histone
+modification signals are highly desirable for understanding how histone
+modifications control the functional heterogeneity of cells through influencing
+differential gene regulation. Recent studies either failed to capture
+combinatorial effects on differential prediction or primarily only focused on
+cell type-specific analysis. In this paper, we develop a novel attention-based
+deep learning architecture, DeepDiff, that provides a unified and end-to-end
+solution to model and to interpret how dependencies among histone modifications
+control the differential patterns of gene regulation. DeepDiff uses a hierarchy
+of multiple Long short-term memory (LSTM) modules to encode the spatial
+structure of input signals and to model how various histone modifications
+cooperate automatically. We introduce and train two levels of attention jointly
+with the target prediction, enabling DeepDiff to attend differentially to
+relevant modifications and to locate important genome positions for each
+modification. Additionally, DeepDiff introduces a novel deep-learning based
+multi-task formulation to use the cell-type-specific gene expression
+predictions as auxiliary tasks, encouraging richer feature embeddings in our
+primary task of differential expression prediction. Using data from Roadmap
+Epigenomics Project (REMC) for ten different pairs of cell types, we show that
+DeepDiff significantly outperforms the state-of-the-art baselines for
+differential gene expression prediction. The learned attention weights are
+validated by observations from previous studies about how epigenetic mechanisms
+connect to differential gene expression. Codes and results are available at
+\url{deepchrome.org}
+",0,0,0,1,0,0
+9826,t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data,"  Modern datasets and models are notoriously difficult to explore and analyze
+due to their inherent high dimensionality and massive numbers of samples.
+Existing visualization methods which employ dimensionality reduction to two or
+three dimensions are often inefficient and/or ineffective for these datasets.
+This paper introduces t-SNE-CUDA, a GPU-accelerated implementation of
+t-distributed Symmetric Neighbor Embedding (t-SNE) for visualizing datasets and
+models. t-SNE-CUDA significantly outperforms current implementations with
+50-700x speedups on the CIFAR-10 and MNIST datasets. These speedups enable, for
+the first time, visualization of the neural network activations on the entire
+ImageNet dataset - a feat that was previously computationally intractable. We
+also demonstrate visualization performance in the NLP domain by visualizing the
+GloVe embedding vectors. From these visualizations, we can draw interesting
+conclusions about using the L2 metric in these embedding spaces. t-SNE-CUDA is
+publicly available atthis https URL
+",1,0,0,1,0,0
+9827,Coma Cluster Ultra-Diffuse Galaxies Are Not Standard Radio Galaxies,"  Matching members in the Coma cluster catalogue of ultra-diffuse galaxies
+(UDGs, Yagi et al. 2016) from SUBARU imaging with a very deep radio continuum
+survey source catalogue of the cluster (Miller et al. 2009) using the Karl G.
+Jansky Very Large Array (VLA) within a rectangular region of ~ 1.19 square
+degrees centred on the cluster core reveals matches consistent with random. An
+overlapping set of 470 UDGs and 696 VLA radio sources in this rectangular area
+finds 33 matches within a separation of 25 arcsec; dividing the sample into
+bins with separations bounded by 5 arcsec, 10 arcsec, 20 arcsec and 25 arcsec
+finds 1, 4, 17 and 11 matches. An analytical model estimate, based on the
+Poisson probability distribution, of the number of randomly expected matches
+within these same separation bounds is 1.7, 4.9, 19.4 and 14.2, each
+respectively consistent with the 95 percent Poisson confidence intervals of the
+observed values. Dividing the data into five clustercentric annuli of 0.1
+degree, and into the four separation bins, finds the same result. This random
+match of UDGs with VLA sources implies that UDGs are not radio galaxies by the
+standard definition. Those VLA sources having integrated flux > 1 mJy at 1.4
+GHz in Miller et al. (2009) without SDSS galaxy matches are consistent with the
+known surface density of background radio sources. We briefly explore the
+possibility that some unresolved VLA sources near UDGs could be young, compact,
+bright, supernova remnants of type Ia events, possibly in the intracluster
+volume.
+",0,1,0,0,0,0
+9828,LD-SDS: Towards an Expressive Spoken Dialogue System based on Linked-Data,"  In this work we discuss the related challenges and describe an approach
+towards the fusion of state-of-the-art technologies from the Spoken Dialogue
+Systems (SDS) and the Semantic Web and Information Retrieval domains. We
+envision a dialogue system named LD-SDS that will support advanced, expressive,
+and engaging user requests, over multiple, complex, rich, and open-domain data
+sources that will leverage the wealth of the available Linked Data.
+Specifically, we focus on: a) improving the identification, disambiguation and
+linking of entities occurring in data sources and user input; b) offering
+advanced query services for exploiting the semantics of the data, with
+reasoning and exploratory capabilities; and c) expanding the typical
+information seeking dialogue model (slot filling) to better reflect real-world
+conversational search scenarios.
+",1,0,0,0,0,0
+9829,On a spiked model for large volatility matrix estimation from noisy high-frequency data,"  Recently, inference about high-dimensional integrated covariance matrices
+(ICVs) based on noisy high-frequency data has emerged as a challenging problem.
+In the literature, a pre-averaging estimator (PA-RCov) is proposed to deal with
+the microstructure noise. Using the large-dimensional random matrix theory, it
+has been established that the eigenvalue distribution of the PA-RCov matrix is
+intimately linked to that of the ICV through the Marcenko-Pastur equation.
+Consequently, the spectrum of the ICV can be inferred from that of the PA-RCov.
+However, extensive data analyses demonstrate that the spectrum of the PA-RCov
+is spiked, that is, a few large eigenvalues (spikes) stay away from the others
+which form a rather continuous distribution with a density function (bulk).
+Therefore, any inference on the ICVs must take into account this spiked
+structure. As a methodological contribution, we propose a spiked model for the
+ICVs where spikes can be inferred from those of the available PA-RCov matrices.
+The consistency of the inference procedure is established and is checked by
+extensive simulation studies. In addition, we apply our method to the real data
+from the US and Hong Kong markets. It is found that our model clearly
+outperforms the existing one in predicting the existence of spikes and in
+mimicking the empirical PA-RCov matrices.
+",0,0,0,1,0,0
+9830,Graph-based Features for Automatic Online Abuse Detection,"  While online communities have become increasingly important over the years,
+the moderation of user-generated content is still performed mostly manually.
+Automating this task is an important step in reducing the financial cost
+associated with moderation, but the majority of automated approaches strictly
+based on message content are highly vulnerable to intentional obfuscation. In
+this paper, we discuss methods for extracting conversational networks based on
+raw multi-participant chat logs, and we study the contribution of graph
+features to a classification system that aims to determine if a given message
+is abusive. The conversational graph-based system yields unexpectedly high
+performance , with results comparable to those previously obtained with a
+content-based approach.
+",1,0,0,0,0,0
+9831,All Classical Adversary Methods are Equivalent for Total Functions,"  We show that all known classical adversary lower bounds on randomized query
+complexity are equivalent for total functions, and are equal to the fractional
+block sensitivity $\text{fbs}(f)$. That includes the Kolmogorov complexity
+bound of Laplante and Magniez and the earlier relational adversary bound of
+Aaronson. This equivalence also implies that for total functions, the
+relational adversary is equivalent to a simpler lower bound, which we call
+rank-1 relational adversary. For partial functions, we show unbounded
+separations between $\text{fbs}(f)$ and other adversary bounds, as well as
+between the adversary bounds themselves.
+We also show that, for partial functions, fractional block sensitivity cannot
+give lower bounds larger than $\sqrt{n \cdot \text{bs}(f)}$, where $n$ is the
+number of variables and $\text{bs}(f)$ is the block sensitivity. Then we
+exhibit a partial function $f$ that matches this upper bound, $\text{fbs}(f) =
+\Omega(\sqrt{n \cdot \text{bs}(f)})$.
+",1,0,0,0,0,0
+9832,A new approach for short-spacing correction of radio interferometric data sets,"  The short-spacing problem describes the inherent inability of
+radio-interferometric arrays to measure the integrated flux and structure of
+diffuse emission associated with extended sources. New interferometric arrays,
+such as SKA, require solutions to efficiently combine interferometer and
+single-dish data.
+We present a new and open source approach for merging single-dish and cleaned
+interferometric data sets requiring a minimum of data manipulation while
+offering a rigid flux determination and full high angular resolution. Our
+approach combines single-dish and cleaned interferometric data in the image
+domain. This approach is tested for both Galactic and extragalactic HI data
+sets. Furthermore, a quantitative comparison of our results to commonly used
+methods is provided. Additionally, for the interferometric data sets of NGC4214
+and NGC5055, we study the impact of different imaging parameters as well as
+their influence on the combination for NGC4214.
+The approach does not require the raw data (visibilities) or any additional
+special information such as antenna patterns. This is advantageous especially
+in the light of upcoming radio surveys with heterogeneous antenna designs.
+",0,1,0,0,0,0
+9833,Data and uncertainty in extreme risks - a nonlinear expectations approach,"  Estimation of tail quantities, such as expected shortfall or Value at Risk,
+is a difficult problem. We show how the theory of nonlinear expectations, in
+particular the Data-robust expectation introduced in [5], can assist in the
+quantification of statistical uncertainty for these problems. However, when we
+are in a heavy-tailed context (in particular when our data are described by a
+Pareto distribution, as is common in much of extreme value theory), the theory
+of [5] is insufficient, and requires an additional regularization step which we
+introduce. By asking whether this regularization is possible, we obtain a
+qualitative requirement for reliable estimation of tail quantities and risk
+measures, in a Pareto setting.
+",0,0,1,1,0,0
+9834,Flow equations for cold Bose gases,"  We derive flow equations for cold atomic gases with one macroscopically
+populated energy level. The generator is chosen such that the ground state
+decouples from all other states in the system as the renormalization group flow
+progresses. We propose a self-consistent truncation scheme for the flow
+equations at the level of three-body operators and show how they can be used to
+calculate the ground state energy of a general $N$-body system. Moreover, we
+provide a general method to estimate the truncation error in the calculated
+energies. Finally, we test our scheme by benchmarking to the exactly solvable
+Lieb-Liniger model and find good agreement for weak and moderate interaction
+strengths.
+",0,1,0,0,0,0
+9835,Fluid flows shaping organism morphology,"  A dynamic self-organized morphology is the hallmark of network-shaped
+organisms like slime moulds and fungi. Organisms continuously re-organize their
+flexible, undifferentiated body plans to forage for food. Among these organisms
+the slime mould Physarum polycephalum has emerged as a model to investigate how
+organism can self-organize their extensive networks and act as a coordinated
+whole. Cytoplasmic fluid flows flowing through the tubular networks have been
+identified as key driver of morphological dynamics. Inquiring how fluid flows
+can shape living matter from small to large scales opens up many new avenues
+for research.
+",0,0,0,0,1,0
+9836,Frames of exponentials and sub-multitiles in LCA groups,"  In this note we investigate the existence of frames of exponentials for
+$L^2(\Omega)$ in the setting of LCA groups. Our main result shows that
+sub-multitiling properties of $\Omega \subset \widehat{G}$ with respect to a
+uniform lattice $\Gamma$ of $\widehat{G}$ guarantee the existence of a frame of
+exponentials with frequencies in a finite number of translates of the
+annihilator of $\Gamma$. We also prove the converse of this result and provide
+conditions for the existence of these frames. These conditions extend recent
+results on Riesz bases of exponentials and multitilings to frames.
+",0,0,1,0,0,0
+9837,Exponential Random Graph Models with Big Networks: Maximum Pseudolikelihood Estimation and the Parametric Bootstrap,"  With the growth of interest in network data across fields, the Exponential
+Random Graph Model (ERGM) has emerged as the leading approach to the
+statistical analysis of network data. ERGM parameter estimation requires the
+approximation of an intractable normalizing constant. Simulation methods
+represent the state-of-the-art approach to approximating the normalizing
+constant, leading to estimation by Monte Carlo maximum likelihood (MCMLE).
+MCMLE is accurate when a large sample of networks is used to approximate the
+normalizing constant. However, MCMLE is computationally expensive, and may be
+prohibitively so if the size of the network is on the order of 1,000 nodes
+(i.e., one million potential ties) or greater. When the network is large, one
+option is maximum pseudolikelihood estimation (MPLE). The standard MPLE is
+simple and fast, but generally underestimates standard errors. We show that a
+resampling method---the parametric bootstrap---results in accurate coverage
+probabilities for confidence intervals. We find that bootstrapped MPLE can be
+run in 1/5th the time of MCMLE. We study the relative performance of MCMLE and
+MPLE with simulation studies, and illustrate the two different approaches by
+applying them to a network of bills introduced in the United State Senate.
+",0,0,0,1,0,0
+9838,Critical behavior of a stochastic anisotropic Bak-Sneppen model,"  In this paper we present our study on the critical behavior of a stochastic
+anisotropic Bak-Sneppen (saBS) model, in which a parameter $\alpha$ is
+introduced to describe the interaction strength among nearest species. We
+estimate the threshold fitness $f_c$ and the critical exponent $\tau_r$ by
+numerically integrating a master equation for the distribution of avalanche
+spatial sizes. Other critical exponents are then evaluated from previously
+known scaling relations. The numerical results are in good agreement with the
+counterparts yielded by the Monte Carlo simulations. Our results indicate that
+all saBS models with nonzero interaction strength exhibit self-organized
+criticality, and fall into the same universality class, by sharing the
+universal critical exponents.
+",0,1,0,0,0,0
+9839,"Probability, Statistics and Planet Earth, I: Geotemporal covariances","  The study of covariances (or positive definite functions) on the sphere (the
+Earth, in our motivation) goes back to Bochner and Schoenberg (1940--42) and to
+the first author (1969, 1973), among others. Extending to the geotemporal case
+(sphere cross line, for position and time) was for a long time an obstacle to
+geostatistical modelling. The characterisation question here was raised by the
+authors and Mijatović in 2016, and answered by Berg and Porcu in 2017.
+Extensions to multiple products (of spheres and lines) follows similarly
+(Guella, Menegatto and Peron, 2016). We survey results of this type, and
+related applications e.g. in numerical weather prediction.
+",0,1,0,1,0,0
+9840,Gang-GC: Locality-aware Parallel Data Placement Optimizations for Key-Value Storages,"  Many cloud applications rely on fast and non-relational storage to aid in the
+processing of large amounts of data. Managed runtimes are now widely used to
+support the execution of several storage solutions of the NoSQL movement,
+particularly when dealing with big data key-value store-driven applications.
+The benefits of these runtimes can however be limited by modern parallel
+throughput-oriented GC algorithms, where related objects have the potential to
+be dispersed in memory, either in the same or different generations. In the
+long run this causes more page faults and degradation of locality on
+system-level memory caches.
+We propose, Gang-CG, an extension to modern heap layouts and to a parallel GC
+algorithm to promote locality between groups of related objects. This is done
+without extensive profiling of the applications and in a way that is
+transparent to the programmer, without the need to use specialized data
+structures. The heap layout and algorithmic extensions were implemented over
+the Parallel Scavenge garbage collector of the HotSpot JVM\@.
+Using microbenchmarks that capture the architecture of several key-value
+stores databases, we show negligible overhead in frequent operations such as
+the allocation of new objects and improvements to the access speed of data,
+supported by lower misses in system-level memory caches. Overall, we show a 6\%
+improvement in the average time of read and update operations and an average
+decrease of 12.4\% in page faults.
+",1,0,0,0,0,0
+9841,Good cyclic codes and the uncertainty principle,"  A long standing problem in the area of error correcting codes asks whether
+there exist good cyclic codes. Most of the known results point in the direction
+of a negative answer.
+The uncertainty principle is a classical result of harmonic analysis
+asserting that given a non-zero function $f$ on some abelian group, either $f$
+or its Fourier transform $\hat{f}$ has large support.
+In this note, we observe a connection between these two subjects. We point
+out that even a weak version of the uncertainty principle for fields of
+positive characteristic would imply that good cyclic codes do exist. We also
+provide some heuristic arguments supporting that this is indeed the case.
+",1,0,1,0,0,0
+9842,An Amateur Drone Surveillance System Based on Cognitive Internet of Things,"  Drones, also known as mini-unmanned aerial vehicles, have attracted
+increasing attention due to their boundless applications in communications,
+photography, agriculture, surveillance and numerous public services. However,
+the deployment of amateur drones poses various safety, security and privacy
+threats. To cope with these challenges, amateur drone surveillance becomes a
+very important but largely unexplored topic. In this article, we firstly
+present a brief survey to show the state-of-the-art studies on amateur drone
+surveillance. Then, we propose a vision, named Dragnet, by tailoring the recent
+emerging cognitive internet of things framework for amateur drone surveillance.
+Next, we discuss the key enabling techniques for Dragnet in details,
+accompanied with the technical challenges and open issues. Furthermore, we
+provide an exemplary case study on the detection and classification of
+authorized and unauthorized amateur drones, where, for example, an important
+event is being held and only authorized drones are allowed to fly over.
+",1,0,0,0,0,0
+9843,Two-species boson mixture on a ring: A group theoretic approach to the quantum dynamics of low-energy excitations,"  We investigate the weak excitations of a system made up of two condensates
+trapped in a Bose-Hubbard ring and coupled by an interspecies repulsive
+interaction. Our approach, based on the Bogoliubov approximation scheme, shows
+that one can reduce the problem Hamiltonian to the sum of sub-Hamiltonians
+$\hat{H}_k$, each one associated to momentum modes $\pm k$. Each $\hat{H}_k$ is
+then recognized to be an element of a dynamical algebra. This uncommon and
+remarkable property allows us to present a straightforward diagonalization
+scheme, to find constants of motion, to highlight the significant microscopic
+processes, and to compute their time evolution. The proposed solution scheme is
+applied to a simple but still very interesting closed circuit, the trimer. The
+dynamics of low-energy excitations, corresponding to weakly-populated vortices,
+is investigated considering different choices of the initial conditions, and
+the angular-momentum transfer between the two condensates is evidenced.
+Finally, the condition for which the spectral collapse and dynamical
+instability are observed is derived analytically.
+",0,1,0,0,0,0
+9844,On a simple model of X_0(N),"  We find plane models for all $X_0(N)$, $N\geq 2$. We observe a map from the
+modular curve $X_0(N)$ to the projective plane constructed using modular forms
+of weight $12$ for the group $\Gamma_0(N)$; the Ramanujan function $\Delta$,
+$\Delta(N\cdot)$ and the third power of Eisestein series of weight $4$,
+$E_4^3$, and prove that this map is birational equivalence for every $N\geq 2$.
+The equation of the model is the minimal polynomial of $\Delta(N\cdot)/\Delta$
+over $\mathbb{C}(j)$.
+",0,0,1,0,0,0
+9845,A Supervised STDP-based Training Algorithm for Living Neural Networks,"  Neural networks have shown great potential in many applications like speech
+recognition, drug discovery, image classification, and object detection. Neural
+network models are inspired by biological neural networks, but they are
+optimized to perform machine learning tasks on digital computers. The proposed
+work explores the possibilities of using living neural networks in vitro as
+basic computational elements for machine learning applications. A new
+supervised STDP-based learning algorithm is proposed in this work, which
+considers neuron engineering constrains. A 74.7% accuracy is achieved on the
+MNIST benchmark for handwritten digit recognition.
+",1,0,0,1,0,0
+9846,Optimal Topology Design for Disturbance Minimization in Power Grids,"  The transient response of power grids to external disturbances influences
+their stable operation. This paper studies the effect of topology in linear
+time-invariant dynamics of different power grids. For a variety of objective
+functions, a unified framework based on $H_2$ norm is presented to analyze the
+robustness to ambient fluctuations. Such objectives include loss reduction,
+weighted consensus of phase angle deviations, oscillations in nodal frequency,
+and other graphical metrics. The framework is then used to study the problem of
+optimal topology design for robust control goals of different grids. For radial
+grids, the problem is shown as equivalent to the hard ""optimum communication
+spanning tree"" problem in graph theory and a combinatorial topology
+construction is presented with bounded approximation gap. Extended to loopy
+(meshed) grids, a greedy topology design algorithm is discussed. The
+performance of the topology design algorithms under multiple control objectives
+are presented on both loopy and radial test grids. Overall, this paper analyzes
+topology design algorithms on a broad class of control problems in power grid
+by exploring their combinatorial and graphical properties.
+",1,0,1,0,0,0
+9847,Asymptotic behaviour of the Christoffel functions on the Unit Ball in the presence of a Mass on the Sphere,"  We present a family of mutually orthogonal polynomials on the unit ball with
+respect to an inner product which includes a mass uniformly distributed on the
+sphere. First, connection formulas relating these multivariate orthogonal
+polynomials and the classical ball polynomials are obtained. Then, using the
+representation formula for these polynomials in terms of spherical harmonics
+analytic properties will be deduced. Finally, we analyze the asymptotic
+behaviour of the Christoffel functions.
+",0,0,1,0,0,0
+9848,Tangent: Automatic Differentiation Using Source Code Transformation in Python,"  Automatic differentiation (AD) is an essential primitive for machine learning
+programming systems. Tangent is a new library that performs AD using source
+code transformation (SCT) in Python. It takes numeric functions written in a
+syntactic subset of Python and NumPy as input, and generates new Python
+functions which calculate a derivative. This approach to automatic
+differentiation is different from existing packages popular in machine
+learning, such as TensorFlow and Autograd. Advantages are that Tangent
+generates gradient code in Python which is readable by the user, easy to
+understand and debug, and has no runtime overhead. Tangent also introduces
+abstractions for easily injecting logic into the generated gradient code,
+further improving usability.
+",1,0,0,1,0,0
+9849,Ca II K 1-A Emission Index Composites,"  We describe here a procedure to combine measurements in the 393.37 nm Ca II K
+spectral line taken at different observatories. Measurements from the National
+Solar Observatory (NSO) Integrated Sunlight Spectrometer (ISS) on the Synoptic
+Optical Long-term Investigations of the Sun (SOLIS) telescope, the NSO/Sac Peak
+Ca II K-Line Monitoring Program, and Ca II K filtergrams from Kodaikanal Solar
+Observatory (KKL) are merged together to create a pair of composites of the Ca
+II K 1-A emission index. These composites are publicly available from the SOLIS
+website at this http URL.
+",0,1,0,0,0,0
+9850,Application of the Bead Perturbation Technique to a Study of a Tunable 5 GHz Annular Cavity,"  Microwave cavities for a Sikivie-type axion search are subject to several
+constraints. In the fabrication and operation of such cavities, often used at
+frequencies where the resonator is highly overmoded, it is important to be able
+to reliably identify several properties of the cavity. Those include
+identifying the symmetry of the mode of interest, confirming its form factor,
+and determining the frequency ranges where mode crossings with intruder levels
+cause unacceptable admixture, thus leading to the loss of purity of the mode of
+interest. A simple and powerful diagnostic for mapping out the electric field
+of a cavity is the bead perturbation technique. While a standard tool in
+accelerator physics, we have, for the first time, applied this technique to
+cavities used in the axion search. We report initial results from an extensive
+study for the initial cavity used in the HAYSTAC experiment. Two effects have
+been investigated: the role of rod misalignment in mode localization, and
+mode-mixing at avoided crossings of TM/TE modes. Future work will extend these
+results by incorporating precision metrology and high-fidelity simulations.
+",0,1,0,0,0,0
+9851,Homogeneous Kobayashi-hyperbolic manifolds with high-dimensional group of holomorphic automorphisms,"  We determine all connected homogeneous Kobayashi-hyperbolic manifolds of
+dimension $n\ge 2$ whose holomorphic automorphism group has dimension $n^2-2$.
+This result complements an existing classification for automorphism group
+dimension $n^2-1$ and greater obtained without the homogeneity assumption.
+",0,0,1,0,0,0
+9852,New estimates for some functions defined over primes,"  In this paper we first establish new explicit estimates for Chebyshev's
+$\vartheta$-function. Applying these new estimates, we derive new upper and
+lower bounds for some functions defined over the prime numbers, for instance
+the prime counting function $\pi(x)$, which improve the currently best ones.
+Furthermore, we use the obtained estimates for the prime counting function to
+give two new results concerning the existence of prime numbers in short
+intervals.
+",0,0,1,0,0,0
+9853,Transferrable End-to-End Learning for Protein Interface Prediction,"  While there has been an explosion in the number of experimentally determined,
+atomically detailed structures of proteins, how to represent these structures
+in a machine learning context remains an open research question. In this work
+we demonstrate that representations learned from raw atomic coordinates can
+outperform hand-engineered structural features while displaying a much higher
+degree of transferrability. To do so, we focus on a central problem in biology:
+predicting how proteins interact with one another--that is, which surfaces of
+one protein bind to which surfaces of another protein. We present Siamese
+Atomic Surfacelet Network (SASNet), the first end-to-end learning method for
+protein interface prediction. Despite using only spatial coordinates and
+identities of atoms as inputs, SASNet outperforms state-of-the-art methods that
+rely on hand-engineered, high-level features. These results are particularly
+striking because we train the method entirely on a significantly biased data
+set that does not account for the fact that proteins deform when binding to one
+another. Demonstrating the first successful application of transfer learning to
+atomic-level data, our network maintains high performance, without retraining,
+when tested on real cases in which proteins do deform.
+",0,0,0,1,1,0
+9854,A new astrophysical solution to the Too Big To Fail problem - Insights from the MoRIA simulations,"  We test whether advanced galaxy models and analysis techniques of simulations
+can alleviate the Too Big To Fail problem (TBTF) for late-type galaxies, which
+states that isolated dwarf galaxy kinematics imply that dwarfs live in
+lower-mass halos than is expected in a {\Lambda}CDM universe. Furthermore, we
+want to explain this apparent tension between theory and observations. To do
+this, we use the MoRIA suite of dwarf galaxy simulations to investigate whether
+observational effects are involved in TBTF for late-type field dwarf galaxies.
+To this end, we create synthetic radio data cubes of the simulated MoRIA
+galaxies and analyse their HI kinematics as if they were real, observed
+galaxies. We find that for low-mass galaxies, the circular velocity profile
+inferred from the HI kinematics often underestimates the true circular velocity
+profile, as derived directly from the enclosed mass. Fitting the HI kinematics
+of MoRIA dwarfs with a theoretical halo profile results in a systematic
+underestimate of the mass of their host halos. We attribute this effect to the
+fact that the interstellar medium of a low-mass late-type dwarf is continuously
+stirred by supernova explosions into a vertically puffed-up, turbulent state to
+the extent that the rotation velocity of the gas is simply no longer a good
+tracer of the underlying gravitational force field. If this holds true for real
+dwarf galaxies as well, it implies that they inhabit more massive dark matter
+halos than would be inferred from their kinematics, solving TBTF for late-type
+field dwarf galaxies.
+",0,1,0,0,0,0
+9855,Friction Variability in Planar Pushing Data: Anisotropic Friction and Data-collection Bias,"  Friction plays a key role in manipulating objects. Most of what we do with
+our hands, and most of what robots do with their grippers, is based on the
+ability to control frictional forces. This paper aims to better understand the
+variability and predictability of planar friction. In particular, we focus on
+the analysis of a recent dataset on planar pushing by Yu et al. [1] devised to
+create a data-driven footprint of planar friction.
+We show in this paper how we can explain a significant fraction of the
+observed unconventional phenomena, e.g., stochasticity and multi-modality, by
+combining the effects of material non-homogeneity, anisotropy of friction and
+biases due to data collection dynamics, hinting that the variability is
+explainable but inevitable in practice.
+We introduce an anisotropic friction model and conduct simulation experiments
+comparing with more standard isotropic friction models. The anisotropic
+friction between object and supporting surface results in convergence of
+initial condition during the automated data collection. Numerical results
+confirm that the anisotropic friction model explains the bias in the dataset
+and the apparent stochasticity in the outcome of a push. The fact that the data
+collection process itself can originate biases in the collected datasets,
+resulting in deterioration of trained models, calls attention to the data
+collection dynamics.
+",1,0,0,0,0,0
+9856,Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers,"  Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC)
+dataset is a collection of automatically categorized and annotated sentences
+obtained from Wikipedia. We constructed large-scale gazetteers by using a graph
+crawler algorithm to extract relevant entity and domain information from a
+semantic knowledge base, Freebase. The constructed gazetteers contains
+approximately 300K entities with thousands of fine-grained entity types under
+77 different domains. Since automated processes are prone to ambiguity, we also
+introduce two new content specific noise reduction methodologies. Moreover, we
+map fine-grained entity types to the equivalent four coarse-grained types:
+person, loc, org, misc. Eventually, we construct six different dataset versions
+and evaluate the quality of annotations by comparing ground truths from human
+annotators. We make these datasets publicly available to support studies on
+Turkish named-entity recognition (NER) and text categorization (TC).
+",1,0,0,0,0,0
+9857,Strong Completeness and the Finite Model Property for Bi-Intuitionistic Stable Tense Logics,"  Bi-Intuitionistic Stable Tense Logics (BIST Logics) are tense logics with a
+Kripke semantics where worlds in a frame are equipped with a pre-order as well
+as with an accessibility relation which is 'stable' with respect to this
+pre-order. BIST logics are extensions of a logic, BiSKt, which arose in the
+semantic context of hypergraphs, since a special case of the pre-order can
+represent the incidence structure of a hypergraph. In this paper we provide,
+for the first time, a Hilbert-style axiomatisation of BISKt and prove the
+strong completeness of BiSKt. We go on to prove strong completeness of a class
+of BIST logics obtained by extending BiSKt by formulas of a certain form.
+Moreover we show that the finite model property and the decidability hold for a
+class of BIST logics.
+",1,0,1,0,0,0
+9858,Using lab notebooks to examine students' engagement in modeling in an upper-division electronics lab course,"  We demonstrate how students' use of modeling can be examined and assessed
+using student notebooks collected from an upper-division electronics lab
+course. The use of models is a ubiquitous practice in undergraduate physics
+education, but the process of constructing, testing, and refining these models
+is much less common. We focus our attention on a lab course that has been
+transformed to engage students in this modeling process during lab activities.
+The design of the lab activities was guided by a framework that captures the
+different components of model-based reasoning, called the Modeling Framework
+for Experimental Physics. We demonstrate how this framework can be used to
+assess students' written work and to identify how students' model-based
+reasoning differed from activity to activity. Broadly speaking, we were able to
+identify the different steps of students' model-based reasoning and assess the
+completeness of their reasoning. Varying degrees of scaffolding present across
+the activities had an impact on how thoroughly students would engage in the
+full modeling process, with more scaffolded activities resulting in more
+thorough engagement with the process. Finally, we identified that the step in
+the process with which students had the most difficulty was the comparison
+between their interpreted data and their model prediction. Students did not use
+sufficiently sophisticated criteria in evaluating such comparisons, which had
+the effect of halting the modeling process. This may indicate that in order to
+engage students further in using model-based reasoning during lab activities,
+the instructor needs to provide further scaffolding for how students make these
+types of experimental comparisons. This is an important design consideration
+for other such courses attempting to incorporate modeling as a learning goal.
+",0,1,0,0,0,0
+9859,An accurate finite element method for the numerical solution of isothermal and incompressible flow of viscous fluid,"  Despite its numerical challenges, finite element method is used to compute
+viscous fluid flow. A consensus on the cause of numerical problems has been
+reached; however, general algorithms---allowing a robust and accurate
+simulation for any process---are still missing. Either a very high
+computational cost is necessary for a direct numerical solution (DNS) or some
+limiting procedure is used by adding artificial dissipation to the system.
+These stabilization methods are useful; however, they are often applied
+relative to the element size such that a local monotonous convergence is
+challenging to acquire. We need a computational strategy for solving viscous
+fluid flow using solely the balance equations. In this work, we present a
+general procedure solving fluid mechanics problems without use of any
+stabilization or splitting schemes. Hence, its generalization to multiphysics
+applications is straightforward. We discuss emerging numerical problems and
+present the methodology rigorously. Implementation is achieved by using
+open-source packages and the accuracy as well as the robustness is demonstrated
+by comparing results to the closed-form solutions and also by solving
+well-known benchmarking problems.
+",1,1,0,0,0,0
+9860,Re-evaluating Evaluation,"  Progress in machine learning is measured by careful evaluation on problems of
+outstanding common interest. However, the proliferation of benchmark suites and
+environments, adversarial attacks, and other complications has diluted the
+basic evaluation model by overwhelming researchers with choices. Deliberate or
+accidental cherry picking is increasingly likely, and designing well-balanced
+evaluation suites requires increasing effort. In this paper we take a step back
+and propose Nash averaging. The approach builds on a detailed analysis of the
+algebraic structure of evaluation in two basic scenarios: agent-vs-agent and
+agent-vs-task. The key strength of Nash averaging is that it automatically
+adapts to redundancies in evaluation data, so that results are not biased by
+the incorporation of easy tasks or weak agents. Nash averaging thus encourages
+maximally inclusive evaluation -- since there is no harm (computational cost
+aside) from including all available tasks and agents.
+",0,0,0,1,0,0
+9861,Ranking and Selection as Stochastic Control,"  Under a Bayesian framework, we formulate the fully sequential sampling and
+selection decision in statistical ranking and selection as a stochastic control
+problem, and derive the associated Bellman equation. Using value function
+approximation, we derive an approximately optimal allocation policy. We show
+that this policy is not only computationally efficient but also possesses both
+one-step-ahead and asymptotic optimality for independent normal sampling
+distributions. Moreover, the proposed allocation policy is easily generalizable
+in the approximate dynamic programming paradigm.
+",1,0,0,1,0,0
+9862,An application of $Γ$-semigroups techniques to the Green's Theorem,"  The concept of a $\Gamma$-semigroup has been introduced by Mridul Kanti Sen
+in the Int. Symp., New Delhi, 1981. It is well known that the Green's relations
+play an essential role in studying the structure of semigroups. In the present
+paper we deal with an application of $\Gamma$-semigroups techniques to the
+Green's Theorem in an attempt to show the way we pass from semigroups to
+$\Gamma$-semigroups.
+",0,0,1,0,0,0
+9863,Reflection from a multi-species material and its transmitted effective wavenumber,"  We formally deduce closed-form expressions for the transmitted effective
+wavenumber of a material comprising multiple types of inclusions or particles
+(multi-species), dispersed in a uniform background medium. The expressions,
+derived here for the first time, are valid for moderate volume fractions and
+without restriction on the frequency. We show that the multi-species effective
+wavenumber is not a straightforward extension of expressions for a single
+species. Comparisons are drawn with state-of-the-art models in acoustics by
+presenting numerical results for a concrete and a water-oil emulsion in two
+dimensions. The limit of when one species is much smaller than the other is
+also discussed and we determine the background medium felt by the larger
+species in this limit. Surprisingly, we show that the answer is not the
+intuitive result predicted by self-consistent multiple scattering theories. The
+derivation presented here applies to the scalar wave equation with cylindrical
+or spherical inclusions, with any distribution of sizes, densities, and wave
+speeds. The reflection coefficient associated with a half-space of
+multi-species cylindrical inclusions is also formally derived.
+",0,1,0,0,0,0
+9864,Colouring perfect graphs with bounded clique number,"  A graph is perfect if the chromatic number of every induced subgraph equals
+the size of its largest clique, and an algorithm of Grötschel, Lovász, and
+Schrijver from 1988 finds an optimal colouring of a perfect graph in polynomial
+time. But this algorithm uses the ellipsoid method, and it is a well-known open
+question to construct a ""combinatorial"" polynomial-time algorithm that yields
+an optimal colouring of a perfect graph.
+A skew partition in $G$ is a partition $(A,B)$ of $V(G)$ such that $G[A]$ is
+not connected and $\bar{G}[B]$ is not connected, where $\bar{G}$ denotes the
+complement graph ; and it is balanced if an additional parity condition of
+paths in $G$ and $\bar{G}$ is satisfied.
+In this paper we first give a polynomial-time algorithm that, with input a
+perfect graph, outputs a balanced skew partition if there is one. Then we use
+this to obtain a combinatorial algorithm that finds an optimal colouring of a
+perfect graph with clique number $k$, in time that is polynomial for fixed $k$.
+",1,0,0,0,0,0
+9865,Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack,"  In this paper, we show synchronization for a group of output passive agents
+that communicate with each other according to an underlying communication graph
+to achieve a common goal. We propose a distributed event-triggered control
+framework that will guarantee synchronization and considerably decrease the
+required communication load on the band-limited network. We define a general
+Byzantine attack on the event-triggered multi-agent network system and
+characterize its negative effects on synchronization. The Byzantine agents are
+capable of intelligently falsifying their data and manipulating the underlying
+communication graph by altering their respective control feedback weights. We
+introduce a decentralized detection framework and analyze its steady-state and
+transient performances. We propose a way of identifying individual Byzantine
+neighbors and a learning-based method of estimating the attack parameters.
+Lastly, we propose learning-based control approaches to mitigate the negative
+effects of the adversarial attack.
+",1,0,0,1,0,0
+9866,EMG-Controlled Hand Teleoperation Using a Continuous Teleoperation Subspace,"  We present a method for EMG-driven teleoperation of non-anthropomorphic robot
+hands. EMG sensors are appealing as a wearable, inexpensive and unobtrusive way
+to gather information about the teleoperator's hand pose. However, mapping from
+EMG signals to the pose space of a non-anthropomorphic hand presents multiple
+challenges. We present a method that first projects from forearm EMG into a
+subspace relevant to teleoperation. To increase robustness, we use a model
+which combines continuous and discrete predictors along different dimensions of
+this subspace. We then project from the teleoperation subspace into the pose
+space of the robot hand. We show that our method is effective and intuitive, as
+it enables novice users to teleoperate pick and place tasks faster and more
+robustly than state-of-the-art EMG teleoperation methods when applied to a
+non-anthropomorphic, multi-DOF robot hand.
+",1,0,0,0,0,0
+9867,Numerical solutions of Hamiltonian PDEs: a multi-symplectic integrator in light-cone coordinates,"  We introduce a novel numerical method to integrate partial differential
+equations representing the Hamiltonian dynamics of field theories. It is a
+multi-symplectic integrator that locally conserves the stress-energy tensor
+with an excellent precision over very long periods. Its major advantage is that
+it is extremely simple (it is basically a centered box scheme) while remaining
+locally well defined. We put it to the test in the case of the non-linear wave
+equation (with quartic potential) in one spatial dimension, and we explain how
+to implement it in higher dimensions. A formal geometric presentation of the
+multi-symplectic structure is also given as well as a technical trick allowing
+to solve the degeneracy problem that potentially accompanies the
+multi-symplectic structure.
+",0,1,1,0,0,0
+9868,Magnetite nano-islands on silicon-carbide with graphene,"  X-ray magnetic circular dichroism (XMCD) measurements of iron nano-islands
+grown on graphene and covered with a Au film for passivation reveal that the
+oxidation through defects in the Au film spontaneously leads to the formation
+of magnetite nano-particles (i.e, $Fe_3$$O_4$). The Fe nano-islands (20 and 75
+monolayers; MLs) are grown on epitaxial graphene formed by thermally annealing
+6H-SiC(0001) and subsequently covered, in the growth chamber, with nominal 20
+layers of Au. Our X-ray absorption spectroscopy and XMCD measurements at
+applied magnetic fields show that the thin film (20 ML) is totally converted to
+magnetite whereas the thicker film (75 ML) exhibits properties of magnetite but
+also those of pure metallic iron. Temperature dependence of the XMCD signal (of
+both samples) shows a clear transition at $T_{\rm V}\approx 120$ K consistent
+with the Verwey transition of bulk magnetite. These results have implications
+on the synthesis of magnetite nano-crystals and also on their regular
+arrangements on functional substrates such as graphene.
+",0,1,0,0,0,0
+9869,Activation Ensembles for Deep Neural Networks,"  Many activation functions have been proposed in the past, but selecting an
+adequate one requires trial and error. We propose a new methodology of
+designing activation functions within a neural network at each layer. We call
+this technique an ""activation ensemble"" because it allows the use of multiple
+activation functions at each layer. This is done by introducing additional
+variables, $\alpha$, at each activation layer of a network to allow for
+multiple activation functions to be active at each neuron. By design,
+activations with larger $\alpha$ values at a neuron is equivalent to having the
+largest magnitude. Hence, those higher magnitude activations are ""chosen"" by
+the network. We implement the activation ensembles on a variety of datasets
+using an array of Feed Forward and Convolutional Neural Networks. By using the
+activation ensemble, we achieve superior results compared to traditional
+techniques. In addition, because of the flexibility of this methodology, we
+more deeply explore activation functions and the features that they capture.
+",0,0,0,1,0,0
+9870,Formation of coalition structures as a non-cooperative game,"  Traditionally social sciences are interested in structuring people in
+multiple groups based on their individual preferences. This pa- per suggests an
+approach to this problem in the framework of a non- cooperative game theory.
+Definition of a suggested finite game includes a family of nested simultaneous
+non-cooperative finite games with intra- and inter-coalition externalities. In
+this family, games differ by the size of maximum coalition, partitions and by
+coalition structure formation rules. A result of every game consists of
+partition of players into coalitions and a payoff? profiles for every player.
+Every game in the family has an equilibrium in mixed strategies with possibly
+more than one coalition. The results of the game differ from those
+conventionally discussed in cooperative game theory, e.g. the Shapley value,
+strong Nash, coalition-proof equilibrium, core, kernel, nucleolus. We discuss
+the following applications of the new game: cooperation as an allocation in one
+coalition, Bayesian games, stochastic games and construction of a
+non-cooperative criterion of coalition structure stability for studying focal
+points.
+",1,0,1,0,0,0
+9871,Smooth and Sparse Optimal Transport,"  Entropic regularization is quickly emerging as a new standard in optimal
+transport (OT). It enables to cast the OT computation as a differentiable and
+unconstrained convex optimization problem, which can be efficiently solved
+using the Sinkhorn algorithm. However, entropy keeps the transportation plan
+strictly positive and therefore completely dense, unlike unregularized OT. This
+lack of sparsity can be problematic in applications where the transportation
+plan itself is of interest. In this paper, we explore regularizing the primal
+and dual OT formulations with a strongly convex term, which corresponds to
+relaxing the dual and primal constraints with smooth approximations. We show
+how to incorporate squared $2$-norm and group lasso regularizations within that
+framework, leading to sparse and group-sparse transportation plans. On the
+theoretical side, we bound the approximation error introduced by regularizing
+the primal and dual formulations. Our results suggest that, for the regularized
+primal, the approximation error can often be smaller with squared $2$-norm than
+with entropic regularization. We showcase our proposed framework on the task of
+color transfer.
+",1,0,0,1,0,0
+9872,The stability and energy exchange mechanism of divergent states with real energy,"  The eigenvalue of the hermitic Hamiltonian is real undoubtedly. Actually, The
+reality can also be guaranteed by the $PT$-symmetry. The hermiticity and the
+$PT$-symmetric quantum theory both have requirements regarding the boundary
+condition. There exists a reverse strategy to investigate the quantum problem.
+Namely, define the eigenvalue as real first, and, meanwhile, open the boundary
+condition. Then the behaviors of the wave function at the boundary become rich
+in meaning. This eigenfunction is generally divergent, and the extent and
+direction of divergence are closely linked to the energy. It was noted that
+these divergent behaviors can be well described by their energy-space
+uncertainty relation which is not trivial anymore. The divergent state is
+unstable and will certainly exchange energy with the outside. The mechanism of
+energy exchange is just in the energy-space uncertainty relation, which will
+benefit dynamic simulation, the many-body problem, and so on. There is no
+distinct dividing line between this kind of divergent unstable state and the
+convergent stable state. Their relationship is like that of the rational and
+irrational numbers. In practice, there are distinct advantages of speed and
+accuracy for the methods based on the laws of divergence.
+",0,1,0,0,0,0
+9873,A bound on partitioning clusters,"  Let $X$ be a finite collection of sets (or ""clusters""). We consider the
+problem of counting the number of ways a cluster $A \in X$ can be partitioned
+into two disjoint clusters $A_1, A_2 \in X$, thus $A = A_1 \uplus A_2$ is the
+disjoint union of $A_1$ and $A_2$; this problem arises in the run time analysis
+of the ASTRAL algorithm in phylogenetic reconstruction. We obtain the bound $$
+| \{ (A_1,A_2,A) \in X \times X \times X: A = A_1 \uplus A_2 \} | \leq
+|X|^{3/p} $$ where $|X|$ denotes the cardinality of $X$, and $p := \log_3
+\frac{27}{4} = 1.73814\dots$, so that $\frac{3}{p} = 1.72598\dots$.
+Furthermore, the exponent $p$ cannot be replaced by any larger quantity. This
+improves upon the trivial bound of $|X|^2$. The argument relies on establishing
+a one-dimensional convolution inequality that can be established by elementary
+calculus combined with some numerical verification.
+In a similar vein, we show that for any subset $A$ of a discrete cube
+$\{0,1\}^n$, the additive energy of $A$ (the number of quadruples
+$(a_1,a_2,a_3,a_4)$ in $A^4$ with $a_1+a_2=a_3+a_4$) is at most $|A|^{\log_2
+6}$, and that this exponent is best possible.
+",0,0,1,0,0,0
+9874,X-ray emission from thin plasmas. Collisional ionization for atoms and ions of H to Zn,"  Every observation of astrophysical objects involving a spectrum requires
+atomic data for the interpretation of line fluxes, line ratios and ionization
+state of the emitting plasma. One of the processes which determines it is
+collisional ionization. In this study an update of the direct ionization (DI)
+and excitation-autoionization (EA) processes is discussed for the H to Zn-like
+isoelectronic sequences. In the last years new laboratory measurements and
+theoretical calculations of ionization cross sections have become available. We
+provide an extension and update of previous published reviews in the
+literature. We include the most recent experimental measurements and fit the
+cross sections of all individual shells of all ions from H to Zn. These data
+are described using an extension of Younger's and Mewe's formula, suitable for
+integration over a Maxwellian velocity distribution to derive the subshell
+ionization rate coefficients. These ionization rate coefficients are
+incorporated in the high-resolution plasma code and spectral fitting tool SPEX
+V3.0.
+",0,1,0,0,0,0
+9875,Support Spinor Machine,"  We generalize a support vector machine to a support spinor machine by using
+the mathematical structure of wedge product over vector machine in order to
+extend field from vector field to spinor field. The separated hyperplane is
+extended to Kolmogorov space in time series data which allow us to extend a
+structure of support vector machine to a support tensor machine and a support
+tensor machine moduli space. Our performance test on support spinor machine is
+done over one class classification of end point in physiology state of time
+series data after empirical mode analysis and compared with support vector
+machine test. We implement algorithm of support spinor machine by using
+Holo-Hilbert amplitude modulation for fully nonlinear and nonstationary time
+series data analysis.
+",1,0,0,1,0,0
+9876,"Various generalizations and deformations of $PSL(2,\mathbb{R})$ surface group representations and their Higgs bundles","  Recall that the group $PSL(2,\mathbb R)$ is isomorphic to $PSp(2,\mathbb R),\
+SO_0(1,2)$ and $PU(1,1).$ The goal of this paper is to examine the various ways
+in which Fuchsian representations of the fundamental group of a closed surface
+of genus $g$ into $PSL(2,\mathbb R)$ and their associated Higgs bundles
+generalize to the higher rank groups $PSL(n,\mathbb R),\ PSp(2n,\mathbb R),\
+SO_0(2,n),\ SO_0(n,n+1)$ and $PU(n,n)$. For the $SO_0(n,n+1)$-character
+variety, we parameterize $n(2g-2)$ new connected components as the total space
+of vector bundles over appropriate symmetric powers of the surface and study
+how these components deform in the $SO_0(n,n+2)$-character variety. This
+generalizes results of Hitchin for $PSL(2,\mathbb R)$.
+",0,0,1,0,0,0
+9877,Redundancy schemes for engineering coherent systems via a signature-based approach,"  This paper proposes a signature-based approach for solving redundancy
+allocation problems when component lifetimes are not only heterogeneous but
+also dependent. The two common schemes for allocations, that is active and
+standby redundancies, are considered. If the component lifetimes are
+independent, the proposed approach leads to simple manipulations. Various
+illustrative examples are also analysed. This method can be implemented for
+practical complex engineering systems.
+",0,0,0,1,0,0
+9878,Anisotropy effects on Baryogenesis in $f(R)$-Theories of Gravity,"  We study the $f(R)$ theory of gravity in an anisotropic metric and its effect
+on the baryon number to entropy ratio. The mechanism of gravitational
+baryogenesis based on the CPT-violating gravitational interaction between
+derivative of the Ricci scalar curvature and the baryon-number current is
+investigated in the context of the $f(R)$ gravity. The gravitational
+baryogenesis in the Bianchi type I universe is examined. We survey the effect
+of anisotropy of the universe on the baryon asymmetry from point of view the
+$f(R)$-theories of gravity and its effect on $n_{b}/s$ for radiation dominant
+regime.
+",0,1,0,0,0,0
+9879,Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation,"  Visual tracking is a fundamental problem in computer vision. Recently, some
+deep-learning-based tracking algorithms have been achieving record-breaking
+performances. However, due to the high complexity of deep learning, most deep
+trackers suffer from low tracking speed, and thus are impractical in many
+real-world applications. Some new deep trackers with smaller network structure
+achieve high efficiency while at the cost of significant decrease on precision.
+In this paper, we propose to transfer the feature for image classification to
+the visual tracking domain via convolutional channel reductions. The channel
+reduction could be simply viewed as an additional convolutional layer with the
+specific task. It not only extracts useful information for object tracking but
+also significantly increases the tracking speed. To better accommodate the
+useful feature of the target in different scales, the adaptation filters are
+designed with different sizes. The yielded visual tracker is real-time and also
+illustrates the state-of-the-art accuracies in the experiment involving two
+well-adopted benchmarks with more than 100 test videos.
+",1,0,0,0,0,0
+9880,Simply Exponential Approximation of the Permanent of Positive Semidefinite Matrices,"  We design a deterministic polynomial time $c^n$ approximation algorithm for
+the permanent of positive semidefinite matrices where $c=e^{\gamma+1}\simeq
+4.84$. We write a natural convex relaxation and show that its optimum solution
+gives a $c^n$ approximation of the permanent. We further show that this factor
+is asymptotically tight by constructing a family of positive semidefinite
+matrices.
+",1,0,0,0,0,0
+9881,Stochastic Variance Reduction for Policy Gradient Estimation,"  Recent advances in policy gradient methods and deep learning have
+demonstrated their applicability for complex reinforcement learning problems.
+However, the variance of the performance gradient estimates obtained from the
+simulation is often excessive, leading to poor sample efficiency. In this
+paper, we apply the stochastic variance reduced gradient descent (SVRG) to
+model-free policy gradient to significantly improve the sample-efficiency. The
+SVRG estimation is incorporated into a trust-region Newton conjugate gradient
+framework for the policy optimization. On several Mujoco tasks, our method
+achieves significantly better performance compared to the state-of-the-art
+model-free policy gradient methods in robotic continuous control such as trust
+region policy optimization (TRPO)
+",1,0,0,1,0,0
+9882,Weighted Low-Rank Approximation of Matrices and Background Modeling,"  We primarily study a special a weighted low-rank approximation of matrices
+and then apply it to solve the background modeling problem. We propose two
+algorithms for this purpose: one operates in the batch mode on the entire data
+and the other one operates in the batch-incremental mode on the data and
+naturally captures more background variations and computationally more
+effective. Moreover, we propose a robust technique that learns the background
+frame indices from the data and does not require any training frames. We
+demonstrate through extensive experiments that by inserting a simple weight in
+the Frobenius norm, it can be made robust to the outliers similar to the
+$\ell_1$ norm. Our methods match or outperform several state-of-the-art online
+and batch background modeling methods in virtually all quantitative and
+qualitative measures.
+",1,0,0,0,0,0
+9883,Master equation for She-Leveque scaling and its classification in terms of other Markov models of developed turbulence,"  We derive the Markov process equivalent to She-Leveque scaling in homogeneous
+and isotropic turbulence. The Markov process is a jump process for velocity
+increments $u(r)$ in scale $r$ in which the jumps occur randomly but with
+deterministic width in $u$. From its master equation we establish a
+prescription to simulate the She-Leveque process and compare it with Kolmogorov
+scaling. To put the She-Leveque process into the context of other established
+turbulence models on the Markov level, we derive a diffusion process for $u(r)$
+from two properties of the Navier-Stokes equation. This diffusion process
+already includes Kolmogorov scaling, extended self-similarity and a class of
+random cascade models. The fluctuation theorem of this Markov process implies a
+""second law"" that puts a loose bound on the multipliers of the random cascade
+models. This bound explicitly allows for inverse cascades, which are necessary
+to satisfy the fluctuation theorem. By adding a jump process to the diffusion
+process, we go beyond Kolmogorov scaling and formulate the most general scaling
+law for the class of Markov processes having both diffusion and jump parts.
+This Markov scaling law includes She-Leveque scaling and a scaling law derived
+by Yakhot.
+",0,1,0,0,0,0
+9884,Scaling Universality at the Dynamic Vortex Mott Transition,"  The dynamic Mott insulator-to-metal transition (DMT) is key to many
+intriguing phenomena in condensed matter physics yet it remains nearly
+unexplored. The cleanest way to observe DMT, without the interference from
+disorder and other effects inherent to electronic and atomic systems, is to
+employ the vortex Mott states formed by superconducting vortices in a regular
+array of pinning sites. The applied electric current delocalizes vortices and
+drives the dynamic vortex Mott transition. Here we report the critical behavior
+of the vortex system as it crosses the DMT line, driven by either current or
+temperature. We find universal scaling with respect to both, expressed by the
+same scaling function and characterized by a single critical exponent
+coinciding with the exponent for the thermodynamic Mott transition. We develop
+a theory for the DMT based on the parity reflection-time reversal (PT) symmetry
+breaking formalism and find that the nonequilibrium-induced Mott transition has
+the same critical behavior as thermal Mott transition. Our findings demonstrate
+the existence of physical systems in which the effect of nonequilibrium drive
+is to generate effective temperature and hence the transition belonging in the
+thermal universality class. We establish PT symmetry-breaking as a universal
+mechanism for out-of-equilibrium phase transitions.
+",0,1,0,0,0,0
+9885,Assessment Formats and Student Learning Performance: What is the Relation?,"  Although compelling assessments have been examined in recent years, more
+studies are required to yield a better understanding of the several methods
+where assessment techniques significantly affect student learning process. Most
+of the educational research in this area does not consider demographics data,
+differing methodologies, and notable sample size. To address these drawbacks,
+the objective of our study is to analyse student learning outcomes of multiple
+assessment formats for a web-facilitated in-class section with an asynchronous
+online class of a core data communications course in the Undergraduate IT
+program of the Information Sciences and Technology (IST) Department at George
+Mason University (GMU). In this study, students were evaluated based on course
+assessments such as home and lab assignments, skill-based assessments, and
+traditional midterm and final exams across all four sections of the course. All
+sections have equivalent content, assessments, and teaching methodologies.
+Student demographics such as exam type and location preferences are considered
+in our study to determine whether they have any impact on their learning
+approach. Large amount of data from the learning management system (LMS),
+Blackboard (BB) Learn, had to be examined to compare the results of several
+assessment outcomes for all students within their respective section and
+amongst students of other sections. To investigate the effect of dissimilar
+assessment formats on student performance, we had to correlate individual
+question formats with the overall course grade. The results show that
+collective assessment formats allow students to be effective in demonstrating
+their knowledge.
+",1,0,0,1,0,0
+9886,Sharing deep generative representation for perceived image reconstruction from human brain activity,"  Decoding human brain activities via functional magnetic resonance imaging
+(fMRI) has gained increasing attention in recent years. While encouraging
+results have been reported in brain states classification tasks, reconstructing
+the details of human visual experience still remains difficult. Two main
+challenges that hinder the development of effective models are the perplexing
+fMRI measurement noise and the high dimensionality of limited data instances.
+Existing methods generally suffer from one or both of these issues and yield
+dissatisfactory results. In this paper, we tackle this problem by casting the
+reconstruction of visual stimulus as the Bayesian inference of missing view in
+a multiview latent variable model. Sharing a common latent representation, our
+joint generative model of external stimulus and brain response is not only
+""deep"" in extracting nonlinear features from visual images, but also powerful
+in capturing correlations among voxel activities of fMRI recordings. The
+nonlinearity and deep structure endow our model with strong representation
+ability, while the correlations of voxel activities are critical for
+suppressing noise and improving prediction. We devise an efficient variational
+Bayesian method to infer the latent variables and the model parameters. To
+further improve the reconstruction accuracy, the latent representations of
+testing instances are enforced to be close to that of their neighbours from the
+training set via posterior regularization. Experiments on three fMRI recording
+datasets demonstrate that our approach can more accurately reconstruct visual
+stimuli.
+",1,0,0,0,0,0
+9887,Two types of criticality in the brain,"  Neural networks with equal excitatory and inhibitory feedback show high
+computational performance. They operate close to a critical point characterized
+by the joint activation of large populations of neurons. Yet, in macaque motor
+cortex we observe very different dynamics with weak fluctuations on the
+population level. This suggests that motor cortex operates in a sub-optimal
+regime. Here we show the opposite: the large dispersion of correlations across
+neurons is a signature of a rich dynamical repertoire, hidden from macroscopic
+brain signals, but essential for high performance in such concepts as reservoir
+computing. Our findings suggest a refinement of the view on criticality in
+neural systems: network topology and heterogeneity endow the brain with two
+complementary substrates for critical dynamics of largely different
+complexities.
+",0,1,0,0,0,0
+9888,Topological networks for quantum communication between distant qubits,"  Efficient communication between qubits relies on robust networks which allow
+for fast and coherent transfer of quantum information. It seems natural to
+harvest the remarkable properties of systems characterized by topological
+invariants to perform this task. Here we show that a linear network of coupled
+bosonic degrees of freedom, characterized by topological bands, can be employed
+for the efficient exchange of quantum information over large distances.
+Important features of our setup are that it is robust against quenched
+disorder, all relevant operations can be performed by global variations of
+parameters, and the time required for communication between distant qubits
+approaches linear scaling with their distance. We demonstrate that our concept
+can be extended to an ensemble of qubits embedded in a two-dimensional network
+to allow for communication between all of them.
+",0,1,0,0,0,0
+9889,How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations,"  Whereas the relationship between criticality of gene regulatory networks
+(GRNs) and dynamics of GRNs at a single cell level has been vigorously studied,
+the relationship between the criticality of GRNs and system properties at a
+higher level has remained unexplored. Here we aim at revealing a potential role
+of criticality of GRNs at a multicellular level which are hard to uncover
+through the single-cell-level studies, especially from an evolutionary
+viewpoint. Our model simulated the growth of a cell population from a single
+seed cell. All the cells were assumed to have identical GRNs. We induced
+genetic perturbations to the GRN of the seed cell by adding, deleting, or
+switching a regulatory link between a pair of genes. From numerical
+simulations, we found that the criticality of GRNs facilitated the formation of
+nontrivial morphologies when the GRNs were critical in the presence of the
+evolutionary perturbations. Moreover, the criticality of GRNs produced
+topologically homogenous cell clusters by adjusting the spatial arrangements of
+cells, which led to the formation of nontrivial morphogenetic patterns. Our
+findings corresponded to an epigenetic viewpoint that heterogeneous and complex
+features emerge from homogeneous and less complex components through the
+interactions among them. Thus, our results imply that highly structured tissues
+or organs in morphogenesis of multicellular organisms might stem from the
+criticality of GRNs.
+",0,0,0,0,1,0
+9890,Task-Driven Convolutional Recurrent Models of the Visual System,"  Feed-forward convolutional neural networks (CNNs) are currently
+state-of-the-art for object classification tasks such as ImageNet. Further,
+they are quantitatively accurate models of temporally-averaged responses of
+neurons in the primate brain's visual system. However, biological visual
+systems have two ubiquitous architectural features not shared with typical
+CNNs: local recurrence within cortical areas, and long-range feedback from
+downstream areas to upstream areas. Here we explored the role of recurrence in
+improving classification performance. We found that standard forms of
+recurrence (vanilla RNNs and LSTMs) do not perform well within deep CNNs on the
+ImageNet task. In contrast, novel cells that incorporated two structural
+features, bypassing and gating, were able to boost task accuracy substantially.
+We extended these design principles in an automated search over thousands of
+model architectures, which identified novel local recurrent cells and
+long-range feedback connections useful for object recognition. Moreover, these
+task-optimized ConvRNNs matched the dynamics of neural activity in the primate
+visual system better than feedforward networks, suggesting a role for the
+brain's recurrent connections in performing difficult visual behaviors.
+",0,0,0,0,1,0
+9891,Self-organization principles of intracellular pattern formation,"  Dynamic patterning of specific proteins is essential for the spatiotemporal
+regulation of many important intracellular processes in procaryotes,
+eucaryotes, and multicellular organisms. The emergence of patterns generated by
+interactions of diffusing proteins is a paradigmatic example for
+self-organization. In this article we review quantitative models for
+intracellular Min protein patterns in E. coli, Cdc42 polarization in S.
+cerevisiae, and the bipolar PAR protein patterns found in C. elegans. By
+analyzing the molecular processes driving these systems we derive a theoretical
+perspective on general principles underlying self-organized pattern formation.
+We argue that intracellular pattern formation is not captured by concepts such
+as ""activators""', ""inhibitors"", or ""substrate-depletion"". Instead,
+intracellular pattern formation is based on the redistribution of proteins by
+cytosolic diffusion, and the cycling of proteins between distinct
+conformational states. Therefore, mass-conserving reaction-diffusion equations
+provide the most appropriate framework to study intracellular pattern
+formation. We conclude that directed transport, e.g. cytosolic diffusion along
+an actively maintained cytosolic gradient, is the key process underlying
+pattern formation. Thus the basic principle of self-organization is the
+establishment and maintenance of directed transport by intracellular protein
+dynamics.
+",0,0,0,0,1,0
+9892,"Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science","  If we pick $n$ random points uniformly in $[0,1]^d$ and connect each point to
+its $k-$nearest neighbors, then it is well known that there exists a giant
+connected component with high probability. We prove that in $[0,1]^d$ it
+suffices to connect every point to $ c_{d,1} \log{\log{n}}$ points chosen
+randomly among its $ c_{d,2} \log{n}-$nearest neighbors to ensure a giant
+component of size $n - o(n)$ with high probability. This construction yields a
+much sparser random graph with $\sim n \log\log{n}$ instead of $\sim n \log{n}$
+edges that has comparable connectivity properties. This result has nontrivial
+implications for problems in data science where an affinity matrix is
+constructed: instead of picking the $k-$nearest neighbors, one can often pick
+$k' \ll k$ random points out of the $k-$nearest neighbors without sacrificing
+efficiency. This can massively simplify and accelerate computation, we
+illustrate this with several numerical examples.
+",1,0,0,1,0,0
+9893,Theory of mechano-chemical patterning in biphasic biological tissues,"  The formation of self-organized patterns is key to the morphogenesis of
+multicellular organisms, although a comprehensive theory of biological pattern
+formation is still lacking. Here, we propose a minimal model combining tissue
+mechanics to morphogen turnover and transport in order to explore new routes to
+patterning. Our active description couples morphogen reaction-diffusion, which
+impact on cell differentiation and tissue mechanics, to a two-phase poroelastic
+rheology, where one tissue phase consists of a poroelastic cell network and the
+other of a permeating extracellular fluid, which provides a feedback by
+actively transporting morphogens. While this model encompasses previous
+theories approximating tissues to inert monophasic media, such as Turing's
+reaction-diffusion model, it overcomes some of their key limitations permitting
+pattern formation via any two-species biochemical kinetics thanks to
+mechanically induced cross-diffusion flows. Moreover, we describe a
+qualitatively different advection-driven Keller-Segel instability which allows
+for the formation of patterns with a single morphogen, and whose fundamental
+mode pattern robustly scales with tissue size. We discuss the potential
+relevance of these findings for tissue morphogenesis.
+",0,0,0,0,1,0
+9894,Diffusion time dependence of microstructural parameters in fixed spinal cord,"  Biophysical modelling of diffusion MRI is necessary to provide specific
+microstructural tissue properties. However, estimating model parameters from
+data with limited diffusion gradient strength, such as clinical scanners, has
+proven unreliable due to a shallow optimization landscape. On the other hand,
+estimation of diffusion kurtosis (DKI) parameters is more robust as the
+clinical acquisitions typically probe a regime in which the associated 4th
+order cumulant expansion is adequate; however, its parameters are not
+microstructurally specific a priori. Given an appropriate biophysical model,
+its parameters may be connected to DKI parameters, but it was previously shown
+that at the DKI level, it still does not provide sufficient information to
+uniquely determine all model parameters. Earlier work has shown that by
+neglecting axonal dispersion, this parameter degeneracy reduces to the question
+of whether intra-axonal diffusivity is larger than or smaller than extra-axonal
+diffusivity. Here we develop a model of diffusion in spinal cord white matter
+including axonal dispersion and demonstrate stable estimation of all model
+parameters from DKI. By employing the recently developed fast axisymmetric DKI,
+we use stimulated echo acquisition mode to collect data over an unprecedented
+diffusion time range with very narrow diffusion gradient pulses, enabling
+finely resolved measurements of diffusion time dependence of both net diffusion
+and kurtosis metrics, as well as model intra- and extra-axonal diffusivities,
+and axonal dispersion. Our results demonstrate substantial time dependence of
+all parameters except volume fractions, and the additional time dimension
+provides support for intra-axonal diffusivity to be larger than extra-axonal
+diffusivity in spinal cord white matter, although not unambiguously. We compare
+our findings to predictions from effective medium theory.
+",0,1,0,0,0,0
+9895,Optimizing Epistemic Model Checking Using Conditional Independence (Extended Abstract),"  This paper shows that conditional independence reasoning can be applied to
+optimize epistemic model checking, in which one verifies that a model for a
+number of agents operating with imperfect information satisfies a formula
+expressed in a modal multi-agent logic of knowledge. The optimization has been
+implemented in the epistemic model checker MCK. The paper reports experimental
+results demonstrating that it can yield multiple orders of magnitude
+performance improvements.
+",1,0,0,0,0,0
+9896,Quickest Localization of Anomalies in Power Grids: A Stochastic Graphical Framework,"  Agile localization of anomalous events plays a pivotal role in enhancing the
+overall reliability of the grid and avoiding cascading failures. This is
+especially of paramount significance in the large-scale grids due to their
+geographical expansions and the large volume of data generated. This paper
+proposes a stochastic graphical framework, by leveraging which it aims to
+localize the anomalies with the minimum amount of data. This framework
+capitalizes on the strong correlation structures observed among the
+measurements collected from different buses. The proposed approach, at its
+core, collects the measurements sequentially and progressively updates its
+decision about the location of the anomaly. The process resumes until the
+location of the anomaly can be identified with desired reliability. We provide
+a general theory for the quickest anomaly localization and also investigate its
+application for quickest line outage localization. Simulations in the IEEE
+118-bus model are provided to establish the gains of the proposed approach.
+",1,0,0,1,0,0
+9897,The trouble with tensor ring decompositions,"  The tensor train decomposition decomposes a tensor into a ""train"" of 3-way
+tensors that are interconnected through the summation of auxiliary indices. The
+decomposition is stable, has a well-defined notion of rank and enables the user
+to perform various linear algebra operations on vectors and matrices of
+exponential size in a computationally efficient manner. The tensor ring
+decomposition replaces the train by a ring through the introduction of one
+additional auxiliary variable. This article discusses a major issue with the
+tensor ring decomposition: its inability to compute an exact minimal-rank
+decomposition from a decomposition with sub-optimal ranks. Both the contraction
+operation and Hadamard product are motivated from applications and it is shown
+through simple examples how the tensor ring-rounding procedure fails to
+retrieve minimal-rank decompositions with these operations. These observations,
+together with the already known issue of not being able to find a best low-rank
+tensor ring approximation to a given tensor indicate that the applicability of
+tensor rings is severely limited.
+",1,0,0,0,0,0
+9898,Direct Measurement of Kramers Turnover with a Levitated Nanoparticle,"  Understanding the thermally activated escape from a metastable state is at
+the heart of important phenomena such as the folding dynamics of proteins, the
+kinetics of chemical reactions or the stability of mechanical systems. In 1940
+Kramers calculated escape rates both in the high damping and the low damping
+regime and suggested that the rate must have a maximum for intermediate
+damping. This phenomenon, today known as the Kramers turnover, has triggered
+important theoretical and numerical studies. However, to date there is no
+direct and quantitative experimental verification of this turnover. Using a
+nanoparticle trapped in a bi-stable optical potential we experimentally measure
+the nanoparticle's transition rates for variable damping and directly resolve
+the Kramers turnover. Our measurements are in agreement with an analytical
+model that is free of adjustable parameters.
+",0,1,0,0,0,0
+9899,An estimator for the tail-index of graphex processes,"  Sparse exchangeable graphs resolve some pathologies in traditional random
+graph models, notably, providing models that are both projective and allow
+sparsity. In a recent paper, Caron and Rousseau (2017) show that for a large
+class of sparse exchangeable models, the sparsity behaviour is governed by a
+single parameter: the tail-index of the function (the graphon) that
+parameterizes the model. We propose an estimator for this parameter and
+quantify its risk. Our estimator is a simple, explicit function of the degrees
+of the observed graph. In many situations of practical interest, the risk
+decays polynomially in the size of the observed graph. Accordingly, the
+estimator is practically useful for estimation of sparse exchangeable models.
+We also derive the analogous results for the bipartite sparse exchangeable
+case.
+",0,0,1,1,0,0
+9900,General tête-à-tête graphs and Seifert manifolds,"  Tête-à-tête graphs and relative tête-à-tête graphs were
+introduced by N. A'Campo in 2010 to model monodromies of isolated plane curves.
+By recent workof Fdez de Bobadilla, Pe Pereira and the author, they provide a
+way of modeling the periodic mapping classes that leave some boundary component
+invariant. In this work we introduce the notion of general tête-à-tête
+graph and prove that they model all periodic mapping classes. We also describe
+algorithms that take a Seifert manifold and a horizontal surface and return a
+tête-à-tête graph and vice versa.
+",0,0,1,0,0,0
+9901,Isometric copies of $l^\infty$ in Cesàro-Orlicz function spaces,"  We characterize Cesàro-Orlicz function spaces $Ces_{\varphi}$ containing
+order isomorphically isometric copy of $l^\infty$. We discuss also some useful
+applicable conditions sufficient for the existence of such a copy.
+",0,0,1,0,0,0
+9902,Modeling non-stationary extreme dependence with stationary max-stable processes and multidimensional scaling,"  Modeling the joint distribution of extreme weather events in multiple
+locations is a challenging task with important applications. In this study, we
+use max-stable models to study extreme daily precipitation events in
+Switzerland. The non-stationarity of the spatial process at hand involves
+important challenges, which are often dealt with by using a stationary model in
+a so-called climate space, with well-chosen covariates. Here, we instead chose
+to warp the weather stations under study in a latent space of higher dimension
+using multidimensional scaling (MDS). The advantage of this approach is its
+improved flexibility to reproduce highly non-stationary phenomena, while
+keeping a tractable stationary spatial model in the latent space. Two model
+fitting approaches, which both use MDS, are presented and compared to a
+classical approach that relies on composite likelihood maximization in a
+climate space. Results suggest that the proposed methods better reproduce the
+observed extremal coefficients and their complex spatial dependence.
+",0,0,0,1,0,0
+9903,MOEMS deformable mirror testing in cryo for future optical instrumentation,"  MOEMS Deformable Mirrors (DM) are key components for next generation
+instruments with innovative adaptive optics systems, in existing telescopes and
+in the future ELTs. These DMs must perform at room temperature as well as in
+cryogenic and vacuum environment. Ideally, the MOEMS-DMs must be designed to
+operate in such environment. We present some major rules for designing /
+operating DMs in cryo and vacuum. We chose to use interferometry for the full
+characterization of these devices, including surface quality measurement in
+static and dynamical modes, at ambient and in vacuum/cryo. Thanks to our
+previous set-up developments, we placed a compact cryo-vacuum chamber designed
+for reaching 10-6 mbar and 160K, in front of our custom Michelson
+interferometer, able to measure performances of the DM at actuator/segment
+level as well as whole mirror level, with a lateral resolution of 2{\mu}m and a
+sub-nanometric z-resolution. Using this interferometric bench, we tested the
+Iris AO PTT111 DM: this unique and robust design uses an array of single
+crystalline silicon hexagonal mirrors with a pitch of 606{\mu}m, able to move
+in tip, tilt and piston with strokes from 5 to 7{\mu}m, and tilt angle in the
+range of +/-5mrad. They exhibit typically an open-loop flat surface figure as
+good as <20nm rms. A specific mount including electronic and opto-mechanical
+interfaces has been designed for fitting in the test chamber. Segment
+deformation, mirror shaping, open-loop operation are tested at room and cryo
+temperature and results are compared. The device could be operated successfully
+at 160K. An additional, mainly focus-like, 500 nm deformation is measured at
+160K; we were able to recover the best flat in cryo by correcting the focus and
+local tip-tilts on some segments. Tests on DM with different mirror thicknesses
+(25{\mu}m and 50{\mu}m) and different coatings (silver and gold) are currently
+under way.
+",0,1,0,0,0,0
+9904,Remote Sensing Image Scene Classification: Benchmark and State of the Art,"  Remote sensing image scene classification plays an important role in a wide
+range of applications and hence has been receiving remarkable attention. During
+the past years, significant efforts have been made to develop various datasets
+or present a variety of approaches for scene classification from remote sensing
+images. However, a systematic review of the literature concerning datasets and
+methods for scene classification is still lacking. In addition, almost all
+existing datasets have a number of limitations, including the small scale of
+scene classes and the image numbers, the lack of image variations and
+diversity, and the saturation of accuracy. These limitations severely limit the
+development of new approaches especially deep learning-based methods. This
+paper first provides a comprehensive review of the recent progress. Then, we
+propose a large-scale dataset, termed ""NWPU-RESISC45"", which is a publicly
+available benchmark for REmote Sensing Image Scene Classification (RESISC),
+created by Northwestern Polytechnical University (NWPU). This dataset contains
+31,500 images, covering 45 scene classes with 700 images in each class. The
+proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total
+image number, (ii) holds big variations in translation, spatial resolution,
+viewpoint, object pose, illumination, background, and occlusion, and (iii) has
+high within-class diversity and between-class similarity. The creation of this
+dataset will enable the community to develop and evaluate various data-driven
+algorithms. Finally, several representative methods are evaluated using the
+proposed dataset and the results are reported as a useful baseline for future
+research.
+",1,0,0,0,0,0
+9905,"The Network of U.S. Mutual Fund Investments: Diversification, Similarity and Fragility throughout the Global Financial Crisis","  Network theory proved recently to be useful in the quantification of many
+properties of financial systems. The analysis of the structure of investment
+portfolios is a major application since their eventual correlation and overlap
+impact the actual risk diversification by individual investors. We investigate
+the bipartite network of US mutual fund portfolios and their assets. We follow
+its evolution during the Global Financial Crisis and analyse the interplay
+between diversification, as understood in classical portfolio theory, and
+similarity of the investments of different funds. We show that, on average,
+portfolios have become more diversified and less similar during the crisis.
+However, we also find that large overlap is far more likely than expected from
+models of random allocation of investments. This indicates the existence of
+strong correlations between fund portfolio strategies. We introduce a
+simplified model of propagation of financial shocks, that we exploit to show
+that a systemic risk component origins from the similarity of portfolios. The
+network is still vulnerable after crisis because of this effect, despite the
+increase in the diversification of portfolios. Our results indicate that
+diversification may even increase systemic risk when funds diversify in the
+same way. Diversification and similarity can play antagonistic roles and the
+trade-off between the two should be taken into account to properly assess
+systemic risk.
+",0,0,0,1,0,1
+9906,Removal of Salt and Pepper noise from Gray-Scale and Color Images: An Adaptive Approach,"  An efficient adaptive algorithm for the removal of Salt and Pepper noise from
+gray scale and color image is presented in this paper. In this proposed method
+first a 3X3 window is taken and the central pixel of the window is considered
+as the processing pixel. If the processing pixel is found as uncorrupted, then
+it is left unchanged. And if the processing pixel is found corrupted one, then
+the window size is increased according to the conditions given in the proposed
+algorithm. Finally the processing pixel or the central pixel is replaced by
+either the mean, median or trimmed value of the elements in the current window
+depending upon different conditions of the algorithm. The proposed algorithm
+efficiently removes noise at all densities with better Peak Signal to Noise
+Ratio (PSNR) and Image Enhancement Factor (IEF). The proposed algorithm is
+compared with different existing algorithms like MF, AMF, MDBUTMF, MDBPTGMF and
+AWMF.
+",1,0,0,0,0,0
+9907,On the distance and algorithms of strong product digraphs,"  Strong product is an efficient way to construct a larger digraph through some
+specific small digraphs. The large digraph constructed by the strong product
+method contains the factor digraphs as its subgraphs, and can retain some good
+properties of the factor digraphs. The distance of digraphs is one of the most
+basic structural parameters in graph theory, and it plays an important role in
+analyzing the effectiveness of interconnection networks. In particular, it
+provides a basis for measuring the transmission delay of networks. When the
+topological structure of an interconnection network is represented by a
+digraph, the average distance of the directed graph is a good measure of the
+communication performance of the network. In this paper, we mainly investigate
+the distance and average distance of strong product digraphs, and give a
+formula for the distance of strong product digraphs and an algorithm for
+solving the average distance of strong product digraphs.
+",1,0,0,0,0,0
+9908,Global weak solution to the viscous two-fluid model with finite energy,"  In this paper, we prove the existence of global weak solutions to the
+compressible two-fluid Navier-Stokes equations in three dimensional space. The
+pressure depends on two different variables from the continuity equations.
+We develop an argument of variable reduction for the pressure law. This
+yields to the strong convergence of the densities, and provides the existence
+of global solutions in time, for the compressible two-fluid Navier-Stokes
+equations, with large data in three dimensional space.
+",0,0,1,0,0,0
+9909,Privacy Preserving and Collusion Resistant Energy Sharing,"  Energy has been increasingly generated or collected by different entities on
+the power grid (e.g., universities, hospitals and householdes) via solar
+panels, wind turbines or local generators in the past decade. With local
+energy, such electricity consumers can be considered as ""microgrids"" which can
+simulataneously generate and consume energy. Some microgrids may have excessive
+energy that can be shared to other power consumers on the grid. To this end,
+all the entities have to share their local private information (e.g., their
+local demand, local supply and power quality data) to each other or a
+third-party to find and implement the optimal energy sharing solution. However,
+such process is constrained by privacy concerns raised by the microgrids. In
+this paper, we propose a privacy preserving scheme for all the microgrids which
+can securely implement their energy sharing against both semi-honest and
+colluding adversaries. The proposed approach includes two secure communication
+protocols that can ensure quantified privacy leakage and handle collusions.
+",1,0,0,0,0,0
+9910,Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes,"  Word embeddings use vectors to represent words such that the geometry between
+vectors captures semantic relationship between the words. In this paper, we
+develop a framework to demonstrate how the temporal dynamics of the embedding
+can be leveraged to quantify changes in stereotypes and attitudes toward women
+and ethnic minorities in the 20th and 21st centuries in the United States. We
+integrate word embeddings trained on 100 years of text data with the U.S.
+Census to show that changes in the embedding track closely with demographic and
+occupation shifts over time. The embedding captures global social shifts --
+e.g., the women's movement in the 1960s and Asian immigration into the U.S --
+and also illuminates how specific adjectives and occupations became more
+closely associated with certain populations over time. Our framework for
+temporal analysis of word embedding opens up a powerful new intersection
+between machine learning and quantitative social science.
+",1,0,0,0,0,0
+9911,Infinite Mixture of Inverted Dirichlet Distributions,"  In this work, we develop a novel Bayesian estimation method for the Dirichlet
+process (DP) mixture of the inverted Dirichlet distributions, which has been
+shown to be very flexible for modeling vectors with positive elements. The
+recently proposed extended variational inference (EVI) framework is adopted to
+derive an analytically tractable solution. The convergency of the proposed
+algorithm is theoretically guaranteed by introducing single lower bound
+approximation to the original objective function in the VI framework. In
+principle, the proposed model can be viewed as an infinite inverted Dirichelt
+mixture model (InIDMM) that allows the automatic determination of the number of
+mixture components from data. Therefore, the problem of pre-determining the
+optimal number of mixing components has been overcome. Moreover, the problems
+of over-fitting and under-fitting are avoided by the Bayesian estimation
+approach. Comparing with several recently proposed DP-related methods, the good
+performance and effectiveness of the proposed method have been demonstrated
+with both synthesized data and real data evaluations.
+",0,0,0,1,0,0
+9912,$Ψ$ec: A Local Spectral Exterior Calculus,"  We introduce $\Psi$ec, a local spectral exterior calculus that provides a
+discretization of Cartan's exterior calculus of differential forms using
+wavelet functions. Our construction consists of differential form wavelets with
+flexible directional localization, between fully isotropic and curvelet- and
+ridgelet-like, that provide tight frames for the spaces of $k$-forms in
+$\mathbb{R}^2$ and $\mathbb{R}^3$. By construction, these wavelets satisfy the
+de Rahm co-chain complex, the Hodge decomposition, and that the integral of a
+$k+1$-form is a $k$-form. They also enforce Stokes' theorem for differential
+forms, and we show that with a finite number of wavelet levels it is most
+efficiently approximated using anisotropic curvelet- or ridgelet-like forms.
+Our construction is based on the intrinsic geometric properties of the exterior
+calculus in the Fourier domain. To reveal these, we extend existing results on
+the Fourier transform of differential forms to a frequency domain description
+of the exterior calculus, including, for example, a Parseval theorem for forms
+and a description of the symbols of all important operators.
+",1,0,0,0,0,0
+9913,Ultraslow fluctuations in the pseudogap states of HgBa$_{2}$CaCu$_{2}$O$_{6+d}$,"  We report the transverse relaxation rates 1/$T_2$'s of the $^{63}$Cu nuclear
+spin-echo envelope for double-layer high-$T_c$ cuprate superconductors
+HgBa$_{2}$CaCu$_{2}$O$_{6+d}$ from underdoped to overdoped. The relaxation rate
+1/$T_{2L}$ of the exponential function (Lorentzian component) shows a peak at
+220$-$240 K in the underdoped ($T_c$ = 103 K) and the optimally doped ($T_c$ =
+127 K) samples but no peak in the overdoped ($T_c$ = 93 K) sample. The
+enhancement in 1/$T_{2L}$ suggests development of the zero frequency components
+of local field fluctuations. Ultraslow fluctuations are hidden in the pseudogap
+states.
+",0,1,0,0,0,0
+9914,Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction,"  Limited-angle computed tomography (CT) is often used in clinical applications
+such as C-arm CT for interventional imaging. However, CT images from limited
+angles suffers from heavy artifacts due to incomplete projection data. Existing
+iterative methods require extensive calculations but can not deliver
+satisfactory results. Based on the observation that the artifacts from limited
+angles have some directional property and are globally distributed, we propose
+a novel multi-scale wavelet domain residual learning architecture, which
+compensates for the artifacts. Experiments have shown that the proposed method
+effectively eliminates artifacts, thereby preserving edge and global structures
+of the image.
+",1,0,0,0,0,0
+9915,Helium-like atoms. The Green's function approach to the Fock expansion calculations,"  The renewed Green's function approach to calculating the angular Fock
+coefficients, $\psi_{k,p}(\alpha,\theta)$ is presented. The final formulas are
+simplified and specified to be applicable for analytical as well as numerical
+calculations. The Green's function formulas with the hyperspherical angles
+$\theta=0,\pi$ (arbitrary $\alpha$) or $\alpha=0,\pi$ (arbitrary $\theta$) are
+indicated as corresponding to the angular Fock coefficients possessing physical
+meaning. The most interesting case of $\theta=0$ corresponding to a collinear
+arrangement of the particles is studied in detail. It is emphasized that this
+case represents the generalization of the specific cases of the
+electron-nucleus ($\alpha=0$) and electron-electron ($\alpha=\pi/2$)
+coalescences. It is shown that the Green's function method for $\theta=0$
+enables us to calculate any component/subcomponent of the angular Fock
+coefficient in the form of a single series representation with arbitrary angle
+$\theta$. Those cases, where the Green's function approach can not be applied,
+are thoroughly studied, and the corresponding solutions are found.
+",0,1,0,0,0,0
+9916,Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation,"  This paper proposes a new algorithm for controlling classification results by
+generating a small additive perturbation without changing the classifier
+network. Our work is inspired by existing works generating adversarial
+perturbation that worsens classification performance. In contrast to the
+existing methods, our work aims to generate perturbations that can enhance
+overall classification performance. To solve this performance enhancement
+problem, we newly propose a perturbation generation network (PGN) influenced by
+the adversarial learning strategy. In our problem, the information in a large
+external dataset is summarized by a small additive perturbation, which helps to
+improve the performance of the classifier trained with the target dataset. In
+addition to this performance enhancement problem, we show that the proposed PGN
+can be adopted to solve the classical adversarial problem without utilizing the
+information on the target classifier. The mentioned characteristics of our
+method are verified through extensive experiments on publicly available visual
+datasets.
+",1,0,0,1,0,0
+9917,Secrecy Outage Analysis for Downlink Transmissions in the Presence of Randomly Located Eavesdroppers,"  We analyze the secrecy outage probability in the downlink for wireless
+networks with spatially (Poisson) distributed eavesdroppers (EDs) under the
+assumption that the base station employs transmit antenna selection (TAS) to
+enhance secrecy performance. We compare the cases where the receiving user
+equipment (UE) operates in half-duplex (HD) mode and full-duplex (FD) mode. In
+the latter case, the UE simultaneously receives the intended downlink message
+and transmits a jamming signal to strengthen secrecy. We investigate two models
+of (semi)passive eavesdropping: (1) EDs act independently and (2) EDs collude
+to intercept the transmitted message. For both of these models, we obtain
+expressions for the secrecy outage probability in the downlink for HD and FD UE
+operation. The expressions for HD systems have very accurate approximate or
+exact forms in terms of elementary and/or special functions for all path loss
+exponents. Those related to the FD systems have exact integral forms for
+general path loss exponents, while exact closed forms are given for specific
+exponents. A closed-form approximation is also derived for the FD case with
+colluding EDs. The resulting analysis shows that the reduction in the secrecy
+outage probability is logarithmic in the number of antennas used for TAS and
+identifies conditions under which HD operation should be used instead of FD
+jamming at the UE. These performance trends and exact relations between system
+parameters can be used to develop adaptive power allocation and duplex
+operation methods in practice. Examples of such techniques are alluded to
+herein.
+",1,0,1,0,0,0
+9918,Defining Equitable Geographic Districts in Road Networks via Stable Matching,"  We introduce a novel method for defining geographic districts in road
+networks using stable matching. In this approach, each geographic district is
+defined in terms of a center, which identifies a location of interest, such as
+a post office or polling place, and all other network vertices must be labeled
+with the center to which they are associated. We focus on defining geographic
+districts that are equitable, in that every district has the same number of
+vertices and the assignment is stable in terms of geographic distance. That is,
+there is no unassigned vertex-center pair such that both would prefer each
+other over their current assignments. We solve this problem using a version of
+the classic stable matching problem, called symmetric stable matching, in which
+the preferences of the elements in both sets obey a certain symmetry. In our
+case, we study a graph-based version of stable matching in which nodes are
+stably matched to a subset of nodes denoted as centers, prioritized by their
+shortest-path distances, so that each center is apportioned a certain number of
+nodes. We show that, for a planar graph or road network with $n$ nodes and $k$
+centers, the problem can be solved in $O(n\sqrt{n}\log n)$ time, which improves
+upon the $O(nk)$ runtime of using the classic Gale-Shapley stable matching
+algorithm when $k$ is large. Finally, we provide experimental results on road
+networks for these algorithms and a heuristic algorithm that performs better
+than the Gale-Shapley algorithm for any range of values of $k$.
+",1,0,0,0,0,0
+9919,End-to-end Lung Nodule Detection in Computed Tomography,"  Computer aided diagnostic (CAD) system is crucial for modern med-ical
+imaging. But almost all CAD systems operate on reconstructed images, which were
+optimized for radiologists. Computer vision can capture features that is subtle
+to human observers, so it is desirable to design a CAD system op-erating on the
+raw data. In this paper, we proposed a deep-neural-network-based detection
+system for lung nodule detection in computed tomography (CT). A
+primal-dual-type deep reconstruction network was applied first to convert the
+raw data to the image space, followed by a 3-dimensional convolutional neural
+network (3D-CNN) for the nodule detection. For efficient network training, the
+deep reconstruction network and the CNN detector was trained sequentially
+first, then followed by one epoch of end-to-end fine tuning. The method was
+evaluated on the Lung Image Database Consortium image collection (LIDC-IDRI)
+with simulated forward projections. With 144 multi-slice fanbeam pro-jections,
+the proposed end-to-end detector could achieve comparable sensitivity with the
+reference detector, which was trained and applied on the fully-sampled image
+data. It also demonstrated superior detection performance compared to detectors
+trained on the reconstructed images. The proposed method is general and could
+be expanded to most detection tasks in medical imaging.
+",1,0,0,1,0,0
+9920,Fairness with Dynamics,"  It has recently been shown that if feedback effects of decisions are ignored,
+then imposing fairness constraints such as demographic parity or equality of
+opportunity can actually exacerbate unfairness. We propose to address this
+challenge by modeling feedback effects as the dynamics of a Markov decision
+processes (MDPs). First, we define analogs of fairness properties that have
+been proposed for supervised learning. Second, we propose algorithms for
+learning fair decision-making policies for MDPs. We also explore extensions to
+reinforcement learning, where parts of the dynamical system are unknown and
+must be learned without violating fairness. Finally, we demonstrate the need to
+account for dynamical effects using simulations on a loan applicant MDP.
+",1,0,0,1,0,0
+9921,Exploring extra dimensions through inflationary tensor modes,"  Predictions of inflationary schemes can be influenced by the presence of
+extra dimensions. This could be of particular relevance for the spectrum of
+gravitational waves in models where the extra dimensions provide a brane-world
+solution to the hierarchy problem. Apart from models of large as well as
+exponentially warped extra dimensions, we analyze the size of tensor modes in
+the Linear Dilaton scheme recently revived in the discussion of the ""clockwork
+mechanism"". The results are model dependent, significantly enhanced tensor
+modes on one side and a suppression on the other. In some cases we are led to a
+scheme of ""remote inflation"", where the expansion is driven by energies at a
+hidden brane. In all cases where tensor modes are enhanced, the requirement of
+perturbativity of gravity leads to a stringent upper limit on the allowed
+Hubble rate during inflation.
+",0,1,0,0,0,0
+9922,Elements of $C^*$-algebras Attaining Their Norm in a Finite-Dimensional Representation,"  We characterize the class of RFD $C^*$-algebras as those containing a dense
+subset of elements that attain their norm under a finite-dimensional
+representation. We show further that this subset is the whole space precisely
+when every irreducible representation of the $C^*$-algebra is
+finite-dimensional, which is equivalent to the $C^*$-algebra having no simple
+infinite-dimensional AF subquotient. We apply techniques from this proof to
+show the existence of elements in more general classes of $C^*$-algebras whose
+norms in finite-dimensional representations fit certain prescribed properties.
+",0,0,1,0,0,0
+9923,A probability inequality for sums of independent Banach space valued random variables,"  Let $(\mathbf{B}, \|\cdot\|)$ be a real separable Banach space. Let
+$\varphi(\cdot)$ and $\psi(\cdot)$ be two continuous and increasing functions
+defined on $[0, \infty)$ such that $\varphi(0) = \psi(0) = 0$, $\lim_{t
+\rightarrow \infty} \varphi(t) = \infty$, and
+$\frac{\psi(\cdot)}{\varphi(\cdot)}$ is a nondecreasing function on $[0,
+\infty)$. Let $\{V_{n};~n \geq 1 \}$ be a sequence of independent and symmetric
+{\bf B}-valued random variables. In this note, we establish a probability
+inequality for sums of independent {\bf B}-valued random variables by showing
+that for every $n \geq 1$ and all $t \geq 0$, \[
+\mathbb{P}\left(\left\|\sum_{i=1}^{n} V_{i} \right\| > t b_{n} \right) \leq 4
+\mathbb{P} \left(\left\|\sum_{i=1}^{n} \varphi\left(\psi^{-1}(\|V_{i}\|)\right)
+\frac{V_{i}}{\|V_{i}\|} \right\| > t a_{n} \right) +
+\sum_{i=1}^{n}\mathbb{P}\left(\|V_{i}\| > b_{n} \right), \] where $a_{n} =
+\varphi(n)$ and $b_{n} = \psi(n)$, $n \geq 1$. As an application of this
+inequality, we establish what we call a comparison theorem for the weak law of
+large numbers for independent and identically distributed ${\bf B}$-valued
+random variables.
+",0,0,1,0,0,0
+9924,Including Uncertainty when Learning from Human Corrections,"  It is difficult for humans to efficiently teach robots how to correctly
+perform a task. One intuitive solution is for the robot to iteratively learn
+the human's preferences from corrections, where the human improves the robot's
+current behavior at each iteration. When learning from corrections, we argue
+that while the robot should estimate the most likely human preferences, it
+should also know what it does not know, and integrate this uncertainty as it
+makes decisions. We advance the state-of-the-art by introducing a Kalman filter
+for learning from corrections: this approach obtains the uncertainty of the
+estimated human preferences. Next, we demonstrate how the estimate uncertainty
+can be leveraged for active learning and risk-sensitive deployment. Our results
+indicate that obtaining and leveraging uncertainty leads to faster learning
+from human corrections.
+",1,0,0,0,0,0
+9925,Gated-Attention Architectures for Task-Oriented Language Grounding,"  To perform tasks specified by natural language instructions, autonomous
+agents need to extract semantically meaningful representations of language and
+map it to visual elements and actions in the environment. This problem is
+called task-oriented language grounding. We propose an end-to-end trainable
+neural architecture for task-oriented language grounding in 3D environments
+which assumes no prior linguistic or perceptual knowledge and requires only raw
+pixels from the environment and the natural language instruction as input. The
+proposed model combines the image and text representations using a
+Gated-Attention mechanism and learns a policy to execute the natural language
+instruction using standard reinforcement and imitation learning methods. We
+show the effectiveness of the proposed model on unseen instructions as well as
+unseen maps, both quantitatively and qualitatively. We also introduce a novel
+environment based on a 3D game engine to simulate the challenges of
+task-oriented language grounding over a rich set of instructions and
+environment states.
+",1,0,0,0,0,0
+9926,Automatic classification of trees using a UAV onboard camera and deep learning,"  Automatic classification of trees using remotely sensed data has been a dream
+of many scientists and land use managers. Recently, Unmanned aerial vehicles
+(UAV) has been expected to be an easy-to-use, cost-effective tool for remote
+sensing of forests, and deep learning has attracted attention for its ability
+concerning machine vision. In this study, using a commercially available UAV
+and a publicly available package for deep learning, we constructed a machine
+vision system for the automatic classification of trees. In our method, we
+segmented a UAV photography image of forest into individual tree crowns and
+carried out object-based deep learning. As a result, the system was able to
+classify 7 tree types at 89.0% accuracy. This performance is notable because we
+only used basic RGB images from a standard UAV. In contrast, most of previous
+studies used expensive hardware such as multispectral imagers to improve the
+performance. This result means that our method has the potential to classify
+individual trees in a cost-effective manner. This can be a usable tool for many
+forest researchers and managements.
+",0,0,0,1,0,0
+9927,Assortative Mixing Equilibria in Social Network Games,"  It is known that individuals in social networks tend to exhibit homophily
+(a.k.a. assortative mixing) in their social ties, which implies that they
+prefer bonding with others of their own kind. But what are the reasons for this
+phenomenon? Is it that such relations are more convenient and easier to
+maintain? Or are there also some more tangible benefits to be gained from this
+collective behaviour?
+The current work takes a game-theoretic perspective on this phenomenon, and
+studies the conditions under which different assortative mixing strategies lead
+to equilibrium in an evolving social network. We focus on a biased preferential
+attachment model where the strategy of each group (e.g., political or social
+minority) determines the level of bias of its members toward other group
+members and non-members. Our first result is that if the utility function that
+the group attempts to maximize is the degree centrality of the group,
+interpreted as the sum of degrees of the group members in the network, then the
+only strategy achieving Nash equilibrium is a perfect homophily, which implies
+that cooperation with other groups is harmful to this utility function. A
+second, and perhaps more surprising, result is that if a reward for inter-group
+cooperation is added to the utility function (e.g., externally enforced by an
+authority as a regulation), then there are only two possible equilibria,
+namely, perfect homophily or perfect heterophily, and it is possible to
+characterize their feasibility spaces. Interestingly, these results hold
+regardless of the minority-majority ratio in the population.
+We believe that these results, as well as the game-theoretic perspective
+presented herein, may contribute to a better understanding of the forces that
+shape the groups and communities of our society.
+",1,1,0,0,0,0
+9928,Texture segmentation with Fully Convolutional Networks,"  In the last decade, deep learning has contributed to advances in a wide range
+computer vision tasks including texture analysis. This paper explores a new
+approach for texture segmentation using deep convolutional neural networks,
+sharing important ideas with classic filter bank based texture segmentation
+methods. Several methods are developed to train Fully Convolutional Networks to
+segment textures in various applications. We show in particular that these
+networks can learn to recognize and segment a type of texture, e.g. wood and
+grass from texture recognition datasets (no training segmentation). We
+demonstrate that Fully Convolutional Networks can learn from repetitive
+patterns to segment a particular texture from a single image or even a part of
+an image. We take advantage of these findings to develop a method that is
+evaluated on a series of supervised and unsupervised experiments and improve
+the state of the art on the Prague texture segmentation datasets.
+",1,0,0,0,0,0
+9929,The Beam and detector of the NA62 experiment at CERN,"  NA62 is a fixed-target experiment at the CERN SPS dedicated to measurements
+of rare kaon decays. Such measurements, like the branching fraction of the
+$K^{+} \rightarrow \pi^{+} \nu \bar\nu$ decay, have the potential to bring
+significant insights into new physics processes when comparison is made with
+precise theoretical predictions. For this purpose, innovative techniques have
+been developed, in particular, in the domain of low-mass tracking devices.
+Detector construction spanned several years from 2009 to 2014. The
+collaboration started detector commissioning in 2014 and will collect data
+until the end of 2018. The beam line and detector components are described
+together with their early performance obtained from 2014 and 2015 data.
+",0,1,0,0,0,0
+9930,A dequantized metaplectic knot invariant,"  Let $K\subset S^3$ be a knot, $X:= S^3\setminus K$ its complement, and
+$\mathbb{T}$ the circle group identified with $\mathbb{R}/\mathbb{Z}$. To any
+oriented long knot diagram of $K$, we associate a quadratic polynomial in
+variables bijectively associated with the bridges of the diagram such that,
+when the variables projected to $\mathbb{T}$ satisfy the linear equations
+characterizing the first homology group $H_1(\tilde{X}_2)$ of the double cyclic
+covering of $X$, the polynomial projects down to a well defined
+$\mathbb{T}$-valued function on $T^1(\tilde{X}_2,\mathbb{T})$ (the dual of the
+torsion part $T_1$ of $H_1$). This function is sensitive to knot chirality, for
+example, it seems to confirm chirality of the knot $10_{71}$. It also
+distinguishes the knots $7_4$ and $9_2$ known to have identical Alexander
+polynomials and the knots $9_2$ and K11n13 known to have identical Jones
+polynomials but does not distinguish $7_4$ and K11n13.
+",0,0,1,0,0,0
+9931,Persian Wordnet Construction using Supervised Learning,"  This paper presents an automated supervised method for Persian wordnet
+construction. Using a Persian corpus and a bi-lingual dictionary, the initial
+links between Persian words and Princeton WordNet synsets have been generated.
+These links will be discriminated later as correct or incorrect by employing
+seven features in a trained classification system. The whole method is just a
+classification system, which has been trained on a train set containing FarsNet
+as a set of correct instances. State of the art results on the automatically
+derived Persian wordnet is achieved. The resulted wordnet with a precision of
+91.18% includes more than 16,000 words and 22,000 synsets.
+",1,0,0,1,0,0
+9932,Anyon condensation and its applications,"  Bose condensation is central to our understanding of quantum phases of
+matter. Here we review Bose condensation in topologically ordered phases (also
+called topological symmetry breaking), where the condensing bosons have
+non-trivial mutual statistics with other quasiparticles in the system. We give
+a non-technical overview of the relationship between the phases before and
+after condensation, drawing parallels with more familiar symmetry-breaking
+transitions. We then review two important applications of this phenomenon.
+First, we describe the equivalence between such condensation transitions and
+pairs of phases with gappable boundaries, as well as examples where multiple
+types of gapped boundary between the same two phases exist. Second, we discuss
+how such transitions can lead to global symmetries which exchange or permute
+anyon types. Finally we discuss the nature of the critical point, which can be
+mapped to a conventional phase transition in some -- but not all -- cases.
+",0,1,0,0,0,0
+9933,Group-velocity-locked vector soliton molecules in a birefringence-enhanced fiber laser,"  Physics phenomena of multi-soliton complexes have enriched the life of
+dissipative solitons in fiber lasers. By developing a birefringence-enhanced
+fiber laser, we report the first experimental observation of
+group-velocity-locked vector soliton (GVLVS) molecules. The
+birefringence-enhanced fiber laser facilitates the generation of GVLVSs, where
+the two orthogonally polarized components are coupled together to form a
+multi-soliton complex. Moreover, the interaction of repulsive and attractive
+forces between multiple pulses binds the particle-like GVLVSs together in time
+domain to further form compound multi-soliton complexes, namely GVLVS
+molecules. By adopting the polarization-resolved measurement, we show that the
+two orthogonally polarized components of the GVLVS molecules are both soliton
+molecules supported by the strongly modulated spectral fringes and the
+double-humped intensity profiles. Additionally, GVLVS molecules with various
+soliton separations are also observed by adjusting the pump power and the
+polarization controller.
+",0,1,0,0,0,0
+9934,Survey of Visual Question Answering: Datasets and Techniques,"  Visual question answering (or VQA) is a new and exciting problem that
+combines natural language processing and computer vision techniques. We present
+a survey of the various datasets and models that have been used to tackle this
+task. The first part of the survey details the various datasets for VQA and
+compares them along some common factors. The second part of this survey details
+the different approaches for VQA, classified into four types: non-deep learning
+models, deep learning models without attention, deep learning models with
+attention, and other models which do not fit into the first three. Finally, we
+compare the performances of these approaches and provide some directions for
+future work.
+",1,0,0,0,0,0
+9935,Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters,"  With the rise of end-to-end learning through deep learning, person detectors
+and re-identification (ReID) models have recently become very strong.
+Multi-camera multi-target (MCMT) tracking has not fully gone through this
+transformation yet. We intend to take another step in this direction by
+presenting a theoretically principled way of integrating ReID with tracking
+formulated as an optimal Bayes filter. This conveniently side-steps the need
+for data-association and opens up a direct path from full images to the core of
+the tracker. While the results are still sub-par, we believe that this new,
+tight integration opens many interesting research opportunities and leads the
+way towards full end-to-end tracking from raw pixels.
+",1,0,0,0,0,0
+9936,Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline,"  From medical charts to national census, healthcare has traditionally operated
+under a paper-based paradigm. However, the past decade has marked a long and
+arduous transformation bringing healthcare into the digital age. Ranging from
+electronic health records, to digitized imaging and laboratory reports, to
+public health datasets, today, healthcare now generates an incredible amount of
+digital information. Such a wealth of data presents an exciting opportunity for
+integrated machine learning solutions to address problems across multiple
+facets of healthcare practice and administration. Unfortunately, the ability to
+derive accurate and informative insights requires more than the ability to
+execute machine learning models. Rather, a deeper understanding of the data on
+which the models are run is imperative for their success. While a significant
+effort has been undertaken to develop models able to process the volume of data
+obtained during the analysis of millions of digitalized patient records, it is
+important to remember that volume represents only one aspect of the data. In
+fact, drawing on data from an increasingly diverse set of sources, healthcare
+data presents an incredibly complex set of attributes that must be accounted
+for throughout the machine learning pipeline. This chapter focuses on
+highlighting such challenges, and is broken down into three distinct
+components, each representing a phase of the pipeline. We begin with attributes
+of the data accounted for during preprocessing, then move to considerations
+during model building, and end with challenges to the interpretation of model
+output. For each component, we present a discussion around data as it relates
+to the healthcare domain and offer insight into the challenges each may impose
+on the efficiency of machine learning techniques.
+",1,0,0,1,0,0
+9937,Redundant Perception and State Estimation for Reliable Autonomous Racing,"  In autonomous racing, vehicles operate close to the limits of handling and a
+sensor failure can have critical consequences. To limit the impact of such
+failures, this paper presents the redundant perception and state estimation
+approaches developed for an autonomous race car. Redundancy in perception is
+achieved by estimating the color and position of the track delimiting objects
+using two sensor modalities independently. Specifically, learning-based
+approaches are used to generate color and pose estimates, from LiDAR and camera
+data respectively. The redundant perception inputs are fused by a particle
+filter based SLAM algorithm that operates in real-time. Velocity is estimated
+using slip dynamics, with reliability being ensured through a probabilistic
+failure detection algorithm. The sub-modules are extensively evaluated in
+real-world racing conditions using the autonomous race car ""gotthard
+driverless"", achieving lateral accelerations up to 1.7G and a top speed of
+90km/h.
+",1,0,0,0,0,0
+9938,Experimental study of extrinsic spin Hall effect in CuPt alloy,"  We have experimentally studied the effects on the spin Hall angle due to
+systematic addition of Pt into the light metal Cu. We perform spin torque
+ferromagnetic resonance measurements on Py/CuPt bilayer and find that as the Pt
+concentration increases, the spin Hall angle of CuPt alloy increases. Moreover,
+only 28% Pt in CuPt alloy can give rise to a spin Hall angle close to that of
+Pt. We further extract the spin Hall resistivity of CuPt alloy for different Pt
+concentrations and find that the contribution of skew scattering is larger for
+lower Pt concentrations, while the side-jump contribution is larger for higher
+Pt concentrations. From technological perspective, since the CuPt alloy can
+sustain high processing temperatures and Cu is the most common metallization
+element in the Si platform, it would be easier to integrate the CuPt alloy
+based spintronic devices into existing Si fabrication technology.
+",0,1,0,0,0,0
+9939,NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm,"  Deep neural networks (DNNs) have begun to have a pervasive impact on various
+applications of machine learning. However, the problem of finding an optimal
+DNN architecture for large applications is challenging. Common approaches go
+for deeper and larger DNN architectures but may incur substantial redundancy.
+To address these problems, we introduce a network growth algorithm that
+complements network pruning to learn both weights and compact DNN architectures
+during training. We propose a DNN synthesis tool (NeST) that combines both
+methods to automate the generation of compact and accurate DNNs. NeST starts
+with a randomly initialized sparse network called the seed architecture. It
+iteratively tunes the architecture with gradient-based growth and
+magnitude-based pruning of neurons and connections. Our experimental results
+show that NeST yields accurate, yet very compact DNNs, with a wide range of
+seed architecture selection. For the LeNet-300-100 (LeNet-5) architecture, we
+reduce network parameters by 70.2x (74.3x) and floating-point operations
+(FLOPs) by 79.4x (43.7x). For the AlexNet and VGG-16 architectures, we reduce
+network parameters (FLOPs) by 15.7x (4.6x) and 30.2x (8.6x), respectively.
+NeST's grow-and-prune paradigm delivers significant additional parameter and
+FLOPs reduction relative to pruning-only methods.
+",1,0,0,0,0,0
+9940,"Resonant Drag Instabilities in protoplanetary disks: the streaming instability and new, faster-growing instabilities","  We identify and study a number of new, rapidly growing instabilities of dust
+grains in protoplanetary disks, which may be important for planetesimal
+formation. The study is based on the recognition that dust-gas mixtures are
+generically unstable to a Resonant Drag Instability (RDI), whenever the gas,
+absent dust, supports undamped linear modes. We show that the ""streaming
+instability"" is an RDI associated with epicyclic oscillations; this provides
+simple interpretations for its mechanisms and accurate analytic expressions for
+its growth rates and fastest-growing wavelengths. We extend this analysis to
+more general dust streaming motions and other waves, including buoyancy and
+magnetohydrodynamic oscillations, finding various new instabilities. Most
+importantly, we identify the disk ""settling instability,"" which occurs as dust
+settles vertically into the midplane of a rotating disk. For small grains, this
+instability grows many orders of magnitude faster than the standard streaming
+instability, with a growth rate that is independent of grain size. Growth
+timescales for realistic dust-to-gas ratios are comparable to the disk orbital
+period, and the characteristic wavelengths are more than an order of magnitude
+larger than the streaming instability (allowing the instability to concentrate
+larger masses). This suggests that in the process of settling, dust will band
+into rings then filaments or clumps, potentially seeding dust traps,
+high-metallicity regions that in turn seed the streaming instability, or even
+overdensities that coagulate or directly collapse to planetesimals.
+",0,1,0,0,0,0
+9941,Nonparametric Neural Networks,"  Automatically determining the optimal size of a neural network for a given
+task without prior information currently requires an expensive global search
+and training many networks from scratch. In this paper, we address the problem
+of automatically finding a good network size during a single training cycle. We
+introduce *nonparametric neural networks*, a non-probabilistic framework for
+conducting optimization over all possible network sizes and prove its soundness
+when network growth is limited via an L_p penalty. We train networks under this
+framework by continuously adding new units while eliminating redundant units
+via an L_2 penalty. We employ a novel optimization algorithm, which we term
+*adaptive radial-angular gradient descent* or *AdaRad*, and obtain promising
+results.
+",1,0,0,0,0,0
+9942,FastTrack: Minimizing Stalls for CDN-based Over-the-top Video Streaming Systems,"  Traffic for internet video streaming has been rapidly increasing and is
+further expected to increase with the higher definition videos and IoT
+applications, such as 360 degree videos and augmented virtual reality
+applications. While efficient management of heterogeneous cloud resources to
+optimize the quality of experience is important, existing work in this problem
+space often left out important factors. In this paper, we present a model for
+describing a today's representative system architecture for video streaming
+applications, typically composed of a centralized origin server and several CDN
+sites. Our model comprehensively considers the following factors: limited
+caching spaces at the CDN sites, allocation of CDN for a video request, choice
+of different ports from the CDN, and the central storage and bandwidth
+allocation. With the model, we focus on minimizing a performance metric, stall
+duration tail probability (SDTP), and present a novel, yet efficient, algorithm
+to solve the formulated optimization problem. The theoretical bounds with
+respect to the SDTP metric are also analyzed and presented. Our extensive
+simulation results demonstrate that the proposed algorithms can significantly
+improve the SDTP metric, compared to the baseline strategies. Small-scale video
+streaming system implementation in a real cloud environment further validates
+our results.
+",1,0,0,0,0,0
+9943,Powerful statistical inference for nested data using sufficient summary statistics,"  Hierarchically-organized data arise naturally in many psychology and
+neuroscience studies. As the standard assumption of independent and identically
+distributed samples does not hold for such data, two important problems are to
+accurately estimate group-level effect sizes, and to obtain powerful
+statistical tests against group-level null hypotheses. A common approach is to
+summarize subject-level data by a single quantity per subject, which is often
+the mean or the difference between class means, and treat these as samples in a
+group-level t-test. This 'naive' approach is, however, suboptimal in terms of
+statistical power, as it ignores information about the intra-subject variance.
+To address this issue, we review several approaches to deal with nested data,
+with a focus on methods that are easy to implement. With what we call the
+sufficient-summary-statistic approach, we highlight a computationally efficient
+technique that can improve statistical power by taking into account
+within-subject variances, and we provide step-by-step instructions on how to
+apply this approach to a number of frequently-used measures of effect size. The
+properties of the reviewed approaches and the potential benefits over a
+group-level t-test are quantitatively assessed on simulated data and
+demonstrated on EEG data from a simulated-driving experiment.
+",0,0,1,1,0,0
+9944,Beam-On-Graph: Simultaneous Channel Estimation for mmWave MIMO Systems with Multiple Users,"  This paper is concerned with the channel estimation problem in multi-user
+millimeter wave (mmWave) wireless systems with large antenna arrays. We develop
+a novel simultaneous-estimation with iterative fountain training (SWIFT)
+framework, in which multiple users estimate their channels at the same time and
+the required number of channel measurements is adapted to various channel
+conditions of different users. To achieve this, we represent the beam direction
+estimation process by a graph, referred to as the beam-on-graph, and associate
+the channel estimation process with a code-on-graph decoding problem.
+Specifically, the base station (BS) and each user measure the channel with a
+series of random combinations of transmit/receive beamforming vectors until the
+channel estimate converges. As the proposed SWIFT does not adapt the BS's beams
+to any single user, we are able to estimate all user channels simultaneously.
+Simulation results show that SWIFT can significantly outperform the existing
+random beamforming-based approaches, which use a predetermined number of
+measurements, over a wide range of signal-to-noise ratios and channel coherence
+time. Furthermore, by utilizing the users' order in terms of completing their
+channel estimation, our SWIFT framework can infer the sequence of users'
+channel quality and perform effective user scheduling to achieve superior
+performance.
+",1,0,1,0,0,0
+9945,On Comparison Of Experts,"  A policy maker faces a sequence of unknown outcomes. At each stage two
+(self-proclaimed) experts provide probabilistic forecasts on the outcome in the
+next stage. A comparison test is a protocol for the policy maker to
+(eventually) decide which of the two experts is better informed. The protocol
+takes as input the sequence of pairs of forecasts and actual realizations and
+(weakly) ranks the two experts. We propose two natural properties that such a
+comparison test must adhere to and show that these essentially uniquely
+determine the comparison test. This test is a function of the derivative of the
+induced pair of measures at the realization.
+",1,0,1,0,0,0
+9946,Multiscale simulation on shearing transitions of thin-film lubrication with multi-layer molecules,"  Shearing transitions of multi-layer molecularly thin-film lubrication systems
+in variations of the film-substrate coupling strength and the load are studied
+by using a multiscale method. Three kinds of the interlayer slips found in
+decreasing the coupling strength are in qualitative agreement with experimental
+results. Although tribological behaviors are almost insensitive to the smaller
+coupling strength, they and the effective film thickness are enlarged more and
+more as the larger one increases. When the load increases, the tribological
+behaviors are similar to those in increasing coupling strength, but the
+effective film thickness is opposite.
+",0,1,0,0,0,0
+9947,Chirality-induced Antisymmetry in Magnetic Domain-Wall Speed,"  In chiral magnetic materials, numerous intriguing phenomena such as built in
+chiral magnetic domain walls (DWs) and skyrmions are generated by the
+Dzyaloshinskii Moriya interaction (DMI). The DMI also results in asymmetric DW
+speed under in plane magnetic field, which provides a useful scheme to measure
+the DMI strengths. However, recent findings of additional asymmetries such as
+chiral damping have disenabled unambiguous DMI determination and the underlying
+mechanism of overall asymmetries becomes under debate. By extracting the
+DMI-induced symmetric contribution, here we experimentally investigated the
+nature of the additional asymmetry. The results revealed that the additional
+asymmetry has a truly antisymmetric nature with the typical behavior governed
+by the DW chirality. In addition, the antisymmetric contribution changes the DW
+speed more than 100 times, which cannot be solely explained by the chiral
+damping scenario. By calibrating such antisymmetric contributions, experimental
+inaccuracies can be largely removed, enabling again the DMI measurement scheme.
+",0,1,0,0,0,0
+9948,Strong interaction between graphene layer and Fano resonance in terahertz metamaterials,"  Graphene has emerged as a promising building block in the modern optics and
+optoelectronics due to its novel optical and electrical properties. In the
+mid-infrared and terahertz (THz) regime, graphene behaves like metals and
+supports surface plasmon resonances (SPRs). Moreover, the continuously tunable
+conductivity of graphene enables active SPRs and gives rise to a range of
+active applications. However, the interaction between graphene and metal-based
+resonant metamaterials has not been fully understood. In this work, a
+simulation investigation on the interaction between the graphene layer and THz
+resonances supported by the two-gap split ring metamaterials is systematically
+conducted. The simulation results show that the graphene layer can
+substantially reduce the Fano resonance and even switch it off, while leave the
+dipole resonance nearly unaffected, which phenomenon is well explained with the
+high conductivity of graphene. With the manipulation of graphene conductivity
+via altering its Fermi energy or layer number, the amplitude of the Fano
+resonance can be modulated. The tunable Fano resonance here together with the
+underlying physical mechanism can be strategically important in designing
+active metal-graphene hybrid metamaterials. In addition, the ""sensitivity"" to
+the graphene layer of the Fano resonance is also highly appreciated in the
+field of ultrasensitive sensing, where the novel physical mechanism can be
+employed in sensing other graphene-like two-dimensional (2D) materials or
+biomolecules with the high conductivity.
+",0,1,0,0,0,0
+9949,DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks,"  Transfer learning through fine-tuning a pre-trained neural network with an
+extremely large dataset, such as ImageNet, can significantly accelerate
+training while the accuracy is frequently bottlenecked by the limited dataset
+size of the new target task. To solve the problem, some regularization methods,
+constraining the outer layer weights of the target network using the starting
+point as references (SPAR), have been studied. In this paper, we propose a
+novel regularized transfer learning framework DELTA, namely DEep Learning
+Transfer using Feature Map with Attention. Instead of constraining the weights
+of neural network, DELTA aims to preserve the outer layer outputs of the target
+network. Specifically, in addition to minimizing the empirical loss, DELTA
+intends to align the outer layer outputs of two networks, through constraining
+a subset of feature maps that are precisely selected by attention that has been
+learned in an supervised learning manner. We evaluate DELTA with the
+state-of-the-art algorithms, including L2 and L2-SP. The experiment results
+show that our proposed method outperforms these baselines with higher accuracy
+for new tasks.
+",1,0,0,1,0,0
+9950,The Burst Failure Influence on the $H_\infty$ Norm,"  In this work, we present an analysis of the Burst failure effect in the
+$H_\infty$ norm. We present a procedure to perform an analysis between
+different Markov Chain models and a numerical example. In the numerical example
+the results obtained pointed out that the burst failure effect in the
+performance does not exceed 6.3%. However, this work is an introduction for a
+wider and more extensive analysis in this subject.
+",1,0,0,0,0,0
+9951,Cosmological Simulations in Exascale Era,"  The architecture of Exascale computing facilities, which involves millions of
+heterogeneous processing units, will deeply impact on scientific applications.
+Future astrophysical HPC applications must be designed to make such computing
+systems exploitable. The ExaNeSt H2020 EU-funded project aims to design and
+develop an exascale ready prototype based on low-energy-consumption ARM64 cores
+and FPGA accelerators. We participate to the design of the platform and to the
+validation of the prototype with cosmological N-body and hydrodynamical codes
+suited to perform large-scale, high-resolution numerical simulations of cosmic
+structures formation and evolution. We discuss our activities on astrophysical
+applications to take advantage of the underlying architecture.
+",1,1,0,0,0,0
+9952,Time evolution of the Luttinger model with nonuniform temperature profile,"  We study the time evolution of a one-dimensional interacting fermion system
+described by the Luttinger model starting from a nonequilibrium state defined
+by a smooth temperature profile $T(x)$. As a specific example we consider the
+case when $T(x)$ is equal to $T_L$ ($T_R$) far to the left (right). Using a
+series expansion in $\epsilon = 2(T_{R} - T_{L})/(T_{L}+T_{R})$, we compute the
+energy density, the heat current density, and the fermion two-point correlation
+function for all times $t \geq 0$. For local (delta-function) interactions, the
+first two are computed to all orders, giving simple exact expressions involving
+the Schwarzian derivative of the integral of $T(x)$. For nonlocal interactions,
+breaking scale invariance, we compute the nonequilibrium steady state (NESS) to
+all orders and the evolution to first order in $\epsilon$. The heat current in
+the NESS is universal even when conformal invariance is broken by the
+interactions, and its dependence on $T_{L,R}$ agrees with numerical results for
+the $XXZ$ spin chain. Moreover, our analytical formulas predict peaks at short
+times in the transition region between different temperatures and show
+dispersion effects that, even if nonuniversal, are qualitatively similar to
+ones observed in numerical simulations for related models, such as spin chains
+and interacting lattice fermions.
+",0,1,1,0,0,0
+9953,Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm,"  The game of chess is the most widely-studied domain in the history of
+artificial intelligence. The strongest programs are based on a combination of
+sophisticated search techniques, domain-specific adaptations, and handcrafted
+evaluation functions that have been refined by human experts over several
+decades. In contrast, the AlphaGo Zero program recently achieved superhuman
+performance in the game of Go, by tabula rasa reinforcement learning from games
+of self-play. In this paper, we generalise this approach into a single
+AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
+many challenging domains. Starting from random play, and given no domain
+knowledge except the game rules, AlphaZero achieved within 24 hours a
+superhuman level of play in the games of chess and shogi (Japanese chess) as
+well as Go, and convincingly defeated a world-champion program in each case.
+",1,0,0,0,0,0
+9954,Some remarks on Huisken's monotonicity formula for mean curvature flow,"  We discuss a monotone quantity related to Huisken's monotonicity formula and
+some technical consequences for mean curvature flow.
+",0,0,1,0,0,0
+9955,Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors,"  We consider forecasting a single time series using high-dimensional
+predictors in the presence of a possible nonlinear forecast function. The
+sufficient forecasting (Fan et al., 2016) used sliced inverse regression to
+estimate lower-dimensional sufficient indices for nonparametric forecasting
+using factor models. However, Fan et al. (2016) is fundamentally limited to the
+inverse first-moment method, by assuming the restricted fixed number of
+factors, linearity condition for factors, and monotone effect of factors on the
+response. In this work, we study the inverse second-moment method using
+directional regression and the inverse third-moment method to extend the
+methodology and applicability of the sufficient forecasting. As the number of
+factors diverges with the dimension of predictors, the proposed method relaxes
+the distributional assumption of the predictor and enhances the capability of
+capturing the non-monotone effect of factors on the response. We not only
+provide a high-dimensional analysis of inverse moment methods such as
+exhaustiveness and rate of convergence, but also prove their model selection
+consistency. The power of our proposed methods is demonstrated in both
+simulation studies and an empirical study of forecasting monthly macroeconomic
+data from Q1 1959 to Q1 2016. During our theoretical development, we prove an
+invariance result for inverse moment methods, which make a separate
+contribution to the sufficient dimension reduction.
+",0,0,1,1,0,0
+9956,Spectral energy distribution and radio halo of NGC 253 at low radio frequencies,"  We present new radio continuum observations of NGC253 from the Murchison
+Widefield Array at frequencies between 76 and 227 MHz. We model the broadband
+radio spectral energy distribution for the total flux density of NGC253 between
+76 MHz and 11 GHz. The spectrum is best described as a sum of central starburst
+and extended emission. The central component, corresponding to the inner 500pc
+of the starburst region of the galaxy, is best modelled as an internally
+free-free absorbed synchrotron plasma, with a turnover frequency around 230
+MHz. The extended emission component of the NGC253 spectrum is best described
+as a synchrotron emission flattening at low radio frequencies. We find that 34%
+of the extended emission (outside the central starburst region) at 1 GHz
+becomes partially absorbed at low radio frequencies. Most of this flattening
+occurs in the western region of the SE halo, and may be indicative of
+synchrotron self-absorption of shock re-accelerated electrons or an intrinsic
+low-energy cut off of the electron distribution. Furthermore, we detect the
+large-scale synchrotron radio halo of NGC253 in our radio images. At 154 - 231
+MHz the halo displays the well known X-shaped/horn-like structure, and extends
+out to ~8kpc in z-direction (from major axis).
+",0,1,0,0,0,0
+9957,Emergent Phases of Fractonic Matter,"  Fractons are emergent particles which are immobile in isolation, but which
+can move together in dipolar pairs or other small clusters. These exotic
+excitations naturally occur in certain quantum phases of matter described by
+tensor gauge theories. Previous research has focused on the properties of small
+numbers of fractons and their interactions, effectively mapping out the
+""Standard Model"" of fractons. In the present work, however, we consider systems
+with a finite density of either fractons or their dipolar bound states, with a
+focus on the $U(1)$ fracton models. We study some of the phases in which
+emergent fractonic matter can exist, thereby initiating the study of the
+""condensed matter"" of fractons. We begin by considering a system with a finite
+density of fractons, which we show can exhibit microemulsion physics, in which
+fractons form small-scale clusters emulsed in a phase dominated by long-range
+repulsion. We then move on to study systems with a finite density of mobile
+dipoles, which have phases analogous to many conventional condensed matter
+phases. We focus on two major examples: Fermi liquids and quantum Hall phases.
+A finite density of fermionic dipoles will form a Fermi surface and enter a
+Fermi liquid phase. Interestingly, this dipolar Fermi liquid exhibits a
+finite-temperature phase transition, corresponding to an unbinding transition
+of fractons. Finally, we study chiral two-dimensional phases corresponding to
+dipoles in ""quantum Hall"" states of their emergent magnetic field. We study
+numerous aspects of these generalized quantum Hall systems, such as their edge
+theories and ground state degeneracies.
+",0,1,0,0,0,0
+9958,Fractional Volterra Hierarchy,"  The generating function of cubic Hodge integrals satisfying the local
+Calabi-Yau condition is conjectured to be a tau function of a new integrable
+system which can be regarded as a fractional generalization of the Volterra
+lattice hierarchy, so we name it the fractional Volterra hierarchy. In this
+paper, we give the definition of this integrable hierarchy in terms of Lax pair
+and Hamiltonian formalisms, construct its tau functions, and present its
+multi-soliton solutions.
+",0,1,1,0,0,0
+9959,Deorbitalization strategies for meta-GGA exchange-correlation functionals,"  We explore the simplification of widely used meta-generalized-gradient
+approximation (mGGA) exchange-correlation functionals to the Laplacian level of
+refinement by use of approximate kinetic energy density functionals (KEDFs).
+Such deorbitalization is motivated by the prospect of reducing computational
+cost while recovering a strictly Kohn-Sham local potential framework (rather
+than the usual generalized Kohn-Sham treatment of mGGAs). A KEDF that has been
+rather successful in solid simulations proves to be inadequate for
+deorbitalization but we produce other forms which, with parametrization to
+Kohn-Sham results (not experimental data) on a small training set, yield rather
+good results on standard molecular test sets when used to deorbitalize the
+meta-GGA made very simple, TPSS, and SCAN functionals. We also study the
+difference between high-fidelity and best-performing deorbitalizations and
+discuss possible implications for use in ab initio molecular dynamics
+simulations of complicated condensed phase systems.
+",0,1,0,0,0,0
+9960,Fast counting of medium-sized rooted subgraphs,"  We prove that counting copies of any graph $F$ in another graph $G$ can be
+achieved using basic matrix operations on the adjacency matrix of $G$.
+Moreover, the resulting algorithm is competitive for medium-sized $F$: our
+algorithm recovers the best known complexity for rooted 6-clique counting and
+improves on the best known for 9-cycle counting. Underpinning our proofs is the
+new result that, for a general class of graph operators, matrix operations are
+homomorphisms for operations on rooted graphs.
+",1,0,1,0,0,0
+9961,First-principles investigation of graphitic carbon nitride monolayer with embedded Fe atom,"  Density-functional theory calculations with spin-polarized generalized
+gradient approximation and Hubbard $U$ correction is carried out to investigate
+the mechanical, structural, electronic and magnetic properties of graphitic
+heptazine with embedded $\mathrm{Fe}$ atom under bi-axial tensile strain and
+applied perpendicular electric field. It was found that the binding energy of
+heptazine with embedded $\mathrm{Fe}$ atom system decreases as more tensile
+strain is applied and increases as more electric field strength is applied. Our
+calculations also predict a band gap at a peak value of 5 tensile strain but at
+expense of the structural stability of the system. The band gap opening at 5
+tensile strain is due to distortion in the structure caused by the repulsive
+effect in the cavity between the lone pairs of edge nitrogen atoms and
+$\mathrm{d}_{xy}/\mathrm{d}_{x^2-y^2}$ orbital of Fe atom, hence the
+unoccupied $\mathrm{p}_z$-orbital is forced to shift towards higher energy. The
+electronic and magnetic properties of the heptazine with embedded $\mathrm{Fe}$
+system under perpendicular electric field up to a peak value of 10
+$\mathrm{V/nm}$ is also well preserved despite obvious buckled structure. Such
+properties may be desirable for diluted magnetic semiconductors, spintronics,
+and sensing devices.
+",0,1,0,0,0,0
+9962,Courant's Nodal Domain Theorem for Positivity Preserving Forms,"  We introduce a notion of nodal domains for positivity preserving forms. This
+notion generalizes the classical ones for Laplacians on domains and on graphs.
+We prove the Courant nodal domain theorem in this generalized setting using
+purely analytical methods.
+",0,0,1,0,0,0
+9963,Intelligent Notification Systems: A Survey of the State of the Art and Research Challenges,"  Notifications provide a unique mechanism for increasing the effectiveness of
+real-time information delivery systems. However, notifications that demand
+users' attention at inopportune moments are more likely to have adverse effects
+and might become a cause of potential disruption rather than proving beneficial
+to users. In order to address these challenges a variety of intelligent
+notification mechanisms based on monitoring and learning users' behavior have
+been proposed. The goal of such mechanisms is maximizing users' receptivity to
+the delivered information by automatically inferring the right time and the
+right context for sending a certain type of information.
+This article provides an overview of the current state of the art in the area
+of intelligent notification mechanisms that relies on the awareness of users'
+context and preferences. More specifically, we first present a survey of
+studies focusing on understanding and modeling users' interruptibility and
+receptivity to notifications from desktops and mobile devices. Then, we discuss
+the existing challenges and opportunities in developing mechanisms for
+intelligent notification systems in a variety of application scenarios.
+",1,0,0,0,0,0
+9964,Using Matching to Detect Infeasibility of Some Integer Programs,"  A novel matching based heuristic algorithm designed to detect specially
+formulated infeasible zero-one IPs is presented. The algorithm input is a set
+of nested doubly stochastic subsystems and a set E of instance defining
+variables set at zero level. The algorithm deduces additional variables at zero
+level until either a constraint is violated (the IP is infeasible), or no more
+variables can be deduced zero (the IP is undecided). All feasible IPs, and all
+infeasible IPs not detected infeasible are undecided. We successfully apply the
+algorithm to a small set of specially formulated infeasible zero-one IP
+instances of the Hamilton cycle decision problem. We show how to model both the
+graph and subgraph isomorphism decision problems for input to the algorithm.
+Increased levels of nested doubly stochastic subsystems can be implemented
+dynamically. The algorithm is designed for parallel processing, and for
+inclusion of techniques in addition to matching.
+",1,0,0,0,0,0
+9965,"α7 nicotinic acetylcholine receptor signaling modulates ovine fetal brain astrocytes transcriptome in response to endotoxin: comparison to microglia, implications for prenatal stress and development of autism spectrum disorder","  Neuroinflammation in utero may result in lifelong neurological disabilities.
+Astrocytes play a pivotal role, but the mechanisms are poorly understood. No
+early postnatal treatment strategies exist to enhance neuroprotective potential
+of astrocytes. We hypothesized that agonism on {\alpha}7 nicotinic
+acetylcholine receptor ({\alpha}7nAChR) in fetal astrocytes will augment their
+neuroprotective transcriptome profile, while the antagonistic stimulation of
+{\alpha}7nAChR will achieve the opposite. Using an in vivo - in vitro model of
+developmental programming of neuroinflammation induced by lipopolysaccharide
+(LPS), we validated this hypothesis in primary fetal sheep astrocytes cultures
+re-exposed to LPS in presence of a selective {\alpha}7nAChR agonist or
+antagonist. Our RNAseq findings show that a pro-inflammatory astrocyte
+transcriptome phenotype acquired in vitro by LPS stimulation is reversed with
+{\alpha}7nAChR agonistic stimulation. Conversely, antagonistic {\alpha}7nAChR
+stimulation potentiates the pro-inflammatory astrocytic transcriptome
+phenotype. Furthermore, we conduct a secondary transcriptome analysis against
+the identical {\alpha}7nAChR experiments in fetal sheep primary microglia
+cultures and against the Simons Simplex Collection for autism spectrum disorder
+and discuss the implications.
+",0,0,0,0,1,0
+9966,Face centered cubic and hexagonal close packed skyrmion crystals in centro-symmetric magnets,"  Skyrmions are disk-like objects that typically form triangular crystals in
+two dimensional systems. This situation is analogous to the so-called ""pancake
+vortices"" of quasi-two dimensional superconductors. The way in which skyrmion
+disks or pancake skyrmions pile up in layered centro-symmetric materials is
+dictated by the inter-layer exchange. Unbiased Monte Carlo simulations and
+simple stabilization arguments reveal face centered cubic and hexagonal close
+packed skyrmion crystals for different choices of the inter-layer exchange, in
+addition to the conventional triangular crystal of skyrmion lines. Moreover, an
+inhomogeneous current induces sliding motion of pancake skyrmions, indicating
+that they behave as effective mesoscale particles.
+",0,1,0,0,0,0
+9967,"Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search","  Deep convolution neural networks demonstrate impressive results in the
+super-resolution domain. A series of studies concentrate on improving peak
+signal noise ratio (PSNR) by using much deeper layers, which are not friendly
+to constrained resources. Pursuing a trade-off between the restoration capacity
+and the simplicity of models is still non-trivial. Recent contributions are
+struggling to manually maximize this balance, while our work achieves the same
+goal automatically with neural architecture search. Specifically, we handle
+super-resolution with a multi-objective approach. We also propose an elastic
+search tactic at both micro and macro level, based on a hybrid controller that
+profits from evolutionary computation and reinforcement learning. Quantitative
+experiments help us to draw a conclusion that our generated models dominate
+most of the state-of-the-art methods with respect to the individual FLOPS.
+",1,0,0,0,0,0
+9968,Robbins-Monro conditions for persistent exploration learning strategies,"  We formulate simple assumptions, implying the Robbins-Monro conditions for
+the $Q$-learning algorithm with the local learning rate, depending on the
+number of visits of a particular state-action pair (local clock) and the number
+of iteration (global clock). It is assumed that the Markov decision process is
+communicating and the learning policy ensures the persistent exploration. The
+restrictions are imposed on the functional dependence of the learning rate on
+the local and global clocks. The result partially confirms the conjecture of
+Bradkte (1994).
+",0,0,0,1,0,0
+9969,Localization in the Disordered Holstein model,"  The Holstein model describes the motion of a tight-binding tracer particle
+interacting with a field of quantum harmonic oscillators. We consider this
+model with an on-site random potential. Provided the hopping amplitude for the
+particle is small, we prove localization for matrix elements of the resolvent,
+in particle position and in the field Fock space. These bounds imply a form of
+dynamical localization for the particle position that leaves open the
+possibility of resonant tunneling in Fock space between equivalent field
+configurations.
+",0,1,1,0,0,0
+9970,Universal Planning Networks,"  A key challenge in complex visuomotor control is learning abstract
+representations that are effective for specifying goals, planning, and
+generalization. To this end, we introduce universal planning networks (UPN).
+UPNs embed differentiable planning within a goal-directed policy. This planning
+computation unrolls a forward model in a latent space and infers an optimal
+action plan through gradient descent trajectory optimization. The
+plan-by-gradient-descent process and its underlying representations are learned
+end-to-end to directly optimize a supervised imitation learning objective. We
+find that the representations learned are not only effective for goal-directed
+visual imitation via gradient-based trajectory optimization, but can also
+provide a metric for specifying goals using images. The learned representations
+can be leveraged to specify distance-based rewards to reach new target states
+for model-free reinforcement learning, resulting in substantially more
+effective learning when solving new tasks described via image-based goals. We
+were able to achieve successful transfer of visuomotor planning strategies
+across robots with significantly different morphologies and actuation
+capabilities.
+",1,0,0,1,0,0
+9971,Adversarial Removal of Demographic Attributes from Text Data,"  Recent advances in Representation Learning and Adversarial Training seem to
+succeed in removing unwanted features from the learned representation. We show
+that demographic information of authors is encoded in -- and can be recovered
+from -- the intermediate representations learned by text-based neural
+classifiers. The implication is that decisions of classifiers trained on
+textual data are not agnostic to -- and likely condition on -- demographic
+attributes. When attempting to remove such demographic information using
+adversarial training, we find that while the adversarial component achieves
+chance-level development-set accuracy during training, a post-hoc classifier,
+trained on the encoded sentences from the first part, still manages to reach
+substantially higher classification accuracies on the same data. This behavior
+is consistent across several tasks, demographic properties and datasets. We
+explore several techniques to improve the effectiveness of the adversarial
+component. Our main conclusion is a cautionary one: do not rely on the
+adversarial training to achieve invariant representation to sensitive features.
+",0,0,0,1,0,0
+9972,Superradiance phase transition in the presence of parameter fluctuations,"  We theoretically analyze the effect of parameter fluctuations on the
+superradiance phase transition in a setup where a large number of
+superconducting qubits are coupled to a single cavity. We include parameter
+fluctuations that are typical of superconducting architectures, such as
+fluctuations in qubit gaps, bias points and qubit-cavity coupling strengths. We
+find that the phase transition should occur in this case, although it manifests
+itself somewhat differently from the case with no fluctuations. We also find
+that fluctuations in the qubit gaps and qubit-cavity coupling strengths do not
+necessarily make it more difficult to reach the transition point. Fluctuations
+in the bias points, however, increase the coupling strength required to reach
+the quantum phase transition point and enter the superradiant phase. Similarly,
+these fluctuations lower the critical temperature for the thermal phase
+transition.
+",0,1,0,0,0,0
+9973,A Decision Support Method for Recommending Degrees of Exploration in Exploratory Testing,"  Exploratory testing is neither black nor white, but rather a continuum of
+exploration exists. In this research we propose an approach for decision
+support helping practitioners to distribute time between different degrees of
+exploratory testing on that continuum. To make the continuum manageable, five
+levels have been defined: freestyle testing, high, medium and low degrees of
+exploration, and scripted testing. The decision support approach is based on
+the repertory grid technique. The approach has been used in one company. The
+method for data collection was focus groups. The results showed that the
+proposed approach aids practitioners in the reflection of what exploratory
+testing levels to use, and aligns their understanding for priorities of
+decision criteria and the performance of exploratory testing levels in their
+contexts. The findings also showed that the participating company, which is
+currently conducting mostly scripted testing, should spend more time on testing
+using higher degrees of exploration in comparison to scripted testing.
+",1,0,0,0,0,0
+9974,Quantum Field Theory and Coalgebraic Logic in Theoretical Computer Science,"  In this paper we suggest that in the framework of the Category Theory it is
+possible to demonstrate the mathematical and logical \textit{dual equivalence}
+between the category of the $q$-deformed Hopf Coalgebras and the category of
+the $q$-deformed Hopf Algebras in QFT, interpreted as a thermal field theory.
+Each pair algebra-coalgebra characterizes, indeed, a QFT system and its
+mirroring thermal bath, respectively, so to model dissipative quantum systems
+persistently in far-from-equilibrium conditions, with an evident significance
+also for biological sciences. The $q$-deformed Hopf Coalgebras and the
+$q$-deformed Hopf Algebras constitute two dual categories because characterized
+by the same functor $T$, related with the Bogoliubov transform, and by its
+contravariant application $T^{op}$, respectively. The \textit{q}-deformation
+parameter, indeed, is related to the Bogoliubov angle, and it is effectively a
+thermal parameter. Therefore, the different values of $q$ identify univocally,
+and then label, the vacua appearing in the foliation process of the quantum
+vacuum. This means that, in the framework of Universal Coalgebra, as general
+theory of dynamic and computing systems (""labelled state-transition systems""),
+the so labelled infinitely many quantum vacua can be interpreted as the Final
+Coalgebra of an ""Infinite State Black-Box Machine"". All this opens the way to
+the possibility of designing a new class of universal quantum computing
+architectures based on this coalgebraic formulation of QFT, as its ability of
+naturally generating a Fibonacci progression demonstrates.
+",1,0,1,0,0,0
+9975,Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression,"  Accurately predicting when and where ambulance call-outs occur can reduce
+response times and ensure the patient receives urgent care sooner. Here we
+present a novel method for ambulance demand prediction using Gaussian process
+regression (GPR) in time and geographic space. The method exhibits superior
+accuracy to MEDIC, a method which has been used in industry. The use of GPR has
+additional benefits such as the quantification of uncertainty with each
+prediction, the choice of kernel functions to encode prior knowledge and the
+ability to capture spatial correlation. Measures to increase the utility of GPR
+in the current context, with large training sets and a Poisson-distributed
+output, are outlined.
+",0,0,0,1,0,0
+9976,ArchiveWeb: collaboratively extending and exploring web archive collections - How would you like to work with your collections?,"  Curated web archive collections contain focused digital content which is
+collected by archiving organizations, groups, and individuals to provide a
+representative sample covering specific topics and events to preserve them for
+future exploration and analysis. In this paper, we discuss how to best support
+collaborative construction and exploration of these collections through the
+ArchiveWeb system. ArchiveWeb has been developed using an iterative
+evaluation-driven design-based research approach, with considerable user
+feedback at all stages. The first part of this paper describes the important
+insights we gained from our initial requirements engineering phase during the
+first year of the project and the main functionalities of the current
+ArchiveWeb system for searching, constructing, exploring, and discussing web
+archive collections. The second part summarizes the feedback we received on
+this version from archiving organizations and libraries, as well as our
+corresponding plans for improving and extending the system for the next
+release.
+",1,0,0,0,0,0
+9977,gl2vec: Learning Feature Representation Using Graphlets for Directed Networks,"  Learning network representations has a variety of applications, such as
+network classification. Most existing work in this area focuses on static
+undirected networks and do not account for presence of directed edges or
+temporarily changes. Furthermore, most work focuses on node representations
+that do poorly on tasks like network classification. In this paper, we propose
+a novel, flexible and scalable network embedding methodology, \emph{gl2vec},
+for network classification in both static and temporal directed networks.
+\emph{gl2vec} constructs vectors for feature representation using static or
+temporal network graphlet distributions and a null model for comparing them
+against random graphs. We argue that \emph{gl2vec} can be used to classify and
+compare networks of varying sizes and time period with high accuracy. We
+demonstrate the efficacy and usability of \emph{gl2vec} over existing
+state-of-the-art methods on network classification tasks such as network type
+classification and subgraph identification in several real-world static and
+temporal directed networks. Experimental results further show that
+\emph{gl2vec}, concatenated with a wide range of state-of-the-art methods,
+improves classification accuracy by up to $10\%$ in real-world applications
+such as detecting departments for subgraphs in an email network or identifying
+mobile users given their app switching behaviors represented as static or
+temporal directed networks.
+",1,0,0,0,0,0
+9978,Evidence of s-wave superconductivity in the noncentrosymmetric La$_7$Ir$_3$,"  Superconductivity in noncentrosymmetric compounds has attracted sustained
+interest in the last decades. Here we present a detailed study on the
+transport, thermodynamic properties and the band structure of the
+noncentrosymmetric superconductor La$_7$Ir$_3$ ($T_c$ $\sim$2.3 K) that was
+recently proposed to break the time-reversal symmetry. It is found that
+La$_7$Ir$_3$ displays a moderately large electronic heat capacity (Sommerfeld
+coefficient $\gamma_n$ $\sim$ 53.1 mJ/mol $\text{K}^2$) and a significantly
+enhanced Kadowaki-Woods ratio (KWR $\sim$ 32 $\mu\Omega$ cm mol$^2$ K$^2$
+J$^{-2}$) that is greater than the typical value ($\sim$ 10 $\mu\Omega$ cm
+mol$^2$ K$^2$ J$^{-2}$) for strongly correlated electron systems. The upper
+critical field $H_{c2}$ was seen to be nicely described by the single-band
+Werthamer-Helfand-Hohenberg model down to very low temperatures. The
+hydrostatic pressure effects on the superconductivity were also investigated.
+The heat capacity below $T_c$ reveals a dominant s-wave gap with the magnitude
+close to the BCS value. The first-principles calculations yield the
+electron-phonon coupling constant $\lambda$ = 0.81 and the logarithmically
+averaged frequency $\omega_{ln}$ = 78.5 K, resulting in a theoretical $T_c$ =
+2.5 K, close to the experimental value. Our calculations suggest that the
+enhanced electronic heat capacity is more likely due to electron-phonon
+coupling, rather than the electron-electron correlation effects. Collectively,
+these results place severe constraints on any theory of exotic
+superconductivity in this system.
+",0,1,0,0,0,0
+9979,Essential Dimension of Generic Symbols in Characteristic p,"  In this article the $p$-essential dimension of generic symbols over fields of
+characteristic $p$ is studied. In particular, the $p$-essential dimension of
+the length $\ell$ generic $p$-symbol of degree $n+1$ is bounded below by
+$n+\ell$ when the base field is algebraically closed of characteristic $p$. The
+proof uses new techniques for working with residues in Milne-Kato
+$p$-cohomology and builds on work of Babic and Chernousov in the Witt group in
+characteristic 2. Two corollaries on $p$-symbol algebras (i.e, degree 2
+symbols) result from this work. The generic $p$-symbol algebra of length $\ell$
+is shown to have $p$-essential dimension equal to $\ell+1$ as a $p$-torsion
+Brauer class. The second is a lower bound of $\ell+1$ on the $p$-essential
+dimension of the functor $\mathrm{Alg}_{p^\ell,p}$. Roughly speaking this says
+that you will need at least $\ell+1$ independent parameters to be able to
+specify any given algebra of degree $p^{\ell}$ and exponent $p$ over a field of
+characteristic $p$ and improves on the previously established lower bound of 3.
+",0,0,1,0,0,0
+9980,Ask less - Scale Market Research without Annoying Your Customers,"  Market research is generally performed by surveying a representative sample
+of customers with questions that includes contexts such as psycho-graphics,
+demographics, attitude and product preferences. Survey responses are used to
+segment the customers into various groups that are useful for targeted
+marketing and communication. Reducing the number of questions asked to the
+customer has utility for businesses to scale the market research to a large
+number of customers. In this work, we model this task using Bayesian networks.
+We demonstrate the effectiveness of our approach using an example market
+segmentation of broadband customers.
+",1,0,0,1,0,0
+9981,Adaptive Regularized Newton Method for Riemannian Optimization,"  Optimization on Riemannian manifolds widely arises in eigenvalue computation,
+density functional theory, Bose-Einstein condensates, low rank nearest
+correlation, image registration, and signal processing, etc. We propose an
+adaptive regularized Newton method which approximates the original objective
+function by the second-order Taylor expansion in Euclidean space but keeps the
+Riemannian manifold constraints. The regularization term in the objective
+function of the subproblem enables us to establish a Cauchy-point like
+condition as the standard trust-region method for proving global convergence.
+The subproblem can be solved inexactly either by first-order methods or a
+modified Riemannian Newton method. In the later case, it can further take
+advantage of negative curvature directions. Both global convergence and
+superlinear local convergence are guaranteed under mild conditions. Extensive
+computational experiments and comparisons with other state-of-the-art methods
+indicate that the proposed algorithm is very promising.
+",0,0,1,0,0,0
+9982,MPC meets SNA: A Privacy Preserving Analysis of Distributed Sensitive Social Networks,"  In this paper, we formalize the notion of distributed sensitive social
+networks (DSSNs), which encompasses networks like enmity networks, financial
+transaction networks, supply chain networks and sexual relationship networks.
+Compared to the well studied traditional social networks, DSSNs are often more
+challenging to study, given the privacy concerns of the individuals on whom the
+network is knit. In the current work, we envision the use of secure multiparty
+tools and techniques for performing privacy preserving social network analysis
+over DSSNs. As a step towards realizing this, we design efficient
+data-oblivious algorithms for computing the K-shell decomposition and the
+PageRank centrality measure for a given DSSN. The designed data-oblivious
+algorithms can be translated into equivalent secure computation protocols. We
+also list a string of challenges that are needed to be addressed, for employing
+secure computation protocols as a practical solution for studying DSSNs.
+",1,0,0,0,0,0
+9983,Lossy Image Compression with Compressive Autoencoders,"  We propose a new approach to the problem of optimizing autoencoders for lossy
+image compression. New media formats, changing hardware technology, as well as
+diverse requirements and content types create a need for compression algorithms
+which are more flexible than existing codecs. Autoencoders have the potential
+to address this need, but are difficult to optimize directly due to the
+inherent non-differentiabilty of the compression loss. We here show that
+minimal changes to the loss are sufficient to train deep autoencoders
+competitive with JPEG 2000 and outperforming recently proposed approaches based
+on RNNs. Our network is furthermore computationally efficient thanks to a
+sub-pixel architecture, which makes it suitable for high-resolution images.
+This is in contrast to previous work on autoencoders for compression using
+coarser approximations, shallower architectures, computationally expensive
+methods, or focusing on small images.
+",1,0,0,1,0,0
+9984,A cost effective and reliable environment monitoring system for HPC applications,"  We present a slow control system to gather all relevant environment
+information necessary to effectively and reliably run an HPC (High Performance
+Computing) system at a high value over price ratio. The scalable and reliable
+overall concept is presented as well as a newly developed hardware device for
+sensor read out. This device incorporates a Raspberry Pi, an Arduino and PoE
+(Power over Ethernet) functionality in a compact form factor. The system is in
+use at the 2 PFLOPS cluster of the Johannes Gutenberg-University and
+Helmholtz-Institute in Mainz.
+",1,0,0,0,0,0
+9985,Quenching the Kitaev honeycomb model,"  I studied the non-equilibrium response of an initial Néel state under
+time evolution with the Kitaev honeycomb model. This time evolution can be
+computed using a random sampling over all relevant flux configurations. With
+isotropic interactions the system quickly equilibrates into a steady state
+valence bond solid. Anisotropy induces an exponentially long prethermal regime
+whose dynamics are governed by an effective toric code. Signatures of topology
+are absent, however, due to the high energy density nature of the initial
+state.
+",0,1,0,0,0,0
+9986,Privacy-Preserving Adversarial Networks,"  We propose a data-driven framework for optimizing privacy-preserving data
+release mechanisms toward the information-theoretically optimal tradeoff
+between minimizing distortion of useful data and concealing sensitive
+information. Our approach employs adversarially-trained neural networks to
+implement randomized mechanisms and to perform a variational approximation of
+mutual information privacy. We empirically validate our Privacy-Preserving
+Adversarial Networks (PPAN) framework with experiments conducted on discrete
+and continuous synthetic data, as well as the MNIST handwritten digits dataset.
+With the synthetic data, we find that our model-agnostic PPAN approach achieves
+tradeoff points very close to the optimal tradeoffs that are
+analytically-derived from model knowledge. In experiments with the MNIST data,
+we visually demonstrate a learned tradeoff between minimizing the pixel-level
+distortion versus concealing the written digit.
+",1,0,0,1,0,0
+9987,Antiferromagnetic structure and electronic properties of BaCr2As2 and BaCrFeAs2,"  The chromium arsenides BaCr2As2 and BaCrFeAs2 with ThCr2Si2 type structure
+(space group I4/mmm; also adopted by '122' iron arsenide superconductors) have
+been suggested as mother compounds for possible new superconductors. DFT-based
+calculations of the electronic structure evidence metallic antiferromagnetic
+ground states for both compounds. By powder neutron diffraction we confirm for
+BaCr2As2 a robust ordering in the antiferromagnetic G-type structure at T_N =
+580 K with mu_Cr = 1.9 mu_B at T = 2K. Anomalies in the lattice parameters
+point to magneto-structural coupling effects. In BaCrFeAs2 the Cr and Fe atoms
+randomly occupy the transition-metal site and G-type order is found below 265 K
+with mu_Cr/Fe = 1.1 mu_B. 57Fe Moessbauer spectroscopy demonstrates that only a
+small ordered moment is associated with the Fe atoms, in agreement with
+electronic structure calculations with mu_Fe ~ 0. The temperature dependence of
+the hyperfine field does not follow that of the total moments. Both compounds
+are metallic but show large enhancements of the linear specific heat
+coefficient gamma with respect to the band structure values. The metallic state
+and the electrical transport in BaCrFeAs2 is dominated by the atomic disorder
+of Cr and Fe and partial magnetic disorder of Fe. Our results indicate that
+Neel-type order is unfavorable for the Fe moments and thus it is destabilized
+with increasing iron content.
+",0,1,0,0,0,0
+9988,Molecular Modeling of the Microstructure Evolution during the Carbonization of PAN-Based Carbon Fibers,"  Development of high strength carbon fibers (CFs) requires an understanding of
+the relationship between the processing conditions, microstructure and
+resulting properties. We developed a molecular model that combines kinetic
+Monte Carlo (KMC) and molecular dynamics (MD) techniques to predict the
+microstructure evolution during the carbonization process of carbon fiber
+manufacturing. The model accurately predicts the cross-sectional microstructure
+of carbon fibers, predicting features such as graphitic sheets and hairpin
+structures that have been observed experimentally. We predict the transverse
+modulus of the resulting fibers and find that the modulus is slightly lower
+than experimental values, but is up to an order of magnitude lower than ideal
+graphite. We attribute this to the perfect longitudinal texture of our
+simulated structures, as well as the chain sliding mechanism that governs the
+deformation of the fibers, rather than the van der Waals interaction that
+governs the modulus for graphite. We also observe that high reaction rates
+result in porous structures that have lower moduli.
+",0,1,0,0,0,0
+9989,Hyperinflation,"  A model of cosmological inflation is proposed in which field space is a
+hyperbolic plane. The inflaton never slow-rolls, and instead orbits the bottom
+of the potential, buoyed by a centrifugal force. Though initial velocities
+redshift away during inflation, in negatively curved spaces angular momentum
+naturally starts exponentially large and remains relevant throughout. Quantum
+fluctuations produce perturbations that are adiabatic and approximately scale
+invariant; strikingly, in a certain parameter regime the perturbations can grow
+double-exponentially during horizon crossing.
+",0,1,0,0,0,0
+9990,Improving on Q & A Recurrent Neural Networks Using Noun-Tagging,"  Often, more time is spent on finding a model that works well, rather than
+tuning the model and working directly with the dataset. Our research began as
+an attempt to improve upon a simple Recurrent Neural Network for answering
+""simple"" first-order questions (QA-RNN), developed by Ferhan Ture and Oliver
+Jojic, from Comcast Labs, using the SimpleQuestions dataset. Their baseline
+model, a bidirectional, 2-layer LSTM RNN and a GRU RNN, have accuracies of 0.94
+and 0.90, for entity detection and relation prediction, respectively. We fine
+tuned these models by doing substantial hyper-parameter tuning, getting
+resulting accuracies of 0.70 and 0.80, for entity detection and relation
+prediction, respectively. An accuracy of 0.984 was obtained on entity detection
+using a 1-layer LSTM, where preprocessing was done by removing all words not
+part of a noun chunk from the question. 100% of the dataset was available for
+relation prediction, but only 20% of the dataset, was available for entity
+detection, which we believe to be much of the reason for our initial
+difficulties in replicating their result, despite the fact we were able to
+improve on their entity detection results.
+",0,0,0,1,0,0
+9991,Injectivity and weak*-to-weak continuity suffice for convergence rates in $\ell^1$-regularization,"  We show that the convergence rate of $\ell^1$-regularization for linear
+ill-posed equations is always $O(\delta)$ if the exact solution is sparse and
+if the considered operator is injective and weak*-to-weak continuous. Under the
+same assumptions convergence rates in case of non-sparse solutions are proven.
+The results base on the fact that certain source-type conditions used in the
+literature for proving convergence rates are automatically satisfied.
+",0,0,1,0,0,0
+9992,A Panel Prototype for the Mu2e Straw Tube Tracker at Fermilab,"  The Mu2e experiment will search for coherent, neutrino-less conversion of
+muons into electrons in the Coulomb field of an aluminum nucleus with a
+sensitivity of four orders of magnitude better than previous experiments. The
+signature of this process is an electron with energy nearly equal to the muon
+mass. Mu2e relies on a precision (0.1%) measurement of the outgoing electron
+momentum to separate signal from background. In order to achieve this goal,
+Mu2e has chosen a very low-mass straw tracker, made of 20,736 5 mm diameter
+thin-walled (15 $\mu$m) Mylar straws, held under tension to avoid the need for
+supports within the active volume, and arranged in an approximately 3 m long by
+0.7 m radius cylinder, operated in vacuum and a 1 T magnetic field. Groups of
+96 straws are assembled into modules, called panels. We present the prototype
+and the assembly procedure for a Mu2e tracker panel built at Fermilab
+",0,1,0,0,0,0
+9993,"A Review on Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT)","  The current prominence and future promises of the Internet of Things (IoT),
+Internet of Everything (IoE) and Internet of Nano Things (IoNT) are extensively
+reviewed and a summary survey report is presented. The analysis clearly
+distinguishes between IoT and IoE which are wrongly considered to be the same
+by many people. Upon examining the current advancement in the fields of IoT,
+IoE and IoNT, the paper presents scenarios for the possible future expansion of
+their applications.
+",1,0,0,0,0,0
+9994,Ensemble Adversarial Training: Attacks and Defenses,"  Adversarial examples are perturbed inputs designed to fool machine learning
+models. Adversarial training injects such examples into training data to
+increase robustness. To scale this technique to large datasets, perturbations
+are crafted using fast single-step methods that maximize a linear approximation
+of the model's loss. We show that this form of adversarial training converges
+to a degenerate global minimum, wherein small curvature artifacts near the data
+points obfuscate a linear approximation of the loss. The model thus learns to
+generate weak perturbations, rather than defend against strong ones. As a
+result, we find that adversarial training remains vulnerable to black-box
+attacks, where we transfer perturbations computed on undefended models, as well
+as to a powerful novel single-step attack that escapes the non-smooth vicinity
+of the input data via a small random step. We further introduce Ensemble
+Adversarial Training, a technique that augments training data with
+perturbations transferred from other models. On ImageNet, Ensemble Adversarial
+Training yields models with strong robustness to black-box attacks. In
+particular, our most robust model won the first round of the NIPS 2017
+competition on Defenses against Adversarial Attacks.
+",1,0,0,1,0,0
+9995,X-Ray bright optically faint active galactic nuclei in the Subaru Hyper Suprime-Cam wide survey,"  We construct a sample of X-ray bright optically faint active galactic nuclei
+by combining Subaru Hyper Suprime-Cam, XMM-Newton, and infrared source
+catalogs. 53 X-ray sources satisfying i band magnitude fainter than 23.5 mag
+and X-ray counts with EPIC-PN detector larger than 70 are selected from 9.1
+deg^2, and their spectral energy distributions (SEDs) and X-ray spectra are
+analyzed. 44 objects with an X-ray to i-band flux ratio F_X/F_i>10 are
+classified as extreme X-ray-to-optical flux sources. SEDs of 48 among 53 are
+represented by templates of type 2 AGNs or starforming galaxies and show
+signature of stellar emission from host galaxies in the optical in the source
+rest frame. Infrared/optical SEDs indicate significant contribution of emission
+from dust to infrared fluxes and that the central AGN is dust obscured.
+Photometric redshifts determined from the SEDs are in the range of 0.6-2.5.
+X-ray spectra are fitted by an absorbed power law model, and the intrinsic
+absorption column densities are modest (best-fit log N_H = 20.5-23.5 cm^-2 in
+most cases). The absorption corrected X-ray luminosities are in the range of
+6x10^42 - 2x10^45 erg s^-1. 20 objects are classified as type 2 quasars based
+on X-ray luminsosity and N_H. The optical faintness is explained by a
+combination of redshifts (mostly z>1.0), strong dust extinction, and in part a
+large ratio of dust/gas.
+",0,1,0,0,0,0
+9996,The Goldman symplectic form on the PSL(V)-Hitchin component,"  This article is the second of a pair of articles about the Goldman symplectic
+form on the PSL(V )-Hitchin component. We show that any ideal triangulation on
+a closed connected surface of genus at least 2, and any compatible bridge
+system determine a symplectic trivialization of the tangent bundle to the
+Hitchin component. Using this, we prove that a large class of flows defined in
+the companion paper [SWZ17] are Hamiltonian. We also construct an explicit
+collection of Hamiltonian vector fields on the Hitchin component that give a
+symplectic basis at every point. These are used in the companion paper to
+compute explicit global Darboux coordinates for the Hitchin component.
+",0,0,1,0,0,0
+9997,"Early Results from TUS, the First Orbital Detector of Extreme Energy Cosmic Rays","  TUS is the world's first orbital detector of extreme energy cosmic rays
+(EECRs), which operates as a part of the scientific payload of the Lomonosov
+satellite since May 19, 2016. TUS employs the nocturnal atmosphere of the Earth
+to register ultraviolet (UV) fluorescence and Cherenkov radiation from
+extensive air showers generated by EECRs as well as UV radiation from lightning
+strikes and transient luminous events, micro-meteors and space debris. The
+first months of its operation in orbit have demonstrated an unexpectedly rich
+variety of UV radiation in the atmosphere. We briefly review the design of TUS
+and present a few examples of events recorded in a mode dedicated to
+registering EECRs.
+",0,1,0,0,0,0
+9998,"Network-based methods for outcome prediction in the ""sample space""","  In this thesis we present the novel semi-supervised network-based algorithm
+P-Net, which is able to rank and classify patients with respect to a specific
+phenotype or clinical outcome under study. The peculiar and innovative
+characteristic of this method is that it builds a network of samples/patients,
+where the nodes represent the samples and the edges are functional or genetic
+relationships between individuals (e.g. similarity of expression profiles), to
+predict the phenotype under study. In other words, it constructs the network in
+the ""sample space"" and not in the ""biomarker space"" (where nodes represent
+biomolecules (e.g. genes, proteins) and edges represent functional or genetic
+relationships between nodes), as usual in state-of-the-art methods. To assess
+the performances of P-Net, we apply it on three different publicly available
+datasets from patients afflicted with a specific type of tumor: pancreatic
+cancer, melanoma and ovarian cancer dataset, by using the data and following
+the experimental set-up proposed in two recently published papers [Barter et
+al., 2014, Winter et al., 2012]. We show that network-based methods in the
+""sample space"" can achieve results competitive with classical supervised
+inductive systems. Moreover, the graph representation of the samples can be
+easily visualized through networks and can be used to gain visual clues about
+the relationships between samples, taking into account the phenotype associated
+or predicted for each sample. To our knowledge this is one of the first works
+that proposes graph-based algorithms working in the ""sample space"" of the
+biomolecular profiles of the patients to predict their phenotype or outcome,
+thus contributing to a novel research line in the framework of the Network
+Medicine.
+",1,0,0,1,0,0
+9999,The geometrical origins of some distributions and the complete concentration of measure phenomenon for mean-values of functionals,"  We derive out naturally some important distributions such as high order
+normal distributions and high order exponent distributions and the Gamma
+distribution from a geometrical way. Further, we obtain the exact mean-values
+of integral form functionals in the balls of continuous functions space with
+$p-$norm, and show the complete concentration of measure phenomenon which means
+that a functional takes its average on a ball with probability 1, from which we
+have nonlinear exchange formula of expectation.
+",0,0,1,1,0,0
+10000,The Signs in Elliptic Nets,"  We give a generalization of a theorem of Silverman and Stephens regarding the
+signs in an elliptic divisibility sequence to the case of an elliptic net. We
+also describe applications of this theorem in the study of the distribution of
+the signs in elliptic nets and generating elliptic nets using the denominators
+of the linear combination of points on elliptic curves.
+",0,0,1,0,0,0
+10001,Codes for Simultaneous Transmission of Quantum and Classical Information,"  We consider the characterization as well as the construction of quantum codes
+that allow to transmit both quantum and classical information, which we refer
+to as `hybrid codes'. We construct hybrid codes $[\![n,k{: }m,d]\!]_q$ with
+length $n$ and distance $d$, that simultaneously transmit $k$ qudits and $m$
+symbols from a classical alphabet of size $q$. Many good codes such as
+$[\![7,1{: }1,3]\!]_2$, $[\![9,2{: }2,3]\!]_2$, $[\![10,3{: }2,3]\!]_2$,
+$[\![11,4{: }2,3]\!]_2$, $[\![11,1{: }2,4]\!]_2$, $[\![13,1{: }4,4]\!]_2$,
+$[\![13,1{: }1,5]\!]_2$, $[\![14,1{: }2,5]\!]_2$, $[\![15,1{: }3,5]\!]_2$,
+$[\![19,9{: }1,4]\!]_2$, $[\![20,9{: }2,4]\!]_2$, $[\![21,9{: }3,4]\!]_2$,
+$[\![22,9{: }4,4]\!]_2$ have been found. All these codes have better parameters
+than hybrid codes obtained from the best known stabilizer quantum codes.
+",1,0,0,0,0,0
+10002,Kinetically constrained lattice gases: tagged particle diffusion,"  Kinetically constrained lattice gases (KCLG) are interacting particle systems
+on the integer lattice $\mathbb Z^d$ with hard core exclusion and
+Kawasaki type dynamics. Their peculiarity is that jumps are allowed only if
+the configuration satisfies a constraint which asks for enough empty sites in a
+certain local neighborhood. KCLG have been introduced and extensively studied
+in physics literature as models of glassy dynamics. We focus on the most
+studied class of KCLG, the Kob Andersen (KA) models. We analyze the behavior of
+a tracer (i.e. a tagged particle) at equilibrium. We prove that for all
+dimensions $d\geq 2$ and for any equilibrium particle density, under diffusive
+rescaling the motion of the tracer converges to a $d$-dimensional Brownian
+motion with non-degenerate diffusion matrix. Therefore we disprove the
+occurrence of a diffusive/non diffusive transition which had been conjectured
+in physics literature. Our technique is flexible enough and can be extended to
+analyse the tracer behavior for other choices of constraints.
+",0,1,1,0,0,0
+10003,An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields,"  Molecular fingerprints, i.e. feature vectors describing atomistic
+neighborhood configurations, is an important abstraction and a key ingredient
+for data-driven modeling of potential energy surface and interatomic force. In
+this paper, we present the Density-Encoded Canonically Aligned Fingerprint
+(DECAF) fingerprint algorithm, which is robust and efficient, for fitting
+per-atom scalar and vector quantities. The fingerprint is essentially a
+continuous density field formed through the superimposition of smoothing
+kernels centered on the atoms. Rotational invariance of the fingerprint is
+achieved by aligning, for each fingerprint instance, the neighboring atoms onto
+a local canonical coordinate frame computed from a kernel minisum optimization
+procedure. We show that this approach is superior over PCA-based methods
+especially when the atomistic neighborhood is sparse and/or contains symmetry.
+We propose that the `distance' between the density fields be measured using a
+volume integral of their pointwise difference. This can be efficiently computed
+using optimal quadrature rules, which only require discrete sampling at a small
+number of grid points. We also experiment on the choice of weight functions for
+constructing the density fields, and characterize their performance for fitting
+interatomic potentials. The applicability of the fingerprint is demonstrated
+through a set of benchmark problems.
+",1,1,0,0,0,0
+10004,Generalized 4 $\times$ 4 Matrix Formalism for Light Propagation in Anisotropic Stratified Media: Study of Surface Phonon Polaritons in Polar Dielectric Heterostructures,"  We present a generalized 4 $\times$ 4 matrix formalism for the description of
+light propagation in birefringent stratified media. In contrast to previous
+work, our algorithm is capable of treating arbitrarily anisotropic or
+isotropic, absorbing or non-absorbing materials and is free of discontinous
+solutions. We calculate the reflection and transmission coefficients and derive
+equations for the electric field distribution for any number of layers. The
+algorithm is easily comprehensible and can be straight forwardly implemented in
+a computer program. To demonstrate the capabilities of the approach, we
+calculate the reflectivities, electric field distributions, and dispersion
+curves for surface phonon polaritons excited in the Otto geometry for selected
+model systems, where we observe several distinct phenomena ranging from
+critical coupling to mode splitting, and surface phonon polaritons in
+hyperbolic media.
+",0,1,0,0,0,0
+10005,Localization of hidden Chua attractors by the describing function method,"  In this paper the Chua circuit with five linear elements and saturation
+non-linearity is studied. Numerical localization of self-excited attractor in
+the Chua circuit model can be done by computation of trajectory with initial
+data in a vicinity of an unstable equilibrium. For a hidden attractor its basin
+of attraction does not overlap with a small vicinity of equilibria, so it is
+difficult to find the corresponding initial data for localization. This survey
+is devoted to the application of describing function method for localization of
+hidden periodic and chaotic attractors in the Chua model. We use a rigorous
+justification of the describing function method, based on the method of small
+parameter, to get the initial data for the visualization of the hidden
+attractors. A new configuration of hidden Chua attractors is presented.
+",0,1,0,0,0,0
+10006,A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models,"  Log-linear models are arguably the most successful class of graphical models
+for large-scale applications because of their simplicity and tractability.
+Learning and inference with these models require calculating the partition
+function, which is a major bottleneck and intractable for large state spaces.
+Importance Sampling (IS) and MCMC-based approaches are lucrative. However, the
+condition of having a ""good"" proposal distribution is often not satisfied in
+practice.
+In this paper, we add a new dimension to efficient estimation via sampling.
+We propose a new sampling scheme and an unbiased estimator that estimates the
+partition function accurately in sub-linear time. Our samples are generated in
+near-constant time using locality sensitive hashing (LSH), and so are
+correlated and unnormalized. We demonstrate the effectiveness of our proposed
+approach by comparing the accuracy and speed of estimating the partition
+function against other state-of-the-art estimation techniques including IS and
+the efficient variant of Gumbel-Max sampling. With our efficient sampling
+scheme, we accurately train real-world language models using only 1-2% of
+computations.
+",1,0,0,1,0,0
+10007,"Effects of interaction strength, doping, and frustration on the antiferromagnetic phase of the two-dimensional Hubbard model","  Recent quantum-gas microscopy of ultracold atoms and scanning tunneling
+microscopy of the cuprates reveal new detailed information about doped Mott
+antiferromagnets, which can be compared with calculations. Using cellular
+dynamical mean-field theory, we map out the antiferromagnetic (AF) phase of the
+two-dimensional Hubbard model as a function of interaction strength $U$, hole
+doping $\delta$ and temperature $T$. The Néel phase boundary is non-monotonic
+as a function of $U$ and $\delta$. Frustration induced by second-neighbor
+hopping reduces Néel order more effectively at small $U$. The doped AF is
+stabilized at large $U$ by kinetic energy and at small $U$ by potential energy.
+The transition between the AF insulator and the doped metallic AF is
+continuous. At large $U$, we find in-gap states similar to those observed in
+scanning tunneling microscopy. We predict that, contrary to the Hubbard bands,
+these states are only slightly spin polarized.
+",0,1,0,0,0,0
+10008,A Coupled Lattice Boltzmann Method and Discrete Element Method for Discrete Particle Simulations of Particulate Flows,"  Discrete particle simulations are widely used to study large-scale
+particulate flows in complex geometries where particle-particle and
+particle-fluid interactions require an adequate representation but the
+computational cost has to be kept low. In this work, we present a novel
+coupling approach for such simulations. A lattice Boltzmann formulation of the
+generalized Navier-Stokes equations is used to describe the fluid motion. This
+promises efficient simulations suitable for high performance computing and,
+since volume displacement effects by the solid phase are considered, our
+approach is also applicable to non-dilute particulate systems. The discrete
+element method is combined with an explicit evaluation of interparticle
+lubrication forces to simulate the motion of individual submerged particles.
+Drag, pressure and added mass forces determine the momentum transfer by
+fluid-particle interactions. A stable coupling algorithm is presented and
+discussed in detail. We demonstrate the validity of our approach for dilute as
+well as dense systems by predicting the settling velocity of spheres over a
+broad range of solid volume fractions in good agreement with semi-empirical
+correlations. Additionally, the accuracy of particle-wall interactions in a
+viscous fluid is thoroughly tested and established. Our approach can thus be
+readily used for various particulate systems and can be extended
+straightforward to e.g. non-spherical particles.
+",1,1,0,0,0,0
+10009,The MERger-event Gamma-Ray (MERGR) Telescope,"  We describe the MERger-event Gamma-Ray (MERGR) Telescope intended for
+deployment by ~2021. MERGR will cover from 20 keV to 2 MeV with a wide field of
+view (6 sr) using nineteen gamma-ray detectors arranged on a section of a
+sphere. The telescope will work as a standalone system or as part of a network
+of sensors, to increase by ~50% the current sky coverage to detect short
+Gamma-Ray Burst (SGRB) counterparts to neutron-star binary mergers within the
+~200 Mpc range of gravitational wave detectors in the early 2020's. Inflight
+software will provide realtime burst detections with mean localization
+uncertainties of 6 deg for a photon fluence of 5 ph cm^-2 (the mean fluence of
+Fermi-GBM SGRBs) and <3 deg for the brightest ~5% of SGRBs to enable rapid
+multi-wavelength follow-up to identify a host galaxy and its redshift. To
+minimize cost and time to first light, MERGR is directly derived from
+demonstrators designed and built at NRL for the DoD Space Test Program (STP).
+We argue that the deployment of a network that provides all-sky coverage for
+SGRB detection is of immediate urgency to the multi-messenger astrophysics
+community.
+",0,1,0,0,0,0
+10010,Graham-Witten's conformal invariant for closed four dimensional submanifolds,"  It was proved by Graham and Witten in 1999 that conformal invariants of
+submanifolds can be obtained via volume renormalization of minimal surfaces in
+conformally compact Einstein manifolds. The conformal invariant of a
+submanifold $\Sigma$ is contained in the volume expansion of the minimal
+surface which is asymptotic to $\Sigma$ when the minimal surface approaches the
+conformaly infinity. In the paper we give the explicit expression of
+Graham-Witten's conformal invariant for closed four dimensional submanifolds
+and find critical points of the conformal invariant in the case of Euclidean
+ambient spaces.
+",0,0,1,0,0,0
+10011,Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating,"  Genealogical networks, also known as family trees or population pedigrees,
+are commonly studied by genealogists wanting to know about their ancestry, but
+they also provide a valuable resource for disciplines such as digital
+demography, genetics, and computational social science. These networks are
+typically constructed by hand through a very time-consuming process, which
+requires comparing large numbers of historical records manually. We develop
+computational methods for automatically inferring large-scale genealogical
+networks. A comparison with human-constructed networks attests to the accuracy
+of the proposed methods. To demonstrate the applicability of the inferred
+large-scale genealogical networks, we present a longitudinal analysis on the
+mating patterns observed in a network. This analysis shows a consistent
+tendency of people choosing a spouse with a similar socioeconomic status, a
+phenomenon known as assortative mating. Interestingly, we do not observe this
+tendency to consistently decrease (nor increase) over our study period of 150
+years.
+",1,0,0,0,1,0
+10012,Nonlinear stability for the Maxwell--Born--Infeld system on a Schwarzschild background,"  In this paper we prove small data global existence for solutions to the
+Maxwell--Born--Infeld (MBI) system on a fixed Schwarzschild background. This
+system has appeared in the context of string theory and can be seen as a
+nonlinear model problem for the stability of the background metric itself, due
+to its tensorial and quasilinear nature. The MBI system models nonlinear
+electromagnetism and does not display birefringence. The key element in our
+proof lies in the observation that there exists a first-order differential
+transformation which brings solutions of the spin $\pm 1$ Teukolsky equations,
+satisfied by the extreme components of the field, into solutions of a ""good""
+equation (the Fackerell--Ipser Equation). This strategy was established in [F.
+Pasqualotto, The spin $\pm 1$ Teukolsky equations and the Maxwell system on
+Schwarzschild, Preprint 2016, arXiv:1612.07244] for the linear Maxwell field on
+Schwarzschild. We show that analogous Fackerell--Ipser equations hold for the
+MBI system on a fixed Schwarzschild background, which are however nonlinearly
+coupled. To essentially decouple these right hand sides, we setup a bootstrap
+argument. We use the $r^p$ method of Dafermos and Rodnianski in [M. Dafermos
+and I. Rodnianski, A new physical-space approach to decay for the wave equation
+with applications to black hole spacetimes, in XVIth International Congress on
+Mathematical Physics, Pavel Exner ed., Prague 2009 pp. 421-433, 2009,
+arXiv:0910.4957] in order to deduce decay of some null components, and we infer
+decay for the remaining quantities by integrating the MBI system as transport
+equations.
+",0,0,1,0,0,0
+10013,"Numerical simulation of BOD5 dynamics in Igapó I lake, Londrina, Paraná, Brazil: Experimental measurement and mathematical modeling","  The concentration of biochemical oxygen demand, BOD5, was studied in order to
+evaluate the water quality of the Igapó I Lake, in Londrina, Paraná State,
+Brazil. The simulation was conducted by means of the discretization in
+curvilinear coordinates of the geometry of Igapó I Lake, together with finite
+difference and finite element methods. The evaluation of the proposed numerical
+model for water quality was performed by comparing the experimental values of
+BOD5 with the numerical results. The evaluation of the model showed
+quantitative results compatible with the actual behavior of Igapó I Lake in
+relation to the simulated parameter. The qualitative analysis of the numerical
+simulations provided a better understanding of the dynamics of the BOD5
+concentration at Igapó I Lake, showing that such concentrations in the
+central regions of the lake have values above those allowed by Brazilian law.
+The results can help to guide choices by public officials, as: (i) improve the
+identification mechanisms of pollutant emitters on Lake Igapó I, (ii)
+contribute to the optimal treatment of the recovery of the polluted environment
+and (iii) provide a better quality of life for the regulars of the lake as well
+as for the residents living on the lakeside.
+",0,0,0,0,1,0
+10014,Sensitivity Analysis for matched pair analysis of binary data: From worst case to average case analysis,"  In matched observational studies where treatment assignment is not
+randomized, sensitivity analysis helps investigators determine how sensitive
+their estimated treatment effect is to some unmeasured con- founder. The
+standard approach calibrates the sensitivity analysis according to the worst
+case bias in a pair. This approach will result in a conservative sensitivity
+analysis if the worst case bias does not hold in every pair. In this paper, we
+show that for binary data, the standard approach can be calibrated in terms of
+the average bias in a pair rather than worst case bias. When the worst case
+bias and average bias differ, the average bias interpretation results in a less
+conservative sensitivity analysis and more power. In many studies, the average
+case calibration may also carry a more natural interpretation than the worst
+case calibration and may also allow researchers to incorporate additional data
+to establish an empirical basis with which to calibrate a sensitivity analysis.
+We illustrate this with a study of the effects of cellphone use on the
+incidence of automobile accidents. Finally, we extend the average case
+calibration to the sensitivity analysis of confidence intervals for
+attributable effects.
+",0,0,0,1,0,0
+10015,"Closed Sets and Operators thereon: Representations, Computability and Complexity","  The TTE approach to Computable Analysis is the study of so-called
+representations (encodings for continuous objects such as reals, functions, and
+sets) with respect to the notions of computability they induce. A rich variety
+of such representations had been devised over the past decades, particularly
+regarding closed subsets of Euclidean space plus subclasses thereof (like
+compact subsets). In addition, they had been compared and classified with
+respect to both non-uniform computability of single sets and uniform
+computability of operators on sets. In this paper we refine these
+investigations from the point of view of computational complexity. Benefiting
+from the concept of second-order representations and complexity recently
+devised by Kawamura & Cook (2012), we determine parameterized complexity bounds
+for operators such as union, intersection, projection, and more generally
+function image and inversion. By indicating natural parameters in addition to
+the output precision, we get a uniform view on results by Ko (1991-2013),
+Braverman (2004/05) and Zhao & Müller (2008), relating these problems to the
+P/UP/NP question in discrete complexity theory.
+",1,0,1,0,0,0
+10016,Propensity score estimation using classification and regression trees in the presence of missing covariate data,"  Data mining and machine learning techniques such as classification and
+regression trees (CART) represent a promising alternative to conventional
+logistic regression for propensity score estimation. Whereas incomplete data
+preclude the fitting of a logistic regression on all subjects, CART is
+appealing in part because some implementations allow for incomplete records to
+be incorporated in the tree fitting and provide propensity score estimates for
+all subjects. Based on theoretical considerations, we argue that the automatic
+handling of missing data by CART may however not be appropriate. Using a series
+of simulation experiments, we examined the performance of different approaches
+to handling missing covariate data; (i) applying the CART algorithm directly to
+the (partially) incomplete data, (ii) complete case analysis, and (iii)
+multiple imputation. Performance was assessed in terms of bias in estimating
+exposure-outcome effects \add{among the exposed}, standard error, mean squared
+error and coverage. Applying the CART algorithm directly to incomplete data
+resulted in bias, even in scenarios where data were missing completely at
+random. Overall, multiple imputation followed by CART resulted in the best
+performance. Our study showed that automatic handling of missing data in CART
+can cause serious bias and does not outperform multiple imputation as a means
+to account for missing data.
+",0,0,0,1,0,0
+10017,Deciding some Maltsev conditions in finite idempotent algebras,"  In this paper we investigate the computational complexity of deciding if a
+given finite algebraic structure satisfies a fixed (strong) Maltsev condition
+$\Sigma$. Our goal in this paper is to show that $\Sigma$-testing can be
+accomplished in polynomial time when the algebras tested are idempotent and the
+Maltsev condition $\Sigma$ can be described using paths. Examples of such path
+conditions are having a Maltsev term, having a majority operation, and having a
+chain of Jónsson (or Gumm) terms of fixed length.
+",1,0,1,0,0,0
+10018,MF traces and the Cuntz semigroup,"  A trace $\tau$ on a separable C*-algebra $A$ is called matricial field (MF)
+if there is a trace-preserving morphism from $A$ to $Q_\omega$, where
+$Q_\omega$ denotes the norm ultrapower of the universal UHF-algebra $Q$. In
+general, the trace $\tau$ induces a state on the Cuntz semigroup $Cu(A)$. We
+show there is always a state-preserving morphism from $Cu(A)$ to
+$Cu(Q_\omega)$.
+As an application, if $A$ is an AI-algebra and $F$ is a free group acting on
+$A$, then every trace on the reduced crossed product $A \rtimes F$ is MF. This
+further implies the same result when $A$ is an AH-algebra with the ideal
+property such that $K_1(A)$ is a torsion group. We also use this to
+characterize when $A \rtimes F$ is MF (i.e. admits an isometric morphism into
+$Q_\omega$) for many simple, nuclear C*-algebras $A$.
+",0,0,1,0,0,0
+10019,Matrix product states for topological phases with parafermions,"  In the Fock representation, we propose a framework to construct the
+generalized matrix product states (MPS) for topological phases with $\mathbb{
+Z}_{p}$ parafermions. Unlike the $\mathbb{Z}_{2}$ Majorana fermions, the $%
+\mathbb{Z}_{p}$ parafermions form intrinsically interacting systems. Here we
+explicitly construct two topologically distinct classes of irreducible $%
+\mathbb{Z}_{3}$ parafermionic MPS wave functions, characterized by one or two
+parafermionic zero modes at each end of an open chain. Their corresponding
+parent Hamiltonians are found as the fixed point models of the single
+$\mathbb{Z}_{3}$ parafermion chain and two-coupled parafermion chains with
+$\mathbb{Z}_{3}\times \mathbb{Z}_{3}$ symmetry. Our results thus pave the road
+to investigate all possible topological phases with $\mathbb{Z}_{p}$
+parafermions within the matrix product representation in one dimension.
+",0,1,0,0,0,0
+10020,F-TRIDYN: A Binary Collision Approximation Code for Simulating Ion Interactions with Rough Surfaces,"  Fractal TRIDYN (F-TRIDYN) is a modified version of the widely used Monte
+Carlo, Binary Collision Approximation code TRIDYN that includes an explicit
+model of surface roughness and additional output modes for coupling to plasma
+edge and material codes. Surface roughness plays an important role in ion
+irradiation processes such as sputtering; roughness can significantly increase
+the angle of maximum sputtering and change the maximum observed sputtering
+yield by a factor of 2 or more. The complete effect of surface roughness on
+sputtering and other ion irradiation phenomena is not completely understood.
+Many rough surfaces can be consistently and realistically modeled by fractals,
+using the fractal dimension and fractal length scale as the sole input
+parameters. F-TRIDYN includes a robust fractal surface algorithm that is more
+computationally efficient than those in previous fractal codes and which
+reproduces available experimental sputtering data from rough surfaces. Fractals
+provide a compelling path toward a complete and concise understanding of the
+effect that surface geometry plays on the behavior of plasma-facing materials.
+F-TRIDYN is a flexible code for simulating ion-solid interactions and coupling
+to plasma and material codes for multiscale modeling.
+",0,1,0,0,0,0
+10021,Measurement of Radon Concentration in Super-Kamiokande's Buffer Gas,"  To precisely measure radon concentrations in purified air supplied to the
+Super-Kamiokande detector as a buffer gas, we have developed a highly sensitive
+radon detector with an intrinsic background as low as 0.33$\pm$0.07
+mBq/m$^{3}$. In this article, we discuss the construction and calibration of
+this detector as well as results of its application to the measurement and
+monitoring of the buffer gas layer above Super-Kamiokande. In March 2013, the
+chilled activated charcoal system used to remove radon in the input buffer gas
+was upgraded. After this improvement, a dramatic reduction in the radon
+concentration of the supply gas down to 0.08 $\pm$ 0.07 mBq/m$^{3}$.
+Additionally, the Rn concentration of the in-situ buffer gas has been measured
+28.8$\pm$1.7 mBq/m$^{3}$ using the new radon detector. Based on these
+measurements we have determined that the dominant source of Rn in the buffer
+gas arises from contamination from the Super-Kamiokande tank itself.
+",0,1,0,0,0,0
+10022,On The Robustness of a Neural Network,"  With the development of neural networks based machine learning and their
+usage in mission critical applications, voices are rising against the
+\textit{black box} aspect of neural networks as it becomes crucial to
+understand their limits and capabilities. With the rise of neuromorphic
+hardware, it is even more critical to understand how a neural network, as a
+distributed system, tolerates the failures of its computing nodes, neurons, and
+its communication channels, synapses. Experimentally assessing the robustness
+of neural networks involves the quixotic venture of testing all the possible
+failures, on all the possible inputs, which ultimately hits a combinatorial
+explosion for the first, and the impossibility to gather all the possible
+inputs for the second.
+In this paper, we prove an upper bound on the expected error of the output
+when a subset of neurons crashes. This bound involves dependencies on the
+network parameters that can be seen as being too pessimistic in the average
+case. It involves a polynomial dependency on the Lipschitz coefficient of the
+neurons activation function, and an exponential dependency on the depth of the
+layer where a failure occurs. We back up our theoretical results with
+experiments illustrating the extent to which our prediction matches the
+dependencies between the network parameters and robustness. Our results show
+that the robustness of neural networks to the average crash can be estimated
+without the need to neither test the network on all failure configurations, nor
+access the training set used to train the network, both of which are
+practically impossible requirements.
+",1,0,0,1,0,0
+10023,The Cut Elimination and the Nonlengthening Property for the Sequent Calculus with Equality,"  We show how Leibnitz.s indiscernibility principle and Gentzen's original work
+lead to extensions of the sequent calculus to first order logic with equality
+and investigate the cut elimination property. Furthermore we discuss and
+improve the nonlengthening property of Lifshitz and Orevkov.
+",1,0,1,0,0,0
+10024,On the Whittaker Plancherel Theorem for Real Reductive Groups,"  The main purpose of this article is to fix several aspects aspects of the
+proof of the Whittaker Plancherel Theorem in Real Reductive Groups II that are
+affected by recently observed errors or gaps . In the process of completing the
+proof of the theorem the paper also gives an exposition of its structure, and
+adds some clarifying new results. It also outlines the steps in the proof of
+the Harish-Chandra Plancherel theorem as they are needed in our proof of the
+Whittaker version.
+",0,0,1,0,0,0
+10025,Poisson distribution for gaps between sums of two squares and level spacings for toral point scatterers,"  We investigate the level spacing distribution for the quantum spectrum of the
+square billiard. Extending work of Connors--Keating, and Smilansky, we
+formulate an analog of the Hardy--Littlewood prime $k$-tuple conjecture for
+sums of two squares, and show that it implies that the spectral gaps, after
+removing degeneracies and rescaling, are Poisson distributed. Consequently, by
+work of Rudnick and Ueberschär, the level spacings of arithmetic toral point
+scatterers, in the weak coupling limit, are also Poisson distributed. We also
+give numerical evidence for the conjecture and its implications.
+",0,1,1,0,0,0
+10026,Maria Krawczyk: friend and physicist,"  With this note, we remember our friend Maria Krawczyk, who passed away this
+year, on May 24th. We briefly outline some of her physics interests and main
+accomplishments, and her great human and moral qualities.
+",0,1,0,0,0,0
+10027,Neural Networks Regularization Through Class-wise Invariant Representation Learning,"  Training deep neural networks is known to require a large number of training
+samples. However, in many applications only few training samples are available.
+In this work, we tackle the issue of training neural networks for
+classification task when few training samples are available. We attempt to
+solve this issue by proposing a new regularization term that constrains the
+hidden layers of a network to learn class-wise invariant representations. In
+our regularization framework, learning invariant representations is generalized
+to the class membership where samples with the same class should have the same
+representation. Numerical experiments over MNIST and its variants showed that
+our proposal helps improving the generalization of neural network particularly
+when trained with few samples. We provide the source code of our framework
+this https URL .
+",1,0,0,1,0,0
+10028,Incorporating Global Visual Features into Attention-Based Neural Machine Translation,"  We introduce multi-modal, attention-based neural machine translation (NMT)
+models which incorporate visual features into different parts of both the
+encoder and the decoder. We utilise global image features extracted using a
+pre-trained convolutional neural network and incorporate them (i) as words in
+the source sentence, (ii) to initialise the encoder hidden state, and (iii) as
+additional data to initialise the decoder hidden state. In our experiments, we
+evaluate how these different strategies to incorporate global image features
+compare and which ones perform best. We also study the impact that adding
+synthetic multi-modal, multilingual data brings and find that the additional
+data have a positive impact on multi-modal models. We report new
+state-of-the-art results and our best models also significantly improve on a
+comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k
+data set according to all metrics evaluated. To the best of our knowledge, it
+is the first time a purely neural model significantly improves over a PBSMT
+model on all metrics evaluated on this data set.
+",1,0,0,0,0,0
+10029,Complete Subgraphs of the Coprime Hypergraph of Integers III: Construction,"  The coprime hypergraph of integers on $n$ vertices $CHI_k(n)$ is defined via
+vertex set $\{1,2,\dots,n\}$ and hyperedge set
+$\{\{v_1,v_2,\dots,v_{k+1}\}\subseteq\{1,2,\dots,n\}:\gcd(v_1,v_2,\dots,v_{k+1})=1\}$.
+In this article we present ideas on how to construct maximal subgraphs in
+$CHI_k(n)$. This continues the author's earlier work, which dealt with bounds
+on the size and structural properties of these subgraphs. We succeed in the
+cases $k\in\{1,2,3\}$ and give promising ideas for $k\geq 4$.
+",0,0,1,0,0,0
+10030,Involvement of Surfactant Protein D in Ebola Virus Infection Enhancement via Glycoprotein Interaction,"  Since the largest 2014-2016 Ebola virus disease outbreak in West Africa,
+understanding of Ebola virus infection has improved, notably the involvement of
+innate immune mediators. Amongst them, collectins are important players in the
+antiviral innate immune defense. A screening of Ebola glycoprotein
+(GP)-collectins interactions revealed the specific interaction of human
+surfactant protein D (hSP-D), a lectin expressed in lung and liver, two
+compartments where Ebola was found in vivo. Further analyses have demonstrated
+an involvement of hSP-D in the enhancement of virus infection in several in
+vitro models. Similar effects were observed for porcine SP-D (pSP-D). In
+addition, both hSP-D and pSP-D interacted with Reston virus (RESTV) GP and
+enhanced pseudoviral infection in pulmonary cells. Thus, our study reveals a
+novel partner of Ebola GP that may participate to enhance viral spread.
+",0,0,0,0,1,0
+10031,Deep Approximately Orthogonal Nonnegative Matrix Factorization for Clustering,"  Nonnegative Matrix Factorization (NMF) is a widely used technique for data
+representation. Inspired by the expressive power of deep learning, several NMF
+variants equipped with deep architectures have been proposed. However, these
+methods mostly use the only nonnegativity while ignoring task-specific features
+of data. In this paper, we propose a novel deep approximately orthogonal
+nonnegative matrix factorization method where both nonnegativity and
+orthogonality are imposed with the aim to perform a hierarchical clustering by
+using different level of abstractions of data. Experiment on two face image
+datasets showed that the proposed method achieved better clustering performance
+than other deep matrix factorization methods and state-of-the-art single layer
+NMF variants.
+",1,0,0,0,0,0
+10032,Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach,"  We consider the problem of recovering the superposition of $R$ distinct
+complex exponential functions from compressed non-uniform time-domain samples.
+Total Variation (TV) minimization or atomic norm minimization was proposed in
+the literature to recover the $R$ frequencies or the missing data. However, it
+is known that in order for TV minimization and atomic norm minimization to
+recover the missing data or the frequencies, the underlying $R$ frequencies are
+required to be well-separated, even when the measurements are noiseless. This
+paper shows that the Hankel matrix recovery approach can super-resolve the $R$
+complex exponentials and their frequencies from compressed non-uniform
+measurements, regardless of how close their frequencies are to each other. We
+propose a new concept of orthonormal atomic norm minimization (OANM), and
+demonstrate that the success of Hankel matrix recovery in separation-free
+super-resolution comes from the fact that the nuclear norm of a Hankel matrix
+is an orthonormal atomic norm. More specifically, we show that, in traditional
+atomic norm minimization, the underlying parameter values $\textbf{must}$ be
+well separated to achieve successful signal recovery, if the atoms are changing
+continuously with respect to the continuously-valued parameter. In contrast,
+for the OANM, it is possible the OANM is successful even though the original
+atoms can be arbitrarily close.
+As a byproduct of this research, we provide one matrix-theoretic inequality
+of nuclear norm, and give its proof from the theory of compressed sensing.
+",1,0,0,0,0,0
+10033,A note on MCMC for nested multilevel regression models via belief propagation,"  In the quest for scalable Bayesian computational algorithms we need to
+exploit the full potential of existing methodologies. In this note we point out
+that message passing algorithms, which are very well developed for inference in
+graphical models, appear to be largely unexplored for scalable inference in
+Bayesian multilevel regression models. We show that nested multilevel
+regression models with Gaussian errors lend themselves very naturally to the
+combined use of belief propagation and MCMC. Specifically, the posterior
+distribution of the regression parameters conditionally on covariance
+hyperparameters is a high-dimensional Gaussian that can be sampled exactly (as
+well as marginalized) using belief propagation at a cost that scales linearly
+in the number of parameters and data. We derive an algorithm that works
+efficiently even for conditionally singular Gaussian distributions, e.g., when
+there are linear constraints between the parameters at different levels. We
+show that allowing for such non-invertible Gaussians is critical for belief
+propagation to be applicable to a large class of nested multilevel models. From
+a different perspective, the methodology proposed can be seen as a
+generalization of forward-backward algorithms for sampling to multilevel
+regressions with tree-structure graphical models, as opposed to single-branch
+trees used in classical Kalman filter contexts.
+",0,0,0,1,0,0
+10034,InGaN Metal-IN Solar Cell: optimized efficiency and fabrication tolerance,"  Choosing the Indium Gallium Nitride (InGaN) ternary alloy for thin films
+solar cells might yield high benefits concerning efficiency and reliability,
+because its bandgap can be tuned through the Indium composition and radiations
+have little destructive effect on it. It may also reveal challenges because
+good quality p-doped InGaN layers are difficult to elaborate. In this letter, a
+new design for an InGaN thin film solar cell is optimized, where the player of
+a PIN structure is replaced by a Schottky contact, leading to a Metal-IN (MIN)
+structure. With a simulated efficiency of 19.8%, the MIN structure performs
+better than the previously studied Schottky structure, while increasing its
+fabrication tolerance and thus functional reliability a. Owing to its good
+tolerance to radiations [1], its high light absorption [2, 3] and its
+Indium-composition-tuned bandgap [4, 5], the Indium Gallium Nitride (InGaN)
+ternary alloy is a good candidate for high-efficiency-high-reliability solar
+cells able to operate in harsh environments. Unfortunately, InGaN p-doping is
+still a challenge, owing to InGaN residual n-doping [6], the lack of dedicated
+ac-ceptors [7] and the complex fabrication process itself [8, 9]. To these
+drawbacks can be added the uneasy fabrication of ohmic contacts [4] and the
+difficulty to grow the high-quality-high-Indium-content thin films [10] which
+would be needed to cover the whole solar spectrum. These drawbacks still
+prevent InGaN solar cells to be competitive with other well established III-V
+and silicon technologies [11]. In this letter, is proposed a new Metal-IN (MIN)
+InGaN solar cell structure where the InGaN p-doped layer is removed and
+replaced by a Schottky contact, lifting one of the above mentioned drawbacks. A
+set of realistic physical models based on actual measurements is used to
+simulate and optimize its behavior and performance using mathematically
+rigorous multi-criteria optimization methods, aiming to show that both
+efficiency and fabrication tolerances are better than the previously described
+simple InGaN Schottky solar cell [12].
+",0,1,0,0,0,0
+10035,Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17),"  With the wide application of machine learning algorithms to the real world,
+class imbalance and concept drift have become crucial learning issues. Class
+imbalance happens when the data categories are not equally represented, i.e.,
+at least one category is minority compared to other categories. It can cause
+learning bias towards the majority class and poor generalization. Concept drift
+is a change in the underlying distribution of the problem, and is a significant
+issue specially when learning from data streams. It requires learners to be
+adaptive to dynamic changes.
+Class imbalance and concept drift can significantly hinder predictive
+performance, and the problem becomes particularly challenging when they occur
+simultaneously. This challenge arises from the fact that one problem can affect
+the treatment of the other. For example, drift detection algorithms based on
+the traditional classification error may be sensitive to the imbalanced degree
+and become less effective; and class imbalance techniques need to be adaptive
+to changing imbalance rates, otherwise the class receiving the preferential
+treatment may not be the correct minority class at the current moment.
+Therefore, the mutual effect of class imbalance and concept drift should be
+considered during algorithm design.
+The aim of this workshop is to bring together researchers from the areas of
+class imbalance learning and concept drift in order to encourage discussions
+and new collaborations on solving the combined issue of class imbalance and
+concept drift. It provides a forum for international researchers and
+practitioners to share and discuss their original work on addressing new
+challenges and research issues in class imbalance learning, concept drift, and
+the combined issues of class imbalance and concept drift. The proceedings
+include 8 papers on these topics.
+",1,0,0,0,0,0
+10036,Nonlinear dynamics on branched structures and networks,"  Nonlinear dynamics on graphs has rapidly become a topical issue with many
+physical applications, ranging from nonlinear optics to Bose-Einstein
+condensation. Whenever in a physical experiment a ramified structure is
+involved, it can prove useful to approximate such a structure by a metric
+graph, or network. For the Schroedinger equation it turns out that the sixth
+power in the nonlinear term of the energy is critical in the sense that below
+that power the constrained energy is lower bounded irrespectively of the value
+of the mass (subcritical case). On the other hand, if the nonlinearity power
+equals six, then the lower boundedness depends on the value of the mass: below
+a critical mass, the constrained energy is lower bounded, beyond it, it is not.
+For powers larger than six the constrained energy functional is never lower
+bounded, so that it is meaningless to speak about ground states (supercritical
+case). These results are the same as in the case of the nonlinear Schrodinger
+equation on the real line. In fact, as regards the existence of ground states,
+the results for systems on graphs differ, in general, from the ones for systems
+on the line even in the subcritical case: in the latter case, whenever the
+constrained energy is lower bounded there always exist ground states (the
+solitons, whose shape is explicitly known), whereas for graphs the existence of
+a ground state is not guaranteed. For the critical case, our results show a
+phenomenology much richer than the analogous on the line.
+",0,0,1,0,0,0
+10037,Rank modulation codes for DNA storage,"  Synthesis of DNA molecules offers unprecedented advances in storage
+technology. Yet, the microscopic world in which these molecules reside induces
+error patterns that are fundamentally different from their digital
+counterparts. Hence, to maintain reliability in reading and writing, new coding
+schemes must be developed. In a reading technique called shotgun sequencing, a
+long DNA string is read in a sliding window fashion, and a profile vector is
+produced. It was recently suggested by Kiah et al. that such a vector can
+represent the permutation which is induced by its entries, and hence a
+rank-modulation scheme arises. Although this interpretation suggests high error
+tolerance, it is unclear which permutations are feasible, and how to produce a
+DNA string whose profile vector induces a given permutation. In this paper, by
+observing some necessary conditions, an upper bound for the number of feasible
+permutations is given. Further, a technique for deciding the feasibility of a
+permutation is devised. By using insights from this technique, an algorithm for
+producing a considerable number of feasible permutations is given, which
+applies to any alphabet size and any window length.
+",1,0,0,0,0,0
+10038,Effect of ion motion on relativistic electron beam driven wakefield in a cold plasma,"  Excitation of relativistic electron beam driven wakefield in a cold plasma is
+studied using 1-D fluid simulation techniques where the effect of ion motion is
+included. We have excited the wakefield using a ultra-relativistic,
+homogeneous, rigid electron beam with different beam densities and mass-ratios
+(ratio of electron's to ion's mass). We have shown that the numerically excited
+wakefield is in a good agreement with the analytical results of Rosenzweig et
+al. \textcolor{blue}{[Physical Review A. 40, 9, (1989)]} for several plasma
+periods. It is shown here that the excited wake wave is equivalent to the
+corresponding ""Khachatryan mode"" \textcolor{blue}{[Physical Review E. 58, 6,
+(1998)]}. After several plasma periods, it is found that the excited wake wave
+gradually modifies and finally breaks, exhibiting sharp spikes in density and
+sawtooth like structure in electric field profile. It is shown here that the
+excited wake wave breaks much below the Khachatryan's wave breaking limit.
+",0,1,0,0,0,0
+10039,Memory-efficient Kernel PCA via Partial Matrix Sampling and Nonconvex Optimization: a Model-free Analysis of Local Minima,"  Kernel PCA is a widely used nonlinear dimension reduction technique in
+machine learning, but storing the kernel matrix is notoriously challenging when
+the sample size is large. Inspired by Yi et al. [2016], where the idea of
+partial matrix sampling followed by nonconvex optimization is proposed for
+matrix completion and robust PCA, we apply a similar approach to
+memory-efficient Kernel PCA. In theory, with no assumptions on the kernel
+matrix in terms of eigenvalues or eigenvectors, we established a model-free
+theory for the low-rank approximation based on any local minimum of the
+proposed objective function. As interesting byproducts, when the underlying
+positive semidefinite matrix is assumed to be low-rank and highly structured,
+corollaries of our main theorem improve the state-of-the-art results of Ge et
+al. [2016, 2017] for nonconvex matrix completion with no spurious local minima.
+Numerical experiments also show that our approach is competitive in terms of
+approximation accuracy compared to the well-known Nyström algorithm for
+Kernel PCA.
+",1,0,0,1,0,0
+10040,Scalable Graph Learning for Anti-Money Laundering: A First Look,"  Organized crime inflicts human suffering on a genocidal scale: the Mexican
+drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people
+per year are ""exported"" in a human trafficking industry enslaving an estimated
+40 million people. These nefarious industries rely on sophisticated money
+laundering schemes to operate. Despite tremendous resources dedicated to
+anti-money laundering (AML) only a tiny fraction of illicit activity is
+prevented. The research community can help. In this brief paper, we map the
+structural and behavioral dynamics driving the technical challenge. We review
+AML methods, current and emergent. We provide a first look at scalable graph
+convolutional neural networks for forensic analysis of financial data, which is
+massive, dense, and dynamic. We report preliminary experimental results using a
+large synthetic graph (1M nodes, 9M edges) generated by a data simulator we
+created called AMLSim. We consider opportunities for high performance
+efficiency, in terms of computation and memory, and we share results from a
+simple graph compression experiment. Our results support our working hypothesis
+that graph deep learning for AML bears great promise in the fight against
+criminal financial activity.
+",1,0,0,0,0,0
+10041,Complete event-by-event $α$/$γ(β)$ separation in a full-size TeO$_2$ CUORE bolometer by Neganov-Luke-magnified light detection,"  In the present work, we describe the results obtained with a large ($\approx
+133$ cm$^3$) TeO$_2$ bolometer, with a view to a search for neutrinoless
+double-beta decay ($0\nu\beta\beta$) of $^{130}$Te. We demonstrate an efficient
+$\alpha$ particle discrimination (99.9\%) with a high acceptance of the
+$0\nu\beta\beta$ signal (about 96\%), expected at $\approx 2.5$ MeV. This
+unprecedented result was possible thanks to the superior performance (10 eV rms
+baseline noise) of a Neganov-Luke-assisted germanium bolometer used to detect a
+tiny (70 eV) light signal from the TeO$_2$ detector, dominated by
+$\gamma$($\beta$)-induced Cherenkov radiation but exhibiting also a clear
+scintillation component. The obtained results represent a major breakthrough
+towards the TeO$_2$-based version of CUORE Upgrade with Particle IDentification
+(CUPID), a ton-scale cryogenic $0\nu\beta\beta$ experiment proposed as a
+follow-up to the CUORE project with particle identification. The CUORE
+experiment began recently a search for neutrinoless double-beta decay of
+$^{130}$Te with an array of 988 125-cm$^3$ TeO$_2$ bolometers. The lack of
+$\alpha$ discrimination in CUORE makes $\alpha$ decays at the detector surface
+the dominant background component, at the level of $\approx 0.01$ counts/(keV
+kg y) in the region of interest. We show here, for the first time with a
+CUORE-size bolometer and using the same technology as CUORE for the readout of
+both heat and light signals, that surface $\alpha$ background can be fully
+rejected.
+",0,1,0,0,0,0
+10042,A Novel Bayesian Multiple Testing Approach to Deregulated miRNA Discovery Harnessing Positional Clustering,"  MicroRNAs (miRNAs) are small non-coding RNAs that function as regulators of
+gene expression. In recent years, there has been a tremendous and growing
+interest among researchers to investigate the role of miRNAs in normal cellular
+as well as in disease processes. Thus to investigate the role of miRNAs in oral
+cancer, we analyse the expression levels of miRNAs to identify miRNAs with
+statistically significant differential expression in cancer tissues.
+In this article, we propose a novel Bayesian hierarchical model of miRNA
+expression data. Compelling evidences have demonstrated that the transcription
+process of miRNAs in human genome is a latent process instrumental for the
+observed expression levels. We take into account positional clustering of the
+miRNAs in the analysis and model the latent transcription phenomenon
+nonparametrically by an appropriate Gaussian process.
+For the testing purpose we employ a novel Bayesian multiple testing method
+where we mainly focus on utilizing the dependence structure between the
+hypotheses for better results, while also ensuring optimality in many respects.
+Indeed, our non-marginal method yielded results in accordance with the
+underlying scientific knowledge which are found to be missed by the very
+popular Benjamini-Hochberg method.
+",0,0,0,1,0,0
+10043,PCN: Point Completion Network,"  Shape completion, the problem of estimating the complete geometry of objects
+from partial observations, lies at the core of many vision and robotics
+applications. In this work, we propose Point Completion Network (PCN), a novel
+learning-based approach for shape completion. Unlike existing shape completion
+methods, PCN directly operates on raw point clouds without any structural
+assumption (e.g. symmetry) or annotation (e.g. semantic class) about the
+underlying shape. It features a decoder design that enables the generation of
+fine-grained completions while maintaining a small number of parameters. Our
+experiments show that PCN produces dense, complete point clouds with realistic
+structures in the missing regions on inputs with various levels of
+incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.
+",1,0,0,0,0,0
+10044,Deep Relaxation: partial differential equations for optimizing deep neural networks,"  In this paper we establish a connection between non-convex optimization
+methods for training deep neural networks and nonlinear partial differential
+equations (PDEs). Relaxation techniques arising in statistical physics which
+have already been used successfully in this context are reinterpreted as
+solutions of a viscous Hamilton-Jacobi PDE. Using a stochastic control
+interpretation allows we prove that the modified algorithm performs better in
+expectation that stochastic gradient descent. Well-known PDE regularity results
+allow us to analyze the geometry of the relaxed energy landscape, confirming
+empirical evidence. The PDE is derived from a stochastic homogenization
+problem, which arises in the implementation of the algorithm. The algorithms
+scale well in practice and can effectively tackle the high dimensionality of
+modern neural networks.
+",1,0,1,0,0,0
+10045,Forbidden Substrings In Circular K-Successions,"  In this note we define circular k-successions in permutations in one-line
+notation and count permutations that avoid substrings j(j+k) and j(j+k) (mod
+n). We also count circular permutations that avoid such substrings, and show
+that for substrings j(j+k) (mod n), the number of permutations depends on
+whether n is prime, and more generally, on whether n and k are relatively
+prime.
+",0,0,1,0,0,0
+10046,Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo,"  We study weighted particle systems in which new generations are resampled
+from current particles with probabilities proportional to their weights. This
+covers a broad class of sequential Monte Carlo (SMC) methods, widely-used in
+applied statistics and cognate disciplines. We consider the genealogical tree
+embedded into such particle systems, and identify conditions, as well as an
+appropriate time-scaling, under which they converge to the Kingman n-coalescent
+in the infinite system size limit in the sense of finite-dimensional
+distributions. Thus, the tractable n-coalescent can be used to predict the
+shape and size of SMC genealogies, as we illustrate by characterising the
+limiting mean and variance of the tree height. SMC genealogies are known to be
+connected to algorithm performance, so that our results are likely to have
+applications in the design of new methods as well. Our conditions for
+convergence are strong, but we show by simulation that they do not appear to be
+necessary.
+",0,0,0,1,1,0
+10047,Symmetry Enforced Stability of Interacting Weyl and Dirac Semimetals,"  The nodal and effectively relativistic dispersion featuring in a range of
+novel materials including two- dimensional graphene and three-dimensional Dirac
+and Weyl semimetals has attracted enormous interest during the past decade.
+Here, by studying the structure and symmetry of the diagrammatic expansion, we
+show that these nodal touching points are in fact perturbatively stable to all
+orders with respect to generic two-body interactions. For effective low-energy
+theories relevant for single and multilayer graphene, type-I and type-II Weyl
+and Dirac semimetals as well as Weyl points with higher topological charge,
+this stability is shown to be a direct consequence of a spatial symmetry that
+anti-commutes with the effective Hamiltonian while leaving the interaction
+invariant. A more refined argument is applied to the honeycomb lattice model of
+graphene showing that its Dirac points are also perturbatively stable to all
+orders. We also give examples of nodal Hamiltonians that acquire a gap from
+interactions as a consequence of symmetries different from those of Weyl and
+Dirac materials.
+",0,1,0,0,0,0
+10048,Luck is Hard to Beat: The Difficulty of Sports Prediction,"  Predicting the outcome of sports events is a hard task. We quantify this
+difficulty with a coefficient that measures the distance between the observed
+final results of sports leagues and idealized perfectly balanced competitions
+in terms of skill. This indicates the relative presence of luck and skill. We
+collected and analyzed all games from 198 sports leagues comprising 1503
+seasons from 84 countries of 4 different sports: basketball, soccer, volleyball
+and handball. We measured the competitiveness by countries and sports. We also
+identify in each season which teams, if removed from its league, result in a
+completely random tournament. Surprisingly, not many of them are needed. As
+another contribution of this paper, we propose a probabilistic graphical model
+to learn about the teams' skills and to decompose the relative weights of luck
+and skill in each game. We break down the skill component into factors
+associated with the teams' characteristics. The model also allows to estimate
+as 0.36 the probability that an underdog team wins in the NBA league, with a
+home advantage adding 0.09 to this probability. As shown in the first part of
+the paper, luck is substantially present even in the most competitive
+championships, which partially explains why sophisticated and complex
+feature-based models hardly beat simple models in the task of forecasting
+sports' outcomes.
+",1,0,0,1,0,0
+10049,Multiscale sequence modeling with a learned dictionary,"  We propose a generalization of neural network sequence models. Instead of
+predicting one symbol at a time, our multi-scale model makes predictions over
+multiple, potentially overlapping multi-symbol tokens. A variation of the
+byte-pair encoding (BPE) compression algorithm is used to learn the dictionary
+of tokens that the model is trained with. When applied to language modelling,
+our model has the flexibility of character-level models while maintaining many
+of the performance benefits of word-level models. Our experiments show that
+this model performs better than a regular LSTM on language modeling tasks,
+especially for smaller models.
+",1,0,0,1,0,0
+10050,Infinite Matrix Product States vs Infinite Projected Entangled-Pair States on the Cylinder: a comparative study,"  In spite of their intrinsic one-dimensional nature matrix product states have
+been systematically used to obtain remarkably accurate results for
+two-dimensional systems. Motivated by basic entropic arguments favoring
+projected entangled-pair states as the method of choice, we assess the relative
+performance of infinite matrix product states and infinite projected
+entangled-pair states on cylindrical geometries. By considering the Heisenberg
+and half-filled Hubbard models on the square lattice as our benchmark cases, we
+evaluate their variational energies as a function of both bond dimension as
+well as cylinder width. In both examples we find crossovers at moderate
+cylinder widths, i.e. for the largest bond dimensions considered we find an
+improvement on the variational energies for the Heisenberg model by using
+projected entangled-pair states at a width of about 11 sites, whereas for the
+half-filled Hubbard model this crossover occurs at about 7 sites.
+",0,1,0,0,0,0
+10051,Dialectometric analysis of language variation in Twitter,"  In the last few years, microblogging platforms such as Twitter have given
+rise to a deluge of textual data that can be used for the analysis of informal
+communication between millions of individuals. In this work, we propose an
+information-theoretic approach to geographic language variation using a corpus
+based on Twitter. We test our models with tens of concepts and their associated
+keywords detected in Spanish tweets geolocated in Spain. We employ
+dialectometric measures (cosine similarity and Jensen-Shannon divergence) to
+quantify the linguistic distance on the lexical level between cells created in
+a uniform grid over the map. This can be done for a single concept or in the
+general case taking into account an average of the considered variants. The
+latter permits an analysis of the dialects that naturally emerge from the data.
+Interestingly, our results reveal the existence of two dialect macrovarieties.
+The first group includes a region-specific speech spoken in small towns and
+rural areas whereas the second cluster encompasses cities that tend to use a
+more uniform variety. Since the results obtained with the two different metrics
+qualitatively agree, our work suggests that social media corpora can be
+efficiently used for dialectometric analyses.
+",1,1,0,0,0,0
+10052,"Single-Pass, Adaptive Natural Language Filtering: Measuring Value in User Generated Comments on Large-Scale, Social Media News Forums","  There are large amounts of insight and social discovery potential in mining
+crowd-sourced comments left on popular news forums like Reddit.com, Tumblr.com,
+Facebook.com and Hacker News. Unfortunately, due the overwhelming amount of
+participation with its varying quality of commentary, extracting value out of
+such data isn't always obvious nor timely. By designing efficient, single-pass
+and adaptive natural language filters to quickly prune spam, noise, copy-cats,
+marketing diversions, and out-of-context posts, we can remove over a third of
+entries and return the comments with a higher probability of relatedness to the
+original article in question. The approach presented here uses an adaptive,
+two-step filtering process. It first leverages the original article posted in
+the thread as a starting corpus to parse comments by matching intersecting
+words and term-ratio balance per sentence then grows the corpus by adding new
+words harvested from high-matching comments to increase filtering accuracy over
+time.
+",1,0,0,0,0,0
+10053,How Do Elements Really Factor in $\mathbb{Z}[\sqrt{-5}]$?,"  Most undergraduate level abstract algebra texts use $\mathbb{Z}[\sqrt{-5}]$
+as an example of an integral domain which is not a unique factorization domain
+(or UFD) by exhibiting two distinct irreducible factorizations of a nonzero
+element. But such a brief example, which requires merely an understanding of
+basic norms, only scratches the surface of how elements actually factor in this
+ring of algebraic integers. We offer here an interactive framework which shows
+that while $\mathbb{Z}[\sqrt{-5}]$ is not a UFD, it does satisfy a slightly
+weaker factorization condition, known as half-factoriality. The arguments
+involved revolve around the Fundamental Theorem of Ideal Theory.
+",0,0,1,0,0,0
+10054,Improvements on lower bounds for the blow-up time under local nonlinear Neumann conditions,"  This paper studies the heat equation $u_t=\Delta u$ in a bounded domain
+$\Omega\subset\mathbb{R}^{n}(n\geq 2)$ with positive initial data and a local
+nonlinear Neumann boundary condition: the normal derivative $\partial
+u/\partial n=u^{q}$ on partial boundary $\Gamma_1\subseteq \partial\Omega$ for
+some $q>1$, while $\partial u/\partial n=0$ on the other part. We investigate
+the lower bound of the blow-up time $T^{*}$ of $u$ in several aspects. First,
+$T^{*}$ is proved to be at least of order $(q-1)^{-1}$ as $q\rightarrow 1^{+}$.
+Since the existing upper bound is of order $(q-1)^{-1}$, this result is sharp.
+Secondly, if $\Omega$ is convex and $|\Gamma_{1}|$ denotes the surface area of
+$\Gamma_{1}$, then $T^{*}$ is shown to be at least of order
+$|\Gamma_{1}|^{-\frac{1}{n-1}}$ for $n\geq 3$ and
+$|\Gamma_{1}|^{-1}\big/\ln\big(|\Gamma_{1}|^{-1}\big)$ for $n=2$ as
+$|\Gamma_{1}|\rightarrow 0$, while the previous result is
+$|\Gamma_{1}|^{-\alpha}$ for any $\alpha<\frac{1}{n-1}$. Finally, we generalize
+the results for convex domains to the domains with only local convexity near
+$\Gamma_{1}$.
+",0,0,1,0,0,0
+10055,Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation,"  This paper addresses the challenge of humanoid robot teleoperation in a
+natural indoor environment via a Brain-Computer Interface (BCI). We leverage
+deep Convolutional Neural Network (CNN) based image and signal understanding to
+facilitate both real-time object detection and dry-Electroencephalography (EEG)
+based human cortical brain bio-signal decoding. We employ recent advances in
+dry-EEG technology to stream and collect the cortical waveforms from subjects
+while the subjects fixate on variable Steady-State Visual Evoked Potential
+(SSVEP) stimuli generated directly from the environment the robot is
+navigating. To these ends, we propose the use of novel variable BCI stimuli by
+utilising the real-time video streamed via the on-board robot camera as visual
+input for SSVEP where the CNN detected natural scene objects are altered and
+flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are
+not akin to traditional stimuli - as both the dimensions of the flicker regions
+and their on-screen position changes depending on the scene objects detected in
+the scene. On-screen object selection via dry-EEG enabled SSVEP in this way,
+facilitates the on-line decoding of human cortical brain signals via a
+secondary CNN approach into teleoperation robot commands (approach object, move
+in a specific direction: right, left or back). This SSVEP decoding model is
+trained via a priori offline experimental data in which very similar visual
+input is present for all subjects. The resulting offline classification
+demonstrates extremely high performance and with mean accuracies of 96% and 90%
+for the real-time robot navigation experiment across multiple test subjects.
+",1,0,0,0,0,0
+10056,Tunable Ampere phase plate for low dose imaging of biomolecular complexes,"  A novel device that can be used as a tunable support-free phase plate for
+transmission electron microscopy of weakly scattering specimens is described.
+The device relies on the generation of a controlled phase shift by the magnetic
+field of a segment of current-carrying wire that is oriented parallel or
+antiparallel to the electron beam. The validity of the concept is established
+using both experimental electron holographic measurements and a theoretical
+model based on Ampere's law. Computer simulations are used to illustrate the
+resulting contrast enhancement for studies of biological cells and
+macromolecules.
+",0,1,0,0,0,0
+10057,An efficient model-free setting for longitudinal and lateral vehicle control. Validation through the interconnected pro-SiVIC/RTMaps prototyping platform,"  In this paper, the problem of tracking desired longitudinal and lateral
+motions for a vehicle is addressed. Let us point out that a ""good"" modeling is
+often quite difficult or even impossible to obtain. It is due for example to
+parametric uncertainties, for the vehicle mass, inertia or for the interaction
+forces between the wheels and the road pavement. To overcome this type of
+difficulties, we consider a model-free control approach leading to
+""intelligent"" controllers. The longitudinal and the lateral motions, on one
+hand, and the driving/braking torques and the steering wheel angle, on the
+other hand, are respectively the output and the input variables. An important
+part of this work is dedicated to present simulation results with actual data.
+Actual data, used in Matlab as reference trajectories, have been previously
+recorded with an instrumented Peugeot 406 experimental car. The simulation
+results show the efficiency of our approach. Some comparisons with a nonlinear
+flatness-based control in one hand, and with a classical PID control in another
+hand confirm this analysis. Other virtual data have been generated through the
+interconnected platform SiVIC/RTMaps, which is a virtual simulation platform
+for prototyping and validation of advanced driving assistance systems.
+",1,0,1,0,0,0
+10058,Genetic fitting techniques for precision ultracold spectroscopy,"  We present development of a genetic algorithm for fitting potential energy
+curves of diatomic molecules to experimental data. Our approach does not
+involve any functional form for fitting, which makes it a general fitting
+procedure. In particular, it takes in a guess potential, perhaps from an $ab \
+initio$ calculation, along with experimental measurements of vibrational
+binding energies, rotational constants, and their experimental uncertainties.
+The fitting procedure is able to modify the guess potential until it converges
+to better than 1% uncertainty, as measured by $\bar{\chi}^2$. We present the
+details of this technique along with a comparison of potentials calculated by
+our genetic algorithm and the state of the art fitting techniques based on
+inverted perturbation approach for the $X \ ^1\Sigma^+$ and $C \ ^1\Sigma^+$
+potentials of lithium-rubidium.
+",0,1,0,0,0,0
+10059,Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph,"  Large datasets represented by multidimensional data point clouds often
+possess non-trivial distributions with branching trajectories and excluded
+regions, with the recent single-cell transcriptomic studies of developing
+embryo being notable examples. Reducing the complexity and producing compact
+and interpretable representations of such data remains a challenging task. Most
+of the existing computational methods are based on exploring the local data
+point neighbourhood relations, a step that can perform poorly in the case of
+multidimensional and noisy data. Here we present ElPiGraph, a scalable and
+robust method for approximation of datasets with complex structures which does
+not require computing the complete data distance matrix or the data point
+neighbourhood graph. This method is able to withstand high levels of noise and
+is capable of approximating complex topologies via principal graph ensembles
+that can be combined into a consensus principal graph. ElPiGraph deals
+efficiently with large and complex datasets in various fields from biology,
+where it can be used to infer gene dynamics from single-cell RNA-Seq, to
+astronomy, where it can be used to explore complex structures in the
+distribution of galaxies.
+",0,0,0,1,1,0
+10060,Epidemic dynamics in open quantum spin systems,"  We explore the non-equilibrium evolution and stationary states of an open
+many-body system which displays epidemic spreading dynamics in a classical and
+a quantum regime. Our study is motivated by recent experiments conducted in
+strongly interacting gases of highly excited Rydberg atoms where the
+facilitated excitation of Rydberg states competes with radiative decay. These
+systems approximately implement open quantum versions of models for population
+dynamics or disease spreading where species can be in a healthy, infected or
+immune state. We show that in a two-dimensional lattice, depending on the
+dominance of either classical or quantum effects, the system may display a
+different kind of non-equilibrium phase transition. We moreover discuss the
+observability of our findings in laser driven Rydberg gases with particular
+focus on the role of long-range interactions.
+",0,1,0,0,0,0
+10061,In-Hand Object Stabilization by Independent Finger Control,"  Grip control during robotic in-hand manipulation is usually modeled as part
+of a monolithic task, relying on complex controllers specialized for specific
+situations. Such approaches do not generalize well and are difficult to apply
+to novel manipulation tasks. Here, we propose a modular object stabilization
+method based on a proposition that explains how humans achieve grasp stability.
+In this bio-mimetic approach, independent tactile grip stabilization
+controllers ensure that slip does not occur locally at the engaged robot
+fingers. Such local slip is predicted from the tactile signals of each
+fingertip sensor i.e., BioTac and BioTac SP by Syntouch. We show that stable
+grasps emerge without any form of central communication when such independent
+controllers are engaged in the control of multi-digit robotic hands. These
+grasps are resistant to external perturbations while being capable of
+stabilizing a large variety of objects.
+",1,0,0,0,0,0
+10062,The X-ray and Mid-Infrared luminosities in Luminous Type 1 Quasars,"  Several recent studies have reported different intrinsic correlations between
+the AGN mid-IR luminosity ($L_{MIR}$) and the rest-frame 2-10 keV luminosity
+($L_{X}$) for luminous quasars. To understand the origin of the difference in
+the observed $L_{X}-L_{MIR}$ relations, we study a sample of 3,247
+spectroscopically confirmed type 1 AGNs collected from Boötes, XMM-COSMOS,
+XMM-XXL-North, and the SDSS quasars in the Swift/XRT footprint spanning over
+four orders of magnitude in luminosity. We carefully examine how different
+observational constraints impact the observed $L_{X}-L_{MIR}$ relations,
+including the inclusion of X-ray non-detected objects, possible X-ray
+absorption in type 1 AGNs, X-ray flux limits, and star formation contamination.
+We find that the primary factor driving the different $L_{X}-L_{MIR}$ relations
+reported in the literature is the X-ray flux limits for different studies. When
+taking these effects into account, we find that the X-ray luminosity and mid-IR
+luminosity (measured at rest-frame $6\mu m$, or $L_{6\mu m}$) of our sample of
+type 1 AGNs follow a bilinear relation in the log-log plane: $\log L_X
+=(0.84\pm0.03)\times\log L_{6\mu m}/10^{45}{\rm erg\;s^{-1}} + (44.60\pm0.01)$
+for $L_{6\mu m} < 10^{44.79}{\rm erg\;s^{-1}} $, and $\log L_X =
+(0.40\pm0.03)\times\log L_{6\mu m}/10^{45}{\rm erg\;s^{-1}} +(44.51\pm0.01)$
+for $L_{6\mu m} \geq 10^{44.79}{\rm erg\;s^{-1}} $. This suggests that the
+luminous type 1 quasars have a shallower $L_{X}-L_{MIR}$ correlation than the
+approximately linear relations found in local Seyfert galaxies. This result is
+consistent with previous studies reporting a luminosity-dependent
+$L_{X}-L_{MIR}$ relation, and implies that assuming a linear $L_{X}-L_{MIR}$
+relation to infer the neutral gas column density for X-ray absorption might
+overestimate the column densities in luminous quasars.
+",0,1,0,0,0,0
+10063,Approximation of general facets by regular facets with respect to anisotropic total variation energies and its application to the crystalline mean curvature flow,"  We show that every bounded subset of an Euclidean space can be approximated
+by a set that admits a certain vector field, the so-called Cahn-Hoffman vector
+field, that is subordinate to a given anisotropic metric and has a
+square-integrable divergence. More generally, we introduce a concept of facets
+as a kind of directed sets, and show that they can be approximated in a similar
+manner.
+We use this approximation to construct test functions necessary to prove the
+comparison principle for viscosity solutions of the level set formulation of
+the crystalline mean curvature flow that were recently introduced by the
+authors. As a consequence, we obtain the well-posedness of the viscosity
+solutions in an arbitrary dimension, which extends the validity of the result
+in the previous paper.
+",0,0,1,0,0,0
+10064,Calibrated Fairness in Bandits,"  We study fairness within the stochastic, \emph{multi-armed bandit} (MAB)
+decision making framework. We adapt the fairness framework of ""treating similar
+individuals similarly"" to this setting. Here, an `individual' corresponds to an
+arm and two arms are `similar' if they have a similar quality distribution.
+First, we adopt a {\em smoothness constraint} that if two arms have a similar
+quality distribution then the probability of selecting each arm should be
+similar. In addition, we define the {\em fairness regret}, which corresponds to
+the degree to which an algorithm is not calibrated, where perfect calibration
+requires that the probability of selecting an arm is equal to the probability
+with which the arm has the best quality realization. We show that a variation
+on Thompson sampling satisfies smooth fairness for total variation distance,
+and give an $\tilde{O}((kT)^{2/3})$ bound on fairness regret. This complements
+prior work, which protects an on-average better arm from being less favored. We
+also explain how to extend our algorithm to the dueling bandit setting.
+",1,0,0,0,0,0
+10065,HTC Vive MeVisLab integration via OpenVR for medical applications,"  Virtual Reality, an immersive technology that replicates an environment via
+computer-simulated reality, gets a lot of attention in the entertainment
+industry. However, VR has also great potential in other areas, like the medical
+domain, Examples are intervention planning, training and simulation. This is
+especially of use in medical operations, where an aesthetic outcome is
+important, like for facial surgeries. Alas, importing medical data into Virtual
+Reality devices is not necessarily trivial, in particular, when a direct
+connection to a proprietary application is desired. Moreover, most researcher
+do not build their medical applications from scratch, but rather leverage
+platforms like MeVisLab, MITK, OsiriX or 3D Slicer. These platforms have in
+common that they use libraries like ITK and VTK, and provide a convenient
+graphical interface. However, ITK and VTK do not support Virtual Reality
+directly. In this study, the usage of a Virtual Reality device for medical data
+under the MeVisLab platform is presented. The OpenVR library is integrated into
+the MeVisLab platform, allowing a direct and uncomplicated usage of the head
+mounted display HTC Vive inside the MeVisLab platform. Medical data coming from
+other MeVisLab modules can directly be connected per drag-and-drop to the
+Virtual Reality module, rendering the data inside the HTC Vive for immersive
+virtual reality inspection.
+",1,0,0,0,0,0
+10066,Explanation of a Polynomial Identity,"  In this note, we provide a conceptual explanation of a well-known polynomial
+identity used in algebraic number theory.
+",0,0,1,0,0,0
+10067,Quantum Stress Tensor Fluctuations and Primordial Gravity Waves,"  We examine the effect of the stress tensor of a quantum matter field, such as
+the electromagnetic field, on the spectrum of primordial gravity waves expected
+in inflationary cosmology. We find that the net effect is a small reduction in
+the power spectrum, especially at higher frequencies, but which has a different
+form from that described by the usual spectral index. Thus this effect has a
+characteristic signature, and is in principle observable. The net effect is a
+sum of two contributions, one of which is due to quantum fluctuations of the
+matter field stress tensor. The other is a quantum correction to the graviton
+field due to coupling to the expectation value of this stress tensor. Both
+contributions are sensitive to initial conditions in the very early universe,
+so this effect has the potential to act as a probe of these initial conditions.
+",0,1,0,0,0,0
+10068,Hyperbolic Pascal simplex,"  In this article we introduce a new geometric object called hyperbolic Pascal
+simplex. This new object is presented by the regular hypercube mosaic in the
+4-dimensional hyperbolic space. The definition of the hyperbolic Pascal
+simplex, whose hyperfaces are hyperbolic Pascal pyramids and faces are
+hyperbolic Pascals triangles, is a natural generalization of the definition of
+the hyperbolic Pascal triangle and pyramid. We describe the growing of the
+hyperbolic Pascal simplex considering the numbers and the values of the
+elements. Further figures illustrate the stepping from a level to the next one.
+",0,0,1,0,0,0
+10069,Small-loss bounds for online learning with partial information,"  We consider the problem of adversarial (non-stochastic) online learning with
+partial information feedback, where at each round, a decision maker selects an
+action from a finite set of alternatives. We develop a black-box approach for
+such problems where the learner observes as feedback only losses of a subset of
+the actions that includes the selected action. When losses of actions are
+non-negative, under the graph-based feedback model introduced by Mannor and
+Shamir, we offer algorithms that attain the so called ""small-loss"" $o(\alpha
+L^{\star})$ regret bounds with high probability, where $\alpha$ is the
+independence number of the graph, and $L^{\star}$ is the loss of the best
+action. Prior to our work, there was no data-dependent guarantee for general
+feedback graphs even for pseudo-regret (without dependence on the number of
+actions, i.e. utilizing the increased information feedback). Taking advantage
+of the black-box nature of our technique, we extend our results to many other
+applications such as semi-bandits (including routing in networks), contextual
+bandits (even with an infinite comparator class), as well as learning with
+slowly changing (shifting) comparators.
+In the special case of classical bandit and semi-bandit problems, we provide
+optimal small-loss, high-probability guarantees of
+$\tilde{O}(\sqrt{dL^{\star}})$ for actual regret, where $d$ is the number of
+actions, answering open questions of Neu. Previous bounds for bandits and
+semi-bandits were known only for pseudo-regret and only in expectation. We also
+offer an optimal $\tilde{O}(\sqrt{\kappa L^{\star}})$ regret guarantee for
+fixed feedback graphs with clique-partition number at most $\kappa$.
+",1,0,0,0,0,0
+10070,Fine-Grained Parameterized Complexity Analysis of Graph Coloring Problems,"  The $q$-Coloring problem asks whether the vertices of a graph can be properly
+colored with $q$ colors. Lokshtanov et al. [SODA 2011] showed that $q$-Coloring
+on graphs with a feedback vertex set of size $k$ cannot be solved in time
+$\mathcal{O}^*((q-\varepsilon)^k)$, for any $\varepsilon > 0$, unless the
+Strong Exponential-Time Hypothesis (SETH) fails. In this paper we perform a
+fine-grained analysis of the complexity of $q$-Coloring with respect to a
+hierarchy of parameters. We show that even when parameterized by the vertex
+cover number, $q$ must appear in the base of the exponent: Unless ETH fails,
+there is no universal constant $\theta$ such that $q$-Coloring parameterized by
+vertex cover can be solved in time $\mathcal{O}^*(\theta^k)$ for all fixed $q$.
+We apply a method due to Jansen and Kratsch [Inform. & Comput. 2013] to prove
+that there are $\mathcal{O}^*((q - \varepsilon)^k)$ time algorithms where $k$
+is the vertex deletion distance to several graph classes $\mathcal{F}$ for
+which $q$-Coloring is known to be solvable in polynomial time. We generalize
+earlier ad-hoc results by showing that if $\mathcal{F}$ is a class of graphs
+whose $(q+1)$-colorable members have bounded treedepth, then there exists some
+$\varepsilon > 0$ such that $q$-Coloring can be solved in time
+$\mathcal{O}^*((q-\varepsilon)^k)$ when parameterized by the size of a given
+modulator to $\mathcal{F}$. In contrast, we prove that if $\mathcal{F}$ is the
+class of paths - some of the simplest graphs of unbounded treedepth - then no
+such algorithm can exist unless SETH fails.
+",1,0,0,0,0,0
+10071,Planar Object Tracking in the Wild: A Benchmark,"  Planar object tracking is an actively studied problem in vision-based robotic
+applications. While several benchmarks have been constructed for evaluating
+state-of-the-art algorithms, there is a lack of video sequences captured in the
+wild rather than in constrained laboratory environment. In this paper, we
+present a carefully designed planar object tracking benchmark containing 210
+videos of 30 planar objects sampled in the natural environment. In particular,
+for each object, we shoot seven videos involving various challenging factors,
+namely scale change, rotation, perspective distortion, motion blur, occlusion,
+out-of-view, and unconstrained. The ground truth is carefully annotated
+semi-manually to ensure the quality. Moreover, eleven state-of-the-art
+algorithms are evaluated on the benchmark using two evaluation metrics, with
+detailed analysis provided for the evaluation results. We expect the proposed
+benchmark to benefit future studies on planar object tracking.
+",1,0,0,0,0,0
+10072,Experimental demonstration of an atomtronic battery,"  Operation of an atomtronic battery is demonstrated where a finite-temperature
+Bose-Einstein condensate stored in one half of a double-well potential is
+coupled to an initially empty load well that is impedance matched by a resonant
+terminator beam. The atom number and temperature of the condensate are
+monitored during the discharge cycle, and are used to calculate fundamental
+properties of the battery. The discharge behavior is analyzed according to a
+Thévenin equivalent circuit that contains a finite internal resistance to
+account for dissipation in the battery. Battery performance at multiple
+discharge rates is characterized by the peak power output, and the current and
+energy capacities of the system.
+",0,1,0,0,0,0
+10073,Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World,"  We address the two fundamental problems of spatial field reconstruction and
+sensor selection in heterogeneous sensor networks: (i) how to efficiently
+perform spatial field reconstruction based on measurements obtained
+simultaneously from networks with both high and low quality sensors; and (ii)
+how to perform query based sensor set selection with predictive MSE performance
+guarantee. For the first problem, we developed a low complexity algorithm based
+on the spatial best linear unbiased estimator (S-BLUE). Next, building on the
+S-BLUE, we address the second problem, and develop an efficient algorithm for
+query based sensor set selection with performance guarantee. Our algorithm is
+based on the Cross Entropy method which solves the combinatorial optimization
+problem in an efficient manner.
+",0,0,0,1,0,0
+10074,A characterization of ordinary abelian varieties by the Frobenius push-forward of the structure sheaf II,"  In this paper, we prove that a smooth projective variety $X$ of
+characteristic $p>0$ is an ordinary abelian variety if and only if $K_X$ is
+pseudo-effective and $F^e_*\mathcal O_X$ splits into a direct sum of line
+bundles for an integer $e$ with $p^e>2$.
+",0,0,1,0,0,0
+10075,On Minimax Optimality of Sparse Bayes Predictive Density Estimates,"  We study predictive density estimation under Kullback-Leibler loss in
+$\ell_0$-sparse Gaussian sequence models. We propose proper Bayes predictive
+density estimates and establish asymptotic minimaxity in sparse models. A
+surprise is the existence of a phase transition in the future-to-past variance
+ratio $r$. For $r < r_0 = (\surd 5 - 1)/4$, the natural discrete prior ceases
+to be asymptotically optimal. Instead, for subcritical $r$, a `bi-grid' prior
+with a central region of reduced grid spacing recovers asymptotic minimaxity.
+This phenomenon seems to have no analog in the otherwise parallel theory of
+point estimation of a multivariate normal mean under quadratic loss. For
+spike-and-slab priors to have any prospect of minimaxity, we show that the
+sparse parameter space needs also to be magnitude constrained. Within a
+substantial range of magnitudes, spike-and-slab priors can attain asymptotic
+minimaxity.
+",0,0,1,1,0,0
+10076,Two forms of minimality in ASPIC+,"  Many systems of structured argumentation explicitly require that the facts
+and rules that make up the argument for a conclusion be the minimal set
+required to derive the conclusion. ASPIC+ does not place such a requirement on
+arguments, instead requiring that every rule and fact that are part of an
+argument be used in its construction. Thus ASPIC+ arguments are minimal in the
+sense that removing any element of the argument would lead to a structure that
+is not an argument. In this brief note we discuss these two types of minimality
+and show how the first kind of minimality can, if desired, be recovered in
+ASPIC+.
+",1,0,0,0,0,0
+10077,Perspectives on constraining a cosmological constant-type parameter with pulsar timing in the Galactic Center,"  Independent tests aiming to constrain the value of the cosmological constant
+$\Lambda$ are usually difficult because of its extreme smallness $\left(\Lambda
+\simeq 1\times 10^{-52}~\textrm{m}^{-2},~\textrm{or}~2.89\times
+10^{-122}~\textrm{in Planck units}\right)$. Bounds on it from Solar System
+orbital motions determined with spacecraft tracking are currently at the
+$\simeq 10^{-43}-10^{-44}~\textrm{m}^{-2}~\left(5-1\times 10^{-113}~\textrm{in
+Planck units}\right)$ level, but they may turn out to be somewhat optimistic
+since $\Lambda$ has not yet been explicitly modeled in the planetary data
+reductions. Accurate $\left(\sigma_{\tau_\textrm{p}}\simeq
+1-10~\mu\textrm{s}\right)$ timing of expected pulsars orbiting the Black Hole
+at the Galactic Center, preferably along highly eccentric and wide orbits,
+might, at least in principle, improve the planetary constraints by several
+orders of magnitude. By looking at the average time shift per orbit
+$\overline{\Delta\delta\tau}^\Lambda_\textrm{p}$, a S2-like orbital
+configuration with $e=0.8839,~P_\textrm{b}=16~\textrm{yr}$ would allow to
+obtain preliminarily an upper bound of the order of
+$\left|\Lambda\right|\lesssim 9\times 10^{-47}~\textrm{m}^{-2}~\left(\lesssim
+2\times 10^{-116}~\textrm{in Planck units}\right)$ if only
+$\sigma_{\tau_\textrm{p}}$ were to be considered. Our results can be easily
+extended to modified models of gravity using $\Lambda-$type parameters.
+",0,1,0,0,0,0
+10078,Flat bundles over some compact complex manifolds,"  We construct examples of flat fiber bundles over the Hopf surface such that
+the total spaces have no pseudoconvex neighborhood basis, admit a complete
+Kähler metric, or are hyperconvex but have no nonconstant holomorphic
+functions. For any compact Riemannian surface of positive genus, we construct a
+flat $\mathbb P^1$ bundle over it and a Stein domain with real analytic bundary
+in it whose closure does not have pseudoconvex neighborhood basis. For a
+compact complex manifold with positive first Betti number, we construct a flat
+disc bundle over it such that the total space is hyperconvex but admits no
+nonconstant holomorphic functions.
+",0,0,1,0,0,0
+10079,EXONEST: The Bayesian Exoplanetary Explorer,"  The fields of astronomy and astrophysics are currently engaged in an
+unprecedented era of discovery as recent missions have revealed thousands of
+exoplanets orbiting other stars. While the Kepler Space Telescope mission has
+enabled most of these exoplanets to be detected by identifying transiting
+events, exoplanets often exhibit additional photometric effects that can be
+used to improve the characterization of exoplanets. The EXONEST Exoplanetary
+Explorer is a Bayesian exoplanet inference engine based on nested sampling and
+originally designed to analyze archived Kepler Space Telescope and CoRoT
+(Convection Rotation et Transits planétaires) exoplanet mission data. We
+discuss the EXONEST software package and describe how it accommodates
+plug-and-play models of exoplanet-associated photometric effects for the
+purpose of exoplanet detection, characterization and scientific hypothesis
+testing. The current suite of models allows for both circular and eccentric
+orbits in conjunction with photometric effects, such as the primary transit and
+secondary eclipse, reflected light, thermal emissions, ellipsoidal variations,
+Doppler beaming and superrotation. We discuss our new efforts to expand the
+capabilities of the software to include more subtle photometric effects
+involving reflected and refracted light. We discuss the EXONEST inference
+engine design and introduce our plans to port the current MATLAB-based EXONEST
+software package over to the next generation Exoplanetary Explorer, which will
+be a Python-based open source project with the capability to employ third-party
+plug-and-play models of exoplanet-related photometric effects.
+",0,1,0,1,0,0
+10080,Intelligence of agents produces a structural phase transition in collective behaviour,"  Living organisms process information to interact and adapt to their changing
+environment with the goal of finding food, mates or averting hazards. The
+structure of their niche has profound repercussions by both selecting their
+internal architecture and also inducing adaptive responses to environmental
+cues and stimuli. Adaptive, collective behaviour underpinned by specialized
+optimization strategies is ubiquitously found in the natural world. This
+exceptional success originates from the processes of fitness and selection.
+Here we prove that a universal physical mechanism of a nonequilibrium
+transition underlies the collective organization of information-processing
+organisms. As cognitive agents build and update an internal, cognitive
+representation of the causal structure of their environment, complex patterns
+emerge in the system, where the onset of pattern formation relates to the
+spatial overlap of cognitive maps. Studying the exchange of information among
+the agents reveals a continuous, order-disorder transition. As a result of the
+spontaneous breaking of translational symmetry, a Goldstone mode emerges, which
+points at a collective mechanism of information transfer among cognitive
+organisms. Taken together, the characteristics of this phase transition
+consolidate different results in cognitive and biological sciences in a
+universal manner. These finding are generally applicable to the design of
+artificial intelligent swarm systems that do not rely on centralized control
+schemes.
+",0,1,0,0,0,0
+10081,Quantifying Filter Bubbles: Analyzing Surprise in Elections,"  This work analyses surprising elections, and attempts to quantify the notion
+of surprise in elections. A voter is surprised if their estimate of the winner
+(assumed to be based on a combination of the preferences of their social
+connections and popular media predictions) is different from the true winner. A
+voter's social connections are assumed to consist of contacts on social media
+and geographically proximate people. We propose a simple mathematical model for
+combining the global information (traditional media) as well as the local
+information (local neighbourhood) of a voter in the case of a two-candidate
+election. We show that an unbiased, influential media can nullify the effect of
+filter bubbles and result in a less surprised populace. Surprisingly, an
+influential media source biased towards the winners of the election also
+results in a less surprised populace. Our model shows that elections will be
+unsurprising for all of the voters with a high probability under certain
+assumptions on the social connection model in the presence of an influential,
+unbiased traditional media source. Our experiments with the UK-EU referendum
+(popularly known as Brexit) dataset support our theoretical predictions. Since
+surprising elections can lead to significant economic movements, it is a
+worthwhile endeavour to figure out the causes of surprising elections.
+",1,0,0,0,0,0
+10082,"Energy transfer, pressure tensor and heating of kinetic plasma","  Kinetic plasma turbulence cascade spans multiple scales ranging from
+macroscopic fluid flow to sub-electron scales. Mechanisms that dissipate large
+scale energy, terminate the inertial range cascade and convert kinetic energy
+into heat are hotly debated. Here we revisit these puzzles using fully kinetic
+simulation. By performing scale-dependent spatial filtering on the Vlasov
+equation, we extract information at prescribed scales and introduce several
+energy transfer functions. This approach allows highly inhomogeneous energy
+cascade to be quantified as it proceeds down to kinetic scales. The pressure
+work, $-\left( \boldsymbol{P} \cdot \nabla \right) \cdot \boldsymbol{u}$, can
+trigger a channel of the energy conversion between fluid flow and random
+motions, which is a collision-free generalization of the viscous dissipation in
+collisional fluid. Both the energy transfer and the pressure work are strongly
+correlated with velocity gradients.
+",0,1,0,0,0,0
+10083,The relation between migration and FDI in the OECD from a complex network perspective,"  We explore the relationship between human migration and OECD's foreign direct
+investment (FDI) using a gravity equation enriched with variables that account
+for complex-network effects. Based on a panel data analysis, we find a strong
+positive correlation between the migration network and the FDI network, which
+can be mostly explained by countries' economic/demographic sizes and
+geographical distance. We highlight the existence of a stronger positive FDI
+relationship in pairs of countries that are more central in the migration
+network. Both intensive and extensive forms of centrality are FDI enhancing.
+Illuminating this result, we show that bilateral FDI between any two countries
+is further affected positively by the complex web of ""third party""
+corridors/migration stocks of the international migration network (IMN). Our
+findings are consistent whether we consider bilateral FDI and bilateral
+migration figures, or we focus on the outward FDI and the respective inward
+migration of the OECD countries.
+",0,1,0,0,0,0
+10084,Thermopower and thermal conductivity in the Weyl semimetal NbP,"  The Weyl semimetal NbP exhibits an extremely large magnetoresistance (MR) and
+an ultra-high mobility. The large MR originates from a combination of the
+nearly perfect compensation between electron- and hole-type charge carriers and
+the high mobility, which is relevant to the topological band structure. In this
+work we report on temperature- and field-dependent thermopower and thermal
+conductivity experiments on NbP. Additionally, we carried out complementary
+heat capacity, magnetization, and electrical resistivity measurements. We found
+a giant adiabatic magnetothermopower with a maximum of 800 $\mu$V/K at 50 K in
+a field of 9 T. Such large effects have been observed rarely in bulk materials.
+We suggest that the origin of this effect might be related to the high
+charge-carrier mobility. We further observe pronounced quantum oscillations in
+both thermal conductivity and thermopower. The obtained frequencies compare
+well with our heat capacity and magnetization data.
+",0,1,0,0,0,0
+10085,Maximum entropy and population heterogeneity in continuous cell cultures,"  Continuous cultures of mammalian cells are complex systems displaying
+hallmark phenomena of nonlinear dynamics, such as multi-stability, hysteresis,
+as well as sharp transitions between different metabolic states. In this
+context mathematical models may suggest control strategies to steer the system
+towards desired states. Although even clonal populations are known to exhibit
+cell-to-cell variability, most of the currently studied models assume that the
+population is homogeneous. To overcome this limitation, we use the maximum
+entropy principle to model the phenotypic distribution of cells in a chemostat
+as a function of the dilution rate. We consider the coupling between cell
+metabolism and extracellular variables describing the state of the bioreactor
+and take into account the impact of toxic byproduct accumulation on cell
+viability. We present a formal solution for the stationary state of the
+chemostat and show how to apply it in two examples. First, a simplified model
+of cell metabolism where the exact solution is tractable, and then a
+genome-scale metabolic network of the Chinese hamster ovary (CHO) cell line.
+Along the way we discuss several consequences of heterogeneity, such as:
+qualitative changes in the dynamical landscape of the system, increasing
+concentrations of byproducts that vanish in the homogeneous case, and larger
+population sizes.
+",0,0,0,0,1,0
+10086,Spin wave propagation and spin polarized electron transport in single crystal iron films,"  The technique of propagating spin wave spectroscopy is applied to a 20 nm
+thick Fe/MgO (001) film. The magnetic parameters extracted from the position of
+the resonance peaks are very close to those tabulated for bulk iron. From the
+propagating waveforms, a group velocity of 4 km/s and an attenuation length of
+about 6 micrometers are extracted for 1.6 micrometers-wavelength spin-wave at
+18 GHz. From the measured current-induced spin-wave Doppler shift, we also
+extract a surprisingly high degree of spin-polarization of the current of 83%.
+This set of results makes single-crystalline iron a promising candidate for
+building devices utilizing high frequency spin-waves and spin-polarized
+currents.
+",0,1,0,0,0,0
+10087,Expert-Driven Genetic Algorithms for Simulating Evaluation Functions,"  In this paper we demonstrate how genetic algorithms can be used to reverse
+engineer an evaluation function's parameters for computer chess. Our results
+show that using an appropriate expert (or mentor), we can evolve a program that
+is on par with top tournament-playing chess programs, outperforming a two-time
+World Computer Chess Champion. This performance gain is achieved by evolving a
+program that mimics the behavior of a superior expert. The resulting evaluation
+function of the evolved program consists of a much smaller number of parameters
+than the expert's. The extended experimental results provided in this paper
+include a report of our successful participation in the 2008 World Computer
+Chess Championship. In principle, our expert-driven approach could be used in a
+wide range of problems for which appropriate experts are available.
+",1,0,0,1,0,0
+10088,A gentle introduction to the minimal Naming Game,"  Social conventions govern countless behaviors all of us engage in every day,
+from how we greet each other to the languages we speak. But how can shared
+conventions emerge spontaneously in the absence of a central coordinating
+authority? The Naming Game model shows that networks of locally interacting
+individuals can spontaneously self-organize to produce global coordination.
+Here, we provide a gentle introduction to the main features of the model, from
+the dynamics observed in homogeneously mixing populations to the role played by
+more complex social networks, and to how slight modifications of the basic
+interaction rules give origin to a richer phenomenology in which more
+conventions can co-exist indefinitely.
+",1,1,0,0,0,0
+10089,"Modelling hidden structure of signals in group data analysis with modified (Lr, 1) and block-term decompositions","  This work is devoted to elaboration on the idea to use block term
+decomposition for group data analysis and to raise the possibility of modelling
+group activity with (Lr, 1) and Tucker blocks. A new generalization of block
+tensor decomposition was considered in application to group data analysis.
+Suggested approach was evaluated on multilabel classification task for a set of
+images. This contribution also reports results of investigation on clustering
+with proposed tensor models in comparison with known matrix models, namely
+common orthogonal basis extraction and group independent component analysis.
+",1,0,0,1,0,0
+10090,ChaLearn Looking at People: A Review of Events and Resources,"  This paper reviews the historic of ChaLearn Looking at People (LAP) events.
+We started in 2011 (with the release of the first Kinect device) to run
+challenges related to human action/activity and gesture recognition. Since then
+we have regularly organized events in a series of competitions covering all
+aspects of visual analysis of humans. So far we have organized more than 10
+international challenges and events in this field. This paper reviews
+associated events, and introduces the ChaLearn LAP platform where public
+resources (including code, data and preprints of papers) related to the
+organized events are available. We also provide a discussion on perspectives of
+ChaLearn LAP activities.
+",1,0,0,0,0,0
+10091,Reinterpreting the Origin of Bifurcation and Chaos by Urbanization Dynamics,"  Chaos associated with bifurcation makes a new science, but the origin and
+essence of chaos are not yet clear. Based on the well-known logistic map, chaos
+used to be regarded as intrinsic randomicity of determinate dynamics systems.
+However, urbanization dynamics indicates new explanation about it. Using
+mathematical derivation, numerical computation, and empirical analysis, we can
+explore chaotic dynamics of urbanization. The key is the formula of
+urbanization level. The urbanization curve can be described with the logistic
+function, which can be transformed into 1-dimensional map and thus produce
+bifurcation and chaos. On the other hand, the logistic model of urbanization
+curve can be derived from the rural-urban population interaction model, and the
+rural-urban interaction model can be discretized to a 2-dimensional map. An
+interesting finding is that the 2-dimensional rural-urban coupling map can
+create the same bifurcation and chaos patterns as those from the 1-dimensional
+logistic map. This suggests that the urban bifurcation and chaos come from
+spatial interaction between rural and urban population rather than pure
+intrinsic randomicity of determinate models. This discovery provides a new way
+of looking at origin and essence of bifurcation and chaos. By analogy with
+urbanization models, the classical predator-prey interaction model can be
+developed to interpret the complex dynamics of the logistic map in physical and
+social sciences.
+",0,1,0,0,0,0
+10092,Biochemical Coupling Through Emergent Conservation Laws,"  Bazhin has analyzed ATP coupling in terms of quasiequilibrium states where
+fast reactions have reached an approximate steady state while slow reactions
+have not yet reached equilibrium. After an expository introduction to the
+relevant aspects of reaction network theory, we review his work and explain the
+role of emergent conserved quantities in coupling. These are quantities, left
+unchanged by fast reactions, whose conservation forces exergonic processes such
+as ATP hydrolysis to drive desired endergonic processes.
+",0,0,0,0,1,0
+10093,Artificial Intelligence as an Enabler for Cognitive Self-Organizing Future Networks,"  The explosive increase in number of smart devices hosting sophisticated
+applications is rapidly affecting the landscape of information communication
+technology industry. Mobile subscriptions, expected to reach 8.9 billion by
+2022, would drastically increase the demand of extra capacity with aggregate
+throughput anticipated to be enhanced by a factor of 1000. In an already
+crowded radio spectrum, it becomes increasingly difficult to meet ever growing
+application demands of wireless bandwidth. It has been shown that the allocated
+spectrum is seldom utilized by the primary users and hence contains spectrum
+holes that may be exploited by the unlicensed users for their communication. As
+we enter the Internet Of Things (IoT) era in which appliances of common use
+will become smart digital devices with rigid performance requirements (such as
+low latency, energy efficiency, etc.), current networks face the vexing problem
+of how to create sufficient capacity for such applications. The fifth
+generation of cellular networks (5G) envisioned to address these challenges are
+thus required to incorporate cognition and intelligence to resolve the
+aforementioned issues.
+",1,0,0,0,0,0
+10094,Localized Thermal States,"  It is believed that thermalization in closed systems of interacting particles
+can occur only when the eigenstates are fully delocalized and chaotic in the
+preferential (unperturbed) basis of the total Hamiltonian. Here we demonstrate
+that at variance with this common belief the typical situation in the systems
+with two-body inter-particle interaction is much more complicated and allows to
+treat as thermal even eigenstates that are not fully delocalized. Using a
+semi-analytical approach we establish the conditions for the emergence of such
+thermal states in a model of randomly interacting bosons. Our numerical data
+show an excellent correspondence with the predicted properties of {\it
+localized thermal eigenstates}.
+",0,1,0,0,0,0
+10095,Interface Phonon Modes in the [AlN/GaN]20 and [Al0.35Ga0.65N/Al0.55Ga0.45N]20 2D Multi Quantum Well Structures,"  Interface phonon (IF) modes of c-plane oriented [AlN/GaN]20 and
+Al0.35Ga0.65N/Al0.55Ga0.45N]20 multi quantum well (MQW) structures grown via
+plasma assisted molecular beam epitaxy are reported. The effect of variation in
+dielectric constant of barrier layers to the IF optical phonon modes of well
+layers periodically arranged in the MQWs investigated.
+",0,1,0,0,0,0
+10096,Predictability of escape for a stochastic saddle-node bifurcation: when rare events are typical,"  Transitions between multiple stable states of nonlinear systems are
+ubiquitous in physics, chemistry, and beyond. Two types of behaviors are
+usually seen as mutually exclusive: unpredictable noise-induced transitions and
+predictable bifurcations of the underlying vector field. Here, we report a new
+situation, corresponding to a fluctuating system approaching a bifurcation,
+where both effects collaborate. We show that the problem can be reduced to a
+single control parameter governing the competition between deterministic and
+stochastic effects. Two asymptotic regimes are identified: when the control
+parameter is small (e.g. small noise), deviations from the deterministic case
+are well described by the Freidlin-Wentzell theory. In particular, escapes over
+the potential barrier are very rare events. When the parameter is large (e.g.
+large noise), such events become typical. Unlike pure noise-induced
+transitions, the distribution of the escape time is peaked around a value which
+is asymptotically predicted by an adiabatic approximation. We show that the two
+regimes are characterized by qualitatively different reacting trajectories,
+with algebraic and exponential divergence, respectively.
+",0,1,0,0,0,0
+10097,Sieving rational points on varieties,"  A sieve for rational points on suitable varieties is developed, together with
+applications to counting rational points in thin sets, the number of varieties
+in a family which are everywhere locally soluble, and to the notion of friable
+rational points with respect to divisors. In the special case of quadrics,
+sharper estimates are obtained by developing a version of the Selberg sieve for
+rational points.
+",0,0,1,0,0,0
+10098,Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability,"  Neural responses in the cortex change over time both systematically, due to
+ongoing plasticity and learning, and seemingly randomly, due to various sources
+of noise and variability. Most previous work considered each of these
+processes, learning and variability, in isolation -- here we study neural
+networks exhibiting both and show that their interaction leads to the emergence
+of powerful computational properties. We trained neural networks on classical
+unsupervised learning tasks, in which the objective was to represent their
+inputs in an efficient, easily decodable form, with an additional cost for
+neural reliability which we derived from basic biophysical considerations. This
+cost on reliability introduced a tradeoff between energetically cheap but
+inaccurate representations and energetically costly but accurate ones. Despite
+the learning tasks being non-probabilistic, the networks solved this tradeoff
+by developing a probabilistic representation: neural variability represented
+samples from statistically appropriate posterior distributions that would
+result from performing probabilistic inference over their inputs. We provide an
+analytical understanding of this result by revealing a connection between the
+cost of reliability, and the objective for a state-of-the-art Bayesian
+inference strategy: variational autoencoders. We show that the same cost leads
+to the emergence of increasingly accurate probabilistic representations as
+networks become more complex, from single-layer feed-forward, through
+multi-layer feed-forward, to recurrent architectures. Our results provide
+insights into why neural responses in sensory areas show signatures of
+sampling-based probabilistic representations, and may inform future deep
+learning algorithms and their implementation in stochastic low-precision
+computing systems.
+",0,0,0,0,1,0
+10099,Microscopic Description of Electric and Magnetic Toroidal Multipoles in Hybrid Orbitals,"  We present a general formalism of multipole descriptions under the space-time
+inversion group. We elucidate that two types of atomic toroidal multipoles,
+i.e., electric and magnetic, are fundamental pieces to express electronic order
+parameters in addition to ordinary electric and magnetic multipoles. By
+deriving quantum-mechanical operators for both toroidal multipoles, we show
+that electric (magnetic) toroidal multipole higher than dipole (monopole) can
+become a primary order parameter in a hybridized-orbital system. We also
+demonstrate emergent cross-correlated couplings between electric, magnetic, and
+elastic degrees of freedom, such as magneto-electric and
+magneto(electro)-elastic couplings, under toroidal multipole orders.
+",0,1,0,0,0,0
+10100,Algorithms to Approximate Column-Sparse Packing Problems,"  Column-sparse packing problems arise in several contexts in both
+deterministic and stochastic discrete optimization. We present two unifying
+ideas, (non-uniform) attenuation and multiple-chance algorithms, to obtain
+improved approximation algorithms for some well-known families of such
+problems. As three main examples, we attain the integrality gap, up to
+lower-order terms, for known LP relaxations for k-column sparse packing integer
+programs (Bansal et al., Theory of Computing, 2012) and stochastic k-set
+packing (Bansal et al., Algorithmica, 2012), and go ""half the remaining
+distance"" to optimal for a major integrality-gap conjecture of Furedi, Kahn and
+Seymour on hypergraph matching (Combinatorica, 1993).
+",1,0,0,0,0,0
+10101,Teaching the Doppler Effect in Astrophysics,"  The Doppler effect is a shift in the frequency of waves emitted from an
+object moving relative to the observer. By observing and analysing the Doppler
+shift in electromagnetic waves from astronomical objects, astronomers gain
+greater insight into the structure and operation of our universe. In this
+paper, a simple technique is described for teaching the basics of the Doppler
+effect to undergraduate astrophysics students using acoustic waves. An
+advantage of the technique is that it produces a visual representation of the
+acoustic Doppler shift. The equipment comprises a 40 kHz acoustic transmitter
+and a microphone. The sound is bounced off a computer fan and the signal
+collected by a DrDAQ ADC and processed by a spectrum analyser. Widening of the
+spectrum is observed as the fan power supply potential is increased from 4 to
+12 V.
+",0,1,0,0,0,0
+10102,Embedding Tarskian Semantics in Vector Spaces,"  We propose a new linear algebraic approach to the computation of Tarskian
+semantics in logic. We embed a finite model M in first-order logic with N
+entities in N-dimensional Euclidean space R^N by mapping entities of M to N
+dimensional one-hot vectors and k-ary relations to order-k adjacency tensors
+(multi-way arrays). Second given a logical formula F in prenex normal form, we
+compile F into a set Sigma_F of algebraic formulas in multi-linear algebra with
+a nonlinear operation. In this compilation, existential quantifiers are
+compiled into a specific type of tensors, e.g., identity matrices in the case
+of quantifying two occurrences of a variable. It is shown that a systematic
+evaluation of Sigma_F in R^N gives the truth value, 1(true) or 0(false), of F
+in M. Based on this framework, we also propose an unprecedented way of
+computing the least models defined by Datalog programs in linear spaces via
+matrix equations and empirically show its effectiveness compared to
+state-of-the-art approaches.
+",1,0,0,0,0,0
+10103,Constraints from Dust Mass and Mass Accretion Rate Measurements on Angular Momentum Transport in Protoplanetary Disks,"  We investigate the relation between disk mass and mass accretion rate to
+constrain the mechanism of angular momentum transport in protoplanetary disks.
+Dust mass and mass accretion rate in Chamaeleon I are correlated with a slope
+close to linear, similar to the one recently identified in Lupus. We
+investigate the effect of stellar mass and find that the intrinsic scatter
+around the best-fit Mdust-Mstar and Macc-Mstar relations is uncorrelated. Disks
+with a constant alpha viscosity can fit the observed relations between dust
+mass, mass accretion rate, and stellar mass, but over-predict the strength of
+the correlation between disk mass and mass accretion rate when using standard
+initial conditions. We find two possible solutions. 1) The observed scatter in
+Mdust and Macc is not primoridal, but arises from additional physical processes
+or uncertainties in estimating the disk gas mass. Most likely grain growth and
+radial drift affect the observable dust mass, while variability on large time
+scales affects the mass accretion rates. 2) The observed scatter is primordial,
+but disks have not evolved substantially at the age of Lupus and Chamaeleon I
+due to a low viscosity or a large initial disk radius. More accurate estimates
+of the disk mass and gas disk sizes in a large sample of protoplanetary disks,
+either through direct observations of the gas or spatially resolved
+multi-wavelength observations of the dust with ALMA, are needed to discriminate
+between both scenarios or to constrain alternative angular momentum transport
+mechanisms such as MHD disk winds.
+",0,1,0,0,0,0
+10104,Character-Word LSTM Language Models,"  We present a Character-Word Long Short-Term Memory Language Model which both
+reduces the perplexity with respect to a baseline word-level language model and
+reduces the number of parameters of the model. Character information can reveal
+structural (dis)similarities between words and can even be used when a word is
+out-of-vocabulary, thus improving the modeling of infrequent and unknown words.
+By concatenating word and character embeddings, we achieve up to 2.77% relative
+improvement on English compared to a baseline model with a similar amount of
+parameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level
+models with a larger number of parameters.
+",1,0,0,0,0,0
+10105,Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution,"  Machine learning methods have found many applications in Raman spectroscopy,
+especially for the identification of chemical species. However, almost all of
+these methods require non-trivial preprocessing such as baseline correction
+and/or PCA as an essential step. Here we describe our unified solution for the
+identification of chemical species in which a convolutional neural network is
+trained to automatically identify substances according to their Raman spectrum
+without the need of ad-hoc preprocessing steps. We evaluated our approach using
+the RRUFF spectral database, comprising mineral sample data. Superior
+classification performance is demonstrated compared with other frequently used
+machine learning algorithms including the popular support vector machine.
+",1,0,0,1,0,0
+10106,Suppression of material transfer at contacting surfaces: The effect of adsorbates on Al/TiN and Cu/diamond interfaces from first-principles calculations,"  The effect of monolayers of oxygen (O) and hydrogen (H) on the possibility of
+material transfer at aluminium/titanium nitride (Al/TiN) and copper/diamond
+(Cu/C$_{\text{dia}}$) interfaces, respectively, were investigated within the
+framework of density functional theory (DFT). To this end the approach,
+contact, and subsequent separation of two atomically flat surfaces consisting
+of the aforementioned pairs of materials were simulated. These calculations
+were performed for the clean as well as oxygenated and hydrogenated Al and
+C$_{\text{dia}}$ surfaces, respectively. Various contact configurations were
+considered by studying several lateral arrangements of the involved surfaces at
+the interface. Material transfer is typically possible at interfaces between
+the investigated clean surfaces; however, the addition of O to the Al and H to
+the C$_{\text{dia}}$ surfaces was found to hinder material transfer. This
+passivation occurs because of a significant reduction of the adhesion energy at
+the examined interfaces, which can be explained by the distinct bonding
+situations.
+",0,1,0,0,0,0
+10107,Fine-grained ECG Classification Based on Deep CNN and Online Decision Fusion,"  Early recognition of abnormal rhythm in ECG signals is crucial for monitoring
+or diagnosing patients' cardiac conditions and increasing the success rate of
+the treatment. Classifying abnormal rhythms into fine-grained categories is
+very challenging due to the the broad taxonomy of rhythms, noises and lack of
+real-world data and annotations from large number of patients. This paper
+presents a new ECG classification method based on Deep Convolutional Neural
+Networks (DCNN) and online decision fusion. Different from previous methods
+which utilize hand-crafted features or learn features from the original signal
+domain, the proposed DCNN based method learns features and classifiers from the
+time-frequency domain in an end-to-end manner. First, the ECG wave signal is
+transformed to time-frequency domain by using Short-Time Fourier Transform.
+Next, specific DCNN models are trained on ECG samples of specific length.
+Finally, an online decision fusion method is proposed to fuse past and current
+decisions from different models into a more accurate one. Experimental results
+on both synthetic and real-world ECG datasets convince the effectiveness and
+efficiency of the proposed method.
+",1,0,0,1,0,0
+10108,A Wavenet for Speech Denoising,"  Currently, most speech processing techniques use magnitude spectrograms as
+front-end and are therefore by default discarding part of the signal: the
+phase. In order to overcome this limitation, we propose an end-to-end learning
+method for speech denoising based on Wavenet. The proposed model adaptation
+retains Wavenet's powerful acoustic modeling capabilities, while significantly
+reducing its time-complexity by eliminating its autoregressive nature.
+Specifically, the model makes use of non-causal, dilated convolutions and
+predicts target fields instead of a single target sample. The discriminative
+adaptation of the model we propose, learns in a supervised fashion via
+minimizing a regression loss. These modifications make the model highly
+parallelizable during both training and inference. Both computational and
+perceptual evaluations indicate that the proposed method is preferred to Wiener
+filtering, a common method based on processing the magnitude spectrogram.
+",1,0,0,0,0,0
+10109,Warped Product Space-times,"  Many classical results in relativity theory concerning spherically symmetric
+space-times have easy generalizations to warped product space-times, with a
+two-dimensional Lorentzian base and arbitrary dimensional Riemannian fibers. We
+first give a systematic presentation of the main geometric constructions, with
+emphasis on the Kodama vector field and the Hawking energy; the construction is
+signature independent. This leads to proofs of general Birkhoff-type theorems
+for warped product manifolds; our theorems in particular apply to situations
+where the warped product manifold is not necessarily Einstein, and thus can be
+applied to solutions with matter content in general relativity. Next we
+specialize to the Lorentzian case and study the propagation of null expansions
+under the assumption of the dominant energy condition. We prove several
+non-existence results relating to the Yamabe class of the fibers, in the spirit
+of the black-hole topology theorem of Hawking-Galloway-Schoen. Finally we
+discuss the effect of the warped product ansatz on matter models. In particular
+we construct several cosmological solutions to the Einstein-Euler equations
+whose spatial geometry is generally not isotropic.
+",0,0,1,0,0,0
+10110,ALMA Observations of the Young Substellar Binary System 2M1207,"  We present ALMA observations of the 2M1207 system, a young binary made of a
+brown dwarf with a planetary-mass companion at a projected separation of about
+40 au. We detect emission from dust continuum at 0.89 mm and from the $J = 3 -
+2$ rotational transition of CO from a very compact disk around the young brown
+dwarf. The small radius found for this brown dwarf disk may be due to
+truncation from the tidal interaction with the planetary-mass companion. Under
+the assumption of optically thin dust emission, we estimated a dust mass of 0.1
+$M_{\oplus}$ for the 2M1207A disk, and a 3$\sigma$ upper limit of $\sim
+1~M_{\rm{Moon}}$ for dust surrounding 2M1207b, which is the tightest upper
+limit obtained so far for the mass of dust particles surrounding a young
+planetary-mass companion. We discuss the impact of this and other
+non-detections of young planetary-mass companions for models of planet
+formation, which predict the presence of circum-planetary material surrounding
+these objects.
+",0,1,0,0,0,0
+10111,Multichannel Linear Prediction for Blind Reverberant Audio Source Separation,"  A class of methods based on multichannel linear prediction (MCLP) can achieve
+effective blind dereverberation of a source, when the source is observed with a
+microphone array. We propose an inventive use of MCLP as a pre-processing step
+for blind source separation with a microphone array. We show theoretically
+that, under certain assumptions, such pre-processing reduces the original blind
+reverberant source separation problem to a non-reverberant one, which in turn
+can be effectively tackled using existing methods. We demonstrate our claims
+using real recordings obtained with an eight-microphone circular array in
+reverberant environments.
+",1,0,0,0,0,0
+10112,SepNE: Bringing Separability to Network Embedding,"  Many successful methods have been proposed for learning low dimensional
+representations on large-scale networks, while almost all existing methods are
+designed in inseparable processes, learning embeddings for entire networks even
+when only a small proportion of nodes are of interest. This leads to great
+inconvenience, especially on super-large or dynamic networks, where these
+methods become almost impossible to implement. In this paper, we formalize the
+problem of separated matrix factorization, based on which we elaborate a novel
+objective function that preserves both local and global information. We further
+propose SepNE, a simple and flexible network embedding algorithm which
+independently learns representations for different subsets of nodes in
+separated processes. By implementing separability, our algorithm reduces the
+redundant efforts to embed irrelevant nodes, yielding scalability to
+super-large networks, automatic implementation in distributed learning and
+further adaptations. We demonstrate the effectiveness of this approach on
+several real-world networks with different scales and subjects. With comparable
+accuracy, our approach significantly outperforms state-of-the-art baselines in
+running times on large networks.
+",1,0,0,0,0,0
+10113,Opinion formation in a locally interacting community with recommender,"  We present a user of model interaction based on the physics of kinetic
+exchange, and extend it to individuals placed in a grid with local interaction.
+We show with numerical analysis and partial analytical results that the
+critical symmetry breaking transitions and percolation effects typical of the
+full interaction model do not take place if the range of interaction is
+limited, allowing for the co-existence of majorty and minority opinions in the
+same community.
+We then introduce a peer recommender system in the model, showing that, even
+with very local iteraction and a small probability of appeal to the
+recommender, its presence is sufficient to make both symmetry breaking and
+percolation reappear. This seems to indicate that one effect of a
+recommendation system is to uniform the opinions of a community, reducing
+minority opinions or making them disappear. Although the recommender system
+does uniform the community opinion, it doesn't constrain it, in the sense that
+all opinions have the same probability of becoming the dominating one. We do a
+partial study, however, that suggests that a ""mischievous"" recommender might be
+able to bias a community so that one opinion will emerge over the opposite with
+overwhelming probability.
+",1,1,0,0,0,0
+10114,Integral representation of shallow neural network that attains the global minimum,"  We consider the supervised learning problem with shallow neural networks.
+According to our unpublished experiments conducted several years prior to this
+study, we had noticed an interesting similarity between the distribution of
+hidden parameters after backprobagation (BP) training, and the ridgelet
+spectrum of the same dataset. Therefore, we conjectured that the distribution
+is expressed as a version of ridgelet transform, but it was not proven until
+this study. One difficulty is that both the local minimizers and the ridgelet
+transforms have an infinite number of varieties, and no relations are known
+between them. By using the integral representation, we reformulate the BP
+training as a strong-convex optimization problem and find a global minimizer.
+Finally, by developing ridgelet analysis on a reproducing kernel Hilbert space
+(RKHS), we write the minimizer explicitly and succeed to prove the conjecture.
+The modified ridgelet transform has an explicit expression that can be computed
+by numerical integration, which suggests that we can obtain the global
+minimizer of BP, without BP.
+",0,0,0,1,0,0
+10115,DoKnowMe: Towards a Domain Knowledge-driven Methodology for Performance Evaluation,"  Software engineering considers performance evaluation to be one of the key
+portions of software quality assurance. Unfortunately, there seems to be a lack
+of standard methodologies for performance evaluation even in the scope of
+experimental computer science. Inspired by the concept of ""instantiation"" in
+object-oriented programming, we distinguish the generic performance evaluation
+logic from the distributed and ad-hoc relevant studies, and develop an abstract
+evaluation methodology (by analogy of ""class"") we name Domain Knowledge-driven
+Methodology (DoKnowMe). By replacing five predefined domain-specific knowledge
+artefacts, DoKnowMe could be instantiated into specific methodologies (by
+analogy of ""object"") to guide evaluators in performance evaluation of different
+software and even computing systems. We also propose a generic validation
+framework with four indicators (i.e.~usefulness, feasibility, effectiveness and
+repeatability), and use it to validate DoKnowMe in the Cloud services
+evaluation domain. Given the positive and promising validation result, we plan
+to integrate more common evaluation strategies to improve DoKnowMe and further
+focus on the performance evaluation of Cloud autoscaler systems.
+",1,0,0,0,0,0
+10116,The Effect of Different Wavelengths on Porous Silicon Formation Process,"  Porous silicon layers (PS) have been prepared in this work via
+Photoelectrochemical etching process (PEC) of n type silicon wafer of 0.8
+ohm.cm resistivity in hydrofluoric (HF) acid of 24.5 precent concentration at
+different etching times (5 to 25 min.). The irradiation has been achieved using
+Tungsten lamp with different wavelengths (450 nm, 535 nm and 700 nm). The
+morphological properties of these layers such as surface morphology, Porosity,
+layer thickness, and also the etching rate have been investigated using optical
+microscopy and the gravimetric method.
+",0,1,0,0,0,0
+10117,Topological dimension tunes activity patterns in hierarchical modular network models,"  Connectivity patterns of relevance in neuroscience and systems biology can be
+encoded in hierarchical modular networks (HMNs). Moreover, recent studies
+highlight the role of hierarchical modular organization in shaping brain
+activity patterns, providing an excellent substrate to promote both the
+segregation and integration of neural information. Here we propose an extensive
+numerical analysis of the critical spreading rate (or ""epidemic"" threshold)
+--separating a phase with endemic persistent activity from one in which
+activity ceases-- on diverse HMNs. By employing analytical and computational
+techniques we determine the nature of such a threshold and scrutinize how it
+depends on general structural features of the underlying HMN. We critically
+discuss the extent to which current graph-spectral methods can be applied to
+predict the onset of spreading in HMNs, and we propose the network topological
+dimension as a relevant and unifying structural parameter, controlling the
+epidemic threshold.
+",0,1,0,0,0,0
+10118,DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition,"  Deep Convolutional Neural Networks (DCNNs) are currently popular in human
+activity recognition applications. However, in the face of modern artificial
+intelligence sensor-based games, many research achievements cannot be
+practically applied on portable devices. DCNNs are typically resource-intensive
+and too large to be deployed on portable devices, thus this limits the
+practical application of complex activity detection. In addition, since
+portable devices do not possess high-performance Graphic Processing Units
+(GPUs), there is hardly any improvement in Action Game (ACT) experience.
+Besides, in order to deal with multi-sensor collaboration, all previous human
+activity recognition models typically treated the representations from
+different sensor signal sources equally. However, distinct types of activities
+should adopt different fusion strategies. In this paper, a novel scheme is
+proposed. This scheme is used to train 2-bit Convolutional Neural Networks with
+weights and activations constrained to {-0.5,0,0.5}. It takes into account the
+correlation between different sensor signal sources and the activity types.
+This model, which we refer to as DFTerNet, aims at producing a more reliable
+inference and better trade-offs for practical applications. Our basic idea is
+to exploit quantization of weights and activations directly in pre-trained
+filter banks and adopt dynamic fusion strategies for different activity types.
+Experiments demonstrate that by using dynamic fusion strategy can exceed the
+baseline model performance by up to ~5% on activity recognition like
+OPPORTUNITY and PAMAP2 datasets. Using the quantization method proposed, we
+were able to achieve performances closer to that of full-precision counterpart.
+These results were also verified using the UniMiB-SHAR dataset. In addition,
+the proposed method can achieve ~9x acceleration on CPUs and ~11x memory
+saving.
+",0,0,0,1,0,0
+10119,Generalized stealthy hyperuniform processes : maximal rigidity and the bounded holes conjecture,"  We study translation invariant stochastic processes on $\mathbb{R}^d$ or
+$\mathbb{Z}^d$ whose diffraction spectrum or structure function $S(k)$, i.e.
+the Fourier transform of the truncated total pair correlation function,
+vanishes on an open set $U$ in the wave space. A key family of such processes
+are stealthy hyperuniform point processes, for which the origin $k=0$ is in
+$U$; these are of much current physical interest. We show that all such
+processes exhibit the following remarkable maximal rigidity : namely, the
+configuration outside a bounded region determines, with probability 1, the
+exact value (or the exact locations of the points) of the process inside the
+region. In particular, such processes are completely determined by their tail.
+In the 1D discrete setting (i.e. $\mathbb{Z}$-valued processes on
+$\mathbb{Z}$), this can also be seen as a consequence of a recent theorem of
+Borichev, Sodin and Weiss; in higher dimensions or in the continuum, such a
+phenomenon seems novel. For stealthy hyperuniform point processes, we prove the
+Zhang-Stillinger-Torquato conjecture that such processes have bounded holes
+(empty regions), with a universal bound that depends inversely on the size of
+$U$.
+",0,1,0,0,0,0
+10120,High-frequency approximation of the interior dirichlet-to-neumann map and applications to the transmission eigenvalues,"  We study the high-frequency behavior of the Dirichlet-to-Neumann map for an
+arbitrary compact Riemannian manifold with a non-empty smooth boundary. We show
+that far from the real axis it can be approximated by a simpler operator. We
+use this fact to get new results concerning the location of the transmission
+eigenvalues on the complex plane. In some cases we obtain optimal transmission
+eigenvalue-free regions.
+",0,0,1,0,0,0
+10121,Conformal scalar curvature equation on S^n: functions with two close critical points (twin pseudo-peaks),"  By using the Lyapunov-Schmidt reduction method without perturbation, we
+consider existence results for the conformal scalar curvature on S^n (n greater
+or equal to 3) when the prescribed function (after being projected to R^n) has
+two close critical points, which have the same value (positive), equal
+""flatness"" (twin, flatness < n - 2), and exhibit maximal behavior in certain
+directions (pseudo-peaks). The proof relies on a balance between the two main
+contributions to the reduced functional - one from the critical points and the
+other from the interaction of the two bubbles.
+",0,0,1,0,0,0
+10122,Equivalence between non-Markovian and Markovian dynamics in epidemic spreading processes,"  A general formalism is introduced to allow the steady state of non-Markovian
+processes on networks to be reduced to equivalent Markovian processes on the
+same substrates. The example of an epidemic spreading process is considered in
+detail, where all the non-Markovian aspects are shown to be captured within a
+single parameter, the effective infection rate. Remarkably, this result is
+independent of the topology of the underlying network, as demonstrated by
+numerical simulations on two-dimensional lattices and various types of random
+networks. Furthermore, an analytic approximation for the effective infection
+rate is introduced, which enables the calculation of the critical point and of
+the critical exponents for the non-Markovian dynamics.
+",0,1,0,0,0,0
+10123,Attacking Binarized Neural Networks,"  Neural networks with low-precision weights and activations offer compelling
+efficiency advantages over their full-precision equivalents. The two most
+frequently discussed benefits of quantization are reduced memory consumption,
+and a faster forward pass when implemented with efficient bitwise operations.
+We propose a third benefit of very low-precision neural networks: improved
+robustness against some adversarial attacks, and in the worst case, performance
+that is on par with full-precision models. We focus on the very low-precision
+case where weights and activations are both quantized to $\pm$1, and note that
+stochastically quantizing weights in just one layer can sharply reduce the
+impact of iterative attacks. We observe that non-scaled binary neural networks
+exhibit a similar effect to the original defensive distillation procedure that
+led to gradient masking, and a false notion of security. We address this by
+conducting both black-box and white-box experiments with binary models that do
+not artificially mask gradients.
+",1,0,0,1,0,0
+10124,On Treewidth and Stable Marriage,"  Stable Marriage is a fundamental problem to both computer science and
+economics. Four well-known NP-hard optimization versions of this problem are
+the Sex-Equal Stable Marriage (SESM), Balanced Stable Marriage (BSM),
+max-Stable Marriage with Ties (max-SMT) and min-Stable Marriage with Ties
+(min-SMT) problems. In this paper, we analyze these problems from the viewpoint
+of Parameterized Complexity. We conduct the first study of these problems with
+respect to the parameter treewidth. First, we study the treewidth $\mathtt{tw}$
+of the primal graph. We establish that all four problems are W[1]-hard. In
+particular, while it is easy to show that all four problems admit algorithms
+that run in time $n^{O(\mathtt{tw})}$, we prove that all of these algorithms
+are likely to be essentially optimal. Next, we study the treewidth
+$\mathtt{tw}$ of the rotation digraph. In this context, the max-SMT and min-SMT
+are not defined. For both SESM and BSM, we design (non-trivial) algorithms that
+run in time $2^{\mathtt{tw}}n^{O(1)}$. Then, for both SESM and BSM, we also
+prove that unless SETH is false, algorithms that run in time
+$(2-\epsilon)^{\mathtt{tw}}n^{O(1)}$ do not exist for any fixed $\epsilon>0$.
+We thus present a comprehensive, complete picture of the behavior of central
+optimization versions of Stable Marriage with respect to treewidth.
+",1,0,0,0,0,0
+10125,A Holistic Approach to Forecasting Wholesale Energy Market Prices,"  Electricity market price predictions enable energy market participants to
+shape their consumption or supply while meeting their economic and
+environmental objectives. By utilizing the basic properties of the
+supply-demand matching process performed by grid operators, we develop a method
+to recover energy market's structure and predict the resulting nodal prices as
+a function of generation mix and system load on the grid. Our methodology uses
+the latest advancements in compressed sensing and statistics to cope with the
+high-dimensional and sparse power grid topologies, underlying physical laws, as
+well as scarce, public market data. Rigorous validations using Southwest Power
+Pool (SPP) market data demonstrate significantly higher accuracy of the
+proposed approach when compared to the state-of-the-art industry benchmark.
+",1,0,0,1,0,0
+10126,Matched bipartite block model with covariates,"  Community detection or clustering is a fundamental task in the analysis of
+network data. Many real networks have a bipartite structure which makes
+community detection challenging. In this paper, we consider a model which
+allows for matched communities in the bipartite setting, in addition to node
+covariates with information about the matching. We derive a simple fast
+algorithm for fitting the model based on variational inference ideas and show
+its effectiveness on both simulated and real data. A variation of the model to
+allow for degree-correction is also considered, in addition to a novel approach
+to fitting such degree-corrected models.
+",1,0,0,1,0,0
+10127,Topological Interference Management with Decoded Message Passing,"  The topological interference management (TIM) problem studies
+partially-connected interference networks with no channel state information
+except for the network topology (i.e., connectivity graph) at the transmitters.
+In this paper, we consider a similar problem in the uplink cellular networks,
+while message passing is enabled at the receivers (e.g., base stations), so
+that the decoded messages can be routed to other receivers via backhaul links
+to help further improve network performance. For this TIM problem with decoded
+message passing (TIM-MP), we model the interference pattern by conflict
+digraphs, connect orthogonal access to the acyclic set coloring on conflict
+digraphs, and show that one-to-one interference alignment boils down to
+orthogonal access because of message passing. With the aid of polyhedral
+combinatorics, we identify the structural properties of certain classes of
+network topologies where orthogonal access achieves the optimal
+degrees-of-freedom (DoF) region in the information-theoretic sense. The
+relation to the conventional index coding with simultaneous decoding is also
+investigated by formulating a generalized index coding problem with successive
+decoding as a result of decoded message passing. The properties of reducibility
+and criticality are also studied, by which we are able to prove the linear
+optimality of orthogonal access in terms of symmetric DoF for the networks up
+to four users with all possible network topologies (218 instances). Practical
+issues of the tradeoff between the overhead of message passing and the
+achievable symmetric DoF are also discussed, in the hope of facilitating
+efficient backhaul utilization.
+",1,0,0,0,0,0
+10128,Robust Regression via Mutivariate Regression Depth,"  This paper studies robust regression in the settings of Huber's
+$\epsilon$-contamination models. We consider estimators that are maximizers of
+multivariate regression depth functions. These estimators are shown to achieve
+minimax rates in the settings of $\epsilon$-contamination models for various
+regression problems including nonparametric regression, sparse linear
+regression, reduced rank regression, etc. We also discuss a general notion of
+depth function for linear operators that has potential applications in robust
+functional linear regression.
+",0,0,1,1,0,0
+10129,Quotients of Buildings as $W$-Groupoids,"  We introduce structures which model the quotients of buildings by
+type-preserving group actions. These structures, which we call W-groupoids,
+generalize Bruhat decompositions, chambers systems of type M, and Tits
+amalgams. We define the fundamental group of a W-groupoid, and characterize
+buildings as connected simply connected W-groupoids. We give a brief outline of
+the covering theory of W-groupoids, which produces buildings as the universal
+covers of W-groupoids. The local-to-global theorem of Tits concerning spherical
+3-resides allows for the construction of W-groupoids by gluing together
+quotients of generalized polygons. In this way, W-groupoids can be used to
+construct exotic, hyperbolic, and wild buildings.
+",0,0,1,0,0,0
+10130,Birth of the GUP and its effect on the entropy of the Universe in Lie-$N$-algebra,"  In this paper, the origin of the generalized uncertainty principle (GUP) in
+an $M$-dimensional theory with Lie-$N$-algebra is considered. This theory which
+we name GLNA(Generalized Lie-$N$-Algebra)-theory can be reduced to $M$-theory
+with $M=11$ and $N=3$. In this theory, at the beginning, two energies with
+positive and negative signs are created from nothing and produce two types of
+branes with opposite quantum numbers and different numbers of timing
+dimensions. Coincidence with the birth of these branes, various derivatives of
+bosonic fields emerge in the action of the system which produce the $r$ GUP for
+bosons. These branes interact with each other, compact and various derivatives
+of spinor fields appear in the action of the system which leads to the creation
+of the GUP for fermions. The previous predicted entropy of branes in the GUP is
+corrected as due to the emergence of higher orders of derivatives and different
+number of timing dimensions.
+",0,1,0,0,0,0
+10131,Data-adaptive smoothing for optimal-rate estimation of possibly non-regular parameters,"  We consider nonparametric inference of finite dimensional, potentially
+non-pathwise differentiable target parameters. In a nonparametric model, some
+examples of such parameters that are always non pathwise differentiable target
+parameters include probability density functions at a point, or regression
+functions at a point. In causal inference, under appropriate causal
+assumptions, mean counterfactual outcomes can be pathwise differentiable or
+not, depending on the degree at which the positivity assumption holds.
+In this paper, given a potentially non-pathwise differentiable target
+parameter, we introduce a family of approximating parameters, that are pathwise
+differentiable. This family is indexed by a scalar. In kernel regression or
+density estimation for instance, a natural choice for such a family is obtained
+by kernel smoothing and is indexed by the smoothing level. For the
+counterfactual mean outcome, a possible approximating family is obtained
+through truncation of the propensity score, and the truncation level then plays
+the role of the index.
+We propose a method to data-adaptively select the index in the family, so as
+to optimize mean squared error. We prove an asymptotic normality result, which
+allows us to derive confidence intervals. Under some conditions, our estimator
+achieves an optimal mean squared error convergence rate. Confidence intervals
+are data-adaptive and have almost optimal width.
+A simulation study demonstrates the practical performance of our estimators
+for the inference of a causal dose-response curve at a given treatment dose.
+",0,0,1,1,0,0
+10132,Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach,"  Knowledge base completion (KBC) aims to predict missing information in a
+knowledge base.In this paper, we address the out-of-knowledge-base (OOKB)
+entity problem in KBC:how to answer queries concerning test entities not
+observed at training time. Existing embedding-based KBC models assume that all
+test entities are available at training time, making it unclear how to obtain
+embeddings for new entities without costly retraining. To solve the OOKB entity
+problem without retraining, we use graph neural networks (Graph-NNs) to compute
+the embeddings of OOKB entities, exploiting the limited auxiliary knowledge
+provided at test time.The experimental results show the effectiveness of our
+proposed model in the OOKB setting.Additionally, in the standard KBC setting in
+which OOKB entities are not involved, our model achieves state-of-the-art
+performance on the WordNet dataset. The code and dataset are available at
+this https URL
+",1,0,0,0,0,0
+10133,Feature Engineering for Predictive Modeling using Reinforcement Learning,"  Feature engineering is a crucial step in the process of predictive modeling.
+It involves the transformation of given feature space, typically using
+mathematical functions, with the objective of reducing the modeling error for a
+given target. However, there is no well-defined basis for performing effective
+feature engineering. It involves domain knowledge, intuition, and most of all,
+a lengthy process of trial and error. The human attention involved in
+overseeing this process significantly influences the cost of model generation.
+We present a new framework to automate feature engineering. It is based on
+performance driven exploration of a transformation graph, which systematically
+and compactly enumerates the space of given options. A highly efficient
+exploration strategy is derived through reinforcement learning on past
+examples.
+",1,0,0,1,0,0
+10134,Cellular automata connections,"  It is shown that any two cellular automata (CA) in rule space can be
+connected by a continuous path parameterized by a real number $\kappa \in (0,
+\infty)$, each point in the path corresponding to a coupled map lattice (CML).
+In the limits $\kappa \to 0$ and $\kappa \to \infty$ the CML becomes each of
+the two CA entering in the connection. A mean-field, reduced model is obtained
+from the connection and allows to gain insight in those parameter regimes at
+intermediate $\kappa$ where the dynamics is approximately homogeneous within
+each neighborhood.
+",0,1,1,0,0,0
+10135,Hypothesis Testing via Euclidean Separation,"  We discuss an ""operational"" approach to testing convex composite hypotheses
+when the underlying distributions are heavy-tailed. It relies upon Euclidean
+separation of convex sets and can be seen as an extension of the approach to
+testing by convex optimization developed in [8, 12]. In particular, we show how
+one can construct quasi-optimal testing procedures for families of
+distributions which are majorated, in a certain precise sense, by a
+sub-spherical symmetric one and study the relationship between tests based on
+Euclidean separation and ""potential-based tests."" We apply the promoted
+methodology in the problem of sequential detection and illustrate its practical
+implementation in an application to sequential detection of changes in the
+input of a dynamic system.
+[8] Goldenshluger, Alexander and Juditsky, Anatoli and Nemirovski, Arkadi,
+Hypothesis testing by convex optimization, Electronic Journal of Statistics,9
+(2):1645-1712, 2015. [12] Juditsky, Anatoli and Nemirovski, Arkadi, Hypothesis
+testing via affine detectors, Electronic Journal of Statistics, 10:2204--2242,
+2016.
+",0,0,1,1,0,0
+10136,Detecting in-plane tension induced crystal plasticity transition with nanoindentation,"  We present experimental data and simulations on the effects of in-plane
+tension on nanoindentation hardness and pop-in noise. Nanoindentation
+experiments using a Berkovich tip are performed on bulk polycrystaline Al
+samples, under tension in a custom 4pt-bending fixture. The hardness displays a
+transition, for indentation depths smaller than 10nm, as function of the
+in-plane stress at a value consistent with the bulk tensile yield stress.
+Displacement bursts appear insensitive to in-plane tension and this transition
+disappears for larger indentation depths. Two dimensional discrete dislocation
+dynamics simulations confirm that a regime exists where hardness is sensitive
+to tension-induced pre-existing dislocations.
+",0,1,0,0,0,0
+10137,A novel approach for fast mining frequent itemsets use N-list structure based on MapReduce,"  Frequent Pattern Mining is a one field of the most significant topics in data
+mining. In recent years, many algorithms have been proposed for mining frequent
+itemsets. A new algorithm has been presented for mining frequent itemsets based
+on N-list data structure called Prepost algorithm. The Prepost algorithm is
+enhanced by implementing compact PPC-tree with the general tree. Prepost
+algorithm can only find a frequent itemsets with required (pre-order and
+post-order) for each node. In this chapter, we improved prepost algorithm based
+on Hadoop platform (HPrepost), proposed using the Mapreduce programming model.
+The main goals of proposed method are efficient mining frequent itemsets
+requiring less running time and memory usage. We have conduct experiments for
+the proposed scheme to compare with another algorithms. With dense datasets,
+which have a large average length of transactions, HPrepost is more effective
+than frequent itemsets algorithms in terms of execution time and memory usage
+for all min-sup. Generally, our algorithm outperforms algorithms in terms of
+runtime and memory usage with small thresholds and large datasets.
+",1,0,0,0,0,0
+10138,Resonant thermalization of periodically driven strongly correlated electrons,"  We study the dynamics of the Fermi-Hubbard model driven by a time-periodic
+modulation of the interaction within nonequilibrium Dynamical Mean-Field
+Theory. For moderate interaction, we find clear evidence of thermalization to a
+genuine infinite-temperature state with no residual oscillations. Quite
+differently, in the strongly correlated regime, we find a quasi-stationary
+extremely long-lived state with oscillations synchronized with the drive
+(Floquet prethermalization). Remarkably, the nature of this state dramatically
+changes upon tuning the drive frequency. In particular, we show the existence
+of a critical frequency at which the system rapidly thermalizes despite the
+large interaction. We characterize this resonant thermalization and provide an
+analytical understanding in terms of a break down of the periodic
+Schrieffer-Wolff transformation.
+",0,1,0,0,0,0
+10139,Fusible HSTs and the randomized k-server conjecture,"  We exhibit an $O((\log k)^6)$-competitive randomized algorithm for the
+$k$-server problem on any metric space. It is shown that a potential-based
+algorithm for the fractional $k$-server problem on hierarchically separated
+trees (HSTs) with competitive ratio $f(k)$ can be used to obtain a randomized
+algorithm for any metric space with competitive ratio $f(k)^2 O((\log k)^2)$.
+Employing the $O((\log k)^2)$-competitive algorithm for HSTs from our joint
+work with Bubeck, Cohen, Lee, and Mądry (2017) yields the claimed bound.
+The best previous result independent of the geometry of the underlying metric
+space is the $2k-1$ competitive ratio established for the deterministic work
+function algorithm by Koutsoupias and Papadimitriou (1995). Even for the
+special case when the underlying metric space is the real line, the best known
+competitive ratio was $k$. Since deterministic algorithms can do no better than
+$k$ on any metric space with at least $k+1$ points, this establishes that for
+every metric space on which the problem is non-trivial, randomized algorithms
+give an exponential improvement over deterministic algorithms.
+",1,0,1,0,0,0
+10140,BPS spectra and 3-manifold invariants,"  We provide a physical definition of new homological invariants $\mathcal{H}_a
+(M_3)$ of 3-manifolds (possibly, with knots) labeled by abelian flat
+connections. The physical system in question involves a 6d fivebrane theory on
+$M_3$ times a 2-disk, $D^2$, whose Hilbert space of BPS states plays the role
+of a basic building block in categorification of various partition functions of
+3d $\mathcal{N}=2$ theory $T[M_3]$: $D^2\times S^1$ half-index, $S^2\times S^1$
+superconformal index, and $S^2\times S^1$ topologically twisted index. The
+first partition function is labeled by a choice of boundary condition and
+provides a refinement of Chern-Simons (WRT) invariant. A linear combination of
+them in the unrefined limit gives the analytically continued WRT invariant of
+$M_3$. The last two can be factorized into the product of half-indices. We show
+how this works explicitly for many examples, including Lens spaces, circle
+fibrations over Riemann surfaces, and plumbed 3-manifolds.
+",0,0,1,0,0,0
+10141,Nonzero positive solutions of a multi-parameter elliptic system with functional BCs,"  We prove, by topological methods, new results on the existence of nonzero
+positive weak solutions for a class of multi-parameter second order elliptic
+systems subject to functional boundary conditions. The setting is fairly
+general and covers the case of multi-point, integral and nonlinear boundary
+conditions. We also present a non-existence result. We provide some examples to
+illustrate the applicability our theoretical results.
+",0,0,1,0,0,0
+10142,Emotion Recognition from Speech based on Relevant Feature and Majority Voting,"  This paper proposes an approach to detect emotion from human speech employing
+majority voting technique over several machine learning techniques. The
+contribution of this work is in two folds: firstly it selects those features of
+speech which is most promising for classification and secondly it uses the
+majority voting technique that selects the exact class of emotion. Here,
+majority voting technique has been applied over Neural Network (NN), Decision
+Tree (DT), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Input
+vector of NN, DT, SVM and KNN consists of various acoustic and prosodic
+features like Pitch, Mel-Frequency Cepstral coefficients etc. From speech
+signal many feature have been extracted and only promising features have been
+selected. To consider a feature as promising, Fast Correlation based feature
+selection (FCBF) and Fisher score algorithms have been used and only those
+features are selected which are highly ranked by both of them. The proposed
+approach has been tested on Berlin dataset of emotional speech [3] and
+Electromagnetic Articulography (EMA) dataset [4]. The experimental result shows
+that majority voting technique attains better accuracy over individual machine
+learning techniques. The employment of the proposed approach can effectively
+recognize the emotion of human beings in case of social robot, intelligent chat
+client, call-center of a company etc.
+",1,0,0,1,0,0
+10143,The basic principles and the structure and algorithmically software of computing by hypercomplex number,"  In article the basic principles put in a basis of algorithmicallysoftware of
+hypercomplex number calculations, structure of a software, structure of
+functional subsystems are considered. The most important procedures included in
+subsystems are considered, program listings and examples of their application
+are given.
+",1,0,0,0,0,0
+10144,Drawing cone spherical metrics via Strebel differentials,"  Cone spherical metrics are conformal metrics with constant curvature one and
+finitely many conical singularities on compact Riemann surfaces. By using
+Strebel differentials as a bridge, we construct a new class of cone spherical
+metrics on compact Riemann surfaces by drawing on the surfaces some class of
+connected metric ribbon graphs.
+",0,0,1,0,0,0
+10145,Thermoelectric radiation detector based on superconductor/ferromagnet systems,"  We suggest a new type of an ultrasensitive detector of electromagnetic fields
+exploiting the giant thermoelectric effect recently found in
+superconductor/ferromagnet hybrid structures. Compared to other types of
+superconducting detectors where the detected signal is based on variations of
+the detector impedance, the thermoelectric detector has the advantage of
+requiring no external driving fields. This becomes especially relevant in
+multi-pixel detectors where the number of bias lines and the heating induced by
+them becomes an issue. We propose different material combinations to implement
+the detector and provide a detailed analysis of its sensitivity and speed. In
+particular, we perform to our knowledge the first proper noise analysis that
+includes the cross correlation between heat and charge current noise and
+thereby describes also thermoelectric detectors with a large thermoelectric
+figure of merit.
+",0,1,0,0,0,0
+10146,Upper estimates of Christoffel function on convex domains,"  New upper bounds on the pointwise behaviour of Christoffel function on convex
+domains in ${\mathbb{R}}^d$ are obtained. These estimates are established by
+explicitly constructing the corresponding ""needle""-like algebraic polynomials
+having small integral norm on the domain, and are stated in terms of few
+easy-to-measure geometric characteristics of the location of the point of
+interest in the domain. Sharpness of the results is shown and examples of
+applications are given.
+",0,0,1,0,0,0
+10147,Special Lagrangian and deformed Hermitian Yang-Mills on tropical manifold,"  From string theory, the notion of deformed Hermitian Yang-Mills connections
+has been introduced by Mariño, Minasian, Moore and Strominger. After that,
+Leung, Yau and Zaslow proved that it naturally appears as mirror objects of
+special Lagrangian submanifolds via Fourier-Mukai transform between dual torus
+fibrations. In their paper, some conditions are imposed for simplicity. In this
+paper, data to glue their construction on tropical manifolds are proposed and a
+generalization of the correspondence is proved without the assumption that the
+Lagrangian submanifold is a section of the torus fibration.
+",0,0,1,0,0,0
+10148,An introduction to the qualitative and quantitative theory of homogenization,"  We present an introduction to periodic and stochastic homogenization of
+ellip- tic partial differential equations. The first part is concerned with the
+qualitative theory, which we present for equations with periodic and random
+coefficients in a unified approach based on Tartar's method of oscillating test
+functions. In partic- ular, we present a self-contained and elementary argument
+for the construction of the sublinear corrector of stochastic homogenization.
+(The argument also applies to elliptic systems and in particular to linear
+elasticity). In the second part we briefly discuss the representation of the
+homogenization error by means of a two- scale expansion. In the last part we
+discuss some results of quantitative stochastic homogenization in a discrete
+setting. In particular, we discuss the quantification of ergodicity via
+concentration inequalities, and we illustrate that the latter in combi- nation
+with elliptic regularity theory leads to a quantification of the growth of the
+sublinear corrector and the homogenization error.
+",0,0,1,0,0,0
+10149,A Computational Study of Yttria-Stabilized Zirconia: I. Using Crystal Chemistry to Search for the Ground State on a Glassy Energy Landscape,"  Yttria-stabilized zirconia (YSZ), a ZrO2-Y2O3 solid solution that contains a
+large population of oxygen vacancies, is widely used in energy and industrial
+applications. Past computational studies correctly predicted the anion
+diffusivity but not the cation diffusivity, which is important for material
+processing and stability. One of the challenges lies in identifying a plausible
+configuration akin to the ground state in a glassy landscape. This is unlikely
+to come from random sampling of even a very large sample space, but the odds
+are much improved by incorporating packing preferences revealed by a modest
+sized configurational library established from empirical potential
+calculations. Ab initio calculations corroborated these preferences, which
+prove remarkably robust extending to the fifth cation-oxygen shell about 8
+{\AA} away. Yet because of frustration there are still rampant violations of
+packing preferences and charge neutrality in the ground state, and the approach
+toward it bears a close analogy to glass relaxations. Fast relaxations proceed
+by fast oxygen movement around cations, while slow relaxations require slow
+cation diffusion. The latter is necessarily cooperative because of strong
+coupling imposed by the long-range packing preferences.
+",0,1,0,0,0,0
+10150,Reactive Power Compensation Game under Prospect-Theoretic Framing Effects,"  Reactive power compensation is an important challenge in current and future
+smart power systems. However, in the context of reactive power compensation,
+most existing studies assume that customers can assess their compensation
+value, i.e., Var unit, objectively. In this paper, customers are assumed to
+make decisions that pertain to reactive power coordination. In consequence, the
+way in which those customers evaluate the compensation value resulting from
+their individual decisions will impact the overall grid performance. In
+particular, a behavioral framework, based on the framing effect of prospect
+theory (PT), is developed to study the effect of both objective value and
+subjective evaluation in a reactive power compensation game. For example, such
+effect allows customers to optimize a subjective value of their utility which
+essentially frames the objective utility with respect to a reference point.
+This game enables customers to coordinate the use of their electrical devices
+to compensate reactive power. For the proposed game, both the objective case
+using expected utility theory (EUT) and the PT consideration are solved via a
+learning algorithm that converges to a mixed-strategy Nash equilibrium. In
+addition, several key properties of this game are derived analytically.
+Simulation results show that, under PT, customers are likely to make decisions
+that differ from those predicted by classical models. For instance, using an
+illustrative two-customer case, we show that a PT customer will increase the
+conservative strategy (achieving a high power factor) by 29% compared to a
+conventional customer. Similar insights are also observed for a case with three
+customers.
+",1,0,0,0,0,0
+10151,Some remarks on protolocalizations and protoadditive reflections,"  We investigate additional properties of protolocalizations, introduced and
+studied by F. Borceux, M. M. Clementino, M. Gran, and L. Sousa, and of
+protoadditive reflections, introduced and studied by T. Everaert and M. Gran.
+Among other things we show that there are no non-trivial (protolocalizations
+and) protoadditive reflections of the category of groups, and establish a
+connection between protolocalizations and Kurosh--Amitsur radicals of groups
+with multiple operators whose semisimple classes form subvarieties.
+",0,0,1,0,0,0
+10152,Resonances near Thresholds in slightly Twisted Waveguides,"  We consider the Dirichlet Laplacian in a straight three dimensional waveguide
+with non-rotationally invariant cross section, perturbed by a twisting of small
+amplitude. It is well known that such a perturbation does not create
+eigenvalues below the essential spectrum. However, around the bottom of the
+spectrum, we provide a meromorphic extension of the weighted resolvent of the
+perturbed operator, and show the existence of exactly one resonance near this
+point. Moreover, we obtain the asymptotic behavior of this resonance as the
+size of the twisting goes to 0. We also extend the analysis to the upper
+eigenvalues of the transversal problem, showing that the number of resonances
+is bounded by the multiplicity of the eigenvalue and obtaining the
+corresponding asymptotic behavior
+",0,0,1,0,0,0
+10153,Trapping and displacement of liquid collars and plugs in rough-walled tubes,"  A liquid film wetting the interior of a long circular cylinder redistributes
+under the action of surface tension to form annular collars or occlusive plugs.
+These equilibrium structures are invariant under axial translation within a
+perfectly smooth uniform tube and therefore can be displaced axially by very
+weak external forcing. We consider how this degeneracy is disrupted when the
+tube wall is rough, and determine threshold conditions under which collars or
+plugs resist displacement under forcing. Wall roughness is modelled as a
+non-axisymmetric Gaussian random field of prescribed correlation length and
+small variance, mimicking some of the geometric irregularities inherent in
+applications such as lung airways. The thin film coating this surface is
+modelled using lubrication theory. When the roughness is weak, we show how the
+locations of equilibrium collars and plugs can be identified in terms of the
+azimuthally averaged tube radius; we derive conditions specifying equilibrium
+collar locations under an externally imposed shear flow, and plug locations
+under an imposed pressure gradient. We use these results to determine the
+probability of external forcing being sufficient to displace a collar or plug
+from a rough-walled tube, when the tube roughness is defined only in
+statistical terms.
+",0,1,0,0,0,0
+10154,Topological Brain Network Distances,"  Existing brain network distances are often based on matrix norms. The
+element-wise differences in the existing matrix norms may fail to capture
+underlying topological differences. Further, matrix norms are sensitive to
+outliers. A major disadvantage to element-wise distance calculations is that it
+could be severely affected even by a small number of extreme edge weights. Thus
+it is necessary to develop network distances that recognize topology. In this
+paper, we provide a survey of bottleneck, Gromov-Hausdorff (GH) and
+Kolmogorov-Smirnov (KS) distances that are adapted for brain networks, and
+compare them against matrix-norm based network distances. Bottleneck and
+GH-distances are often used in persistent homology. However, they were rarely
+utilized to measure similarity between brain networks. KS-distance is recently
+introduced to measure the similarity between networks across different
+filtration values. The performance analysis was conducted using the random
+network simulations with the ground truths. Using a twin imaging study, which
+provides biological ground truth, we demonstrate that the KS distance has the
+ability to determine heritability.
+",0,0,0,0,1,0
+10155,The challenge of decentralized marketplaces,"  Online trust systems are playing an important role in to-days world and face
+various challenges in building them. Billions of dollars of products and
+services are traded through electronic commerce, files are shared among large
+peer-to-peer networks and smart contracts can potentially replace paper
+contracts with digital contracts. These systems rely on trust mechanisms in
+peer-to-peer networks like reputation systems or a trustless public ledger. In
+most cases, reputation systems are build to determine the trustworthiness of
+users and to provide incentives for users to make a fair contribution to the
+peer-to-peer network. The main challenges are how to set up a good trust
+system, how to deal with security issues and how to deal with strategic users
+trying to cheat on the system. The Sybil attack, the most important attack on
+reputation systems is discussed. At last match making in two sided markets and
+the strategy proofness of these markets are discussed.
+",1,0,0,0,0,0
+10156,Radially resolved simulations of collapsing pebble clouds in protoplanetary discs,"  We study the collapse of pebble clouds with a statistical model to find the
+internal structure of comet-sized planetesimals. Pebble-pebble collisions occur
+during the collapse and the outcome of these collisions affect the resulting
+structure of the planetesimal. We expand our previous models by allowing the
+individual pebble sub-clouds to contract at different rates and by including
+the effect of gas drag on the contraction speed and in energy dissipation. Our
+results yield comets that are porous pebble-piles with particle sizes varying
+with depth. In the surface layers there is a mixture of primordial pebbles and
+pebble fragments. The interior, on the other hand, consists only of primordial
+pebbles with a narrower size distribution, yielding higher porosity there. Our
+results imply that the gas in the protoplanetary disc plays an important role
+in determining the radial distribution of pebble sizes and porosity inside
+planetesimals.
+",0,1,0,0,0,0
+10157,Accelerating equilibrium isotope effect calculations: I. Stochastic thermodynamic integration with respect to mass,"  Accurate path integral Monte Carlo or molecular dynamics calculations of
+isotope effects have until recently been expensive because of the necessity to
+reduce three types of errors present in such calculations: statistical errors
+due to sampling, path integral discretization errors, and thermodynamic
+integration errors. While the statistical errors can be reduced with virial
+estimators and path integral discretization errors with high-order
+factorization of the Boltzmann operator, here we propose a method for
+accelerating isotope effect calculations by eliminating the integration error.
+We show that the integration error can be removed entirely by changing particle
+masses stochastically during the calculation and by using a piecewise linear
+umbrella biasing potential. Moreover, we demonstrate numerically that this
+approach does not increase the statistical error. The resulting acceleration of
+isotope effect calculations is demonstrated on a model harmonic system and on
+deuterated species of methane.
+",0,1,0,0,0,0
+10158,CoAP over ICN,"  The Constrained Application Protocol (CoAP) is a specialized Web transfer
+protocol for resource-oriented applications intended to run on constrained
+devices, typically part of the Internet of Things. In this paper we leverage
+Information-Centric Networking (ICN), deployed within the domain of a network
+provider that interconnects, in addition to other terminals, CoAP endpoints in
+order to provide enhanced CoAP services. We present various CoAP-specific
+communication scenarios and discuss how ICN can provide benefits to both
+network providers and CoAP applications, even though the latter are not aware
+of the existence of ICN. In particular, the use of ICN results in smaller state
+management complexity at CoAP endpoints, simpler implementation at CoAP
+endpoints, and less communication overhead in the network.
+",1,0,0,0,0,0
+10159,Nonequilibrium mode-coupling theory for dense active systems of self-propelled particles,"  The physics of active systems of self-propelled particles, in the regime of a
+dense liquid state, is an open puzzle of great current interest, both for
+statistical physics and because such systems appear in many biological
+contexts. We develop a nonequilibrium mode-coupling theory (MCT) for such
+systems, where activity is included as a colored noise with the particles
+having a self-propulsion foce $f_0$ and persistence time $\tau_p$. Using the
+extended MCT and a generalized fluctuation-dissipation theorem, we calculate
+the effective temperature $T_{eff}$ of the active fluid. The nonequilibrium
+nature of the systems is manifested through a time-dependent $T_{eff}$ that
+approaches a constant in the long-time limit, which depends on the activity
+parameters $f_0$ and $\tau_p$. We find, phenomenologically, that this long-time
+limit is captured by the potential energy of a single, trapped active particle
+(STAP). Through a scaling analysis close to the MCT glass transition point, we
+show that $\tau_\alpha$, the $\alpha$-relaxation time, behaves as
+$\tau_\alpha\sim f_0^{-2\gamma}$, where $\gamma=1.74$ is the MCT exponent for
+the passive system. $\tau_\alpha$ may increase or decrease as a function of
+$\tau_p$ depending on the type of active force correlations, but the behavior
+is always governed by the same value of the exponent $\gamma$. Comparison with
+numerical solution of the nonequilibrium MCT as well as simulation results give
+excellent agreement with the scaling analysis.
+",0,1,0,0,0,0
+10160,Symmetry Protected Dynamical Symmetry in the Generalized Hubbard Models,"  In this letter we present a theorem on the dynamics of the generalized
+Hubbard models. This theorem shows that the symmetry of the single particle
+Hamiltonian can protect a kind of dynamical symmetry driven by the
+interactions. Here the dynamical symmetry refers to that the time evolution of
+certain observables are symmetric between the repulsive and attractive Hubbard
+models. We demonstrate our theorem with three different examples in which the
+symmetry involves bipartite lattice symmetry, reflection symmetry and
+translation symmetry, respectively. Each of these examples relates to one
+recent cold atom experiment on the dynamics in the optical lattices where such
+a dynamical symmetry is manifested. These experiments include expansion
+dynamics of cold atoms, chirality of atomic motion within a synthetic magnetic
+field and melting of charge-density-wave order. Therefore, our theorem provides
+a unified view of these seemingly disparate phenomena.
+",0,1,0,0,0,0
+10161,Machine-learning a virus assembly fitness landscape,"  Realistic evolutionary fitness landscapes are notoriously difficult to
+construct. A recent cutting-edge model of virus assembly consists of a
+dodecahedral capsid with $12$ corresponding packaging signals in three affinity
+bands. This whole genome/phenotype space consisting of $3^{12}$ genomes has
+been explored via computationally expensive stochastic assembly models, giving
+a fitness landscape in terms of the assembly efficiency. Using latest
+machine-learning techniques by establishing a neural network, we show that the
+intensive computation can be short-circuited in a matter of minutes to
+astounding accuracy.
+",0,0,0,0,1,0
+10162,A multilevel block building algorithm for fast modeling generalized separable systems,"  Data-driven modeling plays an increasingly important role in different areas
+of engineering. For most of existing methods, such as genetic programming (GP),
+the convergence speed might be too slow for large scale problems with a large
+number of variables. It has become the bottleneck of GP for practical
+applications. Fortunately, in many applications, the target models are
+separable in some sense. In this paper, we analyze different types of
+separability of some real-world engineering equations and establish a
+mathematical model of generalized separable system (GS system). In order to get
+the structure of the GS system, a multilevel block building (MBB) algorithm is
+proposed, in which the target model is decomposed into a number of blocks,
+further into minimal blocks and factors. Compare to the conventional GP, MBB
+can make large reductions to the search space. This makes MBB capable of
+modeling a complex system. The minimal blocks and factors are optimized and
+assembled with a global optimization search engine, low dimensional simplex
+evolution (LDSE). An extensive study between the proposed MBB and a
+state-of-the-art data-driven fitting tool, Eureqa, has been presented with
+several man-made problems, as well as some real-world problems. Test results
+indicate that the proposed method is more effective and efficient under all the
+investigated cases.
+",0,0,1,0,0,0
+10163,Competition and Selection Among Conventions,"  In many domains, a latent competition among different conventions determines
+which one will come to dominate. One sees such effects in the success of
+community jargon, of competing frames in political rhetoric, or of terminology
+in technical contexts. These effects have become widespread in the online
+domain, where the data offers the potential to study competition among
+conventions at a fine-grained level.
+In analyzing the dynamics of conventions over time, however, even with
+detailed on-line data, one encounters two significant challenges. First, as
+conventions evolve, the underlying substance of their meaning tends to change
+as well; and such substantive changes confound investigations of social
+effects. Second, the selection of a convention takes place through the complex
+interactions of individuals within a community, and contention between the
+users of competing conventions plays a key role in the convention's evolution.
+Any analysis must take place in the presence of these two issues.
+In this work we study a setting in which we can cleanly track the competition
+among conventions. Our analysis is based on the spread of low-level authoring
+conventions in the eprint arXiv over 24 years: by tracking the spread of macros
+and other author-defined conventions, we are able to study conventions that
+vary even as the underlying meaning remains constant. We find that the
+interaction among co-authors over time plays a crucial role in the selection of
+them; the distinction between more and less experienced members of the
+community, and the distinction between conventions with visible versus
+invisible effects, are both central to the underlying processes. Through our
+analysis we make predictions at the population level about the ultimate success
+of different synonymous conventions over time--and at the individual level
+about the outcome of ""fights"" between people over convention choices.
+",1,1,0,0,0,0
+10164,Tradeoff Between Delay and High SNR Capacity in Quantized MIMO Systems,"  Analog-to-digital converters (ADCs) are a major contributor to the power
+consumption of multiple-input multiple-output (MIMO) communication systems with
+large number of antennas. Use of low resolution ADCs has been proposed as a
+means to decrease power consumption in MIMO receivers. However, reducing the
+ADC resolution leads to performance loss in terms of achievable transmission
+rates. In order to mitigate the rate-loss, the receiver can perform analog
+processing of the received signals before quantization. Prior works consider
+one-shot analog processing where at each channel-use, analog linear
+combinations of the received signals are fed to a set of one-bit threshold
+ADCs. In this paper, a receiver architecture is proposed which uses a sequence
+of delay elements to allow for blockwise linear combining of the received
+analog signals. In the high signal to noise ratio regime, it is shown that the
+proposed architecture achieves the maximum achievable transmission rate given a
+fixed number of one-bit ADCs. Furthermore, a tradeoff between transmission rate
+and the number of delay elements is identified which quantifies the increase in
+maximum achievable rate as the number of delay elements is increased.
+",1,0,0,0,0,0
+10165,Formal Synthesis of Control Strategies for Positive Monotone Systems,"  We design controllers from formal specifications for positive discrete-time
+monotone systems that are subject to bounded disturbances. Such systems are
+widely used to model the dynamics of transportation and biological networks.
+The specifications are described using signal temporal logic (STL), which can
+express a broad range of temporal properties. We formulate the problem as a
+mixed-integer linear program (MILP) and show that under the assumptions made in
+this paper, which are not restrictive for traffic applications, the existence
+of open-loop control policies is sufficient and almost necessary to ensure the
+satisfaction of STL formulas. We establish a relation between satisfaction of
+STL formulas in infinite time and set-invariance theories and provide an
+efficient method to compute robust control invariant sets in high dimensions.
+We also develop a robust model predictive framework to plan controls optimally
+while ensuring the satisfaction of the specification. Illustrative examples and
+a traffic management case study are included.
+",1,0,1,0,0,0
+10166,Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks,"  Progress in deep learning is slowed by the days or weeks it takes to train
+large models. The natural solution of using more hardware is limited by
+diminishing returns, and leads to inefficient use of additional resources. In
+this paper, we present a large batch, stochastic optimization algorithm that is
+both faster than widely used algorithms for fixed amounts of computation, and
+also scales up substantially better as more computational resources become
+available. Our algorithm implicitly computes the inverse Hessian of each
+mini-batch to produce descent directions; we do so without either an explicit
+approximation to the Hessian or Hessian-vector products. We demonstrate the
+effectiveness of our algorithm by successfully training large ImageNet models
+(Inception-V3, Resnet-50, Resnet-101 and Inception-Resnet-V2) with mini-batch
+sizes of up to 32000 with no loss in validation error relative to current
+baselines, and no increase in the total number of steps. At smaller mini-batch
+sizes, our optimizer improves the validation error in these models by 0.8-0.9%.
+Alternatively, we can trade off this accuracy to reduce the number of training
+steps needed by roughly 10-30%. Our work is practical and easily usable by
+others -- only one hyperparameter (learning rate) needs tuning, and
+furthermore, the algorithm is as computationally cheap as the commonly used
+Adam optimizer.
+",1,0,0,1,0,0
+10167,Signal propagation in sensing and reciprocating cellular systems with spatial and structural heterogeneity,"  Sensing and reciprocating cellular systems (SARs) are important for the
+operation of many biological systems. Production in interferon (IFN) SARs is
+achieved through activation of the Jak-Stat pathway, and downstream
+upregulation of IFN regulatory factor (IRF)-3 and IFN transcription, but the
+role that high and low affinity IFNs play in this process remains unclear. We
+present a comparative between a minimal spatio-temporal partial differential
+equation (PDE) model and a novel spatio-structural-temporal (SST) model for the
+consideration of receptor, binding, and metabolic aspects of SAR behaviour.
+Using the SST framework, we simulate single- and multi-cluster paradigms of IFN
+communication. Simulations reveal a cyclic process between the binding of IFN
+to the receptor, and the consequent increase in metabolism, decreasing the
+propensity for binding due to the internal feed-back mechanism. One observes
+the effect of heterogeneity between cellular clusters, allowing them to
+individualise and increase local production, and within clusters, where we
+observe `sub popular quiescence'; a process whereby intra-cluster
+subpopulations reduce their binding and metabolism such that other such
+subpopulations may augment their production. Finally, we observe the ability
+for low affinity IFN to communicate a long range signal, where high affinity
+cannot, and the breakdown of this relationship through the introduction of cell
+motility. Biological systems may utilise cell motility where environments are
+unrestrictive and may use fixed system, with low affinity communication, where
+a localised response is desirable.
+",0,0,0,0,1,0
+10168,A New Family of Asymmetric Distributions for Modeling Light-Tailed and Right-Skewed Data,"  A new three-parameter cumulative distribution function defined on
+$(\alpha,\infty)$, for some $\alpha\geq0$, with asymmetric probability density
+function and showing exponential decays at its both tails, is introduced. The
+new distribution is near to familiar distributions like the gamma and
+log-normal distributions, but this new one shows own elements and thus does not
+generalize neither of these distributions. Hence, the new distribution
+constitutes a new alternative to fit values showing light-tailed behaviors.
+Further, this new distribution shows great flexibility to fit the bulk of data
+by tuning some parameters. We refer to this new distribution as the generalized
+exponential log-squared distribution (GEL-S). Statistical properties of the
+GEL-S distribution are discussed. The maximum likelihood method is proposed for
+estimating the model parameters, but incorporating adaptations in computational
+procedures due to difficulties in the manipulation of the parameters. The
+perfomance of the new distribution is studied using simulations. Applications
+to real data sets coming from different domains are showed.
+",0,0,1,1,0,0
+10169,Secret-Key-Aided Scheme for Securing Untrusted DF Relaying Networks,"  This paper proposes a new scheme to secure the transmissions in an untrusted
+decode-and-forward (DF) relaying network. A legitimate source node, Alice,
+sends her data to a legitimate destination node, Bob, with the aid of an
+untrusted DF relay node, Charlie. To secure the transmissions from Charlie
+during relaying time slots, each data codeword is secured using a secret-key
+codeword that has been previously shared between Alice and Bob during the
+perfectly secured time slots (i.e., when the channel secrecy rate is positive).
+The secret-key bits exchanged between Alice and Bob are stored in a
+finite-length buffer and are used to secure data transmission whenever needed.
+We model the secret-key buffer as a queueing system and analyze its Markov
+chain. Our numerical results show the gains of our proposed scheme relative to
+benchmarks. Moreover, the proposed scheme achieves an upper bound on the secure
+throughput.
+",1,0,0,0,0,0
+10170,On deep speaker embeddings for text-independent speaker recognition,"  We investigate deep neural network performance in the textindependent speaker
+recognition task. We demonstrate that using angular softmax activation at the
+last classification layer of a classification neural network instead of a
+simple softmax activation allows to train a more generalized discriminative
+speaker embedding extractor. Cosine similarity is an effective metric for
+speaker verification in this embedding space. We also address the problem of
+choosing an architecture for the extractor. We found that deep networks with
+residual frame level connections outperform wide but relatively shallow
+architectures. This paper also proposes several improvements for previous
+DNN-based extractor systems to increase the speaker recognition accuracy. We
+show that the discriminatively trained similarity metric learning approach
+outperforms the standard LDA-PLDA method as an embedding backend. The results
+obtained on Speakers in the Wild and NIST SRE 2016 evaluation sets demonstrate
+robustness of the proposed systems when dealing with close to real-life
+conditions.
+",0,0,0,1,0,0
+10171,Accurate Computation of Marginal Data Densities Using Variational Bayes,"  Bayesian model selection and model averaging rely on estimates of marginal
+data densities (MDDs) also known as marginal likelihoods. Estimation of MDDs is
+often nontrivial and requires elaborate numerical integration methods. We
+propose using the variational Bayes posterior density as a weighting density
+within the class of reciprocal importance sampling MDD estimators. This
+proposal is computationally convenient, is based on variational Bayes posterior
+densities that are available for many models, only requires simulated draws
+from the posterior distribution, and provides accurate estimates with a
+moderate number of posterior draws. We show that this estimator is
+theoretically well-justified, has finite variance, provides a minimum variance
+candidate for the class of reciprocal importance sampling MDD estimators, and
+that its reciprocal is consistent, asymptotically normally distributed and
+unbiased. We also investigate the performance of the variational Bayes
+approximate density as a weighting density within the class of bridge sampling
+estimators. Using several examples, we show that our proposed estimators are at
+least as good as the best existing estimators and outperform many MDD
+estimators in terms of bias and numerical standard errors.
+",0,0,0,1,0,0
+10172,Random matrices and the New York City subway system,"  We analyze subway arrival times in the New York City subway system. We find
+regimes where the gaps between trains exhibit both (unitarily invariant) random
+matrix statistics and Poisson statistics. The departure from random matrix
+statistics is captured by the value of the Coulomb potential along the subway
+route. This departure becomes more pronounced as trains make more stops.
+",0,1,0,0,0,0
+10173,Weakening of the diamagnetic shielding in FeSe$_{1-x}$S$_x$ at high pressures,"  The superconducting transition of FeSe$_{1-x}$S$_x$ with three distinct
+sulphur concentrations $x$ was studied under hydrostatic pressure up to
+$\sim$70 kbar via bulk AC susceptibility. The pressure dependence of the
+superconducting transition temperature ($T_c$) features a small dome-shaped
+variation at low pressures for $x=0.04$ and $x=0.12$, followed by a more
+substantial $T_c$ enhancement to a value of around 30 K at moderate pressures.
+In $x=0.21$, a similar overall pressure dependence of $T_c$ is observed, except
+that the small dome at low pressures is flattened. For all three
+concentrations, a significant weakening of the diamagnetic shielding is
+observed beyond the pressure around which the maximum $T_c$ of 30 K is reached
+near the verge of pressure-induced magnetic phase. This observation points to a
+strong competition between the magnetic and high-$T_c$ superconducting states
+at high pressure in this system.
+",0,1,0,0,0,0
+10174,Incremental Skip-gram Model with Negative Sampling,"  This paper explores an incremental training strategy for the skip-gram model
+with negative sampling (SGNS) from both empirical and theoretical perspectives.
+Existing methods of neural word embeddings, including SGNS, are multi-pass
+algorithms and thus cannot perform incremental model update. To address this
+problem, we present a simple incremental extension of SGNS and provide a
+thorough theoretical analysis to demonstrate its validity. Empirical
+experiments demonstrated the correctness of the theoretical analysis as well as
+the practical usefulness of the incremental algorithm.
+",1,0,0,0,0,0
+10175,Continuous-Time Accelerated Methods via a Hybrid Control Lens,"  Treating optimization methods as dynamical systems can be traced back
+centuries ago in order to comprehend the notions and behaviors of optimization
+methods. Lately, this mind set has become the driving force to design new
+optimization methods. Inspired by the recent dynamical system viewpoint of
+Nesterov's fast method, we propose two classes of fast methods, formulated as
+hybrid control systems, to obtain pre-specified exponential convergence rate.
+Alternative to the existing fast methods which are parametric-in-time second
+order differential equations, we dynamically synthesize feedback controls in a
+state-dependent manner. Namely, in the first class the damping term is viewed
+as the control input, while in the second class the amplitude with which the
+gradient of the objective function impacts the dynamics serves as the
+controller. The objective function requires to satisfy a certain sharpness
+criterion, the so-called Polyak--{\L}ojasiewicz inequality. Moreover, we
+establish that both hybrid structures possess Zeno-free solution trajectories.
+We finally provide a mechanism to determine the discretization step size to
+attain an exponential convergence rate.
+",1,0,0,0,0,0
+10176,Fast-Slow Recurrent Neural Networks,"  Processing sequential data of variable length is a major challenge in a wide
+range of applications, such as speech recognition, language modeling,
+generative image modeling and machine translation. Here, we address this
+challenge by proposing a novel recurrent neural network (RNN) architecture, the
+Fast-Slow RNN (FS-RNN). The FS-RNN incorporates the strengths of both
+multiscale RNNs and deep transition RNNs as it processes sequential data on
+different timescales and learns complex transition functions from one time step
+to the next. We evaluate the FS-RNN on two character level language modeling
+data sets, Penn Treebank and Hutter Prize Wikipedia, where we improve state of
+the art results to $1.19$ and $1.25$ bits-per-character (BPC), respectively. In
+addition, an ensemble of two FS-RNNs achieves $1.20$ BPC on Hutter Prize
+Wikipedia outperforming the best known compression algorithm with respect to
+the BPC measure. We also present an empirical investigation of the learning and
+network dynamics of the FS-RNN, which explains the improved performance
+compared to other RNN architectures. Our approach is general as any kind of RNN
+cell is a possible building block for the FS-RNN architecture, and thus can be
+flexibly applied to different tasks.
+",1,0,0,0,0,0
+10177,On OR Many-Access Channels,"  OR multi-access channel is a simple model where the channel output is the
+Boolean OR among the Boolean channel inputs. We revisit this model, showing
+that employing Bloom filter, a randomized data structure, as channel inputs
+achieves its capacity region with joint decoding and the symmetric sum rate of
+$\ln 2$ bits per channel use without joint decoding. We then proceed to the
+""many-access"" regime where the number of potential users grows without bound,
+treating both activity recognition and message transmission problems,
+establishing scaling laws which are optimal within a constant factor, based on
+Bloom filter channel inputs.
+",1,0,1,0,0,0
+10178,TwiInsight: Discovering Topics and Sentiments from Social Media Datasets,"  Social media platforms contain a great wealth of information which provides
+opportunities for us to explore hidden patterns or unknown correlations, and
+understand people's satisfaction with what they are discussing. As one
+showcase, in this paper, we present a system, TwiInsight which explores the
+insight of Twitter data. Different from other Twitter analysis systems,
+TwiInsight automatically extracts the popular topics under different categories
+(e.g., healthcare, food, technology, sports and transport) discussed in Twitter
+via topic modeling and also identifies the correlated topics across different
+categories. Additionally, it also discovers the people's opinions on the tweets
+and topics via the sentiment analysis. The system also employs an intuitive and
+informative visualization to show the uncovered insight. Furthermore, we also
+develop and compare six most popular algorithms - three for sentiment analysis
+and three for topic modeling.
+",1,0,0,0,0,0
+10179,The Promise and Peril of Human Evaluation for Model Interpretability,"  Transparency, user trust, and human comprehension are popular ethical
+motivations for interpretable machine learning. In support of these goals,
+researchers evaluate model explanation performance using humans and real world
+applications. This alone presents a challenge in many areas of artificial
+intelligence. In this position paper, we propose a distinction between
+descriptive and persuasive explanations. We discuss reasoning suggesting that
+functional interpretability may be correlated with cognitive function and user
+preferences. If this is indeed the case, evaluation and optimization using
+functional metrics could perpetuate implicit cognitive bias in explanations
+that threaten transparency. Finally, we propose two potential research
+directions to disambiguate cognitive function and explanation models, retaining
+control over the tradeoff between accuracy and interpretability.
+",1,0,0,1,0,0
+10180,Guided Unfoldings for Finding Loops in Standard Term Rewriting,"  In this paper, we reconsider the unfolding-based technique that we have
+introduced previously for detecting loops in standard term rewriting. We
+improve it by guiding the unfolding process, using distinguished positions in
+the rewrite rules. This results in a depth-first computation of the unfoldings,
+whereas the original technique was breadth-first. We have implemented this new
+approach in our tool NTI and compared it to the previous one on a bunch of
+rewrite systems. The results we get are promising (better times, more
+successful proofs).
+",1,0,0,0,0,0
+10181,Nutrients and biomass dynamics in photo-sequencing batch reactors treating wastewater with high nutrients loadings,"  The present study investigates different strategies for the treatment of a
+mixture of digestate from an anaerobic digester diluted and secondary effluent
+from a high rate algal pond. To this aim, the performance of two
+photo-sequencing batch reactors (PSBRs) operated at high nutrients loading
+rates and different solids retention times (SRTs) were compared with a
+semi-continuous photobioreactor (SC). Performances were evaluated in terms of
+wastewater treatment, biomass composition and biopolymers accumulation during
+30 days of operation. PSBRs were operated at a hydraulic retention time (HRT)
+of 2 days and SRTs of 10 and 5 days (PSBR2-10 and PSBR2-5, respectively),
+whereas the semi-continuous reactor was operated at a coupled HRT/SRT of 10
+days (SC10-10). Results showed that PSBR2-5 achieved the highest removal rates
+in terms of TN (6.7 mg L-1 d-1), TP (0.31 mg L-1 d-1), TOC (29.32 mg L-1 d-1)
+and TIC (3.91 mg L-1 d-1). These results were in general 3-6 times higher than
+the removal rates obtained in the SC10-10 (TN 29.74 mg L-1 d-1, TP 0.96 mg L-1
+d-1, TOC 29.32 mg L-1 d-1 and TIC 3.91 mg L-1 d-1). Furthermore, both PSBRs
+were able to produce biomass up to 0.09 g L-1 d-1, more than twofold the
+biomass produced by the semi-continuous reactor (0.04 g L-1 d-1), and achieved
+a biomass settleability of 86-92%. This study also demonstrated that the
+microbial composition could be controlled by the nutrients loads, since the
+three reactors were dominated by different species depending on the nutritional
+conditions. Concerning biopolymers accumulation, carbohydrates concentration
+achieved similar values in the three reactors (11%), whereas <0.5 % of
+polyhydrohybutyrates (PHB) was produced. These low values in biopolymers
+production could be related to the lack of microorganisms as cyanobacteria that
+are able to accumulate carbohydrates/PHB.
+",0,0,0,0,1,0
+10182,Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees,"  Random Fourier features is one of the most popular techniques for scaling up
+kernel methods, such as kernel ridge regression. However, despite impressive
+empirical results, the statistical properties of random Fourier features are
+still not well understood. In this paper we take steps toward filling this gap.
+Specifically, we approach random Fourier features from a spectral matrix
+approximation point of view, give tight bounds on the number of Fourier
+features required to achieve a spectral approximation, and show how spectral
+matrix approximation bounds imply statistical guarantees for kernel ridge
+regression.
+Qualitatively, our results are twofold: on the one hand, we show that random
+Fourier feature approximation can provably speed up kernel ridge regression
+under reasonable assumptions. At the same time, we show that the method is
+suboptimal, and sampling from a modified distribution in Fourier space, given
+by the leverage function of the kernel, yields provably better performance. We
+study this optimal sampling distribution for the Gaussian kernel, achieving a
+nearly complete characterization for the case of low-dimensional bounded
+datasets. Based on this characterization, we propose an efficient sampling
+scheme with guarantees superior to random Fourier features in this regime.
+",1,0,0,1,0,0
+10183,Charge reconstruction study of the DAMPE Silicon-Tungsten Tracker with ion beams,"  The DArk Matter Particle Explorer (DAMPE) is one of the four satellites
+within Strategic Pioneer Research Program in Space Science of the Chinese
+Academy of Science (CAS). DAMPE can detect electrons, photons in a wide energy
+range (5 GeV to 10 TeV) and ions up to iron (100GeV to 100 TeV).
+Silicon-Tungsten Tracker (STK) is one of the four subdetectors in DAMPE,
+providing photon-electron conversion, track reconstruction and charge
+identification for ions. Ion beam test was carried out in CERN with 60GeV/u
+Lead primary beams. Charge reconstruction and charge resolution of STK
+detectors were investigated.
+",0,1,0,0,0,0
+10184,Scalable Cryogenic Read-out Circuit for a Superconducting Nanowire Single-Photon Detector System,"  The superconducting nanowire single photon detector (SNSPD) is a leading
+technology for quantum information science applications using photons, and they
+are finding increasing uses in photon-starved classical imaging applications.
+Critical detector characteristics, such as timing resolution (jitter), reset
+time and maximum count rate, are heavily influenced by the readout electronics
+that sense and amplify the photon detection signal. We describe a readout
+circuit for SNSPDs using commercial off-the-shelf amplifiers operating at
+cryogenic temperatures. Our design demonstrates a 35 ps timing resolution and a
+maximum count rate of over 2x10^7 counts per second while maintaining <3 mW
+power consumption per channel, making it suitable for a multichannel readout.
+",0,1,0,0,0,0
+10185,String principal bundles and Courant algebroids,"  Just like Atiyah Lie algebroids encode the infinitesimal symmetries of
+principal bundles, exact Courant algebroids are believed to encode the
+infinitesimal symmetries of $S^1$-gerbes. At the same time, transitive Courant
+algebroids may be viewed as the higher analogue of Atiyah Lie algebroids, and
+the non-commutative analogue of exact Courant algebroids. In this article, we
+explore what the ""principal bundle"" behind transitive Courant algebroids are,
+and they turn out to be principal 2-bundles of string groups. First, we
+construct the stack of principal 2-bundles of string groups with connection
+data. We prove a lifting theorem for the stack of string principal bundles with
+connections and show the multiplicity of the lifts once they exist. This is a
+differential geometrical refinement of what is known for string structures by
+Redden, Waldorf and Stolz-Teichner. We also extend the result of Bressler and
+Chen-Stiénon-Xu on extension obstruction involving transitive Courant
+algebroids to the case of transitive Courant algebroids with connections, as a
+lifting theorem with the description of multiplicity once liftings exist. At
+the end, we build a morphism between these two stacks. The morphism turns out
+to be neither injective nor surjective in general, which shows that the process
+of associating the ""higher Atiyah algebroid"" loses some information and at the
+same time, only some special transitive Courant algebroids come from string
+bundles.
+",0,0,1,0,0,0
+10186,Electroforming-Free TaOx Memristors using Focused Ion Beam Irradiations,"  We demonstrate creation of electroforming-free TaOx memristive devices using
+focused ion beam irradiations to locally define conductive filaments in TaOx
+films. Electrical characterization shows that these irradiations directly
+create fully functional memristors without the need for electroforming. Ion
+beam forming of conductive filaments combined with state-of-the-art
+nano-patterning presents a CMOS compatible approach to wafer level fabrication
+of fully formed and operational memristors.
+",0,1,0,0,0,0
+10187,Optimal hypothesis testing for stochastic block models with growing degrees,"  The present paper considers testing an Erdos--Renyi random graph model
+against a stochastic block model in the asymptotic regime where the average
+degree of the graph grows with the graph size n. Our primary interest lies in
+those cases in which the signal-to-noise ratio is at a constant level. Focusing
+on symmetric two block alternatives, we first derive joint central limit
+theorems for linear spectral statistics of power functions for properly
+rescaled graph adjacency matrices under both the null and local alternative
+hypotheses. The powers in the linear spectral statistics are allowed to grow to
+infinity together with the graph size. In addition, we show that linear
+spectral statistics of Chebyshev polynomials are closely connected to signed
+cycles of growing lengths that determine the asymptotic likelihood ratio test
+for the hypothesis testing problem of interest. This enables us to construct a
+sequence of test statistics that achieves the exact optimal asymptotic power
+within $O(n^3 \log n)$ time complexity in the contiguous regime when $n^2
+p_{n,av}^3 \to\infty$ where $p_{n,av}$ is the average connection probability.
+We further propose a class of adaptive tests that are computationally tractable
+and completely data-driven. They achieve nontrivial powers in the contiguous
+regime and consistency in the singular regime whenever $n p_{n,av} \to\infty$.
+These tests remain powerful when the alternative becomes a more general
+stochastic block model with more than two blocks.
+",1,0,1,1,0,0
+10188,Generalized End-to-End Loss for Speaker Verification,"  In this paper, we propose a new loss function called generalized end-to-end
+(GE2E) loss, which makes the training of speaker verification models more
+efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike
+TE2E, the GE2E loss function updates the network in a way that emphasizes
+examples that are difficult to verify at each step of the training process.
+Additionally, the GE2E loss does not require an initial stage of example
+selection. With these properties, our model with the new loss function
+decreases speaker verification EER by more than 10%, while reducing the
+training time by 60% at the same time. We also introduce the MultiReader
+technique, which allows us to do domain adaptation - training a more accurate
+model that supports multiple keywords (i.e. ""OK Google"" and ""Hey Google"") as
+well as multiple dialects.
+",1,0,0,1,0,0
+10189,Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data,"  The regularization approach for variable selection was well developed for a
+completely observed data set in the past two decades. In the presence of
+missing values, this approach needs to be tailored to different missing data
+mechanisms. In this paper, we focus on a flexible and generally applicable
+missing data mechanism, which contains both ignorable and nonignorable missing
+data mechanism assumptions. We show how the regularization approach for
+variable selection can be adapted to the situation under this missing data
+mechanism. The computational and theoretical properties for variable selection
+consistency are established. The proposed method is further illustrated by
+comprehensive simulation studies and real data analyses, for both low and high
+dimensional settings.
+",0,0,0,1,0,0
+10190,Fast embedding of multilayer networks: An algorithm and application to group fMRI,"  Learning interpretable features from complex multilayer networks is a
+challenging and important problem. The need for such representations is
+particularly evident in multilayer networks of the brain, where nodal
+characteristics may help model and differentiate regions of the brain according
+to individual, cognitive task, or disease. Motivated by this problem, we
+introduce the multi-node2vec algorithm, an efficient and scalable feature
+engineering method that automatically learns continuous node feature
+representations from multilayer networks. Multi-node2vec relies upon a
+second-order random walk sampling procedure that efficiently explores the
+inner- and intra- layer ties of the observed multilayer network is utilized to
+identify multilayer neighborhoods. Maximum likelihood estimators of the nodal
+features are identified through the use of the Skip-gram neural network model
+on the collection of sampled neighborhoods. We investigate the conditions under
+which multi-node2vec is an approximation of a closed-form matrix factorization
+problem. We demonstrate the efficacy of multi-node2vec on a multilayer
+functional brain network from resting state fMRI scans over a group of 74
+healthy individuals. We find that multi-node2vec outperforms contemporary
+methods on complex networks, and that multi-node2vec identifies nodal
+characteristics that closely associate with the functional organization of the
+brain.
+",1,0,0,0,0,0
+10191,Towards Better Summarizing Bug Reports with Crowdsourcing Elicited Attributes,"  Recent years have witnessed the growing demands for resolving numerous bug
+reports in software maintenance. Aiming to reduce the time testers/developers
+take in perusing bug reports, the task of bug report summarization has
+attracted a lot of research efforts in the literature. However, no systematic
+analysis has been conducted on attribute construction which heavily impacts the
+performance of supervised algorithms for bug report summarization. In this
+study, we first conduct a survey to reveal the existing methods for attribute
+construction in mining software repositories. Then, we propose a new method
+named Crowd-Attribute to infer new effective attributes from the crowdgenerated
+data in crowdsourcing and develop a new tool named Crowdsourcing Software
+Engineering Platform to facilitate this method. With Crowd-Attribute, we
+successfully construct 11 new attributes and propose a new supervised algorithm
+named Logistic Regression with Crowdsourced Attributes (LRCA). To evaluate the
+effectiveness of LRCA, we build a series of large scale data sets with 105,177
+bug reports. Experiments over both the public data set SDS with 36 manually
+annotated bug reports and new large-scale data sets demonstrate that LRCA can
+consistently outperform the state-of-the-art algorithms for bug report
+summarization.
+",1,0,0,0,0,0
+10192,Extraordinary linear dynamic range in laser-defined functionalized graphene photodetectors,"  Graphene-based photodetectors have demonstrated mechanical flexibility, large
+operating bandwidth, and broadband spectral response. However, their linear
+dynamic range (LDR) is limited by graphene's intrinsichot-carrier dynamics,
+which causes deviation from a linear photoresponse at low incident powers. At
+the same time, multiplication of hot carriers causes the photoactive region to
+be smeared over distances of a few micro-meters, limiting the use of graphene
+in high-resolution applications. We present a novel method for engineer-ing
+photoactive junctions in FeCl3-intercalated graphene using laser irradiation.
+Photocurrent measured at these planar junctions shows an extraordinary linear
+response with an LDR value at least 4500 times larger than that of other
+graphene devices (44 dB) while maintaining high stability against environmental
+contamination without the need for encapsulation. The observed photoresponse is
+purely photovoltaic, demonstrating complete quenching of hot-carrier effects.
+These results pave the way toward the design of ultrathin photode-tectors with
+unprecedented LDR for high-definition imaging and sensing.
+",0,1,0,0,0,0
+10193,Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network,"  In this paper, we propose an efficient transfer leaning methods for training
+a personalized language model using a recurrent neural network with long
+short-term memory architecture. With our proposed fast transfer learning
+schemes, a general language model is updated to a personalized language model
+with a small amount of user data and a limited computing resource. These
+methods are especially useful for a mobile device environment while the data is
+prevented from transferring out of the device for privacy purposes. Through
+experiments on dialogue data in a drama, it is verified that our transfer
+learning methods have successfully generated the personalized language model,
+whose output is more similar to the personal language style in both qualitative
+and quantitative aspects.
+",1,0,0,0,0,0
+10194,Convergence of ground state solutions for nonlinear Schrödinger equations on graphs,"  We consider the nonlinear Schrödinger equation $-\Delta u+(\lambda
+a(x)+1)u=|u|^{p-1}u$ on a locally finite graph $G=(V,E)$. We prove via the
+Nehari method that if $a(x)$ satisfies certain assumptions, for any
+$\lambda>1$, the equation admits a ground state solution $u_\lambda$. Moreover,
+as $\lambda\rightarrow \infty$, the solution $u_\lambda$ converges to a
+solution of the Dirichlet problem $-\Delta u+u=|u|^{p-1}u$ which is defined on
+the potential well $\Omega$. We also provide a numerical experiment which
+solves the equation on a finite graph to illustrate our results.
+",0,0,1,0,0,0
+10195,Error Analysis of the Stochastic Linear Feedback Particle Filter,"  This paper is concerned with the convergence and long-term stability analysis
+of the feedback particle filter (FPF) algorithm. The FPF is an interacting
+system of $N$ particles where the interaction is designed such that the
+empirical distribution of the particles approximates the posterior
+distribution. It is known that in the mean-field limit ($N=\infty$), the
+distribution of the particles is equal to the posterior distribution. However
+little is known about the convergence to the mean-field limit. In this paper,
+we consider the FPF algorithm for the linear Gaussian setting. In this setting,
+the algorithm is similar to the ensemble Kalman-Bucy filter algorithm. Although
+these algorithms have been numerically evaluated and widely used in
+applications, their convergence and long-term stability analysis remains an
+active area of research. In this paper, we show that, (i) the mean-field limit
+is well-defined with a unique strong solution; (ii) the mean-field process is
+stable with respect to the initial condition; (iii) we provide conditions such
+that the finite-$N$ system is long term stable and we obtain some mean-squared
+error estimates that are uniform in time.
+",1,0,0,0,0,0
+10196,Geometry in the Courtroom,"  There has been a recent media blitz on a cohort of mathematicians valiantly
+working to fix America's democratic system by combatting gerrymandering with
+geometry. While statistics commonly features in the courtroom (forensics, DNA
+analysis, etc.), the gerrymandering news raises a natural question: in what
+other ways has pure math, specifically geometry and topology, been involved in
+court cases and legal scholarship? In this survey article, we collect a few
+examples with topics ranging from the Pythagorean formula to the Ham Sandwich
+Theorem, and we discuss some jurists' perspectives on geometric reasoning in
+the legal realm. One of our goals is to provide math educators with engaging
+real-world instances of some abstract geometric concepts.
+",0,0,1,0,0,0
+10197,Proof of Concept of Wireless TERS Monitoring,"  Temporary earth retaining structures (TERS) help prevent collapse during
+construction excavation. To ensure that these structures are operating within
+design specifications, load forces on supports must be monitored. Current
+monitoring approaches are expensive, sparse, off-line, and thus difficult to
+integrate into predictive models. This work aims to show that wirelessly
+connected battery powered sensors are feasible, practical, and have similar
+accuracy to existing sensor systems. We present the design and validation of
+ReStructure, an end-to-end prototype wireless sensor network for collection,
+communication, and aggregation of strain data. ReStructure was validated
+through a six months deployment on a real-life excavation site with all but one
+node producing valid and accurate strain measurements at higher frequency than
+existing ones. These results and the lessons learnt provide the basis for
+future widespread wireless TERS monitoring that increase measurement density
+and integrate closely with predictive models to provide timely alerts of damage
+or potential failure.
+",1,0,0,0,0,0
+10198,Hyperrigid subsets of Cuntz-Krieger algebras and the property of rigidity at zero,"  A subset $\mathcal{G}$ generating a $C^*$-algebra $A$ is said to be
+hyperrigid if for every faithful nondegenerate $*$-representation $A\subseteq
+B(H)$ and a sequence $\phi_n:B(H) \to B(H)$ of unital completely positive maps,
+we have that \[ \lim_{n\to\infty}\phi_n(g)= g~~\text{for all } g\in \mathcal{G}
+~~ \implies ~~ \lim_{n\to\infty}\phi_n(a)= a~~\text{for all } a\in A \] where
+all convergence are in norm. In this paper, we show that for the Cuntz-Krieger
+algebra $\mathcal{O}(G)$ associated to a row-finite directed graph $G$ with no
+isolated vertices, the set of partial isometries $\mathcal{E}=\{S_e:e\in E\}$
+is hyperrigid.
+In addition, we define and examine a closely related notion: the property of
+rigidity at $0$. A generating subset $\mathcal{G}$ of a $C^*$-algebra $A$ is
+said to be rigid at $0$ if for every sequence of contractive positive maps
+$\varphi_n:A\to \mathbb C$ satisfying $\lim_{n\to \infty}\varphi_n(g)=0$ for
+every $g\in \mathcal{G}$, we have that $\lim_{n\to \infty}\varphi_n(a)=0$ for
+every $a\in A$.
+We show that, when combined, hyperrigidity and rigidity at $0$ are equivalent
+to a somewhat stronger notion of hyperrigidity, and we connect this to the
+unique extension property. This, however, is not the case for the generating
+set $\mathcal{E}$. More precisely, we show that for any graph $G$, subsets of
+the Cuntz-Krieger family generating $\mathcal{O}(G)$ are rigid at $0$ if and
+only if they contain every vertex projection.
+",0,0,1,0,0,0
+10199,Chiral Mott insulators in frustrated Bose-Hubbard models on ladders and two-dimensional lattices: a combined perturbative and density matrix renormalization group study,"  We study the fully gapped chiral Mott insulator (CMI) of frustrated
+Bose-Hubbard models on ladders and two-dimensional lattices by perturbative
+strong-coupling analysis and density matrix renormalization group (DMRG). First
+we show the existence of a low-lying exciton state on all geometries carrying
+the correct quantum numbers responsible for the condensation of excitons and
+formation of the CMI in the intermediate interaction regime. Then we perform
+systematic DMRG simulations on several two-leg ladder systems with $\pi$-flux
+and carefully characterize the two quantum phase transitions. We discuss the
+possibility to extend the generally very small CMI window by including
+repulsive nearest-neighbour interactions or changing density and coupling
+ratios.
+",0,1,0,0,0,0
+10200,Thermodynamic Limit of Interacting Particle Systems over Time-varying Sparse Random Networks,"  We establish a functional weak law of large numbers for observable
+macroscopic state variables of interacting particle systems (e.g., voter and
+contact processes) over fast time-varying sparse random networks of
+interactions. We show that, as the number of agents $N$ grows large, the
+proportion of agents $\left(\overline{Y}_{k}^{N}(t)\right)$ at a certain state
+$k$ converges in distribution -- or, more precisely, weakly with respect to the
+uniform topology on the space of \emph{càdlàg} sample paths -- to the
+solution of an ordinary differential equation over any compact interval
+$\left[0,T\right]$. Although the limiting process is Markov, the prelimit
+processes, i.e., the normalized macrostate vector processes
+$\left(\mathbf{\overline{Y}}^{N}(t)\right)=\left(\overline{Y}_{1}^{N}(t),\ldots,\overline{Y}_{K}^{N}(t)\right)$,
+are non-Markov as they are tied to the \emph{high-dimensional} microscopic
+state of the system, which precludes the direct application of standard
+arguments for establishing weak convergence. The techniques developed in the
+paper for establishing weak convergence might be of independent interest.
+",0,0,1,0,0,0
+10201,Evidence for short-range magnetic order in the nematic phase of FeSe from anisotropic in-plane magnetostriction and susceptibility measurements,"  The nature of the nematic state in FeSe remains one of the major unsolved
+mysteries in Fe- based superconductors. Both spin and orbital physics have been
+invoked to explain the origin of this phase. Here we present experimental
+evidence for frustrated, short-range magnetic order, as suggested by several
+recent theoretical works, in the nematic state of FeSe. We use a combination of
+magnetostriction, susceptibility and resistivity measurements to probe the
+in-plane anisotropies of the nematic state and its associated fluctuations.
+Despite the absence of long-range magnetic order in FeSe, we observe a sizable
+in-plane magnetic susceptibility anisotropy, which is responsible for the
+field-induced in-plane distortion inferred from magnetostriction measurements.
+Further we demonstrate that all three anisotropies in FeSe are very similar to
+those of BaFe2As2, which strongly suggests that the nematic phase in FeSe is
+also of magnetic origin.
+",0,1,0,0,0,0
+10202,Contextual Parameter Generation for Universal Neural Machine Translation,"  We propose a simple modification to existing neural machine translation (NMT)
+models that enables using a single universal model to translate between
+multiple languages while allowing for language specific parameterization, and
+that can also be used for domain adaptation. Our approach requires no changes
+to the model architecture of a standard NMT system, but instead introduces a
+new component, the contextual parameter generator (CPG), that generates the
+parameters of the system (e.g., weights in a neural network). This parameter
+generator accepts source and target language embeddings as input, and generates
+the parameters for the encoder and the decoder, respectively. The rest of the
+model remains unchanged and is shared across all languages. We show how this
+simple modification enables the system to use monolingual data for training and
+also perform zero-shot translation. We further show it is able to surpass
+state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and
+that the learned language embeddings are able to uncover interesting
+relationships between languages.
+",0,0,0,1,0,0
+10203,Opinion Dynamics with Stubborn Agents,"  We consider the problem of optimizing the placement of stubborn agents in a
+social network in order to maximally impact population opinions.
+We assume individuals in a directed social network each have a latent opinion
+that evolves over time in response to social media posts by their neighbors.
+The individuals randomly communicate noisy versions of their latent opinion to
+their neighbors. Each individual updates his opinion using a time-varying
+update rule that has him become more stubborn with time and be less affected by
+new posts. The dynamic update rule is a novel component of our model and
+reflects realistic behaviors observed in many psychological studies.
+We show that in the presence of stubborn agents with immutable opinions and
+under fairly general conditions on the stubbornness rate of the individuals,
+the opinions converge to an equilibrium determined by a linear system. We give
+an interesting electrical network interpretation of the equilibrium. We also
+use this equilibrium to present a simple closed form expression for harmonic
+influence centrality, which is a function that quantifies how much a node can
+affect the mean opinion in a network. We develop a discrete optimization
+formulation for the problem of maximally shifting opinions in a network by
+targeting nodes with stubborn agents. We show that this is an optimization
+problem with a monotone and submodular objective, allowing us to utilize a
+greedy algorithm. Finally, we show that a small number of stubborn agents can
+non-trivially influence a large population using simulated networks.
+",1,0,0,1,0,0
+10204,A Community Microgrid Architecture with an Internal Local Market,"  This work fits in the context of community microgrids, where members of a
+community can exchange energy and services among themselves, without going
+through the usual channels of the public electricity grid. We introduce and
+analyze a framework to operate a community microgrid, and to share the
+resulting revenues and costs among its members. A market-oriented pricing of
+energy exchanges within the community is obtained by implementing an internal
+local market based on the marginal pricing scheme. The market aims at
+maximizing the social welfare of the community, thanks to the more efficient
+allocation of resources, the reduction of the peak power to be paid, and the
+increased amount of reserve, achieved at an aggregate level. A community
+microgrid operator, acting as a benevolent planner, redistributes revenues and
+costs among the members, in such a way that the solution achieved by each
+member within the community is not worse than the solution it would achieve by
+acting individually. In this way, each member is incentivized to participate in
+the community on a voluntary basis. The overall framework is formulated in the
+form of a bilevel model, where the lower level problem clears the market, while
+the upper level problem plays the role of the community microgrid operator.
+Numerical results obtained on a real test case implemented in Belgium show
+significant cost savings on a yearly scale for the community members, as
+compared to the case when they act individually.
+",1,0,0,0,0,1
+10205,Extended nilHecke algebra and symmetric functions in type B,"  We formulate a type B extended nilHecke algebra, following the type A
+construction of Naisse and Vaz. We describe an action of this algebra on
+extended polynomials and describe some results on the structure on the extended
+symmetric polynomials. Finally, following Appel, Egilmez, Hogancamp, and Lauda,
+we prove a result analogous to a classical theorem of Solomon connecting the
+extended symmetric polynomial ring to a ring of usual symmetric polynomials and
+their differentials.
+",0,0,1,0,0,0
+10206,One-Sided Unsupervised Domain Mapping,"  In unsupervised domain mapping, the learner is given two unmatched datasets
+$A$ and $B$. The goal is to learn a mapping $G_{AB}$ that translates a sample
+in $A$ to the analog sample in $B$. Recent approaches have shown that when
+learning simultaneously both $G_{AB}$ and the inverse mapping $G_{BA}$,
+convincing mappings are obtained. In this work, we present a method of learning
+$G_{AB}$ without learning $G_{BA}$. This is done by learning a mapping that
+maintains the distance between a pair of samples. Moreover, good mappings are
+obtained, even by maintaining the distance between different parts of the same
+sample before and after mapping. We present experimental results that the new
+method not only allows for one sided mapping learning, but also leads to
+preferable numerical results over the existing circularity-based constraint.
+Our entire code is made publicly available at
+this https URL .
+",1,0,0,0,0,0
+10207,Adaptive Neural Networks for Efficient Inference,"  We present an approach to adaptively utilize deep neural networks in order to
+reduce the evaluation time on new examples without loss of accuracy. Rather
+than attempting to redesign or approximate existing networks, we propose two
+schemes that adaptively utilize networks. We first pose an adaptive network
+evaluation scheme, where we learn a system to adaptively choose the components
+of a deep network to be evaluated for each example. By allowing examples
+correctly classified using early layers of the system to exit, we avoid the
+computational time associated with full evaluation of the network. We extend
+this to learn a network selection system that adaptively selects the network to
+be evaluated for each example. We show that computational time can be
+dramatically reduced by exploiting the fact that many examples can be correctly
+classified using relatively efficient networks and that complex,
+computationally costly networks are only necessary for a small fraction of
+examples. We pose a global objective for learning an adaptive early exit or
+network selection policy and solve it by reducing the policy learning problem
+to a layer-by-layer weighted binary classification problem. Empirically, these
+approaches yield dramatic reductions in computational cost, with up to a 2.8x
+speedup on state-of-the-art networks from the ImageNet image recognition
+challenge with minimal (<1%) loss of top5 accuracy.
+",1,0,0,1,0,0
+10208,The phylogenetic effective sample size and jumps,"  The phylogenetic effective sample size is a parameter that has as its goal
+the quantification of the amount of independent signal in a phylogenetically
+correlated sample. It was studied for Brownian motion and Ornstein-Uhlenbeck
+models of trait evolution. Here, we study this composite parameter when the
+trait is allowed to jump at speciation points of the phylogeny. Our numerical
+study indicates that there is a non-trivial limit as the effect of jumps grows.
+The limit depends on the value of the drift parameter of the Ornstein-Uhlenbeck
+process.
+",0,0,0,0,1,0
+10209,"Bosonic symmetries of the extended fermionic $(2N,2M)$-Toda hierarchy","  In this paper, we construct the additional symmetries of the fermionic
+$(2N,2M)$-Toda hierarchy basing on the generalization of the $N{=}(1|1)$
+supersymmetric two dimensional Toda lattice hierarchy. These additional flows
+constitute a $w_{\infty}\times w_{\infty}$ Lie algebra. As a Bosonic reduction
+of the $N{=}(1|1)$ supersymmetric two dimensional Toda lattice hierarchy and
+the fermionic $(2N,2M)$-Toda hierarchy, we define a new extended fermionic
+$(2N,2M)$-Toda hierarchy which admits a Bosonic Block type superconformal
+structure.
+",0,1,1,0,0,0
+10210,Single-atom-resolved probing of lattice gases in momentum space,"  Measuring the full distribution of individual particles is of fundamental
+importance to characterize many-body quantum systems through correlation
+functions at any order. Here we demonstrate the possibility to reconstruct the
+momentum-space distribution of three-dimensional interacting lattice gases
+atom-by-atom. This is achieved by detecting individual metastable Helium atoms
+in the far-field regime of expansion, when released from an optical lattice. We
+benchmark our technique with Quantum Monte-Carlo calculations, demonstrating
+the ability to resolve momentum distributions of superfluids occupying $10^5$
+lattice sites. It permits a direct measure of the condensed fraction across
+phase transitions, as we illustrate on the superfluid-to-normal transition. Our
+single-atom-resolved approach opens a new route to investigate interacting
+lattice gases through momentum correlations.
+",0,1,0,0,0,0
+10211,Provability Logics of Hierarchies,"  The branch of provability logic investigates the provability-based behavior
+of the mathematical theories. In a more precise way, it studies the relation
+between a mathematical theory $T$ and a modal logic $L$ via the provability
+interpretation which interprets the modality as the provability predicate of
+$T$. In this paper we will extend this relation to investigate the
+provability-based behavior of a hierarchy of theories. More precisely, using
+the modal language with infinitely many modalities,
+$\{\Box_n\}_{n=0}^{\infty}$, we will define the hierarchical counterparts of
+some of the classical modal theories such as $\mathbf{K4}$, $\mathbf{KD4}$,
+$\mathbf{GL}$ and $\mathbf{S4}$. Then we will define their canonical
+provability interpretations and their corresponding soundness-completeness
+theorems.
+",0,0,1,0,0,0
+10212,On the Difference between Physics and Biology: Logical Branching and Biomolecules,"  Physical emergence - crystals, rocks, sandpiles, turbulent eddies, planets,
+stars - is fundamentally different from biological emergence - amoeba, cells,
+mice, humans - even though the latter is based in the former. This paper points
+out that an essential difference is that as well as involving physical
+causation, causation in biological systems has a logical nature at each level
+of the hierarchy of emergence, from the biomolecular level up. The key link
+between physics and life enabling this to happen is provided by biomolecules,
+such as voltage gated ion channels, which enable branching logic to emerge from
+the underlying physics and hence enable logically based cell processes to take
+place in general, and in neurons in particular. These molecules can only have
+come into being via the contextually dependent processes of natural selection,
+which selects them for their biological function. A further major difference is
+between life in general and intelligent life. We characterise intelligent
+organisms as being engaged in deductive causation, which enables them to
+transcend the physical limitations of their bodies through the power of
+abstract thought, prediction, and planning. Ultimately this is enabled by the
+biomolecules that underlie the propagation of action potentials in neuronal
+axons in the brain.
+",0,1,0,0,0,0
+10213,Spin liquid and infinitesimal-disorder-driven cluster spin glass in the kagome lattice,"  The interplay between geometric frustration (GF) and bond disorder is studied
+in the Ising kagome lattice within a cluster approach. The model considers
+antiferromagnetic (AF) short-range couplings and long-range intercluster
+disordered interactions. The replica formalism is used to obtain an effective
+single cluster model from where the thermodynamics is analyzed by exact
+diagonalization. We found that the presence of GF can introduce cluster
+freezing at very low levels of disorder. The system exhibits an entropy plateau
+followed by a large entropy drop close to the freezing temperature. In this
+scenario, a spin-liquid (SL) behavior prevents conventional long-range order,
+but an infinitesimal disorder picks out uncompensated cluster states from the
+multi degenerate SL regime, potentializing the intercluster disordered coupling
+and bringing the cluster spin-glass state. To summarize, our results suggest
+that the SL state combined with low levels of disorder can activate small
+clusters, providing hypersensitivity to the freezing process in geometrically
+frustrated materials and playing a key role in the glassy stabilization. We
+propose that this physical mechanism could be present in several geometrically
+frustrated materials. In particular, we discuss our results in connection to
+the recent experimental investigations of the Ising kagome compound
+Co$_3$Mg(OH)$_6$Cl$_2$.
+",0,1,0,0,0,0
+10214,A punishment voting algorithm based on super categories construction for acoustic scene classification,"  In acoustic scene classification researches, audio segment is usually split
+into multiple samples. Majority voting is then utilized to ensemble the results
+of the samples. In this paper, we propose a punishment voting algorithm based
+on the super categories construction method for acoustic scene classification.
+Specifically, we propose a DenseNet-like model as the base classifier. The base
+classifier is trained by the CQT spectrograms generated from the raw audio
+segments. Taking advantage of the results of the base classifier, we propose a
+super categories construction method using the spectral clustering. Super
+classifiers corresponding to the constructed super categories are further
+trained. Finally, the super classifiers are utilized to enhance the majority
+voting of the base classifier by punishment voting. Experiments show that the
+punishment voting obviously improves the performances on both the DCASE2017
+Development dataset and the LITIS Rouen dataset.
+",1,0,0,1,0,0
+10215,Strong and Weak Equilibria for Time-Inconsistent Stochastic Control in Continuous Time,"  A new definition of continuous-time equilibrium controls is introduced. As
+opposed to the standard definition, which involves a derivative-type operation,
+the new definition parallels how a discrete-time equilibrium is defined, and
+allows for unambiguous economic interpretation. The terms ""strong equilibria""
+and ""weak equilibria"" are coined for controls under the new and the standard
+definitions, respectively. When the state process is a time-homogeneous
+continuous-time Markov chain, a careful asymptotic analysis gives complete
+characterizations of weak and strong equilibria. Thanks to Kakutani-Fan's
+fixed-point theorem, general existence of weak and strong equilibria is also
+established, under additional compactness assumption. Our theoretic results are
+applied to a two-state model under non-exponential discounting. In particular,
+we demonstrate explicitly that there can be incentive to deviate from a weak
+equilibrium, which justifies the need for strong equilibria. Our analysis also
+provides new results for the existence and characterization of discrete-time
+equilibria under infinite horizon.
+",0,0,0,0,0,1
+10216,Trading algorithms with learning in latent alpha models,"  Alpha signals for statistical arbitrage strategies are often driven by latent
+factors. This paper analyses how to optimally trade with latent factors that
+cause prices to jump and diffuse. Moreover, we account for the effect of the
+trader's actions on quoted prices and the prices they receive from trading.
+Under fairly general assumptions, we demonstrate how the trader can learn the
+posterior distribution over the latent states, and explicitly solve the latent
+optimal trading problem. We provide a verification theorem, and a methodology
+for calibrating the model by deriving a variation of the
+expectation-maximization algorithm. To illustrate the efficacy of the optimal
+strategy, we demonstrate its performance through simulations and compare it to
+strategies which ignore learning in the latent factors. We also provide
+calibration results for a particular model using Intel Corporation stock as an
+example.
+",0,0,0,1,0,1
+10217,Temporal Segment Networks for Action Recognition in Videos,"  Deep convolutional networks have achieved great success for image
+recognition. However, for action recognition in videos, their advantage over
+traditional methods is not so evident. We present a general and flexible
+video-level framework for learning action models in videos. This method, called
+temporal segment network (TSN), aims to model long-range temporal structures
+with a new segment-based sampling and aggregation module. This unique design
+enables our TSN to efficiently learn action models by using the whole action
+videos. The learned models could be easily adapted for action recognition in
+both trimmed and untrimmed videos with simple average pooling and multi-scale
+temporal window integration, respectively. We also study a series of good
+practices for the instantiation of TSN framework given limited training
+samples. Our approach obtains the state-the-of-art performance on four
+challenging action recognition benchmarks: HMDB51 (71.0%), UCF101 (94.9%),
+THUMOS14 (80.1%), and ActivityNet v1.2 (89.6%). Using the proposed RGB
+difference for motion models, our method can still achieve competitive accuracy
+on UCF101 (91.0%) while running at 340 FPS. Furthermore, based on the temporal
+segment networks, we won the video classification track at the ActivityNet
+challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and
+the proposed good practices.
+",1,0,0,0,0,0
+10218,Neural Machine Translation between Herbal Prescriptions and Diseases,"  The current study applies deep learning to herbalism. Toward the goal, we
+acquired the de-identified health insurance reimbursements that were claimed in
+a 10-year period from 2004 to 2013 in the National Health Insurance Database of
+Taiwan, the total number of reimbursement records equaling 340 millions. Two
+artificial intelligence techniques were applied to the dataset: residual
+convolutional neural network multitask classifier and attention-based recurrent
+neural network. The former works to translate from herbal prescriptions to
+diseases; and the latter from diseases to herbal prescriptions. Analysis of the
+classification results indicates that herbal prescriptions are specific to:
+anatomy, pathophysiology, sex and age of the patient, and season and year of
+the prescription. Further analysis identifies temperature and gross domestic
+product as the meteorological and socioeconomic factors that are associated
+with herbal prescriptions. Analysis of the neural machine transitional result
+indicates that the recurrent neural network learnt not only syntax but also
+semantics of diseases and herbal prescriptions.
+",1,0,0,0,0,0
+10219,"A Quantum-Proof Non-Malleable Extractor, With Application to Privacy Amplification against Active Quantum Adversaries","  In privacy amplification, two mutually trusted parties aim to amplify the
+secrecy of an initial shared secret $X$ in order to establish a shared private
+key $K$ by exchanging messages over an insecure communication channel. If the
+channel is authenticated the task can be solved in a single round of
+communication using a strong randomness extractor; choosing a quantum-proof
+extractor allows one to establish security against quantum adversaries.
+In the case that the channel is not authenticated, Dodis and Wichs (STOC'09)
+showed that the problem can be solved in two rounds of communication using a
+non-malleable extractor, a stronger pseudo-random construction than a strong
+extractor.
+We give the first construction of a non-malleable extractor that is secure
+against quantum adversaries. The extractor is based on a construction by Li
+(FOCS'12), and is able to extract from source of min-entropy rates larger than
+$1/2$. Combining this construction with a quantum-proof variant of the
+reduction of Dodis and Wichs, shown by Cohen and Vidick (unpublished), we
+obtain the first privacy amplification protocol secure against active quantum
+adversaries.
+",1,0,0,0,0,0
+10220,A Theory of Reversibility for Erlang,"  In a reversible language, any forward computation can be undone by a finite
+sequence of backward steps. Reversible computing has been studied in the
+context of different programming languages and formalisms, where it has been
+used for testing and verification, among others. In this paper, we consider a
+subset of Erlang, a functional and concurrent programming language based on the
+actor model. We present a formal semantics for reversible computation in this
+language and prove its main properties, including its causal consistency. We
+also build on top of it a rollback operator that can be used to undo the
+actions of a process up to a given checkpoint.
+",1,0,0,0,0,0
+10221,Convergence and Consistency Analysis for A 3D Invariant-EKF SLAM,"  In this paper, we investigate the convergence and consistency properties of
+an Invariant-Extended Kalman Filter (RI-EKF) based Simultaneous Localization
+and Mapping (SLAM) algorithm. Basic convergence properties of this algorithm
+are proven. These proofs do not require the restrictive assumption that the
+Jacobians of the motion and observation models need to be evaluated at the
+ground truth. It is also shown that the output of RI-EKF is invariant under any
+stochastic rigid body transformation in contrast to $\mathbb{SO}(3)$ based EKF
+SLAM algorithm ($\mathbb{SO}(3)$-EKF) that is only invariant under
+deterministic rigid body transformation. Implications of these invariance
+properties on the consistency of the estimator are also discussed. Monte Carlo
+simulation results demonstrate that RI-EKF outperforms $\mathbb{SO}(3)$-EKF,
+Robocentric-EKF and the ""First Estimates Jacobian"" EKF, for 3D point feature
+based SLAM.
+",1,0,0,0,0,0
+10222,Optical Characterization of Electro-spun Polymer Nanofiber based Silver Nanotubes,"  Nanotubes of various kinds have been prepared in the last decade, starting
+from the discovery of carbon nanotubes. Recently other types of nanotubes
+including metallic (Au), inorganic (TiO2, HfS2, V7O16, CdSe, MoS2), and
+polymeric (polyaniline, polyacrylonitrile) have been produced. Herein we
+present a novel synthetic procedure leading to a new kind of porous,
+high-surface-area nanoparticle nanotubes (NPNTs). This study characterizes the
+synthesized silver nanotubes at optical wavelengths. The absorption spectrum of
+PAN washed silver nanotubes shows an extended absorption peak at visible
+wavelengths ranging from 350 to 700 nm. In addition, the absorption spectrum of
+randomly oriented silver nanotubes showed plasmonic behavior, indicating high
+efficient surface enhanced Raman scattering (SERS) performance.
+",0,1,0,0,0,0
+10223,Optimal Prediction for Additive Function-on-Function Regression,"  As with classic statistics, functional regression models are invaluable in
+the analysis of functional data. While there are now extensive tools with
+accompanying theory available for linear models, there is still a great deal of
+work to be done concerning nonlinear models for functional data. In this work
+we consider the Additive Function-on-Function Regression model, a type of
+nonlinear model that uses an additive relationship between the functional
+outcome and functional covariate. We present an estimation methodology built
+upon Reproducing Kernel Hilbert Spaces, and establish optimal rates of
+convergence for our estimates in terms of prediction error. We also discuss
+computational challenges that arise with such complex models, developing a
+representer theorem for our estimate as well as a more practical and
+computationally efficient approximation. Simulations and an application to
+Cumulative Intraday Returns around the 2008 financial crisis are also provided.
+",0,0,1,1,0,0
+10224,Star chromatic index of subcubic multigraphs,"  The star chromatic index of a multigraph $G$, denoted $\chi'_{s}(G)$, is the
+minimum number of colors needed to properly color the edges of $G$ such that no
+path or cycle of length four is bi-colored. A multigraph $G$ is star
+$k$-edge-colorable if $\chi'_{s}(G)\le k$. Dvořák, Mohar and Šámal
+[Star chromatic index, J. Graph Theory 72 (2013), 313--326] proved that every
+subcubic multigraph is star $7$-edge-colorable. They conjectured in the same
+paper that every subcubic multigraph should be star $6$-edge-colorable. In this
+paper, we first prove that it is NP-complete to determine whether
+$\chi'_s(G)\le3$ for an arbitrary graph $G$. This answers a question of Mohar.
+We then establish some structure results on subcubic multigraphs $G$ with
+$\delta(G)\le2$ such that $\chi'_s(G)>k$ but $\chi'_s(G-v)\le k$ for any $v\in
+V(G)$, where $k\in\{5,6\}$. We finally apply the structure results, along with
+a simple discharging method, to prove that every subcubic multigraph $G$ is
+star $6$-edge-colorable if $mad(G)<5/2$, and star $5$-edge-colorable if
+$mad(G)<24/11$, respectively, where $mad(G)$ is the maximum average degree of a
+multigraph $G$. This partially confirms the conjecture of Dvořák, Mohar
+and Šámal.
+",0,0,1,0,0,0
+10225,The absolutely Koszul property of Veronese subrings and Segre products,"  Absolutely Koszul algebras are a class of rings over which any finite graded
+module has a rational Poincaré series. We provide a criterion to detect
+non-absolutely Koszul rings. Combining the criterion with machine computations,
+we identify large families of Veronese subrings and Segre products of
+polynomial rings which are not absolutely Koszul. In particular, we classify
+completely the absolutely Koszul algebras among Segre products of polynomial
+rings, at least in characteristic $0$.
+",0,0,1,0,0,0
+10226,Learning Theory and Algorithms for Revenue Management in Sponsored Search,"  Online advertisement is the main source of revenue for Internet business.
+Advertisers are typically ranked according to a score that takes into account
+their bids and potential click-through rates(eCTR). Generally, the likelihood
+that a user clicks on an ad is often modeled by optimizing for the click
+through rates rather than the performance of the auction in which the click
+through rates will be used. This paper attempts to eliminate this
+dis-connection by proposing loss functions for click modeling that are based on
+final auction performance.In this paper, we address two feasible metrics (AUC^R
+and SAUC) to evaluate the on-line RPM (revenue per mille) directly rather than
+the CTR. And then, we design an explicit ranking function by incorporating the
+calibration fac-tor and price-squashed factor to maximize the revenue. Given
+the power of deep networks, we also explore an implicit optimal ranking
+function with deep model. Lastly, various experiments with two real world
+datasets are presented. In particular, our proposed methods perform better than
+the state-of-the-art methods with regard to the revenue of the platform.
+",0,0,0,1,0,0
+10227,Isotropic functions revisited,"  To a smooth and symmetric function $f$ defined on a symmetric open set
+$\Gamma\subset\mathbb{R}^{n}$ and a real $n$-dimensional vector space $V$ we
+assign an associated operator function $F$ defined on an open subset
+$\Omega\subset\mathcal{L}(V)$ of linear transformations of $V$, such that for
+each inner product $g$ on $V$, on the subspace
+$\Sigma_{g}(V)\subset\mathcal{L}(V)$ of $g$-selfadjoint operators,
+$F_{g}=F_{|\Sigma_{g}(V)}$ is the isotropic function associated to $f$, which
+means that $F_{g}(A)=f(\mathrm{EV}(A))$, where $\mathrm{EV}(A)$ denotes the
+ordered $n$-tuple of real eigenvalues of $A$. We extend some well known
+relations between the derivatives of $f$ and each $F_{g}$ to relations between
+$f$ and $F$. By means of an example we show that well known regularity
+properties of $F_{g}$ do not carry over to $F$.
+",0,0,1,0,0,0
+10228,Condensates in double-well potential with synthetic gauge potentials and vortex seeding,"  We demonstrate an enhancement in the vortex generation when artificial gauge
+potential is introduced to condensates confined in a double well potential.
+This is due to the lower energy required to create a vortex in the low
+condensate density region within the barrier. Furthermore, we study the
+transport of vortices between the two wells, and show that the traverse time
+for vortices is longer for the lower height of the well. We also show that the
+critical value of synthetic magnetic field to inject vortices into the bulk of
+the condensate is lower in the double-well potential compared to the harmonic
+confining potential.
+",0,1,0,0,0,0
+10229,"On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables","  We investigate the sub-Gaussian property for almost surely bounded random
+variables. If sub-Gaussianity per se is de facto ensured by the bounded support
+of said random variables, then exciting research avenues remain open. Among
+these questions is how to characterize the optimal sub-Gaussian proxy variance?
+Another question is how to characterize strict sub-Gaussianity, defined by a
+proxy variance equal to the (standard) variance? We address the questions in
+proposing conditions based on the study of functions variations. A particular
+focus is given to the relationship between strict sub-Gaussianity and symmetry
+of the distribution. In particular, we demonstrate that symmetry is neither
+sufficient nor necessary for strict sub-Gaussianity. In contrast, simple
+necessary conditions on the one hand, and simple sufficient conditions on the
+other hand, for strict sub-Gaussianity are provided. These results are
+illustrated via various applications to a number of bounded random variables,
+including Bernoulli, beta, binomial, uniform, Kumaraswamy, and triangular
+distributions.
+",0,0,1,1,0,0
+10230,Fast Distributed Approximation for Max-Cut,"  Finding a maximum cut is a fundamental task in many computational settings.
+Surprisingly, it has been insufficiently studied in the classic distributed
+settings, where vertices communicate by synchronously sending messages to their
+neighbors according to the underlying graph, known as the $\mathcal{LOCAL}$ or
+$\mathcal{CONGEST}$ models. We amend this by obtaining almost optimal
+algorithms for Max-Cut on a wide class of graphs in these models. In
+particular, for any $\epsilon > 0$, we develop randomized approximation
+algorithms achieving a ratio of $(1-\epsilon)$ to the optimum for Max-Cut on
+bipartite graphs in the $\mathcal{CONGEST}$ model, and on general graphs in the
+$\mathcal{LOCAL}$ model.
+We further present efficient deterministic algorithms, including a
+$1/3$-approximation for Max-Dicut in our models, thus improving the best known
+(randomized) ratio of $1/4$. Our algorithms make non-trivial use of the greedy
+approach of Buchbinder et al. (SIAM Journal on Computing, 2015) for maximizing
+an unconstrained (non-monotone) submodular function, which may be of
+independent interest.
+",1,0,0,0,0,0
+10231,A note on Oliver's p-group conjecture,"  Let $S$ be a $p$-group for an odd prime $p$, Oliver proposed the conjecture
+that the Thompson subgroup $J(S)$ is always contained in the Oliver subgroup
+$\mathfrak{X}(S)$. That means he conjectured that
+$|J(S)\mathfrak{X}(S):\mathfrak{X}(S)|=1$. Let $\mathfrak{X}_1(S)$ be a
+subgroup of $S$ such that $\mathfrak{X}_1(S)/\mathfrak{X}(S)$ is the center of
+$S/\mathfrak{X}(S)$. In this short note, we prove that $J(S)\leq
+\mathfrak{X}(S)$ if and only if $J(S)\leq \mathfrak{X}_1(S)$.
+As an easy application, we prove that
+$|J(S)\mathfrak{X}(S):\mathfrak{X}(S)|\neq p$.
+",0,0,1,0,0,0
+10232,Frobenius elements in Galois representations with SL_n image,"  Suppose we have a elliptic curve over a number field whose mod $l$
+representation has image isomorphic to $SL_2(\mathbb{F}_l)$. We present a
+method to determine Frobenius elements of the associated Galois group which
+incorporates the linear structure available. We are able to distinguish
+$SL_n(\mathbb{F}_l)$-conjugacy from $GL_n(\mathbb{F}_l)$-conjugacy; this can be
+thought of as being analogous to a result which distinguishes $A_n$-conjugacy
+from $S_n$-conjugacy when the Galois group is considered as a permutation
+group.
+",0,0,1,0,0,0
+10233,CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks,"  The precise modeling of subatomic particle interactions and propagation
+through matter is paramount for the advancement of nuclear and particle physics
+searches and precision measurements. The most computationally expensive step in
+the simulation pipeline of a typical experiment at the Large Hadron Collider
+(LHC) is the detailed modeling of the full complexity of physics processes that
+govern the motion and evolution of particle showers inside calorimeters. We
+introduce \textsc{CaloGAN}, a new fast simulation technique based on generative
+adversarial networks (GANs). We apply these neural networks to the modeling of
+electromagnetic showers in a longitudinally segmented calorimeter, and achieve
+speedup factors comparable to or better than existing full simulation
+techniques on CPU ($100\times$-$1000\times$) and even faster on GPU (up to
+$\sim10^5\times$). There are still challenges for achieving precision across
+the entire phase space, but our solution can reproduce a variety of geometric
+shower shape properties of photons, positrons and charged pions. This
+represents a significant stepping stone toward a full neural network-based
+detector simulation that could save significant computing time and enable many
+analyses now and in the future.
+",1,0,0,1,0,0
+10234,Measurement of ultrashort optical pulses via time lens imaging in CMOS compatible waveguides,"  We demonstrate temporal measurements of subpicosecond optical pulses via
+time-to-frequency conversion in a 45cm long CMOS compatible high index glass
+spiral waveguide. The measurements are based on efficient four wave mixing in
+the C-band, using around 1W of peak pump power. We achieve a resolution of
+400fs over a time window of 100ps, representing a time-bandwidth product > 250.
+",0,1,0,0,0,0
+10235,On the next-to-minimal weight of projective Reed-Muller codes,"  In this paper we present several values for the next-to-minimal weights of
+projective Reed-Muller codes. We work over $\mathbb{F}_q$ with $q \geq 3$ since
+in IEEE-IT 62(11) p. 6300-6303 (2016) we have determined the complete values
+for the next-to-minimal weights of binary projective Reed-Muller codes. As in
+loc. cit. here we also find examples of codewords with next-to-minimal weight
+whose set of zeros is not in a hyperplane arrangement.
+",1,0,1,0,0,0
+10236,Scalable Global Grid catalogue for LHC Run3 and beyond,"  The AliEn (ALICE Environment) file catalogue is a global unique namespace
+providing mapping between a UNIX-like logical name structure and the
+corresponding physical files distributed over 80 storage elements worldwide.
+Powerful search tools and hierarchical metadata information are integral parts
+of the system and are used by the Grid jobs as well as local users to store and
+access all files on the Grid storage elements. The catalogue has been in
+production since 2005 and over the past 11 years has grown to more than 2
+billion logical file names. The backend is a set of distributed relational
+databases, ensuring smooth growth and fast access. Due to the anticipated fast
+future growth, we are looking for ways to enhance the performance and
+scalability by simplifying the catalogue schema while keeping the functionality
+intact. We investigated different backend solutions, such as distributed key
+value stores, as replacement for the relational database. This contribution
+covers the architectural changes in the system, together with the technology
+evaluation, benchmark results and conclusions.
+",1,0,0,0,0,0
+10237,A versatile UHV transport and measurement chamber for neutron reflectometry under UHV conditions,"  We report on a versatile mini ultra-high vacuum (UHV) chamber which is
+designed to be used on the MAgnetic Reflectometer with high Incident Angle of
+the Jülich Centre for Neutron Science at Heinz Maier-Leibnitz Zentrum in
+Garching, Germany. Samples are prepared in the adjacent thin film laboratory by
+molecular beam epitaxy and moved into the compact chamber for transfer without
+exposure to ambient air. The chamber is based on DN 40 CF flanges and equipped
+with sapphire view ports, a small getter pump, and a wobble stick, which serves
+also as sample holder. Here, we present polarized neutron reflectivity
+measurements which have been performed on Co thin films at room temperature in
+UHV and in ambient air in a magnetic field of 200 mT and in the Q-range of 0.18
+\AA$^{-1}$. The results confirm that the Co film is not contaminated during the
+polarized neutron reflectivity measurement. Herewith it is demonstrated that
+the mini UHV transport chamber also works as a measurement chamber which opens
+new possibilities for polarized neutron measurements under UHV conditions.
+",0,1,0,0,0,0
+10238,Dual gauge field theory of quantum liquid crystals in three dimensions,"  The dislocation-mediated quantum melting of solids into quantum liquid
+crystals is extended from two to three spatial dimensions, using a
+generalization of boson-vortex or Abelian-Higgs duality. Dislocations are now
+Burgers-vector-valued strings that trace out worldsheets in spacetime while the
+phonons of the solid dualize into two-form (Kalb-Ramond) gauge fields. We
+propose an effective dual Higgs potential that allows for restoring
+translational symmetry in either one, two or three directions, leading to the
+quantum analogues of columnar, smectic or nematic liquid crystals. In these
+phases, transverse phonons turn into gapped, propagating modes while
+compressional stress remains massless. Rotational Goldstone modes emerge
+whenever translational symmetry is restored. We also consider electrically
+charged matter, and find amongst others that as a hard principle only two out
+of the possible three rotational Goldstone modes are observable using
+electromagnetic means.
+",0,1,0,0,0,0
+10239,Deformation mechanism map of Cu/Nb nanoscale metallic multilayers as a function of temperature and layer thickness,"  The mechanical properties and deformation mechanisms of Cu/Nb nanoscale
+metallic multilayers (NMMs) manufactured by accumulative roll bonding (ARB) are
+studied at 25C and 400C. Cu/Nb NMMs with individual layer thicknesses between 7
+and 63 nm were tested by in-situ micropillar compression inside a scanning
+electron microscope Yield strength, strain-rate sensitivities and activation
+volumes were obtained from the pillar compression tests. The deformed
+micropillars were examined under scanning and transmission electron microscopy
+in order to examine the deformation mechanisms active for different layer
+thicknesses and temperatures. The analysis suggests that room temperature
+deformation was determined by dislocation glide at larger layer thicknesses and
+interface-related mechanisms at the thinner layer thicknesses. The high
+temperature compression tests, in contrast, revealed superior thermo-mechanical
+stability and strength retention for the NMMs with larger layer thicknesses
+with deformation controlled by dislocation glide. A remarkable transition in
+deformation mechanism occurred as the layer thickness decreased, to a
+deformation response controlled by diffusion processes along the interfaces,
+which resulted in temperature-induced softening. A deformation mechanism map,
+in terms of layer thickness and temperature, is proposed from the results
+obtained in this investigation.
+",0,1,0,0,0,0
+10240,Relations between Schramm spaces and generalized Wiener classes,"  We give necessary and sufficient conditions for the embeddings
+$\Lambda\text{BV}^{(p)}\subseteq \Gamma\text{BV}^{(q_n\uparrow q)}$ and
+$\Phi\text{BV}\subseteq\text{BV}^{(q_n\uparrow q)}$. As a consequence, a number
+of results in the literature, including a fundamental theorem of Perlman and
+Waterman, are simultaneously extended.
+",0,0,1,0,0,0
+10241,Stability Selection for Structured Variable Selection,"  In variable or graph selection problems, finding a right-sized model or
+controlling the number of false positives is notoriously difficult. Recently, a
+meta-algorithm called Stability Selection was proposed that can provide
+reliable finite-sample control of the number of false positives. Its benefits
+were demonstrated when used in conjunction with the lasso and orthogonal
+matching pursuit algorithms.
+In this paper, we investigate the applicability of stability selection to
+structured selection algorithms: the group lasso and the structured
+input-output lasso. We find that using stability selection often increases the
+power of both algorithms, but that the presence of complex structure reduces
+the reliability of error control under stability selection. We give strategies
+for setting tuning parameters to obtain a good model size under stability
+selection, and highlight its strengths and weaknesses compared to competing
+methods screen and clean and cross-validation. We give guidelines about when to
+use which error control method.
+",1,0,0,1,0,0
+10242,Vafa-Witten invariants for projective surfaces II: semistable case,"  We propose a definition of Vafa-Witten invariants counting semistable Higgs
+pairs on a polarised surface. We use virtual localisation applied to
+Mochizuki/Joyce-Song pairs.
+For $K_S\le0$ we expect our definition coincides with an alternative
+definition using weighted Euler characteristics. We prove this for deg $K_S<0$
+here, and it is proved for $S$ a K3 surface in \cite{MT}.
+For K3 surfaces we calculate the invariants in terms of modular forms which
+generalise and prove conjectures of Vafa and Witten.
+",0,0,1,0,0,0
+10243,Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds,"  The aggregation of many independent estimates can outperform the most
+accurate individual judgment. This centenarian finding, popularly known as the
+wisdom of crowds, has been applied to problems ranging from the diagnosis of
+cancer to financial forecasting. It is widely believed that social influence
+undermines collective wisdom by reducing the diversity of opinions within the
+crowd. Here, we show that if a large crowd is structured in small independent
+groups, deliberation and social influence within groups improve the crowd's
+collective accuracy. We asked a live crowd (N=5180) to respond to
+general-knowledge questions (e.g., what is the height of the Eiffel Tower?).
+Participants first answered individually, then deliberated and made consensus
+decisions in groups of five, and finally provided revised individual estimates.
+We found that averaging consensus decisions was substantially more accurate
+than aggregating the initial independent opinions. Remarkably, combining as few
+as four consensus choices outperformed the wisdom of thousands of individuals.
+",1,1,0,0,0,0
+10244,"Summertime, and the livin is easy: Winter and summer pseudoseasonal life expectancy in the United States","  In temperate climates, mortality is seasonal with a winter-dominant pattern,
+due in part to pneumonia and influenza. Cardiac causes, which are the leading
+cause of death in the United States, are also winter-seasonal although it is
+not clear why. Interactions between circulating respiratory viruses (f.e.,
+influenza) and cardiac conditions have been suggested as a cause of
+winter-dominant mortality patterns. We propose and implement a way to estimate
+an upper bound on mortality attributable to winter-dominant viruses like
+influenza. We calculate 'pseudo-seasonal' life expectancy, dividing the year
+into two six-month spans, one encompassing winter the other summer. During the
+summer when the circulation of respiratory viruses is drastically reduced, life
+expectancy is about one year longer. We also quantify the seasonal mortality
+difference in terms of seasonal ""equivalent ages"" (defined herein) and
+proportional hazards. We suggest that even if viruses cause excess winter
+cardiac mortality, the population-level mortality reduction of a perfect
+influenza vaccine would be much more modest than is often recognized.
+",0,0,0,1,0,0
+10245,Solving Horn Clauses on Inductive Data Types Without Induction,"  We address the problem of verifying the satisfiability of Constrained Horn
+Clauses (CHCs) based on theories of inductively defined data structures, such
+as lists and trees. We propose a transformation technique whose objective is
+the removal of these data structures from CHCs, hence reducing their
+satisfiability to a satisfiability problem for CHCs on integers and booleans.
+We propose a transformation algorithm and identify a class of clauses where it
+always succeeds. We also consider an extension of that algorithm, which
+combines clause transformation with reasoning on integer constraints. Via an
+experimental evaluation we show that our technique greatly improves the
+effectiveness of applying the Z3 solver to CHCs. We also show that our
+verification technique based on CHC transformation followed by CHC solving, is
+competitive with respect to CHC solvers extended with induction. This paper is
+under consideration for acceptance in TPLP.
+",1,0,0,0,0,0
+10246,The Power of Non-Determinism in Higher-Order Implicit Complexity,"  We investigate the power of non-determinism in purely functional programming
+languages with higher-order types. Specifically, we consider cons-free programs
+of varying data orders, equipped with explicit non-deterministic choice.
+Cons-freeness roughly means that data constructors cannot occur in function
+bodies and all manipulation of storage space thus has to happen indirectly
+using the call stack.
+While cons-free programs have previously been used by several authors to
+characterise complexity classes, the work on non-deterministic programs has
+almost exclusively considered programs of data order 0. Previous work has shown
+that adding explicit non-determinism to cons-free programs taking data of order
+0 does not increase expressivity; we prove that this - dramatically - is not
+the case for higher data orders: adding non-determinism to programs with data
+order at least 1 allows for a characterisation of the entire class of
+elementary-time decidable sets.
+Finally we show how, even with non-deterministic choice, the original
+hierarchy of characterisations is restored by imposing different restrictions.
+",1,0,0,0,0,0
+10247,Upper-limit on the Advanced Virgo output mode cleaner cavity length noise,"  The Advanced Virgo detector uses two monolithic optical cavities at its
+output port to suppress higher order modes and radio frequency sidebands from
+the carrier light used for gravitational wave detection. These two cavities in
+series form the output mode cleaner. We present a measured upper limit on the
+length noise of these cavities that is consistent with the thermo-refractive
+noise prediction of $8 \times 10^{-16}\,\textrm{m/Hz}^{1/2}$ at 15 Hz. The
+cavity length is controlled using Peltier cells and piezo-electric actuators to
+maintain resonance on the incoming light. A length lock precision of $3.5
+\times 10^{-13}\,\textrm{m}$ is achieved. These two results are combined to
+demonstrate that the broadband length noise of the output mode cleaner in the
+10-60 Hz band is at least a factor 10 below other expected noise sources in the
+Advanced Virgo detector design configuration.
+",0,1,0,0,0,0
+10248,Mobile impurities in integrable models,"  We use a mobile impurity or depleton model to study elementary excitations in
+one-dimensional integrable systems. For Lieb-Liniger and bosonic Yang-Gaudin
+models we express two phenomenological parameters characterising renormalised
+inter- actions of mobile impurities with superfluid background: the number of
+depleted particles, $N$ and the superfluid phase drop $\pi J$ in terms of the
+corresponding Bethe Ansatz solution and demonstrate, in the leading order, the
+absence of two-phonon scattering resulting in vanishing rates of inelastic
+processes such as viscosity experienced by the mobile impurities
+",0,1,1,0,0,0
+10249,Time-dependent variational principle in matrix-product state manifolds: pitfalls and potential,"  We study the applicability of the time-dependent variational principle in
+matrix product state manifolds for the long time description of quantum
+interacting systems. By studying integrable and nonintegrable systems for which
+the long time dynamics are known we demonstrate that convergence of long time
+observables is subtle and needs to be examined carefully. Remarkably, for the
+disordered nonintegrable system we consider the long time dynamics are in good
+agreement with the rigorously obtained short time behavior and with previous
+obtained numerically exact results, suggesting that at least in this case the
+apparent convergence of this approach is reliable. Our study indicates that
+while great care must be exercised in establishing the convergence of the
+method, it may still be asymptotically accurate for a class of disordered
+nonintegrable quantum systems.
+",0,1,0,0,0,0
+10250,Self-supporting Topology Optimization for Additive Manufacturing,"  The paper presents a topology optimization approach that designs an optimal
+structure, called a self-supporting structure, which is ready to be fabricated
+via additive manufacturing without the usage of additional support structures.
+Such supports in general have to be created during the fabricating process so
+that the primary object can be manufactured layer by layer without collapse,
+which is very time-consuming and waste of material.
+The proposed approach resolves this problem by formulating the
+self-supporting requirements as a novel explicit quadratic continuous
+constraint in the topology optimization problem, or specifically, requiring the
+number of unsupported elements (in terms of the sum of squares of their
+densities) to be zero. Benefiting form such novel formulations, computing
+sensitivity of the self-supporting constraint with respect to the design
+density is straightforward, which otherwise would require lots of research
+efforts in general topology optimization studies. The derived sensitivity for
+each element is only linearly dependent on its sole density, which, different
+from previous layer-based sensitivities, consequently allows for a parallel
+implementation and possible higher convergence rate. In addition, a discrete
+convolution operator is also designed to detect the unsupported elements as
+involved in each step of optimization iteration, and improves the detection
+process 100 times as compared with simply enumerating these elements. The
+approach works for cases of general overhang angle, or general domain, and
+produces an optimized structures, and their associated optimal compliance, very
+close to that of the reference structure obtained without considering the
+self-supporting constraint, as demonstrated by extensive 2D and 3D benchmark
+examples.
+",1,0,0,0,0,0
+10251,Reward Shaping via Meta-Learning,"  Reward shaping is one of the most effective methods to tackle the crucial yet
+challenging problem of credit assignment in Reinforcement Learning (RL).
+However, designing shaping functions usually requires much expert knowledge and
+hand-engineering, and the difficulties are further exacerbated given multiple
+similar tasks to solve. In this paper, we consider reward shaping on a
+distribution of tasks, and propose a general meta-learning framework to
+automatically learn the efficient reward shaping on newly sampled tasks,
+assuming only shared state space but not necessarily action space. We first
+derive the theoretically optimal reward shaping in terms of credit assignment
+in model-free RL. We then propose a value-based meta-learning algorithm to
+extract an effective prior over the optimal reward shaping. The prior can be
+applied directly to new tasks, or provably adapted to the task-posterior while
+solving the task within few gradient updates. We demonstrate the effectiveness
+of our shaping through significantly improved learning efficiency and
+interpretable visualizations across various settings, including notably a
+successful transfer from DQN to DDPG.
+",1,0,0,1,0,0
+10252,Regulous vector bundles,"  Among recently introduced new notions in real algebraic geometry is that of
+regulous functions. Such functions form a foundation for the development of
+regulous geometry. Several interesting results on regulous varieties and
+regulous sheaves are already available. In this paper, we define and
+investigate regulous vector bundles. We establish algebraic and geometric
+properties of such vector bundles, and identify them with stratified-algebraic
+vector bundles. Furthermore, using new results on curve-rational functions, we
+characterize regulous vector bundles among families of vector spaces
+parametrized by an affine regulous variety. We also study relationships between
+regulous and topological vector bundles.
+",0,0,1,0,0,0
+10253,Remarks on the operator-norm convergence of the Trotter product formula,"  We revise the operator-norm convergence of the Trotter product formula for a
+pair {A,B} of generators of semigroups on a Banach space. Operator-norm
+convergence holds true if the dominating operator A generates a holomorphic
+contraction semigroup and B is a A-infinitesimally small generator of a
+contraction semigroup, in particular, if B is a bounded operator. Inspired by
+studies of evolution semigroups it is shown in the present paper that the
+operator-norm convergence generally fails even for bounded operators B if A is
+not a holomorphic generator. Moreover, it is shown that operator norm
+convergence of the Trotter product formula can be arbitrary slow.
+",0,0,1,0,0,0
+10254,Using data-compressors for statistical analysis of problems on homogeneity testing and classification,"  Nowadays data compressors are applied to many problems of text analysis, but
+many such applications are developed outside of the framework of mathematical
+statistics. In this paper we overcome this obstacle and show how several
+methods of classical mathematical statistics can be developed based on
+applications of the data compressors.
+",1,0,1,1,0,0
+10255,Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections,"  Two-dimensional embeddings remain the dominant approach to visualize high
+dimensional data. The choice of embeddings ranges from highly non-linear ones,
+which can capture complex relationships but are difficult to interpret
+quantitatively, to axis-aligned projections, which are easy to interpret but
+are limited to bivariate relationships. Linear project can be considered as a
+compromise between complexity and interpretability, as they allow explicit axes
+labels, yet provide significantly more degrees of freedom compared to
+axis-aligned projections. Nevertheless, interpreting the axes directions, which
+are linear combinations often with many non-trivial components, remains
+difficult. To address this problem we introduce a structure aware decomposition
+of (multiple) linear projections into sparse sets of axis aligned projections,
+which jointly capture all information of the original linear ones. In
+particular, we use tools from Dempster-Shafer theory to formally define how
+relevant a given axis aligned project is to explain the neighborhood relations
+displayed in some linear projection. Furthermore, we introduce a new approach
+to discover a diverse set of high quality linear projections and show that in
+practice the information of $k$ linear projections is often jointly encoded in
+$\sim k$ axis aligned plots. We have integrated these ideas into an interactive
+visualization system that allows users to jointly browse both linear
+projections and their axis aligned representatives. Using a number of case
+studies we show how the resulting plots lead to more intuitive visualizations
+and new insight.
+",1,0,0,1,0,0
+10256,Exact Recovery with Symmetries for the Doubly-Stochastic Relaxation,"  Graph matching or quadratic assignment, is the problem of labeling the
+vertices of two graphs so that they are as similar as possible. A common method
+for approximately solving the NP-hard graph matching problem is relaxing it to
+a convex optimization problem over the set of doubly stochastic (DS) matrices.
+Recent analysis has shown that for almost all pairs of isomorphic and
+asymmetric graphs, the DS relaxation succeeds in correctly retrieving the
+isomorphism between the graphs. Our goal in this paper is to analyze the case
+of symmetric isomorphic graphs. This goal is motivated by shape matching
+applications where the graphs of interest usually have reflective symmetry.
+For symmetric problems the graph matching problem has multiple isomorphisms
+and so convex relaxations admit all convex combinations of these isomorphisms
+as viable solutions. If the convex relaxation does not admit any additional
+superfluous solution we say that it is convex exact. In this case there are
+tractable algorithms to retrieve an isomorphism from the convex relaxation.
+We show that convex exactness depends strongly on the symmetry group of the
+graphs; For a fixed symmetry group $G$, either the DS relaxation will be convex
+exact for almost all pairs of isomorphic graphs with symmetry group $G$, or the
+DS relaxation will fail for all such pairs. We show that for reflective groups
+with at least one full orbit convex exactness holds almost everywhere, and
+provide some simple examples of non-reflective symmetry groups for which convex
+exactness always fails.
+When convex exactness holds, the isomorphisms of the graphs are the extreme
+points of the convex solution set. We suggest an efficient algorithm for
+retrieving an isomorphism in this case. We also show that the ""convex to
+concave"" projection method will also retrieve an isomorphism in this case.
+",0,0,1,0,0,0
+10257,Statistical physics of human cooperation,"  Extensive cooperation among unrelated individuals is unique to humans, who
+often sacrifice personal benefits for the common good and work together to
+achieve what they are unable to execute alone. The evolutionary success of our
+species is indeed due, to a large degree, to our unparalleled other-regarding
+abilities. Yet, a comprehensive understanding of human cooperation remains a
+formidable challenge. Recent research in social science indicates that it is
+important to focus on the collective behavior that emerges as the result of the
+interactions among individuals, groups, and even societies. Non-equilibrium
+statistical physics, in particular Monte Carlo methods and the theory of
+collective behavior of interacting particles near phase transition points, has
+proven to be very valuable for understanding counterintuitive evolutionary
+outcomes. By studying models of human cooperation as classical spin models, a
+physicist can draw on familiar settings from statistical physics. However,
+unlike pairwise interactions among particles that typically govern solid-state
+physics systems, interactions among humans often involve group interactions,
+and they also involve a larger number of possible states even for the most
+simplified description of reality. The complexity of solutions therefore often
+surpasses that observed in physical systems. Here we review experimental and
+theoretical research that advances our understanding of human cooperation,
+focusing on spatial pattern formation, on the spatiotemporal dynamics of
+observed solutions, and on self-organization that may either promote or hinder
+socially favorable states.
+",1,1,0,0,0,0
+10258,Bayesian Hypernetworks,"  We study Bayesian hypernetworks: a framework for approximate Bayesian
+inference in neural networks. A Bayesian hypernetwork $\h$ is a neural network
+which learns to transform a simple noise distribution, $p(\vec\epsilon) =
+\N(\vec 0,\mat I)$, to a distribution $q(\pp) := q(h(\vec\epsilon))$ over the
+parameters $\pp$ of another neural network (the ""primary network"")\@. We train
+$q$ with variational inference, using an invertible $\h$ to enable efficient
+estimation of the variational lower bound on the posterior $p(\pp | \D)$ via
+sampling. In contrast to most methods for Bayesian deep learning, Bayesian
+hypernets can represent a complex multimodal approximate posterior with
+correlations between parameters, while enabling cheap iid sampling of~$q(\pp)$.
+In practice, Bayesian hypernets can provide a better defense against
+adversarial examples than dropout, and also exhibit competitive performance on
+a suite of tasks which evaluate model uncertainty, including regularization,
+active learning, and anomaly detection.
+",1,0,0,1,0,0
+10259,Block Motion Changes in Japan Triggered by the 2011 Great Tohoku Earthquake,"  Plate motions are governed by equilibrium between basal and edge forces.
+Great earthquakes may induce differential static stress changes across tectonic
+plates, enabling a new equilibrium state. Here we consider the torque balance
+for idealized circular plates and find a simple scalar relationship for changes
+in relative plate speed as a function of its size, upper mantle viscosity, and
+coseismic stress changes. Applied to Japan, the 2011
+$\mathrm{M}_{\mathrm{W}}=9.0$ Tohoku earthquake generated coseismic stresses of
+$10^2-10^5$~Pa that could have induced changes in motion of small (radius
+$\sim100$~km) crustal blocks within Honshu. Analysis of time-dependent GPS
+velocities, with corrections for earthquake cycle effects, reveals that plate
+speeds may have changed by up to $\sim3$ mm/yr between $\sim3.75$-year epochs
+bracketing this earthquake, consistent with an upper mantle viscosity of $\sim
+5\times10^{18}$Pa$\cdot$s, suggesting that great earthquakes may modulate
+motions of proximal crustal blocks at frequencies as high as $10^-8$~Hz.
+",0,1,0,0,0,0
+10260,Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes,"  One of the most challenging tasks when adopting Bayesian Networks (BNs) is
+the one of learning their structure from data. This task is complicated by the
+huge search space of possible solutions, and by the fact that the problem is
+NP-hard. Hence, full enumeration of all the possible solutions is not always
+feasible and approximations are often required. However, to the best of our
+knowledge, a quantitative analysis of the performance and characteristics of
+the different heuristics to solve this problem has never been done before.
+For this reason, in this work, we provide a detailed comparison of many
+different state-of-the-arts methods for structural learning on simulated data
+considering both BNs with discrete and continuous variables, and with different
+rates of noise in the data. In particular, we investigate the performance of
+different widespread scores and algorithmic approaches proposed for the
+inference and the statistical pitfalls within them.
+",1,0,0,1,0,0
+10261,Thermal fracturing on comets. Applications to 67P/Churyumov-Gerasimenko,"  We simulate the stresses induced by temperature changes in a putative hard
+layer near the surface of comet 67P/Churyumov--Gerasimenko with a
+thermo-viscoelastic model. Such a layer could be formed by the recondensation
+or sintering of water ice (and dust grains), as suggested by laboratory
+experiments and computer simulations, and would explain the high compressive
+strength encountered by experiments on board the Philae lander. Changes in
+temperature from seasonal insolation variation penetrate into the comet's
+surface to depths controlled by the thermal inertia, causing the material to
+expand and contract. Modelling this with a Maxwellian viscoelastic response on
+a spherical nucleus, we show that a hard, icy layer with similar properties to
+Martian permafrost will experience high stresses: up to tens of MPa, which
+exceed its material strength (a few MPa), down to depths of centimetres to a
+metre. The stress distribution with latitude is confirmed qualitatively when
+taking into account the comet's complex shape but neglecting thermal inertia.
+Stress is found to be comparable to the material strength everywhere for
+sufficient thermal inertia ($\gtrsim50$ J m$^{-2}$ K$^{-1}$ s$^{-1/2}$) and ice
+content ($\gtrsim 45\%$ at the equator). In this case, stresses penetrate to a
+typical depth of $\sim0.25$ m, consistent with the detection of metre-scale
+thermal contraction crack polygons all over the comet. Thermal fracturing may
+be an important erosion process on cometary surfaces which breaks down material
+and weakens cliffs.
+",0,1,0,0,0,0
+10262,ADN: An Information-Centric Networking Architecture for the Internet of Things,"  Forwarding data by name has been assumed to be a necessary aspect of an
+information-centric redesign of the current Internet architecture that makes
+content access, dissemination, and storage more efficient. The Named Data
+Networking (NDN) and Content-Centric Networking (CCNx) architectures are the
+leading examples of such an approach. However, forwarding data by name incurs
+storage and communication complexities that are orders of magnitude larger than
+solutions based on forwarding data using addresses. Furthermore, the specific
+algorithms used in NDN and CCNx have been shown to have a number of
+limitations. The Addressable Data Networking (ADN) architecture is introduced
+as an alternative to NDN and CCNx. ADN is particularly attractive for
+large-scale deployments of the Internet of Things (IoT), because it requires
+far less storage and processing in relaying nodes than NDN. ADN allows things
+and data to be denoted by names, just like NDN and CCNx do. However, instead of
+replacing the waist of the Internet with named-data forwarding, ADN uses an
+address-based forwarding plane and introduces an information plane that
+seamlessly maps names to addresses without the involvement of end-user
+applications. Simulation results illustrate the order of magnitude savings in
+complexity that can be attained with ADN compared to NDN.
+",1,0,0,0,0,0
+10263,Urban Delay Tolerant Network Simulator (UDTNSim v0.1),"  Delay Tolerant Networking (DTN) is an approach to networking which handles
+network disruptions and high delays that may occur in many kinds of
+communication networks. The major reasons for high delay include partial
+connectivity of networks as can be seen in many types of ad hoc wireless
+networks with frequent network partitions, long propagation time as experienced
+in inter-planetary and deep space networks, and frequent link disruptions due
+to the mobility of nodes as observed in terrestrial wireless network
+environments. Experimenting network architectures, protocols, and mobility
+models in such real-world scenarios is difficult due to the complexities
+involved in the network environment. Therefore, in this document, we present
+the documentation of an Urban Delay Tolerant Network Simulator (UDTNSim)
+version 0.1, capable of simulating urban road network environments with DTN
+characteristics including mobility models and routing protocols. The mobility
+models included in this version of UDTNSim are (i) Stationary Movement, (ii)
+Simple Random Movement, (iii) Path Type Based Movememt, (iv) Path Memory Based
+Movement, (v) Path Type with Restricted Movement, and (vi) Path Type with Wait
+Movement. In addition to mobility models, we also provide three routing and
+data hand-off protocols: (i) Epidemic Routing, (ii) Superior Only Handoff, and
+(iii) Superior Peer Handoff. UDTNSim v0.1 is designed using object-oriented
+programming approach in order to provide flexibility in addition of new
+features to the DTN environment. UDTNSim v0.1 is distributed as an open source
+simulator for the use of the research community.
+",1,0,0,0,0,0
+10264,Regularized Ordinal Regression and the ordinalNet R Package,"  Regularization techniques such as the lasso (Tibshirani 1996) and elastic net
+(Zou and Hastie 2005) can be used to improve regression model coefficient
+estimation and prediction accuracy, as well as to perform variable selection.
+Ordinal regression models are widely used in applications where the use of
+regularization could be beneficial; however, these models are not included in
+many popular software packages for regularized regression. We propose a
+coordinate descent algorithm to fit a broad class of ordinal regression models
+with an elastic net penalty. Furthermore, we demonstrate that each model in
+this class generalizes to a more flexible form, for instance to accommodate
+unordered categorical data. We introduce an elastic net penalty class that
+applies to both model forms. Additionally, this penalty can be used to shrink a
+non-ordinal model toward its ordinal counterpart. Finally, we introduce the R
+package ordinalNet, which implements the algorithm for this model class.
+",0,0,0,1,0,0
+10265,Visual Multiple-Object Tracking for Unknown Clutter Rate,"  In multi-object tracking applications, model parameter tuning is a
+prerequisite for reliable performance. In particular, it is difficult to know
+statistics of false measurements due to various sensing conditions and changes
+in the field of views. In this paper we are interested in designing a
+multi-object tracking algorithm that handles unknown false measurement rate.
+Recently proposed robust multi-Bernoulli filter is employed for clutter
+estimation while generalized labeled multi-Bernoulli filter is considered for
+target tracking. Performance evaluation with real videos demonstrates the
+effectiveness of the tracking algorithm for real-world scenarios.
+",1,0,0,0,0,0
+10266,On Integrated $L^{1}$ Convergence Rate of an Isotonic Regression Estimator for Multivariate Observations,"  We consider a general monotone regression estimation where we allow for
+independent and dependent regressors. We propose a modification of the
+classical isotonic least squares estimator and establish its rate of
+convergence for the integrated $L_1$-loss function. The methodology captures
+the shape of the data without assuming additivity or a parametric form for the
+regression function. Furthermore, the degree of smoothing is chosen
+automatically and no auxiliary tuning is required for the theoretical analysis.
+Some simulations and two real data illustrations complement the study of the
+proposed estimator.
+",0,0,1,1,0,0
+10267,"Towards Fast-Convergence, Low-Delay and Low-Complexity Network Optimization","  Distributed network optimization has been studied for well over a decade.
+However, we still do not have a good idea of how to design schemes that can
+simultaneously provide good performance across the dimensions of utility
+optimality, convergence speed, and delay. To address these challenges, in this
+paper, we propose a new algorithmic framework with all these metrics
+approaching optimality. The salient features of our new algorithm are
+three-fold: (i) fast convergence: it converges with only $O(\log(1/\epsilon))$
+iterations that is the fastest speed among all the existing algorithms; (ii)
+low delay: it guarantees optimal utility with finite queue length; (iii) simple
+implementation: the control variables of this algorithm are based on virtual
+queues that do not require maintaining per-flow information. The new technique
+builds on a kind of inexact Uzawa method in the Alternating Directional Method
+of Multiplier, and provides a new theoretical path to prove global and linear
+convergence rate of such a method without requiring the full rank assumption of
+the constraint matrix.
+",1,0,1,0,0,0
+10268,On zeros of polynomials in best $L^p$-approximation and inserting mass points,"  The purpose of this note is to revive in $L^p$ spaces the original A. Markov
+ideas to study monotonicity of zeros of orthogonal polynomials. This allows us
+to prove and improve in a simple and unified way our previous result [Electron.
+Trans. Numer. Anal., 44 (2015), pp. 271-280] concerning the discrete version of
+A. Markov's theorem on monotonicity of zeros.
+",0,0,1,0,0,0
+10269,Empirical likelihood inference for partial functional linear regression models based on B spline,"  In this paper, we apply empirical likelihood method to inference for the
+regression parameters in the partial functional linear regression models based
+on B spline. We prove that the empirical log likelihood ratio for the
+regression parameters converges in law to a weighted sum of independent chi
+square distributions and run simulations to assess the finite sample
+performance of our method.
+",0,0,0,1,0,0
+10270,Dropout Inference in Bayesian Neural Networks with Alpha-divergences,"  To obtain uncertainty estimates with real-world Bayesian deep learning
+models, practical inference approximations are needed. Dropout variational
+inference (VI) for example has been used for machine vision and medical
+applications, but VI can severely underestimates model uncertainty.
+Alpha-divergences are alternative divergences to VI's KL objective, which are
+able to avoid VI's uncertainty underestimation. But these are hard to use in
+practice: existing techniques can only use Gaussian approximating
+distributions, and require existing models to be changed radically, thus are of
+limited use for practitioners. We propose a re-parametrisation of the
+alpha-divergence objectives, deriving a simple inference technique which,
+together with dropout, can be easily implemented with existing models by simply
+changing the loss of the model. We demonstrate improved uncertainty estimates
+and accuracy compared to VI in dropout networks. We study our model's epistemic
+uncertainty far away from the data using adversarial images, showing that these
+can be distinguished from non-adversarial images by examining our model's
+uncertainty.
+",1,0,0,1,0,0
+10271,Semiparametric Mixtures of Regressions with Single-index for Model Based Clustering,"  In this article, we propose two classes of semiparametric mixture regression
+models with single-index for model based clustering. Unlike many
+semiparametric/nonparametric mixture regression models that can only be applied
+to low dimensional predictors, the new semiparametric models can easily
+incorporate high dimensional predictors into the nonparametric components. The
+proposed models are very general, and many of the recently proposed
+semiparametric/nonparametric mixture regression models are indeed special cases
+of the new models. Backfitting estimates and the corresponding modified EM
+algorithms are proposed to achieve optimal convergence rates for both
+parametric and nonparametric parts. We establish the identifiability results of
+the proposed two models and investigate the asymptotic properties of the
+proposed estimation procedures. Simulation studies are conducted to demonstrate
+the finite sample performance of the proposed models. An application of NBA
+data by new models reveals some new findings.
+",0,0,0,1,0,0
+10272,"Deep learning Approach for Classifying, Detecting and Predicting Photometric Redshifts of Quasars in the Sloan Digital Sky Survey Stripe 82","  We apply a convolutional neural network (CNN) to classify and detect quasars
+in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric
+redshifts of quasars. The network takes the variability of objects into account
+by converting light curves into images. The width of the images, noted w,
+corresponds to the five magnitudes ugriz and the height of the images, noted h,
+represents the date of the observation. The CNN provides good results since its
+precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for
+the same recall with a random forest classifier. Moreover 175 new quasar
+candidates are found with the CNN considering a fixed recall of 0.97. The
+combination of probabilities given by the CNN and the random forest makes good
+performance even better with a precision of 0.99 for a recall of 0.90.
+For the redshift predictions, the CNN presents excellent results which are
+higher than those obtained with a feature extraction step and different
+classifiers (a K-nearest-neighbors, a support vector machine, a random forest
+and a gaussian process classifier). Indeed, the accuracy of the CNN within
+|\Delta z|<0.1 can reach 78.09%, within |\Delta z|<0.2 reaches 86.15%, within
+|\Delta z|<0.3 reaches 91.2% and the value of rms is 0.359. The performance of
+the KNN decreases for the three |\Delta z| regions, since within the accuracy
+of |\Delta z|<0.1, |\Delta z|<0.2 and |\Delta z|<0.3 is 73.72%, 82.46% and
+90.09% respectively, and the value of rms amounts to 0.395. So the CNN
+successfully reduces the dispersion and the catastrophic redshifts of quasars.
+This new method is very promising for the future of big databases like the
+Large Synoptic Survey Telescope.
+",0,1,0,0,0,0
+10273,"Sources of inter-model scatter in TRACMIP, the Tropical Rain belts with an Annual cycle and a Continent Model Intercomparison Project","  We analyze the source of inter-model scatter in the surface temperature
+response to quadrupling CO2 in two sets of GCM simulations from the Tropical
+Rain Belts with an Annual cycle and a Continent Model Intercomparison Project
+(TRACMIP; Voigt et al, 2016). TRACMIP provides simulations of idealized
+climates that allow for studying the fundamental dynamics of tropical rainfall
+and its response to climate change. One configuration is an aquaplanet
+atmosphere (i.e., with zonally-symmetric boundary conditions) coupled to a slab
+ocean (AquaCTL and Aqua4x). The other includes an equatorial continent
+represented by a thin slab ocean with increased surface albedo and decreased
+evaporation (LandCTL and Land4x).
+",0,1,0,0,0,0
+10274,Sub-clustering in decomposable graphs and size-varying junction trees,"  This paper proposes a novel representation of decomposable graphs based on
+semi-latent tree-dependent bipartite graphs. The novel representation has two
+main benefits. First, it enables a form of sub-clustering within maximal
+cliques of the graph, adding informational richness to the general use of
+decomposable graphs that could be harnessed in applications with behavioural
+type of data. Second, it allows for a new node-driven Markov chain Monte Carlo
+sampler of decomposable graphs that can easily parallelize and scale. The
+proposed sampler also benefits from the computational efficiency of
+junction-tree-based samplers of decomposable graphs.
+",1,0,0,1,0,0
+10275,The inflation technique solves completely the classical inference problem,"  The causal inference problem consists in determining whether a probability
+distribution over a set of observed variables is compatible with a given causal
+structure. In [arXiv:1609.00672], one of us introduced a hierarchy of necessary
+linear programming constraints which all the observed distributions compatible
+with the considered causal structure must satisfy. In this work, we prove that
+the inflation hierarchy is complete, i.e., any distribution of the observed
+variables which does not admit a realization within the considered causal
+structure will fail one of the inflation tests. More quantitatively, we show
+that any distribution of measurable events satisfying the $n^{th}$ inflation
+test is $O\left(\frac{1}{\sqrt{n}}\right)$-close in Euclidean norm to a
+distribution realizable within the given causal structure. In addition, we show
+that the corresponding $n^{th}$-order relaxation of the dual problem consisting
+in maximizing a $k^{th}$ degree polynomial on the observed variables is
+$O\left(\frac{k^2}{n}\right)$-close to the optimal solution.
+",0,0,1,1,0,0
+10276,Magnetic charge injection in spin ice: a new way to fragmentation,"  The complexity embedded in condensed matter fertilizes the discovery of new
+states of matter, enriched by ingredients like frustration. Illustrating
+examples in magnetic systems are Kitaev spin liquids, skyrmions phases, or spin
+ices. These unconventional ground states support exotic excitations, for
+example the magnetic charges in spin ices, also called monopoles. Beyond their
+discovery, an important challenge is to be able to control and manipulate them.
+Here, we propose a new mechanism to inject monopoles in a spin ice through a
+staggered magnetic field. We show theoretically, and demonstrate experimentally
+in the Ho$_2$Ir$_2$O$_7$ pyrochlore iridate, that it results in the
+stabilization of a monopole crystal, which exhibits magnetic fragmentation. In
+this new state of matter, the magnetic moment fragments into an ordered part
+and a persistently fluctuating one. Compared to conventional spin ices, the
+different nature of the excitations in this fragmented state opens the way to
+novel tunable field-induced and dynamical behaviors.
+",0,1,0,0,0,0
+10277,Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented with Fuzzy Clustering,"  Robotic systems, working together as a team, are becoming valuable players in
+different real-world applications, from disaster response to warehouse
+fulfillment services. Centralized solutions for coordinating multi-robot teams
+often suffer from poor scalability and vulnerability to communication
+disruptions. This paper develops a decentralized multi-agent task allocation
+(Dec-MATA) algorithm for multi-robot applications. The task planning problem is
+posed as a maximum-weighted matching of a bipartite graph, the solution of
+which using the blossom algorithm allows each robot to autonomously identify
+the optimal sequence of tasks it should undertake. The graph weights are
+determined based on a soft clustering process, which also plays a problem
+decomposition role seeking to reduce the complexity of the individual-agents'
+task assignment problems. To evaluate the new Dec-MATA algorithm, a series of
+case studies (of varying complexity) are performed, with tasks being
+distributed randomly over an observable 2D environment. A centralized approach,
+based on a state-of-the-art MILP formulation of the multi-Traveling Salesman
+problem is used for comparative analysis. While getting within 7-28% of the
+optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is
+found to be 1-3 orders of magnitude faster and minimally sensitive to
+task-to-robot ratios, unlike the centralized algorithm.
+",1,0,0,0,0,0
+10278,Combining Static and Dynamic Features for Multivariate Sequence Classification,"  Model precision in a classification task is highly dependent on the feature
+space that is used to train the model. Moreover, whether the features are
+sequential or static will dictate which classification method can be applied as
+most of the machine learning algorithms are designed to deal with either one or
+another type of data. In real-life scenarios, however, it is often the case
+that both static and dynamic features are present, or can be extracted from the
+data. In this work, we demonstrate how generative models such as Hidden Markov
+Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can
+be used to extract temporal information from the dynamic data. We explore how
+the extracted information can be combined with the static features in order to
+improve the classification performance. We evaluate the existing techniques and
+suggest a hybrid approach, which outperforms other methods on several public
+datasets.
+",1,0,0,1,0,0
+10279,Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods,"  Our goal is to improve variance reducing stochastic methods through better
+control variates. We first propose a modification of SVRG which uses the
+Hessian to track gradients over time, rather than to recondition, increasing
+the correlation of the control variates and leading to faster theoretical
+convergence close to the optimum. We then propose accurate and computationally
+efficient approximations to the Hessian, both using a diagonal and a low-rank
+matrix. Finally, we demonstrate the effectiveness of our method on a wide range
+of problems.
+",1,0,0,1,0,0
+10280,On spectral properties of Neuman-Poincare operator and plasmonic resonances in 3D elastostatics,"  We consider plasmon resonances and cloaking for the elastostatic system in
+$\mathbb{R}^3$ via the spectral theory of Neumann-Poincaré operator. We first
+derive the full spectral properties of the Neumann-Poincaré operator for the
+3D elastostatic system in the spherical geometry. The spectral result is of
+significant interest for its own sake, and serves as a highly nontrivial
+extension of the corresponding 2D study in [8]. The derivation of the spectral
+result in 3D involves much more complicated and subtle calculations and
+arguments than that for the 2D case. Then we consider a 3D plasmonic structure
+in elastostatics which takes a general core-shell-matrix form with the
+metamaterial located in the shell. Using the obtained spectral result, we
+provide an accurate characterisation of the anomalous localised resonance and
+cloaking associated to such a plasmonic structure.
+",0,0,1,0,0,0
+10281,A hybridizable discontinuous Galerkin method for the Navier--Stokes equations with pointwise divergence-free velocity field,"  We introduce a hybridizable discontinuous Galerkin method for the
+incompressible Navier--Stokes equations for which the approximate velocity
+field is pointwise divergence-free. The method builds on the method presented
+by Labeur and Wells [SIAM J. Sci. Comput., vol. 34 (2012), pp. A889--A913]. We
+show that with modifications of the function spaces in the method of Labeur and
+Wells it is possible to formulate a simple method with pointwise
+divergence-free velocity fields which is momentum conserving, energy stable,
+and pressure-robust. Theoretical results are supported by two- and
+three-dimensional numerical examples and for different orders of polynomial
+approximation.
+",1,1,0,0,0,0
+10282,Near Perfect Protein Multi-Label Classification with Deep Neural Networks,"  Artificial neural networks (ANNs) have gained a well-deserved popularity
+among machine learning tools upon their recent successful applications in
+image- and sound processing and classification problems. ANNs have also been
+applied for predicting the family or function of a protein, knowing its residue
+sequence. Here we present two new ANNs with multi-label classification ability,
+showing impressive accuracy when classifying protein sequences into 698 UniProt
+families (AUC=99.99%) and 983 Gene Ontology classes (AUC=99.45%).
+",1,0,0,1,0,0
+10283,Comparison of Decoding Strategies for CTC Acoustic Models,"  Connectionist Temporal Classification has recently attracted a lot of
+interest as it offers an elegant approach to building acoustic models (AMs) for
+speech recognition. The CTC loss function maps an input sequence of observable
+feature vectors to an output sequence of symbols. Output symbols are
+conditionally independent of each other under CTC loss, so a language model
+(LM) can be incorporated conveniently during decoding, retaining the
+traditional separation of acoustic and linguistic components in ASR. For fixed
+vocabularies, Weighted Finite State Transducers provide a strong baseline for
+efficient integration of CTC AMs with n-gram LMs. Character-based neural LMs
+provide a straight forward solution for open vocabulary speech recognition and
+all-neural models, and can be decoded with beam search. Finally,
+sequence-to-sequence models can be used to translate a sequence of individual
+sounds into a word string. We compare the performance of these three
+approaches, and analyze their error patterns, which provides insightful
+guidance for future research and development in this important area.
+",1,0,0,0,0,0
+10284,Battery Degradation Maps for Power System Optimization and as a Benchmark Reference,"  This paper presents a novel method to describe battery degradation. We use
+the concept of degradation maps to model the incremental charge capacity loss
+as a function of discrete battery control actions and state of charge. The maps
+can be scaled to represent any battery system in size and power. Their convex
+piece-wise affine representations allow for tractable optimal control
+formulations and can be used in power system simulations to incorporate battery
+degradation. The map parameters for different battery technologies are
+published making them an useful basis to benchmark different battery
+technologies in case studies.
+",1,0,0,0,0,0
+10285,Computer Assisted Localization of a Heart Arrhythmia,"  We consider the problem of locating a point-source heart arrhythmia using
+data from a standard diagnostic procedure, where a reference catheter is placed
+in the heart, and arrival times from a second diagnostic catheter are recorded
+as the diagnostic catheter moves around within the heart. We model this
+situation as a nonconvex feasibility problem, where given a set of arrival
+times, we look for a source location that is consistent with the available
+data. We develop a new optimization approach and fast algorithm to obtain
+online proposals for the next location to suggest to the operator as she
+collects data. We validate the procedure using a Monte Carlo simulation based
+on patients' electrophysiological data. The proposed procedure robustly and
+quickly locates the source of arrhythmias without any prior knowledge of heart
+anatomy.
+",0,0,0,1,0,0
+10286,Solving for high dimensional committor functions using artificial neural networks,"  In this note we propose a method based on artificial neural network to study
+the transition between states governed by stochastic processes. In particular,
+we aim for numerical schemes for the committor function, the central object of
+transition path theory, which satisfies a high-dimensional Fokker-Planck
+equation. By working with the variational formulation of such partial
+differential equation and parameterizing the committor function in terms of a
+neural network, approximations can be obtained via optimizing the neural
+network weights using stochastic algorithms. The numerical examples show that
+moderate accuracy can be achieved for high-dimensional problems.
+",1,0,0,1,0,0
+10287,The possibility of constructing a relativistic space of information states based on the theory of complexity and analogies with physical space-time,"  The possibility of calculation of the conditional and unconditional
+complexity of description of information objects in the algorithmic theory of
+information is connected with the limitations for the set of the used languages
+of programming (description). The results of calculation of the conditional
+complexity allow introducing the fundamental information dimensions and the
+partial ordering in the set of information objects, and the requirement of
+equality of languages allows introducing the vector space. In case of optimum
+compression, the ""prefix"" contains the regular part of the information about
+the object, and is analogous to the classical trajectory of a material point in
+the physical space, and the ""suffix"" contains the random part of the
+information, the quantity of which is analogous to the physical time in the
+intrinsic reference system. Analysis of the mechanism of the ""Einstein's clock""
+allows representing the result of observation of the material point as a word,
+written down in a binary alphabet, thus making the aforesaid analogies more
+clear. The kinematics of the information trajectories is described by the
+Lorentz's transformations, identically to its physical analog. At the same
+time, various languages of description are associated with various reference
+systems in physics. In the present paper, the information analog of the
+principle of least action is found and the main problems of information
+dynamics in the constructed space are formulated.
+",0,1,0,0,0,0
+10288,Problems on Matchings and Independent Sets of a Graph,"  Let $G$ be a finite simple graph. For $X \subset V(G)$, the difference of
+$X$, $d(X) := |X| - |N (X)|$ where $N(X)$ is the neighborhood of $X$ and $\max
+\, \{d(X):X\subset V(G)\}$ is called the critical difference of $G$. $X$ is
+called a critical set if $d(X)$ equals the critical difference and ker$(G)$ is
+the intersection of all critical sets. It is known that ker$(G)$ is an
+independent (vertex) set of $G$. diadem$(G)$ is the union of all critical
+independent sets. An independent set $S$ is an inclusion minimal set with $d(S)
+> 0$ if no proper subset of $S$ has positive difference.
+A graph $G$ is called König-Egerváry if the sum of its independence
+number ($\alpha (G)$) and matching number ($\mu (G)$) equals $|V(G)|$. It is
+known that bipartite graphs are König-Egerváry.
+In this paper, we study independent sets with positive difference for which
+every proper subset has a smaller difference and prove a result conjectured by
+Levit and Mandrescu in 2013. The conjecture states that for any graph, the
+number of inclusion minimal sets $S$ with $d(S) > 0$ is at least the critical
+difference of the graph. We also give a short proof of the inequality
+$|$ker$(G)| + |$diadem$(G)| \le 2\alpha (G)$ (proved by Short in 2016).
+A characterization of unicyclic non-König-Egerváry graphs is also
+presented and a conjecture which states that for such a graph $G$, the critical
+difference equals $\alpha (G) - \mu (G)$, is proved.
+We also make an observation about ker$G)$ using Edmonds-Gallai Structure
+Theorem as a concluding remark.
+",1,0,1,0,0,0
+10289,Nonsequential double ionization of helium in IR+XUV two-color laser fields II: Collision-excitation ionization process,"  The collision-ionization mechanism of nonsequential double ionization (NSDI)
+process in IR+XUV two-color laser fields [\PRA \textbf{93}, 043417 (2016)] has
+been investigated by us recently. Here we extend this work to study the
+collision-excitation-ionization (CEI) mechanism of NSDI processes in the
+two-color laser fields with different laser conditions. It is found that the
+CEI mechanism makes a dominant contribution to the NSDI as the XUV photon
+energy is smaller than the ionization threshold of the He$^+$ ion, and the
+momentum spectrum shows complex interference patterns and symmetrical
+structures. By channel analysis, we find that, as the energy carried by the
+recollision electron is not enough to excite the bound electron, the bound
+electron will absorb XUV photons during their collision, as a result, both
+forward and backward collisions make a comparable contributions to the NSDI
+processes. However, it is found that, as the energy carried by the recollision
+electron is large enough to excite the bound electron, the bound electron does
+not absorb any XUV photon and it is excited only by sharing the energy carried
+by the recollsion electron, hence the forward collision plays a dominant role
+on the NSDI processes. Moreover, we find that the interference patterns of the
+NSDI spectra can be reconstructed by the spectra of two above-threshold
+ionization (ATI) processes, which may be used to analyze the structure of the
+two separate ATI spectra by NSDI processes.
+",0,1,0,0,0,0
+10290,Chaos or Order?,"  What is chaos? Despite several decades of research on this ubiquitous and
+fundamental phenomenon there is yet no agreed-upon answer to this question.
+Recently, it was realized that all stochastic and deterministic differential
+equations, describing all natural and engineered dynamical systems, possess a
+topological supersymmetry. It was then suggested that its spontaneous breakdown
+could be interpreted as the stochastic generalization of deterministic chaos.
+This conclusion stems from the fact that such phenomenon encompasses features
+that are traditionally associated with chaotic dynamics such as
+non-integrability, positive topological entropy, sensitivity to initial
+conditions, and the Poincare-Bendixson theorem. Here, we strengthen and
+complete this picture by showing that the hallmarks of set-theoretic chaos --
+topological transitivity/mixing and dense periodic orbits -- can also be
+attributed to the spontaneous breakdown of topological supersymmetry. We also
+demonstrate that these features, which highlight the noisy character of chaotic
+dynamics, do not actually admit a stochastic generalization. We therefore
+conclude that spontaneous topological symmetry breaking can be considered as
+the most general definition of continuous-time dynamical chaos. Contrary to the
+common perception and semantics of the word ""chaos"", this phenomenon should
+then be truly interpreted as the low-symmetry, or ordered phase of the
+dynamical systems that manifest it. Since the long-range order in this case is
+temporal, we then suggest the word ""chronotaxis"" as a better representation of
+this phenomenon.
+",0,1,1,0,0,0
+10291,Gaia Data Release 1: The archive visualisation service,"  Context: The first Gaia data release (DR1) delivered a catalogue of
+astrometry and photometry for over a billion astronomical sources. Within the
+panoply of methods used for data exploration, visualisation is often the
+starting point and even the guiding reference for scientific thought. However,
+this is a volume of data that cannot be efficiently explored using traditional
+tools, techniques, and habits.
+Aims: We aim to provide a global visual exploration service for the Gaia
+archive, something that is not possible out of the box for most people. The
+service has two main goals. The first is to provide a software platform for
+interactive visual exploration of the archive contents, using common personal
+computers and mobile devices available to most users. The second aim is to
+produce intelligible and appealing visual representations of the enormous
+information content of the archive.
+Methods: The interactive exploration service follows a client-server design.
+The server runs close to the data, at the archive, and is responsible for
+hiding as far as possible the complexity and volume of the Gaia data from the
+client. This is achieved by serving visual detail on demand. Levels of detail
+are pre-computed using data aggregation and subsampling techniques. For DR1,
+the client is a web application that provides an interactive multi-panel
+visualisation workspace as well as a graphical user interface.
+Results: The Gaia archive Visualisation Service offers a web-based
+multi-panel interactive visualisation desktop in a browser tab. It currently
+provides highly configurable 1D histograms and 2D scatter plots of Gaia DR1 and
+the Tycho-Gaia Astrometric Solution (TGAS) with linked views. An innovative
+feature is the creation of ADQL queries from visually defined regions in plots.
+[abridged]
+",0,1,0,0,0,0
+10292,Monte Carlo methods for massively parallel computers,"  Applications that require substantial computational resources today cannot
+avoid the use of heavily parallel machines. Embracing the opportunities of
+parallel computing and especially the possibilities provided by a new
+generation of massively parallel accelerator devices such as GPUs, Intel's Xeon
+Phi or even FPGAs enables applications and studies that are inaccessible to
+serial programs. Here we outline the opportunities and challenges of massively
+parallel computing for Monte Carlo simulations in statistical physics, with a
+focus on the simulation of systems exhibiting phase transitions and critical
+phenomena. This covers a range of canonical ensemble Markov chain techniques as
+well as generalized ensembles such as multicanonical simulations and population
+annealing. While the examples discussed are for simulations of spin systems,
+many of the methods are more general and moderate modifications allow them to
+be applied to other lattice and off-lattice problems including polymers and
+particle systems. We discuss important algorithmic requirements for such highly
+parallel simulations, such as the challenges of random-number generation for
+such cases, and outline a number of general design principles for parallel
+Monte Carlo codes to perform well.
+",0,1,0,0,0,0
+10293,A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform,"  Kiva is an online non-profit crowdsouring microfinance platform that raises
+funds for the poor in the third world. The borrowers on Kiva are small business
+owners and individuals in urgent need of money. To raise funds as fast as
+possible, they have the option to form groups and post loan requests in the
+name of their groups. While it is generally believed that group loans pose less
+risk for investors than individual loans do, we study whether this is the case
+in a philanthropic online marketplace. In particular, we measure the effect of
+group loans on funding time while controlling for the loan sizes and other
+factors. Because loan descriptions (in the form of texts) play an important
+role in lenders' decision process on Kiva, we make use of this information
+through deep learning in natural language processing. In this aspect, this is
+the first paper that uses one of the most advanced deep learning techniques to
+deal with unstructured data in a way that can take advantage of its superior
+prediction power to answer causal questions. We find that on average, forming
+group loans speeds up the funding time by about 3.3 days.
+",0,0,0,1,0,0
+10294,JADE - A Platform for Research on Cooperation of Physical and Virtual Agents,"  In the ICS, WUT a platform for simulation of cooperation of physical and
+virtual mobile agents is under development. The paper describes the motivation
+of the research, an organization of the platform, a model of agent, and the
+principles of design of the platform. Several experimental simulations are
+briefly described.
+",1,0,0,0,0,0
+10295,Analytic Expressions for the Inner-Rim Structure of Passively Heated Protoplanetary Disks,"  We analytically derive the expressions for the structure of the inner region
+of protoplanetary disks based on the results from the recent hydrodynamical
+simulations. The inner part of a disk can be divided into four regions:
+dust-free region with gas temperature in the optically thin limit, optically
+thin dust halo, optically thick condensation front and the classical optically
+thick region in order from the inside. We derive the dust-to-gas mass ratio
+profile in the dust halo using the fact that partial dust condensation
+regulates the temperature to the dust evaporation temperature. Beyond the dust
+halo, there is an optically thick condensation front where all the available
+silicate gas condenses out. The curvature of the condensation surface is
+determined by the condition that the surface temperature must be nearly equal
+to the characteristic temperature $\sim 1200{\,\rm K}$. We derive the mid-plane
+temperature in the outer two regions using the two-layer approximation with the
+additional heating by the condensation front for the outermost region. As a
+result, the overall temperature profile is step-like with steep gradients at
+the borders between the outer three regions. The borders might act as planet
+traps where the inward migration of planets due to gravitational interaction
+with the gas disk stops. The temperature at the border between the two
+outermost regions coincides with the temperature needed to activate
+magnetorotational instability, suggesting that the inner edge of the dead zone
+must lie at this border. The radius of the dead-zone inner edge predicted from
+our solution is $\sim$ 2-3 times larger than that expected from the classical
+optically thick temperature.
+",0,1,0,0,0,0
+10296,X-ray Emission Spectrum of Liquid Ethanol : Origin of Split Peaks,"  The X-ray emission spectrum of liquid ethanol was calculated using density
+functional theory and a semi-classical approximation to the Kramers-Heisenberg
+formula including core-hole-induced dynamics. Our spectrum agrees well with the
+experimental spectrum. We found that the intensity ratio between the two peaks
+at 526 and 527 eV assigned as 10a' and 3a"" depends not only on the hydrogen
+bonding network around the target molecule, but also on the intramolecular
+conformation. This effect is absent in liquid methanol and demonstrates the
+high sensitivity of X-ray emission to molecular structure. The dependence of
+spectral features on hydrogen-bonding as well as on dynamical effects following
+core-excitation are also discussed.
+",0,1,0,0,0,0
+10297,Activation Maximization Generative Adversarial Nets,"  Class labels have been empirically shown useful in improving the sample
+quality of generative adversarial nets (GANs). In this paper, we mathematically
+study the properties of the current variants of GANs that make use of class
+label information. With class aware gradient and cross-entropy decomposition,
+we reveal how class labels and associated losses influence GAN's training.
+Based on that, we propose Activation Maximization Generative Adversarial
+Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been
+conducted to validate our analysis and evaluate the effectiveness of our
+solution, where AM-GAN outperforms other strong baselines and achieves
+state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we
+demonstrate that, with the Inception ImageNet classifier, Inception Score
+mainly tracks the diversity of the generator, and there is, however, no
+reliable evidence that it can reflect the true sample quality. We thus propose
+a new metric, called AM Score, to provide a more accurate estimation of the
+sample quality. Our proposed model also outperforms the baseline methods in the
+new metric.
+",1,0,0,1,0,0
+10298,Job Management and Task Bundling,"  High Performance Computing is often performed on scarce and shared computing
+resources. To ensure computers are used to their full capacity, administrators
+often incentivize large workloads that are not possible on smaller systems.
+Measurements in Lattice QCD frequently do not scale to machine-size workloads.
+By bundling tasks together we can create large jobs suitable for gigantic
+partitions. We discuss METAQ and mpi_jm, software developed to dynamically
+group computational tasks together, that can intelligently backfill to consume
+idle time without substantial changes to users' current workflows or
+executables.
+",0,1,0,0,0,0
+10299,Scintillation based search for off-pulse radio emission from pulsars,"  We propose a new method to detect off-pulse (unpulsed and/or continuous)
+emission from pulsars, using the intensity modulations associated with
+interstellar scintillation. Our technique involves obtaining the dynamic
+spectra, separately for on-pulse window and off-pulse region, with time and
+frequency resolutions to properly sample the intensity variations due to
+diffractive scintillation, and then estimating their mutual correlation as a
+measure of off-pulse emission, if any. We describe and illustrate the essential
+details of this technique with the help of simulations, as well as real data.
+We also discuss advantages of this method over earlier approaches to detect
+off-pulse emission. In particular, we point out how certain non-idealities
+inherent to measurement set-ups could potentially affect estimations in earlier
+approaches, and argue that the present technique is immune to such
+non-idealities. We verify both of the above situations with relevant
+simulations. We apply this method to observation of PSR B0329+54 at frequencies
+730 and 810 MHz, made with the Green Bank Telescope and present upper limits
+for the off-pulse intensity at the two frequencies. We expect this technique to
+pave way for extensive investigations of off-pulse emission with the help of
+even existing dynamic spectral data on pulsars and of course with more
+sensitive long-duration data from new observations.
+",0,1,0,0,0,0
+10300,Machine learning for graph-based representations of three-dimensional discrete fracture networks,"  Structural and topological information play a key role in modeling flow and
+transport through fractured rock in the subsurface. Discrete fracture network
+(DFN) computational suites such as dfnWorks are designed to simulate flow and
+transport in such porous media. Flow and transport calculations reveal that a
+small backbone of fractures exists, where most flow and transport occurs.
+Restricting the flowing fracture network to this backbone provides a
+significant reduction in the network's effective size. However, the particle
+tracking simulations needed to determine the reduction are computationally
+intensive. Such methods may be impractical for large systems or for robust
+uncertainty quantification of fracture networks, where thousands of forward
+simulations are needed to bound system behavior.
+In this paper, we develop an alternative network reduction approach to
+characterizing transport in DFNs, by combining graph theoretical and machine
+learning methods. We consider a graph representation where nodes signify
+fractures and edges denote their intersections. Using random forest and support
+vector machines, we rapidly identify a subnetwork that captures the flow
+patterns of the full DFN, based primarily on node centrality features in the
+graph. Our supervised learning techniques train on particle-tracking backbone
+paths found by dfnWorks, but run in negligible time compared to those
+simulations. We find that our predictions can reduce the network to
+approximately 20% of its original size, while still generating breakthrough
+curves consistent with those of the original network.
+",1,1,0,1,0,0
+10301,VQABQ: Visual Question Answering by Basic Questions,"  Taking an image and question as the input of our method, it can output the
+text-based answer of the query question about the given image, so called Visual
+Question Answering (VQA). There are two main modules in our algorithm. Given a
+natural language question about an image, the first module takes the question
+as input and then outputs the basic questions of the main given question. The
+second module takes the main question, image and these basic questions as input
+and then outputs the text-based answer of the main question. We formulate the
+basic questions generation problem as a LASSO optimization problem, and also
+propose a criterion about how to exploit these basic questions to help answer
+main question. Our method is evaluated on the challenging VQA dataset and
+yields state-of-the-art accuracy, 60.34% in open-ended task.
+",1,0,0,0,0,0
+10302,Power-Constrained Secrecy Rate Maximization for Joint Relay and Jammer Selection Assisted Wireless Networks,"  In this paper, we examine the physical layer security for cooperative
+wireless networks with multiple intermediate nodes, where the
+decode-and-forward (DF) protocol is considered. We propose a new joint relay
+and jammer selection (JRJS) scheme for protecting wireless communications
+against eavesdropping, where an intermediate node is selected as the relay for
+the sake of forwarding the source signal to the destination and meanwhile, the
+remaining intermediate nodes are employed to act as friendly jammers which
+broadcast the artificial noise for disturbing the eavesdropper. We further
+investigate the power allocation among the source, relay and friendly jammers
+for maximizing the secrecy rate of proposed JRJS scheme and derive a
+closed-form sub-optimal solution. Specificially, all the intermediate nodes
+which successfully decode the source signal are considered as relay candidates.
+For each candidate, we derive the sub-optimal closed-form power allocation
+solution and obtain the secrecy rate result of the corresponding JRJS scheme.
+Then, the candidate which is capable of achieving the highest secrecy rate is
+selected as the relay. Two assumptions about the channel state information
+(CSI), namely the full CSI (FCSI) and partial CSI (PCSI), are considered.
+Simulation results show that the proposed JRJS scheme outperforms the
+conventional pure relay selection, pure jamming and GSVD based beamforming
+schemes in terms of secrecy rate. Additionally, the proposed FCSI based power
+allocation (FCSI-PA) and PCSI based power allocation (PCSI-PA) schemes both
+achieve higher secrecy rates than the equal power allocation (EPA) scheme.
+",1,0,1,0,0,0
+10303,Demagnetization of cubic Gd-Ba-Cu-O bulk superconductor by cross-fields: measurements and 3D modelling,"  Superconducting bulks, acting as high-field permanent magnets, are promising
+for many applications. An important effect in bulk permanent magnets is
+crossed-field demagnetization, which can reduce the magnetic field in
+superconductors due to relatively small transverse fields. Crossed-field
+demagnetization has not been studied in sample shapes such as rectangular
+prisms or cubes. This contribution presents a study based on both 3D numerical
+modelling and experiments. We study a cubic Gd-Ba-Cu-O bulk superconductor
+sample of size 6 mm magnetized by field cooling in an external field of around
+1.3 T, which is later submitted to crossed-field magnetic fields of up to 164
+mT. Modelling results agree with experiments, except at transverse fields 50\%
+or above of the initial trapped field. The current paths present a strong 3D
+nature. For instance, at the mid-plane perpendicular to the initial magnetizing
+field, the current density in this direction changes smoothly from the critical
+magnitude, ${J_c}$, at the lateral sides to zero at a certain penetration
+depth. This indicates a rotation of the current density with magnitude ${J_c}$,
+and hence force free effects like flux cutting are expected to play a
+significant role.
+",0,1,0,0,0,0
+10304,Resonance-Free Light Recycling,"  The inability to efficiently tune the optical properties of waveguiding
+structures has been one of the major hurdles for the future scalability of
+integrated photonic systems. In silicon photonics, although dynamic tuning has
+been achieved with various mechanisms, even the most effective thermo-optic
+effect offers a refractive index change of only $1.86 \times 10^{-4} K^{-1}$.
+To enhance this small change, light recycling based on resonators has been
+employed in order to realize efficient modulators, phase shifters, and optical
+switches. However, the resonant enhancement comes at a great cost of optical
+bandwidth, fabrication tolerance and system scalability. Here we demonstrate a
+scalable light recycling approach based on spatial-mode multiplexing. Our
+approach offers a fabrication tolerance of ${\pm}$ 15 nm, in stark contrast to
+the non-scalable subnanometer tolerance in typical silicon resonators. We
+experimentally demonstrate light recycling up to 7 passes with an optical
+bandwidth greater than 100 nm. We realize power-efficient thermo-optic phase
+shifters that require only 1.7 mW per ${\pi}$, representing more than an 8-fold
+reduction in the power consumption.
+",0,1,0,0,0,0
+10305,A Computationally Efficient and Practically Feasible Two Microphones Blind Speech Separation Method,"  Traditionally, Blind Speech Separation techniques are computationally
+expensive as they update the demixing matrix at every time frame index, making
+them impractical to use in many Real-Time applications. In this paper, a robust
+data-driven two-microphone sound source localization method is used as a
+criterion to reduce the computational complexity of the Independent Vector
+Analysis (IVA) Blind Speech Separation (BSS) method. IVA is used to separate
+convolutedly mixed speech and noise sources. The practical feasibility of the
+proposed method is proved by implementing it on a smartphone device to separate
+speech and noise in Real-World scenarios for Hearing-Aid applications. The
+experimental results with objective and subjective tests reveal the practical
+usability of the developed method in many real-world applications.
+",1,0,0,0,0,0
+10306,Magnetic ground state and magnon-phonon interaction in multiferroic h-YMnO$_3$,"  Inelastic neutron scattering has been used to study the magneto-elastic
+excitations in the multiferroic manganite hexagonal YMnO$_3$. An avoided
+crossing is found between magnon and phonon modes close to the Brillouin zone
+boundary in the $(a,b)$-plane. Neutron polarization analysis reveals that this
+mode has mixed magnon-phonon character. An external magnetic field along the
+$c$-axis is observed to cause a linear field-induced splitting of one of the
+spin wave branches. A theoretical description is performed, using a Heisenberg
+model of localized spins, acoustic phonon modes and a magneto-elastic coupling
+via the single-ion magnetostriction. The model quantitatively reproduces the
+dispersion and intensities of all modes in the full Brillouin zone, describes
+the observed magnon-phonon hybridized modes, and quantifies the magneto-elastic
+coupling. The combined information, including the field-induced magnon
+splitting, allows us to exclude several of the earlier proposed models and
+point to the correct magnetic ground state symmetry, and provides an effective
+dynamic model relevant for the multiferroic hexagonal manganites.
+",0,1,0,0,0,0
+10307,Neuromodulation of Neuromorphic Circuits,"  We present a novel methodology to enable control of a neuromorphic circuit in
+close analogy with the physiological neuromodulation of a single neuron. The
+methodology is general in that it only relies on a parallel interconnection of
+elementary voltage-controlled current sources. In contrast to controlling a
+nonlinear circuit through the parameter tuning of a state-space model, our
+approach is purely input-output. The circuit elements are controlled and
+interconnected to shape the current-voltage characteristics (I-V curves) of the
+circuit in prescribed timescales. In turn, shaping those I-V curves determines
+the excitability properties of the circuit. We show that this methodology
+enables both robust and accurate control of the circuit behavior and resembles
+the biophysical mechanisms of neuromodulation. As a proof of concept, we
+simulate a SPICE model composed of MOSFET transconductance amplifiers operating
+in the weak inversion regime.
+",0,0,0,0,1,0
+10308,Frank-Wolfe Style Algorithms for Large Scale Optimization,"  We introduce a few variants on Frank-Wolfe style algorithms suitable for
+large scale optimization. We show how to modify the standard Frank-Wolfe
+algorithm using stochastic gradients, approximate subproblem solutions, and
+sketched decision variables in order to scale to enormous problems while
+preserving (up to constants) the optimal convergence rate
+$\mathcal{O}(\frac{1}{k})$.
+",0,0,0,1,0,0
+10309,Theoretical Description of Micromaser in the Ultrastrong-Coupling Regime,"  We theoretically investigate an ultrastrongly-coupled micromaser based on
+Rydberg atoms interacting with a superconducting LC resonator, where the common
+rotating-wave approximation and slowly-varying-envelope approximation are no
+longer applicable. The effect of counter-rotating terms on the masing dynamics
+is studied in detail. We find that the intraresonator electric energy declines
+and the microwave oscillation frequency shifts significantly in the regime of
+ultrastrong coupling. Additionally, the micromaser phase fluctuation is
+suppressed, resulting in a reduced spectral linewidth.
+",0,1,0,0,0,0
+10310,Compatibility of quasi-orderings and valuations; A Baer-Krull Theorem for quasi-ordered Rings,"  In his work of 1969, Merle E. Manis introduced valuations on commutative
+rings. Recently, the class of totally quasi-ordered rings was developped by the
+second author. In the present paper, we establish the notion of compatibility
+between valuations and quasi-orders on rings, leading to a definition of the
+rank of a quasi-ordered ring. Moreover, we prove a Baer-Krull Theorem for
+quasi-ordered rings: fixing a Manis valuation v on R, we characterize all
+v-compatible quasi-orders of R by lifting the quasi-orders from the residue
+class ring to R itself.
+",0,0,1,0,0,0
+10311,Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms,"  Electron Cryo-Tomography (ECT) allows 3D visualization of subcellular
+structures at the submolecular resolution in close to the native state.
+However, due to the high degree of structural complexity and imaging limits,
+the automatic segmentation of cellular components from ECT images is very
+difficult. To complement and speed up existing segmentation methods, it is
+desirable to develop a generic cell component segmentation method that is 1)
+not specific to particular types of cellular components, 2) able to segment
+unknown cellular components, 3) fully unsupervised and does not rely on the
+availability of training data. As an important step towards this goal, in this
+paper, we propose a saliency detection method that computes the likelihood that
+a subregion in a tomogram stands out from the background. Our method consists
+of four steps: supervoxel over-segmentation, feature extraction, feature matrix
+decomposition, and computation of saliency. The method produces a distribution
+map that represents the regions' saliency in tomograms. Our experiments show
+that our method can successfully label most salient regions detected by a human
+observer, and able to filter out regions not containing cellular components.
+Therefore, our method can remove the majority of the background region, and
+significantly speed up the subsequent processing of segmentation and
+recognition of cellular components captured by ECT.
+",0,0,0,1,1,0
+10312,A Characterization of Integral ISS for Switched and Time-varying Systems,"  Most of the existing characterizations of the integral input-to-state
+stability (iISS) property are not valid for time-varying or switched systems in
+cases where converse Lyapunov theorems for stability are not available. This
+note provides a characterization that is valid for switched and time-varying
+systems, and shows that natural extensions of some of the existing
+characterizations result in only sufficient but not necessary conditions. The
+results provided also pinpoint suitable iISS gains and relate these to supply
+functions and bounds on the function defining the system dynamics.
+",1,0,1,0,0,0
+10313,Low-temperature marginal ferromagnetism explains anomalous scale-free correlations in natural flocks,"  We introduce a new ferromagnetic model capable of reproducing one of the most
+intriguing properties of collective behaviour in starling flocks, namely the
+fact that strong collective order of the system coexists with scale-free
+correlations of the modulus of the microscopic degrees of freedom, that is the
+birds' speeds. The key idea of the new theory is that the single-particle
+potential needed to bound the modulus of the microscopic degrees of freedom
+around a finite value, is marginal, that is has zero curvature. We study the
+model by using mean-field approximation and Monte Carlo simulations in three
+dimensions, complemented by finite-size scaling analysis. While at the standard
+critical temperature, $T_c$, the properties of the marginal model are exactly
+the same as a normal ferromagnet with continuous symmetry-breaking, our results
+show that a novel zero-temperature critical point emerges, so that in its
+deeply ordered phase the marginal model develops divergent susceptibility and
+correlation length of the modulus of the microscopic degrees of freedom, in
+complete analogy with experimental data on natural flocks of starlings.
+",0,0,0,0,1,0
+10314,Historical and personal recollections of Guido Altarelli,"  In this paper I will present a short scientific biography of Guido Altarelli,
+briefly describing some of his most important seminal works. I will analyze in
+great details the paper of the $q^2$ evolution of the effective quark
+distribution: I will put this paper in a historical perspective, describing our
+theoretical understanding at that time and the reasons why the paper was so
+successful.
+",0,1,0,0,0,0
+10315,Segment Parameter Labelling in MCMC Mean-Shift Change Detection,"  This work addresses the problem of segmentation in time series data with
+respect to a statistical parameter of interest in Bayesian models. It is common
+to assume that the parameters are distinct within each segment. As such, many
+Bayesian change point detection models do not exploit the segment parameter
+patterns, which can improve performance. This work proposes a Bayesian
+mean-shift change point detection algorithm that makes use of repetition in
+segment parameters, by introducing segment class labels that utilise a
+Dirichlet process prior. The performance of the proposed approach was assessed
+on both synthetic and real world data, highlighting the enhanced performance
+when using parameter labelling.
+",1,0,0,1,0,0
+10316,M/G/c/c state dependent queuing model for a road traffic system of two sections in tandem,"  We propose in this article a M/G/c/c state dependent queuing model for road
+traffic flow. The model is based on finite capacity queuing theory which
+captures the stationary density-flow relationships. It is also inspired from
+the deterministic Godunov scheme for the road traffic simulation. We first
+present a reformulation of the existing linear case of M/G/c/c state dependent
+model, in order to use flow rather than speed variables. We then extend this
+model in order to consider upstream traffic demand and downstream traffic
+supply. After that, we propose the model for two road sections in tandem where
+both sections influence each other. In order to deal with this mutual
+dependence, we solve an implicit system given by an algebraic equation.
+Finally, we derive some performance measures (throughput and expected travel
+time). A comparison with results predicted by the M/G/c/c state dependent
+queuing networks shows that the model we propose here captures really the
+dynamics of the road traffic.
+",1,0,1,0,0,0
+10317,Laplace approximation and the natural gradient for Gaussian process regression with the heteroscedastic Student-t model,"  This paper considers the Laplace method to derive approximate inference for
+the Gaussian process (GP) regression in the location and scale parameters of
+the Student-t probabilistic model. This allows both mean and variance of the
+data to vary as a function of covariates with the attractive feature that the
+Student-t model has been widely used as a useful tool for robustifying data
+analysis. The challenge in the approximate inference for the GP regression with
+the Student-t probabilistic model, lies in the analytical intractability of the
+posterior distribution and the lack of concavity of the log-likelihood
+function. We present the natural gradient adaptation for the estimation process
+which primarily relies on the property that the Student-t model naturally has
+orthogonal parametrization with respect to the location and scale paramaters.
+Due to this particular property of the model, we also introduce an alternative
+Laplace approximation by using the Fisher information matrix in place of the
+Hessian matrix of the negative log-likelihood function. According to
+experiments this alternative approximation provides very similar posterior
+approximations and predictive performance when compared to the traditional
+Laplace approximation. We also compare both of these Laplace approximations
+with the Monte Carlo Markov Chain (MCMC) method. Moreover, we compare our
+heteroscedastic Student-t model and the GP regression with the heteroscedastic
+Gaussian model. We also discuss how our approach can improve the inference
+algorithm in cases where the probabilistic model assumed for the data is not
+log-concave.
+",0,0,0,1,0,0
+10318,"Fixed points of n-valued maps, the fixed point property and the case of surfaces -- a braid approach","  We study the fixed point theory of n-valued maps of a space X using the fixed
+point theory of maps between X and its configuration spaces. We give some
+general results to decide whether an n-valued map can be deformed to a fixed
+point free n-valued map. In the case of surfaces, we provide an algebraic
+criterion in terms of the braid groups of X to study this problem. If X is
+either the k-dimensional ball or an even-dimensional real or complex projective
+space, we show that the fixed point property holds for n-valued maps for all n
+$\ge$ 1, and we prove the same result for even-dimensional spheres for all n
+$\ge$ 2. If X is the 2-torus, we classify the homotopy classes of 2-valued maps
+in terms of the braid groups of X. We do not currently have a complete
+characterisation of the homotopy classes of split 2-valued maps of the 2-torus
+that contain a fixed point free representative, but we give an infinite family
+of such homotopy classes.
+",0,0,1,0,0,0
+10319,Large Synoptic Survey Telescope Galaxies Science Roadmap,"  The Large Synoptic Survey Telescope (LSST) will enable revolutionary studies
+of galaxies, dark matter, and black holes over cosmic time. The LSST Galaxies
+Science Collaboration has identified a host of preparatory research tasks
+required to leverage fully the LSST dataset for extragalactic science beyond
+the study of dark energy. This Galaxies Science Roadmap provides a brief
+introduction to critical extragalactic science to be conducted ahead of LSST
+operations, and a detailed list of preparatory science tasks including the
+motivation, activities, and deliverables associated with each. The Galaxies
+Science Roadmap will serve as a guiding document for researchers interested in
+conducting extragalactic science in anticipation of the forthcoming LSST era.
+",0,1,0,0,0,0
+10320,Multiresolution Coupled Vertical Equilibrium Model for Fast Flexible Simulation of CO$_2$ Storage,"  CO2 capture and storage is an important technology for mitigating climate
+change. Design of efficient strategies for safe, long-term storage requires the
+capability to efficiently simulate processes taking place on very different
+temporal and spatial scales. The physical laws describing CO2 storage are the
+same as for hydrocarbon recovery, but the characteristic spatial and temporal
+scales are quite different. Petroleum reservoirs seldom extend more than tens
+of kilometers and have operational horizons spanning decades. Injected CO2
+needs to be safely contained for hundreds or thousands of years, during which
+it can migrate hundreds or thousands of kilometers. Because of the vast scales
+involved, conventional 3D reservoir simulation quickly becomes computationally
+unfeasible. Large density difference between injected CO2 and resident brine
+means that vertical segregation will take place relatively quickly, and
+depth-integrated models assuming vertical equilibrium (VE) often represents a
+better strategy to simulate long-term migration of CO2 in large-scale aquifer
+systems. VE models have primarily been formulated for relatively simple rock
+formations and have not been coupled to 3D simulation in a uniform way. In
+particular, known VE simulations have not been applied to models of realistic
+geology in which many flow compartments may exist in-between impermeable
+layers. In this paper, we generalize the concept of VE models, formulated in
+terms of well-proven reservoir simulation technology, to complex aquifer
+systems with multiple layers and regions. We also introduce novel formulations
+for multi-layered VE models by use of both direct spill and diffuse leakage
+between individual layers. This new layered 3D model is then coupled to a
+state-of-the-art, 3D black-oil type model.
+",0,1,0,0,0,0
+10321,Improved GelSight Tactile Sensor for Measuring Geometry and Slip,"  A GelSight sensor uses an elastomeric slab covered with a reflective membrane
+to measure tactile signals. It measures the 3D geometry and contact force
+information with high spacial resolution, and successfully helped many
+challenging robot tasks. A previous sensor, based on a semi-specular membrane,
+produces high resolution but with limited geometry accuracy. In this paper, we
+describe a new design of GelSight for robot gripper, using a Lambertian
+membrane and new illumination system, which gives greatly improved geometric
+accuracy while retaining the compact size. We demonstrate its use in measuring
+surface normals and reconstructing height maps using photometric stereo. We
+also use it for the task of slip detection, using a combination of information
+about relative motions on the membrane surface and the shear distortions. Using
+a robotic arm and a set of 37 everyday objects with varied properties, we find
+that the sensor can detect translational and rotational slip in general cases,
+and can be used to improve the stability of the grasp.
+",1,0,0,0,0,0
+10322,Deep Learning for Design and Retrieval of Nano-photonic Structures,"  Our visual perception of our surroundings is ultimately limited by the
+diffraction limit, which stipulates that optical information smaller than
+roughly half the illumination wavelength is not retrievable. Over the past
+decades, many breakthroughs have led to unprecedented imaging capabilities
+beyond the diffraction-limit, with applications in biology and nanotechnology.
+In this context, nano-photonics has revolutionized the field of optics in
+recent years by enabling the manipulation of light-matter interaction with
+subwavelength structures. However, despite the many advances in this field, its
+impact and penetration in our daily life has been hindered by a convoluted and
+iterative process, cycling through modeling, nanofabrication and
+nano-characterization. The fundamental reason is the fact that not only the
+prediction of the optical response is very time consuming and requires solving
+Maxwell's equations with dedicated numerical packages. But, more significantly,
+the inverse problem, i.e. designing a nanostructure with an on-demand optical
+response, is currently a prohibitive task even with the most advanced numerical
+tools due to the high non-linearity of the problem. Here, we harness the power
+of Deep Learning, a new path in modern machine learning, and show its ability
+to predict the geometry of nanostructures based solely on their far-field
+response. This approach also addresses in a direct way the currently
+inaccessible inverse problem breaking the ground for on-demand design of
+optical response with applications such as sensing, imaging and also for
+plasmon's mediated cancer thermotherapy.
+",0,1,0,0,0,0
+10323,A Sparse Completely Positive Relaxation of the Modularity Maximization for Community Detection,"  In this paper, we consider the community detection problem under either the
+stochastic block model (SBM) assumption or the degree-correlated stochastic
+block model (DCSBM) assumption. The modularity maximization formulation for the
+community detection problem is NP-hard in general. In this paper, we propose a
+sparse and low-rank completely positive relaxation for the modularity
+maximization problem, we then develop an efficient row-by-row (RBR) type block
+coordinate descent (BCD) algorithm to solve the relaxation and prove an
+$\mathcal{O}(1/\sqrt{N})$ convergence rate to a stationary point where $N$ is
+the number of iterations. A fast rounding scheme is constructed to retrieve the
+community structure from the solution. Non-asymptotic high probability bounds
+on the misclassification rate are established to justify our approach. We
+further develop an asynchronous parallel RBR algorithm to speed up the
+convergence. Extensive numerical experiments on both synthetic and real world
+networks show that the proposed approach enjoys advantages in both clustering
+accuracy and numerical efficiency. Our numerical results indicate that the
+newly proposed method is a quite competitive alternative for community
+detection on sparse networks with over 50 million nodes.
+",0,0,1,0,0,0
+10324,Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning,"  In this paper, we build upon previous work on designing informative and
+efficient Exploratory Landscape Analysis features for characterizing problems'
+landscapes and show their effectiveness in automatically constructing algorithm
+selection models in continuous black-box optimization problems. Focussing on
+algorithm performance results of the COCO platform of several years, we
+construct a representative set of high-performing complementary solvers and
+present an algorithm selection model that - compared to the portfolio's single
+best solver - on average requires less than half of the resources for solving a
+given problem. Therefore, there is a huge gain in efficiency compared to
+classical ensemble methods combined with an increased insight into problem
+characteristics and algorithm properties by using informative features. Acting
+on the assumption that the function set of the Black-Box Optimization Benchmark
+is representative enough for practical applications the model allows for
+selecting the best suited optimization algorithm within the considered set for
+unseen problems prior to the optimization itself based on a small sample of
+function evaluations. Note that such a sample can even be reused for the
+initial population of an evolutionary (optimization) algorithm so that even the
+feature costs become negligible.
+",1,0,0,1,0,0
+10325,Car-following behavior of connected vehicles in a mixed traffic flow: modeling and stability analysis,"  Vehicle-to-vehicle communications can change the driving behavior of drivers
+significantly by providing them rich information on downstream traffic flow
+conditions. This study seeks to model the varying car-following behaviors
+involving connected vehicles and human-driving vehicles in mixed traffic flow.
+A revised car-following model is developed using an intelligent driver model
+(IDM) to capture drivers' perceptions of their preceding traffic conditions
+through vehicle-to-vehicle communications. Stability analysis of the mixed
+traffic flow is conducted for a specific case. Numerical results show that the
+stable region is apparently enlarged compared with the IDM.
+",1,0,0,0,0,0
+10326,A note on a two-weight estimate for the dyadic square function,"  We show that the two-weight estimate for the dyadic square function proved by
+Lacey--Li in [2] is sharp.
+",0,0,1,0,0,0
+10327,The Oblique Orbit of WASP-107b from K2 Photometry,"  Observations of nine transits of WASP-107 during the {\it K2} mission reveal
+three separate occasions when the planet crossed in front of a starspot. The
+data confirm the stellar rotation period to be 17 days --- approximately three
+times the planet's orbital period --- and suggest that large spots persist for
+at least one full rotation. If the star had a low obliquity, at least two
+additional spot crossings should have been observed. They were not observed,
+giving evidence for a high obliquity. We use a simple geometric model to show
+that the obliquity is likely in the range 40-140$^\circ$, i.e., both spin-orbit
+alignment and anti-alignment can be ruled out. WASP-107 thereby joins the small
+collection of relatively low-mass stars hosting a giant planet with a high
+obliquity. Most such stars have been observed to have low obliquities; all the
+exceptions, including WASP-107, involve planets with relatively wide orbits
+(""warm Jupiters"", with $a_{\rm min}/R_\star \gtrsim 8$). This demonstrates a
+connection between stellar obliquity and planet properties, in contradiction to
+some theories for obliquity excitation.
+",0,1,0,0,0,0
+10328,A characterization of signed discrete infinitely divisible distributions,"  In this article, we give some reviews concerning negative probabilities model
+and quasi-infinitely divisible at the beginning. We next extend Feller's
+characterization of discrete infinitely divisible distributions to signed
+discrete infinitely divisible distributions, which are discrete pseudo compound
+Poisson (DPCP) distributions with connections to the Lévy-Wiener theorem.
+This is a special case of an open problem which is proposed by Sato(2014),
+Chaumont and Yor(2012). An analogous result involving characteristic functions
+is shown for signed integer-valued infinitely divisible distributions. We show
+that many distributions are DPCP by the non-zero p.g.f. property, such as the
+mixed Poisson distribution and fractional Poisson process. DPCP has some
+bizarre properties, and one is that the parameter $\lambda $ in the DPCP class
+cannot be arbitrarily small.
+",0,0,1,1,0,0
+10329,Frequency Domain Singular Value Decomposition for Efficient Spatial Audio Coding,"  Advances in virtual reality have generated substantial interest in accurately
+reproducing and storing spatial audio in the higher order ambisonics (HOA)
+representation, given its rendering flexibility. Recent standardization for HOA
+compression adopted a framework wherein HOA data are decomposed into principal
+components that are then encoded by standard audio coding, i.e., frequency
+domain quantization and entropy coding to exploit psychoacoustic redundancy. A
+noted shortcoming of this approach is the occasional mismatch in principal
+components across blocks, and the resulting suboptimal transitions in the data
+fed to the audio coder. Instead, we propose a framework where singular value
+decomposition (SVD) is performed after transformation to the frequency domain
+via the modified discrete cosine transform (MDCT). This framework not only
+ensures smooth transition across blocks, but also enables frequency dependent
+SVD for better energy compaction. Moreover, we introduce a novel noise
+substitution technique to compensate for suppressed ambient energy in discarded
+higher order ambisonics channels, which significantly enhances the perceptual
+quality of the reconstructed HOA signal. Objective and subjective evaluation
+results provide evidence for the effectiveness of the proposed framework in
+terms of both higher compression gains and better perceptual quality, compared
+to existing methods.
+",1,0,0,0,0,0
+10330,Functional Dynamical Structures in Complex Systems: an Information-Theoretic Approach,"  Understanding the dynamical behavior of complex systems is of exceptional
+relevance in everyday life, from biology to economy. In order to describe the
+dynamical organization of complex systems, existing methods require the
+knowledge of the network topology. By contrast, in this thesis we develop a new
+method based on Information Theory which does not require any topological
+knowledge. We introduce the Dynamical Cluster Index to detect those groups of
+system elements which have strong mutual interactions, named as Relevant
+Subsets. Among them, we identify those which exchange most information with the
+rest of the system, thus being the most influential for its dynamics. In order
+to detect such Functional Dynamical Structures, we introduce another
+information theoretic measure, called D-index. The experimental results make us
+confident that our method can be effectively used to study both artificial and
+natural complex systems.
+",0,1,0,0,0,0
+10331,Angle-resolved photoemission spectroscopy with quantum gas microscopes,"  Quantum gas microscopes are a promising tool to study interacting quantum
+many-body systems and bridge the gap between theoretical models and real
+materials. So far they were limited to measurements of instantaneous
+correlation functions of the form $\langle \hat{O}(t) \rangle$, even though
+extensions to frequency-resolved response functions $\langle \hat{O}(t)
+\hat{O}(0) \rangle$ would provide important information about the elementary
+excitations in a many-body system. For example, single particle spectral
+functions, which are usually measured using photoemission experiments in
+electron systems, contain direct information about fractionalization and the
+quasiparticle excitation spectrum. Here, we propose a measurement scheme to
+experimentally access the momentum and energy resolved spectral function in a
+quantum gas microscope with currently available techniques. As an example for
+possible applications, we numerically calculate the spectrum of a single hole
+excitation in one-dimensional $t-J$ models with isotropic and anisotropic
+antiferromagnetic couplings. A sharp asymmetry in the distribution of spectral
+weight appears when a hole is created in an isotropic Heisenberg spin chain.
+This effect slowly vanishes for anisotropic spin interactions and disappears
+completely in the case of pure Ising interactions. The asymmetry strongly
+depends on the total magnetization of the spin chain, which can be tuned in
+experiments with quantum gas microscopes. An intuitive picture for the observed
+behavior is provided by a slave-fermion mean field theory. The key properties
+of the spectra are visible at currently accessible temperatures.
+",0,1,0,0,0,0
+10332,A note on MLE of covariance matrix,"  For a multivariate normal set up, it is well known that the maximum
+likelihood estimator of covariance matrix is neither admissible nor minimax
+under the Stein loss function. For the past six decades, a bunch of researches
+have followed along this line for Stein's phenomenon in the literature. In this
+note, the results are two folds: Firstly, with respect to Stein type loss
+function we use the full Iwasawa decomposition to enhance the unpleasant
+phenomenon that the minimum risks of maximum likelihood estimators for the
+different coordinate systems (Cholesky decomposition and full Iwasawa
+decomposition) are different. Secondly, we introduce a new class of loss
+functions to show that the minimum risks of maximum likelihood estimators for
+the different coordinate systems, the Cholesky decomposition and the full
+Iwasawa decomposition, are of the same, and hence the Stein's paradox
+disappears.
+",0,0,1,1,0,0
+10333,Resource Management in Cloud Computing: Classification and Taxonomy,"  Cloud Computing is a new era of remote computing / Internet based computing
+where one can access their personal resources easily from any computer through
+Internet. Cloud delivers computing as a utility as it is available to the cloud
+consumers on demand. It is a simple pay-per-use consumer-provider service
+model. It contains large number of shared resources. So Resource Management is
+always a major issue in cloud computing like any other computing paradigm. Due
+to the availability of finite resources it is very challenging for cloud
+providers to provide all the requested resources. From the cloud providers
+perspective cloud resources must be allocated in a fair and efficient manner.
+Research Survey is not available from the perspective of resource management as
+a process in cloud computing. So this research paper provides a detailed
+sequential view / steps on resource management in cloud computing. Firstly this
+research paper classifies various resources in cloud computing. It also gives
+taxonomy on resource management in cloud computing through which one can do
+further research. Lastly comparisons on various resource management algorithms
+has been presented.
+",1,0,0,0,0,0
+10334,What can the programming language Rust do for astrophysics?,"  The astrophysics community uses different tools for computational tasks such
+as complex systems simulations, radiative transfer calculations or big data.
+Programming languages like Fortran, C or C++ are commonly present in these
+tools and, generally, the language choice was made based on the need for
+performance. However, this comes at a cost: safety. For instance, a common
+source of error is the access to invalid memory regions, which produces random
+execution behaviors and affects the scientific interpretation of the results.
+In 2015, Mozilla Research released the first stable version of a new
+programming language named Rust. Many features make this new language
+attractive for the scientific community, it is open source and it guarantees
+memory safety while offering zero-cost abstraction.
+We explore the advantages and drawbacks of Rust for astrophysics by
+re-implementing the fundamental parts of Mercury-T, a Fortran code that
+simulates the dynamical and tidal evolution of multi-planet systems.
+",1,1,0,0,0,0
+10335,Eliashberg theory with the external pair potential,"  Based on BCS model with the external pair potential formulated in a work
+\emph{K.V. Grigorishin} arXiv:1605.07080, analogous model with electron-phonon
+coupling and Coulomb coupling is proposed. The generalized Eliashberg equations
+in the regime of renormalization of the order parameter are obtained. High
+temperature asymptotics and influence of Coulomb pseudopotential on them are
+investigated: as in the BCS model the order parameter asymptotically tends to
+zero as temperature rises, but the accounting of the Coulomb pseudopotential
+leads to existence of critical temperature. The effective Ginzburg-Landau
+theory is formulated for such model, where the temperature dependencies near
+$T_{c}$ of the basic characteristics of a superconductor (coherence length,
+magnetic penetration depth, GL parameter, the thermodynamical critical field,
+the first and the second critical fields) recovers to the temperature
+dependencies as in the ordinary GL theory after the BCS model with the external
+pair potential.
+",0,1,0,0,0,0
+10336,An exponential limit shape of random $q$-proportion Bulgarian solitaire,"  We introduce \emph{$p_n$-random $q_n$-proportion Bulgarian solitaire}
+($0<p_n,q_n\le 1$), played on $n$ cards distributed in piles. In each pile, a
+number of cards equal to the proportion $q_n$ of the pile size rounded upward
+to the nearest integer are candidates to be picked. Each candidate card is
+picked with probability $p_n$, independently of other candidate cards. This
+generalizes Popov's random Bulgarian solitaire, in which there is a single
+candidate card in each pile. Popov showed that a triangular limit shape is
+obtained for a fixed $p$ as $n$ tends to infinity. Here we let both $p_n$ and
+$q_n$ vary with $n$. We show that under the conditions $q_n^2 p_n n/{\log
+n}\rightarrow \infty$ and $p_n q_n \rightarrow 0$ as $n\to\infty$, the
+$p_n$-random $q_n$-proportion Bulgarian solitaire has an exponential limit
+shape.
+",0,0,1,0,0,0
+10337,"Constraining Effective Temperature, Mass and Radius of Hot White Dwarfs","  By introducing a simplified transport model of outer layers of white dwarfs
+we derive an analytical semi-empirical relation which constrains effective
+temperature-mass-radius for white dwarfs. This relation is used to classify
+recent data of white dwarfs according to their time evolution in non-accretion
+process of cooling. This formula permit us to study the population map of white
+dwarfs in the central temperature and mass plane, and discuss the relation with
+the ignition temperature for C-O material. Our effective
+temperature-mass-radius relation provide a quick method to estimate the mass of
+newly observed white dwarfs from their spectral measurements of effective
+temperature and superficial gravity.
+",0,1,0,0,0,0
+10338,Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables,"  We present a novel approach for estimating conditional probability tables,
+based on a joint, rather than independent, estimate of the conditional
+distributions belonging to the same table. We derive exact analytical
+expressions for the estimators and we analyse their properties both
+analytically and via simulation. We then apply this method to the estimation of
+parameters in a Bayesian network. Given the structure of the network, the
+proposed approach better estimates the joint distribution and significantly
+improves the classification performance with respect to traditional approaches.
+",0,0,0,1,0,0
+10339,Proceedings of the Fifth Workshop on Proof eXchange for Theorem Proving,"  This volume of EPTCS contains the proceedings of the Fifth Workshop on Proof
+Exchange for Theorem Proving (PxTP 2017), held on September 23-24, 2017 as part
+of the Tableaux, FroCoS and ITP conferences in Brasilia, Brazil. The PxTP
+workshop series brings together researchers working on various aspects of
+communication, integration, and cooperation between reasoning systems and
+formalisms, with a special focus on proofs. The progress in computer-aided
+reasoning, both automated and interactive, during the past decades, made it
+possible to build deduction tools that are increasingly more applicable to a
+wider range of problems and are able to tackle larger problems progressively
+faster. In recent years, cooperation between such tools in larger systems has
+demonstrated the potential to reduce the amount of manual intervention.
+Cooperation between reasoning systems relies on availability of theoretical
+formalisms and practical tools to exchange problems, proofs, and models. The
+PxTP workshop series strives to encourage such cooperation by inviting
+contributions on all aspects of cooperation between reasoning tools, whether
+automatic or interactive.
+",1,0,0,0,0,0
+10340,Photoinduced charge-order melting dynamics in a one-dimensional interacting Holstein model,"  Transient quantum dynamics in an interacting fermion-phonon system are
+investigated. In particular, a charge order (CO) melting after a short
+optical-pulse irradiation and roles of the quantum phonons on the transient
+dynamics are focused on. A spinless-fermion model in a one-dimensional chain
+coupled with local phonons is analyzed numerically. The infinite time-evolving
+block decimation algorithm is adopted as a reliable numerical method for
+one-dimensional quantum many-body systems. Numerical results for the
+photoinduced CO melting dynamics without phonons are well interpreted by the
+soliton picture for the CO domains. This interpretation is confirmed by the
+numerical simulation for an artificial local excitation and the classical
+soliton model. In the case of the large phonon frequency corresponding to the
+antiadiabatic condition, the CO melting is induced by propagations of the
+polaronic solitons with the renormalized soliton velocity. On the other hand,
+in the case of the small phonon frequency corresponding to the adiabatic
+condition, the first stage of the CO melting dynamics occurs due to the energy
+transfer from the fermionic to phononic systems, and the second stage is
+brought about by the soliton motions around the bottom of the soliton band.
+Present analyses provide a standard reference for the photoinduced CO melting
+dynamics in low-dimensional many-body quantum systems.
+",0,1,0,0,0,0
+10341,Compactness of the automorphism group of a topological parallelism on real projective 3-space: The disconnected case,"  We prove that the automorphism group of a topological parallelism on real
+projective 3-space is compact. In a preceding article it was proved that at
+least the connected component of the identity is compact. The present proof
+does not depend on that earlier result.
+",0,0,1,0,0,0
+10342,A model provides insight into electric field-induced rupture mechanism of water-in-toluene emulsion films,"  This paper presents the first MD simulations of a model, which we have
+designed for understanding the development of electro-induced instability of a
+thin toluene emulsion film in contact with saline aqueous phase. This study
+demonstrates the charge accumulation role in toluene film rupture when a DC
+electric field is applied. The critical value of the external field at which
+film ruptures, thin film charge distribution, capacitance, number densities and
+film structure have been obtained in simulating the system within NVT and NPT
+ensembles. A mechanism of thin film rupture driven by the electric discharge is
+suggested.We show that NPT ensemble with a constant surface tension is a better
+choice for further modeling of the systems that resemble more close the real
+films.
+",0,1,0,0,0,0
+10343,A Rich-Variant Architecture for a User-Aware multi-tenant SaaS approach,"  Software as a Service cloud computing model favorites the Multi-Tenancy as a
+key factor to exploit economies of scale. However Multi-Tenancy present several
+disadvantages. Therein, our approach comes to assign instances to multi-tenants
+with an optimal solution while ensuring more economies of scale and avoiding
+tenants hesitation to share resources. The present paper present the
+architecture of our user-aware multi-tenancy SaaS approach based on the use of
+rich-variant components. The proposed approach seek to model services
+functional customization as well as automation of computing the optimal
+distribution of instances by tenants. The proposed model takes into
+consideration tenants functional requirements and tenants deployment
+requirements to deduce an optimal distribution using essentially a specific
+variability engine and a graph-based execution framework.
+",1,0,0,0,0,0
+10344,"Comparison of Parallelisation Approaches, Languages, and Compilers for Unstructured Mesh Algorithms on GPUs","  Efficiently exploiting GPUs is increasingly essential in scientific
+computing, as many current and upcoming supercomputers are built using them. To
+facilitate this, there are a number of programming approaches, such as CUDA,
+OpenACC and OpenMP 4, supporting different programming languages (mainly C/C++
+and Fortran). There are also several compiler suites (clang, nvcc, PGI, XL)
+each supporting different combinations of languages. In this study, we take a
+detailed look at some of the currently available options, and carry out a
+comprehensive analysis and comparison using computational loops and
+applications from the domain of unstructured mesh computations. Beyond runtimes
+and performance metrics (GB/s), we explore factors that influence performance
+such as register counts, occupancy, usage of different memory types,
+instruction counts, and algorithmic differences. Results of this work show how
+clang's CUDA compiler frequently outperform NVIDIA's nvcc, performance issues
+with directive-based approaches on complex kernels, and OpenMP 4 support
+maturing in clang and XL; currently around 10% slower than CUDA.
+",1,0,0,0,0,0
+10345,Notes on Growing a Tree in a Graph,"  We study the height of a spanning tree $T$ of a graph $G$ obtained by
+starting with a single vertex of $G$ and repeatedly selecting, uniformly at
+random, an edge of $G$ with exactly one endpoint in $T$ and adding this edge to
+$T$.
+",1,0,0,0,0,0
+10346,Vortex pinning by the point potential in topological superconductors: a scheme for braiding Majorana bound states,"  We propose theoretically an effective scheme for braiding Majorana bound
+states by manipulating the point potential. The vortex pinning effect is
+carefully elucidated. This effect may be used to control the vortices and
+Majorana bound states in topological superconductors. The exchange of two
+vortices induced by moving the potentials is simulated numerically. The
+zero-energy state in the vortex core is robust with respect to the strength of
+the potential. The Majorana bound states in a pinned vortex are identified
+numerically.
+",0,1,0,0,0,0
+10347,T* : A Heuristic Search Based Algorithm for Motion Planning with Temporal Goals,"  Motion planning is the core problem to solve for developing any application
+involving an autonomous mobile robot. The fundamental motion planning problem
+involves generating a trajectory for a robot for point-to-point navigation
+while avoiding obstacles. Heuristic-based search algorithms like A* have been
+shown to be extremely efficient in solving such planning problems. Recently,
+there has been an increased interest in specifying complex motion plans using
+temporal logic. In the state-of-the-art algorithm, the temporal logic motion
+planning problem is reduced to a graph search problem and Dijkstra's shortest
+path algorithm is used to compute the optimal trajectory satisfying the
+specification.
+The A* algorithm when used with a proper heuristic for the distance from the
+destination can generate an optimal path in a graph efficiently. The primary
+challenge for using A* algorithm in temporal logic path planning is that there
+is no notion of a single destination state for the robot. In this thesis, we
+present a novel motion planning algorithm T* that uses the A* search procedure
+in temporal logic path planning \emph{opportunistically} to generate an optimal
+trajectory satisfying a temporal logic query. Our experimental results
+demonstrate that T* achieves an order of magnitude improvement over the
+state-of-the-art algorithm to solve many temporal logic motion planning
+problems.
+",1,0,0,0,0,0
+10348,Discontinuous Homomorphisms of $C(X)$ with $2^{\aleph_0}>\aleph_2$,"  Assume that $M$ is a c.t.m. of $ZFC+CH$ containing a simplified
+$(\omega_1,2)$-morass, $P\in M$ is the poset adding $\aleph_3$ generic reals
+and $G$ is $P$-generic over $M$. In $M$ we construct a function between sets of
+terms in the forcing language, that interpreted in $M[G]$ is an $\mathbb
+R$-linear order-preserving monomorphism from the finite elements of an
+ultrapower of the reals, over a non-principal ultrafilter on $\omega$, into the
+Esterle algebra of formal power series. Therefore it is consistent that
+$2^{\aleph_0}=\aleph_3$ and, for any infinite compact Hausdorff space $X$,
+there exists a discontinuous homomorphism of $C(X)$, the algebra of continuous
+real-valued functions on $X$. For $n\in \mathbb N$, If $M$ contains a
+simplified $(\omega_1,n)$-morass, then in the Cohen extension of $M$ adding
+$\aleph_n$ generic reals there exists a discontinuous homomorphism of $C(X)$,
+for any infinite compact Hausdorff space $X$.
+",0,0,1,0,0,0
+10349,Investigation of beam self-polarization in the future $e^{+}e^{-}$ circular collider,"  The use of resonant depolarization has been suggested for precise beam energy
+measurements (better than 100 keV) in the $e^{+}e^{-}$ Future Circular Collider
+(FCC-$e^{+}e^{-}$) for Z and WW physics at 45 and 80 GeV beam energy
+respectively. Longitudinal beam polarization would benefit the Z peak physics
+program; however it is not essential and therefore it will be not investigated
+here. In this paper the possibility of self-polarized leptons is considered.
+Preliminary results of simulations in presence of quadrupole misalignments and
+beam position monitors (BPMs) errors for a simplified FCC-$e^{+}e^{-}$ ring are
+presented.
+",0,1,0,0,0,0
+10350,Learning to Grasp from a Single Demonstration,"  Learning-based approaches for robotic grasping using visual sensors typically
+require collecting a large size dataset, either manually labeled or by many
+trial and errors of a robotic manipulator in the real or simulated world. We
+propose a simpler learning-from-demonstration approach that is able to detect
+the object to grasp from merely a single demonstration using a convolutional
+neural network we call GraspNet. In order to increase robustness and decrease
+the training time even further, we leverage data from previous demonstrations
+to quickly fine-tune a GrapNet for each new demonstration. We present some
+preliminary results on a grasping experiment with the Franka Panda cobot for
+which we can train a GraspNet with only hundreds of train iterations.
+",1,0,0,0,0,0
+10351,Tensor Minkowski Functionals for random fields on the sphere,"  We generalize the translation invariant tensor-valued Minkowski Functionals
+which are defined on two-dimensional flat space to the unit sphere. We apply
+them to level sets of random fields. The contours enclosing boundaries of level
+sets of random fields give a spatial distribution of random smooth closed
+curves. We obtain analytic expressions for the ensemble expectation values for
+the matrix elements of the tensor-valued Minkowski Functionals for isotropic
+Gaussian and Rayleigh fields. We elucidate the way in which the elements of the
+tensor Minkowski Functionals encode information about the nature and
+statistical isotropy (or departure from isotropy) of the field. We then
+implement our method to compute the tensor-valued Minkowski Functionals
+numerically and demonstrate how they encode statistical anisotropy and
+departure from Gaussianity by applying the method to maps of the Galactic
+foreground emissions from the PLANCK data.
+",0,1,0,0,0,0
+10352,Measuring galaxy cluster masses with CMB lensing using a Maximum Likelihood estimator: Statistical and systematic error budgets for future experiments,"  We develop a Maximum Likelihood estimator (MLE) to measure the masses of
+galaxy clusters through the impact of gravitational lensing on the temperature
+and polarization anisotropies of the cosmic microwave background (CMB). We show
+that, at low noise levels in temperature, this optimal estimator outperforms
+the standard quadratic estimator by a factor of two. For polarization, we show
+that the Stokes Q/U maps can be used instead of the traditional E- and B-mode
+maps without losing information. We test and quantify the bias in the recovered
+lensing mass for a comprehensive list of potential systematic errors. Using
+realistic simulations, we examine the cluster mass uncertainties from
+CMB-cluster lensing as a function of an experiment's beam size and noise level.
+We predict the cluster mass uncertainties will be 3 - 6% for SPT-3G, AdvACT,
+and Simons Array experiments with 10,000 clusters and less than 1% for the
+CMB-S4 experiment with a sample containing 100,000 clusters. The mass
+constraints from CMB polarization are very sensitive to the experimental beam
+size and map noise level: for a factor of three reduction in either the beam
+size or noise level, the lensing signal-to-noise improves by roughly a factor
+of two.
+",0,1,0,0,0,0
+10353,The Incremental Proximal Method: A Probabilistic Perspective,"  In this work, we highlight a connection between the incremental proximal
+method and stochastic filters. We begin by showing that the proximal operators
+coincide, and hence can be realized with, Bayes updates. We give the explicit
+form of the updates for the linear regression problem and show that there is a
+one-to-one correspondence between the proximal operator of the least-squares
+regression and the Bayes update when the prior and the likelihood are Gaussian.
+We then carry out this observation to a general sequential setting: We consider
+the incremental proximal method, which is an algorithm for large-scale
+optimization, and show that, for a linear-quadratic cost function, it can
+naturally be realized by the Kalman filter. We then discuss the implications of
+this idea for nonlinear optimization problems where proximal operators are in
+general not realizable. In such settings, we argue that the extended Kalman
+filter can provide a systematic way for the derivation of practical procedures.
+",0,0,0,1,0,0
+10354,List Decoding of Insertions and Deletions,"  List decoding of insertions and deletions in the Levenshtein metric is
+considered. The Levenshtein distance between two sequences is the minimum
+number of insertions and deletions needed to turn one of the sequences into the
+other. In this paper, a Johnson-like upper bound on the maximum list size when
+list decoding in the Levenshtein metric is derived. This bound depends only on
+the length and minimum Levenshtein distance of the code, the length of the
+received word, and the alphabet size. It shows that polynomial-time list
+decoding beyond half the Levenshtein distance is possible for many parameters.
+Further, we also prove a lower bound on list decoding of deletions with with
+the well-known binary Varshamov-Tenengolts (VT) codes which shows that the
+maximum list size grows exponentially with the number of deletions. Finally, an
+efficient list decoding algorithm for two insertions/deletions with VT codes is
+given. This decoder can be modified to a polynomial-time list decoder of any
+constant number of insertions/deletions.
+",1,0,1,0,0,0
+10355,Almost sure scattering for the energy-critical NLS with radial data below $H^1(\mathbb{R}^4)$,"  We prove almost sure global existence and scattering for the energy-critical
+nonlinear Schrödinger equation with randomized spherically symmetric initial
+data in $H^s(\mathbb{R}^4)$ with $\frac56<s<1$. We were inspired to consider
+this problem by the recent work of Dodson--Lührmann--Mendelson, which treated
+the analogous problem for the energy-critical wave equation.
+",0,0,1,0,0,0
+10356,Almost Boltzmann Exploration,"  Boltzmann exploration is widely used in reinforcement learning to provide a
+trade-off between exploration and exploitation. Recently, in (Cesa-Bianchi et
+al., 2017) it has been shown that pure Boltzmann exploration does not perform
+well from a regret perspective, even in the simplest setting of stochastic
+multi-armed bandit (MAB) problems. In this paper, we show that a simple
+modification to Boltzmann exploration, motivated by a variation of the standard
+doubling trick, achieves $O(K\log^{1+\alpha} T)$ regret for a stochastic MAB
+problem with $K$ arms, where $\alpha>0$ is a parameter of the algorithm. This
+improves on the result in (Cesa-Bianchi et al., 2017), where an algorithm
+inspired by the Gumbel-softmax trick achieves $O(K\log^2 T)$ regret. We also
+show that our algorithm achieves $O(\beta(G) \log^{1+\alpha} T)$ regret in
+stochastic MAB problems with graph-structured feedback, without knowledge of
+the graph structure, where $\beta(G)$ is the independence number of the
+feedback graph. Additionally, we present extensive experimental results on real
+datasets and applications for multi-armed bandits with both traditional bandit
+feedback and graph-structured feedback. In all cases, our algorithm performs as
+well or better than the state-of-the-art.
+",1,0,0,1,0,0
+10357,A Time Localization System in Smart Home Using Hierarchical Structure and Dynamic Frequency,"  Both GPS and WiFi based localization have been exploited in recent years, yet
+most researches focus on localizing at home without environment context.
+Besides, the near home or workplace area is complex and has little attention in
+smart home or IOT. Therefore, after exploring the realistic route in and out of
+building, we conducted a time localization system (TLS) based on off-the-shelf
+smart phones with WiFi identification. TLS can identify the received signal
+strength indication (RSSI) of home and construct radio map of users' time route
+without site survey. As designed to service the smart devices in home, TLS
+applies the time interval as the distance of positions and as the variables of
+WiFi environment to mark time points. Experimental results with real users show
+that TLS as a service system for timeline localization achieves a median
+accuracy of 70 seconds and is more robust compared with nearest neighbor
+localization approach.
+",1,0,0,0,0,0
+10358,Vision-based Real Estate Price Estimation,"  Since the advent of online real estate database companies like Zillow, Trulia
+and Redfin, the problem of automatic estimation of market values for houses has
+received considerable attention. Several real estate websites provide such
+estimates using a proprietary formula. Although these estimates are often close
+to the actual sale prices, in some cases they are highly inaccurate. One of the
+key factors that affects the value of a house is its interior and exterior
+appearance, which is not considered in calculating automatic value estimates.
+In this paper, we evaluate the impact of visual characteristics of a house on
+its market value. Using deep convolutional neural networks on a large dataset
+of photos of home interiors and exteriors, we develop a method for estimating
+the luxury level of real estate photos. We also develop a novel framework for
+automated value assessment using the above photos in addition to home
+characteristics including size, offered price and number of bedrooms. Finally,
+by applying our proposed method for price estimation to a new dataset of real
+estate photos and metadata, we show that it outperforms Zillow's estimates.
+",1,0,0,0,0,0
+10359,Deep Rewiring: Training very sparse deep networks,"  Neuromorphic hardware tends to pose limits on the connectivity of deep
+networks that one can run on them. But also generic hardware and software
+implementations of deep learning run more efficiently for sparse networks.
+Several methods exist for pruning connections of a neural network after it was
+trained without connectivity constraints. We present an algorithm, DEEP R, that
+enables us to train directly a sparsely connected neural network. DEEP R
+automatically rewires the network during supervised training so that
+connections are there where they are most needed for the task, while its total
+number is all the time strictly bounded. We demonstrate that DEEP R can be used
+to train very sparse feedforward and recurrent neural networks on standard
+benchmark tasks with just a minor loss in performance. DEEP R is based on a
+rigorous theoretical foundation that views rewiring as stochastic sampling of
+network configurations from a posterior.
+",1,0,0,1,0,0
+10360,Analyzing and Disentangling Interleaved Interrupt-driven IoT Programs,"  In the Internet of Things (IoT) community, Wireless Sensor Network (WSN) is a
+key technique to enable ubiquitous sensing of environments and provide reliable
+services to applications. WSN programs, typically interrupt-driven, implement
+the functionalities via the collaboration of Interrupt Procedure Instances
+(IPIs, namely executions of interrupt processing logic). However, due to the
+complicated concurrency model of WSN programs, the IPIs are interleaved
+intricately and the program behaviours are hard to predicate from the source
+codes. Thus, to improve the software quality of WSN programs, it is significant
+to disentangle the interleaved executions and develop various IPI-based program
+analysis techniques, including offline and online ones. As the common
+foundation of those techniques, a generic efficient and real-time algorithm to
+identify IPIs is urgently desired. However, the existing
+instance-identification approach cannot satisfy the desires. In this paper, we
+first formally define the concept of IPI. Next, we propose a generic
+IPI-identification algorithm, and prove its correctness, real-time and
+efficiency. We also conduct comparison experiments to illustrate that our
+algorithm is more efficient than the existing one in terms of both time and
+space. As the theoretical analyses and empirical studies exhibit, our algorithm
+provides the groundwork for IPI-based analyses of WSN programs in IoT
+environment.
+",1,0,0,0,0,0
+10361,Absence of replica symmetry breaking in the transverse and longitudinal random field Ising model,"  It is proved that replica symmetry is not broken in the transverse and
+longitudinal random field Ising model. In this model, the variance of spin
+overlap of any component vanishes in any dimension almost everywhere in the
+coupling constant space in the infinite volume limit. The weak
+Fortuin-Kasteleyn-Ginibre property in this model and the Ghirlanda-Guerra
+identities in artificial models in a path integral representation based on the
+Lie-Trotter-Suzuki formula enable us to extend Chatterjee's proof for the
+random field Ising model to the quantum model.
+",0,1,0,0,0,0
+10362,Specialization of Generic Array Accesses After Inlining,"  We have implemented an optimization that specializes type-generic array
+accesses after inlining of polymorphic functions in the native-code OCaml
+compiler. Polymorphic array operations (read and write) in OCaml require
+runtime type dispatch because of ad hoc memory representations of integer and
+float arrays. It cannot be removed even after being monomorphized by inlining
+because the intermediate language is mostly untyped. We therefore extended it
+with explicit type application like System F (while keeping implicit type
+abstraction by means of unique identifiers for type variables). Our
+optimization has achieved up to 21% speed-up of numerical programs.
+",1,0,0,0,0,0
+10363,Exact tensor completion with sum-of-squares,"  We obtain the first polynomial-time algorithm for exact tensor completion
+that improves over the bound implied by reduction to matrix completion. The
+algorithm recovers an unknown 3-tensor with $r$ incoherent, orthogonal
+components in $\mathbb R^n$ from $r\cdot \tilde O(n^{1.5})$ randomly observed
+entries of the tensor. This bound improves over the previous best one of
+$r\cdot \tilde O(n^{2})$ by reduction to exact matrix completion. Our bound
+also matches the best known results for the easier problem of approximate
+tensor completion (Barak & Moitra, 2015).
+Our algorithm and analysis extends seminal results for exact matrix
+completion (Candes & Recht, 2009) to the tensor setting via the sum-of-squares
+method. The main technical challenge is to show that a small number of randomly
+chosen monomials are enough to construct a degree-3 polynomial with precisely
+planted orthogonal global optima over the sphere and that this fact can be
+certified within the sum-of-squares proof system.
+",1,0,0,1,0,0
+10364,Outlier Cluster Formation in Spectral Clustering,"  Outlier detection and cluster number estimation is an important issue for
+clustering real data. This paper focuses on spectral clustering, a time-tested
+clustering method, and reveals its important properties related to outliers.
+The highlights of this paper are the following two mathematical observations:
+first, spectral clustering's intrinsic property of an outlier cluster
+formation, and second, the singularity of an outlier cluster with a valid
+cluster number. Based on these observations, we designed a function that
+evaluates clustering and outlier detection results. In experiments, we prepared
+two scenarios, face clustering in photo album and person re-identification in a
+camera network. We confirmed that the proposed method detects outliers and
+estimates the number of clusters properly in both problems. Our method
+outperforms state-of-the-art methods in both the 128-dimensional sparse space
+for face clustering and the 4,096-dimensional non-sparse space for person
+re-identification.
+",1,0,0,0,0,0
+10365,Stored Electromagnetic Field Energies in General Materials,"  The most general expressions of the stored energies for time-harmonic
+electromagnetic fields are derived from the time-domain Poynting theorem, and
+are valuable in characterizing the energy storage and transport properties of
+complex media. A new energy conservation law for the time-harmonic
+electromagnetic fields, which involves the derived general expressions of the
+stored energies, is introduced. In contrast to the well-established Poynting
+theorem for time-harmonic fields, the real part of the new energy conservation
+law gives an equation for the sum of stored electric and magnetic field
+energies; the imaginary part involves an equation related to the difference
+between the dissipated electric and magnetic field energies. In a lossless
+isotropic and homogeneous medium, the new energy conservation law has a clear
+physical implication: the stored electromagnetic field energy of a radiating
+system enclosed by a surface is equal to the total field energy inside the
+surface subtracted by the field energy flowing out of the surface.
+",0,1,0,0,0,0
+10366,From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles,"  The inference of network topologies from relational data is an important
+problem in data analysis. Exemplary applications include the reconstruction of
+social ties from data on human interactions, the inference of gene
+co-expression networks from DNA microarray data, or the learning of semantic
+relationships based on co-occurrences of words in documents. Solving these
+problems requires techniques to infer significant links in noisy relational
+data. In this short paper, we propose a new statistical modeling framework to
+address this challenge. It builds on generalized hypergeometric ensembles, a
+class of generative stochastic models that give rise to analytically tractable
+probability spaces of directed, multi-edge graphs. We show how this framework
+can be used to assess the significance of links in noisy relational data. We
+illustrate our method in two data sets capturing spatio-temporal proximity
+relations between actors in a social system. The results show that our
+analytical framework provides a new approach to infer significant links from
+relational data, with interesting perspectives for the mining of data on social
+systems.
+",1,1,0,1,0,0
+10367,A Relaxed Kačanov Iteration for the $p$-Poisson Problem,"  In this paper, we introduce an iterative linearization scheme that allows to
+approximate the weak solution of the $p$-Poisson problem
+\begin{align*}
+-\operatorname{div}(|\nabla u|^{p-2}\nabla u) &= f\quad\text{in }\Omega,
+u&= 0\quad\text{on}\partial\Omega
+\end{align*} for $1 < p \leq 2$. The algorithm can be interpreted as a
+relaxed Kačanov iteration. We prove that the algorithm converges at least
+with an algebraic rate.
+",0,0,1,0,0,0
+10368,Explosive Percolation on Directed Networks Due to Monotonic Flow of Activity,"  An important class of real-world networks have directed edges, and in
+addition, some rank ordering on the nodes, for instance the ""popularity"" of
+users in online social networks. Yet, nearly all research related to explosive
+percolation has been restricted to undirected networks. Furthermore,
+information on such rank ordered networks typically flows from higher ranked to
+lower ranked individuals, such as follower relations, replies and retweets on
+Twitter.
+Here we introduce a simple percolation process on an ordered, directed
+network where edges are added monotonically with respect to the rank ordering.
+We show with a numerical approach that the emergence of a dominant strongly
+connected component appears to be discontinuous. Large scale connectivity
+occurs at very high density compared with most percolation processes, and this
+holds not just for the strongly connected component structure but for the
+weakly connected component structure as well. We present analysis with
+branching processes which explains this unusual behavior and gives basic
+intuition for the underlying mechanisms. We also show that before the emergence
+of a dominant strongly connected component, multiple giant strongly connected
+components may exist simultaneously. By adding a competitive percolation rule
+with a small bias to link uses of similar rank, we show this leads to formation
+of two distinct components, one of high ranked users, and one of low ranked
+users, with little flow between the two components.
+",1,1,0,0,0,0
+10369,Experimental observation of self excited co--rotating multiple vortices in a dusty plasma with inhomogeneous plasma background,"  We report an experimental observation of multiple co--rotating vortices in a
+extended dust column in the background of non--uniform diffused plasma.
+Inductively coupled RF discharge is initiated in the background of argon gas in
+the source region which later found to diffuse in the main experimental
+chamber. A secondary DC glow discharge plasma is produced to introduce the dust
+particles into the plasma. These micron sized poly-disperse dust particles get
+charged in the plasma environment and transported by the ambipolar electric
+field of the diffused plasma and found to confine in the potential well, where
+the resultant electric field of the diffused plasma (ambipolar E--field) and
+glass wall charging (sheath E--field) hold the micron sized particles against
+the gravity. Multiple co--rotating (anti--clockwise) dust vortices are observed
+in the dust cloud for a particular discharge condition. The transition from
+multiple to single dust vortex is observed when input RF power is lowered.
+Occurrence of these vortices are explained on the basis of the charge gradient
+of dust particles which is orthogonal to the ion drag force. The charge
+gradient is a consequence of the plasma inhomogeneity along the dust cloud
+length. The detailed nature and the reason for multiple vortices are still
+under investigation through further experiments, however, preliminary
+qualitative understanding is discussed based on characteristic scale length of
+dust vortex. There is a characteristic size of the vortex in the dusty plasma
+so that multiple vortices is possible to form in the extended dusty plasma with
+inhomogeneous plasma background. The experimental results on the vortex motion
+of particles are compared with a theoretical model and found some agreement.
+",0,1,0,0,0,0
+10370,Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory,"  This paper presents results of topic modeling and network models of topics
+using the International Conference on Computational Science corpus, which
+contains domain-specific (computational science) papers over sixteen years (a
+total of 5695 papers). We discuss topical structures of International
+Conference on Computational Science, how these topics evolve over time in
+response to the topicality of various problems, technologies and methods, and
+how all these topics relate to one another. This analysis illustrates
+multidisciplinary research and collaborations among scientific communities, by
+constructing static and dynamic networks from the topic modeling results and
+the keywords of authors. The results of this study give insights about the past
+and future trends of core discussion topics in computational science. We used
+the Non-negative Matrix Factorization topic modeling algorithm to discover
+topics and labeled and grouped results hierarchically.
+",1,0,0,0,0,0
+10371,A maximal Boolean sublattice that is not the range of a Banaschewski function,"  We construct a countable bounded sublattice of the lattice of all subspaces
+of a vector space with two non-isomorphic maximal Boolean sublattice. We
+represent one of them as the range of a Banschewski function and we prove that
+this is not the case of the other. Hereby we solve a problem of F. Wehrung.
+",0,0,1,0,0,0
+10372,Bayesian inference for stationary data on finite state spaces,"  In this work the issue of Bayesian inference for stationary data is
+addressed. Therefor a parametrization of a statistically suitable subspace of
+the the shift-ergodic probability measures on a Cartesian product of some
+finite state space is given using an inverse limit construction. Moreover, an
+explicit model for the prior is given by taking into account an additional step
+in the usual stepwise sampling scheme of data. An update to the posterior is
+defined by exploiting this augmented sample scheme. Thereby, its model-step is
+updated using a measurement of the empirical distances between the model
+classes.
+",0,0,1,1,0,0
+10373,Why is solar cycle 24 an inefficient producer of high-energy particle events?,"  The aim of the study is to investigate the reason for the low productivity of
+high-energy SEPs in the present solar cycle. We employ scaling laws derived
+from diffusive shock acceleration theory and simulation studies including
+proton-generated upstream Alfvén waves to find out how the changes observed
+in the long-term average properties of the erupting and ambient coronal and/or
+solar wind plasma would affect the ability of shocks to accelerate particles to
+the highest energies. Provided that self-generated turbulence dominates
+particle transport around coronal shocks, it is found that the most crucial
+factors controlling the diffusive shock acceleration process are the number
+density of seed particles and the plasma density of the ambient medium.
+Assuming that suprathermal populations provide a fraction of the particles
+injected to shock acceleration in the corona, we show that the lack of most
+energetic particle events as well as the lack of low charge-to-mass ratio ion
+species in the present cycle can be understood as a result of the reduction of
+average coronal plasma and suprathermal densities in the present cycle over the
+previous one.
+",0,1,0,0,0,0
+10374,E2M2: Energy Efficient Mobility Management in Dense Small Cells with Mobile Edge Computing,"  Merging mobile edge computing with the dense deployment of small cell base
+stations promises enormous benefits such as a real proximity, ultra-low latency
+access to cloud functionalities. However, the envisioned integration creates
+many new challenges and one of the most significant is mobility management,
+which is becoming a key bottleneck to the overall system performance. Simply
+applying existing solutions leads to poor performance due to the highly
+overlapped coverage areas of multiple base stations in the proximity of the
+user and the co-provisioning of radio access and computing services. In this
+paper, we develop a novel user-centric mobility management scheme, leveraging
+Lyapunov optimization and multi-armed bandits theories, in order to maximize
+the edge computation performance for the user while keeping the user's
+communication energy consumption below a constraint. The proposed scheme
+effectively handles the uncertainties present at multiple levels in the system
+and provides both short-term and long-term performance guarantee. Simulation
+results show that our proposed scheme can significantly improve the computation
+performance (compared to state of the art) while satisfying the communication
+energy constraint.
+",1,0,0,0,0,0
+10375,Big Data in HEP: A comprehensive use case study,"  Experimental Particle Physics has been at the forefront of analyzing the
+worlds largest datasets for decades. The HEP community was the first to develop
+suitable software and computing tools for this task. In recent times, new
+toolkits and systems collectively called Big Data technologies have emerged to
+support the analysis of Petabyte and Exabyte datasets in industry. While the
+principles of data analysis in HEP have not changed (filtering and transforming
+experiment-specific data formats), these new technologies use different
+approaches and promise a fresh look at analysis of very large datasets and
+could potentially reduce the time-to-physics with increased interactivity. In
+this talk, we present an active LHC Run 2 analysis, searching for dark matter
+with the CMS detector, as a testbed for Big Data technologies. We directly
+compare the traditional NTuple-based analysis with an equivalent analysis using
+Apache Spark on the Hadoop ecosystem and beyond. In both cases, we start the
+analysis with the official experiment data formats and produce publication
+physics plots. We will discuss advantages and disadvantages of each approach
+and give an outlook on further studies needed.
+",1,0,0,0,0,0
+10376,Criticality & Deep Learning II: Momentum Renormalisation Group,"  Guided by critical systems found in nature we develop a novel mechanism
+consisting of inhomogeneous polynomial regularisation via which we can induce
+scale invariance in deep learning systems. Technically, we map our deep
+learning (DL) setup to a genuine field theory, on which we act with the
+Renormalisation Group (RG) in momentum space and produce the flow equations of
+the couplings; those are translated to constraints and consequently interpreted
+as ""critical regularisation"" conditions in the optimiser; the resulting
+equations hence prove to be sufficient conditions for - and serve as an elegant
+and simple mechanism to induce scale invariance in any deep learning setup.
+",1,0,0,0,0,0
+10377,The Price of Diversity in Assignment Problems,"  We introduce and analyze an extension to the matching problem on a weighted
+bipartite graph: Assignment with Type Constraints. The two parts of the graph
+are partitioned into subsets called types and blocks; we seek a matching with
+the largest sum of weights under the constraint that there is a pre-specified
+cap on the number of vertices matched in every type-block pair. Our primary
+motivation stems from the public housing program of Singapore, accounting for
+over 70% of its residential real estate. To promote ethnic diversity within its
+housing projects, Singapore imposes ethnicity quotas: each new housing
+development comprises blocks of flats and each ethnicity-based group in the
+population must not own more than a certain percentage of flats in a block.
+Other domains using similar hard capacity constraints include matching
+prospective students to schools or medical residents to hospitals. Limiting
+agents' choices for ensuring diversity in this manner naturally entails some
+welfare loss. One of our goals is to study the trade-off between diversity and
+social welfare in such settings. We first show that, while the classic
+assignment program is polynomial-time computable, adding diversity constraints
+makes it computationally intractable; however, we identify a
+$\tfrac{1}{2}$-approximation algorithm, as well as reasonable assumptions on
+the weights that permit poly-time algorithms. Next, we provide two upper bounds
+on the price of diversity -- a measure of the loss in welfare incurred by
+imposing diversity constraints -- as functions of natural problem parameters.
+We conclude the paper with simulations based on publicly available data from
+two diversity-constrained allocation problems -- Singapore Public Housing and
+Chicago School Choice -- which shed light on how the constrained maximization
+as well as lottery-based variants perform in practice.
+",1,0,0,0,0,0
+10378,"X-ray spectral analyses of AGNs from the 7Ms Chandra Deep Field-South survey: the distribution, variability, and evolution of AGN's obscuration","  We present a detailed spectral analysis of the brightest Active Galactic
+Nuclei (AGN) identified in the 7Ms Chandra Deep Field South (CDF-S) survey over
+a time span of 16 years. Using a model of an intrinsically absorbed power-law
+plus reflection, with possible soft excess and narrow Fe K$\alpha$ line, we
+perform a systematic X-ray spectral analysis, both on the total 7Ms exposure
+and in four different periods with lengths of 2-21 months. With this approach,
+we not only present the power-law slopes, column densities $N_H$, observed
+fluxes, and absorption-corrected 2-10~keV luminosities $L_X$ for our sample of
+AGNs, but also identify significant spectral variabilities among them on time
+scales of years. We find that the $N_H$ variabilities can be ascribed to two
+different types of mechanisms, either flux-driven or flux-independent. We also
+find that the correlation between the narrow Fe line EW and $N_H$ can be well
+explained by the continuum suppression with increasing $N_H$. Accounting for
+the sample incompleteness and bias, we measure the intrinsic distribution of
+$N_H$ for the CDF-S AGN population and present re-selected subsamples which are
+complete with respect to $N_H$. The $N_H$-complete subsamples enable us to
+decouple the dependences of $N_H$ on $L_X$ and on redshift. Combining our data
+with that from C-COSMOS, we confirm the anti-correlation between the average
+$N_H$ and $L_X$ of AGN, and find a significant increase of the AGN obscured
+fraction with redshift at any luminosity. The obscured fraction can be
+described as $f_{obscured}\thickapprox 0.42\ (1+z)^{0.60}$.
+",0,1,0,0,0,0
+10379,Muon spin relaxation and inelastic neutron scattering investigations of all-in/all-out antiferromagnet Nd2Hf2O7,"  Nd2Hf2O7, belonging to the family of geometrically frustrated cubic rare
+earth pyrochlore oxides, was recently identified to order antiferromagnetically
+below T_N = 0.55 K with an all-in/all-out arrangement of Nd3+ moments, however
+with a much reduced ordered state moment. Herein we investigate the spin
+dynamics and crystal field states of Nd2Hf2O7 using muon spin relaxation (muSR)
+and inelastic neutron scattering (INS) measurements. Our muSR study confirms
+the long range magnetic ordering and shows evidence for coexisting persistent
+dynamic spin fluctuations deep inside the ordered state down to 42 mK. The INS
+data show the crytal electric field (CEF) excitations due to the transitions
+both within the ground state multiplet and to the first excited state
+multiplet. The INS data are analyzed by a model based on CEF and crystal field
+states are determined. Strong Ising-type anisotropy is inferred from the ground
+state wavefunction. The CEF parameters indicate the CEF-split Kramers doublet
+ground state of Nd3+ to be consistent with the dipolar-octupolar character.
+",0,1,0,0,0,0
+10380,Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks,"  Noncritical soft-faults and model deviations are a challenge for Fault
+Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles
+(AUVs). Such systems may have a faster performance degradation due to the
+permanent exposure to the marine environment, and constant monitoring of
+component conditions is required to ensure their reliability. This works
+presents an evaluation of Recurrent Neural Networks (RNNs) for a data-driven
+fault detection and diagnosis scheme for underwater thrusters with empirical
+data. The nominal behavior of the thruster was modeled using the measured
+control input, voltage, rotational speed and current signals. We evaluated the
+performance of fault classification using all the measured signals compared to
+using the computed residuals from the nominal model as features.
+",1,0,0,1,0,0
+10381,Epidemic Spreading on Activity-Driven Networks with Attractiveness,"  We study SIS epidemic spreading processes unfolding on a recent
+generalisation of the activity-driven modelling framework. In this model of
+time-varying networks each node is described by two variables: activity and
+attractiveness. The first, describes the propensity to form connections. The
+second, defines the propensity to attract them. We derive analytically the
+epidemic threshold considering the timescale driving the evolution of contacts
+and the contagion as comparable. The solutions are general and hold for any
+joint distribution of activity and attractiveness. The theoretical picture is
+confirmed via large-scale numerical simulations performed considering
+heterogeneous distributions and different correlations between the two
+variables. We find that heterogeneous distributions of attractiveness alter the
+contagion process. In particular, in case of uncorrelated and positive
+correlations between the two variables, heterogeneous attractiveness
+facilitates the spreading. On the contrary, negative correlations between
+activity and attractiveness hamper the spreading. The results presented
+contribute to the understanding of the dynamical properties of time-varying
+networks and their effects on contagion phenomena unfolding on their fabric.
+",1,1,0,0,0,0
+10382,A generalized family of anisotropic compact object in general relativity,"  We present model for anisotropic compact star under the general theory of
+relativity of Einstein. In the study a 4-dimensional spacetime has been
+considered which is embedded into the 5-dimensional flat metric so that the
+spherically symmetric metric has class 1 when the condition
+$e^{\lambda}=\left(\,1+C\,e^{\nu} \,{\nu'}^2\,\right)$ is satisfied ($\lambda$
+and $\nu$ being the metric potentials along with a constant $C$). A set of
+solutions for the field equations are found depending on the index $n$ involved
+in the physical parameters. The interior solutions have been matched smoothly
+at the boundary of the spherical distribution to the exterior Schwarzschild
+solution which necessarily provides values of the unknown constants. We have
+chosen the values of $n$ as $n=2$ and $n$=10 to 20000 for which interesting and
+physically viable results can be found out. The numerical values of the
+parameters and arbitrary constants for different compact stars are assumed in
+the graphical plots and tables as follows: (i) LMC X-4 : $a=0.0075$,
+$b=0.000821$ for $n=2$ and $a=0.0075$, $nb=0.00164$ for $n\ge 10$, (ii) SMC
+X-1: $a=0.00681$, $b=0.00078$ for $n=2$, and $a=0.00681$, $nb=0.00159$ for $n
+\ge 10$. The investigations on the physical features of the model include
+several astrophysical issues, like (i) regularity behavior of stars at the
+centre, (ii) well behaved condition for velocity of sound, (iii) energy
+conditions, (iv) stabilty of the system via the following three techniques -
+adiabatic index, Herrera cracking concept and TOV equation, (v) total mass,
+effective mass and compactification factor and (vi) surface redshift. Specific
+numerical values of the compact star candidates LMC X-4 and SMC X-1 are
+calculated for central and surface densities as well as central pressure to
+compare the model value with actual observational data.
+",0,1,0,0,0,0
+10383,Automated capture and delivery of assistive task guidance with an eyewear computer: The GlaciAR system,"  In this paper we describe and evaluate a mixed reality system that aims to
+augment users in task guidance applications by combining automated and
+unsupervised information collection with minimally invasive video guides. The
+result is a self-contained system that we call GlaciAR (Glass-enabled
+Contextual Interactions for Augmented Reality), that operates by extracting
+contextual interactions from observing users performing actions. GlaciAR is
+able to i) automatically determine moments of relevance based on a head motion
+attention model, ii) automatically produce video guidance information, iii)
+trigger these video guides based on an object detection method, iv) learn
+without supervision from observing multiple users and v) operate fully on-board
+a current eyewear computer (Google Glass). We describe the components of
+GlaciAR together with evaluations on how users are able to use the system to
+achieve three tasks. We see this work as a first step toward the development of
+systems that aim to scale up the notoriously difficult authoring problem in
+guidance systems and where people's natural abilities are enhanced via
+minimally invasive visual guidance.
+",1,0,0,0,0,0
+10384,V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks,"  Benefited from the widely deployed infrastructure, the LTE network has
+recently been considered as a promising candidate to support the
+vehicle-to-everything (V2X) services. However, with a massive number of devices
+accessing the V2X network in the future, the conventional OFDM-based LTE
+network faces the congestion issues due to its low efficiency of orthogonal
+access, resulting in significant access delay and posing a great challenge
+especially to safety-critical applications. The non-orthogonal multiple access
+(NOMA) technique has been well recognized as an effective solution for the
+future 5G cellular networks to provide broadband communications and massive
+connectivity. In this article, we investigate the applicability of NOMA in
+supporting cellular V2X services to achieve low latency and high reliability.
+Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed
+to tackle the technical hurdles in designing high spectral efficient scheduling
+and resource allocation schemes in the ultra dense topology. We then extend it
+to a more general V2X broadcasting system. Other NOMA-based extended V2X
+applications and some open issues are also discussed.
+",1,0,0,0,0,0
+10385,Theoretical Foundation of Co-Training and Disagreement-Based Algorithms,"  Disagreement-based approaches generate multiple classifiers and exploit the
+disagreement among them with unlabeled data to improve learning performance.
+Co-training is a representative paradigm of them, which trains two classifiers
+separately on two sufficient and redundant views; while for the applications
+where there is only one view, several successful variants of co-training with
+two different classifiers on single-view data instead of two views have been
+proposed. For these disagreement-based approaches, there are several important
+issues which still are unsolved, in this article we present theoretical
+analyses to address these issues, which provides a theoretical foundation of
+co-training and disagreement-based approaches.
+",1,0,0,1,0,0
+10386,Schrödinger's Man,"  What if someone built a ""box"" that applies quantum superposition not just to
+quantum bits in the microscopic but also to macroscopic everyday ""objects"",
+such as Schrödinger's cat or a human being? If that were possible, and if the
+different ""copies"" of a man could exploit quantum interference to synchronize
+and collapse into their preferred state, then one (or they?) could in a sense
+choose their future, win the lottery, break codes and other security devices,
+and become king of the world, or actually of the many-worlds. We set up the
+plot-line of a new episode of Black Mirror to reflect on what might await us if
+one were able to build such a technology.
+",1,0,0,0,0,0
+10387,High-Resolution Multispectral Dataset for Semantic Segmentation,"  Unmanned aircraft have decreased the cost required to collect remote sensing
+imagery, which has enabled researchers to collect high-spatial resolution data
+from multiple sensor modalities more frequently and easily. The increase in
+data will push the need for semantic segmentation frameworks that are able to
+classify non-RGB imagery, but this type of algorithmic development requires an
+increase in publicly available benchmark datasets with class labels. In this
+paper, we introduce a high-resolution multispectral dataset with image labels.
+This new benchmark dataset has been pre-split into training/testing folds in
+order to standardize evaluation and continue to push state-of-the-art
+classification frameworks for non-RGB imagery.
+",1,0,0,0,0,0
+10388,A Methodology for the Selection of Requirement Elicitation Techniques,"  In this paper, we present an approach to select a subset of requirement
+elicitation technique for an optimum result in the requirement elicitation
+process. Our approach consists of three steps. First, we identify various
+attribute in three important dimensions namely project, people and the process
+of software development that can influence the outcome of an elicitation
+process. Second, we construct three p matrix (3PM) separately for each
+dimension, that shows a relation between the elicitation techniques and three
+dimensions of a software. Third, we provide a mapping criteria and use them in
+the selection of a subset of elicitation techniques. We demonstrate the
+applicability of the proposed approach using case studies to evaluate and
+provide the contextual knowledge of selecting requirement elicitation
+technique.
+",1,0,0,0,0,0
+10389,Nonclassical Light Generation from III-V and Group-IV Solid-State Cavity Quantum Systems,"  In this chapter, we present the state-of-the-art in the generation of
+nonclassical states of light using semiconductor cavity quantum electrodynamics
+(QED) platforms. Our focus is on the photon blockade effects that enable the
+generation of indistinguishable photon streams with high purity and efficiency.
+Starting with the leading platform of InGaAs quantum dots in optical
+nanocavities, we review the physics of a single quantum emitter strongly
+coupled to a cavity. Furthermore, we propose a complete model for photon
+blockade and tunneling in III-V quantum dot cavity QED systems. Turning toward
+quantum emitters with small inhomogeneous broadening, we propose a direction
+for novel experiments for nonclassical light generation based on group-IV
+color-center systems. We present a model of a multi-emitter cavity QED
+platform, which features richer dressed-states ladder structures, and show how
+it can offer opportunities for studying new regimes of high-quality photon
+blockade.
+",0,1,0,0,0,0
+10390,INtERAcT: Interaction Network Inference from Vector Representations of Words,"  In recent years, the number of biomedical publications has steadfastly grown,
+resulting in a rich source of untapped new knowledge. Most biomedical facts are
+however not readily available, but buried in the form of unstructured text, and
+hence their exploitation requires the time-consuming manual curation of
+published articles. Here we present INtERAcT, a novel approach to extract
+protein-protein interactions from a corpus of biomedical articles related to a
+broad range of scientific domains in a completely unsupervised way. INtERAcT
+exploits vector representation of words, computed on a corpus of domain
+specific knowledge, and implements a new metric that estimates an interaction
+score between two molecules in the space where the corresponding words are
+embedded. We demonstrate the power of INtERAcT by reconstructing the molecular
+pathways associated to 10 different cancer types using a corpus of
+disease-specific articles for each cancer type. We evaluate INtERAcT using
+STRING database as a benchmark, and show that our metric outperforms currently
+adopted approaches for similarity computation at the task of identifying known
+molecular interactions in all studied cancer types. Furthermore, our approach
+does not require text annotation, manual curation or the definition of semantic
+rules based on expert knowledge, and hence it can be easily and efficiently
+applied to different scientific domains. Our findings suggest that INtERAcT may
+increase our capability to summarize the understanding of a specific disease
+using the published literature in an automated and completely unsupervised
+fashion.
+",0,0,0,0,1,0
+10391,Distributed Deep Transfer Learning by Basic Probability Assignment,"  Transfer learning is a popular practice in deep neural networks, but
+fine-tuning of large number of parameters is a hard task due to the complex
+wiring of neurons between splitting layers and imbalance distributions of data
+in pretrained and transferred domains. The reconstruction of the original
+wiring for the target domain is a heavy burden due to the size of
+interconnections across neurons. We propose a distributed scheme that tunes the
+convolutional filters individually while backpropagates them jointly by means
+of basic probability assignment. Some of the most recent advances in evidence
+theory show that in a vast variety of the imbalanced regimes, optimizing of
+some proper objective functions derived from contingency matrices prevents
+biases towards high-prior class distributions. Therefore, the original filters
+get gradually transferred based on individual contributions to overall
+performance of the target domain. This largely reduces the expected complexity
+of transfer learning whilst highly improves precision. Our experiments on
+standard benchmarks and scenarios confirm the consistent improvement of our
+distributed deep transfer learning strategy.
+",1,0,0,1,0,0
+10392,Parameter Learning and Change Detection Using a Particle Filter With Accelerated Adaptation,"  This paper presents the construction of a particle filter, which incorporates
+elements inspired by genetic algorithms, in order to achieve accelerated
+adaptation of the estimated posterior distribution to changes in model
+parameters. Specifically, the filter is designed for the situation where the
+subsequent data in online sequential filtering does not match the model
+posterior filtered based on data up to a current point in time. The examples
+considered encompass parameter regime shifts and stochastic volatility. The
+filter adapts to regime shifts extremely rapidly and delivers a clear heuristic
+for distinguishing between regime shifts and stochastic volatility, even though
+the model dynamics assumed by the filter exhibit neither of those features.
+",0,0,0,1,0,1
+10393,Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation,"  Enabling robots to autonomously navigate complex environments is essential
+for real-world deployment. Prior methods approach this problem by having the
+robot maintain an internal map of the world, and then use a localization and
+planning method to navigate through the internal map. However, these approaches
+often include a variety of assumptions, are computationally intensive, and do
+not learn from failures. In contrast, learning-based methods improve as the
+robot acts in the environment, but are difficult to deploy in the real-world
+due to their high sample complexity. To address the need to learn complex
+policies with few samples, we propose a generalized computation graph that
+subsumes value-based model-free methods and model-based methods, with specific
+instantiations interpolating between model-free and model-based. We then
+instantiate this graph to form a navigation model that learns from raw images
+and is sample efficient. Our simulated car experiments explore the design
+decisions of our navigation model, and show our approach outperforms
+single-step and $N$-step double Q-learning. We also evaluate our approach on a
+real-world RC car and show it can learn to navigate through a complex indoor
+environment with a few hours of fully autonomous, self-supervised training.
+Videos of the experiments and code can be found at github.com/gkahn13/gcg
+",1,0,0,0,0,0
+10394,Magnetization reversal by superconducting current in $φ_0$ Josephson junctions,"  We study magnetization reversal in a $\varphi_0$ Josephson junction with
+direct coupling between magnetic moment and Josephson current. Our simulations
+of magnetic moment dynamics show that by applying an electric current pulse, we
+can realize the full magnetization reversal. We propose different protocols of
+full magnetization reversal based on the variation of the Josephson junction
+and pulse parameters, particularly, electric current pulse amplitude, damping
+of magnetization and spin-orbit interaction. We discuss experiments which can
+probe the magnetization reversal in $\varphi_0$-junctions.
+",0,1,0,0,0,0
+10395,Higher Order Accurate Space-Time Schemes for Computational Astrophysics -- Part I -- Finite Volume Methods,"  As computational astrophysics comes under pressure to become a precision
+science, there is an increasing need to move to high accuracy schemes for
+computational astrophysics. Hence the need for a specialized review on higher
+order schemes for computational astrophysics.
+The focus here is on weighted essentially non-oscillatory (WENO) schemes,
+discontinuous Galerkin (DG) schemes and PNPM schemes. WENO schemes are higher
+order extensions of traditional second order finite volume schemes which are
+already familiar to most computational astrophysicists. DG schemes, on the
+other hand, evolve all the moments of the solution, with the result that they
+are more accurate than WENO schemes. PNPM schemes occupy a compromise position
+between WENO and PNPM schemes. They evolve an Nth order spatial polynomial,
+while reconstructing higher order terms up to Mth order. As a result, the
+timestep can be larger.
+Time-dependent astrophysical codes need to be accurate in space and time.
+This is realized with the help of SSP-RK (strong stability preserving
+Runge-Kutta) schemes and ADER (Arbitrary DERivative in space and time) schemes.
+The most popular approaches to SSP-RK and ADER schemes are also described.
+The style of this review is to assume that readers have a basic understanding
+of hyperbolic systems and one-dimensional Riemann solvers. Such an
+understanding can be acquired from a sequence of prepackaged lectures available
+from this http URL. We then build on this
+understanding to give the reader a practical introduction to the schemes
+described here. The emphasis is on computer-implementable ideas, not
+necessarily on the underlying theory, because it was felt that this would be
+most interesting to most computational astrophysicists.
+",0,1,0,0,0,0
+10396,Building Robust Deep Neural Networks for Road Sign Detection,"  Deep Neural Networks are built to generalize outside of training set in mind
+by using techniques such as regularization, early stopping and dropout. But
+considerations to make them more resilient to adversarial examples are rarely
+taken. As deep neural networks become more prevalent in mission-critical and
+real-time systems, miscreants start to attack them by intentionally making deep
+neural networks to misclassify an object of one type to be seen as another
+type. This can be catastrophic in some scenarios where the classification of a
+deep neural network can lead to a fatal decision by a machine. In this work, we
+used GTSRB dataset to craft adversarial samples by Fast Gradient Sign Method
+and Jacobian Saliency Method, used those crafted adversarial samples to attack
+another Deep Convolutional Neural Network and built the attacked network to be
+more resilient against adversarial attacks by making it more robust by
+Defensive Distillation and Adversarial Training
+",1,0,0,1,0,0
+10397,Statman's Hierarchy Theorem,"  In the Simply Typed $\lambda$-calculus Statman investigates the reducibility
+relation $\leq_{\beta\eta}$ between types: for $A,B \in \mathbb{T}^0$, types
+freely generated using $\rightarrow$ and a single ground type $0$, define $A
+\leq_{\beta\eta} B$ if there exists a $\lambda$-definable injection from the
+closed terms of type $A$ into those of type $B$. Unexpectedly, the induced
+partial order is the (linear) well-ordering (of order type) $\omega + 4$.
+In the proof a finer relation $\leq_{h}$ is used, where the above injection
+is required to be a Böhm transformation, and an (a posteriori) coarser
+relation $\leq_{h^+}$, requiring a finite family of Böhm transformations that
+is jointly injective.
+We present this result in a self-contained, syntactic, constructive and
+simplified manner. En route similar results for $\leq_h$ (order type $\omega +
+5$) and $\leq_{h^+}$ (order type $8$) are obtained. Five of the equivalence
+classes of $\leq_{h^+}$ correspond to canonical term models of Statman, one to
+the trivial term model collapsing all elements of the same type, and one does
+not even form a model by the lack of closed terms of many types.
+",1,0,0,0,0,0
+10398,Anisotropic Dzyaloshinskii-Moriya Interaction in ultra-thin epitaxial Au/Co/W(110),"  We have used Brillouin Light Scattering spectroscopy to independently
+determine the in-plane Magneto-Crystalline Anisotropy and the
+Dzyaloshinskii-Moriya Interaction (DMI) in out-of-plane magnetized
+Au/Co/W(110). We found that the DMI strength is 2-3 times larger along the
+bcc$[\bar{1}10]$ than along the bcc$[001]$ direction. We use analytical
+considerations to illustrate the relationship between the crystal symmetry of
+the stack and the anisotropy of microscopic DMI. Such an anisotropic DMI is the
+first step to realize isolated elliptical skyrmions or anti-skyrmions in thin
+film systems with $C_{2v}$ symmetry.
+",0,1,0,0,0,0
+10399,Modeling and Simulation of Robotic Finger Powered by Nylon Artificial Muscles- Equations with Simulink model,"  This paper shows a detailed modeling of three-link robotic finger that is
+actuated by nylon artificial muscles and a simulink model that can be used for
+numerical study of a robotic finger. The robotic hand prototype was recently
+demonstrated in recent publication Wu, L., Jung de Andrade, M., Saharan,
+L.,Rome, R., Baughman, R., and Tadesse, Y., 2017, Compact and Low-cost Humanoid
+Hand Powered by Nylon Artificial Muscles, Bioinspiration & Biomimetics, 12 (2).
+The robotic hand is a 3D printed, lightweight and compact hand actuated by
+silver-coated nylon muscles, often called Twisted and coiled Polymer (TCP)
+muscles. TCP muscles are thermal actuators that contract when they are heated
+and they are getting attention for application in robotics. The purpose of this
+paper is to demonstrate the modeling equations that were derived based on Euler
+Lagrangian approach that is suitable for implementation in simulink model.
+",1,0,0,0,0,0
+10400,Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo,"  A key task in Bayesian statistics is sampling from distributions that are
+only specified up to a partition function (i.e., constant of proportionality).
+However, without any assumptions, sampling (even approximately) can be #P-hard,
+and few works have provided ""beyond worst-case"" guarantees for such settings.
+For log-concave distributions, classical results going back to Bakry and
+Émery (1985) show that natural continuous-time Markov chains called Langevin
+diffusions mix in polynomial time. The most salient feature of log-concavity
+violated in practice is uni-modality: commonly, the distributions we wish to
+sample from are multi-modal. In the presence of multiple deep and
+well-separated modes, Langevin diffusion suffers from torpid mixing.
+We address this problem by combining Langevin diffusion with simulated
+tempering. The result is a Markov chain that mixes more rapidly by
+transitioning between different temperatures of the distribution. We analyze
+this Markov chain for the canonical multi-modal distribution: a mixture of
+gaussians (of equal variance). The algorithm based on our Markov chain provably
+samples from distributions that are close to mixtures of gaussians, given
+access to the gradient of the log-pdf. For the analysis, we use a spectral
+decomposition theorem for graphs (Gharan and Trevisan, 2014) and a Markov chain
+decomposition technique (Madras and Randall, 2002).
+",1,0,0,1,0,0
+10401,A Compositional Coalgebraic Semantics of Strategic Games,"  We provide a compositional coalgebraic semantics for strategic games. In our
+framework, like in the semantics of functional programming languages,
+coalgebras represent the observable behaviour of systems derived from the
+behaviour of the parts over an unobservable state space. We use coalgebras to
+describe and program stage games, finitely and potentially infinitely repeated
+hierarchical or parallel games with imperfect and incomplete information based
+on deterministic, non-deterministic or probabilistic decisions of learning
+agents in possibly endogenous networks. Our framework is compositional in that
+arbitrarily complex network of games can be composed. The coalgebraic approach
+allows to represent self-referential or reflexive structures like institutional
+dynamics, strategic network formation from within the network, belief
+formation, learning agents or other self-referential phenomena that
+characterise complex social systems of cognitive agents. And finally our games
+represent directly runnable code in functional programming languages that can
+also be analysed by sophisticated verification and logical tools of software
+engineering.
+",1,0,0,0,0,0
+10402,Informed Sampling for Asymptotically Optimal Path Planning (Consolidated Version),"  Anytime almost-surely asymptotically optimal planners, such as RRT*,
+incrementally find paths to every state in the search domain. This is
+inefficient once an initial solution is found as then only states that can
+provide a better solution need to be considered. Exact knowledge of these
+states requires solving the problem but can be approximated with heuristics.
+This paper formally defines these sets of states and demonstrates how they
+can be used to analyze arbitrary planning problems. It uses the well-known
+$L^2$ norm (i.e., Euclidean distance) to analyze minimum-path-length problems
+and shows that existing approaches decrease in effectiveness factorially (i.e.,
+faster than exponentially) with state dimension. It presents a method to
+address this curse of dimensionality by directly sampling the prolate
+hyperspheroids (i.e., symmetric $n$-dimensional ellipses) that define the $L^2$
+informed set.
+The importance of this direct informed sampling technique is demonstrated
+with Informed RRT*. This extension of RRT* has less theoretical dependence on
+state dimension and problem size than existing techniques and allows for linear
+convergence on some problems. It is shown experimentally to find better
+solutions faster than existing techniques on both abstract planning problems
+and HERB, a two-arm manipulation robot.
+",1,0,0,0,0,0
+10403,Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation,"  We propose a novel approach to 3D human pose estimation from a single depth
+map. Recently, convolutional neural network (CNN) has become a powerful
+paradigm in computer vision. Many of computer vision tasks have benefited from
+CNNs, however, the conventional approach to directly regress 3D body joint
+locations from an image does not yield a noticeably improved performance. In
+contrast, we formulate the problem as estimating per-voxel likelihood of key
+body joints from a 3D occupancy grid. We argue that learning a mapping from
+volumetric input to volumetric output with 3D convolution consistently improves
+the accuracy when compared to learning a regression from depth map to 3D joint
+coordinates. We propose a two-stage approach to reduce the computational
+overhead caused by volumetric representation and 3D convolution: Holistic 2D
+prediction and Local 3D prediction. In the first stage, Planimetric Network
+(P-Net) estimates per-pixel likelihood for each body joint in the holistic 2D
+space. In the second stage, Volumetric Network (V-Net) estimates the per-voxel
+likelihood of each body joints in the local 3D space around the 2D estimations
+of the first stage, effectively reducing the computational cost. Our model
+outperforms existing methods by a large margin in publicly available datasets.
+",1,0,0,0,0,0
+10404,Automated design of collective variables using supervised machine learning,"  Selection of appropriate collective variables for enhancing sampling of
+molecular simulations remains an unsolved problem in computational biophysics.
+In particular, picking initial collective variables (CVs) is particularly
+challenging in higher dimensions. Which atomic coordinates or transforms there
+of from a list of thousands should one pick for enhanced sampling runs? How
+does a modeler even begin to pick starting coordinates for investigation? This
+remains true even in the case of simple two state systems and only increases in
+difficulty for multi-state systems. In this work, we solve the initial CV
+problem using a data-driven approach inspired by the filed of supervised
+machine learning. In particular, we show how the decision functions in
+supervised machine learning (SML) algorithms can be used as initial CVs
+(SML_cv) for accelerated sampling. Using solvated alanine dipeptide and
+Chignolin mini-protein as our test cases, we illustrate how the distance to the
+Support Vector Machines' decision hyperplane, the output probability estimates
+from Logistic Regression, the outputs from deep neural network classifiers, and
+other classifiers may be used to reversibly sample slow structural transitions.
+We discuss the utility of other SML algorithms that might be useful for
+identifying CVs for accelerating molecular simulations.
+",0,0,0,1,1,0
+10405,Berry-Esseen Theorem and Quantitative homogenization for the Random Conductance Model with degenerate Conductances,"  We study the random conductance model on the lattice $\mathbb{Z}^d$, i.e. we
+consider a linear, finite-difference, divergence-form operator with random
+coefficients and the associated random walk under random conductances. We allow
+the conductances to be unbounded and degenerate elliptic, but they need to
+satisfy a strong moment condition and a quantified ergodicity assumption in
+form of a spectral gap estimate. As a main result we obtain in dimension $d\geq
+3$ quantitative central limit theorems for the random walk in form of a
+Berry-Esseen estimate with speed $t^{-\frac 1 5+\varepsilon}$ for $d\geq 4$ and
+$t^{-\frac{1}{10}+\varepsilon}$ for $d=3$. Additionally, in the uniformly
+elliptic case in low dimensions $d=2,3$ we improve the rate in a quantitative
+Berry-Esseen theorem recently obtained by Mourrat. As a central analytic
+ingredient, for $d\geq 3$ we establish near-optimal decay estimates on the
+semigroup associated with the environment process. These estimates also play a
+central role in quantitative stochastic homogenization and extend some recent
+results by Gloria, Otto and the second author to the degenerate elliptic case.
+",0,0,1,0,0,0
+10406,Detecting Multiple Communities Using Quantum Annealing on the D-Wave System,"  A very important problem in combinatorial optimization is partitioning a
+network into communities of densely connected nodes; where the connectivity
+between nodes inside a particular community is large compared to the
+connectivity between nodes belonging to different ones. This problem is known
+as community detection, and has become very important in various fields of
+science including chemistry, biology and social sciences. The problem of
+community detection is a twofold problem that consists of determining the
+number of communities and, at the same time, finding those communities. This
+drastically increases the solution space for heuristics to work on, compared to
+traditional graph partitioning problems. In many of the scientific domains in
+which graphs are used, there is the need to have the ability to partition a
+graph into communities with the ``highest quality'' possible since the presence
+of even small isolated communities can become crucial to explain a particular
+phenomenon. We have explored community detection using the power of quantum
+annealers, and in particular the D-Wave 2X and 2000Q machines. It turns out
+that the problem of detecting at most two communities naturally fits into the
+architecture of a quantum annealer with almost no need of reformulation. This
+paper addresses a systematic study of detecting two or more communities in a
+network using a quantum annealer.
+",1,0,0,0,0,0
+10407,Evaluating Feature Importance Estimates,"  Estimating the influence of a given feature to a model prediction is
+challenging. We introduce ROAR, RemOve And Retrain, a benchmark to evaluate the
+accuracy of interpretability methods that estimate input feature importance in
+deep neural networks. We remove a fraction of input features deemed to be most
+important according to each estimator and measure the change to the model
+accuracy upon retraining. The most accurate estimator will identify inputs as
+important whose removal causes the most damage to model performance relative to
+all other estimators. This evaluation produces thought-provoking results -- we
+find that several estimators are less accurate than a random assignment of
+feature importance. However, averaging a set of squared noisy estimators (a
+variant of a technique proposed by Smilkov et al. (2017)), leads to significant
+gains in accuracy for each method considered and far outperforms such a random
+guess.
+",0,0,0,1,0,0
+10408,Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control,"  For large-scale industrial processes under closed-loop control, process
+dynamics directly resulting from control action are typical characteristics and
+may show different behaviors between real faults and normal changes of
+operating conditions. However, conventional distributed monitoring approaches
+do not consider the closed-loop control mechanism and only explore static
+characteristics, which thus are incapable of distinguishing between real
+process faults and nominal changes of operating conditions, leading to
+unnecessary alarms. In this regard, this paper proposes a distributed
+monitoring method for closed-loop industrial processes by concurrently
+exploring static and dynamic characteristics. First, the large-scale
+closed-loop process is decomposed into several subsystems by developing a
+sparse slow feature analysis (SSFA) algorithm which capture changes of both
+static and dynamic information. Second, distributed models are developed to
+separately capture static and dynamic characteristics from the local and global
+aspects. Based on the distributed monitoring system, a two-level monitoring
+strategy is proposed to check different influences on process characteristics
+resulting from changes of the operating conditions and control action, and thus
+the two changes can be well distinguished from each other. Case studies are
+conducted based on both benchmark data and real industrial process data to
+illustrate the effectiveness of the proposed method.
+",1,0,0,1,0,0
+10409,Learning to compress and search visual data in large-scale systems,"  The problem of high-dimensional and large-scale representation of visual data
+is addressed from an unsupervised learning perspective. The emphasis is put on
+discrete representations, where the description length can be measured in bits
+and hence the model capacity can be controlled. The algorithmic infrastructure
+is developed based on the synthesis and analysis prior models whose
+rate-distortion properties, as well as capacity vs. sample complexity
+trade-offs are carefully optimized. These models are then extended to
+multi-layers, namely the RRQ and the ML-STC frameworks, where the latter is
+further evolved as a powerful deep neural network architecture with fast and
+sample-efficient training and discrete representations. For the developed
+algorithms, three important applications are developed. First, the problem of
+large-scale similarity search in retrieval systems is addressed, where a
+double-stage solution is proposed leading to faster query times and shorter
+database storage. Second, the problem of learned image compression is targeted,
+where the proposed models can capture more redundancies from the training
+images than the conventional compression codecs. Finally, the proposed
+algorithms are used to solve ill-posed inverse problems. In particular, the
+problems of image denoising and compressive sensing are addressed with
+promising results.
+",1,0,0,1,0,0
+10410,Simple Round Compression for Parallel Vertex Cover,"  Recently, Czumaj et.al. (arXiv 2017) presented a parallel (almost)
+$2$-approximation algorithm for the maximum matching problem in only
+$O({(\log\log{n})^2})$ rounds of the massive parallel computation (MPC)
+framework, when the memory per machine is $O(n)$. The main approach in their
+work is a way of compressing $O(\log{n})$ rounds of a distributed algorithm for
+maximum matching into only $O({(\log\log{n})^2})$ MPC rounds.
+In this note, we present a similar algorithm for the closely related problem
+of approximating the minimum vertex cover in the MPC framework. We show that
+one can achieve an $O(\log{n})$ approximation to minimum vertex cover in only
+$O(\log\log{n})$ MPC rounds when the memory per machine is $O(n)$. Our
+algorithm for vertex cover is similar to the maximum matching algorithm of
+Czumaj et.al. but avoids many of the intricacies in their approach and as a
+result admits a considerably simpler analysis (at a cost of a worse
+approximation guarantee). We obtain this result by modifying a previous
+parallel algorithm by Khanna and the author (SPAA 2017) for vertex cover that
+allowed for compressing $O(\log{n})$ rounds of a distributed algorithm into
+constant MPC rounds when the memory allowed per machine is $O(n\sqrt{n})$.
+",1,0,0,0,0,0
+10411,"ELDAR, a new method to identify AGN in multi-filter surveys: the ALHAMBRA test-case","  We present ELDAR, a new method that exploits the potential of medium- and
+narrow-band filter surveys to securely identify active galactic nuclei (AGN)
+and determine their redshifts. Our methodology improves on traditional
+approaches by looking for AGN emission lines expected to be identified against
+the continuum, thanks to the width of the filters. To assess its performance,
+we apply ELDAR to the data of the ALHAMBRA survey, which covered an effective
+area of $2.38\,{\rm deg}^2$ with 20 contiguous medium-band optical filters down
+to F814W$\simeq 24.5$. Using two different configurations of ELDAR in which we
+require the detection of at least 2 and 3 emission lines, respectively, we
+extract two catalogues of type-I AGN. The first is composed of 585 sources
+($79\,\%$ of them spectroscopically-unknown) down to F814W$=22.5$ at $z_{\rm
+phot}>1$, which corresponds to a surface density of $209\,{\rm deg}^{-2}$. In
+the second, the 494 selected sources ($83\,\%$ of them
+spectroscopically-unknown) reach F814W$=23$ at $z_{\rm phot}>1.5$, for a
+corresponding number density of $176\,{\rm deg}^{-2}$. Then, using samples of
+spectroscopically-known AGN in the ALHAMBRA fields, for the two catalogues we
+estimate a completeness of $73\,\%$ and $67\,\%$, and a redshift precision of
+$1.01\,\%$ and $0.86\,\%$ (with outliers fractions of $8.1\,\%$ and $5.8\,\%$).
+At $z>2$, where our selection performs best, we reach $85\,\%$ and $77\,\%$
+completeness and we find no contamination from galaxies.
+",0,1,0,0,0,0
+10412,Identification of multiple hard X-ray sources in solar flares: A Bayesian analysis of the February 20 2002 event,"  The hard X-ray emission in a solar flare is typically characterized by a
+number of discrete sources, each with its own spectral, temporal, and spatial
+variability. Establishing the relationship amongst these sources is critical to
+determine the role of each in the energy release and transport processes that
+occur within the flare. In this paper we present a novel method to identify and
+characterize each source of hard X-ray emission. The method permits a
+quantitative determination of the most likely number of subsources present, and
+of the relative probabilities that the hard X-ray emission in a given subregion
+of the flare is represented by a complicated multiple source structure or by a
+simpler single source. We apply the method to a well-studied flare on
+2002~February~20 in order to assess competing claims as to the number of
+chromospheric footpoint sources present, and hence to the complexity of the
+underlying magnetic geometry/toplogy. Contrary to previous claims of the need
+for multiple sources to account for the chromospheric hard X-ray emission at
+different locations and times, we find that a simple
+two-footpoint-plus-coronal-source model is the most probable explanation for
+the data. We also find that one of the footpoint sources moves quite rapidly
+throughout the event, a factor that presumably complicated previous analyses.
+The inferred velocity of the footpoint corresponds to a very high induced
+electric field, compatible with those in thin reconnecting current sheets.
+",0,0,0,1,0,0
+10413,A General Framework for Robust Interactive Learning,"  We propose a general framework for interactively learning models, such as
+(binary or non-binary) classifiers, orderings/rankings of items, or clusterings
+of data points. Our framework is based on a generalization of Angluin's
+equivalence query model and Littlestone's online learning model: in each
+iteration, the algorithm proposes a model, and the user either accepts it or
+reveals a specific mistake in the proposal. The feedback is correct only with
+probability $p > 1/2$ (and adversarially incorrect with probability $1 - p$),
+i.e., the algorithm must be able to learn in the presence of arbitrary noise.
+The algorithm's goal is to learn the ground truth model using few iterations.
+Our general framework is based on a graph representation of the models and
+user feedback. To be able to learn efficiently, it is sufficient that there be
+a graph $G$ whose nodes are the models and (weighted) edges capture the user
+feedback, with the property that if $s, s^*$ are the proposed and target
+models, respectively, then any (correct) user feedback $s'$ must lie on a
+shortest $s$-$s^*$ path in $G$. Under this one assumption, there is a natural
+algorithm reminiscent of the Multiplicative Weights Update algorithm, which
+will efficiently learn $s^*$ even in the presence of noise in the user's
+feedback.
+From this general result, we rederive with barely any extra effort classic
+results on learning of classifiers and a recent result on interactive
+clustering; in addition, we easily obtain new interactive learning algorithms
+for ordering/ranking.
+",1,0,0,0,0,0
+10414,Predicting Gravitational Lensing by Stellar Remnants,"  Gravitational lensing provides a means to measure mass that does not rely on
+detecting and analysing light from the lens itself. Compact objects are ideal
+gravitational lenses, because they have relatively large masses and are dim. In
+this paper we describe the prospects for predicting lensing events generated by
+the local population of compact objects, consisting of 250 neutron stars, 5
+black holes, and approximately 35,000 white dwarfs. By focusing on a population
+of nearby compact objects with measured proper motions and known distances from
+us, we can measure their masses by studying the characteristics of any lensing
+event they generate. Here we concentrate on shifts in the position of a
+background source due to lensing by a foreground compact object. With HST,
+JWST, and Gaia, measurable centroid shifts caused by lensing are relatively
+frequent occurrences. We find that 30-50 detectable events per decade are
+expected for white dwarfs. Because relatively few neutron stars and black holes
+have measured distances and proper motions, it is more difficult to compute
+realistic rates for them. However, we show that at least one isolated neutron
+star has likely produced detectable events during the past several decades.
+This work is particularly relevant to the upcoming data releases by the Gaia
+mission and also to data that will be collected by JWST. Monitoring predicted
+microlensing events will not only help to determine the masses of compact
+objects, but will also potentially discover dim companions to these stellar
+remnants, including orbiting exoplanets.
+",0,1,0,0,0,0
+10415,Weak separation properties for closed subgroups of locally compact groups,"  Three separation properties for a closed subgroup $H$ of a locally compact
+group $G$ are studied: (1) the existence of a bounded approximate indicator for
+$H$, (2) the existence of a completely bounded invariant projection of
+$VN\left(G\right)$ onto $VN_{H}\left(G\right)$, and (3) the approximability of
+the characteristic function $\chi_{H}$ by functions in $M_{cb}A\left(G\right)$
+with respect to the weak$^{*}$ topology of $M_{cb}A\left(G_{d}\right)$. We show
+that the $H$-separation property of Kaniuth and Lau is characterized by the
+existence of certain bounded approximate indicators for $H$ and that a
+discretized analogue of the $H$-separation property is equivalent to (3).
+Moreover, we give a related characterization of amenability of $H$ in terms of
+any group $G$ containing $H$ as a closed subgroup. The weak amenability of $G$
+or that $G_{d}$ satisfies the approximation property, in combination with the
+existence of a natural projection (in the sense of Lau and Ülger), are shown
+to suffice to conclude (3). Several consequences of (2) involving the
+cb-multiplier completion of $A\left(G\right)$ are given. Finally, a convolution
+technique for averaging over the closed subgroup $H$ is developed and used to
+weaken a condition for the existence of a bounded approximate indicator for
+$H$.
+",0,0,1,0,0,0
+10416,A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics,"  Machine learning (ML) is increasingly deployed in real world contexts,
+supplying actionable insights and forming the basis of automated
+decision-making systems. While issues resulting from biases pre-existing in
+training data have been at the center of the fairness debate, these systems are
+also affected by technical and emergent biases, which often arise as
+context-specific artifacts of implementation. This position paper interprets
+technical bias as an epistemological problem and emergent bias as a dynamical
+feedback phenomenon. In order to stimulate debate on how to change machine
+learning practice to effectively address these issues, we explore this broader
+view on bias, stress the need to reflect on epistemology, and point to
+value-sensitive design methodologies to revisit the design and implementation
+process of automated decision-making systems.
+",1,0,0,1,0,0
+10417,Solution to the relaxation problem for a gas with a distribution function dependent on the velocity modulus,"  The paper presents a solution to the Boltzmann kinetic equation based on the
+construction of its discrete conservative model. Discrete analogue of the
+collision integral is presented as a contraction of a tensor, which is
+independent from the initial distribution function, colliding with a tensor
+composed of medium densities in the cells. Numerical implementation of the
+discrete model is demonstrated on the example of the isotropic gas relaxation
+problem applied to the hard spheres model. The key feature of the method is
+independence of the collision tensor components from the distribution function.
+Consequently the components of the collision tensor are calculated once for
+various initial distribution functions, which substantially increases
+performance of the suggested method.
+",0,1,0,0,0,0
+10418,Robust Regression for Automatic Fusion Plasma Analysis based on Generative Modeling,"  The first step to realize automatic experimental data analysis for fusion
+plasma experiments is fitting noisy data of temperature and density spatial
+profiles, which are obtained routinely. However, it has been difficult to
+construct algorithms that fit all the data without over- and under-fitting. In
+this paper, we show that this difficulty originates from the lack of knowledge
+of the probability distribution that the measurement data follow. We
+demonstrate the use of a machine learning technique to estimate the data
+distribution and to construct an optimal generative model. We show that the
+fitting algorithm based on the generative modeling outperforms classical
+heuristic methods in terms of the stability as well as the accuracy.
+",0,0,0,1,0,0
+10419,Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration,"  This article expands on research that has been done to develop a recurrent
+neural network (RNN) capable of predicting aircraft engine vibrations using
+long short-term memory (LSTM) neurons. LSTM RNNs can provide a more
+generalizable and robust method for prediction over analytical calculations of
+engine vibration, as analytical calculations must be solved iteratively based
+on specific empirical engine parameters, making this approach ungeneralizable
+across multiple engines. In initial work, multiple LSTM RNN architectures were
+proposed, evaluated and compared. This research improves the performance of the
+most effective LSTM network design proposed in the previous work by using a
+promising neuroevolution method based on ant colony optimization (ACO) to
+develop and enhance the LSTM cell structure of the network. A parallelized
+version of the ACO neuroevolution algorithm has been developed and the evolved
+LSTM RNNs were compared to the previously used fixed topology. The evolved
+networks were trained on a large database of flight data records obtained from
+an airline containing flights that suffered from excessive vibration. Results
+were obtained using MPI (Message Passing Interface) on a high performance
+computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168
+cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%,
+reducing prediction error from 5.51% to 4.17% when predicting excessive engine
+vibrations 10 seconds in the future, while at the same time dramatically
+reducing the number of weights from 21,170 to 11,810.
+",1,0,0,0,0,0
+10420,PhyShare: Sharing Physical Interaction in Virtual Reality,"  We present PhyShare, a new haptic user interface based on actuated robots.
+Virtual reality has recently been gaining wide adoption, and an effective
+haptic feedback in these scenarios can strongly support user's sensory in
+bridging virtual and physical world. Since participants do not directly observe
+these robotic proxies, we investigate the multiple mappings between physical
+robots and virtual proxies that can utilize the resources needed to provide a
+well rounded VR experience. PhyShare bots can act either as directly touchable
+objects or invisible carriers of physical objects, depending on different
+scenarios. They also support distributed collaboration, allowing remotely
+located VR collaborators to share the same physical feedback.
+",1,0,0,0,0,0
+10421,Kneser ranks of random graphs and minimum difference representations,"  Every graph $G=(V,E)$ is an induced subgraph of some Kneser graph of rank
+$k$, i.e., there is an assignment of (distinct) $k$-sets $v \mapsto A_v$ to the
+vertices $v\in V$ such that $A_u$ and $A_v$ are disjoint if and only if $uv\in
+E$. The smallest such $k$ is called the Kneser rank of $G$ and denoted by
+$f_{\rm Kneser}(G)$. As an application of a result of Frieze and Reed
+concerning the clique cover number of random graphs we show that for constant
+$0< p< 1$ there exist constants $c_i=c_i(p)>0$, $i=1,2$ such that with high
+probability \[ c_1 n/(\log n)< f_{\rm Kneser}(G) < c_2 n/(\log n). \] We apply
+this for other graph representations defined by Boros, Gurvich and Meshulam. A
+{\em $k$-min-difference representation} of a graph $G$ is an assignment of a
+set $A_i$ to each vertex $i\in V(G)$ such that \[ ij\in E(G) \,\,
+\Leftrightarrow \, \, \min \{|A_i\setminus A_j|,|A_j\setminus A_i| \}\geq k. \]
+The smallest $k$ such that there exists a $k$-min-difference representation of
+$G$ is denoted by $f_{\min}(G)$. Balogh and Prince proved in 2009 that for
+every $k$ there is a graph $G$ with $f_{\min}(G)\geq k$. We prove that there
+are constants $c''_1, c''_2>0$ such that $c''_1 n/(\log n)< f_{\min}(G) <
+c''_2n/(\log n)$ holds for almost all bipartite graphs $G$ on $n+n$ vertices.
+",0,0,1,0,0,0
+10422,Visualizing spreading phenomena on complex networks,"  Graph drawings are useful tools for exploring the structure and dynamics of
+data that can be represented by pair-wise relationships among a set of objects.
+Typical real-world social, biological or technological networks exhibit high
+complexity resulting from a large number and broad heterogeneity of objects and
+relationships. Thus, mapping these networks into a low-dimensional space to
+visualize the dynamics of network-driven processes is a challenging task. Often
+we want to analyze how a single node is influenced by or is influencing its
+local network as the source of a spreading process. Here I present a network
+layout algorithm for graphs with millions of nodes that visualizes spreading
+phenomena from the perspective of a single node. The algorithm consists of
+three stages to allow for an interactive graph exploration: First, a global
+solution for the network layout is found in spherical space that minimizes
+distance errors between all nodes. Second, a focal node is interactively
+selected, and distances to this node are further optimized. Third, node
+coordinates are mapped to a circular representation and drawn with additional
+features to represent the network-driven phenomenon. The effectiveness and
+scalability of this method are shown for a large collaboration network of
+scientists, where we are interested in the citation dynamics around a focal
+author.
+",1,0,0,0,0,0
+10423,In silico evolution of signaling networks using rule-based models: bistable response dynamics,"  One of the ultimate goals in biology is to understand the design principles
+of biological systems. Such principles, if they exist, can help us better
+understand complex, natural biological systems and guide the engineering of de
+novo ones. Towards deciphering design principles, in silico evolution of
+biological systems with proper abstraction is a promising approach. Here, we
+demonstrate the application of in silico evolution combined with rule-based
+modelling for exploring design principles of cellular signaling networks. This
+application is based on a computational platform, called BioJazz, which allows
+in silico evolution of signaling networks with unbounded complexity. We provide
+a detailed introduction to BioJazz architecture and implementation and describe
+how it can be used to evolve and/or design signaling networks with defined
+dynamics. For the latter, we evolve signaling networks with switch-like
+response dynamics and demonstrate how BioJazz can result in new biological
+insights on network structures that can endow bistable response dynamics. This
+example also demonstrated both the power of BioJazz in evolving and designing
+signaling networks and its limitations at the current stage of development.
+",0,0,0,0,1,0
+10424,Deep Recurrent Neural Networks for seizure detection and early seizure detection systems,"  Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world
+population. Epileptic patients suffer from chronic unprovoked seizures, which
+can result in broad spectrum of debilitating medical and social consequences.
+Since seizures, in general, occur infrequently and are unpredictable, automated
+seizure detection systems are recommended to screen for seizures during
+long-term electroencephalogram (EEG) recordings. In addition, systems for early
+seizure detection can lead to the development of new types of intervention
+systems that are designed to control or shorten the duration of seizure events.
+In this article, we investigate the utility of recurrent neural networks (RNNs)
+in designing seizure detection and early seizure detection systems. We propose
+a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for
+seizure detection. We use publicly available data in order to evaluate our
+method and demonstrate very promising evaluation results with overall accuracy
+close to 100 %. We also systematically investigate the application of our
+method for early seizure warning systems. Our method can detect about 98% of
+seizure events within the first 5 seconds of the overall epileptic seizure
+duration.
+",1,0,0,0,0,0
+10425,QUICKAR: Automatic Query Reformulation for Concept Location using Crowdsourced Knowledge,"  During maintenance, software developers deal with numerous change requests
+made by the users of a software system. Studies show that the developers find
+it challenging to select appropriate search terms from a change request during
+concept location. In this paper, we propose a novel technique--QUICKAR--that
+automatically suggests helpful reformulations for a given query by leveraging
+the crowdsourced knowledge from Stack Overflow. It determines semantic
+similarity or relevance between any two terms by analyzing their adjacent word
+lists from the programming questions of Stack Overflow, and then suggests
+semantically relevant queries for concept location. Experiments using 510
+queries from two software systems suggest that our technique can improve or
+preserve the quality of 76% of the initial queries on average which is
+promising. Comparison with one baseline technique validates our preliminary
+findings, and also demonstrates the potential of our technique.
+",1,0,0,0,0,0
+10426,Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation,"  We consider the weighted belief-propagation (WBP) decoder recently proposed
+by Nachmani et al. where different weights are introduced for each Tanner graph
+edge and optimized using machine learning techniques. Our focus is on
+simple-scaling models that use the same weights across certain edges to reduce
+the storage and computational burden. The main contribution is to show that
+simple scaling with few parameters often achieves the same gain as the full
+parameterization. Moreover, several training improvements for WBP are proposed.
+For example, it is shown that minimizing average binary cross-entropy is
+suboptimal in general in terms of bit error rate (BER) and a new ""soft-BER""
+loss is proposed which can lead to better performance. We also investigate
+parameter adapter networks (PANs) that learn the relation between the
+signal-to-noise ratio and the WBP parameters. As an example, for the (32,16)
+Reed-Muller code with a highly redundant parity-check matrix, training a PAN
+with soft-BER loss gives near-maximum-likelihood performance assuming simple
+scaling with only three parameters.
+",1,0,0,1,0,0
+10427,Thick-medium model of transverse pattern formation in optically excited cold two-level atoms with a feedback mirror,"  We study a pattern forming instability in a laser driven optically thick
+cloud of cold two-level atoms with a planar feedback mirror. A theoretical
+model is developed, enabling a full analysis of transverse patterns in a medium
+with saturable nonlinearity, taking into account diffraction within the medium,
+and both the transmission and reflection gratings. Focus of the analysis is on
+combined treatment of nonlinear propagation in a diffractively- and
+optically-thick medium and the boundary condition given by feedback. We
+demonstrate explicitly how diffraction within the medium breaks the degeneracy
+of Talbot modes inherent in thin slice models. Existence of envelope curves
+bounding all possible pattern formation thresholds is predicted. The importance
+of envelope curves and their interaction with threshold curves is illustrated
+by experimental observation of a sudden transition between length scales as
+mirror displacement is varied.
+",0,1,0,0,0,0
+10428,A reinvestigation of the giant Rashba-split states on Bi-covered Si(111),"  We study the electronic and spin structures of the giant Rashba-split surface
+states of the Bi/Si(111)-($\sqrt{3} \times \sqrt{3}$)R30 trimer phase by means
+of spin- and angle-resolved photoelectron spectroscopy (spin-ARPES). Supported
+by tight-binding calculations of the surface state dispersion and spin
+orientation, our findings show that the spin experiences a vortex-like
+structure around the $\bar{\Gamma}$-point of the surface Brillouin zone - in
+accordance with the standard Rashba model. Moreover, we find no evidence of a
+spin vortex around the $\bar{\mathrm{K}}$-point in the hexagonal Brillouin
+zone, and thus no peculiar Rashba split around this point, something that has
+been suggested by previous works. Rather the opposite, our results show that
+the spin structure around $\bar{\mathrm{K}}$ can be fully understood by taking
+into account the symmetry of the Brillouin zone and the intersection of spin
+vortices centered around the $\bar{\Gamma}$-points in neighboring Brillouin
+zones. As a result, the spin structure is consistently explained within the
+standard framework of the Rashba model although the spin-polarized surface
+states experience a more complex dispersion compared to free-electron like
+parabolic states.
+",0,1,0,0,0,0
+10429,Performance Evaluation of Spectrum Mobility in Multi-homed Mobile IPv6 Cognitive Radio Cellular Networks,"  Technological developments alongside VLSI achievements enable mobile devices
+to be equipped with multiple radio interfaces which is known as multihoming. On
+the other hand, the combination of various wireless access technologies, known
+as Next Generation Wireless Networks (NGWNs) has been introduced to provide
+continuous connection to mobile devices in any time and location. Cognitive
+radio networks as a part of NGWNs aroused to overcome spectrum inefficiency and
+spectrum scarcity issues. In order to provide seamless and ubiquitous
+connection across heterogeneous wireless access networks in the context of
+cognitive radio networks, utilizing Mobile IPv6 is beneficial. In this paper, a
+mobile device equipped with two radio interfaces is considered in order to
+evaluate performance of spectrum handover in terms of handover latency. The
+analytical results show that the proposed model can achieve better performance
+compared to other related mobility management protocols mainly in terms of
+handover latency.
+",1,0,0,0,0,0
+10430,Tunable Quantum Criticality and Super-ballistic Transport in a `Charge' Kondo Circuit,"  Quantum phase transitions are ubiquitous in many exotic behaviors of
+strongly-correlated materials. However the microscopic complexity impedes their
+quantitative understanding. Here, we observe thoroughly and comprehend the rich
+strongly-correlated physics in two profoundly dissimilar regimes of quantum
+criticality. With a circuit implementing a quantum simulator for the
+three-channel Kondo model, we reveal the universal scalings toward different
+low-temperature fixed points and along the multiple crossovers from quantum
+criticality. Notably, an unanticipated violation of the maximum conductance for
+ballistic free electrons is uncovered. The present charge pseudospin
+implementation of a Kondo impurity opens access to a broad variety of
+strongly-correlated phenomena.
+",0,1,0,0,0,0
+10431,Exploring Cosmic Origins with CORE: Survey requirements and mission design,"  Future observations of cosmic microwave background (CMB) polarisation have
+the potential to answer some of the most fundamental questions of modern
+physics and cosmology. In this paper, we list the requirements for a future CMB
+polarisation survey addressing these scientific objectives, and discuss the
+design drivers of the CORE space mission proposed to ESA in answer to the ""M5""
+call for a medium-sized mission. The rationale and options, and the
+methodologies used to assess the mission's performance, are of interest to
+other future CMB mission design studies. CORE is designed as a near-ultimate
+CMB polarisation mission which, for optimal complementarity with ground-based
+observations, will perform the observations that are known to be essential to
+CMB polarisation scienceand cannot be obtained by any other means than a
+dedicated space mission.
+",0,1,0,0,0,0
+10432,Influence of material parameters on the performance of niobium based superconducting RF cavities,"  A detailed thermal analysis of a Niobium (Nb) based superconducting radio
+frequency (SRF) cavity in a liquid helium bath is presented, taking into
+account the temperature and magnetic field dependence of the surface resistance
+and thermal conductivity in the superconducting state of the starting Nb
+material (for SRF cavity fabrication) with different impurity levels. The drop
+in SRF cavity quality factor (Q_0) in the high acceleration gradient regime
+(before ultimate breakdown of the SRF cavity) is studied in details. It is
+argued that the high field Q_0-drop in SRF cavity is considerably influenced by
+the intrinsic material parameters such as electrical conductivity, and thermal
+diffusivity. The detail analysis also shows that the current specification on
+the purity of niobium material for SRF cavity fabrication is somewhat over
+specified. Niobium material with a relatively low purity can very well serve
+the purpose for the accelerators dedicated for spallation neutron source (SNS)
+or accelerator driven sub-critical system (ADSS) applications, where the
+required accelerating gradient is typically up to 20 MV/m,. This information
+will have important implication towards the cost reduction of superconducting
+technology based particle accelerators for various applications.
+",0,1,0,0,0,0
+10433,Shear Viscosity of Uniform Fermi Gases with Population Imbalance,"  The shear viscosity plays an important role in studies of transport phenomena
+in ultracold Fermi gases and serves as a diagnostic of various microscopic
+theories. Due to the complicated phase structures of population-imbalanced
+Fermi gases, past works mainly focus on unpolarized Fermi gases. Here we
+investigate the shear viscosity of homogeneous, population-imbalanced Fermi
+gases with tunable attractive interactions at finite temperatures by using a
+pairing fluctuation theory for thermodynamical quantities and a gauge-invariant
+linear response theory for transport coefficients. In the unitary and BEC
+regimes, the shear viscosity increases with the polarization because the excess
+majority fermions cause gapless excitations acting like a normal fluid. In the
+weak BEC regime the excess fermions also suppress the noncondensed pairs at low
+polarization, and we found a minimum in the ratio of shear viscosity and
+relaxation time. To help constrain the relaxation time from linear response
+theory, we derive an exact relation connecting some thermodynamic quantities
+and transport coefficients at the mean-field level for unitary Fermi
+superfluids with population imbalance. An approximate relation beyond
+mean-field theory is proposed and only exhibits mild deviations from numerical
+results.
+",0,1,0,0,0,0
+10434,Equivalence of Intuitionistic Inductive Definitions and Intuitionistic Cyclic Proofs under Arithmetic,"  A cyclic proof system gives us another way of representing inductive
+definitions and efficient proof search. In 2011 Brotherston and Simpson
+conjectured the equivalence between the provability of the classical cyclic
+proof system and that of the classical system of Martin-Lof's inductive
+definitions.
+This paper studies the conjecture for intuitionistic logic.
+This paper first points out that the countermodel of FOSSACS 2017 paper by
+the same authors shows the conjecture for intuitionistic logic is false in
+general. Then this paper shows the conjecture for intuitionistic logic is true
+under arithmetic, namely, the provability of the intuitionistic cyclic proof
+system is the same as that of the intuitionistic system of Martin-Lof's
+inductive definitions when both systems contain Heyting arithmetic HA.
+For this purpose, this paper also shows that HA proves Podelski-Rybalchenko
+theorem for induction and Kleene-Brouwer theorem for induction. These results
+immediately give another proof to the conjecture under arithmetic for classical
+logic shown in LICS 2017 paper by the same authors.
+",1,0,1,0,0,0
+10435,Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent,"  In this paper, we revisit the recently established theoretical guarantees for
+the convergence of the Langevin Monte Carlo algorithm of sampling from a smooth
+and (strongly) log-concave density. We improve the existing results when the
+convergence is measured in the Wasserstein distance and provide further
+insights on the very tight relations between, on the one hand, the Langevin
+Monte Carlo for sampling and, on the other hand, the gradient descent for
+optimization. Finally, we also establish guarantees for the convergence of a
+version of the Langevin Monte Carlo algorithm that is based on noisy
+evaluations of the gradient.
+",0,0,1,1,0,0
+10436,Probing Spatial Locality in Ionic Liquids with the Grand Canonical Adaptive Resolution Molecular Dynamics Technique,"  We employ the Grand Canonical Adaptive Resolution Molecular Dynamics
+Technique (GC-AdResS) to test the spatial locality of the 1-ethyl 3-methyl
+imidazolium chloride liquid. In GC-AdResS atomistic details are kept only in an
+open sub-region of the system while the environment is treated at
+coarse-grained level, thus if spatial quantities calculated in such a
+sub-region agree with the equivalent quantities calculated in a full atomistic
+simulation then the atomistic degrees of freedom outside the sub-region play a
+negligible role. The size of the sub-region fixes the degree of spatial
+locality of a certain quantity. We show that even for sub-regions whose radius
+corresponds to the size of a few molecules, spatial properties are reasonably
+{reproduced} thus suggesting a higher degree of spatial locality, a hypothesis
+put forward also by other {researchers} and that seems to play an important
+role for the characterization of fundamental properties of a large class of
+ionic liquids.
+",0,1,0,0,0,0
+10437,Towards automation of data quality system for CERN CMS experiment,"  Daily operation of a large-scale experiment is a challenging task,
+particularly from perspectives of routine monitoring of quality for data being
+taken. We describe an approach that uses Machine Learning for the automated
+system to monitor data quality, which is based on partial use of data qualified
+manually by detector experts. The system automatically classifies marginal
+cases: both of good an bad data, and use human expert decision to classify
+remaining ""grey area"" cases.
+This study uses collision data collected by the CMS experiment at LHC in
+2010. We demonstrate that proposed workflow is able to automatically process at
+least 20\% of samples without noticeable degradation of the result.
+",1,0,0,0,0,0
+10438,Solving a non-linear model of HIV infection for CD4+T cells by combining Laplace transformation and Homotopy analysis,"  The aim of this paper is to find the approximate solution of HIV infection
+model of CD4+T cells. For this reason, the homotopy analysis transform method
+(HATM) is applied. The presented method is combination of traditional homotopy
+analysis method (HAM) and the Laplace transformation. The convergence of
+presented method is discussed by preparing a theorem which shows the
+capabilities of method. The numerical results are shown for different values of
+iterations. Also, the regions of convergence are demonstrated by plotting
+several h-curves. Furthermore in order to show the efficiency and accuracy of
+method, the residual error for different iterations are presented.
+",0,0,0,0,1,0
+10439,On Store Languages of Language Acceptors,"  It is well known that the ""store language"" of every pushdown automaton -- the
+set of store configurations (state and stack contents) that can appear as an
+intermediate step in accepting computations -- is a regular language. Here many
+models of language acceptors with various data structures are examined, along
+with a study of their store languages. For each model, an attempt is made to
+find the simplest model that accepts their store languages. Some connections
+between store languages of one-way and two-way machines generally are
+demonstrated, as with connections between nondeterministic and deterministic
+machines. A nice application of these store language results is also presented,
+showing a general technique for proving families accepted by many deterministic
+models are closed under right quotient with regular languages, resolving some
+open questions (and significantly simplifying proofs for others that are known)
+in the literature. Lower bounds on the space complexity for recognizing store
+languages for the languages to be non-regular are obtained.
+",1,0,0,0,0,0
+10440,Exchange constants in molecule-based magnets derived from density functional methods,"  Cu(pyz)(NO3)2 is a quasi one-dimensional molecular antiferromagnet that
+exhibits three dimensional long-range magnetic order below TN=110 mK due to the
+presence of weak inter-chain exchange couplings. Here we compare calculations
+of the three largest exchange coupling constants in this system using two
+techniques based on plane-wave basis-set density functional theory: (i) a dimer
+fragment approach and (ii) an approach using periodic boundary conditions. The
+calculated values of the large intrachain coupling constant are found to be
+consistent with experiment, showing the expected level of variation between
+different techniques and implementations. However, the interchain coupling
+constants are found to be smaller than the current limits on the resolution of
+the calculations. This is due to the computational limitations on convergence
+of absolute energy differences with respect to basis set, which are larger than
+the inter-chain couplings themselves. Our results imply that errors resulting
+from such limitations are inherent in the evaluation of small exchange
+constants in systems of this sort, and that many previously reported results
+should therefore be treated with caution.
+",0,1,0,0,0,0
+10441,An Extended Relevance Model for Session Search,"  The session search task aims at best serving the user's information need
+given her previous search behavior during the session. We propose an extended
+relevance model that captures the user's dynamic information need in the
+session. Our relevance modelling approach is directly driven by the user's
+query reformulation (change) decisions and the estimate of how much the user's
+search behavior affects such decisions. Overall, we demonstrate that, the
+proposed approach significantly boosts session search performance.
+",1,0,0,0,0,0
+10442,Hierarchical Graph Representation Learning with Differentiable Pooling,"  Recently, graph neural networks (GNNs) have revolutionized the field of graph
+representation learning through effectively learned node embeddings, and
+achieved state-of-the-art results in tasks such as node classification and link
+prediction. However, current GNN methods are inherently flat and do not learn
+hierarchical representations of graphs---a limitation that is especially
+problematic for the task of graph classification, where the goal is to predict
+the label associated with an entire graph. Here we propose DiffPool, a
+differentiable graph pooling module that can generate hierarchical
+representations of graphs and can be combined with various graph neural network
+architectures in an end-to-end fashion. DiffPool learns a differentiable soft
+cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a
+set of clusters, which then form the coarsened input for the next GNN layer.
+Our experimental results show that combining existing GNN methods with DiffPool
+yields an average improvement of 5-10% accuracy on graph classification
+benchmarks, compared to all existing pooling approaches, achieving a new
+state-of-the-art on four out of five benchmark data sets.
+",1,0,0,1,0,0
+10443,Ranking and Cooperation in Real-World Complex Networks,"  People participate and activate in online social networks and thus tremendous
+amount of network data is generated; data regarding their interactions,
+interests and activities. Some people search for specific questions through
+online social platforms such as forums and they may receive a suitable response
+via experts. To categorize people as experts and to evaluate their willingness
+to cooperate, one can use ranking and cooperation problems from complex
+networks. In this paper, we investigate classical ranking algorithms besides
+the prisoner dilemma game to simulate cooperation and defection of agents. We
+compute the correlation among the node rank and node cooperativity via three
+strategies. The first strategy is involved in node level; however, other
+strategies are calculated regarding neighborhood of nodes. We find out
+correlations among specific ranking algorithms and cooperativtiy of nodes. Our
+observations may be applied to estimate the propensity of people (experts) to
+cooperate in future based on their ranking values.
+",1,0,0,0,0,0
+10444,Remarks on defective Fano manifolds,"  This note continues our previous work on special secant defective
+(specifically, conic connected and local quadratic entry locus) and dual
+defective manifolds. These are now well understood, except for the prime Fano
+ones. Here we add a few remarks on this case, completing the results in our
+papers \cite{LQEL I}, \cite{LQEL II}, \cite{CC}, \cite{HC} and \cite{DD}; see
+also the recent book \cite{Russo}
+",0,0,1,0,0,0
+10445,Precision of the ENDGame: Mixed-precision arithmetic in the iterative solver of the Unified Model,"  The Met Office's weather and climate simulation code the Unified Model is
+used for both operational Numerical Weather Prediction and Climate modelling.
+The computational performance of the model running on parallel supercomputers
+is a key consideration. A Krylov sub-space solver is employed to solve the
+equations of the dynamical core of the model, known as ENDGame. These describe
+the evolution of the Earth's atmosphere. Typically, 64-bit precision is used
+throughout weather and climate applications. This work presents a
+mixed-precision implementation of the solver, the beneficial effect on run-time
+and the impact on solver convergence. The complex interplay of errors arising
+from accumulated round-off in floating-point arithmetic and other numerical
+effects is discussed. A careful analysis is required, however, the
+mixed-precision solver is now employed in the operational forecast to satisfy
+run-time constraints without compromising the accuracy of the solution.
+",1,0,0,0,0,0
+10446,Intra-Cluster Autonomous Coverage Optimization For Dense LTE-A Networks,"  Self Organizing Networks (SONs) are considered as vital deployments towards
+upcoming dense cellular networks. From a mobile carrier point of view,
+continuous coverage optimization is critical for better user perceptions. The
+majority of SON contributions introduce novel algorithms that optimize specific
+performance metrics. However, they require extensive processing delays and
+advanced knowledge of network statistics that may not be available. In this
+work, a progressive Autonomous Coverage Optimization (ACO) method combined with
+adaptive cell dimensioning is proposed. The proposed method emphasizes the fact
+that the effective cell coverage is a variant on actual user distributions. ACO
+algorithm builds a generic Space-Time virtual coverage map per cell to detect
+coverage holes in addition to limited or extended coverage conditions.
+Progressive levels of optimization are followed to timely resolve coverage
+issues with maintaining optimization stability. Proposed ACO is verified under
+both simulations and practical deployment in a pilot cluster for a worldwide
+mobile carrier. Key Performance Indicators show that proposed ACO method
+significantly enhances system coverage and performance.
+",1,0,0,0,0,0
+10447,"Two-dimensional topological nodal line semimetal in layered $X_2Y$ ($X$ = Ca, Sr, and Ba; $Y$ = As, Sb, and Bi)","  In topological semimetals the Dirac points can form zero-dimensional and
+one-dimensional manifolds, as predicted for Dirac/Weyl semimetals and
+topological nodal line semimetals, respectively. Here, based on
+first-principles calculations, we predict a topological nodal line semimetal
+phase in the two-dimensional compounds $X_2Y$ ($X$=Ca, Sr, and Ba; $Y$=As, Sb,
+and Bi) in the absence of spin-orbit coupling (SOC) with a band inversion at
+the M point. The mirror symmetry as well as the electrostatic interaction, that
+can be engineered via strain, are responsible for the nontrivial phase. In
+addition, we demonstrate that the exotic edge states can be also obtained
+without and with SOC although a tiny gap appears at the nodal line for the bulk
+states when SOC is included.
+",0,1,0,0,0,0
+10448,A Real-Valued Modal Logic,"  A many-valued modal logic is introduced that combines the usual Kripke frame
+semantics of the modal logic K with connectives interpreted locally at worlds
+by lattice and group operations over the real numbers. A labelled tableau
+system is provided and a coNEXPTIME upper bound obtained for checking validity
+in the logic. Focussing on the modal-multiplicative fragment, the labelled
+tableau system is then used to establish completeness for a sequent calculus
+that admits cut-elimination and an axiom system that extends the multiplicative
+fragment of Abelian logic.
+",1,0,0,0,0,0
+10449,Embedded tori with prescribed mean curvature,"  We construct a sequence of compact, oriented, embedded, two-dimensional
+surfaces of genus one into Euclidean 3-space with prescribed, almost constant,
+mean curvature of the form $H(X)=1+{A}{|X|^{-\gamma}}$ for $|X|$ large, when
+$A<0$ and $\gamma\in(0,2)$. Such surfaces are close to sections of unduloids
+with small necksize, folded along circumferences centered at the origin and
+with larger and larger radii. The construction involves a deep study of the
+corresponding Jacobi operators, an application of the Lyapunov-Schmidt
+reduction method and some variational argument.
+",0,0,1,0,0,0
+10450,On the reducibility of induced representations for classical p-adic groups and related affine Hecke algebras,"  Let $\pi $ be an irreducible smooth complex representation of a general
+linear $p$-adic group and let $\sigma $ be an irreducible complex supercuspidal
+representation of a classical $p$-adic group of a given type, so that
+$\pi\otimes\sigma $ is a representation of a standard Levi subgroup of a
+$p$-adic classical group of higher rank. We show that the reducibility of the
+representation of the appropriate $p$-adic classical group obtained by
+(normalized) parabolic induction from $\pi\otimes\sigma $ does not depend on
+$\sigma $, if $\sigma $ is ""separated"" from the supercuspidal support of $\pi
+$. (Here, ""separated"" means that, for each factor $\rho $ of a representation
+in the supercuspidal support of $\pi $, the representation parabolically
+induced from $\rho\otimes\sigma $ is irreducible.) This was conjectured by E.
+Lapid and M. Tadić. (In addition, they proved, using results of C. Jantzen,
+that this induced representation is always reducible if the supercuspidal
+support is not separated.)
+More generally, we study, for a given set $I$ of inertial orbits of
+supercuspidal representations of $p$-adic general linear groups, the category
+$\CC _{I,\sigma}$ of smooth complex finitely generated representations of
+classical $p$-adic groups of fixed type, but arbitrary rank, and supercuspidal
+support given by $\sigma $ and $I$, show that this category is equivalent to a
+category of finitely generated right modules over a direct sum of tensor
+products of extended affine Hecke algebras of type $A$, $B$ and $D$ and
+establish functoriality properties, relating categories with disjoint $I$'s. In
+this way, we extend results of C. Jantzen who proved a bijection between
+irreducible representations corresponding to these categories. The proof of the
+above reducibility result is then based on Hecke algebra arguments, using
+Kato's exotic geometry.
+",0,0,1,0,0,0
+10451,On derivations with respect to finite sets of smooth functions,"  The purpose of this paper is to show that functions that derivate the
+two-variable product function and one of the exponential, trigonometric or
+hyperbolic functions are also standard derivations. The more general problem
+considered is to describe finite sets of differentiable functions such that
+derivations with respect to this set are automatically standard derivations.
+",0,0,1,0,0,0
+10452,A new magnetic phase in the nickelate perovskite TlNiO$_3$,"  The RNiO$_3$ perovskites are known to order antiferromagnetically below a
+material-dependent Néel temperature $T_\text{N}$. We report experimental
+evidence indicating the existence of a second magnetically-ordered phase in
+TlNiO$_3$ above $T_\text{N} = 104$ K, obtained using nuclear magnetic resonance
+and muon spin rotation spectroscopy. The new phase, which persists up to a
+temperature $T_\text{N}^* = 202$ K, is suppressed by the application of an
+external magnetic field of approximately 1 T. It is not yet known if such a
+phase also exists in other perovskite nickelates.
+",0,1,0,0,0,0
+10453,On the Inverse of Forward Adjacency Matrix,"  During routine state space circuit analysis of an arbitrarily connected set
+of nodes representing a lossless LC network, a matrix was formed that was
+observed to implicitly capture connectivity of the nodes in a graph similar to
+the conventional incidence matrix, but in a slightly different manner. This
+matrix has only 0, 1 or -1 as its elements. A sense of direction (of the graph
+formed by the nodes) is inherently encoded in the matrix because of the
+presence of -1. It differs from the incidence matrix because of leaving out the
+datum node from the matrix. Calling this matrix as forward adjacency matrix, it
+was found that its inverse also displays useful and interesting physical
+properties when a specific style of node-indexing is adopted for the nodes in
+the graph. The graph considered is connected but does not have any closed
+loop/cycle (corresponding to closed loop of inductors in a circuit) as with its
+presence the matrix is not invertible. Incidentally, by definition the graph
+being considered is a tree. The properties of the forward adjacency matrix and
+its inverse, along with rigorous proof, are presented.
+",1,0,0,0,0,0
+10454,Constructing and Understanding New and Old Scales on Slide Rules,"  We discuss the practical problems arising when constructing any (new or old)
+scales on slide rules, i.e. realizing the theory in the practice. This might
+help anyone in planning and realizing (mainly the magnitude and labeling of)
+new scales on slide rules in the future. In Sections 1-7 we deal with technical
+problems, Section 8 is devoted to the relationship among different scales. In
+the last Section we provide an interesting fact as a surprise to those readers
+who wish to skip this long article.
+",0,0,1,0,0,0
+10455,Non-compact subsets of the Zariski space of an integral domain,"  Let $V$ be a minimal valuation overring of an integral domain $D$ and let
+$\mathrm{Zar}(D)$ be the Zariski space of the valuation overrings of $D$.
+Starting from a result in the theory of semistar operations, we prove a
+criterion under which the set $\mathrm{Zar}(D)\setminus\{V\}$ is not compact.
+We then use it to prove that, in many cases, $\mathrm{Zar}(D)$ is not a
+Noetherian space, and apply it to the study of the spaces of Kronecker function
+rings and of Noetherian overrings.
+",0,0,1,0,0,0
+10456,Finite temperature Green's function approach for excited state and thermodynamic properties of cool to warm dense matter,"  We present a finite-temperature extension of the retarded cumulant Green's
+function for calculations of exited-state and thermodynamic properties of
+electronic systems. The method incorporates a cumulant to leading order in the
+screened Coulomb interaction $W$ and improves excited state properties compared
+to the $GW$ approximation of many-body perturbation theory. Results for the
+homogeneous electron gas are presented for a wide range of densities and
+temperatures, from cool to warm dense matter regime, which reveal several
+hitherto unexpected properties. For example, correlation effects remain strong
+at high $T$ while the exchange-correlation energy becomes small. In addition,
+the spectral function broadens and damping increases with temperature, blurring
+the usual quasi-particle picture. Similarly Compton scattering exhibits
+substantial many-body corrections that persist at normal densities and
+intermediate $T$. Results for exchange-correlation energies and potentials are
+in good agreement with existing theories and finite-temperature DFT
+functionals.
+",0,1,0,0,0,0
+10457,Geometric Embedding of Path and Cycle Graphs in Pseudo-convex Polygons,"  Given a graph $ G $ with $ n $ vertices and a set $ S $ of $ n $ points in
+the plane, a point-set embedding of $ G $ on $ S $ is a planar drawing such
+that each vertex of $ G $ is mapped to a distinct point of $ S $. A
+straight-line point-set embedding is a point-set embedding with no edge bends
+or curves. The point-set embeddability problem is NP-complete, even when $ G $
+is $ 2 $-connected and $ 2 $-outerplanar. It has been solved polynomially only
+for a few classes of planar graphs. Suppose that $ S $ is the set of vertices
+of a simple polygon. A straight-line polygon embedding of a graph is a
+straight-line point-set embedding of the graph onto the vertices of the polygon
+with no crossing between edges of graph and the edges of polygon. In this
+paper, we present $ O(n) $-time algorithms for polygon embedding of path and
+cycle graphs in simple convex polygon and same time algorithms for polygon
+embedding of path and cycle graphs in a large type of simple polygons where $n$
+is the number of vertices of the polygon.
+",1,0,0,0,0,0
+10458,Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification,"  Despite significant recent progress in the area of Brain-Computer Interface,
+there are numerous shortcomings associated with collecting
+Electroencephalography (EEG) signals in real-world environments. These include,
+but are not limited to, subject and session data variance, long and arduous
+calibration processes and performance generalisation issues across
+differentsubjects or sessions. This implies that many downstream applications,
+including Steady State Visual Evoked Potential (SSVEP) based classification
+systems, can suffer from a shortage of reliable data. Generating meaningful and
+realistic synthetic data can therefore be of significant value in circumventing
+this problem. We explore the use of modern neural-based generative models
+trained on a limited quantity of EEG data collected from different subjects to
+generate supplementary synthetic EEG signal vectors subsequently utilised to
+train an SSVEP classifier. Extensive experimental analyses demonstrate the
+efficacy of our generated data, leading to significant improvements across a
+variety of evaluations, with the crucial task of cross-subject generalisation
+improving by over 35% with the use of synthetic data.
+",0,0,0,0,1,0
+10459,An FPTAS for the parametric knapsack problem,"  In this paper, we investigate the parametric knapsack problem, in which the
+item profits are affine functions depending on a real-valued parameter. The aim
+is to provide a solution for all values of the parameter. It is well-known that
+any exact algorithm for the problem may need to output an exponential number of
+knapsack solutions. We present a fully polynomial-time approximation scheme
+(FPTAS) for the problem that, for any desired precision $\varepsilon \in
+(0,1)$, computes $(1-\varepsilon)$-approximate solutions for all values of the
+parameter. This is the first FPTAS for the parametric knapsack problem that
+does not require the slopes and intercepts of the affine functions to be
+non-negative but works for arbitrary integral values. Our FPTAS outputs
+$\mathcal{O}(\frac{n^2}{\varepsilon})$ knapsack solutions and runs in strongly
+polynomial-time $\mathcal{O}(\frac{n^4}{\varepsilon^2})$. Even for the special
+case of positive input data, this is the first FPTAS with a strongly polynomial
+running time. We also show that this time bound can be further improved to
+$\mathcal{O}(\frac{n^2}{\varepsilon} \cdot A(n,\varepsilon))$, where
+$A(n,\varepsilon)$ denotes the running time of any FPTAS for the traditional
+(non-parametric) knapsack problem.
+",1,0,1,0,0,0
+10460,"Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics","  Dozens of new models on fixation prediction are published every year and
+compared on open benchmarks such as MIT300 and LSUN. However, progress in the
+field can be difficult to judge because models are compared using a variety of
+inconsistent metrics. Here we show that no single saliency map can perform well
+under all metrics. Instead, we propose a principled approach to solve the
+benchmarking problem by separating the notions of saliency models, maps and
+metrics. Inspired by Bayesian decision theory, we define a saliency model to be
+a probabilistic model of fixation density prediction and a saliency map to be a
+metric-specific prediction derived from the model density which maximizes the
+expected performance on that metric given the model density. We derive these
+optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC,
+NSS, CC, SIM, KL-Div) and show that they can be computed analytically or
+approximated with high precision. We show that this leads to consistent
+rankings in all metrics and avoids the penalties of using one saliency map for
+all metrics. Our method allows researchers to have their model compete on many
+different metrics with state-of-the-art in those metrics: ""good"" models will
+perform well in all metrics.
+",1,0,0,1,0,0
+10461,Distributed Framework for Optimal Demand Distribution in Self-Balancing Microgrid,"  This study focusses on self-balancing microgrids to smartly utilize and
+prevent overdrawing of available power capacity of the grid. A distributed
+framework for automated distribution of optimal power demand is proposed, where
+all building in a microgrid dynamically and simultaneously adjusts their own
+power consumption to reach their individual optimal power demands while
+cooperatively striving to maintain the overall grid stable. Emphasis has been
+given to aspects of algorithm that yields lower time of convergence and is
+demonstrated through quantitative and qualitative analysis of simulation
+results.
+",1,0,0,0,0,0
+10462,"Fast, precise, and widely tunable frequency control of an optical parametric oscillator referenced to a frequency comb","  Optical frequency combs (OFC) provide a convenient reference for the
+frequency stabilization of continuous-wave lasers. We demonstrate a frequency
+control method relying on tracking over a wide range and stabilizing the beat
+note between the laser and the OFC. The approach combines fast frequency ramps
+on a millisecond timescale in the entire mode-hop free tuning range of the
+laser and precise stabilization to single frequencies. We apply it to a
+commercially available optical parametric oscillator (OPO) and demonstrate
+tuning over more than 60 GHz with a ramping speed up to 3 GHz/ms. Frequency
+ramps spanning 15 GHz are performed in less than 10 ms, with the OPO instantly
+relocked to the OFC after the ramp at any desired frequency. The developed
+control hardware and software is able to stabilize the OPO to sub-MHz precision
+and to perform sequences of fast frequency ramps automatically.
+",0,1,0,0,0,0
+10463,Compact-Like Operators in Lattice-Normed Spaces,"  A linear operator $T$ between two lattice-normed spaces is said to be
+$p$-compact if, for any $p$-bounded net $x_\alpha$, the net $Tx_\alpha$ has a
+$p$-convergent subnet. $p$-Compact operators generalize several known classes
+of operators such as compact, weakly compact, order weakly compact,
+$AM$-compact operators, etc. Similar to $M$-weakly and $L$-weakly compact
+operators, we define $p$-$M$-weakly and $p$-$L$-weakly compact operators and
+study some of their properties. We also study $up$-continuous and $up$-compact
+operators between lattice-normed vector lattices.
+",0,0,1,0,0,0
+10464,Near-infrared spectroscopy of 5 ultra-massive galaxies at 1.7 < z < 2.7,"  We present the results of a pilot near-infrared (NIR) spectroscopic campaign
+of five very massive galaxies ($\log(\text{M}_\star/\text{M}_\odot)>11.45$) in
+the range of $1.7<z<2.7$. We measure an absorption feature redshift for one
+galaxy at $z_\text{spec}=2.000\pm0.006$. For the remaining galaxies, we combine
+the photometry with the continuum from the spectra to estimate continuum
+redshifts and stellar population properties. We define a continuum redshift
+($z_{\rm cont}$ ) as one in which the redshift is estimated probabilistically
+using EAZY from the combination of catalog photometry and the observed
+spectrum. We derive the uncertainties on the stellar population synthesis
+properties using a Monte Carlo simulation and examine the correlations between
+the parameters with and without the use of the spectrum in the modeling of the
+spectral energy distributions (SEDs). The spectroscopic constraints confirm the
+extreme stellar masses of the galaxies in our sample. We find that three out of
+five galaxies are quiescent (star formation rate of $\lesssim 1
+M_\odot~yr^{-1}$) with low levels of dust obscuration ($A_{\rm V} < 1$) , that
+one galaxy displays both high levels of star formation and dust obscuration
+(${\rm SFR} \approx 300 M_\odot~{\rm yr}^{-1}$, $A_{\rm V} \approx 1.7$~mag),
+and that the remaining galaxy has properties that are intermediate between the
+quiescent and star-forming populations.
+",0,1,0,0,0,0
+10465,A hybrid deep learning approach for medical relation extraction,"  Mining relationships between treatment(s) and medical problem(s) is vital in
+the biomedical domain. This helps in various applications, such as decision
+support system, safety surveillance, and new treatment discovery. We propose a
+deep learning approach that utilizes both word level and sentence-level
+representations to extract the relationships between treatment and problem.
+While deep learning techniques demand a large amount of data for training, we
+make use of a rule-based system particularly for relationship classes with
+fewer samples. Our final relations are derived by jointly combining the results
+from deep learning and rule-based models. Our system achieved a promising
+performance on the relationship classes of I2b2 2010 relation extraction task.
+",0,0,0,1,0,0
+10466,Performance Optimization of Network Coding Based Communication and Reliable Storage in Internet of Things,"  Internet or things (IoT) is changing our daily life rapidly. Although new
+technologies are emerging everyday and expanding their influence in this
+rapidly growing area, many classic theories can still find their places. In
+this paper, we study the important applications of the classic network coding
+theory in two important components of Internet of things, including the IoT
+core network, where data is sensed and transmitted, and the distributed cloud
+storage, where the data generated by the IoT core network is stored. First we
+propose an adaptive network coding (ANC) scheme in the IoT core network to
+improve the transmission efficiency. We demonstrate the efficacy of the scheme
+and the performance advantage over existing schemes through simulations. %Next
+we study the application of network coding in the distributed cloud storage.
+Next we introduce the optimal storage allocation problem in the network coding
+based distributed cloud storage, which aims at searching for the most reliable
+allocation that distributes the $n$ data components into $N$ data centers,
+given the failure probability $p$ of each data center. Then we propose a
+polynomial-time optimal storage allocation (OSA) scheme to solve the problem.
+Both the theoretical analysis and the simulation results show that the storage
+reliability could be greatly improved by the OSA scheme.
+",1,0,0,0,0,0
+10467,Time-dependent probability density functions and information geometry in stochastic logistic and Gompertz models,"  A probabilistic description is essential for understanding growth processes
+far from equilibrium. In this paper, we compute time-dependent Probability
+Density Functions (PDFs) in order to investigate stochastic logistic and
+Gompertz models, which are two of the most popular growth models. We consider
+different types of short-correlated internal (multiplicative) and external
+(additive) stochastic noises and compare the time-dependent PDFs in the two
+models, elucidating the effects of the additive and multiplicative noises on
+the form of PDFs. We demonstrate an interesting transition from a unimodal to a
+bimodal PDF as the multiplicative noise increases for a fixed value of the
+additive noise. A much weaker (leaky) attractor in the Gompertz model leads to
+a significant (singular) growth of the population of a very small size. We
+point out the limitation of using stationary PDFs, mean value and variance in
+understanding statistical properties of the growth far from equilibrium,
+highlighting the importance of time-dependent PDFs. We further compare these
+two models from the perspective of information change that occurs during the
+growth process. Specifically, we define an infinitesimal distance at any time
+by comparing two PDFs at times infinitesimally apart and sum these distances in
+time. The total distance along the trajectory quantifies the total number of
+different states that the system undergoes in time, and is called the
+information length. We show that the time-evolution of the two models become
+more similar when measured in units of the information length and point out the
+merit of using the information length in unifying and understanding the dynamic
+evolution of different growth processes.
+",0,1,0,0,0,0
+10468,A Local-Search Algorithm for Steiner Forest,"  In the Steiner Forest problem, we are given a graph and a collection of
+source-sink pairs, and the goal is to find a subgraph of minimum total length
+such that all pairs are connected. The problem is APX-Hard and can be
+2-approximated by, e.g., the elegant primal-dual algorithm of Agrawal, Klein,
+and Ravi from 1995.
+We give a local-search-based constant-factor approximation for the problem.
+Local search brings in new techniques to an area that has for long not seen any
+improvements and might be a step towards a combinatorial algorithm for the more
+general survivable network design problem. Moreover, local search was an
+essential tool to tackle the dynamic MST/Steiner Tree problem, whereas dynamic
+Steiner Forest is still wide open.
+It is easy to see that any constant factor local search algorithm requires
+steps that add/drop many edges together. We propose natural local moves which,
+at each step, either (a) add a shortest path in the current graph and then drop
+a bunch of inessential edges, or (b) add a set of edges to the current
+solution. This second type of moves is motivated by the potential function we
+use to measure progress, combining the cost of the solution with a penalty for
+each connected component. Our carefully-chosen local moves and potential
+function work in tandem to eliminate bad local minima that arise when using
+more traditional local moves.
+",1,0,0,0,0,0
+10469,Fundamental bounds on MIMO antennas,"  Antenna current optimization is often used to analyze the optimal performance
+of antennas. Antenna performance can be quantified in e.g., minimum Q-factor
+and efficiency. The performance of MIMO antennas is more involved and, in
+general, a single parameter is not sufficient to quantify it. Here, the
+capacity of an idealized channel is used as the main performance quantity. An
+optimization problem in the current distribution for optimal capacity, measured
+in spectral efficiency, given a fixed Q-factor and efficiency is formulated as
+a semi-definite optimization problem. A model order reduction based on
+characteristic and energy modes is employed to improve the computational
+efficiency. The performance bound is illustrated by solving the optimization
+problem numerically for rectangular plates and spherical shells.
+",0,1,1,0,0,0
+10470,DeepProteomics: Protein family classification using Shallow and Deep Networks,"  The knowledge regarding the function of proteins is necessary as it gives a
+clear picture of biological processes. Nevertheless, there are many protein
+sequences found and added to the databases but lacks functional annotation. The
+laboratory experiments take a considerable amount of time for annotation of the
+sequences. This arises the need to use computational techniques to classify
+proteins based on their functions. In our work, we have collected the data from
+Swiss-Prot containing 40433 proteins which is grouped into 30 families. We pass
+it to recurrent neural network(RNN), long short term memory(LSTM) and gated
+recurrent unit(GRU) model and compare it by applying trigram with deep neural
+network and shallow neural network on the same dataset. Through this approach,
+we could achieve maximum of around 78% accuracy for the classification of
+protein families.
+",0,0,0,1,0,0
+10471,Designing Deterministic Polynomial-Space Algorithms by Color-Coding Multivariate Polynomials,"  In recent years, several powerful techniques have been developed to design
+{\em randomized} polynomial-space parameterized algorithms. In this paper, we
+introduce an enhancement of color coding to design deterministic
+polynomial-space parameterized algorithms. Our approach aims at reducing the
+number of random choices by exploiting the special structure of a solution.
+Using our approach, we derive the following deterministic algorithms (see
+Introduction for problem definitions).
+1. Polynomial-space $O^*(3.86^k)$-time (exponential-space $O^*(3.41^k)$-time)
+algorithm for {\sc $k$-Internal Out-Branching}, improving upon the previously
+fastest {\em exponential-space} $O^*(5.14^k)$-time algorithm for this problem.
+2. Polynomial-space $O^*((2e)^{k+o(k)})$-time (exponential-space
+$O^*(4.32^k)$-time) algorithm for {\sc $k$-Colorful Out-Branching} on
+arc-colored digraphs and {\sc $k$-Colorful Perfect Matching} on planar
+edge-colored graphs.
+To obtain our polynomial space algorithms, we show that $(n,k,\alpha
+k)$-splitters ($\alpha\ge 1$) and in particular $(n,k)$-perfect hash families
+can be enumerated one by one with polynomial delay.
+",1,0,0,0,0,0
+10472,String Attractors,"  Let $S$ be a string of length $n$. In this paper we introduce the notion of
+\emph{string attractor}: a subset of the string's positions $[1,n]$ such that
+every distinct substring of $S$ has an occurrence crossing one of the
+attractor's elements. We first show that the minimum attractor's size yields
+upper-bounds to the string's repetitiveness as measured by its linguistic
+complexity and by the length of its longest repeated substring. We then prove
+that all known compressors for repetitive strings induce a string attractor
+whose size is bounded by their associated repetitiveness measure, and can
+therefore be considered as approximations of the smallest one. Using further
+reductions, we derive the approximation ratios of these compressors with
+respect to the smallest attractor and solve several open problems related to
+the asymptotic relations between repetitiveness measures (in particular,
+between the the sizes of the Lempel-Ziv factorization, the run-length
+Burrows-Wheeler transform, the smallest grammar, and the smallest macro
+scheme). These reductions directly provide approximation algorithms for the
+smallest string attractor. We then apply string attractors to solve efficiently
+a fundamental problem in the field of compressed computation: we present a
+universal compressed data structure for text extraction that improves existing
+strategies simultaneously for \emph{all} known dictionary compressors and that,
+by recent lower bounds, almost matches the optimal running time within the
+resulting space. To conclude, we consider generalizations of string attractors
+to labeled graphs, show that the attractor problem is NP-complete on trees, and
+provide a logarithmic approximation computable in polynomial time.
+",1,0,0,0,0,0
+10473,Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review,"  Purpose of review: This paper presents a review of the current state of the
+art in remote sensing based monitoring of forest disturbances and forest
+degradation from optical Earth Observation data. Part one comprises an overview
+of currently available optical remote sensing sensors, which can be used for
+forest disturbance and degradation mapping. Part two reviews the two main
+categories of existing approaches: classical image-to-image change detection
+and time series analysis. Recent findings: With the launch of the Sentinel-2a
+satellite and available Landsat imagery, time series analysis has become the
+most promising but also most demanding category of degradation mapping
+approaches. Four time series classification methods are distinguished. The
+methods are explained and their benefits and drawbacks are discussed. A
+separate chapter presents a number of recent forest degradation mapping studies
+for two different ecosystems: temperate forests with a geographical focus on
+Europe and tropical forests with a geographical focus on Africa. Summary: The
+review revealed that a wide variety of methods for the detection of forest
+degradation is already available. Today, the main challenge is to transfer
+these approaches to high resolution time series data from multiple sensors.
+Future research should also focus on the classification of disturbance types
+and the development of robust up-scalable methods to enable near real time
+disturbance mapping in support of operational reactive measures.
+",1,0,0,0,0,0
+10474,Testing for Global Network Structure Using Small Subgraph Statistics,"  We study the problem of testing for community structure in networks using
+relations between the observed frequencies of small subgraphs. We propose a
+simple test for the existence of communities based only on the frequencies of
+three-node subgraphs. The test statistic is shown to be asymptotically normal
+under a null assumption of no community structure, and to have power
+approaching one under a composite alternative hypothesis of a degree-corrected
+stochastic block model. We also derive a version of the test that applies to
+multivariate Gaussian data. Our approach achieves near-optimal detection rates
+for the presence of community structure, in regimes where the signal-to-noise
+is too weak to explicitly estimate the communities themselves, using existing
+computationally efficient algorithms. We demonstrate how the method can be
+effective for detecting structure in social networks, citation networks for
+scientific articles, and correlations of stock returns between companies on the
+S\&P 500.
+",0,0,1,1,0,0
+10475,A new method of correcting radial velocity time series for inhomogeneous convection,"  Magnetic activity strongly impacts stellar RVs and the search for small
+planets. We showed previously that in the solar case it induces RV variations
+with an amplitude over the cycle on the order of 8 m/s, with signals on short
+and long timescales. The major component is the inhibition of the convective
+blueshift due to plages. We explore a new approach to correct for this major
+component of stellar radial velocities in the case of solar-type stars. The
+convective blueshift depends on line depths; we use this property to develop a
+method that will characterize the amplitude of this effect and to correct for
+this RV component. We build realistic RV time series corresponding to RVs
+computed using different sets of lines, including lines in different depth
+ranges. We characterize the performance of the method used to reconstruct the
+signal without the convective component and the detection limits derived from
+the residuals. We identified a set of lines which, combined with a global set
+of lines, allows us to reconstruct the convective component with a good
+precision and to correct for it. For the full temporal sampling, the power in
+the range 100-500~d significantly decreased, by a factor of 100 for a RV noise
+below 30 cm/s. We also studied the impact of noise contributions other than the
+photon noise, which lead to uncertainties on the RV computation, as well as the
+impact of the temporal sampling. We found that these other sources of noise do
+not greatly alter the quality of the correction, although they need a better
+noise level to reach a similar performance level. A very good correction of the
+convective component can be achieved providing very good RV noise levels
+combined with a very good instrumental stability and realistic granulation
+noise. Under the conditions considered in this paper, detection limits at 480~d
+lower than 1 MEarth could be achieved for RV noise below 15 cm/s.
+",0,1,0,0,0,0
+10476,Differential Testing for Variational Analyses: Experience from Developing KConfigReader,"  Differential testing to solve the oracle problem has been applied in many
+scenarios where multiple supposedly equivalent implementations exist, such as
+multiple implementations of a C compiler. If the multiple systems disagree on
+the output for a given test input, we have likely discovered a bug without
+every having to specify what the expected output is. Research on variational
+analyses (or variability-aware or family-based analyses) can benefit from
+similar ideas. The goal of most variational analyses is to perform an analysis,
+such as type checking or model checking, over a large number of configurations
+much faster than an existing traditional analysis could by analyzing each
+configuration separately. Variational analyses are very suitable for
+differential testing, since the existence nonvariational analysis can provide
+the oracle for test cases that would otherwise be tedious or difficult to
+write. In this experience paper, I report how differential testing has helped
+in developing KConfigReader, a tool for translating the Linux kernel's kconfig
+model into a propositional formula. Differential testing allows us to quickly
+build a large test base and incorporate external tests that avoided many
+regressions during development and made KConfigReader likely the most precise
+kconfig extraction tool available.
+",1,0,0,0,0,0
+10477,Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction,"  Explicitly or implicitly, most of dimensionality reduction methods need to
+determine which samples are neighbors and the similarity between the neighbors
+in the original highdimensional space. The projection matrix is then learned on
+the assumption that the neighborhood information (e.g., the similarity) is
+known and fixed prior to learning. However, it is difficult to precisely
+measure the intrinsic similarity of samples in high-dimensional space because
+of the curse of dimensionality. Consequently, the neighbors selected according
+to such similarity might and the projection matrix obtained according to such
+similarity and neighbors are not optimal in the sense of classification and
+generalization. To overcome the drawbacks, in this paper we propose to let the
+similarity and neighbors be variables and model them in low-dimensional space.
+Both the optimal similarity and projection matrix are obtained by minimizing a
+unified objective function. Nonnegative and sum-to-one constraints on the
+similarity are adopted. Instead of empirically setting the regularization
+parameter, we treat it as a variable to be optimized. It is interesting that
+the optimal regularization parameter is adaptive to the neighbors in
+low-dimensional space and has intuitive meaning. Experimental results on the
+YALE B, COIL-100, and MNIST datasets demonstrate the effectiveness of the
+proposed method.
+",0,0,0,1,0,0
+10478,Joint Multichannel Deconvolution and Blind Source Separation,"  Blind Source Separation (BSS) is a challenging matrix factorization problem
+that plays a central role in multichannel imaging science. In a large number of
+applications, such as astrophysics, current unmixing methods are limited since
+real-world mixtures are generally affected by extra instrumental effects like
+blurring. Therefore, BSS has to be solved jointly with a deconvolution problem,
+which requires tackling a new inverse problem: deconvolution BSS (DBSS). In
+this article, we introduce an innovative DBSS approach, called DecGMCA, based
+on sparse signal modeling and an efficient alternative projected least square
+algorithm. Numerical results demonstrate that the DecGMCA algorithm performs
+very well on simulations. It further highlights the importance of jointly
+solving BSS and deconvolution instead of considering these two problems
+independently. Furthermore, the performance of the proposed DecGMCA algorithm
+is demonstrated on simulated radio-interferometric data.
+",1,0,0,1,0,0
+10479,Bimodule monomorphism categories and RSS equivalences via cotilting modules,"  The monomorphism category $\mathscr{S}(A, M, B)$ induced by a bimodule
+$_AM_B$ is the subcategory of $\Lambda$-mod consisting of
+$\left[\begin{smallmatrix} X\\ Y\end{smallmatrix}\right]_{\phi}$ such that
+$\phi: M\otimes_B Y\rightarrow X$ is a monic $A$-map, where
+$\Lambda=\left[\begin{smallmatrix} A&M\\0&B \end{smallmatrix}\right]$. In
+general, it is not the monomorphism categories induced by quivers. It could
+describe the Gorenstein-projective $\m$-modules. This monomorphism category is
+a resolving subcategory of $\modcat{\Lambda}$ if and only if $M_B$ is
+projective. In this case, it has enough injective objects and Auslander-Reiten
+sequences, and can be also described as the left perpendicular category of a
+unique basic cotilting $\Lambda$-module. If $M$ satisfies the condition ${\rm
+(IP)}$, then the stable category of $\mathscr{S}(A, M, B)$ admits a recollement
+of additive categories, which is in fact a recollement of singularity
+categories if $\mathscr{S}(A, M, B)$ is a {\rm Frobenius} category.
+Ringel-Schmidmeier-Simson equivalence between $\mathscr{S}(A, M, B)$ and its
+dual is introduced. If $M$ is an exchangeable bimodule, then an {\rm RSS}
+equivalence is given by a $\Lambda$-$\Lambda$ bimodule which is a two-sided
+cotilting $\Lambda$-module with a special property; and the Nakayama functor
+$\mathcal N_\m$ gives an {\rm RSS} equivalence if and only if both $A$ and $B$
+are Frobenius algebras.
+",0,0,1,0,0,0
+10480,The Vanishing viscosity limit for some symmetric flows,"  The focus of this paper is on the analysis of the boundary layer and the
+associated vanishing viscosity limit for two classes of flows with symmetry,
+namely, Plane-Parallel Channel Flows and Parallel Pipe Flows. We construct
+explicit boundary layer correctors, which approximate the difference between
+the Navier-Stokes and the Euler solutions. Using properties of these
+correctors, we establish convergence of the Navier-Stokes solution to the Euler
+solution as viscosity vanishes with optimal rates of convergence. In addition,
+we investigate vorticity production on the boundary in the limit of vanishing
+viscosity. Our work significantly extends prior work in the literature.
+",0,0,1,0,0,0
+10481,Anisotropy of transport in bulk Rashba metals,"  The recent experimental discovery of three-dimensional (3D) materials hosting
+a strong Rashba spin-orbit coupling calls for the theoretical investigation of
+their transport properties. Here we study the zero temperature dc conductivity
+of a 3D Rashba metal in the presence of static diluted impurities. We show
+that, at variance with the two-dimensional case, in 3D systems spin-orbit
+coupling affects dc charge transport in all density regimes. We find in
+particular that the effect of spin-orbit interaction strongly depends on the
+direction of the current, and we show that this yields strongly anisotropic
+transport characteristics. In the dominant spin-orbit coupling regime where
+only the lowest band is occupied, the SO-induced conductivity anisotropy is
+governed entirely by the anomalous component of the renormalized current. We
+propose that measurements of the conductivity anisotropy in bulk Rashba metals
+may give a direct experimental assessment of the spin-orbit strength.
+",0,1,0,0,0,0
+10482,A quick guide for student-driven community genome annotation,"  High quality gene models are necessary to expand the molecular and genetic
+tools available for a target organism, but these are available for only a
+handful of model organisms that have undergone extensive curation and
+experimental validation over the course of many years. The majority of gene
+models present in biological databases today have been identified in draft
+genome assemblies using automated annotation pipelines that are frequently
+based on orthologs from distantly related model organisms. Manual curation is
+time consuming and often requires substantial expertise, but is instrumental in
+improving gene model structure and identification. Manual annotation may seem
+to be a daunting and cost-prohibitive task for small research communities but
+involving undergraduates in community genome annotation consortiums can be
+mutually beneficial for both education and improved genomic resources. We
+outline a workflow for efficient manual annotation driven by a team of
+primarily undergraduate annotators. This model can be scaled to large teams and
+includes quality control processes through incremental evaluation. Moreover, it
+gives students an opportunity to increase their understanding of genome biology
+and to participate in scientific research in collaboration with peers and
+senior researchers at multiple institutions.
+",0,0,0,0,1,0
+10483,Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data,"  In the past few years, Convolutional Neural Networks (CNNs) have been
+achieving state-of-the-art performance on a variety of problems. Many companies
+employ resources and money to generate these models and provide them as an API,
+therefore it is in their best interest to protect them, i.e., to avoid that
+someone else copies them. Recent studies revealed that state-of-the-art CNNs
+are vulnerable to adversarial examples attacks, and this weakness indicates
+that CNNs do not need to operate in the problem domain (PD). Therefore, we
+hypothesize that they also do not need to be trained with examples of the PD in
+order to operate in it.
+Given these facts, in this paper, we investigate if a target black-box CNN
+can be copied by persuading it to confess its knowledge through random
+non-labeled data. The copy is two-fold: i) the target network is queried with
+random data and its predictions are used to create a fake dataset with the
+knowledge of the network; and ii) a copycat network is trained with the fake
+dataset and should be able to achieve similar performance as the target
+network.
+This hypothesis was evaluated locally in three problems (facial expression,
+object, and crosswalk classification) and against a cloud-based API. In the
+copy attacks, images from both non-problem domain and PD were used. All copycat
+networks achieved at least 93.7% of the performance of the original models with
+non-problem domain data, and at least 98.6% using additional data from the PD.
+Additionally, the copycat CNN successfully copied at least 97.3% of the
+performance of the Microsoft Azure Emotion API. Our results show that it is
+possible to create a copycat CNN by simply querying a target network as
+black-box with random non-labeled data.
+",0,0,0,1,0,0
+10484,Medoids in almost linear time via multi-armed bandits,"  Computing the medoid of a large number of points in high-dimensional space is
+an increasingly common operation in many data science problems. We present an
+algorithm Med-dit which uses O(n log n) distance evaluations to compute the
+medoid with high probability. Med-dit is based on a connection with the
+multi-armed bandit problem. We evaluate the performance of Med-dit empirically
+on the Netflix-prize and the single-cell RNA-Seq datasets, containing hundreds
+of thousands of points living in tens of thousands of dimensions, and observe a
+5-10x improvement in performance over the current state of the art. Med-dit is
+available at this https URL
+",1,0,0,1,0,0
+10485,Early stopping for statistical inverse problems via truncated SVD estimation,"  We consider truncated SVD (or spectral cut-off, projection) estimators for a
+prototypical statistical inverse problem in dimension $D$. Since calculating
+the singular value decomposition (SVD) only for the largest singular values is
+much less costly than the full SVD, our aim is to select a data-driven
+truncation level $\widehat m\in\{1,\ldots,D\}$ only based on the knowledge of
+the first $\widehat m$ singular values and vectors. We analyse in detail
+whether sequential {\it early stopping} rules of this type can preserve
+statistical optimality. Information-constrained lower bounds and matching upper
+bounds for a residual based stopping rule are provided, which give a clear
+picture in which situation optimal sequential adaptation is feasible. Finally,
+a hybrid two-step approach is proposed which allows for classical oracle
+inequalities while considerably reducing numerical complexity.
+",0,0,1,1,0,0
+10486,Algorithmic Theory of ODEs and Sampling from Well-conditioned Logconcave Densities,"  Sampling logconcave functions arising in statistics and machine learning has
+been a subject of intensive study. Recent developments include analyses for
+Langevin dynamics and Hamiltonian Monte Carlo (HMC). While both approaches have
+dimension-independent bounds for the underlying $\mathit{continuous}$ processes
+under sufficiently strong smoothness conditions, the resulting discrete
+algorithms have complexity and number of function evaluations growing with the
+dimension. Motivated by this problem, in this paper, we give a general
+algorithm for solving multivariate ordinary differential equations whose
+solution is close to the span of a known basis of functions (e.g., polynomials
+or piecewise polynomials). The resulting algorithm has polylogarithmic depth
+and essentially tight runtime - it is nearly linear in the size of the
+representation of the solution.
+We apply this to the sampling problem to obtain a nearly linear
+implementation of HMC for a broad class of smooth, strongly logconcave
+densities, with the number of iterations (parallel depth) and gradient
+evaluations being $\mathit{polylogarithmic}$ in the dimension (rather than
+polynomial as in previous work). This class includes the widely-used loss
+function for logistic regression with incoherent weight matrices and has been
+subject of much study recently. We also give a faster algorithm with $
+\mathit{polylogarithmic~depth}$ for the more general and standard class of
+strongly convex functions with Lipschitz gradient. These results are based on
+(1) an improved contraction bound for the exact HMC process and (2) logarithmic
+bounds on the degree of polynomials that approximate solutions of the
+differential equations arising in implementing HMC.
+",1,0,0,0,0,0
+10487,The PSLQ Algorithm for Empirical Data,"  The celebrated integer relation finding algorithm PSLQ has been successfully
+used in many applications. PSLQ was only analyzed theoretically for exact input
+data, however, when the input data are irrational numbers, they must be
+approximate ones due to the finite precision of the computer. When the
+algorithm takes empirical data (inexact data with error bounded) instead of
+exact real numbers as its input, how do we theoretically ensure the output of
+the algorithm to be an exact integer relation?
+In this paper, we investigate the PSLQ algorithm for empirical data as its
+input. Firstly, we give a termination condition for this case. Secondly, we
+analyze a perturbation on the hyperplane matrix constructed from the input data
+and hence disclose a relationship between the accuracy of the input data and
+the output quality (an upper bound on the absolute value of the inner product
+of the exact data and the computed integer relation), which naturally leads to
+an error control strategy for PSLQ. Further, we analyze the complexity bound of
+the PSLQ algorithm for empirical data. Examples on transcendental numbers and
+algebraic numbers show the meaningfulness of our error control strategy.
+",1,0,1,0,0,0
+10488,Tensor Completion Algorithms in Big Data Analytics,"  Tensor completion is a problem of filling the missing or unobserved entries
+of partially observed tensors. Due to the multidimensional character of tensors
+in describing complex datasets, tensor completion algorithms and their
+applications have received wide attention and achievement in areas like data
+mining, computer vision, signal processing, and neuroscience. In this survey,
+we provide a modern overview of recent advances in tensor completion algorithms
+from the perspective of big data analytics characterized by diverse variety,
+large volume, and high velocity. We characterize these advances from four
+perspectives: general tensor completion algorithms, tensor completion with
+auxiliary information (variety), scalable tensor completion algorithms
+(volume), and dynamic tensor completion algorithms (velocity). Further, we
+identify several tensor completion applications on real-world data-driven
+problems and present some common experimental frameworks popularized in the
+literature. Our goal is to summarize these popular methods and introduce them
+to researchers and practitioners for promoting future research and
+applications. We conclude with a discussion of key challenges and promising
+research directions in this community for future exploration.
+",1,0,0,1,0,0
+10489,On Gallai's and Hajós' Conjectures for graphs with treewidth at most 3,"  A path (resp. cycle) decomposition of a graph $G$ is a set of edge-disjoint
+paths (resp. cycles) of $G$ that covers the edge set of $G$. Gallai (1966)
+conjectured that every graph on $n$ vertices admits a path decomposition of
+size at most $\lfloor (n+1)/2\rfloor$, and Hajós (1968) conjectured that
+every Eulerian graph on $n$ vertices admits a cycle decomposition of size at
+most $\lfloor (n-1)/2\rfloor$. Gallai's Conjecture was verified for many
+classes of graphs. In particular, Lovász (1968) verified this conjecture for
+graphs with at most one vertex of even degree, and Pyber (1996) verified it for
+graphs in which every cycle contains a vertex of odd degree. Hajós'
+Conjecture, on the other hand, was verified only for graphs with maximum degree
+$4$ and for planar graphs. In this paper, we verify Gallai's and Hajós'
+Conjectures for graphs with treewidth at most $3$. Moreover, we show that the
+only graphs with treewidth at most $3$ that do not admit a path decomposition
+of size at most $\lfloor n/2\rfloor$ are isomorphic to $K_3$ or $K_5-e$.
+Finally, we use the technique developed in this paper to present new proofs for
+Gallai's and Hajós' Conjectures for graphs with maximum degree at most $4$,
+and for planar graphs with girth at least $6$.
+",1,0,0,0,0,0
+10490,Band and correlated insulators of cold fermions in a mesoscopic lattice,"  We investigate the transport properties of neutral, fermionic atoms passing
+through a one-dimensional quantum wire containing a mesoscopic lattice. The
+lattice is realized by projecting individually controlled, thin optical
+barriers on top of a ballistic conductor. Building an increasingly longer
+lattice, one site after another, we observe and characterize the emergence of a
+band insulating phase, demonstrating control over quantum-coherent transport.
+We explore the influence of atom-atom interactions and show that the insulating
+state persists as contact interactions are tuned from moderately to strongly
+attractive. Using bosonization and classical Monte-Carlo simulations we analyze
+such a model of interacting fermions and find good qualitative agreement with
+the data. The robustness of the insulating state supports the existence of a
+Luther-Emery liquid in the one-dimensional wire. Our work realizes a tunable,
+site-controlled lattice Fermi gas strongly coupled to reservoirs, which is an
+ideal test bed for non-equilibrium many-body physics.
+",0,1,0,0,0,0
+10491,Stochasticity from function - why the Bayesian brain may need no noise,"  An increasing body of evidence suggests that the trial-to-trial variability
+of spiking activity in the brain is not mere noise, but rather the reflection
+of a sampling-based encoding scheme for probabilistic computing. Since the
+precise statistical properties of neural activity are important in this
+context, many models assume an ad-hoc source of well-behaved, explicit noise,
+either on the input or on the output side of single neuron dynamics, most often
+assuming an independent Poisson process in either case. However, these
+assumptions are somewhat problematic: neighboring neurons tend to share
+receptive fields, rendering both their input and their output correlated; at
+the same time, neurons are known to behave largely deterministically, as a
+function of their membrane potential and conductance. We suggest that spiking
+neural networks may, in fact, have no need for noise to perform sampling-based
+Bayesian inference. We study analytically the effect of auto- and
+cross-correlations in functionally Bayesian spiking networks and demonstrate
+how their effect translates to synaptic interaction strengths, rendering them
+controllable through synaptic plasticity. This allows even small ensembles of
+interconnected deterministic spiking networks to simultaneously and
+co-dependently shape their output activity through learning, enabling them to
+perform complex Bayesian computation without any need for noise, which we
+demonstrate in silico, both in classical simulation and in neuromorphic
+emulation. These results close a gap between the abstract models and the
+biology of functionally Bayesian spiking networks, effectively reducing the
+architectural constraints imposed on physical neural substrates required to
+perform probabilistic computing, be they biological or artificial.
+",0,0,0,0,1,0
+10492,Very Asymmetric Collider for Dark Matter Search below 1 GeV,"  Current searches for a dark photon in the mass range below 1 GeV require an
+electron-positron collider with a luminosity at the level of at least $10^{34}$
+cm$^{-2}$s$^{-1}$. The challenge is that, at such low energies, the collider
+luminosity rapidly drops off due to increase in the beam sizes, strong mutual
+focusing of the colliding beams, and enhancement of collective effects. Using
+recent advances in accelerator technology such as the nano-beam scheme of
+SuperKEK-B, high-current Energy Recovery Linacs (ERL), and magnetized beams, we
+propose a new configuration of an electron-positron collider based on a
+positron storage ring and an electron ERL. It allows one to achieve a
+luminosity of $>10^{34}$ cm$^{-2}$s$^{-1}$ at the center of momentum energy of
+<1 GeV. We present general considerations and a specific example of such a
+facility using the parameters of the SuperKEK-B positron storage ring and
+Cornell ERL project.
+",0,1,0,0,0,0
+10493,Some divisibility properties of binomial coefficients,"  In this paper, we gave some properties of binomial coefficient.
+",0,0,1,0,0,0
+10494,An Unsupervised Method for Estimating the Global Horizontal Irradiance from Photovoltaic Power Measurements,"  In this paper, we present a method to determine the global horizontal
+irradiance (GHI) from the power measurements of one or more PV systems, located
+in the same neighborhood. The method is completely unsupervised and is based on
+a physical model of a PV plant. The precise assessment of solar irradiance is
+pivotal for the forecast of the electric power generated by photovoltaic (PV)
+plants. However, on-ground measurements are expensive and are generally not
+performed for small and medium-sized PV plants. Satellite-based services
+represent a valid alternative to on site measurements, but their space-time
+resolution is limited. Results from two case studies located in Switzerland are
+presented. The performance of the proposed method at assessing GHI is compared
+with that of free and commercial satellite services. Our results show that the
+presented method is generally better than satellite-based services, especially
+at high temporal resolutions.
+",0,0,0,1,0,0
+10495,Strong anisotropy effect in iron-based superconductor CaFe$_{0.882}$Co$_{0.118}$AsF,"  The anisotropy of the Fe-based superconductors is much smaller than that of
+the cuprates and the theoretical calculations. A credible understanding for
+this experimental fact is still lacking up to now. Here we experimentally study
+the magnetic-field-angle dependence of electronic resistivity in the
+superconducting phase of iron-based superconductor
+CaFe$_{0.882}$Co$_{0.118}$AsF, and find the strongest anisotropy effect of the
+upper critical field among the iron-based superconductors based on the
+framework of Ginzburg-Landau theory. The evidences of energy band structure and
+charge density distribution from electronic structure calculations demonstrate
+that the observed strong anisotropic effect mainly comes from the strong ionic
+bonding in between the ions of Ca$^{2+}$ and F$^-$, which weakens the
+interlayer coupling between the layers of FeAs and CaF. This finding provides a
+significant insight into the nature of experimentally observed strong
+anisotropic effect of electronic resistivity, and also paves an avenue to
+design exotic two dimensional artificial unconventional superconductors in
+future.
+",0,1,0,0,0,0
+10496,Example of C-rigid polytopes which are not B-rigid,"  A simple polytope $P$ is said to be \emph{B-rigid} if its combinatorial
+structure is characterized by its Tor-algebra, and is said to be \emph{C-rigid}
+if its combinatorial structure is characterized by the cohomology ring of a
+quasitoric manifold over $P$. It is known that a B-rigid simple polytope is
+C-rigid. In this paper, we, further, show that the B-rigidity is not equivalent
+to the C-rigidity.
+",0,0,1,0,0,0
+10497,"Dynamic Switching Networks: A Dynamic, Non-local, and Time-independent Approach to Emergence","  The concept of emergence is a powerful concept to explain very complex
+behaviour by simple underling rules. Existing approaches of producing emergent
+collective behaviour have many limitations making them unable to account for
+the complexity we see in the real world. In this paper we propose a new
+dynamic, non-local, and time independent approach that uses a network like
+structure to implement the laws or the rules, where the mathematical equations
+representing the rules are converted to a series of switching decisions carried
+out by the network on the particles moving in the network. The proposed
+approach is used to generate patterns with different types of symmetry.
+",1,0,0,0,0,0
+10498,The Trouvé group for spaces of test functions,"  The Trouvé group $\mathcal G_{\mathcal A}$ from image analysis consists of
+the flows at a fixed time of all time-dependent vectors fields of a given
+regularity $\mathcal A(\mathbb R^d,\mathbb R^d)$. For a multitude of regularity
+classes $\mathcal A$, we prove that the Trouvé group $\mathcal G_{\mathcal
+A}$ coincides with the connected component of the identity of the group of
+orientation preserving diffeomorphims of $\mathbb R^d$ which differ from the
+identity by a mapping of class $\mathcal A$. We thus conclude that $\mathcal
+G_{\mathcal A}$ has a natural regular Lie group structure. In many cases we
+show that the mapping which takes a time-dependent vector field to its flow is
+continuous. As a consequence we obtain that the scale of Bergman spaces on the
+polystrip with variable width is stable under solving ordinary differential
+equations.
+",0,0,1,0,0,0
+10499,Robust Motion Planning employing Signal Temporal Logic,"  Motion planning classically concerns the problem of accomplishing a goal
+configuration while avoiding obstacles. However, the need for more
+sophisticated motion planning methodologies, taking temporal aspects into
+account, has emerged. To address this issue, temporal logics have recently been
+used to formulate such advanced specifications. This paper will consider Signal
+Temporal Logic in combination with Model Predictive Control. A robustness
+metric, called Discrete Average Space Robustness, is introduced and used to
+maximize the satisfaction of specifications which results in a natural
+robustness against noise. The comprised optimization problem is convex and
+formulated as a Linear Program.
+",1,0,0,0,0,0
+10500,COREclust: a new package for a robust and scalable analysis of complex data,"  In this paper, we present a new R package COREclust dedicated to the
+detection of representative variables in high dimensional spaces with a
+potentially limited number of observations. Variable sets detection is based on
+an original graph clustering strategy denoted CORE-clustering algorithm that
+detects CORE-clusters, i.e. variable sets having a user defined size range and
+in which each variable is very similar to at least another variable.
+Representative variables are then robustely estimate as the CORE-cluster
+centers. This strategy is entirely coded in C++ and wrapped by R using the Rcpp
+package. A particular effort has been dedicated to keep its algorithmic cost
+reasonable so that it can be used on large datasets. After motivating our work,
+we will explain the CORE-clustering algorithm as well as a greedy extension of
+this algorithm. We will then present how to use it and results obtained on
+synthetic and real data.
+",0,0,0,1,0,0
+10501,Deep Neural Networks,"  Deep Neural Networks (DNNs) are universal function approximators providing
+state-of- the-art solutions on wide range of applications. Common perceptual
+tasks such as speech recognition, image classification, and object tracking are
+now commonly tackled via DNNs. Some fundamental problems remain: (1) the lack
+of a mathematical framework providing an explicit and interpretable
+input-output formula for any topology, (2) quantification of DNNs stability
+regarding adversarial examples (i.e. modified inputs fooling DNN predictions
+whilst undetectable to humans), (3) absence of generalization guarantees and
+controllable behaviors for ambiguous patterns, (4) leverage unlabeled data to
+apply DNNs to domains where expert labeling is scarce as in the medical field.
+Answering those points would provide theoretical perspectives for further
+developments based on a common ground. Furthermore, DNNs are now deployed in
+tremendous societal applications, pushing the need to fill this theoretical gap
+to ensure control, reliability, and interpretability.
+",1,0,0,1,0,0
+10502,The Rosetta mission orbiter Science overview the comet phase,"  The International Rosetta Mission was launched in 2004 and consists of the
+orbiter spacecraft Rosetta and the lander Philae. The aim of the mission is to
+map the comet 67P Churyumov Gerasimenko by remote sensing, to examine its
+environment insitu and its evolution in the inner solar system.Rosetta was the
+first spacecraft to rendezvous and orbit a comet, accompanying it as it passes
+through the inner solar system, and to deploy a lander, Philae and perform in
+situ science on the comet surface. The primary goals of the mission were to:
+characterize the comets nucleus; examine the chemical, mineralogical and
+isotopic composition of volatiles and refractories; examine the physical
+properties and interrelation of volatiles and refractories in a cometary
+nucleus; study the development of cometary activity and the processes in the
+surface layer of the nucleus and in the coma; detail the origin of comets, the
+relationship between cometary and interstellar material and the implications
+for the origin of the solar system; characterize asteroids, 2867 Steins and 21
+Lutetia. This paper presents a summary of mission operations and science,
+focusing on the Rosetta orbiter component of the mission during its comet
+phase, from early 2014 up to September 2016.
+",0,1,0,0,0,0
+10503,Back to the Future: an Even More Nearly Optimal Cardinality Estimation Algorithm,"  We describe a new cardinality estimation algorithm that is extremely
+space-efficient. It applies one of three novel estimators to the compressed
+state of the Flajolet-Martin-85 coupon collection process. In an
+apples-to-apples empirical comparison against compressed HyperLogLog sketches,
+the new algorithm simultaneously wins on all three dimensions of the
+time/space/accuracy tradeoff. Our prototype uses the zstd compression library,
+and produces sketches that are smaller than the entropy of HLL, so no possible
+implementation of compressed HLL can match its space efficiency. The paper's
+technical contributions include analyses and simulations of the three new
+estimators, accurate values for the entropies of FM85 and HLL, and a
+non-trivial method for estimating a double asymptotic limit via simulation.
+",1,0,0,0,0,0
+10504,Tracking Systems as Thinging Machine: A Case Study of a Service Company,"  Object tracking systems play important roles in tracking moving objects and
+overcoming problems such as safety, security and other location-related
+applications. Problems arise from the difficulties in creating a well-defined
+and understandable description of tracking systems. Nowadays, describing such
+processes results in fragmental representation that most of the time leads to
+difficulties creating documentation. Additionally, once learned by assigned
+personnel, repeated tasks result in them continuing on autopilot in a way that
+often degrades their effectiveness. This paper proposes the modeling of
+tracking systems in terms of a new diagrammatic methodology to produce
+engineering-like schemata. The resultant diagrams can be used in documentation,
+explanation, communication, education and control.
+",1,0,0,0,0,0
+10505,Spectral Evidence for an Inner Carbon-Rich Circumstellar Dust Belt in the Young HD36546 A-Star System,"  Using the NASA/IRTF SpeX & BASS spectrometers we have obtained novel 0.7 - 13
+um observations of the newly imaged HD36546 debris disk system. The SpeX
+spectrum is most consistent with the photospheric emission expected from an
+Lstar ~ 20 Lsun, solar abundance A1.5V star with little/no extinction and
+excess emission from circumstellar dust detectable beyond 4.5 um.
+Non-detections of CO emission lines and accretion signatures point to the gas
+poor circumstellar environment of a very old transition disk. Combining the
+SpeX and BASS spectra with archival WISE/AKARI/IRAS/Herschel photometery, we
+find an outer cold dust belt at ~135K and 20 - 40 AU from the primary, likely
+coincident with the disk imaged by Subaru (Currie et al. 2017), and a new
+second inner belt with temperature ~570K and an unusual, broad SED maximum in
+the 6 - 9 um region, tracing dust at 1.1 - 2.2 AU. An SED maximum at 6 - 9 um
+has been reported in just two other A-star systems, HD131488 and HD121191, both
+of ~10 Myr age (Melis et al. 2013). From Spitzer, we have also identified the
+~12 Myr old A7V HD148567 system as having similar 5 - 35 um excess spectral
+features (Mittal et al. 2015). The Spitzer data allows us to rule out water
+emission and rule in carbonaceous materials - organics, carbonates, SiC - as
+the source of the 6 - 9 um excess. Assuming a common origin for the 4 young
+Astar systems' disks, we suggest they are experiencing an early era of
+carbon-rich planetesimal processing.
+",0,1,0,0,0,0
+10506,The flyby anomaly: A multivariate analysis approach,"  The flyby anomaly is the unexpected variation of the asymptotic
+post-encounter velocity of a spacecraft with respect to the pre-encounter
+velocity as it performs a slingshot manoeuvre. This effect has been detected
+in, at least, six flybys of the Earth but it has not appeared in other recent
+flybys. In order to find a pattern in these, apparently contradictory, data
+several phenomenological formulas have been proposed but all have failed to
+predict a new result in agreement with the observations. In this paper we use a
+multivariate dimensional analysis approach to propose a fitting of the data in
+terms of the local parameters at perigee, as it would occur if this anomaly
+comes from an unknown fifth force with latitude dependence. Under this
+assumption, we estimate the range of this force around 300 km.
+",0,1,0,0,0,0
+10507,Opto-Valleytronic Spin Injection in Monolayer MoS2/Few-Layer Graphene Hybrid Spin Valves,"  Two dimensional (2D) materials provide a unique platform for spintronics and
+valleytronics due to the ability to combine vastly different functionalities
+into one vertically-stacked heterostructure, where the strengths of each of the
+constituent materials can compensate for the weaknesses of the others. Graphene
+has been demonstrated to be an exceptional material for spin transport at room
+temperature, however it lacks a coupling of the spin and optical degrees of
+freedom. In contrast, spin/valley polarization can be efficiently generated in
+monolayer transition metal dichalcogenides (TMD) such as MoS2 via absorption of
+circularly-polarized photons, but lateral spin or valley transport has not been
+realized at room temperature. In this letter, we fabricate monolayer
+MoS2/few-layer graphene hybrid spin valves and demonstrate, for the first time,
+the opto-valleytronic spin injection across a TMD/graphene interface. We
+observe that the magnitude and direction of spin polarization is controlled by
+both helicity and photon energy. In addition, Hanle spin precession
+measurements confirm optical spin injection, spin transport, and electrical
+detection up to room temperature. Finally, analysis by a one-dimensional
+drift-diffusion model quantifies the optically injected spin current and the
+spin transport parameters. Our results demonstrate a 2D spintronic/valleytronic
+system that achieves optical spin injection and lateral spin transport at room
+temperature in a single device, which paves the way for multifunctional 2D
+spintronic devices for memory and logic applications.
+",0,1,0,0,0,0
+10508,On singular limit equations for incompressible fluids in moving thin domains,"  We consider the incompressible Euler and Navier-Stokes equations in a
+three-dimensional moving thin domain. Under the assumption that the moving thin
+domain degenerates into a two-dimensional moving closed surface as the width of
+the thin domain goes to zero, we give a heuristic derivation of singular limit
+equations on the degenerate moving surface of the Euler and Navier-Stokes
+equations in the moving thin domain and investigate relations between their
+energy structures. We also compare the limit equations with the Euler and
+Navier-Stokes equations on a stationary manifold, which are described in terms
+of the Levi-Civita connection.
+",0,1,1,0,0,0
+10509,"Flying, Hopping Pit-Bots for Cave and Lava Tube Exploration on the Moon and Mars","  Wheeled ground robots are limited from exploring extreme environments such as
+caves, lava tubes and skylights. Small robots that utilize unconventional
+mobility through hopping, flying and rolling can overcome many roughness
+limitations and thus extend exploration sites of interest on Moon and Mars. In
+this paper we introduce a network of 3 kg, 0.30 m diameter ball robots
+(pit-bots) that can fly, hop and roll using an onboard miniature propulsion
+system. These pit-bots can be deployed from a lander or large rover. Each robot
+is equipped with a smartphone sized computer, stereo camera and laser
+rangefinder to per-form navigation and mapping. The ball robot can carry a
+payload of 1 kg or perform sample return. Our studies show a range of 5 km and
+0.7 hours flight time on the Moon.
+",1,1,0,0,0,0
+10510,Design of a Robotic System for Diagnosis and Rehabilitation of Lower Limbs,"  Currently, lower limb robotic rehabilitation is widely developed, However,
+the devices used so far seem to not have a uniform criteria for their design,
+because, on the contrary, each developed mechanism is often presented as if it
+does not take into account the criteria used in previous designs. On the other
+hand, the diagnosis of lower limb from robotic devices has been little studied.
+This chapter presents a guide for the design of robotic devices in diagnosis of
+lower limbs, taking into account the mobility of the human leg and the
+techniques used by physiotherapists in the execution of exercises and the
+rehabilitation of rehabilitation and diagnosis tests, as well as the
+recommendations made by various authors, among other aspects. The proposed
+guide is illustrated through a case study based on a parallel robot RPU+3UPS
+able to make movements that are applied during the processes of rehabilitation
+and diagnosis. The proposal presents advantages over some existing devices such
+as its load capacity that can support, and also allows you to restrict the
+movement in directions required by the rehabilitation and the diagnosis
+movements.
+",1,1,0,0,0,0
+10511,Multi-parametric sensitivity analysis of the band structure for tetrachiral acoustic metamaterials,"  Tetrachiral materials are characterized by a cellular microstructure made by
+a periodic pattern of stiff rings and flexible ligaments. Their mechanical
+behaviour can be described by a planar lattice of rigid massive bodies and
+elastic massless beams. The periodic cell dynamics is governed by a monoatomic
+structural model, conveniently reduced to the only active degrees-of-freedom.
+The paper presents an explicit parametric description of the band structure
+governing the free propagation of elastic waves. By virtue of multiparametric
+perturbation techniques, sensitivity analyses are performed to achieve
+analytical asymptotic approximation of the dispersion functions. The parametric
+conditions for the existence of full band gaps in the low-frequency range are
+established. Furthermore, the band gap amplitude is analytically assessed in
+the admissible parameter range. In tetrachiral acoustic metamaterials, stop
+bands can be opened by the introduction of intra-ring resonators. Perturbation
+methods can efficiently deal with the consequent enlargement of the mechanical
+parameter space. Indeed high-accuracy parametric approximations are achieved
+for the band structure, enriched by the new optical branches related to the
+resonator frequencies. In particular, target stop bands in the metamaterial
+spectrum are analytically designed through the asymptotic solution of inverse
+spectral problems.
+",0,1,0,0,0,0
+10512,What do the US West Coast Public Libraries Post on Twitter?,"  Twitter has provided a great opportunity for public libraries to disseminate
+information for a variety of purposes. Twitter data have been applied in
+different domains such as health, politics, and history. There are thousands of
+public libraries in the US, but no study has yet investigated the content of
+their social media posts like tweets to find their interests. Moreover,
+traditional content analysis of Twitter content is not an efficient task for
+exploring thousands of tweets. Therefore, there is a need for automatic methods
+to overcome the limitations of manual methods. This paper proposes a
+computational approach to collecting and analyzing using Twitter Application
+Programming Interfaces (API) and investigates more than 138,000 tweets from 48
+US west coast libraries using topic modeling. We found 20 topics and assigned
+them to five categories including public relations, book, event, training, and
+social good. Our results show that the US west coast libraries are more
+interested in using Twitter for public relations and book-related events. This
+research has both practical and theoretical applications for libraries as well
+as other organizations to explore social media actives of their customer and
+themselves.
+",0,0,0,1,0,0
+10513,Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions,"  Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict
+time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is
+generated by a Gaussian distribution whose first-order moment (mean)
+dynamically changes over time but its second-order moment (variance) is fixed.
+However, in many financial applications, the assumption is quite limiting in
+two aspects. First, even when the data follows a Gaussian distribution, its
+variance may change over time. Such variance is also related to important
+temporal economic indicators such as the market volatility. Second, financial
+time-series data often requires learning datasets generated by the generalized
+Gaussian distribution with an additional shape parameter that is important to
+approximate heavy-tailed distributions. Addressing those aspects, we show how
+to extend DyBM that results in significant performance improvement in
+predicting financial time-series data.
+",1,0,0,1,0,0
+10514,Edge switching transformations of quantum graphs,"  Discussed here are the effects of basics graph transformations on the spectra
+of associated quantum graphs. In particular it is shown that under an edge
+switch the spectrum of the transformed Schrödinger operator is interlaced
+with that of the original one. By implication, under edge swap the spectra
+before and after the transformation, denoted by $\{ E_n\}_{n=1}^{\infty}$ and
+$\{\widetilde E_n\}_{n=1}^{\infty}$ correspondingly, are level-2 interlaced, so
+that $E_{n-2}\le \widetilde E_n\le E_{n+2}$. The proofs are guided by
+considerations of the quantum graphs' discrete analogs.
+",0,0,1,0,0,0
+10515,Resilient Monotone Submodular Function Maximization,"  In this paper, we focus on applications in machine learning, optimization,
+and control that call for the resilient selection of a few elements, e.g.
+features, sensors, or leaders, against a number of adversarial
+denial-of-service attacks or failures. In general, such resilient optimization
+problems are hard, and cannot be solved exactly in polynomial time, even though
+they often involve objective functions that are monotone and submodular.
+Notwithstanding, in this paper we provide the first scalable,
+curvature-dependent algorithm for their approximate solution, that is valid for
+any number of attacks or failures, and which, for functions with low curvature,
+guarantees superior approximation performance. Notably, the curvature has been
+known to tighten approximations for several non-resilient maximization
+problems, yet its effect on resilient maximization had hitherto been unknown.
+We complement our theoretical analyses with supporting empirical evaluations.
+",1,0,1,0,0,0
+10516,Antireflection Coated Semiconductor Laser Amplifier,"  This paper presents a laser amplifier based on an antireflection coated laser
+diode. This laser amplifier operates without active temperature stabilisation
+at any wavelength within its gain profile without restrictions on the injection
+current. Using a active feedback from an external detector to the laser current
+the power stabilized to better than $10^{-4}$, even after additional optical
+elements such as an optical fiber and/or a polarization cleaner. This power can
+also be modulated and tuned arbitrarily. In the absence of the seeding light,
+the laser amplifier does not lase, thus resulting in an extremely simple setup,
+which requires neither an external Fabry Perot cavity for monitoring the mode
+purity nor a temperature stabilization.
+",0,1,0,0,0,0
+10517,On the structure of radial solutions for some quasilinear elliptic equations,"  In this paper we study entire radial solutions for the quasilinear
+$p$-Laplace equation $\Delta_p u + k(x) f(u) = 0$ where $k$ is a radial
+positive weight and the nonlinearity behaves e.g. as
+$f(u)=u|u|^{q-2}-u|u|^{Q-2}$ with $q<Q$. In particular we focus our attention
+on solutions (positive and sign changing) which are infinitesimal at infinity,
+thus providing an extension of a previous result by Tang (2001).
+",0,0,1,0,0,0
+10518,Bi-class classification of humpback whale sound units against complex background noise with Deep Convolution Neural Network,"  Automatically detecting sound units of humpback whales in complex
+time-varying background noises is a current challenge for scientists. In this
+paper, we explore the applicability of Convolution Neural Network (CNN) method
+for this task. In the evaluation stage, we present 6 bi-class classification
+experimentations of whale sound detection against different background noise
+types (e.g., rain, wind). In comparison to classical FFT-based representation
+like spectrograms, we showed that the use of image-based pretrained CNN
+features brought higher performance to classify whale sounds and background
+noise.
+",1,0,0,1,0,0
+10519,A model for a Lindenmayer reconstruction algorithm,"  Given an input string s and a specific Lindenmayer system (the so-called
+Fibonacci grammar), we define an automaton which is capable of (i) determining
+whether s belongs to the set of strings that the Fibonacci grammar can generate
+(in other words, if s corresponds to a generation of the grammar) and, if so,
+(ii) reconstructing the previous generation.
+",1,0,0,0,0,0
+10520,Spacetime symmetries and conformal data in the continuous multi-scale entanglement renormalization ansatz,"  The generalization of the multi-scale entanglement renormalization ansatz
+(MERA) to continuous systems, or cMERA [Haegeman et al., Phys. Rev. Lett, 110,
+100402 (2013)], is expected to become a powerful variational ansatz for the
+ground state of strongly interacting quantum field theories. In this paper we
+investigate, in the simpler context of Gaussian cMERA for free theories, the
+extent to which the cMERA state $|\Psi^\Lambda\rangle$ with finite UV cut-off
+$\Lambda$ can capture the spacetime symmetries of the ground state
+$|\Psi\rangle$. For a free boson conformal field theory (CFT) in 1+1 dimensions
+as a concrete example, we build a quasi-local unitary transformation $V$ that
+maps $|\Psi\rangle$ into $|\Psi^\Lambda\rangle$ and show two main results. (i)
+Any spacetime symmetry of the ground state $|\Psi\rangle$ is also mapped by $V$
+into a spacetime symmetry of the cMERA $|\Psi^\Lambda\rangle$. However, while
+in the CFT the stress-energy tensor $T_{\mu\nu}(x)$ (in terms of which all the
+spacetime symmetry generators are expressed) is local, the corresponding cMERA
+stress-energy tensor $T_{\mu\nu}^{\Lambda}(x) = V T_{\mu\nu}(x) V^{\dagger}$ is
+quasi-local. (ii) From the cMERA, we can extract quasi-local scaling operators
+$O^{\Lambda}_{\alpha}(x)$ characterized by the exact same scaling dimensions
+$\Delta_{\alpha}$, conformal spins $s_{\alpha}$, operator product expansion
+coefficients $C_{\alpha\beta\gamma}$, and central charge $c$ as the original
+CFT. Finally, we argue that these results should also apply to interacting
+theories.
+",0,1,0,0,0,0
+10521,Augmenting Input Method Language Model with user Location Type Information,"  Geo-tags from micro-blog posts have been shown to be useful in many data
+mining applications. This work seeks to find out if the location type derived
+from these geo-tags can benefit input methods, which attempts to predict the
+next word a user will input during typing. If a correlation between different
+location types and a change in word distribution can be found, the location
+type information can be used to make the input method more accurate. This work
+queried micro-blog posts from Twitter API and location type of these posts from
+Google Place API, forming a dataset of around 500k samples. A statistical study
+on the word distribution found weak support for the assumption. An LSTM based
+prediction experiment found a 2% edge in the accuracy from language models
+leveraging location type information when compared to a baseline without that
+information.
+",1,0,0,0,0,0
+10522,"Cauchy-Lipschitz theory for fractional multi-order dynamics -- State-transition matrices, Duhamel formulas and duality theorems","  The aim of the present paper is to contribute to the development of the study
+of Cauchy problems involving Riemann-Liouville and Caputo fractional
+derivatives. Firstly existence-uniqueness results for solutions of non-linear
+Cauchy problems with vector fractional multi-order are addressed. A qualitative
+result about the behavior of local but non-global solutions is also provided.
+Finally the major aim of this paper is to introduce notions of fractional
+state-transition matrices and to derive fractional versions of the classical
+Duhamel formula. We also prove duality theorems relying left state-transition
+matrices with right state-transition matrices.
+",0,0,1,0,0,0
+10523,Tanaka formula for strictly stable processes,"  For symmetric Lévy processes, if the local times exist, the Tanaka formula
+has already constructed via the techniques in the potential theory by Salminen
+and Yor (2007). In this paper, we study the Tanaka formula for arbitrary
+strictly stable processes with index $\alpha \in (1,2)$ including spectrally
+positive and negative cases in a framework of Itô's stochastic calculus. Our
+approach to the existence of local times for such processes is different from
+Bertoin (1996).
+",0,0,1,0,0,0
+10524,The Classical Complexity of Boson Sampling,"  We study the classical complexity of the exact Boson Sampling problem where
+the objective is to produce provably correct random samples from a particular
+quantum mechanical distribution. The computational framework was proposed by
+Aaronson and Arkhipov in 2011 as an attainable demonstration of `quantum
+supremacy', that is a practical quantum computing experiment able to produce
+output at a speed beyond the reach of classical (that is non-quantum) computer
+hardware. Since its introduction Boson Sampling has been the subject of intense
+international research in the world of quantum computing. On the face of it,
+the problem is challenging for classical computation. Aaronson and Arkhipov
+show that exact Boson Sampling is not efficiently solvable by a classical
+computer unless $P^{\#P} = BPP^{NP}$ and the polynomial hierarchy collapses to
+the third level.
+The fastest known exact classical algorithm for the standard Boson Sampling
+problem takes $O({m + n -1 \choose n} n 2^n )$ time to produce samples for a
+system with input size $n$ and $m$ output modes, making it infeasible for
+anything but the smallest values of $n$ and $m$. We give an algorithm that is
+much faster, running in $O(n 2^n + \operatorname{poly}(m,n))$ time and $O(m)$
+additional space. The algorithm is simple to implement and has low constant
+factor overheads. As a consequence our classical algorithm is able to solve the
+exact Boson Sampling problem for system sizes far beyond current photonic
+quantum computing experimentation, thereby significantly reducing the
+likelihood of achieving near-term quantum supremacy in the context of Boson
+Sampling.
+",1,0,0,1,0,0
+10525,Impurity band conduction in group-IV ferromagnetic semiconductor Ge1-xFex with nanoscale fluctuations in Fe concentration,"  We study the carrier transport and magnetic properties of group-IV-based
+ferromagnetic semiconductor Ge1-xFex thin films (Fe concentration x = 2.3 - 14
+%) with and without boron (B) doping, by measuring their transport
+characteristics; the temperature dependence of resistivity, hole concentration,
+mobility, and the relation between the anomalous Hall conductivity versus
+conductivity. At relatively low x (= 2.3 %), the transport in the undoped
+Ge1-xFex film is dominated by hole hopping between Fe-rich hopping sites in the
+Fe impurity band, whereas that in the B-doped Ge1-xFex film is dominated by the
+holes in the valence band in the degenerated Fe-poor regions. As x increases (x
+= 2.3 - 14 %), the transport in the both undoped and B-doped Ge1-xFex films is
+dominated by hole hopping between the Fe-rich hopping sites of the impurity
+band. The magnetic properties of the Ge1-xFex films are studied by various
+methods including magnetic circular dichroism, magnetization and anomalous Hall
+resistance, and are not influenced by B-doping. We show band profile models of
+both undoped and B-doped Ge1-xFex films, which can explain the transport and
+the magnetic properties of the Ge1-xFex films.
+",0,1,0,0,0,0
+10526,On the metastable Mabillard-Wagner conjecture,"  The purpose of this note is to attract attention to the following conjecture
+(metastable $r$-fold Whitney trick) by clarifying its status as not having a
+complete proof, in the sense described in the paper.
+Assume that $D=D_1\sqcup\ldots\sqcup D_r$ is disjoint union of $r$ disks of
+dimension $s$, $f:D\to B^d$ a proper PL map such that $f\partial
+D_1\cap\ldots\cap f\partial D_r=\emptyset$, $rd\ge (r+1)s+3$ and $d\ge s+3$. If
+the map $$f^r:\partial(D_1\times\ldots\times D_r)\to
+(B^d)^r-\{(x,x,\ldots,x)\in(B^d)^r\ |\ x\in B^d\}$$ extends to
+$D_1\times\ldots\times D_r$, then there is a PL map $\overline f:D\to B^d$ such
+that $$\overline f=f \quad\text{on}\quad D_r\cup\partial D\quad\text{and}\quad
+\overline fD_1\cap\ldots\cap \overline fD_r=\emptyset.$$
+",1,0,1,0,0,0
+10527,Reference Publication Year Spectroscopy (RPYS) of Eugene Garfield's publications,"  Which studies, theories, and ideas have influenced Eugene Garfield's
+scientific work? Recently, the method reference publication year spectroscopy
+(RPYS) has been introduced, which can be used to answer this and related
+questions. Since then, several studies have been published dealing with the
+historical roots of research fields and scientists. The program CRExplorer
+(this http URL) was specifically developed for RPYS. In this study,
+we use this program to investigate the historical roots of Eugene Garfield's
+oeuvre.
+",1,0,0,0,0,0
+10528,Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World,"  In this paper, we focus on online representation learning in non-stationary
+environments which may require continuous adaptation of model architecture. We
+propose a novel online dictionary-learning (sparse-coding) framework which
+incorporates the addition and deletion of hidden units (dictionary elements),
+and is inspired by the adult neurogenesis phenomenon in the dentate gyrus of
+the hippocampus, known to be associated with improved cognitive function and
+adaptation to new environments. In the online learning setting, where new input
+instances arrive sequentially in batches, the neuronal-birth is implemented by
+adding new units with random initial weights (random dictionary elements); the
+number of new units is determined by the current performance (representation
+error) of the dictionary, higher error causing an increase in the birth rate.
+Neuronal-death is implemented by imposing l1/l2-regularization (group sparsity)
+on the dictionary within the block-coordinate descent optimization at each
+iteration of our online alternating minimization scheme, which iterates between
+the code and dictionary updates. Finally, hidden unit connectivity adaptation
+is facilitated by introducing sparsity in dictionary elements. Our empirical
+evaluation on several real-life datasets (images and language) as well as on
+synthetic data demonstrates that the proposed approach can considerably
+outperform the state-of-art fixed-size (nonadaptive) online sparse coding of
+Mairal et al. (2009) in the presence of nonstationary data. Moreover, we
+identify certain properties of the data (e.g., sparse inputs with nearly
+non-overlapping supports) and of the model (e.g., dictionary sparsity)
+associated with such improvements.
+",1,0,0,1,0,0
+10529,Transiently enhanced interlayer tunneling in optically driven high $T_c$ superconductors,"  Recent pump-probe experiments reported an enhancement of superconducting
+transport along the $c$-axis of underdoped YBa$_2$Cu$_3$O$_{6+\delta}$ (YBCO),
+induced by a mid-infrared optical pump pulse tuned to a specific lattice
+vibration. To understand this transient non-equilibrium state, we develop a
+pump-probe formalism for a stack of Josephson junctions, and we consider the
+tunneling strengths in presence of modulation with an ultrashort optical pulse.
+We demonstrate that a transient enhancement of the Josephson coupling can be
+obtained for pulsed excitation and that this can be even larger than in a
+continuously driven steady-state. Especially interesting is the conclusion that
+the effect is largest when the material is parametrically driven at a frequency
+immediately above the plasma frequency, in agreement with what is found
+experimentally. For bilayer Josephson junctions, an enhancement similar to that
+experimentally is predicted below the critical temperature $T_c$. This model
+reproduces the essential features of the enhancement measured below $T_c$. To
+reproduce the experimental results above $T_c$, we will explore extensions of
+this model, such as in-plane and amplitude fluctuations, elsewhere.
+",0,1,0,0,0,0
+10530,An Integer Programming Formulation of the Key Management Problem in Wireless Sensor Networks,"  With the advent of modern communications systems, much attention has been put
+on developing methods for securely transferring information between
+constituents of wireless sensor networks. To this effect, we introduce a
+mathematical programming formulation for the key management problem, which
+broadly serves as a mechanism for encrypting communications. In particular, an
+integer programming model of the q-Composite scheme is proposed and utilized to
+distribute keys among nodes of a network whose topology is known. Numerical
+experiments demonstrating the effectiveness of the proposed model are conducted
+using using a well-known optimization solver package. An illustrative example
+depicting an optimal encryption for a small-scale network is also presented.
+",1,0,0,0,0,0
+10531,Laplacian-Steered Neural Style Transfer,"  Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to
+synthesize a new image that retains the high-level structure of a content
+image, rendered in the low-level texture of a style image. This is achieved by
+constraining the new image to have high-level CNN features similar to the
+content image, and lower-level CNN features similar to the style image. However
+in the traditional optimization objective, low-level features of the content
+image are absent, and the low-level features of the style image dominate the
+low-level detail structures of the new image. Hence in the synthesized image,
+many details of the content image are lost, and a lot of inconsistent and
+unpleasing artifacts appear. As a remedy, we propose to steer image synthesis
+with a novel loss function: the Laplacian loss. The Laplacian matrix
+(""Laplacian"" in short), produced by a Laplacian operator, is widely used in
+computer vision to detect edges and contours. The Laplacian loss measures the
+difference of the Laplacians, and correspondingly the difference of the detail
+structures, between the content image and a new image. It is flexible and
+compatible with the traditional style transfer constraints. By incorporating
+the Laplacian loss, we obtain a new optimization objective for neural style
+transfer named Lapstyle. Minimizing this objective will produce a stylized
+image that better preserves the detail structures of the content image and
+eliminates the artifacts. Experiments show that Lapstyle produces more
+appealing stylized images with less artifacts, without compromising their
+""stylishness"".
+",1,0,0,0,0,0
+10532,A quantized physical framework for understanding the working mechanism of ion channels,"  A quantized physical framework, called the five-anchor model, is developed
+for a general understanding of the working mechanism of ion channels. According
+to the hypotheses of this model, the following two basic physical principles
+are assigned to each anchor: the polarity change induced by an electron
+transition and the mutual repulsion and attraction induced by an electrostatic
+force. Consequently, many unique phenomena, such as fast and slow inactivation,
+the stochastic gating pattern and constant conductance of a single ion channel,
+the difference between electrical and optical stimulation (optogenetics), nerve
+conduction block and the generation of an action potential, become intrinsic
+features of this physical model. Moreover, this model also provides a
+foundation for the probability equation used to calculate the results of
+electrical stimulation in our previous C-P theory.
+",0,0,0,0,1,0
+10533,Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system,"  Reinforcement learning has emerged as a promising methodology for training
+robot controllers. However, most results have been limited to simulation due to
+the need for a large number of samples and the lack of automated-yet-safe data
+collection methods. Model-based reinforcement learning methods provide an
+avenue to circumvent these challenges, but the traditional concern has been the
+mismatch between the simulator and the real world. Here, we show that control
+policies learned in simulation can successfully transfer to a physical system,
+composed of three Phantom robots pushing an object to various desired target
+positions. We use a modified form of the natural policy gradient algorithm for
+learning, applied to a carefully identified simulation model. The resulting
+policies, trained entirely in simulation, work well on the physical system
+without additional training. In addition, we show that training with an
+ensemble of models makes the learned policies more robust to modeling errors,
+thus compensating for difficulties in system identification.
+",1,0,0,0,0,0
+10534,A contour for the entanglement entropies in harmonic lattices,"  We construct a contour function for the entanglement entropies in generic
+harmonic lattices. In one spatial dimension, numerical analysis are performed
+by considering harmonic chains with either periodic or Dirichlet boundary
+conditions. In the massless regime and for some configurations where the
+subsystem is a single interval, the numerical results for the contour function
+are compared to the inverse of the local weight function which multiplies the
+energy-momentum tensor in the corresponding entanglement hamiltonian, found
+through conformal field theory methods, and a good agreement is observed. A
+numerical analysis of the contour function for the entanglement entropy is
+performed also in a massless harmonic chain for a subsystem made by two
+disjoint intervals.
+",0,1,0,0,0,0
+10535,A Constrained Coupled Matrix-Tensor Factorization for Learning Time-evolving and Emerging Topics,"  Topic discovery has witnessed a significant growth as a field of data mining
+at large. In particular, time-evolving topic discovery, where the evolution of
+a topic is taken into account has been instrumental in understanding the
+historical context of an emerging topic in a dynamic corpus. Traditionally,
+time-evolving topic discovery has focused on this notion of time. However,
+especially in settings where content is contributed by a community or a crowd,
+an orthogonal notion of time is the one that pertains to the level of expertise
+of the content creator: the more experienced the creator, the more advanced the
+topic. In this paper, we propose a novel time-evolving topic discovery method
+which, in addition to the extracted topics, is able to identify the evolution
+of that topic over time, as well as the level of difficulty of that topic, as
+it is inferred by the level of expertise of its main contributors. Our method
+is based on a novel formulation of Constrained Coupled Matrix-Tensor
+Factorization, which adopts constraints well-motivated for, and, as we
+demonstrate, are essential for high-quality topic discovery. We qualitatively
+evaluate our approach using real data from the Physics and also Programming
+Stack Exchange forum, and we were able to identify topics of varying levels of
+difficulty which can be linked to external events, such as the announcement of
+gravitational waves by the LIGO lab in Physics forum. We provide a quantitative
+evaluation of our method by conducting a user study where experts were asked to
+judge the coherence and quality of the extracted topics. Finally, our proposed
+method has implications for automatic curriculum design using the extracted
+topics, where the notion of the level of difficulty is necessary for the proper
+modeling of prerequisites and advanced concepts.
+",0,0,0,1,0,0
+10536,"Generalized Commutative Association Schemes, Hypergroups, and Positive Product Formulas","  It is well known that finite commutative association schemes in the sense of
+the monograph of Bannai and Ito lead to finite commutative hypergroups with
+positive dual convolutions and even dual hypergroup structures. In this paper
+we present several discrete generalizations of association schemes which also
+lead to associated hypergroups. We show that discrete commutative hypergroups
+associated with such generalized association schemes admit dual positive
+convolutions at least on the support of the Plancherel measure. We hope that
+examples for this theory will lead to the existence of new dual positive
+product formulas in near future.
+",0,0,1,0,0,0
+10537,BD-19 5044L: discovery of a short-period SB2 system with a magnetic Bp primary in the open cluster IC 4725,"  Until recently almost nothing was known about the evolution of magnetic
+fields found in upper main sequence Ap/Bp stars during their long main sequence
+lifetime. We are thus studying magnetic Ap/Bp stars in open clusters in order
+to obtain observational evidence of how the properties of Ap/Bp magnetic stars,
+such as field strength and structure, evolve with age during the main sequence.
+One important aspect of this study is to search for the very rare examples of
+hot magnetic stars in short-period binary systems among magnetic cluster
+members. In this paper we characterize the object BD-19~5044L, which is both a
+member of the open cluster IC 4725 = M~25, and a short-period SB2 system
+containing a magnetic primary star. We have obtained a series of intensity and
+circular polarisation spectra distributed through the orbital and rotation
+cycles of BD-19 5044L with the ESPaDOnS spectropolarimeter at CFHT. We find
+that the orbit of BD-19 5044L AB is quite eccentric (e = 0.477), with a period
+of 17.63 d. The primary is a magnetic Bp star with a variable longitudinal
+magnetic field, a polar field strength of ~1400 G and a low obliquity, while
+the secondary is probably a hot Am star and does not appear to be magnetic. The
+rotation period of the primary (5.04 d) is not synchronised with the orbit, but
+the rotation angular velocity is close to being synchronised with the orbital
+angular velocity of the secondary at periastron, perhaps as a result of tidal
+interactions. The periastron separation is small enough (about 12 times the
+radius of the primary star) that BD-19 5044L may be one of the very rare known
+cases of a tidally interacting SB2 binary system containing a magnetic Ap/Bp
+star.
+",0,1,0,0,0,0
+10538,A Submillimeter Perspective on the GOODS Fields (SUPER GOODS) - II. The High Radio Power Population in the GOODS-N,"  We use ultradeep 20 cm data from the Karl G. Jansky Very Large Array and 850
+micron data from SCUBA-2 and the Submillimeter Array of an 124 arcmin^2 region
+of the Chandra Deep Field-north to analyze the high radio power (P_20cm>10^31
+erg s^-1 Hz^-1) population. We find that 20 (42+/-9%) of the spectroscopically
+identified z>0.8 sources have consistent star formation rates (SFRs) inferred
+from both submillimeter and radio observations, while the remaining sources
+have lower (mostly undetected) submillimeter fluxes, suggesting that active
+galactic nucleus (AGN) activity dominates the radio power in these sources. We
+develop a classification scheme based on the ratio of submillimeter flux to
+radio power versus radio power and find that it agrees with AGN and
+star-forming galaxy classifications from Very Long Baseline Interferometry. Our
+results provide support for an extremely rapid drop in the number of high SFR
+galaxies above about a thousand solar masses per year (Kroupa initial mass
+function) and for the locally determined relation between X-ray luminosity and
+radio power for star-forming galaxies applying at high redshifts and high radio
+powers. We measure far-infrared (FIR) luminosities and find that some AGNs lie
+on the FIR-radio correlation, while others scatter below. The AGNs that lie on
+the correlation appear to do so based on their emission from the AGN torus. We
+measure a median radio size of 1.0+/-0.3 arcsecond for the star-forming
+galaxies. The radio sizes of the star-forming galaxies are generally larger
+than those of the AGNs.
+",0,1,0,0,0,0
+10539,Discrete Spacetime Quantum Field Theory,"  This paper begins with a theoretical explanation of why spacetime is
+discrete. The derivation shows that there exists an elementary length which is
+essentially Planck's length. We then show how the existence of this length
+affects time dilation in special relativity. We next consider the symmetry
+group for discrete spacetime. This symmetry group gives a discrete version of
+the usual Lorentz group. However, it is much simpler and is actually a discrete
+version of the rotation group. From the form of the symmetry group we deduce a
+possible explanation for the structure of elementary particle classes.
+Energy-momentum space is introduced and mass operators are defined. Discrete
+versions of the Klein-Gordon and Dirac equations are derived. The final section
+concerns discrete quantum field theory. Interaction Hamiltonians and scattering
+operators are considered. In particular, we study the scalar spin~0 and spin~1
+bosons as well as the spin~$1/2$ fermion cases
+",0,1,0,0,0,0
+10540,Neural network-based arithmetic coding of intra prediction modes in HEVC,"  In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is
+adopted as the entropy coding method. CABAC relies on manually designed
+binarization processes as well as handcrafted context models, which may
+restrict the compression efficiency. In this paper, we propose an arithmetic
+coding strategy by training neural networks, and make preliminary studies on
+coding of the intra prediction modes in HEVC. Instead of binarization, we
+propose to directly estimate the probability distribution of the 35 intra
+prediction modes with the adoption of a multi-level arithmetic codec. Instead
+of handcrafted context models, we utilize convolutional neural network (CNN) to
+perform the probability estimation. Simulation results show that our proposed
+arithmetic coding leads to as high as 9.9% bits saving compared with CABAC.
+",1,0,0,0,0,0
+10541,Securing Information-Centric Networking without negating Middleboxes,"  Information-Centric Networking is a promising networking paradigm that
+overcomes many of the limitations of current networking architectures. Various
+research efforts investigate solutions for securing ICN. Nevertheless, most of
+these solutions relax security requirements in favor of network performance. In
+particular, they weaken end-user privacy and the architecture's tolerance to
+security breaches in order to support middleboxes that offer services such as
+caching and content replication. In this paper, we adapt TLS, a widely used
+security standard, to an ICN context. We design solutions that allow session
+reuse and migration among multiple stakeholders and we propose an extension
+that allows authorized middleboxes to lawfully and transparently intercept
+secured communications.
+",1,0,0,0,0,0
+10542,Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo,"  Deep Gaussian Processes (DGPs) are hierarchical generalizations of Gaussian
+Processes that combine well calibrated uncertainty estimates with the high
+flexibility of multilayer models. One of the biggest challenges with these
+models is that exact inference is intractable. The current state-of-the-art
+inference method, Variational Inference (VI), employs a Gaussian approximation
+to the posterior distribution. This can be a potentially poor unimodal
+approximation of the generally multimodal posterior. In this work, we provide
+evidence for the non-Gaussian nature of the posterior and we apply the
+Stochastic Gradient Hamiltonian Monte Carlo method to generate samples. To
+efficiently optimize the hyperparameters, we introduce the Moving Window MCEM
+algorithm. This results in significantly better predictions at a lower
+computational cost than its VI counterpart. Thus our method establishes a new
+state-of-the-art for inference in DGPs.
+",0,0,0,1,0,0
+10543,Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems,"  This paper develops meshless methods for probabilistically describing
+discretisation error in the numerical solution of partial differential
+equations. This construction enables the solution of Bayesian inverse problems
+while accounting for the impact of the discretisation of the forward problem.
+In particular, this drives statistical inferences to be more conservative in
+the presence of significant solver error. Theoretical results are presented
+describing rates of convergence for the posteriors in both the forward and
+inverse problems. This method is tested on a challenging inverse problem with a
+nonlinear forward model.
+",1,0,1,1,0,0
+10544,"Some discussions on the Read Paper ""Beyond subjective and objective in statistics"" by A. Gelman and C. Hennig","  This note is a collection of several discussions of the paper ""Beyond
+subjective and objective in statistics"", read by A. Gelman and C. Hennig to the
+Royal Statistical Society on April 12, 2017, and to appear in the Journal of
+the Royal Statistical Society, Series A.
+",0,0,0,1,0,0
+10545,FASER: ForwArd Search ExpeRiment at the LHC,"  New physics has traditionally been expected in the high-$p_T$ region at
+high-energy collider experiments. If new particles are light and
+weakly-coupled, however, this focus may be completely misguided: light
+particles are typically highly concentrated within a few mrad of the beam line,
+allowing sensitive searches with small detectors, and even extremely
+weakly-coupled particles may be produced in large numbers there. We propose a
+new experiment, ForwArd Search ExpeRiment, or FASER, which would be placed
+downstream of the ATLAS or CMS interaction point (IP) in the very forward
+region and operated concurrently there. Two representative on-axis locations
+are studied: a far location, $400~\text{m}$ from the IP and just off the beam
+tunnel, and a near location, just $150~\text{m}$ from the IP and right behind
+the TAN neutral particle absorber. For each location, we examine leading
+neutrino- and beam-induced backgrounds. As a concrete example of light,
+weakly-coupled particles, we consider dark photons produced through light meson
+decay and proton bremsstrahlung. We find that even a relatively small and
+inexpensive cylindrical detector, with a radius of $\sim 10~\text{cm}$ and
+length of $5-10~\text{m}$, depending on the location, can discover dark photons
+in a large and unprobed region of parameter space with dark photon mass $m_{A'}
+\sim 10~\text{MeV} - 1~\text{GeV}$ and kinetic mixing parameter $\epsilon \sim
+10^{-7} - 10^{-3}$. FASER will clearly also be sensitive to many other forms of
+new physics. We conclude with a discussion of topics for further study that
+will be essential for understanding FASER's feasibility, optimizing its design,
+and realizing its discovery potential.
+",0,1,0,0,0,0
+10546,Size Agnostic Change Point Detection Framework for Evolving Networks,"  Changes in the structure of observed social and complex networks' structure
+can indicate a significant underlying change in an organization, or reflect the
+response of the network to an external event. Automatic detection of change
+points in evolving networks is rudimentary to the research and the
+understanding of the effect of such events on networks. Here we present an
+easy-to-implement and fast framework for change point detection in temporal
+evolving networks. Unlike previous approaches, our method is size agnostic, and
+does not require either prior knowledge about the network's size and structure,
+nor does it require obtaining historical information or nodal identities over
+time. We use both synthetic data derived from dynamic models and two real
+datasets: Enron email exchange and Ask-Ubuntu forum. Our framework succeeds
+with both precision and recall and outperforms previous solutions
+",1,0,0,0,0,0
+10547,"On distributions determined by their upward, space-time Wiener-Hopf factor","  According to the Wiener-Hopf factorization, the characteristic function
+$\varphi$ of any probability distribution $\mu$ on $\mathbb{R}$ can be
+decomposed in a unique way as
+\[1-s\varphi(t)=[1-\chi_-(s,it)][1-\chi_+(s,it)]\,,\;\;\;|s|\le1,\,t\in\mathbb{R}\,,\]
+where $\chi_-(e^{iu},it)$ and $\chi_+(e^{iu},it)$ are the characteristic
+functions of possibly defective distributions in
+$\mathbb{Z}_+\times(-\infty,0)$ and $\mathbb{Z}_+\times[0,\infty)$,
+respectively.
+We prove that $\mu$ can be characterized by the sole data of the upward
+factor $\chi_+(s,it)$, $s\in[0,1)$, $t\in\mathbb{R}$ in many cases including
+the cases where:
+1) $\mu$ has some exponential moments;
+2) the function $t\mapsto\mu(t,\infty)$ is completely monotone on
+$(0,\infty)$;
+3) the density of $\mu$ on $[0,\infty)$ admits an analytic continuation on
+$\mathbb{R}$.
+We conjecture that any probability distribution is actually characterized by
+its upward factor. This conjecture is equivalent to the following: {\it Any
+probability measure $\mu$ on $\mathbb{R}$ whose support is not included in
+$(-\infty,0)$ is determined by its convolution powers $\mu^{*n}$, $n\ge1$
+restricted to $[0,\infty)$}. We show that in many instances, the sole knowledge
+of $\mu$ and $\mu^{*2}$ restricted to $[0,\infty)$ is actually sufficient to
+determine $\mu$. Then we investigate the analogous problem in the framework of
+infinitely divisible distributions.
+",0,0,1,0,0,0
+10548,"Spontaneous Octahedral Tilting in the Cubic Inorganic Caesium Halide Perovskites CsSnX$_3$ and CsPbX$_3$ (X = F, Cl, Br, I)","  The local crystal structures of many perovskite-structured materials deviate
+from the average space group symmetry. We demonstrate, from lattice-dynamics
+calculations based on quantum chemical force constants, that all the
+caesium-lead and caesium-tin halide perovskites exhibit vibrational
+instabilities associated with octahedral titling in their high-temperature
+cubic phase. Anharmonic double-well potentials are found for zone-boundary
+phonon modes in all compounds with barriers ranging from 108 to 512 meV. The
+well depth is correlated with the tolerance factor and the chemistry of the
+composition, but is not proportional to the imaginary harmonic phonon
+frequency. We provide quantitative insights into the thermodynamic driving
+forces and distinguish between dynamic and static disorder based on the
+potential-energy landscape. A positive band gap deformation (spectral
+blueshift) accompanies the structural distortion, with implications for
+understanding the performance of these materials in applications areas
+including solar cells and light-emitting diodes.
+",0,1,0,0,0,0
+10549,Kinetics of the Phospholipid Multilayer Formation at the Surface of the Silica Sol,"  The ordering of a multilayer consisting of DSPC bilayers on a silica sol
+substrate is studied within the model-independent approach to the
+reconstruction of profiles of the electron density from X-ray reflectometry
+data. It is found that the electroporation of bilayers in the field of anion
+silica nanoparticles significantly accelerates the process of their saturation
+with Na+ and H2O, which explains both a relatively small time of formation of
+the structure of the multilayer of 10^5 - 7x10^5 s and ~13 % excess of the
+electron density in it.
+",0,1,0,0,0,0
+10550,Asymmetric Actor Critic for Image-Based Robot Learning,"  Deep reinforcement learning (RL) has proven a powerful technique in many
+sequential decision making domains. However, Robotics poses many challenges for
+RL, most notably training on a physical system can be expensive and dangerous,
+which has sparked significant interest in learning control policies using a
+physics simulator. While several recent works have shown promising results in
+transferring policies trained in simulation to the real world, they often do
+not fully utilize the advantage of working with a simulator. In this work, we
+exploit the full state observability in the simulator to train better policies
+which take as input only partial observations (RGBD images). We do this by
+employing an actor-critic training algorithm in which the critic is trained on
+full states while the actor (or policy) gets rendered images as input. We show
+experimentally on a range of simulated tasks that using these asymmetric inputs
+significantly improves performance. Finally, we combine this method with domain
+randomization and show real robot experiments for several tasks like picking,
+pushing, and moving a block. We achieve this simulation to real world transfer
+without training on any real world data.
+",1,0,0,0,0,0
+10551,Effects of particles on spinodal decomposition: A Phase field study,"  In this thesis, we study the interplay of phase separation and wetting in
+multicomponent systems. For this purpose, we have examined the phase separation
+pattern of a binary mixture (AB) in presence of stationary spherical particles
+(C) which prefers one of the components of the binary (say, A). Binary AB is
+composed of critical composition(50:50) and off-critical compositions(60:40,
+40:60). Particle sizes of 8 units and 16 units are used in the simulations. Two
+types of particle loading are used, 5\% and 10\%. We have employed a ternary
+form of Cahn-Hilliard equation to incorporate immobile fillers in our system.
+To elucidate the effect of wetting on phase separation we have designed three
+sets of $\chi_{ij}$ and $\kappa_{ij}$ to include the effects of neutral
+preference, weak preference and strong preference of the particle for one of
+the binary components. If the particles are preferentially wetted by one of the
+components then early stage microstructures show transient concentric alternate
+layers of preferred and non-preferred phases around the particles. When
+particles are neutral to binary components then such a ring pattern does not
+form. At late times, neutral preference between particles and binary components
+yields a continuous morphology whereas preferential wetting produces isolated
+domains of non-preferred phases dispersed in a continuous matrix of preferred
+phase. For off-critical compositions, if minor component wets the particle then
+a bicontinuous morphology results whereas if major component wets the network a
+droplet morphology is seen. When majority component wets the particle, a
+possibility of double phase separation is reported. In such alloys phase
+separation starts near the particle surface and propagates to the bulk at
+intermediate to late times forming spherical or nearly spherical droplets of
+the minor component.
+",0,1,0,0,0,0
+10552,Tail asymptotics of signal-to-interference ratio distribution in spatial cellular network models,"  We consider a spatial stochastic model of wireless cellular networks, where
+the base stations (BSs) are deployed according to a simple and stationary point
+process on $\mathbb{R}^d$, $d\ge2$. In this model, we investigate tail
+asymptotics of the distribution of signal-to-interference ratio (SIR), which is
+a key quantity in wireless communications. In the case where the path-loss
+function representing signal attenuation is unbounded at the origin, we derive
+the exact tail asymptotics of the SIR distribution under an appropriate
+sufficient condition. While we show that widely-used models based on a Poisson
+point process and on a determinantal point process meet the sufficient
+condition, we also give a counterexample violating it. In the case of bounded
+path-loss functions, we derive a logarithmically asymptotic upper bound on the
+SIR tail distribution for the Poisson-based and $\alpha$-Ginibre-based models.
+A logarithmically asymptotic lower bound with the same order as the upper bound
+is also obtained for the Poisson-based model.
+",1,0,1,0,0,0
+10553,An Optimal Combination of Proportional and Stop-Loss Reinsurance Contracts From Insurer's and Reinsurer's Viewpoints,"  A reinsurance contract should address the conflicting interests of the
+insurer and reinsurer. Most of existing optimal reinsurance contracts only
+considers the interests of one party. This article combines the proportional
+and stop-loss reinsurance contracts and introduces a new reinsurance contract
+called proportional-stop-loss reinsurance. Using the balanced loss function,
+unknown parameters of the proportional-stop-loss reinsurance have been
+estimated such that the expected surplus for both the insurer and reinsurer are
+maximized. Several characteristics for the new reinsurance are provided.
+",0,0,0,1,0,0
+10554,Computational complexity and 3-manifolds and zombies,"  We show the problem of counting homomorphisms from the fundamental group of a
+homology $3$-sphere $M$ to a finite, non-abelian simple group $G$ is
+#P-complete, in the case that $G$ is fixed and $M$ is the computational input.
+Similarly, deciding if there is a non-trivial homomorphism is NP-complete. In
+both reductions, we can guarantee that every non-trivial homomorphism is a
+surjection. As a corollary, for any fixed integer $m \ge 5$, it is NP-complete
+to decide whether $M$ admits a connected $m$-sheeted covering.
+Our construction is inspired by universality results in topological quantum
+computation. Given a classical reversible circuit $C$, we construct $M$ so that
+evaluations of $C$ with certain initialization and finalization conditions
+correspond to homomorphisms $\pi_1(M) \to G$. An intermediate state of $C$
+likewise corresponds to a homomorphism $\pi_1(\Sigma_g) \to G$, where
+$\Sigma_g$ is a pointed Heegaard surface of $M$ of genus $g$. We analyze the
+action on these homomorphisms by the pointed mapping class group
+$\text{MCG}_*(\Sigma_g)$ and its Torelli subgroup $\text{Tor}_*(\Sigma_g)$. By
+results of Dunfield-Thurston, the action of $\text{MCG}_*(\Sigma_g)$ is as
+large as possible when $g$ is sufficiently large; we can pass to the Torelli
+group using the congruence subgroup property of $\text{Sp}(2g,\mathbb{Z})$. Our
+results can be interpreted as a sharp classical universality property of an
+associated combinatorial $(2+1)$-dimensional TQFT.
+",1,0,1,0,0,0
+10555,Learning to Schedule Deadline- and Operator-Sensitive Tasks,"  The use of semi-autonomous and autonomous robotic assistants to aid in care
+of the elderly is expected to ease the burden on human caretakers, with
+small-stage testing already occurring in a variety of countries. Yet, it is
+likely that these robots will need to request human assistance via
+teleoperation when domain expertise is needed for a specific task. As
+deployment of robotic assistants moves to scale, mapping these requests for
+human aid to the teleoperators themselves will be a difficult online
+optimization problem. In this paper, we design a system that allocates requests
+to a limited number of teleoperators, each with different specialities, in an
+online fashion. We generalize a recent model of online job scheduling with a
+worst-case competitive-ratio bound to our setting. Next, we design a scalable
+machine-learning-based teleoperator-aware task scheduling algorithm and show,
+experimentally, that it performs well when compared to an omniscient optimal
+scheduling algorithm.
+",1,0,0,0,0,0
+10556,r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial Patches,"  We start by asking an interesting yet challenging question, ""If an eyewitness
+can only recall the eye features of the suspect, such that the forensic artist
+can only produce a sketch of the eyes (e.g., the top-left sketch shown in Fig.
+1), can advanced computer vision techniques help generate the whole face
+image?"" A more generalized question is that if a large proportion (e.g., more
+than 50%) of the face/sketch is missing, can a realistic whole face
+sketch/image still be estimated. Existing face completion and generation
+methods either do not conduct domain transfer learning or can not handle large
+missing area. For example, the inpainting approach tends to blur the generated
+region when the missing area is large (i.e., more than 50%). In this paper, we
+exploit the potential of deep learning networks in filling large missing region
+(e.g., as high as 95% missing) and generating realistic faces with
+high-fidelity in cross domains. We propose the recursive generation by
+bidirectional transformation networks (r-BTN) that recursively generates a
+whole face/sketch from a small sketch/face patch. The large missing area and
+the cross domain challenge make it difficult to generate satisfactory results
+using a unidirectional cross-domain learning structure. On the other hand, a
+forward and backward bidirectional learning between the face and sketch domains
+would enable recursive estimation of the missing region in an incremental
+manner (Fig. 1) and yield appealing results. r-BTN also adopts an adversarial
+constraint to encourage the generation of realistic faces/sketches. Extensive
+experiments have been conducted to demonstrate the superior performance from
+r-BTN as compared to existing potential solutions.
+",1,0,0,0,0,0
+10557,Regularity of Kleinian limit sets and Patterson-Sullivan measures,"  We consider several (related) notions of geometric regularity in the context
+of limit sets of geometrically finite Kleinian groups and associated
+Patterson-Sullivan measures. We begin by computing the upper and lower
+regularity dimensions of the Patterson-Sullivan measure, which involves
+controlling the relative measure of concentric balls. We then compute the
+Assouad and lower dimensions of the limit set, which involves controlling local
+doubling properties. Unlike the Hausdorff, packing, and box-counting
+dimensions, we show that the Assouad and lower dimensions are not necessarily
+given by the Poincaré exponent.
+",0,0,1,0,0,0
+10558,Large-Scale Sleep Condition Analysis Using Selfies from Social Media,"  Sleep condition is closely related to an individual's health. Poor sleep
+conditions such as sleep disorder and sleep deprivation affect one's daily
+performance, and may also cause many chronic diseases. Many efforts have been
+devoted to monitoring people's sleep conditions. However, traditional
+methodologies require sophisticated equipment and consume a significant amount
+of time. In this paper, we attempt to develop a novel way to predict
+individual's sleep condition via scrutinizing facial cues as doctors would.
+Rather than measuring the sleep condition directly, we measure the
+sleep-deprived fatigue which indirectly reflects the sleep condition. Our
+method can predict a sleep-deprived fatigue rate based on a selfie provided by
+a subject. This rate is used to indicate the sleep condition. To gain deeper
+insights of human sleep conditions, we collected around 100,000 faces from
+selfies posted on Twitter and Instagram, and identified their age, gender, and
+race using automatic algorithms. Next, we investigated the sleep condition
+distributions with respect to age, gender, and race. Our study suggests among
+the age groups, fatigue percentage of the 0-20 youth and adolescent group is
+the highest, implying that poor sleep condition is more prevalent in this age
+group. For gender, the fatigue percentage of females is higher than that of
+males, implying that more females are suffering from sleep issues than males.
+Among ethnic groups, the fatigue percentage in Caucasian is the highest
+followed by Asian and African American.
+",1,0,0,0,0,0
+10559,A note on the Diophantine equation $2^{n-1}(2^{n}-1)=x^3+y^3+z^3$,"  Motivated by the recent result of Farhi we show that for each $n\equiv \pm
+1\pmod{6}$ the title Diophantine equation has at least two solutions in
+integers. As a consequence, we get that each (even) perfect number is a sum of
+three cubes of integers. Moreover, we present some computational results
+concerning the considered equation and state some questions and conjectures.
+",0,0,1,0,0,0
+10560,Modeling of nonlinear audio effects with end-to-end deep neural networks,"  In the context of music production, distortion effects are mainly used for
+aesthetic reasons and are usually applied to electric musical instruments. Most
+existing methods for nonlinear modeling are often either simplified or
+optimized to a very specific circuit. In this work, we investigate deep
+learning architectures for audio processing and we aim to find a general
+purpose end-to-end deep neural network to perform modeling of nonlinear audio
+effects. We show the network modeling various nonlinearities and we discuss the
+generalization capabilities among different instruments.
+",1,0,0,0,0,0
+10561,Weak Adaptive Submodularity and Group-Based Active Diagnosis with Applications to State Estimation with Persistent Sensor Faults,"  In this paper, we consider adaptive decision-making problems for stochastic
+state estimation with partial observations. First, we introduce the concept of
+weak adaptive submodularity, a generalization of adaptive submodularity, which
+has found great success in solving challenging adaptive state estimation
+problems. Then, for the problem of active diagnosis, i.e., discrete state
+estimation via active sensing, we show that an adaptive greedy policy has a
+near-optimal performance guarantee when the reward function possesses this
+property. We further show that the reward function for group-based active
+diagnosis, which arises in applications such as medical diagnosis and state
+estimation with persistent sensor faults, is also weakly adaptive submodular.
+Finally, in experiments of state estimation for an aircraft electrical system
+with persistent sensor faults, we observe that an adaptive greedy policy
+performs equally well as an exhaustive search.
+",1,0,1,1,0,0
+10562,Robust Decentralized Learning Using ADMM with Unreliable Agents,"  Many machine learning problems can be formulated as consensus optimization
+problems which can be solved efficiently via a cooperative multi-agent system.
+However, the agents in the system can be unreliable due to a variety of
+reasons: noise, faults and attacks. Providing erroneous updates leads the
+optimization process in a wrong direction, and degrades the performance of
+distributed machine learning algorithms. This paper considers the problem of
+decentralized learning using ADMM in the presence of unreliable agents. First,
+we rigorously analyze the effect of erroneous updates (in ADMM learning
+iterations) on the convergence behavior of multi-agent system. We show that the
+algorithm linearly converges to a neighborhood of the optimal solution under
+certain conditions and characterize the neighborhood size analytically. Next,
+we provide guidelines for network design to achieve a faster convergence. We
+also provide conditions on the erroneous updates for exact convergence to the
+optimal solution. Finally, to mitigate the influence of unreliable agents, we
+propose \textsf{ROAD}, a robust variant of ADMM, and show its resilience to
+unreliable agents with an exact convergence to the optimum.
+",1,0,0,1,0,0
+10563,Applying the Delta method in metric analytics: A practical guide with novel ideas,"  During the last decade, the information technology industry has adopted a
+data-driven culture, relying on online metrics to measure and monitor business
+performance. Under the setting of big data, the majority of such metrics
+approximately follow normal distributions, opening up potential opportunities
+to model them directly without extra model assumptions and solve big data
+problems via closed-form formulas using distributed algorithms at a fraction of
+the cost of simulation-based procedures like bootstrap. However, certain
+attributes of the metrics, such as their corresponding data generating
+processes and aggregation levels, pose numerous challenges for constructing
+trustworthy estimation and inference procedures. Motivated by four real-life
+examples in metric development and analytics for large-scale A/B testing, we
+provide a practical guide to applying the Delta method, one of the most
+important tools from the classic statistics literature, to address the
+aforementioned challenges. We emphasize the central role of the Delta method in
+metric analytics by highlighting both its classic and novel applications.
+",0,0,0,1,0,0
+10564,Chebyshev Approximation and Higher Order Derivatives of Lyapunov Functions for Estimating the Domain of Attraction,"  Estimating the Domain of Attraction (DA) of non-polynomial systems is a
+challenging problem. Taylor expansion is widely adopted for transforming a
+nonlinear analytic function into a polynomial function, but the performance of
+Taylor expansion is not always satisfactory. This paper provides solvable ways
+for estimating the DA via Chebyshev approximation. Firstly, for Chebyshev
+approximation without the remainder, higher order derivatives of Lyapunov
+functions are used for estimating the DA, and the largest estimate is obtained
+by solving a generalized eigenvalue problem. Moreover, for Chebyshev
+approximation with the remainder, an uncertain polynomial system is
+reformulated, and a condition is proposed for ensuring the convergence to the
+largest estimate with a selected Lyapunov function. Numerical examples
+demonstrate that both accuracy and efficiency are improved compared to Taylor
+approximation.
+",1,0,0,0,0,0
+10565,Ranking Median Regression: Learning to Order through Local Consensus,"  This article is devoted to the problem of predicting the value taken by a
+random permutation $\Sigma$, describing the preferences of an individual over a
+set of numbered items $\{1,\; \ldots,\; n\}$ say, based on the observation of
+an input/explanatory r.v. $X$ e.g. characteristics of the individual), when
+error is measured by the Kendall $\tau$ distance. In the probabilistic
+formulation of the 'Learning to Order' problem we propose, which extends the
+framework for statistical Kemeny ranking aggregation developped in
+\citet{CKS17}, this boils down to recovering conditional Kemeny medians of
+$\Sigma$ given $X$ from i.i.d. training examples $(X_1, \Sigma_1),\; \ldots,\;
+(X_N, \Sigma_N)$. For this reason, this statistical learning problem is
+referred to as \textit{ranking median regression} here. Our contribution is
+twofold. We first propose a probabilistic theory of ranking median regression:
+the set of optimal elements is characterized, the performance of empirical risk
+minimizers is investigated in this context and situations where fast learning
+rates can be achieved are also exhibited. Next we introduce the concept of
+local consensus/median, in order to derive efficient methods for ranking median
+regression. The major advantage of this local learning approach lies in its
+close connection with the widely studied Kemeny aggregation problem. From an
+algorithmic perspective, this permits to build predictive rules for ranking
+median regression by implementing efficient techniques for (approximate) Kemeny
+median computations at a local level in a tractable manner. In particular,
+versions of $k$-nearest neighbor and tree-based methods, tailored to ranking
+median regression, are investigated. Accuracy of piecewise constant ranking
+median regression rules is studied under a specific smoothness assumption for
+$\Sigma$'s conditional distribution given $X$.
+",0,0,1,1,0,0
+10566,Anisotropic power-law inflation in a two-scalar-field model with a mixed kinetic term,"  We examine whether an extended scenario of a two-scalar-field model, in which
+a mixed kinetic term of canonical and phantom scalar fields is involved, admits
+the Bianchi type I metric, which is homogeneous but anisotropic spacetime, as
+its power-law solutions. Then we analyze the stability of the anisotropic
+power-law solutions to see whether these solutions respect the cosmic no-hair
+conjecture or not during the inflationary phase. In addition, we will also
+investigate a special scenario, where the pure kinetic terms of canonical and
+phantom fields disappear altogether in field equations, to test again the
+validity of cosmic no-hair conjecture. As a result, the cosmic no-hair
+conjecture always holds in both these scenarios due to the instability of the
+corresponding anisotropic inflationary solutions.
+",0,1,0,0,0,0
+10567,Higher-dimensional SYK Non-Fermi Liquids at Lifshitz transitions,"  We address the key open problem of a higher dimensional generalization of the
+Sachdev-Ye-Kitaev (SYK) model. We construct a model on a lattice of SYK dots
+with non-random intersite hopping. The crucial feature of the resulting band
+dispersion is the presence of a Lifshitz point where two bands touch with a
+tunable powerlaw divergent density of states (DOS). For a certain regime of the
+powerlaw exponent, we obtain a new class of interaction-dominated non-Fermi
+liquid (NFL) states, which exhibits exciting features such as a
+zero-temperature scaling symmetry, an emergent (approximate) time
+reparameterization invariance, a powerlaw entropy-temperature relationship, and
+a fermion dimension that depends continuously on the DOS exponent. Notably, we
+further demonstrate that these NFL states are fast scramblers with a Lyapunov
+exponent $\lambda_L\propto T$, although they do not saturate the upper bound of
+chaos, rendering them truly unique.
+",0,1,0,0,0,0
+10568,Convex-constrained Sparse Additive Modeling and Its Extensions,"  Sparse additive modeling is a class of effective methods for performing
+high-dimensional nonparametric regression. In this work we show how shape
+constraints such as convexity/concavity and their extensions, can be integrated
+into additive models. The proposed sparse difference of convex additive models
+(SDCAM) can estimate most continuous functions without any a priori smoothness
+assumption. Motivated by a characterization of difference of convex functions,
+our method incorporates a natural regularization functional to avoid
+overfitting and to reduce model complexity. Computationally, we develop an
+efficient backfitting algorithm with linear per-iteration complexity.
+Experiments on both synthetic and real data verify that our method is
+competitive against state-of-the-art sparse additive models, with improved
+performance in most scenarios.
+",1,0,0,1,0,0
+10569,Comparative study of finite element methods using the Time-Accuracy-Size (TAS) spectrum analysis,"  We present a performance analysis appropriate for comparing algorithms using
+different numerical discretizations. By taking into account the total
+time-to-solution, numerical accuracy with respect to an error norm, and the
+computation rate, a cost-benefit analysis can be performed to determine which
+algorithm and discretization are particularly suited for an application. This
+work extends the performance spectrum model in Chang et. al. 2017 for
+interpretation of hardware and algorithmic tradeoffs in numerical PDE
+simulation. As a proof-of-concept, popular finite element software packages are
+used to illustrate this analysis for Poisson's equation.
+",1,0,0,0,0,0
+10570,A sharpened Riesz-Sobolev inequality,"  The Riesz-Sobolev inequality provides an upper bound, in integral form, for
+the convolution of indicator functions of subsets of Euclidean space. We
+formulate and prove a sharper form of the inequality. This can be equivalently
+phrased as a stability result, quantifying an inverse theorem of Burchard that
+characterizes cases of equality.
+",0,0,1,0,0,0
+10571,Unoriented Cobordism Maps on Link Floer Homology,"  We study the problem of defining maps on link Floer homology induced by
+unoriented link cobordisms. We provide a natural notion of link cobordism,
+disoriented link cobordism, which tracks the motion of index zero and index
+three critical points. Then we construct a map on unoriented link Floer
+homology associated to a disoriented link cobordism. Furthermore, we give a
+comparison with Oszváth-Stipsicz-Szabó's and Manolescu's constructions of
+link cobordism maps for an unoriented band move.
+",0,0,1,0,0,0
+10572,3D Object Reconstruction from Hand-Object Interactions,"  Recent advances have enabled 3d object reconstruction approaches using a
+single off-the-shelf RGB-D camera. Although these approaches are successful for
+a wide range of object classes, they rely on stable and distinctive geometric
+or texture features. Many objects like mechanical parts, toys, household or
+decorative articles, however, are textureless and characterized by minimalistic
+shapes that are simple and symmetric. Existing in-hand scanning systems and 3d
+reconstruction techniques fail for such symmetric objects in the absence of
+highly distinctive features. In this work, we show that extracting 3d hand
+motion for in-hand scanning effectively facilitates the reconstruction of even
+featureless and highly symmetric objects and we present an approach that fuses
+the rich additional information of hands into a 3d reconstruction pipeline,
+significantly contributing to the state-of-the-art of in-hand scanning.
+",1,0,0,0,0,0
+10573,Secular dynamics of a planar model of the Sun-Jupiter-Saturn-Uranus system; effective stability into the light of Kolmogorov and Nekhoroshev theories,"  We investigate the long-time stability of the Sun-Jupiter-Saturn-Uranus
+system by considering a planar secular model, that can be regarded as a major
+refinement of the approach first introduced by Lagrange. Indeed, concerning the
+planetary orbital revolutions, we improve the classical circular approximation
+by replacing it with a solution that is invariant up to order two in the
+masses; therefore, we investigate the stability of the secular system for
+rather small values of the eccentricities. First, we explicitly construct a
+Kolmogorov normal form, so as to find an invariant KAM torus which approximates
+very well the secular orbits. Finally, we adapt the approach that is at basis
+of the analytic part of the Nekhoroshev's theorem, so as to show that there is
+a neighborhood of that torus for which the estimated stability time is larger
+than the lifetime of the Solar System. The size of such a neighborhood,
+compared with the uncertainties of the astronomical observations, is about ten
+times smaller.
+",0,0,1,0,0,0
+10574,Variants on the Berz sublinearity theorem,"  We consider variants on the classical Berz sublinearity theorem, using only
+DC, the Axiom of Dependent Choices, rather than AC, the Axiom of Choice which
+Berz used. We consider thinned versions, in which conditions are imposed on
+only part of the domain of the function -- results of quantifier-weakening
+type. There are connections with classical results on subadditivity. We close
+with a discussion of the extensive related literature.
+",0,0,1,0,0,0
+10575,Linguistic Markers of Influence in Informal Interactions,"  There has been a long standing interest in understanding `Social Influence'
+both in Social Sciences and in Computational Linguistics. In this paper, we
+present a novel approach to study and measure interpersonal influence in daily
+interactions. Motivated by the basic principles of influence, we attempt to
+identify indicative linguistic features of the posts in an online knitting
+community. We present the scheme used to operationalize and label the posts
+with indicator features. Experiments with the identified features show an
+improvement in the classification accuracy of influence by 3.15%. Our results
+illustrate the important correlation between the characteristics of the
+language and its potential to influence others.
+",1,0,0,0,0,0
+10576,Finding multiple core-periphery pairs in networks,"  With a core-periphery structure of networks, core nodes are densely
+interconnected, peripheral nodes are connected to core nodes to different
+extents, and peripheral nodes are sparsely interconnected. Core-periphery
+structure composed of a single core and periphery has been identified for
+various networks. However, analogous to the observation that many empirical
+networks are composed of densely interconnected groups of nodes, i.e.,
+communities, a network may be better regarded as a collection of multiple cores
+and peripheries. We propose a scalable algorithm to detect multiple
+non-overlapping groups of core-periphery structure in a network. We illustrate
+our algorithm using synthesised and empirical networks. For example, we find
+distinct core-periphery pairs with different political leanings in a network of
+political blogs and separation between international and domestic subnetworks
+of airports in some single countries in a world-wide airport network.
+",1,1,0,0,0,0
+10577,Central Limit Theorems of Local Polynomial Threshold Estimators for Diffusion Processes with Jumps,"  Central limit theorems play an important role in the study of statistical
+inference for stochastic processes. However, when the nonparametric local
+polynomial threshold estimator, especially local linear case, is employed to
+estimate the diffusion coefficients of diffusion processes, the adaptive and
+predictable structure of the estimator conditionally on the $\sigma-$field
+generated by diffusion processes is destroyed, the classical central limit
+theorem for martingale difference sequences can not work. In this paper, we
+proved the central limit theorems of local polynomial threshold estimators for
+the volatility function in diffusion processes with jumps. We believe that our
+proof for local polynomial threshold estimators provides a new method in this
+fields, especially local linear case.
+",0,0,1,0,0,0
+10578,Honey from the Hives: A Theoretical and Computational Exploration of Combinatorial Hives,"  In the first half of this manuscript, we begin with a brief review of
+combinatorial hives as introduced by Knutson and Tao, and focus on a conjecture
+by Danilov and Koshevoy for generating such a hive from Hermitian matrix pairs
+through an optimization scheme. We examine a proposal by Appleby and Whitehead
+in the spirit of this conjecture and analytically elucidate an obstruction in
+their construction for guaranteeing hive generation, while detailing stronger
+conditions under which we can produce hives with almost certain probability. We
+provide the first mapping of this prescription onto a practical algorithmic
+space that enables us to produce affirming computational results and open a new
+area of research into the analysis of the random geometries and curvatures of
+hive surfaces from select matrix ensembles.
+The second part of this manuscript concerns Littlewood-Richardson
+coefficients and methods of estimating them from the hive construction. We
+illustrate experimental confirmation of two numerical algorithms that we
+provide as tools for the community: one as a rounded estimator on the
+continuous hive polytope volume following a proposal by Narayanan, and the
+other as a novel construction using a coordinate hit-and-run on the hive
+lattice itself. We compare the advantages of each, and include numerical
+results on their accuracies for some tested cases.
+",0,0,0,1,0,0
+10579,Pressure-induced ferromagnetism due to an anisotropic electronic topological transition in Fe1.08Te,"  A rapid and anisotropic modification of the Fermi-surface shape can be
+associated with abrupt changes in crystalline lattice geometry or in the
+magnetic state of a material. In this study we show that such an electronic
+topological transition is at the basis of the formation of an unusual
+pressure-induced tetragonal ferromagnetic phase in Fe$_{1.08}$Te. Around 2 GPa,
+the orthorhombic and incommensurate antiferromagnetic ground-state of
+Fe$_{1.08}$Te is transformed upon increasing pressure into a tetragonal
+ferromagnetic state via a conventional first-order transition. On the other
+hand, an isostructural transition takes place from the paramagnetic
+high-temperature state into the ferromagnetic phase as a rare case of a `type
+0' transformation with anisotropic properties. Electronic-structure
+calculations in combination with electrical resistivity, magnetization, and
+x-ray diffraction experiments show that the electronic system of Fe$_{1.08}$Te
+is instable with respect to profound topological transitions that can drive
+fundamental changes of the lattice anisotropy and the associated magnetic
+order.
+",0,1,0,0,0,0
+10580,DataSlicer: Task-Based Data Selection for Visual Data Exploration,"  In visual exploration and analysis of data, determining how to select and
+transform the data for visualization is a challenge for data-unfamiliar or
+inexperienced users. Our main hypothesis is that for many data sets and common
+analysis tasks, there are relatively few ""data slices"" that result in effective
+visualizations. By focusing human users on appropriate and suitably transformed
+parts of the underlying data sets, these data slices can help the users carry
+their task to correct completion.
+To verify this hypothesis, we develop a framework that permits us to capture
+exemplary data slices for a user task, and to explore and parse
+visual-exploration sequences into a format that makes them distinct and easy to
+compare. We develop a recommendation system, DataSlicer, that matches a
+""currently viewed"" data slice with the most promising ""next effective"" data
+slices for the given exploration task. We report the results of controlled
+experiments with an implementation of the DataSlicer system, using four common
+analytical task types. The experiments demonstrate statistically significant
+improvements in accuracy and exploration speed versus users without access to
+our system.
+",1,0,0,0,0,0
+10581,"Convolutional Analysis Operator Learning: Acceleration, Convergence, Application, and Neural Networks","  Convolutional operator learning is increasingly gaining attention in many
+signal processing and computer vision applications. Learning kernels has mostly
+relied on so-called local approaches that extract and store many overlapping
+patches across training signals. Due to memory demands, local approaches have
+limitations when learning kernels from large datasets -- particularly with
+multi-layered structures, e.g., convolutional neural network (CNN) -- and/or
+applying the learned kernels to high-dimensional signal recovery problems. The
+so-called global approach has been studied within the ""synthesis"" signal model,
+e.g., convolutional dictionary learning, overcoming the memory problems by
+careful algorithmic designs. This paper proposes a new convolutional analysis
+operator learning (CAOL) framework in the global approach, and develops a new
+convergent Block Proximal Gradient method using a Majorizer (BPG-M) to solve
+the corresponding block multi-nonconvex problems. To learn diverse filters
+within the CAOL framework, this paper introduces an orthogonality constraint
+that enforces a tight-frame (TF) filter condition, and a regularizer that
+promotes diversity between filters. Numerical experiments show that, for tight
+majorizers, BPG-M significantly accelerates the CAOL convergence rate compared
+to the state-of-the-art method, BPG. Numerical experiments for sparse-view
+computational tomography show that CAOL using TF filters significantly improves
+reconstruction quality compared to a conventional edge-preserving regularizer.
+Finally, this paper shows that CAOL can be useful to mathematically model a
+CNN, and the corresponding updates obtained via BPG-M coincide with core
+modules of the CNN.
+",0,0,0,1,0,0
+10582,Several Classes of Permutation Trinomials over $\mathbb F_{5^n}$ From Niho Exponents,"  The construction of permutation trinomials over finite fields attracts
+people's interest recently due to their simple form and some additional
+properties. Motivated by some results on the construction of permutation
+trinomials with Niho exponents, by constructing some new fractional polynomials
+that permute the set of the $(q+1)$-th roots of unity in $\mathbb F_{q^2}$, we
+present several classes of permutation trinomials with Niho exponents over
+$\mathbb F_{q^2}$, where $q=5^k$.
+",1,0,0,0,0,0
+10583,Core-powered mass loss and the radius distribution of small exoplanets,"  Recent observations identify a valley in the radius distribution of small
+exoplanets, with planets in the range $1.5-2.0\,{\rm R}_{\oplus}$ significantly
+less common than somewhat smaller or larger planets. This valley may suggest a
+bimodal population of rocky planets that are either engulfed by massive gas
+envelopes that significantly enlarge their radius, or do not have detectable
+atmospheres at all. One explanation of such a bimodal distribution is
+atmospheric erosion by high-energy stellar photons. We investigate an
+alternative mechanism: the luminosity of the cooling rocky core, which can
+completely erode light envelopes while preserving heavy ones, produces a
+deficit of intermediate sized planets. We evolve planetary populations that are
+derived from observations using a simple analytical prescription, accounting
+self-consistently for envelope accretion, cooling and mass loss, and
+demonstrate that core-powered mass loss naturally reproduces the observed
+radius distribution, regardless of the high-energy incident flux. Observations
+of planets around different stellar types may distinguish between
+photoevaporation, which is powered by the high-energy tail of the stellar
+radiation, and core-powered mass loss, which depends on the bolometric flux
+through the planet's equilibrium temperature that sets both its cooling and
+mass-loss rates.
+",0,1,0,0,0,0
+10584,Comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront,"  The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for
+holography, where the gradient modulus of the complex refocused hologram is
+calculated, on which a sparsity metric is applied. Here, we compare two
+different choices of sparsity metrics used in SoG, specifically, the Gini index
+(GI) and the Tamura coefficient (TC), for holographic autofocusing on
+dense/connected or sparse samples. We provide a theoretical analysis predicting
+that for uniformly distributed image data, TC and GI exhibit similar behavior,
+while for naturally sparse images containing few high-valued signal entries and
+many low-valued noisy background pixels, TC is more sensitive to distribution
+changes in the signal and more resistive to background noise. These predictions
+are also confirmed by experimental results using SoG-based holographic
+autofocusing on dense and connected samples (such as stained breast tissue
+sections) as well as highly sparse samples (such as isolated Giardia lamblia
+cysts). Through these experiments, we found that ToG and GoG offer almost
+identical autofocusing performance on dense and connected samples, whereas for
+naturally sparse samples, GoG should be calculated on a relatively small region
+of interest (ROI) closely surrounding the object, while ToG offers more
+flexibility in choosing a larger ROI containing more background pixels.
+",0,1,0,0,0,0
+10585,Probabilistic interpretation of HJB equations by the representation theorem for generators of BSDEs,"  The purpose of this note is to propose a new approach for the probabilistic
+interpretation of Hamilton-Jacobi-Bellman equations associated with stochastic
+recursive optimal control problems, utilizing the representation theorem for
+generators of backward stochastic differential equations. The key idea of our
+approach for proving this interpretation consists of transmitting the signs
+between the solution and generator via the identity given by representation
+theorem. Compared with existing methods, our approach seems to be more
+applicable for general settings. This can also be regarded as a new application
+of such representation theorem.
+",0,0,1,0,0,0
+10586,A Review on Deep Learning Techniques Applied to Semantic Segmentation,"  Image semantic segmentation is more and more being of interest for computer
+vision and machine learning researchers. Many applications on the rise need
+accurate and efficient segmentation mechanisms: autonomous driving, indoor
+navigation, and even virtual or augmented reality systems to name a few. This
+demand coincides with the rise of deep learning approaches in almost every
+field or application target related to computer vision, including semantic
+segmentation or scene understanding. This paper provides a review on deep
+learning methods for semantic segmentation applied to various application
+areas. Firstly, we describe the terminology of this field as well as mandatory
+background concepts. Next, the main datasets and challenges are exposed to help
+researchers decide which are the ones that best suit their needs and their
+targets. Then, existing methods are reviewed, highlighting their contributions
+and their significance in the field. Finally, quantitative results are given
+for the described methods and the datasets in which they were evaluated,
+following up with a discussion of the results. At last, we point out a set of
+promising future works and draw our own conclusions about the state of the art
+of semantic segmentation using deep learning techniques.
+",1,0,0,0,0,0
+10587,Sum-Product Networks for Hybrid Domains,"  While all kinds of mixed data -from personal data, over panel and scientific
+data, to public and commercial data- are collected and stored, building
+probabilistic graphical models for these hybrid domains becomes more difficult.
+Users spend significant amounts of time in identifying the parametric form of
+the random variables (Gaussian, Poisson, Logit, etc.) involved and learning the
+mixed models. To make this difficult task easier, we propose the first
+trainable probabilistic deep architecture for hybrid domains that features
+tractable queries. It is based on Sum-Product Networks (SPNs) with piecewise
+polynomial leave distributions together with novel nonparametric decomposition
+and conditioning steps using the Hirschfeld-Gebelein-Rényi Maximum
+Correlation Coefficient. This relieves the user from deciding a-priori the
+parametric form of the random variables but is still expressive enough to
+effectively approximate any continuous distribution and permits efficient
+learning and inference. Our empirical evidence shows that the architecture,
+called Mixed SPNs, can indeed capture complex distributions across a wide range
+of hybrid domains.
+",1,0,0,1,0,0
+10588,Unified Hypersphere Embedding for Speaker Recognition,"  Incremental improvements in accuracy of Convolutional Neural Networks are
+usually achieved through use of deeper and more complex models trained on
+larger datasets. However, enlarging dataset and models increases the
+computation and storage costs and cannot be done indefinitely. In this work, we
+seek to improve the identification and verification accuracy of a
+text-independent speaker recognition system without use of extra data or deeper
+and more complex models by augmenting the training and testing data, finding
+the optimal dimensionality of embedding space and use of more discriminative
+loss functions. Results of experiments on VoxCeleb dataset suggest that: (i)
+Simple repetition and random time-reversion of utterances can reduce prediction
+errors by up to 18%. (ii) Lower dimensional embeddings are more suitable for
+verification. (iii) Use of proposed logistic margin loss function leads to
+unified embeddings with state-of-the-art identification and competitive
+verification accuracies.
+",1,0,0,0,0,0
+10589,Linearly-Recurrent Autoencoder Networks for Learning Dynamics,"  This paper describes a method for learning low-dimensional approximations of
+nonlinear dynamical systems, based on neural-network approximations of the
+underlying Koopman operator. Extended Dynamic Mode Decomposition (EDMD)
+provides a useful data-driven approximation of the Koopman operator for
+analyzing dynamical systems. This paper addresses a fundamental problem
+associated with EDMD: a trade-off between representational capacity of the
+dictionary and over-fitting due to insufficient data. A new neural network
+architecture combining an autoencoder with linear recurrent dynamics in the
+encoded state is used to learn a low-dimensional and highly informative
+Koopman-invariant subspace of observables. A method is also presented for
+balanced model reduction of over-specified EDMD systems in feature space.
+Nonlinear reconstruction using partially linear multi-kernel regression aims to
+improve reconstruction accuracy from the low-dimensional state when the data
+has complex but intrinsically low-dimensional structure. The techniques
+demonstrate the ability to identify Koopman eigenfunctions of the unforced
+Duffing equation, create accurate low-dimensional models of an unstable
+cylinder wake flow, and make short-time predictions of the chaotic
+Kuramoto-Sivashinsky equation.
+",1,0,0,1,0,0
+10590,Macros to Conduct Tests of Multimodality in SAS,"  The Dip Test of Unimodality and Silverman's Critical Bandwidth Test are two
+popular tests to determine if an unknown density contains more than one mode.
+While the tests can be easily run in R, they are not included in SAS software.
+We provide implementations of the Dip Test and Silverman Test as macros in the
+SAS software, capitalizing on the capability of SAS to execute R code
+internally. Descriptions of the macro parameters, installation steps, and
+sample macro calls are provided, along with an appendix for troubleshooting. We
+illustrate the use of the macros on data simulated from one or more Gaussian
+distributions as well as on the famous $\textit{iris}$ dataset.
+",0,0,0,1,0,0
+10591,The James construction and $π_4(\mathbb{S}^3)$ in homotopy type theory,"  In the first part of this paper we present a formalization in Agda of the
+James construction in homotopy type theory. We include several fragments of
+code to show what the Agda code looks like, and we explain several techniques
+that we used in the formalization. In the second part, we use the James
+construction to give a constructive proof that $\pi_4(\mathbb{S}^3)$ is of the
+form $\mathbb{Z}/n\mathbb{Z}$ (but we do not compute the $n$ here).
+",1,0,1,0,0,0
+10592,Effective One-Dimensional Coupling in the Highly-Frustrated Square-Lattice Itinerant Magnet CaCo$_{\mathrm{2}-y}$As$_{2}$,"  Inelastic neutron scattering measurements on the itinerant antiferromagnet
+(AFM) CaCo$_{\mathrm{2}-y}$As$_{2}$ at a temperature of 8 K reveal two
+orthogonal planes of scattering perpendicular to the Co square lattice in
+reciprocal space, demonstrating the presence of effective one-dimensional spin
+interactions. These results are shown to arise from near-perfect bond
+frustration within the $J_1$-$J_2$ Heisenberg model on a square lattice with
+ferromagnetic $J_1$, and hence indicate that the extensive previous
+experimental and theoretical study of the $J_1$-$J_2$ Heisenberg model on
+local-moment square spin lattices should be expanded to include itinerant spin
+systems.
+",0,1,0,0,0,0
+10593,Graph-Based Ascent Algorithms for Function Maximization,"  We study the problem of finding the maximum of a function defined on the
+nodes of a connected graph. The goal is to identify a node where the function
+obtains its maximum. We focus on local iterative algorithms, which traverse the
+nodes of the graph along a path, and the next iterate is chosen from the
+neighbors of the current iterate with probability distribution determined by
+the function values at the current iterate and its neighbors. We study two
+algorithms corresponding to a Metropolis-Hastings random walk with different
+transition kernels: (i) The first algorithm is an exponentially weighted random
+walk governed by a parameter $\gamma$. (ii) The second algorithm is defined
+with respect to the graph Laplacian and a smoothness parameter $k$. We derive
+convergence rates for the two algorithms in terms of total variation distance
+and hitting times. We also provide simulations showing the relative convergence
+rates of our algorithms in comparison to an unbiased random walk, as a function
+of the smoothness of the graph function. Our algorithms may be categorized as a
+new class of ""descent-based"" methods for function maximization on the nodes of
+a graph.
+",1,0,0,1,0,0
+10594,Multiequilibria analysis for a class of collective decision-making networked systems,"  The models of collective decision-making considered in this paper are
+nonlinear interconnected cooperative systems with saturating interactions.
+These systems encode the possible outcomes of a decision process into different
+steady states of the dynamics. In particular, they are characterized by two
+main attractors in the positive and negative orthant, representing two choices
+of agreement among the agents, associated to the Perron-Frobenius eigenvector
+of the system. In this paper we give conditions for the appearance of other
+equilibria of mixed sign. The conditions are inspired by Perron-Frobenius
+theory and are related to the algebraic connectivity of the network. We also
+show how all these equilibria must be contained in a solid disk of radius given
+by the norm of the equilibrium point which is located in the positive orthant.
+",1,0,1,0,0,0
+10595,Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer,"  This paper focuses on the recognition of Activities of Daily Living (ADL)
+applying pattern recognition techniques to the data acquired by the
+accelerometer available in the mobile devices. The recognition of ADL is
+composed by several stages, including data acquisition, data processing, and
+artificial intelligence methods. The artificial intelligence methods used are
+related to pattern recognition, and this study focuses on the use of Artificial
+Neural Networks (ANN). The data processing includes data cleaning, and the
+feature extraction techniques to define the inputs for the ANN. Due to the low
+processing power and memory of the mobile devices, they should be mainly used
+to acquire the data, applying an ANN previously trained for the identification
+of the ADL. The main purpose of this paper is to present a new method
+implemented with ANN for the identification of a defined set of ADL with a
+reliable accuracy. This paper also presents a comparison of different types of
+ANN in order to choose the type for the implementation of the final method.
+Results of this research probes that the best accuracies are achieved with Deep
+Learning techniques with an accuracy higher than 80%.
+",1,0,0,0,0,0
+10596,Simultaneous dynamic characterization of charge and structural motion during ferroelectric switching,"  Monitoring structural changes in ferroelectric thin films during electric
+field-induced polarization switching is important for a full microscopic
+understanding of the coupled motion of charges, atoms and domain walls. We
+combine standard ferroelectric test-cycles with time-resolved x-ray diffraction
+to investigate the response of a nanoscale ferroelectric oxide capacitor upon
+charging, discharging and switching. Piezoelectric strain develops during the
+electronic RC time constant and additionally during structural domain-wall
+creep. The complex atomic motion during ferroelectric polarization reversal
+starts with a negative piezoelectric response to the charge flow triggered by
+voltage pulses. Incomplete screening limits the compressive strain. The
+piezoelectric modulation of the unit cell tweaks the energy barrier between the
+two polarization states. Domain wall motion is evidenced by a broadening of the
+in-plane components of Bragg reflections. Such simultaneous measurements on a
+working device elucidate and visualize the complex interplay of charge flow and
+structural motion and challenges theoretical modelling.
+",0,1,0,0,0,0
+10597,Continuous Relaxations for the Traveling Salesman Problem,"  In this work, we aim to explore connections between dynamical systems
+techniques and combinatorial optimization problems. In particular, we construct
+heuristic approaches for the traveling salesman problem (TSP) based on
+embedding the relaxed discrete optimization problem into appropriate manifolds.
+We explore multiple embedding techniques -- namely, the construction of new
+dynamical systems on the manifold of orthogonal matrices and associated
+Procrustes approximations of the TSP cost function. Using these dynamical
+systems, we analyze the local neighborhood around the optimal TSP solutions
+(which are equilibria) using computations to approximate the associated
+\emph{stable manifolds}. We find that these flows frequently converge to
+undesirable equilibria. However, the solutions of the dynamical systems and the
+associated Procrustes approximation provide an interesting biasing approach for
+the popular Lin--Kernighan heuristic which yields fast convergence. The
+Lin--Kernighan heuristic is typically based on the computation of edges that
+have a `high probability' of being in the shortest tour, thereby effectively
+pruning the search space. Our new approach, instead, relies on a natural
+relaxation of the combinatorial optimization problem to the manifold of
+orthogonal matrices and the subsequent use of this solution to bias the
+Lin--Kernighan heuristic. Although the initial cost of computing these edges
+using the Procrustes solution is higher than existing methods, we find that the
+Procrustes solution, when coupled with a homotopy computation, contains
+valuable information regarding the optimal edges. We explore the Procrustes
+based approach on several TSP instances and find that our approach often
+requires fewer $k$-opt moves than existing approaches. Broadly, we hope that
+this work initiates more work in the intersection of dynamical systems theory
+and combinatorial optimization.
+",1,0,1,0,0,0
+10598,"Isotopes of Octonion Algebras, G2-Torsors and Triality","  Octonion algebras over rings are, in contrast to those over fields, not
+determined by their norm forms. Octonion algebras whose norm is isometric to
+the norm q of a given algebra C are twisted forms of C by means of the
+Aut(C)-torsor O(q) ->O(q)/Aut(C). We show that, over any commutative unital
+ring, these twisted forms are precisely the isotopes C(a,b) of C, with
+multiplication given by x*y=(xa)(by), for unit norm octonions a,b of C. The
+link is provided by the triality phenomenon, which we study from new and
+classical perspectives. We then study these twisted forms using the interplay,
+thus obtained, between torsor geometry and isotope computations, thus obtaining
+new results on octonion algebras over e.g. rings of (Laurent) polynomials.
+",0,0,1,0,0,0
+10599,Second Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusion,"  Two fundamental approaches to information averaging are based on linear and
+logarithmic combination, yielding the arithmetic average (AA) and geometric
+average (GA) of the fusing initials, respectively. In the context of target
+tracking, the two most common formats of data to be fused are random variables
+and probability density functions, namely $v$-fusion and $f$-fusion,
+respectively. In this work, we analyze and compare the second order statistics
+(including variance and mean square error) of AA and GA in terms of both
+$v$-fusion and $f$-fusion. The case of weighted Gaussian mixtures representing
+multitarget densities in the presence of false alarms and misdetection (whose
+weight sums are not necessarily unit) is also considered, the result of which
+appears significantly different from that for a single target. In addition to
+exact derivation, exemplifying analysis and illustrations are provided.
+",1,0,1,1,0,0
+10600,Phonon lasing from optical frequency comb illumination of a trapped ion,"  An atomic transition can be addressed by a single tooth of an optical
+frequency comb if the excited state lifetime ($\tau$) is significantly longer
+than the pulse repetition period ($T_\mathrm{r}$). In the crossover regime
+between fully-resolved and unresolved comb teeth ($\tau \lessapprox
+T_\mathrm{r}$), we observe Doppler cooling of a pre-cooled trapped atomic ion
+by a single tooth of a frequency-doubled optical frequency comb. We find that
+for initially hot ions, a multi-tooth effect gives rise to lasing of the ion's
+harmonic motion in the trap, verified by acoustic injection locking. The gain
+saturation of this phonon laser action leads to a comb of steady-state
+oscillation amplitudes, allowing hot ions to be loaded directly into the trap
+and laser cooled to crystallization despite the presence of hundreds of
+blue-detuned teeth.
+",0,1,0,0,0,0
+10601,Blocks with the hyperfocal subgroup $Z_{2^n}\times Z_{2^n}$,"  In this paper, we calculate the numbers of irreducible ordinary characters
+and irreducible Brauer characters in a block of a finite group $G$, whose
+associated fusion system over a 2-subgroup $P$ of $G$ (which is a defect group
+of the block) has the hyperfocal subgroup $\mathbb Z_{2^n}\times \mathbb
+Z_{2^n}$ for some $n\geq 2$, when the block is controlled by the normalizer
+$N_G(P)$ and the hyperfocal subgroup is contained in the center of $P$, or when
+the block is not controlled by $N_G(P)$ and the hyperfocal subgroup is
+contained in the center of the unique essential subgroup in the fusion system.
+In particular, Alperin's weight conjecture holds in the considered cases.
+",0,0,1,0,0,0
+10602,Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms,"  This paper investigates asymptotic behaviors of gradient descent algorithms
+(particularly accelerated gradient descent and stochastic gradient descent) in
+the context of stochastic optimization arose in statistics and machine learning
+where objective functions are estimated from available data. We show that these
+algorithms can be modeled by continuous-time ordinary or stochastic
+differential equations, and their asymptotic dynamic evolutions and
+distributions are governed by some linear ordinary or stochastic differential
+equations, as the data size goes to infinity. We illustrate that our study can
+provide a novel unified framework for a joint computational and statistical
+asymptotic analysis on dynamic behaviors of these algorithms with the time (or
+the number of iterations in the algorithms) and large sample behaviors of the
+statistical decision rules (like estimators and classifiers) that the
+algorithms are applied to compute, where the statistical decision rules are the
+limits of the random sequences generated from these iterative algorithms as the
+number of iterations goes to infinity. The analysis results may shed light on
+the empirically observed phenomenon of escaping from saddle points, avoiding
+bad local minimizers, and converging to good local minimizers, which depends on
+local geometry, learning rate and batch size, when stochastic gradient descent
+algorithms are applied to solve non-convex optimization problems.
+",0,0,0,1,0,0
+10603,Pigeonring: A Principle for Faster Thresholded Similarity Search,"  The pigeonhole principle states that if n items are contained in m boxes,
+then at least one box has no more than n/m items. It is utilized to solve many
+data management problems, especially for thresholded similarity searches.
+Despite many pigeonhole principle-based solutions proposed in the last few
+decades, the condition stated by the principle is weak. It only constrains the
+number of items in a single box. By organizing the boxes in a ring, we propose
+a new principle, called the pigeonring principle, which constrains the number
+of items in multiple boxes and yields stronger conditions.
+To utilize the new principle, we focus on problems defined in the form of
+identifying data objects whose similarities or distances to the query is
+constrained by a threshold. Many solutions to these problems utilize the
+pigeonhole principle to find candidates that satisfy a filtering condition. By
+the new principle, stronger filtering conditions can be established. We show
+that the pigeonhole principle is a special case of the new principle. This
+suggests that all the pigeonhole principle-based solutions are possible to be
+accelerated by the new principle. A universal filtering framework is introduced
+to encompass the solutions to these problems based on the new principle.
+Besides, we discuss how to quickly find candidates specified by the new
+principle. The implementation requires only minor modifications on top of
+existing pigeonhole principle-based algorithms. Experimental results on real
+datasets demonstrate the applicability of the new principle as well as the
+superior performance of the algorithms based on the new principle.
+",1,0,0,0,0,0
+10604,Circle compactification and 't Hooft anomaly,"  Anomaly matching constrains low-energy physics of strongly-coupled field
+theories, but it is not useful at finite temperature due to contamination from
+high-energy states. The known exception is an 't Hooft anomaly involving
+one-form symmetries as in pure $SU(N)$ Yang-Mills theory at $\theta=\pi$.
+Recent development about large-$N$ volume independence, however, gives us a
+circumstantial evidence that 't Hooft anomalies can also remain under circle
+compactifications in some theories without one-form symmetries. We develop a
+systematic procedure for deriving an 't Hooft anomaly of the
+circle-compactified theory starting from the anomaly of the original
+uncompactified theory without one-form symmetries, where the twisted boundary
+condition for the compactified direction plays a pivotal role. As an
+application, we consider $\mathbb{Z}_N$-twisted $\mathbb{C}P^{N-1}$ sigma model
+and massless $\mathbb{Z}_N$-QCD, and compute their anomalies explicitly.
+",0,1,0,0,0,0
+10605,High-speed 100 MHz strain monitor using fiber Bragg grating and optical filter for magnetostriction measurements under ultrahigh magnetic fields,"  A high-speed 100 MHz strain monitor using a fiber Bragg grating, an optical
+filter, and a mode-locked optical fiber laser has been devised, which has a
+resolution of $\Delta L/L\sim10^{-4}$. The strain monitor is sufficiently fast
+and robust for the magnetostriction measurements of magnetic materials under
+ultrahigh magnetic fields generated with destructive pulse magnets, where the
+sweep rate is in the range of 10-100 T/$\mu$s. As a working example, the
+magnetostriction of LaCoO$_{3}$ was measured at room temperature, 115 K, and
+7$\sim$4.2 K up to a maximum magnetic field of 150 T. The smooth $B^{2}$
+dependence and the first-order transition were observed at 115 K and 7$\sim$4.2
+K, respectively, reflecting the field-induced spin-state evolution.
+",0,1,0,0,0,0
+10606,Functional limit laws for the increments of Lévy processes,"  We present a functional form of the Erdös-Renyi law of large numbers for
+Levy processes.
+",0,0,1,1,0,0
+10607,R$^3$PUF: A Highly Reliable Memristive Device based Reconfigurable PUF,"  We present a memristive device based R$ ^3 $PUF construction achieving highly
+desired PUF properties, which are not offered by most current PUF designs: (1)
+High reliability, almost 100\% that is crucial for PUF-based cryptographic key
+generations, significantly reducing, or even eliminating the expensive overhead
+of on-chip error correction logic and the associated helper on-chip data
+storage or off-chip storage and transfer. (2) Reconfigurability, while current
+PUF designs rarely exhibit such an attractive property. We validate our R$ ^3
+$PUF via extensive Monte-Carlo simulations in Cadence based on parameters of
+real devices. The R$ ^3 $PUF is simple, cost-effective and easy to manage
+compared to other PUF constructions exhibiting high reliability or
+reconfigurability. None of previous PUF constructions is able to provide both
+desired high reliability and reconfigurability concurrently.
+",1,0,0,0,0,0
+10608,FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go,"  In this paper, we demonstrate the application of Fuzzy Markup Language (FML)
+to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an
+FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The
+proposed FDAA comprises an intelligent decision-making and learning mechanism,
+an intelligent game bot, a proximal development agent, and an intelligent
+agent. The intelligent game bot is based on the open-source code of Facebook
+Darkforest, and it features a representational state transfer application
+programming interface mechanism. The proximal development agent contains a
+dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a
+fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket
+engine and a summarization agent that is based on the estimated win rate,
+real-time simulation number, and matching degree of predicted moves.
+Additionally, the FML for player performance evaluation and linguistic
+descriptions for game results commentary are presented. We experimentally
+verify and validate the performance of the FDAA and variants of the FHMCS by
+testing five games in 2016 and 60 games of Google Master Go, a new version of
+the AlphaGo program, in January 2017. The experimental results demonstrate that
+the proposed FDAA can work effectively for Go applications.
+",1,0,0,0,0,0
+10609,ALMA reveals starburst-like interstellar medium conditions in a compact star-forming galaxy at z ~ 2 using [CI] and CO,"  We present ALMA detections of the [CI] 1-0, CO J=3-2, and CO J=4-3 emission
+lines, as well as the ALMA band 4 continuum for a compact star-forming galaxy
+(cSFG) at z=2.225, 3D-HST GS30274. As is typical for cSFGs, this galaxy has a
+stellar mass of $1.89 \pm 0.47\,\times 10^{11}\,\rm{M}_\odot$, with a star
+formation rate of $214\pm44\,\rm{M}_\odot\,\rm{yr}^{-1}$ putting it on the
+star-forming `main-sequence', but with an H-band effective radius of 2.5 kpc,
+making it much smaller than the bulk of `main-sequence' star-forming galaxies.
+The intensity ratio of the line detections yield an ISM density (~ 6 $\times
+10^{4}\,\rm{cm}^{-3}$) and a UV-radiation field ( ~2 $\times 10^4\,\rm{G}_0$),
+similar to the values in local starburst and ultra-luminous infrared galaxy
+environments. A starburst phase is consistent with the short depletion times
+($t_{\rm H2, dep} \leq 140$ Myr) we find using three different proxies for the
+H2 mass ([CI], CO, dust mass). This depletion time is significantly shorter
+than in more extended SFGs with similar stellar masses and SFRs. Moreover, the
+gas fraction of 3D-HST GS30274 is smaller than typically found in extended
+galaxies. We measure the CO and [CI] kinematics and find a FWHM line width of
+~$750 \pm 41 $ km s$^{-1}$. The CO and [CI] FWHM are consistent with a
+previously measured H$\alpha$ FWHM for this source. The line widths are
+consistent with gravitational motions, suggesting we are seeing a compact
+molecular gas reservoir. A previous merger event, as suggested by the
+asymmetric light profile, may be responsible for the compact distribution of
+gas and has triggered a central starburst event. This event gives rise to the
+starburst-like ISM properties and short depletion times. The centrally located
+and efficient star formation is quickly building up a dense core of stars,
+responsible for the compact distribution of stellar light in 3D-HST GS30274.
+",0,1,0,0,0,0
+10610,Faster Betweenness Centrality Updates in Evolving Networks,"  Finding central nodes is a fundamental problem in network analysis.
+Betweenness centrality is a well-known measure which quantifies the importance
+of a node based on the fraction of shortest paths going though it. Due to the
+dynamic nature of many today's networks, algorithms that quickly update
+centrality scores have become a necessity. For betweenness, several dynamic
+algorithms have been proposed over the years, targeting different update types
+(incremental- and decremental-only, fully-dynamic). In this paper we introduce
+a new dynamic algorithm for updating betweenness centrality after an edge
+insertion or an edge weight decrease. Our method is a combination of two
+independent contributions: a faster algorithm for updating pairwise distances
+as well as number of shortest paths, and a faster algorithm for updating
+dependencies. Whereas the worst-case running time of our algorithm is the same
+as recomputation, our techniques considerably reduce the number of operations
+performed by existing dynamic betweenness algorithms.
+",1,0,0,0,0,0
+10611,Constraining Reionization with the $z \sim 5-6$ Lyman-$α$ Forest Power Spectrum: the Outlook after Planck,"  The latest measurements of CMB electron scattering optical depth reported by
+Planck significantly reduces the allowed space of HI reionization models,
+pointing towards a later ending and/or less extended phase transition than
+previously believed. Reionization impulsively heats the intergalactic medium
+(IGM) to $\sim10^4$ K, and owing to long cooling and dynamical times in the
+diffuse gas, comparable to the Hubble time, memory of reionization heating is
+retained. Therefore, a late ending reionization has significant implications
+for the structure of the $z\sim5-6$ Lyman-$\alpha$ (ly$\alpha$) forest. Using
+state-of-the-art hydrodynamical simulations that allow us to vary the timing of
+reionization and its associated heat injection, we argue that extant thermal
+signatures from reionization can be detected via the ly$\alpha$ forest power
+spectrum at $5< z<6$. This arises because the small-scale cutoff in the power
+depends not only the the IGMs temperature at these epochs, but is also
+particularly sensitive to the pressure smoothing scale set by the IGMs full
+thermal history. Comparing our different reionization models with existing
+measurements of the ly$\alpha$ forest flux power spectrum at $z=5.0-5.4$, we
+find that models satisfying Planck's $\tau_e$ constraint, favor a moderate
+amount of heat injection consistent with galaxies driving reionization, but
+disfavoring quasar driven scenarios. We explore the impact of different
+reionization histories and heating models on the shape of the power spectrum,
+and find that they can produce similar effects, but argue that this degeneracy
+can be broken with high enough quality data. We study the feasibility of
+measuring the flux power spectrum at $z\simeq 6$ using mock quasar spectra and
+conclude that a sample of $\sim10$ high-resolution spectra with attainable S/N
+ratio will allow to discriminate between different reionization scenarios.
+",0,1,0,0,0,0
+10612,Third-Person Imitation Learning,"  Reinforcement learning (RL) makes it possible to train agents capable of
+achiev- ing sophisticated goals in complex and uncertain environments. A key
+difficulty in reinforcement learning is specifying a reward function for the
+agent to optimize. Traditionally, imitation learning in RL has been used to
+overcome this problem. Unfortunately, hitherto imitation learning methods tend
+to require that demonstra- tions are supplied in the first-person: the agent is
+provided with a sequence of states and a specification of the actions that it
+should have taken. While powerful, this kind of imitation learning is limited
+by the relatively hard problem of collect- ing first-person demonstrations.
+Humans address this problem by learning from third-person demonstrations: they
+observe other humans perform tasks, infer the task, and accomplish the same
+task themselves.
+In this paper, we present a method for unsupervised third-person imitation
+learn- ing. Here third-person refers to training an agent to correctly achieve
+a simple goal in a simple environment when it is provided a demonstration of a
+teacher achieving the same goal but from a different viewpoint; and
+unsupervised refers to the fact that the agent receives only these third-person
+demonstrations, and is not provided a correspondence between teacher states and
+student states. Our methods primary insight is that recent advances from domain
+confusion can be utilized to yield domain agnostic features which are crucial
+during the training process. To validate our approach, we report successful
+experiments on learning from third-person demonstrations in a pointmass domain,
+a reacher domain, and inverted pendulum.
+",1,0,0,0,0,0
+10613,Model Trees for Identifying Exceptional Players in the NHL Draft,"  Drafting strong players is crucial for the team success. We describe a new
+data-driven interpretable approach for assessing draft prospects in the
+National Hockey League. Successful previous approaches have built a predictive
+model based on player features, or derived performance predictions from the
+observed performance of comparable players in a cohort. This paper develops
+model tree learning, which incorporates strengths of both model-based and
+cohort-based approaches. A model tree partitions the feature space according to
+the values of discrete features, or learned thresholds for continuous features.
+Each leaf node in the tree defines a group of players, easily described to
+hockey experts, with its own group regression model. Compared to a single
+model, the model tree forms an ensemble that increases predictive power.
+Compared to cohort-based approaches, the groups of comparables are discovered
+from the data, without requiring a similarity metric. The performance
+predictions of the model tree are competitive with the state-of-the-art
+methods, which validates our model empirically. We show in case studies that
+the model tree player ranking can be used to highlight strong and weak points
+of players.
+",1,0,0,0,0,0
+10614,Collapsed Dark Matter Structures,"  The distributions of dark matter and baryons in the Universe are known to be
+very different: the dark matter resides in extended halos, while a significant
+fraction of the baryons have radiated away much of their initial energy and
+fallen deep into the potential wells. This difference in morphology leads to
+the widely held conclusion that dark matter cannot cool and collapse on any
+scale. We revisit this assumption, and show that a simple model where dark
+matter is charged under a ""dark electromagnetism"" can allow dark matter to form
+gravitationally collapsed objects with characteristic mass scales much smaller
+than that of a Milky Way-type galaxy. Though the majority of the dark matter in
+spiral galaxies would remain in the halo, such a model opens the possibility
+that galaxies and their associated dark matter play host to a significant
+number of collapsed substructures. The observational signatures of such
+structures are not well explored, but potentially interesting.
+",0,1,0,0,0,0
+10615,Locally free actions of groupoids and proper topological correspondences,"  Let $(G,\alpha)$ and $(H,\beta)$ be locally compact Hausdorff groupoids with
+Haar systems, and let $(X,\lambda)$ be a topological correspondence from
+$(G,\alpha)$ to $(H,\beta)$ which induce the ${C}^*$-correspondence
+$\mathcal{H}(X)\colon {C}^*(G,\alpha)\to {C}^*(H,\beta)$. We give sufficient
+topological conditions which when satisfied the ${C}^*$-correspondence
+$\mathcal{H}(X)$ is proper, that is, the ${C}^*$-algebra ${C}^*(G,\alpha)$ acts
+on the Hilbert ${C}^*(H,\beta)$-module ${H}(X)$ via the comapct operators. Thus
+a proper topological correspondence produces an element in
+${KK}({C}^*(G,\alpha),{C}^*(H,\beta))$.
+",0,0,1,0,0,0
+10616,Adaptive Lock-Free Data Structures in Haskell: A General Method for Concurrent Implementation Swapping,"  A key part of implementing high-level languages is providing built-in and
+default data structures. Yet selecting good defaults is hard. A mutable data
+structure's workload is not known in advance, and it may shift over its
+lifetime - e.g., between read-heavy and write-heavy, or from heavy contention
+by multiple threads to single-threaded or low-frequency use. One idea is to
+switch implementations adaptively, but it is nontrivial to switch the
+implementation of a concurrent data structure at runtime. Performing the
+transition requires a concurrent snapshot of data structure contents, which
+normally demands special engineering in the data structure's design. However,
+in this paper we identify and formalize an relevant property of lock-free
+algorithms. Namely, lock-freedom is sufficient to guarantee that freezing
+memory locations in an arbitrary order will result in a valid snapshot. Several
+functional languages have data structures that freeze and thaw, transitioning
+between mutable and immutable, such as Haskell vectors and Clojure transients,
+but these enable only single-threaded writers. We generalize this approach to
+augment an arbitrary lock-free data structure with the ability to gradually
+freeze and optionally transition to a new representation. This augmentation
+doesn't require changing the algorithm or code for the data structure, only
+replacing its datatype for mutable references with a freezable variant. In this
+paper, we present an algorithm for lifting plain to adaptive data and prove
+that the resulting hybrid data structure is itself lock-free, linearizable, and
+simulates the original. We also perform an empirical case study in the context
+of heating up and cooling down concurrent maps.
+",1,0,0,0,0,0
+10617,Output feedback exponential stabilization of a nonlinear 1-D wave equation with boundary input,"  This paper develops systematically the output feedback exponential
+stabilization for a one-dimensional unstable/anti-stable wave equation where
+the control boundary suffers from both internal nonlinear uncertainty and
+external disturbance. Using only two displacement signals, we propose a
+disturbance estimator that not only can estimate successfully the disturbance
+in the sense that the error is in $L^2(0,\infty)$ but also is free high-gain.
+With the estimated disturbance, we design a state observer that is
+exponentially convergent to the state of original system. An observer-based
+output feedback stabilizing control law is proposed. The disturbance is then
+canceled in the feedback loop by its approximated value. The closed-loop system
+is shown to be exponentially stable and it can be guaranteed that all internal
+signals are uniformly bounded.
+",0,0,1,0,0,0
+10618,A Microphotonic Astrocomb,"  One of the essential prerequisites for detection of Earth-like extra-solar
+planets or direct measurements of the cosmological expansion is the accurate
+and precise wavelength calibration of astronomical spectrometers. It has
+already been realized that the large number of exactly known optical
+frequencies provided by laser frequency combs ('astrocombs') can significantly
+surpass conventionally used hollow-cathode lamps as calibration light sources.
+A remaining challenge, however, is generation of frequency combs with lines
+resolvable by astronomical spectrometers. Here we demonstrate an astrocomb
+generated via soliton formation in an on-chip microphotonic resonator
+('microresonator') with a resolvable line spacing of 23.7 GHz. This comb is
+providing wavelength calibration on the 10 cm/s radial velocity level on the
+GIANO-B high-resolution near-infrared spectrometer. As such, microresonator
+frequency combs have the potential of providing broadband wavelength
+calibration for the next-generation of astronomical instruments in
+planet-hunting and cosmological research.
+",0,1,0,0,0,0
+10619,Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train,"  For the past 5 years, the ILSVRC competition and the ImageNet dataset have
+attracted a lot of interest from the Computer Vision community, allowing for
+state-of-the-art accuracy to grow tremendously. This should be credited to the
+use of deep artificial neural network designs. As these became more complex,
+the storage, bandwidth, and compute requirements increased. This means that
+with a non-distributed approach, even when using the most high-density server
+available, the training process may take weeks, making it prohibitive.
+Furthermore, as datasets grow, the representation learning potential of deep
+networks grows as well by using more complex models. This synchronicity
+triggers a sharp increase in the computational requirements and motivates us to
+explore the scaling behaviour on petaflop scale supercomputers. In this paper
+we will describe the challenges and novel solutions needed in order to train
+ResNet-50 in this large scale environment. We demonstrate above 90\% scaling
+efficiency and a training time of 28 minutes using up to 104K x86 cores. This
+is supported by software tools from Intel's ecosystem. Moreover, we show that
+with regular 90 - 120 epoch train runs we can achieve a top-1 accuracy as high
+as 77\% for the unmodified ResNet-50 topology. We also introduce the novel
+Collapsed Ensemble (CE) technique that allows us to obtain a 77.5\% top-1
+accuracy, similar to that of a ResNet-152, while training a unmodified
+ResNet-50 topology for the same fixed training budget. All ResNet-50 models as
+well as the scripts needed to replicate them will be posted shortly.
+",1,0,0,1,0,0
+10620,Alphabet-dependent Parallel Algorithm for Suffix Tree Construction for Pattern Searching,"  Suffix trees have recently become very successful data structures in handling
+large data sequences such as DNA or Protein sequences. Consequently parallel
+architectures have become ubiquitous. We present a novel alphabet-dependent
+parallel algorithm which attempts to take advantage of the perverseness of the
+multicore architecture. Microsatellites are important for their biological
+relevance hence our algorithm is based on time efficient construction for
+identification of such. We experimentally achieved up to 15x speedup over the
+sequential algorithm on different input sizes of biological sequences.
+",1,0,0,0,0,0
+10621,A coupled mitral valve -- left ventricle model with fluid-structure interaction,"  Understanding the interaction between the valves and walls of the heart is
+important in assessing and subsequently treating heart dysfunction. With
+advancements in cardiac imaging, nonlinear mechanics and computational
+techniques, it is now possible to explore the mechanics of valve-heart
+interactions using anatomically and physiologically realistic models. This
+study presents an integrated model of the mitral valve (MV) coupled to the left
+ventricle (LV), with the geometry derived from in vivo clinical magnetic
+resonance images. Numerical simulations using this coupled MV-LV model are
+developed using an immersed boundary/finite element method. The model
+incorporates detailed valvular features, left ventricular contraction,
+nonlinear soft tissue mechanics, and fluid-mediated interactions between the MV
+and LV wall. We use the model to simulate the cardiac function from diastole to
+systole, and investigate how myocardial active relaxation function affects the
+LV pump function. The results of the new model agree with in vivo measurements,
+and demonstrate that the diastolic filling pressure increases significantly
+with impaired myocardial active relaxation to maintain the normal cardiac
+output. The coupled model has the potential to advance fundamental knowledge of
+mechanisms underlying MV-LV interaction, and help in risk stratification and
+optimization of therapies for heart diseases.
+",1,1,0,0,0,0
+10622,"Performance of an Algorithm for Estimation of Flux, Background and Location on One-Dimensional Signals","  Optimal estimation of signal amplitude, background level, and photocentre
+location is crucial to the combined extraction of astrometric and photometric
+information from focal plane images, and in particular from the one-dimensional
+measurements performed by Gaia on intermediate to faint magnitude stars. Our
+goal is to define a convenient maximum likelihood framework, suited to
+efficient iterative implementation and to assessment of noise level, bias, and
+correlation among variables. The analytical model is investigated numerically
+and verified by simulation over a range of magnitude and background values. The
+estimates are unbiased, with a well-understood correlation between amplitude
+and background, and with a much lower correlation of either of them with
+location, further alleviated in case of signal symmetry. Two versions of the
+algorithm are implemented and tested against each other, respectively, for
+independent and combined parameter estimation. Both are effective and provide
+consistent results, but the latter is more efficient because it takes into
+account the flux-background estimate correlation.
+",0,1,0,0,0,0
+10623,Depth Creates No Bad Local Minima,"  In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create
+non-convex loss surfaces. Then, does depth alone create bad local minima? In
+this paper, we prove that without nonlinearity, depth alone does not create bad
+local minima, although it induces non-convex loss surface. Using this insight,
+we greatly simplify a recently proposed proof to show that all of the local
+minima of feedforward deep linear neural networks are global minima. Our
+theoretical results generalize previous results with fewer assumptions, and
+this analysis provides a method to show similar results beyond square loss in
+deep linear models.
+",1,0,1,1,0,0
+10624,Some Formulas for Numbers of Restricted Words,"  We define a quantity $c_m(n,k)$ as a generalization of the notion of the
+composition of the positive integer $n$ into $k$ parts. We proceed to derive
+some known properties of this quantity. In particular, we relate two partial
+Bell polynomials, in which the sequence of the variables of one polynomial is
+the invert transform of the sequence of the variables of the other. We connect
+the quantities $c_m(n,k)$ and $c_{m-1}(n,k)$ via Pascal matrices. We then
+relate $c_m(n,k)$ with the numbers of some restricted words over a finite
+alphabet. We develop a method which transfers some properties of restricted
+words over an alphabet of $N$ letters to the restricted words over an alphabet
+of $N+1$ letters. Several examples illustrate our findings. Note that all our
+results depend solely on the initial arithmetic function $f_0$.
+",0,0,1,0,0,0
+10625,Mean Birds: Detecting Aggression and Bullying on Twitter,"  In recent years, bullying and aggression against users on social media have
+grown significantly, causing serious consequences to victims of all
+demographics. In particular, cyberbullying affects more than half of young
+social media users worldwide, and has also led to teenage suicides, prompted by
+prolonged and/or coordinated digital harassment. Nonetheless, tools and
+technologies for understanding and mitigating it are scarce and mostly
+ineffective. In this paper, we present a principled and scalable approach to
+detect bullying and aggressive behavior on Twitter. We propose a robust
+methodology for extracting text, user, and network-based attributes, studying
+the properties of cyberbullies and aggressors, and what features distinguish
+them from regular users. We find that bully users post less, participate in
+fewer online communities, and are less popular than normal users, while
+aggressors are quite popular and tend to include more negativity in their
+posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3
+months, and show that machine learning classification algorithms can accurately
+detect users exhibiting bullying and aggressive behavior, achieving over 90%
+AUC.
+",1,0,0,0,0,0
+10626,"Towards an open standard for assessing the severity of robot security vulnerabilities, the Robot Vulnerability Scoring System (RVSS)","  Robots are typically not created with security as a main concern. Contrasting
+to typical IT systems, cyberphysical systems rely on security to handle safety
+aspects. In light of the former, classic scoring methods such as the Common
+Vulnerability Scoring System (CVSS) are not able to accurately capture the
+severity of robot vulnerabilities. The present research work focuses upon
+creating an open and free to access Robot Vulnerability Scoring System (RVSS)
+that considers major relevant issues in robotics including a) robot safety
+aspects, b) assessment of downstream implications of a given vulnerability, c)
+library and third-party scoring assessments and d) environmental variables,
+such as time since vulnerability disclosure or exposure on the web. Finally, an
+experimental evaluation of RVSS with contrast to CVSS is provided and discussed
+with focus on the robotics security landscape.
+",1,0,0,0,0,0
+10627,Fully Distributed and Asynchronized Stochastic Gradient Descent for Networked Systems,"  This paper considers a general data-fitting problem over a networked system,
+in which many computing nodes are connected by an undirected graph. This kind
+of problem can find many real-world applications and has been studied
+extensively in the literature. However, existing solutions either need a
+central controller for information sharing or requires slot synchronization
+among different nodes, which increases the difficulty of practical
+implementations, especially for a very large and heterogeneous system.
+As a contrast, in this paper, we treat the data-fitting problem over the
+network as a stochastic programming problem with many constraints. By adapting
+the results in a recent paper, we design a fully distributed and asynchronized
+stochastic gradient descent (SGD) algorithm. We show that our algorithm can
+achieve global optimality and consensus asymptotically by only local
+computations and communications. Additionally, we provide a sharp lower bound
+for the convergence speed in the regular graph case. This result fits the
+intuition and provides guidance to design a `good' network topology to speed up
+the convergence. Also, the merit of our design is validated by experiments on
+both synthetic and real-world datasets.
+",1,0,0,0,0,0
+10628,Priv'IT: Private and Sample Efficient Identity Testing,"  We develop differentially private hypothesis testing methods for the small
+sample regime. Given a sample $\cal D$ from a categorical distribution $p$ over
+some domain $\Sigma$, an explicitly described distribution $q$ over $\Sigma$,
+some privacy parameter $\varepsilon$, accuracy parameter $\alpha$, and
+requirements $\beta_{\rm I}$ and $\beta_{\rm II}$ for the type I and type II
+errors of our test, the goal is to distinguish between $p=q$ and
+$d_{\rm{TV}}(p,q) \geq \alpha$.
+We provide theoretical bounds for the sample size $|{\cal D}|$ so that our
+method both satisfies $(\varepsilon,0)$-differential privacy, and guarantees
+$\beta_{\rm I}$ and $\beta_{\rm II}$ type I and type II errors. We show that
+differential privacy may come for free in some regimes of parameters, and we
+always beat the sample complexity resulting from running the $\chi^2$-test with
+noisy counts, or standard approaches such as repetition for endowing
+non-private $\chi^2$-style statistics with differential privacy guarantees. We
+experimentally compare the sample complexity of our method to that of recently
+proposed methods for private hypothesis testing.
+",1,0,1,1,0,0
+10629,Radiative Transfer for Exoplanet Atmospheres,"  Remote sensing of the atmospheres of distant worlds motivates a firm
+understanding of radiative transfer. In this review, we provide a pedagogical
+cookbook that describes the principal ingredients needed to perform a radiative
+transfer calculation and predict the spectrum of an exoplanet atmosphere,
+including solving the radiative transfer equation, calculating opacities (and
+chemistry), iterating for radiative equilibrium (or not), and adapting the
+output of the calculations to the astronomical observations. A review of the
+state of the art is performed, focusing on selected milestone papers.
+Outstanding issues, including the need to understand aerosols or clouds and
+elucidating the assumptions and caveats behind inversion methods, are
+discussed. A checklist is provided to assist referees/reviewers in their
+scrutiny of works involving radiative transfer. A table summarizing the
+methodology employed by past studies is provided.
+",0,1,0,0,0,0
+10630,Computational Results for Extensive-Form Adversarial Team Games,"  We provide, to the best of our knowledge, the first computational study of
+extensive-form adversarial team games. These games are sequential, zero-sum
+games in which a team of players, sharing the same utility function, faces an
+adversary. We define three different scenarios according to the communication
+capabilities of the team. In the first, the teammates can communicate and
+correlate their actions both before and during the play. In the second, they
+can only communicate before the play. In the third, no communication is
+possible at all. We define the most suitable solution concepts, and we study
+the inefficiency caused by partial or null communication, showing that the
+inefficiency can be arbitrarily large in the size of the game tree.
+Furthermore, we study the computational complexity of the equilibrium-finding
+problem in the three scenarios mentioned above, and we provide, for each of the
+three scenarios, an exact algorithm. Finally, we empirically evaluate the
+scalability of the algorithms in random games and the inefficiency caused by
+partial or null communication.
+",1,0,0,0,0,0
+10631,A Parametric MPC Approach to Balancing the Cost of Abstraction for Differential-Drive Mobile Robots,"  When designing control strategies for differential-drive mobile robots, one
+standard tool is the consideration of a point at a fixed distance along a line
+orthogonal to the wheel axis instead of the full pose of the vehicle. This
+abstraction supports replacing the non-holonomic, three-state unicycle model
+with a much simpler two-state single-integrator model (i.e., a
+velocity-controlled point). Yet this transformation comes at a performance
+cost, through the robot's precision and maneuverability. This work contains
+derivations for expressions of these precision and maneuverability costs in
+terms of the transformation's parameters. Furthermore, these costs show that
+only selecting the parameter once over the course of an application may cause
+an undue loss of precision. Model Predictive Control (MPC) represents one such
+method to ameliorate this condition. However, MPC typically realizes a control
+signal, rather than a parameter, so this work also proposes a Parametric Model
+Predictive Control (PMPC) method for parameter and sampling horizon
+optimization. Experimental results are presented that demonstrate the effects
+of the parameterization on the deployment of algorithms developed for the
+single-integrator model on actual differential-drive mobile robots.
+",1,0,0,0,0,0
+10632,A norm knockout method on indirect reciprocity to reveal indispensable norms,"  Although various norms for reciprocity-based cooperation have been suggested
+that are evolutionarily stable against invasion from free riders, the process
+of alternation of norms and the role of diversified norms remain unclear in the
+evolution of cooperation. We clarify the co-evolutionary dynamics of norms and
+cooperation in indirect reciprocity and also identify the indispensable norms
+for the evolution of cooperation. Inspired by the gene knockout method, a
+genetic engineering technique, we developed the norm knockout method and
+clarified the norms necessary for the establishment of cooperation. The results
+of numerical investigations revealed that the majority of norms gradually
+transitioned to tolerant norms after defectors are eliminated by strict norms.
+Furthermore, no cooperation emerges when specific norms that are intolerant to
+defectors are knocked out.
+",1,1,0,0,0,0
+10633,Online control of the false discovery rate with decaying memory,"  In the online multiple testing problem, p-values corresponding to different
+null hypotheses are observed one by one, and the decision of whether or not to
+reject the current hypothesis must be made immediately, after which the next
+p-value is observed. Alpha-investing algorithms to control the false discovery
+rate (FDR), formulated by Foster and Stine, have been generalized and applied
+to many settings, including quality-preserving databases in science and
+multiple A/B or multi-armed bandit tests for internet commerce. This paper
+improves the class of generalized alpha-investing algorithms (GAI) in four
+ways: (a) we show how to uniformly improve the power of the entire class of
+monotone GAI procedures by awarding more alpha-wealth for each rejection,
+giving a win-win resolution to a recent dilemma raised by Javanmard and
+Montanari, (b) we demonstrate how to incorporate prior weights to indicate
+domain knowledge of which hypotheses are likely to be non-null, (c) we allow
+for differing penalties for false discoveries to indicate that some hypotheses
+may be more important than others, (d) we define a new quantity called the
+decaying memory false discovery rate (mem-FDR) that may be more meaningful for
+truly temporal applications, and which alleviates problems that we describe and
+refer to as ""piggybacking"" and ""alpha-death"". Our GAI++ algorithms incorporate
+all four generalizations simultaneously, and reduce to more powerful variants
+of earlier algorithms when the weights and decay are all set to unity. Finally,
+we also describe a simple method to derive new online FDR rules based on an
+estimated false discovery proportion.
+",1,0,1,1,0,0
+10634,Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning,"  Most of the current game-theoretic demand-side management methods focus
+primarily on the scheduling of home appliances, and the related numerical
+experiments are analyzed under various scenarios to achieve the corresponding
+Nash-equilibrium (NE) and optimal results. However, not much work is conducted
+for academic or commercial buildings. The methods for optimizing
+academic-buildings are distinct from the optimal methods for home appliances.
+In my study, we address a novel methodology to control the operation of
+heating, ventilation, and air conditioning system (HVAC). With the development
+of Artificial Intelligence and computer technologies, reinforcement learning
+(RL) can be implemented in multiple realistic scenarios and help people to
+solve thousands of real-world problems. Reinforcement Learning, which is
+considered as the art of future AI, builds the bridge between agents and
+environments through Markov Decision Chain or Neural Network and has seldom
+been used in power system. The art of RL is that once the simulator for a
+specific environment is built, the algorithm can keep learning from the
+environment. Therefore, RL is capable of dealing with constantly changing
+simulator inputs such as power demand, the condition of power system and
+outdoor temperature, etc. Compared with the existing distribution power system
+planning mechanisms and the related game theoretical methodologies, our
+proposed algorithm can plan and optimize the hourly energy usage, and have the
+ability to corporate with even shorter time window if needed.
+",1,0,0,1,0,0
+10635,A locally quasi-convex abelian group without Mackey topology,"  We give the first example of a locally quasi-convex (even countable reflexive
+and $k_\omega$) abelian group $G$ which does not admit the strongest compatible
+locally quasi-convex group topology. Our group $G$ is the Graev free abelian
+group $A_G(\mathbf{s})$ over a convergent sequence $\mathbf{s}$.
+",0,0,1,0,0,0
+10636,How accurate is density functional theory at predicting dipole moments? An assessment using a new database of 200 benchmark values,"  Dipole moments are a simple, global measure of the accuracy of the electron
+density of a polar molecule. Dipole moments also affect the interactions of a
+molecule with other molecules as well as electric fields. To directly assess
+the accuracy of modern density functionals for calculating dipole moments, we
+have developed a database of 200 benchmark dipole moments, using coupled
+cluster theory through triple excitations, extrapolated to the complete basis
+set limit. This new database is used to assess the performance of 88 popular or
+recently developed density functionals. The results suggest that double hybrid
+functionals perform the best, yielding dipole moments within about 3.6-4.5%
+regularized RMS error versus the reference values---which is not very different
+from the 4% regularized RMS error produced by coupled cluster singles and
+doubles. Many hybrid functionals also perform quite well, generating
+regularized RMS errors in the 5-6% range. Some functionals however exhibit
+large outliers and local functionals in general perform less well than hybrids
+or double hybrids.
+",0,1,0,0,0,0
+10637,Understanding the Impact of Early Citers on Long-Term Scientific Impact,"  This paper explores an interesting new dimension to the challenging problem
+of predicting long-term scientific impact (LTSI) usually measured by the number
+of citations accumulated by a paper in the long-term. It is well known that
+early citations (within 1-2 years after publication) acquired by a paper
+positively affects its LTSI. However, there is no work that investigates if the
+set of authors who bring in these early citations to a paper also affect its
+LTSI. In this paper, we demonstrate for the first time, the impact of these
+authors whom we call early citers (EC) on the LTSI of a paper. Note that this
+study of the complex dynamics of EC introduces a brand new paradigm in citation
+behavior analysis. Using a massive computer science bibliographic dataset we
+identify two distinct categories of EC - we call those authors who have high
+overall publication/citation count in the dataset as influential and the rest
+of the authors as non-influential. We investigate three characteristic
+properties of EC and present an extensive analysis of how each category
+correlates with LTSI in terms of these properties. In contrast to popular
+perception, we find that influential EC negatively affects LTSI possibly owing
+to attention stealing. To motivate this, we present several representative
+examples from the dataset. A closer inspection of the collaboration network
+reveals that this stealing effect is more profound if an EC is nearer to the
+authors of the paper being investigated. As an intuitive use case, we show that
+incorporating EC properties in the state-of-the-art supervised citation
+prediction models leads to high performance margins. At the closing, we present
+an online portal to visualize EC statistics along with the prediction results
+for a given query paper.
+",1,0,0,0,0,0
+10638,Cause-Effect Deep Information Bottleneck For Incomplete Covariates,"  Estimating the causal effects of an intervention in the presence of
+confounding is a frequently occurring problem in applications such as medicine.
+The task is challenging since there may be multiple confounding factors, some
+of which may be missing, and inferences must be made from high-dimensional,
+noisy measurements. In this paper, we propose a decision-theoretic approach to
+estimate the causal effects of interventions where a subset of the covariates
+is unavailable for some patients during testing. Our approach uses the
+information bottleneck principle to perform a discrete, low-dimensional
+sufficient reduction of the covariate data to estimate a distribution over
+confounders. In doing so, we can estimate the causal effect of an intervention
+where only partial covariate information is available. Our results on a causal
+inference benchmark and a real application for treating sepsis show that our
+method achieves state-of-the-art performance, without sacrificing
+interpretability.
+",0,0,0,1,0,0
+10639,On the restricted almost unbiased Liu estimator in the Logistic regression model,"  It is known that when the multicollinearity exists in the logistic regression
+model, variance of maximum likelihood estimator is unstable. As a remedy, in
+the context of biased shrinkage ridge estimation, Chang (2015) introduced an
+almost unbiased Liu estimator in the logistic regression model. Making use of
+his approach, when some prior knowledge in the form of linear restrictions are
+also available, we introduce a restricted almost unbiased Liu estimator in the
+logistic regression model. Statistical properties of this newly defined
+estimator are derived and some comparison result are also provided in the form
+of theorems. A Monte Carlo simulation study along with a real data example are
+given to investigate the performance of this estimator.
+",0,0,1,1,0,0
+10640,Camera-trap images segmentation using multi-layer robust principal component analysis,"  The segmentation of animals from camera-trap images is a difficult task. To
+illustrate, there are various challenges due to environmental conditions and
+hardware limitation in these images. We proposed a multi-layer robust principal
+component analysis (multi-layer RPCA) approach for background subtraction. Our
+method computes sparse and low-rank images from a weighted sum of descriptors,
+using color and texture features as case of study for camera-trap images
+segmentation. The segmentation algorithm is composed of histogram equalization
+or Gaussian filtering as pre-processing, and morphological filters with active
+contour as post-processing. The parameters of our multi-layer RPCA were
+optimized with an exhaustive search. The database consists of camera-trap
+images from the Colombian forest taken by the Instituto de Investigación de
+Recursos Biológicos Alexander von Humboldt. We analyzed the performance of
+our method in inherent and therefore challenging situations of camera-trap
+images. Furthermore, we compared our method with some state-of-the-art
+algorithms of background subtraction, where our multi-layer RPCA outperformed
+these other methods. Our multi-layer RPCA reached 76.17 and 69.97% of average
+fine-grained F-measure for color and infrared sequences, respectively. To our
+best knowledge, this paper is the first work proposing multi-layer RPCA and
+using it for camera-trap images segmentation.
+",1,0,0,0,0,0
+10641,The Bi-Lipschitz Equisingularity of Essentially Isolated Determinantal Singularities,"  The bi-Lipschitz geometry is one of the main subjects in the modern approach
+of Singularity Theory. However, it rises from works of important mathematicians
+of the last century, especially Zariski. In this work we investigate the
+Bi-Lipschitz equisingularity of families of Essentially Isolated Determinantal
+Singularities inspired by the approach of Mostowski and Gaffney.
+",0,0,1,0,0,0
+10642,Braid group symmetries of Grassmannian cluster algebras,"  We define an action of the extended affine d-strand braid group on the open
+positroid stratum in the Grassmannian Gr(k,n), for d the greatest common
+divisor of k and n. The action is by quasi-automorphisms of the cluster
+structure on the Grassmannian, determining a homomorphism from the extended
+affine braid group to the cluster modular group. We also define a
+quasi-isomorphism between the Grassmannian Gr(k,rk) and the Fock-Goncharov
+configuration space of 2r-tuples of affine flags for SL(k). This identifies the
+cluster variables, clusters, and cluster modular groups, in these two cluster
+structures.
+Fomin and Pylyavskyy proposed a description of the cluster combinatorics for
+Gr(3,n) in terms of Kuperberg's basis of non-elliptic webs. As our main
+application, we prove many of their conjectures for Gr(3,9) and give a
+presentation for its cluster modular group. We establish similar results for
+Gr(4,8). These results rely on the fact that both of these Grassmannians have
+finite mutation type.
+",0,0,1,0,0,0
+10643,Bi-$s^*$-concave distributions,"  We introduce a new shape-constrained class of distribution functions on R,
+the bi-$s^*$-concave class. In parallel to results of Dümbgen, Kolesnyk, and
+Wilke (2017) for what they called the class of bi-log-concave distribution
+functions, we show that every s-concave density f has a bi-$s^*$-concave
+distribution function $F$ and that every bi-$s^*$-concave distribution function
+satisfies $\gamma (F) \le 1/(1+s)$ where finiteness of $$ \gamma (F) \equiv
+\sup_{x} F(x) (1-F(x)) \frac{| f' (x)|}{f^2 (x)}, $$ the Csörgő -
+Révész constant of F, plays an important role in the theory of quantile
+processes on $R$.
+",0,0,1,1,0,0
+10644,A central limit theorem for the realised covariation of a bivariate Brownian semistationary process,"  This article presents a weak law of large numbers and a central limit theorem
+for the scaled realised covariation of a bivariate Brownian semistationary
+process. The novelty of our results lies in the fact that we derive the
+suitable asymptotic theory both in a multivariate setting and outside the
+classical semimartingale framework. The proofs rely heavily on recent
+developments in Malliavin calculus.
+",0,0,1,1,0,0
+10645,"First principles investigations of electronic, magnetic and bonding peculiarities of uranium nitride-fluoride UNF","  Based on geometry optimization and magnetic structure investigations within
+density functional theory, unique uranium nitride fluoride UNF, isoelectronic
+with UO2, is shown to present peculiar differentiated physical properties. Such
+specificities versus the oxide are related with the mixed anionic sublattices
+and the layered-like tetragonal structure characterized by covalent like
+[U2N2]2+motifs interlayered by ionic like [F2]2- ones and illustrated herein
+with electron localization function graphs. Particularly the ionocovalent
+chemical picture shows, based on overlap population analyses, stronger U-N
+bonding versus N-F and d(U-N) < d(U-F) distances. Based on LDA+U calculations
+the ground state magnetic structure is insulating antiferromagnet with 2 Bohr
+Magnetons magnetization per magnetic subcell and ~2 eV band gap.
+",0,1,0,0,0,0
+10646,Eight-cluster structure of chloroplast genomes differs from similar one observed for bacteria,"  Previously, a seven-cluster pattern claiming to be a universal one in
+bacterial genomes has been reported. Keeping in mind the most popular theory of
+chloroplast origin, we checked whether a similar pattern is observed in
+chloroplast genomes. Surprisingly, eight cluster structure has been found, for
+chloroplasts. The pattern observed for chloroplasts differs rather
+significantly, from bacterial one, and from that latter observed for
+cyanobacteria. The structure is provided by clustering of the fragments of
+equal length isolated within a genome so that each fragment is converted in
+triplet frequency dictionary with non-overlapping triplets with no gaps in
+frame tiling. The points in 63-dimensional space were clustered due to elastic
+map technique. The eight cluster found in chloroplasts comprises the fragments
+of a genome bearing tRNA genes and exhibiting excessively high
+$\mathsf{GC}$-content, in comparison to the entire genome.
+",0,0,0,0,1,0
+10647,Remote Document Encryption - encrypting data for e-passport holders,"  We show how any party can encrypt data for an e-passport holder such that
+only with physical possession of the e-passport decryption is possible. The
+same is possible for electronic identity cards and driver licenses. We also
+indicate possible applications. Dutch passports allow for 160 bit security,
+theoretically giving sufficient security beyond the year 2079, exceeding
+current good practice of 128 bit security. We also introduce the notion of RDE
+Extraction PIN which effectively provides the same security as a regular PIN.
+Our results ironically suggest that carrying a passport when traveling abroad
+might violate export or import laws on strong cryptography.
+",1,0,0,0,0,0
+10648,Full likelihood inference for max-stable data,"  We show how to perform full likelihood inference for max-stable multivariate
+distributions or processes based on a stochastic Expectation-Maximisation
+algorithm, which combines statistical and computational efficiency in
+high-dimensions. The good performance of this methodology is demonstrated by
+simulation based on the popular logistic and Brown--Resnick models, and it is
+shown to provide dramatic computational time improvements with respect to a
+direct computation of the likelihood. Strategies to further reduce the
+computational burden are also discussed.
+",0,0,0,1,0,0
+10649,Nef vector bundles on a projective space with first Chern class 3 and second Chern class 8,"  We describe nef vector bundles on a projective space with first Chern class
+three and second Chern class eight over an algebraically closed field of
+characteristic zero by giving them a minimal resolution in terms of a full
+strong exceptional collection of line bundles.
+",0,0,1,0,0,0
+10650,Performance of irradiated thin n-in-p planar pixel sensors for the ATLAS Inner Tracker upgrade,"  The ATLAS collaboration will replace its tracking detector with new all
+silicon pixel and strip systems. This will allow to cope with the higher
+radiation and occupancy levels expected after the 5-fold increase in the
+luminosity of the LHC accelerator complex (HL-LHC). In the new tracking
+detector (ITk) pixel modules with increased granularity will implement to
+maintain the occupancy with a higher track density. In addition, both sensors
+and read-out chips composing the hybrid modules will be produced employing more
+radiation hard technologies with respect to the present pixel detector. Due to
+their outstanding performance in terms of radiation hardness, thin n-in-p
+sensors are promising candidates to instrument a section of the new pixel
+system. Recently produced and developed sensors of new designs will be
+presented. To test the sensors before interconnection to chips, a punch-through
+biasing structure has been implemented. Its design has been optimized to
+decrease the possible tracking efficiency losses observed. After irradiation,
+they were caused by the punch-through biasing structure. A sensor compatible
+with the ATLAS FE-I4 chip with a pixel size of 50x250 $\mathrm{\mu}$m$^{2}$,
+subdivided into smaller pixel implants of 30x30 $\mathrm{\mu}$m$^{2}$ size was
+designed to investigate the performance of the 50x50 $\mathrm{\mu}$m$^{2}$
+pixel cells foreseen for the HL-LHC. Results on sensor performance of 50x250
+and 50x50 $\mathrm{\mu}$m$^{2}$ pixel cells in terms of efficiency, charge
+collection and electric field properties are obtained with beam tests and the
+Transient Current Technique.
+",0,1,0,0,0,0
+10651,Assessment of sound spatialisation algorithms for sonic rendering with headsets,"  Given an input sound signal and a target virtual sound source, sound
+spatialisation algorithms manipulate the signal so that a listener perceives it
+as though it were emitted from the target source. There exist several
+established spatialisation approaches that deliver satisfactory results when
+loudspeakers are used to playback the manipulated signal. As headphones have a
+number of desirable characteristics over loudspeakers, such as portability,
+isolation from the surrounding environment, cost and ease of use, it is
+interesting to explore how a sense of acoustic space can be conveyed through
+them. This article first surveys traditional spatialisation approaches intended
+for loudspeakers, and then reviews them with regard to their adaptability to
+headphones.
+",1,0,0,0,0,0
+10652,Identification of individual coherent sets associated with flow trajectories using Coherent Structure Coloring,"  We present a method for identifying the coherent structures associated with
+individual Lagrangian flow trajectories even where only sparse particle
+trajectory data is available. The method, based on techniques in spectral graph
+theory, uses the Coherent Structure Coloring vector and associated eigenvectors
+to analyze the distance in higher-dimensional eigenspace between a selected
+reference trajectory and other tracer trajectories in the flow. By analyzing
+this distance metric in a hierarchical clustering, the coherent structure of
+which the reference particle is a member can be identified. This algorithm is
+proven successful in identifying coherent structures of varying complexities in
+canonical unsteady flows. Additionally, the method is able to assess the
+relative coherence of the associated structure in comparison to the surrounding
+flow. Although the method is demonstrated here in the context of fluid flow
+kinematics, the generality of the approach allows for its potential application
+to other unsupervised clustering problems in dynamical systems such as neuronal
+activity, gene expression, or social networks.
+",0,1,0,1,0,0
+10653,Gaussian Processes for HRF estimation for BOLD fMRI,"  We present a non-parametric joint estimation method for fMRI task activation
+values and the hemodynamic response function (HRF). The HRF is modeled as a
+Gaussian process, making continuous evaluation possible for jittered paradigms
+and providing a variance estimate at each point.
+",0,0,0,1,0,0
+10654,Iterated Elliptic and Hypergeometric Integrals for Feynman Diagrams,"  We calculate 3-loop master integrals for heavy quark correlators and the
+3-loop QCD corrections to the $\rho$-parameter. They obey non-factorizing
+differential equations of second order with more than three singularities,
+which cannot be factorized in Mellin-$N$ space either. The solution of the
+homogeneous equations is possible in terms of convergent close integer power
+series as $_2F_1$ Gau\ss{} hypergeometric functions at rational argument. In
+some cases, integrals of this type can be mapped to complete elliptic integrals
+at rational argument. This class of functions appears to be the next one
+arising in the calculation of more complicated Feynman integrals following the
+harmonic polylogarithms, generalized polylogarithms, cyclotomic harmonic
+polylogarithms, square-root valued iterated integrals, and combinations
+thereof, which appear in simpler cases. The inhomogeneous solution of the
+corresponding differential equations can be given in terms of iterative
+integrals, where the new innermost letter itself is not an iterative integral.
+A new class of iterative integrals is introduced containing letters in which
+(multiple) definite integrals appear as factors. For the elliptic case, we also
+derive the solution in terms of integrals over modular functions and also
+modular forms, using $q$-product and series representations implied by Jacobi's
+$\vartheta_i$ functions and Dedekind's $\eta$-function. The corresponding
+representations can be traced back to polynomials out of Lambert--Eisenstein
+series, having representations also as elliptic polylogarithms, a $q$-factorial
+$1/\eta^k(\tau)$, logarithms and polylogarithms of $q$ and their $q$-integrals.
+Due to the specific form of the physical variable $x(q)$ for different
+processes, different representations do usually appear. Numerical results are
+also presented.
+",1,0,0,0,0,0
+10655,Stein Variational Message Passing for Continuous Graphical Models,"  We propose a novel distributed inference algorithm for continuous graphical
+models, by extending Stein variational gradient descent (SVGD) to leverage the
+Markov dependency structure of the distribution of interest. Our approach
+combines SVGD with a set of structured local kernel functions defined on the
+Markov blanket of each node, which alleviates the curse of high dimensionality
+and simultaneously yields a distributed algorithm for decentralized inference
+tasks. We justify our method with theoretical analysis and show that the use of
+local kernels can be viewed as a new type of localized approximation that
+matches the target distribution on the conditional distributions of each node
+over its Markov blanket. Our empirical results show that our method outperforms
+a variety of baselines including standard MCMC and particle message passing
+methods.
+",1,0,0,1,0,0
+10656,PEBP1/RKIP: from multiple functions to a common role in cellular processes,"  PEBPs (PhosphatidylEthanolamine Binding Proteins) form a protein family
+widely present in the living world since they are encountered in
+microorganisms, plants and animals. In all organisms PEBPs appear to regulate
+important mechanisms that govern cell cycle, proliferation, differentiation and
+motility. In humans, three PEBPs have been identified, namely PEBP1, PEBP2 and
+PEBP4. PEBP1 and PEBP4 are the most studied as they are implicated in the
+development of various cancers. PEBP2 is specific of testes in mammals and was
+essentially studied in rats and mice where it is very abundant. A lot of
+information has been gained on PEBP1 also named RKIP (Raf Kinase Inhibitory
+protein) due to its role as a metastasis suppressor in cancer. PEBP1 was also
+demonstrated to be implicated in Alzheimers disease, diabetes and
+nephropathies. Furthermore, PEBP1 was described to be involved in many cellular
+processes, among them are signal transduction, inflammation, cell cycle,
+proliferation, adhesion, differentiation, apoptosis, autophagy, circadian
+rhythm and mitotic spindle checkpoint. On the molecular level, PEBP1 was shown
+to regulate several signaling pathways such as Raf/MEK/ERK, NFkB,
+PI3K/Akt/mTOR, p38, Notch and Wnt. PEBP1 acts by inhibiting most of the kinases
+of these signaling cascades. Moreover, PEBP1 is able to bind to a variety of
+small ligands such as ATP, phospholipids, nucleotides, flavonoids or drugs.
+Considering PEBP1 is a small cytoplasmic protein (21kDa), its involvement in so
+many diseases and cellular mechanisms is amazing. The aim of this review is to
+highlight the molecular systems that are common to all these cellular
+mechanisms in order to decipher the specific role of PEBP1. Recent discoveries
+enable us to propose that PEBP1 is a modulator of molecular interactions that
+control signal transduction during membrane and cytoskeleton reorganization.
+",0,0,0,0,1,0
+10657,A Characterization Theorem for a Modal Description Logic,"  Modal description logics feature modalities that capture dependence of
+knowledge on parameters such as time, place, or the information state of
+agents. E.g., the logic S5-ALC combines the standard description logic ALC with
+an S5-modality that can be understood as an epistemic operator or as
+representing (undirected) change. This logic embeds into a corresponding modal
+first-order logic S5-FOL. We prove a modal characterization theorem for this
+embedding, in analogy to results by van Benthem and Rosen relating ALC to
+standard first-order logic: We show that S5-ALC with only local roles is, both
+over finite and over unrestricted models, precisely the bisimulation invariant
+fragment of S5-FOL, thus giving an exact description of the expressive power of
+S5-ALC with only local roles.
+",1,0,1,0,0,0
+10658,Emergence of magnetic long-range order in kagome quantum antiferromagnets,"  The existence of a spin-liquid ground state of the $s=1/2$ Heisenberg kagome
+antiferromagnet (KAFM) is well established. Meanwhile, also for the $s=1$
+Heisenberg KAFM evidence for the absence of magnetic long-range order (LRO) was
+found. Magnetic LRO in Heisenberg KAFMs can emerge by increasing the spin
+quantum number $s$ to $s>1$ and for $s=1$ by an easy-plane anisotropy. In the
+present paper we discuss the route to magnetic order in $s=1/2$ KAFMs by
+including an isotropic interlayer coupling (ILC) $J_\perp$ as well as an
+easy-plane anisotropy in the kagome layers by using the coupled-cluster method
+to high orders of approximation. We consider ferro- as well as
+antiferromagnetic $J_\perp$. To discuss the general question for the crossover
+from a purely two-dimensional (2D) to a quasi-2D and finally to a
+three-dimensional system we consider the simplest model of stacked (unshifted)
+kagome layers. Although the ILC of real kagome compounds is often more
+sophisticated, such a geometry of the ILC can be relevant for barlowite. We
+find that the spin-liquid ground state present for the strictly 2D $s=1/2$
+$XXZ$ KAFM survives a finite ILC, where the spin-liquid region shrinks
+monotonously with increasing anisotropy. If the ILC becomes large enough (about
+15\% of intralayer coupling for the isotropic Heisenberg case and about 4\% for
+the $XY$ limit) magnetic LRO can be established, where the $q=0$ symmetry is
+favorable if $J_\perp$ is of moderate strength. If the strength of the ILC
+further increases, $\sqrt{3}\times \sqrt{3}$ LRO can become favorable against
+$q=0$ LRO.
+",0,1,0,0,0,0
+10659,"The multidimensional truncated Moment Problem: Atoms, Determinacy, and Core Variety","  This paper is about the moment problem on a finite-dimensional vector space
+of continuous functions. We investigate the structure of the convex cone of
+moment functionals (supporting hyperplanes, exposed faces, inner points) and
+treat various important special topics on moment functionals (determinacy, set
+of atoms of representing measures, core variety).
+",0,0,1,0,0,0
+10660,PROOF OF VALUE ALIENATION (PoVA) - a concept of a cryptocurrency issuance protocol,"  In this paper, we will describe a concept of a cryptocurrency issuance
+protocol which supports digital currencies in a Proof-of-Work (< PoW >) like
+manner. However, the methods assume alternative utilization of assets used for
+cryptocurrency creation (rather than purchasing electricity necessary for <
+mining >).
+",0,0,0,0,0,1
+10661,Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation,"  Pairwise ranking methods are the basis of many widely used discriminative
+training approaches for structure prediction problems in natural language
+processing(NLP). Decomposing the problem of ranking hypotheses into pairwise
+comparisons enables simple and efficient solutions. However, neglecting the
+global ordering of the hypothesis list may hinder learning. We propose a
+listwise learning framework for structure prediction problems such as machine
+translation. Our framework directly models the entire translation list's
+ordering to learn parameters which may better fit the given listwise samples.
+Furthermore, we propose top-rank enhanced loss functions, which are more
+sensitive to ranking errors at higher positions. Experiments on a large-scale
+Chinese-English translation task show that both our listwise learning framework
+and top-rank enhanced listwise losses lead to significant improvements in
+translation quality.
+",1,0,0,0,0,0
+10662,"Performance Limits of Stochastic Sub-Gradient Learning, Part II: Multi-Agent Case","  The analysis in Part I revealed interesting properties for subgradient
+learning algorithms in the context of stochastic optimization when gradient
+noise is present. These algorithms are used when the risk functions are
+non-smooth and involve non-differentiable components. They have been long
+recognized as being slow converging methods. However, it was revealed in Part I
+that the rate of convergence becomes linear for stochastic optimization
+problems, with the error iterate converging at an exponential rate $\alpha^i$
+to within an $O(\mu)-$neighborhood of the optimizer, for some $\alpha \in
+(0,1)$ and small step-size $\mu$. The conclusion was established under weaker
+assumptions than the prior literature and, moreover, several important problems
+(such as LASSO, SVM, and Total Variation) were shown to satisfy these weaker
+assumptions automatically (but not the previously used conditions from the
+literature). These results revealed that sub-gradient learning methods have
+more favorable behavior than originally thought when used to enable continuous
+adaptation and learning. The results of Part I were exclusive to single-agent
+adaptation. The purpose of the current Part II is to examine the implications
+of these discoveries when a collection of networked agents employs subgradient
+learning as their cooperative mechanism. The analysis will show that, despite
+the coupled dynamics that arises in a networked scenario, the agents are still
+able to attain linear convergence in the stochastic case; they are also able to
+reach agreement within $O(\mu)$ of the optimizer.
+",1,0,1,1,0,0
+10663,Aggregating incoherent agents who disagree,"  In this paper, we explore how we should aggregate the degrees of belief of of
+a group of agents to give a single coherent set of degrees of belief, when at
+least some of those agents might be probabilistically incoherent. There are a
+number of way of aggregating degrees of belief, and there are a number of ways
+of fixing incoherent degrees of belief. When we have picked one of each, should
+we aggregate first and then fix, or fix first and then aggregate? Or should we
+try to do both at once? And when do these different procedures agree with one
+another? In this paper, we focus particularly on the final question.
+",1,0,0,1,0,0
+10664,On Fundamental Limits of Robust Learning,"  We consider the problems of robust PAC learning from distributed and
+streaming data, which may contain malicious errors and outliers, and analyze
+their fundamental complexity questions. In particular, we establish lower
+bounds on the communication complexity for distributed robust learning
+performed on multiple machines, and on the space complexity for robust learning
+from streaming data on a single machine. These results demonstrate that gaining
+robustness of learning algorithms is usually at the expense of increased
+complexities. As far as we know, this work gives the first complexity results
+for distributed and online robust PAC learning.
+",1,0,0,1,0,0
+10665,Robust Multi-view Pedestrian Tracking Using Neural Networks,"  In this paper, we present a real-time robust multi-view pedestrian detection
+and tracking system for video surveillance using neural networks which can be
+used in dynamic environments. The proposed system consists of two phases:
+multi-view pedestrian detection and tracking. First, pedestrian detection
+utilizes background subtraction to segment the foreground blob. An adaptive
+background subtraction method where each of the pixel of input image models as
+a mixture of Gaussians and uses an on-line approximation to update the model
+applies to extract the foreground region. The Gaussian distributions are then
+evaluated to determine which are most likely to result from a background
+process. This method produces a steady, real-time tracker in outdoor
+environment that consistently deals with changes of lighting condition, and
+long-term scene change. Second, the Tracking is performed at two phases:
+pedestrian classification and tracking the individual subject. A sliding window
+is applied on foreground binary image to select an input window which is used
+for selecting the input image patches from actually input frame. The neural
+networks is used for classification with PHOG features. Finally, a Kalman
+filter is applied to calculate the subsequent step for tracking that aims at
+finding the exact position of pedestrians in an input image. The experimental
+result shows that the proposed approach yields promising performance on
+multi-view pedestrian detection and tracking on different benchmark datasets.
+",1,0,0,0,0,0
+10666,A Hybrid Algorithm for Period Analysis from Multi-band Data with Sparse and Irregular Sampling for Arbitrary Light Curve Shapes,"  Ongoing and future surveys with repeat imaging in multiple bands are
+producing (or will produce) time-spaced measurements of brightness, resulting
+in the identification of large numbers of variable sources in the sky. A large
+fraction of these are periodic variables: compilations of these are of
+scientific interest for a variety of purposes. Unavoidably, the data-sets from
+many such surveys not only have sparse sampling, but also have embedded
+frequencies in the observing cadence that beat against the natural
+periodicities of any object under investigation. Such limitations can make
+period determination ambiguous and uncertain. For multi-band data sets with
+asynchronous measurements in multiple pass-bands, we want to maximally utilize
+the information on periodicity in a manner that is agnostic of differences in
+the light curve shapes across the different channels. Given large volumes of
+data, computational efficiency is also at a premium. This paper develops and
+presents a computationally economic method for determining periodicity which
+combines the results from two different classes of period determination
+algorithms. The underlying principles are illustrated through examples. The
+effectiveness of this approach for combining asynchronously sampled
+measurements in multiple observables that share an underlying fundamental
+frequency is also demonstrated.
+",0,1,0,0,0,0
+10667,A behavioral interpretation of belief functions,"  Shafer's belief functions were introduced in the seventies of the previous
+century as a mathematical tool in order to model epistemic probability. One of
+the reasons that they were not picked up by mainstream probability was the lack
+of a behavioral interpretation. In this paper we provide such a behavioral
+interpretation, and re-derive Shafer's belief functions via a betting
+interpretation reminiscent of the classical Dutch Book Theorem for probability
+distributions. We relate our betting interpretation of belief functions to the
+existing literature.
+",0,0,1,0,0,0
+10668,Radiation Hardness of Fiber Bragg Grating Thermometers,"  Photonics sensing has long been valued for its tolerance to harsh
+environments where traditional sensing technologies fail. As photonic
+components continue to evolve and find new applications, their tolerance to
+radiation is emerging as an important line of inquiry. Here we report on our
+investigation of the impact of gamma-ray exposure on the temperature response
+of fiber Bragg gratings. At 25 degrees C, exposures leading to an accumulated
+dose of up to 600 kGy result in complex dose-dependent drift in Bragg
+wavelength, significantly increasing the uncertainty in temperature
+measurements obtained if appreciable dose is delivered over the measurement
+interval. We note that temperature sensitivity is not severely impacted by the
+integrated dose, suggesting such devices could be used to measure relative
+changes in temperature.
+",0,1,0,0,0,0
+10669,On the inherent competition between valid and spurious inductive inferences in Boolean data,"  Inductive inference is the process of extracting general rules from specific
+observations. This problem also arises in the analysis of biological networks,
+such as genetic regulatory networks, where the interactions are complex and the
+observations are incomplete. A typical task in these problems is to extract
+general interaction rules as combinations of Boolean covariates, that explain a
+measured response variable. The inductive inference process can be considered
+as an incompletely specified Boolean function synthesis problem. This
+incompleteness of the problem will also generate spurious inferences, which are
+a serious threat to valid inductive inference rules. Using random Boolean data
+as a null model, here we attempt to measure the competition between valid and
+spurious inductive inference rules from a given data set. We formulate two
+greedy search algorithms, which synthesize a given Boolean response variable in
+a sparse disjunct normal form, and respectively a sparse generalized algebraic
+normal form of the variables from the observation data, and we evaluate
+numerically their performance.
+",0,0,0,0,1,0
+10670,Computing Stable Models of Normal Logic Programs Without Grounding,"  We present a method for computing stable models of normal logic programs,
+i.e., logic programs extended with negation, in the presence of predicates with
+arbitrary terms. Such programs need not have a finite grounding, so traditional
+methods do not apply. Our method relies on the use of a non-Herbrand universe,
+as well as coinduction, constructive negation and a number of other novel
+techniques. Using our method, a normal logic program with predicates can be
+executed directly under the stable model semantics without requiring it to be
+grounded either before or during execution and without requiring that its
+variables range over a finite domain. As a result, our method is quite general
+and supports the use of terms as arguments, including lists and complex data
+structures. A prototype implementation and non-trivial applications have been
+developed to demonstrate the feasibility of our method.
+",1,0,0,0,0,0
+10671,Security for 4G and 5G Cellular Networks: A Survey of Existing Authentication and Privacy-preserving Schemes,"  This paper presents a comprehensive survey of existing authentication and
+privacy-preserving schemes for 4G and 5G cellular networks. We start by
+providing an overview of existing surveys that deal with 4G and 5G
+communications, applications, standardization, and security. Then, we give a
+classification of threat models in 4G and 5G cellular networks in four
+categories, including, attacks against privacy, attacks against integrity,
+attacks against availability, and attacks against authentication. We also
+provide a classification of countermeasures into three types of categories,
+including, cryptography methods, humans factors, and intrusion detection
+methods. The countermeasures and informal and formal security analysis
+techniques used by the authentication and privacy preserving schemes are
+summarized in form of tables. Based on the categorization of the authentication
+and privacy models, we classify these schemes in seven types, including,
+handover authentication with privacy, mutual authentication with privacy, RFID
+authentication with privacy, deniable authentication with privacy,
+authentication with mutual anonymity, authentication and key agreement with
+privacy, and three-factor authentication with privacy. In addition, we provide
+a taxonomy and comparison of authentication and privacy-preserving schemes for
+4G and 5G cellular networks in form of tables. Based on the current survey,
+several recommendations for further research are discussed at the end of this
+paper.
+",1,0,0,0,0,0
+10672,Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams,"  Analysis of an organization's computer network activity is a key component of
+early detection and mitigation of insider threat, a growing concern for many
+organizations. Raw system logs are a prototypical example of streaming data
+that can quickly scale beyond the cognitive power of a human analyst. As a
+prospective filter for the human analyst, we present an online unsupervised
+deep learning approach to detect anomalous network activity from system logs in
+real time. Our models decompose anomaly scores into the contributions of
+individual user behavior features for increased interpretability to aid
+analysts reviewing potential cases of insider threat. Using the CERT Insider
+Threat Dataset v6.2 and threat detection recall as our performance metric, our
+novel deep and recurrent neural network models outperform Principal Component
+Analysis, Support Vector Machine and Isolation Forest based anomaly detection
+baselines. For our best model, the events labeled as insider threat activity in
+our dataset had an average anomaly score in the 95.53 percentile, demonstrating
+our approach's potential to greatly reduce analyst workloads.
+",1,0,0,1,0,0
+10673,Singular branched covers of four-manifolds,"  Consider a dihedral cover $f: Y\to X$ with $X$ and $Y$ four-manifolds and $f$
+branched along an oriented surface embedded in $X$ with isolated cone
+singularities. We prove that only a slice knot can arise as the unique
+singularity on an irregular dihedral cover $f: Y\to S^4$ if $Y$ is homotopy
+equivalent to $\mathbb{CP}^2$ and construct an explicit infinite family of such
+covers with $Y$ diffeomorphic to $\mathbb{CP}^2$. An obstruction to a knot
+being homotopically ribbon arises in this setting, and we describe a class of
+potential counter-examples to the Slice-Ribbon Conjecture.
+Our tools include lifting a trisection of a singularly embedded surface in a
+four-manifold $X$ to obtain a trisection of the corresponding irregular
+dihedral branched cover of $X$, when such a cover exists. We also develop a
+combinatorial procedure to compute, using a formula by the second author, the
+contribution to the signature of the covering manifold which results from the
+presence of a singularity on the branching set.
+",0,0,1,0,0,0
+10674,A Sample Complexity Measure with Applications to Learning Optimal Auctions,"  We introduce a new sample complexity measure, which we refer to as
+split-sample growth rate. For any hypothesis $H$ and for any sample $S$ of size
+$m$, the split-sample growth rate $\hat{\tau}_H(m)$ counts how many different
+hypotheses can empirical risk minimization output on any sub-sample of $S$ of
+size $m/2$. We show that the expected generalization error is upper bounded by
+$O\left(\sqrt{\frac{\log(\hat{\tau}_H(2m))}{m}}\right)$. Our result is enabled
+by a strengthening of the Rademacher complexity analysis of the expected
+generalization error. We show that this sample complexity measure, greatly
+simplifies the analysis of the sample complexity of optimal auction design, for
+many auction classes studied in the literature. Their sample complexity can be
+derived solely by noticing that in these auction classes, ERM on any sample or
+sub-sample will pick parameters that are equal to one of the points in the
+sample.
+",1,0,1,1,0,0
+10675,MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network,"  The inability to interpret the model prediction in semantically and visually
+meaningful ways is a well-known shortcoming of most existing computer-aided
+diagnosis methods. In this paper, we propose MDNet to establish a direct
+multimodal mapping between medical images and diagnostic reports that can read
+images, generate diagnostic reports, retrieve images by symptom descriptions,
+and visualize attention, to provide justifications of the network diagnosis
+process. MDNet includes an image model and a language model. The image model is
+proposed to enhance multi-scale feature ensembles and utilization efficiency.
+The language model, integrated with our improved attention mechanism, aims to
+read and explore discriminative image feature descriptions from reports to
+learn a direct mapping from sentence words to image pixels. The overall network
+is trained end-to-end by using our developed optimization strategy. Based on a
+pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we
+conduct sufficient experiments to demonstrate that MDNet outperforms
+comparative baselines. The proposed image model obtains state-of-the-art
+performance on two CIFAR datasets as well.
+",1,0,0,0,0,0
+10676,I0 and rank-into-rank axioms,"  Just a survey on I0: The basics, some things known but never published, some
+things published but not known.
+",0,0,1,0,0,0
+10677,Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN),"  Given the potential X-ray radiation risk to the patient, low-dose CT has
+attracted a considerable interest in the medical imaging field. The current
+main stream low-dose CT methods include vendor-specific sinogram domain
+filtration and iterative reconstruction, but they need to access original raw
+data whose formats are not transparent to most users. Due to the difficulty of
+modeling the statistical characteristics in the image domain, the existing
+methods for directly processing reconstructed images cannot eliminate image
+noise very well while keeping structural details. Inspired by the idea of deep
+learning, here we combine the autoencoder, the deconvolution network, and
+shortcut connections into the residual encoder-decoder convolutional neural
+network (RED-CNN) for low-dose CT imaging. After patch-based training, the
+proposed RED-CNN achieves a competitive performance relative to
+the-state-of-art methods in both simulated and clinical cases. Especially, our
+method has been favorably evaluated in terms of noise suppression, structural
+preservation and lesion detection.
+",1,1,0,0,0,0
+10678,Primes In Arithmetic Progressions And Primitive Roots,"  Let $x\geq 1$ be a large number, and let $1 \leq a <q $ be integers such that
+$\gcd(a,q)=1$ and $q=O(\log^c)$ with $c>0$ constant. This note proves that the
+counting function for the number of primes $p \in \{p=qn+a: n \geq1 \}$ with a
+fixed primitive root $u\ne \pm 1, v^2$ has the asymptotic formula
+$\pi_u(x,q,a)=\delta(u,q,a)x/ \log x +O(x/\log^b x),$ where $\delta(u,q,a)>0$
+is the density, and $b>c+1$ is a constant.
+",0,0,1,0,0,0
+10679,Photometric redshift estimation via deep learning,"  The need to analyze the available large synoptic multi-band surveys drives
+the development of new data-analysis methods. Photometric redshift estimation
+is one field of application where such new methods improved the results,
+substantially. Up to now, the vast majority of applied redshift estimation
+methods have utilized photometric features. We aim to develop a method to
+derive probabilistic photometric redshift directly from multi-band imaging
+data, rendering pre-classification of objects and feature extraction obsolete.
+A modified version of a deep convolutional network was combined with a mixture
+density network. The estimates are expressed as Gaussian mixture models
+representing the probability density functions (PDFs) in the redshift space. In
+addition to the traditional scores, the continuous ranked probability score
+(CRPS) and the probability integral transform (PIT) were applied as performance
+criteria. We have adopted a feature based random forest and a plain mixture
+density network to compare performances on experiments with data from SDSS
+(DR9). We show that the proposed method is able to predict redshift PDFs
+independently from the type of source, for example galaxies, quasars or stars.
+Thereby the prediction performance is better than both presented reference
+methods and is comparable to results from the literature. The presented method
+is extremely general and allows us to solve of any kind of probabilistic
+regression problems based on imaging data, for example estimating metallicity
+or star formation rate of galaxies. This kind of methodology is tremendously
+important for the next generation of surveys.
+",0,1,0,0,0,0
+10680,Effect of antipsychotics on community structure in functional brain networks,"  Schizophrenia, a mental disorder that is characterized by abnormal social
+behavior and failure to distinguish one's own thoughts and ideas from reality,
+has been associated with structural abnormalities in the architecture of
+functional brain networks. Using various methods from network analysis, we
+examine the effect of two classical therapeutic antipsychotics --- Aripiprazole
+and Sulpiride --- on the structure of functional brain networks of healthy
+controls and patients who have been diagnosed with schizophrenia. We compare
+the community structures of functional brain networks of different individuals
+using mesoscopic response functions, which measure how community structure
+changes across different scales of a network. We are able to do a reasonably
+good job of distinguishing patients from controls, and we are most successful
+at this task on people who have been treated with Aripiprazole. We demonstrate
+that this increased separation between patients and controls is related only to
+a change in the control group, as the functional brain networks of the patient
+group appear to be predominantly unaffected by this drug. This suggests that
+Aripiprazole has a significant and measurable effect on community structure in
+healthy individuals but not in individuals who are diagnosed with
+schizophrenia. In contrast, we find for individuals are given the drug
+Sulpiride that it is more difficult to separate the networks of patients from
+those of controls. Overall, we observe differences in the effects of the drugs
+(and a placebo) on community structure in patients and controls and also that
+this effect differs across groups. We thereby demonstrate that different types
+of antipsychotic drugs selectively affect mesoscale structures of brain
+networks, providing support that mesoscale structures such as communities are
+meaningful functional units in the brain.
+",0,0,0,1,1,0
+10681,Unsupervised Learning of Mixture Models with a Uniform Background Component,"  Gaussian Mixture Models are one of the most studied and mature models in
+unsupervised learning. However, outliers are often present in the data and
+could influence the cluster estimation. In this paper, we study a new model
+that assumes that data comes from a mixture of a number of Gaussians as well as
+a uniform ""background"" component assumed to contain outliers and other
+non-interesting observations. We develop a novel method based on robust loss
+minimization that performs well in clustering such GMM with a uniform
+background. We give theoretical guarantees for our clustering algorithm to
+obtain best clustering results with high probability. Besides, we show that the
+result of our algorithm does not depend on initialization or local optima, and
+the parameter tuning is an easy task. By numeric simulations, we demonstrate
+that our algorithm enjoys high accuracy and achieves the best clustering
+results given a large enough sample size. Finally, experimental comparisons
+with typical clustering methods on real datasets witness the potential of our
+algorithm in real applications.
+",0,0,0,1,0,0
+10682,Sound emitted by some grassland animals as an indicator of motion in the surroundings,"  It is argued based on the results of both numerical modelling and the
+experiments performed on an artificial substitute of a meadow that the sound
+emitted by animals living in a dense surrounding such as a meadow or shrubs can
+be used as a tool for detection of motion. Some characteristics of the sound
+emitted by these animals, e.g. its frequency, seem to be adjusted to the meadow
+density to optimize the effectiveness of this skill. This kind of sensing the
+environment could be used as a useful tool improving detection of mates or
+predators. A study thereof would be important both from the basic-knowledge and
+ecological points of view (unnatural environmental changes like increasing of a
+noise or changes in plants species composition can make this sensing
+ineffective).
+",0,1,0,0,0,0
+10683,Monochromaticity of coherent Smith-Purcell radiation from finite size grating,"  Investigation of coherent Smith-Purcell Radiation (SPR) spectral
+characteristics was performed both experimentally and by numerical simulation.
+The measurement of SPR spectral line shapes of different diffraction orders was
+carried out at KEK LUCX facility. A pair of room-temperature Schottky barrier
+diode (SBD) detectors with sensitivity bands of $60-90$~GHz and $320-460$~GHz
+was used in the measurements. Reasonable agreement of experimental results and
+simulations performed with CST Studio Suite justifies the use of different
+narrow-band SBD detectors to investigate different SPR diffraction orders. It
+was shown that monochromaticity of the SPR spectral lines increases with
+diffraction order. The comparison of coherent transition radiation and coherent
+SPR intensities in sub-THz frequency range showed that the brightnesses of both
+radiation mechanisms were comparable. A fine tuning feasibility of the SPR
+spectral lines is discussed.
+",0,1,0,0,0,0
+10684,Search for water vapor in the high-resolution transmission spectrum of HD189733b in the visible,"  Ground-based telescopes equipped with state-of-the-art spectrographs are able
+to obtain high-resolution transmission and emission spectra of exoplanets that
+probe the structure and composition of their atmospheres. Various atomic and
+molecular species, such as Na, CO, H2O have been already detected. Molecular
+species have been observed only in the near-infrared while atomic species have
+been observed in the visible. In particular, the detection and abundance
+determination of water vapor bring important constraints to the planet
+formation process. We search for water vapor in the atmosphere of the exoplanet
+HD189733b using a high-resolution transmission spectrum in the visible obtained
+with HARPS. We use Molecfit to correct for telluric absorption features. Then
+we compute the high-resolution transmission spectrum of the planet using 3
+transit datasets. We finally search for water vapor absorption using a
+cross-correlation technique that combines the signal of 800 individual lines.
+Telluric features are corrected to the noise level. We place a 5-sigma upper
+limit of 100 ppm on the strength of the 6500 A water vapor band. The 1-sigma
+precision of 20 ppm on the transmission spectrum demonstrates that space-like
+sensitivity can be achieved from the ground. This approach opens new
+perspectives to detect various atomic and molecular species with future
+instruments such as ESPRESSO at the VLT. Extrapolating from our results, we
+show that only 1 transit with ESPRESSO would be sufficient to detect water
+vapor on HD189733b-like hot Jupiter with a cloud-free atmosphere. Upcoming
+near-IR spectrographs will be even more efficient and sensitive to a wider
+range of molecular species. Moreover, the detection of the same molecular
+species in different bands (e.g. visible and IR) is key to constrain the
+structure and composition of the atmosphere, such as the presence of Rayleigh
+scattering or aerosols.
+",0,1,0,0,0,0
+10685,The Capacity of Some Classes of Polyhedra,"  K. Borsuk in 1979, in the Topological Conference in Moscow, introduced the
+concept of the capacity of a compactum. In this paper, we compute the capacity
+of the product of two spheres of the same or different dimensions and the
+capacity of lense spaces. Also, we present an upper bound for the capacity of a
+$\mathbb{Z}_n$-complex, i.e., a connected finite 2-dimensional CW-complex with
+finite cyclic fundamental group $\mathbb{Z}_n$.
+",0,0,1,0,0,0
+10686,Network Transplanting (extended abstract),"  This paper focuses on a new task, i.e., transplanting a
+category-and-task-specific neural network to a generic, modular network without
+strong supervision. We design a functionally interpretable structure for the
+generic network. Like building LEGO blocks, we teach the generic network a new
+category by directly transplanting the module corresponding to the category
+from a pre-trained network with a few or even without sample annotations. Our
+method incrementally adds new categories to the generic network but does not
+affect representations of existing categories. In this way, our method breaks
+the typical bottleneck of learning a net for massive tasks and categories,
+i.e., the requirement of collecting samples for all tasks and categories at the
+same time before the learning begins. Thus, we use a new distillation
+algorithm, namely back-distillation, to overcome specific challenges of network
+transplanting. Our method without training samples even outperformed the
+baseline with 100 training samples.
+",1,0,0,1,0,0
+10687,Extracting Build Changes with BUILDDIFF,"  Build systems are an essential part of modern software engineering projects.
+As software projects change continuously, it is crucial to understand how the
+build system changes because neglecting its maintenance can lead to expensive
+build breakage. Recent studies have investigated the (co-)evolution of build
+configurations and reasons for build breakage, but they did this only on a
+coarse grained level. In this paper, we present BUILDDIFF, an approach to
+extract detailed build changes from MAVEN build files and classify them into 95
+change types. In a manual evaluation of 400 build changing commits, we show
+that BUILDDIFF can extract and classify build changes with an average precision
+and recall of 0.96 and 0.98, respectively. We then present two studies using
+the build changes extracted from 30 open source Java projects to study the
+frequency and time of build changes. The results show that the top 10 most
+frequent change types account for 73% of the build changes. Among them, changes
+to version numbers and changes to dependencies of the projects occur most
+frequently. Furthermore, our results show that build changes occur frequently
+around releases. With these results, we provide the basis for further research,
+such as for analyzing the (co-)evolution of build files with other artifacts or
+improving effort estimation approaches. Furthermore, our detailed change
+information enables improvements of refactoring approaches for build
+configurations and improvements of models to identify error-prone build files.
+",1,0,0,0,0,0
+10688,Integrating car path optimization with train formation plan: a non-linear binary programming model and simulated annealing based heuristics,"  An essential issue that a freight transportation system faced is how to
+deliver shipments (OD pairs) on a capacitated physical network optimally; that
+is, to determine the best physical path for each OD pair and assign each OD
+pair into the most reasonable freight train service sequence. Instead of
+pre-specifying or pre-solving the railcar routing beforehand and optimizing the
+train formation plan subsequently, which is a standard practice in China
+railway system and a widely used method in existing literature to reduce the
+problem complexity, this paper proposes a non-linear binary programming model
+to address the integrated railcar itinerary and train formation plan
+optimization problem. The model comprehensively considers various operational
+requirements and a set of capacity constraints, including link capacity, yard
+reclassification capacity and the maximal number of blocks a yard can be
+formed, while trying to minimize the total costs of accumulation,
+reclassification and transportation. An efficient simulated annealing based
+heuristic solution approach is developed to solve the mathematical model. To
+tackle the difficult capacity constraints, we use a penalty function method.
+Furthermore, a customized heuristics for satisfying the operational
+requirements is designed as well.
+",0,0,1,0,0,0
+10689,Modeling of networks and globules of charged domain walls observed in pump and pulse induced states,"  Experiments on optical and STM injection of carriers in layered
+$\mathrm{MX_2}$ materials revealed the formation of nanoscale patterns with
+networks and globules of domain walls. This is thought to be responsible for
+the metallization transition of the Mott insulator and for stabilization of a
+""hidden"" state. In response, here we present studies of the classical charged
+lattice gas model emulating the superlattice of polarons ubiquitous to the
+material of choice $1T-\mathrm{TaS_2}$. The injection pulse was simulated by
+introducing a small random concentration of voids which subsequent evolution
+was followed by means of Monte Carlo cooling. Below the detected phase
+transition, the voids gradually coalesce into domain walls forming locally
+connected globules and then the global network leading to a mosaic
+fragmentation into domains with different degenerate ground states. The
+obtained patterns closely resemble the experimental STM visualizations. The
+surprising aggregation of charged voids is understood by fractionalization of
+their charges across the walls' lines.
+",0,1,0,0,0,0
+10690,cGAN-based Manga Colorization Using a Single Training Image,"  The Japanese comic format known as Manga is popular all over the world. It is
+traditionally produced in black and white, and colorization is time consuming
+and costly. Automatic colorization methods generally rely on greyscale values,
+which are not present in manga. Furthermore, due to copyright protection,
+colorized manga available for training is scarce. We propose a manga
+colorization method based on conditional Generative Adversarial Networks
+(cGAN). Unlike previous cGAN approaches that use many hundreds or thousands of
+training images, our method requires only a single colorized reference image
+for training, avoiding the need of a large dataset. Colorizing manga using
+cGANs can produce blurry results with artifacts, and the resolution is limited.
+We therefore also propose a method of segmentation and color-correction to
+mitigate these issues. The final results are sharp, clear, and in high
+resolution, and stay true to the character's original color scheme.
+",1,0,0,0,0,0
+10691,Cooperation and Environment Characterize the Low-Lying Optical Spectrum of Liquid Water,"  The optical spectrum of liquid water is analyzed by subsystem time-dependent
+density functional theory. We provide simple explanations for several important
+(and so far elusive) features. Due to the disordered environment surrounding
+each water molecule, the joint density of states of the liquid is much broader
+than that of the vapor. This results in a red shifted Urbach tail. Confinement
+effects provided by the first solvation shell are responsible for the blue
+shift of the first absorption peak compared to the vapor. In addition, we also
+characterize many-body excitonic effects. These dramatically affect the
+spectral weights at low frequencies, contributing to the refractive index by a
+small but significant amount.
+",0,1,0,0,0,0
+10692,Possible particle-hole instabilities in interacting type-II Weyl semimetals,"  Type II Weyl semimetal, a three dimensional gapless topological phase, has
+drawn enormous interest recently. These topological semimetals enjoy overtilted
+dispersion and Weyl nodes that separate the particle and hole pocket. Using
+perturbation renormalization group, we identify possible renormalization of the
+interaction vertices, which show a tendency toward instability. We further
+adopt a self-consistent mean-field approach to study possible instability of
+the type II Weyl semimetals under short-range electron-electron interaction. It
+is found that the instabilities are much easier to form in type II Weyl
+semimetals than the type I case. Eight different mean-field orders are
+identified, among which we further show that the polar charge density wave
+(CDW) phase exhibits the lowest energy. This CDW order is originated from the
+nesting of the Fermi surfaces and could be a possible ground state in
+interacting type II Weyl semimetals.
+",0,1,0,0,0,0
+10693,Turbulent shear layers in confining channels,"  We present a simple model for the development of shear layers between
+parallel flows in confining channels. Such flows are important across a wide
+range of topics from diffusers, nozzles and ducts to urban air flow and
+geophysical fluid dynamics. The model approximates the flow in the shear layer
+as a linear profile separating uniform-velocity streams. Both the channel
+geometry and wall drag affect the development of the flow. The model shows good
+agreement with both particle-image-velocimetry experiments and computational
+turbulence modelling. The low computational cost of the model allows it to be
+used for design purposes, which we demonstrate by investigating optimal
+pressure recovery in diffusers with non-uniform inflow.
+",0,1,0,0,0,0
+10694,"Crystal and Magnetic Structures in Layered, Transition Metal Dihalides and Trihalides","  Materials composed of two dimensional layers bonded to one another through
+weak van der Waals interactions often exhibit strongly anisotropic behaviors
+and can be cleaved into very thin specimens and sometimes into monolayer
+crystals. Interest in such materials is driven by the study of low dimensional
+physics and the design of functional heterostructures. Binary compounds with
+the compositions MX2 and MX3 where M is a metal cation and X is a halogen anion
+often form such structures. Magnetism can be incorporated by choosing a
+transition metal with a partially filled d-shell for M, enabling ferroic
+responses for enhanced functionality. Here a brief overview of binary
+transition metal dihalides and trihalides is given, summarizing their
+crystallographic properties and long-range-ordered magnetic structures,
+focusing on those materials with layered crystal structures and partially
+filled d-shells required for combining low dimensionality and cleavability with
+magnetism.
+",0,1,0,0,0,0
+10695,Ramsey theorem for designs,"  We prove that for any choice of parameters $k,t,\lambda$ the class of all
+finite ordered designs with parameters $k,t,\lambda$ is a Ramsey class.
+",1,0,1,0,0,0
+10696,Oracle inequalities for the stochastic differential equations,"  This paper is a survey of recent results on the adaptive robust non
+parametric methods for the continuous time regression model with the semi -
+martingale noises with jumps. The noises are modeled by the Lévy processes,
+the Ornstein -- Uhlenbeck processes and semi-Markov processes. We represent the
+general model selection method and the sharp oracle inequalities methods which
+provide the robust efficient estimation in the adaptive setting. Moreover, we
+present the recent results on the improved model selection methods for the
+nonparametric estimation problems.
+",0,0,1,0,0,0
+10697,Empirical Risk Minimization as Parameter Choice Rule for General Linear Regularization Methods,"  We consider the statistical inverse problem to recover $f$ from noisy
+measurements $Y = Tf + \sigma \xi$ where $\xi$ is Gaussian white noise and $T$
+a compact operator between Hilbert spaces. Considering general reconstruction
+methods of the form $\hat f_\alpha = q_\alpha \left(T^*T\right)T^*Y$ with an
+ordered filter $q_\alpha$, we investigate the choice of the regularization
+parameter $\alpha$ by minimizing an unbiased estimate of the predictive risk
+$\mathbb E\left[\Vert Tf - T\hat f_\alpha\Vert^2\right]$. The corresponding
+parameter $\alpha_{\mathrm{pred}}$ and its usage are well-known in the
+literature, but oracle inequalities and optimality results in this general
+setting are unknown. We prove a (generalized) oracle inequality, which relates
+the direct risk $\mathbb E\left[\Vert f - \hat
+f_{\alpha_{\mathrm{pred}}}\Vert^2\right]$ with the oracle prediction risk
+$\inf_{\alpha>0}\mathbb E\left[\Vert Tf - T\hat f_{\alpha}\Vert^2\right]$. From
+this oracle inequality we are then able to conclude that the investigated
+parameter choice rule is of optimal order.
+Finally we also present numerical simulations, which support the order
+optimality of the method and the quality of the parameter choice in finite
+sample situations.
+",0,0,1,1,0,0
+10698,Disturbance-to-State Stabilization and Quantized Control for Linear Hyperbolic Systems,"  We consider a system of linear hyperbolic PDEs where the state at one of the
+boundary points is controlled using the measurements of another boundary point.
+Because of the disturbances in the measurement, the problem of designing
+dynamic controllers is considered so that the closed-loop system is robust with
+respect to measurement errors. Assuming that the disturbance is a locally
+essentially bounded measurable function of time, we derive a
+disturbance-to-state estimate which provides an upper bound on the maximum norm
+of the state (with respect to the spatial variable) at each time in terms of
+$\mathcal{L}^\infty$-norm of the disturbance up to that time. The analysis is
+based on constructing a Lyapunov function for the closed-loop system, which
+leads to controller synthesis and the conditions on system dynamics required
+for stability. As an application of this stability notion, the problem of
+quantized control for hyperbolic PDEs is considered where the measurements sent
+to the controller are communicated using a quantizer of finite length. The
+presence of quantizer yields practical stability only, and the ultimate bounds
+on the norm of the state trajectory are also derived.
+",1,0,1,0,0,0
+10699,Energy dependent stereodynamics of the Ne($^3$P$_2$)+Ar reaction,"  The stereodynamics of the Ne($^3$P$_2$)+Ar Penning and Associative ionization
+reactions have been studied using a crossed molecular beam apparatus. The
+experiment uses a curved magnetic hexapole to polarise the Ne($^3$P$_2$) which
+is then oriented with a shaped magnetic field in the region where it intersects
+with a beam of Ar($^1$S). The ratios of Penning to associative ionization were
+recorded over a range of collision energies from 320 cm$^{-1}$ to 500 cm$^{-1}$
+and the data was used to obtain $\Omega$ state dependent reactivities for the
+two reaction channels. These reactivities were found to compare favourably to
+those predicted in the theoretical work of Brumer et al.
+",0,1,0,0,0,0
+10700,Qubit dynamics at tunneling Fermi-edge singularity in $\it{a.c.}$ response,"  We consider tunneling of spinless electrons from a single-channel emitter
+into an empty collector through an interacting resonant level of the quantum
+dot. When all Coulomb screening of sudden charge variations of the dot during
+the tunneling is realized by the emitter channel, the system is described with
+an exactly solvable model of a dissipative qubit. To study manifestations of
+the coherent qubit dynamics in the collector $\it{a.c.}$ response we derive
+solution to the corresponding Bloch equation for the model quantum evolution in
+the presence of the oscillating voltage of frequency $% \omega$ and calculate
+perturbatively the $\it{a.c.}$ response in the voltage amplitude. We have shown
+that in a wide range of the model parameters the coherent qubit dynamics
+results in the non-zero frequencies resonances in the amplitudes dependence of
+the $\it{a.c.}$ harmonics and in the jumps of the harmonics phase shifts across
+the resonances. In the first order the $\it{a.c.}$ response is directly related
+to the spectral decomposition of the corresponding transient current and
+contains only the first $\omega$ harmonic, whose amplitude exhibits resonance
+at $\omega =\omega_I $, where $\omega_I$ is the qubit oscillation frequency. In
+the second order we have obtained the $2 \omega$ harmonic of the $\it{a.c.}$
+response with resonances in the frequency dependence of its amplitude at
+$\omega_I$, $\omega_I/2$ and zero frequency and also have found the frequency
+dependent shift of the average steady current.
+",0,1,0,0,0,0
+10701,A general method to describe intersystem crossing dynamics in trajectory surface hopping,"  Intersystem crossing is a radiationless process that can take place in a
+molecule irradiated by UV-Vis light, thereby playing an important role in many
+environmental, biological and technological processes. This paper reviews
+different methods to describe intersystem crossing dynamics, paying attention
+to semiclassical trajectory theories, which are especially interesting because
+they can be applied to large systems with many degrees of freedom. In
+particular, a general trajectory surface hopping methodology recently developed
+by the authors, which is able to include non-adiabatic and spin-orbit couplings
+in excited-state dynamics simulations, is explained in detail. This method,
+termed SHARC, can in principle include any arbitrary coupling, what makes it
+generally applicable to photophysical and photochemical problems, also those
+including explicit laser fields. A step-by-step derivation of the main
+equations of motion employed in surface hopping based on the fewest-switches
+method of Tully, adapted for the inclusion of spin-orbit interactions, is
+provided. Special emphasis is put on describing the different possible choices
+of the electronic bases in which spin-orbit can be included in surface hopping,
+highlighting the advantages and inconsistencies of the different approaches.
+",0,1,0,0,0,0
+10702,Generating Synthetic Data for Real World Detection of DoS Attacks in the IoT,"  Denial of service attacks are especially pertinent to the internet of things
+as devices have less computing power, memory and security mechanisms to defend
+against them. The task of mitigating these attacks must therefore be redirected
+from the device onto a network monitor. Network intrusion detection systems can
+be used as an effective and efficient technique in internet of things systems
+to offload computation from the devices and detect denial of service attacks
+before they can cause harm. However the solution of implementing a network
+intrusion detection system for internet of things networks is not without
+challenges due to the variability of these systems and specifically the
+difficulty in collecting data. We propose a model-hybrid approach to model the
+scale of the internet of things system and effectively train network intrusion
+detection systems. Through bespoke datasets generated by the model, the IDS is
+able to predict a wide spectrum of real-world attacks, and as demonstrated by
+an experiment construct more predictive datasets at a fraction of the time of
+other more standard techniques.
+",1,0,0,0,0,0
+10703,A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments,"  This paper reports on a data-driven, interaction-aware motion prediction
+approach for pedestrians in environments cluttered with static obstacles. When
+navigating in such workspaces shared with humans, robots need accurate motion
+predictions of the surrounding pedestrians. Human navigation behavior is mostly
+influenced by their surrounding pedestrians and by the static obstacles in
+their vicinity. In this paper we introduce a new model based on Long-Short Term
+Memory (LSTM) neural networks, which is able to learn human motion behavior
+from demonstrated data. To the best of our knowledge, this is the first
+approach using LSTMs, that incorporates both static obstacles and surrounding
+pedestrians for trajectory forecasting. As part of the model, we introduce a
+new way of encoding surrounding pedestrians based on a 1d-grid in polar angle
+space. We evaluate the benefit of interaction-aware motion prediction and the
+added value of incorporating static obstacles on both simulation and real-world
+datasets by comparing with state-of-the-art approaches. The results show, that
+our new approach outperforms the other approaches while being very
+computationally efficient and that taking into account static obstacles for
+motion predictions significantly improves the prediction accuracy, especially
+in cluttered environments.
+",1,0,0,0,0,0
+10704,Performance study of SKIROC2 and SKIROC2A with BGA testboard,"  SKIROC2 is an ASIC to readout the silicon pad detectors for the
+electromagnetic calorimeter in the International Linear Collider.
+Characteristics of SKIROC2 and the new version of SKIROC2A, packaged with BGA,
+are measured with testboards and charge injection. The results on the
+signal-to-noise ratio of both trigger and ADC output, threshold tuning
+capability and timing resolution are presented.
+",0,1,0,0,0,0
+10705,Priority effects between annual and perennial plants,"  Dominance by annual plants has traditionally been considered a brief early
+stage of ecological succession preceding inevitable dominance by competitive
+perennials. A more recent, alternative view suggests that interactions between
+annuals and perennials can result in priority effects, causing annual dominance
+to persist if they are initially more common. Such priority effects would
+complicate restoration of native perennial grasslands that have been invaded by
+exotic annuals. However, the conditions under which these priority effects
+occur remain unknown. Using a simple simulation model, we show that long-term
+(500 years) priority effects are possible as long as the plants have low
+fecundity and show an establishment-longevity tradeoff, with annuals having
+competitive advantage over perennial seedlings. We also show that short-term
+(up to 50 years) priority effects arise solely due to low fitness difference in
+cases where perennials dominate in the long term. These results provide a
+theoretical basis for predicting when restoration of annual-invaded grasslands
+requires active removal of annuals and timely reintroduction of perennials.
+",0,0,0,0,1,0
+10706,Joining and decomposing reaction networks,"  In systems and synthetic biology, much research has focused on the behavior
+and design of single pathways, while, more recently, experimental efforts have
+focused on how cross-talk (coupling two or more pathways) or inhibiting
+molecular function (isolating one part of the pathway) affects systems-level
+behavior. However, the theory for tackling these larger systems in general has
+lagged behind. Here, we analyze how joining networks (e.g., cross-talk) or
+decomposing networks (e.g., inhibition or knock-outs) affects three properties
+that reaction networks may possess---identifiability (recoverability of
+parameter values from data), steady-state invariants (relationships among
+species concentrations at steady state, used in model selection), and
+multistationarity (capacity for multiple steady states, which correspond to
+multiple cell decisions). Specifically, we prove results that clarify, for a
+network obtained by joining two smaller networks, how properties of the smaller
+networks can be inferred from or can imply similar properties of the original
+network. Our proofs use techniques from computational algebraic geometry,
+including elimination theory and differential algebra.
+",0,0,0,0,1,0
+10707,Evidence for structural damping in a high-stress silicon nitride nanobeam and its implications for quantum optomechanics,"  We resolve the thermal motion of a high-stress silicon nitride nanobeam at
+frequencies far below its fundamental flexural resonance (3.4 MHz) using
+cavity-enhanced optical interferometry. Over two decades, the displacement
+spectrum is well-modeled by that of a damped harmonic oscillator driven by a
+$1/f$ thermal force, suggesting that the loss angle of the beam material is
+frequency-independent. The inferred loss angle at 3.4 MHz, $\phi = 4.5\cdot
+10^{-6}$, agrees well with the quality factor ($Q$) of the fundamental beam
+mode ($\phi = Q^{-1}$). In conjunction with $Q$ measurements made on higher
+order flexural modes, and accounting for the mode dependence of stress-induced
+loss dilution, we find that the intrinsic (undiluted) loss angle of the beam
+changes by less than a factor of 2 between 50 kHz and 50 MHz. We discuss the
+impact of such ""structural damping"" on experiments in quantum optomechanics, in
+which the thermal force acting on a mechanical oscillator coupled to an optical
+cavity is overwhelmed by radiation pressure shot noise. As an illustration, we
+show that structural damping reduces the bandwidth of ponderomotive squeezing.
+",0,1,0,0,0,0
+10708,"Efficient, sparse representation of manifold distance matrices for classical scaling","  Geodesic distance matrices can reveal shape properties that are largely
+invariant to non-rigid deformations, and thus are often used to analyze and
+represent 3-D shapes. However, these matrices grow quadratically with the
+number of points. Thus for large point sets it is common to use a low-rank
+approximation to the distance matrix, which fits in memory and can be
+efficiently analyzed using methods such as multidimensional scaling (MDS). In
+this paper we present a novel sparse method for efficiently representing
+geodesic distance matrices using biharmonic interpolation. This method exploits
+knowledge of the data manifold to learn a sparse interpolation operator that
+approximates distances using a subset of points. We show that our method is 2x
+faster and uses 20x less memory than current leading methods for solving MDS on
+large point sets, with similar quality. This enables analyses of large point
+sets that were previously infeasible.
+",1,0,0,1,0,0
+10709,A First Principle Study on Iron Substituted LiNi(BO3) to use as Cathode Material for Li-ion Batteries,"  In this work, the structural stability and the electronic properties of
+LiNiBO 3 and LiFe x Ni (1-x) BO 3 are studied using first principle
+calculations based on density functional theory. The calculated structural
+parameters are in good agreement with the available theoretical data. The most
+stable phases of the Fe substituted systems are predicted from the formation
+energy hull generated using the cluster expansion method. The 66% of Fe
+substitution at the Ni site gives the most stable structure among all the Fe
+substituted systems. The bonding mechanisms of the considered systems are
+discussed based on the density of states (DOS) and charge density plot. The
+detailed analysis of the stability, electronic structure, and the bonding
+mechanisms suggests that the systems can be a promising cathode material for Li
+ion battery applications.
+",0,1,0,0,0,0
+10710,Noncommutative Knörrer type equivalences via noncommutative resolutions of singularities,"  We construct Knörrer type equivalences outside of the hypersurface case,
+namely, between singularity categories of cyclic quotient surface singularities
+and certain finite dimensional local algebras. This generalises Knörrer's
+equivalence for singularities of Dynkin type A (between Krull dimensions $2$
+and $0$) and yields many new equivalences between singularity categories of
+finite dimensional algebras.
+Our construction uses noncommutative resolutions of singularities, relative
+singularity categories, and an idea of Hille & Ploog yielding strongly
+quasi-hereditary algebras which we describe explicitly by building on Wemyss's
+work on reconstruction algebras. Moreover, K-theory gives obstructions to
+generalisations of our main result.
+",0,0,1,0,0,0
+10711,Torsion and K-theory for some free wreath products,"  We classify torsion actions of free wreath products of arbitrary compact
+quantum groups and use this to prove that if $\mathbb{G}$ is a torsion-free
+compact quantum group satisfying the strong Baum-Connes property, then
+$\mathbb{G}\wr_{\ast}S_{N}^{+}$ also satisfies the strong Baum-Connes property.
+We then compute the K-theory of free wreath products of classical and quantum
+free groups by $SO_{q}(3)$.
+",0,0,1,0,0,0
+10712,High Dimensional Inference in Partially Linear Models,"  We propose two semiparametric versions of the debiased Lasso procedure for
+the model $Y_i = X_i\beta_0 + g_0(Z_i) + \epsilon_i$, where $\beta_0$ is high
+dimensional but sparse (exactly or approximately). Both versions are shown to
+have the same asymptotic normal distribution and do not require the minimal
+signal condition for statistical inference of any component in $\beta_0$. Our
+method also works when $Z_i$ is high dimensional provided that the function
+classes $E(X_{ij} |Z_i)$s and $E(Y_i|Z_i)$ belong to exhibit certain sparsity
+features, e.g., a sparse additive decomposition structure. We further develop a
+simultaneous hypothesis testing procedure based on multiplier bootstrap. Our
+testing method automatically takes into account of the dependence structure
+within the debiased estimates, and allows the number of tested components to be
+exponentially high.
+",0,0,1,1,0,0
+10713,The Impact of Small-Cell Bandwidth Requirements on Strategic Operators,"  Small-cell deployment in licensed and unlicensed spectrum is considered to be
+one of the key approaches to cope with the ongoing wireless data demand
+explosion. Compared to traditional cellular base stations with large
+transmission power, small-cells typically have relatively low transmission
+power, which makes them attractive for some spectrum bands that have strict
+power regulations, for example, the 3.5GHz band [1]. In this paper we consider
+a heterogeneous wireless network consisting of one or more service providers
+(SPs). Each SP operates in both macro-cells and small-cells, and provides
+service to two types of users: mobile and fixed. Mobile users can only
+associate with macro-cells whereas fixed users can connect to either macro- or
+small-cells. The SP charges a price per unit rate for each type of service.
+Each SP is given a fixed amount of bandwidth and splits it between macro- and
+small-cells. Motivated by bandwidth regulations, such as those for the 3.5Gz
+band, we assume a minimum amount of bandwidth has to be set aside for
+small-cells. We study the optimal pricing and bandwidth allocation strategies
+in both monopoly and competitive scenarios. In the monopoly scenario the
+strategy is unique. In the competitive scenario there exists a unique Nash
+equilibrium, which depends on the regulatory constraints. We also analyze the
+social welfare achieved, and compare it to that without the small-cell
+bandwidth constraints. Finally, we discuss implications of our results on the
+effectiveness of the minimum bandwidth constraint on influencing small-cell
+deployments.
+",1,0,1,0,0,0
+10714,Optimisation approach for the Monge-Ampere equation,"  This paper studies the numerical approximation of solution of the Dirichlet
+problem for the fully nonlinear Monge-Ampere equation. In this approach, we
+take the advantage of reformulation the Monge-Ampere problem as an optimization
+problem, to which we associate a well defined functional whose minimum provides
+us with the solution to the Monge-Ampere problem after resolving a Poisson
+problem by the finite element Galerkin method. We present some numerical
+examples, for which a good approximation is obtained in 68 iterations.
+",0,0,1,0,0,0
+10715,Coupling parallel adaptive mesh refinement with a nonoverlapping domain decomposition solver,"  We study the effect of adaptive mesh refinement on a parallel domain
+decomposition solver of a linear system of algebraic equations. These concepts
+need to be combined within a parallel adaptive finite element software. A
+prototype implementation is presented for this purpose. It uses adaptive mesh
+refinement with one level of hanging nodes. Two and three-level versions of the
+Balancing Domain Decomposition based on Constraints (BDDC) method are used to
+solve the arising system of algebraic equations. The basic concepts are
+recalled and components necessary for the combination are studied in detail. Of
+particular interest is the effect of disconnected subdomains, a typical output
+of the employed mesh partitioning based on space-filling curves, on the
+convergence and solution time of the BDDC method. It is demonstrated using a
+large set of experiments that while both refined meshes and disconnected
+subdomains have a negative effect on the convergence of BDDC, the number of
+iterations remains acceptable. In addition, scalability of the three-level BDDC
+solver remains good on up to a few thousands of processor cores. The largest
+presented problem using adaptive mesh refinement has over 10^9 unknowns and is
+solved on 2048 cores.
+",1,0,1,0,0,0
+10716,The Pragmatics of Indirect Commands in Collaborative Discourse,"  Today's artificial assistants are typically prompted to perform tasks through
+direct, imperative commands such as \emph{Set a timer} or \emph{Pick up the
+box}. However, to progress toward more natural exchanges between humans and
+these assistants, it is important to understand the way non-imperative
+utterances can indirectly elicit action of an addressee. In this paper, we
+investigate command types in the setting of a grounded, collaborative game. We
+focus on a less understood family of utterances for eliciting agent action,
+locatives like \emph{The chair is in the other room}, and demonstrate how these
+utterances indirectly command in specific game state contexts. Our work shows
+that models with domain-specific grounding can effectively realize the
+pragmatic reasoning that is necessary for more robust natural language
+interaction.
+",1,0,0,0,0,0
+10717,The Dawn of the Post-Naturalness Era,"  In an imaginary conversation with Guido Altarelli, I express my views on the
+status of particle physics beyond the Standard Model and its future prospects.
+",0,1,0,0,0,0
+10718,An effective likelihood-free approximate computing method with statistical inferential guarantees,"  Approximate Bayesian computing is a powerful likelihood-free method that has
+grown increasingly popular since early applications in population genetics.
+However, complications arise in the theoretical justification for Bayesian
+inference conducted from this method with a non-sufficient summary statistic.
+In this paper, we seek to re-frame approximate Bayesian computing within a
+frequentist context and justify its performance by standards set on the
+frequency coverage rate. In doing so, we develop a new computational technique
+called approximate confidence distribution computing, yielding theoretical
+support for the use of non-sufficient summary statistics in likelihood-free
+methods. Furthermore, we demonstrate that approximate confidence distribution
+computing extends the scope of approximate Bayesian computing to include
+data-dependent priors without damaging the inferential integrity. This
+data-dependent prior can be viewed as an initial `distribution estimate' of the
+target parameter which is updated with the results of the approximate
+confidence distribution computing method. A general strategy for constructing
+an appropriate data-dependent prior is also discussed and is shown to often
+increase the computing speed while maintaining statistical inferential
+guarantees. We supplement the theory with simulation studies illustrating the
+benefits of the proposed method, namely the potential for broader applications
+and the increased computing speed compared to the standard approximate Bayesian
+computing methods.
+",0,0,1,1,0,0
+10719,The word and conjugacy problems in lacunary hyperbolic groups,"  We study the word and conjugacy problems in lacunary hyperbolic groups
+(briefly, LHG). In particular, we describe a necessary and sufficient condition
+for decidability of the word problem in LHG. Then, based on the graded
+small-cancellation theory of Olshanskii, we develop a general framework which
+allows us to construct lacunary hyperbolic groups with word and conjugacy
+problems highly controllable and flexible both in terms of computability and
+computational complexity.
+As an application, we show that for any recursively enumerable subset
+$\mathcal{L} \subseteq \mathcal{A}^*$, where $\mathcal{A}^*$ is the set of
+words over arbitrarily chosen non-empty finite alphabet $\mathcal{A}$, there
+exists a lacunary hyperbolic group $G_{\mathcal{L}}$ such that the membership
+problem for $ \mathcal{L}$ is `almost' linear time equivalent to the conjugacy
+problem in $G_{\mathcal{L}}$. Moreover, for the mentioned group the word and
+individual conjugacy problems are decidable in `almost' linear time.
+Another application is the construction of a lacunary hyperbolic group with
+`almost' linear time word problem and with all the individual conjugacy
+problems being undecidable except the word problem.
+As yet another application of the developed framework, we construct infinite
+verbally complete groups and torsion free Tarski monsters, i.e. infinite
+torsion-free groups all of whose proper subgroups are cyclic, with `almost'
+linear time word and polynomial time conjugacy problems. These groups are
+constructed as quotients of arbitrarily given non-elementary torsion-free
+hyperbolic groups and are lacunary hyperbolic.
+Finally, as a consequence of the main results, we answer a few open
+questions.
+",0,0,1,0,0,0
+10720,Prediction of ultra-narrow Higgs resonance in magnon Bose-condensates,"  Higgs resonance modes in condensed matter systems are generally broad;
+meaning large decay widths or short relaxation times. This common feature has
+obscured and limited their observation to a select few systems. Contrary to
+this, the present work predicts that Higgs resonances in magnetic field
+induced, three-dimensional magnon Bose-condensates have vanishingly small decay
+widths. Specifically for parameters relating to TlCuCl$_3$, we find an energy
+($\Delta_H$) to width ($\Gamma_H$) ratio $\Delta_H/\Gamma_H\sim500$, making
+this the narrowest predicted Higgs mode in a condensed matter system, some two
+orders of magnitude `narrower' than the sharpest condensed matter Higgs
+observed so far.
+",0,1,0,0,0,0
+10721,Better Text Understanding Through Image-To-Text Transfer,"  Generic text embeddings are successfully used in a variety of tasks. However,
+they are often learnt by capturing the co-occurrence structure from pure text
+corpora, resulting in limitations of their ability to generalize. In this
+paper, we explore models that incorporate visual information into the text
+representation. Based on comprehensive ablation studies, we propose a
+conceptually simple, yet well performing architecture. It outperforms previous
+multimodal approaches on a set of well established benchmarks. We also improve
+the state-of-the-art results for image-related text datasets, using orders of
+magnitude less data.
+",1,0,0,0,0,0
+10722,Two-sample instrumental variable analyses using heterogeneous samples,"  Instrumental variable analysis is a widely used method to estimate causal
+effects in the presence of unmeasured confounding. When the instruments,
+exposure and outcome are not measured in the same sample, Angrist and Krueger
+(1992) suggested to use two-sample instrumental variable (TSIV) estimators that
+use sample moments from an instrument-exposure sample and an instrument-outcome
+sample. However, this method is biased if the two samples are from
+heterogeneous populations so that the distributions of the instruments are
+different. In linear structural equation models, we derive a new class of TSIV
+estimators that are robust to heterogeneous samples under the key assumption
+that the structural relations in the two samples are the same. The widely used
+two-sample two-stage least squares estimator belongs to this class. It is
+generally not asymptotically efficient, although we find that it performs
+similarly to the optimal TSIV estimator in most practical situations. We then
+attempt to relax the linearity assumption. We find that, unlike one-sample
+analyses, the TSIV estimator is not robust to misspecified exposure model.
+Additionally, to nonparametrically identify the magnitude of the causal effect,
+the noise in the exposure must have the same distributions in the two samples.
+However, this assumption is in general untestable because the exposure is not
+observed in one sample. Nonetheless, we may still identify the sign of the
+causal effect in the absence of homogeneity of the noise.
+",0,0,1,1,0,0
+10723,Combinatorial models for Schubert polynomials,"  Schubert polynomials are a basis for the polynomial ring that represent
+Schubert classes for the flag manifold. In this paper, we introduce and develop
+several new combinatorial models for Schubert polynomials that relate them to
+other known bases including key polynomials and fundamental slide polynomials.
+We unify these and existing models by giving simple bijections between the
+combinatorial objects indexing each. In particular, we give a simple bijective
+proof that the balanced tableaux of Edelman and Greene enumerate reduced
+expressions and a direct combinatorial proof of Kohnert's algorithm for
+computing Schubert polynomials. Further, we generalize the insertion algorithm
+of Edelman and Greene to give a bijection between reduced expressions and pairs
+of tableaux of the same key diagram shape and use this to give a simple
+formula, directly in terms of reduced expressions, for the key polynomial
+expansion of a Schubert polynomial.
+",0,0,1,0,0,0
+10724,Phase-tunable Josephson thermal router,"  Since the the first studies of thermodynamics, heat transport has been a
+crucial element for the understanding of any thermal system. Quantum mechanics
+has introduced new appealing ingredients for the manipulation of heat currents,
+such as the long-range coherence of the superconducting condensate. The latter
+has been exploited by phase-coherent caloritronics, a young field of
+nanoscience, to realize Josephson heat interferometers, which can control
+electronic thermal currents as a function of the external magnetic flux. So
+far, only one output temperature has been modulated, while multi-terminal
+devices that allow to distribute the heat flux among different reservoirs are
+still missing. Here, we report the experimental realization of a phase-tunable
+thermal router able to control the heat transferred between two terminals
+residing at different temperatures. Thanks to the Josephson effect, our
+structure allows to regulate the thermal gradient between the output electrodes
+until reaching its inversion. Together with interferometers, heat diodes and
+thermal memories, the thermal router represents a fundamental step towards the
+thermal conversion of non-linear electronic devices, and the realization of
+caloritronic logic components.
+",0,1,0,0,0,0
+10725,Topology Analysis of International Networks Based on Debates in the United Nations,"  In complex, high dimensional and unstructured data it is often difficult to
+extract meaningful patterns. This is especially the case when dealing with
+textual data. Recent studies in machine learning, information theory and
+network science have developed several novel instruments to extract the
+semantics of unstructured data, and harness it to build a network of relations.
+Such approaches serve as an efficient tool for dimensionality reduction and
+pattern detection. This paper applies semantic network science to extract
+ideological proximity in the international arena, by focusing on the data from
+General Debates in the UN General Assembly on the topics of high salience to
+international community. UN General Debate corpus (UNGDC) covers all high-level
+debates in the UN General Assembly from 1970 to 2014, covering all UN member
+states. The research proceeds in three main steps. First, Latent Dirichlet
+Allocation (LDA) is used to extract the topics of the UN speeches, and
+therefore semantic information. Each country is then assigned a vector
+specifying the exposure to each of the topics identified. This intermediate
+output is then used in to construct a network of countries based on information
+theoretical metrics where the links capture similar vectorial patterns in the
+topic distributions. Topology of the networks is then analyzed through network
+properties like density, path length and clustering. Finally, we identify
+specific topological features of our networks using the map equation framework
+to detect communities in our networks of countries.
+",1,0,1,1,0,0
+10726,Solving the incompressible surface Navier-Stokes equation by surface finite elements,"  We consider a numerical approach for the incompressible surface Navier-Stokes
+equation on surfaces with arbitrary genus $g(\mathcal{S})$. The approach is
+based on a reformulation of the equation in Cartesian coordinates of the
+embedding $\mathbb{R}^3$, penalization of the normal component, a Chorin
+projection method and discretization in space by surface finite elements for
+each component. The approach thus requires only standard ingredients which most
+finite element implementations can offer. We compare computational results with
+discrete exterior calculus (DEC) simulations on a torus and demonstrate the
+interplay of the flow field with the topology by showing realizations of the
+Poincaré-Hopf theorem on $n$-tori.
+",0,1,0,0,0,0
+10727,Exploring a potential energy surface by machine learning for characterizing atomic transport,"  We propose a machine-learning method for evaluating the potential barrier
+governing atomic transport based on the preferential selection of dominant
+points for the atomic transport. The proposed method generates numerous random
+samples of the entire potential energy surface (PES) from a probabilistic
+Gaussian process model of the PES, which enables defining the likelihood of the
+dominant points. The robustness and efficiency of the method are demonstrated
+on a dozen model cases for proton diffusion in oxides, in comparison with a
+conventional nudge elastic band method.
+",0,1,0,0,0,0
+10728,Products of random walks on finite groups with moderate growth,"  In this article, we consider products of random walks on finite groups with
+moderate growth and discuss their cutoffs in the total variation. Based on
+several comparison techniques, we are able to identify the total variation
+cutoff of discrete time lazy random walks with the Hellinger distance cutoff of
+continuous time random walks. Along with the cutoff criterion for Laplace
+transforms, we derive a series of equivalent conditions on the existence of
+cutoffs, including the existence of pre-cutoffs, Peres' product condition and a
+formula generated by the graph diameters. For illustration, we consider
+products of Heisenberg groups and randomized products of finite cycles.
+",0,0,1,0,0,0
+10729,A Theoretical Analysis of Sparse Recovery Stability of Dantzig Selector and LASSO,"  Dantzig selector (DS) and LASSO problems have attracted plenty of attention
+in statistical learning, sparse data recovery and mathematical optimization. In
+this paper, we provide a theoretical analysis of the sparse recovery stability
+of these optimization problems in more general settings and from a new
+perspective. We establish recovery error bounds for these optimization problems
+under a mild assumption called weak range space property of a transposed design
+matrix. This assumption is less restrictive than the well known sparse recovery
+conditions such as restricted isometry property (RIP), null space property
+(NSP) or mutual coherence. In fact, our analysis indicates that this assumption
+is tight and cannot be relaxed for the standard DS problems in order to
+maintain their sparse recovery stability. As a result, a series of new
+stability results for DS and LASSO have been established under various matrix
+properties, including the RIP with constant $\delta_{2k}< 1/\sqrt{2}$ and the
+(constant-free) standard NSP of order $k.$ We prove that these matrix
+properties can yield an identical recovery error bound for DS and LASSO with
+stability coefficients being measured by the so-called Robinson's constant,
+instead of the conventional RIP or NSP constant. To our knowledge, this is the
+first time that the stability results with such a unified feature are
+established for DS and LASSO problems. Different from the standard analysis in
+this area of research, our analysis is carried out deterministically, and the
+key analytic tools used in our analysis include the error bound of linear
+systems due to Hoffman and Robinson and polytope approximation of symmetric
+convex bodies due to Barvinok.
+",0,0,1,1,0,0
+10730,Input Perturbations for Adaptive Regulation and Learning,"  Design of adaptive algorithms for simultaneous regulation and estimation of
+MIMO linear dynamical systems is a canonical reinforcement learning problem.
+Efficient policies whose regret (i.e. increase in the cost due to uncertainty)
+scales at a square-root rate of time have been studied extensively in the
+recent literature. Nevertheless, existing strategies are computationally
+intractable and require a priori knowledge of key system parameters. The only
+exception is a randomized Greedy regulator, for which asymptotic regret bounds
+have been recently established. However, randomized Greedy leads to probable
+fluctuations in the trajectory of the system, which renders its finite time
+regret suboptimal.
+This work addresses the above issues by designing policies that utilize input
+signals perturbations. We show that perturbed Greedy guarantees non-asymptotic
+regret bounds of (nearly) square-root magnitude w.r.t. time. More generally, we
+establish high probability bounds on both the regret and the learning accuracy
+under arbitrary input perturbations. The settings where Greedy attains the
+information theoretic lower bound of logarithmic regret are also discussed. To
+obtain the results, state-of-the-art tools from martingale theory together with
+the recently introduced method of policy decomposition are leveraged. Beside
+adaptive regulators, analysis of input perturbations captures key applications
+including remote sensing and distributed control.
+",1,0,0,0,0,0
+10731,Nearly circular domains which are integrable close to the boundary are ellipses,"  The Birkhoff conjecture says that the boundary of a strictly convex
+integrable billiard table is necessarily an ellipse. In this article, we
+consider a stronger notion of integrability, namely integrability close to the
+boundary, and prove a local version of this conjecture: a small perturbation of
+an ellipse of small eccentricity which preserves integrability near the
+boundary, is itself an ellipse. This extends the result in [1], where
+integrability was assumed on a larger set. In particular, it shows that (local)
+integrability near the boundary implies global integrability. One of the
+crucial ideas in the proof consists in analyzing Taylor expansion of the
+corresponding action-angle coordinates with respect to the eccentricity
+parameter, deriving and studying higher order conditions for the preservation
+of integrable rational caustics.
+",0,0,1,0,0,0
+10732,Empirical study on social groups in pedestrian evacuation dynamics,"  Pedestrian crowds often include social groups, i.e. pedestrians that walk
+together because of social relationships. They show characteristic
+configurations and influence the dynamics of the entire crowd. In order to
+investigate the impact of social groups on evacuations we performed an
+empirical study with pupils. Several evacuation runs with groups of different
+sizes and different interactions were performed. New group parameters are
+introduced which allow to describe the dynamics of the groups and the
+configuration of the group members quantitatively. The analysis shows a
+possible decrease of evacuation times for large groups due to self-ordering
+effects. Social groups can be approximated as ellipses that orientate along
+their direction of motion. Furthermore, explicitly cooperative behaviour among
+group members leads to a stronger aggregation of group members and an
+intermittent way of evacuation.
+",0,1,0,0,0,0
+10733,Capacity of the Aperture-Constrained AWGN Free-Space Communication Channel,"  In this paper, we derive upper and lower bounds as well as a simple
+closed-form approximation for the capacity of the continuous-time, bandlimited,
+additive white Gaussian noise channel in a three-dimensional free-space
+electromagnetic propagation environment subject to constraints on the total
+effective antenna aperture area of the link and a total transmitter power
+constraint. We assume that the communication range is much larger than the
+radius of the sphere containing the antennas at both ends of the link, and we
+show that, in general, the capacity can only be achieved by transmitting
+multiple spatially-multiplexed data streams simultaneously over the channel.
+Furthermore, the lower bound on capacity can be approached asymptotically by
+transmitting the data streams between a pair of physically-realizable
+distributed antenna arrays at either end of the link. A consequence of this
+result is that, in general, communication at close to the maximum achievable
+data rate on a deep-space communication link can be achieved in practice if and
+only if the communication system utilizes spatial multiplexing over a
+distributed MIMO antenna array. Such an approach to deep-space communication
+does not appear to be envisioned currently by any of the international space
+agencies or any commercial space companies. A second consequence is that the
+capacity of a long-range free-space communication link, if properly utilized,
+grows asymptotically as a function of the square root of the received SNR
+rather than only logarithmically in the received SNR.
+",1,0,0,0,0,0
+10734,Quasiparticle entropy in superconductor/normal metal/superconductor proximity junctions in the diffusive limit,"  We discuss the quasiparticle entropy and heat capacity of a dirty
+superconductor-normal metal-superconductor junction. In the case of short
+junctions, the inverse proximity effect extending in the superconducting banks
+plays a crucial role in determining the thermodynamic quantities. In this case,
+commonly used approximations can violate thermodynamic relations between
+supercurrent and quasiparticle entropy. We provide analytical and numerical
+results as a function of different geometrical parameters. Quantitative
+estimates for the heat capacity can be relevant for the design of caloritronic
+devices or radiation sensor applications.
+",0,1,0,0,0,0
+10735,A Kepler Study of Starspot Lifetimes with Respect to Light Curve Amplitude and Spectral Type,"  Wide-field high precision photometric surveys such as Kepler have produced
+reams of data suitable for investigating stellar magnetic activity of cooler
+stars. Starspot activity produces quasi-sinusoidal light curves whose phase and
+amplitude vary as active regions grow and decay over time. Here we investigate,
+firstly, whether there is a correlation between the size of starspots - assumed
+to be related to the amplitude of the sinusoid - and their decay timescale and,
+secondly, whether any such correlation depends on the stellar effective
+temperature. To determine this, we computed the autocorrelation functions of
+the light curves of samples of stars from Kepler and fitted them with apodised
+periodic functions. The light curve amplitudes, representing spot size were
+measured from the root-mean-squared scatter of the normalised light curves. We
+used a Monte Carlo Markov Chain to measure the periods and decay timescales of
+the light curves. The results show a correlation between the decay time of
+starspots and their inferred size. The decay time also depends strongly on the
+temperature of the star. Cooler stars have spots that last much longer, in
+particular for stars with longer rotational periods. This is consistent with
+current theories of diffusive mechanisms causing starspot decay. We also find
+that the Sun is not unusually quiet for its spectral type - stars with
+solar-type rotation periods and temperatures tend to have (comparatively)
+smaller starspots than stars with mid-G or later spectral types.
+",0,1,0,0,0,0
+10736,Plasmon-Driven Acceleration in a Photo-Excited Nanotube,"  A plasmon-assisted channeling acceleration can be realized with a large
+channel, possibly at the nanometer scale. Carbon nanotubes (CNTs) are the most
+typical example of nano-channels that can confine a large number of channeled
+particles in a photon-plasmon coupling condition. This paper presents a
+theoretical and numerical study on the concept of high-field charge
+acceleration driven by photo-excited Luttinger-liquid plasmons (LLP) in a
+nanotube. An analytic description of the plasmon-assisted laser acceleration is
+detailed with practical acceleration parameters, in particular with
+specifications of a typical tabletop femtosecond laser system. The maximally
+achievable acceleration gradients and energy gains within dephasing lengths and
+CNT lengths are discussed with respect to laser-incident angles and CNT-filling
+ratios.
+",0,1,0,0,0,0
+10737,Towards Evaluating Size Reduction Techniques for Software Model Checking,"  Formal verification techniques are widely used for detecting design flaws in
+software systems. Formal verification can be done by transforming an already
+implemented source code to a formal model and attempting to prove certain
+properties of the model (e.g. that no erroneous state can occur during
+execution). Unfortunately, transformations from source code to a formal model
+often yield large and complex models, making the verification process
+inefficient and costly. In order to reduce the size of the resulting model,
+optimization transformations can be used. Such optimizations include common
+algorithms known from compiler design and different program slicing techniques.
+Our paper describes a framework for transforming C programs to a formal model,
+enhanced by various optimizations for size reduction. We evaluate and compare
+several optimization algorithms regarding their effect on the size of the model
+and the efficiency of the verification. Results show that different
+optimizations are more suitable for certain models, justifying the need for a
+framework that includes several algorithms.
+",1,0,0,0,0,0
+10738,Quantum-continuum simulation of underpotential deposition at electrified metal-solution interfaces,"  The underpotential deposition of transition metal ions is a critical step in
+many electrosynthetic approaches. While underpotential deposition has been
+intensively studied at the atomic level, first-principles calculations in
+vacuum can strongly underestimate the stability of underpotentially deposited
+metals. It has been shown recently that the consideration of co-adsorbed anions
+can deliver more reliable descriptions of underpotential deposition reactions;
+however, the influence of additional key environmental factors such as the
+electrification of the interface under applied voltage and the activities of
+the ions in solution have yet to be investigated. In this work, copper
+underpotential deposition on gold is studied under realistic electrochemical
+conditions using a quantum-continuum model of the electrochemical interface. We
+report here on the influence of surface electrification, concentration effects,
+and anion co-adsorption on the stability of the copper underpotential
+deposition layer on the gold (100) surface.
+",0,1,0,0,0,0
+10739,Neural Expectation Maximization,"  Many real world tasks such as reasoning and physical interaction require
+identification and manipulation of conceptual entities. A first step towards
+solving these tasks is the automated discovery of distributed symbol-like
+representations. In this paper, we explicitly formalize this problem as
+inference in a spatial mixture model where each component is parametrized by a
+neural network. Based on the Expectation Maximization framework we then derive
+a differentiable clustering method that simultaneously learns how to group and
+represent individual entities. We evaluate our method on the (sequential)
+perceptual grouping task and find that it is able to accurately recover the
+constituent objects. We demonstrate that the learned representations are useful
+for next-step prediction.
+",1,0,0,1,0,0
+10740,Gold Standard Online Debates Summaries and First Experiments Towards Automatic Summarization of Online Debate Data,"  Usage of online textual media is steadily increasing. Daily, more and more
+news stories, blog posts and scientific articles are added to the online
+volumes. These are all freely accessible and have been employed extensively in
+multiple research areas, e.g. automatic text summarization, information
+retrieval, information extraction, etc. Meanwhile, online debate forums have
+recently become popular, but have remained largely unexplored. For this reason,
+there are no sufficient resources of annotated debate data available for
+conducting research in this genre. In this paper, we collected and annotated
+debate data for an automatic summarization task. Similar to extractive gold
+standard summary generation our data contains sentences worthy to include into
+a summary. Five human annotators performed this task. Inter-annotator
+agreement, based on semantic similarity, is 36% for Cohen's kappa and 48% for
+Krippendorff's alpha. Moreover, we also implement an extractive summarization
+system for online debates and discuss prominent features for the task of
+summarizing online debate data automatically.
+",1,0,0,0,0,0
+10741,Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors,"  We show that Entropy-SGD (Chaudhari et al., 2017), when viewed as a learning
+algorithm, optimizes a PAC-Bayes bound on the risk of a Gibbs (posterior)
+classifier, i.e., a randomized classifier obtained by a risk-sensitive
+perturbation of the weights of a learned classifier. Entropy-SGD works by
+optimizing the bound's prior, violating the hypothesis of the PAC-Bayes theorem
+that the prior is chosen independently of the data. Indeed, available
+implementations of Entropy-SGD rapidly obtain zero training error on random
+labels and the same holds of the Gibbs posterior. In order to obtain a valid
+generalization bound, we rely on a result showing that data-dependent priors
+obtained by stochastic gradient Langevin dynamics (SGLD) yield valid PAC-Bayes
+bounds provided the target distribution of SGLD is $\epsilon$-differentially
+private. We observe that test error on MNIST and CIFAR10 falls within the
+(empirically nonvacuous) risk bounds computed under the assumption that SGLD
+reaches stationarity. In particular, Entropy-SGLD can be configured to yield
+relatively tight generalization bounds and still fit real labels, although
+these same settings do not obtain state-of-the-art performance.
+",1,0,0,1,0,0
+10742,DeepFM: A Factorization-Machine based Neural Network for CTR Prediction,"  Learning sophisticated feature interactions behind user behaviors is critical
+in maximizing CTR for recommender systems. Despite great progress, existing
+methods seem to have a strong bias towards low- or high-order interactions, or
+require expertise feature engineering. In this paper, we show that it is
+possible to derive an end-to-end learning model that emphasizes both low- and
+high-order feature interactions. The proposed model, DeepFM, combines the power
+of factorization machines for recommendation and deep learning for feature
+learning in a new neural network architecture. Compared to the latest Wide \&
+Deep model from Google, DeepFM has a shared input to its ""wide"" and ""deep""
+parts, with no need of feature engineering besides raw features. Comprehensive
+experiments are conducted to demonstrate the effectiveness and efficiency of
+DeepFM over the existing models for CTR prediction, on both benchmark data and
+commercial data.
+",1,0,0,0,0,0
+10743,How a small quantum bath can thermalize long localized chains,"  We investigate the stability of the many-body localized (MBL) phase for a
+system in contact with a single ergodic grain, modelling a Griffiths region
+with low disorder. Our numerical analysis provides evidence that even a small
+ergodic grain consisting of only 3 qubits can delocalize a localized chain, as
+soon as the localization length exceeds a critical value separating localized
+and extended regimes of the whole system. We present a simple theory,
+consistent with the arguments in [Phys. Rev. B 95, 155129 (2017)], that assumes
+a system to be locally ergodic unless the local relaxation time, determined by
+Fermi's Golden Rule, is larger than the inverse level spacing. This theory
+predicts a critical value for the localization length that is perfectly
+consistent with our numerical calculations. We analyze in detail the behavior
+of local operators inside and outside the ergodic grain, and find excellent
+agreement of numerics and theory.
+",0,1,0,0,0,0
+10744,Transfer Learning for Brain-Computer Interfaces: An Euclidean Space Data Alignment Approach,"  Almost all EEG-based brain-computer interfaces (BCIs) need some labeled
+subject-specific data to calibrate a new subject, as neural responses are
+different across subjects to even the same stimulus. So, a major challenge in
+developing high-performance and user-friendly BCIs is to cope with such
+individual differences so that the calibration can be reduced or even
+completely eliminated. This paper focuses on the latter. More specifically, we
+consider an offline application scenario, in which we have unlabeled EEG trials
+from a new subject, and would like to accurately label them by leveraging
+auxiliary labeled EEG trials from other subjects in the same task. To
+accommodate the individual differences, we propose a novel unsupervised
+approach to align the EEG trials from different subjects in the Euclidean space
+to make them more consistent. It has three desirable properties: 1) the aligned
+trial lie in the Euclidean space, which can be used by any Euclidean space
+signal processing and machine learning approach; 2) it can be computed very
+efficiently; and, 3) it does not need any labeled trials from the new subject.
+Experiments on motor imagery and event-related potentials demonstrated the
+effectiveness and efficiency of our approach.
+",0,0,0,1,1,0
+10745,Linear and bilinear restriction to certain rotationally symmetric hypersurfaces,"  Conditional on Fourier restriction estimates for elliptic hypersurfaces, we
+prove optimal restriction estimates for polynomial hypersurfaces of revolution
+for which the defining polynomial has non-negative coefficients. In particular,
+we obtain uniform--depending only on the dimension and polynomial
+degree--estimates for restriction with affine surface measure, slightly beyond
+the bilinear range. The main step in the proof of our linear result is an
+(unconditional) bilinear adjoint restriction estimate for pieces at different
+scales.
+",0,0,1,0,0,0
+10746,Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction,"  When a measurement falls outside the quantization or measurable range, it
+becomes saturated and cannot be used in classical reconstruction methods. For
+example, in C-arm angiography systems, which provide projection radiography,
+fluoroscopy, digital subtraction angiography, and are widely used for medical
+diagnoses and interventions, the limited dynamic range of C-arm flat detectors
+leads to overexposure in some projections during an acquisition, such as
+imaging relatively thin body parts (e.g., the knee). Aiming at overexposure
+correction for computed tomography (CT) reconstruction, we in this paper
+propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information
+from both regular and saturated measurements. This method is inspired by the
+recent progress on one-bit compressive sensing, which deals with only sign
+observations. Its successful applications imply that information carried by
+saturated measurements is useful to improve recovery quality. For the proposed
+M1bit-CS model, alternating direction methods of multipliers is developed and
+an iterative saturation detection scheme is established. Then we evaluate
+M1bit-CS on one-dimensional signal recovery tasks. In some experiments, the
+performance of the proposed algorithms on mixed measurements is almost the same
+as recovery on unsaturated ones with the same amount of measurements. Finally,
+we apply the proposed method to overexposure correction for CT reconstruction
+on a phantom and a simulated clinical image. The results are promising, as the
+typical streaking artifacts and capping artifacts introduced by saturated
+projection data are effectively reduced, yielding significant error reduction
+compared with existing algorithms based on extrapolation.
+",1,0,1,0,0,0
+10747,Stochastic Gradient Descent on Highly-Parallel Architectures,"  There is an increased interest in building data analytics frameworks with
+advanced algebraic capabilities both in industry and academia. Many of these
+frameworks, e.g., TensorFlow and BIDMach, implement their compute-intensive
+primitives in two flavors---as multi-thread routines for multi-core CPUs and as
+highly-parallel kernels executed on GPU. Stochastic gradient descent (SGD) is
+the most popular optimization method for model training implemented extensively
+on modern data analytics platforms. While the data-intensive properties of SGD
+are well-known, there is an intense debate on which of the many SGD variants is
+better in practice. In this paper, we perform a comprehensive study of parallel
+SGD for training generalized linear models. We consider the impact of three
+factors -- computing architecture (multi-core CPU or GPU), synchronous or
+asynchronous model updates, and data sparsity -- on three measures---hardware
+efficiency, statistical efficiency, and time to convergence. In the process, we
+design an optimized asynchronous SGD algorithm for GPU that leverages warp
+shuffling and cache coalescing for data and model access. We draw several
+interesting findings from our extensive experiments with logistic regression
+(LR) and support vector machines (SVM) on five real datasets. For synchronous
+SGD, GPU always outperforms parallel CPU---they both outperform a sequential
+CPU solution by more than 400X. For asynchronous SGD, parallel CPU is the
+safest choice while GPU with data replication is better in certain situations.
+The choice between synchronous GPU and asynchronous CPU depends on the task and
+the characteristics of the data. As a reference, our best implementation
+outperforms TensorFlow and BIDMach consistently. We hope that our insights
+provide a useful guide for applying parallel SGD to generalized linear models.
+",1,0,0,0,0,0
+10748,Mixed Precision Training,"  Deep neural networks have enabled progress in a wide variety of applications.
+Growing the size of the neural network typically results in improved accuracy.
+As model sizes grow, the memory and compute requirements for training these
+models also increases. We introduce a technique to train deep neural networks
+using half precision floating point numbers. In our technique, weights,
+activations and gradients are stored in IEEE half-precision format.
+Half-precision floating numbers have limited numerical range compared to
+single-precision numbers. We propose two techniques to handle this loss of
+information. Firstly, we recommend maintaining a single-precision copy of the
+weights that accumulates the gradients after each optimizer step. This
+single-precision copy is rounded to half-precision format during training.
+Secondly, we propose scaling the loss appropriately to handle the loss of
+information with half-precision gradients. We demonstrate that this approach
+works for a wide variety of models including convolution neural networks,
+recurrent neural networks and generative adversarial networks. This technique
+works for large scale models with more than 100 million parameters trained on
+large datasets. Using this approach, we can reduce the memory consumption of
+deep learning models by nearly 2x. In future processors, we can also expect a
+significant computation speedup using half-precision hardware units.
+",1,0,0,1,0,0
+10749,An elementary representation of the higher-order Jacobi-type differential equation,"  We investigate the differential equation for the Jacobi-type polynomials
+which are orthogonal on the interval $[-1,1]$ with respect to the classical
+Jacobi measure and an additional point mass at one endpoint. This scale of
+higher-order equations was introduced by J. and R. Koekoek in 1999 essentially
+by using special function methods. In this paper, a completely elementary
+representation of the Jacobi-type differential operator of any even order is
+given. This enables us to trace the orthogonality relation of the Jacobi-type
+polynomials back to their differential equation. Moreover, we establish a new
+factorization of the Jacobi-type operator which gives rise to a recurrence
+relation with respect to the order of the equation.
+",0,0,1,0,0,0
+10750,Theory of $L$-edge spectroscopy of strongly correlated systems,"  X-ray absorption spectroscopy measured at the $L$-edge of transition metals
+(TMs) is a powerful element-selective tool providing direct information about
+the correlation effects in the $3d$ states. The theoretical modeling of the
+$2p\rightarrow3d$ excitation processes remains to be challenging for
+contemporary \textit{ab initio} electronic structure techniques, due to strong
+core-hole and multiplet effects influencing the spectra. In this work we
+present a realization of the method combining the density-functional theory
+with multiplet ligand field theory, proposed in Haverkort et al.
+(this https URL), Phys. Rev. B 85, 165113
+(2012). In this approach a single-impurity Anderson model (SIAM) is
+constructed, with almost all parameters obtained from first principles, and
+then solved to obtain the spectra. In our implementation we adopt the language
+of the dynamical mean-field theory and utilize the local density of states and
+the hybridization function, projected onto TM $3d$ states, in order to
+construct the SIAM. The developed computational scheme is applied to calculate
+the $L$-edge spectra for several TM monoxides. A very good agreement between
+the theory and experiment is found for all studied systems. The effect of
+core-hole relaxation, hybridization discretization, possible extensions of the
+method as well as its limitations are discussed.
+",0,1,0,0,0,0
+10751,Pseudo-spin Skyrmions in the Phase Diagram of Cuprate Superconductors,"  Topological states of matter are at the root of some of the most fascinating
+phenomena in condensed matter physics. Here we argue that skyrmions in the
+pseudo-spin space related to an emerging SU(2) symmetry enlighten many
+mysterious properties of the pseudogap phase in under-doped cuprates. We detail
+the role of the SU(2) symmetry in controlling the phase diagram of the
+cuprates, in particular how a cascade of phase transitions explains the arising
+of the pseudogap, superconducting and charge modulation phases seen at low
+temperature. We specify the structure of the charge modulations inside the
+vortex core below $T_{c}$, as well as in a wide temperature region above
+$T_{c}$, which is a signature of the skyrmion topological structure. We argue
+that the underlying SU(2) symmetry is the main structure controlling the
+emergent complexity of excitations at the pseudogap scale $T^{*}$. The theory
+yields a gapping of a large part of the anti-nodal region of the Brillouin
+zone, along with $q=0$ phase transitions, of both nematic and loop currents
+characters.
+",0,1,0,0,0,0
+10752,Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method,"  We consider large scale empirical risk minimization (ERM) problems, where
+both the problem dimension and variable size is large. In these cases, most
+second order methods are infeasible due to the high cost in both computing the
+Hessian over all samples and computing its inverse in high dimensions. In this
+paper, we propose a novel adaptive sample size second-order method, which
+reduces the cost of computing the Hessian by solving a sequence of ERM problems
+corresponding to a subset of samples and lowers the cost of computing the
+Hessian inverse using a truncated eigenvalue decomposition. We show that while
+we geometrically increase the size of the training set at each stage, a single
+iteration of the truncated Newton method is sufficient to solve the new ERM
+within its statistical accuracy. Moreover, for a large number of samples we are
+allowed to double the size of the training set at each stage, and the proposed
+method subsequently reaches the statistical accuracy of the full training set
+approximately after two effective passes. In addition to this theoretical
+result, we show empirically on a number of well known data sets that the
+proposed truncated adaptive sample size algorithm outperforms stochastic
+alternatives for solving ERM problems.
+",1,0,1,1,0,0
+10753,Large covers and sharp resonances of hyperbolic surfaces,"  Let $\Gamma$ be a convex co-compact discrete group of isometries of the
+hyperbolic plane $\mathbb{H}^2$, and $X=\Gamma\backslash \mathbb{H}^2$ the
+associated surface. In this paper we investigate the behaviour of resonances of
+the Laplacian for large degree covers of $X$ given by a finite index normal
+subgroup of $\Gamma$. Using various techniques of thermodynamical formalism and
+representation theory, we prove two new existence results of ""sharp non-trivial
+resonances"" close to $\Re(s)=\delta_\Gamma$, both in the large degree limit,
+for abelian covers and also infinite index congruence subgroups of
+$SL2(\mathbb{Z})$.
+",0,0,1,0,0,0
+10754,Computational Models of Tutor Feedback in Language Acquisition,"  This paper investigates the role of tutor feedback in language learning using
+computational models. We compare two dominant paradigms in language learning:
+interactive learning and cross-situational learning - which differ primarily in
+the role of social feedback such as gaze or pointing. We analyze the
+relationship between these two paradigms and propose a new mixed paradigm that
+combines the two paradigms and allows to test algorithms in experiments that
+combine no feedback and social feedback. To deal with mixed feedback
+experiments, we develop new algorithms and show how they perform with respect
+to traditional knn and prototype approaches.
+",1,0,0,0,0,0
+10755,Non-Saturated Throughput Analysis of Coexistence of Wi-Fi and Cellular With Listen-Before-Talk in Unlicensed Spectrum,"  This paper analyzes the coexistence performance of Wi-Fi and cellular
+networks conditioned on non-saturated traffic in the unlicensed spectrum. Under
+the condition, the time-domain behavior of a cellular small-cell base station
+(SCBS) with a listen-before-talk (LBT) procedure is modeled as a Markov chain,
+and it is combined with a Markov chain which describes the time-domain behavior
+of a Wi-Fi access point. Using the proposed model, this study finds the optimal
+contention window size of cellular SCBSs in which total throughput of both
+networks is maximized while satisfying the required throughput of each network,
+under the given traffic densities of both networks. This will serve as a
+guideline for cellular operators with respect to performing LBT at cellular
+SCBSs according to the changes of traffic volumes of both networks over time.
+",1,0,0,0,0,0
+10756,A Dynamic Model for Traffic Flow Prediction Using Improved DRN,"  Real-time traffic flow prediction can not only provide travelers with
+reliable traffic information so that it can save people's time, but also assist
+the traffic management agency to manage traffic system. It can greatly improve
+the efficiency of the transportation system. Traditional traffic flow
+prediction approaches usually need a large amount of data but still give poor
+performances. With the development of deep learning, researchers begin to pay
+attention to artificial neural networks (ANNs) such as RNN and LSTM. However,
+these ANNs are very time-consuming. In our research, we improve the Deep
+Residual Network and build a dynamic model which previous researchers hardly
+use. We firstly integrate the input and output of the $i^{th}$ layer to the
+input of the $i+1^{th}$ layer and prove that each layer will fit a simpler
+function so that the error rate will be much smaller. Then, we use the concept
+of online learning in our model to update pre-trained model during prediction.
+Our result shows that our model has higher accuracy than some state-of-the-art
+models. In addition, our dynamic model can perform better in practical
+applications.
+",0,0,0,1,0,0
+10757,"Think globally, fit locally under the Manifold Setup: Asymptotic Analysis of Locally Linear Embedding","  Since its introduction in 2000, the locally linear embedding (LLE) has been
+widely applied in data science. We provide an asymptotical analysis of the LLE
+under the manifold setup. We show that for the general manifold, asymptotically
+we may not obtain the Laplace-Beltrami operator, and the result may depend on
+the non-uniform sampling, unless a correct regularization is chosen. We also
+derive the corresponding kernel function, which indicates that the LLE is not a
+Markov process. A comparison with the other commonly applied nonlinear
+algorithms, particularly the diffusion map, is provided, and its relationship
+with the locally linear regression is also discussed.
+",0,0,1,1,0,0
+10758,Relative energetics of acetyl-histidine protomers with and without Zn2+ and a benchmark of energy methods,"  We studied acetylhistidine (AcH), bare or microsolvated with a zinc cation by
+simulations in isolation. First, a global search for minima of the potential
+energy surface combining both, empirical and first-principles methods, is
+performed individually for either one of five possible protonation states.
+Comparing the most stable structures between tautomeric forms of negatively
+charged AcH shows a clear preference for conformers with the neutral imidazole
+ring protonated at the N-epsilon-2 atom. When adding a zinc cation to the
+system, the situation is reversed and N-delta-1-protonated structures are
+energetically more favorable. Obtained minima structures then served as basis
+for a benchmark study to examine the goodness of commonly applied levels of
+theory, i.e. force fields, semi-empirical methods, density-functional
+approximations (DFA), and wavefunction-based methods with respect to high-level
+coupled-cluster calculations, i.e. the DLPNO-CCSD(T) method. All tested force
+fields and semi-empirical methods show a poor performance in reproducing the
+energy hierarchies of conformers, in particular of systems involving the zinc
+cation. Meta-GGA, hybrid, double hybrid DFAs, and the MP2 method are able to
+describe the energetics of the reference method within chemical accuracy, i.e.
+with a mean absolute error of less than 1kcal/mol. Best performance is found
+for the double hybrid DFA B3LYP+XYG3 with a mean absolute error of 0.7 kcal/mol
+and a maximum error of 1.8 kcal/mol. While MP2 performs similarly as
+B3LYP+XYG3, computational costs, i.e. timings, are increased by a factor of 4
+in comparison due to the large basis sets required for accurate results.
+",0,0,0,0,1,0
+10759,"""Space is blue and birds fly through it""","  Quantum mechanics is not about 'quantum states': it is about values of
+physical variables. I give a short fresh presentation and update on the
+$relational$ perspective on the theory, and a comment on its philosophical
+implications.
+",0,1,0,0,0,0
+10760,On the Geometry of Nash and Correlated Equilibria with Cumulative Prospect Theoretic Preferences,"  It is known that the set of all correlated equilibria of an n-player
+non-cooperative game is a convex polytope and includes all the Nash equilibria.
+Further, the Nash equilibria all lie on the boundary of this polytope. We study
+the geometry of both these equilibrium notions when the players have cumulative
+prospect theoretic (CPT) preferences. The set of CPT correlated equilibria
+includes all the CPT Nash equilibria but it need not be a convex polytope. We
+show that it can, in fact, be disconnected. However, all the CPT Nash
+equilibria continue to lie on its boundary. We also characterize the sets of
+CPT correlated equilibria and CPT Nash equilibria for all 2x2 games.
+",1,0,0,0,0,0
+10761,Multipoint Radiation Induced Ignition of Dust Explosions: Turbulent Clustering of Particles and Increased Transparency,"  It is known that unconfined dust explosions consist of a relatively weak
+primary (turbulent) deflagrations followed by a devastating secondary
+explosion. The secondary explosion may propagate with a speed of up to 1000 m/s
+producing overpressures of over 8-10 atm. Since detonation is the only
+established theory that allows a rapid burning producing a high pressure that
+can be sustained in open areas, the generally accepted view was that the
+mechanism explaining the high rate of combustion in dust explosions is
+deflagration to detonation transition. In the present work we propose a
+theoretical substantiation of the alternative propagation mechanism explaining
+origin of the secondary explosion producing the high speeds of combustion and
+high overpressures in unconfined dust explosions. We show that clustering of
+dust particles in a turbulent flow gives rise to a significant increase of the
+thermal radiation absorption length ahead of the advancing flame front. This
+effect ensures that clusters of dust particles are exposed to and heated by the
+radiation from hot combustion products of large gaseous explosions sufficiently
+long time to become multi-point ignition kernels in a large volume ahead of the
+advancing flame front. The ignition times of fuel-air mixture by the
+radiatively heated clusters of particles is considerably reduced compared to
+the ignition time by the isolated particle. The radiation-induced multi-point
+ignitions of a large volume of fuel-air ahead of the primary flame efficiently
+increase the total flame area, giving rise to the secondary explosion, which
+results in high rates of combustion and overpressures required to account for
+the observed level of overpressures and damages in unconfined dust explosions,
+such as e.g. the 2005 Buncefield explosion and several vapor cloud explosions
+of severity similar to that of the Buncefield incident.
+",0,1,0,0,0,0
+10762,Modeling of the Latent Embedding of Music using Deep Neural Network,"  While both the data volume and heterogeneity of the digital music content is
+huge, it has become increasingly important and convenient to build a
+recommendation or search system to facilitate surfacing these content to the
+user or consumer community. Most of the recommendation models fall into two
+primary species, collaborative filtering based and content based approaches.
+Variants of instantiations of collaborative filtering approach suffer from the
+common issues of so called ""cold start"" and ""long tail"" problems where there is
+not much user interaction data to reveal user opinions or affinities on the
+content and also the distortion towards the popular content. Content-based
+approaches are sometimes limited by the richness of the available content data
+resulting in a heavily biased and coarse recommendation result. In recent
+years, the deep neural network has enjoyed a great success in large-scale image
+and video recognitions. In this paper, we propose and experiment using deep
+convolutional neural network to imitate how human brain processes hierarchical
+structures in the auditory signals, such as music, speech, etc., at various
+timescales. This approach can be used to discover the latent factor models of
+the music based upon acoustic hyper-images that are extracted from the raw
+audio waves of music. These latent embeddings can be used either as features to
+feed to subsequent models, such as collaborative filtering, or to build
+similarity metrics between songs, or to classify music based on the labels for
+training such as genre, mood, sentiment, etc.
+",1,0,0,0,0,0
+10763,Intense keV isolated attosecond pulse generation by orthogonally polarized multicycle midinfrared two-color laser field,"  We theoretically investigate the generation of intense keV attosecond pulses
+in an orthogonally polarized multicycle midinfrared two-color laser field. It
+is demonstrated that multiple continuum-like humps, which have a spectral width
+of about twenty orders of harmonics and an intensity of about one order higher
+than adjacent normal harmonic peaks, are generated under proper two-color
+delays, owing to the reduction of the number of electron-ion recollisions and
+suppression of inter-half-cycle interference effect of multiple electron
+trajectories when the long wavelength midinfrared driving field is used. Using
+the semiclassical trajectory model, we have revealed the two-dimensional
+manipulation of the electron-ion recollision process, which agrees well with
+the time frequency analysis. By filtering these humps, intense isolated
+attosecond pulses are directly generated without any phase compensation. Our
+proposal provides a simple technique to generate intense isolated attosecond
+pulses with various central photon energies covering the multi-keV spectral
+regime by using multicycle driving pulses with high pump energy in experiment.
+",0,1,0,0,0,0
+10764,Games with Costs and Delays,"  We demonstrate the usefulness of adding delay to infinite games with
+quantitative winning conditions. In a delay game, one of the players may delay
+her moves to obtain a lookahead on her opponent's moves. We show that
+determining the winner of delay games with winning conditions given by parity
+automata with costs is EXPTIME-complete and that exponential bounded lookahead
+is both sufficient and in general necessary. Thus, although the parity
+condition with costs is a quantitative extension of the parity condition, our
+results show that adding costs does not increase the complexity of delay games
+with parity conditions.
+Furthermore, we study a new phenomenon that appears in quantitative delay
+games: lookahead can be traded for the quality of winning strategies and vice
+versa. We determine the extent of this tradeoff. In particular, even the
+smallest lookahead allows to improve the quality of an optimal strategy from
+the worst possible value to almost the smallest possible one. Thus, the benefit
+of introducing lookahead is twofold: not only does it allow the delaying player
+to win games she would lose without, but lookahead also allows her to improve
+the quality of her winning strategies in games she wins even without lookahead.
+",1,0,0,0,0,0
+10765,Compile-Time Extensions to Hybrid ODEs,"  Reachability analysis for hybrid systems is an active area of development and
+has resulted in many promising prototype tools. Most of these tools allow users
+to express hybrid system as automata with a set of ordinary differential
+equations (ODEs) associated with each state, as well as rules for transitions
+between states. Significant effort goes into developing and verifying and
+correctly implementing those tools. As such, it is desirable to expand the
+scope of applicability tools of such as far as possible. With this goal, we
+show how compile-time transformations can be used to extend the basic hybrid
+ODE formalism traditionally supported in hybrid reachability tools such as
+SpaceEx or Flow*. The extension supports certain types of partial derivatives
+and equational constraints. These extensions allow users to express, among
+other things, the Euler-Lagrangian equation, and to capture practically
+relevant constraints that arise naturally in mechanical systems. Achieving this
+level of expressiveness requires using a binding time-analysis (BTA), program
+differentiation, symbolic Gaussian elimination, and abstract interpretation
+using interval analysis. Except for BTA, the other components are either
+readily available or can be easily added to most reachability tools. The paper
+therefore focuses on presenting both the declarative and algorithmic
+specifications for the BTA phase, and establishes the soundness of the
+algorithmic specifications with respect to the declarative one.
+",1,0,0,0,0,0
+10766,Sorted Concave Penalized Regression,"  The Lasso is biased. Concave penalized least squares estimation (PLSE) takes
+advantage of signal strength to reduce this bias, leading to sharper error
+bounds in prediction, coefficient estimation and variable selection. For
+prediction and estimation, the bias of the Lasso can be also reduced by taking
+a smaller penalty level than what selection consistency requires, but such
+smaller penalty level depends on the sparsity of the true coefficient vector.
+The sorted L1 penalized estimation (Slope) was proposed for adaptation to such
+smaller penalty levels. However, the advantages of concave PLSE and Slope do
+not subsume each other. We propose sorted concave penalized estimation to
+combine the advantages of concave and sorted penalizations. We prove that
+sorted concave penalties adaptively choose the smaller penalty level and at the
+same time benefits from signal strength, especially when a significant
+proportion of signals are stronger than the corresponding adaptively selected
+penalty levels. A local convex approximation, which extends the local linear
+and quadratic approximations to sorted concave penalties, is developed to
+facilitate the computation of sorted concave PLSE and proven to possess desired
+prediction and estimation error bounds. We carry out a unified treatment of
+penalty functions in a general optimization setting, including the penalty
+levels and concavity of the above mentioned sorted penalties and mixed
+penalties motivated by Bayesian considerations. Our analysis of prediction and
+estimation errors requires the restricted eigenvalue condition on the design,
+not beyond, and provides selection consistency under a required minimum signal
+strength condition in addition. Thus, our results also sharpens existing
+results on concave PLSE by removing the upper sparse eigenvalue component of
+the sparse Riesz condition.
+",0,0,1,0,0,0
+10767,A New Fully Polynomial Time Approximation Scheme for the Interval Subset Sum Problem,"  The interval subset sum problem (ISSP) is a generalization of the well-known
+subset sum problem. Given a set of intervals
+$\left\{[a_{i,1},a_{i,2}]\right\}_{i=1}^n$ and a target integer $T,$ the ISSP
+is to find a set of integers, at most one from each interval, such that their
+sum best approximates the target $T$ but cannot exceed it. In this paper, we
+first study the computational complexity of the ISSP. We show that the ISSP is
+relatively easy to solve compared to the 0-1 Knapsack problem (KP). We also
+identify several subclasses of the ISSP which are polynomial time solvable
+(with high probability), albeit the problem is generally NP-hard. Then, we
+propose a new fully polynomial time approximation scheme (FPTAS) for solving
+the general ISSP problem. The time and space complexities of the proposed
+scheme are ${\cal O}\left(n \max\left\{1 / \epsilon,\log n\right\}\right)$ and
+${\cal O}\left(n+1/\epsilon\right),$ respectively, where $\epsilon$ is the
+relative approximation error. To the best of our knowledge, the proposed scheme
+has almost the same time complexity but a significantly lower space complexity
+compared to the best known scheme. Both the correctness and efficiency of the
+proposed scheme are validated by numerical simulations. In particular, the
+proposed scheme successfully solves ISSP instances with $n=100,000$ and
+$\epsilon=0.1\%$ within one second.
+",1,0,1,0,0,0
+10768,Data Dependent Kernel Approximation using Pseudo Random Fourier Features,"  Kernel methods are powerful and flexible approach to solve many problems in
+machine learning. Due to the pairwise evaluations in kernel methods, the
+complexity of kernel computation grows as the data size increases; thus the
+applicability of kernel methods is limited for large scale datasets. Random
+Fourier Features (RFF) has been proposed to scale the kernel method for solving
+large scale datasets by approximating kernel function using randomized Fourier
+features. While this method proved very popular, still it exists shortcomings
+to be effectively used. As RFF samples the randomized features from a
+distribution independent of training data, it requires sufficient large number
+of feature expansions to have similar performances to kernelized classifiers,
+and this is proportional to the number samples in the dataset. Thus, reducing
+the number of feature dimensions is necessary to effectively scale to large
+datasets. In this paper, we propose a kernel approximation method in a data
+dependent way, coined as Pseudo Random Fourier Features (PRFF) for reducing the
+number of feature dimensions and also to improve the prediction performance.
+The proposed approach is evaluated on classification and regression problems
+and compared with the RFF, orthogonal random features and Nystr{ö}m approach
+",1,0,0,1,0,0
+10769,Estimating Historical Hourly Traffic Volumes via Machine Learning and Vehicle Probe Data: A Maryland Case Study,"  This paper focuses on the problem of estimating historical traffic volumes
+between sparsely-located traffic sensors, which transportation agencies need to
+accurately compute statewide performance measures. To this end, the paper
+examines applications of vehicle probe data, automatic traffic recorder counts,
+and neural network models to estimate hourly volumes in the Maryland highway
+network, and proposes a novel approach that combines neural networks with an
+existing profiling method. On average, the proposed approach yields 24% more
+accurate estimates than volume profiles, which are currently used by
+transportation agencies across the US to compute statewide performance
+measures. The paper also quantifies the value of using vehicle probe data in
+estimating hourly traffic volumes, which provides important managerial insights
+to transportation agencies interested in acquiring this type of data. For
+example, results show that volumes can be estimated with a mean absolute
+percent error of about 21% at locations where average number of observed probes
+is between 30 and 47 vehicles/hr, which provides a useful guideline for
+assessing the value of probe vehicle data from different vendors.
+",1,0,0,1,0,0
+10770,Monitoring of Wild Pseudomonas Biofilm Strain Conditions Using Statistical Characterisation of Scanning Electron Microscopy Images,"  The present paper proposes a novel method of quantification of the variation
+in biofilm architecture, in correlation with the alteration of growth
+conditions that include, variations of substrate and conditioning layer. The
+polymeric biomaterial serving as substrates are widely used in implants and
+indwelling medical devices, while the plasma proteins serve as the conditioning
+layer. The present method uses descriptive statistics of FESEM images of
+biofilms obtained during a variety of growth conditions. We aim to explore here
+the texture and fractal analysis techniques, to identify the most
+discriminatory features which are capable of predicting the difference in
+biofilm growth conditions. We initially extract some statistical features of
+biofilm images on bare polymer surfaces, followed by those on the same
+substrates adsorbed with two different types of plasma proteins, viz. Bovine
+serum albumin (BSA) and Fibronectin (FN), for two different adsorption times.
+The present analysis has the potential to act as a futuristic technology for
+developing a computerized monitoring system in hospitals with automated image
+analysis and feature extraction, which may be used to predict the growth
+profile of an emerging biofilm on surgical implants or similar medical
+applications.
+",0,0,0,1,1,0
+10771,Continuous Measurement of an Atomic Current,"  We are interested in dynamics of quantum many-body systems under continuous
+observation, and its physical realizations involving cold atoms in lattices. In
+the present work we focus on continuous measurement of atomic currents in
+lattice models, including the Hubbard model. We describe a Cavity QED setup,
+where measurement of a homodyne current provides a faithful representation of
+the atomic current as a function of time. We employ the quantum optical
+description in terms of a diffusive stochastic Schrödinger equation to follow
+the time evolution of the atomic system conditional to observing a given
+homodyne current trajectory, thus accounting for the competition between the
+Hamiltonian evolution and measurement back-action. As an illustration, we
+discuss minimal models of atomic dynamics and continuous current measurement on
+rings with synthetic gauge fields, involving both real space and synthetic
+dimension lattices (represented by internal atomic states). Finally, by `not
+reading' the current measurements the time evolution of the atomic system is
+governed by a master equation, where - depending on the microscopic details of
+our CQED setups - we effectively engineer a current coupling of our system to a
+quantum reservoir. This provides novel scenarios of dissipative dynamics
+generating `dark' pure quantum many-body states.
+",0,1,0,0,0,0
+10772,Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros,"  In finance, durations between successive transactions are usually modeled by
+the autoregressive conditional duration model based on a continuous
+distribution omitting frequent zero values. Zero durations can be caused by
+either split transactions or independent transactions. We propose a discrete
+model allowing for excessive zero values based on the zero-inflated negative
+binomial distribution with score dynamics. We establish the invertibility of
+the score filter. Additionally, we derive sufficient conditions for the
+consistency and asymptotic normality of the maximum likelihood of the model
+parameters. In an empirical study of DJIA stocks, we find that split
+transactions cause on average 63% of zero values. Furthermore, the loss of
+decimal places in the proposed model is less severe than incorrect treatment of
+zero values in continuous models.
+",0,0,0,0,0,1
+10773,Analog Experiments on Tensile Strength of Dusty and Cometary Matter,"  The tensile strength of small dusty bodies in the solar system is determined
+by the interaction between the composing grains. In the transition regime
+between small and sticky dust ($\rm \mu m$) and non cohesive large grains (mm),
+particles still stick to each other but are easily separated. In laboratory
+experiments we find that thermal creep gas flow at low ambient pressure
+generates an overpressure sufficient to overcome the tensile strength. For the
+first time it allows a direct measurement of the tensile strength of
+individual, very small (sub)-mm aggregates which consist of only tens of grains
+in the (sub)-mm size range. We traced the disintegration of aggregates by
+optical imaging in ground based as well as microgravity experiments and present
+first results for basalt, palagonite and vitreous carbon samples with up to a
+few hundred Pa. These measurements show that low tensile strength can be the
+result of building loose aggregates with compact (sub)-mm units. This is in
+favour of a combined cometary formation scenario by aggregation to compact
+aggreates and gravitational instability of these units.
+",0,1,0,0,0,0
+10774,On Compiling DNNFs without Determinism,"  State-of-the-art knowledge compilers generate deterministic subsets of DNNF,
+which have been recently shown to be exponentially less succinct than DNNF. In
+this paper, we propose a new method to compile DNNFs without enforcing
+determinism necessarily. Our approach is based on compiling deterministic DNNFs
+with the addition of auxiliary variables to the input formula. These variables
+are then existentially quantified from the deterministic structure in linear
+time, which would lead to a DNNF that is equivalent to the input formula and
+not necessarily deterministic. On the theoretical side, we show that the new
+method could generate exponentially smaller DNNFs than deterministic ones, even
+by adding a single auxiliary variable. Further, we show that various existing
+techniques that introduce auxiliary variables to the input formulas can be
+employed in our framework. On the practical side, we empirically demonstrate
+that our new method can significantly advance DNNF compilation on certain
+benchmarks.
+",1,0,0,0,0,0
+10775,Algebraic infinite delooping and derived destabilization,"  Working over the prime field of characteristic two, consequences of the
+Koszul duality between the Steenrod algebra and the big Dyer-Lashof algebra are
+studied, with an emphasis on the interplay between instability for the Steenrod
+algebra action and that for the Dyer-Lashof operations. The central algebraic
+framework is the category of length-graded modules over the Steenrod algebra
+equipped with an unstable action of the Dyer-Lashof algebra, with compatibility
+via the Nishida relations.
+A first ingredient is a functor defined on modules over the Steenrod algebra
+that arose in the work of Kuhn and McCarty on the homology of infinite loop
+spaces. This functor is given in terms of derived functors of destabilization
+from the category of modules over the Steenrod algebra to unstable modules,
+enriched by taking into account the action of Dyer-Lashof operations.
+A second ingredient is the derived functors of the Dyer-Lashof
+indecomposables functor to length-graded modules over the Steenrod algebra.
+These are related to functors used by Miller in his study of a spectral
+sequence to calculate the homology of an infinite delooping. An important fact
+is that these functors can be calculated as the homology of an explicit Koszul
+complex with terms expressed as certain Steinberg functors. The latter are
+quadratic dual to the more familiar Singer functors.
+By exploiting the explicit complex built from the Singer functors which
+calculates the derived functors of destabilization, Koszul duality leads to an
+algebraic infinite delooping spectral sequence. This is conceptually similar to
+Miller's spectral sequence, but there seems to be no direct relationship.
+The spectral sequence sheds light on the relationship between unstable
+modules over the Steenrod algebra and all modules.
+",0,0,1,0,0,0
+10776,WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling,"  To train an inference network jointly with a deep generative topic model,
+making it both scalable to big corpora and fast in out-of-sample prediction, we
+develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet
+allocation, which infers posterior samples via a hybrid of stochastic-gradient
+MCMC and autoencoding variational Bayes. The generative network of WHAI has a
+hierarchy of gamma distributions, while the inference network of WHAI is a
+Weibull upward-downward variational autoencoder, which integrates a
+deterministic-upward deep neural network, and a stochastic-downward deep
+generative model based on a hierarchy of Weibull distributions. The Weibull
+distribution can be used to well approximate a gamma distribution with an
+analytic Kullback-Leibler divergence, and has a simple reparameterization via
+the uniform noise, which help efficiently compute the gradients of the evidence
+lower bound with respect to the parameters of the inference network. The
+effectiveness and efficiency of WHAI are illustrated with experiments on big
+corpora.
+",0,0,0,1,0,0
+10777,Constraining the Dynamics of Deep Probabilistic Models,"  We introduce a novel generative formulation of deep probabilistic models
+implementing ""soft"" constraints on their function dynamics. In particular, we
+develop a flexible methodological framework where the modeled functions and
+derivatives of a given order are subject to inequality or equality constraints.
+We then characterize the posterior distribution over model and constraint
+parameters through stochastic variational inference. As a result, the proposed
+approach allows for accurate and scalable uncertainty quantification on the
+predictions and on all parameters. We demonstrate the application of equality
+constraints in the challenging problem of parameter inference in ordinary
+differential equation models, while we showcase the application of inequality
+constraints on the problem of monotonic regression of count data. The proposed
+approach is extensively tested in several experimental settings, leading to
+highly competitive results in challenging modeling applications, while offering
+high expressiveness, flexibility and scalability.
+",0,0,0,1,0,0
+10778,Democratizing Design for Future Computing Platforms,"  Information and communications technology can continue to change our world.
+These advances will partially depend upon designs that synergistically combine
+software with specialized hardware. Today open-source software incubates rapid
+software-only innovation. The government can unleash software-hardware
+innovation with programs to develop open hardware components, tools, and design
+flows that simplify and reduce the cost of hardware design. Such programs will
+speed development for startup companies, established industry leaders,
+education, scientific research, and for government intelligence and defense
+platforms.
+",1,0,0,0,0,0
+10779,EstimatedWold Representation and Spectral Density-Driven Bootstrap for Time Series,"  The second-order dependence structure of purely nondeterministic stationary
+process is described by the coefficients of the famous Wold representation.
+These coefficients can be obtained by factorizing the spectral density of the
+process. This relation together with some spectral density estimator is used in
+order to obtain consistent estimators of these coefficients. A spectral
+density-driven bootstrap for time series is then developed which uses the
+entire sequence of estimated MA coefficients together with appropriately
+generated pseudo innovations in order to obtain a bootstrap pseudo time series.
+It is shown that if the underlying process is linear and if the pseudo
+innovations are generated by means of an i.i.d. wild bootstrap which mimics, to
+the necessary extent, the moment structure of the true innovations, this
+bootstrap proposal asymptotically works for a wide range of statistics. The
+relations of the proposed bootstrap procedure to some other bootstrap
+procedures, including the autoregressive-sieve bootstrap, are discussed. It is
+shown that the latter is a special case of the spectral density-driven
+bootstrap, if a parametric autoregressive spectral density estimator is used.
+Simulations investigate the performance of the new bootstrap procedure in
+finite sample situations. Furthermore, a real-life data example is presented.
+",0,0,1,0,0,0
+10780,A Polynomial Time Match Test for Large Classes of Extended Regular Expressions,"  In the present paper, we study the match test for extended regular
+expressions. We approach this NP-complete problem by introducing a novel
+variant of two-way multihead automata, which reveals that the complexity of the
+match test is determined by a hidden combinatorial property of extended regular
+expressions, and it shows that a restriction of the corresponding parameter
+leads to rich classes with a polynomial time match test. For presentational
+reasons, we use the concept of pattern languages in order to specify extended
+regular expressions. While this decision, formally, slightly narrows the scope
+of our results, an extension of our concepts and results to more general
+notions of extended regular expressions is straightforward.
+",1,0,0,0,0,0
+10781,Anomalous Magnetism for Dirac Electrons in Two Dimensional Rashba Systems,"  Spin-spin correlation function response in the low electronic density regime
+and externally applied electric field is evaluated for 2D metallic crystals
+under Rashba-type coupling, fixed number of particles and two-fold energy band
+structure. Intrinsic Zeeman-like effect on electron spin polarization, density
+of states, Fermi surface topology and transverse magnetic susceptibility are
+analyzed in the zero temperature limit. A possible magnetic state for Dirac
+electrons depending on the zero field band gap magnitude under this conditions
+is found.
+",0,1,0,0,0,0
+10782,Estimating Average Treatment Effects with a Double-Index Propensity Score,"  We consider estimating average treatment effects (ATE) of a binary treatment
+in observational data when data-driven variable selection is needed to select
+relevant covariates from a moderately large number of available covariates
+$\mathbf{X}$. To leverage covariates among $\mathbf{X}$ predictive of the
+outcome for efficiency gain while using regularization to fit a parameteric
+propensity score (PS) model, we consider a dimension reduction of $\mathbf{X}$
+based on fitting both working PS and outcome models using adaptive LASSO. A
+novel PS estimator, the Double-index Propensity Score (DiPS), is proposed, in
+which the treatment status is smoothed over the linear predictors for
+$\mathbf{X}$ from both the initial working models. The ATE is estimated by
+using the DiPS in a normalized inverse probability weighting (IPW) estimator,
+which is found to maintain double-robustness and also local semiparametric
+efficiency with a fixed number of covariates $p$. Under misspecification of
+working models, the smoothing step leads to gains in efficiency and robustness
+over traditional doubly-robust estimators. These results are extended to the
+case where $p$ diverges with sample size and working models are sparse.
+Simulations show the benefits of the approach in finite samples. We illustrate
+the method by estimating the ATE of statins on colorectal cancer risk in an
+electronic medical record (EMR) study and the effect of smoking on C-reactive
+protein (CRP) in the Framingham Offspring Study.
+",0,0,0,1,0,0
+10783,Emotion Detection and Analysis on Social Media,"  In this paper, we address the problem of detection, classification and
+quantification of emotions of text in any form. We consider English text
+collected from social media like Twitter, which can provide information having
+utility in a variety of ways, especially opinion mining. Social media like
+Twitter and Facebook is full of emotions, feelings and opinions of people all
+over the world. However, analyzing and classifying text on the basis of
+emotions is a big challenge and can be considered as an advanced form of
+Sentiment Analysis. This paper proposes a method to classify text into six
+different Emotion-Categories: Happiness, Sadness, Fear, Anger, Surprise and
+Disgust. In our model, we use two different approaches and combine them to
+effectively extract these emotions from text. The first approach is based on
+Natural Language Processing, and uses several textual features like emoticons,
+degree words and negations, Parts Of Speech and other grammatical analysis. The
+second approach is based on Machine Learning classification algorithms. We have
+also successfully devised a method to automate the creation of the training-set
+itself, so as to eliminate the need of manual annotation of large datasets.
+Moreover, we have managed to create a large bag of emotional words, along with
+their emotion-intensities. On testing, it is shown that our model provides
+significant accuracy in classifying tweets taken from Twitter.
+",1,0,0,0,0,0
+10784,HotFlip: White-Box Adversarial Examples for Text Classification,"  We propose an efficient method to generate white-box adversarial examples to
+trick a character-level neural classifier. We find that only a few
+manipulations are needed to greatly decrease the accuracy. Our method relies on
+an atomic flip operation, which swaps one token for another, based on the
+gradients of the one-hot input vectors. Due to efficiency of our method, we can
+perform adversarial training which makes the model more robust to attacks at
+test time. With the use of a few semantics-preserving constraints, we
+demonstrate that HotFlip can be adapted to attack a word-level classifier as
+well.
+",1,0,0,0,0,0
+10785,"Towards fully commercial, UV-compatible fiber patch cords","  We present and analyze two pathways to produce commercial optical-fiber patch
+cords with stable long-term transmission in the ultraviolet (UV) at powers up
+to $\sim$ 200 mW, and typical bulk transmission between 66-75\%. Commercial
+fiber patch cords in the UV are of great interest across a wide variety of
+scientific applications ranging from biology to metrology, and the lack of
+availability has yet to be suitably addressed. We provide a guide to producing
+such solarization-resistant, hydrogen-passivated, polarization-maintaining,
+connectorized and jacketed optical fibers compatible with demanding scientific
+and industrial applications. Our presentation describes the fabrication and
+hydrogen loading procedure in detail and presents a high-pressure vessel
+design, calculations of required \Ht\ loading times, and information on patch
+cord handling and the mitigation of bending sensitivities. Transmission at 313
+nm is measured over many months for cumulative energy on the fiber output of >
+10 kJ with no demonstrable degradation due to UV solarization, in contrast to
+standard uncured fibers. Polarization sensitivity and stability are
+characterized yielding polarization extinction ratios between 15 dB and 25 dB
+at 313 nm, where we find patch cords become linearly polarizing. We observe
+that particle deposition at the fiber facet induced by high-intensity UV
+exposure can (reversibly) deteriorate patch cord performance and describe a
+technique for nitrogen purging of fiber collimators which mitigates this
+phenomenon.
+",0,1,0,0,0,0
+10786,Convex Parameterizations and Fidelity Bounds for Nonlinear Identification and Reduced-Order Modelling,"  Model instability and poor prediction of long-term behavior are common
+problems when modeling dynamical systems using nonlinear ""black-box""
+techniques. Direct optimization of the long-term predictions, often called
+simulation error minimization, leads to optimization problems that are
+generally non-convex in the model parameters and suffer from multiple local
+minima. In this work we present methods which address these problems through
+convex optimization, based on Lagrangian relaxation, dissipation inequalities,
+contraction theory, and semidefinite programming. We demonstrate the proposed
+methods with a model order reduction task for electronic circuit design and the
+identification of a pneumatic actuator from experiment.
+",1,0,1,0,0,0
+10787,Rydberg states of helium in electric and magnetic fields of arbitrary relative orientation,"  A spectroscopic study of Rydberg states of helium ($n$ = 30 and 45) in
+magnetic, electric and combined magnetic and electric fields with arbitrary
+relative orientations of the field vectors is presented. The emphasis is on two
+special cases where (i) the diamagnetic term is negligible and both
+paramagnetic Zeeman and Stark effects are linear ($n$ = 30, $B \leq$ 120 mT and
+$F$ = 0 - 78 V/cm ), and (ii) the diamagnetic term is dominant and the Stark
+effect is linear ($n$ = 45, $B$ = 277 mT and $F$ = 0 - 8 V/cm). Both cases
+correspond to regimes where the interactions induced by the electric and
+magnetic fields are much weaker than the Coulomb interaction, but much stronger
+than the spin-orbit interaction. The experimental spectra are compared to
+spectra calculated by determining the eigenvalues of the Hamiltonian matrix
+describing helium Rydberg states in the external fields. The spectra and the
+calculated energy-level diagrams in external fields reveal avoided crossings
+between levels of different $m_l$ values and pronounced $m_l$-mixing effects at
+all angles between the electric and magnetic field vectors other than 0. These
+observations are discussed in the context of the development of a method to
+generate dense samples of cold atoms and molecules in a magnetic trap following
+Rydberg-Stark deceleration.
+",0,1,0,0,0,0
+10788,Change-point inference on volatility in noisy Itô semimartingales,"  This work is concerned with tests on structural breaks in the spot volatility
+process of a general Itô semimartingale based on discrete observations
+contaminated with i.i.d. microstructure noise. We construct a consistent test
+building up on infill asymptotic results for certain functionals of spectral
+spot volatility estimates. A weak limit theorem is established under the null
+hypothesis relying on extreme value theory. We prove consistency of the test
+and of an associated estimator for the change point. A simulation study
+illustrates the finite-sample performance of the method and efficiency gains
+compared to a skip-sampling approach.
+",0,0,1,1,0,0
+10789,Double-diffusive erosion of the core of Jupiter,"  We present Direct Numerical Simulations of the transport of heat and heavy
+elements across a double-diffusive interface or a double-diffusive staircase,
+in conditions that are close to those one may expect to find near the boundary
+between the heavy-element rich core and the hydrogen-helium envelope of giant
+planets such as Jupiter. We find that the non-dimensional ratio of the buoyancy
+flux associated with heavy element transport to the buoyancy flux associated
+with heat transport lies roughly between 0.5 and 1, which is much larger than
+previous estimates derived by analogy with geophysical double-diffusive
+convection. Using these results in combination with a core-erosion model
+proposed by Guillot et al. (2004), we find that the entire core of Jupiter
+would be eroded within less than 1Myr assuming that the core-envelope boundary
+is composed of a single interface. We also propose an alternative model that is
+more appropriate in the presence of a well-established double-diffusive
+staircase, and find that in this limit a large fraction of the core could be
+preserved. These findings are interesting in the context of Juno's recent
+results, but call for further modeling efforts to better understand the process
+of core erosion from first principles.
+",0,1,0,0,0,0
+10790,Classifying Exoplanets with Gaussian Mixture Model,"  Recently, Odrzywolek and Rafelski (arXiv:1612.03556) have found three
+distinct categories of exoplanets, when they are classified based on density.
+We first carry out a similar classification of exoplanets according to their
+density using the Gaussian Mixture Model, followed by information theoretic
+criterion (AIC and BIC) to determine the optimum number of components. Such a
+one-dimensional classification favors two components using AIC and three using
+BIC, but the statistical significance from both the tests is not significant
+enough to decisively pick the best model between two and three components. We
+then extend this GMM-based classification to two dimensions by using both the
+density and the Earth similarity index (arXiv:1702.03678), which is a measure
+of how similar each planet is compared to the Earth. For this two-dimensional
+classification, both AIC and BIC provide decisive evidence in favor of three
+components.
+",0,1,0,0,0,0
+10791,Competing effects of Hund's splitting and symmetry-breaking perturbations on electronic order in Pb$_{1-x}$Sn$_{x}$Te,"  We study the effect of a uniform external magnetization on p-wave
+superconductivity on the (001) surface of the crystalline topological
+insulator(TCI) Pb$_{1-x}$Sn$_{x}$Te. It was shown by us in an earlier work that
+a chiral p-wave finite momentum pairing (FFLO) state can be stabilized in this
+system in the presence of weak repulsive interparticle interactions. In
+particular, the superconducting instability is very sensitive to the Hund's
+interaction in the multiorbital TCI, and no instabilities are found to be
+possible for the ""wrong"" sign of the Hund's splitting. Here we show that for a
+finite Hund's splitting of interactions, a significant value of the external
+magnetization is needed to degrade the surface superconductivity, while in the
+absence of the Hund's interaction, an arbitrarily small external magnetization
+can destroy the superconductivity. This implies that multiorbital effects in
+this system play an important role in stabilizing electronic order on the
+surface.
+",0,1,0,0,0,0
+10792,Partition function of Chern-Simons theory as renormalized q-dimension,"  We calculate $q$-dimension of $k$-th Cartan power of fundamental
+representation $\Lambda_0$, corresponding to affine root of affine simply laced
+Kac-Moody algebras, and show that in the limit $q\rightarrow 1 $, and with
+natural renormalization, it is equal to universal partition function of
+Chern-Simons theory on three-dimensional sphere.
+",0,0,1,0,0,0
+10793,Task Recommendation in Crowdsourcing Based on Learning Preferences and Reliabilities,"  Workers participating in a crowdsourcing platform can have a wide range of
+abilities and interests. An important problem in crowdsourcing is the task
+recommendation problem, in which tasks that best match a particular worker's
+preferences and reliabilities are recommended to that worker. A task
+recommendation scheme that assigns tasks more likely to be accepted by a worker
+who is more likely to complete it reliably results in better performance for
+the task requester. Without prior information about a worker, his preferences
+and reliabilities need to be learned over time. In this paper, we propose a
+multi-armed bandit (MAB) framework to learn a worker's preferences and his
+reliabilities for different categories of tasks. However, unlike the classical
+MAB problem, the reward from the worker's completion of a task is unobservable.
+We therefore include the use of gold tasks (i.e., tasks whose solutions are
+known \emph{a priori} and which do not produce any rewards) in our task
+recommendation procedure. Our model could be viewed as a new variant of MAB, in
+which the random rewards can only be observed at those time steps where gold
+tasks are used, and the accuracy of estimating the expected reward of
+recommending a task to a worker depends on the number of gold tasks used. We
+show that the optimal regret is $O(\sqrt{n})$, where $n$ is the number of tasks
+recommended to the worker. We develop three task recommendation strategies to
+determine the number of gold tasks for different task categories, and show that
+they are order optimal. Simulations verify the efficiency of our approaches.
+",1,0,0,0,0,0
+10794,Elliptic Weight Functions and Elliptic q-KZ Equation,"  By using representation theory of the elliptic quantum group U_{q,p}(sl_N^),
+we present a systematic method of deriving the weight functions. The resultant
+sl_N type elliptic weight functions are new and give elliptic and dynamical
+analogues of those obtained in the trigonometric case. We then discuss some
+basic properties of the elliptic weight functions. We also present an explicit
+formula for formal elliptic hypergeometric integral solution to the face type,
+i.e. dynamical, elliptic q-KZ equation.
+",0,0,1,0,0,0
+10795,Formal duality in finite cyclic groups,"  The notion of formal duality in finite Abelian groups appeared recently in
+relation to spherical designs, tight sphere packings, and energy minimizing
+configurations in Euclidean spaces. For finite cyclic groups it is conjectured
+that there are no primitive formally dual pairs besides the trivial one and the
+TITO configuration. This conjecture has been verified for cyclic groups of
+prime power order, as well as of square-free order. In this paper, we will
+confirm the conjecture for other classes of cyclic groups, namely almost all
+cyclic groups of order a product of two prime powers, with finitely many
+exceptions for each pair of primes, or whose order $N$ satisfies $p\mid\!\mid
+N$, where $p$ a prime satisfying the so-called self-conjugacy property with
+respect to $N$. For the above proofs, various tools were needed: the field
+descent method, used chiefly for the circulant Hadamard conjecture, the
+techniques of Coven & Meyerowitz for sets that tile $\mathbb{Z}$ or
+$\mathbb{Z}_N$ by translations, dubbed herein as the polynomial method, as well
+as basic number theory of cyclotomic fields, especially the splitting of primes
+in a given cyclotomic extension.
+",0,0,1,0,0,0
+10796,Nonlinear Dynamics of Binocular Rivalry: A Comparative Study,"  When our eyes are presented with the same image, the brain processes it to
+view it as a single coherent one. The lateral shift in the position of our
+eyes, causes the two images to possess certain differences, which our brain
+exploits for the purpose of depth perception and to gauge the size of objects
+at different distances, a process commonly known as stereopsis. However, when
+presented with two different visual stimuli, the visual awareness alternates.
+This phenomenon of binocular rivalry is a result of competition between the
+corresponding neuronal populations of the two eyes. The article presents a
+comparative study of various dynamical models proposed to capture this process.
+It goes on to study the effect of a certain parameter on the rate of perceptual
+alternations and proceeds to disprove the initial propositions laid down to
+characterise this phenomenon. It concludes with a discussion on the possible
+future work that can be conducted to obtain a better picture of the neuronal
+functioning behind this rivalry.
+",0,0,0,0,1,0
+10797,Nonstandard Analysis and Constructivism!,"  Almost two decades ago, Wattenberg published a paper with the title
+'Nonstandard Analysis and Constructivism?' in which he speculates on a possible
+connection between Nonstandard Analysis and constructive mathematics. We study
+Wattenberg's work in light of recent research on the aforementioned connection.
+On one hand, with only slight modification, some of Wattenberg's theorems in
+Nonstandard Analysis are seen to yield effective and constructive theorems (not
+involving Nonstandard Analysis). On the other hand, we establish the
+incorrectness of some of Wattenberg's (explicit and implicit) claims regarding
+the constructive status of the axioms Transfer and Standard Part of Nonstandard
+Analysis.
+",0,0,1,0,0,0
+10798,Text-Independent Speaker Verification Using 3D Convolutional Neural Networks,"  In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN)
+architecture has been proposed for speaker verification in the text-independent
+setting. One of the main challenges is the creation of the speaker models. Most
+of the previously-reported approaches create speaker models based on averaging
+the extracted features from utterances of the speaker, which is known as the
+d-vector system. In our paper, we propose an adaptive feature learning by
+utilizing the 3D-CNNs for direct speaker model creation in which, for both
+development and enrollment phases, an identical number of spoken utterances per
+speaker is fed to the network for representing the speakers' utterances and
+creation of the speaker model. This leads to simultaneously capturing the
+speaker-related information and building a more robust system to cope with
+within-speaker variation. We demonstrate that the proposed method significantly
+outperforms the traditional d-vector verification system. Moreover, the
+proposed system can also be an alternative to the traditional d-vector system
+which is a one-shot speaker modeling system by utilizing 3D-CNNs.
+",1,0,0,0,0,0
+10799,Sequential identification of nonignorable missing data mechanisms,"  With nonignorable missing data, likelihood-based inference should be based on
+the joint distribution of the study variables and their missingness indicators.
+These joint models cannot be estimated from the data alone, thus requiring the
+analyst to impose restrictions that make the models uniquely obtainable from
+the distribution of the observed data. We present an approach for constructing
+classes of identifiable nonignorable missing data models. The main idea is to
+use a sequence of carefully set up identifying assumptions, whereby we specify
+potentially different missingness mechanisms for different blocks of variables.
+We show that the procedure results in models with the desirable property of
+being non-parametric saturated.
+",0,0,1,1,0,0
+10800,Image Stitching by Line-guided Local Warping with Global Similarity Constraint,"  Low-textured image stitching remains a challenging problem. It is difficult
+to achieve good alignment and it is easy to break image structures due to
+insufficient and unreliable point correspondences. Moreover, because of the
+viewpoint variations between multiple images, the stitched images suffer from
+projective distortions. To solve these problems, this paper presents a
+line-guided local warping method with a global similarity constraint for image
+stitching. Line features which serve well for geometric descriptions and scene
+constraints, are employed to guide image stitching accurately. On one hand, the
+line features are integrated into a local warping model through a designed
+weight function. On the other hand, line features are adopted to impose strong
+geometric constraints, including line correspondence and line colinearity, to
+improve the stitching performance through mesh optimization. To mitigate
+projective distortions, we adopt a global similarity constraint, which is
+integrated with the projective warps via a designed weight strategy. This
+constraint causes the final warp to slowly change from a projective to a
+similarity transformation across the image. Finally, the images undergo a
+two-stage alignment scheme that provides accurate alignment and reduces
+projective distortion. We evaluate our method on a series of images and compare
+it with several other methods. The experimental results demonstrate that the
+proposed method provides a convincing stitching performance and that it
+outperforms other state-of-the-art methods.
+",1,0,0,0,0,0
+10801,Large-scale Nonlinear Variable Selection via Kernel Random Features,"  We propose a new method for input variable selection in nonlinear regression.
+The method is embedded into a kernel regression machine that can model general
+nonlinear functions, not being a priori limited to additive models. This is the
+first kernel-based variable selection method applicable to large datasets. It
+sidesteps the typical poor scaling properties of kernel methods by mapping the
+inputs into a relatively low-dimensional space of random features. The
+algorithm discovers the variables relevant for the regression task together
+with learning the prediction model through learning the appropriate nonlinear
+random feature maps. We demonstrate the outstanding performance of our method
+on a set of large-scale synthetic and real datasets.
+",0,0,0,1,0,0
+10802,Couple microscale periodic patches to simulate macroscale emergent dynamics,"  This article proposes a new way to construct computationally efficient
+`wrappers' around fine scale, microscopic, detailed descriptions of dynamical
+systems, such as molecular dynamics, to make predictions at the macroscale
+`continuum' level. It is often significantly easier to code a microscale
+simulator with periodicity: so the challenge addressed here is to develop a
+scheme that uses only a given periodic microscale simulator; specifically, one
+for atomistic dynamics. Numerical simulations show that applying a suitable
+proportional controller within `action regions' of a patch of atomistic
+simulation effectively predicts the macroscale transport of heat. Theoretical
+analysis establishes that such an approach will generally be effective and
+efficient, and also determines good values for the strength of the proportional
+controller. This work has the potential to empower systematic analysis and
+understanding at a macroscopic system level when only a given microscale
+simulator is available.
+",0,0,1,0,0,0
+10803,Determinantal Point Processes for Mini-Batch Diversification,"  We study a mini-batch diversification scheme for stochastic gradient descent
+(SGD). While classical SGD relies on uniformly sampling data points to form a
+mini-batch, we propose a non-uniform sampling scheme based on the Determinantal
+Point Process (DPP). The DPP relies on a similarity measure between data points
+and gives low probabilities to mini-batches which contain redundant data, and
+higher probabilities to mini-batches with more diverse data. This
+simultaneously balances the data and leads to stochastic gradients with lower
+variance. We term this approach Diversified Mini-Batch SGD (DM-SGD). We show
+that regular SGD and a biased version of stratified sampling emerge as special
+cases. Furthermore, DM-SGD generalizes stratified sampling to cases where no
+discrete features exist to bin the data into groups. We show experimentally
+that our method results more interpretable and diverse features in unsupervised
+setups, and in better classification accuracies in supervised setups.
+",1,0,0,1,0,0
+10804,Proton-induced halo formation in charged meteors,"  Despite a very long history of meteor science, our understanding of meteor
+ablation and its shocked plasma physics is still far from satisfactory as we
+are still missing the microphysics of meteor shock formation and its plasma
+dynamics. Here we argue that electrons and ions in the meteor plasma above
+$\sim$100 km altitude undergo spatial separation due to electrons being trapped
+by gyration in the Earth's magnetic field, while the ions are carried by the
+meteor as their dynamics is dictated by collisions. This separation process
+charges the meteor and creates a strong local electric field. We show how
+acceleration of protons in this field leads to the collisional excitation of
+ionospheric N$_2$ on the scale of many 100 m. This mechanism explains the
+puzzling large halo detected around Leonid meteors, while it also fits into the
+theoretical expectations of several other unexplained meteor related phenomena.
+We expect our work to lead to more advanced models of meteor-ionosphere
+interaction, combined with the electrodynamics of meteor trail evolution.
+",0,1,0,0,0,0
+10805,Generalised Seiberg-Witten equations and almost-Hermitian geometry,"  In this article, we study a generalisation of the Seiberg-Witten equations,
+replacing the spinor representation with a hyperKahler manifold equipped with
+certain symmetries. Central to this is the construction of a (non-linear) Dirac
+operator acting on the sections of the non-linear fibre-bundle. For hyperKahler
+manifolds admitting a hyperKahler potential, we derive a transformation formula
+for the Dirac operator under the conformal change of metric on the base
+manifold.
+As an application, we show that when the hyperKahler manifold is of dimension
+four, then away from a singular set, the equations can be expressed as a second
+order PDE in terms of almost-complex structure on the base manifold and a
+conformal factor. This extends a result of Donaldson to generalised
+Seiberg-Witten equations.
+",0,0,1,0,0,0
+10806,Enhanced Photon Traps for Hyper-Kamiokande,"  Hyper-Kamiokande, the next generation large water Cherenkov detector in
+Japan, is planning to use approximately 80,000 20-inch photomultiplier tubes
+(PMTs). They are one of the major cost factors of the experiment. We propose a
+novel enhanced photon trap design based on a smaller and more economical PMT in
+combination with wavelength shifters, dichroic mirrors, and broadband mirrors.
+GEANT4 is utilized to obtain photon collection efficiencies and timing
+resolution of the photon traps. We compare the performance of different trap
+configurations and sizes. Our simulations indicate an enhanced photon trap with
+a 12-inch PMT can match a 20-inch PMTs collection efficiency, however at a cost
+of reduced timing resolution. The photon trap might be suitable as detection
+module for the outer detector with large photo coverage area.
+",0,1,0,0,0,0
+10807,A Deep Reinforcement Learning Chatbot (Short Version),"  We present MILABOT: a deep reinforcement learning chatbot developed by the
+Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize
+competition. MILABOT is capable of conversing with humans on popular small talk
+topics through both speech and text. The system consists of an ensemble of
+natural language generation and retrieval models, including neural network and
+template-based models. By applying reinforcement learning to crowdsourced data
+and real-world user interactions, the system has been trained to select an
+appropriate response from the models in its ensemble. The system has been
+evaluated through A/B testing with real-world users, where it performed
+significantly better than other systems. The results highlight the potential of
+coupling ensemble systems with deep reinforcement learning as a fruitful path
+for developing real-world, open-domain conversational agents.
+",0,0,0,1,0,0
+10808,A Recurrent Neural Network for Sentiment Quantification,"  Quantification is a supervised learning task that consists in predicting,
+given a set of classes C and a set D of unlabelled items, the prevalence (or
+relative frequency) p(c|D) of each class c in C. Quantification can in
+principle be solved by classifying all the unlabelled items and counting how
+many of them have been attributed to each class. However, this ""classify and
+count"" approach has been shown to yield suboptimal quantification accuracy;
+this has established quantification as a task of its own, and given rise to a
+number of methods specifically devised for it. We propose a recurrent neural
+network architecture for quantification (that we call QuaNet) that observes the
+classification predictions to learn higher-order ""quantification embeddings"",
+which are then refined by incorporating quantification predictions of simple
+classify-and-count-like methods. We test {QuaNet on sentiment quantification on
+text, showing that it substantially outperforms several state-of-the-art
+baselines.
+",0,0,0,1,0,0
+10809,Data Noising as Smoothing in Neural Network Language Models,"  Data noising is an effective technique for regularizing neural network
+models. While noising is widely adopted in application domains such as vision
+and speech, commonly used noising primitives have not been developed for
+discrete sequence-level settings such as language modeling. In this paper, we
+derive a connection between input noising in neural network language models and
+smoothing in $n$-gram models. Using this connection, we draw upon ideas from
+smoothing to develop effective noising schemes. We demonstrate performance
+gains when applying the proposed schemes to language modeling and machine
+translation. Finally, we provide empirical analysis validating the relationship
+between noising and smoothing.
+",1,0,0,0,0,0
+10810,Optical Flow-based 3D Human Motion Estimation from Monocular Video,"  We present a generative method to estimate 3D human motion and body shape
+from monocular video. Under the assumption that starting from an initial pose
+optical flow constrains subsequent human motion, we exploit flow to find
+temporally coherent human poses of a motion sequence. We estimate human motion
+by minimizing the difference between computed flow fields and the output of an
+artificial flow renderer. A single initialization step is required to estimate
+motion over multiple frames. Several regularization functions enhance
+robustness over time. Our test scenarios demonstrate that optical flow
+effectively regularizes the under-constrained problem of human shape and motion
+estimation from monocular video.
+",1,0,0,0,0,0
+10811,Quasi-ordered Rings,"  A quasi-order is a binary, reflexive and transitive relation. In the Journal
+of Pure and Applied Algebra 45 (1987), S.M. Fakhruddin introduced the notion of
+(totally) quasi-ordered fields and showed that each such field is either an
+ordered field or else a valued field. Hence, quasi-ordered fields are very well
+suited to treat ordered and valued fields simultaneously.
+In this note, we will prove that the same dichotomy holds for commutative
+rings with 1 as well. For that purpose we first develop an appropriate notion
+of (totally) quasi-ordered rings. Our proof of the dichotomy then exploits
+Fakhruddin's result that was mentioned above.
+",0,0,1,0,0,0
+10812,"Mean-Field Controllability and Decentralized Stabilization of Markov Chains, Part I: Global Controllability and Rational Feedbacks","  In this paper, we study the controllability and stabilizability properties of
+the Kolmogorov forward equation of a continuous time Markov chain (CTMC)
+evolving on a finite state space, using the transition rates as the control
+parameters. Firstly, we prove small-time local and global controllability from
+and to strictly positive equilibrium configurations when the underlying graph
+is strongly connected. Secondly, we show that there always exists a locally
+exponentially stabilizing decentralized linear (density-)feedback law that
+takes zero valu at equilibrium and respects the graph structure, provided that
+the transition rates are allowed to be negative and the desired target density
+lies in the interior of the set of probability densities. For bidirected
+graphs, that is, graphs where a directed edge in one direction implies an edge
+in the opposite direction, we show that this linear control law can be realized
+using a decentralized rational feedback law of the form k(x) = a(x) +
+b(x)f(x)/g(x) that also respects the graph structure and control constraints
+(positivity and zero at equilibrium). This enables the possibility of using
+Linear Matrix Inequality (LMI) based tools to algorithmically construct
+decentralized density feedback controllers for stabilization of a robotic swarm
+to a target task distribution with no task-switching at equilibrium, as we
+demonstrate with several numerical examples.
+",1,0,1,0,0,0
+10813,Sharp total variation results for maximal functions,"  In this article, we prove some total variation inequalities for maximal
+functions. Our results deal with two possible generalizations of the results
+contained in Aldaz and Pérez Lázaro's work, one of whose considers a
+variable truncation of the maximal function, and the other one interpolates the
+centered and the uncentered maximal functions. In both contexts, we find sharp
+constants for the desired inequalities, which can be viewed as progress towards
+the conjecture that the best constant for the variation inequality in the
+centered context is one. We also provide counterexamples showing that our
+methods do not apply outside the stated parameter ranges.
+",0,0,1,0,0,0
+10814,Optimal Bipartite Network Clustering,"  We study bipartite community detection in networks, or more generally the
+network biclustering problem. We present a fast two-stage procedure based on
+spectral initialization followed by the application of a pseudo-likelihood
+classifier twice. Under mild regularity conditions, we establish the weak
+consistency of the procedure (i.e., the convergence of the misclassification
+rate to zero) under a general bipartite stochastic block model. We show that
+the procedure is optimal in the sense that it achieves the optimal convergence
+rate that is achievable by a biclustering oracle, adaptively over the whole
+class, up to constants. This is further formalized by deriving a minimax lower
+bound over a class of biclustering problems. The optimal rate we obtain
+sharpens some of the existing results and generalizes others to a wide regime
+of average degree growth, from sparse networks with average degrees growing
+arbitrarily slowly to fairly dense networks with average degrees of order
+$\sqrt{n}$. As a special case, we recover the known exact recovery threshold in
+the $\log n$ regime of sparsity. To obtain the consistency result, as part of
+the provable version of the algorithm, we introduce a sub-block partitioning
+scheme that is also computationally attractive, allowing for distributed
+implementation of the algorithm without sacrificing optimality. The provable
+algorithm is derived from a general class of pseudo-likelihood biclustering
+algorithms that employ simple EM type updates. We show the effectiveness of
+this general class by numerical simulations.
+",1,0,0,1,0,0
+10815,End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level,"  This paper demonstrates end-to-end neural network architectures for
+Vietnamese named entity recognition. Our best model is a combination of
+bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network
+(CNN), Conditional Random Field (CRF), using pre-trained word embeddings as
+input, which achieves an F1 score of 88.59% on a standard test set. Our system
+is able to achieve a comparable performance to the first-rank system of the
+VLSP campaign without using any syntactic or hand-crafted features. We also
+give an extensive empirical study on using common deep learning models for
+Vietnamese NER, at both word and character level.
+",1,0,0,0,0,0
+10816,The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks,"  We consider the task of estimating a high-dimensional directed acyclic graph,
+given observations from a linear structural equation model with arbitrary noise
+distribution. By exploiting properties of common random graphs, we develop a
+new algorithm that requires conditioning only on small sets of variables. The
+proposed algorithm, which is essentially a modified version of the
+PC-Algorithm, offers significant gains in both computational complexity and
+estimation accuracy. In particular, it results in more efficient and accurate
+estimation in large networks containing hub nodes, which are common in
+biological systems. We prove the consistency of the proposed algorithm, and
+show that it also requires a less stringent faithfulness assumption than the
+PC-Algorithm. Simulations in low and high-dimensional settings are used to
+illustrate these findings. An application to gene expression data suggests that
+the proposed algorithm can identify a greater number of clinically relevant
+genes than current methods.
+",0,0,0,1,1,0
+10817,Theoretical analysis of the electron bridge process in $^{229}$Th$^{3+}$,"  We investigate the deexcitation of the $^{229}$Th nucleus via the excitation
+of an electron. Detailed calculations are performed for the enhancement of the
+nuclear decay width due to this so called electron bridge (EB) compared to the
+direct photoemission from the nucleus. The results are obtianed for triply
+ionized thorium by using a B-spline pseudo basis approach to solve the Dirac
+equation for a local $x_\alpha$ potential. This approach allows for an
+approximation of the full electron propagator including the positive and
+negative continuum. We show that the contribution of continua slightly
+increases the enhancement compared to a propagator calculated by a direct
+summation over bound states. Moreover we put special emphasis on the
+interference between the direct and exchange Feynman diagrams that can have a
+strong influence on the enhancement.
+",0,1,0,0,0,0
+10818,Memory in de Sitter space and BMS-like supertranslations,"  It is well known that the memory effect in flat spacetime is parametrized by
+the BMS supertranslation. We investigate the relation between the memory effect
+and diffeomorphism in de Sitter spacetime. We find that gravitational memory is
+parametrized by a BMS-like supertranslation in the static patch of de Sitter
+spacetime. We also show a diffeomorphism that corresponds to gravitational
+memory in the Poincare/cosmological patch. Our method does not need to assume
+the separation between the source and the detector to be small compared with
+the Hubble radius, and can potentially be applicable to other FLRW universes,
+as well as ""ordinary memory"" mediated by massive messenger particles.
+",0,1,0,0,0,0
+10819,Data-Driven Estimation Of Mutual Information Between Dependent Data,"  We consider the problem of estimating mutual information between dependent
+data, an important problem in many science and engineering applications. We
+propose a data-driven, non-parametric estimator of mutual information in this
+paper. The main novelty of our solution lies in transforming the data to
+frequency domain to make the problem tractable. We define a novel
+metric--mutual information in frequency--to detect and quantify the dependence
+between two random processes across frequency using Cramér's spectral
+representation. Our solution calculates mutual information as a function of
+frequency to estimate the mutual information between the dependent data over
+time. We validate its performance on linear and nonlinear models. In addition,
+mutual information in frequency estimated as a part of our solution can also be
+used to infer cross-frequency coupling in the data.
+",1,0,0,1,0,0
+10820,Wigner functions for gauge equivalence classes of unitary irreducible representations of noncommutative quantum mechanics,"  While Wigner functions forming phase space representation of quantum states
+is a well-known fact, their construction for noncommutative quantum mechanics
+(NCQM) remains relatively lesser known, in particular with respect to gauge
+dependencies. This paper deals with the construction of Wigner functions of
+NCQM for a system of 2-degrees of freedom using 2-parameter families of gauge
+equivalence classes of unitary irreducible representations (UIRs) of the Lie
+group $\g$ which has been identified as the kinematical symmetry group of NCQM
+in an earlier paper. This general construction of Wigner functions for NCQM, in
+turn, yields the special cases of Landau and symmetric gauges of NCQM.
+",0,0,1,0,0,0
+10821,Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks,"  Intracranial carotid artery calcification (ICAC) is a major risk factor for
+stroke, and might contribute to dementia and cognitive decline. Reliance on
+time-consuming manual annotation of ICAC hampers much demanded further research
+into the relationship between ICAC and neurological diseases. Automation of
+ICAC segmentation is therefore highly desirable, but difficult due to the
+proximity of the lesions to bony structures with a similar attenuation
+coefficient. In this paper, we propose a method for automatic segmentation of
+ICAC; the first to our knowledge. Our method is based on a 3D fully
+convolutional neural network that we extend with two regularization techniques.
+Firstly, we use deep supervision (hidden layers supervision) to encourage
+discriminative features in the hidden layers. Secondly, we augment the network
+with skip connections, as in the recently developed ResNet, and dropout layers,
+inserted in a way that skip connections circumvent them. We investigate the
+effect of skip connections and dropout. In addition, we propose a simple
+problem-specific modification of the network objective function that restricts
+the focus to the most important image regions and simplifies the optimization.
+We train and validate our model using 882 CT scans and test on 1,000. Our
+regularization techniques and objective improve the average Dice score by 7.1%,
+yielding an average Dice of 76.2% and 97.7% correlation between predicted ICAC
+volumes and manual annotations.
+",1,0,0,0,0,0
+10822,"HTMoL: full-stack solution for remote access, visualization, and analysis of Molecular Dynamics trajectory data","  The field of structural bioinformatics has seen significant advances with the
+use of Molecular Dynamics (MD) simulations of biological systems. The MD
+methodology has allowed to explain and discover molecular mechanisms in a wide
+range of natural processes. There is an impending need to readily share the
+ever-increasing amount of MD data, which has been hindered by the lack of
+specialized tools in the past. To solve this problem, we present HTMoL, a
+state-of-the-art plug-in-free hardware-accelerated web application specially
+designed to efficiently transfer and visualize raw MD trajectory files on a web
+browser. Now, individual research labs can publish MD data on the Internet, or
+use HTMoL to profoundly improve scientific reports by including supplemental MD
+data in a journal publication. HTMoL can also be used as a visualization
+interface to access MD trajectories generated on a high-performance computer
+center directly.
+Availability: HTMoL is available free of charge for academic use. All major
+browsers are supported. A complete online documentation including instructions
+for download, installation, configuration, and examples is available at the
+HTMoL website this http URL. Supplementary data are available
+online. Corresponding author: mauricio.carrillo@cinvestav.mx
+",1,0,0,0,0,0
+10823,General auction method for real-valued optimal transport,"  The auction method developed by Bertsekas in the late 1970s is a relaxation
+technique for solving integer-valued assignment problems. It resembles a
+competitive bidding process, where unsatisfied persons (bidders) attempt to
+claim the objects (lots) offering the best value. By transforming
+integer-valued transport problems into assignment problems, the auction method
+can be extended to compute optimal transport solutions. We propose a more
+general auction method that can be applied directly to real-valued transport
+problems. We prove termination and provide a priori error bounds for the
+general auction method. Our numerical results indicate that the complexity of
+the general auction is roughly comparable to that of the original auction
+method, when the latter is applicable.
+",1,0,1,0,0,0
+10824,Force sensing with an optically levitated charged nanoparticle,"  Levitated optomechanics is showing potential for precise force measurements.
+Here, we report a case study, to show experimentally the capacity of such a
+force sensor. Using an electric field as a tool to detect a Coulomb force
+applied onto a levitated nanosphere. We experimentally observe the spatial
+displacement of up to 6.6 nm of the levitated nanosphere by imposing a DC
+field. We further apply an AC field and demonstrate resonant enhancement of
+force sensing when a driving frequency, $\omega_{AC}$, and the frequency of the
+levitated mechanical oscillator, $\omega_0$, converge. We directly measure a
+force of $3.0 \pm 1.5 \times 10^{-20}$ N with 10 second integration time, at a
+centre of mass temperature of 3 K and at a pressure of $1.6 \times 10^{-5}$
+mbar.
+",0,1,0,0,0,0
+10825,Unveiling Eilenberg-type Correspondences: Birkhoff's Theorem for (finite) Algebras + Duality,"  The purpose of the present paper is to show that: Eilenberg-type
+correspondences = Birkhoff's theorem for (finite) algebras + duality. We
+consider algebras for a monad T on a category D and we study (pseudo)varieties
+of T-algebras. Pseudovarieties of algebras are also known in the literature as
+varieties of finite algebras. Two well-known theorems that characterize
+varieties and pseudovarieties of algebras play an important role here:
+Birkhoff's theorem and Birkhoff's theorem for finite algebras, the latter also
+known as Reiterman's theorem. We prove, under mild assumptions, a categorical
+version of Birkhoff's theorem for (finite) algebras to establish a one-to-one
+correspondence between (pseudo)varieties of T-algebras and (pseudo)equational
+T-theories. Now, if C is a category that is dual to D and B is the comonad on C
+that is the dual of T, we get a one-to-one correspondence between
+(pseudo)equational T-theories and their dual, (pseudo)coequational B-theories.
+Particular instances of (pseudo)coequational B-theories have been already
+studied in language theory under the name of ""varieties of languages"" to
+establish Eilenberg-type correspondences. All in all, we get a one-to-one
+correspondence between (pseudo)varieties of T-algebras and (pseudo)coequational
+B-theories, which will be shown to be exactly the nature of Eilenberg-type
+correspondences.
+",1,0,1,0,0,0
+10826,Detecting transit signatures of exoplanetary rings using SOAP3.0,"  CONTEXT. It is theoretically possible for rings to have formed around
+extrasolar planets in a similar way to that in which they formed around the
+giant planets in our solar system. However, no such rings have been detected to
+date.
+AIMS: We aim to test the possibility of detecting rings around exoplanets by
+investigating the photometric and spectroscopic ring signatures in
+high-precision transit signals.
+METHODS: The photometric and spectroscopic transit signals of a ringed planet
+is expected to show deviations from that of a spherical planet. We used these
+deviations to quantify the detectability of rings. We present SOAP3.0 which is
+a numerical tool to simulate ringed planet transits and measure ring
+detectability based on amplitudes of the residuals between the ringed planet
+signal and best fit ringless model.
+RESULTS: We find that it is possible to detect the photometric and
+spectroscopic signature of near edge-on rings especially around planets with
+high impact parameter. Time resolution $\leq$ 7 mins is required for the
+photometric detection, while 15 mins is sufficient for the spectroscopic
+detection. We also show that future instruments like CHEOPS and ESPRESSO, with
+precisions that allow ring signatures to be well above their noise-level,
+present good prospects for detecting rings.
+",0,1,0,0,0,0
+10827,MatlabCompat.jl: helping Julia understand Your Matlab/Octave Code,"  Scientific legacy code in MATLAB/Octave not compatible with modernization of
+research workflows is vastly abundant throughout academic community.
+Performance of non-vectorized code written in MATLAB/Octave represents a major
+burden. A new programming language for technical computing Julia, promises to
+address these issues. Although Julia syntax is similar to MATLAB/Octave,
+porting code to Julia may be cumbersome for researchers. Here we present
+MatlabCompat.jl - a library aimed at simplifying the conversion of your
+MATLAB/Octave code to Julia. We show using a simplistic image analysis use case
+that MATLAB/Octave code can be easily ported to high performant Julia using
+MatlabCompat.jl.
+",1,0,0,0,0,0
+10828,Extending the topological analysis and seeking the real-space subsystems in non-Coulombic systems with homogeneous potential energy functions,"  It is customary to conceive the interactions of all the constituents of a
+molecular system, i.e. electrons and nuclei, as Coulombic. However, in a more
+detailed analysis one may always find small but non-negligible non-Coulombic
+interactions in molecular systems originating from the finite size of nuclei,
+magnetic interactions, etc. While such small modifications of the Coulombic
+interactions do not seem to alter the nature of a molecular system in real
+world seriously, they are a serious obstacle for quantum chemical theories and
+methodologies which their formalism is strictly confined to the Coulombic
+interactions. Although the quantum theory of atoms in molecules (QTAIM) has
+been formulated originally for the Coulombic systems, some recent studies have
+demonstrated that apart from basin energy of an atom in a molecule, its
+theoretical ingredients are not sensitive to the explicit form of the potential
+energy operator. In this study, it is demonstrated that the basin energy may be
+defined not only for coulombic systems but for all real-space subsystems of
+those systems that are described by any member of the set of the homogeneous
+potential energy functions. On the other hand, this extension opens the door
+for seeking novel real-space subsystems, apart from atoms in molecules, in
+non-Coulombic systems. These novel real-space subsystems call for an extended
+formalism that goes beyond the orthodox QTAIM, which is not confined to the
+Coulombic systems nor to the atoms in molecules as the sole real-space
+subsystems. It is termed the quantum theory of real-space open subsystems
+(QTROS) and its potential applications are detailed. The harmonic trap model,
+containing non-interacting fermions or bosons, is considered as an example for
+the QTROS analysis. The QTROS analysis of bosonic systems is particularly quite
+unprecedented, not attempted before.
+",0,1,0,0,0,0
+10829,Deep Uncertainty Surrounding Coastal Flood Risk Projections: A Case Study for New Orleans,"  Future sea-level rise drives severe risks for many coastal communities.
+Strategies to manage these risks hinge on a sound characterization of the
+uncertainties. For example, recent studies suggest that large fractions of the
+Antarctic ice sheet (AIS) may rapidly disintegrate in response to rising global
+temperatures, leading to potentially several meters of sea-level rise during
+the next few centuries. It is deeply uncertain, for example, whether such an
+AIS disintegration will be triggered, how much this would increase sea-level
+rise, whether extreme storm surges intensify in a warming climate, or which
+emissions pathway future societies will choose. Here, we assess the impacts of
+these deep uncertainties on projected flooding probabilities for a levee ring
+in New Orleans, Louisiana. We use 18 scenarios, presenting probabilistic
+projections within each one, to sample key deeply uncertain future projections
+of sea-level rise, radiative forcing pathways, storm surge characterization,
+and contributions from rapid AIS mass loss. The implications of these deep
+uncertainties for projected flood risk are thus characterized by a set of 18
+probability distribution functions. We use a global sensitivity analysis to
+assess which mechanisms contribute to uncertainty in projected flood risk over
+the course of a 50-year design life. In line with previous work, we find that
+the uncertain storm surge drives the most substantial risk, followed by general
+AIS dynamics, in our simple model for future flood risk for New Orleans.
+",0,1,0,0,0,0
+10830,Bounds on the Approximation Power of Feedforward Neural Networks,"  The approximation power of general feedforward neural networks with piecewise
+linear activation functions is investigated. First, lower bounds on the size of
+a network are established in terms of the approximation error and network depth
+and width. These bounds improve upon state-of-the-art bounds for certain
+classes of functions, such as strongly convex functions. Second, an upper bound
+is established on the difference of two neural networks with identical weights
+but different activation functions.
+",0,0,0,1,0,0
+10831,Non-classification of free Araki-Woods factors and $τ$-invariants,"  We define the standard Borel space of free Araki-Woods factors and prove that
+their isomorphism relation is not classifiable by countable structures. We also
+prove that equality of $\tau$-topologies, arising as invariants of type III
+factors, as well as coycle and outer conjugacy of actions of abelian groups on
+free product factors are not classifiable by countable structures.
+",0,0,1,0,0,0
+10832,Data-Driven Learning and Planning for Environmental Sampling,"  Robots such as autonomous underwater vehicles (AUVs) and autonomous surface
+vehicles (ASVs) have been used for sensing and monitoring aquatic environments
+such as oceans and lakes. Environmental sampling is a challenging task because
+the environmental attributes to be observed can vary both spatially and
+temporally, and the target environment is usually a large and continuous domain
+whereas the sampling data is typically sparse and limited. The challenges
+require that the sampling method must be informative and efficient enough to
+catch up with the environmental dynamics. In this paper we present a planning
+and learning method that enables a sampling robot to perform persistent
+monitoring tasks by learning and refining a dynamic ""data map"" that models a
+spatiotemporal environment attribute such as ocean salinity content. Our
+environmental sampling framework consists of two components: to maximize the
+information collected, we propose an informative planning component that
+efficiently generates sampling waypoints that contain the maximal information;
+To alleviate the computational bottleneck caused by large-scale data
+accumulated, we develop a component based on a sparse Gaussian Process whose
+hyperparameters are learned online by taking advantage of only a subset of data
+that provides the greatest contribution. We validate our method with both
+simulations running on real ocean data and field trials with an ASV in a lake
+environment. Our experiments show that the proposed framework is both accurate
+in learning the environmental data map and efficient in catching up with the
+dynamic environmental changes.
+",1,0,0,0,0,0
+10833,Global spectral graph wavelet signature for surface analysis of carpal bones,"  In this paper, we present a spectral graph wavelet approach for shape
+analysis of carpal bones of human wrist. We apply a metric called global
+spectral graph wavelet signature for representation of cortical surface of the
+carpal bone based on eigensystem of Laplace-Beltrami operator. Furthermore, we
+propose a heuristic and efficient way of aggregating local descriptors of a
+carpal bone surface to global descriptor. The resultant global descriptor is
+not only isometric invariant, but also much more efficient and requires less
+memory storage. We perform experiments on shape of the carpal bones of ten
+women and ten men from a publicly-available database. Experimental results show
+the excellency of the proposed GSGW compared to recent proposed GPS embedding
+approach for comparing shapes of the carpal bones across populations.
+",1,0,0,0,0,0
+10834,Learning Rates of Regression with q-norm Loss and Threshold,"  This paper studies some robust regression problems associated with the
+$q$-norm loss ($q\ge1$) and the $\epsilon$-insensitive $q$-norm loss in the
+reproducing kernel Hilbert space. We establish a variance-expectation bound
+under a priori noise condition on the conditional distribution, which is the
+key technique to measure the error bound. Explicit learning rates will be given
+under the approximation ability assumptions on the reproducing kernel Hilbert
+space.
+",0,0,1,1,0,0
+10835,"Instability, rupture and fluctuations in thin liquid films: Theory and computations","  Thin liquid films are ubiquitous in natural phenomena and technological
+applications. They have been extensively studied via deterministic hydrodynamic
+equations, but thermal fluctuations often play a crucial role that needs to be
+understood. An example of this is dewetting, which involves the rupture of a
+thin liquid film and the formation of droplets. Such a process is thermally
+activated and requires fluctuations to be taken into account self-consistently.
+In this work we present an analytical and numerical study of a stochastic
+thin-film equation derived from first principles. Following a brief review of
+the derivation, we scrutinise the behaviour of the equation in the limit of
+perfectly correlated noise along the wall-normal direction. The stochastic
+thin-film equation is also simulated by adopting a numerical scheme based on a
+spectral collocation method. The scheme allows us to explore the fluctuating
+dynamics of the thin film and the behaviour of its free energy in the vicinity
+of rupture. Finally, we also study the effect of the noise intensity on the
+rupture time, which is in agreement with previous works.
+",0,1,0,0,0,0
+10836,Fractional Patlak-Keller-Segel equations for chemotactic superdiffusion,"  The long range movement of certain organisms in the presence of a
+chemoattractant can be governed by long distance runs, according to an
+approximate Levy distribution. This article clarifies the form of biologically
+relevant model equations: We derive Patlak-Keller-Segel-like equations
+involving nonlocal, fractional Laplacians from a microscopic model for cell
+movement. Starting from a power-law distribution of run times, we derive a
+kinetic equation in which the collision term takes into account the long range
+behaviour of the individuals. A fractional chemotactic equation is obtained in
+a biologically relevant regime. Apart from chemotaxis, our work has
+implications for biological diffusion in numerous processes.
+",0,1,0,0,0,0
+10837,Adaptive Risk Bounds in Univariate Total Variation Denoising and Trend Filtering,"  We study trend filtering, a relatively recent method for univariate
+nonparametric regression. For a given positive integer $r$, the $r$-th order
+trend filtering estimator is defined as the minimizer of the sum of squared
+errors when we constrain (or penalize) the sum of the absolute $r$-th order
+discrete derivatives of the fitted function at the design points. For $r=1$,
+the estimator reduces to total variation regularization which has received much
+attention in the statistics and image processing literature. In this paper, we
+study the performance of the trend filtering estimator for every positive
+integer $r$, both in the constrained and penalized forms. Our main results show
+that in the strong sparsity setting when the underlying function is a
+(discrete) spline with few ""knots"", the risk (under the global squared error
+loss) of the trend filtering estimator (with an appropriate choice of the
+tuning parameter) achieves the parametric $n^{-1}$ rate, up to a logarithmic
+(multiplicative) factor. Our results therefore provide support for the use of
+trend filtering, for every $r$, in the strong sparsity setting.
+",0,0,1,1,0,0
+10838,Assessment of First-Principles and Semiempirical Methodologies for Absorption and Emission Energies of Ce$^{3+}$-Doped Luminescent Materials,"  In search of a reliable methodology for the prediction of light absorption
+and emission of Ce$^{3+}$-doped luminescent materials, 13 representative
+materials are studied with first-principles and semiempirical approaches. In
+the first-principles approach, that combines constrained density-functional
+theory and $\Delta$SCF, the atomic positions are obtained for both ground and
+excited states of the Ce$^{3+}$ ion. The structural information is fed into
+Dorenbos' semiempirical model. Absorption and emission energies are calculated
+with both methods and compared with experiment. The first-principles approach
+matches experiment within 0.3 eV, with two exceptions at 0.5 eV. In contrast,
+the semiempirical approach does not perform as well (usually more than 0.5 eV
+error). The general applicability of the present first-principles scheme, with
+an encouraging predictive power, opens a novel avenue for crystal site
+engineering and high-throughput search for new phosphors and scintillators.
+",0,1,0,0,0,0
+10839,Regularizing deep networks using efficient layerwise adversarial training,"  Adversarial training has been shown to regularize deep neural networks in
+addition to increasing their robustness to adversarial examples. However, its
+impact on very deep state of the art networks has not been fully investigated.
+In this paper, we present an efficient approach to perform adversarial training
+by perturbing intermediate layer activations and study the use of such
+perturbations as a regularizer during training. We use these perturbations to
+train very deep models such as ResNets and show improvement in performance both
+on adversarial and original test data. Our experiments highlight the benefits
+of perturbing intermediate layer activations compared to perturbing only the
+inputs. The results on CIFAR-10 and CIFAR-100 datasets show the merits of the
+proposed adversarial training approach. Additional results on WideResNets show
+that our approach provides significant improvement in classification accuracy
+for a given base model, outperforming dropout and other base models of larger
+size.
+",1,0,0,1,0,0
+10840,Improving Dynamic Analysis of Android Apps Using Hybrid Test Input Generation,"  The Android OS has become the most popular mobile operating system leading to
+a significant increase in the spread of Android malware. Consequently, several
+static and dynamic analysis systems have been developed to detect Android
+malware. With dynamic analysis, efficient test input generation is needed in
+order to trigger the potential run-time malicious behaviours. Most existing
+dynamic analysis systems employ random-based input generation methods usually
+built using the Android Monkey tool. Random-based input generation has several
+shortcomings including limited code coverage, which motivates us to explore
+combining it with a state-based method in order to improve efficiency. Hence,
+in this paper, we present a novel hybrid test input generation approach
+designed to improve dynamic analysis on real devices. We implemented the hybrid
+system by integrating a random based tool (Monkey) with a state based tool
+(DroidBot) in order to improve code coverage and potentially uncover more
+malicious behaviours. The system is evaluated using 2,444 Android apps
+containing 1222 benign and 1222 malware samples from the Android malware genome
+project. Three scenarios, random only, state-based only, and our proposed
+hybrid approach were investigated to comparatively evaluate their performances.
+Our study shows that the hybrid approach significantly improved the amount of
+dynamic features extracted from both benign and malware samples over the
+state-based and commonly used random test input generation method.
+",1,0,0,0,0,0
+10841,Process Monitoring Using Maximum Sequence Divergence,"  Process Monitoring involves tracking a system's behaviors, evaluating the
+current state of the system, and discovering interesting events that require
+immediate actions. In this paper, we consider monitoring temporal system state
+sequences to help detect the changes of dynamic systems, check the divergence
+of the system development, and evaluate the significance of the deviation. We
+begin with discussions of data reduction, symbolic data representation, and the
+anomaly detection in temporal discrete sequences. Time-series representation
+methods are also discussed and used in this paper to discretize raw data into
+sequences of system states. Markov Chains and stationary state distributions
+are continuously generated from temporal sequences to represent snapshots of
+the system dynamics in different time frames. We use generalized Jensen-Shannon
+Divergence as the measure to monitor changes of the stationary symbol
+probability distributions and evaluate the significance of system deviations.
+We prove that the proposed approach is able to detect deviations of the systems
+we monitor and assess the deviation significance in probabilistic manner.
+",0,0,0,1,0,0
+10842,Some generalizations of Kannan's theorems via $σ_c$-function,"  In this article we go on to discuss about various proper extensions of
+Kannan's two different fixed point theorems, introducing the new concept of
+$\sigma_c$-function; which is independent of the three notions of simulation
+function, manageable functions and R-functions. These results are the analogous
+to some well known theorems, and extends several known results in this
+literature.
+",0,0,1,0,0,0
+10843,On Exact Sequences of the Rigid Fibrations,"  In 2002, Biss investigated on a kind of fibration which is called rigid
+covering fibration (we rename it by rigid fibration) with properties similar to
+covering spaces. In this paper, we obtain a relation between arbitrary
+topological spaces and its rigid fibrations. Using this relation we obtain a
+commutative diagram of homotopy groups and quasitopological homotopy groups and
+deduce some results in this field.
+",0,0,1,0,0,0
+10844,Analytic Gradients for Complete Active Space Pair-Density Functional Theory,"  Analytic gradient routines are a desirable feature for quantum mechanical
+methods, allowing for efficient determination of equilibrium and transition
+state structures and several other molecular properties. In this work, we
+present analytical gradients for multiconfiguration pair-density functional
+theory (MC-PDFT) when used with a state-specific complete active space
+self-consistent field reference wave function. Our approach constructs a
+Lagrangian that is variational in all wave function parameters. We find that
+MC-PDFT locates equilibrium geometries for several small- to medium-sized
+organic molecules that are similar to those located by complete active space
+second-order perturbation theory but that are obtained with decreased
+computational cost.
+",0,1,0,0,0,0
+10845,Northern sky Galactic Cosmic Ray anisotropy between 10-1000 TeV with the Tibet Air Shower Array,"  We report the analysis of the $10-1000$ TeV large-scale sidereal anisotropy
+of Galactic cosmic rays (GCRs) with the data collected by the Tibet Air Shower
+Array from October, 1995 to February, 2010. In this analysis, we improve the
+energy estimate and extend the declination range down to $-30^{\circ}$. We find
+that the anisotropy maps above 100 TeV are distinct from that at multi-TeV
+band. The so-called ""tail-in"" and ""loss-cone"" features identified at low
+energies get less significant and a new component appears at $\sim100$ TeV. The
+spatial distribution of the GCR intensity with an excess (7.2$\sigma$
+pre-trial, 5.2$\sigma$ post-trial) and a deficit ($-5.8\sigma$ pre-trial) are
+observed in the 300 TeV anisotropy map, in a good agreement with IceCube's
+results at 400 TeV. Combining the Tibet results in the northern sky with
+IceCube's results in the southern sky, we establish a full-sky picture of the
+anisotropy in hundreds of TeV band. We further find that the amplitude of the
+first order anisotropy increases sharply above $\sim100$ TeV, indicating a new
+component of the anisotropy. All these results may shed new light on
+understanding the origin and propagation of GCRs.
+",0,1,0,0,0,0
+10846,Count-ception: Counting by Fully Convolutional Redundant Counting,"  Counting objects in digital images is a process that should be replaced by
+machines. This tedious task is time consuming and prone to errors due to
+fatigue of human annotators. The goal is to have a system that takes as input
+an image and returns a count of the objects inside and justification for the
+prediction in the form of object localization. We repose a problem, originally
+posed by Lempitsky and Zisserman, to instead predict a count map which contains
+redundant counts based on the receptive field of a smaller regression network.
+The regression network predicts a count of the objects that exist inside this
+frame. By processing the image in a fully convolutional way each pixel is going
+to be accounted for some number of times, the number of windows which include
+it, which is the size of each window, (i.e., 32x32 = 1024). To recover the true
+count we take the average over the redundant predictions. Our contribution is
+redundant counting instead of predicting a density map in order to average over
+errors. We also propose a novel deep neural network architecture adapted from
+the Inception family of networks called the Count-ception network. Together our
+approach results in a 20% relative improvement (2.9 to 2.3 MAE) over the state
+of the art method by Xie, Noble, and Zisserman in 2016.
+",1,0,0,1,0,0
+10847,A robust inverse scattering transform for the focusing nonlinear Schrödinger equation,"  We propose a modification of the standard inverse scattering transform for
+the focusing nonlinear Schrödinger equation (also other equations by natural
+generalization) formulated with nonzero boundary conditions at infinity. The
+purpose is to deal with arbitrary-order poles and potentially severe spectral
+singularities in a simple and unified way. As an application, we use the
+modified transform to place the Peregrine solution and related higher-order
+""rogue wave"" solutions in an inverse-scattering context for the first time.
+This allows one to directly study properties of these solutions such as their
+dynamical or structural stability, or their asymptotic behavior in the limit of
+high order. The modified transform method also allows rogue waves to be
+generated on top of other structures by elementary Darboux transformations,
+rather than the generalized Darboux transformations in the literature or other
+related limit processes.
+",0,1,0,0,0,0
+10848,Admissible Bayes equivariant estimation of location vectors for spherically symmetric distributions with unknown scale,"  This paper investigates estimation of the mean vector under invariant
+quadratic loss for a spherically symmetric location family with a residual
+vector with density of the form $
+f(x,u)=\eta^{(p+n)/2}f(\eta\{\|x-\theta\|^2+\|u\|^2\}) $, where $\eta$ is
+unknown. We show that the natural estimator $x$ is admissible for $p=1,2$.
+Also, for $p\geq 3$, we find classes of generalized Bayes estimators that are
+admissible within the class of equivariant estimators of the form
+$\{1-\xi(x/\|u\|)\}x$. In the Gaussian case, a variant of the James--Stein
+estimator, $[1-\{(p-2)/(n+2)\}/\{\|x\|^2/\|u\|^2+(p-2)/(n+2)+1\}]x$, which
+dominates the natural estimator $x$, is also admissible within this class. We
+also study the related regression model.
+",0,0,1,1,0,0
+10849,A proof theoretic study of abstract termination principles,"  We define a variety of abstract termination principles which form
+generalisations of simplification orders, and investigate their computational
+content. Simplification orders, which include the well-known multiset and
+lexicographic path orderings, are important techniques for proving that
+computer programs terminate. Moreover, an analysis of the proofs that these
+orders are wellfounded can yield additional quantitative information: namely an
+upper bound on the complexity of programs reducing under these orders. In this
+paper we focus on extracting computational content from the typically
+non-constructive wellfoundedness proofs of termination orders, with an eye
+towards the establishment of general metatheorems which characterise bounds on
+the derivational complexity induced by these orders. However, ultimately we
+have a much broader goal, which is to explore a number of deep mathematical
+concepts which underlie termination orders, including minimal-bad-sequence
+constructions, modified realizability and bar recursion. We aim to describe how
+these concepts all come together to form a particularly elegant illustration of
+the bridge between proofs and programs.
+",1,0,1,0,0,0
+10850,Why exomoons must be rare?,"  The problem of the search for the satellites of the exoplanets (exomoons) is
+discussed recently. There are very many satellites in our Solar System. But in
+contrary of our Solar system, exoplanets have significant eccentricity. In
+process of planetary migration, exoplanets can cross some resonances with
+following growth of their orbital eccentricity. The stability of exomoons
+decreases, and many of satellites were lost. Here we give a simple example of
+loss satellite when eccentricity increased. Finally, we can conclude that
+exomoons must be rare due to observed large eccentricities of exoplanets.
+",0,1,0,0,0,0
+10851,Propagation of regularity in $L^p$-spaces for Kolmogorov type hypoelliptic operators,"  Consider the following Kolmogorov type hypoelliptic operator $$ \mathscr
+L_t:=\mbox{$\sum_{j=2}^n$}x_j\cdot\nabla_{x_{j-1}}+{\rm Tr} (a_t
+\cdot\nabla^2_{x_n}), $$ where $n\geq 2$, $x=(x_1,\cdots,x_n)\in(\mathbb R^d)^n
+=\mathbb R^{nd}$ and $a_t$ is a time-dependent constant symmetric $d\times
+d$-matrix that is uniformly elliptic and bounded.. Let $\{\mathcal T_{s,t};
+t\geq s\}$ be the time-dependent semigroup associated with $\mathscr L_t$; that
+is, $\partial_s {\mathcal T}_{s, t} f = - {\mathscr L}_s {\mathcal T}_{s, t}f$.
+For any $p\in(1,\infty)$, we show that there is a constant $C=C(p,n,d)>0$ such
+that for any $f(t, x)\in L^p(\mathbb R \times \mathbb R^{nd})=L^p(\mathbb
+R^{1+nd})$ and every $\lambda \geq 0$, $$
+\left\|\Delta_{x_j}^{{1}/{(1+2(n-j)})}\int^{\infty}_0 e^{-\lambda t} {\mathcal
+T}_{s, s+t }f(t+s, x)dt\right\|_p\leq C\|f\|_p,\quad j=1,\cdots, n, $$ where
+$\|\cdot\|_p$ is the usual $L^p$-norm in $L^p(\mathbb R^{1+nd}; d s\times d
+x)$. To show this type of estimates, we first study the propagation of
+regularity in $L^2$-space from variable $x_n$ to $x_1$ for the solution of the
+transport equation $\partial_t u+\sum_{j=2}^nx_j\cdot\nabla_{x_{j-1}} u=f$.
+",0,0,1,0,0,0
+10852,On Relation between Constraint Answer Set Programming and Satisfiability Modulo Theories,"  Constraint answer set programming is a promising research direction that
+integrates answer set programming with constraint processing. It is often
+informally related to the field of satisfiability modulo theories. Yet, the
+exact formal link is obscured as the terminology and concepts used in these two
+research areas differ. In this paper, we connect these two research areas by
+uncovering the precise formal relation between them. We believe that this work
+will booster the cross-fertilization of the theoretical foundations and the
+existing solving methods in both areas. As a step in this direction we provide
+a translation from constraint answer set programs with integer linear
+constraints to satisfiability modulo linear integer arithmetic that paves the
+way to utilizing modern satisfiability modulo theories solvers for computing
+answer sets of constraint answer set programs.
+",1,0,0,0,0,0
+10853,The w-effect in interferometric imaging: from a fast sparse measurement operator to super-resolution,"  Modern radio telescopes, such as the Square Kilometre Array (SKA), will probe
+the radio sky over large fields-of-view, which results in large w-modulations
+of the sky image. This effect complicates the relationship between the measured
+visibilities and the image under scrutiny. In algorithmic terms, it gives rise
+to massive memory and computational time requirements. Yet, it can be a
+blessing in terms of reconstruction quality of the sky image. In recent years,
+several works have shown that large w-modulations promote the spread spectrum
+effect. Within the compressive sensing framework, this effect increases the
+incoherence between the sensing basis and the sparsity basis of the signal to
+be recovered, leading to better estimation of the sky image. In this article,
+we revisit the w-projection approach using convex optimisation in realistic
+settings, where the measurement operator couples the w-terms in Fourier and the
+de-gridding kernels. We provide sparse, thus fast, models of the Fourier part
+of the measurement operator through adaptive sparsification procedures.
+Consequently, memory requirements and computational cost are significantly
+alleviated, at the expense of introducing errors on the radio-interferometric
+data model. We present a first investigation of the impact of the sparse
+variants of the measurement operator on the image reconstruction quality. We
+finally analyse the interesting super-resolution potential associated with the
+spread spectrum effect of the w-modulation, and showcase it through
+simulations. Our C++ code is available online on GitHub.
+",0,1,0,0,0,0
+10854,Large Scale Graph Learning from Smooth Signals,"  Graphs are a prevalent tool in data science, as they model the inherent
+structure of the data. They have been used successfully in unsupervised and
+semi-supervised learning. Typically they are constructed either by connecting
+nearest samples, or by learning them from data, solving an optimization
+problem. While graph learning does achieve a better quality, it also comes with
+a higher computational cost. In particular, the current state-of-the-art model
+cost is $\mathcal{O}(n^2)$ for $n$ samples. In this paper, we show how to scale
+it, obtaining an approximation with leading cost of $\mathcal{O}(n\log(n))$,
+with quality that approaches the exact graph learning model. Our algorithm uses
+known approximate nearest neighbor techniques to reduce the number of
+variables, and automatically selects the correct parameters of the model,
+requiring a single intuitive input: the desired edge density.
+",1,0,0,1,0,0
+10855,Power Plant Performance Modeling with Concept Drift,"  Power plant is a complex and nonstationary system for which the traditional
+machine learning modeling approaches fall short of expectations. The
+ensemble-based online learning methods provide an effective way to continuously
+learn from the dynamic environment and autonomously update models to respond to
+environmental changes. This paper proposes such an online ensemble regression
+approach to model power plant performance, which is critically important for
+operation optimization. The experimental results on both simulated and real
+data show that the proposed method can achieve performance with less than 1%
+mean average percentage error, which meets the general expectations in field
+operations.
+",1,0,0,1,0,0
+10856,Does putting your emotions into words make you feel better? Measuring the minute-scale dynamics of emotions from online data,"  Studies of affect labeling, i.e. putting your feelings into words, indicate
+that it can attenuate positive and negative emotions. Here we track the
+evolution of individual emotions for tens of thousands of Twitter users by
+analyzing the emotional content of their tweets before and after they
+explicitly report having a strong emotion. Our results reveal how emotions and
+their expression evolve at the temporal resolution of one minute. While the
+expression of positive emotions is preceded by a short but steep increase in
+positive valence and followed by short decay to normal levels, negative
+emotions build up more slowly, followed by a sharp reversal to previous levels,
+matching earlier findings of the attenuating effects of affect labeling. We
+estimate that positive and negative emotions last approximately 1.25 and 1.5
+hours from onset to evanescence. A separate analysis for male and female
+subjects is suggestive of possible gender-specific differences in emotional
+dynamics.
+",1,0,0,0,0,0
+10857,Axiomatizing Epistemic Logic of Friendship via Tree Sequent Calculus,"  This paper positively solves an open problem if it is possible to provide a
+Hilbert system to Epistemic Logic of Friendship (EFL) by Seligman, Girard and
+Liu. To find a Hilbert system, we first introduce a sound, complete and
+cut-free tree (or nested) sequent calculus for EFL, which is an integrated
+combination of Seligman's sequent calculus for basic hybrid logic and a tree
+sequent calculus for modal logic. Then we translate a tree sequent into an
+ordinary formula to specify a Hilbert system of EFL and finally show that our
+Hilbert system is sound and complete for the intended two-dimensional
+semantics.
+",1,0,1,0,0,0
+10858,A novel approach to the Lindelöf hypothesis,"  Lindel{ö}f's hypothesis, one of the most important open problems in the
+history of mathematics, states that for large $t$, Riemann's zeta function
+$\zeta(\frac{1}{2}+it)$ is of order $O(t^{\varepsilon})$ for any
+$\varepsilon>0$. It is well known that for large $t$, the leading order
+asymptotics of the Riemann zeta function can be expressed in terms of a
+transcendental exponential sum. The usual approach to the Lindelöf hypothesis
+involves the use of ingenious techniques for the estimation of this sum.
+However, since such estimates can not yield an asymptotic formula for the above
+sum, it appears that this approach cannot lead to the proof of the Lindelöf
+hypothesis. Here, a completely different approach is introduced: the Riemann
+zeta function is embedded in a classical problem in the theory of complex
+analysis known as a Riemann-Hilbert problem, and then, the large
+$t$-asymptotics of the associated integral equation is formally computed. This
+yields two different results. First, the formal proof that a certain Riemann
+zeta-type double exponential sum satisfies the asymptotic estimate of the
+Lindelöf hypothesis. Second, it is formally shown that the sum of
+$|\zeta(1/2+it)|^2$ and of a certain sum which depends on $\epsilon$, satisfies
+for large $t$ the estimate of the Lindelöf hypothesis. Hence, since the above
+identity is valid for all $\epsilon$, this asymptotic identity suggests the
+validity of Lindelöf's hypothesis. The completion of the rigorous derivation
+of the above results will be presented in a companion paper.
+",0,0,1,0,0,0
+10859,An Overview of Recent Solutions to and Lower Bounds for the Firing Synchronization Problem,"  Complex systems in a wide variety of areas such as biological modeling, image
+processing, and language recognition can be modeled using networks of very
+simple machines called finite automata. Connecting subsystems modeled using
+finite automata into a network allows for more computational power. One such
+network, called a cellular automaton, consists of an n-dimensional array for n
+> 1 with a single finite automaton located at each point of the array. One of
+the oldest problems associated with cellular automata is the firing
+synchronization problem, originally proposed by John Myhill in 1957. As with
+any long-standing problem, there are a large number of solutions to the firing
+synchronization problem. Our goal, and the contribution of this work, is to
+summarize recent solutions to the problem. We focus primarily on solutions to
+the original problem, that is, the problem where the network is a
+one-dimensional array and there is a single initiator located at one of the
+ends. We summarize both minimal-time and non-minimal-time solutions, with an
+emphasis on solutions that were published after 1998. We also focus on
+solutions that minimize the number of states required by the finite automata.
+In the process we also identify open problems that remain in terms of finding
+minimal-state solutions to the firing synchronization problem.
+",1,1,0,0,0,0
+10860,On Tackling the Limits of Resolution in SAT Solving,"  The practical success of Boolean Satisfiability (SAT) solvers stems from the
+CDCL (Conflict-Driven Clause Learning) approach to SAT solving. However, from a
+propositional proof complexity perspective, CDCL is no more powerful than the
+resolution proof system, for which many hard examples exist. This paper
+proposes a new problem transformation, which enables reducing the decision
+problem for formulas in conjunctive normal form (CNF) to the problem of solving
+maximum satisfiability over Horn formulas. Given the new transformation, the
+paper proves a polynomial bound on the number of MaxSAT resolution steps for
+pigeonhole formulas. This result is in clear contrast with earlier results on
+the length of proofs of MaxSAT resolution for pigeonhole formulas. The paper
+also establishes the same polynomial bound in the case of modern core-guided
+MaxSAT solvers. Experimental results, obtained on CNF formulas known to be hard
+for CDCL SAT solvers, show that these can be efficiently solved with modern
+MaxSAT solvers.
+",1,0,0,0,0,0
+10861,Penalized Estimation in Additive Regression with High-Dimensional Data,"  Additive regression provides an extension of linear regression by modeling
+the signal of a response as a sum of functions of covariates of relatively low
+complexity. We study penalized estimation in high-dimensional nonparametric
+additive regression where functional semi-norms are used to induce smoothness
+of component functions and the empirical $L_2$ norm is used to induce sparsity.
+The functional semi-norms can be of Sobolev or bounded variation types and are
+allowed to be different amongst individual component functions. We establish
+new oracle inequalities for the predictive performance of such methods under
+three simple technical conditions: a sub-gaussian condition on the noise, a
+compatibility condition on the design and the functional classes under
+consideration, and an entropy condition on the functional classes. For random
+designs, the sample compatibility condition can be replaced by its population
+version under an additional condition to ensure suitable convergence of
+empirical norms. In homogeneous settings where the complexities of the
+component functions are of the same order, our results provide a spectrum of
+explicit convergence rates, from the so-called slow rate without requiring the
+compatibility condition to the fast rate under the hard sparsity or certain
+$L_q$ sparsity to allow many small components in the true regression function.
+These results significantly broadens and sharpens existing ones in the
+literature.
+",0,0,1,1,0,0
+10862,Towards Quality Advancement of Underwater Machine Vision with Generative Adversarial Networks,"  Underwater machine vision has attracted significant attention, but its low
+quality has prevented it from a wide range of applications. Although many
+different algorithms have been developed to solve this problem, real-time
+adaptive methods are frequently deficient. In this paper, based on filtering
+and the use of generative adversarial networks (GANs), two approaches are
+proposed for the aforementioned issue, i.e., a filtering-based restoration
+scheme (FRS) and a GAN-based restoration scheme (GAN-RS). Distinct from
+previous methods, FRS restores underwater images in the Fourier domain, which
+is composed of a parameter search, filtering, and enhancement. Aiming to
+further improve the image quality, GAN-RS can adaptively restore underwater
+machine vision in real time without the need for pretreatment. In particular,
+information in the Lab color space and the dark channel is developed as loss
+functions, namely, underwater index loss and dark channel prior loss,
+respectively. More specifically, learning from the underwater index, the
+discriminator is equipped with a carefully crafted underwater branch to predict
+the underwater probability of an image. A multi-stage loss strategy is then
+developed to guarantee the effective training of GANs. Through extensive
+comparisons on the image quality and applications, the superiority of the
+proposed approaches is confirmed. Consequently, the GAN-RS is considerably
+faster and achieves a state-of-the-art performance in terms of the color
+correction, contrast stretch, dehazing, and feature restoration of various
+underwater scenes. The source code will be made available.
+",1,0,0,0,0,0
+10863,Linking Sketches and Diagrams to Source Code Artifacts,"  Recent studies have shown that sketches and diagrams play an important role
+in the daily work of software developers. If these visual artifacts are
+archived, they are often detached from the source code they document, because
+there is no adequate tool support to assist developers in capturing, archiving,
+and retrieving sketches related to certain source code artifacts. This paper
+presents SketchLink, a tool that aims at increasing the value of sketches and
+diagrams created during software development by supporting developers in these
+tasks. Our prototype implementation provides a web application that employs the
+camera of smartphones and tablets to capture analog sketches, but can also be
+used on desktop computers to upload, for instance, computer-generated diagrams.
+We also implemented a plugin for a Java IDE that embeds the links in Javadoc
+comments and visualizes them in situ in the source code editor as graphical
+icons.
+",1,0,0,0,0,0
+10864,Parsimonious Data: How a single Facebook like predicts voting behaviour in multiparty systems,"  Recently, two influential PNAS papers have shown how our preferences for
+'Hello Kitty' and 'Harley Davidson', obtained through Facebook likes, can
+accurately predict details about our personality, religiosity, political
+attitude and sexual orientation (Konsinski et al. 2013; Youyou et al 2015). In
+this paper, we make the claim that though the wide variety of Facebook likes
+might predict such personal traits, even more accurate and generalizable
+results can be reached through applying a contexts-specific, parsimonious data
+strategy. We built this claim by predicting present day voter intention based
+solely on likes directed toward posts from political actors. Combining the
+online and offline, we join a subsample of surveyed respondents to their public
+Facebook activity and apply machine learning classifiers to explore the link
+between their political liking behaviour and actual voting intention. Through
+this work, we show how even a single well-chosen Facebook like, can reveal as
+much about our political voter intention as hundreds of random likes. Further,
+by including the entire political like history of the respondents, our model
+reaches prediction accuracies above previous multiparty studies (60-70%). We
+conclude the paper by discussing how a parsimonious data strategy applied, with
+some limitations, allow us to generalize our findings to the 1,4 million Danes
+with at least one political like and even to other political multiparty
+systems.
+",1,0,0,0,0,0
+10865,The Hidden Vulnerability of Distributed Learning in Byzantium,"  While machine learning is going through an era of celebrated success,
+concerns have been raised about the vulnerability of its backbone: stochastic
+gradient descent (SGD). Recent approaches have been proposed to ensure the
+robustness of distributed SGD against adversarial (Byzantine) workers sending
+poisoned gradients during the training phase. Some of these approaches have
+been proven Byzantine-resilient: they ensure the convergence of SGD despite the
+presence of a minority of adversarial workers.
+We show in this paper that convergence is not enough. In high dimension $d
+\gg 1$, an adver\-sary can build on the loss function's non-convexity to make
+SGD converge to ineffective models. More precisely, we bring to light that
+existing Byzantine-resilient schemes leave a margin of poisoning of
+$\Omega\left(f(d)\right)$, where $f(d)$ increases at least like $\sqrt{d~}$.
+Based on this leeway, we build a simple attack, and experimentally show its
+strong to utmost effectivity on CIFAR-10 and MNIST.
+We introduce Bulyan, and prove it significantly reduces the attackers leeway
+to a narrow $O( \frac{1}{\sqrt{d~}})$ bound. We empirically show that Bulyan
+does not suffer the fragility of existing aggregation rules and, at a
+reasonable cost in terms of required batch size, achieves convergence as if
+only non-Byzantine gradients had been used to update the model.
+",0,0,0,1,0,0
+10866,walk2friends: Inferring Social Links from Mobility Profiles,"  The development of positioning technologies has resulted in an increasing
+amount of mobility data being available. While bringing a lot of convenience to
+people's life, such availability also raises serious concerns about privacy. In
+this paper, we concentrate on one of the most sensitive information that can be
+inferred from mobility data, namely social relationships. We propose a novel
+social relation inference attack that relies on an advanced feature learning
+technique to automatically summarize users' mobility features. Compared to
+existing approaches, our attack is able to predict any two individuals' social
+relation, and it does not require the adversary to have any prior knowledge on
+existing social relations. These advantages significantly increase the
+applicability of our attack and the scope of the privacy assessment. Extensive
+experiments conducted on a large dataset demonstrate that our inference attack
+is effective, and achieves between 13% to 20% improvement over the best
+state-of-the-art scheme. We propose three defense mechanisms -- hiding,
+replacement and generalization -- and evaluate their effectiveness for
+mitigating the social link privacy risks stemming from mobility data sharing.
+Our experimental results show that both hiding and replacement mechanisms
+outperform generalization. Moreover, hiding and replacement achieve a
+comparable trade-off between utility and privacy, the former preserving better
+utility and the latter providing better privacy.
+",1,0,0,0,0,0
+10867,An Optimal Algorithm for Online Unconstrained Submodular Maximization,"  We consider a basic problem at the interface of two fundamental fields:
+submodular optimization and online learning. In the online unconstrained
+submodular maximization (online USM) problem, there is a universe
+$[n]=\{1,2,...,n\}$ and a sequence of $T$ nonnegative (not necessarily
+monotone) submodular functions arrive over time. The goal is to design a
+computationally efficient online algorithm, which chooses a subset of $[n]$ at
+each time step as a function only of the past, such that the accumulated value
+of the chosen subsets is as close as possible to the maximum total value of a
+fixed subset in hindsight. Our main result is a polynomial-time no-$1/2$-regret
+algorithm for this problem, meaning that for every sequence of nonnegative
+submodular functions, the algorithm's expected total value is at least $1/2$
+times that of the best subset in hindsight, up to an error term sublinear in
+$T$. The factor of $1/2$ cannot be improved upon by any polynomial-time online
+algorithm when the submodular functions are presented as value oracles.
+Previous work on the offline problem implies that picking a subset uniformly at
+random in each time step achieves zero $1/4$-regret.
+A byproduct of our techniques is an explicit subroutine for the two-experts
+problem that has an unusually strong regret guarantee: the total value of its
+choices is comparable to twice the total value of either expert on rounds it
+did not pick that expert. This subroutine may be of independent interest.
+",0,0,0,1,0,0
+10868,Modeling Grasp Motor Imagery through Deep Conditional Generative Models,"  Grasping is a complex process involving knowledge of the object, the
+surroundings, and of oneself. While humans are able to integrate and process
+all of the sensory information required for performing this task, equipping
+machines with this capability is an extremely challenging endeavor. In this
+paper, we investigate how deep learning techniques can allow us to translate
+high-level concepts such as motor imagery to the problem of robotic grasp
+synthesis. We explore a paradigm based on generative models for learning
+integrated object-action representations, and demonstrate its capacity for
+capturing and generating multimodal, multi-finger grasp configurations on a
+simulated grasping dataset.
+",1,0,0,1,0,0
+10869,Designing an Effective Metric Learning Pipeline for Speaker Diarization,"  State-of-the-art speaker diarization systems utilize knowledge from external
+data, in the form of a pre-trained distance metric, to effectively determine
+relative speaker identities to unseen data. However, much of recent focus has
+been on choosing the appropriate feature extractor, ranging from pre-trained
+$i-$vectors to representations learned via different sequence modeling
+architectures (e.g. 1D-CNNs, LSTMs, attention models), while adopting
+off-the-shelf metric learning solutions. In this paper, we argue that,
+regardless of the feature extractor, it is crucial to carefully design a metric
+learning pipeline, namely the loss function, the sampling strategy and the
+discrimnative margin parameter, for building robust diarization systems.
+Furthermore, we propose to adopt a fine-grained validation process to obtain a
+comprehensive evaluation of the generalization power of metric learning
+pipelines. To this end, we measure diarization performance across different
+language speakers, and variations in the number of speakers in a recording.
+Using empirical studies, we provide interesting insights into the effectiveness
+of different design choices and make recommendations.
+",1,0,0,0,0,0
+10870,Mining Application-aware Community Organization with Expanded Feature Subspaces from Concerned Attributes in Social Networks,"  Social networks are typical attributed networks with node attributes.
+Different from traditional attribute community detection problem aiming at
+obtaining the whole set of communities in the network, we study an
+application-oriented problem of mining an application-aware community
+organization with respect to specific concerned attributes. The concerned
+attributes are designated based on the requirements of any application by a
+user in advance. The application-aware community organization w.r.t. concerned
+attributes consists of the communities with feature subspaces containing these
+concerned attributes. Besides concerned attributes, feature subspace of each
+required community may contain some other relevant attributes. All relevant
+attributes of a feature subspace jointly describe and determine the community
+embedded in such subspace. Thus the problem includes two subproblems, i.e., how
+to expand the set of concerned attributes to complete feature subspaces and how
+to mine the communities embedded in the expanded subspaces. Two subproblems are
+jointly solved by optimizing a quality function called subspace fitness. An
+algorithm called ACM is proposed. In order to locate the communities
+potentially belonging to the application-aware community organization, cohesive
+parts of a network backbone composed of nodes with similar concerned attributes
+are detected and set as the community seeds. The set of concerned attributes is
+set as the initial subspace for all community seeds. Then each community seed
+and its attribute subspace are adjusted iteratively to optimize the subspace
+fitness. Extensive experiments on synthetic datasets demonstrate the
+effectiveness and efficiency of our method and applications on real-world
+networks show its application values.
+",1,1,0,0,0,0
+10871,A spin-gapped Mott insulator with the dimeric arrangement of twisted molecules Zn(tmdt)$_{2}$,"  $^{13}$C nuclear magnetic resonance measurements were performed for a
+single-component molecular material Zn(tmdt)$_{2}$, in which tmdt's form an
+arrangement similar to the so-called ${\kappa}$-type molecular packing in
+quasi-two-dimensional Mott insulators and superconductors. Detailed analysis of
+the powder spectra uncovered local spin susceptibility in the tmdt ${\pi}$
+orbitals. The obtained shift and relaxation rate revealed the singlet-triplet
+excitations of the ${\pi}$ spins, indicating that Zn(tmdt)$_{2}$ is a
+spin-gapped Mott insulator with exceptionally large electron correlations
+compared to conventional molecular Mott systems.
+",0,1,0,0,0,0
+10872,Li doping kagome spin liquid compounds,"  Herbertsmithite and Zn-doped barlowite are two compounds for experimental
+realization of twodimensional gapped kagome spin liquid. Theoretically, it has
+been proposed that charge doping a quantum spin liquid gives rise to exotic
+metallic states, such as high-temperature superconductivity. However, one
+recent experiment about herbertsmithite with successful Li-doping shows
+surprisingly the insulating state even under the heavy doped scenario, which
+can hardly be explained by many-body physics. Using first-principles
+calculation, we performed a comprehensive study about the Li intercalated
+doping effect of these two compounds. For the Li-doped herbertsmithite, we
+identified the optimized Li position at the Cl-(OH)$_3$-Cl pentahedron site
+instead of previously speculated Cl-(OH)$_3$ tetrahedral site. With the
+increase of Li doping concentration, the saturation magnetization decreases
+linearly due to the charge transfer from Li to Cu ions. Moreover, we found that
+Li forms chemical bonds with the nearby (OH)$^-$ and Cl$^-$ ions, which lowers
+the surrounding chemical potential and traps the electron, as evidenced by the
+localized charge distribution, explaining the insulating behavior measured
+experimentally. Though with different structure from herbertsmithite, Zn-doped
+Barlowite shows the same features upon Li doping. We conclude that Li doping
+this family of kagome spin liquid cannot realize exotic metallic states, other
+methods should be further explored, such as element substitution with different
+valence electrons.
+",0,1,0,0,0,0
+10873,DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion,"  Non-recurring traffic congestion is caused by temporary disruptions, such as
+accidents, sports games, adverse weather, etc. We use data related to real-time
+traffic speed, jam factors (a traffic congestion indicator), and events
+collected over a year from Nashville, TN to train a multi-layered deep neural
+network. The traffic dataset contains over 900 million data records. The
+network is thereafter used to classify the real-time data and identify
+anomalous operations. Compared with traditional approaches of using statistical
+or machine learning techniques, our model reaches an accuracy of 98.73 percent
+when identifying traffic congestion caused by football games. Our approach
+first encodes the traffic across a region as a scaled image. After that the
+image data from different timestamps is fused with event- and time-related
+data. Then a crossover operator is used as a data augmentation method to
+generate training datasets with more balanced classes. Finally, we use the
+receiver operating characteristic (ROC) analysis to tune the sensitivity of the
+classifier. We present the analysis of the training time and the inference time
+separately.
+",0,0,0,1,0,0
+10874,Facets of a mixed-integer bilinear covering set with bounds on variables,"  We derive a closed form description of the convex hull of mixed-integer
+bilinear covering set with bounds on the integer variables. This convex hull
+description is completely determined by considering some orthogonal disjunctive
+sets defined in a certain way. Our description does not introduce any new
+variables. We also derive a linear time separation algorithm for finding the
+facet defining inequalities of this convex hull. We show the effectiveness of
+the new inequalities using some examples.
+",0,0,1,0,0,0
+10875,Universal partial sums of Taylor series as functions of the centre of expansion,"  V. Nestoridis conjectured that if $\Omega$ is a simply connected subset of
+$\mathbb{C}$ that does not contain $0$ and $S(\Omega)$ is the set of all
+functions $f\in \mathcal{H}(\Omega)$ with the property that the set
+$\left\{T_N(f)(z)\coloneqq\sum_{n=0}^N\dfrac{f^{(n)}(z)}{n!} (-z)^n : N =
+0,1,2,\dots \right\}$ is dense in $\mathcal{H}(\Omega)$, then $S(\Omega)$ is a
+dense $G_\delta$ set in $\mathcal{H}(\Omega)$. We answer the conjecture in the
+affirmative in the special case where $\Omega$ is an open disc $D(z_0,r)$ that
+does not contain $0$.
+",0,0,1,0,0,0
+10876,Free Cooling of a Granular Gas in Three Dimensions,"  Granular gases as dilute ensembles of particles in random motion are not only
+at the basis of elementary structure-forming processes in the universe and
+involved in many industrial and natural phenomena, but also excellent models to
+study fundamental statistical dynamics. A vast number of theoretical and
+numerical investigations have dealt with this apparently simple non-equilibrium
+system. The essential difference to molecular gases is the energy dissipation
+in particle collisions, a subtle distinction with immense impact on their
+global dynamics. Its most striking manifestation is the so-called granular
+cooling, the gradual loss of mechanical energy in absence of external
+excitation.
+We report an experimental study of homogeneous cooling of three-dimensional
+(3D) granular gases in microgravity. Surprisingly, the asymptotic scaling
+$E(t)\propto t^{-2}$ obtained by Haff's minimal model [J. Fluid Mech. 134, 401
+(1983)] proves to be robust, despite the violation of several of its central
+assumptions. The shape anisotropy of the grains influences the characteristic
+time of energy loss quantitatively, but not qualitatively. We compare kinetic
+energies in the individual degrees of freedom, and find a slight predominance
+of the translational motions. In addition, we detect a certain preference of
+the grains to align with their long axis in flight direction, a feature known
+from active matter or animal flocks, and the onset of clustering.
+",0,1,0,0,0,0
+10877,The SCUBA-2 Ambitious Sky Survey: a catalogue of beam-sized sources in the Galactic longitude range 120 to 140,"  The SCUBA-2 Ambitious Sky Survey (SASSy) is composed of shallow 850-$\umu$m
+imaging using the Sub-millimetre Common-User Bolometer Array 2 (SCUBA-2) on the
+James Clerk Maxwell Telescope. Here we describe the extraction of a catalogue
+of beam-sized sources from a roughly $120\,{\rm deg}^2$ region of the Galactic
+plane mapped uniformly (to an rms level of about 40\,mJy), covering longitude
+120\degr\,$<$\,\textit{l}\,$<$\,140\degr\ and latitude
+$\abs{\textit{b}}$\,$<$\,2.9\degr. We used a matched-filtering approach to
+increase the signal-to-noise (S/N) ratio in these noisy maps and tested the
+efficiency of our extraction procedure through estimates of the false discovery
+rate, as well as by adding artificial sources to the real images. The primary
+catalogue contains a total of 189 sources at 850\,$\umu$m, down to a S/N
+threshold of approximately 4.6. Additionally, we list 136 sources detected down
+to ${\rm S/N}=4.3$, but recognise that as we go lower in S/N, the reliability
+of the catalogue rapidly diminishes. We perform follow-up observations of some
+of our lower significance sources through small targeted SCUBA-2 images, and
+list 265 sources detected in these maps down to ${\rm S/N}=5$. This illustrates
+the real power of SASSy: inspecting the shallow maps for regions of 850-$\umu$m
+emission and then using deeper targeted images to efficiently find fainter
+sources. We also perform a comparison of the SASSy sources with the Planck
+Catalogue of Compact Sources and the \textit{IRAS} Point Source Catalogue, to
+determine which sources discovered in this field might be new, and hence
+potentially cold regions at an early stage of star formation.
+",0,1,0,0,0,0
+10878,A stable and optimally convergent LaTIn-Cut Finite Element Method for multiple unilateral contact problems,"  In this paper, we propose a novel unfitted finite element method for the
+simulation of multiple body contact. The computational mesh is generated
+independently of the geometry of the interacting solids, which can be
+arbitrarily complex. The key novelty of the approach is the combination of
+elements of the CutFEM technology, namely the enrichment of the solution field
+via the definition of overlapping fictitious domains with a dedicated
+penalty-type regularisation of discrete operators, and the LaTIn hybrid-mixed
+formulation of complex interface conditions. Furthermore, the novel P1-P1
+discretisation scheme that we propose for the unfitted LaTIn solver is shown to
+be stable, robust and optimally convergent with mesh refinement. Finally, the
+paper introduces a high-performance 3D level-set/CutFEM framework for the
+versatile and robust solution of contact problems involving multiple bodies of
+complex geometries, with more than two bodies interacting at a single point.
+",1,0,0,0,0,0
+10879,Non Fermi liquid behavior and continuously tunable resistivity exponents in the Anderson-Hubbard model at finite temperature,"  We employ a recently developed computational many-body technique to study for
+the first time the half-filled Anderson-Hubbard model at finite temperature and
+arbitrary correlation ($U$) and disorder ($V$) strengths. Interestingly, the
+narrow zero temperature metallic range induced by disorder from the Mott
+insulator expands with increasing temperature in a manner resembling a quantum
+critical point. Our study of the resistivity temperature scaling $T^{\alpha}$
+for this metal reveals non Fermi liquid characteristics. Moreover, a continuous
+dependence of $\alpha$ on $U$ and $V$ from linear to nearly quadratic was
+observed. We argue that these exotic results arise from a systematic change
+with $U$ and $V$ of the ""effective"" disorder, a combination of quenched
+disorder and intrinsic localized spins.
+",0,1,0,0,0,0
+10880,Beyond Parity: Fairness Objectives for Collaborative Filtering,"  We study fairness in collaborative-filtering recommender systems, which are
+sensitive to discrimination that exists in historical data. Biased data can
+lead collaborative-filtering methods to make unfair predictions for users from
+minority groups. We identify the insufficiency of existing fairness metrics and
+propose four new metrics that address different forms of unfairness. These
+fairness metrics can be optimized by adding fairness terms to the learning
+objective. Experiments on synthetic and real data show that our new metrics can
+better measure fairness than the baseline, and that the fairness objectives
+effectively help reduce unfairness.
+",1,0,0,1,0,0
+10881,Blind Regression via Nearest Neighbors under Latent Variable Models,"  We consider the setup of nonparametric 'blind regression' for estimating the
+entries of a large $m \times n$ matrix, when provided with a small, random
+fraction of noisy measurements. We assume that all rows $u \in [m]$ and columns
+$i \in [n]$ of the matrix are associated to latent features $x_1(u)$ and
+$x_2(i)$ respectively, and the $(u,i)$-th entry of the matrix, $A(u, i)$ is
+equal to $f(x_1(u), x_2(i))$ for a latent function $f$. Given noisy
+observations of a small, random subset of the matrix entries, our goal is to
+estimate the unobserved entries of the matrix as well as to ""de-noise"" the
+observed entries.
+As the main result of this work, we introduce a neighbor-based estimation
+algorithm inspired by the classical Taylor's series expansion. We establish its
+consistency when the underlying latent function $f$ is Lipschitz, the latent
+features belong to a compact domain, and the fraction of observed entries in
+the matrix is at least $\max \left(m^{-1 + \delta}, n^{-1/2 + \delta} \right)$,
+for any $\delta > 0$. As an important byproduct, our analysis sheds light into
+the performance of the classical collaborative filtering (CF) algorithm for
+matrix completion, which has been widely utilized in practice. Experiments with
+the MovieLens and Netflix datasets suggest that our algorithm provides a
+principled improvement over basic CF and is competitive with matrix
+factorization methods.
+Our algorithm has a natural extension to tensor completion. For a $t$-order
+balanced tensor with total of $N$ entries, we prove that our approach provides
+a consistent estimator when at least $N^{-\frac{\lfloor 2t/3 \rfloor}{2t}+
+\delta}$ fraction of entries are observed, for any $\delta > 0$. When applied
+to the setting of image in-painting (a tensor of order 3), we find that our
+approach is competitive with respect to state-of-art tensor completion
+algorithms across benchmark images.
+",0,0,1,1,0,0
+10882,Metalearning for Feature Selection,"  A general formulation of optimization problems in which various candidate
+solutions may use different feature-sets is presented, encompassing supervised
+classification, automated program learning and other cases. A novel
+characterization of the concept of a ""good quality feature"" for such an
+optimization problem is provided; and a proposal regarding the integration of
+quality based feature selection into metalearning is suggested, wherein the
+quality of a feature for a problem is estimated using knowledge about related
+features in the context of related problems. Results are presented regarding
+extensive testing of this ""feature metalearning"" approach on supervised text
+classification problems; it is demonstrated that, in this context, feature
+metalearning can provide significant and sometimes dramatic speedup over
+standard feature selection heuristics.
+",1,0,0,1,0,0
+10883,Variability-Aware Design for Energy Efficient Computational Artificial Intelligence Platform,"  Portable computing devices, which include tablets, smart phones and various
+types of wearable sensors, experienced a rapid development in recent years. One
+of the most critical limitations for these devices is the power consumption as
+they use batteries as the power supply. However, the bottleneck of the power
+saving schemes in both hardware design and software algorithm is the huge
+variability in power consumption. The variability is caused by a myriad of
+factors, including the manufacturing process, the ambient environment
+(temperature, humidity), the aging effects and etc. As the technology node
+scaled down to 28nm and even lower, the variability becomes more severe. As a
+result, a platform for variability characterization seems to be very necessary
+and helpful.
+",1,0,0,0,0,0
+10884,Quantifying Performance of Bipedal Standing with Multi-channel EMG,"  Spinal cord stimulation has enabled humans with motor complete spinal cord
+injury (SCI) to independently stand and recover some lost autonomic function.
+Quantifying the quality of bipedal standing under spinal stimulation is
+important for spinal rehabilitation therapies and for new strategies that seek
+to combine spinal stimulation and rehabilitative robots (such as exoskeletons)
+in real time feedback. To study the potential for automated electromyography
+(EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients
+undergoing electrical spinal cord stimulation using both video and
+multi-channel surface EMG recordings during spinal stimulation therapy
+sessions. The quality of standing under different stimulation settings was
+quantified manually by experienced clinicians. By correlating features of the
+recorded EMG activity with the expert evaluations, we show that multi-channel
+EMG recording can provide accurate, fast, and robust estimation for the quality
+of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis
+shows that the total number of EMG channels needed to effectively predict
+standing quality can be reduced while maintaining high estimation accuracy,
+which provides more flexibility for rehabilitation robotic systems to
+incorporate EMG recordings.
+",1,0,0,1,0,0
+10885,A framework for on-line calibration of LINAC devices,"  General description of an on-line procedure of calibration for IGRT (Image
+Guided Radiotherapy) is given. The algorithm allows to improve targeting cancer
+by estimating its position in space and suggests appropriate correction of the
+position of the patient. The description is given in the Geometric Algebra
+language which significantly simplifies calculations and clarifies
+presentation.
+",0,1,0,0,0,0
+10886,People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities,"  Media seems to have become more partisan, often providing a biased coverage
+of news catering to the interest of specific groups. It is therefore essential
+to identify credible information content that provides an objective narrative
+of an event. News communities such as digg, reddit, or newstrust offer
+recommendations, reviews, quality ratings, and further insights on journalistic
+works. However, there is a complex interaction between different factors in
+such online communities: fairness and style of reporting, language clarity and
+objectivity, topical perspectives (like political viewpoint), expertise and
+bias of community members, and more. This paper presents a model to
+systematically analyze the different interactions in a news community between
+users, news, and sources. We develop a probabilistic graphical model that
+leverages this joint interaction to identify 1) highly credible news articles,
+2) trustworthy news sources, and 3) expert users who perform the role of
+""citizen journalists"" in the community. Our method extends CRF models to
+incorporate real-valued ratings, as some communities have very fine-grained
+scales that cannot be easily discretized without losing information. To the
+best of our knowledge, this paper is the first full-fledged analysis of
+credibility, trust, and expertise in news communities.
+",1,0,0,1,0,0
+10887,Towards Provably Safe Mixed Transportation Systems with Human-driven and Automated Vehicles,"  Currently, we are in an environment where the fraction of automated vehicles
+is negligibly small. We anticipate that this fraction will increase in coming
+decades before if ever, we have a fully automated transportation system.
+Motivated by this we address the problem of provable safety of mixed traffic
+consisting of both intelligent vehicles (IVs) as well as human-driven vehicles
+(HVs). An important issue that arises is that such mixed systems may well have
+lesser throughput than all human traffic systems if the automated vehicles are
+expected to remain provably safe with respect to human traffic. This
+necessitates the consideration of strategies such as platooning of automated
+vehicles in order to increase the throughput. In this paper, we address the
+design of provably safe systems consisting of a mix of automated and
+human-driven vehicles including the use of platooning by automated vehicles.
+We design motion planing policies and coordination rules for participants in
+this novel mixed system. HVs are considered as nearsighted and modeled with
+relatively loose constraints, while IVs are considered as capable of following
+much tighter constraints. HVs are expected to follow reasonable and simple
+rules. IVs are designed to move under a model predictive control (MPC) based
+motion plans and coordination protocols. Our contribution of this paper is in
+showing how to integrate these two types of models safely into a mixed system.
+System safety is proved in single lane scenarios, as well as in multi-lane
+situations allowing lane changes.
+",1,0,0,0,0,0
+10888,"Streaming kernel regression with provably adaptive mean, variance, and regularization","  We consider the problem of streaming kernel regression, when the observations
+arrive sequentially and the goal is to recover the underlying mean function,
+assumed to belong to an RKHS. The variance of the noise is not assumed to be
+known. In this context, we tackle the problem of tuning the regularization
+parameter adaptively at each time step, while maintaining tight confidence
+bounds estimates on the value of the mean function at each point. To this end,
+we first generalize existing results for finite-dimensional linear regression
+with fixed regularization and known variance to the kernel setup with a
+regularization parameter allowed to be a measurable function of past
+observations. Then, using appropriate self-normalized inequalities we build
+upper and lower bound estimates for the variance, leading to Bersntein-like
+concentration bounds. The later is used in order to define the adaptive
+regularization. The bounds resulting from our technique are valid uniformly
+over all observation points and all time steps, and are compared against the
+literature with numerical experiments. Finally, the potential of these tools is
+illustrated by an application to kernelized bandits, where we revisit the
+Kernel UCB and Kernel Thompson Sampling procedures, and show the benefits of
+the novel adaptive kernel tuning strategy.
+",1,0,0,1,0,0
+10889,Estimating reducible stochastic differential equations by conversion to a least-squares problem,"  Stochastic differential equations (SDEs) are increasingly used in
+longitudinal data analysis, compartmental models, growth modelling, and other
+applications in a number of disciplines. Parameter estimation, however,
+currently requires specialized software packages that can be difficult to use
+and understand. This work develops and demonstrates an approach for estimating
+reducible SDEs using standard nonlinear least squares or mixed-effects
+software. Reducible SDEs are obtained through a change of variables in linear
+SDEs, and are sufficiently flexible for modelling many situations. The approach
+is based on extending a known technique that converts maximum likelihood
+estimation for a Gaussian model with a nonlinear transformation of the
+dependent variable into an equivalent least-squares problem. A similar idea can
+be used for Bayesian maximum a posteriori estimation. It is shown how to obtain
+parameter estimates for reducible SDEs containing both process and observation
+noise, including hierarchical models with either fixed or random group
+parameters. Code and examples in R are given. Univariate SDEs are discussed in
+detail, with extensions to the multivariate case outlined more briefly. The use
+of well tested and familiar standard software should make SDE modelling more
+transparent and accessible. Keywords: stochastic processes; longitudinal data;
+growth curves; compartmental models; mixed-effects; R
+",0,0,0,1,0,0
+10890,Deep Tensor Encoding,"  Learning an encoding of feature vectors in terms of an over-complete
+dictionary or a information geometric (Fisher vectors) construct is wide-spread
+in statistical signal processing and computer vision. In content based
+information retrieval using deep-learning classifiers, such encodings are
+learnt on the flattened last layer, without adherence to the multi-linear
+structure of the underlying feature tensor. We illustrate a variety of feature
+encodings incl. sparse dictionary coding and Fisher vectors along with
+proposing that a structured tensor factorization scheme enables us to perform
+retrieval that can be at par, in terms of average precision, with Fisher vector
+encoded image signatures. In short, we illustrate how structural constraints
+increase retrieval fidelity.
+",1,0,0,1,0,0
+10891,hMDAP: A Hybrid Framework for Multi-paradigm Data Analytical Processing on Spark,"  We propose hMDAP, a hybrid framework for large-scale data analytical
+processing on Spark, to support multi-paradigm process (incl. OLAP, machine
+learning, and graph analysis etc.) in distributed environments. The framework
+features a three-layer data process module and a business process module which
+controls the former. We will demonstrate the strength of hMDAP by using traffic
+scenarios in a real world.
+",1,0,0,0,0,0
+10892,Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment,"  A novel approach for unsupervised domain adaptation for neural networks is
+proposed. It relies on metric-based regularization of the learning process. The
+metric-based regularization aims at domain-invariant latent feature
+representations by means of maximizing the similarity between domain-specific
+activation distributions. The proposed metric results from modifying an
+integral probability metric such that it becomes less translation-sensitive on
+a polynomial function space. The metric has an intuitive interpretation in the
+dual space as the sum of differences of higher order central moments of the
+corresponding activation distributions. Under appropriate assumptions on the
+input distributions, error minimization is proven for the continuous case. As
+demonstrated by an analysis of standard benchmark experiments for sentiment
+analysis, object recognition and digit recognition, the outlined approach is
+robust regarding parameter changes and achieves higher classification
+accuracies than comparable approaches. The source code is available at
+this https URL.
+",1,0,0,1,0,0
+10893,A Convolutional Neural Network For Cosmic String Detection in CMB Temperature Maps,"  We present in detail the convolutional neural network used in our previous
+work to detect cosmic strings in cosmic microwave background (CMB) temperature
+anisotropy maps. By training this neural network on numerically generated CMB
+temperature maps, with and without cosmic strings, the network can produce
+prediction maps that locate the position of the cosmic strings and provide a
+probabilistic estimate of the value of the string tension $G\mu$. Supplying
+noiseless simulations of CMB maps with arcmin resolution to the network
+resulted in the accurate determination both of string locations and string
+tension for sky maps having strings with string tension as low as
+$G\mu=5\times10^{-9}$. The code is publicly available online. Though we trained
+the network with a long straight string toy model, we show the network performs
+well with realistic Nambu-Goto simulations.
+",0,1,0,0,0,0
+10894,Improving Palliative Care with Deep Learning,"  Improving the quality of end-of-life care for hospitalized patients is a
+priority for healthcare organizations. Studies have shown that physicians tend
+to over-estimate prognoses, which in combination with treatment inertia results
+in a mismatch between patients wishes and actual care at the end of life. We
+describe a method to address this problem using Deep Learning and Electronic
+Health Record (EHR) data, which is currently being piloted, with Institutional
+Review Board approval, at an academic medical center. The EHR data of admitted
+patients are automatically evaluated by an algorithm, which brings patients who
+are likely to benefit from palliative care services to the attention of the
+Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR
+data from previous years, to predict all-cause 3-12 month mortality of patients
+as a proxy for patients that could benefit from palliative care. Our
+predictions enable the Palliative Care team to take a proactive approach in
+reaching out to such patients, rather than relying on referrals from treating
+physicians, or conduct time consuming chart reviews of all patients. We also
+present a novel interpretation technique which we use to provide explanations
+of the model's predictions.
+",1,0,0,1,0,0
+10895,Scalable Online Convolutional Sparse Coding,"  Convolutional sparse coding (CSC) improves sparse coding by learning a
+shift-invariant dictionary from the data. However, existing CSC algorithms
+operate in the batch mode and are expensive, in terms of both space and time,
+on large datasets. In this paper, we alleviate these problems by using online
+learning. The key is a reformulation of the CSC objective so that convolution
+can be handled easily in the frequency domain and much smaller history matrices
+are needed. We use the alternating direction method of multipliers (ADMM) to
+solve the resulting optimization problem and the ADMM subproblems have
+efficient closed-form solutions. Theoretical analysis shows that the learned
+dictionary converges to a stationary point of the optimization problem.
+Extensive experiments show that convergence of the proposed method is much
+faster and its reconstruction performance is also better. Moreover, while
+existing CSC algorithms can only run on a small number of images, the proposed
+method can handle at least ten times more images.
+",1,0,0,0,0,0
+10896,Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion,"  It is challenging to recognize facial action unit (AU) from spontaneous
+facial displays, especially when they are accompanied by speech. The major
+reason is that the information is extracted from a single source, i.e., the
+visual channel, in the current practice. However, facial activity is highly
+correlated with voice in natural human communications.
+Instead of solely improving visual observations, this paper presents a novel
+audiovisual fusion framework, which makes the best use of visual and acoustic
+cues in recognizing speech-related facial AUs. In particular, a dynamic
+Bayesian network (DBN) is employed to explicitly model the semantic and dynamic
+physiological relationships between AUs and phonemes as well as measurement
+uncertainty. A pilot audiovisual AU-coded database has been collected to
+evaluate the proposed framework, which consists of a ""clean"" subset containing
+frontal faces under well controlled circumstances and a challenging subset with
+large head movements and occlusions. Experiments on this database have
+demonstrated that the proposed framework yields significant improvement in
+recognizing speech-related AUs compared to the state-of-the-art visual-based
+methods especially for those AUs whose visual observations are impaired during
+speech, and more importantly also outperforms feature-level fusion methods by
+explicitly modeling and exploiting physiological relationships between AUs and
+phonemes.
+",1,0,0,0,0,0
+10897,Re-purposing Compact Neuronal Circuit Policies to Govern Reinforcement Learning Tasks,"  We propose an effective method for creating interpretable control agents, by
+\textit{re-purposing} the function of a biological neural circuit model, to
+govern simulated and real world reinforcement learning (RL) test-beds. Inspired
+by the structure of the nervous system of the soil-worm, \emph{C. elegans}, we
+introduce \emph{Neuronal Circuit Policies} (NCPs) as a novel recurrent neural
+network instance with liquid time-constants, universal approximation
+capabilities and interpretable dynamics. We theoretically show that they can
+approximate any finite simulation time of a given continuous n-dimensional
+dynamical system, with $n$ output units and some hidden units. We model
+instances of the policies and learn their synaptic and neuronal parameters to
+control standard RL tasks and demonstrate its application for autonomous
+parking of a real rover robot on a pre-defined trajectory. For reconfiguration
+of the \emph{purpose} of the neural circuit, we adopt a search-based RL
+algorithm. We show that our neuronal circuit policies perform as good as deep
+neural network policies with the advantage of realizing interpretable dynamics
+at the cell-level. We theoretically find bounds for the time-varying dynamics
+of the circuits, and introduce a novel way to reason about networks' dynamics.
+",1,0,0,1,0,0
+10898,On the Expected Value of the Determinant of Random Sum of Rank-One Matrices,"  We present a simple, yet useful result about the expected value of the
+determinant of random sum of rank-one matrices. Computing such expectations in
+general may involve a sum over exponentially many terms. Nevertheless, we show
+that an interesting and useful class of such expectations that arise in, e.g.,
+D-optimal estimation and random graphs can be computed efficiently via
+computing a single determinant.
+",1,0,1,0,0,0
+10899,A hyperbolic-equation system approach for magnetized electron fluids in quasi-neutral plasmas,"  A new approach using a hyperbolic-equation system (HES) is proposed to solve
+for the electron fluids in quasi-neutral plasmas. The HES approach avoids
+treatments of cross-diffusion terms which cause numerical instabilities in
+conventional approaches using an elliptic equation (EE). A test calculation
+reveals that the HES approach can robustly solve problems of strong magnetic
+confinement by using an upwind method. The computation time of the HES approach
+is compared with that of the EE approach in terms of the size of the problem
+and the strength of magnetic confinement. The results indicate that the HES
+approach can be used to solve problems in a simple structured mesh without
+increasing computational time compared to the EE approach and that it features
+fast convergence in conditions of strong magnetic confinement.
+",0,1,0,0,0,0
+10900,Solving delay differential equations through RBF collocation,"  A general and easy-to-code numerical method based on radial basis functions
+(RBFs) collocation is proposed for the solution of delay differential equations
+(DDEs). It relies on the interpolation properties of infinitely smooth RBFs,
+which allow for a large accuracy over a scattered and relatively small
+discretization support. Hardy's multiquadric is chosen as RBF and combined with
+the Residual Subsampling Algorithm of Driscoll and Heryudono for support
+adaptivity. The performance of the method is very satisfactory, as demonstrated
+over a cross-section of benchmark DDEs, and by comparison with existing
+general-purpose and specialized numerical schemes for DDEs.
+",0,0,1,0,0,0
+10901,Learning Filter Functions in Regularisers by Minimising Quotients,"  Learning approaches have recently become very popular in the field of inverse
+problems. A large variety of methods has been established in recent years,
+ranging from bi-level learning to high-dimensional machine learning techniques.
+Most learning approaches, however, only aim at fitting parametrised models to
+favourable training data whilst ignoring misfit training data completely. In
+this paper, we follow up on the idea of learning parametrised regularisation
+functions by quotient minimisation as established in [3]. We extend the model
+therein to include higher-dimensional filter functions to be learned and allow
+for fit- and misfit-training data consisting of multiple functions. We first
+present results resembling behaviour of well-established derivative-based
+sparse regularisers like total variation or higher-order total variation in
+one-dimension. Our second and main contribution is the introduction of novel
+families of non-derivative-based regularisers. This is accomplished by learning
+favourable scales and geometric properties while at the same time avoiding
+unfavourable ones.
+",0,0,1,0,0,0
+10902,Riemann-Theta Boltzmann Machine,"  A general Boltzmann machine with continuous visible and discrete integer
+valued hidden states is introduced. Under mild assumptions about the connection
+matrices, the probability density function of the visible units can be solved
+for analytically, yielding a novel parametric density function involving a
+ratio of Riemann-Theta functions. The conditional expectation of a hidden state
+for given visible states can also be calculated analytically, yielding a
+derivative of the logarithmic Riemann-Theta function. The conditional
+expectation can be used as activation function in a feedforward neural network,
+thereby increasing the modelling capacity of the network. Both the Boltzmann
+machine and the derived feedforward neural network can be successfully trained
+via standard gradient- and non-gradient-based optimization techniques.
+",1,0,0,1,0,0
+10903,A Joint Quantile and Expected Shortfall Regression Framework,"  We introduce a novel regression framework which simultaneously models the
+quantile and the Expected Shortfall (ES) of a response variable given a set of
+covariates. This regression is based on a strictly consistent loss function for
+the pair quantile and ES, which allows for M- and Z-estimation of the joint
+regression parameters. We show consistency and asymptotic normality for both
+estimators under weak regularity conditions. The underlying loss function
+depends on two specification functions, whose choice affects the properties of
+the resulting estimators. We find that the Z-estimator is numerically unstable
+and thus, we rely on M-estimation of the model parameters. Extensive
+simulations verify the asymptotic properties and analyze the small sample
+behavior of the M-estimator for different specification functions. This joint
+regression framework allows for various applications including estimating,
+forecasting, and backtesting ES, which is particularly relevant in light of the
+recent introduction of ES into the Basel Accords.
+",0,0,1,1,0,0
+10904,Poincaré Embeddings for Learning Hierarchical Representations,"  Representation learning has become an invaluable approach for learning from
+symbolic data such as text and graphs. However, while complex symbolic datasets
+often exhibit a latent hierarchical structure, state-of-the-art methods
+typically learn embeddings in Euclidean vector spaces, which do not account for
+this property. For this purpose, we introduce a new approach for learning
+hierarchical representations of symbolic data by embedding them into hyperbolic
+space -- or more precisely into an n-dimensional Poincaré ball. Due to the
+underlying hyperbolic geometry, this allows us to learn parsimonious
+representations of symbolic data by simultaneously capturing hierarchy and
+similarity. We introduce an efficient algorithm to learn the embeddings based
+on Riemannian optimization and show experimentally that Poincaré embeddings
+outperform Euclidean embeddings significantly on data with latent hierarchies,
+both in terms of representation capacity and in terms of generalization
+ability.
+",1,0,0,1,0,0
+10905,Randomly cross-linked polymer models,"  Polymer models are used to describe chromatin, which can be folded at
+different spatial scales by binding molecules. By folding, chromatin generates
+loops of various sizes. We present here a randomly cross-linked (RCL) polymer
+model, where monomer pairs are connected randomly. We obtain asymptotic
+formulas for the steady-state variance, encounter probability, the radius of
+gyration, instantaneous displacement and the mean first encounter time between
+any two monomers. The analytical results are confirmed by Brownian simulations.
+Finally, the present results can be used to extract the minimum number of
+cross-links in a chromatin region from {conformation capture} data.
+",0,1,0,0,0,0
+10906,Multi-Stage Complex Contagions in Random Multiplex Networks,"  Complex contagion models have been developed to understand a wide range of
+social phenomena such as adoption of cultural fads, the diffusion of belief,
+norms, and innovations in social networks, and the rise of collective action to
+join a riot. Most existing works focus on contagions where individuals' states
+are represented by {\em binary} variables, and propagation takes place over a
+single isolated network. However, characterization of an individual's standing
+on a given matter as a binary state might be overly simplistic as most of our
+opinions, feelings, and perceptions vary over more than two states. Also, most
+real-world contagions take place over multiple networks (e.g., Twitter and
+Facebook) or involve {\em multiplex} networks where individuals engage in
+different {\em types} of relationships (e.g., acquaintance, co-worker, family,
+etc.). To this end, this paper studies {\em multi-stage} complex contagions
+that take place over multi-layer or multiplex networks. Under a linear
+threshold based contagion model, we give analytic results for the probability
+and expected size of \textit{global} cascades, i.e., cases where a randomly
+chosen node can initiate a propagation that eventually reaches a {\em positive}
+fraction of the whole population. Analytic results are also confirmed and
+supported by an extensive numerical study. In particular, we demonstrate how
+the dynamics of complex contagions is affected by the extra weight exerted by
+\textit{hyper-active} nodes and by the structural properties of the networks
+involved. Among other things, we reveal an interesting connection between the
+assortativity of a network and the impact of \textit{hyper-active} nodes on the
+cascade size.
+",1,0,0,0,0,0
+10907,Secrecy and Robustness for Active Attack in Secure Network Coding and its Application to Network Quantum Key Distribution,"  In network coding, we discuss the effect of sequential error injection on
+information leakage. We show that there is no improvement when the operations
+in the network are linear operations. However, when the operations in the
+network contains non-linear operations, we find a counterexample to improve
+Eve's obtained information. Furthermore, we discuss the asymptotic rate in a
+linear network under the secrecy and robustness conditions as well as under the
+secrecy condition alone. Finally, we apply our results to network quantum key
+distribution, which clarifies the type of network that enables us to realize
+secure long distance communication via short distance quantum key distribution.
+",1,0,0,0,0,0
+10908,SLAMBooster: An Application-aware Controller for Approximation in SLAM,"  Simultaneous Localization and Mapping (SLAM) is the problem of constructing a
+map of an agent's environment while localizing or tracking the mobile agent's
+position and orientation within the map. Algorithms for SLAM have high
+computational requirements, which has hindered their use on embedded devices.
+Approximation can be used to reduce the time and energy requirements of SLAM
+implementations as long as the approximations do not prevent the agent from
+navigating correctly through the environment. Previous studies of approximation
+in SLAM have assumed that the entire trajectory of the agent is known before
+the agent starts to move, and they have focused on offline controllers that use
+features of the trajectory to set approximation knobs at the start of the
+trajectory. In practice, the trajectory is not usually known ahead of time, and
+allowing knob settings to change dynamically opens up more opportunities for
+reducing computation time and energy.
+We describe SLAMBooster, an application-aware online control system for SLAM
+that adaptively controls approximation knobs during the motion of the agent.
+SLAMBooster is based on a control technique called hierarchical proportional
+control but our experiments showed this application-agnostic control led to an
+unacceptable reduction in the quality of localization. To address this problem,
+SLAMBooster exploits domain knowledge: it uses features extracted from input
+frames and from the estimated motion of the agent in its algorithm for
+controlling approximation.
+We implemented SLAMBooster in the open-source SLAMBench framework. Our
+experiments show that SLAMBooster reduces the computation time and energy
+consumption by around half on the average on an embedded platform, while
+maintaining the accuracy of the localization within reasonable bounds. These
+improvements make it feasible to deploy SLAM on a wider range of devices.
+",1,0,0,0,0,0
+10909,Modeling sepsis progression using hidden Markov models,"  Characterizing a patient's progression through stages of sepsis is critical
+for enabling risk stratification and adaptive, personalized treatment. However,
+commonly used sepsis diagnostic criteria fail to account for significant
+underlying heterogeneity, both between patients as well as over time in a
+single patient. We introduce a hidden Markov model of sepsis progression that
+explicitly accounts for patient heterogeneity. Benchmarked against two sepsis
+diagnostic criteria, the model provides a useful tool to uncover a patient's
+latent sepsis trajectory and to identify high-risk patients in whom more
+aggressive therapy may be indicated.
+",0,0,0,1,1,0
+10910,Optimized Bacteria are Environmental Prediction Engines,"  Experimentalists have observed phenotypic variability in isogenic bacteria
+populations. We explore the hypothesis that in fluctuating environments this
+variability is tuned to maximize a bacterium's expected log growth rate,
+potentially aided by epigenetic markers that store information about past
+environments. We show that, in a complex, memoryful environment, the maximal
+expected log growth rate is linear in the instantaneous predictive
+information---the mutual information between a bacterium's epigenetic markers
+and future environmental states. Hence, under resource constraints, optimal
+epigenetic markers are causal states---the minimal sufficient statistics for
+prediction. This is the minimal amount of information about the past needed to
+predict the future as well as possible. We suggest new theoretical
+investigations into and new experiments on bacteria phenotypic bet-hedging in
+fluctuating complex environments.
+",0,0,0,0,1,0
+10911,Photo-realistic Facial Texture Transfer,"  Style transfer methods have achieved significant success in recent years with
+the use of convolutional neural networks. However, many of these methods
+concentrate on artistic style transfer with few constraints on the output image
+appearance. We address the challenging problem of transferring face texture
+from a style face image to a content face image in a photorealistic manner
+without changing the identity of the original content image. Our framework for
+face texture transfer (FaceTex) augments the prior work of MRF-CNN with a novel
+facial semantic regularization that incorporates a face prior regularization
+smoothly suppressing the changes around facial meso-structures (e.g eyes, nose
+and mouth) and a facial structure loss function which implicitly preserves the
+facial structure so that face texture can be transferred without changing the
+original identity. We demonstrate results on face images and compare our
+approach with recent state-of-the-art methods. Our results demonstrate superior
+texture transfer because of the ability to maintain the identity of the
+original face image.
+",1,0,0,0,0,0
+10912,Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholding,"  This paper proposes an innovative method for segmentation of skin lesions in
+dermoscopy images developed by the authors, based on fuzzy classification of
+pixels and histogram thresholding.
+",1,0,0,1,0,0
+10913,Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods,"  In this paper, we explore deep reinforcement learning algorithms for
+vision-based robotic grasping. Model-free deep reinforcement learning (RL) has
+been successfully applied to a range of challenging environments, but the
+proliferation of algorithms makes it difficult to discern which particular
+approach would be best suited for a rich, diverse task like grasping. To answer
+this question, we propose a simulated benchmark for robotic grasping that
+emphasizes off-policy learning and generalization to unseen objects. Off-policy
+learning enables utilization of grasping data over a wide variety of objects,
+and diversity is important to enable the method to generalize to new objects
+that were not seen during training. We evaluate the benchmark tasks against a
+variety of Q-function estimation methods, a method previously proposed for
+robotic grasping with deep neural network models, and a novel approach based on
+a combination of Monte Carlo return estimation and an off-policy correction.
+Our results indicate that several simple methods provide a surprisingly strong
+competitor to popular algorithms such as double Q-learning, and our analysis of
+stability sheds light on the relative tradeoffs between the algorithms.
+",1,0,0,1,0,0
+10914,Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification,"  The key issues pertaining to collection of epidemic disease data for our
+analysis purposes are that it is a labour intensive, time consuming and
+expensive process resulting in availability of sparse sample data which we use
+to develop prediction models. To address this sparse data issue, we present
+novel Incremental Transductive methods to circumvent the data collection
+process by applying previously acquired data to provide consistent,
+confidence-based labelling alternatives to field survey research. We
+investigated various reasoning approaches for semisupervised machine learning
+including Bayesian models for labelling data. The results show that using the
+proposed methods, we can label instances of data with a class of vector density
+at a high level of confidence. By applying the Liberal and Strict Training
+Approaches, we provide a labelling and classification alternative to standalone
+algorithms. The methods in this paper are components in the process of reducing
+the proliferation of the Schistosomiasis disease and its effects.
+",1,0,0,0,0,0
+10915,Millimeter-scale layered MoSe2 grown on sapphire and evidence for negative magnetoresistance,"  Molecular beam epitaxy technique has been used to deposit a single layer and
+a bilayer of MoSe 2 on sapphire. Extensive characterizations including in-situ
+and ex-situ measurements show that the layered MoSe 2 grows in a scalable
+manner on the substrate and reveals characteristics of a stoichiometric
+2H-phase. The layered MoSe 2 exhibits polycrystalline features with domains
+separated by defects and boundaries. Temperature and magnetic field dependent
+resistivity measurements unveil a carrier hopping character described within
+two-dimensional variable range hopping mechanism. Moreover, a negative
+magnetoresistance was observed, stressing a fascinating feature of the charge
+transport under the application of a magnetic field in the layered MoSe 2
+system. This negative magnetoresistance observed at millimeter-scale is similar
+to that observed recently at room temperature inWS2 flakes at a micrometer
+scale [Zhang et al., Appl. Phys. Lett. 108, 153114 (2016)]. This scalability
+highlights the fact that the underlying physical mechanism is intrinsic to
+these two-dimensional materials and occurs at very short scale.
+",0,1,0,0,0,0
+10916,Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars,"  We investigate an end-to-end method for automatically inducing task-based
+dialogue systems from small amounts of unannotated dialogue data. It combines
+an incremental semantic grammar - Dynamic Syntax and Type Theory with Records
+(DS-TTR) - with Reinforcement Learning (RL), where language generation and
+dialogue management are a joint decision problem. The systems thus produced are
+incremental: dialogues are processed word-by-word, shown previously to be
+essential in supporting natural, spontaneous dialogue. We hypothesised that the
+rich linguistic knowledge within the grammar should enable a combinatorially
+large number of dialogue variations to be processed, even when trained on very
+few dialogues. Our experiments show that our model can process 74% of the
+Facebook AI bAbI dataset even when trained on only 0.13% of the data (5
+dialogues). It can in addition process 65% of bAbI+, a corpus we created by
+systematically adding incremental dialogue phenomena such as restarts and
+self-corrections to bAbI. We compare our model with a state-of-the-art
+retrieval model, MemN2N. We find that, in terms of semantic accuracy, MemN2N
+shows very poor robustness to the bAbI+ transformations even when trained on
+the full bAbI dataset.
+",1,0,0,0,0,0
+10917,Learning Deep Latent Spaces for Multi-Label Classification,"  Multi-label classification is a practical yet challenging task in machine
+learning related fields, since it requires the prediction of more than one
+label category for each input instance. We propose a novel deep neural networks
+(DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this
+task. Aiming at better relating feature and label domain data for improved
+classification, we uniquely perform joint feature and label embedding by
+deriving a deep latent space, followed by the introduction of label-correlation
+sensitive loss function for recovering the predicted label outputs. Our C2AE is
+achieved by integrating the DNN architectures of canonical correlation analysis
+and autoencoder, which allows end-to-end learning and prediction with the
+ability to exploit label dependency. Moreover, our C2AE can be easily extended
+to address the learning problem with missing labels. Our experiments on
+multiple datasets with different scales confirm the effectiveness and
+robustness of our proposed method, which is shown to perform favorably against
+state-of-the-art methods for multi-label classification.
+",1,0,0,0,0,0
+10918,Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning,"  The main goal of this study is to extract a set of brain networks in multiple
+time-resolutions to analyze the connectivity patterns among the anatomic
+regions for a given cognitive task. We suggest a deep architecture which learns
+the natural groupings of the connectivity patterns of human brain in multiple
+time-resolutions. The suggested architecture is tested on task data set of
+Human Connectome Project (HCP) where we extract multi-resolution networks, each
+of which corresponds to a cognitive task. At the first level of this
+architecture, we decompose the fMRI signal into multiple sub-bands using
+wavelet decompositions. At the second level, for each sub-band, we estimate a
+brain network extracted from short time windows of the fMRI signal. At the
+third level, we feed the adjacency matrices of each mesh network at each
+time-resolution into an unsupervised deep learning algorithm, namely, a Stacked
+De- noising Auto-Encoder (SDAE). The outputs of the SDAE provide a compact
+connectivity representation for each time window at each sub-band of the fMRI
+signal. We concatenate the learned representations of all sub-bands at each
+window and cluster them by a hierarchical algorithm to find the natural
+groupings among the windows. We observe that each cluster represents a
+cognitive task with a performance of 93% Rand Index and 71% Adjusted Rand
+Index. We visualize the mean values and the precisions of the networks at each
+component of the cluster mixture. The mean brain networks at cluster centers
+show the variations among cognitive tasks and the precision of each cluster
+shows the within cluster variability of networks, across the subjects.
+",0,0,0,1,0,0
+10919,Improper Filter Reduction,"  Combinatorial filters have been the subject of increasing interest from the
+robotics community in recent years. This paper considers automatic reduction of
+combinatorial filters to a given size, even if that reduction necessitates
+changes to the filter's behavior. We introduce an algorithmic problem called
+improper filter reduction, in which the input is a combinatorial filter F along
+with an integer k representing the target size. The output is another
+combinatorial filter F' with at most k states, such that the difference in
+behavior between F and F' is minimal. We present two metrics for measuring the
+distance between pairs of filters, describe dynamic programming algorithms for
+computing these distances, and show that improper filter reduction is NP-hard
+under these metrics. We then describe two heuristic algorithms for improper
+filter reduction, one greedy sequential approach, and one randomized global
+approach based on prior work on weighted improper graph coloring. We have
+implemented these algorithms and analyze the results of three sets of
+experiments.
+",1,0,0,0,0,0
+10920,Collective Dynamics of Self-propelled Semiflexible Filaments,"  The collective behavior of active semiflexible filaments is studied with a
+model of tangentially driven self-propelled worm-like chains. The combination
+of excluded-volume interactions and self-propulsion leads to several distinct
+dynamic phases as a function of bending rigidity, activity, and aspect ratio of
+individual filaments. We consider first the case of intermediate filament
+density. For high-aspect-ratio filaments, we identify a transition with
+increasing propulsion from a state of free-swimming filaments to a state of
+spiraled filaments with nearly frozen translational motion. For lower aspect
+ratios, this gas-of-spirals phase is suppressed with growing density due to
+filament collisions; instead, filaments form clusters similar to self-propelled
+rods, as activity increases. Finite bending rigidity strongly effects the
+dynamics and phase behavior. Flexible filaments form small and transient
+clusters, while stiffer filaments organize into giant clusters, similarly as
+self-propelled rods, but with a reentrant phase behavior from giant to smaller
+clusters as activity becomes large enough to bend the filaments. For high
+filament densities, we identify a nearly frozen jamming state at low
+activities, a nematic laning state at intermediate activities, and an
+active-turbulence state at high activities. The latter state is characterized
+by a power-law decay of the energy spectrum as a function of wave number. The
+resulting phase diagrams encapsulate tunable non-equilibrium steady states that
+can be used in the organization of living matter.
+",0,0,0,0,1,0
+10921,Two sources of poor coverage of confidence intervals after model selection,"  We compare the following two sources of poor coverage of post-model-selection
+confidence intervals: the preliminary data-based model selection sometimes
+chooses the wrong model and the data used to choose the model is re-used for
+the construction of the confidence interval.
+",0,0,1,1,0,0
+10922,On the ERM Principle with Networked Data,"  Networked data, in which every training example involves two objects and may
+share some common objects with others, is used in many machine learning tasks
+such as learning to rank and link prediction. A challenge of learning from
+networked examples is that target values are not known for some pairs of
+objects. In this case, neither the classical i.i.d.\ assumption nor techniques
+based on complete U-statistics can be used. Most existing theoretical results
+of this problem only deal with the classical empirical risk minimization (ERM)
+principle that always weights every example equally, but this strategy leads to
+unsatisfactory bounds. We consider general weighted ERM and show new universal
+risk bounds for this problem. These new bounds naturally define an optimization
+problem which leads to appropriate weights for networked examples. Though this
+optimization problem is not convex in general, we devise a new fully
+polynomial-time approximation scheme (FPTAS) to solve it.
+",1,0,0,1,0,0
+10923,Front interaction induces excitable behavior,"  Spatially extended systems can support local transient excitations in which
+just a part of the system is excited. The mechanisms reported so far are local
+excitability and excitation of a localized structure. Here we introduce an
+alternative mechanism based on the coexistence of two homogeneous stable states
+and spatial coupling. We show the existence of a threshold for perturbations of
+the homogeneous state. Sub-threshold perturbations decay exponentially.
+Super-threshold perturbations induce the emergence of a long-lived structure
+formed by two back to back fronts that join the two homogeneous states. While
+in typical excitability the trajectory follows the remnants of a limit cycle,
+here reinjection is provided by front interaction, such that fronts slowly
+approach each other until eventually annihilating. This front-mediated
+mechanism shows that extended systems with no oscillatory regimes can display
+excitability.
+",0,1,1,0,0,0
+10924,Characterization of optimal carbon nanotubes under stretching and validation of the Cauchy-Born rule,"  Carbon nanotubes are modeled as point configurations and investigated by
+minimizing configurational energies including two-and three-body interactions.
+Optimal configurations are identified with local minima and their fine geometry
+is fully characterized in terms of lower-dimensional problems. Under moderate
+tension, we prove the existence of periodic local minimizers, which indeed
+validates the so-called Cauchy-Born rule in this setting.
+",0,1,1,0,0,0
+10925,The Closer the Better: Similarity of Publication Pairs at Different Co-Citation Levels,"  We investigate the similarities of pairs of articles which are co-cited at
+the different co-citation levels of the journal, article, section, paragraph,
+sentence and bracket. Our results indicate that textual similarity,
+intellectual overlap (shared references), author overlap (shared authors),
+proximity in publication time all rise monotonically as the co-citation level
+gets lower (from journal to bracket). While the main gain in similarity happens
+when moving from journal to article co-citation, all level changes entail an
+increase in similarity, especially section to paragraph and paragraph to
+sentence/bracket levels. We compare results from four journals over the years
+2010-2015: Cell, the European Journal of Operational Research, Physics Letters
+B and Research Policy, with consistent general outcomes and some interesting
+differences. Our findings motivate the use of granular co-citation information
+as defined by meaningful units of text, with implications for, among others,
+the elaboration of maps of science and the retrieval of scholarly literature.
+",1,0,0,0,0,0
+10926,MP2-F12 Basis Set Convergence for the S66 Noncovalent Interactions Benchmark: Transferability of the Complementary Auxiliary Basis Set (CABS),"  Complementary auxiliary basis sets for F12 explicitly correlated calculations
+appear to be more transferable between orbital basis sets than has been
+generally assumed. We also find that aVnZ-F12 basis sets, originally developed
+with anionic systems in mind, appear to be superior for noncovalent
+interactions as well, and propose a suitable CABS sequence for them.
+",0,1,0,0,0,0
+10927,Quantized Minimum Error Entropy Criterion,"  Comparing with traditional learning criteria, such as mean square error
+(MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and
+non-Gaussian signal processing and machine learning. The argument of the
+logarithm in Renyis entropy estimator, called information potential (IP), is a
+popular MEE cost in information theoretic learning (ITL). The computational
+complexity of IP is however quadratic in terms of sample number due to double
+summation. This creates computational bottlenecks especially for large-scale
+datasets. To address this problem, in this work we propose an efficient
+quantization approach to reduce the computational burden of IP, which decreases
+the complexity from O(N*N) to O (MN) with M << N. The new learning criterion is
+called the quantized MEE (QMEE). Some basic properties of QMEE are presented.
+Illustrative examples are provided to verify the excellent performance of QMEE.
+",1,0,0,1,0,0
+10928,Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor,"  A novel predictor for traffic flow forecasting, namely spatio-temporal
+Bayesian network predictor, is proposed. Unlike existing methods, our approach
+incorporates all the spatial and temporal information available in a
+transportation network to carry our traffic flow forecasting of the current
+site. The Pearson correlation coefficient is adopted to rank the input
+variables (traffic flows) for prediction, and the best-first strategy is
+employed to select a subset as the cause nodes of a Bayesian network. Given the
+derived cause nodes and the corresponding effect node in the spatio-temporal
+Bayesian network, a Gaussian Mixture Model is applied to describe the
+statistical relationship between the input and output. Finally, traffic flow
+forecasting is performed under the criterion of Minimum Mean Square Error
+(M.M.S.E.). Experimental results with the urban vehicular flow data of Beijing
+demonstrate the effectiveness of our presented spatio-temporal Bayesian network
+predictor.
+",1,0,0,1,0,0
+10929,AlteregoNets: a way to human augmentation,"  A person dependent network, called an AlterEgo net, is proposed for
+development. The networks are created per person. It receives at input an
+object descriptions and outputs a simulation of the internal person's
+representation of the objects. The network generates a textual stream
+resembling the narrative stream of consciousness depicting multitudinous
+thoughts and feelings related to a perceived object. In this way, the object is
+described not by a 'static' set of its properties, like a dictionary, but by
+the stream of words and word combinations referring to the object. The network
+simulates a person's dialogue with a representation of the object. It is based
+on an introduced algorithmic scheme, where perception is modeled by two
+interacting iterative cycles, reminding one respectively the forward and
+backward propagation executed at training convolution neural networks. The
+'forward' iterations generate a stream representing the 'internal world' of a
+human. The 'backward' iterations generate a stream representing an internal
+representation of the object. People perceive the world differently. Tuning
+AlterEgo nets to a specific person or group of persons, will allow simulation
+of their thoughts and feelings. Thereby these nets is potentially a new human
+augmentation technology for various applications.
+",1,0,0,0,0,0
+10930,Modalities in homotopy type theory,"  Univalent homotopy type theory (HoTT) may be seen as a language for the
+category of $\infty$-groupoids. It is being developed as a new foundation for
+mathematics and as an internal language for (elementary) higher toposes. We
+develop the theory of factorization systems, reflective subuniverses, and
+modalities in homotopy type theory, including their construction using a
+""localization"" higher inductive type. This produces in particular the
+($n$-connected, $n$-truncated) factorization system as well as internal
+presentations of subtoposes, through lex modalities. We also develop the
+semantics of these constructions.
+",1,0,1,0,0,0
+10931,Exponentially Slow Heating in Short and Long-range Interacting Floquet Systems,"  We analyze the dynamics of periodically-driven (Floquet) Hamiltonians with
+short- and long-range interactions, finding clear evidence for a thermalization
+time, $\tau^*$, that increases exponentially with the drive frequency. We
+observe this behavior, both in systems with short-ranged interactions, where
+our results are consistent with rigorous bounds, and in systems with long-range
+interactions, where such bounds do not exist at present. Using a combination of
+heating and entanglement dynamics, we explicitly extract the effective energy
+scale controlling the rate of thermalization. Finally, we demonstrate that for
+times shorter than $\tau^*$, the dynamics of the system is well-approximated by
+evolution under a time-independent Hamiltonian $D_{\mathrm{eff}}$, for both
+short- and long-range interacting systems.
+",0,1,0,0,0,0
+10932,High moments of the Estermann function,"  For $a/q\in\mathbb{Q}$ the Estermann function is defined as
+$D(s,a/q):=\sum_{n\geq1}d(n)n^{-s}\operatorname{e}(n\frac aq)$ if $\Re(s)>1$
+and by meromorphic continuation otherwise. For $q$ prime, we compute the
+moments of $D(s,a/q)$ at the central point $s=1/2$, when averaging over $1\leq
+a<q$.
+As a consequence we deduce the asymptotic for the iterated moment of
+Dirichlet $L$-functions $\sum_{\chi_1,\dots,\chi_k\mod
+q}|L(\frac12,\chi_1)|^2\cdots |L(\frac12,\chi_k)|^2|L(\frac12,\chi_1\cdots
+\chi_k)|^2$, obtaining a power saving error term.
+Also, we compute the moments of certain functions defined in terms of
+continued fractions. For example, writing $f_{\pm}(a/q):=\sum_{j=0}^r
+(\pm1)^jb_j$ where $[0;b_0,\dots,b_r]$ is the continued fraction expansion of
+$a/q$ we prove that for $k\geq2$ and $q$ primes one has
+$\sum_{a=1}^{q-1}f_{\pm}(a/q)^k\sim2 \frac{\zeta(k)^2}{\zeta(2k)} q^k$ as
+$q\to\infty$.
+",0,0,1,0,0,0
+10933,On Game-Theoretic Risk Management (Part Three) - Modeling and Applications,"  The game-theoretic risk management framework put forth in the precursor
+reports ""Towards a Theory of Games with Payoffs that are
+Probability-Distributions"" (arXiv:1506.07368 [q-fin.EC]) and ""Algorithms to
+Compute Nash-Equilibria in Games with Distributions as Payoffs""
+(arXiv:1511.08591v1 [q-fin.EC]) is herein concluded by discussing how to
+integrate the previously developed theory into risk management processes. To
+this end, we discuss how loss models (primarily but not exclusively
+non-parametric) can be constructed from data. Furthermore, hints are given on
+how a meaningful game theoretic model can be set up, and how it can be used in
+various stages of the ISO 27000 risk management process. Examples related to
+advanced persistent threats and social engineering are given. We conclude by a
+discussion on the meaning and practical use of (mixed) Nash equilibria
+equilibria for risk management.
+",0,0,1,1,0,0
+10934,"PKS 1954-388: RadioAstron Detection on 80,000 km Baselines and Multiwavelength Observations","  We present results from a multiwavelength study of the blazar PKS 1954-388 at
+radio, UV, X-ray, and gamma-ray energies. A RadioAstron observation at 1.66 GHz
+in June 2012 resulted in the detection of interferometric fringes on baselines
+of 6.2 Earth-diameters. This suggests a source frame brightness temperature of
+greater than 2x10^12 K, well in excess of both equipartition and inverse
+Compton limits and implying the existence of Doppler boosting in the core. An
+8.4 GHz TANAMI VLBI image, made less than a month after the RadioAstron
+observations, is consistent with a previously reported superluminal motion for
+a jet component. Flux density monitoring with the Australia Telescope Compact
+Array confirms previous evidence for long-term variability that increases with
+observing frequency. A search for more rapid variability revealed no evidence
+for significant day-scale flux density variation. The ATCA light-curve reveals
+a strong radio flare beginning in late 2013 which peaks higher, and earlier, at
+higher frequencies. Comparison with the Fermi gamma-ray light-curve indicates
+this followed ~9 months after the start of a prolonged gamma-ray high-state --
+a radio lag comparable to that seen in other blazars. The multiwavelength data
+are combined to derive a Spectral Energy Distribution, which is fitted by a
+one-zone synchrotron-self-Compton (SSC) model with the addition of external
+Compton (EC) emission.
+",0,1,0,0,0,0
+10935,Smart Assessment of and Tutoring for Computational Thinking MOOC Assignments using MindReader,"  One of the major hurdles toward automatic semantic understanding of computer
+programs is the lack of knowledge about what constitutes functional equivalence
+of code segments. We postulate that a sound knowledgebase can be used to
+deductively understand code segments in a hierarchical fashion by first
+de-constructing a code and then reconstructing it from elementary knowledge and
+equivalence rules of elementary code segments. The approach can also be
+engineered to produce computable programs from conceptual and abstract
+algorithms as an inverse function. In this paper, we introduce the core idea
+behind the MindReader online assessment system that is able to understand a
+wide variety of elementary algorithms students learn in their entry level
+programming classes such as Java, C++ and Python. The MindReader system is able
+to assess student assignments and guide them how to develop correct and better
+code in real time without human assistance.
+",1,0,0,0,0,0
+10936,EasyInterface: A toolkit for rapid development of GUIs for research prototype tools,"  In this paper we describe EasyInterface, an open-source toolkit for rapid
+development of web-based graphical user interfaces (GUIs). This toolkit
+addresses the need of researchers to make their research prototype tools
+available to the community, and integrating them in a common environment,
+rapidly and without being familiar with web programming or GUI libraries in
+general. If a tool can be executed from a command-line and its output goes to
+the standard output, then in few minutes one can make it accessible via a
+web-interface or within Eclipse. Moreover, the toolkit defines a text-based
+language that can be used to get more sophisticated GUIs, e.g., syntax
+highlighting, dialog boxes, user interactions, etc. EasyInterface was
+originally developed for building a common frontend for tools developed in the
+Envisage project.
+",1,0,0,0,0,0
+10937,The Cramér-Rao inequality on singular statistical models I,"  We introduce the notion of the essential tangent bundle of a parametrized
+measure model and the notion of reduced Fisher metric on a (possibly singular)
+2-integrable measure model. Using these notions and a new characterization of
+$k$-integrable parametrized measure models, we extend the Cramér-Rao
+inequality to $2$-integrable (possibly singular) statistical models for general
+$\varphi$-estimations, where $\varphi$ is a $V$-valued feature function and $V$
+is a topological vector space. Thus we derive an intrinsic Cramér-Rao
+inequality in the most general terms of parametric statistics.
+",0,0,1,1,0,0
+10938,Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning,"  Machine learning applications often require hyperparameter tuning. The
+hyperparameters usually drive both the efficiency of the model training process
+and the resulting model quality. For hyperparameter tuning, machine learning
+algorithms are complex black-boxes. This creates a class of challenging
+optimization problems, whose objective functions tend to be nonsmooth,
+discontinuous, unpredictably varying in computational expense, and include
+continuous, categorical, and/or integer variables. Further, function
+evaluations can fail for a variety of reasons including numerical difficulties
+or hardware failures. Additionally, not all hyperparameter value combinations
+are compatible, which creates so called hidden constraints. Robust and
+efficient optimization algorithms are needed for hyperparameter tuning. In this
+paper we present an automated parallel derivative-free optimization framework
+called \textbf{Autotune}, which combines a number of specialized sampling and
+search methods that are very effective in tuning machine learning models
+despite these challenges. Autotune provides significantly improved models over
+using default hyperparameter settings with minimal user interaction on
+real-world applications. Given the inherent expense of training numerous
+candidate models, we demonstrate the effectiveness of Autotune's search methods
+and the efficient distributed and parallel paradigms for training and tuning
+models, and also discuss the resource trade-offs associated with the ability to
+both distribute the training process and parallelize the tuning process.
+",0,0,0,1,0,0
+10939,Reduced fusion systems over $p$-groups with abelian subgroup of index $p$: III,"  We finish the classification, begun in two earlier papers, of all simple
+fusion systems over finite nonabelian $p$-groups with an abelian subgroup of
+index $p$. In particular, this gives many new examples illustrating the
+enormous variety of exotic examples that can arise. In addition, we classify
+all simple fusion systems over infinite nonabelian discrete $p$-toral groups
+with an abelian subgroup of index $p$. In all of these cases (finite or
+infinite), we reduce the problem to one of listing all $\mathbb{F}_pG$-modules
+(for $G$ finite) satisfying certain conditions: a problem which was solved in
+the earlier paper by Craven, Oliver, and Semeraro using the classification of
+finite simple groups.
+",0,0,1,0,0,0
+10940,Deep learning for studies of galaxy morphology,"  Establishing accurate morphological measurements of galaxies in a reasonable
+amount of time for future big-data surveys such as EUCLID, the Large Synoptic
+Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge.
+Because of its high level of abstraction with little human intervention, deep
+learning appears to be a promising approach. Deep learning is a rapidly growing
+discipline that models high-level patterns in data as complex multilayered
+networks. In this work we test the ability of deep convolutional networks to
+provide parametric properties of Hubble Space Telescope like galaxies
+(half-light radii, Sersic indices, total flux etc..). We simulate a set of
+galaxies including point spread function and realistic noise from the CANDELS
+survey and try to recover the main galaxy parameters using deep-learning. We
+com- pare the results with the ones obtained with the commonly used profile
+fitting based software GALFIT. This way showing that with our method we obtain
+results at least equally good as the ones obtained with GALFIT but, once
+trained, with a factor 5 hundred time faster.
+",0,1,0,0,0,0
+10941,A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit,"  Multi-start algorithms are a common and effective tool for metaheuristic
+searches. In this paper we amplify multi-start capabilities by employing the
+parallel processing power of the graphics processer unit (GPU) to quickly
+generate a diverse starting set of solutions for the Unconstrained Binary
+Quadratic Optimization Problem which are evaluated and used to implement
+screening methods to select solutions for further optimization. This method is
+implemented as an initial high quality solution generation phase prior to a
+secondary steepest ascent search and a comparison of results to best known
+approaches on benchmark unconstrained binary quadratic problems demonstrates
+that GPU-enabled diversified multi-start with screening quickly yields very
+good results.
+",1,0,0,0,0,0
+10942,Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images,"  Estimation of the number of endmembers existing in a scene constitutes a
+critical task in the hyperspectral unmixing process. The accuracy of this
+estimate plays a crucial role in subsequent unsupervised unmixing steps i.e.,
+the derivation of the spectral signatures of the endmembers (endmembers'
+extraction) and the estimation of the abundance fractions of the pixels. A
+common practice amply followed in literature is to treat endmembers' number
+estimation and unmixing, independently as two separate tasks, providing the
+outcome of the former as input to the latter. In this paper, we go beyond this
+computationally demanding strategy. More precisely, we set forth a multiple
+constrained optimization framework, which encapsulates endmembers' number
+estimation and unsupervised unmixing in a single task. This is attained by
+suitably formulating the problem via a low-rank and sparse nonnegative matrix
+factorization rationale, where low-rankness is promoted with the use of a
+sophisticated $\ell_2/\ell_1$ norm penalty term. An alternating proximal
+algorithm is then proposed for minimizing the emerging cost function. The
+results obtained by simulated and real data experiments verify the
+effectiveness of the proposed approach.
+",1,0,0,1,0,0
+10943,On the Necessity of Structured Codes for Communications over MAC with Feedback,"  The problem of three-user multiple-access channel (MAC) with noiseless
+feedback is investigated. A new coding strategy is presented. The coding scheme
+builds upon the natural extension of the Cover-Leung (CL) scheme; and uses
+quasi-linear codes. A new single-letter achievable rate region is derived. The
+new achievable region strictly contains the CL region. This is shown through an
+example. In this example, the coding scheme achieves optimality in terms of
+transmission rates. It is shown that any optimality achieving scheme for this
+example must have a specific algebraic structure. Particularly, the codebooks
+must be closed under binary addition.
+",1,0,0,0,0,0
+10944,Historical Review of Recurrence Plots,"  In the last two decades recurrence plots (RPs) were introduced in many
+different scientific disciplines. It turned out how powerful this method is.
+After introducing approaches of quantification of RPs and by the study of
+relationships between RPs and fundamental properties of dynamical systems, this
+method attracted even more attention. After 20 years of RPs it is time to
+summarise this development in a historical context.
+",0,1,0,0,0,0
+10945,The Meta Distribution of the SIR for Cellular Networks with Power Control,"  The meta distribution of the signal-to-interference ratio (SIR) provides
+fine-grained information about the performance of individual links in a
+wireless network. This paper focuses on the analysis of the meta distribution
+of the SIR for both the cellular network uplink and downlink with fractional
+power control. For the uplink scenario, an approximation of the interfering
+user point process with a non-homogeneous Poisson point process is used. The
+moments of the meta distribution for both scenarios are calculated. Some
+bounds, the analytical expression, the mean local delay, and the beta
+approximation of the meta distribution are provided. The results give
+interesting insights into the effect of the power control in both the uplink
+and downlink. Detailed simulations show that the approximations made in the
+analysis are well justified.
+",1,0,0,0,0,0
+10946,Temperature induced transition from p-n to n-n electronic behavior in Ni0.07Zn0.93O/Mg0.21Zn0.79O heterojunction,"  The transport characteristics across the pulsed laser deposited
+Ni0.07Zn0.93O/Mg0.21Zn0.79O heterojunction exhibits p-n type semiconducting
+properties at 10 K while at 100 K, its characteristics become similar to that
+of an n-n junction. The reason for the same is attributed to the role of larger
+electronegativity of Ni as compared to Mg at 10 K and ionization of impurity
+states at 100 K. The above behavior is confirmed by performing the Hall
+measurements.
+",0,1,0,0,0,0
+10947,N-body simulations of gravitational redshifts and other relativistic distortions of galaxy clustering,"  Large redshift surveys of galaxies and clusters are providing the first
+opportunities to search for distortions in the observed pattern of large-scale
+structure due to such effects as gravitational redshift. We focus on non-linear
+scales and apply a quasi-Newtonian approach using N-body simulations to predict
+the small asymmetries in the cross-correlation function of two galaxy different
+populations. Following recent work by Bonvin et al., Zhao and Peacock and
+Kaiser on galaxy clusters, we include effects which enter at the same order as
+gravitational redshift: the transverse Doppler effect, light-cone effects,
+relativistic beaming, luminosity distance perturbation and wide-angle effects.
+We find that all these effects cause asymmetries in the cross-correlation
+functions. Quantifying these asymmetries, we find that the total effect is
+dominated by the gravitational redshift and luminosity distance perturbation at
+small and large scales, respectively. By adding additional subresolution
+modelling of galaxy structure to the large-scale structure information, we find
+that the signal is significantly increased, indicating that structure on the
+smallest scales is important and should be included. We report on comparison of
+our simulation results with measurements from the SDSS/BOSS galaxy redshift
+survey in a companion paper.
+",0,1,0,0,0,0
+10948,Few-Shot Learning with Metric-Agnostic Conditional Embeddings,"  Learning high quality class representations from few examples is a key
+problem in metric-learning approaches to few-shot learning. To accomplish this,
+we introduce a novel architecture where class representations are conditioned
+for each few-shot trial based on a target image. We also deviate from
+traditional metric-learning approaches by training a network to perform
+comparisons between classes rather than relying on a static metric comparison.
+This allows the network to decide what aspects of each class are important for
+the comparison at hand. We find that this flexible architecture works well in
+practice, achieving state-of-the-art performance on the Caltech-UCSD birds
+fine-grained classification task.
+",0,0,0,1,0,0
+10949,Gradient Coding from Cyclic MDS Codes and Expander Graphs,"  Gradient coding is a technique for straggler mitigation in distributed
+learning. In this paper we design novel gradient codes using tools from
+classical coding theory, namely, cyclic MDS codes, which compare favourably
+with existing solutions, both in the applicable range of parameters and in the
+complexity of the involved algorithms. Second, we introduce an approximate
+variant of the gradient coding problem, in which we settle for approximate
+gradient computation instead of the exact one. This approach enables graceful
+degradation, i.e., the $\ell_2$ error of the approximate gradient is a
+decreasing function of the number of stragglers. Our main result is that the
+normalized adjacency matrix of an expander graph can yield excellent
+approximate gradient codes, and that this approach allows us to perform
+significantly less computation compared to exact gradient coding. We
+experimentally test our approach on Amazon EC2, and show that the
+generalization error of approximate gradient coding is very close to the full
+gradient while requiring significantly less computation from the workers.
+",0,0,0,1,0,0
+10950,Commissioning of te China-ADS injector-I testing facility,"  The 10 MeV accelerator-driven subcritical system (ADS) Injector-I test stand
+at Institute of High Energy Physics (IHEP) is a testing facility dedicated to
+demonstrate one of the two injector design schemes [Injector Scheme-I, which
+works at 325 MHz], for the ADS project in China. The Injector adopted a four
+vane copper structure RFQ with output energy of 3.2 MeV and a superconducting
+(SC) section accommodating fourteen \b{eta}g=0.12 single spoke cavities,
+fourteen SC solenoids and fourteen cold BPMs. The ion source was installed
+since April of 2014, periods of commissioning are regularly scheduled between
+installation phases of the rest of the injector. Continuous wave (CW) beam was
+shooting through the injector and 10 MeV CW proton beam with average beam
+current around 2 mA was obtained recently. This contribution describe the
+results achieved so far and the difficulties encountered in CW commissioning.
+",0,1,0,0,0,0
+10951,Impact of the Global Crisis on SME Internal vs. External Financing in China,"  Changes in the capital structure before and after the global financial crisis
+for SMEs are studied, emphasizing their financing problems, distinguishing
+between internal financing and external financing determinants. The empirical
+research bears upon 158 small and medium-sized firms listed on Shenzhen and
+Shanghai Stock Exchanges in China over the period of 2004-2014. A regression
+analysis, along the lines of the Trade-Off Theory, shows that the leverage
+decreases with profitability, non-debt tax shields and the liquidity, and
+increases with firm size and tangibility. A positive relationship is found
+between firm growth and debt ratio, though not highly significantly. It is
+shown that the SMEs with high growth rates are those which will more easily
+obtain external financing after a financial crisis. It is recognized that the
+China government should reconsider SMEs taxation laws.
+",0,0,0,1,0,0
+10952,Dipole force free optical control and cooling of nanofiber trapped atoms,"  The evanescent field surrounding nano-scale optical waveguides offers an
+efficient interface between light and mesoscopic ensembles of neutral atoms.
+However, the thermal motion of trapped atoms, combined with the strong radial
+gradients of the guided light, leads to a time-modulated coupling between atoms
+and the light mode, thus giving rise to additional noise and motional dephasing
+of collective states. Here, we present a dipole force free scheme for coupling
+of the radial motional states, utilizing the strong intensity gradient of the
+guided mode and demonstrate all-optical coupling of the cesium hyperfine ground
+states and motional sideband transitions. We utilize this to prolong the trap
+lifetime of an atomic ensemble by Raman sideband cooling of the radial motion,
+which has not been demonstrated in nano-optical structures previously. Our work
+points towards full and independent control of internal and external atomic
+degrees of freedom using guided light modes only.
+",0,1,0,0,0,0
+10953,Control refinement for discrete-time descriptor systems: a behavioural approach via simulation relations,"  The analysis of industrial processes, modelled as descriptor systems, is
+often computationally hard due to the presence of both algebraic couplings and
+difference equations of high order. In this paper, we introduce a control
+refinement notion for these descriptor systems that enables analysis and
+control design over related reduced-order systems. Utilising the behavioural
+framework, we extend upon the standard hierarchical control refinement for
+ordinary systems and allow for algebraic couplings inherent to descriptor
+systems.
+",1,0,0,0,0,0
+10954,Beam Based RF Voltage Measurements and Longitudinal Beam Tomography at the Fermilab Booster,"  Increasing proton beam power on neutrino production targets is one of the
+major goals of the Fermilab long term accelerator programs. In this effort, the
+Fermilab 8 GeV Booster synchrotron plays a critical role for at least the next
+two decades. Therefore, understanding the Booster in great detail is important
+as we continue to improve its performance. For example, it is important to know
+accurately the available RF power in the Booster by carrying out beam-based
+measurements in order to specify the needed upgrades to the Booster RF system.
+Since the Booster magnetic field is changing continuously measuring/calibrating
+the RF voltage is not a trivial task. Here, we present a beam based method for
+the RF voltage measurements. Data analysis is carried out using computer
+programs developed in Python and MATLAB. The method presented here is
+applicable to any RCS which do not have flat-bottom and flat-top in the
+acceleration magnetic ramps. We have also carried out longitudinal beam
+tomography at injection and extraction energies with the data used for RF
+voltage measurements. Beam based RF voltage measurements and beam tomography
+were never done before for the Fermilab Booster. The results from these
+investigations will be very useful in future intensity upgrades.
+",0,1,0,0,0,0
+10955,A framework for quantitative modeling and analysis of highly (re)configurable systems,"  This paper presents our approach to the quantitative modeling and analysis of
+highly (re)configurable systems, such as software product lines. Different
+combinations of the optional features of such a system give rise to
+combinatorially many individual system variants. We use a formal modeling
+language that allows us to model systems with probabilistic behavior, possibly
+subject to quantitative feature constraints, and able to dynamically install,
+remove or replace features. More precisely, our models are defined in the
+probabilistic feature-oriented language QFLAN, a rich domain specific language
+(DSL) for systems with variability defined in terms of features. QFLAN
+specifications are automatically encoded in terms of a process algebra whose
+operational behavior interacts with a store of constraints, and hence allows to
+separate system configuration from system behavior. The resulting probabilistic
+configurations and behavior converge seamlessly in a semantics based on
+discrete-time Markov chains, thus enabling quantitative analysis. Our analysis
+is based on statistical model checking techniques, which allow us to scale to
+larger models with respect to precise probabilistic analysis techniques. The
+analyses we can conduct range from the likelihood of specific behavior to the
+expected average cost, in terms of feature attributes, of specific system
+variants. Our approach is supported by a novel Eclipse-based tool which
+includes state-of-the-art DSL utilities for QFLAN based on the Xtext framework
+as well as analysis plug-ins to seamlessly run statistical model checking
+analyses. We provide a number of case studies that have driven and validated
+the development of our framework.
+",1,0,0,0,0,0
+10956,Generating large misalignments in gapped and binary discs,"  Many protostellar gapped and binary discs show misalignments between their
+inner and outer discs; in some cases, $\sim70$ degree misalignments have been
+observed. Here we show that these misalignments can be generated through a
+""secular precession resonance"" between the nodal precession of the inner disc
+and the precession of the gap-opening (stellar or massive planetary) companion.
+An evolving protostellar system may naturally cross this resonance during its
+lifetime due to disc dissipation and/or companion migration. If resonance
+crossing occurs on the right timescale, of order a few Myrs, characteristic for
+young protostellar systems, the inner and outer discs can become highly
+misaligned ($\gtrsim 60$ degrees). When the primary star has a mass of order a
+solar mass, generating a significant misalignment typically requires the
+companion to have a mass of $\sim 0.01-0.1$ M$_\odot$ and an orbital separation
+of tens of AU. The recently observed companion in the cavity of the gapped,
+highly misaligned system HD 142527 satisfies these requirements, indicating
+that a previous resonance crossing event misaligned the inner and outer discs.
+Our scenario for HD 142527's misaligned discs predicts that the companion's
+orbital plane is aligned with the outer disc's; this prediction should be
+testable with future observations as the companion's orbit is mapped out.
+Misalignments observed in several other gapped disc systems could be generated
+by the same secular resonance mechanism.
+",0,1,0,0,0,0
+10957,Bilinear approach to the supersymmetric Gardner equation,"  We study a supersymmetric version of the Gardner equation (both focusing and
+defocusing) using the superbilinear formalism. This equation is new and cannot
+be obtained from supersymmetric modified Korteweg-de Vries equation with a
+nonzero boundary condition. We construct supersymmetric solitons and then by
+passing to the long-wave limit in the focusing case obtain rational nonsingular
+solutions. We also discuss the supersymmetric version of the defocusing
+equation and the dynamics of its solutions.
+",0,1,0,0,0,0
+10958,"The Effect of Focal Distance, Age, and Brightness on Near-Field Augmented Reality Depth Matching","  Many augmented reality (AR) applications operate within near-field reaching
+distances, and require matching the depth of a virtual object with a real
+object. The accuracy of this matching was measured in three experiments, which
+examined the effect of focal distance, age, and brightness, within distances of
+33.3 to 50 cm, using a custom-built AR haploscope. Experiment I examined the
+effect of focal demand, at the levels of collimated (infinite focal distance),
+consistent with other depth cues, and at the midpoint of reaching distance.
+Observers were too young to exhibit age-related reductions in accommodative
+ability. The depth matches of collimated targets were increasingly
+overestimated with increasing distance, consistent targets were slightly
+underestimated, and midpoint targets were accurately estimated. Experiment II
+replicated Experiment I, with older observers. Results were similar to
+Experiment I. Experiment III replicated Experiment I with dimmer targets, using
+young observers. Results were again consistent with Experiment I, except that
+both consistent and midpoint targets were accurately estimated. In all cases,
+collimated results were explained by a model, where the collimation biases the
+eyes' vergence angle outwards by a constant amount. Focal demand and brightness
+affect near-field AR depth matching, while age-related reductions in
+accommodative ability have no effect.
+",1,0,0,0,0,0
+10959,Small-amplitude steady water waves with critical layers: non-symmetric waves,"  The problem for two-dimensional steady water waves with vorticity is
+considered. Using methods of spatial dynamics, we reduce the problem to a
+finite dimensional Hamiltonian system. As an application, we prove the
+existence of non-symmetric steady water waves when the number of roots of the
+dispersion equation is greater than 1.
+",0,0,1,0,0,0
+10960,Emergence of Leadership in Communication,"  We study a neuro-inspired model that mimics a discussion (or information
+dissemination) process in a network of agents. During their interaction, agents
+redistribute activity and network weights, resulting in emergence of leader(s).
+The model is able to reproduce the basic scenarios of leadership known in
+nature and society: laissez-faire (irregular activity, weak leadership, sizable
+inter-follower interaction, autonomous sub-leaders); participative or
+democratic (strong leadership, but with feedback from followers); and
+autocratic (no feedback, one-way influence). Several pertinent aspects of these
+scenarios are found as well---e.g., hidden leadership (a hidden clique of
+agents driving the official autocratic leader), and successive leadership (two
+leaders influence followers by turns). We study how these scenarios emerge from
+inter-agent dynamics and how they depend on behavior rules of agents---in
+particular, on their inertia against state changes.
+",0,1,0,0,0,0
+10961,Throughput-Improving Control of Highways Facing Stochastic Perturbations,"  In this article, we study the problem of controlling a highway segment facing
+stochastic perturbations, such as recurrent incidents and moving bottlenecks.
+To model traffic flow under perturbations, we use the cell-transmission model
+with Markovian capacities. The control inputs are: (i) the inflows that are
+sent to various on-ramps to the highway (for managing traffic demand), and (ii)
+the priority levels assigned to the on-ramp traffic relative to the mainline
+traffic (for allocating highway capacity). The objective is to maximize the
+throughput while ensuring that on-ramp queues remain bounded in the long-run.
+We develop a computational approach to solving this stability-constrained,
+throughput-maximization problem. Firstly, we use the classical drift condition
+in stability analysis of Markov processes to derive a sufficient condition for
+boundedness of on-ramp queues. Secondly, we show that our control design
+problem can be formulated as a mixed integer program with linear or bilinear
+constraints, depending on the complexity of Lyapunov function involved in the
+stability condition. Finally, for specific types of capacity perturbations, we
+derive intuitive criteria for managing demand and/or selecting priority levels.
+These criteria suggest that inflows and priority levels should be determined
+simultaneously such that traffic queues are placed at locations that discharge
+queues fast. We illustrate the performance benefits of these criteria through a
+computational study of a segment on Interstate 210 in California, USA.
+",1,0,0,0,0,0
+10962,Deep Learning for Accelerated Ultrasound Imaging,"  In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an
+increasing demand to reconstruct high quality images from limited number of
+data. However, the existing solutions require either hardware changes or
+computationally expansive algorithms. To overcome these limitations, here we
+propose a novel deep learning approach that interpolates the missing RF data by
+utilizing the sparsity of the RF data in the Fourier domain. Extensive
+experimental results from sub-sampled RF data from a real US system confirmed
+that the proposed method can effectively reduce the data rate without
+sacrificing the image quality.
+",1,0,0,1,0,0
+10963,Adaptive Estimation for Nonlinear Systems using Reproducing Kernel Hilbert Spaces,"  This paper extends a conventional, general framework for online adaptive
+estimation problems for systems governed by unknown nonlinear ordinary
+differential equations. The central feature of the theory introduced in this
+paper represents the unknown function as a member of a reproducing kernel
+Hilbert space (RKHS) and defines a distributed parameter system (DPS) that
+governs state estimates and estimates of the unknown function. This paper 1)
+derives sufficient conditions for the existence and stability of the infinite
+dimensional online estimation problem, 2) derives existence and stability of
+finite dimensional approximations of the infinite dimensional approximations,
+and 3) determines sufficient conditions for the convergence of finite
+dimensional approximations to the infinite dimensional online estimates. A new
+condition for persistency of excitation in a RKHS in terms of its evaluation
+functionals is introduced in the paper that enables proof of convergence of the
+finite dimensional approximations of the unknown function in the RKHS. This
+paper studies two particular choices of the RKHS, those that are generated by
+exponential functions and those that are generated by multiscale kernels
+defined from a multiresolution analysis.
+",1,0,0,0,0,0
+10964,Weak saturation and weak amalgamation property,"  The two model-theoretic concepts of weak saturation and weak amalgamation
+property are studied in the context of accessible categories. We relate these
+two concepts providing sufficient conditions for existence and uniqueness of
+weakly saturated objects of an accessible category K. We discuss the
+implications of this fact in classical model theory.
+",0,0,1,0,0,0
+10965,Perturbation problems in homogenization of hamilton-jacobi equations,"  This paper is concerned with the behavior of the ergodic constant associated
+with convex and superlinear Hamilton-Jacobi equation in a periodic environment
+which is perturbed either by medium with increasing period or by a random
+Bernoulli perturbation with small parameter. We find a first order Taylor's
+expansion for the ergodic constant which depends on the dimension d. When d = 1
+the first order term is non trivial, while for all d $\ge$ 2 it is always 0.
+Although such questions have been looked at in the context of linear uniformly
+elliptic homogenization, our results are the first of this kind in nonlinear
+settings. Our arguments, which rely on viscosity solutions and the weak KAM
+theory, also raise several new and challenging questions.
+",0,0,1,0,0,0
+10966,Chimera states: Effects of different coupling topologies,"  Collective behavior among coupled dynamical units can emerge in various forms
+as a result of different coupling topologies as well as different types of
+coupling functions. Chimera states have recently received ample attention as a
+fascinating manifestation of collective behavior, in particular describing a
+symmetry breaking spatiotemporal pattern where synchronized and desynchronized
+states coexist in a network of coupled oscillators. In this perspective, we
+review the emergence of different chimera states, focusing on the effects of
+different coupling topologies that describe the interaction network connecting
+the oscillators. We cover chimera states that emerge in local, nonlocal and
+global coupling topologies, as well as in modular, temporal and multilayer
+networks. We also provide an outline of challenges and directions for future
+research.
+",0,1,0,0,0,0
+10967,Human Activity Recognition using Recurrent Neural Networks,"  Human activity recognition using smart home sensors is one of the bases of
+ubiquitous computing in smart environments and a topic undergoing intense
+research in the field of ambient assisted living. The increasingly large amount
+of data sets calls for machine learning methods. In this paper, we introduce a
+deep learning model that learns to classify human activities without using any
+prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent
+Neural Network was applied to three real world smart home datasets. The results
+of these experiments show that the proposed approach outperforms the existing
+ones in terms of accuracy and performance.
+",0,0,0,1,0,0
+10968,Eigenvalues of elliptic operators with density,"  We consider eigenvalue problems for elliptic operators of arbitrary order
+$2m$ subject to Neumann boundary conditions on bounded domains of the Euclidean
+$N$-dimensional space. We study the dependence of the eigenvalues upon
+variations of mass density and in particular we discuss the existence and
+characterization of upper and lower bounds under both the condition that the
+total mass is fixed and the condition that the $L^{\frac{N}{2m}}$-norm of the
+density is fixed. We highlight that the interplay between the order of the
+operator and the space dimension plays a crucial role in the existence of
+eigenvalue bounds.
+",0,0,1,0,0,0
+10969,A projection pursuit framework for testing general high-dimensional hypothesis,"  This article develops a framework for testing general hypothesis in
+high-dimensional models where the number of variables may far exceed the number
+of observations. Existing literature has considered less than a handful of
+hypotheses, such as testing individual coordinates of the model parameter.
+However, the problem of testing general and complex hypotheses remains widely
+open. We propose a new inference method developed around the hypothesis
+adaptive projection pursuit framework, which solves the testing problems in the
+most general case. The proposed inference is centered around a new class of
+estimators defined as $l_1$ projection of the initial guess of the unknown onto
+the space defined by the null. This projection automatically takes into account
+the structure of the null hypothesis and allows us to study formal inference
+for a number of long-standing problems. For example, we can directly conduct
+inference on the sparsity level of the model parameters and the minimum signal
+strength. This is especially significant given the fact that the former is a
+fundamental condition underlying most of the theoretical development in
+high-dimensional statistics, while the latter is a key condition used to
+establish variable selection properties. Moreover, the proposed method is
+asymptotically exact and has satisfactory power properties for testing very
+general functionals of the high-dimensional parameters. The simulation studies
+lend further support to our theoretical claims and additionally show excellent
+finite-sample size and power properties of the proposed test.
+",0,0,1,1,0,0
+10970,On the impact of quantum computing technology on future developments in high-performance scientific computing,"  Quantum computing technologies have become a hot topic in academia and
+industry receiving much attention and financial support from all sides.
+Building a quantum computer that can be used practically is in itself an
+outstanding challenge that has become the 'new race to the moon'. Next to
+researchers and vendors of future computing technologies, national authorities
+are showing strong interest in maturing this technology due to its known
+potential to break many of today's encryption techniques, which would have
+significant impact on our society. It is however quite likely that quantum
+computing has beneficial impact on many computational disciplines.
+In this article we describe our vision of future developments in scientific
+computing that would be enabled by the advent of software-programmable quantum
+computers. We thereby assume that quantum computers will form part of a hybrid
+accelerated computing platform like GPUs and co-processor cards do today. In
+particular, we address the potential of quantum algorithms to bring major
+breakthroughs in applied mathematics and its applications. Finally, we give
+several examples that demonstrate the possible impact of quantum-accelerated
+scientific computing on society.
+",1,0,0,0,0,0
+10971,Local Monotonic Attention Mechanism for End-to-End Speech and Language Processing,"  Recently, encoder-decoder neural networks have shown impressive performance
+on many sequence-related tasks. The architecture commonly uses an attentional
+mechanism which allows the model to learn alignments between the source and the
+target sequence. Most attentional mechanisms used today is based on a global
+attention property which requires a computation of a weighted summarization of
+the whole input sequence generated by encoder states. However, it is
+computationally expensive and often produces misalignment on the longer input
+sequence. Furthermore, it does not fit with monotonous or left-to-right nature
+in several tasks, such as automatic speech recognition (ASR),
+grapheme-to-phoneme (G2P), etc. In this paper, we propose a novel attention
+mechanism that has local and monotonic properties. Various ways to control
+those properties are also explored. Experimental results on ASR, G2P and
+machine translation between two languages with similar sentence structures,
+demonstrate that the proposed encoder-decoder model with local monotonic
+attention could achieve significant performance improvements and reduce the
+computational complexity in comparison with the one that used the standard
+global attention architecture.
+",1,0,0,0,0,0
+10972,"Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming","  Approximate dynamic programming algorithms, such as approximate value
+iteration, have been successfully applied to many complex reinforcement
+learning tasks, and a better approximate dynamic programming algorithm is
+expected to further extend the applicability of reinforcement learning to
+various tasks. In this paper we propose a new, robust dynamic programming
+algorithm that unifies value iteration, advantage learning, and dynamic policy
+programming. We call it generalized value iteration (GVI) and its approximated
+version, approximate GVI (AGVI). We show AGVI's performance guarantee, which
+includes performance guarantees for existing algorithms, as special cases. We
+discuss theoretical weaknesses of existing algorithms, and explain the
+advantages of AGVI. Numerical experiments in a simple environment support
+theoretical arguments, and suggest that AGVI is a promising alternative to
+previous algorithms.
+",1,0,0,1,0,0
+10973,Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting,"  Spatiotemporal forecasting has various applications in neuroscience, climate
+and transportation domain. Traffic forecasting is one canonical example of such
+learning task. The task is challenging due to (1) complex spatial dependency on
+road networks, (2) non-linear temporal dynamics with changing road conditions
+and (3) inherent difficulty of long-term forecasting. To address these
+challenges, we propose to model the traffic flow as a diffusion process on a
+directed graph and introduce Diffusion Convolutional Recurrent Neural Network
+(DCRNN), a deep learning framework for traffic forecasting that incorporates
+both spatial and temporal dependency in the traffic flow. Specifically, DCRNN
+captures the spatial dependency using bidirectional random walks on the graph,
+and the temporal dependency using the encoder-decoder architecture with
+scheduled sampling. We evaluate the framework on two real-world large scale
+road network traffic datasets and observe consistent improvement of 12% - 15%
+over state-of-the-art baselines.
+",1,0,0,1,0,0
+10974,"Machine Learning for Networking: Workflow, Advances and Opportunities","  Recently, machine learning has been used in every possible field to leverage
+its amazing power. For a long time, the net-working and distributed computing
+system is the key infrastructure to provide efficient computational resource
+for machine learning. Networking itself can also benefit from this promising
+technology. This article focuses on the application of Machine Learning
+techniques for Networking (MLN), which can not only help solve the intractable
+old network questions but also stimulate new network applications. In this
+article, we summarize the basic workflow to explain how to apply the machine
+learning technology in the networking domain. Then we provide a selective
+survey of the latest representative advances with explanations on their design
+principles and benefits. These advances are divided into several network design
+objectives and the detailed information of how they perform in each step of MLN
+workflow is presented. Finally, we shed light on the new opportunities on
+networking design and community building of this new inter-discipline. Our goal
+is to provide a broad research guideline on networking with machine learning to
+help and motivate researchers to develop innovative algorithms, standards and
+frameworks.
+",1,0,0,0,0,0
+10975,The WAGGS project - I. The WiFeS Atlas of Galactic Globular cluster Spectra,"  We present the WiFeS Atlas of Galactic Globular cluster Spectra, a library of
+integrated spectra of Milky Way and Local Group globular clusters. We used the
+WiFeS integral field spectrograph on the Australian National University 2.3 m
+telescope to observe the central regions of 64 Milky Way globular clusters and
+22 globular clusters hosted by the Milky Way's low mass satellite galaxies. The
+spectra have wider wavelength coverage (3300 {\AA} to 9050 {\AA}) and higher
+spectral resolution (R = 6800) than existing spectral libraries of Milky Way
+globular clusters. By including Large and Small Magellanic Cloud star clusters,
+we extend the coverage of parameter space of existing libraries towards young
+and intermediate ages. While testing stellar population synthesis models and
+analysis techniques is the main aim of this library, the observations may also
+further our understanding of the stellar populations of Local Group globular
+clusters and make possible the direct comparison of extragalactic globular
+cluster integrated light observations with well understood globular clusters in
+the Milky Way. The integrated spectra are publicly available via the project
+website.
+",0,1,0,0,0,0
+10976,Going Viral: Stability of Consensus-Driven Adoptive Spread,"  The spread of new products in a networked population is often modeled as an
+epidemic. However, in the case of ""complex"" contagion, these models are
+insufficient to properly model adoption behavior. In this paper, we investigate
+a model of complex contagion which allows a coevolutionary interplay between
+adoption, modeled as an SIS epidemic spreading process, and social
+reinforcement effects, modeled as consensus opinion dynamics. Asymptotic
+stability analysis of the all-adopt as well as the none-adopt equilibria of the
+combined opinion-adoption model is provided through the use of Lyapunov
+arguments. In doing so, sufficient conditions are provided which determine the
+stability of the ""flop"" state, where no one adopts the product and everyone's
+opinion of the product is least favorable, and the ""hit"" state, where everyone
+adopts and their opinions are most favorable. These conditions are shown to
+extend to the bounded confidence opinion dynamic under a stronger assumption on
+the model parameters. To conclude, numerical simulations demonstrate behavior
+of the model which reflect findings from the sociology literature on adoption
+behavior.
+",1,0,0,0,0,0
+10977,"What's In A Patch, I: Tensors, Differential Geometry and Statistical Shading Analysis","  We develop a linear algebraic framework for the shape-from-shading problem,
+because tensors arise when scalar (e.g. image) and vector (e.g. surface normal)
+fields are differentiated multiple times. The work is in two parts. In this
+first part we investigate when image derivatives exhibit invariance to changing
+illumination by calculating the statistics of image derivatives under general
+distributions on the light source. We computationally validate the hypothesis
+that image orientations (derivatives) provide increased invariance to
+illumination by showing (for a Lambertian model) that a shape-from-shading
+algorithm matching gradients instead of intensities provides more accurate
+reconstructions when illumination is incorrectly estimated under a flatness
+prior.
+",1,0,0,0,0,0
+10978,Counterexample to Gronwall's Conjecture,"  We present a projectively invariant description of planar linear 3-webs and
+construct a counterexample to Gronwall's conjecture.
+",0,0,1,0,0,0
+10979,Urban Swarms: A new approach for autonomous waste management,"  Modern cities are growing ecosystems that face new challenges due to the
+increasing population demands. One of the many problems they face nowadays is
+waste management, which has become a pressing issue requiring new solutions.
+Swarm robotics systems have been attracting an increasing amount of attention
+in the past years and they are expected to become one of the main driving
+factors for innovation in the field of robotics. The research presented in this
+paper explores the feasibility of a swarm robotics system in an urban
+environment. By using bio-inspired foraging methods such as multi-place
+foraging and stigmergy-based navigation, a swarm of robots is able to improve
+the efficiency and autonomy of the urban waste management system in a realistic
+scenario. To achieve this, a diverse set of simulation experiments was
+conducted using real-world GIS data and implementing different garbage
+collection scenarios driven by robot swarms. Results presented in this research
+show that the proposed system outperforms current approaches. Moreover, results
+not only show the efficiency of our solution, but also give insights about how
+to design and customize these systems.
+",1,0,0,0,0,0
+10980,An Algorithm of Parking Planning for Smart Parking System,"  There are so many vehicles in the world and the number of vehicles is
+increasing rapidly. To alleviate the parking problems caused by that, the smart
+parking system has been developed. The parking planning is one of the most
+important parts of it. An effective parking planning strategy makes the better
+use of parking resources possible. In this paper, we present a feasible method
+to do parking planning. We transform the parking planning problem into a kind
+of linear assignment problem. We take vehicles as jobs and parking spaces as
+agents. We take distances between vehicles and parking spaces as costs for
+agents doing jobs. Then we design an algorithm for this particular assignment
+problem and solve the parking planning problem. The method proposed can give
+timely and efficient guide information to vehicles for a real time smart
+parking system. Finally, we show the effectiveness of the method with
+experiments over some data, which can simulate the situation of doing parking
+planning in the real world.
+",1,0,0,0,0,0
+10981,"Classical Control, Quantum Circuits and Linear Logic in Enriched Category Theory","  We describe categorical models of a circuit-based (quantum) functional pro-
+gramming language. We show that enriched categories play a crucial role.
+Following earlier work on QWire by Paykin et al., we consider both a simple
+first-order linear language for circuits, and a more powerful host language,
+such that the circuit language is embedded inside the host language. Our
+categorical semantics for the host language is standard, and involves cartesian
+closed categories and monads. We interpret the circuit language not in an
+ordinary category, but in a category that is enriched in the host category. We
+show that this structure is also related to linear/non-linear models. As an
+extended example, we recall an earlier result that the category of W*-algebras
+is dcpo-enriched, and we use this model to extend the circuit language with
+some recursive types.
+",1,0,1,0,0,0
+10982,Unifying Map and Landmark Based Representations for Visual Navigation,"  This works presents a formulation for visual navigation that unifies map
+based spatial reasoning and path planning, with landmark based robust plan
+execution in noisy environments. Our proposed formulation is learned from data
+and is thus able to leverage statistical regularities of the world. This allows
+it to efficiently navigate in novel environments given only a sparse set of
+registered images as input for building representations for space. Our
+formulation is based on three key ideas: a learned path planner that outputs
+path plans to reach the goal, a feature synthesis engine that predicts features
+for locations along the planned path, and a learned goal-driven closed loop
+controller that can follow plans given these synthesized features. We test our
+approach for goal-driven navigation in simulated real world environments and
+report performance gains over competitive baseline approaches.
+",1,0,0,0,0,0
+10983,Accounting for Uncertainty About Past Values In Probabilistic Projections of the Total Fertility Rate for All Countries,"  Since the 1940s, population projections have in most cases been produced
+using the deterministic cohort component method. However, in 2015, for the
+first time, in a major advance, the United Nations issued official
+probabilistic population projections for all countries based on Bayesian
+hierarchical models for total fertility and life expectancy. The estimates of
+these models and the resulting projections are conditional on the UN's official
+estimates of past values. However, these past values are themselves uncertain,
+particularly for the majority of the world's countries that do not have
+longstanding high-quality vital registration systems, when they rely on surveys
+and censuses with their own biases and measurement errors. This paper is a
+first attempt to remedy this for total fertility rates, by extending the UN
+model for the future to take account of uncertainty about past values. This is
+done by adding an additional level to the hierarchical model to represent the
+multiple data sources, in each case estimating their bias and measurement error
+variance. We assess the method by out-of-sample predictive validation. While
+the prediction intervals produced by the current method have somewhat less than
+nominal coverage, we find that our proposed method achieves close to nominal
+coverage. The prediction intervals become wider for countries for which the
+estimates of past total fertility rates rely heavily on surveys rather than on
+vital registration data.
+",0,0,0,1,0,0
+10984,Periodic solutions of a perturbed Kepler problem in the plane: from existence to stability,"  The existence of elliptic periodic solutions of a perturbed Kepler problem is
+proved. The equations are in the plane and the perturbation depends
+periodically on time. The proof is based on a local description of the
+symplectic group in two degrees of freedom.
+",0,0,1,0,0,0
+10985,Removal of Narrowband Interference (PLI in ECG Signal) Using Ramanujan Periodic Transform (RPT),"  Suppression of interference from narrowband frequency signals play vital role
+in many signal processing and communication applications. A transform based
+method for suppression of narrow band interference in a biomedical signal is
+proposed. As a specific example Electrocardiogram (ECG) is considered for the
+analysis. ECG is one of the widely used biomedical signal. ECG signal is often
+contaminated with baseline wander noise, powerline interference (PLI) and
+artifacts (bioelectric signals), which complicates the processing of raw ECG
+signal. This work proposes an approach using Ramanujan periodic transform for
+reducing PLI and is tested on a subject data from MIT-BIH Arrhythmia database.
+A sum ($E$) of Euclidean error per block ($e_i$) is used as measure to quantify
+the suppression capability of RPT and notch filter based methods. The
+transformation is performed for different lengths ($N$), namely $36$, $72$,
+$108$, $144$, $180$. Every doubling of $N$-points results in $50{\%}$ reduction
+in error ($E$).
+",0,0,1,1,0,0
+10986,Global regularity and fast small scale formation for Euler patch equation in a disk,"  It is well known that the Euler vortex patch in $\mathbb{R}^{2}$ will remain
+regular if it is regular enough initially. In bounded domains, the regularity
+theory for patch solutions is less complete. We study here the Euler vortex
+patch in a disk. We prove global in time regularity by providing the upper
+bound of the growth of curvature of the patch boundary. For a special symmetric
+scenario, we construct an example of double exponential curvature growth,
+showing that such upper bound is qualitatively sharp.
+",0,0,1,0,0,0
+10987,Effective interaction in a non-Fermi liquid conductor and spin correlations in under-doped cuprates,"  The effective interaction between the itinerant spin degrees of freedom in
+the paramagnetic phases of hole doped quantum Heisenberg antiferromagnets is
+investigated theoretically, based on the single-band t-J model on 1D lattice,
+at zero temperature. The effective spin-spin interaction for this model in the
+strong correlation limit, is studied in terms of the generalized spin stiffness
+constant as a function of doping concentration. The plot of this generalized
+spin stiffness constant against doping shows a very high value of stiffness in
+the vicinity of zero doping and a very sharp fall with increase in doping
+concentration, signifying the rapid decay of original coupling of
+semi-localized spins in the system. Quite interestingly, this plot also shows a
+maximum occurring at a finite value of doping, which strongly suggests the
+tendency of the itinerant spins to couple again in the unconventional
+paramagnetic phase. As the doping is further increased, this new coupling is
+also suppressed and the spin response becomes analogous to almost Pauli-like.
+The last two predictions of ours are quite novel and may be directly tested by
+independent experiments and computational techniques in future. Our results in
+general receive good support from other theoretical works and experimental
+results extracted from the chains of YBa$_2$Cu$_3$O$_{6+x}$.
+",0,1,0,0,0,0
+10988,Planning with Verbal Communication for Human-Robot Collaboration,"  Human collaborators coordinate effectively their actions through both verbal
+and non-verbal communication. We believe that the the same should hold for
+human-robot teams. We propose a formalism that enables a robot to decide
+optimally between doing a task and issuing an utterance. We focus on two types
+of utterances: verbal commands, where the robot expresses how it wants its
+human teammate to behave, and state-conveying actions, where the robot explains
+why it is behaving this way. Human subject experiments show that enabling the
+robot to issue verbal commands is the most effective form of communicating
+objectives, while retaining user trust in the robot. Communicating why
+information should be done judiciously, since many participants questioned the
+truthfulness of the robot statements.
+",1,0,0,0,0,0
+10989,Magnon Condensation and Spin Superfluidity,"  We consider the phenomenon of Bose-Einstein condensation of quasi-equilibrium
+magnons which leads to a spin superfluidity, the coherent quantum transfer of
+magnetization in magnetic materials. These phenomena are beyond the classical
+Landau-Lifshitz-Gilbert paradigm. The critical conditions for excited magnon
+density for ferro- and antiferromagnets, bulk and thin films are estimated and
+discussed. The BEC should occur in the antiferromagnetic hematite at much lower
+excited magnon density compared to the ferromagnetic YIG.
+",0,1,0,0,0,0
+10990,Electroweak Vacuum Metastability and Low-scale Inflation,"  We study the stability of the electroweak vacuum in low-scale inflation
+models whose Hubble parameter is much smaller than the instability scale of the
+Higgs potential. In general, couplings between the inflaton and Higgs are
+present, and hence we study effects of these couplings during and after
+inflation. We derive constraints on the couplings between the inflaton and
+Higgs by requiring that they do not lead to catastrophic electroweak vacuum
+decay, in particular, via resonant production of the Higgs particles.
+",0,1,0,0,0,0
+10991,Spectral selectivity in capillary dye lasers,"  We explore the spectral properties of a capillary dye laser in the highly
+multimode regime. Our experiments indicate that the spectral behavior of the
+laser does not conform with a simple Fabry-Perot analysis; rather, it is
+strongly dictated by a Vernier resonant mechanism involving multiple modes,
+which propagate with different group velocities. The laser operates over a very
+broad spectral range and the Vernier effect gives rise to a free spectral range
+which is orders of magnitude larger than that expected from a simple
+Fabry-Perot mechanism. The presented theoretical calculations confirm the
+experimental results. Propagating modes of the capillary fiber are calculated
+using the finite element method (FEM) and it is shown that the optical
+pathlengths resulting from simultaneous beatings of these modes are in close
+agreement with the optical pathlengths directly extracted from the Fourier
+Transform of the experimentally measured laser emission spectra.
+",0,1,0,0,0,0
+10992,The Mechanism of Electrolyte Gating on High-Tc Cuprates: The Role of Oxygen Migration and Electrostatics,"  Electrolyte gating is widely used to induce large carrier density modulation
+on solid surfaces to explore various properties. Most of past works have
+attributed the charge modulation to electrostatic field effect. However, some
+recent reports have argued that the electrolyte gating effect in VO2, TiO2 and
+SrTiO3 originated from field-induced oxygen vacancy formation. This gives rise
+to a controversy about the gating mechanism, and it is therefore vital to
+reveal the relationship between the role of electrolyte gating and the
+intrinsic properties of materials. Here, we report entirely different
+mechanisms of electrolyte gating on two high-Tc cuprates, NdBa2Cu3O7-{\delta}
+(NBCO) and Pr2-xCexCuO4 (PCCO), with different crystal structures. We show that
+field-induced oxygen vacancy formation in CuO chains of NBCO plays the dominant
+role while it is mainly an electrostatic field effect in the case of PCCO. The
+possible reason is that NBCO has mobile oxygen in CuO chains while PCCO does
+not. Our study helps clarify the controversy relating to the mechanism of
+electrolyte gating, leading to a better understanding of the role of oxygen
+electro migration which is very material specific.
+",0,1,0,0,0,0
+10993,Invariant theory of a special group action on irreducible polynomials over finite fields,"  In the past few years, an action of $\mathrm{PGL}_2(\mathbb F_q)$ on the set
+of irreducible polynomials in $\mathbb F_q[x]$ has been introduced and many
+questions have been discussed, such as the characterization and number of
+invariant elements. In this paper, we analyze some recent works on this action
+and provide full generalizations of them, yielding final theoretical results on
+the characterization and number of invariant elements.
+",0,0,1,0,0,0
+10994,TIP: Typifying the Interpretability of Procedures,"  We provide a novel notion of what it means to be interpretable, looking past
+the usual association with human understanding. Our key insight is that
+interpretability is not an absolute concept and so we define it relative to a
+target model, which may or may not be a human. We define a framework that
+allows for comparing interpretable procedures by linking them to important
+practical aspects such as accuracy and robustness. We characterize many of the
+current state-of-the-art interpretable methods in our framework portraying its
+general applicability. Finally, principled interpretable strategies are
+proposed and empirically evaluated on synthetic data, as well as on the largest
+public olfaction dataset that was made recently available \cite{olfs}. We also
+experiment on MNIST with a simple target model and different oracle models of
+varying complexity. This leads to the insight that the improvement in the
+target model is not only a function of the oracle model's performance, but also
+its relative complexity with respect to the target model. Further experiments
+on CIFAR-10, a real manufacturing dataset and FICO dataset showcase the benefit
+of our methods over Knowledge Distillation when the target models are simple
+and the complex model is a neural network.
+",1,0,0,1,0,0
+10995,Differences in 1D electron plasma wake field acceleration in MeV versus GeV and linear versus blowout regimes,"  In some laboratory and most astrophysical situations plasma wake-field
+acceleration of electrons is one dimensional, i.e. variation transverse to the
+beam's motion can be ignored. Thus, one dimensional (1D), particle-in-cell
+(PIC), fully electromagnetic simulations of electron plasma wake field
+acceleration are conducted in order to study the differences in electron plasma
+wake field acceleration in MeV versus GeV and linear versus blowout regimes.
+First, we show that caution needs to be taken when using fluid simulations, as
+PIC simulations prove that an approximation for an electron bunch not to evolve
+in time for few hundred plasma periods only applies when it is sufficiently
+relativistic. This conclusion is true irrespective of the plasma temperature.
+We find that in the linear regime and GeV energies, the accelerating electric
+field generated by the plasma wake is similar to the linear and MeV regime.
+However, because GeV energy driving bunch stays intact for much longer time,
+the final acceleration energies are much larger in the GeV energies case. In
+the GeV energy range and blowout regime the wake's accelerating electric field
+is much larger in amplitude compared to the linear case and also plasma wake
+geometrical size is much larger. Thus, the correct positioning of the trailing
+bunch is needed to achieve the efficient acceleration. For the considered case,
+optimally there should be approximately $(90-100) c/\omega_{pe}$ distance
+between trailing and driving electron bunches in the GeV blowout regime.
+",0,1,0,0,0,0
+10996,Estimating network memberships by simplex vertex hunting,"  Consider an undirected mixed membership network with $n$ nodes and $K$
+communities. For each node $1 \leq i \leq n$, we model the membership by
+$\pi_{i} = (\pi_{i}(1), \pi_{i}(2), \ldots$, $\pi_{i}(K))'$, where $\pi_{i}(k)$
+is the probability that node $i$ belongs to community $k$, $1 \leq k \leq K$.
+We call node $i$ ""pure"" if $\pi_i$ is degenerate and ""mixed"" otherwise. The
+primary interest is to estimate $\pi_i$, $1 \leq i \leq n$.
+We model the adjacency matrix $A$ with a Degree Corrected Mixed Membership
+(DCMM) model. Let $\hat{\xi}_1, \hat{\xi}_2, \ldots, \hat{\xi}_K$ be the first
+$K$ eigenvectors of $A$. We define a matrix $\hat{R} \in \mathbb{R}^{n, K-1}$
+by $\hat{R}(i,k) = \hat{\xi}_{k+1}(i)/\hat{\xi}_1(i)$, $1 \leq k \leq K-1$, $1
+\leq i \leq n$. The matrix can be viewed as a distorted version of its
+non-stochastic counterpart $R \in \mathbb{R}^{n, K-1}$, which is unknown but
+contains all information we need for the memberships.
+We reveal an interesting insight: There is a simplex ${\cal S}$ in
+$\mathbb{R}^{K-1}$ such that row $i$ of $R$ corresponds to a vertex of ${\cal
+S}$ if node $i$ is pure, and corresponds to an interior point of ${\cal S}$
+otherwise. Vertex Hunting (i.e., estimating the vertices of ${\cal S}$) is thus
+the key to our problem.
+The matrix $\hat{R}$ is a row-wise normalization on the matrix of
+eigenvectors $\hat{\Xi}=[\hat{\xi}_1,\ldots,\hat{\xi}_K]$, first proposed by
+Jin (2015). Alternatively, we may normalize $\hat{\Xi}$ by the row-wise
+$\ell^q$-norms (e.g., Supplement of Jin (2015)), but it won't give rise to a
+simplex so is less convenient.
+We propose a new approach $\textit{Mixed-SCORE}$ to estimating the
+memberships, at the heart of which is an easy-to-use Vertex Hunting algorithm.
+The approach is successfully applied to $4$ network data sets. We also derive
+the rate of convergence for Mixed-SCORE.
+",0,0,0,1,0,0
+10997,"Non-singular Green's functions for the unbounded Poisson equation in one, two and three dimensions","  In this paper, we derive the non-singular Green's functions for the unbounded
+Poisson equation in two and three dimensions using a spectral approach to
+regularize the homogeneous equation. The resulting Green's functions are
+relevant to applications which are restricted to a minimum resolved length
+scale (e.g. a mesh size h) and thus cannot handle the singular Green's function
+of the continuous Poisson equation. We furthermore derive the gradient vector
+of the regularized Green's function, as this is useful in applications where
+the Poisson equation represents potential functions of a vector field.
+",0,1,1,0,0,0
+10998,Evolutionary phases of gas-rich galaxies in a galaxy cluster at z=1.46,"  We report a survey of molecular gas in galaxies in the XMMXCS J2215.9-1738
+cluster at $z=1.46$. We have detected emission lines from 17 galaxies within a
+radius of $R_{200}$ from the cluster center, in Band 3 data of the Atacama
+Large Millimeter/submillimeter Array (ALMA) with a coverage of 93 -- 95 GHz in
+frequency and 2.33 arcmin$^2$ in spatial direction. The lines are all
+identified as CO $J$=2--1 emission lines from cluster members at $z\sim1.46$ by
+their redshifts and the colors of their optical and near-infrared (NIR)
+counterparts. The line luminosities reach down to $L'_{\rm
+CO(2-1)}=4.5\times10^{9}$ K km s$^{-1}$ pc$^2$. The spatial distribution of
+galaxies with a detection of CO(2--1) suggests that they disappear from the
+very center of the cluster. The phase-space diagram showing relative velocity
+versus cluster-centric distance indicates that the gas-rich galaxies have
+entered the cluster more recently than the gas-poor star-forming galaxies and
+passive galaxies located in the virialized region of this cluster. The results
+imply that the galaxies have experienced ram-pressure stripping and/or
+strangulation during the course of infall towards the cluster center and then
+the molecular gas in the galaxies at the cluster center is depleted by star
+formation.
+",0,1,0,0,0,0
+10999,An iterative ensemble Kalman filter in presence of additive model error,"  The iterative ensemble Kalman filter (IEnKF) in a deterministic framework was
+introduced in Sakov et al. (2012) to extend the ensemble Kalman filter (EnKF)
+and improve its performance in mildly up to strongly nonlinear cases.
+However, the IEnKF assumes that the model is perfect. This assumption
+simplified the update of the system at a time different from the observation
+time, which made it natural to apply the IEnKF for smoothing. In this study, we
+generalise the IEnKF to the case of imperfect model with additive model error.
+The new method called IEnKF-Q conducts a Gauss-Newton minimisation in
+ensemble space. It combines the propagated analysed ensemble anomalies from the
+previous cycle and model noise ensemble anomalies into a single ensemble of
+anomalies, and by doing so takes an algebraic form similar to that of the
+IEnKF. The performance of the IEnKF-Q is tested in a number of experiments with
+the Lorenz-96 model, which show that the method consistently outperforms both
+the EnKF and the IEnKF naively modified to accommodate additive model noise.
+",0,1,0,1,0,0
+11000,A Bayesian algorithm for detecting identity matches and fraud in image databases,"  A statistical algorithm for categorizing different types of matches and fraud
+in image databases is presented. The approach is based on a generative model of
+a graph representing images and connections between pairs of identities,
+trained using properties of a matching algorithm between images.
+",1,0,0,1,0,0
+11001,Adaptive Stimulus Selection in ERP-Based Brain-Computer Interfaces by Maximizing Expected Discrimination Gain,"  Brain-computer interfaces (BCIs) can provide an alternative means of
+communication for individuals with severe neuromuscular limitations. The
+P300-based BCI speller relies on eliciting and detecting transient
+event-related potentials (ERPs) in electroencephalography (EEG) data, in
+response to a user attending to rarely occurring target stimuli amongst a
+series of non-target stimuli. However, in most P300 speller implementations,
+the stimuli to be presented are randomly selected from a limited set of options
+and stimulus selection and presentation are not optimized based on previous
+user data. In this work, we propose a data-driven method for stimulus selection
+based on the expected discrimination gain metric. The data-driven approach
+selects stimuli based on previously observed stimulus responses, with the aim
+of choosing a set of stimuli that will provide the most information about the
+user's intended target character. Our approach incorporates knowledge of
+physiological and system constraints imposed due to real-time BCI
+implementation. Simulations were performed to compare our stimulus selection
+approach to the row-column paradigm, the conventional stimulus selection method
+for P300 spellers. Results from the simulations demonstrated that our adaptive
+stimulus selection approach has the potential to significantly improve
+performance from the conventional method: up to 34% improvement in accuracy and
+43% reduction in the mean number of stimulus presentations required to spell a
+character in a 72-character grid. In addition, our greedy approach to stimulus
+selection provides the flexibility to accommodate design constraints.
+",1,0,0,0,0,0
+11002,Bursting dynamics of viscous film without circular symmetry: the effect of confinement,"  We experimentally investigate the bursting dynamics of confined liquid film
+suspended in air and find a viscous dynamics distinctly different from the
+non-confined counterpart, due to lack of circular symmetry in the shape of
+expanding hole: the novel confined-viscous bursting proceeds at a constant
+speed and a rim formed at the bursting tip does not grow. We find a
+confined-viscous to confined-inertial crossover, as well as a
+nonconfined-inertial to confined-inertial crossover, at which bursting speed
+does not change although the circular symmetry in the hole shape breaks
+dynamically.
+",0,1,0,0,0,0
+11003,Low-energy electron-positron collider to search and study (μ^+μ^-) bound state,"  We discuss a low energy $e^+e^-$ collider for production of the not yet
+observed ($\mu^+\mu^-$) bound system (dimuonium). Collider with large crossing
+angle for $e^+e^-$ beams intersection produces dimuonium with non-zero
+momentum, therefore, its decay point is shifted from the beam collision area
+providing effective suppression of the elastic $e^+e^-$ scattering background.
+The experimental constraints define subsequent collider specifications. We show
+preliminary layout of the accelerator and obtained main parameters. High
+luminosity in chosen beam energy range allows to study $\pi^\pm$ and $\eta$
+-mesons.
+",0,1,0,0,0,0
+11004,Følner functions and the generic Word Problem for finitely generated amenable groups,"  We introduce and investigate different definitions of effective amenability,
+in terms of computability of F{\o}lner sets, Reiter functions, and F{\o}lner
+functions. As a consequence, we prove that recursively presented amenable
+groups have subrecursive F{\o}lner function, answering a question of Gromov,
+for the same class of groups we prove that solvability of the Equality Problem
+on a generic set (generic EP) is equivalent to solvability of the Word Problem
+on the whole group (WP), thus providing the first examples of finitely
+presented groups with unsolvable generic EP. In particular, we prove that for
+finitely presented groups, solvability of generic WP doesn't imply solvability
+of generic EP.
+",0,0,1,0,0,0
+11005,Differences between Health Related News Articles from Reliable and Unreliable Media,"  In this study, we examine a collection of health-related news articles
+published by reliable and unreliable media outlets. Our analysis shows that
+there are structural, topical, and semantic differences in the way reliable and
+unreliable media outlets conduct health journalism. We argue that the findings
+from this study will be useful for combating health disinformation problem.
+",1,0,0,0,0,0
+11006,A Numerical Study of Carr and Lee's Correlation Immunization Strategy for Volatility Derivatives,"  In their seminal work `Robust Replication of Volatility Derivatives,' Carr
+and Lee show how to robustly price and replicate a variety of claims written on
+the quadratic variation of a risky asset under the assumption that the asset's
+volatility process is independent of the Brownian motion that drives the
+asset's price. Additionally, they propose a correlation immunization method
+that minimizes the pricing and hedging error that results when the correlation
+between the risky asset's price and volatility is nonzero. In this paper, we
+perform a number of Monte Carlo experiments to test the effectiveness of Carr
+and Lee's immunization strategy. Our results indicate that the correlation
+immunization method is an effective means of reducing pricing and hedging
+errors that result from nonzero correlation.
+",0,0,0,0,0,1
+11007,Functional renormalization group study of parallel double quantum dots: Effects of asymmetric dot-lead couplings,"  We explore the effects of asymmetry of hopping parameters between double
+parallel quantum dots and the leads on the conductance and a possibility of
+local magnetic moment formation in this system using functional renormalization
+group approach with the counterterm. We demonstrate a possibility of a quantum
+phase transition to a local moment regime (so called singular Fermi liquid
+(SFL) state) for various types of hopping asymmetries and discuss respective
+gate voltage dependences of the conductance. It is shown, that depending on the
+type of the asymmetry, the system can demonstrate either a first order quantum
+phase transition to SFL state, accompanied by a discontinuous change of the
+conductance, similarly to the symmetric case, or the second order quantum phase
+transition, in which the conductance is continuous and exhibits Fano-type
+asymmetric resonance near the transition point. A semi-analytical explanation
+of these different types of conductance behavior is presented.
+",0,1,0,0,0,0
+11008,Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System,"  Purpose: We propose a phenotype-based artificial intelligence system that can
+self-learn and is accurate for screening purposes, and test it on a Level IV
+monitoring system. Methods: Based on the physiological knowledge, we
+hypothesize that the phenotype information will allow us to find subjects from
+a well-annotated database that share similar sleep apnea patterns. Therefore,
+for a new-arriving subject, we can establish a prediction model from the
+existing database that is adaptive to the subject. We test the proposed
+algorithm on a database consisting of 62 subjects with the signals recorded
+from a Level IV wearable device measuring the thoracic and abdominal movements
+and the SpO2. Results: With the leave-one cross validation, the accuracy of the
+proposed algorithm to screen subjects with an apnea-hypopnea index greater or
+equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative
+likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and
+show that the proposed algorithm has great potential to screen patients with
+SAS.
+",0,0,0,1,0,0
+11009,Generative adversarial network-based approach to signal reconstruction from magnitude spectrograms,"  In this paper, we address the problem of reconstructing a time-domain signal
+(or a phase spectrogram) solely from a magnitude spectrogram. Since magnitude
+spectrograms do not contain phase information, we must restore or infer phase
+information to reconstruct a time-domain signal. One widely used approach for
+dealing with the signal reconstruction problem was proposed by Griffin and Lim.
+This method usually requires many iterations for the signal reconstruction
+process and depending on the inputs, it does not always produce high-quality
+audio signals. To overcome these shortcomings, we apply a learning-based
+approach to the signal reconstruction problem by modeling the signal
+reconstruction process using a deep neural network and training it using the
+idea of a generative adversarial network. Experimental evaluations revealed
+that our method was able to reconstruct signals faster with higher quality than
+the Griffin-Lim method.
+",0,0,0,1,0,0
+11010,The Braid Shelf,"  The braids of $B\_\infty$ can be equipped with a selfdistributive operation
+$\mathbin{\triangleright}$ enjoying a number of deep properties. This text is a
+survey of known properties and open questions involving this structure, its
+quotients, and its extensions.
+",0,0,1,0,0,0
+11011,"Ultra-Fast Relaxation, Decoherence and Localization of Photoexcited States in $π$-Conjugated Polymers: A TEBD Study","  The exciton relaxation dynamics of photoexcited electronic states in
+poly($p$-phenylenevinylene) (PPV) are theoretically investigated within a
+coarse-grained model, in which both the exciton and nuclear degrees of freedom
+are treated quantum mechanically. The Frenkel-Holstein Hamiltonian is used to
+describe the strong exciton-phonon coupling present in the system, while
+external damping of the internal nuclear degrees of freedom are accounted for
+by a Lindblad master equation. Numerically, the dynamics are computed using the
+time evolving block decimation (TEBD) and quantum jump trajectory techniques.
+The values of the model parameters physically relevant to polymer systems
+naturally lead to a separation of time scales, with the ultra-fast dynamics
+corresponding to energy transfer from the exciton to the internal phonon modes
+(i.e., the C-C bond oscillations), while the longer time dynamics correspond to
+damping of these phonon modes by the external dissipation. Associated with
+these time scales, we investigate the following processes that are indicative
+of the system relaxing onto the emissive chromophores of the polymer: 1)
+Exciton-polaron formation occurs on an ultra-fast time scale, with the
+associated exciton-phonon correlations present within half a vibrational time
+period of the C-C bond oscillations. 2) Exciton decoherence is driven by the
+decay in the vibrational overlaps associated with exciton-polaron formation,
+occurring on the same time scale. 3) Exciton density localization is driven by
+the external dissipation, arising from `wavefunction collapse' occurring as a
+result of the system-environment interactions. Finally, we show how
+fluorescence anisotropy measurements can be used to investigate the exciton
+decoherence process during the relaxation dynamics.
+",0,1,0,0,0,0
+11012,Towards Reverse-Engineering Black-Box Neural Networks,"  Many deployed learned models are black boxes: given input, returns output.
+Internal information about the model, such as the architecture, optimisation
+procedure, or training data, is not disclosed explicitly as it might contain
+proprietary information or make the system more vulnerable. This work shows
+that such attributes of neural networks can be exposed from a sequence of
+queries. This has multiple implications. On the one hand, our work exposes the
+vulnerability of black-box neural networks to different types of attacks -- we
+show that the revealed internal information helps generate more effective
+adversarial examples against the black box model. On the other hand, this
+technique can be used for better protection of private content from automatic
+recognition models using adversarial examples. Our paper suggests that it is
+actually hard to draw a line between white box and black box models.
+",1,0,0,1,0,0
+11013,Learning Dexterous In-Hand Manipulation,"  We use reinforcement learning (RL) to learn dexterous in-hand manipulation
+policies which can perform vision-based object reorientation on a physical
+Shadow Dexterous Hand. The training is performed in a simulated environment in
+which we randomize many of the physical properties of the system like friction
+coefficients and an object's appearance. Our policies transfer to the physical
+robot despite being trained entirely in simulation. Our method does not rely on
+any human demonstrations, but many behaviors found in human manipulation emerge
+naturally, including finger gaiting, multi-finger coordination, and the
+controlled use of gravity. Our results were obtained using the same distributed
+RL system that was used to train OpenAI Five. We also include a video of our
+results: this https URL
+",1,0,0,1,0,0
+11014,Index transforms with Weber type kernels,"  New index transforms with Weber type kernels, consisting of products of
+Bessel functions of the first and second kind are investigated. Mapping
+properties and inversion formulas are established for these transforms in
+Lebesgue spaces. The results are applied to solve a boundary value problem on
+the wedge for a fourth order partial differential equation.
+",0,0,1,0,0,0
+11015,Computationally Efficient Robust Estimation of Sparse Functionals,"  Many conventional statistical procedures are extremely sensitive to seemingly
+minor deviations from modeling assumptions. This problem is exacerbated in
+modern high-dimensional settings, where the problem dimension can grow with and
+possibly exceed the sample size. We consider the problem of robust estimation
+of sparse functionals, and provide a computationally and statistically
+efficient algorithm in the high-dimensional setting. Our theory identifies a
+unified set of deterministic conditions under which our algorithm guarantees
+accurate recovery. By further establishing that these deterministic conditions
+hold with high-probability for a wide range of statistical models, our theory
+applies to many problems of considerable interest including sparse mean and
+covariance estimation; sparse linear regression; and sparse generalized linear
+models.
+",1,0,0,1,0,0
+11016,Resource Sharing Among mmWave Cellular Service Providers in a Vertically Differentiated Duopoly,"  With the increasing interest in the use of millimeter wave bands for 5G
+cellular systems comes renewed interest in resource sharing. Properties of
+millimeter wave bands such as massive bandwidth, highly directional antennas,
+high penetration loss, and susceptibility to shadowing, suggest technical
+advantages to spectrum and infrastructure sharing in millimeter wave cellular
+networks. However, technical advantages do not necessarily translate to
+increased profit for service providers, or increased consumer surplus. In this
+paper, detailed network simulations are used to better understand the economic
+implications of resource sharing in a vertically differentiated duopoly market
+for cellular service. The results suggest that resource sharing is less often
+profitable for millimeter wave service providers compared to microwave cellular
+service providers, and does not necessarily increase consumer surplus.
+",1,0,0,0,0,0
+11017,How to Generate Pseudorandom Permutations Over Other Groups: Even-Mansour and Feistel Revisited,"  Recent results by Alagic and Russell have given some evidence that the
+Even-Mansour cipher may be secure against quantum adversaries with quantum
+queries, if considered over other groups than $(\mathbb{Z}/2)^n$. This prompts
+the question as to whether or not other classical schemes may be generalized to
+arbitrary groups and whether classical results still apply to those generalized
+schemes.
+In this paper, we generalize the Even-Mansour cipher and the Feistel cipher.
+We show that Even and Mansour's original notions of secrecy are obtained on a
+one-key, group variant of the Even-Mansour cipher. We generalize the result by
+Kilian and Rogaway, that the Even-Mansour cipher is pseudorandom, to super
+pseudorandomness, also in the one-key, group case. Using a Slide Attack we
+match the bound found above. After generalizing the Feistel cipher to arbitrary
+groups we resolve an open problem of Patel, Ramzan, and Sundaram by showing
+that the $3$-round Feistel cipher over an arbitrary group is not super
+pseudorandom. Finally, we generalize a result by Gentry and Ramzan showing that
+the Even-Mansour cipher can be implemented using the Feistel cipher as the
+public permutation. In this last result, we also consider the one-key case over
+a group and generalize their bound.
+",1,0,1,0,0,0
+11018,On Weyl's asymptotics and remainder term for the orthogonal and unitary groups,"  We examine the asymptotics of the spectral counting function of a compact
+Riemannian manifold by V.G.~Avakumovic \cite{Avakumovic} and L.~Hörmander
+\cite{Hormander-eigen} and show that for the scale of orthogonal and unitary
+groups ${\bf SO}(N)$, ${\bf SU}(N)$, ${\bf U}(N)$ and ${\bf Spin}(N)$ it is not
+sharp. While for negative sectional curvature improvements are possible and
+known, {\it cf.} e.g., J.J.~Duistermaat $\&$ V.~Guillemin \cite{Duist-Guill},
+here, we give sharp and contrasting examples in the positive Ricci curvature
+case [non-negative for ${\bf U}(N)$]. Furthermore here the improvements are
+sharp and quantitative relating to the dimension and {\it rank} of the group.
+We discuss the implications of these results on the closely related problem of
+closed geodesics and the length spectrum.
+",0,0,1,0,0,0
+11019,Virtual plane-wave imaging via Marchenko redatuming,"  Marchenko redatuming is a novel scheme used to retrieve up- and down-going
+Green's functions in an unknown medium. Marchenko equations are based on
+reciprocity theorems and are derived on the assumption of the existence of so
+called focusing functions, i.e. functions which exhibit time-space focusing
+properties once injected in the subsurface. In contrast to interferometry but
+similarly to standard migration methods, Marchenko redatuming only requires an
+estimate of the direct wave from the virtual source (or to the virtual
+receiver), illumination from only one side of the medium, and no physical
+sources (or receivers) inside the medium. In this contribution we consider a
+different time-focusing condition within the frame of Marchenko redatuming and
+show how this can lead to the retrieval of virtual plane-wave responses, thus
+allowing multiple-free imaging using only a 1 dimensional sampling of the
+targeted model. The potential of the new method is demonstrated on a 2D
+synthetic model.
+",0,1,0,0,0,0
+11020,Probing the local nature of excitons and plasmons in few-layer MoS2,"  Excitons and plasmons are the two most fundamental types of collective
+electronic excitations occurring in solids. Traditionally, they have been
+studied separately using bulk techniques that probe their average energetic
+structure over large spatial regions. However, as the dimensions of materials
+and devices continue to shrink, it becomes crucial to understand how these
+excitations depend on local variations in the crystal- and chemical structure
+on the atomic scale. Here we use monochromated low-loss
+scanning-transmission-electron-microscopy electron-energy-loss (LL-STEM-EEL)
+spectroscopy, providing the best simultaneous energy and spatial resolution
+achieved to-date to unravel the full set of electronic excitations in few-layer
+MoS2 nanosheets over a wide energy range. Using first-principles many-body
+calculations we confirm the excitonic nature of the peaks at ~2eV and ~3eV in
+the experimental EEL spectrum and the plasmonic nature of higher energy-loss
+peaks. We also rationalise the non-trivial dependence of the EEL spectrum on
+beam and sample geometry such as the number of atomic layers and distance to
+steps and edges. Moreover, we show that the excitonic features are dominated by
+the long wavelength (q=0) components of the probing field, while the plasmonic
+features are sensitive to a much broader range of q-vectors, indicating a
+qualitative difference in the spatial character of the two types of collective
+excitations. Our work provides a template protocol for mapping the local nature
+of electronic excitations that open new possibilities for studying
+photo-absorption and energy transfer processes on a nanometer scale.
+",0,1,0,0,0,0
+11021,Flavour composition and entropy increase of cosmological neutrinos after decoherence,"  We investigate the evolution of the flavour composition of the cosmic
+neutrino background from neutrino decoupling until today. The decoherence of
+neutrino mass states is described by means of Lindblad operators. Decoherence
+goes along with the increase of neutrino family entropy, which we obtain as a
+function of initial spectral distortions, mixing angles and CP-violation phase.
+We also present the expected flavour composition of the cosmic neutrino
+background after decoherence is completed. Decoherence is proposed to happen
+after the two heaviest neutrino mass states become non-relativistic. We discuss
+how the associated increase of entropy could be observed (in principle). The
+physics of two- or three-flavour oscillation of cosmological neutrinos
+resembles in many aspects two- or three-level systems in atomic clocks, which
+were recently proposed by Weinberg for the study of decoherence phenomena.
+",0,1,0,0,0,0
+11022,Random non-Abelian G-circulant matrices. Spectrum of random convolution operators on large finite groups,"  We analyse the limiting behavior of the eigenvalue and singular value
+distribution for random convolution operators on large (not necessarily
+Abelian) groups, extending the results by M. Meckes for the Abelian case. We
+show that for regular sequences of groups the limiting distribution of
+eigenvalues (resp. singular values) is a mixture of eigenvalue (resp. singular
+value) distributions of Ginibre matrices with the directing measure being
+related to the limiting behavior of the Plancherel measure of the sequence of
+groups. In particular for the sequence of symmetric groups, the limiting
+distributions are just the circular and quarter circular laws, whereas e.g. for
+the dihedral groups the limiting distributions have unbounded supports but are
+different than in the Abelian case.
+We also prove that under additional assumptions on the sequence of groups (in
+particular for symmetric groups of increasing order) families of stochastically
+independent random projection operators converge in moments to free circular
+elements.
+Finally, in the Gaussian case we provide Central Limit Theorems for linear
+eigenvalue statistics.
+",0,0,1,0,0,0
+11023,"Fog Robotics for Efficient, Fluent and Robust Human-Robot Interaction","  Active communication between robots and humans is essential for effective
+human-robot interaction. To accomplish this objective, Cloud Robotics (CR) was
+introduced to make robots enhance their capabilities. It enables robots to
+perform extensive computations in the cloud by sharing their outcomes. Outcomes
+include maps, images, processing power, data, activities, and other robot
+resources. But due to the colossal growth of data and traffic, CR suffers from
+serious latency issues. Therefore, it is unlikely to scale a large number of
+robots particularly in human-robot interaction scenarios, where responsiveness
+is paramount. Furthermore, other issues related to security such as privacy
+breaches and ransomware attacks can increase. To address these problems, in
+this paper, we have envisioned the next generation of social robotic
+architectures based on Fog Robotics (FR) that inherits the strengths of Fog
+Computing to augment the future social robotic systems. These new architectures
+can escalate the dexterity of robots by shoving the data closer to the robot.
+Additionally, they can ensure that human-robot interaction is more responsive
+by resolving the problems of CR. Moreover, experimental results are further
+discussed by considering a scenario of FR and latency as a primary factor
+comparing to CR models.
+",1,0,0,0,0,0
+11024,An Estimation of the Star Formation Rate in the Perseus Complex,"  We present the results of our investigation of the star-forming potential in
+the Perseus star-forming complex. We build on previous starless core,
+protostellar core, and young stellar object (YSO) catalogs from Spitzer,
+Herschel, and SCUBA observations in the literature. We place the cores and YSOs
+within seven star-forming clumps based on column densities greater than 5x10^21
+cm^-2. We calculate the mean density and free-fall time for 69 starless cores
+as 5.55x10^-19 gcm^-3 and 0.1 Myr,respectively, and we estimate the star
+formation rate for the near future as 150 Msun Myr^-1. According to Bonnor
+Ebert stability analysis, we find that majority of starless cores in Perseus
+are unstable. Broadly, these cores can collapse to form the next generation of
+stars. We found a relation between starless cores and YSOs, where the numbers
+of young protostars (Class 0 + Class I) are similar to the numbers of starless
+cores. This similarity, which shows a one-to-one relation, suggests that these
+starless cores may form the next generation of stars with approximately the
+same formation rate as the current generation, as identified by the Class 0 and
+Class I protostars. It follows that if such a relation between starless cores
+and any YSO stage exists, the SFR values of these two populations must be
+nearly constant. In brief, we propose that this one-to-one relation is an
+important factor in better understanding the star formation process within a
+cloud.
+",0,1,0,0,0,0
+11025,Deep Neural Networks - A Brief History,"  Introduction to deep neural networks and their history.
+",1,0,0,0,0,0
+11026,An advanced active quenching circuit for ultra-fast quantum cryptography,"  Commercial photon-counting modules based on actively quenched solid-state
+avalanche photodiode sensors are used in a wide variety of applications.
+Manufacturers characterize their detectors by specifying a small set of
+parameters, such as detection efficiency, dead time, dark counts rate,
+afterpulsing probability and single-photon arrival-time resolution (jitter).
+However, they usually do not specify the range of conditions over which these
+parameters are constant or present a sufficient description of the
+characterization process. In this work, we perform a few novel tests on two
+commercial detectors and identify an additional set of imperfections that must
+be specified to sufficiently characterize their behavior. These include
+rate-dependence of the dead time and jitter, detection delay shift, and
+""twilighting."" We find that these additional non-ideal behaviors can lead to
+unexpected effects or strong deterioration of the performance of a system using
+these devices. We explain their origin by an in-depth analysis of the active
+quenching process. To mitigate the effects of these imperfections, a
+custom-built detection system is designed using a novel active quenching
+circuit. Its performance is compared against two commercial detectors in a fast
+quantum key distribution system with hyper-entangled photons and a random
+number generator.
+",1,1,0,0,0,0
+11027,Shannon entropy: a study of confined hydrogenic-like atoms,"  The Shannon entropy in the atomic, molecular and chemical physics context is
+presented by using as test cases the hydrogenic-like atoms $H_c$, ${He_c}^+$
+and ${Li_c}^{2+}$ confined by an impenetrable spherical box. Novel expressions
+for entropic uncertainty relation and Shannon entropies $S_r$ and $S_p$ are
+proposed to ensure their physical dimensionless characteristic. The electronic
+ground state energy and the quantities $S_r$, $S_p$ and $S_t$ are calculated
+for the hydrogenic-like atoms to different confinement radii by using a
+variational method. The global behavior of these quantities and different
+conjectures are analyzed. The results are compared, when available, with those
+previously published.
+",0,1,0,0,0,0
+11028,Exploring Halo Substructure with Giant Stars. XV. Discovery of a Connection between the Monoceros Ring and the Triangulum-Andromeda Overdensity?,"  Thanks to modern sky surveys, over twenty stellar streams and overdensity
+structures have been discovered in the halo of the Milky Way. In this paper, we
+present an analysis of spectroscopic observations of individual stars from one
+such structure, ""A13"", first identified as an overdensity using the M giant
+catalog from the Two Micron All-Sky Survey. Our spectroscopic observations show
+that stars identified with A13 have a velocity dispersion of $\lesssim$ 40
+$\mathrm{km~s^{-1}}$, implying that it is a genuine coherent structure rather
+than a chance super-position of random halo stars. From its position on the
+sky, distance ($\sim$15~kpc heliocentric), and kinematical properties, A13 is
+likely to be an extension of another low Galactic latitude substructure -- the
+Galactic Anticenter Stellar Structure (also known as the Monoceros Ring) --
+towards smaller Galactic longitude and farther distance. Furthermore, the
+kinematics of A13 also connect it with another structure in the southern
+Galactic hemisphere -- the Triangulum-Andromeda overdensity. We discuss these
+three connected structures within the context of a previously proposed scenario
+that one or all of these features originate from the disk of the Milky Way.
+",0,1,0,0,0,0
+11029,Universal fitness dynamics through an adaptive resource utilization model,"  The fitness of a species determines its abundance and survival in an
+ecosystem. At the same time, species take up resources for growth, so their
+abundance affects the availability of resources in an ecosystem. We show here
+that such species-resource coupling can be used to assign a quantitative metric
+for fitness to each species. This fitness metric also allows for the modeling
+of drift in species composition, and hence ecosystem evolution through
+speciation and adaptation. Our results provide a foundation for an entirely
+computational exploration of evolutionary ecosystem dynamics on any length or
+time scale. For example, we can evolve ecosystem dynamics even by initiating
+dynamics out of a single primordial ancestor and show that there exists a well
+defined ecosystem-averaged fitness dynamics that is resilient against resource
+shocks.
+",0,0,0,0,1,0
+11030,Integral and measure-turnpike properties for infinite-dimensional optimal control systems,"  We first derive a general integral-turnpike property around a set for
+infinite-dimensional non-autonomous optimal control problems with any possible
+terminal state constraints, under some appropriate assumptions. Roughly
+speaking, the integral-turnpike property means that the time average of the
+distance from any optimal trajectory to the turnpike set con- verges to zero,
+as the time horizon tends to infinity. Then, we establish the measure-turnpike
+property for strictly dissipative optimal control systems, with state and
+control constraints. The measure-turnpike property, which is slightly stronger
+than the integral-turnpike property, means that any optimal (state and control)
+solution remains essentially, along the time frame, close to an optimal
+solution of an associated static optimal control problem, except along a subset
+of times that is of small relative Lebesgue measure as the time horizon is
+large. Next, we prove that strict strong duality, which is a classical notion
+in optimization, implies strict dissipativity, and measure-turnpike. Finally,
+we conclude the paper with several comments and open problems.
+",0,0,1,0,0,0
+11031,Optimal rate of convergence in Stratified Boussinesq system,"  We study the vortex patch problem for $2d-$stratified Navier-Stokes system.
+We aim at extending several results obtained in
+\cite{ad,danchinpoche,hmidipoche} for standard Euler and Navier-Stokes systems.
+We shall deal with smooth initial patches and establish global strong estimates
+uniformly with respect to the viscosity in the spirit of \cite{HZ-poche,
+Z-poche}. This allows to prove the convergence of the viscous solutions towards
+the inviscid one. In the setting of a Rankine vortex, we show that the rate of
+convergence for the vortices is optimal in $L^p$ space and is given by $(\mu
+t)^{\frac{1}{2p}}$. This generalizes the result of \cite{ad} obtained for $L^2$
+space.
+",0,0,1,0,0,0
+11032,On indirect noise in multicomponent nozzle flows,"  A one-dimensional, unsteady nozzle flow is modelled to identify the sources
+of indirect noise in multicomponent gases. First, from non-equilibrium
+thermodynamics relations, it is shown that a compositional inhomogeneity
+advected in an accelerating flow is a source of sound induced by
+inhomogeneities in the mixture (i) chemical potentials and (ii) specific heat
+capacities. Second, it is shown that the acoustic, entropy and compositional
+linear perturbations evolve independently from each other and they become
+coupled through mean-flow gradients and/or at the boundaries. Third, the
+equations are cast in invariant formulation and a mathematical solution is
+found by asymptotic expansion of path-ordered integrals with an infinite radius
+of convergence. Finally, the transfer functions are calculated for a supersonic
+nozzle with finite spatial extent perturbed by a methane-air compositional
+inhomogeneity. The proposed framework will help identify and quantify the
+sources of sound in nozzles with relevance, for example, to aeronautical gas
+turbines.
+",0,1,0,0,0,0
+11033,A note on conditional covariance matrices for elliptical distributions,"  In this short note we provide an analytical formula for the conditional
+covariance matrices of the elliptically distributed random vectors, when the
+conditioning is based on the values of any linear combination of the marginal
+random variables. We show that one could introduce the univariate invariant
+depending solely on the conditioning set, which greatly simplifies the
+calculations. As an application, we show that one could define uniquely defined
+quantile-based sets on which conditional covariance matrices must be equal to
+each other if only the vector is multivariate normal. The similar results are
+obtained for conditional correlation matrices of the general elliptic case.
+",0,0,1,1,0,0
+11034,The Passive Eavesdropper Affects my Channel: Secret-Key Rates under Real-World Conditions (Extended Version),"  Channel-reciprocity based key generation (CRKG) has gained significant
+importance as it has recently been proposed as a potential lightweight security
+solution for IoT devices. However, the impact of the attacker's position in
+close range has only rarely been evaluated in practice, posing an open research
+problem about the security of real-world realizations. Furthermore, this would
+further bridge the gap between theoretical channel models and their
+practice-oriented realizations. For security metrics, we utilize
+cross-correlation, mutual information, and a lower bound on secret-key
+capacity. We design a practical setup of three parties such that the channel
+statistics, although based on joint randomness, are always reproducible. We run
+experiments to obtain channel states and evaluate the aforementioned metrics
+for the impact of an attacker depending on his position. It turns out the
+attacker himself affects the outcome, which has not been adequately regarded
+yet in standard channel models.
+",1,0,1,0,0,0
+11035,On dimensions supporting a rational projective plane,"  A rational projective plane ($\mathbb{QP}^2$) is a simply connected, smooth,
+closed manifold $M$ such that $H^*(M;\mathbb{Q}) \cong
+\mathbb{Q}[\alpha]/\langle \alpha^3 \rangle$. An open problem is to classify
+the dimensions at which such a manifold exists. The Barge-Sullivan rational
+surgery realization theorem provides necessary and sufficient conditions that
+include the Hattori-Stong integrality conditions on the Pontryagin numbers. In
+this article, we simplify these conditions and combine them with the signature
+equation to give a single quadratic residue equation that determines whether a
+given dimension supports a $\mathbb{QP}^2$. We then confirm existence of a
+$\mathbb{QP}^2$ in two new dimensions and prove several non-existence results
+using factorizations of numerators of divided Bernoulli numbers. We also
+resolve the existence question in the Spin case, and we discuss existence
+results for the more general class of rational projective spaces.
+",0,0,1,0,0,0
+11036,Implicit Causal Models for Genome-wide Association Studies,"  Progress in probabilistic generative models has accelerated, developing
+richer models with neural architectures, implicit densities, and with scalable
+algorithms for their Bayesian inference. However, there has been limited
+progress in models that capture causal relationships, for example, how
+individual genetic factors cause major human diseases. In this work, we focus
+on two challenges in particular: How do we build richer causal models, which
+can capture highly nonlinear relationships and interactions between multiple
+causes? How do we adjust for latent confounders, which are variables
+influencing both cause and effect and which prevent learning of causal
+relationships? To address these challenges, we synthesize ideas from causality
+and modern probabilistic modeling. For the first, we describe implicit causal
+models, a class of causal models that leverages neural architectures with an
+implicit density. For the second, we describe an implicit causal model that
+adjusts for confounders by sharing strength across examples. In experiments, we
+scale Bayesian inference on up to a billion genetic measurements. We achieve
+state of the art accuracy for identifying causal factors: we significantly
+outperform existing genetics methods by an absolute difference of 15-45.3%.
+",1,0,0,1,0,0
+11037,Vector Quantization as Sparse Least Square Optimization,"  Vector quantization aims to form new vectors/matrices with shared values
+close to the original. It could compress data with acceptable information loss
+and could be of great usefulness in areas like Image Processing, Pattern
+Recognition, and Machine Learning. In this paper, the problem of vector
+quantization is examined from a new perspective, namely sparse least square
+optimization. Specifically, inspired by the property of sparsity of Lasso, a
+novel quantization algorithm based on $l_1$ least square is proposed and
+implemented. Similar schemes with $l_1 + l_2$ combination penalization and
+$l_0$ regularization are simultaneously proposed. In addition, to produce
+quantization results with given amount of quantized values(instead of
+penalization coefficient $\lambda$), this paper proposed an iterative sparse
+least square method and a cluster-based least square quantization method. It is
+also noticed that the later method is mathematically equivalent to an improved
+version of the existed clustering-based quantization algorithm, although the
+two algorithms originated from different intuitions. The algorithms proposed
+were tested under three scenarios of data and their computational performance,
+including information loss, time consumption and the distribution of the value
+of sparse vectors were compared and analyzed. The paper offers a new
+perspective to probe the area of vector quantization, and the algorithms
+proposed could offer better performance especially when the required
+post-quantization value amounts are not on a tiny scale.
+",1,0,0,1,0,0
+11038,The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios,"  The effects of the spatial scale on the results of the optimisation of
+transmission and generation capacity in Europe are quantified under a 95% CO2
+reduction compared to 1990 levels, interpolating between one-node-per-country
+solutions and many-nodes-per-country. The trade-offs that come with higher
+spatial detail between better exposure of transmission bottlenecks,
+exploitation of sites with good renewable resources (particularly wind power)
+and computational limitations are discussed. It is shown that solutions with no
+grid expansion beyond today's capacities are only around 20% more expensive
+than with cost-optimal grid expansion.
+",0,1,0,0,0,0
+11039,Recovery Guarantees for One-hidden-layer Neural Networks,"  In this paper, we consider regression problems with one-hidden-layer neural
+networks (1NNs). We distill some properties of activation functions that lead
+to $\mathit{local~strong~convexity}$ in the neighborhood of the ground-truth
+parameters for the 1NN squared-loss objective. Most popular nonlinear
+activation functions satisfy the distilled properties, including rectified
+linear units (ReLUs), leaky ReLUs, squared ReLUs and sigmoids. For activation
+functions that are also smooth, we show $\mathit{local~linear~convergence}$
+guarantees of gradient descent under a resampling rule. For homogeneous
+activations, we show tensor methods are able to initialize the parameters to
+fall into the local strong convexity region. As a result, tensor initialization
+followed by gradient descent is guaranteed to recover the ground truth with
+sample complexity $ d \cdot \log(1/\epsilon) \cdot \mathrm{poly}(k,\lambda )$
+and computational complexity $n\cdot d \cdot \mathrm{poly}(k,\lambda) $ for
+smooth homogeneous activations with high probability, where $d$ is the
+dimension of the input, $k$ ($k\leq d$) is the number of hidden nodes,
+$\lambda$ is a conditioning property of the ground-truth parameter matrix
+between the input layer and the hidden layer, $\epsilon$ is the targeted
+precision and $n$ is the number of samples. To the best of our knowledge, this
+is the first work that provides recovery guarantees for 1NNs with both sample
+complexity and computational complexity $\mathit{linear}$ in the input
+dimension and $\mathit{logarithmic}$ in the precision.
+",1,0,0,1,0,0
+11040,Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks,"  With the rising number of interconnected devices and sensors, modeling
+distributed sensor networks is of increasing interest. Recurrent neural
+networks (RNN) are considered particularly well suited for modeling sensory and
+streaming data. When predicting future behavior, incorporating information from
+neighboring sensor stations is often beneficial. We propose a new RNN based
+architecture for context specific information fusion across multiple spatially
+distributed sensor stations. Hereby, latent representations of multiple local
+models, each modeling one sensor station, are jointed and weighted, according
+to their importance for the prediction. The particular importance is assessed
+depending on the current context using a separate attention function. We
+demonstrate the effectiveness of our model on three different real-world sensor
+network datasets.
+",1,0,0,1,0,0
+11041,Kharita: Robust Map Inference using Graph Spanners,"  The widespread availability of GPS information in everyday devices such as
+cars, smartphones and smart watches make it possible to collect large amount of
+geospatial trajectory information. A particularly important, yet technically
+challenging, application of this data is to identify the underlying road
+network and keep it updated under various changes. In this paper, we propose
+efficient algorithms that can generate accurate maps in both batch and online
+settings. Our algorithms utilize techniques from graph spanners so that they
+produce maps can effectively handle a wide variety of road and intersection
+shapes. We conduct a rigorous evaluation of our algorithms over two real-world
+datasets and under a wide variety of performance metrics. Our experiments show
+a significant improvement over prior work. In particular, we observe an
+increase in Biagioni f-score of up to 20% when compared to the state of the art
+while reducing the execution time by an order of magnitude. We also make our
+source code open source for reproducibility and enable other researchers to
+build on our work.
+",1,0,0,0,0,0
+11042,DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks,"  Information extraction and user intention identification are central topics
+in modern query understanding and recommendation systems. In this paper, we
+propose DeepProbe, a generic information-directed interaction framework which
+is built around an attention-based sequence to sequence (seq2seq) recurrent
+neural network. DeepProbe can rephrase, evaluate, and even actively ask
+questions, leveraging the generative ability and likelihood estimation made
+possible by seq2seq models. DeepProbe makes decisions based on a derived
+uncertainty (entropy) measure conditioned on user inputs, possibly with
+multiple rounds of interactions. Three applications, namely a rewritter, a
+relevance scorer and a chatbot for ad recommendation, were built around
+DeepProbe, with the first two serving as precursory building blocks for the
+third. We first use the seq2seq model in DeepProbe to rewrite a user query into
+one of standard query form, which is submitted to an ordinary recommendation
+system. Secondly, we evaluate DeepProbe's seq2seq model-based relevance
+scoring. Finally, we build a chatbot prototype capable of making active user
+interactions, which can ask questions that maximize information gain, allowing
+for a more efficient user intention idenfication process. We evaluate first two
+applications by 1) comparing with baselines by BLEU and AUC, and 2) human judge
+evaluation. Both demonstrate significant improvements compared with current
+state-of-the-art systems, proving their values as useful tools on their own,
+and at the same time laying a good foundation for the ongoing chatbot
+application.
+",1,0,0,1,0,0
+11043,Dictionary-based Monitoring of Premature Ventricular Contractions: An Ultra-Low-Cost Point-of-Care Service,"  While cardiovascular diseases (CVDs) are prevalent across economic strata,
+the economically disadvantaged population is disproportionately affected due to
+the high cost of traditional CVD management. Accordingly, developing an
+ultra-low-cost alternative, affordable even to groups at the bottom of the
+economic pyramid, has emerged as a societal imperative. Against this backdrop,
+we propose an inexpensive yet accurate home-based electrocardiogram(ECG)
+monitoring service. Specifically, we seek to provide point-of-care monitoring
+of premature ventricular contractions (PVCs), high frequency of which could
+indicate the onset of potentially fatal arrhythmia. Note that a traditional
+telecardiology system acquires the ECG, transmits it to a professional
+diagnostic centre without processing, and nearly achieves the diagnostic
+accuracy of a bedside setup, albeit at high bandwidth cost. In this context, we
+aim at reducing cost without significantly sacrificing reliability. To this
+end, we develop a dictionary-based algorithm that detects with high sensitivity
+the anomalous beats only which are then transmitted. We further compress those
+transmitted beats using class-specific dictionaries subject to suitable
+reconstruction/diagnostic fidelity. Such a scheme would not only reduce the
+overall bandwidth requirement, but also localising anomalous beats, thereby
+reducing physicians' burden. Finally, using Monte Carlo cross validation on
+MIT/BIH arrhythmia database, we evaluate the performance of the proposed
+system. In particular, with a sensitivity target of at most one undetected PVC
+in one hundred beats, and a percentage root mean squared difference less than
+9% (a clinically acceptable level of fidelity), we achieved about 99.15%
+reduction in bandwidth cost, equivalent to 118-fold savings over traditional
+telecardiology.
+",1,0,0,0,0,0
+11044,Temporal Multimodal Fusion for Video Emotion Classification in the Wild,"  This paper addresses the question of emotion classification. The task
+consists in predicting emotion labels (taken among a set of possible labels)
+best describing the emotions contained in short video clips. Building on a
+standard framework -- lying in describing videos by audio and visual features
+used by a supervised classifier to infer the labels -- this paper investigates
+several novel directions. First of all, improved face descriptors based on 2D
+and 3D Convo-lutional Neural Networks are proposed. Second, the paper explores
+several fusion methods, temporal and multimodal, including a novel hierarchical
+method combining features and scores. In addition, we carefully reviewed the
+different stages of the pipeline and designed a CNN architecture adapted to the
+task; this is important as the size of the training set is small compared to
+the difficulty of the problem, making generalization difficult. The so-obtained
+model ranked 4th at the 2017 Emotion in the Wild challenge with the accuracy of
+58.8 %.
+",1,0,0,0,0,0
+11045,Bonding charge distribution analysis of molecule by computation of interatomic charge penetration,"  Charge transfer among individual atoms in a molecule is the key concept in
+the modern electronic theory of chemical bonding. In this work, we defined an
+atomic region between two atoms by Slater orbital exponents of valence
+electrons and suggested a method for analytical calculation of charge
+penetration between all atoms in a molecule. Computation of charge penetration
+amount is self-consistently performed until each orbital exponent converges to
+its certain values respectively. Charge penetration matrix was calculated for
+ethylene and MgO, and bonding charge and its distribution were analyzed by
+using the charge penetration matrix and the orbital exponents under the bonding
+state. These results were compared with those by density function method and
+showed that this method is a simple and direct method to obtain bonding charge
+distribution of molecule from atomic orbital functions.
+",0,1,0,0,0,0
+11046,Shatter functions with polynomial growth rates,"  We study how a single value of the shatter function of a set system restricts
+its asymptotic growth. Along the way, we refute a conjecture of Bondy and
+Hajnal which generalizes Sauer's Lemma.
+",1,0,1,0,0,0
+11047,On Newstead's Mayer-Vietoris argument in characteristic 2,"  Consider the moduli space of framed flat $U(2)$ connections with fixed odd
+determinant over a surface. Newstead combined some fundamental facts about this
+moduli space with the Mayer-Vietoris sequence to compute its betti numbers over
+any field not of characteristic two. We adapt his method in characteristic two
+to produce conjectural recursive formulae for the mod two betti numbers of the
+framed moduli space which we partially verify. We also discuss the interplay
+with the mod two cohomology ring structure of the unframed moduli space.
+",0,0,1,0,0,0
+11048,Hunting high and low: Disentangling primordial and late-time non-Gaussianity with cosmic densities in spheres,"  Non-Gaussianities of dynamical origin are disentangled from primordial ones
+using the formalism of large deviation statistics with spherical collapse
+dynamics. This is achieved by relying on accurate analytical predictions for
+the one-point probability distribution function (PDF) and the two-point
+clustering of spherically-averaged cosmic densities (sphere bias). Sphere bias
+extends the idea of halo bias to intermediate density environments and voids as
+underdense regions. In the presence of primordial non-Gaussianity, sphere bias
+displays a strong scale dependence relevant for both high and low density
+regions, which is predicted analytically. The statistics of densities in
+spheres are built to model primordial non-Gaussianity via an initial skewness
+with a scale-dependence that depends on the bispectrum of the underlying model.
+The analytical formulas with the measured nonlinear dark matter variance as
+input are successfully tested against numerical simulations. For local
+non-Gaussianity with a range from $f_{\rm NL}=-100$ to $+100$ they are found to
+agree within 2\% or better for densities $\rho\in[0.5,3]$ in spheres of radius
+15 Mpc$/h$ down to $z=0.35$. The validity of the large deviation statistics
+formalism is thereby established for all observationally relevant local-type
+departures from perfectly Gaussian initial conditions. The corresponding
+estimators for the amplitude of the nonlinear variance $\sigma_8$ and
+primordial skewness $f_{\rm NL}$ are validated using a fiducial joint maximum
+likelihood experiment. The influence of observational effects and the prospects
+for a future detection of primordial non-Gaussianity from joint one- and
+two-point densities-in-spheres statistics are discussed.
+",0,1,0,0,0,0
+11049,Measure Theory and Integration By and For the Learner,"  Measure Theory and Integration is exposed with the clear aim to help
+beginning learners to perfectly master its essence. In opposition of a delivery
+of the contents in an academic and vertical course, the knowledge is broken
+into exercises which are left to the learners for solutions. Hints are present
+at any corner to help readers to achieve the solutions. In that way, the
+knowledge is constructed by the readers by summarizing the results of one or a
+group of exercises.
+Each chapter is organized into Summary documents which contain the knowledge,
+Discovery documents which give the learner the opportunity to extract the
+knowledge himself through exercises and into Solution Documents which offer
+detailed answers for the exercises. Exceptionally, a few number of results (A
+key lemma related the justification of definition of the integral of a
+non-negative function, the Caratheodory's theorem and the Lebesgue-Stieljes
+measure on $\mathbb{R}^d$) are presented in appendix documents and given for
+reading in small groups.
+The full theory is presented in the described way. We highly expect that any
+student who goes through the materials, alone or in a small group or under the
+supervision of an assistant will gain a very solid knowledge in the subject and
+by the way ensure a sound foundation for studying disciplines such as
+Probability Theory, Statistics, Functional Analysis, etc.
+The materials have been successfully used as such in normal real analysis
+classes several times.
+",0,0,1,0,0,0
+11050,Target Tracking for Contextual Bandits: Application to Demand Side Management,"  We propose a contextual-bandit approach for demand side management by
+offering price incentives. More precisely, a target mean consumption is set at
+each round and the mean consumption is modeled as a complex function of the
+distribution of prices sent and of some contextual variables such as the
+temperature, weather, and so on. The performance of our strategies is measured
+in quadratic losses through a regret criterion. We offer $\sqrt{T}$ upper
+bounds on this regret (up to poly-logarithmic terms), for strategies inspired
+by standard strategies for contextual bandits (like LinUCB, Li et al., 2010).
+Simulations on a real data set gathered by UK Power Networks, in which price
+incentives were offered, show that our strategies are effective and may indeed
+manage demand response by suitably picking the price levels.
+",1,0,0,1,0,0
+11051,Quantizing deep convolutional networks for efficient inference: A whitepaper,"  We present an overview of techniques for quantizing convolutional neural
+networks for inference with integer weights and activations. Per-channel
+quantization of weights and per-layer quantization of activations to 8-bits of
+precision post-training produces classification accuracies within 2% of
+floating point networks for a wide variety of CNN architectures. Model sizes
+can be reduced by a factor of 4 by quantizing weights to 8-bits, even when
+8-bit arithmetic is not supported. This can be achieved with simple, post
+training quantization of weights.We benchmark latencies of quantized networks
+on CPUs and DSPs and observe a speedup of 2x-3x for quantized implementations
+compared to floating point on CPUs. Speedups of up to 10x are observed on
+specialized processors with fixed point SIMD capabilities, like the Qualcomm
+QDSPs with HVX.
+Quantization-aware training can provide further improvements, reducing the
+gap to floating point to 1% at 8-bit precision. Quantization-aware training
+also allows for reducing the precision of weights to four bits with accuracy
+losses ranging from 2% to 10%, with higher accuracy drop for smaller
+networks.We introduce tools in TensorFlow and TensorFlowLite for quantizing
+convolutional networks and review best practices for quantization-aware
+training to obtain high accuracy with quantized weights and activations. We
+recommend that per-channel quantization of weights and per-layer quantization
+of activations be the preferred quantization scheme for hardware acceleration
+and kernel optimization. We also propose that future processors and hardware
+accelerators for optimized inference support precisions of 4, 8 and 16 bits.
+",0,0,0,1,0,0
+11052,Angle-dependent electron spin resonance of YbRh$_2$Si$_2$ measured with planar microwave resonators and in-situ rotation,"  We present a new experimental approach to investigate the magnetic properties
+of the anisotropic heavy-fermion system YbRh$_2$Si$_2$ as a function of
+crystallographic orientation. Angle-dependent electron spin resonance (ESR)
+measurements are performed at a low temperature of 1.6 K and at an ESR
+frequency of 4.4 GHz utilizing a superconducting planar microwave resonator in
+a $^4$He-cryostat in combination with in-situ sample rotation. The obtained ESR
+g-factor of YbRh$_2$Si$_2$ as a function of the crystallographic angle is
+consistent with results of previous measurements using conventional ESR
+spectrometers at higher frequencies and fields. Perspectives to implement this
+experimental approach into a dilution refrigerator and to reach the
+magnetically ordered phase of YbRh$_2$Si$_2$ are discussed.
+",0,1,0,0,0,0
+11053,Role of Kohn-Sham Kinetic Energy Density in Designing Asymptotically Correct Semilocal Exchange-Correlation Functionals in Two Dimensions,"  The positive definite Kohn-Sham kinetic energy(KS-KE) density plays crucial
+role in designing semilocal meta generalized gradient approximations(meta-GGAs)
+for low dimensional quantum systems. It has been rigorously shown that near
+nucleus and at the asymptotic region, the KE-KS differ from its von
+Weizsäcker(VW) counterpart as contributions from different orbitals (i.e.,
+s and p orbitals) play important role. This has been explored using two
+dimensional isotropic quantum harmonic oscillator as a test case. Several
+meta-GGA ingredients with different physical behaviors are also constructed and
+further used to design an accurate semilocal functionals at meta-GGA level. In
+the asymptotic region, a new exchange energy functional is constructed using
+the meta-GGA ingredients with formally exact properties of the enhancement
+factor. Also, it has been shown that exact asymptotic behavior of the exchange
+energy density and potential can be attained by choosing accurately the
+enhancement factor as a functional of meta-GGA ingredients.
+",0,1,0,0,0,0
+11054,BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs,"  In this paper we describe our attempt at producing a state-of-the-art Twitter
+sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short
+Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled
+data to pre-train word embeddings. We then use a subset of the unlabeled data
+to fine tune the embeddings using distant supervision. The final CNNs and LSTMs
+are trained on the SemEval-2017 Twitter dataset where the embeddings are fined
+tuned again. To boost performances we ensemble several CNNs and LSTMs together.
+Our approach achieved first rank on all of the five English subtasks amongst 40
+teams.
+",1,0,0,1,0,0
+11055,Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis,"  Discerning how a mutation affects the stability of a protein is central to
+the study of a wide range of diseases. Machine learning and statistical
+analysis techniques can inform how to allocate limited resources to the
+considerable time and cost associated with wet lab mutagenesis experiments. In
+this work we explore the effectiveness of using a neural network classifier to
+predict the change in the stability of a protein due to a mutation. Assessing
+the accuracy of our approach is dependent on the use of experimental data about
+the effects of mutations performed in vitro. Because the experimental data is
+prone to discrepancies when similar experiments have been performed by multiple
+laboratories, the use of the data near the juncture of stabilizing and
+destabilizing mutations is questionable. We address this later problem via a
+systematic approach in which we explore the use of a three-way classification
+scheme with stabilizing, destabilizing, and inconclusive labels. For a
+systematic search of potential classification cutoff values our classifier
+achieved 68 percent accuracy on ternary classification for cutoff values of
+-0.6 and 0.7 with a low rate of classifying stabilizing as destabilizing and
+vice versa.
+",0,0,0,0,1,0
+11056,Starspot activity and superflares on solar-type stars,"  We analyze the correlation between starspots and superflares on solar-type
+stars using observations from the Kepler mission. The analysis shows that the
+observed fraction of stars with superflares decreases as the rotation period
+increases and as the amplitude of photometric variability associated with
+rotation decreases. We found that the fraction of stars with superflares among
+the stars showing large-amplitude rotational variations, which are thought to
+be the signature of the large starspots, also decreases as the rotation period
+increases. The small fraction of superflare stars among the stars with large
+starspots in the longer-period regime suggests that some of the stars with
+large starspots show a much lower flare activity than the superflare stars with
+the same spot area. Assuming simple relations between spot area and lifetime
+and between spot temperature and photospheric temperature, we compared the size
+distribution of large starspot groups on slowly-rotating solar-type stars with
+that of sunspot groups. The size distribution of starspots shows the power-law
+distribution and the size distribution of larger sunspots lies on this
+power-law line. We also found that frequency-energy distributions for flares
+originating from spots with different sizes are the same for solar-type stars
+with superflares and the Sun. These results suggest that the magnetic activity
+we observe on solar-type stars with superflares and that on the Sun is caused
+by the same physical processes.
+",0,1,0,0,0,0
+11057,A Contractive Approach to Separable Lyapunov Functions for Monotone Systems,"  Monotone systems preserve a partial ordering of states along system
+trajectories and are often amenable to separable Lyapunov functions that are
+either the sum or the maximum of a collection of functions of a scalar
+argument. In this paper, we consider constructing separable Lyapunov functions
+for monotone systems that are also contractive, that is, the distance between
+any pair of trajectories exponentially decreases. The distance is defined in
+terms of a possibly state-dependent norm. When this norm is a weighted
+one-norm, we obtain conditions which lead to sum-separable Lyapunov functions,
+and when this norm is a weighted infinity-norm, symmetric conditions lead to
+max-separable Lyapunov functions. In addition, we consider two classes of
+Lyapunov functions: the first class is separable along the system's state, and
+the second class is separable along components of the system's vector field.
+The latter case is advantageous for many practically motivated systems for
+which it is difficult to measure the system's state but easier to measure the
+system's velocity or rate of change. In addition, we present an algorithm based
+on sum-of-squares programming to compute such separable Lyapunov functions. We
+provide several examples to demonstrate our results.
+",1,0,0,0,0,0
+11058,Isotopic ratios in outbursting comet C/2015 ER61,"  Isotopic ratios in comets are critical to understanding the origin of
+cometary material and the physical and chemical conditions in the early solar
+nebula. Comet C/2015 ER61 (PANSTARRS) underwent an outburst with a total
+brightness increase of 2 magnitudes on the night of 2017 April 4. The sharp
+increase in brightness offered a rare opportunity to measure the isotopic
+ratios of the light elements in the coma of this comet. We obtained two
+high-resolution spectra of C/2015 ER61 with UVES/VLT on the nights of 2017
+April 13 and 17. At the time of our observations, the comet was fading
+gradually following the outburst. We measured the nitrogen and carbon isotopic
+ratios from the CN violet (0,0) band and found that $^{12}$C/$^{13}$C=100 $\pm$
+15, $^{14}$N/$^{15}$N=130 $\pm$ 15. In addition, we determined the
+$^{14}$N/$^{15}$N ratio from four pairs of NH$_2$ isotopolog lines and measured
+$^{14}$N/$^{15}$N=140 $\pm$ 28. The measured isotopic ratios of C/2015 ER61 do
+not deviate significantly from those of other comets.
+",0,1,0,0,0,0
+11059,Muon detector for the COSINE-100 experiment,"  The COSINE-100 dark matter search experiment has started taking physics data
+with the goal of performing an independent measurement of the annual modulation
+signal observed by DAMA/LIBRA. A muon detector was constructed by using plastic
+scintillator panels in the outermost layer of the shield surrounding the
+COSINE-100 detector. It is used to detect cosmic ray muons in order to
+understand the impact of the muon annual modulation on dark matter analysis.
+Assembly and initial performance test of each module have been performed at a
+ground laboratory. The installation of the detector in Yangyang Underground
+Laboratory (Y2L) was completed in the summer of 2016. Using three months of
+data, the muon underground flux was measured to be 328 $\pm$ 1(stat.)$\pm$
+10(syst.) muons/m$^2$/day. In this report, the assembly of the muon detector
+and the results from the analysis are presented.
+",0,1,0,0,0,0
+11060,Levitated optomechanics with a fiber Fabry-Perot interferometer,"  In recent years quantum phenomena have been experimentally demonstrated on
+variety of optomechanical systems ranging from micro-oscillators to photonic
+crystals. Since single photon couplings are quite small, most experimental
+approaches rely on the realization of high finesse Fabry-Perot cavities in
+order to enhance the effective coupling. Here we show that by exploiting a,
+long path, low finesse fiber Fabry-Perot interferometer ground state cooling
+can be achieved. We model a 100 m long cavity with a finesse of 10 and analyze
+the impact of additional noise sources arising from the fiber. As a mechanical
+oscillator we consider a levitated microdisk but the same approach could be
+applied to other optomechanical systems.
+",0,1,0,0,0,0
+11061,Spatial cytoskeleton organization supports targeted intracellular transport,"  The efficiency of intracellular cargo transport from specific source to
+target locations is strongly dependent upon molecular motor-assisted motion
+along the cytoskeleton. Radial transport along microtubules and lateral
+transport along the filaments of the actin cortex underneath the cell membrane
+are characteristic for cells with a centrosome. The interplay between the
+specific cytoskeleton organization and the motor performance realizes a
+spatially inhomogeneous intermittent search strategy. In order to analyze the
+efficiency of such intracellular search strategies we formulate a random
+velocity model with intermittent arrest states. We evaluate efficiency in terms
+of mean first passage times for three different, frequently encountered
+intracellular transport tasks: i) the narrow escape problem, which emerges
+during cargo transport to a synapse or other specific region of the cell
+membrane, ii) the reaction problem, which considers the binding time of two
+particles within the cell, and iii) the reaction-escape problem, which arises
+when cargo must be released at a synapse only after pairing with another
+particle. Our results indicate that cells are able to realize efficient search
+strategies for various intracellular transport tasks economically through a
+spatial cytoskeleton organization that involves only a narrow actin cortex
+rather than a cell body filled with randomly oriented actin filaments.
+",0,1,0,0,0,0
+11062,Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation,"  Deep learning models have consistently outperformed traditional machine
+learning models in various classification tasks, including image
+classification. As such, they have become increasingly prevalent in many real
+world applications including those where security is of great concern. Such
+popularity, however, may attract attackers to exploit the vulnerabilities of
+the deployed deep learning models and launch attacks against security-sensitive
+applications. In this paper, we focus on a specific type of data poisoning
+attack, which we refer to as a {\em backdoor injection attack}. The main goal
+of the adversary performing such attack is to generate and inject a backdoor
+into a deep learning model that can be triggered to recognize certain embedded
+patterns with a target label of the attacker's choice. Additionally, a backdoor
+injection attack should occur in a stealthy manner, without undermining the
+efficacy of the victim model. Specifically, we propose two approaches for
+generating a backdoor that is hardly perceptible yet effective in poisoning the
+model. We consider two attack settings, with backdoor injection carried out
+either before model training or during model updating. We carry out extensive
+experimental evaluations under various assumptions on the adversary model, and
+demonstrate that such attacks can be effective and achieve a high attack
+success rate (above $90\%$) at a small cost of model accuracy loss (below
+$1\%$) with a small injection rate (around $1\%$), even under the weakest
+assumption wherein the adversary has no knowledge either of the original
+training data or the classifier model.
+",0,0,0,1,0,0
+11063,Chiral and Topological Orbital Magnetism of Spin Textures,"  Using a semiclassical Green's function formalism, we discover the emergence
+of chiral and topological orbital magnetism in two-dimensional chiral spin
+textures by explicitly finding the corrections to the orbital magnetization,
+proportional to the powers of the gradients of the texture. We show that in the
+absence of spin-orbit coupling, the resulting orbital moment can be understood
+as the electronic response to the emergent magnetic field associated with the
+real-space Berry curvature. By referring to the Rashba model, we demonstrate
+that by tuning the parameters of surface systems the engineering of emergent
+orbital magnetism in spin textures can pave the way to novel concepts in
+orbitronics.
+",0,1,0,0,0,0
+11064,The Mira-Titan Universe II: Matter Power Spectrum Emulation,"  We introduce a new cosmic emulator for the matter power spectrum covering
+eight cosmological parameters. Targeted at optical surveys, the emulator
+provides accurate predictions out to a wavenumber k~5/Mpc and redshift z<=2.
+Besides covering the standard set of LCDM parameters, massive neutrinos and a
+dynamical dark energy of state are included. The emulator is built on a sample
+set of 36 cosmological models, carefully chosen to provide accurate predictions
+over the wide and large parameter space. For each model, we have performed a
+high-resolution simulation, augmented with sixteen medium-resolution
+simulations and TimeRG perturbation theory results to provide accurate coverage
+of a wide k-range; the dataset generated as part of this project is more than
+1.2Pbyte. With the current set of simulated models, we achieve an accuracy of
+approximately 4%. Because the sampling approach used here has established
+convergence and error-control properties, follow-on results with more than a
+hundred cosmological models will soon achieve ~1% accuracy. We compare our
+approach with other prediction schemes that are based on halo model ideas and
+remapping approaches. The new emulator code is publicly available.
+",0,1,0,0,0,0
+11065,k-Anonymously Private Search over Encrypted Data,"  In this paper we compare the performance of various homomorphic encryption
+methods on a private search scheme that can achieve $k$-anonymity privacy. To
+make our benchmarking fair, we use open sourced cryptographic libraries which
+are written by experts and well scrutinized. We find that Goldwasser-Micali
+encryption achieves good enough performance for practical use, whereas fully
+homomorphic encryptions are much slower than partial ones like
+Goldwasser-Micali and Paillier.
+",1,0,0,0,0,0
+11066,Stability of Correction Procedure via Reconstruction With Summation-by-Parts Operators for Burgers' Equation Using a Polynomial Chaos Approach,"  In this paper, we consider Burgers' equation with uncertain boundary and
+initial conditions. The polynomial chaos (PC) approach yields a hyperbolic
+system of deterministic equations, which can be solved by several numerical
+methods. Here, we apply the correction procedure via reconstruction (CPR) using
+summation-by-parts operators. We focus especially on stability, which is proven
+for CPR methods and the systems arising from the PC approach. Due to the usage
+of split-forms, the major challenge is to construct entropy stable numerical
+fluxes. For the first time, such numerical fluxes are constructed for all
+systems resulting from the PC approach for Burgers' equation. In numerical
+tests, we verify our results and show also the advantage of the given ansatz
+using CPR methods. Moreover, one of the simulations, i.e. Burgers' equation
+equipped with an initial shock, demonstrates quite fascinating observations.
+The behaviour of the numerical solutions from several methods (finite volume,
+finite difference, CPR) differ significantly from each other. Through careful
+investigations, we conclude that the reason for this is the high sensitivity of
+the system to varying dissipation. Furthermore, it should be stressed that the
+system is not strictly hyperbolic with genuinely nonlinear or linearly
+degenerate fields.
+",0,0,1,0,0,0
+11067,Special Solutions of Bi-Riccati Delay-Differential Equations,"  Delay-differential equations are functional differential equations that
+involve shifts and derivatives with respect to a single independent variable.
+Some integrability candidates in this class have been identified by various
+means. For three of these equations we consider their elliptic and soliton-type
+solutions. Using Hirota's bilinear method, we find that two of our equations
+possess three-soliton-type solutions.
+",0,1,1,0,0,0
+11068,Signatures of the Kondo effect in VSe2,"  VSe2 is a transition metal dichaclogenide which has a charge-density wave
+transition that has been well studied. We report on a low-temperature upturn in
+the resistivity and, at temperatures below this resistivity minimum, an unusual
+magnetoresistance which is negative at low fields and positive at higher
+fields, in single crystals of VSe2. The negative magnetoresistance has a
+parabolic dependence on the magnetic field and shows little angular dependence.
+The magnetoresistance at temperatures above the resistivity minimum is always
+positive. We interpret these results as signatures of the Kondo effect in VSe2.
+An upturn in the susceptibility indicates the presence of interlayer V ions
+which can provide the localized magnetic moments required for scattering the
+conduction electrons in the Kondo effect. The low-temperature behaviour of the
+heat capacity, including a high value of gamma, along with a deviation from a
+Curie-Weiss law observed in the low-temperature magnetic susceptibility, are
+consistent with the presence of magnetic interactions between the paramagnetic
+interlayer V ions and a Kondo screening of these V moments.
+",0,1,0,0,0,0
+11069,A Procedural Texture Generation Framework Based on Semantic Descriptions,"  Procedural textures are normally generated from mathematical models with
+parameters carefully selected by experienced users. However, for naive users,
+the intuitive way to obtain a desired texture is to provide semantic
+descriptions such as ""regular,"" ""lacelike,"" and ""repetitive"" and then a
+procedural model with proper parameters will be automatically suggested to
+generate the corresponding textures. By contrast, it is less practical for
+users to learn mathematical models and tune parameters based on multiple
+examinations of large numbers of generated textures. In this study, we propose
+a novel framework that generates procedural textures according to user-defined
+semantic descriptions, and we establish a mapping between procedural models and
+semantic texture descriptions. First, based on a vocabulary of semantic
+attributes collected from psychophysical experiments, a multi-label learning
+method is employed to annotate a large number of textures with semantic
+attributes to form a semantic procedural texture dataset. Then, we derive a low
+dimensional semantic space in which the semantic descriptions can be separated
+from one other. Finally, given a set of semantic descriptions, the diverse
+properties of the samples in the semantic space can lead the framework to find
+an appropriate generation model that uses appropriate parameters to produce a
+desired texture. The experimental results show that the proposed framework is
+effective and that the generated textures closely correlate with the input
+semantic descriptions.
+",1,0,0,0,0,0
+11070,Inward Migration of the TRAPPIST-1 Planets as Inferred From Their Water-Rich Compositions,"  Multiple planet systems provide an ideal laboratory for probing exoplanet
+composition, formation history and potential habitability. For the TRAPPIST-1
+planets, the planetary radii are well established from transits (Gillon et al.,
+2016, Gillon et al., 2017), with reasonable mass estimates coming from transit
+timing variations (Gillon et al., 2017, Wang et al., 2017) and dynamical
+modeling (Quarles et al., 2017). The low bulk densities of the TRAPPIST-1
+planets demand significant volatile content. Here we show using
+mass-radius-composition models, that TRAPPIST-1f and g likely contain
+substantial ($\geq50$ wt\%) water/ice, with b and c being significantly drier
+($\leq15$ wt\%). We propose this gradient of water mass fractions implies
+planets f and g formed outside the primordial snow line whereas b and c formed
+inside. We find that compared to planets in our solar system that also formed
+within the snow line, TRAPPIST-1b and c contain hundreds more oceans worth of
+water. We demonstrate the extent and timescale of migration in the TRAPPIST-1
+system depends on how rapidly the planets formed and the relative location of
+the primordial snow line. This work provides a framework for understanding the
+differences between the protoplanetary disks of our solar system versus M
+dwarfs. Our results provide key insights into the volatile budgets, timescales
+of planet formation, and migration history of likely the most common planetary
+host in the Galaxy.
+",0,1,0,0,0,0
+11071,Bidirectional Conditional Generative Adversarial Networks,"  Conditional Generative Adversarial Networks (cGANs) are generative models
+that can produce data samples ($x$) conditioned on both latent variables ($z$)
+and known auxiliary information ($c$). We propose the Bidirectional cGAN
+(BiCoGAN), which effectively disentangles $z$ and $c$ in the generation process
+and provides an encoder that learns inverse mappings from $x$ to both $z$ and
+$c$, trained jointly with the generator and the discriminator. We present
+crucial techniques for training BiCoGANs, which involve an extrinsic factor
+loss along with an associated dynamically-tuned importance weight. As compared
+to other encoder-based cGANs, BiCoGANs encode $c$ more accurately, and utilize
+$z$ and $c$ more effectively and in a more disentangled way to generate
+samples.
+",1,0,0,1,0,0
+11072,"Micromagnetic study of a feasibility of the magnetic anisotropy engineering in nano-structured epitaxial films of (III,Mn)V ferromagnetic semiconductors","  The attainability of modification of the apparent magnetic anisotropy in
+(III,Mn)V ferromagnetic semiconductors is probed by means of the
+finite-elements-based modelling. The most representative case of (Ga,Mn)As and
+its in-plane uniaxial anisotropy is investigated. The hysteresis loops of the
+continuous films of a ferromagnetic semiconductor as well as films structured
+with the elliptic antidots are modelled for various eccentricity, orientation,
+and separation of the anti dots. The effect of anti-dots on the magnetic
+anisotropy is confirmed but overall is found to be very weak. The subsequent
+modelling for (Ga,Mn)As film with the elliptic dots comprising of metallic NiFe
+shows much stronger effect, revealing switching of the magnetic moment in the
+ferromagnetic semiconductor governed by the switching behavior of the metallic
+inclusions.
+",0,1,0,0,0,0
+11073,On the exponential large sieve inequality for sparse sequences modulo primes,"  We complement the argument of M. Z. Garaev (2009) with several other ideas to
+obtain a stronger version of the large sieve inequality with sparse exponential
+sequences of the form $\lambda^{s_n}$. In particular, we obtain a result which
+is non-trivial for monotonically increasing sequences $\cal{S}=\{s_n
+\}_{n=1}^{\infty}$ provided $s_n\le n^{2+o(1)}$, whereas the original argument
+of M. Z. Garaev requires $s_n \le n^{15/14 +o(1)}$ in the same setting. We also
+give an application of our result to arithmetic properties of integers with
+almost all digits prescribed.
+",0,0,1,0,0,0
+11074,Excess conduction of YBaCuO point contacts between 100 and 200 K,"  $YBaCuO-Ag$ pressure point contacts with direct conduction are investigated.
+The excess (relative to the normal state) conductivity mainly caused by
+fluctuational pairing of electrons above $T_c$ is measured in the temperature
+interval 100-200~$K$. The superconductivity above 120~$K$ is found to be of the
+two-dimensional type. The obtained preliminary results indicate the presence of
+small amount of an unknown phase with $T'_c\gtrsim 200~K$ in $YBaCuO$.
+",0,1,0,0,0,0
+11075,Quivers with additive labelings: classification and algebraic entropy,"  We show that Zamolodchikov dynamics of a recurrent quiver has zero algebraic
+entropy only if the quiver has a weakly subadditive labeling, and conjecture
+the converse. By assigning a pair of generalized Cartan matrices of affine type
+to each quiver with an additive labeling, we completely classify such quivers,
+obtaining $40$ infinite families and $13$ exceptional quivers. This completes
+the program of classifying Zamolodchikov periodic and integrable quivers.
+",0,0,1,0,0,0
+11076,"Harnessing bistability for directional propulsion of untethered, soft robots","  In most macro-scale robotics systems , propulsion and controls are enabled
+through a physical tether or complex on-board electronics and batteries. A
+tether simplifies the design process but limits the range of motion of the
+robot, while on-board controls and power supplies are heavy and complicate the
+design process. Here we present a simple design principle for an untethered,
+entirely soft, swimming robot with the ability to achieve preprogrammed,
+directional propulsion without a battery or on-board electronics. Locomotion is
+achieved by employing actuators that harness the large displacements of
+bistable elements, triggered by surrounding temperature changes. Powered by
+shape memory polymer (SMP) muscles, the bistable elements in turn actuates the
+robot's fins. Our robots are fabricated entirely using a commercially available
+3D printer with no post-processing. As a proof-of-concept, we demonstrate the
+ability to program a vessel, which can autonomously deliver a cargo and
+navigate back to the deployment point.
+",1,0,0,0,0,0
+11077,Cs nDJ Rydberg-atom macrodimers formed by long-range multipole interaction,"  Long-range macrodimers formed by D-state cesium Rydberg atoms are studied in
+experiments and in calculations. Cesium 62DJ-62DJ Rydberg-atom macrodimers,
+bonded via long-range multipole interaction, are prepared by two-color
+photo-association in a cesium atom trap. The first color (pulse A) resonantly
+excites seed Rydberg atoms, while the second (pulse B, detuned by the molecular
+binding energy) resonantly excites the Rydberg-atom macrodimer states below the
+62DJ pair asymptotes. The Rydberg-atom molecules are measured by extraction of
+auto-ionization products and Rydberg-atom electric-field ionization, and ion
+detection. Molecular spectra are compared with calculations of adiabatic
+molecular potentials. The lifetime of the molecules is obtained from
+exponential fits to the dependence of the molecular signal on the detection
+delay time; lifetimes of about 6 us are found.
+",0,1,0,0,0,0
+11078,A Brief Review of Galactic Winds,"  Galactic winds from star-forming galaxies play at key role in the evolution
+of galaxies and the inter-galactic medium. They transport metals out of
+galaxies, chemically-enriching the inter-galactic medium and modifying the
+chemical evolution of galaxies. They affect the surrounding inter-stellar and
+circum-galactic media, thereby influencing the growth of galaxies through gas
+accretion and star-formation. In this contribution we first summarize the
+physical mechanisms by which the momentum and energy output from a population
+of massive stars and associated supernovae can drive galactic winds. We use the
+proto-typical example of M82 to illustrate the multiphase nature of galactic
+winds. We then describe how the basic properties of galactic winds are derived
+from the data, and summarize how the properties of galactic winds vary
+systematically with the properties of the galaxies that launch them. We
+conclude with a brief discussion of the broad implications of galactic winds.
+",0,1,0,0,0,0
+11079,Transfer Learning for Speech Recognition on a Budget,"  End-to-end training of automated speech recognition (ASR) systems requires
+massive data and compute resources. We explore transfer learning based on model
+adaptation as an approach for training ASR models under constrained GPU memory,
+throughput and training data. We conduct several systematic experiments
+adapting a Wav2Letter convolutional neural network originally trained for
+English ASR to the German language. We show that this technique allows faster
+training on consumer-grade resources while requiring less training data in
+order to achieve the same accuracy, thereby lowering the cost of training ASR
+models in other languages. Model introspection revealed that small adaptations
+to the network's weights were sufficient for good performance, especially for
+inner layers.
+",1,0,0,1,0,0
+11080,Information-Propogation-Enhanced Neural Machine Translation by Relation Model,"  Even though sequence-to-sequence neural machine translation (NMT) model have
+achieved state-of-art performance in the recent fewer years, but it is widely
+concerned that the recurrent neural network (RNN) units are very hard to
+capture the long-distance state information, which means RNN can hardly find
+the feature with long term dependency as the sequence becomes longer.
+Similarly, convolutional neural network (CNN) is introduced into NMT for
+speeding recently, however, CNN focus on capturing the local feature of the
+sequence; To relieve this issue, we incorporate a relation network into the
+standard encoder-decoder framework to enhance information-propogation in neural
+network, ensuring that the information of the source sentence can flow into the
+decoder adequately. Experiments show that proposed framework outperforms the
+statistical MT model and the state-of-art NMT model significantly on two data
+sets with different scales.
+",1,0,0,0,0,0
+11081,Neural Machine Translation and Sequence-to-sequence Models: A Tutorial,"  This tutorial introduces a new and powerful set of techniques variously
+called ""neural machine translation"" or ""neural sequence-to-sequence models"".
+These techniques have been used in a number of tasks regarding the handling of
+human language, and can be a powerful tool in the toolbox of anyone who wants
+to model sequential data of some sort. The tutorial assumes that the reader
+knows the basics of math and programming, but does not assume any particular
+experience with neural networks or natural language processing. It attempts to
+explain the intuition behind the various methods covered, then delves into them
+with enough mathematical detail to understand them concretely, and culiminates
+with a suggestion for an implementation exercise, where readers can test that
+they understood the content in practice.
+",1,0,0,1,0,0
+11082,Uncountable realtime probabilistic classes,"  We investigate the minimum cases for realtime probabilistic machines that can
+define uncountably many languages with bounded error. We show that logarithmic
+space is enough for realtime PTMs on unary languages. On binary case, we follow
+the same result for double logarithmic space, which is tight. When replacing
+the worktape with some limited memories, we can follow uncountable results on
+unary languages for two counters.
+",1,0,0,0,0,0
+11083,A combinatorial model for the path fibration,"  We introduce the abstract notion of a necklical set in order to describe a
+functorial combinatorial model of the path fibration over the geometric
+realization of a path connected simplicial set. In particular, to any path
+connected simplicial set $X$ we associate a necklical set
+$\widehat{\mathbf{\Omega}}X$ such that its geometric realization
+$|\widehat{\mathbf{\Omega}}X|$, a space built out of gluing cubical cells, is
+homotopy equivalent to the based loop space on $|X|$ and the differential
+graded module of chains $C_*(\widehat{\mathbf{\Omega}}X)$ is a differential
+graded associative algebra generalizing Adams' cobar construction.
+",0,0,1,0,0,0
+11084,A Systematic Evaluation of Static API-Misuse Detectors,"  Application Programming Interfaces (APIs) often have usage constraints, such
+as restrictions on call order or call conditions. API misuses, i.e., violations
+of these constraints, may lead to software crashes, bugs, and vulnerabilities.
+Though researchers developed many API-misuse detectors over the last two
+decades, recent studies show that API misuses are still prevalent. Therefore,
+we need to understand the capabilities and limitations of existing detectors in
+order to advance the state of the art. In this paper, we present the first-ever
+qualitative and quantitative evaluation that compares static API-misuse
+detectors along the same dimensions, and with original author validation. To
+accomplish this, we develop MUC, a classification of API misuses, and
+MUBenchPipe, an automated benchmark for detector comparison, on top of our
+misuse dataset, MUBench. Our results show that the capabilities of existing
+detectors vary greatly and that existing detectors, though capable of detecting
+misuses, suffer from extremely low precision and recall. A systematic
+root-cause analysis reveals that, most importantly, detectors need to go beyond
+the naive assumption that a deviation from the most-frequent usage corresponds
+to a misuse and need to obtain additional usage examples to train their models.
+We present possible directions towards more-powerful API-misuse detectors.
+",1,0,0,0,0,0
+11085,Human life is unlimited - but short,"  Does the human lifespan have an impenetrable biological upper limit which
+ultimately will stop further increase in life lengths? This question is
+important for understanding aging, and for society, and has led to intense
+controversies. Demographic data for humans has been interpreted as showing
+existence of a limit, or even as an indication of a decreasing limit, but also
+as evidence that a limit does not exist. This paper studies what can be
+inferred from data about human mortality at extreme age. We show that in
+western countries and Japan and after age 110 the probability of dying is about
+47% per year. Hence there is no finite upper limit to the human lifespan.
+Still, given the present stage of biotechnology, it is unlikely that during the
+next 25 years anyone will live longer than 128 years in these countries. Data,
+remarkably, shows no difference in mortality after age 110 between sexes,
+between ages, or between different lifestyles or genetic backgrounds. These
+results, and the analysis methods developed in this paper, can help testing
+biological theories of ageing and aid confirmation of success of efforts to
+find a cure for ageing.
+",0,0,0,1,0,0
+11086,Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm,"  Object detection when provided image-level labels instead of instance-level
+labels (i.e., bounding boxes) during training is an important problem in
+computer vision, since large scale image datasets with instance-level labels
+are extremely costly to obtain. In this paper, we address this challenging
+problem by developing an Expectation-Maximization (EM) based object detection
+method using deep convolutional neural networks (CNNs). Our method is
+applicable to both the weakly-supervised and semi-supervised settings.
+Extensive experiments on PASCAL VOC 2007 benchmark show that (1) in the weakly
+supervised setting, our method provides significant detection performance
+improvement over current state-of-the-art methods, (2) having access to a small
+number of strongly (instance-level) annotated images, our method can almost
+match the performace of the fully supervised Fast RCNN. We share our source
+code at this https URL.
+",1,0,0,0,0,0
+11087,Coverage Analysis in Millimeter Wave Cellular Networks with Reflections,"  The coverage probability of a user in a mmwave system depends on the
+availability of line-of-sight paths or reflected paths from any base station.
+Many prior works modelled blockages using random shape theory and analyzed the
+SIR distribution with and without interference. While, it is intuitive that the
+reflected paths do not significantly contribute to the coverage (because of
+longer path lengths), there are no works which provide a model and study the
+coverage with reflections. In this paper, we model and analyze the impact of
+reflectors using stochastic geometry. We observe that the reflectors have very
+little impact on the coverage probability.
+",1,0,0,1,0,0
+11088,Polar Coding for the Binary Erasure Channel with Deletions,"  We study the application of polar codes in deletion channels by analyzing the
+cascade of a binary erasure channel (BEC) and a deletion channel. We show how
+polar codes can be used effectively on a BEC with a single deletion, and
+propose a list decoding algorithm with a cyclic redundancy check for this case.
+The decoding complexity is $O(N^2\log N)$, where $N$ is the blocklength of the
+code. An important contribution is an optimization of the amount of redundancy
+added to minimize the overall error probability. Our theoretical results are
+corroborated by numerical simulations which show that the list size can be
+reduced to one and the original message can be recovered with high probability
+as the length of the code grows.
+",1,0,1,0,0,0
+11089,Efficient Structured Surrogate Loss and Regularization in Structured Prediction,"  In this dissertation, we focus on several important problems in structured
+prediction. In structured prediction, the label has a rich intrinsic
+substructure, and the loss varies with respect to the predicted label and the
+true label pair. Structured SVM is an extension of binary SVM to adapt to such
+structured tasks.
+In the first part of the dissertation, we study the surrogate losses and its
+efficient methods. To minimize the empirical risk, a surrogate loss which upper
+bounds the loss, is used as a proxy to minimize the actual loss. Since the
+objective function is written in terms of the surrogate loss, the choice of the
+surrogate loss is important, and the performance depends on it. Another issue
+regarding the surrogate loss is the efficiency of the argmax label inference
+for the surrogate loss. Efficient inference is necessary for the optimization
+since it is often the most time-consuming step. We present a new class of
+surrogate losses named bi-criteria surrogate loss, which is a generalization of
+the popular surrogate losses. We first investigate an efficient method for a
+slack rescaling formulation as a starting point utilizing decomposability of
+the model. Then, we extend the algorithm to the bi-criteria surrogate loss,
+which is very efficient and also shows performance improvements.
+In the second part of the dissertation, another important issue of
+regularization is studied. Specifically, we investigate a problem of
+regularization in hierarchical classification when a structural imbalance
+exists in the label structure. We present a method to normalize the structure,
+as well as a new norm, namely shared Frobenius norm. It is suitable for
+hierarchical classification that adapts to the data in addition to the label
+structure.
+",0,0,0,1,0,0
+11090,Optimizing the Coherence of Composite Networks,"  We consider how to connect a set of disjoint networks to optimize the
+performance of the resulting composite network. We quantify this performance by
+the coherence of the composite network, which is defined by an $H_2$ norm of
+the system. Two dynamics are considered: noisy consensus dynamics with and
+without stubborn agents. For noisy consensus dynamics without stubborn agents,
+we derive analytical expressions for the coherence of composite networks in
+terms of the coherence of the individual networks and the structure of their
+interconnections. We also identify optimal interconnection topologies and give
+bounds on coherence for general composite graphs. For noisy consensus dynamics
+with stubborn agents, we develop a non-combinatorial algorithm that identifies
+connecting edges such that the composite network coherence closely approximates
+the performance of the optimal composite graph.
+",1,0,1,0,0,0
+11091,Deep Learning based Estimation of Weaving Target Maneuvers,"  In target tracking, the estimation of an unknown weaving target frequency is
+crucial for improving the miss distance. The estimation process is commonly
+carried out in a Kalman framework. The objective of this paper is to examine
+the potential of using neural networks in target tracking applications. To that
+end, we propose estimating the weaving frequency using deep neural networks,
+instead of classical Kalman framework based estimation. Particularly, we focus
+on the case where a set of possible constant target frequencies is known.
+Several neural network architectures, requiring low computational resources
+were designed to estimate the unknown frequency out of the known set of
+frequencies. The proposed approach performance is compared with the multiple
+model adaptive estimation algorithm. Simulation results show that in the
+examined scenarios, deep neural network outperforms multiple model adaptive
+estimation in terms of accuracy and the amount of required measurements to
+convergence.
+",0,0,0,1,0,0
+11092,On tangent cones to length minimizers in Carnot-Carathéodory spaces,"  We give a detailed proof of some facts about the blow-up of horizontal curves
+in Carnot-Carathéodory spaces.
+",0,0,1,0,0,0
+11093,Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer,"  In many machine learning applications, there are multiple decision-makers
+involved, both automated and human. The interaction between these agents often
+goes unaddressed in algorithmic development. In this work, we explore a simple
+version of this interaction with a two-stage framework containing an automated
+model and an external decision-maker. The model can choose to say ""Pass"", and
+pass the decision downstream, as explored in rejection learning. We extend this
+concept by proposing ""learning to defer"", which generalizes rejection learning
+by considering the effect of other agents in the decision-making process. We
+propose a learning algorithm which accounts for potential biases held by
+external decision-makers in a system. Experiments demonstrate that learning to
+defer can make systems not only more accurate but also less biased. Even when
+working with inconsistent or biased users, we show that deferring models still
+greatly improve the accuracy and/or fairness of the entire system.
+",1,0,0,1,0,0
+11094,Approximate and Stochastic Greedy Optimization,"  We consider two greedy algorithms for minimizing a convex function in a
+bounded convex set: an algorithm by Jones [1992] and the Frank-Wolfe (FW)
+algorithm. We first consider approximate versions of these algorithms. For
+smooth convex functions, we give sufficient conditions for convergence, a
+unified analysis for the well-known convergence rate of O(1/k) together with a
+result showing that this rate is the best obtainable from the proof technique,
+and an equivalence result for the two algorithms. We also consider approximate
+stochastic greedy algorithms for minimizing expectations. We show that
+replacing the full gradient by a single stochastic gradient can fail even on
+smooth convex functions. We give a convergent approximate stochastic Jones
+algorithm and a convergent approximate stochastic FW algorithm for smooth
+convex functions. In addition, we give a convergent approximate stochastic FW
+algorithm for nonsmooth convex functions. Convergence rates for these
+algorithms are given and proved.
+",1,0,1,0,0,0
+11095,Phase diagram of the triangular-lattice Potts antiferromagnet,"  We study the phase diagram of the triangular-lattice $Q$-state Potts model in
+the real $(Q,v)$-plane, where $v=e^J-1$ is the temperature variable. Our first
+goal is to provide an obviously missing feature of this diagram: the position
+of the antiferromagnetic critical curve. This curve turns out to possess a
+bifurcation point with two branches emerging from it, entailing important
+consequences for the global phase diagram. We have obtained accurate numerical
+estimates for the position of this curve by combining the transfer-matrix
+approach for strip graphs with toroidal boundary conditions and the recent
+method of critical polynomials. The second goal of this work is to study the
+corresponding $A_{p-1}$ RSOS model on the torus, for integer $p=4,5,\ldots,8$.
+We clarify its relation to the corresponding Potts model, in particular
+concerning the role of boundary conditions. For certain values of $p$, we
+identify several new critical points and regimes for the RSOS model and we
+initiate the study of the flows between the corresponding field theories.
+",0,1,0,0,0,0
+11096,Devam vs. Tamam: 2018 Turkish Elections,"  On June 24, 2018, Turkey held a historical election, transforming its
+parliamentary system to a presidential one. One of the main questions for
+Turkish voters was whether to start this new political era with reelecting its
+long-time political leader Recep Tayyip Erdogan or not. In this paper, we
+analyzed 108M tweets posted in the two months leading to the election to
+understand the groups that supported or opposed Erdogan's reelection. We
+examined the most distinguishing hashtags and retweeted accounts for both
+groups. Our findings indicate strong polarization between both groups as they
+differ in terms of ideology, news sources they follow, and preferred TV
+entertainment.
+",1,0,0,0,0,0
+11097,A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning,"  Insertion is a challenging haptic and visual control problem with significant
+practical value for manufacturing. Existing approaches in the model-based
+robotics community can be highly effective when task geometry is known, but are
+complex and cumbersome to implement, and must be tailored to each individual
+problem by a qualified engineer. Within the learning community there is a long
+history of insertion research, but existing approaches are typically either too
+sample-inefficient to run on real robots, or assume access to high-level object
+features, e.g. socket pose. In this paper we show that relatively minor
+modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a
+small number of human demonstrations, allows the robot to quickly learn to
+solve these tasks efficiently and robustly. Our approach requires no modeling
+or simulation, no parameterized search or alignment behaviors, no vision system
+aside from raw images, and no reward shaping. We evaluate our approach on a
+narrow-clearance peg-insertion task and a deformable clip-insertion task, both
+of which include variability in the socket position. Our results show that
+these tasks can be solved reliably on the real robot in less than 10 minutes of
+interaction time, and that the resulting policies are robust to variance in the
+socket position and orientation.
+",1,0,0,0,0,0
+11098,"Off-axis electron holography of magnetic nanostructures: magnetic behavior of Mn rich nanoprecipitates in (Mn,Ga)As system","  The Lorentz off-axis electron holography technique is applied to study the
+magnetic nature of Mn rich nanoprecipitates in (Mn,Ga)As system. The
+effectiveness of this technique is demonstrated in detection of the magnetic
+field even for small nanocrystals having an average size down to 20 nm.
+",0,1,0,0,0,0
+11099,Predicting interactions between individuals with structural and dynamical information,"  Capturing both the structural and temporal aspects of interactions is crucial
+for many real world datasets like contact between individuals. Using the link
+stream formalism to capture the dynamic of the systems, we tackle the issue of
+activity prediction in link streams, that is to say predicting the number of
+links occurring during a given period of time and we present a protocol that
+takes advantage of the temporal and structural information contained in the
+link stream. Using a supervised learning method, we are able to model the
+dynamic of our system to improve the prediction. We investigate the behavior of
+our algorithm and crucial elements affecting the prediction. By introducing
+different categories of pair of nodes, we are able to improve the quality as
+well as increase the diversity of our prediction.
+",1,0,0,0,0,0
+11100,Projected Shadowing-based Data Assimilation,"  In this article we develop algorithms for data assimilation based upon a
+computational time dependent stable/unstable splitting. Our particular method
+is based upon shadowing refinement and synchronization techniques and is
+motivated by work on Assimilation in the Unstable Subspace (AUS) and
+Pseudo-orbit Data Assimilation (PDA). The algorithm utilizes time dependent
+projections onto the non-stable subspace determined by employing computational
+techniques for Lyapunov exponents/vectors. The method is extended to parameter
+estimation without changing the problem dynamics and we address techniques for
+adapting the method when (as is commonly the case) observations are not
+available in the full model state space. We use a combination of analysis and
+numerical experiments (with the Lorenz 63 and Lorenz 96 models) to illustrate
+the efficacy of the techniques and show that the results compare favorably with
+other variational techniques.
+",0,1,0,0,0,0
+11101,Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images,"  This paper describes a new algorithm for solar energy forecasting from a
+sequence of Cloud Optical Depth (COD) images. The algorithm is based on the
+following simple observation: the dynamics of clouds represented by COD images
+resembles the motion (transport) of a density in a fluid flow. This suggests
+that, to forecast the motion of COD images, it is sufficient to forecast the
+flow. The latter, in turn, can be accomplished by fitting a parametric model of
+the fluid flow to the COD images observed in the past. Namely, the learning
+phase of the algorithm is composed of the following steps: (i) given a sequence
+of COD images, the snapshots of the optical flow are estimated from two
+consecutive COD images; (ii) these snapshots are then assimilated into a
+Navier-Stokes Equation (NSE), i.e. an initial velocity field for NSE is
+selected so that the corresponding NSE' solution is as close as possible to the
+optical flow snapshots. The prediction phase consists of utilizing a linear
+transport equation, which describes the propagation of COD images in the fluid
+flow predicted by NSE, to estimate the future motion of the COD images. The
+algorithm has been tested on COD images provided by two geostationary
+operational environmental satellites from NOAA serving the west-hemisphere.
+",0,1,0,0,0,0
+11102,On Mimura's extension problem,"  We determine the group strucure of the $23$-rd homotopy group $\pi_{23}(G_2 :
+2)$, where $G_2$ is the Lie group of exceptional type, which hasn't been
+determined for $50$ years.
+",0,0,1,0,0,0
+11103,On critical and supercritical pseudo-relativistic nonlinear Schrödinger equations,"  In this paper, we investigate existence and non-existence of a nontrivial
+solution to the pseudo-relativistic nonlinear Schrödinger equation $$\left(
+\sqrt{-c^2\Delta + m^2 c^4}-mc^2\right) u + \mu u = |u|^{p-1}u\quad
+\textrm{in}~\mathbb{R}^n~(n \geq 2)$$ involving an
+$H^{1/2}$-critical/supercritical power-type nonlinearity, i.e., $p \geq
+\frac{n+1}{n-1}$. We prove that in the non-relativistic regime, there exists a
+nontrivial solution provided that the nonlinearity is
+$H^{1/2}$-critical/supercritical but it is $H^1$-subcritical. On the other
+hand, we also show that there is no nontrivial bounded solution either $(i)$ if
+the nonlinearity is $H^{1/2}$-critical/supercritical in the ultra-relativistic
+regime or $(ii)$ if the nonlinearity is $H^1$-critical/supercritical in all
+cases.
+",0,0,1,0,0,0
+11104,Eye Tracker Accuracy: Quantitative Evaluation of the Invisible Eye Center Location,"  Purpose. We present a new method to evaluate the accuracy of an eye tracker
+based eye localization system. Measuring the accuracy of an eye tracker's
+primary intention, the estimated point of gaze, is usually done with volunteers
+and a set of fixation points used as ground truth. However, verifying the
+accuracy of the location estimate of a volunteer's eye center in 3D space is
+not easily possible. This is because the eye center is an intangible point
+hidden by the iris. Methods. We evaluate the eye location accuracy by using an
+eye phantom instead of eyes of volunteers. For this, we developed a testing
+stage with a realistic artificial eye and a corresponding kinematic model,
+which we trained with {\mu}CT data. This enables us to precisely evaluate the
+eye location estimate of an eye tracker. Results. We show that the proposed
+testing stage with the corresponding kinematic model is suitable for such a
+validation. Further, we evaluate a particular eye tracker based navigation
+system and show that this system is able to successfully determine the eye
+center with sub-millimeter accuracy. Conclusions. We show the suitability of
+the evaluated eye tracker for eye interventions, using the proposed testing
+stage and the corresponding kinematic model. The results further enable
+specific enhancement of the navigation system to potentially get even better
+results.
+",1,0,0,0,0,0
+11105,Theoretical Foundations of Forward Feature Selection Methods based on Mutual Information,"  Feature selection problems arise in a variety of applications, such as
+microarray analysis, clinical prediction, text categorization, image
+classification and face recognition, multi-label learning, and classification
+of internet traffic. Among the various classes of methods, forward feature
+selection methods based on mutual information have become very popular and are
+widely used in practice. However, comparative evaluations of these methods have
+been limited by being based on specific datasets and classifiers. In this
+paper, we develop a theoretical framework that allows evaluating the methods
+based on their theoretical properties. Our framework is grounded on the
+properties of the target objective function that the methods try to
+approximate, and on a novel categorization of features, according to their
+contribution to the explanation of the class; we derive upper and lower bounds
+for the target objective function and relate these bounds with the feature
+types. Then, we characterize the types of approximations taken by the methods,
+and analyze how these approximations cope with the good properties of the
+target objective function. Additionally, we develop a distributional setting
+designed to illustrate the various deficiencies of the methods, and provide
+several examples of wrong feature selections. Based on our work, we identify
+clearly the methods that should be avoided, and the methods that currently have
+the best performance.
+",1,0,0,1,0,0
+11106,Robust quantum switch with Rydberg excitations,"  We develop an approach to realize a quantum switch for Rydberg excitation in
+atoms with $Y$-typed level configuration. We find that the steady population on
+two different Rydberg states can be reversibly exchanged in a controllable way
+by properly tuning the Rydberg-Rydberg interaction. Moreover, our numerical
+simulations verify that the switching scheme is robust against spontaneous
+decay, environmental disturbance, as well as the duration of operation on the
+interaction, and also a high switching efficiency is quite attainable, which
+makes it have potential applications in quantum information processing and
+other Rydberg-based quantum technologies.
+",0,1,0,0,0,0
+11107,Non-robust phase transitions in the generalized clock model on trees,"  Pemantle and Steif provided a sharp threshold for the existence of a RPT
+(robust phase transition) for the continuous rotator model and the Potts model
+in terms of the branching number and the second eigenvalue of the transfer
+operator, where a robust phase transition is said to occur if an arbitrarily
+weak coupling with symmetry-breaking boundary conditions suffices to induce
+symmetry breaking in the bulk. They further showed that for the Potts model RPT
+occurs at a different threshold than PT (phase transition in the sense of
+multiple Gibbs measures), and conjectured that RPT and PT should occur at the
+same threshold in the continuous rotator model. We consider the class of 4- and
+5-state rotation-invariant spin models with reflection symmetry on general
+trees which contains the Potts model and the clock model with
+scalarproduct-interaction as limiting cases. The clock model can be viewed as a
+particular discretization which is obtained from the classical rotator model on
+the continuous one-dimensional sphere. We analyze the transition between PT=RPT
+and PT is unequal to RPT, in terms of the eigenvalues of the transfer matrix of
+the model at the critical threshold value for the existence of RPT. The
+transition between the two regimes depends sensitively on the third largest
+eigenvalue.
+",0,0,1,0,0,0
+11108,The Role of Gender in Social Network Organization,"  The digital traces we leave behind when engaging with the modern world offer
+an interesting lens through which we study behavioral patterns as expression of
+gender. Although gender differentiation has been observed in a number of
+settings, the majority of studies focus on a single data stream in isolation.
+Here we use a dataset of high resolution data collected using mobile phones, as
+well as detailed questionnaires, to study gender differences in a large cohort.
+We consider mobility behavior and individual personality traits among a group
+of more than $800$ university students. We also investigate interactions among
+them expressed via person-to-person contacts, interactions on online social
+networks, and telecommunication. Thus, we are able to study the differences
+between male and female behavior captured through a multitude of channels for a
+single cohort. We find that while the two genders are similar in a number of
+aspects, there are robust deviations that include multiple facets of social
+interactions, suggesting the existence of inherent behavioral differences.
+Finally, we quantify how aspects of an individual's characteristics and social
+behavior reveals their gender by posing it as a classification problem. We ask:
+How well can we distinguish between male and female study participants based on
+behavior alone? Which behavioral features are most predictive?
+",1,0,0,0,0,0
+11109,The Schur Lie-Multiplier of Leibinz Algebras,"  For a free presentation $0 \to R \to F \to G \to 0$ of a Leibniz algebra $G$,
+the Baer invariant ${\cal M}^{\sf Lie}(G) = \frac{R \cap [F, F]_{Lie}}{[F,
+R]_{Lie}}$ is called the Schur multiplier of $G$ relative to the Liezation
+functor or Schur Lie-multiplier. For a two-sided ideal $N$ of a Leibniz algebra
+$G$, we construct a four-term exact sequence relating the Schur Lie-multiplier
+of $G$ and $G/N$, which is applied to study and characterize Lie-nilpotency,
+Lie-stem covers and Lie-capability of Leibniz algebras.
+",0,0,1,0,0,0
+11110,Did we learn from LLC Side Channel Attacks? A Cache Leakage Detection Tool for Crypto Libraries,"  This work presents a new tool to verify the correctness of cryptographic
+implementations with respect to cache attacks. Our methodology discovers
+vulnerabilities that are hard to find with other techniques, observed as
+exploitable leakage. The methodology works by identifying secret dependent
+memory and introducing forced evictions inside potentially vulnerable code to
+obtain cache traces that are analyzed using Mutual Information. If dependence
+is observed, the cryptographic implementation is classified as to leak
+information.
+We demonstrate the viability of our technique in the design of the three main
+cryptographic primitives, i.e., AES, RSA and ECC, in eight popular up to date
+cryptographic libraries, including OpenSSL, Libgcrypt, Intel IPP and NSS. Our
+results show that cryptographic code designers are far away from incorporating
+the appropriate countermeasures to avoid cache leakages, as we found that 50%
+of the default implementations analyzed leaked information that lead to key
+extraction. We responsibly notified the designers of all the leakages found and
+suggested patches to solve these vulnerabilities.
+",1,0,0,0,0,0
+11111,Effects of anisotropy in spin molecular-orbital coupling on effective spin models of trinuclear organometallic complexes,"  We consider layered decorated honeycomb lattices at two-thirds filling, as
+realized in some trinuclear organometallic complexes. Localized $S=1$ moments
+with a single-spin anisotropy emerge from the interplay of Coulomb repulsion
+and spin molecular-orbit coupling (SMOC). Magnetic anisotropies with bond
+dependent exchange couplings occur in the honeycomb layers when the direct
+intracluster exchange and the spin molecular-orbital coupling are both present.
+We find that the effective spin exchange model within the layers is an XXZ +
+120$^\circ$ honeycomb quantum compass model. The intrinsic non-spherical
+symmetry of the multinuclear complexes leads to very different transverse and
+longitudinal spin molecular-orbital couplings, which greatly enhances the
+single-spin and exchange coupling anisotropies. The interlayer coupling is
+described by a XXZ model with anisotropic biquadratic terms. As the correlation
+strength increases the systems becomes increasingly one-dimensional. Thus, if
+the ratio of SMOC to the interlayer hopping is small this stabilizes the
+Haldane phase. However, as the ratio increases there is a quantum phase
+transition to the topologically trivial `$D$-phase'. We also predict a quantum
+phase transition from a Haldane phase to a magnetically ordered phase at
+sufficiently strong external magnetic fields.
+",0,1,0,0,0,0
+11112,Design and experimental test of an optical vortex coronagraph,"  The optical vortex coronagraph (OVC) is one of the promising ways for direct
+imaging exoplanets because of its small inner working angle and high
+throughput. This paper presents the design and laboratory demonstration
+performance at 633nm and 1520nm of the OVC based on liquid crystal polymers
+(LCP). Two LCPs has been manufactured in partnership with a commercial vendor.
+The OVC can deliver a good performance in laboratory test and achieve the
+contrast of the order 10^-6 at angular distance 3{\lambda}/D, which is able to
+image the giant exoplanets at a young stage in combination with extreme
+adaptive optics.
+",0,1,0,0,0,0
+11113,Symbolic Computation via Program Transformation,"  Symbolic computation is an important approach in automated program analysis.
+Most state-of-the-art tools perform symbolic computation as interpreters and
+directly maintain symbolic data. In this paper, we show that it is feasible,
+and in fact practical, to use a compiler-based strategy instead. Using compiler
+tooling, we propose and implement a transformation which takes a standard
+program and outputs a program that performs semantically equivalent, but
+partially symbolic, computation. The transformed program maintains symbolic
+values internally and operates directly on them hence the program can be
+processed by a tool without support for symbolic manipulation.
+The main motivation for the transformation is in symbolic verification, but
+there are many other possible use-cases, including test generation and concolic
+testing. Moreover using the transformation simplifies tools, since the symbolic
+computation is handled by the program directly. We have implemented the
+transformation at the level of LLVM bitcode. The paper includes an experimental
+evaluation, based on an explicit-state software model checker as a verification
+backend.
+",1,0,0,0,0,0
+11114,Simulating Dirac models with ultracold atoms in optical lattices,"  We present a general model allowing ""quantum simulation"" of one-dimensional
+Dirac models with 2- and 4-component spinors using ultracold atoms in driven 1D
+tilted optical latices. The resulting Dirac physics is illustrated by one of
+its well-known manifestations, Zitterbewegung. This general model can be
+extended and applied with great flexibility to more complex situations.
+",0,1,0,0,0,0
+11115,Network analysis of Japanese global business using quasi-exhaustive micro-data for Japanese overseas subsidiaries,"  Network analysis techniques remain rarely used for understanding
+international management strategies. Our paper highlights their value as
+research tool in this field of social science using a large set of micro-data
+(20,000) to investigate the presence of networks of subsidiaries overseas. The
+research question is the following: to what extent did/do global Japanese
+business networks mirror organizational models existing in Japan? In
+particular, we would like to assess how much the links building such business
+networks are shaped by the structure of big-size industrial conglomerates of
+firms headquartered in Japan, also described as HK. The major part of the
+academic community in the fields of management and industrial organization
+considers that formal links can be identified among firms belonging to HK. Miwa
+and Ramseyer (Miwa and Ramseyer 2002; Ramseyer 2006) challenge this claim and
+argue that the evidence supporting the existence of HK is weak. So far,
+quantitative empirical investigation has been conducted exclusively using data
+for firms incorporated in Japan. Our study tests the Miwa-Ramseyer hypothesis
+(MRH) at the global level using information on the network of Japanese
+subsidiaries overseas. The results obtained lead us to reject the MRH for the
+global dataset, as well as for subsets restricted to the two main
+regions/countries of destination of Japanese foreign investment. The results
+are robust to the weighting of the links, with different specifications, and
+are observed in most industrial sectors. The global Japanese network became
+increasingly complex during the late 20th century as a consequence of increase
+in the number of Japanese subsidiaries overseas but the key features of the
+structure remained rather stable. We draw implications of these findings for
+academic research in international business and for professionals involved in
+corporate strategy.
+",1,0,0,0,0,0
+11116,A Privacy-preserving Community-based P2P OSNs Using Broadcast Encryption Supporting Recommendation Mechanism,"  Online Social Networks (OSNs) have become one of the most important
+activities on the Internet, such as Facebook and Google+. However, security and
+privacy have become major concerns in existing C/S based OSNs. In this paper,
+we propose a novel scheme called a Privacy-preserving Community-based P2P OSNs
+Using Broadcast Encryption Supporting Recommendation Mechanism (PCBE) that
+supports cross-platform availability in stringent privacy requirements. For the
+first time, we introduce recommendation mechanism into a privacy-preserving P2P
+based OSNs, in which we firstly employ the Open Directory Project to generate
+user interest model. We firstly introduce broadcast encryption into P2P
+community-based social networks together with reputation mechanism to decrease
+the system overhead. We formulate the security requirements and design goals
+for privacy- preserving P2P based OSNs supporting recommendation mechanism. The
+RESTful web-services help to ensure cross-platform availability and
+transmission security. As a result, thorough security analysis and performance
+evaluation on experiments demonstrate that the PCBE scheme indeed accords with
+our proposed design goals.
+",1,0,0,0,0,0
+11117,Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop,"  The stochastic variance-reduced gradient method (SVRG) and its accelerated
+variant (Katyusha) have attracted enormous attention in the machine learning
+community in the last few years due to their superior theoretical properties
+and empirical behaviour on training supervised machine learning models via the
+empirical risk minimization paradigm. A key structural element in both of these
+methods is the inclusion of an outer loop at the beginning of which a full pass
+over the training data is made in order to compute the exact gradient, which is
+then used to construct a variance-reduced estimator of the gradient. In this
+work we design {\em loopless variants} of both of these methods. In particular,
+we remove the outer loop and replace its function by a coin flip performed in
+each iteration designed to trigger, with a small probability, the computation
+of the gradient. We prove that the new methods enjoy the same superior
+theoretical convergence properties as the original methods. However, we
+demonstrate through numerical experiments that our methods have substantially
+superior practical behavior.
+",1,0,0,1,0,0
+11118,Hierarchical Temporal Representation in Linear Reservoir Computing,"  Recently, studies on deep Reservoir Computing (RC) highlighted the role of
+layering in deep recurrent neural networks (RNNs). In this paper, the use of
+linear recurrent units allows us to bring more evidence on the intrinsic
+hierarchical temporal representation in deep RNNs through frequency analysis
+applied to the state signals. The potentiality of our approach is assessed on
+the class of Multiple Superimposed Oscillator tasks. Furthermore, our
+investigation provides useful insights to open a discussion on the main aspects
+that characterize the deep learning framework in the temporal domain.
+",1,0,0,1,0,0
+11119,"Bombieri-Vinogradov for multiplicative functions, and beyond the $x^{1/2}$-barrier","  Part-and-parcel of the study of ""multiplicative number theory"" is the study
+of the distribution of multiplicative functions in arithmetic progressions.
+Although appropriate analogies to the Bombieri-Vingradov Theorem have been
+proved for particular examples of multiplicative functions, there has not
+previously been headway on a general theory; seemingly none of the different
+proofs of the Bombieri-Vingradov Theorem for primes adapt well to this
+situation. In this article we find out why such a result has been so elusive,
+and discover what can be proved along these lines and develop some limitations.
+For a fixed residue class $a$ we extend such averages out to moduli $\leq
+x^{\frac {20}{39}-\delta}$.
+",0,0,1,0,0,0
+11120,Three-component fermions with surface Fermi arcs in topological semimetal tungsten carbide,"  Topological Dirac and Weyl semimetals not only host quasiparticles analogous
+to the elementary fermionic particles in high-energy physics, but also have
+nontrivial band topology manifested by exotic Fermi arcs on the surface. Recent
+advances suggest new types of topological semimetals, in which spatial
+symmetries protect gapless electronic excitations without high-energy analogy.
+Here we observe triply-degenerate nodal points (TPs) near the Fermi level of
+WC, in which the low-energy quasiparticles are described as three-component
+fermions distinct from Dirac and Weyl fermions. We further observe the surface
+states whose constant energy contours are pairs of Fermi arcs connecting the
+surface projection of the TPs, proving the nontrivial topology of the newly
+identified semimetal state.
+",0,1,0,0,0,0
+11121,Detecting stochastic inclusions in electrical impedance tomography,"  This work considers the inclusion detection problem of electrical impedance
+tomography with stochastic conductivities. It is shown that a conductivity
+anomaly with a random conductivity can be identified by applying the
+Factorization Method or the Monotonicity Method to the mean value of the
+corresponding Neumann-to-Dirichlet map provided that the anomaly has high
+enough contrast in the sense of expectation. The theoretical results are
+complemented by numerical examples in two spatial dimensions.
+",0,0,1,0,0,0
+11122,On compact Hermitian manifolds with flat Gauduchon connections,"  Given a Hermitian manifold $(M^n,g)$, the Gauduchon connections are the one
+parameter family of Hermitian connections joining the Chern connection and the
+Bismut connection. We will call $\nabla^s = (1-\frac{s}{2})\nabla^c +
+\frac{s}{2}\nabla^b$ the $s$-Gauduchon connection of $M$, where $\nabla^c$ and
+$\nabla^b$ are respectively the Chern and Bismut connections. It is natural to
+ask when a compact Hermitian manifold could admit a flat $s$-Gauduchon
+connection. This is related to a question asked by Yau \cite{Yau}. The cases
+with $s=0$ (a flat Chern connection) or $s=2$ (a flat Bismut connection) are
+classified respectively by Boothby \cite{Boothby} in the 1950s or by Q. Wang
+and the authors recently \cite{WYZ}. In this article, we observe that if either
+$s\geq 4+2\sqrt{3} \approx 7.46$ or $s\leq 4-2\sqrt{3}\approx 0.54$ and $s\neq
+0$, then $g$ is Kähler. We also show that, when $n=2$, $g$ is always Kähler
+unless $s=2$. Note that non-Kähler compact Bismut flat surfaces are exactly
+those isosceles Hopf surfaces by \cite{WYZ}.
+",0,0,1,0,0,0
+11123,A Support Tensor Train Machine,"  There has been growing interest in extending traditional vector-based machine
+learning techniques to their tensor forms. An example is the support tensor
+machine (STM) that utilizes a rank-one tensor to capture the data structure,
+thereby alleviating the overfitting and curse of dimensionality problems in the
+conventional support vector machine (SVM). However, the expressive power of a
+rank-one tensor is restrictive for many real-world data. To overcome this
+limitation, we introduce a support tensor train machine (STTM) by replacing the
+rank-one tensor in an STM with a tensor train. Experiments validate and confirm
+the superiority of an STTM over the SVM and STM.
+",1,0,0,1,0,0
+11124,Diffusion under confinement: hydrodynamic finite-size effects in simulation,"  We investigate finite-size effects on diffusion in confined fluids using
+molecular dynamics simulations and hydrodynamic calculations. Specifically, we
+consider a Lennard-Jones fluid in slit pores without slip at the interface and
+show that the use of periodic boundary conditions in the directions along the
+surfaces results in dramatic finite-size effects, in addition to that of the
+physically relevant confining length. As in the simulation of bulk fluids,
+these effects arise from spurious hydrodynamic interactions between periodic
+images and from the constraint of total momentum conservation. We derive
+analytical expressions for the correction to the diffusion coefficient in the
+limits of both elongated and flat systems, which are in excellent agreement
+with the molecular simulation results except for the narrowest pores, where the
+discreteness of the fluid particles starts to play a role. The present work
+implies that the diffusion coefficients for wide nanopores computed using
+elongated boxes suffer from finite-size artifacts which had not been previously
+appreciated. In addition, our analytical expression provides the correction to
+be applied to the simulation results for finite (possibly small) systems. It
+applies not only to molecular but also to all mesoscopic hydrodynamic
+simulations, including Lattice-Boltzmann, Multi-Particle Collision Dynamics or
+Dissipative Particle Dynamics, which are often used to investigate confined
+soft matter involving colloidal particles and polymers.
+",0,1,0,0,0,0
+11125,SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning,"  It is known that Boosting can be interpreted as a gradient descent technique
+to minimize an underlying loss function. Specifically, the underlying loss
+being minimized by the traditional AdaBoost is the exponential loss, which is
+proved to be very sensitive to random noise/outliers. Therefore, several
+Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to
+improve the robustness of AdaBoost by replacing the exponential loss with some
+designed robust loss functions. In this work, we present a new way to robustify
+AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning
+(SPL) into Boosting framework. Specifically, we design a new robust Boosting
+algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented
+by slightly modifying off-the-shelf Boosting packages. Extensive experiments
+and a theoretical characterization are also carried out to illustrate the
+merits of the proposed SPLBoost.
+",1,0,0,1,0,0
+11126,Influence of thermal boundary conditions on the current-driven resistive transition in $\mathbf{VO_2}$ microbridges,"  We investigate the resistive switching behaviour of $\mathrm{VO_2}$
+microbridges under current bias as a function of temperature and thermal
+coupling with the heat bath. Upon increasing the electrical current bias, the
+formation of the metallic phase can progress smoothly or through sharp jumps.
+The magnitude and threshold current values of these sharp resistance drops show
+random behaviour and are dramatically influenced by thermal dissipation
+conditions. Our results also evidence how the propagation of the metallic phase
+induced by electrical current in $\mathrm{VO_2}$, and thus the shape of the
+resulting high-conductivity path, are not predictable. We discuss the origin of
+the switching events through a simple electro-thermal model based on the domain
+structure of $\mathrm{VO_2}$ films that can be useful to improve the stability
+and controllability of future $\mathrm{VO_2}$-based devices.
+",0,1,0,0,0,0
+11127,Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction,"  This paper proposes a convolutional neural network (CNN)-based method that
+learns traffic as images and predicts large-scale, network-wide traffic speed
+with a high accuracy. Spatiotemporal traffic dynamics are converted to images
+describing the time and space relations of traffic flow via a two-dimensional
+time-space matrix. A CNN is applied to the image following two consecutive
+steps: abstract traffic feature extraction and network-wide traffic speed
+prediction. The effectiveness of the proposed method is evaluated by taking two
+real-world transportation networks, the second ring road and north-east
+transportation network in Beijing, as examples, and comparing the method with
+four prevailing algorithms, namely, ordinary least squares, k-nearest
+neighbors, artificial neural network, and random forest, and three deep
+learning architectures, namely, stacked autoencoder, recurrent neural network,
+and long-short-term memory network. The results show that the proposed method
+outperforms other algorithms by an average accuracy improvement of 42.91%
+within an acceptable execution time. The CNN can train the model in a
+reasonable time and, thus, is suitable for large-scale transportation networks.
+",1,0,0,1,0,0
+11128,The ZX calculus is a language for surface code lattice surgery,"  Quantum computing is moving rapidly to the point of deployment of technology.
+Functional quantum devices will require the ability to correct error in order
+to be scalable and effective. A leading choice of error correction, in
+particular for modular or distributed architectures, is the surface code with
+logical two-qubit operations realised via ""lattice surgery"". These operations
+consist of ""merges"" and ""splits"" acting non-unitarily on the logical states and
+are not easily captured by standard circuit notation. This raises the question
+of how best to reason about lattice surgery in order efficiently to use quantum
+states and operations in architectures with complex resource management issues.
+In this paper we demonstrate that the operations of the ZX calculus, a form of
+quantum diagrammatic reasoning designed using category theory, match exactly
+the operations of lattice surgery. Red and green ""spider"" nodes match rough and
+smooth merges and splits, and follow the axioms of a dagger special associative
+Frobenius algebra. Some lattice surgery operations can require non-trivial
+correction operations, which are captured natively in the use of the ZX
+calculus in the form of ensembles of diagrams. We give a first taste of the
+power of the calculus as a language for surgery by considering two operations
+(magic state use and producing a CNOT) and show how ZX diagram re-write rules
+give lattice surgery procedures for these operations that are novel, efficient,
+and highly configurable.
+",1,0,0,0,0,0
+11129,Learning One-hidden-layer Neural Networks with Landscape Design,"  We consider the problem of learning a one-hidden-layer neural network: we
+assume the input $x\in \mathbb{R}^d$ is from Gaussian distribution and the
+label $y = a^\top \sigma(Bx) + \xi$, where $a$ is a nonnegative vector in
+$\mathbb{R}^m$ with $m\le d$, $B\in \mathbb{R}^{m\times d}$ is a full-rank
+weight matrix, and $\xi$ is a noise vector. We first give an analytic formula
+for the population risk of the standard squared loss and demonstrate that it
+implicitly attempts to decompose a sequence of low-rank tensors simultaneously.
+Inspired by the formula, we design a non-convex objective function $G(\cdot)$
+whose landscape is guaranteed to have the following properties: 1. All local
+minima of $G$ are also global minima.
+2. All global minima of $G$ correspond to the ground truth parameters.
+3. The value and gradient of $G$ can be estimated using samples.
+With these properties, stochastic gradient descent on $G$ provably converges
+to the global minimum and learn the ground-truth parameters. We also prove
+finite sample complexity result and validate the results by simulations.
+",1,0,0,1,0,0
+11130,Growth and electronic structure of graphene on semiconducting Ge(110),"  The direct growth of graphene on semiconducting or insulating substrates
+might help to overcome main drawbacks of metal-based synthesis, like metal-atom
+contaminations of graphene, transfer issues, etc. Here we present the growth of
+graphene on n-doped semiconducting Ge(110) by using an atomic carbon source and
+the study of the structural and electronic properties of the obtained
+interface. We found that graphene interacts weakly with the underlying Ge(110)
+substrate that keeps graphene's electronic structure almost intact promoting
+this interface for future graphene-semiconductor applications. The effect of
+dopants in Ge on the electronic properties of graphene is also discussed.
+",0,1,0,0,0,0
+11131,Alternative Lagrangians obtained by scalar deformations,"  We study non-conservative like SODEs admitting explicit Lagrangian
+descriptions. Such systems are equivalent to the system of Lagrange equations
+of some Lagrangian $L$, including a covariant force field which represents
+non-conservative forces. We find necessary and sufficient conditions for the
+existence of a differentiable function $\Phi:\mathbb{R}\rightarrow\mathbb{R}$
+such that the initial system is equivalent to the system of Euler-Lagrange
+equations of the deformed Lagrangian $\Phi(L)$. We give various examples of
+such deformations.
+",0,0,1,0,0,0
+11132,Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning,"  Constrained Markov Decision Process (CMDP) is a natural framework for
+reinforcement learning tasks with safety constraints, where agents learn a
+policy that maximizes the long-term reward while satisfying the constraints on
+the long-term cost. A canonical approach for solving CMDPs is the primal-dual
+method which updates parameters in primal and dual spaces in turn. Existing
+methods for CMDPs only use on-policy data for dual updates, which results in
+sample inefficiency and slow convergence. In this paper, we propose a policy
+search method for CMDPs called Accelerated Primal-Dual Optimization (APDO),
+which incorporates an off-policy trained dual variable in the dual update
+procedure while updating the policy in primal space with on-policy likelihood
+ratio gradient. Experimental results on a simulated robot locomotion task show
+that APDO achieves better sample efficiency and faster convergence than
+state-of-the-art approaches for CMDPs.
+",0,0,0,1,0,0
+11133,Toward Common Components for Open Workflow Systems,"  The role of scalable high-performance workflows and flexible workflow
+management systems that can support multiple simulations will continue to
+increase in importance. For example, with the end of Dennard scaling, there is
+a need to substitute a single long running simulation with multiple repeats of
+shorter simulations, or concurrent replicas. Further, many scientific problems
+involve ensembles of simulations in order to solve a higher-level problem or
+produce statistically meaningful results. However most supercomputing software
+development and performance enhancements have focused on optimizing single-
+simulation performance. On the other hand, there is a strong inconsistency in
+the definition and practice of workflows and workflow management systems. This
+inconsistency often centers around the difference between several different
+types of workflows, including modeling and simulation, grid, uncertainty
+quantification, and purely conceptual workflows. This work explores this
+phenomenon by examining the different types of workflows and workflow
+management systems, reviewing the perspective of a large supercomputing
+facility, examining the common features and problems of workflow management
+systems, and finally presenting a proposed solution based on the concept of
+common building blocks. The implications of the continuing proliferation of
+workflow management systems and the lack of interoperability between these
+systems are discussed from a practical perspective. In doing so, we have begun
+an investigation of the design and implementation of open workflow systems for
+supercomputers based upon common components.
+",1,0,0,0,0,0
+11134,On the Sampling Problem for Kernel Quadrature,"  The standard Kernel Quadrature method for numerical integration with random
+point sets (also called Bayesian Monte Carlo) is known to converge in root mean
+square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode
+the smoothness and dimension of the integrand. However, an empirical
+investigation reveals that the rate constant $C$ is highly sensitive to the
+distribution of the random points. In contrast to standard Monte Carlo
+integration, for which optimal importance sampling is well-understood, the
+sampling distribution that minimises $C$ for Kernel Quadrature does not admit a
+closed form. This paper argues that the practical choice of sampling
+distribution is an important open problem. One solution is considered; a novel
+automatic approach based on adaptive tempering and sequential Monte Carlo.
+Empirical results demonstrate a dramatic reduction in integration error of up
+to 4 orders of magnitude can be achieved with the proposed method.
+",1,0,0,1,0,0
+11135,Zeroth order regular approximation approach to electric dipole moment interactions of the electron,"  A quasi-relativistic two-component approach for an efficient calculation of
+$\mathcal{P,T}$-odd interactions caused by a permanent electric dipole moment
+of the electron (eEDM) is presented. The approach uses a (two-component)
+complex generalized Hartree-Fock (cGHF) and a complex generalized Kohn-Sham
+(cGKS) scheme within the zeroth order regular approximation (ZORA). In
+applications to select heavy-elemental polar diatomic molecular radicals, which
+are promising candidates for an eEDM experiment, the method is compared to
+relativistic four-component electron-correlation calculations and confirms
+values for the effective electrical field acting on the unpaired electron for
+RaF, BaF, YbF and HgF. The calculations show that purely relativistic effects,
+involving only the lower component of the Dirac bi-spinor, are well described
+by treating only the upper component explicitly.
+",0,1,0,0,0,0
+11136,Trace your sources in large-scale data: one ring to find them all,"  An important preprocessing step in most data analysis pipelines aims to
+extract a small set of sources that explain most of the data. Currently used
+algorithms for blind source separation (BSS), however, often fail to extract
+the desired sources and need extensive cross-validation. In contrast, their
+rarely used probabilistic counterparts can get away with little
+cross-validation and are more accurate and reliable but no simple and scalable
+implementations are available. Here we present a novel probabilistic BSS
+framework (DECOMPOSE) that can be flexibly adjusted to the data, is extensible
+and easy to use, adapts to individual sources and handles large-scale data
+through algorithmic efficiency. DECOMPOSE encompasses and generalises many
+traditional BSS algorithms such as PCA, ICA and NMF and we demonstrate
+substantial improvements in accuracy and robustness on artificial and real
+data.
+",0,0,0,1,0,0
+11137,The rigorous derivation of the linear Landau equation from a particle system in a weak-coupling limit,"  We consider a system of N particles interacting via a short-range smooth
+potential, in a intermediate regime between the weak-coupling and the
+low-density. We provide a rigorous derivation of the Linear Landau equation
+from this particle system. The strategy of the proof consists in showing the
+asymptotic equivalence between the one-particle marginal and the solution of
+the linear Boltzmann equation with vanishing mean free path.Then, following the
+ideas of Landau, we prove the asympotic equivalence between the solutions of
+the Boltzmann and Landau linear equation in the grazing collision limit.
+",0,0,1,0,0,0
+11138,Fractal dimension and lower bounds for geometric problems,"  We study the complexity of geometric problems on spaces of low fractal
+dimension. It was recently shown by [Sidiropoulos & Sridhar, SoCG 2017] that
+several problems admit improved solutions when the input is a pointset in
+Euclidean space with fractal dimension smaller than the ambient dimension. In
+this paper we prove nearly-matching lower bounds, thus establishing
+nearly-optimal bounds for various problems as a function of the fractal
+dimension.
+More specifically, we show that for any set of $n$ points in $d$-dimensional
+Euclidean space, of fractal dimension $\delta\in (1,d)$, for any $\epsilon >0$
+and $c\geq 1$, any $c$-spanner must have treewidth at least $\Omega \left(
+\frac{n^{1-1/(\delta - \epsilon)}}{c^{d-1}} \right)$, matching the previous
+upper bound. The construction used to prove this lower bound on the treewidth
+of spanners can also be used to derive lower bounds on the running time of
+algorithms for various problems, assuming the Exponential Time Hypothesis. We
+provide two prototypical results of this type. For any $\delta \in (1,d)$ and
+any $\epsilon >0$ we show that:
+1) $d$-dimensional Euclidean TSP on $n$ points with fractal dimension at most
+$\delta$ cannot be solved in time $2^{O\left(n^{1-1/(\delta - \epsilon)}
+\right)}$. The best-known upper bound is $2^{O(n^{1-1/\delta} \log n)}$.
+2) The problem of finding $k$-pairwise non-intersecting $d$-dimensional unit
+balls/axis parallel unit cubes with centers having fractal dimension at most
+$\delta$ cannot be solved in time $f(k)n^{O \left(k^{1-1/(\delta -
+\epsilon)}\right)}$ for any computable function $f$. The best-known upper bound
+is $n^{O(k^{1-1/\delta} \log n)}$.
+The above results nearly match previously known upper bounds from
+[Sidiropoulos & Sridhar, SoCG 2017], and generalize analogous lower bounds for
+the case of ambient dimension due to [Marx & Sidiropoulos, SoCG 2014].
+",1,0,0,0,0,0
+11139,Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization,"  We present a unified framework to analyze the global convergence of Langevin
+dynamics based algorithms for nonconvex finite-sum optimization with $n$
+component functions. At the core of our analysis is a direct analysis of the
+ergodicity of the numerical approximations to Langevin dynamics, which leads to
+faster convergence rates. Specifically, we show that gradient Langevin dynamics
+(GLD) and stochastic gradient Langevin dynamics (SGLD) converge to the almost
+minimizer within $\tilde O\big(nd/(\lambda\epsilon) \big)$ and $\tilde
+O\big(d^7/(\lambda^5\epsilon^5) \big)$ stochastic gradient evaluations
+respectively, where $d$ is the problem dimension, and $\lambda$ is the spectral
+gap of the Markov chain generated by GLD. Both of the results improve upon the
+best known gradient complexity results. Furthermore, for the first time we
+prove the global convergence guarantee for variance reduced stochastic gradient
+Langevin dynamics (VR-SGLD) to the almost minimizer after $\tilde
+O\big(\sqrt{n}d^5/(\lambda^4\epsilon^{5/2})\big)$ stochastic gradient
+evaluations, which outperforms the gradient complexities of GLD and SGLD in a
+wide regime. Our theoretical analyses shed some light on using Langevin
+dynamics based algorithms for nonconvex optimization with provable guarantees.
+",1,0,1,1,0,0
+11140,Eigenvalue Analysis via Kernel Density Estimation,"  In this paper, we propose an eigenvalue analysis -- of system dynamics models
+-- based on the Mutual Information measure, which in turn will be estimated via
+the Kernel Density Estimation method. We postulate that the proposed approach
+represents a novel and efficient multivariate eigenvalue sensitivity analysis.
+",1,0,0,0,0,0
+11141,The Saga of KPR: Theoretical and Experimental developments,"  In this article, we present a brief narration of the origin and the overview
+of the recent developments done on the Kolkata Paise Restaurant (KPR) problem,
+which can serve as a prototype for a broader class of resource allocation
+problems in the presence of a large number of competing agents, typically
+studied using coordination and anti-coordination games. We discuss the KPR and
+its several extensions, as well as its applications in many economic and social
+phenomena. We end the article with some discussions on our ongoing experimental
+analysis of the same problem. We demonstrate that this provides an interesting
+picture of how people analyze complex situations, and design their strategies
+or react to them.
+",1,0,0,0,0,0
+11142,The computational complexity of integer programming with alternations,"  We prove that integer programming with three quantifier alternations is
+$NP$-complete, even for a fixed number of variables. This complements earlier
+results by Lenstra and Kannan, which together say that integer programming with
+at most two quantifier alternations can be done in polynomial time for a fixed
+number of variables. As a byproduct of the proof, we show that for two
+polytopes $P,Q \subset \mathbb{R}^4$ , counting the projection of integer
+points in $Q \backslash P$ is $\#P$-complete. This contrasts the 2003 result by
+Barvinok and Woods, which allows counting in polynomial time the projection of
+integer points in $P$ and $Q$ separately.
+",1,0,1,0,0,0
+11143,Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling,"  Dictionary learning and component analysis are part of one of the most
+well-studied and active research fields, at the intersection of signal and
+image processing, computer vision, and statistical machine learning. In
+dictionary learning, the current methods of choice are arguably K-SVD and its
+variants, which learn a dictionary (i.e., a decomposition) for sparse coding
+via Singular Value Decomposition. In robust component analysis, leading methods
+derive from Principal Component Pursuit (PCP), which recovers a low-rank matrix
+from sparse corruptions of unknown magnitude and support. However, K-SVD is
+sensitive to the presence of noise and outliers in the training set.
+Additionally, PCP does not provide a dictionary that respects the structure of
+the data (e.g., images), and requires expensive SVD computations when solved by
+convex relaxation. In this paper, we introduce a new robust decomposition of
+images by combining ideas from sparse dictionary learning and PCP. We propose a
+novel Kronecker-decomposable component analysis which is robust to gross
+corruption, can be used for low-rank modeling, and leverages separability to
+solve significantly smaller problems. We design an efficient learning algorithm
+by drawing links with a restricted form of tensor factorization. The
+effectiveness of the proposed approach is demonstrated on real-world
+applications, namely background subtraction and image denoising, by performing
+a thorough comparison with the current state of the art.
+",1,0,0,1,0,0
+11144,Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks,"  Despite the wide use of machine learning in adversarial settings including
+computer security, recent studies have demonstrated vulnerabilities to evasion
+attacks---carefully crafted adversarial samples that closely resemble
+legitimate instances, but cause misclassification. In this paper, we examine
+the adequacy of the leading approach to generating adversarial samples---the
+gradient descent approach. In particular (1) we perform extensive experiments
+on three datasets, MNIST, USPS and Spambase, in order to analyse the
+effectiveness of the gradient-descent method against non-linear support vector
+machines, and conclude that carefully reduced kernel smoothness can
+significantly increase robustness to the attack; (2) we demonstrate that
+separated inter-class support vectors lead to more secure models, and propose a
+quantity similar to margin that can efficiently predict potential
+susceptibility to gradient-descent attacks, before the attack is launched; and
+(3) we design a new adversarial sample construction algorithm based on
+optimising the multiplicative ratio of class decision functions.
+",1,0,0,1,0,0
+11145,The way to uncover community structure with core and diversity,"  Communities are ubiquitous in nature and society. Individuals that share
+common properties often self-organize to form communities. Avoiding the
+shortages of computation complexity, pre-given information and unstable results
+in different run, in this paper, we propose a simple and effcient method to
+deepen our understanding of the emergence and diversity of communities in
+complex systems. By introducing the rational random selection, our method
+reveals the hidden deterministic and normal diverse community states of
+community structure. To demonstrate this method, we test it with real-world
+systems. The results show that our method could not only detect community
+structure with high sensitivity and reliability, but also provide instructional
+information about the hidden deterministic community world and our normal
+diverse community world by giving out the core-community, the real-community,
+the tide and the diversity. This is of paramount importance in understanding,
+predicting, and controlling a variety of collective behaviors in complex
+systems.
+",1,1,0,0,0,0
+11146,Stochastic comparisons of the largest claim amounts from two sets of interdependent heterogeneous portfolios,"  Let $ X_{\lambda_1},\ldots,X_{\lambda_n}$ be dependent non-negative random
+variables and $Y_i=I_{p_i} X_{\lambda_i}$, $i=1,\ldots,n$, where
+$I_{p_1},\ldots,I_{p_n}$ are independent Bernoulli random variables independent
+of $X_{\lambda_i}$'s, with ${\rm E}[I_{p_i}]=p_i$, $i=1,\ldots,n$. In actuarial
+sciences, $Y_i$ corresponds to the claim amount in a portfolio of risks. In
+this paper, we compare the largest claim amounts of two sets of interdependent
+portfolios, in the sense of usual stochastic order, when the variables in one
+set have the parameters $\lambda_1,\ldots,\lambda_n$ and $p_1,\ldots,p_n$ and
+the variables in the other set have the parameters
+$\lambda^{*}_1,\ldots,\lambda^{*}_n$ and $p^*_1,\ldots,p^*_n$. For
+illustration, we apply the results to some important models in actuary.
+",0,0,0,0,0,1
+11147,An Achilles' Heel of Term-Resolution,"  Term-resolution provides an elegant mechanism to prove that a quantified
+Boolean formula (QBF) is true. It is a dual to Q-resolution (also referred to
+as clause-resolution) and is practically highly important as it enables
+certifying answers of DPLL-based QBF solvers. While term-resolution and
+Q-resolution are very similar, they're not completely symmetric. In particular,
+Q-resolution operates on clauses and term-resolution operates on models of the
+matrix. This paper investigates what impact this asymmetry has. We'll see that
+there is a large class of formulas (formulas with ""big models"") whose
+term-resolution proofs are exponential. As a possible remedy, the paper
+suggests to prove true QBFs by refuting their negation ({\em negate-refute}),
+rather than proving them by term-resolution. The paper shows that from the
+theoretical perspective this is indeed a favorable approach. In particular,
+negation-refutation can p-simulates term-resolution and there is an exponential
+separation between the two calculi. These observations further our
+understanding of proof systems for QBFs and provide a strong theoretical
+underpinning for the effort towards non-CNF QBF solvers.
+",1,0,0,0,0,0
+11148,Tunneling estimates and approximate controllability for hypoelliptic equations,"  This article is concerned with quantitative unique continuation estimates for
+equations involving a ""sum of squares"" operator $\mathcal{L}$ on a compact
+manifold $\mathcal{M}$ assuming: $(i)$ the Chow-Rashevski-Hörmander condition
+ensuring the hypoellipticity of $\mathcal{L}$, and $(ii)$ the analyticity of
+$\mathcal{M}$ and the coefficients of $\mathcal{L}$.
+The first result is the tunneling estimate $\|\varphi\|_{L^2(\omega)} \geq
+Ce^{- \lambda^{\frac{k}{2}}}$ for normalized eigenfunctions $\varphi$ of
+$\mathcal{L}$ from a nonempty open set $\omega\subset \mathcal{M}$, where $k$
+is the hypoellipticity index of $\mathcal{L}$ and $\lambda$ the eigenvalue.
+The main result is a stability estimate for solutions to the hypoelliptic
+wave equation $(\partial_t^2+\mathcal{L})u=0$: for $T>2 \sup_{x \in
+\mathcal{M}}(dist(x,\omega))$ (here, $dist$ is the sub-Riemannian distance),
+the observation of the solution on $(0,T)\times \omega$ determines the data.
+The constant involved in the estimate is $Ce^{c\Lambda^k}$ where $\Lambda$ is
+the typical frequency of the data.
+We then prove the approximate controllability of the hypoelliptic heat
+equation $(\partial_t+\mathcal{L})v=1_\omega f$ in any time, with appropriate
+(exponential) cost, depending on $k$. In case $k=2$ (Grushin, Heisenberg...),
+we further show approximate controllability to trajectories with polynomial
+cost in large time.
+We also explain how the analyticity assumption can be relaxed, and a boundary
+$\partial \mathcal{M}$ can be added in some situations.
+Most results turn out to be optimal on a family of Grushin-type operators.
+The main proof relies on the general strategy developed by the authors in
+arXiv:1506.04254.
+",0,0,1,0,0,0
+11149,Optimal Resonant Beam Charging for Electronic Vehicles in Internet of Intelligent Vehicles,"  To enable electric vehicles (EVs) to access to the internet of intelligent
+vehicles (IoIV), charging EVs wirelessly anytime and anywhere becomes an urgent
+need. The resonant beam charging (RBC) technology can provide high-power and
+long-range wireless energy for EVs. However, the RBC system is unefficient. To
+improve the RBC power transmission efficiency, the adaptive resonant beam
+charging (ARBC) technology was introduced. In this paper, after analyzing the
+modular model of the ARBC system, we obtain the closed-form formula of the
+end-to-end power transmission efficiency. Then, we prove that the optimal power
+transmission efficiency uniquely exists. Moreover, we analyze the relationships
+among the optimal power transmission efficiency, the source power, the output
+power, and the beam transmission efficiency, which provide the guidelines for
+the optimal ARBC system design and implementation. Hence, perpetual energy can
+be supplied to EVs in IoIV virtually.
+",1,0,0,0,0,0
+11150,An Adaptive Version of Brandes' Algorithm for Betweenness Centrality,"  Betweenness centrality---measuring how many shortest paths pass through a
+vertex---is one of the most important network analysis concepts for assessing
+the relative importance of a vertex. The well-known algorithm of Brandes [2001]
+computes, on an $n$-vertex and $m$-edge graph, the betweenness centrality of
+all vertices in $O(nm)$ worst-case time. In follow-up work, significant
+empirical speedups were achieved by preprocessing degree-one vertices and by
+graph partitioning based on cut vertices. We further contribute an algorithmic
+treatment of degree-two vertices, which turns out to be much richer in
+mathematical structure than the case of degree-one vertices. Based on these
+three algorithmic ingredients, we provide a strengthened worst-case running
+time analysis for betweenness centrality algorithms. More specifically, we
+prove an adaptive running time bound $O(kn)$, where $k < m$ is the size of a
+minimum feedback edge set of the input graph.
+",1,0,0,0,0,0
+11151,Kinetic cascade in solar-wind turbulence: 3D3V hybrid-kinetic simulations with electron inertia,"  Understanding the nature of the turbulent fluctuations below the ion
+gyroradius in solar-wind turbulence is a great challenge. Recent studies have
+been mostly in favor of kinetic Alfvén wave (KAW) type of fluctuations, but
+other kinds of fluctuations with characteristics typical of magnetosonic,
+whistler and ion Bernstein modes, could also play a role depending on the
+plasma parameters. Here we investigate the properties of the sub-proton-scale
+cascade with high-resolution hybrid-kinetic simulations of freely-decaying
+turbulence in 3D3V phase space, including electron inertia effects. Two proton
+plasma beta are explored: the ""intermediate"" $\beta_p=1$ and ""low""
+$\beta_p=0.2$ regimes, both typically observed in solar wind and corona. The
+magnetic energy spectum exhibits $k_\perp^{-8/3}$ and $k_\|^{-7/2}$ power laws
+at $\beta_p=1$, while they are slightly steeper at $\beta_p=0.2$. Nevertheless,
+both regimes develop a spectral anisotropy consistent with $k_\|\sim
+k_\perp^{2/3}$ at $k_\perp\rho_p>1$, and pronounced small-scale intermittency.
+In this context, we find that the kinetic-scale cascade is dominated by
+KAW-like fluctuations at $\beta_p=1$, whereas the low-$\beta$ case presents a
+more complex scenario suggesting the simultaneous presence of different types
+of fluctuations. In both regimes, however, a non-negligible role of ion
+Bernstein type of fluctuations at the smallest scales seems to emerge.
+",0,1,0,0,0,0
+11152,Gamma-Band Correlations in Primary Visual Cortex,"  Neural field theory is used to quantitatively analyze the two-dimensional
+spatiotemporal correlation properties of gamma-band (30 -- 70 Hz) oscillations
+evoked by stimuli arriving at the primary visual cortex (V1), and modulated by
+patchy connectivities that depend on orientation preference (OP). Correlation
+functions are derived analytically under different stimulus and measurement
+conditions. The predictions reproduce a range of published experimental
+results, including the existence of two-point oscillatory temporal
+cross-correlations with zero time-lag between neurons with similar OP, the
+influence of spatial separation of neurons on the strength of the correlations,
+and the effects of differing stimulus orientations.
+",0,0,0,0,1,0
+11153,A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration,"  We address the problem of camera-to-laser-scanner calibration using a
+checkerboard and multiple image-laser scan pairs. Distinguishing which laser
+points measure the checkerboard and which lie on the background is essential to
+any such system. We formulate the checkerboard extraction as a combinatorial
+optimization problem with a clear cut objective function. We propose a
+branch-and-bound technique that deterministically and globally optimizes the
+objective. Unlike what is available in the literature, the proposed method is
+not heuristic and does not require assumptions such as constraints on the
+background or relying on discontinuity of the range measurements to partition
+the data into line segments. The proposed approach is generic and can be
+applied to both 3D or 2D laser scanners as well as the cases where multiple
+checkerboards are present. We demonstrate the effectiveness of the proposed
+approach by providing numerical simulations as well as experimental results.
+",1,0,0,0,0,0
+11154,On the mapping of Points of Interest through StreetView imagery and paid crowdsourcing,"  The use of volunteers has emerged as low-cost alternative to generate
+accurate geographical information, an approach known as Volunteered Geographic
+Information (VGI). However, VGI is limited by the number and availability of
+volunteers in the area to be mapped, hindering scalability for large areas and
+making difficult to map within a time-frame. Fortunately, the availability of
+street-view imagery enables the virtual exploration of urban environments,
+making possible the recruitment of contributors not necessarily located in the
+area to be mapped. In this paper, we describe the design, implementation, and
+evaluation of the Virtual City Explorer (VCE), a system to collect the
+coordinates of Points of Interest within a bounded area on top of a street view
+service with the use of paid crowdworkers. Our evaluation suggests that paid
+crowdworkers are effective for finding PoIs, and cover almost all the area.
+With respect to completeness, our approach does not find all PoIs found by
+experts or VGI communities, but is able to find PoIs that were not found by
+them, suggesting complementarity. We also studied the impact of making PoIs
+already discovered by a certain number of workers \emph{taboo} for incoming
+workers, finding that it encourages more exploration from workers , increase
+the number of detected PoIs , and reduce costs.
+",1,0,0,0,0,0
+11155,Complex tensor factorisation with PARAFAC2 for the estimation of brain connectivity from the EEG,"  Objective: The coupling between neuronal populations and its magnitude have
+been shown to be informative for various clinical applications. One method to
+estimate brain connectivity is with electroencephalography (EEG) from which the
+cross-spectrum between different sensor locations is derived. We wish to test
+the efficacy of tensor factorisation in the estimation of brain connectivity.
+Methods: Complex tensor factorisation based on PARAFAC2 is used to decompose
+the EEG into scalp components described by the spatial, spectral, and complex
+trial profiles. An EEG model in the complex domain was derived that shows the
+suitability of PARAFAC2. A connectivity metric was also derived on the complex
+trial profiles of the extracted components. Results: Results on a benchmark EEG
+dataset confirmed that PARAFAC2 can estimate connectivity better than
+traditional tensor analysis such as PARAFAC within a range of signal-to-noise
+ratios. The analysis of EEG from patients with mild cognitive impairment or
+Alzheimer's disease showed that PARAFAC2 identifies loss of brain connectivity
+better than traditional approaches and agreeing with prior pathological
+knowledge. Conclusion: The complex PARAFAC2 algorithm is suitable for EEG
+connectivity estimation since it allows to extract meaningful coupled sources
+and provides better estimates than complex PARAFAC. Significance: A new
+paradigm that employs complex tensor factorisation has demonstrated to be
+successful in identifying brain connectivity and the location of couples
+sources for both a benchmark and a real-world EEG dataset. This can enable
+future applications and has the potential to solve some the issues that
+deteriorate the performance of traditional connectivity metrics.
+",1,0,0,0,0,0
+11156,A New Approach of Exploiting Self-Adjoint Matrix Polynomials of Large Random Matrices for Anomaly Detection and Fault Location,"  Synchronized measurements of a large power grid enable an unprecedented
+opportunity to study the spatialtemporal correlations. Statistical analytics
+for those massive datasets start with high-dimensional data matrices.
+Uncertainty is ubiquitous in a future's power grid. These data matrices are
+recognized as random matrices. This new point of view is fundamental in our
+theoretical analysis since true covariance matrices cannot be estimated
+accurately in a high-dimensional regime. As an alternative, we consider
+large-dimensional sample covariance matrices in the asymptotic regime to
+replace the true covariance matrices. The self-adjoint polynomials of
+large-dimensional random matrices are studied as statistics for big data
+analytics. The calculation of the asymptotic spectrum distribution (ASD) for
+such a matrix polynomial is understandably challenging. This task is made
+possible by a recent breakthrough in free probability, an active research
+branch in random matrix theory. This is the very reason why the work of this
+paper is inspired initially. The new approach is interesting in many aspects.
+The mathematical reason may be most critical. The real-world problems can be
+solved using this approach, however.
+",0,0,0,1,0,0
+11157,Bayesian Pool-based Active Learning With Abstention Feedbacks,"  We study pool-based active learning with abstention feedbacks, where a
+labeler can abstain from labeling a queried example with some unknown
+abstention rate. This is an important problem with many useful applications. We
+take a Bayesian approach to the problem and develop two new greedy algorithms
+that learn both the classification problem and the unknown abstention rate at
+the same time. These are achieved by simply incorporating the estimated
+abstention rate into the greedy criteria. We prove that both of our algorithms
+have near-optimality guarantees: they respectively achieve a
+${(1-\frac{1}{e})}$ constant factor approximation of the optimal expected or
+worst-case value of a useful utility function. Our experiments show the
+algorithms perform well in various practical scenarios.
+",1,0,0,1,0,0
+11158,Multiconfigurational Short-Range Density-Functional Theory for Open-Shell Systems,"  Many chemical systems cannot be described by quantum chemistry methods based
+on a singlereference wave function. Accurate predictions of energetic and
+spectroscopic properties require a delicate balance between describing the most
+important configurations (static correlation) and obtaining dynamical
+correlation efficiently. The former is most naturally done through a
+multiconfigurational (MC) wave function, whereas the latter can be done by,
+e.g., perturbation theory. We have employed a different strategy, namely, a
+hybrid between multiconfigurational wave functions and density-functional
+theory (DFT) based on range separation. The method is denoted by MC short-range
+(sr) DFT and is more efficient than perturbative approaches as it capitalizes
+on the efficient treatment of the (short-range) dynamical correlation by DFT
+approximations. In turn, the method also improves DFT with standard
+approximations through the ability of multiconfigurational wave functions to
+recover large parts of the static correlation. Until now, our implementation
+was restricted to closed-shell systems, and to lift this restriction, we
+present here the generalization of MC-srDFT to open-shell cases. The additional
+terms required to treat open-shell systems are derived and implemented in the
+DALTON program. This new method for open-shell systems is illustrated on
+dioxygen and [Fe(H2O)6]3+.
+",0,1,0,0,0,0
+11159,Shape recognition of volcanic ash by simple convolutional neural network,"  Shape analyses of tephra grains result in understanding eruption mechanism of
+volcanoes. However, we have to define and select parameter set such as
+convexity for the precise discrimination of tephra grains. Selection of the
+best parameter set for the recognition of tephra shapes is complicated.
+Actually, many shape parameters have been suggested. Recently, neural network
+has made a great success in the field of machine learning. Convolutional neural
+network can recognize the shape of images without human bias and shape
+parameters. We applied the simple convolutional neural network developed for
+the handwritten digits to the recognition of tephra shapes. The network was
+trained by Morphologi tephra images, and it can recognize the tephra shapes
+with approximately 90% of accuracy.
+",1,1,0,0,0,0
+11160,"The Uranie platform: an Open-source software for optimisation, meta-modelling and uncertainty analysis","  The high-performance computing resources and the constant improvement of both
+numerical simulation accuracy and the experimental measurements with which they
+are confronted, bring a new compulsory step to strengthen the credence given to
+the simulation results: uncertainty quantification. This can have different
+meanings, according to the requested goals (rank uncertainty sources, reduce
+them, estimate precisely a critical threshold or an optimal working point) and
+it could request mathematical methods with greater or lesser complexity. This
+paper introduces the Uranie platform, an Open-source framework which is
+currently developed at the Alternative Energies and Atomic Energy Commission
+(CEA), in the nuclear energy division, in order to deal with uncertainty
+propagation, surrogate models, optimisation issues, code calibration... This
+platform benefits from both its dependencies, but also from personal
+developments, to offer an efficient data handling model, a C++ and Python
+interpreter, advanced graphical tools, several parallelisation solutions...
+These methods are very generic and can then be applied to many kinds of code
+(as Uranie considers them as black boxes) so to many fields of physics as well.
+In this paper, the example of thermal exchange between a plate-sheet and a
+fluid is introduced to show how Uranie can be used to perform a large range of
+analysis. The code used to produce the figures of this paper can be found in
+this https URL along with the sources of the
+platform.
+",0,0,0,1,0,0
+11161,Learning Convolutional Text Representations for Visual Question Answering,"  Visual question answering is a recently proposed artificial intelligence task
+that requires a deep understanding of both images and texts. In deep learning,
+images are typically modeled through convolutional neural networks, and texts
+are typically modeled through recurrent neural networks. While the requirement
+for modeling images is similar to traditional computer vision tasks, such as
+object recognition and image classification, visual question answering raises a
+different need for textual representation as compared to other natural language
+processing tasks. In this work, we perform a detailed analysis on natural
+language questions in visual question answering. Based on the analysis, we
+propose to rely on convolutional neural networks for learning textual
+representations. By exploring the various properties of convolutional neural
+networks specialized for text data, such as width and depth, we present our
+""CNN Inception + Gate"" model. We show that our model improves question
+representations and thus the overall accuracy of visual question answering
+models. We also show that the text representation requirement in visual
+question answering is more complicated and comprehensive than that in
+conventional natural language processing tasks, making it a better task to
+evaluate textual representation methods. Shallow models like fastText, which
+can obtain comparable results with deep learning models in tasks like text
+classification, are not suitable in visual question answering.
+",1,0,0,1,0,0
+11162,Cross-stream migration of active particles,"  For natural microswimmers, the interplay of swimming activity and external
+flow can promote robust motion, e.g. propulsion against (""upstream rheotaxis"")
+or perpendicular to the direction of flow. These effects are generally
+attributed to their complex body shapes and flagellar beat patterns. Here,
+using catalytic Janus particles as a model experimental system, we report on a
+strong directional response that occurs for spherical active particles in a
+channel flow. The particles align their propulsion axes to be nearly
+perpendicular to both the direction of flow and the normal vector of a nearby
+bounding surface. We develop a deterministic theoretical model of spherical
+microswimmers near a planar wall that captures the experimental observations.
+We show how the directional response emerges from the interplay of shear flow
+and near-surface swimming activity. Finally, adding the effect of thermal
+noise, we obtain probability distributions for the swimmer orientation that
+semi-quantitatively agree with the experimental distributions.
+",0,1,0,0,0,0
+11163,Analysis of the Polya-Gamma block Gibbs sampler for Bayesian logistic linear mixed models,"  In this article, we construct a two-block Gibbs sampler using Polson et al.
+(2013) data augmentation technique with Polya-Gamma latent variables for
+Bayesian logistic linear mixed models under proper priors. Furthermore, we
+prove the uniform ergodicity of this Gibbs sampler, which guarantees the
+existence of the central limit theorems for MCMC based estimators.
+",0,0,1,1,0,0
+11164,Direct and Simultaneous Observation of Ultrafast Electron and Hole Dynamics in Germanium,"  Understanding excited carrier dynamics in semiconductors is crucial for the
+development of photovoltaics and efficient photonic devices. However,
+overlapping spectral features in optical/NIR pump-probe spectroscopy often
+render assignments of separate electron and hole carrier dynamics ambiguous.
+Here, ultrafast electron and hole dynamics in germanium nanocrystalline thin
+films are directly and simultaneously observed by attosecond transient
+absorption spectroscopy (ATAS) in the extreme ultraviolet at the germanium
+M_{4,5}-edge (~30 eV). We decompose the ATAS spectra into contributions of
+electronic state blocking and photo-induced band shifts at a carrier density of
+8*10^{20}cm^{-3}. Separate electron and hole relaxation times are observed as a
+function of hot carrier energies. A first order electron and hole decay of ~1
+ps suggests a Shockley-Read-Hall recombination mechanism. The simultaneous
+observation of electrons and holes with ATAS paves the way for investigating
+few to sub-femtosecond dynamics of both holes and electrons in complex
+semiconductor materials and across junctions.
+",0,1,0,0,0,0
+11165,"Introducing the Robot Security Framework (RSF), a standardized methodology to perform security assessments in robotics","  Robots have gained relevance in society, increasingly performing critical
+tasks. Nonetheless, robot security is being underestimated. Robotics security
+is a complex landscape, which often requires a cross-disciplinar perspective to
+which classical security lags behind. To address this issue, we present the
+Robot Security Framework (RSF), a methodology to perform systematic security
+assessments in robots. We propose, adapt and develop specific terminology and
+provide guidelines to enable a holistic security assessment following four main
+layers (Physical, Network, Firmware and Application). We argue that modern
+robotics should regard as equally relevant internal and external communication
+security. Finally, we advocate against ""security by obscurity"". We conclude
+that the field of security in robotics deserves further research efforts.
+",1,0,0,0,0,0
+11166,Statistical Verification of Computational Rapport Model,"  Rapport plays an important role during communication because it can help
+people understand each other's feelings or ideas and leads to a smooth
+communication. Computational rapport model has been proposed based on theory in
+previous work. But there lacks solid verification. In this paper, we apply
+structural equation model (SEM) to the theoretical model on both dyads of
+friend and stranger. The results indicate some unfavorable paths. Based on the
+results and more literature, we modify the original model to integrate more
+nonverbal behaviors, including gaze and smile. Fit indices and other
+examination show the goodness of our new models, which can give us more insight
+into rapport management during conversation.
+",1,0,0,0,0,0
+11167,Least models of second-order set theories,"  The main theorems of this paper are (1) there is no least transitive model of
+Kelley--Morse set theory $\mathsf{KM}$ and (2) there is a least
+$\beta$-model---that is, a transitive model which is correct about which of its
+classes are well-founded---of Gödel--Bernays set theory $\mathsf{GBC}$ +
+Elementary Transfinite Recursion. Along the way I characterize when a countable
+model of $\mathsf{ZFC}$ has a least $\mathsf{GBC}$-realization and show that no
+countable model of $\mathsf{ZFC}$ has a least $\mathsf{KM}$-realization. I also
+show that fragments of Elementary Transfinite Recursion have least
+$\beta$-models and, for sufficiently weak fragments, least transitive models.
+These fragments can be separated from each other and from the full principle of
+Elementary Transfinite Recursion by consistency strength. The main question
+left unanswered by this article is whether there is a least transitive model of
+$\mathsf{GBC}$ + Elementary Transfinite Recursion.
+",0,0,1,0,0,0
+11168,Characterizing K2 Candidate Planetary Systems Orbiting Low-Mass Stars I: Classifying Low-mass Host Stars Observed During Campaigns 1-7,"  We present near-infrared spectra for 144 candidate planetary systems
+identified during Campaigns 1-7 of the NASA K2 Mission. The goal of the survey
+was to characterize planets orbiting low-mass stars, but our IRTF/SpeX and
+Palomar/TripleSpec spectroscopic observations revealed that 49% of our targets
+were actually giant stars or hotter dwarfs reddened by interstellar extinction.
+For the 72 stars with spectra consistent with classification as cool dwarfs
+(spectral types K3 - M4), we refined their stellar properties by applying
+empirical relations based on stars with interferometric radius measurements.
+Although our revised temperatures are generally consistent with those reported
+in the Ecliptic Plane Input Catalog (EPIC), our revised stellar radii are
+typically 0.13 solar radii (39%) larger than the EPIC values, which were based
+on model isochrones that have been shown to underestimate the radii of cool
+dwarfs. Our improved stellar characterizations will enable more efficient
+prioritization of K2 targets for follow-up studies.
+",0,1,0,0,0,0
+11169,Binary orbits from combined astrometric and spectroscopic data,"  An efficient Bayesian technique for estimation problems in fundamental
+stellar astronomy is tested on simulated data for a binary observed both
+astrometrically and spectroscopically. Posterior distributions are computed for
+the components' masses and for the binary's parallax. One thousand independent
+repetitions of the simulation demonstrate that the 1- and 2-$\!\sigma$
+credibility intervals for these fundamental quantities have close to the
+correct coverage fractions. In addition, the simulations allow the
+investigation of the statistical properties of a Bayesian goodness-of-fit
+criterion and of the corresponding p-value. The criterion has closely similar
+properties to the traditional chi^{2} test for minimum-chi^{2} solutions.
+",0,1,0,0,0,0
+11170,Gyrotropic Zener tunneling and nonlinear IV curves in the zero-energy Landau level of graphene in a strong magnetic field,"  We have investigated tunneling current through a suspended graphene Corbino
+disk in high magnetic fields at the Dirac point, i.e. at filling factor $\nu$ =
+0. At the onset of the dielectric breakdown the current through the disk grows
+exponentially before ohmic behaviour, but in a manner distinct from thermal
+activation. We find that Zener tunneling between Landau sublevels dominates,
+facilitated by tilting of the source-drain bias potential. According to our
+analytic modelling, the Zener tunneling is strongly affected by the gyrotropic
+force (Lorentz force) due to the high magnetic field
+",0,1,0,0,0,0
+11171,"Accurate, Efficient and Scalable Graph Embedding","  The Graph Convolutional Network (GCN) model and its variants are powerful
+graph embedding tools for facilitating classification and clustering on graphs.
+However, a major challenge is to reduce the complexity of layered GCNs and make
+them parallelizable and scalable on very large graphs --- state-of the art
+techniques are unable to achieve scalability without losing accuracy and
+efficiency. In this paper, we propose novel parallelization techniques for
+graph sampling-based GCNs that achieve superior scalable performance on very
+large graphs without compromising accuracy. Specifically, our GCN guarantees
+work-efficient training and produces order of magnitude savings in computation
+and communication. To scale GCN training on tightly-coupled shared memory
+systems, we develop parallelization strategies for the key steps in training:
+For the graph sampling step, we exploit parallelism within and across multiple
+sampling instances, and devise an efficient data structure for concurrent
+accesses that provides theoretical guarantee of near-linear speedup with number
+of processing units. For the feature propagation step within the sampled graph,
+we improve cache utilization and reduce DRAM communication by data
+partitioning. We prove that our partitioning strategy is a 2-approximation for
+minimizing the communication time compared to the optimal strategy. We
+demonstrate that our parallel graph embedding outperforms state-of-the-art
+methods in scalability (with respect to number of processors, graph size and
+GCN model size), efficiency and accuracy on several large datasets. On a
+40-core Xeon platform, our parallel training achieves 64$\times$ speedup (with
+AVX) in the sampling step and 25$\times$ speedup in the feature propagation
+step, compared to the serial implementation, resulting in a net speedup of
+21$\times$.
+",1,0,0,0,0,0
+11172,A principled methodology for comparing relatedness measures for clustering publications,"  There are many different relatedness measures, based for instance on citation
+relations or textual similarity, that can be used to cluster scientific
+publications. We propose a principled methodology for evaluating the accuracy
+of clustering solutions obtained using these relatedness measures. We formally
+show that the proposed methodology has an important consistency property. The
+empirical analyses that we present are based on publications in the fields of
+cell biology, condensed matter physics, and economics. Using the BM25
+text-based relatedness measure as evaluation criterion, we find that
+bibliographic coupling relations yield more accurate clustering solutions than
+direct citation relations and co-citation relations. The so-called extended
+direct citation approach performs similarly to or slightly better than
+bibliographic coupling in terms of the accuracy of the resulting clustering
+solutions. The other way around, using a citation-based relatedness measure as
+evaluation criterion, BM25 turns out to yield more accurate clustering
+solutions than other text-based relatedness measures.
+",1,0,0,0,0,0
+11173,A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce,"  Nowadays many companies have available large amounts of raw, unstructured
+data. Among Big Data enabling technologies, a central place is held by the
+MapReduce framework and, in particular, by its open source implementation,
+Apache Hadoop. For cost effectiveness considerations, a common approach entails
+sharing server clusters among multiple users. The underlying infrastructure
+should provide every user with a fair share of computational resources,
+ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In
+this paper we consider two mathematical programming problems that model the
+optimal allocation of computational resources in a Hadoop 2.x cluster with the
+aim to develop new capacity allocation techniques that guarantee better
+performance in shared data centers. Our goal is to get a substantial reduction
+of power consumption while respecting the deadlines stated in the SLAs and
+avoiding penalties associated with job rejections. The core of this approach is
+a distributed algorithm for runtime capacity allocation, based on Game Theory
+models and techniques, that mimics the MapReduce dynamics by means of
+interacting players, namely the central Resource Manager and Class Managers.
+",1,0,0,0,0,0
+11174,Holographic Butterfly Effect and Diffusion in Quantum Critical Region,"  We investigate the butterfly effect and charge diffusion near the quantum
+phase transition in holographic approach. We argue that their criticality is
+controlled by the holographic scaling geometry with deformations induced by a
+relevant operator at finite temperature. Specifically, in the quantum critical
+region controlled by a single fixed point, the butterfly velocity decreases
+when deviating from the critical point. While, in the non-critical region, the
+behavior of the butterfly velocity depends on the specific phase at low
+temperature. Moreover, in the holographic Berezinskii-Kosterlitz-Thouless
+transition, the universal behavior of the butterfly velocity is absent.
+Finally, the tendency of our holographic results matches with the numerical
+results of Bose-Hubbard model. A comparison between our result and that in the
+$O(N)$ nonlinear sigma model is also given.
+",0,1,0,0,0,0
+11175,High-temperature charge density wave correlations in La$_{1.875}$Ba$_{0.125}$CuO$_{4}$ without spin-charge locking,"  Although all superconducting cuprates display charge-ordering tendencies,
+their low-temperature properties are distinct, impeding efforts to understand
+the phenomena within a single conceptual framework. While some systems exhibit
+stripes of charge and spin, with a locked periodicity, others host charge
+density waves (CDWs) without any obviously related spin order. Here we use
+resonant inelastic x-ray scattering (RIXS) to follow the evolution of charge
+correlations in the canonical stripe ordered cuprate
+La$_{1.875}$Ba$_{0.125}$CuO$_{4}$ (LBCO~$1/8$) across its ordering transition.
+We find that high-temperature charge correlations are unlocked from the
+wavevector of the spin correlations, signaling analogies to CDW phases in
+various other cuprates. This indicates that stripe order at low temperatures is
+stabilized by the coupling of otherwise independent charge and spin density
+waves, with important implications for the relation between charge and spin
+correlations in the cuprates.
+",0,1,0,0,0,0
+11176,A unified deep artificial neural network approach to partial differential equations in complex geometries,"  In this paper we use deep feedforward artificial neural networks to
+approximate solutions to partial differential equations in complex geometries.
+We show how to modify the backpropagation algorithm to compute the partial
+derivatives of the network output with respect to the space variables which is
+needed to approximate the differential operator. The method is based on an
+ansatz for the solution which requires nothing but feedforward neural networks
+and an unconstrained gradient based optimization method such as gradient
+descent or a quasi-Newton method.
+We show an example where classical mesh based methods cannot be used and
+neural networks can be seen as an attractive alternative. Finally, we highlight
+the benefits of deep compared to shallow neural networks and device some other
+convergence enhancing techniques.
+",1,0,0,1,0,0
+11177,Gradient Flows in Uncertainty Propagation and Filtering of Linear Gaussian Systems,"  The purpose of this work is mostly expository and aims to elucidate the
+Jordan-Kinderlehrer-Otto (JKO) scheme for uncertainty propagation, and a
+variant, the Laugesen-Mehta-Meyn-Raginsky (LMMR) scheme for filtering. We point
+out that these variational schemes can be understood as proximal operators in
+the space of density functions, realizing gradient flows. These schemes hold
+the promise of leading to efficient ways for solving the Fokker-Planck equation
+as well as the equations of non-linear filtering. Our aim in this paper is to
+develop in detail the underlying ideas in the setting of linear stochastic
+systems with Gaussian noise and recover known results.
+",1,0,1,0,0,0
+11178,Investigating Simulation-Based Metrics for Characterizing Linear Iterative Reconstruction in Digital Breast Tomosynthesis,"  Simulation-based image quality metrics are adapted and investigated for
+characterizing the parameter dependences of linear iterative image
+reconstruction for DBT. Three metrics based on 2D DBT simulation are
+investigated: (1) a root-mean-square-error (RMSE) between the test phantom and
+reconstructed image, (2) a gradient RMSE where the comparison is made after
+taking a spatial gradient of both image and phantom, and (3) a
+region-of-interest (ROI) Hotelling observer (HO) for
+signal-known-exactly/background-known-exactly (SKE/BKE) and
+signal-known-exactly/background-known-statistically (SKE/BKS) detection tasks.
+Two simulation studies are performed using the aforementioned metrics, varying
+voxel aspect ratio and regularization strength for two types of Tikhonov
+regularized least-squares optimization. The RMSE metrics are applied to a 2D
+test phantom and the ROI-HO metric is applied to two tasks relevant to DBT:
+large, low contrast lesion detection and small, high contrast
+microcalcification detection. The RMSE metric trends are compared with visual
+assessment of the reconstructed test phantom. The ROI-HO metric trends are
+compared with 3D reconstructed images from ACR phantom data acquired with a
+Hologic Selenia Dimensions DBT system. Sensitivity of image RMSE to mean pixel
+value is found to limit its applicability to the assessment of DBT image
+reconstruction. Image gradient RMSE is insensitive to mean pixel value and
+appears to track better with subjective visualization of the reconstructed
+bar-pattern phantom. The ROI-HO metric shows an increasing trend with
+regularization strength for both forms of Tikhonov-regularized least-squares;
+however, this metric saturates at intermediate regularization strength
+indicating a point of diminishing returns for signal detection. Visualization
+with reconstructed ACR phantom images appears to show a similar dependence with
+regularization strength.
+",0,1,0,0,0,0
+11179,Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem,"  Temporal-Difference learning (TD) [Sutton, 1988] with function approximation
+can converge to solutions that are worse than those obtained by Monte-Carlo
+regression, even in the simple case of on-policy evaluation. To increase our
+understanding of the problem, we investigate the issue of approximation errors
+in areas of sharp discontinuities of the value function being further
+propagated by bootstrap updates. We show empirical evidence of this leakage
+propagation, and show analytically that it must occur, in a simple Markov
+chain, when function approximation errors are present. For reversible policies,
+the result can be interpreted as the tension between two terms of the loss
+function that TD minimises, as recently described by [Ollivier, 2018]. We show
+that the upper bounds from [Tsitsiklis and Van Roy, 1997] hold, but they do not
+imply that leakage propagation occurs and under what conditions. Finally, we
+test whether the problem could be mitigated with a better state representation,
+and whether it can be learned in an unsupervised manner, without rewards or
+privileged information.
+",0,0,0,1,0,0
+11180,First Order Theories of Some Lattices of Open Sets,"  We show that the first order theory of the lattice of open sets in some
+natural topological spaces is $m$-equivalent to second order arithmetic. We
+also show that for many natural computable metric spaces and computable domains
+the first order theory of the lattice of effectively open sets is undecidable.
+Moreover, for several important spaces (e.g., $\mathbb{R}^n$, $n\geq1$, and the
+domain $P\omega$) this theory is $m$-equivalent to first order arithmetic.
+",1,0,1,0,0,0
+11181,Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation,"  A cloud server spent a lot of time, energy and money to train a Viola-Jones
+type object detector with high accuracy. Clients can upload their photos to the
+cloud server to find objects. However, the client does not want the leakage of
+the content of his/her photos. In the meanwhile, the cloud server is also
+reluctant to leak any parameters of the trained object detectors. 10 years ago,
+Avidan & Butman introduced Blind Vision, which is a method for securely
+evaluating a Viola-Jones type object detector. Blind Vision uses standard
+cryptographic tools and is painfully slow to compute, taking a couple of hours
+to scan a single image. The purpose of this work is to explore an efficient
+method that can speed up the process. We propose the Random Base Image (RBI)
+Representation. The original image is divided into random base images. Only the
+base images are submitted randomly to the cloud server. Thus, the content of
+the image can not be leaked. In the meanwhile, a random vector and the secure
+Millionaire protocol are leveraged to protect the parameters of the trained
+object detector. The RBI makes the integral-image enable again for the great
+acceleration. The experimental results reveal that our method can retain the
+detection accuracy of that of the plain vision algorithm and is significantly
+faster than the traditional blind vision, with only a very low probability of
+the information leakage theoretically.
+",1,0,0,0,0,0
+11182,Lagrangian Statistics for Navier-Stokes Turbulence under Fourier-mode reduction: Fractal and Homogeneous Decimations,"  We study small-scale and high-frequency turbulent fluctuations in
+three-dimensional flows under Fourier-mode reduction. The Navier-Stokes
+equations are evolved on a restricted set of modes, obtained as a projection on
+a fractal or homogeneous Fourier set. We find a strong sensitivity (reduction)
+of the high-frequency variability of the Lagrangian velocity fluctuations on
+the degree of mode decimation, similarly to what is already reported for
+Eulerian statistics. This is quantified by a tendency towards a quasi-Gaussian
+statistics, i.e., to a reduction of intermittency, at all scales and
+frequencies. This can be attributed to a strong depletion of vortex filaments
+and of the vortex stretching mechanism. Nevertheless, we found that Eulerian
+and Lagrangian ensembles are still connected by a dimensional bridge-relation
+which is independent of the degree of Fourier-mode decimation.
+",0,1,0,0,0,0
+11183,Conjunctive management of surface and groundwater under severe drought: A case study in southern Iran,"  Hormozgan Province, located in the south of Iran, faces several challenges
+regarding water resources management. The first one is the discharge of a
+massive volume of water to the Persian Gulf because of the concentration of the
+annual rainfalls in a short period of time and the narrow distance between the
+headwater and the coast. The second one is the unbalanced development of
+economic sectors in comparison with distribution of fresh water resources.
+Finally, long-term drought is also common in this area. The construction of a
+carry-over dam (Esteghlal Dam) and several conveyance pipelines and withdrawing
+of the surface water and groundwater resources were considered as the solution
+to deal with those challenges. During recent drought, severe overdraft and
+inefficient use of recourses confirmed the fact that all done before are not
+enough. During this period, there was a tendency to store water in reservoir in
+order to meet the demand of the urban sector. Therefore, the agricultural
+demand was the first victim of water allocation policy. It caused over
+exploitation of the groundwater resources (to meet the agricultural demand) and
+considerable losses (evaporation and leakage) from the reservoir. All of the
+above-mentioned problems confirm the necessity of the development of a
+conjunctive use policy. In this paper, all demand related to the Esteghlal Dam
+and the Minab Aquifer (Bandarabbas City and agriculture in the Minab Plain)
+were considered as the case study. The main objective was to find the best
+applicable conjunctive policy which as well guarantees the conservation of the
+Minab Aquifer. Alternative water allocation policies have been developed based
+on the present capacities and the experience of local operating staff.
+",0,1,0,0,0,0
+11184,Classification in biological networks with hypergraphlet kernels,"  Biological and cellular systems are often modeled as graphs in which vertices
+represent objects of interest (genes, proteins, drugs) and edges represent
+relational ties among these objects (binds-to, interacts-with, regulates). This
+approach has been highly successful owing to the theory, methodology and
+software that support analysis and learning on graphs. Graphs, however, often
+suffer from information loss when modeling physical systems due to their
+inability to accurately represent multiobject relationships. Hypergraphs, a
+generalization of graphs, provide a framework to mitigate information loss and
+unify disparate graph-based methodologies. In this paper, we present a
+hypergraph-based approach for modeling physical systems and formulate vertex
+classification, edge classification and link prediction problems on
+(hyper)graphs as instances of vertex classification on (extended, dual)
+hypergraphs in a semi-supervised setting. We introduce a novel kernel method on
+vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The
+method is based on exact and inexact (via hypergraph edit distances)
+enumeration of small simple hypergraphs, referred to as hypergraphlets, rooted
+at a vertex of interest. We extensively evaluate this method and show its
+potential use in a positive-unlabeled setting to estimate the number of missing
+and false positive links in protein-protein interaction networks.
+",1,0,0,1,0,0
+11185,Pair Correlation and Gap Distributions for Substitution Tilings and Generalized Ulam Sets in the Plane,"  We study empirical statistical and gap distributions of several important
+tilings of the plane. In particular, we consider the slope distributions, the
+angle distributions, pair correlation, squared-distance pair correlation, angle
+gap distributions, and slope gap distributions for the Ammann Chair tiling, the
+recently discovered fifteenth pentagonal tiling, and a few pertinent tilings
+related to these famous examples. We also consider the spatial statistics of
+generalized Ulam sets in two dimensions. Additionally, we carefully prove a
+tight asymptotic formula for the time steps in which Ulam set points at certain
+prescribed geometric positions in their plots in the plane formally enter the
+recursively-defined sets.
+The software we have developed to these generate numerical approximations to
+the distributions for the tilings we consider here is written in Python under
+the Sage environment and is released as open-source software which is available
+freely on our websites. In addition to the small subset of tilings and other
+point sets in the plane we study within the article, our program supports many
+other tiling variants and is easily extended for researchers to explore related
+tilings and iterative sets.
+",0,0,1,0,0,0
+11186,"Pay-with-a-Selfie, a human-centred digital payment system","  Mobile payment systems are increasingly used to simplify the way in which
+money transfers and transactions can be performed. We argue that, to achieve
+their full potential as economic boosters in developing countries, mobile
+payment systems need to rely on new metaphors suitable for the business models,
+lifestyle, and technology availability conditions of the targeted communities.
+The Pay-with-a-Group-Selfie (PGS) project, funded by the Melinda & Bill Gates
+Foundation, has developed a micro-payment system that supports everyday small
+transactions by extending the reach of, rather than substituting, existing
+payment frameworks. PGS is based on a simple gesture and a readily
+understandable metaphor. The gesture - taking a selfie - has become part of the
+lifestyle of mobile phone users worldwide, including non-technology-savvy ones.
+The metaphor likens computing two visual shares of the selfie to ripping a
+banknote in two, a technique used for decades for delayed payment in cash-only
+markets. PGS is designed to work with devices with limited computational power
+and when connectivity is patchy or not always available. Thanks to visual
+cryptography techniques PGS uses for computing the shares, the original selfie
+can be recomposed simply by stacking the shares, preserving the analogy with
+re-joining the two parts of the banknote.
+",1,0,0,0,0,0
+11187,LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems,"  Deep convolutional Neural Networks (CNN) are the state-of-the-art performers
+for object detection task. It is well known that object detection requires more
+computation and memory than image classification. Thus the consolidation of a
+CNN-based object detection for an embedded system is more challenging. In this
+work, we propose LCDet, a fully-convolutional neural network for generic object
+detection that aims to work in embedded systems. We design and develop an
+end-to-end TensorFlow(TF)-based model. Additionally, we employ 8-bit
+quantization on the learned weights. We use face detection as a use case. Our
+TF-Slim based network can predict different faces of different shapes and sizes
+in a single forward pass. Our experimental results show that the proposed
+method achieves comparative accuracy comparing with state-of-the-art CNN-based
+face detection methods, while reducing the model size by 3x and memory-BW by
+~4x comparing with one of the best real-time CNN-based object detector such as
+YOLO. TF 8-bit quantized model provides additional 4x memory reduction while
+keeping the accuracy as good as the floating point model. The proposed model
+thus becomes amenable for embedded implementations.
+",1,0,0,0,0,0
+11188,Phase transitions of a 2D deformed-AKLT model,"  We study spin-2 deformed-AKLT models on the square lattice, specifically a
+two-parameter family of $O(2)$-symmetric ground-state wavefunctions as defined
+by Niggemann, Klümper, and Zittartz, who found previously that the phase
+diagram consists of a Néel-ordered phase and a disordered phase which
+contains the AKLT point. Using tensor-network methods, we not only confirm the
+Néel phase but also find an XY phase with quasi-long-range order and a region
+adjacent to it, within the AKLT phase, with very large correlation length, and
+investigate the consequences of a perfectly factorizable point at the corner of
+that phase.
+",0,1,0,0,0,0
+11189,Statistical estimation of superhedging prices,"  We consider statistical estimation of superhedging prices using historical
+stock returns in a frictionless market with d traded assets. We introduce a
+simple plugin estimator based on empirical measures, show it is consistent but
+lacks suitable robustness. This is addressed by our improved estimators which
+use a larger set of martingale measures defined through a tradeoff between the
+radius of Wasserstein balls around the empirical measure and the allowed norm
+of martingale densities. We also study convergence rates, convergence of
+superhedging strategies, and our study extends, in part, to the case of a
+market with traded options and to a multiperiod setting.
+",0,0,0,0,0,1
+11190,Ore's theorem on subfactor planar algebras,"  This paper proves that an irreducible subfactor planar algebra with a
+distributive biprojection lattice admits a minimal 2-box projection generating
+the identity biprojection. It is a generalization of a theorem of Ore on
+intervals of finite groups, conjectured by the author since 2013. We deduce a
+link between combinatorics and representations in finite groups theory, related
+to an open problem of K.S. Brown in algebraic combinatorics.
+",0,0,1,0,0,0
+11191,Machine Learning CICY Threefolds,"  The latest techniques from Neural Networks and Support Vector Machines (SVM)
+are used to investigate geometric properties of Complete Intersection
+Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model
+building. An advanced neural network classifier and SVM are employed to (1)
+learn Hodge numbers and report a remarkable improvement over previous efforts,
+(2) query for favourability, and (3) predict discrete symmetries, a highly
+imbalanced problem to which both Synthetic Minority Oversampling Technique
+(SMOTE) and permutations of the CICY matrix are used to decrease the class
+imbalance and improve performance. In each case study, we employ a genetic
+algorithm to optimise the hyperparameters of the neural network. We demonstrate
+that our approach provides quick diagnostic tools capable of shortlisting
+quasi-realistic string models based on compactification over smooth CICYs and
+further supports the paradigm that classes of problems in algebraic geometry
+can be machine learned.
+",0,0,0,1,0,0
+11192,Weak lensing deflection of three-point correlation functions,"  Weak gravitational lensing alters the apparent separations between observed
+sources, potentially affecting clustering statistics. We derive a general
+expression for the lensing deflection which is valid for any three-point
+statistic, and investigate its effect on the three-point clustering correlation
+function. We find that deflection of the clustering correlation function is
+greatest at around $z=2$. It is most prominent in regions where the correlation
+function varies rapidly, in particular at the baryon acoustic oscillation scale
+where it smooths out the peaks and troughs, reducing the peak-to-trough
+difference by about 0.1 percent at $z=1$ and around 2.3 percent at $z=10$. The
+modification due to lensing deflection is typically at the per cent level of
+the expected errors in a Euclid-like survey and therefore undetectable.
+",0,1,0,0,0,0
+11193,Solitonic dynamics and excitations of the nonlinear Schrodinger equation with third-order dispersion in non-Hermitian PT-symmetric potentials,"  Solitons are of the important significant in many fields of nonlinear science
+such as nonlinear optics, Bose-Einstein condensates, plamas physics, biology,
+fluid mechanics, and etc.. The stable solitons have been captured not only
+theoretically and experimentally in both linear and nonlinear Schrodinger (NLS)
+equations in the presence of non-Hermitian potentials since the concept of the
+parity-time (PT)-symmetry was introduced in 1998. In this paper, we present
+novel bright solitons of the NLS equation with third-order dispersion in some
+complex PT-symmetric potentials (e.g., physically relevant PT-symmetric
+Scarff-II-like and harmonic-Gaussian potentials). We find stable nonlinear
+modes even if the respective linear PT-symmetric phases are broken. Moreover,
+we also use the adiabatic changes of the control parameters to excite the
+initial modes related to exact solitons to reach stable nonlinear modes. The
+elastic interactions of two solitons are exhibited in the third-order NLS
+equation with PT-symmetric potentials. Our results predict the dynamical
+phenomena of soliton equations in the presence of third-order dispersion and
+PT-symmetric potentials arising in nonlinear fiber optics and other physically
+relevant fields.
+",0,1,1,0,0,0
+11194,Supporting Ruled Polygons,"  We explore several problems related to ruled polygons. Given a ruling of a
+polygon $P$, we consider the Reeb graph of $P$ induced by the ruling. We define
+the Reeb complexity of $P$, which roughly equates to the minimum number of
+points necessary to support $P$. We give asymptotically tight bounds on the
+Reeb complexity that are also tight up to a small additive constant. When
+restricted to the set of parallel rulings, we show that the Reeb complexity can
+be computed in polynomial time.
+",1,0,0,0,0,0
+11195,Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data,"  The recent Nobel-prize-winning detections of gravitational waves from merging
+black holes and the subsequent detection of the collision of two neutron stars
+in coincidence with electromagnetic observations have inaugurated a new era of
+multimessenger astrophysics. To enhance the scope of this emergent field of
+science, we pioneered the use of deep learning with convolutional neural
+networks, that take time-series inputs, for rapid detection and
+characterization of gravitational wave signals. This approach, Deep Filtering,
+was initially demonstrated using simulated LIGO noise. In this article, we
+present the extension of Deep Filtering using real data from LIGO, for both
+detection and parameter estimation of gravitational waves from binary black
+hole mergers using continuous data streams from multiple LIGO detectors. We
+demonstrate for the first time that machine learning can detect and estimate
+the true parameters of real events observed by LIGO. Our results show that Deep
+Filtering achieves similar sensitivities and lower errors compared to
+matched-filtering while being far more computationally efficient and more
+resilient to glitches, allowing real-time processing of weak time-series
+signals in non-stationary non-Gaussian noise with minimal resources, and also
+enables the detection of new classes of gravitational wave sources that may go
+unnoticed with existing detection algorithms. This unified framework for data
+analysis is ideally suited to enable coincident detection campaigns of
+gravitational waves and their multimessenger counterparts in real-time.
+",1,1,0,0,0,0
+11196,Self-doping effect arising from electron correlations in multi-layer cuprates,"  A self-doping effect between outer and inner CuO$_2$ planes (OPs and IPs) in
+multi-layer cuprate superconductors is studied. When one considers a
+three-layer tight-binding model of the Hg-based three-layer cuprate derived
+from the first principle calculations, the electron concentration gets to be
+large in the OP compared to IP. This is inconsistent with the experimental fact
+that more hole carriers tend to be introduced into the OP than IP.We
+investigate a three-layer Hubbard model with the two-particle self-consistent
+approach for multi-layer systems to incorporate electron correlations. We
+observe that the double occupancy (antiferromagnetic instability) in the IP
+decreases (increases) more than the OP, and also reveal that more electrons
+tend to be introduced into the IP than OP to obtain the energy gain from the
+on-site Hubbard interaction. These results are consistent with the experimental
+facts, and this electron distribution between the OP and IP can be interpreted
+as a self-doping effect arising from strong electron correlations.
+",0,1,0,0,0,0
+11197,Increased Prediction Accuracy in the Game of Cricket using Machine Learning,"  Player selection is one the most important tasks for any sport and cricket is
+no exception. The performance of the players depends on various factors such as
+the opposition team, the venue, his current form etc. The team management, the
+coach and the captain select 11 players for each match from a squad of 15 to 20
+players. They analyze different characteristics and the statistics of the
+players to select the best playing 11 for each match. Each batsman contributes
+by scoring maximum runs possible and each bowler contributes by taking maximum
+wickets and conceding minimum runs. This paper attempts to predict the
+performance of players as how many runs will each batsman score and how many
+wickets will each bowler take for both the teams. Both the problems are
+targeted as classification problems where number of runs and number of wickets
+are classified in different ranges. We used naïve bayes, random forest,
+multiclass SVM and decision tree classifiers to generate the prediction models
+for both the problems. Random Forest classifier was found to be the most
+accurate for both the problems.
+",1,0,0,0,0,0
+11198,Learning Probabilistic Programs Using Backpropagation,"  Probabilistic modeling enables combining domain knowledge with learning from
+data, thereby supporting learning from fewer training instances than purely
+data-driven methods. However, learning probabilistic models is difficult and
+has not achieved the level of performance of methods such as deep neural
+networks on many tasks. In this paper, we attempt to address this issue by
+presenting a method for learning the parameters of a probabilistic program
+using backpropagation. Our approach opens the possibility to building deep
+probabilistic programming models that are trained in a similar way to neural
+networks.
+",1,0,0,1,0,0
+11199,Turbulent gas accretion between supermassive black holes and star-forming rings in the circumnuclear disk,"  While supermassive black holes are known to co-evolve with their host galaxy,
+the precise nature and origin of this co-evolution is not clear. We here
+explore the possible connection between star formation and black hole growth in
+the circumnuclear disk (CND) to probe this connection in the vicinity close to
+the black hole. We adopt here the circumnuclear disk model developed by
+Kawakatu & Wada (2008) and Wutschik et al. (2013), and explore both the
+dependence on the star formation recipe as well as the role of the
+gravitational field, which can be dominated by the central black hole, the CND
+itself or the host galaxy. A specific emphasis is put on the turbulence
+regulated star formation model by Krumholz et al. (2005) to explore the impact
+of a realistic star formation recipe. It is shown that this model helps to
+introduce realistic fluctuations in the black hole and star formation rate,
+without overestimating them. Consistent with previous works, we show that the
+final black hole masses are rather insensitive to the masses of the initial
+seeds, even for seed masses of up to 10^6 M_sol. In addition, we apply our
+model to the formation of high-redshift quasars, as well as to the nearby
+system NGC 6951, where a tentative comparison is made in spite of the presence
+of a bar in the galaxy. We show that our model can reproduce the high black
+hole masses of the high-redshift quasars within a sufficiently short time,
+provided a high mass supply rate from the host galaxy. In addition, it
+reproduces several of the properties observed in NGC 6951. With respect to the
+latter system, our analysis suggests that supernova feedback may be important
+to create the observed fluctuations in the star formation history as a result
+of negative feedback effects.
+",0,1,0,0,0,0
+11200,Grayscale Image Authentication using Neural Hashing,"  Many different approaches for neural network based hash functions have been
+proposed. Statistical analysis must correlate security of them. This paper
+proposes novel neural hashing approach for gray scale image authentication. The
+suggested system is rapid, robust, useful and secure. Proposed hash function
+generates hash values using neural network one-way property and non-linear
+techniques. As a result security and performance analysis are performed and
+satisfying results are achieved. These features are dominant reasons for
+preferring against traditional ones.
+",1,0,0,0,0,0
+11201,Binary Image Selection (BISON): Interpretable Evaluation of Visual Grounding,"  Providing systems the ability to relate linguistic and visual content is one
+of the hallmarks of computer vision. Tasks such as image captioning and
+retrieval were designed to test this ability, but come with complex evaluation
+measures that gauge various other abilities and biases simultaneously. This
+paper presents an alternative evaluation task for visual-grounding systems:
+given a caption the system is asked to select the image that best matches the
+caption from a pair of semantically similar images. The system's accuracy on
+this Binary Image SelectiON (BISON) task is not only interpretable, but also
+measures the ability to relate fine-grained text content in the caption to
+visual content in the images. We gathered a BISON dataset that complements the
+COCO Captions dataset and used this dataset in auxiliary evaluations of
+captioning and caption-based retrieval systems. While captioning measures
+suggest visual grounding systems outperform humans, BISON shows that these
+systems are still far away from human performance.
+",1,0,0,0,0,0
+11202,Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization,"  In this paper we study the limitations of parallelization in convex
+optimization. A convenient approach to study parallelization is through the
+prism of \emph{adaptivity} which is an information theoretic measure of the
+parallel runtime of an algorithm. Informally, adaptivity is the number of
+sequential rounds an algorithm needs to make when it can execute
+polynomially-many queries in parallel at every round. For combinatorial
+optimization with black-box oracle access, the study of adaptivity has recently
+led to exponential accelerations in parallel runtime and the natural question
+is whether dramatic accelerations are achievable for convex optimization.
+Our main result is a spoiler. We show that, in general, parallelization does
+not accelerate convex optimization. In particular, for the problem of
+minimizing a non-smooth Lipschitz and strongly convex function with black-box
+oracle access we give information theoretic lower bounds that indicate that the
+number of adaptive rounds of any randomized algorithm exactly match the upper
+bounds of single-query-per-round (i.e. non-parallel) algorithms.
+",0,0,0,1,0,0
+11203,"Velocity dependence of point masses, moving on timelike geodesics, in weak gravitational fields","  Applying the principle of equivalence, analogous to Einstein's original 1907
+approach demonstrating the bending of light in a gravitational field, we deduce
+that radial geodesics of point masses are velocity dependent. Then, using the
+Schwarzschild solution for observers at spatial infinity, we analyze the
+similar case of masses moving on timelike geodesics, rederiving a previous
+result by Hilbert from 1917. We find that the Schwarzschild solution gives more
+than twice the rate of falling than found from the simpler acceleration
+arguments in flat space. We note Einstein also found a similar difference for
+the bending of light between these two approaches and in this case the
+increased deflection of light was due to space curvature. Similarly we find
+that in our case, the discrepancy between the two approaches can be attributed
+to space curvature. Although we have calculated the effect locally for
+observers under a Schwarzschild coordinate system in a weak field, further work
+needs to be carried out to explore the stronger field case.
+",0,1,0,0,0,0
+11204,Demand Response in the Smart Grid: the Impact of Consumers Temporal Preferences,"  In Demand Response programs, price incentives might not be sufficient to
+modify residential consumers load profile. Here, we consider that each consumer
+has a preferred profile and a discomfort cost when deviating from it. Consumers
+can value this discomfort at a varying level that we take as a parameter. This
+work analyses Demand Response as a game theoretic environment. We study the
+equilibria of the game between consumers with preferences within two different
+dynamic pricing mechanisms, respectively the daily proportional mechanism
+introduced by Mohsenian-Rad et al, and an hourly proportional mechanism. We
+give new results about equilibria as functions of the preference level in the
+case of quadratic system costs and prove that, whatever the preference level,
+system costs are smaller with the hourly mechanism. We simulate the Demand
+Response environment using real consumption data from PecanStreet database.
+While the Price of Anarchy remains always close to one up to 0.1% with the
+hourly mechanism, it can be more than 10% bigger with the daily mechanism.
+",1,0,0,0,0,0
+11205,Distributed Nesterov gradient methods over arbitrary graphs,"  In this letter, we introduce a distributed Nesterov method, termed as
+$\mathcal{ABN}$, that does not require doubly-stochastic weight matrices.
+Instead, the implementation is based on a simultaneous application of both row-
+and column-stochastic weights that makes this method applicable to arbitrary
+(strongly-connected) graphs. Since constructing column-stochastic weights needs
+additional information (the number of outgoing neighbors at each agent), not
+available in certain communication protocols, we derive a variation, termed as
+FROZEN, that only requires row-stochastic weights but at the expense of
+additional iterations for eigenvector learning. We numerically study these
+algorithms for various objective functions and network parameters and show that
+the proposed distributed Nesterov methods achieve acceleration compared to the
+current state-of-the-art methods for distributed optimization.
+",1,0,0,1,0,0
+11206,Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots,"  One of the most important features of mobile rescue robots is the ability to
+autonomously detect casualties, i.e. human bodies, which are usually lying on
+the ground. This paper proposes a novel method for autonomously detecting
+casualties lying on the ground using obtained 3D point-cloud data from an
+on-board sensor, such as an RGB-D camera or a 3D LIDAR, on a mobile rescue
+robot. In this method, the obtained 3D point-cloud data is projected onto the
+detected ground plane, i.e. floor, within the point cloud. Then, this projected
+point cloud is converted into a grid-map that is used afterwards as an input
+for the algorithm to detect human body shapes. The proposed method is evaluated
+by performing detection of a human dummy, placed in different random positions
+and orientations, using an on-board RGB-D camera on a mobile rescue robot
+called ResQbot. To evaluate the robustness of the casualty detection method to
+different camera angles, the orientation of the camera is set to different
+angles. The experimental results show that using the point-cloud data from the
+on-board RGB-D camera, the proposed method successfully detects the casualty in
+all tested body positions and orientations relative to the on-board camera, as
+well as in all tested camera angles.
+",1,0,0,0,0,0
+11207,Representations of superconformal algebras and mock theta functions,"  It is well known that the normaized characters of integrable highest weight
+modules of given level over an affine Lie algebra $\hat{\frak{g}}$ span an
+$SL_2(\mathbf{Z})$-invariant space. This result extends to admissible
+$\hat{\frak{g}}$-modules, where $\frak{g}$ is a simple Lie algebra or
+$osp_{1|n}$. Applying the quantum Hamiltonian reduction (QHR) to admissible
+$\hat{\frak{g}}$-modules when $\frak{g} =sl_2$ (resp. $=osp_{1|2}$) one obtains
+minimal series modules over the Virasoro (resp. $N=1$ superconformal algebras),
+which form modular invariant families.
+Another instance of modular invariance occurs for boundary level admissible
+modules, including when $\frak{g}$ is a basic Lie superalgebra. For example, if
+$\frak{g}=sl_{2|1}$ (resp. $=osp_{3|2}$), we thus obtain modular invariant
+families of $\hat{\frak{g}}$-modules, whose QHR produces the minimal series
+modules for the $N=2$ superconformal algebras (resp. a modular invariant family
+of $N=3$ superconformal algebra modules).
+However, in the case when $\frak{g}$ is a basic Lie superalgebra different
+from a simple Lie algebra or $osp_{1|n}$, modular invariance of normalized
+supercharacters of admissible $\hat{\frak{g}}$-modules holds outside of
+boundary levels only after their modification in the spirit of Zwegers'
+modification of mock theta functions. Applying the QHR, we obtain families of
+representations of $N=2,3,4$ and big $N=4$ superconformal algebras, whose
+modified (super)characters span an $SL_2(\mathbf{Z})$-invariant space.
+",0,0,1,0,0,0
+11208,Anchored Network Users: Stochastic Evolutionary Dynamics of Cognitive Radio Network Selection,"  To solve the spectrum scarcity problem, the cognitive radio technology
+involves licensed users and unlicensed users. A fundamental issue for the
+network users is whether it is better to act as a licensed user by using a
+primary network or an unlicensed user by using a secondary network. To model
+the network selection process by the users, the deterministic replicator
+dynamics is often used, but in a less practical way that it requires each user
+to know global information on the network state for reaching a Nash
+equilibrium. This paper addresses the network selection process in a more
+practical way such that only noise-prone estimation of local information is
+required and, yet, it obtains an efficient system performance.
+",1,0,0,0,0,0
+11209,On the rates of convergence of Parallelized Averaged Stochastic Gradient Algorithms,"  The growing interest for high dimensional and functional data analysis led in
+the last decade to an important research developing a consequent amount of
+techniques. Parallelized algorithms, which consist in distributing and treat
+the data into different machines, for example, are a good answer to deal with
+large samples taking values in high dimensional spaces. We introduce here a
+parallelized averaged stochastic gradient algorithm, which enables to treat
+efficiently and recursively the data, and so, without taking care if the
+distribution of the data into the machines is uniform. The rate of convergence
+in quadratic mean as well as the asymptotic normality of the parallelized
+estimates are given, for strongly and locally strongly convex objectives.
+",0,0,1,1,0,0
+11210,On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator,"  Extended Dynamic Mode Decomposition (EDMD) is an algorithm that approximates
+the action of the Koopman operator on an $N$-dimensional subspace of the space
+of observables by sampling at $M$ points in the state space. Assuming that the
+samples are drawn either independently or ergodically from some measure $\mu$,
+it was shown that, in the limit as $M\rightarrow\infty$, the EDMD operator
+$\mathcal{K}_{N,M}$ converges to $\mathcal{K}_N$, where $\mathcal{K}_N$ is the
+$L_2(\mu)$-orthogonal projection of the action of the Koopman operator on the
+finite-dimensional subspace of observables. In this work, we show that, as $N
+\rightarrow \infty$, the operator $\mathcal{K}_N$ converges in the strong
+operator topology to the Koopman operator. This in particular implies
+convergence of the predictions of future values of a given observable over any
+finite time horizon, a fact important for practical applications such as
+forecasting, estimation and control. In addition, we show that accumulation
+points of the spectra of $\mathcal{K}_N$ correspond to the eigenvalues of the
+Koopman operator with the associated eigenfunctions converging weakly to an
+eigenfunction of the Koopman operator, provided that the weak limit of
+eigenfunctions is nonzero. As a by-product, we propose an analytic version of
+the EDMD algorithm which, under some assumptions, allows one to construct
+$\mathcal{K}_N$ directly, without the use of sampling. Finally, under
+additional assumptions, we analyze convergence of $\mathcal{K}_{N,N}$ (i.e.,
+$M=N$), proving convergence, along a subsequence, to weak eigenfunctions (or
+eigendistributions) related to the eigenmeasures of the Perron-Frobenius
+operator. No assumptions on the observables belonging to a finite-dimensional
+invariant subspace of the Koopman operator are required throughout.
+",0,0,1,0,0,0
+11211,Random problems with R,"  R (Version 3.5.1 patched) has an issue with its random sampling
+functionality. R generates random integers between $1$ and $m$ by multiplying
+random floats by $m$, taking the floor, and adding $1$ to the result.
+Well-known quantization effects in this approach result in a non-uniform
+distribution on $\{ 1, \ldots, m\}$. The difference, which depends on $m$, can
+be substantial. Because the sample function in R relies on generating random
+integers, random sampling in R is biased. There is an easy fix: construct
+random integers directly from random bits, rather than multiplying a random
+float by $m$. That is the strategy taken in Python's numpy.random.randint()
+function, among others. Example source code in Python is available at
+this https URL
+(see functions getrandbits() and randbelow_from_randbits()).
+",0,0,0,1,0,0
+11212,Quasinormal modes as a distinguisher between general relativity and f(R) gravity,"  Quasi-Normal Modes (QNM) or ringdown phase of gravitational waves provide
+critical information about the structure of compact objects like Black Holes.
+Thus, QNMs can be a tool to test General Relativity (GR) and possible
+deviations from it. In the case of GR, it is known for a long time that a
+relation between two types of Black Hole perturbations: scalar (Zerilli) and
+vector (Regge-Wheeler), leads to an equal share of emitted gravitational
+energy. With the direct detection of Gravitational waves, it is now natural to
+ask: whether the same relation (between scalar and vector perturbations) holds
+for modified gravity theories? If not, whether one can use this as a way to
+probe deviations from General Relativity. As a first step, we show explicitly
+that the above relation between Regge-Wheeler and Zerilli breaks down for a
+general f (R) model, and hence the two perturbations do not share equal amounts
+of emitted gravitational energy. We discuss the implication of this imbalance
+on observations and the no-hair conjecture.
+",0,1,0,0,0,0
+11213,Polymorphism and the obstinate circularity of second order logic: a victims' tale,"  The investigations on higher-order type theories and on the related notion of
+parametric polymorphism constitute the technical counterpart of the old
+foundational problem of the circularity (or impredicativity) of second and
+higher order logic. However, the epistemological significance of such
+investigations, and of their often non trivial results, has not received much
+attention in the contemporary foundational debate. The results recalled in this
+paper suggest that the question of the circularity of second order logic cannot
+be reduced to the simple assessment of a vicious circle. Through a comparison
+between the faulty consistency arguments given by Frege and Martin-Löf,
+respectively for the logical system of the Grundgesetze (shown inconsistent by
+Russell's paradox) and for the intuitionistic type theory with a type of all
+types (shown inconsistent by Girard's paradox), and the normalization argument
+for second order type theory (or System F), we indicate a bunch of subtle
+mathematical problems and logical concepts hidden behind the hazardous idea of
+impredicative quantification, constituting a vast (and largely unexplored)
+domain for foundational research.
+",1,0,1,0,0,0
+11214,Bayesian significance test for discriminating between survival distributions,"  An evaluation of FBST, Fully Bayesian Significance Test, restricted to
+survival models is the main objective of the present paper. A Survival
+distribution should be chosen among the tree celebrated ones, lognormal, gamma,
+and Weibull. For this discrimination, a linear mixture of the three
+distributions, for which the mixture weights are defined by a Dirichlet
+distribution of order three, is an important tool: the FBST is used to test the
+hypotheses defined on the mixture weights space. Another feature of the paper
+is that all three distributions are reparametrized in that all the six
+parameters - two for each distribution - are written as functions of the mean
+and the variance of the population been studied. Note that the three
+distributions share the same two parameters in the mixture model. The mixture
+density has then four parameters, the same two for the three discriminating
+densities and two for the mixture weights. Some numerical results from
+simulations with some right-censored data are considered. The
+lognormal-gamma-Weibull model is also applied to a real study with dataset
+being composed by patient's survival times of patients in the end-stage of
+chronic kidney failure subjected to hemodialysis procedures; data from Rio de
+Janeiro hospitals. The posterior density of the weights indicates an order of
+the mixture weights and the FBST is used for discriminating between the three
+survival distributions.
+Keywords: Model choice; Separate Models; Survival distributions; Mixture
+model; Significance test; FBST
+",0,0,0,1,0,0
+11215,A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines,"  We propose a minimal solution for pose estimation using both points and lines
+for a multi-perspective camera. In this paper, we treat the multi-perspective
+camera as a collection of rigidly attached perspective cameras. These type of
+imaging devices are useful for several computer vision applications that
+require a large coverage such as surveillance, self-driving cars, and
+motion-capture studios. While prior methods have considered the cases using
+solely points or lines, the hybrid case involving both points and lines has not
+been solved for multi-perspective cameras. We present the solutions for two
+cases. In the first case, we are given 2D to 3D correspondences for two points
+and one line. In the later case, we are given 2D to 3D correspondences for one
+point and two lines. We show that the solution for the case of two points and
+one line can be formulated as a fourth degree equation. This is interesting
+because we can get a closed-form solution and thereby achieve high
+computational efficiency. The later case involving two lines and one point can
+be mapped to an eighth degree equation. We show simulations and real
+experiments to demonstrate the advantages and benefits over existing methods.
+",1,0,0,0,0,0
+11216,Learning K-way D-dimensional Discrete Code For Compact Embedding Representations,"  Embedding methods such as word embedding have become pillars for many
+applications containing discrete structures. Conventional embedding methods
+directly associate each symbol with a continuous embedding vector, which is
+equivalent to applying linear transformation based on ""one-hot"" encoding of the
+discrete symbols. Despite its simplicity, such approach yields number of
+parameters that grows linearly with the vocabulary size and can lead to
+overfitting. In this work we propose a much more compact K-way D-dimensional
+discrete encoding scheme to replace the ""one-hot"" encoding. In ""KD encoding"",
+each symbol is represented by a $D$-dimensional code, and each of its dimension
+has a cardinality of $K$. The final symbol embedding vector can be generated by
+composing the code embedding vectors. To learn the semantically meaningful
+code, we derive a relaxed discrete optimization technique based on stochastic
+gradient descent. By adopting the new coding system, the efficiency of
+parameterization can be significantly improved (from linear to logarithmic),
+and this can also mitigate the over-fitting problem. In our experiments with
+language modeling, the number of embedding parameters can be reduced by 97\%
+while achieving similar or better performance.
+",1,0,0,1,0,0
+11217,Attention based convolutional neural network for predicting RNA-protein binding sites,"  RNA-binding proteins (RBPs) play crucial roles in many biological processes,
+e.g. gene regulation. Computational identification of RBP binding sites on RNAs
+are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence
+motifs. Thus, fast locating those motifs on RNA sequences is crucial and
+time-efficient for determining whether the RNAs interact with the RBPs or not.
+In this study, we present an attention based convolutional neural network,
+iDeepA, to predict RNA-protein binding sites from raw RNA sequences. We first
+encode RNA sequences into one-hot encoding. Next, we design a deep learning
+model with a convolutional neural network (CNN) and an attention mechanism,
+which automatically search for important positions, e.g. binding motifs, to
+learn discriminant high-level features for predicting RBP binding sites. We
+evaluate iDeepA on publicly gold-standard RBP binding sites derived from
+CLIP-seq data. The results demonstrate iDeepA achieves comparable performance
+with other state-of-the-art methods.
+",1,0,0,1,0,0
+11218,Ramp Reversal Memory and Phase-Boundary Scarring in Transition Metal Oxides,"  Transition metal oxides (TMOs) are complex electronic systems which exhibit a
+multitude of collective phenomena. Two archetypal examples are VO2 and NdNiO3,
+which undergo a metal-insulator phase-transition (MIT), the origin of which is
+still under debate. Here we report the discovery of a memory effect in both
+systems, manifest through an increase of resistance at a specific temperature,
+which is set by reversing the temperature-ramp from heating to cooling during
+the MIT. The characteristics of this ramp-reversal memory effect do not
+coincide with any previously reported history or memory effects in manganites,
+electron-glass or magnetic systems. From a broad range of experimental
+features, supported by theoretical modelling, we find that the main ingredients
+for the effect to arise are the spatial phase-separation of metallic and
+insulating regions during the MIT and the coupling of lattice strain to the
+local critical temperature of the phase transition. We conclude that the
+emergent memory effect originates from phase boundaries at the
+reversal-temperature leaving `scars` in the underlying lattice structure,
+giving rise to a local increase in the transition temperature. The universality
+and robustness of the effect shed new light on the MIT in complex oxides.
+",0,1,0,0,0,0
+11219,A multi-channel approach for automatic microseismic event localization using RANSAC-based arrival time event clustering(RATEC),"  In the presence of background noise and interference, arrival times picked
+from a surface microseismic data set usually include a number of false picks
+which lead to uncertainty in location estimation. To eliminate false picks and
+improve the accuracy of location estimates, we develop a classification
+algorithm (RATEC) that clusters picked arrival times into event groups based on
+random sampling and fitting moveout curves that approximate hyperbolas. Arrival
+times far from the fitted hyperbolas are classified as false picks and removed
+from the data set prior to location estimation. Simulations of synthetic data
+for a 1-D linear array show that RATEC is robust under different noise
+conditions and generally applicable to various types of media. By generalizing
+the underlying moveout model, RATEC is extended to the case of a 2-D surface
+monitoring array. The effectiveness of event location for the 2-D case is
+demonstrated using a data set collected by a 5200-element dense 2-D array
+deployed for microearthquake monitoring.
+",1,1,0,0,0,0
+11220,Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms,"  Currently, diagnosis of skin diseases is based primarily on visual pattern
+recognition skills and expertise of the physician observing the lesion. Even
+though dermatologists are trained to recognize patterns of morphology, it is
+still a subjective visual assessment. Tools for automated pattern recognition
+can provide objective information to support clinical decision-making.
+Noninvasive skin imaging techniques provide complementary information to the
+clinician. In recent years, optical coherence tomography has become a powerful
+skin imaging technique. According to specific functional needs, skin
+architecture varies across different parts of the body, as do the textural
+characteristics in OCT images. There is, therefore, a critical need to
+systematically analyze OCT images from different body sites, to identify their
+significant qualitative and quantitative differences. Sixty-three optical and
+textural features extracted from OCT images of healthy and diseased skin are
+analyzed and in conjunction with decision-theoretic approaches used to create
+computational models of the diseases. We demonstrate that these models provide
+objective information to the clinician to assist in the diagnosis of
+abnormalities of cutaneous microstructure, and hence, aid in the determination
+of treatment. Specifically, we demonstrate the performance of this methodology
+on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)
+from healthy tissue.
+",0,1,0,0,0,0
+11221,Batched Large-scale Bayesian Optimization in High-dimensional Spaces,"  Bayesian optimization (BO) has become an effective approach for black-box
+function optimization problems when function evaluations are expensive and the
+optimum can be achieved within a relatively small number of queries. However,
+many cases, such as the ones with high-dimensional inputs, may require a much
+larger number of observations for optimization. Despite an abundance of
+observations thanks to parallel experiments, current BO techniques have been
+limited to merely a few thousand observations. In this paper, we propose
+ensemble Bayesian optimization (EBO) to address three current challenges in BO
+simultaneously: (1) large-scale observations; (2) high dimensional input
+spaces; and (3) selections of batch queries that balance quality and diversity.
+The key idea of EBO is to operate on an ensemble of additive Gaussian process
+models, each of which possesses a randomized strategy to divide and conquer. We
+show unprecedented, previously impossible results of scaling up BO to tens of
+thousands of observations within minutes of computation.
+",1,0,1,1,0,0
+11222,Will a Large Economy Be Stable?,"  We study networks of firms with Leontief production functions. Relying on
+results from Random Matrix Theory, we argue that such networks generically
+become unstable when their size increases, or when the heterogeneity in
+productivities/connectivities becomes too strong. At marginal stability and for
+large heterogeneities, we find that the distribution of firm sizes develops a
+power-law tail, as observed empirically. Crises can be triggered by small
+idiosyncratic shocks, which lead to ""avalanches"" of defaults characterized by a
+power-law distribution of total output losses. We conjecture that evolutionary
+and behavioural forces conspire to keep the economy close to marginal
+stability. This scenario would naturally explain the well-known ""small shocks,
+large business cycles"" puzzle, as anticipated long ago by Bak, Chen, Scheinkman
+and Woodford.
+",0,0,0,0,0,1
+11223,Linear Convergence of Accelerated Stochastic Gradient Descent for Nonconvex Nonsmooth Optimization,"  In this paper, we study the stochastic gradient descent (SGD) method for the
+nonconvex nonsmooth optimization, and propose an accelerated SGD method by
+combining the variance reduction technique with Nesterov's extrapolation
+technique. Moreover, based on the local error bound condition, we establish the
+linear convergence of our method to obtain a stationary point of the nonconvex
+optimization. In particular, we prove that not only the sequence generated
+linearly converges to a stationary point of the problem, but also the
+corresponding sequence of objective values is linearly convergent. Finally,
+some numerical experiments demonstrate the effectiveness of our method. To the
+best of our knowledge, it is first proved that the accelerated SGD method
+converges linearly to the local minimum of the nonconvex optimization.
+",0,0,1,1,0,0
+11224,"TRINITY: Coordinated Performance, Energy and Temperature Management in 3D Processor-Memory Stacks","  The consistent demand for better performance has lead to innovations at
+hardware and microarchitectural levels. 3D stacking of memory and logic dies
+delivers an order of magnitude improvement in available memory bandwidth. The
+price paid however is, tight thermal constraints.
+In this paper, we study the complex multiphysics interactions between
+performance, energy and temperature. Using a cache coherent multicore processor
+cycle level simulator coupled with power and thermal estimation tools, we
+investigate the interactions between (a) thermal behaviors (b) compute and
+memory microarchitecture and (c) application workloads. The key insights from
+this exploration reveal the need to manage performance, energy and temperature
+in a coordinated fashion. Furthermore, we identify the concept of ""effective
+heat capacity"" i.e. the heat generated beyond which no further gains in
+performance is observed with increases in voltage-frequency of the compute
+logic. Subsequently, a real-time, numerical optimization based, application
+agnostic controller (TRINITY) is developed which intelligently manages the
+three parameters of interest. We observe up to $30\%$ improvement in Energy
+Delay$^2$ Product and up to $8$ Kelvin lower core temperatures as compared to
+fixed frequencies. Compared to the \texttt{ondemand} Linux CPU DVFS governor,
+for similar energy efficiency, TRINITY keeps the cores cooler by $6$ Kelvin
+which increases the lifetime reliability by up to 59\%.
+",1,0,0,0,0,0
+11225,Robust Covariate Shift Prediction with General Losses and Feature Views,"  Covariate shift relaxes the widely-employed independent and identically
+distributed (IID) assumption by allowing different training and testing input
+distributions. Unfortunately, common methods for addressing covariate shift by
+trying to remove the bias between training and testing distributions using
+importance weighting often provide poor performance guarantees in theory and
+unreliable predictions with high variance in practice. Recently developed
+methods that construct a predictor that is inherently robust to the
+difficulties of learning under covariate shift are restricted to minimizing
+logloss and can be too conservative when faced with high-dimensional learning
+tasks. We address these limitations in two ways: by robustly minimizing various
+loss functions, including non-convex ones, under the testing distribution; and
+by separately shaping the influence of covariate shift according to different
+feature-based views of the relationship between input variables and example
+labels. These generalizations make robust covariate shift prediction applicable
+to more task scenarios. We demonstrate the benefits on classification under
+covariate shift tasks.
+",1,0,0,1,0,0
+11226,On the Ergodic Control of Ensembles,"  Across smart-grid and smart-city applications, there are problems where an
+ensemble of agents is to be controlled such that both the aggregate behaviour
+and individual-level perception of the system's performance are acceptable. In
+many applications, traditional PI control is used to regulate aggregate
+ensemble performance. Our principal contribution in this note is to demonstrate
+that PI control may not be always suitable for this purpose, and in some
+situations may lead to a loss of ergodicity for closed-loop systems. Building
+on this observation, a theoretical framework is proposed to both analyse and
+design control systems for the regulation of large scale ensembles of agents
+with a probabilistic intent. Examples are given to illustrate our results.
+",1,0,0,0,0,0
+11227,Relative merits of Phononics vs. Plasmonics: the energy balance approach,"  The common feature of various plasmonic schemes is their ability to confine
+optical fields of surface plasmon polaritons (SPPs) into sub-wavelength volumes
+and thus achieve a large enhancement of linear and nonlinear optical
+properties. This ability, however, is severely limited by the large ohmic loss
+inherent to even the best of metals. However, in the mid and far infrared
+ranges of the spectrum there exists a viable alternative to metals, polar
+dielectrics and semiconductors in which dielectric permittivity (the real part)
+turns negative in the Reststrahlen region. This feature engenders the so-called
+surface phonon polaritons (SPhPs) capable of confining the field in a way akin
+to their plasmonic analogues, the SPPs. Since the damping rate of polar phonons
+is substantially less than that of free electrons, it is not unreasonable to
+expect that phononic devices may outperform their plasmonic counterparts. Yet a
+more rigorous analysis of the comparative merits of phononics and plasmonics
+reveals a more nuanced answer, namely that while phononic schemes do exhibit
+narrower resonances and can achieve a very high degree of energy concentration,
+most of the energy is contained in the form of lattice vibrations so that
+enhancement of the electric field, and hence the Purcell factor, is rather
+small compared to what can be achieved with metal nanoantennas. Still, the
+sheer narrowness of phononic resonances is expected to make phononics viable in
+applications where frequency selectivity is important.
+",0,1,0,0,0,0
+11228,Memory footprint reduction for the FFT-based volume integral equation method via tensor decompositions,"  We present a method of memory footprint reduction for FFT-based,
+electromagnetic (EM) volume integral equation (VIE) formulations. The arising
+Green's function tensors have low multilinear rank, which allows Tucker
+decomposition to be employed for their compression, thereby greatly reducing
+the required memory storage for numerical simulations. Consequently, the
+compressed components are able to fit inside a graphical processing unit (GPU)
+on which highly parallelized computations can vastly accelerate the iterative
+solution of the arising linear system. In addition, the element-wise products
+throughout the iterative solver's process require additional flops, thus, we
+provide a variety of novel and efficient methods that maintain the linear
+complexity of the classic element-wise product with an additional
+multiplicative small constant. We demonstrate the utility of our approach via
+its application to VIE simulations for the Magnetic Resonance Imaging (MRI) of
+a human head. For these simulations we report an order of magnitude
+acceleration over standard techniques.
+",1,0,0,0,0,0
+11229,On a property of the nodal set of least energy sign-changing solutions for quasilinear elliptic equations,"  In this note we prove the Payne-type conjecture about the behaviour of the
+nodal set of least energy sign-changing solutions for the equation $-\Delta_p u
+= f(u)$ in bounded Steiner symmetric domains $\Omega \subset \mathbb{R}^N$
+under the zero Dirichlet boundary conditions. The nonlinearity $f$ is assumed
+to be either superlinear or resonant. In the latter case, least energy
+sign-changing solutions are second eigenfunctions of the zero Dirichlet
+$p$-Laplacian in $\Omega$. We show that the nodal set of any least energy
+sign-changing solution intersects the boundary of $\Omega$. The proof is based
+on a moving polarization argument.
+",0,0,1,0,0,0
+11230,Smooth contractible threefolds with hyperbolic $\mathbb{G}_{m}$-actions via ps-divisors,"  The aim of this note is to give an alternative proof of a theorem of Koras
+and Russell, that is, a characterization of smooth contractible affine
+varieties endowed with a hyperbolic action of the group
+$\mathbb{G}_{m}\simeq\mathbb{C}^{\text{*}}$, using the language of polyhedral
+divisors developed by Altmann and Hausen as generalization of
+$\mathbb{Q}$-divisors.
+",0,0,1,0,0,0
+11231,Ergodic Theorems for Nonconventional Arrays and an Extension of the Szemeredi Theorem,"  The paper is primarily concerned with the asymptotic behavior as $N\to\infty$
+of averages of nonconventional arrays having the form
+$N^{-1}\sum_{n=1}^N\prod_{j=1}^\ell T^{P_j(n,N)}f_j$ where $f_j$'s are bounded
+measurable functions, $T$ is an invertible measure preserving transformation
+and $P_j$'s are polynomials of $n$ and $N$ taking on integer values on
+integers. It turns out that when $T$ is weakly mixing and $P_j(n,N)=p_jn+q_jN$
+are linear or, more generally, have the form $P_j(n,N)=P_j(n)+Q_j(N)$ for some
+integer valued polynomials $P_j$ and $Q_j$ then the above averages converge in
+$L^2$ but for general polynomials $P_j$ the $L^2$ convergence can be ensured
+even in the case $\ell=1$ only when $T$ is strongly mixing. Studying also
+weakly mixing and compact extensions and relying on Furstenberg's structure
+theorem we derive an extension of Szemer\' edi's theorem saying that for any
+subset of integers $\Lambda$ with positive upper density there exists a subset
+$\mathcal N_\Lambda$ of positive integers having uniformly bounded gaps such
+that for $N\in\mathcal N_\Lambda$ and at least $\varepsilon N,\,\varepsilon>0$
+of $n$'s all numbers $p_jn+q_jN,\, j=1,...,\ell$ belong to $\Lambda$. We obtain
+also a version of these results for several commuting transformations which
+yields a corresponding extension of the multidimensional Szemer\' edi theorem.
+",0,0,1,0,0,0
+11232,Bellman Gradient Iteration for Inverse Reinforcement Learning,"  This paper develops an inverse reinforcement learning algorithm aimed at
+recovering a reward function from the observed actions of an agent. We
+introduce a strategy to flexibly handle different types of actions with two
+approximations of the Bellman Optimality Equation, and a Bellman Gradient
+Iteration method to compute the gradient of the Q-value with respect to the
+reward function. These methods allow us to build a differentiable relation
+between the Q-value and the reward function and learn an approximately optimal
+reward function with gradient methods. We test the proposed method in two
+simulated environments by evaluating the accuracy of different approximations
+and comparing the proposed method with existing solutions. The results show
+that even with a linear reward function, the proposed method has a comparable
+accuracy with the state-of-the-art method adopting a non-linear reward
+function, and the proposed method is more flexible because it is defined on
+observed actions instead of trajectories.
+",1,0,0,0,0,0
+11233,Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data,"  We consider the question of accurately and efficiently computing low-rank
+matrix or tensor factorizations given data compressed via random projections.
+This problem arises naturally in the many settings in which data is acquired
+via compressive sensing. We examine the approach of first performing
+factorization in the compressed domain, and then reconstructing the original
+high-dimensional factors from the recovered (compressed) factors. In both the
+tensor and matrix settings, we establish conditions under which this natural
+approach will provably recover the original factors. We support these
+theoretical results with experiments on synthetic data and demonstrate the
+practical applicability of our methods on real-world gene expression and EEG
+time series data.
+",1,0,0,1,0,0
+11234,Quantum gravity corrections to the thermodynamics and phase transition of Schwarzschild black hole,"  In this work, we derive a new kind of rainbow functions, which has
+generalized uncertainty principle parameter. Then, we investigate modified
+thermodynamic quantities and phase transition of rainbow Schwarzschild black
+hole by employing this new kind of rainbow functions. Our results demonstrate
+that the effect of rainbow gravity and generalized uncertainty principle have a
+great effect on the picture of Hawking radiation. It prevents black holes from
+total evaporation and causes the remnant. In addition, after analyzing the the
+modified local thermodynamic quantities, we find that effect of rainbow gravity
+and generalized uncertainty principle lead to one first-order phase transition,
+two second-order phase transitions, and two Hawking-Page-type phase transitions
+in the thermodynamic system of rainbow Schwarzschild black hole.
+",0,1,0,0,0,0
+11235,Non-abelian reciprocity laws and higher Brauer-Manin obstructions,"  We reinterpret Kim's non-abelian reciprocity maps for algebraic varieties as
+obstruction towers of mapping spaces of etale homotopy types, removing
+technical hypotheses such as global basepoints and cohomological constraints.
+We then extend the theory by considering alternative natural series of
+extensions, one of which gives an obstruction tower whose first stage is the
+Brauer--Manin obstruction, allowing us to determine when Kim's maps recover the
+Brauer-Manin locus. A tower based on relative completions yields non-trivial
+reciprocity maps even for Shimura varieties; for the stacky modular curve,
+these take values in Galois cohomology of modular forms, and give obstructions
+to an adelic elliptic curve with global Tate module underlying a global
+elliptic curve.
+",0,0,1,0,0,0
+11236,Learning with Bounded Instance- and Label-dependent Label Noise,"  Instance- and label-dependent label noise (ILN) is widely existed in
+real-world datasets but has been rarely studied. In this paper, we focus on a
+particular case of ILN where the label noise rates, representing the
+probabilities that the true labels of examples flip into the corrupted labels,
+have upper bounds. We propose to handle this bounded instance- and
+label-dependent label noise under two different conditions. First,
+theoretically, we prove that when the marginal distributions $P(X|Y=+1)$ and
+$P(X|Y=-1)$ have non-overlapping supports, we can recover every noisy example's
+true label and perform supervised learning directly on the cleansed examples.
+Second, for the overlapping situation, we propose a novel approach to learn a
+well-performing classifier which needs only a few noisy examples to be labeled
+manually. Experimental results demonstrate that our method works well on both
+synthetic and real-world datasets.
+",0,0,0,1,0,0
+11237,"Community Detection in Hypergraphs, Spiked Tensor Models, and Sum-of-Squares","  We study the problem of community detection in hypergraphs under a stochastic
+block model. Similarly to how the stochastic block model in graphs suggests
+studying spiked random matrices, our model motivates investigating statistical
+and computational limits of exact recovery in a certain spiked tensor model. In
+contrast with the matrix case, the spiked model naturally arising from
+community detection in hypergraphs is different from the one arising in the
+so-called tensor Principal Component Analysis model. We investigate the
+effectiveness of algorithms in the Sum-of-Squares hierarchy on these models.
+Interestingly, our results suggest that these two apparently similar models
+exhibit significantly different computational to statistical gaps.
+",1,0,1,1,0,0
+11238,Presentations of the saturated cluster modular groups of finite mutation type $X_6$ and $X_7$,"  We give finite presentations of the saturated cluster modular groups of type
+$X_6$ and $X_7$. We compute the first homology of these groups and conclude
+that they are different from Artin-Tits braid groups and mapping class groups
+of surfaces. We verify that the cluster modular group of type $X_7$ is
+generated by cluster Dehn twists. Further we discuss several relations between
+these cluster modular groups and the mapping class group of an annulus.
+",0,0,1,0,0,0
+11239,Quantum Query Algorithms are Completely Bounded Forms,"  We prove a characterization of $t$-query quantum algorithms in terms of the
+unit ball of a space of degree-$2t$ polynomials. Based on this, we obtain a
+refined notion of approximate polynomial degree that equals the quantum query
+complexity, answering a question of Aaronson et al. (CCC'16). Our proof is
+based on a fundamental result of Christensen and Sinclair (J. Funct. Anal.,
+1987) that generalizes the well-known Stinespring representation for quantum
+channels to multilinear forms. Using our characterization, we show that many
+polynomials of degree four are far from those coming from two-query quantum
+algorithms. We also give a simple and short proof of one of the results of
+Aaronson et al. showing an equivalence between one-query quantum algorithms and
+bounded quadratic polynomials.
+",1,0,1,0,0,0
+11240,Massively-Parallel Feature Selection for Big Data,"  We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for
+feature selection (FS) in Big Data settings (high dimensionality and/or sample
+size). To tackle the challenges of Big Data FS PFBP partitions the data matrix
+both in terms of rows (samples, training examples) as well as columns
+(features). By employing the concepts of $p$-values of conditional independence
+tests and meta-analysis techniques PFBP manages to rely only on computations
+local to a partition while minimizing communication costs. Then, it employs
+powerful and safe (asymptotically sound) heuristics to make early, approximate
+decisions, such as Early Dropping of features from consideration in subsequent
+iterations, Early Stopping of consideration of features within the same
+iteration, or Early Return of the winner in each iteration. PFBP provides
+asymptotic guarantees of optimality for data distributions faithfully
+representable by a causal network (Bayesian network or maximal ancestral
+graph). Our empirical analysis confirms a super-linear speedup of the algorithm
+with increasing sample size, linear scalability with respect to the number of
+features and processing cores, while dominating other competitive algorithms in
+its class.
+",1,0,0,1,0,0
+11241,Gravitational Wave Sources from Pop III Stars are Preferentially Located within the Cores of their Host Galaxies,"  The detection of gravitational waves (GWs) generated by merging black holes
+has recently opened up a new observational window into the Universe. The mass
+of the black holes in the first and third LIGO detections, ($36-29 \,
+\mathrm{M_{\odot}}$ and $32-19 \, \mathrm{M_{\odot}}$), suggests
+low-metallicity stars as their most likely progenitors. Based on
+high-resolution N-body simulations, coupled with state-of-the-art metal
+enrichment models, we find that the remnants of Pop III stars are
+preferentially located within the cores of galaxies. The probability of a GW
+signal to be generated by Pop III stars reaches $\sim 90\%$ at $\sim 0.5 \,
+\mathrm{kpc}$ from the galaxy center, compared to a benchmark value of $\sim
+5\%$ outside the core. The predicted merger rates inside bulges is $\sim 60
+\times \beta_{III} \, \mathrm{Gpc^{-3} \, yr^{-1}}$ ($\beta_{III}$ is the Pop
+III binarity fraction). To match the $90\%$ credible range of LIGO merger
+rates, we obtain: $0.03 < \beta_{III} < 0.88$. Future advances in GW
+observatories and the discovery of possible electromagnetic counterparts could
+allow the localization of such sources within their host galaxies. The
+preferential concentration of GW events within the bulge of galaxies would then
+provide an indirect proof for the existence of Pop III stars.
+",0,1,0,0,0,0
+11242,A Markov Chain Model for the Cure Rate of Non-Performing Loans,"  A Markov-chain model is developed for the purpose estimation of the cure rate
+of non-performing loans. The technique is performed collectively, on portfolios
+and it can be applicable in the process of calculation of credit impairment. It
+is efficient in terms of data manipulation costs which makes it accessible even
+to smaller financial institutions. In addition, several other applications to
+portfolio optimization are suggested.
+",0,0,0,1,0,1
+11243,Query-Efficient Black-box Adversarial Examples (superceded),"  Note that this paper is superceded by ""Black-Box Adversarial Attacks with
+Limited Queries and Information.""
+Current neural network-based image classifiers are susceptible to adversarial
+examples, even in the black-box setting, where the attacker is limited to query
+access without access to gradients. Previous methods --- substitute networks
+and coordinate-based finite-difference methods --- are either unreliable or
+query-inefficient, making these methods impractical for certain problems.
+We introduce a new method for reliably generating adversarial examples under
+more restricted, practical black-box threat models. First, we apply natural
+evolution strategies to perform black-box attacks using two to three orders of
+magnitude fewer queries than previous methods. Second, we introduce a new
+algorithm to perform targeted adversarial attacks in the partial-information
+setting, where the attacker only has access to a limited number of target
+classes. Using these techniques, we successfully perform the first targeted
+adversarial attack against a commercially deployed machine learning system, the
+Google Cloud Vision API, in the partial information setting.
+",1,0,0,1,0,0
+11244,Beyond the Erdős Matching Conjecture,"  A family $\mathcal F\subset {[n]\choose k}$ is $U(s,q)$ of for any
+$F_1,\ldots, F_s\in \mathcal F$ we have $|F_1\cup\ldots\cup F_s|\le q$. This
+notion generalizes the property of a family to be $t$-intersecting and to have
+matching number smaller than $s$.
+In this paper, we find the maximum $|\mathcal F|$ for $\mathcal F$ that are
+$U(s,q)$, provided $n>C(s,q)k$ with moderate $C(s,q)$. In particular, we
+generalize the result of the first author on the Erdős Matching Conjecture
+and prove a generalization of the Erdős-Ko-Rado theorem, which states that
+for $n> s^2k$ the largest family $\mathcal F\subset {[n]\choose k}$ with
+property $U(s,s(k-1)+1)$ is the star and is in particular intersecting.
+(Conversely, it is easy to see that any intersecting family in ${[n]\choose k}$
+is $U(s,s(k-1)+1)$.)
+We investigate the case $k=3$ more thoroughly, showing that, unlike in the
+case of the Erdős Matching Conjecture, in general there may be $3$ extremal
+families.
+",1,0,0,0,0,0
+11245,pH dependence of charge multipole moments in proteins,"  Electrostatic interactions play a fundamental role in the structure and
+function of proteins. Due to ionizable amino acid residues present on the
+solvent-exposed surfaces of proteins, the protein charge is not constant but
+varies with the changes in the environment -- most notably, the pH of the
+surrounding solution. We study the effects of pH on the charge of four globular
+proteins by expanding their surface charge distributions in terms of
+multipoles. The detailed representation of the charges on the proteins is in
+this way replaced by the magnitudes and orientations of the multipole moments
+of varying order. Focusing on the three lowest-order multipoles -- the total
+charge, dipole, and quadrupole moment -- we show that the value of pH
+influences not only their magnitudes, but more notably and importantly also the
+spatial orientation of their principal axes. Our findings imply important
+consequences for the study of protein-protein interactions and the assembly of
+both proteinaceous shells and patchy colloids with dissociable charge groups.
+",0,1,0,0,0,0
+11246,"A streamlined, general approach for computing ligand binding free energies and its application to GPCR-bound cholesterol","  The theory of receptor-ligand binding equilibria has long been
+well-established in biochemistry, and was primarily constructed to describe
+dilute aqueous solutions. Accordingly, few computational approaches have been
+developed for making quantitative predictions of binding probabilities in
+environments other than dilute isotropic solution. Existing techniques, ranging
+from simple automated docking procedures to sophisticated thermodynamics-based
+methods, have been developed with soluble proteins in mind. Biologically and
+pharmacologically relevant protein-ligand interactions often occur in complex
+environments, including lamellar phases like membranes and crowded, non-dilute
+solutions. Here we revisit the theoretical bases of ligand binding equilibria,
+avoiding overly specific assumptions that are nearly always made when
+describing receptor-ligand binding. Building on this formalism, we extend the
+asymptotically exact Alchemical Free Energy Perturbation technique to
+quantifying occupancies of sites on proteins in a complex bulk, including
+phase-separated, anisotropic, or non-dilute solutions, using a
+thermodynamically consistent and easily generalized approach that resolves
+several ambiguities of current frameworks. To incorporate the complex bulk
+without overcomplicating the overall thermodynamic cycle, we simplify the
+common approach for ligand restraints by using a single
+distance-from-bound-configuration (DBC) ligand restraint during AFEP decoupling
+from protein. DBC restraints should be generalizable to binding modes of most
+small molecules, even those with strong orientational dependence. We apply this
+approach to compute the likelihood that membrane cholesterol binds to known
+crystallographic sites on 3 GPCRs at a range of concentrations. Non-ideality of
+cholesterol in a binary cholesterol:POPC bilayer is characterized and
+consistently incorporated into the interpretation.
+",0,0,0,0,1,0
+11247,Dynamics of one-dimensional electrons with broken spin-charge separation,"  Spin-charge separation is known to be broken in many physically interesting
+one-dimensional (1D) and quasi-1D systems with spin-orbit interaction because
+of which spin and charge degrees of freedom are mixed in collective
+excitations. Mixed spin-charge modes carry an electric charge and therefore can
+be investigated by electrical means. We explore this possibility by studying
+the dynamic conductance of a 1D electron system with image-potential-induced
+spin-orbit interaction. The real part of the admittance reveals an oscillatory
+behavior versus frequency that reflects the collective excitation resonances
+for both modes at their respective transit frequencies. By analyzing the
+frequency dependence of the conductance the mode velocities can be found and
+their spin-charge structure can be determined quantitatively.
+",0,1,0,0,0,0
+11248,A Rational Distributed Process-level Account of Independence Judgment,"  It is inconceivable how chaotic the world would look to humans, faced with
+innumerable decisions a day to be made under uncertainty, had they been lacking
+the capacity to distinguish the relevant from the irrelevant---a capacity which
+computationally amounts to handling probabilistic independence relations. The
+highly parallel and distributed computational machinery of the brain suggests
+that a satisfying process-level account of human independence judgment should
+also mimic these features. In this work, we present the first rational,
+distributed, message-passing, process-level account of independence judgment,
+called $\mathcal{D}^\ast$. Interestingly, $\mathcal{D}^\ast$ shows a curious,
+but normatively-justified tendency for quick detection of dependencies,
+whenever they hold. Furthermore, $\mathcal{D}^\ast$ outperforms all the
+previously proposed algorithms in the AI literature in terms of worst-case
+running time, and a salient aspect of it is supported by recent work in
+neuroscience investigating possible implementations of Bayes nets at the neural
+level. $\mathcal{D}^\ast$ nicely exemplifies how the pursuit of cognitive
+plausibility can lead to the discovery of state-of-the-art algorithms with
+appealing properties, and its simplicity makes $\mathcal{D}^\ast$ potentially a
+good candidate for pedagogical purposes.
+",0,0,0,1,1,0
+11249,Automata in the Category of Glued Vector Spaces,"  In this paper we adopt a category-theoretic approach to the conception of
+automata classes enjoying minimization by design. The main instantiation of our
+construction is a new class of automata that are hybrid between deterministic
+automata and automata weighted over a field.
+",1,0,0,0,0,0
+11250,"Second order structural phase transitions, free energy curvature, and temperature-dependent anharmonic phonons in the self-consistent harmonic approximation: theory and stochastic implementation","  The self-consistent harmonic approximation is an effective harmonic theory to
+calculate the free energy of systems with strongly anharmonic atomic
+vibrations, and its stochastic implementation has proved to be an efficient
+method to study, from first-principles, the anharmonic properties of solids.
+The free energy as a function of average atomic positions (centroids) can be
+used to study quantum or thermal lattice instability. In particular the
+centroids are order parameters in second-order structural phase transitions
+such as, e.g., charge-density-waves or ferroelectric instabilities. According
+to Landau's theory, the knowledge of the second derivative of the free energy
+(i.e. the curvature) with respect to the centroids in a high-symmetry
+configuration allows the identification of the phase-transition and of the
+instability modes. In this work we derive the exact analytic formula for the
+second derivative of the free energy in the self-consistent harmonic
+approximation for a generic atomic configuration. The analytic derivative is
+expressed in terms of the atomic displacements and forces in a form that can be
+evaluated by a stochastic technique using importance sampling. Our approach is
+particularly suitable for applications based on first-principles
+density-functional-theory calculations, where the forces on atoms can be
+obtained with a negligible computational effort compared to total energy
+determination. Finally we propose a dynamical extension of the theory to
+calculate spectral properties of strongly anharmonic phonons, as probed by
+inelastic scattering processes. We illustrate our method with a numerical
+application on a toy model that mimics the ferroelectric transition in
+rock-salt crystals such as SnTe or GeTe.
+",0,1,0,0,0,0
+11251,Prioritized Norms in Formal Argumentation,"  To resolve conflicts among norms, various nonmonotonic formalisms can be used
+to perform prioritized normative reasoning. Meanwhile, formal argumentation
+provides a way to represent nonmonotonic logics. In this paper, we propose a
+representation of prioritized normative reasoning by argumentation. Using
+hierarchical abstract normative systems, we define three kinds of prioritized
+normative reasoning approaches, called Greedy, Reduction, and Optimization.
+Then, after formulating an argumentation theory for a hierarchical abstract
+normative system, we show that for a totally ordered hierarchical abstract
+normative system, Greedy and Reduction can be represented in argumentation by
+applying the weakest link and the last link principles respectively, and
+Optimization can be represented by introducing additional defeats capturing the
+idea that for each argument that contains a norm not belonging to the maximal
+obeyable set then this argument should be rejected.
+",1,0,0,0,0,0
+11252,Fast and robust tensor decomposition with applications to dictionary learning,"  We develop fast spectral algorithms for tensor decomposition that match the
+robustness guarantees of the best known polynomial-time algorithms for this
+problem based on the sum-of-squares (SOS) semidefinite programming hierarchy.
+Our algorithms can decompose a 4-tensor with $n$-dimensional orthonormal
+components in the presence of error with constant spectral norm (when viewed as
+an $n^2$-by-$n^2$ matrix). The running time is $n^5$ which is close to linear
+in the input size $n^4$.
+We also obtain algorithms with similar running time to learn sparsely-used
+orthogonal dictionaries even when feature representations have constant
+relative sparsity and non-independent coordinates.
+The only previous polynomial-time algorithms to solve these problem are based
+on solving large semidefinite programs. In contrast, our algorithms are easy to
+implement directly and are based on spectral projections and tensor-mode
+rearrangements.
+Or work is inspired by recent of Hopkins, Schramm, Shi, and Steurer (STOC'16)
+that shows how fast spectral algorithms can achieve the guarantees of SOS for
+average-case problems. In this work, we introduce general techniques to capture
+the guarantees of SOS for worst-case problems.
+",1,0,0,1,0,0
+11253,Distributed Testing of Conductance,"  We study the problem of testing conductance in the setting of distributed
+computing and give a two-sided tester that takes $\mathcal{O}(\log(n) /
+(\epsilon \Phi^2))$ rounds to decide if a graph has conductance at least $\Phi$
+or is $\epsilon$-far from having conductance at least $\Phi^2 / 1000$ in the
+distributed CONGEST model. We also show that $\Omega(\log n)$ rounds are
+necessary for testing conductance even in the LOCAL model. In the case of a
+connected graph, we show that we can perform the test even when the number of
+vertices in the graph is not known a priori. This is the first two-sided tester
+in the distributed model we are aware of. A key observation is that one can
+perform a polynomial number of random walks from a small set of vertices if it
+is sufficient to track only some small statistics of the walks. This greatly
+reduces the congestion on the edges compared to tracking each walk
+individually.
+",1,0,0,0,0,0
+11254,Ultra high stiffness and thermal conductivity of graphene like C3N,"  Recently, single crystalline carbon nitride 2D material with a C3N
+stoichiometry has been synthesized. In this investigation, we explored the
+mechanical response and thermal transport along pristine, free-standing and
+single-layer C3N. To this aim, we conducted extensive first-principles density
+functional theory (DFT) calculations as well as molecular dynamics (MD)
+simulations. DFT results reveal that C3N nanofilms can yield remarkably high
+elastic modulus of 341 GPa.nm and tensile strength of 35 GPa.nm, very close to
+those of defect-free graphene. Classical MD simulations performed at a low
+temperature, predict accurately the elastic modulus of 2D C3N with less than 3%
+difference with the first-principles estimation. The deformation process of C3N
+nanosheets was studied both by the DFT and MD simulations. Ab initio molecular
+dynamics simulations show that single-layer C3N can withstand high temperatures
+like 4000 K. Notably, the phononic thermal conductivity of free-standing C3N
+was predicted to be as high as 815 W/mK. Our atomistic modelling results reveal
+ultra high stiffness and thermal conductivity of C3N nanomembranes and
+therefore propose them as promising candidates for new application such as the
+thermal management in nanoelectronics or simultaneously reinforcing the thermal
+and mechanical properties of polymeric materials.
+",0,1,0,0,0,0
+11255,Automatic Liver Lesion Detection using Cascaded Deep Residual Networks,"  Automatic segmentation of liver lesions is a fundamental requirement towards
+the creation of computer aided diagnosis (CAD) and decision support systems
+(CDS). Traditional segmentation approaches depend heavily upon hand-crafted
+features and a priori knowledge of the user. As such, these methods are
+difficult to adopt within a clinical environment. Recently, deep learning
+methods based on fully convolutional networks (FCNs) have been successful in
+many segmentation problems primarily because they leverage a large labelled
+dataset to hierarchically learn the features that best correspond to the
+shallow visual appearance as well as the deep semantics of the areas to be
+segmented. However, FCNs based on a 16 layer VGGNet architecture have limited
+capacity to add additional layers. Therefore, it is challenging to learn more
+discriminative features among different classes for FCNs. In this study, we
+overcome these limitations using deep residual networks (ResNet) to segment
+liver lesions. ResNet contain skip connections between convolutional layers,
+which solved the problem of the training degradation of training accuracy in
+very deep networks and thereby enables the use of additional layers for
+learning more discriminative features. In addition, we achieve more precise
+boundary definitions through a novel cascaded ResNet architecture with
+multi-scale fusion to gradually learn and infer the boundaries of both the
+liver and the liver lesions. Our proposed method achieved 4th place in the ISBI
+2017 Liver Tumor Segmentation Challenge by the submission deadline.
+",1,0,0,0,0,0
+11256,"Combinatorial metrics: MacWilliams-type identities, isometries and extension property","  In this work we characterize the combinatorial metrics admitting a
+MacWilliams-type identity and describe the group of linear isometries of such
+metrics. Considering coverings that are not connected, we classify the metrics
+satisfying the MacWilliams extension property.
+",1,0,0,0,0,0
+11257,The curtain remains open: NGC 2617 continues in a high state,"  Optical and near-infrared photometry, optical spectroscopy, and soft X-ray
+and UV monitoring of the changing look active galactic nucleus NGC 2617 show
+that it continues to have the appearance of a type-1 Seyfert galaxy. An optical
+light curve for 2010-2016 indicates that the change of type probably occurred
+between 2010 October and 2012 February and was not related to the brightening
+in 2013. In 2016 NGC 2617 brightened again to a level of activity close to that
+in 2013 April. We find variations in all passbands and in both the intensities
+and profiles of the broad Balmer lines. A new displaced emission peak has
+appeared in H$\beta$. X-ray variations are well correlated with UV-optical
+variability and possibly lead by $\sim$ 2-3 d. The $K$ band lags the $J$ band
+by about 21.5 $\pm$ 2.5 d. and lags the combined $B+J$ filters by $\sim$ 25 d.
+$J$ lags $B$ by about 3 d. This could be because $J$-band variability arises
+from the outer part of the accretion disc, while $K$-band variability comes
+from thermal re-emission by dust. We propose that spectral-type changes are a
+result of increasing central luminosity causing sublimation of the innermost
+dust in the hollow biconical outflow. We briefly discuss various other possible
+reasons that might explain the dramatic changes in NGC 2617.
+",0,1,0,0,0,0
+11258,The topology on Berkovich affine lines over complete valuation rings,"  In this article, we give a full description of the topology of the one
+dimensional affine analytic space $\mathbb{A}_R^1$ over a complete valuation
+ring $R$ (i.e. a valuation ring with ""real valued valuation"" which is complete
+under the induced metric), when its field of fractions $K$ is algebraically
+closed. In particular, we show that $\mathbb{A}_R^1$ is both connected and
+locally path connected. Furthermore, $\mathbb{A}_R^1$ is the completion of
+$K\times (1,\infty)$ under a canonical uniform structure. As an application, we
+describe the Berkovich spectrum $\mathfrak{M}(\mathbb{Z}_p[G])$ of the Banach
+group ring $\mathbb{Z}_p[G]$ of a cyclic $p$-group $G$ over the ring
+$\mathbb{Z}_p$ of $p$-adic integers.
+",0,0,1,0,0,0
+11259,An Original Mechanism for the Acceleration of Ultra-High-Energy Cosmic Rays,"  We suggest that ultra-high-energy (UHE) cosmic rays (CRs) may be accelerated
+in ultra-relativistic flows via a one-shot mechanism, the ""espresso""
+acceleration, in which already-energetic particles are generally boosted by a
+factor of $\sim\Gamma^2$ in energy, where $\Gamma$ is the flow Lorentz factor.
+More precisely, we consider blazar-like jets with $\Gamma\gtrsim 30$
+propagating into a halo of ""seed"" CRs produced in supernova remnants, which can
+accelerate UHECRs up to $10^{20}$\,eV. Such a re-acceleration process naturally
+accounts for the chemical composition measured by the Pierre Auger
+Collaboration, which resembles the one around and above the knee in the CR
+spectrum, and is consistent with the distribution of potential sources in the
+local universe, particularly intriguing is the coincidence of the powerful
+blazar Mrk 421 with the hotspot reported by the Telescope Array Collaboration.
+",0,1,0,0,0,0
+11260,Expected Time to Extinction of SIS Epidemic Model Using Quasy Stationary Distribution,"  We study that the breakdown of epidemic depends on some parameters, that is
+expressed in epidemic reproduction ratio number. It is noted that when $R_0 $
+exceeds 1, the stochastic model have two different results. But, eventually the
+extinction will be reached even though the major epidemic occurs. The question
+is how long this process will reach extinction. In this paper, we will focus on
+the Markovian process of SIS model when major epidemic occurs. Using the
+approximation of quasi--stationary distribution, the expected mean time of
+extinction only occurs when the process is one step away from being extinct.
+Combining the theorm from Ethier and Kurtz, we use CLT to find the
+approximation of this quasi distribution and successfully determine the
+asymptotic mean time to extinction of SIS model without demography.
+",0,0,0,0,1,0
+11261,The gyrokinetic limit for the Vlasov-Poisson system with a point charge,"  We consider the asymptotics of large external magnetic field for a 2D
+Vlasov-Poisson system governing the evolution of a bounded density interacting
+with a point charge. In a suitable asymptotical regime, we show that the
+solution converges to a measure-valued solution of the Euler equation with a
+defect measure.
+",0,0,1,0,0,0
+11262,Deriving Verb Predicates By Clustering Verbs with Arguments,"  Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)
+have proved useful, but have limited coverage. Verb classes automatically
+induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other
+hand, can give clusters with much larger coverage, and can be adapted to
+specific corpora such as Twitter. We present a method for clustering the
+outputs of VerbKB: verbs with their multiple argument types, e.g.
+""marry(person, person)"", ""feel(person, emotion)."" We make use of a novel
+low-dimensional embedding of verbs and their arguments to produce high quality
+clusters in which the same verb can be in different clusters depending on its
+argument type. The resulting verb clusters do a better job than hand-built
+clusters of predicting sarcasm, sentiment, and locus of control in tweets.
+",1,0,0,0,0,0
+11263,A propagation tool to connect remote-sensing observations with in-situ measurements of heliospheric structures,"  The remoteness of the Sun and the harsh conditions prevailing in the solar
+corona have so far limited the observational data used in the study of solar
+physics to remote-sensing observations taken either from the ground or from
+space. In contrast, the `solar wind laboratory' is directly measured in situ by
+a fleet of spacecraft measuring the properties of the plasma and magnetic
+fields at specific points in space. Since 2007, the solar-terrestrial relations
+observatory (STEREO) has been providing images of the solar wind that flows
+between the solar corona and spacecraft making in-situ measurements. This has
+allowed scientists to directly connect processes imaged near the Sun with the
+subsequent effects measured in the solar wind. This new capability prompted the
+development of a series of tools and techniques to track heliospheric
+structures through space. This article presents one of these tools, a web-based
+interface called the 'Propagation Tool' that offers an integrated research
+environment to study the evolution of coronal and solar wind structures, such
+as Coronal Mass Ejections (CMEs), Corotating Interaction Regions (CIRs) and
+Solar Energetic Particles (SEPs). These structures can be propagated from the
+Sun outwards to or alternatively inwards from planets and spacecraft situated
+in the inner and outer heliosphere. In this paper, we present the global
+architecture of the tool, discuss some of the assumptions made to simulate the
+evolution of the structures and show how the tool connects to different
+databases.
+",0,1,0,0,0,0
+11264,Chemical-disorder-caused Medium Range Order in Covalent Glass,"  How atoms in covalent solids rearrange over a medium-range length-scale
+during amorphization is a long pursued question whose answer could profoundly
+shape our understanding on amorphous (a-) networks. Based on ab-intio
+calculations and reverse Monte Carlo simulations of experiments, we
+surprisingly find that even though the severe chemical disorder in a-GeTe
+undermined the prevailing medium range order (MRO) picture, it is responsible
+for the experimentally observed MRO. That this thing could happen depends on a
+novel atomic packing scheme. And this scheme results in a kind of homopolar
+bond chain-like polyhedral clusters. Within this scheme, the formation of
+homopolar bonds can be well explained by an electron-counting model and further
+validated by quantitative bond energy analysis based. Our study suggests that
+the underlying physics for chemical disorder in a-GeTe is intrinsic and
+universal to all severely chemically disordered covalent glasses.
+",0,1,0,0,0,0
+11265,"Optimal compromise between incompatible conditional probability distributions, with application to Objective Bayesian Kriging","  Models are often defined through conditional rather than joint distributions,
+but it can be difficult to check whether the conditional distributions are
+compatible, i.e. whether there exists a joint probability distribution which
+generates them. When they are compatible, a Gibbs sampler can be used to sample
+from this joint distribution. When they are not, the Gibbs sampling algorithm
+may still be applied, resulting in a ""pseudo-Gibbs sampler"". We show its
+stationary probability distribution to be the optimal compromise between the
+conditional distributions, in the sense that it minimizes a mean squared misfit
+between them and its own conditional distributions. This allows us to perform
+Objective Bayesian analysis of correlation parameters in Kriging models by
+using univariate conditional Jeffreys-rule posterior distributions instead of
+the widely used multivariate Jeffreys-rule posterior. This strategy makes the
+full-Bayesian procedure tractable. Numerical examples show it has near-optimal
+frequentist performance in terms of prediction interval coverage.
+",0,0,1,1,0,0
+11266,Estimation for the Prediction of Point Processes with Many Covariates,"  Estimation of the intensity of a point process is considered within a
+nonparametric framework. The intensity measure is unknown and depends on
+covariates, possibly many more than the observed number of jumps. Only a single
+trajectory of the counting process is observed. Interest lies in estimating the
+intensity conditional on the covariates. The impact of the covariates is
+modelled by an additive model where each component can be written as a linear
+combination of possibly unknown functions. The focus is on prediction as
+opposed to variable screening. Conditions are imposed on the coefficients of
+this linear combination in order to control the estimation error. The rates of
+convergence are optimal when the number of active covariates is large. As an
+application, the intensity of the buy and sell trades of the New Zealand dollar
+futures is estimated and a test for forecast evaluation is presented. A
+simulation is included to provide some finite sample intuition on the model and
+asymptotic properties.
+",0,0,1,1,0,0
+11267,Chordal SLE$_6$ explorations of a quantum disk,"  We consider a particular type of $\sqrt{8/3}$-Liouville quantum gravity
+surface called a doubly marked quantum disk (equivalently, a Brownian disk)
+decorated by an independent chordal SLE$_6$ curve $\eta$ between its marked
+boundary points. We obtain descriptions of the law of the quantum surfaces
+parameterized by the complementary connected components of $\eta([0,t])$ for
+each time $t \geq 0$ as well as the law of the left/right $\sqrt{8/3}$-quantum
+boundary length process for $\eta$.
+",0,0,1,0,0,0
+11268,Optimal rate list decoding over bounded alphabets using algebraic-geometric codes,"  We give new constructions of two classes of algebraic code families which are
+efficiently list decodable with small output list size from a fraction
+$1-R-\epsilon$ of adversarial errors where $R$ is the rate of the code, for any
+desired positive constant $\epsilon$. The alphabet size depends only $\epsilon$
+and is nearly-optimal.
+The first class of codes are obtained by folding algebraic-geometric codes
+using automorphisms of the underlying function field. The list decoding
+algorithm is based on a linear-algebraic approach, which pins down the
+candidate messages to a subspace with a nice ""periodic"" structure. The list is
+pruned by precoding into a special form of ""subspace-evasive"" sets, which are
+constructed pseudorandomly. Instantiating this construction with the
+Garcia-Stichtenoth function field tower yields codes list-decodable up to a
+$1-R-\epsilon$ error fraction with list size bounded by $O(1/\epsilon)$,
+matching the existential bound up to constant factors. The parameters we
+achieve are thus quite close to the existential bounds in all three aspects:
+error-correction radius, alphabet size, and list-size.
+The second class of codes are obtained by restricting evaluation points of an
+algebraic-geometric code to rational points from a subfield. Once again, the
+linear-algebraic approach to list decoding to pin down candidate messages to a
+periodic subspace. We develop an alternate approach based on ""subspace designs""
+to precode messages. Together with the subsequent explicit constructions of
+subspace designs, this yields a deterministic construction of an algebraic code
+family of rate $R$ with efficient list decoding from $1-R-\epsilon$ fraction of
+errors over a constant-sized alphabet. The list size is bounded by a very
+slowly growing function of the block length $N$; in particular, it is at most
+$O(\log^{(r)} N)$ (the $r$'th iterated logarithm) for any fixed integer $r$.
+",1,0,1,0,0,0
+11269,Quadratic Programming Approach to Fit Protein Complexes into Electron Density Maps,"  The paper investigates the problem of fitting protein complexes into electron
+density maps. They are represented by high-resolution cryoEM density maps
+converted into overlapping matrices and partly show a structure of a complex.
+The general purpose is to define positions of all proteins inside it. This
+problem is known to be NP-hard, since it lays in the field of combinatorial
+optimization over a set of discrete states of the complex. We introduce
+quadratic programming approaches to the problem. To find an approximate
+solution, we convert a density map into an overlapping matrix, which is
+generally indefinite. Since the matrix is indefinite, the optimization problem
+for the corresponding quadratic form is non-convex. To treat non-convexity of
+the optimization problem, we use different convex relaxations to find which set
+of proteins minimizes the quadratic form best.
+",0,0,1,0,0,0
+11270,Conditions for the equivalence between IQC and graph separation stability results,"  This paper provides a link between time-domain and frequency-domain stability
+results in the literature. Specifically, we focus on the comparison between
+stability results for a feedback interconnection of two nonlinear systems
+stated in terms of frequency-domain conditions. While the Integral Quadratic
+Constrain (IQC) theorem can cope with them via a homotopy argument for the
+Lurye problem, graph separation results require the transformation of the
+frequency-domain conditions into truncated time-domain conditions. To date,
+much of the literature focuses on ""hard"" factorizations of the multiplier,
+considering only one of the two frequency-domain conditions. Here it is shown
+that a symmetric, ""doubly-hard"" factorization is required to convert both
+frequency-domain conditions into truncated time-domain conditions. By using the
+appropriate factorization, a novel comparison between the results obtained by
+IQC and separation theories is then provided. As a result, we identify under
+what conditions the IQC theorem may provide some advantage.
+",1,0,0,0,0,0
+11271,Energy-Efficient Wireless Content Delivery with Proactive Caching,"  We propose an intelligent proactive content caching scheme to reduce the
+energy consumption in wireless downlink. We consider an online social network
+(OSN) setting where new contents are generated over time, and remain
+\textit{relevant} to the user for a random lifetime. Contents are downloaded to
+the user equipment (UE) through a time-varying wireless channel at an energy
+cost that depends on the channel state and the number of contents downloaded.
+The user accesses the OSN at random time instants, and consumes all the
+relevant contents. To reduce the energy consumption, we propose
+\textit{proactive caching} of contents under favorable channel conditions to a
+finite capacity cache memory. Assuming that the channel quality (or
+equivalently, the cost of downloading data) is memoryless over time slots, we
+show that the optimal caching policy, which may replace contents in the cache
+with shorter remaining lifetime with contents at the server that remain
+relevant longer, has certain threshold structure with respect to the channel
+quality. Since the optimal policy is computationally demanding in practice, we
+introduce a simplified caching scheme and optimize its parameters using policy
+search. We also present two lower bounds on the energy consumption. We
+demonstrate through numerical simulations that the proposed caching scheme
+significantly reduces the energy consumption compared to traditional reactive
+caching tools, and achieves close-to-optimal performance for a wide variety of
+system parameters.
+",1,0,1,0,0,0
+11272,Low- and high-order gravitational harmonics of rigidly rotating Jupiter,"  The Juno Orbiter has provided improved estimates of the even gravitational
+harmonics J2 to J8 of Jupiter. To compute higher-order moments, new methods
+such as the Concentric Maclaurin Spheroids (CMS) method have been developed
+which surpass the so far commonly used Theory of Figures (ToF) method in
+accuracy. This progress rises the question whether ToF can still provide a
+useful service for deriving the internal structure of giant planets in the
+Solar system. In this paper, I apply both the ToF and the CMS method to compare
+results for polytropic Jupiter and for the physical equation of state
+H/He-REOS.3 based models. An accuracy in the computed values of J2 and J4 of
+0.1% is found to be sufficient in order to obtain the core mass safely within
+0.5 Mearth numerical accuracy and the atmospheric metallicity within about
+0.0004. ToF to 4th order provides that accuracy, while ToF to 3rd order does
+not for J4. Furthermore, I find that the assumption of rigid rotation yields J6
+and J8 values in agreement with the current Juno estimates, and that higher
+order terms (J10 to J18) deviate by about 10% from predictions by polytropic
+models. This work suggests that ToF4 can still be applied to infer the deep
+internal structure, and that the zonal winds on Jupiter reach less deep than
+0.9 RJup.
+",0,1,0,0,0,0
+11273,Language Modeling with Generative Adversarial Networks,"  Generative Adversarial Networks (GANs) have been promising in the field of
+image generation, however, they have been hard to train for language
+generation. GANs were originally designed to output differentiable values, so
+discrete language generation is challenging for them which causes high levels
+of instability in training GANs. Consequently, past work has resorted to
+pre-training with maximum-likelihood or training GANs without pre-training with
+a WGAN objective with a gradient penalty. In this study, we present a
+comparison of those approaches. Furthermore, we present the results of some
+experiments that indicate better training and convergence of Wasserstein GANs
+(WGANs) when a weaker regularization term is enforcing the Lipschitz
+constraint.
+",0,0,0,1,0,0
+11274,How AD Can Help Solve Differential-Algebraic Equations,"  A characteristic feature of differential-algebraic equations is that one
+needs to find derivatives of some of their equations with respect to time, as
+part of so called index reduction or regularisation, to prepare them for
+numerical solution. This is often done with the help of a computer algebra
+system. We show in two significant cases that it can be done efficiently by
+pure algorithmic differentiation. The first is the Dummy Derivatives method,
+here we give a mainly theoretical description, with tutorial examples. The
+second is the solution of a mechanical system directly from its Lagrangian
+formulation. Here we outline the theory and show several non-trivial examples
+of using the ""Lagrangian facility"" of the Nedialkov-Pryce initial-value solver
+DAETS, namely: a spring-mass-multipendulum system, a prescribed-trajectory
+control problem, and long-time integration of a model of the outer planets of
+the solar system, taken from the DETEST testing package for ODE solvers.
+",0,0,1,0,0,0
+11275,Secure Coding Practices in Java: Challenges and Vulnerabilities,"  Java platform and third-party libraries provide various security features to
+facilitate secure coding. However, misusing these features can cost tremendous
+time and effort of developers or cause security vulnerabilities in software.
+Prior research was focused on the misuse of cryptography and SSL APIs, but did
+not explore the key fundamental research question: what are the biggest
+challenges and vulnerabilities in secure coding practices? In this paper, we
+conducted a comprehensive empirical study on StackOverflow posts to understand
+developers' concerns on Java secure coding, their programming obstacles, and
+potential vulnerabilities in their code. We observed that developers have
+shifted their effort to the usage of authentication and authorization features
+provided by Spring security--a third-party framework designed to secure
+enterprise applications. Multiple programming challenges are related to APIs or
+libraries, including the complicated cross-language data handling of
+cryptography APIs, and the complex Java-based or XML-based approaches to
+configure Spring security. More interestingly, we identified security
+vulnerabilities in the suggested code of accepted answers. The vulnerabilities
+included using insecure hash functions such as MD5, breaking SSL/TLS security
+through bypassing certificate validation, and insecurely disabling the default
+protection against Cross Site Request Forgery (CSRF) attacks. Our findings
+reveal the insufficiency of secure coding assistance and education, and the gap
+between security theory and coding practices.
+",1,0,0,0,0,0
+11276,Complex Urban LiDAR Data Set,"  This paper presents a Light Detection and Ranging (LiDAR) data set that
+targets complex urban environments. Urban environments with high-rise buildings
+and congested traffic pose a significant challenge for many robotics
+applications. The presented data set is unique in the sense it is able to
+capture the genuine features of an urban environment (e.g. metropolitan areas,
+large building complexes and underground parking lots). Data of two-dimensional
+(2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors,
+are provided in the data set. The two 16-ray 3D LiDARs are tilted on both sides
+for maximal coverage. One 2D LiDAR faces backward while the other faces
+forwards to collect data of roads and buildings, respectively. Raw sensor data
+from Fiber Optic Gyro (FOG), Inertial Measurement Unit (IMU), and the Global
+Positioning System (GPS) are presented in a file format for vehicle pose
+estimation. The pose information of the vehicle estimated at 100 Hz is also
+presented after applying the graph simultaneous localization and mapping (SLAM)
+algorithm. For the convenience of development, the file player and data viewer
+in Robot Operating System (ROS) environment were also released via the web
+page. The full data sets are available at: this http URL. In
+this website, 3D preview of each data set is provided using WebGL.
+",1,0,0,0,0,0
+11277,Blind Source Separation Using Mixtures of Alpha-Stable Distributions,"  We propose a new blind source separation algorithm based on mixtures of
+alpha-stable distributions. Complex symmetric alpha-stable distributions have
+been recently showed to better model audio signals in the time-frequency domain
+than classical Gaussian distributions thanks to their larger dynamic range.
+However, inference of these models is notoriously hard to perform because their
+probability density functions do not have a closed-form expression in general.
+Here, we introduce a novel method for estimating mixture of alpha-stable
+distributions based on characteristic function matching. We apply this to the
+blind estimation of binary masks in individual frequency bands from
+multichannel convolutive audio mixes. We show that the proposed method yields
+better separation performance than Gaussian-based binary-masking methods.
+",1,0,0,1,0,0
+11278,Student and instructor framing in upper-division physics,"  Upper-division physics students spend much of their time solving problems. In
+addition to their basic skills and background, their epistemic framing can form
+an important part of their ability to learn physics from these problems.
+Encouraging students to move toward productive framing may help them solve
+problems. Thus, an instructor should understand the specifics of how student
+have framed a problem and understand how her interaction with the students will
+impact that framing. In this study we investigate epistemic framing of students
+in problem solving situations where math is applied to physics. To analyze the
+frames and changes in frames, we develop and use a two-axis framework involving
+conceptual and algorithmic physics and math. We examine student and instructor
+framing and the interactions of these frames over a range of problems in an
+upper-division electromagnetic fields course. Within interactions, students and
+instructors generally follow each others' leads in framing.
+",0,1,0,0,0,0
+11279,Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity,"  Accurate and automated detection of anomalous samples in a natural image
+dataset can be accomplished with a probabilistic model for end-to-end modeling
+of images. Such images have heterogeneous complexity, however, and a
+probabilistic model overlooks simply shaped objects with small anomalies. This
+is because the probabilistic model assigns undesirably lower likelihoods to
+complexly shaped objects that are nevertheless consistent with set standards.
+To overcome this difficulty, we propose an unregularized score for deep
+generative models (DGMs), which are generative models leveraging deep neural
+networks. We found that the regularization terms of the DGMs considerably
+influence the anomaly score depending on the complexity of the samples. By
+removing these terms, we obtain an unregularized score, which we evaluated on a
+toy dataset and real-world manufacturing datasets. Empirical results
+demonstrate that the unregularized score is robust to the inherent complexity
+of samples and can be used to better detect anomalies.
+",0,0,0,1,0,0
+11280,Non-collinear magnetic structure and multipolar order in Eu$_2$Ir$_2$O$_7$,"  The magnetic properties of the pyrochlore iridate material Eu$_2$Ir$_2$O$_7$
+(5$d^5$) have been studied based on the first principle calculations, where the
+crystal field splitting $\Delta$, spin-orbit coupling (SOC) $\lambda$ and
+Coulomb interaction $U$ within Ir 5$d$ orbitals are all playing significant
+roles. The ground state phase diagram has been obtained with respect to the
+strength of SOC and Coulomb interaction $U$, where a stable anti-ferromagnetic
+ground state with all-in/all-out (AIAO) spin structure has been found. Besides,
+another anti-ferromagnetic states with close energy to AIAO have also been
+found to be stable. The calculated nonlinear magnetization of the two stable
+states both have the d-wave pattern but with a $\pi/4$ phase difference, which
+can perfectly explain the experimentally observed nonlinear magnetization
+pattern. Compared with the results of the non-distorted structure, it turns out
+that the trigonal lattice distortion is crucial for stabilizing the AIAO state
+in Eu$_2$Ir$_2$O$_7$. Furthermore, besides large dipolar moments, we also find
+considerable octupolar moments in the magnetic states.
+",0,1,0,0,0,0
+11281,OGLE-2013-BLG-1761Lb: A Massive Planet Around an M/K Dwarf,"  We report the discovery and the analysis of the planetary microlensing event,
+OGLE-2013-BLG-1761. There are some degenerate solutions in this event because
+the planetary anomaly is only sparsely sampled. But the detailed light curve
+analysis ruled out all stellar binary models and shows that the lens to be a
+planetary system. There is the so-called close/wide degeneracy in the solutions
+with the planet/host mass ratio of $q \sim (7.5 \pm 1.5) \times 10^{-3}$ and $q
+\sim (9.3 \pm 2.9) \times 10^{-3}$ with the projected separation in Einstein
+radius units of $s = 0.95$ (close) and $s = 1.19$ (wide), respectively. The
+microlens parallax effect is not detected but the finite source effect is
+detected. Our Bayesian analysis indicates that the lens system is located at
+$D_{\rm L}=6.9_{-1.2}^{+1.0} \ {\rm kpc}$ away from us and the host star is an
+M/K-dwarf with the mass of $M_{\rm L}=0.33_{-0.18}^{+0.32} \ M_{\odot}$ orbited
+by a super-Jupiter mass planet with the mass of $m_{\rm P}=2.8_{-1.5}^{+2.5} \
+M_{\rm Jup}$ at the projected separation of $a_{\perp}=1.8_{-0.5}^{+0.5} \ {\rm
+AU}$. The preference of the large lens distance in the Bayesian analysis is due
+to the relatively large observed source star radius. The distance and other
+physical parameters can be constrained by the future high resolution imaging by
+ground large telescopes or HST. If the estimated lens distance is correct, this
+planet provides another sample for testing the claimed deficit of planets in
+the Galactic bulge.
+",0,1,0,0,0,0
+11282,Phase-Aware Single-Channel Speech Enhancement with Modulation-Domain Kalman Filtering,"  We present a single-channel phase-sensitive speech enhancement algorithm that
+is based on modulation-domain Kalman filtering and on tracking the speech phase
+using circular statistics. With Kalman filtering, using that speech and noise
+are additive in the complex STFT domain, the algorithm tracks the speech
+log-spectrum, the noise log-spectrum and the speech phase. Joint amplitude and
+phase estimation of speech is performed. Given the noisy speech signal,
+conventional algorithms use the noisy phase for signal reconstruction
+approximating the speech phase with the noisy phase. In the proposed Kalman
+filtering algorithm, the speech phase posterior is used to create an enhanced
+speech phase spectrum for signal reconstruction. The Kalman filter prediction
+models the temporal/inter-frame correlation of the speech and noise log-spectra
+and of the speech phase, while the Kalman filter update models their nonlinear
+relations. With the proposed algorithm, speech is tracked and estimated both in
+the log-spectral and spectral phase domains. The algorithm is evaluated in
+terms of speech quality and different algorithm configurations, dependent on
+the signal model, are compared in different noise types. Experimental results
+show that the proposed algorithm outperforms traditional enhancement algorithms
+over a range of SNRs for various noise types.
+",1,0,0,0,0,0
+11283,Reduction and specialization of hyperelliptic continued fractions,"  For a monic polynomial $D(X)$ of even degree, express $\sqrt D$ as a Laurent
+series in $X^{-1}$; this yields a continued fraction expansion (similar to
+continued fractions of real numbers): \[\sqrt
+D=a_0+\dfrac{1}{a_1+\dfrac{1}{a_2+\dfrac{1}{\ddots}}},\quad a_i\text{
+polynomials in }X.\] Such continued fractions were first considered by Abel in
+1826, and later by Chebyshev. It turns out they are rarely periodic unless $D$
+is defined over a finite field.
+Around 2001 van der Poorten studied non-periodic continued fractions of
+$\sqrt D$, with $D$ defined over the rationals, and simultaneously the
+continued fraction of $\sqrt D$ modulo a suitable prime $p$; the latter
+continued fraction is automatically periodic. He found that one recovers all
+the convergents (rational function approximations to $\sqrt D$ obtained by
+cutting off the continued fraction) of $\sqrt D \mod{p}$ by appropriately
+normalising and then reducing the convergents of $\sqrt D$.
+By developing a general specialization theory for continued fractions of
+Laurent series, I produced a rigorous proof of this result stated by van der
+Poorten and further was able to show the following:
+If $D$ is defined over the rationals and the continued fraction of $\sqrt D$
+is non-periodic, then for all but finitely many primes $p \in \mathbb Z$, this
+prime $p$ occurs in the denominator of the leading coefficient of infinitely
+many $a_i$.
+For $\mathrm{deg}\,D = 4$, I can even give a description of the orders in
+which the prime appears, and the $p$-adic Gauss norms of the $a_i$ and the
+convergents. These results also generalise to number fields.
+Moreover, I derive optimised formulae for computing quadratic continued
+fractions, along with several example expansions. I discuss a few known results
+on the heights of the convergents, and explain some relations with the
+reduction of hyperelliptic curves and Jacobians.
+",0,0,1,0,0,0
+11284,Three years of SPHERE: the latest view of the morphology and evolution of protoplanetary discs,"  Spatially resolving the immediate surroundings of young stars is a key
+challenge for the planet formation community. SPHERE on the VLT represents an
+important step forward by increasing the opportunities offered by optical or
+near-infrared imaging instruments to image protoplanetary discs. The Guaranteed
+Time Observation Disc team has concentrated much of its efforts on polarimetric
+differential imaging, a technique that enables the efficient removal of stellar
+light and thus facilitates the detection of light scattered by the disc within
+a few au from the central star. These images reveal intriguing complex disc
+structures and diverse morphological features that are possibly caused by
+ongoing planet formation in the disc. An overview of the recent advances
+enabled by SPHERE is presented.
+",0,1,0,0,0,0
+11285,Tensor Methods for Nonlinear Matrix Completion,"  In the low rank matrix completion (LRMC) problem, the low rank assumption
+means that the columns (or rows) of the matrix to be completed are points on a
+low-dimensional linear algebraic variety. This paper extends this thinking to
+cases where the columns are points on a low-dimensional nonlinear algebraic
+variety, a problem we call Low Algebraic Dimension Matrix Completion (LADMC).
+Matrices whose columns belong to a union of subspaces (UoS) are an important
+special case. We propose a LADMC algorithm that leverages existing LRMC methods
+on a tensorized representation of the data. For example, a second-order
+tensorization representation is formed by taking the outer product of each
+column with itself, and we consider higher order tensorizations as well. This
+approach will succeed in many cases where traditional LRMC is guaranteed to
+fail because the data are low-rank in the tensorized representation but not in
+the original representation. We also provide a formal mathematical
+justification for the success of our method. In particular, we show bounds of
+the rank of these data in the tensorized representation, and we prove sampling
+requirements to guarantee uniqueness of the solution. Interestingly, the
+sampling requirements of our LADMC algorithm nearly match the information
+theoretic lower bounds for matrix completion under a UoS model. We also provide
+experimental results showing that the new approach significantly outperforms
+existing state-of-the-art methods for matrix completion in many situations.
+",0,0,0,1,0,0
+11286,Visual Interaction Networks,"  From just a glance, humans can make rich predictions about the future state
+of a wide range of physical systems. On the other hand, modern approaches from
+engineering, robotics, and graphics are often restricted to narrow domains and
+require direct measurements of the underlying states. We introduce the Visual
+Interaction Network, a general-purpose model for learning the dynamics of a
+physical system from raw visual observations. Our model consists of a
+perceptual front-end based on convolutional neural networks and a dynamics
+predictor based on interaction networks. Through joint training, the perceptual
+front-end learns to parse a dynamic visual scene into a set of factored latent
+object representations. The dynamics predictor learns to roll these states
+forward in time by computing their interactions and dynamics, producing a
+predicted physical trajectory of arbitrary length. We found that from just six
+input video frames the Visual Interaction Network can generate accurate future
+trajectories of hundreds of time steps on a wide range of physical systems. Our
+model can also be applied to scenes with invisible objects, inferring their
+future states from their effects on the visible objects, and can implicitly
+infer the unknown mass of objects. Our results demonstrate that the perceptual
+module and the object-based dynamics predictor module can induce factored
+latent representations that support accurate dynamical predictions. This work
+opens new opportunities for model-based decision-making and planning from raw
+sensory observations in complex physical environments.
+",1,0,0,0,0,0
+11287,Using polarimetry to retrieve the cloud coverage of Earth-like exoplanets,"  Context. Clouds have already been detected in exoplanetary atmospheres. They
+play crucial roles in a planet's atmosphere and climate and can also create
+ambiguities in the determination of atmospheric parameters such as trace gas
+mixing ratios. Knowledge of cloud properties is required when assessing the
+habitability of a planet. Aims. We aim to show that various types of cloud
+cover such as polar cusps, subsolar clouds, and patchy clouds on Earth-like
+exoplanets can be distinguished from each other using the polarization and flux
+of light that is reflected by the planet. Methods. We have computed the flux
+and polarization of reflected starlight for different types of (liquid water)
+cloud covers on Earth-like model planets using the adding-doubling method, that
+fully includes multiple scattering and polarization. Variations in cloud-top
+altitudes and planet-wide cloud cover percentages were taken into account.
+Results. We find that the different types of cloud cover (polar cusps, subsolar
+clouds, and patchy clouds) can be distinguished from each other and that the
+percentage of cloud cover can be estimated within 10%. Conclusions. Using our
+proposed observational strategy, one should be able to determine basic orbital
+parameters of a planet such as orbital inclination and estimate cloud coverage
+with reduced ambiguities from the planet's polarization signals along its
+orbit.
+",0,1,0,0,0,0
+11288,Dirac nodal lines and induced spin Hall effect in metallic rutile oxides,"  We have found Dirac nodal lines (DNLs) in the band structures of metallic
+rutile oxides IrO$_2$, OsO$_2$, and RuO$_2$ and revealed a large spin Hall
+conductivity contributed by these nodal lines, which explains a strong spin
+Hall effect (SHE) of IrO$_2$ discovered recently. Two types of DNLs exist. The
+first type forms DNL networks that extend in the whole Brillouin zone and
+appears only in the absence of spin-orbit coupling (SOC), which induces surface
+states on the boundary. Because of SOC-induced band anti-crossing, a large
+intrinsic SHE can be realized in these compounds. The second type appears at
+the Brillouin zone edges and is stable against SOC because of the protection of
+nonsymmorphic symmetry. Besides reporting new DNL materials, our work reveals
+the general relationship between DNLs and the SHE, indicating a way to apply
+Dirac nodal materials for spintronics.
+",0,1,0,0,0,0
+11289,Chaotic Dynamics Enhance the Sensitivity of Inner Ear Hair Cells,"  Hair cells of the auditory and vestibular systems are capable of detecting
+sounds that induce sub-nanometer vibrations of the hair bundle, below the
+stochastic noise levels of the surrounding fluid. Hair cells of certain species
+are also known to oscillate without external stimulation, indicating the
+presence of an underlying active mechanism. We previously demonstrated that
+these spontaneous oscillations exhibit chaotic dynamics. By varying the Calcium
+concentration and the viscosity of the Endolymph solution, we are able to
+modulate the degree of chaos in the hair cell dynamics. We find that the hair
+cell is most sensitive to a stimulus of small amplitude when it is poised in
+the weakly chaotic regime. Further, we show that the response time to a force
+step decreases with increasing levels of chaos. These results agree well with
+our numerical simulations of a chaotic Hopf oscillator and suggest that chaos
+may be responsible for the extreme sensitivity and temporal resolution of hair
+cells.
+",0,0,0,0,1,0
+11290,A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation,"  External localization is an essential part for the indoor operation of small
+or cost-efficient robots, as they are used, for example, in swarm robotics. We
+introduce a two-stage localization and instance identification framework for
+arbitrary robots based on convolutional neural networks. Object detection is
+performed on an external camera image of the operation zone providing robot
+bounding boxes for an identification and orientation estimation convolutional
+neural network. Additionally, we propose a process to generate the necessary
+training data. The framework was evaluated with 3 different robot types and
+various identification patterns. We have analyzed the main framework
+hyperparameters providing recommendations for the framework operation settings.
+We achieved up to 98% mAP@IOU0.5 and only 1.6° orientation error, running
+with a frame rate of 50 Hz on a GPU.
+",1,0,0,0,0,0
+11291,Executable Trigger-Action Comments,"  Natural language elements, e.g., todo comments, are frequently used to
+communicate among the developers and to describe tasks that need to be
+performed (actions) when specific conditions hold in the code repository
+(triggers). As projects evolve, development processes change, and development
+teams reorganize, these comments, because of their informal nature, frequently
+become irrelevant or forgotten.
+We present the first technique, dubbed TrigIt, to specify triggeraction todo
+comments as executable statements. Thus, actions are executed automatically
+when triggers evaluate to true. TrigIt specifications are written in the host
+language (e.g., Java) and are evaluated as part of the build process. The
+triggers are specified as query statements over abstract syntax trees and
+abstract representation of build configuration scripts, and the actions are
+specified as code transformation steps. We implemented TrigIt for the Java
+programming language and migrated 20 existing trigger-action comments from 8
+popular open-source projects. We evaluate the cost of using TrigIt in terms of
+the number of tokens in the executable comments and the time overhead
+introduced in the build process.
+",1,0,0,0,0,0
+11292,Cardinal Virtues: Extracting Relation Cardinalities from Text,"  Information extraction (IE) from text has largely focused on relations
+between individual entities, such as who has won which award. However, some
+facts are never fully mentioned, and no IE method has perfect recall. Thus, it
+is beneficial to also tap contents about the cardinalities of these relations,
+for example, how many awards someone has won. We introduce this novel problem
+of extracting cardinalities and discusses the specific challenges that set it
+apart from standard IE. We present a distant supervision method using
+conditional random fields. A preliminary evaluation results in precision
+between 3% and 55%, depending on the difficulty of relations.
+",1,0,0,0,0,0
+11293,A Decision Tree Approach to Predicting Recidivism in Domestic Violence,"  Domestic violence (DV) is a global social and public health issue that is
+highly gendered. Being able to accurately predict DV recidivism, i.e.,
+re-offending of a previously convicted offender, can speed up and improve risk
+assessment procedures for police and front-line agencies, better protect
+victims of DV, and potentially prevent future re-occurrences of DV. Previous
+work in DV recidivism has employed different classification techniques,
+including decision tree (DT) induction and logistic regression, where the main
+focus was on achieving high prediction accuracy. As a result, even the diagrams
+of trained DTs were often too difficult to interpret due to their size and
+complexity, making decision-making challenging. Given there is often a
+trade-off between model accuracy and interpretability, in this work our aim is
+to employ DT induction to obtain both interpretable trees as well as high
+prediction accuracy. Specifically, we implement and evaluate different
+approaches to deal with class imbalance as well as feature selection. Compared
+to previous work in DV recidivism prediction that employed logistic regression,
+our approach can achieve comparable area under the ROC curve results by using
+only 3 of 11 available features and generating understandable decision trees
+that contain only 4 leaf nodes.
+",0,0,0,1,0,0
+11294,On the Necessity of Superparametric Geometry Representation for Discontinuous Galerkin Methods on Domains with Curved Boundaries,"  We provide numerical evidence demonstrating the necessity of employing a
+superparametric geometry representation in order to obtain optimal convergence
+orders on two-dimensional domains with curved boundaries when solving the Euler
+equations using Discontinuous Galerkin methods. However, concerning the
+obtention of optimal convergence orders for the Navier-Stokes equations, we
+demonstrate numerically that the use of isoparametric geometry representation
+is sufficient for the case considered here.
+",1,0,0,0,0,0
+11295,Low-dose cryo electron ptychography via non-convex Bayesian optimization,"  Electron ptychography has seen a recent surge of interest for phase sensitive
+imaging at atomic or near-atomic resolution. However, applications are so far
+mainly limited to radiation-hard samples because the required doses are too
+high for imaging biological samples at high resolution. We propose the use of
+non-convex, Bayesian optimization to overcome this problem and reduce the dose
+required for successful reconstruction by two orders of magnitude compared to
+previous experiments. We suggest to use this method for imaging single
+biological macromolecules at cryogenic temperatures and demonstrate 2D
+single-particle reconstructions from simulated data with a resolution of 7.9
+\AA$\,$ at a dose of 20 $e^- / \AA^2$. When averaging over only 15 low-dose
+datasets, a resolution of 4 \AA$\,$ is possible for large macromolecular
+complexes. With its independence from microscope transfer function, direct
+recovery of phase contrast and better scaling of signal-to-noise ratio,
+cryo-electron ptychography may become a promising alternative to Zernike
+phase-contrast microscopy.
+",0,1,1,1,0,0
+11296,Efficient Charge Collection in Coplanar Grid Radiation Detectors,"  We have modeled laser-induced transient current waveforms in radiation
+coplanar grid detectors. Poisson's equation has been solved by finite element
+method and currents induced by photo-generated charge were obtained using
+Shockley-Ramo theorem. The spectral response on a radiation flux has been
+modeled by Monte-Carlo simulations. We show 10$\times$ improved spectral
+resolution of coplanar grid detector using differential signal sensing. We
+model the current waveform dependence on doping, depletion width, diffusion and
+detector shielding and their mutual dependence is discussed in terms of
+detector optimization. The numerical simulations are successfully compared to
+experimental data and further model simplifications are proposed. The space
+charge below electrodes and a non-homogeneous electric field on a coplanar grid
+anode are found to be the dominant contributions to laser-induced transient
+current waveforms.
+",0,1,0,0,0,0
+11297,Thermalization near integrability in a dipolar quantum Newton's cradle,"  Isolated quantum many-body systems with integrable dynamics generically do
+not thermalize when taken far from equilibrium. As one perturbs such systems
+away from the integrable point, thermalization sets in, but the nature of the
+crossover from integrable to thermalizing behavior is an unresolved and
+actively discussed question. We explore this question by studying the dynamics
+of the momentum distribution function in a dipolar quantum Newton's cradle
+consisting of highly magnetic dysprosium atoms. This is accomplished by
+creating the first one-dimensional Bose gas with strong magnetic dipole-dipole
+interactions. These interactions provide tunability of both the strength of the
+integrability-breaking perturbation and the nature of the near-integrable
+dynamics. We provide the first experimental evidence that thermalization close
+to a strongly interacting integrable point occurs in two steps:
+prethermalization followed by near-exponential thermalization. Exact numerical
+calculations on a two-rung lattice model yield a similar two-timescale process,
+suggesting that this is generic in strongly interacting near-integrable models.
+Moreover, the measured thermalization rate is consistent with a parameter-free
+theoretical estimate, based on identifying the types of collisions that
+dominate thermalization. By providing tunability between regimes of integrable
+and nonintegrable dynamics, our work sheds light both on the mechanisms by
+which isolated quantum many-body systems thermalize, and on the temporal
+structure of the onset of thermalization.
+",0,1,0,0,0,0
+11298,Hausdorff dimension of the boundary of bubbles of additive Brownian motion and of the Brownian sheet,"  We first consider the additive Brownian motion process $(X(s_1,s_2),\
+(s_1,s_2) \in \mathbb{R}^2)$ defined by $X(s_1,s_2) = Z_1(s_1) - Z_2 (s_2)$,
+where $Z_1$ and $Z_2 $ are two independent (two-sided) Brownian motions. We
+show that with probability one, the Hausdorff dimension of the boundary of any
+connected component of the random set $\{(s_1,s_2)\in \mathbb{R}^2: X(s_1,s_2)
+>0\}$ is equal to $$
+\frac{1}{4}\left(1 + \sqrt{13 + 4 \sqrt{5}}\right) \simeq 1.421\, . $$ Then
+the same result is shown to hold when $X$ is replaced by a standard Brownian
+sheet indexed by the nonnegative quadrant.
+",0,0,1,0,0,0
+11299,Inverse mean curvature flow in quaternionic hyperbolic space,"  In this paper we complete the study started in [Pi2] of evolution by inverse
+mean curvature flow of star-shaped hypersurface in non-compact rank one
+symmetric spaces. We consider the evolution by inverse mean curvature flow of a
+closed, mean convex and star-shaped hypersurface in the quaternionic hyperbolic
+space. We prove that the flow is defined for any positive time, the evolving
+hypersurface stays star-shaped and mean convex. Moreover the induced metric
+converges, after rescaling, to a conformal multiple of the standard
+sub-Riemannian metric on the sphere defined on a codimension 3 distribution.
+Finally we show that there exists a family of examples such that the qc-scalar
+curvature of this sub-Riemannian limit is not constant.
+",0,0,1,0,0,0
+11300,Mosquito detection with low-cost smartphones: data acquisition for malaria research,"  Mosquitoes are a major vector for malaria, causing hundreds of thousands of
+deaths in the developing world each year. Not only is the prevention of
+mosquito bites of paramount importance to the reduction of malaria transmission
+cases, but understanding in more forensic detail the interplay between malaria,
+mosquito vectors, vegetation, standing water and human populations is crucial
+to the deployment of more effective interventions. Typically the presence and
+detection of malaria-vectoring mosquitoes is only quantified by hand-operated
+insect traps or signified by the diagnosis of malaria. If we are to gather
+timely, large-scale data to improve this situation, we need to automate the
+process of mosquito detection and classification as much as possible. In this
+paper, we present a candidate mobile sensing system that acts as both a
+portable early warning device and an automatic acoustic data acquisition
+pipeline to help fuel scientific inquiry and policy. The machine learning
+algorithm that powers the mobile system achieves excellent off-line
+multi-species detection performance while remaining computationally efficient.
+Further, we have conducted preliminary live mosquito detection tests using
+low-cost mobile phones and achieved promising results. The deployment of this
+system for field usage in Southeast Asia and Africa is planned in the near
+future. In order to accelerate processing of field recordings and labelling of
+collected data, we employ a citizen science platform in conjunction with
+automated methods, the former implemented using the Zooniverse platform,
+allowing crowdsourcing on a grand scale.
+",1,0,0,1,0,0
+11301,Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa,"  This paper presents a generic Bayesian framework that enables any deep
+learning model to actively learn from targeted crowds. Our framework inherits
+from recent advances in Bayesian deep learning, and extends existing work by
+considering the targeted crowdsourcing approach, where multiple annotators with
+unknown expertise contribute an uncontrolled amount (often limited) of
+annotations. Our framework leverages the low-rank structure in annotations to
+learn individual annotator expertise, which then helps to infer the true labels
+from noisy and sparse annotations. It provides a unified Bayesian model to
+simultaneously infer the true labels and train the deep learning model in order
+to reach an optimal learning efficacy. Finally, our framework exploits the
+uncertainty of the deep learning model during prediction as well as the
+annotators' estimated expertise to minimize the number of required annotations
+and annotators for optimally training the deep learning model.
+We evaluate the effectiveness of our framework for intent classification in
+Alexa (Amazon's personal assistant), using both synthetic and real-world
+datasets. Experiments show that our framework can accurately learn annotator
+expertise, infer true labels, and effectively reduce the amount of annotations
+in model training as compared to state-of-the-art approaches. We further
+discuss the potential of our proposed framework in bridging machine learning
+and crowdsourcing towards improved human-in-the-loop systems.
+",1,0,0,1,0,0
+11302,Using Convolutional Neural Networks to Count Palm Trees in Satellite Images,"  In this paper we propose a supervised learning system for counting and
+localizing palm trees in high-resolution, panchromatic satellite imagery
+(40cm/pixel to 1.5m/pixel). A convolutional neural network classifier trained
+on a set of palm and no-palm images is applied across a satellite image scene
+in a sliding window fashion. The resultant confidence map is smoothed with a
+uniform filter. A non-maximal suppression is applied onto the smoothed
+confidence map to obtain peaks. Trained with a small dataset of 500 images of
+size 40x40 cropped from satellite images, the system manages to achieve a tree
+count accuracy of over 99%.
+",1,0,0,0,0,0
+11303,The sharp for the Chang model is small,"  Woodin has shown that if there is a measurable Woodin cardinal then there is,
+in an appropriate sense, a sharp for the Chang model. We produce, in a weaker
+sense, a sharp for the Chang model using only the existence of a cardinal
+$\kappa$ having an extender of length $\kappa^{+\omega_1}$.
+",0,0,1,0,0,0
+11304,Weak quadrupole moments,"  Collective effects in deformed atomic nuclei present possible avenues of
+study on the non-spherical distribution of neutrons and the violation of the
+local Lorentz invariance. We introduce the weak quadrupole moment of nuclei,
+related to the quadrupole distribution of the weak charge in the nucleus. The
+weak quadrupole moment produces tensor weak interaction between the nucleus and
+electrons and can be observed in atomic and molecular experiments measuring
+parity nonconservation. The dominating contribution to the weak quadrupole is
+given by the quadrupole moment of the neutron distribution, therefore,
+corresponding experiments should allow one to measure the neutron quadrupoles.
+Using the deformed oscillator model and the Schmidt model we calculate the
+quadrupole distributions of neutrons, $Q_{n}$, the weak quadrupole moments
+,$Q_{W}^{(2)}$, and the Lorentz Innvariance violating energy shifts in
+$^{9}$Be, $^{21}$Ne , $^{27}$Al, $^{131}$Xe, $^{133}$Cs, $^{151}$Eu,
+$^{153}$Eu, $^{163}$Dy, $^{167}$Er, $^{173}$Yb, $^{177}$Hf, $^{179}$Hf,
+$^{181}$Ta, $^{201}$Hg and $^{229}$Th.
+",0,1,0,0,0,0
+11305,Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation,"  We propose an efficient and scalable method for incrementally building a
+dense, semantically annotated 3D map in real-time. The proposed method assigns
+class probabilities to each region, not each element (e.g., surfel and voxel),
+of the 3D map which is built up through a robust SLAM framework and
+incrementally segmented with a geometric-based segmentation method. Differently
+from all other approaches, our method has a capability of running at over 30Hz
+while performing all processing components, including SLAM, segmentation, 2D
+recognition, and updating class probabilities of each segmentation label at
+every incoming frame, thanks to the high efficiency that characterizes the
+computationally intensive stages of our framework. By utilizing a specifically
+designed CNN to improve the frame-wise segmentation result, we can also achieve
+high accuracy. We validate our method on the NYUv2 dataset by comparing with
+the state of the art in terms of accuracy and computational efficiency, and by
+means of an analysis in terms of time and space complexity.
+",1,0,0,0,0,0
+11306,Pumping Lemma for Higher-order Languages,"  We study a pumping lemma for the word/tree languages generated by
+higher-order grammars. Pumping lemmas are known up to order-2 word languages
+(i.e., for regular/context-free/indexed languages), and have been used to show
+that a given language does not belong to the classes of
+regular/context-free/indexed languages. We prove a pumping lemma for word/tree
+languages of arbitrary orders, modulo a conjecture that a higher-order version
+of Kruskal's tree theorem holds. We also show that the conjecture indeed holds
+for the order-2 case, which yields a pumping lemma for order-2 tree languages
+and order-3 word languages.
+",1,0,0,0,0,0
+11307,Generative Bridging Network in Neural Sequence Prediction,"  In order to alleviate data sparsity and overfitting problems in maximum
+likelihood estimation (MLE) for sequence prediction tasks, we propose the
+Generative Bridging Network (GBN), in which a novel bridge module is introduced
+to assist the training of the sequence prediction model (the generator
+network). Unlike MLE directly maximizing the conditional likelihood, the bridge
+extends the point-wise ground truth to a bridge distribution conditioned on it,
+and the generator is optimized to minimize their KL-divergence. Three different
+GBNs, namely uniform GBN, language-model GBN and coaching GBN, are proposed to
+penalize confidence, enhance language smoothness and relieve learning burden.
+Experiments conducted on two recognized sequence prediction tasks (machine
+translation and abstractive text summarization) show that our proposed GBNs can
+yield significant improvements over strong baselines. Furthermore, by analyzing
+samples drawn from different bridges, expected influences on the generator are
+verified.
+",1,0,0,1,0,0
+11308,A Rule-Based Computational Model of Cognitive Arithmetic,"  Cognitive arithmetic studies the mental processes used in solving math
+problems. This area of research explores the retrieval mechanisms and
+strategies used by people during a common cognitive task. Past research has
+shown that human performance in arithmetic operations is correlated to the
+numerical size of the problem. Past research on cognitive arithmetic has
+pinpointed this trend to either retrieval strength, error checking, or
+strategy-based approaches when solving equations. This paper describes a
+rule-based computational model that performs the four major arithmetic
+operations (addition, subtraction, multiplication and division) on two
+operands. We then evaluated our model to probe its validity in representing the
+prevailing concepts observed in psychology experiments from the related works.
+The experiments specifically explore the problem size effect, an
+activation-based model for fact retrieval, backup strategies when retrieval
+fails, and finally optimization strategies when faced with large operands. From
+our experimental results, we concluded that our model's response times were
+comparable to results observed when people performed similar tasks during
+psychology experiments. The fit of our model in reproducing these results and
+incorporating accuracy into our model are discussed.
+",1,0,0,0,0,0
+11309,Modular categories are not determined by their modular data,"  Arbitrarily many pairwise inequivalent modular categories can share the same
+modular data. We exhibit a family of examples that are module categories over
+twisted Drinfeld doubles of finite groups, and thus in particular integral
+modular categories.
+",0,0,1,0,0,0
+11310,Change Detection in a Dynamic Stream of Attributed Networks,"  While anomaly detection in static networks has been extensively studied, only
+recently, researchers have focused on dynamic networks. This trend is mainly
+due to the capacity of dynamic networks in representing complex physical,
+biological, cyber, and social systems. This paper proposes a new methodology
+for modeling and monitoring of dynamic attributed networks for quick detection
+of temporal changes in network structures. In this methodology, the generalized
+linear model (GLM) is used to model static attributed networks. This model is
+then combined with a state transition equation to capture the dynamic behavior
+of the system. Extended Kalman filter (EKF) is used as an online, recursive
+inference procedure to predict and update network parameters over time. In
+order to detect changes in the underlying mechanism of edge formation,
+prediction residuals are monitored through an Exponentially Weighted Moving
+Average (EWMA) control chart. The proposed modeling and monitoring procedure is
+examined through simulations for attributed binary and weighted networks. The
+email communication data from the Enron corporation is used as a case study to
+show how the method can be applied in real-world problems.
+",0,0,0,1,0,0
+11311,Local Differential Privacy for Physical Sensor Data and Sparse Recovery,"  In this work we explore the utility of locally differentially private thermal
+sensor data. We design a locally differentially private recovery algorithm for
+the 1-dimensional, discrete heat source location problem and analyse its
+performance in terms of the Earth Mover Distance error. Our work indicates that
+it is possible to produce locally private sensor measurements that both keep
+the exact locations of the heat sources private and permit recovery of the
+""general geographic vicinity"" of the sources. We also discuss the relationship
+between the property of an inverse problem being ill-conditioned and the amount
+of noise needed to maintain privacy.
+",1,0,0,0,0,0
+11312,Kernel Feature Selection via Conditional Covariance Minimization,"  We propose a method for feature selection that employs kernel-based measures
+of independence to find a subset of covariates that is maximally predictive of
+the response. Building on past work in kernel dimension reduction, we show how
+to perform feature selection via a constrained optimization problem involving
+the trace of the conditional covariance operator. We prove various consistency
+results for this procedure, and also demonstrate that our method compares
+favorably with other state-of-the-art algorithms on a variety of synthetic and
+real data sets.
+",1,0,0,1,0,0
+11313,2s exciton-polariton revealed in an external magnetic field,"  We demonstrate the existence of the excited state of an exciton-polariton in
+a semiconductor microcavity. The strong coupling of the quantum well heavy-hole
+exciton in an excited 2s state to the cavity photon is observed in non-zero
+magnetic field due to surprisingly fast increase of Rabi energy of the 2s
+exciton-polariton in magnetic field. This effect is explained by a strong
+modification of the wave-function of the relative electron-hole motion for the
+2s exciton state.
+",0,1,0,0,0,0
+11314,Weight hierarchy of a class of linear codes relating to non-degenerate quadratic forms,"  In this paper, we discuss the generalized Hamming weights of a class of
+linear codes associated with non-degenerate quadratic forms. In order to do so,
+we study the quadratic forms over subspaces of finite field and obtain some
+interesting results about subspaces and their dual spaces. On this basis, we
+solve all the generalized Hamming weights of these linear codes.
+",0,0,1,0,0,0
+11315,"Cosmological discordances II: Hubble constant, Planck and large-scale-structure data sets","  We examine systematically the (in)consistency between cosmological
+constraints as obtained from various current data sets of the expansion
+history, Large Scale Structure (LSS), and Cosmic Microwave Background (CMB)
+from Planck. We run (dis)concordance tests within each set and across the sets
+using a recently introduced index of inconsistency (IOI) capable of dissecting
+inconsistencies between two or more data sets. First, we compare the
+constraints on $H_0$ from five different methods and find that the IOI drops
+from 2.85 to 0.88 (on Jeffreys' scales) when the local $H_0$ measurements is
+removed. This seems to indicate that the local measurement is an outlier, thus
+favoring a systematics-based explanation. We find a moderate inconsistency
+(IOI=2.61) between Planck temperature and polarization. We find that current
+LSS data sets including WiggleZ, SDSS RSD, CFHTLenS, CMB lensing and SZ cluster
+count, are consistent one with another and when all combined. However, we find
+a persistent moderate inconsistency between Planck and individual or combined
+LSS probes. For Planck TT+lowTEB versus individual LSS probes, the IOI spans
+the range 2.92--3.72 and increases to 3.44--4.20 when the polarization data is
+added in. The joint LSS versus the combined Planck temperature and polarization
+has an IOI of 2.83 in the most conservative case. But if Planck lowTEB is added
+to the joint LSS to constrain $\tau$ and break degeneracies, the inconsistency
+between Planck and joint LSS data increases to the high-end of the moderate
+range with IOI=4.81. Whether due to systematic effects in the data or to the
+underlying model, these inconsistencies need to be resolved. Finally, we
+perform forecast calculations using LSST and find that the discordance between
+Planck and future LSS data, if it persists as present, can rise up to a high
+IOI of 17, thus falling in the strong range of inconsistency. (Abridged).
+",0,1,0,0,0,0
+11316,The perfect spin injection in silicene FS/NS junction,"  We theoretically investigate the spin injection from a ferromagnetic silicene
+to a normal silicene (FS/NS), where the magnetization in the FS is assumed from
+the magnetic proximity effect. Based on a silicene lattice model, we
+demonstrated that the pure spin injection could be obtained by tuning the Fermi
+energy of two spin species, where one is in the spin orbit coupling gap and the
+other one is outside the gap. Moreover, the valley polarity of the spin species
+can be controlled by a perpendicular electric field in the FS region. Our
+findings may shed light on making silicene-based spin and valley devices in the
+spintronics and valleytronics field.
+",0,1,0,0,0,0
+11317,Distance-based Protein Folding Powered by Deep Learning,"  Contact-assisted protein folding has made very good progress, but two
+challenges remain. One is accurate contact prediction for proteins lack of many
+sequence homologs and the other is that time-consuming folding simulation is
+often needed to predict good 3D models from predicted contacts. We show that
+protein distance matrix can be predicted well by deep learning and then
+directly used to construct 3D models without folding simulation at all. Using
+distance geometry to construct 3D models from our predicted distance matrices,
+we successfully folded 21 of the 37 CASP12 hard targets with a median family
+size of 58 effective sequence homologs within 4 hours on a Linux computer of 20
+CPUs. In contrast, contacts predicted by direct coupling analysis (DCA) cannot
+fold any of them in the absence of folding simulation and the best CASP12 group
+folded 11 of them by integrating predicted contacts into complex,
+fragment-based folding simulation. The rigorous experimental validation on 15
+CASP13 targets show that among the 3 hardest targets of new fold our
+distance-based folding servers successfully folded 2 large ones with <150
+sequence homologs while the other servers failed on all three, and that our ab
+initio folding server also predicted the best, high-quality 3D model for a
+large homology modeling target. Further experimental validation in CAMEO shows
+that our ab initio folding server predicted correct fold for a membrane protein
+of new fold with 200 residues and 229 sequence homologs while all the other
+servers failed. These results imply that deep learning offers an efficient and
+accurate solution for ab initio folding on a personal computer.
+",0,0,0,0,1,0
+11318,Double Threshold Digraphs,"  A semiorder is a model of preference relations where each element $x$ is
+associated with a utility value $\alpha(x)$, and there is a threshold $t$ such
+that $y$ is preferred to $x$ iff $\alpha(y) > \alpha(x)+t$. These are motivated
+by the notion that there is some uncertainty in the utility values we assign an
+object or that a subject may be unable to distinguish a preference between
+objects whose values are close. However, they fail to model the well-known
+phenomenon that preferences are not always transitive. Also, if we are
+uncertain of the utility values, it is not logical that preference is
+determined absolutely by a comparison of them with an exact threshold. We
+propose a new model in which there are two thresholds, $t_1$ and $t_2$; if the
+difference $\alpha(y) - \alpha(x)$ less than $t_1$, then $y$ is not preferred
+to $x$; if the difference is greater than $t_2$ then $y$ is preferred to $x$;
+if it is between $t_1$ and $t_2$, then then $y$ may or may not be preferred to
+$x$. We call such a relation a double-threshold semiorder, and the
+corresponding directed graph $G = (V,E)$ a double threshold digraph. Every
+directed acyclic graph is a double threshold graph; bounds on $t_2/t_1$ give a
+nested hierarchy of subclasses of the directed acyclic graphs. In this paper we
+characterize the subclasses in terms of forbidden subgraphs, and give
+algorithms for finding an assignment of of utility values that explains the
+relation in terms of a given $(t_1,t_2)$ or else produces a forbidden subgraph,
+and finding the minimum value $\lambda$ of $t_2/t_1$ that is satisfiable for a
+given directed acyclic graph. We show that $\lambda$ gives a measure of the
+complexity of a directed acyclic graph with respect to several optimization
+problems that are NP-hard on arbitrary directed acyclic graphs.
+",1,0,0,0,0,0
+11319,Directed unions of local quadratic transforms of regular local rings and pullbacks,"  Let $\{ R_n, {\mathfrak m}_n \}_{n \ge 0}$ be an infinite sequence of regular
+local rings with $R_{n+1}$ birationally dominating $R_n$ and ${\mathfrak
+m}_nR_{n+1}$ a principal ideal of $R_{n+1}$ for each $n$. We examine properties
+of the integrally closed local domain $S = \bigcup_{n \ge 0}R_n$.
+",0,0,1,0,0,0
+11320,Lipschitz regularity of deep neural networks: analysis and efficient estimation,"  Deep neural networks are notorious for being sensitive to small well-chosen
+perturbations, and estimating the regularity of such architectures is of utmost
+importance for safe and robust practical applications. In this paper, we
+investigate one of the key characteristics to assess the regularity of such
+methods: the Lipschitz constant of deep learning architectures. First, we show
+that, even for two layer neural networks, the exact computation of this
+quantity is NP-hard and state-of-art methods may significantly overestimate it.
+Then, we both extend and improve previous estimation methods by providing
+AutoLip, the first generic algorithm for upper bounding the Lipschitz constant
+of any automatically differentiable function. We provide a power method
+algorithm working with automatic differentiation, allowing efficient
+computations even on large convolutions. Second, for sequential neural
+networks, we propose an improved algorithm named SeqLip that takes advantage of
+the linear computation graph to split the computation per pair of consecutive
+layers. Third we propose heuristics on SeqLip in order to tackle very large
+networks. Our experiments show that SeqLip can significantly improve on the
+existing upper bounds.
+",0,0,0,1,0,0
+11321,Preference-based Teaching,"  We introduce a new model of teaching named ""preference-based teaching"" and a
+corresponding complexity parameter---the preference-based teaching dimension
+(PBTD)---representing the worst-case number of examples needed to teach any
+concept in a given concept class. Although the PBTD coincides with the
+well-known recursive teaching dimension (RTD) on finite classes, it is
+radically different on infinite ones: the RTD becomes infinite already for
+trivial infinite classes (such as half-intervals) whereas the PBTD evaluates to
+reasonably small values for a wide collection of infinite classes including
+classes consisting of so-called closed sets w.r.t. a given closure operator,
+including various classes related to linear sets over $\mathbb{N}_0$ (whose RTD
+had been studied quite recently) and including the class of Euclidean
+half-spaces. On top of presenting these concrete results, we provide the reader
+with a theoretical framework (of a combinatorial flavor) which helps to derive
+bounds on the PBTD.
+",1,0,0,0,0,0
+11322,Unified description of dynamics of a repulsive two-component Fermi gas,"  We study a binary spin-mixture of a zero-temperature repulsively interacting
+$^6$Li atoms using both the atomic-orbital and the density functional
+approaches. The gas is initially prepared in a configuration of two magnetic
+domains and we determine the frequency of the spin-dipole oscillations which
+are emerging after the repulsive barrier, initially separating the domains, is
+removed. We find, in agreement with recent experiment (G. Valtolina et al.,
+arXiv:1605.07850 (2016)), the occurrence of a ferromagnetic instability in an
+atomic gas while the interaction strength between different spin states is
+increased, after which the system becomes ferromagnetic. The ferromagnetic
+instability is preceded by the softening of the spin-dipole mode.
+",0,1,0,0,0,0
+11323,Effective inertial frame in an atom interferometric test of the equivalence principle,"  In an ideal test of the equivalence principle, the test masses fall in a
+common inertial frame. A real experiment is affected by gravity gradients,
+which introduce systematic errors by coupling to initial kinematic differences
+between the test masses. We demonstrate a method that reduces the sensitivity
+of a dual-species atom interferometer to initial kinematics by using a
+frequency shift of the mirror pulse to create an effective inertial frame for
+both atomic species. This suppresses the gravity-gradient-induced dependence of
+the differential phase on initial kinematic differences by a factor of 100 and
+enables a precise measurement of these differences. We realize a relative
+precision of $\Delta g / g \approx 6 \times 10^{-11}$ per shot, which improves
+on the best previous result for a dual-species atom interferometer by more than
+three orders of magnitude. By suppressing gravity gradient systematic errors to
+below one part in $10^{13}$, these results pave the way for an atomic test of
+the equivalence principle at an accuracy comparable with state-of-the-art
+classical tests.
+",0,1,0,0,0,0
+11324,Phonon-mediated spin-flipping mechanism in the spin ices Dy$_2$Ti$_2$O$_7$ and Ho$_2$Ti$_2$O$_7$,"  To understand emergent magnetic monopole dynamics in the spin ices
+Ho$_2$Ti$_2$O$_7$ and Dy$_2$Ti$_2$O$_7$, it is necessary to investigate the
+mechanisms by which spins flip in these materials. Presently there are thought
+to be two processes: quantum tunneling at low and intermediate temperatures and
+thermally activated at high temperatures. We identify possible couplings
+between crystal field and optical phonon excitations and construct a strictly
+constrained model of phonon-mediated spin flipping that quantitatively
+describes the high-temperature processes in both compounds, as measured by
+quasielastic neutron scattering. We support the model with direct experimental
+evidence of the coupling between crystal field states and optical phonons in
+Ho$_2$Ti$_2$O$_7$.
+",0,1,0,0,0,0
+11325,A hexatic smectic phase with algebraically decaying bond-orientational order,"  The hexatic phase predicted by the theories of two-dimensional melting is
+characterised by the power law decay of the orientational correlations whereas
+the in-layer bond orientational order in all the hexatic smectic phases
+observed so far was found to be long-range. We report a hexatic smectic phase
+where the in-layer bond orientational correlations decay as $\propto r^{-1/4}$,
+in quantitative agreement with the hexatic ordering predicted by the theory for
+two dimensions. The phase was formed in a molecular dynamics simulation of a
+one-component system of particles interacting via a spherically symmetric
+potential. This is the first observation of the theoretically predicted
+two-dimensional hexatic order in a three-dimensional system.
+",0,1,0,0,0,0
+11326,Pebble accretion at the origin of water in Europa,"  Despite the fact that the observed gradient in water content among the
+Galilean satellites is globally consistent with a formation in a circum-Jovian
+disk on both sides of the snowline, the mechanisms that led to a low water mass
+fraction in Europa ($\sim$$8\%$) are not yet understood. Here, we present new
+modeling results of solids transport in the circum-Jovian disk accounting for
+aerodynamic drag, turbulent diffusion, surface temperature evolution and
+sublimation of water ice. We find that the water mass fraction of pebbles
+(e.g., solids with sizes of 10$^{-2}$ -- 1 m) as they drift inward is globally
+consistent with the current water content of the Galilean system. This opens
+the possibility that each satellite could have formed through pebble accretion
+within a delimited region whose boundaries were defined by the position of the
+snowline. This further implies that the migration of the forming satellites was
+tied to the evolution of the snowline so that Europa fully accreted from
+partially dehydrated material in the region just inside of the snowline.
+",0,1,0,0,0,0
+11327,Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting,"  Traffic forecasting is a particularly challenging application of
+spatiotemporal forecasting, due to the complicated spatial dependencies on
+roadway networks and the time-varying traffic patterns. To address this
+challenge, we learn the traffic network as a graph and propose a novel deep
+learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural
+Network (TGC-LSTM), to learn the interactions between roadways in the traffic
+network and forecast the network-wide traffic state. We define the traffic
+graph convolution based on the physical network topology. The relationship
+between traffic graph convolution and the spectral graph convolution is also
+discussed. The proposed model employs L1-norms on the graph convolution weights
+and L2-norms on the extracted features to identify the most influential
+roadways in the traffic network. Experiments show that our TGC-LSTM network is
+able to capture the complex spatial-temporal dependencies efficiently present
+in a vehicle traffic network and consistently outperforms state-of-the-art
+baseline methods on two heterogeneous real-world traffic datasets. The
+visualization of graph convolution weights shows that the proposed framework
+can accurately recognize the most influential roadway segments in real-world
+traffic networks.
+",0,0,0,1,0,0
+11328,Intrinsic Analysis of the Sample Fréchet Mean and Sample Mean of Complex Wishart Matrices,"  We consider two types of averaging of complex covariance matrices, a sample
+mean (average) and the sample Fréchet mean. We analyse the performance of
+these quantities as estimators for the true covariance matrix via `intrinsic'
+versions of bias and mean square error, a methodology which takes account of
+geometric structure. We derive simple expressions for the intrinsic bias in
+both cases, and the simple average is seen to be preferable. The same is true
+for the asymptotic Riemannian risk, and for the Riemannian risk itself in the
+scalar case. Combined with a similar preference for the simple average using
+non-intrinsic analysis, we conclude that the simple average is preferred
+overall to the sample Fréchet mean in this context.
+",0,0,1,1,0,0
+11329,Alternating Optimization for Capacity Region of Gaussian MIMO Broadcast Channels with Per-antenna Power Constraint,"  This paper characterizes the capacity region of Gaussian MIMO broadcast
+channels (BCs) with per-antenna power constraint (PAPC). While the capacity
+region of MIMO BCs with a sum power constraint (SPC) was extensively studied,
+that under PAPC has received less attention. A reason is that efficient
+solutions for this problem are hard to find. The goal of this paper is to
+devise an efficient algorithm for determining the capacity region of Gaussian
+MIMO BCs subject to PAPC, which is scalable to the problem size. To this end,
+we first transform the weighted sum capacity maximization problem, which is
+inherently nonconvex with the input covariance matrices, into a convex
+formulation in the dual multiple access channel by minimax duality. Then we
+derive a computationally efficient algorithm combining the concept of
+alternating optimization and successive convex approximation. The proposed
+algorithm achieves much lower complexity compared to an existing interiorpoint
+method. Moreover, numerical results demonstrate that the proposed algorithm
+converges very fast under various scenarios.
+",1,0,0,0,0,0
+11330,Tales of Two Cities: Using Social Media to Understand Idiosyncratic Lifestyles in Distinctive Metropolitan Areas,"  Lifestyles are a valuable model for understanding individuals' physical and
+mental lives, comparing social groups, and making recommendations for improving
+people's lives. In this paper, we examine and compare lifestyle behaviors of
+people living in cities of different sizes, utilizing freely available social
+media data as a large-scale, low-cost alternative to traditional survey
+methods. We use the Greater New York City area as a representative for large
+cities, and the Greater Rochester area as a representative for smaller cities
+in the United States. We employed matrix factor analysis as an unsupervised
+method to extract salient mobility and work-rest patterns for a large
+population of users within each metropolitan area. We discovered interesting
+human behavior patterns at both a larger scale and a finer granularity than is
+present in previous literature, some of which allow us to quantitatively
+compare the behaviors of individuals of living in big cities to those living in
+small cities. We believe that our social media-based approach to lifestyle
+analysis represents a powerful tool for social computing in the big data age.
+",1,0,0,0,0,0
+11331,Randomized Iterative Reconstruction for Sparse View X-ray Computed Tomography,"  With the availability of more powerful computers, iterative reconstruction
+algorithms are the subject of an ongoing work in the design of more efficient
+reconstruction algorithms for X-ray computed tomography. In this work, we show
+how two analytical reconstruction algorithms can be improved by correcting the
+corresponding reconstructions using a randomized iterative reconstruction
+algorithm. The combined analytical reconstruction followed by randomized
+iterative reconstruction can also be viewed as a reconstruction algorithm
+which, in the experiments we have conducted, uses up to $35\%$ less projection
+angles as compared to the analytical reconstruction algorithms and produces the
+same results in terms of quality of reconstruction, without increasing the
+execution time significantly.
+",1,0,0,0,0,0
+11332,Finding Local Minima via Stochastic Nested Variance Reduction,"  We propose two algorithms that can find local minima faster than the
+state-of-the-art algorithms in both finite-sum and general stochastic nonconvex
+optimization. At the core of the proposed algorithms is
+$\text{One-epoch-SNVRG}^+$ using stochastic nested variance reduction (Zhou et
+al., 2018a), which outperforms the state-of-the-art variance reduction
+algorithms such as SCSG (Lei et al., 2017). In particular, for finite-sum
+optimization problems, the proposed
+$\text{SNVRG}^{+}+\text{Neon2}^{\text{finite}}$ algorithm achieves
+$\tilde{O}(n^{1/2}\epsilon^{-2}+n\epsilon_H^{-3}+n^{3/4}\epsilon_H^{-7/2})$
+gradient complexity to converge to an $(\epsilon, \epsilon_H)$-second-order
+stationary point, which outperforms $\text{SVRG}+\text{Neon2}^{\text{finite}}$
+(Allen-Zhu and Li, 2017) , the best existing algorithm, in a wide regime. For
+general stochastic optimization problems, the proposed
+$\text{SNVRG}^{+}+\text{Neon2}^{\text{online}}$ achieves
+$\tilde{O}(\epsilon^{-3}+\epsilon_H^{-5}+\epsilon^{-2}\epsilon_H^{-3})$
+gradient complexity, which is better than both
+$\text{SVRG}+\text{Neon2}^{\text{online}}$ (Allen-Zhu and Li, 2017) and
+Natasha2 (Allen-Zhu, 2017) in certain regimes. Furthermore, we explore the
+acceleration brought by third-order smoothness of the objective function.
+",0,0,0,1,0,0
+11333,Growth rate of the state vector in a generalized linear stochastic system with symmetric matrix,"  The mean growth rate of the state vector is evaluated for a generalized
+linear stochastic second-order system with a symmetric matrix. Diagonal entries
+of the matrix are assumed to be independent and exponentially distributed with
+different means, while the off-diagonal entries are equal to zero.
+",1,0,0,0,0,0
+11334,Bayesian Patchworks: An Approach to Case-Based Reasoning,"  Doctors often rely on their past experience in order to diagnose patients.
+For a doctor with enough experience, almost every patient would have
+similarities to key cases seen in the past, and each new patient could be
+viewed as a mixture of these key past cases. Because doctors often tend to
+reason this way, an efficient computationally aided diagnostic tool that thinks
+in the same way might be helpful in locating key past cases of interest that
+could assist with diagnosis. This article develops a novel mathematical model
+to mimic the type of logical thinking that physicians use when considering past
+cases. The proposed model can also provide physicians with explanations that
+would be similar to the way they would naturally reason about cases. The
+proposed method is designed to yield predictive accuracy, computational
+efficiency, and insight into medical data; the key element is the insight into
+medical data, in some sense we are automating a complicated process that
+physicians might perform manually. We finally implemented the result of this
+work on two publicly available healthcare datasets, for heart disease
+prediction and breast cancer prediction.
+",0,0,0,1,0,0
+11335,Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models,"  Machine Learning (ML) and Deep Learning (DL) models have achieved
+state-of-the-art performance on multiple learning tasks, from vision to natural
+language modelling. With the growing adoption of ML and DL to many areas of
+computer science, recent research has also started focusing on the security
+properties of these models. There has been a lot of work undertaken to
+understand if (deep) neural network architectures are resilient to black-box
+adversarial attacks which craft perturbed input samples that fool the
+classifier without knowing the architecture used. Recent work has also focused
+on the transferability of adversarial attacks and found that adversarial
+attacks are generally easily transferable between models, datasets, and
+techniques. However, such attacks and their analysis have not been covered from
+the perspective of unsupervised machine learning algorithms. In this paper, we
+seek to bridge this gap through multiple contributions. We first provide a
+strong (iterative) black-box adversarial attack that can craft adversarial
+samples which will be incorrectly clustered irrespective of the choice of
+clustering algorithm. We choose 4 prominent clustering algorithms, and a
+real-world dataset to show the working of the proposed adversarial algorithm.
+Using these clustering algorithms we also carry out a simple study of
+cross-technique adversarial attack transferability.
+",1,0,0,1,0,0
+11336,Formal affine Demazure and Hecke algebras of Kac-Moody root systems,"  We define the formal affine Demazure algebra and formal affine Hecke algebra
+associated to a Kac-Moody root system. We prove the structure theorems of these
+algebras, hence, extending several result and construction (presentation in
+terms of generators and relations, coproduct and product structures, filtration
+by codimension of Bott-Samelson classes, root polynomials and multiplication
+formulas) that were previously known for finite root system.
+",0,0,1,0,0,0
+11337,Handling Homographs in Neural Machine Translation,"  Homographs, words with different meanings but the same surface form, have
+long caused difficulty for machine translation systems, as it is difficult to
+select the correct translation based on the context. However, with the advent
+of neural machine translation (NMT) systems, which can theoretically take into
+account global sentential context, one may hypothesize that this problem has
+been alleviated. In this paper, we first provide empirical evidence that
+existing NMT systems in fact still have significant problems in properly
+translating ambiguous words. We then proceed to describe methods, inspired by
+the word sense disambiguation literature, that model the context of the input
+word with context-aware word embeddings that help to differentiate the word
+sense be- fore feeding it into the encoder. Experiments on three language pairs
+demonstrate that such models improve the performance of NMT systems both in
+terms of BLEU score and in the accuracy of translating homographs.
+",1,0,0,0,0,0
+11338,Simple Length Rigidity for Hitchin Representations,"  We show that a Hitchin representation is determined by the spectral radii of
+the images of simple, non-separating closed curves. As a consequence, we
+classify isometries of the intersection function on Hitchin components of
+dimension 3 and on the self-dual Hitchin components in all dimensions. As an
+important tool in the proof, we establish a transversality result for positive
+quadruples of flags.
+",0,0,1,0,0,0
+11339,Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology,"  Digital pathology is not only one of the most promising fields of diagnostic
+medicine, but at the same time a hot topic for fundamental research. Digital
+pathology is not just the transfer of histopathological slides into digital
+representations. The combination of different data sources (images, patient
+records, and *omics data) together with current advances in artificial
+intelligence/machine learning enable to make novel information accessible and
+quantifiable to a human expert, which is not yet available and not exploited in
+current medical settings. The grand goal is to reach a level of usable
+intelligence to understand the data in the context of an application task,
+thereby making machine decisions transparent, interpretable and explainable.
+The foundation of such an ""augmented pathologist"" needs an integrated approach:
+While machine learning algorithms require many thousands of training examples,
+a human expert is often confronted with only a few data points. Interestingly,
+humans can learn from such few examples and are able to instantly interpret
+complex patterns. Consequently, the grand goal is to combine the possibilities
+of artificial intelligence with human intelligence and to find a well-suited
+balance between them to enable what neither of them could do on their own. This
+can raise the quality of education, diagnosis, prognosis and prediction of
+cancer and other diseases. In this paper we describe some (incomplete) research
+issues which we believe should be addressed in an integrated and concerted
+effort for paving the way towards the augmented pathologist.
+",1,0,0,1,0,0
+11340,Morse Code Datasets for Machine Learning,"  We present an algorithm to generate synthetic datasets of tunable difficulty
+on classification of Morse code symbols for supervised machine learning
+problems, in particular, neural networks. The datasets are spatially
+one-dimensional and have a small number of input features, leading to high
+density of input information content. This makes them particularly challenging
+when implementing network complexity reduction methods. We explore how network
+performance is affected by deliberately adding various forms of noise and
+expanding the feature set and dataset size. Finally, we establish several
+metrics to indicate the difficulty of a dataset, and evaluate their merits. The
+algorithm and datasets are open-source.
+",0,0,0,1,0,0
+11341,Guarantees for Spectral Clustering with Fairness Constraints,"  Given the widespread popularity of spectral clustering (SC) for partitioning
+graph data, we study a version of constrained SC in which we try to incorporate
+the fairness notion proposed by Chierichetti et al. (2017). According to this
+notion, a clustering is fair if every demographic group is approximately
+proportionally represented in each cluster. To this end, we develop variants of
+both normalized and unnormalized constrained SC and show that they help find
+fairer clusterings on both synthetic and real data. We also provide a rigorous
+theoretical analysis of our algorithms. While there have been efforts to
+incorporate various constraints into the SC framework, theoretically analyzing
+them is a challenging problem. We overcome this by proposing a natural variant
+of the stochastic block model where h groups have strong inter-group
+connectivity, but also exhibit a ""natural"" clustering structure which is fair.
+We prove that our algorithms can recover this fair clustering with high
+probability.
+",1,0,0,1,0,0
+11342,Using Maximum Entry-Wise Deviation to Test the Goodness-of-Fit for Stochastic Block Models,"  The stochastic block model is widely used for detecting community structures
+in network data. How to test the goodness-of-fit of the model is one of the
+fundamental problems and has gained growing interests in recent years. In this
+paper, we propose a novel goodness-of-fit test based on the maximum entry of
+the centered and re-scaled adjacency matrix for the stochastic block model. One
+noticeable advantage of the proposed test is that the number of communities can
+be allowed to grow linearly with the number of nodes ignoring a logarithmic
+factor. We prove that the null distribution of the test statistic converges in
+distribution to a Gumbel distribution, and we show that both the number of
+communities and the membership vector can be tested via the proposed method.
+Further, we show that the proposed test has asymptotic power guarantee against
+a class of alternatives. We also demonstrate that the proposed method can be
+extended to the degree-corrected stochastic block model. Both simulation
+studies and real-world data examples indicate that the proposed method works
+well.
+",0,0,0,1,0,0
+11343,Twitter and the Press: an Ego-Centred Analysis,"  Ego networks have proved to be a valuable tool for understanding the
+relationships that individuals establish with their peers, both in offline and
+online social networks. Particularly interesting are the cognitive constraints
+associated with the interactions between the ego and the members of their ego
+network, whereby individuals cannot maintain meaningful interactions with more
+than 150 people, on average. In this work, we focus on the ego networks of
+journalists on Twitter, and we investigate whether they feature the same
+characteristics observed for other relevant classes of Twitter users, like
+politicians and generic users. Our findings are that journalists are generally
+more active and interact with more people than generic users. Their ego network
+structure is very aligned with reference models derived from the social brain
+hypothesis and observed in general human ego networks. Remarkably, the
+similarity is even higher than the one of politicians and generic users ego
+networks. This may imply a greater cognitive involvement with Twitter than with
+other social interaction means. Moreover, the ego networks of journalists are
+much stabler than those of politicians and generic users, and the ego-alter
+ties are often information-driven.
+",1,0,0,0,0,0
+11344,Majorana quasiparticles in condensed matter,"  In the space of less than one decade, the search for Majorana quasiparticles
+in condensed matter has become one of the hottest topics in physics. The aim of
+this review is to provide a brief perspective of where we are with strong focus
+on artificial implementations of one-dimensional topological superconductivity.
+After a self-contained introduction and some technical parts, an overview of
+the current experimental status is given and some of the most successful
+experiments of the last few years are discussed in detail. These include the
+novel generation of ballistic InSb nanowire devices, epitaxial Al-InAs
+nanowires and Majorana boxes, high frequency experiments with proximitized
+quantum spin Hall insulators realised in HgTe quantum wells and recent
+experiments on ferromagnetic atomic chains on top of superconducting surfaces.
+",0,1,0,0,0,0
+11345,Proper orthogonal decomposition vs. Fourier analysis for extraction of large-scale structures of thermal convection,"  We performed a comparative study of extraction of large-scale flow structures
+in Rayleigh Bénard convection using proper orthogonal decomposition (POD) and
+{\em Fourier analysis}. We show that the free-slip basis functions capture the
+flow profiles successfully for the no-slip boundary conditions. We observe that
+the large-scale POD modes capture a larger fraction of total energy than the
+Fourier modes. However, the Fourier modes capture the rarer flow structures
+like flow reversals better. The flow profiles of the dominant POD and Fourier
+modes are quite similar. Our results show that the Fourier analysis provides an
+attractive alternative to POD analysis for capturing large-scale flow
+structures.
+",0,1,0,0,0,0
+11346,On Gromov--Witten invariants of $\mathbb{P}^1$,"  We propose a conjectural explicit formula of generating series of a new type
+for Gromov--Witten invariants of $\mathbb{P}^1$ of all degrees in full genera.
+",0,1,1,0,0,0
+11347,Downwash-Aware Trajectory Planning for Large Quadrotor Teams,"  We describe a method for formation-change trajectory planning for large
+quadrotor teams in obstacle-rich environments. Our method decomposes the
+planning problem into two stages: a discrete planner operating on a graph
+representation of the workspace, and a continuous refinement that converts the
+non-smooth graph plan into a set of C^k-continuous trajectories, locally
+optimizing an integral-squared-derivative cost. We account for the downwash
+effect, allowing safe flight in dense formations. We demonstrate the
+computational efficiency in simulation with up to 200 robots and the physical
+plausibility with an experiment with 32 nano-quadrotors. Our approach can
+compute safe and smooth trajectories for hundreds of quadrotors in dense
+environments with obstacles in a few minutes.
+",1,0,0,0,0,0
+11348,Flow simulation in a 2D bubble column with the Euler-Lagrange and Euler-Euler method,"  Bubbly flows, as present in bubble column reactors, can be simulated using a
+variety of simulation techniques. In order to gain high resolution CFD methods
+are used to simulate a pseudo 2D bubble column using EL and EE techniques. The
+forces on bubble dynamics are solved within open access software OpenFOAM with
+bubble interactions computed via Monte Carlo methods. The estimated bubble size
+distribution and the predicted hold-up are compared to experimental data and
+other simulative work using EE approach and show reasonable consensus for both.
+Benchmarks with state of the art EE simulations shows that the EL approach is
+advantageous if the bubble number stays at a certain level, as the EL approach
+scales linearly with the number of bubbles simulated. Therefore, different
+computational meshes have been used to also account for influence of the
+resolution quality. The EL approach indicated faster solution for all realistic
+cases, only deliberate decrease of coalescence rates could push CPU time to the
+limits. Critical bubble number - when EE becomes advantageous over the EL
+approach - was estimated to be 40.000 in this particular case.
+",0,1,0,0,0,0
+11349,User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient,"  In this paper, we study the problem of sampling from a given probability
+density function that is known to be smooth and strongly log-concave. We
+analyze several methods of approximate sampling based on discretizations of the
+(highly overdamped) Langevin diffusion and establish guarantees on its error
+measured in the Wasserstein-2 distance. Our guarantees improve or extend the
+state-of-the-art results in three directions. First, we provide an upper bound
+on the error of the first-order Langevin Monte Carlo (LMC) algorithm with
+optimized varying step-size. This result has the advantage of being horizon
+free (we do not need to know in advance the target precision) and to improve by
+a logarithmic factor the corresponding result for the constant step-size.
+Second, we study the case where accurate evaluations of the gradient of the
+log-density are unavailable, but one can have access to approximations of the
+aforementioned gradient. In such a situation, we consider both deterministic
+and stochastic approximations of the gradient and provide an upper bound on the
+sampling error of the first-order LMC that quantifies the impact of the
+gradient evaluation inaccuracies. Third, we establish upper bounds for two
+versions of the second-order LMC, which leverage the Hessian of the
+log-density. We nonasymptotic guarantees on the sampling error of these
+second-order LMCs. These guarantees reveal that the second-order LMC algorithms
+improve on the first-order LMC in ill-conditioned settings.
+",1,0,1,1,0,0
+11350,Attacking the Madry Defense Model with $L_1$-based Adversarial Examples,"  The Madry Lab recently hosted a competition designed to test the robustness
+of their adversarially trained MNIST model. Attacks were constrained to perturb
+each pixel of the input image by a scaled maximal $L_\infty$ distortion
+$\epsilon$ = 0.3. This discourages the use of attacks which are not optimized
+on the $L_\infty$ distortion metric. Our experimental results demonstrate that
+by relaxing the $L_\infty$ constraint of the competition, the elastic-net
+attack to deep neural networks (EAD) can generate transferable adversarial
+examples which, despite their high average $L_\infty$ distortion, have minimal
+visual distortion. These results call into question the use of $L_\infty$ as a
+sole measure for visual distortion, and further demonstrate the power of EAD at
+generating robust adversarial examples.
+",1,0,0,1,0,0
+11351,Quantum sensors for the generating functional of interacting quantum field theories,"  Difficult problems described in terms of interacting quantum fields evolving
+in real time or out of equilibrium are abound in condensed-matter and
+high-energy physics. Addressing such problems via controlled experiments in
+atomic, molecular, and optical physics would be a breakthrough in the field of
+quantum simulations. In this work, we present a quantum-sensing protocol to
+measure the generating functional of an interacting quantum field theory and,
+with it, all the relevant information about its in or out of equilibrium
+phenomena. Our protocol can be understood as a collective interferometric
+scheme based on a generalization of the notion of Schwinger sources in quantum
+field theories, which make it possible to probe the generating functional. We
+show that our scheme can be realized in crystals of trapped ions acting as
+analog quantum simulators of self-interacting scalar quantum field theories.
+",0,1,0,0,0,0
+11352,Sockeye: A Toolkit for Neural Machine Translation,"  We describe Sockeye (version 1.12), an open-source sequence-to-sequence
+toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready
+framework for training and applying models as well as an experimental platform
+for researchers. Written in Python and built on MXNet, the toolkit offers
+scalable training and inference for the three most prominent encoder-decoder
+architectures: attentional recurrent neural networks, self-attentional
+transformers, and fully convolutional networks. Sockeye also supports a wide
+range of optimizers, normalization and regularization techniques, and inference
+improvements from current NMT literature. Users can easily run standard
+training recipes, explore different model settings, and incorporate new ideas.
+In this paper, we highlight Sockeye's features and benchmark it against other
+NMT toolkits on two language arcs from the 2017 Conference on Machine
+Translation (WMT): English-German and Latvian-English. We report competitive
+BLEU scores across all three architectures, including an overall best score for
+Sockeye's transformer implementation. To facilitate further comparison, we
+release all system outputs and training scripts used in our experiments. The
+Sockeye toolkit is free software released under the Apache 2.0 license.
+",1,0,0,1,0,0
+11353,Bayesian shape modelling of cross-sectional geological data,"  Shape information is of great importance in many applications. For example,
+the oil-bearing capacity of sand bodies, the subterranean remnants of ancient
+rivers, is related to their cross-sectional shapes. The analysis of these
+shapes is therefore of some interest, but current classifications are
+simplistic and ad hoc. In this paper, we describe the first steps towards a
+coherent statistical analysis of these shapes by deriving the integrated
+likelihood for data shapes given class parameters. The result is of interest
+beyond this particular application.
+",0,0,0,1,0,0
+11354,Analysing Relations involving small number of Monomials in AES S- Box,"  In the present day, AES is one the most widely used and most secure
+Encryption Systems prevailing. So, naturally lots of research work is going on
+to mount a significant attack on AES. Many different forms of Linear and
+differential cryptanalysis have been performed on AES. Of late, an active area
+of research has been Algebraic Cryptanalysis of AES, where although fast
+progress is being made, there are still numerous scopes for research and
+improvement. One of the major reasons behind this being that algebraic
+cryptanalysis mainly depends on I/O relations of the AES S- Box (a major
+component of the AES). As, already known, that the key recovery algorithm of
+AES can be broken down as an MQ problem which is itself considered hard.
+Solving these equations depends on our ability reduce them into linear forms
+which are easily solvable under our current computational prowess. The lower
+the degree of these equations, the easier it is for us to linearlize hence the
+attack complexity reduces. The aim of this paper is to analyze the various
+relations involving small number of monomials of the AES S- Box and to answer
+the question whether it is actually possible to have such monomial equations
+for the S- Box if we restrict the degree of the monomials. In other words this
+paper aims to study such equations and see if they can be applicable for AES.
+",1,0,0,0,0,0
+11355,q-Virasoro algebra and affine Kac-Moody Lie algebras,"  We establish a natural connection of the $q$-Virasoro algebra $D_{q}$
+introduced by Belov and Chaltikian with affine Kac-Moody Lie algebras. More
+specifically, for each abelian group $S$ together with a one-to-one linear
+character $\chi$, we define an infinite-dimensional Lie algebra $D_{S}$ which
+reduces to $D_{q}$ when $S=\mathbb{Z}$. Guided by the theory of equivariant
+quasi modules for vertex algebras, we introduce another Lie algebra
+${\mathfrak{g}}_{S}$ with $S$ as an automorphism group and we prove that
+$D_{S}$ is isomorphic to the $S$-covariant algebra of the affine Lie algebra
+$\widehat{\mathfrak{g}_{S}}$. We then relate restricted $D_{S}$-modules of
+level $\ell\in \mathbb{C}$ to equivariant quasi modules for the vertex algebra
+$V_{\widehat{\mathfrak{g}_{S}}}(\ell,0)$ associated to
+$\widehat{\mathfrak{g}_{S}}$ with level $\ell$. Furthermore, we show that if
+$S$ is a finite abelian group of order $2l+1$, $D_{S}$ is isomorphic to the
+affine Kac-Moody algebra of type $B^{(1)}_{l}$.
+",0,0,1,0,0,0
+11356,The Noise Handling Properties of the Talbot Algorithm for Numerically Inverting the Laplace Transform,"  This paper examines the noise handling properties of three of the most widely
+used algorithms for numerically inverting the Laplace Transform. After
+examining the genesis of the algorithms, the regularization properties are
+evaluated through a series of standard test functions in which noise is added
+to the inverse transform. Comparisons are then made with the exact data. Our
+main finding is that the Talbot inversion algorithm is very good at handling
+noisy data and performs much better than the Fourier Series and Stehfest
+numerical inversion schemes as outlined in this paper. This offers a
+considerable advantage for it's use in inverting the Laplace Transform when
+seeking numerical solutions to time dependent differential equations.
+",0,0,1,0,0,0
+11357,Short-term Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks,"  Despite its ubiquity in our daily lives, AI is only just starting to make
+advances in what may arguably have the largest societal impact thus far, the
+nascent field of autonomous driving. In this work we discuss this important
+topic and address one of crucial aspects of the emerging area, the problem of
+predicting future state of autonomous vehicle's surrounding necessary for safe
+and efficient operations. We introduce a deep learning-based approach that
+takes into account current world state and produces rasterized representations
+of each actor's vicinity. The raster images are then used by deep convolutional
+models to infer future movement of actors while accounting for inherent
+uncertainty of the prediction task. Extensive experiments on real-world data
+strongly suggest benefits of the proposed approach. Moreover, following
+successful tests the system was deployed to a fleet of autonomous vehicles.
+",1,0,0,1,0,0
+11358,"Tropicalization, symmetric polynomials, and complexity","  D. Grigoriev-G. Koshevoy recently proved that tropical Schur polynomials have
+(at worst) polynomial tropical semiring complexity. They also conjectured
+tropical skew Schur polynomials have at least exponential complexity; we
+establish a polynomial complexity upper bound. Our proof uses results about
+(stable) Schubert polynomials, due to R. P. Stanley and S. Billey-W.
+Jockusch-R. P. Stanley, together with a sufficient condition for polynomial
+complexity that is connected to the saturated Newton polytope property.
+",1,0,0,0,0,0
+11359,"The normal closure of big Dehn twists, and plate spinning with rotating families","  We study the normal closure of a big power of one or several Dehn twists in a
+Mapping Class Group. We prove that it has a presentation whose relators
+consists only of commutators between twists of disjoint support, thus answering
+a question of Ivanov. Our method is to use the theory of projection complexes
+of Bestvina Bromberg and Fujiwara, together with the theory of rotating
+families, simultaneously on several spaces.
+",0,0,1,0,0,0
+11360,Secure Minimum Time Planning Under Environmental Uncertainty: an Extended Treatment,"  Cyber Physical Systems (CPS) are becoming ubiquitous and affect the physical
+world, yet security is seldom at the forefront of their design. This is
+especially true of robotic control algorithms which seldom consider the effect
+of a cyber attack on mission objectives and success. This work presents a
+secure optimal control algorithm in the face of a cyber attack on a robot's
+knowledge of the environment. This work focuses on cyber attack, but the
+results generalize to incomplete or outdated information of an environment.
+This work fuses ideas from robust control, optimal control, and sensor based
+planning to provide a generalization of stopping distance in 3D. The planner is
+implemented in simulation and its properties are analyzed.
+",1,0,0,0,0,0
+11361,"Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions","  Treatment effects can be estimated from observational data as the difference
+in potential outcomes. In this paper, we address the challenge of estimating
+the potential outcome when treatment-dose levels can vary continuously over
+time. Further, the outcome variable may not be measured at a regular frequency.
+Our proposed solution represents the treatment response curves using linear
+time-invariant dynamical systems---this provides a flexible means for modeling
+response over time to highly variable dose curves. Moreover, for multivariate
+data, the proposed method: uncovers shared structure in treatment response and
+the baseline across multiple markers; and, flexibly models challenging
+correlation structure both across and within signals over time. For this, we
+build upon the framework of multiple-output Gaussian Processes. On simulated
+and a challenging clinical dataset, we show significant gains in accuracy over
+state-of-the-art models.
+",1,0,0,1,0,0
+11362,Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling,"  Analytical electron microscopy and spectroscopy of biological specimens,
+polymers, and other beam sensitive materials has been a challenging area due to
+irradiation damage. There is a pressing need to develop novel imaging and
+spectroscopic imaging methods that will minimize such sample damage as well as
+reduce the data acquisition time. The latter is useful for high-throughput
+analysis of materials structure and chemistry. In this work, we present a novel
+machine learning based method for dynamic sparse sampling of EDS data using a
+scanning electron microscope. Our method, based on the supervised learning
+approach for dynamic sampling algorithm and neural networks based
+classification of EDS data, allows a dramatic reduction in the total sampling
+of up to 90%, while maintaining the fidelity of the reconstructed elemental
+maps and spectroscopic data. We believe this approach will enable imaging and
+elemental mapping of materials that would otherwise be inaccessible to these
+analysis techniques.
+",1,0,0,0,0,0
+11363,Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates,"  We consider the problem of global optimization of an unknown non-convex
+smooth function with zeroth-order feedback. In this setup, an algorithm is
+allowed to adaptively query the underlying function at different locations and
+receives noisy evaluations of function values at the queried points (i.e. the
+algorithm has access to zeroth-order information). Optimization performance is
+evaluated by the expected difference of function values at the estimated
+optimum and the true optimum. In contrast to the classical optimization setup,
+first-order information like gradients are not directly accessible to the
+optimization algorithm. We show that the classical minimax framework of
+analysis, which roughly characterizes the worst-case query complexity of an
+optimization algorithm in this setting, leads to excessively pessimistic
+results. We propose a local minimax framework to study the fundamental
+difficulty of optimizing smooth functions with adaptive function evaluations,
+which provides a refined picture of the intrinsic difficulty of zeroth-order
+optimization. We show that for functions with fast level set growth around the
+global minimum, carefully designed optimization algorithms can identify a near
+global minimizer with many fewer queries. For the special case of strongly
+convex and smooth functions, our implied convergence rates match the ones
+developed for zeroth-order convex optimization problems. At the other end of
+the spectrum, for worst-case smooth functions no algorithm can converge faster
+than the minimax rate of estimating the entire unknown function in the
+$\ell_\infty$-norm. We provide an intuitive and efficient algorithm that
+attains the derived upper error bounds.
+",0,0,0,1,0,0
+11364,Raw Waveform-based Speech Enhancement by Fully Convolutional Networks,"  This study proposes a fully convolutional network (FCN) model for raw
+waveform-based speech enhancement. The proposed system performs speech
+enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which
+dif-fers from most existing denoising methods that process the magnitude
+spectrum (e.g., log power spectrum (LPS)) only. Because the fully connected
+layers, which are involved in deep neural networks (DNN) and convolutional
+neural networks (CNN), may not accurately characterize the local information of
+speech signals, particularly with high frequency components, we employed fully
+convolutional layers to model the waveform. More specifically, FCN consists of
+only convolutional layers and thus the local temporal structures of speech
+signals can be efficiently and effectively preserved with relatively few
+weights. Experimental results show that DNN- and CNN-based models have limited
+capability to restore high frequency components of waveforms, thus leading to
+decreased intelligibility of enhanced speech. By contrast, the proposed FCN
+model can not only effectively recover the waveforms but also outperform the
+LPS-based DNN baseline in terms of short-time objective intelligibility (STOI)
+and perceptual evaluation of speech quality (PESQ). In addition, the number of
+model parameters in FCN is approximately only 0.2% compared with that in both
+DNN and CNN.
+",1,0,0,1,0,0
+11365,Kinetic Theory for Finance Brownian Motion from Microscopic Dynamics,"  Recent technological development has enabled researchers to study social
+phenomena scientifically in detail and financial markets has particularly
+attracted physicists since the Brownian motion has played the key role as in
+physics. In our previous report (arXiv:1703.06739; to appear in Phys. Rev.
+Lett.), we have presented a microscopic model of trend-following high-frequency
+traders (HFTs) and its theoretical relation to the dynamics of financial
+Brownian motion, directly supported by a data analysis of tracking trajectories
+of individual HFTs in a financial market. Here we show the mathematical
+foundation for the HFT model paralleling to the traditional kinetic theory in
+statistical physics. We first derive the time-evolution equation for the
+phase-space distribution for the HFT model exactly, which corresponds to the
+Liouville equation in conventional analytical mechanics. By a systematic
+reduction of the Liouville equation for the HFT model, the
+Bogoliubov-Born-Green-Kirkwood-Yvon hierarchal equations are derived for
+financial Brownian motion. We then derive the Boltzmann-like and Langevin-like
+equations for the order-book and the price dynamics by making the assumption of
+molecular chaos. The qualitative behavior of the model is asymptotically
+studied by solving the Boltzmann-like and Langevin-like equations for the large
+number of HFTs, which is numerically validated through the Monte-Carlo
+simulation. Our kinetic description highlights the parallel mathematical
+structure between the financial Brownian motion and the physical Brownian
+motion.
+",0,0,0,0,0,1
+11366,Assessing Uncertainties in X-ray Single-particle Three-dimensional reconstructions,"  Modern technology for producing extremely bright and coherent X-ray laser
+pulses provides the possibility to acquire a large number of diffraction
+patterns from individual biological nanoparticles, including proteins, viruses,
+and DNA. These two-dimensional diffraction patterns can be practically
+reconstructed and retrieved down to a resolution of a few \angstrom. In
+principle, a sufficiently large collection of diffraction patterns will contain
+the required information for a full three-dimensional reconstruction of the
+biomolecule. The computational methodology for this reconstruction task is
+still under development and highly resolved reconstructions have not yet been
+produced.
+We analyze the Expansion-Maximization-Compression scheme, the current state
+of the art approach for this very challenging application, by isolating
+different sources of uncertainty. Through numerical experiments on synthetic
+data we evaluate their respective impact. We reach conclusions of relevance for
+handling actual experimental data, as well as pointing out certain improvements
+to the underlying estimation algorithm.
+We also introduce a practically applicable computational methodology in the
+form of bootstrap procedures for assessing reconstruction uncertainty in the
+real data case. We evaluate the sharpness of this approach and argue that this
+type of procedure will be critical in the near future when handling the
+increasing amount of data.
+",1,1,0,1,0,0
+11367,Learning Hawkes Processes from Short Doubly-Censored Event Sequences,"  Many real-world applications require robust algorithms to learn point
+processes based on a type of incomplete data --- the so-called short
+doubly-censored (SDC) event sequences. We study this critical problem of
+quantitative asynchronous event sequence analysis under the framework of Hawkes
+processes by leveraging the idea of data synthesis. Given SDC event sequences
+observed in a variety of time intervals, we propose a sampling-stitching data
+synthesis method --- sampling predecessors and successors for each SDC event
+sequence from potential candidates and stitching them together to synthesize
+long training sequences. The rationality and the feasibility of our method are
+discussed in terms of arguments based on likelihood. Experiments on both
+synthetic and real-world data demonstrate that the proposed data synthesis
+method improves learning results indeed for both time-invariant and
+time-varying Hawkes processes.
+",0,0,1,1,0,0
+11368,"Unveiling the Role of Dopant Polarity on the Recombination, and Performance of Organic Light-Emitting Diodes","  The recombination of charges is an important process in organic photonic
+devices because the process influences the device characteristics such as the
+driving voltage, efficiency and lifetime. By combining the dipole trap theory
+with the drift-diffusion model, we report that the stationary dipole moment
+({\mu}0) of the dopant is a major factor determining the recombination
+mechanism in the dye-doped organic light emitting diodes when the trap depth
+({\Delta}Et) is larger than 0.3 eV where any de-trapping effect becomes
+negligible. Dopants with large {\mu}0 (e.g., homoleptic Ir(III) dyes) induce
+large charge trapping on them, resulting in high driving voltage and
+trap-assisted-recombination dominated emission. On the other hand, dyes with
+small {\mu}0 (e.g., heteroleptic Ir(III) dyes) show much less trapping on them
+no matter what {\Delta}Et is, leading to lower driving voltage, higher
+efficiencies and Langevin recombination dominated emission characteristics.
+This finding will be useful in any organic photonic devices where trapping and
+recombination sites play key roles.
+",0,1,0,0,0,0
+11369,Sliced Wasserstein Distance for Learning Gaussian Mixture Models,"  Gaussian mixture models (GMM) are powerful parametric tools with many
+applications in machine learning and computer vision. Expectation maximization
+(EM) is the most popular algorithm for estimating the GMM parameters. However,
+EM guarantees only convergence to a stationary point of the log-likelihood
+function, which could be arbitrarily worse than the optimal solution. Inspired
+by the relationship between the negative log-likelihood function and the
+Kullback-Leibler (KL) divergence, we propose an alternative formulation for
+estimating the GMM parameters using the sliced Wasserstein distance, which
+gives rise to a new algorithm. Specifically, we propose minimizing the
+sliced-Wasserstein distance between the mixture model and the data distribution
+with respect to the GMM parameters. In contrast to the KL-divergence, the
+energy landscape for the sliced-Wasserstein distance is more well-behaved and
+therefore more suitable for a stochastic gradient descent scheme to obtain the
+optimal GMM parameters. We show that our formulation results in parameter
+estimates that are more robust to random initializations and demonstrate that
+it can estimate high-dimensional data distributions more faithfully than the EM
+algorithm.
+",1,0,0,1,0,0
+11370,How constant shifts affect the zeros of certain rational harmonic functions,"  We study the effect of constant shifts on the zeros of rational harmomic
+functions $f(z) = r(z) - \conj{z}$. In particular, we characterize how shifting
+through the caustics of $f$ changes the number of zeros and their respective
+orientations. This also yields insight into the nature of the singular zeros of
+$f$. Our results have applications in gravitational lensing theory, where
+certain such functions $f$ represent gravitational point-mass lenses, and a
+constant shift can be interpreted as the position of the light source of the
+lens.
+",0,1,1,0,0,0
+11371,Discovery and usage of joint attention in images,"  Joint visual attention is characterized by two or more individuals looking at
+a common target at the same time. The ability to identify joint attention in
+scenes, the people involved, and their common target, is fundamental to the
+understanding of social interactions, including others' intentions and goals.
+In this work we deal with the extraction of joint attention events, and the use
+of such events for image descriptions. The work makes two novel contributions.
+First, our extraction algorithm is the first which identifies joint visual
+attention in single static images. It computes 3D gaze direction, identifies
+the gaze target by combining gaze direction with a 3D depth map computed for
+the image, and identifies the common gaze target. Second, we use a human study
+to demonstrate the sensitivity of humans to joint attention, suggesting that
+the detection of such a configuration in an image can be useful for
+understanding the image, including the goals of the agents and their joint
+activity, and therefore can contribute to image captioning and related tasks.
+",1,0,0,0,1,0
+11372,Singular p-Laplacian parabolic system in exterior domains: higher regularity of solutions and related properties of extinction and asymptotic behavior in time,"  We consider the IBVP in exterior domains for the p-Laplacian parabolic
+system. We prove regularity up to the boundary, extinction properties for p \in
+( 2n/(n+2) , 2n/(n+1) ) and exponential decay for p= 2n/(n+1) .
+",0,0,1,0,0,0
+11373,Size distribution of galaxies in SDSS DR7: weak dependence on halo environment,"  Using a sample of galaxies selected from the Sloan Digital Sky Survey Data
+Release 7 (SDSS DR7) and a catalog of bulge-disk decompositions, we study how
+the size distribution of galaxies depends on the intrinsic properties of
+galaxies, such as concentration, morphology, specific star formation rate
+(sSFR), and bulge fraction, and on the large-scale environments in the context
+of central/satellite decomposition, halo environment, the cosmic web: \cluster,
+\filament, \sheet ~and \void, as well as galaxy number density. We find that
+there is a strong dependence of the luminosity- or mass-size relation on the
+galaxy concentration, morphology, sSFR, and bulge fraction. Compared with
+late-type (spiral) galaxies, there is a clear trend of smaller sizes and
+steeper slope for early-type (elliptical) galaxies. Similarly, galaxies with
+high bulge fraction have smaller sizes and steeper slope than those with low
+bulge fraction. Fitting formula of the average luminosity- and mass-size
+relations are provided for galaxies of these different intrinsic properties.
+Examining galaxies in terms of their large scale environments, we find that the
+mass-size relation has some weak dependence on the halo mass and
+central/satellite segregation for galaxies within mass range $9.0\le \log
+M_{\ast} \le 10.5$, where satellites or galaxies in more massive halos have
+slightly smaller sizes than their counterparts. While the cosmic web and local
+number density dependence of the mass-size relation is almost negligible.
+",0,1,0,0,0,0
+11374,Towards thinner convolutional neural networks through Gradually Global Pruning,"  Deep network pruning is an effective method to reduce the storage and
+computation cost of deep neural networks when applying them to resource-limited
+devices. Among many pruning granularities, neuron level pruning will remove
+redundant neurons and filters in the model and result in thinner networks. In
+this paper, we propose a gradually global pruning scheme for neuron level
+pruning. In each pruning step, a small percent of neurons were selected and
+dropped across all layers in the model. We also propose a simple method to
+eliminate the biases in evaluating the importance of neurons to make the scheme
+feasible. Compared with layer-wise pruning scheme, our scheme avoid the
+difficulty in determining the redundancy in each layer and is more effective
+for deep networks. Our scheme would automatically find a thinner sub-network in
+original network under a given performance.
+",1,0,0,0,0,0
+11375,Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars,"  We propose a systematic learning-based approach to the generation of massive
+quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D
+images thereof, with associated ground truth information, for the purposes of
+training, benchmarking, and diagnosing learning-based computer vision and
+robotics algorithms. In particular, we devise a learning-based pipeline of
+algorithms capable of automatically generating and rendering a potentially
+infinite variety of indoor scenes by using a stochastic grammar, represented as
+an attributed Spatial And-Or Graph, in conjunction with state-of-the-art
+physics-based rendering. Our pipeline is capable of synthesizing scene layouts
+with high diversity, and it is configurable inasmuch as it enables the precise
+customization and control of important attributes of the generated scenes. It
+renders photorealistic RGB images of the generated scenes while automatically
+synthesizing detailed, per-pixel ground truth data, including visible surface
+depth and normal, object identity, and material information (detailed to object
+parts), as well as environments (e.g., illuminations and camera viewpoints). We
+demonstrate the value of our synthesized dataset, by improving performance in
+certain machine-learning-based scene understanding tasks--depth and surface
+normal prediction, semantic segmentation, reconstruction, etc.--and by
+providing benchmarks for and diagnostics of trained models by modifying object
+attributes and scene properties in a controllable manner.
+",1,0,0,1,0,0
+11376,Multiband NFC for High-Throughput Wireless Computer Vision Sensor Network,"  Vision sensors lie in the heart of computer vision. In many computer vision
+applications, such as AR/VR, non-contacting near-field communication (NFC) with
+high throughput is required to transfer information to algorithms. In this
+work, we proposed a novel NFC system which utilizes multiple frequency bands to
+achieve high throughput.
+",1,0,0,0,0,0
+11377,Learning Rare Word Representations using Semantic Bridging,"  We propose a methodology that adapts graph embedding techniques (DeepWalk
+(Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as
+cross-lingual vector space mapping approaches (Least Squares and Canonical
+Correlation Analysis) in order to merge the corpus and ontological sources of
+lexical knowledge. We also perform comparative analysis of the used algorithms
+in order to identify the best combination for the proposed system. We then
+apply this to the task of enhancing the coverage of an existing word
+embedding's vocabulary with rare and unseen words. We show that our technique
+can provide considerable extra coverage (over 99%), leading to consistent
+performance gain (around 10% absolute gain is achieved with w2v-gn-500K cf.§
+3.3) on the Rare Word Similarity dataset.
+",1,0,0,0,0,0
+11378,Effect of annealing temperatures on the electrical conductivity and dielectric properties of Ni1.5Fe1.5O4 spinel ferrite prepared by chemical reaction at different pH values,"  The electrical conductivity and dielectric properties of Ni1.5Fe1.5O4 ferrite
+has been controlled by varying the annealing temperature of the chemical routed
+samples. The frequency activated conductivity obeyed Jonschers power law and
+universal scaling suggested semiconductor nature. An unusual metal like state
+has been revealed in the measurement temperature scale in between two
+semiconductor states with different activation energy. The metal like state has
+been affected by thermal annealing of the material. The analysis of electrical
+impedance and modulus spectra has confirmed non-Debye dielectric relaxation
+with contributions from grains and grain boundaries. The dielectric relaxation
+process is thermally activated in terms of measurement temperature and
+annealing temperature of the samples. The hole hopping process, due to presence
+of Ni3+ ions in the present Ni rich ferrite, played a significant role in
+determining the thermal activated conduction mechanism. This work has
+successfully applied the technique of a combined variation of annealing
+temperature and pH value during chemical reaction for tuning electrical
+parameters in a wide range; for example dc limit of conductivity 10power(-4)
+-10power(-12) S/cm, and unusually high activation energy 0.17-1.36 eV.
+",0,1,0,0,0,0
+11379,Molecular dynamic simulation of water vapor interaction with blind pore of dead-end and saccate type,"  One of the varieties of pores, often found in natural or artificial building
+materials, are the so-called blind pores of dead-end or saccate type.
+Three-dimensional model of such kind of pore has been developed in this work.
+This model has been used for simulation of water vapor interaction with
+individual pore by molecular dynamics in combination with the diffusion
+equation method. Special investigations have been done to find dependencies
+between thermostats implementations and conservation of thermodynamic and
+statistical values of water vapor - pore system. The two types of evolution of
+water-pore system have been investigated: drying and wetting of the pore. Full
+research of diffusion coefficient, diffusion velocity and other diffusion
+parameters has been made.
+",1,1,0,0,0,0
+11380,Learning Program Component Order,"  Successful programs are written to be maintained. One aspect to this is that
+programmers order the components in the code files in a particular way. This is
+part of programming style. While the conventions for ordering are sometimes
+given as part of a style guideline, such guidelines are often incomplete and
+programmers tend to have their own more comprehensive orderings in mind. This
+paper defines a model for ordering program components and shows how this model
+can be learned from sample code. Such a model is a useful tool for a
+programming environment in that it can be used to find the proper location for
+inserting new components or for reordering files to better meet the needs of
+the programmer. The model is designed so that it can be fine- tuned by the
+programmer. The learning framework is evaluated both by looking at code with
+known style guidelines and by testing whether it inserts existing components
+into a file correctly.
+",1,0,0,0,0,0
+11381,Random Euler Complex-Valued Nonlinear Filters,"  Over the last decade, both the neural network and kernel adaptive filter have
+successfully been used for nonlinear signal processing. However, they suffer
+from high computational cost caused by their complex/growing network
+structures. In this paper, we propose two random Euler filters for
+complex-valued nonlinear filtering problem, i.e., linear random Euler
+complex-valued filter (LRECF) and its widely-linear version (WLRECF), which
+possess a simple and fixed network structure. The transient and steady-state
+performances are studied in a non-stationary environment. The analytical
+minimum mean square error (MSE) and optimum step-size are derived. Finally,
+numerical simulations on complex-valued nonlinear system identification and
+nonlinear channel equalization are presented to show the effectiveness of the
+proposed methods.
+",0,0,0,1,0,0
+11382,Memory effects on epidemic evolution: The susceptible-infected-recovered epidemic model,"  Memory has a great impact on the evolution of every process related to human
+societies. Among them, the evolution of an epidemic is directly related to the
+individuals' experiences. Indeed, any real epidemic process is clearly
+sustained by a non-Markovian dynamics: memory effects play an essential role in
+the spreading of diseases. Including memory effects in the
+susceptible-infected-recovered (SIR) epidemic model seems very appropriate for
+such an investigation. Thus, the memory prone SIR model dynamics is
+investigated using fractional derivatives. The decay of long-range memory,
+taken as a power-law function, is directly controlled by the order of the
+fractional derivatives in the corresponding nonlinear fractional differential
+evolution equations. Here we assume ""fully mixed"" approximation and show that
+the epidemic threshold is shifted to higher values than those for the
+memoryless system, depending on this memory ""length"" decay exponent. We also
+consider the SIR model on structured networks and study the effect of topology
+on threshold points in a non- Markovian dynamics. Furthermore, the lack of
+access to the precise information about the initial conditions or the past
+events plays a very relevant role in the correct estimation or prediction of
+the epidemic evolution. Such a ""constraint"" is analyzed and discussed.
+",0,1,0,0,0,0
+11383,On the equivalence of Eulerian and Lagrangian variables for the two-component Camassa-Holm system,"  The Camassa-Holm equation and its two-component Camassa-Holm system
+generalization both experience wave breaking in finite time. To analyze this,
+and to obtain solutions past wave breaking, it is common to reformulate the
+original equation given in Eulerian coordinates, into a system of ordinary
+differential equations in Lagrangian coordinates. It is of considerable
+interest to study the stability of solutions and how this is manifested in
+Eulerian and Lagrangian variables. We identify criteria of convergence, such
+that convergence in Eulerian coordinates is equivalent to convergence in
+Lagrangian coordinates. In addition, we show how one can approximate global
+conservative solutions of the scalar Camassa-Holm equation by smooth solutions
+of the two-component Camassa-Holm system that do not experience wave breaking.
+",0,0,1,0,0,0
+11384,Bulk diffusion in a kinetically constrained lattice gas,"  In the hydrodynamic regime, the evolution of a stochastic lattice gas with
+symmetric hopping rules is described by a diffusion equation with
+density-dependent diffusion coefficient encapsulating all microscopic details
+of the dynamics. This diffusion coefficient is, in principle, determined by a
+Green-Kubo formula. In practice, even when the equilibrium properties of a
+lattice gas are analytically known, the diffusion coefficient cannot be
+computed except when a lattice gas additionally satisfies the gradient
+condition. We develop a procedure to systematically obtain analytical
+approximations for the diffusion coefficient for non-gradient lattice gases
+with known equilibrium. The method relies on a variational formula found by
+Varadhan and Spohn which is a version of the Green-Kubo formula particularly
+suitable for diffusive lattice gases. Restricting the variational formula to
+finite-dimensional sub-spaces allows one to perform the minimization and gives
+upper bounds for the diffusion coefficient. We apply this approach to a
+kinetically constrained non-gradient lattice gas, viz. to the Kob-Andersen
+model on the square lattice.
+",0,1,0,0,0,0
+11385,Censored pairwise likelihood-based tests for mixing coefficient of spatial max-mixture models,"  Max-mixture processes are defined as Z = max(aX, (1 -- a)Y) with X an
+asymptotic dependent (AD) process, Y an asymptotic independent (AI) process and
+a $\in$ [0, 1]. So that, the mixing coefficient a may reveal the strength of
+the AD part present in the max-mixture process. In this paper we focus on two
+tests based on censored pairwise likelihood estimates. We compare their
+performance through an extensive simulation study. Monte Carlo simulation plays
+a fundamental tool for asymptotic variance calculations. We apply our tests to
+daily precipitations from the East of Australia. Drawbacks and possible
+developments are discussed.
+",0,0,1,1,0,0
+11386,From rate distortion theory to metric mean dimension: variational principle,"  The purpose of this paper is to point out a new connection between
+information theory and dynamical systems. In the information theory side, we
+consider rate distortion theory, which studies lossy data compression of
+stochastic processes under distortion constraints. In the dynamical systems
+side, we consider mean dimension theory, which studies how many parameters per
+second we need to describe a dynamical system. The main results are new
+variational principles connecting rate distortion function to metric mean
+dimension.
+",1,0,1,0,0,0
+11387,Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks,"  Quantized Neural Networks (QNNs), which use low bitwidth numbers for
+representing parameters and performing computations, have been proposed to
+reduce the computation complexity, storage size and memory usage. In QNNs,
+parameters and activations are uniformly quantized, such that the
+multiplications and additions can be accelerated by bitwise operations.
+However, distributions of parameters in Neural Networks are often imbalanced,
+such that the uniform quantization determined from extremal values may under
+utilize available bitwidth. In this paper, we propose a novel quantization
+method that can ensure the balance of distributions of quantized values. Our
+method first recursively partitions the parameters by percentiles into balanced
+bins, and then applies uniform quantization. We also introduce computationally
+cheaper approximations of percentiles to reduce the computation overhead
+introduced. Overall, our method improves the prediction accuracies of QNNs
+without introducing extra computation during inference, has negligible impact
+on training speed, and is applicable to both Convolutional Neural Networks and
+Recurrent Neural Networks. Experiments on standard datasets including ImageNet
+and Penn Treebank confirm the effectiveness of our method. On ImageNet, the
+top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is
+superior to the state-of-the-arts of QNNs.
+",1,0,0,0,0,0
+11388,The Causal Frame Problem: An Algorithmic Perspective,"  The Frame Problem (FP) is a puzzle in philosophy of mind and epistemology,
+articulated by the Stanford Encyclopedia of Philosophy as follows: ""How do we
+account for our apparent ability to make decisions on the basis only of what is
+relevant to an ongoing situation without having explicitly to consider all that
+is not relevant?"" In this work, we focus on the causal variant of the FP, the
+Causal Frame Problem (CFP). Assuming that a reasoner's mental causal model can
+be (implicitly) represented by a causal Bayes net, we first introduce a notion
+called Potential Level (PL). PL, in essence, encodes the relative position of a
+node with respect to its neighbors in a causal Bayes net. Drawing on the
+psychological literature on causal judgment, we substantiate the claim that PL
+may bear on how time is encoded in the mind. Using PL, we propose an inference
+framework, called the PL-based Inference Framework (PLIF), which permits a
+boundedly-rational approach to the CFP to be formally articulated at Marr's
+algorithmic level of analysis. We show that our proposed framework, PLIF, is
+consistent with a wide range of findings in causal judgment literature, and
+that PL and PLIF make a number of predictions, some of which are already
+supported by existing findings.
+",1,0,0,1,0,0
+11389,A Visual Representation of Wittgenstein's Tractatus Logico-Philosophicus,"  In this paper we present a data visualization method together with its
+potential usefulness in digital humanities and philosophy of language. We
+compile a multilingual parallel corpus from different versions of
+Wittgenstein's Tractatus Logico-Philosophicus, including the original in German
+and translations into English, Spanish, French, and Russian. Using this corpus,
+we compute a similarity measure between propositions and render a visual
+network of relations for different languages.
+",1,0,0,0,0,0
+11390,Primordial perturbations generated by Higgs field and $R^2$ operator,"  If the very early Universe is dominated by the non-minimally coupled Higgs
+field and Starobinsky's curvature-squared term together, the potential diagram
+would mimic the landscape of a valley, serving as a cosmological attractor. The
+inflationary dynamics along this valley is studied, model parameters are
+constrained against observational data, and the isocurvature perturbation is
+evaluated.
+",0,1,0,0,0,0
+11391,"Schubert polynomials, theta and eta polynomials, and Weyl group invariants","  We examine the relationship between the (double) Schubert polynomials of
+Billey-Haiman and Ikeda-Mihalcea-Naruse and the (double) theta and eta
+polynomials of Buch-Kresch-Tamvakis and Wilson from the perspective of Weyl
+group invariants. We obtain generators for the kernel of the natural map from
+the corresponding ring of Schubert polynomials to the (equivariant) cohomology
+ring of symplectic and orthogonal flag manifolds.
+",0,0,1,0,0,0
+11392,Massive Fields as Systematics for Single Field Inflation,"  During inflation, massive fields can contribute to the power spectrum of
+curvature perturbation via a dimension-5 operator. This contribution can be
+considered as a bias for the program of using $n_s$ and $r$ to select inflation
+models. Even the dimension-5 operator is suppressed by $\Lambda = M_p$, there
+is still a significant shift on the $n_s$-$r$ diagram if the massive fields
+have $m\sim H$. On the other hand, if the heavy degree of freedom appears only
+at the same energy scale as the suppression scale of the dimension-5 operator,
+then significant shift on the $n_s$-$r$ diagram takes place at $m=\Lambda \sim
+70H$, which is around the inflationary time-translation symmetry breaking
+scale. Hence, the systematics from massive fields pose a greater challenge for
+future high precision experiments for inflationary model selection. This result
+can be thought of as the impact of UV sensitivity to inflationary observables.
+",0,1,0,0,0,0
+11393,The second boundary value problem of the prescribed affine mean curvature equation and related linearized Monge-Ampère equation,"  These lecture notes are concerned with the solvability of the second boundary
+value problem of the prescribed affine mean curvature equation and related
+regularity theory of the Monge-Ampère and linearized Monge-Ampère
+equations. The prescribed affine mean curvature equation is a fully nonlinear,
+fourth order, geometric partial differential equation of the following form
+$$\sum_{i, j=1}^n U^{ij}\frac{\partial^2}{\partial
+{x_i}\partial{x_j}}\left[(\det D^2 u)^{-\frac{n+1}{n+2}}\right]=f$$ where
+$(U^{ij})$ is the cofactor matrix of the Hessian matrix $D^2 u$ of a locally
+uniformly convex function $u$. Its variant is related to the problem of finding
+Kähler metrics of constant scalar curvature in complex geometry. We first
+introduce the background of the prescribed affine mean curvature equation which
+can be viewed as a coupled system of Monge-Ampère and linearized
+Monge-Ampère equations. Then we state key open problems and present the
+solution of the second boundary value problem that prescribes the boundary
+values of the solution $u$ and its Hessian determinant $\det D^2 u$. Its proof
+uses important tools from the boundary regularity theory of the Monge-Ampère
+and linearized Monge-Ampère equations that we will present in the lecture
+notes.
+",0,0,1,0,0,0
+11394,Additive Combinatorics: A Menu of Research Problems,"  This text contains over three hundred specific open questions on various
+topics in additive combinatorics, each placed in context by reviewing all
+relevant results. While the primary purpose is to provide an ample supply of
+problems for student research, it is hopefully also useful for a wider
+audience. It is the author's intention to keep the material current, thus all
+feedback and updates are greatly appreciated.
+",0,0,1,0,0,0
+11395,NMR evidence for static local nematicity and its cooperative interplay with low-energy magnetic fluctuations in FeSe under pressure,"  We present $^{77}$Se-NMR measurements on single-crystalline FeSe under
+pressures up to 2 GPa. Based on the observation of the splitting and broadening
+of the NMR spectrum due to structural twin domains, we discovered that static,
+local nematic ordering exists well above the bulk nematic ordering temperature,
+$T_{\rm s}$. The static, local nematic order and the low-energy stripe-type
+antiferromagnetic spin fluctuations, as revealed by NMR spin-lattice relaxation
+rate measurements, are both insensitive to pressure application. These NMR
+results provide clear evidence for the microscopic cooperation between
+magnetism and local nematicity in FeSe.
+",0,1,0,0,0,0
+11396,LitStoryTeller: An Interactive System for Visual Exploration of Scientific Papers Leveraging Named entities and Comparative Sentences,"  The present study proposes LitStoryTeller, an interactive system for visually
+exploring the semantic structure of a scientific article. We demonstrate how
+LitStoryTeller could be used to answer some of the most fundamental research
+questions, such as how a new method was built on top of existing methods, based
+on what theoretical proof and experimental evidences. More importantly,
+LitStoryTeller can assist users to understand the full and interesting story a
+scientific paper, with a concise outline and important details. The proposed
+system borrows a metaphor from screen play, and visualizes the storyline of a
+scientific paper by arranging its characters (scientific concepts or
+terminologies) and scenes (paragraphs/sentences) into a progressive and
+interactive storyline. Such storylines help to preserve the semantic structure
+and logical thinking process of a scientific paper. Semantic structures, such
+as scientific concepts and comparative sentences, are extracted using existing
+named entity recognition APIs and supervised classifiers, from a scientific
+paper automatically. Two supplementary views, ranked entity frequency view and
+entity co-occurrence network view, are provided to help users identify the
+""main plot"" of such scientific storylines. When collective documents are ready,
+LitStoryTeller also provides a temporal entity evolution view and entity
+community view for collection digestion.
+",1,0,0,0,0,0
+11397,Acoustic Metacages for Omnidirectional Sound Shielding,"  Conventional sound shielding structures typically prevent fluid transport
+between the exterior and interior. A design of a two-dimensional acoustic
+metacage with subwavelength thickness which can shield acoustic waves from all
+directions while allowing steady fluid flow is presented in this paper. The
+structure is designed based on acoustic gradient-index metasurfaces composed of
+open channels and shunted Helmholtz resonators. The strong parallel momentum on
+the metacage surface rejects in-plane sound at an arbitrary angle of incidence
+which leads to low sound transmission through the metacage. The performance of
+the proposed metacage is verified by numerical simulations and measurements on
+a three-dimensional printed prototype. The acoustic metacage has potential
+applications in sound insulation where steady fluid flow is necessary or
+advantageous.
+",0,1,0,0,0,0
+11398,Concave losses for robust dictionary learning,"  Traditional dictionary learning methods are based on quadratic convex loss
+function and thus are sensitive to outliers. In this paper, we propose a
+generic framework for robust dictionary learning based on concave losses. We
+provide results on composition of concave functions, notably regarding
+super-gradient computations, that are key for developing generic dictionary
+learning algorithms applicable to smooth and non-smooth losses. In order to
+improve identification of outliers, we introduce an initialization heuristic
+based on undercomplete dictionary learning. Experimental results using
+synthetic and real data demonstrate that our method is able to better detect
+outliers, is capable of generating better dictionaries, outperforming
+state-of-the-art methods such as K-SVD and LC-KSVD.
+",1,0,0,1,0,0
+11399,"Target-Quality Image Compression with Recurrent, Convolutional Neural Networks","  We introduce a stop-code tolerant (SCT) approach to training recurrent
+convolutional neural networks for lossy image compression. Our methods
+introduce a multi-pass training method to combine the training goals of
+high-quality reconstructions in areas around stop-code masking as well as in
+highly-detailed areas. These methods lead to lower true bitrates for a given
+recursion count, both pre- and post-entropy coding, even using unstructured
+LZ77 code compression. The pre-LZ77 gains are achieved by trimming stop codes.
+The post-LZ77 gains are due to the highly unequal distributions of 0/1 codes
+from the SCT architectures. With these code compressions, the SCT architecture
+maintains or exceeds the image quality at all compression rates compared to
+JPEG and to RNN auto-encoders across the Kodak dataset. In addition, the SCT
+coding results in lower variance in image quality across the extent of the
+image, a characteristic that has been shown to be important in human ratings of
+image quality
+",1,0,0,0,0,0
+11400,Embedding simply connected 2-complexes in 3-space -- IV. Dual matroids,"  We introduce dual matroids of 2-dimensional simplicial complexes. Under
+certain necessary conditions, duals matroids are used to characterise
+embeddability in 3-space in a way analogous to Whitney's planarity criterion.
+We further use dual matroids to extend a 3-dimensional analogue of
+Kuratowski's theorem to the class of 2-dimensional simplicial complexes
+obtained from simply connected ones by identifying vertices or edges.
+",0,0,1,0,0,0
+11401,Excitonic effects in third harmonic generation: the case of carbon nanotubes and nanoribbons,"  Linear and nonlinear optical properties of low dimensional nanostructures
+have attracted a large interest in the scientific community as tools to probe
+the strong confinement of the electrons and for possible applications in
+optoelectronic devices. In particular it has been shown that the linear optical
+response of carbon nanotubes [Science 308, 838 (2005)] and graphene nanoribbons
+[Nat. Comm. 5, 4253 (2014)] is dominated by bounded electron-hole pairs, the
+excitons. The role of excitons in linear response has been widely studied, but
+still little is known on their effect on nonlinear susceptibilities. Using a
+recently developed methodology [Phys. Rev. B 88, 235113 (2013)] based on
+well-established ab-initio many-body perturbation theory approaches, we find
+that quasiparticle shifts and excitonic effects significantly modify the
+third-harmonic generation in carbon nanotubes and graphene nanoribbons. For
+both systems the net effect of many-body effects is to reduce the intensity of
+the main peak in the independent particle spectrum and redistribute the
+spectral weight among several excitonic resonances.
+",0,1,0,0,0,0
+11402,A general family of congruences for Bernoulli numbers,"  We prove a general family of congruences for Bernoulli numbers whose index is
+a polynomial function of a prime, modulo a power of that prime. Our family
+generalizes many known results, including the von Staudt--Clausen theorem and
+Kummer's congruence.
+",0,0,1,0,0,0
+11403,The Fourier algebra of a rigid $C^{\ast}$-tensor category,"  Completely positive and completely bounded mutlipliers on rigid
+$C^{\ast}$-tensor categories were introduced by Popa and Vaes. Using these
+notions, we define and study the Fourier-Stieltjes algebra, the Fourier algebra
+and the algebra of completely bounded multipliers of a rigid $C^{\ast}$-tensor
+category. The rich structure that these algebras have in the setting of locally
+compact groups is still present in the setting of rigid $C^{\ast}$-tensor
+categories. We also prove that Leptin's characterization of amenability still
+holds in this setting, and we collect some natural observations on property
+(T).
+",0,0,1,0,0,0
+11404,On lattice path matroid polytopes: integer points and Ehrhart polynomial,"  In this paper we investigate the number of integer points lying in dilations
+of lattice path matroid polytopes. We give a characterization of such points as
+polygonal paths in the diagram of the lattice path matroid. Furthermore, we
+prove that lattice path matroid polytopes are affinely equivalent to a family
+of distributive polytopes. As applications we obtain two new infinite families
+of matroids verifying a conjecture of De Loera et.~al. and present an explicit
+formula of the Ehrhart polynomial for one of them.
+",0,0,1,0,0,0
+11405,Quantum effects and magnetism in the spatially distributed DNA molecules,"  Electronic and magnetic properties of DNA structures doped by simple and
+transition d- and f-metal ions (Gd, La, Cu, Zn, Au) are reviewed. Both one- and
+two dimensional systems are considered. A particular attention is paid to
+gadolinium and copper doped DNA systems, their unusual magnetism being treated.
+The problem of classical and quantum transport (including transfer of genetic
+information during replication and transcription) and electron localization in
+biological systems is discussed.
+",0,1,0,0,0,0
+11406,A Stochastic Model for Short-Term Probabilistic Forecast of Solar Photo-Voltaic Power,"  In this paper, a stochastic model with regime switching is developed for
+solar photo-voltaic (PV) power in order to provide short-term probabilistic
+forecasts. The proposed model for solar PV power is physics inspired and
+explicitly incorporates the stochasticity due to clouds using different
+parameters addressing the attenuation in power.Based on the statistical
+behavior of parameters, a simple regime-switching process between the three
+classes of sunny, overcast and partly cloudy is proposed. Then, probabilistic
+forecasts of solar PV power are obtained by identifying the present regime
+using PV power measurements and assuming persistence in this regime. To
+illustrate the technique developed, a set of solar PV power data from a single
+rooftop installation in California is analyzed and the effectiveness of the
+model in fitting the data and in providing short-term point and probabilistic
+forecasts is verified. The proposed forecast method outperforms a variety of
+reference models that produce point and probabilistic forecasts and therefore
+portrays the merits of employing the proposed approach.
+",0,0,0,1,0,0
+11407,Stability and elasticity of metastable solid solutions and superlattices in the MoN-TaN system: a first-principles study,"  Employing ab initio calculations, we discuss chemical, mechanical, and
+dynamical stability of MoN-TaN solid solutions together with cubic-like MoN/TaN
+superlattices, as another materials design concept. Hexagonal-type structures
+based on low-energy modifications of MoN and TaN are the most stable ones over
+the whole composition range. Despite being metastable, disordered cubic
+polymorphs are energetically significantly preferred over their ordered
+counterparts. An in-depth analysis of atomic environments in terms of bond
+lengths and angles reveals that the chemical disorder results in (partially)
+broken symmetry, i.e., the disordered cubic structure relaxes towards a
+hexagonal NiAs-type phase, the ground state of MoN. Surprisingly, also the
+superlattice architecture is clearly favored over the ordered cubic solid
+solution. We show that the bi-axial coherency stresses in superlattices break
+the cubic symmetry beyond simple tetragonal distortions and lead to a new
+tetragonal $\zeta$-phase (space group P4/nmm), which exhibits a more negative
+formation energy than the symmetry-stabilized cubic structures of MoN and TaN.
+Unlike cubic TaN, the $\zeta\text{-TaN}$ is elastically and vibrationally
+stable, while $\zeta$-MoN is stabilized only by the superlattice structure. To
+map compositional trends in elasticity, we establish mechanical stability of
+various Mo$_{1-x}$Ta$_x$N systems and find the closest high-symmetry
+approximants of the corresponding elastic tensors. According to the estimated
+polycrystalline moduli, the hexagonal polymorphs are predicted to be extremely
+hard, however, less ductile than the cubic phases and superlattices. The trends
+in stability based on energetics and elasticity are corroborated by density of
+electronic states.
+",0,1,0,0,0,0
+11408,High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in $^{123}$I-Ioflupane SPECT Imaging,"  Early and accurate identification of parkinsonian syndromes (PS) involving
+presynaptic degeneration from non-degenerative variants such as Scans Without
+Evidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for
+effective patient management as the course, therapy and prognosis differ
+substantially between the two groups. In this study, we use Single Photon
+Emission Computed Tomography (SPECT) images from healthy normal, early PD and
+SWEDD subjects, as obtained from the Parkinson's Progression Markers Initiative
+(PPMI) database, and process them to compute shape- and surface fitting-based
+features for the three groups. We use these features to develop and compare
+various classification models that can discriminate between scans showing
+dopaminergic deficit, as in PD, from scans without the deficit, as in healthy
+normal or SWEDD. Along with it, we also compare these features with Striatal
+Binding Ratio (SBR)-based features, which are well-established and clinically
+used, by computing a feature importance score using Random forests technique.
+We observe that the Support Vector Machine (SVM) classifier gave the best
+performance with an accuracy of 97.29%. These features also showed higher
+importance than the SBR-based features. We infer from the study that shape
+analysis and surface fitting are useful and promising methods for extracting
+discriminatory features that can be used to develop diagnostic models that
+might have the potential to help clinicians in the diagnostic process.
+",1,0,0,1,0,0
+11409,End-to-End Learning for Structured Prediction Energy Networks,"  Structured Prediction Energy Networks (SPENs) are a simple, yet expressive
+family of structured prediction models (Belanger and McCallum, 2016). An energy
+function over candidate structured outputs is given by a deep network, and
+predictions are formed by gradient-based optimization. This paper presents
+end-to-end learning for SPENs, where the energy function is discriminatively
+trained by back-propagating through gradient-based prediction. In our
+experience, the approach is substantially more accurate than the structured SVM
+method of Belanger and McCallum (2016), as it allows us to use more
+sophisticated non-convex energies. We provide a collection of techniques for
+improving the speed, accuracy, and memory requirements of end-to-end SPENs, and
+demonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005
+semantic role labeling tasks. In both, inexact minimization of non-convex SPEN
+energies is superior to baseline methods that use simplistic energy functions
+that can be minimized exactly.
+",1,0,0,1,0,0
+11410,Self-compression of spatially limited laser pulses in a system of coupled light-guides,"  The self-action features of wave packets propagating in a two-dimensional
+system of equidistantly arranged fibers are studied analytically and
+numerically on the basis of the discrete nonlinear Schrödinger equation.
+Self-consistent equations for the characteristic scales of a Gaussian wave
+packet are derived on the basis of the variational approach, which are proved
+numerically for powers $\mathcal{P} < 10 \mathcal{P}_\text{cr}$ exceeding
+slightly the critical one for self-focusing. At higher powers, the wave beams
+become filamented, and their amplitude is limited due to nonlinear breaking of
+the interaction between neighbor light-guides. This make impossible to collect
+a powerful wave beam into the single light-guide. The variational analysis show
+the possibility of adiabatic self-compression of soliton-like laser pulses in
+the process of their three-dimensional self-focusing to the central
+light-guide. However, the further increase of the field amplitude during
+self-compression leads to the longitudinal modulation instability development
+and formation of a set of light bullets in the central fiber. In the regime of
+hollow wave beams, filamentation instability becomes predominant. As a result,
+it becomes possible to form a set of light bullets in optical fibers located on
+the ring.
+",0,1,0,0,0,0
+11411,A Hand-Held Multimedia Translation and Interpretation System with Application to Diet Management,"  We propose a network independent, hand-held system to translate and
+disambiguate foreign restaurant menu items in real-time. The system is based on
+the use of a portable multimedia device, such as a smartphones or a PDA. An
+accurate and fast translation is obtained using a Machine Translation engine
+and a context-specific corpora to which we apply two pre-processing steps,
+called translation standardization and $n$-gram consolidation. The phrase-table
+generated is orders of magnitude lighter than the ones commonly used in market
+applications, thus making translations computationally less expensive, and
+decreasing the battery usage. Translation ambiguities are mitigated using
+multimedia information including images of dishes and ingredients, along with
+ingredient lists. We implemented a prototype of our system on an iPod Touch
+Second Generation for English speakers traveling in Spain. Our tests indicate
+that our translation method yields higher accuracy than translation engines
+such as Google Translate, and does so almost instantaneously. The memory
+requirements of the application, including the database of images, are also
+well within the limits of the device. By combining it with a database of
+nutritional information, our proposed system can be used to help individuals
+who follow a medical diet maintain this diet while traveling.
+",0,0,0,1,0,0
+11412,Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees,"  In this paper, we show how controllers created using data driven designs,
+such as neural networks, can be used together with model based controllers in a
+way that combines the performance guarantees of the model based controllers
+with the efficiency of the data driven controllers. The considered performance
+guarantees include both safety, in terms of avoiding designated unsafe parts of
+the state space, and convergence, in terms of reaching a given beneficial part
+of the state space. Using the framework Behavior Trees, we are able to show how
+this can be done on the top level, concerning just two controllers, as
+described above, but also note that the same approach can be used in arbitrary
+sub-trees. The price for introducing the new controller is that the upper bound
+on the time needed to reach the desired part of the state space increases. The
+approach is illustrated with an inverted pendulum example.
+",1,0,0,0,0,0
+11413,Drop pattern resulting from the breakup of a bidimensional grid of liquid filaments,"  A rectangular grid formed by liquid filaments on a partially wetting
+substrate evolves in a series of breakups leading to arrays of drops with
+different shapes distributed in a rather regular bidimensional pattern. Our
+study is focused on the configuration produced when two long parallel filaments
+of silicone oil, which are placed upon a glass substrate previously coated with
+a fluorinated solution, are crossed perpendicularly by another pair of long
+parallel filaments. A remarkable feature of this kind of grids is that there
+are two qualitatively different types of drops. While one set is formed at the
+crossing points, the rest are consequence of the breakup of shorter filaments
+formed between the crossings. Here, we analyze the main geometric features of
+all types of drops, such as shape of the footprint and contact angle
+distribution along the drop periphery. The formation of a series of short
+filaments with similar geometric and physical properties allows us to have
+simultaneously quasi identical experiments to study the subsequent breakups. We
+develop a simple hydrodynamic model to predict the number of drops that results
+from a filament of given initial length and width. This model is able to yield
+the length intervals corresponding to a small number of drops and its
+predictions are successfully compared with the experimental data as well as
+with numerical simulations of the full Navier--Stokes equation that provide a
+detailed time evolution of the dewetting motion of the filament till the
+breakup into drops. Finally, the prediction for finite filaments is contrasted
+with the existing theories for infinite ones.
+",0,1,0,0,0,0
+11414,FIRED: Frequent Inertial Resets with Diversification for Emerging Commodity Cyber-Physical Systems,"  A Cyber-Physical System (CPS) is defined by its unique characteristics
+involving both the cyber and physical domains. Their hybrid nature introduces
+new attack vectors, but also provides an opportunity to design new security
+defenses. In this paper, we present a new domain-specific security mechanism,
+FIRED, that leverages physical properties such as inertia of the CPS to improve
+security.
+FIRED is simple to describe and implement. It goes through two operations:
+Reset and Diversify, as frequently as possible -- typically in the order of
+seconds or milliseconds. The combined effect of these operations is that
+attackers are unable to gain persistent control of the system. The CPS stays
+safe and stable even under frequent resets because of the inertia present.
+Further, resets simplify certain diversification mechanisms and makes them
+feasible to implement in CPSs with limited computing resources.
+We evaluate our idea on two real-world systems: an engine management unit of
+a car and a flight controller of a quadcopter. Roughly speaking, these two
+systems provide typical and extreme operational requirements for evaluating
+FIRED in terms of stability, algorithmic complexity, and safety requirements.
+We show that FIRED provides robust security guarantees against hijacking
+attacks and persistent CPS threats. We find that our defense is suitable for
+emerging CPS such as commodity unmanned vehicles that are currently unregulated
+and cost sensitive.
+",1,0,0,0,0,0
+11415,Modelling of Dictyostelium Discoideum Movement in Linear Gradient of Chemoattractant,"  Chemotaxis is a ubiquitous biological phenomenon in which cells detect a
+spatial gradient of chemoattractant, and then move towards the source. Here we
+present a position-dependent advection-diffusion model that quantitatively
+describes the statistical features of the chemotactic motion of the social
+amoeba {\it Dictyostelium discoideum} in a linear gradient of cAMP (cyclic
+adenosine monophosphate). We fit the model to experimental trajectories that
+are recorded in a microfluidic setup with stationary cAMP gradients and extract
+the diffusion and drift coefficients in the gradient direction. Our analysis
+shows that for the majority of gradients, both coefficients decrease in time
+and become negative as the cells crawl up the gradient. The extracted model
+parameters also show that besides the expected drift in the direction of
+chemoattractant gradient, we observe a nonlinear dependency of the
+corresponding variance in time, which can be explained by the model.
+Furthermore, the results of the model show that the non-linear term in the mean
+squared displacement of the cell trajectories can dominate the linear term on
+large time scales.
+",0,0,0,0,1,0
+11416,Cycles of Activity in the Jovian Atmosphere,"  Jupiter's banded appearance may appear unchanging to the casual observer, but
+closer inspection reveals a dynamic, ever-changing system of belts and zones
+with distinct cycles of activity. Identification of these long-term cycles
+requires access to datasets spanning multiple jovian years, but explaining them
+requires multi-spectral characterization of the thermal, chemical, and aerosol
+changes associated with visible color variations. The Earth-based support
+campaign for Juno's exploration of Jupiter has already characterized two
+upheaval events in the equatorial and temperate belts that are part of
+long-term jovian cycles, whose underlying sources could be revealed by Juno's
+exploration of Jupiter's deep atmosphere.
+",0,1,0,0,0,0
+11417,Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information,"  This paper offers a methodological contribution at the intersection of
+machine learning and operations research. Namely, we propose a methodology to
+quickly predict tactical solutions to a given operational problem. In this
+context, the tactical solution is less detailed than the operational one but it
+has to be computed in very short time and under imperfect information. The
+problem is of importance in various applications where tactical and operational
+planning problems are interrelated and information about the operational
+problem is revealed over time. This is for instance the case in certain
+capacity planning and demand management systems.
+We formulate the problem as a two-stage optimal prediction stochastic program
+whose solution we predict with a supervised machine learning algorithm. The
+training data set consists of a large number of deterministic (second stage)
+problems generated by controlled probabilistic sampling. The labels are
+computed based on solutions to the deterministic problems (solved independently
+and offline) employing appropriate aggregation and subselection methods to
+address uncertainty. Results on our motivating application in load planning for
+rail transportation show that deep learning algorithms produce highly accurate
+predictions in very short computing time (milliseconds or less). The prediction
+accuracy is comparable to solutions computed by sample average approximation of
+the stochastic program.
+",1,0,0,1,0,0
+11418,Consistent Approval-Based Multi-Winner Rules,"  This paper is an axiomatic study of consistent approval-based multi-winner
+rules, i.e., voting rules that select a fixed-size group of candidates based on
+approval ballots. We introduce the class of counting rules, provide an
+axiomatic characterization of this class and, in particular, show that counting
+rules are consistent. Building upon this result, we axiomatically characterize
+three important consistent multi-winner rules: Proportional Approval Voting,
+Multi-Winner Approval Voting and the Approval Chamberlin-Courant rule. Our
+results demonstrate the variety of multi-winner rules and illustrate three
+different, orthogonal principles that multi-winner voting rules may represent:
+individual excellence, diversity, and proportionality.
+",1,0,0,0,0,0
+11419,Bridge functional for the molecular density functional theory with consistent pressure and surface tension and its importance for solvation in water,"  We address the problem of predicting the solvation free energy and
+equilibrium solvent density profile in fews minutes from the molecular density
+functional theory beyond the usual hypernetted-chain approximation. We
+introduce a bridge functional of a coarse-grained, weighted solvent density. In
+few minutes at most, for solutes of sizes ranging from small compounds to large
+proteins, we produce (i) an estimation of the free energy of solvation within 1
+kcal/mol of the experimental data for the hydrophobic solutes presented here,
+and (ii) the solvent distribution around the solute. Contrary to previous
+propositions, this bridge functional is thermodynamically consistent in that it
+produces the correct liquid-vapor coexistence and the experimental surface
+tension. We show this consistency to be of crucial importance for water at room
+temperature and pressure. This bridge functional is designed to be simple,
+local, and thus numerically efficient. Finally, we illustrate this new level of
+molecular theory of solutions with the study of the hydration shell of a
+protein.
+",0,1,0,0,0,0
+11420,Fourier multiplier theorems for Triebel-Lizorkin spaces,"  In this paper we study sharp generalizations of $\dot{F}_p^{0,q}$ multiplier
+theorem of Mikhlin-Hörmander type. The class of multipliers that we consider
+involves Herz spaces $K_u^{s,t}$. Plancherel's theorem proves
+$\widehat{L_s^2}=K_2^{s,2}$ and we study the optimal triple $(u,t,s)$ for which
+$\sup_{k\in\mathbb{Z}}{\big\Vert \big(
+m(2^k\cdot)\varphi\big)^{\vee}\big\Vert_{K_u^{s,t}}}<\infty$ implies
+$\dot{F}_p^{0,q}$ boundedness of multiplier operator $T_m$ where $\varphi$ is a
+cutoff function. Our result also covers the $BMO$-type space
+$\dot{F}_{\infty}^{0,q}$.
+",0,0,1,0,0,0
+11421,Individual dynamic predictions using landmarking and joint modelling: validation of estimators and robustness assessment,"  After the diagnosis of a disease, one major objective is to predict
+cumulative probabilities of events such as clinical relapse or death from the
+individual information collected up to a prediction time, including usually
+biomarker repeated measurements. Several competing estimators have been
+proposed to calculate these individual dynamic predictions, mainly from two
+approaches: joint modelling and landmarking. These approaches differ by the
+information used, the model assumptions and the complexity of the computational
+procedures. It is essential to properly validate the estimators derived from
+joint models and landmark models, quantify their variability and compare them
+in order to provide key elements for the development and use of individual
+dynamic predictions in clinical follow-up of patients. Motivated by the
+prediction of two competing causes of progression of prostate cancer from the
+history of prostate-specific antigen, we conducted an in-depth simulation study
+to validate and compare the dynamic predictions derived from these two methods.
+Specifically, we formally defined the quantity to estimate and its estimators,
+proposed techniques to assess the uncertainty around predictions and validated
+them. We also compared the individual dynamic predictions derived from joint
+models and landmark models in terms of prediction error, discriminatory power,
+efficiency and robustness to model assumptions. We show that these prediction
+tools should be handled with care, in particular by properly specifying models
+and estimators.
+",0,0,0,1,0,0
+11422,A partial converse to the Andreotti-Grauert theorem,"  Let $X$ be a smooth projective manifold with $\dim_\mathbb{C} X=n$. We show
+that if a line bundle $L$ is $(n-1)$-ample, then it is $(n-1)$-positive. This
+is a partial converse to the Andreotti-Grauert theorem. As an application, we
+show that a projective manifold $X$ is uniruled if and only if there exists a
+Hermitian metric $\omega$ on $X$ such that its Ricci curvature
+$\mathrm{Ric}(\omega)$ has at least one positive eigenvalue everywhere.
+",0,0,1,0,0,0
+11423,"Ab initio study of magnetocrystalline anisotropy, magnetostriction, and Fermi surface of L10 FeNi (tetrataenite)","  The ordered L1$_0$ FeNi phase (tetrataenite) is recently considered as a
+promising candidate for the rare-earth free permanent magnets applications. In
+this work we calculate several characteristics of the L1$_0$ FeNi, where most
+of the results come form the fully relativistic full potential FPLO method with
+the generalized gradient approximation (GGA). A special attention deserves the
+summary of the magnetocrystalline anisotropy energies (MAE's), the full
+potential calculations of the anisotropy constant $K_3$, and the combined
+analysis of the Fermi surface and three-dimensional $\mathbf{k}$-resolved MAE.
+Other calculated parameters presented in this article are the magnetic moments
+$m_{s}$ and $m_{l}$, magnetostrictive coefficient $\lambda_{001}$, bulk modulus
+B$_0$, and lattice parameters. The MAE's summary shows rather big discrepancies
+between the experimental MAE's from literature and also between the calculated
+MAE's.
+",0,1,0,0,0,0
+11424,"Modular groups, Hurwitz classes and dynamic portraits of NET maps","  An orientation-preserving branched covering $f: S^2 \to S^2$ is a nearly
+Euclidean Thurston (NET) map if each critical point is simple and its
+postcritical set has exactly four points. Inspired by classical, non-dynamical
+notions such as Hurwitz equivalence of branched covers of surfaces, we develop
+invariants for such maps. We then apply these notions to the classification and
+enumeration of NET maps. As an application, we obtain a complete classification
+of the dynamic critical orbit portraits of NET maps.
+",0,0,1,0,0,0
+11425,Proximal Planar Shape Signatures. Homology Nerves and Descriptive Proximity,"  This article introduces planar shape signatures derived from homology nerves,
+which are intersecting 1-cycles in a collection of homology groups endowed with
+a proximal relator (set of nearness relations) that includes a descriptive
+proximity. A 1-cycle is a closed, connected path with a zero boundary in a
+simplicial complex covering a finite, bounded planar shape. The signature of a
+shape sh A (denoted by sig(sh A)) is a feature vector that describes sh A. A
+signature sig(sh A) is derived from the geometry, homology nerves, Betti
+number, and descriptive CW topology on the shape sh A. Several main results are
+given, namely, (a) every finite, bounded planar shape has a signature derived
+from the homology group on the shape, (b) a homology group equipped with a
+proximal relator defines a descriptive Leader uniform topology and (c) a
+description of a homology nerve and union of the descriptions of the 1-cycles
+in the nerve have same homotopy type.
+",0,0,1,0,0,0
+11426,Asymptotic analysis of a 2D overhead crane with input delays in the boundary control,"  The paper investigates the asymptotic behavior of a 2D overhead crane with
+input delays in the boundary control. A linear boundary control is proposed.
+The main feature of such a control lies in the facts that it solely depends on
+the velocity but under the presence of time-delays. We end-up with a
+closed-loop system where no displacement term is involved. It is shown that the
+problem is well-posed in the sense of semigroups theory. LaSalle's invariance
+principle is invoked in order to establish the asymptotic convergence for the
+solutions of the system to a stationary position which depends on the initial
+data. Using a resolvent method it is proved that the convergence is indeed
+polynomial.
+",0,0,1,0,0,0
+11427,Abrupt disappearance and reemergence of the SU(2) and SU(4) Kondo effects due to population inversion,"  The interplay of almost degenerate levels in quantum dots and molecular
+junctions with possibly different couplings to the reservoirs has lead to many
+observable phenomena, such as the Fano effect, transmission phase slips and the
+SU(4) Kondo effect. Here we predict a dramatic repeated disappearance and
+reemergence of the SU(4) and anomalous SU(2) Kondo effects with increasing gate
+voltage. This phenomenon is attributed to the level occupation switching which
+has been previously invoked to explain the universal transmission phase slips
+in the conductance through a quantum dot. We use analytical arguments and
+numerical renormalization group calculations to explain the observations and
+discuss their experimental relevance and dependence on the physical parameters.
+",0,1,0,0,0,0
+11428,Stochastic Global Optimization Algorithms: A Systematic Formal Approach,"  As we know, some global optimization problems cannot be solved using analytic
+methods, so numeric/algorithmic approaches are used to find near to the optimal
+solutions for them. A stochastic global optimization algorithm (SGoal) is an
+iterative algorithm that generates a new population (a set of candidate
+solutions) from a previous population using stochastic operations. Although
+some research works have formalized SGoals using Markov kernels, such
+formalization is not general and sometimes is blurred. In this paper, we
+propose a comprehensive and systematic formal approach for studying SGoals.
+First, we present the required theory of probability (\sigma-algebras,
+measurable functions, kernel, markov chain, products, convergence and so on)
+and prove that some algorithmic functions like swapping and projection can be
+represented by kernels. Then, we introduce the notion of join-kernel as a way
+of characterizing the combination of stochastic methods. Next, we define the
+optimization space, a formal structure (a set with a \sigma-algebra that
+contains strict \epsilon-optimal states) for studying SGoals, and we develop
+kernels, like sort and permutation, on such structure. Finally, we present some
+popular SGoals in terms of the developed theory, we introduce sufficient
+conditions for convergence of a SGoal, and we prove convergence of some popular
+SGoals.
+",1,0,1,0,0,0
+11429,A complete characterization of optimal dictionaries for least squares representation,"  Dictionaries are collections of vectors used for representations of elements
+in Euclidean spaces. While recent research on optimal dictionaries is focussed
+on providing sparse (i.e., $\ell_0$-optimal,) representations, here we consider
+the problem of finding optimal dictionaries such that representations of
+samples of a random vector are optimal in an $\ell_2$-sense. For us, optimality
+of representation is equivalent to minimization of the average $\ell_2$-norm of
+the coefficients used to represent the random vector, with the lengths of the
+dictionary vectors being specified a priori. With the help of recent results on
+rank-$1$ decompositions of symmetric positive semidefinite matrices and the
+theory of majorization, we provide a complete characterization of
+$\ell_2$-optimal dictionaries. Our results are accompanied by polynomial time
+algorithms that construct $\ell_2$-optimal dictionaries from given data.
+",1,0,0,1,0,0
+11430,Least Square Variational Bayesian Autoencoder with Regularization,"  In recent years Variation Autoencoders have become one of the most popular
+unsupervised learning of complicated distributions.Variational Autoencoder
+(VAE) provides more efficient reconstructive performance over a traditional
+autoencoder. Variational auto enocders make better approximaiton than MCMC. The
+VAE defines a generative process in terms of ancestral sampling through a
+cascade of hidden stochastic layers. They are a directed graphic models.
+Variational autoencoder is trained to maximise the variational lower bound.
+Here we are trying maximise the likelihood and also at the same time we are
+trying to make a good approximation of the data. Its basically trading of the
+data log-likelihood and the KL divergence from the true posterior. This paper
+describes the scenario in which we wish to find a point-estimate to the
+parameters $\theta$ of some parametric model in which we generate each
+observations by first sampling a local latent variable and then sampling the
+associated observation. Here we use least square loss function with
+regularization in the the reconstruction of the image, the least square loss
+function was found to give better reconstructed images and had a faster
+training time.
+",1,0,0,1,0,0
+11431,Rational points of rationally simply connected varieties over global function fields,"  A complex projective manifold is rationally connected, resp. rationally
+simply connected, if finite subsets are connected by a rational curve, resp.
+the spaces parameterizing these connecting rational curves are themselves
+rationally connected. We prove that a projective scheme over a global function
+field with vanishing ""elementary obstruction"" has a rational point if it
+deforms to a rationally simply connected variety in characteristic 0. This
+gives new, uniform proofs over these fields of the Period-Index Theorem, the
+quasi-split case of Serre's ""Conjecture II"", and Lang's $C_2$ property.
+",0,0,1,0,0,0
+11432,A Koszul sign map,"  We define a Koszul sign map encoding the Koszul sign convention. A
+cohomological interpretation is given.
+",0,0,1,0,0,0
+11433,Combining low- to high-resolution transit spectroscopy of HD 189733b. Linking the troposphere and the thermosphere of a hot gas giant,"  Space-borne low-to medium-resolution (R~10^2-10^3) transmission spectroscopy
+of atmospheres detect the broadest spectral features (alkali doublets,
+molecular bands, scattering), while high-resolution (R~10^5), ground-based
+observations probe the sharpest features (cores of the alkali lines, molecular
+lines).The two techniques differ by:(1) The LSF of ground-based observations is
+10^3 times narrower than for space-borne observations;(2)Space-borne
+transmission spectra probe up to the base of thermosphere, while ground-based
+observations can reach pressures down to 10^(-11);(3)Space-borne observations
+directly yield the transit depth of the planet, while ground-based observations
+measure differences in the radius of the planet at different wavelengths.It is
+challenging to combine both techniques.We develop a method to compare
+theoretical models with observations at different resolutions.We introduce
+PyETA, a line-by-line 1D radiative transfer code to compute transmission
+spectra at R~10^6 (0.01 A) over a broad wavelength range.An hybrid forward
+modeling/retrieval optimization scheme is devised to deal with the large
+computational resources required by modeling a broad wavelength range (0.3-2
+$\mu$m) at high resolution.We apply our technique to HD189733b.Here, HST
+observations reveal a flattened spectrum due to scattering by aerosols, while
+high-resolution ground-based HARPS observations reveal the sharp cores of
+sodium lines.We reconcile these results by building models that reproduce
+simultaneously both data sets, from the troposphere to the thermosphere. We
+confirm:(1)the presence of scattering by tropospheric aerosols;(2)that the
+sodium core feature is of thermospheric origin.Accounting for aerosols, the
+sodium cores indicate T up to 10000K in the thermosphere.The precise value of
+the thermospheric temperature is degenerate with the abundance of sodium and
+altitude of the aerosol deck.
+",0,1,0,0,0,0
+11434,Factorizations in Modules and Splitting Multiplicatively Closed Subsets,"  We introduce the concept of multiplicatively closed subsets of a commutative
+ring $R$ which split an $R$-module $M$ and study factorization properties of
+elements of $M$ with respect to such a set. Also we demonstrate how one can
+utilize this concept to investigate factorization properties of $R$ and deduce
+some Nagata type theorems relating factorization properties of $R$ to those of
+its localizations, when $R$ is an integral domain.
+",0,0,1,0,0,0
+11435,"ROPPERI - A TPC readout with GEMs, pads and Timepix","  The concept of a hybrid readout of a time projection chamber is presented. It
+combines a GEM-based amplification and a pad-based anode plane with a pixel
+chip as readout electronics. This way, a high granularity enabling to identify
+electron clusters from the primary ionisation is achieved as well as
+flexibility and large anode coverage. The benefits of this high granularity, in
+particular for dE/dx measurements, are outlined and the current software and
+hardware development status towards a proof-of-principle is given.
+",0,1,0,0,0,0
+11436,A Shared Task on Bandit Learning for Machine Translation,"  We introduce and describe the results of a novel shared task on bandit
+learning for machine translation. The task was organized jointly by Amazon and
+Heidelberg University for the first time at the Second Conference on Machine
+Translation (WMT 2017). The goal of the task is to encourage research on
+learning machine translation from weak user feedback instead of human
+references or post-edits. On each of a sequence of rounds, a machine
+translation system is required to propose a translation for an input, and
+receives a real-valued estimate of the quality of the proposed translation for
+learning. This paper describes the shared task's learning and evaluation setup,
+using services hosted on Amazon Web Services (AWS), the data and evaluation
+metrics, and the results of various machine translation architectures and
+learning protocols.
+",1,0,0,1,0,0
+11437,Double Sparsity Kernel Learning with Automatic Variable Selection and Data Extraction,"  Learning with Reproducing Kernel Hilbert Spaces (RKHS) has been widely used
+in many scientific disciplines. Because a RKHS can be very flexible, it is
+common to impose a regularization term in the optimization to prevent
+overfitting. Standard RKHS learning employs the squared norm penalty of the
+learning function. Despite its success, many challenges remain. In particular,
+one cannot directly use the squared norm penalty for variable selection or data
+extraction. Therefore, when there exists noise predictors, or the underlying
+function has a sparse representation in the dual space, the performance of
+standard RKHS learning can be suboptimal. In the literature,work has been
+proposed on how to perform variable selection in RKHS learning, and a data
+sparsity constraint was considered for data extraction. However, how to learn
+in a RKHS with both variable selection and data extraction simultaneously
+remains unclear. In this paper, we propose a unified RKHS learning method,
+namely, DOuble Sparsity Kernel (DOSK) learning, to overcome this challenge. An
+efficient algorithm is provided to solve the corresponding optimization
+problem. We prove that under certain conditions, our new method can
+asymptotically achieve variable selection consistency. Simulated and real data
+results demonstrate that DOSK is highly competitive among existing approaches
+for RKHS learning.
+",0,0,0,1,0,0
+11438,Controlling of blow-up responses by a nonlinear $\cal{PT}$ symmetric coupling,"  We investigate the dynamics of a coupled waveguide system with competing
+linear and nonlinear loss-gain profiles which can facilitate power saturation.
+We show the usefulness of the model in achieving unidirectional beam
+propagation. In this regard, the considered type of coupled waveguide system
+has two drawbacks, (i) difficulty in achieving perfect isolation of light in a
+waveguide and (ii) existence of blow-up type behavior for certain input power
+situations. We here show a nonlinear $\cal{PT}$ symmetric coupling that helps
+to overcome these two drawbacks. Such a nonlinear coupling has close connection
+with the phenomenon of stimulated Raman scattering. In particular, we have
+elucidated the role of this nonlinear coupling using an integrable $\cal{PT}$
+symmetric situation. In particular, using the integrals of motion, we have
+reduced this coupled waveguide problem to the problem of dynamics of a particle
+in a potential. With the latter picture, we have clearly illustrated the role
+of the considered nonlinear coupling. The above $\cal{PT}$ symmetric case
+corresponds to a limiting form of a general equation describing the phenomenon
+of stimulated Raman scattering. We also point out the ability to transport
+light unidirectionally even in this general case.
+",0,1,0,0,0,0
+11439,Text Extraction From Texture Images Using Masked Signal Decomposition,"  Text extraction is an important problem in image processing with applications
+from optical character recognition to autonomous driving. Most of the
+traditional text segmentation algorithms consider separating text from a simple
+background (which usually has a different color from texts). In this work we
+consider separating texts from a textured background, that has similar color to
+texts. We look at this problem from a signal decomposition perspective, and
+consider a more realistic scenario where signal components are overlaid on top
+of each other (instead of adding together). When the signals are overlaid, to
+separate signal components, we need to find a binary mask which shows the
+support of each component. Because directly solving the binary mask is
+intractable, we relax this problem to the approximated continuous problem, and
+solve it by alternating optimization method. We show that the proposed
+algorithm achieves significantly better results than other recent works on
+several challenging images.
+",1,0,0,0,0,0
+11440,Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting,"  Long-lead forecasting for spatio-temporal systems can often entail complex
+nonlinear dynamics that are difficult to specify it a priori. Current
+statistical methodologies for modeling these processes are often highly
+parameterized and thus, challenging to implement from a computational
+perspective. One potential parsimonious solution to this problem is a method
+from the dynamical systems and engineering literature referred to as an echo
+state network (ESN). ESN models use so-called {\it reservoir computing} to
+efficiently compute recurrent neural network (RNN) forecasts. Moreover,
+so-called ""deep"" models have recently been shown to be successful at predicting
+high-dimensional complex nonlinear processes, particularly those with multiple
+spatial and temporal scales of variability (such as we often find in
+spatio-temporal environmental data). Here we introduce a deep ensemble ESN
+(D-EESN) model. We present two versions of this model for spatio-temporal
+processes that both produce forecasts and associated measures of uncertainty.
+The first approach utilizes a bootstrap ensemble framework and the second is
+developed within a hierarchical Bayesian framework (BD-EESN). This more general
+hierarchical Bayesian framework naturally accommodates non-Gaussian data types
+and multiple levels of uncertainties. The methodology is first applied to a
+data set simulated from a novel non-Gaussian multiscale Lorenz-96 dynamical
+system simulation model and then to a long-lead United States (U.S.) soil
+moisture forecasting application.
+",0,0,0,1,0,0
+11441,Using Convex Optimization of Autocorrelation with Constrained Support and Windowing for Improved Phase Retrieval Accuracy,"  In imaging modalities recording diffraction data, the original image can be
+reconstructed assuming known phases. When phases are unknown, oversampling and
+a constraint on the support region in the original object can be used to solve
+a non-convex optimization problem.
+Such schemes are ill-suited to find the optimum solution for sparse data,
+since the recorded image does not correspond exactly to the original wave
+function. We construct a convex optimization problem using a relaxed support
+constraint and a maximum-likelihood treatment of the recorded data as a sample
+from the underlying wave function. We also stress the need to use relevant
+windowing techniques to account for the sampled pattern being finite.
+On simulated data, we demonstrate the benefits of our approach in terms of
+visual quality and an improvement in the crystallographic R-factor from .4 to
+.1 for highly noisy data.
+",1,0,0,0,0,0
+11442,"Parametrizations, weights, and optimal prediction: Part 1","  We consider the problem of the annual mean temperature prediction. The years
+taken into account and the corresponding annual mean temperatures are denoted
+by $0,\ldots, n$ and $t_0$, $\ldots$, $t_n$, respectively. We propose to
+predict the temperature $t_{n+1}$ using the data $t_0$, $\ldots$, $t_n$. For
+each $0\leq l\leq n$ and each parametrization $\Theta^{(l)}$ of the Euclidean
+space $\mathbb{R}^{l+1}$ we construct a list of weights for the data
+$\{t_0,\ldots, t_l\}$ based on the rows of $\Theta^{(l)}$ which are correlated
+with the constant trend. Using these weights we define a list of predictors of
+$t_{l+1}$ from the data $t_0$, $\ldots$, $t_l$. We analyse how the
+parametrization affects the prediction, and provide three optimality criteria
+for the selection of weights and parametrization. We illustrate our results for
+the annual mean temperature of France and Morocco.
+",0,0,0,1,0,0
+11443,Time irreversibility from symplectic non-squeezing,"  The issue of how time reversible microscopic dynamics gives rise to
+macroscopic irreversible processes has been a recurrent issue in Physics since
+the time of Boltzmann whose ideas shaped, and essentially resolved, such an
+apparent contradiction. Following Boltzmann's spirit and ideas, but employing
+Gibbs's approach, we advance the view that macroscopic irreversibility of
+Hamiltonian systems of many degrees of freedom can be also seen as a result of
+the symplectic non-squeezing theorem.
+",0,1,1,0,0,0
+11444,On Optimal Weighted-Delay Scheduling in Input-Queued Switches,"  Motivated by relatively few delay-optimal scheduling results, in comparison
+to results on throughput optimality, we investigate an input-queued switch
+scheduling problem in which the objective is to minimize a linear function of
+the queue-length vector. Theoretical properties of variants of the well-known
+MaxWeight scheduling algorithm are established within this context, which
+includes showing that these algorithms exhibit optimal heavy-traffic
+queue-length scaling. For the case of $2 \times 2$ input-queued switches, we
+derive an optimal scheduling policy and establish its theoretical properties,
+demonstrating fundamental differences with the variants of MaxWeight
+scheduling. Our theoretical results are expected to be of interest more broadly
+than input-queued switches. Computational experiments demonstrate and quantify
+the benefits of our optimal scheduling policy.
+",0,0,1,0,0,0
+11445,"Hausdorff dimension, projections, intersections, and Besicovitch sets","  This is a survey on recent developments on the Hausdorff dimension of
+projections and intersections for general subsets of Euclidean spaces, with an
+emphasis on estimates of the Hausdorff dimension of exceptional sets and on
+restricted projection families. We shall also discuss relations between
+projections and Hausdorff dimension of Besicovitch sets.
+",0,0,1,0,0,0
+11446,Interpolating between matching and hedonic pricing models,"  We consider the theoretical properties of a model which encompasses
+bi-partite matching under transferable utility on the one hand, and hedonic
+pricing on the other. This framework is intimately connected to tripartite
+matching problems (known as multi-marginal optimal transport problems in the
+mathematical literature). We exploit this relationship in two ways; first, we
+show that a known structural result from multi-marginal optimal transport can
+be used to establish an upper bound on the dimension of the support of stable
+matchings. Next, assuming the distribution of agents on one side of the market
+is continuous, we identify a condition on their preferences that ensures purity
+and uniqueness of the stable matching; this condition is a variant of a known
+condition in the mathematical literature, which guarantees analogous properties
+in the multi-marginal optimal transport problem. We exhibit several examples of
+surplus functions for which our condition is satisfied, as well as some for
+which it fails.
+",0,0,1,0,0,0
+11447,Modeling and predicting the short term evolution of the Geomagnetic field,"  The coupled evolution of the magnetic field and the flow at the Earth's core
+mantle boundary is modeled within the 1900.0-2014.0 time period. To constraint
+the dynamical behavior of the system with a core field model deriving from
+direct measurements of the Earth's magnetic field we used an Ensemble Kalman
+filter algorithm. By simulating an ensemble of possible states, access to the
+complete statistical properties of the considered fields is available.
+Furthermore, the method enables to provide predictions and to assess their
+reliability. In this study, we could highlight the cohabitation of two distinct
+flow regimes. One associated with the large scale part of the eccentric gyre,
+which evolves slowly in time and posses a very long memory of its past, and a
+faster one associated with the small scale velocity field. We show that the
+latter can exhibit rapid variations in localized areas. The combination of the
+two regimes can predict quite well the decadal variations in length of day, but
+it can also explain the discrepancies between the physically predicted and the
+observed trend in these variations. Hindcast tests demonstrate that the model
+is well balanced and that it can provide accurate short term predictions of a
+mean state and its associated uncertainties. However, magnetic field
+predictions are limited by the high randomization rate of the different
+velocity field scales, and after approximately 2000 years of forecast, no
+reliable information on the core field can be recovered.
+",0,1,0,0,0,0
+11448,Analysing the Potential of BLE to Support Dynamic Broadcasting Scenarios,"  In this paper, we present a novel approach for broadcasting information based
+on a Bluetooth Low Energy (BLE) ibeacon technology. We propose a dynamic method
+that uses a combination of Wi-Fi and BLE technology where every technology
+plays a part in a user discovery and broadcasting process. In such system, a
+specific ibeacon device broadcasts the information when a user is in proximity.
+Using experiments, we conduct a scenario where the system discovers users,
+disseminates information, and later we use collected data to examine the system
+performance and capability. The results show that our proposed approach has a
+promising potential to become a powerful tool in the discovery and broadcasting
+concept that can be easily implemented and used in business environments.
+",1,0,0,0,0,0
+11449,$\texttt{PyTranSpot}$ - A tool for multiband light curve modeling of planetary transits and stellar spots,"  Several studies have shown that stellar activity features, such as occulted
+and non-occulted starspots, can affect the measurement of transit parameters
+biasing studies of transit timing variations and transmission spectra. We
+present $\texttt{PyTranSpot}$, which we designed to model multiband transit
+light curves showing starspot anomalies, inferring both transit and spot
+parameters. The code follows a pixellation approach to model the star with its
+corresponding limb darkening, spots, and transiting planet on a two dimensional
+Cartesian coordinate grid. We combine $\texttt{PyTranSpot}$ with an MCMC
+framework to study and derive exoplanet transmission spectra, which provides
+statistically robust values for the physical properties and uncertainties of a
+transiting star-planet system. We validate $\texttt{PyTranSpot}$'s performance
+by analyzing eleven synthetic light curves of four different star-planet
+systems and 20 transit light curves of the well-studied WASP-41b system. We
+also investigate the impact of starspots on transit parameters and derive
+wavelength dependent transit depth values for WASP-41b covering a range of
+6200-9200 $\AA$, indicating a flat transmission spectrum.
+",0,1,0,0,0,0
+11450,Interplay of spatial dynamics and local adaptation shapes species lifetime distributions and species-area relationships,"  The distributions of species lifetimes and species in space are related,
+since species with good local survival chances have more time to colonize new
+habitats and species inhabiting large areas have higher chances to survive
+local disturbances. Yet, both distributions have been discussed in mostly
+separate communities. Here, we study both patterns simultaneously using a
+spatially explicit, evolutionary community assembly approach. We present and
+investigate a metacommunity model, consisting of a grid of patches, where each
+patch contains a local food web. Species survival depends on predation and
+competition interactions, which in turn depend on species body masses as the
+key traits. The system evolves due to the migration of species to neighboring
+patches, the addition of new species as modifications of existing species, and
+local extinction events. The structure of each local food web thus emerges in a
+self-organized manner as the highly non-trivial outcome of the relative time
+scales of these processes. Our model generates a large variety of complex,
+multi-trophic networks and therefore serves as a powerful tool to investigate
+ecosystems on long temporal and large spatial scales. We find that the observed
+lifetime distributions and species-area relations resemble power laws over
+appropriately chosen parameter ranges and thus agree qualitatively with
+empirical findings. Moreover, we observe strong finite-size effects, and a
+dependence of the relationships on the trophic level of the species. By
+comparing our results to simple neutral models found in the literature, we
+identify the features that are responsible for the values of the exponents.
+",0,0,0,0,1,0
+11451,Routing Symmetric Demands in Directed Minor-Free Graphs with Constant Congestion,"  The problem of routing in graphs using node-disjoint paths has received a lot
+of attention and a polylogarithmic approximation algorithm with constant
+congestion is known for undirected graphs [Chuzhoy and Li 2016] and [Chekuri
+and Ene 2013]. However, the problem is hard to approximate within polynomial
+factors on directed graphs, for any constant congestion [Chuzhoy, Kim and Li
+2016].
+Recently, [Chekuri, Ene and Pilipczuk 2016] have obtained a polylogarithmic
+approximation with constant congestion on directed planar graphs, for the
+special case of symmetric demands. We extend their result by obtaining a
+polylogarithmic approximation with constant congestion on arbitrary directed
+minor-free graphs, for the case of symmetric demands.
+",1,0,0,0,0,0
+11452,Fast sampling of parameterised Gaussian random fields,"  Gaussian random fields are popular models for spatially varying
+uncertainties, arising for instance in geotechnical engineering, hydrology or
+image processing. A Gaussian random field is fully characterised by its mean
+function and covariance operator. In more complex models these can also be
+partially unknown. In this case we need to handle a family of Gaussian random
+fields indexed with hyperparameters. Sampling for a fixed configuration of
+hyperparameters is already very expensive due to the nonlocal nature of many
+classical covariance operators. Sampling from multiple configurations increases
+the total computational cost severely. In this report we employ parameterised
+Karhunen-Loève expansions for sampling. To reduce the cost we construct a
+reduced basis surrogate built from snapshots of Karhunen-Loève eigenvectors.
+In particular, we consider Matérn-type covariance operators with unknown
+correlation length and standard deviation. We suggest a linearisation of the
+covariance function and describe the associated online-offline decomposition.
+In numerical experiments we investigate the approximation error of the reduced
+eigenpairs. As an application we consider forward uncertainty propagation and
+Bayesian inversion with an elliptic partial differential equation where the
+logarithm of the diffusion coefficient is a parameterised Gaussian random
+field. In the Bayesian inverse problem we employ Markov chain Monte Carlo on
+the reduced space to generate samples from the posterior measure. All numerical
+experiments are conducted in 2D physical space, with non-separable covariance
+operators, and finite element grids with $\sim 10^4$ degrees of freedom.
+",1,0,0,1,0,0
+11453,Universal Scalable Robust Solvers from Computational Information Games and fast eigenspace adapted Multiresolution Analysis,"  We show how the discovery of robust scalable numerical solvers for arbitrary
+bounded linear operators can be automated as a Game Theory problem by
+reformulating the process of computing with partial information and limited
+resources as that of playing underlying hierarchies of adversarial information
+games. When the solution space is a Banach space $B$ endowed with a quadratic
+norm $\|\cdot\|$, the optimal measure (mixed strategy) for such games (e.g. the
+adversarial recovery of $u\in B$, given partial measurements $[\phi_i, u]$ with
+$\phi_i\in B^*$, using relative error in $\|\cdot\|$-norm as a loss) is a
+centered Gaussian field $\xi$ solely determined by the norm $\|\cdot\|$, whose
+conditioning (on measurements) produces optimal bets. When measurements are
+hierarchical, the process of conditioning this Gaussian field produces a
+hierarchy of elementary bets (gamblets). These gamblets generalize the notion
+of Wavelets and Wannier functions in the sense that they are adapted to the
+norm $\|\cdot\|$ and induce a multi-resolution decomposition of $B$ that is
+adapted to the eigensubspaces of the operator defining the norm $\|\cdot\|$.
+When the operator is localized, we show that the resulting gamblets are
+localized both in space and frequency and introduce the Fast Gamblet Transform
+(FGT) with rigorous accuracy and (near-linear) complexity estimates. As the FFT
+can be used to solve and diagonalize arbitrary PDEs with constant coefficients,
+the FGT can be used to decompose a wide range of continuous linear operators
+(including arbitrary continuous linear bijections from $H^s_0$ to $H^{-s}$ or
+to $L^2$) into a sequence of independent linear systems with uniformly bounded
+condition numbers and leads to $\mathcal{O}(N \operatorname{polylog} N)$
+solvers and eigenspace adapted Multiresolution Analysis (resulting in near
+linear complexity approximation of all eigensubspaces).
+",0,0,1,1,0,0
+11454,Preliminary Experiments using Subjective Logic for the Polyrepresentation of Information Needs,"  According to the principle of polyrepresentation, retrieval accuracy may
+improve through the combination of multiple and diverse information object
+representations about e.g. the context of the user, the information sought, or
+the retrieval system. Recently, the principle of polyrepresentation was
+mathematically expressed using subjective logic, where the potential
+suitability of each representation for improving retrieval performance was
+formalised through degrees of belief and uncertainty. No experimental evidence
+or practical application has so far validated this model. We extend the work of
+Lioma et al. (2010), by providing a practical application and analysis of the
+model. We show how to map the abstract notions of belief and uncertainty to
+real-life evidence drawn from a retrieval dataset. We also show how to estimate
+two different types of polyrepresentation assuming either (a) independence or
+(b) dependence between the information objects that are combined. We focus on
+the polyrepresentation of different types of context relating to user
+information needs (i.e. work task, user background knowledge, ideal answer) and
+show that the subjective logic model can predict their optimal combination
+prior and independently to the retrieval process.
+",1,0,0,0,0,0
+11455,General Robust Bayes Pseudo-Posterior: Exponential Convergence results with Applications,"  Although Bayesian inference is an immensely popular paradigm among a large
+segment of scientists including statisticians, most of the applications
+consider the objective priors and need critical investigations (Efron, 2013,
+Science). And although it has several optimal properties, one major drawback of
+Bayesian inference is the lack of robustness against data contamination and
+model misspecification, which becomes pernicious in the use of objective
+priors. This paper presents the general formulation of a Bayes pseudo-posterior
+distribution yielding robust inference. Exponential convergence results related
+to the new pseudo-posterior and the corresponding Bayes estimators are
+established under the general parametric set-up and illustrations are provided
+for the independent stationary models and the independent non-homogenous
+models. For the first case, the discrete priors and the corresponding maximum
+posterior estimators are discussed with additional details. We further apply
+this new pseudo-posterior to propose robust versions of the Bayes predictive
+density estimators and the expected Bayes estimator for the fixed-design
+(normal) linear regression models; their properties are illustrated both
+theoretically as well as empirically.
+",0,0,1,1,0,0
+11456,The $H_0$ tension in light of vacuum dynamics in the Universe,"  Despite the outstanding achievements of modern cosmology, the classical
+dispute on the precise value of $H_0$, which is the first ever parameter of
+modern cosmology and one of the prime parameters in the field, still goes on
+and on after over half a century of measurements. Recently the dispute came to
+the spotlight with renewed strength owing to the significant tension (at
+$>3\sigma$ c.l.) between the latest Planck determination obtained from the CMB
+anisotropies and the local (distance ladder) measurement from the Hubble Space
+Telescope (HST), based on Cepheids. In this work, we investigate the impact of
+the running vacuum model (RVM) and related models on such a controversy. For
+the RVM, the vacuum energy density $\rho_{\Lambda}$ carries a mild dependence
+on the cosmic expansion rate, i.e. $\rho_{\Lambda}(H)$, which allows to
+ameliorate the fit quality to the overall $SNIa+BAO+H(z)+LSS+CMB$ cosmological
+observations as compared to the concordance $\Lambda$CDM model. By letting the
+RVM to deviate from the vacuum option, the equation of state $w=-1$ continues
+to be favored by the overall fit. Vacuum dynamics also predicts the following:
+i) the CMB range of values for $H_0$ is more favored than the local ones, and
+ii) smaller values for $\sigma_8(0)$. As a result, a better account for the LSS
+structure formation data is achieved as compared to the $\Lambda$CDM, which is
+based on a rigid (i.e. non-dynamical) $\Lambda$ term.
+",0,1,0,0,0,0
+11457,On certain type of difference polynomials of meromorphic functions,"  In this paper, we investigate zeros of difference polynomials of the form
+$f(z)^nH(z, f)-s(z)$, where $f(z)$ is a meromorphic function, $H(z, f)$ is a
+difference polynomial of $f(z)$ and $s(z)$ is a small function. We first obtain
+some inequalities for the relationship of the zero counting function of
+$f(z)^nH(z, f)-s(z)$ and the characteristic function and pole counting function
+of $f(z)$. Based on these inequalities, we establish some difference analogues
+of a classical result of Hayman for meromorphic functions. Some special cases
+are also investigated. These results improve previous findings.
+",0,0,1,0,0,0
+11458,Teaching methods are erroneous: approaches which lead to erroneous end-user computing,"  If spreadsheets are not erroneous then who, or what, is? Research has found
+that end-users are. If end-users are erroneous then why are they? Research has
+found that responsibility lies with human beings' fast and slow thinking modes
+and the inappropriate way they use them. If we are aware of this peculiarity of
+human thinking, then why do we not teach students how to train their brains?
+This is the main problem, this is the weakest link in the process: teaching. We
+have to make teachers realize that end-users are erroneous because of the
+erroneous teaching approaches to end-user computing. The proportion of fast and
+slow thinking modes is not constant, and teachers are mistaken when they apply
+the same proportion in both the teaching and end-user roles. Teachers should
+believe in the incremental nature of science and have high self-efficacy to
+make students understand and appreciate science. This is not currently the case
+in ICT and CS, and it is high time fundamental changes were introduced.
+",1,0,0,0,0,0
+11459,Modulational Instability in Linearly Coupled Asymmetric Dual-Core Fibers,"  We investigate modulational instability (MI) in asymmetric dual-core
+nonlinear directional couplers incorporating the effects of the differences in
+effective mode areas and group velocity dispersions, as well as phase- and
+group-velocity mismatches. Using coupled-mode equations for this system, we
+identify MI conditions from the linearization with respect to small
+perturbations. First, we compare the MI spectra of the asymmetric system and
+its symmetric counterpart in the case of the anomalous group-velocity
+dispersion (GVD). In particular, it is demonstrated that the increase of the
+inter-core linear-coupling coefficient leads to a reduction of the MI gain
+spectrum in the asymmetric coupler. The analysis is extended for the asymmetric
+system in the normal-GVD regime, where the coupling induces and controls the
+MI, as well as for the system with opposite GVD signs in the two cores.
+Following the analytical consideration of the MI, numerical simulations are
+carried out to explore nonlinear development of the MI, revealing the
+generation of periodic chains of localized peaks with growing amplitudes, which
+may transform into arrays of solitons.
+",0,1,0,0,0,0
+11460,Discovering Visual Concept Structure with Sparse and Incomplete Tags,"  Discovering automatically the semantic structure of tagged visual data (e.g.
+web videos and images) is important for visual data analysis and
+interpretation, enabling the machine intelligence for effectively processing
+the fast-growing amount of multi-media data. However, this is non-trivial due
+to the need for jointly learning underlying correlations between heterogeneous
+visual and tag data. The task is made more challenging by inherently sparse and
+incomplete tags. In this work, we develop a method for modelling the inherent
+visual data concept structures based on a novel Hierarchical-Multi-Label Random
+Forest model capable of correlating structured visual and tag information so as
+to more accurately interpret the visual semantics, e.g. disclosing meaningful
+visual groups with similar high-level concepts, and recovering missing tags for
+individual visual data samples. Specifically, our model exploits hierarchically
+structured tags of different semantic abstractness and multiple tag statistical
+correlations in addition to modelling visual and tag interactions. As a result,
+our model is able to discover more accurate semantic correlation between
+textual tags and visual features, and finally providing favourable visual
+semantics interpretation even with highly sparse and incomplete tags. We
+demonstrate the advantages of our proposed approach in two fundamental
+applications, visual data clustering and missing tag completion, on
+benchmarking video (i.e. TRECVID MED 2011) and image (i.e. NUS-WIDE) datasets.
+",1,0,0,0,0,0
+11461,GALILEO: A Generalized Low-Entropy Mixture Model,"  We present a new method of generating mixture models for data with
+categorical attributes. The keys to this approach are an entropy-based density
+metric in categorical space and annealing of high-entropy/low-density
+components from an initial state with many components. Pruning of low-density
+components using the entropy-based density allows GALILEO to consistently find
+high-quality clusters and the same optimal number of clusters. GALILEO has
+shown promising results on a range of test datasets commonly used for
+categorical clustering benchmarks. We demonstrate that the scaling of GALILEO
+is linear in the number of records in the dataset, making this method suitable
+for very large categorical datasets.
+",1,0,0,1,0,0
+11462,Approximation by mappings with singular Hessian minors,"  Let $\Omega\subset\mathbb R^n$ be a Lipschitz domain. Given $1\leq p<k\leq n$
+and any $u\in W^{2,p}(\Omega)$ belonging to the little Hölder class
+$c^{1,\alpha}$, we construct a sequence $u_j$ in the same space with
+$\operatorname{rank}D^2u_j<k$ almost everywhere such that $u_j\to u$ in
+$C^{1,\alpha}$ and weakly in $W^{2,p}$. This result is in strong contrast with
+known regularity behavior of functions in $W^{2,p}$, $p\geq k$, satisfying the
+same rank inequality.
+",0,0,1,0,0,0
+11463,Adaptive Cardinality Estimation,"  In this paper we address cardinality estimation problem which is an important
+subproblem in query optimization. Query optimization is a part of every
+relational DBMS responsible for finding the best way of the execution for the
+given query. These ways are called plans. The execution time of different plans
+may differ by several orders, so query optimizer has a great influence on the
+whole DBMS performance. We consider cost-based query optimization approach as
+the most popular one. It was observed that cost-based optimization quality
+depends much on cardinality estimation quality. Cardinality of the plan node is
+the number of tuples returned by it.
+In the paper we propose a novel cardinality estimation approach with the use
+of machine learning methods. The main point of the approach is using query
+execution statistics of the previously executed queries to improve cardinality
+estimations. We called this approach adaptive cardinality estimation to reflect
+this point. The approach is general, flexible, and easy to implement. The
+experimental evaluation shows that this approach significantly increases the
+quality of cardinality estimation, and therefore increases the DBMS performance
+for some queries by several times or even by several dozens of times.
+",1,0,0,1,0,0
+11464,"Non-stationary Stochastic Optimization under $L_{p,q}$-Variation Measures","  We consider a non-stationary sequential stochastic optimization problem, in
+which the underlying cost functions change over time under a variation budget
+constraint. We propose an $L_{p,q}$-variation functional to quantify the
+change, which yields less variation for dynamic function sequences whose
+changes are constrained to short time periods or small subsets of input domain.
+Under the $L_{p,q}$-variation constraint, we derive both upper and matching
+lower regret bounds for smooth and strongly convex function sequences, which
+generalize previous results in Besbes et al. (2015). Furthermore, we provide an
+upper bound for general convex function sequences with noisy gradient feedback,
+which matches the optimal rate as $p\to\infty$. Our results reveal some
+surprising phenomena under this general variation functional, such as the curse
+of dimensionality of the function domain. The key technical novelties in our
+analysis include affinity lemmas that characterize the distance of the
+minimizers of two convex functions with bounded Lp difference, and a cubic
+spline based construction that attains matching lower bounds.
+",1,0,0,1,0,0
+11465,Spin conductance of YIG thin films driven from thermal to subthermal magnons regime by large spin-orbit torque,"  We report a study on spin conductance in ultra-thin films of Yttrium Iron
+Garnet (YIG), where spin transport is provided by propagating spin waves, that
+are generated and detected by direct and inverse spin Hall effects in two Pt
+wires deposited on top. While at low current the spin conductance is dominated
+by transport of thermal magnons, at high current, the spin conductance is
+dominated by low-damping non-equilibrium magnons thermalized near the spectral
+bottom by magnon-magnon interaction, with consequent a sensitivity to the
+applied magnetic field and a longer decay length. This picture is supported by
+microfocus Brillouin Light Scattering spectroscopy.
+",0,1,0,0,0,0
+11466,Mathematical renormalization in quantum electrodynamics via noncommutative generating series,"  In this work, we focus on on the approach by noncommutative formal power
+series to study the combinatorial aspects of the renormalization at the
+singularities in $\{0,1,+\infty\}$ of the solutions of nonlinear differential
+equations involved in quantum electrodynamics.
+",0,0,1,0,0,0
+11467,Inference in Deep Networks in High Dimensions,"  Deep generative networks provide a powerful tool for modeling complex data in
+a wide range of applications. In inverse problems that use these networks as
+generative priors on data, one must often perform inference of the inputs of
+the networks from the outputs. Inference is also required for sampling during
+stochastic training on these generative models. This paper considers inference
+in a deep stochastic neural network where the parameters (e.g., weights, biases
+and activation functions) are known and the problem is to estimate the values
+of the input and hidden units from the output. While several approximate
+algorithms have been proposed for this task, there are few analytic tools that
+can provide rigorous guarantees in the reconstruction error. This work presents
+a novel and computationally tractable output-to-input inference method called
+Multi-Layer Vector Approximate Message Passing (ML-VAMP). The proposed
+algorithm, derived from expectation propagation, extends earlier AMP methods
+that are known to achieve the replica predictions for optimality in simple
+linear inverse problems. Our main contribution shows that the mean-squared
+error (MSE) of ML-VAMP can be exactly predicted in a certain large system limit
+(LSL) where the numbers of layers is fixed and weight matrices are random and
+orthogonally-invariant with dimensions that grow to infinity. ML-VAMP is thus a
+principled method for output-to-input inference in deep networks with a
+rigorous and precise performance achievability result in high dimensions.
+",1,0,0,1,0,0
+11468,Differentially Private Variational Dropout,"  Deep neural networks with their large number of parameters are highly
+flexible learning systems. The high flexibility in such networks brings with
+some serious problems such as overfitting, and regularization is used to
+address this problem. A currently popular and effective regularization
+technique for controlling the overfitting is dropout. Often, large data
+collections required for neural networks contain sensitive information such as
+the medical histories of patients, and the privacy of the training data should
+be protected. In this paper, we modify the recently proposed variational
+dropout technique which provided an elegant Bayesian interpretation to dropout,
+and show that the intrinsic noise in the variational dropout can be exploited
+to obtain a degree of differential privacy. The iterative nature of training
+neural networks presents a challenge for privacy-preserving estimation since
+multiple iterations increase the amount of noise added. We overcome this by
+using a relaxed notion of differential privacy, called concentrated
+differential privacy, which provides tighter estimates on the overall privacy
+loss. We demonstrate the accuracy of our privacy-preserving variational dropout
+algorithm on benchmark datasets.
+",1,0,0,1,0,0
+11469,Persistent Currents in Ferromagnetic Condensates,"  Persistent currents in Bose condensates with a scalar order parameter are
+stabilized by the topology of the order parameter manifold. In condensates with
+multicomponent order parameters it is topologically possible for supercurrents
+to `unwind' without leaving the manifold. We study the energetics of this
+process in the case of ferromagnetic condensates using a long wavelength energy
+functional that includes both the superfluid and spin stiffnesses. Exploiting
+analogies to an elastic rod and rigid body motion, we show that the current
+carrying state in a 1D ring geometry transitions between a spin helix in the
+energy minima and a soliton-like configuration at the maxima. The relevance to
+recent experiments in ultracold atoms is briefly discussed.
+",0,1,0,0,0,0
+11470,Parameter Adaptation and Criticality in Particle Swarm Optimization,"  Generality is one of the main advantages of heuristic algorithms, as such,
+multiple parameters are exposed to the user with the objective of allowing them
+to shape the algorithms to their specific needs. Parameter selection,
+therefore, becomes an intrinsic problem of every heuristic algorithm. Selecting
+good parameter values relies not only on knowledge related to the problem at
+hand, but to the algorithms themselves. This research explores the usage of
+self-organized criticality to reduce user interaction in the process of
+selecting suitable parameters for particle swarm optimization (PSO) heuristics.
+A particle swarm variant (named Adaptive PSO) with self-organized criticality
+is developed and benchmarked against the standard PSO. Criticality is observed
+in the dynamic behaviour of this swarm and excellent results are observed in
+the long run. In contrast with the standard PSO, the Adaptive PSO does not
+stagnate at any point in time, balancing the concepts of exploration and
+exploitation better. A software platform for experimenting with particle
+swarms, called PSO Laboratory, is also developed. This software is used to test
+the standard PSO as well as all other PSO variants developed in the process of
+creating the Adaptive PSO. As the software is intended to be of aid to future
+and related research, special attention has been put in the development of a
+friendly graphical user interface. Particle swarms are executed in real time,
+allowing users to experiment by changing parameters on-the-fly.
+",1,0,0,0,0,0
+11471,Model Predictive Control meets robust Kalman filtering,"  Model Predictive Control (MPC) is the principal control technique used in
+industrial applications. Although it offers distinguishable qualities that make
+it ideal for industrial applications, it can be questioned its robustness
+regarding model uncertainties and external noises. In this paper we propose a
+robust MPC controller that merges the simplicity in the design of MPC with
+added robustness. In particular, our control system stems from the idea of
+adding robustness in the prediction phase of the algorithm through a specific
+robust Kalman filter recently introduced. Notably, the overall result is an
+algorithm very similar to classic MPC but that also provides the user with the
+possibility to tune the robustness of the control. To test the ability of the
+controller to deal with errors in modeling, we consider a servomechanism system
+characterized by nonlinear dynamics.
+",0,0,1,0,0,0
+11472,Election forensic analysis of the Turkish Constitutional Referendum 2017,"  With a majority of 'Yes' votes in the Constitutional Referendum of 2017,
+Turkey continues its transition from democracy to autocracy. By the will of the
+Turkish people, this referendum transferred practically all executive power to
+president Erdogan. However, the referendum was confronted with a substantial
+number of allegations of electoral misconducts and irregularities, ranging from
+state coercion of 'No' supporters to the controversial validity of unstamped
+ballots. In this note we report the results of an election forensic analysis of
+the 2017 referendum to clarify to what extent these voting irregularities were
+present and if they were able to influence the outcome of the referendum. We
+specifically apply novel statistical forensics tests to further identify the
+specific nature of electoral malpractices. In particular, we test whether the
+data contains fingerprints for ballot-stuffing (submission of multiple ballots
+per person during the vote) and voter rigging (coercion and intimidation of
+voters). Additionally, we perform tests to identify numerical anomalies in the
+election results. We find systematic and highly significant support for the
+presence of both, ballot-stuffing and voter rigging. In 6% of stations we find
+signs for ballot-stuffing with an error (probability of ballot-stuffing not
+happening) of 0.15% (3 sigma event). The influence of these vote distortions
+were large enough to tip the overall balance from 'No' to a majority of 'Yes'
+votes.
+",0,1,0,1,0,0
+11473,Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage,"  This paper discusses the efficient Bayesian estimation of a multivariate
+factor stochastic volatility (Factor MSV) model with leverage. We propose a
+novel approach to construct the sampling schemes that converges to the
+posterior distribution of the latent volatilities and the parameters of
+interest of the Factor MSV model based on recent advances in Particle Markov
+chain Monte Carlo (PMCMC). As opposed to the approach of Chib et al. (2006} and
+Omori et al. (2007}, our approach does not require approximating the joint
+distribution of outcome and volatility innovations by a mixture of bivariate
+normal distributions. To sample the free elements of the loading matrix we
+employ the interweaving method used in Kastner et al. (2017} in the Particle
+Metropolis within Gibbs (PMwG) step. The proposed method is illustrated
+empirically using a simulated dataset and a sample of daily US stock returns.
+",0,0,0,1,0,0
+11474,Next Steps for the Colorado Risk-Limiting Audit (CORLA) Program,"  Colorado conducted risk-limiting tabulation audits (RLAs) across the state in
+2017, including both ballot-level comparison audits and ballot-polling audits.
+Those audits only covered contests restricted to a single county; methods to
+efficiently audit contests that cross county boundaries and combine ballot
+polling and ballot-level comparisons have not been available.
+Colorado's current audit software (RLATool) needs to be improved to audit
+these contests that cross county lines and to audit small contests efficiently.
+This paper addresses these needs. It presents extremely simple but
+inefficient methods, more efficient methods that combine ballot polling and
+ballot-level comparisons using stratified samples, and methods that combine
+ballot-level comparison and variable-size batch comparison audits in a way that
+does not require stratified sampling.
+We conclude with some recommendations, and illustrate our recommended method
+using examples that compare them to existing approaches. Exemplar open-source
+code and interactive Jupyter notebooks are provided that implement the methods
+and allow further exploration.
+",0,0,0,1,0,0
+11475,HD 202206 : A Circumbinary Brown Dwarf System,"  With Hubble Space Telescope Fine Guidance Sensor astrometry and previously
+published radial velocity measures we explore the exoplanetary system HD
+202206. Our modeling results in a parallax, $\pi_{abs} = 21.96\pm0.12$
+milliseconds of arc, a mass for HD 202206 B of M$_B = 0.089^{ +0.007}_{-0.006}$
+Msun, and a mass for HD 202206 c of M$_c = 17.9 ^{ +2.9}_{-1.8}$ MJup. HD
+202206 is a nearly face-on G+M binary orbited by a brown dwarf. The system
+architecture we determine supports past assertions that stability requires a
+5:1 mean motion resonance (we find a period ratio, $P_c/P_B = 4.92\pm0.04$) and
+coplanarity (we find a mutual inclination, Phi = 6 \arcdeg \pm 2 \arcdeg).
+",0,1,0,0,0,0
+11476,On Optimal Group Claims at Voting in a Stochastic Environment,"  There is a paradox in the model of social dynamics determined by voting in a
+stochastic environment (the ViSE model) called ""pit of losses."" It consists in
+the fact that a series of democratic decisions may systematically lead the
+society to the states unacceptable for all the voters. The paper examines how
+this paradox can be neutralized by the presence in society of a group that
+votes for its benefit and can regulate the threshold of its claims. We obtain
+and analyze analytical results characterizing the welfare of the whole society,
+the group, and the other participants as functions of the said claims
+threshold.
+",1,0,1,0,0,0
+11477,Exploring many body localization and thermalization using semiclassical method,"  The Discrete Truncated Wigner Approximation (DTWA) is a semi-classical phase
+space method useful for the exploration of Many-body quantum dynamics. In this
+work we investigate Many-Body Localization (MBL) and thermalization using DTWA
+and compare its performance to exact numerical solutions. By taking as a
+benchmark case a 1D random field Heisenberg spin chain with short range
+interactions, and by comparing to numerically exact techniques, we show that
+DTWA is able to reproduce dynamical signatures that characterize both the
+thermal and the MBL phases. It exhibits the best quantitative agreement at
+short times deep in each of the phases and larger mismatches close to the phase
+transition. The DTWA captures the logarithmic growth of entanglement in the MBL
+phase, even though a pure classical mean-field analysis would lead to no
+dynamics at all. Our results suggest the DTWA can become a useful method to
+investigate MBL and thermalization in experimentally relevant settings
+intractable with exact numerical techniques, such as systems with long range
+interactions and/or systems in higher dimensions.
+",0,1,0,0,0,0
+11478,An FPTAS for the Knapsack Problem with Parametric Weights,"  In this paper, we investigate the parametric weight knapsack problem, in
+which the item weights are affine functions of the form $w_i(\lambda) = a_i +
+\lambda \cdot b_i$ for $i \in \{1,\ldots,n\}$ depending on a real-valued
+parameter $\lambda$. The aim is to provide a solution for all values of the
+parameter. It is well-known that any exact algorithm for the problem may need
+to output an exponential number of knapsack solutions. We present the first
+fully polynomial-time approximation scheme (FPTAS) for the problem that, for
+any desired precision $\varepsilon \in (0,1)$, computes
+$(1-\varepsilon)$-approximate solutions for all values of the parameter. Our
+FPTAS is based on two different approaches and achieves a running time of
+$\mathcal{O}(n^3/\varepsilon^2 \cdot \min\{ \log^2 P, n^2 \} \cdot \min\{\log
+M, n \log (n/\varepsilon) / \log(n \log (n/\varepsilon) )\})$ where $P$ is an
+upper bound on the optimal profit and $M := \max\{W, n \cdot \max\{a_i,b_i: i
+\in \{1,\ldots,n\}\}\}$ for a knapsack with capacity $W$.
+",1,0,1,0,0,0
+11479,Mellin and Wiener-Hopf operators in a non-classical boundary value problem describing a Lévy process,"  Markov processes are well understood in the case when they take place in the
+whole Euclidean space. However, the situation becomes much more complicated if
+a Markov process is restricted to a domain with a boundary, and then a
+satisfactory theory only exists for processes with continuous trajectories.
+This research, into non-classical boundary value problems, is motivated by the
+study of stochastic processes, restricted to a domain, that can have
+discontinuous trajectories.
+To make this general problem more tractable, we consider a particular
+operator, $\mathcal{A}$, which is chosen to be the generator of a certain
+stable Lévy process restricted to the positive half-line. We are able to
+represent $\mathcal{A}$ as a (hyper-) singular integral and, using this
+representation, deduce simple conditions for its boundedness, between Bessel
+potential spaces. Moreover, from energy estimates, we prove that, under certain
+conditions, $\mathcal{A}$ has a trivial kernel.
+A central feature of this research is our use of Mellin operators to deal
+with the leading singular terms that combine, and cancel, at the boundary.
+Indeed, after considerable analysis, the problem is reformulated in the context
+of an algebra of multiplication, Wiener-Hopf and Mellin operators, acting on a
+Lebesgue space. The resulting generalised symbol is examined and, it turns out,
+that a certain transcendental equation, involving gamma and trigonometric
+functions with complex arguments, plays a pivotal role. Following detailed
+consideration of this transcendental equation, we are able to determine when
+our operator is Fredholm and, in that case, calculate its index. Finally,
+combining information on the kernel with the Fredholm index, we establish
+precise conditions for the invertibility of $\mathcal{A}$.
+",0,0,1,0,0,0
+11480,Long-term photometric behavior of the eclipsing cataclysmic variable V729 Sgr,"  We present the analysis results of an eclipsing cataclysmic variable (CV)
+V729 Sgr, based on our observations and AAVSO data. Some outburst parameters
+were determined such as outburst amplitude ($A_{n}$) and recurrence time
+($T_{n}$), and then the relationship between $A_{n}$ and $T_{n}$ is discussed.
+A cursory examination for the long-term light curves reveals that there are
+small-amplitude outbursts and dips present, which is similar to the behaviors
+seen in some nova-like CVs (NLs). More detailed inspection suggests that the
+outbursts in V729 Sgr may be Type A (outside-in) with a rise time $\sim1.76$ d.
+Further analysis also shows that V729 Sgr is an intermediate between dwarf nova
+and NLs, and we constrain its mass transfer rate to $1.59\times10^{-9} <
+\dot{M}_{2} < 5.8\times10^{-9}M_{\odot}yr^{-1}$ by combining the theory for Z
+Cam type stars with observations. Moreover, the rapid oscillations in V729 Sgr
+were detected and analyzed for the first time. Our results indicate that the
+oscillation at $\sim 25.5$ s is a true DNO, being associated with the accretion
+events. The classification of the oscillations at $\sim 136$ and $154$ s as
+lpDNOs is based on the relation between $P_{lpDNOs}$ and $P_{DNOs}$. Meanwhile,
+the QPOs at the period of hundreds of seconds are also detected.
+",0,1,0,0,0,0
+11481,SPUX: Scalable Particle Markov Chain Monte Carlo for uncertainty quantification in stochastic ecological models,"  Calibration of individual based models (IBMs), successful in modeling complex
+ecological dynamical systems, is often performed only ad-hoc. Bayesian
+inference can be used for both parameter estimation and uncertainty
+quantification, but its successful application to realistic scenarios has been
+hindered by the complex stochastic nature of IBMs. Computationally expensive
+techniques such as Particle Filter (PF) provide marginal likelihood estimates,
+where multiple model simulations (particles) are required to get a sample from
+the state distribution conditional on the observed data. Particle ensembles are
+re-sampled at each data observation time, requiring particle destruction and
+replication, which lead to an increase in algorithmic complexity. We present
+SPUX, a Python implementation of parallel Particle Markov Chain Monte Carlo
+(PMCMC) algorithm, which mitigates high computational costs by distributing
+particles over multiple computational units. Adaptive load re-balancing
+techniques are used to mitigate computational work imbalances introduced by
+re-sampling. Framework performance is investigated and significant speed-ups
+are observed for a simple predator-prey IBM model.
+",1,0,0,1,0,0
+11482,A Social Network Analysis Framework for Modeling Health Insurance Claims Data,"  Health insurance companies in Brazil have their data about claims organized
+having the view only for providers. In this way, they loose the physician view
+and how they share patients. Partnership between physicians can view as a
+fruitful work in most of the cases but sometimes this could be a problem for
+health insurance companies and patients, for example a recommendation to visit
+another physician only because they work in same clinic. The focus of the work
+is to better understand physicians activities and how these activities are
+represented in the data. Our approach considers three aspects: the
+relationships among physicians, the relationships between physicians and
+patients, and the relationships between physicians and health providers. We
+present the results of an analysis of a claims database (detailing 18 months of
+activity) from a large health insurance company in Brazil. The main
+contribution presented in this paper is a set of models to represent: mutual
+referral between physicians, patient retention, and physician centrality in the
+health insurance network. Our results show the proposed models based on social
+network frameworks, extracted surprising insights about physicians from real
+health insurance claims data.
+",1,0,0,0,0,0
+11483,Congruences for Restricted Plane Overpartitions Modulo 4 and 8,"  In 2009, Corteel, Savelief and Vuletić generalized the concept of
+overpartitions to a new object called plane overpartitions. In recent work, the
+author considered a restricted form of plane overpartitions called $k$-rowed
+plane overpartions and proved a method to obtain congruences for these and
+other types of combinatorial generating functions. In this paper, we prove
+several restricted and unrestricted plane overpartition congruences modulo $4$
+and $8$ using other techniques.
+",0,0,1,0,0,0
+11484,AndroVault: Constructing Knowledge Graph from Millions of Android Apps for Automated Analysis,"  Data driven research on Android has gained a great momentum these years. The
+abundance of data facilitates knowledge learning, however, also increases the
+difficulty of data preprocessing. Therefore, it is non-trivial to prepare a
+demanding and accurate set of data for research. In this work, we put forward
+AndroVault, a framework for the Android research composing of data collection,
+knowledge representation and knowledge extraction. It has started with a
+long-running web crawler for data collection (both apps and description) since
+2013, which guarantees the timeliness of data; With static analysis and dynamic
+analysis of the collected data, we compute a variety of attributes to
+characterize Android apps. After that, we employ a knowledge graph to connect
+all these apps by computing their correlation in terms of attributes; Last, we
+leverage multiple technologies such as logical inference, machine learning, and
+correlation analysis to extract facts (more accurate and demanding, either high
+level or not, data) that are beneficial for a specific research problem. With
+the produced data of high quality, we have successfully conducted many research
+works including malware detection, code generation, and Android testing. We
+would like to release our data to the research community in an authenticated
+manner, and encourage them to conduct productive research.
+",1,0,0,0,0,0
+11485,Universal and generalizable restoration strategies for degraded ecological networks,"  Humans are increasingly stressing ecosystems via habitat destruction, climate
+change and global population movements leading to the widespread loss of
+biodiversity and the disruption of key ecological services. Ecosystems
+characterized primarily by mutualistic relationships between species such as
+plant-pollinator interactions may be particularly vulnerable to such
+perturbations because the loss of biodiversity can cause extinction cascades
+that can compromise the entire network. Here, we develop a general restoration
+strategy based on network-science for degraded ecosystems. Specifically, we
+show that network topology can be used to identify the optimal sequence of
+species reintroductions needed to maximize biodiversity gains following partial
+and full ecosystem collapse. This restoration strategy generalizes across
+topologically-disparate and geographically-distributed ecosystems.
+Additionally, we find that although higher connectance and diversity promote
+persistence in pristine ecosystems, these attributes reduce the effectiveness
+of restoration efforts in degraded networks. Hence, focusing on restoring the
+factors that promote persistence in pristine ecosystems may yield suboptimal
+recovery strategies for degraded ecosystems. Overall, our results have
+important insights for designing effective ecosystem restoration strategies to
+preserve biodiversity and ensure the delivery of critical natural services that
+fuel economic development, food security and human health around the globe
+",0,0,0,0,1,0
+11486,Information transmission and signal permutation in active flow networks,"  Recent experiments show that both natural and artificial microswimmers in
+narrow channel-like geometries will self-organise to form steady, directed
+flows. This suggests that networks of flowing active matter could function as
+novel autonomous microfluidic devices. However, little is known about how
+information propagates through these far-from-equilibrium systems. Through a
+mathematical analogy with spin-ice vertex models, we investigate here the
+input-output characteristics of generic incompressible active flow networks
+(AFNs). Our analysis shows that information transport through an AFN is
+inherently different from conventional pressure or voltage driven networks.
+Active flows on hexagonal arrays preserve input information over longer
+distances than their passive counterparts and are highly sensitive to bulk
+topological defects, whose presence can be inferred from marginal input-output
+distributions alone. This sensitivity further allows controlled permutations on
+parallel inputs, revealing an unexpected link between active matter and group
+theory that can guide new microfluidic mixing strategies facilitated by active
+matter and aid the design of generic autonomous information transport networks.
+",1,1,0,0,0,0
+11487,Heuristic Framework for Multi-Scale Testing of the Multi-Manifold Hypothesis,"  When analyzing empirical data, we often find that global linear models
+overestimate the number of parameters required. In such cases, we may ask
+whether the data lies on or near a manifold or a set of manifolds (a so-called
+multi-manifold) of lower dimension than the ambient space. This question can be
+phrased as a (multi-) manifold hypothesis. The identification of such intrinsic
+multiscale features is a cornerstone of data analysis and representation and
+has given rise to a large body of work on manifold learning. In this work, we
+review key results on multi-scale data analysis and intrinsic dimension
+followed by the introduction of a heuristic, multiscale framework for testing
+the multi-manifold hypothesis. Our method implements a hypothesis test on a set
+of spline-interpolated manifolds constructed from variance-based intrinsic
+dimensions. The workflow is suitable for empirical data analysis as we
+demonstrate on two use cases.
+",0,0,0,1,0,0
+11488,Kitting in the Wild through Online Domain Adaptation,"  Technological developments call for increasing perception and action
+capabilities of robots. Among other skills, vision systems that can adapt to
+any possible change in the working conditions are needed. Since these
+conditions are unpredictable, we need benchmarks which allow to assess the
+generalization and robustness capabilities of our visual recognition
+algorithms. In this work we focus on robotic kitting in unconstrained
+scenarios. As a first contribution, we present a new visual dataset for the
+kitting task. Differently from standard object recognition datasets, we provide
+images of the same objects acquired under various conditions where camera,
+illumination and background are changed. This novel dataset allows for testing
+the robustness of robot visual recognition algorithms to a series of different
+domain shifts both in isolation and unified. Our second contribution is a novel
+online adaptation algorithm for deep models, based on batch-normalization
+layers, which allows to continuously adapt a model to the current working
+conditions. Differently from standard domain adaptation algorithms, it does not
+require any image from the target domain at training time. We benchmark the
+performance of the algorithm on the proposed dataset, showing its capability to
+fill the gap between the performances of a standard architecture and its
+counterpart adapted offline to the given target domain.
+",1,0,0,0,0,0
+11489,Event Analysis of Pulse-reclosers in Distribution Systems Through Sparse Representation,"  The pulse-recloser uses pulse testing technology to verify that the line is
+clear of faults before initiating a reclose operation, which significantly
+reduces stress on the system components (e.g. substation transformers) and
+voltage sags on adjacent feeders. Online event analysis of pulse-reclosers are
+essential to increases the overall utility of the devices, especially when
+there are numerous devices installed throughout the distribution system. In
+this paper, field data recorded from several devices were analyzed to identify
+specific activity and fault locations. An algorithm is developed to screen the
+data to identify the status of each pole and to tag time windows with a
+possible pulse event. In the next step, selected time windows are further
+analyzed and classified using a sparse representation technique by solving an
+l1-regularized least-square problem. This classification is obtained by
+comparing the pulse signature with the reference dictionary to find a set that
+most closely matches the pulse features. This work also sheds additional light
+on the possibility of fault classification based on the pulse signature. Field
+data collected from a distribution system are used to verify the effectiveness
+and reliability of the proposed method.
+",1,0,0,0,0,0
+11490,Projected Power Iteration for Network Alignment,"  The network alignment problem asks for the best correspondence between two
+given graphs, so that the largest possible number of edges are matched. This
+problem appears in many scientific problems (like the study of protein-protein
+interactions) and it is very closely related to the quadratic assignment
+problem which has graph isomorphism, traveling salesman and minimum bisection
+problems as particular cases. The graph matching problem is NP-hard in general.
+However, under some restrictive models for the graphs, algorithms can
+approximate the alignment efficiently. In that spirit the recent work by Feizi
+and collaborators introduce EigenAlign, a fast spectral method with convergence
+guarantees for Erdős-Renyí graphs. In this work we propose the algorithm
+Projected Power Alignment, which is a projected power iteration version of
+EigenAlign. We numerically show it improves the recovery rates of EigenAlign
+and we describe the theory that may be used to provide performance guarantees
+for Projected Power Alignment.
+",1,0,1,1,0,0
+11491,3D mean Projective Shape Difference for Face Differentiation from Multiple Digital Camera Images,"  We give a nonparametric methodology for hypothesis testing for equality of
+extrinsic mean objects on a manifold embedded in a numerical spaces. The
+results obtained in the general setting are detailed further in the case of 3D
+projective shapes represented in a space of symmetric matrices via the
+quadratic Veronese-Whitney (VW) embedding. Large sample and nonparametric
+bootstrap confidence regions are derived for the common VW-mean of random
+projective shapes for finite 3D configurations. As an example, the VW MANOVA
+testing methodology is applied to the multi-sample mean problem for independent
+projective shapes of $3D$ facial configurations retrieved from digital images,
+via Agisoft PhotoScan technology.
+",0,0,0,1,0,0
+11492,Video Highlight Prediction Using Audience Chat Reactions,"  Sports channel video portals offer an exciting domain for research on
+multimodal, multilingual analysis. We present methods addressing the problem of
+automatic video highlight prediction based on joint visual features and textual
+analysis of the real-world audience discourse with complex slang, in both
+English and traditional Chinese. We present a novel dataset based on League of
+Legends championships recorded from North American and Taiwanese Twitch.tv
+channels (will be released for further research), and demonstrate strong
+results on these using multimodal, character-level CNN-RNN model architectures.
+",1,0,0,0,0,0
+11493,Effects of Interactions on Dynamic Correlations of Hard-Core Bosons at Finite Temperatures,"  We investigate how dynamic correlations of hard-core bosonic excitation at
+finite temperature are affected by additional interactions besides the
+hard-core repulsion which prevents them from occupying the same site. We focus
+especially on dimerized spin systems, where these additional interactions
+between the elementary excitations, triplons, lead to the formation of bound
+states, relevant for the correct description of scattering processes. In order
+to include these effects quantitatively we extend the previously developed
+Brückner approach to include also nearest-neighbor (NN) and next-nearest
+neighbor (NNN) interactions correctly in a low-temperature expansion. This
+leads to the extension of the scalar Bethe-Salpeter equation to a matrix-valued
+equation. Exemplarily, we consider the Heisenberg spin ladder to illustrate the
+significance of the additional interactions on the spectral functions at finite
+temperature which are proportional to inelastic neutron scattering rates.
+",0,1,0,0,0,0
+11494,The Frechet distribution: Estimation and Application an Overview,"  In this article, we consider the problem of estimating the parameters of the
+Fréchet distribution from both frequentist and Bayesian points of view. First
+we briefly describe different frequentist approaches, namely, maximum
+likelihood, method of moments, percentile estimators, L-moments, ordinary and
+weighted least squares, maximum product of spacings, maximum goodness-of-fit
+estimators and compare them with respect to mean relative estimates, mean
+squared errors and the 95\% coverage probability of the asymptotic confidence
+intervals using extensive numerical simulations. Next, we consider the Bayesian
+inference approach using reference priors. The Metropolis-Hasting algorithm is
+used to draw Markov Chain Monte Carlo samples, and they have in turn been used
+to compute the Bayes estimates and also to construct the corresponding credible
+intervals. Five real data sets related to the minimum flow of water on
+Piracicaba river in Brazil are used to illustrate the applicability of the
+discussed procedures.
+",0,0,0,1,0,0
+11495,Prediction of Sea Surface Temperature using Long Short-Term Memory,"  This letter adopts long short-term memory(LSTM) to predict sea surface
+temperature(SST), which is the first attempt, to our knowledge, to use
+recurrent neural network to solve the problem of SST prediction, and to make
+one week and one month daily prediction. We formulate the SST prediction
+problem as a time series regression problem. LSTM is a special kind of
+recurrent neural network, which introduces gate mechanism into vanilla RNN to
+prevent the vanished or exploding gradient problem. It has strong ability to
+model the temporal relationship of time series data and can handle the
+long-term dependency problem well. The proposed network architecture is
+composed of two kinds of layers: LSTM layer and full-connected dense layer.
+LSTM layer is utilized to model the time series relationship. Full-connected
+layer is utilized to map the output of LSTM layer to a final prediction. We
+explore the optimal setting of this architecture by experiments and report the
+accuracy of coastal seas of China to confirm the effectiveness of the proposed
+method. In addition, we also show its online updated characteristics.
+",1,0,0,0,0,0
+11496,An Operational Framework for Specifying Memory Models using Instantaneous Instruction Execution,"  There has been great progress recently in formally specifying the memory
+model of microprocessors like ARM and POWER. These specifications are, however,
+too complicated for reasoning about program behaviors, verifying compilers
+etc., because they involve microarchitectural details like the reorder buffer
+(ROB), partial and speculative execution, instruction replay on speculation
+failure, etc. In this paper we present a new Instantaneous Instruction
+Execution (I2E) framework which allows us to specify weak memory models in the
+same style as SC and TSO. Each instruction in I2E is executed instantaneously
+and in-order such that the state of the processor is always correct. The effect
+of instruction reordering is captured by the way data is moved between the
+processors and the memory non-deterministically, using three conceptual
+devices: invalidation buffers, timestamps and dynamic store buffers. We prove
+that I2E models capture the behaviors of modern microarchitectures and
+cache-coherent memory systems accurately, thus eliminating the need to think
+about microarchitectural details.
+",1,0,0,0,0,0
+11497,Efficient Algorithms for Moral Lineage Tracing,"  Lineage tracing, the joint segmentation and tracking of living cells as they
+move and divide in a sequence of light microscopy images, is a challenging
+task. Jug et al. have proposed a mathematical abstraction of this task, the
+moral lineage tracing problem (MLTP), whose feasible solutions define both a
+segmentation of every image and a lineage forest of cells. Their branch-and-cut
+algorithm, however, is prone to many cuts and slow convergence for large
+instances. To address this problem, we make three contributions: (i) we devise
+the first efficient primal feasible local search algorithms for the MLTP, (ii)
+we improve the branch-and-cut algorithm by separating tighter cutting planes
+and by incorporating our primal algorithms, (iii) we show in experiments that
+our algorithms find accurate solutions on the problem instances of Jug et al.
+and scale to larger instances, leveraging moral lineage tracing to practical
+significance.
+",1,0,0,0,0,0
+11498,Interpolating between $k$-Median and $k$-Center: Approximation Algorithms for Ordered $k$-Median,"  We consider a generalization of $k$-median and $k$-center, called the {\em
+ordered $k$-median} problem. In this problem, we are given a metric space
+$(\mathcal{D},\{c_{ij}\})$ with $n=|\mathcal{D}|$ points, and a non-increasing
+weight vector $w\in\mathbb{R}_+^n$, and the goal is to open $k$ centers and
+assign each point each point $j\in\mathcal{D}$ to a center so as to minimize
+$w_1\cdot\text{(largest assignment cost)}+w_2\cdot\text{(second-largest
+assignment cost)}+\ldots+w_n\cdot\text{($n$-th largest assignment cost)}$. We
+give an $(18+\epsilon)$-approximation algorithm for this problem. Our
+algorithms utilize Lagrangian relaxation and the primal-dual schema, combined
+with an enumeration procedure of Aouad and Segev. For the special case of
+$\{0,1\}$-weights, which models the problem of minimizing the $\ell$ largest
+assignment costs that is interesting in and of by itself, we provide a novel
+reduction to the (standard) $k$-median problem showing that LP-relative
+guarantees for $k$-median translate to guarantees for the ordered $k$-median
+problem; this yields a nice and clean $(8.5+\epsilon)$-approximation algorithm
+for $\{0,1\}$ weights.
+",1,0,0,0,0,0
+11499,Statistical Challenges in Modeling Big Brain Signals,"  Brain signal data are inherently big: massive in amount, complex in
+structure, and high in dimensions. These characteristics impose great
+challenges for statistical inference and learning. Here we review several key
+challenges, discuss possible solutions, and highlight future research
+directions.
+",0,0,0,1,0,0
+11500,Injective and Automorphism-Invariant Non-Singular Modules,"  Every automorphism-invariant right non-singular $A$-module is injective if
+and only if the factor ring of the ring $A$ with respect to its right Goldie
+radical is a right strongly semiprime ring.
+",0,0,1,0,0,0
+11501,Visible transitions in Ag-like and Cd-like lanthanide ions,"  We present visible spectra of Ag-like ($4d^{10}4f$) and Cd-like
+($4d^{10}4f^2$) ions of Ho (atomic number $Z=67$), Er (68), and Tm (69)
+observed with a compact electron beam ion trap. For Ag-like ions, prominent
+emission corresponding to the M1 transitions between the ground state fine
+structure splitting $4f_{5/2}$--$4f_{7/2}$ is identified. For Cd-like ions,
+several M1 transitions in the ground state configuration are identified. The
+transition wavelength and the transition probability are calculated with the
+relativistic many-body perturbation theory and the relativistic CI + all-order
+approach. Comparisons between the experiments and the calculations show good
+agreement.
+",0,1,0,0,0,0
+11502,A gradient flow approach to linear Boltzmann equations,"  We introduce a gradient flow formulation of linear Boltzmann equations. Under
+a diffusive scaling we derive a diffusion equation by using the machinery of
+gradient flows.
+",0,0,1,0,0,0
+11503,Constraints on neutrino masses from Lyman-alpha forest power spectrum with BOSS and XQ-100,"  We present constraints on masses of active and sterile neutrinos. We use the
+one-dimensional Ly$\alpha$-forest power spectrum from the Baryon Oscillation
+Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey (SDSS-III) and from
+the VLT/XSHOOTER legacy survey (XQ-100). In this paper, we present our own
+measurement of the power spectrum with the publicly released XQ-100 quasar
+spectra.
+Fitting Ly$\alpha$ data alone leads to cosmological parameters in excellent
+agreement with the values derived independently from Planck 2015 Cosmic
+Microwave Background (CMB) data. Combining BOSS and XQ-100 Ly$\alpha$ power
+spectra, we constrain the sum of neutrino masses to $\sum m_\nu < 0.8$ eV (95\%
+C.L). With the addition of CMB data, this bound is tightened to $\sum m_\nu <
+0.14$ eV (95\% C.L.).
+With their sensitivity to small scales, Ly$\alpha$ data are ideal to
+constrain $\Lambda$WDM models. Using XQ-100 alone, we issue lower bounds on
+pure dark matter particles: $m_X \gtrsim 2.08 \: \rm{keV}$ (95\% C.L.) for
+early decoupled thermal relics, and $m_s \gtrsim 10.2 \: \rm{keV}$ (95\% C.L.)
+for non-resonantly produced right-handed neutrinos. Combining the 1D Ly$\alpha$
+forest power spectrum measured by BOSS and XQ-100, we improve the two bounds to
+$m_X \gtrsim 4.17 \: \rm{keV}$ and $m_s \gtrsim 25.0 \: \rm{keV}$ (95\% C.L.).
+The $3~\sigma$ bound shows a more significant improvement, increasing from $m_X
+\gtrsim 2.74 \: \rm{keV}$ for BOSS alone to $m_X \gtrsim 3.10 \: \rm{keV}$ for
+the combined BOSS+XQ-100 data set.
+Finally, we include in our analysis the first two redshift bins ($z=4.2$ and
+$z=4.6$) of the power spectrum measured with the high-resolution HIRES/MIKE
+spectrographs. The addition of HIRES/MIKE power spectrum allows us to further
+improve the two limits to $m_X \gtrsim 4.65 \: \rm{keV}$ and $m_s \gtrsim 28.8
+\: \rm{keV}$ (95\% C.L.).
+",0,1,0,0,0,0
+11504,BubbleView: an interface for crowdsourcing image importance maps and tracking visual attention,"  In this paper, we present BubbleView, an alternative methodology for eye
+tracking using discrete mouse clicks to measure which information people
+consciously choose to examine. BubbleView is a mouse-contingent, moving-window
+interface in which participants are presented with a series of blurred images
+and click to reveal ""bubbles"" - small, circular areas of the image at original
+resolution, similar to having a confined area of focus like the eye fovea.
+Across 10 experiments with 28 different parameter combinations, we evaluated
+BubbleView on a variety of image types: information visualizations, natural
+images, static webpages, and graphic designs, and compared the clicks to eye
+fixations collected with eye-trackers in controlled lab settings. We found that
+BubbleView clicks can both (i) successfully approximate eye fixations on
+different images, and (ii) be used to rank image and design elements by
+importance. BubbleView is designed to collect clicks on static images, and
+works best for defined tasks such as describing the content of an information
+visualization or measuring image importance. BubbleView data is cleaner and
+more consistent than related methodologies that use continuous mouse movements.
+Our analyses validate the use of mouse-contingent, moving-window methodologies
+as approximating eye fixations for different image and task types.
+",1,0,0,0,0,0
+11505,Effects of Disorder on the Pressure-Induced Mott Transition in $κ$-BEDT-TTF)$_2$Cu[N(CN)$_2$]Cl,"  We present a study of the influence of disorder on the Mott metal-insulator
+transition for the organic charge-transfer salt
+$\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Cl. To this end, disorder was introduced
+into the system in a controlled way by exposing the single crystals to x-ray
+irradiation. The crystals were then fine-tuned across the Mott transition by
+the application of continuously controllable He-gas pressure at low
+temperatures. Measurements of the thermal expansion and resistance show that
+the first-order character of the Mott transition prevails for low irradiation
+doses achieved by irradiation times up to 100 h. For these crystals with a
+moderate degree of disorder, we find a first-order transition line which ends
+in a second-order critical endpoint, akin to the pristine crystals. Compared to
+the latter, however, we observe a significant reduction of both, the critical
+pressure $p_c$ and the critical temperature $T_c$. This result is consistent
+with the theoretically-predicted formation of a soft Coulomb gap in the
+presence of strong correlations and small disorder. Furthermore, we
+demonstrate, similar to the observation for the pristine sample, that the Mott
+transition after 50 h of irradiation is accompanied by sizable lattice effects,
+the critical behavior of which can be well described by mean-field theory. Our
+results demonstrate that the character of the Mott transition remains
+essentially unchanged at a low disorder level. However, after an irradiation
+time of 150 h, no clear signatures of a discontinuous metal-insulator
+transition could be revealed anymore. These results suggest that, above a
+certain disorder level, the metal-insulator transition becomes a smeared
+first-order transition with some residual hysteresis.
+",0,1,0,0,0,0
+11506,Dark Energy Survey Year 1 Results: Multi-Probe Methodology and Simulated Likelihood Analyses,"  We present the methodology for and detail the implementation of the Dark
+Energy Survey (DES) 3x2pt DES Year 1 (Y1) analysis, which combines
+configuration-space two-point statistics from three different cosmological
+probes: cosmic shear, galaxy-galaxy lensing, and galaxy clustering, using data
+from the first year of DES observations. We have developed two independent
+modeling pipelines and describe the code validation process. We derive
+expressions for analytical real-space multi-probe covariances, and describe
+their validation with numerical simulations. We stress-test the inference
+pipelines in simulated likelihood analyses that vary 6-7 cosmology parameters
+plus 20 nuisance parameters and precisely resemble the analysis to be presented
+in the DES 3x2pt analysis paper, using a variety of simulated input data
+vectors with varying assumptions.
+We find that any disagreement between pipelines leads to changes in assigned
+likelihood $\Delta \chi^2 \le 0.045$ with respect to the statistical error of
+the DES Y1 data vector. We also find that angular binning and survey mask do
+not impact our analytic covariance at a significant level. We determine lower
+bounds on scales used for analysis of galaxy clustering (8 Mpc$~h^{-1}$) and
+galaxy-galaxy lensing (12 Mpc$~h^{-1}$) such that the impact of modeling
+uncertainties in the non-linear regime is well below statistical errors, and
+show that our analysis choices are robust against a variety of systematics.
+These tests demonstrate that we have a robust analysis pipeline that yields
+unbiased cosmological parameter inferences for the flagship 3x2pt DES Y1
+analysis. We emphasize that the level of independent code development and
+subsequent code comparison as demonstrated in this paper is necessary to
+produce credible constraints from increasingly complex multi-probe analyses of
+current data.
+",0,1,0,0,0,0
+11507,Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples,"  Synthesizing user-intended programs from a small number of input-output
+examples is a challenging problem with several important applications like
+spreadsheet manipulation, data wrangling and code refactoring. Existing
+synthesis systems either completely rely on deductive logic techniques that are
+extensively hand-engineered or on purely statistical models that need massive
+amounts of data, and in general fail to provide real-time synthesis on
+challenging benchmarks. In this work, we propose Neural Guided Deductive Search
+(NGDS), a hybrid synthesis technique that combines the best of both symbolic
+logic techniques and statistical models. Thus, it produces programs that
+satisfy the provided specifications by construction and generalize well on
+unseen examples, similar to data-driven systems. Our technique effectively
+utilizes the deductive search framework to reduce the learning problem of the
+neural component to a simple supervised learning setup. Further, this allows us
+to both train on sparingly available real-world data and still leverage
+powerful recurrent neural network encoders. We demonstrate the effectiveness of
+our method by evaluating on real-world customer scenarios by synthesizing
+accurate programs with up to 12x speed-up compared to state-of-the-art systems.
+",1,0,0,0,0,0
+11508,Coupled Electron-Ion Monte Carlo simulation of hydrogen molecular crystals,"  We performed simulations for solid molecular hydrogen at high pressures
+(250GPa$\leq$P$\leq$500GPa) along two isotherms at T=200 K (phases III and VI)
+and at T=414 K (phase IV). At T=200K we considered likely candidates for phase
+III, the C2c and Cmca12 structures, while at T=414K in phase IV we studied the
+Pc48 structure. We employed both Coupled Electron-Ion Monte Carlo (CEIMC) and
+Path Integral Molecular Dynamics (PIMD) based on Density Functional Theory
+(DFT) using the vdW-DF approximation. The comparison between the two methods
+allows us to address the question of the accuracy of the xc approximation of
+DFT for thermal and quantum protons without recurring to perturbation theories.
+In general, we find that atomic and molecular fluctuations in PIMD are larger
+than in CEIMC which suggests that the potential energy surface from vdW-DF is
+less structured than the one from Quantum Monte Carlo. We find qualitatively
+different behaviors for systems prepared in the C2c structure for increasing
+pressure. Within PIMD the C2c structure is dynamically partially stable for
+P$\leq$250GPa only: it retains the symmetry of the molecular centers but not
+the molecular orientation; at intermediate pressures it develops layered
+structures like Pbcn or Ibam and transforms to the metallic Cmca-4 structure at
+P$\geq$450GPa. Instead, within CEIMC, the C2c structure is found to be
+dynamically stable at least up to 450GPa; at increasing pressure the molecular
+bond length increases and the nuclear correlation decreases. For the other two
+structures the two methods are in qualitative agreement although quantitative
+differences remain. We discuss various structural properties and the electrical
+conductivity. We find these structures become conducting around 350GPa but the
+metallic Drude-like behavior is reached only at around 500GPa, consistent with
+recent experimental claims.
+",0,1,0,0,0,0
+11509,Spline Based Search Method For Unmodeled Transient Gravitational Wave Chirps,"  A method is described for the detection and estimation of transient chirp
+signals that are characterized by smoothly evolving, but otherwise unmodeled,
+amplitude envelopes and instantaneous frequencies. Such signals are
+particularly relevant for gravitational wave searches, where they may arise in
+a wide range of astrophysical scenarios. The method uses splines with
+continuously adjustable breakpoints to represent the amplitude envelope and
+instantaneous frequency of a signal, and estimates them from noisy data using
+penalized least squares and model selection. Simulations based on waveforms
+spanning a wide morphological range show that the method performs well in a
+signal-to-noise ratio regime where the time-frequency signature of a signal is
+highly degraded, thereby extending the coverage of current unmodeled
+gravitational wave searches to a wider class of signals.
+",0,1,0,0,0,0
+11510,Quantum mechanics from an epistemic state space,"  We derive the Hilbert space formalism of quantum mechanics from epistemic
+principles. A key assumption is that a physical theory that relies on entities
+or distinctions that are unknowable in principle gives rise to wrong
+predictions. An epistemic formalism is developed, where concepts like
+individual and collective knowledge are used, and knowledge may be actual or
+potential. The physical state $S$ corresponds to the collective potential
+knowledge. The state $S$ is a subset of a state space $\mathcal{S}=\{Z\}$, such
+that $S$ always contains several elements $Z$, which correspond to unattainable
+states of complete potential knowledge of the world. The evolution of $S$
+cannot be determined in terms of the individual evolution of the elements $Z$,
+unlike the evolution of an ensemble in classical phase space. The evolution of
+$S$ is described in terms of sequential time $n\in \mathbf{\mathbb{N}}$, which
+is updated according to $n\rightarrow n+1$ each time potential knowledge
+changes. In certain experimental contexts $C$, there is initial knowledge at
+time $n$ that a given series of properties $P,P',\ldots$ will be observed
+within a given time frame, meaning that a series of values $p,p',\ldots$ of
+these properties will become known. At time $n$, it is just known that these
+values belong to predefined, finite sets $\{p\},\{p'\},\ldots$. In such a
+context $C$, it is possible to define a complex Hilbert space $\mathcal{H}_{C}$
+on top of $\mathcal{S}$, in which the elements are contextual state vectors
+$\bar{S}_{C}$. Born's rule to calculate the probabilities to find the values
+$p,p',\ldots$ is derived as the only generally applicable such rule. Also, we
+can associate a self-adjoint operator $\bar{P}$ with eigenvalues $\{p\}$ to
+each property $P$ observed within $C$. These operators obey
+$[\bar{P},\bar{P}']=0$ if and only if the precise values of $P$ and $P'$ are
+simultaneoulsy knowable.
+",0,1,0,0,0,0
+11511,Self-duality and scattering map for the hyperbolic van Diejen systems with two coupling parameters (with an appendix by S. Ruijsenaars),"  In this paper, we construct global action-angle variables for a certain
+two-parameter family of hyperbolic van Diejen systems. Following Ruijsenaars'
+ideas on the translation invariant models, the proposed action-angle variables
+come from a thorough analysis of the commutation relation obeyed by the Lax
+matrix, whereas the proof of their canonicity is based on the study of the
+scattering theory. As a consequence, we show that the van Diejen system of our
+interest is self-dual with a factorized scattering map. Also, in an appendix by
+S. Ruijsenaars, a novel proof of the spectral asymptotics of certain
+exponential type matrix flows is presented. This result is of crucial
+importance in our scattering-theoretical analysis.
+",0,1,1,0,0,0
+11512,Inference for heavy tailed stationary time series based on sliding blocks,"  The block maxima method in extreme value theory consists of fitting an
+extreme value distribution to a sample of block maxima extracted from a time
+series. Traditionally, the maxima are taken over disjoint blocks of
+observations. Alternatively, the blocks can be chosen to slide through the
+observation period, yielding a larger number of overlapping blocks. Inference
+based on sliding blocks is found to be more efficient than inference based on
+disjoint blocks. The asymptotic variance of the maximum likelihood estimator of
+the Fréchet shape parameter is reduced by more than 18%. Interestingly, the
+amount of the efficiency gain is the same whatever the serial dependence of the
+underlying time series: as for disjoint blocks, the asymptotic distribution
+depends on the serial dependence only through the sequence of scaling
+constants. The findings are illustrated by simulation experiments and are
+applied to the estimation of high return levels of the daily log-returns of the
+Standard & Poor's 500 stock market index.
+",0,0,1,1,0,0
+11513,Active tuning of high-Q dielectric metasurfaces,"  We demonstrate the active tuning of all-dielectric metasurfaces exhibiting
+high-quality factor (high-Q) resonances. The active control is provided by
+embedding the asymmetric silicon meta-atoms with liquid crystals, which allows
+the relative index of refraction to be controlled through heating. It is found
+that high quality factor resonances ($Q=270\pm30$) can be tuned over more than
+three resonance widths. Our results demonstrate the feasibility of using
+all-dielectric metasurfaces to construct tunable narrow-band filters.
+",0,1,0,0,0,0
+11514,Semi-Supervised QA with Generative Domain-Adaptive Nets,"  We study the problem of semi-supervised question answering----utilizing
+unlabeled text to boost the performance of question answering models. We
+propose a novel training framework, the Generative Domain-Adaptive Nets. In
+this framework, we train a generative model to generate questions based on the
+unlabeled text, and combine model-generated questions with human-generated
+questions for training question answering models. We develop novel domain
+adaptation algorithms, based on reinforcement learning, to alleviate the
+discrepancy between the model-generated data distribution and the
+human-generated data distribution. Experiments show that our proposed framework
+obtains substantial improvement from unlabeled text.
+",1,0,0,0,0,0
+11515,Rationality proofs by curve counting,"  We propose an approach for showing rationality of an algebraic variety $X$.
+We try to cover $X$ by rational curves of certain type and count how many
+curves pass through a generic point. If the answer is $1$, then we can
+sometimes reduce the question of rationality of $X$ to the question of
+rationality of a closed subvariety of $X$. This approach is applied to the case
+of the so-called Ueno-Campana manifolds. Our experiments indicate that the
+previously open cases $X_{4,6}$ and $X_{5,6}$ are both rational. However, this
+result is not rigorously justified and depends on a heuristic argument and a
+Monte Carlo type computer simulation. In an unexpected twist, existence of
+lattices $D_6$, $E_8$ and $\Lambda_{10}$ turns out to be crucial.
+",0,0,1,0,0,0
+11516,"Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models","  With the availability of large databases and recent improvements in deep
+learning methodology, the performance of AI systems is reaching or even
+exceeding the human level on an increasing number of complex tasks. Impressive
+examples of this development can be found in domains such as image
+classification, sentiment analysis, speech understanding or strategic game
+playing. However, because of their nested non-linear structure, these highly
+successful machine learning and artificial intelligence models are usually
+applied in a black box manner, i.e., no information is provided about what
+exactly makes them arrive at their predictions. Since this lack of transparency
+can be a major drawback, e.g., in medical applications, the development of
+methods for visualizing, explaining and interpreting deep learning models has
+recently attracted increasing attention. This paper summarizes recent
+developments in this field and makes a plea for more interpretability in
+artificial intelligence. Furthermore, it presents two approaches to explaining
+predictions of deep learning models, one method which computes the sensitivity
+of the prediction with respect to changes in the input and one approach which
+meaningfully decomposes the decision in terms of the input variables. These
+methods are evaluated on three classification tasks.
+",1,0,0,1,0,0
+11517,The study on quantum material WTe2,"  WTe2 and its sister alloys have attracted tremendous attentions recent years
+due to the large non-saturating magnetoresistance and topological non-trivial
+properties. Herein, we briefly review the electrical property studies on this
+new quantum material.
+",0,1,0,0,0,0
+11518,Learning Social Image Embedding with Deep Multimodal Attention Networks,"  Learning social media data embedding by deep models has attracted extensive
+research interest as well as boomed a lot of applications, such as link
+prediction, classification, and cross-modal search. However, for social images
+which contain both link information and multimodal contents (e.g., text
+description, and visual content), simply employing the embedding learnt from
+network structure or data content results in sub-optimal social image
+representation. In this paper, we propose a novel social image embedding
+approach called Deep Multimodal Attention Networks (DMAN), which employs a deep
+model to jointly embed multimodal contents and link information. Specifically,
+to effectively capture the correlations between multimodal contents, we propose
+a multimodal attention network to encode the fine-granularity relation between
+image regions and textual words. To leverage the network structure for
+embedding learning, a novel Siamese-Triplet neural network is proposed to model
+the links among images. With the joint deep model, the learnt embedding can
+capture both the multimodal contents and the nonlinear network information.
+Extensive experiments are conducted to investigate the effectiveness of our
+approach in the applications of multi-label classification and cross-modal
+search. Compared to state-of-the-art image embeddings, our proposed DMAN
+achieves significant improvement in the tasks of multi-label classification and
+cross-modal search.
+",1,0,0,1,0,0
+11519,DOC: Deep Open Classification of Text Documents,"  Traditional supervised learning makes the closed-world assumption that the
+classes appeared in the test data must have appeared in training. This also
+applies to text learning or text classification. As learning is used
+increasingly in dynamic open environments where some new/test documents may not
+belong to any of the training classes, identifying these novel documents during
+classification presents an important problem. This problem is called open-world
+classification or open classification. This paper proposes a novel deep
+learning based approach. It outperforms existing state-of-the-art techniques
+dramatically.
+",1,0,0,0,0,0
+11520,When Is the First Spurious Variable Selected by Sequential Regression Procedures?,"  Applied statisticians use sequential regression procedures to produce a
+ranking of explanatory variables and, in settings of low correlations between
+variables and strong true effect sizes, expect that variables at the very top
+of this ranking are truly relevant to the response. In a regime of certain
+sparsity levels, however, three examples of sequential procedures--forward
+stepwise, the lasso, and least angle regression--are shown to include the first
+spurious variable unexpectedly early. We derive a rigorous, sharp prediction of
+the rank of the first spurious variable for these three procedures,
+demonstrating that the first spurious variable occurs earlier and earlier as
+the regression coefficients become denser. This counterintuitive phenomenon
+persists for statistically independent Gaussian random designs and an
+arbitrarily large magnitude of the true effects. We gain a better understanding
+of the phenomenon by identifying the underlying cause and then leverage the
+insights to introduce a simple visualization tool termed the double-ranking
+diagram to improve on sequential methods. As a byproduct of these findings, we
+obtain the first provable result certifying the exact equivalence between the
+lasso and least angle regression in the early stages of solution paths beyond
+orthogonal designs. This equivalence can seamlessly carry over many important
+model selection results concerning the lasso to least angle regression.
+",0,0,1,1,0,0
+11521,Interpreting Blackbox Models via Model Extraction,"  Interpretability has become incredibly important as machine learning is
+increasingly used to inform consequential decisions. We propose to construct
+global explanations of complex, blackbox models in the form of a decision tree
+approximating the original model---as long as the decision tree is a good
+approximation, then it mirrors the computation performed by the blackbox model.
+We devise a novel algorithm for extracting decision tree explanations that
+actively samples new training points to avoid overfitting. We evaluate our
+algorithm on a random forest to predict diabetes risk and a learned controller
+for cart-pole. Compared to several baselines, our decision trees are both
+substantially more accurate and equally or more interpretable based on a user
+study. Finally, we describe several insights provided by our interpretations,
+including a causal issue validated by a physician.
+",1,0,0,0,0,0
+11522,Extending holomorphic motions and monodromy,"  Let $E$ be a closed set in the Riemann sphere $\widehat{\mathbb{C}}$. We
+consider a holomorphic motion $\phi$ of $E$ over a complex manifold $M$, that
+is, a holomorphic family of injections on $E$ parametrized by $M$. It is known
+that if $M$ is the unit disk $\Delta$ in the complex plane, then any
+holomorphic motion of $E$ over $\Delta$ can be extended to a holomorphic motion
+of the Riemann sphere over $\Delta$. In this paper, we consider conditions
+under which a holomorphic motion of $E$ over a non-simply connected Riemann
+surface $X$ can be extended to a holomorphic motion of $\widehat{\mathbb{C}}$
+over $X$. Our main result shows that a topological condition, the triviality of
+the monodromy, gives a necessary and sufficient condition for a holomorphic
+motion of $E$ over $X$ to be extended to a holomorphic motion of
+$\widehat{\mathbb{C}}$ over $X$. We give topological and geometric conditions
+for a holomorphic motion over a Riemann surface to be extended. We also apply
+our result to a lifting problem for holomorphic maps to Teichmüller spaces.
+",0,0,1,0,0,0
+11523,A/D Converter Architectures for Energy-Efficient Vision Processor,"  AI applications have emerged in current world. Among AI applications,
+computer-vision (CV) related applications have attracted high interest.
+Hardware implementation of CV processors necessitates a high performance but
+low-power image detector. The key to energy-efficiency work lies in
+analog-digital converting, where output of imaging detectors is transferred to
+digital domain and CV algorithms can be performed on data. In this paper,
+analog-digital converter architectures are compared, and an example ADC design
+is proposed which achieves both good performance and low power consumption.
+",1,0,0,0,0,0
+11524,Quotients in monadic programming: Projective algebras are equivalent to coalgebras,"  In monadic programming, datatypes are presented as free algebras, generated
+by data values, and by the algebraic operations and equations capturing some
+computational effects. These algebras are free in the sense that they satisfy
+just the equations imposed by their algebraic theory, and remain free of any
+additional equations. The consequence is that they do not admit quotient types.
+This is, of course, often inconvenient. Whenever a computation involves data
+with multiple representatives, and they need to be identified according to some
+equations that are not satisfied by all data, the monadic programmer has to
+leave the universe of free algebras, and resort to explicit destructors. We
+characterize the situation when these destructors are preserved under all
+operations, and the resulting quotients of free algebras are also their
+subalgebras. Such quotients are called *projective*. Although popular in
+universal algebra, projective algebras did not attract much attention in the
+monadic setting, where they turn out to have a surprising avatar: for any given
+monad, a suitable category of projective algebras is equivalent with the
+category of coalgebras for the comonad induced by any monad resolution. For a
+monadic programmer, this equivalence provides a convenient way to implement
+polymorphic quotients as coalgebras. The dual correspondence of injective
+coalgebras and all algebras leads to a different family of quotient types,
+which seems to have a different family of applications. Both equivalences also
+entail several general corollaries concerning monadicity and comonadicity.
+",1,0,1,0,0,0
+11525,Density Independent Algorithms for Sparsifying $k$-Step Random Walks,"  We give faster algorithms for producing sparse approximations of the
+transition matrices of $k$-step random walks on undirected, weighted graphs.
+These transition matrices also form graphs, and arise as intermediate objects
+in a variety of graph algorithms. Our improvements are based on a better
+understanding of processes that sample such walks, as well as tighter bounds on
+key weights underlying these sampling processes. On a graph with $n$ vertices
+and $m$ edges, our algorithm produces a graph with about $n\log{n}$ edges that
+approximates the $k$-step random walk graph in about $m + n \log^4{n}$ time. In
+order to obtain this runtime bound, we also revisit ""density independent""
+algorithms for sparsifying graphs whose runtime overhead is expressed only in
+terms of the number of vertices.
+",1,0,0,0,0,0
+11526,On a generalized $k$-FL sequence and its applications,"  We introduce a generalized $k$-FL sequence and special kind of pairs of real
+numbers that are related to it, and give an application on the integral
+solutions of a certain equation using those pairs. Also, we associate skew
+circulant and circulant matrices to each generalized $k$-FL sequence, and study
+the determinantal variety of those matrices as an application.
+",0,0,1,0,0,0
+11527,Supervised Metric Learning with Generalization Guarantees,"  The crucial importance of metrics in machine learning algorithms has led to
+an increasing interest in optimizing distance and similarity functions, an area
+of research known as metric learning. When data consist of feature vectors, a
+large body of work has focused on learning a Mahalanobis distance. Less work
+has been devoted to metric learning from structured objects (such as strings or
+trees), most of it focusing on optimizing a notion of edit distance. We
+identify two important limitations of current metric learning approaches.
+First, they allow to improve the performance of local algorithms such as
+k-nearest neighbors, but metric learning for global algorithms (such as linear
+classifiers) has not been studied so far. Second, the question of the
+generalization ability of metric learning methods has been largely ignored. In
+this thesis, we propose theoretical and algorithmic contributions that address
+these limitations. Our first contribution is the derivation of a new kernel
+function built from learned edit probabilities. Our second contribution is a
+novel framework for learning string and tree edit similarities inspired by the
+recent theory of (e,g,t)-good similarity functions. Using uniform stability
+arguments, we establish theoretical guarantees for the learned similarity that
+give a bound on the generalization error of a linear classifier built from that
+similarity. In our third contribution, we extend these ideas to metric learning
+from feature vectors by proposing a bilinear similarity learning method that
+efficiently optimizes the (e,g,t)-goodness. Generalization guarantees are
+derived for our approach, highlighting that our method minimizes a tighter
+bound on the generalization error of the classifier. Our last contribution is a
+framework for establishing generalization bounds for a large class of existing
+metric learning algorithms based on a notion of algorithmic robustness.
+",1,0,0,0,0,0
+11528,Symbolic dynamics for Kuramoto-Sivashinsky PDE on the line --- a computer-assisted proof,"  The Kuramoto-Sivashinsky PDE on the line with odd and periodic boundary
+conditions and with parameter $\nu=0.1212$ is considered. We give a
+computer-assisted proof the existence of symbolic dynamics and countable
+infinity of periodic orbits with arbitrary large periods.
+",0,1,0,0,0,0
+11529,Individual Dynamical Masses of Ultracool Dwarfs,"  We present the full results of our decade-long astrometric monitoring
+programs targeting 31 ultracool binaries with component spectral types M7-T5.
+Joint analysis of resolved imaging from Keck Observatory and Hubble Space
+Telescope and unresolved astrometry from CFHT/WIRCam yields parallactic
+distances for all systems, robust orbit determinations for 23 systems, and
+photocenter orbits for 19 systems. As a result, we measure 38 precise
+individual masses spanning 30-115 $M_{\rm Jup}$. We determine a
+model-independent substellar boundary that is $\approx$70 $M_{\rm Jup}$ in mass
+($\approx$L4 in spectral type), and we validate Baraffe et al. (2015)
+evolutionary model predictions for the lithium-depletion boundary (60 $M_{\rm
+Jup}$ at field ages). Assuming each binary is coeval, we test models of the
+substellar mass-luminosity relation and find that in the L/T transition, only
+the Saumon & Marley (2008) ""hybrid"" models accounting for cloud clearing match
+our data. We derive a precise, mass-calibrated spectral type-effective
+temperature relation covering 1100-2800 K. Our masses enable a novel direct
+determination of the age distribution of field brown dwarfs spanning L4-T5 and
+30-70 $M_{\rm Jup}$. We determine a median age of 1.3 Gyr, and our population
+synthesis modeling indicates our sample is consistent with a constant star
+formation history modulated by dynamical heating in the Galactic disk. We
+discover two triple-brown-dwarf systems, the first with directly measured
+masses and eccentricities. We examine the eccentricity distribution, carefully
+considering biases and completeness, and find that low-eccentricity orbits are
+significantly more common among ultracool binaries than solar-type binaries,
+possibly indicating the early influence of long-lived dissipative gas disks.
+Overall, this work represents a major advance in the empirical view of very
+low-mass stars and brown dwarfs.
+",0,1,0,0,0,0
+11530,Beta Dips in the Gaia Era: Simulation Predictions of the Galactic Velocity Anisotropy Parameter for Stellar Halos,"  The velocity anisotropy parameter, beta, is a measure of the kinematic state
+of orbits in the stellar halo which holds promise for constraining the merger
+history of the Milky Way (MW). We determine global trends for beta as a
+function of radius from three suites of simulations, including accretion only
+and cosmological hydrodynamic simulations. We find that both types of
+simulations are consistent and predict strong radial anisotropy (<beta>~0.7)
+for Galactocentric radii greater than 10 kpc. Previous observations of beta for
+the MW's stellar halo claim a detection of an isotropic or tangential ""dip"" at
+r~20 kpc. Using the N-body+SPH simulations, we investigate the temporal
+persistence, population origin, and severity of ""dips"" in beta. We find dips in
+the in situ stellar halo are long-lived, while dips in the accreted stellar
+halo are short-lived and tied to the recent accretion of satellite material. We
+also find that a major merger as early as z~1 can result in a present day low
+(isotropic to tangential) value of beta over a wide range of radii and angular
+expanse. While all of these mechanisms are plausible drivers for the beta dip
+observed in the MW, in the simulations, each mechanism has a unique metallicity
+signature associated with it, implying that future spectroscopic surveys could
+distinguish between them. Since an accurate knowledge of beta(r) is required
+for measuring the mass of the MW halo, we note significant transient dips in
+beta could cause an overestimate of the halo's mass when using spherical Jeans
+equation modeling.
+",0,1,0,0,0,0
+11531,Computational Flows in Arithmetic,"  A computational flow is a pair consisting of a sequence of computational
+problems of a certain sort and a sequence of computational reductions among
+them. In this paper we will develop a theory for these computational flows and
+we will use it to make a sound and complete interpretation for bounded theories
+of arithmetic. This property helps us to decompose a first order arithmetical
+proof to a sequence of computational reductions by which we can extract the
+computational content of low complexity statements in some bounded theories of
+arithmetic such as $I\Delta_0$, $T^k_n$, $I\Delta_0+EXP$ and $PRA$. In the last
+section, by generalizing term-length flows to ordinal-length flows, we will
+extend our investigation from bounded theories to strong unbounded ones such as
+$I\Sigma_n$ and $PA+TI(\alpha)$ and we will capture their total $NP$ search
+problems as a consequence.
+",1,0,1,0,0,0
+11532,Injective homomorphisms of mapping class groups of non-orientable surfaces,"  Let $N$ be a compact, connected, non-orientable surface of genus $\rho$ with
+$n$ boundary components, with $\rho \ge 5$ and $n \ge 0$, and let $\mathcal{M}
+(N)$ be the mapping class group of $N$. We show that, if $\mathcal{G}$ is a
+finite index subgroup of $\mathcal{M} (N)$ and $\varphi: \mathcal{G} \to
+\mathcal{M} (N)$ is an injective homomorphism, then there exists $f_0 \in
+\mathcal{M} (N)$ such that $\varphi (g) = f_0 g f_0^{-1}$ for all $g \in
+\mathcal{G}$. We deduce that the abstract commensurator of $\mathcal{M} (N)$
+coincides with $\mathcal{M} (N)$.
+",0,0,1,0,0,0
+11533,Transport in a disordered $ν=2/3$ fractional quantum Hall junction,"  Electric and thermal transport properties of a $\nu=2/3$ fractional quantum
+Hall junction are analyzed. We investigate the evolution of the electric and
+thermal two-terminal conductances, $G$ and $G^Q$, with system size $L$ and
+temperature $T$. This is done both for the case of strong interaction between
+the 1 and 1/ 3 modes (when the low-temperature physics of the interacting
+segment of the device is controlled by the vicinity of the strong-disorder
+Kane-Fisher-Polchinski fixed point) and for relatively weak interaction, for
+which the disorder is irrelevant at $T=0$ in the renormalization-group sense.
+The transport properties in both cases are similar in several respects. In
+particular, $G(L)$ is close to 4/3 (in units of $e^2/h$) and $G^Q$ to 2 (in
+units of $\pi T / 6 \hbar$) for small $L$, independently of the interaction
+strength. For large $L$ the system is in an incoherent regime, with $G$ given
+by 2/3 and $G^Q$ showing the Ohmic scaling, $G^Q\propto 1/L$, again for any
+interaction strength. The hallmark of the strong-disorder fixed point is the
+emergence of an intermediate range of $L$, in which the electric conductance
+shows strong mesoscopic fluctuations and the thermal conductance is $G^Q=1$.
+The analysis is extended also to a device with floating 1/3 mode, as studied in
+a recent experiment [A. Grivnin et al, Phys. Rev. Lett. 113, 266803 (2014)].
+",0,1,0,0,0,0
+11534,Giant Field Enhancement in Longitudinal Epsilon Near Zero Films,"  We report that a longitudinal epsilon-near-zero (LENZ) film leads to giant
+field enhancement and strong radiation emission of sources in it and that these
+features are superior to what found in previous studies related to isotropic
+ENZ. LENZ films are uniaxially anisotropic films where relative permittivity
+along the normal direction to the film is much smaller than unity, while the
+permittivity in the transverse plane of the film is not vanishing. It has been
+shown previously that realistic isotropic ENZ films do not provide large field
+enhancement due to material losses, however, we show the loss effects can be
+overcome using LENZ films. We also prove that in comparison to the (isotropic)
+ENZ case, the LENZ film field enhancement is not only remarkably larger but it
+also occurs for a wider range of angles of incidence. Importantly, the field
+enhancement near the interface of the LENZ film is almost independent of the
+thickness unlike for the isotropic ENZ case where extremely small thickness is
+required. We show that for a LENZ structure consisting of a multilayer of
+dysprosium-doped cadmium oxide and silicon accounting for realistic losses,
+field intensity enhancement of 30 is obtained which is almost 10 times larger
+than that obtained with realistic ENZ materials
+",0,1,0,0,0,0
+11535,Analysis and optimal individual pitch control decoupling by inclusion of an azimuth offset in the multi-blade coordinate transformation,"  With the trend of increasing wind turbine rotor diameters, the mitigation of
+blade fatigue loadings is of special interest to extend the turbine lifetime.
+Fatigue load reductions can be partly accomplished using Individual Pitch
+Control (IPC) facilitated by the so-called Multi-Blade Coordinate (MBC)
+transformation. This operation transforms and decouples the blade load signals
+in a yaw- and tilt-axis. However, in practical scenarios, the resulting
+transformed system still shows coupling between the axes, posing a need for
+more advanced Multiple-Input Multiple-Output (MIMO) control architectures. This
+paper presents a novel analysis and design framework for decoupling of the
+non-rotating axes by the inclusion of an azimuth offset in the reverse MBC
+transformation, enabling the application of simple Single-Input Single-Output
+(SISO) controllers. A thorough analysis is given by including the azimuth
+offset in a frequency-domain representation. The result is evaluated on
+simplified blade models, as well as linearizations obtained from the
+NREL~5\nobreakdash-MW reference wind turbine. A sensitivity and decoupling
+assessment justify the application of decentralized SISO control loops for IPC.
+Furthermore, closed-loop high-fidelity simulations show beneficial effects on
+pitch actuation and blade fatigue load reductions.
+",1,0,0,0,0,0
+11536,Deep Learning-aided Application Scheduler for Vehicular Safety Communication,"  802.11p based V2X communication uses stochastic medium access control, which
+cannot prevent broadcast packet collision, in particular during high channel
+load. Wireless congestion control has been designed to keep the channel load at
+an optimal point. However, vehicles' lack of precise and granular knowledge
+about true channel activity, in time and space, makes it impossible to fully
+avoid packet collisions. In this paper, we propose a machine learning approach
+using deep neural network for learning the vehicles' transmit patterns, and as
+such predicting future channel activity in space and time. We evaluate the
+performance of our proposal via simulation considering multiple safety-related
+V2X services involving heterogeneous transmit patterns. Our results show that
+predicting channel activity, and transmitting accordingly, reduces collisions
+and significantly improves communication performance.
+",1,0,0,0,0,0
+11537,Convergence rates in the central limit theorem for weighted sums of Bernoulli random fields,"  We prove moment inequalities for a class of functionals of i.i.d. random
+fields. We then derive rates in the central limit theorem for weighted sums of
+such randoms fields via an approximation by $m$-dependent random fields.
+",0,0,1,1,0,0
+11538,On Adaptive Estimation for Dynamic Bernoulli Bandits,"  The multi-armed bandit (MAB) problem is a classic example of the
+exploration-exploitation dilemma. It is concerned with maximising the total
+rewards for a gambler by sequentially pulling an arm from a multi-armed slot
+machine where each arm is associated with a reward distribution. In static
+MABs, the reward distributions do not change over time, while in dynamic MABs,
+each arm's reward distribution can change, and the optimal arm can switch over
+time. Motivated by many real applications where rewards are binary, we focus on
+dynamic Bernoulli bandits. Standard methods like $\epsilon$-Greedy and Upper
+Confidence Bound (UCB), which rely on the sample mean estimator, often fail to
+track changes in the underlying reward for dynamic problems. In this paper, we
+overcome the shortcoming of slow response to change by deploying adaptive
+estimation in the standard methods and propose a new family of algorithms,
+which are adaptive versions of $\epsilon$-Greedy, UCB, and Thompson sampling.
+These new methods are simple and easy to implement. Moreover, they do not
+require any prior knowledge about the dynamic reward process, which is
+important for real applications. We examine the new algorithms numerically in
+different scenarios and the results show solid improvements of our algorithms
+in dynamic environments.
+",1,0,0,1,0,0
+11539,Near Optimal Sketching of Low-Rank Tensor Regression,"  We study the least squares regression problem \begin{align*} \min_{\Theta \in
+\mathcal{S}_{\odot D,R}} \|A\Theta-b\|_2, \end{align*} where
+$\mathcal{S}_{\odot D,R}$ is the set of $\Theta$ for which $\Theta =
+\sum_{r=1}^{R} \theta_1^{(r)} \circ \cdots \circ \theta_D^{(r)}$ for vectors
+$\theta_d^{(r)} \in \mathbb{R}^{p_d}$ for all $r \in [R]$ and $d \in [D]$, and
+$\circ$ denotes the outer product of vectors. That is, $\Theta$ is a
+low-dimensional, low-rank tensor. This is motivated by the fact that the number
+of parameters in $\Theta$ is only $R \cdot \sum_{d=1}^D p_d$, which is
+significantly smaller than the $\prod_{d=1}^{D} p_d$ number of parameters in
+ordinary least squares regression. We consider the above CP decomposition model
+of tensors $\Theta$, as well as the Tucker decomposition. For both models we
+show how to apply data dimensionality reduction techniques based on {\it
+sparse} random projections $\Phi \in \mathbb{R}^{m \times n}$, with $m \ll n$,
+to reduce the problem to a much smaller problem $\min_{\Theta} \|\Phi A \Theta
+- \Phi b\|_2$, for which if $\Theta'$ is a near-optimum to the smaller problem,
+then it is also a near optimum to the original problem. We obtain significantly
+smaller dimension and sparsity in $\Phi$ than is possible for ordinary least
+squares regression, and we also provide a number of numerical simulations
+supporting our theory.
+",1,0,0,1,0,0
+11540,Fast computation of p-values for the permutation test based on Pearson's correlation coefficient and other statistical tests,"  Permutation tests are among the simplest and most widely used statistical
+tools. Their p-values can be computed by a straightforward sampling of
+permutations. However, this way of computing p-values is often so slow that it
+is replaced by an approximation, which is accurate only for part of the
+interesting range of parameters. Moreover, the accuracy of the approximation
+can usually not be improved by increasing the computation time.
+We introduce a new sampling-based algorithm which uses the fast Fourier
+transform to compute p-values for the permutation test based on Pearson's
+correlation coefficient. The algorithm is practically and asymptotically faster
+than straightforward sampling. Typically, its complexity is logarithmic in the
+input size, while the complexity of straightforward sampling is linear. The
+idea behind the algorithm can also be used to accelerate the computation of
+p-values for many other common statistical tests. The algorithm is easy to
+implement, but its analysis involves results from the representation theory of
+the symmetric group.
+",0,0,0,1,0,0
+11541,Convergence analysis of the information matrix in Gaussian belief propagation,"  Gaussian belief propagation (BP) has been widely used for distributed
+estimation in large-scale networks such as the smart grid, communication
+networks, and social networks, where local measurements/observations are
+scattered over a wide geographical area. However, the convergence of Gaus- sian
+BP is still an open issue. In this paper, we consider the convergence of
+Gaussian BP, focusing in particular on the convergence of the information
+matrix. We show analytically that the exchanged message information matrix
+converges for arbitrary positive semidefinite initial value, and its dis- tance
+to the unique positive definite limit matrix decreases exponentially fast.
+",1,0,0,0,0,0
+11542,Vortex pairs in a spin-orbit coupled Bose-Einstein condensate,"  Static and dynamic properties of vortices in a two-component Bose-Einstein
+condensate with Rashba spin-orbit coupling are investigated. The mass current
+around a vortex core in the plane-wave phase is found to be deformed by the
+spin-orbit coupling, and this makes the dynamics of the vortex pairs quite
+different from those in a scalar Bose-Einstein condensate. The velocity of a
+vortex-antivortex pair is much smaller than that without spin-orbit coupling,
+and there exist stationary states. Two vortices with the same circulation move
+away from each other or unite to form a stationary state.
+",0,1,0,0,0,0
+11543,GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery,"  Advances in remote sensing technologies have made it possible to use
+high-resolution visual data for weather observation and forecasting tasks. We
+propose the use of multi-layer neural networks for understanding complex
+atmospheric dynamics based on multichannel satellite images. The capability of
+our model was evaluated by using a linear regression task for single typhoon
+coordinates prediction. A specific combination of models and different
+activation policies enabled us to obtain an interesting prediction result in
+the northeastern hemisphere (ENH).
+",1,0,0,0,0,0
+11544,The Incremental Multiresolution Matrix Factorization Algorithm,"  Multiresolution analysis and matrix factorization are foundational tools in
+computer vision. In this work, we study the interface between these two
+distinct topics and obtain techniques to uncover hierarchical block structure
+in symmetric matrices -- an important aspect in the success of many vision
+problems. Our new algorithm, the incremental multiresolution matrix
+factorization, uncovers such structure one feature at a time, and hence scales
+well to large matrices. We describe how this multiscale analysis goes much
+farther than what a direct global factorization of the data can identify. We
+evaluate the efficacy of the resulting factorizations for relative leveraging
+within regression tasks using medical imaging data. We also use the
+factorization on representations learned by popular deep networks, providing
+evidence of their ability to infer semantic relationships even when they are
+not explicitly trained to do so. We show that this algorithm can be used as an
+exploratory tool to improve the network architecture, and within numerous other
+settings in vision.
+",1,0,0,1,0,0
+11545,Carlsson's rank conjecture and a conjecture on square-zero upper triangular matrices,"  Let $k$ be an algebraically closed field and $A$ the polynomial algebra in
+$r$ variables with coefficients in $k$. In case the characteristic of $k$ is
+$2$, Carlsson conjectured that for any $DG$-$A$-module $M$ of dimension $N$ as
+a free $A$-module, if the homology of $M$ is nontrivial and finite dimensional
+as a $k$-vector space, then $2^r\leq N$. Here we state a stronger conjecture
+about varieties of square-zero upper-triangular $N\times N$ matrices with
+entries in $A$. Using stratifications of these varieties via Borel orbits, we
+show that the stronger conjecture holds when $N < 8$ or $r < 3$ without any
+restriction on the characteristic of $k$. As a consequence, we attain a new
+proof for many of the known cases of Carlsson's conjecture and give new results
+when $N > 4$ and $r = 2$.
+",0,0,1,0,0,0
+11546,Effect of Anodizing Parameters on Corrosion Resistance of Coated Purified Magnesium,"  Magnesium and its alloys are being considered for biodegradable biomaterials.
+However, high and uncontrollable corrosion rates have limited the use of
+magnesium and its alloys in biological environments. In this research, high
+purified magnesium (HP-Mg) was coated with stearic acid in order to improve the
+corrosion resistance of magnesium. Anodization and immersion in stearic acid
+were used to form a hydrophobic layer on magnesium substrate. Different DC
+voltages, times, electrolytes, and temperatures were tested. Electrochemical
+impedance spectroscopy and potentiodynamic polarization were used to measure
+the corrosion rates of the coated HP-Mg. The results showed that optimum
+corrosion resistance occurred for specimens anodized at +4 volts for 4 minutes
+at 70°C in borate benzoate. The corrosion resistance was temporarily
+enhanced by 1000x.
+",0,1,0,0,0,0
+11547,The probabilistic nature of McShane's identity: planar tree coding of simple loops,"  In this article, we discuss a probabilistic interpretation of McShane's
+identity as describing a finite measure on the space of embedded paths though a
+point.
+",0,0,1,0,0,0
+11548,RT-DAP: A Real-Time Data Analytics Platform for Large-scale Industrial Process Monitoring and Control,"  In most process control systems nowadays, process measurements are
+periodically collected and archived in historians. Analytics applications
+process the data, and provide results offline or in a time period that is
+considerably slow in comparison to the performance of the manufacturing
+process. Along with the proliferation of Internet-of-Things (IoT) and the
+introduction of ""pervasive sensors"" technology in process industries,
+increasing number of sensors and actuators are installed in process plants for
+pervasive sensing and control, and the volume of produced process data is
+growing exponentially. To digest these data and meet the ever-growing
+requirements to increase production efficiency and improve product quality,
+there needs to be a way to both improve the performance of the analytics system
+and scale the system to closely monitor a much larger set of plant resources.
+In this paper, we present a real-time data analytics platform, called RT-DAP,
+to support large-scale continuous data analytics in process industries. RT-DAP
+is designed to be able to stream, store, process and visualize a large volume
+of realtime data flows collected from heterogeneous plant resources, and
+feedback to the control system and operators in a realtime manner. A prototype
+of the platform is implemented on Microsoft Azure. Our extensive experiments
+validate the design methodologies of RT-DAP and demonstrate its efficiency in
+both component and system levels.
+",1,0,0,0,0,0
+11549,BFGS convergence to nonsmooth minimizers of convex functions,"  The popular BFGS quasi-Newton minimization algorithm under reasonable
+conditions converges globally on smooth convex functions. This result was
+proved by Powell in 1976: we consider its implications for functions that are
+not smooth. In particular, an analogous convergence result holds for functions,
+like the Euclidean norm, that are nonsmooth at the minimizer.
+",0,0,1,0,0,0
+11550,A collaborative citizen science platform for real-time volunteer computing and games,"  Volunteer computing (VC) or distributed computing projects are common in the
+citizen cyberscience (CCS) community and present extensive opportunities for
+scientists to make use of computing power donated by volunteers to undertake
+large-scale scientific computing tasks. Volunteer computing is generally a
+non-interactive process for those contributing computing resources to a project
+whereas volunteer thinking (VT) or distributed thinking, which allows
+volunteers to participate interactively in citizen cyberscience projects to
+solve human computation tasks. In this paper we describe the integration of
+three tools, the Virtual Atom Smasher (VAS) game developed by CERN, LiveQ, a
+job distribution middleware, and CitizenGrid, an online platform for hosting
+and providing computation to CCS projects. This integration demonstrates the
+combining of volunteer computing and volunteer thinking to help address the
+scientific and educational goals of games like VAS. The paper introduces the
+three tools and provides details of the integration process along with further
+potential usage scenarios for the resulting platform.
+",1,0,0,0,0,0
+11551,Quasiparticle interference in multiband superconductors with strong coupling,"  We develop a theory of the quasiparticle interference (QPI) in multiband
+superconductors based on strong-coupling Eliashberg approach within the Born
+approximation. In the framework of this theory, we study dependencies of the
+QPI response function in the multiband superconductors with nodeless s-wave
+superconductive order parameter. We pay a special attention to the difference
+of the quasiparticle scattering between the bands having the same and opposite
+signs of the order parameter. We show that, at the momentum values close to the
+momentum transfer between two bands, the energy dependence of the quasiparticle
+interference response function has three singularities. Two of these correspond
+to the values of the gap functions and the third one depends on both the gaps
+and the transfer momentum. We argue that only the singularity near the smallest
+band gap may be used as an universal tool to distinguish between $s_{++}$ and
+$s_{\pm}$ order parameters. The robustness of the sign of the response function
+peak near the smaller gap value, irrespective of the change in parameters, in
+both the symmetry cases is a promising feature that can be harnessed
+experimentally.
+",0,1,0,0,0,0
+11552,Trading Strategies Generated by Path-dependent Functionals of Market Weights,"  Almost twenty years ago, E.R. Fernholz introduced portfolio generating
+functions which can be used to construct a variety of portfolios, solely in the
+terms of the individual companies' market weights. I. Karatzas and J. Ruf
+recently developed another methodology for the functional construction of
+portfolios, which leads to very simple conditions for strong relative arbitrage
+with respect to the market. In this paper, both of these notions of functional
+portfolio generation are generalized in a pathwise, probability-free setting;
+portfolio generating functions are substituted by path-dependent functionals,
+which involve the current market weights, as well as additional
+bounded-variation functions of past and present market weights. This
+generalization leads to a wider class of functionally-generated portfolios than
+was heretofore possible, and yields improved conditions for outperforming the
+market portfolio over suitable time-horizons.
+",0,0,0,0,0,1
+11553,General Bayesian Inference over the Stiefel Manifold via the Givens Representation,"  We introduce an approach based on the Givens representation that allows for a
+routine, reliable, and flexible way to infer Bayesian models with orthogonal
+matrix parameters. This class of models most notably includes models from
+multivariate statistics such factor models and probabilistic principal
+component analysis (PPCA). Our approach overcomes several of the practical
+barriers to using the Givens representation in a general Bayesian inference
+framework. In particular, we show how to inexpensively compute the
+change-of-measure term necessary for transformations of random variables. We
+also show how to overcome specific topological pathologies that arise when
+representing circular random variables in an unconstrained space. In addition,
+we discuss how the alternative parameterization can be used to define new
+distributions over orthogonal matrices as well as to constrain parameter space
+to eliminate superfluous posterior modes in models such as PPCA. While previous
+inference approaches to this problem involved specialized updates to the
+orthogonal matrix parameters, our approach lets us represent these constrained
+parameters in an unconstrained form. Unlike previous approaches, this allows
+for the inference of models with orthogonal matrix parameters using any modern
+inference algorithm including those available in modern Bayesian modeling
+frameworks such as Stan, Edward, or PyMC3. We illustrate with examples how our
+approach can be used in practice in Stan to infer models with orthogonal matrix
+parameters, and we compare to existing methods.
+",0,0,0,1,0,0
+11554,Sine wave gating Silicon single-photon detectors for multiphoton entanglement experiments,"  Silicon single-photon detectors (SPDs) are the key devices for detecting
+single photons in the visible wavelength range. Here we present high detection
+efficiency silicon SPDs dedicated to the generation of multiphoton entanglement
+based on the technique of high-frequency sine wave gating. The silicon
+single-photon avalanche diodes (SPADs) components are acquired by disassembling
+6 commercial single-photon counting modules (SPCMs). Using the new quenching
+electronics, the average detection efficiency of SPDs is increased from 68.6%
+to 73.1% at a wavelength of 785 nm. These sine wave gating SPDs are then
+applied in a four-photon entanglement experiment, and the four-fold coincidence
+count rate is increased by 30% without degrading its visibility compared with
+the original SPCMs.
+",0,1,0,0,0,0
+11555,A Tutorial on Fisher Information,"  In many statistical applications that concern mathematical psychologists, the
+concept of Fisher information plays an important role. In this tutorial we
+clarify the concept of Fisher information as it manifests itself across three
+different statistical paradigms. First, in the frequentist paradigm, Fisher
+information is used to construct hypothesis tests and confidence intervals
+using maximum likelihood estimators; second, in the Bayesian paradigm, Fisher
+information is used to define a default prior; lastly, in the minimum
+description length paradigm, Fisher information is used to measure model
+complexity.
+",0,0,1,1,0,0
+11556,A statistical approach to identify superluminous supernovae and probe their diversity,"  We investigate the identification of hydrogen-poor superluminous supernovae
+(SLSNe I) using a photometric analysis, without including an arbitrary
+magnitude threshold. We assemble a homogeneous sample of previously classified
+SLSNe I from the literature, and fit their light curves using Gaussian
+processes. From the fits, we identify four photometric parameters that have a
+high statistical significance when correlated, and combine them in a parameter
+space that conveys information on their luminosity and color evolution. This
+parameter space presents a new definition for SLSNe I, which can be used to
+analyse existing and future transient datasets. We find that 90% of previously
+classified SLSNe I meet our new definition. We also examine the evidence for
+two subclasses of SLSNe I, combining their photometric evolution with
+spectroscopic information, namely the photospheric velocity and its gradient. A
+cluster analysis reveals the presence of two distinct groups. `Fast' SLSNe show
+fast light curves and color evolution, large velocities, and a large velocity
+gradient. `Slow' SLSNe show slow light curve and color evolution, small
+expansion velocities, and an almost non-existent velocity gradient. Finally, we
+discuss the impact of our analyses in the understanding of the powering engine
+of SLSNe, and their implementation as cosmological probes in current and future
+surveys.
+",0,1,0,0,0,0
+11557,Low fertility rate reversal: a feature of interactions between Biological and Economic systems,"  An empirical relation indicates that an increase of living standard decreases
+the Total Fertility Rate (TFR), but this trend was broken in highly developed
+countries in 2005. The reversal of the TFR was associated with the continuous
+economic and social development expressed by the Human Development Index (HDI).
+We have investigated how universal and persistent the TFR reversal is. The
+results show that in highly developed countries, $ \mathrm{HDI}>0.85 $, the TFR
+and the HDI are not correlated in 2010-2014. Detailed analyses of correlations
+and differences of the TFR and the HDI indicate a decrease of the TFR if the
+HDI increases in this period. However, we found that a reversal of the TFR as a
+consequence of economic development started at medium levels of the HDI, i.e. $
+0.575<\mathrm{HDI}<0.85 $, in many countries. Our results show a transient
+nature of the TFR reversal in highly developed countries in 2010-2014 and a
+relative stable trend of the TFR increase in medium developed countries in
+longer time periods. We believe that knowledge of the fundamental nature of the
+TFR is very important for the survival of medium and highly developed
+societies.
+",0,1,0,0,0,0
+11558,Analysis of $p$-Laplacian Regularization in Semi-Supervised Learning,"  We investigate a family of regression problems in a semi-supervised setting.
+The task is to assign real-valued labels to a set of $n$ sample points,
+provided a small training subset of $N$ labeled points. A goal of
+semi-supervised learning is to take advantage of the (geometric) structure
+provided by the large number of unlabeled data when assigning labels. We
+consider random geometric graphs, with connection radius $\epsilon(n)$, to
+represent the geometry of the data set. Functionals which model the task reward
+the regularity of the estimator function and impose or reward the agreement
+with the training data. Here we consider the discrete $p$-Laplacian
+regularization.
+We investigate asymptotic behavior when the number of unlabeled points
+increases, while the number of training points remains fixed. We uncover a
+delicate interplay between the regularizing nature of the functionals
+considered and the nonlocality inherent to the graph constructions. We
+rigorously obtain almost optimal ranges on the scaling of $\epsilon(n)$ for the
+asymptotic consistency to hold. We prove that the minimizers of the discrete
+functionals in random setting converge uniformly to the desired continuum
+limit. Furthermore we discover that for the standard model used there is a
+restrictive upper bound on how quickly $\epsilon(n)$ must converge to zero as
+$n \to \infty$. We introduce a new model which is as simple as the original
+model, but overcomes this restriction.
+",1,0,1,1,0,0
+11559,Spectral stability of shifted states on star graphs,"  We consider the nonlinear Schrödinger (NLS) equation with the subcritical
+power nonlinearity on a star graph consisting of $N$ edges and a single vertex
+under generalized Kirchhoff boundary conditions. The stationary NLS equation
+may admit a family of solitary waves parameterized by a translational
+parameter, which we call the shifted states. The two main examples include (i)
+the star graph with even $N$ under the classical Kirchhoff boundary conditions
+and (ii) the star graph with one incoming edge and $N-1$ outgoing edges under a
+single constraint on coefficients of the generalized Kirchhoff boundary
+conditions. We obtain the general counting results on the Morse index of the
+shifted states and apply them to the two examples. In the case of (i), we prove
+that the shifted states with even $N \geq 4$ are saddle points of the action
+functional which are spectrally unstable under the NLS flow. In the case of
+(ii), we prove that the shifted states with the monotone profiles in the $N-1$
+outgoing edges are spectrally stable, whereas the shifted states with
+non-monotone profiles in the $N-1$ outgoing edges are spectrally unstable, the
+two families intersect at the half-soliton states which are spectrally stable
+but nonlinearly unstable. Since the NLS equation on a star graph with shifted
+states can be reduced to the homogeneous NLS equation on a line, the spectral
+instability of shifted states is due to the perturbations breaking this
+reduction. We give a simple argument suggesting that the spectrally stable
+shifted states are nonlinear unstable under the NLS flow due to the
+perturbations breaking the reduction to the NLS equation on a line.
+",0,1,0,0,0,0
+11560,Estimation of the multifractional function and the stability index of linear multifractional stable processes,"  In this paper we are interested in multifractional stable processes where the
+self-similarity index $H$ is a function of time, in other words $H$ becomes
+time changing, and the stability index $\alpha$ is a constant. Using $\beta$-
+negative power variations ($-1/2<\beta<0$), we propose estimators for the value
+of the multifractional function $H$ at a fixed time $t_0$ and for $\alpha$ for
+two cases: multifractional Brownian motion ($\alpha=2$) and linear
+multifractional stable motion ($0<\alpha<2$). We get the consistency of our
+estimates for the underlying processes with the rate of convergence.
+",0,0,1,1,0,0
+11561,"Observation of ""Topological"" Microflares in the Solar Atmosphere","  We report on observation of the unusual kind of solar microflares, presumably
+associated with the so-called ""topological trigger"" of magnetic reconnection,
+which was theoretically suggested long time ago by Gorbachev et al. (Sov. Ast.
+1988, v.32, p.308) but has not been clearly identified so far by observations.
+As can be seen in pictures by Hinode SOT in CaII line, there may be a bright
+loop connecting two sunspots, which looks at the first sight just as a magnetic
+field line connecting the opposite poles. However, a closer inspection of SDO
+HMI magnetograms shows that the respective arc is anchored in the regions of
+the same polarity near the sunspot boundaries. Yet another peculiar feature is
+that the arc flashes almost instantly as a thin strip and then begins to expand
+and decay, while the typical chromospheric flares in CaII line are much wider
+and propagate progressively in space. A qualitative explanation of the unusual
+flare can be given by the above-mentioned model of topological trigger. Namely,
+there are such configurations of the magnetic sources on the surface of
+photosphere that their tiny displacements result in the formation and fast
+motion of a 3D null point along the arc located well above the plane of the
+sources. So, such a null point can quickly ignite a magnetic reconnection along
+the entire its trajectory. Pictorially, this can be presented as flipping the
+so-called two-dome magnetic-field structure (which is just the reason why such
+mechanism was called topological). The most important prerequisite for the
+development of topological instability in the two-dome structure is a cruciform
+arrangement of the magnetic sources in its base, and this condition is really
+satisfied in the case under consideration.
+",0,1,0,0,0,0
+11562,Commutative positive varieties of languages,"  We study the commutative positive varieties of languages closed under various
+operations: shuffle, renaming and product over one-letter alphabets.
+",1,0,1,0,0,0
+11563,A generalized model of social and biological contagion,"  We present a model of contagion that unifies and generalizes existing models
+of the spread of social influences and micro-organismal infections. Our model
+incorporates individual memory of exposure to a contagious entity (e.g., a
+rumor or disease), variable magnitudes of exposure (dose sizes), and
+heterogeneity in the susceptibility of individuals. Through analysis and
+simulation, we examine in detail the case where individuals may recover from an
+infection and then immediately become susceptible again (analogous to the
+so-called SIS model). We identify three basic classes of contagion models which
+we call \textit{epidemic threshold}, \textit{vanishing critical mass}, and
+\textit{critical mass} classes, where each class of models corresponds to
+different strategies for prevention or facilitation. We find that the
+conditions for a particular contagion model to belong to one of the these three
+classes depend only on memory length and the probabilities of being infected by
+one and two exposures respectively. These parameters are in principle
+measurable for real contagious influences or entities, thus yielding empirical
+implications for our model. We also study the case where individuals attain
+permanent immunity once recovered, finding that epidemics inevitably die out
+but may be surprisingly persistent when individuals possess memory.
+",0,1,0,0,0,0
+11564,Luminous Efficiency Estimates of Meteors -I. Uncertainty analysis,"  The luminous efficiency of meteors is poorly known, but critical for
+determining the meteoroid mass. We present an uncertainty analysis of the
+luminous efficiency as determined by the classical ablation equations, and
+suggest a possible method for determining the luminous efficiency of real
+meteor events. We find that a two-term exponential fit to simulated lag data is
+able to reproduce simulated luminous efficiencies reasonably well.
+",0,1,0,0,0,0
+11565,Application of Van Der Waals Density Functionals to Two Dimensional Systems Based on a Mixed Basis Approach,"  A van der Waals (vdW) density functional was implemented in the mixed basis
+approach previously developed for studying two dimensional systems, in which
+the vdW interaction plays an important role. The basis functions here are taken
+to be the localized B-splines for the finite non-periodic dimension and plane
+waves for the two periodic directions. This approach will significantly reduce
+the size of the basis set, especially for large systems, and therefore is
+computationally efficient for the diagonalization of the Kohn-Sham Hamiltonian.
+We applied the present algorithm to calculate the binding energy for the
+two-layer graphene case and the results are consistent with data reported
+earlier. We also found that, due to the relatively weak vdW interaction, the
+charge density obtained self-consistently for the whole bi-layer graphene
+system is not significantly different from the simple addition of those for the
+two individual one-layer system, except when the interlayer separation is close
+enough that the strong electron-repulsion dominates. This finding suggests an
+efficient way to calculate the vdW interaction for large complex systems
+involving the Moire pattern configurations.
+",0,1,0,0,0,0
+11566,Secondary atmospheres on HD 219134 b and c,"  We analyze the interiors of HD~219134~b and c, which are among the coolest
+super Earths detected thus far. Without using spectroscopic measurements, we
+aim at constraining if the possible atmospheres are hydrogen-rich or
+hydrogen-poor. In a first step, we employ a full probabilistic Bayesian
+inference analysis in order to rigorously quantify the degeneracy of interior
+parameters given the data of mass, radius, refractory element abundances,
+semi-major axes, and stellar irradiation. We obtain constraints on structure
+and composition for core, mantle, ice layer, and atmosphere. In a second step,
+we aim to draw conclusions on the nature of possible atmospheres by considering
+atmospheric escape. Specifically, we compare the actual possible atmospheres to
+a threshold thickness above which a primordial (H$_2$-dominated) atmosphere can
+be retained against evaporation over the planet's lifetime. The best
+constrained parameters are the individual layer thicknesses. The maximum radius
+fraction of possible atmospheres are 0.18 and 0.13 $R$ (radius), for planets b
+and c, respectively. These values are significantly smaller than the threshold
+thicknesses of primordial atmospheres: 0.28 and 0.19 $R$, respectively. Thus,
+the possible atmospheres of planets b and c are unlikely to be H$_2$-dominated.
+However, whether possible volatile layers are made of gas or liquid/solid water
+cannot be uniquely determined. Our main conclusions are: (1) the possible
+atmospheres for planets b and c are enriched and thus possibly secondary in
+nature, and (2) both planets may contain a gas layer, whereas the layer of HD
+219134 b must be larger. HD 219134 c can be rocky.
+",0,1,0,0,0,0
+11567,Classification of rank two Lie conformal algebras,"  We give a complete classification (up to isomorphism) of Lie conformal
+algebras which are free of rank two as $\C[\partial]$-modules, and determine
+their automorphism groups.
+",0,0,1,0,0,0
+11568,Smooth equivalence of deformations of domains in complex euclidean spaces,"  We prove that two smooth families of 2-connected domains in $\cc$ are
+smoothly equivalent if they are equivalent under a possibly discontinuous
+family of biholomorphisms. We construct, for $m \geq 3$, two smooth families of
+smoothly bounded $m$-connected domains in $\cc$, and for $n\geq2$, two families
+of strictly pseudoconvex domains in $\cc^n$, that are equivalent under
+discontinuous families of biholomorphisms but not under any continuous family
+of biholomorphisms. Finally, we give sufficient conditions for the smooth
+equivalence of two smooth families of domains.
+",0,0,1,0,0,0
+11569,A lower bound of the hyperbolic dimension for meromorphic functions having a logarithmic Hölder tract,"  We improve existing lower bounds of the hyperbolic dimension for meromophic
+functions that have a logarithmic tract {\Omega} which is a Hölder domain.
+These bounds are given in terms of the fractal behavior, measured with integral
+means, of the boundary of {\Omega} at infinity.
+",0,0,1,0,0,0
+11570,Invariant Causal Prediction for Nonlinear Models,"  An important problem in many domains is to predict how a system will respond
+to interventions. This task is inherently linked to estimating the system's
+underlying causal structure. To this end, Invariant Causal Prediction (ICP)
+(Peters et al., 2016) has been proposed which learns a causal model exploiting
+the invariance of causal relations using data from different environments. When
+considering linear models, the implementation of ICP is relatively
+straightforward. However, the nonlinear case is more challenging due to the
+difficulty of performing nonparametric tests for conditional independence. In
+this work, we present and evaluate an array of methods for nonlinear and
+nonparametric versions of ICP for learning the causal parents of given target
+variables. We find that an approach which first fits a nonlinear model with
+data pooled over all environments and then tests for differences between the
+residual distributions across environments is quite robust across a large
+variety of simulation settings. We call this procedure ""invariant residual
+distribution test"". In general, we observe that the performance of all
+approaches is critically dependent on the true (unknown) causal structure and
+it becomes challenging to achieve high power if the parental set includes more
+than two variables. As a real-world example, we consider fertility rate
+modelling which is central to world population projections. We explore
+predicting the effect of hypothetical interventions using the accepted models
+from nonlinear ICP. The results reaffirm the previously observed central causal
+role of child mortality rates.
+",0,0,0,1,0,0
+11571,"Geometric Ergodicity of the MUCOGARCH(1,1) process","  For the multivariate COGARCH(1,1) volatility process we show sufficient
+conditions for the existence of a unique stationary distribution, for the
+geometric ergodicity and for the finiteness of moments of the stationary
+distribution. One of the conditions demands a sufficiently fast exponential
+decay of the MUCOGARCH(1,1) volatility process. Furthermore, we show easily
+applicable sufficient conditions for the needed irreducibility of the
+volatility process living in the cone of positive semidefinite matrices, if the
+driving Lévy process is a compound Poisson process.
+",0,0,1,0,0,0
+11572,"Cognitive networks: brains, internet, and civilizations","  In this short essay, we discuss some basic features of cognitive activity at
+several different space-time scales: from neural networks in the brain to
+civilizations. One motivation for such comparative study is its heuristic
+value. Attempts to better understand the functioning of ""wetware"" involved in
+cognitive activities of central nervous system by comparing it with a computing
+device have a long tradition. We suggest that comparison with Internet might be
+more adequate. We briefly touch upon such subjects as encoding, compression,
+and Saussurean trichotomy langue/langage/parole in various environments.
+",1,0,0,0,0,0
+11573,Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection,"  The trigram `I love being' is expected to be followed by positive words such
+as `happy'. In a sarcastic sentence, however, the word `ignored' may be
+observed. The expected and the observed words are, thus, incongruous. We model
+sarcasm detection as the task of detecting incongruity between an observed and
+an expected word. In order to obtain the expected word, we use Context2Vec, a
+sentence completion library based on Bidirectional LSTM. However, since the
+exact word where such an incongruity occurs may not be known in advance, we
+present two approaches: an All-words approach (which consults sentence
+completion for every content word) and an Incongruous words-only approach
+(which consults sentence completion for the 50% most incongruous content
+words). The approaches outperform reported values for tweets but not for
+discussion forum posts. This is likely to be because of redundant consultation
+of sentence completion for discussion forum posts. Therefore, we consider an
+oracle case where the exact incongruous word is manually labeled in a corpus
+reported in past work. In this case, the performance is higher than the
+all-words approach. This sets up the promise for using sentence completion for
+sarcasm detection.
+",1,0,0,0,0,0
+11574,Sensitivity Analysis of Deep Neural Networks,"  Deep neural networks (DNNs) have achieved superior performance in various
+prediction tasks, but can be very vulnerable to adversarial examples or
+perturbations. Therefore, it is crucial to measure the sensitivity of DNNs to
+various forms of perturbations in real applications. We introduce a novel
+perturbation manifold and its associated influence measure to quantify the
+effects of various perturbations on DNN classifiers. Such perturbations include
+various external and internal perturbations to input samples and network
+parameters. The proposed measure is motivated by information geometry and
+provides desirable invariance properties. We demonstrate that our influence
+measure is useful for four model building tasks: detecting potential
+'outliers', analyzing the sensitivity of model architectures, comparing network
+sensitivity between training and test sets, and locating vulnerable areas.
+Experiments show reasonably good performance of the proposed measure for the
+popular DNN models ResNet50 and DenseNet121 on CIFAR10 and MNIST datasets.
+",1,0,0,1,0,0
+11575,An application of the Hylleraas-B-splines basis set: High accuracy calculations of the static dipole polarizabilities of helium,"  The Hylleraas-B-splines basis set is introduced in this paper, which can be
+used to obtain the eigenvalues and eigenstates of helium-like system's
+Hamiltonian. Comparing with traditional B-splines basis, the rate of
+convergence of our results has been significantly improved. Through combine
+this method and pseudo-states sum over scheme, we obtained the high precision
+values of static dipole porlarizabilities of the $1{}^1S-5{}^1S$,
+$2{}^3S-6{}^3S$ states of helium in length and velocity gauges respectively,
+and the results get good agreements. The final extrapolate results of
+porlarizabilities in different quantum states arrived eight significant digits
+at least, which fully illustrates the advantage and convenience of this method
+in the problems involving continuous states.
+",0,1,0,0,0,0
+11576,"Jupiter's South Equatorial Belt cycle in 2009-2011: II, The SEB Revival","  A Revival of the South Equatorial Belt (SEB) is an organised disturbance on a
+grand scale. It starts with a single vigorous outbreak from which energetic
+storms and disturbances spread around the planet in the different zonal
+currents. The Revival that began in 2010 was better observed than any before
+it. The observations largely validate the historical descriptions of these
+events: the major features portrayed therein, albeit at lower resolution, are
+indeed the large structural features described here. Our major conclusions
+about the 2010 SEB Revival are as follows, and we show that most of them may be
+typical of SEB Revivals.
+1) The Revival started with a bright white plume.
+2) The initial plume erupted in a pre-existing cyclonic oval ('barge').
+Subsequent white plumes continued to appear on the track of this barge, which
+was the location of the sub-surface source of the whole Revival.
+3) These plumes were extremely bright in the methane absorption band, i.e.
+thrusting up to very high altitudes, especially when new.
+4) Brilliant, methane-bright plumes also appeared along the leading edge of
+the central branch. Altogether, 7 plumes appeared at the source and at least 6
+along the leading edge.
+5) The central branch of the outbreak was composed of large convective cells,
+each initiated by a bright plume, which only occupied a part of each cell,
+while a very dark streak defined its west edge.
+6) The southern branch began with darkening and sudden acceleration of
+pre-existing faint spots in a slowly retrograding wave-train.
+7) Subsequent darker spots in the southern branch were complex structures,
+not coherent vortices.
+8) Dark spots in the southern branch had typical SEBs jetstream speeds but
+were unusually far south....
+",0,1,0,0,0,0
+11577,Improved Regularization Techniques for End-to-End Speech Recognition,"  Regularization is important for end-to-end speech models, since the models
+are highly flexible and easy to overfit. Data augmentation and dropout has been
+important for improving end-to-end models in other domains. However, they are
+relatively under explored for end-to-end speech models. Therefore, we
+investigate the effectiveness of both methods for end-to-end trainable, deep
+speech recognition models. We augment audio data through random perturbations
+of tempo, pitch, volume, temporal alignment, and adding random noise.We further
+investigate the effect of dropout when applied to the inputs of all layers of
+the network. We show that the combination of data augmentation and dropout give
+a relative performance improvement on both Wall Street Journal (WSJ) and
+LibriSpeech dataset of over 20%. Our model performance is also competitive with
+other end-to-end speech models on both datasets.
+",1,0,0,1,0,0
+11578,On the limits of coercivity in permanent magnets,"  The maximum coercivity that can be achieved for a given hard magnetic alloy
+is estimated by computing the energy barrier for the nucleation of a reversed
+domain in an idealized microstructure without any structural defects and
+without any soft magnetic secondary phases. For
+Sm$_{1-z}$Zr$_z$(Fe$_{1-y}$Co$_y$)$_{12-x}$Ti$_x$ based alloys, which are
+considered an alternative to Nd$_2$Fe$_{14}$B magnets with lower rare-earth
+content, the coercive field of a small magnetic cube is reduced to 60 percent
+of the anisotropy field at room temperature and to 50 percent of the anisotropy
+field at elevated temperature (473K). This decrease of the coercive field is
+caused by misorientation, demagnetizing fields and thermal fluctuations.
+",0,1,0,0,0,0
+11579,$N$-soliton formula and blowup result of the Wadati-Konno-Ichikawa equation,"  We formulate the $N$ soliton solution of the Wadati-Konno-Ichikawa equation
+that is determined by purely algebraic equations. Derivation is based on the
+matrix Riemann-Hilbert problem. We give examples of one soliton solution that
+include smooth soliton, bursting soliton, and loop type soliton. In addition,
+we give an explicit example for two soliton solution that blows up in a finite
+time.
+",0,1,1,0,0,0
+11580,Introduction of Improved Repairing Locality into Secret Sharing Schemes with Perfect Security,"  Repairing locality is an appreciated feature for distributed storage, in
+which a damaged or lost data share can be repaired by accessing a subset of
+other shares much smaller than is required for decoding the complete data.
+However for Secret Sharing (SS) schemes, it has been proven theoretically that
+local repairing can not be achieved with perfect security for the majority of
+threshold SS schemes, where all the shares are equally regarded in both secret
+recovering and share repairing. In this paper we make an attempt on decoupling
+the two processes to make secure local repairing possible. Dedicated repairing
+redundancies only for the repairing process are generated, which are random
+numbers to the original secret. Through this manner a threshold SS scheme with
+improved repairing locality is achieved on the condition that security of
+repairing redundancies is ensured, or else our scheme degenerates into a
+perfect access structure that is equivalent to the best existing schemes can
+do. To maximize security of the repairing redundancies, a random placement
+mechanism is also proposed.
+",1,0,0,0,0,0
+11581,"A counterexample to a conjecture of Kiyota, Murai and Wada","  Kiyota, Murai and Wada conjectured in 2002 that the largest eigenvalue of the
+Cartan matrix C of a block of a finite group is rational if and only if all
+eigenvalues of C are rational. We provide a counterexample to this conjecture
+and discuss related questions.
+",0,0,1,0,0,0
+11582,Minimal Hermite-type eigenbasis of the discrete Fourier transform,"  There exist many ways to build an orthonormal basis of $\mathbb{R}^N$,
+consisting of the eigenvectors of the discrete Fourier transform (DFT). In this
+paper we show that there is only one such orthonormal eigenbasis of the DFT
+that is optimal in the sense of an appropriate uncertainty principle. Moreover,
+we show that these optimal eigenvectors of the DFT are direct analogues of the
+Hermite functions, that they also satisfy a three-term recurrence relation and
+that they converge to Hermite functions as $N$ increases to infinity.
+",0,0,1,0,0,0
+11583,Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm,"  We design new algorithms for the combinatorial pure exploration problem in
+the multi-arm bandit framework. In this problem, we are given $K$ distributions
+and a collection of subsets $\mathcal{V} \subset 2^{[K]}$ of these
+distributions, and we would like to find the subset $v \in \mathcal{V}$ that
+has largest mean, while collecting, in a sequential fashion, as few samples
+from the distributions as possible. In both the fixed budget and fixed
+confidence settings, our algorithms achieve new sample-complexity bounds that
+provide polynomial improvements on previous results in some settings. Via an
+information-theoretic lower bound, we show that no approach based on uniform
+sampling can improve on ours in any regime, yielding the first interactive
+algorithms for this problem with this basic property. Computationally, we show
+how to efficiently implement our fixed confidence algorithm whenever
+$\mathcal{V}$ supports efficient linear optimization. Our results involve
+precise concentration-of-measure arguments and a new algorithm for linear
+programming with exponentially many constraints.
+",1,0,0,1,0,0
+11584,Insulator to Metal Transition in WO$_3$ Induced by Electrolyte Gating,"  Tungsten oxide and its associated bronzes (compounds of tungsten oxide and an
+alkali metal) are well known for their interesting optical and electrical
+characteristics. We have modified the transport properties of thin WO$_3$ films
+by electrolyte gating using both ionic liquids and polymer electrolytes. We are
+able to tune the resistivity of the gated film by more than five orders of
+magnitude, and a clear insulator-to-metal transition is observed. To clarify
+the doping mechanism, we have performed a series of incisive operando
+experiments, ruling out both a purely electronic effect (charge accumulation
+near the interface) and oxygen-related mechanisms. We propose instead that
+hydrogen intercalation is responsible for doping WO$_3$ into a highly
+conductive ground state and provide evidence that it can be described as a
+dense polaronic gas.
+",0,1,0,0,0,0
+11585,Extensions and Exact Solutions to the Quaternion-Based RMSD Problem,"  We examine the problem of transforming matching collections of data points
+into optimal correspondence. The classic RMSD (root-mean-square deviation)
+method calculates a 3D rotation that minimizes the RMSD of a set of test data
+points relative to a reference set of corresponding points. Similar literature
+in aeronautics, photogrammetry, and proteomics employs numerical methods to
+find the maximal eigenvalue of a particular $4\!\times\! 4$ quaternion-based
+matrix, thus specifying the quaternion eigenvector corresponding to the optimal
+3D rotation. Here we generalize this basic problem, sometimes referred to as
+the ""Procrustes Problem,"" and present algebraic solutions that exhibit
+properties that are inaccessible to traditional numerical methods. We begin
+with the 4D data problem, a problem one dimension higher than the conventional
+3D problem, but one that is also solvable by quaternion methods, we then study
+the 3D and 2D data problems as special cases. In addition, we consider data
+that are themselves quaternions isomorphic to orthonormal triads describing 3
+coordinate frames (amino acids in proteins possess such frames). Adopting a
+reasonable approximation to the exact quaternion-data minimization problem, we
+find a novel closed form ""quaternion RMSD"" (QRMSD) solution for the optimal
+rotation from a quaternion data set to a reference set. We observe that
+composites of the RMSD and QRMSD measures, combined with problem-dependent
+parameters including scaling factors to make their incommensurate dimensions
+compatible, could be suitable for certain matching tasks.
+",0,0,0,0,1,0
+11586,"Particles, Cutoffs and Inequivalent Representations. Fraser andWallace on Quantum Field Theory","  We critically review the recent debate between Doreen Fraser and David
+Wallace on the interpretation of quantum field theory, with the aim of
+identifying where the core of the disagreement lies. We show that, despite
+appearances, their conflict does not concern the existence of particles or the
+occurrence of unitarily inequivalent representations. Instead, the dispute
+ultimately turns on the very definition of what a quantum field theory is. We
+further illustrate the fundamental differences between the two approaches by
+comparing them both to the Bohmian program in quantum field theory.
+",0,1,0,0,0,0
+11587,Dynamic Analysis of Executables to Detect and Characterize Malware,"  It is needed to ensure the integrity of systems that process sensitive
+information and control many aspects of everyday life. We examine the use of
+machine learning algorithms to detect malware using the system calls generated
+by executables-alleviating attempts at obfuscation as the behavior is monitored
+rather than the bytes of an executable. We examine several machine learning
+techniques for detecting malware including random forests, deep learning
+techniques, and liquid state machines. The experiments examine the effects of
+concept drift on each algorithm to understand how well the algorithms
+generalize to novel malware samples by testing them on data that was collected
+after the training data. The results suggest that each of the examined machine
+learning algorithms is a viable solution to detect malware-achieving between
+90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the
+performance evaluation on an operational network may not match the performance
+achieved in training. Namely, the CAA may be about the same, but the values for
+precision and recall over the malware can change significantly. We structure
+experiments to highlight these caveats and offer insights into expected
+performance in operational environments. In addition, we use the induced models
+to gain a better understanding about what differentiates the malware samples
+from the goodware, which can further be used as a forensics tool to understand
+what the malware (or goodware) was doing to provide directions for
+investigation and remediation.
+",1,0,0,1,0,0
+11588,Network support of talented people,"  Network support is a key success factor for talented people. As an example,
+the Hungarian Talent Support Network involves close to 1500 Talent Points and
+more than 200,000 people. This network started the Hungarian Templeton Program
+identifying and helping 315 exceptional cognitive talents. This network is a
+part of the European Talent Support Network initiated by the European Council
+for High Ability involving more than 300 organizations in over 30 countries in
+Europe and extending in other continents. These networks are giving good
+examples that talented people often occupy a central, but highly dynamic
+position in social networks. The involvement of such 'creative nodes' in
+network-related decision making processes is vital, especially in novel
+environmental challenges. Such adaptive/learning responses characterize a large
+variety of complex systems from proteins, through brains to society. It is
+crucial for talent support programs to use these networking and learning
+processes to increase their efficiency further.
+",1,1,0,0,0,0
+11589,How to Escape Saddle Points Efficiently,"  This paper shows that a perturbed form of gradient descent converges to a
+second-order stationary point in a number iterations which depends only
+poly-logarithmically on dimension (i.e., it is almost ""dimension-free""). The
+convergence rate of this procedure matches the well-known convergence rate of
+gradient descent to first-order stationary points, up to log factors. When all
+saddle points are non-degenerate, all second-order stationary points are local
+minima, and our result thus shows that perturbed gradient descent can escape
+saddle points almost for free. Our results can be directly applied to many
+machine learning applications, including deep learning. As a particular
+concrete example of such an application, we show that our results can be used
+directly to establish sharp global convergence rates for matrix factorization.
+Our results rely on a novel characterization of the geometry around saddle
+points, which may be of independent interest to the non-convex optimization
+community.
+",1,0,1,1,0,0
+11590,Geert Hofstede et al's set of national cultural dimensions - popularity and criticisms,"  This article outlines different stages in development of the national culture
+model, created by Geert Hofstede and his affiliates. This paper reveals and
+synthesizes the contemporary review of the application spheres of this
+framework. Numerous applications of the dimensions set are used as a source of
+identifying significant critiques, concerning different aspects in model's
+operation. These critiques are classified and their underlying reasons are also
+outlined by means of a fishbone diagram.
+",0,0,0,0,0,1
+11591,Deep and Confident Prediction for Time Series at Uber,"  Reliable uncertainty estimation for time series prediction is critical in
+many fields, including physics, biology, and manufacturing. At Uber,
+probabilistic time series forecasting is used for robust prediction of number
+of trips during special events, driver incentive allocation, as well as
+real-time anomaly detection across millions of metrics. Classical time series
+models are often used in conjunction with a probabilistic formulation for
+uncertainty estimation. However, such models are hard to tune, scale, and add
+exogenous variables to. Motivated by the recent resurgence of Long Short Term
+Memory networks, we propose a novel end-to-end Bayesian deep model that
+provides time series prediction along with uncertainty estimation. We provide
+detailed experiments of the proposed solution on completed trips data, and
+successfully apply it to large-scale time series anomaly detection at Uber.
+",0,0,0,1,0,0
+11592,Coverage Analysis of a Vehicular Network Modeled as Cox Process Driven by Poisson Line Process,"  In this paper, we consider a vehicular network in which the wireless nodes
+are located on a system of roads. We model the roadways, which are
+predominantly straight and randomly oriented, by a Poisson line process (PLP)
+and the locations of nodes on each road as a homogeneous 1D Poisson point
+process (PPP). Assuming that each node transmits independently, the locations
+of transmitting and receiving nodes are given by two Cox processes driven by
+the same PLP. For this setup, we derive the coverage probability of a typical
+receiver, which is an arbitrarily chosen receiving node, assuming independent
+Nakagami-$m$ fading over all wireless channels. Assuming that the typical
+receiver connects to its closest transmitting node in the network, we first
+derive the distribution of the distance between the typical receiver and the
+serving node to characterize the desired signal power. We then characterize
+coverage probability for this setup, which involves two key technical
+challenges. First, we need to handle several cases as the serving node can
+possibly be located on any line in the network and the corresponding
+interference experienced at the typical receiver is different in each case.
+Second, conditioning on the serving node imposes constraints on the spatial
+configuration of lines, which require careful analysis of the conditional
+distribution of the lines. We address these challenges in order to accurately
+characterize the interference experienced at the typical receiver. We then
+derive an exact expression for coverage probability in terms of the derivative
+of Laplace transform of interference power distribution. We analyze the trends
+in coverage probability as a function of the network parameters: line density
+and node density. We also study the asymptotic behavior of this model and
+compare the coverage performance with that of a homogeneous 2D PPP model with
+the same node density.
+",1,0,0,0,0,0
+11593,Database Engines: Evolution of Greenness,"  Context: Information Technology consumes up to 10\% of the world's
+electricity generation, contributing to CO2 emissions and high energy costs.
+Data centers, particularly databases, use up to 23% of this energy. Therefore,
+building an energy-efficient (green) database engine could reduce energy
+consumption and CO2 emissions.
+Goal: To understand the factors driving databases' energy consumption and
+execution time throughout their evolution.
+Method: We conducted an empirical case study of energy consumption by two
+MySQL database engines, InnoDB and MyISAM, across 40 releases. We examined the
+relationships of four software metrics to energy consumption and execution time
+to determine which metrics reflect the greenness and performance of a database.
+Results: Our analysis shows that database engines' energy consumption and
+execution time increase as databases evolve. Moreover, the Lines of Code metric
+is correlated moderately to strongly with energy consumption and execution time
+in 88% of cases.
+Conclusions: Our findings provide insights to both practitioners and
+researchers. Database administrators may use them to select a fast, green
+release of the MySQL database engine. MySQL database-engine developers may use
+the software metric to assess products' greenness and performance. Researchers
+may use our findings to further develop new hypotheses or build models to
+predict greenness and performance of databases.
+",1,0,0,0,0,0
+11594,Parseval Networks: Improving Robustness to Adversarial Examples,"  We introduce Parseval networks, a form of deep neural networks in which the
+Lipschitz constant of linear, convolutional and aggregation layers is
+constrained to be smaller than 1. Parseval networks are empirically and
+theoretically motivated by an analysis of the robustness of the predictions
+made by deep neural networks when their input is subject to an adversarial
+perturbation. The most important feature of Parseval networks is to maintain
+weight matrices of linear and convolutional layers to be (approximately)
+Parseval tight frames, which are extensions of orthogonal matrices to
+non-square matrices. We describe how these constraints can be maintained
+efficiently during SGD. We show that Parseval networks match the
+state-of-the-art in terms of accuracy on CIFAR-10/100 and Street View House
+Numbers (SVHN) while being more robust than their vanilla counterpart against
+adversarial examples. Incidentally, Parseval networks also tend to train faster
+and make a better usage of the full capacity of the networks.
+",1,0,0,1,0,0
+11595,Production of 82Se enriched Zinc Selenide (ZnSe) crystals for the study of neutrinoless double beta decay,"  High purity Zinc Selenide (ZnSe) crystals are produced starting from
+elemental Zn and Se to be used for the search of the neutrinoless double beta
+decay (0{\nu}DBD) of 82Se. In order to increase the number of emitting
+nuclides, enriched 82Se is used. Dedicated production lines for the synthesis
+and conditioning of the Zn82Se powder in order to make it suitable for crystal
+growth were assembled compliant with radio-purity constraints specific to rare
+event physics experiments. Besides routine check of impurities concentration,
+high sensitivity measurements are made for radio-isotope concentrations in raw
+materials, reactants, consumables, ancillaries and intermediary products used
+for ZnSe crystals production. Indications are given on the crystals perfection
+and how it is achieved. Since very expensive isotopically enriched material
+(82Se) is used, a special attention is given for acquiring the maximum yield in
+the mass balance of all production stages. Production and certification
+protocols are presented and resulting ready-to-use Zn82Se crystals are
+described.
+",0,1,0,0,0,0
+11596,Computing Human-Understandable Strategies,"  Algorithms for equilibrium computation generally make no attempt to ensure
+that the computed strategies are understandable by humans. For instance the
+strategies for the strongest poker agents are represented as massive binary
+files. In many situations, we would like to compute strategies that can
+actually be implemented by humans, who may have computational limitations and
+may only be able to remember a small number of features or components of the
+strategies that have been computed. We study poker games where private
+information distributions can be arbitrary. We create a large training set of
+game instances and solutions, by randomly selecting the information
+probabilities, and present algorithms that learn from the training instances in
+order to perform well in games with unseen information distributions. We are
+able to conclude several new fundamental rules about poker strategy that can be
+easily implemented by humans.
+",0,0,0,1,0,0
+11597,A statistical model for aggregating judgments by incorporating peer predictions,"  We propose a probabilistic model to aggregate the answers of respondents
+answering multiple-choice questions. The model does not assume that everyone
+has access to the same information, and so does not assume that the consensus
+answer is correct. Instead, it infers the most probable world state, even if
+only a minority vote for it. Each respondent is modeled as receiving a signal
+contingent on the actual world state, and as using this signal to both
+determine their own answer and predict the answers given by others. By
+incorporating respondent's predictions of others' answers, the model infers
+latent parameters corresponding to the prior over world states and the
+probability of different signals being received in all possible world states,
+including counterfactual ones. Unlike other probabilistic models for
+aggregation, our model applies to both single and multiple questions, in which
+case it estimates each respondent's expertise. The model shows good
+performance, compared to a number of other probabilistic models, on data from
+seven studies covering different types of expertise.
+",0,0,0,1,0,0
+11598,The Inner 25 AU Debris Distribution in the epsilon Eri System,"  Debris disk morphology is wavelength dependent due to the wide range of
+particle sizes and size-dependent dynamics influenced by various forces.
+Resolved images of nearby debris disks reveal complex disk structures that are
+difficult to distinguish from their spectral energy distributions. Therefore,
+multi-wavelength resolved images of nearby debris systems provide an essential
+foundation to understand the intricate interplay between collisional,
+gravitational, and radiative forces that govern debris disk structures. We
+present the SOFIA 35 um resolved disk image of epsilon Eri, the closest debris
+disk around a star similar to the early Sun. Combining with the Spitzer
+resolved image at 24 um and 15-38 um excess spectrum, we examine two proposed
+origins of the inner debris in epsilon Eri: (1) in-situ planetesimal belt(s)
+and (2) dragged-in grains from the cold outer belt. We find that the presence
+of in-situ dust-producing planetesmial belt(s) is the most likely source of the
+excess emission in the inner 25 au region. Although a small amount of
+dragged-in grains from the cold belt could contribute to the excess emission in
+the inner region, the resolution of the SOFIA data is high enough to rule out
+the possibility that the entire inner warm excess results from dragged-in
+grains, but not enough to distinguish one broad inner disk from two narrow
+belts.
+",0,1,0,0,0,0
+11599,Model-Based Clustering of Nonparametric Weighted Networks,"  Water pollution is a major global environmental problem, and it poses a great
+environmental risk to public health and biological diversity. This work is
+motivated by assessing the potential environmental threat of coal mining
+through increased sulfate concentrations in river networks, which do not belong
+to any simple parametric distribution. However, existing network models mainly
+focus on binary or discrete networks and weighted networks with known
+parametric weight distributions. We propose a principled nonparametric weighted
+network model based on exponential-family random graph models and local
+likelihood estimation and study its model-based clustering with application to
+large-scale water pollution network analysis. We do not require any parametric
+distribution assumption on network weights. The proposed method greatly extends
+the methodology and applicability of statistical network models. Furthermore,
+it is scalable to large and complex networks in large-scale environmental
+studies and geoscientific research. The power of our proposed methods is
+demonstrated in simulation studies.
+",0,0,0,1,0,0
+11600,FPGA-Based Tracklet Approach to Level-1 Track Finding at CMS for the HL-LHC,"  During the High Luminosity LHC, the CMS detector will need charged particle
+tracking at the hardware trigger level to maintain a manageable trigger rate
+and achieve its physics goals. The tracklet approach is a track-finding
+algorithm based on a road-search algorithm that has been implemented on
+commercially available FPGA technology. The tracklet algorithm has achieved
+high performance in track-finding and completes tracking within 3.4 $\mu$s on a
+Xilinx Virtex-7 FPGA. An overview of the algorithm and its implementation on an
+FPGA is given, results are shown from a demonstrator test stand and system
+performance studies are presented.
+",0,1,0,0,0,0
+11601,Isolating effects of age with fair representation learning when assessing dementia,"  One of the most prevalent symptoms among the elderly population, dementia,
+can be detected by classifiers trained on linguistic features extracted from
+narrative transcripts. However, these linguistic features are impacted in a
+similar but different fashion by the normal aging process. Aging is therefore a
+confounding factor, whose effects have been hard for machine learning
+classifiers to isolate. In this paper, we show that deep neural network (DNN)
+classifiers can infer ages from linguistic features, which is an entanglement
+that could lead to unfairness across age groups. We show this problem is caused
+by undesired activations of v-structures in causality diagrams, and it could be
+addressed with fair representation learning. We build neural network
+classifiers that learn low-dimensional representations reflecting the impacts
+of dementia yet discarding the effects of age. To evaluate these classifiers,
+we specify a model-agnostic score $\Delta_{eo}^{(N)}$ measuring how classifier
+results are disentangled from age. Our best models outperform baseline neural
+network classifiers in disentanglement, while compromising accuracy by as
+little as 2.56\% and 2.25\% on DementiaBank and the Famous People dataset
+respectively.
+",0,0,0,1,0,0
+11602,The Trimmed Lasso: Sparsity and Robustness,"  Nonconvex penalty methods for sparse modeling in linear regression have been
+a topic of fervent interest in recent years. Herein, we study a family of
+nonconvex penalty functions that we call the trimmed Lasso and that offers
+exact control over the desired level of sparsity of estimators. We analyze its
+structural properties and in doing so show the following:
+1) Drawing parallels between robust statistics and robust optimization, we
+show that the trimmed-Lasso-regularized least squares problem can be viewed as
+a generalized form of total least squares under a specific model of
+uncertainty. In contrast, this same model of uncertainty, viewed instead
+through a robust optimization lens, leads to the convex SLOPE (or OWL) penalty.
+2) Further, in relating the trimmed Lasso to commonly used sparsity-inducing
+penalty functions, we provide a succinct characterization of the connection
+between trimmed-Lasso- like approaches and penalty functions that are
+coordinate-wise separable, showing that the trimmed penalties subsume existing
+coordinate-wise separable penalties, with strict containment in general.
+3) Finally, we describe a variety of exact and heuristic algorithms, both
+existing and new, for trimmed Lasso regularized estimation problems. We include
+a comparison between the different approaches and an accompanying
+implementation of the algorithms.
+",0,0,1,1,0,0
+11603,Mobile phone identification through the built-in magnetometers,"  Mobile phones identification through their built in components has been
+demonstrated in literature for various types of sensors including the camera,
+microphones and accelerometers. The identification is performed by the
+exploitation of the small but significant differences in the electronic
+circuits generated during the production process. Thus, these differences
+become an intrinsic property of the electronic components, which can be
+detected and become an unique fingerprint of the component and of the mobile
+phone. In this paper, we investigate the identification of mobile phones
+through their builtin magnetometers, which has not been reported in literature
+yet. Magnetometers are stimulated with different waveforms using a solenoid
+connected to a computer s audio board. The identification is performed
+analyzing the digital output of the magnetometer through the use of statistical
+features and the Support Vector Machine (SVM) machine learning algorithm. We
+prove that this technique can distinguish different models and brands with very
+high accuracy but it can only distinguish phones of the same model with limited
+accuracy.
+",1,0,0,0,0,0
+11604,A Machine Learning Framework for Stock Selection,"  This paper demonstrates how to apply machine learning algorithms to
+distinguish good stocks from the bad stocks. To this end, we construct 244
+technical and fundamental features to characterize each stock, and label stocks
+according to their ranking with respect to the return-to-volatility ratio.
+Algorithms ranging from traditional statistical learning methods to recently
+popular deep learning method, e.g. Logistic Regression (LR), Random Forest
+(RF), Deep Neural Network (DNN), and the Stacking, are trained to solve the
+classification task. Genetic Algorithm (GA) is also used to implement feature
+selection. The effectiveness of the stock selection strategy is validated in
+Chinese stock market in both statistical and practical aspects, showing that:
+1) Stacking outperforms other models reaching an AUC score of 0.972; 2) Genetic
+Algorithm picks a subset of 114 features and the prediction performances of all
+models remain almost unchanged after the selection procedure, which suggests
+some features are indeed redundant; 3) LR and DNN are radical models; RF is
+risk-neutral model; Stacking is somewhere between DNN and RF. 4) The portfolios
+constructed by our models outperform market average in back tests.
+",0,0,0,1,0,1
+11605,Zampa's systems theory: a comprehensive theory of measurement in dynamic systems,"  The article outlines in memoriam Prof. Pavel Zampa's concepts of system
+theory which enable to devise a measurement in dynamic systems independently of
+the particular system behaviour. From the point of view of Zampa's theory,
+terms like system time, system attributes, system link, system element, input,
+output, subsystems, and state variables are defined. In Conclusions, Zampa's
+theory is discussed together with another mathematical approaches of
+qualitative dynamics known since the 19th century. In Appendices, we present
+applications of Zampa's technical approach to measurement of complex dynamical
+(chemical and biological) systems at the Institute of Complex Systems,
+University of South Bohemia in Ceske Budejovice.
+",1,0,0,0,0,0
+11606,Learning Data Manifolds with a Cutting Plane Method,"  We consider the problem of classifying data manifolds where each manifold
+represents invariances that are parameterized by continuous degrees of freedom.
+Conventional data augmentation methods rely upon sampling large numbers of
+training examples from these manifolds; instead, we propose an iterative
+algorithm called M_{CP} based upon a cutting-plane approach that efficiently
+solves a quadratic semi-infinite programming problem to find the maximum margin
+solution. We provide a proof of convergence as well as a polynomial bound on
+the number of iterations required for a desired tolerance in the objective
+function. The efficiency and performance of M_{CP} are demonstrated in
+high-dimensional simulations and on image manifolds generated from the ImageNet
+dataset. Our results indicate that M_{CP} is able to rapidly learn good
+classifiers and shows superior generalization performance compared with
+conventional maximum margin methods using data augmentation methods.
+",1,0,0,1,0,0
+11607,Theory of magnetism in La$_2$NiMnO$_6$,"  The magnetism of ordered and disordered La$_2$NiMnO$_6$ is explained using a
+model involving double exchange and superexchange. The concept of majority spin
+hybridization in the large coupling limit is used to explain the ferromagnetism
+of La$_2$NiMnO$_6$ as compared to the ferrimagnetism of Sr$_{2}$FeMoO$_{6}$.
+The ferromagnetic insulating ground state in the ordered phase is explained.
+The essential role played by the Ni-Mn superexchange between the Ni $e_{g}$
+electron spins and the Mn $t_{2g}$ core electron spins in realizing this ground
+state, is outlined. In presence of antisite disorder, the model system is found
+to exhibit a tendency of becoming a spin-glass at low temperatures, while it
+continues to retain a ferromagnetic transition at higher temperatures, similar
+to recent experimental observations [D. Choudhury .et.al., Phys. Rev. Lett.
+108, 127201 (2012)]. This reentrant spin-glass or reentrant ferromagnetic
+behaviour is explained in terms of the competition of the ferromagnetic double
+exchange between the Ni $e_{g}$ and the Mn $e_{g}$ electrons, and the
+ferromagnetic Ni-Mn superexchange, with the antiferromagnetic antisite Mn-Mn
+superexchange.
+",0,1,0,0,0,0
+11608,Counterexample-guided Abstraction Refinement for POMDPs,"  Partially Observable Markov Decision Process (POMDP) is widely used to model
+probabilistic behavior for complex systems. Compared with MDPs, POMDP models a
+system more accurate but solving a POMDP generally takes exponential time in
+the size of its state space. This makes the formal verification and synthesis
+problems much more challenging for POMDPs, especially when multiple system
+components are involved. As a promising technique to reduce the verification
+complexity, the abstraction method tries to find an abstract system with a
+smaller state space but preserves enough properties for the verification
+purpose. While abstraction based verification has been explored extensively for
+MDPs, in this paper, we present the first result of POMDP abstraction and its
+refinement techniques. The main idea follows the counterexample-guided
+abstraction refinement (CEGAR) framework. Starting with a coarse guess for the
+POMDP abstraction, we iteratively use counterexamples from formal verification
+to refine the abstraction until the abstract system can be used to infer the
+verification result for the original POMDP. Our main contributions have two
+folds: 1) we propose a novel abstract system model for POMDP and a new
+simulation relation to capture the partial observability then prove the
+preservation on a fragment of Probabilistic Computation Tree Logic (PCTL); 2)
+to find a proper abstract system that can prove or disprove the satisfaction
+relation on the concrete POMDP, we develop a novel refinement algorithm. Our
+work leads to a sound and complete CEGAR framework for POMDP.
+",1,0,0,0,0,0
+11609,An Overview of Multi-Task Learning in Deep Neural Networks,"  Multi-task learning (MTL) has led to successes in many applications of
+machine learning, from natural language processing and speech recognition to
+computer vision and drug discovery. This article aims to give a general
+overview of MTL, particularly in deep neural networks. It introduces the two
+most common methods for MTL in Deep Learning, gives an overview of the
+literature, and discusses recent advances. In particular, it seeks to help ML
+practitioners apply MTL by shedding light on how MTL works and providing
+guidelines for choosing appropriate auxiliary tasks.
+",1,0,0,1,0,0
+11610,Low temperature synthesis of heterostructures of transition metal dichalcogenide alloys (WxMo1-xS2) and graphene with superior catalytic performance for hydrogen evolution,"  Large-area ($\sim$cm$^2$) films of vertical heterostructures formed by
+alternating graphene and transition-metal dichalcogenide(TMD) alloys are
+obtained by wet chemical routes followed by a thermal treatment at low
+temperature (300 $^\circ$C). In particular, we synthesized stacked graphene and
+W$_x$Mo$_{1-x}$S$_2$ alloy phases that were used as hydrogen evolution
+catalysts. We observed a Tafel slope of 38.7 mV dec$^{-1}$ and 96 mV onset
+potential (at current density of 10 mA cm$^{-2}$) when the heterostructure
+alloy is annealed at 300 $^o$C. These results indicate that heterostructure
+formed by graphene and W$_{0.4}$Mo$_{0.6}$S$_2$ alloys are far more efficient
+than WS$_2$ and MoS$_2$ by at least a factor of two, and it is superior than
+any other reported TMD system. This strategy offers a cheap and low temperature
+synthesis alternative able to replace Pt in the hydrogen evolution reaction
+(HER). Furthermore, the catalytic activity of the alloy is stable over time,
+i.e. the catalytic activity does not experience a significant change even after
+1000 cycles. Using density functional theory calculations, we found that this
+enhanced hydrogen evolution in the W$_x$Mo$_{1-x}$S$_2$ alloys is mainly due to
+the lower energy barrier created by a favorable overlap of the d-orbitals from
+the transition metals and the s-orbitals of H$_2$, with the lowest energy
+barrier occurring for W$_{0.4}$Mo$_{0.6}$S$_2$ alloy. Thus, it is now possible
+to further improve the performance of the ""inert"" TMD basal plane via metal
+alloying, in addition to the previously reported strategies of creation of
+point defects, vacancies and edges. The synthesis of
+graphene/W$_{0.4}$Mo$_{0.6}$S$_2$ produced at relatively low temperatures is
+scalable and could be used as an effective low cost Pt-free catalyst.
+",0,1,0,0,0,0
+11611,Virtual refinements of the Vafa-Witten formula,"  We conjecture a formula for the generating function of virtual
+$\chi_y$-genera of moduli spaces of rank 2 sheaves on arbitrary surfaces with
+holomorphic 2-form. Specializing the conjecture to minimal surfaces of general
+type and to virtual Euler characteristics, we recover (part of) a formula of C.
+Vafa and E. Witten.
+These virtual $\chi_y$-genera can be written in terms of descendent Donaldson
+invariants. Using T. Mochizuki's formula, the latter can be expressed in terms
+of Seiberg-Witten invariants and certain explicit integrals over Hilbert
+schemes of points. These integrals are governed by seven universal functions,
+which are determined by their values on $\mathbb{P}^2$ and $\mathbb{P}^1 \times
+\mathbb{P}^1$. Using localization we calculate these functions up to some
+order, which allows us to check our conjecture in many cases.
+In an appendix by H. Nakajima and the first named author, the virtual Euler
+characteristic specialization of our conjecture is extended to include
+$\mu$-classes, thereby interpolating between Vafa-Witten's formula and Witten's
+conjecture for Donaldson invariants.
+",0,0,1,0,0,0
+11612,Uncertainty in Multitask Transfer Learning,"  Using variational Bayes neural networks, we develop an algorithm capable of
+accumulating knowledge into a prior from multiple different tasks. The result
+is a rich and meaningful prior capable of few-shot learning on new tasks. The
+posterior can go beyond the mean field approximation and yields good
+uncertainty on the performed experiments. Analysis on toy tasks shows that it
+can learn from significantly different tasks while finding similarities among
+them. Experiments of Mini-Imagenet yields the new state of the art with 74.5%
+accuracy on 5 shot learning. Finally, we provide experiments showing that other
+existing methods can fail to perform well in different benchmarks.
+",0,0,0,1,0,0
+11613,Geared Rotationally Identical and Invariant Convolutional Neural Network Systems,"  Theorems and techniques to form different types of transformationally
+invariant processing and to produce the same output quantitatively based on
+either transformationally invariant operators or symmetric operations have
+recently been introduced by the authors. In this study, we further propose to
+compose a geared rotationally identical CNN system (GRI-CNN) with a small step
+angle by connecting networks of participated processes at the first flatten
+layer. Using an ordinary CNN structure as a base, requirements for constructing
+a GRI-CNN include the use of either symmetric input vector or kernels with an
+angle increment that can form a complete cycle as a ""gearwheel"". Four basic
+GRI-CNN structures were studied. Each of them can produce quantitatively
+identical output results when a rotation angle of the input vector is evenly
+divisible by the step angle of the gear. Our study showed when an input vector
+rotated with an angle does not match to a step angle, the GRI-CNN can also
+produce a highly consistent result. With a design of using an ultra-fine
+gear-tooth step angle (e.g., 1 degree or 0.1 degree), all four GRI-CNN systems
+can be constructed virtually isotropically.
+",1,0,0,1,0,0
+11614,Conditional quantum one-time pad,"  Suppose that Alice and Bob are located in distant laboratories, which are
+connected by an ideal quantum channel. Suppose further that they share many
+copies of a quantum state $\rho_{ABE}$, such that Alice possesses the $A$
+systems and Bob the $BE$ systems. In our model, there is an identifiable part
+of Bob's laboratory that is insecure: a third party named Eve has infiltrated
+Bob's laboratory and gained control of the $E$ systems. Alice, knowing this,
+would like use their shared state and the ideal quantum channel to communicate
+a message in such a way that Bob, who has access to the whole of his laboratory
+($BE$ systems), can decode it, while Eve, who has access only to a sector of
+Bob's laboratory ($E$ systems) and the ideal quantum channel connecting Alice
+to Bob, cannot learn anything about Alice's transmitted message. We call this
+task the conditional one-time pad, and in this paper, we prove that the optimal
+rate of secret communication for this task is equal to the conditional quantum
+mutual information $I(A;B|E)$ of their shared state. We thus give the
+conditional quantum mutual information an operational meaning that is different
+from those given in prior works, via state redistribution, conditional erasure,
+or state deconstruction. We also generalize the model and method in several
+ways, one of which demonstrates that the negative tripartite interaction
+information $-I_{3}(A;B;E) = I(A;BE)-I(A;B)-I(A;E)$ of a tripartite state
+$\rho_{ABE}$ is an achievable rate for a secret-sharing task, i.e., the case in
+which Alice's message should be secure from someone possessing only the $AB$ or
+$AE$ systems but should be decodable by someone possessing all systems $A$,
+$B$, and $E$.
+",1,0,0,0,0,0
+11615,The Representation Theory of 2-Sylow Subgroups of the Symmetric Group,"  We study the Bratteli diagram of 2-Sylow subgroups of symmetric groups. We
+show that it is simple, has a recursive structure, and self-similarities at all
+scales. We contrast its subgraph of one-dimensional representations with the
+Macdonald tree.
+",0,0,1,0,0,0
+11616,Debiasing the Debiased Lasso with Bootstrap,"  In this paper, we prove that under proper conditions, bootstrap can further
+debias the debiased Lasso estimator for statistical inference of
+low-dimensional parameters in high-dimensional linear regression. We prove that
+the required sample size for inference with bootstrapped debiased Lasso, which
+involves the number of small coefficients, can be of smaller order than the
+existing ones for the debiased Lasso. Therefore, our results reveal the
+benefits of having strong signals. Our theory is supported by results of
+simulation experiments, which compare coverage probabilities and lengths of
+confidence intervals with and without bootstrap, with and without debiasing.
+",0,0,1,1,0,0
+11617,Asymptotic Normality of Extensible Grid Sampling,"  Recently, He and Owen (2016) proposed the use of Hilbert's space filling
+curve (HSFC) in numerical integration as a way of reducing the dimension from
+$d>1$ to $d=1$. This paper studies the asymptotic normality of the HSFC-based
+estimate when using scrambled van der Corput sequence as input. We show that
+the estimate has an asymptotic normal distribution for functions in
+$C^1([0,1]^d)$, excluding the trivial case of constant functions. The
+asymptotic normality also holds for discontinuous functions under mild
+conditions. It was previously known only that scrambled $(0,m,d)$-net
+quadratures enjoy the asymptotic normality for smooth enough functions, whose
+mixed partial gradients satisfy a Hölder condition. As a by-product, we find
+lower bounds for the variance of the HSFC-based estimate. Particularly, for
+nontrivial functions in $C^1([0,1]^d)$, the low bound is of order $n^{-1-2/d}$,
+which matches the rate of the upper bound established in He and Owen (2016).
+",0,0,1,1,0,0
+11618,"Hardening Stratum, the Bitcoin Pool Mining Protocol","  Stratum, the de-facto mining communication protocol used by blockchain based
+cryptocurrency systems, enables miners to reliably and efficiently fetch jobs
+from mining pool servers. In this paper we exploit Stratum's lack of encryption
+to develop passive and active attacks on Bitcoin's mining protocol, with
+important implications on the privacy, security and even safety of mining
+equipment owners. We introduce StraTap and ISP Log attacks, that infer miner
+earnings if given access to miner communications, or even their logs. We
+develop BiteCoin, an active attack that hijacks shares submitted by miners, and
+their associated payouts. We build BiteCoin on WireGhost, a tool we developed
+to hijack and surreptitiously maintain Stratum connections. Our attacks reveal
+that securing Stratum through pervasive encryption is not only undesirable (due
+to large overheads), but also ineffective: an adversary can predict miner
+earnings even when given access to only packet timestamps. Instead, we devise
+Bedrock, a minimalistic Stratum extension that protects the privacy and
+security of mining participants. We introduce and leverage the mining cookie
+concept, a secret that each miner shares with the pool and includes in its
+puzzle computations, and that prevents attackers from reconstructing or
+hijacking the puzzles. We have implemented our attacks and collected 138MB of
+Stratum protocol traffic from mining equipment in the US and Venezuela. We show
+that Bedrock is resilient to active attacks even when an adversary breaks the
+crypto constructs it uses. Bedrock imposes a daily overhead of 12.03s on a
+single pool server that handles mining traffic from 16,000 miners.
+",1,0,0,0,0,0
+11619,Representations associated to small nilpotent orbits for complex Spin groups,"  This paper provides a comparison between the $K$-structure of unipotent
+representations and regular sections of bundles on nilpotent orbits for complex
+groups of type $D$. Precisely, let $ G_ 0 =Spin(2n,\mathbb C)$ be the Spin
+complex group viewed as a real group, and $K\cong G_0$ be the complexification
+of the maximal compact subgroup of $G_0$. We compute $K$-spectra of the regular
+functions on some small nilpotent orbits $\mathcal O$ transforming according to
+characters $\psi$ of $C_{ K}(\mathcal O)$ trivial on the connected component of
+the identity $C_{ K}(\mathcal O)^0$. We then match them with the ${K}$-types of
+the genuine (i.e. representations which do not factor to $SO(2n,\mathbb C)$)
+unipotent representations attached to $\mathcal O$.
+",0,0,1,0,0,0
+11620,Fast Linear Transformations in Python,"  This paper introduces a new free library for the Python programming language,
+which provides a collection of structured linear transforms, that are not
+represented as explicit two dimensional arrays but in a more efficient way by
+exploiting the structural knowledge.
+This allows fast and memory savy forward and backward transformations while
+also provding a clean but still flexible interface to these effcient
+algorithms, thus making code more readable, scable and adaptable.
+We first outline the goals of this library, then how they were achieved and
+lastly we demonstrate the performance compared to current state of the art
+packages available for Python.
+This library is released and distributed under a free license.
+",1,0,0,0,0,0
+11621,Physical description of nature from a system-internal viewpoint,"  Objectivity is often considered as an ideal for scientific description of
+nature. When we describe physical phenomena, thus, we have exclusively taken an
+objective viewpoint by excluding a subject. Here we consider how nature can be
+described from a subjective viewpoint and how it is related to the objective
+description. To this end, we introduce a viewpoint-shift operation that sets
+perspective within a system, and subject the system to the laws of
+thermodynamics. We consider a situation in which the activation of an active
+part of the system starts to influence an objective part at t = 0, bringing the
+system into non-equilibrium. We find that the perspective alters physical state
+functions of the system, or leaves a viewpoint-dependent physical trace that is
+detectable. In the system-internal viewpoint, a system in the heat bath
+self-organizes to maximize the free energy, creating order. The active part
+keeps increasing a gap from an initial equilibrium state as long as the energy
+is available, forming a memory in the form of organized matter. This outcome of
+a system-internal viewpoint in physics matches our intuition coming from our
+daily-life experience that our subjective action leads to a change of our
+environment. This suggests that this system-internal viewpoint may provide a
+clue to understand a long-standing problem on the physical meaning to be
+subjective.
+",0,1,0,0,0,0
+11622,Training-induced inversion of spontaneous exchange bias field on La1.5Ca0.5CoMnO6,"  In this work we report the synthesis and structural, electronic and magnetic
+properties of La1.5Ca0.5CoMnO6 double-perovskite. This is a re-entrant spin
+cluster material which exhibits a non-negligible negative exchange bias effect
+when it is cooled in zero magnetic field from an unmagnetized state down to low
+temperature. X-ray powder diffraction, X-ray photoelectron spectroscopy and
+magnetometry results indicate mixed valence state at Co site, leading to
+competing magnetic phases and uncompensated spins at the magnetic interfaces.
+We compare the results for this Ca-doped material with those reported for the
+resemblant compound La1.5Sr0.5CoMnO6, and discuss the much smaller spontaneous
+exchange bias effect observed for the former in terms of its structural and
+magnetic particularities. For La1.5Ca0.5CoMnO6, when successive magnetization
+loops are carried, the spontaneous exchange bias field inverts its sign from
+negative to positive from the first to the second measurement. We discuss this
+behavior based on the disorder at the magnetic interfaces, related to the
+presence of a glassy phase. This compound also exhibits a large conventional
+exchange bias, for which there is no sign inversion of the exchange bias field
+for consecutive cycles.
+",0,1,0,0,0,0
+11623,Efficient and Adaptive Linear Regression in Semi-Supervised Settings,"  We consider the linear regression problem under semi-supervised settings
+wherein the available data typically consists of: (i) a small or moderate sized
+'labeled' data, and (ii) a much larger sized 'unlabeled' data. Such data arises
+naturally from settings where the outcome, unlike the covariates, is expensive
+to obtain, a frequent scenario in modern studies involving large databases like
+electronic medical records (EMR). Supervised estimators like the ordinary least
+squares (OLS) estimator utilize only the labeled data. It is often of interest
+to investigate if and when the unlabeled data can be exploited to improve
+estimation of the regression parameter in the adopted linear model.
+In this paper, we propose a class of 'Efficient and Adaptive Semi-Supervised
+Estimators' (EASE) to improve estimation efficiency. The EASE are two-step
+estimators adaptive to model mis-specification, leading to improved (optimal in
+some cases) efficiency under model mis-specification, and equal (optimal)
+efficiency under a linear model. This adaptive property, often unaddressed in
+the existing literature, is crucial for advocating 'safe' use of the unlabeled
+data. The construction of EASE primarily involves a flexible
+'semi-non-parametric' imputation, including a smoothing step that works well
+even when the number of covariates is not small; and a follow up 'refitting'
+step along with a cross-validation (CV) strategy both of which have useful
+practical as well as theoretical implications towards addressing two important
+issues: under-smoothing and over-fitting. We establish asymptotic results
+including consistency, asymptotic normality and the adaptive properties of
+EASE. We also provide influence function expansions and a 'double' CV strategy
+for inference. The results are further validated through extensive simulations,
+followed by application to an EMR study on auto-immunity.
+",0,0,1,1,0,0
+11624,Small Moving Window Calibration Models for Soft Sensing Processes with Limited History,"  Five simple soft sensor methodologies with two update conditions were
+compared on two experimentally-obtained datasets and one simulated dataset. The
+soft sensors investigated were moving window partial least squares regression
+(and a recursive variant), moving window random forest regression, the mean
+moving window of $y$, and a novel random forest partial least squares
+regression ensemble (RF-PLS), all of which can be used with small sample sizes
+so that they can be rapidly placed online. It was found that, on two of the
+datasets studied, small window sizes led to the lowest prediction errors for
+all of the moving window methods studied. On the majority of datasets studied,
+the RF-PLS calibration method offered the lowest one-step-ahead prediction
+errors compared to those of the other methods, and it demonstrated greater
+predictive stability at larger time delays than moving window PLS alone. It was
+found that both the random forest and RF-PLS methods most adequately modeled
+the datasets that did not feature purely monotonic increases in property
+values, but that both methods performed more poorly than moving window PLS
+models on one dataset with purely monotonic property values. Other data
+dependent findings are presented and discussed.
+",1,0,0,1,0,0
+11625,Deep Learning the Physics of Transport Phenomena,"  We have developed a new data-driven paradigm for the rapid inference,
+modeling and simulation of the physics of transport phenomena by deep learning.
+Using conditional generative adversarial networks (cGAN), we train models for
+the direct generation of solutions to steady state heat conduction and
+incompressible fluid flow purely on observation without knowledge of the
+underlying governing equations. Rather than using iterative numerical methods
+to approximate the solution of the constitutive equations, cGANs learn to
+directly generate the solutions to these phenomena, given arbitrary boundary
+conditions and domain, with high test accuracy (MAE$<$1\%) and state-of-the-art
+computational performance. The cGAN framework can be used to learn causal
+models directly from experimental observations where the underlying physical
+model is complex or unknown.
+",1,1,0,0,0,0
+11626,A Survey on Methods and Theories of Quantized Neural Networks,"  Deep neural networks are the state-of-the-art methods for many real-world
+tasks, such as computer vision, natural language processing and speech
+recognition. For all its popularity, deep neural networks are also criticized
+for consuming a lot of memory and draining battery life of devices during
+training and inference. This makes it hard to deploy these models on mobile or
+embedded devices which have tight resource constraints. Quantization is
+recognized as one of the most effective approaches to satisfy the extreme
+memory requirements that deep neural network models demand. Instead of adopting
+32-bit floating point format to represent weights, quantized representations
+store weights using more compact formats such as integers or even binary
+numbers. Despite a possible degradation in predictive performance, quantization
+provides a potential solution to greatly reduce the model size and the energy
+consumption. In this survey, we give a thorough review of different aspects of
+quantized neural networks. Current challenges and trends of quantized neural
+networks are also discussed.
+",0,0,0,1,0,0
+11627,Constructing confidence sets for the matrix completion problem,"  In the present note we consider the problem of constructing honest and
+adaptive confidence sets for the matrix completion problem. For the Bernoulli
+model with known variance of the noise we provide a realizable method for
+constructing confidence sets that adapt to the unknown rank of the true matrix.
+",0,0,1,1,0,0
+11628,Ring objects in the equivariant derived Satake category arising from Coulomb branches (with an appendix by Gus Lonergan),"  This is the second companion paper of arXiv:1601.03586. We consider the
+morphism from the variety of triples introduced in arXiv:1601.03586 to the
+affine Grassmannian. The direct image of the dualizing complex is a ring object
+in the equivariant derived category on the affine Grassmannian (equivariant
+derived Satake category). We show that various constructions in
+arXiv:1601.03586 work for an arbitrary commutative ring object.
+The second purpose of this paper is to study Coulomb branches associated with
+star shaped quivers, which are expected to be conjectural Higgs branches of
+$3d$ Sicilian theories in type $A$ by arXiv:1007.0992.
+",0,0,1,0,0,0
+11629,Well-Posedness of a Navier-Stokes/Mean Curvature Flow system,"  We consider a two-phase flow of two incompressible, viscous and immiscible
+fluids which are separated by a sharp interface in the case of a simple phase
+transition. In this model the interface is no longer material and its evolution
+is governed by a convective mean curvature flow equation, which is coupled to a
+two-phase Navier-Stokes equation with Young-Laplace law. The problem arises as
+a sharp interface limit of a diffuse interface model, which consists of a
+Navier-Stokes system coupled with an Allen-Cahn equation. We prove existence of
+strong solutions for sufficiently small times and regular initial data.
+",0,0,1,0,0,0
+11630,Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control,"  MOBAs represent a huge segment of online gaming and are growing as both an
+eSport and a casual genre. The natural starting point for AI researchers
+interested in MOBAs is to develop an AI to play the game better than a human -
+but MOBAs have many more challenges besides adversarial AI. In this paper we
+introduce the reader to the wider context of MOBA culture, propose a range of
+challenges faced by the community today, and posit concrete AI projects that
+can be undertaken to begin solving them.
+",1,0,0,0,0,0
+11631,Domains for Higher-Order Games,"  We study two-player inclusion games played over word-generating higher-order
+recursion schemes. While inclusion checks are known to capture verification
+problems, two-player games generalize this relationship to program synthesis.
+In such games, non-terminals of the grammar are controlled by opposing players.
+The goal of the existential player is to avoid producing a word that lies
+outside of a regular language of safe words.
+We contribute a new domain that provides a representation of the winning
+region of such games. Our domain is based on (functions over) potentially
+infinite Boolean formulas with words as atomic propositions. We develop an
+abstract interpretation framework that we instantiate to abstract this domain
+into a domain where the propositions are replaced by states of a finite
+automaton. This second domain is therefore finite and we obtain, via standard
+fixed-point techniques, a direct algorithm for the analysis of two-player
+inclusion games. We show, via a second instantiation of the framework, that our
+finite domain can be optimized, leading to a (k+1)EXP algorithm for order-k
+recursion schemes. We give a matching lower bound, showing that our approach is
+optimal. Since our approach is based on standard Kleene iteration, existing
+techniques and tools for fixed-point computations can be applied.
+",1,0,0,0,0,0
+11632,Improved torque formula for low and intermediate mass planetary migration,"  The migration of planets on nearly circular, non-inclined orbits in
+protoplanetary discs is entirely described by the disc's torque. This torque is
+a complex function of the disc parameters, and essentially amounts to the sum
+of two components: the Lindblad torque and the corotation torque. Known torque
+formulae do not reproduce accurately the torque actually experienced in
+numerical simulations by low- and intermediate- mass planets in radiative
+discs. One of the main reasons for this inaccuracy is that these formulae have
+been worked out in two-dimensional analyses. Here we revisit the torque formula
+and update many of its dimensionless coefficients by means of tailored, three-
+dimensional numerical simulations. In particular, we derive the dependence of
+the Lindblad torque on the temperature gradient, the dependence of the
+corotation torque on the radial entropy gradient (and work out a suitable
+expression of this gradient in a three-dimensional disc). We also work out the
+dependence of the corotation torque on the radial temperature gradient,
+overlooked so far. Corotation torques are known to scale very steeply with the
+width of the horseshoe region. We extend the expression of this width to the
+domain of intermediate mass planets, so that our updated torque formula remains
+valid for planets up to typically several tens of Earth masses, provided these
+relatively massive planets do not significantly deplete their coorbital region.
+Our torque expression can be applied to low- and intermediate-mass planets in
+optically thick protoplanetary discs, as well as protomoons embedded in
+circumplanetary discs.
+",0,1,0,0,0,0
+11633,The fluid running in the subnanochannel with functional surface,"  We have researched the motion of gas in the subnanochannel with functional
+surface which wettability has a gradient for the fluid by using molecular
+dynamics simulation. The results show that the gas is driven to flow under a
+single heat source and without any other work or energy applied to the system.
+The driving source is owed to the potential gradient of the functional face
+which keeps the fluid running in the subnanochannel. The width of the channel
+and the pressure of the reservoir has a significant influence on the flow
+velocity, which, respectively, has an optimal value for the maximum velocity.
+The flow velocity grows with the increasing temperature.
+",0,1,0,0,0,0
+11634,Data-Driven Decentralized Optimal Power Flow,"  The implementation of optimal power flow (OPF) methods to perform voltage and
+power flow regulation in electric networks is generally believed to require
+communication. We consider distribution systems with multiple controllable
+Distributed Energy Resources (DERs) and present a data-driven approach to learn
+control policies for each DER to reconstruct and mimic the solution to a
+centralized OPF problem from solely locally available information.
+Collectively, all local controllers closely match the centralized OPF solution,
+providing near-optimal performance and satisfaction of system constraints. A
+rate distortion framework facilitates the analysis of how well the resulting
+fully decentralized control policies are able to reconstruct the OPF solution.
+Our methodology provides a natural extension to decide what buses a DER should
+communicate with to improve the reconstruction of its individual policy. The
+method is applied on both single- and three-phase test feeder networks using
+data from real loads and distributed generators. It provides a framework for
+Distribution System Operators to efficiently plan and operate the contributions
+of DERs to active distribution networks.
+",0,0,0,1,0,0
+11635,Thermal-induced stress of plasmonic magnetic nanocomposites,"  We present theoretical calculations to interpret optical and mechanical
+properties of Ag@Fe3O4 nanoflowers. The microstructures and nature of optical
+peaks of nanoflowers are determined by means of the Mie theory associated with
+effective dielectric approximation and the experimental absorption spectrum.
+Under laser illumination, the thermal strain fields inside and outside the
+structure due to the absorbed optical energy are studied using continuum
+mechanics approach. Our findings provide simple but comprehensive description
+of the elastic behaviors of previous experiments.
+",0,1,0,0,0,0
+11636,High-resolution photoelectron-spectroscopic investigation of the H$_2$O$^+$ cation in its ${\mathrm {\tilde A^+}}$ electronic state,"  The photoelectron spectrum of water has been recorded in the vicinity of the
+${\mathrm {\tilde A^+}}$ $\leftarrow$ $\tilde{\mathrm{X}}$ transition between
+112 000 and 116 000 cm$^{-1}$ (13.89-14.38 eV). The high-resolution allowed the
+observation of the rotational structure of several bands. Rotational
+assignments of the transitions involving the $\Pi(080)$, $\Sigma(070)$ and
+$\Pi(060)$ vibronic states of the $\tilde{\mathrm{A}}^+$ electronic state are
+deduced from previous studies of the $\tilde{\mathrm{A}}^+ -
+\tilde{\mathrm{X}}^+$ band system of H$_2$O$^+$ (Lew, Can. J. Phys. 54, 2028
+(1976) and Huet et al., J. Chem. Phys. 107, 5645 (1997)) and photoionization
+selection rules. The transition to the $\Sigma(030)$ vibronic state is
+tentatively assigned.
+",0,1,0,0,0,0
+11637,Connectedness of the Balmer spectra of right bounded derived categories,"  By virtue of Balmer's celebrated theorem, the classification of thick tensor
+ideals of a tensor triangulated category $\T$ is equivalent to the topological
+structure of its Balmer spectrum $\spc \T$. Motivated by this theorem, we
+discuss connectedness and noetherianity of the Balmer spectrum of a right
+bounded derived category of finitely generated modules over a commutative ring.
+",0,0,1,0,0,0
+11638,Underdamped Langevin MCMC: A non-asymptotic analysis,"  We study the underdamped Langevin diffusion when the log of the target
+distribution is smooth and strongly concave. We present a MCMC algorithm based
+on its discretization and show that it achieves $\varepsilon$ error (in
+2-Wasserstein distance) in $\mathcal{O}(\sqrt{d}/\varepsilon)$ steps. This is a
+significant improvement over the best known rate for overdamped Langevin MCMC,
+which is $\mathcal{O}(d/\varepsilon^2)$ steps under the same
+smoothness/concavity assumptions.
+The underdamped Langevin MCMC scheme can be viewed as a version of
+Hamiltonian Monte Carlo (HMC) which has been observed to outperform overdamped
+Langevin MCMC methods in a number of application areas. We provide quantitative
+rates that support this empirical wisdom.
+",1,0,0,1,0,0
+11639,Two-dimensional compressible viscous flow around a circular cylinder,"  Direct numerical simulation is performed to study compressible, viscous flow
+around a circular cylinder. The present study considers two-dimensional,
+shock-free continuum flow by varying the Reynolds number between 20 and 100 and
+the freestream Mach number between 0 and 0.5. The results indicate that
+compressibility effects elongate the near wake for cases above and below the
+critical Reynolds number for two-dimensional flow under shedding. The wake
+elongation becomes more pronounced as the Reynolds number approaches this
+critical value. Moreover, we determine the growth rate and frequency of linear
+instability for cases above the critical Reynolds number. From the analysis, it
+is observed that the frequency of the Bénard-von Kármán vortex street in
+the time-periodic, fully-saturated flow increases from the dominant unstable
+frequency found from the linear stability analysis as the Reynolds number
+increases from its critical value, even for the low range of Reynolds numbers
+considered. We also notice that the compressibility effects reduce the growth
+rate and dominant frequency in the linear growth stage. Semi-empirical
+functional relationships for the growth rate and the dominant frequency in
+linearized flow around the cylinder in terms of the Reynolds number and
+freestream Mach number are presented.
+",0,1,0,0,0,0
+11640,BindsNET: A machine learning-oriented spiking neural networks library in Python,"  The development of spiking neural network simulation software is a critical
+component enabling the modeling of neural systems and the development of
+biologically inspired algorithms. Existing software frameworks support a wide
+range of neural functionality, software abstraction levels, and hardware
+devices, yet are typically not suitable for rapid prototyping or application to
+problems in the domain of machine learning. In this paper, we describe a new
+Python package for the simulation of spiking neural networks, specifically
+geared towards machine learning and reinforcement learning. Our software,
+called BindsNET, enables rapid building and simulation of spiking networks and
+features user-friendly, concise syntax. BindsNET is built on top of the PyTorch
+deep neural networks library, enabling fast CPU and GPU computation for large
+spiking networks. The BindsNET framework can be adjusted to meet the needs of
+other existing computing and hardware environments, e.g., TensorFlow. We also
+provide an interface into the OpenAI gym library, allowing for training and
+evaluation of spiking networks on reinforcement learning problems. We argue
+that this package facilitates the use of spiking networks for large-scale
+machine learning experimentation, and show some simple examples of how we
+envision BindsNET can be used in practice. BindsNET code is available at
+this https URL
+",0,0,0,0,1,0
+11641,Monaural Audio Speaker Separation with Source Contrastive Estimation,"  We propose an algorithm to separate simultaneously speaking persons from each
+other, the ""cocktail party problem"", using a single microphone. Our approach
+involves a deep recurrent neural networks regression to a vector space that is
+descriptive of independent speakers. Such a vector space can embed empirically
+determined speaker characteristics and is optimized by distinguishing between
+speaker masks. We call this technique source-contrastive estimation. The
+methodology is inspired by negative sampling, which has seen success in natural
+language processing, where an embedding is learned by correlating and
+de-correlating a given input vector with output weights. Although the matrix
+determined by the output weights is dependent on a set of known speakers, we
+only use the input vectors during inference. Doing so will ensure that source
+separation is explicitly speaker-independent. Our approach is similar to recent
+deep neural network clustering and permutation-invariant training research; we
+use weighted spectral features and masks to augment individual speaker
+frequencies while filtering out other speakers. We avoid, however, the severe
+computational burden of other approaches with our technique. Furthermore, by
+training a vector space rather than combinations of different speakers or
+differences thereof, we avoid the so-called permutation problem during
+training. Our algorithm offers an intuitive, computationally efficient response
+to the cocktail party problem, and most importantly boasts better empirical
+performance than other current techniques.
+",1,0,0,1,0,0
+11642,Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks,"  The quest for biologically plausible deep learning is driven, not just by the
+desire to explain experimentally-observed properties of biological neural
+networks, but also by the hope of discovering more efficient methods for
+training artificial networks. In this paper, we propose a new algorithm named
+Variational Probably Flow (VPF), an extension of minimum probability flow for
+training binary Deep Boltzmann Machines (DBMs). We show that weight updates in
+VPF are local, depending only on the states and firing rates of the adjacent
+neurons. Unlike contrastive divergence, there is no need for Gibbs
+confabulations; and unlike backpropagation, alternating feedforward and
+feedback phases are not required. Moreover, the learning algorithm is effective
+for training DBMs with intra-layer connections between the hidden nodes.
+Experiments with MNIST and Fashion MNIST demonstrate that VPF learns reasonable
+features quickly, reconstructs corrupted images more accurately, and generates
+samples with a high estimated log-likelihood. Lastly, we note that,
+interestingly, if an asymmetric version of VPF exists, the weight updates
+directly explain experimental results in Spike-Timing-Dependent Plasticity
+(STDP).
+",1,0,0,1,0,0
+11643,Node classification for signed networks using diffuse interface methods,"  Signed networks are a crucial tool when modeling friend and foe
+relationships. In contrast to classical undirected, weighted graphs, the edge
+weights for signed graphs are positive and negative. Crucial network properties
+are often derived from the study of the associated graph Laplacians. We here
+study several different signed network Laplacians with a focus on the task of
+classifying the nodes of the graph. We here extend a recently introduced
+technique based on a partial differential equation defined on the signed
+network, namely the Allen-Cahn-equation, to classify the nodes into two or more
+classes. We illustrate the performance of this approach on several real-world
+networks.
+",1,0,0,1,0,0
+11644,Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods,"  Recently we proposed a general, ensemble-based feature engineering wrapper
+(FEW) that was paired with a number of machine learning methods to solve
+regression problems. Here, we adapt FEW for supervised classification and
+perform a thorough analysis of fitness and survival methods within this
+framework. Our tests demonstrate that two fitness metrics, one introduced as an
+adaptation of the silhouette score, outperform the more commonly used Fisher
+criterion. We analyze survival methods and demonstrate that $\epsilon$-lexicase
+survival works best across our test problems, followed by random survival which
+outperforms both tournament and deterministic crowding. We conduct a benchmark
+comparison to several classification methods using a large set of problems and
+show that FEW can improve the best classifier performance in several cases. We
+show that FEW generates consistent, meaningful features for a biomedical
+problem with different ML pairings.
+",1,0,0,1,0,0
+11645,Anisotropic Fermi surface probed by the de Haas-van Alphen oscillation in proposed Dirac Semimetal TaSb$_{2}$,"  TaSb$_{2}$ has been predicted theoretically and proposed through
+magnetotransport experiment to be a topological semimetal. In earlier reports,
+the Shubnikov-de Haas oscillation has been analyzed to probe the Fermi surface,
+with magnetic field along a particular crystallographic axis only. By employing
+a sample rotator, we reveal highly anisotropic transverse magnetoresistance by
+rotating the magnetic field along different crystallographic directions. To
+probe the anisotropy in the Fermi surface, we have performed magnetization
+measurements and detected strong de Haas-van Alphen (dHvA) oscillations for the
+magnetic field applied along \textbf{b} and \textbf{c} axes as well as
+perpendicular to \textbf{bc} plane of the crystals. Three Fermi pockets have
+been identified by analyzing the dHvA oscillations. Hall measurement reveals
+electron as the only charge carrier, i.e., all the three Fermi pockets are
+electron type. With the application of magnetic field along different crystal
+directions, the cross sectional areas of the Fermi pockets have been found
+significantly different. Other physical parameters, such as the effective mass
+of the charge carrier and Fermi velocity have also been calculated using the
+Lifshitz-Kosevich formula.
+",0,1,0,0,0,0
+11646,Stochastic Deconvolutional Neural Network Ensemble Training on Generative Pseudo-Adversarial Networks,"  The training of Generative Adversarial Networks is a difficult task mainly
+due to the nature of the networks. One such issue is when the generator and
+discriminator start oscillating, rather than converging to a fixed point.
+Another case can be when one agent becomes more adept than the other which
+results in the decrease of the other agent's ability to learn, reducing the
+learning capacity of the system as a whole. Additionally, there exists the
+problem of Mode Collapse which involves the generators output collapsing to a
+single sample or a small set of similar samples. To train GANs a careful
+selection of the architecture that is used along with a variety of other
+methods to improve training. Even when applying these methods there is low
+stability of training in relation to the parameters that are chosen. Stochastic
+ensembling is suggested as a method for improving the stability while training
+GANs.
+",0,0,0,1,0,0
+11647,A Local Prime Factor Decomposition Algorithm for Strong Product Graphs,"  This work is concerned with the prime factor decomposition (PFD) of strong
+product graphs. A new quasi-linear time algorithm for the PFD with respect to
+the strong product for arbitrary, finite, connected, undirected graphs is
+derived. Moreover, since most graphs are prime although they can have a
+product-like structure, also known as approximate graph products, the practical
+application of the well-known ""classical"" prime factorization algorithm is
+strictly limited. This new PFD algorithm is based on a local approach that
+covers a graph by small factorizable subgraphs and then utilizes this
+information to derive the global factors. Therefore, we can take advantage of
+this approach and derive in addition a method for the recognition of
+approximate graph products.
+",1,0,0,0,0,0
+11648,Dispersion for the wave equation outside a ball and counterexamples,"  The purpose of this note is to prove dispersive estimates for the wave
+equation outside a ball in R^d. If d = 3, we show that the linear flow
+satisfies the dispersive estimates as in R^3. In higher dimensions d $\ge$ 4 we
+show that losses in dispersion do appear and this happens at the Poisson spot.
+",0,0,1,0,0,0
+11649,Minimal surfaces near short geodesics in hyperbolic $3$-manifolds,"  If $M$ is a finite volume complete hyperbolic $3$-manifold, the quantity
+$\mathcal A_1(M)$ is defined as the infimum of the areas of closed minimal
+surfaces in $M$. In this paper we study the continuity property of the
+functional $\mathcal A_1$ with respect to the geometric convergence of
+hyperbolic manifolds. We prove that it is lower semi-continuous and even
+continuous if $\mathcal A_1(M)$ is realized by a minimal surface satisfying
+some hypotheses. Understanding the interaction between minimal surfaces and
+short geodesics in $M$ is the main theme of this paper
+",0,0,1,0,0,0
+11650,Softening and Yielding of Soft Glassy Materials,"  Solids deform and fluids flow, but soft glassy materials, such as emulsions,
+foams, suspensions, and pastes, exhibit an intricate mix of solid and
+liquid-like behavior. While much progress has been made to understand their
+elastic (small strain) and flow (infinite strain) properties, such
+understanding is lacking for the softening and yielding phenomena that connect
+these asymptotic regimes. Here we present a comprehensive framework for
+softening and yielding of soft glassy materials, based on extensive numerical
+simulations of oscillatory rheological tests, and show that two distinct
+scenarios unfold depending on the material's packing density. For dense
+systems, there is a single, pressure-independent strain where the elastic
+modulus drops and the particle motion becomes diffusive. In contrast, for
+weakly jammed systems, a two-step process arises: at an intermediate softening
+strain, the elastic and loss moduli both drop down and then reach a new plateau
+value, whereas the particle motion becomes diffusive at the distinctly larger
+yield strain. We show that softening is associated with an extensive number of
+microscopic contact changes leading to a non-analytic rheological signature.
+Moreover, the scaling of the softening strain with pressure suggest the
+existence of a novel pressure scale above which softening and yielding
+coincide, and we verify the existence of this crossover scale numerically. Our
+findings thus evidence the existence of two distinct classes of soft glassy
+materials -- jamming dominated and dense -- and show how these can be
+distinguished by their rheological fingerprint.
+",0,1,0,0,0,0
+11651,Seemless Utilization of Heterogeneous XSede Resources to Accelerate Processing of a High Value Functional Neuroimaging Dataset,"  We describe the technical effort used to process a voluminous high value
+human neuroimaging dataset on the Open Science Grid with opportunistic use of
+idle HPC resources to boost computing capacity more than 5-fold. With minimal
+software development effort and no discernable competitive interference with
+other HPC users, this effort delivered 15,000,000 core hours over 7 months.
+",0,0,0,0,1,0
+11652,Nonlinear Network description for many-body quantum systems in continuous space,"  We show that the recently introduced iterative backflow renormalization can
+be interpreted as a general neural network in continuum space with non-linear
+functions in the hidden units. We use this wave function within Variational
+Monte Carlo for liquid $^4$He in two and three dimensions, where we typically
+find a tenfold increase in accuracy over currently used wave functions.
+Furthermore, subsequent stages of the iteration procedure define a set of
+increasingly good wave functions, each with its own variational energy and
+variance of the local energy: extrapolation of these energies to zero variance
+gives values in close agreement with the exact values. For two dimensional
+$^4$He, we also show that the iterative backflow wave function can describe
+both the liquid and the solid phase with the same functional form -a feature
+shared with the Shadow Wave Function, but now joined by much higher accuracy.
+We also achieve significant progress for liquid $^3$He in three dimensions,
+improving previous variational and fixed-node energies for this very
+challenging fermionic system.
+",0,1,0,0,0,0
+11653,Winding number $m$ and $-m$ patterns acting on concordance,"  We prove that for any winding number $m>0$ pattern $P$ and winding number
+$-m$ pattern $Q$, there exist knots $K$ such that the minimal genus of a
+cobordism between $P(K)$ and $Q(K)$ is arbitrarily large. This answers a
+question posed by Cochran-Harvey [CH17] and generalizes a result of
+Kim-Livingston [KL05].
+",0,0,1,0,0,0
+11654,Averages of shifted convolution sums for $GL(3) \times GL(2)$,"  Let $A_f(1,n)$ be the normalized Fourier coefficients of a $GL(3)$ Maass cusp
+form $f$ and let $a_g(n)$ be the normalized Fourier coefficients of a $GL(2)$
+cusp form $g$. Let $\lambda(n)$ be either $A_f(1,n)$ or the triple divisor
+function $d_3(n)$. It is proved that for any $\epsilon>0$, any integer $r\geq
+1$ and $r^{5/2}X^{1/4+7\delta/2}\leq H\leq X$ with $\delta>0$, $$
+\frac{1}{H}\sum_{h\geq 1}W\left(\frac{h}{H}\right) \sum_{n\geq
+1}\lambda(n)a_g(rn+h)V\left(\frac{n}{X}\right)\ll X^{1-\delta+\epsilon}, $$
+where $V$ and $W$ are smooth compactly supported functions, and the implied
+constants depend only on the associated forms and $\epsilon$.
+",0,0,1,0,0,0
+11655,"Reply to Hicks et al 2017, Reply to Morrison et al 2016 Refining the relevant population in forensic voice comparison, Reply to Hicks et al 2015 The importance of distinguishing info from evidence/observations when formulating propositions","  The present letter to the editor is one in a series of publications
+discussing the formulation of hypotheses (propositions) for the evaluation of
+strength of forensic evidence. In particular, the discussion focusses on the
+issue of what information may be used to define the relevant population
+specified as part of the different-speaker hypothesis in forensic voice
+comparison. The previous publications in the series are: Hicks et al. 2015
+<this http URL>; Morrison et al. (2016)
+<this http URL>; Hicks et al. (2017)
+<this http URL>. The latter letter to the
+editor mostly resolves the apparent disagreement between the two groups of
+authors. We briefly discuss one outstanding point of apparent disagreement, and
+attempt to correct a misinterpretation of our earlier remarks. We believe that
+at this point there is no actual disagreement, and that both groups of authors
+are calling for greater collaboration in order to reduce the likelihood of
+future misunderstandings.
+",0,0,0,1,0,0
+11656,Fourier Multipliers on the Heisenberg groups revisited,"  In this paper, we give explicit expressions of differential-difference
+operators appeared in the hypothesis of the general Fourier multiplier theorem
+associated to the Heisenberg groups proved by Mauceri and De Micheal for one
+dimension and C. Lin for higher dimension. We also give a much shorter proof of
+the above-mentioned theorem. Then we obtain a sharp weighted estimate for
+Fourier multipliers on the Heisenberg groups.
+",0,0,1,0,0,0
+11657,Free Boundary Minimal Surfaces in the Unit Three-Ball via Desingularization of the Critical Catenoid and the Equatorial Disk,"  We construct a new family of high genus examples of free boundary minimal
+surfaces in the Euclidean unit 3-ball by desingularizing the intersection of a
+coaxial pair of a critical catenoid and an equatorial disk. The surfaces are
+constructed by singular perturbation methods and have three boundary
+components. They are the free boundary analogue of the Costa-Hoffman-Meeks
+surfaces and the surfaces constructed by Kapouleas by desingularizing coaxial
+catenoids and planes. It is plausible that the minimal surfaces we constructed
+here are the same as the ones obtained recently by Ketover using the min-max
+method.
+",0,0,1,0,0,0
+11658,Multi-scale Transactive Control In Interconnected Bulk Power Systems Under High Renewable Energy Supply and High Demand Response Scenarios,"  This thesis presents the design, analysis, and validation of a hierarchical
+transactive control system that engages demand response resources to enhance
+the integration of renewable electricity generation resources. This control
+system joins energy, capacity and regulation markets together in a unified
+homeostatic and economically efficient electricity operation that increases
+total surplus while improving reliability and decreasing carbon emissions from
+fossil-based generation resources.
+The work encompasses: (1) the derivation of a short-term demand response
+model suitable for transactive control systems and its validation with field
+demonstration data; (2) an aggregate load model that enables effective control
+of large populations of thermal loads using a new type of thermostat (discrete
+time with zero deadband); (3) a methodology for optimally controlling response
+to frequency deviations while tracking schedule area exports in areas that have
+high penetration of both intermittent renewable resources and fast-acting
+demand response; and (4) the development of a system-wide (continental
+interconnection) scale strategy for optimal power trajectory and resource
+dispatch based on a shift from primarily energy cost-based approach to a
+primarily ramping cost-based one.
+The results show that multi-layer transactive control systems can be
+constructed, will enhance renewable resource utilization, and will operate in a
+coordinated manner with bulk power systems that include both regions with and
+without organized power markets. Estimates of Western Electric Coordionating
+Council (WECC) system cost savings under target renewable energy generation
+levels resulting from the proposed system exceed US$150B annually by the year
+2024, when compared to the existing control system.
+",0,1,0,0,0,0
+11659,Constraining the contribution of active galactic nuclei to reionisation,"  Recent results have suggested that active galactic nuclei (AGN) could provide
+enough photons to reionise the Universe. We assess the viability of this
+scenario using a semi-numerical framework for modeling reionisation, to which
+we add a quasar contribution by constructing a Quasar Halo Occupation
+Distribution (QHOD) based on Giallongo et al. observations. Assuming a constant
+QHOD, we find that an AGN-only model cannot simultaneously match observations
+of the optical depth $\tau_e$, neutral fraction, and ionising emissivity. Such
+a model predicts $\tau_e$ too low by $\sim 2\sigma$ relative to Planck
+constraints, and reionises the Universe at $z\lesssim 5$. Arbitrarily
+increasing the AGN emissivity to match these results yields a strong mismatch
+with the observed ionising emissivity at $z\sim 5$. If we instead assume a
+redshift-independent AGN luminosity function yielding an emissivity evolution
+like that assumed in Madau & Haardt model, then we can match $\tau_e$ albeit
+with late reionisation, however such evolution is inconsistent with
+observations at $z\sim 4-6$ and poorly motivated physically. These results
+arise because AGN are more biased towards massive halos than typical reionising
+galaxies, resulting in stronger clustering and later formation times.
+AGN-dominated models produce larger ionising bubbles that are reflected in
+$\sim\times 2$ more 21cm power on all scales. A model with equal parts galaxies
+and AGN contribution is still (barely) consistent with observations, but could
+be distinguished using next-generation 21cm experiments HERA and SKA-low. We
+conclude that, even with recent claims of more faint AGN than previously
+thought, AGN are highly unlikely to dominate the ionising photon budget for
+reionisation.
+",0,1,0,0,0,0
+11660,Two-walks degree assortativity in graphs and networks,"  Degree ssortativity is the tendency for nodes of high degree (resp.low
+degree) in a graph to be connected to high degree nodes (resp. to low degree
+ones). It is sually quantified by the Pearson correlation coefficient of the
+degree-degree correlation. Here we extend this concept to account for the
+effect of second neighbours to a given node in a graph. That is, we consider
+the two-walks degree of a node as the sum of all the degrees of its adjacent
+nodes. The two-walks degree assortativity of a graph is then the Pearson
+correlation coefficient of the two-walks degree-degree correlation. We found
+here analytical expression for this two-walks degree assortativity index as a
+function of contributing subgraphs. We then study all the 261,000 connected
+graphs with 9 nodes and observe the existence of assortative-assortative and
+disassortative-disassortative graphs according to degree and two-walks degree,
+respectively. More surprinsingly, we observe a class of graphs which are degree
+disassortative and two-walks degree assortative. We explain the existence of
+some of these graphs due to the presence of certain topological features, such
+as a node of low-degree connected to high-degree ones. More importantly, we
+study a series of 49 real-world networks, where we observe the existence of the
+disassortative-assortative class in several of them. In particular, all
+biological networks studied here were in this class. We also conclude that no
+graphs/networks are possible with assortative-disassortative structure.
+",1,1,0,0,0,0
+11661,Variational Analysis of Constrained M-Estimators,"  We propose a unified framework for establishing existence of nonparametric
+M-estimators, computing the corresponding estimates, and proving their strong
+consistency when the class of functions is exceptionally rich. In particular,
+the framework addresses situations where the class of functions is complex
+involving information and assumptions about shape, pointwise bounds, location
+of modes, height at modes, location of level-sets, values of moments, size of
+subgradients, continuity, distance to a `prior' function, multivariate total
+positivity, and any combination of the above. The class might be engineered to
+perform well in a specific setting even in the presence of little data. The
+framework views the class of functions as a subset of a particular metric space
+of upper semicontinuous functions under the Attouch-Wets distance. In addition
+to allowing a systematic treatment of numerous M-estimators, the framework
+yields consistency of plug-in estimators of modes of densities, maximizers of
+regression functions, and related quantities, and also enables computation by
+means of approximating parametric classes. We establish consistency through a
+one-sided law of large numbers, here extended to sieves, that relaxes
+assumptions of uniform laws, while ensuring global approximations even under
+model misspecification.
+",0,0,1,1,0,0
+11662,Minimally-Supervised Attribute Fusion for Data Lakes,"  Aggregate analysis, such as comparing country-wise sales versus global market
+share across product categories, is often complicated by the unavailability of
+common join attributes, e.g., category, across diverse datasets from different
+geographies or retail chains, even after disparate data is technically ingested
+into a common data lake. Sometimes this is a missing data issue, while in other
+cases it may be inherent, e.g., the records in different geographical databases
+may actually describe different product 'SKUs', or follow different norms for
+categorization. Record linkage techniques can be used to automatically map
+products in different data sources to a common set of global attributes,
+thereby enabling federated aggregation joins to be performed. Traditional
+record-linkage techniques are typically unsupervised, relying textual
+similarity features across attributes to estimate matches. In this paper, we
+present an ensemble model combining minimal supervision using Bayesian network
+models together with unsupervised textual matching for automating such
+'attribute fusion'. We present results of our approach on a large volume of
+real-life data from a market-research scenario and compare with a standard
+record matching algorithm. Finally we illustrate how attribute fusion using
+machine learning could be included as a data-lake management feature,
+especially as our approach also provides confidence values for matches,
+enabling human intervention, if required.
+",1,0,0,0,0,0
+11663,Diffusivities bounds in the presence of Weyl corrections,"  In this paper, we investigate the behavior of the thermoelectric DC
+conductivities in the presence of Weyl corrections with momentum dissipation in
+the incoherent limit. Moreover, we compute the butterfly velocity and study the
+charge and energy diffusion with broken translational symmetry. Our results
+show that the Weyl coupling $\gamma$, violates the bounds on the charge and
+energy diffusivity. It is also shown that the Weyl corrections violate the
+bound on the DC electrical conductivity in the incoherent limit.
+",0,1,0,0,0,0
+11664,Family-specific scaling laws in bacterial genomes,"  Among several quantitative invariants found in evolutionary genomics, one of
+the most striking is the scaling of the overall abundance of proteins, or
+protein domains, sharing a specific functional annotation across genomes of
+given size. The size of these functional categories change, on average, as
+power-laws in the total number of protein-coding genes. Here, we show that such
+regularities are not restricted to the overall behavior of high-level
+functional categories, but also exist systematically at the level of single
+evolutionary families of protein domains. Specifically, the number of proteins
+within each family follows family-specific scaling laws with genome size.
+Functionally similar sets of families tend to follow similar scaling laws, but
+this is not always the case. To understand this systematically, we provide a
+comprehensive classification of families based on their scaling properties.
+Additionally, we develop a quantitative score for the heterogeneity of the
+scaling of families belonging to a given category or predefined group. Under
+the common reasonable assumption that selection is driven solely or mainly by
+biological function, these findings point to fine-tuned and interdependent
+functional roles of specific protein domains, beyond our current functional
+annotations. This analysis provides a deeper view on the links between
+evolutionary expansion of protein families and the functional constraints
+shaping the gene repertoire of bacterial genomes.
+",0,1,0,0,0,0
+11665,Crowdsourcing Multiple Choice Science Questions,"  We present a novel method for obtaining high-quality, domain-targeted
+multiple choice questions from crowd workers. Generating these questions can be
+difficult without trading away originality, relevance or diversity in the
+answer options. Our method addresses these problems by leveraging a large
+corpus of domain-specific text and a small set of existing questions. It
+produces model suggestions for document selection and answer distractor choice
+which aid the human question generation process. With this method we have
+assembled SciQ, a dataset of 13.7K multiple choice science exam questions
+(Dataset available at this http URL). We demonstrate that the
+method produces in-domain questions by providing an analysis of this new
+dataset and by showing that humans cannot distinguish the crowdsourced
+questions from original questions. When using SciQ as additional training data
+to existing questions, we observe accuracy improvements on real science exams.
+",1,0,0,1,0,0
+11666,Ce 3$p$ hard x-ray photoelectron spectroscopy study of the topological Kondo insulator CeRu$_4$Sn$_6$,"  Bulk sensitive hard x-ray photoelectron spectroscopy data of the Ce 3$p$ core
+level of CeRu$_4$Sn$_6$ are presented. Using a combination of full multiplet
+and configuration iteration model we were able to obtain an accurate lineshape
+analysis of the data, thereby taking into account correlations for the strong
+plasmon intensities. We conclude that CeRu$_4$Sn$_6$ is a moderately mixed
+valence compound with a weight of 8% for the Ce $f^0$ configuration in the
+ground state.
+",0,1,0,0,0,0
+11667,A short proof of the middle levels theorem,"  Consider the graph that has as vertices all bitstrings of length $2n+1$ with
+exactly $n$ or $n+1$ entries equal to 1, and an edge between any two bitstrings
+that differ in exactly one bit. The well-known middle levels conjecture asserts
+that this graph has a Hamilton cycle for any $n\geq 1$. In this paper we
+present a new proof of this conjecture, which is much shorter and more
+accessible than the original proof.
+",1,0,0,0,0,0
+11668,Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification,"  After being trained, classifiers must often operate on data that has been
+corrupted by noise. In this paper, we consider the impact of such noise on the
+features of binary classifiers. Inspired by tools for classifier robustness, we
+introduce the same classification probability (SCP) to measure the resulting
+distortion on the classifier outputs. We introduce a low-complexity estimate of
+the SCP based on quantization and polynomial multiplication. We also study
+channel coding techniques based on replication error-correcting codes. In
+contrast to the traditional channel coding approach, where error-correction is
+meant to preserve the data and is agnostic to the application, our schemes
+specifically aim to maximize the SCP (equivalently minimizing the distortion of
+the classifier output) for the same redundancy overhead.
+",1,0,0,1,0,0
+11669,Kropina change of a Finsler space with m-th root metric,"  In this paper, we find a condition under which a Finsler space with Kropina
+change of mth-root metric is projectively related to a mth-root metric and also
+we find a condition under which this Kropina transformed mth-root metric is
+locally dually flat. Moreover we find the condition for its Projective
+flatness.
+",0,0,1,0,0,0
+11670,Direct Visualization of 2D Topological Insulator in Single-layer 1T'-WTe2,"  We grow nearly freestanding single-layer 1T'-WTe2 on graphitized 6H-SiC(0001)
+by using molecular beam epitaxy (MBE), and characterize its electronic
+structure with scanning tunneling microscopy / spectroscopy (STM/STS). We
+demonstrate the existence of topological edge states at the periphery of
+single-layer WTe2 islands. Surprisingly, we also find a band gap in the bulk
+and the semiconducting behaviors of the single-layer WTe2 at low temperature,
+which is likely resulted from an incommensurate charge density wave (CDW)
+transition. The realization of two-dimensional topological insulators (2D TIs)
+in single-layer transition metal dichalcogenide (TMD) thus provides a promising
+platform for further exploration of the 2D TIs' physics and related
+applications.
+",0,1,0,0,0,0
+11671,Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fields,"  This paper is concerned with structured machine learning, in a supervised
+machine learning context. It discusses how to make joint structured learning on
+interdependent objects of different nature, as well as how to enforce logical
+con-straints when predicting labels. We explain how this need arose in a
+Document Understanding task. We then discuss a general extension to Conditional
+Random Field (CRF) for this purpose and present the contributed open source
+implementation on top of the open source PyStruct library. We evaluate its
+performance on a publicly available dataset.
+",1,0,0,1,0,0
+11672,Mobile big data analysis with machine learning,"  This paper investigates to identify the requirement and the development of
+machine learning-based mobile big data analysis through discussing the insights
+of challenges in the mobile big data (MBD). Furthermore, it reviews the
+state-of-the-art applications of data analysis in the area of MBD. Firstly, we
+introduce the development of MBD. Secondly, the frequently adopted methods of
+data analysis are reviewed. Three typical applications of MBD analysis, namely
+wireless channel modeling, human online and offline behavior analysis, and
+speech recognition in the internet of vehicles, are introduced respectively.
+Finally, we summarize the main challenges and future development directions of
+mobile big data analysis.
+",0,0,0,1,0,0
+11673,Enhancement of Galaxy Overdensity around Quasar Pairs at z<3.6 based on the Hyper Suprime-Cam Subaru Strategic Program Survey,"  We investigate the galaxy overdensity around proto-cluster scale quasar pairs
+at high (z>3) and low (z~1) redshift based on the unprecedentedly wide and deep
+optical survey of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP).
+Using the first-year survey data covering effectively ~121 deg^2 with the
+5sigma depth of i~26.4 and the SDSS DR12Q catalog, we find two luminous pairs
+at z~3.3 and 3.6 which reside in >5sigma overdense regions of g-dropout
+galaxies at i<25. The projected separations of the two pairs are R_perp=1.75
+and 1.04 proper Mpc, and their velocity offsets are Delta V=692 and 1448 km
+s^{-1}, respectively. This result is in clear contrast to the average z~4
+quasar environments as discussed in Uchiyama et al. (2017) and implies that the
+quasar activities of the pair members are triggered via major mergers in
+proto-clusters, unlike the vast majority of isolated quasars in general fields
+that may turn on via non-merger events such as bar and disk instabilities. At
+z~1, we find 37 pairs with R_perp<2 pMpc and Delta V<2300 km s^{-1} in the
+current HSC-Wide coverage, including four from Hennawi et al. (2006). The
+distribution of the peak overdensity significance within two arcminutes around
+the pairs has a long tail toward high density (>4sigma) regions. Thanks to the
+large sample size, we find a statistical evidence that this excess is unique to
+the pair environments when compared to single quasar and randomly selected
+galaxy environments at the same redshift range. Moreover, there are nine
+small-scale (R_perp<1 pMpc) pairs, two of which are found to reside in cluster
+fields. Our results demonstrate that <2 pMpc-scale quasar pairs at both
+redshift range tend to occur in massive haloes, although perhaps not the most
+massive ones, and that they are useful to search for rare density peaks.
+",0,1,0,0,0,0
+11674,Search for cosmic dark matter by means of ultra high purity NaI(Tl) scintillator,"  The dark matter search project by means of ultra high purity NaI(Tl)
+scintillator is now underdevelopment. An array of large volume NaI(Tl)
+detectors whose volume is 12.7 cm$\phi\times$12.7 cm is applied to search for
+dark matter signal. To remove radioactive impurities in NaI(Tl) crystal is one
+of the most important task to find small number of dark matter signals. We have
+developed high purity NaI(Tl) crystal which contains small amounts of
+radioactive impurities, $<4$ ppb of $^{nat}$K, 0.3 ppt of Th chain, 58
+$\mu$Bq/kg of $^{226}$Ra and 30 $\mu$Bq/kg of $^{210}$Pb. Future prospects to
+search for dark matter by means of a large volume and high purity NaI(Tl)
+scintillator is discussed.
+",0,1,0,0,0,0
+11675,On Training Recurrent Networks with Truncated Backpropagation Through Time in Speech Recognition,"  Recurrent neural networks have been the dominant models for many speech and
+language processing tasks. However, we understand little about the behavior and
+the class of functions recurrent networks can realize. Moreover, the heuristics
+used during training complicate the analyses. In this paper, we study recurrent
+networks' ability to learn long-term dependency in the context of speech
+recognition. We consider two decoding approaches, online and batch decoding,
+and show the classes of functions to which the decoding approaches correspond.
+We then draw a connection between batch decoding and a popular training
+approach for recurrent networks, truncated backpropagation through time.
+Changing the decoding approach restricts the amount of past history recurrent
+networks can use for prediction, allowing us to analyze their ability to
+remember. Empirically, we utilize long-term dependency in subphonetic states,
+phonemes, and words, and show how the design decisions, such as the decoding
+approach, lookahead, context frames, and consecutive prediction, characterize
+the behavior of recurrent networks. Finally, we draw a connection between
+Markov processes and vanishing gradients. These results have implications for
+studying the long-term dependency in speech data and how these properties are
+learned by recurrent networks.
+",1,0,0,0,0,0
+11676,Wavelength Dependence of Picosecond Laser-Induced Periodic Surface Structures on Copper,"  The physical mechanisms of the laser-induced periodic surface structures
+(LIPSS) formation are studied in this paper for single-pulse irradiation
+regimes. The change in the LIPSS period with wavelength of incident laser
+radiation is investigated experimentally, using a picosecond laser system,
+which provides 7-ps pulses in near-IR, visible, and UV spectral ranges. The
+experimental results are compared with predictions made under the assumption
+that the surface-scattered waves are involved in the LIPSS formation.
+Considerable disagreement suggests that hydrodynamic mechanisms can be
+responsible for the observed pattern periodicity.
+",0,1,0,0,0,0
+11677,Time-efficient Garbage Collection in SSDs,"  SSDs are currently replacing magnetic disks in many application areas. A
+challenge of the underlying flash technology is that data cannot be updated
+in-place. A block consisting of many pages must be completely erased before a
+single page can be rewritten. This victim block can still contain valid pages
+which need to be copied to other blocks before erasure. The objective of
+garbage collection strategies is to minimize write amplification induced by
+copying valid pages from victim blocks while minimizing the performance
+overhead of the victim selection. Victim selection strategies minimizing write
+amplification, like the cost-benefit approach, have linear runtime, while the
+write amplifications of time-efficient strategies, like the greedy strategy,
+significantly reduce the lifetime of SSDs. In this paper, we propose two
+strategies which optimize the performance of cost-benefit, while (almost)
+preserving its write amplification. Trace-driven simulations for single- and
+multi-channel SSDs show that the optimizations help to keep the write
+amplification low while improving the runtime by up to 24-times compared to the
+original cost-benefit strategy, so that the new strategies can be used in
+multi-TByte SSDs.
+",1,0,0,0,0,0
+11678,Annealed limit theorems for the ising model on random regular graphs,"  In a recent paper [15], Giardin{à}, Giberti, Hofstad, Prioriello have
+proved a law of large number and a central limit theorem with respect to the
+annealed measure for the magnetization of the Ising model on some random graphs
+including the random 2-regular graph. We present a new proof of their results,
+which applies to all random regular graphs. In addition, we prove the existence
+of annealed pressure in the case of configuration model random graphs.
+",0,1,1,0,0,0
+11679,Multi-Task Learning of Keyphrase Boundary Classification,"  Keyphrase boundary classification (KBC) is the task of detecting keyphrases
+in scientific articles and labelling them with respect to predefined types.
+Although important in practice, this task is so far underexplored, partly due
+to the lack of labelled data. To overcome this, we explore several auxiliary
+tasks, including semantic super-sense tagging and identification of multi-word
+expressions, and cast the task as a multi-task learning problem with deep
+recurrent neural networks. Our multi-task models perform significantly better
+than previous state of the art approaches on two scientific KBC datasets,
+particularly for long keyphrases.
+",1,0,0,1,0,0
+11680,On the interior motive of certain Shimura varieties : the case of Picard varieties,"  The aim of this article is the construction of the interior motive of a
+Picard variety. Those are Shimura varieties of PEL type. Our result is an
+application of the strategy developed by Wildeshaus to construct a
+Hecke-invariant motive whose realizations correspond to interior cohomology.
+",0,0,1,0,0,0
+11681,SenGen: Sentence Generating Neural Variational Topic Model,"  We present a new topic model that generates documents by sampling a topic for
+one whole sentence at a time, and generating the words in the sentence using an
+RNN decoder that is conditioned on the topic of the sentence. We argue that
+this novel formalism will help us not only visualize and model the topical
+discourse structure in a document better, but also potentially lead to more
+interpretable topics since we can now illustrate topics by sampling
+representative sentences instead of bag of words or phrases. We present a
+variational auto-encoder approach for learning in which we use a factorized
+variational encoder that independently models the posterior over topical
+mixture vectors of documents using a feed-forward network, and the posterior
+over topic assignments to sentences using an RNN. Our preliminary experiments
+on two different datasets indicate early promise, but also expose many
+challenges that remain to be addressed.
+",1,0,0,1,0,0
+11682,Atomistic-continuum multiscale modelling of magnetisation dynamics at non-zero temperature,"  In this article, a few problems related to multiscale modelling of magnetic
+materials at finite temperatures and possible ways of solving these problems
+are discussed. The discussion is mainly centred around two established
+multiscale concepts: the partitioned domain and the upscaling-based
+methodologies. The major challenge for both multiscale methods is to capture
+the correct value of magnetisation length accurately, which is affected by a
+random temperature-dependent force. Moreover, general limitations of these
+multiscale techniques in application to spin systems are discussed.
+",0,1,1,0,0,0
+11683,"Regularly Varying Functions, Generalized contents, and the spectrum of fractal strings","  We revisit the problem of characterizing the eigenvalue distribution of the
+Dirichlet-Laplacian on bounded open sets $\Omega\subset\mathbb{R}$ with fractal
+boundaries. It is well-known from the results of Lapidus and Pomerance
+\cite{LapPo1} that the asymptotic second term of the eigenvalue counting
+function can be described in terms of the Minkowski content of the boundary of
+$\Omega$ provided it exists. He and Lapidus \cite{HeLap2} discussed a
+remarkable extension of this characterization to sets $\Omega$ with boundaries
+that are not necessarily Minkowski measurable. They employed so-called
+generalized Minkowski contents given in terms of gauge functions more general
+than the usual power functions. The class of valid gauge functions in their
+theory is characterized by some technical conditions, the geometric meaning and
+necessity of which is not obvious. Therefore, it is not completely clear how
+general the approach is and which sets $\Omega$ are covered. Here we revisit
+these results and put them in the context of regularly varying functions. Using
+Karamata theory, it is possible to get rid of most of the technical conditions
+and simplify the proofs given by He and Lapidus, revealing thus even more of
+the beauty of their results. Further simplifications arise from
+characterization results for Minkowski contents obtained in \cite{RW13}. We
+hope our new point of view on these spectral problems will initiate some
+further investigations of this beautiful theory.
+",0,0,1,0,0,0
+11684,The Geodesic Distance between $\mathcal{G}_I^0$ Models and its Application to Region Discrimination,"  The $\mathcal{G}_I^0$ distribution is able to characterize different regions
+in monopolarized SAR imagery. It is indexed by three parameters: the number of
+looks (which can be estimated in the whole image), a scale parameter and a
+texture parameter. This paper presents a new proposal for feature extraction
+and region discrimination in SAR imagery, using the geodesic distance as a
+measure of dissimilarity between $\mathcal{G}_I^0$ models. We derive geodesic
+distances between models that describe several practical situations, assuming
+the number of looks known, for same and different texture and for same and
+different scale. We then apply this new tool to the problems of (i)~identifying
+edges between regions with different texture, and (ii)~quantify the
+dissimilarity between pairs of samples in actual SAR data. We analyze the
+advantages of using the geodesic distance when compared to stochastic
+distances.
+",1,0,0,1,0,0
+11685,Search for Common Minima in Joint Optimization of Multiple Cost Functions,"  We present a novel optimization method, named the Combined Optimization
+Method (COM), for the joint optimization of two or more cost functions. Unlike
+the conventional joint optimization schemes, which try to find minima in a
+weighted sum of cost functions, the COM explores search space for common minima
+shared by all the cost functions. Given a set of multiple cost functions that
+have qualitatively different distributions of local minima with each other, the
+proposed method finds the common minima with a high success rate without the
+help of any metaheuristics. As a demonstration, we apply the COM to the crystal
+structure prediction in materials science. By introducing the concept of data
+assimilation, i.e., adopting the theoretical potential energy of the crystal
+and the crystallinity, which characterizes the agreement with the theoretical
+and experimental X-ray diffraction patterns, as cost functions, we show that
+the correct crystal structures of Si diamond, low quartz, and low cristobalite
+can be predicted with significantly higher success rates than the previous
+methods.
+",0,0,0,1,0,0
+11686,Statistical Inferences for Polarity Identification in Natural Language,"  Information forms the basis for all human behavior, including the ubiquitous
+decision-making that people constantly perform in their every day lives. It is
+thus the mission of researchers to understand how humans process information to
+reach decisions. In order to facilitate this task, this work proposes a novel
+method of studying the reception of granular expressions in natural language.
+The approach utilizes LASSO regularization as a statistical tool to extract
+decisive words from textual content and draw statistical inferences based on
+the correspondence between the occurrences of words and an exogenous response
+variable. Accordingly, the method immediately suggests significant implications
+for social sciences and Information Systems research: everyone can now identify
+text segments and word choices that are statistically relevant to authors or
+readers and, based on this knowledge, test hypotheses from behavioral research.
+We demonstrate the contribution of our method by examining how authors
+communicate subjective information through narrative materials. This allows us
+to answer the question of which words to choose when communicating negative
+information. On the other hand, we show that investors trade not only upon
+facts in financial disclosures but are distracted by filler words and
+non-informative language. Practitioners - for example those in the fields of
+investor communications or marketing - can exploit our insights to enhance
+their writings based on the true perception of word choice.
+",1,0,0,1,0,0
+11687,Effect of Adaptive and Cooperative Adaptive Cruise Control on Throughput of Signalized Arterials,"  The paper evaluates the influence of the maximum vehicle acceleration and
+variable proportions of ACC/CACC vehicles on the throughput of an intersection.
+Two cases are studied: (1) free road downstream of the intersection; and (2)
+red light at some distance downstream of the intersection. Simulation of a
+4-mile stretch of an arterial with 13 signalized intersections is used to
+evaluate the impact of (C)ACC vehicles on the mean and standard deviation of
+travel time as the proportion of (C)ACC vehicles is increased. The results
+suggest a very high urban mobility benefit of (C)ACC vehicles at little or no
+cost in infrastructure.
+",1,0,0,0,0,0
+11688,Very cost effective bipartition in Gamma(Z_n),"  Let Z_n be the finite commutative ring of residue classes modulo n and
+Gamma(Z_n) be its zero-divisor graph. The nilradical graph and non-nilradical
+graph of Z_n are denoted by N(Z_n) and Omega(Z_n) respectively. In 2012, Haynes
+et al. [5] introduced the concept of very cost effective graph. For a graph G =
+(V,E) and a set of vertices S subset of V, a vertex v in S is said to be very
+cost effective if it is adjacent to more vertices in V§than in S. A
+bipartition Pi = {S, V§} is called very cost effective if both S and V§are
+very cost effective sets [5,6]. In this paper, we investigate the very cost
+effective bipartition of Gamma(Z_n), where n = p_1 p_2 ... p_m, here all p_i's
+are distinct primes. In addition, we discuss the cases in which N(Z_n) and
+Omega(Z_n) graphs have very cost effective bipartition for different n.
+Finally, we derive some results for very cost effective bipartition of the Line
+graph and Total graph of Gamma(Z_n), denoted by L(Gamma(Z_n)) and T(Gamma(Z_n))
+respectively.
+",0,0,1,0,0,0
+11689,Doubly dressed bosons - exciton-polaritons in a strong terahertz field,"  We demonstrate the existence of a novel quasiparticle: an exciton in a
+semiconductor doubly dressed with two photons of different wavelengths: near
+infrared cavity photon and terahertz (THz) photon, with the THz coupling
+strength approaching the ultra-strong coupling regime. This quasiparticle is
+composed of three different bosons, being a mixture of a matter-light
+quasiparticle. Our observations are confirmed by a detailed theoretical
+analysis, treating quantum mechanically all three bosonic fields. The doubly
+dressed quasiparticles retain the bosonic nature of their constituents, but
+their internal quantum structure strongly depends on the intensity of the
+applied terahertz field.
+",0,1,0,0,0,0
+11690,Fast Stability Scanning for Future Grid Scenario Analysis,"  Future grid scenario analysis requires a major departure from conventional
+power system planning, where only a handful of most critical conditions is
+typically analyzed. To capture the inter-seasonal variations in renewable
+generation of a future grid scenario necessitates the use of computationally
+intensive time-series analysis. In this paper, we propose a planning framework
+for fast stability scanning of future grid scenarios using a novel feature
+selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm.
+To achieve the computational speed-up, the stability analysis is performed only
+on small number of representative cluster centroids instead of on the full set
+of operating conditions. As a case study, we perform small-signal stability and
+steady-state voltage stability scanning of a simplified model of the Australian
+National Electricity Market with significant penetration of renewable
+generation. The simulation results show the effectiveness of the proposed
+approach. Compared to an exhaustive time series scanning, the proposed
+framework reduced the computational burden up to ten times, with an acceptable
+level of accuracy.
+",1,0,0,1,0,0
+11691,Femtosecond Mega-electron-volt Electron Energy-Loss Spectroscopy,"  Pump-probe electron energy-loss spectroscopy (EELS) with femtosecond temporal
+resolution will be a transformative research tool for studying non-equilibrium
+chemistry and electronic dynamics of matter. In this paper, we propose a new
+concept of femtosecond EELS utilizing mega-electron-volt electron beams from a
+radio-frequency (rf) photocathode source. The high acceleration gradient and
+high beam energy of the rf gun are critical to the generation of 10-femtosecond
+electron beams, which enables improvement of the temporal resolution by more
+than one order of magnitude beyond the state of the art. The major innovation
+in our proposal - the `reference-beam technique', relaxes the energy stability
+requirement on the rf power source by roughly two orders of magnitude.
+Requirements on the electron beam quality, photocathode, spectrometer and
+detector are also discussed. Supported by particle-tracking simulations, we
+demonstrate the feasibility of achieving sub-electron-volt energy resolution
+and ~10-femtosecond temporal resolution with existing or near-future hardware
+performances.
+",0,1,0,0,0,0
+11692,On the Power of Symmetric Linear Programs,"  We consider families of symmetric linear programs (LPs) that decide a
+property of graphs (or other relational structures) in the sense that, for each
+size of graph, there is an LP defining a polyhedral lift that separates the
+integer points corresponding to graphs with the property from those
+corresponding to graphs without the property. We show that this is equivalent,
+with at most polynomial blow-up in size, to families of symmetric Boolean
+circuits with threshold gates. In particular, when we consider polynomial-size
+LPs, the model is equivalent to definability in a non-uniform version of
+fixed-point logic with counting (FPC). Known upper and lower bounds for FPC
+apply to the non-uniform version. In particular, this implies that the class of
+graphs with perfect matchings has polynomial-size symmetric LPs while we obtain
+an exponential lower bound for symmetric LPs for the class of Hamiltonian
+graphs. We compare and contrast this with previous results (Yannakakis 1991)
+showing that any symmetric LPs for the matching and TSP polytopes have
+exponential size. As an application, we establish that for random, uniformly
+distributed graphs, polynomial-size symmetric LPs are as powerful as general
+Boolean circuits. We illustrate the effect of this on the well-studied
+planted-clique problem.
+",1,0,0,0,0,0
+11693,Pulsar science with the CHIME telescope,"  The CHIME telescope (the Canadian Hydrogen Intensity Mapping Experiment)
+recently built in Penticton, Canada, is currently being commissioned.
+Originally designed as a cosmology experiment, it was soon recognized that
+CHIME has the potential to simultaneously serve as an incredibly useful radio
+telescope for pulsar science. CHIME operates across a wide bandwidth of 400-800
+MHz and will have a collecting area and sensitivity comparable to that of the
+100-m class radio telescopes. CHIME has a huge field of view of ~250 square
+degrees. It will be capable of observing 10 pulsars simultaneously, 24-hours
+per day, every day, while still accomplishing its missions to study Baryon
+Acoustic Oscillations and Fast Radio Bursts. It will carry out daily monitoring
+of roughly half of all pulsars in the northern hemisphere, including all
+NANOGrav pulsars employed in the Pulsar Timing Array project. It will cycle
+through all pulsars in the northern hemisphere with a range of cadence of no
+more than 10 days.
+",0,1,0,0,0,0
+11694,Approximation by generalized Kantorovich sampling type series,"  In the present article, we analyse the behaviour of a new family of
+Kantorovich type sampling operators $(K_w^{\varphi}f)_{w>0}.$ First, we give a
+Voronovskaya type theorem for these Kantorovich generalized sampling series and
+a corresponding quantitative version in terms of the first order of modulus of
+continuity. Further, we study the order of approximation in $C({\mathbb{R}})$
+(the set of all uniformly continuous and bounded functions on ${\mathbb{R}}$)
+for the family $(K_w^{\varphi}f)_{w>0}.$ Finally, we give some examples of
+kernels such as B-spline kernels and Blackman-Harris kernel to which the theory
+can be applied.
+",0,0,1,0,0,0
+11695,Mechanical Instability Leading Epithelial Cell Delamination,"  We theoretically investigate the mechanical stability of three-dimensional
+(3D) foam geometry in a cell sheet and apply its understandings to epithelial
+integrity and cell delamination. Analytical calculations revealed that the
+monolayer integrity of cell sheet is lost to delamination by a spontaneous
+symmetry breaking, inherently depending on the 3D foam geometry of cells; i.e.,
+the instability spontaneously appears when the cell density in the sheet plane
+increases and/or when the number of neighboring cells decreases, as observed in
+vivo. The instability is also facilitated by the delaminated cell-specific
+force generation upon lateral surfaces, which are driven by cell-intrinsic
+genetic programs during cell invasion and apoptosis in physiology. In
+principle, this instability emerges from the force balance on the lateral
+boundaries among cells. Additionally, taking into account the cell-intrinsic
+force generation on apical and basal sides, which are also broadly observed in
+morphogenesis, homeostasis, and carcinogenesis, we found apically/basally
+directed cell delaminations and pseudostratified structures, which could
+universally explain mechanical regulations of epithelial geometries in both
+physiology and pathophysiology.
+",0,0,0,0,1,0
+11696,A two-layer shallow water model for bedload sediment transport: convergence to Saint-Venant-Exner model,"  A two-layer shallow water type model is proposed to describe bedload sediment
+transport. The upper layer is filled by water and the lower one by sediment.
+The key point falls on the definition of the friction laws between the two
+layers, which are a generalization of those introduced in Fernández-Nieto et
+al. (ESAIM: M2AN, 51:115-145, 2017). This definition allows to apply properly
+the two-layer shallow water model for the case of intense and slow bedload
+sediment transport. Moreover, we prove that the two-layer model converges to a
+Saint-Venant-Exner system (SVE) including gravitational effects when the ratio
+between the hydrodynamic and morphodynamic time scales is small. The SVE with
+gravitational effects is a degenerated nonlinear parabolic system. This means
+that its numerical approximation is very expensive from a computational point
+of view, see for example T. Morales de Luna et al. (J. Sci. Comp., 48(1):
+258-273, 2011). In this work, gravitational effects are introduced into the
+two-layer system without such extra computational cost. Finally, we also
+consider a generalization of the model that includes a non-hydrostatic pressure
+correction for the fluid layer and the boundary condition at the sediment
+surface. Numerical tests show that the model provides promising results and
+behave well in low transport rate regimes as well as in many other situations.
+",0,1,0,0,0,0
+11697,Chondrule Accretion with a Growing Protoplanet,"  Chondrules are primitive materials in the Solar System. They are formed in
+the first about 3 Myr of the Solar System's history. This timescale is longer
+than that of Mars formation, and it is conceivable that protoplanets,
+planetesimals and chondrules might have existed simultaneously in the solar
+nebula. Due to protoplanets perturbation on the planetesimal dynamics and
+chondrule accretion on them, all the formed chondrules are unlikely to be
+accreted by planetesimals. We investigate the amount of chondrules accreted by
+planetesimals in such a condition. We assume that a protoplanet is in
+oligarchic growth, and we perform analytical calculations of chondrule
+accretion both by a protoplanet and by planetesimals. Through the oligarchic
+growth stage, planetesimals accrete about half of the formed chondrules. The
+smallest planetesimals get the largest amount of the chondrules, compared with
+the amount accreted by more massive planetesimals. We perform a parameter study
+and find that this fraction is not largely changed for a wide range of
+parameter sets.
+",0,1,0,0,0,0
+11698,Exemplar or Matching: Modeling DCJ Problems with Unequal Content Genome Data,"  The edit distance under the DCJ model can be computed in linear time for
+genomes with equal content or with Indels. But it becomes NP-Hard in the
+presence of duplications, a problem largely unsolved especially when Indels are
+considered. In this paper, we compare two mainstream methods to deal with
+duplications and associate them with Indels: one by deletion, namely
+DCJ-Indel-Exemplar distance; versus the other by gene matching, namely
+DCJ-Indel-Matching distance. We design branch-and-bound algorithms with set of
+optimization methods to compute exact distances for both. Furthermore, median
+problems are discussed in alignment with both of these distance methods, which
+are to find a median genome that minimizes distances between itself and three
+given genomes. Lin-Kernighan (LK) heuristic is leveraged and powered up by
+sub-graph decomposition and search space reduction technologies to handle
+median computation. A wide range of experiments are conducted on synthetic data
+sets and real data sets to show pros and cons of these two distance metrics per
+se, as well as putting them in the median computation scenario.
+",1,0,0,0,0,0
+11699,MIHash: Online Hashing with Mutual Information,"  Learning-based hashing methods are widely used for nearest neighbor
+retrieval, and recently, online hashing methods have demonstrated good
+performance-complexity trade-offs by learning hash functions from streaming
+data. In this paper, we first address a key challenge for online hashing: the
+binary codes for indexed data must be recomputed to keep pace with updates to
+the hash functions. We propose an efficient quality measure for hash functions,
+based on an information-theoretic quantity, mutual information, and use it
+successfully as a criterion to eliminate unnecessary hash table updates. Next,
+we also show how to optimize the mutual information objective using stochastic
+gradient descent. We thus develop a novel hashing method, MIHash, that can be
+used in both online and batch settings. Experiments on image retrieval
+benchmarks (including a 2.5M image dataset) confirm the effectiveness of our
+formulation, both in reducing hash table recomputations and in learning
+high-quality hash functions.
+",1,0,0,0,0,0
+11700,On the Dedekind different of a Cayley-Bacharach scheme,"  Given a 0-dimensional scheme $\mathbb{X}$ in a projective space
+$\mathbb{P}^n_K$ over a field $K$, we characterize the Cayley-Bacharach
+property of $\mathbb{X}$ in terms of the algebraic structure of the Dedekind
+different of its homogeneous coordinate ring. Moreover, we characterize
+Cayley-Bacharach schemes by Dedekind's formula for the conductor and the
+complementary module, we study schemes with minimal Dedekind different using
+the trace of the complementary module, and we prove various results about
+almost Gorenstein and nearly Gorenstein schemes.
+",0,0,1,0,0,0
+11701,A note on the role of projectivity in likelihood-based inference for random graph models,"  There is widespread confusion about the role of projectivity in
+likelihood-based inference for random graph models. The confusion is rooted in
+claims that projectivity, a form of marginalizability, may be necessary for
+likelihood-based inference and consistency of maximum likelihood estimators. We
+show that likelihood-based superpopulation inference is not affected by lack of
+projectivity and that projectivity is not a necessary condition for consistency
+of maximum likelihood estimators.
+",0,0,1,1,0,0
+11702,"Alternating Double Euler Sums, Hypergeometric Identities and a Theorem of Zagier","  In this work, we derive relations between generating functions of double
+stuffle relations and double shuffle relations to express the alternating
+double Euler sums $\zeta\left(\overline{r}, s\right)$, $\zeta\left(r,
+\overline{s}\right)$ and $\zeta\left(\overline{r}, \overline{s}\right)$ with
+$r+s$ odd in terms of zeta values. We also give a direct proof of a
+hypergeometric identity which is a limiting case of a basic hypergeometric
+identity of Andrews. Finally, we gave another proof for the formula of Zagier
+on the multiple zeta values $\zeta(2,\ldots,2,3,2,\ldots,2)$.
+",0,0,1,0,0,0
+11703,Achieving Dilution without Knowledge of Coordinates in the SINR Model,"  Considerable literature has been developed for various fundamental
+distributed problems in the SINR (Signal-to-Interference-plus-Noise-Ratio)
+model for radio transmission. A setting typically studied is when all nodes
+transmit a signal of the same strength, and each device only has access to
+knowledge about the total number of nodes in the network $n$, the range from
+which each node's label is taken $[1,\dots,N]$, and the label of the device
+itself. In addition, an assumption is made that each node also knows its
+coordinates in the Euclidean plane. In this paper, we create a technique which
+allows algorithm designers to remove that last assumption. The assumption about
+the unavailability of the knowledge of the physical coordinates of the nodes
+truly captures the `ad-hoc' nature of wireless networks.
+Previous work in this area uses a flavor of a technique called dilution, in
+which nodes transmit in a (predetermined) round-robin fashion, and are able to
+reach all their neighbors. However, without knowing the physical coordinates,
+it's not possible to know the coordinates of their containing (pivotal) grid
+box and seemingly not possible to use dilution (to coordinate their
+transmissions). We propose a new technique to achieve dilution without using
+the knowledge of physical coordinates. This technique exploits the
+understanding that the transmitting nodes lie in 2-D space, segmented by an
+appropriate pivotal grid, without explicitly referring to the actual physical
+coordinates of these nodes. Using this technique, it is possible for every weak
+device to successfully transmit its message to all of its neighbors in
+$\Theta(\lg N)$ rounds, as long as the density of transmitting nodes in any
+physical grid box is bounded by a known constant. This technique, we feel, is
+an important generic tool for devising practical protocols when physical
+coordinates of the nodes are not known.
+",1,0,0,0,0,0
+11704,Probing the topology of density matrices,"  The mixedness of a quantum state is usually seen as an adversary to
+topological quantization of observables. For example, exact quantization of the
+charge transported in a so-called Thouless adiabatic pump is lifted at any
+finite temperature in symmetry-protected topological insulators. Here, we show
+that certain directly observable many-body correlators preserve the integrity
+of topological invariants for mixed Gaussian quantum states in one dimension.
+Our approach relies on the expectation value of the many-body
+momentum-translation operator, and leads to a physical observable --- the
+""ensemble geometric phase"" (EGP) --- which represents a bona fide geometric
+phase for mixed quantum states, in the thermodynamic limit. In cyclic
+protocols, the EGP provides a topologically quantized observable which detects
+encircled spectral singularities (""purity-gap"" closing points) of density
+matrices. While we identify the many-body nature of the EGP as a key
+ingredient, we propose a conceptually simple, interferometric setup to directly
+measure the latter in experiments with mesoscopic ensembles of ultracold atoms.
+",0,1,0,0,0,0
+11705,Stable Limit Theorems for Empirical Processes under Conditional Neighborhood Dependence,"  This paper introduces a new concept of stochastic dependence among many
+random variables which we call conditional neighborhood dependence (CND).
+Suppose that there are a set of random variables and a set of sigma algebras
+where both sets are indexed by the same set endowed with a neighborhood system.
+When the set of random variables satisfies CND, any two non-adjacent sets of
+random variables are conditionally independent given sigma algebras having
+indices in one of the two sets' neighborhood. Random variables with CND include
+those with conditional dependency graphs and a class of Markov random fields
+with a global Markov property. The CND property is useful for modeling
+cross-sectional dependence governed by a complex, large network. This paper
+provides two main results. The first result is a stable central limit theorem
+for a sum of random variables with CND. The second result is a Donsker-type
+result of stable convergence of empirical processes indexed by a class of
+functions satisfying a certain bracketing entropy condition when the random
+variables satisfy CND.
+",0,0,1,1,0,0
+11706,Topological Maxwell Metal Bands in a Superconducting Qutrit,"  We experimentally explore the topological Maxwell metal bands by mapping the
+momentum space of condensed-matter models to the tunable parameter space of
+superconducting quantum circuits. An exotic band structure that is effectively
+described by the spin-1 Maxwell equations is imaged. Three-fold degenerate
+points dubbed Maxwell points are observed in the Maxwell metal bands. Moreover,
+we engineer and observe the topological phase transition from the topological
+Maxwell metal to a trivial insulator, and report the first experiment to
+measure the Chern numbers that are higher than one.
+",0,1,0,0,0,0
+11707,Pressure effect and Superconductivity in $β$-Bi$_4$I$_4$ Topological Insulator,"  We report a detailed study of the transport coefficients of
+$\beta$-Bi$_4$I$_4$ quasi-one dimensional topological insulator. Electrical
+resistivity, thermoelectric power, thermal conductivity and Hall coefficient
+measurements are consistent with the possible appearance of a charge density
+wave order at low temperatures. Both electrons and holes contribute to the
+conduction in $\beta$-Bi$_4$I$_4$ and the dominant type of charge carrier
+changes with temperature as a consequence of temperature-dependent carrier
+densities and mobilities. Measurements of resistivity and Seebeck coefficient
+under hydrostatic pressure up to 2 GPa show a shift of the charge density wave
+order to higher temperatures suggesting a strongly one-dimensional character at
+ambient pressure. Surprisingly, superconductivity is induced in
+$\beta$-Bi$_4$I$_4$ above 10 GPa with of 4.0 K which is slightly decreasing
+upon increasing the pressure up to 20 GPa. Chemical characterisation of the
+pressure-treated samples shows amorphization of $\beta$-Bi$_4$I$_4$ under
+pressure and rules out decomposition into Bi and BiI$_3$ at room-temperature
+conditions.
+",0,1,0,0,0,0
+11708,Backprop-Q: Generalized Backpropagation for Stochastic Computation Graphs,"  In real-world scenarios, it is appealing to learn a model carrying out
+stochastic operations internally, known as stochastic computation graphs
+(SCGs), rather than learning a deterministic mapping. However, standard
+backpropagation is not applicable to SCGs. We attempt to address this issue
+from the angle of cost propagation, with local surrogate costs, called
+Q-functions, constructed and learned for each stochastic node in an SCG. Then,
+the SCG can be trained based on these surrogate costs using standard
+backpropagation. We propose the entire framework as a solution to generalize
+backpropagation for SCGs, which resembles an actor-critic architecture but
+based on a graph. For broad applicability, we study a variety of SCG structures
+from one cost to multiple costs. We utilize recent advances in reinforcement
+learning (RL) and variational Bayes (VB), such as off-policy critic learning
+and unbiased-and-low-variance gradient estimation, and review them in the
+context of SCGs. The generalized backpropagation extends transported learning
+signals beyond gradients between stochastic nodes while preserving the benefit
+of backpropagating gradients through deterministic nodes. Experimental
+suggestions and concerns are listed to help design and test any specific model
+using this framework.
+",0,0,0,1,0,0
+11709,Development of a low-alpha-emitting μ-PIC for NEWAGE direction-sensitive dark-matter search,"  NEWAGE is a direction-sensitive dark-matter-search experiment that uses a
+micro-patterned gaseous detector, or {\mu}-PIC, as the readout. The main
+background sources are {\alpha}-rays from radioactive contaminants in the
+{\mu}-PIC. We have therefore developed a low-alpha-emitting {\mu}-PICs and
+measured its performances. We measured the surface {\alpha}-ray emission rate
+of the {\mu}-PIC in the Kamioka mine using a surface {\alpha}-ray counter based
+on a micro TPC.
+",0,1,0,0,0,0
+11710,"Online characterization of planetary surfaces: PlanetServer, an open-source analysis and visualization tool","  The lack of open-source tools for hyperspectral data visualization and
+analysiscreates a demand for new tools. In this paper we present the new
+PlanetServer,a set of tools comprising a web Geographic Information System
+(GIS) and arecently developed Python Application Programming Interface (API)
+capableof visualizing and analyzing a wide variety of hyperspectral data from
+differentplanetary bodies. Current WebGIS open-source tools are evaluated in
+orderto give an overview and contextualize how PlanetServer can help in this
+mat-ters. The web client is thoroughly described as well as the datasets
+availablein PlanetServer. Also, the Python API is described and exposed the
+reason ofits development. Two different examples of mineral characterization of
+differenthydrosilicates such as chlorites, prehnites and kaolinites in the Nili
+Fossae areaon Mars are presented. As the obtained results show positive outcome
+in hyper-spectral analysis and visualization compared to previous literature,
+we suggestusing the PlanetServer approach for such investigations.
+",1,1,0,0,0,0
+11711,GM-PHD Filter for Searching and Tracking an Unknown Number of Targets with a Mobile Sensor with Limited FOV,"  We study the problem of searching for and tracking a collection of moving
+targets using a robot with a limited Field-Of-View (FOV) sensor. The actual
+number of targets present in the environment is not known a priori. We propose
+a search and tracking framework based on the concept of Bayesian Random Finite
+Sets (RFSs). Specifically, we generalize the Gaussian Mixture Probability
+Hypothesis Density (GM-PHD) filter which was previously applied for tracking
+problems to allow for simultaneous search and tracking with a limited FOV
+sensor. The proposed framework can extract individual target tracks as well as
+estimate the number and the spatial density of targets. We also show how to use
+the Gaussian Process (GP) regression to extract and predict non-linear target
+trajectories in this framework. We demonstrate the efficacy of our techniques
+through representative simulations and a real data collected from an aerial
+robot.
+",1,0,0,0,0,0
+11712,Regularity of solutions to scalar conservation laws with a force,"  We prove regularity estimates for entropy solutions to scalar conservation
+laws with a force. Based on the kinetic form of a scalar conservation law, a
+new decomposition of entropy solutions is introduced, by means of a
+decomposition in the velocity variable, adapted to the non-degeneracy
+properties of the flux function. This allows a finer control of the degeneracy
+behavior of the flux. In addition, this decomposition allows to make use of the
+fact that the entropy dissipation measure has locally finite singular moments.
+Based on these observations, improved regularity estimates for entropy
+solutions to (forced) scalar conservation laws are obtained.
+",0,0,1,0,0,0
+11713,Coupled identical localized fermionic chains with quasi-random disorder,"  We analyze the ground state localization properties of an array of identical
+interacting spinless fermionic chains with quasi-random disorder, using
+non-perturbative Renormalization Group methods. In the single or two chains
+case localization persists while for a larger number of chains a different
+qualitative behavior is generically expected, unless the many body interaction
+is vanishing. This is due to number theoretical properties of the frequency,
+similar to the ones assumed in KAM theory, and cancellations due to Pauli
+principle which in the single or two chains case imply that all the effective
+interactions are irrelevant; in contrast for a larger number of chains relevant
+effective interactions are present.
+",0,1,0,0,0,0
+11714,Calibrated Filtered Reduced Order Modeling,"  We propose a calibrated filtered reduced order model (CF-ROM) framework for
+the numerical simulation of general nonlinear PDEs that are amenable to reduced
+order modeling. The novel CF-ROM framework consists of two steps: (i) In the
+first step, we use explicit ROM spatial filtering of the nonlinear PDE to
+construct a filtered ROM. This filtered ROM is low-dimensional, but is not
+closed (because of the nonlinearity in the given PDE). (ii) In the second step,
+we use a calibration procedure to close the filtered ROM, i.e., to model the
+interaction between the resolved and unresolved modes. To this end, we use a
+linear or quadratic ansatz to model this interaction and close the filtered
+ROM. To find the new coefficients in the closed filtered ROM, we solve an
+optimization problem that minimizes the difference between the full order model
+data and our ansatz. Although we use a fluid dynamics setting to illustrate how
+to construct and use the CF-ROM framework, we emphasize that it is built on
+general ideas of spatial filtering and optimization and is independent of
+(restrictive) phenomenological arguments. Thus, the CF-ROM framework can be
+applied to a wide variety of PDEs.
+",0,1,1,0,0,0
+11715,Population of collective modes in light scattering by many atoms,"  The interaction of light with an atomic sample containing a large number of
+particles gives rise to many collective (or cooperative) effects, such as
+multiple scattering, superradiance and subradiance, even if the atomic density
+is low and the incident optical intensity weak (linear optics regime). Tracing
+over the degrees of freedom of the light field, the system can be well
+described by an effective atomic Hamiltonian, which contains the light-mediated
+dipole-dipole interaction between atoms. This long-range interaction is at the
+origin of the various collective effects, or of collective excitation modes of
+the system. Even though an analysis of the eigenvalues and eigenfunctions of
+these collective modes does allow distinguishing superradiant modes, for
+instance, from other collective modes, this is not sufficient to understand the
+dynamics of a driven system, as not all collective modes are significantly
+populated. Here, we study how the excitation parameters, i.e. the driving
+field, determines the population of the collective modes. We investigate in
+particular the role of the laser detuning from the atomic transition, and
+demonstrate a simple relation between the detuning and the steady-state
+population of the modes. This relation allows understanding several properties
+of cooperative scattering, such as why superradiance and subradiance become
+independent of the detuning at large enough detuning without vanishing, and why
+superradiance, but not subradiance, is suppressed near resonance.
+",0,1,0,0,0,0
+11716,A Framework for Accurate Drought Forecasting System Using Semantics-Based Data Integration Middleware,"  Technological advancement in Wireless Sensor Networks (WSN) has made it
+become an invaluable component of a reliable environmental monitoring system;
+they form the digital skin' through which to 'sense' and collect the context of
+the surroundings and provides information on the process leading to complex
+events such as drought. However, these environmental properties are measured by
+various heterogeneous sensors of different modalities in distributed locations
+making up the WSN, using different abstruse terms and vocabulary in most cases
+to denote the same observed property, causing data heterogeneity. Adding
+semantics and understanding the relationships that exist between the observed
+properties, and augmenting it with local indigenous knowledge is necessary for
+an accurate drought forecasting system. In this paper, we propose the framework
+for the semantic representation of sensor data and integration with indigenous
+knowledge on drought using a middleware for an efficient drought forecasting
+system.
+",1,0,0,0,0,0
+11717,Readings and Misreadings of J. Willard Gibbs Elementary Principles in Statistical Mechanics,"  J. Willard Gibbs' Elementary Principles in Statistical Mechanics was the
+definitive work of one of America's greatest physicists. Gibbs' book on
+statistical mechanics establishes the basic principles and fundamental results
+that have flowered into the modern field of statistical mechanics. However, at
+a number of points, Gibbs' teachings on statistical mechanics diverge from
+positions on the canonical ensemble found in more recent works, at points where
+seemingly there should be agreement. The objective of this paper is to note
+some of these points, so that Gibbs' actual positions are not misrepresented to
+future generations of students.
+",0,1,0,0,0,0
+11718,Multilingual Adaptation of RNN Based ASR Systems,"  In this work, we focus on multilingual systems based on recurrent neural
+networks (RNNs), trained using the Connectionist Temporal Classification (CTC)
+loss function. Using a multilingual set of acoustic units poses difficulties.
+To address this issue, we proposed Language Feature Vectors (LFVs) to train
+language adaptive multilingual systems. Language adaptation, in contrast to
+speaker adaptation, needs to be applied not only on the feature level, but also
+to deeper layers of the network. In this work, we therefore extended our
+previous approach by introducing a novel technique which we call ""modulation"".
+Based on this method, we modulated the hidden layers of RNNs using LFVs. We
+evaluated this approach in both full and low resource conditions, as well as
+for grapheme and phone based systems. Lower error rates throughout the
+different conditions could be achieved by the use of the modulation.
+",1,0,0,0,0,0
+11719,SPIDERS: Selection of spectroscopic targets using AGN candidates detected in all-sky X-ray surveys,"  SPIDERS (SPectroscopic IDentification of eROSITA Sources) is an SDSS-IV
+survey running in parallel to the eBOSS cosmology project. SPIDERS will obtain
+optical spectroscopy for large numbers of X-ray-selected AGN and galaxy cluster
+members detected in wide area eROSITA, XMM-Newton and ROSAT surveys. We
+describe the methods used to choose spectroscopic targets for two
+sub-programmes of SPIDERS: X-ray selected AGN candidates detected in the ROSAT
+All Sky and the XMM-Newton Slew surveys. We have exploited a Bayesian
+cross-matching algorithm, guided by priors based on mid-IR colour-magnitude
+information from the WISE survey, to select the most probable optical
+counterpart to each X-ray detection. We empirically demonstrate the high
+fidelity of our counterpart selection method using a reference sample of bright
+well-localised X-ray sources collated from XMM-Newton, Chandra and Swift-XRT
+serendipitous catalogues, and also by examining blank-sky locations. We
+describe the down-selection steps which resulted in the final set of
+SPIDERS-AGN targets put forward for spectroscopy within the eBOSS/TDSS/SPIDERS
+survey, and present catalogues of these targets. We also present catalogues of
+~12000 ROSAT and ~1500 XMM-Newton Slew survey sources which have existing
+optical spectroscopy from SDSS-DR12, including the results of our visual
+inspections. On completion of the SPIDERS program, we expect to have collected
+homogeneous spectroscopic redshift information over a footprint of ~7500
+deg$^2$ for >85 percent of the ROSAT and XMM-Newton Slew survey sources having
+optical counterparts in the magnitude range 17<r<22.5, producing a large and
+highly complete sample of bright X-ray-selected AGN suitable for statistical
+studies of AGN evolution and clustering.
+",0,1,0,0,0,0
+11720,Task-specific Word Identification from Short Texts Using a Convolutional Neural Network,"  Task-specific word identification aims to choose the task-related words that
+best describe a short text. Existing approaches require well-defined seed words
+or lexical dictionaries (e.g., WordNet), which are often unavailable for many
+applications such as social discrimination detection and fake review detection.
+However, we often have a set of labeled short texts where each short text has a
+task-related class label, e.g., discriminatory or non-discriminatory, specified
+by users or learned by classification algorithms. In this paper, we focus on
+identifying task-specific words and phrases from short texts by exploiting
+their class labels rather than using seed words or lexical dictionaries. We
+consider the task-specific word and phrase identification as feature learning.
+We train a convolutional neural network over a set of labeled texts and use
+score vectors to localize the task-specific words and phrases. Experimental
+results on sentiment word identification show that our approach significantly
+outperforms existing methods. We further conduct two case studies to show the
+effectiveness of our approach. One case study on a crawled tweets dataset
+demonstrates that our approach can successfully capture the
+discrimination-related words/phrases. The other case study on fake review
+detection shows that our approach can identify the fake-review words/phrases.
+",1,0,0,0,0,0
+11721,Merlin-Arthur with efficient quantum Merlin and quantum supremacy for the second level of the Fourier hierarchy,"  We introduce a simple sub-universal quantum computing model, which we call
+the Hadamard-classical circuit with one-qubit (HC1Q) model. It consists of a
+classical reversible circuit sandwiched by two layers of Hadamard gates, and
+therefore it is in the second level of the Fourier hierarchy. We show that
+output probability distributions of the HC1Q model cannot be classically
+efficiently sampled within a multiplicative error unless the polynomial-time
+hierarchy collapses to the second level. The proof technique is different from
+those used for previous sub-universal models, such as IQP, Boson Sampling, and
+DQC1, and therefore the technique itself might be useful for finding other
+sub-universal models that are hard to classically simulate. We also study the
+classical verification of quantum computing in the second level of the Fourier
+hierarchy. To this end, we define a promise problem, which we call the
+probability distribution distinguishability with maximum norm (PDD-Max). It is
+a promise problem to decide whether output probability distributions of two
+quantum circuits are far apart or close. We show that PDD-Max is BQP-complete,
+but if the two circuits are restricted to some types in the second level of the
+Fourier hierarchy, such as the HC1Q model or the IQP model, PDD-Max has a
+Merlin-Arthur system with quantum polynomial-time Merlin and classical
+probabilistic polynomial-time Arthur.
+",1,0,0,0,0,0
+11722,"Light sterile neutrinos, dark matter, and new resonances in a $U(1)$ extension of the MSSM","  We present $\psi'$MSSM, a model based on a $U(1)_{\psi'}$ extension of the
+minimal supersymmetric standard model. The gauge symmetry $U(1)_{\psi'}$, also
+known as $U(1)_N$, is a linear combination of the $U(1)_\chi$ and $U(1)_\psi$
+subgroups of $E_6$. The model predicts the existence of three sterile neutrinos
+with masses $\lesssim 0.1~{\rm eV}$, if the $U(1)_{\psi'}$ breaking scale is of
+order 10 TeV. Their contribution to the effective number of neutrinos at
+nucleosynthesis is $\Delta N_{\nu}\simeq 0.29$. The model can provide a variety
+of possible cold dark matter candidates including the lightest sterile
+sneutrino. If the $U(1)_{\psi'}$ breaking scale is increased to $10^3~{\rm
+TeV}$, the sterile neutrinos, which are stable on account of a $Z_2$ symmetry,
+become viable warm dark matter candidates. The observed value of the standard
+model Higgs boson mass can be obtained with relatively light stop quarks thanks
+to the D-term contribution from $U(1)_{\psi'}$. The model predicts diquark and
+diphoton resonances which may be found at an updated LHC. The well-known $\mu$
+problem is resolved and the observed baryon asymmetry of the universe can be
+generated via leptogenesis. The breaking of $U(1)_{\psi'}$ produces
+superconducting strings that may be present in our galaxy. A $U(1)$ R symmetry
+plays a key role in keeping the proton stable and providing the light sterile
+neutrinos.
+",0,1,0,0,0,0
+11723,Accurate halo-galaxy mocks from automatic bias estimation and particle mesh gravity solvers,"  Reliable extraction of cosmological information from clustering measurements
+of galaxy surveys requires estimation of the error covariance matrices of
+observables. The accuracy of covariance matrices is limited by our ability to
+generate sufficiently large number of independent mock catalogs that can
+describe the physics of galaxy clustering across a wide range of scales.
+Furthermore, galaxy mock catalogs are required to study systematics in galaxy
+surveys and to test analysis tools. In this investigation, we present a fast
+and accurate approach for generation of mock catalogs for the upcoming galaxy
+surveys. Our method relies on low-resolution approximate gravity solvers to
+simulate the large scale dark matter field, which we then populate with halos
+according to a flexible nonlinear and stochastic bias model. In particular, we
+extend the \textsc{patchy} code with an efficient particle mesh algorithm to
+simulate the dark matter field (the \textsc{FastPM} code), and with a robust
+MCMC method relying on the \textsc{emcee} code for constraining the parameters
+of the bias model. Using the halos in the BigMultiDark high-resolution $N$-body
+simulation as a reference catalog, we demonstrate that our technique can model
+the bivariate probability distribution function (counts-in-cells), power
+spectrum, and bispectrum of halos in the reference catalog. Specifically, we
+show that the new ingredients permit us to reach percentage accuracy in the
+power spectrum up to $k\sim 0.4\; \,h\,{\rm Mpc}^{-1}$ (within 5\% up to $k\sim
+0.6\; \,h\,{\rm Mpc}^{-1}$) with accurate bispectra improving previous results
+based on Lagrangian perturbation theory.
+",0,1,0,0,0,0
+11724,Time-Optimal Path Tracking via Reachability Analysis,"  Given a geometric path, the Time-Optimal Path Tracking problem consists in
+finding the control strategy to traverse the path time-optimally while
+regulating tracking errors. A simple yet effective approach to this problem is
+to decompose the controller into two components: (i)~a path controller, which
+modulates the parameterization of the desired path in an online manner,
+yielding a reference trajectory; and (ii)~a tracking controller, which takes
+the reference trajectory and outputs joint torques for tracking. However, there
+is one major difficulty: the path controller might not find any feasible
+reference trajectory that can be tracked by the tracking controller because of
+torque bounds. In turn, this results in degraded tracking performances. Here,
+we propose a new path controller that is guaranteed to find feasible reference
+trajectories by accounting for possible future perturbations. The main
+technical tool underlying the proposed controller is Reachability Analysis, a
+new method for analyzing path parameterization problems. Simulations show that
+the proposed controller outperforms existing methods.
+",1,0,0,0,0,0
+11725,Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians,"  We consider deep classifying neural networks. We expose a structure in the
+derivative of the logits with respect to the parameters of the model, which is
+used to explain the existence of outliers in the spectrum of the Hessian.
+Previous works decomposed the Hessian into two components, attributing the
+outliers to one of them, the so-called Covariance of gradients. We show this
+term is not a Covariance but a second moment matrix, i.e., it is influenced by
+means of gradients. These means possess an additive two-way structure that is
+the source of the outliers in the spectrum. This structure can be used to
+approximate the principal subspace of the Hessian using certain ""averaging""
+operations, avoiding the need for high-dimensional eigenanalysis. We
+corroborate this claim across different datasets, architectures and sample
+sizes.
+",1,0,0,1,0,0
+11726,Discrete-Time Statistical Inference for Multiscale Diffusions,"  We study statistical inference for small-noise-perturbed multiscale dynamical
+systems under the assumption that we observe a single time series from the slow
+process only. We construct estimators for both averaging and homogenization
+regimes, based on an appropriate misspecified model motivated by a second-order
+stochastic Taylor expansion of the slow process with respect to a function of
+the time-scale separation parameter. In the case of a fixed number of
+observations, we establish consistency, asymptotic normality, and asymptotic
+statistical efficiency of a minimum contrast estimator (MCE), the limiting
+variance having been identified explicitly; we furthermore establish
+consistency and asymptotic normality of a simplified minimum constrast
+estimator (SMCE), which is however not in general efficient. These results are
+then extended to the case of high-frequency observations under a condition
+restricting the rate at which the number of observations may grow vis-à-vis
+the separation of scales. Numerical simulations illustrate the theoretical
+results.
+",0,0,1,1,0,0
+11727,Taggle: Scalable Visualization of Tabular Data through Aggregation,"  Visualization of tabular data---for both presentation and exploration
+purposes---is a well-researched area. Although effective visual presentations
+of complex tables are supported by various plotting libraries, creating such
+tables is a tedious process and requires scripting skills. In contrast,
+interactive table visualizations that are designed for exploration purposes
+either operate at the level of individual rows, where large parts of the table
+are accessible only via scrolling, or provide a high-level overview that often
+lacks context-preserving drill-down capabilities. In this work we present
+Taggle, a novel visualization technique for exploring and presenting large and
+complex tables that are composed of individual columns of categorical or
+numerical data and homogeneous matrices. The key contribution of Taggle is the
+hierarchical aggregation of data subsets, for which the user can also choose
+suitable visual representations.The aggregation strategy is complemented by the
+ability to sort hierarchically such that groups of items can be flexibly
+defined by combining categorical stratifications and by rich data selection and
+filtering capabilities. We demonstrate the usefulness of Taggle for interactive
+analysis and presentation of complex genomics data for the purpose of drug
+discovery.
+",1,0,0,0,0,0
+11728,A parity-breaking electronic nematic phase transition in the spin-orbit coupled metal Cd$_2$Re$_2$O$_7$,"  Strong electron interactions can drive metallic systems toward a variety of
+well-known symmetry-broken phases, but the instabilities of correlated metals
+with strong spin-orbit coupling have only recently begun to be explored. We
+uncovered a multipolar nematic phase of matter in the metallic pyrochlore
+Cd$_2$Re$_2$O$_7$ using spatially resolved second-harmonic optical anisotropy
+measurements. Like previously discovered electronic nematic phases, this
+multipolar phase spontaneously breaks rotational symmetry while preserving
+translational invariance. However, it has the distinguishing property of being
+odd under spatial inversion, which is allowed only in the presence of
+spin-orbit coupling. By examining the critical behavior of the multipolar
+nematic order parameter, we show that it drives the thermal phase transition
+near 200 kelvin in Cd$_2$Re$_2$O$_7$ and induces a parity-breaking lattice
+distortion as a secondary order.
+",0,1,0,0,0,0
+11729,Sparse bounds for a prototypical singular Radon transform,"  We use a variant of the technique in [Lac17a] to give sparse L^p(log(L))^4
+bounds for a class of model singular and maximal Radon transforms
+",0,0,1,0,0,0
+11730,Sterile neutrinos in cosmology,"  Sterile neutrinos are natural extensions to the standard model of particle
+physics in neutrino mass generation mechanisms. If they are relatively light,
+less than approximately 10 keV, they can alter cosmology significantly, from
+the early Universe to the matter and radiation energy density today. Here, we
+review the cosmological role such light sterile neutrinos can play from the
+early Universe, including production of keV-scale sterile neutrinos as dark
+matter candidates, and dynamics of light eV-scale sterile neutrinos during the
+weakly-coupled active neutrino era. We review proposed signatures of light
+sterile neutrinos in cosmic microwave background and large scale structure
+data. We also discuss keV-scale sterile neutrino dark matter decay signatures
+in X-ray observations, including recent candidate $\sim$3.5 keV X-ray line
+detections consistent with the decay of a $\sim$7 keV sterile neutrino dark
+matter particle.
+",0,1,0,0,0,0
+11731,A Statistical Approach to Increase Classification Accuracy in Supervised Learning Algorithms,"  Probabilistic mixture models have been widely used for different machine
+learning and pattern recognition tasks such as clustering, dimensionality
+reduction, and classification. In this paper, we focus on trying to solve the
+most common challenges related to supervised learning algorithms by using
+mixture probability distribution functions. With this modeling strategy, we
+identify sub-labels and generate synthetic data in order to reach better
+classification accuracy. It means we focus on increasing the training data
+synthetically to increase the classification accuracy.
+",1,0,0,1,0,0
+11732,Kinematics and workspace analysis of a 3ppps parallel robot with u-shaped base,"  This paper presents the kinematic analysis of the 3-PPPS parallel robot with
+an equilateral mobile platform and a U-shape base. The proposed design and
+appropriate selection of parameters allow to formulate simpler direct and
+inverse kinematics for the manipulator under study. The parallel singularities
+associated with the manipulator depend only on the orientation of the
+end-effector, and thus depend only on the orientation of the end effector. The
+quaternion parameters are used to represent the aspects, i.e. the singularity
+free regions of the workspace. A cylindrical algebraic decomposition is used to
+characterize the workspace and joint space with a low number of cells. The
+dis-criminant variety is obtained to describe the boundaries of each cell. With
+these simplifications, the 3-PPPS parallel robot with proposed design can be
+claimed as the simplest 6 DOF robot, which further makes it useful for the
+industrial applications.
+",1,0,0,0,0,0
+11733,Accurate spectroscopic redshift of the multiply lensed quasar PSOJ0147 from the Pan-STARRS survey,"  Context: The gravitational lensing time delay method provides a one-step
+determination of the Hubble constant (H0) with an uncertainty level on par with
+the cosmic distance ladder method. However, to further investigate the nature
+of the dark energy, a H0 estimate down to 1% level is greatly needed. This
+requires dozens of strongly lensed quasars that are yet to be delivered by
+ongoing and forthcoming all-sky surveys.
+Aims: In this work we aim to determine the spectroscopic redshift of
+PSOJ0147, the first strongly lensed quasar candidate found in the Pan-STARRS
+survey. The main goal of our work is to derive an accurate redshift estimate of
+the background quasar for cosmography.
+Methods: To obtain timely spectroscopically follow-up, we took advantage of
+the fast-track service programme that is carried out by the Nordic Optical
+Telescope. Using a grism covering 3200 - 9600 A, we identified prominent
+emission line features, such as Ly-alpha, N V, O I, C II, Si IV, C IV, and [C
+III] in the spectra of the background quasar of the PSOJ0147 lens system. This
+enables us to determine accurately the redshift of the background quasar.
+Results: The spectrum of the background quasar exhibits prominent absorption
+features bluewards of the strong emission lines, such as Ly-alpha, N V, and C
+IV. These blue absorption lines indicate that the background source is a broad
+absorption line (BAL) quasar. Unfortunately, the BAL features hamper an
+accurate determination of redshift using the above-mentioned strong emission
+lines. Nevertheless, we are able to determine a redshift of 2.341+/-0.001 from
+three of the four lensed quasar images with the clean forbidden line [C III].
+In addition, we also derive a maximum outflow velocity of ~ 9800 km/s with the
+broad absorption features bluewards of the C IV emission line. This value of
+maximum outflow velocity is in good agreement with other BAL quasars.
+",0,1,0,0,0,0
+11734,Upper bounds on the smallest size of a saturating set in projective planes and spaces of even dimension,"  In a projective plane $\Pi_{q}$ (not necessarily Desarguesian) of order $q$,
+a point subset $\mathcal{S}$ is saturating (or dense) if any point of
+$\Pi_{q}\setminus \mathcal{S}$ is collinear with two points in $\mathcal{S}$.
+Modifying an approach of [31], we proved the following upper bound on the
+smallest size $s(2,q)$ of a saturating set in $\Pi_{q}$: \begin{equation*}
+s(2,q)\leq \sqrt{(q+1)\left(3\ln q+\ln\ln q
++\ln\frac{3}{4}\right)}+\sqrt{\frac{q}{3\ln q}}+3. \end{equation*} The bound
+holds for all q, not necessarily large.
+By using inductive constructions, upper bounds on the smallest size of a
+saturating set in the projective space $\mathrm{PG}(N,q)$ with even dimension
+$N$ are obtained.
+All the results are also stated in terms of linear covering codes.
+",1,0,1,0,0,0
+11735,A Neural Representation of Sketch Drawings,"  We present sketch-rnn, a recurrent neural network (RNN) able to construct
+stroke-based drawings of common objects. The model is trained on thousands of
+crude human-drawn images representing hundreds of classes. We outline a
+framework for conditional and unconditional sketch generation, and describe new
+robust training methods for generating coherent sketch drawings in a vector
+format.
+",1,0,0,1,0,0
+11736,Privileged Multi-label Learning,"  This paper presents privileged multi-label learning (PrML) to explore and
+exploit the relationship between labels in multi-label learning problems. We
+suggest that for each individual label, it cannot only be implicitly connected
+with other labels via the low-rank constraint over label predictors, but also
+its performance on examples can receive the explicit comments from other labels
+together acting as an \emph{Oracle teacher}. We generate privileged label
+feature for each example and its individual label, and then integrate it into
+the framework of low-rank based multi-label learning. The proposed algorithm
+can therefore comprehensively explore and exploit label relationships by
+inheriting all the merits of privileged information and low-rank constraints.
+We show that PrML can be efficiently solved by dual coordinate descent
+algorithm using iterative optimization strategy with cheap updates. Experiments
+on benchmark datasets show that through privileged label features, the
+performance can be significantly improved and PrML is superior to several
+competing methods in most cases.
+",1,0,0,1,0,0
+11737,A Diophantine approximation problem with two primes and one $k$-th power of a prime,"  We refine a result of the last two Authors of [8] on a Diophantine
+approximation problem with two primes and a $k$-th power of a prime which was
+only proved to hold for $1<k<4/3$. We improve the $k$-range to $1<k\le 3$ by
+combining Harman's technique on the minor arc with a suitable estimate for the
+$L^4$-norm of the relevant exponential sum over primes $S_k$. In the common
+range we also give a stronger bound for the approximation.
+",0,0,1,0,0,0
+11738,Reexamining Low Rank Matrix Factorization for Trace Norm Regularization,"  Trace norm regularization is a widely used approach for learning low rank
+matrices. A standard optimization strategy is based on formulating the problem
+as one of low rank matrix factorization which, however, leads to a non-convex
+problem. In practice this approach works well, and it is often computationally
+faster than standard convex solvers such as proximal gradient methods.
+Nevertheless, it is not guaranteed to converge to a global optimum, and the
+optimization can be trapped at poor stationary points. In this paper we show
+that it is possible to characterize all critical points of the non-convex
+problem. This allows us to provide an efficient criterion to determine whether
+a critical point is also a global minimizer. Our analysis suggests an iterative
+meta-algorithm that dynamically expands the parameter space and allows the
+optimization to escape any non-global critical point, thereby converging to a
+global minimizer. The algorithm can be applied to problems such as matrix
+completion or multitask learning, and our analysis holds for any random
+initialization of the factor matrices. Finally, we confirm the good performance
+of the algorithm on synthetic and real datasets.
+",1,0,0,1,0,0
+11739,Tier structure of strongly endotactic reaction networks,"  Reaction networks are mainly used to model the time-evolution of molecules of
+interacting chemical species. Stochastic models are typically used when the
+counts of the molecules are low, whereas deterministic models are used when the
+counts are in high abundance. In 2011, the notion of `tiers' was introduced to
+study the long time behavior of deterministically modeled reaction networks
+that are weakly reversible and have a single linkage class. This `tier' based
+argument was analytical in nature. Later, in 2014, the notion of a strongly
+endotactic network was introduced in order to generalize the previous results
+from weakly reversible networks with a single linkage class to this wider
+family of networks. The point of view of this later work was more geometric and
+algebraic in nature. The notion of strongly endotactic networks was later used
+in 2018 to prove a large deviation principle for a class of stochastically
+modeled reaction networks.
+We provide an analytical characterization of strongly endotactic networks in
+terms of tier structures. By doing so, we shed light on the connection between
+the two points of view, and also make available a new proof technique for the
+study of strongly endotactic networks. We show the power of this new technique
+in two distinct ways. First, we demonstrate how the main previous results
+related to strongly endotactic networks, both for the deterministic and
+stochastic modeling choices, can be quickly obtained from our characterization.
+Second, we demonstrate how new results can be obtained by proving that a
+sub-class of strongly endotactic networks, when modeled stochastically, is
+positive recurrent. Finally, and similarly to recent independent work by Agazzi
+and Mattingly, we provide an example which closes a conjecture in the negative
+by showing that stochastically modeled strongly endotactic networks can be
+transient (and even explosive).
+",0,0,0,0,1,0
+11740,Relative FP-injective and FP-flat complexes and their model structures,"  In this paper, we introduce the notions of ${\rm FP}_n$-injective and ${\rm
+FP}_n$-flat complexes in terms of complexes of type ${\rm FP}_n$. We show that
+some characterizations analogous to that of injective, FP-injective and flat
+complexes exist for ${\rm FP}_n$-injective and ${\rm FP}_n$-flat complexes. We
+also introduce and study ${\rm FP}_n$-injective and ${\rm FP}_n$-flat
+dimensions of modules and complexes, and give a relation between them in terms
+of Pontrjagin duality. The existence of pre-envelopes and covers in this
+setting is discussed, and we prove that any complex has an ${\rm FP}_n$-flat
+cover and an ${\rm FP}_n$-flat pre-envelope, and in the case $n \geq 2$ that
+any complex has an ${\rm FP}_n$-injective cover and an ${\rm FP}_n$-injective
+pre-envelope. Finally, we construct model structures on the category of
+complexes from the classes of modules with bounded ${\rm FP}_n$-injective and
+${\rm FP}_n$-flat dimensions, and analyze several conditions under which it is
+possible to connect these model structures via Quillen functors and Quillen
+equivalences.
+",0,0,1,0,0,0
+11741,An Interactive Tool to Explore and Improve the Ply Number of Drawings,"  Given a straight-line drawing $\Gamma$ of a graph $G=(V,E)$, for every vertex
+$v$ the ply disk $D_v$ is defined as a disk centered at $v$ where the radius of
+the disk is half the length of the longest edge incident to $v$. The ply number
+of a given drawing is defined as the maximum number of overlapping disks at
+some point in $\mathbb{R}^2$. Here we present a tool to explore and evaluate
+the ply number for graphs with instant visual feedback for the user. We
+evaluate our methods in comparison to an existing ply computation by De Luca et
+al. [WALCOM'17]. We are able to reduce the computation time from seconds to
+milliseconds for given drawings and thereby contribute to further research on
+the ply topic by providing an efficient tool to examine graphs extensively by
+user interaction as well as some automatic features to reduce the ply number.
+",1,0,0,0,0,0
+11742,Distinguishing the albedo of exoplanets from stellar activity,"  Light curves show the flux variation from the target star and its orbiting
+planets as a function of time. In addition to the transit features created by
+the planets, the flux also includes the reflected light component of each
+planet, which depends on the planetary albedo. This signal is typically
+referred to as phase curve and could be easily identified if there were no
+additional noise. As well as instrumental noise, stellar activity, such as
+spots, can create a modulation in the data, which may be very difficult to
+distinguish from the planetary signal. We analyze the limitations imposed by
+the stellar activity on the detection of the planetary albedo, considering the
+limitations imposed by the predicted level of instrumental noise and the short
+duration of the observations planned in the context of the CHEOPS mission. As
+initial condition, we have assumed that each star is characterized by just one
+orbiting planet. We built mock light curves that included a realistic stellar
+activity pattern, the reflected light component of the planet and an
+instrumental noise level, which we have chosen to be at the same level as
+predicted for CHEOPS. We then fit these light curves to try to recover the
+reflected light component, assuming the activity patterns can be modeled with a
+Gaussian process.We estimate that at least one full stellar rotation is
+necessary to obtain a reliable detection of the planetary albedo. This result
+is independent of the level of noise, but it depends on the limitation of the
+Gaussian process to describe the stellar activity when the light curve
+time-span is shorter than the stellar rotation. Finally, in presence of typical
+CHEOPS gaps in the simulations, we confirm that it is still possible to obtain
+a reliable albedo.
+",0,1,0,0,0,0
+11743,Inverse Reinforcement Learning Under Noisy Observations,"  We consider the problem of performing inverse reinforcement learning when the
+trajectory of the expert is not perfectly observed by the learner. Instead, a
+noisy continuous-time observation of the trajectory is provided to the learner.
+This problem exhibits wide-ranging applications and the specific application we
+consider here is the scenario in which the learner seeks to penetrate a
+perimeter patrolled by a robot. The learner's field of view is limited due to
+which it cannot observe the patroller's complete trajectory. Instead, we allow
+the learner to listen to the expert's movement sound, which it can also use to
+estimate the expert's state and action using an observation model. We treat the
+expert's state and action as hidden data and present an algorithm based on
+expectation maximization and maximum entropy principle to solve the non-linear,
+non-convex problem. Related work considers discrete-time observations and an
+observation model that does not include actions. In contrast, our technique
+takes expectations over both state and action of the expert, enabling learning
+even in the presence of extreme noise and broader applications.
+",1,0,0,0,0,0
+11744,Using Perturbed Underdamped Langevin Dynamics to Efficiently Sample from Probability Distributions,"  In this paper we introduce and analyse Langevin samplers that consist of
+perturbations of the standard underdamped Langevin dynamics. The perturbed
+dynamics is such that its invariant measure is the same as that of the
+unperturbed dynamics. We show that appropriate choices of the perturbations can
+lead to samplers that have improved properties, at least in terms of reducing
+the asymptotic variance. We present a detailed analysis of the new Langevin
+sampler for Gaussian target distributions. Our theoretical results are
+supported by numerical experiments with non-Gaussian target measures.
+",0,0,1,1,0,0
+11745,Meta learning Framework for Automated Driving,"  The success of automated driving deployment is highly depending on the
+ability to develop an efficient and safe driving policy. The problem is well
+formulated under the framework of optimal control as a cost optimization
+problem. Model based solutions using traditional planning are efficient, but
+require the knowledge of the environment model. On the other hand, model free
+solutions suffer sample inefficiency and require too many interactions with the
+environment, which is infeasible in practice. Methods under the Reinforcement
+Learning framework usually require the notion of a reward function, which is
+not available in the real world. Imitation learning helps in improving sample
+efficiency by introducing prior knowledge obtained from the demonstrated
+behavior, on the risk of exact behavior cloning without generalizing to unseen
+environments. In this paper we propose a Meta learning framework, based on data
+set aggregation, to improve generalization of imitation learning algorithms.
+Under the proposed framework, we propose MetaDAgger, a novel algorithm which
+tackles the generalization issues in traditional imitation learning. We use The
+Open Race Car Simulator (TORCS) to test our algorithm. Results on unseen test
+tracks show significant improvement over traditional imitation learning
+algorithms, improving the learning time and sample efficiency in the same time.
+The results are also supported by visualization of the learnt features to prove
+generalization of the captured details.
+",1,0,0,1,0,0
+11746,MOBILITY21: Strategic Investments for Transportation Infrastructure & Technology,"  America's transportation infrastructure is the backbone of our economy. A
+strong infrastructure means a strong America - an America that competes
+globally, supports local and regional economic development, and creates jobs.
+Strategic investments in our transportation infrastructure are vital to our
+national security, economic growth, transportation safety and our technology
+leadership. This document outlines critical needs for our transportation
+infrastructure, identifies new technology drivers and proposes strategic
+investments for safe and efficient air, ground, rail and marine mobility of
+people and goods.
+",1,0,0,0,0,0
+11747,Hysteretic behaviour of metal connectors for hybrid (high- and low-grade mixed species) cross laminated timber,"  Cross-laminated timber (CLT) is a prefabricated solid engineered wood product
+made of at least three orthogonally bonded layers of solid-sawn lumber that are
+laminated by gluing longitudinal and transverse layers with structural
+adhesives to form a solid panel. Previous studies have shown that the CLT
+buildings can perform well in seismic loading and are recognized as the
+essential role of connector performance in structural design, modelling, and
+analysis of CLT buildings. When CLT is composed of high-grade/high-density
+layers for the outer lamellas and low-grade/low-density for the core of the
+panels, the CLT panels are herein designated as hybrid CLT panels as opposed to
+conventional CLT panels that are built using one lumber type for both outer and
+core lamellas. This paper presents results of a testing program developed to
+estimate the cyclic performance of CLT connectors applied on hybrid CLT layups.
+Two connectors are selected, which can be used in wall-to-floor connections.
+These are readily available in the North American market. Characterization of
+the performance of connectors is done in two perpendicular directions under a
+modified CUREE cyclic loading protocol. Depending on the mode of failure, in
+some cases, testing results indicate that when the nails or screws penetrate
+the low-grade/low-density core lumber, a statistically significant difference
+is obtained between hybrid and conventional layups. However, in other cases,
+due to damage in the face layer or in the connection, force-displacement
+results for conventional and hybrid CLT layups were not statistically
+significant.
+",0,1,0,0,0,0
+11748,Partial Knowledge In Embeddings,"  Representing domain knowledge is crucial for any task. There has been a wide
+range of techniques developed to represent this knowledge, from older logic
+based approaches to the more recent deep learning based techniques (i.e.
+embeddings). In this paper, we discuss some of these methods, focusing on the
+representational expressiveness tradeoffs that are often made. In particular,
+we focus on the the ability of various techniques to encode `partial knowledge'
+- a key component of successful knowledge systems. We introduce and describe
+the concepts of `ensembles of embeddings' and `aggregate embeddings' and
+demonstrate how they allow for partial knowledge.
+",1,0,0,0,0,0
+11749,Impact of the latest measurement of Hubble constant on constraining inflation models,"  We investigate how the constraint results of inflation models are affected by
+considering the latest local measurement of $H_0$ in the global fit. We use the
+observational data, including the Planck CMB full data, the BICEP2 and Keck
+Array CMB B-mode data, the BAO data, and the latest measurement of Hubble
+constant, to constrain the $\Lambda$CDM+$r$+$N_{\rm eff}$ model, and the
+obtained 1$\sigma$ and 2$\sigma$ contours of $(n_s, r)$ are compared to the
+theoretical predictions of selected inflationary models. We find that, in this
+fit, the scale invariance is only excluded at the 3.3$\sigma$ level, and
+$\Delta N_{\rm eff}>0$ is favored at the 1.6$\sigma$ level. The natural
+inflation model is now excluded at more than 2$\sigma$ level; the Starobinsky
+$R^2$ model becomes only favored at around 2$\sigma$ level; the most favored
+model becomes the spontaneously broken SUSY inflation model; and, the brane
+inflation model is also well consistent with the current data, in this case.
+",0,1,0,0,0,0
+11750,Ultra-light and strong: the massless harmonic oscillator and its singular path integral,"  In classical mechanics, a light particle bound by a strong elastic force just
+oscillates at high frequency in the region allowed by its initial position and
+velocity. In quantum mechanics, instead, the ground state of the particle
+becomes completely de-localized in the limit $m \to 0$. The harmonic oscillator
+thus ceases to be a useful microscopic physical model in the limit $m \to 0$,
+but its Feynman path integral has interesting singularities which make it a
+prototype of other systems exhibiting a ""quantum runaway"" from the classical
+configurations near the minimum of the action. The probability density of the
+coherent runaway modes can be obtained as the solution of a Fokker-Planck
+equation associated to the condition $S=S_{min}$. This technique can be applied
+also to other systems, notably to a dimensional reduction of the
+Einstein-Hilbert action.
+",0,1,0,0,0,0
+11751,The Shannon-McMillan-Breiman theorem beyond amenable groups,"  We introduce a new isomorphism-invariant notion of entropy for measure
+preserving actions of arbitrary countable groups on probability spaces, which
+we call cocycle entropy. We develop methods to show that cocycle entropy
+satisfies many of the properties of classical amenable entropy theory, but
+applies in much greater generality to actions of non-amenable groups. One key
+ingredient in our approach is a proof of a subadditive convergence principle
+which is valid for measure-preserving amenable equivalence relations, going
+beyond the Ornstein-Weiss Lemma for amenable groups.
+For a large class of countable groups, which may in fact include all of them,
+we prove the Shannon-McMillan-Breiman pointwise convergence theorem for cocycle
+entropy in their measure-preserving actions.
+We also compare cocycle entropy to Rokhlin entropy, and using an important
+recent result of Seward we show that they coincide for free, ergodic actions of
+any countable group in the class. Finally, we use the example of the free group
+to demonstrate the geometric significance of the entropy equipartition property
+implied by the Shannon-McMillan-Breiman theorem.
+",0,0,1,0,0,0
+11752,Assistive robotic device: evaluation of intelligent algorithms,"  Assistive robotic devices can be used to help people with upper body
+disabilities gaining more autonomy in their daily life. Although basic motions
+such as positioning and orienting an assistive robot gripper in space allow
+performance of many tasks, it might be time consuming and tedious to perform
+more complex tasks. To overcome these difficulties, improvements can be
+implemented at different levels, such as mechanical design, control interfaces
+and intelligent control algorithms. In order to guide the design of solutions,
+it is important to assess the impact and potential of different innovations.
+This paper thus presents the evaluation of three intelligent algorithms aiming
+to improve the performance of the JACO robotic arm (Kinova Robotics). The
+evaluated algorithms are 'preset position', 'fluidity filter' and 'drinking
+mode'. The algorithm evaluation was performed with 14 motorized wheelchair's
+users and showed a statistically significant improvement of the robot's
+performance.
+",1,0,0,0,0,0
+11753,Solving the Brachistochrone Problem by an Influence Diagram,"  Influence diagrams are a decision-theoretic extension of probabilistic
+graphical models. In this paper we show how they can be used to solve the
+Brachistochrone problem. We present results of numerical experiments on this
+problem, compare the solution provided by the influence diagram with the
+optimal solution. The R code used for the experiments is presented in the
+Appendix.
+",1,0,1,0,0,0
+11754,OH Survey along Sightlines of Galactic Observations of Terahertz C+,"  We have obtained OH spectra of four transitions in the $^2\Pi_{3/2}$ ground
+state, at 1612, 1665, 1667, and 1720 MHz, toward 51 sightlines that were
+observed in the Herschel project Galactic Observations of Terahertz C+. The
+observations cover the longitude range of (32$^\circ$, 64$^\circ$) and
+(189$^\circ$, 207$^\circ$) in the northern Galactic plane. All of the diffuse
+OH emissions conform to the so-called 'Sum Rule' of the four brightness
+temperatures, indicating optically thin emission condition for OH from diffuse
+clouds in the Galactic plane. The column densities of the HI `halos' N(HI)
+surrounding molecular clouds increase monotonically with OH column density,
+N(OH), until saturating when N(HI)=1.0 x 10$^{21}$ cm$^{-2}$ and N (OH) $\geq
+4.5\times 10^{15}$ cm$^{-2}$, indicating the presence of molecular gas that
+cannot be traced by HI. Such a linear correlation, albeit weak, is suggestive
+of HI halos' contribution to the UV shielding required for molecular formation.
+About 18% of OH clouds have no associated CO emission (CO-dark) at a
+sensitivity of 0.07 K but are associated with C$^+$ emission. A weak
+correlation exists between C$^+$ intensity and OH column density for CO-dark
+molecular clouds. These results imply that OH seems to be a better tracer of
+molecular gas than CO in diffuse molecular regions.
+",0,1,0,0,0,0
+11755,Strain manipulation of Majorana fermions in graphene armchair nanoribbons,"  Graphene nanoribbons with armchair edges are studied for externally enhanced,
+but realistic parameter values: enhanced Rashba spin-orbit coupling due to
+proximity to a transition metal dichalcogenide like WS$_{2}$, and enhanced
+Zeeman field due to exchange coupling with a magnetic insulator like EuS under
+applied magnetic field. The presence of s--wave superconductivity, induced
+either by proximity or by decoration with alkali metal atoms like Ca or Li,
+leads to a topological superconducting phase with Majorana end modes. The
+topological phase is highly sensitive to the application of uniaxial strain,
+with a transition to the trivial state above a critical strain well below
+$0.1\%$. This sensitivity allows for real space manipulation of Majorana
+fermions by applying non-uniform strain profiles. Similar manipulation is also
+possible by applying inhomogeneous Zeeman field or chemical potential.
+",0,1,0,0,0,0
+11756,Relativistic wide-angle galaxy bispectrum on the light-cone,"  Given the important role that the galaxy bispectrum has recently acquired in
+cosmology and the scale and precision of forthcoming galaxy clustering
+observations, it is timely to derive the full expression of the large-scale
+bispectrum going beyond approximated treatments which neglect integrated terms
+or higher-order bias terms or use the Limber approximation. On cosmological
+scales, relativistic effects that arise from observing on the past light-cone
+alter the observed galaxy number counts, therefore leaving their imprints on
+N-point correlators at all orders. In this paper we compute for the first time
+the bispectrum including all general relativistic, local and integrated,
+effects at second order, the tracers' bias at second order, geometric effects
+as well as the primordial non-Gaussianity contribution. This is timely
+considering that future surveys will probe scales comparable to the horizon
+where approximations widely used currently may not hold; neglecting these
+effects may introduce biases in estimation of cosmological parameters as well
+as primordial non-Gaussianity.
+",0,1,0,0,0,0
+11757,Recent progress in many-body localization,"  This article is a brief introduction to the rapidly evolving field of
+many-body localization. Rather than giving an in-depth review of the subject,
+our aspiration here is simply to introduce the problem and its general context,
+outlining a few directions where notable progress has been achieved in recent
+years. We hope that this will prepare the readers for the more specialized
+articles appearing in the forthcoming dedicated volume of Annalen der Physik,
+where these developments are discussed in more detail.
+",0,1,0,0,0,0
+11758,Magnetic Field Dependence of Spin Glass Free Energy Barriers,"  We measure the field dependence of spin glass free energy barriers in a thin
+amorphous Ge:Mn film through the time dependence of the magnetization. After
+the correlation length $\xi(t, T)$ has reached the film thickness $\mathcal
+{L}=155$~\AA~so that the dynamics are activated, we change the initial magnetic
+field by $\delta H$. In agreement with the scaling behavior exhibited in a
+companion Letter [Janus collaboration: M. Baity-Jesi {\it et al.}, Phys. Rev.
+Lett. {\bf 118}, 157202 (2017)], we find the activation energy is increased
+when $\delta H < 0$. The change is proportional to $(\delta H)^2$ with the
+addition of a small $(\delta H)^4$ term. The magnitude of the change of the
+spin glass free energy barriers is in near quantitative agreement with the
+prediction of a barrier model.
+",0,1,0,0,0,0
+11759,Topological Structures on DMC spaces,"  Two channels are said to be equivalent if they are degraded from each other.
+The space of equivalent channels with input alphabet $X$ and output alphabet
+$Y$ can be naturally endowed with the quotient of the Euclidean topology by the
+equivalence relation. A topology on the space of equivalent channels with fixed
+input alphabet $X$ and arbitrary but finite output alphabet is said to be
+natural if and only if it induces the quotient topology on the subspaces of
+equivalent channels sharing the same output alphabet. We show that every
+natural topology is $\sigma$-compact, separable and path-connected. On the
+other hand, if $|X|\geq 2$, a Hausdorff natural topology is not Baire and it is
+not locally compact anywhere. This implies that no natural topology can be
+completely metrized if $|X|\geq 2$. The finest natural topology, which we call
+the strong topology, is shown to be compactly generated, sequential and $T_4$.
+On the other hand, the strong topology is not first-countable anywhere, hence
+it is not metrizable. We show that in the strong topology, a subspace is
+compact if and only if it is rank-bounded and strongly-closed. We introduce a
+metric distance on the space of equivalent channels which compares the noise
+levels between channels. The induced metric topology, which we call the
+noisiness topology, is shown to be natural. We also study topologies that are
+inherited from the space of meta-probability measures by identifying channels
+with their Blackwell measures. We show that the weak-* topology is exactly the
+same as the noisiness topology and hence it is natural. We prove that if
+$|X|\geq 2$, the total variation topology is not natural nor Baire, hence it is
+not completely metrizable. Moreover, it is not locally compact anywhere.
+Finally, we show that the Borel $\sigma$-algebra is the same for all Hausdorff
+natural topologies.
+",1,0,1,0,0,0
+11760,The spectral element method as an efficient tool for transient simulations of hydraulic systems,"  This paper presents transient numerical simulations of hydraulic systems in
+engineering applications using the spectral element method (SEM). Along with a
+detailed description of the underlying numerical method, it is shown that the
+SEM yields highly accurate numerical approximations at modest computational
+costs, which is in particular useful for optimization-based control
+applications. In order to enable fast explicit time stepping methods, the
+boundary conditions are imposed weakly using a numerically stable upwind
+discretization. The benefits of the SEM in the area of hydraulic system
+simulations are demonstrated in various examples including several simulations
+of strong water hammer effects. Due to its exceptional convergence
+characteristics, the SEM is particularly well suited to be used in real-time
+capable control applications. As an example, it is shown that the time
+evolution of pressure waves in a large scale pumped-storage power plant can be
+well approximated using a low-dimensional system representation utilizing a
+minimum number of dynamical states.
+",0,1,0,0,0,0
+11761,"Re-entrant charge order in overdoped (Bi,Pb)$_{2.12}$Sr$_{1.88}$CuO$_{6+δ}$ outside the pseudogap regime","  Charge modulations are considered as a leading competitor of high-temperature
+superconductivity in the underdoped cuprates, and their relationship to Fermi
+surface reconstructions and to the pseudogap state is an important subject of
+current research. Overdoped cuprates, on the other hand, are widely regarded as
+conventional Fermi liquids without collective electronic order. For the
+overdoped (Bi,Pb)2.12Sr1.88CuO6+{\delta} (Bi2201) high-temperature
+superconductor, here we report resonant x-ray scattering measurements revealing
+incommensurate charge order reflections, with correlation lengths of 40-60
+lattice units, that persist up to at least 250K. Charge order is markedly more
+robust in the overdoped than underdoped regime but the incommensurate wave
+vectors follow a common trend; moreover it coexists with a single,
+unreconstructed Fermi surface, without pseudogap or nesting features, as
+determined from angle-resolved photoemission spectroscopy. This re-entrant
+charge order is reproduced by model calculations that consider a strong van
+Hove singularity within a Fermi liquid framework.
+",0,1,0,0,0,0
+11762,"A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms","  This paper proposes a new approach to construct high quality space-filling
+sample designs. First, we propose a novel technique to quantify the
+space-filling property and optimally trade-off uniformity and randomness in
+sample designs in arbitrary dimensions. Second, we connect the proposed metric
+(defined in the spatial domain) to the objective measure of the design
+performance (defined in the spectral domain). This connection serves as an
+analytic framework for evaluating the qualitative properties of space-filling
+designs in general. Using the theoretical insights provided by this
+spatial-spectral analysis, we derive the notion of optimal space-filling
+designs, which we refer to as space-filling spectral designs. Third, we propose
+an efficient estimator to evaluate the space-filling properties of sample
+designs in arbitrary dimensions and use it to develop an optimization framework
+to generate high quality space-filling designs. Finally, we carry out a
+detailed performance comparison on two different applications in 2 to 6
+dimensions: a) image reconstruction and b) surrogate modeling on several
+benchmark optimization functions and an inertial confinement fusion (ICF)
+simulation code. We demonstrate that the propose spectral designs significantly
+outperform existing approaches especially in high dimensions.
+",1,0,0,1,0,0
+11763,Detection and Tracking of General Movable Objects in Large 3D Maps,"  This paper studies the problem of detection and tracking of general objects
+with long-term dynamics, observed by a mobile robot moving in a large
+environment. A key problem is that due to the environment scale, it can only
+observe a subset of the objects at any given time. Since some time passes
+between observations of objects in different places, the objects might be moved
+when the robot is not there. We propose a model for this movement in which the
+objects typically only move locally, but with some small probability they jump
+longer distances, through what we call global motion. For filtering, we
+decompose the posterior over local and global movements into two linked
+processes. The posterior over the global movements and measurement associations
+is sampled, while we track the local movement analytically using Kalman
+filters. This novel filter is evaluated on point cloud data gathered
+autonomously by a mobile robot over an extended period of time. We show that
+tracking jumping objects is feasible, and that the proposed probabilistic
+treatment outperforms previous methods when applied to real world data. The key
+to efficient probabilistic tracking in this scenario is focused sampling of the
+object posteriors.
+",1,0,0,0,0,0
+11764,Mapping the aberrations of a wide-field spectrograph using a photonic comb,"  We demonstrate a new approach to calibrating the spectral-spatial response of
+a wide-field spectrograph using a fibre etalon comb. Conventional wide-field
+instruments employed on front-line telescopes are mapped with a grid of
+diffraction-limited holes cut into a focal plane mask. The aberrated grid
+pattern in the image plane typically reveals n-symmetric (e.g. pincushion)
+distortion patterns over the field arising from the optical train. This
+approach is impractical in the presence of a dispersing element because the
+diffraction-limited spots in the focal plane are imaged as an array of
+overlapping spectra. Instead we propose a compact solution that builds on
+recent developments in fibre-based Fabry-Perot etalons. We introduce a novel
+approach to near-field illumination that exploits a 25cm commercial telescope
+and the propagation of skew rays in a multimode fibre. The mapping of the
+optical transfer function across the full field is represented accurately
+(<0.5% rms residual) by an orthonormal set of Chebyshev moments. Thus we are
+able to reconstruct the full 4Kx4K CCD image of the dispersed output from the
+optical fibres using this mapping, as we demonstrate. Our method removes one of
+the largest sources of systematic error in multi-object spectroscopy.
+",0,1,0,0,0,0
+11765,On The Asymptotic Efficiency of Selection Procedures for Independent Gaussian Populations,"  The field of discrete event simulation and optimization techniques motivates
+researchers to adjust classic ranking and selection (R&S) procedures to the
+settings where the number of populations is large. We use insights from extreme
+value theory in order to reveal the asymptotic properties of R&S procedures.
+Namely, we generalize the asymptotic result of Robbins and Siegmund regarding
+selection from independent Gaussian populations with known constant variance by
+their means to the case of selecting a subset of varying size out of a given
+set of populations. In addition, we revisit the problem of selecting the
+population with the highest mean among independent Gaussian populations with
+unknown and possibly different variances. Particularly, we derive the relative
+asymptotic efficiency of Dudewicz and Dalal's and Rinott's procedures, showing
+that the former can be asymptotically superior by a multiplicative factor which
+is larger than one, but this factor may be reduced by proper choice of
+parameters. We also use our asymptotic results to suggest that the sample size
+in the first stage of the two procedures should be logarithmic in the number of
+populations.
+",0,0,1,1,0,0
+11766,DSOD: Learning Deeply Supervised Object Detectors from Scratch,"  We present Deeply Supervised Object Detector (DSOD), a framework that can
+learn object detectors from scratch. State-of-the-art object objectors rely
+heavily on the off-the-shelf networks pre-trained on large-scale classification
+datasets like ImageNet, which incurs learning bias due to the difference on
+both the loss functions and the category distributions between classification
+and detection tasks. Model fine-tuning for the detection task could alleviate
+this bias to some extent but not fundamentally. Besides, transferring
+pre-trained models from classification to detection between discrepant domains
+is even more difficult (e.g. RGB to depth images). A better solution to tackle
+these two critical problems is to train object detectors from scratch, which
+motivates our proposed DSOD. Previous efforts in this direction mostly failed
+due to much more complicated loss functions and limited training data in object
+detection. In DSOD, we contribute a set of design principles for training
+object detectors from scratch. One of the key findings is that deep
+supervision, enabled by dense layer-wise connections, plays a critical role in
+learning a good detector. Combining with several other principles, we develop
+DSOD following the single-shot detection (SSD) framework. Experiments on PASCAL
+VOC 2007, 2012 and MS COCO datasets demonstrate that DSOD can achieve better
+results than the state-of-the-art solutions with much more compact models. For
+instance, DSOD outperforms SSD on all three benchmarks with real-time detection
+speed, while requires only 1/2 parameters to SSD and 1/10 parameters to Faster
+RCNN. Our code and models are available at: this https URL .
+",1,0,0,0,0,0
+11767,Data Capture & Analysis to Assess Impact of Carbon Credit Schemes,"  Data enables Non-Governmental Organisations (NGOs) to quantify the impact of
+their initiatives to themselves and to others. The increasing amount of data
+stored today can be seen as a direct consequence of the falling costs in
+obtaining it. Cheap data acquisition harnesses existing communications networks
+to collect information. Globally, more people are connected by the mobile phone
+network than by the Internet. We worked with Vita, a development organisation
+implementing green initiatives to develop an SMS-based data collection
+application to collect social data surrounding the impacts of their
+initiatives. We present our system design and lessons learned from
+on-the-ground testing.
+",1,0,0,0,0,0
+11768,Conducting Simulations in Causal Inference with Networks-Based Structural Equation Models,"  The past decade has seen an increasing body of literature devoted to the
+estimation of causal effects in network-dependent data. However, the validity
+of many classical statistical methods in such data is often questioned. There
+is an emerging need for objective and practical ways to assess which causal
+methodologies might be applicable and valid in network-dependent data. This
+paper describes a set of tools implemented in the simcausal R package that
+allow simulating data based on user-specified structural equation model for
+connected units. Specification and simulation of counterfactual data is
+implemented for static, dynamic and stochastic interventions. A new interface
+aims to simplify the specification of network-based functional relationships
+between connected units. A set of examples illustrates how these simulations
+may be applied to evaluation of different statistical methods for estimation of
+causal effects in network-dependent data.
+",0,0,0,1,0,0
+11769,Finite Semihypergroups Built From Groups,"  Necessary and sufficient conditions for finite semihypergroups to be built
+from groups of the same order are established
+",0,0,1,0,0,0
+11770,Auslander Modules,"  In this paper, we introduce the notion of Auslander modules, inspired from
+Auslander's zero-divisor conjecture (theorem) and give some interesting results
+for these modules. We also investigate torsion-free modules.
+",0,0,1,0,0,0
+11771,Verifying Security Protocols using Dynamic Strategies,"  Current formal approaches have been successfully used to find design flaws in
+many security protocols. However, it is still challenging to automatically
+analyze protocols due to their large or infinite state spaces. In this paper,
+we propose a novel framework that can automatically verifying security
+protocols without any human intervention. Experimental results show that
+SmartVerif automatically verifies security protocols that cannot be
+automatically verified by existing approaches. The case studies also validate
+the effectiveness of our dynamic strategy.
+",1,0,0,0,0,0
+11772,Distribution uniformity of laser-accelerated proton beams,"  Compared with conventional accelerators, laser plasma accelerators can
+generate high energy ions at a greatly reduced scale, due to their TV/m
+acceleration gradient. A compact laser plasma accelerator (CLAPA) has been
+built at the Institute of Heavy Ion Physics at Peking University. It will be
+used for applied research like biological irradiation, astrophysics
+simulations, etc. A beamline system with multiple quadrupoles and an analyzing
+magnet for laser-accelerated ions is proposed here. Since laser-accelerated ion
+beams have broad energy spectra and large angular divergence, the parameters
+(beam waist position in the Y direction, beam line layout, drift distance,
+magnet angles etc.) of the beamline system are carefully designed and optimised
+to obtain a radially symmetric proton distribution at the irradiation platform.
+Requirements of energy selection and differences in focusing or defocusing in
+application systems greatly influence the evolution of proton distributions.
+With optimal parameters, radially symmetric proton distributions can be
+achieved and protons with different energy spread within 5% have similar
+transverse areas at the experiment target.
+",0,1,0,0,0,0
+11773,Learning to Detect Human-Object Interactions,"  We study the problem of detecting human-object interactions (HOI) in static
+images, defined as predicting a human and an object bounding box with an
+interaction class label that connects them. HOI detection is a fundamental
+problem in computer vision as it provides semantic information about the
+interactions among the detected objects. We introduce HICO-DET, a new large
+benchmark for HOI detection, by augmenting the current HICO classification
+benchmark with instance annotations. To solve the task, we propose Human-Object
+Region-based Convolutional Neural Networks (HO-RCNN). At the core of our
+HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the
+spatial relations between two bounding boxes. Experiments on HICO-DET
+demonstrate that our HO-RCNN, by exploiting human-object spatial relations
+through Interaction Patterns, significantly improves the performance of HOI
+detection over baseline approaches.
+",1,0,0,0,0,0
+11774,Data clustering with edge domination in complex networks,"  This paper presents a model for a dynamical system where particles dominate
+edges in a complex network. The proposed dynamical system is then extended to
+an application on the problem of community detection and data clustering. In
+the case of the data clustering problem, 6 different techniques were simulated
+on 10 different datasets in order to compare with the proposed technique. The
+results show that the proposed algorithm performs well when prior knowledge of
+the number of clusters is known to the algorithm.
+",1,1,0,0,0,0
+11775,sWSI: A Low-cost and Commercial-quality Whole Slide Imaging System on Android and iOS Smartphones,"  In this paper, scalable Whole Slide Imaging (sWSI), a novel high-throughput,
+cost-effective and robust whole slide imaging system on both Android and iOS
+platforms is introduced and analyzed. With sWSI, most mainstream smartphone
+connected to a optical eyepiece of any manually controlled microscope can be
+automatically controlled to capture sequences of mega-pixel fields of views
+that are synthesized into giga-pixel virtual slides. Remote servers carry out
+the majority of computation asynchronously to support clients running at
+satisfying frame rates without sacrificing image quality nor robustness. A
+typical 15x15mm sample can be digitized in 30 seconds with 4X or in 3 minutes
+with 10X object magnification, costing under $1. The virtual slide quality is
+considered comparable to existing high-end scanners thus satisfying for
+clinical usage by surveyed pathologies. The scan procedure with features such
+as supporting magnification up to 100x, recoding z-stacks,
+specimen-type-neutral and giving real-time feedback, is deemed
+work-flow-friendly and reliable.
+",1,1,0,0,0,0
+11776,Predicting multicellular function through multi-layer tissue networks,"  Motivation: Understanding functions of proteins in specific human tissues is
+essential for insights into disease diagnostics and therapeutics, yet
+prediction of tissue-specific cellular function remains a critical challenge
+for biomedicine.
+Results: Here we present OhmNet, a hierarchy-aware unsupervised node feature
+learning approach for multi-layer networks. We build a multi-layer network,
+where each layer represents molecular interactions in a different human tissue.
+OhmNet then automatically learns a mapping of proteins, represented as nodes,
+to a neural embedding based low-dimensional space of features. OhmNet
+encourages sharing of similar features among proteins with similar network
+neighborhoods and among proteins activated in similar tissues. The algorithm
+generalizes prior work, which generally ignores relationships between tissues,
+by modeling tissue organization with a rich multiscale tissue hierarchy. We use
+OhmNet to study multicellular function in a multi-layer protein interaction
+network of 107 human tissues. In 48 tissues with known tissue-specific cellular
+functions, OhmNet provides more accurate predictions of cellular function than
+alternative approaches, and also generates more accurate hypotheses about
+tissue-specific protein actions. We show that taking into account the tissue
+hierarchy leads to improved predictive power. Remarkably, we also demonstrate
+that it is possible to leverage the tissue hierarchy in order to effectively
+transfer cellular functions to a functionally uncharacterized tissue. Overall,
+OhmNet moves from flat networks to multiscale models able to predict a range of
+phenotypes spanning cellular subsystems
+",1,0,0,1,0,0
+11777,Toward Microphononic Circuits on Chip: An Evaluation of Components based on High-Contrast Evanescent Confinement of Acoustic Waves,"  We investigate the prospects for micron-scale acoustic wave components and
+circuits on chip in solid planar structures that do not require suspension. We
+leverage evanescent guiding of acoustic waves by high slowness contrast
+materials readily available in silicon complementary metal-oxide semiconductor
+(CMOS) processes. High slowness contrast provides strong confinement of GHz
+frequency acoustic fields in micron-scale structures. We address the
+fundamental implications of intrinsic material and radiation losses on
+operating frequency, bandwidth, device size and as a result practicality of
+multi-element microphononic circuits based on solid embedded waveguides. We
+show that a family of acoustic components based on evanescently guided acoustic
+waves, including waveguide bends, evanescent couplers, Y-splitters, and
+acoustic-wave microring resonators, can be realized in compact, micron-scale
+structures, and provide basic scaling and performance arguments for these
+components based on material properties and simulations. We further find that
+wave propagation losses are expected to permit high quality factor (Q),
+narrowband resonators and propagation lengths allowing delay lines and the
+coupling or cascading of multiple components to form functional circuits, of
+potential utility in guided acoustic signal processing on chip. We also address
+and simulate bends and radiation loss, providing insight into routing and
+resonators. Such circuits could be monolithically integrated with electronic
+and photonic circuits on a single chip with expanded capabilities.
+",0,1,0,0,0,0
+11778,Adaptive Submodular Influence Maximization with Myopic Feedback,"  This paper examines the problem of adaptive influence maximization in social
+networks. As adaptive decision making is a time-critical task, a realistic
+feedback model has been considered, called myopic. In this direction, we
+propose the myopic adaptive greedy policy that is guaranteed to provide a (1 -
+1/e)-approximation of the optimal policy under a variant of the independent
+cascade diffusion model. This strategy maximizes an alternative utility
+function that has been proven to be adaptive monotone and adaptive submodular.
+The proposed utility function considers the cumulative number of active nodes
+through the time, instead of the total number of the active nodes at the end of
+the diffusion. Our empirical analysis on real-world social networks reveals the
+benefits of the proposed myopic strategy, validating our theoretical results.
+",1,0,0,0,0,0
+11779,Evaluation of Direct Haptic 4D Volume Rendering of Partially Segmented Data for Liver Puncture Simulation,"  This work presents an evaluation study using a force feedback evaluation
+framework for a novel direct needle force volume rendering concept in the
+context of liver puncture simulation. PTC/PTCD puncture interventions targeting
+the bile ducts have been selected to illustrate this concept. The haptic
+algorithms of the simulator system are based on (1) partially segmented patient
+image data and (2) a non-linear spring model effective at organ borders. The
+primary aim is to quantitatively evaluate force errors caused by our patient
+modeling approach, in comparison to haptic force output obtained from using
+gold-standard, completely manually-segmented data. The evaluation of the force
+algorithms compared to a force output from fully manually segmented
+gold-standard patient models, yields a low mean of 0.12 N root mean squared
+force error and up to 1.6 N for systematic maximum absolute errors. Force
+errors were evaluated on 31,222 preplanned test paths from 10 patients. Only
+twelve percent of the emitted forces along these paths were affected by errors.
+This is the first study evaluating haptic algorithms with deformable virtual
+patients in silico. We prove haptic rendering plausibility on a very high
+number of test paths. Important errors are below just noticeable differences
+for the hand-arm system.
+",1,1,0,0,0,0
+11780,The Role of Big Data on Smart Grid Transition,"  Despite being popularly referred to as the ultimate solution for all problems
+of our current electric power system, smart grid is still a growing and
+unstable concept. It is usually considered as a set of advanced features
+powered by promising technological solutions. In this paper, we describe smart
+grid as a socio-technical transition and illustrate the evolutionary path on
+which a smart grid can be realized. Through this conceptual lens, we reveal the
+role of big data, and how it can fuel the organic growth of smart grid. We also
+provide a rough estimate of how much data will be potentially generated from
+different data sources, which helps clarify the big data challenges during the
+evolutionary process.
+",1,0,0,0,0,0
+11781,Learning Deep ResNet Blocks Sequentially using Boosting Theory,"  Deep neural networks are known to be difficult to train due to the
+instability of back-propagation. A deep \emph{residual network} (ResNet) with
+identity loops remedies this by stabilizing gradient computations. We prove a
+boosting theory for the ResNet architecture. We construct $T$ weak module
+classifiers, each contains two of the $T$ layers, such that the combined strong
+learner is a ResNet. Therefore, we introduce an alternative Deep ResNet
+training algorithm, \emph{BoostResNet}, which is particularly suitable in
+non-differentiable architectures. Our proposed algorithm merely requires a
+sequential training of $T$ ""shallow ResNets"" which are inexpensive. We prove
+that the training error decays exponentially with the depth $T$ if the
+\emph{weak module classifiers} that we train perform slightly better than some
+weak baseline. In other words, we propose a weak learning condition and prove a
+boosting theory for ResNet under the weak learning condition. Our results apply
+to general multi-class ResNets. A generalization error bound based on margin
+theory is proved and suggests ResNet's resistant to overfitting under network
+with $l_1$ norm bounded weights.
+",1,0,0,0,0,0
+11782,Total energy of radial mappings,"  The main aim of this paper is to extend one of the main results of Iwaniec
+and Onninen (Arch. Ration. Mech. Anal., 194: 927-986, 2009). We prove that, the
+so called total energy functional defined on the class of radial streachings
+between annuli attains its minimum on a total energy diffeomorphism between
+annuli. This involves a subtle analysis of some special ODE.
+",0,0,1,0,0,0
+11783,Divergence and Sufficiency for Convex Optimization,"  Logarithmic score and information divergence appear in information theory,
+statistics, statistical mechanics, and portfolio theory. We demonstrate that
+all these topics involve some kind of optimization that leads directly to
+regret functions and such regret functions are often given by a Bregman
+divergence. If the regret function also fulfills a sufficiency condition it
+must be proportional to information divergence. We will demonstrate that
+sufficiency is equivalent to the apparently weaker notion of locality and it is
+also equivalent to the apparently stronger notion of monotonicity. These
+sufficiency conditions have quite different relevance in the different areas of
+application, and often they are not fulfilled. Therefore sufficiency conditions
+can be used to explain when results from one area can be transferred directly
+to another and when one will experience differences.
+",1,1,1,0,0,0
+11784,Compact linear programs for 2SAT,"  For each integer $n$ we present an explicit formulation of a compact linear
+program, with $O(n^3)$ variables and constraints, which determines the
+satisfiability of any 2SAT formula with $n$ boolean variables by a single
+linear optimization. This contrasts with the fact that the natural polytope for
+this problem, formed from the convex hull of all satisfiable formulas and their
+satisfying assignments, has superpolynomial extension complexity. Our
+formulation is based on multicommodity flows. We also discuss connections of
+these results to the stable matching problem.
+",1,0,1,0,0,0
+11785,Distributed Protocols at the Rescue for Trustworthy Online Voting,"  While online services emerge in all areas of life, the voting procedure in
+many democracies remains paper-based as the security of current online voting
+technology is highly disputed. We address the issue of trustworthy online
+voting protocols and recall therefore their security concepts with its trust
+assumptions. Inspired by the Bitcoin protocol, the prospects of distributed
+online voting protocols are analysed. No trusted authority is assumed to ensure
+ballot secrecy. Further, the integrity of the voting is enforced by all voters
+themselves and without a weakest link, the protocol becomes more robust. We
+introduce a taxonomy of notions of distribution in online voting protocols that
+we apply on selected online voting protocols. Accordingly, blockchain-based
+protocols seem to be promising for online voting due to their similarity with
+paper-based protocols.
+",1,0,0,0,0,0
+11786,Isomonodromy aspects of the tt* equations of Cecotti and Vafa III. Iwasawa factorization and asymptotics,"  This paper, the third in a series, completes our description of all (radial)
+solutions on C* of the tt*-Toda equations, using a combination of methods from
+p.d.e., isomonodromic deformations (Riemann-Hilbert method), and loop groups.
+We place these global solutions into the broader context of solutions which are
+smooth near 0. For such solutions, we compute explicitly the Stokes data and
+connection matrix of the associated meromorphic system, in the resonant cases
+as well as the non-resonant case. This allows us to give a complete picture of
+the monodromy data of the global solutions.
+",0,0,1,0,0,0
+11787,Decorative Plasmonic Surfaces,"  Low-profile patterned plasmonic surfaces are synergized with a broad class of
+silicon microstructures to greatly enhance near-field nanoscale imaging,
+sensing, and energy harvesting coupled with far-field free-space detection.
+This concept has a clear impact on several key areas of interest for the MEMS
+community, including but not limited to ultra-compact microsystems for
+sensitive detection of small number of target molecules, and surface devices
+for optical data storage, micro-imaging and displaying. In this paper, we
+review the current state-of-the-art in plasmonic theory as well as derive
+design guidance for plasmonic integration with microsystems, fabrication
+techniques, and selected applications in biosensing, including refractive-index
+based label-free biosensing, plasmonic integrated lab-on-chip systems,
+plasmonic near-field scanning optical microscopy and plasmonics on-chip systems
+for cellular imaging. This paradigm enables low-profile conformal surfaces on
+microdevices, rather than bulk material or coatings, which provide clear
+advantages for physical, chemical and biological-related sensing, imaging, and
+light harvesting, in addition to easier realization, enhanced flexibility, and
+tunability.
+",0,1,0,0,0,0
+11788,Hamiltonian approach to slip-stacking dynamics,"  Hamiltonian dynamics has been applied to study the slip-stacking dynamics.
+The canonical-perturbation method is employed to obtain the second-harmonic
+correction term in the slip-stacking Hamiltonian. The Hamiltonian approach
+provides a clear optimal method for choosing the slip-stacking parameter and
+improving stacking efficiency. The dynamics are applied specifically to the
+Fermilab Booster-Recycler complex. The dynamics can also be applied to other
+accelerator complexes.
+",0,1,0,0,0,0
+11789,Materials processing with intense pulsed ion beams and masked targets,"  Intense, pulsed ion beams locally heat materials and deliver dense electronic
+excitations that can induce materials modifications and phase transitions.
+Materials properties can potentially be stabilized by rapid quenching. Pulsed
+ion beams with (sub-) ns pulse lengths have recently become available for
+materials processing. Here, we optimize mask geometries for local modification
+of materials by intense ion pulses. The goal is to rapidly excite targets
+volumetrically to the point where a phase transition or local lattice
+reconstruction is induced followed by rapid cooling that stabilizes desired
+materials properties fast enough before the target is altered or damaged by e.
+g. hydrodynamic expansion. We performed HYDRA simulations that calculate peak
+temperatures for a series of excitation conditions and cooling rates of silicon
+targets with micro-structured masks and compare these to a simple analytical
+model. The model gives scaling laws that can guide the design of targets over a
+wide range of pulsed ion beam parameters.
+",0,1,0,0,0,0
+11790,Distributions of Historic Market Data -- Implied and Realized Volatility,"  We undertake a systematic comparison between implied volatility, as
+represented by VIX (new methodology) and VXO (old methodology), and realized
+volatility. We compare visually and statistically distributions of realized and
+implied variance (volatility squared) and study the distribution of their
+ratio. We find that the ratio is best fitted by heavy-tailed -- lognormal and
+fat-tailed (power-law) -- distributions, depending on whether preceding or
+concurrent month of realized variance is used. We do not find substantial
+difference in accuracy between VIX and VXO. Additionally, we study the variance
+of theoretical realized variance for Heston and multiplicative models of
+stochastic volatility and compare those with realized variance obtained from
+historic market data.
+",0,0,0,0,0,1
+11791,Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net,"  We propose a novel method to directly learn a stochastic transition operator
+whose repeated application provides generated samples. Traditional undirected
+graphical models approach this problem indirectly by learning a Markov chain
+model whose stationary distribution obeys detailed balance with respect to a
+parameterized energy function. The energy function is then modified so the
+model and data distributions match, with no guarantee on the number of steps
+required for the Markov chain to converge. Moreover, the detailed balance
+condition is highly restrictive: energy based models corresponding to neural
+networks must have symmetric weights, unlike biological neural circuits. In
+contrast, we develop a method for directly learning arbitrarily parameterized
+transition operators capable of expressing non-equilibrium stationary
+distributions that violate detailed balance, thereby enabling us to learn more
+biologically plausible asymmetric neural networks and more general non-energy
+based dynamical systems. The proposed training objective, which we derive via
+principled variational methods, encourages the transition operator to ""walk
+back"" in multi-step trajectories that start at data-points, as quickly as
+possible back to the original data points. We present a series of experimental
+results illustrating the soundness of the proposed approach, Variational
+Walkback (VW), on the MNIST, CIFAR-10, SVHN and CelebA datasets, demonstrating
+superior samples compared to earlier attempts to learn a transition operator.
+We also show that although each rapid training trajectory is limited to a
+finite but variable number of steps, our transition operator continues to
+generate good samples well past the length of such trajectories, thereby
+demonstrating the match of its non-equilibrium stationary distribution to the
+data distribution. Source Code: this http URL
+",1,0,0,1,0,0
+11792,Toward a language-theoretic foundation for planning and filtering,"  We address problems underlying the algorithmic question of automating the
+co-design of robot hardware in tandem with its apposite software. Specifically,
+we consider the impact that degradations of a robot's sensor and actuation
+suites may have on the ability of that robot to complete its tasks. We
+introduce a new formal structure that generalizes and consolidates a variety of
+well-known structures including many forms of plans, planning problems, and
+filters, into a single data structure called a procrustean graph, and give
+these graph structures semantics in terms of ideas based in formal language
+theory. We describe a collection of operations on procrustean graphs (both
+semantics-preserving and semantics-mutating), and show how a family of
+questions about the destructiveness of a change to the robot hardware can be
+answered by applying these operations. We also highlight the connections
+between this new approach and existing threads of research, including
+combinatorial filtering, Erdmann's strategy complexes, and hybrid automata.
+",1,0,0,0,0,0
+11793,Algebraic relations between solutions of Painlevé equations,"  We calculate model theoretic ranks of Painlevé equations in this article,
+showing in particular, that any equation in any of the Painlevé families has
+Morley rank one, extending results of Nagloo and Pillay (2011). We show that
+the type of the generic solution of any equation in the second Painlevé
+family is geometrically trivial, extending a result of Nagloo (2015).
+We also establish the orthogonality of various pairs of equations in the
+Painlevé families, showing at least generically, that all instances of
+nonorthogonality between equations in the same Painlevé family come from
+classically studied B{ä}cklund transformations. For instance, we show that if
+at least one of $\alpha, \beta$ is transcendental, then $P_{II} (\alpha)$ is
+nonorthogonal to $P_{II} ( \beta )$ if and only if $\alpha+ \beta \in \mathbb
+Z$ or $\alpha - \beta \in \mathbb Z$. Our results have concrete interpretations
+in terms of characterizing the algebraic relations between solutions of
+Painlevé equations. We give similar results for orthogonality relations
+between equations in different Painlevé families, and formulate some general
+questions which extend conjectures of Nagloo and Pillay (2011) on transcendence
+and algebraic independence of solutions to Painlevé equations. We also apply
+our analysis of ranks to establish some orthogonality results for pairs of
+Painlevé equations from different families. For instance, we answer several
+open questions of Nagloo (2016), and in the process answer a question of Boalch
+(2012).
+",0,0,1,0,0,0
+11794,Pore lifetimes in cell electroporation: Complex dark pores?,"  We review some of the basic concepts and the possible pore structures
+associated with electroporation (EP) for times after electrical pulsing. We
+purposefully give only a short description of pore creation and subsequent
+evolution of pore populations, as these are adequately discussed in both
+reviews and original research reports. In contrast, post-pulse pore concepts
+have changed dramatically. For perspective we note that pores are not directly
+observed. Instead understanding of pores is based on inference from experiments
+and, increasingly, molecular dynamics (MD) simulations. In the past decade
+concepts for post-pulse pores have changed significantly: The idea of pure
+lipidic transient pores (TPs) that exist for milliseconds or longer post-pulse
+has become inconsistent with MD results, which support TP lifetimes of only
+$\sim$100 ns. A typical large TP number during cell EP pulsing is of order
+$10^6$. In twenty MD-based TP lifetimes (2 us total), the TP number plummets to
+$\sim$0.001. In short, TPs vanish 2 us after a pulse ends, and cannot account
+for post-pulse behavior such as large and relatively non-specific ionic and
+molecular transport. Instead, an early conjecture of complex pores (CPs) with
+both lipidic and other molecule should be taken seriously. Indeed, in the past
+decade several experiments provide partial support for complex pores (CPs).
+Presently, CPs are ""dark"", in the sense that while some CP functions are known,
+little is known about their structure(s). There may be a wide range of
+lifetimes and permeabilities, not yet revealed by experiments. Like cosmology's
+dark matter, these unseen pores present us with an outstanding problem.
+",0,1,0,0,0,0
+11795,Data-driven causal path discovery without prior knowledge - a benchmark study,"  Causal discovery broadens the inference possibilities, as correlation does
+not inform about the relationship direction. The common approaches were
+proposed for cases in which prior knowledge is desired, when the impact of a
+treatment/intervention variable is discovered or to analyze time-related
+dependencies. In some practical applications, more universal techniques are
+needed and have already been presented. Therefore, the aim of the study was to
+assess the accuracies in determining causal paths in a dataset without
+considering the ground truth and the contextual information. This benchmark was
+performed on the database with cause-effect pairs, using a framework consisting
+of generalized correlations (GC), kernel regression gradients (GR) and absolute
+residuals criteria (AR), along with causal additive modeling (CAM). The best
+overall accuracy, 80%, was achieved for the (majority voting) combination of
+GC, AR, and CAM, however, the most similar sensitivity and specificity values
+were obtained for AR. Bootstrap simulation established the probability of
+correct causal path determination (which pairs should remain indeterminate).
+The mean accuracy was then improved to 83% for the selected subset of pairs.
+The described approach can be used for preliminary dependence assessment, as an
+initial step for commonly used causality assessment frameworks or for
+comparison with prior assumptions.
+",0,0,0,1,0,0
+11796,Computation of Ground States of the Gross-Pitaevskii Functional via Riemannian Optimization,"  In this paper we combine concepts from Riemannian Optimization and the theory
+of Sobolev gradients to derive a new conjugate gradient method for direct
+minimization of the Gross-Pitaevskii energy functional with rotation. The
+conservation of the number of particles constrains the minimizers to lie on a
+manifold corresponding to the unit $L^2$ norm. The idea developed here is to
+transform the original constrained optimization problem to an unconstrained
+problem on this (spherical) Riemannian manifold, so that fast minimization
+algorithms can be applied as alternatives to more standard constrained
+formulations. First, we obtain Sobolev gradients using an equivalent definition
+of an $H^1$ inner product which takes into account rotation. Then, the
+Riemannian gradient (RG) steepest descent method is derived based on projected
+gradients and retraction of an intermediate solution back to the constraint
+manifold. Finally, we use the concept of the Riemannian vector transport to
+propose a Riemannian conjugate gradient (RCG) method for this problem. It is
+derived at the continuous level based on the ""optimize-then-discretize""
+paradigm instead of the usual ""discretize-then-optimize"" approach, as this
+ensures robustness of the method when adaptive mesh refinement is performed in
+computations. We evaluate various design choices inherent in the formulation of
+the method and conclude with recommendations concerning selection of the best
+options. Numerical tests demonstrate that the proposed RCG method outperforms
+the simple gradient descent (RG) method in terms of rate of convergence. While
+on simple problems a Newton-type method implemented in the {\tt Ipopt} library
+exhibits a faster convergence than the (RCG) approach, the two methods perform
+similarly on more complex problems requiring the use of mesh adaptation. At the
+same time the (RCG) approach has far fewer tunable parameters.
+",0,1,1,0,0,0
+11797,On the symplectic size of convex polytopes,"  In this paper we introduce a combinatorial formula for the
+Ekeland-Hofer-Zehnder capacity of a convex polytope in $\mathbb{R}^{2n}$. One
+application of this formula is a certain subadditivity property of this
+capacity.
+",0,0,1,0,0,0
+11798,A Fully Convolutional Neural Network Approach to End-to-End Speech Enhancement,"  This paper will describe a novel approach to the cocktail party problem that
+relies on a fully convolutional neural network (FCN) architecture. The FCN
+takes noisy audio data as input and performs nonlinear, filtering operations to
+produce clean audio data of the target speech at the output. Our method learns
+a model for one specific speaker, and is then able to extract that speakers
+voice from babble background noise. Results from experimentation indicate the
+ability to generalize to new speakers and robustness to new noise environments
+of varying signal-to-noise ratios. A potential application of this method would
+be for use in hearing aids. A pre-trained model could be quickly fine tuned for
+an individuals family members and close friends, and deployed onto a hearing
+aid to assist listeners in noisy environments.
+",1,0,0,1,0,0
+11799,Tunnelling Spectroscopy of Andreev States in Graphene,"  A normal conductor placed in good contact with a superconductor can inherit
+its remarkable electronic properties. This proximity effect microscopically
+originates from the formation in the conductor of entangled electron-hole
+states, called Andreev states. Spectroscopic studies of Andreev states have
+been performed in just a handful of systems. The unique geometry, electronic
+structure and high mobility of graphene make it a novel platform for studying
+Andreev physics in two dimensions. Here we use a full van der Waals
+heterostructure to perform tunnelling spectroscopy measurements of the
+proximity effect in superconductor-graphene-superconductor junctions. The
+measured energy spectra, which depend on the phase difference between the
+superconductors, reveal the presence of a continuum of Andreev bound states.
+Moreover, our device heterostructure geometry and materials enable us to
+measure the Andreev spectrum as a function of the graphene Fermi energy,
+showing a transition between different mesoscopic regimes. Furthermore, by
+experimentally introducing a novel concept, the supercurrent spectral density,
+we determine the supercurrent-phase relation in a tunnelling experiment, thus
+establishing the connection between Andreev physics at finite energy and the
+Josephson effect. This work opens up new avenues for probing exotic topological
+phases of matter in hybrid superconducting Dirac materials.
+",0,1,0,0,0,0
+11800,Volatility estimation for stochastic PDEs using high-frequency observations,"  We study the parameter estimation for parabolic, linear, second order,
+stochastic partial differential equations (SPDEs) observing a mild solution on
+a discrete grid in time and space. A high-frequency regime is considered where
+the mesh of the grid in the time variable goes to zero. Focusing on volatility
+estimation, we provide an explicit and easy to implement method of moments
+estimator based on squared increments. The estimator is consistent and admits a
+central limit theorem. This is established moreover for the estimation of the
+integrated volatility in a semi-parametric framework and for the joint
+estimation of the volatility and an unknown parameter in the differential
+operator. Starting from a representation of the solution as an infinite factor
+model and exploiting mixing-type properties of time series, the theory
+considerably differs from the statistics for semi-martingales literature. The
+performance of the method is illustrated in a simulation study.
+",0,0,1,1,0,0
+11801,A Conic Integer Programming Approach to Constrained Assortment Optimization under the Mixed Multinomial Logit Model,"  We consider the constrained assortment optimization problem under the mixed
+multinomial logit model. Even moderately sized instances of this problem are
+challenging to solve directly using standard mixed-integer linear optimization
+formulations. This has motivated recent research exploring customized
+optimization strategies and approximation techniques. In contrast, we develop a
+novel conic quadratic mixed-integer formulation. This new formulation, together
+with McCormick inequalities exploiting the capacity constraints, enables the
+solution of large instances using commercial optimization software.
+",0,0,1,0,0,0
+11802,"Voyager 1 Measurements Beyond the Heliopause of Galactic Cosmic Ray Helium, Boron, Carbon, Oxygen, Magnesium, Silicon and Iron Nuclei with Energies 0.5 to >1.5 GeV/nuc","  We have obtained the energy spectra of cosmic ray He, B, C, O, Mg, S and Fe
+nuclei in the range 0.5-1.5 GeV/nuc and above using the penetrating particle
+mode of the High Energy Telescope, part of the Cosmic Ray Science (CRS)
+experiment on Voyagers 1 and 2. The data analysis procedures are the same as
+those used to obtain similar spectra from the identical V2 HET telescope while
+it was in the heliosphere between about 23 and 54 AU. The time period of
+analysis includes 4 years of data beyond the heliopause (HP). These new
+interstellar spectra are compared with various earlier experiments at the same
+energies at the Earth to determine the solar modulation parameter, phi. These
+new spectra are also compared with recent measurements of the spectra of the
+same nuclei measured by the same telescope at low energies. It is found that
+the ratio of intensities at 100 MeV/nuc to those at 1.0 GeV/nuc are
+significantly Z dependent. Some of this Z dependence can be explained by the Z2
+dependence of energy loss by ionization in the 7-10 g/cm2 of interstellar H and
+He traversed by cosmic rays of these energies in the galaxy; some by the Z
+dependent loss due to nuclear interactions in this same material; some by
+possible differences in the source spectra of these nuclei and some by the
+non-uniformity of the source distribution and propagation conditions. The
+observed features of the spectra, also including a Z dependence of the peak
+intensities of the various nuclei, pose interesting problems related to the
+propagation and source distribution of these cosmic rays.
+",0,1,0,0,0,0
+11803,Block Mean Approximation for Efficient Second Order Optimization,"  Advanced optimization algorithms such as Newton method and AdaGrad benefit
+from second order derivative or second order statistics to achieve better
+descent directions and faster convergence rates. At their heart, such
+algorithms need to compute the inverse or inverse square root of a matrix whose
+size is quadratic of the dimensionality of the search space. For high
+dimensional search spaces, the matrix inversion or inversion of square root
+becomes overwhelming which in turn demands for approximate methods. In this
+work, we propose a new matrix approximation method which divides a matrix into
+blocks and represents each block by one or two numbers. The method allows
+efficient computation of matrix inverse and inverse square root. We apply our
+method to AdaGrad in training deep neural networks. Experiments show
+encouraging results compared to the diagonal approximation.
+",0,0,0,1,0,0
+11804,A Minimum Discounted Reward Hamilton-Jacobi Formulation for Computing Reachable Sets,"  We propose a novel formulation for approximating reachable sets through a
+minimum discounted reward optimal control problem. The formulation yields a
+continuous solution that can be obtained by solving a Hamilton-Jacobi equation.
+Furthermore, the numerical approximation to this solution can be obtained as
+the unique fixed-point to a contraction mapping. This allows for more efficient
+solution methods that could not be applied under traditional formulations for
+solving reachable sets. In addition, this formulation provides a link between
+reinforcement learning and learning reachable sets for systems with unknown
+dynamics, allowing algorithms from the former to be applied to the latter. We
+use two benchmark examples, double integrator, and pursuit-evasion games, to
+show the correctness of the formulation as well as its strengths in comparison
+to previous work.
+",1,0,0,0,0,0
+11805,Imbedding results in Musielak-Orlicz spaces with an application to anisotropic nonlinear Neumann problems,"  We prove a continuous embedding that allows us to obtain a boundary trace
+imbedding result for anisotropic Musielak-Orlicz spaces, which we then apply to
+obtain an existence result for Neumann problems with nonlinearities on the
+boundary associated to some anisotropic nonlinear elliptic equations in
+Musielak-Orlicz spaces constructed from Musielak-Orlicz functions on which and
+on their conjugates we do not assume the $\Delta_2$-condition. The uniqueness
+is also studied.
+",0,0,1,0,0,0
+11806,Defend against advanced persistent threats: An optimal control approach,"  The new cyber attack pattern of advanced persistent threat (APT) has posed a
+serious threat to modern society. This paper addresses the APT defense problem,
+i.e., the problem of how to effectively defend against an APT campaign. Based
+on a novel APT attack-defense model, the effectiveness of an APT defense
+strategy is quantified. Thereby, the APT defense problem is modeled as an
+optimal control problem, in which an optimal control stands for a most
+effective APT defense strategy. The existence of an optimal control is proved,
+and an optimality system is derived. Consequently, an optimal control can be
+figured out by solving the optimality system. Some examples of the optimal
+control are given. Finally, the influence of some factors on the effectiveness
+of an optimal control is examined through computer experiments. These findings
+help organizations to work out policies of defending against APTs.
+",1,0,0,0,0,0
+11807,Challenges in Designing Datasets and Validation for Autonomous Driving,"  Autonomous driving is getting a lot of attention in the last decade and will
+be the hot topic at least until the first successful certification of a car
+with Level 5 autonomy. There are many public datasets in the academic
+community. However, they are far away from what a robust industrial production
+system needs. There is a large gap between academic and industrial setting and
+a substantial way from a research prototype, built on public datasets, to a
+deployable solution which is a challenging task. In this paper, we focus on bad
+practices that often happen in the autonomous driving from an industrial
+deployment perspective. Data design deserves at least the same amount of
+attention as the model design. There is very little attention paid to these
+issues in the scientific community, and we hope this paper encourages better
+formalization of dataset design. More specifically, we focus on the datasets
+design and validation scheme for autonomous driving, where we would like to
+highlight the common problems, wrong assumptions, and steps towards avoiding
+them, as well as some open problems.
+",1,0,0,1,0,0
+11808,Improving Adversarial Robustness via Promoting Ensemble Diversity,"  Though deep neural networks have achieved significant progress on various
+tasks, often enhanced by model ensemble, existing high-performance models can
+be vulnerable to adversarial attacks. Many efforts have been devoted to
+enhancing the robustness of individual networks and then constructing a
+straightforward ensemble, e.g., by directly averaging the outputs, which
+ignores the interaction among networks. This paper presents a new method that
+explores the interaction among individual networks to improve robustness for
+ensemble models. Technically, we define a new notion of ensemble diversity in
+the adversarial setting as the diversity among non-maximal predictions of
+individual members, and present an adaptive diversity promoting (ADP)
+regularizer to encourage the diversity, which leads to globally better
+robustness for the ensemble by making adversarial examples difficult to
+transfer among individual members. Our method is computationally efficient and
+compatible with the defense methods acting on individual networks. Empirical
+results on various datasets verify that our method can improve adversarial
+robustness while maintaining state-of-the-art accuracy on normal examples.
+",1,0,0,1,0,0
+11809,Majorana stripe order on the surface of a three-dimensional topological insulator,"  The issue on the effect of interactions in topological states concerns not
+only interacting topological phases but also novel symmetry-breaking phases and
+phase transitions. Here we study the interaction effect on Majorana zero modes
+(MZMs) bound to a square vortex lattice in two-dimensional (2D) topological
+superconductors. Under the neutrality condition, where single-body
+hybridization between MZMs is prohibited by an emergent symmetry, a minimal
+square-lattice model for MZMs can be faithfully mapped to a quantum spin model,
+which has no sign problem in the world-line quantum Monte Carlo simulation.
+Guided by an insight from a further duality mapping, we demonstrate that the
+interaction induces a Majorana stripe state, a gapped state spontaneously
+breaking lattice translational and rotational symmetries, as opposed to the
+previously conjectured topological quantum criticality. Away from neutrality, a
+mean-field theory suggests a quantum critical point induced by hybridization.
+",0,1,0,0,0,0
+11810,Academic Engagement and Commercialization in an Institutional Transition Environment: Evidence from Shanghai Maritime University,"  Does academic engagement accelerate or crowd out the commercialization of
+university knowledge? Research on this topic seldom considers the impact of the
+institutional environment, especially when a formal institution for encouraging
+the commercial activities of scholars has not yet been established. This study
+investigates this question in the context of China, which is in the
+institutional transition stage. Based on a survey of scholars from Shanghai
+Maritime University, we demonstrate that academic engagement has a positive
+impact on commercialization and that this impact is greater for risk-averse
+scholars than for other risk-seeking scholars. Our results suggest that in an
+institutional transition environment, the government should consider
+encouraging academic engagement to stimulate the commercialization activities
+of conservative scholars.
+",0,0,0,0,0,1
+11811,Affine-Gradient Based Local Binary Pattern Descriptor for Texture Classiffication,"  We present a novel Affine-Gradient based Local Binary Pattern (AGLBP)
+descriptor for texture classification. It is very hard to describe complicated
+texture using single type information, such as Local Binary Pattern (LBP),
+which just utilizes the sign information of the difference between the pixel
+and its local neighbors. Our descriptor has three characteristics: 1) In order
+to make full use of the information contained in the texture, the
+Affine-Gradient, which is different from Euclidean-Gradient and invariant to
+affine transformation is incorporated into AGLBP. 2) An improved method is
+proposed for rotation invariance, which depends on the reference direction
+calculating respect to local neighbors. 3) Feature selection method,
+considering both the statistical frequency and the intraclass variance of the
+training dataset, is also applied to reduce the dimensionality of descriptors.
+Experiments on three standard texture datasets, Outex12, Outex10 and KTH-TIPS2,
+are conducted to evaluate the performance of AGLBP. The results show that our
+proposed descriptor gets better performance comparing to some state-of-the-art
+rotation texture descriptors in texture classification.
+",1,0,0,0,0,0
+11812,Decomposition theorems for asymptotic property C and property A,"  We combine aspects of the notions of finite decomposition complexity and
+asymptotic property C into a notion that we call finite APC-decomposition
+complexity. Any space with finite decomposition complexity has finite
+APC-decomposition complexity and any space with asymptotic property C has
+finite APC-decomposition complexity. Moreover, finite APC-decomposition
+complexity implies property A for metric spaces. We also show that finite
+APC-decomposition complexity is preserved by direct products of groups and
+spaces, amalgamated products of groups, and group extensions, among other
+constructions.
+",0,0,1,0,0,0
+11813,Three dimensional free-surface flow over arbitrary bottom topography,"  We consider steady nonlinear free surface flow past an arbitrary bottom
+topography in three dimensions, concentrating on the shape of the wave pattern
+that forms on the surface of the fluid. Assuming ideal fluid flow, the problem
+is formulated using a boundary integral method and discretised to produce a
+nonlinear system of algebraic equations. The Jacobian of this system is dense
+due to integrals being evaluated over the entire free surface. To overcome the
+computational difficulty and large memory requirements, a Jacobian-free Newton
+Krylov (JFNK) method is utilised. Using a block-banded approximation of the
+Jacobian from the linearised system as a preconditioner for the JFNK scheme, we
+find significant reductions in computational time and memory required for
+generating numerical solutions. These improvements also allow for a larger
+number of mesh points over the free surface and the bottom topography. We
+present a range of numerical solutions for both subcritical and supercritical
+regimes, and for a variety of bottom configurations. We discuss nonlinear
+features of the wave patterns as well as their relationship to ship wakes.
+",0,1,0,0,0,0
+11814,Spin-polaron formation and magnetic state diagram in La doped $CaMnO_3$,"  $La_xCa_{1-x}MnO_3$ (LCMO) has been studied in the framework of density
+functional theory (DFT) using Hubbard-U correction. We show that the formation
+of spin-polarons of different configurations is possible in the G-type
+antiferromagentic phase. We also show that the spin-polaron (SP) solutions are
+stabilized due to an interplay of magnetic and lattice effects at lower La
+concentrations and mostly due to the lattice contribution at larger
+concentrations. Our results indicate that the development of SPs is unfavorable
+in the C- and A-type antiferromagnetic phases. The theoretically obtained
+magnetic state diagram is in good agreement with previously reported
+experimental results
+",0,1,0,0,0,0
+11815,Power-law citation distributions are not scale-free,"  We analyze time evolution of statistical distributions of citations to
+scientific papers published in one year. While these distributions can be
+fitted by a power-law dependence we find that they are nonstationary and the
+exponent of the power law fit decreases with time and does not come to
+saturation. We attribute the nonstationarity of citation distributions to
+different longevity of the low-cited and highly-cited papers. By measuring
+citation trajectories of papers we found that citation careers of the low-cited
+papers come to saturation after 10-15 years while those of the highly-cited
+papers continue to increase indefinitely: the papers that exceed some citation
+threshold become runaways. Thus, we show that although citation distribution
+can look as a power-law, it is not scale-free and there is a hidden dynamic
+scale associated with the onset of runaways. We compare our measurements to our
+recently developed model of citation dynamics based on
+copying/redirection/triadic closure and find explanations to our empirical
+observations.
+",1,0,0,0,0,0
+11816,"Atmospheric stellar parameters for large surveys using FASMA, a new spectral synthesis package","  In the era of vast spectroscopic surveys focusing on Galactic stellar
+populations, astronomers want to exploit the large quantity and good quality of
+data to derive their atmospheric parameters without losing precision from
+automatic procedures. In this work, we developed a new spectral package, FASMA,
+to estimate the stellar atmospheric parameters (namely effective temperature,
+surface gravity, and metallicity) in a fast and robust way. This method is
+suitable for spectra of FGK-type stars in medium and high resolution. The
+spectroscopic analysis is based on the spectral synthesis technique using the
+radiative transfer code, MOOG. The line list is comprised of mainly iron lines
+in the optical spectrum. The atomic data are calibrated after the Sun and
+Arcturus. We use two comparison samples to test our method, i) a sample of 451
+FGK-type dwarfs from the high resolution HARPS spectrograph, and ii) the
+Gaia-ESO benchmark stars using both high and medium resolution spectra. We
+explore biases in our method from the analysis of synthetic spectra covering
+the parameter space of our interest. We show that our spectral package is able
+to provide reliable results for a wide range of stellar parameters, different
+rotational velocities, different instrumental resolutions, and for different
+spectral regions of the VLT-GIRAFFE spectrographs, used among others for the
+Gaia-ESO survey. FASMA estimates stellar parameters in less than 15 min for
+high resolution and 3 min for medium resolution spectra. The complete package
+is publicly available to the community.
+",0,1,0,0,0,0
+11817,Bingham flow in porous media with obstacles of different size,"  By using the unfolding operators for periodic homogenization, we give a
+general compactness result for a class of functions defined on bounded domains
+presenting perforations of two different size. Then we apply this result to the
+homogenization of the flow of a Bingham fluid in a porous medium with solid
+obstacles of different size. Next we give the interpretation of the limit
+problem in term of a non linear Darcy law.
+",0,0,1,0,0,0
+11818,Eigenstate entanglement in the Sachdev-Ye-Kitaev model,"  In the Sachdev-Ye-Kitaev model, we argue that the entanglement entropy of any
+eigenstate (including the ground state) obeys a volume law, whose coefficient
+can be calculated analytically from the energy and subsystem size. We expect
+that the argument applies to a broader class of chaotic models with all-to-all
+interactions.
+",0,1,0,0,0,0
+11819,Finite presheaves and $A$-finite generation of unstable algebras mod nilpotents,"  Inspired by the work of Henn, Lannes and Schwartz on unstable algebras over
+the Steenrod algebra modulo nilpotents, a characterization of unstable algebras
+that are $A$-finitely generated up to nilpotents is given in terms of the
+associated presheaf, by introducing the notion of a finite presheaf. In
+particular, this gives the natural characterization of the (co)analytic
+presheaves that are important in the theory of Henn, Lannes and Schwartz.
+However, finite presheaves remain imperfectly understood, as illustrated by
+examples. One important class of examples is shown to be provided by unstable
+algebras of finite transcendence degree (under a necessary weak finiteness
+condition).
+For unstable Hopf algebras, it is shown that the situation is much better:
+the associated presheaf is finite if and only if its growth function is
+polynomial. This leads to a description of unstable Hopf algebras modulo
+nilpotents in the spirit of Henn, Lannes and Schwartz.
+",0,0,1,0,0,0
+11820,Ejection of rocky and icy material from binary star systems: Implications for the origin and composition of 1I/`Oumuamua,"  In single star systems like our own Solar system, comets dominate the mass
+budget of bodies that are ejected into interstellar space, since they form
+further away and are less tightly bound. However 1I/`Oumuamua, the first
+interstellar object detected, appears asteroidal in its spectra and in its lack
+of detectable activity. We argue that the galactic budget of interstellar
+objects like 1I/`Oumuamua should be dominated by planetesimal material ejected
+during planet formation in circumbinary systems, rather than in single star
+systems or widely separated binaries. We further show that in circumbinary
+systems, rocky bodies should be ejected in comparable numbers to icy ones. This
+suggests that a substantial fraction of additional interstellar objects
+discovered in the future should display an active coma. We find that the rocky
+population, of which 1I/`Oumuamua seems to be a member, should be predominantly
+sourced from A-type and late B-star binaries.
+",0,1,0,0,0,0
+11821,Two-photon excitation of rubidium atoms inside porous glass,"  We study the two-photon laser excitation to the 5D$_{5/2}$ energy level of
+$^{85}$Rb atoms contained in the interstices of a porous material made from
+sintered ground glass with typical pore dimensions in the 10 - 100 $\mu$m
+range. The excitation spectra show unusual flat-top lineshapes which are shown
+to be the consequence of wave-vector randomization of the laser light in the
+porous material. For large atomic densities, the spectra are affected by
+radiation trapping around the D2 transitions. The effect of the transient
+atomic response limited by time of flight between pores walls appears to have a
+minor influence in the excitation spectra. It is however revealed by the
+shortening of the temporal evolution of the emitted blue light following a
+sudden switch-off of the laser excitation.
+",0,1,0,0,0,0
+11822,Readout of the atomtronic quantum interference device,"  A Bose-Einstein condensate confined in ring shaped lattices interrupted by a
+weak link and pierced by an effective magnetic flux defines the atomic
+counterpart of the superconducting quantum interference device: the atomtronic
+quantum interference device (AQUID). In this paper, we report on the detection
+of current states in the system through a self-heterodyne protocol. Following
+the original proposal of the NIST and Paris groups, the ring-condensate
+many-body wave function interferes with a reference condensate expanding from
+the center of the ring. We focus on the rf-AQUID which realizes effective qubit
+dynamics. Both the Bose-Hubbard and Gross-Pitaevskii dynamics are studied. For
+the Bose-Hubbard dynamics, we demonstrate that the self-heterodyne protocol can
+be applied, but higher-order correlations in the evolution of the interfering
+condensates are measured to readout of the current states of the system. We
+study how states with macroscopic quantum coherence can be told apart analyzing
+the noise in the time of flight of the ring condensate.
+",0,1,0,0,0,0
+11823,Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy,"  Deep learning networks have achieved state-of-the-art accuracies on computer
+vision workloads like image classification and object detection. The performant
+systems, however, typically involve big models with numerous parameters. Once
+trained, a challenging aspect for such top performing models is deployment on
+resource constrained inference systems - the models (often deep networks or
+wide networks or both) are compute and memory intensive. Low-precision numerics
+and model compression using knowledge distillation are popular techniques to
+lower both the compute requirements and memory footprint of these deployed
+models. In this paper, we study the combination of these two techniques and
+show that the performance of low-precision networks can be significantly
+improved by using knowledge distillation techniques. Our approach, Apprentice,
+achieves state-of-the-art accuracies using ternary precision and 4-bit
+precision for variants of ResNet architecture on ImageNet dataset. We present
+three schemes using which one can apply knowledge distillation techniques to
+various stages of the train-and-deploy pipeline.
+",1,0,0,0,0,0
+11824,An IDE-Based Context-Aware Meta Search Engine,"  Traditional web search forces the developers to leave their working
+environments and look for solutions in the web browsers. It often does not
+consider the context of their programming problems. The context-switching
+between the web browser and the working environment is time-consuming and
+distracting, and the keyword-based traditional search often does not help much
+in problem solving. In this paper, we propose an Eclipse IDE-based web search
+solution that collects the data from three web search APIs-- Google, Yahoo,
+Bing and a programming Q & A site-- Stack Overflow. It then provides search
+results within IDE taking not only the content of the selected error into
+account but also the problem context, popularity and search engine
+recommendation of the result links. Experiments with 25 run time errors and
+exceptions show that the proposed approach outperforms the keyword-based search
+approaches with a recommendation accuracy of 96%. We also validate the results
+with a user study involving five prospective participants where we get a result
+agreement of 64.28%. While the preliminary results are promising, the approach
+needs to be further validated with more errors and exceptions followed by a
+user study with more participants to establish itself as a complete IDE-based
+web search solution.
+",1,0,0,0,0,0
+11825,Sharing Data Homomorphically Encrypted with Different Encryption Keys,"  In this paper, we propose the first homomorphic based proxy re-encryption
+(HPRE) solution that allows different users to share data they outsourced
+homomorphically encrypted using their respective public keys with the
+possibility to process such data remotely. More clearly, this scheme makes
+possible to switch the public encryption key to another one without the help of
+a trusted third party. Its originality stands on a method we propose so as to
+compute the difference between two encrypted data without decrypting them and
+with no extra communications. Basically, in our HPRE scheme, the two users, the
+delegator and the delegate, ask the cloud server to generate an encrypted noise
+based on a secret key, both users previously agreed on. Based on our solution
+for comparing encrypted data, the cloud computes in clear the differences
+in-between the encrypted noise and the encrypted data of the delegator,
+obtaining thus blinded data. By next the cloud encrypts these differences with
+the public key of the delegate. As the noise is also known of the delegate,
+this one just has to remove it to get access to the data encrypted with his
+public key. This solution has been experimented in the case of the sharing of
+images outsourced into a semihonest cloud server.
+",1,0,0,0,0,0
+11826,Low temperature features in the heat capacity of unary metals and intermetallics for the example of bulk aluminum and Al$_3$Sc,"  We explore the competition and coupling of vibrational and electronic
+contributions to the heat capacity of Al and Al$_3$Sc at temperatures below 50
+K combining experimental calorimetry with highly converged finite temperature
+density functional theory calculations. We find that semilocal exchange
+correlation functionals accurately describe the rich feature set observed for
+these temperatures, including electron-phonon coupling. Using different
+representations of the heat capacity, we are therefore able to identify and
+explain deviations from the Debye behaviour in the low-temperature limit and in
+the temperature regime 30 - 50 K as well as the reduction of these features due
+to the addition of Sc.
+",0,1,0,0,0,0
+11827,Designing spin and orbital exchange Hamiltonians with ultrashort electric field transients,"  We demonstrate how electric fields with arbitrary time profile can be used to
+control the time-dependent parameters of spin and orbital exchange
+Hamiltonians. Analytic expressions for the exchange constants are derived from
+a time-dependent Schrieffer-Wolff transformation, and the validity of the
+resulting effective Hamiltonian is verified for the case of a quarter-filled
+two-orbital Hubbard model, by comparing to the results of a full nonequilibrium
+dynamical mean-field theory simulation. The ability to manipulate Hamiltonians
+with arbitrary time-dependent fields, beyond the paradigm of Floquet
+engineering, opens the possibility to control intertwined spin and orbital
+order using laser or THz pulses which are tailored to minimize electronic
+excitations.
+",0,1,0,0,0,0
+11828,A Novel Comprehensive Approach for Estimating Concept Semantic Similarity in WordNet,"  Computation of semantic similarity between concepts is an important
+foundation for many research works. This paper focuses on IC computing methods
+and IC measures, which estimate the semantic similarities between concepts by
+exploiting the topological parameters of the taxonomy. Based on analyzing
+representative IC computing methods and typical semantic similarity measures,
+we propose a new hybrid IC computing method. Through adopting the parameter
+dhyp and lch, we utilize the new IC computing method and propose a novel
+comprehensive measure of semantic similarity between concepts. An experiment
+based on WordNet ""is a"" taxonomy has been designed to test representative
+measures and our measure on benchmark dataset R&G, and the results show that
+our measure can obviously improve the similarity accuracy. We evaluate the
+proposed approach by comparing the correlation coefficients between five
+measures and the artificial data. The results show that our proposal
+outperforms the previous measures.
+",1,0,0,0,0,0
+11829,Tannakian duality for affine homogeneous spaces,"  Associated to any closed quantum subgroup $G\subset U_N^+$ and any index set
+$I\subset\{1,\ldots,N\}$ is a certain homogeneous space $X_{G,I}\subset
+S^{N-1}_{\mathbb C,+}$, called affine homogeneous space. We discuss here the
+abstract axiomatization of the algebraic manifolds $X\subset S^{N-1}_{\mathbb
+C,+}$ which can appear in this way, by using Tannakian duality methods.
+",0,0,1,0,0,0
+11830,Strong comparison principle for the fractional $p$-Laplacian and applications to starshaped rings,"  In the following we show the strong comparison principle for the fractional
+$p$-Laplacian, i.e. we analyze functions $v,w$ which satisfy $v\geq w$ in
+$\mathbb{R}^N$ and
+\[
+(-\Delta)^s_pv+q(x)|v|^{p-2}v\geq (-\Delta)^s_pw+q(x)|w|^{p-2}w \quad
+\text{in $D$,}
+\] where $s\in(0,1)$, $p>1$, $D\subset \mathbb{R}^N$ is an open set, and
+$q\in L^{\infty}(\mathbb{R}^N)$ is a nonnegative function. Under suitable
+conditions on $s,p$ and some regularity assumptions on $v,w$ we show that
+either $v\equiv w$ in $\mathbb{R}^N$ or $v>w$ in $D$. Moreover, we apply this
+result to analyze the geometry of nonnegative solutions in starshaped rings and
+in the half space.
+",0,0,1,0,0,0
+11831,The uniformity and time-invariance of the intra-cluster metal distribution in galaxy clusters from the IllustrisTNG simulations,"  The distribution of metals in the intra-cluster medium encodes important
+information about the enrichment history and formation of galaxy clusters. Here
+we explore the metal content of clusters in IllustrisTNG - a new suite of
+galaxy formation simulations building on the Illustris project. Our cluster
+sample contains 20 objects in TNG100 - a ~(100 Mpc)^3 volume simulation with
+2x1820^3 resolution elements, and 370 objects in TNG300 - a ~(300 Mpc)^3 volume
+simulation with 2x2500^3 resolution elements. The z=0 metallicity profiles
+agree with observations, and the enrichment history is consistent with
+observational data going beyond z~1, showing nearly no metallicity evolution.
+The abundance profiles vary only minimally within the cluster samples,
+especially in the outskirts with a relative scatter of ~15%. The average
+metallicity profile flattens towards the center, where we find a logarithmic
+slope of -0.1 compared to -0.5 in the outskirts. Cool core clusters have more
+centrally peaked metallicity profiles (~0.8 solar) compared to non-cool core
+systems (~0.5 solar), similar to observational trends. Si/Fe and O/Fe radial
+profiles follow positive gradients. The outer abundance profiles do not evolve
+below z~2, whereas the inner profiles flatten towards z=0. More than ~80% of
+the metals in the intra-cluster medium have been accreted from the
+proto-cluster environment, which has been enriched to ~0.1 solar already at
+z~2. We conclude that the intra-cluster metal distribution is uniform among our
+cluster sample, nearly time-invariant in the outskirts for more than 10 Gyr,
+and forms through a universal enrichment history.
+",0,1,0,0,0,0
+11832,Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks,"  Click through rate (CTR) prediction is very important for Native
+advertisement but also hard as there is no direct query intent. In this paper
+we propose a large-scale event embedding scheme to encode the each user
+browsing event by training a Siamese network with weak supervision on the
+users' consecutive events. The CTR prediction problem is modeled as a
+supervised recurrent neural network, which naturally model the user history as
+a sequence of events. Our proposed recurrent models utilizing pretrained event
+embedding vectors and an attention layer to model the user history. Our
+experiments demonstrate that our model significantly outperforms the baseline
+and some variants.
+",0,0,0,1,0,0
+11833,On the Communication Cost of Determining an Approximate Nearest Lattice Point,"  We consider the closest lattice point problem in a distributed network
+setting and study the communication cost and the error probability for
+computing an approximate nearest lattice point, using the nearest-plane
+algorithm, due to Babai. Two distinct communication models, centralized and
+interactive, are considered. The importance of proper basis selection is
+addressed. Assuming a reduced basis for a two-dimensional lattice, we determine
+the approximation error of the nearest plane algorithm. The communication cost
+for determining the Babai point, or equivalently, for constructing the
+rectangular nearest-plane partition, is calculated in the interactive setting.
+For the centralized model, an algorithm is presented for reducing the
+communication cost of the nearest plane algorithm in an arbitrary number of
+dimensions.
+",1,0,0,0,0,0
+11834,One shot entanglement assisted classical and quantum communication over noisy quantum channels: A hypothesis testing and convex split approach,"  Capacity of a quantum channel characterizes the limits of reliable
+communication through a noisy quantum channel. This fundamental information
+theoretic question is very well studied specially in the setting of many
+independent uses of the channel. An important scenario, both from practical and
+conceptual point of view, is when the channel can be used only once. This is
+known as the one-shot channel coding problem. We provide a tight
+characterization of the one-shot entanglement assisted classical capacity of a
+quantum channel. We arrive at our result by introducing a simple decoding
+technique which we refer to as position-based decoding. We also consider two
+other important quantum network scenarios: quantum channel with a jammer and
+quantum broadcast channel. For these problems, we use the recently introduced
+convex split technique [Anshu, Devabathini and Jain 2014] in addition to
+position based decoding. Our approach exhibits that the simultaneous use of
+these two techniques provides a uniform and conceptually simple framework for
+designing communication protocols for quantum networks.
+",1,0,0,0,0,0
+11835,Conformally variational Riemannian invariants,"  Conformally variational Riemannian invariants (CVIs), such as the scalar
+curvature, are homogeneous scalar invariants which arise as the gradient of a
+Riemannian functional. We establish a wide range of stability and rigidity
+results involving CVIs, generalizing many such results for the scalar
+curvature.
+",0,0,1,0,0,0
+11836,Caching Meets Millimeter Wave Communications for Enhanced Mobility Management in 5G Networks,"  One of the most promising approaches to overcome the uncertainty and dynamic
+channel variations of millimeter wave (mmW) communications is to deploy
+dual-mode base stations that integrate both mmW and microwave ($\mu$W)
+frequencies. If properly designed, such dual-mode base stations can enhance
+mobility and handover in highly mobile wireless environments. In this paper, a
+novel approach for analyzing and managing mobility in joint $\mu$W-mmW networks
+is proposed. The proposed approach leverages device-level caching along with
+the capabilities of dual-mode base stations to minimize handover failures,
+reduce inter-frequency measurement energy consumption, and provide seamless
+mobility in emerging dense heterogeneous networks. First, fundamental results
+on the caching capabilities, including caching probability and cache duration
+are derived for the proposed dual-mode network scenario. Second, the average
+achievable rate of caching is derived for mobile users. Third, the proposed
+cache-enabled mobility management problem is formulated as a dynamic matching
+game between mobile user equipments (MUEs) and small base stations (SBSs). The
+goal of this game is to find a distributed handover mechanism that subject to
+the network constraints on HOFs and limited cache sizes, allows each MUE to
+choose between executing an HO to a target SBS, being connected to the
+macrocell base station (MBS), or perform a transparent HO by using the cached
+content. The formulated matching game allows capturing the dynamics of the
+mobility management problem caused by HOFs. To solve this dynamic matching
+problem, a novel algorithm is proposed and its convergence to a two-sided
+dynamically stable HO policy is proved. Numerical results corroborate the
+analytical derivations and show that the proposed solution will provides
+significant reductions in both the HOF and energy consumption by MUEs.
+",1,0,0,0,0,0
+11837,Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks,"  Data poisoning is an attack on machine learning models wherein the attacker
+adds examples to the training set to manipulate the behavior of the model at
+test time. This paper explores poisoning attacks on neural nets. The proposed
+attacks use ""clean-labels""; they don't require the attacker to have any control
+over the labeling of training data. They are also targeted; they control the
+behavior of the classifier on a $\textit{specific}$ test instance without
+degrading overall classifier performance. For example, an attacker could add a
+seemingly innocuous image (that is properly labeled) to a training set for a
+face recognition engine, and control the identity of a chosen person at test
+time. Because the attacker does not need to control the labeling function,
+poisons could be entered into the training set simply by leaving them on the
+web and waiting for them to be scraped by a data collection bot.
+We present an optimization-based method for crafting poisons, and show that
+just one single poison image can control classifier behavior when transfer
+learning is used. For full end-to-end training, we present a ""watermarking""
+strategy that makes poisoning reliable using multiple ($\approx$50) poisoned
+training instances. We demonstrate our method by generating poisoned frog
+images from the CIFAR dataset and using them to manipulate image classifiers.
+",0,0,0,1,0,0
+11838,Sorting Phenomena in a Mathematical Model For Two Mutually Attracting/Repelling Species,"  Macroscopic models for systems involving diffusion, short-range repulsion,
+and long-range attraction have been studied extensively in the last decades. In
+this paper we extend the analysis to a system for two species interacting with
+each other according to different inner- and intra-species attractions. Under
+suitable conditions on this self- and crosswise attraction an interesting
+effect can be observed, namely phase separation into neighbouring regions, each
+of which contains only one of the species. We prove that the intersection of
+the support of the stationary solutions of the continuum model for the two
+species has zero Lebesgue measure, while the support of the sum of the two
+densities is simply connected.
+Preliminary results indicate the existence of phase separation, i.e. spatial
+sorting of the different species. A detailed analysis in one spatial dimension
+follows. The existence and shape of segregated stationary solutions is shown
+via the Krein-Rutman theorem. Moreover, for small repulsion/nonlinear
+diffusion, also uniqueness of these stationary states is proved.
+",0,0,1,0,0,0
+11839,Ab initio design of drug carriers for zoledronate guest molecule using phosphonated and sulfonated calix[4]arene and calix[4]resorcinarene host molecules,"  Monomolecular drug carriers based on calix[n]-arenes and -resorcinarenes
+containing the interior cavity can enhance the affinity and specificity of the
+osteoporosis inhibitor drug zoledronate (ZOD). In this work we investigate the
+suitability of nine different calix[4]-arenes and -resorcinarenes based
+macrocycles as hosts for the ZOD guest molecule by conducting {\it ab initio}
+density functional theory calculations for structures and energetics of
+eighteen different host-guest complexes. For the optimized molecular structures
+of the free, phosphonated, sulfonated calix[4]-arenes and -resorcinarenes, the
+geometric sizes of their interior cavities are measured and compared with those
+of the host-guest complexes in order to check the appropriateness for
+host-guest complex formation. Our calculations of binding energies indicate
+that in gaseous states some of the complexes might be unstable but in aqueous
+states almost all of the complexes can be formed spontaneously. Of the two
+different docking ways, the insertion of ZOD with the \ce{P-C-P} branch into
+the cavity of host is easier than that with the nitrogen containing heterocycle
+of ZOD. The work will open a way for developing effective drug delivering
+systems for the ZOD drug and promote experimentalists to synthesize them.
+",0,1,0,0,0,0
+11840,Skeleton-based Action Recognition of People Handling Objects,"  In visual surveillance systems, it is necessary to recognize the behavior of
+people handling objects such as a phone, a cup, or a plastic bag. In this
+paper, to address this problem, we propose a new framework for recognizing
+object-related human actions by graph convolutional networks using human and
+object poses. In this framework, we construct skeletal graphs of reliable human
+poses by selectively sampling the informative frames in a video, which include
+human joints with high confidence scores obtained in pose estimation. The
+skeletal graphs generated from the sampled frames represent human poses related
+to the object position in both the spatial and temporal domains, and these
+graphs are used as inputs to the graph convolutional networks. Through
+experiments over an open benchmark and our own data sets, we verify the
+validity of our framework in that our method outperforms the state-of-the-art
+method for skeleton-based action recognition.
+",1,0,0,0,0,0
+11841,Active classification with comparison queries,"  We study an extension of active learning in which the learning algorithm may
+ask the annotator to compare the distances of two examples from the boundary of
+their label-class. For example, in a recommendation system application (say for
+restaurants), the annotator may be asked whether she liked or disliked a
+specific restaurant (a label query); or which one of two restaurants did she
+like more (a comparison query).
+We focus on the class of half spaces, and show that under natural
+assumptions, such as large margin or bounded bit-description of the input
+examples, it is possible to reveal all the labels of a sample of size $n$ using
+approximately $O(\log n)$ queries. This implies an exponential improvement over
+classical active learning, where only label queries are allowed. We complement
+these results by showing that if any of these assumptions is removed then, in
+the worst case, $\Omega(n)$ queries are required.
+Our results follow from a new general framework of active learning with
+additional queries. We identify a combinatorial dimension, called the
+\emph{inference dimension}, that captures the query complexity when each
+additional query is determined by $O(1)$ examples (such as comparison queries,
+each of which is determined by the two compared examples). Our results for half
+spaces follow by bounding the inference dimension in the cases discussed above.
+",1,0,0,0,0,0
+11842,Behavior of digital sequences through exotic numeration systems,"  Many digital functions studied in the literature, e.g., the summatory
+function of the base-$k$ sum-of-digits function, have a behavior showing some
+periodic fluctuation. Such functions are usually studied using techniques from
+analytic number theory or linear algebra. In this paper we develop a method
+based on exotic numeration systems and we apply it on two examples motivated by
+the study of generalized Pascal triangles and binomial coefficients of words.
+",1,0,0,0,0,0
+11843,Quantum cognition goes beyond-quantum: modeling the collective participant in psychological measurements,"  In psychological measurements, two levels should be distinguished: the
+'individual level', relative to the different participants in a given cognitive
+situation, and the 'collective level', relative to the overall statistics of
+their outcomes, which we propose to associate with a notion of 'collective
+participant'. When the distinction between these two levels is properly
+formalized, it reveals why the modeling of the collective participant generally
+requires beyond-quantum - non-Bornian - probabilistic models, when sequential
+measurements at the individual level are considered, and this though a pure
+quantum description remains valid for single measurement situations.
+",0,0,0,0,1,0
+11844,Geometry of Projective Perfectoid and Integer Partitions,"  Line bundles of rational degree are defined using Perfectoid spaces, and
+their co-homology computed via standard Čech complex along with Kunneth
+formula. A new concept of `braided dimension' is introduced, which helps
+convert the curse of infinite dimensionality into a boon, which is then used to
+do Bezout type computations, define euler characters, describe ampleness and
+link integer partitions with geometry. This new concept of 'Braided dimension'
+gives a space within a space within a space an infinite tower of spaces, all
+intricately braided into each other. Finally, the concept of Blow Up over
+perfectoid space is introduced.
+",0,0,1,0,0,0
+11845,Determinantal Generalizations of Instrumental Variables,"  Linear structural equation models relate the components of a random vector
+using linear interdependencies and Gaussian noise. Each such model can be
+naturally associated with a mixed graph whose vertices correspond to the
+components of the random vector. The graph contains directed edges that
+represent the linear relationships between components, and bidirected edges
+that encode unobserved confounding. We study the problem of generic
+identifiability, that is, whether a generic choice of linear and confounding
+effects can be uniquely recovered from the joint covariance matrix of the
+observed random vector. An existing combinatorial criterion for establishing
+generic identifiability is the half-trek criterion (HTC), which uses the
+existence of trek systems in the mixed graph to iteratively discover
+generically invertible linear equation systems in polynomial time. By focusing
+on edges one at a time, we establish new sufficient and necessary conditions
+for generic identifiability of edge effects extending those of the HTC. In
+particular, we show how edge coefficients can be recovered as quotients of
+subdeterminants of the covariance matrix, which constitutes a determinantal
+generalization of formulas obtained when using instrumental variables for
+identification.
+",0,0,1,1,0,0
+11846,Computational insights and the observation of SiC nanograin assembly: towards 2D silicon carbide,"  While an increasing number of two-dimensional (2D) materials, including
+graphene and silicene, have already been realized, others have only been
+predicted. An interesting example is the two-dimensional form of silicon
+carbide (2D-SiC). Here, we present an observation of atomically thin and
+hexagonally bonded nanosized grains of SiC assembling temporarily in graphene
+oxide pores during an atomic resolution scanning transmission electron
+microscopy experiment. Even though these small grains do not fully represent
+the bulk crystal, simulations indicate that their electronic structure already
+approaches that of 2D-SiC. This is predicted to be flat, but some doubts have
+remained regarding the preference of Si for sp$^{3}$ hybridization. Exploring a
+number of corrugated morphologies, we find completely flat 2D-SiC to have the
+lowest energy. We further compute its phonon dispersion, with a Raman-active
+transverse optical mode, and estimate the core level binding energies. Finally,
+we study the chemical reactivity of 2D-SiC, suggesting it is like silicene
+unstable against molecular absorption or interlayer linking. Nonetheless, it
+can form stable van der Waals-bonded bilayers with either graphene or hexagonal
+boron nitride, promising to further enrich the family of two-dimensional
+materials once bulk synthesis is achieved.
+",0,1,0,0,0,0
+11847,Non-Homogeneous Hydrodynamic Systems and Quasi-Stäckel Hamiltonians,"  In this paper we present a novel construction of non-homogeneous hydrodynamic
+equations from what we call quasi-Stäckel systems, that is non-commutatively
+integrable systems constructed from appropriate maximally superintegrable
+Stäckel systems. We describe the relations between Poisson algebras generated
+by quasi-Stäckel Hamiltonians and the corresponding Lie algebras of vector
+fields of non-homogeneous hydrodynamic systems. We also apply Stäckel
+transform to obtain new non-homogeneous equations of considered type.
+",0,1,0,0,0,0
+11848,Latest results of the Tunka Radio Extension (ISVHECRI2016),"  The Tunka Radio Extension (Tunka-Rex) is an antenna array consisting of 63
+antennas at the location of the TAIGA facility (Tunka Advanced Instrument for
+cosmic ray physics and Gamma Astronomy) in Eastern Siberia, nearby Lake Baikal.
+Tunka-Rex is triggered by the air-Cherenkov array Tunka-133 during clear and
+moonless winter nights and by the scintillator array Tunka-Grande during the
+remaining time. Tunka-Rex measures the radio emission from the same air-showers
+as Tunka-133 and Tunka-Grande, but with a higher threshold of about 100 PeV.
+During the first stages of its operation, Tunka-Rex has proven, that sparse
+radio arrays can measure air-showers with an energy resolution of better than
+15\% and the depth of the shower maximum with a resolution of better than 40
+g/cm\textsuperscript{2}. To improve and interpret our measurements as well as
+to study systematic uncertainties due to interaction models, we perform radio
+simulations with CORSIKA and CoREAS. In this overview we present the setup of
+Tunka-Rex, discuss the achieved results and the prospects of mass-composition
+studies with radio arrays.
+",0,1,0,0,0,0
+11849,The Rabi frequency on the $H^3Δ_1$ to $C^1Π$ transition in ThO: influence of interaction with electric and magnetic fields,"  Calculations of the correlations between the Rabi frequency on the
+$H^3\Delta_1$ to $C^1\Pi$ transition in ThO molecule and experimental setup
+parameters in the electron electric dipole moment (eEDM) search experiment is
+performed. Calculations are required for estimations of systematic errors in
+the experiment due to imperfections in laser beams used to prepare the molecule
+and read out the eEDM signal.
+",0,1,0,0,0,0
+11850,Deriving mesoscopic models of collective behaviour for finite populations,"  Animal groups exhibit emergent properties that are a consequence of local
+interactions. Linking individual-level behaviour to coarse-grained descriptions
+of animal groups has been a question of fundamental interest. Here, we present
+two complementary approaches to deriving coarse-grained descriptions of
+collective behaviour at so-called mesoscopic scales, which account for the
+stochasticity arising from the finite sizes of animal groups. We construct
+stochastic differential equations (SDEs) for a coarse-grained variable that
+describes the order/consensus within a group. The first method of construction
+is based on van Kampen's system-size expansion of transition rates. The second
+method employs Gillespie's chemical Langevin equations. We apply these two
+methods to two microscopic models from the literature, in which organisms
+stochastically interact and choose between two directions/choices of foraging.
+These `binary-choice' models differ only in the types of interactions between
+individuals, with one assuming simple pair-wise interactions, and the other
+incorporating higher-order effects. In both cases, the derived mesoscopic SDEs
+have multiplicative, or state-dependent, noise. However, the different models
+demonstrate the contrasting effects of noise: increasing order in the pair-wise
+interaction model, whilst reducing order in the higher-order interaction model.
+Although both methods yield identical SDEs for such binary-choice, or
+one-dimensional, systems, the relative tractability of the chemical Langevin
+approach is beneficial in generalizations to higher-dimensions. In summary,
+this book chapter provides a pedagogical review of two complementary methods to
+construct mesoscopic descriptions from microscopic rules and demonstrates how
+resultant multiplicative noise can have counter-intuitive effects on shaping
+collective behaviour.
+",0,0,0,0,1,0
+11851,First Indirect X-Ray Imaging Tests With An 88-mm Diameter Single Crystal,"  Using the 1-BM-C beamline at the Advanced Photon Source (APS), we have
+performed the initial indirect x-ray imaging point-spread-function (PSF) test
+of a unique 88-mm diameter YAG:Ce single crystal of only 100-micron thickness.
+The crystal was bonded to a fiber optic plate (FOP) for mechanical support and
+to allow the option for FO coupling to a large format camera. This
+configuration resolution was compared to that of self-supported 25-mm diameter
+crystals, with and without an Al reflective coating. An upstream monochromator
+was used to select 17-keV x-rays from the broadband APS bending magnet source
+of synchrotron radiation. The upstream, adjustable Mo collimators were then
+used to provide a series of x-ray source transverse sizes from 200 microns down
+to about 15-20 microns (FWHM) at the crystal surface. The emitted scintillator
+radiation was in this case lens coupled to the ANDOR Neo sCMOS camera, and the
+indirect x-ray images were processed offline by a MATLAB-based image processing
+program. Based on single Gaussian peak fits to the x-ray image projected
+profiles, we observed a 10.5 micron PSF. This sample thus exhibited superior
+spatial resolution to standard P43 polycrystalline phosphors of the same
+thickness which would have about a 100-micron PSF. This single crystal
+resolution combined with the 88-mm diameter makes it a candidate to support
+future x-ray diffraction or wafer topography experiments.
+",0,1,0,0,0,0
+11852,A randomized Halton algorithm in R,"  Randomized quasi-Monte Carlo (RQMC) sampling can bring orders of magnitude
+reduction in variance compared to plain Monte Carlo (MC) sampling. The extent
+of the efficiency gain varies from problem to problem and can be hard to
+predict. This article presents an R function rhalton that produces scrambled
+versions of Halton sequences. On some problems it brings efficiency gains of
+several thousand fold. On other problems, the efficiency gain is minor. The
+code is designed to make it easy to determine whether a given integrand will
+benefit from RQMC sampling. An RQMC sample of n points in $[0,1]^d$ can be
+extended later to a larger n and/or d.
+",1,0,0,1,0,0
+11853,Leveraging Node Attributes for Incomplete Relational Data,"  Relational data are usually highly incomplete in practice, which inspires us
+to leverage side information to improve the performance of community detection
+and link prediction. This paper presents a Bayesian probabilistic approach that
+incorporates various kinds of node attributes encoded in binary form in
+relational models with Poisson likelihood. Our method works flexibly with both
+directed and undirected relational networks. The inference can be done by
+efficient Gibbs sampling which leverages sparsity of both networks and node
+attributes. Extensive experiments show that our models achieve the
+state-of-the-art link prediction results, especially with highly incomplete
+relational data.
+",1,0,0,1,0,0
+11854,pMR: A high-performance communication library,"  On many parallel machines, the time LQCD applications spent in communication
+is a significant contribution to the total wall-clock time, especially in the
+strong-scaling limit. We present a novel high-performance communication library
+that can be used as a de facto drop-in replacement for MPI in existing
+software. Its lightweight nature that avoids some of the unnecessary overhead
+introduced by MPI allows us to improve the communication performance of
+applications without any algorithmic or complicated implementation changes. As
+a first real-world benchmark, we make use of the pMR library in the coarse-grid
+solve of the Regensburg implementation of the DD-$\alpha$AMG algorithm. On
+realistic lattices, we see an improvement of a factor 2x in pure communication
+time and total execution time savings of up to 20%.
+",1,1,0,0,0,0
+11855,Extending a Function Just by Multiplying and Dividing Function Values: Smoothness and Prime Identities,"  We describe a purely-multiplicative method for extending an analytic
+function. It calculates the value of an analytic function at a point, merely by
+multiplying together function values and reciprocals of function values at
+other points closer to the origin. The function values are taken at the points
+of geometric sequences, independent of the function, whose geometric ratios are
+arbitrary. The method exposes an ""elastic invariance"" property of all analytic
+functions. We show how to simplify and truncate multiplicative function
+extensions for practical calculations. If we choose each geometric ratio to be
+the reciprocal of a power of a prime number, we obtain a prime functional
+identity, which contains a generalization of the Möbius function (with the
+same denominator as the Rieman zeta function), and generates prime number
+identities.
+",0,0,1,0,0,0
+11856,Towards Modeling the Interaction of Spatial-Associative Neural Network Representations for Multisensory Perception,"  Our daily perceptual experience is driven by different neural mechanisms that
+yield multisensory interaction as the interplay between exogenous stimuli and
+endogenous expectations. While the interaction of multisensory cues according
+to their spatiotemporal properties and the formation of multisensory
+feature-based representations have been widely studied, the interaction of
+spatial-associative neural representations has received considerably less
+attention. In this paper, we propose a neural network architecture that models
+the interaction of spatial-associative representations to perform causal
+inference of audiovisual stimuli. We investigate the spatial alignment of
+exogenous audiovisual stimuli modulated by associative congruence. In the
+spatial layer, topographically arranged networks account for the interaction of
+audiovisual input in terms of population codes. In the associative layer,
+congruent audiovisual representations are obtained via the experience-driven
+development of feature-based associations. Levels of congruency are obtained as
+a by-product of the neurodynamics of self-organizing networks, where the amount
+of neural activation triggered by the input can be expressed via a nonlinear
+distance function. Our novel proposal is that activity-driven levels of
+congruency can be used as top-down modulatory projections to spatially
+distributed representations of sensory input, e.g. semantically related
+audiovisual pairs will yield a higher level of integration than unrelated
+pairs. Furthermore, levels of neural response in unimodal layers may be seen as
+sensory reliability for the dynamic weighting of crossmodal cues. We describe a
+series of planned experiments to validate our model in the tasks of
+multisensory interaction on the basis of semantic congruence and unimodal cue
+reliability.
+",0,0,0,0,1,0
+11857,Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics,"  To reduce data collection time for deep learning of robust robotic grasp
+plans, we explore training from a synthetic dataset of 6.7 million point
+clouds, grasps, and analytic grasp metrics generated from thousands of 3D
+models from Dex-Net 1.0 in randomized poses on a table. We use the resulting
+dataset, Dex-Net 2.0, to train a Grasp Quality Convolutional Neural Network
+(GQ-CNN) model that rapidly predicts the probability of success of grasps from
+depth images, where grasps are specified as the planar position, angle, and
+depth of a gripper relative to an RGB-D sensor. Experiments with over 1,000
+trials on an ABB YuMi comparing grasp planning methods on singulated objects
+suggest that a GQ-CNN trained with only synthetic data from Dex-Net 2.0 can be
+used to plan grasps in 0.8sec with a success rate of 93% on eight known objects
+with adversarial geometry and is 3x faster than registering point clouds to a
+precomputed dataset of objects and indexing grasps. The Dex-Net 2.0 grasp
+planner also has the highest success rate on a dataset of 10 novel rigid
+objects and achieves 99% precision (one false positive out of 69 grasps
+classified as robust) on a dataset of 40 novel household objects, some of which
+are articulated or deformable. Code, datasets, videos, and supplementary
+material are available at this http URL .
+",1,0,0,0,0,0
+11858,Effects of temperature and strain rate on mechanical behaviors of Stone-Wales defective monolayer black phosphorene,"  The mechanical behaviors of monolayer black phosphorene (MBP) are explored by
+molecular dynamics (MD) simulations using reactive force field. It is revealed
+that the temperature and strain rate have significant influence on mechanical
+behaviors of MBP, and they are further weakened by SW (Stone-Wales) defects. In
+general, the tensile strength for both of the pristine and SW defective MBP
+decreases with the increase of temperature or decreasing of strain rate.
+Surprisingly, for relatively high temperature and low strain rate, phase
+transition from the black phosphorene to a mixture of {\beta}-phase ({\beta}-P)
+and {\gamma}-phase ({\gamma}-P) is observed for the SW-2 defective MBP under
+armchair tension, while self-healing of the SW-2 defect is observed under
+zigzag tension. A deformation map of SW-2 defective MBP under armchair tension
+at different temperature and strain rate is established, which is useful for
+the design of phosphorene allotropes by strain. The results presented herein
+yield useful insights for designing and tuning the structure, and the
+mechanical and physical properties of phosphorene.
+",0,1,0,0,0,0
+11859,Ward identities for charge and heat currents of particle-particle and particle-hole pairs,"  The Ward identities for the charge and heat currents are derived for
+particle-particle and particle-hole pairs. They are the exact constraints on
+the current-vertex functions imposed by conservation laws and should be
+satisfied by consistent theories. While the Ward identity for the charge
+current of electrons is well established, that for the heat current is not
+understood correctly. Thus the correct interpretation is presented. On this
+firm basis the Ward identities for pairs are discussed. As the application of
+the identity we criticize some inconsistent results in the studies of the
+superconducting fluctuation transport and the transport anomaly in the normal
+state of high-Tc superconductors.
+",0,1,0,0,0,0
+11860,Ultrafast imprinting of topologically protected magnetic textures via pulsed electrons,"  Short electron pulses are demonstrated to trigger and control magnetic
+excitations, even at low electron current densities.
+We show that the tangential magnetic field surrounding a picosecond electron
+pulse can imprint topologically protected magnetic textures such as skyrmions
+in a sample with a residual Dzyaloshinskii-Moriya spin-orbital coupling.
+Characteristics of the created excitations such as the topological charge can
+be steered via the duration and the strength of the electron pulses. The study
+points to a possible way for a spatio-temporally controlled generation of
+skyrmionic excitations.
+",0,1,0,0,0,0
+11861,Recurrent Environment Simulators,"  Models that can simulate how environments change in response to actions can
+be used by agents to plan and act efficiently. We improve on previous
+environment simulators from high-dimensional pixel observations by introducing
+recurrent neural networks that are able to make temporally and spatially
+coherent predictions for hundreds of time-steps into the future. We present an
+in-depth analysis of the factors affecting performance, providing the most
+extensive attempt to advance the understanding of the properties of these
+models. We address the issue of computationally inefficiency with a model that
+does not need to generate a high-dimensional image at each time-step. We show
+that our approach can be used to improve exploration and is adaptable to many
+diverse environments, namely 10 Atari games, a 3D car racing environment, and
+complex 3D mazes.
+",1,0,0,1,0,0
+11862,Exceeding the Shockley-Queisser limit within the detailed balance framework,"  The Shockley-Queisser limit is one of the most fundamental results in the
+field of photovoltaics. Based on the principle of detailed balance, it defines
+an upper limit for a single junction solar cell that uses an absorber material
+with a specific band gap. Although methods exist that allow a solar cell to
+exceed the Shockley-Queisser limit, here we show that it is possible to exceed
+the Shockley-Queisser limit without considering any of these additions. Merely
+by introducing an absorptivity that does not assume that every photon with an
+energy above the band gap is absorbed, efficiencies above the Shockley-Queisser
+limit are obtained. This is related to the fact that assuming optimal
+absorption properties also maximizes the recombination current within the
+detailed balance approach. We conclude that considering a finite thickness for
+the absorber layer allows the efficiency to exceed the Shockley-Queisser limit,
+and that this is more likely to occur for materials with small band gaps.
+",0,1,0,0,0,0
+11863,Effect of disorder on the optical response of NiPt and Ni$_3$Pt alloys,"  In this communication we present a detailed study of the effect of chemical
+disorder on the optical response of Ni$_{1-x}$Pt$_x$ (0.1$\leq$ x $\leq$0.75)
+and Ni$_{3(1-x)/3}$Pt$_x$ (0.1$\leq$ x $\leq$0.3). We shall propose a formalism
+which will combine a Kubo-Greenwood approach with a DFT based tight-binding
+linear muffin-tin orbitals (TB-LMTO) basis and augmented space recursion (ASR)
+technique to explicitly incorporate the effect of disorder. We show that
+chemical disorder has a large impact on optical response of Ni-Pt systems. In
+ordered Ni-Pt alloys, the optical conductivity peaks are sharp. But as we
+switch on chemical disorder, the UV peak becomes broadened and its position as
+a function of composition and disorder carries the signature of a phase
+transition from NiPt to Ni$_3$Pt with decreasing Pt concentration.
+Quantitatively this agrees well with Massalski's Ni-Pt phase diagram
+\cite{massal}. Both ordered NiPt and Ni$_3$Pt have an optical conductivity
+transition at 4.12 eV. But disordered NiPt has an optical conductivity
+transition at 3.93 eV. If we decrease the Pt content, it results a chemical
+phase transition from NiPt to Ni$_3$Pt and shifts the peak position by 1.67 eV
+to the ultraviolet range at 5.6 eV. There is a significant broadening of UV
+peak with increasing Pt content due to enhancement of 3d(Ni)-5d(Pt) bonding.
+Chemical disorder enhances the optical response of NiPt alloys nearly one order
+of magnitude. Our study also shows the fragile magnetic effect on optical
+response of disordered Ni$_{1-x}$Pt$_x$ (0.4$<$ x $<$0.6) binary alloys. Our
+theoretical predictions agree more than reasonably well with both earlier
+experimental as well as theoretical investigations.
+",0,1,0,0,0,0
+11864,Train on Validation: Squeezing the Data Lemon,"  Model selection on validation data is an essential step in machine learning.
+While the mixing of data between training and validation is considered taboo,
+practitioners often violate it to increase performance. Here, we offer a
+simple, practical method for using the validation set for training, which
+allows for a continuous, controlled trade-off between performance and
+overfitting of model selection. We define the notion of
+on-average-validation-stable algorithms as one in which using small portions of
+validation data for training does not overfit the model selection process. We
+then prove that stable algorithms are also validation stable. Finally, we
+demonstrate our method on the MNIST and CIFAR-10 datasets using stable
+algorithms as well as state-of-the-art neural networks. Our results show
+significant increase in test performance with a minor trade-off in bias
+admitted to the model selection process.
+",0,0,0,1,0,0
+11865,Local migration quantification method for scratch assays,"  Motivation: The scratch assay is a standard experimental protocol used to
+characterize cell migration. It can be used to identify genes that regulate
+migration and evaluate the efficacy of potential drugs that inhibit cancer
+invasion. In these experiments, a scratch is made on a cell monolayer and
+recolonisation of the scratched region is imaged to quantify cell migration
+rates. A drawback of this methodology is the lack of its reproducibility
+resulting in irregular cell-free areas with crooked leading edges. Existing
+quantification methods deal poorly with such resulting irregularities present
+in the data. Results: We introduce a new quantification method that can analyse
+low quality experimental data. By considering in-silico and in-vitro data, we
+show that the method provides a more accurate statistical classification of the
+migration rates than two established quantification methods. The application of
+this method will enable the quantification of migration rates of scratch assay
+data previously unsuitable for analysis. Availability and Implementation: The
+source code and the implementation of the algorithm as a GUI along with an
+example dataset and user instructions, are available in
+this https URL.
+The datasets are available in
+this https URL.
+",0,0,0,0,1,0
+11866,Assessing the level of merging errors for coauthorship data: a Bayesian model,"  Robust analysis of coauthorship networks is based on high quality data.
+However, ground-truth data are usually unavailable. Empirical data suffer
+several types of errors, a typical one of which is called merging error,
+identifying different persons as one entity. Specific features of authors have
+been used to reduce these errors. We proposed a Bayesian model to calculate the
+information of any given features of authors. Based on the features, the model
+can be utilized to calculate the rate of merging errors for entities.
+Therefore, the model helps to find informative features for detecting heavily
+compromised entities. It has potential contributions to improving the quality
+of empirical data.
+",1,0,0,0,0,0
+11867,Blood-based metabolic signatures in Alzheimer's disease,"  Introduction: Identification of blood-based metabolic changes might provide
+early and easy-to-obtain biomarkers.
+Methods: We included 127 AD patients and 121 controls with
+CSF-biomarker-confirmed diagnosis (cut-off tau/A$\beta_{42}$: 0.52). Mass
+spectrometry platforms determined the concentrations of 53 amine, 22 organic
+acid, 120 lipid, and 40 oxidative stress compounds. Multiple signatures were
+assessed: differential expression (nested linear models), classification
+(logistic regression), and regulatory (network extraction).
+Results: Twenty-six metabolites were differentially expressed. Metabolites
+improved the classification performance of clinical variables from 74% to 79%.
+Network models identified 5 hubs of metabolic dysregulation: Tyrosine,
+glycylglycine, glutamine, lysophosphatic acid C18:2 and platelet activating
+factor C16:0. The metabolite network for APOE $\epsilon$4 negative AD patients
+was less cohesive compared to the network for APOE $\epsilon$4 positive AD
+patients.
+Discussion: Multiple signatures point to various promising peripheral markers
+for further validation. The network differences in AD patients according to
+APOE genotype may reflect different pathways to AD.
+",0,0,0,1,0,0
+11868,The Indecomposable Solutions of Linear Congruences,"  This article considers the minimal non-zero (= indecomposable) solutions of
+the linear congruence $1\cdot x_1 + \cdots + (m-1)\cdot x_{m-1} \equiv 0 \pmod
+m$ for unknown non-negative integers $x_1, \ldots, x_n$, and characterizes the
+solutions that attain the Eggleton-Erdős bound. Furthermore it discusses
+the asymptotic behaviour of the number of indecomposable solutions. The results
+have direct interpretations in terms of zero-sum sequences and invariant
+theory.
+",0,0,1,0,0,0
+11869,Weak multiplier Hopf algebras III. Integrals and duality,"  Let $(A,\Delta)$ be a weak multiplier Hopf algebra. It is a pair of a
+non-degenerate algebra $A$, with or without identity, and a coproduct $\Delta$
+on $A$, satisfying certain properties. The main difference with multiplier Hopf
+algebras is that now, the canonical maps $T_1$ and $T_2$ on $A\otimes A$,
+defined by $$T_1(a\otimes b)=\Delta(a)(1\otimes b)
+\qquad\quad\text{and}\qquad\quad T_2(c\otimes a)=(c\otimes 1)\Delta(a),$$ are
+no longer assumed to be bijective. Also recall that a weak multiplier Hopf
+algebra is called regular if its antipode is a bijective map from $A$ to
+itself.
+In this paper, we introduce and study the notion of integrals on such regular
+weak multiplier Hopf algebras. A left integral is a non-zero linear functional
+on $A$ that is left invariant (in an appropriate sense). Similarly for a right
+integral. For a regular weak multiplier Hopf algebra $(A,\Delta)$ with
+(sufficiently many) integrals, we construct the dual $(\widehat
+A,\widehat\Delta)$. It is again a regular weak multiplier Hopf algebra with
+(sufficiently many) integrals. This duality extends the known duality of
+finite-dimensional weak Hopf algebras to this more general case. It also
+extends the duality of multiplier Hopf algebras with integrals, the so-called
+algebraic quantum groups. For this reason, we will sometimes call a regular
+weak multiplier Hopf algebra with enough integrals an algebraic quantum
+groupoid.
+We discuss the relation of our work with the work on duality for algebraic
+quantum groupoids by Timmermann.
+We also illustrate this duality with a particular example in a separate
+paper. In this paper, we only mention the main definitions and results for this
+example. However, we do consider the two natural weak multiplier Hopf algebras
+associated with a groupoid in detail and show that they are dual to each other
+in the sense of the above duality.
+",0,0,1,0,0,0
+11870,Monte-Carlo Tree Search by Best Arm Identification,"  Recent advances in bandit tools and techniques for sequential learning are
+steadily enabling new applications and are promising the resolution of a range
+of challenging related problems. We study the game tree search problem, where
+the goal is to quickly identify the optimal move in a given game tree by
+sequentially sampling its stochastic payoffs. We develop new algorithms for
+trees of arbitrary depth, that operate by summarizing all deeper levels of the
+tree into confidence intervals at depth one, and applying a best arm
+identification procedure at the root. We prove new sample complexity guarantees
+with a refined dependence on the problem instance. We show experimentally that
+our algorithms outperform existing elimination-based algorithms and match
+previous special-purpose methods for depth-two trees.
+",1,0,0,1,0,0
+11871,Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation,"  Modern reinforcement learning algorithms reach super-human performance on
+many board and video games, but they are sample inefficient, i.e. they
+typically require significantly more playing experience than humans to reach an
+equal performance level. To improve sample efficiency, an agent may build a
+model of the environment and use planning methods to update its policy. In this
+article we introduce Variational State Tabulation (VaST), which maps an
+environment with a high-dimensional state space (e.g. the space of visual
+inputs) to an abstract tabular model. Prioritized sweeping with small backups,
+a highly efficient planning method, can then be used to update state-action
+values. We show how VaST can rapidly learn to maximize reward in tasks like 3D
+navigation and efficiently adapt to sudden changes in rewards or transition
+probabilities.
+",0,0,0,1,0,0
+11872,Griffiths Singularities in the Random Quantum Ising Antiferromagnet: A Tree Tensor Network Renormalization Group Study,"  The antiferromagnetic Ising chain in both transverse and longitudinal
+magnetic fields is one of the paradigmatic models of a quantum phase
+transition. The antiferromagnetic system exhibits a zero-temperature critical
+line separating an antiferromagnetic phase and a paramagnetic phase; the
+critical line connects an integrable quantum critical point at zero
+longitudinal field and a classical first-order transition point at zero
+transverse field. Using a strong-disorder renormalization group method
+formulated as a tree tensor network, we study the zero-temperature phase of the
+quantum Ising chain with bond randomness. We introduce a new matrix product
+operator representation of high-order moments, which provides an efficient and
+accurate tool for determining quantum phase transitions via the Binder cumulant
+of the order parameter. Our results demonstrate an infinite-randomness quantum
+critical point in zero longitudinal field accompanied by pronounced quantum
+Griffiths singularities, arising from rare ordered regions with anomalously
+slow fluctuations inside the paramagnetic phase. The strong Griffiths effects
+are signaled by a large dynamical exponent $z>1$, which characterizes a
+power-law density of low-energy states of the localized rare regions and
+becomes infinite at the quantum critical point. Upon application of a
+longitudinal field, the quantum phase transition between the paramagnetic phase
+and the antiferromagnetic phase is completely destroyed. Furthermore, quantum
+Griffiths effects are suppressed, showing $z<1$, when the dynamics of the rare
+regions is hampered by the longitudinal field.
+",0,1,0,0,0,0
+11873,Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients?,"  We give a rigorous analysis of the statistical behavior of gradients in a
+randomly initialized fully connected network N with ReLU activations. Our
+results show that the empirical variance of the squares of the entries in the
+input-output Jacobian of N is exponential in a simple architecture-dependent
+constant beta, given by the sum of the reciprocals of the hidden layer widths.
+When beta is large, the gradients computed by N at initialization vary wildly.
+Our approach complements the mean field theory analysis of random networks.
+From this point of view, we rigorously compute finite width corrections to the
+statistics of gradients at the edge of chaos.
+",0,0,0,1,0,0
+11874,Curriculum-Based Neighborhood Sampling For Sequence Prediction,"  The task of multi-step ahead prediction in language models is challenging
+considering the discrepancy between training and testing. At test time, a
+language model is required to make predictions given past predictions as input,
+instead of the past targets that are provided during training. This difference,
+known as exposure bias, can lead to the compounding of errors along a generated
+sequence at test time.
+In order to improve generalization in neural language models and address
+compounding errors, we propose a curriculum learning based method that
+gradually changes an initially deterministic teacher policy to a gradually more
+stochastic policy, which we refer to as \textit{Nearest-Neighbor Replacement
+Sampling}. A chosen input at a given timestep is replaced with a sampled
+nearest neighbor of the past target with a truncated probability proportional
+to the cosine similarity between the original word and its top $k$ most similar
+words. This allows the teacher to explore alternatives when the teacher
+provides a sub-optimal policy or when the initial policy is difficult for the
+learner to model. The proposed strategy is straightforward, online and requires
+little additional memory requirements. We report our main findings on two
+language modelling benchmarks and find that the proposed approach performs
+particularly well when used in conjunction with scheduled sampling, that too
+attempts to mitigate compounding errors in language models.
+",0,0,0,1,0,0
+11875,Randomized Composable Coresets for Matching and Vertex Cover,"  A common approach for designing scalable algorithms for massive data sets is
+to distribute the computation across, say $k$, machines and process the data
+using limited communication between them. A particularly appealing framework
+here is the simultaneous communication model whereby each machine constructs a
+small representative summary of its own data and one obtains an
+approximate/exact solution from the union of the representative summaries. If
+the representative summaries needed for a problem are small, then this results
+in a communication-efficient and round-optimal protocol. While many fundamental
+graph problems admit efficient solutions in this model, two prominent problems
+are notably absent from the list of successes, namely, the maximum matching
+problem and the minimum vertex cover problem. Indeed, it was shown recently
+that for both these problems, even achieving a polylog$(n)$ approximation
+requires essentially sending the entire input graph from each machine.
+The main insight of our work is that the intractability of matching and
+vertex cover in the simultaneous communication model is inherently connected to
+an adversarial partitioning of the underlying graph across machines. We show
+that when the underlying graph is randomly partitioned across machines, both
+these problems admit randomized composable coresets of size $\widetilde{O}(n)$
+that yield an $\widetilde{O}(1)$-approximate solution. This results in an
+$\widetilde{O}(1)$-approximation simultaneous protocol for these problems with
+$\widetilde{O}(nk)$ total communication when the input is randomly partitioned
+across $k$ machines. We further prove the optimality of our results. Finally,
+by a standard application of composable coresets, our results also imply
+MapReduce algorithms with the same approximation guarantee in one or two rounds
+of communication
+",1,0,0,0,0,0
+11876,simode: R Package for statistical inference of ordinary differential equations using separable integral-matching,"  In this paper we describe simode: Separable Integral Matching for Ordinary
+Differential Equations. The statistical methodologies applied in the package
+focus on several minimization procedures of an integral-matching criterion
+function, taking advantage of the mathematical structure of the differential
+equations like separability of parameters from equations. Application of
+integral based methods to parameter estimation of ordinary differential
+equations was shown to yield more accurate and stable results comparing to
+derivative based ones. Linear features such as separability were shown to ease
+optimization and inference. We demonstrate the functionalities of the package
+using various systems of ordinary differential equations.
+",0,0,0,1,0,0
+11877,Positive scalar curvature and the Euler class,"  We prove the following generalization of the classical Lichnerowicz vanishing
+theorem: if $F$ is an oriented flat vector bundle over a closed spin manifold
+$M$ such that $TM$ carries a metric of positive scalar curvature, then
+$<\widehat A(TM)e(F),[M]>=0$, where $e(F)$ is the Euler class of $F$.
+",0,0,1,0,0,0
+11878,Online Service with Delay,"  In this paper, we introduce the online service with delay problem. In this
+problem, there are $n$ points in a metric space that issue service requests
+over time, and a server that serves these requests. The goal is to minimize the
+sum of distance traveled by the server and the total delay in serving the
+requests. This problem models the fundamental tradeoff between batching
+requests to improve locality and reducing delay to improve response time, that
+has many applications in operations management, operating systems, logistics,
+supply chain management, and scheduling.
+Our main result is to show a poly-logarithmic competitive ratio for the
+online service with delay problem. This result is obtained by an algorithm that
+we call the preemptive service algorithm. The salient feature of this algorithm
+is a process called preemptive service, which uses a novel combination of
+(recursive) time forwarding and spatial exploration on a metric space. We hope
+this technique will be useful for related problems such as reordering buffer
+management, online TSP, vehicle routing, etc. We also generalize our results to
+$k > 1$ servers.
+",1,0,0,0,0,0
+11879,Comparison of dynamic mechanical properties of non-superheated and superheated A357 alloys,"  The influence of superheat treatment on the microstructure and dynamic
+mechanical properties of A357 alloys has been investigated. The study of
+microstructure was performed by the optical microscope. Dynamic mechanical
+properties (storage modulus, loss modulus, and damping capacity) were measured
+by the dynamic mechanical analyzer (DMA). Microstructure showed coarser and
+angular eutectic Si particles with larger {\alpha}-Al dendrites in the
+non-superheated A357 alloy. In contrast, finer and rounded eutectic Si
+particles together with smaller and preferred oriented {\alpha}-Al dendrites
+have been observed in the superheated A357 alloy. Dynamic mechanical properties
+showed an increasing trend of loss modulus and damping capacity meanwhile a
+decreasing trend of storage modulus at elevated temperatures for superheated
+and non-superheated A357 alloys. The high damping capacity of superheated A357
+has been ascribed to the grain boundary damping at elevated temperatures.
+",0,1,0,0,0,0
+11880,On the accuracy and usefulness of analytic energy models for contemporary multicore processors,"  This paper presents refinements to the execution-cache-memory performance
+model and a previously published power model for multicore processors. The
+combination of both enables a very accurate prediction of performance and
+energy consumption of contemporary multicore processors as a function of
+relevant parameters such as number of active cores as well as core and Uncore
+frequencies. Model validation is performed on the Sandy Bridge-EP and
+Broadwell-EP microarchitectures. Production-related variations in chip quality
+are demonstrated through a statistical analysis of the fit parameters obtained
+on one hundred Broadwell-EP CPUs of the same model. Insights from the models
+are used to explain the performance- and energy-related behavior of the
+processors for scalable as well as saturating (i.e., memory-bound) codes. In
+the process we demonstrate the models' capability to identify optimal operating
+points with respect to highest performance, lowest energy-to-solution, and
+lowest energy-delay product and identify a set of best practices for
+energy-efficient execution.
+",1,0,0,0,0,0
+11881,A Simple Solution for Maximum Range Flight,"  Within the standard framework of quasi-steady flight, this paper derives a
+speed that realizes the maximal obtainable range per unit of fuel. If this
+speed is chosen at each instant of a flight plan $h(x)$ giving altitude $h$ as
+a function of distance $x$, a variational problem for finding an optimal $h(x)$
+can be formulated and solved. It yields flight plans with maximal range, and
+these turn out to consist of mainly three phases using the optimal speed:
+starting with a climb at maximal continuous admissible thrust, ending with a
+continuous descent at idle thrust, and in between with a transition based on a
+solution of the Euler-Lagrange equation for the variational problem. A similar
+variational problem is derived and solved for speed-restricted flights, e.g. at
+250 KIAS below 10000 ft. In contrast to the literature, the approach of this
+paper does not need more than standard ordinary differential equations solving
+variational problems to derive range-optimal trajectories. Various numerical
+examplesbased on a Standard Business Jet are added for illustration.
+",0,0,1,0,0,0
+11882,Multiscale Residual Mixture of PCA: Dynamic Dictionaries for Optimal Basis Learning,"  In this paper we are interested in the problem of learning an over-complete
+basis and a methodology such that the reconstruction or inverse problem does
+not need optimization. We analyze the optimality of the presented approaches,
+their link to popular already known techniques s.a. Artificial Neural
+Networks,k-means or Oja's learning rule. Finally, we will see that one approach
+to reach the optimal dictionary is a factorial and hierarchical approach. The
+derived approach lead to a formulation of a Deep Oja Network. We present
+results on different tasks and present the resulting very efficient learning
+algorithm which brings a new vision on the training of deep nets. Finally, the
+theoretical work shows that deep frameworks are one way to efficiently have
+over-complete (combinatorially large) dictionary yet allowing easy
+reconstruction. We thus present the Deep Residual Oja Network (DRON). We
+demonstrate that a recursive deep approach working on the residuals allow
+exponential decrease of the error w.r.t. the depth.
+",1,0,0,1,0,0
+11883,On factorizations of graphical maps,"  We study the categories governing infinity (wheeled) properads. The graphical
+category, which was already known to be generalized Reedy, is in fact an
+Eilenberg-Zilber category. A minor alteration to the definition of the wheeled
+graphical category allows us to show that it is a generalized Reedy category.
+Finally, we present model structures for Segal properads and Segal wheeled
+properads.
+",0,0,1,0,0,0
+11884,Raking-ratio empirical process with auxiliary information learning,"  The raking-ratio method is a statistical and computational method which
+adjusts the empirical measure to match the true probability of sets in a finite
+partition. We study the asymptotic behavior of the raking-ratio empirical
+process indexed by a class of functions when the auxiliary information is given
+by the learning of the probability of sets in partitions from another sample
+larger than the sample of the statistician. Under some metric entropy
+hypothesis and conditions on the size of the independent samples, we establish
+the strong approximation of this process with estimated auxiliary information
+and show in particular that weak convergence is the same as the classical
+raking-ratio empirical process. We also give possible statistical applications
+of these results like strengthening the $Z$-test and the chi-square goodness of
+fit test.
+",0,0,1,1,0,0
+11885,Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation,"  Predicting the efficacy of a drug for a given individual, using
+high-dimensional genomic measurements, is at the core of precision medicine.
+However, identifying features on which to base the predictions remains a
+challenge, especially when the sample size is small. Incorporating expert
+knowledge offers a promising alternative to improve a prediction model, but
+collecting such knowledge is laborious to the expert if the number of candidate
+features is very large. We introduce a probabilistic model that can incorporate
+expert feedback about the impact of genomic measurements on the sensitivity of
+a cancer cell for a given drug. We also present two methods to intelligently
+collect this feedback from the expert, using experimental design and
+multi-armed bandit models. In a multiple myeloma blood cancer data set (n=51),
+expert knowledge decreased the prediction error by 8%. Furthermore, the
+intelligent approaches can be used to reduce the workload of feedback
+collection to less than 30% on average compared to a naive approach.
+",1,0,0,1,0,0
+11886,On Vague Computers,"  Vagueness is something everyone is familiar with. In fact, most people think
+that vagueness is closely related to language and exists only there. However,
+vagueness is a property of the physical world. Quantum computers harness
+superposition and entanglement to perform their computational tasks. Both
+superposition and entanglement are vague processes. Thus quantum computers,
+which process exact data without ""exploiting"" vagueness, are actually vague
+computers.
+",1,0,0,0,0,0
+11887,Exploiting OxRAM Resistive Switching for Dynamic Range Improvement of CMOS Image Sensors,"  We present a unique application of OxRAM devices in CMOS Image Sensors (CIS)
+for dynamic range (DR) improvement. We propose a modified 3T-APS (Active Pixel
+Sensor) circuit that incorporates OxRAM in 1T-1R configuration. DR improvement
+is achieved by resistive compression of the pixel output signal through
+autonomous programming of OxRAM device resistance during exposure. We show that
+by carefully preconditioning the OxRAM resistance, pixel DR can be enhanced.
+Detailed impact of OxRAM SET-to-RESET and RESET-to-SET transitions on pixel DR
+is discussed. For experimental validation with specific OxRAM preprogrammed
+states, a 4 Kb 10 nm thick HfOx (1T-1R) matrix was fabricated and
+characterized. Best case, relative pixel DR improvement of ~ 50 dB was obtained
+for our design.
+",1,0,0,0,0,0
+11888,Magnetic control of Goos-Hanchen shifts in a yttrium-iron-garnet film,"  We investigate the Goos-Hanchen (G-H) shifts reflected and transmitted by a
+yttrium-iron-garnet (YIG) film for both normal and oblique incidence. It is
+found that the nonreciprocity effect of the MO material does not only result in
+a nonvanishing reflected shift at normal incidence, but also leads to a
+slab-thickness-independent term which breaks the symmetry between the reflected
+and transmitted shifts at oblique incidence. The asymptotic behaviors of the
+normal-incidence reflected shift are obtained in the vicinity of two
+characteristic frequencies corresponding to a minimum reflectivity and a total
+reflection, respectively. Moreover, the coexistence of two types of
+negative-reflected-shift (NRS) at oblique incidence is discussed. We show that
+the reversal of the shifts from positive to negative values can be realized by
+tuning the magnitude of applied magnetic field, the frequency of incident wave
+and the slab thickness as well as the incident angle. In addition, we further
+investigate two special cases for practical purposes: the reflected shift with
+a total reflection and the transmitted shift with a total transmission.
+Numerical simulations are also performed to verify our analytical results.
+",0,1,0,0,0,0
+11889,Effective mass of quasiparticles from thermodynamics,"  We discuss the potential advantages of calculating the effective mass of
+quasiparticles in the interacting electron liquid from the low-temperature free
+energy vis-a-vis the conventional approach, in which the effective mass is
+obtained from approximate calculations of the self-energy, or from a quantum
+Monte Carlo evaluation of the energy of a variational ""quasiparticle wave
+function"". While raw quantum Monte Carlo data are presently too sparse to allow
+for an accurate determination of the effective mass, the values estimated by
+this method are numerically close to the ones obtained in previous calculations
+using diagrammatic many-body theory. In contrast to this, a recently published
+parametrization of quantum Monte Carlo data for the free energy of the
+homogeneous electron liquid yields effective masses that considerably deviate
+from previous calculations and even change sign for low densities, reflecting
+an unphysical negative entropy. We suggest that this anomaly is related to the
+treatment of the exchange energy at finite temperature.
+",0,1,0,0,0,0
+11890,Dynamics over Signed Networks,"  A signed network is a network with each link associated with a positive or
+negative sign. Models for nodes interacting over such signed networks, where
+two different types of interactions take place along the positive and negative
+links, respectively, arise from various biological, social, political, and
+economic systems. As modifications to the conventional DeGroot dynamics for
+positive links, two basic types of negative interactions along negative links,
+namely the opposing rule and the repelling rule, have been proposed and studied
+in the literature. This paper reviews a few fundamental convergence results for
+such dynamics over deterministic or random signed networks under a unified
+algebraic-graphical method. We show that a systematic tool of studying node
+state evolution over signed networks can be obtained utilizing generalized
+Perron-Frobenius theory, graph theory, and elementary algebraic recursions.
+",1,0,0,0,0,0
+11891,On the Estimation of Entropy in the FastICA Algorithm,"  The fastICA algorithm is a popular dimension reduction technique used to
+reveal patterns in data. Here we show that the approximations used in fastICA
+can result in patterns not being successfully recognised. We demonstrate this
+problem using a two-dimensional example where a clear structure is immediately
+visible to the naked eye, but where the projection chosen by fastICA fails to
+reveal this structure. This implies that care is needed when applying fastICA.
+We discuss how the problem arises and how it is intrinsically connected to the
+approximations that form the basis of the computational efficiency of fastICA.
+",0,0,0,1,0,0
+11892,Spatial-Temporal Imaging of Anisotropic Photocarrier Dynamics in Black Phosphorus,"  As an emerging single elemental layered material with a low symmetry in-plane
+crystal lattice, black phosphorus (BP) has attracted significant research
+interest owing to its unique electronic and optoelectronic properties,
+including its widely tunable bandgap, polarization dependent photoresponse and
+highly anisotropic in-plane charge transport. Despite extensive study of the
+steady-state charge transport in BP, there has not been direct characterization
+and visualization of the hot carriers dynamics in BP immediately after
+photoexcitation, which is crucial to understanding the performance of BP-based
+optoelectronic devices. Here we use the newly developed scanning ultrafast
+electron microscopy (SUEM) to directly visualize the motion of photo-excited
+hot carriers on the surface of BP in both space and time. We observe highly
+anisotropic in-plane diffusion of hot holes, with a 15-times higher diffusivity
+along the armchair (x-) direction than that along the zigzag (y-) direction.
+Our results provide direct evidence of anisotropic hot carrier transport in BP
+and demonstrate the capability of SUEM to resolve ultrafast hot carrier
+dynamics in layered two-dimensional materials.
+",0,1,0,0,0,0
+11893,The maximal order of iterated multiplicative functions,"  Following Wigert, a great number of authors including Ramanujan, Gronwall,
+Erdős, Ivić, Heppner, J. Knopfmacher, Nicolas, Schwarz, Wirsing,
+Freiman, Shiu et al. determined the maximal order of several multiplicative
+functions, generalizing Wigert's result \[\max_{n\leq x} \log d(n)= (\log
+2+o(1))\frac{\log x}{\log \log x}.\]
+On the contrary, for many multiplicative functions, the maximal order of
+iterations of the functions remains wide open. The case of the iterated divisor
+function was only recently solved, answering a question of Ramanujan (1915).
+Here, we determine the maximal order of $\log f(f(n))$ for a class of
+multiplicative functions $f$ which are related to the divisor function. As a
+corollary, we apply this to the function counting representations as sums of
+two squares of non-negative integers, also known as $r_2(n)/4$, and obtain an
+asymptotic formula: \[\max_{n\leq x} \log f(f(n))= (c+o(1))\frac{\sqrt{\log
+x}}{\log \log x},\] with some explicitly given positive constant $c$.
+",0,0,1,0,0,0
+11894,Extending applicability of bimetric theory: chameleon bigravity,"  This article extends bimetric formulations of massive gravity to make the
+mass of the graviton to depend on its environment. This minimal extension
+offers a novel way to reconcile massive gravity with local tests of general
+relativity without invoking the Vainshtein mechanism. On cosmological scales,
+it is argued that the model is stable and that it circumvents the Higuchi
+bound, hence relaxing the constraints on the parameter space. Moreover, with
+this extension the strong coupling scale is also environmentally dependent in
+such a way that it is kept sufficiently higher than the expansion rate all the
+way up to the very early universe, while the present graviton mass is low
+enough to be phenomenologically interesting. In this sense the extended
+bigravity theory serves as a partial UV completion of the standard bigravity
+theory. This extension is very generic and robust and a simple specific example
+is described.
+",0,1,0,0,0,0
+11895,The Scaling Limit of High-Dimensional Online Independent Component Analysis,"  We analyze the dynamics of an online algorithm for independent component
+analysis in the high-dimensional scaling limit. As the ambient dimension tends
+to infinity, and with proper time scaling, we show that the time-varying joint
+empirical measure of the target feature vector and the estimates provided by
+the algorithm will converge weakly to a deterministic measured-valued process
+that can be characterized as the unique solution of a nonlinear PDE. Numerical
+solutions of this PDE, which involves two spatial variables and one time
+variable, can be efficiently obtained. These solutions provide detailed
+information about the performance of the ICA algorithm, as many practical
+performance metrics are functionals of the joint empirical measures. Numerical
+simulations show that our asymptotic analysis is accurate even for moderate
+dimensions. In addition to providing a tool for understanding the performance
+of the algorithm, our PDE analysis also provides useful insight. In particular,
+in the high-dimensional limit, the original coupled dynamics associated with
+the algorithm will be asymptotically ""decoupled"", with each coordinate
+independently solving a 1-D effective minimization problem via stochastic
+gradient descent. Exploiting this insight to design new algorithms for
+achieving optimal trade-offs between computational and statistical efficiency
+may prove an interesting line of future research.
+",1,1,0,1,0,0
+11896,Direct characterization of a nonlinear photonic circuit's wave function with laser light,"  Integrated photonics is a leading platform for quantum technologies including
+nonclassical state generation \cite{Vergyris:2016-35975:SRP,
+Solntsev:2014-31007:PRX, Silverstone:2014-104:NPHOT, Solntsev:2016:RPH},
+demonstration of quantum computational complexity \cite{Lamitral_NJP2016} and
+secure quantum communications \cite{Zhang:2014-130501:PRL}. As photonic
+circuits grow in complexity, full quantum tomography becomes impractical, and
+therefore an efficient method for their characterization
+\cite{Lobino:2008-563:SCI, Rahimi-Keshari:2011-13006:NJP} is essential. Here we
+propose and demonstrate a fast, reliable method for reconstructing the
+two-photon state produced by an arbitrary quadratically nonlinear optical
+circuit. By establishing a rigorous correspondence between the generated
+quantum state and classical sum-frequency generation measurements from laser
+light, we overcome the limitations of previous approaches for lossy multimode
+devices \cite{Liscidini:2013-193602:PRL, Helt:2015-1460:OL}. We applied this
+protocol to a multi-channel nonlinear waveguide network, and measured a
+99.28$\pm$0.31\% fidelity between classical and quantum characterization. This
+technique enables fast and precise evaluation of nonlinear quantum photonic
+networks, a crucial step towards complex, large-scale, device production.
+",0,1,0,0,0,0
+11897,The Accuracy of Confidence Intervals for Field Normalised Indicators,"  When comparing the average citation impact of research groups, universities
+and countries, field normalisation reduces the influence of discipline and
+time. Confidence intervals for these indicators can help with attempts to infer
+whether differences between sets of publications are due to chance factors.
+Although both bootstrapping and formulae have been proposed for these, their
+accuracy is unknown. In response, this article uses simulated data to
+systematically compare the accuracy of confidence limits in the simplest
+possible case, a single field and year. The results suggest that the MNLCS
+(Mean Normalised Log-transformed Citation Score) confidence interval formula is
+conservative for large groups but almost always safe, whereas bootstrap MNLCS
+confidence intervals tend to be accurate but can be unsafe for smaller world or
+group sample sizes. In contrast, bootstrap MNCS (Mean Normalised Citation
+Score) confidence intervals can be very unsafe, although their accuracy
+increases with sample sizes.
+",1,0,0,0,0,0
+11898,Generalized Theta Functions. I,"  Generalizations of classical theta functions are proposed that include any
+even number of analytic parameters for which conditions of quasi-periodicity
+are fulfilled and that are representations of extended Heisenberg group.
+Differential equations for generalized theta functions and finite non-unitary
+representations of extended Heisenberg group are presented so as other
+properties and possible applications are pointed out such as a projective
+embedding of tori by means of generalized theta functions.
+",0,1,0,0,0,0
+11899,Constructing grids for molecular quantum dynamics using an autoencoder,"  A challenge for molecular quantum dynamics (QD) calculations is the curse of
+dimensionality with respect to the nuclear degrees of freedom. A common
+approach that works especially well for fast reactive processes is to reduce
+the dimensionality of the system to a few most relevant coordinates.
+Identifying these can become a very difficult task, since they often are highly
+unintuitive. We present a machine learning approach that utilizes an
+autoencoder that is trained to find a low-dimensional representation of a set
+of molecular configurations. These configurations are generated by trajectory
+calculations performed on the reactive molecular systems of interest. The
+resulting low-dimensional representation can be used to generate a potential
+energy surface grid in the desired subspace. Using the G-matrix formalism to
+calculate the kinetic energy operator, QD calculations can be carried out on
+this grid. In addition to step-by-step instructions for the grid construction,
+we present the application to a test system.
+",0,1,0,0,0,0
+11900,3D Pursuit-Evasion for AUVs,"  In this paper, we consider the problem of pursuit-evasion using multiple
+Autonomous Underwater Vehicles (AUVs) in a 3D water volume, with and without
+simple obstacles. Pursuit-evasion is a well studied topic in robotics, but the
+results are mostly set in 2D environments, using unlimited line of sight
+sensing. We propose an algorithm for range limited sensing in 3D environments
+that captures a finite speed evader based on one single previous observation of
+its location. The pursuers are first moved to form a maximal cage formation,
+based on their number and sensor ranges, containing all of the possible evader
+locations. The cage is then shrunk until every part of that volume is sensed,
+thereby capturing the evader. The pursuers need only limited sensing range and
+low bandwidth communication, making the algorithm well suited for an underwater
+environment.
+",1,0,0,0,0,0
+11901,Local Gradient Estimates for Second-Order Nonlinear Elliptic and Parabolic Equations by the Weak Bernstein's Method,"  In the theory of second-order, nonlinear elliptic and parabolic equations,
+obtaining local or global gradient bounds is often a key step for proving the
+existence of solutions but it may be even more useful in many applications, for
+example to singular perturbations problems. The classical Bernstein's method is
+the well-known tool to obtain these bounds but, in most cases, it has the
+defect of providing only a priori estimates. The ""weak Bernstein's method"" ,
+based on viscosity solutions' theory, is an alternative way to prove the global
+Lipschitz regularity of solutions together with some estimates but it is not so
+easy to perform in the case of local bounds. The aim of this paper is to
+provide an extension of the ""weak Bernstein's method"" which allows to prove
+local gradient bounds with reasonnable technicalities. The classical
+Bernstein's method is a well-known tool for obtaining gradient estimates for
+solutions of second-order, elliptic and parabolic equations
+",0,0,1,0,0,0
+11902,Dynamic patterns of knowledge flows across technological domains: empirical results and link prediction,"  The purpose of this study is to investigate the structure and evolution of
+knowledge spillovers across technological domains. Specifically, dynamic
+patterns of knowledge flow among 29 technological domains, measured by patent
+citations for eight distinct periods, are identified and link prediction is
+tested for capability for forecasting the evolution in these cross-domain
+patent networks. The overall success of the predictions using the Katz metric
+implies that there is a tendency to generate increased knowledge flows mostly
+within the set of previously linked technological domains. This study
+contributes to innovation studies by characterizing the structural change and
+evolutionary behaviors in dynamic technology networks and by offering the basis
+for predicting the emergence of future technological knowledge flows.
+",1,1,0,0,0,0
+11903,Accurate calculation of oblate spheroidal wave functions,"  Alternative expressions for calculating the oblate spheroidal radial
+functions of both kinds R1ml and R2ml are shown to provide accurate values over
+very large parameter ranges using 64 bit arithmetic, even where the traditional
+expressions fail. First is the expansion of the product of a radial function
+and the angular function of the first kind in a series of products of the
+corresponding spherical functions, with the angular coordinate being a free
+parameter. Setting the angular coordinate equal to zero leads to accurate
+values for R2ml when the radial coordinate xi is larger than 0.01 and l is
+somewhat larger than m. Allowing it to vary with increasing l leads to highly
+accurate values for R1ml over all parameter ranges. Next is the calculation of
+R2ml as an integral of the product of S1ml and a spherical Neumann function
+kernel. This is useful for smaller values of xi. Also used is the near equality
+of pairs of low order eigenvalues when the size parameter c is large that leads
+to accurate values for R2ml using neighboring accurate values for R1ml. A
+modified method is described that provides accurate values for the necessary
+expansion coefficients when c is large and l is near m and traditional methods
+fail. A resulting Fortran computer program Oblfcn almost always provides radial
+function values with at least 8 accurate decimal digits using 64 bit arithmetic
+for m up to at least 1000 with c up to at least 2000 when xi is greater than
+0.000001 and c up to at least 5000 when xi is greater than 0.01. Use of 128 bit
+arithmetic extends the accuracy to 15 or more digits and extends xi to all
+values other than zero. Oblfcn is freely available.
+",0,0,1,0,0,0
+11904,Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach,"  Learning cooperative policies for multi-agent systems is often challenged by
+partial observability and a lack of coordination. In some settings, the
+structure of a problem allows a distributed solution with limited
+communication. Here, we consider a scenario where no communication is
+available, and instead we learn local policies for all agents that collectively
+mimic the solution to a centralized multi-agent static optimization problem.
+Our main contribution is an information theoretic framework based on rate
+distortion theory which facilitates analysis of how well the resulting fully
+decentralized policies are able to reconstruct the optimal solution. Moreover,
+this framework provides a natural extension that addresses which nodes an agent
+should communicate with to improve the performance of its individual policy.
+",1,1,1,0,0,0
+11905,Ion distribution and ablation depth measurements of a fs-ps laser-irradiated solid tin target,"  The ablation of solid tin surfaces by an 800-nanometer-wavelength laser is
+studied for a pulse length range from 500 fs to 4.5 ps and a fluence range
+spanning 0.9 to 22 J/cm^2. The ablation depth and volume are obtained employing
+a high-numerical-aperture optical microscope, while the ion yield and energy
+distributions are obtained from a set of Faraday cups set up under various
+angles. We found a slight increase of the ion yield for an increasing pulse
+length, while the ablation depth is slightly decreasing. The ablation volume
+remained constant as a function of pulse length. The ablation depth follows a
+two-region logarithmic dependence on the fluence, in agreement with the
+available literature and theory. In the examined fluence range, the ion yield
+angular distribution is sharply peaked along the target normal at low fluences
+but rapidly broadens with increasing fluence. The total ionization fraction
+increases monotonically with fluence to a 5-6% maximum, which is substantially
+lower than the typical ionization fractions obtained with nanosecond-pulse
+ablation. The angular distribution of the ions does not depend on the laser
+pulse length within the measurement uncertainty. These results are of
+particular interest for the possible utilization of fs-ps laser systems in
+plasma sources of extreme ultraviolet light for nanolithography.
+",0,1,0,0,0,0
+11906,Measuring the Hubble constant with Type Ia supernovae as near-infrared standard candles,"  The most precise local measurements of $H_0$ rely on observations of Type Ia
+supernovae (SNe Ia) coupled with Cepheid distances to SN Ia host galaxies.
+Recent results have shown tension comparing $H_0$ to the value inferred from
+CMB observations assuming $\Lambda$CDM, making it important to check for
+potential systematic uncertainties in either approach. To date, precise local
+$H_0$ measurements have used SN Ia distances based on optical photometry, with
+corrections for light curve shape and colour. Here, we analyse SNe Ia as
+standard candles in the near-infrared (NIR), where intrinsic variations in the
+supernovae and extinction by dust are both reduced relative to the optical.
+From a combined fit to 9 nearby calibrator SNe with host Cepheid distances from
+Riess et al. (2016) and 27 SNe in the Hubble flow, we estimate the absolute
+peak $J$ magnitude $M_J = -18.524\;\pm\;0.041$ mag and $H_0 = 72.8\;\pm\;1.6$
+(statistical) $\pm$ 2.7 (systematic) km s$^{-1}$ Mpc$^{-1}$. The 2.2 $\%$
+statistical uncertainty demonstrates that the NIR provides a compelling avenue
+to measuring SN Ia distances, and for our sample the intrinsic (unmodeled) peak
+$J$ magnitude scatter is just $\sim$0.10 mag, even without light curve shape or
+colour corrections. Our results do not vary significantly with different sample
+selection criteria, though photometric calibration in the NIR may be a dominant
+systematic uncertainty. Our findings suggest that tension in the competing
+$H_0$ distance ladders is likely not a result of supernova systematics that
+could be expected to vary between optical and NIR wavelengths, like dust
+extinction. We anticipate further improvements in $H_0$ with a larger
+calibrator sample of SNe Ia with Cepheid distances, more Hubble flow SNe Ia
+with NIR light curves, and better use of the full NIR photometric data set
+beyond simply the peak $J$-band magnitude.
+",0,1,0,0,0,0
+11907,"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","  Deep learning thrives with large neural networks and large datasets. However,
+larger networks and larger datasets result in longer training times that impede
+research and development progress. Distributed synchronous SGD offers a
+potential solution to this problem by dividing SGD minibatches over a pool of
+parallel workers. Yet to make this scheme efficient, the per-worker workload
+must be large, which implies nontrivial growth in the SGD minibatch size. In
+this paper, we empirically show that on the ImageNet dataset large minibatches
+cause optimization difficulties, but when these are addressed the trained
+networks exhibit good generalization. Specifically, we show no loss of accuracy
+when training with large minibatch sizes up to 8192 images. To achieve this
+result, we adopt a hyper-parameter-free linear scaling rule for adjusting
+learning rates as a function of minibatch size and develop a new warmup scheme
+that overcomes optimization challenges early in training. With these simple
+techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of
+8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using
+commodity hardware, our implementation achieves ~90% scaling efficiency when
+moving from 8 to 256 GPUs. Our findings enable training visual recognition
+models on internet-scale data with high efficiency.
+",1,0,0,0,0,0
+11908,Universal Construction of Cheater-Identifiable Secret Sharing Against Rushing Cheaters Based on Message Authentication,"  For conventional secret sharing, if cheaters can submit possibly forged
+shares after observing shares of the honest users in the reconstruction phase
+then they cannot only disturb the protocol but also only they may reconstruct
+the true secret. To overcome the problem, secret sharing scheme with properties
+of cheater-identification have been proposed. Existing protocols for
+cheater-identifiable secret sharing assumed non-rushing cheaters or honest
+majority. In this paper, we remove both conditions simultaneously, and give its
+universal construction from any secret sharing scheme. To resolve this end, we
+propose the concepts of ""individual identification"" and ""agreed
+identification"".
+",1,0,0,0,0,0
+11909,Regrasp Planning Considering Bipedal Stability Constraints,"  This paper presents a Center of Mass (CoM) based manipulation and regrasp
+planner that implements stability constraints to preserve the robot balance.
+The planner provides a graph of IK-feasible, collision-free and stable motion
+sequences, constructed using an energy based motion planning algorithm. It
+assures that the assembly motions are stable and prevent the robot from falling
+while performing dexterous tasks in different situations. Furthermore, the
+constraints are also used to perform an RRT-inspired task-related stability
+estimation in several simulations. The estimation can be used to select between
+single-arm and dual-arm regrasping configurations to achieve more stability and
+robustness for a given manipulation task. To validate the planner and the
+task-related stability estimations, several tests are performed in simulations
+and real-world experiments involving the HRP5P humanoid robot, the 5th
+generation of the HRP robot family. The experiment results suggest that the
+planner and the task-related stability estimation provide robust behavior for
+the humanoid robot while performing regrasp tasks.
+",1,0,0,0,0,0
+11910,Unsaturated deformable porous media flow with phase transition,"  In the present paper, a continuum model is introduced for fluid flow in a
+deformable porous medium, where the fluid may undergo phase transitions.
+Typically, such problems arise in modeling liquid-solid phase transformations
+in groundwater flows. The system of equations is derived here from the
+conservation principles for mass, momentum, and energy and from the
+Clausius-Duhem inequality for entropy. It couples the evolution of the
+displacement in the matrix material, of the capillary pressure, of the absolute
+temperature, and of the phase fraction. Mathematical results are proved under
+the additional hypothesis that inertia effects and shear stresses can be
+neglected. For the resulting highly nonlinear system of two PDEs, one ODE and
+one ordinary differential inclusion with natural initial and boundary
+conditions, existence of global in time solutions is proved by means of cut-off
+techniques and suitable Moser-type estimates.
+",0,0,1,0,0,0
+11911,Active Mini-Batch Sampling using Repulsive Point Processes,"  The convergence speed of stochastic gradient descent (SGD) can be improved by
+actively selecting mini-batches. We explore sampling schemes where similar data
+points are less likely to be selected in the same mini-batch. In particular, we
+prove that such repulsive sampling schemes lowers the variance of the gradient
+estimator. This generalizes recent work on using Determinantal Point Processes
+(DPPs) for mini-batch diversification (Zhang et al., 2017) to the broader class
+of repulsive point processes. We first show that the phenomenon of variance
+reduction by diversified sampling generalizes in particular to non-stationary
+point processes. We then show that other point processes may be computationally
+much more efficient than DPPs. In particular, we propose and investigate
+Poisson Disk sampling---frequently encountered in the computer graphics
+community---for this task. We show empirically that our approach improves over
+standard SGD both in terms of convergence speed as well as final model
+performance.
+",0,0,0,1,0,0
+11912,Customizing First Person Image Through Desired Actions,"  This paper studies a problem of inverse visual path planning: creating a
+visual scene from a first person action. Our conjecture is that the spatial
+arrangement of a first person visual scene is deployed to afford an action, and
+therefore, the action can be inversely used to synthesize a new scene such that
+the action is feasible. As a proof-of-concept, we focus on linking visual
+experiences induced by walking.
+A key innovation of this paper is a concept of ActionTunnel---a 3D virtual
+tunnel along the future trajectory encoding what the wearer will visually
+experience as moving into the scene. This connects two distinctive first person
+images through similar walking paths. Our method takes a first person image
+with a user defined future trajectory and outputs a new image that can afford
+the future motion. The image is created by combining present and future
+ActionTunnels in 3D where the missing pixels in adjoining area are computed by
+a generative adversarial network. Our work can provide a travel across
+different first person experiences in diverse real world scenes.
+",1,0,0,0,0,0
+11913,Michell trusses in two dimensions as a Gamma-limit of optimal design problems in linear elasticity,"  We reconsider the minimization of the compliance of a two dimensional elastic
+body with traction boundary conditions for a given weight. It is well known how
+to rewrite this optimal design problem as a nonlinear variational problem. We
+take the limit of vanishing weight by sending a suitable Lagrange multiplier to
+infinity in the variational formulation. We show that the limit, in the sense
+of $\Gamma$-convergence, is a certain Michell truss problem. This proves a
+conjecture by Kohn and Allaire.
+",0,0,1,0,0,0
+11914,Moment analysis of highway-traffic clearance distribution,"  To help with the planning of inter-vehicular communication networks, an
+accurate understanding of traffic behavior and traffic phase transition is
+required. We calculate inter-vehicle spacings from empirical data collected in
+a multi-lane highway in California, USA. We calculate the correlation
+coefficients for spacings between vehicles in individual lanes to show that the
+flows are independent. We determine the first four moments for individual lanes
+at regular time intervals, namely the mean, variance, skewness and kurtosis. We
+follow the evolution of these moments as the traffic condition changes from the
+low-density free flow to high-density congestion. We find that the higher
+moments of inter-vehicle spacings have a well defined dependence on the mean
+value. The variance of the spacing distribution monotonously increases with the
+mean vehicle spacing. In contrast, our analysis suggests that the skewness and
+kurtosis provide one of the most sensitive probes towards the search for the
+critical points. We find two significant results. First, the kurtosis
+calculated in different time intervals for different lanes varies smoothly with
+the skewness. They share the same behavior with the skewness and kurtosis
+calculated for probability density functions that depend on a single parameter.
+Second, the skewness and kurtosis as functions of the mean intervehicle spacing
+show sharp peaks at critical densities expected for transitions between
+different traffic phases. The data show a considerable scatter near the peak
+positions, which suggests that the critical behavior may depend on other
+parameters in addition to the traffic density.
+",0,1,0,0,0,0
+11915,Single-image Tomography: 3D Volumes from 2D Cranial X-Rays,"  As many different 3D volumes could produce the same 2D x-ray image, inverting
+this process is challenging. We show that recent deep learning-based
+convolutional neural networks can solve this task. As the main challenge in
+learning is the sheer amount of data created when extending the 2D image into a
+3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which
+is then fused in a second step with the input x-ray into a high-resolution
+volume. To train and validate our approach we introduce a new dataset that
+comprises of close to half a million computer-simulated 2D x-ray images of 3D
+volumes scanned from 175 mammalian species. Applications of our approach
+include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays
+including changes of illumination, view pose or geometry. Our evaluation
+includes comparison to previous tomography work, previous learning methods
+using our data, a user study and application to a set of real x-rays.
+",1,0,0,0,0,0
+11916,Stochastic Methods for Composite and Weakly Convex Optimization Problems,"  We consider minimization of stochastic functionals that are compositions of a
+(potentially) non-smooth convex function $h$ and smooth function $c$ and, more
+generally, stochastic weakly-convex functionals. We develop a family of
+stochastic methods---including a stochastic prox-linear algorithm and a
+stochastic (generalized) sub-gradient procedure---and prove that, under mild
+technical conditions, each converges to first-order stationary points of the
+stochastic objective. We provide experiments further investigating our methods
+on non-smooth phase retrieval problems; the experiments indicate the practical
+effectiveness of the procedures.
+",0,0,1,1,0,0
+11917,Thermal Expansion of the Heavy-fermion Superconductor PuCoGa$_{5}$,"  We have performed high-resolution powder x-ray diffraction measurements on a
+sample of $^{242}$PuCoGa$_{5}$, the heavy-fermion superconductor with the
+highest critical temperature $T_{c}$ = 18.7 K. The results show that the
+tetragonal symmetry of its crystallographic lattice is preserved down to 2 K.
+Marginal evidence is obtained for an anomalous behaviour below $T_{c}$ of the
+$a$ and $c$ lattice parameters. The observed thermal expansion is isotropic
+down to 150 K, and becomes anisotropic for lower temperatures. This gives a
+$c/a$ ratio that decreases with increasing temperature to become almost
+constant above $\sim$150 K. The volume thermal expansion coefficient
+$\alpha_{V}$ has a jump at $T_{c}$, a factor $\sim$20 larger than the change
+predicted by the Ehrenfest relation for a second order phase transition. The
+volume expansion deviates from the curve expected for the conventional
+anharmonic behaviour described by a simple Grüneisen-Einstein model. The
+observed differences are about ten times larger than the statistical error bars
+but are too small to be taken as an indication for the proximity of the system
+to a valence instability that is avoided by the superconducting state.
+",0,1,0,0,0,0
+11918,"Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarios","  Conditional generators learn the data distribution for each class in a
+multi-class scenario and generate samples for a specific class given the right
+input from the latent space. In this work, a method known as ""Versatile
+Auxiliary Classifier with Generative Adversarial Network"" for multi-class
+scenarios is presented. In this technique, the Generative Adversarial Networks
+(GAN)'s generator is turned into a conditional generator by placing a
+multi-class classifier in parallel with the discriminator network and
+backpropagate the classification error through the generator. This technique is
+versatile enough to be applied to any GAN implementation. The results on two
+databases and comparisons with other method are provided as well.
+",0,0,0,1,0,0
+11919,A Lagrangian Model to Predict Microscallop Motion in non Newtonian Fluids,"  The need to develop models to predict the motion of microrobots, or robots of
+a much smaller scale, moving in fluids in a low Reynolds number regime, and in
+particular, in non Newtonian fluids, cannot be understated. The article
+develops a Lagrangian based model for one such mechanism - a two-link mechanism
+termed a microscallop, moving in a low Reynolds number environment in a non
+Newtonian fluid. The modelling proceeds through the conventional Lagrangian
+construction for a two-link mechanism and then goes on to model the external
+fluid forces using empirically based models for viscosity to complete the
+dynamic model. The derived model is then simulated for different initial
+conditions and key parameters of the non Newtonian fluid, and the results are
+corroborated with a few existing experimental results on a similar mechanism
+under identical conditions.
+",1,0,0,0,0,0
+11920,Towards a Bootstrap approach to higher orders of epsilon expansion,"  We employ a hybrid approach in determining the anomalous dimension and OPE
+coefficient of higher spin operators in the Wilson-Fisher theory. First we do a
+large spin analysis for CFT data where we use results obtained from the usual
+and the Mellin Bootstrap and also from Feynman diagram literature. This gives
+new predictions at $O(\epsilon^4)$ and $O(\epsilon^5)$ for anomalous dimensions
+and OPE coefficients, and also provides a cross-check for the results from
+Mellin Bootstrap. These higher orders get contributions from all higher spin
+operators in the crossed channel. We also use the Bootstrap in Mellin space
+method for $\phi^3$ in $d=6-\epsilon$ CFT where we calculate general higher
+spin OPE data. We demonstrate a higher loop order calculation in this approach
+by summing over contributions from higher spin operators of the crossed channel
+in the same spirit as before.
+",0,1,0,0,0,0
+11921,"Small-scale Effects of Thermal Inflation on Halo Abundance at High-$z$, Galaxy Substructure Abundance and 21-cm Power Spectrum","  We study the impact of thermal inflation on the formation of cosmological
+structures and present astrophysical observables which can be used to constrain
+and possibly probe the thermal inflation scenario. These are dark matter halo
+abundance at high redshifts, satellite galaxy abundance in the Milky Way, and
+fluctuation in the 21-cm radiation background before the epoch of reionization.
+The thermal inflation scenario leaves a characteristic signature on the matter
+power spectrum by boosting the amplitude at a specific wavenumber determined by
+the number of e-foldings during thermal inflation ($N_{\rm bc}$), and strongly
+suppressing the amplitude for modes at smaller scales. For a reasonable range
+of parameter space, one of the consequences is the suppression of minihalo
+formation at high redshifts and that of satellite galaxies in the Milky Way.
+While this effect is substantial, it is degenerate with other cosmological or
+astrophysical effects. The power spectrum of the 21-cm background probes this
+impact more directly, and its observation may be the best way to constrain the
+thermal inflation scenario due to the characteristic signature in the power
+spectrum. The Square Kilometre Array (SKA) in phase 1 (SKA1) has sensitivity
+large enough to achieve this goal for models with $N_{\rm bc}\gtrsim 26$ if a
+10000-hr observation is performed. The final phase SKA, with anticipated
+sensitivity about an order of magnitude higher, seems more promising and will
+cover a wider parameter space.
+",0,1,0,0,0,0
+11922,Finiteness theorems for holomorphic mappings from products of hyperbolic Riemann surfaces,"  We prove that the space of dominant/non-constant holomorphic mappings from a
+product of hyperbolic Riemann surfaces of finite type into certain hyperbolic
+manifolds with universal cover a bounded domain is a finite set.
+",0,0,1,0,0,0
+11923,Time and media-use of Italian Generation Y: dimensions of leisure preferences,"  Time spent in leisure is not a minor research question as it is acknowledged
+as a key aspect of one's quality of life. The primary aim of this article is to
+qualify time and Internet use of Italian Generation Y beyond media hype and
+assumptions. To this aim, we apply a multidimensional extension of Item
+Response Theory models to the Italian ""Multipurpose survey on households:
+aspects of daily life"" to ascertain the relevant dimensions of Generation Y
+time-use. We show that the use of technology is neither the first nor the
+foremost time-use activity of Italian Generation Y, who still prefers to use
+its time to socialise and have fun with friends in a non media-medalled manner.
+",1,0,0,1,0,0
+11924,Recurrent Multimodal Interaction for Referring Image Segmentation,"  In this paper we are interested in the problem of image segmentation given
+natural language descriptions, i.e. referring expressions. Existing works
+tackle this problem by first modeling images and sentences independently and
+then segment images by combining these two types of representations. We argue
+that learning word-to-image interaction is more native in the sense of jointly
+modeling two modalities for the image segmentation task, and we propose
+convolutional multimodal LSTM to encode the sequential interactions between
+individual words, visual information, and spatial information. We show that our
+proposed model outperforms the baseline model on benchmark datasets. In
+addition, we analyze the intermediate output of the proposed multimodal LSTM
+approach and empirically explain how this approach enforces a more effective
+word-to-image interaction.
+",1,0,0,0,0,0
+11925,Motion of a thin elliptic plate under symmetric and asymmetric orthotropic friction forces,"  Anisotropy of friction force is proved to be an important factor in various
+contact problems. We study dynamical behavior of thin plates with respect to
+symmetric and asymmetric orthotropic friction. Terminal motion of plates with
+circular and elliptic contact areas is mainly analyzed. Evaluation of friction
+forces for both symmetric and asymmetric orthotropic cases are shown. Regular
+pressure distribution is considered. Differential equations are formulated and
+solved numerically for a number of initial conditions. Examples show
+significant influence of friction force asymmetry on the motion.
+",0,1,0,0,0,0
+11926,Combinatorial Auctions with Online XOS Bidders,"  In combinatorial auctions, a designer must decide how to allocate a set of
+indivisible items amongst a set of bidders. Each bidder has a valuation
+function which gives the utility they obtain from any subset of the items. Our
+focus is specifically on welfare maximization, where the objective is to
+maximize the sum of valuations that the bidders place on the items that they
+were allocated (the valuation functions are assumed to be reported truthfully).
+We analyze an online problem in which the algorithm is not given the set of
+bidders in advance. Instead, the bidders are revealed sequentially in a
+uniformly random order, similarly to secretary problems. The algorithm must
+make an irrevocable decision about which items to allocate to the current
+bidder before the next one is revealed. When the valuation functions lie in the
+class $XOS$ (which includes submodular functions), we provide a black box
+reduction from offline to online optimization. Specifically, given an
+$\alpha$-approximation algorithm for offline welfare maximization, we show how
+to create a $(0.199 \alpha)$-approximation algorithm for the online problem.
+Our algorithm draws on connections to secretary problems; in fact, we show that
+the online welfare maximization problem itself can be viewed as a particular
+kind of secretary problem with nonuniform arrival order.
+",1,0,0,0,0,0
+11927,Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient,"  The adaptability of the convolutional neural network (CNN) technique for
+aerodynamic meta-modeling tasks is probed in this work. The primary objective
+is to develop suitable CNN architecture for variable flow conditions and object
+geometry, in addition to identifying a sufficient data preparation process.
+Multiple CNN structures were trained to learn the lift coefficients of the
+airfoils with a variety of shapes in multiple flow Mach numbers, Reynolds
+numbers, and diverse angles of attack. This is conducted to illustrate the
+concept of the technique. A multi-layered perceptron (MLP) is also used for the
+training sets. The MLP results are compared with that of the CNN results. The
+newly proposed meta-modeling concept has been found to be comparable with the
+MLP in learning capability; and more importantly, our CNN model exhibits a
+competitive prediction accuracy with minimal constraints in a geometric
+representation.
+",1,0,0,1,0,0
+11928,Bayesian inference in Y-linked two-sex branching processes with mutations: ABC approach,"  A Y-linked two-sex branching process with mutations and blind choice of males
+is a suitable model for analyzing the evolution of the number of carriers of an
+allele and its mutations of a Y-linked gene. Considering a two-sex monogamous
+population, in this model each female chooses her partner from among the male
+population without caring about his type (i.e., the allele he carries). In this
+work, we deal with the problem of estimating the main parameters of such model
+developing the Bayesian inference in a parametric framework. Firstly, we
+consider, as sample scheme, the observation of the total number of females and
+males up to some generation as well as the number of males of each genotype at
+last generation. Later, we introduce the information of the mutated males only
+in the last generation obtaining in this way a second sample scheme. For both
+samples, we apply the Approximate Bayesian Computation (ABC) methodology to
+approximate the posterior distributions of the main parameters of this model.
+The accuracy of the procedure based on these samples is illustrated and
+discussed by way of simulated examples.
+",0,0,0,1,1,0
+11929,Effective Theories for 2+1 Dimensional Non-Abelian Topological Spin Liquids,"  In this work we propose an effective low-energy theory for a large class of
+2+1 dimensional non-Abelian topological spin liquids whose edge states are
+conformal degrees of freedom with central charges corresponding to the coset
+structure $su(2)_k\oplus su(2)_{k'}/su(2)_{k+k'}$. For particular values of
+$k'$ it furnishes the series for unitary minimal and superconformal models.
+These gapped phases were recently suggested to be obtained from an array of
+one-dimensional coupled quantum wires. In doing so we provide an explicit
+relationship between two distinct approaches: quantum wires and Chern-Simons
+bulk theory. We firstly make a direct connection between the interacting
+quantum wires and the corresponding conformal field theory at the edges, which
+turns out to be given in terms of chiral gauged WZW models. Relying on the
+bulk-edge correspondence we are able to construct the underlying non-Abelian
+Chern-Simons effective field theory.
+",0,1,0,0,0,0
+11930,Coset space construction for the conformal group. II. Spontaneously broken phase and inverse Higgs phenomenon,"  A self-contained method of obtaining effective theories resulting from the
+spontaneous breakdown of conformal invariance is developed. It allows to
+demonstrate that the Nambu-Goldstone fields for special conformal
+transformations always represent non-dynamical degrees of freedom. The standard
+approach to the same question, which includes the imposition of the inverse
+Higgs constraints, is shown to follow from the developed technique. This
+provides an alternative view on the nature of the inverse Higgs constraints for
+the conformal group.
+",0,1,0,0,0,0
+11931,The morphodynamics of 3D migrating cancer cells,"  Cell shape is an important biomarker. Previously extensive studies have
+established the relation between cell shape and cell function. However, the
+morphodynamics, namely the temporal fluctuation of cell shape is much less
+understood. We study the morphodynamics of MDA-MB-231 cells in type I collagen
+extracellular matrix (ECM). We find ECM mechanics, as tuned by collagen
+concentration, controls the morphodynamics but not the static cell morphology.
+By employing machine learning techniques, we classify cell shape into five
+different morphological phenotypes corresponding to different migration modes.
+As a result, cell morphodynamics is mapped into temporal evolution of
+morphological phenotypes. We systematically characterize the phenotype dynamics
+including occurrence probability, dwell time, transition flux, and also obtain
+the invasion characteristics of each phenotype. Using a tumor organoid model,
+we show that the distinct invasion potentials of each phenotype modulate the
+phenotype homeostasis. Overall invasion of a tumor organoid is facilitated by
+individual cells searching for and committing to phenotypes of higher invasive
+potential. In conclusion, we show that 3D migrating cancer cells exhibit rich
+morphodynamics that is regulated by ECM mechanics and is closely related with
+cell motility. Our results pave the way to systematic characterization and
+functional understanding of cell morphodynamics.
+",0,0,0,0,1,0
+11932,Evolution Strategies as a Scalable Alternative to Reinforcement Learning,"  We explore the use of Evolution Strategies (ES), a class of black box
+optimization algorithms, as an alternative to popular MDP-based RL techniques
+such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show
+that ES is a viable solution strategy that scales extremely well with the
+number of CPUs available: By using a novel communication strategy based on
+common random numbers, our ES implementation only needs to communicate scalars,
+making it possible to scale to over a thousand parallel workers. This allows us
+to solve 3D humanoid walking in 10 minutes and obtain competitive results on
+most Atari games after one hour of training. In addition, we highlight several
+advantages of ES as a black box optimization technique: it is invariant to
+action frequency and delayed rewards, tolerant of extremely long horizons, and
+does not need temporal discounting or value function approximation.
+",1,0,0,1,0,0
+11933,IoT Localization for Bistatic Passive UHF RFID Systems with 3D Radiation Pattern,"  Passive Radio-Frequency IDentification (RFID) systems carry critical
+importance for Internet of Things (IoT) applications due to their energy
+harvesting capabilities. RFID based position estimation, in particular, is
+expected to facilitate a wide array of location based services for IoT
+applications with low-power requirements. In this paper, considering monostatic
+and bistatic configurations and 3D antenna radiation pattern, we investigate
+the accuracy of received signal strength based wireless localization using
+passive ultra high frequency (UHF) RFID systems. The Cramer-Rao Lower Bound
+(CRLB) for the localization accuracy is derived, and is compared with the
+accuracy of maximum likelihood estimators for various RFID antenna
+configurations. Numerical results show that due to RFID tag/antenna
+sensitivity, and the directional antenna pattern, the localization accuracy can
+degrade at blind locations that remain outside of the RFID reader antennas'
+main beam patterns. In such cases optimizing elevation angle of antennas are
+shown to improve localization coverage, while using bistatic configuration
+improves localization accuracy significantly.
+",1,0,0,0,0,0
+11934,Multidimensional upwind hydrodynamics on unstructured meshes using Graphics Processing Units I. Two-dimensional uniform meshes,"  We present a new method for numerical hydrodynamics which uses a
+multidimensional generalisation of the Roe solver and operates on an
+unstructured triangular mesh. The main advantage over traditional methods based
+on Riemann solvers, which commonly use one-dimensional flux estimates as
+building blocks for a multidimensional integration, is its inherently
+multidimensional nature, and as a consequence its ability to recognise
+multidimensional stationary states that are not hydrostatic. A second novelty
+is the focus on Graphics Processing Units (GPUs). By tailoring the algorithms
+specifically to GPUs we are able to get speedups of 100-250 compared to a
+desktop machine. We compare the multidimensional upwind scheme to a
+traditional, dimensionally split implementation of the Roe solver on several
+test problems, and we find that the new method significantly outperforms the
+Roe solver in almost all cases. This comes with increased computational costs
+per time step, which makes the new method approximately a factor of 2 slower
+than a dimensionally split scheme acting on a structured grid.
+",0,1,0,0,0,0
+11935,Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders,"  An effective approach to non-parallel voice conversion (VC) is to utilize
+deep neural networks (DNNs), specifically variational auto encoders (VAEs), to
+model the latent structure of speech in an unsupervised manner. A previous
+study has confirmed the ef- fectiveness of VAE using the STRAIGHT spectra for
+VC. How- ever, VAE using other types of spectral features such as mel- cepstral
+coefficients (MCCs), which are related to human per- ception and have been
+widely used in VC, have not been prop- erly investigated. Instead of using one
+specific type of spectral feature, it is expected that VAE may benefit from
+using multi- ple types of spectral features simultaneously, thereby improving
+the capability of VAE for VC. To this end, we propose a novel VAE framework
+(called cross-domain VAE, CDVAE) for VC. Specifically, the proposed framework
+utilizes both STRAIGHT spectra and MCCs by explicitly regularizing multiple
+objectives in order to constrain the behavior of the learned encoder and de-
+coder. Experimental results demonstrate that the proposed CD- VAE framework
+outperforms the conventional VAE framework in terms of subjective tests.
+",1,0,0,0,0,0
+11936,Density matrix expansion based semi-local exchange hole applied to range separated density functional theory,"  Exchange hole is the principle constituent in density functional theory,
+which can be used to accurately design exchange energy functional and range
+separated hybrid functionals coupled with some appropriate correlation.
+Recently, density matrix expansion (DME) based semi-local exchange hole
+proposed by Tao-Mo gained attention due to its fulfillment of some exact
+constraints. We propose a new long-range corrected (LC) scheme that combines
+meta-generalized gradient approximation (meta-GGA) exchange functionals
+designed from DME exchange hole coupled with the ab-initio Hartree-Fock (HF)
+exchange integral by separating the Coulomb interaction operator using standard
+error function. Associate with Lee-Yang-Parr (LYP) correlation functional,
+assessment and benchmarking of our functional using well-known test set shows
+that it performs remarkably well for a broad range of molecular properties,
+such as thermochemistry, noncovalent interaction and barrier height of chemical
+reactions.
+",0,1,0,0,0,0
+11937,CANDELS Sheds Light on the Environmental Quenching of Low-mass Galaxies,"  We investigate the environmental quenching of galaxies, especially those with
+stellar masses (M*)$<10^{9.5} M_\odot$, beyond the local universe. Essentially
+all local low-mass quenched galaxies (QGs) are believed to live close to
+massive central galaxies, which is a demonstration of environmental quenching.
+We use CANDELS data to test {\it whether or not} such a dwarf QG--massive
+central galaxy connection exists beyond the local universe. To this purpose, we
+only need a statistically representative, rather than a complete, sample of
+low-mass galaxies, which enables our study to $z\gtrsim1.5$. For each low-mass
+galaxy, we measure the projected distance ($d_{proj}$) to its nearest massive
+neighbor (M*$>10^{10.5} M_\odot$) within a redshift range. At a given redshift
+and M*, the environmental quenching effect is considered to be observed if the
+$d_{proj}$ distribution of QGs ($d_{proj}^Q$) is significantly skewed toward
+lower values than that of star-forming galaxies ($d_{proj}^{SF}$). For galaxies
+with $10^{8} M_\odot < M* < 10^{10} M_\odot$, such a difference between
+$d_{proj}^Q$ and $d_{proj}^{SF}$ is detected up to $z\sim1$. Also, about 10\%
+of the quenched galaxies in our sample are located between two and four virial
+radii ($R_{Vir}$) of the massive halos. The median projected distance from
+low-mass QGs to their massive neighbors, $d_{proj}^Q / R_{Vir}$, decreases with
+satellite M* at $M* \lesssim 10^{9.5} M_\odot$, but increases with satellite M*
+at $M* \gtrsim 10^{9.5} M_\odot$. This trend suggests a smooth, if any,
+transition of the quenching timescale around $M* \sim 10^{9.5} M_\odot$ at
+$0.5<z<1.0$.
+",0,1,0,0,0,0
+11938,BP-homology of elementary abelian 2-groups: BP-module structure,"  We determine the BP-module structure, mod higher filtration, of the main part
+of the BP-homology of elementary abelian 2-groups. The action is related to
+symmetric polynomials and to Dickson invariants.
+",0,0,1,0,0,0
+11939,Probabilistic Active Learning of Functions in Structural Causal Models,"  We consider the problem of learning the functions computing children from
+parents in a Structural Causal Model once the underlying causal graph has been
+identified. This is in some sense the second step after causal discovery.
+Taking a probabilistic approach to estimating these functions, we derive a
+natural myopic active learning scheme that identifies the intervention which is
+optimally informative about all of the unknown functions jointly, given
+previously observed data. We test the derived algorithms on simple examples, to
+demonstrate that they produce a structured exploration policy that
+significantly improves on unstructured base-lines.
+",1,0,0,1,0,0
+11940,Neural Episodic Control,"  Deep reinforcement learning methods attain super-human performance in a wide
+range of environments. Such methods are grossly inefficient, often taking
+orders of magnitudes more data than humans to achieve reasonable performance.
+We propose Neural Episodic Control: a deep reinforcement learning agent that is
+able to rapidly assimilate new experiences and act upon them. Our agent uses a
+semi-tabular representation of the value function: a buffer of past experience
+containing slowly changing state representations and rapidly updated estimates
+of the value function. We show across a wide range of environments that our
+agent learns significantly faster than other state-of-the-art, general purpose
+deep reinforcement learning agents.
+",1,0,0,1,0,0
+11941,School bus routing by maximizing trip compatibility,"  School bus planning is usually divided into routing and scheduling due to the
+complexity of solving them concurrently. However, the separation between these
+two steps may lead to worse solutions with higher overall costs than that from
+solving them together. When finding the minimal number of trips in the routing
+problem, neglecting the importance of trip compatibility may increase the
+number of buses actually needed in the scheduling problem. This paper proposes
+a new formulation for the multi-school homogeneous fleet routing problem that
+maximizes trip compatibility while minimizing total travel time. This
+incorporates the trip compatibility for the scheduling problem in the routing
+problem. Since the problem is inherently just a routing problem, finding a good
+solution is not cumbersome. To compare the performance of the model with
+traditional routing problems, we generate eight mid-size data sets. Through
+importing the generated trips of the routing problems into the bus scheduling
+(blocking) problem, it is shown that the proposed model uses up to 13% fewer
+buses than the common traditional routing models.
+",1,0,0,0,0,0
+11942,Solvability of abstract semilinear equations by a global diffeomorphism theorem,"  In this work we proivied a new simpler proof of the global diffeomorphism
+theorem from [9] which we further apply to consider unique solvability of some
+abstract semilinear equations. Applications to the second order Dirichlet
+problem driven by the Laplace operator are given.
+",0,0,1,0,0,0
+11943,Learning a Generative Model for Validity in Complex Discrete Structures,"  Deep generative models have been successfully used to learn representations
+for high-dimensional discrete spaces by representing discrete objects as
+sequences and employing powerful sequence-based deep models. Unfortunately,
+these sequence-based models often produce invalid sequences: sequences which do
+not represent any underlying discrete structure; invalid sequences hinder the
+utility of such models. As a step towards solving this problem, we propose to
+learn a deep recurrent validator model, which can estimate whether a partial
+sequence can function as the beginning of a full, valid sequence. This
+validator provides insight as to how individual sequence elements influence the
+validity of the overall sequence, and can be used to constrain sequence based
+models to generate valid sequences -- and thus faithfully model discrete
+objects. Our approach is inspired by reinforcement learning, where an oracle
+which can evaluate validity of complete sequences provides a sparse reward
+signal. We demonstrate its effectiveness as a generative model of Python 3
+source code for mathematical expressions, and in improving the ability of a
+variational autoencoder trained on SMILES strings to decode valid molecular
+structures.
+",1,0,0,1,0,0
+11944,On discrete structures in finite Hilbert spaces,"  We present a brief review of discrete structures in a finite Hilbert space,
+relevant for the theory of quantum information. Unitary operator bases,
+mutually unbiased bases, Clifford group and stabilizer states, discrete Wigner
+function, symmetric informationally complete measurements, projective and
+unitary t--designs are discussed. Some recent results in the field are covered
+and several important open questions are formulated. We advocate a geometric
+approach to the subject and emphasize numerous links to various mathematical
+problems.
+",0,0,1,0,0,0
+11945,Selected topics on Toric Varieties,"  This article is based on a series of lectures on toric varieties given at
+RIMS, Kyoto. We start by introducing toric varieties, their basic properties
+and later pass to more advanced topics relating mostly to combinatorics.
+",0,0,1,0,0,0
+11946,Control Capacity,"  Feedback control actively dissipates uncertainty from a dynamical system by
+means of actuation. We develop a notion of ""control capacity"" that gives a
+fundamental limit (in bits) on the rate at which a controller can dissipate the
+uncertainty from a system, i.e. stabilize to a known fixed point. We give a
+computable single-letter characterization of control capacity for memoryless
+stationary scalar multiplicative actuation channels. Control capacity allows us
+to answer questions of stabilizability for scalar linear systems: a system with
+actuation uncertainty is stabilizable if and only if the control capacity is
+larger than the log of the unstable open-loop eigenvalue.
+For second-moment senses of stability, we recover the classic uncertainty
+threshold principle result. However, our definition of control capacity can
+quantify the stabilizability limits for any moment of stability. Our
+formulation parallels the notion of Shannon's communication capacity, and thus
+yields both a strong converse and a way to compute the value of
+side-information in control. The results in our paper are motivated by
+bit-level models for control that build on the deterministic models that are
+widely used to understand information flows in wireless network information
+theory.
+",1,0,1,0,0,0
+11947,Benefits from Superposed Hawkes Processes,"  The superposition of temporal point processes has been studied for many
+years, although the usefulness of such models for practical applications has
+not be fully developed. We investigate superposed Hawkes process as an
+important class of such models, with properties studied in the framework of
+least squares estimation. The superposition of Hawkes processes is demonstrated
+to be beneficial for tightening the upper bound of excess risk under certain
+conditions, and we show the feasibility of the benefit in typical situations.
+The usefulness of superposed Hawkes processes is verified on synthetic data,
+and its potential to solve the cold-start problem of recommendation systems is
+demonstrated on real-world data.
+",0,0,0,1,0,0
+11948,"Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec","  Since the invention of word2vec, the skip-gram model has significantly
+advanced the research of network embedding, such as the recent emergence of the
+DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of
+the aforementioned models with negative sampling can be unified into the matrix
+factorization framework with closed forms. Our analysis and proofs reveal that:
+(1) DeepWalk empirically produces a low-rank transformation of a network's
+normalized Laplacian matrix; (2) LINE, in theory, is a special case of DeepWalk
+when the size of vertices' context is set to one; (3) As an extension of LINE,
+PTE can be viewed as the joint factorization of multiple networks' Laplacians;
+(4) node2vec is factorizing a matrix related to the stationary distribution and
+transition probability tensor of a 2nd-order random walk. We further provide
+the theoretical connections between skip-gram based network embedding
+algorithms and the theory of graph Laplacian. Finally, we present the NetMF
+method as well as its approximation algorithm for computing network embedding.
+Our method offers significant improvements over DeepWalk and LINE for
+conventional network mining tasks. This work lays the theoretical foundation
+for skip-gram based network embedding methods, leading to a better
+understanding of latent network representation learning.
+",1,0,0,1,0,0
+11949,Differentially Private Query Learning: from Data Publishing to Model Publishing,"  With the development of Big Data and cloud data sharing, privacy preserving
+data publishing becomes one of the most important topics in the past decade. As
+one of the most influential privacy definitions, differential privacy provides
+a rigorous and provable privacy guarantee for data publishing. Differentially
+private interactive publishing achieves good performance in many applications;
+however, the curator has to release a large number of queries in a batch or a
+synthetic dataset in the Big Data era. To provide accurate non-interactive
+publishing results in the constraint of differential privacy, two challenges
+need to be tackled: one is how to decrease the correlation between large sets
+of queries, while the other is how to predict on fresh queries. Neither is easy
+to solve by the traditional differential privacy mechanism. This paper
+transfers the data publishing problem to a machine learning problem, in which
+queries are considered as training samples and a prediction model will be
+released rather than query results or synthetic datasets. When the model is
+published, it can be used to answer current submitted queries and predict
+results for fresh queries from the public. Compared with the traditional
+method, the proposed prediction model enhances the accuracy of query results
+for non-interactive publishing. Experimental results show that the proposed
+solution outperforms traditional differential privacy in terms of Mean Absolute
+Value on a large group of queries. This also suggests the learning model can
+successfully retain the utility of published queries while preserving privacy.
+",1,0,0,0,0,0
+11950,Flexible Level-1 Consensus Ensuring Stable Social Choice: Analysis and Algorithms,"  Level-1 Consensus is a property of a preference-profile. Intuitively, it
+means that there exists a preference relation which induces an ordering of all
+other preferences such that frequent preferences are those that are more
+similar to it. This is a desirable property, since it enhances the stability of
+social choice by guaranteeing that there exists a Condorcet winner and it is
+elected by all scoring rules.
+In this paper, we present an algorithm for checking whether a given
+preference profile exhibits level-1 consensus. We apply this algorithm to a
+large number of preference profiles, both real and randomly-generated, and find
+that level-1 consensus is very improbable. We support these empirical findings
+theoretically, by showing that, under the impartial culture assumption, the
+probability of level-1 consensus approaches zero when the number of individuals
+approaches infinity.
+Motivated by these observations, we show that the level-1 consensus property
+can be weakened while retaining its stability implications. We call this weaker
+property Flexible Consensus. We show, both empirically and theoretically, that
+it is considerably more probable than the original level-1 consensus. In
+particular, under the impartial culture assumption, the probability for
+Flexible Consensus converges to a positive number when the number of
+individuals approaches infinity.
+",1,0,0,0,0,0
+11951,Fracture imaging within a granitic rock aquifer using multiple-offset single-hole and cross-hole GPR reflection data,"  The sparsely spaced highly permeable fractures of the granitic rock aquifer
+at Stang-er-Brune (Brittany, France) form a well-connected fracture network of
+high permeability but unknown geometry. Previous work based on optical and
+acoustic logging together with single-hole and cross-hole flowmeter data
+acquired in 3 neighboring boreholes (70-100 m deep) have identified the most
+important permeable fractures crossing the boreholes and their hydraulic
+connections. To constrain possible flow paths by estimating the geometries of
+known and previously unknown fractures, we have acquired, processed and
+interpreted multifold, single- and cross-hole GPR data using 100 and 250 MHz
+antennas. The GPR data processing scheme consisting of time-zero corrections,
+scaling, bandpass filtering and F-X deconvolution, eigenvector filtering,
+muting, pre-stack Kirchhoff depth migration and stacking was used to
+differentiate fluid-filled fracture reflections from source-generated noise.
+The final stacked and pre-stack depth-migrated GPR sections provide
+high-resolution images of individual fractures (dipping 30-90°) in the
+surroundings (2-20 m for the 100 MHz antennas; 2-12 m for the 250 MHz antennas)
+of each borehole in a 2D plane projection that are of superior quality to those
+obtained from single-offset sections. Most fractures previously identified from
+hydraulic testing can be correlated to reflections in the single-hole data.
+Several previously unknown major near vertical fractures have also been
+identified away from the boreholes.
+",0,1,0,0,0,0
+11952,The asymptotic behavior of automorphism groups of function fields over finite fields,"  The purpose of this paper is to investigate the asymptotic behavior of
+automorphism groups of function fields when genus tends to infinity.
+Motivated by applications in coding and cryptography, we consider the maximum
+size of abelian subgroups of the automorphism group
+$\mbox{Aut}(F/\mathbb{F}_q)$ in terms of genus ${g_F}$ for a function field $F$
+over a finite field $\mathbb{F}_q$. Although the whole group
+$\mbox{Aut}(F/\mathbb{F}_q)$ could have size $\Omega({g_F}^4)$, the maximum
+size $m_F$ of abelian subgroups of the automorphism group
+$\mbox{Aut}(F/\mathbb{F}_q)$ is upper bounded by $4g_F+4$ for $g_F\ge 2$. In
+the present paper, we study the asymptotic behavior of $m_F$ by defining
+$M_q=\limsup_{{g_F}\rightarrow\infty}\frac{m_F \cdot \log_q m_F}{g_F}$, where
+$F$ runs through all function fields over $\mathbb{F}_q$. We show that $M_q$
+lies between $2$ and $3$ (or $4$) for odd characteristic (or for even
+characteristic, respectively). This means that $m_F$ grows much more slowly
+than genus does asymptotically.
+The second part of this paper is to study the maximum size $b_F$ of subgroups
+of $\mbox{Aut}(F/\mathbb{F}_q)$ whose order is coprime to $q$. The Hurwitz
+bound gives an upper bound $b_F\le 84(g_F-1)$ for every function field
+$F/\mathbb{F}_q$ of genus $g_F\ge 2$. We investigate the asymptotic behavior of
+$b_F$ by defining ${B_q}=\limsup_{{g_F}\rightarrow\infty}\frac{b_F}{g_F}$,
+where $F$ runs through all function fields over $\mathbb{F}_q$. Although the
+Hurwitz bound shows ${B_q}\le 84$, there are no lower bounds on $B_q$ in
+literature. One does not even know if ${B_q}=0$. For the first time, we show
+that ${B_q}\ge 2/3$ by explicitly constructing some towers of function fields
+in this paper.
+",0,0,1,0,0,0
+11953,Bootstrap Robust Prescriptive Analytics,"  We address the problem of prescribing an optimal decision in a framework
+where its cost depends on uncertain problem parameters $Y$ that need to be
+learned from data. Earlier work by Bertsimas and Kallus (2014) transforms
+classical machine learning methods that merely predict $Y$ from supervised
+training data $[(x_1, y_1), \dots, (x_n, y_n)]$ into prescriptive methods
+taking optimal decisions specific to a particular covariate context $X=\bar x$.
+Their prescriptive methods factor in additional observed contextual information
+on a potentially large number of covariates $X=\bar x$ to take context specific
+actions $z(\bar x)$ which are superior to any static decision $z$. Any naive
+use of limited training data may, however, lead to gullible decisions
+over-calibrated to one particular data set. In this paper, we borrow ideas from
+distributionally robust optimization and the statistical bootstrap of Efron
+(1982) to propose two novel prescriptive methods based on (nw) Nadaraya-Watson
+and (nn) nearest-neighbors learning which safeguard against overfitting and
+lead to improved out-of-sample performance. Both resulting robust prescriptive
+methods reduce to tractable convex optimization problems and enjoy a limited
+disappointment on bootstrap data. We illustrate the data-driven decision-making
+framework and our novel robustness notion on a small news vendor problem as
+well as a small portfolio allocation problem.
+",0,0,0,1,0,0
+11954,Tverberg type theorems for matroids,"  In this paper we show a variant of colorful Tverberg's theorem which is valid
+in any matroid: Let $S$ be a sequence of non-loops in a matroid $M$ of finite
+rank $m$ with closure operator cl. Suppose that $S$ is colored in such a way
+that the first color does not appear more than $r$-times and each other color
+appears at most $(r-1)$-times. Then $S$ can be partitioned into $r$ rainbow
+subsequences $S_1,\ldots, S_r$ such that $cl\,\emptyset\subsetneq
+cl\,S_1\subseteq cl\, S_2\subseteq \ldots \subseteq cl\,S_r$. In particular,
+$\emptyset\neq \bigcap_{i=1}^r cl\,S_i$. A subsequence is called rainbow if it
+contains each color at most once.
+The conclusion of our theorem is weaker than the conclusion of the original
+Tverberg's theorem in $\mathbb R^d$, which states that $\bigcap conv\,S_i\neq
+\emptyset$, whereas we only claim that $\bigcap aff\,S_i\neq \emptyset$. On the
+other hand, our theorem strengthens the Tverberg's theorem in several other
+ways: 1) it is applicable to any matroid (whereas Tverberg's theorem can only
+be used in $\mathbb R^d$), 2) instead of $\bigcap cl\,S_i\neq \emptyset$ we
+have the stronger condition $cl\,\emptyset\subsetneq cl\,S_1\subseteq
+cl\,S_2\subseteq \ldots \subseteq cl\,S_r$, and 3) we add a color constraints
+that are even stronger than the color constraints in the colorful version of
+Tverberg's theorem.
+Recently, the author together with Goaoc, Mabillard, Patáková, Tancer and
+Wagner used the first property and applied the non-colorful version of this
+theorem to homology groups with $GF(p)$ coefficients to obtain several
+non-embeddability results, for details we refer to arXiv:1610.09063.
+",0,0,1,0,0,0
+11955,Dynamic Erdős-Rényi graphs,"  We propose two classes of dynamic versions of the classical Erdős-Rényi
+graph: one in which the transition rates are governed by an external regime
+process, and one in which the transition rates are periodically resampled. For
+both models we consider the evolution of the number of edges present, with
+explicit results for the corresponding moments, functional central limit
+theorems and large deviations asymptotics.
+",0,0,1,0,0,0
+11956,Mining Density Contrast Subgraphs,"  Dense subgraph discovery is a key primitive in many graph mining
+applications, such as detecting communities in social networks and mining gene
+correlation from biological data. Most studies on dense subgraph mining only
+deal with one graph. However, in many applications, we have more than one graph
+describing relations among a same group of entities. In this paper, given two
+graphs sharing the same set of vertices, we investigate the problem of
+detecting subgraphs that contrast the most with respect to density. We call
+such subgraphs Density Contrast Subgraphs, or DCS in short. Two widely used
+graph density measures, average degree and graph affinity, are considered. For
+both density measures, mining DCS is equivalent to mining the densest subgraph
+from a ""difference"" graph, which may have both positive and negative edge
+weights. Due to the existence of negative edge weights, existing dense subgraph
+detection algorithms cannot identify the subgraph we need. We prove the
+computational hardness of mining DCS under the two graph density measures and
+develop efficient algorithms to find DCS. We also conduct extensive experiments
+on several real-world datasets to evaluate our algorithms. The experimental
+results show that our algorithms are both effective and efficient.
+",1,0,0,0,0,0
+11957,Unsupervised Learning by Predicting Noise,"  Convolutional neural networks provide visual features that perform remarkably
+well in many computer vision applications. However, training these networks
+requires significant amounts of supervision. This paper introduces a generic
+framework to train deep networks, end-to-end, with no supervision. We propose
+to fix a set of target representations, called Noise As Targets (NAT), and to
+constrain the deep features to align to them. This domain agnostic approach
+avoids the standard unsupervised learning issues of trivial solutions and
+collapsing of features. Thanks to a stochastic batch reassignment strategy and
+a separable square loss function, it scales to millions of images. The proposed
+approach produces representations that perform on par with state-of-the-art
+unsupervised methods on ImageNet and Pascal VOC.
+",1,0,0,1,0,0
+11958,Trajectory Generation for Millimeter Scale Ferromagnetic Swimmers: Theory and Experiments,"  Microrobots have the potential to impact many areas such as microsurgery,
+micromanipulation and minimally invasive sensing. Due to their small size,
+microrobots swim in a regime that is governed by low Reynolds number
+hydrodynamics. In this paper, we consider small scale artificial swimmers that
+are fabricated using ferromagnetic filaments and locomote in response to time
+varying external magnetic fields. We motivate the design of previously proposed
+control laws using tools from geometric mechanics and also demonstrate how new
+control laws can be synthesized to generate net translation in such swimmers.
+We further describe how to modify these control inputs to make the swimmers
+track rich trajectories in the workspace by investigating stability properties
+of their limit cycles in the orientation angles phase space. Following a
+systematic design optimization, we develop a principled approach to encode
+internal magnetization distributions in millimeter scale ferromagnetic
+filaments. We verify and demonstrate this procedure experimentally and finally
+show translation, trajectory tracking and turning in place locomotion in these
+optimal swimmers using a Helmholtz coils setup.
+",1,0,0,0,0,0
+11959,Decoupled Block-Wise ILU(k) Preconditioner on GPU,"  This research investigates the implementation mechanism of block-wise ILU(k)
+preconditioner on GPU. The block-wise ILU(k) algorithm requires both the level
+k and the block size to be designed as variables. A decoupled ILU(k) algorithm
+consists of a symbolic phase and a factorization phase. In the symbolic phase,
+a ILU(k) nonzero pattern is established from the point-wise structure extracted
+from a block-wise matrix. In the factorization phase, the block-wise matrix
+with a variable block size is factorized into a block lower triangular matrix
+and a block upper triangular matrix. And a further diagonal factorization is
+required to perform on the block upper triangular matrix for adapting a
+parallel triangular solver on GPU.We also present the numerical experiments to
+study the preconditioner actions on different k levels and block sizes.
+",1,0,0,0,0,0
+11960,Multi-agent Economics and the Emergence of Critical Markets,"  The dual crises of the sub-prime mortgage crisis and the global financial
+crisis has prompted a call for explanations of non-equilibrium market dynamics.
+Recently a promising approach has been the use of agent based models (ABMs) to
+simulate aggregate market dynamics. A key aspect of these models is the
+endogenous emergence of critical transitions between equilibria, i.e. market
+collapses, caused by multiple equilibria and changing market parameters.
+Several research themes have developed microeconomic based models that include
+multiple equilibria: social decision theory (Brock and Durlauf), quantal
+response models (McKelvey and Palfrey), and strategic complementarities
+(Goldstein). A gap that needs to be filled in the literature is a unified
+analysis of the relationship between these models and how aggregate criticality
+emerges from the individual agent level. This article reviews the agent-based
+foundations of markets starting with the individual agent perspective of
+McFadden and the aggregate perspective of catastrophe theory emphasising
+connections between the different approaches. It is shown that changes in the
+uncertainty agents have in the value of their interactions with one another,
+even if these changes are one-sided, plays a central role in systemic market
+risks such as market instability and the twin crises effect. These interactions
+can endogenously cause crises that are an emergent phenomena of markets.
+",0,0,0,0,0,1
+11961,Error-Correcting Neural Sequence Prediction,"  In this paper we propose a novel neural language modelling (NLM) method based
+on \textit{error-correcting output codes} (ECOC), abbreviated as ECOC-NLM. This
+latent variable based approach provides a principled way to choose a varying
+amount of latent output codes and avoids exact softmax normalization. Instead
+of minimizing measures between the predicted probability distribution and true
+distribution, we use error-correcting codes to represent both predictions and
+outputs. Secondly, we propose multiple ways to improve accuracy and convergence
+rates by maximizing the separability between codes that correspond to classes
+proportional to word embedding similarities. Lastly, we introduce a novel
+method called \textit{Latent Mixture Sampling}, a technique that is used to
+mitigate exposure bias and can be integrated into training latent-based neural
+language models. This involves mixing the latent codes (i.e variables) of past
+predictions and past targets in one of two ways: (1) according to a predefined
+sampling schedule or (2) a differentiable sampling procedure whereby the mixing
+probability is learned throughout training by replacing the greedy argmax
+operation with a smooth approximation. In evaluating Codeword Mixture Sampling
+for ECOC-NLM, we also baseline it against CWMS in a closely related Hierarhical
+Softmax-based NLM.
+",1,0,0,1,0,0
+11962,Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem,"  We study sampling as optimization in the space of measures. We focus on
+gradient flow-based optimization with the Langevin dynamics as a case study. We
+investigate the source of the bias of the unadjusted Langevin algorithm (ULA)
+in discrete time, and consider how to remove or reduce the bias. We point out
+the difficulty is that the heat flow is exactly solvable, but neither its
+forward nor backward method is implementable in general, except for Gaussian
+data. We propose the symmetrized Langevin algorithm (SLA), which should have a
+smaller bias than ULA, at the price of implementing a proximal gradient step in
+space. We show SLA is in fact consistent for Gaussian target measure, whereas
+ULA is not. We also illustrate various algorithms explicitly for Gaussian
+target measure, including gradient descent, proximal gradient, and
+Forward-Backward, and show they are all consistent.
+",0,0,0,1,0,0
+11963,Deep laser cooling in optical trap: two-level quantum model,"  We study laser cooling of $^{24}$Mg atoms in dipole optical trap with pumping
+field resonant to narrow $(3s3s)\,^1S_0 \rightarrow \, (3s3p)\,^{3}P_1$
+($\lambda = 457$ nm) optical transition. For description of laser cooling of
+atoms in the optical trap with taking into account quantum recoil effects we
+consider two quantum models. The first one is based on direct numerical
+solution of quantum kinetic equation for atom density matrix and the second one
+is simplified model based on decomposition of atom density matrix over
+vibration states in the dipole trap. We search pumping field intensity and
+detuning for minimum cooling energy and fast laser cooling.
+",0,1,0,0,0,0
+11964,JADE: Joint Autoencoders for Dis-Entanglement,"  The problem of feature disentanglement has been explored in the literature,
+for the purpose of image and video processing and text analysis.
+State-of-the-art methods for disentangling feature representations rely on the
+presence of many labeled samples. In this work, we present a novel method for
+disentangling factors of variation in data-scarce regimes. Specifically, we
+explore the application of feature disentangling for the problem of supervised
+classification in a setting where few labeled samples exist, and there are no
+unlabeled samples for use in unsupervised training. Instead, a similar datasets
+exists which shares at least one direction of variation with the
+sample-constrained datasets. We train our model end-to-end using the framework
+of variational autoencoders and are able to experimentally demonstrate that
+using an auxiliary dataset with similar variation factors contribute positively
+to classification performance, yielding competitive results with the
+state-of-the-art in unsupervised learning.
+",1,0,0,1,0,0
+11965,Numerical Simulations of Collisional Cascades at the Roche Limits of White Dwarf Stars,"  We consider the long-term collisional and dynamical evolution of solid
+material orbiting in a narrow annulus near the Roche limit of a white dwarf.
+With orbital velocities of 300 km/sec, systems of solids with initial
+eccentricity $e \gtrsim 10^{-3}$ generate a collisional cascade where objects
+with radii $r \lesssim$ 100--300 km are ground to dust. This process converts
+1-100 km asteroids into 1 $\mu$m particles in $10^2 - 10^6$ yr. Throughout this
+evolution, the swarm maintains an initially large vertical scale height $H$.
+Adding solids at a rate $\dot{M}$ enables the system to find an equilibrium
+where the mass in solids is roughly constant. This equilibrium depends on
+$\dot{M}$ and $r_0$, the radius of the largest solid added to the swarm. When
+$r_0 \lesssim$ 10 km, this equilibrium is stable. For larger $r_0$, the mass
+oscillates between high and low states; the fraction of time spent in high
+states ranges from 100% for large $\dot{M}$ to much less than 1% for small
+$\dot{M}$. During high states, the stellar luminosity reprocessed by the solids
+is comparable to the excess infrared emission observed in many metallic line
+white dwarfs.
+",0,1,0,0,0,0
+11966,Causal Effect Inference with Deep Latent-Variable Models,"  Learning individual-level causal effects from observational data, such as
+inferring the most effective medication for a specific patient, is a problem of
+growing importance for policy makers. The most important aspect of inferring
+causal effects from observational data is the handling of confounders, factors
+that affect both an intervention and its outcome. A carefully designed
+observational study attempts to measure all important confounders. However,
+even if one does not have direct access to all confounders, there may exist
+noisy and uncertain measurement of proxies for confounders. We build on recent
+advances in latent variable modeling to simultaneously estimate the unknown
+latent space summarizing the confounders and the causal effect. Our method is
+based on Variational Autoencoders (VAE) which follow the causal structure of
+inference with proxies. We show our method is significantly more robust than
+existing methods, and matches the state-of-the-art on previous benchmarks
+focused on individual treatment effects.
+",1,0,0,1,0,0
+11967,Gamma-ray bursts and their relation to astroparticle physics and cosmology,"  This article gives an overview of gamma-ray bursts (GRBs) and their relation
+to astroparticle physics and cosmology. GRBs are the most powerful explosions
+in the universe that occur roughly once per day and are characterized by
+flashes of gamma-rays typically lasting from a fraction of a second to
+thousands of seconds. Even after more than four decades since their discovery
+they still remain not fully understood. Two types of GRBs are observed:
+spectrally harder short duration bursts and softer long duration bursts. The
+long GRBs originate from the collapse of massive stars whereas the preferred
+model for the short GRBs is coalescence of compact objects such as two neutron
+stars or a neutron star and a black hole. There were suggestions that GRBs can
+produce ultra-high energy cosmic rays and neutrinos. Also a certain sub-type of
+GRBs may serve as a new standard candle that can help constrain and measure the
+cosmological parameters to much higher redshift than what was possible so far.
+I will review the recent experimental observations.
+",0,1,0,0,0,0
+11968,Discrete-time Risk-sensitive Mean-field Games,"  In this paper, we study a class of discrete-time mean-field games under the
+infinite-horizon risk-sensitive discounted-cost optimality criterion.
+Risk-sensitivity is introduced for each agent (player) via an exponential
+utility function. In this game model, each agent is coupled with the rest of
+the population through the empirical distribution of the states, which affects
+both the agent's individual cost and its state dynamics. Under mild
+assumptions, we establish the existence of a mean-field equilibrium in the
+infinite-population limit as the number of agents ($N$) goes to infinity, and
+then show that the policy obtained from the mean-field equilibrium constitutes
+an approximate Nash equilibrium when $N$ is sufficiently large.
+",1,0,0,0,0,0
+11969,Glasner's problem for Polish groups with metrizable universal minimal flow,"  A problem of Glasner, now known as Glasner's problem, asks whether every
+minimally almost periodic, monothetic, Polish groups is extremely amenable. The
+purpose of this short note is to observe that a positive answer is obtained
+under the additional assumption that the universal minimal flow is metrizable.
+",0,0,1,0,0,0
+11970,VAE with a VampPrior,"  Many different methods to train deep generative models have been introduced
+in the past. In this paper, we propose to extend the variational auto-encoder
+(VAE) framework with a new type of prior which we call ""Variational Mixture of
+Posteriors"" prior, or VampPrior for short. The VampPrior consists of a mixture
+distribution (e.g., a mixture of Gaussians) with components given by
+variational posteriors conditioned on learnable pseudo-inputs. We further
+extend this prior to a two layer hierarchical model and show that this
+architecture with a coupled prior and posterior, learns significantly better
+models. The model also avoids the usual local optima issues related to useless
+latent dimensions that plague VAEs. We provide empirical studies on six
+datasets, namely, static and binary MNIST, OMNIGLOT, Caltech 101 Silhouettes,
+Frey Faces and Histopathology patches, and show that applying the hierarchical
+VampPrior delivers state-of-the-art results on all datasets in the unsupervised
+permutation invariant setting and the best results or comparable to SOTA
+methods for the approach with convolutional networks.
+",1,0,0,1,0,0
+11971,Fast and Strong Convergence of Online Learning Algorithms,"  In this paper, we study the online learning algorithm without explicit
+regularization terms. This algorithm is essentially a stochastic gradient
+descent scheme in a reproducing kernel Hilbert space (RKHS). The polynomially
+decaying step size in each iteration can play a role of regularization to
+ensure the generalization ability of online learning algorithm. We develop a
+novel capacity dependent analysis on the performance of the last iterate of
+online learning algorithm. The contribution of this paper is two-fold. First,
+our nice analysis can lead to the convergence rate in the standard mean square
+distance which is the best so far. Second, we establish, for the first time,
+the strong convergence of the last iterate with polynomially decaying step
+sizes in the RKHS norm. We demonstrate that the theoretical analysis
+established in this paper fully exploits the fine structure of the underlying
+RKHS, and thus can lead to sharp error estimates of online learning algorithm.
+",1,0,0,1,0,0
+11972,A new continuum theory for incompressible swelling materials,"  Swelling media (e.g. gels, tumors) are usually described by mechanical
+constitutive laws (e.g. Hooke or Darcy laws). However, constitutive relations
+of real swelling media are not well known. Here, we take an opposite route and
+consider a simple packing heuristics, i.e. the particles can't overlap. We
+deduce a formula for the equilibrium density under a confining potential. We
+then consider its evolution when the average particle volume and confining
+potential depend on time under two additional heuristics: (i) any two particles
+can't swap their position; (ii) motion should obey some energy minimization
+principle. These heuristics determine the medium velocity consistently with the
+continuity equation. In the direction normal to the potential level sets the
+velocity is related with that of the level sets while in the parallel
+direction, it is determined by a Laplace-Beltrami operator on these sets. This
+complex geometrical feature cannot be recovered using a simple Darcy law.
+",0,0,1,0,0,0
+11973,The Hidden Binary Search Tree:A Balanced Rotation-Free Search Tree in the AVL RAM Model,"  In this paper we generalize the definition of ""Search Trees"" (ST) to enable
+reference values other than the key of prior inserted nodes. The idea builds on
+the assumption an $n$-node AVL (or Red-Black) requires to assure $O(\log_2n)$
+worst-case search time, namely, a single comparison between two keys takes
+constant time. This means the size of each key in bits is fixed to $B=c\log_2
+n$ ($c\geq1$) once $n$ is determined, otherwise the $O(1)$-time comparison
+assumption does not hold. Based on this we calculate \emph{ideal} reference
+values from the mid-point of the interval $0..2^B$. This idea follows
+`recursively' to assure each node along the search path is provided a reference
+value that guarantees an overall logarithmic time. Because the search tree
+property works only when keys are compared to reference values and these values
+are calculated only during searches, we term the data structure as the Hidden
+Binary Search Tree (HBST). We show elementary functions to maintain the HSBT
+height $O(B)=O(\log_2n)$. This result requires no special order on the input --
+as does BST -- nor self-balancing procedures, as do AVL and Red-Black.
+",1,0,0,0,0,0
+11974,The sdB pulsating star V391 Peg and its putative giant planet revisited after 13 years of time-series photometric data,"  V391 Peg (alias HS2201+2610) is a subdwarf B (sdB) pulsating star that shows
+both p- and g-modes. By studying the arrival times of the p-mode maxima and
+minima through the O-C method, in a previous article the presence of a planet
+was inferred with an orbital period of 3.2 yr and a minimum mass of 3.2 M_Jup.
+Here we present an updated O-C analysis using a larger data set of 1066 hours
+of photometric time series (~2.5x larger in terms of the number of data
+points), which covers the period between 1999 and 2012 (compared with 1999-2006
+of the previous analysis). Up to the end of 2008, the new O-C diagram of the
+main pulsation frequency (f1) is compatible with (and improves) the previous
+two-component solution representing the long-term variation of the pulsation
+period (parabolic component) and the giant planet (sine wave component). Since
+2009, the O-C trend of f1 changes, and the time derivative of the pulsation
+period (p_dot) passes from positive to negative; the reason of this change of
+regime is not clear and could be related to nonlinear interactions between
+different pulsation modes. With the new data, the O-C diagram of the secondary
+pulsation frequency (f2) continues to show two components (parabola and sine
+wave), like in the previous analysis. Various solutions are proposed to fit the
+O-C diagrams of f1 and f2, but in all of them, the sinusoidal components of f1
+and f2 differ or at least agree less well than before. The nice agreement found
+previously was a coincidence due to various small effects that are carefully
+analysed. Now, with a larger dataset, the presence of a planet is more
+uncertain and would require confirmation with an independent method. The new
+data allow us to improve the measurement of p_dot for f1 and f2: using only the
+data up to the end of 2008, we obtain p_dot_1=(1.34+-0.04)x10**-12 and
+p_dot_2=(1.62+-0.22)x10**-12 ...
+",0,1,0,0,0,0
+11975,Rates of convergence for inexact Krasnosel'skii-Mann iterations in Banach spaces,"  We study the convergence of an inexact version of the classical
+Krasnosel'skii-Mann iteration for computing fixed points of nonexpansive maps.
+Our main result establishes a new metric bound for the fixed-point residuals,
+from which we derive their rate of convergence as well as the convergence of
+the iterates towards a fixed point. The results are applied to three variants
+of the basic iteration: infeasible iterations with approximate projections, the
+Ishikawa iteration, and diagonal Krasnosels'kii-Mann schemes. The results are
+also extended to continuous time in order to study the asymptotics of
+nonautonomous evolution equations governed by nonexpansive operators.
+",0,0,1,0,0,0
+11976,Automatic Estimation of Fetal Abdominal Circumference from Ultrasound Images,"  Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal
+biometry, and owing to its time-consuming process, there has been a great
+demand for automatic estimation. However, the automated analysis of ultrasound
+images is complicated because they are patient-specific, operator-dependent,
+and machine-specific. Among various types of fetal biometry, the accurate
+estimation of abdominal circumference (AC) is especially difficult to perform
+automatically because the abdomen has low contrast against surroundings,
+non-uniform contrast, and irregular shape compared to other parameters.We
+propose a method for the automatic estimation of the fetal AC from 2D
+ultrasound data through a specially designed convolutional neural network
+(CNN), which takes account of doctors' decision process, anatomical structure,
+and the characteristics of the ultrasound image. The proposed method uses CNN
+to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical
+vein) and Hough transformation for measuring AC. We test the proposed method
+using clinical ultrasound data acquired from 56 pregnant women. Experimental
+results show that, with relatively small training samples, the proposed CNN
+provides sufficient classification results for AC estimation through the Hough
+transformation. The proposed method automatically estimates AC from ultrasound
+images. The method is quantitatively evaluated, and shows stable performance in
+most cases and even for ultrasound images deteriorated by shadowing artifacts.
+As a result of experiments for our acceptance check, the accuracies are 0.809
+and 0.771 with the expert 1 and expert 2, respectively, while the accuracy
+between the two experts is 0.905. However, for cases of oversized fetus, when
+the amniotic fluid is not observed or the abdominal area is distorted, it could
+not correctly estimate AC.
+",1,0,0,1,0,0
+11977,Heavy fermion quantum criticality at dilute carrier limit in CeNi$_{2-δ}$(As$_{1-x}$P$_{x}$)$_{2}$,"  We study the quantum phase transitions in the nickel pnctides,
+CeNi$_{2-\delta}$(As$_{1-x}$P$_{x}$)$_{2}$ ($\delta$ $\approx$ 0.07-0.22). This
+series displays the distinct heavy fermion behavior in the rarely studied
+parameter regime of dilute carrier limit. We systematically investigate the
+magnetization, specific heat and electrical transport down to low temperatures.
+Upon increasing the P-content, the antiferromagnetic order of the Ce-4$f$
+moment is suppressed continuously and vanishes at $x_c \sim$ 0.55. At this
+doping, the temperature dependences of the specific heat and longitudinal
+resistivity display non-Fermi liquid behavior. Both the residual resistivity
+$\rho_0$ and the Sommerfeld coefficient $\gamma_0$ are sharply peaked around
+$x_c$. When the P-content reaches close to 100\%, we observe a clear
+low-temperature crossover into the Fermi liquid regime. In contrast to what
+happens in the parent compound $x$ = 0.0 as a function of pressure, we find a
+surprising result that the non-Fermi liquid behavior persists over a nonzero
+range of doping concentration, $x_c<x<0.9$. In this doping range, at the lowest
+measured temperatures, the temperature dependence of the specific-heat
+coefficient is logarithmically divergent and that of the electrical resistivity
+is linear. We discuss the properties of
+CeNi$_{2-\delta}$(As$_{1-x}$P$_{x}$)$_{2}$ in comparison with those of its 1111
+counterpart, CeNi(As$_{1-x}$P$_{x}$)O. Our results indicate a non-Fermi liquid
+phase in the global phase diagram of heavy fermion metals.
+",0,1,0,0,0,0
+11978,Invariant Gibbs measures for the 2-d defocusing nonlinear wave equations,"  We consider the defocusing nonlinear wave equations (NLW) on the
+two-dimensional torus. In particular, we construct invariant Gibbs measures for
+the renormalized so-called Wick ordered NLW. We then prove weak universality of
+the Wick ordered NLW, showing that the Wick ordered NLW naturally appears as a
+suitable scaling limit of non-renormalized NLW with Gaussian random initial
+data.
+",0,0,1,0,0,0
+11979,Topological Analysis and Synthesis of Structures related to Certain Classes of K-Geodetic Computer Networks,"  A fundamental characteristic of computer networks is their topological
+structure. The question of the description of the structural characteristics of
+computer networks represents a problem that is not completely solved. Search
+methods for structures of computer networks, for which the values of the
+selected parameters of their operation quality are extreme, have not been
+completely developed. The construction of computer networks with optimum
+indices of their operation quality is reduced to the solution of discrete
+optimization problems over graphs. This paper describes in detail the
+advantages of the practical use of k-geodetic graphs [2, 3] in the topological
+design of computer networks as an alternative for the solution of the
+fundamental problems mentioned above which, we believe, are still open. Also,
+the topological analysis and synthesis of some classes of these networks have
+been performed.
+",1,0,0,0,0,0
+11980,A distributed-memory hierarchical solver for general sparse linear systems,"  We present a parallel hierarchical solver for general sparse linear systems
+on distributed-memory machines. For large-scale problems, this fully algebraic
+algorithm is faster and more memory-efficient than sparse direct solvers
+because it exploits the low-rank structure of fill-in blocks. Depending on the
+accuracy of low-rank approximations, the hierarchical solver can be used either
+as a direct solver or as a preconditioner. The parallel algorithm is based on
+data decomposition and requires only local communication for updating boundary
+data on every processor. Moreover, the computation-to-communication ratio of
+the parallel algorithm is approximately the volume-to-surface-area ratio of the
+subdomain owned by every processor. We present various numerical results to
+demonstrate the versatility and scalability of the parallel algorithm.
+",1,0,0,0,0,0
+11981,Quasar: Datasets for Question Answering by Search and Reading,"  We present two new large-scale datasets aimed at evaluating systems designed
+to comprehend a natural language query and extract its answer from a large
+corpus of text. The Quasar-S dataset consists of 37000 cloze-style
+(fill-in-the-gap) queries constructed from definitions of software entity tags
+on the popular website Stack Overflow. The posts and comments on the website
+serve as the background corpus for answering the cloze questions. The Quasar-T
+dataset consists of 43000 open-domain trivia questions and their answers
+obtained from various internet sources. ClueWeb09 serves as the background
+corpus for extracting these answers. We pose these datasets as a challenge for
+two related subtasks of factoid Question Answering: (1) searching for relevant
+pieces of text that include the correct answer to a query, and (2) reading the
+retrieved text to answer the query. We also describe a retrieval system for
+extracting relevant sentences and documents from the corpus given a query, and
+include these in the release for researchers wishing to only focus on (2). We
+evaluate several baselines on both datasets, ranging from simple heuristics to
+powerful neural models, and show that these lag behind human performance by
+16.4% and 32.1% for Quasar-S and -T respectively. The datasets are available at
+this https URL .
+",1,0,0,0,0,0
+11982,Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems,"  As robotic systems are moved out of factory work cells into human-facing
+environments questions of choreography become central to their design,
+placement, and application. With a human viewer or counterpart present, a
+system will automatically be interpreted within context, style of movement, and
+form factor by human beings as animate elements of their environment. The
+interpretation by this human counterpart is critical to the success of the
+system's integration: knobs on the system need to make sense to a human
+counterpart; an artificial agent should have a way of notifying a human
+counterpart of a change in system state, possibly through motion profiles; and
+the motion of a human counterpart may have important contextual clues for task
+completion. Thus, professional choreographers, dance practitioners, and
+movement analysts are critical to research in robotics. They have design
+methods for movement that align with human audience perception, can identify
+simplified features of movement for human-robot interaction goals, and have
+detailed knowledge of the capacity of human movement. This article provides
+approaches employed by one research lab, specific impacts on technical and
+artistic projects within, and principles that may guide future such work. The
+background section reports on choreography, somatic perspectives,
+improvisation, the Laban/Bartenieff Movement System, and robotics. From this
+context methods including embodied exercises, writing prompts, and community
+building activities have been developed to facilitate interdisciplinary
+research. The results of this work is presented as an overview of a smattering
+of projects in areas like high-level motion planning, software development for
+rapid prototyping of movement, artistic output, and user studies that help
+understand how people interpret movement. Finally, guiding principles for other
+groups to adopt are posited.
+",1,0,0,0,0,0
+11983,Development of verification system of socio-demographic data of virtual community member,"  The important task of developing verification system of data of virtual
+community member on the basis of computer-linguistic analysis of the content of
+a large sample of Ukrainian virtual communities is solved. The subject of
+research is methods and tools for verification of web-members socio-demographic
+characteristics based on computer-linguistic analysis of their communicative
+interaction results. The aim of paper is to verifying web-user personal data on
+the basis of computer-linguistic analysis of web-members information tracks.
+The structure of verification software for web-user profile is designed for a
+practical implementation of assigned tasks. The method of personal data
+verification of web-members by analyzing information track of virtual community
+member is conducted. For the first time the method for checking the
+authenticity of web members personal data, which helped to design of
+verification tool for socio-demographic characteristics of web-member is
+developed. The verification system of data of web-members, which forms the
+verified socio-demographic profiles of web-members, is developed as a result of
+conducted experiments. Also the user interface of the developed verification
+system web-members data is presented. Effectiveness and efficiency of use of
+the developed methods and means for solving tasks in web-communities
+administration is proved by their approbation. The number of false results of
+verification system is 18%.
+",1,0,0,0,0,0
+11984,"Demonstration of an efficient, photonic-based astronomical spectrograph on an 8-m telescope","  We demonstrate for the first time an efficient, photonic-based astronomical
+spectrograph on the 8-m Subaru Telescope. An extreme adaptive optics system is
+combined with pupil apodiziation optics to efficiently inject light directly
+into a single-mode fiber, which feeds a compact cross-dispersed spectrograph
+based on array waveguide grating technology. The instrument currently offers a
+throughput of 5% from sky-to-detector which we outline could easily be upgraded
+to ~13% (assuming a coupling efficiency of 50%). The isolated spectrograph
+throughput from the single-mode fiber to detector was 42% at 1550 nm. The
+coupling efficiency into the single-mode fiber was limited by the achievable
+Strehl ratio on a given night. A coupling efficiency of 47% has been achieved
+with ~60% Strehl ratio on-sky to date. Improvements to the adaptive optics
+system will enable 90% Strehl ratio and a coupling of up to 67% eventually.
+This work demonstrates that the unique combination of advanced technologies
+enables the realization of a compact and highly efficient spectrograph, setting
+a precedent for future instrument design on very-large and extremely-large
+telescopes.
+",0,1,0,0,0,0
+11985,A Game of Random Variables,"  This paper analyzes a simple game with $n$ players. We fix a mean, $\mu$, in
+the interval $[0, 1]$ and let each player choose any random variable
+distributed on that interval with the given mean. The winner of the zero-sum
+game is the player whose random variable has the highest realization. We show
+that the position of the mean within the interval is paramount. Remarkably, if
+the given mean is above a crucial threshold then the unique equilibrium must
+contain a point mass on $1$. The cutoff is strictly decreasing in the number of
+players, $n$; and for fixed $\mu$, as the number of players is increased, each
+player places more weight on $1$ at equilibrium. We characterize the
+equilibrium as the number of players goes to infinity.
+",1,0,0,0,0,0
+11986,Parallel Structure from Motion from Local Increment to Global Averaging,"  In this paper, we tackle the accurate and consistent Structure from Motion
+(SfM) problem, in particular camera registration, far exceeding the memory of a
+single computer in parallel. Different from the previous methods which
+drastically simplify the parameters of SfM and sacrifice the accuracy of the
+final reconstruction, we try to preserve the connectivities among cameras by
+proposing a camera clustering algorithm to divide a large SfM problem into
+smaller sub-problems in terms of camera clusters with overlapping. We then
+exploit a hybrid formulation that applies the relative poses from local
+incremental SfM into a global motion averaging framework and produce accurate
+and consistent global camera poses. Our scalable formulation in terms of camera
+clusters is highly applicable to the whole SfM pipeline including track
+generation, local SfM, 3D point triangulation and bundle adjustment. We are
+even able to reconstruct the camera poses of a city-scale data-set containing
+more than one million high-resolution images with superior accuracy and
+robustness evaluated on benchmark, Internet, and sequential data-sets.
+",1,0,0,0,0,0
+11987,Proceedings 2nd Workshop on Models for Formal Analysis of Real Systems,"  This volume contains the proceedings of MARS 2017, the second workshop on
+Models for Formal Analysis of Real Systems, held on April 29, 2017 in Uppala,
+Sweden, as an affiliated workshop of ETAPS 2017, the European Joint Conferences
+on Theory and Practice of Software.
+The workshop emphasises modelling over verification. It aims at discussing
+the lessons learned from making formal methods for the verification and
+analysis of realistic systems. Examples are:
+(1) Which formalism is chosen, and why?
+(2) Which abstractions have to be made and why?
+(3) How are important characteristics of the system modelled?
+(4) Were there any complications while modelling the system?
+(5) Which measures were taken to guarantee the accuracy of the model?
+We invited papers that present full models of real systems, which may lay the
+basis for future comparison and analysis. An aim of the workshop is to present
+different modelling approaches and discuss pros and cons for each of them.
+Alternative formal descriptions of the systems presented at this workshop are
+encouraged, which should foster the development of improved specification
+formalisms.
+",1,0,0,0,0,0
+11988,Negative electronic compressibility and nanoscale inhomogeneity in ionic-liquid gated two-dimensional superconductors,"  When the electron density of highly crystalline thin films is tuned by
+chemical doping or ionic liq- uid gating, interesting effects appear including
+unconventional superconductivity, sizeable spin-orbit coupling, competition
+with charge-density waves, and a debated low-temperature metallic state that
+seems to avoid the superconducting or insulating fate of standard
+two-dimensional electron systems. Some experiments also find a marked tendency
+to a negative electronic compressibility. We suggest that this indicates an
+inclination for electronic phase separation resulting in a nanoscopic inhomo-
+geneity. Although the mild modulation of the inhomogeneous landscape is
+compatible with a high electron mobility in the metallic state, this
+intrinsically inhomogeneous character is highlighted by the peculiar behaviour
+of the metal-to-superconductor transition. Modelling the system with super-
+conducting puddles embedded in a metallic matrix, we fit the peculiar
+resistance vs. temperature curves of systems like TiSe2, MoS2, and ZrNCl. In
+this framework also the low-temperature debated metallic state finds a natural
+explanation in terms of the pristine metallic background embedding
+non-percolating superconducting clusters. An intrinsically inhomogeneous
+character naturally raises the question of the formation mechanism(s). We
+propose a mechanism based on the interplay be- tween electrons and the charges
+of the gating ionic liquid.
+",0,1,0,0,0,0
+11989,Symbolic Music Genre Transfer with CycleGAN,"  Deep generative models such as Variational Autoencoders (VAEs) and Generative
+Adversarial Networks (GANs) have recently been applied to style and domain
+transfer for images, and in the case of VAEs, music. GAN-based models employing
+several generators and some form of cycle consistency loss have been among the
+most successful for image domain transfer. In this paper we apply such a model
+to symbolic music and show the feasibility of our approach for music genre
+transfer. Evaluations using separate genre classifiers show that the style
+transfer works well. In order to improve the fidelity of the transformed music,
+we add additional discriminators that cause the generators to keep the
+structure of the original music mostly intact, while still achieving strong
+genre transfer. Visual and audible results further show the potential of our
+approach. To the best of our knowledge, this paper represents the first
+application of GANs to symbolic music domain transfer.
+",1,0,0,0,0,0
+11990,"Methodology for Multi-stage, Operations- and Uncertainty-Aware Placement and Sizing of FACTS Devices in a Large Power Transmission System","  We develop new optimization methodology for planning installation of Flexible
+Alternating Current Transmission System (FACTS) devices of the parallel and
+shunt types into large power transmission systems, which allows to delay or
+avoid installations of generally much more expensive power lines. Methodology
+takes as an input projected economic development, expressed through a paced
+growth of the system loads, as well as uncertainties, expressed through
+multiple scenarios of the growth. We price new devices according to their
+capacities. Installation cost contributes to the optimization objective in
+combination with the cost of operations integrated over time and averaged over
+the scenarios. The multi-stage (-time-frame) optimization aims to achieve a
+gradual distribution of new resources in space and time. Constraints on the
+investment budget, or equivalently constraint on building capacity, is
+introduced at each time frame. Our approach adjusts operationally not only
+newly installed FACTS devices but also other already existing flexible degrees
+of freedom. This complex optimization problem is stated using the most general
+AC Power Flows. Non-linear, non-convex, multiple-scenario and multi-time-frame
+optimization is resolved via efficient heuristics, consisting of a sequence of
+alternating Linear Programmings or Quadratic Programmings (depending on the
+generation cost) and AC-PF solution steps designed to maintain operational
+feasibility for all scenarios. Computational scalability and application of the
+newly developed approach is illustrated on the example of the 2736-nodes large
+Polish system. One most important advantage of the framework is that the
+optimal capacity of FACTS is build up gradually at each time frame in a limited
+number of locations, thus allowing to prepare the system better for possible
+congestion due to future economic and other uncertainties.
+",1,1,1,0,0,0
+11991,PC Proxy: A New Method of Dynamical Tracer Reconstruction,"  A detailed development of the principal component proxy method of dynamical
+tracer reconstruction is presented, including error analysis. The method works
+by correlating the largest principal components of a matrix representation of
+the transport dynamics with a set of sparse measurements. The Lyapunov spectrum
+was measured and used to quantify the lifetime of each principal component. The
+method was tested on the 500 K isentropic surface with ozone measurements from
+the Polar Aerosol and Ozone Measurement (POAM) III satellite instrument during
+October and November 1998 and compared with the older proxy tracer method which
+works by correlating measurements with a single other tracer or proxy. Using a
+60 day integration time and five (5) principal components, cross validation of
+globally reconstructed ozone and comparison with ozone sondes returned
+root-mean-square errors of 0.16 ppmv and 0.36 ppmv, respectively. This compares
+favourably with the classic proxy tracer method in which a passive tracer
+equivalent latitude field was used for the proxy and which returned RMS errors
+of 0.30 ppmv and 0.59 ppmv for cross-validation and sonde validation
+respectively. The method seems especially effective for shorter lived tracers
+and was far more accurate than the classic method at predicting ozone
+concentration in the Southern hemisphere at the end of winter.
+",0,1,0,0,0,0
+11992,Magnetic Correlations in the Two-dimensional Repulsive Fermi Hubbard Model,"  The repulsive Fermi Hubbard model on the square lattice has a rich phase
+diagram near half-filling (corresponding to the particle density per lattice
+site $n=1$): for $n=1$ the ground state is an antiferromagnetic insulator, at
+$0.6 < n \lesssim 0.8$, it is a $d_{x^2-y^2}$-wave superfluid (at least for
+moderately strong interactions $U \lesssim 4t$ in terms of the hopping $t$),
+and the region $1-n \ll 1$ is most likely subject to phase separation. Much of
+this physics is preempted at finite temperatures and to an extent driven by
+strong magnetic fluctuations, their quantitative characteristics and how they
+change with the doping level being much less understood. Experiments on
+ultra-cold atoms have recently gained access to this interesting fluctuation
+regime, which is now under extensive investigation. In this work we employ a
+self-consistent skeleton diagrammatic approach to quantify the characteristic
+temperature scale $T_{M}(n)$ for the onset of magnetic fluctuations with a
+large correlation length and identify their nature. Our results suggest that
+the strongest fluctuations---and hence highest $T_{M}$ and easiest experimental
+access to this regime---are observed at $U/t \approx 4-6$.
+",0,1,0,0,0,0
+11993,Bridge type classification: supervised learning on a modified NBI dataset,"  A key phase in the bridge design process is the selection of the structural
+system. Due to budget and time constraints, engineers typically rely on
+engineering judgment and prior experience when selecting a structural system,
+often considering a limited range of design alternatives. The objective of this
+study was to explore the suitability of supervised machine learning as a
+preliminary design aid that provides guidance to engineers with regards to the
+statistically optimal bridge type to choose, ultimately improving the
+likelihood of optimized design, design standardization, and reduced maintenance
+costs. In order to devise this supervised learning system, data for over
+600,000 bridges from the National Bridge Inventory database were analyzed. Key
+attributes for determining the bridge structure type were identified through
+three feature selection techniques. Potentially useful attributes like seismic
+intensity and historic data on the cost of materials (steel and concrete) were
+then added from the US Geological Survey (USGS) database and Engineering News
+Record. Decision tree, Bayes network and Support Vector Machines were used for
+predicting the bridge design type. Due to state-to-state variations in material
+availability, material costs, and design codes, supervised learning models
+based on the complete data set did not yield favorable results. Supervised
+learning models were then trained and tested using 10-fold cross validation on
+data for each state. Inclusion of seismic data improved the model performance
+noticeably. The data was then resampled to reduce the bias of the models
+towards more common design types, and the supervised learning models thus
+constructed showed further improvements in performance. The average recall and
+precision for the state models was 88.6% and 88.0% using Decision Trees, 84.0%
+and 83.7% using Bayesian Networks, and 80.8% and 75.6% using SVM.
+",0,0,0,1,0,0
+11994,Automatic Question-Answering Using A Deep Similarity Neural Network,"  Automatic question-answering is a classical problem in natural language
+processing, which aims at designing systems that can automatically answer a
+question, in the same way as human does. In this work, we propose a deep
+learning based model for automatic question-answering. First the questions and
+answers are embedded using neural probabilistic modeling. Then a deep
+similarity neural network is trained to find the similarity score of a pair of
+answer and question. Then for each question, the best answer is found as the
+one with the highest similarity score. We first train this model on a
+large-scale public question-answering database, and then fine-tune it to
+transfer to the customer-care chat data. We have also tested our framework on a
+public question-answering database and achieved very good performance.
+",1,0,0,0,0,0
+11995,Long ties accelerate noisy threshold-based contagions,"  Changes to network structure can substantially affect when and how widely new
+ideas, products, and conventions are adopted. In models of biological
+contagion, interventions that randomly rewire edges (making them ""longer"")
+accelerate spread. However, there are other models relevant to social
+contagion, such as those motivated by myopic best-response in games with
+strategic complements, in which individual's behavior is described by a
+threshold number of adopting neighbors above which adoption occurs (i.e.,
+complex contagions). Recent work has argued that highly clustered, rather than
+random, networks facilitate spread of these complex contagions. Here we show
+that minor modifications of prior analyses, which make them more realistic,
+reverse this result. The modification is that we allow very rarely below
+threshold adoption, i.e., very rarely adoption occurs, where there is only one
+adopting neighbor. To model the trade-off between long and short edges we
+consider networks that are the union of cycle-power-$k$ graphs and random
+graphs on $n$ nodes. We study how the time to global spread changes as we
+replace the cycle edges with (random) long ties. Allowing adoptions below
+threshold to occur with order $1/\sqrt{n}$ probability is enough to ensure that
+random rewiring accelerates spread. Simulations illustrate the robustness of
+these results to other commonly-posited models for noisy best-response
+behavior. We then examine empirical social networks, where we find that
+hypothetical interventions that (a) randomly rewire existing edges or (b) add
+random edges reduce time to spread compared with the original network or
+addition of ""short"", triad-closing edges, respectively. This substantially
+revises conclusions about how interventions change the spread of behavior,
+suggesting that those wanting to increase spread should induce formation of
+long ties, rather than triad-closing ties.
+",1,0,0,0,0,0
+11996,Detecting topological transitions in two dimensions by Hamiltonian evolution,"  We show that the evolution of two-component particles governed by a
+two-dimensional spin-orbit lattice Hamiltonian can reveal transitions between
+topological phases. A kink in the mean width of the particle distribution
+signals the closing of the band gap, a prerequisite for a quantum phase
+transition between topological phases. Furthermore, for realistic and
+experimentally motivated Hamiltonians the density profile in topologically
+non-trivial phases displays characteristic rings in the vicinity of the origin
+that are absent in trivial phases. The results are expected to have immediate
+application to systems of ultracold atoms and photonic lattices.
+",0,1,0,0,0,0
+11997,A search for optical bursts from the repeating fast radio burst FRB 121102,"  We present a search for optical bursts from the repeating fast radio burst
+FRB 121102 using simultaneous observations with the high-speed optical camera
+ULTRASPEC on the 2.4-m Thai National Telescope and radio observations with the
+100-m Effelsberg Radio Telescope. A total of 13 radio bursts were detected, but
+we found no evidence for corresponding optical bursts in our 70.7-ms frames.
+The 5-sigma upper limit to the optical flux density during our observations is
+0.33 mJy at 767nm. This gives an upper limit for the optical burst fluence of
+0.046 Jy ms, which constrains the broadband spectral index of the burst
+emission to alpha < -0.2. Two of the radio pulses are separated by just 34 ms,
+which may represent an upper limit on a possible underlying periodicity (a
+rotation period typical of pulsars), or these pulses may have come from a
+single emission window that is a small fraction of a possible period.
+",0,1,0,0,0,0
+11998,de Haas-van Alphen measurement of the antiferromagnet URhIn$_5$,"  We report on the results of a de Haas-van Alphen (dHvA) measurement performed
+on the recently discovered antiferromagnet URhIn$_5$ ($T_N$ = 98 K), a
+5\textit{f}-analogue of the well studied heavy fermion antiferromagnet
+CeRhIn$_5$. The Fermi surface is found to consist of four surfaces: a roughly
+spherical pocket $\beta$, with $F_\beta \simeq 0.3$ kT; a pillow-shaped closed
+surface, $\alpha$, with $F_\alpha \simeq 1.1$ kT; and two higher frequencies
+$\gamma_1$ with $F_{\gamma_1} \simeq 3.2$ kT and $\gamma_2$ with $F_{\gamma_2}
+\simeq 3.5$ kT that are seen only near the \textit{c}-axis, and that may arise
+on cylindrical Fermi surfaces. The measured cyclotron masses range from 1.9
+$m_e$ to 4.3 $m_e$. A simple LDA+SO calculation performed for the paramagnetic
+ground state shows a very different Fermi surface topology, demonstrating a
+need for more advanced electronic structure calculations.
+",0,1,0,0,0,0
+11999,Indoor Frame Recovery from Refined Line Segments,"  An important yet challenging problem in understanding indoor scene is
+recovering indoor frame structure from a monocular image. It is more difficult
+when occlusions and illumination vary, and object boundaries are weak. To
+overcome these difficulties, a new approach based on line segment refinement
+with two constraints is proposed. First, the line segments are refined by four
+consecutive operations, i.e., reclassifying, connecting, fitting, and voting.
+Specifically, misclassified line segments are revised by the reclassifying
+operation, some short line segments are joined by the connecting operation, the
+undetected key line segments are recovered by the fitting operation with the
+help of the vanishing points, the line segments converging on the frame are
+selected by the voting operation. Second, we construct four frame models
+according to four classes of possible shooting angles of the monocular image,
+the natures of all frame models are introduced via enforcing the cross ratio
+and depth constraints. The indoor frame is then constructed by fitting those
+refined line segments with related frame model under the two constraints, which
+jointly advance the accuracy of the frame. Experimental results on a collection
+of over 300 indoor images indicate that our algorithm has the capability of
+recovering the frame from complex indoor scenes.
+",1,0,0,0,0,0
+12000,Fairly Allocating Contiguous Blocks of Indivisible Items,"  In this paper, we study the classic problem of fairly allocating indivisible
+items with the extra feature that the items lie on a line. Our goal is to find
+a fair allocation that is contiguous, meaning that the bundle of each agent
+forms a contiguous block on the line. While allocations satisfying the
+classical fairness notions of proportionality, envy-freeness, and equitability
+are not guaranteed to exist even without the contiguity requirement, we show
+the existence of contiguous allocations satisfying approximate versions of
+these notions that do not degrade as the number of agents or items increases.
+We also study the efficiency loss of contiguous allocations due to fairness
+constraints.
+",1,0,0,0,0,0
+12001,Optimization and Testing in Linear Non-Gaussian Component Analysis,"  Independent component analysis (ICA) decomposes multivariate data into
+mutually independent components (ICs). The ICA model is subject to a constraint
+that at most one of these components is Gaussian, which is required for model
+identifiability. Linear non-Gaussian component analysis (LNGCA) generalizes the
+ICA model to a linear latent factor model with any number of both non-Gaussian
+components (signals) and Gaussian components (noise), where observations are
+linear combinations of independent components. Although the individual Gaussian
+components are not identifiable, the Gaussian subspace is identifiable. We
+introduce an estimator along with its optimization approach in which
+non-Gaussian and Gaussian components are estimated simultaneously, maximizing
+the discrepancy of each non-Gaussian component from Gaussianity while
+minimizing the discrepancy of each Gaussian component from Gaussianity. When
+the number of non-Gaussian components is unknown, we develop a statistical test
+to determine it based on resampling and the discrepancy of estimated
+components. Through a variety of simulation studies, we demonstrate the
+improvements of our estimator over competing estimators, and we illustrate the
+effectiveness of the test to determine the number of non-Gaussian components.
+Further, we apply our method to real data examples and demonstrate its
+practical value.
+",0,0,1,1,0,0
+12002,New Horizons Ring Collision Hazard: Constraints from Earth-based Observations,"  The New Horizons spacecraft's nominal trajectory crosses the planet's
+satellite plane at $\sim 10,000\ \rm{km}$ from the barycenter, between the
+orbits of Pluto and Charon. I have investigated the risk to the spacecraft
+based on observational limits of rings and dust within this region, assuming
+various particle size distributions. The best limits are placed by 2011 and
+2012 HST observations, which significantly improve on the limits from stellar
+occultations, although they do not go as close to the planet. From the HST data
+and assuming a `reasonable worst case' for the size distribution, we place a
+limit of $N < 20$ damaging impacts by grains of radius $> 0.2\ \textrm{mm}$
+onto the spacecraft during the encounter. The number of hits is $\approx$
+200$\times$ above the NH mission requirement, and $\approx$ $2000\times$ above
+the mission's desired level. Stellar occultations remain valuable because they
+are able to measure $N$ closer to the Pluto surface than direct imaging,
+although with a sensitivity limit several orders of magnitude higher than that
+from HST imaging. Neither HST nor occultations are sensitive enough to place
+limits on $N$ at or below the mission requirements.
+",0,1,0,0,0,0
+12003,Dependability of Sensor Networks for Industrial Prognostics and Health Management,"  Maintenance is an important activity in industry. It is performed either to
+revive a machine/component or to prevent it from breaking down. Different
+strategies have evolved through time, bringing maintenance to its current
+state: condition-based and predictive maintenances. This evolution was due to
+the increasing demand of reliability in industry. The key process of
+condition-based and predictive maintenances is prognostics and health
+management, and it is a tool to predict the remaining useful life of
+engineering assets. Nowadays, plants are required to avoid shutdowns while
+offering safety and reliability. Nevertheless, planning a maintenance activity
+requires accurate information about the system/component health state. Such
+information is usually gathered by means of independent sensor nodes. In this
+study, we consider the case where the nodes are interconnected and form a
+wireless sensor network. As far as we know, no research work has considered
+such a case of study for prognostics. Regarding the importance of data
+accuracy, a good prognostics requires reliable sources of information. This is
+why, in this paper, we will first discuss the dependability of wireless sensor
+networks, and then present a state of the art in prognostic and health
+management activities.
+",1,0,0,0,0,0
+12004,On Markov Chain Gradient Descent,"  Stochastic gradient methods are the workhorse (algorithms) of large-scale
+optimization problems in machine learning, signal processing, and other
+computational sciences and engineering. This paper studies Markov chain
+gradient descent, a variant of stochastic gradient descent where the random
+samples are taken on the trajectory of a Markov chain. Existing results of this
+method assume convex objectives and a reversible Markov chain and thus have
+their limitations. We establish new non-ergodic convergence under wider step
+sizes, for nonconvex problems, and for non-reversible finite-state Markov
+chains. Nonconvexity makes our method applicable to broader problem classes.
+Non-reversible finite-state Markov chains, on the other hand, can mix
+substatially faster. To obtain these results, we introduce a new technique that
+varies the mixing levels of the Markov chains. The reported numerical results
+validate our contributions.
+",0,0,0,1,0,0
+12005,Reduction of Second-Order Network Systems with Structure Preservation,"  This paper proposes a general framework for structure-preserving model
+reduction of a secondorder network system based on graph clustering. In this
+approach, vertex dynamics are captured by the transfer functions from inputs to
+individual states, and the dissimilarities of vertices are quantified by the
+H2-norms of the transfer function discrepancies. A greedy hierarchical
+clustering algorithm is proposed to place those vertices with similar dynamics
+into same clusters. Then, the reduced-order model is generated by the
+Petrov-Galerkin method, where the projection is formed by the characteristic
+matrix of the resulting network clustering. It is shown that the simplified
+system preserves an interconnection structure, i.e., it can be again
+interpreted as a second-order system evolving over a reduced graph.
+Furthermore, this paper generalizes the definition of network controllability
+Gramian to second-order network systems. Based on it, we develop an efficient
+method to compute H2-norms and derive the approximation error between the
+full-order and reduced-order models. Finally, the approach is illustrated by
+the example of a small-world network.
+",1,0,0,0,0,0
+12006,Higher-rank graph algebras are iterated Cuntz-Pimsner algebras,"  Given a finitely aligned $k$-graph $\Lambda$, we let $\Lambda^i$ denote the
+$(k-1)$-graph formed by removing all edges of degree $e_i$ from $\Lambda$. We
+show that the Toeplitz-Cuntz-Krieger algebra of $\Lambda$, denoted by
+$\mathcal{T}C^*(\Lambda)$, may be realised as the Toeplitz algebra of a Hilbert
+$\mathcal{T}C^*(\Lambda^i)$-bimodule. When $\Lambda$ is locally-convex, we show
+that the Cuntz-Krieger algebra of $\Lambda$, which we denote by $C^*(\Lambda)$,
+may be realised as the Cuntz-Pimsner algebra of a Hilbert
+$C^*(\Lambda^i)$-bimodule. Consequently, $\mathcal{T}C^*(\Lambda)$ and
+$C^*(\Lambda)$ may be viewed as iterated Toeplitz and iterated Cuntz-Pimsner
+algebras over $c_0(\Lambda^0)$ respectively.
+",0,0,1,0,0,0
+12007,Ultracold Atomic Gases in Artificial Magnetic Fields (PhD thesis),"  A phenomenon can hardly be found that accompanied physical paradigms and
+theoretical concepts in a more reflecting way than magnetism. From the
+beginnings of metaphysics and the first classical approaches to magnetic poles
+and streamlines of the field, it has inspired modern physics on its way to the
+classical field description of electrodynamics, and further to the quantum
+mechanical description of internal degrees of freedom of elementary particles.
+Meanwhile, magnetic manifestations have posed and still do pose complex and
+often controversially debated questions. This regards so various and utterly
+distinct topics as quantum spin systems and the grand unification theory. This
+may be foremost caused by the fact that all of these effects are based on
+correlated structures, which are induced by the interplay of dynamics and
+elementary interactions. It is strongly correlated systems that certainly
+represent one of the most fascinating and universal fields of research. In
+particular, low dimensional systems are in the focus of interest, as they
+reveal strongly pronounced correlations of counterintuitive nature. As regards
+this framework, the quantum Hall effect must be seen as one of the most
+intriguing and complex problems of modern solid state physics. Even after two
+decades and the same number of Nobel prizes, it still keeps researchers of
+nearly all fields of physics occupied. In spite of seminal progress, its
+inherent correlated order still lacks understanding on a microscopic level.
+Despite this, it is obvious that the phenomenon is thoroughly fundamental of
+nature. To resolve some puzzles of this nature is a key topic of this thesis.
+(excerpt from abstract)
+",0,1,0,0,0,0
+12008,Stream VByte: Faster Byte-Oriented Integer Compression,"  Arrays of integers are often compressed in search engines. Though there are
+many ways to compress integers, we are interested in the popular byte-oriented
+integer compression techniques (e.g., VByte or Google's Varint-GB). They are
+appealing due to their simplicity and engineering convenience. Amazon's
+varint-G8IU is one of the fastest byte-oriented compression technique published
+so far. It makes judicious use of the powerful single-instruction-multiple-data
+(SIMD) instructions available in commodity processors. To surpass varint-G8IU,
+we present Stream VByte, a novel byte-oriented compression technique that
+separates the control stream from the encoded data. Like varint-G8IU, Stream
+VByte is well suited for SIMD instructions. We show that Stream VByte decoding
+can be up to twice as fast as varint-G8IU decoding over real data sets. In this
+sense, Stream VByte establishes new speed records for byte-oriented integer
+compression, at times exceeding the speed of the memcpy function. On a 3.4GHz
+Haswell processor, it decodes more than 4 billion differentially-coded integers
+per second from RAM to L1 cache.
+",1,0,0,0,0,0
+12009,Kondo Signatures of a Quantum Magnetic Impurity in Topological Superconductors,"  We study the Kondo physics of a quantum magnetic impurity in two-dimensional
+topological superconductors (TSCs), either intrinsic or induced on the surface
+of a bulk topological insulator, using a numerical renormalization group
+technique. We show that, despite sharing the p + ip pairing symmetry, intrinsic
+and extrinsic TSCs host different physical processes that produce distinct
+Kondo signatures. Extrinsic TSCs harbor an unusual screening mechanism
+involving both electron and orbital degrees of freedom that produces rich and
+prominent Kondo phenomena, especially an intriguing pseudospin Kondo singlet
+state in the superconducting gap and a spatially anisotropic spin correlation.
+In sharp contrast, intrinsic TSCs support a robust impurity spin doublet ground
+state and an isotropic spin correlation. These findings advance fundamental
+knowledge of novel Kondo phenomena in TSCs and suggest experimental avenues for
+their detection and distinction.
+",0,1,0,0,0,0
+12010,Waldschmidt constants for Stanley-Reisner ideals of a class of graphs,"  In the present note we study Waldschmidt constants of Stanley-Reisner ideals
+of a hypergraph and a graph with vertices forming a bipyramid over a planar
+n-gon. The case of the hypergraph has been studied by Bocci and Franci. We
+reprove their main result. The case of the graph is new. Interestingly, both
+cases provide series of ideals with Waldschmidt constants descending to 1. It
+would be interesting to known if there are bounded ascending sequences of
+Waldschmidt constants.
+",0,0,1,0,0,0
+12011,FO model checking of geometric graphs,"  Over the past two decades the main focus of research into first-order (FO)
+model checking algorithms has been on sparse relational structures -
+culminating in the FPT algorithm by Grohe, Kreutzer and Siebertz for FO model
+checking of nowhere dense classes of graphs. On contrary to that, except the
+case of locally bounded clique-width only little is currently known about FO
+model checking of dense classes of graphs or other structures. We study the FO
+model checking problem for dense graph classes definable by geometric means
+(intersection and visibility graphs). We obtain new nontrivial FPT results,
+e.g., for restricted subclasses of circular-arc, circle, box, disk, and
+polygon-visibility graphs. These results use the FPT algorithm by Gajarský et
+al. for FO model checking of posets of bounded width. We also complement the
+tractability results by related hardness reductions.
+",1,0,0,0,0,0
+12012,Pseudo-linear regression identification based on generalized orthonormal transfer functions: Convergence conditions and bias distribution,"  In this paper we generalize three identification recursive algorithms
+belonging to the pseudo-linear class, by introducing a predictor established on
+a generalized orthonormal function basis. Contrary to the existing
+identification schemes that use such functions, no constraint on the model
+poles is imposed. Not only this predictor parameterization offers the
+opportunity to relax the convergence conditions of the associated recursive
+schemes, but it entails a modification of the bias distribution linked to the
+basis poles. This result is specific to pseudo-linear regression properties,
+and cannot be transposed to most of prediction error method algorithms. A
+detailed bias distribution is provided, using the concept of equivalent
+prediction error, which reveals strong analogies between the three proposed
+schemes, corresponding to ARMAX, Output Error and a generalization of ARX
+models. That leads to introduce an indicator of the basis poles location effect
+on the bias distribution in the frequency domain. As shown by the simulations,
+the said basis poles play the role of tuning parameters, allowing to manage the
+model fit in the frequency domain, and allowing efficient identification of
+fast sampled or stiff discrete-time systems.
+",1,0,0,0,0,0
+12013,Lower bounds for weak approximation errors for spatial spectral Galerkin approximations of stochastic wave equations,"  Although for a number of semilinear stochastic wave equations existence and
+uniqueness results for corresponding solution processes are known from the
+literature, these solution processes are typically not explicitly known and
+numerical approximation methods are needed in order for mathematical modelling
+with stochastic wave equations to become relevant for real world applications.
+This, in turn, requires the numerical analysis of convergence rates for such
+numerical approximation processes. A recent article by the authors proves upper
+bounds for weak errors for spatial spectral Galerkin approximations of a class
+of semilinear stochastic wave equations. The findings there are complemented by
+the main result of this work, that provides lower bounds for weak errors which
+show that in the general framework considered the established upper bounds can
+essentially not be improved.
+",0,0,1,0,0,0
+12014,Structured low-rank matrix learning: algorithms and applications,"  We consider the problem of learning a low-rank matrix, constrained to lie in
+a linear subspace, and introduce a novel factorization for modeling such
+matrices. A salient feature of the proposed factorization scheme is it
+decouples the low-rank and the structural constraints onto separate factors. We
+formulate the optimization problem on the Riemannian spectrahedron manifold,
+where the Riemannian framework allows to develop computationally efficient
+conjugate gradient and trust-region algorithms. Experiments on problems such as
+standard/robust/non-negative matrix completion, Hankel matrix learning and
+multi-task learning demonstrate the efficacy of our approach. A shorter version
+of this work has been published in ICML'18.
+",0,0,0,1,0,0
+12015,Greed Works - Online Algorithms For Unrelated Machine Stochastic Scheduling,"  This paper establishes the first performance guarantees for a combinatorial
+online algorithm that schedules stochastic, nonpreemptive jobs on unrelated
+machines to minimize the expected total weighted completion time. Prior work on
+unrelated machine scheduling with stochastic jobs was restricted to the offline
+case, and required sophisticated linear or convex programming relaxations for
+the assignment of jobs to machines. The algorithm introduced in this paper is
+based on a purely combinatorial assignment of jobs to machines, hence it also
+works online. The performance bounds are of the same order of magnitude as
+those of earlier work, and depend linearly on an upper bound $\Delta$ on the
+squared coefficient of variation of the jobs' processing times. They are
+$4+2\Delta$ when there are no release dates, and $12+6\Delta$ when jobs are
+released over time. For the special case of deterministic processing times,
+without and with release times, this paper shows that the same combinatorial
+greedy algorithm has a competitive ratio of 4 and 6, respectively. As to the
+technical contribution, the paper shows for the first time how dual fitting
+techniques can be used for stochastic and nonpreemptive scheduling problems.
+",1,0,0,0,0,0
+12016,On a combinatorial curvature for surfaces with inversive distance circle packing metrics,"  In this paper, we introduce a new combinatorial curvature on triangulated
+surfaces with inversive distance circle packing metrics. Then we prove that
+this combinatorial curvature has global rigidity. To study the Yamabe problem
+of the new curvature, we introduce a combinatorial Ricci flow, along which the
+curvature evolves almost in the same way as that of scalar curvature along the
+surface Ricci flow obtained by Hamilton \cite{Ham1}. Then we study the long
+time behavior of the combinatorial Ricci flow and obtain that the existence of
+a constant curvature metric is equivalent to the convergence of the flow on
+triangulated surfaces with nonpositive Euler number. We further generalize the
+combinatorial curvature to $\alpha$-curvature and prove that it is also
+globally rigid, which is in fact a generalized Bower-Stephenson conjecture
+\cite{BS}. We also use the combinatorial Ricci flow to study the corresponding
+$\alpha$-Yamabe problem.
+",0,0,1,0,0,0
+12017,Improving End-to-End Speech Recognition with Policy Learning,"  Connectionist temporal classification (CTC) is widely used for maximum
+likelihood learning in end-to-end speech recognition models. However, there is
+usually a disparity between the negative maximum likelihood and the performance
+metric used in speech recognition, e.g., word error rate (WER). This results in
+a mismatch between the objective function and metric during training. We show
+that the above problem can be mitigated by jointly training with maximum
+likelihood and policy gradient. In particular, with policy learning we are able
+to directly optimize on the (otherwise non-differentiable) performance metric.
+We show that joint training improves relative performance by 4% to 13% for our
+end-to-end model as compared to the same model learned through maximum
+likelihood. The model achieves 5.53% WER on Wall Street Journal dataset, and
+5.42% and 14.70% on Librispeech test-clean and test-other set, respectively.
+",1,0,0,1,0,0
+12018,Better Protocol for XOR Game using Communication Protocol and Nonlocal Boxes,"  Buhrman showed that an efficient communication protocol implies a reliable
+XOR game protocol. This idea rederives Linial and Shraibman's lower bounds of
+communication complexity, which was derived by using factorization norms, with
+worse constant factor in much more intuitive way. In this work, we improve and
+generalize Buhrman's idea, and obtain a class of lower bounds for classical
+communication complexity including an exact Linial and Shraibman's lower bound
+as a special case. In the proof, we explicitly construct a protocol for XOR
+game from a classical communication protocol by using a concept of nonlocal
+boxes and Paw{\l}owski et al.'s elegant protocol, which was used for showing
+the violation of information causality in superquantum theories.
+",1,0,1,0,0,0
+12019,DTN: A Learning Rate Scheme with Convergence Rate of $\mathcal{O}(1/t)$ for SGD,"  We propose a novel diminishing learning rate scheme, coined
+Decreasing-Trend-Nature (DTN), which allows us to prove fast convergence of the
+Stochastic Gradient Descent (SGD) algorithm to a first-order stationary point
+for smooth general convex and some class of nonconvex including neural network
+applications for classification problems. We are the first to prove that SGD
+with diminishing learning rate achieves a convergence rate of
+$\mathcal{O}(1/t)$ for these problems. Our theory applies to neural network
+applications for classification problems in a straightforward way.
+",1,0,0,1,0,0
+12020,Minimax Distribution Estimation in Wasserstein Distance,"  The Wasserstein metric is an important measure of distance between
+probability distributions, with applications in machine learning, statistics,
+probability theory, and data analysis. This paper provides upper and lower
+bounds on statistical minimax rates for the problem of estimating a probability
+distribution under Wasserstein loss, using only metric properties, such as
+covering and packing numbers, of the sample space, and weak moment assumptions
+on the probability distributions.
+",0,0,0,1,0,0
+12021,Uniformization and Steinness,"  It is shown that the unit ball in ${\mathbb C}^n$ is the only complex
+manifold that can universally cover both Stein and non-Stein strictly
+pseudoconvex domains.
+",0,0,1,0,0,0
+12022,Recent progress on conditional randomness,"  In this article, recent progress on ML-randomness with respect to conditional
+probabilities is reviewed. In particular a new result of conditional randomness
+with respect to mutually singular probabilities are shown, which is a
+generalization of Hanssen's result (2010) for Bernoulli processes.
+",1,0,1,0,0,0
+12023,The evolution of red supergiants to supernovae,"  With red supergiants (RSGs) predicted to end their lives as Type IIP core
+collapse supernova (CCSN), their behaviour before explosion needs to be fully
+understood. Mass loss rates govern RSG evolution towards SN and have strong
+implications on the appearance of the resulting explosion. To study how the
+mass-loss rates change with the evolution of the star, we have measured the
+amount of circumstellar material around 19 RSGs in a coeval cluster. Our study
+has shown that mass loss rates ramp up throughout the lifetime of an RSG, with
+more evolved stars having mass loss rates a factor of 40 higher than early
+stage RSGs. Interestingly, we have also found evidence for an increase in
+circumstellar extinction throughout the RSG lifetime, meaning the most evolved
+stars are most severely affected. We find that, were the most evolved RSGs in
+NGC2100 to go SN, this extra extinction would cause the progenitor's initial
+mass to be underestimated by up to 9M$_\odot$.
+",0,1,0,0,0,0
+12024,Conformally invariant elliptic Liouville equation and its symmetry preserving discretization,"  The symmetry algebra of the real elliptic Liouville equation is an
+infinite-dimensional loop algebra with the simple Lie algebra $o(3,1)$ as its
+maximal finite-dimensional subalgebra. The entire algebra generates the
+conformal group of the Euclidean plane $E_2$. This infinite-dimensional algebra
+distinguishes the elliptic Liouville equation from the hyperbolic one with its
+symmetry algebra that is the direct sum of two Virasoro algebras. Following a
+discretisation procedure developed earlier, we present a difference scheme that
+is invariant under the group $O(3,1)$ and has the elliptic Liouville equation
+in polar coordinates as its continuous limit. The lattice is a solution of an
+equation invariant under $O(3,1)$ and is itself invariant under a subgroup of
+$O(3,1)$, namely the $O(2)$ rotations of the Euclidean plane.
+",0,1,1,0,0,0
+12025,Discrete diffusion Lyman-alpha radiative transfer,"  Due to its accuracy and generality, Monte Carlo radiative transfer (MCRT) has
+emerged as the prevalent method for Ly$\alpha$ radiative transfer in arbitrary
+geometries. The standard MCRT encounters a significant efficiency barrier in
+the high optical depth, diffusion regime. Multiple acceleration schemes have
+been developed to improve the efficiency of MCRT but the noise from photon
+packet discretization remains a challenge. The discrete diffusion Monte Carlo
+(DDMC) scheme has been successfully applied in state-of-the-art radiation
+hydrodynamics (RHD) simulations. Still, the established framework is not
+optimal for resonant line transfer. Inspired by the DDMC paradigm, we present a
+novel extension to resonant DDMC (rDDMC) in which diffusion in space and
+frequency are treated on equal footing. We explore the robustness of our new
+method and demonstrate a level of performance that justifies incorporating the
+method into existing Ly$\alpha$ codes. We present computational speedups of
+$\sim 10^2$-$10^6$ relative to contemporary MCRT implementations with schemes
+that skip scattering in the core of the line profile. This is because the rDDMC
+runtime scales with the spatial and frequency resolution rather than the number
+of scatterings - the latter is typically $\propto \tau_0$ for static media, or
+$\propto (a \tau_0)^{2/3}$ with core-skipping. We anticipate new frontiers in
+which on-the-fly Ly$\alpha$ radiative transfer calculations are feasible in 3D
+RHD. More generally, rDDMC is transferable to any computationally demanding
+problem amenable to a Fokker-Planck approximation of frequency redistribution.
+",0,1,0,0,0,0
+12026,Kosterlitz-Thouless transition and vortex-antivortex lattice melting in two-dimensional Fermi gases with $p$- or $d$-wave pairing,"  We present a theoretical study of the finite-temperature Kosterlitz-Thouless
+(KT) and vortex-antivortex lattice (VAL) melting transitions in two-dimensional
+Fermi gases with $p$- or $d$-wave pairing. For both pairings, when the
+interaction is tuned from weak to strong attractions, we observe a quantum
+phase transition from the Bardeen-Cooper-Schrieffer (BCS) superfluidity to the
+Bose-Einstein condensation (BEC) of difermions. The KT and VAL transition
+temperatures increase during this BCS-BEC transition and approach constant
+values in the deep BEC region. The BCS-BEC transition is characterized by the
+non-analyticities of the chemical potential, the superfluid order parameter,
+and the sound velocities as functions of the interaction strength at both zero
+and finite temperatures; however, the temperature effect tends to weaken the
+non-analyticities comparing to the zero temperature case. The effect of
+mismatched Fermi surfaces on the $d$-wave pairing is also studied.
+",0,1,0,0,0,0
+12027,Chiral Optical Tamm States: Temporal Coupled-Mode Theory,"  The chiral optical Tamm state (COTS) is a special localized state at the
+interface of a handedness-preserving mirror and a structurally chiral medium
+such as a cholesteric liquid crystal or a chiral sculptured thin film. The
+spectral behavior of COTS, observed as reflection resonances, is described by
+the temporal coupled-mode theory. Mode coupling is different for two circular
+light polarizations because COTS has a helix structure replicating that of the
+cholesteric. The mode coupling for co-handed circularly polarized light
+exponentially attenuates with the cholesteric layer thickness since the COTS
+frequency falls into the stop band. Cross-handed circularly polarized light
+freely goes through the cholesteric layer and can excite COTS when reflected
+from the handedness-preserving mirror. The coupling in this case is
+proportional to anisotropy of the cholesteric and theoretically it is only
+anisotropy of magnetic permittivity that can ultimately cancel this coupling.
+These two couplings being equal results in a polarization crossover (the
+Kopp--Genack effect) for which a linear polarization is optimal to excite COTS.
+The corresponding cholesteric thickness and scattering matrix for COTS are
+generally described by simple expressions.
+",0,1,0,0,0,0
+12028,Functional inequalities for Fox-Wright functions,"  In this paper, our aim is to show some mean value inequalities for the
+Fox-Wright functions, such as Turán--type inequalities, Lazarević and
+Wilker--type inequalities. As applications we derive some new type inequalities
+for hypergeometric functions and the four--parametric Mittag--Leffler
+functions. Furthermore, we prove monotonicity of ratios for sections of series
+of Fox-Wright functions, the results is also closely connected with
+Turán--type inequalities. Moreover, some other type inequalities are also
+presented. At the end of the paper, some problems stated, which may be of
+interest for further research.
+",0,0,1,0,0,0
+12029,Collaboration Spheres: a Visual Metaphor to Share and Reuse Research Objects,"  Research Objects (ROs) are semantically enhanced aggregations of resources
+associated to scientific experiments, such as data, provenance of these data,
+the scientific workflow used to run the experiment, intermediate results, logs
+and the interpretation of the results. As the number of ROs increases, it is
+becoming difficult to find ROs to be used, reused or re-purposed. New search
+and retrieval techniques are required to find the most appropriate ROs for a
+given researcher, paying attention to provide an intuitive user interface. In
+this paper we show CollabSpheres, a user interface that provides a new visual
+metaphor to find ROs by means of a recommendation system that takes advantage
+of the social aspects of ROs. The experimental evaluation of this tool shows
+that users perceive high values of usability, user satisfaction, usefulness and
+ease of use. From the analysis of these results we argue that users perceive
+the simplicity, intuitiveness and cleanness of this tool, as well as this tool
+increases collaboration and reuse of research objects.
+",1,0,0,0,0,0
+12030,Intelligent flat-and-textureless object manipulation in Service Robots,"  This work introduces our approach to the flat and textureless object grasping
+problem. In particular, we address the tableware and cutlery manipulation
+problem where a service robot has to clean up a table. Our solution integrates
+colour and 2D and 3D geometry information to describe objects, and this
+information is given to the robot action planner to find the best grasping
+trajectory depending on the object class. Furthermore, we use visual feedback
+as a verification step to determine if the grasping process has successfully
+occurred. We evaluate our approach in both an open and a standard service robot
+platform following the RoboCup@Home international tournament regulations.
+",1,0,0,0,0,0
+12031,Constraints on the Intergalactic Magnetic Field from Bow Ties in the Gamma-ray Sky,"  Pair creation on the cosmic infrared background and subsequent
+inverse-Compton scattering on the CMB potentially reprocesses the TeV emission
+of blazars into faint GeV halos with structures sensitive to intergalactic
+magnetic fields (IGMF). We attempt to detect such halos exploiting their highly
+anisotropic shape. Their persistent nondetection excludes at greater than
+$3.9\sigma$ an IGMF with correlation lengths >100 Mpc and current-day strengths
+in the range $10^{-16}$ to $10^{-15}$ G, and at 2 sigma from $10^{-17}$ to
+$10^{-14}$ G, covering the range implied by gamma-ray spectra of nearby TeV
+emitters. Alternatively, plasma processes could pre-empt the inverse-Compton
+cascade.
+",0,1,0,0,0,0
+12032,Learning Deep Representations with Probabilistic Knowledge Transfer,"  Knowledge Transfer (KT) techniques tackle the problem of transferring the
+knowledge from a large and complex neural network into a smaller and faster
+one. However, existing KT methods are tailored towards classification tasks and
+they cannot be used efficiently for other representation learning tasks. In
+this paper a novel knowledge transfer technique, that is capable of training a
+student model that maintains the same amount of mutual information between the
+learned representation and a set of (possible unknown) labels as the teacher
+model, is proposed. Apart from outperforming existing KT techniques, the
+proposed method allows for overcoming several limitations of existing methods
+providing new insight into KT as well as novel KT applications, ranging from
+knowledge transfer from handcrafted feature extractors to {cross-modal} KT from
+the textual modality into the representation extracted from the visual modality
+of the data.
+",0,0,0,1,0,0
+12033,Pluricanonical Periods over Compact Riemann Surfaces of Genus at least 2,"  This article is an attempt to generalize Riemann's bilinear relations on
+compact Riemann surface of genus at least 2, which may lead to new structures
+in the theory of hyperbolic Riemann surfaces. No significant result is
+obtained, the article serves to bring the readers' attention to the observation
+made by [Bol-1949], and some easy consequences.
+",0,0,1,0,0,0
+12034,Stochastic Gradient Descent: Going As Fast As Possible But Not Faster,"  When applied to training deep neural networks, stochastic gradient descent
+(SGD) often incurs steady progression phases, interrupted by catastrophic
+episodes in which loss and gradient norm explode. A possible mitigation of such
+events is to slow down the learning process. This paper presents a novel
+approach to control the SGD learning rate, that uses two statistical tests. The
+first one, aimed at fast learning, compares the momentum of the normalized
+gradient vectors to that of random unit vectors and accordingly gracefully
+increases or decreases the learning rate. The second one is a change point
+detection test, aimed at the detection of catastrophic learning episodes; upon
+its triggering the learning rate is instantly halved. Both abilities of
+speeding up and slowing down the learning rate allows the proposed approach,
+called SALeRA, to learn as fast as possible but not faster. Experiments on
+standard benchmarks show that SALeRA performs well in practice, and compares
+favorably to the state of the art.
+",0,0,0,1,0,0
+12035,Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection,"  This paper studies the detection of bird calls in audio segments using
+stacked convolutional and recurrent neural networks. Data augmentation by
+blocks mixing and domain adaptation using a novel method of test mixing are
+proposed and evaluated in regard to making the method robust to unseen data.
+The contributions of two kinds of acoustic features (dominant frequency and log
+mel-band energy) and their combinations are studied in the context of bird
+audio detection. Our best achieved AUC measure on five cross-validations of the
+development data is 95.5% and 88.1% on the unseen evaluation data.
+",1,0,0,0,0,0
+12036,Gibbs posterior convergence and the thermodynamic formalism,"  In this paper we consider a Bayesian framework for making inferences about
+dynamical systems from ergodic observations. The proposed Bayesian procedure is
+based on the Gibbs posterior, a decision theoretic generalization of standard
+Bayesian inference. We place a prior over a model class consisting of a
+parametrized family of Gibbs measures on a mixing shift of finite type. This
+model class generalizes (hidden) Markov chain models by allowing for long range
+dependencies, including Markov chains of arbitrarily large orders. We
+characterize the asymptotic behavior of the Gibbs posterior distribution on the
+parameter space as the number of observations tends to infinity. In particular,
+we define a limiting variational problem over the space of joinings of the
+model system with the observed system, and we show that the Gibbs posterior
+distributions concentrate around the solution set of this variational problem.
+In the case of properly specified models our convergence results may be used to
+establish posterior consistency. This work establishes tight connections
+between Gibbs posterior inference and the thermodynamic formalism, which may
+inspire new proof techniques in the study of Bayesian posterior consistency for
+dependent processes.
+",0,0,1,1,0,0
+12037,Retrieval Analysis of the Emission Spectrum of WASP-12b: Sensitivity of Outcomes to Prior Assumptions and Implications for Formation History,"  We analyze the emission spectrum of the hot Jupiter WASP-12b using our
+HELIOS-R retrieval code and HELIOS-K opacity calculator. When interpreting
+Hubble and Spitzer data, the retrieval outcomes are found to be
+prior-dominated. When the prior distributions of the molecular abundances are
+assumed to be log-uniform, the volume mixing ratio of HCN is found to be
+implausibly high. A VULCAN chemical kinetics model of WASP-12b suggests that
+chemical equilibrium is a reasonable assumption even when atmospheric mixing is
+implausibly rigorous. Guided by (exo)planet formation theory, we set Gaussian
+priors on the elemental abundances of carbon, oxygen and nitrogen with the
+Gaussian peaks being centered on the measured C/H, O/H and N/H values of the
+star. By enforcing chemical equilibrium, we find substellar O/H and stellar to
+slightly superstellar C/H for the dayside atmosphere of WASP-12b. The
+superstellar carbon-to-oxygen ratio is just above unity, regardless of whether
+clouds are included in the retrieval analysis, consistent with Madhusudhan et
+al. (2011). Furthermore, whether a temperature inversion exists in the
+atmosphere depends on one's assumption for the Gaussian width of the priors.
+Our retrieved posterior distributions are consistent with the formation of
+WASP-12b in a solar-composition protoplanetary disk, beyond the water iceline,
+via gravitational instability or pebble accretion (without core erosion) and
+migration inwards to its present orbital location via a disk-free mechanism,
+and are inconsistent with both in-situ formation and core accretion with disk
+migration, as predicted by Madhusudhan et al. (2017). We predict that the
+interpretation of James Webb Space Telescope WASP-12b data will not be
+prior-dominated.
+",0,1,0,0,0,0
+12038,Model Learning for Look-ahead Exploration in Continuous Control,"  We propose an exploration method that incorporates look-ahead search over
+basic learnt skills and their dynamics, and use it for reinforcement learning
+(RL) of manipulation policies . Our skills are multi-goal policies learned in
+isolation in simpler environments using existing multigoal RL formulations,
+analogous to options or macroactions. Coarse skill dynamics, i.e., the state
+transition caused by a (complete) skill execution, are learnt and are unrolled
+forward during lookahead search. Policy search benefits from temporal
+abstraction during exploration, though itself operates over low-level primitive
+actions, and thus the resulting policies does not suffer from suboptimality and
+inflexibility caused by coarse skill chaining. We show that the proposed
+exploration strategy results in effective learning of complex manipulation
+policies faster than current state-of-the-art RL methods, and converges to
+better policies than methods that use options or parametrized skills as
+building blocks of the policy itself, as opposed to guiding exploration. We
+show that the proposed exploration strategy results in effective learning of
+complex manipulation policies faster than current state-of-the-art RL methods,
+and converges to better policies than methods that use options or parameterized
+skills as building blocks of the policy itself, as opposed to guiding
+exploration.
+",1,0,0,0,0,0
+12039,Efficient and Scalable View Generation from a Single Image using Fully Convolutional Networks,"  Single-image-based view generation (SIVG) is important for producing 3D
+stereoscopic content. Here, handling different spatial resolutions as input and
+optimizing both reconstruction accuracy and processing speed is desirable.
+Latest approaches are based on convolutional neural network (CNN), and they
+generate promising results. However, their use of fully connected layers as
+well as pre-trained VGG forces a compromise between reconstruction accuracy and
+processing speed. In addition, this approach is limited to the use of a
+specific spatial resolution. To remedy these problems, we propose exploiting
+fully convolutional networks (FCN) for SIVG. We present two FCN architectures
+for SIVG. The first one is based on combination of an FCN and a view-rendering
+network called DeepView$_{ren}$. The second one consists of decoupled networks
+for luminance and chrominance signals, denoted by DeepView$_{dec}$. To train
+our solutions we present a large dataset of 2M stereoscopic images. Results
+show that both of our architectures improve accuracy and speed over the state
+of the art. DeepView$_{ren}$ generates competitive accuracy to the state of the
+art, however, with the fastest processing speed of all. That is x5 times faster
+speed and x24 times lower memory consumption compared to the state of the art.
+DeepView$_{dec}$ has much higher accuracy, but with x2.5 times faster speed and
+x12 times lower memory consumption. We evaluated our approach with both
+objective and subjective studies.
+",1,0,0,0,0,0
+12040,An Empirical Analysis of Approximation Algorithms for the Euclidean Traveling Salesman Problem,"  With applications to many disciplines, the traveling salesman problem (TSP)
+is a classical computer science optimization problem with applications to
+industrial engineering, theoretical computer science, bioinformatics, and
+several other disciplines. In recent years, there have been a plethora of novel
+approaches for approximate solutions ranging from simplistic greedy to
+cooperative distributed algorithms derived from artificial intelligence. In
+this paper, we perform an evaluation and analysis of cornerstone algorithms for
+the Euclidean TSP. We evaluate greedy, 2-opt, and genetic algorithms. We use
+several datasets as input for the algorithms including a small dataset, a
+mediumsized dataset representing cities in the United States, and a synthetic
+dataset consisting of 200 cities to test algorithm scalability. We discover
+that the greedy and 2-opt algorithms efficiently calculate solutions for
+smaller datasets. Genetic algorithm has the best performance for optimality for
+medium to large datasets, but generally have longer runtime. Our
+implementations is public available.
+",1,0,0,0,0,0
+12041,"Dynamical compensation and structural identifiability: analysis, implications, and reconciliation","  The concept of dynamical compensation has been recently introduced to
+describe the ability of a biological system to keep its output dynamics
+unchanged in the face of varying parameters. Here we show that, according to
+its original definition, dynamical compensation is equivalent to lack of
+structural identifiability. This is relevant if model parameters need to be
+estimated, which is often the case in biological modelling. This realization
+prompts us to warn that care should we taken when using an unidentifiable model
+to extract biological insight: the estimated values of structurally
+unidentifiable parameters are meaningless, and model predictions about
+unmeasured state variables can be wrong. Taking this into account, we explore
+alternative definitions of dynamical compensation that do not necessarily imply
+structural unidentifiability. Accordingly, we show different ways in which a
+model can be made identifiable while exhibiting dynamical compensation. Our
+analyses enable the use of the new concept of dynamical compensation in the
+context of parameter identification, and reconcile it with the desirable
+property of structural identifiability.
+",1,0,0,0,0,0
+12042,Characterization of the beam from the RFQ of the PIP-II Injector Test,"  A 2.1 MeV, 10 mA CW RFQ has been installed and commissioned at Fermilab's
+test accelerator known as PIP-II Injector Test. This report describes the
+measurements of the beam properties after acceleration in the RFQ, including
+the energy and emittance.
+",0,1,0,0,0,0
+12043,Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy,"  Softmax is a standard final layer used in Neural Nets (NNs) to summarize
+information encoded in the trained NN and return a prediction. However, Softmax
+leverages only a subset of the class-specific structure encoded in the trained
+model and ignores potentially valuable information: During training, models
+encode an array $D$ of class response distributions, where $D_{ij}$ is the
+distribution of the $j^{th}$ pre-Softmax readout neuron's responses to the
+$i^{th}$ class. Given a test sample, Softmax implicitly uses only the row of
+this array $D$ that corresponds to the readout neurons' responses to the
+sample's true class. Leveraging more of this array $D$ can improve classifier
+accuracy, because the likelihoods of two competing classes can be encoded in
+other rows of $D$.
+To explore this potential resource, we develop a hybrid classifier
+(Softmax-Pooling Hybrid, $SPH$) that uses Softmax on high-scoring samples, but
+on low-scoring samples uses a log-likelihood method that pools the information
+from the full array $D$. We apply $SPH$ to models trained on a vectorized MNIST
+dataset to varying levels of accuracy. $SPH$ replaces only the final Softmax
+layer in the trained NN, at test time only. All training is the same as for
+Softmax. Because the pooling classifier performs better than Softmax on
+low-scoring samples, $SPH$ reduces test set error by 6% to 23%, using the exact
+same trained model, whatever the baseline Softmax accuracy. This reduction in
+error reflects hidden capacity of the trained NN that is left unused by
+Softmax.
+",1,0,0,1,0,0
+12044,Generalized Stieltjes constants and integrals involving the log-log function: Kummer's Theorem in action,"  In this note, we recall Kummer's Fourier series expansion of the 1-periodic
+function that coincides with the logarithm of the Gamma function on the unit
+interval $(0,1)$, and we use it to find closed forms for some numerical series
+related to the generalized Stieltjes constants, and some integrals involving
+the function $x\mapsto \ln \ln(1/x)$.
+",0,0,1,0,0,0
+12045,On the Combinatorial Lower Bound for the Extension Complexity of the Spanning Tree Polytope,"  In the study of extensions of polytopes of combinatorial optimization
+problems, a notorious open question is that for the size of the smallest
+extended formulation of the Minimum Spanning Tree problem on a complete graph
+with $n$ nodes. The best known lower bound is the trival (dimension) bound,
+$\Omega(n^2)$, the best known upper bound is the extended formulation by Wong
+(1980) of size $O(n^3)$ (also Martin, 1991).
+In this note we give a nondeterministic communication protocol with cost
+$\log_2(n^2\log n)+O(1)$ for the support of the spanning tree slack matrix.
+This means that the combinatorial lower bounds can improve the trivial lower
+bound only by a factor of (at most) $O(\log n)$.
+",1,0,1,0,0,0
+12046,An Introduction to Classic DEVS,"  DEVS is a popular formalism for modelling complex dynamic systems using a
+discrete-event abstraction. At this abstraction level, a timed sequence
+ofpertinent ""events"" input to a system (or internal, in the case of timeouts)
+cause instantaneous changes to the state of the system. Between events, the
+state does not change, resulting in a a piecewise constant state trajectory.
+Main advantages of DEVS are its rigorous formal definition, and its support for
+modular composition.
+This chapter introduces the Classic DEVS formalism in a bottom-up fashion,
+using a simple traffic light example. The syntax and operational semantics of
+Atomic (i.e., non-hierarchical) models are intruced first. The semantics of
+Coupled (hierarchical) models is then given by translation into Atomic DEVS
+models. As this formal ""flattening"" is not efficient, a modular abstract
+simulator which operates directly on the coupled model is also presented. This
+is the common basis for subsequent efficient implementations. We continue to
+actual applications of DEVS modelling and simulation, as seen in performance
+analysis for queueing systems. Finally, we present some of the shortcomings in
+the Classic DEVS formalism, and show solutions to them in the form of variants
+of the original formalism.
+",1,0,0,0,0,0
+12047,Nanoscale Solid State Batteries Enabled By Thermal Atomic Layer Deposition of a Lithium Polyphosphazene Solid State Electrolyte,"  Several active areas of research in novel energy storage technologies,
+including three-dimensional solid state batteries and passivation coatings for
+reactive battery electrode components, require conformal solid state
+electrolytes. We describe an atomic layer deposition (ALD) process for a member
+of the lithium phosphorus oxynitride (LiPON) family, which is employed as a
+thin film lithium-conducting solid electrolyte. The reaction between lithium
+tert-butoxide (LiO$^t$Bu) and diethyl phosphoramidate (DEPA) produces
+conformal, ionically conductive thin films with a stoichiometry close to
+Li$_2$PO$_2$N between 250 and 300$^\circ$C. The P/N ratio of the films is
+always 1, indicative of a particular polymorph of LiPON which closely resembles
+a polyphosphazene. Films grown at 300$^\circ$C have an ionic conductivity of
+$6.51\:(\pm0.36)\times10^{-7}$ S/cm at 35$^\circ$C, and are functionally
+electrochemically stable in the window from 0 to 5.3V vs. Li/Li$^+$. We
+demonstrate the viability of the ALD-grown electrolyte by integrating it into
+full solid state batteries, including thin film devices using LiCoO$_2$ as the
+cathode and Si as the anode operating at up to 1 mA/cm$^2$. The high quality of
+the ALD growth process allows pinhole-free deposition even on rough crystalline
+surfaces, and we demonstrate the fabrication and operation of thin film
+batteries with the thinnest (<100nm) solid state electrolytes yet reported.
+Finally, we show an additional application of the moderate-temperature ALD
+process by demonstrating a flexible solid state battery fabricated on a polymer
+substrate.
+",0,1,0,0,0,0
+12048,Classification of crystallization outcomes using deep convolutional neural networks,"  The Machine Recognition of Crystallization Outcomes (MARCO) initiative has
+assembled roughly half a million annotated images of macromolecular
+crystallization experiments from various sources and setups. Here,
+state-of-the-art machine learning algorithms are trained and tested on
+different parts of this data set. We find that more than 94% of the test images
+can be correctly labeled, irrespective of their experimental origin. Because
+crystal recognition is key to high-density screening and the systematic
+analysis of crystallization experiments, this approach opens the door to both
+industrial and fundamental research applications.
+",0,0,0,1,1,0
+12049,"Private Information, Credit Risk and Graph Structure in P2P Lending Networks","  This research investigated the potential for improving Peer-to-Peer (P2P)
+credit scoring by using ""private information"" about communications and travels
+of borrowers. We found that P2P borrowers' ego networks exhibit scale-free
+behavior driven by underlying preferential attachment mechanisms that connect
+borrowers in a fashion that can be used to predict loan profitability. The
+projection of these private networks onto networks of mobile phone
+communication and geographical locations from mobile phone GPS potentially give
+loan providers access to private information through graph and location metrics
+which we used to predict loan profitability. Graph topology was found to be an
+important predictor of loan profitability, explaining over 5.5% of variability.
+Networks of borrower location information explain an additional 19% of the
+profitability. Machine learning algorithms were applied to the data set
+previously analyzed to develop the predictive model and resulted in a 4%
+reduction in mean squared error.
+",1,0,0,0,0,1
+12050,Nonmonotonous classical magneto-conductivity of a two-dimensional electron gas in a disordered array of obstacles,"  Magnetotransport measurements in combination with molecular dynamics (MD)
+simulations on two-dimensional disordered Lorentz gases in the classical regime
+are reported. In quantitative agreement between experiment and simulation, the
+magnetoconductivity displays a pronounced peak as a function of perpendicular
+magnetic field $B$ which cannot be explained in the framework of existing
+kinetic theories. We show that this peak is linked to the onset of a directed
+motion of the electrons along the contour of the disordered obstacle matrix
+when the cyclotron radius becomes smaller than the size of the obstacles. This
+directed motion leads to transient superdiffusive motion and strong scaling
+corrections in the vicinity of the insulator-to-conductor transitions of the
+Lorentz gas.
+",0,1,0,0,0,0
+12051,Pushing STEM-education through a social-media-based contest format - experiences and lessons-learned from the H2020-project SciChallenge,"  Science education is a crucial issue with long-term impacts for Europe as the
+low enrolment rates in the STEM-fields, including (natural) science,
+technology, engineering and mathematics, will lead to a workforce problem in
+research and development. In order to address this challenge, the EU-funded
+research project SciChallenge (project.scichallenge.eu) aims to find a new way
+for getting young people more interested in STEM. For this purpose, the project
+developed and implemented a social-media-based STEM-contest for young people,
+which aims at increasing the attractiveness of science education and careers
+among young people. In the first two parts, the paper reflects on the problem,
+introduces the project and highlights the main steps of the preparation of the
+contest. The third section of the paper presents the idea, design and
+implementation of the digital contest platform (www.scichallenge.eu), which
+serves as the core of the challenge. The fourth part of the paper will provide
+a status update on the contest pilot. It will provide a summary of the
+experiences that the consortium made with this novel approach as well as the
+main obstacles that the consortium was facing. The paper will conclude with a
+preliminary reflection on the question if such an approach can help to increase
+the interest of young people in STEM-education and careers.
+",1,0,0,0,0,0
+12052,Hidden area and mechanical nonlinearities in freestanding graphene,"  We investigated the effect of out-of-plane crumpling on the mechanical
+response of graphene membranes. In our experiments, stress was applied to
+graphene membranes using pressurized gas while the strain state was monitored
+through two complementary techniques: interferometric profilometry and Raman
+spectroscopy. By comparing the data obtained through these two techniques, we
+determined the geometric hidden area which quantifies the crumpling strength.
+While the devices with hidden area $\sim0~\%$ obeyed linear mechanics with
+biaxial stiffness $428\pm10$ N/m, specimens with hidden area in the range
+$0.5-1.0~\%$ were found to obey an anomalous Hooke's law with an exponent
+$\sim0.1$.
+",0,1,0,0,0,0
+12053,Solving Partial Differential Equations on Manifolds From Incomplete Inter-Point Distance,"  Solutions of partial differential equations (PDEs) on manifolds have provided
+important applications in different fields in science and engineering. Existing
+methods are majorly based on discretization of manifolds as implicit functions,
+triangle meshes, or point clouds, where the manifold structure is approximated
+by either zero level set of an implicit function or a set of points. In many
+applications, manifolds might be only provided as an inter-point distance
+matrix with possible missing values. This paper discusses a framework to
+discretize PDEs on manifolds represented as incomplete inter-point distance
+information. Without conducting a time-consuming global coordinates
+reconstruction, we propose a more efficient strategy by discretizing
+differential operators only based on point-wisely local reconstruction. Our
+local reconstruction model is based on the recent advances of low-rank matrix
+completion theory, where only a very small random portion of distance
+information is required. This method enables us to conduct analyses of
+incomplete distance data using solutions of special designed PDEs such as the
+Laplace-Beltrami (LB) eigen-system. As an application, we demonstrate a new way
+of manifold reconstruction from an incomplete distance by stitching patches
+using the spectrum of the LB operator. Intensive numerical experiments
+demonstrate the effectiveness of the proposed methods.
+",1,0,1,0,0,0
+12054,Zonal Flow Magnetic Field Interaction in the Semi-Conducting Region of Giant Planets,"  All four giant planets in the Solar System feature zonal flows on the order
+of 100 m/s in the cloud deck, and large-scale intrinsic magnetic fields on the
+order of 1 Gauss near the surface. The vertical structure of the zonal flows
+remains obscure. The end-member scenarios are shallow flows confined in the
+radiative atmosphere and deep flows throughout the entire planet. The
+electrical conductivity increases rapidly yet smoothly as a function of depth
+inside Jupiter and Saturn. Deep zonal flows will inevitably interact with the
+magnetic field, at depth with even modest electrical conductivity. Here we
+investigate the interaction between zonal flows and magnetic fields in the
+semi-conducting region of giant planets. Employing mean-field electrodynamics,
+we show that the interaction will generate detectable poloidal magnetic field
+perturbations spatially correlated with the deep zonal flows. Assuming the peak
+amplitude of the dynamo alpha-effect to be 0.1 mm/s, deep zonal flows on the
+order of 0.1 - 1 m/s in the semi-conducting region of Jupiter and Saturn would
+generate poloidal magnetic perturbations on the order of 0.01% - 1% of the
+background dipole field. These poloidal perturbations should be detectable with
+the in-situ magnetic field measurements from the Juno mission and the Cassini
+Grand Finale. This implies that magnetic field measurements can be employed to
+constrain the properties of deep zonal flows in the semi-conducting region of
+giant planets.
+",0,1,0,0,0,0
+12055,Superpixel-based Semantic Segmentation Trained by Statistical Process Control,"  Semantic segmentation, like other fields of computer vision, has seen a
+remarkable performance advance by the use of deep convolution neural networks.
+However, considering that neighboring pixels are heavily dependent on each
+other, both learning and testing of these methods have a lot of redundant
+operations. To resolve this problem, the proposed network is trained and tested
+with only 0.37% of total pixels by superpixel-based sampling and largely
+reduced the complexity of upsampling calculation. The hypercolumn feature maps
+are constructed by pyramid module in combination with the convolution layers of
+the base network. Since the proposed method uses a very small number of sampled
+pixels, the end-to-end learning of the entire network is difficult with a
+common learning rate for all the layers. In order to resolve this problem, the
+learning rate after sampling is controlled by statistical process control (SPC)
+of gradients in each layer. The proposed method performs better than or equal
+to the conventional methods that use much more samples on Pascal Context,
+SUN-RGBD dataset.
+",1,0,0,0,0,0
+12056,Photometric Stereo by Hemispherical Metric Embedding,"  Photometric Stereo methods seek to reconstruct the 3d shape of an object from
+motionless images obtained with varying illumination. Most existing methods
+solve a restricted problem where the physical reflectance model, such as
+Lambertian reflectance, is known in advance. In contrast, we do not restrict
+ourselves to a specific reflectance model. Instead, we offer a method that
+works on a wide variety of reflectances. Our approach uses a simple yet
+uncommonly used property of the problem - the sought after normals are points
+on a unit hemisphere. We present a novel embedding method that maps pixels to
+normals on the unit hemisphere. Our experiments demonstrate that this approach
+outperforms existing manifold learning methods for the task of hemisphere
+embedding. We further show successful reconstructions of objects from a wide
+variety of reflectances including smooth, rough, diffuse and specular surfaces,
+even in the presence of significant attached shadows. Finally, we empirically
+prove that under these challenging settings we obtain more accurate shape
+reconstructions than existing methods.
+",1,0,0,0,0,0
+12057,"Time Reversal, SU(N) Yang-Mills and Cobordisms: Interacting Topological Superconductors/Insulators and Quantum Spin Liquids in 3+1D","  We introduce a web of strongly correlated interacting 3+1D topological
+superconductors/insulators of 10 particular global symmetry groups of Cartan
+classes, realizable in electronic condensed matter systems, and their new SU(N)
+generalizations. The symmetries include SU(N), SU(2), U(1), fermion parity,
+time reversal and relate to each other through symmetry embeddings. We overview
+the lattice Hamiltonian formalism. We complete the list of field theories of
+bulk symmetry-protected topological invariants (SPT invariants/partition
+functions that exhibit boundary 't Hooft anomalies) via cobordism calculations,
+matching their full classification. We also present explicit 4-manifolds that
+detect these SPTs. On the other hand, once we dynamically gauge part of their
+global symmetries, we arrive in various new phases of SU(N) Yang-Mills (YM)
+gauge theories, analogous to quantum spin liquids with emergent gauge fields.
+We discuss how coupling YM theories to time reversal-SPTs affects the strongly
+coupled theories at low energy. For example, we point out a possibility of
+having two deconfined gapless time-reversal symmetric SU(2) YM theories at
+$\theta=\pi$ as two distinct conformal field theories, which although are
+secretly indistinguishable by correlators of local operators on orientable
+spacetimes nor by gapped SPT states, can be distinguished on non-orientable
+spacetimes or potentially by correlators of extended operators.
+",0,1,1,0,0,0
+12058,Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting,"  Rainfall ensemble forecasts have to be skillful for both low precipitation
+and extreme events. We present statistical post-processing methods based on
+Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric
+extension for heavy-tailed distributions. Our goal is to improve ensemble
+quality for all types of precipitation events, heavy-tailed included, subject
+to a good overall performance. Our hybrid proposed methods are applied to daily
+51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France
+using the M{é}t{é}o-France ensemble prediction system called PEARP. They
+provide calibrated pre-dictive distributions and compete favourably with
+state-of-the-art methods like Analogs method or Ensemble Model Output
+Statistics. In particular, hybrid forest-based procedures appear to bring an
+added value to the forecast of heavy rainfall.
+",0,0,1,1,0,0
+12059,Non interactive simulation of correlated distributions is decidable,"  A basic problem in information theory is the following: Let $\mathbf{P} =
+(\mathbf{X}, \mathbf{Y})$ be an arbitrary distribution where the marginals
+$\mathbf{X}$ and $\mathbf{Y}$ are (potentially) correlated. Let Alice and Bob
+be two players where Alice gets samples $\{x_i\}_{i \ge 1}$ and Bob gets
+samples $\{y_i\}_{i \ge 1}$ and for all $i$, $(x_i, y_i) \sim \mathbf{P}$. What
+joint distributions $\mathbf{Q}$ can be simulated by Alice and Bob without any
+interaction?
+Classical works in information theory by G{á}cs-K{ö}rner and Wyner answer
+this question when at least one of $\mathbf{P}$ or $\mathbf{Q}$ is the
+distribution on $\{0,1\} \times \{0,1\}$ where each marginal is unbiased and
+identical. However, other than this special case, the answer to this question
+is understood in very few cases. Recently, Ghazi, Kamath and Sudan showed that
+this problem is decidable for $\mathbf{Q}$ supported on $\{0,1\} \times
+\{0,1\}$. We extend their result to $\mathbf{Q}$ supported on any finite
+alphabet.
+We rely on recent results in Gaussian geometry (by the authors) as well as a
+new \emph{smoothing argument} inspired by the method of \emph{boosting} from
+learning theory and potential function arguments from complexity theory and
+additive combinatorics.
+",1,0,1,0,0,0
+12060,Effective Completeness for S4.3.1-Theories with Respect to Discrete Linear Models,"  The computable model theory of modal logic was initiated by Suman Ganguli and
+Anil Nerode in [4]. They use an effective Henkin-type construction to
+effectivize various completeness theorems from classical modal logic. This
+construction has the feature of only producing models whose frames can be
+obtained by adding edges to a tree digraph. Consequently, this construction
+cannot prove an effective version of a well-known completeness theorem which
+states that every $\mathsf{S4.3.1}$-theory has a model whose accessibility
+relation is a linear order of order type $\omega$. We prove an effectivization
+of that theorem by means of a new construction adapted from that of Ganguli and
+Nerode.
+",0,0,1,0,0,0
+12061,DeepTingle,"  DeepTingle is a text prediction and classification system trained on the
+collected works of the renowned fantastic gay erotica author Chuck Tingle.
+Whereas the writing assistance tools you use everyday (in the form of
+predictive text, translation, grammar checking and so on) are trained on
+generic, purportedly ""neutral"" datasets, DeepTingle is trained on a very
+specific, internally consistent but externally arguably eccentric dataset. This
+allows us to foreground and confront the norms embedded in data-driven
+creativity and productivity assistance tools. As such tools effectively
+function as extensions of our cognition into technology, it is important to
+identify the norms they embed within themselves and, by extension, us.
+DeepTingle is realized as a web application based on LSTM networks and the
+GloVe word embedding, implemented in JavaScript with Keras-JS.
+",1,0,0,0,0,0
+12062,Degrees of Freedom in Cached MIMO Relay Networks With Multiple Base Stations,"  The ability of physical layer relay caching to increase the degrees of
+freedom (DoF) of a single cell was recently illustrated. In this paper, we
+extend this result to the case of multiple cells in which a caching relay is
+shared among multiple non-cooperative base stations (BSs). In particular, we
+show that a large DoF gain can be achieved by exploiting the benefits of having
+a shared relay that cooperates with the BSs. We first propose a cache-assisted
+relaying protocol that improves the cooperation opportunity between the BSs and
+the relay. Next, we consider the cache content placement problem that aims to
+design the cache content at the relay such that the DoF gain is maximized. We
+propose an optimal algorithm and a near-optimal low-complexity algorithm for
+the cache content placement problem. Simulation results show significant
+improvement in the DoF gain using the proposed relay-caching protocol.
+",1,0,0,0,0,0
+12063,A perturbation theory for water with an associating reference fluid,"  The theoretical description of the thermodynamics of water is challenged by
+the structural transition towards tetrahedral symmetry at ambient conditions.
+As perturbation theories typically assume a spherically symmetric reference
+fluid, they are incapable of accurately describing the liquid properties of
+water at ambient conditions. In this paper we solve this problem, by
+introducing the concept of an associated reference perturbation theory (APT).
+In APT we treat the reference fluid as an associating hard sphere fluid which
+transitions to tetrahedral symmetry in the fully hydrogen bonded limit. We
+calculate this transition in a theoretically self-consistent manner without
+appealing to molecular simulations. This associated reference provides the
+reference fluid for a second order Barker-Hendersen perturbative treatment of
+the long-range attractions. We demonstrate that this new approach gives a
+significantly improved description of water as compared to standard
+perturbation theories.
+",0,1,0,0,0,0
+12064,Finite Sample Complexity of Sequential Monte Carlo Estimators,"  We present bounds for the finite sample error of sequential Monte Carlo
+samplers on static spaces. Our approach explicitly relates the performance of
+the algorithm to properties of the chosen sequence of distributions and mixing
+properties of the associated Markov kernels. This allows us to give the first
+finite sample comparison to other Monte Carlo schemes. We obtain bounds for the
+complexity of sequential Monte Carlo approximations for a variety of target
+distributions including finite spaces, product measures, and log-concave
+distributions including Bayesian logistic regression. The bounds obtained are
+within a logarithmic factor of similar bounds obtainable for Markov chain Monte
+Carlo.
+",0,0,0,1,0,0
+12065,Reduced chemistry for butanol isomers at engine-relevant conditions,"  Butanol has received significant research attention as a second-generation
+biofuel in the past few years. In the present study, skeletal mechanisms for
+four butanol isomers were generated from two widely accepted, well-validated
+detailed chemical kinetic models for the butanol isomers. The detailed models
+were reduced using a two-stage approach consisting of the directed relation
+graph with error propagation and sensitivity analysis. During the reduction
+process, issues were encountered with pressure-dependent reactions formulated
+using the logarithmic pressure interpolation approach; these issues are
+discussed and recommendations made to avoid ambiguity in its future
+implementation in mechanism development. The performance of the skeletal
+mechanisms generated here was compared with that of detailed mechanisms in
+simulations of autoignition delay times, laminar flame speeds, and perfectly
+stirred reactor temperature response curves and extinction residence times,
+over a wide range of pressures, temperatures, and equivalence ratios. The
+detailed and skeletal mechanisms agreed well, demonstrating the adequacy of the
+resulting reduced chemistry for all the butanol isomers in predicting global
+combustion phenomena. In addition, the skeletal mechanisms closely predicted
+the time-histories of fuel mass fractions in homogeneous compression-ignition
+engine simulations. The performance of each butanol isomer was additionally
+compared with that of a gasoline surrogate with an antiknock index of 87 in a
+homogeneous compression-ignition engine simulation. The gasoline surrogate was
+consumed faster than any of the butanol isomers, with tert-butanol exhibiting
+the slowest fuel consumption rate. While n-butanol and isobutanol displayed the
+most similar consumption profiles relative to the gasoline surrogate, the two
+literature chemical kinetic models predicted different orderings.
+",0,1,0,0,0,0
+12066,A Measure of Dependence Between Discrete and Continuous Variables,"  Mutual Information (MI) is an useful tool for the recognition of mutual
+dependence berween data sets. Differen methods for the estimation of MI have
+been developed when both data sets are discrete or when both data sets are
+continuous. The MI estimation between a discrete data set and a continuous data
+set has not received so much attention. We present here a method for the
+estimation of MI for this last case based on the kernel density approximation.
+The calculation may be of interest in diverse contexts. Since MI is closely
+related to Jensen Shannon divergence, the method here developed is of
+particular interest in the problem of sequence segmentation.
+",0,0,0,1,0,0
+12067,Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks,"  Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a
+rigorous and viable way to identity changepoints in complex systems. In this
+work, we introduce a Stein variational online changepoint detection (SVOCD)
+method to provide a computationally tractable generalization of BOCPD beyond
+the exponential family of probability distributions. We integrate the recently
+developed Stein variational Newton (SVN) method (Detommaso et al., 2018) and
+BOCPD to offer a full online Bayesian treatment for a large number of
+situations with significant importance in practice. We apply the resulting
+method to two challenging and novel applications: Hawkes processes and long
+short-term memory (LSTM) neural networks. In both cases, we successfully
+demonstrate the efficacy of our method on real data.
+",1,0,0,1,0,0
+12068,Quantum Teleportation and Super-dense Coding in Operator Algebras,"  Let $\mathcal{B}_d$ be the unital $C^*$-algebra generated by the elements
+$u_{jk}, \, 0 \le i, j \le d-1$, satisfying the relations that $[u_{j,k}]$ is a
+unitary operator, and let $C^*(\mathbb{F}_{d^2})$ be the full group
+$C^*$-algebra of free group of $d^2$ generators. Based on the idea of
+teleportation and super-dense coding in quantum information theory, we exhibit
+the two $*$-isomorphisms $M_d(C^*(\mathbb{F}_{d^2}))\cong \mathcal{B}_d\rtimes
+\mathbb{Z}_d\rtimes \mathbb{Z}_d$ and $M_d(\mathcal{B}_d)\cong
+C^*(\mathbb{F}_{d^2})\rtimes \mathbb{Z}_d\rtimes \mathbb{Z}_d$, for certain
+actions of $\mathbb{Z}_d$. As an application, we show that for any $d,m\ge 2$
+with $(d,m)\neq (2,2)$, the matrix-valued generalization of the (tensor
+product) quantum correlation set of $d$ inputs and $m$ outputs is not closed.
+",0,0,1,0,0,0
+12069,A Practical Randomized CP Tensor Decomposition,"  The CANDECOMP/PARAFAC (CP) decomposition is a leading method for the analysis
+of multiway data. The standard alternating least squares algorithm for the CP
+decomposition (CP-ALS) involves a series of highly overdetermined linear least
+squares problems. We extend randomized least squares methods to tensors and
+show the workload of CP-ALS can be drastically reduced without a sacrifice in
+quality. We introduce techniques for efficiently preprocessing, sampling, and
+computing randomized least squares on a dense tensor of arbitrary order, as
+well as an efficient sampling-based technique for checking the stopping
+condition. We also show more generally that the Khatri-Rao product (used within
+the CP-ALS iteration) produces conditions favorable for direct sampling. In
+numerical results, we see improvements in speed, reductions in memory
+requirements, and robustness with respect to initialization.
+",1,0,0,0,0,0
+12070,Two dimensional potential flow around a rectangular pole solved by a multiple linear regression,"  A potential flow around a circular cylinder is a commonly examined problem in
+an introductory physics class. We pose a similar problem but with different
+boundary conditions where a rectangular pole replaces a circular cylinder. We
+demonstrate to solve the problem by deriving a general solution for the flow in
+the form of an infinite series and determining the coefficients in the series
+using a multiple linear regression. When the size of a pole is specified, our
+solution provides a quantitative estimate of the characteristic length scale of
+the potential flow. Our analysis implies that the potential flow around a
+rectangular pole of the diagonal 1 is equivalent to the potential flow around a
+circle of diameter 0.78 to a distant observer.
+",0,1,0,1,0,0
+12071,Fixed Price Approximability of the Optimal Gain From Trade,"  Bilateral trade is a fundamental economic scenario comprising a strategically
+acting buyer and seller, each holding valuations for the item, drawn from
+publicly known distributions. A mechanism is supposed to facilitate trade
+between these agents, if such trade is beneficial. It was recently shown that
+the only mechanisms that are simultaneously DSIC, SBB, and ex-post IR, are
+fixed price mechanisms, i.e., mechanisms that are parametrised by a price p,
+and trade occurs if and only if the valuation of the buyer is at least p and
+the valuation of the seller is at most p. The gain from trade is the increase
+in welfare that results from applying a mechanism; here we study the gain from
+trade achievable by fixed price mechanisms. We explore this question for both
+the bilateral trade setting, and a double auction setting where there are
+multiple buyers and sellers. We first identify a fixed price mechanism that
+achieves a gain from trade of at least 2/r times the optimum, where r is the
+probability that the seller's valuation does not exceed the buyer's valuation.
+This extends a previous result by McAfee. Subsequently, we improve this
+approximation factor in an asymptotic sense, by showing that a more
+sophisticated rule for setting the fixed price results in an expected gain from
+trade within a factor O(log(1/r)) of the optimal gain from trade. This is
+asymptotically the best approximation factor possible. Lastly, we extend our
+study of fixed price mechanisms to the double auction setting defined by a set
+of multiple i.i.d. unit demand buyers, and i.i.d. unit supply sellers. We
+present a fixed price mechanism that achieves a gain from trade that achieves
+for all epsilon > 0 a gain from trade of at least (1-epsilon) times the
+expected optimal gain from trade with probability 1 - 2/e^{#T epsilon^2 /2},
+where #T is the expected number of trades resulting from the double auction.
+",1,0,0,0,0,0
+12072,$^{139}$La and $^{63}$Cu NMR investigation of charge order in La$_{2}$CuO$_{4+y}$ ($T_{c}=42$K),"  We report $^{139}$La and $^{63}$Cu NMR investigation of the successive charge
+order, spin order, and superconducting transitions in super-oxygenated
+La$_2$CuO$_{4+y}$ single crystal with stage-4 excess oxygen order at
+$T_{stage}\simeq 290$ K. We show that the stage-4 order induces tilting of
+CuO$_6$ octahedra below $T_{stage}$, which in turn causes $^{139}$La NMR line
+broadening. The structural distortion continues to develop far below
+$T_{stage}$, and completes at $T_{charge}\simeq 60$ K, where charge order sets
+in. This sequence is reminiscent of the the charge order transition in Nd
+co-doped La$_{1.88}$Sr$_{0.12}$CuO$_4$ that sets in once the low temperature
+tetragonal (LTT) phase is established. We also show that the paramagnetic
+$^{63}$Cu NMR signals are progressively wiped out below $T_{charge}$ due to
+enhanced low frequency spin fluctuations, but the residual $^{63}$Cu NMR
+signals continue to exhibit the characteristics expected for optimally doped
+superconducting CuO$_2$ planes. This indicates that charge order in
+La$_2$CuO$_{4+y}$ does not take place uniformly in space. Low frequency Cu spin
+fluctuations as probed by $^{139}$La nuclear spin-lattice relaxation rate are
+mildly glassy, and do not exhibit critical divergence at $T_{spin}$($\simeq
+T_{c}$)=42 K. These findings, including the spatially inhomogeneous nature of
+the charge ordered state, are qualitatively similar to the case of
+La$_{1.885}$Sr$_{0.115}$CuO$_4$ [T. Imai et al., Phys. Rev. B 96 (2017) 224508,
+and A. Arsenault et al., Phys. Rev. B 97 (2018) 064511], but both charge and
+spin order take place more sharply in the present case.
+",0,1,0,0,0,0
+12073,An Exploratory Study of Field Failures,"  Field failures, that is, failures caused by faults that escape the testing
+phase leading to failures in the field, are unavoidable. Improving verification
+and validation activities before deployment can identify and timely remove many
+but not all faults, and users may still experience a number of annoying
+problems while using their software systems. This paper investigates the nature
+of field failures, to understand to what extent further improving in-house
+verification and validation activities can reduce the number of failures in the
+field, and frames the need of new approaches that operate in the field. We
+report the results of the analysis of the bug reports of five applications
+belonging to three different ecosystems, propose a taxonomy of field failures,
+and discuss the reasons why failures belonging to the identified classes cannot
+be detected at design time but shall be addressed at runtime. We observe that
+many faults (70%) are intrinsically hard to detect at design-time.
+",1,0,0,0,0,0
+12074,Crowdsourcing Predictors of Residential Electric Energy Usage,"  Crowdsourcing has been successfully applied in many domains including
+astronomy, cryptography and biology. In order to test its potential for useful
+application in a Smart Grid context, this paper investigates the extent to
+which a crowd can contribute predictive hypotheses to a model of residential
+electric energy consumption. In this experiment, the crowd generated hypotheses
+about factors that make one home different from another in terms of monthly
+energy usage. To implement this concept, we deployed a web-based system within
+which 627 residential electricity customers posed 632 questions that they
+thought predictive of energy usage. While this occurred, the same group
+provided 110,573 answers to these questions as they accumulated. Thus users
+both suggested the hypotheses that drive a predictive model and provided the
+data upon which the model is built. We used the resulting question and answer
+data to build a predictive model of monthly electric energy consumption, using
+random forest regression. Because of the sparse nature of the answer data,
+careful statistical work was needed to ensure that these models are valid. The
+results indicate that the crowd can generate useful hypotheses, despite the
+sparse nature of the dataset.
+",1,0,0,1,0,0
+12075,One-dimensional fluids with positive potentials,"  We study a class of one-dimensional classical fluids with penetrable
+particles interacting through positive, purely repulsive, pair-potentials.
+Starting from some lower bounds to the total potential energy, we draw results
+on the thermodynamic limit of the given model.
+",0,1,1,0,0,0
+12076,Recovery of Sparse and Low Rank Components of Matrices Using Iterative Method with Adaptive Thresholding,"  In this letter, we propose an algorithm for recovery of sparse and low rank
+components of matrices using an iterative method with adaptive thresholding. In
+each iteration, the low rank and sparse components are obtained using a
+thresholding operator. This algorithm is fast and can be implemented easily. We
+compare it with one of the most common fast methods in which the rank and
+sparsity are approximated by $\ell_1$ norm. We also apply it to some real
+applications where the noise is not so sparse. The simulation results show that
+it has a suitable performance with low run-time.
+",1,0,0,1,0,0
+12077,Reduced Order Modelling for the Simulation of Quenches in Superconducting Magnets,"  This contributions discusses the simulation of magnetothermal effects in
+superconducting magnets as used in particle accelerators. An iterative coupling
+scheme using reduced order models between a magnetothermal partial differential
+model and an electrical lumped-element circuit is demonstrated. The
+multiphysics, multirate and multiscale problem requires a consistent
+formulation and framework to tackle the challenging transient effects occurring
+at both system and device level.
+",1,1,0,0,0,0
+12078,"Life in the ""Matrix"": Human Mobility Patterns in the Cyber Space","  With the wide adoption of the multi-community setting in many popular social
+media platforms, the increasing user engagements across multiple online
+communities warrant research attention. In this paper, we introduce a novel
+analogy between the movements in the cyber space and the physical space. This
+analogy implies a new way of studying human online activities by modelling the
+activities across online communities in a similar fashion as the movements
+among locations. First, we quantitatively validate the analogy by comparing
+several important properties of human online activities and physical movements.
+Our experiments reveal striking similarities between the cyber space and the
+physical space. Next, inspired by the established methodology on human mobility
+in the physical space, we propose a framework to study human ""mobility"" across
+online platforms. We discover three interesting patterns of user engagements in
+online communities. Furthermore, our experiments indicate that people with
+different mobility patterns also exhibit divergent preferences to online
+communities. This work not only attempts to achieve a better understanding of
+human online activities, but also intends to open a promising research
+direction with rich implications and applications.
+",1,0,0,0,0,0
+12079,A Novel Subclass of Univalent Functions Involving Operators of Fractional Calculus,"  In this paper, we introduce and investigate a novel class of analytic and
+univalent functions of negative coefficients in the open unit disk. For this
+function class, we obtain characterization and distortion theorems as well as
+the radii of close-to-convexity, starlikeness and convexity by using fractional
+calculus techniques.
+",0,0,1,0,0,0
+12080,Local bandwidth selection for kernel density estimation in bifurcating Markov chain model,"  We propose an adaptive estimator for the stationary distribution of a
+bifurcating Markov Chain on $\mathbb R^d$. Bifurcating Markov chains (BMC for
+short) are a class of stochastic processes indexed by regular binary trees. A
+kernel estimator is proposed whose bandwidth is selected by a method inspired
+by the works of Goldenshluger and Lepski [18]. Drawing inspiration from
+dimension jump methods for model selection, we also provide an algorithm to
+select the best constant in the penalty.
+",0,0,1,1,0,0
+12081,Sparse geometries handling in lattice-Boltzmann method implementation for graphic processors,"  We describe a high-performance implementation of the lattice-Boltzmann method
+(LBM) for sparse geometries on graphic processors. In our implementation we
+cover the whole geometry with a uniform mesh of small tiles and carry out
+calculations for each tile independently with a proper data synchronization at
+tile edges. For this method we provide both the theoretical analysis of
+complexity and the results for real implementations for 2D and 3D geometries.
+Based on the theoretical model, we show that tiles offer significantly smaller
+bandwidth overhead than solutions based on indirect addressing. For
+2-dimensional lattice arrangements a reduction of memory usage is also
+possible, though at the cost of diminished performance. We reached the
+performance of 682 MLUPS on GTX Titan (72\% of peak theoretical memory
+bandwidth) for D3Q19 lattice arrangement and double precision data.
+",1,0,0,0,0,0
+12082,From Multimodal to Unimodal Webpages for Developing Countries,"  The multimodal web elements such as text and images are associated with
+inherent memory costs to store and transfer over the Internet. With the limited
+network connectivity in developing countries, webpage rendering gets delayed in
+the presence of high-memory demanding elements such as images (relative to
+text). To overcome this limitation, we propose a Canonical Correlation Analysis
+(CCA) based computational approach to replace high-cost modality with an
+equivalent low-cost modality. Our model learns a common subspace for low-cost
+and high-cost modalities that maximizes the correlation between their visual
+features. The obtained common subspace is used for determining the low-cost
+(text) element of a given high-cost (image) element for the replacement. We
+analyze the cost-saving performance of the proposed approach through an
+eye-tracking experiment conducted on real-world webpages. Our approach reduces
+the memory-cost by at least 83.35% by replacing images with text.
+",1,0,0,1,0,0
+12083,Light curves of hydrogen-poor Superluminous Supernovae from the Palomar Transient Factory,"  We investigate the light-curve properties of a sample of 26 spectroscopically
+confirmed hydrogen-poor superluminous supernovae (SLSNe-I) in the Palomar
+Transient Factory (PTF) survey. These events are brighter than SNe Ib/c and SNe
+Ic-BL, on average, by about 4 and 2~mag, respectively. The peak absolute
+magnitudes of SLSNe-I in rest-frame $g$ band span $-22\lesssim M_g
+\lesssim-20$~mag, and these peaks are not powered by radioactive $^{56}$Ni,
+unless strong asymmetries are at play. The rise timescales are longer for SLSNe
+than for normal SNe Ib/c, by roughly 10 days, for events with similar decay
+times. Thus, SLSNe-I can be considered as a separate population based on
+photometric properties. After peak, SLSNe-I decay with a wide range of slopes,
+with no obvious gap between rapidly declining and slowly declining events. The
+latter events show more irregularities (bumps) in the light curves at all
+times. At late times, the SLSN-I light curves slow down and cluster around the
+$^{56}$Co radioactive decay rate. Powering the late-time light curves with
+radioactive decay would require between 1 and 10${\rm M}_\odot$ of Ni masses.
+Alternatively, a simple magnetar model can reasonably fit the majority of
+SLSNe-I light curves, with four exceptions, and can mimic the radioactive decay
+of $^{56}$Co, up to $\sim400$ days from explosion. The resulting spin values do
+not correlate with the host-galaxy metallicities. Finally, the analysis of our
+sample cannot strengthen the case for using SLSNe-I for cosmology.
+",0,1,0,0,0,0
+12084,Sharing Means Renting?: An Entire-marketplace Analysis of Airbnb,"  Airbnb, an online marketplace for accommodations, has experienced a
+staggering growth accompanied by intense debates and scattered regulations
+around the world. Current discourses, however, are largely focused on opinions
+rather than empirical evidences. Here, we aim to bridge this gap by presenting
+the first large-scale measurement study on Airbnb, using a crawled data set
+containing 2.3 million listings, 1.3 million hosts, and 19.3 million reviews.
+We measure several key characteristics at the heart of the ongoing debate and
+the sharing economy. Among others, we find that Airbnb has reached a global yet
+heterogeneous coverage. The majority of its listings across many countries are
+entire homes, suggesting that Airbnb is actually more like a rental marketplace
+rather than a spare-room sharing platform. Analysis on star-ratings reveals
+that there is a bias toward positive ratings, amplified by a bias toward using
+positive words in reviews. The extent of such bias is greater than Yelp
+reviews, which were already shown to exhibit a positive bias. We investigate a
+key issue---commercial hosts who own multiple listings on Airbnb---repeatedly
+discussed in the current debate. We find that their existence is prevalent,
+they are early-movers towards joining Airbnb, and their listings are
+disproportionately entire homes and located in the US. Our work advances the
+current understanding of how Airbnb is being used and may serve as an
+independent and empirical reference to inform the debate.
+",1,1,0,0,0,0
+12085,What Happens - After the First Race? Enhancing the Predictive Power of Happens - Before Based Dynamic Race Detection,"  Dynamic race detection is the problem of determining if an observed program
+execution reveals the presence of a data race in a program. The classical
+approach to solving this problem is to detect if there is a pair of conflicting
+memory accesses that are unordered by Lamport's happens-before (HB) relation.
+HB based race detection is known to not report false positives, i.e., it is
+sound. However, the soundness guarantee of HB only promises that the first pair
+of unordered, conflicting events is a schedulable data race. That is, there can
+be pairs of HB-unordered conflicting data accesses that are not schedulable
+races because there is no reordering of the events of the execution, where the
+events in race can be executed immediately after each other. We introduce a new
+partial order, called schedulable happens-before (SHB) that exactly
+characterizes the pairs of schedulable data races --- every pair of conflicting
+data accesses that are identified by SHB can be scheduled, and every HB-race
+that can be scheduled is identified by SHB. Thus, the SHB partial order is
+truly sound. We present a linear time, vector clock algorithm to detect
+schedulable races using SHB. Our experiments demonstrate the value of our
+algorithm for dynamic race detection --- SHB incurs only little performance
+overhead and can scale to executions from real-world software applications
+without compromising soundness.
+",1,0,0,0,0,0
+12086,Proceedings of the Workshop on Data Mining for Oil and Gas,"  The process of exploring and exploiting Oil and Gas (O&G) generates a lot of
+data that can bring more efficiency to the industry. The opportunities for
+using data mining techniques in the ""digital oil-field"" remain largely
+unexplored or uncharted. With the high rate of data expansion, companies are
+scrambling to develop ways to develop near-real-time predictive analytics, data
+mining and machine learning capabilities, and are expanding their data storage
+infrastructure and resources. With these new goals, come the challenges of
+managing data growth, integrating intelligence tools, and analyzing the data to
+glean useful insights. Oil and Gas companies need data solutions to
+economically extract value from very large volumes of a wide variety of data
+generated from exploration, well drilling and production devices and sensors.
+Data mining for oil and gas industry throughout the lifecycle of the
+reservoir includes the following roles: locating hydrocarbons, managing
+geological data, drilling and formation evaluation, well construction, well
+completion, and optimizing production through the life of the oil field. For
+each of these phases during the lifecycle of oil field, data mining play a
+significant role. Based on which phase were talking about, knowledge creation
+through scientific models, data analytics and machine learning, a effective,
+productive, and on demand data insight is critical for decision making within
+the organization.
+The significant challenges posed by this complex and economically vital field
+justify a meeting of data scientists that are willing to share their experience
+and knowledge. Thus, the Worskhop on Data Mining for Oil and Gas (DM4OG) aims
+to provide a quality forum for researchers that work on the significant
+challenges arising from the synergy between data science, machine learning, and
+the modeling and optimization problems in the O&G industry.
+",1,0,0,1,0,0
+12087,Chiral magnetic effect of light,"  We study a photonic analog of the chiral magnetic (vortical) effect. We
+discuss that the vector component of magnetoelectric tensors plays a role of
+""vector potential,"" and its rotation is understood as ""magnetic field"" of a
+light. Using the geometrical optics approximation, we show that ""magnetic
+fields"" cause an anomalous shift of a wave packet of a light through an
+interplay with the Berry curvature of photons. The mechanism is the same as
+that of the chiral magnetic (vortical) effect of a chiral fermion, so that we
+term the anomalous shift ""chiral magnetic effect of a light."" We further study
+the chiral magnetic effect of a light beyond geometric optics by directly
+solving the transmission problem of a wave packet at a surface of a
+magnetoelectric material. We show that the experimental signal of the chiral
+magnetic effect of a light is the nonvanishing of transverse displacements for
+the beam normally incident to a magnetoelectric material.
+",0,1,0,0,0,0
+12088,New Pressure-Induced Polymorphic Transitions of Anhydrous Magnesium Sulfate,"  The effects of pressure on the crystal structure of the three known
+polymorphs of magnesium sulfate have been theoretically study by means of DFT
+calculations up to 45 GPa. We determined that at ambient conditions gamma MgSO4
+is an unstable polymorph, which decompose into MgO and SO3, and that the
+response of the other two polymorphs to hydrostatic pressure is non isotropic.
+Additionally we found that at all pressures beta MgSO4 has a largest enthalpy
+than alpha MgSO4. This indicates that beta MgSO4 is thermodynamically unstable
+versus alpha MgSO4 and predicts the occurrence of a beta alpha phase transition
+under moderate compression. Our calculations also predict the existence under
+pressure of additional phase transitions to two new polymorphs of MgSO4, which
+we named as delta MgSO4 and epsilon MgSO4. The alpha delta transition is
+predicted to occur at 17.5 GPa, and the delta epsilon transition at 35 GPa,
+pressures that nowadays can be experimentally easily achieved. All the
+predicted structural transforma ions are characterized as first order
+transitions. This suggests that they can be non reversible, and therefore the
+new polymorphs could be recovered as metastable polymorphs at ambient
+conditions. The crystal structure of the two new polymorphs is reported. In
+them, the coordination number of sulfur is four as in the previously known
+polymorphs, but the coordination number of magnesium is eight instead of six.
+In the article we will report the axial and bond compressibility for the four
+polymorphs of MgSO4. The pressure volume equation of state of each phase is
+also given. The values obtained for the bulk modulus are 62 GPa, 57 GPa, 102
+GPa, and 119 GPa for alpha MgSO4, beta MgSO4, delta MgSO4, and epsilon MgSO4,
+respectively. Finally, the electronic band structure of these four polymorphs
+of MgSO4 has been calculated by the first time.
+",0,1,0,0,0,0
+12089,Optimal transport and integer partitions,"  We link the theory of optimal transportation to the theory of integer
+partitions. Let $\mathscr P(n)$ denote the set of integer partitions of $n \in
+\mathbb N$ and write partitions $\pi \in \mathscr P(n)$ as $(n_1, \dots,
+n_{k(\pi)})$. Using terminology from optimal transport, we characterize certain
+classes of partitions like symmetric partitions and those in Euler's identity
+$|\{ \pi \in \mathscr P(n) |$ all $ n_i $ distinct $ \} | = | \{ \pi \in
+\mathscr P(n) | $ all $ n_i $ odd $ \}|$.
+Then we sketch how optimal transport might help to understand higher
+dimensional partitions.
+",0,0,1,0,0,0
+12090,An Automated Auto-encoder Correlation-based Health-Monitoring and Prognostic Method for Machine Bearings,"  This paper studies an intelligent ultimate technique for health-monitoring
+and prognostic of common rotary machine components, particularly bearings.
+During a run-to-failure experiment, rich unsupervised features from vibration
+sensory data are extracted by a trained sparse auto-encoder. Then, the
+correlation of the extracted attributes of the initial samples (presumably
+healthy at the beginning of the test) with the succeeding samples is calculated
+and passed through a moving-average filter. The normalized output is named
+auto-encoder correlation-based (AEC) rate which stands for an informative
+attribute of the system depicting its health status and precisely identifying
+the degradation starting point. We show that AEC technique well-generalizes in
+several run-to-failure tests. AEC collects rich unsupervised features form the
+vibration data fully autonomous. We demonstrate the superiority of the AEC over
+many other state-of-the-art approaches for the health monitoring and prognostic
+of machine bearings.
+",1,0,0,1,0,0
+12091,Temporal Type Theory: A topos-theoretic approach to systems and behavior,"  This book introduces a temporal type theory, the first of its kind as far as
+we know. It is based on a standard core, and as such it can be formalized in a
+proof assistant such as Coq or Lean by adding a number of axioms. Well-known
+temporal logics---such as Linear and Metric Temporal Logic (LTL and
+MTL)---embed within the logic of temporal type theory.
+The types in this theory represent ""behavior types"". The language is rich
+enough to allow one to define arbitrary hybrid dynamical systems, which are
+mixtures of continuous dynamics---e.g. as described by a differential
+equation---and discrete jumps. In particular, the derivative of a continuous
+real-valued function is internally defined.
+We construct a semantics for the temporal type theory in the topos of sheaves
+on a translation-invariant quotient of the standard interval domain. In fact,
+domain theory plays a recurring role in both the semantics and the type theory.
+",0,0,1,0,0,0
+12092,On Poletsky theory of discs in compact manifolds,"  We provide a direct construction of Poletsky discs via local arc
+approximation and a Runge-type theorem by A. Gournay.
+",0,0,1,0,0,0
+12093,Continuous DR-submodular Maximization: Structure and Algorithms,"  DR-submodular continuous functions are important objectives with wide
+real-world applications spanning MAP inference in determinantal point processes
+(DPPs), and mean-field inference for probabilistic submodular models, amongst
+others. DR-submodularity captures a subclass of non-convex functions that
+enables both exact minimization and approximate maximization in polynomial
+time.
+In this work we study the problem of maximizing non-monotone DR-submodular
+continuous functions under general down-closed convex constraints. We start by
+investigating geometric properties that underlie such objectives, e.g., a
+strong relation between (approximately) stationary points and global optimum is
+proved. These properties are then used to devise two optimization algorithms
+with provable guarantees. Concretely, we first devise a ""two-phase"" algorithm
+with $1/4$ approximation guarantee. This algorithm allows the use of existing
+methods for finding (approximately) stationary points as a subroutine, thus,
+harnessing recent progress in non-convex optimization. Then we present a
+non-monotone Frank-Wolfe variant with $1/e$ approximation guarantee and
+sublinear convergence rate. Finally, we extend our approach to a broader class
+of generalized DR-submodular continuous functions, which captures a wider
+spectrum of applications. Our theoretical findings are validated on synthetic
+and real-world problem instances.
+",1,0,0,1,0,0
+12094,A Structural Characterization for Certifying Robinsonian Matrices,"  A symmetric matrix is Robinsonian if its rows and columns can be
+simultaneously reordered in such a way that entries are monotone nondecreasing
+in rows and columns when moving toward the diagonal. The adjacency matrix of a
+graph is Robinsonian precisely when the graph is a unit interval graph, so that
+Robinsonian matrices form a matrix analogue of the class of unit interval
+graphs. Here we provide a structural characterization for Robinsonian matrices
+in terms of forbidden substructures, extending the notion of asteroidal triples
+to weighted graphs. This implies the known characterization of unit interval
+graphs and leads to an efficient algorithm for certifying that a matrix is not
+Robinsonian.
+",1,0,1,0,0,0
+12095,A new scenario for gravity detection in plants: the position sensor hypothesis,"  The detection of gravity plays a fundamental role during the growth and
+evolution of plants. Although progress has been made in our understanding of
+the molecular, cellular and physical mechanisms involved in the gravity
+detection, a coherent scenario consistent with all the observations is still
+lacking. In this perspective paper we discuss recent experiments showing that
+the response to inclination of shoots is independent of the gravity intensity,
+meaning that the gravity sensor detects an inclination and not a force. This
+result questions some of the commonly accepted hypotheses and leads to propose
+a new ""position sensor hypothesis"". The implications of this new scenario are
+discussed in the light of different observations available in the literature.
+",0,1,0,0,0,0
+12096,Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods,"  Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding
+widespread use in applied statistics and machine learning. These often lead to
+difficult computational problems, which are increasingly being solved on
+parallel and distributed systems such as compute clusters. Recent work has
+proposed running iterative algorithms such as gradient descent and MCMC in
+parallel asynchronously for increased performance, with good empirical results
+in certain problems. Unfortunately, for MCMC this parallelization technique
+requires new convergence theory, as it has been explicitly demonstrated to lead
+to divergence on some examples. Recent theory on Asynchronous Gibbs sampling
+describes why these algorithms can fail, and provides a way to alter them to
+make them converge. In this article, we describe how to apply this theory in a
+generic setting, to understand the asynchronous behavior of any MCMC algorithm,
+including those implemented using parameter servers, and those not based on
+Gibbs sampling.
+",1,0,0,1,0,0
+12097,Predicting Hurricane Trajectories using a Recurrent Neural Network,"  Hurricanes are cyclones circulating about a defined center whose closed wind
+speeds exceed 75 mph originating over tropical and subtropical waters. At
+landfall, hurricanes can result in severe disasters. The accuracy of predicting
+their trajectory paths is critical to reduce economic loss and save human
+lives. Given the complexity and nonlinearity of weather data, a recurrent
+neural network (RNN) could be beneficial in modeling hurricane behavior. We
+propose the application of a fully connected RNN to predict the trajectory of
+hurricanes. We employed the RNN over a fine grid to reduce typical truncation
+errors. We utilized their latitude, longitude, wind speed, and pressure
+publicly provided by the National Hurricane Center (NHC) to predict the
+trajectory of a hurricane at 6-hour intervals. Results show that this proposed
+technique is competitive to methods currently employed by the NHC and can
+predict up to approximately 120 hours of hurricane path.
+",0,0,0,1,0,0
+12098,HJB equations in infinite dimension and optimal control of stochastic evolution equations via generalized Fukushima decomposition,"  A stochastic optimal control problem driven by an abstract evolution equation
+in a separable Hilbert space is considered. Thanks to the identification of the
+mild solution of the state equation as $\nu$-weak Dirichlet process, the value
+processes is proved to be a real weak Dirichlet process. The uniqueness of the
+corresponding decomposition is used to prove a verification theorem. Through
+that technique several of the required assumptions are milder than those
+employed in previous contributions about non-regular solutions of
+Hamilton-Jacobi-Bellman equations.
+",0,0,1,0,0,0
+12099,Developing a Method to Determine Electrical Conductivity in Meteoritic Materials with Applications to Induction Heating Theory (2008 Student Thesis),"  Magnetic induction was first proposed as a planetary heating mechanism by
+Sonett and Colburn in 1968, in recent years this theory has lost favor as a
+plausible source of heating in the early solar system. However, new models of
+proto-planetary disk evolution suggest that magnetic fields play an important
+role in solar system formation. In particular, the magneto-hydrodynamic
+behavior of proto-planetary disks is believed to be responsible for the net
+outward flow of angular momentum in the solar system. It is important to
+re-evaluate the plausibility of magnetic induction based on the intense
+magnetic field environments described by the most recent models of
+proto-planetary disk evolution.
+In order to re-evaluate electromagnetic induction theory the electrical
+conductivity of meteorites must be determined. To develop a technique capable
+of making these measurements, a time-varying magnetic field was generated to
+inductively heat metallic control samples. The thermal response of each sample,
+which depends on electrical conductivity, was monitored until a thermal steady
+state was achieved. The relationship between conductivity and thermal response
+can be exploited to estimate the electrical conductivity of unknown samples.
+After applying the technique to various metals it was recognized that this
+method is not capable of making precise electrical conductivity measurements.
+However, this method can constrain the product of the electrical conductivity
+and the square of the magnetic permeability, or ${\sigma}{{\mu}^2}$, for
+meteoritic and metallic samples alike. The results also illustrate that along
+with electrical conductivity {\sigma}, the magnetic permeability {\mu} of a
+substance has an important effect on induction heating phenomena for
+paramagnetic ({\mu}/{\mu}0 > 1) and especially ferromagnetic materials
+({\mu}/{\mu}0 >> 1).
+",0,1,0,0,0,0
+12100,A KiDS weak lensing analysis of assembly bias in GAMA galaxy groups,"  We investigate possible signatures of halo assembly bias for
+spectroscopically selected galaxy groups from the GAMA survey using weak
+lensing measurements from the spatially overlapping regions of the deeper,
+high-imaging-quality photometric KiDS survey. We use GAMA groups with an
+apparent richness larger than 4 to identify samples with comparable mean host
+halo masses but with a different radial distribution of satellite galaxies,
+which is a proxy for the formation time of the haloes. We measure the weak
+lensing signal for groups with a steeper than average and with a shallower than
+average satellite distribution and find no sign of halo assembly bias, with the
+bias ratio of $0.85^{+0.37}_{-0.25}$, which is consistent with the $\Lambda$CDM
+prediction. Our galaxy groups have typical masses of $10^{13} M_{\odot}/h$,
+naturally complementing previous studies of halo assembly bias on galaxy
+cluster scales.
+",0,1,0,0,0,0
+12101,VIP: Vortex Image Processing package for high-contrast direct imaging,"  We present the Vortex Image Processing (VIP) library, a python package
+dedicated to astronomical high-contrast imaging. Our package relies on the
+extensive python stack of scientific libraries and aims to provide a flexible
+framework for high-contrast data and image processing. In this paper, we
+describe the capabilities of VIP related to processing image sequences acquired
+using the angular differential imaging (ADI) observing technique. VIP
+implements functionalities for building high-contrast data processing
+pipelines, encompass- ing pre- and post-processing algorithms, potential
+sources position and flux estimation, and sensitivity curves generation. Among
+the reference point-spread function subtraction techniques for ADI
+post-processing, VIP includes several flavors of principal component analysis
+(PCA) based algorithms, such as annular PCA and incremental PCA algorithm
+capable of processing big datacubes (of several gigabytes) on a computer with
+limited memory. Also, we present a novel ADI algorithm based on non-negative
+matrix factorization (NMF), which comes from the same family of low-rank matrix
+approximations as PCA and provides fairly similar results. We showcase the ADI
+capabilities of the VIP library using a deep sequence on HR8799 taken with the
+LBTI/LMIRCam and its recently commissioned L-band vortex coronagraph. Using VIP
+we investigated the presence of additional companions around HR8799 and did not
+find any significant additional point source beyond the four known planets. VIP
+is available at this http URL and is accompanied with
+Jupyter notebook tutorials illustrating the main functionalities of the
+library.
+",0,1,0,0,0,0
+12102,Domain-Sharding for Faster HTTP/2 in Lossy Cellular Networks,"  HTTP/2 (h2) is a new standard for Web communications that already delivers a
+large share of Web traffic. Unlike HTTP/1, h2 uses only one underlying TCP
+connection. In a cellular network with high loss and sudden spikes in latency,
+which the TCP stack might interpret as loss, using a single TCP connection can
+negatively impact Web performance. In this paper, we perform an extensive
+analysis of real world cellular network traffic and design a testbed to emulate
+loss characteristics in cellular networks. We use the emulated cellular network
+to measure h2 performance in comparison to HTTP/1.1, for webpages synthesized
+from HTTP Archive repository data.
+Our results show that, in lossy conditions, h2 achieves faster page load
+times (PLTs) for webpages with small objects. For webpages with large objects,
+h2 degrades the PLT. We devise a new domain-sharding technique that isolates
+large and small object downloads on separate connections. Using sharding, we
+show that under lossy cellular conditions, h2 over multiple connections
+improves the PLT compared to h2 with one connection and HTTP/1.1 with six
+connections. Finally, we recommend content providers and content delivery
+networks to apply h2-aware domain-sharding on webpages currently served over h2
+for improved mobile Web performance.
+",1,0,0,0,0,0
+12103,Extremal copositive matrices with minimal zero supports of cardinality two,"  Let $A \in {\cal C}^n$ be an extremal copositive matrix with unit diagonal.
+Then the minimal zeros of $A$ all have supports of cardinality two if and only
+if the elements of $A$ are all from the set $\{-1,0,1\}$. Thus the extremal
+copositive matrices with minimal zero supports of cardinality two are exactly
+those matrices which can be obtained by diagonal scaling from the extremal
+$\{-1,0,1\}$ unit diagonal matrices characterized by Hoffman and Pereira in
+1973.
+",0,0,1,0,0,0
+12104,A Projected Inverse Dynamics Approach for Dual-arm Cartesian Impedance Control,"  We propose a method for dual-arm manipulation of rigid objects, subject to
+external disturbance. The problem is formulated as a Cartesian impedance
+controller within a projected inverse dynamics framework. We use the
+constrained component of the controller to enforce contact and the
+unconstrained controller to accomplish the task with a desired 6-DOF impedance
+behaviour. Furthermore, the proposed method optimises the torque required to
+maintain contact, subject to unknown disturbances, and can do so without direct
+measurement of external force. The techniques are evaluated on a single-arm
+wiping a table and a dual-arm platform manipulating a rigid object of unknown
+mass and with human interaction.
+",1,0,0,0,0,0
+12105,AC-Biased Shift Registers as Fabrication Process Benchmark Circuits and Flux Trapping Diagnostic Tool,"  We develop an ac-biased shift register introduced in our previous work (V.K.
+Semenov et al., IEEE Trans. Appl. Supercond., vol. 25, no. 3, 1301507, June
+2015) into a benchmark circuit for evaluation of superconductor electronics
+fabrication technology. The developed testing technique allows for extracting
+margins of all individual cells in the shift register, which in turn makes it
+possible to estimate statistical distribution of Josephson junctions in the
+circuit. We applied this approach to successfully test registers having 8, 16,
+36, and 202 thousand cells and, respectively, about 33000, 65000, 144000, and
+809000 Josephson junctions. The circuits were fabricated at MIT Lincoln
+Laboratory, using a fully planarized process, 0.4 {\mu}m inductor linewidth,
+and 1.33x10^6 cm^-2 junction density. They are presently the largest
+operational superconducting SFQ circuits ever made. The developed technique
+distinguishes between hard defects (fabrication-related) and soft defects
+(measurement-related) and locates them in the circuit. The soft defects are
+specific to superconducting circuits and caused by magnetic flux trapping
+either inside the active cells or in the dedicated flux-trapping moats near the
+cells. The number and distribution of soft defects depend on the ambient
+magnetic field and vary with thermal cycling even if done in the same magnetic
+environment.
+",0,1,0,0,0,0
+12106,Designing diagnostic platforms for analysis of disease patterns and probing disease emergence,"  The emerging era of personalized medicine relies on medical decisions,
+practices, and products being tailored to the individual patient. Point-of-care
+systems, at the heart of this model, play two important roles. First, they are
+required for identifying subjects for optimal therapies based on their genetic
+make-up and epigenetic profile. Second, they will be used for assessing the
+progression of such therapies. Central to this vision is designing systems
+that, with minimal user-intervention, can transduce complex signals from
+biosystems in complement with clinical information to inform medical decision
+within point-of-care settings. To reach our ultimate goal of developing
+point-of-care systems and realizing personalized medicine, we are taking a
+multistep systems-level approach towards understanding cellular processes and
+biomolecular profiles, to quantify disease states and external interventions.
+",0,0,0,0,1,0
+12107,Stopping GAN Violence: Generative Unadversarial Networks,"  While the costs of human violence have attracted a great deal of attention
+from the research community, the effects of the network-on-network (NoN)
+violence popularised by Generative Adversarial Networks have yet to be
+addressed. In this work, we quantify the financial, social, spiritual,
+cultural, grammatical and dermatological impact of this aggression and address
+the issue by proposing a more peaceful approach which we term Generative
+Unadversarial Networks (GUNs). Under this framework, we simultaneously train
+two models: a generator G that does its best to capture whichever data
+distribution it feels it can manage, and a motivator M that helps G to achieve
+its dream. Fighting is strictly verboten and both models evolve by learning to
+respect their differences. The framework is both theoretically and electrically
+grounded in game theory, and can be viewed as a winner-shares-all two-player
+game in which both players work as a team to achieve the best score.
+Experiments show that by working in harmony, the proposed model is able to
+claim both the moral and log-likelihood high ground. Our work builds on a rich
+history of carefully argued position-papers, published as anonymous YouTube
+comments, which prove that the optimal solution to NoN violence is more GUNs.
+",1,0,0,1,0,0
+12108,Cas d'existence de solutions d'EDP,"  We give some examples of the existence of solutions of geometric PDEs (Yamabe
+equation, Prescribed Scalar Curvature Equation, Gaussian curvature).We also
+give some remarks on second order PDE and Green functions and on the maximum
+principles.
+",0,0,1,0,0,0
+12109,The Onset of Thermally Unstable Cooling from the Hot Atmospheres of Giant Galaxies in Clusters - Constraints on Feedback Models,"  We present accurate mass and thermodynamic profiles for a sample of 56 galaxy
+clusters observed with the Chandra X-ray Observatory. We investigate the
+effects of local gravitational acceleration in central cluster galaxies, and we
+explore the role of the local free-fall time (t$_{\rm ff}$) in thermally
+unstable cooling. We find that the local cooling time (t$_{\rm cool}$) is as
+effective an indicator of cold gas, traced through its nebular emission, as the
+ratio of t$_{\rm cool}$/t$_{\rm ff}$. Therefore, t$_{\rm cool}$ alone
+apparently governs the onset of thermally unstable cooling in hot atmospheres.
+The location of the minimum t$_{\rm cool}$/t$_{\rm ff}$, a thermodynamic
+parameter that simulations suggest may be key in driving thermal instability,
+is unresolved in most systems. As a consequence, selection effects bias the
+value and reduce the observed range in measured t$_{\rm cool}$/t$_{\rm ff}$
+minima. The entropy profiles of cool-core clusters are characterized by broken
+power-laws down to our resolution limit, with no indication of isentropic
+cores. We show, for the first time, that mass isothermality and the $K \propto
+r^{2/3}$ entropy profile slope imply a floor in t$_{\rm cool}$/t$_{\rm ff}$
+profiles within central galaxies. No significant departures of t$_{\rm
+cool}$/t$_{\rm ff}$ below 10 are found, which is inconsistent with many recent
+feedback models. The inner densities and cooling times of cluster atmospheres
+are resilient to change in response to powerful AGN activity, suggesting that
+the energy coupling between AGN heating and atmospheric gas is gentler than
+most models predict.
+",0,1,0,0,0,0
+12110,The linearized Calderon problem in transversally anisotropic geometries,"  In this article we study the linearized anisotropic Calderon problem. In a
+compact manifold with boundary, this problem amounts to showing that products
+of harmonic functions form a complete set. Assuming that the manifold is
+transversally anisotropic, we show that the boundary measurements determine an
+FBI type transform at certain points in the transversal manifold. This leads to
+recovery of transversal singularities in the linearized problem. The method
+requires a geometric condition on the transversal manifold related to pairs of
+intersecting geodesics, but it does not involve the geodesic X-ray transform
+which has limited earlier results on this problem.
+",0,0,1,0,0,0
+12111,Almost isometries between Teichmüller spaces,"  We prove that the Teichmüller space of surfaces with given boundary lengths
+equipped with the arc metric (resp. the Teichmüller metric) is almost
+isometric to the Teichmüller space of punctured surfaces equipped with the
+Thurston metric (resp. the Teichmüller metric).
+",0,0,1,0,0,0
+12112,Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies,"  Decision making in multi-agent systems (MAS) is a great challenge due to
+enormous state and joint action spaces as well as uncertainty, making
+centralized control generally infeasible. Decentralized control offers better
+scalability and robustness but requires mechanisms to coordinate on joint tasks
+and to avoid conflicts. Common approaches to learn decentralized policies for
+cooperative MAS suffer from non-stationarity and lacking credit assignment,
+which can lead to unstable and uncoordinated behavior in complex environments.
+In this paper, we propose Strong Emergent Policy approximation (STEP), a
+scalable approach to learn strong decentralized policies for cooperative MAS
+with a distributed variant of policy iteration. For that, we use function
+approximation to learn from action recommendations of a decentralized
+multi-agent planning algorithm. STEP combines decentralized multi-agent
+planning with centralized learning, only requiring a generative model for
+distributed black box optimization. We experimentally evaluate STEP in two
+challenging and stochastic domains with large state and joint action spaces and
+show that STEP is able to learn stronger policies than standard multi-agent
+reinforcement learning algorithms, when combining multi-agent open-loop
+planning with centralized function approximation. The learned policies can be
+reintegrated into the multi-agent planning process to further improve
+performance.
+",1,0,0,0,0,0
+12113,Integrable Floquet dynamics,"  We discuss several classes of integrable Floquet systems, i.e. systems which
+do not exhibit chaotic behavior even under a time dependent perturbation. The
+first class is associated with finite-dimensional Lie groups and
+infinite-dimensional generalization thereof. The second class is related to the
+row transfer matrices of the 2D statistical mechanics models. The third class
+of models, called here ""boost models"", is constructed as a periodic interchange
+of two Hamiltonians - one is the integrable lattice model Hamiltonian, while
+the second is the boost operator. The latter for known cases coincides with the
+entanglement Hamiltonian and is closely related to the corner transfer matrix
+of the corresponding 2D statistical models. We present several explicit
+examples. As an interesting application of the boost models we discuss a
+possibility of generating periodically oscillating states with the period
+different from that of the driving field. In particular, one can realize an
+oscillating state by performing a static quench to a boost operator. We term
+this state a ""Quantum Boost Clock"". All analyzed setups can be readily realized
+experimentally, for example in cod atoms.
+",0,1,1,0,0,0
+12114,Topologically independent sets in precompact groups,"  It is a simple fact that a subgroup generated by a subset $A$ of an abelian
+group is the direct sum of the cyclic groups $\langle a\rangle$, $a\in A$ if
+and only if the set $A$ is independent. In [5] the concept of an $independent$
+set in an abelian group was generalized to a $topologically$ $independent$
+$set$ in a topological abelian group (these two notions coincide in discrete
+abelian groups). It was proved that a topological subgroup generated by a
+subset $A$ of an abelian topological group is the Tychonoff direct sum of the
+cyclic topological groups $\langle a\rangle$, $a\in A$ if and only if the set
+$A$ is topologically independent and absolutely Cauchy summable. Further, it
+was shown, that the assumption of absolute Cauchy summability of $A$ can not be
+removed in general in this result. In our paper we show that it can be removed
+in precompact groups.
+In other words, we prove that if $A$ is a subset of a {\em precompact}
+abelian group, then the topological subgroup generated by $A$ is the Tychonoff
+direct sum of the topological cyclic subgroups $\langle a\rangle$, $a\in A$ if
+and only if $A$ is topologically independent. We show that precompactness can
+not be replaced by local compactness in this result.
+",0,0,1,0,0,0
+12115,Efficient Simulation of Temperature Evolution of Overhead Transmission Lines Based on Analytical Solution and NWP,"  Transmission lines are vital components in power systems. Tripping of
+transmission lines caused by over-temperature is a major threat to the security
+of system operations, so it is necessary to efficiently simulate line
+temperature under both normal operation conditions and foreseen fault
+conditions. Existing methods based on thermal-steady-state analyses cannot
+reflect transient temperature evolution, and thus cannot provide timing
+information needed for taking remedial actions. Moreover, conventional
+numerical method requires huge computational efforts and barricades system-wide
+analysis. In this regard, this paper derives an approximate analytical solution
+of transmission-line temperature evolution enabling efficient analysis on
+multiple operation states. Considering the uncertainties in environmental
+parameters, the region of over-temperature is constructed in the environmental
+parameter space to realize the over-temperature risk assessment in both the
+planning stage and real-time operations. A test on a typical conductor model
+verifies the accuracy of the approximate analytical solution. Based on the
+analytical solution and numerical weather prediction (NWP) data, an efficient
+simulation method for temperature evolution of transmission systems under
+multiple operation states is proposed. As demonstrated on an NPCC 140-bus
+system, it achieves over 1000 times of efficiency enhancement, verifying its
+potentials in online risk assessment and decision support.
+",1,0,0,0,0,0
+12116,Multiuser Communication Based on the DFT Eigenstructure,"  The eigenstructure of the discrete Fourier transform (DFT) is examined and
+new systematic procedures to generate eigenvectors of the unitary DFT are
+proposed. DFT eigenvectors are suggested as user signatures for data
+communication over the real adder channel (RAC). The proposed multiuser
+communication system over the 2-user RAC is detailed.
+",1,0,0,1,0,0
+12117,Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification,"  This paper introduces a new Urban Point Cloud Dataset for Automatic
+Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We
+describe how the dataset is obtained from acquisition to post-processing and
+labeling. This dataset can be used to learn classification algorithm, however,
+given that a great attention has been paid to the split between the different
+objects, this dataset can also be used to learn the segmentation. The dataset
+consists of around 2km of MLS point cloud acquired in two cities. The number of
+points and range of classes make us consider that it can be used to train
+Deep-Learning methods. Besides we show some results of automatic segmentation
+and classification. The dataset is available at:
+this http URL
+",1,0,0,1,0,0
+12118,Viden: Attacker Identification on In-Vehicle Networks,"  Various defense schemes --- which determine the presence of an attack on the
+in-vehicle network --- have recently been proposed. However, they fail to
+identify which Electronic Control Unit (ECU) actually mounted the attack.
+Clearly, pinpointing the attacker ECU is essential for fast/efficient forensic,
+isolation, security patch, etc. To meet this need, we propose a novel scheme,
+called Viden (Voltage-based attacker identification), which can identify the
+attacker ECU by measuring and utilizing voltages on the in-vehicle network. The
+first phase of Viden, called ACK learning, determines whether or not the
+measured voltage signals really originate from the genuine message transmitter.
+Viden then exploits the voltage measurements to construct and update the
+transmitter ECUs' voltage profiles as their fingerprints. It finally uses the
+voltage profiles to identify the attacker ECU. Since Viden adapts its profiles
+to changes inside/outside of the vehicle, it can pinpoint the attacker ECU
+under various conditions. Moreover, its efficiency and design-compliance with
+modern in-vehicle network implementations make Viden practical and easily
+deployable. Our extensive experimental evaluations on both a CAN bus prototype
+and two real vehicles have shown that Viden can accurately fingerprint ECUs
+based solely on voltage measurements and thus identify the attacker ECU with a
+low false identification rate of 0.2%.
+",1,0,0,0,0,0
+12119,Constraining black hole spins with low-frequency quasi-periodic oscillations in soft states,"  Black hole X-ray transients show a variety of state transitions during their
+outburst phases, characterized by changes in their spectral and timing
+properties. In particular, power density spectra (PDS) show quasi periodic
+oscillations (QPOs) that can be related to the accretion regime of the source.
+We looked for type-C QPOs in the disc-dominated state (i.e. the high soft
+state) and in the ultra-luminous state in the RXTE archival data of 12
+transient black hole X-ray binaries known to show QPOs during their outbursts.
+We detected 6 significant QPOs in the soft state that can be classified as
+type-C QPOs. Under the assumption that the accretion disc in disc-dominated
+states extends down or close to the innermost stable circular orbit (ISCO) and
+that type-C QPOs would arise at the inner edge of the accretion flow, we use
+the relativistic precession model (RPM) to place constraints on the black hole
+spin. We were able to place lower limits on the spin value for all the 12
+sources of our sample while we could place also an upper limit on the spin for
+5 sources.
+",0,1,0,0,0,0
+12120,On a generalization of Lie($k$): a CataLAnKe theorem,"  We define a generalization of the free Lie algebra based on an $n$-ary
+commutator and call it the free LAnKe. We show that the action of the symmetric
+group $S_{2n-1}$ on the multilinear component with $2n-1$ generators is given
+by the representation $S^{2^{n-1}1}$, whose dimension is the $n$th Catalan
+number. An application involving Specht modules of staircase shape is
+presented. We also introduce a conjecture that extends the relation between the
+Whitehouse representation and Lie($k$).
+",0,0,1,0,0,0
+12121,Inference for Multiple Change-points in Linear and Non-linear Time Series Models,"  In this paper we develop a generalized likelihood ratio scan method (GLRSM)
+for multiple change-points inference in piecewise stationary time series, which
+estimates the number and positions of change-points and provides a confidence
+interval for each change-point. The computational complexity of using GLRSM for
+multiple change-points detection is as low as $O(n(\log n)^3)$ for a series of
+length $n$. Consistency of the estimated numbers and positions of the
+change-points is established. Extensive simulation studies are provided to
+demonstrate the effectiveness of the proposed methodology under different
+scenarios.
+",0,0,1,1,0,0
+12122,Acceleration of Convergence of Some Infinite Sequences $\boldsymbol{\{A_n\}}$ Whose Asymptotic Expansions Involve Fractional Powers of $\boldsymbol{n}$,"  In this paper, we deal with the acceleration of the convergence of infinite
+series $\sum^\infty_{n=1}a_n$, when the terms $a_n$ are in general complex and
+have asymptotic expansions that can be expressed in the form $$
+a_n\sim[\Gamma(n)]^{s/m}\exp\left[Q(n)\right]\sum^\infty_{i=0}w_i
+n^{\gamma-i/m}\quad\text{as $n\to\infty$},$$ where $\Gamma(z)$ is the gamma
+function, $m\geq1$ is an arbitrary integer, $Q(n)=\sum^{m}_{i=0}q_in^{i/m}$ is
+a polynomial of degree at most $m$ in $n^{1/m}$, $s$ is an arbitrary integer,
+and $\gamma$ is an arbitrary complex number. This can be achieved effectively
+by applying the $\tilde{d}^{(m)}$ transformation of the author to the sequence
+$\{A_n\}$ of the partial sums $A_n=\sum^n_{k=1}a_k$, $n=1,2,\dots\ .$
+We give a detailed review of the properties of such series and of the
+$\tilde{d}^{(m)}$ transformation and the recursive W-algorithm that implements
+it. We illustrate with several numerical examples of varying nature the
+remarkable performance of this transformation on both convergent and divergent
+series. We also show that the $\tilde{d}^{(m)}$ transformation can be used
+efficiently to accelerate the convergence of some infinite products of the form
+$\prod^\infty_{n=1}(1+v_n)$, where $$v_n\sim
+\sum^\infty_{i=0}e_in^{-t/m-i/m}\quad \text{as $n\to\infty$,\ \ $t\geq m+1$ an
+integer,}$$ and illustrate this with numerical examples. We put special
+emphasis on the issue of numerical stability, we show how to monitor stability,
+or lack thereof, numerically, and discuss how it can be achieved/improved in
+suitable ways.
+",0,0,1,0,0,0
+12123,Fiber Orientation Estimation Guided by a Deep Network,"  Diffusion magnetic resonance imaging (dMRI) is currently the only tool for
+noninvasively imaging the brain's white matter tracts. The fiber orientation
+(FO) is a key feature computed from dMRI for fiber tract reconstruction.
+Because the number of FOs in a voxel is usually small, dictionary-based sparse
+reconstruction has been used to estimate FOs with a relatively small number of
+diffusion gradients. However, accurate FO estimation in regions with complex FO
+configurations in the presence of noise can still be challenging. In this work
+we explore the use of a deep network for FO estimation in a dictionary-based
+framework and propose an algorithm named Fiber Orientation Reconstruction
+guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a
+smaller dictionary encoding coarse basis FOs to represent the diffusion
+signals. To estimate the mixture fractions of the dictionary atoms (and thus
+coarse FOs), a deep network is designed specifically for solving the sparse
+reconstruction problem. Here, the smaller dictionary is used to reduce the
+computational cost of training. Second, the coarse FOs inform the final FO
+estimation, where a larger dictionary encoding dense basis FOs is used and a
+weighted l1-norm regularized least squares problem is solved to encourage FOs
+that are consistent with the network output. FORDN was evaluated and compared
+with state-of-the-art algorithms that estimate FOs using sparse reconstruction
+on simulated and real dMRI data, and the results demonstrate the benefit of
+using a deep network for FO estimation.
+",1,0,0,0,0,0
+12124,The hypotensive effect of activated apelin receptor is correlated with \b{eta}-arrestin recruitment,"  The apelinergic system is an important player in the regulation of both
+vascular tone and cardiovascular function, making this physiological system an
+attractive target for drug development for hypertension, heart failure and
+ischemic heart disease. Indeed, apelin exerts a positive inotropic effect in
+humans whilst reducing peripheral vascular resistance. In this study, we
+investigated the signaling pathways through which apelin exerts its hypotensive
+action. We synthesized a series of apelin-13 analogs whereby the C-terminal
+Phe13 residue was replaced by natural or unnatural amino acids. In HEK293 cells
+expressing APJ, we evaluated the relative efficacy of these compounds to
+activate G{\alpha}i1 and G{\alpha}oA G-proteins, recruit \b{eta}-arrestins 1
+and 2 (\b{eta}arrs), and inhibit cAMP production. Calculating the transduction
+ratio for each pathway allowed us to identify several analogs with distinct
+signaling profiles. Furthermore, we found that these analogs delivered i.v. to
+Sprague-Dawley rats exerted a wide range of hypotensive responses. Indeed, two
+compounds lost their ability to lower blood pressure, while other analogs
+significantly reduced blood pressure as apelin-13. Interestingly, analogs that
+did not lower blood pressure were less effective at recruiting \b{eta}arrs.
+Finally, using Spearman correlations, we established that the hypotensive
+response was significantly correlated with \b{eta}arr recruitment but not with
+G protein- dependent signaling. In conclusion, our results demonstrated that
+the \b{eta}arr recruitment potency is involved in the hypotensive efficacy of
+activated APJ.
+",0,0,0,0,1,0
+12125,The Shape of Bouncing Universes,"  What happens to the most general closed oscillating universes in general
+relativity? We sketch the development of interest in cyclic universes from the
+early work of Friedmann and Tolman to modern variations introduced by the
+presence of a cosmological constant. Then we show what happens in the cyclic
+evolution of the most general closed anisotropic universes provided by the
+Mixmaster universe. We show that in the presence of entropy increase its cycles
+grow in size and age, increasingly approaching flatness. But these cycles also
+grow increasingly anisotropic at their expansion maxima. If there is a positive
+cosmological constant, or dark energy, present then these oscillations always
+end and the last cycle evolves from an anisotropic inflexion point towards a de
+Sitter future of everlasting expansion.
+",0,1,0,0,0,0
+12126,Multi-sensor authentication to improve smartphone security,"  The widespread use of smartphones gives rise to new security and privacy
+concerns. Smartphone thefts account for the largest percentage of thefts in
+recent crime statistics. Using a victim's smartphone, the attacker can launch
+impersonation attacks, which threaten the security of the victim and other
+users in the network. Our threat model includes the attacker taking over the
+phone after the user has logged on with his password or pin. Our goal is to
+design a mechanism for smartphones to better authenticate the current user,
+continuously and implicitly, and raise alerts when necessary. In this paper, we
+propose a multi-sensors-based system to achieve continuous and implicit
+authentication for smartphone users. The system continuously learns the owner's
+behavior patterns and environment characteristics, and then authenticates the
+current user without interrupting user-smartphone interactions. Our method can
+adaptively update a user's model considering the temporal change of user's
+patterns. Experimental results show that our method is efficient, requiring
+less than 10 seconds to train the model and 20 seconds to detect the abnormal
+user, while achieving high accuracy (more than 90%). Also the combination of
+more sensors provide better accuracy. Furthermore, our method enables adjusting
+the security level by changing the sampling rate.
+",1,0,0,0,0,0
+12127,The ALF (Algorithms for Lattice Fermions) project release 1.0. Documentation for the auxiliary field quantum Monte Carlo code,"  The Algorithms for Lattice Fermions package provides a general code for the
+finite temperature auxiliary field quantum Monte Carlo algorithm. The code is
+engineered to be able to simulate any model that can be written in terms of
+sums of single-body operators, of squares of single-body operators and
+single-body operators coupled to an Ising field with given dynamics. We provide
+predefined types that allow the user to specify the model, the Bravais lattice
+as well as equal time and time displaced observables. The code supports an MPI
+implementation. Examples such as the Hubbard model on the honeycomb lattice and
+the Hubbard model on the square lattice coupled to a transverse Ising field are
+provided and discussed in the documentation. We furthermore discuss how to use
+the package to implement the Kondo lattice model and the
+$SU(N)$-Hubbard-Heisenberg model. One can download the code from our Git
+instance at this https URL and sign in to file issues.
+",0,1,0,0,0,0
+12128,A Survey on Blockchain Technology and Its Potential Applications in Distributed Control and Cooperative Robots,"  As a disruptive technology, blockchain, particularly its original form of
+bitcoin as a type of digital currency, has attracted great attentions. The
+innovative distributed decision making and security mechanism lay the technical
+foundation for its success, making us consider to penetrate the power of
+blockchain technology to distributed control and cooperative robotics, in which
+the distributed and secure mechanism is also highly demanded. Actually,
+security and distributed communication have long been unsolved problems in the
+field of distributed control and cooperative robotics. It has been reported on
+the network failure and intruder attacks of distributed control and
+multi-robotic systems. Blockchain technology provides promise to remedy this
+situation thoroughly. This work is intended to create a global picture of
+blockchain technology on its working principle and key elements in the language
+of control and robotics, to provide a shortcut for beginners to step into this
+research field.
+",1,0,0,0,0,0
+12129,Toroidal trapped surfaces and isoperimetric inequalities,"  We analytically construct an infinite number of trapped toroids in
+spherically symmetric Cauchy hypersurfaces of the Einstein equations. We focus
+on initial data which represent ""constant density stars"" momentarily at rest.
+There exists an infinite number of constant mean curvature tori, but we also
+deal with more general configurations. The marginally trapped toroids have been
+found analytically and numerically; they are unstable. The topologically
+toroidal trapped surfaces appear in a finite region surrounded by the
+Schwarzschild horizon.
+",0,0,1,0,0,0
+12130,Federated Tensor Factorization for Computational Phenotyping,"  Tensor factorization models offer an effective approach to convert massive
+electronic health records into meaningful clinical concepts (phenotypes) for
+data analysis. These models need a large amount of diverse samples to avoid
+population bias. An open challenge is how to derive phenotypes jointly across
+multiple hospitals, in which direct patient-level data sharing is not possible
+(e.g., due to institutional policies). In this paper, we developed a novel
+solution to enable federated tensor factorization for computational phenotyping
+without sharing patient-level data. We developed secure data harmonization and
+federated computation procedures based on alternating direction method of
+multipliers (ADMM). Using this method, the multiple hospitals iteratively
+update tensors and transfer secure summarized information to a central server,
+and the server aggregates the information to generate phenotypes. We
+demonstrated with real medical datasets that our method resembles the
+centralized training model (based on combined datasets) in terms of accuracy
+and phenotypes discovery while respecting privacy.
+",1,0,0,1,0,0
+12131,Optical fluxes in coupled $\cal PT$-symmetric photonic structures,"  In this work we first examine transverse and longitudinal fluxes in a $\cal
+PT$-symmetric photonic dimer using a coupled-mode theory. Several surprising
+understandings are obtained from this perspective: The longitudinal flux shows
+that the $\cal PT$ transition in a dimer can be regarded as a classical effect,
+despite its analogy to $\cal PT$-symmetric quantum mechanics. The longitudinal
+flux also indicates that the so-called giant amplification in the $\cal
+PT$-symmetric phase is a sub-exponential behavior and does not outperform a
+single gain waveguide. The transverse flux, on the other hand, reveals that the
+apparent power oscillations between the gain and loss waveguides in the $\cal
+PT$-symmetric phase can be deceiving in certain cases, where the transverse
+power transfer is in fact unidirectional. We also show that this power transfer
+cannot be arbitrarily fast even when the exceptional point is approached.
+Finally, we go beyond the coupled-mode theory by using the paraxial wave
+equation and also extend our discussions to a $\cal PT$ diamond and a
+one-dimensional periodic lattice.
+",0,1,0,0,0,0
+12132,QCRI Machine Translation Systems for IWSLT 16,"  This paper describes QCRI's machine translation systems for the IWSLT 2016
+evaluation campaign. We participated in the Arabic->English and English->Arabic
+tracks. We built both Phrase-based and Neural machine translation models, in an
+effort to probe whether the newly emerged NMT framework surpasses the
+traditional phrase-based systems in Arabic-English language pairs. We trained a
+very strong phrase-based system including, a big language model, the Operation
+Sequence Model, Neural Network Joint Model and Class-based models along with
+different domain adaptation techniques such as MML filtering, mixture modeling
+and using fine tuning over NNJM model. However, a Neural MT system, trained by
+stacking data from different genres through fine-tuning, and applying ensemble
+over 8 models, beat our very strong phrase-based system by a significant 2 BLEU
+points margin in Arabic->English direction. We did not obtain similar gains in
+the other direction but were still able to outperform the phrase-based system.
+We also applied system combination on phrase-based and NMT outputs.
+",1,0,0,0,0,0
+12133,Detecting the direction of a signal on high-dimensional spheres: Non-null and Le Cam optimality results,"  We consider one of the most important problems in directional statistics,
+namely the problem of testing the null hypothesis that the spike direction
+${\pmb \theta}$ of a Fisher-von Mises-Langevin distribution on the
+$p$-dimensional unit hypersphere is equal to a given direction ${\pmb
+\theta}_0$. After a reduction through invariance arguments, we derive local
+asymptotic normality (LAN) results in a general high-dimensional framework
+where the dimension $p_n$ goes to infinity at an arbitrary rate with the sample
+size $n$, and where the concentration $\kappa_n$ behaves in a completely free
+way with $n$, which offers a spectrum of problems ranging from arbitrarily easy
+to arbitrarily challenging ones. We identify seven asymptotic regimes,
+depending on the convergence/divergence properties of $(\kappa_n)$, that yield
+different contiguity rates and different limiting experiments. In each regime,
+we derive Le Cam optimal tests under specified $\kappa_n$ and we compute, from
+the Le Cam third lemma, asymptotic powers of the classical Watson test under
+contiguous alternatives. We further establish LAN results with respect to both
+spike direction and concentration, which allows us to discuss optimality also
+under unspecified $\kappa_n$. To obtain a full understanding of the non-null
+behavior of the Watson test, we use martingale CLTs to derive its local
+asymptotic powers in the broader, semiparametric, model of rotationally
+symmetric distributions. A Monte Carlo study shows that the finite-sample
+behaviors of the various tests remarkably agree with our asymptotic results.
+",0,0,1,1,0,0
+12134,"Hamiltonicity is Hard in Thin or Polygonal Grid Graphs, but Easy in Thin Polygonal Grid Graphs","  In 2007, Arkin et al. initiated a systematic study of the complexity of the
+Hamiltonian cycle problem on square, triangular, or hexagonal grid graphs,
+restricted to polygonal, thin, superthin, degree-bounded, or solid grid graphs.
+They solved many combinations of these problems, proving them either
+polynomially solvable or NP-complete, but left three combinations open. In this
+paper, we prove two of these unsolved combinations to be NP-complete:
+Hamiltonicity of Square Polygonal Grid Graphs and Hamiltonicity of Hexagonal
+Thin Grid Graphs. We also consider a new restriction, where the grid graph is
+both thin and polygonal, and prove that Hamiltonicity then becomes polynomially
+solvable for square, triangular, and hexagonal grid graphs.
+",1,0,0,0,0,0
+12135,More on cyclic amenability of the Lau product of Banach algebras defined by a Banach algebra morphism,"  For two Banach algebras $A$ and $B$, the $T$-Lau product $A\times_T B$, was
+recently introduced and studied for some bounded homomorphism $T:B\to A$ with
+$\|T\|\leq 1$. Here, we give general nessesary and sufficent conditions for
+$A\times_T B$ to be (approximately) cyclic amenable. In particular, we extend
+some recent results on (approximate) cyclic amenability of direct product
+$A\oplus B$ and $T$-Lau product $A\times_T B$ and answer a question on cyclic
+amenability of $A\times_T B$.
+",0,0,1,0,0,0
+12136,Data-Augmented Contact Model for Rigid Body Simulation,"  Accurately modeling contact behaviors for real-world, near-rigid materials
+remains a grand challenge for existing rigid-body physics simulators. This
+paper introduces a data-augmented contact model that incorporates analytical
+solutions with observed data to predict the 3D contact impulse which could
+result in rigid bodies bouncing, sliding or spinning in all directions. Our
+method enhances the expressiveness of the standard Coulomb contact model by
+learning the contact behaviors from the observed data, while preserving the
+fundamental contact constraints whenever possible. For example, a classifier is
+trained to approximate the transitions between static and dynamic frictions,
+while non-penetration constraint during collision is enforced analytically. Our
+method computes the aggregated effect of contact for the entire rigid body,
+instead of predicting the contact force for each contact point individually,
+removing the exponential decline in accuracy as the number of contact points
+increases.
+",1,0,0,0,0,0
+12137,Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem,"  In retailer management, the Newsvendor problem has widely attracted attention
+as one of basic inventory models. In the traditional approach to solving this
+problem, it relies on the probability distribution of the demand. In theory, if
+the probability distribution is known, the problem can be considered as fully
+solved. However, in any real world scenario, it is almost impossible to even
+approximate or estimate a better probability distribution for the demand. In
+recent years, researchers start adopting machine learning approach to learn a
+demand prediction model by using other feature information. In this paper, we
+propose a supervised learning that optimizes the demand quantities for products
+based on feature information. We demonstrate that the original Newsvendor loss
+function as the training objective outperforms the recently suggested quadratic
+loss function. The new algorithm has been assessed on both the synthetic data
+and real-world data, demonstrating better performance.
+",1,0,0,1,0,0
+12138,STWalk: Learning Trajectory Representations in Temporal Graphs,"  Analyzing the temporal behavior of nodes in time-varying graphs is useful for
+many applications such as targeted advertising, community evolution and outlier
+detection. In this paper, we present a novel approach, STWalk, for learning
+trajectory representations of nodes in temporal graphs. The proposed framework
+makes use of structural properties of graphs at current and previous time-steps
+to learn effective node trajectory representations. STWalk performs random
+walks on a graph at a given time step (called space-walk) as well as on graphs
+from past time-steps (called time-walk) to capture the spatio-temporal behavior
+of nodes. We propose two variants of STWalk to learn trajectory
+representations. In one algorithm, we perform space-walk and time-walk as part
+of a single step. In the other variant, we perform space-walk and time-walk
+separately and combine the learned representations to get the final trajectory
+embedding. Extensive experiments on three real-world temporal graph datasets
+validate the effectiveness of the learned representations when compared to
+three baseline methods. We also show the goodness of the learned trajectory
+embeddings for change point detection, as well as demonstrate that arithmetic
+operations on these trajectory representations yield interesting and
+interpretable results.
+",1,0,0,1,0,0
+12139,A Study on Performance and Power Efficiency of Dense Non-Volatile Caches in Multi-Core Systems,"  In this paper, we present a novel cache design based on Multi-Level Cell
+Spin-Transfer Torque RAM (MLC STTRAM) that can dynamically adapt the set
+capacity and associativity to use efficiently the full potential of MLC STTRAM.
+We exploit the asymmetric nature of the MLC storage scheme to build cache lines
+featuring heterogeneous performances, that is, half of the cache lines are
+read-friendly, while the other is write-friendly. Furthermore, we propose to
+opportunistically deactivate ways in underutilized sets to convert MLC to
+Single-Level Cell (SLC) mode, which features overall better performance and
+lifetime. Our ultimate goal is to build a cache architecture that combines the
+capacity advantages of MLC and performance/energy advantages of SLC. Our
+experiments show an improvement of 43% in total numbers of conflict misses, 27%
+in memory access latency, 12% in system performance, and 26% in LLC access
+energy, with a slight degradation in cache lifetime (about 7%) compared to an
+SLC cache.
+",1,0,0,0,0,0
+12140,Characterization theorems for $Q$-independent random variables with values in a locally compact Abelian group,"  Let $X$ be a locally compact Abelian group, $Y$ be its character group.
+Following A. Kagan and G. Székely we introduce a notion of $Q$-independence
+for random variables with values in $X$. We prove group analogues of the
+Cramér, Kac-Bernstein, Skitovich-Darmois and Heyde theorems for
+$Q$-independent random variables with values in $X$. The proofs of these
+theorems are reduced to solving some functional equations on the group $Y$.
+",0,0,1,0,0,0
+12141,Controlling competing orders via non-equilibrium acoustic phonons: emergence of anisotropic electronic temperature,"  Ultrafast perturbations offer a unique tool to manipulate correlated systems
+due to their ability to promote transient behaviors with no equilibrium
+counterpart. A widely employed strategy is the excitation of coherent optical
+phonons, as they can cause significant changes in the electronic structure and
+interactions on short time scales. Here, we explore a promising alternative
+route: the non-equilibrium excitation of acoustic phonons. We demonstrate that
+it leads to the remarkable phenomenon of a momentum-dependent temperature, by
+which electronic states at different regions of the Fermi surface are subject
+to distinct local temperatures. Such an anisotropic electronic temperature can
+have a profound effect on the delicate balance between competing ordered states
+in unconventional superconductors, opening a novel avenue to control correlated
+phases.
+",0,1,0,0,0,0
+12142,"The Junk News Aggregator: Examining junk news posted on Facebook, starting with the 2018 US Midterm Elections","  In recent years, the phenomenon of online misinformation and junk news
+circulating on social media has come to constitute an important and widespread
+problem affecting public life online across the globe, particularly around
+important political events such as elections. At the same time, there have been
+calls for more transparency around misinformation on social media platforms, as
+many of the most popular social media platforms function as ""walled gardens,""
+where it is impossible for researchers and the public to readily examine the
+scale and nature of misinformation activity as it is unfolding on the
+platforms. In order to help address this, this paper, we present the Junk News
+Aggregator, an interactive web tool made publicly available, which allows the
+public to examine, in near real-time, all of the public content posted to
+Facebook by important junk news sources in the US. It allows the public to gain
+access to and examine the latest articles posted on Facebook (the most popular
+social media platform in the US and one where content is not readily accessible
+at scale from the open Web), as well as organise them by time, news publisher,
+and keywords of interest, and sort them based on all eight engagement metrics
+available on Facebook. Therefore, the Aggregator allows the public to gain
+insights on the volume, content, key themes, and types and volumes of
+engagement received by content posted by junk news publishers, in near real
+time, hence opening up and offering transparency in these activities, at scale
+across the top most popular junk news publishers and in near real time. In this
+way, the Aggregator can help increase transparency around the nature, volume,
+and engagement with junk news on social media, and serve as a media literacy
+tool for the public.
+",1,0,0,0,0,0
+12143,Quantum Blockchain using entanglement in time,"  A conceptual design for a quantum blockchain is proposed. Our method involves
+encoding the blockchain into a temporal GHZ (Greenberger-Horne-Zeilinger) state
+of photons that do not simultaneously coexist. It is shown that the
+entanglement in time, as opposed to an entanglement in space, provides the
+crucial quantum advantage. All the subcomponents of this system have already
+been shown to be experimentally realized. Perhaps more shockingly, our encoding
+procedure can be interpreted as non-classically influencing the past; hence
+this decentralized quantum blockchain can be viewed as a quantum networked time
+machine.
+",0,0,0,0,0,1
+12144,Sensor Transformation Attention Networks,"  Recent work on encoder-decoder models for sequence-to-sequence mapping has
+shown that integrating both temporal and spatial attention mechanisms into
+neural networks increases the performance of the system substantially. In this
+work, we report on the application of an attentional signal not on temporal and
+spatial regions of the input, but instead as a method of switching among inputs
+themselves. We evaluate the particular role of attentional switching in the
+presence of dynamic noise in the sensors, and demonstrate how the attentional
+signal responds dynamically to changing noise levels in the environment to
+achieve increased performance on both audio and visual tasks in three
+commonly-used datasets: TIDIGITS, Wall Street Journal, and GRID. Moreover, the
+proposed sensor transformation network architecture naturally introduces a
+number of advantages that merit exploration, including ease of adding new
+sensors to existing architectures, attentional interpretability, and increased
+robustness in a variety of noisy environments not seen during training.
+Finally, we demonstrate that the sensor selection attention mechanism of a
+model trained only on the small TIDIGITS dataset can be transferred directly to
+a pre-existing larger network trained on the Wall Street Journal dataset,
+maintaining functionality of switching between sensors to yield a dramatic
+reduction of error in the presence of noise.
+",1,0,0,0,0,0
+12145,Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer,"  The capacity of a neural network to absorb information is limited by its
+number of parameters. Conditional computation, where parts of the network are
+active on a per-example basis, has been proposed in theory as a way of
+dramatically increasing model capacity without a proportional increase in
+computation. In practice, however, there are significant algorithmic and
+performance challenges. In this work, we address these challenges and finally
+realize the promise of conditional computation, achieving greater than 1000x
+improvements in model capacity with only minor losses in computational
+efficiency on modern GPU clusters. We introduce a Sparsely-Gated
+Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward
+sub-networks. A trainable gating network determines a sparse combination of
+these experts to use for each example. We apply the MoE to the tasks of
+language modeling and machine translation, where model capacity is critical for
+absorbing the vast quantities of knowledge available in the training corpora.
+We present model architectures in which a MoE with up to 137 billion parameters
+is applied convolutionally between stacked LSTM layers. On large language
+modeling and machine translation benchmarks, these models achieve significantly
+better results than state-of-the-art at lower computational cost.
+",1,0,0,1,0,0
+12146,Finite Size Corrections and Likelihood Ratio Fluctuations in the Spiked Wigner Model,"  In this paper we study principal components analysis in the regime of high
+dimensionality and high noise. Our model of the problem is a rank-one
+deformation of a Wigner matrix where the signal-to-noise ratio (SNR) is of
+constant order, and we are interested in the fundamental limits of detection of
+the spike. Our main goal is to gain a fine understanding of the asymptotics for
+the log-likelihood ratio process, also known as the free energy, as a function
+of the SNR. Our main results are twofold. We first prove that the free energy
+has a finite-size correction to its limit---the replica-symmetric
+formula---which we explicitly compute. This provides a formula for the
+Kullback-Leibler divergence between the planted and null models. Second, we
+prove that below the reconstruction threshold, where it becomes impossible to
+reconstruct the spike, the log-likelihood ratio has fluctuations of constant
+order and converges in distribution to a Gaussian under both the planted and
+(under restrictions) the null model. As a consequence, we provide a general
+proof of contiguity between these two distributions that holds up to the
+reconstruction threshold, and is valid for an arbitrary separable prior on the
+spike. Formulae for the total variation distance, and the Type-I and Type-II
+errors of the optimal test are also given. Our proofs are based on Gaussian
+interpolation methods and a rigorous incarnation of the cavity method, as
+devised by Guerra and Talagrand in their study of the Sherrington--Kirkpatrick
+spin-glass model.
+",0,0,1,1,0,0
+12147,Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory,"  In meta-learning an agent extracts knowledge from observed tasks, aiming to
+facilitate learning of novel future tasks. Under the assumption that future
+tasks are 'related' to previous tasks, the accumulated knowledge should be
+learned in a way which captures the common structure across learned tasks,
+while allowing the learner sufficient flexibility to adapt to novel aspects of
+new tasks. We present a framework for meta-learning that is based on
+generalization error bounds, allowing us to extend various PAC-Bayes bounds to
+meta-learning. Learning takes place through the construction of a distribution
+over hypotheses based on the observed tasks, and its utilization for learning a
+new task. Thus, prior knowledge is incorporated through setting an
+experience-dependent prior for novel tasks. We develop a gradient-based
+algorithm which minimizes an objective function derived from the bounds and
+demonstrate its effectiveness numerically with deep neural networks. In
+addition to establishing the improved performance available through
+meta-learning, we demonstrate the intuitive way by which prior information is
+manifested at different levels of the network.
+",1,0,0,1,0,0
+12148,Accurate Optical Flow via Direct Cost Volume Processing,"  We present an optical flow estimation approach that operates on the full
+four-dimensional cost volume. This direct approach shares the structural
+benefits of leading stereo matching pipelines, which are known to yield high
+accuracy. To this day, such approaches have been considered impractical due to
+the size of the cost volume. We show that the full four-dimensional cost volume
+can be constructed in a fraction of a second due to its regularity. We then
+exploit this regularity further by adapting semi-global matching to the
+four-dimensional setting. This yields a pipeline that achieves significantly
+higher accuracy than state-of-the-art optical flow methods while being faster
+than most. Our approach outperforms all published general-purpose optical flow
+methods on both Sintel and KITTI 2015 benchmarks.
+",1,0,0,0,0,0
+12149,Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls,"  We propose a rank-$k$ variant of the classical Frank-Wolfe algorithm to solve
+convex optimization over a trace-norm ball. Our algorithm replaces the top
+singular-vector computation ($1$-SVD) in Frank-Wolfe with a top-$k$
+singular-vector computation ($k$-SVD), which can be done by repeatedly applying
+$1$-SVD $k$ times. Alternatively, our algorithm can be viewed as a rank-$k$
+restricted version of projected gradient descent. We show that our algorithm
+has a linear convergence rate when the objective function is smooth and
+strongly convex, and the optimal solution has rank at most $k$. This improves
+the convergence rate and the total time complexity of the Frank-Wolfe method
+and its variants.
+",1,0,0,1,0,0
+12150,A Note on Exponential Inequalities in Hilbert Spaces for Spatial Processes with Applications to the Functional Kernel Regression Model,"  In this manuscript we present exponential inequalities for spatial lattice
+processes which take values in a separable Hilbert space and satisfy certain
+dependence conditions. We consider two types of dependence: spatial data under
+$\alpha$-mixing conditions and spatial data which satisfies a weak dependence
+condition introduced by Dedecker and Prieur [2005]. We demonstrate their
+usefulness in the functional kernel regression model of Ferraty and Vieu [2004]
+where we study uniform consistency properties of the estimated regression
+operator on increasing subsets of the underlying function space.
+",0,0,1,1,0,0
+12151,Polygons pulled from an adsorbing surface,"  We consider self-avoiding lattice polygons, in the hypercubic lattice, as a
+model of a ring polymer adsorbed at a surface and either being desorbed by the
+action of a force, or pushed towards the surface. We show that, when there is
+no interaction with the surface, then the response of the polygon to the
+applied force is identical (in the thermodynamic limit) for two ways in which
+we apply the force. When the polygon is attracted to the surface then, when the
+dimension is at least 3, we have a complete characterization of the critical
+force--temperature curve in terms of the behaviour, (a) when there is no force,
+and, (b) when there is no surface interaction. For the 2-dimensional case we
+have upper and lower bounds on the free energy. We use both Monte Carlo and
+exact enumeration and series analysis methods to investigate the form of the
+phase diagram in two dimensions. We find evidence for the existence of a
+\emph{mixed phase} where the free energy depends on the strength of the
+interaction with the adsorbing line and on the applied force.
+",0,1,0,0,0,0
+12152,O$^2$TD: (Near)-Optimal Off-Policy TD Learning,"  Temporal difference learning and Residual Gradient methods are the most
+widely used temporal difference based learning algorithms; however, it has been
+shown that none of their objective functions is optimal w.r.t approximating the
+true value function $V$. Two novel algorithms are proposed to approximate the
+true value function $V$. This paper makes the following contributions: (1) A
+batch algorithm that can help find the approximate optimal off-policy
+prediction of the true value function $V$. (2) A linear computational cost (per
+step) near-optimal algorithm that can learn from a collection of off-policy
+samples. (3) A new perspective of the emphatic temporal difference learning
+which bridges the gap between off-policy optimality and off-policy stability.
+",1,0,0,1,0,0
+12153,Comprehensive classification for Bose-Fermi mixtures,"  We present analytical studies of a boson-fermion mixture at zero temperature
+with spin-polarized fermions. Using the Thomas-Fermi approximation for bosons
+and the local-density approximation for fermions, we find a large variety of
+different density shapes. In the case of continuous density, we obtain analytic
+conditions for each configuration for attractive as well as repulsive
+boson-fermion interaction. Furthermore, we analytically show that all the
+scenarios we describe are minima of the grand-canonical energy functional.
+Finally, we provide a full classification of all possible ground states in the
+interpenetrative regime. Our results also apply to binary mixtures of bosons.
+",0,1,0,0,0,0
+12154,Navigation Objects Extraction for Better Content Structure Understanding,"  Existing works for extracting navigation objects from webpages focus on
+navigation menus, so as to reveal the information architecture of the site.
+However, web 2.0 sites such as social networks, e-commerce portals etc. are
+making the understanding of the content structure in a web site increasingly
+difficult. Dynamic and personalized elements such as top stories, recommended
+list in a webpage are vital to the understanding of the dynamic nature of web
+2.0 sites. To better understand the content structure in web 2.0 sites, in this
+paper we propose a new extraction method for navigation objects in a webpage.
+Our method will extract not only the static navigation menus, but also the
+dynamic and personalized page-specific navigation lists. Since the navigation
+objects in a webpage naturally come in blocks, we first cluster hyperlinks into
+different blocks by exploiting spatial locations of hyperlinks, the
+hierarchical structure of the DOM-tree and the hyperlink density. Then we
+identify navigation objects from those blocks using the SVM classifier with
+novel features such as anchor text lengths etc. Experiments on real-world data
+sets with webpages from various domains and styles verified the effectiveness
+of our method.
+",1,0,0,0,0,0
+12155,Source Selection for Cluster Weak Lensing Measurements in the Hyper Suprime-Cam Survey,"  We present optimized source galaxy selection schemes for measuring cluster
+weak lensing (WL) mass profiles unaffected by cluster member dilution from the
+Subaru Hyper Suprime-Cam Strategic Survey Program (HSC-SSP). The ongoing
+HSC-SSP survey will uncover thousands of galaxy clusters to $z\lesssim1.5$. In
+deriving cluster masses via WL, a critical source of systematics is
+contamination and dilution of the lensing signal by cluster {members, and by
+foreground galaxies whose photometric redshifts are biased}. Using the
+first-year CAMIRA catalog of $\sim$900 clusters with richness larger than 20
+found in $\sim$140 deg$^2$ of HSC-SSP data, we devise and compare several
+source selection methods, including selection in color-color space (CC-cut),
+and selection of robust photometric redshifts by applying constraints on their
+cumulative probability distribution function (PDF; P-cut). We examine the
+dependence of the contamination on the chosen limits adopted for each method.
+Using the proper limits, these methods give mass profiles with minimal dilution
+in agreement with one another. We find that not adopting either the CC-cut or
+P-cut methods results in an underestimation of the total cluster mass
+($13\pm4\%$) and the concentration of the profile ($24\pm11\%$). The level of
+cluster contamination can reach as high as $\sim10\%$ at $R\approx 0.24$
+Mpc/$h$ for low-z clusters without cuts, while employing either the P-cut or
+CC-cut results in cluster contamination consistent with zero to within the 0.5%
+uncertainties. Our robust methods yield a $\sim60\sigma$ detection of the
+stacked CAMIRA surface mass density profile, with a mean mass of
+$M_\mathrm{200c} = (1.67\pm0.05({\rm {stat}}))\times 10^{14}\,M_\odot/h$.
+",0,1,0,0,0,0
+12156,Dense blowup for parabolic SPDEs,"  The main result of this paper is that there are examples of stochastic
+partial differential equations [hereforth, SPDEs] of the type $$ \partial_t
+u=\frac12\Delta u +\sigma(u)\eta \qquad\text{on
+$(0\,,\infty)\times\mathbb{R}^3$}$$ such that the solution exists and is unique
+as a random field in the sense of Dalang and Walsh, yet the solution has
+unbounded oscillations in every open neighborhood of every space-time point. We
+are not aware of the existence of such a construction in spatial dimensions
+below $3$. En route, it will be proved that there exist a large family of
+parabolic SPDEs whose moment Lyapunov exponents grow at least sub exponentially
+in its order parameter in the sense that there exist $A_1,\beta\in(0\,,1)$ such
+that \[
+\underline{\gamma}(k) :=
+\liminf_{t\to\infty}t^{-1}\inf_{x\in\mathbb{R}^3}
+\log\mathbb{E}\left(|u(t\,,x)|^k\right) \ge A_1\exp(A_1 k^\beta)
+\qquad\text{for all $k\ge 2$}.
+\] This sort of ""super intermittency"" is combined with a local linearization
+of the solution, and with techniques from Gaussian analysis in order to
+establish the unbounded oscillations of the sample functions of the solution to
+our SPDE.
+",0,0,1,0,0,0
+12157,Batched High-dimensional Bayesian Optimization via Structural Kernel Learning,"  Optimization of high-dimensional black-box functions is an extremely
+challenging problem. While Bayesian optimization has emerged as a popular
+approach for optimizing black-box functions, its applicability has been limited
+to low-dimensional problems due to its computational and statistical challenges
+arising from high-dimensional settings. In this paper, we propose to tackle
+these challenges by (1) assuming a latent additive structure in the function
+and inferring it properly for more efficient and effective BO, and (2)
+performing multiple evaluations in parallel to reduce the number of iterations
+required by the method. Our novel approach learns the latent structure with
+Gibbs sampling and constructs batched queries using determinantal point
+processes. Experimental validations on both synthetic and real-world functions
+demonstrate that the proposed method outperforms the existing state-of-the-art
+approaches.
+",1,0,1,1,0,0
+12158,Classifying Time-Varying Complex Networks on the Tensor Manifold,"  At the core of understanding dynamical systems is the ability to maintain and
+control the systems behavior that includes notions of robustness,
+heterogeneity, and/or regime-shift detection. Recently, to explore such
+functional properties, a convenient representation has been to model such
+dynamical systems as a weighted graph consisting of a finite, but very large
+number of interacting agents. This said, there exists very limited relevant
+statistical theory that is able cope with real-life data, i.e., how does
+perform simple analysis and/or statistics over a family of networks as opposed
+to a specific network or network-to-network variation. Here, we are interested
+in the analysis of network families whereby each network represents a point on
+an underlying statistical manifold. From this, we explore the Riemannian
+structure of the statistical (tensor) manifold in order to define notions of
+geodesics or shortest distance amongst such points as well as a statistical
+framework for time-varying complex networks for which we can utilize in higher
+order classification tasks.
+",1,0,0,0,0,0
+12159,An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks,"  We propose a method for semi-supervised training of structured-output neural
+networks. Inspired by the framework of Generative Adversarial Networks (GAN),
+we train a discriminator network to capture the notion of a quality of network
+output. To this end, we leverage the qualitative difference between outputs
+obtained on the labelled training data and unannotated data. We then use the
+discriminator as a source of error signal for unlabelled data. This effectively
+boosts the performance of a network on a held out test set. Initial experiments
+in image segmentation demonstrate that the proposed framework enables achieving
+the same network performance as in a fully supervised scenario, while using two
+times less annotations.
+",1,0,0,0,0,0
+12160,Elliptic operators on refined Sobolev scales on vector bundles,"  We introduce a refined Sobolev scale on a vector bundle over a closed
+infinitely smooth manifold. This scale consists of inner product Hörmander
+spaces parametrized with a real number and a function varying slowly at
+infinity in the sense of Karamata. We prove that these spaces are obtained by
+the interpolation with a function parameter between inner product Sobolev
+spaces. An arbitrary classical elliptic pseudodifferential operator acting
+between vector bundles of the same rank is investigated on this scale. We prove
+that this operator is bounded and Fredholm on pairs of appropriate Hörmander
+spaces. We also prove that the solutions to the corresponding elliptic equation
+satisfy a certain a priori estimate on these spaces. The local regularity of
+these solutions is investigated on the refined Sobolev scale. We find new
+sufficient conditions for the solutions to have continuous derivatives of a
+given order.
+",0,0,1,0,0,0
+12161,CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication,"  In this work, we propose a goal-driven collaborative task that contains
+language, vision, and action in a virtual environment as its core components.
+Specifically, we develop a Collaborative image-Drawing game between two agents,
+called CoDraw. Our game is grounded in a virtual world that contains movable
+clip art objects. The game involves two players: a Teller and a Drawer. The
+Teller sees an abstract scene containing multiple clip art pieces in a
+semantically meaningful configuration, while the Drawer tries to reconstruct
+the scene on an empty canvas using available clip art pieces. The two players
+communicate via two-way communication using natural language. We collect the
+CoDraw dataset of ~10K dialogs consisting of ~138K messages exchanged between
+human agents. We define protocols and metrics to evaluate the effectiveness of
+learned agents on this testbed, highlighting the need for a novel crosstalk
+condition which pairs agents trained independently on disjoint subsets of the
+training data for evaluation. We present models for our task, including simple
+but effective nearest-neighbor techniques and neural network approaches trained
+using a combination of imitation learning and goal-driven training. All models
+are benchmarked using both fully automated evaluation and by playing the game
+with live human agents.
+",1,0,0,0,0,0
+12162,Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection,"  In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology
+and framework for efficient and effective real-time malware detection,
+leveraging the best of conventional machine learning (ML) and deep learning
+(DL) algorithms. In PROPEDEUTICA, all software processes in the system start
+execution subjected to a conventional ML detector for fast classification. If a
+piece of software receives a borderline classification, it is subjected to
+further analysis via more performance expensive and more accurate DL methods,
+via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays
+to the execution of software subjected to deep learning analysis as a way to
+""buy time"" for DL analysis and to rate-limit the impact of possible malware in
+the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and
+877 commonly used benign software samples from various categories for the
+Windows OS. Our results show that the false positive rate for conventional ML
+methods can reach 20%, and for modern DL methods it is usually below 6%.
+However, the classification time for DL can be 100X longer than conventional ML
+methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional
+ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the
+percentage of software subjected to DL analysis was approximately 40% on
+average. Further, the application of delays in software subjected to ML reduced
+the detection time by approximately 10%. Finally, we found and discussed a
+discrepancy between the detection accuracy offline (analysis after all traces
+are collected) and on-the-fly (analysis in tandem with trace collection). Our
+insights show that conventional ML and modern DL-based malware detectors in
+isolation cannot meet the needs of efficient and effective malware detection:
+high accuracy, low false positive rate, and short classification time.
+",1,0,0,1,0,0
+12163,Metachronal motion of artificial magnetic cilia,"  Organisms use hair-like cilia that beat in a metachronal fashion to actively
+transport fluid and suspended particles. Metachronal motion emerges due to a
+phase difference between beating cycles of neighboring cilia and appears as
+traveling waves propagating along ciliary carpet. In this work, we demonstrate
+biomimetic artificial cilia capable of metachronal motion. The cilia are
+micromachined magnetic thin filaments attached at one end to a substrate and
+actuated by a uniform rotating magnetic field. We show that the difference in
+magnetic cilium length controls the phase of the beating motion. We use this
+property to induce metachronal waves within a ciliary array and explore the
+effect of operation parameters on the wave motion. The metachronal motion in
+our artificial system is shown to depend on the magnetic and elastic properties
+of the filaments, unlike natural cilia, where metachronal motion arises due to
+fluid coupling. Our approach enables an easy integration of metachronal
+magnetic cilia in lab-on-a-chip devices for enhanced fluid and particle
+manipulations.
+",0,0,0,0,1,0
+12164,Power-of-$d$-Choices with Memory: Fluid Limit and Optimality,"  In multi-server distributed queueing systems, the access of stochastically
+arriving jobs to resources is often regulated by a dispatcher, also known as
+load balancer. A fundamental problem consists in designing a load balancing
+algorithm that minimizes the delays experienced by jobs. During the last two
+decades, the power-of-$d$-choice algorithm, based on the idea of dispatching
+each job to the least loaded server out of $d$ servers randomly sampled at the
+arrival of the job itself, has emerged as a breakthrough in the foundations of
+this area due to its versatility and appealing asymptotic properties. In this
+paper, we consider the power-of-$d$-choice algorithm with the addition of a
+local memory that keeps track of the latest observations collected over time on
+the sampled servers. Then, each job is sent to a server with the lowest
+observation. We show that this algorithm is asymptotically optimal in the sense
+that the load balancer can always assign each job to an idle server in the
+large-server limit. This holds true if and only if the system load $\lambda$ is
+less than $1-\frac{1}{d}$. If this condition is not satisfied, we show that
+queue lengths are bounded by $j^\star+1$, where $j^\star\in\mathbb{N}$ is given
+by the solution of a polynomial equation. This is in contrast with the classic
+version of the power-of-$d$-choice algorithm, where queue lengths are
+unbounded. Our upper bound on the size of the most loaded server, $j^*+1$, is
+tight and increases slowly when $\lambda$ approaches its critical value from
+below. For instance, when $\lambda= 0.995$ and $d=2$ (respectively, $d=3$), we
+find that no server will contain more than just $5$ ($3$) jobs in equilibrium.
+Our results quantify and highlight the importance of using memory as a means to
+enhance performance in randomized load balancing.
+",1,0,0,0,0,0
+12165,Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning,"  Convolutional neural networks (CNNs) have state-of-the-art performance on
+many problems in machine vision. However, networks with superior performance
+often have millions of weights so that it is difficult or impossible to use
+CNNs on computationally limited devices or to humanly interpret them. A myriad
+of CNN compression approaches have been proposed and they involve pruning and
+compressing the weights and filters. In this article, we introduce a greedy
+structural compression scheme that prunes filters in a trained CNN. We define a
+filter importance index equal to the classification accuracy reduction (CAR) of
+the network after pruning that filter (similarly defined as RAR for
+regression). We then iteratively prune filters based on the CAR index. This
+algorithm achieves substantially higher classification accuracy in AlexNet
+compared to other structural compression schemes that prune filters. Pruning
+half of the filters in the first or second layer of AlexNet, our CAR algorithm
+achieves 26% and 20% higher classification accuracies respectively, compared to
+the best benchmark filter pruning scheme. Our CAR algorithm, combined with
+further weight pruning and compressing, reduces the size of first or second
+convolutional layer in AlexNet by a factor of 42, while achieving close to
+original classification accuracy through retraining (or fine-tuning) network.
+Finally, we demonstrate the interpretability of CAR-compressed CNNs by showing
+that our algorithm prunes filters with visually redundant functionalities. In
+fact, out of top 20 CAR-pruned filters in AlexNet, 17 of them in the first
+layer and 14 of them in the second layer are color-selective filters as opposed
+to shape-selective filters. To our knowledge, this is the first reported result
+on the connection between compression and interpretability of CNNs.
+",1,0,0,0,0,0
+12166,Testing the Young Neutron Star Scenario with Persistent Radio Emission Associated with FRB 121102,"  Recently a repeating fast radio burst (FRB) 121102 has been confirmed to be
+an extragalactic event and a persistent radio counterpart has been identified.
+While other possibilities are not ruled out, the emission properties are
+broadly consistent with Murase et al. (2016) that theoretically proposed
+quasi-steady radio emission as a counterpart of both FRBs and pulsar-driven
+supernovae. Here we constrain the model parameters of such a young neutron star
+scenario for FRB 121102. If the associated supernova has a conventional ejecta
+mass of $M_{\rm ej}\gtrsim{\rm a \ few}\ M_\odot$, a neutron star with an age
+of $t_{\rm age} \sim 10-100 \ \rm yrs$, an initial spin period of $P_{i}
+\lesssim$ a few ms, and a dipole magnetic field of $B_{\rm dip} \lesssim {\rm a
+\ few} \times 10^{13} \ \rm G$ can be compatible with the observations.
+However, in this case, the magnetically-powered scenario may be favored as an
+FRB energy source because of the efficiency problem in the rotation-powered
+scenario. On the other hand, if the associated supernova is an ultra-stripped
+one or the neutron star is born by the accretion-induced collapse with $M_{\rm
+ej} \sim 0.1 \ M_\odot$, a younger neutron star with $t_{\rm age} \sim 1-10$
+yrs can be the persistent radio source and might produce FRBs with the
+spin-down power. These possibilities can be distinguished by the decline rate
+of the quasi-steady radio counterpart.
+",0,1,0,0,0,0
+12167,Critical Vertices and Edges in $H$-free Graphs,"  A vertex or edge in a graph is critical if its deletion reduces the chromatic
+number of the graph by 1. We consider the problems of deciding whether a graph
+has a critical vertex or edge, respectively. We give a complexity dichotomy for
+both problems restricted to $H$-free graphs, that is, graphs with no induced
+subgraph isomorphic to $H$. Moreover, we show that an edge is critical if and
+only if its contraction reduces the chromatic number by 1. Hence, we also
+obtain a complexity dichotomy for the problem of deciding if a graph has an
+edge whose contraction reduces the chromatic number by 1.
+",1,0,0,0,0,0
+12168,Transverse Weitzenböck formulas and de Rham cohomology of totally geodesic foliations,"  We prove transverse Weitzenböck identities for the horizontal Laplacians of
+a totally geodesic foliation. As a consequence, we obtain nullity theorems for
+the de Rham cohomology assuming only the positivity of curvature quantities
+transverse to the leaves. Those curvature quantities appear in the adiabatic
+limit of the canonical variation of the metric.
+",0,0,1,0,0,0
+12169,An adaptive Newton algorithm for optimal control problems with application to optimal electrode design,"  In this work we present an adaptive Newton-type method to solve nonlinear
+constrained optimization problems in which the constraint is a system of
+partial differential equations discretized by the finite element method. The
+adaptive strategy is based on a goal-oriented a posteriori error estimation for
+the discretization and for the iteration error. The iteration error stems from
+an inexact solution of the nonlinear system of first order optimality
+conditions by the Newton-type method. This strategy allows to balance the two
+errors and to derive effective stopping criteria for the Newton-iterations. The
+algorithm proceeds with the search of the optimal point on coarse grids which
+are refined only if the discretization error becomes dominant. Using computable
+error indicators the mesh is refined locally leading to a highly efficient
+solution process. The performance of the algorithm is shown with several
+examples and in particular with an application in the neurosciences: the
+optimal electrode design for the study of neuronal networks.
+",0,0,1,0,0,0
+12170,Characterization of the Two-Dimensional Five-Fold Lattice Tiles,"  In 1885, Fedorov discovered that a convex domain can form a lattice tiling of
+the Euclidean plane if and only if it is a parallelogram or a centrally
+symmetric hexagon. It is known that there is no other convex domain which can
+form a two-, three- or four-fold lattice tiling in the Euclidean plane, but
+there is a centrally symmetric convex decagon which can form a five-fold
+lattice tiling. This paper characterizes all the convex domains which can form
+a five-fold lattice tiling of the Euclidean plane.
+",0,0,1,0,0,0
+12171,"Projectors separating spectra for $L^2$ on pseudounitary groups $U(p,q)$","  The spectrum of $L^2$ on a pseudo-unitary group $U(p,q)$ (we assume $p\ge q$
+naturally splits into $q+1$ types. We write explicitly orthogonal projectors in
+$L^2$ to subspaces with uniform spectra (this is an old question formulated by
+Gelfand and Gindikin). We also write two finer separations of $L^2$. In the
+first case pieces are enumerated by $r=0$, 1,..., $q$ and representations of
+discrete series of $U(p-r,q-r)$, where $r=0$, \dots, $q$. In the second case
+pieces are enumerated by all discrete parameters of the tempered spectrum of
+$U(p,q)$.
+",0,0,1,0,0,0
+12172,Predicting Individual Physiologically Acceptable States for Discharge from a Pediatric Intensive Care Unit,"  Objective: Predict patient-specific vitals deemed medically acceptable for
+discharge from a pediatric intensive care unit (ICU). Design: The means of each
+patient's hr, sbp and dbp measurements between their medical and physical
+discharge from the ICU were computed as a proxy for their physiologically
+acceptable state space (PASS) for successful ICU discharge. These individual
+PASS values were compared via root mean squared error (rMSE) to population
+age-normal vitals, a polynomial regression through the PASS values of a
+Pediatric ICU (PICU) population and predictions from two recurrent neural
+network models designed to predict personalized PASS within the first twelve
+hours following ICU admission. Setting: PICU at Children's Hospital Los Angeles
+(CHLA). Patients: 6,899 PICU episodes (5,464 patients) collected between 2009
+and 2016. Interventions: None. Measurements: Each episode data contained 375
+variables representing vitals, labs, interventions, and drugs. They also
+included a time indicator for PICU medical discharge and physical discharge.
+Main Results: The rMSEs between individual PASS values and population
+age-normals (hr: 25.9 bpm, sbp: 13.4 mmHg, dbp: 13.0 mmHg) were larger than the
+rMSEs corresponding to the polynomial regression (hr: 19.1 bpm, sbp: 12.3 mmHg,
+dbp: 10.8 mmHg). The rMSEs from the best performing RNN model were the lowest
+(hr: 16.4 bpm; sbp: 9.9 mmHg, dbp: 9.0 mmHg). Conclusion: PICU patients are a
+unique subset of the general population, and general age-normal vitals may not
+be suitable as target values indicating physiologic stability at discharge.
+Age-normal vitals that were specifically derived from the medical-to-physical
+discharge window of ICU patients may be more appropriate targets for
+'acceptable' physiologic state for critical care patients. Going beyond simple
+age bins, an RNN model can provide more personalized target values.
+",1,0,0,1,0,0
+12173,Unsupervised learning of object frames by dense equivariant image labelling,"  One of the key challenges of visual perception is to extract abstract models
+of 3D objects and object categories from visual measurements, which are
+affected by complex nuisance factors such as viewpoint, occlusion, motion, and
+deformations. Starting from the recent idea of viewpoint factorization, we
+propose a new approach that, given a large number of images of an object and no
+other supervision, can extract a dense object-centric coordinate frame. This
+coordinate frame is invariant to deformations of the images and comes with a
+dense equivariant labelling neural network that can map image pixels to their
+corresponding object coordinates. We demonstrate the applicability of this
+method to simple articulated objects and deformable objects such as human
+faces, learning embeddings from random synthetic transformations or optical
+flow correspondences, all without any manual supervision.
+",1,0,0,1,0,0
+12174,Channel surfaces in Lie sphere geometry,"  We discuss channel surfaces in the context of Lie sphere geometry and
+characterise them as certain $\Omega_{0}$-surfaces. Since $\Omega_{0}$-surfaces
+possess a rich transformation theory, we study the behaviour of channel
+surfaces under these transformations. Furthermore, by using certain Dupin
+cyclide congruences, we characterise Ribaucour pairs of channel surfaces.
+",0,0,1,0,0,0
+12175,A Parallelizable Acceleration Framework for Packing Linear Programs,"  This paper presents an acceleration framework for packing linear programming
+problems where the amount of data available is limited, i.e., where the number
+of constraints m is small compared to the variable dimension n. The framework
+can be used as a black box to speed up linear programming solvers dramatically,
+by two orders of magnitude in our experiments. We present worst-case guarantees
+on the quality of the solution and the speedup provided by the algorithm,
+showing that the framework provides an approximately optimal solution while
+running the original solver on a much smaller problem. The framework can be
+used to accelerate exact solvers, approximate solvers, and parallel/distributed
+solvers. Further, it can be used for both linear programs and integer linear
+programs.
+",1,0,0,1,0,0
+12176,$L^p$ estimates for the Bergman projection on some Reinhardt domains,"  We obtain $L^p$ regularity for the Bergman projection on some Reinhardt
+domains. We start with a bounded initial domain $\Omega$ with some symmetry
+properties and generate successor domains in higher {dimensions}. We prove: If
+the Bergman kernel on $\Omega$ satisfies appropriate estimates, then the
+Bergman projection on the successor is $L^p$ bounded. For example, the Bergman
+projection on successors of strictly pseudoconvex initial domains is bounded on
+$L^p$ for $1<p<\infty$. The successor domains need not have smooth boundary nor
+be strictly pseudoconvex.
+",0,0,1,0,0,0
+12177,Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing,"  We present a simple encoding for unlabeled noncrossing graphs and show how
+its latent counterpart helps us to represent several families of directed and
+undirected graphs used in syntactic and semantic parsing of natural language as
+context-free languages. The families are separated purely on the basis of
+forbidden patterns in latent encoding, eliminating the need to differentiate
+the families of non-crossing graphs in inference algorithms: one algorithm
+works for all when the search space can be controlled in parser input.
+",1,0,0,0,0,0
+12178,Computation of Optimal Transport on Discrete Metric Measure Spaces,"  In this paper we investigate the numerical approximation of an analogue of
+the Wasserstein distance for optimal transport on graphs that is defined via a
+discrete modification of the Benamou--Brenier formula. This approach involves
+the logarithmic mean of measure densities on adjacent nodes of the graph. For
+this model a variational time discretization of the probability densities on
+graph nodes and the momenta on graph edges is proposed. A robust descent
+algorithm for the action functional is derived, which in particular uses a
+proximal splitting with an edgewise nonlinear projection on the convex subgraph
+of the logarithmic mean. Thereby, suitable chosen slack variables avoid a
+global coupling of probability densities on all graph nodes in the projection
+step. For the time discrete action functional $\Gamma$--convergence to the time
+continuous action is established. Numerical results for a selection of test
+cases show qualitative and quantitative properties of the optimal transport on
+graphs. Finally, we use our algorithm to implement a JKO scheme for the
+gradient flow of the entropy in the discrete transportation distance, which is
+known to coincide with the underlying Markov semigroup, and test our results
+against a classical backward Euler discretization of this discrete heat flow.
+",0,0,1,0,0,0
+12179,Torsions of integral homology and cohomology of real Grassmannians,"  According to a result of Ehresmann, the torsions of integral homology of real
+Grassmannian are all of order $2$. In this note, We compute the
+$\mathbb{Z}_2$-dimensions of torsions in the integral homology and cohomology
+of real Grassmannian.
+",0,0,1,0,0,0
+12180,PACO: Signal Restoration via PAtch COnsensus,"  Many signal processing algorithms operate by breaking the target signal into
+possibly overlapping segments (typically called windows or patches), processing
+them separately, and then stitching them back into place to produce a unified
+output. In most cases where pach overlapping occurs, the final value of those
+samples that are estimated by more than one patch is resolved by averaging
+those estimates; this includes many recent image processing algorithms. In
+other cases, typically frequency-based restoration methods, the average is
+implicitly weighted by some window function such as Hanning, Blackman, etc.
+which is applied prior to the Fourier/DCT transform in order to avoid Gibbs
+oscillations in the processed patches. Such averaging may incidentally help in
+covering up artifacts in the restoration process, but more often will simply
+degrade the overall result, posing an upper limit to the size of the patches
+that can be used. In order to avoid such drawbacks, we propose a new
+methodology where the different estimates of any given sample are forced to be
+identical. We show that, together, these consensus constraints constitute a
+non-empty convex feasible set, provide a general formulation of the resulting
+constrained optimization problem which can be applied to a wide variety of
+signal restoration tasks, and propose an efficient algorithm for finding the
+corresponding solutions. Finally, we describe in detail the application of the
+proposed methodology to three different signal processing problems, in some
+cases surpassing the state of the art by a significant margin.
+",0,0,0,1,0,0
+12181,Hyperplane arrangements associated to symplectic quotient singularities,"  We study the hyperplane arrangements associated, via the minimal model
+programme, to symplectic quotient singularities. We show that this hyperplane
+arrangement equals the arrangement of CM-hyperplanes coming from the
+representation theory of restricted rational Cherednik algebras. We explain
+some of the interesting consequences of this identification for the
+representation theory of restricted rational Cherednik algebras. We also show
+that the Calogero-Moser space is smooth if and only if the Calogero-Moser
+families are trivial. We describe the arrangements of CM-hyperplanes associated
+to several exceptional complex reflection groups, some of which are free.
+",0,0,1,0,0,0
+12182,CTCModel: a Keras Model for Connectionist Temporal Classification,"  We report an extension of a Keras Model, called CTCModel, to perform the
+Connectionist Temporal Classification (CTC) in a transparent way. Combined with
+Recurrent Neural Networks, the Connectionist Temporal Classification is the
+reference method for dealing with unsegmented input sequences, i.e. with data
+that are a couple of observation and label sequences where each label is
+related to a subset of observation frames. CTCModel makes use of the CTC
+implementation in the Tensorflow backend for training and predicting sequences
+of labels using Keras. It consists of three branches made of Keras models: one
+for training, computing the CTC loss function; one for predicting, providing
+sequences of labels; and one for evaluating that returns standard metrics for
+analyzing sequences of predictions.
+",1,0,0,1,0,0
+12183,Software Distribution Transparency and Auditability,"  A large user base relies on software updates provided through package
+managers. This provides a unique lever for improving the security of the
+software update process. We propose a transparency system for software updates
+and implement it for a widely deployed Linux package manager, namely APT. Our
+system is capable of detecting targeted backdoors without producing overhead
+for maintainers. In addition, in our system, the availability of source code is
+ensured, the binding between source and binary code is verified using
+reproducible builds, and the maintainer responsible for distributing a specific
+package can be identified. We describe a novel ""hidden version"" attack against
+current software transparency systems and propose as well as integrate a
+suitable defense. To address equivocation attacks by the transparency log
+server, we introduce tree root cross logging, where the log's Merkle tree root
+is submitted into a separately operated log server. This significantly relaxes
+the inter-operator cooperation requirements compared to other systems. Our
+implementation is evaluated by replaying over 3000 updates of the Debian
+operating system over the course of two years, demonstrating its viability and
+identifying numerous irregularities.
+",1,0,0,0,0,0
+12184,Imputation Approaches for Animal Movement Modeling,"  The analysis of telemetry data is common in animal ecological studies. While
+the collection of telemetry data for individual animals has improved
+dramatically, the methods to properly account for inherent uncertainties (e.g.,
+measurement error, dependence, barriers to movement) have lagged behind. Still,
+many new statistical approaches have been developed to infer unknown quantities
+affecting animal movement or predict movement based on telemetry data.
+Hierarchical statistical models are useful to account for some of the
+aforementioned uncertainties, as well as provide population-level inference,
+but they often come with an increased computational burden. For certain types
+of statistical models, it is straightforward to provide inference if the latent
+true animal trajectory is known, but challenging otherwise. In these cases,
+approaches related to multiple imputation have been employed to account for the
+uncertainty associated with our knowledge of the latent trajectory. Despite the
+increasing use of imputation approaches for modeling animal movement, the
+general sensitivity and accuracy of these methods have not been explored in
+detail. We provide an introduction to animal movement modeling and describe how
+imputation approaches may be helpful for certain types of models. We also
+assess the performance of imputation approaches in a simulation study. Our
+simulation study suggests that inference for model parameters directly related
+to the location of an individual may be more accurate than inference for
+parameters associated with higher-order processes such as velocity or
+acceleration. Finally, we apply these methods to analyze a telemetry data set
+involving northern fur seals (Callorhinus ursinus) in the Bering Sea.
+",0,0,0,1,0,0
+12185,Motion planning in high-dimensional spaces,"  Motion planning is a key tool that allows robots to navigate through an
+environment without collisions. The problem of robot motion planning has been
+studied in great detail over the last several decades, with researchers
+initially focusing on systems such as planar mobile robots and low
+degree-of-freedom (DOF) robotic arms. The increased use of high DOF robots that
+must perform tasks in real time in complex dynamic environments spurs the need
+for fast motion planning algorithms. In this overview, we discuss several types
+of strategies for motion planning in high dimensional spaces and dissect some
+of them, namely grid search based, sampling based and trajectory optimization
+based approaches. We compare them and outline their advantages and
+disadvantages, and finally, provide an insight into future research
+opportunities.
+",1,0,0,0,0,0
+12186,Emergent low-energy bound states in the two-orbital Hubbard model,"  A repulsive Coulomb interaction between electrons in different orbitals in
+correlated materials can give rise to bound quasiparticle states. We study the
+non-hybridized two-orbital Hubbard model with intra (inter)-orbital interaction
+$U$ ($U_{12}$) and different band widths using an improved dynamical mean field
+theory numerical technique which leads to reliable spectra on the real energy
+axis directly at zero temperature. We find that a finite density of states at
+the Fermi energy in one band is correlated with the emergence of well defined
+quasiparticle states at excited energies $\Delta=U-U_{12}$ in the other band.
+These excitations are inter-band holon-doublon bound states. At the symmetric
+point $U=U_{12}$, the quasiparticle peaks are located at the Fermi energy,
+leading to a simultaneous and continuous Mott transition settling a
+long-standing controversy.
+",0,1,0,0,0,0
+12187,Improved thermal lattice Boltzmann model for simulation of liquid-vapor phase change,"  In this paper, an improved thermal lattice Boltzmann (LB) model is proposed
+for simulating liquid-vapor phase change, which is aimed at improving an
+existing thermal LB model for liquid-vapor phase change [S. Gong and P. Cheng,
+Int. J. Heat Mass Transfer 55, 4923 (2012)]. First, we emphasize that the
+replacement of \[{\left( {\rho {c_V}} \right)^{ - 1}}\nabla \cdot \left(
+{\lambda \nabla T} \right)\] with \[\nabla \cdot \left( {\chi \nabla T}
+\right)\] is an inappropriate treatment for diffuse interface modeling of
+liquid-vapor phase change. Furthermore, the error terms ${\partial_{t0}}\left(
+{Tv} \right) + \nabla \cdot \left( {Tvv} \right)$, which exist in the
+macroscopic temperature equation recovered from the standard thermal LB
+equation, are eliminated in the present model through a way that is consistent
+with the philosophy of the LB method. In addition, the discrete effect of the
+source term is also eliminated in the present model. Numerical simulations are
+performed for droplet evaporation and bubble nucleation to validate the
+capability of the improved model for simulating liquid-vapor phase change.
+Numerical comparisons show that the aforementioned replacement leads to
+significant numerical errors and the error terms in the recovered macroscopic
+temperature equation also result in considerable errors.
+",0,1,0,0,0,0
+12188,A fresh look at effect aliasing and interactions: some new wine in old bottles,"  Interactions and effect aliasing are among the fundamental concepts in
+experimental design. In this paper, some new insights and approaches are
+provided on these subjects. In the literature, the ""de-aliasing"" of aliased
+effects is deemed to be impossible. We argue that this ""impossibility"" can
+indeed be resolved by employing a new approach which consists of
+reparametrization of effects and exploitation of effect non-orthogonality. This
+approach is successfully applied to three classes of designs: regular and
+nonregular two-level fractional factorial designs, and three-level fractional
+factorial designs. For reparametrization, the notion of conditional main
+effects (cme's) is employed for two-level regular designs, while the
+linear-quadratic system is used for three-level designs. For nonregular
+two-level designs, reparametrization is not needed because the partial aliasing
+of their effects already induces non-orthogonality. The approach can be
+extended to general observational data by using a new bi-level variable
+selection technique based on the cme's. A historical recollection is given on
+how these ideas were discovered.
+",0,0,0,1,0,0
+12189,Effects of sampling skewness of the importance-weighted risk estimator on model selection,"  Importance-weighting is a popular and well-researched technique for dealing
+with sample selection bias and covariate shift. It has desirable
+characteristics such as unbiasedness, consistency and low computational
+complexity. However, weighting can have a detrimental effect on an estimator as
+well. In this work, we empirically show that the sampling distribution of an
+importance-weighted estimator can be skewed. For sample selection bias
+settings, and for small sample sizes, the importance-weighted risk estimator
+produces overestimates for datasets in the body of the sampling distribution,
+i.e. the majority of cases, and large underestimates for data sets in the tail
+of the sampling distribution. These over- and underestimates of the risk lead
+to suboptimal regularization parameters when used for importance-weighted
+validation.
+",0,0,0,1,0,0
+12190,Minimizing the Cost of Team Exploration,"  A group of mobile agents is given a task to explore an edge-weighted graph
+$G$, i.e., every vertex of $G$ has to be visited by at least one agent. There
+is no centralized unit to coordinate their actions, but they can freely
+communicate with each other. The goal is to construct a deterministic strategy
+which allows agents to complete their task optimally. In this paper we are
+interested in a cost-optimal strategy, where the cost is understood as the
+total distance traversed by agents coupled with the cost of invoking them. Two
+graph classes are analyzed, rings and trees, in the off-line and on-line
+setting, i.e., when a structure of a graph is known and not known to agents in
+advance. We present algorithms that compute the optimal solutions for a given
+ring and tree of order $n$, in $O(n)$ time units. For rings in the on-line
+setting, we give the $2$-competitive algorithm and prove the lower bound of
+$3/2$ for the competitive ratio for any on-line strategy. For every strategy
+for trees in the on-line setting, we prove the competitive ratio to be no less
+than $2$, which can be achieved by the $DFS$ algorithm.
+",1,0,0,0,0,0
+12191,Exponential Source/Channel Duality,"  We propose a source/channel duality in the exponential regime, where
+success/failure in source coding parallels error/correctness in channel coding,
+and a distortion constraint becomes a log-likelihood ratio (LLR) threshold. We
+establish this duality by first deriving exact exponents for lossy coding of a
+memoryless source P, at distortion D, for a general i.i.d. codebook
+distribution Q, for both encoding success (R < R(P,Q,D)) and failure (R >
+R(P,Q,D)). We then turn to maximum likelihood (ML) decoding over a memoryless
+channel P with an i.i.d. input Q, and show that if we substitute P=QP, Q=Q, and
+D=0 under the LLR distortion measure, then the exact exponents for
+decoding-error (R < I(Q, P)) and strict correct-decoding (R > I(Q, P)) follow
+as special cases of the exponents for source encoding success/failure,
+respectively. Moreover, by letting the threshold D take general values, the
+exact random-coding exponents for erasure (D > 0) and list decoding (D < 0)
+under the simplified Forney decoder are obtained. Finally, we derive the exact
+random-coding exponent for Forney's optimum tradeoff erasure/list decoder, and
+show that at the erasure regime it coincides with Forney's lower bound and with
+the simplified decoder exponent.
+",1,0,0,0,0,0
+12192,Delta Theorem in the Age of High Dimensions,"  We provide a new version of delta theorem, that takes into account of high
+dimensional parameter estimation. We show that depending on the structure of
+the function, the limits of functions of estimators have faster or slower rate
+of convergence than the limits of estimators. We illustrate this via two
+examples. First, we use it for testing in high dimensions, and second in
+estimating large portfolio risk. Our theorem works in the case of larger number
+of parameters, $p$, than the sample size, $n$: $p>n$.
+",0,0,1,1,0,0
+12193,Deep Learning Interior Tomography for Region-of-Interest Reconstruction,"  Interior tomography for the region-of-interest (ROI) imaging has advantages
+of using a small detector and reducing X-ray radiation dose. However, standard
+analytic reconstruction suffers from severe cupping artifacts due to existence
+of null space in the truncated Radon transform. Existing penalized
+reconstruction methods may address this problem but they require extensive
+computations due to the iterative reconstruction. Inspired by the recent deep
+learning approaches to low-dose and sparse view CT, here we propose a deep
+learning architecture that removes null space signals from the FBP
+reconstruction. Experimental results have shown that the proposed method
+provides near-perfect reconstruction with about 7-10 dB improvement in PSNR
+over existing methods in spite of significantly reduced run-time complexity.
+",1,0,0,1,0,0
+12194,Structured Parallel Programming for Monte Carlo Tree Search,"  In this paper, we present a new algorithm for parallel Monte Carlo tree
+search (MCTS). It is based on the pipeline pattern and allows flexible
+management of the control flow of the operations in parallel MCTS. The pipeline
+pattern provides for the first structured parallel programming approach to
+MCTS. Moreover, we propose a new lock-free tree data structure for parallel
+MCTS which removes synchronization overhead. The Pipeline Pattern for Parallel
+MCTS algorithm (called 3PMCTS), scales very well to higher numbers of cores
+when compared to the existing methods.
+",1,0,0,0,0,0
+12195,"Single-Atom Scale Structural Selectivity in Te Nanowires Encapsulated inside Ultra-Narrow, Single-Walled Carbon Nanotubes","  Extreme nanowires (ENs) represent the ultimate class of crystals: They are
+the smallest possible periodic materials. With atom-wide motifs repeated in one
+dimension (1D), they offer a privileged perspective into the Physics and
+Chemistry of low-dimensional systems. Single-walled carbon nanotubes (SWCNTs)
+provide ideal environments for the creation of such materials. Here we present
+a comprehensive study of Te ENs encapsulated inside ultra- narrow SWCNTs with
+diameters between 0.7 nm and 1.1 nm. We combine state-of-the-art imaging
+techniques and 1D-adapted ab initio structure prediction to treat both
+confinement and periodicity effects. The studied Te ENs adopt a variety of
+structures, exhibiting a true 1D realisation of a Peierls structural distortion
+and transition from metallic to insulating behaviour as a function of
+encapsulating diameter. We analyse the mechanical stability of the encapsulated
+ENs and show that nanoconfinement is not only a useful means to produce ENs,
+but may actually be necessary, in some cases, to prevent them from
+disintegrating. The ability to control functional properties of these ENs with
+confinement has numerous applications in future device technologies, and we
+anticipate that our study will set the basic paradigm to be adopted in the
+characterisation and understanding of such systems.
+",0,1,0,0,0,0
+12196,Navigating through the R packages for movement,"  The advent of miniaturized biologging devices has provided ecologists with
+unparalleled opportunities to record animal movement across scales, and led to
+the collection of ever-increasing quantities of tracking data. In parallel,
+sophisticated tools to process, visualize and analyze tracking data have been
+developed in abundance. Within the R software alone, we listed 57 focused on
+these tasks, called here tracking packages. Here, we reviewed these tracking
+packages, as an introduction to this set of packages for researchers, and to
+provide feedback and recommendations to package developers, from a user
+perspective. We described each package based on a workflow centered around
+tracking data (i.e. (x,y,t)), broken down in three stages: pre-processing,
+post-processing, and analysis (data visualization, track description, path
+reconstruction, behavioral pattern identification, space use characterization,
+trajectory simulation and others).
+Supporting documentation is key to the accessibility of a package for users.
+Based on a user survey, we reviewed the quality of packages' documentation, and
+identified $12$ packages with good or excellent documentation. Links between
+packages were assessed through a network graph analysis. Although a large group
+of packages shows some degree of connectivity (either depending on functions or
+suggesting the use of another tracking package), a third of tracking packages
+work on isolation, reflecting a fragmentation in the R Movement-Ecology
+programming community.
+Finally, we provide recommendations for users to choose packages, and for
+developers to maximize usefulness of their contribution and strengthen the
+links between the programming community.
+",0,0,0,0,1,0
+12197,Image classification and retrieval with random depthwise signed convolutional neural networks,"  We study image classification and retrieval performance in a feature space
+given by random depthwise convolutional neural networks. Intuitively our
+network can be interpreted as applying random hyperplanes to the space of all
+patches of input images followed by average pooling to obtain final features.
+We show that the ratio of image pixel distribution similarity across classes to
+within classes and the average margin of the linear support vector machine on
+test data are both higher in our network's final layer compared to the input
+space. We then apply the linear support vector machine for image classification
+and $k$-nearest neighbor for image similarity detection on our network's final
+layer. We show that for classification our network attains higher accuracies
+than previous random networks and is not far behind in accuracy to trained
+state of the art networks, especially in the top-k setting. For example the
+top-2 accuracy of our network is near 90\% on both CIFAR10 and a 10-class mini
+ImageNet, and 85\% on STL10. In the problem of image similarity we find that
+$k$-nearest neighbor gives a comparable precision on the Corel Princeton Image
+Similarity Benchmark than if we were to use the last hidden layer of trained
+networks. We highlight sensitivity of our network to background color as a
+potential pitfall. Overall our work pushes the boundary of what can be achieved
+with random weights.
+",0,0,0,1,0,0
+12198,Spatial dynamics of flower organ formation,"  Understanding the emergence of biological structures and their changes is a
+complex problem. On a biochemical level, it is based on gene regulatory
+networks (GRN) consisting on interactions between the genes responsible for
+cell differentiation and coupled in a greater scale with external factors. In
+this work we provide a systematic methodological framework to construct
+Waddington's epigenetic landscape of the GRN involved in cellular determination
+during the early stages of development of angiosperms. As a specific example we
+consider the flower of the plant \textit{Arabidopsis thaliana}. Our model,
+which is based on experimental data, recovers accurately the spatial
+configuration of the flower during cell fate determination, not only for the
+wild type, but for its homeotic mutants as well. The method developed in this
+project is general enough to be used in the study of the relationship between
+genotype-phenotype in other living organisms.
+",0,0,0,0,1,0
+12199,Percent Change Estimation in Large Scale Online Experiments,"  Online experiments are a fundamental component of the development of
+web-facing products. Given the large user-base, even small product improvements
+can have a large impact on an absolute scale. As a result, accurately
+estimating the relative impact of these changes is extremely important. I
+propose an approach based on an objective Bayesian model to improve the
+sensitivity of percent change estimation in A/B experiments. Leveraging
+pre-period information, this approach produces more robust and accurate point
+estimates and up to 50% tighter credible intervals than traditional methods.
+The R package abpackage provides an implementation of the approach.
+",0,0,0,1,0,0
+12200,Characterization of Near-Earth Asteroids using KMTNet-SAAO,"  We present here VRI spectrophotometry of 39 near-Earth asteroids (NEAs)
+observed with the Sutherland, South Africa, node of the Korea Microlensing
+Telescope Network (KMTNet). Of the 39 NEAs, 19 were targeted, but because of
+KMTNet's large 2 deg by 2 deg field of view, 20 serendipitous NEAs were also
+captured in the observing fields. Targeted observations were performed within
+44 days (median: 16 days, min: 4 days) of each NEA's discovery date. Our
+broadband spectrophotometry is reliable enough to distinguish among four
+asteroid taxonomies and we were able to confidently categorize 31 of the 39
+observed targets as either a S-, C-, X- or D-type asteroid by means of a
+Machine Learning (ML) algorithm approach. Our data suggest that the ratio
+between ""stony"" S-type NEAs and ""not-stony"" (C+X+D)-type NEAs, with H
+magnitudes between 15 and 25, is roughly 1:1. Additionally, we report ~1-hour
+light curve data for each NEA and of the 39 targets we were able to resolve the
+complete rotation period and amplitude for six targets and report lower limits
+for the remaining targets.
+",0,1,0,0,0,0
+12201,Stochastic comparisons of series and parallel systems with heterogeneous components,"  In this paper, we discuss stochastic comparisons of parallel systems with
+independent heterogeneous exponentiated Nadarajah-Haghighi (ENH) components in
+terms of the usual stochastic order, dispersive order, convex transform order
+and the likelihood ratio order. In the presence of the Archimedean copula, we
+study stochastic comparison of series dependent systems in terms of the usual
+stochastic order.
+",0,0,1,1,0,0
+12202,Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score,"  Mendelian randomization (MR) is a method of exploiting genetic variation to
+unbiasedly estimate a causal effect in presence of unmeasured confounding. MR
+is being widely used in epidemiology and other related areas of population
+science. In this paper, we study statistical inference in the increasingly
+popular two-sample summary-data MR design. We show a linear model for the
+observed associations approximately holds in a wide variety of settings when
+all the genetic variants satisfy the exclusion restriction assumption, or in
+genetic terms, when there is no pleiotropy. In this scenario, we derive a
+maximum profile likelihood estimator with provable consistency and asymptotic
+normality. However, through analyzing real datasets, we find strong evidence of
+both systematic and idiosyncratic pleiotropy in MR, echoing the omnigenic model
+of complex traits that is recently proposed in genetics. We model the
+systematic pleiotropy by a random effects model, where no genetic variant
+satisfies the exclusion restriction condition exactly. In this case we propose
+a consistent and asymptotically normal estimator by adjusting the profile
+score. We then tackle the idiosyncratic pleiotropy by robustifying the adjusted
+profile score. We demonstrate the robustness and efficiency of the proposed
+methods using several simulated and real datasets.
+",0,0,0,1,0,0
+12203,Edgeworth correction for the largest eigenvalue in a spiked PCA model,"  We study improved approximations to the distribution of the largest
+eigenvalue $\hat{\ell}$ of the sample covariance matrix of $n$ zero-mean
+Gaussian observations in dimension $p+1$. We assume that one population
+principal component has variance $\ell > 1$ and the remaining `noise'
+components have common variance $1$. In the high dimensional limit $p/n \to
+\gamma > 0$, we begin study of Edgeworth corrections to the limiting Gaussian
+distribution of $\hat{\ell}$ in the supercritical case $\ell > 1 + \sqrt
+\gamma$. The skewness correction involves a quadratic polynomial as in
+classical settings, but the coefficients reflect the high dimensional
+structure. The methods involve Edgeworth expansions for sums of independent
+non-identically distributed variates obtained by conditioning on the sample
+noise eigenvalues, and limiting bulk properties \textit{and} fluctuations of
+these noise eigenvalues.
+",0,0,1,1,0,0
+12204,"On one nearly everywhere continuous and nowhere differentiable function, that defined by automaton with finite memory","  This paper is devoted to the investigation of the following function $$ f:
+x=\Delta^{3}_{\alpha_{1}\alpha_{2}...\alpha_{n}...}{\rightarrow}
+\Delta^{3}_{\varphi(\alpha_{1})\varphi(\alpha_{2})...\varphi(\alpha_{n})...}=f(x)=y,
+$$ where $\varphi(i)=\frac{-3i^{2}+7i}{2}$, $ i \in N^{0}_{2}=\{0,1,2\}$, and
+$\Delta^{3}_{\alpha_{1}\alpha_{2}...\alpha_{n}...}$ is the ternary
+representation of $x \in [0;1]$. That is values of this function are obtained
+from the ternary representation of the argument by the following change of
+digits: 0 by 0, 1 by 2, and 2 by 1. This function preserves the ternary digit
+$0$.
+Main mapping properties and differential, integral, fractal properties of the
+function are studied. Equivalent representations by additionally defined
+auxiliary functions of this function are proved.
+This paper is the paper translated from Ukrainian (the Ukrainian variant
+available at this https URL). In 2012, the
+Ukrainian variant of this paper was represented by the author in the
+International Scientific Conference ""Asymptotic Methods in the Theory of
+Differential Equations"" dedicated to 80th anniversary of M. I. Shkil (the
+conference paper available at
+this https URL). In 2013, the
+investigations of the present article were generalized by the author in the
+paper ""One one class of functions with complicated local structure""
+(this https URL) and in the several conference papers
+(available at: this https URL,
+this https URL).
+",0,0,1,0,0,0
+12205,Hochschild cohomology for periodic algebras of polynomial growth,"  We describe the dimensions of low Hochschild cohomology spaces of exceptional
+periodic representation-infinite algebras of polynomial growth. As an
+application we obtain that an indecomposable non-standard periodic
+representation-infinite algebra of polynomial growth is not derived equivalent
+to a standard self-injective algebra.
+",0,0,1,0,0,0
+12206,Fracton Models on General Three-Dimensional Manifolds,"  Fracton models, a collection of exotic gapped lattice Hamiltonians recently
+discovered in three spatial dimensions, contain some 'topological' features:
+they support fractional bulk excitations (dubbed fractons), and a ground state
+degeneracy that is robust to local perturbations. However, because previous
+fracton models have only been defined and analyzed on a cubic lattice with
+periodic boundary conditions, it is unclear to what extent a notion of topology
+is applicable. In this paper, we demonstrate that the X-cube model, a
+prototypical type-I fracton model, can be defined on general three-dimensional
+manifolds. Our construction revolves around the notion of a singular compact
+total foliation of the spatial manifold, which constructs a lattice from
+intersecting stacks of parallel surfaces called leaves. We find that the ground
+state degeneracy depends on the topology of the leaves and the pattern of leaf
+intersections. We further show that such a dependence can be understood from a
+renormalization group transformation for the X-cube model, wherein the system
+size can be changed by adding or removing 2D layers of topological states. Our
+results lead to an improved definition of fracton phase and bring to the fore
+the topological nature of fracton orders.
+",0,1,0,0,0,0
+12207,Topic Modeling on Health Journals with Regularized Variational Inference,"  Topic modeling enables exploration and compact representation of a corpus.
+The CaringBridge (CB) dataset is a massive collection of journals written by
+patients and caregivers during a health crisis. Topic modeling on the CB
+dataset, however, is challenging due to the asynchronous nature of multiple
+authors writing about their health journeys. To overcome this challenge we
+introduce the Dynamic Author-Persona topic model (DAP), a probabilistic
+graphical model designed for temporal corpora with multiple authors. The
+novelty of the DAP model lies in its representation of authors by a persona ---
+where personas capture the propensity to write about certain topics over time.
+Further, we present a regularized variational inference algorithm, which we use
+to encourage the DAP model's personas to be distinct. Our results show
+significant improvements over competing topic models --- particularly after
+regularization, and highlight the DAP model's unique ability to capture common
+journeys shared by different authors.
+",0,0,0,1,0,0
+12208,On the Taylor coefficients of a subclass of meromorphic univalent functions,"  Let $\mathcal{V}_p(\lambda)$ be the collection of all functions $f$ defined
+in the unit disc $\ID$ having a simple pole at $z=p$ where $0<p<1$ and analytic
+in $\ID\setminus\{p\}$ with $f(0)=0=f'(0)-1$ and satisfying the differential
+inequality $|(z/f(z))^2 f'(z)-1|< \lambda $ for $z\in \ID$, $0<\lambda\leq 1$.
+Each $f\in\mathcal{V}_p(\lambda)$ has the following Taylor expansion:
+$$
+f(z)=z+\sum_{n=2}^{\infty}a_n(f) z^n, \quad |z|<p.
+$$
+In \cite{BF-3}, we conjectured that
+$$
+|a_n(f)|\leq \frac{1-(\lambda p^2)^n}{p^{n-1}(1-\lambda p^2)}\quad
+\mbox{for}\quad n\geq3. $$ In the present article, we first obtain a
+representation formula for functions in the class $\mathcal{V}_p(\lambda)$.
+Using this representation, we prove the aforementioned conjecture for $n=3,4,5$
+whenever $p$ belongs to certain subintervals of $(0,1)$. Also we determine non
+sharp bounds for $|a_n(f)|,\,n\geq 3$ and for $|a_{n+1}(f)-a_n(f)/p|,\,n\geq
+2$.
+",0,0,1,0,0,0
+12209,"Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs","  The loss functions of deep neural networks are complex and their geometric
+properties are not well understood. We show that the optima of these complex
+loss functions are in fact connected by simple curves over which training and
+test accuracy are nearly constant. We introduce a training procedure to
+discover these high-accuracy pathways between modes. Inspired by this new
+geometric insight, we also propose a new ensembling method entitled Fast
+Geometric Ensembling (FGE). Using FGE we can train high-performing ensembles in
+the time required to train a single model. We achieve improved performance
+compared to the recent state-of-the-art Snapshot Ensembles, on CIFAR-10,
+CIFAR-100, and ImageNet.
+",0,0,0,1,0,0
+12210,On the scaling patterns of infectious disease incidence in cities,"  Urban areas with larger and more connected populations offer an auspicious
+environment for contagion processes such as the spread of pathogens. Empirical
+evidence reveals a systematic increase in the rates of certain sexually
+transmitted diseases (STDs) with larger urban population size. However, the
+main drivers of these systemic infection patterns are still not well
+understood, and rampant urbanization rates worldwide makes it critical to
+advance our understanding on this front. Using confirmed-cases data for three
+STDs in US metropolitan areas, we investigate the scaling patterns of
+infectious disease incidence in urban areas. The most salient features of these
+patterns are that, on average, the incidence of infectious diseases that
+transmit with less ease-- either because of a lower inherent transmissibility
+or due to a less suitable environment for transmission-- scale more steeply
+with population size, are less predictable across time and more variable across
+cities of similar size. These features are explained, first, using a simple
+mathematical model of contagion, and then through the lens of a new theory of
+urban scaling. These theoretical frameworks help us reveal the links between
+the factors that determine the transmissibility of infectious diseases and the
+properties of their scaling patterns across cities.
+",0,0,0,0,1,0
+12211,Homological subsets of Spec,"  We investigate homological subsets of the prime spectrum of a ring, defined
+by the help of the Ext-family $\{\Ext^i_R(-,R)\}$. We extend Grothendieck's
+calculation of $\dim(\Ext^g_R(M,R))$. We compute support of $\Ext^i_R(M,R)$ in
+many cases. Also, we answer a low-dimensional case of a problem posed by
+Vasconcelos on the finiteness of associated prime ideals of
+$\{\Ext^i_R(M,R)\}$. An application is given.
+",0,0,1,0,0,0
+12212,Mean field repulsive Kuramoto models: Phase locking and spatial signs,"  The phenomenon of self-synchronization in populations of oscillatory units
+appears naturally in neurosciences. However, in some situations, the formation
+of a coherent state is damaging. In this article we study a repulsive
+mean-field Kuramoto model that describes the time evolution of n points on the
+unit circle, which are transformed into incoherent phase-locked states. It has
+been recently shown that such systems can be reduced to a three-dimensional
+system of ordinary differential equations, whose mathematical structure is
+strongly related to hyperbolic geometry. The orbits of the Kuramoto dynamical
+system are then described by a ow of Möbius transformations. We show this
+underlying dynamic performs statistical inference by computing dynamically
+M-estimates of scatter matrices. We also describe the limiting phase-locked
+states for random initial conditions using Tyler's transformation matrix.
+Moreover, we show the repulsive Kuramoto model performs dynamically not only
+robust covariance matrix estimation, but also data processing: the initial
+configuration of the n points is transformed by the dynamic into a limiting
+phase-locked state that surprisingly equals the spatial signs from
+nonparametric statistics. That makes the sign empirical covariance matrix to
+equal 1 2 id2, the variance-covariance matrix of a random vector that is
+uniformly distributed on the unit circle.
+",0,0,0,0,1,0
+12213,Signal tracking beyond the time resolution of an atomic sensor by Kalman filtering,"  We study causal waveform estimation (tracking) of time-varying signals in a
+paradigmatic atomic sensor, an alkali vapor monitored by Faraday rotation
+probing. We use Kalman filtering, which optimally tracks known linear Gaussian
+stochastic processes, to estimate stochastic input signals that we generate by
+optical pumping. Comparing the known input to the estimates, we confirm the
+accuracy of the atomic statistical model and the reliability of the Kalman
+filter, allowing recovery of waveform details far briefer than the sensor's
+intrinsic time resolution. With proper filter choice, we obtain similar
+benefits when tracking partially-known and non-Gaussian signal processes, as
+are found in most practical sensing applications. The method evades the
+trade-off between sensitivity and time resolution in coherent sensing.
+",0,1,0,0,0,0
+12214,Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients,"  In this report, we applied integrated gradients to explaining a neural
+network for diabetic retinopathy detection. The integrated gradient is an
+attribution method which measures the contributions of input to the quantity of
+interest. We explored some new ways for applying this method such as explaining
+intermediate layers, filtering out unimportant units by their attribution value
+and generating contrary samples. Moreover, the visualization results extend the
+use of diabetic retinopathy detection model from merely predicting to assisting
+finding potential lesions.
+",1,0,0,0,0,0
+12215,6.2-GHz modulated terahertz light detection using fast terahertz quantum well photodetectors,"  The fast detection of terahertz radiation is of great importance for various
+applications such as fast imaging, high speed communications, and spectroscopy.
+Most commercial products capable of sensitively responding the terahertz
+radiation are thermal detectors, i.e., pyroelectric sensors and bolometers.
+This class of terahertz detectors is normally characterized by low modulation
+frequency (dozens or hundreds of Hz). Here we demonstrate the first fast
+semiconductor-based terahertz quantum well photodetectors by carefully
+designing the device structure and microwave transmission line for high
+frequency signal extraction. Modulation response bandwidth of gigahertz level
+is obtained. As an example, the 6.2-GHz modulated terahertz light emitted from
+a Fabry-Pérot terahertz quantum cascade laser is successfully detected
+using the fast terahertz quantum well photodetector. In addition to the fast
+terahertz detection, the technique presented in this work can also facilitate
+the frequency stability or phase noise characterizations for terahertz quantum
+cascade lasers.
+",0,1,0,0,0,0
+12216,Inference via low-dimensional couplings,"  We investigate the low-dimensional structure of deterministic transformations
+between random variables, i.e., transport maps between probability measures. In
+the context of statistics and machine learning, these transformations can be
+used to couple a tractable ""reference"" measure (e.g., a standard Gaussian) with
+a target measure of interest. Direct simulation from the desired measure can
+then be achieved by pushing forward reference samples through the map. Yet
+characterizing such a map---e.g., representing and evaluating it---grows
+challenging in high dimensions. The central contribution of this paper is to
+establish a link between the Markov properties of the target measure and the
+existence of low-dimensional couplings, induced by transport maps that are
+sparse and/or decomposable. Our analysis not only facilitates the construction
+of transformations in high-dimensional settings, but also suggests new
+inference methodologies for continuous non-Gaussian graphical models. For
+instance, in the context of nonlinear state-space models, we describe new
+variational algorithms for filtering, smoothing, and sequential parameter
+inference. These algorithms can be understood as the natural
+generalization---to the non-Gaussian case---of the square-root
+Rauch-Tung-Striebel Gaussian smoother.
+",0,0,0,1,0,0
+12217,A unified thermostat scheme for efficient configurational sampling for classical/quantum canonical ensembles via molecular dynamics,"  We show a unified second-order scheme for constructing simple, robust and
+accurate algorithms for typical thermostats for configurational sampling for
+the canonical ensemble. When Langevin dynamics is used, the scheme leads to the
+BAOAB algorithm that has been recently investigated. We show that the scheme is
+also useful for other types of thermostat, such as the Andersen thermostat and
+Nosé-Hoover chain. Two 1-dimensional models and three typical realistic
+molecular systems that range from the gas phase, clusters, to the condensed
+phase are used in numerical examples for demonstration. Accuracy may be
+increased by an order of magnitude for estimating coordinate-dependent
+properties in molecular dynamics (when the same time interval is used),
+irrespective of which type of thermostat is applied. The scheme is especially
+useful for path integral molecular dynamics, because it consistently improves
+the efficiency for evaluating all thermodynamic properties for any type of
+thermostat.
+",0,1,0,0,0,0
+12218,"Wide Bandwidth, Frequency Modulated Free Electron Laser","  It is shown via theory and simulation that the resonant frequency of a Free
+Electron Laser may be modulated to obtain an FEL interaction with a frequency
+bandwidth which is at least an order of magnitude greater than normal FEL
+operation. The system is described in the linear regime by a summation over
+exponential gain modes, allowing the amplification of multiple light
+frequencies simultaneously. Simulation in 3D demonstrates the process for
+parameters of the UK's CLARA FEL test facility currently under construction.
+This new mode of FEL operation has close analogies to Frequency Modulation in a
+conventional cavity laser. This new, wide bandwidth mode of FEL operation
+scales well for X-ray generation and offers users a new form of high-power FEL
+output.
+",0,1,0,0,0,0
+12219,A Transformation-Proximal Bundle Algorithm for Solving Large-Scale Multistage Adaptive Robust Optimization Problems,"  This paper presents a novel transformation-proximal bundle algorithm to solve
+multistage adaptive robust mixed-integer linear programs (MARMILPs). By
+explicitly partitioning recourse decisions into state decisions and local
+decisions, the proposed algorithm applies affine decision rule only to state
+decisions and allows local decisions to be fully adaptive. In this way, the
+MARMILP is proved to be transformed into an equivalent two-stage adaptive
+robust optimization (ARO) problem. The proposed multi-to-two transformation
+scheme remains valid for other types of non-anticipative decision rules besides
+the affine one, and it is general enough to be employed with existing two-stage
+ARO algorithms for solving MARMILPs. The proximal bundle method is developed
+for the resulting two-stage ARO problem. We perform a theoretical analysis to
+show finite convergence of the proposed algorithm with any positive tolerance.
+To quantitatively assess solution quality, we develop a scenario-tree-based
+lower bounding technique. Computational studies on multiperiod inventory
+management and process network planning are presented to demonstrate its
+effectiveness and computational scalability. In the inventory management
+application, the affine decision rule method suffers from a severe
+suboptimality with an average gap of 34.88%, while the proposed algorithm
+generates near-optimal solutions with an average gap of merely 1.68%.
+",1,0,0,0,0,0
+12220,Dual SVM Training on a Budget,"  We present a dual subspace ascent algorithm for support vector machine
+training that respects a budget constraint limiting the number of support
+vectors. Budget methods are effective for reducing the training time of kernel
+SVM while retaining high accuracy. To date, budget training is available only
+for primal (SGD-based) solvers. Dual subspace ascent methods like sequential
+minimal optimization are attractive for their good adaptation to the problem
+structure, their fast convergence rate, and their practical speed. By
+incorporating a budget constraint into a dual algorithm, our method enjoys the
+best of both worlds. We demonstrate considerable speed-ups over primal budget
+training methods.
+",0,0,0,1,0,0
+12221,"AWAKE readiness for the study of the seeded self-modulation of a 400\,GeV proton bunch","  AWAKE is a proton-driven plasma wakefield acceleration experiment. % We show
+that the experimental setup briefly described here is ready for systematic
+study of the seeded self-modulation of the 400\,GeV proton bunch in the
+10\,m-long rubidium plasma with density adjustable from 1 to
+10$\times10^{14}$\,cm$^{-3}$. % We show that the short laser pulse used for
+ionization of the rubidium vapor propagates all the way along the column,
+suggesting full ionization of the vapor. % We show that ionization occurs along
+the proton bunch, at the laser time and that the plasma that follows affects
+the proton bunch. %
+",0,1,0,0,0,0
+12222,Exploring the Psychological Basis for Transitions in the Archaeological Record,"  In lieu of an abstract here is the first paragraph: No other species remotely
+approaches the human capacity for the cultural evolution of novelty that is
+accumulative, adaptive, and open-ended (i.e., with no a priori limit on the
+size or scope of possibilities). By culture we mean extrasomatic
+adaptations--including behavior and technology--that are socially rather than
+sexually transmitted. This chapter synthesizes research from anthropology,
+psychology, archaeology, and agent-based modeling into a speculative yet
+coherent account of two fundamental cognitive transitions underlying human
+cultural evolution that is consistent with contemporary psychology. While the
+chapter overlaps with a more technical paper on this topic (Gabora & Smith
+2018), it incorporates new research and elaborates a genetic component to our
+overall argument. The ideas in this chapter grew out of a non-Darwinian
+framework for cultural evolution, referred to as the Self-other Reorganization
+(SOR) theory of cultural evolution (Gabora, 2013, in press; Smith, 2013), which
+was inspired by research on the origin and earliest stage in the evolution of
+life (Cornish-Bowden & Cárdenas 2017; Goldenfeld, Biancalani, & Jafarpour,
+2017, Vetsigian, Woese, & Goldenfeld 2006; Woese, 2002). SOR bridges
+psychological research on fundamental aspects of our human nature such as
+creativity and our proclivity to reflect on ideas from different perspectives,
+with the literature on evolutionary approaches to cultural evolution that
+aspire to synthesize the behavioral sciences much as has been done for the
+biological scientists. The current chapter is complementary to this effort, but
+less abstract; it attempts to ground the theory of cultural evolution in terms
+of cognitive transitions as suggested by archaeological evidence.
+",0,0,0,0,1,0
+12223,Well quasi-orders and the functional interpretation,"  The purpose of this article is to study the role of Gödel's functional
+interpretation in the extraction of programs from proofs in well quasi-order
+theory. The main focus is on the interpretation of Nash-Williams' famous
+minimal bad sequence construction, and the exploration of a number of much
+broader problems which are related to this, particularly the question of the
+constructive meaning of Zorn's lemma and the notion of recursion over the
+non-wellfounded lexicographic ordering on infinite sequences.
+",1,0,1,0,0,0
+12224,A local limit theorem for Quicksort key comparisons via multi-round smoothing,"  As proved by Régnier and Rösler, the number of key comparisons required
+by the randomized sorting algorithm QuickSort to sort a list of $n$ distinct
+items (keys) satisfies a global distributional limit theorem. Fill and Janson
+proved results about the limiting distribution and the rate of convergence, and
+used these to prove a result part way towards a corresponding local limit
+theorem. In this paper we use a multi-round smoothing technique to prove the
+full local limit theorem.
+",0,0,1,0,0,0
+12225,On the Computation of Kantorovich-Wasserstein Distances between 2D-Histograms by Uncapacitated Minimum Cost Flows,"  In this work, we present a method to compute the Kantorovich distance, that
+is, the Wasserstein distance of order one, between a pair of two-dimensional
+histograms. Recent works in Computer Vision and Machine Learning have shown the
+benefits of measuring Wasserstein distances of order one between histograms
+with $N$ bins, by solving a classical transportation problem on (very large)
+complete bipartite graphs with $N$ nodes and $N^2$ edges. The main contribution
+of our work is to approximate the original transportation problem by an
+uncapacitated min cost flow problem on a reduced flow network of size $O(N)$.
+More precisely, when the distance among the bin centers is measured with the
+1-norm or the $\infty$-norm, our approach provides an optimal solution. When
+the distance amongst bins is measured with the 2-norm: (i) we derive a
+quantitative estimate on the error between optimal and approximate solution;
+(ii) given the error, we construct a reduced flow network of size $O(N)$. We
+numerically show the benefits of our approach by computing Wasserstein
+distances of order one on a set of grey scale images used as benchmarks in the
+literature. We show how our approach scales with the size of the images with
+1-norm, 2-norm and $\infty$-norm ground distances.
+",0,0,0,1,0,0
+12226,Efficient anchor loss suppression in coupled near-field optomechanical resonators,"  Elastic dissipation through radiation towards the substrate is a major loss
+channel in micro- and nanomechanical resonators. Engineering the coupling of
+these resonators with optical cavities further complicates and constrains the
+design of low-loss optomechanical devices. In this work we rely on the coherent
+cancellation of mechanical radiation to demonstrate material and surface
+absorption limited silicon near-field optomechanical resonators oscillating at
+tens of MHz. The effectiveness of our dissipation suppression scheme is
+investigated at room and cryogenic temperatures. While at room temperature we
+can reach a maximum quality factor of 7.61k ($fQ$-product of the order of
+$10^{11}$~Hz), at 22~K the quality factor increases to 37k, resulting in a
+$fQ$-product of $2\times10^{12}$~Hz.
+",0,1,0,0,0,0
+12227,Searching for previously unknown classes of objects in the AKARI-NEP Deep data with fuzzy logic SVM algorithm,"  In this proceedings application of a fuzzy Support Vector Machine (FSVM)
+learning algorithm, to classify mid-infrared (MIR) sources from the AKARI NEP
+Deep field into three classes: stars, galaxies and AGNs, is presented. FSVM is
+an improved version of the classical SVM algorithm, incorporating measurement
+errors into the classification process; this is the first successful
+application of this algorithm in the astronomy. We created reliable catalogues
+of galaxies, stars and AGNs consisting of objects with MIR measurements, some
+of them with no optical counterparts. Some examples of identified objects are
+shown, among them O-rich and C-rich AGB stars.
+",0,1,0,0,0,0
+12228,Stability of laminar Couette flow of compressible fluids,"  Cylindrical Couette flow is a subject where the main focus has long been on
+the onset of turbulence or, more precisely, the limit of stability of the
+simplest laminar flow. The theoretical framework of this paper is a recently
+developed action principle for hydrodynamics. It incorporates Euler-Lagrange
+equations that are in essential agreement with the Navier-Stokes equation, but
+applicable to the general case of a compressible fluid. The variational
+principle incorporates the equation of continuity, a canonical structure and a
+conserved Hamiltonian. The density is compressible, characterized by a general
+(non-polar) equation of state, and homogeneous. The onset of instability is
+often accompanied by bubble formation. It is proposed that the limit of
+stability of laminar Couette flow may some times be related to cavitation. In
+contrast to traditional stability theory we are not looking for mathematical
+instabilities of a system of differential equations, but instead for the
+possibility that the system is driven to a metastable or unstable
+configuration. The application of this idea to cylindrical Couette flow
+reported here turns out to account rather well for the observations. The
+failure of a famous criterion due to Rayleigh is well known. It is here shown
+that it may be due to the use of methods that are appropriate only in the case
+that the equations of motion are derived from an action principle.
+",0,1,0,0,0,0
+12229,A Hierarchical Max-infinitely Divisible Process for Extreme Areal Precipitation Over Watersheds,"  Understanding the spatial extent of extreme precipitation is necessary for
+determining flood risk and adequately designing infrastructure (e.g.,
+stormwater pipes) to withstand such hazards. While environmental phenomena
+typically exhibit weakening spatial dependence at increasingly extreme levels,
+limiting max-stable process models for block maxima have a rigid dependence
+structure that does not capture this type of behavior. We propose a flexible
+Bayesian model from a broader family of max-infinitely divisible processes that
+allows for weakening spatial dependence at increasingly extreme levels, and due
+to a hierarchical representation of the likelihood in terms of random effects,
+our inference approach scales to large datasets. The proposed model is
+constructed using flexible random basis functions that are estimated from the
+data, allowing for straightforward inspection of the predominant spatial
+patterns of extremes. In addition, the described process possesses
+max-stability as a special case, making inference on the tail dependence class
+possible. We apply our model to extreme precipitation in eastern North America,
+and show that the proposed model adequately captures the extremal behavior of
+the data.
+",0,0,0,1,0,0
+12230,The dependence of cluster galaxy properties on the central entropy of their host cluster,"  We present a study of the connection between brightest cluster galaxies
+(BCGs) and their host galaxy clusters. Using galaxy clusters at $0.1<z<0.3$
+from the Hectospec Cluster Survey (HeCS) with X-ray information from the
+Archive of {\it Chandra} Cluster Entropy Profile Tables (ACCEPT), we confirm
+that BCGs in low central entropy clusters are well aligned with the X-ray
+center. Additionally, the magnitude difference between BCG and the 2nd
+brightest one also correlates with the central entropy of the intracluster
+medium. From the red-sequence (RS) galaxies, we cannot find significant
+dependence of RS color scatter and stellar population on the central entropy of
+the intracluster medium of their host cluster. However, BCGs in low entropy
+clusters are systematically less massive than those in high entropy clusters,
+although this is dependent on the method used to derive the stellar mass of
+BCGs. In contrast, the stellar velocity dispersion of BCGs shows no dependence
+on BCG activity and cluster central entropy. This implies that the potential of
+the BCG is established earlier and the activity leading to optical emission
+lines is dictated by the properties of the intracluster medium in the cluster
+core.
+",0,1,0,0,0,0
+12231,Gaussian Prototypical Networks for Few-Shot Learning on Omniglot,"  We propose a novel architecture for $k$-shot classification on the Omniglot
+dataset. Building on prototypical networks, we extend their architecture to
+what we call Gaussian prototypical networks. Prototypical networks learn a map
+between images and embedding vectors, and use their clustering for
+classification. In our model, a part of the encoder output is interpreted as a
+confidence region estimate about the embedding point, and expressed as a
+Gaussian covariance matrix. Our network then constructs a direction and class
+dependent distance metric on the embedding space, using uncertainties of
+individual data points as weights. We show that Gaussian prototypical networks
+are a preferred architecture over vanilla prototypical networks with an
+equivalent number of parameters. We report state-of-the-art performance in
+1-shot and 5-shot classification both in 5-way and 20-way regime (for 5-shot
+5-way, we are comparable to previous state-of-the-art) on the Omniglot dataset.
+We explore artificially down-sampling a fraction of images in the training set,
+which improves our performance even further. We therefore hypothesize that
+Gaussian prototypical networks might perform better in less homogeneous,
+noisier datasets, which are commonplace in real world applications.
+",1,0,0,1,0,0
+12232,Uncertainty principle and geometry of the infinite Grassmann manifold,"  We study the pairs of projections $$ P_If=\chi_If ,\ \ Q_Jf= \left(\chi_J
+\hat{f}\right)\check{\ } , \ \ f\in L^2(\mathbb{R}^n), $$ where $I, J\subset
+\mathbb{R}^n$ are sets of finite Lebesgue measure, $\chi_I, \chi_J$ denote the
+corresponding characteristic functions and $\hat{\ } , \check{\ }$ denote the
+Fourier-Plancherel transformation $L^2(\mathbb{R}^n)\to L^2(\mathbb{R}^n)$ and
+its inverse. These pairs of projections have been widely studied by several
+authors in connection with the mathematical formulation of Heisenberg's
+uncertainty principle. Our study is done from a differential geometric point of
+view. We apply known results on the Finsler geometry of the Grassmann manifold
+${\cal P}({\cal H})$ of a Hilbert space ${\cal H}$ to establish that there
+exists a unique minimal geodesic of ${\cal P}({\cal H})$, which is a curve of
+the form $$ \delta(t)=e^{itX_{I,J}}P_Ie^{-itX_{I,J}} $$ which joins $P_I$ and
+$Q_J$ and has length $\pi/2$. As a consequence we obtain that if $H$ is the
+logarithm of the Fourier-Plancherel map, then $$ \|[H,P_I]\|\ge \pi/2. $$ The
+spectrum of $X_{I,J}$ is denumerable and symmetric with respect to the origin,
+it has a smallest positive eigenvalue $\gamma(X_{I,J})$ which satisfies $$
+\cos(\gamma(X_{I,J}))=\|P_IQ_J\|. $$
+",0,0,1,0,0,0
+12233,Existence and symmetry of solutions for critical fractional Schrödinger equations with bounded potentials,"  This paper is concerned with the following fractional Schrödinger
+equations involving critical exponents: \begin{eqnarray*}
+(-\Delta)^{\alpha}u+V(x)u=k(x)f(u)+\lambda|u|^{2_{\alpha}^{*}-2}u\quad\quad
+\mbox{in}\ \mathbb{R}^{N}, \end{eqnarray*} where $(-\Delta)^{\alpha}$ is the
+fractional Laplacian operator with $\alpha\in(0,1)$, $N\geq2$, $\lambda$ is a
+positive real parameter and $2_{\alpha}^{*}=2N/(N-2\alpha)$ is the critical
+Sobolev exponent, $V(x)$ and $k(x)$ are positive and bounded functions
+satisfying some extra hypotheses. Based on the principle of concentration
+compactness in the fractional Sobolev space and the minimax arguments, we
+obtain the existence of a nontrivial radially symmetric weak solution for the
+above-mentioned equations without assuming the Ambrosetti-Rabinowitz condition
+on the subcritical nonlinearity.
+",0,0,1,0,0,0
+12234,Early Detection of Promoted Campaigns on Social Media,"  Social media expose millions of users every day to information campaigns ---
+some emerging organically from grassroots activity, others sustained by
+advertising or other coordinated efforts. These campaigns contribute to the
+shaping of collective opinions. While most information campaigns are benign,
+some may be deployed for nefarious purposes. It is therefore important to be
+able to detect whether a meme is being artificially promoted at the very moment
+it becomes wildly popular. This problem has important social implications and
+poses numerous technical challenges. As a first step, here we focus on
+discriminating between trending memes that are either organic or promoted by
+means of advertisement. The classification is not trivial: ads cause bursts of
+attention that can be easily mistaken for those of organic trends. We designed
+a machine learning framework to classify memes that have been labeled as
+trending on Twitter.After trending, we can rely on a large volume of activity
+data. Early detection, occurring immediately at trending time, is a more
+challenging problem due to the minimal volume of activity data that is
+available prior to trending.Our supervised learning framework exploits hundreds
+of time-varying features to capture changing network and diffusion patterns,
+content and sentiment information, timing signals, and user meta-data. We
+explore different methods for encoding feature time series. Using millions of
+tweets containing trending hashtags, we achieve 75% AUC score for early
+detection, increasing to above 95% after trending. We evaluate the robustness
+of the algorithms by introducing random temporal shifts on the trend time
+series. Feature selection analysis reveals that content cues provide
+consistently useful signals; user features are more informative for early
+detection, while network and timing features are more helpful once more data is
+available.
+",1,0,0,0,0,0
+12235,Discovery of statistical equivalence classes using computer algebra,"  Discrete statistical models supported on labelled event trees can be
+specified using so-called interpolating polynomials which are generalizations
+of generating functions. These admit a nested representation. A new algorithm
+exploits the primary decomposition of monomial ideals associated with an
+interpolating polynomial to quickly compute all nested representations of that
+polynomial. It hereby determines an important subclass of all trees
+representing the same statistical model. To illustrate this method we analyze
+the full polynomial equivalence class of a staged tree representing the best
+fitting model inferred from a real-world dataset.
+",0,0,1,1,0,0
+12236,Can Boltzmann Machines Discover Cluster Updates ?,"  Boltzmann machines are physics informed generative models with wide
+applications in machine learning. They can learn the probability distribution
+from an input dataset and generate new samples accordingly. Applying them back
+to physics, the Boltzmann machines are ideal recommender systems to accelerate
+Monte Carlo simulation of physical systems due to their flexibility and
+effectiveness. More intriguingly, we show that the generative sampling of the
+Boltzmann Machines can even discover unknown cluster Monte Carlo algorithms.
+The creative power comes from the latent representation of the Boltzmann
+machines, which learn to mediate complex interactions and identify clusters of
+the physical system. We demonstrate these findings with concrete examples of
+the classical Ising model with and without four spin plaquette interactions.
+Our results endorse a fresh research paradigm where intelligent machines are
+designed to create or inspire human discovery of innovative algorithms.
+",0,1,0,1,0,0
+12237,Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction,"  Graph Signal Processing (GSP) is a promising framework to analyze
+multi-dimensional neuroimaging datasets, while taking into account both the
+spatial and functional dependencies between brain signals. In the present work,
+we apply dimensionality reduction techniques based on graph representations of
+the brain to decode brain activity from real and simulated fMRI datasets. We
+introduce seven graphs obtained from a) geometric structure and/or b)
+functional connectivity between brain areas at rest, and compare them when
+performing dimension reduction for classification. We show that mixed graphs
+using both a) and b) offer the best performance. We also show that graph
+sampling methods perform better than classical dimension reduction including
+Principal Component Analysis (PCA) and Independent Component Analysis (ICA).
+",1,0,0,1,0,0
+12238,Weak subsolutions to complex Monge-Ampère equations,"  We compare various notions of weak subsolutions to degenerate complex
+Monge-Ampère equations, showing that they all coincide. This allows us to
+give an alternative proof of mixed Monge-Ampère inequalities due to Kolodziej
+and Dinew.
+",0,0,1,0,0,0
+12239,Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction,"  Efficient assessment of convolved hidden Markov models is discussed. The
+bottom-layer is defined as an unobservable categorical first-order Markov
+chain, while the middle-layer is assumed to be a Gaussian spatial variable
+conditional on the bottom-layer. Hence, this layer appear as a Gaussian mixture
+spatial variable unconditionally. We observe the top-layer as a convolution of
+the middle-layer with Gaussian errors. Focus is on assessment of the
+categorical and Gaussian mixture variables given the observations, and we
+operate in a Bayesian inversion framework. The model is defined to make
+inversion of subsurface seismic AVO data into lithology/fluid classes and to
+assess the associated elastic material properties. Due to the spatial coupling
+in the likelihood functions, evaluation of the posterior normalizing constant
+is computationally demanding, and brute-force, single-site updating Markov
+chain Monte Carlo algorithms converges far too slow to be useful. We construct
+two classes of approximate posterior models which we assess analytically and
+efficiently using the recursive Forward-Backward algorithm. These approximate
+posterior densities are used as proposal densities in an independent proposal
+Markov chain Monte Carlo algorithm, to assess the correct posterior model. A
+set of synthetic realistic examples are presented. The proposed approximations
+provides efficient proposal densities which results in acceptance probabilities
+in the range 0.10-0.50 in the Markov chain Monte Carlo algorithm. A case study
+of lithology/fluid seismic inversion is presented. The lithology/fluid classes
+and the elastic material properties can be reliably predicted.
+",0,1,0,1,0,0
+12240,On the post-Keplerian corrections to the orbital periods of a two-body system and their application to the Galactic Center,"  Detailed numerical analyses of the orbital motion of a test particle around a
+spinning primary are performed. They aim to investigate the possibility of
+using the post-Keplerian (pK) corrections to the orbiter's periods (draconitic,
+anomalistic and sidereal) as a further opportunity to perform new tests of
+post-Newtonian (pN) gravity. As a specific scenario, the S-stars orbiting the
+Massive Black Hole (MBH) supposedly lurking in Sgr A$^\ast$ at the center of
+the Galaxy is adopted. We, first, study the effects of the pK Schwarzchild,
+Lense-Thirring and quadrupole moment accelerations experienced by a target star
+for various possible initial orbital configurations. It turns out that the
+results of the numerical simulations are consistent with the analytical ones in
+the small eccentricity approximation for which almost all the latter ones were
+derived. For highly elliptical orbits, the size of all the three pK corrections
+considered turn out to increase remarkably. The periods of the observed S2 and
+S0-102 stars as functions of the MBH's spin axis orientation are considered as
+well. The pK accelerations considered lead to corrections of the orbital
+periods of the order of 1-100d (Schwarzschild), 0.1-10h (Lense-Thirring) and
+1-10^3s (quadrupole) for a target star with a=300-800~AU and e ~ 0.8, which
+could be possibly measurable by the future facilities.
+",0,1,0,0,0,0
+12241,Disentangling top-down vs. bottom-up and low-level vs. high-level influences on eye movements over time,"  Bottom-up and top-down, as well as low-level and high-level factors influence
+where we fixate when viewing natural scenes. However, the importance of each of
+these factors and how they interact remains a matter of debate. Here, we
+disentangle these factors by analysing their influence over time. For this
+purpose we develop a saliency model which is based on the internal
+representation of a recent early spatial vision model to measure the low-level
+bottom-up factor. To measure the influence of high-level bottom-up features, we
+use a recent DNN-based saliency model. To account for top-down influences, we
+evaluate the models on two large datasets with different tasks: first, a
+memorisation task and, second, a search task. Our results lend support to a
+separation of visual scene exploration into three phases: The first saccade, an
+initial guided exploration characterised by a gradual broadening of the
+fixation density, and an steady state which is reached after roughly 10
+fixations. Saccade target selection during the initial exploration and in the
+steady state are related to similar areas of interest, which are better
+predicted when including high-level features. In the search dataset, fixation
+locations are determined predominantly by top-down processes. In contrast, the
+first fixation follows a different fixation density and contains a strong
+central fixation bias. Nonetheless, first fixations are guided strongly by
+image properties and as early as 200 ms after image onset, fixations are better
+predicted by high-level information. We conclude that any low-level bottom-up
+factors are mainly limited to the generation of the first saccade. All saccades
+are better explained when high-level features are considered, and later this
+high-level bottom-up control can be overruled by top-down influences.
+",0,0,0,0,1,0
+12242,Sharp estimates for oscillatory integral operators via polynomial partitioning,"  The sharp range of $L^p$-estimates for the class of Hörmander-type
+oscillatory integral operators is established in all dimensions under a
+positive-definite assumption on the phase. This is achieved by generalising a
+recent approach of the first author for studying the Fourier extension
+operator, which utilises polynomial partitioning arguments.
+",0,0,1,0,0,0
+12243,Inhomogeneous Heisenberg Spin Chain and Quantum Vortex Filament as Non-Holonomically Deformed NLS Systems,"  Through the Hasimoto map, various dynamical systems can be mapped to
+different integrodifferential generalizations of Nonlinear Schrodinger (NLS)
+family of equations some of which are known to be integrable. Two such
+continuum limits, corresponding to the inhomogeneous XXX Heisenberg spin chain
+[Balakrishnan, J. Phys. C 15, L1305 (1982)] and that of a thin vortex filament
+moving in a superfluid with drag [Shivamoggi, Eur. Phys. J. B 86, 275 (2013)
+86; Van Gorder, Phys. Rev. E 91, 053201 (2015)], are shown to be particular
+non-holonomic deformations (NHDs) of the standard NLS system involving
+generalized parameterizations. Crucially, such NHDs of the NLS system are
+restricted to specific spectral orders that exactly complements NHDs of the
+original physical systems. The specific non-holonomic constraints associated
+with these integrodifferential generalizations additionally posses distinct
+semi-classical signature.
+",0,1,0,0,0,0
+12244,An Information-Theoretic Analysis of Deduplication,"  Deduplication finds and removes long-range data duplicates. It is commonly
+used in cloud and enterprise server settings and has been successfully applied
+to primary, backup, and archival storage. Despite its practical importance as a
+source-coding technique, its analysis from the point of view of information
+theory is missing. This paper provides such an information-theoretic analysis
+of data deduplication. It introduces a new source model adapted to the
+deduplication setting. It formalizes the two standard fixed-length and
+variable-length deduplication schemes, and it introduces a novel multi-chunk
+deduplication scheme. It then provides an analysis of these three deduplication
+variants, emphasizing the importance of boundary synchronization between source
+blocks and deduplication chunks. In particular, under fairly mild assumptions,
+the proposed multi-chunk deduplication scheme is shown to be order optimal.
+",1,0,1,0,0,0
+12245,A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception,"  While deep neural networks take loose inspiration from neuroscience, it is an
+open question how seriously to take the analogies between artificial deep
+networks and biological neuronal systems. Interestingly, recent work has shown
+that deep convolutional neural networks (CNNs) trained on large-scale image
+recognition tasks can serve as strikingly good models for predicting the
+responses of neurons in visual cortex to visual stimuli, suggesting that
+analogies between artificial and biological neural networks may be more than
+superficial. However, while CNNs capture key properties of the average
+responses of cortical neurons, they fail to explain other properties of these
+neurons. For one, CNNs typically require large quantities of labeled input data
+for training. Our own brains, in contrast, rarely have access to this kind of
+supervision, so to the extent that representations are similar between CNNs and
+brains, this similarity must arise via different training paths. In addition,
+neurons in visual cortex produce complex time-varying responses even to static
+inputs, and they dynamically tune themselves to temporal regularities in the
+visual environment. We argue that these differences are clues to fundamental
+differences between the computations performed in the brain and in deep
+networks. To begin to close the gap, here we study the emergent properties of a
+previously-described recurrent generative network that is trained to predict
+future video frames in a self-supervised manner. Remarkably, the model is able
+to capture a wide variety of seemingly disparate phenomena observed in visual
+cortex, ranging from single unit response dynamics to complex perceptual motion
+illusions. These results suggest potentially deep connections between recurrent
+predictive neural network models and the brain, providing new leads that can
+enrich both fields.
+",0,0,0,0,1,0
+12246,Triangulum II: Not Especially Dense After All,"  Among the Milky Way satellites discovered in the past three years, Triangulum
+II has presented the most difficulty in revealing its dynamical status. Kirby
+et al. (2015a) identified it as the most dark matter-dominated galaxy known,
+with a mass-to-light ratio within the half-light radius of 3600 +3500 -2100
+M_sun/L_sun. On the other hand, Martin et al. (2016) measured an outer velocity
+dispersion that is 3.5 +/- 2.1 times larger than the central velocity
+dispersion, suggesting that the system might not be in equilibrium. From new
+multi-epoch Keck/DEIMOS measurements of 13 member stars in Triangulum II, we
+constrain the velocity dispersion to be sigma_v < 3.4 km/s (90% C.L.). Our
+previous measurement of sigma_v, based on six stars, was inflated by the
+presence of a binary star with variable radial velocity. We find no evidence
+that the velocity dispersion increases with radius. The stars display a wide
+range of metallicities, indicating that Triangulum II retained supernova ejecta
+and therefore possesses or once possessed a massive dark matter halo. However,
+the detection of a metallicity dispersion hinges on the membership of the two
+most metal-rich stars. The stellar mass is lower than galaxies of similar mean
+stellar metallicity, which might indicate that Triangulum II is either a star
+cluster or a tidally stripped dwarf galaxy. Detailed abundances of one star
+show heavily depressed neutron-capture abundances, similar to stars in most
+other ultra-faint dwarf galaxies but unlike stars in globular clusters.
+",0,1,0,0,0,0
+12247,Improving the upper bound on the length of the shortest reset words,"  We improve the best known upper bound on the length of the shortest reset
+words of synchronizing automata. The new bound is slightly better than $114 n^3
+/ 685 + O(n^2)$. The Černý conjecture states that $(n-1)^2$ is an upper
+bound. So far, the best general upper bound was $(n^3-n)/6-1$ obtained by
+J.-E.~Pin and P.~Frankl in 1982. Despite a number of efforts, it remained
+unchanged for about 35 years.
+To obtain the new upper bound we utilize avoiding words. A word is avoiding
+for a state $q$ if after reading the word the automaton cannot be in $q$. We
+obtain upper bounds on the length of the shortest avoiding words, and using the
+approach of Trahtman from 2011 combined with the well known Frankl theorem from
+1982, we improve the general upper bound on the length of the shortest reset
+words. For all the bounds, there exist polynomial algorithms finding a word of
+length not exceeding the bound.
+",1,0,0,0,0,0
+12248,Graphite: Iterative Generative Modeling of Graphs,"  Graphs are a fundamental abstraction for modeling relational data. However,
+graphs are discrete and combinatorial in nature, and learning representations
+suitable for machine learning tasks poses statistical and computational
+challenges. In this work, we propose Graphite an algorithmic framework for
+unsupervised learning of representations over nodes in a graph using deep
+latent variable generative models. Our model is based on variational
+autoencoders (VAE), and uses graph neural networks for parameterizing both the
+generative model (i.e., decoder) and inference model (i.e., encoder). The use
+of graph neural networks directly incorporates inductive biases due to the
+spatial, local structure of graphs directly in the generative model. We draw
+novel connections of our framework with approximate inference via kernel
+embeddings. Empirically, Graphite outperforms competing approaches for the
+tasks of density estimation, link prediction, and node classification on
+synthetic and benchmark datasets.
+",1,0,0,1,0,0
+12249,Characteristic classes in general relativity on a modified Poincare curvature bundle,"  Characteristic classes in space-time manifolds are discussed for both even-
+and odd-dimensional spacetimes. In particular, it is shown that the
+Einstein--Hilbert action is equivalent to a second Chern-class on a modified
+Poincare bundle in four dimensions. Consequently, the cosmological constant and
+the trace of an energy-momentum tensor become divisible modulo R/Z.
+",0,1,0,0,0,0
+12250,Observation of a 3D magnetic null point,"  We describe high resolution observations of a GOES B-class flare
+characterized by a circular ribbon at chromospheric level, corresponding to the
+network at photospheric level. We interpret the flare as a consequence of a
+magnetic reconnection event occurred at a three-dimensional (3D) coronal null
+point located above the supergranular cell. The potential field extrapolation
+of the photospheric magnetic field indicates that the circular chromospheric
+ribbon is cospatial with the fan footpoints, while the ribbons of the inner and
+outer spines look like compact kernels. We found new interesting observational
+aspects that need to be explained by models: 1) a loop corresponding to the
+outer spine became brighter a few minutes before the onset of the flare; 2) the
+circular ribbon was formed by several adjacent compact kernels characterized by
+a size of 1""-2""; 3) the kernels with stronger intensity emission were located
+at the outer footpoint of the darker filaments departing radially from the
+center of the supergranular cell; 4) these kernels start to brighten
+sequentially in clockwise direction; 5) the site of the 3D null point and the
+shape of the outer spine were detected by RHESSI in the low energy channel
+between 6.0 and 12.0 keV. Taking into account all these features and the length
+scales of the magnetic systems involved by the event we argued that the low
+intensity of the flare may be ascribed to the low amount of magnetic flux and
+to its symmetric configuration.
+",0,1,0,0,0,0
+12251,3D spatial exploration by E. coli echoes motor temporal variability,"  Unraveling bacterial strategies for spatial exploration is crucial to
+understand the complexity of the organi- zation of life. Currently, a
+cornerstone for quantitative modeling of bacterial transport, is their
+run-and-tumble strategy to explore their environment. For Escherichia coli, the
+run time distribution was reported to follow a Poisson process with a single
+characteristic time related to the rotational switching of the flagellar motor.
+Direct measurements on flagellar motors show, on the contrary, heavy-tailed
+distributions of rotation times stemming from the intrinsic noise in the
+chemotactic mechanism. The crucial role of stochasticity on the chemotactic
+response has also been highlighted by recent modeling, suggesting its
+determinant influence on motility. In stark contrast with the accepted vision
+of run-and-tumble, here we report a large behavioral variability of wild-type
+E. coli, revealed in their three-dimensional trajectories. At short times, a
+broad distribution of run times is measured on a population and attributed to
+the slow fluctuations of a signaling protein triggering the flagellar motor
+reversal. Over long times, individual bacteria undergo significant changes in
+motility. We demonstrate that such a large distribution introduces measurement
+biases in most practical situations. These results reconcile the notorious
+conundrum between run time observations and motor switching statistics. We
+finally propose that statistical modeling of transport properties currently
+undertaken in the emerging framework of active matter studies should be
+reconsidered under the scope of this large variability of motility features.
+",0,0,0,0,1,0
+12252,Unsupervised Latent Behavior Manifold Learning from Acoustic Features: audio2behavior,"  Behavioral annotation using signal processing and machine learning is highly
+dependent on training data and manual annotations of behavioral labels.
+Previous studies have shown that speech information encodes significant
+behavioral information and be used in a variety of automated behavior
+recognition tasks. However, extracting behavior information from speech is
+still a difficult task due to the sparseness of training data coupled with the
+complex, high-dimensionality of speech, and the complex and multiple
+information streams it encodes. In this work we exploit the slow varying
+properties of human behavior. We hypothesize that nearby segments of speech
+share the same behavioral context and hence share a similar underlying
+representation in a latent space. Specifically, we propose a Deep Neural
+Network (DNN) model to connect behavioral context and derive the behavioral
+manifold in an unsupervised manner. We evaluate the proposed manifold in the
+couples therapy domain and also provide examples from publicly available data
+(e.g. stand-up comedy). We further investigate training within the couples'
+therapy domain and from movie data. The results are extremely encouraging and
+promise improved behavioral quantification in an unsupervised manner and
+warrants further investigation in a range of applications.
+",1,0,0,0,0,0
+12253,Test map characterizations of local properties of fundamental groups,"  Local properties of the fundamental group of a path-connected topological
+space can pose obstructions to the applicability of covering space theory. A
+generalized covering map is a generalization of the classical notion of
+covering map defined in terms of unique lifting properties. The existence of
+generalized covering maps depends entirely on the verification of the unique
+path lifting property for a standard covering construction. Given any
+path-connected metric space $X$, and a subgroup $H\leq\pi_1(X,x_0)$, we
+characterize the unique path lifting property relative to $H$ in terms of a new
+closure operator on the $\pi_1$-subgroup lattice that is induced by maps from a
+fixed ""test"" domain into $X$. Using this test map framework, we develop a
+unified approach to comparing the existence of generalized coverings with a
+number of related properties.
+",0,0,1,0,0,0
+12254,Adaptive channel selection for DOA estimation in MIMO radar,"  We present adaptive strategies for antenna selection for Direction of Arrival
+(DoA) estimation of a far-field source using TDM MIMO radar with linear arrays.
+Our treatment is formulated within a general adaptive sensing framework that
+uses one-step ahead predictions of the Bayesian MSE using a parametric family
+of Weiss-Weinstein bounds that depend on previous measurements. We compare in
+simulations our strategy with adaptive policies that optimize the Bobrovsky-
+Zaka{\i} bound and the Expected Cramér-Rao bound, and show the performance
+for different levels of measurement noise.
+",1,0,0,0,0,0
+12255,Photometric characterization of the Dark Energy Camera,"  We characterize the variation in photometric response of the Dark Energy
+Camera (DECam) across its 520~Mpix science array during 4 years of operation.
+These variations are measured using high signal-to-noise aperture photometry of
+$>10^7$ stellar images in thousands of exposures of a few selected fields, with
+the telescope dithered to move the sources around the array. A calibration
+procedure based on these results brings the RMS variation in aperture
+magnitudes of bright stars on cloudless nights down to 2--3 mmag, with <1 mmag
+of correlated photometric errors for stars separated by $\ge20$"". On cloudless
+nights, any departures of the exposure zeropoints from a secant airmass law
+exceeding >1 mmag are plausibly attributable to spatial/temporal variations in
+aperture corrections. These variations can be inferred and corrected by
+measuring the fraction of stellar light in an annulus between 6"" and 8""
+diameter. Key elements of this calibration include: correction of amplifier
+nonlinearities; distinguishing pixel-area variations and stray light from
+quantum-efficiency variations in the flat fields; field-dependent color
+corrections; and the use of an aperture-correction proxy. The DECam response
+pattern across the 2-degree field drifts over months by up to $\pm7$ mmag, in a
+nearly-wavelength-independent low-order pattern. We find no fundamental
+barriers to pushing global photometric calibrations toward mmag accuracy.
+",0,1,0,0,0,0
+12256,Tough self-healing elastomers by molecular enforced integration of covalent and reversible networks,"  Self-healing polymers crosslinked by solely reversible bonds are
+intrinsically weaker than common covalently crosslinked networks. Introducing
+covalent crosslinks into a reversible network would improve mechanical
+strength. It is challenging, however, to apply this design concept to dry
+elastomers, largely because reversible crosslinks such as hydrogen bonds are
+often polar motifs, whereas covalent crosslinks are non-polar motifs, and these
+two types of bonds are intrinsically immiscible without co-solvents. Here we
+design and fabricate a hybrid polymer network by crosslinking randomly branched
+polymers carrying motifs that can form both reversible hydrogen bonds and
+permanent covalent crosslinks. The randomly branched polymer links such two
+types of bonds and forces them to mix on the molecular level without
+co-solvents. This allows us to create a hybrid dry elastomer that is very tough
+with a fracture energy $13,500J/m^2$ comparable to that of natural rubber;
+moreover, the elastomer can self-heal at room temperature with a recovered
+tensile strength 4 MPa similar to that of existing self-healing elastomers. The
+concept of forcing covalent and reversible bonds to mix at molecular scale to
+create a homogenous network is quite general and should enable development of
+tough, self-healing polymers of practical usage.
+",0,1,0,0,0,0
+12257,SYK Models and SYK-like Tensor Models with Global Symmetry,"  In this paper, we study an SYK model and an SYK-like tensor model with global
+symmetry. First, we study the large $N$ expansion of the bi-local collective
+action for the SYK model with manifest global symmetry. We show that the global
+symmetry is enhanced to a local symmetry at strong coupling limit, and the
+corresponding symmetry algebra is the Kac-Moody algebra. The emergent local
+symmetry together with the emergent reparametrization is spontaneously and
+explicit broken. This leads to a low energy effective action. We evaluate four
+point functions, and obtain spectrum of our model. We derive the low energy
+effective action and analyze the chaotic behavior of the four point functions.
+We also consider the recent 3D gravity conjecture for our model.
+We also introduce an SYK-like tensor model with global symmetry. We first
+study chaotic behavior of four point functions in various channels for the
+rank-3 case, and generalize this into a rank-$(q-1)$ tensor model.
+",0,1,0,0,0,0
+12258,Which Stars are Ionizing the Orion Nebula ?,"  The common assumption that Theta-1-Ori C is the dominant ionizing source for
+the Orion Nebula is critically examined. This assumption underlies much of the
+existing analysis of the nebula. In this paper we establish through comparison
+of the relative strengths of emission lines with expectations from Cloudy
+models and through the direction of the bright edges of proplyds that
+Theta-2-Ori-A, which lies beyond the Bright Bar, also plays an important role.
+Theta-1-Ori-C does dominate ionization in the inner part of the Orion Nebula,
+but outside of the Bright Bar as far as the southeast boundary of the Extended
+Orion Nebula, Theta-2-Ori-A is the dominant source. In addition to identifying
+the ionizing star in sample regions, we were able to locate those portions of
+the nebula in 3-D. This analysis illustrates the power of MUSE spectral imaging
+observations in identifying sources of ionization in extended regions.
+",0,1,0,0,0,0
+12259,FLaapLUC: a pipeline for the generation of prompt alerts on transient Fermi-LAT $γ$-ray sources,"  The large majority of high energy sources detected with Fermi-LAT are
+blazars, which are known to be very variable sources. High cadence long-term
+monitoring simultaneously at different wavelengths being prohibitive, the study
+of their transient activities can help shedding light on our understanding of
+these objects. The early detection of such potentially fast transient events is
+the key for triggering follow-up observations at other wavelengths. A Python
+tool, FLaapLUC, built on top of the Science Tools provided by the Fermi Science
+Support Center and the Fermi-LAT collaboration, has been developed using a
+simple aperture photometry approach. This tool can effectively detect relative
+flux variations in a set of predefined sources and alert potential users. Such
+alerts can then be used to trigger target of opportunity observations with
+other facilities. It is shown that FLaapLUC is an efficient tool to reveal
+transient events in Fermi-LAT data, providing quick results which can be used
+to promptly organise follow-up observations. Results from this simple aperture
+photometry method are also compared to full likelihood analyses. The FLaapLUC
+package is made available on GitHub and is open to contributions by the
+community.
+",0,1,0,0,0,0
+12260,A network approach to topic models,"  One of the main computational and scientific challenges in the modern age is
+to extract useful information from unstructured texts. Topic models are one
+popular machine-learning approach which infers the latent topical structure of
+a collection of documents. Despite their success --- in particular of its most
+widely used variant called Latent Dirichlet Allocation (LDA) --- and numerous
+applications in sociology, history, and linguistics, topic models are known to
+suffer from severe conceptual and practical problems, e.g. a lack of
+justification for the Bayesian priors, discrepancies with statistical
+properties of real texts, and the inability to properly choose the number of
+topics. Here we obtain a fresh view on the problem of identifying topical
+structures by relating it to the problem of finding communities in complex
+networks. This is achieved by representing text corpora as bipartite networks
+of documents and words. By adapting existing community-detection methods --
+using a stochastic block model (SBM) with non-parametric priors -- we obtain a
+more versatile and principled framework for topic modeling (e.g., it
+automatically detects the number of topics and hierarchically clusters both the
+words and documents). The analysis of artificial and real corpora demonstrates
+that our SBM approach leads to better topic models than LDA in terms of
+statistical model selection. More importantly, our work shows how to formally
+relate methods from community detection and topic modeling, opening the
+possibility of cross-fertilization between these two fields.
+",1,0,0,1,0,0
+12261,CRPropa 3.1 -- A low energy extension based on stochastic differential equations,"  The propagation of charged cosmic rays through the Galactic environment
+influences all aspects of the observation at Earth. Energy spectrum,
+composition and arrival directions are changed due to deflections in magnetic
+fields and interactions with the interstellar medium. Today the transport is
+simulated with different simulation methods either based on the solution of a
+transport equation (multi-particle picture) or a solution of an equation of
+motion (single-particle picture).
+We developed a new module for the publicly available propagation software
+CRPropa 3.1, where we implemented an algorithm to solve the transport equation
+using stochastic differential equations. This technique allows us to use a
+diffusion tensor which is anisotropic with respect to an arbitrary magnetic
+background field. The source code of CRPropa is written in C++ with python
+steering via SWIG which makes it easy to use and computationally fast.
+In this paper, we present the new low-energy propagation code together with
+validation procedures that are developed to proof the accuracy of the new
+implementation. Furthermore, we show first examples of the cosmic ray density
+evolution, which depends strongly on the ratio of the parallel
+$\kappa_\parallel$ and perpendicular $\kappa_\perp$ diffusion coefficients.
+This dependency is systematically examined as well the influence of the
+particle rigidity on the diffusion process.
+",0,1,0,0,0,0
+12262,Isometries in spaces of Kähler potentials,"  The space of Kähler potentials in a compact Kähler manifold, endowed with
+Mabuchi's metric, is an infinite dimensional Riemannian manifold. We
+characterize local isometries between spaces of Kähler potentials, and prove
+existence and uniqueness for such isometries.
+",0,0,1,0,0,0
+12263,On the Statistical Challenges of Echo State Networks and Some Potential Remedies,"  Echo state networks are powerful recurrent neural networks. However, they are
+often unstable and shaky, making the process of finding an good ESN for a
+specific dataset quite hard. Obtaining a superb accuracy by using the Echo
+State Network is a challenging task. We create, develop and implement a family
+of predictably optimal robust and stable ensemble of Echo State Networks via
+regularizing the training and perturbing the input. Furthermore, several
+distributions of weights have been tried based on the shape to see if the shape
+of the distribution has the impact for reducing the error. We found ESN can
+track in short term for most dataset, but it collapses in the long run.
+Short-term tracking with large size reservoir enables ESN to perform strikingly
+with superior prediction. Based on this scenario, we go a further step to
+aggregate many of ESNs into an ensemble to lower the variance and stabilize the
+system by stochastic replications and bootstrapping of input data.
+",0,0,0,1,0,0
+12264,Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances,"  In this paper, we prove that some Gaussian structural equation models with
+dependent errors having equal variances are identifiable from their
+corresponding Gaussian distributions. Specifically, we prove identifiability
+for the Gaussian structural equation models that can be represented as
+Andersson-Madigan-Perlman chain graphs (Andersson et al., 2001). These chain
+graphs were originally developed to represent independence models. However,
+they are also suitable for representing causal models with additive noise
+(Peña, 2016. Our result implies then that these causal models can be
+identified from observational data alone. Our result generalizes the result by
+Peters and Bühlmann (2014), who considered independent errors having equal
+variances. The suitability of the equal error variances assumption should be
+assessed on a per domain basis.
+",0,0,0,1,0,0
+12265,Smart Contract SLAs for Dense Small-Cell-as-a-Service,"  The disruptive power of blockchain technologies represents a great
+opportunity to re-imagine standard practices of telecommunication networks and
+to identify critical areas that can benefit from brand new approaches. As a
+starting point for this debate, we look at the current limits of infrastructure
+sharing, and specifically at the Small-Cell-as-a-Service trend, asking
+ourselves how we could push it to its natural extreme: a scenario in which any
+individual home or business user can become a service provider for mobile
+network operators, freed from all the scalability and legal constraints that
+are inherent to the current modus operandi. We propose the adoption of smart
+contracts to implement simple but effective Service Level Agreements (SLAs)
+between small cell providers and mobile operators, and present an example
+contract template based on the Ethereum blockchain.
+",1,0,0,0,0,0
+12266,On Kiguradze theorem for linear boundary value problems,"  We investigate the limiting behavior of solutions of nonhomogeneous boundary
+value problems for the systems of linear ordinary differential equations. The
+generalization of Kiguradze theorem (1987) on passage to the limit is obtained.
+",0,0,1,0,0,0
+12267,Fidelity Lower Bounds for Stabilizer and CSS Quantum Codes,"  In this paper we estimate the fidelity of stabilizer and CSS codes. First, we
+derive a lower bound on the fidelity of a stabilizer code via its quantum
+enumerator. Next, we find the average quantum enumerators of the ensembles of
+finite length stabilizer and CSS codes. We use the average quantum enumerators
+for obtaining lower bounds on the average fidelity of these ensembles. We
+further improve the fidelity bounds by estimating the quantum enumerators of
+expurgated ensembles of stabilizer and CSS codes. Finally, we derive fidelity
+bounds in the asymptotic regime when the code length tends to infinity.
+These results tell us which code rate we can afford for achieving a target
+fidelity with codes of a given length. The results also show that in symmetric
+depolarizing channel a typical stabilizer code has better performance, in terms
+of fidelity and code rate, compared with a typical CSS codes, and that balanced
+CSS codes significantly outperform other CSS codes. Asymptotic results
+demonstrate that CSS codes have a fundamental performance loss compared to
+stabilizer codes.
+",1,0,0,0,0,0
+12268,Cross-validation improved by aggregation: Agghoo,"  Cross-validation is widely used for selecting among a family of learning
+rules. This paper studies a related method, called aggregated hold-out
+(Agghoo), which mixes cross-validation with aggregation; Agghoo can also be
+related to bagging. According to numerical experiments, Agghoo can improve
+significantly cross-validation's prediction error, at the same computational
+cost; this makes it very promising as a general-purpose tool for prediction. We
+provide the first theoretical guarantees on Agghoo, in the supervised
+classification setting, ensuring that one can use it safely: at worse, Agghoo
+performs like the hold-out, up to a constant factor. We also prove a
+non-asymptotic oracle inequality, in binary classification under the margin
+condition, which is sharp enough to get (fast) minimax rates.
+",0,0,1,1,0,0
+12269,MAT: A Multimodal Attentive Translator for Image Captioning,"  In this work we formulate the problem of image captioning as a multimodal
+translation task. Analogous to machine translation, we present a
+sequence-to-sequence recurrent neural networks (RNN) model for image caption
+generation. Different from most existing work where the whole image is
+represented by convolutional neural network (CNN) feature, we propose to
+represent the input image as a sequence of detected objects which feeds as the
+source sequence of the RNN model. In this way, the sequential representation of
+an image can be naturally translated to a sequence of words, as the target
+sequence of the RNN model. To represent the image in a sequential way, we
+extract the objects features in the image and arrange them in a order using
+convolutional neural networks. To further leverage the visual information from
+the encoded objects, a sequential attention layer is introduced to selectively
+attend to the objects that are related to generate corresponding words in the
+sentences. Extensive experiments are conducted to validate the proposed
+approach on popular benchmark dataset, i.e., MS COCO, and the proposed model
+surpasses the state-of-the-art methods in all metrics following the dataset
+splits of previous work. The proposed approach is also evaluated by the
+evaluation server of MS COCO captioning challenge, and achieves very
+competitive results, e.g., a CIDEr of 1.029 (c5) and 1.064 (c40).
+",1,0,0,0,0,0
+12270,A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games,"  Mobile gaming has emerged as a promising market with billion-dollar revenues.
+A variety of mobile game platforms and services have been developed around the
+world. One critical challenge for these platforms and services is to understand
+user churn behavior in mobile games. Accurate churn prediction will benefit
+many stakeholders such as game developers, advertisers, and platform operators.
+In this paper, we present the first large-scale churn prediction solution for
+mobile games. In view of the common limitations of the state-of-the-art methods
+built upon traditional machine learning models, we devise a novel
+semi-supervised and inductive embedding model that jointly learns the
+prediction function and the embedding function for user-app relationships. We
+model these two functions by deep neural networks with a unique edge embedding
+technique that is able to capture both contextual information and relationship
+dynamics. We also design a novel attributed random walk technique that takes
+into consideration both topological adjacency and attribute similarities. To
+evaluate the performance of our solution, we collect real-world data from the
+Samsung Game Launcher platform that includes tens of thousands of games and
+hundreds of millions of user-app interactions. The experimental results with
+this data demonstrate the superiority of our proposed model against existing
+state-of-the-art methods.
+",0,0,0,1,0,0
+12271,Sequential rerandomization,"  The seminal work of Morgan and Rubin (2012) considers rerandomization for all
+the units at one time. In practice, however, experimenters may have to
+rerandomize units sequentially. For example, a clinician studying a rare
+disease may be unable to wait to perform an experiment until all the
+experimental units are recruited. Our work offers a mathematical framework for
+sequential rerandomization designs, where the experimental units are enrolled
+in groups. We formulate an adaptive rerandomization procedure for balancing
+treatment/control assignments over some continuous or binary covariates, using
+Mahalanobis distance as the imbalance measure. We prove in our key result,
+Theorem 3, that given the same number of rerandomizations (in expected value),
+under certain mild assumptions, sequential rerandomization achieves better
+covariate balance than rerandomization at one time.
+",0,0,0,1,0,0
+12272,Neural SLAM: Learning to Explore with External Memory,"  We present an approach for agents to learn representations of a global map
+from sensor data, to aid their exploration in new environments. To achieve
+this, we embed procedures mimicking that of traditional Simultaneous
+Localization and Mapping (SLAM) into the soft attention based addressing of
+external memory architectures, in which the external memory acts as an internal
+representation of the environment. This structure encourages the evolution of
+SLAM-like behaviors inside a completely differentiable deep neural network. We
+show that this approach can help reinforcement learning agents to successfully
+explore new environments where long-term memory is essential. We validate our
+approach in both challenging grid-world environments and preliminary Gazebo
+experiments. A video of our experiments can be found at: this https URL.
+",1,0,0,0,0,0
+12273,Information Storage and Retrieval using Macromolecules as Storage Media,"  To store information at extremely high-density and data-rate, we propose to
+adapt, integrate, and extend the techniques developed by chemists and molecular
+biologists for the purpose of manipulating biological and other macromolecules.
+In principle, volumetric densities in excess of 10^21 bits/cm^3 can be achieved
+when individual molecules having dimensions below a nanometer or so are used to
+encode the 0's and 1's of a binary string of data. In practice, however, given
+the limitations of electron-beam lithography, thin film deposition and
+patterning technologies, molecular manipulation in submicron dimensions, etc.,
+we believe that volumetric storage densities on the order of 10^16 bits/cm^3
+(i.e., petabytes per cubic centimeter) should be readily attainable, leaving
+plenty of room for future growth. The unique feature of the proposed new
+approach is its focus on the feasibility of storing bits of information in
+individual molecules, each only a few angstroms in size.
+",1,1,0,0,0,0
+12274,Morgan type uncertainty principle and unique continuation properties for abstract Schrödinger equations,"  In this paper, Morgan type uncertainty principle and unique continuation
+properties of abstract Schrödinger equations with time dependent potentials
+in vector-valued classes are obtained. The equation involves a possible linear
+operators considered in the Hilbert spaces. So, by choosing the corresponding
+spaces H and operators we derived unique continuation properties for numerous
+classes of Schrödinger type equations and its systems which occur in a wide
+variety of physical systems
+",0,0,1,0,0,0
+12275,Can the Journal Impact Factor Be Used as a Criterion for the Selection of Junior Researchers? A Large-Scale Empirical Study Based on ResearcherID Data,"  Early in researchers' careers, it is difficult to assess how good their work
+is or how important or influential the scholars will eventually be. Hence,
+funding agencies, academic departments, and others often use the Journal Impact
+Factor (JIF) of where the authors have published to assess their work and
+provide resources and rewards for future work. The use of JIFs in this way has
+been heavily criticized, however. Using a large data set with many thousands of
+publication profiles of individual researchers, this study tests the ability of
+the JIF (in its normalized variant) to identify, at the beginning of their
+careers, those candidates who will be successful in the long run. Instead of
+bare JIFs and citation counts, the metrics used here are standardized according
+to Web of Science subject categories and publication years. The results of the
+study indicate that the JIF (in its normalized variant) is able to discriminate
+between researchers who published papers later on with a citation impact above
+or below average in a field and publication year - not only in the short term,
+but also in the long term. However, the low to medium effect sizes of the
+results also indicate that the JIF (in its normalized variant) should not be
+used as the sole criterion for identifying later success: other criteria, such
+as the novelty and significance of the specific research, academic
+distinctions, and the reputation of previous institutions, should also be
+considered.
+",1,1,0,0,0,0
+12276,Deep MIMO Detection,"  In this paper, we consider the use of deep neural networks in the context of
+Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction
+to deep learning and propose a modern neural network architecture suitable for
+this detection task. First, we consider the case in which the MIMO channel is
+constant, and we learn a detector for a specific system. Next, we consider the
+harder case in which the parameters are known yet changing and a single
+detector must be learned for all multiple varying channels. We demonstrate the
+performance of our deep MIMO detector using numerical simulations in comparison
+to competing methods including approximate message passing and semidefinite
+relaxation. The results show that deep networks can achieve state of the art
+accuracy with significantly lower complexity while providing robustness against
+ill conditioned channels and mis-specified noise variance.
+",1,0,0,1,0,0
+12277,Cross-View Image Matching for Geo-localization in Urban Environments,"  In this paper, we address the problem of cross-view image geo-localization.
+Specifically, we aim to estimate the GPS location of a query street view image
+by finding the matching images in a reference database of geo-tagged bird's eye
+view images, or vice versa. To this end, we present a new framework for
+cross-view image geo-localization by taking advantage of the tremendous success
+of deep convolutional neural networks (CNNs) in image classification and object
+detection. First, we employ the Faster R-CNN to detect buildings in the query
+and reference images. Next, for each building in the query image, we retrieve
+the $k$ nearest neighbors from the reference buildings using a Siamese network
+trained on both positive matching image pairs and negative pairs. To find the
+correct NN for each query building, we develop an efficient multiple nearest
+neighbors matching method based on dominant sets. We evaluate the proposed
+framework on a new dataset that consists of pairs of street view and bird's eye
+view images. Experimental results show that the proposed method achieves better
+geo-localization accuracy than other approaches and is able to generalize to
+images at unseen locations.
+",1,0,0,0,0,0
+12278,Understanding the Feedforward Artificial Neural Network Model From the Perspective of Network Flow,"  In recent years, deep learning based on artificial neural network (ANN) has
+achieved great success in pattern recognition. However, there is no clear
+understanding of such neural computational models. In this paper, we try to
+unravel ""black-box"" structure of Ann model from network flow. Specifically, we
+consider the feed forward Ann as a network flow model, which consists of many
+directional class-pathways. Each class-pathway encodes one class. The
+class-pathway of a class is obtained by connecting the activated neural nodes
+in each layer from input to output, where activation value of neural node
+(node-value) is defined by the weights of each layer in a trained
+ANN-classifier. From the perspective of the class-pathway, training an
+ANN-classifier can be regarded as the formulation process of class-pathways of
+different classes. By analyzing the the distances of each two class-pathways in
+a trained ANN-classifiers, we try to answer the questions, why the classifier
+performs so? At last, from the neural encodes view, we define the importance of
+each neural node through the class-pathways, which is helpful to optimize the
+structure of a classifier. Experiments for two types of ANN model including
+multi-layer MLP and CNN verify that the network flow based on class-pathway is
+a reasonable explanation for ANN models.
+",1,0,0,0,0,0
+12279,Excitation of multiple 2-mode parametric resonances by a single driven mode,"  We demonstrate autoparametric excitation of two distinct sub-harmonic
+mechanical modes by the same driven mechanical mode corresponding to different
+drive frequencies within its resonance dispersion band. This experimental
+observation is used to motivate a more general physical picture wherein
+multiple mechanical modes could be excited by the same driven primary mode
+within the same device as long as the frequency spacing between the
+sub-harmonic modes is less than half the dispersion bandwidth of the driven
+primary mode. The excitation of both modes is seen to be threshold-dependent
+and a parametric back-action is observed impacting on the response of the
+driven primary mode. Motivated by this experimental observation, modified
+dynamical equations specifying 2-mode auto-parametric excitation for such
+systems are presented.
+",0,1,0,0,0,0
+12280,Solar system science with the Wide-Field InfraRed Survey Telescope (WFIRST),"  We present a community-led assessment of the solar system investigations
+achievable with NASA's next-generation space telescope, the Wide Field InfraRed
+Survey Telescope (WFIRST). WFIRST will provide imaging, spectroscopic, and
+coronagraphic capabilities from 0.43-2.0 $\mu$m and will be a potential
+contemporary and eventual successor to JWST. Surveys of irregular satellites
+and minor bodies are where WFIRST will excel with its 0.28 deg$^2$ field of
+view Wide Field Instrument (WFI). Potential ground-breaking discoveries from
+WFIRST could include detection of the first minor bodies orbiting in the Inner
+Oort Cloud, identification of additional Earth Trojan asteroids, and the
+discovery and characterization of asteroid binary systems similar to
+Ida/Dactyl. Additional investigations into asteroids, giant planet satellites,
+Trojan asteroids, Centaurs, Kuiper Belt Objects, and comets are presented.
+Previous use of astrophysics assets for solar system science and synergies
+between WFIRST, LSST, JWST, and the proposed NEOCam mission are discussed. We
+also present the case for implementation of moving target tracking, a feature
+that will benefit from the heritage of JWST and enable a broader range of solar
+system observations.
+",0,1,0,0,0,0
+12281,Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition,"  Unsupervised dependency parsing aims to learn a dependency parser from
+unannotated sentences. Existing work focuses on either learning generative
+models using the expectation-maximization algorithm and its variants, or
+learning discriminative models using the discriminative clustering algorithm.
+In this paper, we propose a new learning strategy that learns a generative
+model and a discriminative model jointly based on the dual decomposition
+method. Our method is simple and general, yet effective to capture the
+advantages of both models and improve their learning results. We tested our
+method on the UD treebank and achieved a state-of-the-art performance on thirty
+languages.
+",1,0,0,0,0,0
+12282,Near-Infrared Knots and Dense Fe Ejecta in the Cassiopeia A Supernova Remnant,"  We report the results of broadband (0.95--2.46 $\mu$m) near-infrared
+spectroscopic observations of the Cassiopeia A supernova remnant. Using a
+clump-finding algorithm in two-dimensional dispersed images, we identify 63
+""knots"" from eight slit positions and derive their spectroscopic properties.
+All of the knots emit [Fe II] lines together with other ionic forbidden lines
+of heavy elements, and some of them also emit H and He lines. We identify 46
+emission line features in total from the 63 knots and measure their fluxes and
+radial velocities. The results of our analyses of the emission line features
+based on principal component analysis show that the knots can be classified
+into three groups: (1) He-rich, (2) S-rich, and (3) Fe-rich knots. The He-rich
+knots have relatively small, $\lesssim 200~{\rm km~s}^{-1}$, line-of-sight
+speeds and radiate strong He I and [Fe II] lines resembling closely optical
+quasi-stationary flocculi of circumstellar medium, while the S-rich knots show
+strong lines from O-burning material with large radial velocities up to $\sim
+2000~{\rm km~s}^{-1}$ indicating that they are supernova ejecta material known
+as fast-moving knots. The Fe-rich knots also have large radial velocities but
+show no lines from O-burning material. We discuss the origin of the Fe-rich
+knots and conclude that they are most likely ""pure"" Fe ejecta synthesized in
+the innermost region during the supernova explosion. The comparison of [Fe II]
+images with other waveband images shows that these dense Fe ejecta are mainly
+distributed along the southwestern shell just outside the unshocked $^{44}$Ti
+in the interior, supporting the presence of unshocked Fe associated with
+$^{44}$Ti.
+",0,1,0,0,0,0
+12283,On the conjecture of Jeśmanowicz,"  We give a survey on some results covering the last 60 years concerning
+Jeśmanowicz' conjecture. Moreover, we conclude the survey with a new result
+by showing that the special Diophantine equation $$(20k)^x+(99k)^y=(101k)^z$$
+has no solution other than $(x,y,z)=(2,2,2)$.
+",0,0,1,0,0,0
+12284,Lattice implementation of Abelian gauge theories with Chern-Simons number and an axion field,"  Real time evolution of classical gauge fields is relevant for a number of
+applications in particle physics and cosmology, ranging from the early Universe
+to dynamics of quark-gluon plasma. We present a lattice formulation of the
+interaction between a $shift$-symmetric field and some $U(1)$ gauge sector,
+$a(x)\tilde{F}_{\mu\nu}F^{\mu\nu}$, reproducing the continuum limit to order
+$\mathcal{O}(dx_\mu^2)$ and obeying the following properties: (i) the system is
+gauge invariant and (ii) shift symmetry is exact on the lattice. For this end
+we construct a definition of the {\it topological number density} $Q =
+\tilde{F}_{\mu\nu}F^{\mu\nu}$ that admits a lattice total derivative
+representation $Q = \Delta_\mu^+ K^\mu$, reproducing to order
+$\mathcal{O}(dx_\mu^2)$ the continuum expression $Q = \partial_\mu K^\mu
+\propto \vec E \cdot \vec B$. If we consider a homogeneous field $a(x) = a(t)$,
+the system can be mapped into an Abelian gauge theory with Hamiltonian
+containing a Chern-Simons term for the gauge fields. This allow us to study in
+an accompanying paper the real time dynamics of fermion number non-conservation
+(or chirality breaking) in Abelian gauge theories at finite temperature. When
+$a(x) = a(\vec x,t)$ is inhomogeneous, the set of lattice equations of motion
+do not admit however a simple explicit local solution (while preserving an
+$\mathcal{O}(dx_\mu^2)$ accuracy). We discuss an iterative scheme allowing to
+overcome this difficulty.
+",0,1,0,0,0,0
+12285,II-FCN for skin lesion analysis towards melanoma detection,"  Dermoscopy image detection stays a tough task due to the weak distinguishable
+property of the object.Although the deep convolution neural network
+signifigantly boosted the performance on prevelance computer vision tasks in
+recent years,there remains a room to explore more robust and precise models to
+the problem of low contrast image segmentation.Towards the challenge of Lesion
+Segmentation in ISBI 2017,we built a symmetrical identity inception fully
+convolution network which is based on only 10 reversible inception blocks,every
+block composed of four convolution branches with combination of different layer
+depth and kernel size to extract sundry semantic features.Then we proposed an
+approximate loss function for jaccard index metrics to train our model.To
+overcome the drawbacks of traditional convolution,we adopted the dilation
+convolution and conditional random field method to rectify our segmentation.We
+also introduced multiple ways to prevent the problem of overfitting.The
+experimental results shows that our model achived jaccard index of 0.82 and
+kept learning from epoch to epoch.
+",1,0,0,0,0,0
+12286,Zero-Shot Visual Imitation,"  The current dominant paradigm for imitation learning relies on strong
+supervision of expert actions to learn both 'what' and 'how' to imitate. We
+pursue an alternative paradigm wherein an agent first explores the world
+without any expert supervision and then distills its experience into a
+goal-conditioned skill policy with a novel forward consistency loss. In our
+framework, the role of the expert is only to communicate the goals (i.e., what
+to imitate) during inference. The learned policy is then employed to mimic the
+expert (i.e., how to imitate) after seeing just a sequence of images
+demonstrating the desired task. Our method is 'zero-shot' in the sense that the
+agent never has access to expert actions during training or for the task
+demonstration at inference. We evaluate our zero-shot imitator in two
+real-world settings: complex rope manipulation with a Baxter robot and
+navigation in previously unseen office environments with a TurtleBot. Through
+further experiments in VizDoom simulation, we provide evidence that better
+mechanisms for exploration lead to learning a more capable policy which in turn
+improves end task performance. Videos, models, and more details are available
+at this https URL
+",1,0,0,1,0,0
+12287,Spreading in kinetic reaction-transport equations in higher velocity dimensions,"  In this paper, we extend and complement previous works about propagation in
+kinetic reaction-transport equations. The model we study describes particles
+moving according to a velocity-jump process, and proliferating according to a
+reaction term of monostable type. We focus on the case of bounded velocities,
+having dimension higher than one. We extend previous results obtained by the
+first author with Calvez and Nadin in dimension one. We study the large
+time/large scale hyperbolic limit via an Hamilton-Jacobi framework together
+with the half-relaxed limits method. We deduce spreading results and the
+existence of travelling wave solutions. A crucial difference with the
+mono-dimensional case is the resolution of the spectral problem at the edge of
+the front, that yields potential singular velocity distributions. As a
+consequence, the minimal speed of propagation may not be determined by a first
+order condition.
+",0,0,1,0,0,0
+12288,Matching of orbital integrals (transfer) and Roche Hecke algebra isomorphisms,"  Let $F$ be a non-Archimedan local field, $G$ a connected reductive group
+defined and split over $F$, and $T$ a maximal $F$-split torus in $G$. Let
+$\chi_0$ be a depth zero character of the maximal compact subgroup
+$\mathcal{T}$ of $T(F)$. It gives by inflation a character $\rho$ of an Iwahori
+subgroup $\mathcal{I}$ of $G(F)$ containing $\mathcal{T}$. From Roche, $\chi_0$
+defines a split endoscopic group $G'$ of $G$, and there is an injective
+morphism of ${\Bbb C}$-algebras $\mathcal{H}(G(F),\rho) \rightarrow
+\mathcal{H}(G'(F),1_{\mathcal{I}'})$ where $\mathcal{H}(G(F),\rho)$ is the
+Hecke algebra of compactly supported $\rho^{-1}$-spherical functions on $G(F)$
+and $\mathcal{I}'$ is an Iwahori subgroup of $G'(F)$. This morphism restricts
+to an injective morphism $\zeta: \mathcal{Z}(G(F),\rho)\rightarrow
+\mathcal{Z}(G'(F),1_{\mathcal{I}'})$ between the centers of the Hecke algebras.
+We prove here that a certain linear combination of morphisms analogous to
+$\zeta$ realizes the transfer (matching of strongly $G$-regular semisimple
+orbital integrals). If ${\rm char}(F)=p>0$, our result is unconditional only if
+$p$ is large enough.
+",0,0,1,0,0,0
+12289,Testing atomic collision theory with the two-photon continuum of astrophysical nebulae,"  Accurate rates for energy-degenerate l-changing collisions are needed to
+determine cosmological abundances and recombination. There are now several
+competing theories for the treatment of this process, and it is not possible to
+test these experimentally. We show that the H I two-photon continuum produced
+by astrophysical nebulae is strongly affected by l-changing collisions. We
+perform an analysis of the different underlying atomic processes and simulate
+the recombination and two-photon spectrum of a nebula containing H and He. We
+provide an extended set of effective recombination coefficients and updated
+l-changing 2s-2p transition rates using several competing theories. In
+principle, accurate astronomical observations could determine which theory is
+correct.
+",0,1,0,0,0,0
+12290,Metrologically useful states of spin-1 Bose condensates with macroscopic magnetization,"  We study theoretically the usefulness of spin-1 Bose condensates with
+macroscopic magnetization in a homogeneous magnetic field for quantum
+metrology. We demonstrate Heisenberg scaling of the quantum Fisher information
+for states in thermal equilibrium. The scaling applies to both
+antiferromagnetic and ferromagnetic interactions. The effect preserves as long
+as fluctuations of magnetization are sufficiently small. Scaling of the quantum
+Fisher information with the total particle number is derived within the
+mean-field approach in the zero temperature limit and exactly in the high
+magnetic field limit for any temperature. The precision gain is intuitively
+explained owing to subtle features of the quasi-distribution function in phase
+space.
+",0,1,0,0,0,0
+12291,The Final Chapter In The Saga Of YIG,"  The magnetic insulator Yttrium Iron Garnet can be grown with exceptional
+quality, has a ferrimagnetic transition temperature of nearly 600 K, and is
+used in microwave and spintronic devices that can operate at room temperature.
+The most accurate prior measurements of the magnon spectrum date back nearly 40
+years, but cover only 3 of the lowest energy modes out of 20 distinct magnon
+branches. Here we have used time-of-flight inelastic neutron scattering to
+measure the full magnon spectrum throughout the Brillouin zone. We find that
+the existing model of the excitation spectrum, well known from an earlier work
+titled ""The Saga of YIG"", fails to describe the optical magnon modes. Using a
+very general spin Hamiltonian, we show that the magnetic interactions are both
+longer-ranged and more complex than was previously understood. The results
+provide the basis for accurate microscopic models of the finite temperature
+magnetic properties of Yttrium Iron Garnet, necessary for next-generation
+electronic devices.
+",0,1,0,0,0,0
+12292,Modeling and Analysis of HetNets with mm-Wave Multi-RAT Small Cells Deployed Along Roads,"  We characterize a multi tier network with classical macro cells, and multi
+radio access technology (RAT) small cells, which are able to operate in
+microwave and millimeter-wave (mm-wave) bands. The small cells are assumed to
+be deployed along roads modeled as a Poisson line process. This
+characterization is more realistic as compared to the classical Poisson point
+processes typically used in literature. In this context, we derive the
+association and RAT selection probabilities of the typical user under various
+system parameters such as the small cell deployment density and mm-wave antenna
+gain, and with varying street densities. Finally, we calculate the signal to
+interference plus noise ratio (SINR) coverage probability for the typical user
+considering a tractable dominant interference based model for mm-wave
+interference. Our analysis reveals the need of deploying more small cells per
+street in cities with more streets to maintain coverage, and highlights that
+mm-wave RAT in small cells can help to improve the SINR performance of the
+users.
+",1,0,0,0,0,0
+12293,Ermakov-Painlevé II Symmetry Reduction of a Korteweg Capillarity System,"  A class of nonlinear Schrödinger equations involving a triad of power law
+terms together with a de Broglie-Bohm potential is shown to admit symmetry
+reduction to a hybrid Ermakov-Painlevé II equation which is linked, in turn,
+to the integrable Painlevé XXXIV equation. A nonlinear Schrödinger
+encapsulation of a Korteweg-type capillary system is thereby used in the
+isolation of such a Ermakov-Painlevé II reduction valid for a multi-parameter
+class of free energy functions. Iterated application of a Bäcklund
+transformation then allows the construction of novel classes of exact solutions
+of the nonlinear capillarity system in terms of Yablonskii-Vorob'ev polynomials
+or classical Airy functions. A Painlevé XXXIV equation is derived for the
+density in the capillarity system and seen to correspond to the symmetry
+reduction of its Bernoulli integral of motion.
+",0,1,1,0,0,0
+12294,Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks,"  Matrix completion models are among the most common formulations of
+recommender systems. Recent works have showed a boost of performance of these
+techniques when introducing the pairwise relationships between users/items in
+the form of graphs, and imposing smoothness priors on these graphs. However,
+such techniques do not fully exploit the local stationarity structures of
+user/item graphs, and the number of parameters to learn is linear w.r.t. the
+number of users and items. We propose a novel approach to overcome these
+limitations by using geometric deep learning on graphs. Our matrix completion
+architecture combines graph convolutional neural networks and recurrent neural
+networks to learn meaningful statistical graph-structured patterns and the
+non-linear diffusion process that generates the known ratings. This neural
+network system requires a constant number of parameters independent of the
+matrix size. We apply our method on both synthetic and real datasets, showing
+that it outperforms state-of-the-art techniques.
+",1,0,0,1,0,0
+12295,Value Asymptotics in Dynamic Games on Large Horizons,"  This paper is concerned with two-person dynamic zero-sum games. Let games for
+some family have common dynamics, running costs and capabilities of players,
+and let these games differ in densities only. We show that the Dynamic
+Programming Principle directly leads to the General Tauberian Theorem---that
+the existence of a uniform limit of the value functions for uniform
+distribution or for exponential distribution implies that the value functions
+uniformly converge to the same limit for arbitrary distribution from large
+class. No assumptions on strategies are necessary. Applications to differential
+games and stochastic statement are considered.
+",0,0,1,0,0,0
+12296,Classical counterparts of quantum attractors in generic dissipative systems,"  In the context of dissipative systems, we show that for any quantum chaotic
+attractor a corre- sponding classical chaotic attractor can always be found. We
+provide with a general way to locate them, rooted in the structure of the
+parameter space (which is typically bidimensional, accounting for the forcing
+strength and dissipation parameters). In the cases where an approximate point
+like quantum distribution is found, it can be associated to exceptionally large
+regular structures. Moreover, supposedly anomalous quantum chaotic behaviour
+can be very well reproduced by the classical dynamics plus Gaussian noise of
+the size of an effective Planck constant $\hbar_{\rm eff}$. We give support to
+our conjectures by means of two paradigmatic examples of quantum chaos and
+transport theory. In particular, a dissipative driven system becomes
+fundamental in order to extend their validity to generic cases.
+",0,1,0,0,0,0
+12297,Marginal likelihood based model comparison in Fuzzy Bayesian Learning,"  In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL)
+paradigm where expert opinions can be encoded in the form of fuzzy rule bases
+and the hyper-parameters of the fuzzy sets can be learned from data using a
+Bayesian approach. The present paper extends this work for selecting the most
+appropriate rule base among a set of competing alternatives, which best
+explains the data, by calculating the model evidence or marginal likelihood. We
+explain why this is an attractive alternative over simply minimizing a mean
+squared error metric of prediction and show the validity of the proposition
+using synthetic examples and a real world case study in the financial services
+sector.
+",0,0,0,1,0,0
+12298,Position-sensitive propagation of information on social media using social physics approach,"  The excitement and convergence of tweets on specific topics are well studied.
+However, by utilizing the position information of Tweet, it is also possible to
+analyze the position-sensitive tweet. In this research, we focus on bomb
+terrorist attacks and propose a method for separately analyzing the number of
+tweets at the place where the incident occurred, nearby, and far. We made
+measurements of position-sensitive tweets and suggested a theory to explain it.
+This theory is an extension of the mathematical model of the hit phenomenon.
+",1,1,0,0,0,0
+12299,The application of Monte Carlo methods for learning generalized linear model,"  Monte Carlo method is a broad class of computational algorithms that rely on
+repeated random sampling to obtain numerical results. They are often used in
+physical and mathematical problems and are most useful when it is difficult or
+impossible to use other mathematical methods. Basically, many statisticians
+have been increasingly drawn to Monte Carlo method in three distinct problem
+classes: optimization, numerical integration, and generating draws from a
+probability distribution. In this paper, we will introduce the Monte Carlo
+method for calculating coefficients in Generalized Linear Model(GLM),
+especially for Logistic Regression. Our main methods are Metropolis
+Hastings(MH) Algorithms and Stochastic Approximation in Monte Carlo
+Computation(SAMC). For comparison, we also get results automatically using MLE
+method in R software.
+",0,0,0,1,0,0
+12300,Language Bootstrapping: Learning Word Meanings From Perception-Action Association,"  We address the problem of bootstrapping language acquisition for an
+artificial system similarly to what is observed in experiments with human
+infants. Our method works by associating meanings to words in manipulation
+tasks, as a robot interacts with objects and listens to verbal descriptions of
+the interactions. The model is based on an affordance network, i.e., a mapping
+between robot actions, robot perceptions, and the perceived effects of these
+actions upon objects. We extend the affordance model to incorporate spoken
+words, which allows us to ground the verbal symbols to the execution of actions
+and the perception of the environment. The model takes verbal descriptions of a
+task as the input and uses temporal co-occurrence to create links between
+speech utterances and the involved objects, actions, and effects. We show that
+the robot is able form useful word-to-meaning associations, even without
+considering grammatical structure in the learning process and in the presence
+of recognition errors. These word-to-meaning associations are embedded in the
+robot's own understanding of its actions. Thus, they can be directly used to
+instruct the robot to perform tasks and also allow to incorporate context in
+the speech recognition task. We believe that the encouraging results with our
+approach may afford robots with a capacity to acquire language descriptors in
+their operation's environment as well as to shed some light as to how this
+challenging process develops with human infants.
+",1,0,0,1,0,0
+12301,Parallel implementation of the coupled harmonic oscillator,"  This article presents the parallel implementation of the coupled harmonic
+oscillator. From the analytical solution of the coupled harmonic oscillator,
+the design parameters are obtained. After that, a numerical integration of the
+system with MATLAB, which is used as a tool of benchmark evaluation, is
+performed. Next, parallel implementation is performed using a well-known
+approach like OpenMP and WinAPI. Taking into account the errors of basic
+parameters of the simulated process, the generated oscillations of the proposed
+parallel realization are almost identical to the actual solution of the
+harmonic oscillator model. Test ways to optimize the parallel architecture of
+computing processes for software implementations of the considered application
+is carried out. The developed model is used to study a fixed priority
+scheduling algorithm for real-time parallel threads execution. The proposed
+parallel implementation of the considered dynamic system has an independent
+value and can be considered as a test for determining the characteristics of
+multi-core systems for time-critical simulation problems. Keywords: Harmonic
+oscillator, model, SMP, parallel programming, OpenMP;
+",1,0,0,0,0,0
+12302,TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China,"  TiEV is an autonomous driving platform implemented by Tongji University of
+China. The vehicle is drive-by-wire and is fully powered by electricity. We
+devised the software system of TiEV from scratch, which is capable of driving
+the vehicle autonomously in urban paths as well as on fast express roads. We
+describe our whole system, especially novel modules of probabilistic perception
+fusion, incremental mapping, the 1st and the 2nd planning and the overall
+safety concern. TiEV finished 2016 and 2017 Intelligent Vehicle Future
+Challenge of China held at Changshu. We show our experiences on the development
+of autonomous vehicles and future trends.
+",1,0,0,0,0,0
+12303,Towards Decoding as Continuous Optimization in Neural Machine Translation,"  We propose a novel decoding approach for neural machine translation (NMT)
+based on continuous optimisation. We convert decoding - basically a discrete
+optimization problem - into a continuous optimization problem. The resulting
+constrained continuous optimisation problem is then tackled using
+gradient-based methods. Our powerful decoding framework enables decoding
+intractable models such as the intersection of left-to-right and right-to-left
+(bidirectional) as well as source-to-target and target-to-source (bilingual)
+NMT models. Our empirical results show that our decoding framework is
+effective, and leads to substantial improvements in translations generated from
+the intersected models where the typical greedy or beam search is not feasible.
+We also compare our framework against reranking, and analyse its advantages and
+disadvantages.
+",1,0,0,0,0,0
+12304,A tail cone version of the Halpern-Läuchli theorem at a large cardinal,"  The classical Halpern-Läuchli theorem states that for any finite coloring
+of a finite product of finitely branching perfect trees of height $\omega$,
+there exist strong subtrees sharing the same level set such that tuples
+consisting of elements lying on the same level get the same color. Relative to
+large cardinals, we establish the consistency of a tail cone version of the
+Halpern-Läuchli theorem at large cardinal, which, roughly speaking, deals
+with many colorings simultaneously and diagonally. Among other applications, we
+generalize a polarized partition relation on rational numbers due to Laver and
+Galvin to one on linear orders of larger saturation.
+",0,0,1,0,0,0
+12305,Over the Air Deep Learning Based Radio Signal Classification,"  We conduct an in depth study on the performance of deep learning based radio
+signal classification for radio communications signals. We consider a rigorous
+baseline method using higher order moments and strong boosted gradient tree
+classification and compare performance between the two approaches across a
+range of configurations and channel impairments. We consider the effects of
+carrier frequency offset, symbol rate, and multi-path fading in simulation and
+conduct over-the-air measurement of radio classification performance in the lab
+using software radios and compare performance and training strategies for both.
+Finally we conclude with a discussion of remaining problems, and design
+considerations for using such techniques.
+",1,0,0,0,0,0
+12306,On conditional parity as a notion of non-discrimination in machine learning,"  We identify conditional parity as a general notion of non-discrimination in
+machine learning. In fact, several recently proposed notions of
+non-discrimination, including a few counterfactual notions, are instances of
+conditional parity. We show that conditional parity is amenable to statistical
+analysis by studying randomization as a general mechanism for achieving
+conditional parity and a kernel-based test of conditional parity.
+",1,0,0,1,0,0
+12307,A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints,"  We consider caching in cellular networks in which each base station is
+equipped with a cache that can store a limited number of files. The popularity
+of the files is known and the goal is to place files in the caches such that
+the probability that a user at an arbitrary location in the plane will find the
+file that she requires in one of the covering caches is maximized.
+We develop distributed asynchronous algorithms for deciding which contents to
+store in which cache. Such cooperative algorithms require communication only
+between caches with overlapping coverage areas and can operate in asynchronous
+manner. The development of the algorithms is principally based on an
+observation that the problem can be viewed as a potential game. Our basic
+algorithm is derived from the best response dynamics. We demonstrate that the
+complexity of each best response step is independent of the number of files,
+linear in the cache capacity and linear in the maximum number of base stations
+that cover a certain area. Then, we show that the overall algorithm complexity
+for a discrete cache placement is polynomial in both network size and catalog
+size. In practical examples, the algorithm converges in just a few iterations.
+Also, in most cases of interest, the basic algorithm finds the best Nash
+equilibrium corresponding to the global optimum. We provide two extensions of
+our basic algorithm based on stochastic and deterministic simulated annealing
+which find the global optimum.
+Finally, we demonstrate the hit probability evolution on real and synthetic
+networks numerically and show that our distributed caching algorithm performs
+significantly better than storing the most popular content, probabilistic
+content placement policy and Multi-LRU caching policies.
+",1,0,0,0,0,0
+12308,"Linear, Second order and Unconditionally Energy Stable schemes for a phase-field moving contact line Model","  In this paper, we consider the numerical approximations for solving a
+hydrodynamics coupled phase field model consisting of incompressible
+Navier-Stokes equations with generalized Navier boundary conditions, and the
+Cahn-Hilliard equation with dynamic moving contact line boundary conditions.
+The main challenging issue for solving this model numerically is the time
+marching problem, i.e., how to develop suitable higher order temporal schemes
+while preserving the unconditional energy stability at the discrete level. We
+solve this issue by developing two linear, second-order schemes based on the
+""Invariant Energy Quadratization"" method for the nonlinear terms in the bulk
+and on the boundary, the projection method for the Navier-Stokes equations, and
+a subtle implicit-explicit treatment for the stress and convective terms.
+Rigorous proofs of the well-posedness of the linear system and the
+unconditional energy stabilities are provided. A spectral-Galerkin spatial
+discretization is implemented and various numerical results are presented to
+verify the second order accuracy and the efficiency of the proposed schemes.
+",0,0,1,0,0,0
+12309,On Security and Sparsity of Linear Classifiers for Adversarial Settings,"  Machine-learning techniques are widely used in security-related applications,
+like spam and malware detection. However, in such settings, they have been
+shown to be vulnerable to adversarial attacks, including the deliberate
+manipulation of data at test time to evade detection. In this work, we focus on
+the vulnerability of linear classifiers to evasion attacks. This can be
+considered a relevant problem, as linear classifiers have been increasingly
+used in embedded systems and mobile devices for their low processing time and
+memory requirements. We exploit recent findings in robust optimization to
+investigate the link between regularization and security of linear classifiers,
+depending on the type of attack. We also analyze the relationship between the
+sparsity of feature weights, which is desirable for reducing processing cost,
+and the security of linear classifiers. We further propose a novel octagonal
+regularizer that allows us to achieve a proper trade-off between them. Finally,
+we empirically show how this regularizer can improve classifier security and
+sparsity in real-world application examples including spam and malware
+detection.
+",1,0,0,0,0,0
+12310,Sharp-interface limits of a phase-field model with a generalized Navier slip boundary condition for moving contact lines,"  The sharp-interface limits of a phase-field model with a generalized Navier
+slip boundary condition for moving contact line problem are studied by
+asymptotic analysis and numerical simulations. The effects of the {mobility}
+number as well as a phenomenological relaxation parameter in the boundary
+condition are considered. In asymptotic analysis, we focus on the case that the
+{mobility} number is the same order of the Cahn number and derive the
+sharp-interface limits for several setups of the boundary relaxation parameter.
+It is shown that the sharp interface limit of the phase field model is the
+standard two-phase incompressible Navier-Stokes equations coupled with several
+different slip boundary conditions. Numerical results are consistent with the
+analysis results and also illustrate the different convergence rates of the
+sharp-interface limits for different scalings of the two parameters.
+",0,1,0,0,0,0
+12311,Notes on Discrete Compound Poisson Point Process and Its Concentration Inequalities,"  The first part of this notes provides a new characterization for discrete
+compound Poisson point process (proposed by {Acz{é}l}
+[Acta~Math.~Hungar.~3(3)(1952), 219-224.]), which extends the characterization
+of Poisson point process given by Copeland and Regan [Ann.~Math.~(1936):
+357-362.]. Next, we derive some concentration inequalities for discrete
+compound Poisson random variable and discrete compound Poisson point process
+(Poisson and negative binomial are the special cases). These concentration
+inequalities are potentially useful. In high-dimensional negative binomial
+regression with weighted Lasso penalty, we give the application that KKT
+conditions of penalized likelihood holds with high probability.
+",0,0,1,1,0,0
+12312,Characteristic cycles of highest weight Harish-Chandra modules,"  Characteristic cycles and leading term cycles of irreducible highest weight
+Harish-Chandra modules of regular integral infinitesimal character are
+determined. In the simply laced cases they are irreducible, but in the
+nonsimply laced cases they are more complicated.
+",0,0,1,0,0,0
+12313,Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model,"  We propose a robust implementation of the Nerlove--Arrow model using a
+Bayesian structural time series model to explain the relationship between
+advertising expenditures of a country-wide fast-food franchise network with its
+weekly sales. Thanks to the flexibility and modularity of the model, it is well
+suited to generalization to other markets or situations. Its Bayesian nature
+facilitates incorporating \emph{a priori} information (the manager's views),
+which can be updated with relevant data. This aspect of the model will be used
+to present a strategy of budget scheduling across time and channels.
+",0,0,0,1,0,1
+12314,Anytime Exact Belief Propagation,"  Statistical Relational Models and, more recently, Probabilistic Programming,
+have been making strides towards an integration of logic and probabilistic
+reasoning. A natural expectation for this project is that a probabilistic logic
+reasoning algorithm reduces to a logic reasoning algorithm when provided a
+model that only involves 0-1 probabilities, exhibiting all the advantages of
+logic reasoning such as short-circuiting, intelligibility, and the ability to
+provide proof trees for a query answer. In fact, we can take this further and
+require that these characteristics be present even for probabilistic models
+with probabilities \emph{near} 0 and 1, with graceful degradation as the model
+becomes more uncertain. We also seek inference that has amortized constant time
+complexity on a model's size (even if still exponential in the induced width of
+a more directly relevant portion of it) so that it can be applied to huge
+knowledge bases of which only a relatively small portion is relevant to typical
+queries. We believe that, among the probabilistic reasoning algorithms, Belief
+Propagation is the most similar to logic reasoning: messages are propagated
+among neighboring variables, and the paths of message-passing are similar to
+proof trees. However, Belief Propagation is either only applicable to tree
+models, or approximate (and without guarantees) for precision and convergence.
+In this paper we present work in progress on an Anytime Exact Belief
+Propagation algorithm that is very similar to Belief Propagation but is exact
+even for graphical models with cycles, while exhibiting soft short-circuiting,
+amortized constant time complexity in the model size, and which can provide
+probabilistic proof trees.
+",1,0,0,0,0,0
+12315,Appropriate conditions to realize a $p$-wave superfluid state starting from a spin-orbit coupled $s$-wave superfluid Fermi gas,"  We theoretically investigate a spin-orbit coupled $s$-wave superfluid Fermi
+gas, to examine the time evolution of the system, after an $s$-wave pairing
+interaction is replaced by a $p$-wave one at $t=0$. In our recent paper, we
+proposed that this manipulation may realize a $p$-wave superfluid Fermi gas,
+because the $p$-wave pair amplitude that is induced in the $s$-wave superfluid
+state by a parity-broken antisymmetric spin-orbit interaction gives a
+non-vanishing $p$-wave superfluid order parameter, immediately after the
+$p$-wave interaction is turned on. In this paper, using a time-dependent
+Bogoliubov-de Gennes theory, we assess this idea under various conditions with
+respect to the $s$-wave and $p$-wave interaction strengths, as well as the
+spin-orbit coupling strength. From these, we clarify that the momentum
+distribution of Fermi atoms in the initial $s$-wave state ($t<0$) is a key to
+produce a large $p$-wave superfluid order parameter. Since the realization of a
+$p$-wave superfluid state is one of the most exciting and difficult challenges
+in cold Fermi gas physics, our results may provide a possible way to accomplish
+this.
+",0,1,0,0,0,0
+12316,On the vanishing of self extensions over Cohen-Macaulay local rings,"  The celebrated Auslander-Reiten Conjecture, on the vanishing of self
+extensions of a module, is one of the long-standing conjectures in ring theory.
+Although it is still open, there are several results in the literature that
+establish the conjecture over Gorenstein rings under certain conditions. The
+purpose of this article is to obtain extensions of such results over
+Cohen-Macaulay local rings that admit canonical modules. In particular, our
+main result recovers theorems of Araya, and Ono and Yoshino simultaneously.
+",0,0,1,0,0,0
+12317,Finite Temperature Phase Diagrams of a Two-band Model of Superconductivity,"  We explore the temperature effects in the superconducting phases of a
+hybridized two-band system. We show that for zero hybridization between the
+bands, there are two different critical temperatures. However, for any finite
+hybridization there are only one critical temperature at which the two gaps
+vanish simultaneously. We construct the phase diagrams of the critical
+temperature versus hybridization parameter $\alpha$ and critical temperature
+versus critical chemical potential asymmetry $\delta \mu$ between the bands,
+identifying the superconductor and normal phases in the system. We find an
+interesting reentrant behavior in the superconducting phase as the parameters
+$\alpha$ or $\delta \mu$, which drive the phase transitions, increase. We also
+find that for optimal values of both $\alpha$ and $\delta \mu$ there is a
+significant enhancement of the critical temperature of the model.
+",0,1,0,0,0,0
+12318,Detecting Multiple Change Points Using Adaptive Regression Splines with Application to Neural Recordings,"  Time series, as frequently the case in neuroscience, are rarely stationary,
+but often exhibit abrupt changes due to attractor transitions or bifurcations
+in the dynamical systems producing them. A plethora of methods for detecting
+such change points in time series statistics have been developed over the
+years, in addition to test criteria to evaluate their significance. Issues to
+consider when developing change point analysis methods include computational
+demands, difficulties arising from either limited amount of data or a large
+number of covariates, and arriving at statistical tests with sufficient power
+to detect as many changes as contained in potentially high-dimensional time
+series. Here, a general method called Paired Adaptive Regressors for Cumulative
+Sum is developed for detecting multiple change points in the mean of
+multivariate time series. The method's advantages over alternative approaches
+are demonstrated through a series of simulation experiments. This is followed
+by a real data application to neural recordings from rat medial prefrontal
+cortex during learning. Finally, the method's flexibility to incorporate useful
+features from state-of-the-art change point detection techniques is discussed,
+along with potential drawbacks and suggestions to remedy them.
+",0,0,0,0,1,0
+12319,The Automorphism Group of Hall's Universal Group,"  We study the automorphism group of Hall's universal locally finite group $H$.
+We show that in $Aut(H)$ every subgroup of index $< 2^\omega$ lies between the
+pointwise and the setwise stabilizer of a unique finite subgroup $A$ of $H$,
+and use this to prove that $Aut(H)$ is complete. We further show that $Inn(H)$
+is the largest locally finite normal subgroup of $Aut(H)$. Finally, we observe
+that from the work of [Sh:312] it follows that for every countable locally
+finite $G$ there exists $G \cong G' \leq H$ such that every $f \in Aut(G')$
+extends to an $\hat{f} \in Aut(H)$ in such a way that $f \mapsto \hat{f}$
+embeds $Aut(G')$ into $Aut(H)$. In particular, we solve the three open
+questions of Hickin on $Aut(H)$ from [3], and give a partial answer to Question
+VI.5 of Kegel and Wehrfritz from [6].
+",0,0,1,0,0,0
+12320,On Atiyah-Singer and Atiyah-Bott for finite abstract simplicial complexes,"  A linear or multi-linear valuation on a finite abstract simplicial complex
+can be expressed as an analytic index dim(ker(D)) -dim(ker(D^*)) of a
+differential complex D:E -> F. In the discrete, a complex D can be called
+elliptic if a McKean-Singer spectral symmetry applies as this implies
+str(exp(-t D^2)) is t-independent. In that case, the analytic index of D is the
+sum of (-1)^k b_k(D), where b_k(D) is the k'th Betti number, which by Hodge is
+the nullity of the (k+1)'th block of the Hodge operator L=D^2. It can also be
+written as a topological index summing K(v) over the set of zero-dimensional
+simplices in G and where K is an Euler type curvature defined by G and D. This
+can be interpreted as a Atiyah-Singer type correspondence between analytic and
+topological index. Examples are the de Rham differential complex for the Euler
+characteristic X(G) or the connection differential complex for Wu
+characteristic w_k(G). Given an endomorphism T of an elliptic complex, the
+Lefschetz number X(T,G,D) is defined as the super trace of T acting on
+cohomology defined by E. It is equal to the sum i(v) over V which are contained
+in fixed simplices of T, and i is a Brouwer type index. This Atiyah-Bott result
+generalizes the Brouwer-Lefschetz fixed point theorem for an endomorphism of
+the simplicial complex G. In both the static and dynamic setting, the proof is
+done by heat deforming the Koopman operator U(T) to get the cohomological
+picture str(exp(-t D^2) U(T)) in the limit t to infinity and then use Hodge,
+and then by applying a discrete gradient flow to the simplex data defining the
+valuation to push str(U(T)) to V, getting curvature K(v) or the Brouwer type
+index i(v).
+",1,0,1,0,0,0
+12321,Evolution of macromolecular structure: a 'double tale' of biological accretion,"  The evolution of structure in biology is driven by accretion and change.
+Accretion brings together disparate parts to form bigger wholes. Change
+provides opportunities for growth and innovation. Here we review patterns and
+processes that are responsible for a 'double tale' of evolutionary accretion at
+various levels of complexity, from proteins and nucleic acids to high-rise
+building structures in cities. Parts are at first weakly linked and associate
+variously. As they diversify, they compete with each other and are selected for
+performance. The emerging interactions constrain their structure and
+associations. This causes parts to self-organize into modules with tight
+linkage. In a second phase, variants of the modules evolve and become new parts
+for a new generative cycle of higher-level organization. Evolutionary genomics
+and network biology support the 'double tale' of structural module creation and
+validate an evolutionary principle of maximum abundance that drives the gain
+and loss of modules.
+",0,0,0,0,1,0
+12322,An estimate of the root mean square error incurred when approximating an $f \in L^2({\mathbb{R}})$ by a partial sum of its Hermite series,"  Let $f$ be a band-limited function in $L^2({\mathbb{R}})$. Fix $T >0$ and
+suppose $f^{\prime}$ exists and is integrable on $[-T, T]$. This paper gives a
+concrete estimate of the error incurred when approximating $f$ in the root mean
+square by a partial sum of its Hermite series.
+Specifically, we show, for $K=2n, \quad n \in Z_+,$
+$$
+\left[\frac{1}{2T}\int_{-T}^T[f(t)-(S_Kf)(t)]^2dt\right]^{1/2}\leq
+\left(1+\frac 1K\right)\left(\left[ \frac{1}{2T}\int_{|t|>
+T}f(t)^2dt\right]^{1/2} +\left[\frac{1}{2T} \int_{|\omega|>N}|\hat
+f(\omega)|^2d\omega\right]^{1/2} \right)
++\frac{1}{K}\left[\frac{1}{2T}\int_{|t|\leq T}f_N(t)^2dt\right]^{1/2}
++\frac{1}{\pi}\left(1+\frac{1}{2K}\right)S_a(K,T), $$ in which $S_Kf$ is the
+$K$-th partial sum of the Hermite series of $f, \hat f $ is the Fourier
+transform of $f$, $\displaystyle{N=\frac{\sqrt{2K+1}+% \sqrt{2K+3}}{2}}$ and
+$f_N=(\hat f
+\chi_{(-N,N)})^\vee(t)=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{\sin
+(N(t-s))}{t-s}f(s)ds$. An explicit upper bound is obtained for $S_{a}(K,T)$.
+",0,0,1,0,0,0
+12323,Learning Topic-Sensitive Word Representations,"  Distributed word representations are widely used for modeling words in NLP
+tasks. Most of the existing models generate one representation per word and do
+not consider different meanings of a word. We present two approaches to learn
+multiple topic-sensitive representations per word by using Hierarchical
+Dirichlet Process. We observe that by modeling topics and integrating topic
+distributions for each document we obtain representations that are able to
+distinguish between different meanings of a given word. Our models yield
+statistically significant improvements for the lexical substitution task
+indicating that commonly used single word representations, even when combined
+with contextual information, are insufficient for this task.
+",1,0,0,0,0,0
+12324,Celestial Walk: A Terminating Oblivious Walk for Convex Subdivisions,"  We present a new oblivious walking strategy for convex subdivisions. Our walk
+is faster than the straight walk and more generally applicable than the
+visibility walk. To prove termination of our walk we use a novel monotonically
+decreasing distance measure.
+",1,0,0,0,0,0
+12325,Kernel k-Groups via Hartigan's Method,"  Energy statistics was proposed by Székely in the 80's inspired by
+Newton's gravitational potential in classical mechanics, and it provides a
+model-free hypothesis test for equality of distributions. In its original form,
+energy statistics was formulated in Euclidean spaces. More recently, it was
+generalized to metric spaces of negative type. In this paper, we consider a
+formulation for the clustering problem using a weighted version of energy
+statistics in spaces of negative type. We show that this approach leads to a
+quadratically constrained quadratic program in the associated kernel space,
+establishing connections with graph partitioning problems and kernel methods in
+unsupervised machine learning. To find local solutions of such an optimization
+problem, we propose an extension of Hartigan's method to kernel spaces. Our
+method has the same computational cost as kernel k-means algorithm, which is
+based on Lloyd's heuristic, but our numerical results show an improved
+performance, especially in high dimensions.
+",1,0,1,1,0,0
+12326,Estimators of the correlation coefficient in the bivariate exponential distribution,"  A finite-support constraint on the parameter space is used to derive a lower
+bound on the error of an estimator of the correlation coefficient in the
+bivariate exponential distribution. The bound is then exploited to examine
+optimality of three estimators, each being a nonlinear function of moments of
+exponential or Rayleigh observables. The estimator based on a measure of cosine
+similarity is shown to be highly efficient for values of the correlation
+coefficient greater than 0.35; for smaller values, however, it is the
+transformed Pearson correlation coefficient that exhibits errors closer to the
+derived bound.
+",0,0,0,1,0,0
+12327,A theoretical framework for retinal computations: insights from textbook knowledge,"  Neural circuits in the retina divide the incoming visual scene into more than
+a dozen distinct representations that are sent on to central brain areas, such
+as the lateral geniculate nucleus and the superior colliculus. The retina can
+be viewed as a parallel image processor made of a multitude of small
+computational devices. Neural circuits of the retina are constituted by various
+cell types that separate the incoming visual information in different channels.
+Visual information is processed by retinal neural circuits and several
+computations are performed extracting distinct features from the visual scene.
+The aim of this article is to understand the computational basis involved in
+processing visual information which finally leads to several feature detectors.
+Therefore, the elements that form the basis of retinal computations will be
+explored by explaining how oscillators can lead to a final output with
+computational meaning. Linear versus nonlinear systems will be presented and
+the retina will be placed in the context of a nonlinear system. Finally,
+simulations will be presented exploring the concept of the retina as a
+nonlinear system which can perform understandable computations converting a
+known input into a predictable output.
+",0,0,0,0,1,0
+12328,Learning to Associate Words and Images Using a Large-scale Graph,"  We develop an approach for unsupervised learning of associations between
+co-occurring perceptual events using a large graph. We applied this approach to
+successfully solve the image captcha of China's railroad system. The approach
+is based on the principle of suspicious coincidence. In this particular
+problem, a user is presented with a deformed picture of a Chinese phrase and
+eight low-resolution images. They must quickly select the relevant images in
+order to purchase their train tickets. This problem presents several
+challenges: (1) the teaching labels for both the Chinese phrases and the images
+were not available for supervised learning, (2) no pre-trained deep
+convolutional neural networks are available for recognizing these Chinese
+phrases or the presented images, and (3) each captcha must be solved within a
+few seconds. We collected 2.6 million captchas, with 2.6 million deformed
+Chinese phrases and over 21 million images. From these data, we constructed an
+association graph, composed of over 6 million vertices, and linked these
+vertices based on co-occurrence information and feature similarity between
+pairs of images. We then trained a deep convolutional neural network to learn a
+projection of the Chinese phrases onto a 230-dimensional latent space. Using
+label propagation, we computed the likelihood of each of the eight images
+conditioned on the latent space projection of the deformed phrase for each
+captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on
+average. Our work, in answering this practical challenge, illustrates the power
+of this class of unsupervised association learning techniques, which may be
+related to the brain's general strategy for associating language stimuli with
+visual objects on the principle of suspicious coincidence.
+",1,0,0,0,0,0
+12329,A dichotomy theorem for nonuniform CSPs,"  In this paper we prove the Dichotomy Conjecture on the complexity of
+nonuniform constraint satisfaction problems posed by Feder and Vardi.
+",1,0,0,0,0,0
+12330,Transition from Weak Wave Turbulence to Soliton-Gas,"  We report an experimental investigation of the effect of finite depth on the
+statistical properties of wave turbulence at the surface of water in the
+gravity-capillary range. We tune the wave dispersion and the level of
+nonlinearity by modifying the depth of water and the forcing respectively. We
+use space-time resolved profilometry to reconstruct the deformed surface of
+water. When decreasing the water depth, we observe a drastic transition between
+weak turbulence at the weakest forcing and a solitonic regime at stronger
+forcing. We characterize the transition between both states by studying their
+Fourier Spectra. We also study the efficiency of energy transfer in the weak
+turbulence regime. We report a loss of efficiency of angular transfer as the
+dispersion of the wave is reduced until the system bifurcates into the
+solitonic regime.
+",0,1,0,0,0,0
+12331,Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model,"  A model-based approach to forecasting chaotic dynamical systems utilizes
+knowledge of the physical processes governing the dynamics to build an
+approximate mathematical model of the system. In contrast, machine learning
+techniques have demonstrated promising results for forecasting chaotic systems
+purely from past time series measurements of system state variables (training
+data), without prior knowledge of the system dynamics. The motivation for this
+paper is the potential of machine learning for filling in the gaps in our
+underlying mechanistic knowledge that cause widely-used knowledge-based models
+to be inaccurate. Thus we here propose a general method that leverages the
+advantages of these two approaches by combining a knowledge-based model and a
+machine learning technique to build a hybrid forecasting scheme. Potential
+applications for such an approach are numerous (e.g., improving weather
+forecasting). We demonstrate and test the utility of this approach using a
+particular illustrative version of a machine learning known as reservoir
+computing, and we apply the resulting hybrid forecaster to a low-dimensional
+chaotic system, as well as to a high-dimensional spatiotemporal chaotic system.
+These tests yield extremely promising results in that our hybrid technique is
+able to accurately predict for a much longer period of time than either its
+machine-learning component or its model-based component alone.
+",0,0,0,1,0,0
+12332,Field-free perpendicular magnetization switching through domain wall motion in Pt/Co/Cr racetracks by spin orbit torques with the assistance of accompanying Joule heating effect,"  Heavy metal/ferromagnetic layers with perpendicular magnetic anisotropy (PMA)
+have potential applications for high-density information storage in racetrack
+memories and nonvolatile magnetic random access memories. Writing and erasing
+of information in these devices are carried out by domain wall (DW) motion and
+deterministic magnetization switching via electric current generated spin
+orbital torques (SOTs) with an assistance of in-plane bias field to break the
+symmetry. Improvements in energy efficiency could be obtained when the
+switching of perpendicular magnetization is controlled by an electric current
+generated SOTs without the in-plane bias fields. Here, we report on reversible
+electric-current-driven magnetization switching through DW motion in Pt/Co/Cr
+trilayers with PMA at room temperature due to the formation of homochiral
+Neel-type domain, in which an in-plane effective Dzyaloshinskii-Moriya
+interaction field exists. Fully deterministic magnetic magnetization switching
+in this trilayers is based on the enhancement of SOTs from a dedicated design
+of Pt/Co/Cr structures with two heavy metals Pt and Cr which show the opposite
+sign of spin Hall angles. We also demonstrated that the simultaneously
+accompanying Joule heating effect also plays a key role for field-free
+magnetization switching through the decrease of the propagation field.
+",0,1,0,0,0,0
+12333,On Generalizing Decidable Standard Prefix Classes of First-Order Logic,"  Recently, the separated fragment (SF) of first-order logic has been
+introduced. Its defining principle is that universally and existentially
+quantified variables may not occur together in atoms. SF properly generalizes
+both the Bernays-Schönfinkel-Ramsey (BSR) fragment and the relational monadic
+fragment. In this paper the restrictions on variable occurrences in SF
+sentences are relaxed such that universally and existentially quantified
+variables may occur together in the same atom under certain conditions. Still,
+satisfiability can be decided. This result is established in two ways: firstly,
+by an effective equivalence-preserving translation into the BSR fragment, and,
+secondly, by a model-theoretic argument.
+Slight modifications to the described concepts facilitate the definition of
+other decidable classes of first-order sentences. The paper presents a second
+fragment which is novel, has a decidable satisfiability problem, and properly
+contains the Ackermann fragment and---once more---the relational monadic
+fragment. The definition is again characterized by restrictions on the
+occurrences of variables in atoms. More precisely, after certain
+transformations, Skolemization yields only unary functions and constants, and
+every atom contains at most one universally quantified variable. An effective
+satisfiability-preserving translation into the monadic fragment is devised and
+employed to prove decidability of the associated satisfiability problem.
+",1,0,0,0,0,0
+12334,"Poisson traces, D-modules, and symplectic resolutions","  We survey the theory of Poisson traces (or zeroth Poisson homology) developed
+by the authors in a series of recent papers. The goal is to understand this
+subtle invariant of (singular) Poisson varieties, conditions for it to be
+finite-dimensional, its relationship to the geometry and topology of symplectic
+resolutions, and its applications to quantizations. The main technique is the
+study of a canonical D-module on the variety. In the case the variety has
+finitely many symplectic leaves (such as for symplectic singularities and
+Hamiltonian reductions of symplectic vector spaces by reductive groups), the
+D-module is holonomic, and hence the space of Poisson traces is
+finite-dimensional. As an application, there are finitely many irreducible
+finite-dimensional representations of every quantization of the variety.
+Conjecturally, the D-module is the pushforward of the canonical D-module under
+every symplectic resolution of singularities, which implies that the space of
+Poisson traces is dual to the top cohomology of the resolution. We explain many
+examples where the conjecture is proved, such as symmetric powers of du Val
+singularities and symplectic surfaces and Slodowy slices in the nilpotent cone
+of a semisimple Lie algebra. We compute the D-module in the case of surfaces
+with isolated singularities, and show it is not always semisimple. We also
+explain generalizations to arbitrary Lie algebras of vector fields, connections
+to the Bernstein-Sato polynomial, relations to two-variable special polynomials
+such as Kostka polynomials and Tutte polynomials, and a conjectural
+relationship with deformations of symplectic resolutions. In the appendix we
+give a brief recollection of the theory of D-modules on singular varieties that
+we require.
+",0,0,1,0,0,0
+12335,Nonequilibrium transport and Electron-Glass effects in thin GexTe films,"  We report on results of nonequilibrium transport measurements made on thin
+films of germanium-telluride (Ge_xTe) at cryogenic temperatures. Owing to a
+rather large deviation from stoichiometry (app. 10% of Ge vacancies), these
+films exhibit p-type conductivity with carrier-concentration N>10^20cm^(-3) and
+can be made either in the diffusive or strongly-localized regime by a judicious
+choice of preparation and post-treatment conditions. In both regimes the system
+shows persistent photoconductivity following excitation by a brief exposure to
+infrared radiation. Persistent photoconductivity is also observed in GexTe
+samples alloyed with Mn. However, in both Ge_xTe and GeMn_xTe_y the effect is
+much weaker than that observable in GeSb_xTe_y alloys suggesting that antimony
+plays an important role in the phenomenon. Structural studies of these films
+reveal an unusual degree of texture that is rarely realized in
+strongly-disordered systems with high carrier-concentrations.
+Anderson-localized samples of Ge_xTe exhibit non-ergodic transport which are
+characteristic of intrinsic electron-glasses, including a well developed
+memory-dip and slow relaxation of the excess conductance created in the excited
+state. These results support the conjecture that electron-glass effects with
+inherently long relaxation times is a generic property of all
+Anderson-localized systems with large carrier-concentration.
+",0,1,0,0,0,0
+12336,Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations,"  When performing a time series analysis of continuous data, for example from
+climate or environmental problems, the assumption that the process is Gaussian
+is often violated. Therefore, we introduce two non-Gaussian autoregressive time
+series models that are able to fit skewed and heavy-tailed time series data.
+Our two models are based on the Tukey g-and-h transformation. We discuss
+parameter estimation, order selection, and forecasting procedures for our
+models and examine their performances in a simulation study. We demonstrate the
+usefulness of our models by applying them to two sets of wind speed data.
+",0,0,0,1,0,0
+12337,Frequency responses of the K-Rb-$^{21}$Ne co-magnetometer,"  The frequency responses of the K-Rb-$^{21}$Ne co-magnetometer to magnetic
+field and exotic spin dependent forces are experimentally studied and simulated
+in this paper. Both the relationship between the output amplitude, the phase
+shift and frequencies are studied. The responses of magnetic field are
+experimentally investigated. Due to a lack of input methods, others are
+numerically simulated.
+",0,1,0,0,0,0
+12338,Some new bounds of placement delivery arrays,"  Coded caching scheme is a technique which reduce the load during peak traffic
+times in a wireless network system. Placement delivery array (PDA in short) was
+first introduced by Yan et al.. It can be used to design coded caching scheme.
+In this paper, we prove some lower bounds of PDA on the element and some lower
+bounds of PDA on the column. We also give some constructions for optimal PDA.
+",1,0,0,0,0,0
+12339,Itineraries for Inverse Limits of Tent Maps: a Backward View,"  Previously published admissibility conditions for an element of
+$\{0,1\}^{\mathbb{Z}}$ to be the itinerary of a point of the inverse limit of a
+tent map are expressed in terms of forward orbits. We give necessary and
+sufficient conditions in terms of backward orbits, which is more natural for
+inverse limits. These backward admissibility conditions are not symmetric
+versions of the forward ones: in particular, the maximum backward itinerary
+which can be realised by a tent map mode locks on intervals of kneading
+sequences.
+",0,0,1,0,0,0
+12340,EmbedInsight: Automated Grading of Embedded Systems Assignments,"  Grading in embedded systems courses typically requires a face-to-face
+appointment between the student and the instructor because of experimental
+setups that are only available in laboratory facilities. Such a manual grading
+process is an impediment to both students and instructors. Students have to
+wait for several days to get feedback, and instructors may spend valuable time
+evaluating trivial aspects of the assignment. As seen with software courses, an
+automated grading system can significantly improve the insights available to
+the instructor and encourage students to learn quickly with iterative testing.
+We have designed and implemented EmbedInsight, an automated grading system for
+embedded system courses that accommodates a wide variety of experimental setups
+and is scalable to MOOC-style courses. EmbedInsight employs a modular web
+services design that separates the user interface and the experimental setup
+that evaluates student assignments. We deployed and evaluated EmbedInsight for
+our university embedded systems course. We show that our system scales well to
+a large number of submissions, and students are satisfied with their overall
+experience.
+",1,0,0,0,0,0
+12341,On the $p'$-subgraph of the Young graph,"  Let $p$ be a prime number. In this article we study the restriction to
+$\mathfrak{S}_{n-1}$ of irreducible characters of degree coprime to $p$ of
+$\mathfrak{S}_n$. In particular, we study the combinatorial properties of the
+subgraph $\mathbb{Y}_{p'}$ of the Young graph $\mathbb{Y}$. This is an
+extension to odd primes of the work done by Ayyer, Prasad and Spallone for
+$p=2$.
+",0,0,1,0,0,0
+12342,Electronic origin of melting T-P curves of alkali metals with negative slope and minimum,"  Group I elements - alkali metals Li, Na, K, Rb and Cs - are examples of
+simple metals with one s electron in the valence band. Under pressure these
+elements display unusually complex structural behaviour transforming from
+close-packed to low symmetry open structures. Unexpectedly complex form was
+found for melting curves of alkalis under compression with initial increasing
+in accordance to Lindemann criterion and further decreasing to very low melting
+point. To understand complex and low symmetry structures in compressed alkalis
+a transformation of the electron energy levels was suggested which involves an
+overlap between the valence band and outer core electrons. Within the model of
+the Fermi sphere - Brillouin zone interaction one can understand the complex
+melting curve of alkalis.
+",0,1,0,0,0,0
+12343,Gene Shaving using influence function of a kernel method,"  Identifying significant subsets of the genes, gene shaving is an essential
+and challenging issue for biomedical research for a huge number of genes and
+the complex nature of biological networks,. Since positive definite kernel
+based methods on genomic information can improve the prediction of diseases, in
+this paper we proposed a new method, ""kernel gene shaving (kernel canonical
+correlation analysis (kernel CCA) based gene shaving). This problem is
+addressed using the influence function of the kernel CCA. To investigate the
+performance of the proposed method in a comparison of three popular gene
+selection methods (T-test, SAM and LIMMA), we were used extensive simulated and
+real microarray gene expression datasets. The performance measures AUC was
+computed for each of the methods. The achievement of the proposed method has
+improved than the three well-known gene selection methods. In real data
+analysis, the proposed method identified a subsets of $210$ genes out of $2000$
+genes. The network of these genes has significantly more interactions than
+expected, which indicates that they may function in a concerted effort on colon
+cancer.
+",0,0,0,1,1,0
+12344,Compressing Green's function using intermediate representation between imaginary-time and real-frequency domains,"  New model-independent compact representations of imaginary-time data are
+presented in terms of the intermediate representation (IR) of analytical
+continuation. This is motivated by a recent numerical finding by the authors
+[J. Otsuki et al., arXiv:1702.03056]. We demonstrate the efficiency of the IR
+through continuous-time quantum Monte Carlo calculations of an Anderson
+impurity model. We find that the IR yields a significantly compact form of
+various types of correlation functions. The present framework will provide
+general ways to boost the power of cutting-edge diagrammatic/quantum Monte
+Carlo treatments of many-body systems.
+",0,1,0,1,0,0
+12345,Machine Learning for the Geosciences: Challenges and Opportunities,"  Geosciences is a field of great societal relevance that requires solutions to
+several urgent problems facing our humanity and the planet. As geosciences
+enters the era of big data, machine learning (ML) -- that has been widely
+successful in commercial domains -- offers immense potential to contribute to
+problems in geosciences. However, problems in geosciences have several unique
+challenges that are seldom found in traditional applications, requiring novel
+problem formulations and methodologies in machine learning. This article
+introduces researchers in the machine learning (ML) community to these
+challenges offered by geoscience problems and the opportunities that exist for
+advancing both machine learning and geosciences. We first highlight typical
+sources of geoscience data and describe their properties that make it
+challenging to use traditional machine learning techniques. We then describe
+some of the common categories of geoscience problems where machine learning can
+play a role, and discuss some of the existing efforts and promising directions
+for methodological development in machine learning. We conclude by discussing
+some of the emerging research themes in machine learning that are applicable
+across all problems in the geosciences, and the importance of a deep
+collaboration between machine learning and geosciences for synergistic
+advancements in both disciplines.
+",1,1,0,0,0,0
+12346,Generalized singular value thresholding operator to affine matrix rank minimization problem,"  It is well known that the affine matrix rank minimization problem is NP-hard
+and all known algorithms for exactly solving it are doubly exponential in
+theory and in practice due to the combinational nature of the rank function. In
+this paper, a generalized singular value thresholding operator is generated to
+solve the affine matrix rank minimization problem. Numerical experiments show
+that our algorithm performs effectively in finding a low-rank matrix compared
+with some state-of-art methods.
+",0,0,1,0,0,0
+12347,Unifying the Brascamp-Lieb Inequality and the Entropy Power Inequality,"  The entropy power inequality (EPI) and the Brascamp-Lieb inequality (BLI) can
+be viewed as information inequalities concerning entropies of linear
+transformations of random variables. The EPI provides lower bounds for the
+entropy of linear transformations of random vectors with independent
+components. The BLI, on the other hand, provides upper bounds on the entropy of
+a random vector in terms of the entropies of its linear transformations. In
+this paper, we present a new entropy inequality that generalizes both the BLI
+and EPI by considering a variety of independence relations among the components
+of a random vector. Our main technical contribution is in the proof strategy
+that leverages the ""doubling trick"" to prove Gaussian optimality for certain
+entropy expressions under independence constraints.
+",1,0,0,0,0,0
+12348,Risk ratios for contagious outcomes,"  The risk ratio is a popular tool for summarizing the relationship between a
+binary covariate and outcome, even when outcomes may be dependent.
+Investigations of infectious disease outcomes in cohort studies of individuals
+embedded within clusters -- households, villages, or small groups -- often
+report risk ratios. Epidemiologists have warned that risk ratios may be
+misleading when outcomes are contagious, but the nature and severity of this
+error is not well understood. In this study, we assess the epidemiologic
+meaning of the risk ratio when outcomes are contagious. We first give a
+structural definition of infectious disease transmission within clusters, based
+on the canonical susceptible-infective epidemic model. From this standard
+characterization, we define the individual-level ratio of instantaneous risks
+(hazard ratio) as the inferential target, and evaluate the properties of the
+risk ratio as an estimate of this quantity. We exhibit analytically and by
+simulation the circumstances under which the risk ratio implies an effect whose
+direction is opposite that of the true individual-level hazard ratio. In
+particular, the risk ratio can be greater than one even when the covariate of
+interest reduces both individual-level susceptibility to infection, and
+transmissibility once infected. We explain these findings in the epidemiologic
+language of confounding and relate the direction bias to Simpson's paradox.
+",0,0,0,1,0,0
+12349,Instrument Orientation-Based Metrics for Surgical Skill Evaluation in Robot-Assisted and Open Needle Driving,"  The technical skill of surgeons directly impacts patient outcomes. Advanced
+tracking systems enable the development of objective motion-based metrics for
+skill evaluation, but these metrics are not sufficient to evaluate the
+performance in complex surgical tasks. In this study, we developed metrics for
+surgical skill evaluation that are based on the orientation of the surgical
+instruments. Experienced robotic surgeons and novice users performed
+teleoperated (using the da Vinci Research Kit) and open needle-driving. Task
+time and the rate of orientation change successfully distinguished between
+experienced surgeons and novice users. Path length and the normalized angular
+displacement allowed for a good separation only in part of the experiment. Our
+new promising metrics for surgical skill evaluation captured technical aspects
+that are taught during surgeons' training. They provide complementing
+evaluation to those of classical metrics. Orientation-based metrics add value
+to skill assessment and may be an adjunct to classic objective metrics
+providing more granular discrimination of skills.
+",1,0,0,0,0,0
+12350,Formal Methods for Adaptive Control of Dynamical Systems,"  We develop a method to control discrete-time systems with constant but
+initially unknown parameters from linear temporal logic (LTL) specifications.
+We introduce the notions of (non-deterministic) parametric and adaptive
+transition systems and show how to use tools from formal methods to compute
+adaptive control strategies for finite systems. For infinite systems, we first
+compute abstractions in the form of parametric finite quotient transition
+systems and then apply the techniques for finite systems. Unlike traditional
+adaptive control methods, our approach is correct by design, does not require a
+reference model, and can deal with a much wider range of systems and
+specifications. Illustrative case studies are included.
+",1,0,1,0,0,0
+12351,Sublayer of Prandtl boundary layers,"  The aim of this paper is to investigate the stability of Prandtl boundary
+layers in the vanishing viscosity limit: $\nu \to 0$. In \cite{Grenier}, one of
+the authors proved that there exists no asymptotic expansion involving one
+Prandtl's boundary layer with thickness of order $\sqrt\nu$, which describes
+the inviscid limit of Navier-Stokes equations. The instability gives rise to a
+viscous boundary sublayer whose thickness is of order $\nu^{3/4}$. In this
+paper, we point out how the stability of the classical Prandtl's layer is
+linked to the stability of this sublayer. In particular, we prove that the two
+layers cannot both be nonlinearly stable in $L^\infty$. That is, either the
+Prandtl's layer or the boundary sublayer is nonlinearly unstable in the sup
+norm.
+",0,0,1,0,0,0
+12352,Effects of a Price limit Change on Market Stability at the Intraday Horizon in the Korean Stock Market,"  This paper investigates the effects of a price limit change on the volatility
+of the Korean stock market's (KRX) intraday stock price process. Based on the
+most recent transaction data from the KRX, which experienced a change in the
+price limit on June 15, 2015, we examine the change in realized variance after
+the price limit change to investigate the overall effects of the change on the
+intraday market volatility. We then analyze the effects in more detail by
+applying the discrete Fourier transform (DFT) to the data set. We find evidence
+that the market becomes more volatile in the intraday horizon because of the
+increase in the amplitudes of the low-frequency components of the price
+processes after the price limit change. Therefore, liquidity providers are in a
+worse situation than they were prior to the change.
+",0,0,0,0,0,1
+12353,Adversarial Perturbations Against Real-Time Video Classification Systems,"  Recent research has demonstrated the brittleness of machine learning systems
+to adversarial perturbations. However, the studies have been mostly limited to
+perturbations on images and more generally, classification that does not deal
+with temporally varying inputs. In this paper we ask ""Are adversarial
+perturbations possible in real-time video classification systems and if so,
+what properties must they satisfy?"" Such systems find application in
+surveillance applications, smart vehicles, and smart elderly care and thus,
+misclassification could be particularly harmful (e.g., a mishap at an elderly
+care facility may be missed). We show that accounting for temporal structure is
+key to generating adversarial examples in such systems. We exploit recent
+advances in generative adversarial network (GAN) architectures to account for
+temporal correlations and generate adversarial samples that can cause
+misclassification rates of over 80% for targeted activities. More importantly,
+the samples also leave other activities largely unaffected making them
+extremely stealthy. Finally, we also surprisingly find that in many scenarios,
+the same perturbation can be applied to every frame in a video clip that makes
+the adversary's ability to achieve misclassification relatively easy.
+",0,0,0,1,0,0
+12354,Wasserstein Identity Testing,"  Uniformity testing and the more general identity testing are well studied
+problems in distributional property testing. Most previous work focuses on
+testing under $L_1$-distance. However, when the support is very large or even
+continuous, testing under $L_1$-distance may require a huge (even infinite)
+number of samples. Motivated by such issues, we consider the identity testing
+in Wasserstein distance (a.k.a. transportation distance and earthmover
+distance) on a metric space (discrete or continuous).
+In this paper, we propose the Wasserstein identity testing problem (Identity
+Testing in Wasserstein distance). We obtain nearly optimal worst-case sample
+complexity for the problem. Moreover, for a large class of probability
+distributions satisfying the so-called ""Doubling Condition"", we provide nearly
+instance-optimal sample complexity.
+",1,0,1,0,0,0
+12355,Concordances from differences of torus knots to $L$-space knots,"  It is known that connected sums of positive torus knots are not concordant to
+$L$-space knots. Here we consider differences of torus knots. The main result
+states that the subgroup of the concordance group generated by two positive
+torus knots contains no nontrivial $L$-space knots other than the torus knots
+themselves. Generalizations to subgroups generated by more than two torus knots
+are also considered.
+",0,0,1,0,0,0
+12356,The classification of Rokhlin flows on C*-algebras,"  We study flows on C*-algebras with the Rokhlin property. We show that every
+Kirchberg algebra carries a unique Rokhlin flow up to cocycle conjugacy, which
+confirms a long-standing conjecture of Kishimoto. We moreover present a
+classification theory for Rokhlin flows on C*-algebras satisfying certain
+technical properties, which hold for many C*-algebras covered by the Elliott
+program. As a consequence, we obtain the following further classification
+theorems for Rokhlin flows. Firstly, we extend the statement of Kishimoto's
+conjecture to the non-simple case: Up to cocycle conjugacy, a Rokhlin flow on a
+separable, nuclear, strongly purely infinite C*-algebra is uniquely determined
+by its induced action on the prime ideal space. Secondly, we give a complete
+classification of Rokhlin flows on simple classifiable $KK$-contractible
+C*-algebras: Two Rokhlin flows on such a C*-algebra are cocycle conjugate if
+and only if their induced actions on the cone of lower-semicontinuous traces
+are affinely conjugate.
+",0,0,1,0,0,0
+12357,Unusual evolution of B_{c2} and T_c with inclined fields in restacked TaS_2 nanosheets,"  Recently we reported an enhanced superconductivity in restacked monolayer
+TaS_2 nanosheets compared with the bulk TaS_2, pointing to the exotic physical
+properties of low dimensional systems. Here we tune the superconducting
+properties of this system with magnetic field along different directions, where
+a strong Pauli paramagnetic spin-splitting effect is found in this system.
+Importantly, an unusual enhancement as high as 3.8 times of the upper critical
+field B_{c2}, as compered with the Ginzburg-Landau (GL) model and Tinkham
+model, is observed under the inclined external magnetic field. Moreover, with
+the out-of-plane field fixed, we find that the superconducting transition
+temperature T_c can be enhanced by increasing the in-plane field and forms a
+dome-shaped phase diagram. An extended GL model considering the special
+microstructure with wrinkles was proposed to describe the results. The
+restacked crystal structure without inversion center along with the strong
+spin-orbit coupling may also play an important role for our observations.
+",0,1,0,0,0,0
+12358,Bounded game-theoretic semantics for modal mu-calculus,"  We introduce a new game-theoretic semantics (GTS) for the modal mu-calculus.
+Our so-called bounded GTS replaces parity games with novel alternative
+evaluation games where only finite paths arise. Infinite paths are not needed
+even when the considered transition system is infinite.
+",1,0,1,0,0,0
+12359,Putative spin liquid in the triangle-based iridate Ba$_3$IrTi$_2$O$_9$,"  We report on thermodynamic, magnetization, and muon spin relaxation
+measurements of the strong spin-orbit coupled iridate Ba$_3$IrTi$_2$O$_9$,
+which constitutes a new frustration motif made up a mixture of edge- and
+corner-sharing triangles. In spite of strong antiferromagnetic exchange
+interaction of the order of 100~K, we find no hint for long-range magnetic
+order down to 23 mK. The magnetic specific heat data unveil the $T$-linear and
+-squared dependences at low temperatures below 1~K. At the respective
+temperatures, the zero-field muon spin relaxation features a persistent spin
+dynamics, indicative of unconventional low-energy excitations. A comparison to
+the $4d$ isostructural compound Ba$_3$RuTi$_2$O$_9$ suggests that a concerted
+interplay of compass-like magnetic interactions and frustrated geometry
+promotes a dynamically fluctuating state in a triangle-based iridate.
+",0,1,0,0,0,0
+12360,Herding behavior in cryptocurrency markets,"  There are no solid arguments to sustain that digital currencies are the
+future of online payments or the disruptive technology that some of its former
+participants declared when used to face critiques. This paper aims to solve the
+cryptocurrency puzzle from a behavioral finance perspective by finding the
+parallelism between biases present in financial markets that could be applied
+to cryptomarkets. Moreover, it is suggested that cryptocurrencies' prices are
+driven by herding, hence this study test herding behavior under asymmetric and
+symmetric conditions and the existence of different herding regimes by
+employing the Markov-Switching approach.
+",0,0,0,0,0,1
+12361,Los agujeros negros y las ondas del Doctor Einstein,"  We describe the main scientific developments that lead LIGO project to the
+detection of the gravitational waves: general relativity, black holes and
+gravitational waves predictions; numerical relativity and the collision and
+coalescence simulations of binary black holes and the development of different
+kind of gravitational wave detectors. Most important, this detection is
+confirming the existence of the enigmatic black holes.
+",0,1,0,0,0,0
+12362,"Symmetries of flat manifolds, Jordan property and the general Zimmer program","  We obtain a sufficient and necessary condition for a finite group that could
+act effectively on closed flat manifolds. Let $G=E_{n}(R)$ the elementary
+subgroup of a linear group, $EU_{n}(R,\Lambda )$ the elementary subgroup of a
+unitary group, $\mathrm{SAut}(F_{n})$ the special automorphism group of a free
+group or $\mathrm{SOut}(F_{n})$ the special outer automorphism group of a free
+group. As applications, we prove that when $n\geq 3$ every group action of $G$
+on a closed flat manifold $M^{k}$ ($k<n$) by homeomorphisms is trivial. This
+confirms a conjecture related to Zimmer's program for flat manifolds. Moreover,
+it is also proved that the group of homeomorphisms of closed flat manifolds are
+Jordan with Jordan constants depending only on dimensions.
+",0,0,1,0,0,0
+12363,Hidden Fermi Liquidity and Topological Criticality in the Finite Temperature Kitaev Model,"  The fate of exotic spin liquid states with fractionalized excitations at
+finite temperature ($T$) is of great interest, since signatures of
+fractionalization manifest in finite-temperature ($T$) dynamics in real
+systems, above the tiny magnetic ordering scales. Here, we study a
+Jordan-Wigner fermionized Kitaev spin liquid at finite $T$ employing combined
+Exact diagonalization and Monte Carlo simulation methods. We uncover $(i)$
+checkerboard or stripy-ordered flux crystals depending on density of flux, and
+$(ii)$ establish, surprisingly, that: $(a)$ the finite-$T$ version of the $T=0$
+transition from a gapless to gapped phases in the Kitaev model is a Mott
+transition of the fermions, belonging to the two-dimensional Ising universality
+class. These transitions correspond to a topological transition between a
+string condensate and a dilute closed string state $(b)$ the Mott ""insulator""
+phase is a precise realization of Laughlin's gossamer (here, p-wave)
+superconductor (g-SC), and $(c)$ the Kitaev Toric Code phase (TC) is a {\it
+fully} Gutzwiller-projected p-wave SC. These findings establish the finite-$T$
+QSL phases in the $d = 2$ to be {\it hidden} Fermi liquid(s) of neutral
+fermions.
+",0,1,0,0,0,0
+12364,Semi-automated labelling of medical images: benefits of a collaborative work in the evaluation of prostate cancer in MRI,"  Purpose: The goal of this study is to show the advantage of a collaborative
+work in the annotation and evaluation of prostate cancer tissues from
+T2-weighted MRI compared to the commonly used double blind evaluation.
+Methods: The variability of medical findings focused on the prostate gland
+(central gland, peripheral and tumoural zones) by two independent experts was
+firstly evaluated, and secondly compared with a consensus of these two experts.
+Using a prostate MRI database, experts drew regions of interest (ROIs)
+corresponding to healthy prostate (peripheral and central zones) and cancer
+using a semi-automated tool. One of the experts then drew the ROI with
+knowledge of the other expert's ROI.
+Results: The surface area of each ROI as the Hausdorff distance and the Dice
+coefficient for each contour were evaluated between the different experiments,
+taking the drawing of the second expert as the reference. The results showed
+that the significant differences between the two experts became non-significant
+with a collaborative work.
+Conclusions: This study shows that collaborative work with a dedicated tool
+allows a better consensus between expertise than using a double blind
+evaluation. Although we show this for prostate cancer evaluation in T2-weighted
+MRI, the results of this research can be extrapolated to other diseases and
+kind of medical images.
+",1,1,0,0,0,0
+12365,Lexical Features in Coreference Resolution: To be Used With Caution,"  Lexical features are a major source of information in state-of-the-art
+coreference resolvers. Lexical features implicitly model some of the linguistic
+phenomena at a fine granularity level. They are especially useful for
+representing the context of mentions. In this paper we investigate a drawback
+of using many lexical features in state-of-the-art coreference resolvers. We
+show that if coreference resolvers mainly rely on lexical features, they can
+hardly generalize to unseen domains. Furthermore, we show that the current
+coreference resolution evaluation is clearly flawed by only evaluating on a
+specific split of a specific dataset in which there is a notable overlap
+between the training, development and test sets.
+",1,0,0,0,0,0
+12366,Complete Semantics to empower Touristic Service Providers,"  The tourism industry has a significant impact on the world's economy,
+contributes 10.2% of the world's gross domestic product in 2016. It becomes a
+very competitive industry, where having a strong online presence is an
+essential aspect for business success. To achieve this goal, the proper usage
+of latest Web technologies, particularly schema.org annotations is crucial. In
+this paper, we present our effort to improve the online visibility of touristic
+service providers in the region of Tyrol, Austria, by creating and deploying a
+substantial amount of semantic annotations according to schema.org, a widely
+used vocabulary for structured data on the Web. We started our work from
+Tourismusverband (TVB) Mayrhofen-Hippach and all touristic service providers in
+the Mayrhofen-Hippach region and applied the same approach to other TVBs and
+regions, as well as other use cases. The rationale for doing this is
+straightforward. Having schema.org annotations enables search engines to
+understand the content better, and provide better results for end users, as
+well as enables various intelligent applications to utilize them. As a direct
+consequence, the region of Tyrol and its touristic service increase their
+online visibility and decrease the dependency on intermediaries, i.e. Online
+Travel Agency (OTA).
+",1,0,0,0,0,0
+12367,Reducing variance in importance-weighted cross-validation under covariate shift,"  Covariate shift classification problems can in principle be tackled by
+importance-weighting of training samples. However, the sampling variance of the
+risk estimator is often scaled up dramatically by employing such weighting. One
+of the consequences of this is that during cross-validation -- when the
+importance-weighted risk is repeatedly estimated -- suboptimal hyperparameter
+estimates are produced. We study the sampling variance of the
+importance-weighted risk estimator as a function of the width of the source
+distribution. We show that introducing a control variate can reduce its
+sampling variance, which leads to improved regularization parameter estimates
+when the training data is smaller in scale than the test data.
+",1,0,0,1,0,0
+12368,Comparing Aggregators for Relational Probabilistic Models,"  Relational probabilistic models have the challenge of aggregation, where one
+variable depends on a population of other variables. Consider the problem of
+predicting gender from movie ratings; this is challenging because the number of
+movies per user and users per movie can vary greatly. Surprisingly, aggregation
+is not well understood. In this paper, we show that existing relational models
+(implicitly or explicitly) either use simple numerical aggregators that lose
+great amounts of information, or correspond to naive Bayes, logistic
+regression, or noisy-OR that suffer from overconfidence. We propose new simple
+aggregators and simple modifications of existing models that empirically
+outperform the existing ones. The intuition we provide on different (existing
+or new) models and their shortcomings plus our empirical findings promise to
+form the foundation for future representations.
+",1,0,0,1,0,0
+12369,The Sad State of Entrepreneurship in America: What Educators Can Do About It,"  The entrepreneurial scene suffers from a sick venture capital industry, a
+number of imponderable illogics, and, maybe, misplaced adulation from students
+and the public. The paper details these problems, finds root causes, and
+prescribes action for higher education professionals and institutions.
+",1,0,0,0,0,0
+12370,Filtering Variational Objectives,"  When used as a surrogate objective for maximum likelihood estimation in
+latent variable models, the evidence lower bound (ELBO) produces
+state-of-the-art results. Inspired by this, we consider the extension of the
+ELBO to a family of lower bounds defined by a particle filter's estimator of
+the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs
+take the same arguments as the ELBO, but can exploit a model's sequential
+structure to form tighter bounds. We present results that relate the tightness
+of FIVO's bound to the variance of the particle filter's estimator by
+considering the generic case of bounds defined as log-transformed likelihood
+estimators. Experimentally, we show that training with FIVO results in
+substantial improvements over training the same model architecture with the
+ELBO on sequential data.
+",1,0,0,1,0,0
+12371,Annealing stability of magnetic tunnel junctions based on dual MgO free layers and [Co/Ni] based thin synthetic antiferromagnet fixed system,"  We study the annealing stability of bottom-pinned perpendicularly magnetized
+magnetic tunnel junctions based on dual MgO free layers and thin fixed systems
+comprising a hard [Co/Ni] multilayer antiferromagnetically coupled to thin a Co
+reference layer and a FeCoB polarizing layer. Using conventional magnetometry
+and advanced broadband ferromagnetic resonance, we identify the properties of
+each sub-unit of the magnetic tunnel junction and demonstrate that this
+material option can ensure a satisfactory resilience to the 400$^\circ$C
+thermal annealing needed in solid-state magnetic memory applications. The dual
+MgO free layer possesses an anneal-robust 0.4 T effective anisotropy and
+suffers only a minor increase of its Gilbert damping from 0.007 to 0.010 for
+the toughest annealing conditions. Within the fixed system, the ferro-coupler
+and texture-breaking TaFeCoB layer keeps an interlayer exchange above 0.8
+mJ/m$^2$, while the Ru antiferrocoupler layer within the synthetic
+antiferromagnet maintains a coupling above -0.5 mJ/m$^2$. These two strong
+couplings maintain the overall functionality of the tunnel junction upon the
+toughest annealing despite the gradual degradation of the thin Co layer
+anisotropy that may reduce the operation margin in spin torque memory
+applications. Based on these findings, we propose further optimization routes
+for the next generation magnetic tunnel junctions.
+",0,1,0,0,0,0
+12372,The Eigenoption-Critic Framework,"  Eigenoptions (EOs) have been recently introduced as a promising idea for
+generating a diverse set of options through the graph Laplacian, having been
+shown to allow efficient exploration. Despite its initial promising results, a
+couple of issues in current algorithms limit its application, namely: (1) EO
+methods require two separate steps (eigenoption discovery and reward
+maximization) to learn a control policy, which can incur a significant amount
+of storage and computation; (2) EOs are only defined for problems with discrete
+state-spaces and; (3) it is not easy to take the environment's reward function
+into consideration when discovering EOs. To addresses these issues, we
+introduce an algorithm termed eigenoption-critic (EOC) based on the
+Option-critic (OC) framework [Bacon17], a general hierarchical reinforcement
+learning (RL) algorithm that allows learning the intra-option policies
+simultaneously with the policy over options. We also propose a generalization
+of EOC to problems with continuous state-spaces through the Nyström
+approximation. EOC can also be seen as extending OC to nonstationary settings,
+where the discovered options are not tailored for a single task.
+",1,0,0,0,0,0
+12373,A perturbation analysis of some Markov chains models with time-varying parameters,"  We study some regularity properties in locally stationary Markov models which
+are fundamental for controlling the bias of nonparametric kernel estimators. In
+particular, we provide an alternative to the standard notion of derivative
+process developed in the literature and that can be used for studying a wide
+class of Markov processes. To this end, for some families of V-geometrically
+ergodic Markov kernels indexed by a real parameter u, we give conditions under
+which the invariant probability distribution is differentiable with respect to
+u, in the sense of signed measures. Our results also complete the existing
+literature for the perturbation analysis of Markov chains, in particular when
+exponential moments are not finite. Our conditions are checked on several
+original examples of locally stationary processes such as integer-valued
+autoregressive processes, categorical time series or threshold autoregressive
+processes.
+",0,0,1,1,0,0
+12374,Exploring patterns of demand in bike sharing systems via replicated point process models,"  Understanding patterns of demand is fundamental for fleet management of bike
+sharing systems. In this paper we analyze data from the Divvy system of the
+city of Chicago. We show that the demand of bicycles can be modeled as a
+multivariate temporal point process, with each dimension corresponding to a
+bike station in the network. The availability of daily replications of the
+process allows nonparametric estimation of the intensity functions, even for
+stations with low daily counts, and straightforward estimation of pairwise
+correlations between stations. These correlations are then used for clustering,
+revealing different patterns of bike usage.
+",0,0,0,1,0,0
+12375,High-performance nanoscale topological energy transduction,"  The realization of high-performance, small-footprint, on-chip inductors
+remains a challenge in radio-frequency and power microelectronics, where they
+perform vital energy transduction in filters and power converters. Modern
+planar inductors consist of metallic spirals that consume significant chip
+area, resulting in low inductance densities. We present a novel method for
+magnetic energy transduction that utilizes ferromagnetic islands (FIs) on the
+surface of a 3D time-reversal-invariant topological insulator (TI) to produce
+paradigmatically different inductors. Depending on the chemical potential, the
+FIs induce either an anomalous or quantum anomalous Hall effect in the
+topological surface states. These Hall effects direct current around the FIs,
+concentrating magnetic flux and producing a highly inductive device. Using a
+novel self-consistent simulation that couples AC non-equilibrium Green
+functions to fully electrodynamic solutions of Maxwell's equations, we
+demonstrate excellent inductance densities up to terahertz frequencies, thus
+harnessing the unique properties of topological materials for practical device
+applications.
+",0,1,0,0,0,0
+12376,Multigrid-based inversion for volumetric radar imaging with asteroid interior reconstruction as a potential application,"  This study concentrates on advancing mathematical and computational
+methodology for radar tomography imaging in which the unknown volumetric
+velocity distribution of a wave within a bounded domain is to be reconstructed.
+Our goal is to enable effective simulation and inversion of a large amount of
+full-wave data within a realistic 2D or 3D geometry. For propagating and
+inverting the wave, we present a rigorous multigrid-based forward approach
+which utilizes the finite-difference time-domain method and a nested finite
+element grid structure. Based on the multigrid approach, we introduce and
+validate a multiresolution algorithm which allows regularization of the unknown
+distribution through a coarse-to-fine inversion scheme. In this approach,
+sparse signals can be effectively inverted, as the coarse fluctuations are
+reconstructed before the finer ones. Furthermore, the number of nonzero entries
+in the system matrix can be compressed and thus the inversion procedure can be
+speeded up. As a test scenario we investigate satellite-based asteroid interior
+reconstruction. We use both full-wave and projected wave data and estimate the
+accuracy of the inversion under different error sources: noise and positioning
+inaccuracies. The results suggest that the present full-wave inversion approach
+allows recovering the interior with a single satellite recording backscattering
+data. It seems that robust results can be achieved, when the peak-to-peak
+signal-to-noise ratio is above 10 dB. Furthermore, it seems that reconstructing
+the deep interior can be enhanced if two satellites can be utilized in the
+measurements.
+",0,1,0,0,0,0
+12377,Fluid dynamics of diving wedges,"  Diving induces large pressures during water entry, accompanied by the
+creation of cavity and water splash ejected from the free water surface. To
+minimize impact forces, divers streamline their shape at impact. Here, we
+investigate the impact forces and splash evolution of diving wedges as a
+function of the wedge opening angle. A gradual transition from impactful to
+smooth entry is observed as the wedge angle decreases. After submersion, diving
+wedges experience significantly smaller drag forces (two-fold smaller) than
+immersed wedges. Our experimental findings compare favorably with existing
+force models upon the introduction of empirically-based corrections. We
+experimentally characterize the shapes of the cavity and splash created by the
+wedge and find that they are independent of the entry velocity at short times,
+but that the splash exhibits distinct variations in shape at later times. We
+propose a one-dimensional model of the splash that takes into account gravity,
+surface tension and aerodynamics forces. The model shows, in conjunction with
+experimental data, that the splash shape is dominated by the interplay between
+a destabilizing Venturi-suction force due to air rushing between the splash and
+the water surface and a stabilizing force due to surface tension. Taken
+together, these findings could direct future research aimed at understanding
+and combining the mechanisms underlying all stages of water entry in
+application to engineering and bio-related problems, including naval
+engineering, disease spreading or platform diving.
+",0,1,0,0,0,0
+12378,Scenic: Language-Based Scene Generation,"  Synthetic data has proved increasingly useful in both training and testing
+machine learning models such as neural networks. The major problem in synthetic
+data generation is producing meaningful data that is not simply random but
+reflects properties of real-world data or covers particular cases of interest.
+In this paper, we show how a probabilistic programming language can be used to
+guide data synthesis by encoding domain knowledge about what data is useful.
+Specifically, we focus on data sets arising from ""scenes"", configurations of
+physical objects; for example, images of cars on a road. We design a
+domain-specific language, Scenic, for describing ""scenarios"" that are
+distributions over scenes. The syntax of Scenic makes it easy to specify
+complex relationships between the positions and orientations of objects. As a
+probabilistic programming language, Scenic allows assigning distributions to
+features of the scene, as well as declaratively imposing hard and soft
+constraints over the scene. A Scenic scenario thereby implicitly defines a
+distribution over scenes, and we formulate the problem of sampling from this
+distribution as ""scene improvisation"". We implement an improviser for Scenic
+scenarios and apply it in a case study generating synthetic data sets for a
+convolutional neural network designed to detect cars in road images. Our
+experiments demonstrate the usefulness of our approach by using Scenic to
+analyze and improve the performance of the network in various scenarios.
+",1,0,0,0,0,0
+12379,An exactly solvable model for Dynamic Nuclear polarization,"  We introduce a solvable model of driven fermions that elucidates the role of
+the localization transition in driven disordered magnets, as used in the
+context of dynamic nuclear polarization. Instead of spins, we study a set of
+non-interacting fermions that are coupled locally to nuclear spins and tend to
+hyperpolarize them. The induced hyperpolarization is a fingerprint of the
+driven steady state of the fermions, which undergo an Anderson Localization
+(AL) transition upon increasing the disorder. Our central result is that the
+maximal hyperpolarization level is always found close to the localization
+transition. In the limit of small nuclear moments the maximum is pinned to the
+transition, and the hyperpolarization is strongly enhanced by multi-fractal
+correlations in the critical state of the nearly localized driven system, its
+magnitude reflecting multi-fractal scaling.
+",0,1,0,0,0,0
+12380,The Research Data Alliance: Building Bridges to Enable Scientific Data Sharing,"  The Research Data Alliance is an international organization which aims at
+building the technical and sociological bridges that enable the open sharing of
+scientific data. It is a remarkable forum to discuss all the aspects of
+scientific data sharing with colleagues from all around the world: in November
+2016, it has 4 500 members from 115 countries. The biannual Plenary meetings,
+which gather several hundred participants, are rotating between different
+regions. The March 2017 one will be held in Barcelona and the September 2017
+one in Montreal, after Tokyo and Denver in 2016. The RDA work is organized
+bottom-up, with Working Groups which have 18 months to produce implementable
+deliverables and Interest Groups which serve as platforms of communication and
+discussion and also produce important outputs such as surveys and
+recommendations. There are currently 27 Working Groups and 45 Interest Groups,
+tackling a wide diversity of subjects, including community needs, reference for
+sharing, data stewardship and services, and topics related to the base
+infrastructure of data sharing. Some scientific communities use the RDA as a
+neutral forum to define their own disciplinary data sharing framework, with
+major successes such as the Wheat Data Interoperability Working Group which
+worked in coordination with the International Wheat Initiative. Astronomy has
+the IVOA to define its interoperability standards, and so we do not need to
+create a Group for that purpose in the RDA. But many topics discussed in the
+RDA have a strong interest for us, for instance on data citation or
+certification of data repositories. We have a lot to share from what we have
+learnt in building our disciplinary global data infrastructure; we also have a
+lot to learn from others. The paper discusses RDA current themes or results of
+interest for astronomy data providers, and current liaisons with astronomy.
+",0,1,0,0,0,0
+12381,Modulated magnetic structure of Fe3PO7 as seen by 57Fe Mössbauer spectroscopy,"  The paper reports new results of the 57Fe Mössbauer measurements on
+Fe3PO4O3 powder sample recorded at various temperatures including the point of
+magnetic phase transition TN ~ 163K. The spectra measured above TN consist of
+quadrupole doublet with high quadrupole splitting of D300K ~ 1.10 mm/s,
+emphasizing that Fe3+ ions are located in crystal positions with a strong
+electric field gradient (EFG). In order to predict the sign and orientation of
+the main components of the EFG tensor we calculated monopole lattice
+contributions to the EFG. In the temperature range T < TN, the experimental
+spectra were fitted assuming that the electric hyperfine interactions are
+modulated when the Fe3+ spin (S) rotates with respect to the EFG axis and
+emergence of spatial anisotropy of the hyperfine field Hhf = SÃI at 57Fe
+nuclei. These data were analyzed to estimate the components of the anisotropic
+hyperfine coupling tensor (Ã). The large anharmonicity parameter, m ~ 0.94,
+of the spiral spin structure results from easy-axis anisotropy in the plane of
+the iron spin rotation. The temperature evolution of the hyperfine field Hhf(T)
+was described by Bean-Rodbell model that takes into account that the exchange
+magnetic interactions are strong function of the lattice spacing. The obtained
+Mössbauer data are in qualitative agreement with previous neutron diffraction
+data for a modulated helical magnetic structure in strongly frustrated
+Fe3PO4O3.
+",0,1,0,0,0,0
+12382,Mutation invariance for the zeroth coefficients of the colored HOMFLY polynomial,"  We show that the zeroth coefficient of the cables of the HOMFLY polynomial
+(colored HOMFLY polynomials) does not distinguish mutants. This makes a sharp
+contrast with the total HOMFLY polynomial whose 3-cables can distinguish
+mutants.
+",0,0,1,0,0,0
+12383,Seasonal Variation of the Underground Cosmic Muon Flux Observed at Daya Bay,"  The Daya Bay Experiment consists of eight identically designed detectors
+located in three underground experimental halls named as EH1, EH2, EH3, with
+250, 265 and 860 meters of water equivalent vertical overburden, respectively.
+Cosmic muon events have been recorded over a two-year period. The underground
+muon rate is observed to be positively correlated with the effective
+atmospheric temperature and to follow a seasonal modulation pattern. The
+correlation coefficient $\alpha$, describing how a variation in the muon rate
+relates to a variation in the effective atmospheric temperature, is found to be
+$\alpha_{\text{EH1}} = 0.362\pm0.031$, $\alpha_{\text{EH2}} = 0.433\pm0.038$
+and $\alpha_{\text{EH3}} = 0.641\pm0.057$ for each experimental hall.
+",0,1,0,0,0,0
+12384,Valid Inference Corrected for Outlier Removal,"  Ordinary least square (OLS) estimation of a linear regression model is
+well-known to be highly sensitive to outliers. It is common practice to first
+identify and remove outliers by looking at the data then to fit OLS and form
+confidence intervals and p-values on the remaining data as if this were the
+original data collected. We show in this paper that this ""detect-and-forget""
+approach can lead to invalid inference, and we propose a framework that
+properly accounts for outlier detection and removal to provide valid confidence
+intervals and hypothesis tests. Our inferential procedures apply to any outlier
+removal procedure that can be characterized by a set of quadratic constraints
+on the response vector, and we show that several of the most commonly used
+outlier detection procedures are of this form. Our methodology is built upon
+recent advances in selective inference (Taylor & Tibshirani 2015), which are
+focused on inference corrected for variable selection. We conduct simulations
+to corroborate the theoretical results, and we apply our method to two classic
+data sets considered in the outlier detection literature to illustrate how our
+inferential results can differ from the traditional detect-and-forget strategy.
+A companion R package, outference, implements these new procedures with an
+interface that matches the functions commonly used for inference with lm in R.
+",0,0,1,1,0,0
+12385,"On the error term of a lattice counting problem, II","  Under the Riemann Hypothesis, we improve the error term in the asymptotic
+formula related to the counting lattice problem studied in a first part of this
+work. The improvement comes from the use of Weyl's bound for exponential sums
+of polynomials and a device due to Popov allowing us to get an improved main
+term in the sums of certain fractional parts of polynomials.
+",0,0,1,0,0,0
+12386,Gamma-Ray Emission from Arp 220: Indications of an Active Galactic Nucleus,"  Extragalactic cosmic ray populations are important diagnostic tools for
+tracking the distribution of energy in nuclei and for distinguishing between
+activity powered by star formation versus active galactic nuclei (AGNs). Here,
+we compare different diagnostics of the cosmic ray populations of the nuclei of
+Arp 220 based on radio synchrotron observations and the recent gamma-ray
+detection. We find the gamma-ray and radio emission to be incompatible; a joint
+solution requires at minimum a factor of 4 - 8 times more energy coming from
+supernovae and a factor of 40 - 70 more mass in molecular gas than is observed.
+We conclude that this excess of gamma-ray flux in comparison to all other
+diagnostics of star-forming activity indicates that there is an AGN present
+that is providing the extra cosmic rays, likely in the western nucleus.
+",0,1,0,0,0,0
+12387,The fundamental factor of optical interference,"  It has been widely accepted that electric field alone is the fundamental
+factor for optical interference, since Wiener's experiments in 1890 proved that
+the electric field plays such a dominant role. A group of experiments were
+demonstrated against Wiener's experiments under the condition that the
+interference fringes made by optical standing waves could have been
+distinguished from the fringes of equal thickness between the inner surface of
+emulsion and the plane mirror used to build the optical standing waves. It was
+found that the Bragg diffraction from the interference fringes formed by the
+standing waves did not exist. This means optical standing waves did not blacken
+the photographic emulsion, or the electric field did not play such a dominant
+role. Therefore, instead of the electric-field energy density solely in
+proportion to the electric-field square, Energy Flux in Interference was
+proposed to represent the intensity of optical interference-field and approved
+in the derivation of equations for the interference. The derived equations
+indicate that both the electric-field vector and the magnetic-field vector are
+in phase and have equal amount of energy densities at the interference maxima
+of two light beams. Thus, the magnetic-field vector acts the same role as the
+electric-field vector on light interacting with substance. The fundamental
+factor of optical interference is electromagnetic energy flux densities rather
+than electric-field alone, or the intensity of optical interference fringes
+should be the energy flux density, not electric-field energy density.
+",0,1,0,0,0,0
+12388,Evidence for a Dusty Dark Dwarf Galaxy in the Quadruple Lens MG0414+0534,"  We report the $4 \, \sigma$ detection of a faint object with a flux of ~ 0.3
+mJy, in the vicinity of the quadruply lensed QSO MG0414+0534 using the Atacama
+Large Millimeter/submillimeter array (ALMA) Band 7. The object is most probably
+a dusty dark dwarf galaxy, which has not been detected in either the optical,
+near-infrared (NIR) or radio (cm) bands. An anomaly in the flux ratio of the
+lensed images observed in Band 7 and the mid-infrared (MIR) band and the
+reddening of the QSO light color can be simultaneously explained if we consider
+the object as a lensing substructure with an ellipticity ~ 0.7 at a redshift of
+$0.5 \lesssim z \lesssim 1$. Using the best-fit lens models with three lenses,
+we find that the dark matter plus baryon mass associated with the object is
+$\sim 10^9\, M_{\odot}$, the dust mass is $\sim 10^7\,M_{\odot}$ and the linear
+size is $\gtrsim 5\,$kpc. Thus our findings suggest that the object is a dusty
+dark dwarf galaxy. A substantial portion of faint submillimeter galaxies (SMGs)
+in the universe may be attributed to such dark objects.
+",0,1,0,0,0,0
+12389,Universal abstract elementary classes and locally multipresentable categories,"  We exhibit an equivalence between the model-theoretic framework of universal
+classes and the category-theoretic framework of locally multipresentable
+categories. We similarly give an equivalence between abstract elementary
+classes (AECs) admitting intersections and locally polypresentable categories.
+We use these results to shed light on Shelah's presentation theorem for AECs.
+",0,0,1,0,0,0
+12390,"q-Viscous Burgers' Equation: Dynamical Symmetry, Shock Solitons and q-Semiclassical Expansion","  We propose new type of $q$-diffusive heat equation with nonsymmetric
+$q$-extension of the diffusion term. Written in relative gradient variables
+this system appears as the $q$- viscous Burgers' equation. Exact solutions of
+this equation in polynomial form as generalized Kampe de Feriet polynomials,
+corresponding dynamical symmetry and description in terms of Bell polynomials
+are derived. We found the generating function for these polynomials by
+application of dynamical symmetry and the Zassenhaus formula. We have
+constructed and analyzed shock solitons and their interactions with different
+$q$. We obtain modification of the soliton relative speeds depending on value
+of $q$.For $q< 1$ the soliton speed becomes bounded from above and as a result
+in addition to usual Burgers soliton process of fusion, we found a new
+phenomena, when soliton with higher amplitude but smaller velocity is fissing
+to two solitons. q-Semiclassical expansion of these equations are found in
+terms of Bernoulli polynomials in power of $\ln q$.
+",0,1,0,0,0,0
+12391,Comparison of invariant metrics and distances on strongly pseudoconvex domains and worm domains,"  We prove that for a strongly pseudoconvex domain $D\subset\mathbb C^n$, the
+infinitesimal Carathéodory metric $g_C(z,v)$ and the infinitesimal Kobayashi
+metric $g_K(z,v)$ coincide if $z$ is sufficiently close to $bD$ and if $v$ is
+sufficiently close to being tangential to $bD$. Also, we show that every two
+close points of $D$ sufficiently close to the boundary and whose difference is
+almost tangential to $bD$ can be joined by a (unique up to reparameterization)
+complex geodesic of $D$ which is also a holomorphic retract of $D$.
+The same continues to hold if $D$ is a worm domain, as long as the points are
+sufficiently close to a strongly pseudoconvex boundary point. We also show that
+a strongly pseudoconvex boundary point of a worm domain can be globally
+exposed, this has consequences for the behavior of the squeezing function.
+",0,0,1,0,0,0
+12392,The strong ring of simplicial complexes,"  We define a ring R of geometric objects G generated by finite abstract
+simplicial complexes. To every G belongs Hodge Laplacian H as the square of the
+Dirac operator determining its cohomology and a unimodular connection matrix
+L). The sum of the matrix entries of the inverse of L is the Euler
+characteristic. The spectra of H as well as inductive dimension add under
+multiplication while the spectra of L multiply. The nullity of the Hodge of H
+are the Betti numbers which can now be signed. The map assigning to G its
+Poincare polynomial is a ring homomorphism from R the polynomials. Especially
+the Euler characteristic is a ring homomorphism. Also Wu characteristic
+produces a ring homomorphism. The Kuenneth correspondence between cohomology
+groups is explicit as a basis for the product can be obtained from a basis of
+the factors. The product in R produces the strong product for the connection
+graphs and leads to tensor products of connection Laplacians. The strong ring R
+is also a subring of the full Stanley-Reisner ring S Every element G can be
+visualized by its Barycentric refinement graph G1 and its connection graph G'.
+Gauss-Bonnet, Poincare-Hopf or the Brouwer-Lefschetz extend to the strong ring.
+The isomorphism of R with a subring of the strong Sabidussi ring shows that the
+multiplicative primes in R are the simplicial complexes and that every
+connected element in the strong ring has a unique prime factorization. The
+Sabidussi ring is dual to the Zykov ring, in which the Zykov join is the
+addition. The connection Laplacian of the d-dimensional lattice remains
+invertible in the infinite volume limit: there is a mass gap in any dimension.
+",1,0,1,0,0,0
+12393,Holonomy representation of quasi-projective leaves of codimension one foliations,"  We prove that a representation of the fundamental group of a quasi-projective
+manifold into the group of formal diffeomorphisms of one variable either is
+virtually abelian or, after taking the quotient by its center, factors through
+an orbicurve.
+",0,0,1,0,0,0
+12394,Super Extensions of the Short Pulse Equation,"  From a super extension of the Wadati, Konno and Ichikawa scheme for
+integrable systems and using a $\mathrm{osp(1,2)}$ valued connection 1-form we
+obtain super generalizations for the Short Pulse equation as well for the
+Elastic Beam equation.
+",0,1,0,0,0,0
+12395,Nonequational Stable Groups,"  We introduce a combinatorial criterion for verifying whether a formula is not
+the conjunction of an equation and a co-equation. Using this, we give a
+transparent proof for the nonequationality of the free group, which was
+originally proved by Sela. Furthermore, we extend this result to arbitrary free
+products of groups (except $\mathbb{Z}_2*\mathbb{Z}_2$), providing an abundance
+of new stable nonequational theories.
+",0,0,1,0,0,0
+12396,Analyzing biological and artificial neural networks: challenges with opportunities for synergy?,"  Deep neural networks (DNNs) transform stimuli across multiple processing
+stages to produce representations that can be used to solve complex tasks, such
+as object recognition in images. However, a full understanding of how they
+achieve this remains elusive. The complexity of biological neural networks
+substantially exceeds the complexity of DNNs, making it even more challenging
+to understand the representations that they learn. Thus, both machine learning
+and computational neuroscience are faced with a shared challenge: how can we
+analyze their representations in order to understand how they solve complex
+tasks?
+We review how data-analysis concepts and techniques developed by
+computational neuroscientists can be useful for analyzing representations in
+DNNs, and in turn, how recently developed techniques for analysis of DNNs can
+be useful for understanding representations in biological neural networks. We
+explore opportunities for synergy between the two fields, such as the use of
+DNNs as in-silico model systems for neuroscience, and how this synergy can lead
+to new hypotheses about the operating principles of biological neural networks.
+",0,0,0,0,1,0
+12397,Recurrences in an isolated quantum many-body system,"  Even though the evolution of an isolated quantum system is unitary, the
+complexity of interacting many-body systems prevents the observation of
+recurrences of quantum states for all but the smallest systems. For large
+systems one can not access the full complexity of the quantum states and the
+requirements to observe a recurrence in experiments reduces to being close to
+the initial state with respect to the employed observable. Selecting an
+observable connected to the collective excitations in one-dimensional
+superfluids, we demonstrate recurrences of coherence and long range order in an
+interacting quantum many-body system containing thousands of particles. This
+opens up a new window into the dynamics of large quantum systems even after
+they reached a transient thermal-like state.
+",0,1,0,0,0,0
+12398,Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization,"  We tackle the issue of classifier combinations when observations have
+multiple views. Our method jointly learns view-specific weighted majority vote
+classifiers (i.e. for each view) over a set of base voters, and a second
+weighted majority vote classifier over the set of these view-specific weighted
+majority vote classifiers. We show that the empirical risk minimization of the
+final majority vote given a multiview training set can be cast as the
+minimization of Bregman divergences. This allows us to derive a parallel-update
+optimization algorithm for learning our multiview model. We empirically study
+our algorithm with a particular focus on the impact of the training set size on
+the multiview learning results. The experiments show that our approach is able
+to overcome the lack of labeled information.
+",0,0,0,1,0,0
+12399,Temporal Overbooking of Lambda Functions in the Cloud,"  We consider the problem of scheduling ""serverless computing"" instances such
+as Amazon Lambda functions. Instead of a quota per tenant/customer, we assume
+demand for Lambda functions is modulated by token-bucket mechanisms per tenant.
+Based on an upper bound on the stationary number of active ""Lambda servers""
+considering the execution-time distribution of Lambda functions, we describe an
+approach that the cloud could use to overbook Lambda functions for improved
+utilization of IT resources. An earlier bound for a single service tier is
+extended to the case of multiple service tiers.
+",1,0,0,0,0,0
+12400,Orbital degeneracy loci and applications,"  Degeneracy loci of morphisms between vector bundles have been used in a wide
+variety of situations. We introduce a vast generalization of this notion, based
+on orbit closures of algebraic groups in their linear representations. A
+preferred class of our orbital degeneracy loci is characterized by a certain
+crepancy condition on the orbit closure, that allows to get some control on the
+canonical sheaf. This condition is fulfilled for Richardson nilpotent orbits,
+and also for partially decomposable skew-symmetric three-forms in six
+variables. In order to illustrate the efficiency and flexibility of our
+methods, we construct in both situations many Calabi--Yau manifolds of
+dimension three and four, as well as a few Fano varieties, including some new
+Fano fourfolds.
+",0,0,1,0,0,0
+12401,Homology of the family of hyperelliptic curves,"  Homology of braid groups and Artin groups can be related to the study of
+spaces of curves. We completely calculate the integral homology of the family
+of smooth curves of genus $g$ with one boundary component, that are double
+coverings of the disk ramified over $n = 2g + 1$ points. The main part of such
+homology is described by the homology of the braid group with coefficients in a
+symplectic representation, namely the braid group $\mathrm{Br}_n$ acts on the
+first homology group of a genus $g$ surface via Dehn twists. Our computations
+shows that such groups have only $2$-torsion. We also investigate stabilization
+properties and provide Poincaré series, both for unstable and stable
+homology.
+",0,0,1,0,0,0
+12402,Simulation of Drop Impact on a Hot Wall using SPH Method with Peng-Robinson Equation of State,"  This study presents a smoothed particle hydrodynamics (SPH) method with
+Peng-Robinson equation of state for simulating drop vaporization and drop
+impact on a hot surface. The conservation equations of momentum and energy and
+Peng-Robinson equation of state are applied to describe both the liquid and gas
+phases. The governing equations are solved numerically by the SPH method. The
+phase change between the liquid and gas phases are simulated directly without
+using any phase change models. The numerical method is validated by comparing
+numerical results with analytical solutions for the vaporization of n-heptane
+drops at different temperatures. Using the SPH method, the processes of
+n-heptane drops impacting on a solid wall with different temperatures are
+studied numerically. The results show that the size of the film formed by drop
+impact decreases when temperature increases. When the temperature is high
+enough, the drop will rebound.
+",0,1,0,0,0,0
+12403,Generation of attosecond electron beams in relativistic ionization by short laser pulses,"  Ionization by relativistically intense short laser pulses is studied in the
+framework of strong-field quantum electrodynamics. Distinctive patterns are
+found in the energy probability distributions of photoelectrons. Except of the
+already observed patterns, which were studied in Phys. Rev. A {\bf 94}, 013402
+(2016), we discover an additional interference-free smooth supercontinuum in
+the high-energy portion of the spectrum, reaching tens of kiloelectronovolts.
+As we show, the latter is sensitive to the driving field intensity and it can
+be detected in a narrow polar-angular window. Once these high-energy electrons
+are collected, they can form solitary attosecond pulses. This is particularly
+important in light of various applications of attosecond electron beams such as
+in ultrafast electron diffraction and crystallography, or in time-resolved
+electron microscopy of physical, chemical, and biological processes.
+",0,1,0,0,0,0
+12404,Surface defects and elliptic quantum groups,"  A brane construction of an integrable lattice model is proposed. The model is
+composed of Belavin's R-matrix, Felder's dynamical R-matrix, the
+Bazhanov-Sergeev-Derkachov-Spiridonov R-operator and some intertwining
+operators. This construction implies that a family of surface defects act on
+supersymmetric indices of four-dimensional $\mathcal{N} = 1$ supersymmetric
+field theories as transfer matrices related to elliptic quantum groups.
+",0,0,1,0,0,0
+12405,Disentangling and Assessing Uncertainties in Multiperiod Corporate Default Risk Predictions,"  Measuring the corporate default risk is broadly important in economics and
+finance. Quantitative methods have been developed to predictively assess future
+corporate default probabilities. However, as a more difficult yet crucial
+problem, evaluating the uncertainties associated with the default predictions
+remains little explored. In this paper, we attempt to fill this blank by
+developing a procedure for quantifying the level of associated uncertainties
+upon carefully disentangling multiple contributing sources. Our framework
+effectively incorporates broad information from historical default data,
+corporates' financial records, and macroeconomic conditions by a)
+characterizing the default mechanism, and b) capturing the future dynamics of
+various features contributing to the default mechanism. Our procedure overcomes
+the major challenges in this large scale statistical inference problem and
+makes it practically feasible by using parsimonious models, innovative methods,
+and modern computational facilities. By predicting the marketwide total number
+of defaults and assessing the associated uncertainties, our method can also be
+applied for evaluating the aggregated market credit risk level. Upon analyzing
+a US market data set, we demonstrate that the level of uncertainties associated
+with default risk assessments is indeed substantial. More informatively, we
+also find that the level of uncertainties associated with the default risk
+predictions is correlated with the level of default risks, indicating potential
+for new scopes in practical applications including improving the accuracy of
+default risk assessments.
+",0,0,0,1,0,1
+12406,The XXL Survey: XVII. X-ray and Sunyaev-Zel'dovich Properties of the Redshift 2.0 Galaxy Cluster XLSSC 122,"  We present results from a 100 ks XMM-Newton observation of galaxy cluster
+XLSSC 122, the first massive cluster discovered through its X-ray emission at
+$z\approx2$. The data provide the first precise constraints on the bulk
+thermodynamic properties of such a distant cluster, as well as an X-ray
+spectroscopic confirmation of its redshift. We measure an average temperature
+of $kT=5.0\pm0.7$ keV; a metallicity with respect to solar of
+$Z/Z_{\odot}=0.33^{+0.19}_{-0.17}$, consistent with lower-redshift clusters;
+and a redshift of $z=1.99^{+0.07}_{-0.06}$, consistent with the earlier photo-z
+estimate. The measured gas density profile leads to a mass estimate at
+$r_{500}$ of $M_{500}=(6.3\pm1.5)\times10^{13}M_{\odot}$. From CARMA 30 GHz
+data, we measure the spherically integrated Compton parameter within $r_{500}$
+to be $Y_{500}=(3.6\pm0.4)\times10^{-12}$. We compare the measured properties
+of XLSSC 122 to lower-redshift cluster samples, and find good agreement when
+assuming the simplest (self-similar) form for the evolution of cluster scaling
+relations. While a single cluster provides limited information, this result
+suggests that the evolution of the intracluster medium in the most massive,
+well developed clusters is remarkably simple, even out to the highest redshifts
+where they have been found. At the same time, our data reaffirm the previously
+reported spatial offset between the centers of the X-ray and SZ signals for
+XLSSC 122, suggesting a disturbed configuration. Higher spatial resolution data
+could thus provide greater insights into the internal dynamics of this system.
+",0,1,0,0,0,0
+12407,A bound for rational Thurston-Bennequin invariants,"  In this paper, we introduce a rational $\tau$ invariant for rationally
+null-homologous knots in contact 3-manifolds with nontrivial
+Ozsváth-Szabó contact invariants. Such an invariant is an upper bound
+for the sum of rational Thurston-Bennequin invariant and the rational rotation
+number of the Legendrian representatives of the knot. In the special case of
+Floer simple knots in L-spaces, we can compute the rational $\tau$ invariants
+by correction terms.
+",0,0,1,0,0,0
+12408,Generalized Fréchet Bounds for Cell Entries in Multidimensional Contingency Tables,"  We consider the lattice, $\mathcal{L}$, of all subsets of a multidimensional
+contingency table and establish the properties of monotonicity and
+supermodularity for the marginalization function, $n(\cdot)$, on $\mathcal{L}$.
+We derive from the supermodularity of $n(\cdot)$ some generalized Fréchet
+inequalities complementing and extending inequalities of Dobra and Fienberg.
+Further, we construct new monotonic and supermodular functions from $n(\cdot)$,
+and we remark on the connection between supermodularity and some correlation
+inequalities for probability distributions on lattices. We also apply an
+inequality of Ky Fan to derive a new approach to Fréchet inequalities for
+multidimensional contingency tables.
+",0,0,1,1,0,0
+12409,Signaling on the Continuous Spectrum of Nonlinear Optical fiber,"  This paper studies different signaling techniques on the continuous spectrum
+(CS) of nonlinear optical fiber defined by nonlinear Fourier transform. Three
+different signaling techniques are proposed and analyzed based on the
+statistics of the noise added to CS after propagation along the nonlinear
+optical fiber. The proposed methods are compared in terms of error performance,
+distance reach, and complexity. Furthermore, the effect of chromatic dispersion
+on the data rate and noise in nonlinear spectral domain is investigated. It is
+demonstrated that, for a given sequence of CS symbols, an optimal bandwidth (or
+symbol rate) can be determined so that the temporal duration of the propagated
+signal at the end of the fiber is minimized. In effect, the required guard
+interval between the subsequently transmitted data packets in time is minimized
+and the effective data rate is significantly enhanced. Moreover, by selecting
+the proper signaling method and design criteria a reach distance of 7100 km is
+reported by only singling on the CS at a rate of 9.6 Gbps.
+",1,1,0,0,0,0
+12410,Symmetry analysis and soliton solution of (2+1)- dimensional Zoomeron equation,"  Traveling wave solutions of (2 + 1)-dimensional Zoomeron equation(ZE) are
+developed in terms of exponential functions involving free parameters. It is
+shown that the novel Lie group of transformations method is a competent and
+prominent tool in solving nonlinear partial differential equations(PDEs) in
+mathematical physics. The similarity transformation method(STM) is applied
+first on (2 + 1)-dimensional ZE to find the infinitesimal generators.
+Discussing the different cases on these infinitesimal generators, STM reduce (2
++ 1)-dimensional ZE into (1 + 1)-dimensional PDEs, later it reduces these PDEs
+into various ordinary differential equations(ODEs) and help to find exact
+solutions of (2 + 1)-dimensional ZE.
+",0,1,1,0,0,0
+12411,Estimation in the convolution structure density model. Part II: adaptation over the scale of anisotropic classes,"  This paper continues the research started in \cite{LW16}. In the framework of
+the convolution structure density model on $\bR^d$, we address the problem of
+adaptive minimax estimation with $\bL_p$--loss over the scale of anisotropic
+Nikol'skii classes. We fully characterize the behavior of the minimax risk for
+different relationships between regularity parameters and norm indexes in the
+definitions of the functional class and of the risk. In particular, we show
+that the boundedness of the function to be estimated leads to an essential
+improvement of the asymptotic of the minimax risk. We prove that the selection
+rule proposed in Part I leads to the construction of an optimally or nearly
+optimally (up to logarithmic factor) adaptive estimator.
+",0,0,1,1,0,0
+12412,Explaining Recurrent Neural Network Predictions in Sentiment Analysis,"  Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown
+to deliver insightful explanations in the form of input space relevances for
+understanding feed-forward neural network classification decisions. In the
+present work, we extend the usage of LRP to recurrent neural networks. We
+propose a specific propagation rule applicable to multiplicative connections as
+they arise in recurrent network architectures such as LSTMs and GRUs. We apply
+our technique to a word-based bi-directional LSTM model on a five-class
+sentiment prediction task, and evaluate the resulting LRP relevances both
+qualitatively and quantitatively, obtaining better results than a
+gradient-based related method which was used in previous work.
+",1,0,0,1,0,0
+12413,Generalized Coherence Concurrence and Path distinguishability,"  We propose a new family of coherence monotones, named the \emph{generalized
+coherence concurrence} (or coherence $k$-concurrence), which is an analogous
+concept to the generalized entanglement concurrence. The coherence
+$k$-concurrence of a state is nonzero if and only if the coherence number (a
+recently introduced discrete coherence monotone) of the state is not smaller
+than $k$, and a state can be converted to a state with nonzero entanglement
+$k$-concurrence via incoherent operations if and only if the state has nonzero
+coherence $k$-concurrence. We apply the coherence concurrence family to the
+problem of wave-particle duality in multi-path interference phenomena. We
+obtain a sharper equation for path distinguishability (which witness the
+duality) than the known value and show that the amount of each concurrence for
+the quanton state determines the number of slits which are identified
+unambiguously.
+",1,0,0,0,0,0
+12414,Passivity Based Whole-body Control for Quadrupedal Locomotion on Challenging Terrain,"  We present a passivity-based Whole-Body Control approach for quadruped robots
+that achieves dynamic locomotion while compliantly balancing the robot's trunk.
+We formulate the motion tracking as a Quadratic Program that takes into account
+the full robot rigid body dynamics, the actuation limit, the joint limits and
+the contact interaction. We analyze the controller robustness against
+inaccurate friction coefficient estimates and unstable footholds, as well as
+its capability to redistribute the load as a consequence of enforcing actuation
+limits. Additionally, we present some practical implementation details gained
+from the experience with the real platform. Extensive experimental trials on
+the 90 Kg Hydraulically actuated Quadruped robot validate the capabilities of
+this controller under various terrain conditions and gaits. The proposed
+approach is expedient for accurate execution of high dynamic motions with
+respect to the current state of the art.
+",1,0,0,0,0,0
+12415,Accelerations for Graph Isomorphism,"  In this paper, we present two main results. First, by only one conjecture
+(Conjecture 2.9) for recognizing a vertex symmetric graph, which is the hardest
+task for our problem, we construct an algorithm for finding an isomorphism
+between two graphs in polynomial time $ O(n^{3}) $. Second, without that
+conjecture, we prove the algorithm to be of quasi-polynomial time $
+O(n^{1.5\log n}) $. The conjectures in this paper are correct for all graphs of
+size no larger than $ 5 $ and all graphs we have encountered. At least the
+conjecture for determining if a graph is vertex symmetric is quite true
+intuitively. We are not able to prove them by hand, so we have planned to find
+possible counterexamples by a computer. We also introduce new concepts like
+collapse pattern and collapse tomography, which play important roles in our
+algorithms.
+",1,0,0,0,0,0
+12416,Multi-Objective Maximization of Monotone Submodular Functions with Cardinality Constraint,"  We consider the problem of multi-objective maximization of monotone
+submodular functions subject to cardinality constraint, often formulated as
+$\max_{|A|=k}\min_{i\in\{1,\dots,m\}}f_i(A)$. While it is widely known that
+greedy methods work well for a single objective, the problem becomes much
+harder with multiple objectives. In fact, Krause et al.\ (2008) showed that
+when the number of objectives $m$ grows as the cardinality $k$ i.e.,
+$m=\Omega(k)$, the problem is inapproximable (unless $P=NP$). On the other
+hand, when $m$ is constant Chekuri et al.\ (2010) showed a randomized
+$(1-1/e)-\epsilon$ approximation with runtime (number of queries to function
+oracle) $n^{m/\epsilon^3}$. %In fact, the result of Chekuri et al.\ (2010) is
+for the far more general case of matroid constant.
+We focus on finding a fast and practical algorithm that has (asymptotic)
+approximation guarantees even when $m$ is super constant. We first modify the
+algorithm of Chekuri et al.\ (2010) to achieve a $(1-1/e)$ approximation for
+$m=o(\frac{k}{\log^3 k})$. This demonstrates a steep transition from constant
+factor approximability to inapproximability around $m=\Omega(k)$. Then using
+Multiplicative-Weight-Updates (MWU), we find a much faster
+$\tilde{O}(n/\delta^3)$ time asymptotic $(1-1/e)^2-\delta$ approximation. While
+the above results are all randomized, we also give a simple deterministic
+$(1-1/e)-\epsilon$ approximation with runtime $kn^{m/\epsilon^4}$. Finally, we
+run synthetic experiments using Kronecker graphs and find that our MWU inspired
+heuristic outperforms existing heuristics.
+",1,0,0,1,0,0
+12417,Cosmology from conservation of global energy,"  It is argued that many of the problems and ambiguities of standard cosmology
+derive from a single one: violation of conservation of energy in the standard
+paradigm. Standard cosmology satisfies conservation of local energy, however
+disregards the inherent global aspect of energy. We therefore explore
+conservation of the quasi-local Misner-Sharp energy within the causal horizon,
+which, as we argue, is necessarily an apparent horizon. Misner-Sharp energy
+assumes the presence of arbitrary mass-energy. Its conservation, however,
+yields ""empty"" de Sitter (open, flat, closed) as single cosmological solution,
+where Misner-Sharp total energy acts as cosmological constant and where the
+source of curvature energy is unidentified. It is argued that de Sitter is only
+apparently empty of matter. That is, total matter energy scales as curvature
+energy in open de Sitter, which causes evolution of the cosmic potential and
+induces gravitational time dilation. Curvature of time accounts completely for
+the extrinsic curvature, i.e., renders open de Sitter spatially flat. This
+explains the well known, surprising, spatial flatness of Misner-Sharp energy,
+even if extrinsic curvature is non-zero. The general relativistic derivation
+from Misner-Sharp energy is confirmed by a Machian equation of recessional and
+peculiar energy, which explicitly assumes the presence of matter. This
+relational model enhances interpretation. Time-dilated open de Sitter is
+spatially flat, dynamically close to $\Lambda$CDM, and is shown to be without
+the conceptual problems of concordance cosmology.
+",0,1,0,0,0,0
+12418,Electric field modulation of the non-linear areal magnetic anisotropy energy,"  We study the ferromagnetic layer thickness dependence of the
+voltage-controlled magnetic anisotropy (VCMA) in gated CoFeB/MgO
+heterostructures with heavy metal underlayers. When the effective CoFeB
+thickness is below ~1 nm, the VCMA efficiency of Ta/CoFeB/MgO heterostructures
+considerably decreases with decreasing CoFeB thickness. We find that a high
+order phenomenological term used to describe the thickness dependence of the
+areal magnetic anisotropy energy can also account for the change in the areal
+VCMA efficiency. In this structure, the higher order term competes against the
+common interfacial VCMA, thereby reducing the efficiency at lower CoFeB
+thickness. The areal VCMA efficiency does not saturate even when the effective
+CoFeB thickness exceeds ~1 nm. We consider the higher order term is related to
+the strain that develops at the CoFeB/MgO interface: as the average strain of
+the CoFeB layer changes with its thickness, the electronic structure of the
+CoFeB/MgO interface varies leading to changes in areal magnetic anisotropy
+energy and VCMA efficiency.
+",0,1,0,0,0,0
+12419,Gapless surface states originated from accidentally degenerate quadratic band touching in a three-dimensional tetragonal photonic crystal,"  A tetragonal photonic crystal composed of high-index pillars can exhibit a
+frequency-isolated accidental degeneracy at a high-symmetry point in the first
+Brillouin zone. A photonic band gap can be formed there by introducing a
+geometrical anisotropy in the pillars. In this gap, gapless surface/domain-wall
+states emerge under a certain condition. We analyze their physical property in
+terms of an effective hamiltonian, and a good agreement between the effective
+theory and numerical calculation is obtained.
+",0,1,0,0,0,0
+12420,On the impact of pull request decisions on future contributions,"  The pull-based development process has become prevalent on platforms such as
+GitHub as a form of distributed software development. Potential contributors
+can create and submit a set of changes to a software project through pull
+requests. These changes can be accepted, discussed or rejected by the
+maintainers of the software project, and can influence further contribution
+proposals. As such, it is important to examine the practices that encourage
+contributors to a project to submit pull requests. Specifically, we consider
+the impact of prior pull requests on the acceptance or rejection of subsequent
+pull requests. We also consider the potential effect of rejecting or ignoring
+pull requests on further contributions. In this preliminary research, we study
+three large projects on \textsf{GitHub}, using pull request data obtained
+through the \textsf{GitHub} API, and we perform empirical analyses to
+investigate the above questions. Our results show that continued contribution
+to a project is correlated with higher pull request acceptance rates and that
+pull request rejections lead to fewer future contributions.
+",1,0,0,0,0,0
+12421,The unreasonable effectiveness of the forget gate,"  Given the success of the gated recurrent unit, a natural question is whether
+all the gates of the long short-term memory (LSTM) network are necessary.
+Previous research has shown that the forget gate is one of the most important
+gates in the LSTM. Here we show that a forget-gate-only version of the LSTM
+with chrono-initialized biases, not only provides computational savings but
+outperforms the standard LSTM on multiple benchmark datasets and competes with
+some of the best contemporary models. Our proposed network, the JANET, achieves
+accuracies of 99% and 92.5% on the MNIST and pMNIST datasets, outperforming the
+standard LSTM which yields accuracies of 98.5% and 91%.
+",0,0,0,1,0,0
+12422,WMRB: Learning to Rank in a Scalable Batch Training Approach,"  We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch
+loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss
+(WARP). WMRB uses a new rank estimator and an efficient batch training
+algorithm. The approach allows more accurate item rank approximation and
+explicit utilization of parallel computation to accelerate training. In three
+item recommendation tasks, WMRB consistently outperforms WARP and other
+baselines. Moreover, WMRB shows clear time efficiency advantages as data scale
+increases.
+",1,0,0,1,0,0
+12423,Learning Features from Co-occurrences: A Theoretical Analysis,"  Representing a word by its co-occurrences with other words in context is an
+effective way to capture the meaning of the word. However, the theory behind
+remains a challenge. In this work, taking the example of a word classification
+task, we give a theoretical analysis of the approaches that represent a word X
+by a function f(P(C|X)), where C is a context feature, P(C|X) is the
+conditional probability estimated from a text corpus, and the function f maps
+the co-occurrence measure to a prediction score. We investigate the impact of
+context feature C and the function f. We also explain the reasons why using the
+co-occurrences with multiple context features may be better than just using a
+single one. In addition, some of the results shed light on the theory of
+feature learning and machine learning in general.
+",1,0,1,1,0,0
+12424,Azumaya algebras and canonical components,"  Let $M$ be a compact 3-manifold and $\Gamma=\pi_1(M)$. The work of Thurston
+and Culler--Shalen established the $\mathrm{SL}_2(\mathbb{C})$ character
+variety $X(\Gamma)$ as fundamental tool in the study of the geometry and
+topology of $M$. This is particularly so in the case when $M$ is the exterior
+of a hyperbolic knot $K$ in $S^3$. The main goals of this paper are to bring to
+bear tools from algebraic and arithmetic geometry to understand algebraic and
+number theoretic properties of the so-called canonical component of $X(\Gamma)$
+as well as distinguished points on the canonical component when $\Gamma$ is a
+knot group. In particular, we study how the theory of quaternion Azumaya
+algebras can be used to obtain algebraic and arithmetic information about Dehn
+surgeries, and perhaps of most interest, to construct new knot invariants that
+lie in the Brauer groups of curves over number fields.
+",0,0,1,0,0,0
+12425,On the Spectrum of Multi-Frequency Quasiperiodic Schrödinger Operators with Large Coupling,"  We study multi-frequency quasiperiodic Schrödinger operators on
+$\mathbb{Z} $. We prove that for a large real analytic potential satisfying
+certain restrictions the spectrum consists of a single interval. The result is
+a consequence of a criterion for the spectrum to contain an interval at a given
+location that we establish non-perturbatively in the regime of positive
+Lyapunov exponent.
+",0,0,1,0,0,0
+12426,Model Spaces of Regularity Structures for Space-Fractional SPDEs,"  We study model spaces, in the sense of Hairer, for stochastic partial
+differential equations involving the fractional Laplacian. We prove that the
+fractional Laplacian is a singular kernel suitable to apply the theory of
+regularity structures. Our main contribution is to study the dependence of the
+model space for a regularity structure on the three-parameter problem involving
+the spatial dimension, the polynomial order of the nonlinearity, and the
+exponent of the fractional Laplacian. The goal is to investigate the growth of
+the model space under parameter variation. In particular, we prove several
+results in the approaching subcriticality limit leading to universal growth
+exponents of the regularity structure. A key role is played by the viewpoint
+that model spaces can be identified with families of rooted trees. Our proofs
+are based upon a geometrical construction similar to Newton polygons for
+classical Taylor series and various combinatorial arguments. We also present
+several explicit examples listing all elements with negative homogeneity by
+implementing a new symbolic software package to work with regularity
+structures. We use this package to illustrate our analytical results and to
+obtain new conjectures regarding coarse-grained network measures for model
+spaces.
+",0,0,1,0,0,0
+12427,"Transit Detection of a ""Starshade"" at the Inner Lagrange Point of an Exoplanet","  All water-covered rocky planets in the inner habitable zones of solar-type
+stars will inevitably experience a catastrophic runaway climate due to
+increasing stellar luminosity and limits to outgoing infrared radiation from
+wet greenhouse atmospheres. Reflectors or scatterers placed near Earth's inner
+Lagrange point (L1) have been proposed as a 'geo-engineering"" solution to
+anthropogenic climate change and an advanced version of this could modulate
+incident irradiation over many Gyr or ""rescue"" a planet from the interior of
+the habitable zone. The distance of the starshade from the planet that
+minimizes its mass is 1.6 times the Earth-L1 distance. Such a starshade would
+have to be similar in size to the planet and the mutual occultations during
+planetary transits could produce a characteristic maximum at mid-transit in the
+light-curve. Because of a fortuitous ratio of densities, Earth-size planets
+around G dwarf stars present the best opportunity to detect such an artifact.
+The signal would be persistent and is potentially detectable by a future space
+photometry mission to characterize transiting planets. The signal could be
+distinguished from natural phenomenon, i.e. starspots or cometary dust clouds,
+by its shape, persistence, and transmission spectrum.
+",0,1,0,0,0,0
+12428,Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality,"  In the context of fitness coaching or for rehabilitation purposes, the motor
+actions of a human participant must be observed and analyzed for errors in
+order to provide effective feedback. This task is normally carried out by human
+coaches, and it needs to be solved automatically in technical applications that
+are to provide automatic coaching (e.g. training environments in VR). However,
+most coaching systems only provide coarse information on movement quality, such
+as a scalar value per body part that describes the overall deviation from the
+correct movement. Further, they are often limited to static body postures or
+rather simple movements of single body parts. While there are many approaches
+to distinguish between different types of movements (e.g., between walking and
+jumping), the detection of more subtle errors in a motor performance is less
+investigated. We propose a novel approach to classify errors in sports or
+rehabilitation exercises such that feedback can be delivered in a rapid and
+detailed manner: Homogeneous sub-sequences of exercises are first temporally
+aligned via Dynamic Time Warping. Next, we extract a feature vector from the
+aligned sequences, which serves as a basis for feature selection using Random
+Forests. The selected features are used as input for Support Vector Machines,
+which finally classify the movement errors. We compare our algorithm to a well
+established state-of-the-art approach in time series classification, 1-Nearest
+Neighbor combined with Dynamic Time Warping, and show our algorithm's
+superiority regarding classification quality as well as computational cost.
+",1,0,0,0,0,0
+12429,A mechanism of synaptic clock underlying subjective time perception,"  Temporal resolution of visual information processing is thought to be an
+important factor in predator-prey interactions, shaped in the course of
+evolution by animals' ecology. Here I show that light can be considered to have
+a dual role of a source of information, which guides motor actions, and an
+environmental feedback for those actions. I consequently show how temporal
+perception might depend on behavioral adaptations realized by the nervous
+system. I propose an underlying mechanism of synaptic clock, with every synapse
+having its characteristic time unit, determined by the persistence of memory
+traces of synaptic inputs, which is used by the synapse to tell time. The
+present theory offers a testable framework, which may account for numerous
+experimental findings, including the interspecies variation in temporal
+resolution and the properties of subjective time perception, specifically the
+variable speed of perceived time passage, depending on emotional and
+attentional states or tasks performed.
+",0,0,0,0,1,0
+12430,Optimal Packings of Two to Four Equal Circles on Any Flat Torus,"  We find explicit formulas for the radii and locations of the circles in all
+the optimally dense packings of two, three or four equal circles on any flat
+torus, defined to be the quotient of the Euclidean plane by the lattice
+generated by two independent vectors. We prove the optimality of the
+arrangements using techniques from rigidity theory and topological graph
+theory.
+",0,0,1,0,0,0
+12431,Obfuscation in Bitcoin: Techniques and Politics,"  In the cryptographic currency Bitcoin, all transactions are recorded in the
+blockchain - a public, global, and immutable ledger. Because transactions are
+public, Bitcoin and its users employ obfuscation to maintain a degree of
+financial privacy. Critically, and in contrast to typical uses of obfuscation,
+in Bitcoin obfuscation is not aimed against the system designer but is instead
+enabled by design. We map sixteen proposed privacy-preserving techniques for
+Bitcoin on an obfuscation-vs.-cryptography axis, and find that those that are
+used in practice tend toward obfuscation. We argue that this has led to a
+balance between privacy and regulatory acceptance.
+",1,0,0,0,0,0
+12432,Path Planning and Controlled Crash Landing of a Quadcopter in case of a Rotor Failure,"  This paper presents a framework for controlled emergency landing of a
+quadcopter, experiencing a rotor failure, away from sensitive areas. A complete
+mathematical model capturing the dynamics of the system is presented that takes
+the asymmetrical aerodynamic load on the propellers into account. An
+equilibrium state of the system is calculated around which a linear
+time-invariant control strategy is developed to stabilize the system. By
+utilizing the proposed model, a specific configuration for a quadcopter is
+introduced that leads to the minimum power consumption during a
+yaw-rate-resolved hovering after a rotor failure. Furthermore, given a 3D
+representation of the environment, an optimal flight trajectory towards a safe
+crash landing spot, while avoiding collision with obstacles, is developed using
+an RRT* approach. The cost function for determining the best landing spot
+consists of: (i) finding the safest landing spot with the largest clearance
+from the obstacles; and (ii) finding the most energy-efficient trajectory
+towards the landing spot. The performance of the proposed framework is tested
+via simulations.
+",1,0,0,0,0,0
+12433,Exact short-time height distribution in 1D KPZ equation with Brownian initial condition,"  The early time regime of the Kardar-Parisi-Zhang (KPZ) equation in $1+1$
+dimension, starting from a Brownian initial condition with a drift $w$, is
+studied using the exact Fredholm determinant representation. For large drift we
+recover the exact results for the droplet initial condition, whereas a
+vanishingly small drift describes the stationary KPZ case, recently studied by
+weak noise theory (WNT). We show that for short time $t$, the probability
+distribution $P(H,t)$ of the height $H$ at a given point takes the large
+deviation form $P(H,t) \sim \exp{\left(-\Phi(H)/\sqrt{t} \right)}$. We obtain
+the exact expressions for the rate function $\Phi(H)$ for $H<H_{c2}$. Our exact
+expression for $H_{c2}$ numerically coincides with the value at which WNT was
+found to exhibit a spontaneous reflection symmetry breaking. We propose two
+continuations for $H>H_{c2}$, which apparently correspond to the symmetric and
+asymmetric WNT solutions. The rate function $\Phi(H)$ is Gaussian in the
+center, while it has asymmetric tails, $|H|^{5/2}$ on the negative $H$ side and
+$H^{3/2}$ on the positive $H$ side.
+",0,1,0,0,0,0
+12434,The role of cosmology in modern physics,"  Subject of this article is the relationship between modern cosmology and
+fundamental physics, in particular general relativity as a theory of gravity on
+one side, together with its unique application in cosmology, and the formation
+of structures and their statistics on the other. It summarises arguments for
+the formulation for a metric theory of gravity and the uniqueness of the
+construction of general relativity. It discusses symmetry arguments in the
+construction of Friedmann-Lemaître cosmologies as well as assumptions in
+relation to the presence of dark matter, when adopting general relativity as
+the gravitational theory. A large section is dedicated to $\Lambda$CDM as the
+standard model for structure formation and the arguments that led to its
+construction, and to the of role statistics and to the problem of scientific
+inference in cosmology as an empirical science. The article concludes with an
+outlook on current and future developments in cosmology.
+",0,1,0,0,0,0
+12435,TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals,"  Temporal Action Proposal (TAP) generation is an important problem, as fast
+and accurate extraction of semantically important (e.g. human actions) segments
+from untrimmed videos is an important step for large-scale video analysis. We
+propose a novel Temporal Unit Regression Network (TURN) model. There are two
+salient aspects of TURN: (1) TURN jointly predicts action proposals and refines
+the temporal boundaries by temporal coordinate regression; (2) Fast computation
+is enabled by unit feature reuse: a long untrimmed video is decomposed into
+video units, which are reused as basic building blocks of temporal proposals.
+TURN outperforms the state-of-the-art methods under average recall (AR) by a
+large margin on THUMOS-14 and ActivityNet datasets, and runs at over 880 frames
+per second (FPS) on a TITAN X GPU. We further apply TURN as a proposal
+generation stage for existing temporal action localization pipelines, it
+outperforms state-of-the-art performance on THUMOS-14 and ActivityNet.
+",1,0,0,0,0,0
+12436,Efficiently Learning Nonstationary Gaussian Processes for Real World Impact,"  Most real world phenomena such as sunlight distribution under a forest
+canopy, minerals concentration, stock valuation, exhibit nonstationary dynamics
+i.e. phenomenon variation changes depending on the locality. Nonstationary
+dynamics pose both theoretical and practical challenges to statistical machine
+learning algorithms that aim to accurately capture the complexities governing
+the evolution of such processes. Typically the nonstationary dynamics are
+modeled using nonstationary Gaussian Process models (NGPS) that employ local
+latent dynamics parameterization to correspondingly model the nonstationary
+real observable dynamics. Recently, an approach based on most likely induced
+latent dynamics representation attracted research community's attention for a
+while. The approach could not be employed for large scale real world
+applications because learning a most likely latent dynamics representation
+involves maximization of marginal likelihood of the observed real dynamics that
+becomes intractable as the number of induced latent points grows with problem
+size. We have established a direct relationship between informativeness of the
+induced latent dynamics and the marginal likelihood of the observed real
+dynamics. This opens up the possibility of maximizing marginal likelihood of
+observed real dynamics indirectly by near optimally maximizing entropy or
+mutual information gain on the induced latent dynamics using greedy algorithms.
+Therefore, for an efficient yet accurate inference, we propose to build an
+induced latent dynamics representation using a novel algorithm LISAL that
+adaptively maximizes entropy or mutual information on the induced latent
+dynamics and marginal likelihood of observed real dynamics in an iterative
+manner. The relevance of LISAL is validated using real world datasets.
+",0,0,0,1,0,0
+12437,Causal Holography in Application to the Inverse Scattering Problems,"  For a given smooth compact manifold $M$, we introduce an open class $\mathcal
+G(M)$ of Riemannian metrics, which we call \emph{metrics of the gradient type}.
+For such metrics $g$, the geodesic flow $v^g$ on the spherical tangent bundle
+$SM \to M$ admits a Lyapunov function (so the $v^g$-flow is traversing). It
+turns out, that metrics of the gradient type are exactly the non-trapping
+metrics.
+For every $g \in \mathcal G(M)$, the geodesic scattering along the boundary
+$\partial M$ can be expressed in terms of the \emph{scattering map} $C_{v^g}:
+\partial_1^+(SM) \to \partial_1^-(SM)$. It acts from a domain
+$\partial_1^+(SM)$ in the boundary $\partial(SM)$ to the complementary domain
+$\partial_1^-(SM)$, both domains being diffeomorphic. We prove that, for a
+\emph{boundary generic} metric $g \in \mathcal G(M)$ the map $C_{v^g}$ allows
+for a reconstruction of $SM$ and of the geodesic foliation $\mathcal F(v^g)$ on
+it, up to a homeomorphism (often a diffeomorphism).
+Also, for such $g$, the knowledge of the scattering map $C_{v^g}$ makes it
+possible to recover the homology of $M$, the Gromov simplicial semi-norm on it,
+and the fundamental group of $M$. Additionally, $C_{v^g}$ allows to reconstruct
+the naturally stratified topological type of the space of geodesics on $M$.
+",0,0,1,0,0,0
+12438,Inferring short-term volatility indicators from Bitcoin blockchain,"  In this paper, we study the possibility of inferring early warning indicators
+(EWIs) for periods of extreme bitcoin price volatility using features obtained
+from Bitcoin daily transaction graphs. We infer the low-dimensional
+representations of transaction graphs in the time period from 2012 to 2017
+using Bitcoin blockchain, and demonstrate how these representations can be used
+to predict extreme price volatility events. Our EWI, which is obtained with a
+non-negative decomposition, contains more predictive information than those
+obtained with singular value decomposition or scalar value of the total Bitcoin
+transaction volume.
+",1,0,0,0,0,1
+12439,Discrete structure of the brain rhythms,"  Neuronal activity in the brain generates synchronous oscillations of the
+Local Field Potential (LFP). The traditional analyses of the LFPs are based on
+decomposing the signal into simpler components, such as sinusoidal harmonics.
+However, a common drawback of such methods is that the decomposition primitives
+are usually presumed from the onset, which may bias our understanding of the
+signal's structure. Here, we introduce an alternative approach that allows an
+impartial, high resolution, hands-off decomposition of the brain waves into a
+small number of discrete, frequency-modulated oscillatory processes, which we
+call oscillons. In particular, we demonstrate that mouse hippocampal LFP
+contain a single oscillon that occupies the $\theta$-frequency band and a
+couple of $\gamma$-oscillons that correspond, respectively, to slow and fast
+$\gamma$-waves. Since the oscillons were identified empirically, they may
+represent the actual, physical structure of synchronous oscillations in
+neuronal ensembles, whereas Fourier-defined ""brain waves"" are nothing but
+poorly resolved oscillons.
+",0,0,0,0,1,0
+12440,A new class of solutions for the multi-component extended Harry Dym equation,"  We construct a point transformation between two integrable systems, the
+multi-component Harry Dym equation and the multi-component extended Harry Dym
+equation, that does not preserve the class of multi-phase solutions. As a
+consequence we obtain a new type of wave-like solutions, generalising
+the~multi-phase solutions of the multi-component extended Harry Dym equation.
+Our construction is easily transferable to other integrable systems with
+analogous properties.
+",0,1,0,0,0,0
+12441,Multivariate Generalized Linear Mixed Models for Joint Estimation of Sporting Outcomes,"  This paper explores improvements in prediction accuracy and inference
+capability when allowing for potential correlation in team-level random effects
+across multiple game-level responses from different assumed distributions.
+First-order and fully exponential Laplace approximations are used to fit
+normal-binary and Poisson-binary multivariate generalized linear mixed models
+with non-nested random effects structures. We have built these models into the
+R package mvglmmRank, which is used to explore several seasons of American
+college football and basketball data.
+",0,0,0,1,0,0
+12442,Accelerator Codesign as Non-Linear Optimization,"  We propose an optimization approach for determining both hardware and
+software parameters for the efficient implementation of a (family of)
+applications called dense stencil computations on programmable GPGPUs. We first
+introduce a simple, analytical model for the silicon area usage of accelerator
+architectures and a workload characterization of stencil computations. We
+combine this characterization with a parametric execution time model and
+formulate a mathematical optimization problem. That problem seeks to maximize a
+common objective function of 'all the hardware and software parameters'. The
+solution to this problem, therefore ""solves"" the codesign problem:
+simultaneously choosing software-hardware parameters to optimize total
+performance.
+We validate this approach by proposing architectural variants of the NVIDIA
+Maxwell GTX-980 (respectively, Titan X) specifically tuned to a predetermined
+workload of four common 2D stencils (Heat, Jacobi, Laplacian, and Gradient) and
+two 3D ones (Heat and Laplacian). Our model predicts that performance would
+potentially improve by 28% (respectively, 33%) with simple tweaks to the
+hardware parameters such as adapting coarse and fine-grained parallelism by
+changing the number of streaming multiprocessors and the number of compute
+cores each contains. We propose a set of Pareto-optimal design points to
+exploit the trade-off between performance and silicon area and show that by
+additionally eliminating GPU caches, we can get a further 2-fold improvement.
+",1,0,0,0,0,0
+12443,Riddim: A Rhythm Analysis and Decomposition Tool Based On Independent Subspace Analysis,"  The goal of this thesis was to implement a tool that, given a digital audio
+input, can extract and represent rhythm and musical time. The purpose of the
+tool is to help develop better models of rhythm for real-time computer based
+performance and composition. This analysis tool, Riddim, uses Independent
+Subspace Analysis (ISA) and a robust onset detection scheme to separate and
+detect salient rhythmic and timing information from different sonic sources
+within the input. This information is then represented in a format that can be
+used by a variety of algorithms that interpret timing information to infer
+rhythmic and musical structure. A secondary objective of this work is a ""proof
+of concept"" as a non-real-time rhythm analysis system based on ISA. This is a
+necessary step since ultimately it is desirable to incorporate this
+functionality in a real-time plug-in for live performance and improvisation.
+",1,0,0,0,0,0
+12444,Cholesterol modulates acetylcholine receptor diffusion by tuning confinement sojourns and nanocluster stability,"  Translational motion of neurotransmitter receptors is key for determining
+receptor number at the synapse and hence, synaptic efficacy. We combine
+live-cell STORM superresolution microscopy of nicotinic acetylcholine receptor
+(nAChR) with single-particle tracking, mean-squared displacement (MSD), turning
+angle, ergodicity, and clustering analyses to characterize the lateral motion
+of individual molecules and their collective behaviour. nAChR diffusion is
+highly heterogeneous: subdiffusive, Brownian and, less frequently,
+superdiffusive. At the single-track level, free walks are transiently
+interrupted by ms-long confinement sojourns occurring in nanodomains of ~36 nm
+radius. Cholesterol modulates the time and the area spent in confinement.
+Turning angle analysis reveals anticorrelated steps with time-lag dependence,
+in good agreement with the permeable fence model. At the ensemble level,
+nanocluster assembly occurs in second-long bursts separated by periods of
+cluster disassembly. Thus, millisecond-long confinement sojourns and
+second-long reversible nanoclustering with similar cholesterol sensitivities
+affect all trajectories; the proportion of the two regimes determines the
+resulting macroscopic motional mode and breadth of heterogeneity in the
+ensemble population.
+",0,1,0,0,0,0
+12445,Incidence systems on Cartesian powers of algebraic curves,"  We show that a reduct of the Zariski structure of an algebraic curve which is
+not locally modular interprets a field, answering a question of Zilber's.
+",0,0,1,0,0,0
+12446,Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition,"  Statisticians have made great progress in creating methods that reduce our
+reliance on parametric assumptions. However this explosion in research has
+resulted in a breadth of inferential strategies that both create opportunities
+for more reliable inference as well as complicate the choices that an applied
+researcher has to make and defend. Relatedly, researchers advocating for new
+methods typically compare their method to at best 2 or 3 other causal inference
+strategies and test using simulations that may or may not be designed to
+equally tease out flaws in all the competing methods. The causal inference data
+analysis challenge, ""Is Your SATT Where It's At?"", launched as part of the 2016
+Atlantic Causal Inference Conference, sought to make progress with respect to
+both of these issues. The researchers creating the data testing grounds were
+distinct from the researchers submitting methods whose efficacy would be
+evaluated. Results from 30 competitors across the two versions of the
+competition (black box algorithms and do-it-yourself analyses) are presented
+along with post-hoc analyses that reveal information about the characteristics
+of causal inference strategies and settings that affect performance. The most
+consistent conclusion was that methods that flexibly model the response surface
+perform better overall than methods that fail to do so. Finally new methods are
+proposed that combine features of several of the top-performing submitted
+methods.
+",0,0,0,1,0,0
+12447,Certified Computation from Unreliable Datasets,"  A wide range of learning tasks require human input in labeling massive data.
+The collected data though are usually low quality and contain inaccuracies and
+errors. As a result, modern science and business face the problem of learning
+from unreliable data sets.
+In this work, we provide a generic approach that is based on
+\textit{verification} of only few records of the data set to guarantee high
+quality learning outcomes for various optimization objectives. Our method,
+identifies small sets of critical records and verifies their validity. We show
+that many problems only need $\text{poly}(1/\varepsilon)$ verifications, to
+ensure that the output of the computation is at most a factor of $(1 \pm
+\varepsilon)$ away from the truth. For any given instance, we provide an
+\textit{instance optimal} solution that verifies the minimum possible number of
+records to approximately certify correctness. Then using this instance optimal
+formulation of the problem we prove our main result: ""every function that
+satisfies some Lipschitz continuity condition can be certified with a small
+number of verifications"". We show that the required Lipschitz continuity
+condition is satisfied even by some NP-complete problems, which illustrates the
+generality and importance of this theorem.
+In case this certification step fails, an invalid record will be identified.
+Removing these records and repeating until success, guarantees that the result
+will be accurate and will depend only on the verified records. Surprisingly, as
+we show, for several computation tasks more efficient methods are possible.
+These methods always guarantee that the produced result is not affected by the
+invalid records, since any invalid record that affects the output will be
+detected and verified.
+",1,0,0,0,0,0
+12448,A sparse grid approach to balance sheet risk measurement,"  In this work, we present a numerical method based on a sparse grid
+approximation to compute the loss distribution of the balance sheet of a
+financial or an insurance company. We first describe, in a stylised way, the
+assets and liabilities dynamics that are used for the numerical estimation of
+the balance sheet distribution. For the pricing and hedging model, we chose a
+classical Black & Scholes model with a stochastic interest rate following a
+Hull & White model. The risk management model describing the evolution of the
+parameters of the pricing and hedging model is a Gaussian model. The new
+numerical method is compared with the traditional nested simulation approach.
+We review the convergence of both methods to estimate the risk indicators under
+consideration. Finally, we provide numerical results showing that the sparse
+grid approach is extremely competitive for models with moderate dimension.
+",0,0,0,0,0,1
+12449,Towards Metamerism via Foveated Style Transfer,"  The problem of $\textit{visual metamerism}$ is defined as finding a family of
+perceptually indistinguishable, yet physically different images. In this paper,
+we propose our NeuroFovea metamer model, a foveated generative model that is
+based on a mixture of peripheral representations and style transfer
+forward-pass algorithms. Our gradient-descent free model is parametrized by a
+foveated VGG19 encoder-decoder which allows us to encode images in high
+dimensional space and interpolate between the content and texture information
+with adaptive instance normalization anywhere in the visual field. Our
+contributions include: 1) A framework for computing metamers that resembles a
+noisy communication system via a foveated feed-forward encoder-decoder network
+-- We observe that metamerism arises as a byproduct of noisy perturbations that
+partially lie in the perceptual null space; 2) A perceptual optimization scheme
+as a solution to the hyperparametric nature of our metamer model that requires
+tuning of the image-texture tradeoff coefficients everywhere in the visual
+field which are a consequence of internal noise; 3) An ABX psychophysical
+evaluation of our metamers where we also find that the rate of growth of the
+receptive fields in our model match V1 for reference metamers and V2 between
+synthesized samples. Our model also renders metamers at roughly a second,
+presenting a $\times1000$ speed-up compared to the previous work, which allows
+for tractable data-driven metamer experiments.
+",1,0,0,0,0,0
+12450,"Modeling Sheep pox Disease from the 1994-1998 Epidemic in Evros Prefecture, Greece","  Sheep pox is a highly transmissible disease which can cause serious loss of
+livestock and can therefore have major economic impact. We present data from
+sheep pox epidemics which occurred between 1994 and 1998. The data include
+weekly records of infected farms as well as a number of covariates. We
+implement Bayesian stochastic regression models which, in addition to various
+explanatory variables like seasonal and environmental/meteorological factors,
+also contain serial correlation structure based on variants of the
+Ornstein-Uhlenbeck process. We take a predictive view in model selection by
+utilizing deviance-based measures. The results indicate that seasonality and
+the number of infected farms are important predictors for sheep pox incidence.
+",0,0,0,1,0,0
+12451,Minimal hard surface-unlink and classical unlink diagrams,"  We describe a method for generating minimal hard prime surface-link diagrams.
+We extend the known examples of minimal hard prime classical unknot and unlink
+diagrams up to three components and generate figures of all minimal hard prime
+surface-unknot and surface-unlink diagrams with prime base surface components
+up to ten crossings.
+",0,0,1,0,0,0
+12452,Fading of collective attention shapes the evolution of linguistic variants,"  Language change involves the competition between alternative linguistic forms
+(1). The spontaneous evolution of these forms typically results in monotonic
+growths or decays (2, 3) like in winner-take-all attractor behaviors. In the
+case of the Spanish past subjunctive, the spontaneous evolution of its two
+competing forms (ended in -ra and -se) was perturbed by the appearance of the
+Royal Spanish Academy in 1713, which enforced the spelling of both forms as
+perfectly interchangeable variants (4), at a moment in which the -ra form was
+dominant (5). Time series extracted from a massive corpus of books (6) reveal
+that this regulation in fact produced a transient renewed interest for the old
+form -se which, once faded, left the -ra again as the dominant form up to the
+present day. We show that time series are successfully explained by a
+two-dimensional linear model that integrates an imitative and a novelty
+component. The model reveals that the temporal scale over which collective
+attention fades is in inverse proportion to the verb frequency. The integration
+of the two basic mechanisms of imitation and attention to novelty allows to
+understand diverse competing objects, with lifetimes that range from hours for
+memes and news (7, 8) to decades for verbs, suggesting the existence of a
+general mechanism underlying cultural evolution.
+",0,0,0,0,1,0
+12453,Continuous-Time User Modeling in the Presence of Badges: A Probabilistic Approach,"  User modeling plays an important role in delivering customized web services
+to the users and improving their engagement. However, most user models in the
+literature do not explicitly consider the temporal behavior of users. More
+recently, continuous-time user modeling has gained considerable attention and
+many user behavior models have been proposed based on temporal point processes.
+However, typical point process based models often considered the impact of peer
+influence and content on the user participation and neglected other factors.
+Gamification elements, are among those factors that are neglected, while they
+have a strong impact on user participation in online services. In this paper,
+we propose interdependent multi-dimensional temporal point processes that
+capture the impact of badges on user participation besides the peer influence
+and content factors. We extend the proposed processes to model user actions
+over the community based question and answering websites, and propose an
+inference algorithm based on Variational-EM that can efficiently learn the
+model parameters. Extensive experiments on both synthetic and real data
+gathered from Stack Overflow show that our inference algorithm learns the
+parameters efficiently and the proposed method can better predict the user
+behavior compared to the alternatives.
+",1,0,0,0,0,0
+12454,Testing for observation-dependent regime switching in mixture autoregressive models,"  Testing for regime switching when the regime switching probabilities are
+specified either as constants (`mixture models') or are governed by a
+finite-state Markov chain (`Markov switching models') are long-standing
+problems that have also attracted recent interest. This paper considers testing
+for regime switching when the regime switching probabilities are time-varying
+and depend on observed data (`observation-dependent regime switching').
+Specifically, we consider the likelihood ratio test for observation-dependent
+regime switching in mixture autoregressive models. The testing problem is
+highly nonstandard, involving unidentified nuisance parameters under the null,
+parameters on the boundary, singular information matrices, and higher-order
+approximations of the log-likelihood. We derive the asymptotic null
+distribution of the likelihood ratio test statistic in a general mixture
+autoregressive setting using high-level conditions that allow for various forms
+of dependence of the regime switching probabilities on past observations, and
+we illustrate the theory using two particular mixture autoregressive models.
+The likelihood ratio test has a nonstandard asymptotic distribution that can
+easily be simulated, and Monte Carlo studies show the test to have satisfactory
+finite sample size and power properties.
+",0,0,1,1,0,0
+12455,The finiteness dimension of modules and relative Cohen-Macaulayness,"  Let $R$ be a commutative Noetherian ring, $\mathfrak a$ and $\mathfrak b$
+ideals of $R$. In this paper, we study the finiteness dimension $f_{\mathfrak
+a}(M)$ of $M$ relative to $\mathfrak a$ and the $\mathfrak b$-minimum
+$\mathfrak a$-adjusted depth $\lambda_{\mathfrak a}^{\mathfrak b}(M)$ of $M$,
+where the underlying module $M$ is relative Cohen-Macaulay w.r.t $\mathfrak a$.
+Some applications of such modules are given.
+",0,0,1,0,0,0
+12456,On Ladder Logic Bombs in Industrial Control Systems,"  In industrial control systems, devices such as Programmable Logic Controllers
+(PLCs) are commonly used to directly interact with sensors and actuators, and
+perform local automatic control. PLCs run software on two different layers: a)
+firmware (i.e. the OS) and b) control logic (processing sensor readings to
+determine control actions). In this work, we discuss ladder logic bombs, i.e.
+malware written in ladder logic (or one of the other IEC 61131-3-compatible
+languages). Such malware would be inserted by an attacker into existing control
+logic on a PLC, and either persistently change the behavior, or wait for
+specific trigger signals to activate malicious behaviour. For example, the LLB
+could replace legitimate sensor readings with manipulated values. We see the
+concept of LLBs as a generalization of attacks such as the Stuxnet attack. We
+introduce LLBs on an abstract level, and then demonstrate several designs based
+on real PLC devices in our lab. In particular, we also focus on stealthy LLBs,
+i.e. LLBs that are hard to detect by human operators manually validating the
+program running in PLCs. In addition to introducing vulnerabilities on the
+logic layer, we also discuss countermeasures and we propose two detection
+techniques.
+",1,0,0,0,0,0
+12457,Stochastic variance reduced multiplicative update for nonnegative matrix factorization,"  Nonnegative matrix factorization (NMF), a dimensionality reduction and factor
+analysis method, is a special case in which factor matrices have low-rank
+nonnegative constraints. Considering the stochastic learning in NMF, we
+specifically address the multiplicative update (MU) rule, which is the most
+popular, but which has slow convergence property. This present paper introduces
+on the stochastic MU rule a variance-reduced technique of stochastic gradient.
+Numerical comparisons suggest that our proposed algorithms robustly outperform
+state-of-the-art algorithms across different synthetic and real-world datasets.
+",1,0,0,1,0,0
+12458,Bi-National Delay Pattern Analysis For Commercial and Passenger Vehicles at Niagara Frontier Border,"  Border crossing delays between New York State and Southern Ontario cause
+problems like enormous economic loss and massive environmental pollutions. In
+this area, there are three border-crossing ports: Peace Bridge (PB), Rainbow
+Bridge (RB) and Lewiston-Queenston Bridge (LQ) at Niagara Frontier border. The
+goals of this paper are to figure out whether the distributions of bi-national
+wait times for commercial and passenger vehicles are evenly distributed among
+the three ports and uncover the hidden significant influential factors that
+result in the possible insufficient utilization. The historical border wait
+time data from 7:00 to 21:00 between 08/22/2016 and 06/20/2017 are archived, as
+well as the corresponding temporal and weather data. For each vehicle type
+towards each direction, a Decision Tree is built to identify the various border
+delay patterns over the three bridges. We find that for the passenger vehicles
+to the USA, the convenient connections between the Canada freeways with USA
+I-190 by LQ and PB may cause these two bridges more congested than RB,
+especially when it is a holiday in Canada. For the passenger vehicles in the
+other bound, RB is much more congested than LQ and PB in some cases, and the
+visitors to Niagara Falls in the USA in summer may be a reason. For the
+commercial trucks to the USA, the various delay patterns show PB is always more
+congested than LQ. Hour interval and weekend are the most significant factors
+appearing in all the four Decision Trees. These Decision Trees can help the
+authorities to make specific routing suggestions when the corresponding
+conditions are satisfied.
+",1,0,0,0,0,0
+12459,A Scalable and Adaptive Method for Finding Semantically Equivalent Cue Words of Uncertainty,"  Scientific knowledge is constantly subject to a variety of changes due to new
+discoveries, alternative interpretations, and fresh perspectives. Understanding
+uncertainties associated with various stages of scientific inquiries is an
+integral part of scientists' domain expertise and it serves as the core of
+their meta-knowledge of science. Despite the growing interest in areas such as
+computational linguistics, systematically characterizing and tracking the
+epistemic status of scientific claims and their evolution in scientific
+disciplines remains a challenge. We present a unifying framework for the study
+of uncertainties explicitly and implicitly conveyed in scientific publications.
+The framework aims to accommodate a wide range of uncertain types, from
+speculations to inconsistencies and controversies. We introduce a scalable and
+adaptive method to recognize semantically equivalent cues of uncertainty across
+different fields of research and accommodate individual analysts' unique
+perspectives. We demonstrate how the new method can be used to expand a small
+seed list of uncertainty cue words and how the validity of the expanded
+candidate cue words are verified. We visualize the mixture of the original and
+expanded uncertainty cue words to reveal the diversity of expressions of
+uncertainty. These cue words offer a novel resource for the study of
+uncertainty in scientific assertions.
+",1,0,0,0,0,0
+12460,An optimization method to simultaneously estimate electrophysiology and connectivity in a model central pattern generator,"  Central pattern generators (CPGs) appear to have evolved multiple times
+throughout the animal kingdom, indicating that their design imparts a
+significant evolutionary advantage. Insight into how this design is achieved is
+hindered by the difficulty inherent in examining relationships among
+electrophysiological properties of the constituent cells of a CPG and their
+functional connectivity. That is: experimentally it is challenging to estimate
+the values of more than two or three of these properties simultaneously. We
+employ a method of statistical data assimilation (D.A.) to estimate the
+synaptic weights, synaptic reversal potentials, and maximum conductances of ion
+channels of the constituent neurons in a multi-modal network model. We then use
+these estimates to predict the functional mode of activity that the network is
+expressing. The measurements used are the membrane voltage time series of all
+neurons in the circuit. We find that these measurements provide sufficient
+information to yield accurate predictions of the network's associated
+electrical activity. This experiment can apply directly in a real laboratory
+using intracellular recordings from a known biological CPG whose structural
+mapping is known, and which can be completely isolated from the animal. The
+simulated results in this paper suggest that D.A. might provide a tool for
+simultaneously estimating tens to hundreds of CPG properties, thereby offering
+the opportunity to seek possible systematic relationships among these
+properties and the emergent electrical activity.
+",0,1,0,0,0,0
+12461,"Overdensities of SMGs around WISE-selected, ultra-luminous, high-redshift AGN","  We investigate extremely luminous dusty galaxies in the environments around
+WISE-selected hot dust obscured galaxies (Hot DOGs) and WISE/radio-selected
+active galactic nuclei (AGNs) at average redshifts of z = 2.7 and z = 1.7,
+respectively. Previous observations have detected overdensities of companion
+submillimetre-selected sources around 10 Hot DOGs and 30 WISE/radio AGNs, with
+overdensities of ~ 2 - 3 and ~ 5 - 6 , respectively. We find that the space
+densities in both samples to be overdense compared to normal star-forming
+galaxies and submillimetre galaxies (SMGs) in the SCUBA-2 Cosmology Legacy
+Survey (S2CLS). Both samples of companion sources have consistent mid-IR
+colours and mid-IR to submm ratios as SMGs. The brighter population around
+WISE/radio AGNs could be responsible for the higher overdensity reported. We
+also find the star formation rate density (SFRDs) are higher than the field,
+but consistent with clusters of dusty galaxies. WISE-selected AGNs appear to be
+good signposts for protoclusters at high redshift on arcmin scales. The results
+reported here provide an upper limit to the strength of angular clustering
+using the two-point correlation function. Monte Carlo simulations show no
+angular correlation, which could indicate protoclusters on scales larger than
+the SCUBA-2 1.5arcmin scale maps.
+",0,1,0,0,0,0
+12462,Constrained Best Linear Unbiased Estimation,"  The least squares (LS) estimator and the best linear unbiased estimator
+(BLUE) are two well-studied approaches for the estimation of a deterministic
+but unknown parameter vector. In many applications it is known that the
+parameter vector fulfills some constraints, e.g., linear constraints. For such
+situations the constrained LS estimator, which is a simple extension of the LS
+estimator, can be employed. In this paper we derive the constrained version of
+the BLUE. It will turn out that the incorporation of the linear constraints
+into the derivation of the BLUE is not straight forward as for the constrained
+LS estimator, but the final expression for the constrained BLUE is closely
+related to that of the constrained LS estimator.
+",0,0,1,1,0,0
+12463,Compact Tensor Pooling for Visual Question Answering,"  Performing high level cognitive tasks requires the integration of feature
+maps with drastically different structure. In Visual Question Answering (VQA)
+image descriptors have spatial structures, while lexical inputs inherently
+follow a temporal sequence. The recently proposed Multimodal Compact Bilinear
+pooling (MCB) forms the outer products, via count-sketch approximation, of the
+visual and textual representation at each spatial location. While this
+procedure preserves spatial information locally, outer-products are taken
+independently for each fiber of the activation tensor, and therefore do not
+include spatial context. In this work, we introduce multi-dimensional sketch
+({MD-sketch}), a novel extension of count-sketch to tensors. Using this new
+formulation, we propose Multimodal Compact Tensor Pooling (MCT) to fully
+exploit the global spatial context during bilinear pooling operations.
+Contrarily to MCB, our approach preserves spatial context by directly
+convolving the MD-sketch from the visual tensor features with the text vector
+feature using higher order FFT. Furthermore we apply MCT incrementally at each
+step of the question embedding and accumulate the multi-modal vectors with a
+second LSTM layer before the final answer is chosen.
+",1,0,0,0,0,0
+12464,Beam-induced Back-streaming Electron Suppression Analysis for Accelerator Type Neutron Generators,"  A facility based on a next-generation, high-flux D-D neutron generator has
+been commissioned and it is now operational at the University of California,
+Berkeley. The current generator design produces near monoenergetic 2.45 MeV
+neutrons at outputs of 10^8 n/s. Calculations provided show that future
+conditioning at higher currents and voltages will allow for a production rate
+over 10^10 n/s. A significant problem encountered was beam-induced electron
+backstreaming, that needed to be resolved to achieve meaningful beam currents.
+Two methods of suppressing secondary electrons resulting from the deuterium
+beam striking the target were tested: the application of static electric and
+magnetic fields. Computational simulations of both techniques were done using a
+finite element analysis in COMSOL Multiphysics. Experimental tests verified
+these simulation results. The most reliable suppression was achieved via the
+implementation of an electrostatic shroud with a voltage offset of -800 V
+relative to the target.
+",0,1,0,0,0,0
+12465,Vertical stratification of forest canopy for segmentation of under-story trees within small-footprint airborne LiDAR point clouds,"  Airborne LiDAR point cloud representing a forest contains 3D data, from which
+vertical stand structure even of understory layers can be derived. This paper
+presents a tree segmentation approach for multi-story stands that stratifies
+the point cloud to canopy layers and segments individual tree crowns within
+each layer using a digital surface model based tree segmentation method. The
+novelty of the approach is the stratification procedure that separates the
+point cloud to an overstory and multiple understory tree canopy layers by
+analyzing vertical distributions of LiDAR points within overlapping locales.
+The procedure does not make a priori assumptions about the shape and size of
+the tree crowns and can, independent of the tree segmentation method, be
+utilized to vertically stratify tree crowns of forest canopies. We applied the
+proposed approach to the University of Kentucky Robinson Forest - a natural
+deciduous forest with complex and highly variable terrain and vegetation
+structure. The segmentation results showed that using the stratification
+procedure strongly improved detecting understory trees (from 46% to 68%) at the
+cost of introducing a fair number of over-segmented understory trees (increased
+from 1% to 16%), while barely affecting the overall segmentation quality of
+overstory trees. Results of vertical stratification of the canopy showed that
+the point density of understory canopy layers were suboptimal for performing a
+reasonable tree segmentation, suggesting that acquiring denser LiDAR point
+clouds would allow more improvements in segmenting understory trees. As shown
+by inspecting correlations of the results with forest structure, the
+segmentation approach is applicable to a variety of forest types.
+",1,0,0,0,0,0
+12466,Temperature effect observed by the Nagoya muon telescope,"  The temperature coefficients for all the directions of the Nagoya muon
+telescope were obtained. The zenith angular dependence of the temperature
+coefficients was studied.
+",0,1,0,0,0,0
+12467,Generalizing Hamiltonian Monte Carlo with Neural Networks,"  We present a general-purpose method to train Markov chain Monte Carlo
+kernels, parameterized by deep neural networks, that converge and mix quickly
+to their target distribution. Our method generalizes Hamiltonian Monte Carlo
+and is trained to maximize expected squared jumped distance, a proxy for mixing
+speed. We demonstrate large empirical gains on a collection of simple but
+challenging distributions, for instance achieving a 106x improvement in
+effective sample size in one case, and mixing when standard HMC makes no
+measurable progress in a second. Finally, we show quantitative and qualitative
+gains on a real-world task: latent-variable generative modeling. We release an
+open source TensorFlow implementation of the algorithm.
+",1,0,0,1,0,0
+12468,Multiplicities of Character Values of Binary Sidel'nikov-Lempel-Cohn-Eastman Sequences,"  Binary Sidel'nikov-Lempel-Cohn-Eastman sequences (or SLCE sequences) over F 2
+have even period and almost perfect autocorrelation. However, the evaluation of
+the linear complexity of these sequences is really difficult. In this paper, we
+continue the study of [1]. We first express the multiple roots of character
+polynomials of SLCE sequences into certain kinds of Jacobi sums. Then by making
+use of Gauss sums and Jacobi sums in the ""semiprimitive"" case, we derive new
+divisibility results for SLCE sequences.
+",1,0,1,0,0,0
+12469,Complex and Holographic Embeddings of Knowledge Graphs: A Comparison,"  Embeddings of knowledge graphs have received significant attention due to
+their excellent performance for tasks like link prediction and entity
+resolution. In this short paper, we are providing a comparison of two
+state-of-the-art knowledge graph embeddings for which their equivalence has
+recently been established, i.e., ComplEx and HolE [Nickel, Rosasco, and Poggio,
+2016; Trouillon et al., 2016; Hayashi and Shimbo, 2017]. First, we briefly
+review both models and discuss how their scoring functions are equivalent. We
+then analyze the discrepancy of results reported in the original articles, and
+show experimentally that they are likely due to the use of different loss
+functions. In further experiments, we evaluate the ability of both models to
+embed symmetric and antisymmetric patterns. Finally, we discuss advantages and
+disadvantages of both models and under which conditions one would be preferable
+to the other.
+",1,0,0,1,0,0
+12470,Deep Image Prior,"  Deep convolutional networks have become a popular tool for image generation
+and restoration. Generally, their excellent performance is imputed to their
+ability to learn realistic image priors from a large number of example images.
+In this paper, we show that, on the contrary, the structure of a generator
+network is sufficient to capture a great deal of low-level image statistics
+prior to any learning. In order to do so, we show that a randomly-initialized
+neural network can be used as a handcrafted prior with excellent results in
+standard inverse problems such as denoising, super-resolution, and inpainting.
+Furthermore, the same prior can be used to invert deep neural representations
+to diagnose them, and to restore images based on flash-no flash input pairs.
+Apart from its diverse applications, our approach highlights the inductive
+bias captured by standard generator network architectures. It also bridges the
+gap between two very popular families of image restoration methods:
+learning-based methods using deep convolutional networks and learning-free
+methods based on handcrafted image priors such as self-similarity. Code and
+supplementary material are available at
+this https URL .
+",0,0,0,1,0,0
+12471,Efficient SMC$^2$ schemes for stochastic kinetic models,"  Fitting stochastic kinetic models represented by Markov jump processes within
+the Bayesian paradigm is complicated by the intractability of the observed data
+likelihood. There has therefore been considerable attention given to the design
+of pseudo-marginal Markov chain Monte Carlo algorithms for such models.
+However, these methods are typically computationally intensive, often require
+careful tuning and must be restarted from scratch upon receipt of new
+observations. Sequential Monte Carlo (SMC) methods on the other hand aim to
+efficiently reuse posterior samples at each time point. Despite their appeal,
+applying SMC schemes in scenarios with both dynamic states and static
+parameters is made difficult by the problem of particle degeneracy. A
+principled approach for overcoming this problem is to move each parameter
+particle through a Metropolis-Hastings kernel that leaves the target invariant.
+This rejuvenation step is key to a recently proposed SMC$^2$ algorithm, which
+can be seen as the pseudo-marginal analogue of an idealised scheme known as
+iterated batch importance sampling. Computing the parameter weights in SMC$^2$
+requires running a particle filter over dynamic states to unbiasedly estimate
+the intractable observed data likelihood contributions at each time point. In
+this paper, we propose to use an auxiliary particle filter inside the SMC$^2$
+scheme. Our method uses two recently proposed constructs for sampling
+conditioned jump processes and we find that the resulting inference schemes
+typically require fewer state particles than when using a simple bootstrap
+filter. Using two applications, we compare the performance of the proposed
+approach with various competing methods, including two global MCMC schemes.
+",0,0,0,1,0,0
+12472,Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks,"  Image-to-image translation is a class of vision and graphics problems where
+the goal is to learn the mapping between an input image and an output image
+using a training set of aligned image pairs. However, for many tasks, paired
+training data will not be available. We present an approach for learning to
+translate an image from a source domain $X$ to a target domain $Y$ in the
+absence of paired examples. Our goal is to learn a mapping $G: X \rightarrow Y$
+such that the distribution of images from $G(X)$ is indistinguishable from the
+distribution $Y$ using an adversarial loss. Because this mapping is highly
+under-constrained, we couple it with an inverse mapping $F: Y \rightarrow X$
+and introduce a cycle consistency loss to push $F(G(X)) \approx X$ (and vice
+versa). Qualitative results are presented on several tasks where paired
+training data does not exist, including collection style transfer, object
+transfiguration, season transfer, photo enhancement, etc. Quantitative
+comparisons against several prior methods demonstrate the superiority of our
+approach.
+",1,0,0,0,0,0
+12473,Consistent structure estimation of exponential-family random graph models with block structure,"  We consider the challenging problem of statistical inference for
+exponential-family random graph models based on a single observation of a
+random graph with complex dependence. To facilitate statistical inference, we
+consider random graphs with additional structure in the form of block
+structure. We have shown elsewhere that when the block structure is known, it
+facilitates consistency results for $M$-estimators of canonical and curved
+exponential-family random graph models with complex dependence, such as
+transitivity. In practice, the block structure is known in some applications
+(e.g., multilevel networks), but is unknown in others. When the block structure
+is unknown, the first and foremost question is whether it can be recovered with
+high probability based on a single observation of a random graph with complex
+dependence. The main consistency results of the paper show that it is possible
+to do so provided the number of blocks grows as fast as in high-dimensional
+stochastic block models. These results confirm that exponential-family random
+graph models with block structure constitute a promising direction of
+statistical network analysis.
+",0,0,1,1,0,0
+12474,Taskonomy: Disentangling Task Transfer Learning,"  Do visual tasks have a relationship, or are they unrelated? For instance,
+could having surface normals simplify estimating the depth of an image?
+Intuition answers these questions positively, implying existence of a structure
+among visual tasks. Knowing this structure has notable values; it is the
+concept underlying transfer learning and provides a principled way for
+identifying redundancies across tasks, e.g., to seamlessly reuse supervision
+among related tasks or solve many tasks in one system without piling up the
+complexity.
+We proposes a fully computational approach for modeling the structure of
+space of visual tasks. This is done via finding (first and higher-order)
+transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D,
+and semantic tasks in a latent space. The product is a computational taxonomic
+map for task transfer learning. We study the consequences of this structure,
+e.g. nontrivial emerged relationships, and exploit them to reduce the demand
+for labeled data. For example, we show that the total number of labeled
+datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3
+(compared to training independently) while keeping the performance nearly the
+same. We provide a set of tools for computing and probing this taxonomical
+structure including a solver that users can employ to devise efficient
+supervision policies for their use cases.
+",1,0,0,0,0,0
+12475,The Energy Measure for the Euler and Navier-Stokes Equations,"  The potential failure of energy equality for a solution $u$ of the Euler or
+Navier-Stokes equations can be quantified using a so-called `energy measure':
+the weak-$*$ limit of the measures $|u(t)|^2\,\mbox{d}x$ as $t$ approaches the
+first possible blowup time. We show that membership of $u$ in certain (weak or
+strong) $L^q L^p$ classes gives a uniform lower bound on the lower local
+dimension of $\mathcal{E}$; more precisely, it implies uniform boundedness of a
+certain upper $s$-density of $\mathcal{E}$. We also define and give lower
+bounds on the `concentration dimension' associated to $\mathcal{E}$, which is
+the Hausdorff dimension of the smallest set on which energy can concentrate.
+Both the lower local dimension and the concentration dimension of $\mathcal{E}$
+measure the departure from energy equality. As an application of our estimates,
+we prove that any solution to the $3$-dimensional Navier-Stokes Equations which
+is Type-I in time must satisfy the energy equality at the first blowup time.
+",0,0,1,0,0,0
+12476,Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering,"  Inference of space-time varying signals on graphs emerges naturally in a
+plethora of network science related applications. A frequently encountered
+challenge pertains to reconstructing such dynamic processes, given their values
+over a subset of vertices and time instants. The present paper develops a
+graph-aware kernel-based kriged Kalman filter that accounts for the
+spatio-temporal variations, and offers efficient online reconstruction, even
+for dynamically evolving network topologies. The kernel-based learning
+framework bypasses the need for statistical information by capitalizing on the
+smoothness that graph signals exhibit with respect to the underlying graph. To
+address the challenge of selecting the appropriate kernel, the proposed filter
+is combined with a multi-kernel selection module. Such a data-driven method
+selects a kernel attuned to the signal dynamics on-the-fly within the linear
+span of a pre-selected dictionary. The novel multi-kernel learning algorithm
+exploits the eigenstructure of Laplacian kernel matrices to reduce
+computational complexity. Numerical tests with synthetic and real data
+demonstrate the superior reconstruction performance of the novel approach
+relative to state-of-the-art alternatives.
+",1,0,0,1,0,0
+12477,"Z2-Thurston Norm and Complexity of 3-Manifolds, II","  In this sequel to earlier papers by three of the authors, we obtain a new
+bound on the complexity of a closed 3--manifold, as well as a characterisation
+of manifolds realising our complexity bounds. As an application, we obtain the
+first infinite families of minimal triangulations of Seifert fibred spaces
+modelled on Thurston's geometry $\widetilde{\text{SL}_2(\mathbb{R})}.$
+",0,0,1,0,0,0
+12478,Discriminative k-shot learning using probabilistic models,"  This paper introduces a probabilistic framework for k-shot image
+classification. The goal is to generalise from an initial large-scale
+classification task to a separate task comprising new classes and small numbers
+of examples. The new approach not only leverages the feature-based
+representation learned by a neural network from the initial task
+(representational transfer), but also information about the classes (concept
+transfer). The concept information is encapsulated in a probabilistic model for
+the final layer weights of the neural network which acts as a prior for
+probabilistic k-shot learning. We show that even a simple probabilistic model
+achieves state-of-the-art on a standard k-shot learning dataset by a large
+margin. Moreover, it is able to accurately model uncertainty, leading to well
+calibrated classifiers, and is easily extensible and flexible, unlike many
+recent approaches to k-shot learning.
+",1,0,0,1,0,0
+12479,Comparative analysis of criteria for filtering time series of word usage frequencies,"  This paper describes a method of nonlinear wavelet thresholding of time
+series. The Ramachandran-Ranganathan runs test is used to assess the quality of
+approximation. To minimize the objective function, it is proposed to use
+genetic algorithms - one of the stochastic optimization methods. The suggested
+method is tested both on the model series and on the word frequency series
+using the Google Books Ngram data. It is shown that method of filtering which
+uses the runs criterion shows significantly better results compared with the
+standard wavelet thresholding. The method can be used when quality of filtering
+is of primary importance but not the speed of calculations.
+",0,0,0,1,0,0
+12480,Extend of the $\mathbb{Z}_2$-spin liquid phase on the Kagomé-lattice,"  The $\mathbb{Z}_2$ topological phase in the quantum dimer model on the
+Kagomé-lattice is a candidate for the description of the low-energy physics
+of the anti-ferromagnetic Heisenberg model on the same lattice. We study the
+extend of the topological phase by interpolating between the exactly solvable
+parent Hamiltonian of the topological phase and an effective low-energy
+description of the Heisenberg model in terms of a quantum-dimer Hamiltonian.
+Therefore, we perform a perturbative treatment of the low-energy excitations in
+the topological phase including free and interacting quasi-particles. We find a
+phase transition out of the topological phase far from the Heisenberg point.
+The resulting phase is characterized by a spontaneously broken rotational
+symmetry and a unit cell involving six sites.
+",0,1,0,0,0,0
+12481,Accelerated Stochastic Quasi-Newton Optimization on Riemann Manifolds,"  We propose an L-BFGS optimization algorithm on Riemannian manifolds using
+minibatched stochastic variance reduction techniques for fast convergence with
+constant step sizes, without resorting to linesearch methods designed to
+satisfy Wolfe conditions. We provide a new convergence proof for strongly
+convex functions without using curvature conditions on the manifold, as well as
+a convergence discussion for nonconvex functions. We discuss a couple of ways
+to obtain the correction pairs used to calculate the product of the gradient
+with the inverse Hessian, and empirically demonstrate their use in synthetic
+experiments on computation of Karcher means for symmetric positive definite
+matrices and leading eigenvalues of large scale data matrices. We compare our
+method to VR-PCA for the latter experiment, along with Riemannian SVRG for both
+cases, and show strong convergence results for a range of datasets.
+",0,0,1,1,0,0
+12482,Bridging trees for posterior inference on Ancestral Recombination Graphs,"  We present a new Markov chain Monte Carlo algorithm, implemented in software
+Arbores, for inferring the history of a sample of DNA sequences. Our principal
+innovation is a bridging procedure, previously applied only for simple
+stochastic processes, in which the local computations within a bridge can
+proceed independently of the rest of the DNA sequence, facilitating large-scale
+parallelisation.
+",0,0,0,0,1,0
+12483,Strong magnetic frustration in Y$_{3}$Cu$_{9}$(OH)$_{19}$Cl$_{8}$: a distorted kagome antiferromagnet,"  We present the crystal structure and magnetic properties of
+Y$_{3}$Cu$_{9}$(OH)$_{19}$Cl$_{8}$, a stoichiometric frustrated quantum spin
+system with slightly distorted kagome layers. Single crystals of
+Y$_{3}$Cu$_{9}$(OH)$_{19}$Cl$_{8}$ were grown under hydrothermal conditions.
+The structure was determined from single crystal X-ray diffraction and
+confirmed by neutron powder diffraction. The observed structure reveals two
+different Cu-positions leading to a slightly distored kagome layer in contrast
+to the closely related YCu$_{3}$(OH)$_{6}$Cl$_{3}$. Curie-Weiss behavior at
+high-temperatures with a Weiss-temperature $\theta_{W}$ of the order of $-100$
+K, shows a large dominant antiferromagnetic coupling within the kagome planes.
+Specific-heat and magnetization measurements on single crystals reveal an
+antiferromagnetic transition at T$_{N}=2.2$ K indicating a pronounced
+frustration parameter of $\theta_{W}/T_{N}\approx50$. Optical transmission
+experiments on powder samples and single crystals confirm the structural
+findings. Specific-heat measurements on YCu$_{3}$(OH)$_{6}$Cl$_{3}$ down to 0.4
+K confirm the proposed quantum spin-liquid state of that system. Therefore, the
+two Y-Cu-OH-Cl compounds present a unique setting to investigate closely
+related structures with a spin-liquid state and a strongly frustrated AFM
+ordered state, by slightly releasing the frustration in a kagome lattice.
+",0,1,0,0,0,0
+12484,The Character Field Theory and Homology of Character Varieties,"  We construct an extended oriented $(2+\epsilon)$-dimensional topological
+field theory, the character field theory $X_G$ attached to a affine algebraic
+group in characteristic zero, which calculates the homology of character
+varieties of surfaces. It is a model for a dimensional reduction of
+Kapustin-Witten theory ($N=4$ $d=4$ super-Yang-Mills in the GL twist), and a
+universal version of the unipotent character field theory introduced in
+arXiv:0904.1247. Boundary conditions in $X_G$ are given by quantum Hamiltonian
+$G$-spaces, as captured by de Rham (or strong) $G$-categories, i.e., module
+categories for the monoidal dg category $D(G)$ of $D$-modules on $G$. We show
+that the circle integral $X_G(S^1)$ (the center and trace of $D(G)$) is
+identified with the category $D(G/G)$ of ""class $D$-modules"", while for an
+oriented surface $S$ (with arbitrary decorations at punctures) we show that
+$X_G(S)\simeq{\rm H}_*^{BM}(Loc_G(S))$ is the Borel-Moore homology of the
+corresponding character stack. We also describe the ""Hodge filtration"" on the
+character theory, a one parameter degeneration to a TFT whose boundary
+conditions are given by classical Hamiltonian $G$-spaces, and which encodes a
+variant of the Hodge filtration on character varieties.
+",0,0,1,0,0,0
+12485,A new generator of chaotic bit sequences with mixed-mode inputs,"  This paper presents a new generator of chaotic bit sequences with mixed-mode
+(continuous and discrete) inputs. The generator has an improved level of
+chaotic properties in comparison with the existing single source (input)
+digital chaotic bit generators. The 0-1 test is used to show the improved
+chaotic behavior of our generator having a chaotic continuous input (Chua,
+Rössler or Lorenz system) intermingled with a discrete input (logistic,
+Tinkerbell or Henon map) with various parameters. The obtained sequences of
+chaotic bits show some features of random processes with increased entropy
+levels, even in the cases of small numbers of bit representations. The
+properties of the new generator and its binary sequences compare well with
+those obtained from a truly random binary reference quantum generator, as
+evidenced by the results of the $ent$ tests.
+",0,1,0,0,0,0
+12486,HAWC response to atmospheric electricity activity,"  The HAWC Gamma Ray observatory consists of 300 water Cherenkov detectors
+(WCD) instrumented with four photo multipliers tubes (PMT) per WCD. HAWC is
+located between two of the highest mountains in Mexico. The high altitude (4100
+m asl), the relatively short distance to the Gulf of Mexico (~100 km), the
+large detecting area (22 000 m$^2$) and its high sensitivity, make HAWC a good
+instrument to explore the acceleration of particles due to the electric fields
+existing inside storm clouds. In particular, the scaler system of HAWC records
+the output of each one of the 1200 PMTs as well as the 2, 3, and 4-fold
+multiplicities (logic AND in a time window of 30 ns) of each WCD with a
+sampling rate of 40 Hz. Using the scaler data, we have identified 20
+enhancements of the observed rate during periods when storm clouds were over
+HAWC but without cloud-earth discharges. These enhancements can be produced by
+electrons with energy of tens of MeV, accelerated by the electric fields of
+tens of kV/m measured at the site during the storm periods. In this work, we
+present the recorded data, the method of analysis and our preliminary
+conclusions on the electron acceleration by the electric fields inside the
+clouds.
+",0,1,0,0,0,0
+12487,Optical Music Recognition with Convolutional Sequence-to-Sequence Models,"  Optical Music Recognition (OMR) is an important technology within Music
+Information Retrieval. Deep learning models show promising results on OMR
+tasks, but symbol-level annotated data sets of sufficient size to train such
+models are not available and difficult to develop. We present a deep learning
+architecture called a Convolutional Sequence-to-Sequence model to both move
+towards an end-to-end trainable OMR pipeline, and apply a learning process that
+trains on full sentences of sheet music instead of individually labeled
+symbols. The model is trained and evaluated on a human generated data set, with
+various image augmentations based on real-world scenarios. This data set is the
+first publicly available set in OMR research with sufficient size to train and
+evaluate deep learning models. With the introduced augmentations a pitch
+recognition accuracy of 81% and a duration accuracy of 94% is achieved,
+resulting in a note level accuracy of 80%. Finally, the model is compared to
+commercially available methods, showing a large improvements over these
+applications.
+",1,0,0,0,0,0
+12488,Fractional Cable Model for Signal Conduction in Spiny Neuronal Dendrites,"  The cable model is widely used in several fields of science to describe the
+propagation of signals. A relevant medical and biological example is the
+anomalous subdiffusion in spiny neuronal dendrites observed in several studies
+of the last decade. Anomalous subdiffusion can be modelled in several ways
+introducing some fractional component into the classical cable model. The
+Chauchy problem associated to these kind of models has been investigated by
+many authors, but up to our knowledge an explicit solution for the signalling
+problem has not yet been published. Here we propose how this solution can be
+derived applying the generalized convolution theorem (known as Efros theorem)
+for Laplace transforms. The fractional cable model considered in this paper is
+defined by replacing the first order time derivative with a fractional
+derivative of order $\alpha\in(0,1)$ of Caputo type. The signalling problem is
+solved for any input function applied to the accessible end of a semi-infinite
+cable, which satisfies the requirements of the Efros theorem. The solutions
+corresponding to the simple cases of impulsive and step inputs are explicitly
+calculated in integral form containing Wright functions. Thanks to the
+variability of the parameter $\alpha$, the corresponding solutions are expected
+to adapt to the qualitative behaviour of the membrane potential observed in
+experiments better than in the standard case $\alpha=1$.
+",0,1,0,0,0,0
+12489,Hedging in fractional Black-Scholes model with transaction costs,"  We consider conditional-mean hedging in a fractional Black-Scholes pricing
+model in the presence of proportional transaction costs. We develop an explicit
+formula for the conditional-mean hedging portfolio in terms of the recently
+discovered explicit conditional law of the fractional Brownian motion.
+",0,0,1,1,0,0
+12490,Finite flat spaces,"  We say that a finite metric space $X$ can be embedded almost isometrically
+into a class of metric spaces $C$, if for every $\epsilon > 0$ there exists an
+embedding of $X$ into one of the elements of $C$ with the bi-Lipschitz
+distortion less then $1 + \epsilon$. We show that almost isometric
+embeddability conditions are equal for following classes of spaces
+(a) Quotients of Euclidean spaces by isometric actions of finite groups,
+(b) $L_2$-Wasserstein spaces over Euclidean spaces,
+(c) Compact flat manifolds,
+(d) Compact flat orbifolds,
+(e) Quotients of bi-invariant Lie groups by isometric actions of compact Lie
+groups. (This one is the most surprising.)
+We call spaces which satisfy this conditions finite flat spaces. The question
+about synthetic definition naturally arises.
+Since Markov type constants depend only on finite subsets we can conclude
+that bi-invariant Lie groups and their quotients have Markov type $2$ with
+constant $1$.
+",0,0,1,0,0,0
+12491,On The Equivalence of Projections In Relative $α$-Entropy and Rényi Divergence,"  The aim of this work is to establish that two recently published projection
+theorems, one dealing with a parametric generalization of relative entropy and
+another dealing with Rényi divergence, are equivalent under a
+correspondence on the space of probability measures. Further, we demonstrate
+that the associated ""Pythagorean"" theorems are equivalent under this
+correspondence. Finally, we apply Eguchi's method of obtaining Riemannian
+metrics from general divergence functions to show that the geometry arising
+from the above divergences are equivalent under the aforementioned
+correspondence.
+",1,0,0,0,0,0
+12492,"Comparing high dimensional partitions, with the Coclustering Adjusted Rand Index","  The popular Adjusted Rand Index (ARI) is extended to the task of simultaneous
+clustering of the rows and columns of a given matrix. This new index called
+Coclustering Adjusted Rand Index (CARI) remains convenient and competitive
+facing other indices. Indeed, partitions with high number of clusters can be
+considered and it does not require any convention when the numbers of clusters
+in partitions are different. Experiments on simulated partitions are presented
+and the performance of this index to measure the agreement between two pairs of
+partitions is assessed. Comparison with other indices is discussed.
+",0,0,0,1,0,0
+12493,Dependence between Path-length and Size in Random Digital Trees,"  We study the size and the external path length of random tries and show that
+they are asymptotically independent in the asymmetric case but strongly
+dependent with small periodic fluctuations in the symmetric case. Such an
+unexpected behavior is in sharp contrast to the previously known results on
+random tries that the size is totally positively correlated to the internal
+path length and that both tend to the same normal limit law. These two
+dependence examples provide concrete instances of bivariate normal
+distributions (as limit laws) whose correlation is $0$, $1$ and periodically
+oscillating. Moreover, the same type of behaviors is also clarified for other
+classes of digital trees such as bucket digital trees and Patricia tries.
+",0,0,1,0,0,0
+12494,Analysing Shortcomings of Statistical Parametric Speech Synthesis,"  Output from statistical parametric speech synthesis (SPSS) remains noticeably
+worse than natural speech recordings in terms of quality, naturalness, speaker
+similarity, and intelligibility in noise. There are many hypotheses regarding
+the origins of these shortcomings, but these hypotheses are often kept vague
+and presented without empirical evidence that could confirm and quantify how a
+specific shortcoming contributes to imperfections in the synthesised speech.
+Throughout speech synthesis literature, surprisingly little work is dedicated
+towards identifying the perceptually most important problems in speech
+synthesis, even though such knowledge would be of great value for creating
+better SPSS systems.
+In this book chapter, we analyse some of the shortcomings of SPSS. In
+particular, we discuss issues with vocoding and present a general methodology
+for quantifying the effect of any of the many assumptions and design choices
+that hold SPSS back. The methodology is accompanied by an example that
+carefully measures and compares the severity of perceptual limitations imposed
+by vocoding as well as other factors such as the statistical model and its use.
+",1,0,0,0,0,0
+12495,On Meshfree GFDM Solvers for the Incompressible Navier-Stokes Equations,"  Meshfree solution schemes for the incompressible Navier--Stokes equations are
+usually based on algorithms commonly used in finite volume methods, such as
+projection methods, SIMPLE and PISO algorithms. However, drawbacks of these
+algorithms that are specific to meshfree methods have often been overlooked. In
+this paper, we study the drawbacks of conventionally used meshfree Generalized
+Finite Difference Method~(GFDM) schemes for Lagrangian incompressible
+Navier-Stokes equations, both operator splitting schemes and monolithic
+schemes. The major drawback of most of these schemes is inaccurate local
+approximations to the mass conservation condition. Further, we propose a new
+modification of a commonly used monolithic scheme that overcomes these problems
+and shows a better approximation for the velocity divergence condition. We then
+perform a numerical comparison which shows the new monolithic scheme to be more
+accurate than existing schemes.
+",0,1,1,0,0,0
+12496,Using a new parsimonious AHP methodology combined with the Choquet integral: An application for evaluating social housing initiatives,"  We propose a development of the Analytic Hierarchy Process (AHP) permitting
+to use the methodology also in cases of decision problems with a very large
+number of alternatives evaluated with respect to several criteria. While the
+application of the original AHP method involves many pairwise comparisons
+between alternatives and criteria, our proposal is composed of three steps: (i)
+direct evaluation of the alternatives at hand on the considered criteria, (ii)
+selection of some reference evaluations; (iii) application of the original AHP
+method to reference evaluations; (iv) revision of the direct evaluation on the
+basis of the prioritization supplied by AHP on reference evaluations. The new
+proposal has been tested and validated in an experiment conducted on a sample
+of university students. The new methodology has been therefore applied to a
+real world problem involving the evaluation of 21 Social Housing initiatives
+sited in the Piedmont region (Italy). To take into account interaction between
+criteria, the Choquet integral preference model has been considered within a
+Non Additive Robust Ordinal Regression approach.
+",1,0,1,0,0,0
+12497,Importance Sketching of Influence Dynamics in Billion-scale Networks,"  The blooming availability of traces for social, biological, and communication
+networks opens up unprecedented opportunities in analyzing diffusion processes
+in networks. However, the sheer sizes of the nowadays networks raise serious
+challenges in computational efficiency and scalability.
+In this paper, we propose a new hyper-graph sketching framework for inflence
+dynamics in networks. The central of our sketching framework, called SKIS, is
+an efficient importance sampling algorithm that returns only non-singular
+reverse cascades in the network. Comparing to previously developed sketches
+like RIS and SKIM, our sketch significantly enhances estimation quality while
+substantially reducing processing time and memory-footprint. Further, we
+present general strategies of using SKIS to enhance existing algorithms for
+influence estimation and influence maximization which are motivated by
+practical applications like viral marketing. Using SKIS, we design high-quality
+influence oracle for seed sets with average estimation error up to 10x times
+smaller than those using RIS and 6x times smaller than SKIM. In addition, our
+influence maximization using SKIS substantially improves the quality of
+solutions for greedy algorithms. It achieves up to 10x times speed-up and 4x
+memory reduction for the fastest RIS-based DSSA algorithm, while maintaining
+the same theoretical guarantees.
+",1,0,0,0,0,0
+12498,"Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies","  Advances in artificial intelligence have renewed interest in conversational
+agents. So-called chatbots have reached maturity for industrial applications.
+German insurance companies are interested in improving their customer service
+and digitizing their business processes. In this work we investigate the
+potential use of conversational agents in insurance companies by determining
+which classes of agents are of interest to insurance companies, finding
+relevant use cases and requirements, and developing a prototype for an
+exemplary insurance scenario. Based on this approach, we derive key findings
+for conversational agent implementation in insurance companies.
+",1,0,0,0,0,0
+12499,Dark Matter and Neutrinos,"  The Keplerian distribution of velocities is not observed in the rotation of
+large scale structures, such as found in the rotation of spiral galaxies. The
+deviation from Keplerian distribution provides compelling evidence of the
+presence of non-luminous matter i.e. called dark matter. There are several
+astrophysical motivations for investigating the dark matter in and around the
+galaxy as halo. In this work we address various theoretical and experimental
+indications pointing towards the existence of this unknown form of matter.
+Amongst its constituents neutrino is one of the most prospective candidates. We
+know the neutrinos oscillate and have tiny masses, but there are also
+signatures for existence of heavy and light sterile neutrinos and possibility
+of their mixing. Altogether, the role of neutrinos is of great interests in
+cosmology and understanding dark matter.
+",0,1,0,0,0,0
+12500,Variational Bi-LSTMs,"  Recurrent neural networks like long short-term memory (LSTM) are important
+architectures for sequential prediction tasks. LSTMs (and RNNs in general)
+model sequences along the forward time direction. Bidirectional LSTMs
+(Bi-LSTMs) on the other hand model sequences along both forward and backward
+directions and are generally known to perform better at such tasks because they
+capture a richer representation of the data. In the training of Bi-LSTMs, the
+forward and backward paths are learned independently. We propose a variant of
+the Bi-LSTM architecture, which we call Variational Bi-LSTM, that creates a
+channel between the two paths (during training, but which may be omitted during
+inference); thus optimizing the two paths jointly. We arrive at this joint
+objective for our model by minimizing a variational lower bound of the joint
+likelihood of the data sequence. Our model acts as a regularizer and encourages
+the two networks to inform each other in making their respective predictions
+using distinct information. We perform ablation studies to better understand
+the different components of our model and evaluate the method on various
+benchmarks, showing state-of-the-art performance.
+",1,0,0,1,0,0
+12501,Riemann-Langevin Particle Filtering in Track-Before-Detect,"  Track-before-detect (TBD) is a powerful approach that consists in providing
+the tracker with sensor measurements directly without pre-detection. Due to the
+measurement model non-linearities, online state estimation in TBD is most
+commonly solved via particle filtering. Existing particle filters for TBD do
+not incorporate measurement information in their proposal distribution. The
+Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to
+exploit all available knowledge of the posterior (that is, both prior and
+measurement information). This letter synthesizes recent advances in LMC-based
+filtering to describe the Riemann-Langevin particle filter and introduces its
+novel application to TBD. The benefits of our approach are illustrated in a
+challenging low-noise scenario.
+",0,0,0,1,0,0
+12502,The universal connection for principal bundles over homogeneous spaces and twistor space of coadjoint orbits,"  Given a holomorphic principal bundle $Q\, \longrightarrow\, X$, the universal
+space of holomorphic connections is a torsor $C_1(Q)$ for $\text{ad} Q \otimes
+T^*X$ such that the pullback of $Q$ to $C_1(Q)$ has a tautological holomorphic
+connection. When $X\,=\, G/P$, where $P$ is a parabolic subgroup of a complex
+simple group $G$, and $Q$ is the frame bundle of an ample line bundle, we show
+that $C_1(Q)$ may be identified with $G/L$, where $L\, \subset\, P$ is a Levi
+factor. We use this identification to construct the twistor space associated to
+a natural hyper-Kähler metric on $T^*(G/P)$, recovering Biquard's description
+of this twistor space, but employing only finite-dimensional, Lie-theoretic
+means.
+",0,0,1,0,0,0
+12503,Equivariance Through Parameter-Sharing,"  We propose to study equivariance in deep neural networks through parameter
+symmetries. In particular, given a group $\mathcal{G}$ that acts discretely on
+the input and output of a standard neural network layer $\phi_{W}: \Re^{M} \to
+\Re^{N}$, we show that $\phi_{W}$ is equivariant with respect to
+$\mathcal{G}$-action iff $\mathcal{G}$ explains the symmetries of the network
+parameters $W$. Inspired by this observation, we then propose two
+parameter-sharing schemes to induce the desirable symmetry on $W$. Our
+procedures for tying the parameters achieve $\mathcal{G}$-equivariance and,
+under some conditions on the action of $\mathcal{G}$, they guarantee
+sensitivity to all other permutation groups outside $\mathcal{G}$.
+",1,0,0,1,0,0
+12504,Quantum light in curved low dimensional hexagonal boron nitride systems,"  Low-dimensional wide bandgap semiconductors open a new playing field in
+quantum optics using sub-bandgap excitation. In this field, hexagonal boron
+nitride (h-BN) has been reported to host single quantum emitters (QEs), linking
+QE density to perimeters. Furthermore, curvature/perimeters in transition metal
+dichalcogenides (TMDCs) have demonstrated a key role in QE formation. We
+investigate a curvature-abundant BN system - quasi one-dimensional BN nanotubes
+(BNNTs) fabricated via a catalyst-free method. We find that non-treated BNNT is
+an abundant source of stable QEs and analyze their emission features down to
+single nanotubes, comparing dispersed/suspended material. Combining high
+spatial resolution of a scanning electron microscope, we categorize and
+pin-point emission origin to a scale of less than 20 nm, giving us a one-to-one
+validation of emission source with dimensions smaller than the laser excitation
+wavelength, elucidating nano-antenna effects. Two emission origins emerge:
+hybrid/entwined BNNT. By artificially curving h-BN flakes, similar QE spectral
+features are observed. The impact on emission of solvents used in commercial
+products and curved regions is also demonstrated. The 'out of the box'
+availability of QEs in BNNT, lacking processing contamination, is a milestone
+for unraveling their atomic features. These findings open possibilities for
+precision engineering of QEs, puts h-BN under a similar 'umbrella' of TMDC's
+QEs and provides a model explaining QEs spatial localization/formation using
+electron/ion irradiation and chemical etching.
+",0,1,0,0,0,0
+12505,Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation,"  While recent developments in autonomous vehicle (AV) technology highlight
+substantial progress, we lack tools for rigorous and scalable testing.
+Real-world testing, the $\textit{de facto}$ evaluation environment, places the
+public in danger, and, due to the rare nature of accidents, will require
+billions of miles in order to statistically validate performance claims. We
+implement a simulation framework that can test an entire modern autonomous
+driving system, including, in particular, systems that employ deep-learning
+perception and control algorithms. Using adaptive importance-sampling methods
+to accelerate rare-event probability evaluation, we estimate the probability of
+an accident under a base distribution governing standard traffic behavior. We
+demonstrate our framework on a highway scenario, accelerating system evaluation
+by $2$-$20$ times over naive Monte Carlo sampling methods and $10$-$300
+\mathsf{P}$ times (where $\mathsf{P}$ is the number of processors) over
+real-world testing.
+",1,0,0,0,0,0
+12506,Exact time-dependent exchange-correlation potential in electron scattering processes,"  We identify peak and valley structures in the exact exchange-correlation
+potential of time-dependent density functional theory that are crucial for
+time-resolved electron scattering in a model one-dimensional system. These
+structures are completely missed by adiabatic approximations which consequently
+significantly underestimate the scattering probability. A recently-proposed
+non-adiabatic approximation is shown to correctly capture the approach of the
+electron to the target when the initial Kohn-Sham state is chosen judiciously,
+and is more accurate than standard adiabatic functionals, but it ultimately
+fails to accurately capture reflection. These results may explain the
+underestimate of scattering probabilities in some recent studies on molecules
+and surfaces.
+",0,1,0,0,0,0
+12507,Polynomial-Time Algorithms for Sliding Tokens on Cactus Graphs and Block Graphs,"  Given two independent sets $I, J$ of a graph $G$, and imagine that a token
+(coin) is placed at each vertex of $I$. The Sliding Token problem asks if one
+could transform $I$ to $J$ via a sequence of elementary steps, where each step
+requires sliding a token from one vertex to one of its neighbors so that the
+resulting set of vertices where tokens are placed remains independent. This
+problem is $\mathsf{PSPACE}$-complete even for planar graphs of maximum degree
+$3$ and bounded-treewidth. In this paper, we show that Sliding Token can be
+solved efficiently for cactus graphs and block graphs, and give upper bounds on
+the length of a transformation sequence between any two independent sets of
+these graph classes. Our algorithms are designed based on two main
+observations. First, all structures that forbid the existence of a sequence of
+token slidings between $I$ and $J$, if exist, can be found in polynomial time.
+A sufficient condition for determining no-instances can be easily derived using
+this characterization. Second, without such forbidden structures, a sequence of
+token slidings between $I$ and $J$ does exist. In this case, one can indeed
+transform $I$ to $J$ (and vice versa) using a polynomial number of
+token-slides.
+",1,0,0,0,0,0
+12508,Summarized Network Behavior Prediction,"  This work studies the entity-wise topical behavior from massive network logs.
+Both the temporal and the spatial relationships of the behavior are explored
+with the learning architectures combing the recurrent neural network (RNN) and
+the convolutional neural network (CNN). To make the behavioral data appropriate
+for the spatial learning in CNN, several reduction steps are taken to form the
+topical metrics and place them homogeneously like pixels in the images. The
+experimental result shows both the temporal- and the spatial- gains when
+compared to a multilayer perceptron (MLP) network. A new learning framework
+called spatially connected convolutional networks (SCCN) is introduced to more
+efficiently predict the behavior.
+",1,0,0,1,0,0
+12509,Stabiliser states are efficiently PAC-learnable,"  The exponential scaling of the wave function is a fundamental property of
+quantum systems with far reaching implications in our ability to process
+quantum information. A problem where these are particularly relevant is quantum
+state tomography. State tomography, whose objective is to obtain a full
+description of a quantum system, can be analysed in the framework of
+computational learning theory. In this model, quantum states have been shown to
+be Probably Approximately Correct (PAC)-learnable with sample complexity linear
+in the number of qubits. However, it is conjectured that in general quantum
+states require an exponential amount of computation to be learned. Here, using
+results from the literature on the efficient classical simulation of quantum
+systems, we show that stabiliser states are efficiently PAC-learnable. Our
+results solve an open problem formulated by Aaronson [Proc. R. Soc. A, 2088,
+(2007)] and propose learning theory as a tool for exploring the power of
+quantum computation.
+",1,0,0,0,0,0
+12510,A bound for the shortest reset words for semisimple synchronizing automata via the packing number,"  We show that if a semisimple synchronizing automaton with $n$ states has a
+minimal reachable non-unary subset of cardinality $r\ge 2$, then there is a
+reset word of length at most $(n-1)D(2,r,n)$, where $D(2,r,n)$ is the
+$2$-packing number for families of $r$-subsets of $[1,n]$.
+",1,0,1,0,0,0
+12511,HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT,"  Recent developments within memory-augmented neural networks have solved
+sequential problems requiring long-term memory, which are intractable for
+traditional neural networks. However, current approaches still struggle to
+scale to large memory sizes and sequence lengths. In this paper we show how
+access to memory can be encoded geometrically through a HyperNEAT-based Neural
+Turing Machine (HyperENTM). We demonstrate that using the indirect HyperNEAT
+encoding allows for training on small memory vectors in a bit-vector copy task
+and then applying the knowledge gained from such training to speed up training
+on larger size memory vectors. Additionally, we demonstrate that in some
+instances, networks trained to copy bit-vectors of size 9 can be scaled to
+sizes of 1,000 without further training. While the task in this paper is
+simple, these results could open up the problems amendable to networks with
+external memories to problems with larger memory vectors and theoretically
+unbounded memory sizes.
+",1,0,0,0,0,0
+12512,X-Ray and Gamma-Ray Emission from Middle-aged Supernova Remnants in Cavities. I. Spherical Symmetry,"  We present analytical and numerical studies of models of supernova-remnant
+(SNR) blast waves expanding into uniform media and interacting with a denser
+cavity wall, in one spatial dimension. We predict the nonthermal emission from
+such blast waves: synchrotron emission at radio and X-ray energies, and
+bremsstrahlung, inverse-Compton emission (from cosmic-microwave-background seed
+photons, ICCMB), and emission from the decay of $\pi^0$ mesons produced in
+inelastic collisions between accelerated ions and thermal gas, at GeV and TeV
+energies. Accelerated particle spectra are assumed to be power-laws with
+exponential cutoffs at energies limited by the remnant age or (for electrons,
+if lower) by radiative losses. We compare the results with those from
+homogeneous (""one-zone"") models. Such models give fair representations of the
+1-D results for uniform media, but cavity-wall interactions produce effects for
+which one-zone models are inadequate. We study the time evolution of SNR
+morphology and emission with time. Strong morphological differences exist
+between ICCMB and $\pi^0$-decay emission, at some stages, the TeV emission can
+be dominated by the former and the GeV by the latter, resulting in strong
+energy-dependence of morphology. Integrated gamma-ray spectra show apparent
+power-laws of slopes that vary with time, but do not indicate the energy
+distribution of a single population of particles. As observational capabilities
+at GeV and TeV energies improve, spatial inhomogeneity in SNRs will need to be
+accounted for.
+",0,1,0,0,0,0
+12513,An adverse selection approach to power pricing,"  We study the optimal design of electricity contracts among a population of
+consumers with different needs. This question is tackled within the framework
+of Principal-Agent problems in presence of adverse selection. The particular
+features of electricity induce an unusual structure on the production cost,
+with no decreasing return to scale. We are nevertheless able to provide an
+explicit solution for the problem at hand. The optimal contracts are either
+linear or polynomial with respect to the consumption. Whenever the outside
+options offered by competitors are not uniform among the different type of
+consumers, we exhibit situations where the electricity provider should contract
+with consumers with either low or high appetite for electricity.
+",0,0,1,0,0,0
+12514,Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains,"  We consider the minimization of an objective function given access to
+unbiased estimates of its gradient through stochastic gradient descent (SGD)
+with constant step-size. While the detailed analysis was only performed for
+quadratic functions, we provide an explicit asymptotic expansion of the moments
+of the averaged SGD iterates that outlines the dependence on initial
+conditions, the effect of noise and the step-size, as well as the lack of
+convergence in the general (non-quadratic) case. For this analysis, we bring
+tools from Markov chain theory into the analysis of stochastic gradient. We
+then show that Richardson-Romberg extrapolation may be used to get closer to
+the global optimum and we show empirical improvements of the new extrapolation
+scheme.
+",0,0,1,1,0,0
+12515,Learning Overcomplete HMMs,"  We study the problem of learning overcomplete HMMs---those that have many
+hidden states but a small output alphabet. Despite having significant practical
+importance, such HMMs are poorly understood with no known positive or negative
+results for efficient learning. In this paper, we present several new
+results---both positive and negative---which help define the boundaries between
+the tractable and intractable settings. Specifically, we show positive results
+for a large subclass of HMMs whose transition matrices are sparse,
+well-conditioned, and have small probability mass on short cycles. On the other
+hand, we show that learning is impossible given only a polynomial number of
+samples for HMMs with a small output alphabet and whose transition matrices are
+random regular graphs with large degree. We also discuss these results in the
+context of learning HMMs which can capture long-term dependencies.
+",1,0,0,1,0,0
+12516,Migration barriers for surface diffusion on a rigid lattice: challenges and solutions,"  Atomistic rigid lattice Kinetic Monte Carlo is an efficient method for
+simulating nano-objects and surfaces at timescales much longer than those
+accessible by molecular dynamics. A laborious part of constructing any Kinetic
+Monte Carlo model is, however, to calculate all migration barriers that are
+needed to give the probabilities for any atom jump event to occur in the
+simulations. One of the common methods of barrier calculations is Nudged
+Elastic Band. The number of barriers needed to fully describe simulated systems
+is typically between hundreds of thousands and millions. Calculations of such a
+large number of barriers of various processes is far from trivial. In this
+paper, we will discuss the challenges arising during barriers calculations on a
+surface and present a systematic and reliable tethering force approach to
+construct a rigid lattice barrier parameterization of face-centred and
+body-centred cubic metal lattices. We have produced several different barrier
+sets for Cu and for Fe that can be used for KMC simulations of processes on
+arbitrarily rough surfaces. The sets are published as Data in Brief articles
+and available for the use.
+",0,1,0,0,0,0
+12517,On the Quest for an Acyclic Graph,"  The paper aims at finding acyclic graphs under a given set of constraints.
+More specifically, given a propositional formula {\phi} over edges of a
+fixed-size graph, the objective is to find a model of {\phi} that corresponds
+to a graph that is acyclic. The paper proposes several encodings of the problem
+and compares them in an experimental evaluation using stateof-the-art SAT
+solvers.
+",1,0,0,0,0,0
+12518,Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning,"  This paper presents a robust matrix elastic net based canonical correlation
+analysis (RMEN-CCA) for multiple view unsupervised learning problems, which
+emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used
+as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN
+to distill naturally meaningful features without any prior assumption and to
+measure effectively correlations between different 'views'. We can further
+employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario
+with theoretical guarantees, which takes advantage of the kernel trick for
+highly complicated nonlinear feature learning. Rather than simply incorporating
+existing regularization minimization terms into CCA, this paper provides a new
+learning paradigm for CCA and is the first to derive a coupled feature
+selection based CCA algorithm that guarantees convergence. More significantly,
+for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of
+relevance and coupled feature selection. Moreover, it is nontrivial to tackle
+directly the RMEN-CCA by previous optimization approaches derived from its
+sophisticated model architecture. Therefore, this paper further offers a bridge
+between a new optimization problem and an existing efficient iterative
+approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and
+address large-scale and streaming data problems. Experimental results on four
+popular competing datasets illustrate that the RMEN-CCA performs more
+effectively and efficiently than do state-of-the-art approaches.
+",1,0,0,1,0,0
+12519,C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset,"  Visual Question Answering (VQA) has received a lot of attention over the past
+couple of years. A number of deep learning models have been proposed for this
+task. However, it has been shown that these models are heavily driven by
+superficial correlations in the training data and lack compositionality -- the
+ability to answer questions about unseen compositions of seen concepts. This
+compositionality is desirable and central to intelligence. In this paper, we
+propose a new setting for Visual Question Answering where the test
+question-answer pairs are compositionally novel compared to training
+question-answer pairs. To facilitate developing models under this setting, we
+present a new compositional split of the VQA v1.0 dataset, which we call
+Compositional VQA (C-VQA). We analyze the distribution of questions and answers
+in the C-VQA splits. Finally, we evaluate several existing VQA models under
+this new setting and show that the performances of these models degrade by a
+significant amount compared to the original VQA setting.
+",1,0,0,0,0,0
+12520,Spherical Planetary Robot for Rugged Terrain Traversal,"  Wheeled planetary rovers such as the Mars Exploration Rovers (MERs) and Mars
+Science Laboratory (MSL) have provided unprecedented, detailed images of the
+Mars surface. However, these rovers are large and are of high-cost as they need
+to carry sophisticated instruments and science laboratories. We propose the
+development of low-cost planetary rovers that are the size and shape of
+cantaloupes and that can be deployed from a larger rover. The rover named
+SphereX is 2 kg in mass, is spherical, holonomic and contains a hopping
+mechanism to jump over rugged terrain. A small low-cost rover complements a
+larger rover, particularly to traverse rugged terrain or roll down a canyon,
+cliff or crater to obtain images and science data. While it may be a one-way
+journey for these small robots, they could be used tactically to obtain
+high-reward science data. The robot is equipped with a pair of stereo cameras
+to perform visual navigation and has room for a science payload. In this paper,
+we analyze the design and development of a laboratory prototype. The results
+show a promising pathway towards development of a field system.
+",1,1,0,0,0,0
+12521,Active Learning for Accurate Estimation of Linear Models,"  We explore the sequential decision making problem where the goal is to
+estimate uniformly well a number of linear models, given a shared budget of
+random contexts independently sampled from a known distribution. The decision
+maker must query one of the linear models for each incoming context, and
+receives an observation corrupted by noise levels that are unknown, and depend
+on the model instance. We present Trace-UCB, an adaptive allocation algorithm
+that learns the noise levels while balancing contexts accordingly across the
+different linear functions, and derive guarantees for simple regret in both
+expectation and high-probability. Finally, we extend the algorithm and its
+guarantees to high dimensional settings, where the number of linear models
+times the dimension of the contextual space is higher than the total budget of
+samples. Simulations with real data suggest that Trace-UCB is remarkably
+robust, outperforming a number of baselines even when its assumptions are
+violated.
+",1,0,0,1,0,0
+12522,Revisiting Frequency Reuse towards Supporting Ultra-Reliable Ubiquitous-Rate Communication,"  One of the goals of 5G wireless systems stated by the NGMN alliance is to
+provide moderate rates (50+ Mbps) everywhere and with very high reliability. We
+term this service Ultra-Reliable Ubiquitous-Rate Communication (UR2C). This
+paper investigates the role of frequency reuse in supporting UR2C in the
+downlink. To this end, two frequency reuse schemes are considered:
+user-specific frequency reuse (FRu) and BS-specific frequency reuse (FRb). For
+a given unit frequency channel, FRu reduces the number of serving user
+equipments (UEs), whereas FRb directly decreases the number of interfering base
+stations (BSs). This increases the distance from the interfering BSs and the
+signal-to-interference ratio (SIR) attains ultra-reliability, e.g. 99% SIR
+coverage at a randomly picked UE. The ultra-reliability is, however, achieved
+at the cost of the reduced frequency allocation, which may degrade overall
+downlink rate. To fairly capture this reliability-rate tradeoff, we propose
+ubiquitous rate defined as the maximum downlink rate whose required SIR can be
+achieved with ultra-reliability. By using stochastic geometry, we derive
+closed-form ubiquitous rate as well as the optimal frequency reuse rules for
+UR2C.
+",1,0,0,0,0,0
+12523,Local isometric immersions of pseudo-spherical surfaces and k-th order evolution equations,"  We consider the class of evolution equations that describe pseudo-spherical
+surfaces of the form u\_t = F (u, $\partial$u/$\partial$x, ..., $\partial$^k
+u/$\partial$x^k), k $\ge$ 2 classified by Chern-Tenenblat. This class of
+equations is characterized by the property that to each solution of a
+differential equation within this class, there corresponds a 2-dimensional
+Riemannian metric of curvature-1. We investigate the following problem: given
+such a metric, is there a local isometric immersion in R 3 such that the
+coefficients of the second fundamental form of the surface depend on a jet of
+finite order of u? By extending our previous result for second order evolution
+equation to k-th order equations, we prove that there is only one type of
+equations that admit such an isometric immersion. We prove that the
+coefficients of the second fundamental forms of the local isometric immersion
+determined by the solutions u are universal, i.e., they are independent of u.
+Moreover, we show that there exists a foliation of the domain of the parameters
+of the surface by straight lines with the property that the mean curvature of
+the surface is constant along the images of these straight lines under the
+isometric immersion.
+",0,0,1,0,0,0
+12524,Integrating Flexible Normalization into Mid-Level Representations of Deep Convolutional Neural Networks,"  Deep convolutional neural networks (CNNs) are becoming increasingly popular
+models to predict neural responses in visual cortex. However, contextual
+effects, which are prevalent in neural processing and in perception, are not
+explicitly handled by current CNNs, including those used for neural prediction.
+In primary visual cortex, neural responses are modulated by stimuli spatially
+surrounding the classical receptive field in rich ways. These effects have been
+modeled with divisive normalization approaches, including flexible models,
+where spatial normalization is recruited only to the degree responses from
+center and surround locations are deemed statistically dependent. We propose a
+flexible normalization model applied to mid-level representations of deep CNNs
+as a tractable way to study contextual normalization mechanisms in mid-level
+cortical areas. This approach captures non-trivial spatial dependencies among
+mid-level features in CNNs, such as those present in textures and other visual
+stimuli, that arise from tiling high order features, geometrically. We expect
+that the proposed approach can make predictions about when spatial
+normalization might be recruited in mid-level cortical areas. We also expect
+this approach to be useful as part of the CNN toolkit, therefore going beyond
+more restrictive fixed forms of normalization.
+",0,0,0,0,1,0
+12525,Moments and non-vanishing of Hecke $L$-functions with quadratic characters in $\mathbb{Q}(i)$ at the central point,"  In this paper, we study the moments of central values of Hecke $L$-functions
+associated with quadratic characters in $\mq(i)$, and establish quantitative
+non-vanishing result for the $L$-values.
+",0,0,1,0,0,0
+12526,Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning,"  Graphs are a commonly used construct for representing relationships between
+elements in complex high dimensional datasets. Many real-world phenomenon are
+dynamic in nature, meaning that any graph used to represent them is inherently
+temporal. However, many of the machine learning models designed to capture
+knowledge about the structure of these graphs ignore this rich temporal
+information when creating representations of the graph. This results in models
+which do not perform well when used to make predictions about the future state
+of the graph -- especially when the delta between time stamps is not small. In
+this work, we explore a novel training procedure and an associated unsupervised
+model which creates graph representations optimised to predict the future state
+of the graph. We make use of graph convolutional neural networks to encode the
+graph into a latent representation, which we then use to train our temporal
+offset reconstruction method, inspired by auto-encoders, to predict a later
+time point -- multiple time steps into the future. Using our method, we
+demonstrate superior performance for the task of future link prediction
+compared with none-temporal state-of-the-art baselines. We show our approach to
+be capable of outperforming non-temporal baselines by 38% on a real world
+dataset.
+",1,0,0,0,0,0
+12527,Decentralized Tube-based Model Predictive Control of Uncertain Nonlinear Multi-Agent Systems,"  This paper addresses the problem of decentralized tube-based nonlinear Model
+Predictive Control (NMPC) for a class of uncertain nonlinear continuous-time
+multi-agent systems with additive and bounded disturbance. In particular, the
+problem of robust navigation of a multi-agent system to predefined states of
+the workspace while using only local information is addressed, under certain
+distance and control input constraints. We propose a decentralized feedback
+control protocol that consists of two terms: a nominal control input, which is
+computed online and is the outcome of a Decentralized Finite Horizon Optimal
+Control Problem (DFHOCP) that each agent solves at every sampling time, for its
+nominal system dynamics; and an additive state feedback law which is computed
+offline and guarantees that the real trajectories of each agent will belong to
+a hyper-tube centered along the nominal trajectory, for all times. The volume
+of the hyper-tube depends on the upper bound of the disturbances as well as the
+bounds of the derivatives of the dynamics. In addition, by introducing certain
+distance constraints, the proposed scheme guarantees that the initially
+connected agents remain connected for all times. Under standard assumptions
+that arise in nominal NMPC schemes, controllability assumptions as well as
+communication capabilities between the agents, we guarantee that the
+multi-agent system is ISS (Input to State Stable) with respect to the
+disturbances, for all initial conditions satisfying the state constraints.
+Simulation results verify the correctness of the proposed framework.
+",1,0,0,0,0,0
+12528,Cognition of the circle in ancient India,"  We discuss the understanding of geometry of the circle in ancient India, in
+terms of enunciation of various principles, constructions, applications etc.
+during various phases of history and cultural contexts.
+",0,0,1,0,0,0
+12529,Estimates for $π(x)$ for large values of $x$ and Ramanujan's prime counting inequality,"  In this paper we use refined approximations for Chebyshev's
+$\vartheta$-function to establish new explicit estimates for the prime counting
+function $\pi(x)$, which improve the current best estimates for large values of
+$x$. As an application we find an upper bound for the number $H_0$ which is
+defined to be the smallest positive integer so that Ramanujan's prime counting
+inequality holds for every $x \geq H_0$.
+",0,0,1,0,0,0
+12530,Unveiled electric profiles within hydrogen bonds suggest DNA base pairs with similar bond strengths,"  Electrical forces are the background of all the interactions occurring in
+biochemical systems. From here and by using a combination of ab-initio and
+ad-hoc models, we introduce the first description of electric field profiles
+with intrabond resolution to support a characterization of single bond forces
+attending to its electrical origin. This fundamental issue has eluded a
+physical description so far. Our method is applied to describe hydrogen bonds
+(HB) in DNA base pairs. Numerical results reveal that base pairs in DNA could
+be equivalent considering HB strength contributions, which challenges previous
+interpretations of thermodynamic properties of DNA based on the assumption that
+Adenine/Thymine pairs are weaker than Guanine/Cytosine pairs due to the sole
+difference in the number of HB. Thus, our methodology provides solid
+foundations to support the development of extended models intended to go deeper
+into the molecular mechanisms of DNA functioning.
+",0,0,0,0,1,0
+12531,Mimetization of the elastic properties of cancellous bone via a parameterized cellular material,"  Bone tissue mechanical properties and trabecular microarchitecture are the
+main factors that determine the biomechanical properties of cancellous bone.
+Artificial cancellous microstructures, typically described by a reduced number
+of geometrical parameters, can be designed to obtain a mechanical behavior
+mimicking that of natural bone. In this work, we assess the ability of the
+parameterized microstructure introduced by Kowalczyk (2006) to mimic the
+elastic response of cancellous bone. Artificial microstructures are compared
+with actual bone samples in terms of elasticity matrices and their symmetry
+classes. The capability of the parameterized microstructure to combine the
+dominant isotropic, hexagonal, tetragonal and orthorhombic symmetry classes in
+the proportions present in the cancellous bone is shown. Based on this finding,
+two optimization approaches are devised to find the geometrical parameters of
+the artificial microstructure that better mimics the elastic response of a
+target natural bone specimen: a Sequential Quadratic Programming algorithm that
+minimizes the norm of the difference between the elasticity matrices, and a
+Pattern Search algorithm that minimizes the difference between the symmetry
+class decompositions. The pattern search approach is found to produce the best
+results. The performance of the method is demonstrated via analyses for 146
+bone samples.
+",0,1,0,0,0,0
+12532,Extended Vertical Lists for Temporal Pattern Mining from Multivariate Time Series,"  Temporal Pattern Mining (TPM) is the problem of mining predictive complex
+temporal patterns from multivariate time series in a supervised setting. We
+develop a new method called the Fast Temporal Pattern Mining with Extended
+Vertical Lists. This method utilizes an extension of the Apriori property which
+requires a more complex pattern to appear within records only at places where
+all of its subpatterns are detected as well. The approach is based on a novel
+data structure called the Extended Vertical List that tracks positions of the
+first state of the pattern inside records. Extensive computational results
+indicate that the new method performs significantly faster than the previous
+version of the algorithm for TMP. However, the speed-up comes at the expense of
+memory usage.
+",0,0,0,1,0,0
+12533,PowerAlert: An Integrity Checker using Power Measurement,"  We propose PowerAlert, an efficient external integrity checker for untrusted
+hosts. Current attestation systems suffer from shortcomings in requiring
+complete checksum of the code segment, being static, use of timing information
+sourced from the untrusted machine, or use of timing information with high
+error (network round trip time). We address those shortcomings by (1) using
+power measurements from the host to ensure that the checking code is executed
+and (2) checking a subset of the kernel space over a long period of time. We
+compare the power measurement against a learned power model of the execution of
+the machine and validate that the execution was not tampered. Finally, power
+diversifies the integrity checking program to prevent the attacker from
+adapting. We implement a prototype of PowerAlert using Raspberry pi and
+evaluate the performance of the integrity checking program generation. We model
+the interaction between PowerAlert and an attacker as a game. We study the
+effectiveness of the random initiation strategy in deterring the attacker. The
+study shows that \power forces the attacker to trade-off stealthiness for the
+risk of detection, while still maintaining an acceptable probability of
+detection given the long lifespan of stealthy attacks.
+",1,0,0,0,0,0
+12534,Extension complexities of Cartesian products involving a pyramid,"  It is an open question whether the linear extension complexity of the
+Cartesian product of two polytopes P, Q is the sum of the extension
+complexities of P and Q. We give an affirmative answer to this question for the
+case that one of the two polytopes is a pyramid.
+",1,0,1,0,0,0
+12535,Speech recognition for medical conversations,"  In this work we explored building automatic speech recognition models for
+transcribing doctor patient conversation. We collected a large scale dataset of
+clinical conversations ($14,000$ hr), designed the task to represent the real
+word scenario, and explored several alignment approaches to iteratively improve
+data quality. We explored both CTC and LAS systems for building speech
+recognition models. The LAS was more resilient to noisy data and CTC required
+more data clean up. A detailed analysis is provided for understanding the
+performance for clinical tasks. Our analysis showed the speech recognition
+models performed well on important medical utterances, while errors occurred in
+causal conversations. Overall we believe the resulting models can provide
+reasonable quality in practice.
+",1,0,0,1,0,0
+12536,Changing users' security behaviour towards security questions: A game based learning approach,"  Fallback authentication is used to retrieve forgotten passwords. Security
+questions are one of the main techniques used to conduct fallback
+authentication. In this paper, we propose a serious game design that uses
+system-generated security questions with the aim of improving the usability of
+fallback authentication. For this purpose, we adopted the popular picture-based
+""4 Pics 1 word"" mobile game. This game was selected because of its use of
+pictures and cues, which previous psychology research found to be crucial to
+aid memorability. This game asks users to pick the word that relates to the
+given pictures. We then customized this game by adding features which help
+maximize the following memory retrieval skills: (a) verbal cues - by providing
+hints with verbal descriptions, (b) spatial cues - by maintaining the same
+order of pictures, (c) graphical cues - by showing 4 images for each challenge,
+(d) interactivity/engaging nature of the game.
+",1,0,0,0,0,0
+12537,Understanding Web Archiving Services and Their (Mis)Use on Social Media,"  Web archiving services play an increasingly important role in today's
+information ecosystem, by ensuring the continuing availability of information,
+or by deliberately caching content that might get deleted or removed. Among
+these, the Wayback Machine has been proactively archiving, since 2001, versions
+of a large number of Web pages, while newer services like archive.is allow
+users to create on-demand snapshots of specific Web pages, which serve as time
+capsules that can be shared across the Web. In this paper, we present a
+large-scale analysis of Web archiving services and their use on social media,
+shedding light on the actors involved in this ecosystem, the content that gets
+archived, and how it is shared. We crawl and study: 1) 21M URLs from
+archive.is, spanning almost two years, and 2) 356K archive.is plus 391K Wayback
+Machine URLs that were shared on four social networks: Reddit, Twitter, Gab,
+and 4chan's Politically Incorrect board (/pol/) over 14 months. We observe that
+news and social media posts are the most common types of content archived,
+likely due to their perceived ephemeral and/or controversial nature. Moreover,
+URLs of archiving services are extensively shared on ""fringe"" communities
+within Reddit and 4chan to preserve possibly contentious content. Lastly, we
+find evidence of moderators nudging or even forcing users to use archives,
+instead of direct links, for news sources with opposing ideologies, potentially
+depriving them of ad revenue.
+",1,0,0,0,0,0
+12538,Mining a Sub-Matrix of Maximal Sum,"  Biclustering techniques have been widely used to identify homogeneous
+subgroups within large data matrices, such as subsets of genes similarly
+expressed across subsets of patients. Mining a max-sum sub-matrix is a related
+but distinct problem for which one looks for a (non-necessarily contiguous)
+rectangular sub-matrix with a maximal sum of its entries. Le Van et al. (Ranked
+Tiling, 2014) already illustrated its applicability to gene expression analysis
+and addressed it with a constraint programming (CP) approach combined with
+large neighborhood search (CP-LNS). In this work, we exhibit some key
+properties of this NP-hard problem and define a bounding function such that
+larger problems can be solved in reasonable time. Two different algorithms are
+proposed in order to exploit the highlighted characteristics of the problem: a
+CP approach with a global constraint (CPGC) and mixed integer linear
+programming (MILP). Practical experiments conducted both on synthetic and real
+gene expression data exhibit the characteristics of these approaches and their
+relative benefits over the original CP-LNS method. Overall, the CPGC approach
+tends to be the fastest to produce a good solution. Yet, the MILP formulation
+is arguably the easiest to formulate and can also be competitive.
+",1,0,0,1,0,0
+12539,Training Deep Networks without Learning Rates Through Coin Betting,"  Deep learning methods achieve state-of-the-art performance in many
+application scenarios. Yet, these methods require a significant amount of
+hyperparameters tuning in order to achieve the best results. In particular,
+tuning the learning rates in the stochastic optimization process is still one
+of the main bottlenecks. In this paper, we propose a new stochastic gradient
+descent procedure for deep networks that does not require any learning rate
+setting. Contrary to previous methods, we do not adapt the learning rates nor
+we make use of the assumed curvature of the objective function. Instead, we
+reduce the optimization process to a game of betting on a coin and propose a
+learning-rate-free optimal algorithm for this scenario. Theoretical convergence
+is proven for convex and quasi-convex functions and empirical evidence shows
+the advantage of our algorithm over popular stochastic gradient algorithms.
+",1,0,1,1,0,0
+12540,Joint Beamforming and Antenna Selection for Sum Rate Maximization in Cognitive Radio Networks,"  This letter studies joint transmit beamforming and antenna selection at a
+secondary base station (BS) with multiple primary users (PUs) in an underlay
+cognitive radio multiple-input single-output broadcast channel. The objective
+is to maximize the sum rate subject to the secondary BS transmit power, minimum
+required rates for secondary users, and PUs' interference power constraints.
+The utility function of interest is nonconcave and the involved constraints are
+nonconvex, so this problem is hard to solve. Nevertheless, we propose a new
+iterative algorithm that finds local optima at the least. We use an inner
+approximation method to construct and solve a simple convex quadratic program
+of moderate dimension at each iteration of the proposed algorithm. Simulation
+results indicate that the proposed algorithm converges quickly and outperforms
+existing approaches.
+",1,0,0,0,0,0
+12541,Multi-sequence segmentation via score and higher-criticism tests,"  We propose local segmentation of multiple sequences sharing a common time- or
+location-index, building upon the single sequence local segmentation methods of
+Niu and Zhang (2012) and Fang, Li and Siegmund (2016). We also propose reverse
+segmentation of multiple sequences that is new even in the single sequence
+context. We show that local segmentation estimates change-points consistently
+for both single and multiple sequences, and that both methods proposed here
+detect signals well, with the reverse segmentation method outperforming a large
+number of known segmentation methods on a commonly used single sequence test
+scenario. We show that on a recent allele-specific copy number study involving
+multiple cancer patients, the simultaneous segmentations of the DNA sequences
+of all the patients provide information beyond that obtained by segmentation of
+the sequences one at a time.
+",0,0,1,1,0,0
+12542,Evidence for structural transition in crystalline tantalum pentoxide films grown by RF magnetron sputtering,"  We investigate the effect of annealing temperature on the crystalline
+structure and physical properties of tantalum-pentoxide films grown by radio
+frequency magnetron sputtering. For this purpose, several tantalum films were
+deposited and the Ta$_2$O$_5$ crystalline phase was induced by exposing the
+samples to heat treatments in air in the temperature range from (575 to
+1000)$^\circ$C. Coating characterization was performed using X-ray diffraction,
+scanning electron microscopy, Raman spectroscopy and UV-VIS spectroscopy. By
+X-ray diffraction analysis we found that a hexagonal Ta$_2$O$_5$ phase
+generates at temperatures above $675^\circ$C. As the annealing temperature
+raises, we observe peak sharpening and new peaks in the corresponding
+diffraction patterns indicating a possible structural transition from hexagonal
+to orthorhombic. The microstructure of the films starts with flake-like
+structures formed on the surface and evolves, as the temperature is further
+increased, to round grains. We found out that, according to the features
+exhibited in the corresponding spectra, Raman spectroscopy can be sensitive
+enough to discriminate between the orthorhombic and hexagonal phases of
+Ta$_2$O$_5$. Finally, as the films crystallize the magnitude of the optical
+band gap increases from 2.4 eV to the typical reported value of 3.8 eV.
+",0,1,0,0,0,0
+12543,Emergence and Reductionism: an awkward Baconian alliance,"  This article discusses the relationship between emergence and reductionism
+from the perspective of a condensed matter physicist. Reductionism and
+emergence play an intertwined role in the everyday life of the physicist, yet
+we rarely stop to contemplate their relationship: indeed, the two are often
+regarded as conflicting world-views of science. I argue that in practice, they
+compliment one-another, forming an awkward alliance in a fashion envisioned by
+the Renaissance scientist, Francis Bacon. Looking at the historical record in
+classical and quantum physics, I discuss how emergence fits into a reductionist
+view of nature. Often, a deep understanding of reductionist physics depends on
+the understanding of its emergent consequences. Thus the concept of energy was
+unknown to Newton, Leibnitz, Lagrange or Hamilton, because they did not
+understand heat. Similarly, the understanding of the weak force awaited an
+understanding of the Meissner effect in superconductivity. Emergence can thus
+be likened to an encrypted consequence of reductionism. Taking examples from
+current research, including topological insulators and strange metals, I show
+that the convection between emergence and reductionism continues to provide a
+powerful driver for frontier scientific research, linking the lab with the
+cosmos.
+",0,1,0,0,0,0
+12544,Pipelined Parallel FFT Architecture,"  In this paper, an optimized efficient VLSI architecture of a pipeline Fast
+Fourier transform (FFT) processor capable of producing the reverse output order
+sequence is presented. Paper presents Radix-2 multipath delay architecture for
+FFT calculation. The implementation of FFT in hardware is very critical because
+for calculation of FFT number of butterfly operations i.e. number of
+multipliers requires due to which hardware gets increased means indirectly cost
+of hardware is automatically gets increased. Also multiplier operations are
+slow that's why it limits the speed of operation of architecture. The optimized
+VLSI implementation of FFT algorithm is presented in this paper. Here
+architecture is pipelined to optimize it and to increase the speed of
+operation. Also to increase the speed of operation 2 levels parallel processing
+is used.
+",1,0,1,0,0,0
+12545,Global smoothing of a subanalytic set,"  We give rather simple answers to two long-standing questions in real-analytic
+geometry, on global smoothing of a subanalytic set, and on transformation of a
+proper real-analytic mapping to a mapping with equidimensional fibres by global
+blowings-up of the target. These questions are related: a positive answer to
+the second can be used to reduce the first to the simpler semianalytic case. We
+show that the second question has a negative answer, in general, and that the
+first problem nevertheless has a positive solution.
+",0,0,1,0,0,0
+12546,Coupled Compound Poisson Factorization,"  We present a general framework, the coupled compound Poisson factorization
+(CCPF), to capture the missing-data mechanism in extremely sparse data sets by
+coupling a hierarchical Poisson factorization with an arbitrary data-generating
+model. We derive a stochastic variational inference algorithm for the resulting
+model and, as examples of our framework, implement three different
+data-generating models---a mixture model, linear regression, and factor
+analysis---to robustly model non-random missing data in the context of
+clustering, prediction, and matrix factorization. In all three cases, we test
+our framework against models that ignore the missing-data mechanism on large
+scale studies with non-random missing data, and we show that explicitly
+modeling the missing-data mechanism substantially improves the quality of the
+results, as measured using data log likelihood on a held-out test set.
+",1,0,0,1,0,0
+12547,A model bridging chimera state and explosive synchronization,"  Global and partial synchronization are the two distinctive forms of
+synchronization in coupled oscillators and have been well studied in the past
+decades. Recent attention on synchronization is focused on the chimera state
+(CS) and explosive synchronization (ES), but little attention has been paid to
+their relationship. We here study this topic by presenting a model to bridge
+these two phenomena, which consists of two groups of coupled oscillators and
+its coupling strength is adaptively controlled by a local order parameter. We
+find that this model displays either CS or ES in two limits. In between the two
+limits, this model exhibits both CS and ES, where CS can be observed for a
+fixed coupling strength and ES appears when the coupling is increased
+adiabatically. Moreover, we show both theoretically and numerically that there
+are a variety of CS basin patterns for the case of identical oscillators,
+depending on the distributions of both the initial order parameters and the
+initial average phases. This model suggests a way to easily observe CS, in
+contrast to others models having some (weak or strong) dependence on initial
+conditions.
+",0,1,0,0,0,0
+12548,Spatial modeling of shot conversion in soccer to single out goalscoring ability,"  Goals are results of pin-point shots and it is a pivotal decision in soccer
+when, how and where to shoot. The main contribution of this study is two-fold.
+At first, after showing that there exists high spatial correlation in the data
+of shots across games, we introduce a spatial process in the error structure to
+model the probability of conversion from a shot depending on positional and
+situational covariates. The model is developed using a full Bayesian framework.
+Secondly, based on the proposed model, we define two new measures that can
+appropriately quantify the impact of an individual in soccer, by evaluating the
+positioning senses and shooting abilities of the players. As a practical
+application, the method is implemented on Major League Soccer data from 2016/17
+season.
+",0,0,0,1,0,0
+12549,"Metrics for Formal Structures, with an Application to Kripke Models and their Dynamics","  This report introduces and investigates a family of metrics on sets of
+pointed Kripke models. The metrics are generalizations of the Hamming distance
+applicable to countably infinite binary strings and, by extension, logical
+theories or semantic structures. We first study the topological properties of
+the resulting metric spaces. A key result provides sufficient conditions for
+spaces having the Stone property, i.e., being compact, totally disconnected and
+Hausdorff. Second, we turn to mappings, where it is shown that a widely used
+type of model transformations, product updates, give rise to continuous maps in
+the induced topology.
+",0,0,1,0,0,0
+12550,Hyperbolic Dispersion Dominant Regime Identified through Spontaneous Emission Variations near Metamaterial Interfaces,"  Surface plasmon polariton, hyberbolic dispersion of energy and momentum, and
+emission interference provide opportunities to control photoluminescence
+properties. However, the interplays between these regimes need to be understood
+to take advantage of them in optoelectronic applications. Here, we investigate
+broadband variations induced by hyperbolic metamaterial (HMM) multilayer
+nanostructures on the spontaneous emission of selected organic chromophores.
+Experimental and calculated spontaneous emission lifetimes are shown to vary
+non-monotonously near HMM interfaces. With the SPP and interference dominant
+regimes. With the HMM number of pairs used as the analysis parameter, the
+lifetime is shown to be independent of the number of pairs in the surface
+plasmon polaritons, and emission interference dominant regimes, while it
+decreases in the Hyperbolic Dispersion dominant regime. We also show that the
+spontaneous emission lifetime is similarly affected by transverse positive and
+transverse negative HMMs. This work has broad implications on the rational
+design of functional photonic surfaces to control the luminescence of
+semiconductor chromophores.
+",0,1,0,0,0,0
+12551,Legendrian Satellites and Decomposable Concordances,"  We investigate the ramifications of the Legendrian satellite construction on
+the relation of Lagrangian cobordism between Legendrian knots. Under a simple
+hypothesis, we construct a Lagrangian concordance between two Legendrian
+satellites by stacking up a sequence of elementary cobordisms. This
+construction narrows the search for ""non-decomposable"" Lagrangian cobordisms
+and yields new families of decomposable Lagrangian slice knots. Finally, we
+show that the maximum Thurston-Bennequin number of a smoothly slice knot
+provides an obstruction to any Legendrian satellite of that knot being
+Lagrangian slice.
+",0,0,1,0,0,0
+12552,Plausible Deniability for Privacy-Preserving Data Synthesis,"  Releasing full data records is one of the most challenging problems in data
+privacy. On the one hand, many of the popular techniques such as data
+de-identification are problematic because of their dependence on the background
+knowledge of adversaries. On the other hand, rigorous methods such as the
+exponential mechanism for differential privacy are often computationally
+impractical to use for releasing high dimensional data or cannot preserve high
+utility of original data due to their extensive data perturbation.
+This paper presents a criterion called plausible deniability that provides a
+formal privacy guarantee, notably for releasing sensitive datasets: an output
+record can be released only if a certain amount of input records are
+indistinguishable, up to a privacy parameter. This notion does not depend on
+the background knowledge of an adversary. Also, it can efficiently be checked
+by privacy tests. We present mechanisms to generate synthetic datasets with
+similar statistical properties to the input data and the same format. We study
+this technique both theoretically and experimentally. A key theoretical result
+shows that, with proper randomization, the plausible deniability mechanism
+generates differentially private synthetic data. We demonstrate the efficiency
+of this generative technique on a large dataset; it is shown to preserve the
+utility of original data with respect to various statistical analysis and
+machine learning measures.
+",1,0,0,1,0,0
+12553,The Co-Evolution of Test Maintenance and Code Maintenance through the lens of Fine-Grained Semantic Changes,"  Automatic testing is a widely adopted technique for improving software
+quality. Software developers add, remove and update test methods and test
+classes as part of the software development process as well as during the
+evolution phase, following the initial release. In this work we conduct a large
+scale study of 61 popular open source projects and report the relationships we
+have established between test maintenance, production code maintenance, and
+semantic changes (e.g, statement added, method removed, etc.). performed in
+developers' commits.
+We build predictive models, and show that the number of tests in a software
+project can be well predicted by employing code maintenance profiles (i.e., how
+many commits were performed in each of the maintenance activities: corrective,
+perfective, adaptive). Our findings also reveal that more often than not,
+developers perform code fixes without performing complementary test maintenance
+in the same commit (e.g., update an existing test or add a new one). When
+developers do perform test maintenance, it is likely to be affected by the
+semantic changes they perform as part of their commit.
+Our work is based on studying 61 popular open source projects, comprised of
+over 240,000 commits consisting of over 16,000,000 semantic change type
+instances, performed by over 4,000 software engineers.
+",1,0,0,0,0,0
+12554,Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning,"  Autonomous vehicles (AVs) are on the road. To safely and efficiently interact
+with other road participants, AVs have to accurately predict the behavior of
+surrounding vehicles and plan accordingly. Such prediction should be
+probabilistic, to address the uncertainties in human behavior. Such prediction
+should also be interactive, since the distribution over all possible
+trajectories of the predicted vehicle depends not only on historical
+information, but also on future plans of other vehicles that interact with it.
+To achieve such interaction-aware predictions, we propose a probabilistic
+prediction approach based on hierarchical inverse reinforcement learning (IRL).
+First, we explicitly consider the hierarchical trajectory-generation process of
+human drivers involving both discrete and continuous driving decisions. Based
+on this, the distribution over all future trajectories of the predicted vehicle
+is formulated as a mixture of distributions partitioned by the discrete
+decisions. Then we apply IRL hierarchically to learn the distributions from
+real human demonstrations. A case study for the ramp-merging driving scenario
+is provided. The quantitative results show that the proposed approach can
+accurately predict both the discrete driving decisions such as yield or pass as
+well as the continuous trajectories.
+",1,0,0,1,0,0
+12555,Fast-slow asymptotics for a Markov chain model of fast sodium current,"  We explore the feasibility of using fast-slow asymptotic to eliminate the
+computational stiffness of the discrete-state, continuous-time deterministic
+Markov chain models of ionic channels underlying cardiac excitability. We focus
+on a Markov chain model of the fast sodium current, and investigate its
+asymptotic behaviour with respect to small parameters identified in different
+ways.
+",0,1,0,0,0,0
+12556,Beating the bookies with their own numbers - and how the online sports betting market is rigged,"  The online sports gambling industry employs teams of data analysts to build
+forecast models that turn the odds at sports games in their favour. While
+several betting strategies have been proposed to beat bookmakers, from expert
+prediction models and arbitrage strategies to odds bias exploitation, their
+returns have been inconsistent and it remains to be shown that a betting
+strategy can outperform the online sports betting market. We designed a
+strategy to beat football bookmakers with their own numbers. Instead of
+building a forecasting model to compete with bookmakers predictions, we
+exploited the probability information implicit in the odds publicly available
+in the marketplace to find bets with mispriced odds. Our strategy proved
+profitable in a 10-year historical simulation using closing odds, a 6-month
+historical simulation using minute to minute odds, and a 5-month period during
+which we staked real money with the bookmakers (we made code, data and models
+publicly available). Our results demonstrate that the football betting market
+is inefficient - bookmakers can be consistently beaten across thousands of
+games in both simulated environments and real-life betting. We provide a
+detailed description of our betting experience to illustrate how the sports
+gambling industry compensates these market inefficiencies with discriminatory
+practices against successful clients.
+",1,0,0,1,0,0
+12557,Fixation probabilities for the Moran process in evolutionary games with two strategies: graph shapes and large population asymptotics,"  This paper is based on the complete classification of evolutionary scenarios
+for the Moran process with two strategies given by Taylor et al. (B. Math.
+Biol. 66(6): 1621--1644, 2004). Their classification is based on whether each
+strategy is a Nash equilibrium and whether the fixation probability for a
+single individual of each strategy is larger or smaller than its value for
+neutral evolution. We improve on this analysis by showing that each
+evolutionary scenario is characterized by a definite graph shape for the
+fixation probability function. A second class of results deals with the
+behavior of the fixation probability when the population size tends to
+infinity. We develop asymptotic formulae that approximate the fixation
+probability in this limit and conclude that some of the evolutionary scenarios
+cannot exist when the population size is large.
+",0,0,0,0,1,0
+12558,FPGA Design Techniques for Stable Cryogenic Operation,"  In this paper we show how a deep-submicron FPGA can be modified to operate at
+extremely low temperatures through modifications in the supporting hardware and
+in the firmware programming it. Though FPGAs are not designed to operate at a
+few Kelvin, it is possible to do so on virtue of the extremely high doping
+levels found in deep-submicron CMOS technology nodes. First, any PCB component,
+that does not conform with this requirement, is removed. Both the majority of
+decoupling capacitor types and voltage regulators are not well behaved at
+cryogenic temperatures, asking for an ad-hoc solution to stabilize the FPGA
+supply voltage, especially for sensitive applications. Therefore, we have
+designed a firmware that enforces a constant power consumption, so as to
+stabilize the supply voltage in the interior of the FPGA chip. The FPGA is
+powered with a supply at several meters distance, causing significant IR drop
+and thus fluctuations on the local supply voltage. To achieve the
+stabilization, the variation in digital logic speed, which directly corresponds
+to changes in supply voltage, is constantly measured and corrected for through
+a tunable oscillator farm, implemented on the FPGA. The method is versatile and
+robust, enabling seamless porting to other FPGA families and configurations.
+",0,1,0,0,0,0
+12559,Curvature-aided Incremental Aggregated Gradient Method,"  We propose a new algorithm for finite sum optimization which we call the
+curvature-aided incremental aggregated gradient (CIAG) method. Motivated by the
+problem of training a classifier for a d-dimensional problem, where the number
+of training data is $m$ and $m \gg d \gg 1$, the CIAG method seeks to
+accelerate incremental aggregated gradient (IAG) methods using aids from the
+curvature (or Hessian) information, while avoiding the evaluation of matrix
+inverses required by the incremental Newton (IN) method. Specifically, our idea
+is to exploit the incrementally aggregated Hessian matrix to trace the full
+gradient vector at every incremental step, therefore achieving an improved
+linear convergence rate over the state-of-the-art IAG methods. For strongly
+convex problems, the fast linear convergence rate requires the objective
+function to be close to quadratic, or the initial point to be close to optimal
+solution. Importantly, we show that running one iteration of the CIAG method
+yields the same improvement to the optimality gap as running one iteration of
+the full gradient method, while the complexity is $O(d^2)$ for CIAG and $O(md)$
+for the full gradient. Overall, the CIAG method strikes a balance between the
+high computation complexity incremental Newton-type methods and the slow IAG
+method. Our numerical results support the theoretical findings and show that
+the CIAG method often converges with much fewer iterations than IAG, and
+requires much shorter running time than IN when the problem dimension is high.
+",1,0,0,1,0,0
+12560,"The composition of Solar system asteroids and Earth/Mars moons, and the Earth-Moon composition similarity","  [abridged] In the typical giant-impact scenario for the Moon formation most
+of the Moon's material originates from the impactor. Any Earth-impactor
+composition difference should, therefore, correspond to a comparable Earth-Moon
+composition difference. Analysis of Moon rocks shows a close Earth-Moon
+composition similarity, posing a challenge for the giant-impact scenario, given
+that impactors were thought to significantly differ in composition from the
+planets they impact. Here we use a large set of 140 simulations to show that
+the composition of impactors could be very similar to that of the planets they
+impact; in $4.9\%$-$18.2\%$ ($1.9\%$-$6.7\%$) of the cases the resulting
+composition of the Moon is consistent with the observations of
+$\Delta^{17}O<15$ ($\Delta^{17}O<6$ ppm). These findings suggest that the
+Earth-Moon composition similarity could be resolved as to arise from the
+primordial Earth-impactor composition similarity. Note that although we find
+the likelihood for the suggested competing model of very high mass-ratio
+impacts (producing significant Earth-impactor composition mixing) is comparable
+($<6.7\%$), this scenario also requires additional fine-tuned requirements of a
+very fast spinning Earth. Using the same simulations we also explore the
+composition of giant-impact formed Mars-moons as well as Vesta-like asteroids.
+We find that the Mars-moon composition difference should be large, but smaller
+than expected if the moons are captured asteroids. Finally, we find that the
+left-over planetesimals ('asteroids') in our simulations are frequently
+scattered far away from their initial positions, thus potentially explaining
+the mismatch between the current position and composition of the Vesta
+asteroid.
+",0,1,0,0,0,0
+12561,Maximum Margin Interval Trees,"  Learning a regression function using censored or interval-valued output data
+is an important problem in fields such as genomics and medicine. The goal is to
+learn a real-valued prediction function, and the training output labels
+indicate an interval of possible values. Whereas most existing algorithms for
+this task are linear models, in this paper we investigate learning nonlinear
+tree models. We propose to learn a tree by minimizing a margin-based
+discriminative objective function, and we provide a dynamic programming
+algorithm for computing the optimal solution in log-linear time. We show
+empirically that this algorithm achieves state-of-the-art speed and prediction
+accuracy in a benchmark of several data sets.
+",1,0,0,1,0,0
+12562,Provable and practical approximations for the degree distribution using sublinear graph samples,"  The degree distribution is one of the most fundamental properties used in the
+analysis of massive graphs. There is a large literature on graph sampling,
+where the goal is to estimate properties (especially the degree distribution)
+of a large graph through a small, random sample. The degree distribution
+estimation poses a significant challenge, due to its heavy-tailed nature and
+the large variance in degrees.
+We design a new algorithm, SADDLES, for this problem, using recent
+mathematical techniques from the field of sublinear algorithms. The SADDLES
+algorithm gives provably accurate outputs for all values of the degree
+distribution. For the analysis, we define two fatness measures of the degree
+distribution, called the $h$-index and the $z$-index. We prove that SADDLES is
+sublinear in the graph size when these indices are large. A corollary of this
+result is a provably sublinear algorithm for any degree distribution bounded
+below by a power law.
+We deploy our new algorithm on a variety of real datasets and demonstrate its
+excellent empirical behavior. In all instances, we get extremely accurate
+approximations for all values in the degree distribution by observing at most
+$1\%$ of the vertices. This is a major improvement over the state-of-the-art
+sampling algorithms, which typically sample more than $10\%$ of the vertices to
+give comparable results. We also observe that the $h$ and $z$-indices of real
+graphs are large, validating our theoretical analysis.
+",1,0,1,0,0,0
+12563,Efficient and Robust Polylinear Analysis of Noisy Time Series,"  A method is proposed to generate an optimal fit of a number of connected
+linear trend segments onto time-series data. To be able to efficiently handle
+many lines, the method employs a stochastic search procedure to determine
+optimal transition point locations. Traditional methods use exhaustive grid
+searches, which severely limit the scale of the problems for which they can be
+utilized. The proposed approach is tried against time series with severe noise
+to demonstrate its robustness, and then it is applied to real medical data as
+an illustrative example.
+",0,0,0,1,0,0
+12564,On the optimal design of wall-to-wall heat transport,"  We consider the problem of optimizing heat transport through an
+incompressible fluid layer. Modeling passive scalar transport by
+advection-diffusion, we maximize the mean rate of total transport by a
+divergence-free velocity field. Subject to various boundary conditions and
+intensity constraints, we prove that the maximal rate of transport scales
+linearly in the r.m.s. kinetic energy and, up to possible logarithmic
+corrections, as the $1/3$rd power of the mean enstrophy in the advective
+regime. This makes rigorous a previous prediction on the near optimality of
+convection rolls for energy-constrained transport. Optimal designs for
+enstrophy-constrained transport are significantly more difficult to describe:
+we introduce a ""branching"" flow design with an unbounded number of degrees of
+freedom and prove it achieves nearly optimal transport. The main technical tool
+behind these results is a variational principle for evaluating the transport of
+candidate designs. The principle admits dual formulations for bounding
+transport from above and below. While the upper bound is closely related to the
+""background method"", the lower bound reveals a connection between the optimal
+design problems considered herein and other apparently related model problems
+from mathematical materials science. These connections serve to motivate
+designs.
+",0,1,0,0,0,0
+12565,On the decay rate for the wave equation with viscoelastic boundary damping,"  We consider the wave equation with a boundary condition of memory type. Under
+natural conditions on the acoustic impedance $\hat{k}$ of the boundary one can
+define a corresponding semigroup of contractions (Desch, Fasangova, Milota,
+Probst 2010). With the help of Tauberian theorems we establish energy decay
+rates via resolvent estimates on the generator $-\mathcal{A}$ of the semigroup.
+We reduce the problem of estimating the resolvent of $-\mathcal{A}$ to the
+problem of estimating the resolvent of the corresponding stationary problem.
+Under not too strict additional assumptions on $\hat{k}$ we establish an upper
+bound on the resolvent. For the wave equation on the interval or the disk we
+prove our estimates to be sharp.
+",0,0,1,0,0,0
+12566,Code Reuse With Transformation Objects,"  We present an approach for a lightweight datatype-generic programming in
+Objective Caml programming language aimed at better code reuse. We show, that a
+large class of transformations usually expressed via recursive functions with
+pattern matching can be implemented using the single per-type traversal
+function and the set of object-encoded transformations, which we call
+transformation objects. Object encoding allows transformations to be modified,
+inherited and extended in a conventional object-oriented manner. However, the
+data representation is kept untouched which preserves the ability to construct
+and pattern-match it in the usual way. Our approach equally works for regular
+and polymorphic variant types which makes it possible to combine data types and
+their transformations from statically typed and separately compiled components.
+We also present an implementation which allows us to automatically derive most
+functionality from a slightly augmented type descriptions.
+",1,0,0,0,0,0
+12567,Fixed effects testing in high-dimensional linear mixed models,"  Many scientific and engineering challenges -- ranging from pharmacokinetic
+drug dosage allocation and personalized medicine to marketing mix (4Ps)
+recommendations -- require an understanding of the unobserved heterogeneity in
+order to develop the best decision making-processes. In this paper, we develop
+a hypothesis test and the corresponding p-value for testing for the
+significance of the homogeneous structure in linear mixed models. A robust
+matching moment construction is used for creating a test that adapts to the
+size of the model sparsity. When unobserved heterogeneity at a cluster level is
+constant, we show that our test is both consistent and unbiased even when the
+dimension of the model is extremely high. Our theoretical results rely on a new
+family of adaptive sparse estimators of the fixed effects that do not require
+consistent estimation of the random effects. Moreover, our inference results do
+not require consistent model selection. We showcase that moment matching can be
+extended to nonlinear mixed effects models and to generalized linear mixed
+effects models. In numerical and real data experiments, we find that the
+developed method is extremely accurate, that it adapts to the size of the
+underlying model and is decidedly powerful in the presence of irrelevant
+covariates.
+",1,0,1,1,0,0
+12568,Ubiquitous quasi-Fuchsian surfaces in cusped hyperbolic 3-manifolds,"  This paper proves that every finite volume hyperbolic 3-manifold M contains a
+ubiquitous collection of closed, immersed, quasi-Fuchsian surfaces. These
+surfaces are ubiquitous in the sense that their preimages in the universal
+cover separate any pair of disjoint, non-asymptotic geodesic planes. The proof
+relies in a crucial way on the corresponding theorem of Kahn and Markovic for
+closed 3-manifolds. As a corollary of this result and a companion statement
+about surfaces with cusps, we recover Wise's theorem that the fundamental group
+of M acts freely and cocompactly on a CAT(0) cube complex.
+",0,0,1,0,0,0
+12569,Generic Cospark of a Matrix Can Be Computed in Polynomial Time,"  The cospark of a matrix is the cardinality of the sparsest vector in the
+column space of the matrix. Computing the cospark of a matrix is well known to
+be an NP hard problem. Given the sparsity pattern (i.e., the locations of the
+non-zero entries) of a matrix, if the non-zero entries are drawn from
+independently distributed continuous probability distributions, we prove that
+the cospark of the matrix equals, with probability one, to a particular number
+termed the generic cospark of the matrix. The generic cospark also equals to
+the maximum cospark of matrices consistent with the given sparsity pattern. We
+prove that the generic cospark of a matrix can be computed in polynomial time,
+and offer an algorithm that achieves this.
+",1,0,0,0,0,0
+12570,Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder,"  Neural networks based vocoders, typically the WaveNet, have achieved
+spectacular performance for text-to-speech (TTS) in recent years. Although
+state-of-the-art parallel WaveNet has addressed the issue of real-time waveform
+generation, there remains problems. Firstly, due to the noisy input signal of
+the model, there is still a gap between the quality of generated and natural
+waveforms. Secondly, a parallel WaveNet is trained under a distilled training
+framework, which makes it tedious to adapt a well trained model to a new
+speaker. To address these two problems, this paper proposes an end-to-end
+adaptation method based on the generative adversarial network (GAN), which can
+reduce the computational cost for the training of new speaker adaptation. Our
+subjective experiments shows that the proposed training method can further
+reduce the quality gap between generated and natural waveforms.
+",1,0,0,0,0,0
+12571,CitizenGrid: An Online Middleware for Crowdsourcing Scientific Research,"  In the last few years, contributions of the general public in scientific
+projects has increased due to the advancement of communication and computing
+technologies. Internet played an important role in connecting scientists and
+volunteers who are interested in participating in their scientific projects.
+However, despite potential benefits, only a limited number of crowdsourcing
+based large-scale science (citizen science) projects have been deployed due to
+the complexity involved in setting them up and running them. In this paper, we
+present CitizenGrid - an online middleware platform which addresses security
+and deployment complexity issues by making use of cloud computing and
+virtualisation technologies. CitizenGrid incentivises scientists to make their
+small-to-medium scale applications available as citizen science projects by: 1)
+providing a directory of projects through a web-based portal that makes
+applications easy to discover; 2) providing flexibility to participate in,
+monitor, and control multiple citizen science projects from a common interface;
+3) supporting diverse categories of citizen science projects. The paper
+describes the design, development and evaluation of CitizenGrid and its use
+cases.
+",1,0,0,0,0,0
+12572,Singular sensitivity in a Keller-Segel-fluid system,"  In bounded smooth domains $\Omega\subset\mathbb{R}^N$, $N\in\{2,3\}$,
+considering the chemotaxis--fluid system
+\[ \begin{cases} \begin{split} & n_t + u\cdot \nabla n &= \Delta n - \chi
+\nabla \cdot(\frac{n}{c}\nabla c) &\\ & c_t + u\cdot \nabla c &= \Delta c - c +
+n &\\ & u_t + \kappa (u\cdot \nabla) u &= \Delta u + \nabla P + n\nabla \Phi &
+\end{split}\end{cases} \] with singular sensitivity, we prove global existence
+of classical solutions for given $\Phi\in C^2(\bar{\Omega})$, for $\kappa=0$
+(Stokes-fluid) if $N=3$ and $\kappa\in\{0,1\}$ (Stokes- or Navier--Stokes
+fluid) if $N=2$ and under the condition that \[
+0<\chi<\sqrt{\frac{2}{N}}. \]
+",0,0,1,0,0,0
+12573,Asymptotic theory of multiple-set linear canonical analysis,"  This paper deals with asymptotics for multiple-set linear canonical analysis
+(MSLCA). A definition of this analysis, that adapts the classical one to the
+context of Euclidean random variables, is given and properties of the related
+canonical coefficients are derived. Then, estimators of the MSLCA's elements,
+based on empirical covariance operators, are proposed and asymptotics for these
+estimators are obtained. More precisely, we prove their consistency and we
+obtain asymptotic normality for the estimator of the operator that gives MSLCA,
+and also for the estimator of the vector of canonical coefficients. These
+results are then used to obtain a test for mutual non-correlation between the
+involved Euclidean random variables.
+",0,0,1,1,0,0
+12574,Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography,"  The rapid development of deep learning, a family of machine learning
+techniques, has spurred much interest in its application to medical imaging
+problems. Here, we develop a deep learning algorithm that can accurately detect
+breast cancer on screening mammograms using an ""end-to-end"" training approach
+that efficiently leverages training datasets with either complete clinical
+annotation or only the cancer status (label) of the whole image. In this
+approach, lesion annotations are required only in the initial training stage,
+and subsequent stages require only image-level labels, eliminating the reliance
+on rarely available lesion annotations. Our all convolutional network method
+for classifying screening mammograms attained excellent performance in
+comparison with previous methods. On an independent test set of digitized film
+mammograms from Digital Database for Screening Mammography (DDSM), the best
+single model achieved a per-image AUC of 0.88, and four-model averaging
+improved the AUC to 0.91 (sensitivity: 86.1%, specificity: 80.1%). On a
+validation set of full-field digital mammography (FFDM) images from the
+INbreast database, the best single model achieved a per-image AUC of 0.95, and
+four-model averaging improved the AUC to 0.98 (sensitivity: 86.7%, specificity:
+96.1%). We also demonstrate that a whole image classifier trained using our
+end-to-end approach on the DDSM digitized film mammograms can be transferred to
+INbreast FFDM images using only a subset of the INbreast data for fine-tuning
+and without further reliance on the availability of lesion annotations. These
+findings show that automatic deep learning methods can be readily trained to
+attain high accuracy on heterogeneous mammography platforms, and hold
+tremendous promise for improving clinical tools to reduce false positive and
+false negative screening mammography results.
+",1,0,0,1,0,0
+12575,Nviz - A General Purpse Visualization tool for Wireless Sensor Networks,"  In a Wireless Sensor Network (WSN), data manipulation and representation is a
+crucial part and can take a lot of time to be developed from scratch. Although
+various visualization tools have been created for certain projects so far,
+these tools can only be used in certain scenarios, due to their hard-coded
+packet formats and network's properties. To speed up the development process, a
+visualization tool which can adapt to any kind of WSN is essentially necessary.
+For this purpose, a general-purpose visualization tool - NViz, which can
+represent and visualize data for any kind of WSN, is proposed. NViz allows
+users to set their network's properties and packet formats through XML files.
+Based on properties defined, users can choose the meaning of them and let NViz
+represents the data respectively. Furthermore, a better Replay mechanism, which
+lets researchers and developers debug their WSN easily, is also integrated in
+this tool. NViz is designed based on a layered architecture which allows for
+clear and well-defined interrelationships and interfaces between each
+component.
+",1,0,0,0,0,0
+12576,Transient photon echoes from donor-bound excitons in ZnO epitaxial layers,"  The coherent optical response from 140~nm and 65~nm thick ZnO epitaxial
+layers is studied using transient four-wave-mixing spectroscopy with picosecond
+temporal resolution. Resonant excitation of neutral donor-bound excitons
+results in two-pulse and three-pulse photon echoes. For the donor-bound A
+exciton (D$^0$X$_\text{A}$) at temperature of 1.8~K we evaluate optical
+coherence times $T_2=33-50$~ps corresponding to homogeneous linewidths of
+$13-19~\mu$eV, about two orders of magnitude smaller as compared with the
+inhomogeneous broadening of the optical transitions. The coherent dynamics is
+determined mainly by the population decay with time $T_1=30-40$~ps, while pure
+dephasing is negligible in the studied high quality samples even for strong
+optical excitation. Temperature increase leads to a significant shortening of
+$T_2$ due to interaction with acoustic phonons. In contrast, the loss of
+coherence of the donor-bound B exciton (D$^0$X$_\text{B}$) is significantly
+faster ($T_2=3.6$~ps) and governed by pure dephasing processes.
+",0,1,0,0,0,0
+12577,The Quasar Luminosity Function at Redshift 4 with Hyper Suprime-Cam Wide Survey,"  We present the luminosity function of z=4 quasars based on the Hyper
+Suprime-Cam Subaru Strategic Program Wide layer imaging data in the g, r, i, z,
+and y bands covering 339.8 deg^2. From stellar objects, 1666 z~4 quasar
+candidates are selected by the g-dropout selection down to i=24.0 mag. Their
+photometric redshifts cover the redshift range between 3.6 and 4.3 with an
+average of 3.9. In combination with the quasar sample from the Sloan Digital
+Sky Survey in the same redshift range, the quasar luminosity function covering
+the wide luminosity range of M1450=-22 to -29 mag is constructed. It is well
+described by a double power-law model with a knee at M1450=-25.36+-0.13 mag and
+a flat faint-end slope with a power-law index of -1.30+-0.05. The knee and
+faint-end slope show no clear evidence of redshift evolution from those at z~2.
+The flat slope implies that the UV luminosity density of the quasar population
+is dominated by the quasars around the knee, and does not support the steeper
+faint-end slope at higher redshifts reported at z>5. If we convert the M1450
+luminosity function to the hard X-ray 2-10keV luminosity function using the
+relation between UV and X-ray luminosity of quasars and its scatter, the number
+density of UV-selected quasars matches well with that of the X-ray-selected
+AGNs above the knee of the luminosity function. Below the knee, the UV-selected
+quasars show a deficiency compared to the hard X-ray luminosity function. The
+deficiency can be explained by the lack of obscured AGNs among the UV-selected
+quasars.
+",0,1,0,0,0,0
+12578,Towards Industry 4.0: Gap Analysis between Current Automotive MES and Industry Standards using Model-Based Requirement Engineering,"  The dawn of the fourth industrial revolution, Industry 4.0 has created great
+enthusiasm among companies and researchers by giving them an opportunity to
+pave the path towards the vision of a connected smart factory ecosystem.
+However, in context of automotive industry there is an evident gap between the
+requirements supported by the current automotive manufacturing execution
+systems (MES) and the requirements proposed by industrial standards from the
+International Society of Automation (ISA) such as, ISA-95, ISA-88 over which
+the Industry 4.0 is being built on. In this paper, we bridge this gap by
+following a model-based requirements engineering approach along with a gap
+analysis process. Our work is mainly divided into three phases, (i) automotive
+MES tool selection phase, (ii) requirements modeling phase, (iii) and gap
+analysis phase based on the modeled requirements. During the MES tool selection
+phase, we used known reliable sources such as, MES product survey reports,
+white papers that provide in-depth and comprehensive information about various
+comparison criteria and tool vendors list for the current MES landscape. During
+the requirement modeling phase, we specified requirements derived from the
+needs of ISA-95 and ISA-88 industrial standards using the general purpose
+Systems Modeling Language (SysML). During the gap analysis phase, we find the
+misalignment between standard requirements and the compliance of the existing
+software tools to those standards.
+",1,0,0,0,0,0
+12579,A Constrained Conditional Likelihood Approach for Estimating the Means of Selected Populations,"  Given p independent normal populations, we consider the problem of estimating
+the mean of those populations, that based on the observed data, give the
+strongest signals. We explicitly condition on the ranking of the sample means,
+and consider a constrained conditional maximum likelihood (CCMLE) approach,
+avoiding the use of any priors and of any sparsity requirement between the
+population means. Our results show that if the observed means are too close
+together, we should in fact use the grand mean to estimate the mean of the
+population with the larger sample mean. If they are separated by more than a
+certain threshold, we should shrink the observed means towards each other. As
+intuition suggests, it is only if the observed means are far apart that we
+should conclude that the magnitude of separation and consequent ranking are not
+due to chance. Unlike other methods, our approach does not need to pre-specify
+the number of selected populations and the proposed CCMLE is able to perform
+simultaneous inference. Our method, which is conceptually straightforward, can
+be easily adapted to incorporate other selection criteria.
+Selected populations, Maximum likelihood, Constrained MLE, Post-selection
+inference
+",0,0,0,1,0,0
+12580,Phase transition in the spiked random tensor with Rademacher prior,"  We consider the problem of detecting a deformation from a symmetric Gaussian
+random $p$-tensor $(p\geq 3)$ with a rank-one spike sampled from the Rademacher
+prior. Recently in Lesieur et al. (2017), it was proved that there exists a
+critical threshold $\beta_p$ so that when the signal-to-noise ratio exceeds
+$\beta_p$, one can distinguish the spiked and unspiked tensors and weakly
+recover the prior via the minimal mean-square-error method. On the other side,
+Perry, Wein, and Bandeira (2017) proved that there exists a $\beta_p'<\beta_p$
+such that any statistical hypothesis test can not distinguish these two
+tensors, in the sense that their total variation distance asymptotically
+vanishes, when the signa-to-noise ratio is less than $\beta_p'$. In this work,
+we show that $\beta_p$ is indeed the critical threshold that strictly separates
+the distinguishability and indistinguishability between the two tensors under
+the total variation distance. Our approach is based on a subtle analysis of the
+high temperature behavior of the pure $p$-spin model with Ising spin, arising
+initially from the field of spin glasses. In particular, we identify the
+signal-to-noise criticality $\beta_p$ as the critical temperature,
+distinguishing the high and low temperature behavior, of the Ising pure
+$p$-spin mean-field spin glass model.
+",0,0,1,0,0,0
+12581,Extended depth-range profilometry using the phase-difference and phase-sum of two close-sensitivity projected fringes,"  We propose a high signal-to-noise extended depth-range three-dimensional (3D)
+profilometer projecting two linear-fringes with close phase-sensitivity. We use
+temporal phase-shifting algorithms (PSAs) to phase-demodulate the two close
+sensitivity phases. Then we calculate their phase-difference and their
+phase-sum. If the sensitivity between the two phases is close enough, their
+phase-difference is not-wrapped. The non-wrapped phase-difference as
+extended-range profilometry is well known and has been widely used. However as
+this paper shows, the closeness between the two demodulated phases makes their
+difference quite noisy. On the other hand, as we show, their phase-sum has a
+much higher phase-sensitivity and signal-to-noise ratio but it is highly
+wrapped. Spatial unwrapping of the phase-sum is precluded for separate or
+highly discontinuous objects. However it is possible to unwrap the phase-sum by
+using the phase-difference as first approximation and our previously published
+2-step temporal phase-unwrapping. Therefore the proposed profilometry technique
+allows unwrapping the higher sensitivity phase-sum using the noisier
+phase-difference as stepping stone. Due to the non-linear nature of the
+extended 2-steps temporal-unwrapper, the harmonics and noise errors in the
+phase-difference do not propagate towards the unwrapping phase-sum. To the best
+of our knowledge this is the highest signal-to-noise ratio, extended
+depth-range, 3D digital profilometry technique reported to this date.
+",0,1,0,0,0,0
+12582,A Language for Probabilistically Oblivious Computation,"  An oblivious computation is one that is free of direct and indirect
+information leaks, e.g., due to observable differences in timing and memory
+access patterns. This paper presents Lobliv, a core language whose type system
+enforces obliviousness. Prior work on type-enforced oblivious computation has
+focused on deterministic programs. Lobliv is new in its consideration of
+programs that implement probabilistic algorithms, such as those involved in
+cryptography. Lobliv employs a substructural type system and a novel notion of
+probability region to ensure that information is not leaked via the
+distribution of visible events. The use of regions was motivated by a source of
+unsoundness that we discovered in the type system of ObliVM, a language for
+implementing state of the art oblivious algorithms and data structures. We
+prove that Lobliv's type system enforces obliviousness and show that it is
+nevertheless powerful enough to check state-of-the-art, efficient oblivious
+data structures, such as stacks and queues, and even tree-based oblivious RAMs.
+",1,0,0,0,0,0
+12583,"Single shot, double differential spectral measurements of inverse Compton scattering in linear and nonlinear regimes","  Inverse Compton scattering (ICS) is a unique mechanism for producing fast
+pulses - picosecond and below - of bright X- to gamma-rays. These nominally
+narrow spectral bandwidth electromagnetic radiation pulses are efficiently
+produced in the interaction between intense, well-focused electron and laser
+beams. The spectral characteristics of such sources are affected by many
+experimental parameters, such as the bandwidth of the laser, and the angles of
+both the electrons and laser photons at collision. The laser field amplitude
+induces harmonic generation and importantly, for the present work, nonlinear
+red shifting, both of which dilute the spectral brightness of the radiation. As
+the applications enabled by this source often depend sensitively on its
+spectra, it is critical to resolve the details of the wavelength and angular
+distribution obtained from ICS collisions. With this motivation, we present
+here an experimental study that greatly improves on previous spectral
+measurement methods based on X-ray K-edge filters, by implementing a
+multi-layer bent-crystal X-ray spectrometer. In tandem with a collimating slit,
+this method reveals a projection of the double-differential angular-wavelength
+spectrum of the ICS radiation in a single shot. The measurements enabled by
+this diagnostic illustrate the combined off-axis and nonlinear-field-induced
+red shifting in the ICS emission process. They reveal in detail the strength of
+the normalized laser vector potential, and provide a non-destructive measure of
+the temporal and spatial electron-laser beam overlap.
+",0,1,0,0,0,0
+12584,Yarkovsky Drift Detections for 159 Near-Earth Asteroids,"  The Yarkovsky effect is a thermal process acting upon the orbits of small
+celestial bodies, which can cause these orbits to slowly expand or contract
+with time. The effect is subtle -- typical drift rates lie near $10^{-4}$ au/My
+for a $\sim$1 km diameter object -- and is thus generally difficult to measure.
+However, objects with long observation intervals, as well as objects with radar
+detections, serve as excellent candidates for the observation of this effect.
+We analyzed both optical and radar astrometry for all numbered Near-Earth
+Asteroids (NEAs), as well as several un-numbered NEAs, for the purpose of
+detecting and quantifying the Yarkovsky effect. We present 159 objects with
+measured drift rates. Our Yarkovsky sample is the largest published set of such
+detections, and presents an opportunity to examine the physical properties of
+these NEAs and the Yarkovsky effect in a statistical manner. In particular, we
+confirm the Yarkovsky effect's theoretical size dependence of 1/$D$, where $D$
+is diameter. We also examine the efficiency with which this effect acts on our
+sample objects and find typical efficiencies of around 12%. We interpret this
+efficiency with respect to the typical spin and thermal properties of objects
+in our sample. We report the ratio of negative to positive drift rates in our
+sample as $N_R/N_P = 2.9 \pm 0.7$ and interpret this ratio in terms of
+retrograde/prograde rotators and main belt escape routes. The observed ratio
+has a probability of 1 in 46 million of occurring by chance, which confirms the
+presence of a non-gravitational influence. We examine how the presence of radar
+data affects the strength and precision of our detections. We find that, on
+average, the precision of radar+optical detections improves by a factor of
+approximately 1.6 for each additional apparition with ranging data compared to
+that of optical-only solutions.
+",0,1,0,0,0,0
+12585,A short note on Godbersen's Conjecture,"  In this short note we improve the best to date bound in Godbersen's
+conjecture, and show some implications for unbalanced difference bodies.
+",0,0,1,0,0,0
+12586,The derivative NLS equation: global existence with solitons,"  We extend the global existence result for the derivative NLS equation to the
+case when the initial datum includes a finite number of solitons. This is
+achieved by an application of the Bäcklund transformation that removes a
+finite number of zeros of the scattering coefficient. By means of this
+transformation, the Riemann--Hilbert problem for meromorphic functions can be
+formulated as the one for analytic functions, the solvability of which was
+obtained recently.
+",0,1,1,0,0,0
+12587,Missing dust signature in the cosmic microwave background,"  I examine a possible spectral distortion of the Cosmic Microwave Background
+(CMB) due to its absorption by galactic and intergalactic dust. I show that
+even subtle intergalactic opacity of $1 \times 10^{-7}\, \mathrm{mag}\, h\,
+\mathrm{Gpc}^{-1}$ at the CMB wavelengths in the local Universe causes
+non-negligible CMB absorption and decline of the CMB intensity because the
+opacity steeply increases with redshift. The CMB should be distorted even
+during the epoch of the Universe defined by redshifts $z < 10$. For this epoch,
+the maximum spectral distortion of the CMB is at least $20 \times 10^{-22}
+\,\mathrm{Wm}^{-2}\, \mathrm{Hz}^{-1}\, \mathrm{sr}^{-1}$ at 300 GHz being well
+above the sensitivity of the COBE/FIRAS, WMAP or Planck flux measurements. If
+dust mass is considered to be redshift dependent with noticeable dust abundance
+at redshifts 2-4, the predicted CMB distortion is even higher. The CMB would be
+distorted also in a perfectly transparent universe due to dust in galaxies but
+this effect is lower by one order than that due to intergalactic opacity. The
+fact that the distortion of the CMB by dust is not observed is intriguing and
+questions either opacity and extinction law measurements or validity of the
+current model of the Universe.
+",0,1,0,0,0,0
+12588,Option market (in)efficiency and implied volatility dynamics after return jumps,"  In informationally efficient financial markets, option prices and this
+implied volatility should immediately be adjusted to new information that
+arrives along with a jump in underlying's return, whereas gradual changes in
+implied volatility would indicate market inefficiency. Using minute-by-minute
+data on S&P 500 index options, we provide evidence regarding delayed and
+gradual movements in implied volatility after the arrival of return jumps.
+These movements are directed and persistent, especially in the case of negative
+return jumps. Our results are significant when the implied volatilities are
+extracted from at-the-money options and out-of-the-money puts, while the
+implied volatility obtained from out-of-the-money calls converges to its new
+level immediately rather than gradually. Thus, our analysis reveals that the
+implied volatility smile is adjusted to jumps in underlying's return
+asymmetrically. Finally, it would be possible to have statistical arbitrage in
+zero-transaction-cost option markets, but under actual option price spreads,
+our results do not imply abnormal option returns.
+",0,0,0,0,0,1
+12589,Combining Alchemical Transformation with Physical Pathway to Accurately Compute Absolute Binding Free Energy,"  We present a new method that combines alchemical transformation with physical
+pathway to accurately and efficiently compute the absolute binding free energy
+of receptor-ligand complex. Currently, the double decoupling method (DDM) and
+the potential of mean force approach (PMF) methods are widely used to compute
+the absolute binding free energy of biomolecules. The DDM relies on
+alchemically decoupling the ligand from its environments, which can be
+computationally challenging for large ligands and charged ligands because of
+the large magnitude of the decoupling free energies involved. On the other
+hand, the PMF approach uses physical pathway to extract the ligand out of the
+binding site, thus avoids the alchemical decoupling of the ligand. However, the
+PMF method has its own drawback because of the reliance on a ligand
+binding/unbinding pathway free of steric obstruction from the receptor atoms.
+Therefore, in the presence of deeply buried ligand functional groups the
+convergence of the PMF calculation can be very slow leading to large errors in
+the computed binding free energy. Here we develop a new method called AlchemPMF
+by combining alchemical transformation with physical pathway to overcome the
+major drawback in the PMF method. We have tested the new approach on the
+binding of a charged ligand to an allosteric site on HIV-1 Integrase. After 20
+ns of simulation per umbrella sampling window, the new method yields absolute
+binding free energies within ~1 kcal/mol from the experimental result, whereas
+the standard PMF approach and the DDM calculations result in errors of ~5
+kcal/mol and > 2 kcal/mol, respectively. Furthermore, the binding free energy
+computed using the new method is associated with smaller statistical error
+compared with those obtained from the existing methods.
+",0,0,0,0,1,0
+12590,Embedding Feature Selection for Large-scale Hierarchical Classification,"  Large-scale Hierarchical Classification (HC) involves datasets consisting of
+thousands of classes and millions of training instances with high-dimensional
+features posing several big data challenges. Feature selection that aims to
+select the subset of discriminant features is an effective strategy to deal
+with large-scale HC problem. It speeds up the training process, reduces the
+prediction time and minimizes the memory requirements by compressing the total
+size of learned model weight vectors. Majority of the studies have also shown
+feature selection to be competent and successful in improving the
+classification accuracy by removing irrelevant features. In this work, we
+investigate various filter-based feature selection methods for dimensionality
+reduction to solve the large-scale HC problem. Our experimental evaluation on
+text and image datasets with varying distribution of features, classes and
+instances shows upto 3x order of speed-up on massive datasets and upto 45% less
+memory requirements for storing the weight vectors of learned model without any
+significant loss (improvement for some datasets) in the classification
+accuracy. Source Code: this https URL.
+",1,0,0,1,0,0
+12591,Orientably-regular maps on twisted linear fractional groups,"  We present an enumeration of orientably-regular maps with automorphism group
+isomorphic to the twisted linear fractional group $M(q^2)$ for any odd prime
+power $q$.
+",0,0,1,0,0,0
+12592,Deep Mean Functions for Meta-Learning in Gaussian Processes,"  Fitting machine learning models in the low-data limit is challenging. The
+main challenge is to obtain suitable prior knowledge and encode it into the
+model, for instance in the form of a Gaussian process prior. Recent advances in
+meta-learning offer powerful methods for extracting such prior knowledge from
+data acquired in related tasks. When it comes to meta-learning in Gaussian
+process models, approaches in this setting have mostly focused on learning the
+kernel function of the prior, but not on learning its mean function. In this
+work, we propose to parameterize the mean function of a Gaussian process with a
+deep neural network and train it with a meta-learning procedure. We present
+analytical and empirical evidence that mean function learning can be superior
+to kernel learning alone, particularly if data is scarce.
+",1,0,0,1,0,0
+12593,"Projectors separating spectra for $L^2$ on symmetric spaces $GL(n,\C)/GL(n,\R)$","  The Plancherel decomposition of $L^2$ on a pseudo-Riemannian symmetric space
+$GL(n,C)/GL(n,R)$ has spectrum of $[n/2]$ types. We write explicitly orthogonal
+projectors separating spectrum into uniform pieces
+",0,0,1,0,0,0
+12594,On the continued fraction expansion of absolutely normal numbers,"  We construct an absolutely normal number whose continued fraction expansion
+is normal in the sense that it contains all finite patterns of partial
+quotients with the expected asymptotic frequency as given by the Gauss-Kuzmin
+measure. The construction is based on ideas of Sierpinski and uses a large
+deviations theorem for sums of mixing random variables.
+",0,0,1,0,0,0
+12595,Talking Open Data,"  Enticing users into exploring Open Data remains an important challenge for
+the whole Open Data paradigm. Standard stock interfaces often used by Open Data
+portals are anything but inspiring even for tech-savvy users, let alone those
+without an articulated interest in data science. To address a broader range of
+citizens, we designed an open data search interface supporting natural language
+interactions via popular platforms like Facebook and Skype. Our data-aware
+chatbot answers search requests and suggests relevant open datasets, bringing
+fun factor and a potential of viral dissemination into Open Data exploration.
+The current system prototype is available for Facebook
+(this https URL) and Skype
+(this https URL) users.
+",1,0,0,0,0,0
+12596,Static non-reciprocity in mechanical metamaterials,"  Reciprocity is a fundamental principle governing various physical systems,
+which ensures that the transfer function between any two points in space is
+identical, regardless of geometrical or material asymmetries. Breaking this
+transmission symmetry offers enhanced control over signal transport, isolation
+and source protection. So far, devices that break reciprocity have been mostly
+considered in dynamic systems, for electromagnetic, acoustic and mechanical
+wave propagation associated with spatio-temporal variations. Here we show that
+it is possible to strongly break reciprocity in static systems, realizing
+mechanical metamaterials that, by combining large nonlinearities with suitable
+geometrical asymmetries, and possibly topological features, exhibit vastly
+different output displacements under excitation from different sides, as well
+as one-way displacement amplification. In addition to extending non-reciprocity
+and isolation to statics, our work sheds new light on the understanding of
+energy propagation in non-linear materials with asymmetric crystalline
+structures and topological properties, opening avenues for energy absorption,
+conversion and harvesting, soft robotics, prosthetics and optomechanics.
+",0,1,0,0,0,0
+12597,A forward-adjoint operator pair based on the elastic wave equation for use in transcranial photoacoustic tomography,"  Photoacoustic computed tomography (PACT) is an emerging imaging modality that
+exploits optical contrast and ultrasonic detection principles to form images of
+the photoacoustically induced initial pressure distribution within tissue. The
+PACT reconstruction problem corresponds to an inverse source problem in which
+the initial pressure distribution is recovered from measurements of the
+radiated wavefield.
+A major challenge in transcranial PACT brain imaging is compensation for
+aberrations in the measured data due to the presence of the skull. Ultrasonic
+waves undergo absorption, scattering and longitudinal-to-shear wave mode
+conversion as they propagate through the skull. To properly account for these
+effects, a wave-equation-based inversion method should be employed that can
+model the heterogeneous elastic properties of the skull. In this work, a
+forward model based on a finite-difference time-domain discretization of the
+three-dimensional elastic wave equation is established and a procedure for
+computing the corresponding adjoint of the forward operator is presented.
+Massively parallel implementations of these operators employing multiple
+graphics processing units (GPUs) are also developed. The developed numerical
+framework is validated and investigated in computer-simulation and experimental
+phantom studies whose designs are motivated by transcranial PACT applications.
+",0,1,0,0,0,0
+12598,Sewing Riemannian Manifolds with Positive Scalar Curvature,"  We explore to what extent one may hope to preserve geometric properties of
+three dimensional manifolds with lower scalar curvature bounds under
+Gromov-Hausdorff and Intrinsic Flat limits. We introduce a new construction,
+called sewing, of three dimensional manifolds that preserves positive scalar
+curvature. We then use sewing to produce sequences of such manifolds which
+converge to spaces that fail to have nonnegative scalar curvature in a standard
+generalized sense. Since the notion of nonnegative scalar curvature is not
+strong enough to persist alone, we propose that one pair a lower scalar
+curvature bound with a lower bound on the area of a closed minimal surface when
+taking sequences as this will exclude the possibility of sewing of manifolds.
+",0,0,1,0,0,0
+12599,Concentration of quadratic forms under a Bernstein moment assumption,"  A concentration result for quadratic form of independent subgaussian random
+variables is derived. If the moments of the random variables satisfy a
+""Bernstein condition"", then the variance term of the Hanson-Wright inequality
+can be improved. The Bernstein condition is satisfied, for instance, by all
+log-concave subgaussian distributions.
+",0,0,1,1,0,0
+12600,Short DNA persistence length in a mesoscopic helical model,"  The flexibility of short DNA chains is investigated via computation of the
+average correlation function between dimers which defines the persistence
+length. Path integration techniques have been applied to confine the phase
+space available to base pair fluctuations and derive the partition function.
+The apparent persistence lengths of a set of short chains have been computed as
+a function of the twist conformation both in the over-twisted and the untwisted
+regimes, whereby the equilibrium twist is selected by free energy minimization.
+The obtained values are significantly lower than those generally attributed to
+kilo-base long DNA. This points to an intrinsic helix flexibility at short
+length scales, arising from large fluctuational effects and local bending, in
+line with recent experimental indications. The interplay between helical
+untwisting and persistence length has been discussed for a heterogeneous
+fragment by weighing the effects of the sequence specificities through the
+non-linear stacking potential.
+",0,0,0,0,1,0
+12601,Diffuse Gamma Rays in 3D Galactic Cosmic-ray Propagation Models,"  The Picard code for the numerical solution of the Galactic cosmic ray
+propagation problem allows for high-resolution models that acknowledge the 3D
+structure of our Galaxy. Picard was used to determine diffuse gamma-ray
+emission of the Galaxy over the energy range from 100 MeV to 100 TeV. We
+discuss the impact of a cosmic-ray source distribution aligned with the
+Galactic spiral arms for a range of such spiral-arm models. As expected, the
+impact on the gamma-ray emission is most distinct in the inverse-Compton
+channel, where imprints of the spiral arms are visible and yield predictions
+that are no longer symmetric to the rotational axis of the Milkyway. We will
+illustrate these differences by a direct comparison to results from previous
+axially symmetric Galactic propagation models: we find differences in the
+gamma-ray flux both on global scales and on local scales related to the spiral
+arm tangents. We compare gamma-ray flux and spectra at on-arm vs. off-arm
+projections and characterize the differences to axially symmetric models.
+",0,1,0,0,0,0
+12602,"Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference","  We propose a simple and general variant of the standard reparameterized
+gradient estimator for the variational evidence lower bound. Specifically, we
+remove a part of the total derivative with respect to the variational
+parameters that corresponds to the score function. Removing this term produces
+an unbiased gradient estimator whose variance approaches zero as the
+approximate posterior approaches the exact posterior. We analyze the behavior
+of this gradient estimator theoretically and empirically, and generalize it to
+more complex variational distributions such as mixtures and importance-weighted
+posteriors.
+",1,0,0,1,0,0
+12603,Consistent hydrodynamic theory of chiral electrons in Weyl semimetals,"  The complete set of Maxwell's and hydrodynamic equations for the chiral
+electrons in Weyl semimetals is presented. The formulation of the Euler
+equation takes into account the explicit breaking of the Galilean invariance by
+the ion lattice. It is shown that the Chern-Simons (or Bardeen-Zumino)
+contributions should be added to the electric current and charge densities in
+Maxwell's equations that provide the information on the separation of Weyl
+nodes in energy and momentum. On the other hand, these topological
+contributions do not directly affect the Euler equation and the energy
+conservation relation for the electron fluid. By making use of the proposed
+consistent hydrodynamic framework, we show that the Chern-Simons contributions
+strongly modify the dispersion relations of collective modes in Weyl
+semimetals. This is reflected, in particular, in the existence of distinctive
+anomalous Hall waves, which are sustained by the local anomalous Hall currents.
+",0,1,0,0,0,0
+12604,Design and Processing of Invertible Orientation Scores of 3D Images for Enhancement of Complex Vasculature,"  The enhancement and detection of elongated structures in noisy image data is
+relevant for many biomedical imaging applications. To handle complex crossing
+structures in 2D images, 2D orientation scores $U: \mathbb{R} ^ 2\times S ^ 1
+\rightarrow \mathbb{C}$ were introduced, which already showed their use in a
+variety of applications. Here we extend this work to 3D orientation scores $U:
+\mathbb{R} ^ 3 \times S ^ 2\rightarrow \mathbb{C}$. First, we construct the
+orientation score from a given dataset, which is achieved by an invertible
+coherent state type of transform. For this transformation we introduce 3D
+versions of the 2D cake-wavelets, which are complex wavelets that can
+simultaneously detect oriented structures and oriented edges. Here we introduce
+two types of cake-wavelets, the first uses a discrete Fourier transform, the
+second is designed in the 3D generalized Zernike basis, allowing us to
+calculate analytical expressions for the spatial filters. Finally, we show two
+applications of the orientation score transformation. In the first application
+we propose an extension of crossing-preserving coherence enhancing diffusion
+via our invertible orientation scores of 3D images which we apply to real
+medical image data. In the second one we develop a new tubularity measure using
+3D orientation scores and apply the tubularity measure to both artificial and
+real medical data.
+",1,0,0,0,0,0
+12605,The origin and early evolution of life in chemical complexity space,"  Life can be viewed as a localized chemical system that sits on, or in the
+basin of attraction of, a metastable dynamical attractor state that remains out
+of equilibrium with the environment. Such a view of life allows that new living
+states can arise through chance changes in local chemical concentration
+(=mutations) that move points in space into the basin of attraction of a life
+state - the attractor being an autocatalytic sets whose essential (=keystone)
+species are produced at a higher rate than they are lost to the environment by
+diffusion, such that growth in expected. This conception of life yields several
+new insights and conjectures. (1) This framework suggests that the first new
+life states to arise are likely at interfaces where the rate of diffusion of
+keystone species is tied to a low-diffusion regime, while precursors and waste
+products diffuse at a higher rate. (2) There are reasons to expect that once
+the first life state arises, most likely on a mineral surface, additional
+mutations will generate derived life states with which the original state will
+compete. (3) I propose that in the resulting adaptive process there is a
+general tendency for higher complexity life states (i.e., ones that are further
+from being at equilibrium with the environment) to dominate a given mineral
+surface. (4) The framework suggests a simple and predictable path by which
+cells evolve and provides pointers on why such cells are likely to acquire
+particulate inheritance. Overall, the dynamical systems theoretical framework
+developed provides an integrated view of the origin and early evolution of life
+and supports novel empirical approaches.
+",0,0,0,0,1,0
+12606,Principal Component Analysis for Functional Data on Riemannian Manifolds and Spheres,"  Functional data analysis on nonlinear manifolds has drawn recent interest.
+Sphere-valued functional data, which are encountered for example as movement
+trajectories on the surface of the earth, are an important special case. We
+consider an intrinsic principal component analysis for smooth Riemannian
+manifold-valued functional data and study its asymptotic properties. Riemannian
+functional principal component analysis (RFPCA) is carried out by first mapping
+the manifold-valued data through Riemannian logarithm maps to tangent spaces
+around the time-varying Fréchet mean function, and then performing a
+classical multivariate functional principal component analysis on the linear
+tangent spaces. Representations of the Riemannian manifold-valued functions and
+the eigenfunctions on the original manifold are then obtained with exponential
+maps. The tangent-space approximation through functional principal component
+analysis is shown to be well-behaved in terms of controlling the residual
+variation if the Riemannian manifold has nonnegative curvature. Specifically,
+we derive a central limit theorem for the mean function, as well as root-$n$
+uniform convergence rates for other model components, including the covariance
+function, eigenfunctions, and functional principal component scores. Our
+applications include a novel framework for the analysis of longitudinal
+compositional data, achieved by mapping longitudinal compositional data to
+trajectories on the sphere, illustrated with longitudinal fruit fly behavior
+patterns. RFPCA is shown to be superior in terms of trajectory recovery in
+comparison to an unrestricted functional principal component analysis in
+applications and simulations and is also found to produce principal component
+scores that are better predictors for classification compared to traditional
+functional functional principal component scores.
+",0,0,1,1,0,0
+12607,Braids with as many full twists as strands realize the braid index,"  We characterize the fractional Dehn twist coefficient of a braid in terms of
+a slope of the homogenization of the Upsilon function, where Upsilon is the
+function-valued concordance homomorphism defined by Ozsváth, Stipsicz, and
+Szabó. We use this characterization to prove that $n$-braids with fractional
+Dehn twist coefficient larger than $n-1$ realize the braid index of their
+closure. As a consequence, we are able to prove a conjecture of Malyutin and
+Netsvetaev stating that $n$-times twisted braids realize the braid index of
+their closure. We provide examples that address the optimality of our results.
+The paper ends with an appendix about the homogenization of knot concordance
+homomorphisms.
+",0,0,1,0,0,0
+12608,The Formal Semantics of Rascal Light,"  Rascal is a high-level transformation language that aims to simplify software
+language engineering tasks like defining program syntax, analyzing and
+transforming programs, and performing code generation. The language provides
+several features including built-in collections (lists, sets, maps), algebraic
+data-types, powerful pattern matching operations with backtracking, and
+high-level traversals supporting multiple strategies. Interaction between
+different language features can be difficult to comprehend, since most features
+are semantically rich. The report provides a well-defined formal semantics for
+a large subset of Rascal, called Rascal Light, suitable for developing formal
+techniques, e.g., type systems and static analyses. Additionally, the report
+states and proofs a series of interesting properties of the semantics,
+including purity of backtracking, strong typing, partial progress and the
+existence of a terminating subset.
+",1,0,0,0,0,0
+12609,On inverse and right inverse ordered semigroups,"  A regular ordered semigroup $S$ is called right inverse if every principal
+left ideal of $S$ is generated by an $\mathcal{R}$-unique ordered idempotent.
+Here we explore the theory of right inverse ordered semigroups. We show that a
+regular ordered semigroup is right inverse if and only if any two right
+inverses of an element $a\in S$ are $\mathcal{R}$-related. Furthermore,
+different characterizations of right Clifford, right group-like, group like
+ordered semigroups are done by right inverse ordered semigroups. Thus a
+foundation of right inverse semigroups has been developed.
+",0,0,1,0,0,0
+12610,Refining the Two-Dimensional Signed Small Ball Inequality,"  The two-dimensional signed small ball inequality states that for all possible
+choices of signs, $$ \left\| \sum_{|R| = 2^{-n}}{ \varepsilon_R h_R}
+\right\|_{L^{\infty}} \gtrsim n,$$ where the summation runs over all dyadic
+rectangles in the unit square and $h_R$ denotes the associated Haar function.
+This inequality first appeared in the work of Talagrand, and alternative proofs
+are due to Temlyakov and Bilyk & Feldheim (who showed that the supremum equals
+$n+1$ in all cases). We prove that for all integers $0\leq k \leq n+1$ and all
+possible choices of signs, $$ \left| \left\{ x \in [0,1)^2: \sum_{|R| =
+2^{-n}}{ \varepsilon_R h_R} = n + 1 - 2k\right\} \right| =
+\frac{1}{2^{n+1}}\binom{n+1}{k}.$$
+",0,0,1,0,0,0
+12611,"The structure, capability and the Schur multiplier of generalized Heisenberg Lie algebras","  From [Problem 1729, Groups of prime power order, Vol. 3], Berkovich et al.
+asked to obtain the Schur multiplier and the representation of a group $G$,
+when $G$ is a special $p$-group minimally generated by $d$ elements and
+$|G'|=p^{\frac{1}{2}d(d-1)}$. Since there are analogies between groups and Lie
+algebras, we intend to give an answer to this question similarly for nilpotent
+Lie algebras. Furthermore, we give some results about the tensor square and the
+Schur multiplier of some nilpotent Lie algebras of class two.
+",0,0,1,0,0,0
+12612,Fractional Brownian markets with time-varying volatility and high-frequency data,"  Diffusion processes driven by Fractional Brownian motion (FBM) have often
+been considered in modeling stock price dynamics in order to capture the long
+range dependence of stock price observed in reality. Option prices for such
+models had been obtained by Necula (2002) under constant drift and volatility.
+We obtain option prices under time varying volatility model. The expression
+depends on volatility and the Hurst parameter in a complicated manner. We
+derive a central limit theorem for the quadratic variation as an estimator for
+volatility for both the cases, constant as well as time varying volatility.
+That will help us to find estimators of the option prices and to find their
+asymptotic distributions.
+",0,0,1,1,0,0
+12613,Experimental GHZ Entanglement beyond Qubits,"  The Greenberger-Horne-Zeilinger (GHZ) argument provides an all-or-nothing
+contradiction between quantum mechanics and local-realistic theories. In its
+original formulation, GHZ investigated three and four particles entangled in
+two dimensions only. Very recently, higher dimensional contradictions
+especially in three dimensions and three particles have been discovered but it
+has remained unclear how to produce such states. In this article we
+experimentally show how to generate a three-dimensional GHZ state from
+two-photon orbital-angular-momentum entanglement. The first suggestion for a
+setup which generates three-dimensional GHZ entanglement from these entangled
+pairs came from using the computer algorithm Melvin. The procedure employs
+novel concepts significantly beyond the qubit case. Our experiment opens up the
+possibility of a truly high-dimensional test of the GHZ-contradiction which,
+interestingly, employs non-Hermitian operators.
+",0,1,0,0,0,0
+12614,On global Okounkov bodies of spherical varieties,"  We define and study the global Okounkov moment cone of a projective spherical
+variety X, generalizing both the global Okounkov body and the moment body of X
+defined by Kaveh and Khovanskii. Under mild assumptions on X we show that the
+global Okounkov moment cone of X is rational polyhedral. As a consequence, also
+the global Okounkov body of X, with respect to a particular valuation, is
+rational polyhedral.
+",0,0,1,0,0,0
+12615,From the simple reacting sphere kinetic model to the reaction-diffusion system of Maxwell-Stefan type,"  In this paper we perform a formal asymptotic analysis on a kinetic model for
+reactive mixtures in order to derive a reaction-diffusion system of
+Maxwell-Stefan type. More specifically, we start from the kinetic model of
+simple reacting spheres for a quaternary mixture of monatomic ideal gases that
+undergoes a reversible chemical reaction of bimolecular type. Then, we consider
+a scaling describing a physical situation in which mechanical collisions play a
+dominant role in the evolution process, while chemical reactions are slow, and
+compute explicitly the production terms associated to the concentration and
+momentum balance equations for each species in the reactive mixture. Finally,
+we prove that, under isothermal assumptions, the limit equations for the scaled
+kinetic model is the reaction diffusion system of Maxwell-Stefan type.
+",0,1,0,0,0,0
+12616,Origin of soft glassy rheology in the cytoskeleton,"  Dynamically crosslinked semiflexible biopolymers such as the actin
+cytoskeleton govern the mechanical behavior of living cells. Semiflexible
+biopolymers stiffen nonlinearly in response to mechanical loads, whereas the
+crosslinker dynamics allow for stress relaxation over time. Here we show,
+through rheology and theoretical modeling, that the combined nonlinearity in
+time and stress leads to an unexpectedly slow stress relaxation, similar to the
+dynamics of disordered systems close to the glass transition. Our work suggests
+that transient crosslinking combined with internal stress is the microscopic
+origin for the universal glassy dynamics as frequently observed in cellular
+mechanics.
+",0,0,0,0,1,0
+12617,Pattern Search Multidimensional Scaling,"  We present a novel view of nonlinear manifold learning using derivative-free
+optimization techniques. Specifically, we propose an extension of the classical
+multi-dimensional scaling (MDS) method, where instead of performing gradient
+descent, we sample and evaluate possible ""moves"" in a sphere of fixed radius
+for each point in the embedded space. A fixed-point convergence guarantee can
+be shown by formulating the proposed algorithm as an instance of General
+Pattern Search (GPS) framework. Evaluation on both clean and noisy synthetic
+datasets shows that pattern search MDS can accurately infer the intrinsic
+geometry of manifolds embedded in high-dimensional spaces. Additionally,
+experiments on real data, even under noisy conditions, demonstrate that the
+proposed pattern search MDS yields state-of-the-art results.
+",0,0,0,1,0,0
+12618,MH370 Burst Frequency Offset Analysis and Implications on Descent Rate at End-of-Flight,"  Malaysian Airlines flight MH370 veered off course unexpectedly during a
+scheduled trip from Kuala Lumpur to Beijing on the 7th of March 2014. MH370 was
+tracked via military radar into the Malacca Straits and, after disappearing
+from radar, was subsequently believed to have turned south towards the southern
+Indian Ocean before crashing approximately 6 hours later. This article
+discusses specifically the analysis of burst frequency offset (BFO) metadata
+from the SATCOM messages. It is shown that the BFOs corresponding to the last
+two SATCOM messages from the plane at 00:19:29Z and 00:19:37Z 8th March 2014
+suggest that flight MH370 was rapidly descending and accelerating downwards
+when message exchange with the ground station ceased.
+",0,0,0,1,0,0
+12619,Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks,"  We investigate the addition of symmetry and temporal context information to a
+deep Convolutional Neural Network (CNN) with the purpose of detecting malignant
+soft tissue lesions in mammography. We employ a simple linear mapping that
+takes the location of a mass candidate and maps it to either the contra-lateral
+or prior mammogram and Regions Of Interest (ROI) are extracted around each
+location. We subsequently explore two different architectures (1) a fusion
+model employing two datastreams were both ROIs are fed to the network during
+training and testing and (2) a stage-wise approach where a single ROI CNN is
+trained on the primary image and subsequently used as feature extractor for
+both primary and symmetrical or prior ROIs. A 'shallow' Gradient Boosted Tree
+(GBT) classifier is then trained on the concatenation of these features and
+used to classify the joint representation. Results shown a significant increase
+in performance using the first architecture and symmetry information, but only
+marginal gains in performance using temporal data and the other setting. We
+feel results are promising and can greatly be improved when more temporal data
+becomes available.
+",1,0,0,0,0,0
+12620,Deep Echo State Network (DeepESN): A Brief Survey,"  The study of deep recurrent neural networks (RNNs) and, in particular, of
+deep Reservoir Computing (RC) is gaining an increasing research attention in
+the neural networks community. The recently introduced deep Echo State Network
+(deepESN) model opened the way to an extremely efficient approach for designing
+deep neural networks for temporal data. At the same time, the study of deepESNs
+allowed to shed light on the intrinsic properties of state dynamics developed
+by hierarchical compositions of recurrent layers, i.e. on the bias of depth in
+RNNs architectural design. In this paper, we summarize the advancements in the
+development, analysis and applications of deepESNs.
+",1,0,0,1,0,0
+12621,Computational topology of graphs on surfaces,"  Computational topology is an area that revisits topological problems from an
+algorithmic point of view, and develops topological tools for improved
+algorithms. We survey results in computational topology that are concerned with
+graphs drawn on surfaces. Typical questions include representing surfaces and
+graphs embedded on them computationally, deciding whether a graph embeds on a
+surface, solving computational problems related to homotopy, optimizing curves
+and graphs on surfaces, and solving standard graph algorithm problems more
+efficiently in the case of surface-embedded graphs.
+",1,0,1,0,0,0
+12622,Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study,"  In this work, we formulated a real-world problem related to sewer pipeline
+gas detection using the classification-based approaches. The primary goal of
+this work was to identify the hazardousness of sewer pipeline to offer safe and
+non-hazardous access to sewer pipeline workers so that the human fatalities,
+which occurs due to the toxic exposure of sewer gas components, can be avoided.
+The dataset acquired through laboratory tests, experiments, and various
+literature sources was organized to design a predictive model that was able to
+identify/classify hazardous and non-hazardous situation of sewer pipeline. To
+design such prediction model, several classification algorithms were used and
+their performances were evaluated and compared, both empirically and
+statistically, over the collected dataset. In addition, the performances of
+several ensemble methods were analyzed to understand the extent of improvement
+offered by these methods. The result of this comprehensive study showed that
+the instance-based learning algorithm performed better than many other
+algorithms such as multilayer perceptron, radial basis function network,
+support vector machine, reduced pruning tree. Similarly, it was observed that
+multi-scheme ensemble approach enhanced the performance of base predictors.
+",1,0,0,0,0,0
+12623,The rational points on certain Abelian varieties over function fields,"  In this paper, we consider Abelian varieties over function fields that arise
+as twists of Abelian varieties by cyclic covers of irreducible quasi-projective
+varieties. Then, in terms of Prym varieties associated to the cyclic covers, we
+prove a structure theorem on their Mordell-Weil group. Our results give an
+explicit method for construction of elliptic curves, hyper- and super-elliptic
+Jacobians that have large ranks over function fields of certain varieties.
+",0,0,1,0,0,0
+12624,Multivariate inhomogeneous diffusion models with covariates and mixed effects,"  Modeling of longitudinal data often requires diffusion models that
+incorporate overall time-dependent, nonlinear dynamics of multiple components
+and provide sufficient flexibility for subject-specific modeling. This
+complexity challenges parameter inference and approximations are inevitable. We
+propose a method for approximate maximum-likelihood parameter estimation in
+multivariate time-inhomogeneous diffusions, where subject-specific flexibility
+is accounted for by incorporation of multidimensional mixed effects and
+covariates. We consider $N$ multidimensional independent diffusions $X^i =
+(X^i_t)_{0\leq t\leq T^i}, 1\leq i\leq N$, with common overall model structure
+and unknown fixed-effects parameter $\mu$. Their dynamics differ by the
+subject-specific random effect $\phi^i$ in the drift and possibly by (known)
+covariate information, different initial conditions and observation times and
+duration. The distribution of $\phi^i$ is parametrized by an unknown
+$\vartheta$ and $\theta = (\mu, \vartheta)$ is the target of statistical
+inference. Its maximum likelihood estimator is derived from the continuous-time
+likelihood. We prove consistency and asymptotic normality of $\hat{\theta}_N$
+when the number $N$ of subjects goes to infinity using standard techniques and
+consider the more general concept of local asymptotic normality for less
+regular models. The bias induced by time-discretization of sufficient
+statistics is investigated. We discuss verification of conditions and
+investigate parameter estimation and hypothesis testing in simulations.
+",0,0,1,1,0,0
+12625,Stability of patterns in the Abelian sandpile,"  We show that the patterns in the Abelian sandpile are stable. The proof
+combines the structure theory for the patterns with the regularity machinery
+for non-divergence form elliptic equations. The stability results allows one to
+improve weak-* convergence of the Abelian sandpile to pattern convergence for
+certain classes of solutions.
+",0,0,1,0,0,0
+12626,Position Heaps for Parameterized Strings,"  We propose a new indexing structure for parameterized strings, called
+parameterized position heap. Parameterized position heap is applicable for
+parameterized pattern matching problem, where the pattern matches a substring
+of the text if there exists a bijective mapping from the symbols of the pattern
+to the symbols of the substring. We propose an online construction algorithm of
+parameterized position heap of a text and show that our algorithm runs in
+linear time with respect to the text size. We also show that by using
+parameterized position heap, we can find all occurrences of a pattern in the
+text in linear time with respect to the product of the pattern size and the
+alphabet size.
+",1,0,0,0,0,0
+12627,Quasi-Steady Model of a Pumping Kite Power System,"  The traction force of a kite can be used to drive a cyclic motion for
+extracting wind energy from the atmosphere. This paper presents a novel
+quasi-steady modelling framework for predicting the power generated over a full
+pumping cycle. The cycle is divided into traction, retraction and transition
+phases, each described by an individual set of analytic equations. The effect
+of gravity on the airborne system components is included in the framework. A
+trade-off is made between modelling accuracy and computation speed such that
+the model is specifically useful for system optimisation and scaling in
+economic feasibility studies. Simulation results are compared to experimental
+measurements of a 20 kW kite power system operated up to a tether length of 720
+m. Simulation and experiment agree reasonably well, both for moderate and for
+strong wind conditions, indicating that the effect of gravity has to be taken
+into account for a predictive performance simulation.
+",1,0,1,0,0,0
+12628,Expansion of pinched hypersurfaces of the Euclidean and hyperbolic space by high powers of curvature,"  We prove convergence results for expanding curvature flows in the Euclidean
+and hyperbolic space. The flow speeds have the form $F^{-p}$, where $p>1$ and
+$F$ is a positive, strictly monotone and 1-homogeneous curvature function. In
+particular this class includes the mean curvature $F=H$. We prove that a
+certain initial pinching condition is preserved and the properly rescaled
+hypersurfaces converge smoothly to the unit sphere. We show that an example due
+to Andrews-McCoy-Zheng can be used to construct strictly convex initial
+hypersurfaces, for which the inverse mean curvature flow to the power $p>1$
+loses convexity, justifying the necessity to impose a certain pinching
+condition on the initial hypersurface.
+",0,0,1,0,0,0
+12629,Sound Mixed-Precision Optimization with Rewriting,"  Finite-precision arithmetic computations face an inherent tradeoff between
+accuracy and efficiency. The points in this tradeoff space are determined,
+among other factors, by different data types but also evaluation orders. To put
+it simply, the shorter a precision's bit-length, the larger the roundoff error
+will be, but the faster the program will run. Similarly, the fewer arithmetic
+operations the program performs, the faster it will run; however, the effect on
+the roundoff error is less clear-cut. Manually optimizing the efficiency of
+finite-precision programs while ensuring that results remain accurate enough is
+challenging. The unintuitive and discrete nature of finite-precision makes
+estimation of roundoff errors difficult; furthermore the space of possible data
+types and evaluation orders is prohibitively large. We present the first fully
+automated and sound technique and tool for optimizing the performance of
+floating-point and fixed-point arithmetic kernels. Our technique combines
+rewriting and mixed-precision tuning. Rewriting searches through different
+evaluation orders to find one which minimizes the roundoff error at no
+additional runtime cost. Mixed-precision tuning assigns different finite
+precisions to different variables and operations and thus provides
+finer-grained control than uniform precision. We show that when these two
+techniques are designed and applied together, they can provide higher
+performance improvements than each alone.
+",1,0,0,0,0,0
+12630,Negative thermal expansion and metallophilicity in Cu$_3$[Co(CN)$_6$],"  We report the synthesis and structural characterisation of the molecular
+framework copper(I) hexacyanocobaltate(III), Cu$_3$[Co(CN)$_6$], which we find
+to be isostructural to H$_3$[Co(CN)$_6$] and the colossal negative thermal
+expansion material Ag$_3$[Co(CN)$_6$]. Using synchrotron X-ray powder
+diffraction measurements, we find strong positive and negative thermal
+expansion behaviour respectively perpendicular and parallel to the trigonal
+crystal axis: $\alpha_a$ = 25.4(5)\,MK$^{-1}$ and $\alpha_c$ =
+$-$43.5(8)\,MK$^{-1}$. These opposing effects collectively result in a volume
+expansivity $\alpha_V$ = 7.4(11)\,MK$^{-1}$ that is remarkably small for an
+anisotropic molecular framework. This thermal response is discussed in the
+context of the behaviour of the analogous H- and Ag-containing systems. We make
+use of density-functional theory with many-body dispersion interactions
+(DFT+MBD) to demonstrate that Cu$\ldots$Cu metallophilic (`cuprophilic')
+interactions are significantly weaker in Cu$_3$[Co(CN)$_6$] than Ag$\ldots$Ag
+interactions in Ag$_3$[Co(CN)$_6$], but that this lowering of energy scale
+counterintuitively translates to a more moderate---rather than
+enhanced---degree of structural flexibility. The same conclusion is drawn from
+consideration of a simple lattice dynamical model, which we also present here.
+Our results demonstrate that strong interactions can actually be exploited in
+the design of ultra-responsive materials if those interactions are set up to
+act in tension.
+",0,1,0,0,0,0
+12631,Quantum Fluctuations along Symmetry Crossover in Kondo-correlated Quantum Dot,"  Universal properties of entangled many-body states are controlled by their
+symmetry and quantum fluctuations. By magnetic-field tuning of the spin-orbital
+degeneracy in a Kondo-correlated quantum dot, we have modified quantum
+fluctuations to directly measure their influence on the many-body properties
+along the crossover from $SU(4)$ to $SU(2)$ symmetry of the ground state.
+High-sensitive current noise measurements combined with the non-equilibrium
+Fermi liquid theory clarify that the Kondo resonance and electron correlations
+are enhanced as the fluctuations, measured by the Wilson ratio, increase along
+the symmetry crossover. Our achievement demonstrates that non-linear noise
+constitutes a measure of quantum fluctuations that can be used to tackle
+quantum phase transitions.
+",0,1,0,0,0,0
+12632,Impossibility results on stability of phylogenetic consensus methods,"  We answer two questions raised by Bryant, Francis and Steel in their work on
+consensus methods in phylogenetics. Consensus methods apply to every practical
+instance where it is desired to aggregate a set of given phylogenetic trees
+(say, gene evolution trees) into a resulting, ""consensus"" tree (say, a species
+tree). Various stability criteria have been explored in this context, seeking
+to model desirable consistency properties of consensus methods as the
+experimental data is updated (e.g., more taxa, or more trees, are mapped).
+However, such stability conditions can be incompatible with some basic
+regularity properties that are widely accepted to be essential in any
+meaningful consensus method. Here, we prove that such an incompatibility does
+arise in the case of extension stability on binary trees and in the case of
+associative stability. Our methods combine general theoretical considerations
+with the use of computer programs tailored to the given stability requirements.
+",0,0,0,0,1,0
+12633,Improved Distributed Degree Splitting and Edge Coloring,"  The degree splitting problem requires coloring the edges of a graph red or
+blue such that each node has almost the same number of edges in each color, up
+to a small additive discrepancy. The directed variant of the problem requires
+orienting the edges such that each node has almost the same number of incoming
+and outgoing edges, again up to a small additive discrepancy.
+We present deterministic distributed algorithms for both variants, which
+improve on their counterparts presented by Ghaffari and Su [SODA'17]: our
+algorithms are significantly simpler and faster, and have a much smaller
+discrepancy. This also leads to a faster and simpler deterministic algorithm
+for $(2+o(1))\Delta$-edge-coloring, improving on that of Ghaffari and Su.
+",1,0,0,0,0,0
+12634,Global geometry and $C^1$ convex extensions of $1$-jets,"  Let $E$ be an arbitrary subset of $\mathbb{R}^n$ (not necessarily bounded),
+and $f:E\to\mathbb{R}$, $G:E\to\mathbb{R}^n$ be functions. We provide necessary
+and sufficient conditions for the $1$-jet $(f,G)$ to have an extension $(F,
+\nabla F)$ with $F:\mathbb{R}^n\to\mathbb{R}$ convex and of class $C^{1}$.
+Besides, if $G$ is bounded we can take $F$ so that $\textrm{Lip}(F)\lesssim
+\|G\|_{\infty}$. As an application we also solve a similar problem about
+finding convex hypersurfaces of class $C^1$ with prescribed normals at the
+points of an arbitrary subset of $\mathbb{R}^n$.
+",0,0,1,0,0,0
+12635,Interpretable Deep Learning applied to Plant Stress Phenotyping,"  Availability of an explainable deep learning model that can be applied to
+practical real world scenarios and in turn, can consistently, rapidly and
+accurately identify specific and minute traits in applicable fields of
+biological sciences, is scarce. Here we consider one such real world example
+viz., accurate identification, classification and quantification of biotic and
+abiotic stresses in crop research and production. Up until now, this has been
+predominantly done manually by visual inspection and require specialized
+training. However, such techniques are hindered by subjectivity resulting from
+inter- and intra-rater cognitive variability. Here, we demonstrate the ability
+of a machine learning framework to identify and classify a diverse set of
+foliar stresses in the soybean plant with remarkable accuracy. We also present
+an explanation mechanism using gradient-weighted class activation mapping that
+isolates the visual symptoms used by the model to make predictions. This
+unsupervised identification of unique visual symptoms for each stress provides
+a quantitative measure of stress severity, allowing for identification,
+classification and quantification in one framework. The learnt model appears to
+be agnostic to species and make good predictions for other (non-soybean)
+species, demonstrating an ability of transfer learning.
+",1,0,0,1,0,0
+12636,Differentiable Compositional Kernel Learning for Gaussian Processes,"  The generalization properties of Gaussian processes depend heavily on the
+choice of kernel, and this choice remains a dark art. We present the Neural
+Kernel Network (NKN), a flexible family of kernels represented by a neural
+network. The NKN architecture is based on the composition rules for kernels, so
+that each unit of the network corresponds to a valid kernel. It can compactly
+approximate compositional kernel structures such as those used by the Automatic
+Statistician (Lloyd et al., 2014), but because the architecture is
+differentiable, it is end-to-end trainable with gradient-based optimization. We
+show that the NKN is universal for the class of stationary kernels. Empirically
+we demonstrate pattern discovery and extrapolation abilities of NKN on several
+tasks that depend crucially on identifying the underlying structure, including
+time series and texture extrapolation, as well as Bayesian optimization.
+",0,0,0,1,0,0
+12637,Airy structures and symplectic geometry of topological recursion,"  We propose a new approach to the topological recursion of Eynard-Orantin
+based on the notion of Airy structure, which we introduce in the paper. We
+explain why Airy structure is a more fundamental object than the one of the
+spectral curve. We explain how the concept of quantization of Airy structure
+leads naturally to the formulas of topological recursion as well as their
+generalizations. The notion of spectral curve is also considered in a more
+general framework of Poisson surfaces endowed with foliation. We explain how
+the deformation theory of spectral curves is related to Airy structures. Few
+other topics (e.g. the Holomorphic Anomaly Equation) are also discussed from
+the general point of view of Airy structures.
+",0,0,1,0,0,0
+12638,Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes,"  Exploiting the theory of state space models, we derive the exact expressions
+of the information transfer, as well as redundant and synergistic transfer, for
+coupled Gaussian processes observed at multiple temporal scales. All of the
+terms, constituting the frameworks known as interaction information
+decomposition and partial information decomposition, can thus be analytically
+obtained for different time scales from the parameters of the VAR model that
+fits the processes. We report the application of the proposed methodology
+firstly to benchmark Gaussian systems, showing that this class of systems may
+generate patterns of information decomposition characterized by mainly
+redundant or synergistic information transfer persisting across multiple time
+scales or even by the alternating prevalence of redundant and synergistic
+source interaction depending on the time scale. Then, we apply our method to an
+important topic in neuroscience, i.e., the detection of causal interactions in
+human epilepsy networks, for which we show the relevance of partial information
+decomposition to the detection of multiscale information transfer spreading
+from the seizure onset zone.
+",0,0,1,1,0,0
+12639,Transforming Speed Sequences into Road Rays on the Map with Elastic Pathing,"  Advances in technology have provided ways to monitor and measure driving
+behavior. Recently, this technology has been applied to usage-based automotive
+insurance policies that offer reduced insurance premiums to policy holders who
+opt-in to automotive monitoring. Several companies claim to measure only speed
+data, which they further claim preserves privacy. However, we have developed an
+algorithm - elastic pathing - that successfully tracks drivers' locations from
+speed data. The algorithm tracks drivers by assuming a start position, such as
+the driver's home address (which is typically known to insurance companies),
+and then estimates the possible routes by fitting the speed data to map data.
+To demonstrate the algorithm's real-world applicability, we evaluated its
+performance with driving datasets from central New Jersey and Seattle,
+Washington, representing suburban and urban areas. We are able to estimate
+destinations with error within 250 meters for 17% of the traces and within 500
+meters for 24% of the traces in the New Jersey dataset, and with error within
+250 and 500 meters for 15.5% and 27.5% of the traces, respectively, in the
+Seattle dataset. Our work shows that these insurance schemes enable a
+substantial breach of privacy.
+",1,0,0,0,0,0
+12640,Solitons and geometrical structures in a perfect fluid spacetime,"  Geometrical aspects of a perfect fluid spacetime are described in terms of
+different curvature tensors and $\eta$-Ricci and $\eta$-Einstein solitons in a
+perfect fluid spacetime are determined. Conditions for the Ricci soliton to be
+steady, expanding or shrinking are also given. In a particular case when the
+potential vector field $\xi$ of the soliton is of gradient type,
+$\xi:=grad(f)$, we derive from the soliton equation a Laplacian equation
+satisfied by $f$.
+",0,0,1,0,0,0
+12641,Hemodynamics of a Bileaflet Mechanical Heart Valve with Different Levels of Dysfunction,"  Heart disease is one of leading causes of mortality worldwide. Healthy heart
+valves are key for proper heart function. When these valves dysfunction, a
+replacement is often necessary in severe cases. The current study presents an
+investigation of the pulsatile blood flow through a bileaflet mechanical heart
+valve (BMHV) where one leaflet is healthy and can fully open and the other
+leaflet cannot fully open with different levels of dysfunction. To better
+understand the implications that a dysfunctional leaflet has on the blood flow
+through these valves, analysis of flow characteristics such as velocity,
+pressure drop, wall shear stress and vorticity profiles was performed. Results
+suggested that leaflet dysfunction caused increased local velocities,
+separation regions and wall shear stresses. For example, the maximum velocity
+increased from 2.53 m/s to 4.9 m/s when dysfunction increased from 0% to 100%.
+The pressure drop increased (by up to 300%) with dysfunctionality. Results
+suggested that leaflet dysfunction also caused increased wall shear stresses on
+the valve frame where higher stresses developed around the hinges (at 75% and
+100% dysfunctions). Analysis also showed that increased dysfunctionality of one
+leaflet led to higher net shear forces on both the healthy and dysfunctional
+leaflets (by up to 200% and 600%, respectively).
+",0,1,0,0,0,0
+12642,Nonnegative Hermitian vector bundles and Chern numbers,"  We show in this article that if a holomorphic vector bundle has a nonnegative
+Hermitian metric in the sense of Bott and Chern, which always exists on
+globally generated holomorphic vector bundles, then some special linear
+combinations of Chern forms are strongly nonnegative. This particularly implies
+that all the Chern numbers of such a holomorphic vector bundle are nonnegative
+and can be bounded below and above respectively by two special Chern numbers.
+As applications, we obtain a family of new results on compact connected complex
+manifolds which are homogeneous or can be holomorphically immersed into complex
+tori, some of which improve several classical results.
+",0,0,1,0,0,0
+12643,The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing,"  Graph processing is becoming increasingly prevalent across many application
+domains. In spite of this prevalence, there is little research about how graphs
+are actually used in practice. We conducted an online survey aimed at
+understanding: (i) the types of graphs users have; (ii) the graph computations
+users run; (iii) the types of graph software users use; and (iv) the major
+challenges users face when processing their graphs. We describe the
+participants' responses to our questions highlighting common patterns and
+challenges. We further reviewed user feedback in the mailing lists, bug
+reports, and feature requests in the source repositories of a large suite of
+software products for processing graphs. Through our review, we were able to
+answer some new questions that were raised by participants' responses and
+identify specific challenges that users face when using different classes of
+graph software. The participants' responses and data we obtained revealed
+surprising facts about graph processing in practice. In particular, real-world
+graphs represent a very diverse range of entities and are often very large, and
+scalability and visualization are undeniably the most pressing challenges faced
+by participants. We hope these findings can guide future research.
+",1,0,0,0,0,0
+12644,On the classification of four-dimensional gradient Ricci solitons,"  In this paper, we prove some classification results for four-dimensional
+gradient Ricci solitons. For a four-dimensional gradient shrinking Ricci
+soliton with $div^4Rm^\pm=0$, we show that it is either Einstein or a finite
+quotient of $\mathbb{R}^4$, $\mathbb{S}^2\times\mathbb{R}^2$ or
+$\mathbb{S}^3\times\mathbb{R}$. The same result can be obtained under the
+condition of $div^4W^\pm=0$. We also present some classification results of
+four-dimensional complete non-compact gradient expanding Ricci soliton with
+non-negative Ricci curvature and gradient steady Ricci solitons under certain
+curvature conditions.
+",0,0,1,0,0,0
+12645,In-gap bound states induced by a single nonmagnetic impurity in sign-preserving s-wave superconductors with incipient bands,"  We have investigated the in-gap bound states (IGBS) induced by a single
+nonmagnetic impurity in multiband superconductors with incipient bands.
+Contrary to the naive expectation, we found that even if the superconducting
+(SC) order parameter is sign-preserving s-wave on the Fermi surfaces, the
+incipient bands may still affect the appearance and locations of the IGBS,
+although the gap between the incipient bands and the Fermi level is much larger
+than the SC gap. Therefore in scanning tunneling microscopy experiments, the
+IGBS induced by a single nonmagnetic impurity are not the definitive evidences
+for the sign-changing order parameter on the Fermi surfaces. Our findings have
+special implications for the experimental determination of the pairing symmetry
+in the FeSe-based superconductors.
+",0,1,0,0,0,0
+12646,Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system,"  It is true that the ""best"" neural network is not necessarily the one with the
+most ""brain-like"" behavior. Understanding biological intelligence, however, is
+a fundamental goal for several distinct disciplines. Translating our
+understanding of intelligence to machines is a fundamental problem in robotics.
+Propelled by new advancements in Neuroscience, we developed a spiking neural
+network (SNN) that draws from mounting experimental evidence that a number of
+individual neurons is associated with spatial navigation. By following the
+brain's structure, our model assumes no initial all-to-all connectivity, which
+could inhibit its translation to a neuromorphic hardware, and learns an
+uncharted territory by mapping its identified components into a limited number
+of neural representations, through spike-timing dependent plasticity (STDP). In
+our ongoing effort to employ a bioinspired SNN-controlled robot to real-world
+spatial mapping applications, we demonstrate here how an SNN may robustly
+control an autonomous robot in mapping and exploring an unknown environment,
+while compensating for its own intrinsic hardware imperfections, such as
+partial or total loss of visual input.
+",0,0,0,0,1,0
+12647,Calculation of thallium hyperfine anomaly,"  We suggest a method to calculate hyperfine anomaly for many-electron atoms
+and ions. At first, we tested this method by calculating hyperfine anomaly for
+hydrogen-like thallium ion and obtained fairly good agreement with analytical
+expressions. Then we did calculations for the neutral thallium and tested an
+assumption, that the the ratio between the anomalies for $s$ and $p_{1/2}$
+states is the same for these two systems. Finally, we come up with
+recommendations about preferable atomic states for the precision measurements
+of the nuclear $g$ factors.
+",0,1,0,0,0,0
+12648,Elliptic supersymmetric integrable model and multivariable elliptic functions,"  We investigate the elliptic integrable model introduced by Deguchi and
+Martin, which is an elliptic extension of the Perk-Schultz model. We introduce
+and study a class of partition functions of the elliptic model by using the
+Izergin-Korepin analysis. We show that the partition functions are expressed as
+a product of elliptic factors and elliptic Schur-type symmetric functions. This
+result resembles the recent works by number theorists in which the
+correspondence between the partition functions of trigonometric models and the
+product of the deformed Vandermonde determinant and Schur functions were
+established.
+",0,1,0,0,0,0
+12649,Crystalline Electric Field Randomness in the Triangular Lattice Spin-Liquid YbMgGaO$_4$,"  We apply moderate-high-energy inelastic neutron scattering (INS) measurements
+to investigate Yb$^{3+}$ crystalline electric field (CEF) levels in the
+triangular spin-liquid candidate YbMgGaO$_4$. Three CEF excitations from the
+ground-state Kramers doublet are centered at the energies $\hbar \omega$ = 39,
+61, and 97\,meV in agreement with the effective \mbox{spin-1/2} $g$-factors and
+experimental heat capacity, but reveal sizable broadening. We argue that this
+broadening originates from the site mixing between Mg$^{2+}$ and Ga$^{3+}$
+giving rise to a distribution of Yb--O distances and orientations and, thus, of
+CEF parameters that account for the peculiar energy profile of the CEF
+excitations. The CEF randomness gives rise to a distribution of the effective
+spin-1/2 $g$-factors and explains the unprecedented broadening of low-energy
+magnetic excitations in the fully polarized ferromagnetic phase of YbMgGaO$_4$,
+although a distribution of magnetic couplings due to the Mg/Ga disorder may be
+important as well.
+",0,1,0,0,0,0
+12650,Quadrics and Scherk towers,"  We investigate the relation between quadrics and their Christoffel duals on
+the one hand, and certain zero mean curvature surfaces and their Gauss maps on
+the other hand. To study the relation between timelike minimal surfaces and the
+Christoffel duals of 1-sheeted hyperboloids we introduce para-holomorphic
+elliptic functions. The curves of type change for real isothermic surfaces of
+mixed causal type turn out to be aligned with the real curvature line net.
+",0,0,1,0,0,0
+12651,Scalable Greedy Feature Selection via Weak Submodularity,"  Greedy algorithms are widely used for problems in machine learning such as
+feature selection and set function optimization. Unfortunately, for large
+datasets, the running time of even greedy algorithms can be quite high. This is
+because for each greedy step we need to refit a model or calculate a function
+using the previously selected choices and the new candidate.
+Two algorithms that are faster approximations to the greedy forward selection
+were introduced recently ([Mirzasoleiman et al. 2013, 2015]). They achieve
+better performance by exploiting distributed computation and stochastic
+evaluation respectively. Both algorithms have provable performance guarantees
+for submodular functions.
+In this paper we show that divergent from previously held opinion,
+submodularity is not required to obtain approximation guarantees for these two
+algorithms. Specifically, we show that a generalized concept of weak
+submodularity suffices to give multiplicative approximation guarantees. Our
+result extends the applicability of these algorithms to a larger class of
+functions. Furthermore, we show that a bounded submodularity ratio can be used
+to provide data dependent bounds that can sometimes be tighter also for
+submodular functions. We empirically validate our work by showing superior
+performance of fast greedy approximations versus several established baselines
+on artificial and real datasets.
+",1,0,0,1,0,0
+12652,Budget-Constrained Multi-Armed Bandits with Multiple Plays,"  We study the multi-armed bandit problem with multiple plays and a budget
+constraint for both the stochastic and the adversarial setting. At each round,
+exactly $K$ out of $N$ possible arms have to be played (with $1\leq K \leq N$).
+In addition to observing the individual rewards for each arm played, the player
+also learns a vector of costs which has to be covered with an a-priori defined
+budget $B$. The game ends when the sum of current costs associated with the
+played arms exceeds the remaining budget.
+Firstly, we analyze this setting for the stochastic case, for which we assume
+each arm to have an underlying cost and reward distribution with support
+$[c_{\min}, 1]$ and $[0, 1]$, respectively. We derive an Upper Confidence Bound
+(UCB) algorithm which achieves $O(NK^4 \log B)$ regret.
+Secondly, for the adversarial case in which the entire sequence of rewards
+and costs is fixed in advance, we derive an upper bound on the regret of order
+$O(\sqrt{NB\log(N/K)})$ utilizing an extension of the well-known
+$\texttt{Exp3}$ algorithm. We also provide upper bounds that hold with high
+probability and a lower bound of order $\Omega((1 - K/N)^2 \sqrt{NB/K})$.
+",1,0,0,1,0,0
+12653,Thermalized Axion Inflation,"  We analyze the dynamics of inflationary models with a coupling of the
+inflaton $\phi$ to gauge fields of the form $\phi F \tilde{F}/f$, as in the
+case of axions. It is known that this leads to an instability, with exponential
+amplification of gauge fields, controlled by the parameter $\xi=
+\dot{\phi}/(2fH)$, which can strongly affect the generation of cosmological
+perturbations and even the background. We show that scattering rates involving
+gauge fields can become larger than the expansion rate $H$, due to the very
+large occupation numbers, and create a thermal bath of particles of temperature
+$T$ during inflation. In the thermal regime, energy is transferred to smaller
+scales, radically modifying the predictions of this scenario. We thus argue
+that previous constraints on $\xi$ are alleviated. If the gauge fields have
+Standard Model interactions, which naturally provides reheating, they
+thermalize already at $\xi\gtrsim2.9$, before perturbativity constraints and
+also before backreaction takes place. In absence of SM interactions (i.e. for a
+dark photon), we find that gauge fields and inflaton perturbations thermalize
+if $\xi\gtrsim3.4$; however, observations require $\xi\gtrsim6$, which is above
+the perturbativity and backreaction bounds and so a dedicated study is
+required. After thermalization, though, the system should evolve non-trivially
+due to the competition between the instability and the gauge field thermal
+mass. If the thermal mass and the instabilities equilibrate, we expect an
+equilibrium temperature of $T_{eq} \simeq \xi H/\bar{g}$ where $\bar{g}$ is the
+effective gauge coupling. Finally, we estimate the spectrum of perturbations if
+$\phi$ is thermal and find that the tensor to scalar ratio is suppressed by
+$H/(2T)$, if tensors do not thermalize.
+",0,1,0,0,0,0
+12654,A Data-Driven Approach to Extract Connectivity Structures from Diffusion Tensor Imaging Data,"  Diffusion Tensor Imaging (DTI) is an effective tool for the analysis of
+structural brain connectivity in normal development and in a broad range of
+brain disorders. However efforts to derive inherent characteristics of
+structural brain networks have been hampered by the very high dimensionality of
+the data, relatively small sample sizes, and the lack of widely acceptable
+connectivity-based regions of interests (ROIs). Typical approaches have focused
+either on regions defined by standard anatomical atlases that do not
+incorporate anatomical connectivity, or have been based on voxel-wise analysis,
+which results in loss of statistical power relative to structure-wise
+connectivity analysis. In this work, we propose a novel, computationally
+efficient iterative clustering method to generate connectivity-based
+whole-brain parcellations that converge to a stable parcellation in a few
+iterations. Our algorithm is based on a sparse representation of the whole
+brain connectivity matrix, which reduces the number of edges from around a half
+billion to a few million while incorporating the necessary spatial constraints.
+We show that the resulting regions in a sense capture the inherent connectivity
+information present in the data, and are stable with respect to initialization
+and the randomization scheme within the algorithm. These parcellations provide
+consistent structural regions across the subjects of population samples that
+are homogeneous with respect to anatomic connectivity. Our method also derives
+connectivity structures that can be used to distinguish between population
+samples with known different structural connectivity. In particular, new
+results in structural differences for different population samples such as
+Females vs Males, Normal Controls vs Schizophrenia, and different age groups in
+Normal Controls are also shown.
+",0,0,0,1,1,0
+12655,A survey of location inference techniques on Twitter,"  The increasing popularity of the social networking service, Twitter, has made
+it more involved in day-to-day communications, strengthening social
+relationships and information dissemination. Conversations on Twitter are now
+being explored as indicators within early warning systems to alert of imminent
+natural disasters such earthquakes and aid prompt emergency responses to crime.
+Producers are privileged to have limitless access to market perception from
+consumer comments on social media and microblogs. Targeted advertising can be
+made more effective based on user profile information such as demography,
+interests and location. While these applications have proven beneficial, the
+ability to effectively infer the location of Twitter users has even more
+immense value. However, accurately identifying where a message originated from
+or author's location remains a challenge thus essentially driving research in
+that regard. In this paper, we survey a range of techniques applied to infer
+the location of Twitter users from inception to state-of-the-art. We find
+significant improvements over time in the granularity levels and better
+accuracy with results driven by refinements to algorithms and inclusion of more
+spatial features.
+",1,0,0,0,0,0
+12656,Detection and segmentation of the Left Ventricle in Cardiac MRI using Deep Learning,"  Manual segmentation of the Left Ventricle (LV) is a tedious and meticulous
+task that can vary depending on the patient, the Magnetic Resonance Images
+(MRI) cuts and the experts. Still today, we consider manual delineation done by
+experts as being the ground truth for cardiac diagnosticians. Thus, we are
+reviewing the paper - written by Avendi and al. - who presents a combined
+approach with Convolutional Neural Networks, Stacked Auto-Encoders and
+Deformable Models, to try and automate the segmentation while performing more
+accurately. Furthermore, we have implemented parts of the paper (around three
+quarts) and experimented both the original method and slightly modified
+versions when changing the architecture and the parameters.
+",0,0,0,1,0,0
+12657,Curvature-driven stability of defects in nematic textures over spherical disks,"  Stabilizing defects in liquid-crystal systems is crucial for many physical
+processes and applications ranging from functionalizing liquid-crystal textures
+to recently reported command of chaotic behaviors of active matters. In this
+work, we perform analytical calculations to study the curvature driven
+stability mechanism of defects based on the isotropic nematic disk model that
+is free of any topological constraint. We show that in a growing spherical disk
+covering a sphere the accumulation of curvature effect can prevent typical +1
+and +1/2 defects from forming boojum textures where the defects are repelled to
+the boundary of the disk. Our calculations reveal that the movement of the
+equilibrium position of the +1 defect from the boundary to the center of the
+spherical disk occurs in a very narrow window of the disk area, exhibiting the
+first-order phase-transition-like behavior. For the pair of +1/2 defects by
+splitting a +1 defect, we find the curvature driven alternating repulsive and
+attractive interactions between the two defects. With the growth of the
+spherical disk these two defects tend to approach and finally recombine towards
+a +1 defect texture. The sensitive response of defects to curvature and the
+curvature driven stability mechanism demonstrated in this work in nematic disk
+systems may have implications towards versatile control and engineering of
+liquid crystal textures in various applications.
+",0,1,0,0,0,0
+12658,Heterogeneous nucleation of catalyst-free InAs nanowires on silicon,"  We report on the heterogeneous nucleation of catalyst-free InAs nanowires on
+Si (111) substrates by chemical beam epitaxy. We show that nanowire nucleation
+is enhanced by sputtering the silicon substrate with energetic particles. We
+argue that particle bombardment introduces lattice defects on the silicon
+surface that serve as preferential nucleation sites. The formation of these
+nucleation sites can be controlled by the sputtering parameters, allowing the
+control of nanowire density in a wide range. Nanowire nucleation is accompanied
+by unwanted parasitic islands, but by careful choice of annealing and growth
+temperature allows to strongly reduce the relative density of these islands and
+to realize samples with high nanowire yield.
+",0,1,0,0,0,0
+12659,Random Transverse Field Spin-Glass Model on the Cayley tree : phase transition between the two Many-Body-Localized Phases,"  The quantum Ising model with random couplings and random transverse fields on
+the Cayley tree is studied by Real-Space-Renormalization in order to construct
+the whole set of eigenstates. The renormalization rules are analyzed via large
+deviations. The phase transition between the paramagnetic and the spin-glass
+Many-Body-Localized phases involves the activated exponent $\psi=1$ and the
+correlation length exponent $\nu=1$. The spin-glass-ordered cluster containing
+$N_{SG}$ spins is found to be extremely sparse with respect to the total number
+$N$ of spins : its size grows only logarithmically at the critical point
+$N_{SG}^{criti} \propto \ln N$, and it is sub-extensive $N_{SG} \propto
+N^{\theta}$ in the finite region of the spin-glass phase where the continuously
+varying exponent $\theta$ remains in the interval $0<\theta<1$.
+",0,1,0,0,0,0
+12660,Femtosecond laser inscription of Bragg grating waveguides in bulk diamond,"  Femtosecond laser writing is applied to form Bragg grating waveguides in the
+diamond bulk. Type II waveguides are integrated with a single pulse
+point-by-point periodic laser modification positioned towards the edge of the
+waveguide core. These photonic devices, operating in the telecommunications
+band, allow for simultaneous optical waveguiding and narrowband reflection from
+a 4th order grating. This fabrication technology opens the way towards advanced
+3D photonic networks in diamond for a range of applications.
+",0,1,0,0,0,0
+12661,FORM version 4.2,"  We introduce FORM 4.2, a new minor release of the symbolic manipulation
+toolkit. We demonstrate several new features, such as a new pattern matching
+option, new output optimization, and automatic expansion of rational functions.
+",1,0,0,0,0,0
+12662,An Analysis of Two Common Reference Points for EEGs,"  Clinical electroencephalographic (EEG) data varies significantly depending on
+a number of operational conditions (e.g., the type and placement of electrodes,
+the type of electrical grounding used). This investigation explores the
+statistical differences present in two different referential montages: Linked
+Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately
+45% of the data in the TUH EEG Corpus. In this study, we explore the impact
+this variability has on machine learning performance. We compare the
+statistical properties of features generated using these two montages, and
+explore the impact of performance on our standard Hidden Markov Model (HMM)
+based classification system. We show that a system trained on LE data
+significantly outperforms one trained only on AR data (77.2% vs. 61.4%). We
+also demonstrate that performance of a system trained on both data sets is
+somewhat compromised (71.4% vs. 77.2%). A statistical analysis of the data
+suggests that mean, variance and channel normalization should be considered.
+However, cepstral mean subtraction failed to produce an improvement in
+performance, suggesting that the impact of these statistical differences is
+subtler.
+",0,0,0,1,0,0
+12663,A Concurrency-Optimal Binary Search Tree,"  The paper presents the first \emph{concurrency-optimal} implementation of a
+binary search tree (BST). The implementation, based on a standard sequential
+implementation of an internal tree, ensures that every \emph{schedule} is
+accepted, i.e., interleaving of steps of the sequential code, unless
+linearizability is violated. To ensure this property, we use a novel read-write
+locking scheme that protects tree \emph{edges} in addition to nodes.
+Our implementation outperforms the state-of-the art BSTs on most basic
+workloads, which suggests that optimizing the set of accepted schedules of the
+sequential code can be an adequate design principle for efficient concurrent
+data structures.
+",1,0,0,0,0,0
+12664,Controlling seizure propagation in large-scale brain networks,"  Information transmission in the human brain is a fundamentally dynamic
+network process. In partial epilepsy, this process is perturbed and highly
+synchronous seizures originate in a local network, the so-called epileptogenic
+zone (EZ), before recruiting other close or distant brain regions. We studied
+patient-specific brain network models of 15 drug-resistant epilepsy patients
+with implanted stereotactic electroencephalography (SEEG) electrodes. Each
+personalized brain model was derived from structural data of magnetic resonance
+imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes
+equipped with region specific neural mass models capable of demonstrating a
+range of epileptiform discharges. Each patients virtual brain was further
+personalized through the integration of the clinically hypothesized EZ.
+Subsequent simulations and connectivity modulations were performed and
+uncovered a finite repertoire of seizure propagation patterns. Across patients,
+we found that (i) patient-specific network connectivity is predictive for the
+subsequent seizure propagation pattern; (ii)seizure propagation is
+characterized by a systematic sequence of brain states; (iii) propagation can
+be controlled by an optimal intervention on the connectivity matrix; (iv) the
+degree of invasiveness can be significantly reduced via the here proposed
+seizure control as compared to traditional resective surgery. To stop seizures,
+neurosurgeons typically resect the EZ completely. We showed that stability
+analysis of the network dynamics using graph theoretical metrics estimates
+reliably the spatiotemporal properties of seizure propagation. This suggests
+novel less invasive paradigms of surgical interventions to treat and manage
+partial epilepsy.
+",0,0,0,0,1,0
+12665,DroidStar: Callback Typestates for Android Classes,"  Event-driven programming frameworks, such as Android, are based on components
+with asynchronous interfaces. The protocols for interacting with these
+components can often be described by finite-state machines we dub *callback
+typestates*. Callback typestates are akin to classical typestates, with the
+difference that their outputs (callbacks) are produced asynchronously. While
+useful, these specifications are not commonly available, because writing them
+is difficult and error-prone.
+Our goal is to make the task of producing callback typestates significantly
+easier. We present a callback typestate assistant tool, DroidStar, that
+requires only limited user interaction to produce a callback typestate. Our
+approach is based on an active learning algorithm, L*. We improved the
+scalability of equivalence queries (a key component of L*), thus making active
+learning tractable on the Android system.
+We use DroidStar to learn callback typestates for Android classes both for
+cases where one is already provided by the documentation, and for cases where
+the documentation is unclear. The results show that DroidStar learns callback
+typestates accurately and efficiently. Moreover, in several cases, the
+synthesized callback typestates uncovered surprising and undocumented
+behaviors.
+",1,0,0,0,0,0
+12666,Piezoelectricity for Nondestructive Testing of Crystal Surfaces,"  A stress is applied at the flat face and the apex of a prismatic
+piezoelectric crystal. The voltage generated at these points differs in order
+of magnitude. The result may be used to nondestructively test the uniformity of
+surfaces of piezoelectric crystals.
+",0,1,0,0,0,0
+12667,ALMA Observations of Starless Core Substructure in Ophiuchus,"  Compact substructure is expected to arise in a starless core as mass becomes
+concentrated in the central region likely to form a protostar. Additionally,
+multiple peaks may form if fragmentation occurs. We present ALMA Cycle 2
+observations of 60 starless and protostellar cores in the Ophiuchus molecular
+cloud. We detect eight compact substructures which are >15 arcsec from the
+nearest Spitzer YSO. Only one of these has strong evidence for being truly
+starless after considering ancillary data, e.g., from Herschel and X-ray
+telescopes. An additional extended emission structure has tentative evidence
+for starlessness. The number of our detections is consistent with estimates
+from a combination of synthetic observations of numerical simulations and
+analytical arguments. This result suggests that a similar ALMA study in the
+Chamaeleon I cloud, which detected no compact substructure in starless cores,
+may be due to the peculiar evolutionary state of cores in that cloud.
+",0,1,0,0,0,0
+12668,Neuron-inspired flexible memristive device on silicon (100),"  Comprehensive understanding of the world's most energy efficient powerful
+computer, the human brain, is an elusive scientific issue. Still, already
+gained knowledge indicates memristors can be used as a building block to model
+the brain. At the same time, brain cortex is folded allowing trillions of
+neurons to be integrated in a compact volume. Therefore, we report flexible
+aluminium oxide based memristive devices fabricated and then derived from
+widely used bulk mono-crystalline silicon (100). We use complementary metal
+oxide semiconductor based processes to layout the foundation for ultra large
+scale integration (ULSI) of such memory devices to advance the task of
+comprehending a physical model of human brain.
+",0,1,0,0,0,0
+12669,Detecting laws in power subgroups,"  A group law is said to be detectable in power subgroups if, for all coprime
+$m$ and $n$, a group $G$ satisfies the law if and only if the power subgroups
+$G^m$ and $G^n$ both satisfy the law. We prove that for all positive integers
+$c$, nilpotency of class at most $c$ is detectable in power subgroups, as is
+the $k$-Engel law for $k$ at most 4. In contrast, detectability in power
+subgroups fails for solvability of given derived length: we construct a finite
+group $W$ such that $W^2$ and $W^3$ are metabelian but $W$ has derived length
+$3$. We analyse the complexity of the detectability of commutativity in power
+subgroups, in terms of finite presentations that encode a proof of the result.
+",0,0,1,0,0,0
+12670,On the Reliable Detection of Concept Drift from Streaming Unlabeled Data,"  Classifiers deployed in the real world operate in a dynamic environment,
+where the data distribution can change over time. These changes, referred to as
+concept drift, can cause the predictive performance of the classifier to drop
+over time, thereby making it obsolete. To be of any real use, these classifiers
+need to detect drifts and be able to adapt to them, over time. Detecting drifts
+has traditionally been approached as a supervised task, with labeled data
+constantly being used for validating the learned model. Although effective in
+detecting drifts, these techniques are impractical, as labeling is a difficult,
+costly and time consuming activity. On the other hand, unsupervised change
+detection techniques are unreliable, as they produce a large number of false
+alarms. The inefficacy of the unsupervised techniques stems from the exclusion
+of the characteristics of the learned classifier, from the detection process.
+In this paper, we propose the Margin Density Drift Detection (MD3) algorithm,
+which tracks the number of samples in the uncertainty region of a classifier,
+as a metric to detect drift. The MD3 algorithm is a distribution independent,
+application independent, model independent, unsupervised and incremental
+algorithm for reliably detecting drifts from data streams. Experimental
+evaluation on 6 drift induced datasets and 4 additional datasets from the
+cybersecurity domain demonstrates that the MD3 approach can reliably detect
+drifts, with significantly fewer false alarms compared to unsupervised feature
+based drift detectors. The reduced false alarms enables the signaling of drifts
+only when they are most likely to affect classification performance. As such,
+the MD3 approach leads to a detection scheme which is credible, label efficient
+and general in its applicability.
+",1,0,0,1,0,0
+12671,Exploiting generalization in the subspaces for faster model-based learning,"  Due to the lack of enough generalization in the state-space, common methods
+in Reinforcement Learning (RL) suffer from slow learning speed especially in
+the early learning trials. This paper introduces a model-based method in
+discrete state-spaces for increasing learning speed in terms of required
+experience (but not required computational time) by exploiting generalization
+in the experiences of the subspaces. A subspace is formed by choosing a subset
+of features in the original state representation (full-space). Generalization
+and faster learning in a subspace are due to many-to-one mapping of experiences
+from the full-space to each state in the subspace. Nevertheless, due to
+inherent perceptual aliasing in the subspaces, the policy suggested by each
+subspace does not generally converge to the optimal policy. Our approach,
+called Model Based Learning with Subspaces (MoBLeS), calculates confidence
+intervals of the estimated Q-values in the full-space and in the subspaces.
+These confidence intervals are used in the decision making, such that the agent
+benefits the most from the possible generalization while avoiding from
+detriment of the perceptual aliasing in the subspaces. Convergence of MoBLeS to
+the optimal policy is theoretically investigated. Additionally, we show through
+several experiments that MoBLeS improves the learning speed in the early
+trials.
+",1,0,0,1,0,0
+12672,Quasitriangular structure and twisting of the 2+1 bicrossproduct model,"  We show that the bicrossproduct model
+$C[SU_2^*]{\blacktriangleright\!\!\triangleleft} U(su_2)$ quantum Poincare
+group in 2+1 dimensions acting on the quantum spacetime $[x_i,t]=\imath\lambda
+x_i$ is related by a Drinfeld and module-algebra twist to the quantum double
+$U(su_2)\ltimes C[SU_2]$ acting on the quantum spacetime
+$[x_\mu,x_\nu]=\imath\lambda\epsilon_{\mu\nu\rho}x_\rho$. We obtain this twist
+by taking a scaling limit as $q\to 1$ of the $q$-deformed version of the above
+where it corresponds to a previous theory of $q$-deformed Wick rotation from
+$q$-Euclidean to $q$-Minkowski space. We also recover the twist result at the
+Lie bialgebra level.
+",0,0,1,0,0,0
+12673,Can simple transmission chains foster collective intelligence in binary-choice tasks?,"  In many social systems, groups of individuals can find remarkably efficient
+solutions to complex cognitive problems, sometimes even outperforming a single
+expert. The success of the group, however, crucially depends on how the
+judgments of the group members are aggregated to produce the collective answer.
+A large variety of such aggregation methods have been described in the
+literature, such as averaging the independent judgments, relying on the
+majority or setting up a group discussion. In the present work, we introduce a
+novel approach for aggregating judgments - the transmission chain - which has
+not yet been consistently evaluated in the context of collective intelligence.
+In a transmission chain, all group members have access to a unique collective
+solution and can improve it sequentially. Over repeated improvements, the
+collective solution that emerges reflects the judgments of every group members.
+We address the question of whether such a transmission chain can foster
+collective intelligence for binary-choice problems. In a series of numerical
+simulations, we explore the impact of various factors on the performance of the
+transmission chain, such as the group size, the model parameters, and the
+structure of the population. The performance of this method is compared to
+those of the majority rule and the confidence-weighted majority. Finally, we
+rely on two existing datasets of individuals performing a series of binary
+decisions to evaluate the expected performances of the three methods
+empirically. We find that the parameter space where the transmission chain has
+the best performance rarely appears in real datasets. We conclude that the
+transmission chain is best suited for other types of problems, such as those
+that have cumulative properties.
+",1,1,0,0,0,0
+12674,A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution,"  Entity resolution (ER) is the task of identifying all records in a database
+that refer to the same underlying entity, and are therefore duplicates of each
+other. Due to inherent ambiguity of data representation and poor data quality,
+ER is a challenging task for any automated process. As a remedy, human-powered
+ER via crowdsourcing has become popular in recent years. Using crowd to answer
+queries is costly and time consuming. Furthermore, crowd-answers can often be
+faulty. Therefore, crowd-based ER methods aim to minimize human participation
+without sacrificing the quality and use a computer generated similarity matrix
+actively. While, some of these methods perform well in practice, no theoretical
+analysis exists for them, and further their worst case performances do not
+reflect the experimental findings. This creates a disparity in the
+understanding of the popular heuristics for this problem. In this paper, we
+make the first attempt to close this gap. We provide a thorough analysis of the
+prominent heuristic algorithms for crowd-based ER. We justify experimental
+observations with our analysis and information theoretic lower bounds.
+",1,0,0,0,0,0
+12675,Dependency resolution and semantic mining using Tree Adjoining Grammars for Tamil Language,"  Tree adjoining grammars (TAGs) provide an ample tool to capture syntax of
+many Indian languages. Tamil represents a special challenge to computational
+formalisms as it has extensive agglutinative morphology and a comparatively
+difficult argument structure. Modelling Tamil syntax and morphology using TAG
+is an interesting problem which has not been in focus even though TAGs are over
+4 decades old, since its inception. Our research with Tamil TAGs have shown us
+that we can not only represent syntax of the language, but to an extent mine
+out semantics through dependency resolution of the sentence. But in order to
+demonstrate this phenomenal property, we need to parse Tamil language sentences
+using TAGs we have built and through parsing obtain a derivation we could use
+to resolve dependencies, thus proving the semantic property. We use an in-house
+developed pseudo lexical TAG chart parser; algorithm given by Schabes and Joshi
+(1988), for generating derivations of sentences. We do not use any statistics
+to rank out ambiguous derivations but rather use all of them to understand the
+mentioned semantic relation with in TAGs for Tamil. We shall also present a
+brief parser analysis for the completeness of our discussions.
+",1,0,0,0,0,0
+12676,Loop conditions,"  We discuss such Maltsev conditions that consist of just one linear equation,
+we call them loop conditions. To every such condition can be assigned a graph.
+We provide a classification of conditions with undirected graphs. It follows
+that the Siggers term is the weakest non-trivial loop condition.
+",0,0,1,0,0,0
+12677,Impact of Feature Selection on Micro-Text Classification,"  Social media datasets, especially Twitter tweets, are popular in the field of
+text classification. Tweets are a valuable source of micro-text (sometimes
+referred to as ""micro-blogs""), and have been studied in domains such as
+sentiment analysis, recommendation systems, spam detection, clustering, among
+others. Tweets often include keywords referred to as ""Hashtags"" that can be
+used as labels for the tweet. Using tweets encompassing 50 labels, we studied
+the impact of word versus character-level feature selection and extraction on
+different learners to solve a multi-class classification task. We show that
+feature extraction of simple character-level groups performs better than simple
+word groups and pre-processing methods like normalizing using Porter's Stemming
+and Part-of-Speech (""POS"")-Lemmatization.
+",1,0,0,0,0,0
+12678,One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video,"  Due to burdensome data requirements, learning from demonstration often falls
+short of its promise to allow users to quickly and naturally program robots.
+Demonstrations are inherently ambiguous and incomplete, making correct
+generalization to unseen situations difficult without a large number of
+demonstrations in varying conditions. By contrast, humans are often able to
+learn complex tasks from a single demonstration (typically observations without
+action labels) by leveraging context learned over a lifetime. Inspired by this
+capability, our goal is to enable robots to perform one-shot learning of
+multi-step tasks from observation by leveraging auxiliary video data as
+context. Our primary contribution is a novel system that achieves this goal by:
+(1) using a single user-segmented demonstration to define the primitive actions
+that comprise a task, (2) localizing additional examples of these actions in
+unsegmented auxiliary videos via a metalearning-based approach, (3) using these
+additional examples to learn a reward function for each action, and (4)
+performing reinforcement learning on top of the inferred reward functions to
+learn action policies that can be combined to accomplish the task. We
+empirically demonstrate that a robot can learn multi-step tasks more
+effectively when provided auxiliary video, and that performance greatly
+improves when localizing individual actions, compared to learning from
+unsegmented videos.
+",1,0,0,1,0,0
+12679,Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic Optimization with Progressive Variance Reduction,"  In this paper, we propose a simple variant of the original stochastic
+variance reduction gradient (SVRG), where hereafter we refer to as the variance
+reduced stochastic gradient descent (VR-SGD). Different from the choices of the
+snapshot point and starting point in SVRG and its proximal variant, Prox-SVRG,
+the two vectors of each epoch in VR-SGD are set to the average and last iterate
+of the previous epoch, respectively. This setting allows us to use much larger
+learning rates or step sizes than SVRG, e.g., 3/(7L) for VR-SGD vs 1/(10L) for
+SVRG, and also makes our convergence analysis more challenging. In fact, a
+larger learning rate enjoyed by VR-SGD means that the variance of its
+stochastic gradient estimator asymptotically approaches zero more rapidly.
+Unlike common stochastic methods such as SVRG and proximal stochastic methods
+such as Prox-SVRG, we design two different update rules for smooth and
+non-smooth objective functions, respectively. In other words, VR-SGD can tackle
+non-smooth and/or non-strongly convex problems directly without using any
+reduction techniques such as quadratic regularizers. Moreover, we analyze the
+convergence properties of VR-SGD for strongly convex problems, which show that
+VR-SGD attains a linear convergence rate. We also provide the convergence
+guarantees of VR-SGD for non-strongly convex problems. Experimental results
+show that the performance of VR-SGD is significantly better than its
+counterparts, SVRG and Prox-SVRG, and it is also much better than the best
+known stochastic method, Katyusha.
+",1,0,1,1,0,0
+12680,Concerning the Neural Code,"  The central problem with understanding brain and mind is the neural code
+issue: understanding the matter of our brain as basis for the phenomena of our
+mind. The richness with which our mind represents our environment, the
+parsimony of genetic data, the tremendous efficiency with which the brain
+learns from scant sensory input and the creativity with which our mind
+constructs mental worlds all speak in favor of mind as an emergent phenomenon.
+This raises the further issue of how the neural code supports these processes
+of organization. The central point of this communication is that the neural
+code has the form of structured net fragments that are formed by network
+self-organization, activate and de-activate on the functional time scale, and
+spontaneously combine to form larger nets with the same basic structure.
+",0,0,0,0,1,0
+12681,Community Question Answering Platforms vs. Twitter for Predicting Characteristics of Urban Neighbourhoods,"  In this paper, we investigate whether text from a Community Question
+Answering (QA) platform can be used to predict and describe real-world
+attributes. We experiment with predicting a wide range of 62 demographic
+attributes for neighbourhoods of London. We use the text from QA platform of
+Yahoo! Answers and compare our results to the ones obtained from Twitter
+microblogs. Outcomes show that the correlation between the predicted
+demographic attributes using text from Yahoo! Answers discussions and the
+observed demographic attributes can reach an average Pearson correlation
+coefficient of \r{ho} = 0.54, slightly higher than the predictions obtained
+using Twitter data. Our qualitative analysis indicates that there is semantic
+relatedness between the highest correlated terms extracted from both datasets
+and their relative demographic attributes. Furthermore, the correlations
+highlight the different natures of the information contained in Yahoo! Answers
+and Twitter. While the former seems to offer a more encyclopedic content, the
+latter provides information related to the current sociocultural aspects or
+phenomena.
+",1,0,0,0,0,0
+12682,SPASS: Scientific Prominence Active Search System with Deep Image Captioning Network,"  Planetary exploration missions with Mars rovers are complicated, which
+generally require elaborated task planning by human experts, from the path to
+take to the images to capture. NASA has been using this process to acquire over
+22 million images from the planet Mars. In order to improve the degree of
+automation and thus efficiency in this process, we propose a system for
+planetary rovers to actively search for prominence of prespecified scientific
+features in captured images. Scientists can prespecify such search tasks in
+natural language and upload them to a rover, on which the deployed system
+constantly captions captured images with a deep image captioning network and
+compare the auto-generated captions to the prespecified search tasks by certain
+metrics so as to prioritize those images for transmission. As a beneficial side
+effect, the proposed system can also be deployed to ground-based planetary data
+systems as a content-based search engine.
+",1,0,0,1,0,0
+12683,Nonlinear Sequential Accepts and Rejects for Identification of Top Arms in Stochastic Bandits,"  We address the M-best-arm identification problem in multi-armed bandits. A
+player has a limited budget to explore K arms (M<K), and once pulled, each arm
+yields a reward drawn (independently) from a fixed, unknown distribution. The
+goal is to find the top M arms in the sense of expected reward. We develop an
+algorithm which proceeds in rounds to deactivate arms iteratively. At each
+round, the budget is divided by a nonlinear function of remaining arms, and the
+arms are pulled correspondingly. Based on a decision rule, the deactivated arm
+at each round may be accepted or rejected. The algorithm outputs the accepted
+arms that should ideally be the top M arms. We characterize the decay rate of
+the misidentification probability and establish that the nonlinear budget
+allocation proves to be useful for different problem environments (described by
+the number of competitive arms). We provide comprehensive numerical experiments
+showing that our algorithm outperforms the state-of-the-art using suitable
+nonlinearity.
+",1,0,0,1,0,0
+12684,A Machine Learning Framework to Forecast Wave Conditions,"  A~machine learning framework is developed to estimate ocean-wave conditions.
+By supervised training of machine learning models on many thousands of
+iterations of a physics-based wave model, accurate representations of
+significant wave heights and period can be used to predict ocean conditions. A
+model of Monterey Bay was used as the example test site; it was forced by
+measured wave conditions, ocean-current nowcasts, and reported winds. These
+input data along with model outputs of spatially variable wave heights and
+characteristic period were aggregated into supervised learning training and
+test data sets, which were supplied to machine learning models. These machine
+learning models replicated wave heights with a root-mean-squared error of 9cm
+and correctly identify over 90% of the characteristic periods for the test-data
+sets. Impressively, transforming model inputs to outputs through matrix
+operations requires only a fraction (<1/1,000) of the computation time compared
+to forecasting with the physics-based model.
+",0,1,0,0,0,0
+12685,Unobtrusive Deferred Update Stabilization for Efficient Geo-Replication,"  In this paper we propose a novel approach to manage the throughput vs latency
+tradeoff that emerges when managing updates in geo-replicated systems. Our
+approach consists in allowing full concurrency when processing local updates
+and using a deferred local serialisation procedure before shipping updates to
+remote datacenters. This strategy allows to implement inexpensive mechanisms to
+ensure system consistency requirements while avoiding intrusive effects on
+update operations, a major performance limitation of previous systems. We have
+implemented our approach as a variant of Riak KV. Our extensive evaluation
+shows that we outperform sequencer-based approaches by almost an order of
+magnitude in the maximum achievable throughput. Furthermore, unlike previous
+sequencer-free solutions, our approach reaches nearly optimal remote update
+visibility latencies without limiting throughput.
+",1,0,0,0,0,0
+12686,Exploratory Analysis of Pairwise Interactions in Online Social Networks,"  In the last few decades sociologists were trying to explain human behaviour
+by analysing social networks, which requires access to data about interpersonal
+relationships. This represented a big obstacle in this research field until the
+emergence of online social networks (OSNs), which vastly facilitated the
+process of collecting such data. Nowadays, by crawling public profiles on OSNs,
+it is possible to build a social graph where ""friends"" on OSN become
+represented as connected nodes. OSN connection does not necessarily indicate a
+close real-life relationship, but using OSN interaction records may reveal
+real-life relationship intensities, a topic which inspired a number of recent
+researches. Still, published research currently lacks an extensive exploratory
+analysis of OSN interaction records, i.e. a comprehensive overview of users'
+interaction via different ways of OSN interaction. In this paper we provide
+such an overview by leveraging results of conducted extensive social experiment
+which managed to collect records for over 3,200 Facebook users interacting with
+over 1,400,000 of their friends. Our exploratory analysis focuses on extracting
+population distributions and correlation parameters for 13 interaction
+parameters, providing valuable insight in online social network interaction for
+future researches aimed at this field of study.
+",1,0,0,0,0,0
+12687,Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation,"  Across a variety of scientific disciplines, sparse inverse covariance
+estimation is a popular tool for capturing the underlying dependency
+relationships in multivariate data. Unfortunately, most estimators are not
+scalable enough to handle the sizes of modern high-dimensional data sets (often
+on the order of terabytes), and assume Gaussian samples. To address these
+deficiencies, we introduce HP-CONCORD, a highly scalable optimization method
+for estimating a sparse inverse covariance matrix based on a regularized
+pseudolikelihood framework, without assuming Gaussianity. Our parallel proximal
+gradient method uses a novel communication-avoiding linear algebra algorithm
+and runs across a multi-node cluster with up to 1k nodes (24k cores), achieving
+parallel scalability on problems with up to ~819 billion parameters (1.28
+million dimensions); even on a single node, HP-CONCORD demonstrates
+scalability, outperforming a state-of-the-art method. We also use HP-CONCORD to
+estimate the underlying dependency structure of the brain from fMRI data, and
+use the result to identify functional regions automatically. The results show
+good agreement with a clustering from the neuroscience literature.
+",1,0,0,1,0,0
+12688,Thought Viruses and Asset Prices,"  We use insights from epidemiology, namely the SIR model, to study how agents
+infect each other with ""investment ideas."" Once an investment idea ""goes
+viral,"" equilibrium prices exhibit the typical ""fever peak,"" which is
+characteristic for speculative excesses. Using our model, we identify a time
+line of symptoms that indicate whether a boom is in its early or later stages.
+Regarding the market's top, we find that prices start to decline while the
+number of infected agents, who buy the asset, is still rising. Moreover, the
+presence of fully rational agents (i) accelerates booms (ii) lowers peak prices
+and (iii) produces broad, drawn-out, market tops.
+",0,0,0,0,0,1
+12689,Walking Through Waypoints,"  We initiate the study of a fundamental combinatorial problem: Given a
+capacitated graph $G=(V,E)$, find a shortest walk (""route"") from a source $s\in
+V$ to a destination $t\in V$ that includes all vertices specified by a set
+$\mathscr{W}\subseteq V$: the \emph{waypoints}. This waypoint routing problem
+finds immediate applications in the context of modern networked distributed
+systems. Our main contribution is an exact polynomial-time algorithm for graphs
+of bounded treewidth. We also show that if the number of waypoints is
+logarithmically bounded, exact polynomial-time algorithms exist even for
+general graphs. Our two algorithms provide an almost complete characterization
+of what can be solved exactly in polynomial-time: we show that more general
+problems (e.g., on grid graphs of maximum degree 3, with slightly more
+waypoints) are computationally intractable.
+",1,0,0,0,0,0
+12690,"Imaging a Central Ionized Component, a Narrow Ring, and the CO Snowline in the Multi-Gapped Disk of HD 169142","  We report Very Large Array observations at 7 mm, 9 mm, and 3 cm toward the
+pre-transitional disk of the Herbig Ae star HD 169142. These observations have
+allowed us to study the mm emission of this disk with the highest angular
+resolution so far ($0\rlap.""12\times0\rlap.""09$, or 14 au$\times$11 au, at 7
+mm). Our 7 and 9 mm images show a narrow ring of emission at a radius of
+$\sim25$ au tracing the outer edge of the inner gap. This ring presents an
+asymmetric morphology that could be produced by dynamical interactions between
+the disk and forming planets. Additionally, the azimuthally averaged radial
+intensity profiles of the 7 and 9 mm images confirm the presence of the
+previously reported gap at $\sim45$ au, and reveal a new gap at $\sim85$ au. We
+analyzed archival DCO$^+$(3-2) and C$^{18}$O(2-1) ALMA observations, showing
+that the CO snowline is located very close to this third outer gap. This
+suggests that growth and accumulation of large dust grains close to the CO
+snowline could be the mechanism responsible for this proposed outer gap.
+Finally, a compact source of emission is detected at 7 mm, 9 mm, and 3 cm
+toward the center of the disk. Its flux density and spectral index indicate
+that it is dominated by free-free emission from ionized gas, which could be
+associated with either the photoionization of the inner disk, an independent
+object, or an ionized jet.
+",0,1,0,0,0,0
+12691,Viconmavlink: A software tool for indoor positioning using a motion capture system,"  Motion capture is a widely-used technology in robotics research thanks to its
+precise posi tional measurements with real-time performance. This paper
+presents ViconMAVLink, a cross-platform open-source software tool that provides
+indoor positioning services to networked robots. ViconMAVLink converts Vicon
+motion capture data into proper pose and motion data formats and send
+localization information to robots using the MAVLink protocol. The software is
+a convenient tool for mobile robotics researchers to conduct experiments in a
+controlled indoor environment.
+",1,0,0,0,0,0
+12692,On the set of optimal homeomorphisms for the natural pseudo-distance associated with the Lie group S^1,"  If $\varphi$ and $\psi$ are two continuous real-valued functions defined on a
+compact topological space $X$ and $G$ is a subgroup of the group of all
+homeomorphisms of $X$ onto itself, the natural pseudo-distance
+$d_G(\varphi,\psi)$ is defined as the infimum of $\mathcal{L}(g)=\|\varphi-\psi
+\circ g \|_\infty$, as $g$ varies in $G$. In this paper, we make a first step
+towards extending the study of this concept to the case of Lie groups, by
+assuming $X=G=S^1$. In particular, we study the set of the optimal
+homeomorphisms for $d_G$, i.e. the elements $\rho_\alpha$ of $S^1$ such that
+$\mathcal{L}(\rho_\alpha)$ is equal to $d_G(\varphi,\psi)$. As our main
+results, we give conditions that a homeomorphism has to meet in order to be
+optimal, and we prove that the set of the optimal homeomorphisms is finite
+under suitable conditions.
+",1,0,1,0,0,0
+12693,A generalization of an identity due to Kimura and Ruehr,"  An identity stated by Kimura and proved by Ruehr, Kimura and others
+stipulates that for any function $f$ continuous on $[-\frac{1}{2},
+\frac{3}{2}]$ one has $$ \int_{-1/2}^{3/2} f(3x^2 - 2x^3) dx = 2 \int_0^1
+f(3x^2 - 2x^3) dx. $$ We prove that this equality is not an isolated example by
+providing a family of polynomials, related to the Tchebychev polynomials and of
+which $(3x^2 - 2x^3)$ is a particular case, giving rise to similar identities.
+",0,0,1,0,0,0
+12694,Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification,"  The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is
+the main computational bottleneck in spectral clustering. In this work, we
+introduce a highly-scalable, spectrum-preserving graph sparsification algorithm
+that enables to build ultra-sparse NN (u-NN) graphs with guaranteed
+preservation of the original graph spectrums, such as the first few
+eigenvectors of the original graph Laplacian. Our approach can immediately lead
+to scalable spectral clustering of large data networks without sacrificing
+solution quality. The proposed method starts from constructing low-stretch
+spanning trees (LSSTs) from the original graphs, which is followed by
+iteratively recovering small portions of ""spectrally critical"" off-tree edges
+to the LSSTs by leveraging a spectral off-tree embedding scheme. To determine
+the suitable amount of off-tree edges to be recovered to the LSSTs, an
+eigenvalue stability checking scheme is proposed, which enables to robustly
+preserve the first few Laplacian eigenvectors within the sparsified graph.
+Additionally, an incremental graph densification scheme is proposed for
+identifying extra edges that have been missing in the original NN graphs but
+can still play important roles in spectral clustering tasks. Our experimental
+results for a variety of well-known data sets show that the proposed method can
+dramatically reduce the complexity of NN graphs, leading to significant
+speedups in spectral clustering.
+",1,0,0,1,0,0
+12695,Deep-Learnt Classification of Light Curves,"  Astronomy light curves are sparse, gappy, and heteroscedastic. As a result
+standard time series methods regularly used for financial and similar datasets
+are of little help and astronomers are usually left to their own instruments
+and techniques to classify light curves. A common approach is to derive
+statistical features from the time series and to use machine learning methods,
+generally supervised, to separate objects into a few of the standard classes.
+In this work, we transform the time series to two-dimensional light curve
+representations in order to classify them using modern deep learning
+techniques. In particular, we show that convolutional neural networks based
+classifiers work well for broad characterization and classification. We use
+labeled datasets of periodic variables from CRTS survey and show how this opens
+doors for a quick classification of diverse classes with several possible
+exciting extensions.
+",0,1,0,0,0,0
+12696,Bulk crystalline optomechanics,"  Brillouin processes couple light and sound through optomechanical three-wave
+interactions. Within bulk solids, this coupling is mediated by the intrinsic
+photo-elastic material response yielding coherent emission of high frequency
+(GHz) acoustic phonons. This same interaction produces strong optical
+nonlinearities that overtake both Raman or Kerr nonlinearities in practically
+all solids. In this paper, we show that the strength and character of Brillouin
+interactions are radically altered at low temperatures when the phonon
+coherence length surpasses the system size. In this limit, the solid becomes a
+coherent optomechanical system with macroscopic (cm-scale) phonon modes
+possessing large ($60\ \mu \rm{g}$) motional masses. These phonon modes, which
+are formed by shaping the surfaces of the crystal into a confocal phononic
+resonator, yield appreciable optomechanical coupling rates (${\sim}100$ Hz),
+providing access to ultra-high $Q$-factor ($4.2{\times}10^7$) phonon modes at
+high ($12$ GHz) carrier frequencies. The single-pass nonlinear optical
+susceptibility is enhanced from its room temperature value by more than four
+orders of magnitude. Through use of bulk properties, rather than
+nano-structural control, this comparatively simple approach is enticing for the
+ability to engineer optomechanical coupling at high frequencies and with high
+power handling. In contrast to cavity optomechanics, we show that this system
+yields a unique form of dispersive symmetry breaking that enables selective
+phonon heating or cooling without an optical cavity (i.e., cavity-less
+optomechanics). Extending these results, practically any transparent
+crystalline material can be shaped into an optomechanical system as the basis
+for materials spectroscopy, new regimes of laser physics, precision metrology,
+quantum information processing, and for studies of macroscopic quantum
+coherence.
+",0,1,0,0,0,0
+12697,Swift Linked Data Miner: Mining OWL 2 EL class expressions directly from online RDF datasets,"  In this study, we present Swift Linked Data Miner, an interruptible algorithm
+that can directly mine an online Linked Data source (e.g., a SPARQL endpoint)
+for OWL 2 EL class expressions to extend an ontology with new SubClassOf:
+axioms. The algorithm works by downloading only a small part of the Linked Data
+source at a time, building a smart index in the memory and swiftly iterating
+over the index to mine axioms. We propose a transformation function from mined
+axioms to RDF Data Shapes. We show, by means of a crowdsourcing experiment,
+that most of the axioms mined by Swift Linked Data Miner are correct and can be
+added to an ontology. We provide a ready to use Protégé plugin implementing
+the algorithm, to support ontology engineers in their daily modeling work.
+",1,0,0,0,0,0
+12698,Political Footprints: Political Discourse Analysis using Pre-Trained Word Vectors,"  In this paper, we discuss how machine learning could be used to produce a
+systematic and more objective political discourse analysis. Political
+footprints are vector space models (VSMs) applied to political discourse. Each
+of their vectors represents a word, and is produced by training the English
+lexicon on large text corpora. This paper presents a simple implementation of
+political footprints, some heuristics on how to use them, and their application
+to four cases: the U.N. Kyoto Protocol and Paris Agreement, and two U.S.
+presidential elections. The reader will be offered a number of reasons to
+believe that political footprints produce meaningful results, along with some
+suggestions on how to improve their implementation.
+",1,0,0,0,0,0
+12699,Predicting the Quality of Short Narratives from Social Media,"  An important and difficult challenge in building computational models for
+narratives is the automatic evaluation of narrative quality. Quality evaluation
+connects narrative understanding and generation as generation systems need to
+evaluate their own products. To circumvent difficulties in acquiring
+annotations, we employ upvotes in social media as an approximate measure for
+story quality. We collected 54,484 answers from a crowd-powered
+question-and-answer website, Quora, and then used active learning to build a
+classifier that labeled 28,320 answers as stories. To predict the number of
+upvotes without the use of social network features, we create neural networks
+that model textual regions and the interdependence among regions, which serve
+as strong benchmarks for future research. To our best knowledge, this is the
+first large-scale study for automatic evaluation of narrative quality.
+",1,0,0,0,0,0
+12700,Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis,"  Optimization algorithms that leverage gradient covariance information, such
+as variants of natural gradient descent (Amari, 1998), offer the prospect of
+yielding more effective descent directions. For models with many parameters,
+the covariance matrix they are based on becomes gigantic, making them
+inapplicable in their original form. This has motivated research into both
+simple diagonal approximations and more sophisticated factored approximations
+such as KFAC (Heskes, 2000; Martens & Grosse, 2015; Grosse & Martens, 2016). In
+the present work we draw inspiration from both to propose a novel approximation
+that is provably better than KFAC and amendable to cheap partial updates. It
+consists in tracking a diagonal variance, not in parameter coordinates, but in
+a Kronecker-factored eigenbasis, in which the diagonal approximation is likely
+to be more effective. Experiments show improvements over KFAC in optimization
+speed for several deep network architectures.
+",0,0,0,1,0,0
+12701,Detecting Casimir torque with an optically levitated nanorod,"  The linear momentum and angular momentum of virtual photons of quantum vacuum
+fluctuations can induce the Casimir force and the Casimir torque, respectively.
+While the Casimir force has been measured extensively, the Casimir torque has
+not been observed experimentally though it was predicted over forty years ago.
+Here we propose to detect the Casimir torque with an optically levitated
+nanorod near a birefringent plate in vacuum. The axis of the nanorod tends to
+align with the polarization direction of the linearly polarized optical
+tweezer. When its axis is not parallel or perpendicular to the optical axis of
+the birefringent crystal, it will experience a Casimir torque that shifts its
+orientation slightly. We calculate the Casimir torque and Casimir force acting
+on a levitated nanorod near a birefringent crystal. We also investigate the
+effects of thermal noise and photon recoils on the torque and force detection.
+We prove that a levitated nanorod in vacuum will be capable of detecting the
+Casimir torque under realistic conditions.
+",0,1,0,0,0,0
+12702,About small eigenvalues of Witten Laplacian,"  We study the eigenvalues of the semiclassical Witten Laplacian $\Delta_\phi$
+associated to a potential $\phi$. We consider the case where the sequence of
+Arrhenius numbers $S_1\leq \ldots\leq S_n$ associated to $\phi$ is degenerated,
+that is the preceding inequality are not necessarily strict.
+",0,0,1,0,0,0
+12703,Partial control of delay-coordinate maps,"  Delay-coordinate maps have been widely used recently to study nonlinear
+dynamical systems, where there is only access to the time series of one of
+their variables. Here, we show how the partial control method can be applied in
+this kind of framework in order to prevent undesirable situations for the
+system or even to reduce the variability of the observable time series
+associated with it. The main advantage of this control method, is that it
+allows to control delay-coordinate maps even if the control applied is smaller
+than the external disturbances present in the system. To illustrate how it
+works, we have applied it to three well-known models in Nonlinear Dynamics with
+different delays such as the two-dimensional cubic map, the standard map and
+the three-dimensional hyperchaotic Hénon map. For the first time we show here
+how hyperchaotic systems can be partially controlled.
+",0,1,0,0,0,0
+12704,Detecting hip fractures with radiologist-level performance using deep neural networks,"  We developed an automated deep learning system to detect hip fractures from
+frontal pelvic x-rays, an important and common radiological task. Our system
+was trained on a decade of clinical x-rays (~53,000 studies) and can be applied
+to clinical data, automatically excluding inappropriate and technically
+unsatisfactory studies. We demonstrate diagnostic performance equivalent to a
+human radiologist and an area under the ROC curve of 0.994. Translated to
+clinical practice, such a system has the potential to increase the efficiency
+of diagnosis, reduce the need for expensive additional testing, expand access
+to expert level medical image interpretation, and improve overall patient
+outcomes.
+",0,0,0,1,0,0
+12705,Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models,"  Background: In this paper we present the approaches and methods employed in
+order to deal with a large scale multi-label semantic indexing task of
+biomedical papers. This work was mainly implemented within the context of the
+BioASQ challenge of 2014. Methods: The main contribution of this work is a
+multi-label ensemble method that incorporates a McNemar statistical
+significance test in order to validate the combination of the constituent
+machine learning algorithms. Some secondary contributions include a study on
+the temporal aspects of the BioASQ corpus (observations apply also to the
+BioASQ's super-set, the PubMed articles collection) and the proper adaptation
+of the algorithms used to deal with this challenging classification task.
+Results: The ensemble method we developed is compared to other approaches in
+experimental scenarios with subsets of the BioASQ corpus giving positive
+results. During the BioASQ 2014 challenge we obtained the first place during
+the first batch and the third in the two following batches. Our success in the
+BioASQ challenge proved that a fully automated machine-learning approach, which
+does not implement any heuristics and rule-based approaches, can be highly
+competitive and outperform other approaches in similar challenging contexts.
+",0,0,0,1,0,0
+12706,Modeling Temporally Evolving and Spatially Globally Dependent Data,"  The last decades have seen an unprecedented increase in the availability of
+data sets that are inherently global and temporally evolving, from remotely
+sensed networks to climate model ensembles. This paper provides a view of
+statistical modeling techniques for space-time processes, where space is the
+sphere representing our planet. In particular, we make a distintion between (a)
+second order-based, and (b) practical approaches to model temporally evolving
+global processes. The former are based on the specification of a class of
+space-time covariance functions, with space being the two-dimensional sphere.
+The latter are based on explicit description of the dynamics of the space-time
+process, i.e., by specifying its evolution as a function of its past history
+with added spatially dependent noise.
+We especially focus on approach (a), where the literature has been sparse. We
+provide new models of space-time covariance functions for random fields defined
+on spheres cross time. Practical approaches, (b), are also discussed, with
+special emphasis on models built directly on the sphere, without projecting the
+spherical coordinate on the plane.
+We present a case study focused on the analysis of air pollution from the
+2015 wildfires in Equatorial Asia, an event which was classified as the year's
+worst environmental disaster. The paper finishes with a list of the main
+theoretical and applied research problems in the area, where we expect the
+statistical community to engage over the next decade.
+",0,0,1,1,0,0
+12707,A New Representation of Skeleton Sequences for 3D Action Recognition,"  This paper presents a new method for 3D action recognition with skeleton
+sequences (i.e., 3D trajectories of human skeleton joints). The proposed method
+first transforms each skeleton sequence into three clips each consisting of
+several frames for spatial temporal feature learning using deep neural
+networks. Each clip is generated from one channel of the cylindrical
+coordinates of the skeleton sequence. Each frame of the generated clips
+represents the temporal information of the entire skeleton sequence, and
+incorporates one particular spatial relationship between the joints. The entire
+clips include multiple frames with different spatial relationships, which
+provide useful spatial structural information of the human skeleton. We propose
+to use deep convolutional neural networks to learn long-term temporal
+information of the skeleton sequence from the frames of the generated clips,
+and then use a Multi-Task Learning Network (MTLN) to jointly process all frames
+of the generated clips in parallel to incorporate spatial structural
+information for action recognition. Experimental results clearly show the
+effectiveness of the proposed new representation and feature learning method
+for 3D action recognition.
+",1,0,0,0,0,0
+12708,From Abstract Entities in Mathematics to Superposition States in Quantum Mechanics,"  Given an equivalence relation ~ on a set U, there are two abstract notions of
+an element of the quotient set U/~. The #1 abstract notion is a set S=[u] of
+equivalent elements of U (an equivalence class); the #2 notion is an abstract
+entity u_{S} that is definite on what is common to the elements of the
+equivalence class S but is otherwise indefinite on the differences between
+those elements. For instance, the #1 interpretation of a homotopy type is an
+equivalence class of homotopic spaces, but the #2 interpretation, e.g., as
+developed in homotopy type theory, is an abstract space (without points) that
+has the properties that are in common to the spaces in the equivalence class
+but is otherwise indefinite. In philosophy, the #2 abstract entities might be
+called paradigm-universals, e.g., `the white thing' as opposed to the #1
+abstract notion of ""the set of white things"" (out of some given collection U).
+The paper shows how this #2 notion of a paradigm may be mathematically modeled
+using incidence matrices in Boolean logic and density matrices in probability
+theory. Then we cross the bridge to the density matrix treatment of the
+indefinite superposition states in quantum mechanics (QM). This connection
+between the #2 abstracts in mathematics and ontic indefinite states in QM
+elucidates Abner Shimony's literal or objective indefiniteness interpretation
+of QM.
+",0,1,1,0,0,0
+12709,A note on effective descent for overconvergent isocrystals,"  In this short note we explain the proof that proper surjective and faithfully
+flat maps are morphisms of effective descent for overconvergent isocrystals. We
+then show how to deduce the folklore theorem that for an arbitrary variety over
+a perfect field of characteristic $p$, the Frobenius pull-back functor is an
+equivalence on the overconvergent category.
+",0,0,1,0,0,0
+12710,Banach strong Novikov conjecture for polynomially contractible groups,"  We prove the Banach strong Novikov conjecture for groups having polynomially
+bounded higher-order combinatorial functions. This includes all automatic
+groups.
+",0,0,1,0,0,0
+12711,"Fabrication of quencher-free liquid scintillator-based, high-activity $^{222}$Rn calibration sources for the Borexino detector","  A reliable and consistently reproducible technique to fabricate
+$^{222}$Rn-loaded radioactive sources ($\sim$0.5-1 kBq just after fabrication)
+based on liquid scintillator (LS), with negligible amounts of LS quencher
+contaminants, was implemented. This work demonstrates the process that will be
+used during the Borexino detector's upcoming calibration campaign, with one or
+several $\sim$100 Bq such sources will be deployed at different positions in
+its fiducial volume, currently showing unprecedented levels of radiopurity.
+These sources need to fulfill stringent requirements of $^{222}$Rn activity,
+transparency to the radiations of interest and complete removability from the
+detector to ensure their impact on Borexino's radiopurity is negligible.
+Moreover, the need for a clean, undistorted spectral signal for the
+calibrations imposes a tight requirement to minimize quenching agents
+(""quenchers"") to null or extremely low levels.
+",0,1,0,0,0,0
+12712,Identification of Voice Utterance with Aging Factor Using the Method of MFCC Multichannel,"  This research was conducted to develop a method to identify voice utterance.
+For voice utterance that encounters change caused by aging factor, with the
+interval of 10 to 25 years. The change of voice utterance influenced by aging
+factor might be extracted by MFCC (Mel Frequency Cepstrum Coefficient).
+However, the level of the compatibility of the feature may be dropped down to
+55%. While the ones which do not encounter it may reach 95%. To improve the
+compatibility of the changing voice feature influenced by aging factor, then
+the method of the more specific feature extraction is developed: which is by
+separating the voice into several channels, suggested as MFCC multichannel,
+consisting of multichannel 5 filterbank (M5FB), multichannel 2 filterbank
+(M2FB) and multichannel 1 filterbank (M1FB). The result of the test shows that
+for model M5FB and M2FB have the highest score in the level of compatibility
+with 85% and 82% with 25 years interval. While model M5FB gets the highest
+score of 86% for 10 years time interval.
+",1,0,0,0,0,0
+12713,Single Letter Expression of Capacity for a Class of Channels with Memory,"  We study finite alphabet channels with Unit Memory on the previous Channel
+Outputs called UMCO channels. We identify necessary and sufficient conditions,
+to test whether the capacity achieving channel input distributions with
+feedback are time-invariant, and whether feedback capacity is characterized by
+single letter, expressions, similar to that of memoryless channels. The method
+is based on showing that a certain dynamic programming equation, which in
+general, is a nested optimization problem over the sequence of channel input
+distributions, reduces to a non-nested optimization problem. Moreover, for UMCO
+channels, we give a simple expression for the ML error exponent, and we
+identify sufficient conditions to test whether feedback does not increase
+capacity. We derive similar results, when transmission cost constraints are
+imposed. We apply the results to a special class of the UMCO channels, the
+Binary State Symmetric Channel (BSSC) with and without transmission cost
+constraints, to show that the optimization problem of feedback capacity is
+non-nested, the capacity achieving channel input distribution and the
+corresponding channel output transition probability distribution are
+time-invariant, and feedback capacity is characterized by a single letter
+formulae, precisely as Shannon's single letter characterization of capacity of
+memoryless channels. Then we derive closed form expressions for the capacity
+achieving channel input distribution and feedback capacity. We use the closed
+form expressions to evaluate an error exponent for ML decoding.
+",1,0,1,0,0,0
+12714,Spectra of quadratic vector fields on $\mathbb{C}^2$: The missing relation,"  Consider a quadratic vector field on $\mathbb{C}^2$ having an invariant line
+at infinity and isolated singularities only. We define the extended spectra of
+singularities to be the collection of the spectra of the linearization matrices
+of each of the singular points over the affine part, together with all the
+characteristic numbers (i.e. Camacho-Sad indices) at infinity. This collection
+consists of 11 complex numbers, and is invariant under affine equivalence of
+vector fields.
+In this paper we describe all polynomial relations among these numbers. There
+are 5 independent polynomial relations; four of them follow from the
+Euler-Jacobi, the Baum-Bott and the Camacho-Sad index theorems, and are well
+known. The fifth relation was, until now, completely unknown. We provide an
+explicit formula for the missing 5th relation, discuss it's meaning and prove
+that it cannot be formulated as an index theorem.
+",0,0,1,0,0,0
+12715,A Controlled Set-Up Experiment to Establish Personalized Baselines for Real-Life Emotion Recognition,"  We design, conduct and present the results of a highly personalized baseline
+emotion recognition experiment, which aims to set reliable ground-truth
+estimates for the subject's emotional state for real-life prediction under
+similar conditions using a small number of physiological sensors. We also
+propose an adaptive stimuli-selection mechanism that would use the user's
+feedback as guide for future stimuli selection in the controlled-setup
+experiment and generate optimal ground-truth personalized sessions
+systematically. Initial results are very promising (85% accuracy) and variable
+importance analysis shows that only a few features, which are easy-to-implement
+in portable devices, would suffice to predict the subject's emotional state.
+",1,0,0,1,0,0
+12716,Continuity of the Green function in meromorphic families of polynomials,"  We prove that along any marked point the Green function of a meromorphic
+family of polynomials parameterized by the punctured unit disk explodes
+exponentially fast near the origin with a continuous error term.
+",0,0,1,0,0,0
+12717,"The World's First Real-Time Testbed for Massive MIMO: Design, Implementation, and Validation","  This paper sets up a framework for designing a massive multiple-input
+multiple-output (MIMO) testbed by investigating hardware (HW) and system-level
+requirements such as processing complexity, duplexing mode and frame structure.
+Taking these into account, a generic system and processing partitioning is
+proposed which allows flexible scaling and processing distribution onto a
+multitude of physically separated devices. Based on the given HW constraints
+such as maximum number of links and maximum throughput for peer-to-peer
+interconnections combined with processing capabilities, the framework allows to
+evaluate modular HW components. To verify our design approach, we present the
+LuMaMi (Lund University Massive MIMO) testbed which constitutes the first
+reconfigurable real-time HW platform for prototyping massive MIMO. Utilizing up
+to 100 base station antennas and more than 50 Field Programmable Gate Arrays,
+up to 12 user equipments are served on the same time/frequency resource using
+an LTE-like Orthogonal Frequency Division Multiplexing time-division
+duplex-based transmission scheme. Proof-of-concept tests with this system show
+that massive MIMO can simultaneously serve a multitude of users in a static
+indoor and static outdoor environment utilizing the same time/frequency
+resource.
+",1,0,1,0,0,0
+12718,Fuel-Efficient En Route Formation of Truck Platoons,"  The problem of how to coordinate a large fleet of trucks with given itinerary
+to enable fuel-efficient platooning is considered. Platooning is a promising
+technology that enables trucks to save significant amounts of fuel by driving
+close together and thus reducing air drag. A setting is considered in which
+each truck in a fleet is provided with a start location, a destination, a
+departure time, and an arrival deadline from a higher planning level.
+Fuel-efficient plans should be computed. The plans consist of routes and speed
+profiles that allow trucks to arrive by their arrival deadlines. Hereby, trucks
+can meet on common parts of their routes and form platoons, resulting in
+decreased fuel consumption.
+We formulate a combinatorial optimization problem that combines plans
+involving only two vehicles. We show that this problem is hard to solve for
+large problem instances. Hence a heuristic algorithm is proposed. The resulting
+plans are further optimized using convex optimization techniques. The method is
+evaluated with Monte Carlo simulations in a realistic setting. We demonstrate
+that the proposed algorithm can compute plans for thousands of trucks and that
+significant fuel savings can be achieved.
+",1,0,0,0,0,0
+12719,Context-Independent Polyphonic Piano Onset Transcription with an Infinite Training Dataset,"  Many of the recent approaches to polyphonic piano note onset transcription
+require training a machine learning model on a large piano database. However,
+such approaches are limited by dataset availability; additional training data
+is difficult to produce, and proposed systems often perform poorly on novel
+recording conditions. We propose a method to quickly synthesize arbitrary
+quantities of training data, avoiding the need for curating large datasets.
+Various aspects of piano note dynamics - including nonlinearity of note
+signatures with velocity, different articulations, temporal clustering of
+onsets, and nonlinear note partial interference - are modeled to match the
+characteristics of real pianos. Our method also avoids the disentanglement
+problem, a recently noted issue affecting machine-learning based approaches. We
+train a feed-forward neural network with two hidden layers on our generated
+training data and achieve both good transcription performance on the large MAPS
+piano dataset and excellent generalization qualities.
+",1,0,0,1,0,0
+12720,On the Implementation of a Scalable Simulator for Multiscale Hybrid-Mixed Methods,"  The family of Multiscale Hybrid-Mixed (MHM) finite element methods has
+received considerable attention from the mathematics and engineering community
+in the last few years. The MHM methods allow solving highly heterogeneous
+problems on coarse meshes while providing solutions with high-order precision.
+It embeds independent local problems which are responsible for upscaling
+unresolved scales into the numerical solution. These local contributions are
+brought together through a global problem defined on the skeleton of the coarse
+partition. Since the local problems are completely independent, they can be
+easily computed in parallel. In this paper, we present two simulator prototypes
+specifically crafted for the MHM methods, which adopt two different
+implementation strategies: (i) a multi-programming language approach, each
+language tackling different simulation issues; and (ii) a classical,
+single-programming language approach. Specifically, we use C++ for numerical
+computation of the global and local problems in a modular way; for process
+distribution in the simulator, we adopt the Erlang concurrent language in the
+first approach, and the MPI standard in the second approach. The aim of
+exploring these different approaches is twofold: (i) allow for the deployment
+of the simulator both in high-performance computing (with MPI) and in cloud
+computing environments (with Erlang); and (ii) pave the way for further
+exploration of quality attributes related to software productivity and
+fault-tolerance, which are key to Exascale systems. We present a performance
+evaluation of the two simulator prototypes taking into account their
+efficiency.
+",1,0,1,0,0,0
+12721,Deformation theory of the blown-up Seiberg-Witten equation in dimension three,"  Associated with every quaternionic representation of a compact, connected Lie
+group there is a Seiberg-Witten equation in dimension three. The moduli spaces
+of solutions to these equations are typically non-compact. We construct
+Kuranishi models around boundary points of a partially compactified moduli
+space. The Haydys correspondence identifies such boundary points with Fueter
+sections - solutions of a non-linear Dirac equation - of the bundle of
+hyperkähler quotients associated with the quaternionic representation. We
+discuss when such a Fueter section can be deformed to a solution of the
+Seiberg-Witten equation.
+",0,0,1,0,0,0
+12722,On a Fractional Stochastic Hodgkin-Huxley Model,"  The model studied in this paper is a stochastic extension of the so-called
+neuron model introduced by Hodgkin and Huxley. In the sense of rough paths, the
+model is perturbed by a multiplicative noise driven by a fractional Brownian
+motion, with a vector field satisfying the viability condition of Coutin and
+Marie for $\mathbb R\times [0,1]^3$. An application to the modeling of the
+membrane potential of nerve fibers damaged by a neuropathy is provided.
+",0,0,1,0,0,0
+12723,"Approximations and Bounds for (n, k) Fork-Join Queues: A Linear Transformation Approach","  Compared to basic fork-join queues, a job in (n, k) fork-join queues only
+needs its k out of all n sub-tasks to be finished. Since (n, k) fork-join
+queues are prevalent in popular distributed systems, erasure coding based cloud
+storages, and modern network protocols like multipath routing, estimating the
+sojourn time of such queues is thus critical for the performance measurement
+and resource plan of computer clusters. However, the estimating keeps to be a
+well-known open challenge for years, and only rough bounds for a limited range
+of load factors have been given. In this paper, we developed a closed-form
+linear transformation technique for jointly-identical random variables: An
+order statistic can be represented by a linear combination of maxima. This
+brand-new technique is then used to transform the sojourn time of non-purging
+(n, k) fork-join queues into a linear combination of the sojourn times of basic
+(k, k), (k+1, k+1), ..., (n, n) fork-join queues. Consequently, existing
+approximations for basic fork-join queues can be bridged to the approximations
+for non-purging (n, k) fork-join queues. The uncovered approximations are then
+used to improve the upper bounds for purging (n, k) fork-join queues.
+Simulation experiments show that this linear transformation approach is
+practiced well for moderate n and relatively large k.
+",1,0,0,1,0,0
+12724,Integrated analysis of energy transfers in elastic-wave turbulence,"  In elastic-wave turbulence, strong turbulence appears in small wave numbers
+while weak turbulence does in large wave numbers. Energy transfers in the
+coexistence of these turbulent states are numerically investigated in both of
+the Fourier space and the real space. An analytical expression of a detailed
+energy balance reveals from which mode to which mode energy is transferred in
+the triad interaction. Stretching energy excited by external force is
+transferred nonlocally and intermittently to large wave numbers as the kinetic
+energy in the strong turbulence. In the weak turbulence, the resonant
+interactions according to the weak turbulence theory produces cascading net
+energy transfer to large wave numbers. Because the system's nonlinearity shows
+strong temporal intermittency, the energy transfers are investigated at active
+and moderate phases separately. The nonlocal interactions in the Fourier space
+are characterized by the intermittent bundles of fibrous structures in the real
+space.
+",0,1,0,0,0,0
+12725,Effect of annealing on the magnetic properties of zinc ferrite thin films,"  We report on the magnetic properties of zinc ferrite thin film deposited on
+SrTiO$_3$ single crystal using pulsed laser deposition. X-ray diffraction
+result indicates the highly oriented single phase growth of the film along with
+the presence of the strain. In comparison to the bulk antiferromagnetic order,
+the as-deposited film has been found to exhibit ferrimagnetic ordering with a
+coercive field of 1140~Oe at 5~K. A broad maximum, at $\approx$105~K, observed
+in zero-field cooled magnetization curve indicates the wide grain size
+distribution for the as-deposited film. Reduction in magnetization and blocking
+temperature has been observed after annealing in both argon as well as oxygen
+atmospheres, where the variation was found to be dependent on the annealing
+temperature.
+",0,1,0,0,0,0
+12726,Statistical Speech Model Description with VMF Mixture Model,"  In this paper, we present the LSF parameters by a unit vector form, which has
+directional characteristics. The underlying distribution of this unit vector
+variable is modeled by a von Mises-Fisher mixture model (VMM). With the high
+rate theory, the optimal inter-component bit allocation strategy is proposed
+and the distortion-rate (D-R) relation is derived for the VMM based-VQ (VVQ).
+Experimental results show that the VVQ outperforms our recently introduced DVQ
+and the conventional GVQ.
+",1,0,0,0,0,0
+12727,Predicting Atomic Decay Rates Using an Informational-Entropic Approach,"  We show that a newly proposed Shannon-like entropic measure of shape
+complexity applicable to spatially-localized or periodic mathematical functions
+known as configurational entropy (CE) can be used as a predictor of spontaneous
+decay rates for one-electron atoms. The CE is constructed from the Fourier
+transform of the atomic probability density. For the hydrogen atom with
+degenerate states labeled with the principal quantum number n, we obtain a
+scaling law relating the n-averaged decay rates to the respective CE. The
+scaling law allows us to predict the n-averaged decay rate without relying on
+the traditional computation of dipole matrix elements. We tested the predictive
+power of our approach up to n=20, obtaining an accuracy better than 3.7% within
+our numerical precision, as compared to spontaneous decay tables listed in the
+literature.
+",0,1,0,0,0,0
+12728,A Fast Numerical Scheme for the Godunov-Peshkov-Romenski Model of Continuum Mechanics,"  A new second-order numerical scheme based on an operator splitting is
+proposed for the Godunov-Peshkov-Romenski model of continuum mechanics. The
+homogeneous part of the system is solved with a finite volume method based on a
+WENO reconstruction, and the temporal ODEs are solved using some analytic
+results presented here. Whilst it is not possible to attain arbitrary-order
+accuracy with this scheme (as with ADER-WENO schemes used previously), the
+attainable order of accuracy is often sufficient, and solutions are
+computationally cheap when compared with other available schemes. The new
+scheme is compared with an ADER-WENO scheme for various test cases, and a
+convergence study is undertaken to demonstrate its order of accuracy.
+",0,1,1,0,0,0
+12729,Explaining Transition Systems through Program Induction,"  Explaining and reasoning about processes which underlie observed black-box
+phenomena enables the discovery of causal mechanisms, derivation of suitable
+abstract representations and the formulation of more robust predictions. We
+propose to learn high level functional programs in order to represent abstract
+models which capture the invariant structure in the observed data. We introduce
+the $\pi$-machine (program-induction machine) -- an architecture able to induce
+interpretable LISP-like programs from observed data traces. We propose an
+optimisation procedure for program learning based on backpropagation, gradient
+descent and A* search. We apply the proposed method to three problems: system
+identification of dynamical systems, explaining the behaviour of a DQN agent
+and learning by demonstration in a human-robot interaction scenario. Our
+experimental results show that the $\pi$-machine can efficiently induce
+interpretable programs from individual data traces.
+",1,0,0,0,0,0
+12730,Approximate Gradient Coding via Sparse Random Graphs,"  Distributed algorithms are often beset by the straggler effect, where the
+slowest compute nodes in the system dictate the overall running time.
+Coding-theoretic techniques have been recently proposed to mitigate stragglers
+via algorithmic redundancy. Prior work in coded computation and gradient coding
+has mainly focused on exact recovery of the desired output. However, slightly
+inexact solutions can be acceptable in applications that are robust to noise,
+such as model training via gradient-based algorithms. In this work, we present
+computationally simple gradient codes based on sparse graphs that guarantee
+fast and approximately accurate distributed computation. We demonstrate that
+sacrificing a small amount of accuracy can significantly increase algorithmic
+robustness to stragglers.
+",1,0,0,1,0,0
+12731,Word Embeddings via Tensor Factorization,"  Most popular word embedding techniques involve implicit or explicit
+factorization of a word co-occurrence based matrix into low rank factors. In
+this paper, we aim to generalize this trend by using numerical methods to
+factor higher-order word co-occurrence based arrays, or \textit{tensors}. We
+present four word embeddings using tensor factorization and analyze their
+advantages and disadvantages. One of our main contributions is a novel joint
+symmetric tensor factorization technique related to the idea of coupled tensor
+factorization. We show that embeddings based on tensor factorization can be
+used to discern the various meanings of polysemous words without being
+explicitly trained to do so, and motivate the intuition behind why this works
+in a way that doesn't with existing methods. We also modify an existing word
+embedding evaluation metric known as Outlier Detection [Camacho-Collados and
+Navigli, 2016] to evaluate the quality of the order-$N$ relations that a word
+embedding captures, and show that tensor-based methods outperform existing
+matrix-based methods at this task. Experimentally, we show that all of our word
+embeddings either outperform or are competitive with state-of-the-art baselines
+commonly used today on a variety of recent datasets. Suggested applications of
+tensor factorization-based word embeddings are given, and all source code and
+pre-trained vectors are publicly available online.
+",1,0,0,1,0,0
+12732,Lower bounds for the index of compact constant mean curvature surfaces in $\mathbb R^{3}$ and $\mathbb S^{3}$,"  Let $M$ be a compact constant mean curvature surface either in $\mathbb{S}^3$
+or $\mathbb{R}^3$. In this paper we prove that the stability index of $M$ is
+bounded below by a linear function of the genus. As a by product we obtain a
+comparison theorem between the spectrum of the Jacobi operator of $M$ and those
+of Hodge Laplacian of $1$-forms on $M$.
+",0,0,1,0,0,0
+12733,On the maximum principle for a time-fractional diffusion equation,"  In this paper, we discuss the maximum principle for a time-fractional
+diffusion equation $$ \partial_t^\alpha u(x,t) = \sum_{i,j=1}^n
+\partial_i(a_{ij}(x)\partial_j u(x,t)) + c(x)u(x,t) + F(x,t),\ t>0,\ x \in
+\Omega \subset {\mathbb R}^n$$ with the Caputo time-derivative of the order
+$\alpha \in (0,1)$ in the case of the homogeneous Dirichlet boundary condition.
+Compared to the already published results, our findings have two important
+special features. First, we derive a maximum principle for a suitably defined
+weak solution in the fractional Sobolev spaces, not for the strong solution.
+Second, for the non-negative source functions $F = F(x,t)$ we prove the
+non-negativity of the weak solution to the problem under consideration without
+any restrictions on the sign of the coefficient $c=c(x)$ by the derivative of
+order zero in the spatial differential operator. Moreover, we prove the
+monotonicity of the solution with respect to the coefficient $c=c(x)$.
+",0,0,1,0,0,0
+12734,Hydrophobic Ice Confined between Graphene and MoS2,"  The structure and nature of water confined between hydrophobic molybdenum
+disulfide (MoS2) and graphene (Gr) are investigated at room temperature by
+means of atomic force microscopy. We find the formation of two-dimensional (2D)
+crystalline ice layers. In contrast to the hexagonal ice 'bilayers' of bulk
+ice, these 2D crystalline ice phases consist of two planar hexagonal layers.
+Additional water condensation leads to either lateral expansion of the ice
+layers or to the formation of three-dimensional water droplets on top or at the
+edges of the two-layer ice, indicating that water does not wet these planar ice
+films. The results presented here are in line with a recent theory suggesting
+that water confined between hydrophobic walls forms 2D crystalline two-layer
+ice with a nontetrahedral geometry and intrahydrogen bonding. The lack of
+dangling bonds on either surface of the ice film gives rise to a hydrophobic
+character. The unusual geometry of these ice films is of great potential
+importance in biological systems with water in direct contact with hydrophobic
+surfaces.
+",0,1,0,0,0,0
+12735,Changes in the flagellar bundling time account for variations in swimming behavior of flagellated bacteria in viscous media,"  Although the motility of the flagellated bacteria, Escherichia coli, has been
+widely studied, the effect of viscosity on swimming speed remains
+controversial. The swimming mode of wild-type E.coli is often idealized as a
+""run-and- tumble"" sequence in which periods of swimming at a constant speed are
+randomly interrupted by a sudden change of direction at a very low speed. Using
+a tracking microscope, we follow cells for extended periods of time in
+Newtonian liquids of varying viscosity, and find that the swimming behavior of
+a single cell can exhibit a variety of behaviors including run-and-tumble and
+""slow-random-walk"" in which the cells move at relatively low speed. Although
+the characteristic swimming speed varies between individuals and in different
+polymer solutions, we find that the skewness of the speed distribution is
+solely a function of viscosity and can be used, in concert with the measured
+average swimming speed, to determine the effective running speed of each cell.
+We hypothesize that differences in the swimming behavior observed in solutions
+of different viscosity are due to changes in the flagellar bundling time, which
+increases as the viscosity rises, due to the lower rotation rate of the
+flagellar motor. A numerical simulation and the use of Resistive Force theory
+provide support for this hypothesis.
+",0,1,0,0,0,0
+12736,Exploring Heritability of Functional Brain Networks with Inexact Graph Matching,"  Data-driven brain parcellations aim to provide a more accurate representation
+of an individual's functional connectivity, since they are able to capture
+individual variability that arises due to development or disease. This renders
+comparisons between the emerging brain connectivity networks more challenging,
+since correspondences between their elements are not preserved. Unveiling these
+correspondences is of major importance to keep track of local functional
+connectivity changes. We propose a novel method based on graph edit distance
+for the comparison of brain graphs directly in their domain, that can
+accurately reflect similarities between individual networks while providing the
+network element correspondences. This method is validated on a dataset of 116
+twin subjects provided by the Human Connectome Project.
+",1,0,0,0,0,0
+12737,Bianchi type-II universe with wet dark fluid in General Theory of Relativity,"  In this paper, dark energy models of the universe filled with wet dark fluid
+are constructed in the framework of LRS Bianchi type-II space-time in General
+Theory of Relativity. A new equation of state modeled on the equation of state
+$p$=$\gamma(\rho - \rho_*)$, which can describe a liquid including water, is
+used. The exact solutions of Einstein's field equations are obtained in
+quadrature form and the models corresponding to the cases $\gamma = 0$ and
+$\gamma = 1$ are discussed in detail.
+",0,1,0,0,0,0
+12738,Solutions of generic bilinear master equations for a quantum oscillator -- positive and factorized conditions on stationary states,"  We obtain the solutions of the generic bilinear master equation for a quantum
+oscillator with constant coefficients in the Gaussian form. The
+well-behavedness and positive semidefiniteness of the stationary states could
+be characterized by a three-dimensional Minkowski vector. By requiring the
+stationary states to satisfy a factorized condition, we obtain a generic class
+of master equations that includes the well-known ones and their
+generalizations, some of which are completely positive. A further subset of the
+master equations with the Gibbs states as stationary states is also obtained.
+For master equations with not completely positive generators, an analysis on
+the stationary states suggests conditions on the coefficients of the master
+equations that generate positive evolution for a given initial state.
+",0,1,0,0,0,0
+12739,Semantic Instance Segmentation with a Discriminative Loss Function,"  Semantic instance segmentation remains a challenging task. In this work we
+propose to tackle the problem with a discriminative loss function, operating at
+the pixel level, that encourages a convolutional network to produce a
+representation of the image that can easily be clustered into instances with a
+simple post-processing step. The loss function encourages the network to map
+each pixel to a point in feature space so that pixels belonging to the same
+instance lie close together while different instances are separated by a wide
+margin. Our approach of combining an off-the-shelf network with a principled
+loss function inspired by a metric learning objective is conceptually simple
+and distinct from recent efforts in instance segmentation. In contrast to
+previous works, our method does not rely on object proposals or recurrent
+mechanisms. A key contribution of our work is to demonstrate that such a simple
+setup without bells and whistles is effective and can perform on par with more
+complex methods. Moreover, we show that it does not suffer from some of the
+limitations of the popular detect-and-segment approaches. We achieve
+competitive performance on the Cityscapes and CVPPP leaf segmentation
+benchmarks.
+",1,0,0,0,0,0
+12740,A Cost-Sensitive Deep Belief Network for Imbalanced Classification,"  Imbalanced data with a skewed class distribution are common in many
+real-world applications. Deep Belief Network (DBN) is a machine learning
+technique that is effective in classification tasks. However, conventional DBN
+does not work well for imbalanced data classification because it assumes equal
+costs for each class. To deal with this problem, cost-sensitive approaches
+assign different misclassification costs for different classes without
+disrupting the true data sample distributions. However, due to lack of prior
+knowledge, the misclassification costs are usually unknown and hard to choose
+in practice. Moreover, it has not been well studied as to how cost-sensitive
+learning could improve DBN performance on imbalanced data problems. This paper
+proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for
+imbalanced classification. ECS-DBN uses adaptive differential evolution to
+optimize the misclassification costs based on training data, that presents an
+effective approach to incorporating the evaluation measure (i.e. G-mean) into
+the objective function. We first optimize the misclassification costs, then
+apply them to deep belief network. Adaptive differential evolution optimization
+is implemented as the optimization algorithm that automatically updates its
+corresponding parameters without the need of prior domain knowledge. The
+experiments have shown that the proposed approach consistently outperforms the
+state-of-the-art on both benchmark datasets and real-world dataset for fault
+diagnosis in tool condition monitoring.
+",0,0,0,1,0,0
+12741,Ordered p-median problems with neighborhoods,"  In this paper, we introduce a new variant of the $p$-median facility location
+problem in which it is assumed that the exact location of the potential
+facilities is unknown. Instead, each of the facilities must be located in a
+region around their initially assigned location (the neighborhood). In this
+problem, two main decisions have to be made simultaneously: the determination
+of the potential facilities that must be open to serve the demands of the
+customers and the location of the open facilities in their neighborhoods, at
+global minimum cost. We present several mixed integer non-linear programming
+formulations for a wide family of objective functions which are common in
+Location Analysis: ordered median functions. We also develop two math-heuristic
+approaches for solving the problem. We report the results of extensive
+computational experiments.
+",0,0,1,0,0,0
+12742,Multi-Block Interleaved Codes for Local and Global Read Access,"  We define multi-block interleaved codes as codes that allow reading
+information from either a small sub-block or from a larger full block. The
+former offers faster access, while the latter provides better reliability. We
+specify the correction capability of the sub-block code through its gap $t$
+from optimal minimum distance, and look to have full-block minimum distance
+that grows with the parameter $t$. We construct two families of such codes when
+the number of sub-blocks is $3$. The codes match the distance properties of
+known integrated-interleaving codes, but with the added feature of mapping the
+same number of information symbols to each sub-block. As such, they are the
+first codes that provide read access in multiple size granularities and
+correction capabilities.
+",1,0,0,0,0,0
+12743,Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion,"  This paper proposes a new method for solving the well-known rank aggregation
+problem from pairwise comparisons using the method of low-rank matrix
+completion. The partial and noisy data of pairwise comparisons is transformed
+into a matrix form. We then use tools from matrix completion, which has served
+as a major component in the low-rank completion solution of the Netflix
+challenge, to construct the preference of the different objects. In our
+approach, the data of multiple comparisons is used to create an estimate of the
+probability of object i to win (or be chosen) over object j, where only a
+partial set of comparisons between N objects is known. The data is then
+transformed into a matrix form for which the noiseless solution has a known
+rank of one. An alternating minimization algorithm, in which the target matrix
+takes a bilinear form, is then used in combination with maximum likelihood
+estimation for both factors. The reconstructed matrix is used to obtain the
+true underlying preference intensity. This work demonstrates the improvement of
+our proposed algorithm over the current state-of-the-art in both simulated
+scenarios and real data.
+",1,0,0,1,0,0
+12744,Distributions of a particle's position and their asymptotics in the $q$-deformed totally asymmetric zero range process with site dependent jumping rates,"  In this paper we study the probability distribution of the position of a
+tagged particle in the $q$-deformed Totally Asymmetric Zero Range Process
+($q$-TAZRP) with site dependent jumping rates. For a finite particle system, it
+is derived from the transition probability previously obtained by Wang and
+Waugh. We also provide the probability distribution formula for a tagged
+particle in the $q$-TAZRP with the so-called step initial condition in which
+infinitely many particles occupy one single site and all other sites are
+unoccupied. For the $q$-TAZRP with step initial condition, we provide a
+Fredholm determinant representation for the probability distribution function
+of the position of a tagged particle, and moreover we obtain the limiting
+distribution function as the time goes to infinity. Our asymptotic result for
+$q$-TAZRP with step initial condition is comparable to the limiting
+distribution function obtained by Tracy and Widom for the $k$-th leftmost
+particle in the asymmetric simple exclusion process with step initial condition
+(Theorem 2 in Commun. Math. Phys. 290, 129--154 (2009)).
+",0,1,1,0,0,0
+12745,Electrically controllable spin filtering based on superconducting helical states,"  The magnetoelectric effects in the surface states of the 3D TI are extremely
+strong due to the full spin-momentum locking. Here the microscopic theory of
+S/3D TI bilayer structures in terms of quasiclassical Green's functions is
+developed. On the basis of the developed formalism it is shown that the DOS in
+the S/TI bilayer manifests giant magnetoelectric behavior and, as a result,
+S/3D TI heterostructures can work as non-magnetic fully electrically
+controllable spin filters. It is shown that due to the full spin-momentum
+locking the amplitudes of the odd-frequency singlet and triplet components of
+the consensate wave function are equal. The same is valid for the even
+frequency singlet and triplet components. We unveil the connection between the
+odd-frequency pairing in S/3D TI heterostructures and magnetoelectric effects
+in the DOS.
+",0,1,0,0,0,0
+12746,An Online Development Environment for Answer Set Programming,"  Recent progress in logic programming (e.g., the development of the Answer Set
+Programming paradigm) has made it possible to teach it to general undergraduate
+and even high school students. Given the limited exposure of these students to
+computer science, the complexity of downloading, installing and using tools for
+writing logic programs could be a major barrier for logic programming to reach
+a much wider audience. We developed an online answer set programming
+environment with a self contained file system and a simple interface, allowing
+users to write logic programs and perform several tasks over the programs.
+",1,0,0,0,0,0
+12747,Experience-based Optimization: A Coevolutionary Approach,"  This paper studies improving solvers based on their past solving experiences,
+and focuses on improving solvers by offline training. Specifically, the key
+issues of offline training methods are discussed, and research belonging to
+this category but from different areas are reviewed in a unified framework.
+Existing training methods generally adopt a two-stage strategy in which
+selecting the training instances and training instances are treated in two
+independent phases. This paper proposes a new training method, dubbed LiangYi,
+which addresses these two issues simultaneously. LiangYi includes a training
+module for a population-based solver and an instance sampling module for
+updating the training instances. The idea behind LiangYi is to promote the
+population-based solver by training it (with the training module) to improve
+its performance on those instances (discovered by the sampling module) on which
+it performs badly, while keeping the good performances obtained by it on
+previous instances. An instantiation of LiangYi on the Travelling Salesman
+Problem is also proposed. Empirical results on a huge testing set containing
+10000 instances showed LiangYi could train solvers that perform significantly
+better than the solvers trained by other state-of-the-art training method.
+Moreover, empirical investigation of the behaviours of LiangYi confirmed it was
+able to continuously improve the solver through training.
+",1,0,0,0,0,0
+12748,Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation,"  Online social networking sites are experimenting with the following
+crowd-powered procedure to reduce the spread of fake news and misinformation:
+whenever a user is exposed to a story through her feed, she can flag the story
+as misinformation and, if the story receives enough flags, it is sent to a
+trusted third party for fact checking. If this party identifies the story as
+misinformation, it is marked as disputed. However, given the uncertain number
+of exposures, the high cost of fact checking, and the trade-off between flags
+and exposures, the above mentioned procedure requires careful reasoning and
+smart algorithms which, to the best of our knowledge, do not exist to date.
+In this paper, we first introduce a flexible representation of the above
+procedure using the framework of marked temporal point processes. Then, we
+develop a scalable online algorithm, Curb, to select which stories to send for
+fact checking and when to do so to efficiently reduce the spread of
+misinformation with provable guarantees. In doing so, we need to solve a novel
+stochastic optimal control problem for stochastic differential equations with
+jumps, which is of independent interest. Experiments on two real-world datasets
+gathered from Twitter and Weibo show that our algorithm may be able to
+effectively reduce the spread of fake news and misinformation.
+",1,0,0,1,0,0
+12749,Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study,"  Bayesian optimization is proposed for automatic learning of optimal
+controller parameters from experimental data. A probabilistic description (a
+Gaussian process) is used to model the unknown function from controller
+parameters to a user-defined cost. The probabilistic model is updated with
+data, which is obtained by testing a set of parameters on the physical system
+and evaluating the cost. In order to learn fast, the Bayesian optimization
+algorithm selects the next parameters to evaluate in a systematic way, for
+example, by maximizing information gain about the optimum. The algorithm thus
+iteratively finds the globally optimal parameters with only few experiments.
+Taking throttle valve control as a representative industrial control example,
+the proposed auto-tuning method is shown to outperform manual calibration: it
+consistently achieves better performance with a low number of experiments. The
+proposed auto-tuning framework is flexible and can handle different control
+structures and objectives.
+",1,0,0,0,0,0
+12750,The Global Optimization Geometry of Low-Rank Matrix Optimization,"  This paper considers general rank-constrained optimization problems that
+minimize a general objective function $f(X)$ over the set of rectangular
+$n\times m$ matrices that have rank at most $r$. To tackle the rank constraint
+and also to reduce the computational burden, we factorize $X$ into $UV^T$ where
+$U$ and $V$ are $n\times r$ and $m\times r$ matrices, respectively, and then
+optimize over the small matrices $U$ and $V$. We characterize the global
+optimization geometry of the nonconvex factored problem and show that the
+corresponding objective function satisfies the robust strict saddle property as
+long as the original objective function $f$ satisfies restricted strong
+convexity and smoothness properties, ensuring global convergence of many local
+search algorithms (such as noisy gradient descent) in polynomial time for
+solving the factored problem. We also provide a comprehensive analysis for the
+optimization geometry of a matrix factorization problem where we aim to find
+$n\times r$ and $m\times r$ matrices $U$ and $V$ such that $UV^T$ approximates
+a given matrix $X^\star$. Aside from the robust strict saddle property, we show
+that the objective function of the matrix factorization problem has no spurious
+local minima and obeys the strict saddle property not only for the
+exact-parameterization case where $rank(X^\star) = r$, but also for the
+over-parameterization case where $rank(X^\star) < r$ and the
+under-parameterization case where $rank(X^\star) > r$. These geometric
+properties imply that a number of iterative optimization algorithms (such as
+gradient descent) converge to a global solution with random initialization.
+",1,0,1,0,0,0
+12751,Which bridge estimator is optimal for variable selection?,"  We study the problem of variable selection for linear models under the
+high-dimensional asymptotic setting, where the number of observations $n$ grows
+at the same rate as the number of predictors $p$. We consider two-stage
+variable selection techniques (TVS) in which the first stage uses bridge
+estimators to obtain an estimate of the regression coefficients, and the second
+stage simply thresholds this estimate to select the ""important"" predictors. The
+asymptotic false discovery proportion (AFDP) and true positive proportion
+(ATPP) of these TVS are evaluated. We prove that for a fixed ATTP, in order to
+obtain a smaller AFDP, one should pick a bridge estimator with smaller
+asymptotic mean square error in the first stage of TVS. Based on such
+principled discovery, we present a sharp comparison of different TVS, via an
+in-depth investigation of the estimation properties of bridge estimators.
+Rather than ""order-wise"" error bounds with loose constants, our analysis
+focuses on precise error characterization. Various interesting signal-to-noise
+ratio and sparsity settings are studied. Our results offer new and thorough
+insights into high-dimensional variable selection. For instance, we prove that
+a TVS with Ridge in its first stage outperforms TVS with other bridge
+estimators in large noise settings; two-stage LASSO becomes inferior when the
+signal is rare and weak. As a by-product, we show our proposed two-stage
+methods outperform some standard variable selection techniques, such as LASSO
+and Sure Independence Screening, under certain conditions.
+",0,0,1,1,0,0
+12752,Network Systems and String Stability,"  Network systems and their control are highly important and appear in a
+variety of applications, including vehicle platooning and formation con- trol.
+Especially vehicle platoons are highly investigated and an interesting problem
+that arises in this area is string stability, which broadly spoken means that a
+input signal amplifies unbounded as it travels through the vehicle string.
+However, various definitions are commonly used. In this paper, we aim to
+formalise the notion of string stability and illustrate the importance of those
+distinctions on simulation examples. A second goal is to generalise the found
+definitions for general network systems.
+",1,0,0,0,0,0
+12753,Exploring the Function Space of Deep-Learning Machines,"  The function space of deep-learning machines is investigated by studying
+growth in the entropy of functions of a given error with respect to a reference
+function, realized by a deep-learning machine. Using physics-inspired methods
+we study both sparsely and densely-connected architectures to discover a
+layer-wise convergence of candidate functions, marked by a corresponding
+reduction in entropy when approaching the reference function, gain insight into
+the importance of having a large number of layers, and observe phase
+transitions as the error increases.
+",1,1,0,0,0,0
+12754,On Geometry of Manifolds with Some Tensor Structures and Metrics of Norden Type,"  The object of study in the present dissertation are some topics in
+differential geometry of smooth manifolds with additional tensor structures and
+metrics of Norden type. There are considered four cases depending on the
+dimension of the manifold: 2n, 2n + 1, 4n and 4n + 3. The studied tensor
+structures, which are counterparts in the different related dimensions, are the
+almost complex/contact/hypercomplex structure and the almost contact
+3-structure. The considered metric on the 2n-dimensional case is the Norden
+metric, and the metrics in the other three cases are generated by it. The
+purpose of the dissertation is to carry out the following: 1. Further
+investigations of almost complex manifolds with Norden metric including
+studying of natural connections with conditions for their torsion and invariant
+tensors under the twin interchange of Norden metrics. 2. Further investigations
+of almost contact manifolds with B-metric including studying of natural
+connections with conditions for their torsion and associated Schouten-van
+Kampen connections as well as a classification of affine connections. 3.
+Introducing and studying of Sasaki-like almost contact complex Riemannian
+manifolds. 4. Further investigations of almost hypercomplex manifolds with
+Hermitian-Norden metrics including studying of integrable structures of the
+considered type on 4-dimensional Lie algebra and tangent bundles with the
+complete lift of the base metric; introducing of associated Nijenhuis tensors
+in relation with natural connections having totally skew-symmetric torsion as
+well as quaternionic Kähler manifolds with Hermitian-Norden metrics. 5.
+Introducing and studying of manifolds with almost contact 3-structures and
+metrics of Hermitian-Norden type and, in particular, associated Nijenhuis
+tensors and their relationship with natural connections having totally
+skew-symmetric torsion.
+",0,0,1,0,0,0
+12755,Backward-emitted sub-Doppler fluorescence from an optically thick atomic vapor,"  Literature mentions only incidentally a sub-Doppler contribution in the
+excitation spectrum of the backward fluorescence of a dense vapor. This
+contribution is here investigated on Cs vapor, both on the first resonance line
+(894 nm) and on the weaker second resonance line (459 nm). We show that in a
+strongly absorbing medium, the quenching of excited atoms moving towards a
+window irradiated under near normal incidence reduces the fluorescence on the
+red side of the excitation spectrum. Atoms moving slowly towards the window
+produce a sub- Doppler velocity-selective contribution, whose visibility is
+here improved by applying a frequency-modulation technique. This sub-Doppler
+feature, induced by a surface quenching combined with a short absorption length
+for the incident irradiation, exhibits close analogies with the narrow spectra
+appearing with thin vapor cells. We also show that a normal incidence
+irradiation is essential for the sub-Doppler feature to be observed, while it
+should be independent of the detection geometry
+",0,1,0,0,0,0
+12756,Dust Density Distribution and Imaging Analysis of Different Ice Lines in Protoplanetary Disks,"  Recent high angular resolution observations of protoplanetary disks at
+different wavelengths have revealed several kinds of structures, including
+multiple bright and dark rings. Embedded planets are the most used explanation
+for such structures, but there are alternative models capable of shaping the
+dust in rings as it has been observed. We assume a disk around a Herbig star
+and investigate the effect that ice lines have on the dust evolution, following
+the growth, fragmentation, and dynamics of multiple dust size particles,
+covering from 1 $\mu$m to 2 m sized objects. We use simplified prescriptions of
+the fragmentation velocity threshold, which is assumed to change radially at
+the location of one, two, or three ice lines. We assume changes at the radial
+location of main volatiles, specifically H$_2$O, CO$_2$, and NH$_3$. Radiative
+transfer calculations are done using the resulting dust density distributions
+in order to compare with current multiwavelength observations. We find that the
+structures in the dust density profiles and radial intensities at different
+wavelengths strongly depend on the disk viscosity. A clear gap of emission can
+be formed between ice lines and be surrounded by ring-like structures, in
+particular between the H$_2$O and CO$_2$ (or CO). The gaps are expected to be
+shallower and narrower at millimeter emission than at near-infrared, opposite
+to model predictions of particle trapping. In our models, the total gas surface
+density is not expected to show strong variations, in contrast to other
+gap-forming scenarios such as embedded giant planets or radial variations of
+the disk viscosity.
+",0,1,0,0,0,0
+12757,nIFTy Cosmology: the clustering consistency of galaxy formation models,"  We present a clustering comparison of 12 galaxy formation models (including
+Semi-Analytic Models (SAMs) and Halo Occupation Distribution (HOD) models) all
+run on halo catalogues and merger trees extracted from a single {\Lambda}CDM
+N-body simulation. We compare the results of the measurements of the mean halo
+occupation numbers, the radial distribution of galaxies in haloes and the
+2-Point Correlation Functions (2PCF). We also study the implications of the
+different treatments of orphan (galaxies not assigned to any dark matter
+subhalo) and non-orphan galaxies in these measurements. Our main result is that
+the galaxy formation models generally agree in their clustering predictions but
+they disagree significantly between HOD and SAMs for the orphan satellites.
+Although there is a very good agreement between the models on the 2PCF of
+central galaxies, the scatter between the models when orphan satellites are
+included can be larger than a factor of 2 for scales smaller than 1 Mpc/h. We
+also show that galaxy formation models that do not include orphan satellite
+galaxies have a significantly lower 2PCF on small scales, consistent with
+previous studies. Finally, we show that the 2PCF of orphan satellites is
+remarkably different between SAMs and HOD models. Orphan satellites in SAMs
+present a higher clustering than in HOD models because they tend to occupy more
+massive haloes. We conclude that orphan satellites have an important role on
+galaxy clustering and they are the main cause of the differences in the
+clustering between HOD models and SAMs.
+",0,1,0,0,0,0
+12758,The near-critical Gibbs measure of the branching random walk,"  Consider the supercritical branching random walk on the real line in the
+boundary case and the associated Gibbs measure $\nu_{n,\beta}$ on the
+$n^\text{th}$ generation, which is also the polymer measure on a disordered
+tree with inverse temperature $\beta$. The convergence of the partition
+function $W_{n,\beta}$, after rescaling, towards a nontrivial limit has been
+proved by A\""{\i}dékon and Shi in the critical case $\beta = 1$ and by
+Madaule when $\beta >1$. We study here the near-critical case, where $\beta_n
+\to 1$, and prove the convergence of $W_{n,\beta_n}$, after rescaling, towards
+a constant multiple of the limit of the derivative martingale. Moreover,
+trajectories of particles chosen according to the Gibbs measure $\nu_{n,\beta}$
+have been studied by Madaule in the critical case, with convergence towards the
+Brownian meander, and by Chen, Madaule and Mallein in the strong disorder
+regime, with convergence towards the normalized Brownian excursion. We prove
+here the convergence for trajectories of particles chosen according to the
+near-critical Gibbs measure and display continuous families of processes from
+the meander to the excursion or to the Brownian motion.
+",0,0,1,0,0,0
+12759,Graded Lie algebras and regular prehomogeneous vector spaces with one-dimensional scalar multiplication,"  The aim of this paper is to study relations between regular reductive PVs
+with one-dimensional scalar multiplication and the structure of graded Lie
+algebras. We will show that the regularity of such PVs is described by an
+$\mathfrak{sl}_2$-triplet of a graded Lie algebra.
+",0,0,1,0,0,0
+12760,"EPIC 210894022b - A short period super-Earth transiting a metal poor, evolved old star","  The star EPIC 210894022 has been identified from a light curve acquired
+through the K2 space mission as possibly orbited by a transiting planet. Our
+aim is to confirm the planetary nature of the object and derive its fundamental
+parameters. We combine the K2 photometry with reconnaissance spectroscopy and
+radial velocity (RV) measurements obtained using three separate telescope and
+spectrograph combinations. The spectroscopic synthesis package SME has been
+used to derive the stellar photospheric parameters that were used as input to
+various stellar evolutionary tracks in order to derive the parameters of the
+system. The planetary transit was also validated to occur on the assumed host
+star through adaptive imaging and statistical analysis. The star is found to be
+located in the background of the Hyades cluster at a distance at least 4 times
+further away from Earth than the cluster itself. The spectrum and the space
+velocities of EPIC 210894022 strongly suggest it to be a member of the thick
+disk population. We find that the star is a metal poor ([Fe/H]=-0.53+/-0.05
+dex) and alpha-rich somewhat evolved solar-like object of spectral type G3 with
+Teff=5730+/-50 K, logg=4.15+/-0.1 (cgs), radius of 1.3+/-0.1 R_Sun, and mass of
+0.88+/-0.02 M_Sun. The RV detection together with the imaging confirms with a
+high level of significance that the transit signature is caused by a
+super-Earth orbiting the star EPIC 210894022. We measure a mass of 8.6+/-3.9
+M_Earth and a radius of 1.9+/-0.2 R_Earth. A second more massive object with a
+period longer than about 120 days is indicated by a long term linear
+acceleration. With an age of > 10 Gyrs this system is one of the oldest where
+planets is hitherto detected. Further studies of this planetary system is
+important since it contains information about the planetary formation process
+during a very early epoch of the history of our Galaxy.
+",0,1,0,0,0,0
+12761,Efficient computation of pi by the Newton - Raphson iteration and a two-term Machin-like formula,"  In our recent publication we have proposed a new methodology for
+determination of the two-term Machin-like formula for pi with small arguments
+of the arctangent function of kind $$ \frac{\pi }{4} = {2^{k - 1}}\arctan
+\left( {\frac{1}{\beta_1}} \right) + \arctan \left( {\frac{1}{\beta_2}}
+\right), $$ where $k$ and ${\beta_1}$ are some integers and ${\beta_2}$ is a
+rational number, dependent upon ${\beta_1}$ and $k$. Although
+${1/\left|\beta_2\right|}$ may be significantly smaller than ${1/\beta_1}$, the
+large numbers in the numerator and denominator of $\beta_2$ decelerate the
+computation. In this work we show how this problem can be effectively resolved
+by the Newton--Raphson iteration method.
+",0,0,1,0,0,0
+12762,Quantum Simulation and Spectroscopy of Entanglement Hamiltonians,"  Entanglement is central to our understanding of many-body quantum matter. In
+particular, the entanglement spectrum, as eigenvalues of the reduced density
+matrix of a subsystem, provides a unique footprint of properties of strongly
+correlated quantum matter from detection of topological order to
+characterisation of quantum critical systems. However, direct experimental
+measurement of the entanglement spectrum has so far remained elusive due to
+lack of direct experimental probes. Here we show that the entanglement spectrum
+of the ground state of a broad class of Hamiltonians becomes directly
+accessible as quantum simulation and spectroscopy of an entanglement Hamil-
+tonian, building on the Bisognano-Wichmann (BW) theorem of axiomatic quantum
+field theory. Remarkably, this theorem gives an explicit physical construction
+of the entanglement Hamiltonian, identified as Hamiltonian of the many-body
+system of interest with spatially varying couplings. Building on this, we
+propose an immediate, scalable recipe for implementation of the entanglement
+Hamiltonian, and measurement of the corresponding entanglement spectrum as
+spectroscopy of the Bisognano-Wichmann Hamiltonian with synthetic quantum
+systems, including atoms in optical lat- tices and trapped ions. We illustrate
+and benchmark this scenario on a variety of models, spanning phenomena as
+diverse as conformal field theories, topological order, and quantum phase
+transitions.
+",0,1,0,0,0,0
+12763,Persistence-like distance on Tamarkin's category and symplectic displacement energy,"  We introduce a persistence-like pseudo-distance on Tamarkin's category and
+prove that the distance between an object and its Hamiltonian deformation is at
+most the Hofer norm of the Hamiltonian function. Using the distance, we show a
+quantitative version of Tamarkin's non-displaceability theorem, which gives a
+lower bound of the displacement energy of compact subsets in a cotangent
+bundle.
+",0,0,1,0,0,0
+12764,On $q$-commutative power and Laurent series rings at roots of unity,"  We continue the first and second authors' study of $q$-commutative power
+series rings $R=k_q[[x_1,\ldots,x_n]]$ and Laurent series rings $L=k_q[[x^{\pm
+1}_1,\ldots,x^{\pm 1}_n]]$, specializing to the case in which the commutation
+parameters $q_{ij}$ are all roots of unity. In this setting, $R$ is a PI
+algebra, and we can apply results of De Concini, Kac, and Procesi to show that
+$L$ is an Azumaya algebra whose degree can be inferred from the $q_{ij}$. Our
+main result establishes an exact criterion (dependent on the $q_{ij}$) for
+determining when the centers of $L$ and $R$ are commutative Laurent series and
+commutative power series rings, respectively. In the event this criterion is
+satisfied, it follows that $L$ is a unique factorization ring in the sense of
+Chatters and Jordan, and it further follows, by results of Dumas, Launois,
+Lenagan, and Rigal, that $R$ is a unique factorization ring. We thus produce
+new examples of complete, local, noetherian, noncommutative, unique
+factorization rings (that are PI domains).
+",0,0,1,0,0,0
+12765,On risk-sensitive piecewise deterministic Markov decision processes,"  We consider a piecewise deterministic Markov decision process, where the
+expected exponential utility of total (nonnegative) cost is to be minimized.
+The cost rate, transition rate and post-jump distributions are under control.
+The state space is Borel, and the transition and cost rates are locally
+integrable along the drift. Under natural conditions, we establish the
+optimality equation, justify the value iteration algorithm, and show the
+existence of a deterministic stationary optimal policy. Applied to special
+cases, the obtained results already significantly improve some existing results
+in the literature on finite horizon and infinite horizon discounted
+risk-sensitive continuous-time Markov decision processes.
+",0,0,1,0,0,0
+12766,Spectral Clustering Methods for Multiplex Networks,"  Multiplex networks offer an important tool for the study of complex systems
+and extending techniques originally designed for single--layer networks is an
+important area of study. One of the most important methods for analyzing
+networks is clustering the nodes into communities that represent common
+connectivity patterns. In this paper we extend spectral clustering to multiplex
+structures and discuss some of the difficulties that arise in attempting to
+define a natural generalization. In order to analyze our approach, we describe
+three simple, synthetic multiplex networks and compare the performance of
+different multiplex models. Our results suggest that a dynamically motivated
+model is more successful than a structurally motivated model in discovering the
+appropriate communities.
+",1,1,0,0,0,0
+12767,Logical properties of random graphs from small addable classes,"  We establish zero-one laws and convergence laws for monadic second-order
+logic (MSO) (and, a fortiori, first-order logic) on a number of interesting
+graph classes. In particular, we show that MSO obeys a zero-one law on the
+class of connected planar graphs, the class of connected graphs of tree-width
+at most k and the class of connected graphs excluding the k-clique as a minor.
+In each of these cases, dropping the connectivity requirement leads to a class
+where the zero-one law fails but a convergence law for MSO still holds.
+",1,0,1,0,0,0
+12768,Unidirectional zero reflection as gauged parity-time symmetry,"  We introduce here the concept of establishing Parity-time symmetry through a
+gauge transformation involving a shift of the mirror plane for the Parity
+operation. The corresponding unitary transformation on the system's
+constitutive matrix allows us to generate and explore a family of equivalent
+Parity-time symmetric systems. We further derive that unidirectional zero
+reflection can always be associated with a gauged PT-symmetry and demonstrate
+this experimentally using a microstrip transmission-line with magnetoelectric
+coupling. This study allows us to use bianisotropy as a simple route to realize
+and explore exceptional point behaviour of PT-symmetric or generally
+non-Hermitian systems.
+",0,1,0,0,0,0
+12769,Gaussian curvature directs the distribution of spontaneous curvature on bilayer membrane necks,"  Formation of membrane necks is crucial for fission and fusion in lipid
+bilayers. In this work, we seek to answer the following fundamental question:
+what is the relationship between protein-induced spontaneous mean curvature and
+the Gaussian curvature at a membrane neck? Using an augmented Helfrich model
+for lipid bilayers to include membrane-protein interaction, we solve the shape
+equation on catenoids to find the field of spontaneous curvature that satisfies
+mechanical equilibrium of membrane necks. In this case, the shape equation
+reduces to a variable coefficient Helmholtz equation for spontaneous curvature,
+where the source term is proportional to the Gaussian curvature. We show how
+this latter quantity is responsible for non-uniform distribution of spontaneous
+curvature in minimal surfaces. We then explore the energetics of catenoids with
+different spontaneous curvature boundary conditions and geometric asymmetries
+to show how heterogeneities in spontaneous curvature distribution can couple
+with Gaussian curvature to result in membrane necks of different geometries.
+",0,1,0,0,0,0
+12770,"Divergence, Entropy, Information: An Opinionated Introduction to Information Theory","  Information theory is a mathematical theory of learning with deep connections
+with topics as diverse as artificial intelligence, statistical physics, and
+biological evolution. Many primers on the topic paint a broad picture with
+relatively little mathematical sophistication, while many others develop
+specific application areas in detail. In contrast, these informal notes aim to
+outline some elements of the information-theoretic ""way of thinking,"" by
+cutting a rapid and interesting path through some of the theory's foundational
+concepts and theorems. We take the Kullback-Leibler divergence as our
+foundational concept, and then proceed to develop the entropy and mutual
+information. We discuss some of the main foundational results, including the
+Chernoff bounds as a characterization of the divergence; Gibbs' Theorem; and
+the Data Processing Inequality. A recurring theme is that the definitions of
+information theory support natural theorems that sound ""obvious"" when
+translated into English. More pithily, ""information theory makes common sense
+precise."" Since the focus of the notes is not primarily on technical details,
+proofs are provided only where the relevant techniques are illustrative of
+broader themes. Otherwise, proofs and intriguing tangents are referenced in
+liberally-sprinkled footnotes. The notes close with a highly nonexhaustive list
+of references to resources and other perspectives on the field.
+",0,0,1,1,0,0
+12771,Stochastic Constraint Programming as Reinforcement Learning,"  Stochastic Constraint Programming (SCP) is an extension of Constraint
+Programming (CP) used for modelling and solving problems involving constraints
+and uncertainty. SCP inherits excellent modelling abilities and filtering
+algorithms from CP, but so far it has not been applied to large problems.
+Reinforcement Learning (RL) extends Dynamic Programming to large stochastic
+problems, but is problem-specific and has no generic solvers. We propose a
+hybrid combining the scalability of RL with the modelling and constraint
+filtering methods of CP. We implement a prototype in a CP system and
+demonstrate its usefulness on SCP problems.
+",1,0,0,0,0,0
+12772,Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary,"  Kernel adaptive filters, a class of adaptive nonlinear time-series models,
+are known by their ability to learn expressive autoregressive patterns from
+sequential data. However, for trivial monotonic signals, they struggle to
+perform accurate predictions and at the same time keep computational complexity
+within desired boundaries. This is because new observations are incorporated to
+the dictionary when they are far from what the algorithm has seen in the past.
+We propose a novel approach to kernel adaptive filtering that compares new
+observations against dictionary samples in terms of their unit-norm
+(normalised) versions, meaning that new observations that look like previous
+samples but have a different magnitude are not added to the dictionary. We
+achieve this by proposing the unit-norm Gaussian kernel and define a
+sparsification criterion for this novel kernel. This new methodology is
+validated on two real-world datasets against standard KAF in terms of the
+normalised mean square error and the dictionary size.
+",1,0,0,1,0,0
+12773,An analyst's take on the BPHZ theorem,"  We provide a self-contained formulation of the BPHZ theorem in the Euclidean
+context, which yields a systematic procedure to ""renormalise"" otherwise
+divergent integrals appearing in generalised convolutions of functions with a
+singularity of prescribed order at their origin. We hope that the formulation
+given in this article will appeal to an analytically minded audience and that
+it will help to clarify to what extent such renormalisations are arbitrary (or
+not). In particular, we do not assume any background whatsoever in quantum
+field theory and we stay away from any discussion of the physical context in
+which such problems typically arise.
+",0,0,1,0,0,0
+12774,A variational-geometric approach for the optimal control of nonholonomic systems,"  Necessary conditions for existence of normal extremals in optimal control of
+systems subject to nonholonomic constraints are derived as solutions of a
+constrained second order variational problems. In this work, a geometric
+interpretation of the derivation is studied from the theory of Lie algebroids.
+We employ such a framework to describe the problem into a unifying formalism
+for normal extremals in optimal control of nonholonomic systems and including
+situations that have not been considered before in the literature from this
+perspective. We show that necessary conditions for existence of extremals in
+the optimal control problem can be also determined by a Hamiltonian system on
+the cotangent bundle of a skew-symmetric algebroid.
+",0,0,1,0,0,0
+12775,Development of a compact ExB microchannel plate detector for beam imaging,"  A beam imaging detector was developed by coupling a multi-strip anode with
+delay line readout to an E$\times$B microchannel plate (MCP) detector. This
+detector is capable of measuring the incident position of the beam particles in
+one-dimension. To assess the spatial resolution, the detector was illuminated
+by an $\alpha$-source with an intervening mask that consists of a series of
+precisely-machined slits. The measured spatial resolution was 520$\mu$m FWHM,
+which was improved to 413$\mu$m FWHM by performing an FFT of the signals,
+rejecting spurious signals on the delay line, and requiring a minimum signal
+amplitude. This measured spatial resolution of 413$\mu$m FWHM corresponds to an
+intrinsic resolution of 334$\mu$m FWHM when the effect of the finite slit width
+is de-convoluted. To understand the measured resolution, the performance of the
+detector is simulated with the ion-trajectory code SIMION.
+",0,1,0,0,0,0
+12776,StackSeq2Seq: Dual Encoder Seq2Seq Recurrent Networks,"  A widely studied non-deterministic polynomial time (NP) hard problem lies in
+finding a route between the two nodes of a graph. Often meta-heuristics
+algorithms such as $A^{*}$ are employed on graphs with a large number of nodes.
+Here, we propose a deep recurrent neural network architecture based on the
+Sequence-2-Sequence (Seq2Seq) model, widely used, for instance in text
+translation. Particularly, we illustrate that utilising a context vector that
+has been learned from two different recurrent networks enables increased
+accuracies in learning the shortest route of a graph. Additionally, we show
+that one can boost the performance of the Seq2Seq network by smoothing the loss
+function using a homotopy continuation of the decoder's loss function.
+",1,0,0,1,0,0
+12777,Supervised Deep Hashing for Hierarchical Labeled Data,"  Recently, hashing methods have been widely used in large-scale image
+retrieval. However, most existing hashing methods did not consider the
+hierarchical relation of labels, which means that they ignored the rich
+information stored in the hierarchy. Moreover, most of previous works treat
+each bit in a hash code equally, which does not meet the scenario of
+hierarchical labeled data. In this paper, we propose a novel deep hashing
+method, called supervised hierarchical deep hashing (SHDH), to perform hash
+code learning for hierarchical labeled data. Specifically, we define a novel
+similarity formula for hierarchical labeled data by weighting each layer, and
+design a deep convolutional neural network to obtain a hash code for each data
+point. Extensive experiments on several real-world public datasets show that
+the proposed method outperforms the state-of-the-art baselines in the image
+retrieval task.
+",1,0,0,0,0,0
+12778,The growth rates of automaton groups generated by reset automata,"  We give sufficient conditions for when groups generated by automata in a
+class $\mathcal{C}$ of transducers, which contains the class of reset automata
+transducers, have infinite order. As a consequence we also demonstrate that if
+a group generated by an automata in $\mathcal{C}$ is infinite, then it contains
+a free semigroup of rank at least 2. This gives a new proof, in the context of
+groups generated by automaton in $\mathcal{C}$, of a result of Chou showing
+that finitely generated elementary amenable groups either have polynomial
+growth or contain a free semigroup of rank at least 2.
+We also study what we call the `core growth rate' of elements of
+$\mathcal{C}$. This turns out to be equivalent to the growth rate of certain
+initial transducers. We give examples of transducers with exponential core
+growth rate, and conjecture that all infinite order transducers in the class
+$\mathcal{C}$ have exponential core growth rate.
+",0,0,1,0,0,0
+12779,Combinatorial identities and Chern numbers of complex flag manifolds,"  We present in this article a family of new combinatorial identities via
+purely differential/complex geometry methods, which include as a speical case a
+unified and explicit formula for Chern numbers of all complex flag manifolds.
+Our strategy is to construct concrete circle actions with isolated fixed points
+on these manifolds and explicitly determine their weights. Then applying Bott's
+residue formula to these models yields the desired results.
+",0,0,1,0,0,0
+12780,Impact splash chondrule formation during planetesimal recycling,"  Chondrules are the dominant bulk silicate constituent of chondritic
+meteorites and originate from highly energetic, local processes during the
+first million years after the birth of the Sun. So far, an astrophysically
+consistent chondrule formation scenario, explaining major chemical, isotopic
+and textural features, remains elusive. Here, we examine the prospect of
+forming chondrules from planetesimal collisions. We show that intensely melted
+bodies with interior magma oceans became rapidly chemically equilibrated and
+physically differentiated. Therefore, collisional interactions among such
+bodies would have resulted in chondrule-like but basaltic spherules, which are
+not observed in the meteoritic record. This inconsistency with the expected
+dynamical interactions hints at an incomplete understanding of the planetary
+growth regime during the protoplanetary disk phase. To resolve this conundrum,
+we examine how the observed chemical and isotopic features of chondrules
+constrain the dynamical environment of accreting chondrite parent bodies by
+interpreting the meteoritic record as an impact-generated proxy of
+planetesimals that underwent repeated collision and reaccretion cycles. Using a
+coupled evolution-collision model we demonstrate that the vast majority of
+collisional debris feeding the asteroid main belt must be derived from
+planetesimals which were partially molten at maximum. Therefore, the precursors
+of chondrite parent bodies either formed primarily small, from sub-canonical
+aluminum-26 reservoirs, or collisional destruction mechanisms were efficient
+enough to shatter planetesimals before they reached the magma ocean phase.
+Finally, we outline the window in parameter space for which chondrule formation
+from planetesimal collisions can be reconciled with the meteoritic record and
+how our results can be used to further constrain early solar system dynamics.
+",0,1,0,0,0,0
+12781,Z-checker: A Framework for Assessing Lossy Compression of Scientific Data,"  Because of vast volume of data being produced by today's scientific
+simulations and experiments, lossy data compressor allowing user-controlled
+loss of accuracy during the compression is a relevant solution for
+significantly reducing the data size. However, lossy compressor developers and
+users are missing a tool to explore the features of scientific datasets and
+understand the data alteration after compression in a systematic and reliable
+way. To address this gap, we have designed and implemented a generic framework
+called Z-checker. On the one hand, Z-checker combines a battery of data
+analysis components for data compression. On the other hand, Z-checker is
+implemented as an open-source community tool to which users and developers can
+contribute and add new analysis components based on their additional analysis
+demands. In this paper, we present a survey of existing lossy compressors. Then
+we describe the design framework of Z-checker, in which we integrated
+evaluation metrics proposed in prior work as well as other analysis tools.
+Specifically, for lossy compressor developers, Z-checker can be used to
+characterize critical properties of any dataset to improve compression
+strategies. For lossy compression users, Z-checker can detect the compression
+quality, provide various global distortion analysis comparing the original data
+with the decompressed data and statistical analysis of the compression error.
+Z-checker can perform the analysis with either coarse granularity or fine
+granularity, such that the users and developers can select the best-fit,
+adaptive compressors for different parts of the dataset. Z-checker features a
+visualization interface displaying all analysis results in addition to some
+basic views of the datasets such as time series. To the best of our knowledge,
+Z-checker is the first tool designed to assess lossy compression
+comprehensively for scientific datasets.
+",1,1,0,0,0,0
+12782,Combining the Transcorrelated method with Full Configuration Interaction Quantum Monte Carlo: application to the homogeneous electron gas,"  We suggest an efficient method to resolve electronic cusps in electronic
+structure calculations by using an effective transcorrelated Hamiltonian. This
+effective Hamiltonian takes a simple form for plane wave bases, containing up
+to two-body operators only, and its use incurs almost no additional
+computational overhead compared to that of the original Hamiltonian. We apply
+this method in combination with the full configuration interaction quantum
+Monte Carlo (FCIQMC) method to the homogeneous electron gas. As a projection
+technique, the non-Hermitian nature of the transcorrelated Hamiltonian does not
+cause complications or numerical difficulties for FCIQMC. The rate of
+convergence of the total energy to the complete basis set limit is improved
+from ${\cal O}(M^{-1})$ to ${\cal O}\left({M^{-5/3}}\right)$, where $M$ is the
+total number of orbital basis functions.
+",0,1,0,0,0,0
+12783,Dynamics of quantum information in many-body localized systems,"  We characterize the information dynamics of strongly disordered systems using
+a combination of analytics, exact diagonalization, and matrix product operator
+simulations. More specifically, we study the spreading of quantum information
+in three different scenarios: thermalizing, Anderson localized, and many-body
+localized. We qualitatively distinguish these cases by quantifying the amount
+of remnant information in a local region. The nature of the dynamics is further
+explored by computing the propagation of mutual information with respect to
+varying partitions. Finally, we demonstrate that classical simulability, as
+captured by the magnitude of MPO truncation errors, exhibits enhanced
+fluctuations near the localization transition, suggesting the possibility of
+its use as a diagnostic of the critical point.
+",0,1,0,0,0,0
+12784,Correlations in suspensions confined between viscoelastic surfaces: Noncontact microrheology,"  We study theoretically the velocity cross-correlations of a viscous fluid
+confined in a slit between two viscoelastic media. We analyze the effect of
+these correlations on the motions of particles suspended in the fluid. The
+compliance of the confining boundaries gives rise to a long-ranged pair
+correlation, decaying only as $1/r$ with the interparticle distance $r$. We
+show how this long-ranged effect may be used to extract the viscoelastic
+properties of the confining media without embedding tracer particles in them.
+We discuss the remarkable robustness of such a potential technique with respect
+to details of the confinement, and its expected statistical advantages over
+standard two-point microrheology.
+",0,1,0,0,0,0
+12785,The Cosmic V-Web,"  The network of filaments with embedded clusters surrounding voids seen in
+maps derived from redshift surveys and reproduced in simulations has been
+referred to as the cosmic web. A complementary description is provided by
+considering the shear in the velocity field of galaxies. The eigenvalues of the
+shear provide information on whether a region is collapsing in three
+dimensions, the condition for a knot, expanding in three-dimensions, the
+condition for a void, or in the intermediate condition of a filament or sheet.
+The structures that are quantitatively defined by the eigenvalues can be
+approximated by iso-contours that provide a visual representation of the cosmic
+velocity (V) web. The current application is based on radial peculiar
+velocities from the Cosmicflows-2 collection of distances. The
+three-dimensional velocity field is constructed using the Wiener filter
+methodology in the linear approximation. Eigenvalues of the velocity shear are
+calculated at each point on a grid. Here, knots and filaments are visualized
+across a local domain of diameter ~0.1c.
+",0,1,0,0,0,0
+12786,Irreducibility and r-th root finding over finite fields,"  Constructing $r$-th nonresidue over a finite field is a fundamental
+computational problem. A related problem is to construct an irreducible
+polynomial of degree $r^e$ (where $r$ is a prime) over a given finite field
+$\mathbb{F}_q$ of characteristic $p$ (equivalently, constructing the bigger
+field $\mathbb{F}_{q^{r^e}}$). Both these problems have famous randomized
+algorithms but the derandomization is an open question. We give some new
+connections between these two problems and their variants.
+In 1897, Stickelberger proved that if a polynomial has an odd number of even
+degree factors, then its discriminant is a quadratic nonresidue in the field.
+We give an extension of Stickelberger's Lemma; we construct $r$-th nonresidues
+from a polynomial $f$ for which there is a $d$, such that, $r|d$ and
+$r\nmid\,$#(irreducible factor of $f(x)$ of degree $d$). Our theorem has the
+following interesting consequences: (1) we can construct $\mathbb{F}_{q^m}$ in
+deterministic poly(deg($f$),$m\log q$)-time if $m$ is an $r$-power and $f$ is
+known; (2) we can find $r$-th roots in $\mathbb{F}_{p^m}$ in deterministic
+poly($m\log p$)-time if $r$ is constant and $r|\gcd(m,p-1)$.
+We also discuss a conjecture significantly weaker than the Generalized
+Riemann hypothesis to get a deterministic poly-time algorithm for $r$-th root
+finding.
+",1,0,1,0,0,0
+12787,Competing magnetic interactions in spin-1/2 square lattice: hidden order in Sr$_2$VO$_4$,"  With decreasing temperature Sr$_2$VO$_4$ undergoes two structural phase
+transitions, tetragonal-to-orthorhombic-to-tetragonal, without long-range
+magnetic order. Recent experiments suggest, that only at very low temperature
+Sr$_{2}$VO$_{4}$ might enter some, yet unknown, phase with long-range magnetic
+order, but without orthorhombic distortion. By combining relativistic density
+functional theory with an extended spin-1/2 compass-Heisenberg model we find an
+antiferromagnetic single-stripe ground state with highly competing exchange
+interactions, involving a non negligible inter-layer coupling, which places the
+system at the crossover between between the XY and Heisenberg picture. Most
+strikingly, we find a strong two-site ""spin-compass"" exchange anisotropy which
+is relieved by the orthorhombic distortion induced by the spin stripe order.
+Based on these results we discuss the origin of the hidden order phase and the
+possible formation of a spin-liquid at low temperatures.
+",0,1,0,0,0,0
+12788,Adaptive Matching for Expert Systems with Uncertain Task Types,"  A matching in a two-sided market often incurs an externality: a matched
+resource may become unavailable to the other side of the market, at least for a
+while. This is especially an issue in online platforms involving human experts
+as the expert resources are often scarce. The efficient utilization of experts
+in these platforms is made challenging by the fact that the information
+available about the parties involved is usually limited.
+To address this challenge, we develop a model of a task-expert matching
+system where a task is matched to an expert using not only the prior
+information about the task but also the feedback obtained from the past
+matches. In our model the tasks arrive online while the experts are fixed and
+constrained by a finite service capacity. For this model, we characterize the
+maximum task resolution throughput a platform can achieve. We show that the
+natural greedy approaches where each expert is assigned a task most suitable to
+her skill is suboptimal, as it does not internalize the above externality. We
+develop a throughput optimal backpressure algorithm which does so by accounting
+for the `congestion' among different task types. Finally, we validate our model
+and confirm our theoretical findings with data-driven simulations via logs of
+Math.StackExchange, a StackOverflow forum dedicated to mathematics.
+",1,0,0,1,0,0
+12789,The t-t'-J model in one dimension using extremely correlated Fermi liquid theory and time dependent density matrix renormalization group,"  We study the one dimensional t-t'-J model for generic couplings using two
+complementary theories, the extremely correlated Fermi liquid theory and
+time-dependent density matrix renormalization group over a broad energy scale.
+The two methods provide a unique insight into the strong momentum dependence of
+the self-energy of this prototypical non-Fermi liquid, described at low
+energies as a Tomonaga-Luttinger liquid. We also demonstrate its intimate
+relationship to spin-charge separation, i.e. the splitting of Landau
+quasiparticles of higher dimensions into two constituents, driven by strong
+quantum fluctuations inherent in one dimension. The momentum distribution
+function, the spectral function, and the excitation dispersion of these two
+methods also compare well.
+",0,1,0,0,0,0
+12790,CAOS: Concurrent-Access Obfuscated Store,"  This paper proposes Concurrent-Access Obfuscated Store (CAOS), a construction
+for remote data storage that provides access-pattern obfuscation in a
+honest-but-curious adversarial model, while allowing for low bandwidth overhead
+and client storage. Compared to the state of the art, the main advantage of
+CAOS is that it supports concurrent access without a proxy, for multiple
+read-only clients and a single read-write client. Concurrent access is achieved
+by letting clients maintain independent maps that describe how the data is
+stored. These maps might diverge from client to client, but it is guaranteed
+that no client will ever lose track of current data. We achieve efficiency and
+concurrency at the expense of perfect obfuscation: in CAOS the extent to which
+access patterns are hidden is determined by the resources allocated to its
+built-in obfuscation mechanism. To assess this trade-off we provide both a
+security and a performance analysis of our protocol instance. We additionally
+provide a proof-of-concept implementation.
+",1,0,0,0,0,0
+12791,Safe Execution of Concurrent Programs by Enforcement of Scheduling Constraints,"  Automated software verification of concurrent programs is challenging because
+of exponentially growing state spaces. Verification techniques such as model
+checking need to explore a large number of possible executions that are
+possible under a non-deterministic scheduler. State space reduction techniques
+such as partial order reduction simplify the verification problem, however, the
+reduced state space may still be exponentially large and intractable.
+This paper discusses Iteratively Relaxed Scheduling, a framework that uses
+scheduling constraints in order to simplify the verification problem and enable
+automated verification of programs which could not be handled with fully
+non-deterministic scheduling. Program executions are safe as long as the same
+scheduling constraints are enforced under which the program has been verified,
+e.g., by instrumenting a program with additional synchronization. As strict
+enforcement of scheduling constraints may induce a high execution time
+overhead, we present optimizations over a naive solution that reduce this
+overhead. Our evaluation of a prototype implementation on well-known benchmark
+programs shows the effect of scheduling constraints on the execution time
+overhead and how this overhead can be reduced by relaxing and choosing
+constraints.
+",1,0,0,0,0,0
+12792,A Short-Term Voltage Stability Index and case studies,"  The short-term voltage stability (SVS) problem in large-scale receiving-end
+power systems is serious due to the increasing load demand, the increasing use
+of electronically controlled loads and so on. Some serious blackouts are
+considered to be related to short-term voltage instability. In China, the East
+China Grid (ECG) is especially vulnerable to short-term voltage instability due
+the its increasing dependence on power injection from external grids through
+HVDC links. However, the SVS criteria used in practice are all qualitative and
+the SVS indices proposed in previous researches are mostly based on the
+qualitative SVS criteria. So a Short-Term Voltage Stability Index (SVSI), which
+is continuous, quantitative and multi-dimensional, is proposed in this paper.
+The SVSI consists of three components, which reflects the transient voltage
+restoration, the transient voltage oscillation and the steady-state recovery
+ability of the voltage signal respectively after the contingency has been
+cleared. The theoretical backgrounds and affected factors of these three
+components of SVSI are analyzed, together with some feasible applications. The
+verification of the validity of SVSI are tested through more 10,000 cases based
+on ECG. Additionally, a simple case of selecting candidate locations to install
+dynamic var using SVSI is presented to show its feasibility to solve the
+optimization problem for dynamic var allocation.
+",1,0,1,0,0,0
+12793,The Structure of the Inverse System of Level $K$-Algebras,"  Macaulay's inverse system is an effective method to construct Artinian
+K-algebras with additional properties like, Gorenstein, level, more generally
+with any socle type. Recently, Elias and Rossi gave the structure of the
+inverse system of $d$-dimensional Gorenstein K-algebras for any $d>0$. In this
+paper we extend their result by establishing a one-to-one correspondence
+between $d$-dimensional level K-algebras and certain submodules of the divided
+power ring. We give several examples to illustrate our result.
+",0,0,1,0,0,0
+12794,Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks,"  In many complex networked systems, such as online social networks, activity
+originates at certain nodes and subsequently spreads on the network through
+influence. In this work, we consider the problem of modeling the spread of
+influence and the identification of influential entities in a complex network
+when nodal activation can happen via two different mechanisms. The first
+mechanism of activation stems from factors that are intrinsic to the node. The
+second mechanism comes from the influence of connected neighbors. After
+introducing the model, we provide an algorithm to mine for the influential
+nodes in such a scenario by modifying the well-known influence maximization
+algorithm to work with our model that incorporates both forms of activation.
+Our model can be considered as a variation of the independent cascade diffusion
+model. We provide small motivating examples to facilitate an intuitive
+understanding of the effect of including the intrinsic activation mechanism. We
+sketch a proof of the submodularity of the influence function under the new
+formulation and demonstrate the same on larger graphs. Based on the model, we
+explain how influential content creators can drive engagement on social media
+platforms. Using additional experiments on a Twitter dataset, we then show how
+the formulation can be applied to real-world social media datasets. Finally, we
+derive a centrality metric that takes into account, both the mechanisms of
+activation and provides for an accurate, computationally efficient, alternate
+approach to the problem of identifying influencers under intrinsic activation.
+",1,0,0,0,0,0
+12795,The border support rank of two-by-two matrix multiplication is seven,"  We show that the border support rank of the tensor corresponding to
+two-by-two matrix multiplication is seven over the complex numbers. We do this
+by constructing two polynomials that vanish on all complex tensors with format
+four-by-four-by-four and border rank at most six, but that do not vanish
+simultaneously on any tensor with the same support as the two-by-two matrix
+multiplication tensor. This extends the work of Hauenstein, Ikenmeyer, and
+Landsberg. We also give two proofs that the support rank of the two-by-two
+matrix multiplication tensor is seven over any field: one proof using a result
+of De Groote saying that the decomposition of this tensor is unique up to
+sandwiching, and another proof via the substitution method. These results
+answer a question asked by Cohn and Umans. Studying the border support rank of
+the matrix multiplication tensor is relevant for the design of matrix
+multiplication algorithms, because upper bounds on the border support rank of
+the matrix multiplication tensor lead to upper bounds on the computational
+complexity of matrix multiplication, via a construction of Cohn and Umans.
+Moreover, support rank has applications in quantum communication complexity.
+",1,0,1,0,0,0
+12796,"TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs","  Given a large graph, how can we determine similarity between nodes in a fast
+and accurate way? Random walk with restart (RWR) is a popular measure for this
+purpose and has been exploited in numerous data mining applications including
+ranking, anomaly detection, link prediction, and community detection. However,
+previous methods for computing exact RWR require prohibitive storage sizes and
+computational costs, and alternative methods which avoid such costs by
+computing approximate RWR have limited accuracy. In this paper, we propose TPA,
+a fast, scalable, and highly accurate method for computing approximate RWR on
+large graphs. TPA exploits two important properties in RWR: 1) nodes close to a
+seed node are likely to be revisited in following steps due to block-wise
+structure of many real-world graphs, and 2) RWR scores of nodes which reside
+far from the seed node are proportional to their PageRank scores. Based on
+these two properties, TPA divides approximate RWR problem into two subproblems
+called neighbor approximation and stranger approximation. In the neighbor
+approximation, TPA estimates RWR scores of nodes close to the seed based on
+scores of few early steps from the seed. In the stranger approximation, TPA
+estimates RWR scores for nodes far from the seed using their PageRank. The
+stranger and neighbor approximations are conducted in the preprocessing phase
+and the online phase, respectively. Through extensive experiments, we show that
+TPA requires up to 3.5x less time with up to 40x less memory space than other
+state-of-the-art methods for the preprocessing phase. In the online phase, TPA
+computes approximate RWR up to 30x faster than existing methods while
+maintaining high accuracy.
+",1,0,0,0,0,0
+12797,Novel polystyrene-based nanocomposites by phosphorene dispersion,"  Polystyrene-based phosphorene nanocomposites were prepared by a solvent
+blending procedure allowing the embedding of black phosphorus (BP) nanoflakes
+in the polymer matrix. Raman spectroscopy, X Ray Diffraction and TEM microscopy
+were employed to characterize the structural and the morphological
+characteristics of the achieved hybrids, with the aim to evaluate the
+dispersion level of black phosphorus layers. TGA, DSC analysis as well as
+thermal oxidation and photo-degradation techniques were employed to investigate
+the thermal- and the photo-stability of the samples. The collected results
+evidenced better thermal and photostability of both polymer matrix and
+dispersed layered phosphorus, suggesting really interesting polymer-nanofiller
+synergic effects ascribable to the presence and the good dispersion of the
+2D-nanomaterial.
+",0,1,0,0,0,0
+12798,Analysis and Applications of Delay Differential Equations in Biology and Medicine,"  The main purpose of this paper is to provide a summary of the fundamental
+methods for analyzing delay differential equations arising in biology and
+medicine. These methods are employed to illustrate the effects of time delay on
+the behavior of solutions, which include destabilization of steady states,
+periodic and oscillatory solutions, bifurcations, and stability switches. The
+biological interpretations of delay effects are briefly discussed.
+",0,0,1,0,0,0
+12799,Planar Drawings of Fixed-Mobile Bigraphs,"  A fixed-mobile bigraph G is a bipartite graph such that the vertices of one
+partition set are given with fixed positions in the plane and the mobile
+vertices of the other part, together with the edges, must be added to the
+drawing. We assume that G is planar and study the problem of finding, for a
+given k >= 0, a planar poly-line drawing of G with at most k bends per edge. In
+the most general case, we show NP-hardness. For k=0 and under additional
+constraints on the positions of the fixed or mobile vertices, we either prove
+that the problem is polynomial-time solvable or prove that it belongs to NP.
+Finally, we present a polynomial-time testing algorithm for a certain type of
+""layered"" 1-bend drawings.
+",1,0,0,0,0,0
+12800,Recovery of Architecture Module Views using an Optimized Algorithm Based on Design Structure Matrices,"  Design structure matrices (DSMs) are useful to represent high-level system
+structure, modeling interactions between design entities. DSMs are used for
+many visualization and abstraction activities. In this work, we propose the use
+of an existing DSM clustering algorithm to recover software architecture module
+views. To make it suitable to this domain, optimization has proved necessary.
+It was achieved through performance analysis and parameter tuning on the
+original algorithm. Results show that DSM clustering can be an alternative to
+other clustering algorithms.
+",1,0,0,0,0,0
+12801,Implementation and Analysis of QUIC for MQTT,"  Transport and security protocols are essential to ensure reliable and secure
+communication between two parties. For IoT applications, these protocols must
+be lightweight, since IoT devices are usually resource constrained.
+Unfortunately, the existing transport and security protocols -- namely TCP/TLS
+and UDP/DTLS -- fall short in terms of connection overhead, latency, and
+connection migration when used in IoT applications. In this paper, after
+studying the root causes of these shortcomings, we show how utilizing QUIC in
+IoT scenarios results in a higher performance. Based on these observations, and
+given the popularity of MQTT as an IoT application layer protocol, we integrate
+MQTT with QUIC. By presenting the main APIs and functions developed, we explain
+how connection establishment and message exchange functionalities work. We
+evaluate the performance of MQTTw/QUIC versus MQTTw/TCP using wired, wireless,
+and long-distance testbeds. Our results show that MQTTw/QUIC reduces connection
+overhead in terms of the number of packets exchanged with the broker by up to
+56%. In addition, by eliminating half-open connections, MQTTw/QUIC reduces
+processor and memory usage by up to 83% and 50%, respectively. Furthermore, by
+removing the head-of-line blocking problem, delivery latency is reduced by up
+to 55%. We also show that the throughput drops experienced by MQTTw/QUIC when a
+connection migration happens is considerably lower than that of MQTTw/TCP.
+",1,0,0,0,0,0
+12802,A Language for Function Signature Representations,"  Recent work by (Richardson and Kuhn, 2017a,b; Richardson et al., 2018) looks
+at semantic parser induction and question answering in the domain of source
+code libraries and APIs. In this brief note, we formalize the representations
+being learned in these studies and introduce a simple domain specific language
+and a systematic translation from this language to first-order logic. By
+recasting the target representations in terms of classical logic, we aim to
+broaden the applicability of existing code datasets for investigating more
+complex natural language understanding and reasoning problems in the software
+domain.
+",1,0,0,0,0,0
+12803,Link Adaptation for Wireless Video Communication Systems,"  This PhD thesis considers the performance evaluation and enhancement of video
+communication over wireless channels. The system model considers hybrid
+automatic repeat request (HARQ) with Chase combining and turbo product codes
+(TPC). The thesis proposes algorithms and techniques to optimize the
+throughput, transmission power and complexity of HARQ-based wireless video
+communication. A semi-analytical solution is developed to model the performance
+of delay-constrained HARQ systems. The semi-analytical and Monte Carlo
+simulation results reveal that significant complexity reduction can be achieved
+by noting that the coding gain advantage of the soft over hard decoding is
+reduced when Chase combining is used, and it actually vanishes completely for
+particular codes. Moreover, the thesis proposes a novel power optimization
+algorithm that achieves a significant power saving of up to 80%. Joint
+throughput maximization and complexity reduction is considered as well. A CRC
+(cyclic redundancy check)-free HARQ is proposed to improve the system
+throughput when short packets are transmitted. In addition, the computational
+complexity/delay is reduced when the packets transmitted are long. Finally, a
+content-aware and occupancy-based HARQ scheme is proposed to ensure minimum
+video quality distortion with continuous playback.
+",1,0,0,0,0,0
+12804,On the Complexity of Polytopes in $LI(2)$,"  In this paper we consider polytopes given by systems of $n$ inequalities in
+$d$ variables, where every inequality has at most two variables with nonzero
+coefficient. We denote this family by $LI(2)$. We show that despite of the easy
+algebraic structure, polytopes in $LI(2)$ can have high complexity. We
+construct a polytope in $LI(2)$, whose number of vertices is almost the number
+of vertices of the dual cyclic polytope, the difference is a multiplicative
+factor of depending on $d$ and in particular independent of $n$. Moreover we
+show that the dual cyclic polytope can not be realized in $LI(2)$.
+",1,0,0,0,0,0
+12805,Dynamic Sensitivity Study of MEMS Capacitive Acceleration Transducer Based on Analytical Squeeze Film Damping and Mechanical Thermoelasticity Approaches,"  The dynamic behavior of a capacitive micro-electro-mechanical (MEMS)
+accelerometer is evaluated by using a theoretical approach which makes use of a
+squeeze film damping (SFD) model and ideal gas approach. The study investigates
+the performance of the device as a function of the temperature, from 228 K to
+398 K, and pressure, from 20 to 1000 Pa, observing the damping gas trapped
+inside de mechanical transducer. Thermoelastic properties of the silicon bulk
+are considered for the entire range of temperature. The damping gases
+considered are Air, Helium and Argon. The global behavior of the system is
+evaluated considering the electro-mechanical sensitivity (SEM) as the main
+figure of merit in frequency domain. The results show the behavior of the main
+mechanism losses of SFD, as well as the dynamic sensitivity of the MEMS
+transducer system, and are in good agreement with experimental dynamic results
+behavior.
+",0,1,0,0,0,0
+12806,Nature of the electromagnetic force between classical magnetic dipoles,"  The Lorentz force law of classical electrodynamics states that the force F
+exerted by the magnetic induction B on a particle of charge q moving with
+velocity V is given by F=qVxB. Since this force is orthogonal to the direction
+of motion, the magnetic field is said to be incapable of performing mechanical
+work. Yet there is no denying that a permanent magnet can readily perform
+mechanical work by pushing/pulling on another permanent magnet -- or by
+attracting pieces of magnetizable material such as scrap iron or iron filings.
+We explain this apparent contradiction by examining the magnetic Lorentz force
+acting on an Amperian current loop, which is the model for a magnetic dipole.
+We then extend the discussion by analyzing the Einstein-Laub model of magnetic
+dipoles in the presence of external magnetic fields.
+",0,1,0,0,0,0
+12807,Tractability of $\mathbb{L}_2$-approximation in hybrid function spaces,"  We consider multivariate $\mathbb{L}_2$-approximation in reproducing kernel
+Hilbert spaces which are tensor products of weighted Walsh spaces and weighted
+Korobov spaces. We study the minimal worst-case error
+$e^{\mathbb{L}_2-\mathrm{app},\Lambda}(N,d)$ of all algorithms that use $N$
+information evaluations from the class $\Lambda$ in the $d$-dimensional case.
+The two classes $\Lambda$ considered in this paper are the class $\Lambda^{\rm
+all}$ consisting of all linear functionals and the class $\Lambda^{\rm std}$
+consisting only of function evaluations.
+The focus lies on the dependence of
+$e^{\mathbb{L}_2-\mathrm{app},\Lambda}(N,d)$ on the dimension $d$. The main
+results are conditions for weak, polynomial, and strong polynomial
+tractability.
+",0,0,1,0,0,0
+12808,The Linearity of the Mitchell Order,"  We show from a weak comparison principle (the Ultrapower Axiom) that the
+Mitchell order is linear on certain kinds of ultrafilters: normal ultrafilters,
+Dodd solid ultrafilters, and assuming GCH, generalized normal ultrafilters. In
+the process we prove a generalization of Solovay's lemma to singular cardinals.
+",0,0,1,0,0,0
+12809,Atomic Order in Non-Equilibrium Silicon-Germanium-Tin Semiconductors,"  The precise knowledge of the atomic order in monocrystalline alloys is
+fundamental to understand and predict their physical properties. With this
+perspective, we utilized laser-assisted atom probe tomography to investigate
+the three-dimensional distribution of atoms in non-equilibrium epitaxial
+Sn-rich group IV SiGeSn ternary semiconductors. Different atom probe
+statistical analysis tools including frequency distribution analysis, partial
+radial distribution functions, and nearest neighbor analysis were employed in
+order to evaluate and compare the behavior of the three elements to their
+spatial distributions in an ideal solid solution. This atomistic-level analysis
+provided clear evidence of an unexpected repulsive interaction between Sn and
+Si leading to the deviation of Si atoms from the theoretical random
+distribution. This departure from an ideal solid solution is supported by first
+principal calculations and attributed to the tendency of the system to reduce
+its mixing enthalpy throughout the layer-by-layer growth process.
+",0,1,0,0,0,0
+12810,On the Laws of Large Numbers in Possibility Theory,"  In this paper we obtain some possibilistic variants of the probabilistic laws
+of large numbers, different from those obtained by other authors, but very
+natural extensions of the corresponding ones in probability theory. Our results
+are based on the possibility measure and on the ""maxitive"" definitions for
+possibility expectation and possibility variance. Also, we show that in this
+frame, the weak form of the law of large numbers, implies the strong law of
+large numbers.
+",0,0,1,0,0,0
+12811,Finite Sample Inference for Targeted Learning,"  The Highly-Adaptive-Lasso(HAL)-TMLE is an efficient estimator of a pathwise
+differentiable parameter in a statistical model that at minimal (and possibly
+only) assumes that the sectional variation norm of the true nuisance parameters
+are finite. It relies on an initial estimator (HAL-MLE) of the nuisance
+parameters by minimizing the empirical risk over the parameter space under the
+constraint that sectional variation norm is bounded by a constant, where this
+constant can be selected with cross-validation. In the formulation of the
+HALMLE this sectional variation norm corresponds with the sum of absolute value
+of coefficients for an indicator basis. Due to its reliance on machine
+learning, statistical inference for the TMLE has been based on its normal limit
+distribution, thereby potentially ignoring a large second order remainder in
+finite samples.
+In this article, we present four methods for construction of a finite sample
+0.95-confidence interval that use the nonparametric bootstrap to estimate the
+finite sample distribution of the HAL-TMLE or a conservative distribution
+dominating the true finite sample distribution. We prove that it consistently
+estimates the optimal normal limit distribution, while its approximation error
+is driven by the performance of the bootstrap for a well behaved empirical
+process. We demonstrate our general inferential methods for 1) nonparametric
+estimation of the average treatment effect based on observing on each unit a
+covariate vector, binary treatment, and outcome, and for 2) nonparametric
+estimation of the integral of the square of the multivariate density of the
+data distribution.
+",0,0,1,1,0,0
+12812,A Short and Elementary Proof of the Two-sidedness of the Matrix-Inverse,"  An elementary proof of the two-sidedness of the matrix-inverse is given using
+only linear independence and the reduced row-echelon form of a matrix. In
+addition, it is shown that a matrix is invertible if and only if it is
+row-equivalent to the identity matrix without appealing to elementary matrices.
+This proof underscores the importance of a basis and provides a proof of the
+invertible matrix theorem.
+",0,0,1,0,0,0
+12813,Rich-Club Ordering and the Dyadic Effect: Two Interrelated Phenomena,"  Rich-club ordering and the dyadic effect are two phenomena observed in
+complex networks that are based on the presence of certain substructures
+composed of specific nodes. Rich-club ordering represents the tendency of
+highly connected and important elements to form tight communities with other
+central elements. The dyadic effect denotes the tendency of nodes that share a
+common property to be much more interconnected than expected. In this study, we
+consider the interrelation between these two phenomena, which until now have
+always been studied separately. We contribute with a new formulation of the
+rich-club measures in terms of the dyadic effect. Moreover, we introduce
+certain measures related to the analysis of the dyadic effect, which are useful
+in confirming the presence and relevance of rich-clubs in complex networks. In
+addition, certain computational experiences show the usefulness of the
+introduced quantities with regard to different classes of real networks.
+",1,1,0,0,0,0
+12814,The global dust modelling framework THEMIS (The Heterogeneous dust Evolution Model for Interstellar Solids),"  Here we introduce the interstellar dust modelling framework THEMIS (The
+Heterogeneous dust Evolution Model for Interstellar Solids), which takes a
+global view of dust and its evolution in response to the local conditions in
+interstellar media. This approach is built upon a core model that was developed
+to explain the dust extinction and emission in the diffuse interstellar medium.
+The model was then further developed to self-consistently include the effects
+of dust evolution in the transition to denser regions. The THEMIS approach is
+under continuous development and currently we are extending the framework to
+explore the implications of dust evolution in HII regions and the
+photon-dominated regions associated with star formation. We provide links to
+the THEMIS, DustEM and DustPedia websites where more information about the
+model, its input data and applications can be found.
+",0,1,0,0,0,0
+12815,Variable Selection Methods for Model-based Clustering,"  Model-based clustering is a popular approach for clustering multivariate data
+which has seen applications in numerous fields. Nowadays, high-dimensional data
+are more and more common and the model-based clustering approach has adapted to
+deal with the increasing dimensionality. In particular, the development of
+variable selection techniques has received a lot of attention and research
+effort in recent years. Even for small size problems, variable selection has
+been advocated to facilitate the interpretation of the clustering results. This
+review provides a summary of the methods developed for variable selection in
+model-based clustering. Existing R packages implementing the different methods
+are indicated and illustrated in application to two data analysis examples.
+",0,0,0,1,0,0
+12816,Synergies between Asteroseismology and Exoplanetary Science,"  Over the past decade asteroseismology has become a powerful method to
+systematically characterize host stars and dynamical architectures of exoplanet
+systems. In this contribution I review current key synergies between
+asteroseismology and exoplanetary science such as the precise determination of
+planet radii and ages, the measurement of orbital eccentricities, stellar
+obliquities and their impact on hot Jupiter formation theories, and the
+importance of asteroseismology on spectroscopic analyses of exoplanet hosts. I
+also give an outlook on future synergies such as the characterization of
+sub-Neptune-size planets orbiting solar-type stars, the study of planet
+populations orbiting evolved stars, and the determination of ages of
+intermediate-mass stars hosting directly imaged planets.
+",0,1,0,0,0,0
+12817,The stellar contents and star formation in the NGC 7538 region,"  Deep optical photometric data on the NGC 7538 region were collected and
+combined with archival data sets from $Chandra$, 2MASS and {\it Spitzer}
+surveys in order to generate a new catalog of young stellar objects (YSOs)
+including those not showing IR excess emission. This new catalog is complete
+down to 0.8 M$_\odot$. The nature of the YSOs associated with the NGC 7538
+region and their spatial distribution are used to study the star formation
+process and the resultant mass function (MF) in the region. Out of the 419
+YSOs, $\sim$91\% have ages between 0.1 to 2.5 Myr and $\sim$86\% have masses
+between 0.5 to 3.5 M$_\odot$, as derived by spectral energy distribution
+fitting analysis. Around 24\%, 62\% and 2\% of these YSOs are classified to be
+the Class I, Class II and Class III sources, respectively. The X-ray activity
+in the Class I, Class II and Class III objects is not significantly different
+from each other. This result implies that the enhanced X-ray surface flux due
+to the increase in the rotation rate may be compensated by the decrease in the
+stellar surface area during the pre-main sequence evolution. Our analysis shows
+that the O3V type high mass star `IRS 6' might have triggered the formation of
+young low mass stars up to a radial distance of 3 pc. The MF shows a turn-off
+at around 1.5 M$_\odot$ and the value of its slope `$\Gamma$' in the mass range
+$1.5 <$M/M$_\odot < 6$ comes out to be $-1.76\pm0.24$, which is steeper than
+the Salpeter value.
+",0,1,0,0,0,0
+12818,Ambiguity set and learning via Bregman and Wasserstein,"  Construction of ambiguity set in robust optimization relies on the choice of
+divergences between probability distributions. In distribution learning,
+choosing appropriate probability distributions based on observed data is
+critical for approximating the true distribution. To improve the performance of
+machine learning models, there has recently been interest in designing
+objective functions based on Lp-Wasserstein distance rather than the classical
+Kullback-Leibler (KL) divergence. In this paper, we derive concentration and
+asymptotic results using Bregman divergence. We propose a novel asymmetric
+statistical divergence called Wasserstein-Bregman divergence as a
+generalization of L2-Wasserstein distance. We discuss how these results can be
+applied to the construction of ambiguity set in robust optimization.
+",1,0,0,1,0,0
+12819,1-bit Massive MU-MIMO Precoding in VLSI,"  Massive multiuser (MU) multiple-input multiple-output (MIMO) will be a core
+technology in fifth-generation (5G) wireless systems as it offers significant
+improvements in spectral efficiency compared to existing multi-antenna
+technologies. The presence of hundreds of antenna elements at the base station
+(BS), however, results in excessively high hardware costs and power
+consumption, and requires high interconnect throughput between the
+baseband-processing unit and the radio unit. Massive MU-MIMO that uses
+low-resolution analog-to-digital and digital-to-analog converters (DACs) has
+the potential to address all these issues. In this paper, we focus on downlink
+precoding for massive MU-MIMO systems with 1-bit DACs at the BS. The objective
+is to design precoders that simultaneously mitigate multi-user interference
+(MUI) and quantization artifacts. We propose two nonlinear 1-bit precoding
+algorithms and corresponding very-large scale integration (VLSI) designs. Our
+algorithms rely on biconvex relaxation, which enables the design of efficient
+1-bit precoding algorithms that achieve superior error-rate performance
+compared to that of linear precoding algorithms followed by quantization. To
+showcase the efficacy of our algorithms, we design VLSI architectures that
+enable efficient 1-bit precoding for massive MU-MIMO systems in which hundreds
+of antennas serve tens of user equipments. We present corresponding
+field-programmable gate array (FPGA) implementations to demonstrate that 1-bit
+precoding enables reliable and high-rate downlink data transmission in
+practical systems.
+",1,0,0,0,0,0
+12820,Step evolution in two-dimensional diblock copolymer films,"  The formation and dynamics of free-surface structures, such as steps or
+terraces and their interplay with the phase separation in the bulk are key
+features of diblock copolymer films. We present a phase-field model with an
+obstacle potential which follows naturally from derivations of the
+Ohta-Kawasaki energy functional via self-consistent field theory. The free
+surface of the film is incorporated into the phase-field model by including a
+third phase for the void. The resulting model and its sharp interface limit are
+shown to capture the energetics of films with steps in two dimensions. For this
+model, we then develop a numerical approach that is capable of resolving the
+long-time complex free-surface structures that arise in diblock copolymer
+films.
+",0,1,0,0,0,0
+12821,The univalence axiom in cubical sets,"  In this note we show that Voevodsky's univalence axiom holds in the model of
+type theory based on symmetric cubical sets. We will also discuss Swan's
+construction of the identity type in this variation of cubical sets. This
+proves that we have a model of type theory supporting dependent products,
+dependent sums, univalent universes, and identity types with the usual
+judgmental equality, and this model is formulated in a constructive metatheory.
+",1,0,1,0,0,0
+12822,Assessing the Privacy Cost in Centralized Event-Based Demand Response for Microgrids,"  Demand response (DR) programs have emerged as a potential key enabling
+ingredient in the context of smart grid (SG). Nevertheless, the rising concerns
+over privacy issues raised by customers subscribed to these programs constitute
+a major threat towards their effective deployment and utilization. This has
+driven extensive research to resolve the hindrance confronted, resulting in a
+number of methods being proposed for preserving customers' privacy. While these
+methods provide stringent privacy guarantees, only limited attention has been
+paid to their computational efficiency and performance quality. Under the
+paradigm of differential privacy, this paper initiates a systematic empirical
+study on quantifying the trade-off between privacy and optimality in
+centralized DR systems for maximizing cumulative customer utility. Aiming to
+elucidate the factors governing this trade-off, we analyze the cost of privacy
+in terms of the effect incurred on the objective value of the DR optimization
+problem when applying the employed privacy-preserving strategy based on Laplace
+mechanism. The theoretical results derived from the analysis are complemented
+with empirical findings, corroborated extensively by simulations on a 4-bus MG
+system with up to thousands of customers. By evaluating the impact of privacy,
+this pilot study serves DR practitioners when considering the social and
+economic implications of deploying privacy-preserving DR programs in practice.
+Moreover, it stimulates further research on exploring more efficient approaches
+with bounded performance guarantees for optimizing energy procurement of MGs
+without infringing the privacy of customers on demand side.
+",1,0,1,0,0,0
+12823,Threat Modeling Data Analysis in Socio-technical Systems,"  Our decision-making processes are becoming more data driven, based on data
+from multiple sources, of different types, processed by a variety of
+technologies. As technology becomes more relevant for decision processes, the
+more likely they are to be subjects of attacks aimed at disrupting their
+execution or changing their outcome. With the increasing complexity and
+dependencies on technical components, such attempts grow more sophisticated and
+their impact will be more severe. This is especially important in scenarios
+with shared goals, which had to be previously agreed to, or decisions with
+broad social impact. We need to think about our decisions-making and underlying
+data analysis processes in a systemic way to correctly evaluate benefits and
+risks of specific solutions and to design them to be resistant to attacks. To
+reach these goals, we can apply experiences from threat modeling analysis used
+in software security. We will need to adapt these practices to new types of
+threats, protecting different assets and operating in socio-technical systems.
+With these changes, threat modeling can become a foundation for implementing
+detailed technical, organizational or legal mitigations and making our
+decisions more reliable and trustworthy.
+",1,0,0,0,0,0
+12824,Nonlinear Instability of Half-Solitons on Star Graphs,"  We consider a half-soliton stationary state of the nonlinear Schrodinger
+equation with the power nonlinearity on a star graph consisting of N edges and
+a single vertex. For the subcritical power nonlinearity, the half-soliton state
+is a degenerate critical point of the action functional under the mass
+constraint such that the second variation is nonnegative. By using normal
+forms, we prove that the degenerate critical point is a nonlinear saddle point,
+for which the small perturbations to the half-soliton state grow slowly in time
+resulting in the nonlinear instability of the half-soliton state. The result
+holds for any $N \geq 3$ and arbitrary subcritical power nonlinearity. It gives
+a precise dynamical characterization of the previous result of Adami {\em et
+al.}, where the half-soliton state was shown to be a saddle point of the action
+functional under the mass constraint for $N = 3$ and for cubic nonlinearity.
+",0,1,1,0,0,0
+12825,Demographics and discussion influence views on algorithmic fairness,"  The field of algorithmic fairness has highlighted ethical questions which may
+not have purely technical answers. For example, different algorithmic fairness
+constraints are often impossible to satisfy simultaneously, and choosing
+between them requires value judgments about which people may disagree.
+Achieving consensus on algorithmic fairness will be difficult unless we
+understand why people disagree in the first place. Here we use a series of
+surveys to investigate how two factors affect disagreement: demographics and
+discussion. First, we study whether disagreement on algorithmic fairness
+questions is caused partially by differences in demographic backgrounds. This
+is a question of interest because computer science is demographically
+non-representative. If beliefs about algorithmic fairness correlate with
+demographics, and algorithm designers are demographically non-representative,
+decisions made about algorithmic fairness may not reflect the will of the
+population as a whole. We show, using surveys of three separate populations,
+that there are gender differences in beliefs about algorithmic fairness. For
+example, women are less likely to favor including gender as a feature in an
+algorithm which recommends courses to students if doing so would make female
+students less likely to be recommended science courses. Second, we investigate
+whether people's views on algorithmic fairness can be changed by discussion and
+show, using longitudinal surveys of students in two computer science classes,
+that they can.
+",1,0,0,0,0,0
+12826,The stifness of the supranuclear equation of state (once again),"  We revisit the present status of the stiffness of the supranuclear equations
+of state, particularly the solutions that increase the stiffness in the
+presence of hyperons, the putative transition to a quark matter phase and the
+robustness of massive compact star observations.
+",0,1,0,0,0,0
+12827,Stable Desynchronization for Wireless Sensor Networks: (III) Stability Analysis,"  In this paper, we use dynamical systems to analyze stability of
+desynchronization algorithms at equilibrium. We start by illustrating the
+equilibrium of a dynamic systems and formalizing force components and time
+phases. Then, we use Linear Approximation to obtain Jaconian (J) matrixes which
+are used to find the eigenvalues. Next, we employ the Hirst and Macey theorem
+and Gershgorins theorem to find the bounds of those eigenvalues. Finally, if
+the number of nodes (n) is within such bounds, the systems are stable at
+equilibrium. (This paper is the last part of the series Stable
+Desynchronization for Wireless Sensor Networks - (I) Concepts and Algorithms
+(II) Performance Evaluation (III) Stability Analysis)
+",1,0,0,0,0,0
+12828,Optimizing Adiabatic Quantum Program Compilation using a Graph-Theoretic Framework,"  Adiabatic quantum computing has evolved in recent years from a theoretical
+field into an immensely practical area, a change partially sparked by D-Wave
+System's quantum annealing hardware. These multimillion-dollar quantum
+annealers offer the potential to solve optimization problems millions of times
+faster than classical heuristics, prompting researchers at Google, NASA and
+Lockheed Martin to study how these computers can be applied to complex
+real-world problems such as NASA rover missions. Unfortunately, compiling
+(embedding) an optimization problem into the annealing hardware is itself a
+difficult optimization problem and a major bottleneck currently preventing
+widespread adoption. Additionally, while finding a single embedding is
+difficult, no generalized method is known for tuning embeddings to use minimal
+hardware resources. To address these barriers, we introduce a graph-theoretic
+framework for developing structured embedding algorithms. Using this framework,
+we introduce a biclique virtual hardware layer to provide a simplified
+interface to the physical hardware. Additionally, we exploit bipartite
+structure in quantum programs using odd cycle transversal (OCT) decompositions.
+By coupling an OCT-based embedding algorithm with new, generalized reduction
+methods, we develop a new baseline for embedding a wide range of optimization
+problems into fault-free D-Wave annealing hardware. To encourage the reuse and
+extension of these techniques, we provide an implementation of the framework
+and embedding algorithms.
+",1,0,0,0,0,0
+12829,Analysis of pedestrian behaviors through non-invasive Bluetooth monitoring,"  This paper analyzes pedestrians' behavioral patterns in the pedestrianized
+shopping environment in the historical center of Barcelona, Spain. We employ a
+Bluetooth detection technique to capture a large-scale dataset of pedestrians'
+behavior over a one-month period, including during a key sales period. We
+focused on comparing particular behaviors before, during, and after the
+discount sales by analyzing this large-scale dataset, which is different but
+complementary to the conventionally used small-scale samples. Our results
+uncover pedestrians actively exploring a wider area of the district during a
+discount period compared to weekdays, giving rise to strong underlying mobility
+patterns.
+",1,1,0,0,0,0
+12830,Open Gromov-Witten theory without Obstruction,"  We define Open Gromov-Witten invariants counting psudoholomorphic curves with
+boundary of a fixed Euler characteristic. There is not obstruction in the
+construction of the invariant.
+",0,0,1,0,0,0
+12831,Resilience of Complex Networks,"  This article determines and characterizes the minimal number of actuators
+needed to ensure structural controllability of a linear system under structural
+alterations that can severe the connection between any two states. We assume
+that initially the system is structurally controllable with respect to a given
+set of controls, and propose an efficient system-synthesis mechanism to find
+the minimal number of additional actuators required for resilience of the
+system w.r.t such structural changes. The effectiveness of this approach is
+demonstrated by using standard IEEE power networks.
+",1,0,1,0,0,0
+12832,Forward Thinking: Building and Training Neural Networks One Layer at a Time,"  We present a general framework for training deep neural networks without
+backpropagation. This substantially decreases training time and also allows for
+construction of deep networks with many sorts of learners, including networks
+whose layers are defined by functions that are not easily differentiated, like
+decision trees. The main idea is that layers can be trained one at a time, and
+once they are trained, the input data are mapped forward through the layer to
+create a new learning problem. The process is repeated, transforming the data
+through multiple layers, one at a time, rendering a new data set, which is
+expected to be better behaved, and on which a final output layer can achieve
+good performance. We call this forward thinking and demonstrate a proof of
+concept by achieving state-of-the-art accuracy on the MNIST dataset for
+convolutional neural networks. We also provide a general mathematical
+formulation of forward thinking that allows for other types of deep learning
+problems to be considered.
+",1,0,0,1,0,0
+12833,Non-negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games,"  Multiplayer online battle arena has become a popular game genre. It also
+received increasing attention from our research community because they provide
+a wealth of information about human interactions and behaviors. A major problem
+is extracting meaningful patterns of activity from this type of data, in a way
+that is also easy to interpret. Here, we propose to exploit tensor
+decomposition techniques, and in particular Non-negative Tensor Factorization,
+to discover hidden correlated behavioral patterns of play in a popular game:
+League of Legends. We first collect the entire gaming history of a group of
+about one thousand players, totaling roughly $100K$ matches. By applying our
+methodological framework, we then separate players into groups that exhibit
+similar features and playing strategies, as well as similar temporal
+trajectories, i.e., behavioral progressions over the course of their gaming
+history: this will allow us to investigate how players learn and improve their
+skills.
+",1,1,0,0,0,0
+12834,Graph-Cut RANSAC,"  A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in
+short, is introduced. To separate inliers and outliers, it runs the graph-cut
+algorithm in the local optimization (LO) step which is applied when a
+so-far-the-best model is found. The proposed LO step is conceptually simple,
+easy to implement, globally optimal and efficient. GC-RANSAC is shown
+experimentally, both on synthesized tests and real image pairs, to be more
+geometrically accurate than state-of-the-art methods on a range of problems,
+e.g. line fitting, homography, affine transformation, fundamental and essential
+matrix estimation. It runs in real-time for many problems at a speed
+approximately equal to that of the less accurate alternatives (in milliseconds
+on standard CPU).
+",1,0,0,0,0,0
+12835,Mixed measurements and the detection of phase synchronization in networks,"  Multivariate singular spectrum analysis (M-SSA), with a varimax rotation of
+eigenvectors, was recently proposed to provide detailed information about phase
+synchronization in networks of nonlinear oscillators without any a priori need
+for phase estimation. The discriminatory power of M-SSA is often enhanced by
+using only the time series of the variable that provides the best observability
+of the node dynamics. In practice, however, diverse factors could prevent one
+to have access to this variable in some nodes and other variables should be
+used, resulting in a mixed set of variables. In the present work, the impact of
+this mixed measurement approach on the M-SSA is numerically investigated in
+networks of Rössler systems and cord oscillators. The results are threefold.
+First, a node measured by a poor variable, in terms of observability, becomes
+virtually invisible to the technique. Second, a side effect of using a poor
+variable is that the characterization of phase synchronization clustering of
+the {\it other}\, nodes is hindered by a small amount. This suggests that,
+given a network, synchronization analysis with M-SSA could be more reliable by
+not measuring those nodes that are accessible only through poor variables.
+Third, global phase synchronization could be detected even using only poor
+variables, given enough of them are measured. These insights could be useful in
+defining measurement strategies for both experimental design and real world
+applications for use with M-SSA.
+",0,1,0,0,0,0
+12836,A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip,"  In building intelligent transportation systems such as taxi or rideshare
+services, accurate prediction of travel time and distance is crucial for
+customer experience and resource management. Using the NYC taxi dataset, which
+contains taxi trips data collected from GPS-enabled taxis [23], this paper
+investigates the use of deep neural networks to jointly predict taxi trip time
+and distance. We propose a model, called ST-NN (Spatio-Temporal Neural
+Network), which first predicts the travel distance between an origin and a
+destination GPS coordinate, then combines this prediction with the time of day
+to predict the travel time. The beauty of ST-NN is that it uses only the raw
+trips data without requiring further feature engineering and provides a joint
+estimate of travel time and distance. We compare the performance of ST-NN to
+that of state-of-the-art travel time estimation methods, and we observe that
+the proposed approach generalizes better than state-of-the-art methods. We show
+that ST-NN approach significantly reduces the mean absolute error for both
+predicted travel time and distance, about 17% for travel time prediction. We
+also observe that the proposed approach is more robust to outliers present in
+the dataset by testing the performance of ST-NN on the datasets with and
+without outliers.
+",1,0,0,1,0,0
+12837,Lion and man in non-metric spaces,"  A lion and a man move continuously in a space $X$. The aim of the lion is to
+capture his prey while the man wants to escape forever. Which of them has a
+strategy? This question has been studied for different metric domains. In this
+article we consider the case of general topological spaces.
+",0,0,1,0,0,0
+12838,Banach Algebra of Complex Bounded Radon Measures on Homogeneous Space,"  Let $ H $ be a compact subgroup of a locally compact group $G$. In this paper
+we define a convolution on $ M(G/H) $, the space of all complex bounded Radon
+measures on the homogeneous space G/H. Then we prove that the measure space $
+M(G/H, *) $ is a non-unital Banach algebra that possesses an approximate
+identity. Finally, it is shown that the Banach algebra $ M(G/H, *) $ is not
+involutive and also $ L^1(G/H, *) $ is a two-sided ideal of it.
+",0,0,1,0,0,0
+12839,A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification,"  In the past, Acoustic Scene Classification systems have been based on hand
+crafting audio features that are input to a classifier. Nowadays, the common
+trend is to adopt data driven techniques, e.g., deep learning, where audio
+representations are learned from data. In this paper, we propose a system that
+consists of a simple fusion of two methods of the aforementioned types: a deep
+learning approach where log-scaled mel-spectrograms are input to a
+convolutional neural network, and a feature engineering approach, where a
+collection of hand-crafted features is input to a gradient boosting machine. We
+first show that both methods provide complementary information to some extent.
+Then, we use a simple late fusion strategy to combine both methods. We report
+classification accuracy of each method individually and the combined system on
+the TUT Acoustic Scenes 2017 dataset. The proposed fused system outperforms
+each of the individual methods and attains a classification accuracy of 72.8%
+on the evaluation set, improving the baseline system by 11.8%.
+",0,0,0,1,0,0
+12840,Online Convex Optimization with Unconstrained Domains and Losses,"  We propose an online convex optimization algorithm (RescaledExp) that
+achieves optimal regret in the unconstrained setting without prior knowledge of
+any bounds on the loss functions. We prove a lower bound showing an exponential
+separation between the regret of existing algorithms that require a known bound
+on the loss functions and any algorithm that does not require such knowledge.
+RescaledExp matches this lower bound asymptotically in the number of
+iterations. RescaledExp is naturally hyperparameter-free and we demonstrate
+empirically that it matches prior optimization algorithms that require
+hyperparameter optimization.
+",1,0,0,1,0,0
+12841,A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM,"  This paper presents a feature encoding method of complex 3D objects for
+high-level semantic features. Recent approaches to object recognition methods
+become important for semantic simultaneous localization and mapping (SLAM).
+However, there is a lack of consideration of the probabilistic observation
+model for 3D objects, as the shape of a 3D object basically follows a complex
+probability distribution. Furthermore, since the mobile robot equipped with a
+range sensor observes only a single view, much information of the object shape
+is discarded. These limitations are the major obstacles to semantic SLAM and
+view-independent loop closure using 3D object shapes as features. In order to
+enable the numerical analysis for the Bayesian inference, we approximate the
+true observation model of 3D objects to tractable distributions. Since the
+observation likelihood can be obtained from the generative model, we formulate
+the true generative model for 3D object with the Bayesian networks. To capture
+these complex distributions, we apply a variational auto-encoder. To analyze
+the approximated distributions and encoded features, we perform classification
+with maximum likelihood estimation and shape retrieval.
+",1,0,0,0,0,0
+12842,Dimensionality reduction methods for molecular simulations,"  Molecular simulations produce very high-dimensional data-sets with millions
+of data points. As analysis methods are often unable to cope with so many
+dimensions, it is common to use dimensionality reduction and clustering methods
+to reach a reduced representation of the data. Yet these methods often fail to
+capture the most important features necessary for the construction of a Markov
+model. Here we demonstrate the results of various dimensionality reduction
+methods on two simulation data-sets, one of protein folding and another of
+protein-ligand binding. The methods tested include a k-means clustering
+variant, a non-linear auto encoder, principal component analysis and tICA. The
+dimension-reduced data is then used to estimate the implied timescales of the
+slowest process by a Markov state model analysis to assess the quality of the
+projection. The projected dimensions learned from the data are visualized to
+demonstrate which conformations the various methods choose to represent the
+molecular process.
+",1,0,0,1,0,0
+12843,Exploration--Exploitation in MDPs with Options,"  While a large body of empirical results show that temporally-extended actions
+and options may significantly affect the learning performance of an agent, the
+theoretical understanding of how and when options can be beneficial in online
+reinforcement learning is relatively limited. In this paper, we derive an upper
+and lower bound on the regret of a variant of UCRL using options. While we
+first analyze the algorithm in the general case of semi-Markov decision
+processes (SMDPs), we show how these results can be translated to the specific
+case of MDPs with options and we illustrate simple scenarios in which the
+regret of learning with options can be \textit{provably} much smaller than the
+regret suffered when learning with primitive actions.
+",1,0,0,1,0,0
+12844,Pressure induced spin crossover in disordered α-LiFeO2,"  Structural, magnetic and electrical-transport properties of {\alpha}-LiFeO2,
+crystallizing in the rock salt structure with random distribution of Li and Fe
+ions, have been studied by synchrotron X-ray diffraction, 57Fe Mössbauer
+spectroscopy and electrical resistance measurements at pressures up to 100 GPa
+using diamond anvil cells. It was found that the crystal structure is stable at
+least to 82 GPa, though a significant change in compressibility has been
+observed above 50 GPa. The changes in the structural properties are found to be
+on a par with a sluggish Fe3+ high- to low-spin (HS-LS) transition (S=5/2 to
+S=1/2) starting at 50 GPa and not completed even at ~100 GPa. The HS-LS
+transition is accompanied by an appreciable resistance decrease remaining a
+semiconductor up to 115 GPa and is not expected to be metallic even at about
+200 GPa. The observed feature of the pressure-induced HS-LS transition is not
+an ordinary behavior of ferric oxides at high pressures. The effect of Fe3+
+nearest and next nearest neighbors on the features of the spin crossover is
+discussed.
+",0,1,0,0,0,0
+12845,Central Limit Theorem for empirical transportation cost in general dimension,"  We consider the problem of optimal transportation with quadratic cost between
+a empirical measure and a general target probability on R d , with d $\ge$ 1.
+We provide new results on the uniqueness and stability of the associated
+optimal transportation potentials , namely, the minimizers in the dual
+formulation of the optimal transportation problem. As a consequence, we show
+that a CLT holds for the empirical transportation cost under mild moment and
+smoothness requirements. The limiting distributions are Gaussian and admit a
+simple description in terms of the optimal transportation potentials.
+",0,0,1,1,0,0
+12846,Characterization of temperatures associated to Schrödinger operators with initial data in Morrey spaces,"  Let $\mathcal{L}$ be a Schrödinger operator of the form $\mathcal{L} =
+-\Delta+V$ acting on $L^2(\mathbb R^n)$ where the nonnegative potential $V$
+belongs to the reverse Hölder class $B_q$ for some $q\geq n.$ Let
+$L^{p,\lambda}(\mathbb{R}^{n})$, $0\le \lambda<n$ denote the Morrey space on
+$\mathbb{R}^{n}$. In this paper, we will show that a function $f\in
+L^{2,\lambda}(\mathbb{R}^{n})$ is the trace of the solution of ${\mathbb
+L}u=u_{t}+{\mathcal{L}}u=0, u(x,0)= f(x),$ where $u$ satisfies a Carleson-type
+condition \begin{eqnarray*} \sup_{x_B, r_B}
+r_B^{-\lambda}\int_0^{r_B^2}\int_{B(x_B, r_B)} |\nabla u(x,t)|^2 {dx dt} \leq C
+<\infty. \end{eqnarray*} Conversely, this Carleson-type condition characterizes
+all the ${\mathbb L}$-carolic functions whose traces belong to the Morrey space
+$L^{2,\lambda}(\mathbb{R}^{n})$ for all $0\le \lambda<n$. This result extends
+the analogous characterization founded by Fabes and Neri for the classical BMO
+space of John and Nirenberg.
+",0,0,1,0,0,0
+12847,Dynamic-sensitive cooperation in the presence of multiple strategy updating rules,"  The importance of microscopic details on cooperation level is an intensively
+studied aspect of evolutionary game theory. Interestingly, these details become
+crucial on heterogeneous populations where individuals may possess diverse
+traits. By introducing a coevolutionary model in which not only strategies but
+also individual dynamical features may evolve we revealed that the formerly
+established conclusion is not necessarily true when different updating rules
+are on stage. In particular, we apply two strategy updating rules, imitation
+and Death-Birth rule, which allow local selection in a spatial system. Our
+observation highlights that the microscopic feature of dynamics, like the level
+of learning activity, could be a fundamental factor even if all players share
+the same trait uniformly.
+",0,0,0,0,1,0
+12848,Inference in Graphical Models via Semidefinite Programming Hierarchies,"  Maximum A posteriori Probability (MAP) inference in graphical models amounts
+to solving a graph-structured combinatorial optimization problem. Popular
+inference algorithms such as belief propagation (BP) and generalized belief
+propagation (GBP) are intimately related to linear programming (LP) relaxation
+within the Sherali-Adams hierarchy. Despite the popularity of these algorithms,
+it is well understood that the Sum-of-Squares (SOS) hierarchy based on
+semidefinite programming (SDP) can provide superior guarantees. Unfortunately,
+SOS relaxations for a graph with $n$ vertices require solving an SDP with
+$n^{\Theta(d)}$ variables where $d$ is the degree in the hierarchy. In
+practice, for $d\ge 4$, this approach does not scale beyond a few tens of
+variables. In this paper, we propose binary SDP relaxations for MAP inference
+using the SOS hierarchy with two innovations focused on computational
+efficiency. Firstly, in analogy to BP and its variants, we only introduce
+decision variables corresponding to contiguous regions in the graphical model.
+Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style
+method, and develop a sequential rounding procedure. We demonstrate that the
+resulting algorithm can solve problems with tens of thousands of variables
+within minutes, and outperforms BP and GBP on practical problems such as image
+denoising and Ising spin glasses. Finally, for specific graph types, we
+establish a sufficient condition for the tightness of the proposed partial SOS
+relaxation.
+",1,0,0,1,0,0
+12849,A Hybrid Approach using Ontology Similarity and Fuzzy Logic for Semantic Question Answering,"  One of the challenges in information retrieval is providing accurate answers
+to a user's question often expressed as uncertainty words. Most answers are
+based on a Syntactic approach rather than a Semantic analysis of the query. In
+this paper, our objective is to present a hybrid approach for a Semantic
+question answering retrieval system using Ontology Similarity and Fuzzy logic.
+We use a Fuzzy Co-clustering algorithm to retrieve the collection of documents
+based on Ontology Similarity. The Fuzzy Scale uses Fuzzy type-1 for documents
+and Fuzzy type-2 for words to prioritize answers. The objective of this work is
+to provide retrieval system with more accurate answers than non-fuzzy Semantic
+Ontology approach.
+",1,0,0,0,0,0
+12850,Scalable multimodal convolutional networks for brain tumour segmentation,"  Brain tumour segmentation plays a key role in computer-assisted surgery. Deep
+neural networks have increased the accuracy of automatic segmentation
+significantly, however these models tend to generalise poorly to different
+imaging modalities than those for which they have been designed, thereby
+limiting their applications. For example, a network architecture initially
+designed for brain parcellation of monomodal T1 MRI can not be easily
+translated into an efficient tumour segmentation network that jointly utilises
+T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable
+multimodal deep learning architecture using new nested structures that
+explicitly leverage deep features within or across modalities. This aims at
+making the early layers of the architecture structured and sparse so that the
+final architecture becomes scalable to the number of modalities. We evaluate
+the scalable architecture for brain tumour segmentation and give evidence of
+its regularisation effect compared to the conventional concatenation approach.
+",1,0,0,0,0,0
+12851,Fermionic projected entangled-pair states and topological phases,"  We study fermionic matrix product operator algebras and identify the
+associated algebraic data. Using this algebraic data we construct fermionic
+tensor network states in two dimensions that have non-trivial
+symmetry-protected or intrinsic topological order. The tensor network states
+allow us to relate physical properties of the topological phases to the
+underlying algebraic data. We illustrate this by calculating defect properties
+and modular matrices of supercohomology phases. Our formalism also captures
+Majorana defects as we show explicitly for a class of $\mathbb{Z}_2$
+symmetry-protected and intrinsic topological phases. The tensor networks states
+presented here are well-suited for numerical applications and hence open up new
+possibilities for studying interacting fermionic topological phases.
+",0,1,0,0,0,0
+12852,Exact collisional moments for plasma fluid theories,"  The velocity-space moments of the often troublesome nonlinear Landau
+collision operator are expressed exactly in terms of multi-index
+Hermite-polynomial moments of the distribution functions. The collisional
+moments are shown to be generated by derivatives of two well-known functions,
+namely the Rosenbluth-MacDonald-Judd-Trubnikov potentials for a Gaussian
+distribution. The resulting formula has a nonlinear dependency on the relative
+mean flow of the colliding species normalised to the root-mean-square of the
+corresponding thermal velocities, and a bilinear dependency on densities and
+higher-order velocity moments of the distribution functions, with no
+restriction on temperature, flow or mass ratio of the species. The result can
+be applied to both the classic transport theory of plasmas, that relies on the
+Chapman-Enskog method, as well as to deriving collisional fluid equations that
+follow Grad's moment approach. As an illustrative example, we provide the
+collisional ten-moment equations with exact conservation laws for momentum- and
+energy-transfer rate.
+",0,1,1,0,0,0
+12853,What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis,"  Machine learning (ML) techniques such as (deep) artificial neural networks
+(DNN) are solving very successfully a plethora of tasks and provide new
+predictive models for complex physical, chemical, biological and social
+systems. However, in most cases this comes with the disadvantage of acting as a
+black box, rarely providing information about what made them arrive at a
+particular prediction. This black box aspect of ML techniques can be
+problematic especially in medical diagnoses, so far hampering a clinical
+acceptance. The present paper studies the uniqueness of individual gait
+patterns in clinical biomechanics using DNNs. By attributing portions of the
+model predictions back to the input variables (ground reaction forces and
+full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique
+reliably demonstrates which variables at what time windows of the gait cycle
+are most relevant for the characterisation of gait patterns from a certain
+individual. By measuring the timeresolved contribution of each input variable
+to the prediction of ML techniques such as DNNs, our method describes the first
+general framework that enables to understand and interpret non-linear ML
+methods in (biomechanical) gait analysis and thereby supplies a powerful tool
+for analysis, diagnosis and treatment of human gait.
+",0,0,0,1,0,0
+12854,On the ground state of spiking network activity in mammalian cortex,"  Electrophysiological recordings of spiking activity are limited to a small
+number of neurons. This spatial subsampling has hindered characterizing even
+most basic properties of collective spiking in cortical networks. In
+particular, two contradictory hypotheses prevailed for over a decade: the first
+proposed an asynchronous irregular state, the second a critical state. While
+distinguishing them is straightforward in models, we show that in experiments
+classical approaches fail to correctly infer network dynamics because of
+subsampling. Deploying a novel, subsampling-invariant estimator, we find that
+in vivo dynamics do not comply with either hypothesis, but instead occupy a
+narrow ""reverberating"" state consistently across multiple mammalian species and
+cortical areas. A generic model tuned to this reverberating state predicts
+single neuron, pairwise, and population properties. With these predictions we
+first validate the model and then deduce network properties that are
+challenging to obtain experimentally, like the network timescale and strength
+of cortical input.
+",0,0,0,1,1,0
+12855,Inferring Information Flow in Spike-train Data Sets using a Trial-Shuffle Method,"  Understanding information processing in the brain requires the ability to
+determine the functional connectivity between the different regions of the
+brain. We present a method using transfer entropy to extract this flow of
+information between brain regions from spike-train data commonly obtained in
+neurological experiments. Transfer entropy is a statistical measure based in
+information theory that attempts to quantify the information flow from one
+process to another, and has been applied to find connectivity in simulated
+spike-train data. Due to statistical error in the estimator, inferring
+functional connectivity requires a method for determining significance in the
+transfer entropy values. We discuss the issues with numerical estimation of
+transfer entropy and resulting challenges in determining significance before
+presenting the trial-shuffle method as a viable option. The trial-shuffle
+method, for spike-train data that is split into multiple trials, determines
+significant transfer entropy values independently for each individual pair of
+neurons by comparing to a created baseline distribution using a rigorous
+statistical test. This is in contrast to either globally comparing all neuron
+transfer entropy values or comparing pairwise values to a single baseline
+value.
+In establishing the viability of this method by comparison to several
+alternative approaches in the literature, we find evidence that preserving the
+inter-spike-interval timing is important.
+We then use the trial-shuffle method to investigate information flow within a
+model network as we vary model parameters. This includes investigating the
+global flow of information within a connectivity network divided into two
+well-connected subnetworks, going beyond local transfer of information between
+pairs of neurons.
+",0,0,0,0,1,0
+12856,Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data,"  How does one find dimensions in multivariate data that are reliably expressed
+across repetitions? For example, in a brain imaging study one may want to
+identify combinations of neural signals that are reliably expressed across
+multiple trials or subjects. For a behavioral assessment with multiple ratings,
+one may want to identify an aggregate score that is reliably reproduced across
+raters. Correlated Components Analysis (CorrCA) addresses this problem by
+identifying components that are maximally correlated between repetitions (e.g.
+trials, subjects, raters). Here we formalize this as the maximization of the
+ratio of between-repetition to within-repetition covariance. We show that this
+criterion maximizes repeat-reliability, defined as mean over variance across
+repeats, and that it leads to CorrCA or to multi-set Canonical Correlation
+Analysis, depending on the constraints. Surprisingly, we also find that CorrCA
+is equivalent to Linear Discriminant Analysis for zero-mean signals, which
+provides an unexpected link between classic concepts of multivariate analysis.
+We present an exact parametric test of statistical significance based on the
+F-statistic for normally distributed independent samples, and present and
+validate shuffle statistics for the case of dependent samples. Regularization
+and extension to non-linear mappings using kernels are also presented. The
+algorithms are demonstrated on a series of data analysis applications, and we
+provide all code and data required to reproduce the results.
+",0,0,0,1,0,0
+12857,Defining and estimating stochastic rate change in a dynamic general insurance portfolio,"  Rate change calculations in the literature involve deterministic methods that
+measure the change in premium for a given policy. The definition of rate change
+as a statistical parameter is proposed to address the stochastic nature of the
+premium charged for a policy. It promotes the idea that rate change is a
+property of an asymptotic population to be estimated, not just a property to
+measure or monitor in the sample of observed policies that are written. Various
+models and techniques are given for estimating this stochastic rate change and
+quantifying the uncertainty in the estimates. The use of matched sampling is
+emphasized for rate change estimation, as it adjusts for changes in policy
+characteristics by directly searching for similar policies across policy years.
+This avoids any of the assumptions and recipes that are required to re-rate
+policies in years where they were not written, as is common with deterministic
+methods. Such procedures can be subjective or implausible if the structure of
+rating algorithms change or there are complex and heterogeneous exposure bases
+and coverages. The methods discussed are applied to a motor premium database.
+The application includes the use of a genetic algorithm with parallel
+computations to automatically optimize the matched sampling.
+",0,0,0,0,0,1
+12858,Teacher Improves Learning by Selecting a Training Subset,"  We call a learner super-teachable if a teacher can trim down an iid training
+set while making the learner learn even better. We provide sharp super-teaching
+guarantees on two learners: the maximum likelihood estimator for the mean of a
+Gaussian, and the large margin classifier in 1D. For general learners, we
+provide a mixed-integer nonlinear programming-based algorithm to find a super
+teaching set. Empirical experiments show that our algorithm is able to find
+good super-teaching sets for both regression and classification problems.
+",0,0,0,1,0,0
+12859,Photospheric Emission of Gamma-Ray Bursts,"  We review the physics of GRB production by relativistic jets that start
+highly opaque near the central source and then expand to transparency. We
+discuss dissipative and radiative processes in the jet and how radiative
+transfer shapes the observed nonthermal spectrum released at the photosphere. A
+comparison of recent detailed models with observations gives estimates for
+important parameters of GRB jets, such as the Lorentz factor and magnetization.
+We also discuss predictions for GRB polarization and neutrino emission.
+",0,1,0,0,0,0
+12860,GPU acceleration and performance of the particle-beam-dynamics code Elegant,"  Elegant is an accelerator physics and particle-beam dynamics code widely used
+for modeling and design of a variety of high-energy particle accelerators and
+accelerator-based systems. In this paper we discuss a recently developed
+version of the code that can take advantage of CUDA-enabled graphics processing
+units (GPUs) to achieve significantly improved performance for a large class of
+simulations that are important in practice. The GPU version is largely defined
+by a framework that simplifies implementations of the fundamental kernel types
+that are used by Elegant: particle operations, reductions, particle loss,
+histograms, array convolutions and random number generation. Accelerated
+performance on the Titan Cray XK-7 supercomputer is approximately 6-10 times
+better with the GPU than all the CPU cores associated with the same node count.
+In addition to performance, the maintainability of the GPU-accelerated version
+of the code was considered a key design objective. Accuracy with respect to the
+CPU implementation is also a core consideration. Four different methods are
+used to ensure that the accelerated code faithfully reproduces the CPU results.
+",0,1,0,0,0,0
+12861,Short-distance breakdown of the Higgs mechanism and the robustness of the BCS theory for charged superconductors,"  Through the Higgs mechanism, the long-range Coulomb interaction eliminates
+the low-energy Goldstone phase mode in superconductors and transfers spectral
+weight all the way up to the plasma frequency. Here we show that the Higgs
+mechanism breaks down for length scales shorter than the superconducting
+coherence length while it stays intact, even at high energies, in the
+long-wavelength limit. This effect is a consequence of the composite nature of
+the Higgs field of superconductivity and the broken Lorentz invariance in a
+solid. Most importantly, the breakdown of the Higgs mechanism inside the
+superconducting coherence volume is crucial to ensure the stability of the BCS
+mean-field theory in the weak-coupling limit. We also show that changes in the
+gap equation due to plasmon-induced fluctuations can lead to significant
+corrections to the mean-field theory and reveal that changes in the
+density-fluctuation spectrum of a superconductor are not limited to the
+vicinity of the gap.
+",0,1,0,0,0,0
+12862,The Massey's method for sport rating: a network science perspective,"  We revisit the Massey's method for rating and ranking in sports and
+contextualize it as a general centrality measure in network science.
+",1,1,0,0,0,0
+12863,Pharmacokinetics Simulations for Studying Correlates of Prevention Efficacy of Passive HIV-1 Antibody Prophylaxis in the Antibody Mediated Prevention (AMP) Study,"  A key objective in two phase 2b AMP clinical trials of VRC01 is to evaluate
+whether drug concentration over time, as estimated by non-linear mixed effects
+pharmacokinetics (PK) models, is associated with HIV infection rate. We
+conducted a simulation study of marker sampling designs, and evaluated the
+effect of study adherence and sub-cohort sample size on PK model estimates in
+multiple-dose studies. With m=120, even under low adherence (about half of
+study visits missing per participant), reasonably unbiased and consistent
+estimates of most fixed and random effect terms were obtained. Coarsened marker
+sampling schedules were also studied.
+",0,0,0,1,1,0
+12864,Magellan/M2FS Spectroscopy of Galaxy Clusters: Stellar Population Model and Application to Abell 267,"  We report the results of a pilot program to use the Magellan/M2FS
+spectrograph to survey the galactic populations and internal kinematics of
+galaxy clusters. For this initial study, we present spectroscopic measurements
+for $223$ quiescent galaxies observed along the line of sight to the galaxy
+cluster Abell 267 ($z\sim0.23$). We develop a Bayesian method for modeling the
+integrated light from each galaxy as a simple stellar population, with free
+parameters that specify redshift ($v_\mathrm{los}/c$) and characteristic age,
+metallicity ($\mathrm{[Fe/H]}$), alpha-abundance ($[\alpha/\mathrm{Fe}]$), and
+internal velocity dispersion ($\sigma_\mathrm{int}$) for individual galaxies.
+Parameter estimates derived from our 1.5-hour observation of A267 have median
+random errors of $\sigma_{v_\mathrm{los}}=20\ \mathrm{km\ s^{-1}}$,
+$\sigma_{\mathrm{Age}}=1.2\ \mathrm{Gyr}$, $\sigma_{\mathrm{[Fe/H]}}=0.11\
+\mathrm{dex}$, $\sigma_{[\alpha/\mathrm{Fe}]}=0.07\ \mathrm{dex}$, and
+$\sigma_{\sigma_\mathrm{int}}=20\ \mathrm{km\ s^{-1}}$. In a companion paper,
+we use these results to model the structure and internal kinematics of A267.
+",0,1,0,0,0,0
+12865,Parametric Decay Instability and Dissipation of Low-frequency Alfvén Waves in Low-beta Turbulent Plasmas,"  Evolution of the parametric decay instability (PDI) of a circularly polarized
+Aflvén wave in a turbulent low-beta plasma background is investigated using
+3D hybrid simulations. It is shown that the turbulence reduces the growth rate
+of PDI as compared to the linear theory predictions, but PDI can still exist.
+Interestingly, the damping rate of ion acoustic mode (as the product of PDI) is
+also reduced as compared to the linear Vlasov predictions. Nonetheless,
+significant heating of ions in the direction parallel to the background
+magnetic field is observed due to resonant Landau damping of the ion acoustic
+waves. In low-beta turbulent plasmas, PDI can provide an important channel for
+energy dissipation of low-frequency Alfvén waves at a scale much larger than
+the ion kinetic scales, different from the traditional turbulence dissipation
+models.
+",0,1,0,0,0,0
+12866,Generalised model-independent characterisation of strong gravitational lenses II: Transformation matrix between multiple images,"  (shortened) We determine the transformation matrix T that maps multiple
+images with resolved features onto one another and that is based on a
+Taylor-expanded lensing potential close to a point on the critical curve within
+our model-independent lens characterisation approach. From T, the same
+information about the critical curve at fold and cusp points is derived as
+determined by the quadrupole moment of the individual images as observables. In
+addition, we read off the relative parities between the images, so that the
+parity of all images is determined, when one is known. We compare all
+retrievable ratios of potential derivatives to the actual ones and to those
+obtained by using the quadrupole moment as observable for two and three image
+configurations generated by a galaxy-cluster scale singular isothermal ellipse.
+We conclude that using the quadrupole moments as observables, the properties of
+the critical curve at the cusp points are retrieved to higher accuracy, at the
+fold points to lower accuracy, and the ratios of second order potential
+derivatives to comparable accuracy. We show that the approach using ratios of
+convergences and reduced shear is equivalent to ours close to the critical
+curve but yields more accurate results and is more robust because it does not
+require a special coordinate system like the approach using potential
+derivatives. T is determined by mapping manually assigned reference points in
+the images onto each other. If the assignment of reference points is subject to
+measurement uncertainties under noise, we find that the confidence intervals of
+the lens parameters can be as large as the values, when the uncertainties are
+larger than one pixel. Observed multiple images with resolved features are more
+extended than unresolved ones, so that higher order moments should be taken
+into account to improve the reconstruction.
+",0,1,0,0,0,0
+12867,"Properties of interaction networks, structure coefficients, and benefit-to-cost ratios","  In structured populations the spatial arrangement of cooperators and
+defectors on the interaction graph together with the structure of the graph
+itself determines the game dynamics and particularly whether or not fixation of
+cooperation (or defection) is favored. For a single cooperator (and a single
+defector) and a network described by a regular graph the question of fixation
+can be addressed by a single parameter, the structure coefficient. As this
+quantity is generic for any regular graph, we may call it the generic structure
+coefficient. For two and more cooperators (or several defectors) fixation
+properties can also be assigned by structure coefficients. These structure
+coefficients, however, depend on the arrangement of cooperators and defectors
+which we may interpret as a configuration of the game. Moreover, the
+coefficients are specific for a given interaction network modeled as regular
+graph, which is why we may call them specific structure coefficients. In this
+paper, we study how specific structure coefficients vary over interaction
+graphs and link the distributions obtained over different graphs to spectral
+properties of interaction networks. We also discuss implications for the
+benefit-to-cost ratios of donation games.
+",0,0,0,0,1,0
+12868,Optimized Household Demand Management with Local Solar PV Generation,"  Demand Side Management (DSM) strategies are of-ten associated with the
+objectives of smoothing the load curve and reducing peak load. Although the
+future of demand side manage-ment is technically dependent on remote and
+automatic control of residential loads, the end-users play a significant role
+by shifting the use of appliances to the off-peak hours when they are exposed
+to Day-ahead market price. This paper proposes an optimum so-lution to the
+problem of scheduling of household demand side management in the presence of PV
+generation under a set of tech-nical constraints such as dynamic electricity
+pricing and voltage deviation. The proposed solution is implemented based on
+the Clonal Selection Algorithm (CSA). This solution is evaluated through a set
+of scenarios and simulation results show that the proposed approach results in
+the reduction of electricity bills and the import of energy from the grid.
+",1,0,0,0,0,0
+12869,Interaction-Based Distributed Learning in Cyber-Physical and Social Networks,"  In this paper we consider a network scenario in which agents can evaluate
+each other according to a score graph that models some physical or social
+interaction. The goal is to design a distributed protocol, run by the agents,
+allowing them to learn their unknown state among a finite set of possible
+values. We propose a Bayesian framework in which scores and states are
+associated to probabilistic events with unknown parameters and hyperparameters
+respectively. We prove that each agent can learn its state by means of a local
+Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of
+the parameter-hyperparameter that combines plain ML and Empirical Bayes
+approaches. By using tools from graphical models, which allow us to gain
+insight on conditional dependences of scores and states, we provide two relaxed
+probabilistic models that ultimately lead to ML parameter-hyperparameter
+estimators amenable to distributed computation. In order to highlight the
+appropriateness of the proposed relaxations, we demonstrate the distributed
+estimators on a machine-to-machine testing set-up for anomaly detection and on
+a social interaction set-up for user profiling.
+",1,0,1,1,0,0
+12870,EC3: Combining Clustering and Classification for Ensemble Learning,"  Classification and clustering algorithms have been proved to be successful
+individually in different contexts. Both of them have their own advantages and
+limitations. For instance, although classification algorithms are more powerful
+than clustering methods in predicting class labels of objects, they do not
+perform well when there is a lack of sufficient manually labeled reliable data.
+On the other hand, although clustering algorithms do not produce label
+information for objects, they provide supplementary constraints (e.g., if two
+objects are clustered together, it is more likely that the same label is
+assigned to both of them) that one can leverage for label prediction of a set
+of unknown objects. Therefore, systematic utilization of both these types of
+algorithms together can lead to better prediction performance. In this paper,
+We propose a novel algorithm, called EC3 that merges classification and
+clustering together in order to support both binary and multi-class
+classification. EC3 is based on a principled combination of multiple
+classification and multiple clustering methods using an optimization function.
+We theoretically show the convexity and optimality of the problem and solve it
+by block coordinate descent method. We additionally propose iEC3, a variant of
+EC3 that handles imbalanced training data. We perform an extensive experimental
+analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known
+standalone classifiers, 5 ensemble classifiers, and 2 existing methods that
+merge classification and clustering) on 13 standard benchmark datasets. We show
+that our methods outperform other baselines for every single dataset, achieving
+at most 10% higher AUC. Moreover our methods are faster (1.21 times faster than
+the best baseline), more resilient to noise and class imbalance than the best
+baseline method.
+",1,0,0,1,0,0
+12871,A review of possible effects of cognitive biases on interpretation of rule-based machine learning models,"  This paper investigates to what extent cognitive biases may affect human
+understanding of interpretable machine learning models, in particular of rules
+discovered from data. Twenty cognitive biases are covered, as are possible
+debiasing techniques that can be adopted by designers of machine learning
+algorithms and software. Our review transfers results obtained in cognitive
+psychology to the domain of machine learning, aiming to bridge the current gap
+between these two areas. It needs to be followed by empirical studies
+specifically aimed at the machine learning domain.
+",0,0,0,1,0,0
+12872,High-Tc superconductivity up to 55 K under high pressure in the heavily electron doped Lix(NH3)yFe2Se2 single crystal,"  We report a high-pressure study on the heavily electron doped Lix(NH3)yFe2Se2
+single crystal by using the cubic anvil cell apparatus. The superconducting
+transition temperature Tc = 44 K at ambient pressure is first suppressed to
+below 20 K upon increasing pressure to Pc = 2 GPa, above which the pressure
+dependence of Tc(P) reverses and Tc increases steadily to ca. 55 K at 11 GPa.
+These results thus evidenced a pressure-induced second high-Tc superconducting
+(SC-II) phase in Lix(NH3)yFe2Se2 with the highest Tcmax = 55K among the
+FeSe-based bulk materials. Hall data confirm that in the emergent SC-II phase
+the dominant electron-type carrier density undergoes a fourfold enhancement and
+tracks the same trend as Tc(P). Interesting, we find a nearly parallel scaling
+behavior between Tc and the inverse Hall coefficient for the SC-II phases of
+both Lix(NH3)yFe2Se2 and (Li,Fe)OHFeSe. The present work demonstrates that high
+pressure offers a distinctive means to further raising the maximum Tc of
+heavily electron doped FeSe-based materials by increasing the effective charge
+carrier concentration via a plausible Fermi surface reconstruction at Pc.
+",0,1,0,0,0,0
+12873,Existence theorems for a nonlinear second-order distributional differential equation,"  In this work, we are concerned with existence of solutions for a nonlinear
+second-order distributional differential equation, which contains measure
+differential equations and stochastic differential equations as special cases.
+The proof is based on the Leray--Schauder nonlinear alternative and
+Kurzweil--Henstock--Stieltjes integrals. Meanwhile, examples are worked out to
+demonstrate that the main results are sharp.
+",0,0,1,0,0,0
+12874,Spatial coherence measurement and partially coherent diffractive imaging,"  The complete characterization of spatial coherence is difficult because the
+mutual coherence function is a complex-valued function of four independent
+variables. This difficulty limits the ability of controlling and optimizing
+spatial coherence in a broad range of key applications. Here we propose a
+method for measuring the complete mutual coherence function, which does not
+require any prior knowledge and can be scaled to measure arbitrary coherence
+properties for any wavelength. Our method can also be used to retrieve objects
+illuminated by partially coherent beam with unknown coherence properties. This
+study is particularly useful for coherent diffractive imaging of nanoscale
+structures in the X-ray or electron regime. Our method is not limited by any
+assumption about the illumination and hence lays the foundation for a branch of
+new diffractive imaging algorithms.
+",0,1,0,0,0,0
+12875,SPECTRE: Seedless Network Alignment via Spectral Centralities,"  Network alignment consists of finding a correspondence between the nodes of
+two networks. From aligning proteins in computational biology, to
+de-anonymization of social networks, to recognition tasks in computer vision,
+this problem has applications in many diverse areas. The current approaches to
+network alignment mostly focus on the case where prior information is
+available, either in the form of a seed set of correctly-matched nodes or
+attributes on the nodes and/or edges. Moreover, those approaches which assume
+no such prior information tend to be computationally expensive and do not scale
+to large-scale networks. However, many real-world networks are very large in
+size, and prior information can be expensive, if not impossible, to obtain. In
+this paper we introduce SPECTRE, a scalable, accurate algorithm able to solve
+the network alignment problem with no prior information. SPECTRE makes use of
+spectral centrality measures and percolation techniques to robustly align nodes
+across networks, even if those networks exhibit only moderate correlation.
+Through extensive numerical experiments, we show that SPECTRE is able to
+recover high-accuracy alignments on both synthetic and real-world networks, and
+outperforms other algorithms in the seedless case.
+",1,0,0,0,0,0
+12876,An efficient genetic algorithm for large-scale planning of robust industrial wireless networks,"  An industrial indoor environment is harsh for wireless communications
+compared to an office environment, because the prevalent metal easily causes
+shadowing effects and affects the availability of an industrial wireless local
+area network (IWLAN). On the one hand, it is costly, time-consuming, and
+ineffective to perform trial-and-error manual deployment of wireless nodes. On
+the other hand, the existing wireless planning tools only focus on office
+environments such that it is hard to plan IWLANs due to the larger problem size
+and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh
+industrial indoor environments. To fill this gap, this paper proposes an
+overdimensioning model and a genetic algorithm based over-dimensioning (GAOD)
+algorithm for deploying large-scale robust IWLANs. As a progress beyond the
+state-of-the-art wireless planning, two full coverage layers are created. The
+second coverage layer serves as redundancy in case of shadowing. Meanwhile, the
+deployment cost is reduced by minimizing the number of access points (APs); the
+hard constraint of minimal inter-AP spatial paration avoids multiple APs
+covering the same area to be simultaneously shadowed by the same obstacle. The
+computation time and occupied memory are dedicatedly considered in the design
+of GAOD for large-scale optimization. A greedy heuristic based
+over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as
+benchmarks. In two vehicle manufacturers with a small and large indoor
+environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD
+outputted up to 25% less APs than a random OD algorithm. Furthermore, the
+effectiveness of this model and GAOD was experimentally validated with a real
+deployment system.
+",1,0,0,0,0,0
+12877,Feature selection algorithm based on Catastrophe model to improve the performance of regression analysis,"  In this paper we introduce a new feature selection algorithm to remove the
+irrelevant or redundant features in the data sets. In this algorithm the
+importance of a feature is based on its fitting to the Catastrophe model.
+Akaike information crite- rion value is used for ranking the features in the
+data set. The proposed algorithm is compared with well-known RELIEF feature
+selection algorithm. Breast Cancer, Parkinson Telemonitoring data and Slice
+locality data sets are used to evaluate the model.
+",1,0,0,1,0,0
+12878,Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model,"  This paper describes a neural-network model which performed competitively
+(top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS)
+task. Our system employs an attention-based recurrent neural network model that
+optimizes the sentence similarity. In this paper, we describe our participation
+in the multilingual STS task which measures similarity across English, Spanish,
+and Arabic.
+",1,0,0,0,0,0
+12879,"Coherent extension of partial automorphisms, free amalgamation, and automorphism groups","  We give strengthened versions of the Herwig-Lascar and Hodkinson-Otto
+extension theorems for partial automorphisms of finite structures. Such
+strengthenings yield several combinatorial and group-theoretic consequences for
+homogeneous structures. For instance, we establish a coherent form of the
+extension property for partial automorphisms for certain Fraisse classes. We
+deduce from these results that the isometry group of the rational Urysohn
+space, the automorphism group of the Fraisse limit of any Fraisse class that is
+the class of all $\mathcal{F}$-free structures (in the Herwig--Lascar sense),
+and the automorphism group of any free homogeneous structure over a finite
+relational language, all contain a dense locally finite subgroup. We also show
+that any free homogeneous structure admits ample generics.
+",0,0,1,0,0,0
+12880,Homological indices of collections of 1-forms,"  Homological index of a holomorphic 1-form on a complex analytic variety with
+an isolated singular point is an analogue of the usual index of a 1-form on a
+non-singular manifold. One can say that it corresponds to the top Chern number
+of a manifold. We offer a definition of homological indices for collections of
+1-forms on a (purely dimensional) complex analytic variety with an isolated
+singular point corresponding to other Chern numbers. We also define new
+invariants of germs of complex analytic varieties with isolated singular points
+related to ""vanishing Chern numbers"" at them.
+",0,0,1,0,0,0
+12881,An age-structured continuum model for myxobacteria,"  Myxobacteria are social bacteria, that can glide in 2D and form
+counter-propagating, interacting waves. Here we present a novel age-structured,
+continuous macroscopic model for the movement of myxobacteria. The derivation
+is based on microscopic interaction rules that can be formulated as a
+particle-based model and set within the SOH (Self-Organized Hydrodynamics)
+framework. The strength of this combined approach is that microscopic knowledge
+or data can be incorporated easily into the particle model, whilst the
+continuous model allows for easy numerical analysis of the different effects.
+However we found that the derived macroscopic model lacks a diffusion term in
+the density equations, which is necessary to control the number of waves,
+indicating that a higher order approximation during the derivation is crucial.
+Upon ad-hoc addition of the diffusion term, we found very good agreement
+between the age-structured model and the biology. In particular we analyzed the
+influence of a refractory (insensitivity) period following a reversal of
+movement. Our analysis reveals that the refractory period is not necessary for
+wave formation, but essential to wave synchronization, indicating separate
+molecular mechanisms.
+",0,1,1,0,0,0
+12882,Smoothing Properties of Bilinear Operators and Leibniz-Type Rules in Lebesgue and Mixed Lebesgue Spaces,"  We prove that bilinear fractional integral operators and similar multipliers
+are smoothing in the sense that they improve the regularity of functions. We
+also treat bilinear singular multiplier operators which preserve regularity and
+obtain several Leibniz-type rules in the contexts of Lebesgue and mixed
+Lebesgue spaces.
+",0,0,1,0,0,0
+12883,Local equilibrium in the Bak-Sneppen model,"  The Bak Sneppen (BS) model is a very simple model that exhibits all the
+richness of self-organized criticality theory. At the thermodynamic limit, the
+BS model converges to a situation where all particles have a fitness that is
+uniformly distributed between a critical value $p_c$ and 1. The $p_c$ value is
+unknown, as are the variables that influence and determine this value. Here, we
+study the Bak Sneppen model in the case in which the lowest fitness particle
+interacts with an arbitrary even number of $m$ nearest neighbors. We show that
+$p_{c,m}$ verifies a simple local equilibrium relationship. Based on this
+relationship, we can determine bounds for $p_{c,m}$.
+",0,1,0,0,0,0
+12884,Ribbon structures of the Drinfeld center of a finite tensor category,"  We classify the ribbon structures of the Drinfeld center
+$\mathcal{Z}(\mathcal{C})$ of a finite tensor category $\mathcal{C}$. Our
+result generalizes Kauffman and Radford's classification result of the ribbon
+elements of the Drinfeld double of a finite-dimensional Hopf algebra. As a
+consequence, we see that $\mathcal{Z}(\mathcal{C})$ is a modular tensor
+category in the sense of Lyubashenko if $\mathcal{C}$ is a spherical finite
+tensor category in the sense of Douglas, Schommer-Pries and Snyder.
+",0,0,1,0,0,0
+12885,X-ray luminescence computed tomography using a focused X-ray beam,"  Due to the low X-ray photon utilization efficiency and low measurement
+sensitivity of the electron multiplying charge coupled device (EMCCD) camera
+setup, the collimator based narrow beam X-ray luminescence computed tomography
+(XLCT) usually requires a long measurement time. In this paper, we, for the
+first time, report a focused X-ray beam based XLCT imaging system with
+measurements by a single optical fiber bundle and a photomultiplier tube (PMT).
+An X-ray tube with a polycapillary lens was used to generate a focused X-ray
+beam whose X-ray photon density is 1200 times larger than a collimated X-ray
+beam. An optical fiber bundle was employed to collect and deliver the emitted
+photons on the phantom surface to the PMT. The total measurement time was
+reduced to 12.5 minutes. For numerical simulations of both single and six fiber
+bundle cases, we were able to reconstruct six targets successfully. For the
+phantom experiment, two targets with an edge-to-edge distance of 0.4 mm and a
+center-to-center distance of 0.8 mm were successfully reconstructed by the
+measurement setup with a single fiber bundle and a PMT.
+",0,1,0,0,0,0
+12886,Perturbed Kitaev model: excitation spectrum and long-ranged spin correlations,"  We developed general approach to the calculation of power-law infrared
+asymptotics of spin-spin correlation functions in the Kitaev honeycomb model
+with different types of perturbations. We have shown that in order to find
+these correlation functions, one can perform averaging of some bilinear forms
+composed out of free Majorana fermions, and we presented the method for
+explicit calculation of these fermionic densities. We demonstrated how to
+derive an effective Hamiltonian for the Majorana fermions, including the
+effects of perturbations. For specific application of the general theory, we
+have studied the effect of the Dzyaloshinskii-Moriya anisotropic spin-spin
+interaction; we demonstrated that it leads, already in the second order over
+its relative magnitude $D/K$, to a power-law spin correlation functions, and
+calculated dynamical spin structure factor of the system. We have shown that an
+external magnetic field $h$ in presence of the DM interaction, opens a gap in
+the excitation spectrum of magnitude $\Delta \propto D h$.
+",0,1,0,0,0,0
+12887,FreezeOut: Accelerate Training by Progressively Freezing Layers,"  The early layers of a deep neural net have the fewest parameters, but take up
+the most computation. In this extended abstract, we propose to only train the
+hidden layers for a set portion of the training run, freezing them out
+one-by-one and excluding them from the backward pass. Through experiments on
+CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20%
+wall-clock time during training with 3% loss in accuracy for DenseNets, a 20%
+speedup without loss of accuracy for ResNets, and no improvement for VGG
+networks. Our code is publicly available at
+this https URL
+",1,0,0,1,0,0
+12888,Stochastic Subsampling for Factorizing Huge Matrices,"  We present a matrix-factorization algorithm that scales to input matrices
+with both huge number of rows and columns. Learned factors may be sparse or
+dense and/or non-negative, which makes our algorithm suitable for dictionary
+learning, sparse component analysis, and non-negative matrix factorization. Our
+algorithm streams matrix columns while subsampling them to iteratively learn
+the matrix factors. At each iteration, the row dimension of a new sample is
+reduced by subsampling, resulting in lower time complexity compared to a simple
+streaming algorithm. Our method comes with convergence guarantees to reach a
+stationary point of the matrix-factorization problem. We demonstrate its
+efficiency on massive functional Magnetic Resonance Imaging data (2 TB), and on
+patches extracted from hyperspectral images (103 GB). For both problems, which
+involve different penalties on rows and columns, we obtain significant
+speed-ups compared to state-of-the-art algorithms.
+",1,0,1,1,0,0
+12889,Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding,"  This paper presents the design of the machine learning architecture that
+underlies the Alexa Skills Kit (ASK) a large scale Spoken Language
+Understanding (SLU) Software Development Kit (SDK) that enables developers to
+extend the capabilities of Amazon's virtual assistant, Alexa. At Amazon, the
+infrastructure powers over 25,000 skills deployed through the ASK, as well as
+AWS's Amazon Lex SLU Service. The ASK emphasizes flexibility, predictability
+and a rapid iteration cycle for third party developers. It imposes inductive
+biases that allow it to learn robust SLU models from extremely small and sparse
+datasets and, in doing so, removes significant barriers to entry for software
+developers and dialogue systems researchers.
+",1,0,0,0,0,0
+12890,"Scaling, Scattering, and Blackbody Radiation in Classical Physics","  Here we discuss blackbody radiation within the context of classical theory.
+We note that nonrelativistic classical mechanics and relativistic classical
+electrodynamics have contrasting scaling symmetries which influence the
+scattering of radiation. Also, nonrelativistic mechanical systems can be
+accurately combined with relativistic electromagnetic radiation only provided
+the nonrelativistic mechanical systems are the low-velocity limits of fully
+relativistic systems. Application of the no-interaction theorem for
+relativistic systems limits the scattering mechanical systems for thermal
+radiation to relativistic classical electrodynamic systems, which involve the
+Coulomb potential. Whereas the naive use of nonrelativistic scatterers or
+nonrelativistic classical statistical mechanics leads to the Rayleigh-Jeans
+spectrum, the use of fully relativistic scatterers leads to the Planck spectrum
+for blackbody radiation within classical physics.
+",0,1,0,0,0,0
+12891,Critical binomial ideals of Norhtcott type,"  In this paper, we study a family of binomial ideals defining monomial curves
+in the $n-$dimensional affine space determined by $n$ hypersurfaces of the form
+$x_i^{c_i} - x_1^{u_{i1}} \cdots x_n^{u_{1n}} \in k[x_1, \ldots, x_n]$ with
+$u_{ii} = 0$, $i\in \{ 1, \ldots, n\}$. We prove that, the monomial curves in
+that family are set-theoretic complete intersection. Moreover, if the monomial
+curve is irreducible, we compute some invariants such as genus, type and
+Fröbenius number of the corresponding numerical semigroup. We also describe a
+method to produce set-theoretic complete intersection semigroup ideals of
+arbitrary large height.
+",0,0,1,0,0,0
+12892,Clustering of Magnetic Swimmers in a Poiseuille Flow,"  We investigate the collective behavior of magnetic swimmers, which are
+suspended in a Poiseuille flow and placed under an external magnetic field,
+using analytical techniques and Brownian dynamics simulations. We find that the
+interplay between intrinsic activity, external alignment, and magnetic
+dipole-dipole interactions leads to longitudinal structure formation. Our work
+sheds light on a recent experimental observation of a clustering instability in
+this system.
+",0,1,0,0,0,0
+12893,A Time-Spectral Method for Initial-Value Problems Using a Novel Spatial Subdomain Scheme,"  We analyse a new subdomain scheme for a time-spectral method for solving
+initial boundary value problems. Whilst spectral methods are commonplace for
+spatially dependent systems, finite difference schemes are typically applied
+for the temporal domain. The Generalized Weighted Residual Method (GWRM) is a
+fully spectral method in that it spectrally decomposes all specified domains,
+including the temporal domain, with multivariate Chebyshev polynomials. The
+Common Boundary-Condition method (CBC) is a spatial subdomain scheme that
+solves the physical equations independently from the global connection of
+subdomains. It is here evaluated against two finite difference methods. For the
+linearised Burger equation the CBC-GWRM is $\sim30\%$ faster and $\sim50\%$
+more memory efficient than the semi implicit Crank-Nicolson method at a maximum
+error $\sim10^{-5}$. For a forced wave equation the CBC-GWRM manages to average
+efficiently over the small time-scale in the entire temporal domain. The
+CBC-GWRM is also applied to the linearised ideal magnetohydrodynamic (MHD)
+equations for a screw pinch equilibrium. The growth rate of the most unstable
+mode was efficiently computed with an error $<0.1\%$.
+",0,1,0,0,0,0
+12894,Stein Variational Adaptive Importance Sampling,"  We propose a novel adaptive importance sampling algorithm which incorporates
+Stein variational gradient decent algorithm (SVGD) with importance sampling
+(IS). Our algorithm leverages the nonparametric transforms in SVGD to
+iteratively decrease the KL divergence between our importance proposal and the
+target distribution. The advantages of this algorithm are twofold: first, our
+algorithm turns SVGD into a standard IS algorithm, allowing us to use standard
+diagnostic and analytic tools of IS to evaluate and interpret the results;
+second, we do not restrict the choice of our importance proposal to predefined
+distribution families like traditional (adaptive) IS methods. Empirical
+experiments demonstrate that our algorithm performs well on evaluating
+partition functions of restricted Boltzmann machines and testing likelihood of
+variational auto-encoders.
+",0,0,0,1,0,0
+12895,Closure operators on dcpos,"  We examine collective properties of closure operators on posets that are at
+least dcpos. The first theorem sets the tone of the paper: it tells how a set
+of preclosure maps on a dcpo determines the least closure operator above them,
+and pronounces the related induction principle, and its companion, the obverse
+induction principle. Using this theorem we prove that the poset of closure
+operators on a dcpo is a complete lattice, and then provide a constructive
+proof of the Tarski's theorem for dcpos. We go on to construct the joins in the
+complete lattice of Scott-continuous closure operators on a dcpo, and to prove
+that the complete lattice of nuclei on a preframe is a frame, giving some
+constructions in the special case of the frame of all nuclei on a frame. In the
+rather drawn-out proof if the Hofmann-Mislove-Johnstone theorem we show off the
+utility of the obverse induction, applying it in the proof of the crucial
+lemma. After that we shift a viewpoint and prove some results, analogous to
+results about dcpos, for posets in which certain special subsets have enough
+maximal elements; these results actually specialize to dcpos, but at the price
+of using the axiom of choice. We conclude by pointing out two convex geometries
+associated with closure operators on a dcpo.
+",0,0,1,0,0,0
+12896,Multi-Agent Coverage Control with Energy Depletion and Repletion,"  We develop a hybrid system model to describe the behavior of multiple agents
+cooperatively solving an optimal coverage problem under energy depletion and
+repletion constraints. The model captures the controlled switching of agents
+between coverage (when energy is depleted) and battery charging (when energy is
+replenished) modes. It guarantees the feasibility of the coverage problem by
+defining a guard function on each agent's battery level to prevent it from
+dying on its way to a charging station. The charging station plays the role of
+a centralized scheduler to solve the contention problem of agents competing for
+the only charging resource in the mission space. The optimal coverage problem
+is transformed into a parametric optimization problem to determine an optimal
+recharging policy. This problem is solved through the use of Infinitesimal
+Perturbation Analysis (IPA), with simulation results showing that a full
+recharging policy is optimal.
+",1,0,0,0,0,0
+12897,Self-injective commutative rings have no nontrivial rigid ideals,"  We establish a link between trace modules and rigidity in modules over
+Noetherian rings. Using the theory of trace ideals we make partial progress on
+a question of Dao, and on the Auslander-Reiten conjecture over Artinian
+Gorenstein rings.
+",0,0,1,0,0,0
+12898,Origin of the Drude peak and of zero sound in probe brane holography,"  At zero temperature, the charge current operator appears to be conserved,
+within linear response, in certain holographic probe brane models of strange
+metals. At small but finite temperature, we analytically show that the weak
+non-conservation of this current leads to both a collective ""zero sound"" mode
+and a Drude peak in the electrical conductivity. This simultaneously resolves
+two outstanding puzzles about probe brane theories. The nonlinear dynamics of
+the current operator itself appears qualitatively different.
+",0,1,0,0,0,0
+12899,Multiscale dynamical network mechanisms underlying aging from birth to death,"  How self-organized networks develop, mature and degenerate is a key question
+for sociotechnical, cyberphysical and biological systems with potential
+applications from tackling violent extremism through to neurological diseases.
+So far, it has proved impossible to measure the continuous-time evolution of
+any in vivo organism network from birth to death. Here we provide such a study
+which crosses all organizational and temporal scales, from individual
+components (10^1) through to the mesoscopic (10^3) and entire system scale
+(10^6). These continuous-time data reveal a lifespan driven by punctuated,
+real-time co-evolution of the structural and functional networks. Aging sees
+these structural and functional networks gradually diverge in terms of their
+small-worldness and eventually their connectivity. Dying emerges as an extended
+process associated with the formation of large but disjoint functional
+sub-networks together with an increasingly detached core. Our mathematical
+model quantifies the very different impacts that interventions will have on the
+overall lifetime, period of initial growth, peak of potency, and duration of
+old age, depending on when and how they are administered. In addition to their
+direct relevance to online extremism, our findings offer fresh insight into
+aging in any network system of comparable complexity for which extensive in
+vivo data is not yet available.
+",0,1,0,0,0,0
+12900,Theoretical calculations for precision polarimetry based on Mott scattering,"  Electron polarimeters based on Mott scattering are extensively used in
+different fields in physics such as atomic, nuclear or particle physics. This
+is because spin-dependent measurements gives additional information on the
+physical processes under study. The main quantity that needs to be understood
+in very much detail, both experimentally and theoretically, is the
+spin-polarization function, so called analyzing power or Sherman function. A
+detailed theoretical analysis on all the contributions to the effective
+interaction potential that are relevant at the typical electron beam energies
+and angles commonly used in the calibration of the experimental apparatus is
+presented. The main contribution leading the theoretical error on the Sherman
+function is found to correspond to radiative corrections that have been
+qualitatively estimated to be below the 0.5% for the considered kinematical
+conditions: unpolarized electron beams of few MeV elastically scattered from a
+gold and silver targets at backward angles.
+",0,1,0,0,0,0
+12901,An Online Hierarchical Algorithm for Extreme Clustering,"  Many modern clustering methods scale well to a large number of data items, N,
+but not to a large number of clusters, K. This paper introduces PERCH, a new
+non-greedy algorithm for online hierarchical clustering that scales to both
+massive N and K--a problem setting we term extreme clustering. Our algorithm
+efficiently routes new data points to the leaves of an incrementally-built
+tree. Motivated by the desire for both accuracy and speed, our approach
+performs tree rotations for the sake of enhancing subtree purity and
+encouraging balancedness. We prove that, under a natural separability
+assumption, our non-greedy algorithm will produce trees with perfect dendrogram
+purity regardless of online data arrival order. Our experiments demonstrate
+that PERCH constructs more accurate trees than other tree-building clustering
+algorithms and scales well with both N and K, achieving a higher quality
+clustering than the strongest flat clustering competitor in nearly half the
+time.
+",1,0,0,1,0,0
+12902,Wave and Dirac equations on manifolds,"  We review some recent results on geometric equations on Lorentzian manifolds
+such as the wave and Dirac equations. This includes well-posedness and
+stability for various initial value problems, as well as results on the
+structure of these equations on black-hole spacetimes (in particular, on the
+Kerr solution), the index theorem for hyperbolic Dirac operators and properties
+of the class of Green-hyperbolic operators.
+",0,0,1,0,0,0
+12903,The Efimov effect for heteronuclear three-body systems at positive scattering length and finite temperature,"  We study the recombination process of three atoms scattering into an atom and
+diatomic molecule in heteronuclear mixtures of ultracold atomic gases with
+large and positive interspecies scattering length at finite temperature. We
+calculate the temperature dependence of the three-body recombination rates by
+extracting universal scaling functions that parametrize the energy dependence
+of the scattering matrix. We compare our results to experimental data for the
+40K-87Rb mixture and make a prediction for 6Li-87Rb. We find that contributions
+from higher partial wave channels significantly impact the total rate and, in
+systems with particularly large mass imbalance, can even obliterate the
+recombination minima associated with the Efimov effect.
+",0,1,0,0,0,0
+12904,"Mean-Field Controllability and Decentralized Stabilization of Markov Chains, Part II: Asymptotic Controllability and Polynomial Feedbacks","  This paper, the second of a two-part series, presents a method for mean-field
+feedback stabilization of a swarm of agents on a finite state space whose time
+evolution is modeled as a continuous time Markov chain (CTMC). The resulting
+(mean-field) control problem is that of controlling a nonlinear system with
+desired global stability properties. We first prove that any probability
+distribution with a strongly connected support can be stabilized using
+time-invariant inputs. Secondly, we show the asymptotic controllability of all
+possible probability distributions, including distributions that assign zero
+density to some states and which do not necessarily have a strongly connected
+support. Lastly, we demonstrate that there always exists a globally
+asymptotically stabilizing decentralized density feedback law with the
+additional property that the control inputs are zero at equilibrium, whenever
+the graph is strongly connected and bidirected. Then the problem of
+synthesizing closed-loop polynomial feedback is framed as a optimization
+problem using state-of-the-art sum-of-squares optimization tools. The
+optimization problem searches for polynomial feedback laws that make the
+candidate Lyapunov function a stability certificate for the resulting
+closed-loop system. Our methodology is tested for two cases on a five vertex
+graph, and the stabilization properties of the constructed control laws are
+validated with numerical simulations of the corresponding system of ordinary
+differential equations.
+",1,0,1,0,0,0
+12905,The kinematics of σ-drop bulges from spectral synthesis modelling of a hydrodynamical simulation,"  A minimum in stellar velocity dispersion is often observed in the central
+regions of disc galaxies. To investigate the origin of this feature, known as a
+{\sigma}-drop, we analyse the stellar kinematics of a high-resolution N-body +
+smooth particle hydrodynamical simulation, which models the secular evolution
+of an unbarred disc galaxy. We compared the intrinsic mass-weighted kinematics
+to the recovered luminosity-weighted ones. The latter were obtained by
+analysing synthetic spectra produced by a new code, SYNTRA, that generates
+synthetic spectra by assigning a stellar population synthesis model to each
+star particle based on its age and metallicity. The kinematics were derived
+from the synthetic spectra as in real spectra to mimic the kinematic analysis
+of real galaxies. We found that the recovered luminosity-weighted kinematics in
+the centre of the simulated galaxy are biased to higher rotation velocities and
+lower velocity dispersions due to the presence of young stars in a thin and
+kinematically cool disc, and are ultimately responsible for the {\sigma}-drop.
+",0,1,0,0,0,0
+12906,Algorithms and Architecture for Real-time Recommendations at News UK,"  Recommendation systems are recognised as being hugely important in industry,
+and the area is now well understood. At News UK, there is a requirement to be
+able to quickly generate recommendations for users on news items as they are
+published. However, little has been published about systems that can generate
+recommendations in response to changes in recommendable items and user
+behaviour in a very short space of time. In this paper we describe a new
+algorithm for updating collaborative filtering models incrementally, and
+demonstrate its effectiveness on clickstream data from The Times. We also
+describe the architecture that allows recommendations to be generated on the
+fly, and how we have made each component scalable. The system is currently
+being used in production at News UK.
+",1,0,0,0,0,0
+12907,A Physics Tragedy,"  The measurement problem and three other vexing experiments in quantum physics
+are described. It is shown how Quantum Field Theory, as formulated by Julian
+Schwinger, provides simple solutions for all four experiments. It is also shown
+how this theory resolves many other problems of Quantum Mechanics and
+Relativity, including a new and simple derivation of E = mc2.
+",0,1,0,0,0,0
+12908,Dynamical Mass Generation in Pseudo Quantum Electrodynamics with Four-Fermion Interactions,"  We describe dynamical symmetry breaking in a system of massless Dirac
+fermions with both electromagnetic and four-fermion interactions in (2+1)
+dimensions. The former is described by the Pseudo Quantum Electrodynamics
+(PQED) and the latter is given by the so-called Gross-Neveu action. We apply
+the Hubbard-Stratonovich transformation and the large$-N_f$ expansion in our
+model to obtain a Yukawa action. Thereafter, the presence of a symmetry broken
+phase is inferred from the non-perturbative Schwinger-Dyson equation for the
+electron propagator. This is the physical solution whenever the fine-structure
+constant is larger than a critical value $\alpha_c(D N_f)$. In particular, we
+obtain the critical coupling constant $\alpha_c\approx 0.36$ for $D N_f=8$.,
+where $D=2,4$ corresponds to the SU(2) and SU(4) cases, respectively, and $N_f$
+is the flavor number. Our results show a decreasing of the critical coupling
+constant in comparison with the case of pure electromagnetic interaction, thus
+yielding a more favorable scenario for the occurrence of dynamical symmetry
+breaking. For two-dimensional materials,in application in condensed matter
+systems, it implies an energy gap at the Dirac points or valleys of the
+honeycomb lattice.
+",0,1,0,0,0,0
+12909,Computational Methods for Path-based Robust Flows,"  Real world networks are often subject to severe uncertainties which need to
+be addressed by any reliable prescriptive model. In the context of the maximum
+flow problem subject to arc failure, robust models have gained particular
+attention. For a path-based model, the resulting optimization problem is
+assumed to be difficult in the literature, yet the complexity status is widely
+unknown. We present a computational approach to solve the robust flow problem
+to optimality by simultaneous primal and dual separation, the practical
+efficacy of which is shown by a computational study.
+Furthermore, we introduce a novel model of robust flows which provides a
+compromise between stochastic and robust optimization by assigning
+probabilities to groups of scenarios. The new model can be solved by the same
+computational techniques as the robust model. A bound on the generalization
+error is proven for the case that the probabilities are determined empirically.
+The suggested model as well as the computational approach extend to linear
+optimization problems more general than robust flows.
+",1,0,1,0,0,0
+12910,A game theoretic approach to a network cloud storage problem,"  The use of game theory in the design and control of large scale networked
+systems is becoming increasingly more important. In this paper, we follow this
+approach to efficiently solve a network allocation problem motivated by
+peer-to- peer cloud storage models as alternatives to classical centralized
+cloud storage services. To this aim, we propose an allocation algorithm that
+allows the units to use their neighbors to store a back up of their data. We
+prove convergence, characterize the final allocation, and corroborate our
+analysis with extensive numerical simulation that shows the good performance of
+the algorithm in terms of scalability, complexity and structure of the
+solution.
+",1,0,1,0,0,0
+12911,A Survey of the State-of-the-Art Parallel Multiple Sequence Alignment Algorithms on Multicore Systems,"  Evolutionary modeling applications are the best way to provide full
+information to support in-depth understanding of evaluation of organisms. These
+applications mainly depend on identifying the evolutionary history of existing
+organisms and understanding the relations between them, which is possible
+through the deep analysis of their biological sequences. Multiple Sequence
+Alignment (MSA) is considered an important tool in such applications, where it
+gives an accurate representation of the relations between different biological
+sequences. In literature, many efforts have been put into presenting a new MSA
+algorithm or even improving existing ones. However, little efforts on
+optimizing parallel MSA algorithms have been done. Nowadays, large datasets
+become a reality, and big data become a primary challenge in various fields,
+which should be also a new milestone for new bioinformatics algorithms. This
+survey presents four of the state-of-the-art parallel MSA algorithms, TCoffee,
+MAFFT, MSAProbs, and M2Align. We provide a detailed discussion of each
+algorithm including its strengths, weaknesses, and implementation details and
+the effectiveness of its parallel implementation compared to the other
+algorithms, taking into account the MSA accuracy on two different datasets,
+BAliBASE and OXBench.
+",0,0,0,0,1,0
+12912,Learning Wasserstein Embeddings,"  The Wasserstein distance received a lot of attention recently in the
+community of machine learning, especially for its principled way of comparing
+distributions. It has found numerous applications in several hard problems,
+such as domain adaptation, dimensionality reduction or generative models.
+However, its use is still limited by a heavy computational cost. Our goal is to
+alleviate this problem by providing an approximation mechanism that allows to
+break its inherent complexity. It relies on the search of an embedding where
+the Euclidean distance mimics the Wasserstein distance. We show that such an
+embedding can be found with a siamese architecture associated with a decoder
+network that allows to move from the embedding space back to the original input
+space. Once this embedding has been found, computing optimization problems in
+the Wasserstein space (e.g. barycenters, principal directions or even
+archetypes) can be conducted extremely fast. Numerical experiments supporting
+this idea are conducted on image datasets, and show the wide potential benefits
+of our method.
+",1,0,0,1,0,0
+12913,Contextual Stochastic Block Models,"  We provide the first information theoretic tight analysis for inference of
+latent community structure given a sparse graph along with high dimensional
+node covariates, correlated with the same latent communities. Our work bridges
+recent theoretical breakthroughs in the detection of latent community structure
+without nodes covariates and a large body of empirical work using diverse
+heuristics for combining node covariates with graphs for inference. The
+tightness of our analysis implies in particular, the information theoretical
+necessity of combining the different sources of information. Our analysis holds
+for networks of large degrees as well as for a Gaussian version of the model.
+",1,0,0,1,0,0
+12914,Tensor Regression Meets Gaussian Processes,"  Low-rank tensor regression, a new model class that learns high-order
+correlation from data, has recently received considerable attention. At the
+same time, Gaussian processes (GP) are well-studied machine learning models for
+structure learning. In this paper, we demonstrate interesting connections
+between the two, especially for multi-way data analysis. We show that low-rank
+tensor regression is essentially learning a multi-linear kernel in Gaussian
+processes, and the low-rank assumption translates to the constrained Bayesian
+inference problem. We prove the oracle inequality and derive the average case
+learning curve for the equivalent GP model. Our finding implies that low-rank
+tensor regression, though empirically successful, is highly dependent on the
+eigenvalues of covariance functions as well as variable correlations.
+",1,0,0,1,0,0
+12915,Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition,"  The Canonical Polyadic decomposition (CPD) is a convenient and intuitive tool
+for tensor factorization; however, for higher-order tensors, it often exhibits
+high computational cost and permutation of tensor entries, these undesirable
+effects grow exponentially with the tensor order. Prior compression of tensor
+in-hand can reduce the computational cost of CPD, but this is only applicable
+when the rank $R$ of the decomposition does not exceed the tensor dimensions.
+To resolve these issues, we present a novel method for CPD of higher-order
+tensors, which rests upon a simple tensor network of representative
+inter-connected core tensors of orders not higher than 3. For rigour, we
+develop an exact conversion scheme from the core tensors to the factor matrices
+in CPD, and an iterative algorithm with low complexity to estimate these factor
+matrices for the inexact case. Comprehensive simulations over a variety of
+scenarios support the approach.
+",1,0,0,0,0,0
+12916,Social Learning and Diffusion of Pervasive Goods: An Empirical Study of an African App Store,"  In this study, the authors develop a structural model that combines a macro
+diffusion model with a micro choice model to control for the effect of social
+influence on the mobile app choices of customers over app stores. Social
+influence refers to the density of adopters within the proximity of other
+customers. Using a large data set from an African app store and Bayesian
+estimation methods, the authors quantify the effect of social influence and
+investigate the impact of ignoring this process in estimating customer choices.
+The findings show that customer choices in the app store are explained better
+by offline than online density of adopters and that ignoring social influence
+in estimations results in biased estimates. Furthermore, the findings show that
+the mobile app adoption process is similar to adoption of music CDs, among all
+other classic economy goods. A counterfactual analysis shows that the app store
+can increase its revenue by 13.6% through a viral marketing policy (e.g., a
+sharing with friends and family button).
+",1,0,0,1,0,0
+12917,Dynamic correlations at different time-scales with Empirical Mode Decomposition,"  The Empirical Mode Decomposition (EMD) provides a tool to characterize time
+series in terms of its implicit components oscillating at different
+time-scales. We apply this decomposition to intraday time series of the
+following three financial indices: the S\&P 500 (USA), the IPC (Mexico) and the
+VIX (volatility index USA), obtaining time-varying multidimensional
+cross-correlations at different time-scales. The correlations computed over a
+rolling window are compared across the three indices, across the components at
+different time-scales, at different lags and over time. We uncover a rich
+heterogeneity of interactions which depends on the time-scale and has important
+led-lag relations which can have practical use for portfolio management, risk
+estimation and investments.
+",1,0,0,0,0,0
+12918,Order-disorder transitions in lattice gases with annealed reactive constraints,"  We study equilibrium properties of catalytically-activated $A + A \to
+\oslash$ reactions taking place on a lattice of adsorption sites. The particles
+undergo continuous exchanges with a reservoir maintained at a constant chemical
+potential $\mu$ and react when they appear at the neighbouring sites, provided
+that some reactive conditions are fulfilled. We model the latter in two
+different ways: In the Model I some fraction $p$ of the {\em bonds} connecting
+neighbouring sites possesses special catalytic properties such that any two
+$A$s appearing on the sites connected by such a bond instantaneously react and
+desorb. In the Model II some fraction $p$ of the adsorption {\em sites}
+possesses such properties and neighbouring particles react if at least one of
+them resides on a catalytic site. For the case of \textit{annealed} disorder in
+the distribution of the catalyst, which is tantamount to the situation when the
+reaction may take place at any point on the lattice but happens with a finite
+probability $p$, we provide an exact solution for both models for the interior
+of an infinitely large Cayley tree - the so-called Bethe lattice. We show that
+both models exhibit a rich critical behaviour: For the annealed Model I it is
+characterised by a transition into an ordered state and a re-entrant transition
+into a disordered phase, which both are continuous. For the annealed Model II,
+which represents a rather exotic model of statistical mechanics in which
+interactions of any particle with its environment have a peculiar Boolean form,
+the transition to an ordered state is always continuous, while the re-entrant
+transition into the disordered phase may be either continuous or discontinuous,
+depending on the value of $p$.
+",0,1,0,0,0,0
+12919,Spherical CNNs,"  Convolutional Neural Networks (CNNs) have become the method of choice for
+learning problems involving 2D planar images. However, a number of problems of
+recent interest have created a demand for models that can analyze spherical
+images. Examples include omnidirectional vision for drones, robots, and
+autonomous cars, molecular regression problems, and global weather and climate
+modelling. A naive application of convolutional networks to a planar projection
+of the spherical signal is destined to fail, because the space-varying
+distortions introduced by such a projection will make translational weight
+sharing ineffective.
+In this paper we introduce the building blocks for constructing spherical
+CNNs. We propose a definition for the spherical cross-correlation that is both
+expressive and rotation-equivariant. The spherical correlation satisfies a
+generalized Fourier theorem, which allows us to compute it efficiently using a
+generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We
+demonstrate the computational efficiency, numerical accuracy, and effectiveness
+of spherical CNNs applied to 3D model recognition and atomization energy
+regression.
+",0,0,0,1,0,0
+12920,The Kelvin-Helmholtz instability in the Orion nebula: The effect of radiation pressure,"  The recent observations of rippled structures on the surface of the Orion
+molecular cloud (Berné et al. 2010), have been attributed to the
+Kelvin-Helmholtz (KH) instability. The wavelike structures which have mainly
+seen near star-forming regions taking place at the interface between the hot
+diffuse gas, which is ionized by massive stars, and the cold dense molecular
+clouds. The radiation pressure of massive stars and stellar clusters is one of
+the important issues that has been considered frequently in the dynamics of
+clouds. Here, we investigate the influence of radiation pressure, from
+well-known Trapezium cluster in the Orion nebula, on the evolution of KH
+instability. The stability of the interface between HII region and molecular
+clouds in the presence of the radiation pressure, has been studied using the
+linear perturbation analysis for the certain range of the wavelengths. The
+linear analysis show that consideration of the radiation pressure intensifies
+the growth rate of KH modes and consequently decreases the e-fold time-scale of
+the instability. On the other hand the domain of the instability is extended
+and includes the more wavelengths, consisting of smaller ones rather than the
+case when the effect of the radiation pressure is not considered. Our results
+shows that for $\lambda_{\rm KH}>0.15\rm pc$, the growth rate of KH instability
+dose not depend to the radiation pressure. Based on our results, the radiation
+pressure is a triggering mechanism in development of the KH instability and
+subsequently formation of turbulent sub-structures in the molecular clouds near
+massive stars. The role of magnetic fields in the presence of the radiation
+pressure is also investigated and it is resulted that the magnetic field
+suppresses the effects induced by the radiation pressure.
+",0,1,0,0,0,0
+12921,Holography and Koszul duality: the example of the $M2$ brane,"  Si Li and author suggested in that, in some cases, the AdS/CFT correspondence
+can be formulated in terms of the algebraic operation of Koszul duality. In
+this paper this suggestion is checked explicitly for $M2$ branes in an
+$\Omega$-background. The algebra of supersymmetric operators on a stack of $K$
+$M2$ branes is shown to be Koszul dual, in large $K$, to the algebra of
+supersymmetric operators of $11$-dimensional supergravity in an
+$\Omega$-background (using the formulation of supergravity in an
+$\Omega$-background presented in arXiv:1610.04144).
+The twisted form of supergravity that is used here can be quantized to all
+orders in perturbation theory. We find that the Koszul duality result holds to
+all orders in perturbation theory, in both the gravitational theory and the
+theory on the $M2$. (However, there is a certain non-linear identification of
+the coupling constants on each side which I was unable to determine
+explicitly).
+It is also shown that the algebra of operators on $K$ $M2$ branes, as $K \to
+\infty$, is a quantum double-loop algebra (a two-variable analog of the
+Yangian). This algebra is also the Koszul dual of the algebra of operators on
+the gravitational theory. An explicit presentation for this algebra is
+presented, and it is shown that this algebra is the unique quantization of its
+classical limit. Some conjectural applications to enumerative geometry of
+Calabi-Yau threefolds are also presented.
+",0,0,1,0,0,0
+12922,Uniqueness of the power of a meromorphic functions with its differential polynomial sharing a set,"  This paper is devoted to the uniqueness problem of the power of a meromorphic
+function with its differential polynomial sharing a set. Our result will extend
+a number of results obtained in the theory of normal families. Some questions
+are posed for future research.
+",0,0,1,0,0,0
+12923,Link between the Superconducting Dome and Spin-Orbit Interaction in the (111) LaAlO$_3$/SrTiO$_3$ Interface,"  We measure the gate voltage ($V_g$) dependence of the superconducting
+properties and the spin-orbit interaction in the (111)-oriented
+LaAlO$_3$/SrTiO$_3$ interface. Superconductivity is observed in a dome-shaped
+region in the carrier density-temperature phase diagram with the maxima of
+superconducting transition temperature $T_c$ and the upper critical fields
+lying at the same $V_g$. The spin-orbit interaction determined from the
+superconducting parameters and confirmed by weak-antilocalization measurements
+follows the same gate voltage dependence as $T_c$. The correlation between the
+superconductivity and spin-orbit interaction as well as the enhancement of the
+parallel upper critical field, well beyond the Chandrasekhar-Clogston limit
+suggest that superconductivity and the spin-orbit interaction are linked in a
+nontrivial fashion. We propose possible scenarios to explain this
+unconventional behavior.
+",0,1,0,0,0,0
+12924,On the Dynamics of Deterministic Epidemic Propagation over Networks,"  In this work we review a class of deterministic nonlinear models for the
+propagation of infectious diseases over contact networks with
+strongly-connected topologies. We consider network models for
+susceptible-infected (SI), susceptible-infected-susceptible (SIS), and
+susceptible-infected-recovered (SIR) settings. In each setting, we provide a
+comprehensive nonlinear analysis of equilibria, stability properties,
+convergence, monotonicity, positivity, and threshold conditions. For the
+network SI setting, specific contributions include establishing its equilibria,
+stability, and positivity properties. For the network SIS setting, we review a
+well-known deterministic model, provide novel results on the computation and
+characterization of the endemic state (when the system is above the epidemic
+threshold), and present alternative proofs for some of its properties. Finally,
+for the network SIR setting, we propose novel results for transient behavior,
+threshold conditions, stability properties, and asymptotic convergence. These
+results are analogous to those well-known for the scalar case. In addition, we
+provide a novel iterative algorithm to compute the asymptotic state of the
+network SIR system.
+",1,1,1,0,0,0
+12925,Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification,"  Text-dependent speaker verification is becoming popular in the speaker
+recognition society. However, the conventional i-vector framework which has
+been successful for speaker identification and other similar tasks works
+relatively poorly in this task. Researchers have proposed several new methods
+to improve performance, but it is still unclear that which model is the best
+choice, especially when the pass-phrases are prompted during enrollment and
+test. In this paper, we introduce four modeling methods and compare their
+performance on the newly published RedDots dataset. To further explore the
+influence of different frame alignments, Viterbi and forward-backward
+algorithms are both used in the HMM-based models. Several bottleneck features
+are also investigated. Our experiments show that, by explicitly modeling the
+lexical content, the HMM-based modeling achieves good results in the
+fixed-phrase condition. In the prompted-phrase condition, GMM-HMM and
+i-vector/HMM are not as successful. In both conditions, the forward-backward
+algorithm brings more benefits to the i-vector/HMM system. Additionally, we
+also find that even though bottleneck features perform well for
+text-independent speaker verification, they do not outperform MFCCs on the most
+challenging Imposter-Correct trials on RedDots.
+",1,0,0,0,0,0
+12926,Estimation of lactate threshold with machine learning techniques in recreational runners,"  Lactate threshold is considered an essential parameter when assessing
+performance of elite and recreational runners and prescribing training
+intensities in endurance sports. However, the measurement of blood lactate
+concentration requires expensive equipment and the extraction of blood samples,
+which are inconvenient for frequent monitoring. Furthermore, most recreational
+runners do not have access to routine assessment of their physical fitness by
+the aforementioned equipment so they are not able to calculate the lactate
+threshold without resorting to an expensive and specialized centre. Therefore,
+the main objective of this study is to create an intelligent system capable of
+estimating the lactate threshold of recreational athletes participating in
+endurance running sports. The solution here proposed is based on a machine
+learning system which models the lactate evolution using recurrent neural
+networks and includes the proposal of standardization of the temporal axis as
+well as a modification of the stratified sampling method. The results show that
+the proposed system accurately estimates the lactate threshold of 89.52% of the
+athletes and its correlation with the experimentally measured lactate threshold
+is very high (R=0,89). Moreover, its behaviour with the test dataset is as good
+as with the training set, meaning that the generalization power of the model is
+high. Therefore, in this study a machine learning based system is proposed as
+alternative to the traditional invasive lactate threshold measurement tests for
+recreational runners.
+",0,0,0,1,0,0
+12927,A Coin-Tossing Conundrum,"  It is shown that an equiprobability hypothesis leads to a scenario in which
+it is possible to predict the outcome of a single toss of a fair coin with a
+success probability greater than 50%. We discuss whether this hypothesis might
+be independent of the usual hypotheses governing probability, as well as
+whether this hypothesis might be assumed as a result of the Principle of
+Indifference. Also discussed are ways to implement or circumvent the
+hypothesis.
+",0,0,0,1,0,0
+12928,Meridian Surfaces on Rotational Hypersurfaces with Lightlike Axis in ${\mathbb E}^4_2$,"  We construct a special class of Lorentz surfaces in the pseudo-Euclidean
+4-space with neutral metric which are one-parameter systems of meridians of
+rotational hypersurfaces with lightlike axis and call them meridian surfaces.
+We give the complete classification of the meridian surfaces with constant
+Gauss curvature and prove that there are no meridian surfaces with parallel
+mean curvature vector field other than CMC surfaces lying in a hyperplane. We
+also classify the meridian surfaces with parallel normalized mean curvature
+vector field. We show that in the family of the meridian surfaces there exist
+Lorentz surfaces which have parallel normalized mean curvature vector field but
+not parallel mean curvature vector.
+",0,0,1,0,0,0
+12929,Hybrid Optimization Method for Reconfiguration of AC/DC Microgrids in All-Electric Ships,"  Since the limited power capacity, finite inertia, and dynamic loads make the
+shipboard power system (SPS) vulnerable, the automatic reconfiguration for
+failure recovery in SPS is an extremely significant but still challenging
+problem. It is not only required to operate accurately and optimally, but also
+to satisfy operating constraints. In this paper, we consider the
+reconfiguration optimization for hybrid AC/DC microgrids in all-electric ships.
+Firstly, the multi-zone medium voltage DC (MVDC) SPS model is presented. In
+this model, the DC power flow for reconfiguration and a generalized AC/DC
+converter are modeled for accurate reconfiguration. Secondly, since this
+problem is mixed integer nonlinear programming (MINLP), a hybrid method based
+on Newton Raphson and Biogeography based Optimization (NRBBO) is designed
+according to the characteristics of system, loads, and faults. This method
+facilitates to maximize the weighted load restoration while satisfying
+operating constraints. Finally, the simulation results demonstrate this method
+has advantages in terms of power restoration and convergence speed.
+",1,0,0,0,0,0
+12930,Looking at Outfit to Parse Clothing,"  This paper extends fully-convolutional neural networks (FCN) for the clothing
+parsing problem. Clothing parsing requires higher-level knowledge on clothing
+semantics and contextual cues to disambiguate fine-grained categories. We
+extend FCN architecture with a side-branch network which we refer outfit
+encoder to predict a consistent set of clothing labels to encourage
+combinatorial preference, and with conditional random field (CRF) to explicitly
+consider coherent label assignment to the given image. The empirical results
+using Fashionista and CFPD datasets show that our model achieves
+state-of-the-art performance in clothing parsing, without additional
+supervision during training. We also study the qualitative influence of
+annotation on the current clothing parsing benchmarks, with our Web-based tool
+for multi-scale pixel-wise annotation and manual refinement effort to the
+Fashionista dataset. Finally, we show that the image representation of the
+outfit encoder is useful for dress-up image retrieval application.
+",1,0,0,0,0,0
+12931,"Mermin-Wagner physics, (H,T) phase diagram, and candidate quantum spin-liquid phase in the spin-1/2 triangular-lattice antiferromagnet Ba8CoNb6O24","  Ba$_8$CoNb$_6$O$_{24}$ presents a system whose Co$^{2+}$ ions have an
+effective spin 1/2 and construct a regular triangular-lattice antiferromagnet
+(TLAFM) with a very large interlayer spacing, ensuring purely two-dimensional
+character. We exploit this ideal realization to perform a detailed experimental
+analysis of the $S = 1/2$ TLAFM, which is one of the keystone models in
+frustrated quantum magnetism. We find strong low-energy spin fluctuations and
+no magnetic ordering, but a diverging correlation length down to 0.1 K,
+indicating a Mermin-Wagner trend towards zero-temperature order. Below 0.1 K,
+however, our low-field measurements show an nexpected magnetically disordered
+state, which is a candidate quantum spin liquid. We establish the $(H,T)$ phase
+diagram, mapping in detail the quantum fluctuation corrections to the available
+theoretical analysis. These include a strong upshift in field of the maximum
+ordering temperature, qualitative changes to both low- and high-field phase
+boundaries, and an ordered regime apparently dominated by the collinear
+""up-up-down"" state. Ba$_8$CoNb$_6$O$_{24}$ therefore offers fresh input for the
+development of theoretical approaches to the field-induced quantum phase
+transitions of the $S = 1/2$ Heisenberg TLAFM.
+",0,1,0,0,0,0
+12932,Most Complex Non-Returning Regular Languages,"  A regular language $L$ is non-returning if in the minimal deterministic
+finite automaton accepting it there are no transitions into the initial state.
+Eom, Han and Jirásková derived upper bounds on the state complexity of
+boolean operations and Kleene star, and proved that these bounds are tight
+using two different binary witnesses. They derived upper bounds for
+concatenation and reversal using three different ternary witnesses. These five
+witnesses use a total of six different transformations. We show that for each
+$n\ge 4$ there exists a ternary witness of state complexity $n$ that meets the
+bound for reversal and that at least three letters are needed to meet this
+bound. Moreover, the restrictions of this witness to binary alphabets meet the
+bounds for product, star, and boolean operations. We also derive tight upper
+bounds on the state complexity of binary operations that take arguments with
+different alphabets. We prove that the maximal syntactic semigroup of a
+non-returning language has $(n-1)^n$ elements and requires at least
+$\binom{n}{2}$ generators. We find the maximal state complexities of atoms of
+non-returning languages. Finally, we show that there exists a most complex
+non-returning language that meets the bounds for all these complexity measures.
+",1,0,0,0,0,0
+12933,ArtGAN: Artwork Synthesis with Conditional Categorical GANs,"  This paper proposes an extension to the Generative Adversarial Networks
+(GANs), namely as ARTGAN to synthetically generate more challenging and complex
+images such as artwork that have abstract characteristics. This is in contrast
+to most of the current solutions that focused on generating natural images such
+as room interiors, birds, flowers and faces. The key innovation of our work is
+to allow back-propagation of the loss function w.r.t. the labels (randomly
+assigned to each generated images) to the generator from the discriminator.
+With the feedback from the label information, the generator is able to learn
+faster and achieve better generated image quality. Empirically, we show that
+the proposed ARTGAN is capable to create realistic artwork, as well as generate
+compelling real world images that globally look natural with clear shape on
+CIFAR-10.
+",1,0,0,0,0,0
+12934,Polynomial bound for the nilpotency index of finitely generated nil algebras,"  Working over an infinite field of positive characteristic, an upper bound is
+given for the nilpotency index of a finitely generated nil algebra of bounded
+nil index $n$ in terms of the maximal degree in a minimal homogenous generating
+system of the ring of simultaneous conjugation invariants of tuples of $n$ by
+$n$ matrices. This is deduced from a result of Zubkov. As a consequence, a
+recent degree bound due to Derksen and Makam for the generators of the ring of
+matrix invariants yields an upper bound for the nilpotency index of a finitely
+generated nil algebra that is polynomial in the number of generators and the
+nil index. Furthermore, a characteristic free treatment is given to Kuzmin's
+lower bound for the nilpotency index.
+",0,0,1,0,0,0
+12935,CFAAR: Control Flow Alteration to Assist Repair,"  We present CFAAR, a program repair assistance technique that operates by
+selectively altering the outcome of suspicious predicates in order to yield
+expected behavior. CFAAR is applicable to defects that are repairable by
+negating predicates under specific conditions. CFAAR proceeds as follows: 1) it
+identifies predicates such that negating them at given instances would make the
+failing tests exhibit correct behavior; 2) for each candidate predicate, it
+uses the program state information to build a classifier that dictates when the
+predicate should be negated; 3) for each classifier, it leverages a Decision
+Tree to synthesize a patch to be presented to the developer. We evaluated our
+toolset using 149 defects from the IntroClass and Siemens benchmarks. CFAAR
+identified 91 potential candidate defects and generated plausible patches for
+41 of them. Twelve of the patches are believed to be correct, whereas the rest
+provide repair assistance to the developer.
+",1,0,0,0,0,0
+12936,Cross-Sentence N-ary Relation Extraction with Graph LSTMs,"  Past work in relation extraction has focused on binary relations in single
+sentences. Recent NLP inroads in high-value domains have sparked interest in
+the more general setting of extracting n-ary relations that span multiple
+sentences. In this paper, we explore a general relation extraction framework
+based on graph long short-term memory networks (graph LSTMs) that can be easily
+extended to cross-sentence n-ary relation extraction. The graph formulation
+provides a unified way of exploring different LSTM approaches and incorporating
+various intra-sentential and inter-sentential dependencies, such as sequential,
+syntactic, and discourse relations. A robust contextual representation is
+learned for the entities, which serves as input to the relation classifier.
+This simplifies handling of relations with arbitrary arity, and enables
+multi-task learning with related relations. We evaluate this framework in two
+important precision medicine settings, demonstrating its effectiveness with
+both conventional supervised learning and distant supervision. Cross-sentence
+extraction produced larger knowledge bases. and multi-task learning
+significantly improved extraction accuracy. A thorough analysis of various LSTM
+approaches yielded useful insight the impact of linguistic analysis on
+extraction accuracy.
+",1,0,0,0,0,0
+12937,Community Interaction and Conflict on the Web,"  Users organize themselves into communities on web platforms. These
+communities can interact with one another, often leading to conflicts and toxic
+interactions. However, little is known about the mechanisms of interactions
+between communities and how they impact users.
+Here we study intercommunity interactions across 36,000 communities on
+Reddit, examining cases where users of one community are mobilized by negative
+sentiment to comment in another community. We show that such conflicts tend to
+be initiated by a handful of communities---less than 1% of communities start
+74% of conflicts. While conflicts tend to be initiated by highly active
+community members, they are carried out by significantly less active members.
+We find that conflicts are marked by formation of echo chambers, where users
+primarily talk to other users from their own community. In the long-term,
+conflicts have adverse effects and reduce the overall activity of users in the
+targeted communities.
+Our analysis of user interactions also suggests strategies for mitigating the
+negative impact of conflicts---such as increasing direct engagement between
+attackers and defenders. Further, we accurately predict whether a conflict will
+occur by creating a novel LSTM model that combines graph embeddings, user,
+community, and text features. This model can be used toreate early-warning
+systems for community moderators to prevent conflicts. Altogether, this work
+presents a data-driven view of community interactions and conflict, and paves
+the way towards healthier online communities.
+",1,0,0,0,0,0
+12938,Recurrence network measures for hypothesis testing using surrogate data: application to black hole light curves,"  Recurrence networks and the associated statistical measures have become
+important tools in the analysis of time series data. In this work, we test how
+effective the recurrence network measures are in analyzing real world data
+involving two main types of noise, white noise and colored noise. We use two
+prominent network measures as discriminating statistic for hypothesis testing
+using surrogate data for a specific null hypothesis that the data is derived
+from a linear stochastic process. We show that the characteristic path length
+is especially efficient as a discriminating measure with the conclusions
+reasonably accurate even with limited number of data points in the time series.
+We also highlight an additional advantage of the network approach in
+identifying the dimensionality of the system underlying the time series through
+a convergence measure derived from the probability distribution of the local
+clustering coefficients. As examples of real world data, we use the light
+curves from a prominent black hole system and show that a combined analysis
+using three primary network measures can provide vital information regarding
+the nature of temporal variability of light curves from different spectroscopic
+classes.
+",0,1,0,0,0,0
+12939,Diffraction-limited plenoptic imaging with correlated light,"  Traditional optical imaging faces an unavoidable trade-off between resolution
+and depth of field (DOF). To increase resolution, high numerical apertures (NA)
+are needed, but the associated large angular uncertainty results in a limited
+range of depths that can be put in sharp focus. Plenoptic imaging was
+introduced a few years ago to remedy this trade off. To this aim, plenoptic
+imaging reconstructs the path of light rays from the lens to the sensor.
+However, the improvement offered by standard plenoptic imaging is practical and
+not fundamental: the increased DOF leads to a proportional reduction of the
+resolution well above the diffraction limit imposed by the lens NA. In this
+paper, we demonstrate that correlation measurements enable pushing plenoptic
+imaging to its fundamental limits of both resolution and DOF. Namely, we
+demonstrate to maintain the imaging resolution at the diffraction limit while
+increasing the depth of field by a factor of 7. Our results represent the
+theoretical and experimental basis for the effective development of the
+promising applications of plenoptic imaging.
+",0,1,0,0,0,0
+12940,"Binary Classification with Karmic, Threshold-Quasi-Concave Metrics","  Complex performance measures, beyond the popular measure of accuracy, are
+increasingly being used in the context of binary classification. These complex
+performance measures are typically not even decomposable, that is, the loss
+evaluated on a batch of samples cannot typically be expressed as a sum or
+average of losses evaluated at individual samples, which in turn requires new
+theoretical and methodological developments beyond standard treatments of
+supervised learning. In this paper, we advance this understanding of binary
+classification for complex performance measures by identifying two key
+properties: a so-called Karmic property, and a more technical
+threshold-quasi-concavity property, which we show is milder than existing
+structural assumptions imposed on performance measures. Under these properties,
+we show that the Bayes optimal classifier is a threshold function of the
+conditional probability of positive class. We then leverage this result to come
+up with a computationally practical plug-in classifier, via a novel threshold
+estimator, and further, provide a novel statistical analysis of classification
+error with respect to complex performance measures.
+",0,0,0,1,0,0
+12941,Learning from Noisy Label Distributions,"  In this paper, we consider a novel machine learning problem, that is,
+learning a classifier from noisy label distributions. In this problem, each
+instance with a feature vector belongs to at least one group. Then, instead of
+the true label of each instance, we observe the label distribution of the
+instances associated with a group, where the label distribution is distorted by
+an unknown noise. Our goals are to (1) estimate the true label of each
+instance, and (2) learn a classifier that predicts the true label of a new
+instance. We propose a probabilistic model that considers true label
+distributions of groups and parameters that represent the noise as hidden
+variables. The model can be learned based on a variational Bayesian method. In
+numerical experiments, we show that the proposed model outperforms existing
+methods in terms of the estimation of the true labels of instances.
+",1,0,0,1,0,0
+12942,Commissioning of FLAG: A phased array feed for the GBT,"  Phased Array Feed (PAF) technology is the next major advancement in radio
+astronomy in terms of combining high sensitivity and large field of view. The
+Focal L-band Array for the Green Bank Telescope (FLAG) is one of the most
+sensitive PAFs developed so far. It consists of 19 dual-polarization elements
+mounted on a prime focus dewar resulting in seven beams on the sky. Its
+unprecedented system temperature of$\sim$17 K will lead to a 3 fold increase in
+pulsar survey speeds as compared to contemporary single pixel feeds. Early
+science observations were conducted in a recently concluded commissioning phase
+of the FLAG where we clearly demonstrated its science capabilities. We observed
+a selection of normal and millisecond pulsars and detected giant pulses from
+PSR B1937+21.
+",0,1,0,0,0,0
+12943,Arithmetic representations of fundamental groups I,"  Let $X$ be a normal algebraic variety over a finitely generated field $k$ of
+characteristic zero, and let $\ell$ be a prime. Say that a continuous
+$\ell$-adic representation $\rho$ of $\pi_1^{\text{ét}}(X_{\bar k})$ is
+arithmetic if there exists a representation $\tilde \rho$ of a finite index
+subgroup of $\pi_1^{\text{ét}}(X)$, with $\rho$ a subquotient of
+$\tilde\rho|_{\pi_1(X_{\bar k})}$. We show that there exists an integer $N=N(X,
+\ell)$ such that every nontrivial, semisimple arithmetic representation of
+$\pi_1^{\text{ét}}(X_{\bar k})$ is nontrivial mod $\ell^N$. As a corollary,
+we prove that any nontrivial semisimple representation of
+$\pi_1^{\text{ét}}(X_{\bar k})$, which arises from geometry, is nontrivial
+mod $\ell^N$.
+",0,0,1,0,0,0
+12944,Optimization over Degree Sequences,"  We introduce and study the problem of optimizing arbitrary functions over
+degree sequences of hypergraphs and multihypergraphs. We show that over
+multihypergraphs the problem can be solved in polynomial time. For hypergraphs,
+we show that deciding if a given sequence is the degree sequence of a
+3-hypergraph is NP-complete, thereby solving a 30 year long open problem. This
+implies that optimization over hypergraphs is hard already for simple concave
+functions. In contrast, we show that for graphs, if the functions at vertices
+are the same, then the problem is polynomial time solvable. We also provide
+positive results for convex optimization over multihypergraphs and graphs and
+exploit connections to degree sequence polytopes and threshold graphs. We then
+elaborate on connections to the emerging theory of shifted combinatorial
+optimization.
+",1,0,1,0,0,0
+12945,Interferometric Monitoring of Gamma-ray Bright AGNs: S5 0716+714,"  We present the results of very long baseline interferometry (VLBI)
+observations of gamma-ray bright blazar S5 0716+714 using the Korean VLBI
+Network (KVN) at the 22, 43, 86, and 129 GHz bands, as part of the
+Interferometric Monitoring of Gamma-ray Bright AGNs (iMOGABA) KVN key science
+program. Observations were conducted in 29 sessions from January 16, 2013 to
+March 1, 2016, with the source being detected and imaged at all available
+frequencies. In all epochs, the source was compact on the milliarcsecond (mas)
+scale, yielding a compact VLBI core dominating the synchrotron emission on
+these scales. Based on the multi-wavelength data between 15 GHz (Owens Valley
+Radio Observatory) and 230 GHz (Submillimeter Array), we found that the source
+shows multiple prominent enhancements of the flux density at the centimeter
+(cm) and millimeter (mm) wavelengths, with mm enhancements leading cm
+enhancements by -16$\pm$8 days. The turnover frequency was found to vary
+between 21 to 69GHz during our observations. By assuming a synchrotron
+self-absorption model for the relativistic jet emission in S5 0716+714, we
+found the magnetic field strength in the mas emission region to be $\le$5 mG
+during the observing period, yielding a weighted mean of 1.0$\pm$0.6 mG for
+higher turnover frequencies (e.g., >45 GHz).
+",0,1,0,0,0,0
+12946,Parabolic equations with natural growth approximated by nonlocal equations,"  In this paper we study several aspects related with solutions of nonlocal
+problems whose prototype is $$ u_t =\displaystyle \int_{\mathbb{R}^N} J(x-y)
+\big( u(y,t) -u(x,t) \big) \mathcal G\big( u(y,t) -u(x,t) \big) dy \qquad
+\mbox{ in } \, \Omega \times (0,T)\,, $$ being $ u (x,t)=0 \mbox{ in }
+(\mathbb{R}^N\setminus \Omega )\times (0,T)\,$ and $ u(x,0)=u_0 (x) \mbox{ in }
+\Omega$. We take, as the most important instance, $\mathcal G (s) \sim 1+
+\frac{\mu}{2} \frac{s}{1+\mu^2 s^2 }$ with $\mu\in \mathbb{R}$ as well as $u_0
+\in L^1 (\Omega)$, $J$ is a smooth symmetric function with compact support and
+$\Omega$ is either a bounded smooth subset of $\mathbb{R}^N$, with nonlocal
+Dirichlet boundary condition, or $\mathbb{R}^N$ itself.
+The results deal with existence, uniqueness, comparison principle and
+asymptotic behavior. Moreover we prove that if the kernel rescales in a
+suitable way, the unique solution of the above problem converges to a solution
+of the deterministic Kardar-Parisi-Zhang equation.
+",0,0,1,0,0,0
+12947,Excitonic gap generation in thin-film topological insulators,"  In this work, we analyze the excitonic gap generation in the strong-coupling
+regime of thin films of three-dimensional time-reversal-invariant topological
+insulators. We start by writing down the effective gauge theory in
+2+1-dimensions from the projection of the 3+1-dimensional quantum
+electrodynamics. Within this method, we obtain a short-range interaction, which
+has the form of a Thirring-like term, and a long-range one. The interaction
+between the two surface states of the material induces an excitonic gap. By
+using the large-$N$ approximation in the strong-coupling limit, we find that
+there is a dynamical mass generation for the excitonic states that preserves
+time-reversal symmetry and is related to the dynamical chiral-symmetry breaking
+of our model. This symmetry breaking occurs only for values of the
+fermion-flavor number smaller than $N_{c}\approx 11.8$. Our results show that
+the inclusion of the full dynamical interaction strongly modifies the critical
+number of flavors for the occurrence of exciton condensation, and therefore,
+cannot be neglected.
+",0,1,0,0,0,0
+12948,Constructive Preference Elicitation over Hybrid Combinatorial Spaces,"  Preference elicitation is the task of suggesting a highly preferred
+configuration to a decision maker. The preferences are typically learned by
+querying the user for choice feedback over pairs or sets of objects. In its
+constructive variant, new objects are synthesized ""from scratch"" by maximizing
+an estimate of the user utility over a combinatorial (possibly infinite) space
+of candidates. In the constructive setting, most existing elicitation
+techniques fail because they rely on exhaustive enumeration of the candidates.
+A previous solution explicitly designed for constructive tasks comes with no
+formal performance guarantees, and can be very expensive in (or unapplicable
+to) problems with non-Boolean attributes. We propose the Choice Perceptron, a
+Perceptron-like algorithm for learning user preferences from set-wise choice
+feedback over constructive domains and hybrid Boolean-numeric feature spaces.
+We provide a theoretical analysis on the attained regret that holds for a large
+class of query selection strategies, and devise a heuristic strategy that aims
+at optimizing the regret in practice. Finally, we demonstrate its effectiveness
+by empirical evaluation against existing competitors on constructive scenarios
+of increasing complexity.
+",1,0,0,0,0,0
+12949,Ginzburg-Landau equations on Riemann surfaces of higher genus,"  We study the Ginzburg-Landau equations on Riemann surfaces of arbitrary
+genus. In particular: we explicitly construct the (local moduli space of
+gauge-equivalent) solutions in a neighbourhood of a constant curvature branch
+of solutions; in linearizing the problem, we find a relation with de Rham
+cohomology groups of the surface; we classify holomorphic structures on line
+bundles arising as solutions to the equations in terms of the degree, the
+Abel-Jacobi map, and symmetric products of the surface; we construct explicitly
+the automorphy factors and the equivariant connection on the trivial bundle
+over the Poincaré upper complex half plane.
+",0,0,1,0,0,0
+12950,On the Hamming Auto- and Cross-correlation Functions of a Class of Frequency Hopping Sequences of Length $ p^{n} $,"  In this paper, a new class of frequency hopping sequences (FHSs) of length $
+p^{n} $ is constructed by using Ding-Helleseth generalized cyclotomic classes
+of order 2, of which the Hamming auto- and cross-correlation functions are
+investigated (for the Hamming cross-correlation, only the case $ p\equiv 3\pmod
+4 $ is considered). It is shown that the set of the constructed FHSs is optimal
+with respect to the average Hamming correlation functions.
+",1,0,0,0,0,0
+12951,Scikit-Multiflow: A Multi-output Streaming Framework,"  Scikit-multiflow is a multi-output/multi-label and stream data mining
+framework for the Python programming language. Conceived to serve as a platform
+to encourage democratization of stream learning research, it provides multiple
+state of the art methods for stream learning, stream generators and evaluators.
+scikit-multiflow builds upon popular open source frameworks including
+scikit-learn, MOA and MEKA. Development follows the FOSS principles and quality
+is enforced by complying with PEP8 guidelines and using continuous integration
+and automatic testing. The source code is publicly available at
+this https URL.
+",0,0,0,1,0,0
+12952,Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows,"  Distributed computing platforms provide a robust mechanism to perform
+large-scale computations by splitting the task and data among multiple
+locations, possibly located thousands of miles apart geographically. Although
+such distribution of resources can lead to benefits, it also comes with its
+associated problems such as rampant duplication of file transfers increasing
+congestion, long job completion times, unexpected site crashing, suboptimal
+data transfer rates, unpredictable reliability in a time range, and suboptimal
+usage of storage elements. In addition, each sub-system becomes a potential
+failure node that can trigger system wide disruptions. In this vision paper, we
+outline our approach to leveraging Deep Learning algorithms to discover
+solutions to unique problems that arise in a system with computational
+infrastructure that is spread over a wide area. The presented vision, motivated
+by a real scientific use case from Belle II experiments, is to develop
+multilayer neural networks to tackle forecasting, anomaly detection and
+optimization challenges in a complex and distributed data movement environment.
+Through this vision based on Deep Learning principles, we aim to achieve
+reduced congestion events, faster file transfer rates, and enhanced site
+reliability.
+",1,0,0,0,0,0
+12953,The Rational Sectional Category of Certain Universal Fibrations,"  We prove that the sectional category of the universal fibration with fibre X,
+for X any space that satisfies a well-known conjecture of Halperin, equals one
+after rationalization.
+",0,0,1,0,0,0
+12954,Characterising exo-ringsystems around fast-rotating stars using the Rossiter-McLaughlin effect,"  Planetary rings produce a distinct shape distortion in transit lightcurves.
+However, to accurately model such lightcurves the observations need to cover
+the entire transit, especially ingress and egress, as well as an out-of-transit
+baseline. Such observations can be challenging for long period planets, where
+the transits may last for over a day. Planetary rings will also impact the
+shape of absorption lines in the stellar spectrum, as the planet and rings
+cover different parts of the rotating star (the Rossiter-McLaughlin effect).
+These line-profile distortions depend on the size, structure, opacity,
+obliquity and sky projected angle of the ring system. For slow rotating stars,
+this mainly impacts the amplitude of the induced velocity shift, however, for
+fast rotating stars the large velocity gradient across the star allows the line
+distortion to be resolved, enabling direct determination of the ring
+parameters. We demonstrate that by modeling these distortions we can recover
+ring system parameters (sky-projected angle, obliquity and size) using only a
+small part of the transit. Substructure in the rings, e.g. gaps, can be
+recovered if the width of the features ($\delta W$) relative to the size of the
+star is similar to the intrinsic velocity resolution (set by the width of the
+local stellar profile, $\gamma$) relative to the stellar rotation velocity ($v$
+sin$i$, i.e. $\delta W / R_* \gtrsim v$sin$i$/$\gamma$). This opens up a new
+way to study the ring systems around planets with long orbital periods, where
+observations of the full transit, covering the ingress and egress, are not
+always feasible.
+",0,1,0,0,0,0
+12955,Learning a Generative Model of Cancer Metastasis,"  We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer
+Genome Atlas (TCGA). We demonstrate that the UFDN learns a biologically
+relevant latent space of gene expression data by applying our network to two
+classification tasks of cancer status and cancer type. Our UFDN specific
+algorithms perform comparably to random forest methods. The UFDN allows for
+continuous, partial interpolation between distinct cancer types. Furthermore,
+we perform an analysis of differentially expressed genes between skin cutaneous
+melanoma(SKCM) samples and the same samples interpolated into glioblastoma
+(GBM). We demonstrate that our interpolations learn relevant metagenes that
+recapitulate known glioblastoma mechanisms and suggest possible starting points
+for investigations into the metastasis of SKCM into GBM.
+",0,0,0,0,1,0
+12956,Joint User Selection and Energy Minimization for Ultra-Dense Multi-channel C-RAN with Incomplete CSI,"  This paper provides a unified framework to deal with the challenges arising
+in dense cloud radio access networks (C-RAN), which include huge power
+consumption, limited fronthaul capacity, heavy computational complexity,
+unavailability of full channel state information (CSI), etc. Specifically, we
+aim to jointly optimize the remote radio head (RRH) selection, user equipment
+(UE)-RRH associations and beam-vectors to minimize the total network power
+consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI
+subject to per-RRH power constraints, each UE's total rate requirement, and
+fronthaul link capacity constraints. This optimization problem is NP-hard. In
+addition, due to the incomplete CSI, the exact expression of UEs' rate
+expression is intractable. We first conservatively replace UEs' rate expression
+with its lower-bound. Then, based on the successive convex approximation (SCA)
+technique and the relationship between the data rate and the mean square error
+(MSE), we propose a single-layer iterative algorithm to solve the NPC
+minimization problem with convergence guarantee. In each iteration of the
+algorithm, the Lagrange dual decomposition method is used to derive the
+structure of the optimal beam-vectors, which facilitates the parallel
+computations at the Baseband unit (BBU) pool. Furthermore, a bisection UE
+selection algorithm is proposed to guarantee the feasibility of the problem.
+Simulation results show the benefits of the proposed algorithms and the fact
+that a limited amount of CSI is sufficient to achieve performance close to that
+obtained when perfect CSI is possessed.
+",1,0,0,0,0,0
+12957,On Sidorenko's conjecture for determinants and Gaussian Markov random fields,"  We study a class of determinant inequalities that are closely related to
+Sidorenko's famous conjecture (Also conjectured by Erd\H os and Simonovits in a
+different form). Our results can also be interpreted as entropy inequalities
+for Gaussian Markov random fields (GMRF). We call a GMRF on a finite graph $G$
+homogeneous if the marginal distributions on the edges are all identical. We
+show that if $G$ satisfies Sidorenko's conjecture then the differential entropy
+of any homogeneous GMRF on $G$ is at least $|E(G)|$ times the edge entropy plus
+$|V(G)|-2|E(G)|$ times the point entropy. We also prove this inequality in a
+large class of graphs for which Sidorenko's conjecture is not verified
+including the so-called Möbius ladder: $K_{5,5}\setminus C_{10}$. The
+connection between Sidorenko's conjecture and GMRF's is established via a large
+deviation principle on high dimensional spheres combined with graph limit
+theory.
+",0,0,1,0,0,0
+12958,Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations,"  Power grids are critical infrastructure assets that face non-technical losses
+(NTL) such as electricity theft or faulty meters. NTL may range up to 40% of
+the total electricity distributed in emerging countries. Industrial NTL
+detection systems are still largely based on expert knowledge when deciding
+whether to carry out costly on-site inspections of customers. Electricity
+providers are reluctant to move to large-scale deployments of automated systems
+that learn NTL profiles from data due to the latter's propensity to suggest a
+large number of unnecessary inspections. In this paper, we propose a novel
+system that combines automated statistical decision making with expert
+knowledge. First, we propose a machine learning framework that classifies
+customers into NTL or non-NTL using a variety of features derived from the
+customers' consumption data. The methodology used is specifically tailored to
+the level of noise in the data. Second, in order to allow human experts to feed
+their knowledge in the decision loop, we propose a method for visualizing
+prediction results at various granularity levels in a spatial hologram. Our
+approach allows domain experts to put the classification results into the
+context of the data and to incorporate their knowledge for making the final
+decisions of which customers to inspect. This work has resulted in appreciable
+results on a real-world data set of 3.6M customers. Our system is being
+deployed in a commercial NTL detection software.
+",1,0,0,0,0,0
+12959,A model of reward-modulated motor learning with parallelcortical and basal ganglia pathways,"  Many recent studies of the motor system are divided into two distinct
+approaches: Those that investigate how motor responses are encoded in cortical
+neurons' firing rate dynamics and those that study the learning rules by which
+mammals and songbirds develop reliable motor responses. Computationally, the
+first approach is encapsulated by reservoir computing models, which can learn
+intricate motor tasks and produce internal dynamics strikingly similar to those
+of motor cortical neurons, but rely on biologically unrealistic learning rules.
+The more realistic learning rules developed by the second approach are often
+derived for simplified, discrete tasks in contrast to the intricate dynamics
+that characterize real motor responses. We bridge these two approaches to
+develop a biologically realistic learning rule for reservoir computing. Our
+algorithm learns simulated motor tasks on which previous reservoir computing
+algorithms fail, and reproduces experimental findings including those that
+relate motor learning to Parkinson's disease and its treatment.
+",0,0,0,0,1,0
+12960,A de Sitter limit analysis for dark energy and modified gravity models,"  The effective field theory of dark energy and modified gravity is supposed to
+well describe, at low energies, the behaviour of the gravity modifications due
+to one extra scalar degree of freedom. The usual curvature perturbation is very
+useful when studying the conditions for the avoidance of ghost instabilities as
+well as the positivity of the squared speeds of propagation for both the scalar
+and tensor modes, or the Stückelberg field performs perfectly when
+investigating the evolution of linear perturbations. We show that the viable
+parameters space identified by requiring no-ghost instabilities and positive
+squared speeds of propagation does not change by performing a field
+redefinition, while the requirement of the avoidance of tachyonic instability
+might instead be different. Therefore, we find interesting to associate to the
+general modified gravity theory described in the effective field theory
+framework, a perturbation field which will inherit the whole properties of the
+theory. In the present paper we address the following questions: 1) how can we
+define such a field? and 2) what is the mass of such a field as the background
+approaches a final de Sitter state? We define a gauge invariant quantity which
+identifies the density of the dark energy perturbation field valid for any
+background. We derive the mass associated to the gauge invariant dark energy
+field on a de Sitter background, which we retain to be still a good
+approximation also at very low redshift ($z\simeq 0$). On this background we
+also investigate the value of the speed of propagation and we find that there
+exist classes of theories which admit a non-vanishing speed of propagation,
+even among the Horndeski model, for which in literature it has previously been
+found a zero speed. We finally apply our results to specific well known models.
+",0,1,0,0,0,0
+12961,Empirical Analysis on Comparing the Performance of Alpha Miner Algorithm in SQL Query Language and NoSQL Column-Oriented Databases Using Apache Phoenix,"  Process-Aware Information Systems (PAIS) is an IT system that support
+business processes and generate large amounts of event logs from the execution
+of business processes. An event log is represented as a tuple of CaseID,
+Timestamp, Activity and Actor. Process Mining is a new and emerging field that
+aims at analyzing the event logs to discover, enhance and improve business
+processes and check conformance between run time and design time business
+processes. The large volume of event logs generated are stored in the
+databases. Relational databases perform well for a certain class of
+applications. However, there are a certain class of applications for which
+relational databases are not able to scale. To handle such class of
+applications, NoSQL database systems emerged. Discovering a process model
+(workflow model) from event logs is one of the most challenging and important
+Process Mining task. The $\alpha$-miner algorithm is one of the first and most
+widely used Process Discovery technique. Our objective is to investigate which
+of the databases (Relational or NoSQL) performs better for a Process Discovery
+application under Process Mining. We implement the $\alpha$-miner algorithm on
+relational (row-oriented) and NoSQL (column-oriented) databases in database
+query languages so that our algorithm is tightly coupled to the database. We
+present a performance benchmarking and comparison of the $\alpha$-miner
+algorithm on row-oriented database and NoSQL column-oriented database so that
+we can compare which database can efficiently store massive event logs and
+analyze it in seconds to discover a process model.
+",1,0,0,0,0,0
+12962,Cross-modal Deep Metric Learning with Multi-task Regularization,"  DNN-based cross-modal retrieval has become a research hotspot, by which users
+can search results across various modalities like image and text. However,
+existing methods mainly focus on the pairwise correlation and reconstruction
+error of labeled data. They ignore the semantically similar and dissimilar
+constraints between different modalities, and cannot take advantage of
+unlabeled data. This paper proposes Cross-modal Deep Metric Learning with
+Multi-task Regularization (CDMLMR), which integrates quadruplet ranking loss
+and semi-supervised contrastive loss for modeling cross-modal semantic
+similarity in a unified multi-task learning architecture. The quadruplet
+ranking loss can model the semantically similar and dissimilar constraints to
+preserve cross-modal relative similarity ranking information. The
+semi-supervised contrastive loss is able to maximize the semantic similarity on
+both labeled and unlabeled data. Compared to the existing methods, CDMLMR
+exploits not only the similarity ranking information but also unlabeled
+cross-modal data, and thus boosts cross-modal retrieval accuracy.
+",1,0,0,1,0,0
+12963,$W$-entropy formulas on super Ricci flows and Langevin deformation on Wasserstein space over Riemannian manifolds,"  In this survey paper, we give an overview of our recent works on the study of
+the $W$-entropy for the heat equation associated with the Witten Laplacian on
+super-Ricci flows and the Langevin deformation on Wasserstein space over
+Riemannian manifolds. Inspired by Perelman's seminal work on the entropy
+formula for the Ricci flow, we prove the $W$-entropy formula for the heat
+equation associated with the Witten Laplacian on $n$-dimensional complete
+Riemannian manifolds with the $CD(K, m)$-condition, and the $W$-entropy formula
+for the heat equation associated with the time dependent Witten Laplacian on
+$n$-dimensional compact manifolds equipped with a $(K, m)$-super Ricci flow,
+where $K\in \mathbb{R}$ and $m\in [n, \infty]$. Furthermore, we prove an
+analogue of the $W$-entropy formula for the geodesic flow on the Wasserstein
+space over Riemannian manifolds. Our result recaptures an important result due
+to Lott and Villani on the displacement convexity of the Boltzmann-Shannon
+entropy on Riemannian manifolds with non-negative Ricci curvature. To better
+understand the similarity between above two $W$-entropy formulas, we introduce
+the Langevin deformation of geometric flows on the cotangent bundle over the
+Wasserstein space and prove an extension of the $W$-entropy formula for the
+Langevin deformation. Finally, we make a discussion on the $W$-entropy for the
+Ricci flow from the point of view of statistical mechanics and probability
+theory.
+",0,0,1,0,0,0
+12964,Cancellable elements of the lattice of semigroup varieties,"  We completely determine all commutative semigroup varieties that are
+cancellable elements of the lattice SEM of all semigroup varieties. In
+particular, we prove that, for commutative varieties, the properties of being
+cancellable and modular elements of SEM are equivalent.
+",0,0,1,0,0,0
+12965,Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs,"  The availability of large scale event data with time stamps has given rise to
+dynamically evolving knowledge graphs that contain temporal information for
+each edge. Reasoning over time in such dynamic knowledge graphs is not yet well
+understood. To this end, we present Know-Evolve, a novel deep evolutionary
+knowledge network that learns non-linearly evolving entity representations over
+time. The occurrence of a fact (edge) is modeled as a multivariate point
+process whose intensity function is modulated by the score for that fact
+computed based on the learned entity embeddings. We demonstrate significantly
+improved performance over various relational learning approaches on two large
+scale real-world datasets. Further, our method effectively predicts occurrence
+or recurrence time of a fact which is novel compared to prior reasoning
+approaches in multi-relational setting.
+",1,0,0,0,0,0
+12966,Mechanisms of dimensionality reduction and decorrelation in deep neural networks,"  Deep neural networks are widely used in various domains. However, the nature
+of computations at each layer of the deep networks is far from being well
+understood. Increasing the interpretability of deep neural networks is thus
+important. Here, we construct a mean-field framework to understand how compact
+representations are developed across layers, not only in deterministic deep
+networks with random weights but also in generative deep networks where an
+unsupervised learning is carried out. Our theory shows that the deep
+computation implements a dimensionality reduction while maintaining a finite
+level of weak correlations between neurons for possible feature extraction.
+Mechanisms of dimensionality reduction and decorrelation are unified in the
+same framework. This work may pave the way for understanding how a sensory
+hierarchy works.
+",1,0,0,1,0,0
+12967,Detecting Strong Ties Using Network Motifs,"  Detecting strong ties among users in social and information networks is a
+fundamental operation that can improve performance on a multitude of
+personalization and ranking tasks. Strong-tie edges are often readily obtained
+from the social network as users often participate in multiple overlapping
+networks via features such as following and messaging. These networks may vary
+greatly in size, density and the information they carry. This setting leads to
+a natural strong tie detection task: given a small set of labeled strong tie
+edges, how well can one detect unlabeled strong ties in the remainder of the
+network?
+This task becomes particularly daunting for the Twitter network due to scant
+availability of pairwise relationship attribute data, and sparsity of strong
+tie networks such as phone contacts. Given these challenges, a natural approach
+is to instead use structural network features for the task, produced by {\em
+combining} the strong and ""weak"" edges. In this work, we demonstrate via
+experiments on Twitter data that using only such structural network features is
+sufficient for detecting strong ties with high precision. These structural
+network features are obtained from the presence and frequency of small network
+motifs on combined strong and weak ties. We observe that using motifs larger
+than triads alleviate sparsity problems that arise for smaller motifs, both due
+to increased combinatorial possibilities as well as benefiting strongly from
+searching beyond the ego network. Empirically, we observe that not all motifs
+are equally useful, and need to be carefully constructed from the combined
+edges in order to be effective for strong tie detection. Finally, we reinforce
+our experimental findings with providing theoretical justification that
+suggests why incorporating these larger sized motifs as features could lead to
+increased performance in planted graph models.
+",1,1,0,0,0,0
+12968,Streaming Weak Submodularity: Interpreting Neural Networks on the Fly,"  In many machine learning applications, it is important to explain the
+predictions of a black-box classifier. For example, why does a deep neural
+network assign an image to a particular class? We cast interpretability of
+black-box classifiers as a combinatorial maximization problem and propose an
+efficient streaming algorithm to solve it subject to cardinality constraints.
+By extending ideas from Badanidiyuru et al. [2014], we provide a constant
+factor approximation guarantee for our algorithm in the case of random stream
+order and a weakly submodular objective function. This is the first such
+theoretical guarantee for this general class of functions, and we also show
+that no such algorithm exists for a worst case stream order. Our algorithm
+obtains similar explanations of Inception V3 predictions $10$ times faster than
+the state-of-the-art LIME framework of Ribeiro et al. [2016].
+",1,0,0,1,0,0
+12969,Staging superstructures in high-$T_c$ Sr/O co-doped La$_{2-x}$Sr$_x$CuO$_{4+y}$,"  We present high energy X-ray diffraction studies on the structural phases of
+an optimal high-$T_c$ superconductor La$_{2-x}$Sr$_x$CuO$_{4+y}$ tailored by
+co-hole-doping. This is specifically done by varying the content of two very
+different chemical species, Sr and O, respectively, in order to study the
+influence of each. A superstructure known as staging is observed in all
+samples, with the staging number $n$ increasing for higher Sr dopings $x$. We
+find that the staging phases emerge abruptly with temperature, and can be
+described as a second order phase transition with transition temperatures
+slightly depending on the Sr doping. The Sr appears to correlate the
+interstitial oxygen in a way that stabilises the reproducibility of the staging
+phase both in terms of staging period and volume fraction in a specific sample.
+The structural details as investigated in this letter appear to have no direct
+bearing on the electronic phase separation previously observed in the same
+samples. This provides new evidence that the electronic phase separation is
+determined by the overall hole concentration rather than specific Sr/O content
+and concommittant structural details.
+",0,1,0,0,0,0
+12970,Interface magnetism and electronic structure: ZnO(0001)/Co3O4(111),"  We have studied the structural, electronic and magnetic properties of spinel
+$\rm Co_3O_4$(111) surfaces and their interfaces with ZnO (0001) using density
+functional theory (DFT) within the Generalized Gradient Approximation with
+on-site Coulomb repulsion term (GGA+U). Two possible forms of spinel surface,
+containing $\rm Co^{2+} $ and $\rm Co^{3+} $ ions and terminated with either
+cobalt or oxygen ions were considered, as well as their interface with zinc
+oxide. Our calculations demonstrate that $\rm Co^{3+} $ ions attain non-zero
+magnetic moments at the surface and interface, in contrast to the bulk, where
+they are not magnetic, leading to the ferromagnetic ordering. Since heavily
+Co-doped ZnO samples can contain $\rm Co_3O_4 $ secondary phase, such a
+magnetic ordering at the interface might explain the origin of the magnetism in
+these diluted magnetic semiconductors (DMS).
+",0,1,0,0,0,0
+12971,Bloch-type spaces and extended Cesàro operators in the unit ball of a complex Banach space,"  Let $\mathbb{B}$ be the unit ball of a complex Banach space $X$. In this
+paper, we will generalize the Bloch-type spaces and the little Bloch-type
+spaces to the open unit ball $\mathbb{B}$ by using the radial derivative. Next,
+we define an extended Cesàro operator $T_{\varphi}$ with holomorphic symbol
+$\varphi$ and characterize those $\varphi$ for which $T_{\varphi}$ is bounded
+between the Bloch-type spaces and the little Bloch-type spaces. We also
+characterize those $\varphi$ for which $T_{\varphi}$ is compact between the
+Bloch-type spaces and the little Bloch-type spaces under some additional
+assumption on the symbol $\varphi$. When $\mathbb{B}$ is the open unit ball of
+a finite dimensional complex Banach space $X$, this additional assumption is
+automatically satisfied.
+",0,0,1,0,0,0
+12972,Alchemist: An Apache Spark <=> MPI Interface,"  The Apache Spark framework for distributed computation is popular in the data
+analytics community due to its ease of use, but its MapReduce-style programming
+model can incur significant overheads when performing computations that do not
+map directly onto this model. One way to mitigate these costs is to off-load
+computations onto MPI codes. In recent work, we introduced Alchemist, a system
+for the analysis of large-scale data sets. Alchemist calls MPI-based libraries
+from within Spark applications, and it has minimal coding, communication, and
+memory overheads. In particular, Alchemist allows users to retain the
+productivity benefits of working within the Spark software ecosystem without
+sacrificing performance efficiency in linear algebra, machine learning, and
+other related computations.
+In this paper, we discuss the motivation behind the development of Alchemist,
+and we provide a detailed overview its design and usage. We also demonstrate
+the efficiency of our approach on medium-to-large data sets, using some
+standard linear algebra operations, namely matrix multiplication and the
+truncated singular value decomposition of a dense matrix, and we compare the
+performance of Spark with that of Spark+Alchemist. These computations are run
+on the NERSC supercomputer Cori Phase 1, a Cray XC40.
+",0,0,0,1,0,0
+12973,Compositional (In)Finite Abstractions for Large-Scale Interconnected Stochastic Systems,"  This paper is concerned with a compositional approach for constructing both
+infinite (reduced-order models) and finite abstractions (a.k.a. finite Markov
+decision processes) of large-scale interconnected discrete-time stochastic
+control systems. The proposed framework is based on the notion of stochastic
+simulation functions enabling us to use an abstract system as a substitution of
+the original one in the controller design process with guaranteed error bounds.
+In the first part of the paper, we derive sufficient small-gain type conditions
+for the compositional quantification of the probabilistic distance between the
+interconnection of stochastic control subsystems and that of their infinite
+abstractions. We then construct infinite abstractions together with their
+corresponding stochastic simulation functions for a class of discrete-time
+nonlinear stochastic control systems. In the second part of the paper, we
+leverage small-gain type conditions for the compositional construction of
+finite abstractions. We propose an approach to construct finite Markov decision
+processes (MDPs) of the concrete models (or their reduced-order versions)
+satisfying an incremental input-to-state stability property. We also show that
+for a particular class of nonlinear stochastic control systems, the
+aforementioned property can be readily checked by matrix inequalities. We
+demonstrate the effectiveness of the proposed results by applying our
+approaches to the temperature regulation in a circular building and
+constructing compositionally a finite abstraction of a network containing 1000
+rooms. We also apply our proposed techniques to a fully connected network of 20
+nonlinear subsystems (totally 100 dimensions) and construct finite MDPs from
+their reduced-order versions (together 20 dimensions) with guaranteed error
+bounds on their output trajectories.
+",1,0,0,0,0,0
+12974,Infinitely generated symbolic Rees algebras over finite fields,"  For the polynomial ring over an arbitrary field with twelve variables, there
+exists a prime ideal whose symbolic Rees algebra is not finitely generated.
+",0,0,1,0,0,0
+12975,Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models,"  Deep generative neural networks have proven effective at both conditional and
+unconditional modeling of complex data distributions. Conditional generation
+enables interactive control, but creating new controls often requires expensive
+retraining. In this paper, we develop a method to condition generation without
+retraining the model. By post-hoc learning latent constraints, value functions
+that identify regions in latent space that generate outputs with desired
+attributes, we can conditionally sample from these regions with gradient-based
+optimization or amortized actor functions. Combining attribute constraints with
+a universal ""realism"" constraint, which enforces similarity to the data
+distribution, we generate realistic conditional images from an unconditional
+variational autoencoder. Further, using gradient-based optimization, we
+demonstrate identity-preserving transformations that make the minimal
+adjustment in latent space to modify the attributes of an image. Finally, with
+discrete sequences of musical notes, we demonstrate zero-shot conditional
+generation, learning latent constraints in the absence of labeled data or a
+differentiable reward function. Code with dedicated cloud instance has been
+made publicly available (this https URL).
+",1,0,0,1,0,0
+12976,Characterization of Zinc oxide & Aluminum Ferrite and Simulation studies of M-H plots of Cobalt/Cobaltoxide,"  Zinc oxide and Aluminum Ferrite were prepared Chemical route. The samples
+were characterized by XRD and VSM. Simulation of M-H plots of Co/CoO thin films
+were performed. Effect of parameters was observed on saturation magnetization.
+",0,1,0,0,0,0
+12977,Poisson Structures and Potentials,"  We introduce a notion of weakly log-canonical Poisson structures on positive
+varieties with potentials. Such a Poisson structure is log-canonical up to
+terms dominated by the potential. To a compatible real form of a weakly
+log-canonical Poisson variety we assign an integrable system on the product of
+a certain real convex polyhedral cone (the tropicalization of the variety) and
+a compact torus.
+We apply this theory to the dual Poisson-Lie group $G^*$ of a
+simply-connected semisimple complex Lie group $G$. We define a positive
+structure and potential on $G^*$ and show that the natural Poisson-Lie
+structure on $G^*$ is weakly log-canonical with respect to this positive
+structure and potential.
+For $K \subset G$ the compact real form, we show that the real form $K^*
+\subset G^*$ is compatible and prove that the corresponding integrable system
+is defined on the product of the decorated string cone and the compact torus of
+dimension $\frac{1}{2}({\rm dim} \, G - {\rm rank} \, G)$.
+",0,0,1,0,0,0
+12978,The closure of ideals of $\boldsymbol{\ell^1(Σ)}$ in its enveloping $\boldsymbol{\mathrm{C}^\ast}$-algebra,"  If $X$ is a compact Hausdorff space and $\sigma$ is a homeomorphism of $X$,
+then an involutive Banach algebra $\ell^1(\Sigma)$ of crossed product type is
+naturally associated with the topological dynamical system $\Sigma=(X,\sigma)$.
+We initiate the study of the relation between two-sided ideals of
+$\ell^1(\Sigma)$ and ${\mathrm C}^\ast(\Sigma)$, the enveloping
+$\mathrm{C}^\ast$-algebra ${\mathrm C}(X)\rtimes_\sigma \mathbb Z$ of
+$\ell^1(\Sigma)$. Among others, we prove that the closure of a proper two-sided
+ideal of $\ell^1(\Sigma)$ in ${\mathrm C}^\ast(\Sigma)$ is again a proper
+two-sided ideal of ${\mathrm C}^\ast(\Sigma)$.
+",0,0,1,0,0,0
+12979,An alternative quadratic formula,"  The classical quadratic formula and some of its lesser known variants for
+solving the quadratic equation are reviewed. Then, a new formula for the roots
+of a quadratic polynomial is presented.
+",0,0,1,0,0,0
+12980,"Sound event detection using weakly-labeled semi-supervised data with GCRNNS, VAT and Self-Adaptive Label Refinement","  In this paper, we present a gated convolutional recurrent neural network
+based approach to solve task 4, large-scale weakly labelled semi-supervised
+sound event detection in domestic environments, of the DCASE 2018 challenge.
+Gated linear units and a temporal attention layer are used to predict the onset
+and offset of sound events in 10s long audio clips. Whereby for training only
+weakly-labelled data is used. Virtual adversarial training is used for
+regularization, utilizing both labelled and unlabeled data. Furthermore, we
+introduce self-adaptive label refinement, a method which allows unsupervised
+adaption of our trained system to refine the accuracy of frame-level class
+predictions. The proposed system reaches an overall macro averaged event-based
+F-score of 34.6%, resulting in a relative improvement of 20.5% over the
+baseline system.
+",1,0,0,0,0,0
+12981,Inflationary $α$-attractor cosmology: A global dynamical systems perspective,"  We study flat FLRW $\alpha$-attractor $\mathrm{E}$- and $\mathrm{T}$-models
+by introducing a dynamical systems framework that yields regularized
+unconstrained field equations on two-dimensional compact state spaces. This
+results in both illustrative figures and a complete description of the entire
+solution spaces of these models, including asymptotics. In particular, it is
+shown that observational viability, which requires a sufficient number of
+$e$-folds, is associated with a solution given by a one-dimensional center
+manifold of a past asymptotic de Sitter state, where the center manifold
+structure also explains why nearby solutions are attracted to this
+`inflationary attractor solution.' A center manifold expansion yields a
+description of the inflationary regime with arbitrary analytic accuracy, where
+the slow-roll approximation asymptotically describes the tangency condition of
+the center manifold at the asymptotic de Sitter state.
+",0,1,0,0,0,0
+12982,Symmetric Convex Sets with Minimal Gaussian Surface Area,"  Let $\Omega\subset\mathbb{R}^{n+1}$ have minimal Gaussian surface area among
+all sets satisfying $\Omega=-\Omega$ with fixed Gaussian volume. Let $A=A_{x}$
+be the second fundamental form of $\partial\Omega$ at $x$, i.e. $A$ is the
+matrix of first order partial derivatives of the unit normal vector at
+$x\in\partial\Omega$. For any $x=(x_{1},\ldots,x_{n+1})\in\mathbb{R}^{n+1}$,
+let $\gamma_{n}(x)=(2\pi)^{-n/2}e^{-(x_{1}^{2}+\cdots+x_{n+1}^{2})/2}$. Let
+$\|A\|^{2}$ be the sum of the squares of the entries of $A$, and let
+$\|A\|_{2\to 2}$ denote the $\ell_{2}$ operator norm of $A$.
+It is shown that if $\Omega$ or $\Omega^{c}$ is convex, and if either
+$$\int_{\partial\Omega}(\|A_{x}\|^{2}-1)\gamma_{n}(x)dx>0\qquad\mbox{or}\qquad
+\int_{\partial\Omega}\Big(\|A_{x}\|^{2}-1+2\sup_{y\in\partial\Omega}\|A_{y}\|_{2\to
+2}^{2}\Big)\gamma_{n}(x)dx<0,$$ then $\partial\Omega$ must be a round cylinder.
+That is, except for the case that the average value of $\|A\|^{2}$ is slightly
+less than $1$, we resolve the convex case of a question of Barthe from 2001.
+The main tool is the Colding-Minicozzi theory for Gaussian minimal surfaces,
+which studies eigenfunctions of the Ornstein-Uhlenbeck type operator $L=
+\Delta-\langle x,\nabla \rangle+\|A\|^{2}+1$ associated to the surface
+$\partial\Omega$. A key new ingredient is the use of a randomly chosen degree 2
+polynomial in the second variation formula for the Gaussian surface area. Our
+actual results are a bit more general than the above statement. Also, some of
+our results hold without the assumption of convexity.
+",1,0,1,0,0,0
+12983,Advancements in Continuum Approximation Models for Logistics and Transportation Systems: 1996 - 2016,"  Continuum Approximation (CA) is an efficient and parsimonious technique for
+modeling complex logistics problems. In this paper,we review recent studies
+that develop CA models for transportation, distribution and logistics problems
+with the aim of synthesizing recent advancements and identifying current
+research gaps. This survey focuses on important principles and key results from
+CA models. In particular, we consider how these studies fill the gaps
+identified by the most recent literature reviews in this field. We observe that
+CA models are used in a wider range of applications, especially in the areas of
+facility location and integrated supply chain management. Most studies use CA
+as an alternative to exact solution approaches; however, CA can also be used in
+combination with exact approaches. We also conclude with promising areas of
+future work.
+",0,0,1,0,0,0
+12984,On a free boundary problem and minimal surfaces,"  From minimal surfaces such as Simons' cone and catenoids, using refined
+Lyapunov-Schmidt reduction method, we construct new solutions for a free
+boundary problem whose free boundary has two components. In dimension $8$,
+using variational arguments, we also obtain solutions which are global
+minimizers of the corresponding energy functional. This shows that Savin's
+theorem is optimal.
+",0,0,1,0,0,0
+12985,Storing and retrieving long-term memories: cooperation and competition in synaptic dynamics,"  We first review traditional approaches to memory storage and formation,
+drawing on the literature of quantitative neuroscience as well as statistical
+physics. These have generally focused on the fast dynamics of neurons; however,
+there is now an increasing emphasis on the slow dynamics of synapses, whose
+weight changes are held to be responsible for memory storage. An important
+first step in this direction was taken in the context of Fusi's cascade model,
+where complex synaptic architectures were invoked, in particular, to store
+long-term memories. No explicit synaptic dynamics were, however, invoked in
+that work. These were recently incorporated theoretically using the techniques
+used in agent-based modelling, and subsequently, models of competing and
+cooperating synapses were formulated. It was found that the key to the storage
+of long-term memories lay in the competitive dynamics of synapses. In this
+review, we focus on models of synaptic competition and cooperation, and look at
+the outstanding challenges that remain.
+",0,0,0,0,1,0
+12986,An algorithm for removing sensitive information: application to race-independent recidivism prediction,"  Predictive modeling is increasingly being employed to assist human
+decision-makers. One purported advantage of replacing or augmenting human
+judgment with computer models in high stakes settings-- such as sentencing,
+hiring, policing, college admissions, and parole decisions-- is the perceived
+""neutrality"" of computers. It is argued that because computer models do not
+hold personal prejudice, the predictions they produce will be equally free from
+prejudice. There is growing recognition that employing algorithms does not
+remove the potential for bias, and can even amplify it if the training data
+were generated by a process that is itself biased. In this paper, we provide a
+probabilistic notion of algorithmic bias. We propose a method to eliminate bias
+from predictive models by removing all information regarding protected
+variables from the data to which the models will ultimately be trained. Unlike
+previous work in this area, our framework is general enough to accommodate data
+on any measurement scale. Motivated by models currently in use in the criminal
+justice system that inform decisions on pre-trial release and parole, we apply
+our proposed method to a dataset on the criminal histories of individuals at
+the time of sentencing to produce ""race-neutral"" predictions of re-arrest. In
+the process, we demonstrate that a common approach to creating ""race-neutral""
+models-- omitting race as a covariate-- still results in racially disparate
+predictions. We then demonstrate that the application of our proposed method to
+these data removes racial disparities from predictions with minimal impact on
+predictive accuracy.
+",0,0,0,1,0,0
+12987,A Question Answering Approach to Emotion Cause Extraction,"  Emotion cause extraction aims to identify the reasons behind a certain
+emotion expressed in text. It is a much more difficult task compared to emotion
+classification. Inspired by recent advances in using deep memory networks for
+question answering (QA), we propose a new approach which considers emotion
+cause identification as a reading comprehension task in QA. Inspired by
+convolutional neural networks, we propose a new mechanism to store relevant
+context in different memory slots to model context information. Our proposed
+approach can extract both word level sequence features and lexical features.
+Performance evaluation shows that our method achieves the state-of-the-art
+performance on a recently released emotion cause dataset, outperforming a
+number of competitive baselines by at least 3.01% in F-measure.
+",1,0,0,0,0,0
+12988,Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting,"  Machine Learning on graph-structured data is an important and omnipresent
+task for a vast variety of applications including anomaly detection and dynamic
+network analysis. In this paper, a deep generative model is introduced to
+capture continuous probability densities corresponding to the nodes of an
+arbitrary graph. In contrast to all learning formulations in the area of
+discriminative pattern recognition, we propose a scalable generative
+optimization/algorithm theoretically proved to capture distributions at the
+nodes of a graph. Our model is able to generate samples from the probability
+densities learned at each node. This probabilistic data generation model, i.e.
+convolutional graph auto-encoder (CGAE), is devised based on the localized
+first-order approximation of spectral graph convolutions, deep learning, and
+the variational Bayesian inference. We apply our CGAE to a new problem, the
+spatio-temporal probabilistic solar irradiance prediction. Multiple solar
+radiation measurement sites in a wide area in northern states of the US are
+modeled as an undirected graph. Using our proposed model, the distribution of
+future irradiance given historical radiation observations is estimated for
+every site/node. Numerical results on the National Solar Radiation Database
+show state-of-the-art performance for probabilistic radiation prediction on
+geographically distributed irradiance data in terms of reliability, sharpness,
+and continuous ranked probability score.
+",0,0,0,1,0,0
+12989,Quasisymmetrically co-Hopfian Sierpiński Spaces and Menger Curve,"  A metric space $X$ is quasisymmetrically co-Hopfian if every quasisymmetric
+embedding of $X$ into itself is onto. We construct the first examples of metric
+spaces homeomorphic to the universal Menger curve and higher dimensional
+Sierpiński spaces, which are quasisymmetrically co-Hopfian. We also show that
+the collection of quasisymmetric equivalence classes of spaces homeomorphic to
+the Menger curve is uncountable. These results answer a problem and generalize
+results of Merenkov from \cite{Mer:coHopf}.
+",0,0,1,0,0,0
+12990,Localization properties and high-fidelity state transfer in electronic hopping models with correlated disorder,"  We investigate a tight-binding electronic chain featuring diagonal and
+off-diagonal disorder, these being modelled through the long-range-correlated
+fractional Brownian motion. Particularly, by employing exact diagonalization
+methods, we evaluate how the eigenstate spectrum of the system and its related
+single-particle dynamics respond to both competing sources of disorder.
+Moreover, we report the possibility of carrying out efficient end-to-end
+quantum-state transfer protocols even in the presence of such generalized
+disorder due to the appearance of extended states around the middle of the band
+in the limit of strong correlations.
+",0,1,0,0,0,0
+12991,On consistency of optimal pricing algorithms in repeated posted-price auctions with strategic buyer,"  We study revenue optimization learning algorithms for repeated posted-price
+auctions where a seller interacts with a single strategic buyer that holds a
+fixed private valuation for a good and seeks to maximize his cumulative
+discounted surplus. For this setting, first, we propose a novel algorithm that
+never decreases offered prices and has a tight strategic regret bound in
+$\Theta(\log\log T)$ under some mild assumptions on the buyer surplus
+discounting. This result closes the open research question on the existence of
+a no-regret horizon-independent weakly consistent pricing. The proposed
+algorithm is inspired by our observation that a double decrease of offered
+prices in a weakly consistent algorithm is enough to cause a linear regret.
+This motivates us to construct a novel transformation that maps a
+right-consistent algorithm to a weakly consistent one that never decreases
+offered prices.
+Second, we outperform the previously known strategic regret upper bound of
+the algorithm PRRFES, where the improvement is achieved by means of a finer
+constant factor $C$ of the principal term $C\log\log T$ in this upper bound.
+Finally, we generalize results on strategic regret previously known for
+geometric discounting of the buyer's surplus to discounting of other types,
+namely: the optimality of the pricing PRRFES to the case of geometrically
+concave decreasing discounting; and linear lower bound on the strategic regret
+of a wide range of horizon-independent weakly consistent algorithms to the case
+of arbitrary discounts.
+",1,0,0,1,0,0
+12992,City-wide Analysis of Electronic Health Records Reveals Gender and Age Biases in the Administration of Known Drug-Drug Interactions,"  The occurrence of drug-drug-interactions (DDI) from multiple drug
+prescriptions is a serious problem, both for individuals and health-care
+systems, since patients with complications due to DDI are likely to re-enter
+the system at a costlier level. We present a large-scale longitudinal study of
+the DDI phenomenon at the primary- and secondary-care level using electronic
+health records from the city of Blumenau in Southern Brazil (pop. ~340,000).
+This is the first study of DDI we are aware of that follows an entire city
+longitudinally for 18 months. We found that 181 distinct drug pairs known to
+interact were dispensed concomitantly to 12% of the patients in the city's
+public health-care system. Further, 4% of the patients were dispensed major DDI
+combinations, likely to result in very serious adverse reactions and costs we
+estimate to be larger than previously reported. DDI results are integrated into
+associative networks for inference and visualization, revealing key medications
+and interactions. Analysis reveals that women have a 60% increased risk of DDI
+as compared to men; the increase becomes 90% when only major DDI are
+considered. Furthermore, DDI risk increases substantially with age. Patients
+aged 70-79 years have a 34% risk of DDI when they are prescribed two or more
+drugs concomitantly. Interestingly, a null model demonstrates that age and
+women-specific risks from increased polypharmacy far exceed expectations in
+those populations. This suggests that social and biological factors are at
+play. Finally, we demonstrate that machine learning classifiers accurately
+predict patients likely to be administered DDI given their history of
+prescribed drugs, gender, and age (MCC=.7,AUC=.97). These results demonstrate
+that accurate warning systems for known DDI can be devised for health-care
+systems leading to substantial reduction of DDI-related adverse reactions and
+health-care savings.
+",1,0,0,1,1,0
+12993,A remark on the disorienting of species due to the fluctuating environment,"  In this article we study the stabilizing of a primitive pattern of behaviour
+for the two-species community with chemotaxis due to the short-wavelength
+external signal. We use a system of Patlak-Keller-Segel type as a model of the
+community. It is well-known that such systems can produce complex unsteady
+patterns of behaviour which are usually explained mathematically by
+bifurcations of some basic solutions that describe simpler patterns. As far as
+we aware, all such bifurcations in the models of the Patlak-Keller-Segel type
+had been found for homogeneous (i.e. translationally invariant) systems where
+the basic solutions are equilibria with homogeneous distributions of all
+species. The model considered in the present paper does not possess the
+translational invariance: one of species (the predators) is assumed to be
+capable of moving in response to a signal produced externally in addition to
+the signal emitted by another species (the prey). For instance, the external
+signal may arise from the inhomogeneity of the distribution of an environmental
+characteristic such as temperature, salinity, terrain relief, etc. Our goal is
+to examine the effect of short-wavelength inhomogeneity. To do this, we employ
+a certain homogenization procedure. We separate the short-wavelength and smooth
+components of the system response and derive a slow system governing the latter
+one. Analysing the slow system and comparing it with the case of homogeneous
+environment shows that, generically, a short-wavelength inhomogeneity results
+in an exponential decrease in the motility of the predators. The loss of
+motility prevents, to a great extent, the occurrence of complex unsteady
+patterns and dramatically stabilizes the primitive basic solution. In some
+sense, the necessity of dealing with intensive small-scale changes of the
+environment makes the system unable to respond to other challenges.
+",0,0,0,0,1,0
+12994,Fourier optimization and prime gaps,"  We investigate some extremal problems in Fourier analysis and their
+connection to a problem in prime number theory. In particular, we improve the
+current bounds for the largest possible gap between consecutive primes assuming
+the Riemann hypothesis.
+",0,0,1,0,0,0
+12995,An Interpretable Knowledge Transfer Model for Knowledge Base Completion,"  Knowledge bases are important resources for a variety of natural language
+processing tasks but suffer from incompleteness. We propose a novel embedding
+model, \emph{ITransF}, to perform knowledge base completion. Equipped with a
+sparse attention mechanism, ITransF discovers hidden concepts of relations and
+transfer statistical strength through the sharing of concepts. Moreover, the
+learned associations between relations and concepts, which are represented by
+sparse attention vectors, can be interpreted easily. We evaluate ITransF on two
+benchmark datasets---WN18 and FB15k for knowledge base completion and obtains
+improvements on both the mean rank and Hits@10 metrics, over all baselines that
+do not use additional information.
+",1,0,0,0,0,0
+12996,Vecchia approximations of Gaussian-process predictions,"  Gaussian processes (GPs) are highly flexible function estimators used for
+geospatial analysis, nonparametric regression, and machine learning, but they
+are computationally infeasible for large datasets. Vecchia approximations of
+GPs have been used to enable fast evaluation of the likelihood for parameter
+inference. Here, we study Vecchia approximations of spatial predictions at
+observed and unobserved locations, including obtaining joint predictive
+distributions at large sets of locations. We propose a general Vecchia
+framework for GP predictions, which contains some novel and some existing
+special cases. We study the accuracy and computational properties of these
+approaches theoretically and numerically. We show that our new approaches
+exhibit linear computational complexity in the total number of spatial
+locations. We also apply our methods to a satellite dataset of chlorophyll
+fluorescence.
+",0,0,0,1,0,0
+12997,Long-Term Sequential Prediction Using Expert Advice,"  For the prediction with experts' advice setting, we consider some methods to
+construct forecasting algorithms that suffer loss not much more than any expert
+in the pool. In contrast to the standard approach, we investigate the case of
+long-term forecasting of time series. This approach implies that each expert
+issues a forecast for a time point ahead (or a time interval), and then the
+master algorithm combines these forecasts into one aggregated forecast
+(sequence of forecasts). We introduce two new approaches to aggregating
+experts' long-term interval predictions. Both are based on Vovk's aggregating
+algorithm. The first approach applies the method of Mixing Past Posteriors
+method to the long-term prediction. The second approach is used for the
+interval forecasting and considers overlapping experts. The upper bounds for
+regret of these algorithms for adversarial case are obtained. We also present
+the results of numerical experiments on time series long-term prediction.
+",1,0,0,1,0,0
+12998,AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control,"  In this paper, a new adaptive multi-batch experience replay scheme is
+proposed for proximal policy optimization (PPO) for continuous action control.
+On the contrary to original PPO, the proposed scheme uses the batch samples of
+past policies as well as the current policy for the update for the next policy,
+where the number of the used past batches is adaptively determined based on the
+oldness of the past batches measured by the average importance sampling (IS)
+weight. The new algorithm constructed by combining PPO with the proposed
+multi-batch experience replay scheme maintains the advantages of original PPO
+such as random mini-batch sampling and small bias due to low IS weights by
+storing the pre-computed advantages and values and adaptively determining the
+mini-batch size. Numerical results show that the proposed method significantly
+increases the speed and stability of convergence on various continuous control
+tasks compared to original PPO.
+",1,0,0,0,0,0
+12999,Magnetic states of MnP: muon-spin rotation studies,"  Muon-spin rotation data collected at ambient pressure ($p$) and at $p=2.42$
+GPa in MnP were analyzed to check their consistency with various low- and
+high-pressure magnetic structures reported in the literature. Our analysis
+confirms that in MnP the low-temperature and low-pressure helimagnetic phase is
+characterised by an increased value of the average magnetic moment compared to
+the high-temperature ferromagnetic phase. An elliptical double-helical
+structure with a propagation vector ${\bf Q}=(0,0,0.117)$, an $a-$axis moment
+elongated by approximately 18% and an additional tilt of the rotation plane
+towards $c-$direction by $\simeq 4-8^{\rm o}$ leads to a good agreement between
+the theory and the experiment. The analysis of the high-pressure $\mu$SR data
+reveals that the new magnetic order appearing for pressures exceeding $1.5$ GPa
+can not be described by keeping the propagation vector ${\bf Q} \parallel c$.
+Even the extreme case -- decoupling the double-helical structure into four
+individual helices -- remains inconsistent with the experiment. It is shown
+that the high-pressure magnetic phase which is a precursor of superconductivity
+is an incommensurate helical state with ${\bf Q} \parallel b$.
+",0,1,0,0,0,0
+13000,The Hyper Suprime-Cam Software Pipeline,"  In this paper, we describe the optical imaging data processing pipeline
+developed for the Subaru Telescope's Hyper Suprime-Cam (HSC) instrument. The
+HSC Pipeline builds on the prototype pipeline being developed by the Large
+Synoptic Survey Telescope's Data Management system, adding customizations for
+HSC, large-scale processing capabilities, and novel algorithms that have since
+been reincorporated into the LSST codebase. While designed primarily to reduce
+HSC Subaru Strategic Program (SSP) data, it is also the recommended pipeline
+for reducing general-observer HSC data. The HSC pipeline includes high level
+processing steps that generate coadded images and science-ready catalogs as
+well as low-level detrending and image characterizations.
+",0,1,0,0,0,0
+13001,Plasma-based wakefield accelerators as sources of axion-like particles,"  We estimate the average flux density of minimally-coupled axion-like
+particles generated by a laser-driven plasma wakefield propagating along a
+constant strong magnetic field. Our calculations suggest that a terrestrial
+source based on this approach could generate a pulse of axion-like particles
+whose flux density is comparable to that of solar axion-like particles at
+Earth. This mechanism is optimal for axion-like particles with mass in the
+range of interest of contemporary experiments designed to detect dark matter
+using microwave cavities.
+",0,1,0,0,0,0
+13002,The Large D Limit of Planar Diagrams,"  We show that in $\text{O}(D)$ invariant matrix theories containing a large
+number $D$ of complex or Hermitian matrices, one can define a
+$D\rightarrow\infty$ limit for which the sum over planar diagrams truncates to
+a tractable, yet non-trivial, sum over melon diagrams. In particular, results
+obtained recently in SYK and tensor models can be generalized to traditional,
+string-inspired matrix quantum mechanical models of black holes.
+",0,0,1,0,0,0
+13003,"Trajectory Tracking Control of a Flexible Spine Robot, With and Without a Reference Input","  The Underactuated Lightweight Tensegrity Robotic Assistive Spine (ULTRA
+Spine) project is an ongoing effort to develop a flexible, actuated backbone
+for quadruped robots. In this work, model-predictive control is used to track a
+trajectory in the robot's state space, in simulation. The state trajectory used
+here corresponds to a bending motion of the spine, with translations and
+rotations of the moving vertebrae. Two different controllers are presented in
+this work: one that does not use a reference input but includes smoothing
+constrants, and a second one that uses a reference input without smoothing. For
+the smoothing controller, without reference inputs, the error converges to
+zero, while the simpler-to-tune controller with an input reference shows small
+errors but not complete convergence. It is expected that this controller will
+converge as it is improved further.
+",1,0,0,0,0,0
+13004,Noncoherent Analog Network Coding using LDPC-coded FSK,"  Analog network coding (ANC) is a throughput increasing technique for the
+two-way relay channel (TWRC) whereby two end nodes transmit simultaneously to a
+relay at the same time and band, followed by the relay broadcasting the
+received sum of signals to the end nodes. Coherent reception under ANC is
+challenging due to requiring oscillator synchronization for all nodes, a
+problem further exacerbated by Doppler shift. This work develops a noncoherent
+M-ary frequency-shift keyed (FSK) demodulator implementing ANC. The demodulator
+produces soft outputs suitable for use with capacity-approaching channel codes
+and supports information feedback from the channel decoder. A unique aspect of
+the formulation is the presence of an infinite summation in the received symbol
+probability density function. Detection and channel decoding succeed when the
+truncated summation contains a sufficient number of terms. Bit error rate
+performance is investigated by Monte Carlo simulation, considering modulation
+orders two, four and eight, channel coded and uncoded operation, and with and
+without information feedback from decoder to demodulator. The channel code
+considered for simulation is the LDPC code defined by the DVB-S2 standard. To
+our knowledge this work is the first to develop a noncoherent soft-output
+demodulator for ANC.
+",1,0,0,0,0,0
+13005,Feature selection in weakly coherent matrices,"  A problem of paramount importance in both pure (Restricted Invertibility
+problem) and applied mathematics (Feature extraction) is the one of selecting a
+submatrix of a given matrix, such that this submatrix has its smallest singular
+value above a specified level. Such problems can be addressed using
+perturbation analysis. In this paper, we propose a perturbation bound for the
+smallest singular value of a given matrix after appending a column, under the
+assumption that its initial coherence is not large, and we use this bound to
+derive a fast algorithm for feature extraction.
+",0,0,0,1,0,0
+13006,Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications,"  Security threats such as jamming and route manipulation can have significant
+consequences on the performance of modern wireless networks. To increase the
+efficacy and stealthiness of such threats, a number of extremely challenging,
+cross-layer attacks have been recently unveiled. Although existing research has
+thoroughly addressed many single-layer attacks, the problem of detecting and
+mitigating cross-layer attacks still remains unsolved. For this reason, in this
+paper we propose a novel framework to analyze and address cross-layer attacks
+in wireless networks. Specifically, our framework consists of a detection and a
+mitigation component. The attack detection component is based on a Bayesian
+learning detection scheme that constructs a model of observed evidence to
+identify stealthy attack activities. The mitigation component comprises a
+scheme that achieves the desired trade-off between security and performance. We
+specialize and evaluate the proposed framework by considering a specific
+cross-layer attack that uses jamming as an auxiliary tool to achieve route
+manipulation. Simulations and experimental results obtained with a test-bed
+made up by USRP software-defined radios demonstrate the effectiveness of the
+proposed methodology.
+",1,0,0,0,0,0
+13007,MHD Turbulence in spin-down flows of liquid metals,"  Intense spin-down flows allow one to reach high Rm in relatively small
+laboratory setups using moderate mass of liquid metals. The spin-down flow in
+toroidal channels was the first flow configuration used for studying dynamo
+effects in non-stationary flows. In this paper, we estimate the effect of
+small-scale dynamo in liquid metal spin-down flows realized in laboratory
+experiments. Our simulations have confirmed the conclusion that the dynamo
+effects observed in the experiments done on gallium are weak -- a slight burst
+of small-scale magnetic energy arises only at the highest available rotation
+velocity of the channel. In sodium flows, the induction effects are quite
+strong -- an essential part of kinetic energy of sodium spin-down flows is
+converted into magnetic energy and dissipates because of Joule heat losses. We
+have extended our simulations beyond the capabilities of existing laboratory
+facilities and examined the spin-down flows at the channel rotation velocity
+Omega>> 50 rps. It has been found that $\Omega\approx 100$rps is enough to
+reach the equipartition of magnetic and kinetic spectral power density at the
+lowest wave numbers (largest scales), whereas at Omega >= 200rps the intensity
+of the magnetic field becomes comparable to the intensity of velocity field
+fluctuations. We have also studied the influence of the Pm on the efficiency of
+small-scale dynamo in spin-down flows. In the experimental spin-down flows, the
+small-scale dynamo remains in a quasi-kinematic regime, and magnetic energy is
+mainly dissipated at the same scale, wherein it is converted from kinetic
+energy. The real small-scale dynamo starts to operate at Pm>10^{-4}, and the
+inertial range of the magnetic energy spectrum appears. Thereupon the energy
+dissipation is postponed to a later time and smaller scales, and the peak of
+turbulent energy (both kinetic and magnetic) slightly increases with Pm.
+",0,1,0,0,0,0
+13008,Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks,"  The multi-agent path-finding (MAPF) problem has recently received a lot of
+attention. However, it does not capture important characteristics of many
+real-world domains, such as automated warehouses, where agents are constantly
+engaged with new tasks. In this paper, we therefore study a lifelong version of
+the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In
+the MAPD problem, agents have to attend to a stream of delivery tasks in an
+online setting. One agent has to be assigned to each delivery task. This agent
+has to first move to a given pickup location and then to a given delivery
+location while avoiding collisions with other agents. We present two decoupled
+MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS).
+Theoretically, we show that they solve all well-formed MAPD instances, a
+realistic subclass of MAPD instances. Experimentally, we compare them against a
+centralized strawman MAPD algorithm without this guarantee in a simulated
+warehouse system. TP can easily be extended to a fully distributed MAPD
+algorithm and is the best choice when real-time computation is of primary
+concern since it remains efficient for MAPD instances with hundreds of agents
+and tasks. TPTS requires limited communication among agents and balances well
+between TP and the centralized MAPD algorithm.
+",1,0,0,0,0,0
+13009,Examples of finite dimensional algebras which do not satisfy the derived Jordan--Hölder property,"  We construct a matrix algebra $\Lambda(A,B)$ from two given finite
+dimensional elementary algebras $A$ and $B$ and give some sufficient conditions
+on $A$ and $B$ under which the derived Jordan--Hölder property (DJHP) fails
+for $\Lambda(A,B)$. This provides finite dimensional algebras of finite global
+dimension which do not satisfy DJHP.
+",0,0,1,0,0,0
+13010,Learning retrosynthetic planning through self-play,"  The problem of retrosynthetic planning can be framed as one player game, in
+which the chemist (or a computer program) works backwards from a molecular
+target to simpler starting materials though a series of choices regarding which
+reactions to perform. This game is challenging as the combinatorial space of
+possible choices is astronomical, and the value of each choice remains
+uncertain until the synthesis plan is completed and its cost evaluated. Here,
+we address this problem using deep reinforcement learning to identify policies
+that make (near) optimal reaction choices during each step of retrosynthetic
+planning. Using simulated experience or self-play, we train neural networks to
+estimate the expected synthesis cost or value of any given molecule based on a
+representation of its molecular structure. We show that learned policies based
+on this value network outperform heuristic approaches in synthesizing
+unfamiliar molecules from available starting materials using the fewest number
+of reactions. We discuss how the learned policies described here can be
+incorporated into existing synthesis planning tools and how they can be adapted
+to changes in the synthesis cost objective or material availability.
+",1,0,0,1,0,0
+13011,Real-space analysis of scanning tunneling microscopy topography datasets using sparse modeling approach,"  A sparse modeling approach is proposed for analyzing scanning tunneling
+microscopy topography data, which contains numerous peaks corresponding to
+surface atoms. The method, based on the relevance vector machine with
+$\mathrm{L}_1$ regularization and $k$-means clustering, enables separation of
+the peaks and atomic center positioning with accuracy beyond the resolution of
+the measurement grid. The validity and efficiency of the proposed method are
+demonstrated using synthetic data in comparison to the conventional
+least-square method. An application of the proposed method to experimental data
+of a metallic oxide thin film clearly indicates the existence of defects and
+corresponding local lattice deformations.
+",0,1,0,0,0,0
+13012,Scattered light intensity measurements of plasma treated Polydimethylsiloxane films: A measure to detect surface modification,"  Polydimethylsiloxane (PDMS) films possess different chemical and physical
+properties based on surface modification. The bond structure of pristine PDMS
+films and plasma treated PDMS films differ in a particular region of silicate
+bonds. We have studied the surface physical properties of pristine PDMS films
+and plasma treated PDMS films through optical technique. It is already known
+that plasma treated PDMS films forms very thin SiO2 layer on its surface. Due
+to difference in coefficient of thermal expansion of the surface SiO2 and the
+remaining bulk layer, the SiO2 layer develops cracks. These formations are
+explored to characterize PDMS surface by observing intensity of scattered light
+while the films are stretched. Pristine PDMS films do not show such optical
+scattering. Further the intensity measurements were repeated over period of
+time to monitor surface properties with time. It was observed that plasma
+treated PDMS film; the scattered light intensity is linearly dependent on the
+applied force for stretching. After a substantial time, scattering intensity is
+reduced to the value which is almost equal to that of pristine PDMS surface. It
+is proposed that basic light scattering through plasma treated PDMS occurs due
+to the decrease in the width of the cracks when the PDMS film is stretched. The
+change in the scattering property of plasma treated PDMS surface over time
+could be attributed to healing of cracks by the migration of polymer chain
+molecules from the bulk to the surface. It is also reported that with reference
+to various bonds present on surface plasma treated PDMS surface regains its
+original properties as that of pristine PDMS with time. Therefore the optical
+technique could be employed to study surface characteristics of PDMS surface as
+an alternative approach to conventional spectroscopic techniques.
+",0,1,0,0,0,0
+13013,A hierarchical Bayesian model for predicting ecological interactions using evolutionary relationships,"  Identifying undocumented or potential future interactions among species is a
+challenge facing modern ecologists. Recent link prediction methods rely on
+trait data, however large species interaction databases are typically sparse
+and covariates are limited to only a fraction of species. On the other hand,
+evolutionary relationships, encoded as phylogenetic trees, can act as proxies
+for underlying traits and historical patterns of parasite sharing among hosts.
+We show that using a network-based conditional model, phylogenetic information
+provides strong predictive power in a recently published global database of
+host-parasite interactions. By scaling the phylogeny using an evolutionary
+model, our method allows for biological interpretation often missing from
+latent variable models. To further improve on the phylogeny-only model, we
+combine a hierarchical Bayesian latent score framework for bipartite graphs
+that accounts for the number of interactions per species with the host
+dependence informed by phylogeny. Combining the two information sources yields
+significant improvement in predictive accuracy over each of the submodels
+alone. As many interaction networks are constructed from presence-only data, we
+extend the model by integrating a correction mechanism for missing
+interactions, which proves valuable in reducing uncertainty in unobserved
+interactions.
+",0,0,0,1,0,0
+13014,Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images,"  Pathological lung segmentation (PLS) is an important, yet challenging,
+medical image application due to the wide variability of pathological lung
+appearance and shape. Because PLS is often a pre-requisite for other imaging
+analytics, methodological simplicity and generality are key factors in
+usability. Along those lines, we present a bottom-up deep-learning based
+approach that is expressive enough to handle variations in appearance, while
+remaining unaffected by any variations in shape. We incorporate the deeply
+supervised learning framework, but enhance it with a simple, yet effective,
+progressive multi-path scheme, which more reliably merges outputs from
+different network stages. The result is a deep model able to produce finer
+detailed masks, which we call progressive holistically-nested networks
+(P-HNNs). Using extensive cross-validation, our method is tested on
+multi-institutional datasets comprising 929 CT scans (848 publicly available),
+of pathological lungs, reporting mean dice scores of 0.985 and demonstrating
+significant qualitative and quantitative improvements over state-of-the art
+approaches.
+",1,0,0,0,0,0
+13015,On the Helium fingers in the intracluster medium,"  In this paper we investigate the convection phenomenon in the intracluster
+medium (the weakly-collisional magnetized inhomogeneous plasma permeating
+galaxy clusters) where the concentration gradient of the Helium ions is not
+ignorable. To this end, we build upon the general machinery employed to study
+the salt finger instability found in the oceans. The salt finger instability is
+a form of double diffusive convection where the diffusions of two physical
+quantities---heat and salt concentrations---occur with different diffusion
+rates. The analogous instability in the intracluster medium may result owing to
+the magnetic field mediated anisotropic diffusions of the heat and the Helium
+ions (in the sea of the Hydrogen ions and the free electrons). These two
+diffusions have inherently different diffusion rates. Hence the convection
+caused by the onset of this instability is an example of double diffusive
+convection in the astrophysical settings. A consequence of this instability is
+the formation of the vertical filamentary structures having more concentration
+of the Helium ions with respect to the immediate neighbourhoods of the
+filaments. We term these structures as Helium fingers in analogy with the salt
+fingers found in the case of the salt finger instability. Here we show that the
+width of a Helium finger scales as one-fourth power of the radius of the inner
+region of the intracluster medium in the supercritical regime. We also
+determine the explicit mathematical expression of the criterion for the onset
+of the heat-flux-driven buoyancy instability modified by the presence of
+inhomogeneously distributed Helium ions.
+",0,1,0,0,0,0
+13016,Absolute frequency determination of molecular transition in the Doppler regime at kHz level of accuracy,"  We present absolute frequency measurement of the unperturbed P7 P7 O$_2$
+B-band transition with relative standard uncertainty of $2\times10^{-11}$. We
+reached the level of accuracy typical for Doppler-free techniques, with
+Doppler-limited spectroscopy. The Doppler-limited shapes of the P7 P7 spectral
+line were measured with a frequency-stabilized cavity ring-down spectrometer
+referenced to an $^{88}$Sr optical atomic clock by an optical frequency comb.
+",0,1,0,0,0,0
+13017,Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification,"  Similarity search is essential to many important applications and often
+involves searching at scale on high-dimensional data based on their similarity
+to a query. In biometric applications, recent vulnerability studies have shown
+that adversarial machine learning can compromise biometric recognition systems
+by exploiting the biometric similarity information. Existing methods for
+biometric privacy protection are in general based on pairwise matching of
+secured biometric templates and have inherent limitations in search efficiency
+and scalability. In this paper, we propose an inference-based framework for
+privacy-preserving similarity search in Hamming space. Our approach builds on
+an obfuscated distance measure that can conceal Hamming distance in a dynamic
+interval. Such a mechanism enables us to systematically design statistically
+reliable methods for retrieving most likely candidates without knowing the
+exact distance values. We further propose to apply Montgomery multiplication
+for generating search indexes that can withstand adversarial similarity
+analysis, and show that information leakage in randomized Montgomery domains
+can be made negligibly small. Our experiments on public biometric datasets
+demonstrate that the inference-based approach can achieve a search accuracy
+close to the best performance possible with secure computation methods, but the
+associated cost is reduced by orders of magnitude compared to cryptographic
+primitives.
+",1,0,0,0,0,0
+13018,Sublinear elliptic problems under radiality. Harmonic $NA$ groups and Euclidean spaces,"  Let $\L $ be the Laplace operator on $\R ^d$, $d\geq 3$ or the Laplace
+Beltrami operator on the harmonic $NA$ group (in particular on a rank one
+noncompact symmetric space).
+For the equation $ \L u - \varphi(\cdot,u)=0$ we give necessary and
+sufficient conditions for the existence of entire bounded or large solutions
+under the hypothesis of radiality of $\varphi$ with respect to the first
+variable.
+A Harnack-type inequality for positive continuous solutions is also proved.
+",0,0,1,0,0,0
+13019,Bayesian Uncertainty Directed Trial Designs,"  Most Bayesian response-adaptive designs unbalance randomization rates towards
+the most promising arms with the goal of increasing the number of positive
+treatment outcomes during the study, even though the primary aim of the trial
+is different. We discuss Bayesian uncertainty directed designs (BUD), a class
+of Bayesian designs in which the investigator specifies an information measure
+tailored to the experiment. All decisions during the trial are selected to
+optimize the available information at the end of the study. The approach can be
+applied to several designs, ranging from early stage multi-arm trials to
+biomarker-driven and multi-endpoint studies. We discuss the asymptotic limit of
+the patient allocation proportion to treatments, and illustrate the
+finite-sample operating characteristics of BUD designs through examples,
+including multi-arm trials, biomarker-stratified trials, and trials with
+multiple co-primary endpoints.
+",0,0,0,1,0,0
+13020,Wasserstein Learning of Deep Generative Point Process Models,"  Point processes are becoming very popular in modeling asynchronous sequential
+data due to their sound mathematical foundation and strength in modeling a
+variety of real-world phenomena. Currently, they are often characterized via
+intensity function which limits model's expressiveness due to unrealistic
+assumptions on its parametric form used in practice. Furthermore, they are
+learned via maximum likelihood approach which is prone to failure in
+multi-modal distributions of sequences. In this paper, we propose an
+intensity-free approach for point processes modeling that transforms nuisance
+processes to a target one. Furthermore, we train the model using a
+likelihood-free leveraging Wasserstein distance between point processes.
+Experiments on various synthetic and real-world data substantiate the
+superiority of the proposed point process model over conventional ones.
+",1,0,0,1,0,0
+13021,Interior Structures and Tidal Heating in the TRAPPIST-1 Planets,"  With seven planets, the TRAPPIST-1 system has the largest number of
+exoplanets discovered in a single system so far. The system is of
+astrobiological interest, because three of its planets orbit in the habitable
+zone of the ultracool M dwarf. Assuming the planets are composed of
+non-compressible iron, rock, and H$_2$O, we determine possible interior
+structures for each planet. To determine how much tidal heat may be dissipated
+within each planet, we construct a tidal heat generation model using a single
+uniform viscosity and rigidity for each planet based on the planet's
+composition. With the exception of TRAPPIST-1c, all seven of the planets have
+densities low enough to indicate the presence of significant H$_2$O in some
+form. Planets b and c experience enough heating from planetary tides to
+maintain magma oceans in their rock mantles; planet c may have eruptions of
+silicate magma on its surface, which may be detectable with next-generation
+instrumentation. Tidal heat fluxes on planets d, e, and f are lower, but are
+still twenty times higher than Earth's mean heat flow. Planets d and e are the
+most likely to be habitable. Planet d avoids the runaway greenhouse state if
+its albedo is $\gtrsim$ 0.3. Determining the planet's masses within $\sim0.1$
+to 0.5 Earth masses would confirm or rule out the presence of H$_2$O and/or
+iron in each planet, and permit detailed models of heat production and
+transport in each planet. Understanding the geodynamics of ice-rich planets f,
+g, and h requires more sophisticated modeling that can self-consistently
+balance heat production and transport in both rock and ice layers.
+",0,1,0,0,0,0
+13022,A Driver-in-the Loop Fuel Economic Control Strategy for Connected Vehicles in Urban Roads,"  In this paper, we focus on developing driver-in-the loop fuel economic
+control strategy for multiple connected vehicles. The control strategy is
+considered to work in a driver assistance framework where the controller gives
+command to a driver to follow while considering the ability of the driver in
+following control commands. Our proposed method uses vehicle-to-vehicle (V2V)
+communication, exploits traffic lights' Signal Phase and Timing (SPAT)
+information, models driver error injection with Markov chain, and employs
+scenario tree based stochastic model predictive control to improve vehicle fuel
+economy and traffic mobility. The proposed strategy is decentralized in nature
+as every vehicle evaluates its own strategy using only local information.
+Simulation results show the effect of consideration of driver error injection
+when synthesizing fuel economic controllers in a driver assistance fashion.
+",1,0,0,0,0,0
+13023,A General and Adaptive Robust Loss Function,"  We present a generalization of the Cauchy/Lorentzian, Geman-McClure,
+Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2
+loss functions. By introducing robustness as a continous parameter, our loss
+function allows algorithms built around robust loss minimization to be
+generalized, which improves performance on basic vision tasks such as
+registration and clustering. Interpreting our loss as the negative log of a
+univariate density yields a general probability distribution that includes
+normal and Cauchy distributions as special cases. This probabilistic
+interpretation enables the training of neural networks in which the robustness
+of the loss automatically adapts itself during training, which improves
+performance on learning-based tasks such as generative image synthesis and
+unsupervised monocular depth estimation, without requiring any manual parameter
+tuning.
+",1,0,0,1,0,0
+13024,Asymptotically Efficient Estimation of Smooth Functionals of Covariance Operators,"  Let $X$ be a centered Gaussian random variable in a separable Hilbert space
+${\mathbb H}$ with covariance operator $\Sigma.$ We study a problem of
+estimation of a smooth functional of $\Sigma$ based on a sample $X_1,\dots
+,X_n$ of $n$ independent observations of $X.$ More specifically, we are
+interested in functionals of the form $\langle f(\Sigma), B\rangle,$ where
+$f:{\mathbb R}\mapsto {\mathbb R}$ is a smooth function and $B$ is a nuclear
+operator in ${\mathbb H}.$ We prove concentration and normal approximation
+bounds for plug-in estimator $\langle f(\hat \Sigma),B\rangle,$ $\hat
+\Sigma:=n^{-1}\sum_{j=1}^n X_j\otimes X_j$ being the sample covariance based on
+$X_1,\dots, X_n.$ These bounds show that $\langle f(\hat \Sigma),B\rangle$ is
+an asymptotically normal estimator of its expectation ${\mathbb E}_{\Sigma}
+\langle f(\hat \Sigma),B\rangle$ (rather than of parameter of interest $\langle
+f(\Sigma),B\rangle$) with a parametric convergence rate $O(n^{-1/2})$ provided
+that the effective rank ${\bf r}(\Sigma):= \frac{{\bf tr}(\Sigma)}{\|\Sigma\|}$
+(${\rm tr}(\Sigma)$ being the trace and $\|\Sigma\|$ being the operator norm of
+$\Sigma$) satisfies the assumption ${\bf r}(\Sigma)=o(n).$ At the same time, we
+show that the bias of this estimator is typically as large as $\frac{{\bf
+r}(\Sigma)}{n}$ (which is larger than $n^{-1/2}$ if ${\bf r}(\Sigma)\geq
+n^{1/2}$). In the case when ${\mathbb H}$ is finite-dimensional space of
+dimension $d=o(n),$ we develop a method of bias reduction and construct an
+estimator $\langle h(\hat \Sigma),B\rangle$ of $\langle f(\Sigma),B\rangle$
+that is asymptotically normal with convergence rate $O(n^{-1/2}).$ Moreover, we
+study asymptotic properties of the risk of this estimator and prove minimax
+lower bounds for arbitrary estimators showing the asymptotic efficiency of
+$\langle h(\hat \Sigma),B\rangle$ in a semi-parametric sense.
+",0,0,1,1,0,0
+13025,Evolution of magnetic and dielectric properties in Sr-substituted high temperature multiferroic YBaCuFeO5,"  We report the evolution of structural, magnetic and dielectric properties due
+to partial substitution of Ba by Sr in the high temperature multiferroic
+YBaCuFeO5. This compound exhibits ferroelectric and antiferromagnetic
+transitions around 200 K and these two phenomena are presumed to be coupled
+with each other. Our studies on magnetic and dielectric properties of the
+YBa1-xSrxCuFeO5 (x = 0.0, 0.25 and 0.5) show that substitution of Sr shifts
+magnetic transition towards higher temperature whereas dielectric transition to
+lower temperature. These results points to the fact that magnetic and
+dielectric transitions get decoupled as a result of chemical pressure in form
+of Sr substitution. The nature of magnetodielectric coupling changes across the
+series with the presence of higher order coupling terms. Additionally in these
+compounds glassy dynamics of electric dipoles is observed at low temperatures.
+",0,1,0,0,0,0
+13026,Regular Intersecting Families,"  We call a family of sets intersecting, if any two sets in the family
+intersect. In this paper we investigate intersecting families $\mathcal{F}$ of
+$k$-element subsets of $[n]:=\{1,\ldots, n\},$ such that every element of $[n]$
+lies in the same (or approximately the same) number of members of
+$\mathcal{F}$. In particular, we show that we can guarantee $|\mathcal{F}| =
+o({n-1\choose k-1})$ if and only if $k=o(n)$.
+",1,0,0,0,0,0
+13027,Stochastic Maximum Likelihood Optimization via Hypernetworks,"  This work explores maximum likelihood optimization of neural networks through
+hypernetworks. A hypernetwork initializes the weights of another network, which
+in turn can be employed for typical functional tasks such as regression and
+classification. We optimize hypernetworks to directly maximize the conditional
+likelihood of target variables given input. Using this approach we obtain
+competitive empirical results on regression and classification benchmarks.
+",1,0,0,1,0,0
+13028,Magnetic resonance of rubidium atoms passing through a multi-layered transmission magnetic grating,"  We measured the magnetic resonance of rubidium atoms passing through periodic
+magnetic fields generated by two types of multilayered transmission magnetic
+grating. One of the gratings reported here was assembled by stacking four
+layers of magnetic films so that the direction of magnetization alternated at
+each level. The other grating was assembled so that the magnetization at each
+level was aligned. For both types of grating, the experimental results were in
+good agreement with our calculations. We studied the feasibility of extending
+the frequency band of the grating and narrowing its resonance linewidth by
+performing calculations. For magnetic resonance precision spectroscopy, we
+conclude that the multi-layered transmission magnetic grating can generate
+periodic fields with narrower linewidths at higher frequencies when a larger
+number of layers is assembled at a shorter period length. Moreover, the
+frequency band of this type of grating can potentially achieve frequencies of
+up to hundreds of PHz.
+",0,1,0,0,0,0
+13029,Fast multi-output relevance vector regression,"  This paper aims to decrease the time complexity of multi-output relevance
+vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output
+dimensions, M is the number of basis functions, and V<M. The experimental
+results demonstrate that the proposed method is more competitive than the
+existing method, with regard to computation time. MATLAB codes are available at
+this http URL.
+",1,0,0,1,0,0
+13030,Computing LPMLN Using ASP and MLN Solvers,"  LPMLN is a recent addition to probabilistic logic programming languages. Its
+main idea is to overcome the rigid nature of the stable model semantics by
+assigning a weight to each rule in a way similar to Markov Logic is defined. We
+present two implementations of LPMLN, $\text{LPMLN2ASP}$ and
+$\text{LPMLN2MLN}$. System $\text{LPMLN2ASP}$ translates LPMLN programs into
+the input language of answer set solver $\text{CLINGO}$, and using weak
+constraints and stable model enumeration, it can compute most probable stable
+models as well as exact conditional and marginal probabilities. System
+$\text{LPMLN2MLN}$ translates LPMLN programs into the input language of Markov
+Logic solvers, such as $\text{ALCHEMY}$, $\text{TUFFY}$, and $\text{ROCKIT}$,
+and allows for performing approximate probabilistic inference on LPMLN
+programs. We also demonstrate the usefulness of the LPMLN systems for computing
+other languages, such as ProbLog and Pearl's Causal Models, that are shown to
+be translatable into LPMLN. (Under consideration for acceptance in TPLP)
+",1,0,0,0,0,0
+13031,Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences,"  In his seminal book `The Inmates are Running the Asylum: Why High-Tech
+Products Drive Us Crazy And How To Restore The Sanity' [2004, Sams
+Indianapolis, IN, USA], Alan Cooper argues that a major reason why software is
+often poorly designed (from a user perspective) is that programmers are in
+charge of design decisions, rather than interaction designers. As a result,
+programmers design software for themselves, rather than for their target
+audience, a phenomenon he refers to as the `inmates running the asylum'. This
+paper argues that explainable AI risks a similar fate. While the re-emergence
+of explainable AI is positive, this paper argues most of us as AI researchers
+are building explanatory agents for ourselves, rather than for the intended
+users. But explainable AI is more likely to succeed if researchers and
+practitioners understand, adopt, implement, and improve models from the vast
+and valuable bodies of research in philosophy, psychology, and cognitive
+science, and if evaluation of these models is focused more on people than on
+technology. From a light scan of literature, we demonstrate that there is
+considerable scope to infuse more results from the social and behavioural
+sciences into explainable AI, and present some key results from these fields
+that are relevant to explainable AI.
+",1,0,0,0,0,0
+13032,Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks,"  An image is a very effective tool for conveying emotions. Many researchers
+have investigated in computing the image emotions by using various features
+extracted from images. In this paper, we focus on two high level features, the
+object and the background, and assume that the semantic information of images
+is a good cue for predicting emotion. An object is one of the most important
+elements that define an image, and we find out through experiments that there
+is a high correlation between the object and the emotion in images. Even with
+the same object, there may be slight difference in emotion due to different
+backgrounds, and we use the semantic information of the background to improve
+the prediction performance. By combining the different levels of features, we
+build an emotion based feed forward deep neural network which produces the
+emotion values of a given image. The output emotion values in our framework are
+continuous values in the 2-dimensional space (Valence and Arousal), which are
+more effective than using a few number of emotion categories in describing
+emotions. Experiments confirm the effectiveness of our network in predicting
+the emotion of images.
+",1,0,0,0,0,0
+13033,Soft Rough Graphs,"  Soft set theory and rough set theory are mathematical tools to deal with
+uncertainties. In [3], authors combined these concepts and introduced soft
+rough sets. In this paper, we introduce the concepts of soft rough graphs,
+vertex and edge induced soft rough graphs and soft rough trees. We define some
+products with examples in soft rough graphs.
+",0,0,1,0,0,0
+13034,PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction,"  To date, developing a good model for early intensive care unit (ICU)
+mortality prediction is still challenging. This paper presents a patient based
+predictive modeling framework (PPMF) to improve the performance of ICU
+mortality prediction using data collected during the first 48 hours of ICU
+admission. PPMF consists of three main components verifying three related
+research hypotheses. The first component captures dynamic changes of patients
+status in the ICU using their time series data (e.g., vital signs and
+laboratory tests). The second component is a local approximation algorithm that
+classifies patients based on their similarities. The third component is a
+Gradient Decent wrapper that updates feature weights according to the
+classification feedback. Experiments using data from MIMICIII show that PPMF
+significantly outperforms: (1) the severity score systems, namely SASP III,
+APACHE IV, and MPM0III, (2) the aggregation based classifiers that utilize
+summarized time series, and (3) baseline feature selection methods.
+",1,0,0,0,0,0
+13035,Zeros of real random polynomials spanned by OPUC,"  Let \( \{\varphi_i\}_{i=0}^\infty \) be a sequence of orthonormal polynomials
+on the unit circle with respect to a probability measure \( \mu \). We study
+zero distribution of random linear combinations of the form \[
+P_n(z)=\sum_{i=0}^{n-1}\eta_i\varphi_i(z), \] where \( \eta_0,\dots,\eta_{n-1}
+\) are i.i.d. standard Gaussian variables. We use the Christoffel-Darboux
+formula to simplify the density functions provided by Vanderbei for the
+expected number real and complex of zeros of \( P_n \). From these expressions,
+under the assumption that \( \mu \) is in the Nevai class, we deduce the
+limiting value of these density functions away from the unit circle. Under the
+mere assumption that \( \mu \) is doubling on subarcs of \( \T \) centered at
+\( 1 \) and \( -1 \), we show that the expected number of real zeros of \( P_n
+\) is at most \[ (2/\pi) \log n +O(1), \] and that the asymptotic equality
+holds when the corresponding recurrence coefficients decay no slower than \(
+n^{-(3+\epsilon)/2} \), \( \epsilon>0 \). We conclude with providing results
+that estimate the expected number of complex zeros of \( P_n \) in shrinking
+neighborhoods of compact subsets of \( \T \).
+",0,0,1,0,0,0
+13036,Indirect observation of molecular disassociation in solid benzene at low temperatures,"  The molecular dynamics of solid benzene are extremely complex; especially
+below 77 K, its inner mechanics remain mostly unexplored. Benzene is also a
+prototypical molecular crystal that becomes energetically frustrated at low
+temperatures and usually unusual phenomena accompanies such scenarios. We
+performed dielectric constant measurements on solid benzene down to 5 K and
+observed a previously unidentified minimum in the imaginary part of the
+dielectric constant at Tm=17.9 K. Results obtained on deuterated solid benzene
+(C6D6, where D is deuterium) show an isotope effect in the form of a shift of
+the critical temperature to Tm'=18.9 K. Our findings indicate that at Tm, only
+the protons without the carbon atoms continue again to undergo rotational
+tunneling about the hexad axes. The deuterons appear to do the same accounting
+for an indirect observation of a continued and sustained 12-body tunneling
+event. We discuss how similar experiments performed on hydrogen-based molecular
+crystals can be exploited to help us obtain more insight on the quantum
+mechanics of many-body tunneling.
+",0,1,0,0,0,0
+13037,Green's function-based control-oriented modeling of electric field for dielectrophoresis,"  In this paper, we propose a novel approach to obtaining a reliable and simple
+mathematical model of a dielectrophoretic force for model-based feedback
+micromanipulation. Any such model is expected to sufficiently accurately relate
+the voltages (electric potentials) applied to the electrodes to the resulting
+forces exerted on microparticles at given locations in the workspace. This
+model also has to be computationally simple enough to be used in real time as
+required by model-based feedback control. Most existing models involve solving
+two- or three-dimensional mixed boundary value problems. As such, they are
+usually analytically intractable and have to be solved numerically instead. A
+numerical solution is, however, infeasible in real time, hence such models are
+not suitable for feedback control. We present a novel approximation of the
+boundary value data for which a closed-form analytical solution is feasible; we
+solve a mixed boundary value problem numerically off-line only once, and based
+on this solution we approximate the mixed boundary conditions by Dirichlet
+boundary conditions. This way we get an approximated boundary value problem
+allowing the application of the analytical framework of Green's functions. Thus
+obtained closed-form analytical solution is amenable to real-time use and
+closely matches the numerical solution of the original exact problem.
+",0,1,0,0,0,0
+13038,Hilbert Bases and Lecture Hall Partitions,"  In the interest of finding the minimum additive generating set for the set of
+$\boldsymbol{s}$-lecture hall partitions, we compute the Hilbert bases for the
+$\boldsymbol{s}$-lecture hall cones in certain cases. In particular, we compute
+the Hilbert bases for two well-studied families of sequences, namely the $1\mod
+k$ sequences and the $\ell$-sequences. Additionally, we provide a
+characterization of the Hilbert bases for $\boldsymbol{u}$-generated Gorenstein
+$\boldsymbol{s}$-lecture hall cones in low dimensions.
+",0,0,1,0,0,0
+13039,CLUBB-SILHS: A parameterization of subgrid variability in the atmosphere,"  This document provides a detailed overview of the CLUBB-SILHS cloud and
+turbulence parameterization, including theoretical background, model equations,
+closure assumptions, simulation results, comparison with other parameterization
+methods, FAQs, and source code documentation.
+",0,1,0,0,0,0
+13040,Relative Entropy in CFT,"  By using Araki's relative entropy, Lieb's convexity and the theory of
+singular integrals, we compute the mutual information associated with free
+fermions, and we deduce many results about entropies for chiral CFT's which are
+embedded into free fermions, and their extensions. Such relative entropies in
+CFT are here computed explicitly for the first time in a mathematical rigorous
+way. Our results agree with previous computations by physicists based on
+heuristic arguments; in addition we uncover a surprising connection with the
+theory of subfactors, in particular by showing that a certain duality, which is
+argued to be true on physical grounds, is in fact violated if the global
+dimension of the conformal net is greater than $1.$
+",0,0,1,0,0,0
+13041,Approximate Supermodularity Bounds for Experimental Design,"  This work provides performance guarantees for the greedy solution of
+experimental design problems. In particular, it focuses on A- and E-optimal
+designs, for which typical guarantees do not apply since the mean-square error
+and the maximum eigenvalue of the estimation error covariance matrix are not
+supermodular. To do so, it leverages the concept of approximate supermodularity
+to derive non-asymptotic worst-case suboptimality bounds for these greedy
+solutions. These bounds reveal that as the SNR of the experiments decreases,
+these cost functions behave increasingly as supermodular functions. As such,
+greedy A- and E-optimal designs approach (1-1/e)-optimality. These results
+reconcile the empirical success of greedy experimental design with the
+non-supermodularity of the A- and E-optimality criteria.
+",1,0,1,1,0,0
+13042,A new precision measurement of the α-decay half-life of 190Pt,"  A laboratory measurement of the $\alpha$-decay half-life of $^{190}$Pt has
+been performed using a low background Frisch grid ionisation chamber. A total
+amount of 216.60(17) mg of natural platinum has been measured for 75.9 days.
+The resulting half-life is $(4.97\pm0.16)\times 10^{11}$ years, with a total
+uncertainty of 3.2%. This number is in good agreement with the half-life
+obtained using the geological comparison method.
+",0,1,0,0,0,0
+13043,Online Inverse Reinforcement Learning via Bellman Gradient Iteration,"  This paper develops an online inverse reinforcement learning algorithm aimed
+at efficiently recovering a reward function from ongoing observations of an
+agent's actions. To reduce the computation time and storage space in reward
+estimation, this work assumes that each observed action implies a change of the
+Q-value distribution, and relates the change to the reward function via the
+gradient of Q-value with respect to reward function parameter. The gradients
+are computed with a novel Bellman Gradient Iteration method that allows the
+reward function to be updated whenever a new observation is available. The
+method's convergence to a local optimum is proved.
+This work tests the proposed method in two simulated environments, and
+evaluates the algorithm's performance under a linear reward function and a
+non-linear reward function. The results show that the proposed algorithm only
+requires a limited computation time and storage space, but achieves an
+increasing accuracy as the number of observations grows. We also present a
+potential application to robot cleaners at home.
+",1,0,0,0,0,0
+13044,SchNet: A continuous-filter convolutional neural network for modeling quantum interactions,"  Deep learning has the potential to revolutionize quantum chemistry as it is
+ideally suited to learn representations for structured data and speed up the
+exploration of chemical space. While convolutional neural networks have proven
+to be the first choice for images, audio and video data, the atoms in molecules
+are not restricted to a grid. Instead, their precise locations contain
+essential physical information, that would get lost if discretized. Thus, we
+propose to use continuous-filter convolutional layers to be able to model local
+correlations without requiring the data to lie on a grid. We apply those layers
+in SchNet: a novel deep learning architecture modeling quantum interactions in
+molecules. We obtain a joint model for the total energy and interatomic forces
+that follows fundamental quantum-chemical principles. This includes
+rotationally invariant energy predictions and a smooth, differentiable
+potential energy surface. Our architecture achieves state-of-the-art
+performance for benchmarks of equilibrium molecules and molecular dynamics
+trajectories. Finally, we introduce a more challenging benchmark with chemical
+and structural variations that suggests the path for further work.
+",0,1,0,1,0,0
+13045,Generalized magnetic mirrors,"  We propose generalized magnetic mirrors that can be achieved by excitations
+of sole electric resonances. Conventional approaches to obtain magnetic mirrors
+rely heavily on exciting the fundamental magnetic dipoles, whereas here we
+reveal that besides magnetic resonances, electric resonances of higher orders
+can be also employed to obtain highly efficient magnetic mirrors. Based on the
+electromagnetic duality, it is also shown that electric mirrors can be achieved
+by exciting magnetic resonances. We provide direct demonstrations of the
+generalized mirrors proposed in a simple system of one-dimensional periodic
+array of all-dielectric wires, which may shed new light to many advanced fields
+of photonics related to resonant multipolar excitations and interferences.
+",0,1,0,0,0,0
+13046,Cryogenic readout for multiple VUV4 Multi-Pixel Photon Counters in liquid xenon,"  We present the performances and characterization of an array made of
+S13370-3050CN (VUV4 generation) Multi-Pixel Photon Counters manufactured by
+Hamamatsu and equipped with a low power consumption preamplifier operating at
+liquid xenon temperature (~ 175 K). The electronics is designed for the readout
+of a matrix of maximum dimension of 8 x 8 individual photosensors and it is
+based on a single operational amplifier. The detector prototype presented in
+this paper utilizes the Analog Devices AD8011 current feedback operational
+amplifier, but other models can be used depending on the application. A biasing
+correction circuit has been implemented for the gain equalization of
+photosensors operating at different voltages. The results show single photon
+detection capability making this device a promising choice for future
+generation of large scale dark matter detectors based on liquid xenon, such as
+DARWIN.
+",0,1,0,0,0,0
+13047,A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting,"  One of the key technologies for future large-scale location-aware services
+covering a complex of multi-story buildings --- e.g., a big shopping mall and a
+university campus --- is a scalable indoor localization technique. In this
+paper, we report the current status of our investigation on the use of deep
+neural networks (DNNs) for scalable building/floor classification and
+floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the
+hierarchical nature of the building/floor estimation and floor-level
+coordinates estimation of a location, we propose a new DNN architecture
+consisting of a stacked autoencoder for the reduction of feature space
+dimension and a feed-forward classifier for multi-label classification of
+building/floor/location, on which the multi-building and multi-floor indoor
+localization system based on Wi-Fi fingerprinting is built. Experimental
+results for the performance of building/floor estimation and floor-level
+coordinates estimation of a given location demonstrate the feasibility of the
+proposed DNN-based indoor localization system, which can provide near
+state-of-the-art performance using a single DNN, for the implementation with
+lower complexity and energy consumption at mobile devices.
+",1,0,0,1,0,0
+13048,A latent spatial factor approach for synthesizing opioid associated deaths and treatment admissions in Ohio counties,"  Background: Opioid misuse is a major public health issue in the United States
+and in particular Ohio. However, the burden of the epidemic is challenging to
+quantify as public health surveillance measures capture different aspects of
+the problem. Here we synthesize county-level death and treatment counts to
+compare the relative burden across counties and assess associations with social
+environmental covariates. Methods: We construct a generalized spatial factor
+model to jointly model death and treatment rates for each county. For each
+outcome, we specify a spatial rates parameterization for a Poisson regression
+model with spatially varying factor loadings. We use a conditional
+autoregressive model to account for spatial dependence within a Bayesian
+framework. Results: The estimated spatial factor was highest in the southern
+and southwestern counties of the state, representing a higher burden of the
+opioid epidemic. We found that relatively high rates of treatment contributed
+to the factor in the southern part of the state; whereas, relatively higher
+rates of death contributed in the southwest. The estimated factor was also
+positively associated with the proportion of residents aged 18-64 on disability
+and negatively associated with the proportion of residents reporting white
+race. Conclusions: We synthesized the information in the opioid associated
+death and treatment counts through a spatial factor model to estimate a latent
+factor representing the consensus between the two surveillance measures. We
+believe this framework provides a coherent approach to describe the epidemic
+while leveraging information from multiple surveillance measures.
+",0,0,0,1,0,0
+13049,The Cost of Transportation : Spatial Analysis of US Fuel Prices,"  The geography of fuel prices has many various implications, from its
+significant impact on accessibility to being an indicator of territorial equity
+and transportation policy. In this paper, we study the spatio-temporal patterns
+of fuel price in the US at a very high resolution using a newly constructed
+dataset collecting daily oil prices for two months, on a significant proportion
+of US gas facilities. These data have been collected using a
+specifically-designed large scale data crawling technology that we describe. We
+study the influence of socio-economic variables, by using complementary
+methods: Geographically Weighted Regression to take into account spatial
+non-stationarity, and linear econometric modeling to condition at the state and
+test county level characteristics. The former yields an optimal spatial range
+roughly corresponding to stationarity scale, and significant influence of
+variables such as median income or wage per job, with a non-simple spatial
+behavior that confirms the importance of geographical particularities. On the
+other hand, multi-level modeling reveals a strong state fixed effect, while
+county specific characteristics still have significant impact. Through the
+combination of such methods, we unveil the superposition of a governance
+process with a local socio- economical spatial process. We discuss one
+important application that is the elaboration of locally parametrized
+car-regulation policies.
+",0,1,0,1,0,0
+13050,Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors,"  Shape priors have been widely utilized in medical image segmentation to
+improve segmentation accuracy and robustness. A major way to encode such a
+prior shape model is to use a mesh representation, which is prone to causing
+self-intersection or mesh folding. Those problems require complex and expensive
+algorithms to mitigate. In this paper, we propose a novel shape prior directly
+embedded in the voxel grid space, based on gradient vector flows of a
+pre-segmentation. The flexible and powerful prior shape representation is ready
+to be extended to simultaneously segmenting multiple interacting objects with
+minimum separation distance constraint. The problem is formulated as a Markov
+random field problem whose exact solution can be efficiently computed with a
+single minimum s-t cut in an appropriately constructed graph. The proposed
+algorithm is validated on two multi-object segmentation applications: the brain
+tissue segmentation in MRI images, and the bladder/prostate segmentation in CT
+images. Both sets of experiments show superior or competitive performance of
+the proposed method to other state-of-the-art methods.
+",1,0,0,0,0,0
+13051,Discovery of water at high spectral resolution in the atmosphere of 51 Peg b,"  We report the detection of water absorption features in the dayside spectrum
+of the first-known hot Jupiter, 51 Peg b, confirming the star-planet system to
+be a double-lined spectroscopic binary. We used high-resolution (R~100,000),
+3.2 micron spectra taken with CRIRES/VLT to trace the radial-velocity shift of
+the water features in the planet's dayside atmosphere during 4 hours of its
+4.23-day orbit after superior conjunction. We detect the signature of molecular
+absorption by water at a significance of 5.6 sigma at a systemic velocity of
+Vsys=-33+/-2 km/s, coincident with the host star, with a corresponding orbital
+velocity Kp = 133^+4.3_-3.5 km/s. This translates directly to a planet mass of
+Mp=0.476^+0.032_-0.031MJ, placing it at the transition boundary between Jovian
+and Neptunian worlds. We determine upper and lower limits on the orbital
+inclination of the system of 70<i (deg)<82.2. We also provide an updated
+orbital solution for 51 Peg b, using an extensive set of 639 stellar radial
+velocities measured between 1994 and 2013, finding no significant evidence of
+an eccentric orbit. We find no evidence of significant absorption or emission
+from other major carbon-bearing molecules of the planet, including methane and
+carbon dioxide. The atmosphere is non-inverted in the temperature-pressure
+region probed by these observations. The deepest absorption lines reach an
+observed relative contrast of 0.9x10^-3 with respect to the host star continuum
+flux, at an angular separation of 3 milliarcseconds. This work is consistent
+with a previous tentative report of K-band molecular absorption for 51 Peg b by
+Brogi et al. (2013).
+",0,1,0,0,0,0
+13052,A strengthened inequality of Alon-Babai-Suzuki's conjecture on set systems with restricted intersections modulo p,"  Let $K=\{k_1,k_2,\ldots,k_r\}$ and $L=\{l_1,l_2,\ldots,l_s\}$ be disjoint
+subsets of $\{0,1,\ldots,p-1\}$, where $p$ is a prime and
+$A=\{A_1,A_2,\ldots,A_m\}$ be a family of subsets of $[n]$ such that
+$|A_i|\pmod{p}\in K$ for all $A_i\in A$ and $|A_i\cap A_j|\pmod{p}\in L$ for
+$i\ne j$. In 1991, Alon, Babai and Suzuki conjectured that if $n\geq
+s+\max_{1\leq i\leq r} k_i$, then $|A|\leq {n\choose s}+{n\choose
+s-1}+\cdots+{n\choose s-r+1}$. In 2000, Qian and Ray-Chaudhuri proved the
+conjecture under the condition $n\geq 2s-r$. In 2015, Hwang and Kim verified
+the conjecture of Alon, Babai and Suzuki.
+In this paper, we will prove that if $n\geq 2s-2r+1$ or $n\geq s+\max_{1\leq
+i\leq r}k_i$, then \[ |A|\leq{n-1\choose s}+{n-1\choose s-1}+\cdots+{n-1\choose
+s-2r+1}. \] This result strengthens the upper bound of Alon, Babai and Suzuki's
+conjecture when $n\geq 2s-2$.
+",0,0,1,0,0,0
+13053,Traditional and Heavy-Tailed Self Regularization in Neural Network Models,"  Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep
+Neural Networks (DNNs), including both production quality, pre-trained models
+such as AlexNet and Inception, and smaller models trained from scratch, such as
+LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly
+indicate that the empirical spectral density (ESD) of DNN layer matrices
+displays signatures of traditionally-regularized statistical models, even in
+the absence of exogenously specifying traditional forms of regularization, such
+as Dropout or Weight Norm constraints. Building on recent results in RMT, most
+notably its extension to Universality classes of Heavy-Tailed matrices, we
+develop a theory to identify \emph{5+1 Phases of Training}, corresponding to
+increasing amounts of \emph{Implicit Self-Regularization}. For smaller and/or
+older DNNs, this Implicit Self-Regularization is like traditional Tikhonov
+regularization, in that there is a `size scale' separating signal from noise.
+For state-of-the-art DNNs, however, we identify a novel form of
+\emph{Heavy-Tailed Self-Regularization}, similar to the self-organization seen
+in the statistical physics of disordered systems. This implicit
+Self-Regularization can depend strongly on the many knobs of the training
+process. By exploiting the generalization gap phenomena, we demonstrate that we
+can cause a small model to exhibit all 5+1 phases of training simply by
+changing the batch size.
+",1,0,0,1,0,0
+13054,Interfacial Mechanical Behaviors in Carbon Nanotube Assemblies,"  Interface widely exists in carbon nanotube (CNT) assembly materials, taking
+place at different length scales. It determines severely the mechanical
+properties of these assembly materials. Here I assess the mechanical properties
+of individual CNTs and CNT bundles, the inter-layer or inter-shell mechanics in
+multi-walled CNTs, the shear properties between adjacent CNTs, and the
+assembly-dependent mechanical and multifunctional properties of macroscopic CNT
+fibers and films.
+",0,1,0,0,0,0
+13055,Learning a Latent Space of Multitrack Measures,"  Discovering and exploring the underlying structure of multi-instrumental
+music using learning-based approaches remains an open problem. We extend the
+recent MusicVAE model to represent multitrack polyphonic measures as vectors in
+a latent space. Our approach enables several useful operations such as
+generating plausible measures from scratch, interpolating between measures in a
+musically meaningful way, and manipulating specific musical attributes. We also
+introduce chord conditioning, which allows all of these operations to be
+performed while keeping harmony fixed, and allows chords to be changed while
+maintaining musical ""style"". By generating a sequence of measures over a
+predefined chord progression, our model can produce music with convincing
+long-term structure. We demonstrate that our latent space model makes it
+possible to intuitively control and generate musical sequences with rich
+instrumentation (see this https URL for generated audio).
+",0,0,0,1,0,0
+13056,Multimodal Affect Analysis for Product Feedback Assessment,"  Consumers often react expressively to products such as food samples, perfume,
+jewelry, sunglasses, and clothing accessories. This research discusses a
+multimodal affect recognition system developed to classify whether a consumer
+likes or dislikes a product tested at a counter or kiosk, by analyzing the
+consumer's facial expression, body posture, hand gestures, and voice after
+testing the product. A depth-capable camera and microphone system - Kinect for
+Windows - is utilized. An emotion identification engine has been developed to
+analyze the images and voice to determine affective state of the customer. The
+image is segmented using skin color and adaptive threshold. Face, body and
+hands are detected using the Haar cascade classifier. Canny edges are
+identified and the lip, body and hand contours are extracted using spatial
+filtering. Edge count and orientation around the mouth, cheeks, eyes,
+shoulders, fingers and the location of the edges are used as features.
+Classification is done by an emotion template mapping algorithm and training a
+classifier using support vector machines. The real-time performance, accuracy
+and feasibility for multimodal affect recognition in feedback assessment are
+evaluated.
+",1,0,0,0,0,0
+13057,Distributed Learning for Cooperative Inference,"  We study the problem of cooperative inference where a group of agents
+interact over a network and seek to estimate a joint parameter that best
+explains a set of observations. Agents do not know the network topology or the
+observations of other agents. We explore a variational interpretation of the
+Bayesian posterior density, and its relation to the stochastic mirror descent
+algorithm, to propose a new distributed learning algorithm. We show that, under
+appropriate assumptions, the beliefs generated by the proposed algorithm
+concentrate around the true parameter exponentially fast. We provide explicit
+non-asymptotic bounds for the convergence rate. Moreover, we develop explicit
+and computationally efficient algorithms for observation models belonging to
+exponential families.
+",1,0,1,1,0,0
+13058,Selection of training populations (and other subset selection problems) with an accelerated genetic algorithm (STPGA: An R-package for selection of training populations with a genetic algorithm),"  Optimal subset selection is an important task that has numerous algorithms
+designed for it and has many application areas. STPGA contains a special
+genetic algorithm supplemented with a tabu memory property (that keeps track of
+previously tried solutions and their fitness for a number of iterations), and
+with a regression of the fitness of the solutions on their coding that is used
+to form the ideal estimated solution (look ahead property) to search for
+solutions of generic optimal subset selection problems. I have initially
+developed the programs for the specific problem of selecting training
+populations for genomic prediction or association problems, therefore I give
+discussion of the theory behind optimal design of experiments to explain the
+default optimization criteria in STPGA, and illustrate the use of the programs
+in this endeavor. Nevertheless, I have picked a few other areas of application:
+supervised and unsupervised variable selection based on kernel alignment,
+supervised variable selection with design criteria, influential observation
+identification for regression, solving mixed integer quadratic optimization
+problems, balancing gains and inbreeding in a breeding population. Some of
+these illustrations pertain new statistical approaches.
+",0,0,0,1,0,0
+13059,CERES in Propositional Proof Schemata,"  Cut-elimination is one of the most famous problems in proof theory, and it
+was defined and solved for first-order sequent calculus by Gentzen in his
+celebrated Hauptsatz. Ceres is a different cut-elimination algorithm for first-
+and higher-order classical logic. Ceres was extended to proof schemata, which
+are templates for usual first-order proofs, with parameters for natural
+numbers. However, while Ceres is known to be a complete cut-elimination
+algorithm for first-order logic, it is not clear whether this holds for
+first-order schemata too: given in input a proof schema with cuts, does Ceres
+always produce a schema for a cut-free proof? The difficult step is finding and
+representing an appropriate refutation schema for the characteristic term
+schema of a proof schema. In this thesis, we progress in solving this problem
+by restricting Ceres to propositional schemata, which are templates for
+propositional proofs. By limiting adequately the expressivity of propositional
+schemata and proof schemata, we aim at providing a version of schematic Ceres
+which is a complete cut-elimination algorithm for propositional schemata. We
+focus on one particular step of Ceres: resolution refutation schemata. First,
+we prove that by naively adapting Ceres for first-order schemata to our case,
+we end up with an incomplete algorithm. Then, we modify slightly the concept of
+resolution refutation schema: to refute a clause set, first we bring it to a
+generic form, and then we use a fixed refutation of that generic clause set.
+Our variation of schematic Ceres is the first step towards completeness with
+respect to propositional schemata.
+",1,0,1,0,0,0
+13060,"A Schur decomposition reveals the richness of structure in homogeneous, isotropic turbulence as a consequence of localised shear","  An improved understanding of turbulence is essential for the effective
+modelling and control of industrial and geophysical processes. Homogeneous,
+isotropic turbulence (HIT) is the archetypal field for developing turbulence
+physics theory. Based on the Schur transform, we introduce an additive
+decomposition of the velocity gradient tensor into a normal part (containing
+the eigenvalues) and a non-normal or shear-related tensor. We re-interrogate
+some key properties of HIT and show that the the tendency of the flow to form
+disc-like structures is not a property of the normal tensor; it emerges from an
+interaction with the non-normality. Also, the alignment between the vorticity
+vector and the second eigenvector of the strain tensor is another consequence
+of local shear processes.
+",0,1,0,0,0,0
+13061,Parametrizing filters of a CNN with a GAN,"  It is commonly agreed that the use of relevant invariances as a good
+statistical bias is important in machine-learning. However, most approaches
+that explicitly incorporate invariances into a model architecture only make use
+of very simple transformations, such as translations and rotations. Hence,
+there is a need for methods to model and extract richer transformations that
+capture much higher-level invariances. To that end, we introduce a tool
+allowing to parametrize the set of filters of a trained convolutional neural
+network with the latent space of a generative adversarial network. We then show
+that the method can capture highly non-linear invariances of the data by
+visualizing their effect in the data space.
+",1,0,0,1,0,0
+13062,Monolayer FeSe on SrTiO$_3$,"  Epitaxial engineering of solid-state heterointerfaces is a leading avenue to
+realizing enhanced or novel electronic states of matter. As a recent example,
+bulk FeSe is an unconventional superconductor with a modest transition
+temperature ($T_c$) of 9 K. When a single atomic layer of FeSe is grown on
+SrTiO$_3$, however, its $T_c$ can skyrocket by an order of magnitude to 65 K or
+109 K. Since this discovery in 2012, efforts to reproduce, understand, and
+extend these findings continue to draw both excitement and scrutiny. In this
+review, we first present a critical survey of experimental measurements
+performed using a wide range of techniques. We then turn to the open question
+of microscopic mechanisms of superconductivity. We examine contrasting
+indications for both phononic (conventional) and magnetic/orbital
+(unconventional) means of electron pairing, and speculations about whether they
+could work cooperatively to boost $T_c$ in a monolayer of FeSe.
+",0,1,0,0,0,0
+13063,A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models,"  Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many
+related tasks (large $K$) under a high-dimensional (large $p$) situation is an
+important task. Most previous studies for the joint estimation of multiple
+sGGMs rely on penalized log-likelihood estimators that involve expensive and
+difficult non-smooth optimizations. We propose a novel approach, FASJEM for
+\underline{fa}st and \underline{s}calable \underline{j}oint
+structure-\underline{e}stimation of \underline{m}ultiple sGGMs at a large
+scale. As the first study of joint sGGM using the Elementary Estimator
+framework, our work has three major contributions: (1) We solve FASJEM through
+an entry-wise manner which is parallelizable. (2) We choose a proximal
+algorithm to optimize FASJEM. This improves the computational efficiency from
+$O(Kp^3)$ to $O(Kp^2)$ and reduces the memory requirement from $O(Kp^2)$ to
+$O(K)$. (3) We theoretically prove that FASJEM achieves a consistent estimation
+with a convergence rate of $O(\log(Kp)/n_{tot})$. On several synthetic and four
+real-world datasets, FASJEM shows significant improvements over baselines on
+accuracy, computational complexity, and memory costs.
+",1,0,0,1,0,0
+13064,Quantum Spin Liquids Unveil the Genuine Mott State,"  The Widom line identifies the locus in the phase diagram where a
+supercritical gas crosses over from gas-like to a more liquid-like behavior. A
+similar transition exists in correlated electron liquids, where the interplay
+of Coulomb repulsion, bandwidth and temperature triggers between the Mott
+insulating state and an incoherent conduction regime. Here we explore the
+electrodynamic response of three organic quantum spin liquids with different
+degrees of effective correlation, where the absence of magnetic order enables
+unique insight into the nature of the genuine Mott state down to the most
+relevant low-temperature region. Combining optical spectroscopy with
+pressure-dependent dc transport and theoretical calculations, we succeeded to
+construct a phase diagram valid for all Mott insulators on a quantitative
+scale. In the vicinity of the low-temperature phase boundary, we discover
+metallic fluctuations within the Mott gap, exhibiting enhanced absorption upon
+cooling that is not present in antiferromagnetic Mott insulators. Our findings
+reveal the phase coexistence region and Pomeranchuk-like anomaly of the Mott
+transition, previously predicted but never observed.
+",0,1,0,0,0,0
+13065,Parameter and State Estimation in Queues and Related Stochastic Models: A Bibliography,"  This is an annotated bibliography on estimation and inference results for
+queues and related stochastic models. The purpose of this document is to
+collect and categorise works in the field, allowing for researchers and
+practitioners to explore the various types of results that exist. This
+bibliography attempts to include all known works that satisfy both of these
+requirements: -Works that deal with queueing models. -Works that contain
+contributions related to the methodology of parameter estimation, state
+estimation, hypothesis testing, confidence interval and/or actual datasets of
+application areas. Our attempt is to make this bibliography exhaustive, yet
+there are possibly some papers that we have missed. As it is updated
+continuously, additions and comments are welcomed. The sections below
+categorise the works based on several categories. A single paper may appear in
+several categories simultaneously. The final section lists all works in
+chronological order along with short descriptions of the contributions. This
+bibliography is maintained at
+this http URL and may be cited as such.
+We welcome additions and corrections.
+",1,0,1,1,0,0
+13066,Towards a Social Virtual Reality Learning Environment in High Fidelity,"  Virtual Learning Environments (VLEs) are spaces designed to educate students
+remotely via online platforms. Although traditional VLEs such as iSocial have
+shown promise in educating students, they offer limited immersion that
+diminishes learning effectiveness. This paper outlines a virtual reality
+learning environment (VRLE) over a high-speed network, which promotes
+educational effectiveness and efficiency via our creation of flexible content
+and infrastructure which meet established VLE standards with improved
+immersion. This paper further describes our implementation of multiple learning
+modules developed in High Fidelity, a ""social VR"" platform. Our experiment
+results show that the VR mode of content delivery better stimulates the
+generalization of lessons to the real world than non-VR lessons and provides
+improved immersion when compared to an equivalent desktop version.
+",1,0,0,0,0,0
+13067,L2 Regularization versus Batch and Weight Normalization,"  Batch Normalization is a commonly used trick to improve the training of deep
+neural networks. These neural networks use L2 regularization, also called
+weight decay, ostensibly to prevent overfitting. However, we show that L2
+regularization has no regularizing effect when combined with normalization.
+Instead, regularization has an influence on the scale of weights, and thereby
+on the effective learning rate. We investigate this dependence, both in theory,
+and experimentally. We show that popular optimization methods such as ADAM only
+partially eliminate the influence of normalization on the learning rate. This
+leads to a discussion on other ways to mitigate this issue.
+",1,0,0,1,0,0
+13068,Bitwise Operations of Cellular Automaton on Gray-scale Images,"  Cellular Automata (CA) theory is a discrete model that represents the state
+of each of its cells from a finite set of possible values which evolve in time
+according to a pre-defined set of transition rules. CA have been applied to a
+number of image processing tasks such as Convex Hull Detection, Image Denoising
+etc. but mostly under the limitation of restricting the input to binary images.
+In general, a gray-scale image may be converted to a number of different binary
+images which are finally recombined after CA operations on each of them
+individually. We have developed a multinomial regression based weighed
+summation method to recombine binary images for better performance of CA based
+Image Processing algorithms. The recombination algorithm is tested for the
+specific case of denoising Salt and Pepper Noise to test against standard
+benchmark algorithms such as the Median Filter for various images and noise
+levels. The results indicate several interesting invariances in the application
+of the CA, such as the particular noise realization and the choice of
+sub-sampling of pixels to determine recombination weights. Additionally, it
+appears that simpler algorithms for weight optimization which seek local minima
+work as effectively as those that seek global minima such as Simulated
+Annealing.
+",1,0,0,0,0,0
+13069,Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems,"  Recently, deep learning approaches with various network architectures have
+achieved significant performance improvement over existing iterative
+reconstruction methods in various imaging problems. However, it is still
+unclear why these deep learning architectures work for specific inverse
+problems. To address these issues, here we show that the long-searched-for
+missing link is the convolution framelets for representing a signal by
+convolving local and non-local bases. The convolution framelets was originally
+developed to generalize the theory of low-rank Hankel matrix approaches for
+inverse problems, and this paper further extends the idea so that we can obtain
+a deep neural network using multilayer convolution framelets with perfect
+reconstruction (PR) under rectilinear linear unit nonlinearity (ReLU). Our
+analysis also shows that the popular deep network components such as residual
+block, redundant filter channels, and concatenated ReLU (CReLU) do indeed help
+to achieve the PR, while the pooling and unpooling layers should be augmented
+with high-pass branches to meet the PR condition. Moreover, by changing the
+number of filter channels and bias, we can control the shrinkage behaviors of
+the neural network. This discovery leads us to propose a novel theory for deep
+convolutional framelets neural network. Using numerical experiments with
+various inverse problems, we demonstrated that our deep convolution framelets
+network shows consistent improvement over existing deep architectures.This
+discovery suggests that the success of deep learning is not from a magical
+power of a black-box, but rather comes from the power of a novel signal
+representation using non-local basis combined with data-driven local basis,
+which is indeed a natural extension of classical signal processing theory.
+",1,0,0,1,0,0
+13070,Enhanced bacterial swimming speeds in macromolecular polymer solutions,"  The locomotion of swimming bacteria in simple Newtonian fluids can
+successfully be described within the framework of low Reynolds number
+hydrodynamics. The presence of polymers in biofluids generally increases the
+viscosity, which is expected to lead to slower swimming for a constant
+bacterial motor torque. Surprisingly, however, several experiments have shown
+that bacterial speeds increase in polymeric fluids, and there is no clear
+understanding why. Therefore we perform extensive coarse-grained simulations of
+a bacterium swimming in explicitly modeled solutions of macromolecular polymers
+of different lengths and densities. We observe an increase of up to 60% in
+swimming speed with polymer density and demonstrate that this is due to a
+depletion of polymers in the vicinity of the bacterium leading to an effective
+slip. However this in itself cannot predict the large increase in swimming
+velocity: coupling to the chirality of the bacterial flagellum is also
+necessary.
+",0,1,0,0,0,0
+13071,An Algebraic Treatment of Recursion,"  I review the three principal methods to assign meaning to recursion in
+process algebra: the denotational, the operational and the algebraic approach,
+and I extend the latter to unguarded recursion.
+",1,0,0,0,0,0
+13072,On Approximation for Fractional Stochastic Partial Differential Equations on the Sphere,"  This paper gives the exact solution in terms of the Karhunen-Loève
+expansion to a fractional stochastic partial differential equation on the unit
+sphere $\mathbb{S}^{2}\subset \mathbb{R}^{3}$ with fractional Brownian motion
+as driving noise and with random initial condition given by a fractional
+stochastic Cauchy problem. A numerical approximation to the solution is given
+by truncating the Karhunen-Loève expansion. We show the convergence rates
+of the truncation errors in degree and the mean square approximation errors in
+time. Numerical examples using an isotropic Gaussian random field as initial
+condition and simulations of evolution of cosmic microwave background (CMB) are
+given to illustrate the theoretical results.
+",0,0,1,1,0,0
+13073,Learning Structural Node Embeddings Via Diffusion Wavelets,"  Nodes residing in different parts of a graph can have similar structural
+roles within their local network topology. The identification of such roles
+provides key insight into the organization of networks and can be used for a
+variety of machine learning tasks. However, learning structural representations
+of nodes is a challenging problem, and it has typically involved manually
+specifying and tailoring topological features for each node. In this paper, we
+develop GraphWave, a method that represents each node's network neighborhood
+via a low-dimensional embedding by leveraging heat wavelet diffusion patterns.
+Instead of training on hand-selected features, GraphWave learns these
+embeddings in an unsupervised way. We mathematically prove that nodes with
+similar network neighborhoods will have similar GraphWave embeddings even
+though these nodes may reside in very different parts of the network, and our
+method scales linearly with the number of edges. Experiments in a variety of
+different settings demonstrate GraphWave's real-world potential for capturing
+structural roles in networks, and our approach outperforms existing
+state-of-the-art baselines in every experiment, by as much as 137%.
+",1,0,0,1,0,0
+13074,Excitonic mass gap in uniaxially strained graphene,"  We study the conditions for spontaneously generating an excitonic mass gap
+due to Coulomb interactions between anisotropic Dirac fermions in uniaxially
+strained graphene. The mass gap equation is realized as a self-consistent
+solution for the self-energy within the Hartree-Fock mean-field and static
+random phase approximations. It depends not only on momentum, due to the
+long-range nature of the interaction, but also on the velocity anisotropy
+caused by the presence of uniaxial strain. We solve the nonlinear integral
+equation self-consistently by performing large scale numerical calculations on
+variable grid sizes. We evaluate the mass gap at the charge neutrality (Dirac)
+point as a function of the dimensionless coupling constant and anisotropy
+parameter. We also obtain the phase diagram of the critical coupling, at which
+the gap becomes finite, against velocity anisotropy. Our numerical study
+indicates that with an increase in uniaxial strain in graphene the strength of
+critical coupling decreases, which suggests anisotropy supports formation of
+excitonic mass gap in graphene.
+",0,1,0,0,0,0
+13075,On spectral properties of high-dimensional spatial-sign covariance matrices in elliptical distributions with applications,"  Spatial-sign covariance matrix (SSCM) is an important substitute of sample
+covariance matrix (SCM) in robust statistics. This paper investigates the SSCM
+on its asymptotic spectral behaviors under high-dimensional elliptical
+populations, where both the dimension $p$ of observations and the sample size
+$n$ tend to infinity with their ratio $p/n\to c\in (0, \infty)$. The empirical
+spectral distribution of this nonparametric scatter matrix is shown to converge
+in distribution to a generalized Marčenko-Pastur law. Beyond this, a new
+central limit theorem (CLT) for general linear spectral statistics of the SSCM
+is also established. For polynomial spectral statistics, explicit formulae of
+the limiting mean and covarance functions in the CLT are provided. The derived
+results are then applied to an estimation procedure and a test procedure for
+the spectrum of the shape component of population covariance matrices.
+",0,0,1,1,0,0
+13076,Decoupled Potential Integral Equations for Electromagnetic Scattering from Dielectric Objects,"  Recent work on developing novel integral equation formulations has involved
+using potentials as opposed to fields. This is a consequence of the additional
+flexibility offered by using potentials to develop well conditioned systems.
+Most of the work in this arena has wrestled with developing this formulation
+for perfectly conducting objects (Vico et al., 2014 and Liu et al., 2015), with
+recent effort made to addressing similar problems for dielectrics (Li et al.,
+2017). In this paper, we present well-conditioned decoupled potential integral
+equation (DPIE) formulated for electromagnetic scattering from homogeneous
+dielectric objects, a fully developed version of that presented in the
+conference communication (Li et al., 2017). The formulation is based on
+boundary conditions derived for decoupled boundary conditions on the scalar and
+vector potentials. The resulting DPIE is the second kind integral equation, and
+does not suffer from either low frequency or dense mesh breakdown. Analytical
+properties of the DPIE are studied. Results on the sphere analysis are provided
+to demonstrate the conditioning and spectrum of the resulting linear system.
+",0,1,0,0,0,0
+13077,High-field transport properties of a P-doped BaFe2As2 film on technical substrate,"  High temperature (high-Tc) superconductors like cuprates have superior
+critical current properties in magnetic fields over other superconductors.
+However, superconducting wires for high-field-magnet applications are still
+dominated by low-Tc Nb3Sn due probably to cost and processing issues. The
+recent discovery of a second class of high-Tc materials, Fe-based
+superconductors, may provide another option for high-field-magnet wires. In
+particular, AEFe2As2 (AE: Alkali earth elements, AE-122) is one of the best
+candidates for high-field-magnet applications because of its high upper
+critical field, Hc2, moderate Hc2 anisotropy, and intermediate Tc. Here we
+report on in-field transport properties of P-doped BaFe2As2 (Ba-122) thin films
+grown on technical substrates (i.e., biaxially textured oxides templates on
+metal tapes) by pulsed laser deposition. The P-doped Ba-122 coated conductor
+sample exceeds a transport Jc of 10^5 A/cm^2 at 15 T for both major
+crystallographic directions of the applied magnetic field, which is favourable
+for practical applications. Our P-doped Ba-122 coated conductors show a
+superior in-field Jc over MgB2 and NbTi, and a comparable level to Nb3Sn above
+20 T. By analysing the E-J curves for determining Jc, a non-Ohmic linear
+differential signature is observed at low field due to flux flow along the
+grain boundaries. However, grain boundaries work as flux pinning centres as
+demonstrated by the pinning force analysis.
+",0,1,0,0,0,0
+13078,Construction of and efficient sampling from the simplicial configuration model,"  Simplicial complexes are now a popular alternative to networks when it comes
+to describing the structure of complex systems, primarily because they encode
+multi-node interactions explicitly. With this new description comes the need
+for principled null models that allow for easy comparison with empirical data.
+We propose a natural candidate, the simplicial configuration model. The core of
+our contribution is an efficient and uniform Markov chain Monte Carlo sampler
+for this model. We demonstrate its usefulness in a short case study by
+investigating the topology of three real systems and their randomized
+counterparts (using their Betti numbers). For two out of three systems, the
+model allows us to reject the hypothesis that there is no organization beyond
+the local scale.
+",0,1,1,1,0,0
+13079,Local and 2-local derivations and automorphisms on simple Leibniz algebras,"  The present paper is devoted to local and 2-local derivations and
+automorphism of complex finite-dimensional simple Leibniz algebras. We prove
+that all local derivations and 2-local derivations on a finite-dimensional
+complex simple Leibniz algebra are automatically derivations. We show that
+nilpotent Leibniz algebras as a rule admit local derivations and 2-local
+derivations which are not derivations. Further we consider automorphisms of
+simple Leibniz algebras. We prove that every 2-local automorphism on a complex
+finite-dimensional simple Leibniz algebra is an automorphism and show that
+nilpotent Leibniz algebras admit 2-local automorphisms which are not
+automorphisms. A similar problem concerning local automorphism on simple
+Leibniz algebras is reduced to the case of simple Lie algebras.
+",0,0,1,0,0,0
+13080,RodFIter: Attitude Reconstruction from Inertial Measurement by Functional Iteration,"  Rigid motion computation or estimation is a cornerstone in numerous fields.
+Attitude computation can be achieved by integrating the angular velocity
+measured by gyroscopes, the accuracy of which is crucially important for the
+dead-reckoning inertial navigation. The state-of-the-art attitude algorithms
+have unexceptionally relied on the simplified differential equation of the
+rotation vector to obtain the attitude. This paper proposes a Functional
+Iteration technique with the Rodrigues vector (named the RodFIter method) to
+analytically reconstruct the attitude from gyroscope measurements. The RodFIter
+method is provably exact in reconstructing the incremental attitude as long as
+the angular velocity is exact. Notably, the Rodrigues vector is analytically
+obtained and can be used to update the attitude over the considered time
+interval. The proposed method gives birth to an ultimate attitude algorithm
+scheme that can be naturally extended to the general rigid motion computation.
+It is extensively evaluated under the attitude coning motion and compares
+favorably in accuracy with the mainstream attitude algorithms. This work is
+believed having eliminated the long-standing theoretical barrier in exact
+motion integration from inertial measurements.
+",1,0,0,0,0,0
+13081,Influence Networks in International Relations,"  Measuring influence and determining what drives it are persistent questions
+in political science and in network analysis more generally. Herein we focus on
+the domain of international relations. Our major substantive question is: How
+can we determine what characteristics make an actor influential? To address the
+topic of influence, we build on a multilinear tensor regression framework
+(MLTR) that captures influence relationships using a tensor generalization of a
+vector autoregression model. Influence relationships in that approach are
+captured in a pair of n x n matrices and provide measurements of how the
+network actions of one actor may influence the future actions of another. A
+limitation of the MLTR and earlier latent space approaches is that there are no
+direct mechanisms through which to explain why a certain actor is more or less
+influential than others. Our new framework, social influence regression,
+provides a way to statistically model the influence of one actor on another as
+a function of characteristics of the actors. Thus we can move beyond just
+estimating that an actor influences another to understanding why. To highlight
+the utility of this approach, we apply it to studying monthly-level conflictual
+events between countries as measured through the Integrated Crisis Early
+Warning System (ICEWS) event data project.
+",0,1,0,1,0,0
+13082,Incorporating Feedback into Tree-based Anomaly Detection,"  Anomaly detectors are often used to produce a ranked list of statistical
+anomalies, which are examined by human analysts in order to extract the actual
+anomalies of interest. Unfortunately, in realworld applications, this process
+can be exceedingly difficult for the analyst since a large fraction of
+high-ranking anomalies are false positives and not interesting from the
+application perspective. In this paper, we aim to make the analyst's job easier
+by allowing for analyst feedback during the investigation process. Ideally, the
+feedback influences the ranking of the anomaly detector in a way that reduces
+the number of false positives that must be examined before discovering the
+anomalies of interest. In particular, we introduce a novel technique for
+incorporating simple binary feedback into tree-based anomaly detectors. We
+focus on the Isolation Forest algorithm as a representative tree-based anomaly
+detector, and show that we can significantly improve its performance by
+incorporating feedback, when compared with the baseline algorithm that does not
+incorporate feedback. Our technique is simple and scales well as the size of
+the data increases, which makes it suitable for interactive discovery of
+anomalies in large datasets.
+",1,0,0,1,0,0
+13083,Towards An Adaptive Compliant Aerial Manipulator for Contact-Based Interaction,"  As roles for unmanned aerial vehicles (UAV) continue to diversify, the
+ability to sense and interact closely with the environment becomes increasingly
+important. Within this paper we report on the initial flight tests of a novel
+adaptive compliant actuator which will allow a UAV to carry out such tasks as
+the ""pick and placement"" of remote sensors, structural testing and
+contact-based inspection. Three key results are discussed and presented; the
+ability to physically compensate impact forces or apply interaction forces by
+the UAV through the use of the active compliant manipulator; to be able to
+tailor these forces through tuning of the manipulator controller gains; and the
+ability to apply a rapid series of physical pulses in order to excite remotely
+placed sensors, e.g. vibration sensors. The paper describes the overall system
+requirements and system modelling considerations which have driven the concept
+through to flight testing. A series of over sixty flight tests have been used
+to generate initial results which clearly demonstrate the potential of this new
+type of compliant aerial actuator. Results are discussed in line with potential
+applications; and a series of future flight tests are described which will
+enable us to refine and characterise the overall system.
+",1,0,0,0,0,0
+13084,On the number of inequivalent Gabidulin codes,"  Maximum rank-distance (MRD) codes are extremal codes in the space of $m\times
+n$ matrices over a finite field, equipped with the rank metric. Up to
+generalizations, the classical examples of such codes were constructed in the
+1970s and are today known as Gabidulin codes. Motivated by several recent
+approaches to construct MRD codes that are inequivalent to Gabidulin codes, we
+study the equivalence issue for Gabidulin codes themselves. This shows in
+particular that the family of Gabidulin codes already contains a huge subset of
+MRD codes that are pairwise inequivalent, provided that $2\le m\le n-2$.
+",1,0,1,0,0,0
+13085,A Connection Between Mixing and Kac's Chaos,"  The Boltzmann equation is an integro-differential equation which describes
+the density function of the distribution of the velocities of the molecules of
+dilute monoatomic gases under the assumption that the energy is only
+transferred via collisions between the molecules. In 1956 Kac studied the
+Boltzmann equation and defined a property of the density function that he
+called the ""Boltzmann property"" which describes the behavior of the density
+function at a given fixed time as the number of particles tends to infinity.
+The Boltzmann property has been studied extensively since then, and now it is
+simply called chaos, or Kac's chaos. On the other hand, in ergodic theory,
+chaos usually refers to the mixing properties of a dynamical system as time
+tends to infinity. A relationship is derived between Kac's chaos and the notion
+of mixing.
+",0,0,1,0,0,0
+13086,The aCORN Backscatter-Suppressed Beta Spectrometer,"  Backscatter of electrons from a beta spectrometer, with incomplete energy
+deposition, can lead to undesirable effects in many types of experiments. We
+present and discuss the design and operation of a backscatter-suppressed beta
+spectrometer that was developed as part of a program to measure the
+electron-antineutrino correlation coefficient in neutron beta decay (aCORN). An
+array of backscatter veto detectors surrounds a plastic scintillator beta
+energy detector. The spectrometer contains an axial magnetic field gradient, so
+electrons are efficiently admitted but have a low probability for escaping back
+through the entrance after backscattering. The design, construction,
+calibration, and performance of the spectrometer are discussed.
+",0,1,0,0,0,0
+13087,Deterministic Browser,"  Timing attacks have been a continuous threat to users' privacy in modern
+browsers. To mitigate such attacks, existing approaches, such as Tor Browser
+and Fermata, add jitters to the browser clock so that an attacker cannot
+accurately measure an event. However, such defenses only raise the bar for an
+attacker but do not fundamentally mitigate timing attacks, i.e., it just takes
+longer than previous to launch a timing attack. In this paper, we propose a
+novel approach, called deterministic browser, which can provably prevent timing
+attacks in modern browsers. Borrowing from Physics, we introduce several
+concepts, such as an observer and a reference frame. Specifically, a snippet of
+JavaScript, i.e., an observer in JavaScript reference frame, will always obtain
+the same, fixed timing information so that timing attacks are prevented; at
+contrast, a user, i.e., an oracle observer, will perceive the JavaScript
+differently and do not experience the performance slowdown. We have implemented
+a prototype called DeterFox and our evaluation shows that the prototype can
+defend against browser-related timing attacks.
+",1,0,0,0,0,0
+13088,Can Transfer Entropy Infer Causality in Neuronal Circuits for Cognitive Processing?,"  Finding the causes to observed effects and establishing causal relationships
+between events is (and has been) an essential element of science and
+philosophy. Automated methods that can detect causal relationships would be
+very welcome, but practical methods that can infer causality are difficult to
+find, and the subject of ongoing research. While Shannon information only
+detects correlation, there are several information-theoretic notions of
+""directed information"" that have successfully detected causality in some
+systems, in particular in the neuroscience community. However, recent work has
+shown that some directed information measures can sometimes inadequately
+estimate the extent of causal relations, or even fail to identify existing
+cause-effect relations between components of systems, especially if neurons
+contribute in a cryptographic manner to influence the effector neuron. Here, we
+test how often cryptographic logic emerges in an evolutionary process that
+generates artificial neural circuits for two fundamental cognitive tasks:
+motion detection and sound localization. Our results suggest that whether or
+not transfer entropy measures of causality are misleading depends strongly on
+the cognitive task considered. These results emphasize the importance of
+understanding the fundamental logic processes that contribute to cognitive
+processing, and quantifying their relevance in any given nervous system.
+",1,0,0,0,1,0
+13089,Solvability of the Stokes Immersed Boundary Problem in Two Dimensions,"  We study coupled motion of a 1-D closed elastic string immersed in a 2-D
+Stokes flow, known as the Stokes immersed boundary problem in two dimensions.
+Using the fundamental solution of the Stokes equation and the Lagrangian
+coordinate of the string, we write the problem into a contour dynamic
+formulation, which is a nonlinear non-local equation solely keeping track of
+evolution of the string configuration. We prove existence and uniqueness of
+local-in-time solution starting from an arbitrary initial configuration that is
+an $H^{5/2}$-function in the Lagrangian coordinate satisfying the so-called
+well-stretched assumption. We also prove that when the initial string
+configuration is sufficiently close to an equilibrium, which is an evenly
+parameterized circular configuration, then global-in-time solution uniquely
+exists and it will converge to an equilibrium configuration exponentially as
+$t\rightarrow +\infty$. The technique in this paper may also apply to the
+Stokes immersed boundary problem in three dimensions.
+",0,0,1,0,0,0
+13090,Nonlinear Field Space Cosmology,"  We consider the FRW cosmological model in which the matter content of
+universe (playing a role of inflaton or quintessence) is given by a novel
+generalization of the massive scalar field. The latter is a scalar version of
+the recently introduced Nonlinear Field Space Theory (NFST), where physical
+phase space of a given field is assumed to be compactified at large energies.
+For our analysis we choose the simple case of a field with the spherical phase
+space and endow it with the generalized Hamiltonian analogous to the XXZ
+Heisenberg model, normally describing a system of spins in condensed matter
+physics. Subsequently, we study both the homogenous cosmological sector and
+linear perturbations of such a test field. In the homogenous sector we find
+that nonlinearity of the field phase space is becoming relevant for large
+volumes of universe and then it can lead to a recollapse, and possibly also at
+very high energies, leading to the phase of a bounce. Quantization of the field
+is performed in the limit where nontrivial nature of its phase space can be
+neglected, while there is a non-vanishing contribution from the Lorentz
+symmetry breaking term of the Hamiltonian. As a result, in the leading order of
+the XXZ anisotropy parameter, we find that the inflationary spectral index
+remains unmodified with respect to the standard case but the total amplitude of
+perturbations is subject to a correction. The Bunch-Davies vacuum state also
+becomes appropriately corrected. The proposed new approach is bringing
+cosmology and condensed matter physics closer together, which may turn out to
+be beneficial for both disciplines.
+",0,1,0,0,0,0
+13091,Futuristic Classification with Dynamic Reference Frame Strategy,"  Classification is one of the widely used analytical techniques in data
+science domain across different business to associate a pattern which
+contribute to the occurrence of certain event which is predicted with some
+likelihood. This Paper address a lacuna of creating some time window before the
+prediction actually happen to enable organizations some space to act on the
+prediction. There are some really good state of the art machine learning
+techniques to optimally identify the possible churners in either customer base
+or employee base, similarly for fault prediction too if the prediction does not
+come with some buffer time to act on the fault it is very difficult to provide
+a seamless experience to the user. New concept of reference frame creation is
+introduced to solve this problem in this paper
+",0,0,0,1,0,0
+13092,On the normal centrosymmetric Nonnegative inverse eigenvalue problem,"  We give sufficient conditions of the nonnegative inverse eigenvalue problem
+(NIEP) for normal centrosymmetric matrices. These sufficient conditions are
+analogous to the sufficient conditions of the NIEP for normal matrices given by
+Xu [16] and Julio, Manzaneda and Soto [2].
+",0,0,1,0,0,0
+13093,Spectral Dynamics of Learning Restricted Boltzmann Machines,"  The Restricted Boltzmann Machine (RBM), an important tool used in machine
+learning in particular for unsupervized learning tasks, is investigated from
+the perspective of its spectral properties. Starting from empirical
+observations, we propose a generic statistical ensemble for the weight matrix
+of the RBM and characterize its mean evolution. This let us show how in the
+linear regime, in which the RBM is found to operate at the beginning of the
+training, the statistical properties of the data drive the selection of the
+unstable modes of the weight matrix. A set of equations characterizing the
+non-linear regime is then derived, unveiling in some way how the selected modes
+interact in later stages of the learning procedure and defining a deterministic
+learning curve for the RBM.
+",1,1,0,0,0,0
+13094,Local energy decay for Lipschitz wavespeeds,"  We prove a logarithmic local energy decay rate for the wave equation with a
+wavespeed that is a compactly supported Lipschitz perturbation of unity. The
+key is to establish suitable resolvent estimates at high and low energy for the
+meromorphic continuation of the cutoff resolvent. The decay rate is the same as
+that proved by Burq for a smooth perturbation of the Laplacian outside an
+obstacle.
+",0,0,1,0,0,0
+13095,Linear stability and stability of Lazarsfeld-Mukai bundles,"  Let $C$ be a smooth irreducible projective curve and let $(L,H^0(C,L))$ be a
+complete and generated linear series on $C$. Denote by $M_L$ the kernel of the
+evaluation map $H^0(C,L)\otimes\mathcal O_C\to L$. The exact sequence $0\to
+M_L\to H^0(C,L)\otimes\mathcal O_C\to L\to 0$ fits into a commutative diagram
+that we call the Butler's diagram. This diagram induces in a natural way a
+multiplication map on global sections $m_W: W^{\vee}\otimes H^0(K_C)\to
+H^0(S^{\vee}\otimes K_C)$, where $W\subseteq H^0(C,L)$ is a subspace and
+$S^{\vee}$ is the dual of a subbundle $S\subset M_L$. When the subbundle $S$ is
+a stable bundle, we show that the map $m_W$ is surjective. When $C$ is a
+Brill-Noether general curve, we use the surjectivity of $m_W$ to give another
+proof on the semistability of $M_L$, moreover we fill up a gap of an incomplete
+argument by Butler: With the surjectivity of $m_W$ we give conditions to
+determinate the stability of $M_L$, and such conditions implies the well known
+stability conditions for $M_L$ stated precisely by Butler. Finally we obtain
+the equivalence between the stability of $M_L$ and the linear stability of
+$(L,H^0(L))$ on $\gamma$-gonal curves.
+",0,0,1,0,0,0
+13096,Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped,"  Controllers in robotics often consist of expert-designed heuristics, which
+can be hard to tune in higher dimensions. It is typical to use simulation to
+learn these parameters, but controllers learned in simulation often don't
+transfer to hardware. This necessitates optimization directly on hardware.
+However, collecting data on hardware can be expensive. This has led to a recent
+interest in adapting data-efficient learning techniques to robotics. One
+popular method is Bayesian Optimization (BO), a sample-efficient black-box
+optimization scheme, but its performance typically degrades in higher
+dimensions. We aim to overcome this problem by incorporating domain knowledge
+to reduce dimensionality in a meaningful way, with a focus on bipedal
+locomotion. In previous work, we proposed a transformation based on knowledge
+of human walking that projected a 16-dimensional controller to a 1-dimensional
+space. In simulation, this showed enhanced sample efficiency when optimizing
+human-inspired neuromuscular walking controllers on a humanoid model. In this
+paper, we present a generalized feature transform applicable to non-humanoid
+robot morphologies and evaluate it on the ATRIAS bipedal robot -- in simulation
+and on hardware. We present three different walking controllers; two are
+evaluated on the real robot. Our results show that this feature transform
+captures important aspects of walking and accelerates learning on hardware and
+simulation, as compared to traditional BO.
+",1,0,0,0,0,0
+13097,Comparison of multi-task convolutional neural network (MT-CNN) and a few other methods for toxicity prediction,"  Toxicity analysis and prediction are of paramount importance to human health
+and environmental protection. Existing computational methods are built from a
+wide variety of descriptors and regressors, which makes their performance
+analysis difficult. For example, deep neural network (DNN), a successful
+approach in many occasions, acts like a black box and offers little conceptual
+elegance or physical understanding. The present work constructs a common set of
+microscopic descriptors based on established physical models for charges,
+surface areas and free energies to assess the performance of multi-task
+convolutional neural network (MT-CNN) architectures and a few other approaches,
+including random forest (RF) and gradient boosting decision tree (GBDT), on an
+equal footing. Comparison is also given to convolutional neural network (CNN)
+and non-convolutional deep neural network (DNN) algorithms. Four benchmark
+toxicity data sets (i.e., endpoints) are used to evaluate various approaches.
+Extensive numerical studies indicate that the present MT-CNN architecture is
+able to outperform the state-of-the-art methods.
+",1,0,0,1,0,0
+13098,SIFM: A network architecture for seamless flow mobility between LTE and WiFi networks - Analysis and Testbed Implementation,"  This paper deals with cellular (e.g. LTE) networks that selectively offload
+the mobile data traffic onto WiFi (IEEE 802.11) networks to improve network
+performance. We propose the Seamless Internetwork Flow Mobility (SIFM)
+architecture that provides seamless flow-mobility support using concepts of
+Software Defined Networking (SDN). The SDN paradigm decouples the control and
+data plane, leading to a centralized network intelligence and state. The SIFM
+architecture utilizes this aspect of SDN and moves the mobility decisions to a
+centralized Flow Controller (FC). This provides a global network view while
+making mobility decisions and also reduces the complexity at the PGW. We
+implement and evaluate both basic PMIPv6 and the SIFM architectures by
+incorporating salient LTE and WiFi network features in the ns-3 simulator.
+Performance experiments validate that seamless mobility is achieved. Also, the
+SIFM architecture shows an improved network performance when compared to the
+base PMIPv6 architecture. A proof-of-concept prototype of the SIFM architecture
+has been implemented on an experimental testbed. The LTE network is emulated by
+integrating USRP B210x with the OpenLTE eNodeB and OpenLTE EPC. The WiFi
+network is emulated using hostapd and dnsmasq daemons running on Ubuntu 12.04.
+An off-the-shelf LG G2 mobile phone running Android 4.2.2 is used as the user
+equipment. We demonstrate seamless mobility between the LTE network and the
+WiFi network with the help of ICMP ping and a TCP chat application.
+",1,0,0,0,0,0
+13099,Graph sampling with determinantal processes,"  We present a new random sampling strategy for k-bandlimited signals defined
+on graphs, based on determinantal point processes (DPP). For small graphs, ie,
+in cases where the spectrum of the graph is accessible, we exhibit a DPP
+sampling scheme that enables perfect recovery of bandlimited signals. For large
+graphs, ie, in cases where the graph's spectrum is not accessible, we
+investigate, both theoretically and empirically, a sub-optimal but much faster
+DPP based on loop-erased random walks on the graph. Preliminary experiments
+show promising results especially in cases where the number of measurements
+should stay as small as possible and for graphs that have a strong community
+structure. Our sampling scheme is efficient and can be applied to graphs with
+up to $10^6$ nodes.
+",1,0,0,1,0,0
+13100,Grundy dominating sequences and zero forcing sets,"  In a graph $G$ a sequence $v_1,v_2,\dots,v_m$ of vertices is Grundy
+dominating if for all $2\le i \le m$ we have $N[v_i]\not\subseteq
+\cup_{j=1}^{i-1}N[v_j]$ and is Grundy total dominating if for all $2\le i \le
+m$ we have $N(v_i)\not\subseteq \cup_{j=1}^{i-1}N(v_j)$. The length of the
+longest Grundy (total) dominating sequence has been studied by several authors.
+In this paper we introduce two similar concepts when the requirement on the
+neighborhoods is changed to $N(v_i)\not\subseteq \cup_{j=1}^{i-1}N[v_j]$ or
+$N[v_i]\not\subseteq \cup_{j=1}^{i-1}N(v_j)$. In the former case we establish a
+strong connection to the zero forcing number of a graph, while we determine the
+complexity of the decision problem in the latter case. We also study the
+relationships among the four concepts, and discuss their computational
+complexities.
+",0,0,1,0,0,0
+13101,Comments on `High-dimensional simultaneous inference with the bootstrap',"  We provide comments on the article ""High-dimensional simultaneous inference
+with the bootstrap"" by Ruben Dezeure, Peter Buhlmann and Cun-Hui Zhang.
+",0,0,0,1,0,0
+13102,Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model,"  Variable clustering is important for explanatory analysis. However, only few
+dedicated methods for variable clustering with the Gaussian graphical model
+have been proposed. Even more severe, small insignificant partial correlations
+due to noise can dramatically change the clustering result when evaluating for
+example with the Bayesian Information Criteria (BIC). In this work, we try to
+address this issue by proposing a Bayesian model that accounts for negligible
+small, but not necessarily zero, partial correlations. Based on our model, we
+propose to evaluate a variable clustering result using the marginal likelihood.
+To address the intractable calculation of the marginal likelihood, we propose
+two solutions: one based on a variational approximation, and another based on
+MCMC. Experiments on simulated data shows that the proposed method is similarly
+accurate as BIC in the no noise setting, but considerably more accurate when
+there are noisy partial correlations. Furthermore, on real data the proposed
+method provides clustering results that are intuitively sensible, which is not
+always the case when using BIC or its extensions.
+",0,0,0,1,0,0
+13103,On the universality of MOG weak field approximation at galaxy cluster scale,"  In its weak field limit, Scalar-tensor-vector gravity theory introduces a
+Yukawa-correction to the gravitational potential. Such a correction depends on
+the two parameters, $\alpha$ which accounts for the modification of the
+gravitational constant, and $\mu^{*-1}$ which represents the scale length on
+which the scalar field propagates. These parameters were found to be universal
+when the modified gravitational potential was used to fit the galaxy rotation
+curves and the mass profiles of galaxy clusters, both without Dark Matter. We
+test the universality of these parameters using the the temperature
+anisotropies due to the thermal Sunyaev-Zeldovich effect. In our model the
+intra-cluster gas is in hydrostatic equilibrium within the modified
+gravitational potential well and it is described by a polytropic equation of
+state. We predict the thermal Sunyaev-Zeldovich temperature anisotropies
+produced by Coma cluster, and we compare them with those obtained using the
+Planck 2013 Nominal maps. In our analysis, we find $\alpha$ and the scale
+length, respectively, to be consistent and to depart from their universal
+values. Our analysis points out that the assumption of the universality of the
+Yukawa-correction to the gravitational potential is ruled out at more than
+$3.5\sigma$ at galaxy clusters scale, while demonstrating that such a theory of
+gravity is capable to fit the cluster profile if the scale dependence of the
+gravitational potential is restored.
+",0,1,0,0,0,0
+13104,Much Faster Algorithms for Matrix Scaling,"  We develop several efficient algorithms for the classical \emph{Matrix
+Scaling} problem, which is used in many diverse areas, from preconditioning
+linear systems to approximation of the permanent. On an input $n\times n$
+matrix $A$, this problem asks to find diagonal (scaling) matrices $X$ and $Y$
+(if they exist), so that $X A Y$ $\varepsilon$-approximates a doubly
+stochastic, or more generally a matrix with prescribed row and column sums.
+We address the general scaling problem as well as some important special
+cases. In particular, if $A$ has $m$ nonzero entries, and if there exist $X$
+and $Y$ with polynomially large entries such that $X A Y$ is doubly stochastic,
+then we can solve the problem in total complexity $\tilde{O}(m + n^{4/3})$.
+This greatly improves on the best known previous results, which were either
+$\tilde{O}(n^4)$ or $O(m n^{1/2}/\varepsilon)$.
+Our algorithms are based on tailor-made first and second order techniques,
+combined with other recent advances in continuous optimization, which may be of
+independent interest for solving similar problems.
+",1,0,1,0,0,0
+13105,"Cooperative ""folding transition"" in the sequence space facilitates function-driven evolution of protein families","  In the protein sequence space, natural proteins form clusters of families
+which are characterized by their unique native folds whereas the great majority
+of random polypeptides are neither clustered nor foldable to unique structures.
+Since a given polypeptide can be either foldable or unfoldable, a kind of
+""folding transition"" is expected at the boundary of a protein family in the
+sequence space. By Monte Carlo simulations of a statistical mechanical model of
+protein sequence alignment that coherently incorporates both short-range and
+long-range interactions as well as variable-length insertions to reproduce the
+statistics of the multiple sequence alignment of a given protein family, we
+demonstrate the existence of such transition between natural-like sequences and
+random sequences in the sequence subspaces for 15 domain families of various
+folds. The transition was found to be highly cooperative and two-state-like.
+Furthermore, enforcing or suppressing consensus residues on a few of the
+well-conserved sites enhanced or diminished, respectively, the natural-like
+pattern formation over the entire sequence. In most families, the key sites
+included ligand binding sites. These results suggest some selective pressure on
+the key residues, such as ligand binding activity, may cooperatively facilitate
+the emergence of a protein family during evolution. From a more practical
+aspect, the present results highlight an essential role of long-range effects
+in precisely defining protein families, which are absent in conventional
+sequence models.
+",0,1,0,0,0,0
+13106,Thermal Molecular Focusing: Tunable Cross Effect of Phoresis and Advection,"  The control of solute fluxes through either microscopic phoresis or
+hydrodynamic advection is a fundamental way to transport molecules, which are
+ubiquitously present in nature and technology. We study the transport of large
+solute such as DNA driven by a time-dependent thermal field in a polymer
+solution. Heat propagation of a single heat spot moving back and forth gives
+rise to the molecular focusing of DNA with frequency-tunable control. We
+developed a theoretical model, where heat conduction, viscoelastic expansion of
+walls, and the viscosity gradient of a smaller solute are coupled, and that can
+explain the underlying hydrodynamic focusing and its interplay with phoretic
+transports. This cross effect may allow one to design a unique miniaturized
+pump in microfluidics.
+",0,1,0,0,0,0
+13107,Shallow water modeling of rolling pad instability in liquid metal batteries,"  Magnetohydrodynamically induced interface instability in liquid metal
+batteries is analyzed. The batteries are represented by a simplified system in
+the form of a rectangular cell, in which strong vertical electric current flows
+through three horizontal layers: the layer of a heavy metal at the bottom, the
+layer of a light metal at the top, and the layer of electrolyte in the middle.
+A new two-dimensional nonlinear model based on the conservative shallow water
+approximation is derived and utilized in a numerical study. It is found that in
+the case of small density difference between the electrolyte and one of the
+metals, the instability closely resembles the rolling pad instability observed
+earlier in the aluminum reduction cells. When the two electrolyte-metal density
+differences are comparable, the dynamics of unstable systems is more complex
+and characterized by interaction between two nearly symmetric or antisymmetric
+interfacial waves.
+",0,1,0,0,0,0
+13108,Near-Optimal Clustering in the $k$-machine model,"  The clustering problem, in its many variants, has numerous applications in
+operations research and computer science (e.g., in applications in
+bioinformatics, image processing, social network analysis, etc.). As sizes of
+data sets have grown rapidly, researchers have focused on designing algorithms
+for clustering problems in models of computation suited for large-scale
+computation such as MapReduce, Pregel, and streaming models. The $k$-machine
+model (Klauck et al., SODA 2015) is a simple, message-passing model for
+large-scale distributed graph processing. This paper considers three of the
+most prominent examples of clustering problems: the uncapacitated facility
+location problem, the $p$-median problem, and the $p$-center problem and
+presents $O(1)$-factor approximation algorithms for these problems running in
+$\tilde{O}(n/k)$ rounds in the $k$-machine model. These algorithms are optimal
+up to polylogarithmic factors because this paper also shows
+$\tilde{\Omega}(n/k)$ lower bounds for obtaining polynomial-factor
+approximation algorithms for these problems. These are the first results for
+clustering problems in the $k$-machine model.
+We assume that the metric provided as input for these clustering problems in
+only implicitly provided, as an edge-weighted graph and in a nutshell, our main
+technical contribution is to show that constant-factor approximation algorithms
+for all three clustering problems can be obtained by learning only a small
+portion of the input metric.
+",1,0,0,0,0,0
+13109,Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval,"  We develop procedures, based on minimization of the composition $f(x) =
+h(c(x))$ of a convex function $h$ and smooth function $c$, for solving random
+collections of quadratic equalities, applying our methodology to phase
+retrieval problems. We show that the prox-linear algorithm we develop can solve
+phase retrieval problems---even with adversarially faulty measurements---with
+high probability as soon as the number of measurements $m$ is a constant factor
+larger than the dimension $n$ of the signal to be recovered. The algorithm
+requires essentially no tuning---it consists of solving a sequence of convex
+problems---and it is implementable without any particular assumptions on the
+measurements taken. We provide substantial experiments investigating our
+methods, indicating the practical effectiveness of the procedures and showing
+that they succeed with high probability as soon as $m / n \ge 2$ when the
+signal is real-valued.
+",0,0,1,1,0,0
+13110,Edge-Based Recognition of Novel Objects for Robotic Grasping,"  In this paper, we investigate the problem of grasping novel objects in
+unstructured environments. To address this problem, consideration of the object
+geometry, reachability and force closure analysis are required. We propose a
+framework for grasping unknown objects by localizing contact regions on the
+contours formed by a set of depth edges in a single view 2D depth image.
+According to the edge geometric features obtained from analyzing the data of
+the depth map, the contact regions are determined. Finally,We validate the
+performance of the approach by applying it to the scenes with both single and
+multiple objects, using Baxter manipulator.
+",1,0,0,0,0,0
+13111,Surface Normals in the Wild,"  We study the problem of single-image depth estimation for images in the wild.
+We collect human annotated surface normals and use them to train a neural
+network that directly predicts pixel-wise depth. We propose two novel loss
+functions for training with surface normal annotations. Experiments on NYU
+Depth and our own dataset demonstrate that our approach can significantly
+improve the quality of depth estimation in the wild.
+",1,0,0,0,0,0
+13112,Piecewise excluding geodesic languages,"  The complexity of a geodesic language has connections to algebraic properties
+of the group. Gilman, Hermiller, Holt, and Rees show that a finitely generated
+group is virtually free if and only if its geodesic language is locally
+excluding for some finite inverse-closed generating set. The existence of such
+a correspondence and the result of Hermiller, Holt, and Rees that finitely
+generated abelian groups have piecewise excluding geodesic language for all
+finite inverse-closed generating sets motivated our work. We show that a
+finitely generated group with piecewise excluding geodesic language need not be
+abelian and give a class of infinite non-abelian groups which have piecewise
+excluding geodesic languages for certain generating sets. The quaternion group
+is shown to be the only non-abelian 2-generator group with piecewise excluding
+geodesic language for all finite inverse-closed generating sets. We also show
+that there are virtually abelian groups with geodesic languages which are not
+piecewise excluding for any finite inverse-closed generating set.
+",0,0,1,0,0,0
+13113,Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR,"  Purpose: The radial k-space trajectory is a well-established sampling
+trajectory used in conjunction with magnetic resonance imaging. However, the
+radial k-space trajectory requires a large number of radial lines for
+high-resolution reconstruction. Increasing the number of radial lines causes
+longer acquisition time, making it more difficult for routine clinical use. On
+the other hand, if we reduce the number of radial lines, streaking artifact
+patterns are unavoidable. To solve this problem, we propose a novel deep
+learning approach with domain adaptation to restore high-resolution MR images
+from under-sampled k-space data.
+Methods: The proposed deep network removes the streaking artifacts from the
+artifact corrupted images. To address the situation given the limited available
+data, we propose a domain adaptation scheme that employs a pre-trained network
+using a large number of x-ray computed tomography (CT) or synthesized radial MR
+datasets, which is then fine-tuned with only a few radial MR datasets.
+Results: The proposed method outperforms existing compressed sensing
+algorithms, such as the total variation and PR-FOCUSS methods. In addition, the
+calculation time is several orders of magnitude faster than the total variation
+and PR-FOCUSS methods.Moreover, we found that pre-training using CT or MR data
+from similar organ data is more important than pre-training using data from the
+same modality for different organ.
+Conclusion: We demonstrate the possibility of a domain-adaptation when only a
+limited amount of MR data is available. The proposed method surpasses the
+existing compressed sensing algorithms in terms of the image quality and
+computation time.
+",1,0,0,0,0,0
+13114,Automation in Human-Machine Networks: How Increasing Machine Agency Affects Human Agency,"  Efficient human-machine networks require productive interaction between human
+and machine actors. In this study, we address how a strengthening of machine
+agency, for example through increasing levels of automation, affect the human
+actors of the networks. Findings from case studies within air traffic
+management, crisis management, and crowd evacuation are presented, exemplifying
+how automation may strengthen the agency of human actors in the network through
+responsibility sharing and task allocation, and serve as a needed prerequisite
+of innovation and change.
+",1,0,0,0,0,0
+13115,"Comment on ""On the nature of magnetic stripes in cuprate superconductors,"" by H. Jacobsen et al., arXiv:1704.08528v2","  Dynamics reduces the orthorhombicity of magnetic stripes in La_2CuO_4+y. The
+measured stripe incommensuration can be used to determine the oxygen content of
+the sample.
+",0,1,0,0,0,0
+13116,"Logically Isolated, Actually Unpredictable? Measuring Hypervisor Performance in Multi-Tenant SDNs","  Ideally, by enabling multi-tenancy, network virtualization allows to improve
+resource utilization, while providing performance isolation: although the
+underlying resources are shared, the virtual network appears as a dedicated
+network to the tenant. However, providing such an illusion is challenging in
+practice, and over the last years, many expedient approaches have been proposed
+to provide performance isolation in virtual networks, by enforcing bandwidth
+reservations. We in this paper study another source for overheads and
+unpredictable performance in virtual networks: the hypervisor.
+The hypervisor is a critical component in multi-tenant environments, but its
+overhead and influence on performance are hardly understood today. In
+particular, we focus on OpenFlow-based virtualized Software Defined Networks
+(vSDNs). Network virtualization is considered a killer application for SDNs: a
+vSDN allows each tenant to flexibly manage its network from a logically
+centralized perspective, via a simple API. For the purpose of our study, we
+developed a new benchmarking tool for OpenFlow control and data planes,
+enabling high and consistent OpenFlow message rates. Using our tool, we
+identify and measure controllable and uncontrollable effects on performance and
+overhead, including the hypervisor technology, the number of tenants as well as
+the tenant type, as well as the type of OpenFlow messages.
+",1,0,0,0,0,0
+13117,Analysis of a nonlinear importance sampling scheme for Bayesian parameter estimation in state-space models,"  The Bayesian estimation of the unknown parameters of state-space (dynamical)
+systems has received considerable attention over the past decade, with a
+handful of powerful algorithms being introduced. In this paper we tackle the
+theoretical analysis of the recently proposed {\it nonlinear} population Monte
+Carlo (NPMC). This is an iterative importance sampling scheme whose key
+features, compared to conventional importance samplers, are (i) the approximate
+computation of the importance weights (IWs) assigned to the Monte Carlo samples
+and (ii) the nonlinear transformation of these IWs in order to prevent the
+degeneracy problem that flaws the performance of conventional importance
+samplers. The contribution of the present paper is a rigorous proof of
+convergence of the nonlinear IS (NIS) scheme as the number of Monte Carlo
+samples, $M$, increases. Our analysis reveals that the NIS approximation errors
+converge to 0 almost surely and with the optimal Monte Carlo rate of
+$M^{-\frac{1}{2}}$. Moreover, we prove that this is achieved even when the mean
+estimation error of the IWs remains constant, a property that has been termed
+{\it exact approximation} in the Markov chain Monte Carlo literature. We
+illustrate these theoretical results by means of a computer simulation example
+involving the estimation of the parameters of a state-space model typically
+used for target tracking.
+",0,0,0,1,0,0
+13118,On Axiomatizability of the Multiplicative Theory of Numbers,"  The multiplicative theory of a set of numbers (which could be natural,
+integer, rational, real or complex numbers) is the first-order theory of the
+structure of that set with (solely) the multiplication operation (that set is
+taken to be multiplicative, i.e., closed under multiplication). In this paper
+we study the multiplicative theories of the complex, real and (positive)
+rational numbers. These theories (and also the multiplicative theories of
+natural and integer numbers) are known to be decidable (i.e., there exists an
+algorithm that decides whether a given sentence is derivable form the theory);
+here we present explicit axiomatizations for them and show that they are not
+finitely axiomatizable. For each of these sets (of complex, real and [positive]
+rational numbers) a language, including the multiplication operation, is
+introduced in a way that it allows quantifier elimination (for the theory of
+that set).
+",1,0,1,0,0,0
+13119,On Bivariate Discrete Weibull Distribution,"  Recently, Lee and Cha (2015, `On two generalized classes of discrete
+bivariate distributions', {\it American Statistician}, 221 - 230) proposed two
+general classes of discrete bivariate distributions. They have discussed some
+general properties and some specific cases of their proposed distributions. In
+this paper we have considered one model, namely bivariate discrete Weibull
+distribution, which has not been considered in the literature yet. The proposed
+bivariate discrete Weibull distribution is a discrete analogue of the
+Marshall-Olkin bivariate Weibull distribution. We study various properties of
+the proposed distribution and discuss its interesting physical interpretations.
+The proposed model has four parameters, and because of that it is a very
+flexible distribution. The maximum likelihood estimators of the parameters
+cannot be obtained in closed forms, and we have proposed a very efficient
+nested EM algorithm which works quite well for discrete data. We have also
+proposed augmented Gibbs sampling procedure to compute Bayes estimates of the
+unknown parameters based on a very flexible set of priors. Two data sets have
+been analyzed to show how the proposed model and the method work in practice.
+We will see that the performances are quite satisfactory. Finally, we conclude
+the paper.
+",0,0,0,1,0,0
+13120,Parabolic induction in characteristic p,"  Let G be the group of rational points of a reductive connected group over a
+finite field (resp. nonarchimedean local field of characteristic p) and R a
+commutative ring. The unipotent (resp. pro-p Iwahori) invariant functor takes a
+smooth representation of G to a module over the unipotent (resp. pro-p Iwahori)
+Hecke R-algebra H of G. We prove that these functors for G and for a Levi
+subgroup of G commute with the parabolic induction functors, as well as with
+the right adjoints of the parabolic induction functors. However, they do not
+commute with the left adjoints of the parabolic induction functors in general;
+they do if p is invertible in R.
+When R is an algebraically closed field of characteristic p, we show in the
+local case that an irreducible admissible R-representation V of G is
+supercuspidal (or equivalently supersingular) if and only if the H-module V^I
+of its invariants by the pro-p Iwahori I admits a supersingular subquotient, if
+and only if V^I is supersingular.
+",0,0,1,0,0,0
+13121,Linear polygraphs applied to categorification,"  We introduce two applications of polygraphs to categorification problems. We
+compute first, from a coherent presentation of an $n$-category, a coherent
+presentation of its Karoubi envelope. For this, we extend the construction of
+Karoubi envelope to $n$-polygraphs and linear $(n,n-1)$-polygraphs. The second
+problem treated in this paper is the construction of Grothendieck
+decategorifications for $(n,n-1)$-polygraphs. This construction yields a
+rewriting system presenting for example algebras categorified by a linear
+monoidal category. We finally link quasi-convergence of such rewriting systems
+to the uniqueness of direct sum decompositions for linear
+$(n-1,n-1)$-categories.
+",0,0,1,0,0,0
+13122,Viable tensor-to-scalar ratio in a symmetric matter bounce,"  Matter bounces refer to scenarios wherein the universe contracts at early
+times as in a matter dominated epoch until the scale factor reaches a minimum,
+after which it starts expanding. While such scenarios are known to lead to
+scale invariant spectra of primordial perturbations after the bounce, the
+challenge has been to construct completely symmetric bounces that lead to a
+tensor-to-scalar ratio which is small enough to be consistent with the recent
+cosmological data. In this work, we construct a model involving two scalar
+fields (a canonical field and a non-canonical ghost field) to drive the
+symmetric matter bounce and study the evolution of the scalar perturbations in
+the model. If we consider the scale associated with the bounce to be of the
+order of the Planck scale, the model is completely described in terms of only
+one parameter, viz the value of the scale factor at the bounce. We evolve the
+scalar perturbations numerically across the bounce and evaluate the scalar
+power spectra after the bounce. We show that, while the scalar and tensor
+perturbation spectra are scale invariant over scales of cosmological interest,
+the tensor-to-scalar ratio proves to be much smaller than the current upper
+bound from the observations of the cosmic microwave background anisotropies by
+the Planck mission. We also support our numerical analysis with analytical
+arguments.
+",0,1,0,0,0,0
+13123,Approximate Ripple Carry and Carry Lookahead Adders - A Comparative Analysis,"  Approximate ripple carry adders (RCAs) and carry lookahead adders (CLAs) are
+presented which are compared with accurate RCAs and CLAs for performing a
+32-bit addition. The accurate and approximate RCAs and CLAs are implemented
+using a 32/28nm CMOS process. Approximations ranging from 4- to 20-bits are
+considered for the less significant adder bit positions. The simulation results
+show that approximate RCAs report reductions in the power-delay product (PDP)
+ranging from 19.5% to 82% than the accurate RCA for approximation sizes varying
+from 4- to 20-bits. Also, approximate CLAs report reductions in PDP ranging
+from 16.7% to 74.2% than the accurate CLA for approximation sizes varying from
+4- to 20-bits. On average, for the approximation sizes considered, it is
+observed that approximate CLAs achieve a 46.5% reduction in PDP compared to the
+approximate RCAs. Hence, approximate CLAs are preferable over approximate RCAs
+for the low power implementation of approximate computer arithmetic.
+",1,0,0,0,0,0
+13124,On the Robustness and Asymptotic Properties for Maximum Likelihood Estimators of Parameters in Exponential Power and its Scale Mixture Form Distributions,"  The normality assumption on data set is very restrictive approach for
+modelling. The generalized form of normal distribution, named as an exponential
+power (EP) distribution, and its scale mixture form have been considered
+extensively to overcome the problem for modelling non-normal data set since
+last decades. However, examining the robustness properties of maximum
+likelihood (ML) estimators of parameters in these distributions, such as the in
+uence function, gross-error sensitivity, breakdown point and
+information-standardized sensitivity, has not been considered together. The
+well-known asymptotic properties of ML estimators of location, scale and added
+skewness parameters in EP and its scale mixture form distributions are studied
+and also these ML estimators for location, scale and scale variant (skewness)
+parameters can be represented as an iterative reweighting algorithm to compute
+the estimates of these parameters simultaneously.
+",0,0,1,1,0,0
+13125,Solving internal covariate shift in deep learning with linked neurons,"  This work proposes a novel solution to the problem of internal covariate
+shift and dying neurons using the concept of linked neurons. We define the
+neuron linkage in terms of two constraints: first, all neuron activations in
+the linkage must have the same operating point. That is to say, all of them
+share input weights. Secondly, a set of neurons is linked if and only if there
+is at least one member of the linkage that has a non-zero gradient in regard to
+the input of the activation function. This means that for any input in the
+activation function, there is at least one member of the linkage that operates
+in a non-flat and non-zero area. This simple change has profound implications
+in the network learning dynamics. In this article we explore the consequences
+of this proposal and show that by using this kind of units, internal covariate
+shift is implicitly solved. As a result of this, the use of linked neurons
+allows to train arbitrarily large networks without any architectural or
+algorithmic trick, effectively removing the need of using re-normalization
+schemes such as Batch Normalization, which leads to halving the required
+training time. It also solves the problem of the need for standarized input
+data. Results show that the units using the linkage not only do effectively
+solve the aforementioned problems, but are also a competitive alternative with
+respect to state-of-the-art with very promising results.
+",1,0,0,1,0,0
+13126,Correlating the nanostructure of Al-oxide with deposition conditions and dielectric contributions of two-level systems in perspective of superconducting quantum circuits,"  This work is concerned with Al/Al-oxide(AlO$_{x}$)/Al-layer systems which are
+important for Josephson-junction-based superconducting devices such as quantum
+bits. The device performance is limited by noise, which has been to a large
+degree assigned to the presence and properties of two-level tunneling systems
+in the amorphous AlO$_{x}$ tunnel barrier. The study is focused on the
+correlation of the fabrication conditions, nanostructural and nanochemical
+properties and the occurrence of two-level tunneling systems with particular
+emphasis on the AlO$_{x}$-layer. Electron-beam evaporation with two different
+processes and sputter deposition were used for structure fabrication, and the
+effect of illumination by ultraviolet light during Al-oxide formation is
+elucidated. Characterization was performed by analytical transmission electron
+microscopy and low-temperature dielectric measurements. We show that the
+fabrication conditions have a strong impact on the nanostructural and
+nanochemical properties of the layer systems and the properties of two-level
+tunneling systems. Based on the understanding of the observed structural
+characteristics, routes are derived towards the fabrication of
+Al/AlO$_{x}$/Al-layers systems with improved properties.
+",0,1,0,0,0,0
+13127,"Senior Project Management System: Requirements, Specification, and Design Issues","  Senior project is a typical essential course in computing educational
+programs. The course involves the selection of a project problem, the
+submission of various documents, and intensive communication among the project
+team members and between them and the course instructors. To facilitate all
+these tasks, we introduce the senior project management system (SPMS) that
+organizes and manages previous, current, and proposed senior projects in all of
+their stages along with proper ways of communication between the students and
+course instructors. This paper explains the system requirements and
+specifications and discusses related design issues. The paper shows the
+importance of well documenting the specifications and requirements of software
+systems and paying considerable attention to system design, which has a
+positive impact on implementing high quality systems.
+",1,0,0,0,0,0
+13128,Simulation of Matrix Product State on a Quantum Computer,"  The study of tensor network theory is an important field and promises a wide
+range of experimental and quantum information theoretical applications. Matrix
+product state is the most well-known example of tensor network states, which
+provides an effective and efficient representation of one-dimensional quantum
+systems. Indeed, it lies at the heart of density matrix renormalization group
+(DMRG), a most common method for simulation of one-dimensional strongly
+correlated quantum systems. It has got attention from several areas varying
+from solid-state systems to quantum computing and quantum simulators. We have
+considered maximally entangled matrix product states (GHZ and W). Here, we
+designed the quantum circuits for implementing the matrix product states. In
+this paper, we simulated the matrix product states in customized IBM (2-qubit,
+3-qubit and 4-qubit) quantum systems and determined the probability
+distribution among the quantum states.
+",1,0,0,0,0,0
+13129,Deep Generative Models with Learnable Knowledge Constraints,"  The broad set of deep generative models (DGMs) has achieved remarkable
+advances. However, it is often difficult to incorporate rich structured domain
+knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a
+principled framework to impose structured constraints on probabilistic models,
+but has limited applicability to the diverse DGMs that can lack a Bayesian
+formulation or even explicit density evaluation. PR also requires constraints
+to be fully specified a priori, which is impractical or suboptimal for complex
+knowledge with learnable uncertain parts. In this paper, we establish
+mathematical correspondence between PR and reinforcement learning (RL), and,
+based on the connection, expand PR to learn constraints as the extrinsic reward
+in RL. The resulting algorithm is model-agnostic to apply to any DGMs, and is
+flexible to adapt arbitrary constraints with the model jointly. Experiments on
+human image generation and templated sentence generation show models with
+learned knowledge constraints by our algorithm greatly improve over base
+generative models.
+",0,0,0,1,0,0
+13130,A Proximity-Aware Hierarchical Clustering of Faces,"  In this paper, we propose an unsupervised face clustering algorithm called
+""Proximity-Aware Hierarchical Clustering"" (PAHC) that exploits the local
+structure of deep representations. In the proposed method, a similarity measure
+between deep features is computed by evaluating linear SVM margins. SVMs are
+trained using nearest neighbors of sample data, and thus do not require any
+external training data. Clusters are then formed by thresholding the similarity
+scores. We evaluate the clustering performance using three challenging
+unconstrained face datasets, including Celebrity in Frontal-Profile (CFP),
+IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3)
+datasets. Experimental results demonstrate that the proposed approach can
+achieve significant improvements over state-of-the-art methods. Moreover, we
+also show that the proposed clustering algorithm can be applied to curate a set
+of large-scale and noisy training dataset while maintaining sufficient amount
+of images and their variations due to nuisance factors. The face verification
+performance on JANUS CS3 improves significantly by finetuning a DCNN model with
+the curated MS-Celeb-1M dataset which contains over three million face images.
+",1,0,0,0,0,0
+13131,Acoustic emission source localization in thin metallic plates: a single-sensor approach based on multimodal edge reflections,"  This paper presents a new acoustic emission (AE) source localization for
+isotropic plates with reflecting boundaries. This approach that has no blind
+spot leverages multimodal edge reflections to identify AE sources with only a
+single sensor. The implementation of the proposed approach involves three main
+steps. First, the continuous wavelet transform (CWT) and the dispersion curves
+of the fundamental Lamb wave modes are utilized to estimate the distance
+between an AE source and a sensor. This step uses a modal acoustic emission
+approach. Then, an analytical model is proposed that uses the estimated
+distances to simulate the edge-reflected waves. Finally, the correlation
+between the experimental and the simulated waveforms is used to estimate the
+location of AE sources. Hsu-Nielson pencil lead break (PLB) tests were
+performed on an aluminum plate to validate this algorithm and promising results
+were achieved. Based on these results, the paper reports the statistics of the
+localization errors.
+",0,1,0,0,0,0
+13132,Asymptotically Optimal Multi-Paving,"  Anderson's paving conjecture, now known to hold due to the resolution of the
+Kadison-Singer problem asserts that every zero diagonal Hermitian matrix admits
+non-trivial pavings with dimension independent bounds. In this paper, we
+develop a technique extending the arguments of Marcus, Spielman and Srivastava
+in their solution of the Kadison-Singer problem to show the existence of
+non-trivial pavings for collections of matrices. We show that given zero
+diagonal Hermitian contractions $A^{(1)}, \cdots, A^{(k)} \in M_n(\mathbb{C})$
+and $\epsilon > 0$, one may find a paving $X_1 \amalg \cdots \amalg X_r = [n]$
+where $r \leq 18k\epsilon^{-2}$ such that, \[\lambda_{max} (P_{X_i} A^{(j)}
+P_{X_i}) < \epsilon, \quad i \in [r], \, j \in [k].\] As a consequence, we get
+the correct asymptotic estimates for paving general zero diagonal matrices;
+zero diagonal contractions can be $(O(\epsilon^{-2}),\epsilon)$ paved. As an
+application, we give a simplified proof wth slightly better estimates of a
+theorem of Johnson, Ozawa and Schechtman concerning commutator representations
+of zero trace matrices.
+",0,0,1,0,0,0
+13133,"Meeting the Challenges of Modeling Astrophysical Thermonuclear Explosions: Castro, Maestro, and the AMReX Astrophysics Suite","  We describe the AMReX suite of astrophysics codes and their application to
+modeling problems in stellar astrophysics. Maestro is tuned to efficiently
+model subsonic convective flows while Castro models the highly compressible
+flows associated with stellar explosions. Both are built on the
+block-structured adaptive mesh refinement library AMReX. Together, these codes
+enable a thorough investigation of stellar phenomena, including Type Ia
+supernovae and X-ray bursts. We describe these science applications and the
+approach we are taking to make these codes performant on current and future
+many-core and GPU-based architectures.
+",0,1,0,0,0,0
+13134,Eliminating the unit constant in the Lambek calculus with brackets,"  We present a translation of the Lambek calculus with brackets and the unit
+constant, $\mathbf{Lb}^{\boldsymbol{*}}_{\mathbf{1}}$, into the Lambek calculus
+with brackets allowing empty antecedents, but without the unit constant,
+$\mathbf{Lb}^{\boldsymbol{*}}$. Using this translation, we extend previously
+known results for $\mathbf{Lb}^{\boldsymbol{*}}$ to
+$\mathbf{Lb}^{\boldsymbol{*}}_{\mathbf{1}}$: (1) languages generated by
+categorial grammars based on the Lambek calculus with brackets are context-free
+(Kanazawa 2017); (2) the polynomial-time algorithm for deciding derivability of
+bounded depth sequents (Kanovich et al. 2017).
+",1,0,1,0,0,0
+13135,Robust Distributed Planar Formation Control for Higher-Order Holonomic and Nonholonomic Agents,"  We present a distributed formation control strategy for agents with a variety
+of dynamics to achieve a desired planar formation. The proposed strategy is
+fully distributed, does not require inter-agent communication or a common sense
+of orientation, and can be implemented using relative position measurements
+acquired by agents in their local coordinate frames. We show how the control
+designed for agents with the simplest dynamical model, i.e., the
+single-integrator dynamics, can be extended to holonomic agents with
+higher-order dynamics such as quadrotors, and nonholonomic agents such as
+unicycles and cars. We prove that the proposed strategy is robust to
+saturations in the input, unmodeled dynamics, and switches in the sensing
+topology. We further show that the control is relaxed in the sense that agents
+can move along a rotated and scaled control direction without affecting the
+convergence to the desired formation. This observation is used to design a
+distributed collision avoidance strategy. We demonstrate the proposed approach
+in simulations and further present a distributed robotic platform to test the
+strategy experimentally. Our experimental platform consists of off-the-shelf
+equipment that can be used to test and validate other multi-agent algorithms.
+The code and implementation instructions for this platform are available online
+and free.
+",1,0,0,0,0,0
+13136,Dual Based DSP Bidding Strategy and its Application,"  In recent years, RTB(Real Time Bidding) becomes a popular online
+advertisement trading method. During the auction, each DSP(Demand Side
+Platform) is supposed to evaluate current opportunity and respond with an ad
+and corresponding bid price. It's essential for DSP to find an optimal ad
+selection and bid price determination strategy which maximizes revenue or
+performance under budget and ROI(Return On Investment) constraints in P4P(Pay
+For Performance) or P4U(Pay For Usage) mode. We solve this problem by 1)
+formalizing the DSP problem as a constrained optimization problem, 2) proposing
+the augmented MMKP(Multi-choice Multi-dimensional Knapsack Problem) with
+general solution, 3) and demonstrating the DSP problem is a special case of the
+augmented MMKP and deriving specialized strategy. Our strategy is verified
+through simulation and outperforms state-of-the-art strategies in real
+application. To the best of our knowledge, our solution is the first dual based
+DSP bidding framework that is derived from strict second price auction
+assumption and generally applicable to the multiple ads scenario with various
+objectives and constraints.
+",1,0,0,1,0,0
+13137,Residual Gated Graph ConvNets,"  Graph-structured data such as social networks, functional brain networks,
+gene regulatory networks, communications networks have brought the interest in
+generalizing deep learning techniques to graph domains. In this paper, we are
+interested to design neural networks for graphs with variable length in order
+to solve learning problems such as vertex classification, graph classification,
+graph regression, and graph generative tasks. Most existing works have focused
+on recurrent neural networks (RNNs) to learn meaningful representations of
+graphs, and more recently new convolutional neural networks (ConvNets) have
+been introduced. In this work, we want to compare rigorously these two
+fundamental families of architectures to solve graph learning tasks. We review
+existing graph RNN and ConvNet architectures, and propose natural extension of
+LSTM and ConvNet to graphs with arbitrary size. Then, we design a set of
+analytically controlled experiments on two basic graph problems, i.e. subgraph
+matching and graph clustering, to test the different architectures. Numerical
+results show that the proposed graph ConvNets are 3-17% more accurate and
+1.5-4x faster than graph RNNs. Graph ConvNets are also 36% more accurate than
+variational (non-learning) techniques. Finally, the most effective graph
+ConvNet architecture uses gated edges and residuality. Residuality plays an
+essential role to learn multi-layer architectures as they provide a 10% gain of
+performance.
+",1,0,0,1,0,0
+13138,User-driven mobile robot storyboarding: Learning image interest and saliency from pairwise image comparisons,"  This paper describes a novel storyboarding scheme that uses a model trained
+on pairwise image comparisons to identify images likely to be of interest to a
+mobile robot user. Traditional storyboarding schemes typically attempt to
+summarise robot observations using predefined novelty or image quality
+objectives, but we propose a user training stage that allows the incorporation
+of user interest when storyboarding. Our approach dramatically reduces the
+number of image comparisons required to infer image interest by applying a
+Gaussian process smoothing algorithm on image features extracted using a
+pre-trained convolutional neural network. As a particularly valuable
+by-product, the proposed approach allows the generation of user-specific
+saliency or attention maps.
+",1,0,0,0,0,0
+13139,Habitability of Exoplanetary Systems,"  The aim of my dissertation is to investigate habitability in extra-Solar
+Systems. Most of the time, only planets are considered as possible places where
+extraterrestrial life can emerge and evolve, however, their moons could be
+inhabited, too. I present a comprehensive study, which considers habitability
+not only on planets, but on satellites, as well. My research focuses on three
+closely related topics. The first one is the circumstellar habitable zone,
+which is usually used as a first proxy for determining the habitability of a
+planet around the host star. The word habitability is used in the sense that
+liquid water, which is essential for life as we know it, may be present on the
+planetary surface. Whether the planet is habitable or not, its moon might have
+a suitable surface temperature for holding water reservoirs, providing that
+tidal heating is in action. Tidal heating is generated inside the satellite and
+its source is the strong gravitational force of the nearby planet. The second
+topic of my research explores tidal heating and the habitability of extra-solar
+moons with and without stellar radiation and other related energy sources. Life
+is possible to form even on icy planetary bodies, inside tidally heated
+subsurface oceans. The third topic probes the possibility of identifying an
+ice-covered satellite from photometric observations. A strong indication of
+surface ice is the high reflectance of the body, which may be measured when the
+moon disappears behind the host star, so its reflected light is blocked out by
+the star.
+",0,1,0,0,0,0
+13140,The HST Large Program on Omega Centauri. I. Multiple stellar populations at the bottom of the main sequence probed in NIR-Optical,"  As part of a large investigation with Hubble Space Telescope to study the
+faintest stars within the globular cluster Omega Centauri, in this work we
+present early results on the multiplicity of its main sequence (MS) stars,
+based on deep optical and near-infrared observations. By using appropriate
+color-magnitude diagrams we have identified, for the first time, the two main
+stellar populations I, and II along the entire MS, from the turn-off towards
+the hydrogen-burning limit. We have compared the observations with suitable
+synthetic spectra of MS stars and conclude that the two MSs are consistent with
+stellar populations with different metallicity, helium, and light-element
+abundance. Specifically, MS-I corresponds to a metal-poor stellar population
+([Fe/H]~-1.7) with Y~ 0.25 and [O/Fe]~0.30. The MS-II hosts helium-rich
+(Y~0.37-0.40) stars with metallicity ranging from [Fe/H]~-1.7 to -1.4. Below
+the MS knee (mF160W~19.5, our photometry reveals that each of the two main MSs
+hosts stellar subpopulations with different oxygen abundances, with very O-poor
+stars ([O/Fe]~-0.5) populating the MS-II. Such a complexity has never been
+observed in previous studies of M-dwarfs in globular clusters. A few months
+before the lunch of the James Webb Space Telescope, these results demonstrate
+the power of optical and near-infrared photometry in the study of multiple
+stellar populations in globular clusters.
+",0,1,0,0,0,0
+13141,On Natural Language Generation of Formal Argumentation,"  In this paper we provide a first analysis of the research questions that
+arise when dealing with the problem of communicating pieces of formal
+argumentation through natural language interfaces. It is a generally held
+opinion that formal models of argumentation naturally capture human argument,
+and some preliminary studies have focused on justifying this view.
+Unfortunately, the results are not only inconclusive, but seem to suggest that
+explaining formal argumentation to humans is a rather articulated task.
+Graphical models for expressing argumentation-based reasoning are appealing,
+but often humans require significant training to use these tools effectively.
+We claim that natural language interfaces to formal argumentation systems offer
+a real alternative, and may be the way forward for systems that capture human
+argument.
+",1,0,0,0,0,0
+13142,Supervised Learning Based Algorithm Selection for Deep Neural Networks,"  Many recent deep learning platforms rely on third-party libraries (such as
+cuBLAS) to utilize the computing power of modern hardware accelerators (such as
+GPUs). However, we observe that they may achieve suboptimal performance because
+the library functions are not used appropriately. In this paper, we target at
+optimizing the operations of multiplying a matrix with the transpose of another
+matrix (referred to as NT operation hereafter), which contribute about half of
+the training time of fully connected deep neural networks. Rather than directly
+calling the library function, we propose a supervised learning based algorithm
+selection approach named MTNN, which uses a gradient boosted decision tree to
+select one from two alternative NT implementations intelligently: (1) calling
+the cuBLAS library function; (2) calling our proposed algorithm TNN that uses
+an efficient out-of-place matrix transpose. We evaluate the performance of MTNN
+on two modern GPUs: NVIDIA GTX 1080 and NVIDIA Titan X Pascal. MTNN can achieve
+96\% of prediction accuracy with very low computational overhead, which results
+in an average of 54\% performance improvement on a range of NT operations. To
+further evaluate the impact of MTNN on the training process of deep neural
+networks, we have integrated MTNN into a popular deep learning platform Caffe.
+Our experimental results show that the revised Caffe can outperform the
+original one by an average of 28\%. Both MTNN and the revised Caffe are
+open-source.
+",1,0,0,0,0,0
+13143,Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach,"  A significant amount of search queries originate from some real world
+information need or tasks. In order to improve the search experience of the end
+users, it is important to have accurate representations of tasks. As a result,
+significant amount of research has been devoted to extracting proper
+representations of tasks in order to enable search systems to help users
+complete their tasks, as well as providing the end user with better query
+suggestions, for better recommendations, for satisfaction prediction, and for
+improved personalization in terms of tasks. Most existing task extraction
+methodologies focus on representing tasks as flat structures. However, tasks
+often tend to have multiple subtasks associated with them and a more
+naturalistic representation of tasks would be in terms of a hierarchy, where
+each task can be composed of multiple (sub)tasks. To this end, we propose an
+efficient Bayesian nonparametric model for extracting hierarchies of such tasks
+\& subtasks. We evaluate our method based on real world query log data both
+through quantitative and crowdsourced experiments and highlight the importance
+of considering task/subtask hierarchies.
+",1,0,0,0,0,0
+13144,The unpolarized Shafarevich Conjecture for K3 Surfaces,"  We prove the unpolarized Shafarevich conjecture for K3 surfaces: the set of
+isomorphism classes of K3 surfaces over a fixed number field with good
+reduction away from a fixed and finite set of places is finite. Our proof is
+based on the theorems of Faltings and André, as well as the Kuga-Satake
+construction.
+",0,0,1,0,0,0
+13145,Unbounded product-form Petri nets,"  Computing steady-state distributions in infinite-state stochastic systems is
+in general a very dificult task. Product-form Petri nets are those Petri nets
+for which the steady-state distribution can be described as a natural product
+corresponding, up to a normalising constant, to an exponentiation of the
+markings. However, even though some classes of nets are known to have a
+product-form distribution, computing the normalising constant can be hard. The
+class of (closed) {\Pi}3-nets has been proposed in an earlier work, for which
+it is shown that one can compute the steady-state distribution efficiently.
+However these nets are bounded. In this paper, we generalise queuing Markovian
+networks and closed {\Pi}3-nets to obtain the class of open {\Pi}3-nets, that
+generate infinite-state systems. We show interesting properties of these nets:
+(1) we prove that liveness can be decided in polynomial time, and that
+reachability in live {\Pi}3-nets can be decided in polynomial time; (2) we show
+that we can decide ergodicity of such nets in polynomial time as well; (3) we
+provide a pseudo-polynomial time algorithm to compute the normalising constant.
+",1,0,0,0,0,0
+13146,QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds,"  This paper presents a discrete-time option pricing model that is rooted in
+Reinforcement Learning (RL), and more specifically in the famous Q-Learning
+method of RL. We construct a risk-adjusted Markov Decision Process for a
+discrete-time version of the classical Black-Scholes-Merton (BSM) model, where
+the option price is an optimal Q-function. Pricing is done by learning to
+dynamically optimize risk-adjusted returns for an option replicating portfolio,
+as in the Markowitz portfolio theory. Using Q-Learning and related methods,
+once created in a parametric setting, the model is able to go model-free and
+learn to price and hedge an option directly from data generated from a dynamic
+replicating portfolio which is rebalanced at discrete times. If the world is
+according to BSM, our risk-averse Q-Learner converges, given enough training
+data, to the true BSM price and hedge ratio of the option in the continuous
+time limit, even if hedges applied at the stage of data generation are
+completely random (i.e. it can learn the BSM model itself, too!), because
+Q-Learning is an off-policy algorithm. If the world is different from a BSM
+world, the Q-Learner will find it out as well, because Q-Learning is a
+model-free algorithm. For finite time steps, the Q-Learner is able to
+efficiently calculate both the optimal hedge and optimal price for the option
+directly from trading data, and without an explicit model of the world. This
+suggests that RL may provide efficient data-driven and model-free methods for
+optimal pricing and hedging of options, once we depart from the academic
+continuous-time limit, and vice versa, option pricing methods developed in
+Mathematical Finance may be viewed as special cases of model-based
+Reinforcement Learning. Our model only needs basic linear algebra (plus Monte
+Carlo simulation, if we work with synthetic data).
+",1,0,0,0,0,0
+13147,End-to-end Networks for Supervised Single-channel Speech Separation,"  The performance of single channel source separation algorithms has improved
+greatly in recent times with the development and deployment of neural networks.
+However, many such networks continue to operate on the magnitude spectrogram of
+a mixture, and produce an estimate of source magnitude spectrograms, to perform
+source separation. In this paper, we interpret these steps as additional neural
+network layers and propose an end-to-end source separation network that allows
+us to estimate the separated speech waveform by operating directly on the raw
+waveform of the mixture. Furthermore, we also propose the use of masking based
+end-to-end separation networks that jointly optimize the mask and the latent
+representations of the mixture waveforms. These networks show a significant
+improvement in separation performance compared to existing architectures in our
+experiments. To train these end-to-end models, we investigate the use of
+composite cost functions that are derived from objective evaluation metrics as
+measured on waveforms. We present subjective listening test results that
+demonstrate the improvement attained by using masking based end-to-end networks
+and also reveal insights into the performance of these cost functions for
+end-to-end source separation.
+",1,0,0,0,0,0
+13148,A note on searching sorted unbalanced three-dimensional arrays,"  We examine the problem of searching sequentially for a desired real value (a
+key) within a sorted unbalanced three-dimensional finite real array. This
+classic problem can be viewed as determining the correct dimensional threshold
+function from a finite class of such functions within the array, based on
+sequential queries that take the form of point samples. This note addresses the
+challenge of constructing algorithms that require the minimum number of queries
+necessary in the worst case, to search for a given key in arrays that have
+three dimensions with sizes that are not necessarily equal.
+",1,0,0,0,0,0
+13149,Isomorphism classes of four dimensional nilpotent associative algebras over a field,"  In this paper we classify the isomorphism classes of four dimensional
+nilpotent associative algebras over a field F, studying regular subgroups of
+the affine group AGL_4(F). In particular we provide explicit representatives
+for such classes when F is a finite field, the real field R or an algebraically
+closed field.
+",0,0,1,0,0,0
+13150,"Contemporary facets of business successes among leading companies, operating in Bulgaria","  The current article unveils and analyzes some important factors, influencing
+diversity in strategic decision-making approaches in local companies.
+Researcher's attention is oriented to survey important characteristics of the
+strategic moves, undertaken by leading companies in Bulgaria.
+",0,0,0,0,0,1
+13151,Ensemble of Part Detectors for Simultaneous Classification and Localization,"  Part-based representation has been proven to be effective for a variety of
+visual applications. However, automatic discovery of discriminative parts
+without object/part-level annotations is challenging. This paper proposes a
+discriminative mid-level representation paradigm based on the responses of a
+collection of part detectors, which only requires the image-level labels.
+Towards this goal, we first develop a detector-based spectral clustering method
+to mine the representative and discriminative mid-level patterns for detector
+initialization. The advantage of the proposed pattern mining technology is that
+the distance metric based on detectors only focuses on discriminative details,
+and a set of such grouped detectors offer an effective way for consistent
+pattern mining. Relying on the discovered patterns, we further formulate the
+detector learning process as a confidence-loss sparse Multiple Instance
+Learning (cls-MIL) task, which considers the diversity of the positive samples,
+while avoid drifting away the well localized ones by assigning a confidence
+value to each positive sample. The responses of the learned detectors can form
+an effective mid-level image representation for both image classification and
+object localization. Experiments conducted on benchmark datasets demonstrate
+the superiority of our method over existing approaches.
+",1,0,0,0,0,0
+13152,Graph Model Selection via Random Walks,"  In this paper, we present a novel approach based on the random walk process
+for finding meaningful representations of a graph model. Our approach leverages
+the transient behavior of many short random walks with novel initialization
+mechanisms to generate model discriminative features. These features are able
+to capture a more comprehensive structural signature of the underlying graph
+model. The resulting representation is invariant to both node permutation and
+the size of the graph, allowing direct comparison between large classes of
+graphs. We test our approach on two challenging model selection problems: the
+discrimination in the sparse regime of an Erdös-Renyi model from a
+stochastic block model and the planted clique problem. Our representation
+approach achieves performance that closely matches known theoretical limits in
+addition to being computationally simple and scalable to large graphs.
+",1,0,0,0,0,0
+13153,On irrationality measure of Thue-Morse constant,"  We provide a non-trivial measure of irrationality for a class of Mahler
+numbers defined with infinite products which cover the Thue-Morse constant.
+",0,0,1,0,0,0
+13154,Adiabatic approach for natural gas pipeline computations,"  We consider slowly evolving, i.e. ADIABATIC, operational regime within a
+transmission level (continental scale) natural gas pipeline system. This allows
+us to introduce a set of nodal equations of reduced complexity describing gas
+transients in injection/consumption UNBALANCED (so-called line-pack) cases. We
+discuss, in details, construction of the UNBALANCED ADIABATIC (UA)
+approximation on the basic example of a single pipe. The UA approximation is
+expected to play a significant ""model reduction"" role in solving control,
+optimization and planning problems relevant for flawless functioning of modern
+natural gas networks.
+",1,1,0,0,0,0
+13155,Clustering and Labelling Auction Fraud Data,"  Although shill bidding is a common auction fraud, it is however very tough to
+detect. Due to the unavailability and lack of training data, in this study, we
+build a high-quality labeled shill bidding dataset based on recently collected
+auctions from eBay. Labeling shill biding instances with multidimensional
+features is a critical phase for the fraud classification task. For this
+purpose, we introduce a new approach to systematically label the fraud data
+with the help of the hierarchical clustering CURE that returns remarkable
+results as illustrated in the experiments.
+",0,0,0,1,0,0
+13156,Investigation of channel model for weakly coupled multicore fiber,"  We investigate the evolution of decorrelation bandwidth of inter core
+crosstalk (IC-XT) in homogeneous weakly coupled multicore fibers (WC-MCFs). The
+modified mode-coupled equations (MCEs) are numerically solved by combining the
+fourth order Runge-Kutta method and compound Simpson integral method. It can be
+theoretically and numerically observed that the decorrelation bandwidth of
+IC-XT decreases with transmission distance by fractional linear function. The
+evolution rule of IC-XT's decorrelation bandwidth is further confirmed by
+experiments, which can be used as an evaluation criterion for channel model.
+Finally, we propose a new channel model with the coupling matrix of IC-XT
+generated automatically by phase transfer function (PTF), which is in good
+agreement with the above evaluation criterion. We believe the proposed channel
+model can provide a good simulation platform for homogeneous WC-MCF based
+communication systems.
+",0,1,0,0,0,0
+13157,Palindromic Subsequences in Finite Words,"  In 1999 Lyngs{\o} and Pedersen proposed a conjecture stating that every
+binary circular word of length $n$ with equal number of zeros and ones has an
+antipalindromic linear subsequence of length at least $\frac{2}{3}n$. No
+progress over a trivial $\frac{1}{2}n$ bound has been achieved since then. We
+suggest a palindromic counterpart to this conjecture and provide a non-trivial
+infinite series of circular words which prove the upper bound of $\frac{2}{3}n$
+for both conjectures at the same time. The construction also works for words
+over an alphabet of size $k$ and gives rise to a generalization of the
+conjecture by Lyngs{\o} and Pedersen. Moreover, we discuss some possible
+strengthenings and weakenings of the named conjectures. We also propose two
+similar conjectures for linear words and provide some evidences for them.
+",1,0,0,0,0,0
+13158,Risk quantification for the thresholding rule for multiple testing using Gaussian scale mixtures,"  In this paper we study the asymptotic properties of Bayesian multiple testing
+procedures for a large class of Gaussian scale mixture pri- ors. We study two
+types of multiple testing risks: a Bayesian risk proposed in Bogdan et al.
+(2011) where the data are assume to come from a mixture of normal, and a
+frequentist risk similar to the one proposed by Arias-Castro and Chen (2017).
+Following the work of van der Pas et al. (2016), we give general conditions on
+the prior such that both risks can be bounded. For the Bayesian risk, the bound
+is almost sharp. This result show that under these conditions, the considered
+class of continuous prior can be competitive with the usual two-group model
+(e.g. spike and slab priors). We also show that if the non-zeros component of
+the parameter are large enough, the minimax risk can be made asymptotically
+null. The separation rates obtained are consistent with the one that could be
+guessed from the existing literature (see van der Pas et al., 2017b). For both
+problems, we then give conditions under which an adaptive version of the result
+can be obtained.
+",0,0,1,1,0,0
+13159,"Some recent results on the Dirichlet problem for (p,q)-Laplace equations","  A short account of recent existence and multiplicity theorems on the
+Dirichlet problem for an elliptic equation with $(p,q)$-Laplacian in a bounded
+domain is performed. Both eigenvalue problems and different types of
+perturbation terms are briefly discussed. Special attention is paid to possibly
+coercive, resonant, subcritical, critical, or asymmetric right-hand sides.
+",0,0,1,0,0,0
+13160,A convex penalty for switching control of partial differential equations,"  A convex penalty for promoting switching controls for partial differential
+equations is introduced; such controls consist of an arbitrary number of
+components of which at most one should be simultaneously active. Using a
+Moreau-Yosida approximation, a family of approximating problems is obtained
+that is amenable to solution by a semismooth Newton method. The efficiency of
+this approach and the structure of the obtained controls are demonstrated by
+numerical examples.
+",0,0,1,0,0,0
+13161,Workload Analysis of Blue Waters,"  Blue Waters is a Petascale-level supercomputer whose mission is to enable the
+national scientific and research community to solve ""grand challenge"" problems
+that are orders of magnitude more complex than can be carried out on other high
+performance computing systems. Given the important and unique role that Blue
+Waters plays in the U.S. research portfolio, it is important to have a detailed
+understanding of its workload in order to guide performance optimization both
+at the software and system configuration level as well as inform architectural
+balance tradeoffs. Furthermore, understanding the computing requirements of the
+Blue Water's workload (memory access, IO, communication, etc.), which is
+comprised of some of the most computationally demanding scientific problems,
+will help drive changes in future computing architectures, especially at the
+leading edge. With this objective in mind, the project team carried out a
+detailed workload analysis of Blue Waters.
+",1,0,0,0,0,0
+13162,Model-Independent Analytic Nonlinear Blind Source Separation,"  Consider a time series of measurements of the state of an evolving system,
+x(t), where x has two or more components. This paper shows how to perform
+nonlinear blind source separation; i.e., how to determine if these signals are
+equal to linear or nonlinear mixtures of the state variables of two or more
+statistically independent subsystems. First, the local distributions of
+measurement velocities are processed in order to derive vectors at each point
+in x-space. If the data are separable, each of these vectors must be directed
+along a subspace of x-space that is traversed by varying the state variable of
+one subsystem, while all other subsystems are kept constant. Because of this
+property, these vectors can be used to construct a small set of mappings, which
+must contain the unmixing function, if it exists. Therefore, nonlinear blind
+source separation can be performed by examining the separability of the data
+after it has been transformed by each of these mappings. The method is
+analytic, constructive, and model-independent. It is illustrated by blindly
+recovering the separate utterances of two speakers from nonlinear combinations
+of their audio waveforms.
+",0,0,0,1,0,0
+13163,Seven Lessons from Manyfield Inflation in Random Potentials,"  We study inflation in models with many interacting fields subject to randomly
+generated scalar potentials. We use methods from non-equilibrium random matrix
+theory to construct the potentials and an adaption of the 'transport method' to
+evolve the two-point correlators during inflation. This construction allows,
+for the first time, for an explicit study of models with up to 100 interacting
+fields supporting a period of 'approximately saddle-point' inflation. We
+determine the statistical predictions for observables by generating over 30,000
+models with 2-100 fields supporting at least 60 efolds of inflation. These
+studies lead us to seven lessons: i) Manyfield inflation is not single-field
+inflation, ii) The larger the number of fields, the simpler and sharper the
+predictions, iii) Planck compatibility is not rare, but future experiments may
+rule out this class of models, iv) The smoother the potentials, the sharper the
+predictions, v) Hyperparameters can transition from stiff to sloppy, vi)
+Despite tachyons, isocurvature can decay, vii) Eigenvalue repulsion drives the
+predictions. We conclude that many of the 'generic predictions' of single-field
+inflation can be emergent features of complex inflation models.
+",0,1,0,0,0,0
+13164,Steering Social Activity: A Stochastic Optimal Control Point Of View,"  User engagement in online social networking depends critically on the level
+of social activity in the corresponding platform--the number of online actions,
+such as posts, shares or replies, taken by their users. Can we design
+data-driven algorithms to increase social activity? At a user level, such
+algorithms may increase activity by helping users decide when to take an action
+to be more likely to be noticed by their peers. At a network level, they may
+increase activity by incentivizing a few influential users to take more
+actions, which in turn will trigger additional actions by other users. In this
+paper, we model social activity using the framework of marked temporal point
+processes, derive an alternate representation of these processes using
+stochastic differential equations (SDEs) with jumps and, exploiting this
+alternate representation, develop two efficient online algorithms with provable
+guarantees to steer social activity both at a user and at a network level. In
+doing so, we establish a previously unexplored connection between optimal
+control of jump SDEs and doubly stochastic marked temporal point processes,
+which is of independent interest. Finally, we experiment both with synthetic
+and real data gathered from Twitter and show that our algorithms consistently
+steer social activity more effectively than the state of the art.
+",1,0,0,1,0,0
+13165,Adaptive Clustering Using Kernel Density Estimators,"  We investigate statistical properties of a clustering algorithm that receives
+level set estimates from a kernel density estimator and then estimates the
+first split in the density level cluster tree if such a split is present or
+detects the absence of such a split. Key aspects of our analysis include finite
+sample guarantees, consistency, rates of convergence, and an adaptive
+data-driven strategy for chosing the kernel bandwidth. For the rates and the
+adaptivity we do not need continuity assumptions on the density such as
+Hölder continuity, but only require intuitive geometric assumptions of
+non-parametric nature.
+",0,0,0,1,0,0
+13166,Realization of functions on the symmetrized bidisc,"  We prove a realization formula and a model formula for analytic functions
+with modulus bounded by $1$ on the symmetrized bidisc \[ G\stackrel{\rm def}{=}
+\{(z+w,zw): |z|<1, \, |w| < 1\}. \] As an application we prove a Pick-type
+theorem giving a criterion for the existence of such a function satisfying a
+finite set of interpolation conditions.
+",0,0,1,0,0,0
+13167,Ground state properties of 3d metals from self-consistent GW approach,"  Self consistent GW approach (scGW) has been applied to calculate the ground
+state properties (equilibrium Wigner-Seitz radius $S_{WZ}$ and bulk modulus
+$B$) of 3d transition metals Sc, Ti, V, Fe, Co, Ni, and Cu. The approach
+systematically underestimates $S_{WZ}$ with average relative deviation from the
+experimental data about 1% and it overestimates the calculated bulk modulus
+with relative error about 25%. It is shown that scGW is superior in accuracy as
+compared to the local density approximation (LDA) but it is less accurate than
+the generalized gradient approach (GGA) for the materials studied. If compared
+to the random phase approximation (RPA), scGW is slightly less accurate, but
+its error for the 3d metals looks more systematic. The systematic nature of the
+deviation from the experimental data suggests that the next order of the
+perturbation theory should allow one to reduce the error.
+",0,1,0,0,0,0
+13168,Survival Trees for Interval-Censored Survival data,"  Interval-censored data, in which the event time is only known to lie in some
+time interval, arise commonly in practice; for example, in a medical study in
+which patients visit clinics or hospitals at pre-scheduled times, and the
+events of interest occur between visits. Such data are appropriately analyzed
+using methods that account for this uncertainty in event time measurement. In
+this paper we propose a survival tree method for interval-censored data based
+on the conditional inference framework. Using Monte Carlo simulations we find
+that the tree is effective in uncovering underlying tree structure, performs
+similarly to an interval-censored Cox proportional hazards model fit when the
+true relationship is linear, and performs at least as well as (and in the
+presence of right-censoring outperforms) the Cox model when the true
+relationship is not linear. Further, the interval-censored tree outperforms
+survival trees based on imputing the event time as an endpoint or the midpoint
+of the censoring interval. We illustrate the application of the method on tooth
+emergence data.
+",0,0,0,1,0,0
+13169,Bayesian LSTMs in medicine,"  The medical field stands to see significant benefits from the recent advances
+in deep learning. Knowing the uncertainty in the decision made by any machine
+learning algorithm is of utmost importance for medical practitioners. This
+study demonstrates the utility of using Bayesian LSTMs for classification of
+medical time series. Four medical time series datasets are used to show the
+accuracy improvement Bayesian LSTMs provide over standard LSTMs. Moreover, we
+show cherry-picked examples of confident and uncertain classifications of the
+medical time series. With simple modifications of the common practice for deep
+learning, significant improvements can be made for the medical practitioner and
+patient.
+",1,0,0,1,0,0
+13170,Motion Planning for a UAV with a Straight or Kinked Tether,"  This paper develops and compares two motion planning algorithms for a
+tethered UAV with and without the possibility of the tether contacting the
+confined and cluttered environment. Tethered aerial vehicles have been studied
+due to their advantages such as power duration, stability, and safety. However,
+the disadvantages brought in by the extra tether have not been well
+investigated by the robotic locomotion community, especially when the tethered
+agent is locomoting in a non-free space occupied with obstacles. In this work,
+we propose two motion planning frameworks that (1) reduce the reachable
+configuration space by taking into account the tether and (2) deliberately plan
+(and relax) the contact point(s) of the tether with the environment and enable
+an equivalent reachable configuration space as the non-tethered counterpart
+would have. Both methods are tested on a physical robot, Fotokite Pro. With our
+approaches, tethered aerial vehicles could find their applications in confined
+and cluttered environments with obstacles as opposed to ideal free space, while
+still maintaining the advantages from the usage of a tether. The motion
+planning strategies are particularly suitable for marsupial heterogeneous
+robotic teams, such as visual servoing/assisting for another mobile,
+tele-operated primary robot.
+",1,0,0,0,0,0
+13171,Star formation driven galactic winds in UGC 10043,"  We study the galactic wind in the edge-on spiral galaxy UGC 10043 with the
+combination of the CALIFA integral field spectroscopy data, scanning
+Fabry-Perot interferometry (FPI), and multiband photometry. We detect ionized
+gas in the extraplanar regions reaching a relatively high distance, up to ~ 4
+kpc above the galactic disk. The ionized gas line ratios ([N ii]/Ha, [S ii]/Ha
+and [O i]/Ha) present an enhancement along the semi minor axis, in contrast
+with the values found at the disk, where they are compatible with ionization
+due to H ii-regions. These differences, together with the biconic symmetry of
+the extra-planar ionized structure, makes UGC 10043 a clear candidate for a
+galaxy with gas outflows ionizated by shocks. From the comparison of shock
+models with the observed line ratios, and the kinematics observed from the FPI
+data, we constrain the physical properties of the observed outflow. The data
+are compatible with a velocity increase of the gas along the extraplanar
+distances up to < 400 km/s and the preshock density decreasing in the same
+direction. We also observe a discrepancy in the SFR estimated based on Ha (0.36
+Msun/yr ) and the estimated with the CIGALE code, being the latter 5 times
+larger. Nevertheless, this SFR is still not enough to drive the observed
+galactic wind if we do not take into account the filling factor. We stress that
+the combination of the three techniques of observation with models is a
+powerful tool to explore galactic winds in the Local Universe.
+",0,1,0,0,0,0
+13172,Drive and measurement electrode patterns for electrode impedance tomography (EIT) imaging of neural activity in peripheral nerve,"  Objective: To establish the performance of several drive and measurement
+patterns in EIT imaging of neural activity in peripheral nerve, which involves
+large impedance change in the nerve's anisotropic length axis. Approach: Eight
+drive and measurement electrode patterns are compared using a finite element
+(FE) four cylindrical shell model of a peripheral nerve and a 32 channel
+dual-ring nerve cuff. The central layer of the FE model contains impedance
+changes representative of neural activity of -0.3 in the length axis and -8.8 x
+10-4 in the radial axis. Four of the electrode patterns generate longitudinal
+drive current, which runs perpendicular to the anisotropic axis. Main results:
+Transverse current patterns produce higher resolution than longitudinal
+patterns but are also more susceptible to noise and errors, and exhibit poorer
+sensitivity to impedance changes in central sample locations. Three of the four
+longitudinal current patterns considered can reconstruct fascicle level
+impedance changes with up to 0.2 mV noise and error, which corresponds to
+between -5.5 and +0.18 dB of the normalised signal standard deviation. Reducing
+the spacing between the two electrode rings in all longitudinal current
+patterns reduced the signal to error ratio across all depth locations of the
+sample. Significance: Electrode patterns which target the large impedance
+change in the anisotropic length axis can provide improved robustness against
+noise and errors, which is a critical step towards real time EIT imaging of
+neural activity in peripheral nerve.
+",0,0,0,0,1,0
+13173,Remarks on Liouville Type Theorems for Steady-State Navier-Stokes Equations,"  Liouville type theorems for the stationary Navier-Stokes equations are proven
+under certain assumptions. These assumptions are motivated by conditions that
+appear in Liouvile type theorems for the heat equations with a given divergence
+free drift.
+",0,0,1,0,0,0
+13174,The Homogeneous Broadcast Problem in Narrow and Wide Strips,"  Let $P$ be a set of nodes in a wireless network, where each node is modeled
+as a point in the plane, and let $s\in P$ be a given source node. Each node $p$
+can transmit information to all other nodes within unit distance, provided $p$
+is activated. The (homogeneous) broadcast problem is to activate a minimum
+number of nodes such that in the resulting directed communication graph, the
+source $s$ can reach any other node. We study the complexity of the regular and
+the hop-bounded version of the problem (in the latter, $s$ must be able to
+reach every node within a specified number of hops), with the restriction that
+all points lie inside a strip of width $w$. We almost completely characterize
+the complexity of both the regular and the hop-bounded versions as a function
+of the strip width $w$.
+",1,0,0,0,0,0
+13175,Extreme CO Isotopic Abundances in the ULIRG IRAS 13120-5453: An Extremely Young Starburst or Top-Heavy Initial Mass Function,"  We present ALMA $^{12}$CO (J=1-0, 3-2 and 6-5), $^{13}$CO (J=1-0) and
+C$^{18}$O (J=1-0) observations of the local Ultra Luminous Infrared Galaxy,
+IRAS 13120-5453 (dubbed ""The Yo-yo""). The morphologies of the three isotopic
+species differ, where $^{13}$CO shows a hole in emission towards the center. We
+measure integrated brightness temperature line ratios of $^{12}$CO/$^{13}$CO
+$\geq$ 60 (exceeding 200) and $^{13}$CO/C$^{18}$O $\leq$ 1 in the central
+region. Assuming optical thin emission, C$^{18}$O is more abundant than
+$^{13}$CO in several regions. The abundances within the central 500 pc are
+consistent with enrichment of the ISM via a young starburst ($<$7Myr), a
+top-heavy initial mass function or a combination of both.
+",0,1,0,0,0,0
+13176,Computing Nearby Non-trivial Smith Forms,"  We consider the problem of computing the nearest matrix polynomial with a
+non-trivial Smith Normal Form. We show that computing the Smith form of a
+matrix polynomial is amenable to numeric computation as an optimization
+problem. Furthermore, we describe an effective optimization technique to find a
+nearby matrix polynomial with a non-trivial Smith form. The results are then
+generalized to include the computation of a matrix polynomial having a maximum
+specified number of ones in the Smith Form (i.e., with a maximum specified
+McCoy rank).
+We discuss the geometry and existence of solutions and how our results can
+used for an error analysis. We develop an optimization-based approach and
+demonstrate an iterative numerical method for computing a nearby matrix
+polynomial with the desired spectral properties. We also describe an
+implementation of our algorithms and demonstrate the robustness with examples
+in Maple.
+",1,0,0,0,0,0
+13177,Quasinonexpansive Iterations on the Affine Hull of Orbits: From Mann's Mean Value Algorithm to Inertial Methods,"  Fixed point iterations play a central role in the design and the analysis of
+a large number of optimization algorithms. We study a new iterative scheme in
+which the update is obtained by applying a composition of quasinonexpansive
+operators to a point in the affine hull of the orbit generated up to the
+current iterate. This investigation unifies several algorithmic constructs,
+including Mann's mean value method, inertial methods, and multi-layer
+memoryless methods. It also provides a framework for the development of new
+algorithms, such as those we propose for solving monotone inclusion and
+minimization problems.
+",0,0,1,0,0,0
+13178,Decentralized Optimal Control for Connected Automated Vehicles at Intersections Including Left and Right Turns,"  In prior work, we addressed the problem of optimally controlling on line
+connected and automated vehicles crossing two adjacent intersections in an
+urban area to minimize fuel consumption while achieving maximal throughput
+without any explicit traffic signaling and without considering left and right
+turns. In this paper, we extend the solution of this problem to account for
+left and right turns under hard safety constraints. Furthermore, we formulate
+and solve another optimization problem to minimize a measure of passenger
+discomfort while the vehicle turns at the intersection and we investigate the
+associated tradeoff between minimizing fuel consumption and passenger
+discomfort.
+",0,0,1,0,0,0
+13179,The Rational Distance Problem for Equilateral Triangles,"  Let (P) denote the problem of existence of a point in the plane of a given
+triangle T, that is at rational distance from all the vertices of T. In this
+article, we provide a complete solution to (P) for all equilateral triangles.
+",0,0,1,0,0,0
+13180,"Compact Hausdorff MV-algebras: Structure, Duality and Projectivity","  It is proved that the category $\mathbb{EM}$ of extended multisets is dually
+equivalent to the category $\mathbb{CHMV}$ of compact Hausdorff MV-algebras
+with continuous homomorphisms, which is in turn equivalent to the category of
+complete and completely distributive MV-algebras with homomorphisms that
+reflect principal maximal ideals. Urysohn-Strauss's Lemma, Gleason's Theorem,
+and projective objects are also investigated for topological MV-algebras.
+",0,0,1,0,0,0
+13181,Nonvanishing theorems for twisted L-functions on unitary groups,"  We prove the nonvanishing of the twisted central critical values of a class
+of automorphic L-functions for twists by all but finitely many unitary
+characters in particular infinite families. The methods build on a
+non-archimedean approach introduced by Greenberg in the context of the Birch
+and Swinnerton-Dyer Conjecture. While this paper focuses on L-functions
+associated to certain automorphic representations of unitary groups, it
+illustrates how decades-old methods from Iwasawa theory can be combined with
+the output of new machinery to achieve broader nonvanishing results.
+",0,0,1,0,0,0
+13182,Force-induced elastic matrix-mediated interactions in the presence of a rigid wall,"  We consider an elastic composite material containing particulate inclusions
+in a soft elastic matrix that is bounded by a rigid wall, e.g., the substrate.
+If such a composite serves as a soft actuator, forces are imposed on or induced
+between the embedded particles. We investigate how the presence of the rigid
+wall affects the interactions between the inclusions in the elastic matrix. For
+no-slip boundary conditions, we transfer Blake's derivation of a corresponding
+Green's function from low-Reynolds-number hydrodynamics to the linearly elastic
+case. Results for no-slip and free-slip surface conditions are compared to each
+other and to the bulk behavior. Our results suggest that walls with free-slip
+surface conditions are preferred when they serve as substrates for soft
+actuators made from elastic composite materials. As we further demonstrate, the
+presence of a rigid wall can qualitatively change the interactions between the
+inclusions. In effect, it can switch attractive interactions into repulsive
+ones (and vice versa). It should be straightforward to observe the effects in
+future experiments and to combine our results, e.g., with the modeling of
+biological cells and tissue on rigid surfaces.
+",0,1,0,0,0,0
+13183,One-way quantum computing in superconducting circuits,"  We propose a method for the implementation of one-way quantum computing in
+superconducting circuits. Measurement-based quantum computing is a universal
+quantum computation paradigm in which an initial cluster-state provides the
+quantum resource, while the iteration of sequential measurements and local
+rotations encodes the quantum algorithm. Up to now, technical constraints have
+limited a scalable approach to this quantum computing alternative. The initial
+cluster state can be generated with available controlled-phase gates, while the
+quantum algorithm makes use of high-fidelity readout and coherent feedforward.
+With current technology, we estimate that quantum algorithms with above 20
+qubits may be implemented in the path towards quantum supremacy. Moreover, we
+propose an alternative initial state with properties of maximal persistence and
+maximal connectedness, reducing the required resources of one-way quantum
+computing protocols.
+",0,1,0,0,0,0
+13184,Smooth solution to higher dimensional complex Plateau problem,"  Let $X$ be a compact connected strongly pseudoconvex $CR$ manifold of real
+dimension $2n-1$ in $\mathbb{C}^{N}$. For $n\ge 3$, Yau solved the complex
+Plateau problem of hypersurface type by checking a bunch of Kohn-Rossi
+cohomology groups in 1981. In this paper, we generalize Yau's conjecture on
+some numerical invariant of every isolated surface singularity defined by Yau
+and the author to any dimension and prove that the conjecture is true for local
+complete intersection singularities of dimension $n\ge 3$. As a direct
+application, we solved complex Plateau problem of hypersurface type for any
+dimension $n\ge 3$ by checking only one numerical invariant.
+",0,0,1,0,0,0
+13185,Parameterized Approximation Schemes for Steiner Trees with Small Number of Steiner Vertices,"  We study the Steiner Tree problem, in which a set of terminal vertices needs
+to be connected in the cheapest possible way in an edge-weighted graph. This
+problem has been extensively studied from the viewpoint of approximation and
+also parametrization. In particular, on one hand Steiner Tree is known to be
+APX-hard, and W[2]-hard on the other, if parameterized by the number of
+non-terminals (Steiner vertices) in the optimum solution. In contrast to this
+we give an efficient parameterized approximation scheme (EPAS), which
+circumvents both hardness results. Moreover, our methods imply the existence of
+a polynomial size approximate kernelization scheme (PSAKS) for the considered
+parameter.
+We further study the parameterized approximability of other variants of
+Steiner Tree, such as Directed Steiner Tree and Steiner Forest. For neither of
+these an EPAS is likely to exist for the studied parameter: for Steiner Forest
+an easy observation shows that the problem is APX-hard, even if the input graph
+contains no Steiner vertices. For Directed Steiner Tree we prove that
+approximating within any function of the studied parameter is W[1]-hard.
+Nevertheless, we show that an EPAS exists for Unweighted Directed Steiner Tree,
+but a PSAKS does not. We also prove that there is an EPAS and a PSAKS for
+Steiner Forest if in addition to the number of Steiner vertices, the number of
+connected components of an optimal solution is considered to be a parameter.
+",1,0,0,0,0,0
+13186,Nerve impulse propagation and wavelet theory,"  A luminous stimulus which penetrates in a retina is converted to a nerve
+message. Ganglion cells give a response that may be approximated by a wavelet.
+We determine a function PSI which is associated with the propagation of nerve
+impulses along an axon. Each kind of channel (inward and outward) may be open
+or closed, depending on the transmembrane potential. The transition between
+these states is a random event. Using quantum relations, we estimate the number
+of channels susceptible to switch between the closed and open states. Our
+quantum approach was first to calculate the energy level distribution in a
+channel. We obtain, for each kind of channel, the empty level density and the
+filled level density of the open and closed conformations. The joint density of
+levels provides the transition number between the closed and open
+conformations. The algebraic sum of inward and outward open channels is a
+function PSI of the normalized energy E. The function PSI verifies the major
+properties of a wavelet. We calculate the functional dependence of the axon
+membrane conductance with the transmembrane energy.
+",0,0,0,0,1,0
+13187,Generation of unipolar half-cycle pulse via unusual reflection of a single-cycle pulse from an optically thin metallic or dielectric layer,"  We present a significantly different reflection process from an optically
+thin flat metallic or dielectric layer and propose a strikingly simple method
+to form approximately unipolar half-cycle optical pulses via reflection of a
+single-cycle optical pulse. Unipolar pulses in reflection arise due to
+specifics of effectively one-dimensional pulse propagation. Namely, we show
+that in considered system the field emitted by a flat medium layer is
+proportional to the velocity of oscillating medium charges instead of their
+acceleration as it is usually the case. When the single-cycle pulse interacts
+with linear optical medium, the oscillation velocity of medium charges can be
+then forced to keep constant sign throughout the pulse duration. Our results
+essentially differ from the direct mirror reflection and suggest a possibility
+of unusual transformations of the few-cycle light pulses in linear optical
+systems.
+",0,1,0,0,0,0
+13188,The Implicit Bias of Gradient Descent on Separable Data,"  We examine gradient descent on unregularized logistic regression problems,
+with homogeneous linear predictors on linearly separable datasets. We show the
+predictor converges to the direction of the max-margin (hard margin SVM)
+solution. The result also generalizes to other monotone decreasing loss
+functions with an infimum at infinity, to multi-class problems, and to training
+a weight layer in a deep network in a certain restricted setting. Furthermore,
+we show this convergence is very slow, and only logarithmic in the convergence
+of the loss itself. This can help explain the benefit of continuing to optimize
+the logistic or cross-entropy loss even after the training error is zero and
+the training loss is extremely small, and, as we show, even if the validation
+loss increases. Our methodology can also aid in understanding implicit
+regularization n more complex models and with other optimization methods.
+",1,0,0,1,0,0
+13189,Floquet prethermalization in the resonantly driven Hubbard model,"  We demonstrate the existence of long-lived prethermalized states in the Mott
+insulating Hubbard model driven by periodic electric fields. These states,
+which also exist in the resonantly driven case with a large density of
+photo-induced doublons and holons, are characterized by a nonzero current and
+an effective temperature of the doublons and holons which depends sensitively
+on the driving condition. Focusing on the specific case of resonantly driven
+models whose effective time-independent Hamiltonian in the high-frequency
+driving limit corresponds to noninteracting fermions, we show that the time
+evolution of the double occupation can be reproduced by the effective
+Hamiltonian, and that the prethermalization plateaus at finite driving
+frequency are controlled by the next-to-leading order correction in the
+high-frequency expansion of the effective Hamiltonian. We propose a numerical
+procedure to determine an effective Hubbard interaction that mimics the
+correlation effects induced by these higher order terms.
+",0,1,0,0,0,0
+13190,Continuous vibronic symmetries in Jahn-Teller models,"  We develop a systematic study of Jahn-Teller (JT) models with continuous
+symmetries by explor- ing their algebraic properties. The compact symmetric
+spaces corresponding to JT models carrying a Lie group symmetry are identified,
+and their invariants used to reduce their adiabatic potential energy surfaces
+into orbit spaces. Each orbit consists of a set of JT distorted molecular
+structures with equal adiabatic electronic spectrum. Molecular motion may be
+decomposed into pseudorota- tional and radial. The former preserves the orbit,
+while the latter maps an orbit into another. The dimensionality and topology of
+the internal space of each orbit depends on the number of degener- ate states
+in its adiabatic electronic spectra. Furthermore, qualitatively different
+pseudorotational modes occur in orbits of different types. We also provide a
+simple proof that the electronic spectrum for the space of JT minimum-energy
+structures (trough) displays a universality predicted by the epikernel
+principle. This result is in turn used to prove the topological equivalence
+between bosonic (fermionic) JT troughs and real (quaternionic) projective
+spaces, a conclusion which has outstanding physical consequences, as explained
+in our work. The relevance of our study for the more common case of JT systems
+with only discrete point group symmetry, and for generic asymmetric molecular
+systems with conical intersections involving more than two states is likewise
+discussed. In particular, we show that JT models with continuous symmetries
+present the simplest models of conical intersections among an arbitrary number
+of electronic state crossings.
+",0,1,0,0,0,0
+13191,Augmented Lagrangian Functions for Cone Constrained Optimization: the Existence of Global Saddle Points and Exact Penalty Property,"  In the article we present a general theory of augmented Lagrangian functions
+for cone constrained optimization problems that allows one to study almost all
+known augmented Lagrangians for cone constrained programs within a unified
+framework. We develop a new general method for proving the existence of global
+saddle points of augmented Lagrangian functions, called the localization
+principle. The localization principle unifies, generalizes and sharpens most of
+the known results on existence of global saddle points, and, in essence,
+reduces the problem of the existence of saddle points to a local analysis of
+optimality conditions. With the use of the localization principle we obtain
+first necessary and sufficient conditions for the existence of a global saddle
+point of an augmented Lagrangian for cone constrained minimax problems via both
+second and first order optimality conditions. In the second part of the paper,
+we present a general approach to the construction of globally exact augmented
+Lagrangian functions. The general approach developed in this paper allowed us
+not only to sharpen most of the existing results on globally exact augmented
+Lagrangians, but also to construct first globally exact augmented Lagrangian
+functions for equality constrained optimization problems, for nonlinear second
+order cone programs and for nonlinear semidefinite programs. These globally
+exact augmented Lagrangians can be utilized in order to design new
+superlinearly (or even quadratically) convergent optimization methods for cone
+constrained optimization problems.
+",0,0,1,0,0,0
+13192,Spin $q$-Whittaker polynomials,"  We introduce and study a one-parameter generalization of the q-Whittaker
+symmetric functions. This is a family of multivariate symmetric polynomials,
+whose construction may be viewed as an application of the procedure of fusion
+from integrable lattice models to a vertex model interpretation of a
+one-parameter generalization of Hall-Littlewood polynomials from [Bor17, BP16a,
+BP16b].
+We prove branching and Pieri rules, standard and dual (skew) Cauchy summation
+identities, and an integral representation for the new polynomials.
+",0,1,1,0,0,0
+13193,Lagrangians of hypergraphs: The Frankl-Füredi conjecture holds almost everywhere,"  Frankl and Füredi conjectured in 1989 that the maximum Lagrangian of all
+$r$-uniform hypergraphs of fixed size $m$ is realised by the initial segment of
+the colexicographic order. In particular, in the principal case
+$m=\binom{t}{r}$ their conjecture states that every $H\subseteq
+\mathbb{N}^{(r)}$ of size $\binom{t}{r}$ satisfies \begin{align*} \max
+\{\sum_{A \in H}\prod_{i\in A} y_i \ \colon \ y_1,y_2,\ldots \geq 0; \sum_{i\in
+\mathbb{N}} y_i=1 \}&\leq \frac{1}{t^r}\binom{t}{r}. \end{align*}
+We prove the above statement for all $r\geq 4$ and large values of $t$ (the
+case $r=3$ was settled by Talbot in 2002). More generally, we show for any
+$r\geq 4$ that the Frankl-Füredi conjecture holds whenever $\binom{t-1}{r}
+\leq m \leq \binom{t}{r}- \gamma_r t^{r-2}$ for a constant $\gamma_r>0$,
+thereby verifying it for `most' $m\in \mathbb{N}$.
+Furthermore, for $r=3$ we make an improvement on the results of
+Talbot~\cite{Tb} and Tang, Peng, Zhang and Zhao~\cite{TPZZ}.
+",0,0,1,0,0,0
+13194,Adversarial Attacks and Defences Competition,"  To accelerate research on adversarial examples and robustness of machine
+learning classifiers, Google Brain organized a NIPS 2017 competition that
+encouraged researchers to develop new methods to generate adversarial examples
+as well as to develop new ways to defend against them. In this chapter, we
+describe the structure and organization of the competition and the solutions
+developed by several of the top-placing teams.
+",0,0,0,1,0,0
+13195,Community structure of copper supply networks in the prehistoric Balkans: An independent evaluation of the archaeological record from the 7th to the 4th millennium BC,"  Complex networks analyses of many physical, biological and social phenomena
+show remarkable structural regularities, yet, their application in studying
+human past interaction remains underdeveloped. Here, we present an innovative
+method for identifying community structures in the archaeological record that
+allow for independent evaluation of the copper using societies in the Balkans,
+from c. 6200 to c. 3200 BC. We achieve this by exploring modularity of
+networked systems of these societies across an estimated 3000 years. We employ
+chemical data of copper-based objects from 79 archaeological sites as the
+independent variable for detecting most densely interconnected sets of nodes
+with a modularity maximization method. Our results reveal three dominant
+modular structures across the entire period, which exhibit strong spatial and
+temporal significance. We interpret patterns of copper supply among prehistoric
+societies as reflective of social relations, which emerge as equally important
+as physical proximity. Although designed on a variable isolated from any
+archaeological and spatiotemporal information, our method provides
+archaeologically and spatiotemporally meaningful results. It produces models of
+human interaction and cooperation that can be evaluated independently of
+established archaeological systematics, and can find wide application on any
+quantitative data from archaeological and historical record.
+",1,1,0,0,0,0
+13196,Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm,"  This paper describes a method for clustering data that are spread out over
+large regions and which dimensions are on different scales of measurement. Such
+an algorithm was developed to implement a robotics application consisting in
+sorting and storing objects in an unsupervised way. The toy dataset used to
+validate such application consists of Lego bricks of different shapes and
+colors. The uncontrolled lighting conditions together with the use of RGB color
+features, respectively involve data with a large spread and different levels of
+measurement between data dimensions. To overcome the combination of these two
+characteristics in the data, we have developed a new weighted K-means
+algorithm, called gap-ratio K-means, which consists in weighting each dimension
+of the feature space before running the K-means algorithm. The weight
+associated with a feature is proportional to the ratio of the biggest gap
+between two consecutive data points, and the average of all the other gaps.
+This method is compared with two other variants of K-means on the Lego bricks
+clustering problem as well as two other common classification datasets.
+",1,0,0,1,0,0
+13197,Eigenvalue Dynamics of a PT-symmetric Sturm-Liouville Operator. Criteria of the Similarity to a Self-adjoint or Normal Operator,"  The goal of the paper is to investigate the dynamics of the eigenvalues of
+the Sturm-Liouville operator with summable PT-symmetric potential on the finite
+interval. It turns out that the case of a complex Airy operator presents an
+exactly solvable model which allows us to trace the dynamics of the movement of
+the eigenvalues in all details and to find explicitly the critical parameter
+values, in particular, to specify precisely the number $\varepsilon_1$ such
+that for $0<\varepsilon<\varepsilon_1$ the operator has a real spectrum and is
+similar to a self-adjoint operator.
+",0,0,1,0,0,0
+13198,Automaton Semigroups and Groups: on the Undecidability of Problems Related to Freeness and Finiteness,"  In this paper, we study algorithmic problems for automaton semigroups and
+automaton groups related to freeness and finiteness. In the course of this
+study, we also exhibit some connections between the algebraic structure of
+automaton (semi)groups and their dynamics on the boundary.
+First, we show that it is undecidable to check whether the group generated by
+a given invertible automaton has a positive relation, i. e. a relation p = 1
+such that p only contains positive generators. Besides its obvious relation to
+the freeness of the group, the absence of positive relations has previously
+been studied and is connected to the triviality of some stabilizers of the
+boundary. We show that the emptiness of the set of positive relations is
+equivalent to the dynamical property that all (directed positive) orbital
+graphs centered at non-singular points are acyclic. Our approach also works to
+show undecidability of the freeness problem for automaton semigroups; in fact,
+it shows undecidability of a strengthened version where the input automaton is
+complete and invertible.
+Gillibert showed that the finiteness problem for automaton semigroups is
+undecidable. In the second part of the paper, we show that this undecidability
+result also holds if the input is restricted to be bi-reversible and invertible
+(but, in general, not complete). As an immediate consequence, we obtain that
+the finiteness problem for automaton subsemigroups of semigroups generated by
+invertible, yet partial automata, so called automaton-inverse semigroups, is
+also undecidable.
+",1,0,1,0,0,0
+13199,Terrestrial effects of moderately nearby supernovae,"  Recent data indicate one or more moderately nearby supernovae in the early
+Pleistocene, with additional events likely in the Miocene. This has motivated
+more detailed computations, using new information about the nature of
+supernovae and the distances of these events to describe in more detail the
+sorts of effects that are indicated at the Earth. This short
+communication/review is designed to describe some of these effects so that they
+may possibly be related to changes in the biota around these times.
+",0,1,0,0,0,0
+13200,A Variational Inequality Perspective on Generative Adversarial Networks,"  Generative adversarial networks (GANs) form a generative modeling approach
+known for producing appealing samples, but they are notably difficult to train.
+One common way to tackle this issue has been to propose new formulations of the
+GAN objective. Yet, surprisingly few studies have looked at optimization
+methods designed for this adversarial training. In this work, we cast GAN
+optimization problems in the general variational inequality framework. Tapping
+into the mathematical programming literature, we counter some common
+misconceptions about the difficulties of saddle point optimization and propose
+to extend techniques designed for variational inequalities to the training of
+GANs. We apply averaging, extrapolation and a novel computationally cheaper
+variant that we call extrapolation from the past to the stochastic gradient
+method (SGD) and Adam.
+",0,0,0,1,0,0
+13201,Integrating electricity markets: Impacts of increasing trade on prices and emissions in the western United States,"  This paper analyzes the market impacts of expanding California's centralized
+electricity market across the western United States and provides the first
+statistical assessment of this issue. Using market data from 2015-2018, I
+estimate the short-term effects of increasing regional electricity trade
+between California and neighboring states on prices, emissions, and generation.
+Consistent with economic theory, I find negative price impacts from regional
+trade, with each 1 gigawatt-hour (GWh) increase in California electricity
+imports associated with an average 0.15 dollar decrease in CAISO price. The
+price effect yields significant consumer savings well in excess of
+implementation costs required to set up a regional market. I find a short-term
+decrease in California carbon dioxide emissions associated with trading that is
+partially offset by increased emissions in neighboring regions. Specifically,
+each 1 GWh increase in regional trade is associated with a net 70-ton average
+decrease in CO2 emissions across the western U.S. A small amount of increased
+SO2 and NOx emissions are also observed in neighboring states associated with
+increased exports to California. This implies a small portion (less than 10
+percent) of electricity exports to California are supplied by coal generation.
+This study identifies substantial short-term monetary benefits from market
+regionalization for California consumers. It also shows that California's cap
+and trade program is relatively effective in limiting the carbon content of
+imported electricity, even absent a regional cap on CO2. The conclusions
+suggest efforts to reduce trade barriers should move forward in parallel with
+strong greenhouse gas policies that cap emissions levels across the market
+region.
+",0,0,0,0,0,1
+13202,Impact of Continuous Integration on Code Reviews,"  Peer code review and continuous integration often interleave with each other
+in the modern software quality management. Although several studies investigate
+how non-technical factors (e.g., reviewer workload), developer participation
+and even patch size affect the code review process, the impact of continuous
+integration on code reviews is not yet properly understood. In this paper, we
+report an exploratory study using 578K automated build entries where we
+investigate the impact of automated builds on the code reviews. Our
+investigation suggests that successfully passed builds are more likely to
+encourage new code review participation in a pull request. Frequently built
+projects are found to be maintaining a steady level of reviewing activities
+over the years, which was quite missing from the rarely built projects.
+Experiments with 26,516 automated build entries reported that our proposed
+model can identify 64% of the builds that triggered new code reviews later.
+",1,0,0,0,0,0
+13203,New Algorithms for Unordered Tree Inclusion,"  The tree inclusion problem is, given two node-labeled trees $P$ and $T$ (the
+""pattern tree"" and the ""text tree""), to locate every minimal subtree in $T$ (if
+any) that can be obtained by applying a sequence of node insertion operations
+to $P$. The ordered tree inclusion problem is known to be solvable in
+polynomial time while the unordered tree inclusion problem is NP-hard. The
+currently fastest algorithm for the latter is from 1995 and runs in
+$O(poly(m,n) \cdot 2^{2d}) = O^{\ast}(4^{d})$ time, where $m$ and $n$ are the
+sizes of the pattern and text trees, respectively, and $d$ is the degree of the
+pattern tree. Here, we develop a new algorithm that improves the exponent $2d$
+to $d$ by considering a particular type of ancestor-descendant relationships
+and applying dynamic programming, thus reducing the time complexity to
+$O^{\ast}(2^{d})$. We then study restricted variants of the unordered tree
+inclusion problem where the number of occurrences of different node labels
+and/or the input trees' heights are bounded and show that although the problem
+remains NP-hard in many such cases, if the leaves of $P$ are distinctly labeled
+and each label occurs at most $c$ times in $T$ then it can be solved in
+polynomial time for $c = 2$ and in $O^{\ast}(1.8^d)$ time for $c = 3$.
+",1,0,0,0,0,0
+13204,Learning to Price with Reference Effects,"  As a firm varies the price of a product, consumers exhibit reference effects,
+making purchase decisions based not only on the prevailing price but also the
+product's price history. We consider the problem of learning such behavioral
+patterns as a monopolist releases, markets, and prices products. This context
+calls for pricing decisions that intelligently trade off between maximizing
+revenue generated by a current product and probing to gain information for
+future benefit. Due to dependence on price history, realized demand can reflect
+delayed consequences of earlier pricing decisions. As such, inference entails
+attribution of outcomes to prior decisions and effective exploration requires
+planning price sequences that yield informative future outcomes. Despite the
+considerable complexity of this problem, we offer a tractable systematic
+approach. In particular, we frame the problem as one of reinforcement learning
+and leverage Thompson sampling. We also establish a regret bound that provides
+graceful guarantees on how performance improves as data is gathered and how
+this depends on the complexity of the demand model. We illustrate merits of the
+approach through simulations.
+",1,0,0,0,0,0
+13205,Inverse Reinforce Learning with Nonparametric Behavior Clustering,"  Inverse Reinforcement Learning (IRL) is the task of learning a single reward
+function given a Markov Decision Process (MDP) without defining the reward
+function, and a set of demonstrations generated by humans/experts. However, in
+practice, it may be unreasonable to assume that human behaviors can be
+explained by one reward function since they may be inherently inconsistent.
+Also, demonstrations may be collected from various users and aggregated to
+infer and predict user's behaviors. In this paper, we introduce the
+Non-parametric Behavior Clustering IRL algorithm to simultaneously cluster
+demonstrations and learn multiple reward functions from demonstrations that may
+be generated from more than one behaviors. Our method is iterative: It
+alternates between clustering demonstrations into different behavior clusters
+and inverse learning the reward functions until convergence. It is built upon
+the Expectation-Maximization formulation and non-parametric clustering in the
+IRL setting. Further, to improve the computation efficiency, we remove the need
+of completely solving multiple IRL problems for multiple clusters during the
+iteration steps and introduce a resampling technique to avoid generating too
+many unlikely clusters. We demonstrate the convergence and efficiency of the
+proposed method through learning multiple driver behaviors from demonstrations
+generated from a grid-world environment and continuous trajectories collected
+from autonomous robot cars using the Gazebo robot simulator.
+",1,0,0,0,0,0
+13206,$z^\circ$-ideals in intermediate rings of ordered field valued continuous functions,"  A proper ideal $I$ in a commutative ring with unity is called a
+$z^\circ$-ideal if for each $a$ in $I$, the intersection of all minimal prime
+ideals in $R$ which contain $a$ is contained in $I$. For any totally ordered
+field $F$ and a completely $F$-regular topological space $X$, let $C(X,F)$ be
+the ring of all $F$-valued continuous functions on $X$ and $B(X,F)$ the
+aggregate of all those functions which are bounded over $X$. An explicit
+formula for all the $z^\circ$-ideals in $A(X,F)$ in terms of ideals of closed
+sets in $X$ is given. It turns out that an intermediate ring $A(X,F)\neq
+C(X,F)$ is never regular in the sense of Von-Neumann. This property further
+characterizes $C(X,F)$ amongst the intermediate rings within the class of
+$P_F$-spaces $X$. It is also realized that $X$ is an almost $P_F$-space if and
+only if each maximal ideal in $C(X,F)$ is $z^\circ$-ideal. Incidentally this
+property also characterizes $C(X,F)$ amongst the intermediate rings within the
+family of almost $P_F$-spaces.
+",0,0,1,0,0,0
+13207,Mosquito Detection with Neural Networks: The Buzz of Deep Learning,"  Many real-world time-series analysis problems are characterised by scarce
+data. Solutions typically rely on hand-crafted features extracted from the time
+or frequency domain allied with classification or regression engines which
+condition on this (often low-dimensional) feature vector. The huge advances
+enjoyed by many application domains in recent years have been fuelled by the
+use of deep learning architectures trained on large data sets. This paper
+presents an application of deep learning for acoustic event detection in a
+challenging, data-scarce, real-world problem. Our candidate challenge is to
+accurately detect the presence of a mosquito from its acoustic signature. We
+develop convolutional neural networks (CNNs) operating on wavelet
+transformations of audio recordings. Furthermore, we interrogate the network's
+predictive power by visualising statistics of network-excitatory samples. These
+visualisations offer a deep insight into the relative informativeness of
+components in the detection problem. We include comparisons with conventional
+classifiers, conditioned on both hand-tuned and generic features, to stress the
+strength of automatic deep feature learning. Detection is achieved with
+performance metrics significantly surpassing those of existing algorithmic
+methods, as well as marginally exceeding those attained by individual human
+experts.
+",1,0,0,1,0,0
+13208,The inseparability of sampling and time and its influence on attempts to unify the molecular and fossil records,"  The two major approaches to studying macroevolution in deep time are the
+fossil record and reconstructed relationships among extant taxa from molecular
+data. Results based on one approach sometimes conflict with those based on the
+other, with inconsistencies often attributed to inherent flaws of one (or the
+other) data source. What is unquestionable is that both the molecular and
+fossil records are limited reflections of the same evolutionary history, and
+any contradiction between them represents a failure of our existing models to
+explain the patterns we observe. Fortunately, the different limitations of each
+record provide an opportunity to test or calibrate the other, and new
+methodological developments leverage both records simultaneously. However, we
+must reckon with the distinct relationships between sampling and time in the
+fossil record and molecular phylogenies. These differences impact our
+recognition of baselines, and the analytical incorporation of age estimate
+uncertainty. These differences in perspective also influence how different
+practitioners view the past and evolutionary time itself, bearing important
+implications for the generality of methodological advancements, and differences
+in the philosophical approach to macroevolutionary theory across fields.
+",0,0,0,0,1,0
+13209,The imprints of bars on the vertical stellar population gradients of galactic bulges,"  This is the second paper of a series aimed to study the stellar kinematics
+and population properties of bulges in highly-inclined barred galaxies. In this
+work, we carry out a detailed analysis of the stellar age, metallicity and
+[Mg/Fe] of 28 highly-inclined ($i > 65^{o}$) disc galaxies, from S0 to S(B)c,
+observed with the SAURON integral-field spectrograph. The sample is divided
+into two clean samples of barred and unbarred galaxies, on the basis of the
+correlation between the stellar velocity and h$_3$ profiles, as well as the
+level of cylindrical rotation within the bulge region. We find that while the
+mean stellar age, metallicity and [Mg/Fe] in the bulges of barred and unbarred
+galaxies are not statistically distinct, the [Mg/Fe] gradients along the minor
+axis (away from the disc) of barred galaxies are significantly different than
+those without bars. For barred galaxies, stars that are vertically further away
+from the midplane are in general more [Mg/Fe]--enhanced and thus the vertical
+gradients in [Mg/Fe] for barred galaxies are mostly positive, while for
+unbarred bulges the [Mg/Fe] profiles are typically negative or flat. This
+result, together with the old populations observed in the barred sample,
+indicates that bars are long-lasting structures, and therefore are not easily
+destroyed. The marked [Mg/Fe] differences with the bulges of unbarred galaxies
+indicate that different formation/evolution scenarios are required to explain
+their build-up, and emphasizes the role of bars in redistributing stellar
+material in the bulge dominated regions.
+",0,1,0,0,0,0
+13210,A structure-preserving split finite element discretization of the split 1D wave equations,"  We introduce a new finite element (FE) discretization framework applicable
+for covariant split equations. The introduction of additional differential
+forms (DF) that form pairs with the original ones permits the splitting of the
+equations into topological momentum and continuity equations and
+metric-dependent closure equations that apply the Hodge-star operator. Our
+discretization framework conserves this geometrical structure and provides for
+all DFs proper FE spaces such that the differential operators hold in strong
+form. We introduce lowest possible order discretizations of the split 1D wave
+equations, in which the discrete momentum and continuity equations follow by
+trivial projections onto piecewise constant FE spaces, omitting partial
+integrations. Approximating the Hodge-star by nontrivial Galerkin projections
+(GP), the two discrete metric equations follow by projections onto either the
+piecewise constant (GP0) or piecewise linear (GP1) space.
+Our framework gives us three schemes with significantly different behavior.
+The split scheme using twice GP1 is unstable and shares the dispersion relation
+with the P1-P1 FE scheme that approximates both variables by piecewise linear
+spaces (P1). The split schemes that apply a mixture of GP1 and GP0 share the
+dispersion relation with the stable P1-P0 FE scheme that applies piecewise
+linear and piecewise constant (P0) spaces. However, the split schemes exhibit
+second order convergence for both quantities of interest. For the split scheme
+applying twice GP0, we are not aware of a corresponding standard formulation to
+compare with. Though it does not provide a satisfactory approximation of the
+dispersion relation as short waves are propagated much too fast, the discovery
+of the new scheme illustrates the potential of our discretization framework as
+a toolbox to study and find FE schemes by new combinations of FE spaces.
+",0,0,1,0,0,0
+13211,Quantum quench dynamics,"  Quench dynamics is an active area of study encompassing condensed matter
+physics and quantum information, with applications to cold-atomic gases and
+pump-probe spectroscopy of materials. Recent theoretical progress in studying
+quantum quenches is reviewed. Quenches in interacting one dimensional systems
+as well as systems in higher spatial dimensions are covered. The appearance of
+non-trivial steady states following a quench in exactly solvable models is
+discussed, and the stability of these states to perturbations is described.
+Proper conserving approximations needed to capture the onset of thermalization
+at long times are outlined. The appearance of universal scaling for quenches
+near critical points, and the role of the renormalization group in capturing
+the transient regime, are reviewed. Finally the effect of quenches near
+critical points on the dynamics of entanglement entropy and entanglement
+statistics is discussed. The extraction of critical exponents from the
+entanglement statistics is outlined.
+",0,1,0,0,0,0
+13212,R&D On Beam Injection and Bunching Schemes In The Fermilab Booster,"  Fermilab is committed to upgrade its accelerator complex to support HEP
+experiments at the intensity frontier. The ongoing Proton Improvement Plan
+(PIP) enables us to reach 700 kW beam power on the NuMI neutrino targets. By
+the end of the next decade, the current 400 MeV normal conducting LINAC will be
+replaced by an 800 MeV superconducting LINAC (PIP-II) with an increased beam
+power >50% of the PIP design goal. Both in PIP and PIP-II era, the existing
+Booster is going to play a very significant role, at least for next two
+decades. In the meanwhile, we have recently developed an innovative beam
+injection and bunching scheme for the Booster called ""early injection scheme""
+that continues to use the existing 400 MeV LINAC and implemented into
+operation. This scheme has the potential to increase the Booster beam intensity
+by >40% from the PIP design goal. Some benefits from the scheme have already
+been seen. In this paper, I will describe the basic principle of the scheme,
+results from recent beam experiments, our experience with the new scheme in
+operation, current status, issues and future plans. This scheme fits well with
+the current and future intensity upgrade programs at Fermilab.
+",0,1,0,0,0,0
+13213,Deep Structured Generative Models,"  Deep generative models have shown promising results in generating realistic
+images, but it is still non-trivial to generate images with complicated
+structures. The main reason is that most of the current generative models fail
+to explore the structures in the images including spatial layout and semantic
+relations between objects. To address this issue, we propose a novel deep
+structured generative model which boosts generative adversarial networks (GANs)
+with the aid of structure information. In particular, the layout or structure
+of the scene is encoded by a stochastic and-or graph (sAOG), in which the
+terminal nodes represent single objects and edges represent relations between
+objects. With the sAOG appropriately harnessed, our model can successfully
+capture the intrinsic structure in the scenes and generate images of
+complicated scenes accordingly. Furthermore, a detection network is introduced
+to infer scene structures from a image. Experimental results demonstrate the
+effectiveness of our proposed method on both modeling the intrinsic structures,
+and generating realistic images.
+",0,0,0,1,0,0
+13214,Machine Learning Techniques for Stellar Light Curve Classification,"  We apply machine learning techniques in an attempt to predict and classify
+stellar properties from noisy and sparse time series data. We preprocessed over
+94 GB of Kepler light curves from MAST to classify according to ten distinct
+physical properties using both representation learning and feature engineering
+approaches. Studies using machine learning in the field have been primarily
+done on simulated data, making our study one of the first to use real light
+curve data for machine learning approaches. We tuned our data using previous
+work with simulated data as a template and achieved mixed results between the
+two approaches. Representation learning using a Long Short-Term Memory (LSTM)
+Recurrent Neural Network (RNN) produced no successful predictions, but our work
+with feature engineering was successful for both classification and regression.
+In particular, we were able to achieve values for stellar density, stellar
+radius, and effective temperature with low error (~ 2 - 4%) and good accuracy
+(~ 75%) for classifying the number of transits for a given star. The results
+show promise for improvement for both approaches upon using larger datasets
+with a larger minority class. This work has the potential to provide a
+foundation for future tools and techniques to aid in the analysis of
+astrophysical data.
+",0,1,0,0,0,0
+13215,Frequency measurement of the clock transition of an indium ion sympathetically-cooled in a linear trap,"  We report frequency measurement of the clock transition in an 115In+ ion
+sympathetically-cooled with Ca+ ions in a linear rf trap. The Ca+ ions are used
+as a probe of the external electromagnetic field and as the coolant for
+preparing the cold In+. The frequency is determined to be 1 267 402 452 901
+049.9 (6.9) Hz by averaging 36 measurements using an optical frequency comb
+referenced to the frequency standards located in the same site.
+",0,1,0,0,0,0
+13216,Sentence-level dialects identification in the greater China region,"  Identifying the different varieties of the same language is more challenging
+than unrelated languages identification. In this paper, we propose an approach
+to discriminate language varieties or dialects of Mandarin Chinese for the
+Mainland China, Hong Kong, Taiwan, Macao, Malaysia and Singapore, a.k.a., the
+Greater China Region (GCR). When applied to the dialects identification of the
+GCR, we find that the commonly used character-level or word-level uni-gram
+feature is not very efficient since there exist several specific problems such
+as the ambiguity and context-dependent characteristic of words in the dialects
+of the GCR. To overcome these challenges, we use not only the general features
+like character-level n-gram, but also many new word-level features, including
+PMI-based and word alignment-based features. A series of evaluation results on
+both the news and open-domain dataset from Wikipedia show the effectiveness of
+the proposed approach.
+",1,0,0,0,0,0
+13217,Measuring abstract reasoning in neural networks,"  Whether neural networks can learn abstract reasoning or whether they merely
+rely on superficial statistics is a topic of recent debate. Here, we propose a
+dataset and challenge designed to probe abstract reasoning, inspired by a
+well-known human IQ test. To succeed at this challenge, models must cope with
+various generalisation `regimes' in which the training and test data differ in
+clearly-defined ways. We show that popular models such as ResNets perform
+poorly, even when the training and test sets differ only minimally, and we
+present a novel architecture, with a structure designed to encourage reasoning,
+that does significantly better. When we vary the way in which the test
+questions and training data differ, we find that our model is notably
+proficient at certain forms of generalisation, but notably weak at others. We
+further show that the model's ability to generalise improves markedly if it is
+trained to predict symbolic explanations for its answers. Altogether, we
+introduce and explore ways to both measure and induce stronger abstract
+reasoning in neural networks. Our freely-available dataset should motivate
+further progress in this direction.
+",0,0,0,1,0,0
+13218,On fractional powers of Bessel operators,"  This paper was published in the special issue of the Journal of Inequalities
+and Special Functions dedicated to Professor Ivan Dimovski's contributions to
+different fields of mathematics: transmutation theory, special functions,
+integral transforms, function theory etc.
+In this paper we study fractional powers of the Bessel differential operator.
+The fractional powers are defined explicitly in the integral form without use
+of integral transforms in its definitions. Some general properties of the
+fractional powers of the Bessel differential operator are proved and some are
+listed. Among them are different variations of definitions, relations with the
+Mellin and Hankel transforms, group property, generalized Taylor formula with
+Bessel operators, evaluation of resolvent integral operator in terms of the
+Wright or generalized Mittag--Leffler functions. At the end, some topics are
+indicated for further study and possible generalizations. Also the aim of the
+paper is to attract attention and give references to not widely known results
+on fractional powers of the Bessel differential operator.
+",0,0,1,0,0,0
+13219,Small Telescope Exoplanet Transit Surveys: XO,"  The XO project aims at detecting transiting exoplanets around bright stars
+from the ground using small telescopes. The original configuration of XO
+(McCullough et al. 2005) has been changed and extended as described here. The
+instrumental setup consists of three identical units located at different
+sites, each composed of two lenses equipped with CCD cameras mounted on the
+same mount. We observed two strips of the sky covering an area of 520 deg$^2$
+for twice nine months. We build lightcurves for ~20,000 stars up to magnitude
+R~12.5 using a custom-made photometric data reduction pipeline. The photometric
+precision is around 1-2% for most stars, and the large quantity of data allows
+us to reach a millimagnitude precision when folding the lightcurves on
+timescales that are relevant to exoplanetary transits. We search for periodic
+signals and identify several hundreds of variable stars and a few tens of
+transiting planet candidates. Follow-up observations are underway to confirm or
+reject these candidates. We found two close-in gas giant planets so far, in
+line with the expected yield.
+",0,1,0,0,0,0
+13220,Divide-and-Conquer Reinforcement Learning,"  Standard model-free deep reinforcement learning (RL) algorithms sample a new
+initial state for each trial, allowing them to optimize policies that can
+perform well even in highly stochastic environments. However, problems that
+exhibit considerable initial state variation typically produce high-variance
+gradient estimates for model-free RL, making direct policy or value function
+optimization challenging. In this paper, we develop a novel algorithm that
+instead partitions the initial state space into ""slices"", and optimizes an
+ensemble of policies, each on a different slice. The ensemble is gradually
+unified into a single policy that can succeed on the whole state space. This
+approach, which we term divide-and-conquer RL, is able to solve complex tasks
+where conventional deep RL methods are ineffective. Our results show that
+divide-and-conquer RL greatly outperforms conventional policy gradient methods
+on challenging grasping, manipulation, and locomotion tasks, and exceeds the
+performance of a variety of prior methods. Videos of policies learned by our
+algorithm can be viewed at this http URL
+",1,0,0,0,0,0
+13221,Whipping of electrified visco-capillary jets in airflows,"  An electrified visco-capillary jet shows different dynamic behavior, such as
+cone forming, breakage into droplets, whipping and coiling, depending on the
+considered parameter regime. The whipping instability that is of fundamental
+importance for electrospinning has been approached by means of stability
+analysis in previous papers. In this work we alternatively propose a model
+framework in which the instability can be computed straightforwardly as the
+stable stationary solution of an asymptotic Cosserat rod description. For this
+purpose, we adopt a procedure by Ribe (Proc. Roy. Soc. Lond. A, 2004)
+describing the jet dynamics with respect to a frame rotating with the a priori
+unknown whipping frequency that itself becomes part of the solution. The rod
+model allows for stretching, bending and torsion, taking into account inertia,
+viscosity, surface tension, electric field and air drag. For the resulting
+parametric boundary value problem of ordinary differential equations we present
+a continuation-collocation method. On top of an implicit Runge-Kutta scheme of
+fifth order, our developed continuation procedure makes the efficient and
+robust simulation and navigation through a high-dimensional parameter space
+possible. Despite the simplicity of the employed electric force model the
+numerical results are convincing, the whipping effect is qualitatively well
+characterized.
+",0,1,1,0,0,0
+13222,On Optimizing Feedback Interval for Temporally Correlated MIMO Channels With Transmit Beamforming And Finite-Rate Feedback,"  A receiver with perfect channel state information (CSI) in a point-to-point
+multiple-input multiple-output (MIMO) channel can compute the transmit
+beamforming vector that maximizes the transmission rate. For frequency-division
+duplex, a transmitter is not able to estimate CSI directly and has to obtain a
+quantized transmit beamforming vector from the receiver via a rate-limited
+feedback channel. We assume that time evolution of MIMO channels is modeled as
+a Gauss-Markov process parameterized by a temporal-correlation coefficient.
+Since feedback rate is usually low, we assume rank-one transmit beamforming or
+transmission with single data stream. For given feedback rate, we analyze the
+optimal feedback interval that maximizes the average received power of the
+systems with two transmit or two receive antennas. For other system sizes, the
+optimal feedback interval is approximated by maximizing the rate difference in
+a large system limit. Numerical results show that the large system
+approximation can predict the optimal interval for finite-size system quite
+accurately. Numerical results also show that quantizing transmit beamforming
+with the optimal feedback interval gives larger rate than the existing
+Kalman-filter scheme does by as much as 10% and than feeding back for every
+block does by 44% when the number of feedback bits is small.
+",1,0,0,0,0,0
+13223,RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans,"  We describe a deep learning approach for automated brain hemorrhage detection
+from computed tomography (CT) scans. Our model emulates the procedure followed
+by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists,
+the model sifts through 2D cross-sectional slices while paying close attention
+to potential hemorrhagic regions. Further, the model utilizes 3D context from
+neighboring slices to improve predictions at each slice and subsequently,
+aggregates the slice-level predictions to provide diagnosis at CT level. We
+refer to our proposed approach as Recurrent Attention DenseNet (RADnet) as it
+employs original DenseNet architecture along with adding the components of
+attention for slice level predictions and recurrent neural network layer for
+incorporating 3D context. The real-world performance of RADnet has been
+benchmarked against independent analysis performed by three senior radiologists
+for 77 brain CTs. RADnet demonstrates 81.82% hemorrhage prediction accuracy at
+CT level that is comparable to radiologists. Further, RADnet achieves higher
+recall than two of the three radiologists, which is remarkable.
+",0,0,0,1,0,0
+13224,Median statistics estimates of Hubble and Newton's Constant,"  Robustness of any statistics depends upon the number of assumptions it makes
+about the measured data. We point out the advantages of median statistics using
+toy numerical experiments and demonstrate its robustness, when the number of
+assumptions we can make about the data are limited. We then apply the median
+statistics technique to obtain estimates of two constants of nature, Hubble
+Constant ($H_0$) and Newton's Gravitational Constant($G$), both of which show
+significant differences between different measurements. For $H_0$, we update
+the analysis done by Chen and Ratra (2011) and Gott et al. (2001) using $576$
+measurements. We find after grouping the different results according to their
+primary type of measurement, the median estimates are given by
+$H_0=72.5^{+2.5}_{-8}$ km/sec/Mpc with errors corresponding to 95% c.l.
+(2$\sigma$) and $G=6.674702^{+0.0014}_{-0.0009} \times 10^{-11} \mathrm{N
+m^{2}kg^{-2}}$ corresponding to 68% c.l. (1$\sigma$).
+",0,1,0,0,0,0
+13225,Re-parameterizing and reducing families of normal operators,"  We present a new proof of results of Kurdyka & Paunescu, and of Rainer, about
+real-analytic multi-parameters generalizations of classical results by Rellich
+and Kato about the reduction in families of univariate deformations of normal
+operators over real or complex vector spaces of finite dimensions.
+Given a real analytic family of normal operators over a finite dimensional
+real or complex vector space, there exists a locally finite composition of
+blowings-up with smooth centers re-parameterizing the given family such that at
+each point of the source space of the re-parameterizing mapping, there exists a
+neighbourhood of any given point over which exists a real analytic orthonormal
+frame in which the pull back of the operator is in reduced form at every point
+of the neighbourhood.
+A free by-product of our proof is the local real analyticity of the
+eigen-values, which in all prior works was a prerequisite step to get local
+regular reducing bases.
+",0,0,1,0,0,0
+13226,Urban Scene Segmentation with Laser-Constrained CRFs,"  Robots typically possess sensors of different modalities, such as colour
+cameras, inertial measurement units, and 3D laser scanners. Often, solving a
+particular problem becomes easier when more than one modality is used. However,
+while there are undeniable benefits to combine sensors of different modalities
+the process tends to be complicated. Segmenting scenes observed by the robot
+into a discrete set of classes is a central requirement for autonomy as
+understanding the scene is the first step to reason about future situations.
+Scene segmentation is commonly performed using either image data or 3D point
+cloud data. In computer vision many successful methods for scene segmentation
+are based on conditional random fields (CRF) where the maximum a posteriori
+(MAP) solution to the segmentation can be obtained by inference. In this paper
+we devise a new CRF inference method for scene segmentation that incorporates
+global constraints, enforcing the sets of nodes are assigned the same class
+label. To do this efficiently, the CRF is formulated as a relaxed quadratic
+program whose MAP solution is found using a gradient-based optimisation
+approach. The proposed method is evaluated on images and 3D point cloud data
+gathered in urban environments where image data provides the appearance
+features needed by the CRF, while the 3D point cloud data provides global
+spatial constraints over sets of nodes. Comparisons with belief propagation,
+conventional quadratic programming relaxation, and higher order potential CRF
+show the benefits of the proposed method.
+",1,0,0,0,0,0
+13227,"The GAPS Programme with HARPS-N at TNG. XIII. The orbital obliquity of three close-in massive planets hosted by dwarf K-type stars: WASP-43, HAT-P-20 and Qatar-2","  In the framework of the GAPS project, we are conducting an observational
+programme aimed at the determination of the orbital obliquity of known
+transiting exoplanets. The targets are selected to probe the obliquity against
+a wide range of stellar and planetary physical parameters. We exploit
+high-precision radial velocity (RV) measurements, delivered by the HARPS-N
+spectrograph at the 3.6m Telescopio Nazionale Galileo, to measure the
+Rossiter-McLaughlin (RM) effect in RV time-series bracketing planet transits,
+and to refine the orbital parameters determinations with out-of-transit RV
+data. We also analyse new transit light curves obtained with several 1-2m class
+telescopes to better constrain the physical fundamental parameters of the
+planets and parent stars. We report here on new transit spectroscopic
+observations for three very massive close-in giant planets: WASP43b, HATP20b
+and Qatar2b orbiting dwarf K-type stars with effective temperature well below
+5000K. We find lambda = 3.5pm6.8 deg for WASP43b and lambda = -8.0pm6.9 deg for
+HATP20b, while for Qatar2, our faintest target, the RM effect is only
+marginally detected, though our best-fit value lambda = 15pm20 deg is in
+agreement with a previous determination. In combination with stellar rotational
+periods derived photometrically, we estimate the true spin-orbit angle, finding
+that WASP43b is aligned while the orbit of HATP20b presents a small but
+significant obliquity (Psi=36 _{-12}^{+10} deg). By analyzing the CaII H&K
+chromospheric emission lines for HATP20 and WASP43, we find evidence for an
+enhanced level of stellar activity which is possibly induced by star-planet
+interactions.
+",0,1,0,0,0,0
+13228,Probabilistic Program Equivalence for NetKAT,"  We tackle the problem of deciding whether two probabilistic programs are
+equivalent in Probabilistic NetKAT, a formal language for specifying and
+reasoning about the behavior of packet-switched networks. We show that the
+problem is decidable for the history-free fragment of the language by
+developing an effective decision procedure based on stochastic matrices. The
+main challenge lies in reasoning about iteration, which we address by designing
+an encoding of the program semantics as a finite-state absorbing Markov chain,
+whose limiting distribution can be computed exactly. In an extended case study
+on a real-world data center network, we automatically verify various
+quantitative properties of interest, including resilience in the presence of
+failures, by analyzing the Markov chain semantics.
+",1,0,0,0,0,0
+13229,Spatially Adaptive Colocalization Analysis in Dual-Color Fluorescence Microscopy,"  Colocalization analysis aims to study complex spatial associations between
+bio-molecules via optical imaging techniques. However, existing colocalization
+analysis workflows only assess an average degree of colocalization within a
+certain region of interest and ignore the unique and valuable spatial
+information offered by microscopy. In the current work, we introduce a new
+framework for colocalization analysis that allows us to quantify colocalization
+levels at each individual location and automatically identify pixels or regions
+where colocalization occurs. The framework, referred to as spatially adaptive
+colocalization analysis (SACA), integrates a pixel-wise local kernel model for
+colocalization quantification and a multi-scale adaptive propagation-separation
+strategy for utilizing spatial information to detect colocalization in a
+spatially adaptive fashion. Applications to simulated and real biological
+datasets demonstrate the practical merits of SACA in what we hope to be an
+easily applicable and robust colocalization analysis method. In addition,
+theoretical properties of SACA are investigated to provide rigorous statistical
+justification.
+",0,0,0,1,0,0
+13230,On consistent vertex nomination schemes,"  Given a vertex of interest in a network $G_1$, the vertex nomination problem
+seeks to find the corresponding vertex of interest (if it exists) in a second
+network $G_2$. A vertex nomination scheme produces a list of the vertices in
+$G_2$, ranked according to how likely they are judged to be the corresponding
+vertex of interest in $G_2$. The vertex nomination problem and related
+information retrieval tasks have attracted much attention in the machine
+learning literature, with numerous applications to social and biological
+networks. However, the current framework has often been confined to a
+comparatively small class of network models, and the concept of statistically
+consistent vertex nomination schemes has been only shallowly explored. In this
+paper, we extend the vertex nomination problem to a very general statistical
+model of graphs. Further, drawing inspiration from the long-established
+classification framework in the pattern recognition literature, we provide
+definitions for the key notions of Bayes optimality and consistency in our
+extended vertex nomination framework, including a derivation of the Bayes
+optimal vertex nomination scheme. In addition, we prove that no universally
+consistent vertex nomination schemes exist. Illustrative examples are provided
+throughout.
+",0,0,0,1,0,0
+13231,Converse passivity theorems,"  Passivity is an imperative concept and a widely utilized tool in the analysis
+and control of interconnected systems. It naturally arises in the modelling of
+physical systems involving passive elements and dynamics. While many theorems
+on passivity are known in the theory of robust control, very few converse
+passivity results exist. This paper establishes various versions of converse
+passivity theorems for nonlinear feedback systems. In particular, open-loop
+passivity is shown to be necessary to ensure closed-loop passivity from an
+input-output perspective. Moreover, the stability of the feedback
+interconnection of a specific system with an arbitrary passive system is shown
+to imply passivity of the system itself.
+",0,0,1,0,0,0
+13232,Neural Networks retrieving Boolean patterns in a sea of Gaussian ones,"  Restricted Boltzmann Machines are key tools in Machine Learning and are
+described by the energy function of bipartite spin-glasses. From a statistical
+mechanical perspective, they share the same Gibbs measure of Hopfield networks
+for associative memory. In this equivalence, weights in the former play as
+patterns in the latter. As Boltzmann machines usually require real weights to
+be trained with gradient descent like methods, while Hopfield networks
+typically store binary patterns to be able to retrieve, the investigation of a
+mixed Hebbian network, equipped with both real (e.g., Gaussian) and discrete
+(e.g., Boolean) patterns naturally arises. We prove that, in the challenging
+regime of a high storage of real patterns, where retrieval is forbidden, an
+extra load of Boolean patterns can still be retrieved, as long as the ratio
+among the overall load and the network size does not exceed a critical
+threshold, that turns out to be the same of the standard
+Amit-Gutfreund-Sompolinsky theory. Assuming replica symmetry, we study the case
+of a low load of Boolean patterns combining the stochastic stability and
+Hamilton-Jacobi interpolating techniques. The result can be extended to the
+high load by a non rigorous but standard replica computation argument.
+",0,1,0,0,0,0
+13233,Link colorings and the Goeritz matrix,"  We discuss the connection between colorings of a link diagram and the Goeritz
+matrix.
+",0,0,1,0,0,0
+13234,Left-invariant Grauert tubes on SU(2),"  Let M be a real analytic Riemannian manifold. An adapted complex structure on
+TM is a complex structure on a neighborhood of the zero section such that the
+leaves of the Riemann foliation are complex submanifolds. This structure is
+called entire if it may be extended to the whole of TM. We call such manifolds
+Grauert tubes, or simply tubes. We consider here the case of M = G a compact
+connected Lie group with a left-invariant metric, and try to determine for
+which such metrics the associated tube is entire. It is well-known that the
+Grauert tube of a bi-invariant metric on a Lie group is entire. The case of the
+smallest group SU(2) is treated completely, thanks to the complete
+integrability of the geodesic flow for such a metric, a standard result in
+classical mechanics. Along the way we find a new obstruction to tubes being
+entire which is made visible by the complete integrability. (New reference and
+exposition shortened, 11/17/2017.)
+",0,0,1,0,0,0
+13235,Ricci flow on cone surfaces and a three-dimensional expanding soliton,"  The main objective of this thesis is the study of the evolution under the
+Ricci flow of surfaces with singularities of cone type. A second objective,
+emerged from the techniques we use, is the study of families of Ricci flow
+solitons in dimension 2 and 3. The Ricci flow is an evolution equation for
+Riemannian manifolds, introduced by R. Hamilton in 1982. It is from the
+achievements made by G. Perelman with this technique in 2002 when the Ricci
+flow has been established in a discipline itself, generating a great interest
+in the community. This thesis contains four original results. First result is a
+complete classification of solitons in smooth and cone surfaces. This
+classification completes the preceding results found by Hamilton, Chow and Wu
+and others, and we obtain explicit descriptions of all solitons in dimension 2.
+Second result is a Geometrization of cone surfaces by Ricci flow. This result,
+which uses the aforementioned first result, extends the theory of Hamilton to
+the singular case. This is the most comprehensive result in the thesis, for
+which we use and develop analysis and PDE techniques, as well as comparison
+geometry techniques. Third result is the existence of a Ricci flow that removes
+cone singularities. This clearly exposes the non-uniqueness of solutions to the
+flow , in analogy to the Ricci flow with cusps of P. Topping. The fourth result
+is the construction of a new expanding gradient Ricci soliton in dimension 3.
+Just as we do with solitons on cone surfaces, we give an explicit construction
+using techniques of phase portraits. We also prove that this is the only
+soliton with its topology and its lower bound of the curvature, and besides
+this is a critical case amongst all expanding solitons in dimension 3 with
+curvature bounded below.
+",0,0,1,0,0,0
+13236,Do metric fluctuations affect the Higgs dynamics during inflation?,"  We show that the dynamics of the Higgs field during inflation is not affected
+by metric fluctuations if the Higgs is an energetically subdominant light
+spectator. For Standard Model parameters we find that couplings between Higgs
+and metric fluctuations are suppressed by $\mathcal{O}(10^{-7})$. They are
+negligible compared to both pure Higgs terms in the effective potential and the
+unavoidable non-minimal Higgs coupling to background scalar curvature. The
+question of the electroweak vacuum instability during high energy scale
+inflation can therefore be studied consistently using the Jordan frame action
+in a Friedmann--Lemaître--Robertson--Walker metric, where the Higgs-curvature
+coupling enters as an effective mass contribution. Similar results apply for
+other light spectator scalar fields during inflation.
+",0,1,0,0,0,0
+13237,Quantum Origami: Transversal Gates for Quantum Computation and Measurement of Topological Order,"  In topology, a torus remains invariant under certain non-trivial
+transformations known as modular transformations. In the context of
+topologically ordered quantum states of matter, these transformations encode
+the braiding statistics and fusion rules of emergent anyonic excitations and
+thus serve as a diagnostic of topological order. Moreover, modular
+transformations of higher genus surfaces, e.g. a torus with multiple handles,
+can enhance the computational power of a topological state, in many cases
+providing a universal fault-tolerant set of gates for quantum computation.
+However, due to the intrusive nature of modular transformations, which
+abstractly involve global operations and manifold surgery, physical
+implementations of them in local systems have remained elusive. Here, we show
+that by folding manifolds, modular transformations can be applied in a single
+shot by independent local unitaries, providing a novel class of transversal
+logic gates for fault-tolerant quantum computation. Specifically, we
+demonstrate that multi-layer topological states with appropriate boundary
+conditions and twist defects allow modular transformations to be effectively
+implemented by a finite sequence of local SWAP gates between the layers. We
+further provide methods to directly measure the modular matrices, and thus the
+fractional statistics of anyonic excitations, providing a novel way to directly
+measure topological order.
+",0,1,0,0,0,0
+13238,A Decentralized Framework for Real-Time Energy Trading in Distribution Networks with Load and Generation Uncertainty,"  The proliferation of small-scale renewable generators and price-responsive
+loads makes it a challenge for distribution network operators (DNOs) to
+schedule the controllable loads of the load aggregators and the generation of
+the generators in real-time. Additionally, the high computational burden and
+violation of the entities' (i.e., load aggregators' and generators') privacy
+make a centralized framework impractical. In this paper, we propose a
+decentralized energy trading algorithm that can be executed by the entities in
+a real-time fashion. To address the privacy issues, the DNO provides the
+entities with proper control signals using the Lagrange relaxation technique to
+motivate them towards an operating point with maximum profit for entities. To
+deal with uncertainty issues, we propose a probabilistic load model and robust
+framework for renewable generation. The performance of the proposed algorithm
+is evaluated on an IEEE 123-node test feeder. When compared with a benchmark of
+not performing load management for the aggregators, the proposed algorithm
+benefits both the load aggregators and generators by increasing their profit by
+17.8%and 10.3%, respectively. When compared with a centralized approach, our
+algorithm converges to the solution of the DNO's centralized problem with a
+significantly lower running time in 50 iterations per time slot.
+",1,0,0,0,0,0
+13239,White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions,"  Electrical brain stimulation is currently being investigated as a therapy for
+neurological disease. However, opportunities to optimize such therapies are
+challenged by the fact that the beneficial impact of focal stimulation on both
+neighboring and distant regions is not well understood. Here, we use network
+control theory to build a model of brain network function that makes
+predictions about how stimulation spreads through the brain's white matter
+network and influences large-scale dynamics. We test these predictions using
+combined electrocorticography (ECoG) and diffusion weighted imaging (DWI) data
+who volunteered to participate in an extensive stimulation regimen. We posit a
+specific model-based manner in which white matter tracts constrain stimulation,
+defining its capacity to drive the brain to new states, including states
+associated with successful memory encoding. In a first validation of our model,
+we find that the true pattern of white matter tracts can be used to more
+accurately predict the state transitions induced by direct electrical
+stimulation than the artificial patterns of null models. We then use a targeted
+optimal control framework to solve for the optimal energy required to drive the
+brain to a given state. We show that, intuitively, our model predicts larger
+energy requirements when starting from states that are farther away from a
+target memory state. We then suggest testable hypotheses about which structural
+properties will lead to efficient stimulation for improving memory based on
+energy requirements. Our work demonstrates that individual white matter
+architecture plays a vital role in guiding the dynamics of direct electrical
+stimulation, more generally offering empirical support for the utility of
+network control theoretic models of brain response to stimulation.
+",0,0,0,0,1,0
+13240,Community detection in networks via nonlinear modularity eigenvectors,"  Revealing a community structure in a network or dataset is a central problem
+arising in many scientific areas. The modularity function $Q$ is an established
+measure quantifying the quality of a community, being identified as a set of
+nodes having high modularity. In our terminology, a set of nodes with positive
+modularity is called a \textit{module} and a set that maximizes $Q$ is thus
+called \textit{leading module}. Finding a leading module in a network is an
+important task, however the dimension of real-world problems makes the
+maximization of $Q$ unfeasible. This poses the need of approximation techniques
+which are typically based on a linear relaxation of $Q$, induced by the
+spectrum of the modularity matrix $M$. In this work we propose a nonlinear
+relaxation which is instead based on the spectrum of a nonlinear modularity
+operator $\mathcal M$. We show that extremal eigenvalues of $\mathcal M$
+provide an exact relaxation of the modularity measure $Q$, however at the price
+of being more challenging to be computed than those of $M$. Thus we extend the
+work made on nonlinear Laplacians, by proposing a computational scheme, named
+\textit{generalized RatioDCA}, to address such extremal eigenvalues. We show
+monotonic ascent and convergence of the method. We finally apply the new method
+to several synthetic and real-world data sets, showing both effectiveness of
+the model and performance of the method.
+",1,0,0,1,0,0
+13241,Scale-invariant unconstrained online learning,"  We consider a variant of online convex optimization in which both the
+instances (input vectors) and the comparator (weight vector) are unconstrained.
+We exploit a natural scale invariance symmetry in our unconstrained setting:
+the predictions of the optimal comparator are invariant under any linear
+transformation of the instances. Our goal is to design online algorithms which
+also enjoy this property, i.e. are scale-invariant. We start with the case of
+coordinate-wise invariance, in which the individual coordinates (features) can
+be arbitrarily rescaled. We give an algorithm, which achieves essentially
+optimal regret bound in this setup, expressed by means of a coordinate-wise
+scale-invariant norm of the comparator. We then study general invariance with
+respect to arbitrary linear transformations. We first give a negative result,
+showing that no algorithm can achieve a meaningful bound in terms of
+scale-invariant norm of the comparator in the worst case. Next, we compliment
+this result with a positive one, providing an algorithm which ""almost"" achieves
+the desired bound, incurring only a logarithmic overhead in terms of the norm
+of the instances.
+",1,0,0,1,0,0
+13242,Tuning Pairing Amplitude and Spin-Triplet Texture by Curving Superconducting Nanostructures,"  We investigate the nature of the superconducting state in curved
+nanostructures with Rashba spin-orbit coupling (RSOC). In bent nanostructures
+with inhomogeneous curvature we find a local enhancement or suppression of the
+superconducting order parameter, with the effect that can be tailored by tuning
+either the RSOC strength or the carrier density. Apart from the local
+superconducting spin-singlet amplitude control, the geometric curvature
+generates non-trivial textures of the spin-triplet pairs through a spatial
+variation of the d-vector. By employing the representative case of an
+elliptically deformed quantum ring, we demonstrate that the amplitude of the
+d-vector strongly depends on the strength of the local curvature and it
+generally exhibits a three-dimensional profile whose winding is tied to that of
+the single electron spin in the normal state. Our findings unveil novel paths
+to manipulate the quantum structure of the superconducting state in RSOC
+nanostructures through their geometry.
+",0,1,0,0,0,0
+13243,Thermal conductivity changes across a structural phase transition: the case of high-pressure silica,"  By means of first-principles calculations, we investigate the thermal
+properties of silica as it evolves, under hydrostatic compression, from a
+stishovite phase into a CaCl$_2$-type structure. We compute the thermal
+conductivity tensor by solving the linearized Boltzmann transport equation
+iteratively in a wide temperature range, using for this the pressure-dependent
+harmonic and anharmonic interatomic couplings obtained from first principles.
+Most remarkably, we find that, at low temperatures, SiO$_2$ displays a large
+peak in the in-plane thermal conductivity and a highly anisotropic behavior
+close to the structural transformation. We trace back the origin of these
+features by analyzing the phonon contributions to the conductivity. We discuss
+the implications of our results in the general context of continuous structural
+transformations in solids, as well as the potential geological interest of our
+results for silica.
+",0,1,0,0,0,0
+13244,Enemy At the Gateways: A Game Theoretic Approach to Proxy Distribution,"  A core technique used by popular proxy-based circumvention systems like Tor,
+Psiphon, and Lantern is to secretly share the IP addresses of circumvention
+proxies with the censored clients for them to be able to use such systems. For
+instance, such secretly shared proxies are known as bridges in Tor. However, a
+key challenge to this mechanism is the insider attack problem: censoring agents
+can impersonate as benign censored clients in order to obtain (and then block)
+such secretly shared circumvention proxies.
+In this paper, we perform a fundamental study on the problem of insider
+attack on proxy-based circumvention systems. We model the proxy distribution
+problem using game theory, based on which we derive the optimal strategies of
+the parties involved, i.e., the censors and circumvention system operators.
+That is, we derive the optimal proxy distribution mechanism of a
+circumvention system like Tor, against the censorship adversary who also takes
+his optimal censorship strategies.
+This is unlike previous works that design ad hoc mechanisms for proxy
+distribution, against non-optimal censors.
+We perform extensive simulations to evaluate our optimal proxy assignment
+algorithm under various adversarial and network settings. Comparing with the
+state-of-the-art prior work, we show that our optimal proxy assignment
+algorithm has superior performance, i.e., better resistance to censorship even
+against the strongest censorship adversary who takes her optimal actions. We
+conclude with lessons and recommendation for the design of proxy-based
+circumvention systems.
+",1,0,0,0,0,0
+13245,Mod-$p$ isogeny classes on Shimura varieties with parahoric level structure,"  We study the special fiber of the integral models for Shimura varieties of
+Hodge type with parahoric level structure constructed by Kisin and Pappas in
+[KP]. We show that when the group is residually split, the points in the mod
+$p$ isogeny classes have the form predicted by the Langlands Rapoport
+conjecture in [LR].
+We also verify most of the He-Rapoport axioms for these integral models
+without the residually split assumption. This allows us to prove that all
+Newton strata are non-empty for these models.
+",0,0,1,0,0,0
+13246,Bayesian Optimization with Gradients,"  Bayesian optimization has been successful at global optimization of
+expensive-to-evaluate multimodal objective functions. However, unlike most
+optimization methods, Bayesian optimization typically does not use derivative
+information. In this paper we show how Bayesian optimization can exploit
+derivative information to decrease the number of objective function evaluations
+required for good performance. In particular, we develop a novel Bayesian
+optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for
+which we show one-step Bayes-optimality, asymptotic consistency, and greater
+one-step value of information than is possible in the derivative-free setting.
+Our procedure accommodates noisy and incomplete derivative information, comes
+in both sequential and batch forms, and can optionally reduce the computational
+cost of inference through automatically selected retention of a single
+directional derivative. We also compute the d-KG acquisition function and its
+gradient using a novel fast discretization-free technique. We show d-KG
+provides state-of-the-art performance compared to a wide range of optimization
+procedures with and without gradients, on benchmarks including logistic
+regression, deep learning, kernel learning, and k-nearest neighbors.
+",1,0,1,1,0,0
+13247,How to cut a cake with a gram matrix,"  In this article we study the problem of fair division. In particular we study
+a notion introduced by J. Barbanel that generalizes super envy-free fair
+division. We give a new proof of his result. Our approach allows us to give an
+explicit bound for this kind of fair division. Furthermore, we also give a
+theoretical answer to an open problem posed by Barbanel in 1996. Roughly
+speaking, this question is: how can we decide if there exists a fair division
+satisfying some inequalities constraints? Furthermore, when all the measures
+are given with piecewise constant density functions then we show how to
+construct effectively such a fair division.
+",1,0,0,0,0,0
+13248,Magnetic MIMO Signal Processing and Optimization for Wireless Power Transfer,"  In magnetic resonant coupling (MRC) enabled multiple-input multiple-output
+(MIMO) wireless power transfer (WPT) systems, multiple transmitters (TXs) each
+with one single coil are used to enhance the efficiency of simultaneous power
+transfer to multiple single-coil receivers (RXs) by constructively combining
+their induced magnetic fields at the RXs, a technique termed ""magnetic
+beamforming"". In this paper, we study the optimal magnetic beamforming design
+in a multi-user MIMO MRC-WPT system. We introduce the multi-user power region
+that constitutes all the achievable power tuples for all RXs, subject to the
+given total power constraint over all TXs as well as their individual peak
+voltage and current constraints. We characterize each boundary point of the
+power region by maximizing the sum-power deliverable to all RXs subject to
+their minimum harvested power constraints. For the special case without the TX
+peak voltage and current constraints, we derive the optimal TX current
+allocation for the single-RX setup in closed-form as well as that for the
+multi-RX setup. In general, the problem is a non-convex quadratically
+constrained quadratic programming (QCQP), which is difficult to solve. For the
+case of one single RX, we show that the semidefinite relaxation (SDR) of the
+problem is tight. For the general case with multiple RXs, based on SDR we
+obtain two approximate solutions by applying time-sharing and randomization,
+respectively. Moreover, for practical implementation of magnetic beamforming,
+we propose a novel signal processing method to estimate the magnetic MIMO
+channel due to the mutual inductances between TXs and RXs. Numerical results
+show that our proposed magnetic channel estimation and adaptive beamforming
+schemes are practically effective, and can significantly improve the power
+transfer efficiency and multi-user performance trade-off in MIMO MRC-WPT
+systems.
+",1,0,0,0,0,0
+13249,Bouncy Hybrid Sampler as a Unifying Device,"  This work introduces a class of rejection-free Markov chain Monte Carlo
+(MCMC) samplers, named the Bouncy Hybrid Sampler, which unifies several
+existing methods from the literature. Examples include the Bouncy Particle
+Sampler of Peters and de With (2012), Bouchard-Cote et al. (2015) and the
+Hamiltonian MCMC. Following the introduced general framework, we derive a new
+sampler called the Quadratic Bouncy Hybrid Sampler. We apply this novel sampler
+to the problem of sampling from a truncated Gaussian distribution.
+",0,0,0,1,0,0
+13250,Lifshitz interaction can promote ice growth at water-silica interfaces,"  At air-water interfaces, the Lifshitz interaction by itself does not promote
+ice growth. On the contrary, we find that the Lifshitz force promotes the
+growth of an ice film, up to 1-8 nm thickness, near silica-water interfaces at
+the triple point of water. This is achieved in a system where the combined
+effect of the retardation and the zero frequency mode influences the
+short-range interactions at low temperatures, contrary to common understanding.
+Cancellation between the positive and negative contributions in the Lifshitz
+spectral function is reversed in silica with high porosity. Our results provide
+a model for how water freezes on glass and other surfaces.
+",0,1,0,0,0,0
+13251,Reverse iterative volume sampling for linear regression,"  We study the following basic machine learning task: Given a fixed set of
+$d$-dimensional input points for a linear regression problem, we wish to
+predict a hidden response value for each of the points. We can only afford to
+attain the responses for a small subset of the points that are then used to
+construct linear predictions for all points in the dataset. The performance of
+the predictions is evaluated by the total square loss on all responses (the
+attained as well as the hidden ones). We show that a good approximate solution
+to this least squares problem can be obtained from just dimension $d$ many
+responses by using a joint sampling technique called volume sampling. Moreover,
+the least squares solution obtained for the volume sampled subproblem is an
+unbiased estimator of optimal solution based on all n responses. This
+unbiasedness is a desirable property that is not shared by other common subset
+selection techniques.
+Motivated by these basic properties, we develop a theoretical framework for
+studying volume sampling, resulting in a number of new matrix expectation
+equalities and statistical guarantees which are of importance not only to least
+squares regression but also to numerical linear algebra in general. Our methods
+also lead to a regularized variant of volume sampling, and we propose the first
+efficient algorithms for volume sampling which make this technique a practical
+tool in the machine learning toolbox. Finally, we provide experimental evidence
+which confirms our theoretical findings.
+",0,0,0,1,0,0
+13252,Learning from Complementary Labels,"  Collecting labeled data is costly and thus a critical bottleneck in
+real-world classification tasks. To mitigate this problem, we propose a novel
+setting, namely learning from complementary labels for multi-class
+classification. A complementary label specifies a class that a pattern does not
+belong to. Collecting complementary labels would be less laborious than
+collecting ordinary labels, since users do not have to carefully choose the
+correct class from a long list of candidate classes. However, complementary
+labels are less informative than ordinary labels and thus a suitable approach
+is needed to better learn from them. In this paper, we show that an unbiased
+estimator to the classification risk can be obtained only from complementarily
+labeled data, if a loss function satisfies a particular symmetric condition. We
+derive estimation error bounds for the proposed method and prove that the
+optimal parametric convergence rate is achieved. We further show that learning
+from complementary labels can be easily combined with learning from ordinary
+labels (i.e., ordinary supervised learning), providing a highly practical
+implementation of the proposed method. Finally, we experimentally demonstrate
+the usefulness of the proposed methods.
+",1,0,0,1,0,0
+13253,Dusty winds in active galactic nuclei: reconciling observations with models,"  This letter presents a revised radiative transfer model for the infrared (IR)
+emission of active galactic nuclei (AGN). While current models assume that the
+IR is emitted from a dusty torus in the equatorial plane of the AGN, spatially
+resolved observations indicate that the majority of the IR emission from 100 pc
+in many AGN originates from the polar region, contradicting classical torus
+models. The new model CAT3D-WIND builds upon the suggestion that the dusty gas
+around the AGN consists of an inflowing disk and an outflowing wind. Here, it
+is demonstrated that (1) such disk+wind models cover overall a similar
+parameter range of observed spectral features in the IR as classical clumpy
+torus models, e.g. the silicate feature strengths and mid-IR spectral slopes,
+(2) they reproduce the 3-5{\mu}m bump observed in many type 1 AGN unlike torus
+models, and (3) they are able to explain polar emission features seen in IR
+interferometry, even for type 1 AGN at relatively low inclination, as
+demonstrated for NGC3783. These characteristics make it possible to reconcile
+radiative transfer models with observations and provide further evidence of a
+two-component parsec-scaled dusty medium around AGN: the disk gives rise to the
+3-5{\mu}m near-IR component, while the wind produces the mid-IR emission. The
+model SEDs will be made available for download.
+",0,1,0,0,0,0
+13254,Continuity of Utility Maximization under Weak Convergence,"  In this paper we find sufficient conditions for the continuity of the value
+of the utility maximization problem from terminal wealth with respect to the
+convergence in distribution of the underlying processes. We provide several
+examples which illustrate that without these conditions, we cannot generally
+expect continuity to hold. Finally, we apply our results to the computation of
+the minimum shortfall in the Heston model by building an appropriate lattice
+approximation.
+",0,0,0,0,0,1
+13255,Introducing the anatomy of disciplinary discernment: an example from astronomy,"  Education is increasingly being framed by a competence mindset; the value of
+knowledge lies much more in competence performativity and innovation than in
+simply knowing. Reaching such competency in areas such as astronomy and physics
+has long been known to be challenging. The movement from everyday conceptions
+of the world around us to a disciplinary interpretation is fraught with
+pitfalls and problems. Thus, what underpins the characteristics of the
+disciplinary trajectory to competence becomes an important educational
+consideration. In this article we report on a study involving what students and
+lecturers discern from the same disciplinary semiotic resource. We use this to
+propose an Anatomy of Disciplinary Discernment (ADD), a hierarchy of what is
+focused on and how it is interpreted in an appropriate, disciplinary manner, as
+an overarching fundamental aspect of disciplinary learning. Students and
+lecturers in astronomy and physics were asked to describe what they could
+discern from a video simulation of travel through our Galaxy and beyond. 137
+people from nine countries participated. The descriptions were analysed using a
+hermeneutic interpretive study approach. The analysis resulted in the
+formulation of five qualitatively different categories of discernment; the ADD,
+reflecting a view of participants' competence levels. The ADD reveals four
+increasing levels of disciplinary discernment: Identification, Explanation,
+Appreciation, and Evaluation. This facilitates the identification of a clear
+relationship between educational level and the level of disciplinary
+discernment. The analytical outcomes of the study suggest how teachers of
+science, after using the ADD to assess the students disciplinary knowledge, may
+attain new insights into how to create more effective learning environments by
+explicitly crafting their teaching to support the crossing of boundaries in the
+ADD model.
+",0,1,0,0,0,0
+13256,Well-posedness and dispersive decay of small data solutions for the Benjamin-Ono equation,"  This article represents a first step toward understanding the long time
+dynamics of solutions for the Benjamin-Ono equation. While this problem is
+known to be both completely integrable and globally well-posed in $L^2$, much
+less seems to be known concerning its long time dynamics. Here, we prove that
+for small localized data the solutions have (nearly) dispersive dynamics almost
+globally in time. An additional objective is to revisit the $L^2$ theory for
+the Benjamin-Ono equation and provide a simpler, self-contained approach.
+",0,0,1,0,0,0
+13257,A vehicle-to-infrastructure communication based algorithm for urban traffic control,"  We present in this paper a new algorithm for urban traffic light control with
+mixed traffic (communicating and non communicating vehicles) and mixed
+infrastructure (equipped and unequipped junctions). We call equipped junction
+here a junction with a traffic light signal (TLS) controlled by a road side
+unit (RSU). On such a junction, the RSU manifests its connectedness to equipped
+vehicles by broadcasting its communication address and geographical
+coordinates. The RSU builds a map of connected vehicles approaching and leaving
+the junction. The algorithm allows the RSU to select a traffic phase, based on
+the built map. The selected traffic phase is applied by the TLS; and both
+equipped and unequipped vehicles must respect it. The traffic management is in
+feedback on the traffic demand of communicating vehicles. We simulated the
+vehicular traffic as well as the communications. The two simulations are
+combined in a closed loop with visualization and monitoring interfaces. Several
+indicators on vehicular traffic (mean travel time, ended vehicles) and IEEE
+802.11p communication performances (end-to-end delay, throughput) are derived
+and illustrated in three dimension maps. We then extended the traffic control
+to a urban road network where we also varied the number of equipped junctions.
+Other indicators are shown for road traffic performances in the road network
+case, where high gains are experienced in the simulation results.
+",1,0,1,0,0,0
+13258,Some Theorems on Optimality of a Single Observation Confidence Interval for the Mean of a Normal Distribution,"  We consider the problem of finding a proper confidence interval for the mean
+based on a single observation from a normal distribution with both mean and
+variance unknown. Portnoy (2017) characterizes the scale-sign invariant rules
+and shows that the Hunt-Stein construction provides a randomized invariant rule
+that improves on any given randomized rule in the sense that it has greater
+minimal coverage among all procedures with a fixed expected length.
+Mathematical results here provide a specific mixture of two non-randomized
+invariant rules that achieve the minimax optimality. A multivariate confidence
+set based on a single observation vector is also developed.
+",0,0,1,1,0,0
+13259,"Reliability and applicability of magnetic force linear response theory: Numerical parameters, predictability, and orbital resolution","  We investigated the reliability and applicability of so-called magnetic force
+linear response method to calculate spin-spin interaction strengths from
+first-principles. We examined the dependence on the numerical parameters
+including the number of basis orbitals and their cutoff radii within
+non-orthogonal LCPAO (linear combination of pseudo-atomic orbitals) formalism.
+It is shown that the parameter dependence and the ambiguity caused by these
+choices are small enough in comparison to the other computation approach and
+experiments. Further, we tried to pursue the possible extension of this
+technique to a wider range of applications. We showed that magnetic force
+theorem can provide the reasonable estimation especially for the case of
+strongly localized moments even when the ground state configuration is unknown
+or the total energy value is not accessible. The formalism is extended to carry
+the orbital resolution from which the matrix form of the magnetic coupling
+constant is calculated. From the applications to Fe-based superconductors
+including LaFeAsO, NaFeAs, BaFe$_2$As$_2$ and FeTe, the distinctive
+characteristics of orbital-resolved interactions are clearly noticed in between
+single-stripe pnictides and double-stripe chalcogenides.
+",0,1,0,0,0,0
+13260,Verifying Probabilistic Timed Automata Against Omega-Regular Dense-Time Properties,"  Probabilistic timed automata (PTAs) are timed automata (TAs) extended with
+discrete probability distributions.They serve as a mathematical model for a
+wide range of applications that involve both stochastic and timed behaviours.
+In this work, we consider the problem of model-checking linear
+\emph{dense-time} properties over {PTAs}. In particular, we study linear
+dense-time properties that can be encoded by TAs with infinite acceptance
+criterion.First, we show that the problem of model-checking PTAs against
+deterministic-TA specifications can be solved through a product construction.
+Based on the product construction, we prove that the computational complexity
+of the problem with deterministic-TA specifications is EXPTIME-complete. Then
+we show that when relaxed to general (nondeterministic) TAs, the model-checking
+problem becomes undecidable.Our results substantially extend state of the art
+with both the dense-time feature and the nondeterminism in TAs.
+",1,0,0,0,0,0
+13261,On the number of integer polynomials with multiplicatively dependent roots,"  In this paper, we give some counting results on integer polynomials of fixed
+degree and bounded height whose distinct non-zero roots are multiplicatively
+dependent. These include sharp lower bounds, upper bounds and asymptotic
+formulas for various cases, although in general there is a logarithmic gap
+between lower and upper bounds.
+",0,0,1,0,0,0
+13262,New irreducible tensor product modules for the Virasoro algebra,"  In this paper, we obtain a class of Virasoro modules by taking tensor
+products of the irreducible Virasoro modules $\Omega(\lambda,\alpha,h)$ defined
+in \cite{CG}, with irreducible highest weight modules $V(\theta,h)$ or with
+irreducible Virasoro modules Ind$_{\theta}(N)$ defined in \cite{MZ2}. We obtain
+the necessary and sufficient conditions for such tensor product modules to be
+irreducible, and determine the necessary and sufficient conditions for two of
+them to be isomorphic. These modules are not isomorphic to any other known
+irreducible Virasoro modules.
+",0,0,1,0,0,0
+13263,Polarization dynamics in a photon BEC,"  It has previously been shown that a dye-filled microcavity can produce a
+Bose-Einstein condensate of photons. Thermalization of photons is possible via
+repeated absorption and re-emission by the dye molecules. In this paper, we
+theoretically explore the behavior of the polarization of light in this system.
+We find that in contrast to the near complete thermalization between different
+spatial modes of light, thermalization of polarization states is expected to
+generally be incomplete. We show that the polarization degree changes
+significantly from below to above threshold, and explain the dependence of
+polarization on all relevant material parameters.
+",0,1,0,0,0,0
+13264,Combining symmetry breaking and restoration with configuration interaction: extension to z-signature symmetry in the case of the Lipkin Model,"  Background: Ab initio many-body methods whose numerical cost scales
+polynomially with the number of particles have been developed over the past
+fifteen years to tackle closed-shell mid-mass nuclei. Open-shell nuclei have
+been further addressed by implementing variants based on the concept of
+spontaneous symmetry breaking (and restoration).
+Purpose: In order to access the spectroscopy of open-shell nuclei more
+systematically while controlling the numerical cost, we design a novel
+many-body method that combines the merit of breaking and restoring symmetries
+with those brought about by low-rank individual excitations.
+Methods: The recently proposed truncated configuration-interaction method
+based on optimized symmetry-broken and -restored states is extended to the
+z-signature symmetry associated with a discrete subgroup of SU(2). The
+highly-truncated N-body Hilbert subspace within which the Hamiltonian is
+diagonalized is spanned by a z-signature broken and restored Slater determinant
+vacuum and associated low-rank excitations.
+Results: The proposed method provides an excellent reproduction of the
+ground-state energy and of low-lying excitation energies of various
+z-signatures and total angular momenta. In doing so, the successive benefits of
+(i) breaking the symmetry, (ii) restoring the symmetry, (iii) including
+low-rank particle-hole excitations and (iv) optimizing the amount by which the
+underlying vacuum breaks the symmetry are illustrated.
+Conclusions: The numerical cost of the newly designed variational method is
+polynomial with respect to the system size. The present study confirms the
+results obtained previously for the attractive pairing Hamiltonian in
+connection with the breaking and restoration of U(1) global gauge symmetry.
+These two studies constitute a strong motivation to apply this method to
+realistic nuclear Hamiltonians.
+",0,1,0,0,0,0
+13265,Nonlocal Cauchy problems for wave equations and applications,"  In this paper, the existence, the uniqueness and estimates of solution to the
+integral Cauchy problem for linear and nonlinear abstract wave equations are
+proved. The equation includes a linear operator A defined in a Banach space E,
+in which by choosing E and A we can obtain numerous classis of nonlocal initial
+value problems for wave equations which occur in a wide variety of physical
+systems.
+",0,0,1,0,0,0
+13266,A two-phase gradient method for quadratic programming problems with a single linear constraint and bounds on the variables,"  We propose a gradient-based method for quadratic programming problems with a
+single linear constraint and bounds on the variables. Inspired by the GPCG
+algorithm for bound-constrained convex quadratic programming [J.J. Moré and
+G. Toraldo, SIAM J. Optim. 1, 1991], our approach alternates between two phases
+until convergence: an identification phase, which performs gradient projection
+iterations until either a candidate active set is identified or no reasonable
+progress is made, and an unconstrained minimization phase, which reduces the
+objective function in a suitable space defined by the identification phase, by
+applying either the conjugate gradient method or a recently proposed spectral
+gradient method. However, the algorithm differs from GPCG not only because it
+deals with a more general class of problems, but mainly for the way it stops
+the minimization phase. This is based on a comparison between a measure of
+optimality in the reduced space and a measure of bindingness of the variables
+that are on the bounds, defined by extending the concept of proportioning,
+which was proposed by some authors for box-constrained problems. If the
+objective function is bounded, the algorithm converges to a stationary point
+thanks to a suitable application of the gradient projection method in the
+identification phase. For strictly convex problems, the algorithm converges to
+the optimal solution in a finite number of steps even in case of degeneracy.
+Extensive numerical experiments show the effectiveness of the proposed
+approach.
+",0,0,1,0,0,0
+13267,Euler characteristic and Akashi series for Selmer groups over global function fields,"  Let $A$ be an abelian variety defined over a global function field $F$ of
+positive characteristic $p$ and let $K/F$ be a $p$-adic Lie extension with
+Galois group $G$. We provide a formula for the Euler characteristic
+$\chi(G,Sel_A(K)_p)$ of the $p$-part of the Selmer group of $A$ over $K$. In
+the special case $G=\mathbb{Z}_p^d$ and $A$ a constant ordinary variety, using
+Akashi series, we show how the Euler characteristic of the dual of $Sel_A(K)_p$
+is related to special values of a $p$-adic $\mathcal{L}$-function.
+",0,0,1,0,0,0
+13268,The effect of boundary conditions on mixing of 2D Potts models at discontinuous phase transitions,"  We study Swendsen--Wang dynamics for the critical $q$-state Potts model on
+the square lattice. For $q=2,3,4$, where the phase transition is continuous,
+the mixing time $t_{\textrm{mix}}$ is expected to obey a universal power-law
+independent of the boundary conditions. On the other hand, for large $q$, where
+the phase transition is discontinuous, the authors recently showed that
+$t_{\textrm{mix}}$ is highly sensitive to boundary conditions:
+$t_{\textrm{mix}} \geq \exp(cn)$ on an $n\times n$ box with periodic boundary,
+yet under free or monochromatic boundary conditions, $t_{\textrm{mix}}
+\leq\exp(n^{o(1)})$.
+In this work we classify this effect under boundary conditions that
+interpolate between these two (torus vs. free/monochromatic). Specifically, if
+one of the $q$ colors is red, mixed boundary conditions such as
+red-free-red-free on the 4 sides of the box induce $t_{\textrm{mix}} \geq
+\exp(cn)$, yet Dobrushin boundary conditions such as red-red-free-free, as well
+as red-periodic-red-periodic, induce sub-exponential mixing.
+",0,0,1,0,0,0
+13269,Number of thermodynamic states in the three-dimensional Edwards-Anderson spin glass,"  The question of the number of thermodynamic states present in the
+low-temperature phase of the three-dimensional Edwards-Anderson Ising spin
+glass is addressed by studying spin and link overlap distributions using
+population annealing Monte Carlo simulations. We consider overlaps between
+systems with the same boundary condition-which are the usual quantities
+measured-and also overlaps between systems with different boundary conditions,
+both for the full systems and also within a smaller window within the system.
+Our results appear to be fully compatible with a single pair of pure states
+such as in the droplet/scaling picture. However, our results for whether or not
+domain walls induced by changing boundary conditions are space filling or not
+are also compatible with scenarios having many thermodynamic states, such as
+the chaotic pairs picture and the replica symmetry breaking picture. The
+differing results for spin overlaps in same and different boundary conditions
+suggest that finite-size effects are very large for the system sizes currently
+accessible in low-temperature simulations.
+",0,1,0,0,0,0
+13270,Mechanics of disordered auxetic metamaterials,"  Auxetic materials are of great engineering interest not only because of their
+fascinating negative Poisson's ratio, but also due to their increased toughness
+and indentation resistance. These materials are typically synthesized polyester
+foams with a very heterogeneous structure, but the role of disorder in auxetic
+behavior is not fully understood. Here, we provide a systematic theoretical and
+experimental investigation in to the effect of disorder on the mechanical
+properties of a paradigmatic auxetic lattice with a re-entrant hexagonal
+geometry. We show that disorder has a marginal effect on the Poisson's ratio
+unless the lattice topology is altered, and in all cases examined the disorder
+preserves the auxetic characteristics. Depending on the direction of loading
+applied to these disordered auxetic lattices, either brittle or ductile failure
+is observed. It is found that brittle failure is associated with a
+disorder-dependent tensile strength, whereas in ductile failure disorder does
+not affect strength. Our work thus provides general guidelines to optimize
+elasticity and strength of disordered auxetic metamaterials.
+",0,1,0,0,0,0
+13271,Coherent Oscillations of Driven rf SQUID Metamaterials,"  Through experiments and numerical simulations we explore the behavior of rf
+SQUID (radio frequency superconducting quantum interference device)
+metamaterials, which show extreme tunability and nonlinearity. The emergent
+electromagnetic properties of this metamaterial are sensitive to the degree of
+coherent response of the driven interacting SQUIDs. Coherence suffers in the
+presence of disorder, which is experimentally found to be mainly due to a dc
+flux gradient. We demonstrate methods to recover the coherence, specifically by
+varying the coupling between the SQUID meta-atoms and increasing the
+temperature or the amplitude of the applied rf flux.
+",0,1,0,0,0,0
+13272,Localizing virtual structure sheaves by cosections,"  We construct a cosection localized virtual structure sheaf when a
+Deligne-Mumford stack is equipped with a perfect obstruction theory and a
+cosection of the obstruction sheaf.
+",0,0,1,0,0,0
+13273,Regularized Greedy Column Subset Selection,"  The Column Subset Selection Problem provides a natural framework for
+unsupervised feature selection. Despite being a hard combinatorial optimization
+problem, there exist efficient algorithms that provide good approximations. The
+drawback of the problem formulation is that it incorporates no form of
+regularization, and is therefore very sensitive to noise when presented with
+scarce data. In this paper we propose a regularized formulation of this
+problem, and derive a correct greedy algorithm that is similar in efficiency to
+existing greedy methods for the unregularized problem. We study its adequacy
+for feature selection and propose suitable formulations. Additionally, we
+derive a lower bound for the error of the proposed problems. Through various
+numerical experiments on real and synthetic data, we demonstrate the
+significantly increased robustness and stability of our method, as well as the
+improved conditioning of its output, all while remaining efficient for
+practical use.
+",0,0,0,1,0,0
+13274,The Cartan Algorithm in Five Dimensions,"  In this paper we introduce an algorithm to determine the equivalence of five
+dimensional spacetimes, which generalizes the Karlhede algorithm for four
+dimensional general relativity. As an alternative to the Petrov type
+classification, we employ the alignment classification to algebraically
+classify the Weyl tensor. To illustrate the algorithm we discuss three
+examples: the singly rotating Myers-Perry solution, the Kerr (anti) de Sitter
+solution, and the rotating black ring solution. We briefly discuss some
+applications of the Cartan algorithm in five dimensions.
+",0,0,1,0,0,0
+13275,Uniform convergence for the incompressible limit of a tumor growth model,"  We study a model introduced by Perthame and Vauchelet that describes the
+growth of a tumor governed by Brinkman's Law, which takes into account friction
+between the tumor cells. We adopt the viscosity solution approach to establish
+an optimal uniform convergence result of the tumor density as well as the
+pressure in the incompressible limit. The system lacks standard maximum
+principle, and thus modification of the usual approach is necessary.
+",0,0,1,0,0,0
+13276,Clustering in Hilbert space of a quantum optimization problem,"  The solution space of many classical optimization problems breaks up into
+clusters which are extensively distant from one another in the Hamming metric.
+Here, we show that an analogous quantum clustering phenomenon takes place in
+the ground state subspace of a certain quantum optimization problem. This
+involves extending the notion of clustering to Hilbert space, where the
+classical Hamming distance is not immediately useful. Quantum clusters
+correspond to macroscopically distinct subspaces of the full quantum ground
+state space which grow with the system size. We explicitly demonstrate that
+such clusters arise in the solution space of random quantum satisfiability
+(3-QSAT) at its satisfiability transition. We estimate both the number of these
+clusters and their internal entropy. The former are given by the number of
+hardcore dimer coverings of the core of the interaction graph, while the latter
+is related to the underconstrained degrees of freedom not touched by the
+dimers. We additionally provide new numerical evidence suggesting that the
+3-QSAT satisfiability transition may coincide with the product satisfiability
+transition, which would imply the absence of an intermediate entangled
+satisfiable phase.
+",1,1,0,0,0,0
+13277,"Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship","  Echo chambers, i.e., situations where one is exposed only to opinions that
+agree with their own, are an increasing concern for the political discourse in
+many democratic countries. This paper studies the phenomenon of political echo
+chambers on social media. We identify the two components in the phenomenon: the
+opinion that is shared ('echo'), and the place that allows its exposure
+('chamber' --- the social network), and examine closely at how these two
+components interact. We define a production and consumption measure for
+social-media users, which captures the political leaning of the content shared
+and received by them. By comparing the two, we find that Twitter users are, to
+a large degree, exposed to political opinions that agree with their own. We
+also find that users who try to bridge the echo chambers, by sharing content
+with diverse leaning, have to pay a 'price of bipartisanship' in terms of their
+network centrality and content appreciation. In addition, we study the role of
+'gatekeepers', users who consume content with diverse leaning but produce
+partisan content (with a single-sided leaning), in the formation of echo
+chambers. Finally, we apply these findings to the task of predicting partisans
+and gatekeepers from social and content features. While partisan users turn out
+relatively easy to identify, gatekeepers prove to be more challenging.
+",1,0,0,0,0,0
+13278,Resonance enhancement of two photon absorption by magnetically trapped atoms in strong rf-fields,"  Applying a many mode Floquet formalism for magnetically trapped atoms
+interacting with a polychromatic rf-field, we predict a large two photon
+transition probability in the atomic system of cold $^{87}Rb$ atoms. The
+physical origin of this enormous increase in the two photon transition
+probability is due to the formation of avoided crossings between eigen-energy
+levels originating from different Floquet sub-manifolds and redistribution of
+population in the resonant intermediate levels to give rise to the resonance
+enhancement effect. Other exquisite features of the studied atom-field
+composite system include the splitting of the generated avoided crossings at
+the strong field strength limit and a periodic variation of the single and two
+photon transition probabilities with the mode separation frequency of the
+polychromatic rf-field. This work can find applications to characterize
+properties of cold atom clouds in the magnetic traps using rf-spectroscopy
+techniques.
+",0,1,0,0,0,0
+13279,Unveiling the AGN in IC 883: discovery of a parsec-scale radio jet,"  IC883 is a luminous infrared galaxy (LIRG) classified as a starburst-active
+galactic nucleus (AGN) composite. In a previous study we detected a
+low-luminosity AGN (LLAGN) radio candidate. Here we report on our radio
+follow-up at three frequencies which provides direct and unequivocal evidence
+of the AGN activity in IC883. Our analysis of archival X-ray data, together
+with the detection of a transient radio source with luminosity typical of
+bright supernovae, give further evidence of the ongoing star formation
+activity, which dominates the energetics of the system. At sub-parsec scales,
+the radio nucleus has a core-jet morphology with the jet being a newly ejected
+component showing a subluminal proper motion of 0.6c-1c. The AGN contributes
+less than two per cent of the total IR luminosity of the system. The
+corresponding Eddington factor is ~1E-3, suggesting this is a low-accretion
+rate engine, as often found in LLAGNs. However, its high bolometric luminosity
+(~10E44erg/s) agrees better with a normal AGN. This apparent discrepancy may
+just be an indication of the transition nature of the nucleus from a system
+dominated by star-formation, to an AGN-dominated system. The nucleus has a
+strongly inverted spectrum and a turnover at ~4.4GHz, thus qualifying as a
+candidate for the least luminous (L_5.0GHz ~ 6.3E28erg/s/Hz) and one of the
+youngest (~3000yr) gigahertz-peaked spectrum (GPS) sources. If the GPS origin
+for the IC883 nucleus is confirmed, then advanced mergers in the LIRG category
+are potentially key environments to unveil the evolution of GPS sources into
+more powerful radio galaxies.
+",0,1,0,0,0,0
+13280,"Counting triangles, tunable clustering and the small-world property in random key graphs (Extended version)","  Random key graphs were introduced to study various properties of the
+Eschenauer-Gligor key predistribution scheme for wireless sensor networks
+(WSNs). Recently this class of random graphs has received much attention in
+contexts as diverse as recommender systems, social network modeling, and
+clustering and classification analysis. This paper is devoted to analyzing
+various properties of random key graphs. In particular, we establish a zero-one
+law for the the existence of triangles in random key graphs, and identify the
+corresponding critical scaling. This zero-one law exhibits significant
+differences with the corresponding result in Erdos-Renyi (ER) graphs. We also
+compute the clustering coefficient of random key graphs, and compare it to that
+of ER graphs in the many node regime when their expected average degrees are
+asymptotically equivalent. For the parameter range of practical relevance in
+both wireless sensor network and social network applications, random key graphs
+are shown to be much more clustered than the corresponding ER graphs. We also
+explore the suitability of random key graphs as small world models in the sense
+of Watts and Strogatz.
+",1,0,1,0,0,0
+13281,Convergence Rates of Variational Posterior Distributions,"  We study convergence rates of variational posterior distributions for
+nonparametric and high-dimensional inference. We formulate general conditions
+on prior, likelihood, and variational class that characterize the convergence
+rates. Under similar ""prior mass and testing"" conditions considered in the
+literature, the rate is found to be the sum of two terms. The first term stands
+for the convergence rate of the true posterior distribution, and the second
+term is contributed by the variational approximation error. For a class of
+priors that admit the structure of a mixture of product measures, we propose a
+novel prior mass condition, under which the variational approximation error of
+the generalized mean-field class is dominated by convergence rate of the true
+posterior. We demonstrate the applicability of our general results for various
+models, prior distributions and variational classes by deriving convergence
+rates of the corresponding variational posteriors.
+",0,0,1,1,0,0
+13282,"V773 Cas, QS Aql, and BR Ind: Eclipsing Binaries as Parts of Multiple Systems","  Eclipsing binaries remain crucial objects for our understanding of the
+universe. In particular, those that are components of multiple systems can help
+us solve the problem of the formation of these systems. Analysis of the radial
+velocities together with the light curve produced for the first time precise
+physical parameters of the components of the multiple systems V773 Cas, QS Aql,
+and BR Ind. Their visual orbits were also analyzed, which resulted in slightly
+improved orbital elements. What is typical for all these systems is that their
+most dominant source is the third distant component. The system V773 Cas
+consists of two similar G1-2V stars revolving in a circular orbit and a more
+distant component of the A3V type. Additionally, the improved value of parallax
+was calculated to be 17.6 mas. Analysis of QS Aql resulted in the following:
+the inner eclipsing pair is composed of B6V and F1V stars, and the third
+component is of about the B6 spectral type. The outer orbit has high
+eccentricity of about 0.95, and observations near its upcoming periastron
+passage between the years 2038 and 2040 are of high importance. Also, the
+parallax of the system was derived to be about 2.89 mas, moving the star much
+closer to the Sun than originally assumed. The system BR Ind was found to be a
+quadruple star consisting of two eclipsing K dwarfs orbiting each other with a
+period of 1.786 days; the distant component is a single-lined spectroscopic
+binary with an orbital period of about 6 days. Both pairs are moving around
+each other on their 148 year orbit.
+",0,1,0,0,0,0
+13283,Variance bounding of delayed-acceptance kernels,"  A delayed-acceptance version of a Metropolis--Hastings algorithm can be
+useful for Bayesian inference when it is computationally expensive to calculate
+the true posterior, but a computationally cheap approximation is available; the
+delayed-acceptance kernel targets the same posterior as its parent
+Metropolis-Hastings kernel. Although the asymptotic variance of any functional
+of the chain cannot be less than that obtained using its parent, the average
+computational time per iteration can be much smaller and so for a given
+computational budget the delayed-acceptance kernel can be more efficient.
+When the asymptotic variance of all $L^2$ functionals of the chain is finite,
+the kernel is said to be variance bounding. It has recently been noted that a
+delayed-acceptance kernel need not be variance bounding even when its parent
+is. We provide sufficient conditions for inheritance: for global algorithms,
+such as the independence sampler, the error in the approximation should be
+bounded; for local algorithms, two alternative sets of conditions are provided.
+As a by-product of our initial, general result we also supply sufficient
+conditions on any pair of proposals such that, for any shared target
+distribution, if a Metropolis-Hastings kernel using one of the proposals is
+variance bounding then so is the Metropolis-Hastings kernel using the other
+proposal.
+",0,0,1,1,0,0
+13284,Positive Herz-Schur multipliers and approximation properties of crossed products,"  For a $C^*$-algebra $A$ and a set $X$ we give a Stinespring-type
+characterisation of the completely positive Schur $A$-multipliers on
+$K(\ell^2(X))\otimes A$. We then relate them to completely positive Herz-Schur
+multipliers on $C^*$-algebraic crossed products of the form
+$A\rtimes_{\alpha,r} G$, with $G$ a discrete group, whose various versions were
+considered earlier by Anantharaman-Delaroche, Bédos and Conti, and Dong and
+Ruan. The latter maps are shown to implement approximation properties, such as
+nuclearity or the Haagerup property, for $A\rtimes_{\alpha,r} G$.
+",0,0,1,0,0,0
+13285,Bias-Variance Tradeoff of Graph Laplacian Regularizer,"  This paper presents a bias-variance tradeoff of graph Laplacian regularizer,
+which is widely used in graph signal processing and semi-supervised learning
+tasks. The scaling law of the optimal regularization parameter is specified in
+terms of the spectral graph properties and a novel signal-to-noise ratio
+parameter, which suggests selecting a mediocre regularization parameter is
+often suboptimal. The analysis is applied to three applications, including
+random, band-limited, and multiple-sampled graph signals. Experiments on
+synthetic and real-world graphs demonstrate near-optimal performance of the
+established analysis.
+",1,0,0,1,0,0
+13286,Dual Discriminator Generative Adversarial Nets,"  We propose in this paper a novel approach to tackle the problem of mode
+collapse encountered in generative adversarial network (GAN). Our idea is
+intuitive but proven to be very effective, especially in addressing some key
+limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and
+reverse KL divergences into a unified objective function, thus it exploits the
+complementary statistical properties from these divergences to effectively
+diversify the estimated density in capturing multi-modes. We term our method
+dual discriminator generative adversarial nets (D2GAN) which, unlike GAN, has
+two discriminators; and together with a generator, it also has the analogy of a
+minimax game, wherein a discriminator rewards high scores for samples from data
+distribution whilst another discriminator, conversely, favoring data from the
+generator, and the generator produces data to fool both two discriminators. We
+develop theoretical analysis to show that, given the maximal discriminators,
+optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL
+divergences between data distribution and the distribution induced from the
+data generated by the generator, hence effectively avoiding the mode collapsing
+problem. We conduct extensive experiments on synthetic and real-world
+large-scale datasets (MNIST, CIFAR-10, STL-10, ImageNet), where we have made
+our best effort to compare our D2GAN with the latest state-of-the-art GAN's
+variants in comprehensive qualitative and quantitative evaluations. The
+experimental results demonstrate the competitive and superior performance of
+our approach in generating good quality and diverse samples over baselines, and
+the capability of our method to scale up to ImageNet database.
+",1,0,0,1,0,0
+13287,A Compressive Sensing Approach to Community Detection with Applications,"  The community detection problem for graphs asks one to partition the n
+vertices V of a graph G into k communities, or clusters, such that there are
+many intracluster edges and few intercluster edges. Of course this is
+equivalent to finding a permutation matrix P such that, if A denotes the
+adjacency matrix of G, then PAP^T is approximately block diagonal. As there are
+k^n possible partitions of n vertices into k subsets, directly determining the
+optimal clustering is clearly infeasible. Instead one seeks to solve a more
+tractable approximation to the clustering problem. In this paper we reformulate
+the community detection problem via sparse solution of a linear system
+associated with the Laplacian of a graph G and then develop a two-stage
+approach based on a thresholding technique and a compressive sensing algorithm
+to find a sparse solution which corresponds to the community containing a
+vertex of interest in G. Crucially, our approach results in an algorithm which
+is able to find a single cluster of size n_0 in O(nlog(n)n_0) operations and
+all k clusters in fewer than O(n^2ln(n)) operations. This is a marked
+improvement over the classic spectral clustering algorithm, which is unable to
+find a single cluster at a time and takes approximately O(n^3) operations to
+find all k clusters. Moreover, we are able to provide robust guarantees of
+success for the case where G is drawn at random from the Stochastic Block
+Model, a popular model for graphs with clusters. Extensive numerical results
+are also provided, showing the efficacy of our algorithm on both synthetic and
+real-world data sets.
+",1,0,0,1,0,0
+13288,Uncertainty quantification for radio interferometric imaging: I. proximal MCMC methods,"  Uncertainty quantification is a critical missing component in radio
+interferometric imaging that will only become increasingly important as the
+big-data era of radio interferometry emerges. Since radio interferometric
+imaging requires solving a high-dimensional, ill-posed inverse problem,
+uncertainty quantification is difficult but also critical to the accurate
+scientific interpretation of radio observations. Statistical sampling
+approaches to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC)
+sampling, can in principle recover the full posterior distribution of the
+image, from which uncertainties can then be quantified. However, traditional
+high-dimensional sampling methods are generally limited to smooth (e.g.
+Gaussian) priors and cannot be used with sparsity-promoting priors. Sparse
+priors, motivated by the theory of compressive sensing, have been shown to be
+highly effective for radio interferometric imaging. In this article proximal
+MCMC methods are developed for radio interferometric imaging, leveraging
+proximal calculus to support non-differential priors, such as sparse priors, in
+a Bayesian framework. Furthermore, three strategies to quantify uncertainties
+using the recovered posterior distribution are developed: (i) local
+(pixel-wise) credible intervals to provide error bars for each individual
+pixel; (ii) highest posterior density credible regions; and (iii) hypothesis
+testing of image structure. These forms of uncertainty quantification provide
+rich information for analysing radio interferometric observations in a
+statistically robust manner.
+",0,1,0,1,0,0
+13289,"Fairer and more accurate, but for whom?","  Complex statistical machine learning models are increasingly being used or
+considered for use in high-stakes decision-making pipelines in domains such as
+financial services, health care, criminal justice and human services. These
+models are often investigated as possible improvements over more classical
+tools such as regression models or human judgement. While the modeling approach
+may be new, the practice of using some form of risk assessment to inform
+decisions is not. When determining whether a new model should be adopted, it is
+therefore essential to be able to compare the proposed model to the existing
+approach across a range of task-relevant accuracy and fairness metrics. Looking
+at overall performance metrics, however, may be misleading. Even when two
+models have comparable overall performance, they may nevertheless disagree in
+their classifications on a considerable fraction of cases. In this paper we
+introduce a model comparison framework for automatically identifying subgroups
+in which the differences between models are most pronounced. Our primary focus
+is on identifying subgroups where the models differ in terms of
+fairness-related quantities such as racial or gender disparities. We present
+experimental results from a recidivism prediction task and a hypothetical
+lending example.
+",1,0,0,1,0,0
+13290,Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks,"  This article is motivated by soccer positional passing networks collected
+across multiple games. We refer to these data as replicated spatial passing
+networks---to accurately model such data it is necessary to take into account
+the spatial positions of the passer and receiver for each passing event. This
+spatial registration and replicates that occur across games represent key
+differences with usual social network data. As a key step before investigating
+how the passing dynamics influence team performance, we focus on developing
+methods for summarizing different team's passing strategies. Our proposed
+approach relies on a novel multiresolution data representation framework and
+Poisson nonnegative block term decomposition model, which automatically
+produces coarse-to-fine low-rank network motifs. The proposed methods are
+applied to detailed passing record data collected from the 2014 FIFA World Cup.
+",1,0,0,1,0,0
+13291,Fast-slow asymptotic for semi-analytical ignition criteria in FitzHugh-Nagumo system,"  We study the problem of initiation of excitation waves in the FitzHugh-Nagumo
+model. Our approach follows earlier works and is based on the idea of
+approximating the boundary between basins of attraction of propagating waves
+and of the resting state as the stable manifold of a critical solution. Here,
+we obtain analytical expressions for the essential ingredients of the theory by
+singular perturbation using two small parameters, the separation of time scales
+of the activator and inhibitor, and the threshold in the activator's kinetics.
+This results in a closed analytical expression for the strength-duration curve.
+",0,1,0,0,0,0
+13292,What drives galactic magnetism?,"  We aim to use statistical analysis of a large number of various galaxies to
+probe, model, and understand relations between different galaxy properties and
+magnetic fields. We have compiled a sample of 55 galaxies including low-mass
+dwarf and Magellanic-types, normal spirals and several massive starbursts, and
+applied principal component analysis (PCA) and regression methods to assess the
+impact of various galaxy properties on the observed magnetic fields. According
+to PCA the global galaxy parameters (like HI, H2, and dynamical mass, star
+formation rate (SFR), near-infrared luminosity, size, and rotational velocity)
+are all mutually correlated and can be reduced to a single principal component.
+Further PCA performed for global and intensive (not size related) properties of
+galaxies (such as gas density, and surface density of the star formation rate,
+SSFR), indicates that magnetic field strength B is connected mainly to the
+intensive parameters, while the global parameters have only weak relationships
+with B. We find that the tightest relationship of B is with SSFR, which is
+described by a power-law with an index of 0.33+-0.03. The observed weaker
+associations of B with galaxy dynamical mass and the rotational velocity we
+interpret as indirect ones, resulting from the observed connection of the
+global SFR with the available total H2 mass in galaxies. Using our sample we
+constructed a diagram of B across the Hubble sequence which reveals that high
+values of B are not restricted by the Hubble type. However, weaker fields
+appear exclusively in later Hubble types and B as low as about 5muG is not seen
+among typical spirals. The processes of generation of magnetic field in the
+dwarf and Magellanic-type galaxies are similar to those in the massive spirals
+and starbursts and are mainly coupled to local star-formation activity
+involving the small-scale dynamo mechanism.
+",0,1,0,0,0,0
+13293,Regular Separability of One Counter Automata,"  The regular separability problem asks, for two given languages, if there
+exists a regular language including one of them but disjoint from the other.
+Our main result is decidability, and PSpace-completeness, of the regular
+separability problem for languages of one counter automata without zero tests
+(also known as one counter nets). This contrasts with undecidability of the
+regularity problem for one counter nets, and with undecidability of the regular
+separability problem for one counter automata, which is our second result.
+",1,0,0,0,0,0
+13294,Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel,"  Support vector data description (SVDD) is a machine learning technique that
+is used for single-class classification and outlier detection. The idea of SVDD
+is to find a set of support vectors that defines a boundary around data. When
+dealing with online or large data, existing batch SVDD methods have to be rerun
+in each iteration. We propose an incremental learning algorithm for SVDD that
+uses the Gaussian kernel. This algorithm builds on the observation that all
+support vectors on the boundary have the same distance to the center of sphere
+in a higher-dimensional feature space as mapped by the Gaussian kernel
+function. Each iteration involves only the existing support vectors and the new
+data point. Moreover, the algorithm is based solely on matrix manipulations;
+the support vectors and their corresponding Lagrange multiplier $\alpha_i$'s
+are automatically selected and determined in each iteration. It can be seen
+that the complexity of our algorithm in each iteration is only $O(k^2)$, where
+$k$ is the number of support vectors. Experimental results on some real data
+sets indicate that FISVDD demonstrates significant gains in efficiency with
+almost no loss in either outlier detection accuracy or objective function
+value.
+",0,0,0,1,0,0
+13295,Comparing distributions by multiple testing across quantiles or CDF values,"  When comparing two distributions, it is often helpful to learn at which
+quantiles or values there is a statistically significant difference. This
+provides more information than the binary ""reject"" or ""do not reject"" decision
+of a global goodness-of-fit test. Framing our question as multiple testing
+across the continuum of quantiles $\tau\in(0,1)$ or values $r\in\mathbb{R}$, we
+show that the Kolmogorov--Smirnov test (interpreted as a multiple testing
+procedure) achieves strong control of the familywise error rate. However, its
+well-known flaw of low sensitivity in the tails remains. We provide an
+alternative method that retains such strong control of familywise error rate
+while also having even sensitivity, i.e., equal pointwise type I error rates at
+each of $n\to\infty$ order statistics across the distribution. Our one-sample
+method computes instantly, using our new formula that also instantly computes
+goodness-of-fit $p$-values and uniform confidence bands. To improve power, we
+also propose stepdown and pre-test procedures that maintain control of the
+asymptotic familywise error rate. One-sample and two-sample cases are
+considered, as well as extensions to regression discontinuity designs and
+conditional distributions. Simulations, empirical examples, and code are
+provided.
+",0,0,1,1,0,0
+13296,Magnetic droplet nucleation with homochiral Neel domain wall,"  We investigate the effect of the Dzyaloshinskii Moriya interaction (DMI) on
+magnetic domain nucleation in a ferromagnetic thin film with perpendicular
+magnetic anisotropy. We propose an extended droplet model to determine the
+nucleation field as a function of the in-plane field. The model can explain the
+experimentally observed nucleation in a CoNi microstrip with the interfacial
+DMI. The results are also reproduced by micromagnetic simulation based on the
+string model. The electrical measurement method proposed in this study can be
+widely used to quantitatively determine the DMI energy density.
+",0,1,0,0,0,0
+13297,Cost-complexity pruning of random forests,"  Random forests perform bootstrap-aggregation by sampling the training samples
+with replacement. This enables the evaluation of out-of-bag error which serves
+as a internal cross-validation mechanism. Our motivation lies in using the
+unsampled training samples to improve each decision tree in the ensemble. We
+study the effect of using the out-of-bag samples to improve the generalization
+error first of the decision trees and second the random forest by post-pruning.
+A preliminary empirical study on four UCI repository datasets show consistent
+decrease in the size of the forests without considerable loss in accuracy.
+",1,0,0,1,0,0
+13298,Chemical abundances of fast-rotating massive stars. I. Description of the methods and individual results,"  Aims: Recent observations have challenged our understanding of rotational
+mixing in massive stars by revealing a population of fast-rotating objects with
+apparently normal surface nitrogen abundances. However, several questions have
+arisen because of a number of issues, which have rendered a reinvestigation
+necessary; these issues include the presence of numerous upper limits for the
+nitrogen abundance, unknown multiplicity status, and a mix of stars with
+different physical properties, such as their mass and evolutionary state, which
+are known to control the amount of rotational mixing. Methods: We have
+carefully selected a large sample of bright, fast-rotating early-type stars of
+our Galaxy (40 objects with spectral types between B0.5 and O4). Their
+high-quality, high-resolution optical spectra were then analysed with the
+stellar atmosphere modelling codes DETAIL/SURFACE or CMFGEN, depending on the
+temperature of the target. Several internal and external checks were performed
+to validate our methods; notably, we compared our results with literature data
+for some well-known objects, studied the effect of gravity darkening, or
+confronted the results provided by the two codes for stars amenable to both
+analyses. Furthermore, we studied the radial velocities of the stars to assess
+their binarity. Results: This first part of our study presents our methods and
+provides the derived stellar parameters, He, CNO abundances, and the
+multiplicity status of every star of the sample. It is the first time that He
+and CNO abundances of such a large number of Galactic massive fast rotators are
+determined in a homogeneous way.
+",0,1,0,0,0,0
+13299,Morphology and Motility of Cells on Soft Substrates,"  Recent experiments suggest that the interplay between cells and the mechanics
+of their substrate gives rise to a diversity of morphological and migrational
+behaviors. Here, we develop a Cellular Potts Model of polarizing cells on a
+visco-elastic substrate. We compare our model with experiments on endothelial
+cells plated on polyacrylamide hydrogels to constrain model parameters and test
+predictions. Our analysis reveals that morphology and migratory behavior are
+determined by an intricate interplay between cellular polarization and
+substrate strain gradients generated by traction forces exerted by cells
+(self-haptotaxis).
+",0,0,0,0,1,0
+13300,A domain-specific language and matrix-free stencil code for investigating electronic properties of Dirac and topological materials,"  We introduce PVSC-DTM (Parallel Vectorized Stencil Code for Dirac and
+Topological Materials), a library and code generator based on a domain-specific
+language tailored to implement the specific stencil-like algorithms that can
+describe Dirac and topological materials such as graphene and topological
+insulators in a matrix-free way. The generated hybrid-parallel (MPI+OpenMP)
+code is fully vectorized using Single Instruction Multiple Data (SIMD)
+extensions. It is significantly faster than matrix-based approaches on the node
+level and performs in accordance with the roofline model. We demonstrate the
+chip-level performance and distributed-memory scalability of basic building
+blocks such as sparse matrix-(multiple-) vector multiplication on modern
+multicore CPUs. As an application example, we use the PVSC-DTM scheme to (i)
+explore the scattering of a Dirac wave on an array of gate-defined quantum
+dots, to (ii) calculate a bunch of interior eigenvalues for strong topological
+insulators, and to (iii) discuss the photoemission spectra of a disordered Weyl
+semimetal.
+",1,1,0,0,0,0
+13301,A cross-correlation-based estimate of the galaxy luminosity function,"  We extend existing methods for using cross-correlations to derive redshift
+distributions for photometric galaxies, without using photometric redshifts.
+The model presented in this paper simultaneously yields highly accurate and
+unbiased redshift distributions and, for the first time, redshift-dependent
+luminosity functions, using only clustering information and the apparent
+magnitudes of the galaxies as input. In contrast to many existing techniques
+for recovering unbiased redshift distributions, the output of our method is not
+degenerate with the galaxy bias b(z), which is achieved by modelling the shape
+of the luminosity bias. We successfully apply our method to a mock galaxy
+survey and discuss improvements to be made before applying our model to real
+data.
+",0,1,0,0,0,0
+13302,A Novel Partitioning Method for Accelerating the Block Cimmino Algorithm,"  We propose a novel block-row partitioning method in order to improve the
+convergence rate of the block Cimmino algorithm for solving general sparse
+linear systems of equations. The convergence rate of the block Cimmino
+algorithm depends on the orthogonality among the block rows obtained by the
+partitioning method. The proposed method takes numerical orthogonality among
+block rows into account by proposing a row inner-product graph model of the
+coefficient matrix. In the graph partitioning formulation defined on this graph
+model, the partitioning objective of minimizing the cutsize directly
+corresponds to minimizing the sum of inter-block inner products between block
+rows thus leading to an improvement in the eigenvalue spectrum of the iteration
+matrix. This in turn leads to a significant reduction in the number of
+iterations required for convergence. Extensive experiments conducted on a large
+set of matrices confirm the validity of the proposed method against a
+state-of-the-art method.
+",1,0,0,0,0,0
+13303,Visualized Insights into the Optimization Landscape of Fully Convolutional Networks,"  Many image processing tasks involve image-to-image mapping, which can be
+addressed well by fully convolutional networks (FCN) without any heavy
+preprocessing. Although empirically designing and training FCNs can achieve
+satisfactory results, reasons for the improvement in performance are slightly
+ambiguous. Our study is to make progress in understanding their generalization
+abilities through visualizing the optimization landscapes. The visualization of
+objective functions is obtained by choosing a solution and projecting its
+vicinity onto a 3D space. We compare three FCN-based networks (two existing
+models and a new proposed in this paper for comparison) on multiple datasets.
+It has been observed in practice that the connections from the pre-pooled
+feature maps to the post-upsampled can achieve better results. We investigate
+the cause and provide experiments to shows that the skip-layer connections in
+FCN can promote flat optimization landscape, which is well known to generalize
+better. Additionally, we explore the relationship between the models
+generalization ability and loss surface under different batch sizes. Results
+show that large-batch training makes the model converge to sharp minimizers
+with chaotic vicinities while small-batch method leads the model to flat
+minimizers with smooth and nearly convex regions. Our work may contribute to
+insights and analysis for designing and training FCNs.
+",1,0,0,1,0,0
+13304,Exact upper and lower bounds on the misclassification probability,"  Exact lower and upper bounds on the best possible misclassification
+probability for a finite number of classes are obtained in terms of the total
+variation norms of the differences between the sub-distributions over the
+classes. These bounds are compared with the exact bounds in terms of the
+conditional entropy obtained by Feder and Merhav.
+",1,0,1,1,0,0
+13305,BB-Graph: A Subgraph Isomorphism Algorithm for Efficiently Querying Big Graph Databases,"  The big graph database model provides strong modeling for complex
+applications and efficient querying. However, it is still a big challenge to
+find all exact matches of a query graph in a big graph database, which is known
+as the subgraph isomorphism problem. The current subgraph isomorphism
+approaches are built on Ullmann's idea of focusing on the strategy of pruning
+out the irrelevant candidates. Nevertheless, the existing pruning techniques
+need much more improvement to efficiently handle complex queries. Moreover,
+many of those existing algorithms need large indices requiring extra memory
+consumption. Motivated by these, we introduce a new subgraph isomorphism
+algorithm, named as BB-Graph, for querying big graph databases efficiently
+without requiring a large data structure to be stored in main memory. We test
+and compare our proposed BB-Graph algorithm with two popular existing
+approaches, GraphQL and Cypher. Our experiments are done on three different
+data sets; (1) a very big graph database of a real-life population database,
+(2) a graph database of a simulated bank database, and (3) the publicly
+available World Cup big graph database. We show that our solution performs
+better than those algorithms mentioned here for most of the query types
+experimented on these big databases.
+",1,0,0,0,0,0
+13306,Analytic solutions of the Madelung equation,"  We present analytic self-similar solutions for the one, two and three
+dimensional Madelung hydrodynamical equation for a free particle. There is a
+direct connection between the zeros of the Madelung fluid density and the
+magnitude of the quantum potential.
+",0,0,1,0,0,0
+13307,Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders,"  We employ unsupervised machine learning techniques to learn latent parameters
+which best describe states of the two-dimensional Ising model and the
+three-dimensional XY model. These methods range from principal component
+analysis to artificial neural network based variational autoencoders. The
+states are sampled using a Monte-Carlo simulation above and below the critical
+temperature. We find that the predicted latent parameters correspond to the
+known order parameters. The latent representation of the states of the models
+in question are clustered, which makes it possible to identify phases without
+prior knowledge of their existence or the underlying Hamiltonian. Furthermore,
+we find that the reconstruction loss function can be used as a universal
+identifier for phase transitions.
+",1,0,0,1,0,0
+13308,A novel online scheduling protocol for energy-efficient TWDM-OLT design,"  Design of energy-efficient access networks has emerged as an important area
+of research, since access networks consume $80-90\%$ of the overall Internet
+power consumption. TWDM-PON is envisaged to be one of the widely accepted
+future access technologies. TWDM-PON offers an additional opportunity to save
+energy at the OLT along with the existing energy-efficient ONU design. In this
+paper, we focus on the energy-efficient OLT design in a TWDM-PON. While most of
+the conventional methods employ a minimization of the number of wavelengths, we
+propose a novel approach which aims at minimizing the number of voids created
+due to scheduling. In the process, for the first time, we present a
+low-complexity on-line scheduling algorithm for the upstream traffic
+considering delay constraints. Our extensive simulations demonstrate a
+significant improvement in energy efficiency of $\sim 25\%$ for high load at
+the OLT receivers. Furthermore, we provide an analytical upper-bound on the
+energy-efficiency of the OLT receivers and demonstrate that the proposed
+protocol achieves an energy efficiency very close to the bound with a maximum
+deviation $\sim 2\%$ for $64$ ONUs.
+",1,0,0,0,0,0
+13309,Machines and Algorithms,"  I discuss the evolution of computer architectures with a focus on QCD and
+with reference to the interplay between architecture, engineering, data motion
+and algorithms. New architectures are discussed and recent performance results
+are displayed. I also review recent progress in multilevel solver and
+integation algorithms.
+",1,1,0,0,0,0
+13310,Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification,"  Person re-identification (Re-ID) aims at matching images of the same person
+across disjoint camera views, which is a challenging problem in multimedia
+analysis, multimedia editing and content-based media retrieval communities. The
+major challenge lies in how to preserve similarity of the same person across
+video footages with large appearance variations, while discriminating different
+individuals. To address this problem, conventional methods usually consider the
+pairwise similarity between persons by only measuring the point to point (P2P)
+distance. In this paper, we propose to use deep learning technique to model a
+novel set to set (S2S) distance, in which the underline objective focuses on
+preserving the compactness of intra-class samples for each camera view, while
+maximizing the margin between the intra-class set and inter-class set. The S2S
+distance metric is consisted of three terms, namely the class-identity term,
+the relative distance term and the regularization term. The class-identity term
+keeps the intra-class samples within each camera view gathering together, the
+relative distance term maximizes the distance between the intra-class class set
+and inter-class set across different camera views, and the regularization term
+smoothness the parameters of deep convolutional neural network (CNN). As a
+result, the final learned deep model can effectively find out the matched
+target to the probe object among various candidates in the video gallery by
+learning discriminative and stable feature representations. Using the CUHK01,
+CUHK03, PRID2011 and Market1501 benchmark datasets, we extensively conducted
+comparative evaluations to demonstrate the advantages of our method over the
+state-of-the-art approaches.
+",1,0,0,1,0,0
+13311,Scheduling Constraint Based Abstraction Refinement for Multi-Threaded Program Verification,"  Bounded model checking is among the most efficient techniques for the
+automatic verification of concurrent programs. However, encoding all possible
+interleavings often requires a huge and complex formula, which significantly
+limits the salability. This paper proposes a novel and efficient abstraction
+refinement method for multi-threaded program verification. Observing that the
+huge formula is usually dominated by the exact encoding of the scheduling
+constraint, this paper proposes a \tsc based abstraction refinement method,
+which avoids the huge and complex encoding of BMC. In addition, to obtain an
+effective refinement, we have devised two graph-based algorithms over event
+order graph for counterexample validation and refinement generation, which can
+always obtain a small yet effective refinement constraint. Enhanced by two
+constraint-based algorithms for counterexample validation and refinement
+generation, we have proved that our method is sound and complete w.r.t. the
+given loop unwinding depth. Experimental results on \svcompc benchmarks
+indicate that our method is promising and significantly outperforms the
+existing state-of-the-art tools.
+",1,0,0,0,0,0
+13312,The braid group for a quiver with superpotential,"  We survey and compare various generalizations of braid groups for quivers
+with superpotential and focus on the cluster braid groups, which are introduced
+in a joint work with A.~King. Our motivations come from the study of cluster
+algebras, Calabi-Yau categories and Bridgeland stability conditions.
+",0,0,1,0,0,0
+13313,The infinitesimal characters of discrete series for real spherical spaces,"  Let $Z=G/H$ be the homogeneous space of a real reductive group and a
+unimodular real spherical subgroup, and consider the regular representation of
+$G$ on $L^2(Z)$. It is shown that all representations of the discrete series,
+that is, the irreducible subrepresentations of $L^2(Z)$, have infinitesimal
+characters which are real and belong to a lattice. Moreover, let $K$ be a
+maximal compact subgroup of $G$. Then each irreducible representation of $K$
+occurs in a finite set of such discrete series representations only. Similar
+results are obtained for the twisted discrete series, that is, the discrete
+components of the space of square integrable sections of a line bundle, given
+by a unitary character on an abelian extension of $H$.
+",0,0,1,0,0,0
+13314,Coincidence point results involving a generalized class of simulation functions,"  The purpose of this work is to introduce a general class of $C_G$-simulation
+functions and obtained some new coincidence and common fixed points results in
+metric spaces. Some useful examples are presented to illustrate our theorems.
+Results obtained in this paper extend, generalize and unify some well known
+fixed and common fixed point results.
+",0,0,1,0,0,0
+13315,A Simulated Cyberattack on Twitter: Assessing Partisan Vulnerability to Spear Phishing and Disinformation ahead of the 2018 U.S. Midterm Elections,"  State-sponsored ""bad actors"" increasingly weaponize social media platforms to
+launch cyberattacks and disinformation campaigns during elections. Social media
+companies, due to their rapid growth and scale, struggle to prevent the
+weaponization of their platforms. This study conducts an automated spear
+phishing and disinformation campaign on Twitter ahead of the 2018 United States
+Midterm Elections. A fake news bot account - the @DCNewsReport - was created
+and programmed to automatically send customized tweets with a ""breaking news""
+link to 138 Twitter users, before being restricted by Twitter.
+Overall, one in five users clicked the link, which could have potentially led
+to the downloading of ransomware or the theft of private information. However,
+the link in this experiment was non-malicious and redirected users to a Google
+Forms survey. In predicting users' likelihood to click the link on Twitter, no
+statistically significant differences were observed between right-wing and
+left-wing partisans, or between Web users and mobile users. The findings signal
+that politically expressive Americans on Twitter, regardless of their party
+preferences or the devices they use to access the platform, are at risk of
+being spear phishing on social media.
+",1,0,0,0,0,0
+13316,Asteroid mass estimation using Markov-chain Monte Carlo,"  Estimates for asteroid masses are based on their gravitational perturbations
+on the orbits of other objects such as Mars, spacecraft, or other asteroids
+and/or their satellites. In the case of asteroid-asteroid perturbations, this
+leads to an inverse problem in at least 13 dimensions where the aim is to
+derive the mass of the perturbing asteroid(s) and six orbital elements for both
+the perturbing asteroid(s) and the test asteroid(s) based on astrometric
+observations. We have developed and implemented three different mass estimation
+algorithms utilizing asteroid-asteroid perturbations: the very rough 'marching'
+approximation, in which the asteroids' orbital elements are not fitted, thereby
+reducing the problem to a one-dimensional estimation of the mass, an
+implementation of the Nelder-Mead simplex method, and most significantly, a
+Markov-chain Monte Carlo (MCMC) approach. We describe each of these algorithms
+with particular focus on the MCMC algorithm, and present example results using
+both synthetic and real data. Our results agree with the published mass
+estimates, but suggest that the published uncertainties may be misleading as a
+consequence of using linearized mass-estimation methods. Finally, we discuss
+remaining challenges with the algorithms as well as future plans.
+",0,1,0,0,0,0
+13317,Asymptotic Properties of the Maximum Likelihood Estimator in Regime Switching Econometric Models,"  Markov regime switching models have been widely used in numerous empirical
+applications in economics and finance. However, the asymptotic distribution of
+the maximum likelihood estimator (MLE) has not been proven for some empirically
+popular Markov regime switching models. In particular, the asymptotic
+distribution of the MLE has been unknown for models in which some elements of
+the transition probability matrix have the value of zero, as is commonly
+assumed in empirical applications with models with more than two regimes. This
+also includes models in which the regime-specific density depends on both the
+current and the lagged regimes such as the seminal model of Hamilton (1989) and
+switching ARCH model of Hamilton and Susmel (1994). This paper shows the
+asymptotic normality of the MLE and consistency of the asymptotic covariance
+matrix estimate of these models.
+",0,0,1,1,0,0
+13318,Fleet management for autonomous vehicles: Online PDP under special constraints,"  The VIPAFLEET project consists in developing models and algorithms for man-
+aging a fleet of Individual Public Autonomous Vehicles (VIPA). Hereby, we
+consider a fleet of cars distributed at specified stations in an industrial
+area to supply internal transportation, where the cars can be used in different
+modes of circulation (tram mode, elevator mode, taxi mode). One goal is to
+develop and implement suitable algorithms for each mode in order to satisfy all
+the requests under an economic point of view by minimizing the total tour
+length. The innovative idea and challenge of the project is to develop and
+install a dynamic fleet management system that allows the operator to switch
+between the different modes within the different periods of the day according
+to the dynamic transportation demands of the users. We model the underlying
+online transportation system and propose a correspond- ing fleet management
+framework, to handle modes, demands and commands. We consider two modes of
+circulation, tram and elevator mode, propose for each mode appropriate on- line
+algorithms and evaluate their performance, both in terms of competitive
+analysis and practical behavior.
+",1,0,0,0,0,0
+13319,Bayesian Model Selection for Misspecified Models in Linear Regression,"  While the Bayesian Information Criterion (BIC) and Akaike Information
+Criterion (AIC) are powerful tools for model selection in linear regression,
+they are built on different prior assumptions and thereby apply to different
+data generation scenarios. We show that in the finite-dimensional case their
+respective assumptions can be unified within an augmented model-plus-noise
+space and construct a prior in this space which inherits the beneficial
+properties of both AIC and BIC. This allows us to adapt the BIC to be robust
+against misspecified models where the signal to noise ratio is low.
+",0,0,0,1,0,0
+13320,Near-optimal sample complexity for convex tensor completion,"  We analyze low rank tensor completion (TC) using noisy measurements of a
+subset of the tensor. Assuming a rank-$r$, order-$d$, $N \times N \times \cdots
+\times N$ tensor where $r=O(1)$, the best sampling complexity that was achieved
+is $O(N^{\frac{d}{2}})$, which is obtained by solving a tensor nuclear-norm
+minimization problem. However, this bound is significantly larger than the
+number of free variables in a low rank tensor which is $O(dN)$. In this paper,
+we show that by using an atomic-norm whose atoms are rank-$1$ sign tensors, one
+can obtain a sample complexity of $O(dN)$. Moreover, we generalize the matrix
+max-norm definition to tensors, which results in a max-quasi-norm (max-qnorm)
+whose unit ball has small Rademacher complexity. We prove that solving a
+constrained least squares estimation using either the convex atomic-norm or the
+nonconvex max-qnorm results in optimal sample complexity for the problem of
+low-rank tensor completion. Furthermore, we show that these bounds are nearly
+minimax rate-optimal. We also provide promising numerical results for max-qnorm
+constrained tensor completion, showing improved recovery results compared to
+matricization and alternating least squares.
+",1,0,0,1,0,0
+13321,Conditional Lower Bounds for Space/Time Tradeoffs,"  In recent years much effort has been concentrated towards achieving
+polynomial time lower bounds on algorithms for solving various well-known
+problems. A useful technique for showing such lower bounds is to prove them
+conditionally based on well-studied hardness assumptions such as 3SUM, APSP,
+SETH, etc. This line of research helps to obtain a better understanding of the
+complexity inside P.
+A related question asks to prove conditional space lower bounds on data
+structures that are constructed to solve certain algorithmic tasks after an
+initial preprocessing stage. This question received little attention in
+previous research even though it has potential strong impact.
+In this paper we address this question and show that surprisingly many of the
+well-studied hard problems that are known to have conditional polynomial time
+lower bounds are also hard when concerning space. This hardness is shown as a
+tradeoff between the space consumed by the data structure and the time needed
+to answer queries. The tradeoff may be either smooth or admit one or more
+singularity points.
+We reveal interesting connections between different space hardness
+conjectures and present matching upper bounds. We also apply these hardness
+conjectures to both static and dynamic problems and prove their conditional
+space hardness.
+We believe that this novel framework of polynomial space conjectures can play
+an important role in expressing polynomial space lower bounds of many important
+algorithmic problems. Moreover, it seems that it can also help in achieving a
+better understanding of the hardness of their corresponding problems in terms
+of time.
+",1,0,0,0,0,0
+13322,Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness,"  Cardiac left ventricle (LV) quantification is among the most clinically
+important tasks for identification and diagnosis of cardiac diseases, yet still
+a challenge due to the high variability of cardiac structure and the complexity
+of temporal dynamics. Full quantification, i.e., to simultaneously quantify all
+LV indices including two areas (cavity and myocardium), six regional wall
+thicknesses (RWT), three LV dimensions, and one cardiac phase, is even more
+challenging since the uncertain relatedness intra and inter each type of
+indices may hinder the learning procedure from better convergence and
+generalization. In this paper, we propose a newly-designed multitask learning
+network (FullLVNet), which is constituted by a deep convolution neural network
+(CNN) for expressive feature embedding of cardiac structure; two followed
+parallel recurrent neural network (RNN) modules for temporal dynamic modeling;
+and four linear models for the final estimation. During the final estimation,
+both intra- and inter-task relatedness are modeled to enforce improvement of
+generalization: 1) respecting intra-task relatedness, group lasso is applied to
+each of the regression tasks for sparse and common feature selection and
+consistent prediction; 2) respecting inter-task relatedness, three phase-guided
+constraints are proposed to penalize violation of the temporal behavior of the
+obtained LV indices. Experiments on MR sequences of 145 subjects show that
+FullLVNet achieves high accurate prediction with our intra- and inter-task
+relatedness, leading to MAE of 190mm$^2$, 1.41mm, 2.68mm for average areas,
+RWT, dimensions and error rate of 10.4\% for the phase classification. This
+endows our method a great potential in comprehensive clinical assessment of
+global, regional and dynamic cardiac function.
+",1,0,0,0,0,0
+13323,On sparsity and power-law properties of graphs based on exchangeable point processes,"  This paper investigates properties of the class of graphs based on
+exchangeable point processes. We provide asymptotic expressions for the number
+of edges, number of nodes and degree distributions, identifying four regimes: a
+dense regime, a sparse, almost dense regime, a sparse regime with power-law
+behavior, and an almost extremely sparse regime. Our results allow us to derive
+a consistent estimator for the scalar parameter tuning the sparsity of the
+graph. We also propose a class of models within this framework where one can
+separately control the local, latent structure and the global
+sparsity/power-law properties of the graph.
+",0,0,1,1,0,0
+13324,Information transmission on hybrid networks,"  Many real-world communication networks often have hybrid nature with both
+fixed nodes and moving modes, such as the mobile phone networks mainly composed
+of fixed base stations and mobile phones. In this paper, we discuss the
+information transmission process on the hybrid networks with both fixed and
+mobile nodes. The fixed nodes (base stations) are connected as a spatial
+lattice on the plane forming the information-carrying backbone, while the
+mobile nodes (users), which are the sources and destinations of information
+packets, connect to their current nearest fixed nodes respectively to deliver
+and receive information packets. We observe the phase transition of traffic
+load in the hybrid network when the packet generation rate goes from below and
+then above a critical value, which measures the network capacity of packets
+delivery. We obtain the optimal speed of moving nodes leading to the maximum
+network capacity. We further improve the network capacity by rewiring the fixed
+nodes and by considering the current load of fixed nodes during packets
+transmission. Our purpose is to optimize the network capacity of hybrid
+networks from the perspective of network science, and provide some insights for
+the construction of future communication infrastructures.
+",1,1,0,0,0,0
+13325,Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN,"  We propose a novel technique to make neural network robust to adversarial
+examples using a generative adversarial network. We alternately train both
+classifier and generator networks. The generator network generates an
+adversarial perturbation that can easily fool the classifier network by using a
+gradient of each image. Simultaneously, the classifier network is trained to
+classify correctly both original and adversarial images generated by the
+generator. These procedures help the classifier network to become more robust
+to adversarial perturbations. Furthermore, our adversarial training framework
+efficiently reduces overfitting and outperforms other regularization methods
+such as Dropout. We applied our method to supervised learning for CIFAR
+datasets, and experimantal results show that our method significantly lowers
+the generalization error of the network. To the best of our knowledge, this is
+the first method which uses GAN to improve supervised learning.
+",1,0,0,1,0,0
+13326,Bad Primes in Computational Algebraic Geometry,"  Computations over the rational numbers often suffer from intermediate
+coefficient swell. One solution to this problem is to apply the given algorithm
+modulo a number of primes and then lift the modular results to the rationals.
+This method is guaranteed to work if we use a sufficiently large set of good
+primes. In many applications, however, there is no efficient way of excluding
+bad primes. In this note, we describe a technique for rational reconstruction
+which will nevertheless return the correct result, provided the number of good
+primes in the selected set of primes is large enough. We give a number of
+illustrating examples which are implemented using the computer algebra system
+Singular and the programming language Julia. We discuss applications of our
+technique in computational algebraic geometry.
+",1,0,1,0,0,0
+13327,Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation,"  Collaborative filtering often suffers from sparsity and cold start problems
+in real recommendation scenarios, therefore, researchers and engineers usually
+use side information to address the issues and improve the performance of
+recommender systems. In this paper, we consider knowledge graphs as the source
+of side information. We propose MKR, a Multi-task feature learning approach for
+Knowledge graph enhanced Recommendation. MKR is a deep end-to-end framework
+that utilizes knowledge graph embedding task to assist recommendation task. The
+two tasks are associated by cross&compress units, which automatically share
+latent features and learn high-order interactions between items in recommender
+systems and entities in the knowledge graph. We prove that cross&compress units
+have sufficient capability of polynomial approximation, and show that MKR is a
+generalized framework over several representative methods of recommender
+systems and multi-task learning. Through extensive experiments on real-world
+datasets, we demonstrate that MKR achieves substantial gains in movie, book,
+music, and news recommendation, over state-of-the-art baselines. MKR is also
+shown to be able to maintain a decent performance even if user-item
+interactions are sparse.
+",1,0,0,1,0,0
+13328,On Consistency of Graph-based Semi-supervised Learning,"  Graph-based semi-supervised learning is one of the most popular methods in
+machine learning. Some of its theoretical properties such as bounds for the
+generalization error and the convergence of the graph Laplacian regularizer
+have been studied in computer science and statistics literatures. However, a
+fundamental statistical property, the consistency of the estimator from this
+method has not been proved. In this article, we study the consistency problem
+under a non-parametric framework. We prove the consistency of graph-based
+learning in the case that the estimated scores are enforced to be equal to the
+observed responses for the labeled data. The sample sizes of both labeled and
+unlabeled data are allowed to grow in this result. When the estimated scores
+are not required to be equal to the observed responses, a tuning parameter is
+used to balance the loss function and the graph Laplacian regularizer. We give
+a counterexample demonstrating that the estimator for this case can be
+inconsistent. The theoretical findings are supported by numerical studies.
+",0,0,0,1,0,0
+13329,Modeling Label Ambiguity for Neural List-Wise Learning to Rank,"  List-wise learning to rank methods are considered to be the state-of-the-art.
+One of the major problems with these methods is that the ambiguous nature of
+relevance labels in learning to rank data is ignored. Ambiguity of relevance
+labels refers to the phenomenon that multiple documents may be assigned the
+same relevance label for a given query, so that no preference order should be
+learned for those documents. In this paper we propose a novel sampling
+technique for computing a list-wise loss that can take into account this
+ambiguity. We show the effectiveness of the proposed method by training a
+3-layer deep neural network. We compare our new loss function to two strong
+baselines: ListNet and ListMLE. We show that our method generalizes better and
+significantly outperforms other methods on the validation and test sets.
+",1,0,0,1,0,0
+13330,Whitehead torsion of inertial h-cobordisms,"  We study the Whitehead torsions of inertial h-cobordisms, and identify
+various types representing a nested sequence of subsets of the Whitehead group.
+A number of examples are given to show that these subsets are all different in
+general.
+",0,0,1,0,0,0
+13331,"Propagation from Deceptive News Sources: Who Shares, How Much, How Evenly, and How Quickly?","  As people rely on social media as their primary sources of news, the spread
+of misinformation has become a significant concern. In this large-scale study
+of news in social media we analyze eleven million posts and investigate
+propagation behavior of users that directly interact with news accounts
+identified as spreading trusted versus malicious content. Unlike previous work,
+which looks at specific rumors, topics, or events, we consider all content
+propagated by various news sources. Moreover, we analyze and contrast
+population versus sub-population behaviour (by demographics) when spreading
+misinformation, and distinguish between two types of propagation, i.e., direct
+retweets and mentions. Our evaluation examines how evenly, how many, how
+quickly, and which users propagate content from various types of news sources
+on Twitter.
+Our analysis has identified several key differences in propagation behavior
+from trusted versus suspicious news sources. These include high inequity in the
+diffusion rate based on the source of disinformation, with a small group of
+highly active users responsible for the majority of disinformation spread
+overall and within each demographic. Analysis by demographics showed that users
+with lower annual income and education share more from disinformation sources
+compared to their counterparts. News content is shared significantly more
+quickly from trusted, conspiracy, and disinformation sources compared to
+clickbait and propaganda. Older users propagate news from trusted sources more
+quickly than younger users, but they share from suspicious sources after longer
+delays. Finally, users who interact with clickbait and conspiracy sources are
+likely to share from propaganda accounts, but not the other way around.
+",1,0,0,0,0,0
+13332,Self-protected nanoscale thermometry based on spin defects in silicon carbide,"  Quantum sensors with solid state electron spins have attracted considerable
+interest due to their nanoscale spatial resolution.A critical requirement is to
+suppress the environment noise of the solid state spin sensor.Here we
+demonstrate a nanoscale thermometer based on silicon carbide (SiC) electron
+spins.We experimentally demonstrate that the performance of the spin sensor is
+robust against dephasing due to a self protected machenism. The SiC thermometry
+may provide a promising platform for sensing in a noisy environment ,e.g.
+biological system sensing.
+",0,1,0,0,0,0
+13333,Robot Composite Learning and the Nunchaku Flipping Challenge,"  Advanced motor skills are essential for robots to physically coexist with
+humans. Much research on robot dynamics and control has achieved success on
+hyper robot motor capabilities, but mostly through heavily case-specific
+engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous
+manner, robot learning from human demonstration (LfD) has achieved great
+progress, but still has limitations handling dynamic skills and compound
+actions. In this paper, we present a composite learning scheme which goes
+beyond LfD and integrates robot learning from human definition, demonstration,
+and evaluation. The method tackles advanced motor skills that require dynamic
+time-critical maneuver, complex contact control, and handling partly soft
+partly rigid objects. We also introduce the ""nunchaku flipping challenge"", an
+extreme test that puts hard requirements to all these three aspects. Continued
+from our previous presentations, this paper introduces the latest update of the
+composite learning scheme and the physical success of the nunchaku flipping
+challenge.
+",1,0,0,0,0,0
+13334,Querying Best Paths in Graph Databases,"  Querying graph databases has recently received much attention. We propose a
+new approach to this problem, which balances competing goals of expressive
+power, language clarity and computational complexity. A distinctive feature of
+our approach is the ability to express properties of minimal (e.g. shortest)
+and maximal (e.g. most valuable) paths satisfying given criteria. To express
+complex properties in a modular way, we introduce labelling-generating
+ontologies. The resulting formalism is computationally attractive -- queries
+can be answered in non-deterministic logarithmic space in the size of the
+database.
+",1,0,0,0,0,0
+13335,Communication Complexity of Correlated Equilibrium in Two-Player Games,"  We show a communication complexity lower bound for finding a correlated
+equilibrium of a two-player game. More precisely, we define a two-player $N
+\times N$ game called the 2-cycle game and show that the randomized
+communication complexity of finding a 1/poly($N$)-approximate correlated
+equilibrium of the 2-cycle game is $\Omega(N)$. For small approximation values,
+this answers an open question of Babichenko and Rubinstein (STOC 2017). Our
+lower bound is obtained via a direct reduction from the unique set disjointness
+problem.
+",1,0,0,0,0,0
+13336,On the computability of graph Turing machines,"  We consider graph Turing machines, a model of parallel computation on a
+graph, in which each vertex is only capable of performing one of a finite
+number of operations. This model of computation is a natural generalization of
+several well-studied notions of computation, including ordinary Turing
+machines, cellular automata, and parallel graph dynamical systems. We analyze
+the power of computations that can take place in this model, both in terms of
+the degrees of computability of the functions that can be computed, and the
+time and space resources needed to carry out these computations. We further
+show that properties of the underlying graph have significant consequences for
+the power of computation thereby obtained. In particular, we show that every
+arithmetically definable set can be computed by a graph Turing machine in
+constant time, and that every computably enumerable Turing degree can be
+computed in constant time and linear space by a graph Turing machine whose
+underlying graph has finite degree.
+",1,0,1,0,0,0
+13337,SCRank: Spammer and Celebrity Ranking in Directed Social Networks,"  Many online social networks allow directed edges: Alice can unilaterally add
+an ""edge"" to Bob, typically indicating interest in Bob or Bob's content,
+without Bob's permission or reciprocation. In directed social networks we
+observe the rise of two distinctive classes of users: celebrities who accrue
+unreciprocated incoming links, and follow spammers, who generate unreciprocated
+outgoing links. Identifying users in these two classes is important for abuse
+detection, user and content ranking, privacy choices, and other social network
+features.
+In this paper we develop SCRank, an iterative algorithm to identify such
+users. We analyze SCRank both theoretically and experimentally. The
+spammer-celebrity definition is not amenable to analysis using standard power
+iteration, so we develop a novel potential function argument to show
+convergence to an approximate equilibrium point for a class of algorithms
+including SCRank. We then use experimental evaluation on a real global-scale
+social network and on synthetically generated graphs to observe that the
+algorithm converges quickly and consistently. Using synthetic data with
+built-in ground truth, we also experimentally show that the algorithm provides
+a good approximation to planted celebrities and spammers.
+",1,0,0,0,0,0
+13338,Taylor series and twisting-index invariants of coupled spin-oscillators,"  About six years ago, semitoric systems on 4-dimensional manifolds were
+classified by Pelayo & Vu Ngoc by means of five invariants. A standard example
+of such a system is the coupled spin-oscillator on $\mathbb{S}^2 \times
+\mathbb{R}^2$. Calculations of three of the five semitoric invariants of this
+system (namely the number of focus-focus singularities, the generalised
+semitoric polygon, and the height invariant) already appeared in the
+literature, but the so-called twisting index was not yet computed and, of the
+so-called Taylor series invariant, only the linear terms were known.
+In the present paper, we complete the list of invariants for the coupled
+spin-oscillator by calculating higher order terms of the Taylor series
+invariant and by computing the twisting index. Moreover, we prove that the
+Taylor series invariant has certain symmetry properties that make the even
+powers in one of the variables vanish and allow us to show superintegrability
+of the coupled spin-oscillator on the zero energy level.
+",0,0,1,0,0,0
+13339,Optimal DoF region of the K-User MISO BC with Partial CSIT,"  We consider the $K$-User Multiple-Input-Single-Output (MISO) Broadcast
+Channel (BC) where the transmitter, equipped with $M$ antennas, serves $K$
+users, with $K \leq M$. The transmitter has access to a partial channel state
+information of the users. This is modelled by letting the variance of the
+Channel State Information at the Transmitter (CSIT) error of user $i$ scale as
+$O(P^{-\alpha_i}$) for the Signal-to-Noise Ratio (SNR) $P$ and some constant
+$\alpha_i \geq 0$. In this work we derive the optimal Degrees-of-Freedom (DoF)
+region in such setting and we show that Rate-Splitting (RS) is the key scheme
+to achieve such a region.
+",1,0,0,0,0,0
+13340,Active learning of constitutive relation from mesoscopic dynamics for macroscopic modeling of non-Newtonian flows,"  We simulate complex fluids by means of an on-the-fly coupling of the bulk
+rheology to the underlying microstructure dynamics. In particular, a
+macroscopic continuum model of polymeric fluids is constructed without a
+pre-specified constitutive relation, but instead it is actively learned from
+mesoscopic simulations where the dynamics of polymer chains is explicitly
+computed. To couple the macroscopic rheology of polymeric fluids and the
+microscale dynamics of polymer chains, the continuum approach (based on the
+finite volume method) provides the transient flow field as inputs for the
+(mesoscopic) dissipative particle dynamics (DPD), and in turn DPD returns an
+effective constitutive relation to close the continuum equations. In this
+multiscale modeling procedure, we employ an active learning strategy based on
+Gaussian process regression (GPR) to minimize the number of expensive DPD
+simulations, where adaptively selected DPD simulations are performed only as
+necessary. Numerical experiments are carried out for flow past a circular
+cylinder of a non-Newtonian fluid, modeled at the mesoscopic level by
+bead-spring chains. The results show that only five DPD simulations are
+required to achieve an effective closure of the continuum equations at Reynolds
+number Re=10. Furthermore, when Re is increased to 100, only one additional DPD
+simulation is required for constructing an extended GPR-informed model closure.
+Compared to traditional message-passing multiscale approaches, applying an
+active learning scheme to multiscale modeling of non-Newtonian fluids can
+significantly increase the computational efficiency. Although the method
+demonstrated here obtains only a local viscosity from the mesoscopic model, it
+can be extended to other multiscale models of complex fluids whose
+macro-rheology is unknown.
+",0,1,0,0,0,0
+13341,Collective search with finite perception: transient dynamics and search efficiency,"  Motile organisms often use finite spatial perception of their surroundings to
+navigate and search their habitats. Yet standard models of search are usually
+based on purely local sensory information. To model how a finite perceptual
+horizon affects ecological search, we propose a framework for optimal
+navigation that combines concepts from random walks and optimal control theory.
+We show that, while local strategies are optimal on asymptotically long and
+short search times, finite perception yields faster convergence and increased
+search efficiency over transient time scales relevant in biological systems.
+The benefit of the finite horizon can be maintained by the searchers tuning
+their response sensitivity to the length scale of the stimulant in the
+environment, and is enhanced when the agents interact as a result of increased
+consensus within subpopulations. Our framework sheds light on the role of
+spatial perception and transients in search movement and collective sensing of
+the environment.
+",0,0,0,0,1,0
+13342,The first moment of cusp form L-functions in weight aspect on average,"  We study the asymptotic behaviour of the twisted first moment of central
+$L$-values associated to cusp forms in weight aspect on average. Our estimate
+of the error term allows extending the logarithmic length of mollifier $\Delta$
+up to 2. The best previously known result, due to Iwaniec and Sarnak, was
+$\Delta<1$. The proof is based on a representation formula for the error in
+terms of Legendre polynomials.
+",0,0,1,0,0,0
+13343,Analyzing Boltzmann Samplers for Bose-Einstein Condensates with Dirichlet Generating Functions,"  Boltzmann sampling is commonly used to uniformly sample objects of a
+particular size from large combinatorial sets. For this technique to be
+effective, one needs to prove that (1) the sampling procedure is efficient and
+(2) objects of the desired size are generated with sufficiently high
+probability. We use this approach to give a provably efficient sampling
+algorithm for a class of weighted integer partitions related to Bose-Einstein
+condensation from statistical physics. Our sampling algorithm is a
+probabilistic interpretation of the ordinary generating function for these
+objects, derived from the symbolic method of analytic combinatorics. Using the
+Khintchine-Meinardus probabilistic method to bound the rejection rate of our
+Boltzmann sampler through singularity analysis of Dirichlet generating
+functions, we offer an alternative approach to analyze Boltzmann samplers for
+objects with multiplicative structure.
+",1,0,0,0,0,0
+13344,On Invariant Random Subgroups of Block-Diagonal Limits of Symmetric Groups,"  We classify the ergodic invariant random subgroups of block-diagonal limits
+of symmetric groups in the cases when the groups are simple and the associated
+dimension groups have finite dimensional state spaces. These block-diagonal
+limits arise as the transformation groups (full groups) of Bratteli diagrams
+that preserve the cofinality of infinite paths in the diagram. Given a simple
+full group $G$ admitting only a finite number of ergodic measures on the
+path-space $X$ of the associated Bratteli digram, we prove that every non-Dirac
+ergodic invariant random subgroup of $G$ arises as the stabilizer distribution
+of the diagonal action on $X^n$ for some $n\geq 1$. As a corollary, we
+establish that every group character $\chi$ of $G$ has the form $\chi(g) =
+Prob(g\in K)$, where $K$ is a conjugation-invariant random subgroup of $G$.
+",0,0,1,0,0,0
+13345,Theoretical Analysis of Sparse Subspace Clustering with Missing Entries,"  Sparse Subspace Clustering (SSC) is a popular unsupervised machine learning
+method for clustering data lying close to an unknown union of low-dimensional
+linear subspaces; a problem with numerous applications in pattern recognition
+and computer vision. Even though the behavior of SSC for complete data is by
+now well-understood, little is known about its theoretical properties when
+applied to data with missing entries. In this paper we give theoretical
+guarantees for SSC with incomplete data, and analytically establish that
+projecting the zero-filled data onto the observation pattern of the point being
+expressed leads to a substantial improvement in performance. The main insight
+that stems from our analysis is that even though the projection induces
+additional missing entries, this is counterbalanced by the fact that the
+projected and zero-filled data are in effect incomplete points associated with
+the union of the corresponding projected subspaces, with respect to which the
+point being expressed is complete. The significance of this phenomenon
+potentially extends to the entire class of self-expressive methods.
+",0,0,0,1,0,0
+13346,On certain geometric properties in Banach spaces of vector-valued functions,"  We consider a certain type of geometric properties of Banach spaces, which
+includes for instance octahedrality, almost squareness, lushness and the
+Daugavet property. For this type of properties, we obtain a general reduction
+theorem, which, roughly speaking, states the following: if the property in
+question is stable under certain finite absolute sums (for example finite
+$\ell^p$-sums), then it is also stable under the formation of corresponding
+Köthe-Bochner spaces (for example $L^p$-Bochner spaces). From this general
+theorem, we obtain as corollaries a number of new results as well as some
+alternative proofs of already known results concerning octahedral and almost
+square spaces and their relatives, diameter-two-properties, lush spaces and
+other classes.
+",0,0,1,0,0,0
+13347,Social Media Analysis based on Semanticity of Streaming and Batch Data,"  Languages shared by people differ in different regions based on their
+accents, pronunciation and word usages. In this era sharing of language takes
+place mainly through social media and blogs. Every second swing of such a micro
+posts exist which induces the need of processing those micro posts, in-order to
+extract knowledge out of it. Knowledge extraction differs with respect to the
+application in which the research on cognitive science fed the necessities for
+the same. This work further moves forward such a research by extracting
+semantic information of streaming and batch data in applications like Named
+Entity Recognition and Author Profiling. In the case of Named Entity
+Recognition context of a single micro post has been utilized and context that
+lies in the pool of micro posts were utilized to identify the sociolect aspects
+of the author of those micro posts. In this work Conditional Random Field has
+been utilized to do the entity recognition and a novel approach has been
+proposed to find the sociolect aspects of the author (Gender, Age group).
+",1,0,0,0,0,0
+13348,Chiral Topological Superconductors Enhanced by Long-Range Interactions,"  We study the phase diagram and edge states of a two-dimensional p-wave
+superconductor with long-range hopping and pairing amplitudes. New topological
+phases and quasiparticles different from the usual short-range model are
+obtained. When both hopping and pairing terms decay with the same exponent, one
+of the topological chiral phases with propagating Majorana edge states gets
+significantly enhanced by long-range couplings. On the other hand, when the
+long-range pairing amplitude decays more slowly than the hopping, we discover
+new topological phases where propagating Majorana fermions at each edge pair
+nonlocally and become gapped even in the thermodynamic limit. Remarkably, these
+nonlocal edge states are still robust, remain separated from the bulk, and are
+localized at both edges at the same time. The inclusion of long-range effects
+is potentially applicable to recent experiments with magnetic impurities and
+islands in 2D superconductors.
+",0,1,0,0,0,0
+13349,An Algebraic Glimpse at Bunched Implications and Separation Logic,"  We overview the logic of Bunched Implications (BI) and Separation Logic (SL)
+from a perspective inspired by Hiroakira Ono's algebraic approach to
+substructural logics. We propose generalized BI algebras (GBI-algebras) as a
+common framework for algebras arising via ""declarative resource reading"",
+intuitionistic generalizations of relation algebras and arrow logics and the
+distributive Lambek calculus with intuitionistic implication. Apart from
+existing models of BI (in particular, heap models and effect algebras), we also
+cover models arising from weakening relations, formal languages or more
+fine-grained treatment of labelled trees and semistructured data. After briefly
+discussing the lattice of subvarieties of GBI, we present a suitable duality
+for GBI along the lines of Esakia and Priestley and an algebraic proof of cut
+elimination in the setting of residuated frames of Galatos and Jipsen. We also
+show how the algebraic approach allows generic results on decidability, both
+positive and negative ones. In the final part of the paper, we gently introduce
+the substructural audience to some theory behind state-of-art tools,
+culminating with an algebraic and proof-theoretic presentation of
+(bi-)abduction.
+",1,0,0,0,0,0
+13350,The adapted hyper-Kähler structure on the crown domain,"  Let $\,\Xi\,$ be the crown domain associated with a non-compact irreducible
+hermitian symmetric space $\,G/K$. We give an explicit description of the
+unique $\,G$-invariant adapted hyper-Kähler structure on $\,\Xi$,$\
+$i.$\,$e.$\ $compatible with the adapted complex structure $\,J_{ad}\,$ and
+with the $\,G$-invariant Kähler structure of $\,G/K$. We also compute
+invariant potentials of the involved Kähler metrics and the associated moment
+maps.
+",0,0,1,0,0,0
+13351,Exact traveling wave solutions of 1D model of cancer invasion,"  In this paper we consider the continuous mathematical model of tumour growth
+and invasion based on the model introduced by Anderson, Chaplain et al.
+\cite{Anderson&Chaplain2000}, for the case of one space dimension. The model
+consists of a system of three coupled nonlinear reaction-diffusion-taxis
+partial differential equations describing the interactions between cancer
+cells, the matrix degrading enzyme and the tissue. For this model under certain
+conditions on the model parameters we obtain the exact analytical solutions in
+terms of traveling wave variables. These solutions are smooth positive definite
+functions whose profiles agree with those obtained from numerical computations
+\cite{Chaplain&Lolas2006} for not very large time intervals.
+",0,0,0,0,1,0
+13352,On purity theorem of Lusztig's perverse sheaves,"  Let $Q$ be a finite quiver without loops and $\mathcal{Q}_{\alpha}$ be the
+Lusztig category for any dimension vector $\alpha$. The purpose of this paper
+is to prove that all Frobenius eigenvalues of the $i$-th cohomology
+$\mathcal{H}^i(\mathcal{L})|_x$ for a simple perverse sheaf $\mathcal{L}\in
+\mathcal{Q}_{\alpha}$ and $x\in
+\mathbb{E}_{\alpha}^{F^n}=\mathbb{E}_{\alpha}(\mathbb{F}_{q^n})$ are equal to
+$(\sqrt{q^n})^{i}$ as a conjecture given by Schiffmann (\cite{Schiffmann2}). As
+an application, we prove the existence of a class of Hall polynomials.
+",0,0,1,0,0,0
+13353,Abstract Interpretation using a Language of Symbolic Approximation,"  The traditional abstract domain framework for imperative programs suffers
+from several shortcomings; in particular it does not allow precise symbolic
+abstractions. To solve these problems, we propose a new abstract interpretation
+framework, based on symbolic expressions used both as an abstraction of the
+program, and as the input analyzed by abstract domains. We demonstrate new
+applications of the frame- work: an abstract domain that efficiently propagates
+constraints across the whole program; a new formalization of functor domains as
+approximate translation, which allows the production of approximate programs,
+on which we can perform classical symbolic techniques. We used these to build a
+complete analyzer for embedded C programs, that demonstrates the practical
+applicability of the framework.
+",1,0,0,0,0,0
+13354,The Integral Transform of N.I.Akhiezer,"  We study the integral transform which appeared in a different form in
+Akhiezer's textbook ""Lectures on Integral Transforms"".
+",0,0,1,0,0,0
+13355,Quantitative CBA: Small and Comprehensible Association Rule Classification Models,"  Quantitative CBA is a postprocessing algorithm for association rule
+classification algorithm CBA (Liu et al, 1998). QCBA uses original,
+undiscretized numerical attributes to optimize the discovered association
+rules, refining the boundaries of literals in the antecedent of the rules
+produced by CBA. Some rules as well as literals from the rules can consequently
+be removed, which makes the resulting classifier smaller. One-rule
+classification and crisp rules make CBA classification models possibly most
+comprehensible among all association rule classification algorithms. These
+viable properties are retained by QCBA. The postprocessing is conceptually
+fast, because it is performed on a relatively small number of rules that passed
+data coverage pruning in CBA. Benchmark of our QCBA approach on 22 UCI datasets
+shows average 53% decrease in the total size of the model as measured by the
+total number of conditions in all rules. Model accuracy remains on the same
+level as for CBA.
+",1,0,0,1,0,0
+13356,Quermassintegral preserving curvature flow in Hyperbolic space,"  We consider the quermassintegral preserving flow of closed \emph{h-convex}
+hypersurfaces in hyperbolic space with the speed given by any positive power of
+a smooth symmetric, strictly increasing, and homogeneous of degree one function
+$f$ of the principal curvatures which is inverse concave and has dual $f_*$
+approaching zero on the boundary of the positive cone. We prove that if the
+initial hypersurface is \emph{h-convex}, then the solution of the flow becomes
+strictly \emph{h-convex} for $t>0$, the flow exists for all time and converges
+to a geodesic sphere exponentially in the smooth topology.
+",0,0,1,0,0,0
+13357,Cloaking for a quasi-linear elliptic partial differential equation,"  In this article we consider cloaking for a quasi-linear elliptic partial
+differential equation of divergence type defined on a bounded domain in
+$\mathbb{R}^N$ for $N=2,3$. We show that a perfect cloak can be obtained via a
+singular change of variables scheme and an approximate cloak can be achieved
+via a regular change of variables scheme. These approximate cloaks though
+non-degenerate are anisotropic. We also show, within the framework of
+homogenization, that it is possible to get isotropic regular approximate
+cloaks. This work generalizes to quasi-linear settings previous work on
+cloaking in the context of Electrical Impedance Tomography for the conductivity
+equation.
+",0,0,1,0,0,0
+13358,Good Arm Identification via Bandit Feedback,"  We consider a novel stochastic multi-armed bandit problem called {\em good
+arm identification} (GAI), where a good arm is defined as an arm with expected
+reward greater than or equal to a given threshold. GAI is a pure-exploration
+problem that a single agent repeats a process of outputting an arm as soon as
+it is identified as a good one before confirming the other arms are actually
+not good. The objective of GAI is to minimize the number of samples for each
+process. We find that GAI faces a new kind of dilemma, the {\em
+exploration-exploitation dilemma of confidence}, which is different difficulty
+from the best arm identification. As a result, an efficient design of
+algorithms for GAI is quite different from that for the best arm
+identification. We derive a lower bound on the sample complexity of GAI that is
+tight up to the logarithmic factor $\mathrm{O}(\log \frac{1}{\delta})$ for
+acceptance error rate $\delta$. We also develop an algorithm whose sample
+complexity almost matches the lower bound. We also confirm experimentally that
+our proposed algorithm outperforms naive algorithms in synthetic settings based
+on a conventional bandit problem and clinical trial researches for rheumatoid
+arthritis.
+",0,0,0,1,0,0
+13359,Learning compressed representations of blood samples time series with missing data,"  Clinical measurements collected over time are naturally represented as
+multivariate time series (MTS), which often contain missing data. An
+autoencoder can learn low dimensional vectorial representations of MTS that
+preserve important data characteristics, but cannot deal explicitly with
+missing data. In this work, we propose a new framework that combines an
+autoencoder with the Time series Cluster Kernel (TCK), a kernel that accounts
+for missingness patterns in MTS. Via kernel alignment, we incorporate TCK in
+the autoencoder to improve the learned representations in presence of missing
+data. We consider a classification problem of MTS with missing values,
+representing blood samples of patients with surgical site infection. With our
+approach, rather than with a standard autoencoder, we learn representations in
+low dimensions that can be classified better.
+",1,0,0,1,0,0
+13360,Fuzzy Galois connections on fuzzy sets,"  In fairly elementary terms this paper presents how the theory of preordered
+fuzzy sets, more precisely quantale-valued preorders on quantale-valued fuzzy
+sets, is established under the guidance of enriched category theory. Motivated
+by several key results from the theory of quantaloid-enriched categories, this
+paper develops all needed ingredients purely in order-theoretic languages for
+the readership of fuzzy set theorists, with particular attention paid to fuzzy
+Galois connections between preordered fuzzy sets.
+",1,0,0,0,0,0
+13361,QWIRE Practice: Formal Verification of Quantum Circuits in Coq,"  We describe an embedding of the QWIRE quantum circuit language in the Coq
+proof assistant. This allows programmers to write quantum circuits using
+high-level abstractions and to prove properties of those circuits using Coq's
+theorem proving features. The implementation uses higher-order abstract syntax
+to represent variable binding and provides a type-checking algorithm for linear
+wire types, ensuring that quantum circuits are well-formed. We formalize a
+denotational semantics that interprets QWIRE circuits as superoperators on
+density matrices, and prove the correctness of some simple quantum programs.
+",1,0,0,0,0,0
+13362,The cost of fairness in classification,"  We study the problem of learning classifiers with a fairness constraint, with
+three main contributions towards the goal of quantifying the problem's inherent
+tradeoffs. First, we relate two existing fairness measures to cost-sensitive
+risks. Second, we show that for cost-sensitive classification and fairness
+measures, the optimal classifier is an instance-dependent thresholding of the
+class-probability function. Third, we show how the tradeoff between accuracy
+and fairness is determined by the alignment between the class-probabilities for
+the target and sensitive features. Underpinning our analysis is a general
+framework that casts the problem of learning with a fairness requirement as one
+of minimising the difference of two statistical risks.
+",1,0,0,0,0,0
+13363,The algebraic structure of cut Feynman integrals and the diagrammatic coaction,"  We study the algebraic and analytic structure of Feynman integrals by
+proposing an operation that maps an integral into pairs of integrals obtained
+from a master integrand and a corresponding master contour. This operation is a
+coaction. It reduces to the known coaction on multiple polylogarithms, but
+applies more generally, e.g. to hypergeometric functions. The coaction also
+applies to generic one-loop Feynman integrals with any configuration of
+internal and external masses, and in dimensional regularization. In this case,
+we demonstrate that it can be given a diagrammatic representation purely in
+terms of operations on graphs, namely contractions and cuts of edges. The
+coaction gives direct access to (iterated) discontinuities of Feynman integrals
+and facilitates a straightforward derivation of the differential equations they
+admit. In particular, the differential equations for any one-loop integral are
+determined by the diagrammatic coaction using limited information about their
+maximal, next-to-maximal, and next-to-next-to-maximal cuts.
+",0,0,1,0,0,0
+13364,Implementing universal nonadiabatic holonomic quantum gates with transmons,"  Geometric phases are well known to be noise-resilient in quantum
+evolutions/operations. Holonomic quantum gates provide us with a robust way
+towards universal quantum computation, as these quantum gates are actually
+induced by nonabelian geometric phases. Here we propose and elaborate how to
+efficiently implement universal nonadiabatic holonomic quantum gates on simpler
+superconducting circuits, with a single transmon serving as a qubit. In our
+proposal, an arbitrary single-qubit holonomic gate can be realized in a
+single-loop scenario, by varying the amplitudes and phase difference of two
+microwave fields resonantly coupled to a transmon, while nontrivial two-qubit
+holonomic gates may be generated with a transmission-line resonator being
+simultaneously coupled to the two target transmons in an effective resonant
+way. Moreover, our scenario may readily be scaled up to a two-dimensional
+lattice configuration, which is able to support large scalable quantum
+computation, paving the way for practically implementing universal nonadiabatic
+holonomic quantum computation with superconducting circuits.
+",0,1,0,0,0,0
+13365,"Fine cophasing of segmented aperture telescopes with ZELDA, a Zernike wavefront sensor in the diffraction-limited regime","  Segmented aperture telescopes require an alignment procedure with successive
+steps from coarse alignment to monitoring process in order to provide very high
+optical quality images for stringent science operations such as exoplanet
+imaging. The final step, referred to as fine phasing, calls for a high
+sensitivity wavefront sensing and control system in a diffraction-limited
+regime to achieve segment alignment with nanometric accuracy. In this context,
+Zernike wavefront sensors represent promising options for such a calibration. A
+concept called the Zernike unit for segment phasing (ZEUS) was previously
+developed for ground-based applications to operate under seeing-limited images.
+Such a concept is, however, not suitable for fine cophasing with
+diffraction-limited images. We revisit ZELDA, a Zernike sensor that was
+developed for the measurement of residual aberrations in exoplanet direct
+imagers, to measure segment piston, tip, and tilt in the diffraction-limited
+regime. We introduce a novel analysis scheme of the sensor signal that relies
+on piston, tip, and tilt estimators for each segment, and provide probabilistic
+insights to predict the success of a closed-loop correction as a function of
+the initial wavefront error. The sensor unambiguously and simultaneously
+retrieves segment piston and tip-tilt misalignment. Our scheme allows for
+correction of these errors in closed-loop operation down to nearly zero
+residuals in a few iterations. This sensor also shows low sensitivity to
+misalignment of its parts and high ability for operation with a relatively
+bright natural guide star. Our cophasing sensor relies on existing mask
+technologies that make the concept already available for segmented apertures in
+future space missions.
+",0,1,0,0,0,0
+13366,Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification,"  We consider the problem of active feature acquisition, where we sequentially
+select the subset of features in order to achieve the maximum prediction
+performance in the most cost-effective way. In this work, we formulate this
+active feature acquisition problem as a reinforcement learning problem, and
+provide a novel framework for jointly learning both the RL agent and the
+classifier (environment). We also introduce a more systematic way of encoding
+subsets of features that can properly handle innate challenge with missing
+entries in active feature acquisition problems, that uses the orderless
+LSTM-based set encoding mechanism that readily fits in the joint learning
+framework. We evaluate our model on a carefully designed synthetic dataset for
+the active feature acquisition as well as several real datasets such as
+electric health record (EHR) datasets, on which it outperforms all baselines in
+terms of prediction performance as well feature acquisition cost.
+",1,0,0,1,0,0
+13367,The Hasse Norm Principle For Biquadratic Extensions,"  We give an asymptotic formula for the number of biquadratic extensions of the
+rationals of bounded discriminant that fail the Hasse norm principle.
+",0,0,1,0,0,0
+13368,Static vs Adaptive Strategies for Optimal Execution with Signals,"  We consider an optimal execution problem in which a trader is looking at a
+short-term price predictive signal while trading. In the case where the trader
+is creating an instantaneous market impact, we show that transactions costs
+resulting from the optimal adaptive strategy are substantially lower than the
+corresponding costs of the optimal static strategy. Later, we investigate the
+case where the trader is creating transient market impact. We show that
+strategies in which the trader is observing the signal a number of times during
+the trading period, can dramatically reduce the transaction costs and improve
+the performance of the optimal static strategy. These results answer a question
+which was raised by Brigo and Piat [6], by analyzing two cases where adaptive
+strategies can improve the performance of the execution.
+",0,0,0,0,0,1
+13369,New descriptions of the weighted Reed-Muller codes and the homogeneous Reed-Muller codes,"  We give a description of the weighted Reed-Muller codes over a prime field in
+a modular algebra. A description of the homogeneous Reed-Muller codes in the
+same ambient space is presented for the binary case. A decoding procedure using
+the Landrock-Manz method is developed.
+",1,0,1,0,0,0
+13370,Criteria for the Absence and Existence of Bounded Solutions at the Threshold Frequency in a Junction of Quantum Waveguides,"  In the junction $\Omega$ of several semi-infinite cylindrical waveguides we
+consider the Dirichlet Laplacian whose continuous spectrum is the ray
+$[\lambda_\dagger, +\infty)$ with a positive cut-off value $\lambda_\dagger$.
+We give two different criteria for the threshold resonance generated by
+nontrivial bounded solutions to the Dirichlet problem for the Helmholtz
+equation $-\Delta u=\lambda_\dagger u$ in $\Omega$. The first criterion is
+quite simple and is convenient to disprove the existence of bounded solutions.
+The second criterion is rather involved but can help to detect concrete shapes
+supporting the resonance. Moreover, the latter distinguishes in a natural way
+between stabilizing, i.e., bounded but non-descending solutions and trapped
+modes with exponential decay at infinity.
+",0,0,1,0,0,0
+13371,Stochastic Feedback Control of Systems with Unknown Nonlinear Dynamics,"  This paper studies the stochastic optimal control problem for systems with
+unknown dynamics. First, an open-loop deterministic trajectory optimization
+problem is solved without knowing the explicit form of the dynamical system.
+Next, a Linear Quadratic Gaussian (LQG) controller is designed for the nominal
+trajectory-dependent linearized system, such that under a small noise
+assumption, the actual states remain close to the optimal trajectory. The
+trajectory-dependent linearized system is identified using input-output
+experimental data consisting of the impulse responses of the nominal system. A
+computational example is given to illustrate the performance of the proposed
+approach.
+",1,0,0,0,0,0
+13372,Micrometer-Sized Water Ice Particles for Planetary Science Experiments: Influence of Surface Structure on Collisional Properties,"  Models and observations suggest that ice-particle aggregation at and beyond
+the snowline dominates the earliest stages of planet-formation, which therefore
+is subject to many laboratory studies. However, the pressure-temperature
+gradients in proto-planetary disks mean that the ices are constantly processed,
+undergoing phase changes between different solid phases and the gas phase. Open
+questions remain as to whether the properties of the icy particles themselves
+dictate collision outcomes and therefore how effectively collision experiments
+reproduce conditions in pro- toplanetary environments. Previous experiments
+often yielded apparently contradictory results on collision outcomes, only
+agreeing in a temperature dependence setting in above $\approx$ 210 K. By
+exploiting the unique capabilities of the NIMROD neutron scattering instrument,
+we characterized the bulk and surface structure of icy particles used in
+collision experiments, and studied how these structures alter as a function of
+temperature at a constant pressure of around 30 mbar. Our icy grains, formed
+under liquid nitrogen, undergo changes in the crystalline ice-phase,
+sublimation, sintering and surface pre-melting as they are heated from 103 to
+247 K. An increase in the thickness of the diffuse surface layer from $\approx$
+10 to $\approx$ 30 {\AA} ($\approx$ 2.5 to 12 bilayers) proves increased
+molecular mobility at temperatures above $\approx$ 210 K. As none of the other
+changes tie-in with the temperature trends in collisional outcomes, we conclude
+that the surface pre-melting phenomenon plays a key role in collision
+experiments at these temperatures. Consequently, the pressure-temperature
+environment, may have a larger influence on collision outcomes than previously
+thought.
+",0,1,0,0,0,0
+13373,"The splashback radius of halos from particle dynamics. II. Dependence on mass, accretion rate, redshift, and cosmology","  The splashback radius $R_{\rm sp}$, the apocentric radius of particles on
+their first orbit after falling into a dark matter halo, has recently been
+suggested as a physically motivated halo boundary that separates accreting from
+orbiting material. Using the SPARTA code presented in Paper I, we analyze the
+orbits of billions of particles in cosmological simulations of structure
+formation and measure $R_{\rm sp}$ for a large sample of halos that span a mass
+range from dwarf galaxy to massive cluster halos, reach redshift 8, and include
+WMAP, Planck, and self-similar cosmologies. We analyze the dependence of
+$R_{\rm sp}/R_{\rm 200m}$ and $M_{\rm sp}/M_{\rm 200m}$ on the mass accretion
+rate $\Gamma$, halo mass, redshift, and cosmology. The scatter in these
+relations varies between 0.02 and 0.1 dex. While we confirm the known trend
+that $R_{\rm sp}/R_{\rm 200m}$ decreases with $\Gamma$, the relationships turn
+out to be more complex than previously thought, demonstrating that $R_{\rm sp}$
+is an independent definition of the halo boundary that cannot trivially be
+reconstructed from spherical overdensity definitions. We present fitting
+functions for $R_{\rm sp}/R_{\rm 200m}$ and $M_{\rm sp}/M_{\rm 200m}$ as a
+function of accretion rate, peak height, and redshift, achieving an accuracy of
+5% or better everywhere in the parameter space explored. We discuss the
+physical meaning of the distribution of particle apocenters and show that the
+previously proposed definition of $R_{\rm sp}$ as the radius of the steepest
+logarithmic density slope encloses roughly three-quarters of the apocenters.
+Finally, we conclude that no analytical model presented thus far can fully
+explain our results.
+",0,1,0,0,0,0
+13374,Empirical Evaluation of Parallel Training Algorithms on Acoustic Modeling,"  Deep learning models (DLMs) are state-of-the-art techniques in speech
+recognition. However, training good DLMs can be time consuming especially for
+production-size models and corpora. Although several parallel training
+algorithms have been proposed to improve training efficiency, there is no clear
+guidance on which one to choose for the task in hand due to lack of systematic
+and fair comparison among them. In this paper we aim at filling this gap by
+comparing four popular parallel training algorithms in speech recognition,
+namely asynchronous stochastic gradient descent (ASGD), blockwise model-update
+filtering (BMUF), bulk synchronous parallel (BSP) and elastic averaging
+stochastic gradient descent (EASGD), on 1000-hour LibriSpeech corpora using
+feed-forward deep neural networks (DNNs) and convolutional, long short-term
+memory, DNNs (CLDNNs). Based on our experiments, we recommend using BMUF as the
+top choice to train acoustic models since it is most stable, scales well with
+number of GPUs, can achieve reproducible results, and in many cases even
+outperforms single-GPU SGD. ASGD can be used as a substitute in some cases.
+",1,0,0,0,0,0
+13375,Distributed Impedance Control of Latency-Prone Robotic Systems with Series Elastic Actuation,"  Robotic systems are increasingly relying on distributed feedback controllers
+to tackle complex and latency-prone sensing and decision problems. These
+demands come at the cost of a growing computational burden and, as a result,
+larger controller latencies. To maximize robustness to mechanical disturbances
+and achieve high control performance, we emphasize the necessity for executing
+damping feedback in close proximity to the control plant while allocating
+stiffness feedback in a latency-prone centralized control process.
+Additionally, series elastic actuators (SEAs) are becoming prevalent in
+torque-controlled robots during recent years to achieve compliant interactions
+with environments and humans. However, designing optimal impedance controllers
+and characterizing impedance performance for SEAs with time delays and
+filtering are still under-explored problems. The presented study addresses the
+optimal controller design problem by devising a critically-damped gain design
+method for a class of SEA cascaded control architectures, which is composed of
+outer-impedance and inner-torque feedback loops. Via the proposed controller
+design criterion, we adopt frequency-domain methods to thoroughly analyze the
+effects of time delays, filtering and load inertia on SEA impedance
+performance. These results are further validated through the analysis,
+simulation, and experimental testing on high-performance actuators and on an
+omnidirectional mobile base.
+",1,0,0,0,0,0
+13376,Simulation Methods for Stochastic Storage Problems: A Statistical Learning Perspective,"  We consider solution of stochastic storage problems through regression Monte
+Carlo (RMC) methods. Taking a statistical learning perspective, we develop the
+dynamic emulation algorithm (DEA) that unifies the different existing
+approaches in a single modular template. We then investigate the two central
+aspects of regression architecture and experimental design that constitute DEA.
+For the regression piece, we discuss various non-parametric approaches, in
+particular introducing the use of Gaussian process regression in the context of
+stochastic storage. For simulation design, we compare the performance of
+traditional design (grid discretization), against space-filling, and several
+adaptive alternatives. The overall DEA template is illustrated with multiple
+examples drawing from natural gas storage valuation and optimal control of
+back-up generator in a microgrid.
+",0,0,0,0,0,1
+13377,Flashes of Hidden Worlds at Colliders,"  (This is a general physics level overview article about hidden sectors, and
+how they motivate searches for long-lived particles. Intended for publication
+in Physics Today.)
+Searches for new physics at the Large Hadron Collider have so far come up
+empty, but we just might not be looking in the right place. Spectacular bursts
+of particles appearing seemingly out of nowhere could shed light on some of
+nature's most profound mysteries.
+",0,1,0,0,0,0
+13378,Espresso: Brewing Java For More Non-Volatility with Non-volatile Memory,"  Fast, byte-addressable non-volatile memory (NVM) embraces both near-DRAM
+latency and disk-like persistence, which has generated considerable interests
+to revolutionize system software stack and programming models. However, it is
+less understood how NVM can be combined with managed runtime like Java virtual
+machine (JVM) to ease persistence management. This paper proposes Espresso, a
+holistic extension to Java and its runtime, to enable Java programmers to
+exploit NVM for persistence management with high performance. Espresso first
+provides a general persistent heap design called Persistent Java Heap (PJH) to
+manage persistent data as normal Java objects. The heap is then strengthened
+with a recoverable mechanism to provide crash consistency for heap metadata. It
+then provides a new abstraction called Persistent Java Object (PJO) to provide
+an easy-to-use but safe persistent programming model for programmers to persist
+application data. The evaluation confirms that Espresso significantly
+outperforms state-of-art NVM support for Java (i.e., JPA and PCJ) while being
+compatible to existing data structures in Java programs.
+",1,0,0,0,0,0
+13379,Multimodal Machine Learning: A Survey and Taxonomy,"  Our experience of the world is multimodal - we see objects, hear sounds, feel
+texture, smell odors, and taste flavors. Modality refers to the way in which
+something happens or is experienced and a research problem is characterized as
+multimodal when it includes multiple such modalities. In order for Artificial
+Intelligence to make progress in understanding the world around us, it needs to
+be able to interpret such multimodal signals together. Multimodal machine
+learning aims to build models that can process and relate information from
+multiple modalities. It is a vibrant multi-disciplinary field of increasing
+importance and with extraordinary potential. Instead of focusing on specific
+multimodal applications, this paper surveys the recent advances in multimodal
+machine learning itself and presents them in a common taxonomy. We go beyond
+the typical early and late fusion categorization and identify broader
+challenges that are faced by multimodal machine learning, namely:
+representation, translation, alignment, fusion, and co-learning. This new
+taxonomy will enable researchers to better understand the state of the field
+and identify directions for future research.
+",1,0,0,0,0,0
+13380,The Motivic Cofiber of $τ$,"  Consider the Tate twist $\tau \in H^{0,1}(S^{0,0})$ in the mod 2 cohomology
+of the motivic sphere. After 2-completion, the motivic Adams spectral sequence
+realizes this element as a map $\tau \colon S^{0,-1} \to S^{0,0}$, with cofiber
+$C\tau$. We show that this motivic 2-cell complex can be endowed with a unique
+$E_{\infty}$ ring structure. Moreover, this promotes the known isomorphism
+$\pi_{\ast,\ast} C\tau \cong
+\mathrm{Ext}^{\ast,\ast}_{BP_{\ast}BP}(BP_{\ast},BP_{\ast})$ to an isomorphism
+of rings which also preserves higher products.
+We then consider the closed symmetric monoidal category $({
+}_{C\tau}\textbf{Mod}, - \wedge_{C\tau} -)$ which lives in the kernel of Betti
+realization. Given a motivic spectrum $X$, the $C\tau$-induced spectrum $X
+\wedge C\tau$ is usually better behaved and easier to understand than $X$
+itself. We specifically illustrate this concept in the examples of the mod 2
+Eilenberg-Maclane spectrum $H\mathbb{F}_2$, the mod 2 Moore spectrum
+$S^{0,0}/2$ and the connective hermitian $K$-theory spectrum $kq$.
+",0,0,1,0,0,0
+13381,EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression,"  Reliable diagnosis of depressive disorder is essential for both optimal
+treatment and prevention of fatal outcomes. In this study, we aimed to
+elucidate the effectiveness of two non-linear measures, Higuchi Fractal
+Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders
+when applied on EEG. HFD and SampEn of EEG signals were used as features for
+seven machine learning algorithms including Multilayer Perceptron, Logistic
+Regression, Support Vector Machines with the linear and polynomial kernel,
+Decision Tree, Random Forest, and Naive Bayes classifier, discriminating EEG
+between healthy control subjects and patients diagnosed with depression. We
+confirmed earlier observations that both non-linear measures can discriminate
+EEG signals of patients from healthy control subjects. The results suggest that
+good classification is possible even with a small number of principal
+components. Average accuracy among classifiers ranged from 90.24% to 97.56%.
+Among the two measures, SampEn had better performance. Using HFD and SampEn and
+a variety of machine learning techniques we can accurately discriminate
+patients diagnosed with depression vs controls which can serve as a highly
+sensitive, clinically relevant marker for the diagnosis of depressive
+disorders.
+",0,0,0,1,1,0
+13382,On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook,"  Targeted advertising is meant to improve the efficiency of matching
+advertisers to their customers. However, targeted advertising can also be
+abused by malicious advertisers to efficiently reach people susceptible to
+false stories, stoke grievances, and incite social conflict. Since targeted ads
+are not seen by non-targeted and non-vulnerable people, malicious ads are
+likely to go unreported and their effects undetected. This work examines a
+specific case of malicious advertising, exploring the extent to which political
+ads from the Russian Intelligence Research Agency (IRA) run prior to 2016 U.S.
+elections exploited Facebook's targeted advertising infrastructure to
+efficiently target ads on divisive or polarizing topics (e.g., immigration,
+race-based policing) at vulnerable sub-populations. In particular, we do the
+following: (a) We conduct U.S. census-representative surveys to characterize
+how users with different political ideologies report, approve, and perceive
+truth in the content of the IRA ads. Our surveys show that many ads are
+""divisive"": they elicit very different reactions from people belonging to
+different socially salient groups. (b) We characterize how these divisive ads
+are targeted to sub-populations that feel particularly aggrieved by the status
+quo. Our findings support existing calls for greater transparency of content
+and targeting of political ads. (c) We particularly focus on how the Facebook
+ad API facilitates such targeting. We show how the enormous amount of personal
+data Facebook aggregates about users and makes available to advertisers enables
+such malicious targeting.
+",1,0,0,0,0,0
+13383,On Helmholtz free energy for finite abstract simplicial complexes,"  We prove a Gauss-Bonnet formula X(G) = sum_x K(x), where K(x)=(-1)^dim(x)
+(1-X(S(x))) is a curvature of a vertex x with unit sphere S(x) in the
+Barycentric refinement G1 of a simplicial complex G. K(x) is dual to
+(-1)^dim(x) for which Gauss-Bonnet is the definition of Euler characteristic X.
+Because the connection Laplacian L'=1+A' of G is unimodular, where A' is the
+adjacency matrix of of the connection graph G', the Green function values
+g(x,y) = (1+A')^-1_xy are integers and 1-X(S(x))=g(x,x). Gauss-Bonnet for K^+
+reads therefore as str(g)=X(G), where str is the super trace. As g is a
+time-discrete heat kernel, this is a cousin to McKean-Singer str(exp(-Lt)) =
+X(G) for the Hodge Laplacian L=dd^* +d^*d which lives on the same Hilbert space
+than L'. Both formulas hold for an arbitrary finite abstract simplicial complex
+G. Writing V_x(y)= g(x,y) for the Newtonian potential of the connection
+Laplacian, we prove sum_y V_x(y) = K(x), so that by the new Gauss-Bonnet
+formula, the Euler characteristic of G agrees with the total potential
+theoretic energy sum_x,y g(x,y)=X(G) of G. The curvature K now relates to the
+probability measure p minimizing the internal energy U(p)=sum_x,y g(x,y) p(x)
+p(y) of the complex. Since both the internal energy (here linked to topology)
+and Shannon entropy are natural and unique in classes of functionals, we then
+look at critical points p the Helmholtz free energy F(p)=(1-T) U(p)-T S(p)
+which combines the energy functional U and the entropy functional S(p)=-sum_x
+p(x) log(p(x)). As the temperature T changes, we observe bifurcation phenomena.
+Already for G=K_3 both a saddle node bifurcation and a pitchfork bifurcation
+occurs. The saddle node bifurcation leads to a catastrophe: the function T ->
+F(p(T),T) is discontinuous if p(T) is a free energy minimizer.
+",1,0,1,0,0,0
+13384,Global Patterns of Synchronization in Human Communications,"  Social media are transforming global communication and coordination. The data
+derived from social media can reveal patterns of human behavior at all levels
+and scales of society. Using geolocated Twitter data, we have quantified
+collective behaviors across multiple scales, ranging from the commutes of
+individuals, to the daily pulse of 50 major urban areas and global patterns of
+human coordination. Human activity and mobility patterns manifest the synchrony
+required for contingency of actions between individuals. Urban areas show
+regular cycles of contraction and expansion that resembles heartbeats linked
+primarily to social rather than natural cycles. Business hours and circadian
+rhythms influence daily cycles of work, recreation, and sleep. Different urban
+areas have characteristic signatures of daily collective activities. The
+differences are consistent with a new emergent global synchrony that couples
+behavior in distant regions across the world. A globally synchronized peak that
+includes exchange of ideas and information across Europe, Africa, Asia and
+Australasia. We propose a dynamical model to explain the emergence of global
+synchrony in the context of increasing global communication and reproduce the
+observed behavior. The collective patterns we observe show how social
+interactions lead to interdependence of behavior manifest in the
+synchronization of communication. The creation and maintenance of temporally
+sensitive social relationships results in the emergence of complexity of the
+larger scale behavior of the social system.
+",1,1,0,0,0,0
+13385,On the predictability of infectious disease outbreaks,"  Infectious disease outbreaks recapitulate biology: they emerge from the
+multi-level interaction of hosts, pathogens, and their shared environment. As a
+result, predicting when, where, and how far diseases will spread requires a
+complex systems approach to modeling. Recent studies have demonstrated that
+predicting different components of outbreaks--e.g., the expected number of
+cases, pace and tempo of cases needing treatment, demand for prophylactic
+equipment, importation probability etc.--is feasible. Therefore, advancing both
+the science and practice of disease forecasting now requires testing for the
+presence of fundamental limits to outbreak prediction. To investigate the
+question of outbreak prediction, we study the information theoretic limits to
+forecasting across a broad set of infectious diseases using permutation entropy
+as a model independent measure of predictability. Studying the predictability
+of a diverse collection of historical outbreaks--including, chlamydia, dengue,
+gonorrhea, hepatitis A, influenza, measles, mumps, polio, and whooping
+cough--we identify a fundamental entropy barrier for infectious disease time
+series forecasting. However, we find that for most diseases this barrier to
+prediction is often well beyond the time scale of single outbreaks. We also
+find that the forecast horizon varies by disease and demonstrate that both
+shifting model structures and social network heterogeneity are the most likely
+mechanisms for the observed differences across contagions. Our results
+highlight the importance of moving beyond time series forecasting, by embracing
+dynamic modeling approaches, and suggest challenges for performing model
+selection across long time series. We further anticipate that our findings will
+contribute to the rapidly growing field of epidemiological forecasting and may
+relate more broadly to the predictability of complex adaptive systems.
+",0,1,0,0,0,0
+13386,Multi-Player Bandits Revisited,"  Multi-player Multi-Armed Bandits (MAB) have been extensively studied in the
+literature, motivated by applications to Cognitive Radio systems. Driven by
+such applications as well, we motivate the introduction of several levels of
+feedback for multi-player MAB algorithms. Most existing work assume that
+sensing information is available to the algorithm. Under this assumption, we
+improve the state-of-the-art lower bound for the regret of any decentralized
+algorithms and introduce two algorithms, RandTopM and MCTopM, that are shown to
+empirically outperform existing algorithms. Moreover, we provide strong
+theoretical guarantees for these algorithms, including a notion of asymptotic
+optimality in terms of the number of selections of bad arms. We then introduce
+a promising heuristic, called Selfish, that can operate without sensing
+information, which is crucial for emerging applications to Internet of Things
+networks. We investigate the empirical performance of this algorithm and
+provide some first theoretical elements for the understanding of its behavior.
+",1,0,0,1,0,0
+13387,Model Averaging for Generalized Linear Model with Covariates that are Missing completely at Random,"  In this paper, we consider the estimation of generalized linear models with
+covariates that are missing completely at random. We propose a model averaging
+estimation method and prove that the corresponding model averaging estimator is
+asymptotically optimal under certain assumptions. Simulaiton results illustrate
+that this method has better performance than other alternatives under most
+situations.
+",0,0,1,1,0,0
+13388,Demonstration of the length stability requirements for ALPS II with a high finesse 9.2m cavity,"  Light-shining-through-a-wall experiments represent a new experimental
+approach in the search for undiscovered elementary particles not accessible
+with accelerator based experiments. The next generation of these experiments,
+such as ALPS~II, require high finesse, long baseline optical cavities with fast
+length control. In this paper we report on a length stabilization control loop
+used to keep a 9.2\,m cavity resonant. It achieves a unity-gain-frequency of
+4\,kHz and actuates on a mirror with a diameter of 50.8\,mm. The finesse of
+this cavity was measured to be 101,304$\pm$540 for 1064\,nm light. The
+differential cavity length noise between 1064\,nm and 532\,nm light was also
+measured since 532\,nm light will be used to sense the length of the
+regeneration cavity. Out-of-loop noise sources and different control strategies
+are discussed, in order to fulfill the length stability requirements for
+ALPS~II.
+",0,1,0,0,0,0
+13389,Shrub-depth: Capturing Height of Dense Graphs,"  The recent increase of interest in the graph invariant called tree-depth and
+in its applications in algorithms and logic on graphs led to a natural
+question: is there an analogously useful ""depth"" notion also for dense graphs
+(say; one which is stable under graph complementation)? To this end, in a 2012
+conference paper, a new notion of shrub-depth has been introduced, such that it
+is related to the established notion of clique-width in a similar way as
+tree-depth is related to tree-width. Since then shrub-depth has been
+successfully used in several research papers. Here we provide an in-depth
+review of the definition and basic properties of shrub-depth, and we focus on
+its logical aspects which turned out to be most useful. In particular, we use
+shrub-depth to give a characterization of the lower ${\omega}$ levels of the
+MSO1 transduction hierarchy of simple graphs.
+",1,0,0,0,0,0
+13390,Detecting Statistical Interactions from Neural Network Weights,"  Interpreting neural networks is a crucial and challenging task in machine
+learning. In this paper, we develop a novel framework for detecting statistical
+interactions captured by a feedforward multilayer neural network by directly
+interpreting its learned weights. Depending on the desired interactions, our
+method can achieve significantly better or similar interaction detection
+performance compared to the state-of-the-art without searching an exponential
+solution space of possible interactions. We obtain this accuracy and efficiency
+by observing that interactions between input features are created by the
+non-additive effect of nonlinear activation functions, and that interacting
+paths are encoded in weight matrices. We demonstrate the performance of our
+method and the importance of discovered interactions via experimental results
+on both synthetic datasets and real-world application datasets.
+",1,0,0,1,0,0
+13391,Making intersections safer with I2V communication,"  Intersections are hazardous places. Threats arise from interactions among
+pedestrians, bicycles and vehicles, more complicated vehicle trajectories in
+the absence of lane markings, phases that prevent determining who has the right
+of way, invisible vehicle approaches, vehicle obstructions, and illegal
+movements. These challenges are not fully addressed by the ""road diet"" and road
+redesign prescribed in Vision Zero plans, nor will they be completely overcome
+by autonomous vehicles with their many sensors and tireless attention to
+surroundings. Accidents can also occur because drivers, cyclists and
+pedestrians do not have the information they need to avoid wrong decisions. In
+these cases, the missing information can be computed and broadcast by an
+intelligent intersection. The information gives the current full signal phase,
+an estimate of the time when the phase will change, and the occupancy of the
+blind spots of the driver or autonomous vehicle. The paper develops a design of
+the intelligent intersection, motivated by the analysis of an accident at an
+intersection in Tempe, AZ, between an automated Uber Volvo and a manual Honda
+CRV and culminates in a proposal for an intelligent intersection
+infrastructure. The intelligent intersection also serves as a software-enabled
+version of the `protected intersection' design to improve the passage of
+cyclists and pedestrians through an intersection.
+",1,0,0,0,0,0
+13392,Convolutional Sparse Representations with Gradient Penalties,"  While convolutional sparse representations enjoy a number of useful
+properties, they have received limited attention for image reconstruction
+problems. The present paper compares the performance of block-based and
+convolutional sparse representations in the removal of Gaussian white noise.
+While the usual formulation of the convolutional sparse coding problem is
+slightly inferior to the block-based representations in this problem, the
+performance of the convolutional form can be boosted beyond that of the
+block-based form by the inclusion of suitable penalties on the gradients of the
+coefficient maps.
+",1,0,0,0,0,0
+13393,On the origin of the crescent-shaped distributions observed by MMS at the magnetopause,"  MMS observations recently confirmed that crescent-shaped electron velocity
+distributions in the plane perpendicular to the magnetic field occur in the
+electron diffusion region near reconnection sites at Earth's magnetopause. In
+this paper, we re-examine the origin of the crescent-shaped distributions in
+the light of our new finding that ions and electrons are drifting in opposite
+directions when displayed in magnetopause boundary-normal coordinates.
+Therefore, ExB drifts cannot cause the crescent shapes. We performed a
+high-resolution multi-scale simulation capturing sub-electron skin depth
+scales. The results suggest that the crescent-shaped distributions are caused
+by meandering orbits without necessarily requiring any additional processes
+found at the magnetopause such as the highly asymmetric magnetopause ambipolar
+electric field. We use an adiabatic Hamiltonian model of particle motion to
+confirm that conservation of canonical momentum in the presence of magnetic
+field gradients causes the formation of crescent shapes without invoking
+asymmetries or the presence of an ExB drift. An important consequence of this
+finding is that we expect crescent-shaped distributions also to be observed in
+the magnetotail, a prediction that MMS will soon be able to test.
+",0,1,0,0,0,0
+13394,Projected support points: a new method for high-dimensional data reduction,"  In an era where big and high-dimensional data is readily available, data
+scientists are inevitably faced with the challenge of reducing this data for
+expensive downstream computation or analysis. To this end, we present here a
+new method for reducing high-dimensional big data into a representative point
+set, called projected support points (PSPs). A key ingredient in our method is
+the so-called sparsity-inducing (SpIn) kernel, which encourages the
+preservation of low-dimensional features when reducing high-dimensional data.
+We begin by introducing a unifying theoretical framework for data reduction,
+connecting PSPs with fundamental sampling principles from experimental design
+and Quasi-Monte Carlo. Through this framework, we then derive sparsity
+conditions under which the curse-of-dimensionality in data reduction can be
+lifted for our method. Next, we propose two algorithms for one-shot and
+sequential reduction via PSPs, both of which exploit big data subsampling and
+majorization-minimization for efficient optimization. Finally, we demonstrate
+the practical usefulness of PSPs in two real-world applications, the first for
+data reduction in kernel learning, and the second for reducing Markov Chain
+Monte Carlo (MCMC) chains.
+",0,0,0,1,0,0
+13395,AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints,"  We present AutonoVi:, a novel algorithm for autonomous vehicle navigation
+that supports dynamic maneuvers and satisfies traffic constraints and norms.
+Our approach is based on optimization-based maneuver planning that supports
+dynamic lane-changes, swerving, and braking in all traffic scenarios and guides
+the vehicle to its goal position. We take into account various traffic
+constraints, including collision avoidance with other vehicles, pedestrians,
+and cyclists using control velocity obstacles. We use a data-driven approach to
+model the vehicle dynamics for control and collision avoidance. Furthermore,
+our trajectory computation algorithm takes into account traffic rules and
+behaviors, such as stopping at intersections and stoplights, based on an
+arc-spline representation. We have evaluated our algorithm in a simulated
+environment and tested its interactive performance in urban and highway driving
+scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios
+include jaywalking pedestrians, sudden stops from high speeds, safely passing
+cyclists, a vehicle suddenly swerving into the roadway, and high-density
+traffic where the vehicle must change lanes to progress more effectively.
+",1,0,0,0,0,0
+13396,On SGD's Failure in Practice: Characterizing and Overcoming Stalling,"  Stochastic Gradient Descent (SGD) is widely used in machine learning problems
+to efficiently perform empirical risk minimization, yet, in practice, SGD is
+known to stall before reaching the actual minimizer of the empirical risk. SGD
+stalling has often been attributed to its sensitivity to the conditioning of
+the problem; however, as we demonstrate, SGD will stall even when applied to a
+simple linear regression problem with unity condition number for standard
+learning rates. Thus, in this work, we numerically demonstrate and
+mathematically argue that stalling is a crippling and generic limitation of SGD
+and its variants in practice. Once we have established the problem of stalling,
+we generalize an existing framework for hedging against its effects, which (1)
+deters SGD and its variants from stalling, (2) still provides convergence
+guarantees, and (3) makes SGD and its variants more practical methods for
+minimization.
+",0,0,1,1,0,0
+13397,Composite Rational Functions and Arithmetic Progressions,"  In this paper we deal with composite rational functions having zeros and
+poles forming consecutive elements of an arithmetic progression. We also
+correct a result published earlier related to composite rational functions
+having a fixed number of zeros and poles.
+",0,0,1,0,0,0
+13398,The Geometry of Concurrent Interaction: Handling Multiple Ports by Way of Multiple Tokens (Long Version),"  We introduce a geometry of interaction model for Mazza's multiport
+interaction combinators, a graph-theoretic formalism which is able to
+faithfully capture concurrent computation as embodied by process algebras like
+the $\pi$-calculus. The introduced model is based on token machines in which
+not one but multiple tokens are allowed to traverse the underlying net at the
+same time. We prove soundness and adequacy of the introduced model. The former
+is proved as a simulation result between the token machines one obtains along
+any reduction sequence. The latter is obtained by a fine analysis of
+convergence, both in nets and in token machines.
+",1,0,0,0,0,0
+13399,A High Space Density of Luminous Lyman Alpha Emitters at z~6.5,"  We present the results of a systematic search for Lyman-alpha emitters (LAEs)
+at $6 \lesssim z \lesssim 7.6$ using the HST WFC3 Infrared Spectroscopic
+Parallel (WISP) Survey. Our total volume over this redshift range is $\sim 8
+\times10^5$ Mpc$^3$, comparable to many of the narrowband surveys despite their
+larger area coverage. We find two LAEs at $z=6.38$ and $6.44$ with line
+luminosities of L$_{\mathrm{Ly}\alpha} \sim 4.7 \times 10^{43}$ erg s$^{-1}$,
+putting them among the brightest LAEs discovered at these redshifts. Taking
+advantage of the broad spectral coverage of WISP, we are able to rule out
+almost all lower-redshift contaminants. The WISP LAEs have a high number
+density of $7.7\times10^{-6}$ Mpc$^{-3}$. We argue that the LAEs reside in
+Mpc-scale ionized bubbles that allow the Lyman-alpha photons to redshift out of
+resonance before encountering the neutral IGM. We discuss possible ionizing
+sources and conclude that the observed LAEs alone are not sufficient to ionize
+the bubbles.
+",0,1,0,0,0,0
+13400,SYZ transforms for immersed Lagrangian multi-sections,"  In this paper, we study the geometry of the SYZ transform on a semi-flat
+Lagrangian torus fibration. Our starting point is an investigation on the
+relation between Lagrangian surgery of a pair of straight lines in a symplectic
+2-torus and extension of holomorphic vector bundles over the mirror elliptic
+curve, via the SYZ transform for immersed Lagrangian multi-sections. This study
+leads us to a new notion of equivalence between objects in the immersed Fukaya
+category of a general compact symplectic manifold $(M, \omega)$, under which
+the immersed Floer cohomology is invariant; in particular, this provides an
+answer to a question of Akaho-Joyce. Furthermore, if $M$ admits a Lagrangian
+torus fibration over an integral affine manifold, we prove, under some
+additional assumptions, that this new equivalence is mirror to isomorphism
+between holomorphic vector bundles over the dual torus fibration via the SYZ
+transform.
+",0,0,1,0,0,0
+13401,Topological Representation of the Transit Sets of k-Point Crossover Operators,"  $k$-point crossover operators and their recombination sets are studied from
+different perspectives. We show that transit functions of $k$-point crossover
+generate, for all $k>1$, the same convexity as the interval function of the
+underlying graph. This settles in the negative an open problem by Mulder about
+whether the geodesic convexity of a connected graph $G$ is uniquely determined
+by its interval function $I$. The conjecture of Gitchoff and Wagner that for
+each transit set $R_k(x,y)$ distinct from a hypercube there is a unique pair of
+parents from which it is generated is settled affirmatively. Along the way we
+characterize transit functions whose underlying graphs are Hamming graphs, and
+those with underlying partial cube graphs. For general values of $k$ it is
+shown that the transit sets of $k$-point crossover operators are the subsets
+with maximal Vapnik-Chervonenkis dimension. Moreover, the transit sets of
+$k$-point crossover on binary strings form topes of uniform oriented matroid of
+VC-dimension $k+1$. The Topological Representation Theorem for oriented
+matroids therefore implies that $k$-point crossover operators can be
+represented by pseudosphere arrangements. This provides the tools necessary to
+study the special case $k=2$ in detail.
+",1,0,1,0,0,0
+13402,Human-Robot Collaboration: From Psychology to Social Robotics,"  With the advances in robotic technology, research in human-robot
+collaboration (HRC) has gained in importance. For robots to interact with
+humans autonomously they need active decision making that takes human partners
+into account. However, state-of-the-art research in HRC does often assume a
+leader-follower division, in which one agent leads the interaction. We believe
+that this is caused by the lack of a reliable representation of the human and
+the environment to allow autonomous decision making. This problem can be
+overcome by an embodied approach to HRC which is inspired by psychological
+studies of human-human interaction (HHI). In this survey, we review
+neuroscientific and psychological findings of the sensorimotor patterns that
+govern HHI and view them in a robotics context. Additionally, we study the
+advances made by the robotic community into the direction of embodied HRC. We
+focus on the mechanisms that are required for active, physical human-robot
+collaboration. Finally, we discuss the similarities and differences in the two
+fields of study which pinpoint directions of future research.
+",1,0,0,0,0,0
+13403,Enhancing TCP End-to-End Performance in Millimeter-Wave Communications,"  Recently, millimeter-wave (mmWave) communications have received great
+attention due to the availability of large spectrum resources. Nevertheless,
+their impact on TCP performance has been overlooked, which is observed that the
+said TCP performance collapse occurs owing to the significant difference in
+signal quality between LOS and NLOS links. We propose a novel TCP design for
+mmWave communications, a mmWave performance enhancing proxy (mmPEP), enabling
+not only to overcome TCP performance collapse but also exploit the properties
+of mmWave channels. The base station installs the TCP proxy to operate the two
+functionalities called Ack management and batch retransmission. Specifically,
+the proxy sends the said early-Ack to the server not to decrease its sending
+rate even in the NLOS status. In addition, when a packet-loss is detected, the
+proxy retransmits not only lost packets but also the certain number of the
+following packets expected to be lost too. It is verified by ns-3 simulation
+that compared with benchmark, mmPEP enhances the end-to-end rate and packet
+delivery ratio by maintaining high sending rate with decreasing the loss
+recovery time.
+",1,0,0,0,0,0
+13404,Adaptive recurrence quantum entanglement distillation for two-Kraus-operator channels,"  Quantum entanglement serves as a valuable resource for many important quantum
+operations. A pair of entangled qubits can be shared between two agents by
+first preparing a maximally entangled qubit pair at one agent, and then sending
+one of the qubits to the other agent through a quantum channel. In this
+process, the deterioration of entanglement is inevitable since the noise
+inherent in the channel contaminates the qubit. To address this challenge,
+various quantum entanglement distillation (QED) algorithms have been developed.
+Among them, recurrence algorithms have advantages in terms of implementability
+and robustness. However, the efficiency of recurrence QED algorithms has not
+been investigated thoroughly in the literature. This paper put forth two
+recurrence QED algorithms that adapt to the quantum channel to tackle the
+efficiency issue. The proposed algorithms have guaranteed convergence for
+quantum channels with two Kraus operators, which include phase-damping and
+amplitude-damping channels. Analytical results show that the convergence speed
+of these algorithms is improved from linear to quadratic and one of the
+algorithms achieves the optimal speed. Numerical results confirm that the
+proposed algorithms significantly improve the efficiency of QED.
+",0,0,1,0,0,0
+13405,Optimised surface-electrode ion-trap junctions for experiments with cold molecular ions,"  We discuss the design and optimisation of two types of junctions between
+surface-electrode radiofrequency ion-trap arrays that enable the integration of
+experiments with sympathetically cooled molecular ions on a monolithic chip
+device. A detailed description of a multi-objective optimisation procedure
+applicable to an arbitrary planar junction is presented, and the results for a
+cross junction between four quadrupoles as well as a quadrupole-to-octupole
+junction are discussed. Based on these optimised functional elements, we
+propose a multi-functional ion-trap chip for experiments with translationally
+cold molecular ions at temperatures in the millikelvin range. This study opens
+the door to extending complex chip-based trapping techniques to
+Coulomb-crystallised molecular ions with potential applications in mass
+spectrometry, spectroscopy, controlled chemistry and quantum technology.
+",0,1,0,0,0,0
+13406,A simple anisotropic three-dimensional quantum spin liquid with fracton topological order,"  We present a three-dimensional cubic lattice spin model, anisotropic in the
+$\hat{z}$ direction, that exhibits fracton topological order. The latter is a
+novel type of topological order characterized by the presence of immobile
+pointlike excitations, named fractons, residing at the corners of an operator
+with two-dimensional support. As other recent fracton models, ours exhibits a
+subextensive ground state degeneracy: On an $L_x\times L_y\times L_z$
+three-torus, it has a $2^{2L_z}$ topological degeneracy, and an additional
+non-topological degeneracy equal to $2^{L_xL_y-2}$. The fractons can be
+combined into composite excitations that move either in a straight line along
+the $\hat{z}$ direction, or freely in the $xy$ plane at a given height $z$.
+While our model draws inspiration from the toric code, we demonstrate that it
+cannot be adiabatically connected to a layered toric code construction.
+Additionally, we investigate the effects of imposing open boundary conditions
+on our system. We find zero energy modes on the surfaces perpendicular to
+either the $\hat{x}$ or $\hat{y}$ directions, and their absence on the surfaces
+normal to $\hat{z}$. This result can be explained using the properties of the
+two kinds of composite two-fracton mobile excitations.
+",0,1,0,0,0,0
+13407,Onsets and Frames: Dual-Objective Piano Transcription,"  We advance the state of the art in polyphonic piano music transcription by
+using a deep convolutional and recurrent neural network which is trained to
+jointly predict onsets and frames. Our model predicts pitch onset events and
+then uses those predictions to condition framewise pitch predictions. During
+inference, we restrict the predictions from the framewise detector by not
+allowing a new note to start unless the onset detector also agrees that an
+onset for that pitch is present in the frame. We focus on improving onsets and
+offsets together instead of either in isolation as we believe this correlates
+better with human musical perception. Our approach results in over a 100%
+relative improvement in note F1 score (with offsets) on the MAPS dataset.
+Furthermore, we extend the model to predict relative velocities of normalized
+audio which results in more natural-sounding transcriptions.
+",1,0,0,1,0,0
+13408,Modified mean curvature flow of entire locally Lipschitz radial graphs in hyperbolic space,"  In a previous joint work of Xiao and the second author, the modified mean
+curvature flow (MMCF) in hyperbolic space $\mathbb{H}^{n+1}$: $$\frac{\partial
+\mathbf{F}}{\partial t} = (H-\sigma)\,\vnu\,,\quad \quad \sigma\in (-n,n)$$ was
+first introduced and the flow starting from an entire Lipschitz continuous
+radial graph with uniform local ball condition on the asymptotic boundary was
+shown to exist for all time and converge to a complete hypersurface of constant
+mean curvature with prescribed asymptotic boundary at infinity. In this paper,
+we remove the uniform local ball condition on the asymptotic boundary of the
+initial hypersurface, and prove that the MMCF starting from an entire locally
+Lipschitz continuous radial graph exists and stays radially graphic for all
+time.
+",0,0,1,0,0,0
+13409,Calibration of atomic trajectories in a large-area dual-atom-interferometer gyroscope,"  We propose and demonstrate a method for calibrating atomic trajectories in a
+large-area dual-atom-interferometer gyroscope. The atom trajectories are
+monitored by modulating and delaying the Raman transition, and they are
+precisely calibrated by controlling the laser orientation and the bias magnetic
+field. To improve the immunity to the gravity effect and the common phase
+noise, the symmetry and the overlapping of two large-area atomic interference
+loops are optimized by calibrating the atomic trajectories and by aligning the
+Raman-laser orientations. The dual-atom-interferometer gyroscope is applied in
+the measurement of the Earth rotation. The sensitivity is $1.2\times10^{-6}$
+rad/s/$\sqrt{Hz}$, and the long-term stability is $6.2\times10^{-8}$ rad/s $@$
+$2000$ s.
+",0,1,0,0,0,0
+13410,Self-adjoint and skew-symmetric extensions of the Laplacian with singular Robin boundary condition,"  We study the Laplacian in a smooth bounded domain, with a varying Robin
+boundary condition singular at one point. The associated quadratic form is not
+semi-bounded from below, and the corresponding Laplacian is not self-adjoint,
+it has the residual spectrum covering the whole complex plane. We describe its
+self-adjoint extensions and exhibit a physically relevant skew-symmetric one.
+We approximate the boundary condition, giving rise to a family of self-adjoint
+operators, and we describe their eigenvalues by the method of matched
+asymptotic expansions. These eigenvalues acquire a strange behaviour when the
+small perturbation parameter $\varepsilon>0$ tends to zero, namely they become
+almost periodic in the logarithmic scale $|\ln \epsilon|$ and, in this way,
+""wander"" along the real axis at a speed $O(\eps^{-1})$.
+",0,0,1,0,0,0
+13411,Experimental observation of fractional topological phases with photonic qudits,"  Geometrical and topological phases play a fundamental role in quantum theory.
+Geometric phases have been proposed as a tool for implementing unitary gates
+for quantum computation. A fractional topological phase has been recently
+discovered for bipartite systems. The dimension of the Hilbert space determines
+the topological phase of entangled qudits under local unitary operations. Here
+we investigate fractional topological phases acquired by photonic entangled
+qudits. Photon pairs prepared as spatial qudits are operated inside a Sagnac
+interferometer and the two-photon interference pattern reveals the topological
+phase as fringes shifts when local operations are performed. Dimensions $d = 2,
+3$ and $4$ were tested, showing the expected theoretical values.
+",0,1,0,0,0,0
+13412,Homotopy groups of generic leaves of logarithmic foliations,"  We study the homotopy groups of generic leaves of logarithmic foliations on
+complex projective manifolds. We exhibit a relation between the homotopy groups
+of a generic leaf and of the complement of the polar divisor of the logarithmic
+foliation.
+",0,0,1,0,0,0
+13413,"On van Kampen-Flores, Conway-Gordon-Sachs and Radon theorems","  We exhibit relations between van Kampen-Flores, Conway-Gordon-Sachs and Radon
+theorems, by presenting direct proofs of some implications between them. The
+key idea is an interesting relation between the van Kampen and the
+Conway-Gordon-Sachs numbers for restrictions of a map of $(d+2)$-simplex to
+$\mathbb R^d$ to the $(d+1)$-face and to the $[d/2]$-skeleton.
+",1,0,1,0,0,0
+13414,"Superzeta functions, regularized products, and the Selberg zeta function on hyperbolic manifolds with cusps","  Let $\Lambda = \{\lambda_{k}\}$ denote a sequence of complex numbers and
+assume that that the counting function $#\{\lambda_{k} \in \Lambda : |
+\lambda_{k}| < T\} =O(T^{n})$ for some integer $n$. From Hadamard's theorem, we
+can construct an entire function $f$ of order at most $n$ such that $\Lambda$
+is the divisor $f$. In this article we prove, under reasonably general
+conditions, that the superzeta function $\Z_{f}(s,z)$ associated to $\Lambda$
+admits a meromorphic continuation. Furthermore, we describe the relation
+between the regularized product of the sequence $z-\Lambda$ and the function
+$f$ as constructed as a Weierstrass product. In the case $f$ admits a Dirichlet
+series expansion in some right half-plane, we derive the meromorphic
+continuation in $s$ of $\Z_{f}(s,z)$ as an integral transform of $f'/f$. We
+apply these results to obtain superzeta product evaluations of Selberg zeta
+function associated to finite volume hyperbolic manifolds with cusps.
+",0,0,1,0,0,0
+13415,Fine Selmer Groups and Isogeny Invariance,"  We investigate fine Selmer groups for elliptic curves and for Galois
+representations over a number field. More specifically, we discuss Conjecture
+A, which states that the fine Selmer group of an elliptic curve over the
+cyclotomic extension is a finitely generated $\mathbb{Z}_p$-module. The
+relationship between this conjecture and Iwasawa's classical $\mu=0$ conjecture
+is clarified. We also present some partial results towards the question whether
+Conjecture A is invariant under isogenies.
+",0,0,1,0,0,0
+13416,Persistence paths and signature features in topological data analysis,"  We introduce a new feature map for barcodes that arise in persistent homology
+computation. The main idea is to first realize each barcode as a path in a
+convenient vector space, and to then compute its path signature which takes
+values in the tensor algebra of that vector space. The composition of these two
+operations - barcode to path, path to tensor series - results in a feature map
+that has several desirable properties for statistical learning, such as
+universality and characteristicness, and achieves state-of-the-art results on
+common classification benchmarks.
+",0,0,0,1,0,0
+13417,New Integral representations for the Fox-Wright functions and its applications,"  Our aim in this paper is to derive several new integral representations of
+the Fox-Wright functions. In particular, we give new Laplace and Stieltjes
+transform for this special functions under a special restriction on parameters.
+From the positivity conditions for the weight in these representations, we
+found sufficient conditions to be imposed on the parameters of the Fox-Wright
+functions that it be completely monotonic. As applications, we derive a class
+of function related to the Fox H-functions is positive definite and an
+investigation of a class of the Fox H-function is non-negative. Moreover, we
+extended the Luke's inequalities and we establish a new Turán type
+inequalities for the Fox-Wright function. Finally, by appealing to each of the
+Luke's inequalities, two sets of two-sided bounding inequalities for the
+generalized Mathieu's type series are proved.
+",0,0,1,0,0,0
+13418,Ultra-Fast Reactive Transport Simulations When Chemical Reactions Meet Machine Learning: Chemical Equilibrium,"  During reactive transport modeling, the computational cost associated with
+chemical reaction calculations is often 10-100 times higher than that of
+transport calculations. Most of these costs results from chemical equilibrium
+calculations that are performed at least once in every mesh cell and at every
+time step of the simulation. Calculating chemical equilibrium is an iterative
+process, where each iteration is in general so computationally expensive that
+even if every calculation converged in a single iteration, the resulting
+speedup would not be significant. Thus, rather than proposing a fast-converging
+numerical method for solving chemical equilibrium equations, we present a
+machine learning method that enables new equilibrium states to be quickly and
+accurately estimated, whenever a previous equilibrium calculation with similar
+input conditions has been performed. We demonstrate the use of this smart
+chemical equilibrium method in a reactive transport modeling example and show
+that, even at early simulation times, the majority of all equilibrium
+calculations are quickly predicted and, after some time steps, the
+machine-learning-accelerated chemical solver has been fully trained to rapidly
+perform all subsequent equilibrium calculations, resulting in speedups of
+almost two orders of magnitude. We remark that our new on-demand machine
+learning method can be applied to any case in which a massive number of
+sequential/parallel evaluations of a computationally expensive function $f$
+needs to be done, $y=f(x)$. We remark, that, in contrast to traditional machine
+learning algorithms, our on-demand training approach does not require a
+statistics-based training phase before the actual simulation of interest
+commences. The introduced on-demand training scheme requires, however, the
+first-order derivatives $\partial f/\partial x$ for later smart predictions.
+",0,1,0,1,0,0
+13419,Pseudo-edge unfoldings of convex polyhedra,"  A pseudo-edge graph of a convex polyhedron K is a 3-connected embedded graph
+in K whose vertices coincide with those of K, whose edges are distance
+minimizing geodesics, and whose faces are convex. We construct a convex
+polyhedron K in Euclidean 3-space with a pseudo-edge graph E with respect to
+which K is not unfoldable. The proof is based on a result of Pogorelov on
+convex caps with prescribed curvature, and an unfoldability criterion for
+almost flat convex caps due to Tarasov. Our example, which has 340 vertices,
+significantly simplifies an earlier construction by Tarasov, and confirms that
+Durer's conjecture does not hold for pseudo-edge unfoldings.
+",0,0,1,0,0,0
+13420,Learning Structured Text Representations,"  In this paper, we focus on learning structure-aware document representations
+from data without recourse to a discourse parser or additional annotations.
+Drawing inspiration from recent efforts to empower neural networks with a
+structural bias, we propose a model that can encode a document while
+automatically inducing rich structural dependencies. Specifically, we embed a
+differentiable non-projective parsing algorithm into a neural model and use
+attention mechanisms to incorporate the structural biases. Experimental
+evaluation across different tasks and datasets shows that the proposed model
+achieves state-of-the-art results on document modeling tasks while inducing
+intermediate structures which are both interpretable and meaningful.
+",1,0,0,0,0,0
+13421,Semi-Parametric Empirical Best Prediction for small area estimation of unemployment indicators,"  The Italian National Institute for Statistics regularly provides estimates of
+unemployment indicators using data from the Labor Force Survey. However, direct
+estimates of unemployment incidence cannot be released for Local Labor Market
+Areas. These are unplanned domains defined as clusters of municipalities; many
+are out-of-sample areas and the majority is characterized by a small sample
+size, which render direct estimates inadequate. The Empirical Best Predictor
+represents an appropriate, model-based, alternative. However, for non-Gaussian
+responses, its computation and the computation of the analytic approximation to
+its Mean Squared Error require the solution of (possibly) multiple integrals
+that, generally, have not a closed form. To solve the issue, Monte Carlo
+methods and parametric bootstrap are common choices, even though the
+computational burden is a non trivial task. In this paper, we propose a
+Semi-Parametric Empirical Best Predictor for a (possibly) non-linear mixed
+effect model by leaving the distribution of the area-specific random effects
+unspecified and estimating it from the observed data. This approach is known to
+lead to a discrete mixing distribution which helps avoid unverifiable
+parametric assumptions and heavy integral approximations. We also derive a
+second-order, bias-corrected, analytic approximation to the corresponding Mean
+Squared Error. Finite sample properties of the proposed approach are tested via
+a large scale simulation study. Furthermore, the proposal is applied to
+unit-level data from the 2012 Italian Labor Force Survey to estimate
+unemployment incidence for 611 Local Labor Market Areas using auxiliary
+information from administrative registers and the 2011 Census.
+",0,0,0,1,0,0
+13422,"The asymptotic coarse-graining formulation of slender-rods, bio-filaments and flagella","  The inertialess fluid-structure interactions of active and passive
+inextensible filaments and slender- rods are ubiquitous in nature, from the
+dynamics of semi-flexible polymers and cytoskeletal filaments to cellular
+mechanics and flagella. The coupling between the geometry of deformation and
+the phys- ical interaction governing the dynamics of bio-filaments is complex.
+Governing equations negotiate elastohydrodynamical interactions with
+non-holonomic constraints arising from the filament inex- tensibility. Such
+elastohydrodynamic systems are structurally convoluted, prone to numerical
+erros, thus requiring penalization methods and high-order spatiotemporal
+propagators. The asymptotic coarse-graining formulation presented here exploits
+the momentum balance in the asymptotic limit of small rod-like elements which
+are integrated semi-analytically. This greatly simplifies the elas-
+tohydrodynamic interactions and overcomes previous numerical instability. The
+resulting matricial system is straightforward and intuitive to implement, and
+allows for a fast and efficient computation, over than a hundred times faster
+than previous schemes. Only basic knowledge of systems of linear equations is
+required, and implementation achieved with any solver of choice.
+Generalisations for complex interaction of multiple rods, Brownian polymer
+dynamics, active filaments and non-local hydrodynamics are also
+straightforward. We demonstrate these in four examples commonly found in
+biological systems, including the dynamics of filaments and flagella. Three of
+these systems are novel in the literature. We additionally provide a Matlab
+code that can be used as a basis for further generalisations.
+",0,1,0,0,0,0
+13423,Performance of two-dimensional tidal turbine arrays in free surface flow,"  Encouraged by recent studies on the performance of tidal turbine arrays, we
+extend the classical momentum actuator disc theory to include the free surface
+effects and allow the vertical arrangement of turbines. Most existing
+literatures concern one dimensional arrays with single turbine in the vertical
+direction, while the arrays in this work are two dimensional (with turbines in
+both the vertical and lateral directions) and also partially block the channel
+which width is far larger than height. The vertical mixing of array scale flow
+is assumed to take place much faster than lateral one. This assumption has been
+verified by numerical simulations. Fixing the total turbine area and utilized
+width, the comparison between two-dimensional and traditional one-dimensional
+arrays is investigated. The results suggest that the two dimensional
+arrangements of smaller turbines are preferred to one dimensional arrays from
+both the power coefficient and efficiency perspectives. When channel dynamics
+are considered, the power increase would be partly offset according to the
+parameters of the channel but the optimal arrangement is unchangeable.
+Furthermore, we consider how to arrange finite number of turbines in a channel.
+It is shown that an optimal distribution of turbines in two directions is
+found. Finally, the scenario of arranging turbines in infinite flow, which is
+the limiting condition of small blockages, is analysed. A new maximum power
+coefficient 0.869 occurs when $Fr=0.2$, greatly increasing the peak power
+compared with existing results.
+",0,1,0,0,0,0
+13424,Elicitability and its Application in Risk Management,"  Elicitability is a property of $\mathbb{R}^k$-valued functionals defined on a
+set of distribution functions. These functionals represent statistical
+properties of a distribution, for instance its mean, variance, or median. They
+are called elicitable if there exists a scoring function such that the expected
+score under a distribution takes its unique minimum at the functional value of
+this distribution. If such a scoring function exists, it is called strictly
+consistent for the functional. Motivated by the recent findings of Fissler and
+Ziegel concerning higher order elicitability, this thesis reviews the most
+important results, examples, and applications which are found in the relevant
+literature. Moreover, we also contribute our own examples and findings in order
+to give the reader a well-founded overview of the topic as well as of the most
+used tools and techniques. We include necessary and sufficient conditions for
+strictly consistent scoring functions, several elicitable as well as
+non-elicitable functionals and the use of elicitability in forecast comparison,
+regression, and estimation. Special emphasis is placed on quantitative risk
+management and the result that Value at Risk and Expected Shortfall are jointly
+elicitable.
+",0,0,1,1,0,0
+13425,Diffusion of particles with short-range interactions,"  A system of interacting Brownian particles subject to short-range repulsive
+potentials is considered. A continuum description in the form of a nonlinear
+diffusion equation is derived systematically in the dilute limit using the
+method of matched asymptotic expansions. Numerical simulations are performed to
+compare the results of the model with those of the commonly used mean-field and
+Kirkwood-superposition approximations, as well as with Monte Carlo simulation
+of the stochastic particle system, for various interaction potentials. Our
+approach works best for very repulsive short-range potentials, while the
+mean-field approximation is suitable for long-range interactions. The Kirkwood
+superposition approximation provides an accurate description for both short-
+and long-range potentials, but is considerably more computationally intensive.
+",0,1,0,0,0,0
+13426,Birman-Murakami-Wenzl type algebras for arbitrary Coxeter systems,"  In this paper we first present a Birman-Murakami-Wenzl type algebra for every
+Coxeter system of rank 2 (corresponding to dihedral groups). We prove they have
+semisimple for generic parameters, and having natural cellular structures. And
+classcify their irreducible representations. Among them there is one serving as
+a generalization of the Lawrence-Krammer representation with quite neat shape
+and the ""correct"" dimension. We conjecture they are isomorphic to the
+generalized Lawrence-Krammer representaions defined by I.Marin as monodromy of
+certain KZ connections. We prove these representations are irreducible for
+generic parameters, and find a quite neat invariant bilinear form on them.
+Based on above constructions for rank 2, we introduce a Birman-Murakami-Wenzl
+type algebra for an arbitrary Coxeter system. For every Coxeter system, the
+introduced algebra is a quotient of group algebra of the Artin group
+(associated with this Coxeter system), having the corresponding Hecke algebra
+as a quotient. The simple generators of the Artin group have degree 3
+annihiating polynomials in this algebra.
+",0,0,1,0,0,0
+13427,Protein Classification using Machine Learning and Statistical Techniques: A Comparative Analysis,"  In recent era prediction of enzyme class from an unknown protein is one of
+the challenging tasks in bioinformatics. Day to day the number of proteins is
+increases as result the prediction of enzyme class gives a new opportunity to
+bioinformatics scholars. The prime objective of this article is to implement
+the machine learning classification technique for feature selection and
+predictions also find out an appropriate classification technique for function
+prediction. In this article the seven different classification technique like
+CRT, QUEST, CHAID, C5.0, ANN (Artificial Neural Network), SVM and Bayesian has
+been implemented on 4368 protein data that has been extracted from UniprotKB
+databank and categories into six different class. The proteins data is high
+dimensional sequence data and contain a maximum of 48 features.To manipulate
+the high dimensional sequential protein data with different classification
+technique, the SPSS has been used as an experimental tool. Different
+classification techniques give different results for every model and shows that
+the data are imbalanced for class C4, C5 and C6. The imbalanced data affect the
+performance of model. In these three classes the precision and recall value is
+very less or negligible. The experimental results highlight that the C5.0
+classification technique accuracy is more suited for protein feature
+classification and predictions. The C5.0 classification technique gives 95.56%
+accuracy and also gives high precision and recall value. Finally, we conclude
+that the features that is selected can be used for function prediction.
+",0,0,0,0,1,0
+13428,The Emergence of Consensus: A Primer,"  The origin of population-scale coordination has puzzled philosophers and
+scientists for centuries. Recently, game theory, evolutionary approaches and
+complex systems science have provided quantitative insights on the mechanisms
+of social consensus. However, the literature is vast and widely scattered
+across fields, making it hard for the single researcher to navigate it. This
+short review aims to provide a compact overview of the main dimensions over
+which the debate has unfolded and to discuss some representative examples. It
+focuses on those situations in which consensus emerges 'spontaneously' in
+absence of centralised institutions and covers topic that include the
+macroscopic consequences of the different microscopic rules of behavioural
+contagion, the role of social networks, and the mechanisms that prevent the
+formation of a consensus or alter it after it has emerged. Special attention is
+devoted to the recent wave of experiments on the emergence of consensus in
+social systems.
+",1,1,0,0,0,0
+13429,ProSLAM: Graph SLAM from a Programmer's Perspective,"  In this paper we present ProSLAM, a lightweight stereo visual SLAM system
+designed with simplicity in mind. Our work stems from the experience gathered
+by the authors while teaching SLAM to students and aims at providing a highly
+modular system that can be easily implemented and understood. Rather than
+focusing on the well known mathematical aspects of Stereo Visual SLAM, in this
+work we highlight the data structures and the algorithmic aspects that one
+needs to tackle during the design of such a system. We implemented ProSLAM
+using the C++ programming language in combination with a minimal set of well
+known used external libraries. In addition to an open source implementation, we
+provide several code snippets that address the core aspects of our approach
+directly in this paper. The results of a thorough validation performed on
+standard benchmark datasets show that our approach achieves accuracy comparable
+to state of the art methods, while requiring substantially less computational
+resources.
+",1,0,0,0,0,0
+13430,Right Amenability And Growth Of Finitely Right Generated Left Group Sets,"  We introduce right generating sets, Cayley graphs, growth functions, types
+and rates, and isoperimetric constants for left homogeneous spaces equipped
+with coordinate systems; characterise right amenable finitely right generated
+left homogeneous spaces with finite stabilisers as those whose isoperimetric
+constant is $0$; and prove that finitely right generated left homogeneous
+spaces with finite stabilisers of sub-exponential growth are right amenable, in
+particular, quotient sets of groups of sub-exponential growth by finite
+subgroups are right amenable.
+",0,0,1,0,0,0
+13431,Convex Hull of the Quadratic Branch AC Power Flow Equations and Its Application in Radial Distribution Networks,"  A branch flow model (BFM) is used to formulate the AC power flow in general
+networks. For each branch/line, the BFM contains a nonconvex quadratic
+equality. A mathematical formulation of its convex hull is proposed, which is
+the tightest convex relaxation of this quadratic equation. The convex hull
+formulation consists of a second order cone inequality and a linear inequality
+within the physical bounds of power flows. The convex hull formulation is
+analytically proved and geometrically validated. An optimal scheduling problem
+of distributed energy storage (DES) in radial distribution systems with high
+penetration of photovoltaic resources is investigated in this paper. To capture
+the performance of both the battery and converter, a second-order DES model is
+proposed. Following the convex hull of the quadratic branch flow equation, the
+convex hull formulation of the nonconvex constraint in the DES model is also
+derived. The proposed convex hull models are used to generate a tight convex
+relaxation of the DES optimal scheduling (DESOS) problem. The proposed approach
+is tested on several radial systems. A discussion on the extension to meshed
+networks is provided.
+",0,0,1,0,0,0
+13432,Multi-Relevance Transfer Learning,"  Transfer learning aims to faciliate learning tasks in a label-scarce target
+domain by leveraging knowledge from a related source domain with plenty of
+labeled data. Often times we may have multiple domains with little or no
+labeled data as targets waiting to be solved. Most existing efforts tackle
+target domains separately by modeling the `source-target' pairs without
+exploring the relatedness between them, which would cause loss of crucial
+information, thus failing to achieve optimal capability of knowledge transfer.
+In this paper, we propose a novel and effective approach called Multi-Relevance
+Transfer Learning (MRTL) for this purpose, which can simultaneously transfer
+different knowledge from the source and exploits the shared common latent
+factors between target domains. Specifically, we formulate the problem as an
+optimization task based on a collective nonnegative matrix tri-factorization
+framework. The proposed approach achieves both source-target transfer and
+target-target leveraging by sharing multiple decomposed latent subspaces.
+Further, an alternative minimization learning algorithm is developed with
+convergence guarantee. Empirical study validates the performance and
+effectiveness of MRTL compared to the state-of-the-art methods.
+",1,0,0,1,0,0
+13433,When confidence and competence collide: Effects on online decision-making discussions,"  Group discussions are a way for individuals to exchange ideas and arguments
+in order to reach better decisions than they could on their own. One of the
+premises of productive discussions is that better solutions will prevail, and
+that the idea selection process is mediated by the (relative) competence of the
+individuals involved. However, since people may not know their actual
+competence on a new task, their behavior is influenced by their self-estimated
+competence --- that is, their confidence --- which can be misaligned with their
+actual competence.
+Our goal in this work is to understand the effects of confidence-competence
+misalignment on the dynamics and outcomes of discussions. To this end, we
+design a large-scale natural setting, in the form of an online team-based
+geography game, that allows us to disentangle confidence from competence and
+thus separate their effects.
+We find that in task-oriented discussions, the more-confident individuals
+have a larger impact on the group's decisions even when these individuals are
+at the same level of competence as their teammates. Furthermore, this
+unjustified role of confidence in the decision-making process often leads teams
+to under-perform. We explore this phenomenon by investigating the effects of
+confidence on conversational dynamics.
+",1,1,0,0,0,0
+13434,NIP formulas and Baire 1 definability,"  In this short note, using results of Bourgain, Fremlin, and Talagrand
+\cite{BFT}, we show that for a countable structure $M$, a saturated elementary
+extension $M^*$ of $M$ and a formula $\phi(x,y)$ the following are equivalent:
+(i) $\phi(x,y)$ is NIP on $M$ (in the sense of Definition 2.1).
+(ii) Whenever $p(x)\in S_\phi(M^*)$ is finitely satisfiable in $M$ then it is
+Baire 1 definable over $M$ (in sense of Definition 2.5).
+",0,0,1,0,0,0
+13435,Lee-Carter method for forecasting mortality for Peruvian Population,"  In this article, we have modeled mortality rates of Peruvian female and male
+populations during the period of 1950-2017 using the Lee-Carter (LC) model. The
+stochastic mortality model was introduced by Lee and Carter (1992) and has been
+used by many authors for fitting and forecasting the human mortality rates. The
+Singular Value Decomposition (SVD) approach is used for estimation of the
+parameters of the LC model. Utilizing the best fitted auto regressive
+integrated moving average (ARIMA) model we forecast the values of the time
+dependent parameter of the LC model for the next thirty years. The forecasted
+values of life expectancy at different age group with $95\%$ confidence
+intervals are also reported for the next thirty years. In this research we use
+the data, obtained from the Peruvian National Institute of Statistics (INEI).
+",0,0,0,0,0,1
+13436,Kernel Recursive ABC: Point Estimation with Intractable Likelihood,"  We propose a novel approach to parameter estimation for simulator-based
+statistical models with intractable likelihood. Our proposed method involves
+recursive application of kernel ABC and kernel herding to the same observed
+data. We provide a theoretical explanation regarding why the approach works,
+showing (for the population setting) that, under a certain assumption, point
+estimates obtained with this method converge to the true parameter, as
+recursion proceeds. We have conducted a variety of numerical experiments,
+including parameter estimation for a real-world pedestrian flow simulator, and
+show that in most cases our method outperforms existing approaches.
+",0,0,0,1,0,0
+13437,More declarative tabling in Prolog using multi-prompt delimited control,"  Several Prolog implementations include a facility for tabling, an alternative
+resolution strategy which uses memoisation to avoid redundant duplication of
+computations. Until relatively recently, tabling has required either low-level
+support in the underlying Prolog engine, or extensive program transormation (de
+Guzman et al., 2008). An alternative approach is to augment Prolog with low
+level support for continuation capturing control operators, particularly
+delimited continuations, which have been investigated in the field of
+functional programming and found to be capable of supporting a wide variety of
+computational effects within an otherwise declarative language.
+This technical report describes an implementation of tabling in SWI Prolog
+based on delimited control operators for Prolog recently introduced by
+Schrijvers et al. (2013). In comparison with a previous implementation of
+tabling for SWI Prolog using delimited control (Desouter et al., 2015), this
+approach, based on the functional memoising parser combinators of Johnson
+(1995), stays closer to the declarative core of Prolog, requires less code, and
+is able to deliver solutions from systems of tabled predicates incrementally
+(as opposed to finding all solutions before delivering any to the rest of the
+program).
+A collection of benchmarks shows that a small number of carefully targeted
+optimisations yields performance within a factor of about 2 of the optimised
+version of Desouter et al.'s system currently included in SWI Prolog.
+",1,0,0,0,0,0
+13438,Emergent topology and dynamical quantum phase transitions in two-dimensional closed quantum systems,"  We introduce the notion of a dynamical topological order parameter (DTOP)
+that characterises dynamical quantum phase transitions (DQPTs) occurring in the
+subsequent temporal evolution of ""two dimensional"" closed quantum systems,
+following a quench (or ramping) of a parameter of the Hamiltonian, {which
+generalizes the notion of DTOP introduced in Budich and Heyl, Phys. Rev. B 93,
+085416 (2016) for one-dimensional situations}. This DTOP is obtained from the
+""gauge-invariant"" Pancharatnam phase extracted from the Loschmidt overlap,
+i.e., the modulus of the overlap between the initially prepared state and its
+time evolved counterpart reached following a temporal evolution generated by
+the time-independent final Hamiltonian. This generic proposal is illustrated
+considering DQPTs occurring in the subsequent temporal evolution following a
+sudden quench of the staggered mass of the topological Haldane model on a
+hexagonal lattice where it stays fixed to zero or unity and makes a
+discontinuous jump between these two values at critical times at which DQPTs
+occur.
+",0,1,0,0,0,0
+13439,"A pictorial introduction to differential geometry, leading to Maxwell's equations as three pictures","  In this article we present pictorially the foundation of differential
+geometry which is a crucial tool for multiple areas of physics, notably general
+and special relativity, but also mechanics, thermodynamics and solving
+differential equations. As all the concepts are presented as pictures, there
+are no equations in this article. As such this article may be read by
+pre-university students who enjoy physics, mathematics and geometry. However it
+will also greatly aid the intuition of an undergraduate and masters students,
+learning general relativity and similar courses. It concentrates on the tools
+needed to understand Maxwell's equations thus leading to the goal of presenting
+Maxwell's equations as 3 pictures.
+",0,1,1,0,0,0
+13440,Regularization by noise in (2x 2) hyperbolic systems of conservation law,"  In this paper we study a non strictly systems of conservation law by
+stochastic perturbation. We show the existence and uniqueness of the solution.
+We do not assume that $BV$-regularity for the initial conditions. The proofs
+are based on the concept of entropy solution and in the characteristics method
+(in the influence of noise). This is the first result on the regularization by
+noise in hyperbolic systems of conservation law.
+",0,0,1,0,0,0
+13441,Large deviations of a tracer in the symmetric exclusion process,"  The one-dimensional symmetric exclusion process, the simplest interacting
+particle process, is a lattice-gas made of particles that hop symmetrically on
+a discrete line respecting hard-core exclusion. The system is prepared on the
+infinite lattice with a step initial profile with average densities $\rho_{+}$
+and $\rho_{-}$ on the right and on the left of the origin. When $\rho_{+} =
+\rho_{-}$, the gas is at equilibrium and undergoes stationary fluctuations.
+When these densities are unequal, the gas is out of equilibrium and will remain
+so forever. A tracer, or a tagged particle, is initially located at the
+boundary between the two domains; its position $X_t$ is a random observable in
+time, that carries information on the non-equilibrium dynamics of the whole
+system. We derive an exact formula for the cumulant generating function and the
+large deviation function of $X_t$, in the long time limit, and deduce the full
+statistical properties of the tracer's position. The equilibrium fluctuations
+of the tracer's position, when the density is uniform, are obtained as an
+important special case.
+",0,1,0,0,0,0
+13442,Unitary Representations with non-zero Dirac cohomology for complex $E_6$,"  This paper classifies the equivalence classes of irreducible unitary
+representations with nonvanishing Dirac cohomology for complex $E_6$. This is
+achieved by using our finiteness result, and by improving the computing method.
+",0,0,1,0,0,0
+13443,Revisiting Lie integrability by quadratures from a geometric perspective,"  After a short review of the classical Lie theorem, a finite dimensional Lie
+algebra of vector fields is considered and the most general conditions under
+which the integral curves of one of the fields can be obtained by quadratures
+in a prescribed way will be discussed, determining also the number of
+quadratures needed to integrate the system. The theory will be illustrated with
+examples andbn an extension of the theorem where the Lie algebras are replaced
+by some distributions will also be presented.
+",0,1,1,0,0,0
+13444,Intersubband polarons in oxides,"  Intersubband (ISB) polarons result from the interaction of an ISB transition
+and the longitudinal optical (LO) phonons in a semiconductor quantum well (QW).
+Their observation requires a very dense two dimensional electron gas (2DEG) in
+the QW and a polar or highly ionic semiconductor. Here we show that in
+ZnO/MgZnO QWs the strength of such a coupling can be as high as 1.5 times the
+LO-phonon frequency due to the very dense 2DEG achieved and the large
+difference between the static and high-frequency dielectric constants in ZnO.
+The ISB polaron is observed optically in multiple QW structures with 2DEG
+densities ranging from $5\times 10^{12}$ to $5\times 10^{13}$ cm$^{-2}$, where
+an unprecedented regime is reached in which the frequency of the upper ISB
+polaron branch is three times larger than that of the bare ISB transition. This
+study opens new prospects to the exploitation of oxides in phenomena happening
+in the ultrastrong coupling regime.
+",0,1,0,0,0,0
+13445,Control Variates for Stochastic Gradient MCMC,"  It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly
+with dataset size. A popular class of methods for solving this issue is
+stochastic gradient MCMC. These methods use a noisy estimate of the gradient of
+the log posterior, which reduces the per iteration computational cost of the
+algorithm. Despite this, there are a number of results suggesting that
+stochastic gradient Langevin dynamics (SGLD), probably the most popular of
+these methods, still has computational cost proportional to the dataset size.
+We suggest an alternative log posterior gradient estimate for stochastic
+gradient MCMC, which uses control variates to reduce the variance. We analyse
+SGLD using this gradient estimate, and show that, under log-concavity
+assumptions on the target distribution, the computational cost required for a
+given level of accuracy is independent of the dataset size. Next we show that a
+different control variate technique, known as zero variance control variates
+can be applied to SGMCMC algorithms for free. This post-processing step
+improves the inference of the algorithm by reducing the variance of the MCMC
+output. Zero variance control variates rely on the gradient of the log
+posterior; we explore how the variance reduction is affected by replacing this
+with the noisy gradient estimate calculated by SGMCMC.
+",1,0,0,1,0,0
+13446,Augmented Reality for Depth Cues in Monocular Minimally Invasive Surgery,"  One of the major challenges in Minimally Invasive Surgery (MIS) such as
+laparoscopy is the lack of depth perception. In recent years, laparoscopic
+scene tracking and surface reconstruction has been a focus of investigation to
+provide rich additional information to aid the surgical process and compensate
+for the depth perception issue. However, robust 3D surface reconstruction and
+augmented reality with depth perception on the reconstructed scene are yet to
+be reported. This paper presents our work in this area. First, we adopt a
+state-of-the-art visual simultaneous localization and mapping (SLAM) framework
+- ORB-SLAM - and extend the algorithm for use in MIS scenes for reliable
+endoscopic camera tracking and salient point mapping. We then develop a robust
+global 3D surface reconstruction frame- work based on the sparse point clouds
+extracted from the SLAM framework. Our approach is to combine an outlier
+removal filter within a Moving Least Squares smoothing algorithm and then
+employ Poisson surface reconstruction to obtain smooth surfaces from the
+unstructured sparse point cloud. Our proposed method has been quantitatively
+evaluated compared with ground-truth camera trajectories and the organ model
+surface we used to render the synthetic simulation videos. In vivo laparoscopic
+videos used in the tests have demonstrated the robustness and accuracy of our
+proposed framework on both camera tracking and surface reconstruction,
+illustrating the potential of our algorithm for depth augmentation and
+depth-corrected augmented reality in MIS with monocular endoscopes.
+",1,0,0,0,0,0
+13447,"General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline","  This paper describes Task 2 of the DCASE 2018 Challenge, titled
+""General-purpose audio tagging of Freesound content with AudioSet labels"". This
+task was hosted on the Kaggle platform as ""Freesound General-Purpose Audio
+Tagging Challenge"". The goal of the task is to build an audio tagging system
+that can recognize the category of an audio clip from a subset of 41 diverse
+categories drawn from the AudioSet Ontology. We present the task, the dataset
+prepared for the competition, and a baseline system.
+",1,0,0,1,0,0
+13448,Semantical Equivalence of the Control Flow Graph and the Program Dependence Graph,"  The program dependence graph (PDG) represents data and control dependence
+between statements in a program. This paper presents an operational semantics
+of program dependence graphs. Since PDGs exclude artificial order of statements
+that resides in sequential programs, executions of PDGs are not unique.
+However, we identified a class of PDGs that have unique final states of
+executions, called deterministic PDGs. We prove that the operational semantics
+of control flow graphs is equivalent to that of deterministic PDGs. The class
+of deterministic PDGs properly include PDGs obtained from well-structured
+programs. Thus, our operational semantics of PDGs is more general than that of
+PDGs for well-structured programs, which are already established in literature.
+",1,0,0,0,0,0
+13449,On the smallest non-trivial quotients of mapping class groups,"  We prove that the smallest non-trivial quotient of the mapping class group of
+a connected orientable surface of genus at least 3 without punctures is
+$\mathrm{Sp}_{2g}(2)$, thus confirming a conjecture of Zimmermann. In the
+process, we generalise Korkmaz's results on $\mathbb{C}$-linear representations
+of mapping class groups to projective representations over any field.
+",0,0,1,0,0,0
+13450,A Gronwall inequality for a general Caputo fractional operator,"  In this paper we present a new type of fractional operator, which is a
+generalization of the Caputo and Caputo--Hadamard fractional derivative
+operators. We study some properties of the operator, namely we prove that it is
+the inverse operation of a generalized fractional integral. A relation between
+this operator and a Riemann--Liouville type is established. We end with a
+fractional Gronwall inequality type, which is useful to compare solutions of
+fractional differential equations.
+",0,0,1,0,0,0
+13451,First-principles insights into ultrashort laser spectroscopy of molecular nitrogen,"  In this research, we employ accurate time-dependent density functional
+calculations for ultrashort laser spectroscopy of nitrogen molecule. Laser
+pulses with different frequencies, intensities, and durations are applied to
+the molecule and the resulting photoelectron spectra are analyzed. It is argued
+that relative orientation of the molecule in the laser pulse significantly
+influence the orbital character of the emitted photoelectrons. Moreover, the
+duration of the laser pulse is also found to be very effective in controlling
+the orbital resolution and intensity of photoelectrons. Angular resolved
+distribution of photoelectrons are computed at different pulse frequencies and
+recording times. By exponential growth of the laser pulse intensity, the
+theoretical threshold of two photons absorption in nitrogen molecule is
+determined.
+",0,1,0,0,0,0
+13452,Borel class and Cartan involution,"  In this note we prove that the Borel class of representations of 3-manifold
+groups to PGL(n,C) is preserved under Cartan involution up to sign. For
+representations to PGL(3,C) this is implied by a more general result of E.
+Falbel and Q. Wang, however our proof appears to be much shorter for that
+special case.
+",0,0,1,0,0,0
+13453,Network Dimensions in the Getty Provenance Index,"  In this article we make a case for a systematic application of complex
+network science to study art market history and more general collection
+dynamics. We reveal social, temporal, spatial, and conceptual network
+dimensions, i.e. network node and link types, previously implicit in the Getty
+Provenance Index (GPI). As a pioneering art history database active since the
+1980s, the GPI provides online access to source material relevant for research
+in the history of collecting and art markets. Based on a subset of the GPI, we
+characterize an aggregate of more than 267,000 sales transactions connected to
+roughly 22,000 actors in four countries over 20 years at daily resolution from
+1801 to 1820. Striving towards a deeper understanding on multiple levels we
+disambiguate social dynamics of buying, brokering, and selling, while observing
+a general broadening of the market, where large collections are split into
+smaller lots. Temporally, we find annual market cycles that are shifted by
+country and obviously favor international exchange. Spatially, we differentiate
+near-monopolies from regions driven by competing sub-centers, while uncovering
+asymmetries of international market flux. Conceptually, we track dynamics of
+artist attribution that clearly behave like product categories in a very slow
+supermarket. Taken together, we introduce a number of meaningful network
+perspectives dealing with historical art auction data, beyond the analysis of
+social networks within a single market region. The results presented here have
+inspired a Linked Open Data conversion of the GPI, which is currently in
+process and will allow further analysis by a broad set of researchers.
+",0,1,0,0,0,0
+13454,Maximum-order Complexity and Correlation Measures,"  We estimate the maximum-order complexity of a binary sequence in terms of its
+correlation measures. Roughly speaking, we show that any sequence with small
+correlation measure up to a sufficiently large order $k$ cannot have very small
+maximum-order complexity.
+",0,0,1,0,0,0
+13455,On Sampling Strategies for Neural Network-based Collaborative Filtering,"  Recent advances in neural networks have inspired people to design hybrid
+recommendation algorithms that can incorporate both (1) user-item interaction
+information and (2) content information including image, audio, and text.
+Despite their promising results, neural network-based recommendation algorithms
+pose extensive computational costs, making it challenging to scale and improve
+upon. In this paper, we propose a general neural network-based recommendation
+framework, which subsumes several existing state-of-the-art recommendation
+algorithms, and address the efficiency issue by investigating sampling
+strategies in the stochastic gradient descent training for the framework. We
+tackle this issue by first establishing a connection between the loss functions
+and the user-item interaction bipartite graph, where the loss function terms
+are defined on links while major computation burdens are located at nodes. We
+call this type of loss functions ""graph-based"" loss functions, for which varied
+mini-batch sampling strategies can have different computational costs. Based on
+the insight, three novel sampling strategies are proposed, which can
+significantly improve the training efficiency of the proposed framework (up to
+$\times 30$ times speedup in our experiments), as well as improving the
+recommendation performance. Theoretical analysis is also provided for both the
+computational cost and the convergence. We believe the study of sampling
+strategies have further implications on general graph-based loss functions, and
+would also enable more research under the neural network-based recommendation
+framework.
+",1,0,0,1,0,0
+13456,Non-integrable dynamics of matter-wave solitons in a density-dependent gauge theory,"  We study interactions between bright matter-wave solitons which acquire
+chiral transport dynamics due to an optically-induced density-dependent gauge
+potential. Through numerical simulations, we find that the collision dynamics
+feature several non-integrable phenomena, from inelastic collisions including
+population transfer and radiation losses to short-lived bound states and
+soliton fission. An effective quasi-particle model for the interaction between
+the solitons is derived by means of a variational approximation, which
+demonstrates that the inelastic nature of the collision arises from a coupling
+of the gauge field to velocities of the solitons. In addition, we derive a set
+of interaction potentials which show that the influence of the gauge field
+appears as a short-range potential, that can give rise to both attractive and
+repulsive interactions.
+",0,1,0,0,0,0
+13457,Geometric theories of patch and Lawson topologies,"  We give geometric characterisations of patch and Lawson topologies in the
+context of predicative point-free topology using the constructive notion of
+located subset. We present the patch topology of a stably locally compact
+formal topology by a geometric theory whose models are the points of the given
+topology that are located, and the Lawson topology of a continuous lattice by a
+geometric theory whose models are the located subsets of the given lattice. We
+also give a predicative presentation of the frame of perfect nuclei on a stably
+locally compact formal topology, and show that it is essentially the same as
+our geometric presentation of the patch topology. Moreover, the construction of
+Lawson topologies naturally induces a monad on the category of compact regular
+formal topologies, which is shown to be isomorphic to the Vietoris monad.
+",1,0,1,0,0,0
+13458,Active galactic nuclei in the era of the Imaging X-ray Polarimetry Explorer,"  In about four years, the National Aeronautics and Space Administration (NASA)
+will launch a small explorer mission named the Imaging X-ray Polarimetry
+Explorer (IXPE). IXPE is a satellite dedicated to the observation of X-ray
+polarization from bright astronomical sources in the 2-8 keV energy range.
+Using Gas Pixel Detectors (GPD), the mission will allow for the first time to
+acquire X-ray polarimetric imaging and spectroscopy of about a hundred of
+sources during its first two years of operation. Among them are the most
+powerful sources of light in the Universe: active galactic nuclei (AGN). In
+this proceedings, we summarize the scientific exploration we aim to achieve in
+the field of AGN using IXPE, describing the main discoveries that this new
+generation of X-ray polarimeters will be able to make. Among these discoveries,
+we expect to detect indisputable signatures of strong gravity, quantifying the
+amount and importance of scattering on distant cold material onto the iron
+K_alpha line observed at 6.4 keV. IXPE will also be able to probe the
+morphology of parsec-scale AGN regions, the magnetic field strength and
+direction in quasar jets, and, among the most important results, deliver an
+independent measurement of the spin of black holes.
+",0,1,0,0,0,0
+13459,Exact completion and constructive theories of sets,"  In the present paper we use the theory of exact completions to study
+categorical properties of small setoids in Martin-Löf type theory and, more
+generally, of models of the Constructive Elementary Theory of the Category of
+Sets, in terms of properties of their subcategories of choice objects (i.e.
+objects satisfying the axiom of choice). Because of these intended
+applications, we deal with categories that lack equalisers and just have weak
+ones, but whose objects can be regarded as collections of global elements. In
+this context, we study the internal logic of the categories involved, and
+employ this analysis to give a sufficient condition for the local cartesian
+closure of an exact completion. Finally, we apply these results to show when an
+exact completion produces a model of CETCS.
+",0,0,1,0,0,0
+13460,Proceedings 5th Workshop on Horn Clauses for Verification and Synthesis,"  Many Program Verification and Synthesis problems of interest can be modeled
+directly using Horn clauses and many recent advances in the CLP and CAV
+communities have centered around efficiently solving problems presented as Horn
+clauses.
+The HCVS series of workshops aims to bring together researchers working in
+the two communities of Constraint/Logic Programming (e.g., ICLP and CP),
+Program Verification (e.g., CAV, TACAS, and VMCAI), and Automated Deduction
+(e.g., CADE, IJCAR), on the topic of Horn clause based analysis, verification,
+and synthesis.
+Horn clauses for verification and synthesis have been advocated by these
+communities in different times and from different perspectives and HCVS is
+organized to stimulate interaction and a fruitful exchange and integration of
+experiences.
+",1,0,0,0,0,0
+13461,Conditional Neural Processes,"  Deep neural networks excel at function approximation, yet they are typically
+trained from scratch for each new function. On the other hand, Bayesian
+methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly
+infer the shape of a new function at test time. Yet GPs are computationally
+expensive, and it can be hard to design appropriate priors. In this paper we
+propose a family of neural models, Conditional Neural Processes (CNPs), that
+combine the benefits of both. CNPs are inspired by the flexibility of
+stochastic processes such as GPs, but are structured as neural networks and
+trained via gradient descent. CNPs make accurate predictions after observing
+only a handful of training data points, yet scale to complex functions and
+large datasets. We demonstrate the performance and versatility of the approach
+on a range of canonical machine learning tasks, including regression,
+classification and image completion.
+",0,0,0,1,0,0
+13462,ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring,"  Many earth science applications require data at both high spatial and
+temporal resolution for effective monitoring of various ecosystem resources.
+Due to practical limitations in sensor design, there is often a trade-off in
+different resolutions of spatio-temporal datasets and hence a single sensor
+alone cannot provide the required information. Various data fusion methods have
+been proposed in the literature that mainly rely on individual timesteps when
+both datasets are available to learn a mapping between features values at
+different resolutions using local relationships between pixels. Earth
+observation data is often plagued with spatially and temporally correlated
+noise, outliers and missing data due to atmospheric disturbances which pose a
+challenge in learning the mapping from a local neighborhood at individual
+timesteps. In this paper, we aim to exploit time-independent global
+relationships between pixels for robust transfer of information across
+different scales. Specifically, we propose a new framework, ORBIT (Ordering
+Based Information Transfer) that uses relative ordering constraint among pixels
+to transfer information across both time and scales. The effectiveness of the
+framework is demonstrated for global surface water monitoring using both
+synthetic and real-world datasets.
+",1,0,0,0,0,0
+13463,The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling,"  Psychiatric neuroscience is increasingly aware of the need to define
+psychopathology in terms of abnormal neural computation. The central tool in
+this endeavour is the fitting of computational models to behavioural data. The
+most prominent example of this procedure is fitting reinforcement learning (RL)
+models to decision-making data collected from mentally ill and healthy subject
+populations. These models are generative models of the decision-making data
+themselves, and the parameters we seek to infer can be psychologically and
+neurobiologically meaningful. Currently, the gold standard approach to this
+inference procedure involves Monte-Carlo sampling, which is robust but
+computationally intensive---rendering additional procedures, such as
+cross-validation, impractical. Searching for point estimates of model
+parameters using optimization procedures remains a popular and interesting
+option. On a novel testbed simulating parameter estimation from a common RL
+task, we investigated the effects of smooth vs. boundary constraints on
+parameter estimation using interior point and deterministic direct search
+algorithms for optimization. Ultimately, we show that the use of boundary
+constraints can lead to substantial truncation effects. Our results discourage
+the use of boundary constraints for these applications.
+",0,0,0,1,1,0
+13464,Kinetic Effects in Dynamic Wetting,"  The maximum speed at which a liquid can wet a solid is limited by the need to
+displace gas lubrication films in front of the moving contact line. The
+characteristic height of these films is often comparable to the mean free path
+in the gas so that hydrodynamic models do not adequately describe the flow
+physics. This Letter develops a model which incorporates kinetic effects in the
+gas, via the Boltzmann equation, and can predict experimentally-observed
+increases in the maximum speed of wetting when (a) the liquid's viscosity is
+varied, (b) the ambient gas pressure is reduced or (c) the meniscus is
+confined.
+",0,1,0,0,0,0
+13465,The spread of low-credibility content by social bots,"  The massive spread of digital misinformation has been identified as a major
+global risk and has been alleged to influence elections and threaten
+democracies. Communication, cognitive, social, and computer scientists are
+engaged in efforts to study the complex causes for the viral diffusion of
+misinformation online and to develop solutions, while search and social media
+platforms are beginning to deploy countermeasures. With few exceptions, these
+efforts have been mainly informed by anecdotal evidence rather than systematic
+data. Here we analyze 14 million messages spreading 400 thousand articles on
+Twitter during and following the 2016 U.S. presidential campaign and election.
+We find evidence that social bots played a disproportionate role in amplifying
+low-credibility content. Accounts that actively spread articles from
+low-credibility sources are significantly more likely to be bots. Automated
+accounts are particularly active in amplifying content in the very early
+spreading moments, before an article goes viral. Bots also target users with
+many followers through replies and mentions. Humans are vulnerable to this
+manipulation, retweeting bots who post links to low-credibility content.
+Successful low-credibility sources are heavily supported by social bots. These
+results suggest that curbing social bots may be an effective strategy for
+mitigating the spread of online misinformation.
+",1,0,0,0,0,0
+13466,Beltrami vector fields with an icosahedral symmetry,"  A vector field is called a Beltrami vector field, if $B\times(\nabla\times
+B)=0$. In this paper we construct two unique Beltrami vector fields
+$\mathfrak{I}$ and $\mathfrak{Y}$, such that
+$\nabla\times\mathfrak{I}=\mathfrak{I}$,
+$\nabla\times\mathfrak{Y}=\mathfrak{Y}$, and such that both have an
+orientation-preserving icosahedral symmetry. Both of them have an additional
+symmetry with respect to a non-trivial automorphism of the number field
+$\mathbb{Q}(\,\sqrt{5}\,)$.
+",0,0,1,0,0,0
+13467,A model for Faraday pilot waves over variable topography,"  Couder and Fort discovered that droplets walking on a vibrating bath possess
+certain features previously thought to be exclusive to quantum systems. These
+millimetric droplets synchronize with their Faraday wavefield, creating a
+macroscopic pilot-wave system. In this paper we exploit the fact that the waves
+generated are nearly monochromatic and propose a hydrodynamic model capable of
+quantitatively capturing the interaction between bouncing drops and a variable
+topography. We show that our reduced model is able to reproduce some important
+experiments involving the drop-topography interaction, such as non-specular
+reflection and single-slit diffraction.
+",0,1,0,0,0,0
+13468,Regular Separability of Well Structured Transition Systems,"  We investigate the languages recognized by well-structured transition systems
+(WSTS) with upward and downward compatibility. Our first result shows that,
+under very mild assumptions, every two disjoint WSTS languages are regular
+separable: There is a regular language containing one of them and being
+disjoint from the other. As a consequence, if a language as well as its
+complement are both recognized by WSTS, then they are necessarily regular. In
+particular, no subclass of WSTS languages beyond the regular languages is
+closed under complement. Our second result shows that for Petri nets, the
+complexity of the backwards coverability algorithm yields a bound on the size
+of the regular separator. We complement it by a lower bound construction.
+",1,0,0,0,0,0
+13469,Dynamical Stochastic Higher Spin Vertex Models,"  We introduce a new family of integrable stochastic processes, called
+\textit{dynamical stochastic higher spin vertex models}, arising from fused
+representations of Felder's elliptic quantum group $E_{\tau, \eta}
+(\mathfrak{sl}_2)$. These models simultaneously generalize the stochastic
+higher spin vertex models, studied by Corwin-Petrov and Borodin-Petrov, and are
+dynamical in the sense of Borodin's recent stochastic interaction round-a-face
+models.
+We provide explicit contour integral identities for observables of these
+models (when run under specific types of initial data) that characterize the
+distributions of their currents. Through asymptotic analysis of these
+identities in a special case, we evaluate the scaling limit for the current of
+a dynamical version of a discrete-time partial exclusion process. In
+particular, we show that its scaling exponent is $1 / 4$ and that its one-point
+marginal converges (in a sense of moments) to that of a non-trivial random
+variable, which we determine explicitly.
+",0,1,1,0,0,0
+13470,Risk-Averse Matchings over Uncertain Graph Databases,"  A large number of applications such as querying sensor networks, and
+analyzing protein-protein interaction (PPI) networks, rely on mining uncertain
+graph and hypergraph databases. In this work we study the following problem:
+given an uncertain, weighted (hyper)graph, how can we efficiently find a
+(hyper)matching with high expected reward, and low risk?
+This problem naturally arises in the context of several important
+applications, such as online dating, kidney exchanges, and team formation. We
+introduce a novel formulation for finding matchings with maximum expected
+reward and bounded risk under a general model of uncertain weighted
+(hyper)graphs that we introduce in this work. Our model generalizes
+probabilistic models used in prior work, and captures both continuous and
+discrete probability distributions, thus allowing to handle privacy related
+applications that inject appropriately distributed noise to (hyper)edge
+weights. Given that our optimization problem is NP-hard, we turn our attention
+to designing efficient approximation algorithms. For the case of uncertain
+weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a
+$\frac{1}{5}$-approximation algorithm with near optimal run time. For the case
+of uncertain weighted hypergraphs, we provide a
+$\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the
+hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in
+almost (modulo log factors) linear time.
+We complement our theoretical results by testing our approximation algorithms
+on a wide variety of synthetic experiments, where we observe in a controlled
+setting interesting findings on the trade-off between reward, and risk. We also
+provide an application of our formulation for providing recommendations of
+teams that are likely to collaborate, and have high impact.
+",1,0,0,0,0,0
+13471,Higher Order Context Transformations,"  The context transformation and generalized context transformation methods, we
+introduced recently, were able to reduce zero order entropy by exchanging
+digrams, and as a consequence, they were removing mutual information between
+consecutive symbols of the input message. These transformations were intended
+to be used as a preprocessor for zero-order entropy coding algorithms like
+Arithmetic or Huffman coding, since we know, that especially Arithmetic coding
+can achieve a compression rate almost of the size of Shannon's entropy.
+This paper introduces a novel algorithm based on the concept of generalized
+context transformation, that allows transformation of words longer than simple
+digrams. The higher order contexts are exploited using recursive form of a
+generalized context transformation. It is shown that the zero order entropy of
+transformed data drops significantly, but on the other hand, the overhead given
+by a description of individual transformations increases and it has become a
+limiting factor in a successful transformation of smaller files.
+",1,0,1,0,0,0
+13472,DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks,"  In recent years numerous advanced malware, aka advanced persistent threats
+(APT) are allegedly developed by nation-states. The task of attributing an APT
+to a specific nation-state is extremely challenging for several reasons. Each
+nation-state has usually more than a single cyber unit that develops such
+advanced malware, rendering traditional authorship attribution algorithms
+useless. Furthermore, those APTs use state-of-the-art evasion techniques,
+making feature extraction challenging. Finally, the dataset of such available
+APTs is extremely small.
+In this paper we describe how deep neural networks (DNN) could be
+successfully employed for nation-state APT attribution. We use sandbox reports
+(recording the behavior of the APT when run dynamically) as raw input for the
+neural network, allowing the DNN to learn high level feature abstractions of
+the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs,
+we achieved an accuracy rate of 94.6%.
+",1,0,0,1,0,0
+13473,Cathode signal in a TPC directional detector: implementation and validation measuring the drift velocity,"  Low-pressure gaseous TPCs are well suited detectors to correlate the
+directions of nuclear recoils to the galactic Dark Matter (DM) halo. Indeed, in
+addition to providing a measure of the energy deposition due to the elastic
+scattering of a DM particle on a nucleus in the target gas, they allow for the
+reconstruction of the track of the recoiling nucleus. In order to exclude the
+background events originating from radioactive decays on the surfaces of the
+detector materials within the drift volume, efforts are ongoing to precisely
+localize the track nuclear recoil in the drift volume along the axis
+perpendicular to the cathode plane. We report here the implementation of the
+measure of the signal induced on the cathode by the motion of the primary
+electrons toward the anode in a MIMAC chamber. As a validation, we performed an
+independent measurement of the drift velocity of the electrons in the
+considered gas mixture, correlating in time the cathode signal with the measure
+of the arrival times of the electrons on the anode.
+",0,1,0,0,0,0
+13474,Fast Reconstruction of High-qubit Quantum States via Low Rate Measurements,"  Due to the exponential complexity of the resources required by quantum state
+tomography (QST), people are interested in approaches towards identifying
+quantum states which require less effort and time. In this paper, we provide a
+tailored and efficient method for reconstructing mixed quantum states up to
+$12$ (or even more) qubits from an incomplete set of observables subject to
+noises. Our method is applicable to any pure or nearly pure state $\rho$, and
+can be extended to many states of interest in quantum information processing,
+such as multi-particle entangled $W$ state, GHZ state and cluster states that
+are matrix product operators of low dimensions. The method applies the quantum
+density matrix constraints to a quantum compressive sensing optimization
+problem, and exploits a modified Quantum Alternating Direction Multiplier
+Method (Quantum-ADMM) to accelerate the convergence. Our algorithm takes $8,35$
+and $226$ seconds respectively to reconstruct superposition state density
+matrices of $10,11,12$ qubits with acceptable fidelity, using less than $1 \%$
+of measurements of expectation. To our knowledge it is the fastest realization
+that people can achieve using a normal desktop. We further discuss applications
+of this method using experimental data of mixed states obtained in an ion trap
+experiment of up to $8$ qubits.
+",1,0,1,0,0,0
+13475,Inferring network connectivity from event timing patterns,"  Reconstructing network connectivity from the collective dynamics of a system
+typically requires access to its complete continuous-time evolution although
+these are often experimentally inaccessible. Here we propose a theory for
+revealing physical connectivity of networked systems only from the event time
+series their intrinsic collective dynamics generate. Representing the patterns
+of event timings in an event space spanned by inter-event and cross-event
+intervals, we reveal which other units directly influence the inter-event times
+of any given unit. For illustration, we linearize an event space mapping
+constructed from the spiking patterns in model neural circuits to reveal the
+presence or absence of synapses between any pair of neurons as well as whether
+the coupling acts in an inhibiting or activating (excitatory) manner. The
+proposed model-independent reconstruction theory is scalable to larger networks
+and may thus play an important role in the reconstruction of networks from
+biology to social science and engineering.
+",0,0,0,1,1,0
+13476,FADE: Fast and Asymptotically efficient Distributed Estimator for dynamic networks,"  Consider a set of agents that wish to estimate a vector of parameters of
+their mutual interest. For this estimation goal, agents can sense and
+communicate. When sensing, an agent measures (in additive gaussian noise)
+linear combinations of the unknown vector of parameters. When communicating, an
+agent can broadcast information to a few other agents, by using the channels
+that happen to be randomly at its disposal at the time.
+To coordinate the agents towards their estimation goal, we propose a novel
+algorithm called FADE (Fast and Asymptotically efficient Distributed
+Estimator), in which agents collaborate at discrete time-steps; at each
+time-step, agents sense and communicate just once, while also updating their
+own estimate of the unknown vector of parameters.
+FADE enjoys five attractive features: first, it is an intuitive estimator,
+simple to derive; second, it withstands dynamic networks, that is, networks
+whose communication channels change randomly over time; third, it is strongly
+consistent in that, as time-steps play out, each agent's local estimate
+converges (almost surely) to the true vector of parameters; fourth, it is both
+asymptotically unbiased and efficient, which means that, across time, each
+agent's estimate becomes unbiased and the mean-square error (MSE) of each
+agent's estimate vanishes to zero at the same rate of the MSE of the optimal
+estimator at an almighty central node; fifth, and most importantly, when
+compared with a state-of-art consensus+innovation (CI) algorithm, it yields
+estimates with outstandingly lower mean-square errors, for the same number of
+communications -- for example, in a sparsely connected network model with 50
+agents, we find through numerical simulations that the reduction can be
+dramatic, reaching several orders of magnitude.
+",1,0,0,0,0,0
+13477,TextRank Based Search Term Identification for Software Change Tasks,"  During maintenance, software developers deal with a number of software change
+requests. Each of those requests is generally written using natural language
+texts, and it involves one or more domain related concepts. A developer needs
+to map those concepts to exact source code locations within the project in
+order to implement the requested change. This mapping generally starts with a
+search within the project that requires one or more suitable search terms.
+Studies suggest that the developers often perform poorly in coming up with good
+search terms for a change task. In this paper, we propose and evaluate a novel
+TextRank-based technique that automatically identifies and suggests search
+terms for a software change task by analyzing its task description. Experiments
+with 349 change tasks from two subject systems and comparison with one of the
+latest and closely related state-of-the-art approaches show that our technique
+is highly promising in terms of suggestion accuracy, mean average precision and
+recall.
+",1,0,0,0,0,0
+13478,Natural Time Analysis of Seismicity in California: The epicenter of an impending mainshock,"  Upon employing the analysis in a new time domain, termed natural time, it has
+been recently demonstrated that a remarkable change of seismicity emerges
+before major mainshocks in California. What constitutes this change is that the
+fluctuations of the order parameter of seismicity exhibit a clearly detectable
+minimum. This is identified by using a natural time window sliding event by
+event through the time series of the earthquakes in a wide area and comprising
+a number of events that would occur on the average within a few months or so.
+Here, we suggest a method to estimate the epicentral area of an impending
+mainshock by an additional study of this minimum using an area window sliding
+through the wide area. We find that when this area window surrounds (or is
+adjacent to) the future epicentral area, the minimum of the order parameter
+fluctuations in this area appears at a date very close to the one at which the
+minimum is observed in the wide area. The method is applied here to major
+earthquakes that occurred in California during the recent decades including the
+largest one, i.e., the 1992 Landers earthquake.
+",0,1,0,0,0,0
+13479,Atomistic study of hardening mechanism in Al-Cu nanostructure,"  Nanostructures have the immense potential to supplant the traditional
+metallic structure as they show enhanced mechanical properties through strain
+hardening. In this paper, the effect of grain size on the hardening mechanism
+of Al-Cu nanostructure is elucidated by molecular dynamics simulation. Al-Cu
+(50-54% Cu by weight) nanostructure having an average grain size of 4.57 to
+7.26 nm are investigated for tensile simulation at different strain rate using
+embedded atom method (EAM) potential at a temperature of 50~500K. It is found
+that the failure mechanism of the nanostructure is governed by the temperature,
+grain size as well as strain rate effect. At the high temperature of 300-500K,
+the failure strength of Al-Cu nanostructure increases with the decrease of
+average grain size following Hall-Petch relation. Dislocation motions are
+hindered significantly when the grain size is decreased which play a vital role
+on the hardening of the nanostructure. The failure is always found to initiate
+at a particular Al grain due to its weak link and propagates through grain
+boundary (GB) sliding, diffusion, dislocation nucleation and propagation. We
+also visualize the dislocation density at different grain size to show how the
+dislocation affects the material properties at the nanoscale. These results
+will further aid investigation on the deformation mechanism of nanostructure.
+",0,1,0,0,0,0
+13480,Maximum redshift of gravitational wave merger events,"  Future generation of gravitational wave detectors will have the sensitivity
+to detect gravitational wave events at redshifts far beyond any detectable
+electromagnetic sources. We show that if the observed event rate is greater
+than one event per year at redshifts z > 40, then the probability distribution
+of primordial density fluctuations must be significantly non-Gaussian or the
+events originate from primordial black holes. The nature of the excess events
+can be determined from the redshift distribution of the merger rate.
+",0,1,0,0,0,0
+13481,The altmetric performance of publications authored by Brazilian researchers: analysis of CNPq productivity scholarship holders,"  The present work seeks to analyse the altmetric performance of Brazilian
+publications authored by researchers who are productivity scholarship holders
+(PQ) of the National Council of Scientific and Technological Development
+(CNPq). It was considered, within the scope of this research, the PQs in
+activity in October, 2017 (n = 14.609). The scientific production registered on
+Lattes was collected via GetLattesData and filtered by articles from academic
+journals published between 2016 and October 2017 that hold the Digital Object
+Identifier (n = 99064). The online attention data are analysed according to
+their distribution by density and variation; language of the publication and
+field of knowledge; and by average performance of the type of source that has
+provided its altmetric values. The density evidences the long tail behavior of
+the variable, with most part of the articles with altmetrics score = 0, while
+few articles have a high index. The average of the online attention indicates a
+better performance of articles written in English and belonging to the Health
+and Biological Sciences field of knowledge. As for the sources, there was a
+good performance from Mendeley, followed by Twitter and a low coverage from
+Facebook
+",1,0,0,0,0,0
+13482,The Impact of Information Dissemination on Vaccinating Epidemics in Multiplex Networks,"  The impact of information dissemination on epidemic control essentially
+affects individual behaviors. Among the information-driven behaviors,
+vaccination is determined by the cost-related factors, and the correlation with
+information dissemination is not clear yet. To this end, we present a model to
+integrate the information-epidemic spread process into an evolutionary
+vaccination game in multiplex networks, and explore how the spread of
+information on epidemic influences the vaccination behavior. We propose a
+two-layer coupled susceptible-alert-infected-susceptible (SAIS) model on a
+multiplex network, where the strength coefficient is defined to characterize
+the tendency and intensity of information dissemination. By means of the
+evolutionary game theory, we get the equilibrium vaccination level (the
+evolutionary stable strategy) for the vaccination game. After exploring the
+influence of the strength coefficient on the equilibrium vaccination level, we
+reach a counter-intuitive conclusion that more information transmission cannot
+promote vaccination. Specifically, when the vaccination cost is within a
+certain range, increasing information dissemination even leads to a decline in
+the equilibrium vaccination level. Moreover, we study the influence of the
+strength coefficient on the infection density and social cost, and unveil the
+role of information dissemination in controlling the epidemic with numerical
+simulations.
+",0,0,0,0,1,0
+13483,Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory,"  As artificial intelligence is increasingly affecting all parts of society and
+life, there is growing recognition that human interpretability of machine
+learning models is important. It is often argued that accuracy or other similar
+generalization performance metrics must be sacrificed in order to gain
+interpretability. Such arguments, however, fail to acknowledge that the overall
+decision-making system is composed of two entities: the learned model and a
+human who fuses together model outputs with his or her own information. As
+such, the relevant performance criteria should be for the entire system, not
+just for the machine learning component. In this work, we characterize the
+performance of such two-node tandem data fusion systems using the theory of
+distributed detection. In doing so, we work in the population setting and model
+interpretable learned models as multi-level quantizers. We prove that under our
+abstraction, the overall system of a human with an interpretable classifier
+outperforms one with a black box classifier.
+",0,0,0,1,0,0
+13484,Online Estimation and Adaptive Control for a Class of History Dependent Functional Differential Equations,"  This paper presents sufficient conditions for the convergence of online
+estimation methods and the stability of adaptive control strategies for a class
+of history dependent, functional differential equations. The study is motivated
+by the increasing interest in estimation and control techniques for robotic
+systems whose governing equations include history dependent nonlinearities. The
+functional differential equations in this paper are constructed using integral
+operators that depend on distributed parameters. As a consequence the resulting
+estimation and control equations are examples of distributed parameter systems
+whose states and distributed parameters evolve in finite and infinite
+dimensional spaces, respectively. suWell-posedness, existence, and uniqueness
+are discussed for the class of fully actuated robotic systems with history
+dependent forces in their governing equation of motion. By deriving rates of
+approximation for the class of history dependent operators in this paper,
+sufficient conditions are derived that guarantee that finite dimensional
+approximations of the online estimation equations converge to the solution of
+the infinite dimensional, distributed parameter system. The convergence and
+stability of a sliding mode adaptive control strategy for the history
+dependent, functional differential equations is established using Barbalat's
+lemma.
+",0,0,1,0,0,0
+13485,The Kinematics of the Permitted C II $λ$ 6578 Line in a Large Sample of Planetary Nebulae,"  We present spectroscopic observations of the C II $\lambda$6578 permitted
+line for 83 lines of sight in 76 planetary nebulae at high spectral resolution,
+most of them obtained with the Manchester Echelle Spectrograph on the 2.1\,m
+telescope at the Observatorio Astronómico Nacional on the Sierra San Pedro
+Mártir. We study the kinematics of the C II $\lambda$6578 permitted line with
+respect to other permitted and collisionally-excited lines. Statistically, we
+find that the kinematics of the C II $\lambda$6578 line are not those expected
+if this line arises from the recombination of C$^{2+}$ ions or the fluorescence
+of C$^+$ ions in ionization equilibrium in a chemically-homogeneous nebular
+plasma, but instead its kinematics are those appropriate for a volume more
+internal than expected. The planetary nebulae in this sample have well-defined
+morphology and are restricted to a limited range in H$\alpha$ line widths (no
+large values) compared to their counterparts in the Milky Way bulge, both of
+which could be interpreted as the result of young nebular shells, an inference
+that is also supported by nebular modeling. Concerning the long-standing
+discrepancy between chemical abundances inferred from permitted and
+collisionally-excited emission lines in photoionized nebulae, our results imply
+that multiple plasma components occur commonly in planetary nebulae.
+",0,1,0,0,0,0
+13486,From parabolic-trough to metasurface-concentrator,"  Metasurfaces are promising tools towards novel designs for flat optics
+applications. As such their quality and tolerance to fabrication imperfections
+need to be evaluated with specific tools. However, most such tools rely on the
+geometrical optics approximation and are not straightforwardly applicable to
+metasurfaces. In this Letter, we introduce and evaluate, for metasurfaces,
+parameters such as the intercept factor and the slope error usually defined for
+solar concentrators in the realm of ray-optics. After proposing definitions
+valid in physical optics, we put forward an approach to calculate them. As
+examples, we design three different concentrators based on three specific unit
+cells and assess them numerically. The concept allows for the comparison of the
+efficiency of the metasurfaces, their sensitivities to fabrication
+imperfections and will be critical for practical systems.
+",0,1,0,0,0,0
+13487,Correspondence Theorem between Holomorphic Discs and Tropical Discs on K3 Surfaces,"  We prove that the open Gromov-Witten invariants on K3 surfaces satisfy the
+Kontsevich-Soibelman wall-crossing formula. One one hand, this gives a
+geometric interpretation of the slab functions in Gross-Siebert program. On the
+other hands, the open Gromov-Witten invariants coincide with the weighted
+counting of tropical discs. This is an analog of the corresponding theorem on
+toric varieties \cite{M2}\cite{NS} but on compact Calabi-Yau surfaces.
+",0,0,1,0,0,0
+13488,A review and comparative study on functional time series techniques,"  This paper reviews the main estimation and prediction results derived in the
+context of functional time series, when Hilbert and Banach spaces are
+considered, specially, in the context of autoregressive processes of order one
+(ARH(1) and ARB(1) processes, for H and B being a Hilbert and Banach space,
+respectively). Particularly, we pay attention to the estimation and prediction
+results, and statistical tests, derived in both parametric and non-parametric
+frameworks. A comparative study between different ARH(1) prediction approaches
+is developed in the simulation study undertaken.
+",0,0,1,1,0,0
+13489,Surface depression with double-angle geometry during the discharge of close-packed grains from a silo,"  When rough grains in standard packing conditions are discharged from a silo,
+a conical depression with a single slope is formed at the surface. We observed
+that the increase of the volume fraction generates a more complex depression
+characterized by two angles of discharge: a lower angle close to the one
+measured for standard packing and a considerably larger upper angle. The change
+in slope appears at the boundary between a densely packed stagnant region at
+the periphery and the central flowing channel formed over the aperture. Since
+the material in the latter zone is always fluidized, the flow rate is
+unaffected by the initial packing of the bed. On the other hand, the contrast
+between both angles is markedly smaller when smooth particles of the same size
+and density are used, which reveals that high volume fraction and friction must
+combine to produce the observed geometry. Our results show that the surface
+profile helps to identify by simple visual inspection the packing conditions of
+a granular bed, and this can be useful to prevent undesirable collapses during
+silo discharge in industry.
+",0,1,0,0,0,0
+13490,Answer Set Programming for Non-Stationary Markov Decision Processes,"  Non-stationary domains, where unforeseen changes happen, present a challenge
+for agents to find an optimal policy for a sequential decision making problem.
+This work investigates a solution to this problem that combines Markov Decision
+Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming
+(ASP) in a method we call ASP(RL). In this method, Answer Set Programming is
+used to find the possible trajectories of an MDP, from where Reinforcement
+Learning is applied to learn the optimal policy of the problem. Results show
+that ASP(RL) is capable of efficiently finding the optimal solution of an MDP
+representing non-stationary domains.
+",1,0,0,0,0,0
+13491,Space-Bounded OTMs and REG$^{\infty}$,"  An important theorem in classical complexity theory is that LOGLOGSPACE=REG,
+i.e. that languages decidable with double-logarithmic space bound are regular.
+We consider a transfinite analogue of this theorem. To this end, we introduce
+deterministic ordinal automata (DOAs), show that they satisfy many of the basic
+statements of the theory of deterministic finite automata and regular
+languages. We then consider languages decidable by an ordinal Turing machine
+(OTM), introduced by P. Koepke in 2005 and show that if the working space of an
+OTM is of strictly smaller cardinality than the input length for all
+sufficiently long inputs, the language so decided is also decidable by a DOA.
+",0,0,1,0,0,0
+13492,Linear-time approximation schemes for planar minimum three-edge connected and three-vertex connected spanning subgraphs,"  We present the first polynomial-time approximation schemes, i.e., (1 +
+{\epsilon})-approximation algorithm for any constant {\epsilon} > 0, for the
+minimum three-edge connected spanning subgraph problem and the minimum
+three-vertex connected spanning subgraph problem in undirected planar graphs.
+Both the approximation schemes run in linear time.
+",1,0,0,0,0,0
+13493,Towards Wi-Fi AP-Assisted Content Prefetching for On-Demand TV Series: A Reinforcement Learning Approach,"  The emergence of smart Wi-Fi APs (Access Point), which are equipped with huge
+storage space, opens a new research area on how to utilize these resources at
+the edge network to improve users' quality of experience (QoE) (e.g., a short
+startup delay and smooth playback). One important research interest in this
+area is content prefetching, which predicts and accurately fetches contents
+ahead of users' requests to shift the traffic away during peak periods.
+However, in practice, the different video watching patterns among users, and
+the varying network connection status lead to the time-varying server load,
+which eventually makes the content prefetching problem challenging. To
+understand this challenge, this paper first performs a large-scale measurement
+study on users' AP connection and TV series watching patterns using
+real-traces. Then, based on the obtained insights, we formulate the content
+prefetching problem as a Markov Decision Process (MDP). The objective is to
+strike a balance between the increased prefetching&storage cost incurred by
+incorrect prediction and the reduced content download delay because of
+successful prediction. A learning-based approach is proposed to solve this
+problem and another three algorithms are adopted as baselines. In particular,
+first, we investigate the performance lower bound by using a random algorithm,
+and the upper bound by using an ideal offline approach. Then, we present a
+heuristic algorithm as another baseline. Finally, we design a reinforcement
+learning algorithm that is more practical to work in the online manner. Through
+extensive trace-based experiments, we demonstrate the performance gain of our
+design. Remarkably, our learning-based algorithm achieves a better precision
+and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to
+the random (resp. heuristic) algorithm.
+",1,0,0,0,0,0
+13494,Schematic Polymorphism in the Abella Proof Assistant,"  The Abella interactive theorem prover has proven to be an effective vehicle
+for reasoning about relational specifications. However, the system has a
+limitation that arises from the fact that it is based on a simply typed logic:
+formalizations that are identical except in the respect that they apply to
+different types have to be repeated at each type. We develop an approach that
+overcomes this limitation while preserving the logical underpinnings of the
+system. In this approach object constructors, formulas and other relevant
+logical notions are allowed to be parameterized by types, with the
+interpretation that they stand for the (infinite) collection of corresponding
+constructs that are obtained by instantiating the type parameters. The proof
+structures that we consider for formulas that are schematized in this fashion
+are limited to ones whose type instances are valid proofs in the simply typed
+logic. We develop schematic proof rules that ensure this property, a task that
+is complicated by the fact that type information influences the notion of
+unification that plays a key role in the logic. Our ideas, which have been
+implemented in an updated version of the system, accommodate schematic
+polymorphism both in the core logic of Abella and in the executable
+specification logic that it embeds.
+",1,0,0,0,0,0
+13495,Representations of weakly multiplicative arithmetic matroids are unique,"  An arithmetic matroid is weakly multiplicative if the multiplicity of at
+least one of its bases is equal to the product of the multiplicities of its
+elements. We show that if such an arithmetic matroid can be represented by an
+integer matrix, then this matrix is uniquely determined. This implies that the
+integer cohomology ring of a centred toric arrangement whose arithmetic matroid
+is weakly multiplicative is determined by its poset of layers. This partially
+answers a question asked by Callegaro-Delucchi.
+",0,0,1,0,0,0
+13496,Memory Efficient Max Flow for Multi-label Submodular MRFs,"  Multi-label submodular Markov Random Fields (MRFs) have been shown to be
+solvable using max-flow based on an encoding of the labels proposed by
+Ishikawa, in which each variable $X_i$ is represented by $\ell$ nodes (where
+$\ell$ is the number of labels) arranged in a column. However, this method in
+general requires $2\,\ell^2$ edges for each pair of neighbouring variables.
+This makes it inapplicable to realistic problems with many variables and
+labels, due to excessive memory requirement. In this paper, we introduce a
+variant of the max-flow algorithm that requires much less storage.
+Consequently, our algorithm makes it possible to optimally solve multi-label
+submodular problems involving large numbers of variables and labels on a
+standard computer.
+",1,0,0,0,0,0
+13497,Double Covers of Cartan Modular Curves,"  We present a strategy to obtain explicit equations for the modular double
+covers associated respectively to both a split and a non-split Cartan subgroup
+of $\text{GL}_2(\mathbb F_{p})$ with $p$ prime. Then we apply it successfully
+to the level $13$ case.
+",0,0,1,0,0,0
+13498,A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer,"  In this paper, we present a simple analysis of {\bf fast rates} with {\it
+high probability} of {\bf empirical minimization} for {\it stochastic composite
+optimization} over a finite-dimensional bounded convex set with exponential
+concave loss functions and an arbitrary convex regularization. To the best of
+our knowledge, this result is the first of its kind. As a byproduct, we can
+directly obtain the fast rate with {\it high probability} for exponential
+concave empirical risk minimization with and without any convex regularization,
+which not only extends existing results of empirical risk minimization but also
+provides a unified framework for analyzing exponential concave empirical risk
+minimization with and without {\it any} convex regularization. Our proof is
+very simple only exploiting the covering number of a finite-dimensional bounded
+set and a concentration inequality of random vectors.
+",0,0,0,1,0,0
+13499,Generation of surface plasmon-polaritons by edge effects,"  By using numerical and analytical methods, we describe the generation of
+fine-scale lateral electromagnetic waves, called surface plasmon-polaritons
+(SPPs), on atomically thick, metamaterial conducting sheets in two spatial
+dimensions (2D). Our computations capture the two-scale character of the total
+field and reveal how each edge of the sheet acts as a source of an SPP that may
+dominate the diffracted field. We use the finite element method to numerically
+implement a variational formulation for a weak discontinuity of the tangential
+magnetic field across a hypersurface. An adaptive, local mesh refinement
+strategy based on a posteriori error estimators is applied to resolve the
+pronounced two-scale character of wave propagation and radiation over the
+metamaterial sheet. We demonstrate by numerical examples how a singular
+geometry, e.g., sheets with sharp edges, and sharp spatial changes in the
+associated surface conductivity may significantly influence surface plasmons in
+nanophotonics.
+",0,1,1,0,0,0
+13500,PowerAI DDL,"  As deep neural networks become more complex and input datasets grow larger,
+it can take days or even weeks to train a deep neural network to the desired
+accuracy. Therefore, distributed Deep Learning at a massive scale is a critical
+capability, since it offers the potential to reduce the training time from
+weeks to hours. In this paper, we present a software-hardware co-optimized
+distributed Deep Learning system that can achieve near-linear scaling up to
+hundreds of GPUs. The core algorithm is a multi-ring communication pattern that
+provides a good tradeoff between latency and bandwidth and adapts to a variety
+of system configurations. The communication algorithm is implemented as a
+library for easy use. This library has been integrated into Tensorflow, Caffe,
+and Torch. We train Resnet-101 on Imagenet 22K with 64 IBM Power8 S822LC
+servers (256 GPUs) in about 7 hours to an accuracy of 33.8 % validation
+accuracy. Microsoft's ADAM and Google's DistBelief results did not reach 30 %
+validation accuracy for Imagenet 22K. Compared to Facebook AI Research's recent
+paper on 256 GPU training, we use a different communication algorithm, and our
+combined software and hardware system offers better communication overhead for
+Resnet-50. A PowerAI DDL enabled version of Torch completed 90 epochs of
+training on Resnet 50 for 1K classes in 50 minutes using 64 IBM Power8 S822LC
+servers (256 GPUs).
+",1,0,0,0,0,0
+13501,A truncated $\mathcal{V}$-fractional derivative in $\mathbb{R}^n$,"  Using the six parameters truncated Mittag-Leffler function, we introduce a
+convenient truncated function to define the so-called truncated
+$\mathcal{V}$-fractional derivative type. After a discussion involving some
+properties associated with this derivative, we propose the derivative of a
+vector valued function and define the $\mathcal{V}$-fractional Jacobian matrix
+whose properties allow us to say that: the multivariable truncated
+$\mathcal{V}$-fractional derivative type, as proposed here, generalizes the
+truncated $\mathcal{V}$-fractional derivative type and can bee extended to
+obtain a truncated $\mathcal{V}$-fractional partial derivative type. As
+applications we discuss and prove the change of order associated with two index
+i.e., the commutativity of two truncated $\mathcal{V}$-fractional partial
+derivative type and propose the truncated $\mathcal{V}$-fractional Green's
+theorem.
+",0,0,1,0,0,0
+13502,Statistical Analysis on Bangla Newspaper Data to Extract Trending Topic and Visualize Its Change Over Time,"  Trending topic of newspapers is an indicator to understand the situation of a
+country and also a way to evaluate the particular newspaper. This paper
+represents a model describing few techniques to select trending topics from
+Bangla Newspaper. Topics that are discussed more frequently than other in
+Bangla newspaper will be marked and how a very famous topic loses its
+importance with the change of time and another topic takes its place will be
+demonstrated. Data from two popular Bangla Newspaper with date and time were
+collected. Statistical analysis was performed after on these data after
+preprocessing. Popular and most used keywords were extracted from the stream of
+Bangla keyword with this analysis. This model can also cluster category wise
+news trend or a list of news trend in daily or weekly basis with enough data. A
+pattern can be found on their news trend too. Comparison among past news trend
+of Bangla newspapers will give a visualization of the situation of Bangladesh.
+This visualization will be helpful to predict future trending topics of Bangla
+Newspaper.
+",1,0,0,0,0,0
+13503,Effects of global gas flows on type I migration,"  Magnetically-driven disk winds would alter the surface density slope of gas
+in the inner region of a protoplanetary disk $(r \lesssim 1 {\rm au})$. This in
+turn affects planet formation. Recently, the effect of disk wind torque has
+been considered with the suggestion that it would carve out the surface density
+of the disk from inside and would induce global gas flows (wind-driven
+accretion). We aim to investigate effects of global gas flows on type I
+migration and also examine planet formation. A simplified approach was taken to
+address this issue, and N-body simulations with isolation-mass planets were
+also performed. In previous studies, the effect of gas flow induced by
+turbulence-driven accretion has been taken into account for its desaturation
+effect of the corotation torque. If more rapid gas flows (e.g., wind-driven
+accretion) are considered, the desaturation effect can be modified. In
+MRI-inactive disks, in which the wind-driven accretion dominates the disk
+evolution, the gas flow at the midplane plays an important role. If this flow
+is fast, the corotation torque is efficiently desaturated. Then, the fact that
+the surface density slope can be positive in the inner region due to the wind
+torque can generate an outward migration region extended to super-Earth mass
+planets. In this case, we observe that no planets fall onto the central star in
+N-body simulations with migration forces imposed to reproduce such migration
+pattern. We also see that super-Earth mass planets can undergo outward
+migration. Relatively rapid gas flows affects type I migration and thus the
+formation of close-in planets.
+",0,1,0,0,0,0
+13504,Warp: a method for neural network interpretability applied to gene expression profiles,"  We show a proof of principle for warping, a method to interpret the inner
+working of neural networks in the context of gene expression analysis. Warping
+is an efficient way to gain insight to the inner workings of neural nets and
+make them more interpretable. We demonstrate the ability of warping to recover
+meaningful information for a given class on a samplespecific individual basis.
+We found warping works well in both linearly and nonlinearly separable
+datasets. These encouraging results show that warping has a potential to be the
+answer to neural networks interpretability in computational biology.
+",1,0,0,0,0,0
+13505,Tilings of convex sets by mutually incongruent equilateral triangles contain arbitrarily small tiles,"  We show that every tiling of a convex set in the Euclidean plane
+$\mathbb{R}^2$ by equilateral triangles of mutually different sizes contains
+arbitrarily small tiles. The proof is purely elementary up to the discussion of
+one family of tilings of the full plane $\mathbb{R}^2$, which is based on a
+surprising connection to a random walk on a directed graph.
+",0,0,1,0,0,0
+13506,The decomposition of 0-Hecke modules associated to quasisymmetric Schur functions,"  Recently Tewari and van Willigenburg constructed modules of the 0-Hecke
+algebra that are mapped to the quasisymmetric Schur functions by the
+quasisymmetric characteristic and decomposed them into a direct sum of certain
+submodules. We show that these submodules are indecomposable by determining
+their endomorphism rings.
+",0,0,1,0,0,0
+13507,Bayesian Uncertainty Quantification and Information Fusion in CALPHAD-based Thermodynamic Modeling,"  Calculation of phase diagrams is one of the fundamental tools in alloy
+design---more specifically under the framework of Integrated Computational
+Materials Engineering. Uncertainty quantification of phase diagrams is the
+first step required to provide confidence for decision making in property- or
+performance-based design. As a manner of illustration, a thorough probabilistic
+assessment of the CALPHAD model parameters is performed against the available
+data for a Hf-Si binary case study using a Markov Chain Monte Carlo sampling
+approach. The plausible optimum values and uncertainties of the parameters are
+thus obtained, which can be propagated to the resulting phase diagram. Using
+the parameter values obtained from deterministic optimization in a
+computational thermodynamic assessment tool (in this case Thermo-Calc) as the
+prior information for the parameter values and ranges in the sampling process
+is often necessary to achieve a reasonable cost for uncertainty quantification.
+This brings up the problem of finding an appropriate CALPHAD model with
+high-level of confidence which is a very hard and costly task that requires
+considerable expert skill. A Bayesian hypothesis testing based on Bayes'
+factors is proposed to fulfill the need of model selection in this case, which
+is applied to compare four recommended models for the Hf-Si system. However, it
+is demonstrated that information fusion approaches, i.e., Bayesian model
+averaging and an error correlation-based model fusion, can be used to combine
+the useful information existing in all the given models rather than just using
+the best selected model, which may lack some information about the system being
+modelled.
+",0,0,0,1,0,0
+13508,Modern Data Formats for Big Bioinformatics Data Analytics,"  Next Generation Sequencing (NGS) technology has resulted in massive amounts
+of proteomics and genomics data. This data is of no use if it is not properly
+analyzed. ETL (Extraction, Transformation, Loading) is an important step in
+designing data analytics applications. ETL requires proper understanding of
+features of data. Data format plays a key role in understanding of data,
+representation of data, space required to store data, data I/O during
+processing of data, intermediate results of processing, in-memory analysis of
+data and overall time required to process data. Different data mining and
+machine learning algorithms require input data in specific types and formats.
+This paper explores the data formats used by different tools and algorithms and
+also presents modern data formats that are used on Big Data Platform. It will
+help researchers and developers in choosing appropriate data format to be used
+for a particular tool or algorithm.
+",1,0,0,0,0,0
+13509,Nearest-Neighbor Based Non-Parametric Probabilistic Forecasting with Applications in Photovoltaic Systems,"  The present contribution offers a simple methodology for the obtainment of
+data-driven interval forecasting models by combining pairs of quantile
+regressions. Those regressions are created without the usage of the
+non-differentiable pinball-loss function, but through a k-nearest-neighbors
+based training set transformation and traditional regression approaches. By
+leaving the underlying training algorithms of the data mining techniques
+unchanged, the presented approach simplifies the creation of quantile
+regressions with more complex techniques (e.g. artificial neural networks). The
+quality of the presented methodology is tested on the usecase of photovoltaic
+power forecasting, for which quantile regressions using polynomial models as
+well as artificial neural networks and support vector regressions are created.
+From the resulting evaluation values it can be concluded that acceptable
+interval forecasting models are created.
+",1,0,0,0,0,0
+13510,A thermodynamic parallel of the Braess road-network paradox,"  We provide here a thermodynamic analog of the Braess road-network paradox
+with irreversible engines working between reservoirs that are placed at
+vertices of the network. Paradoxes of different kinds reappear, emphasizing the
+specialty of the network.
+",0,1,0,0,0,0
+13511,Weighted gevrey class regularity of euler equation in the whole space,"  In this paper we study the weighted Gevrey class regularity of Euler equation
+in the whole space R 3. We first establish the local existence of Euler
+equation in weighted Sobolev space, then obtain the weighted Gevrey regularity
+of Euler equation. We will use the weighted Sobolev-Gevrey space method to
+obtain the results of Gevrey regularity of Euler equation, and the use of the
+property of singular operator in the estimate of the pressure term is the
+improvement of our work.
+",0,0,1,0,0,0
+13512,Temporal connectivity in finite networks with non-uniform measures,"  Soft Random Geometric Graphs (SRGGs) have been widely applied to various
+models including those of wireless sensor, communication, social and neural
+networks. SRGGs are constructed by randomly placing nodes in some space and
+making pairwise links probabilistically using a connection function that is
+system specific and usually decays with distance. In this paper we focus on the
+application of SRGGs to wireless communication networks where information is
+relayed in a multi hop fashion, although the analysis is more general and can
+be applied elsewhere by using different distributions of nodes and/or
+connection functions. We adopt a general non-uniform density which can model
+the stationary distribution of different mobility models, with the interesting
+case being when the density goes to zero along the boundaries. The global
+connectivity properties of these non-uniform networks are likely to be
+determined by highly isolated nodes, where isolation can be caused by the
+spatial distribution or the local geometry (boundaries). We extend the analysis
+to temporal-spatial networks where we fix the underlying non-uniform
+distribution of points and the dynamics are caused by the temporal variations
+in the link set, and explore the probability a node near the corner is isolated
+at time $T$. This work allows for insight into how non-uniformity (caused by
+mobility) and boundaries impact the connectivity features of temporal-spatial
+networks. We provide a simple method for approximating these probabilities for
+a range of different connection functions and verify them against simulations.
+Boundary nodes are numerically shown to dominate the connectivity properties of
+these finite networks with non-uniform measure.
+",1,0,0,0,0,0
+13513,Matching Media Contents with User Profiles by means of the Dempster-Shafer Theory,"  The media industry is increasingly personalizing the offering of contents in
+attempt to better target the audience. This requires to analyze the
+relationships that goes established between users and content they enjoy,
+looking at one side to the content characteristics and on the other to the user
+profile, in order to find the best match between the two. In this paper we
+suggest to build that relationship using the Dempster-Shafer's Theory of
+Evidence, proposing a reference model and illustrating its properties by means
+of a toy example. Finally we suggest possible applications of the model for
+tasks that are common in the modern media industry.
+",1,0,0,0,0,0
+13514,Separability by Piecewise Testable Languages is PTime-Complete,"  Piecewise testable languages form the first level of the Straubing-Thérien
+hierarchy. The membership problem for this level is decidable and testing if
+the language of a DFA is piecewise testable is NL-complete. The question has
+not yet been addressed for NFAs. We fill in this gap by showing that it is
+PSpace-complete. The main result is then the lower-bound complexity of
+separability of regular languages by piecewise testable languages. Two regular
+languages are separable by a piecewise testable language if the piecewise
+testable language includes one of them and is disjoint from the other. For
+languages represented by NFAs, separability by piecewise testable languages is
+known to be decidable in PTime. We show that it is PTime-hard and that it
+remains PTime-hard even for minimal DFAs.
+",1,0,0,0,0,0
+13515,"Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages","  We present SuperPivot, an analysis method for low-resource languages that
+occur in a superparallel corpus, i.e., in a corpus that contains an order of
+magnitude more languages than parallel corpora currently in use. We show that
+SuperPivot performs well for the crosslingual analysis of the linguistic
+phenomenon of tense. We produce analysis results for more than 1000 languages,
+conducting - to the best of our knowledge - the largest crosslingual
+computational study performed to date. We extend existing methodology for
+leveraging parallel corpora for typological analysis by overcoming a limiting
+assumption of earlier work: We only require that a linguistic feature is
+overtly marked in a few of thousands of languages as opposed to requiring that
+it be marked in all languages under investigation.
+",1,0,0,0,0,0
+13516,A Stochastic Control Approach to Managed Futures Portfolios,"  We study a stochastic control approach to managed futures portfolios.
+Building on the Schwartz 97 stochastic convenience yield model for commodity
+prices, we formulate a utility maximization problem for dynamically trading a
+single-maturity futures or multiple futures contracts over a finite horizon. By
+analyzing the associated Hamilton-Jacobi-Bellman (HJB) equation, we solve the
+investor's utility maximization problem explicitly and derive the optimal
+dynamic trading strategies in closed form. We provide numerical examples and
+illustrate the optimal trading strategies using WTI crude oil futures data.
+",0,0,0,0,0,1
+13517,Attention-Set based Metric Learning for Video Face Recognition,"  Face recognition has made great progress with the development of deep
+learning. However, video face recognition (VFR) is still an ongoing task due to
+various illumination, low-resolution, pose variations and motion blur. Most
+existing CNN-based VFR methods only obtain a feature vector from a single image
+and simply aggregate the features in a video, which less consider the
+correlations of face images in one video. In this paper, we propose a novel
+Attention-Set based Metric Learning (ASML) method to measure the statistical
+characteristics of image sets. It is a promising and generalized extension of
+Maximum Mean Discrepancy with memory attention weighting. First, we define an
+effective distance metric on image sets, which explicitly minimizes the
+intra-set distance and maximizes the inter-set distance simultaneously. Second,
+inspired by Neural Turing Machine, a Memory Attention Weighting is proposed to
+adapt set-aware global contents. Then ASML is naturally integrated into CNNs,
+resulting in an end-to-end learning scheme. Our method achieves
+state-of-the-art performance for the task of video face recognition on the
+three widely used benchmarks including YouTubeFace, YouTube Celebrities and
+Celebrity-1000.
+",1,0,0,0,0,0
+13518,Variable Annealing Length and Parallelism in Simulated Annealing,"  In this paper, we propose: (a) a restart schedule for an adaptive simulated
+annealer, and (b) parallel simulated annealing, with an adaptive and
+parameter-free annealing schedule. The foundation of our approach is the
+Modified Lam annealing schedule, which adaptively controls the temperature
+parameter to track a theoretically ideal rate of acceptance of neighboring
+states. A sequential implementation of Modified Lam simulated annealing is
+almost parameter-free. However, it requires prior knowledge of the annealing
+length. We eliminate this parameter using restarts, with an exponentially
+increasing schedule of annealing lengths. We then extend this restart schedule
+to parallel implementation, executing several Modified Lam simulated annealers
+in parallel, with varying initial annealing lengths, and our proposed parallel
+annealing length schedule. To validate our approach, we conduct experiments on
+an NP-Hard scheduling problem with sequence-dependent setup constraints. We
+compare our approach to fixed length restarts, both sequentially and in
+parallel. Our results show that our approach can achieve substantial
+performance gains, throughout the course of the run, demonstrating our approach
+to be an effective anytime algorithm.
+",1,0,0,0,0,0
+13519,Low-Shapiro hydrostatic reconstruction technique for blood flow simulation in large arteries with varying geometrical and mechanical properties,"  The purpose of this work is to construct a simple, efficient and accurate
+well-balanced numerical scheme for one-dimensional (1D) blood flow in large
+arteries with varying geometrical and mechanical properties. As the steady
+states at rest are not relevant for blood flow, we construct two well-balanced
+hydrostatic reconstruction techniques designed to preserve low-Shapiro number
+steady states that may occur in large network simulations. The Shapiro number S
+h = u/c is the equivalent of the Froude number for shallow water equations and
+the Mach number for compressible Euler equations. The first is the low-Shapiro
+hydrostatic reconstruction (HR-LS), which is a simple and efficient method,
+inspired from the hydrostatic reconstruction technique (HR). The second is the
+subsonic hydrostatic reconstruction (HR-S), adapted here to blood flow and
+designed to exactly preserve all subcritical steady states. We systematically
+compare HR, HR-LS and HR-S in a series of single artery and arterial network
+numerical tests designed to evaluate their well-balanced and wave-capturing
+properties. The results indicate that HR is not adapted to compute blood flow
+in large arteries as it is unable to capture wave reflections and transmissions
+when large variations of the arteries' geometrical and mechanical properties
+are considered. On the contrary, HR-S is exactly well-balanced and is the most
+accurate hydrostatic reconstruction technique. However, HR-LS is able to
+compute low-Shapiro number steady states as well as wave reflections and
+transmissions with satisfying accuracy and is simpler and computationally less
+expensive than HR-S. We therefore recommend using HR-LS for 1D blood flow
+simulations in large arterial network simulations.
+",0,1,1,0,0,0
+13520,Anisotropic two-gap superconductivity and the absence of a Pauli paramagnetic limit in single-crystalline LaO$_{0.5}$F$_{0.5}$BiS$_2$,"  Ambient-pressure-grown LaO$_{0.5}$F$_{0.5}$BiS$_2$ with a superconducting
+transition temperature $T_{c}\sim$3K possesses a highly anisotropic normal
+state. By a series of electrical resistivity measurements with a magnetic field
+direction varying between the crystalline $c$-axis and the $ab$-plane, we
+present the first datasets displaying the temperature dependence of the
+out-of-plane upper critical field $H_{c2}^{\perp}(T)$, the in-plane upper
+critical field $H_{c2}^{\parallel}(T)$, as well as the angular dependence of
+$H_{c2}$ at fixed temperatures for ambient-pressure-grown
+LaO$_{0.5}$F$_{0.5}$BiS$_2$ single crystals. The anisotropy of the
+superconductivity, $H_{c2}^{\parallel}/H_{c2}^{\perp}$, reaches $\sim$16 on
+approaching 0 K, but it decreases significantly near $T_{c}$. A pronounced
+upward curvature of $H_{c2}^{\parallel}(T)$ is observed near $T_{c}$, which we
+analyze using a two-gap model. Moreover, $H_{c2}^{\parallel}(0)$ is found to
+exceed the Pauli paramagnetic limit, which can be understood by considering the
+strong spin-orbit coupling associated with Bi as well as the breaking of the
+local inversion symmetry at the electronically active BiS$_2$ bilayers. Hence,
+LaO$_{0.5}$F$_{0.5}$BiS$_2$ with a centrosymmetric lattice structure is a
+unique platform to explore the physics associated with local parity violation
+in the bulk crystal.
+",0,1,0,0,0,0
+13521,Discrete Invariants of Generically Inconsistent Systems of Laurent Polynomials,"  Let $ \mathcal{A}_1, \ldots, \mathcal{A}_k $ be finite sets in $ \mathbb{Z}^n
+$ and let $ Y \subset (\mathbb{C}^*)^n $ be an algebraic variety defined by a
+system of equations \[ f_1 = \ldots = f_k = 0, \] where $ f_1, \ldots, f_k $
+are Laurent polynomials with supports in $\mathcal{A}_1, \ldots,
+\mathcal{A}_k$. Assuming that $ f_1, \ldots, f_k $ are sufficiently generic,
+the Newton polyhedron theory computes discrete invariants of $ Y $ in terms of
+the Newton polyhedra of $ f_1, \ldots, f_k $. It may appear that the generic
+system with fixed supports $ \mathcal{A}_1, \ldots, \mathcal{A}_k $ is
+inconsistent. In this paper, we compute discrete invariants of algebraic
+varieties defined by system of equations which are generic in the set of
+consistent system with support in $\mathcal{A}_1, \ldots, \mathcal{A}_k$ by
+reducing the question to the Newton polyhedra theory. Unlike the classical
+situation, not only the Newton polyhedra of $f_1,\dots,f_k$, but also the
+supports $\mathcal{A}_1, \ldots, \mathcal{A}_k$ themselves appear in the
+answers.
+",0,0,1,0,0,0
+13522,Enabling near real-time remote search for fast transient events with lossy data compression,"  We present a systematic evaluation of JPEG2000 (ISO/IEC 15444) as a transport
+data format to enable rapid remote searches for fast transient events as part
+of the Deeper Wider Faster program (DWF). DWF uses ~20 telescopes from radio to
+gamma-rays to perform simultaneous and rapid-response follow-up searches for
+fast transient events on millisecond-to-hours timescales. DWF search demands
+have a set of constraints that is becoming common amongst large collaborations.
+Here, we focus on the rapid optical data component of DWF led by the Dark
+Energy Camera (DECam) at CTIO. Each DECam image has 70 total CCDs saved as a
+~1.2 gigabyte FITS file. Near real-time data processing and fast transient
+candidate identifications -- in minutes for rapid follow-up triggers on other
+telescopes -- requires computational power exceeding what is currently
+available on-site at CTIO. In this context, data files need to be transmitted
+rapidly to a foreign location for supercomputing post-processing, source
+finding, visualization and analysis. This step in the search process poses a
+major bottleneck, and reducing the data size helps accommodate faster data
+transmission. To maximise our gain in transfer time and still achieve our
+science goals, we opt for lossy data compression -- keeping in mind that raw
+data is archived and can be evaluated at a later time. We evaluate how lossy
+JPEG2000 compression affects the process of finding transients, and find only a
+negligible effect for compression ratios up to ~25:1. We also find a linear
+relation between compression ratio and the mean estimated data transmission
+speed-up factor. Adding highly customized compression and decompression steps
+to the science pipeline considerably reduces the transmission time --
+validating its introduction to the DWF science pipeline and enabling science
+that was otherwise too difficult with current technology.
+",0,1,0,0,0,0
+13523,Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning,"  Deep Reinforcement Learning (DRL) has been applied successfully to many
+robotic applications. However, the large number of trials needed for training
+is a key issue. Most of existing techniques developed to improve training
+efficiency (e.g. imitation) target on general tasks rather than being tailored
+for robot applications, which have their specific context to benefit from. We
+propose a novel framework, Assisted Reinforcement Learning, where a classical
+controller (e.g. a PID controller) is used as an alternative, switchable policy
+to speed up training of DRL for local planning and navigation problems. The
+core idea is that the simple control law allows the robot to rapidly learn
+sensible primitives, like driving in a straight line, instead of random
+exploration. As the actor network becomes more advanced, it can then take over
+to perform more complex actions, like obstacle avoidance. Eventually, the
+simple controller can be discarded entirely. We show that not only does this
+technique train faster, it also is less sensitive to the structure of the DRL
+network and consistently outperforms a standard Deep Deterministic Policy
+Gradient network. We demonstrate the results in both simulation and real-world
+experiments.
+",1,0,0,0,0,0
+13524,The Partition Rank of a Tensor and $k$-Right Corners in $\mathbb{F}_{q}^{n}$,"  Following the breakthrough of Croot, Lev, and Pach, Tao introduced a
+symmetrized version of their argument, which is now known as the slice rank
+method. In this paper, we introduce a more general version of the slice rank of
+a tensor, which we call the Partition Rank. This allows us to extend the slice
+rank method to problems that require the variables to be distinct. Using the
+partition rank, we generalize a recent result of Ge and Shangguan, and prove
+that any set $A\subset\mathbb{F}_{q}^{n}$ of size
+\[|A|>(k+1)\cdot\binom{n+(k-1)q}{(k-1)(q-1)}\] contains a $k$-right-corner,
+that is distinct vectors $x_{1},\dots,x_{k},x_{k+1}$ where
+$x_{1}-x_{k+1},\dots,x_{k}-x_{k+1}$ are mutually orthogonal, for $q=p^{r}$, a
+prime power with $p>k$.
+",0,0,1,0,0,0
+13525,Tunable Optoelectronic Properties of Triply-Bonded Carbon Molecules with Linear and Graphyne Substructures,"  In this paper we present a detailed computational study of the electronic
+structure and optical properties of triply-bonded hydrocarbons with linear, and
+graphyne substructures, with the aim of identifying their potential in
+opto-electronic device applications. For the purpose, we employed a correlated
+electron methodology based upon the Pariser-Parr-Pople model Hamiltonian,
+coupled with the configuration interaction (CI) approach, and studied
+structures containing up to 42 carbon atoms. Our calculations, based upon
+large-scale CI expansions, reveal that the linear structures have intense
+optical absorption at the HOMO-LUMO gap, while the graphyne ones have those at
+higher energies. Thus, the opto-electronic properties depend on the topology of
+the {graphyne substructures, suggesting that they can be tuned by means of
+structural modifications. Our results are in very good agreement with the
+available experimental data.
+",0,1,0,0,0,0
+13526,Higher zigzag algebras,"  Given any Koszul algebra of finite global dimension one can define a new
+algebra, which we call a higher zigzag algebra, as a twisted trivial extension
+of the Koszul dual of our original algebra. If our original algebra is the path
+algebra of a quiver whose underlying graph is a tree, this construction
+recovers the zigzag algebras of Huerfano and Khovanov. We study examples of
+higher zigzag algebras coming from Iyama's iterative construction of type A
+higher representation finite algebras. We give presentations of these algebras
+by quivers and relations, and describe relations between spherical twists
+acting on their derived categories. We then make a connection to the McKay
+correspondence in higher dimensions: if G is a finite abelian subgroup of the
+special linear group acting on affine space, then the skew group algebra which
+controls the category of G-equivariant sheaves is Koszul dual to a higher
+zigzag algebra. Using this, we show that our relations between spherical twists
+appear naturally in examples from algebraic geometry.
+",0,0,1,0,0,0
+13527,Motivic modular forms from equivariant stable homotopy theory,"  In this paper, we produce a cellular motivic spectrum of motivic modular
+forms over $\R$ and $\C$, answering positively to a conjecture of Dan Isaksen.
+This spectrum is constructed to have the appropriate cohomology, as a module
+over the relevant motivic Steenrod algebra. We first produce a $\G$-equivariant
+version of this spectrum, and then use a machinery to construct a motivic
+spectrum from an equivariant one. We believe that this machinery will be of
+independent interest.
+",0,0,1,0,0,0
+13528,Topology and experimental distinguishability,"  In this work we introduce the idea that the primary application of topology
+in experimental sciences is to keep track of what can be distinguished through
+experimentation. This link provides understanding and justification as to why
+topological spaces and continuous functions are pervasive tools in science. We
+first define an experimental observation as a statement that can be verified
+using an experimental procedure and show that observations are closed under
+finite conjunction and countable disjunction. We then consider observations
+that identify elements within a set and show how they induce a Hausdorff and
+second-countable topology on that set, thus identifying an open set as one that
+can be associated with an experimental observation. We then show that
+experimental relationships are continuous functions, as they must preserve
+experimental distinguishability, and that they are themselves experimentally
+distinguishable by defining a Hausdorff and second-countable topology for this
+collection.
+",0,0,1,0,0,0
+13529,Edge fracture in complex fluids,"  We study theoretically the edge fracture instability in sheared complex
+fluids, by means of linear stability analysis and direct nonlinear simulations.
+We derive an exact analytical expression for the onset of edge fracture in
+terms of the shear-rate derivative of the fluid's second normal stress
+difference, the shear-rate derivative of the shear stress, the jump in shear
+stress across the interface between the fluid and the outside medium (usually
+air), the surface tension of that interface, and the rheometer gap size. We
+provide a full mechanistic understanding of the edge fracture instability,
+carefully validated against our simulations. These findings, which are robust
+with respect to choice of rheological constitutive model, also suggest a
+possible route to mitigating edge fracture, potentially allowing
+experimentalists to achieve and accurately measure stronger flows than
+hitherto.
+",0,1,0,0,0,0
+13530,Testing statistical Isotropy in Cosmic Microwave Background Polarization maps,"  We apply our symmetry based Power tensor technique to test conformity of
+PLANCK Polarization maps with statistical isotropy. On a wide range of angular
+scales (l=40-150), our preliminary analysis detects many statistically
+anisotropic multipoles in foreground cleaned full sky PLANCK polarization maps
+viz., COMMANDER and NILC. We also study the effect of residual foregrounds that
+may still be present in the galactic plane using both common UPB77 polarization
+mask, as well as the individual component separation method specific
+polarization masks. However some of the statistically anisotropic modes still
+persist, albeit significantly in NILC map. We further probed the data for any
+coherent alignments across multipoles in several bins from the chosen multipole
+range.
+",0,1,0,0,0,0
+13531,Generalised Lyapunov Functions and Functionally Generated Trading Strategies,"  This paper investigates the dependence of functional portfolio generation,
+introduced by Fernholz (1999), on an extra finite variation process. The
+framework of Karatzas and Ruf (2017) is used to formulate conditions on trading
+strategies to be strong arbitrage relative to the market over sufficiently
+large time horizons. A mollification argument and Komlos theorem yield a
+general class of potential arbitrage strategies. These theoretical results are
+complemented by several empirical examples using data from the S&P 500 stocks.
+",0,0,0,0,0,1
+13532,Local optima of the Sherrington-Kirkpatrick Hamiltonian,"  We study local optima of the Hamiltonian of the Sherrington-Kirkpatrick
+model. We compute the exponent of the expected number of local optima and
+determine the ""typical"" value of the Hamiltonian.
+",1,0,0,0,0,0
+13533,Joining Extractions of Regular Expressions,"  Regular expressions with capture variables, also known as ""regex formulas,""
+extract relations of spans (interval positions) from text. These relations can
+be further manipulated via Relational Algebra as studied in the context of
+document spanners, Fagin et al.'s formal framework for information extraction.
+We investigate the complexity of querying text by Conjunctive Queries (CQs) and
+Unions of CQs (UCQs) on top of regex formulas. We show that the lower bounds
+(NP-completeness and W[1]-hardness) from the relational world also hold in our
+setting; in particular, hardness hits already single-character text! Yet, the
+upper bounds from the relational world do not carry over. Unlike the relational
+world, acyclic CQs, and even gamma-acyclic CQs, are hard to compute. The source
+of hardness is that it may be intractable to instantiate the relation defined
+by a regex formula, simply because it has an exponential number of tuples. Yet,
+we are able to establish general upper bounds. In particular, UCQs can be
+evaluated with polynomial delay, provided that every CQ has a bounded number of
+atoms (while unions and projection can be arbitrary). Furthermore, UCQ
+evaluation is solvable with FPT (Fixed-Parameter Tractable) delay when the
+parameter is the size of the UCQ.
+",1,0,0,0,0,0
+13534,Learning Generalized Reactive Policies using Deep Neural Networks,"  We present a new approach to learning for planning, where knowledge acquired
+while solving a given set of planning problems is used to plan faster in
+related, but new problem instances. We show that a deep neural network can be
+used to learn and represent a \emph{generalized reactive policy} (GRP) that
+maps a problem instance and a state to an action, and that the learned GRPs
+efficiently solve large classes of challenging problem instances. In contrast
+to prior efforts in this direction, our approach significantly reduces the
+dependence of learning on handcrafted domain knowledge or feature selection.
+Instead, the GRP is trained from scratch using a set of successful execution
+traces. We show that our approach can also be used to automatically learn a
+heuristic function that can be used in directed search algorithms. We evaluate
+our approach using an extensive suite of experiments on two challenging
+planning problem domains and show that our approach facilitates learning
+complex decision making policies and powerful heuristic functions with minimal
+human input. Videos of our results are available at goo.gl/Hpy4e3.
+",1,0,0,0,0,0
+13535,Symplectic Coarse-Grained Dynamics: Chalkboard Motion in Classical and Quantum Mechanics,"  In the usual approaches to mechanics (classical or quantum) the primary
+object of interest is the Hamiltonian, from which one tries to deduce the
+solutions of the equations of motion (Hamilton or Schrödinger). In the
+present work we reverse this paradigm and view the motions themselves as being
+the primary objects. This is made possible by studying arbitrary phase space
+motions, not of points, but of (small) ellipsoids with the requirement that the
+symplectic capacity of these ellipsoids is preserved. This allows us to guide
+and control these motions as we like. In the classical case these ellipsoids
+correspond to a symplectic coarse graining of phase space, and in the quantum
+case they correspond to the ""quantum blobs"" we defined in previous work, and
+which can be viewed as minimum uncertainty phase space cells which are in a
+one-to-one correspondence with Gaussian pure states.
+",0,0,1,0,0,0
+13536,Characteristic functions as bounded multipliers on anisotropic spaces,"  We show that characteristic functions of domains with boundaries transversal
+to stable cones are bounded multipliers on a recently introduced scale
+$U^{t,s}_p$ of anisotropic Banach spaces, under the conditions -1+1/p<s<-t<0
+and -(r-1)+t<s, with 1<p<infty. (Amended after comments from the referee and M.
+Jézéquel, January 10, 2018)
+",0,1,0,0,0,0
+13537,Pathwise Least Angle Regression and a Significance Test for the Elastic Net,"  Least angle regression (LARS) by Efron et al. (2004) is a novel method for
+constructing the piece-wise linear path of Lasso solutions. For several years,
+it remained also as the de facto method for computing the Lasso solution before
+more sophisticated optimization algorithms preceded it. LARS method has
+recently again increased its popularity due to its ability to find the values
+of the penalty parameters, called knots, at which a new parameter enters the
+active set of non-zero coefficients. Significance test for the Lasso by
+Lockhart et al. (2014), for example, requires solving the knots via the LARS
+algorithm. Elastic net (EN), on the other hand, is a highly popular extension
+of Lasso that uses a linear combination of Lasso and ridge regression
+penalties. In this paper, we propose a new novel algorithm, called pathwise
+(PW-)LARS-EN, that is able to compute the EN knots over a grid of EN tuning
+parameter {\alpha} values. The developed PW-LARS-EN algorithm decreases the EN
+tuning parameter and exploits the previously found knot values and the original
+LARS algorithm. A covariance test statistic for the Lasso is then generalized
+to the EN for testing the significance of the predictors. Our simulation
+studies validate the fact that the test statistic has an asymptotic Exp(1)
+distribution.
+",0,0,1,1,0,0
+13538,2MTF VI. Measuring the velocity power spectrum,"  We present measurements of the velocity power spectrum and constraints on the
+growth rate of structure $f\sigma_{8}$, at redshift zero, using the peculiar
+motions of 2,062 galaxies in the completed 2MASS Tully-Fisher survey (2MTF). To
+accomplish this we introduce a model for fitting the velocity power spectrum
+including the effects of non-linear Redshift Space Distortions (RSD), allowing
+us to recover unbiased fits down to scales $k=0.2\,h\,{\rm Mpc}^{-1}$ without
+the need to smooth or grid the data. Our fitting methods are validated using a
+set of simulated 2MTF surveys. Using these simulations we also identify that
+the Gaussian distributed estimator for peculiar velocities of
+\cite{Watkins2015} is suitable for measuring the velocity power spectrum, but
+sub-optimal for the 2MTF data compared to using magnitude fluctuations $\delta
+m$, and that, whilst our fits are robust to a change in fiducial cosmology,
+future peculiar velocity surveys with more constraining power may have to
+marginalise over this. We obtain \textit{scale-dependent} constraints on the
+growth rate of structure in two bins, finding $f\sigma_{8} =
+[0.55^{+0.16}_{-0.13},0.40^{+0.16}_{-0.17}]$ in the ranges $k = [0.007-0.055,
+0.55-0.150]\,h\,{\rm Mpc}^{-1}$. We also find consistent results using four
+bins. Assuming scale-\textit{independence} we find a value $f\sigma_{8} =
+0.51^{+0.09}_{-0.08}$, a $\sim16\%$ measurement of the growth rate. Performing
+a consistency check of General Relativity (GR) and combining our results with
+CMB data only we find $\gamma = 0.45^{+0.10}_{-0.11}$, a remarkable constraint
+considering the small number of galaxies. All of our results are completely
+independent of the effects of galaxy bias, and fully consistent with the
+predictions of GR (scale-independent $f\sigma_{8}$ and $\gamma\approx0.55$).
+",0,1,0,0,0,0
+13539,Gradient weighted norm inequalities for very weak solutions of linear parabolic equations with BMO coefficients,"  In this paper, we prove the Lorentz space $L^{q,p}$-estimates for gradients
+of very weak solutions to the linear parabolic equations with
+$\mathbf{A}_q$-weights $$u_t-\operatorname{div}(A(x,t)\nabla
+u)=\operatorname{div}(F),$$ in a bounded domain $\Omega\times
+(0,T)\subset\mathbb{R}^{N+1}$, where $A$ has a small mean oscillation, and
+$\Omega$ is a Lipchistz domain with a small Lipschitz constant.
+",0,0,1,0,0,0
+13540,Local incompressibility estimates for the Laughlin phase,"  We prove sharp density upper bounds on optimal length-scales for the ground
+states of classical 2D Coulomb systems and generalizations thereof. Our method
+is new, based on an auxiliary Thomas-Fermi-like variational model. Moreover, we
+deduce density upper bounds for the related low-temperature Gibbs states. Our
+motivation comes from fractional quantum Hall physics, more precisely, the
+perturbation of the Laughlin state by external potentials or impurities. These
+give rise to a class of many-body wave-functions that have the form of a
+product of the Laughlin state and an analytic function of many variables. This
+class is related via Laughlin's plasma analogy to Gibbs states of the
+generalized classical Coulomb systems we consider. Our main result shows that
+the perturbation of the Laughlin state cannot increase the particle density
+anywhere, with implications for the response of FQHE systems to external
+perturbations.
+",0,1,1,0,0,0
+13541,Learning Non-local Image Diffusion for Image Denoising,"  Image diffusion plays a fundamental role for the task of image denoising.
+Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a
+simple but very effective framework for image denoising. However, as the TNRD
+model is a local model, the diffusion behavior of which is purely controlled by
+information of local patches, it is prone to create artifacts in the homogenous
+regions and over-smooth highly textured regions, especially in the case of
+strong noise levels. Meanwhile, it is widely known that the non-local
+self-similarity (NSS) prior stands as an effective image prior for image
+denoising, which has been widely exploited in many non-local methods. In this
+work, we are highly motivated to embed the NSS prior into the TNRD model to
+tackle its weaknesses. In order to preserve the expected property that
+end-to-end training is available, we exploit the NSS prior by a set of
+non-local filters, and derive our proposed trainable non-local reaction
+diffusion (TNLRD) model for image denoising. Together with the local filters
+and influence functions, the non-local filters are learned by employing
+loss-specific training. The experimental results show that the trained TNLRD
+model produces visually plausible recovered images with more textures and less
+artifacts, compared to its local versions. Moreover, the trained TNLRD model
+can achieve strongly competitive performance to recent state-of-the-art image
+denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural
+similarity index (SSIM).
+",1,0,0,0,0,0
+13542,Deep vs. Diverse Architectures for Classification Problems,"  This study compares various superlearner and deep learning architectures
+(machine-learning-based and neural-network-based) for classification problems
+across several simulated and industrial datasets to assess performance and
+computational efficiency, as both methods have nice theoretical convergence
+properties. Superlearner formulations outperform other methods at small to
+moderate sample sizes (500-2500) on nonlinear and mixed linear/nonlinear
+predictor relationship datasets, while deep neural networks perform well on
+linear predictor relationship datasets of all sizes. This suggests faster
+convergence of the superlearner compared to deep neural network architectures
+on many messy classification problems for real-world data.
+Superlearners also yield interpretable models, allowing users to examine
+important signals in the data; in addition, they offer flexible formulation,
+where users can retain good performance with low-computational-cost base
+algorithms.
+K-nearest-neighbor (KNN) regression demonstrates improvements using the
+superlearner framework, as well; KNN superlearners consistently outperform deep
+architectures and KNN regression, suggesting that superlearners may be better
+able to capture local and global geometric features through utilizing a variety
+of algorithms to probe the data space.
+",1,0,0,1,0,0
+13543,Means Moments and Newton's Inequalities,"  It is shown that Newton's inequalities and the related Maclaurin's
+inequalities provide several refinements of the fundamental Arithmetic mean -
+Geometric mean - Harmonic mean inequality in terms of the means and variance of
+positive real numbers. We also obtain some inequalities involving third and
+fourth central moments of real numbers.
+",0,0,1,1,0,0
+13544,Automatic Exploration of Machine Learning Experiments on OpenML,"  Understanding the influence of hyperparameters on the performance of a
+machine learning algorithm is an important scientific topic in itself and can
+help to improve automatic hyperparameter tuning procedures. Unfortunately,
+experimental meta data for this purpose is still rare. This paper presents a
+large, free and open dataset addressing this problem, containing results on 38
+OpenML data sets, six different machine learning algorithms and many different
+hyperparameter configurations. Results where generated by an automated random
+sampling strategy, termed the OpenML Random Bot. Each algorithm was
+cross-validated up to 20.000 times per dataset with different hyperparameters
+settings, resulting in a meta dataset of around 2.5 million experiments
+overall.
+",0,0,0,1,0,0
+13545,Generalized Expectation Consistent Signal Recovery for Nonlinear Measurements,"  In this paper, we propose a generalized expectation consistent signal
+recovery algorithm to estimate the signal $\mathbf{x}$ from the nonlinear
+measurements of a linear transform output $\mathbf{z}=\mathbf{A}\mathbf{x}$.
+This estimation problem has been encountered in many applications, such as
+communications with front-end impairments, compressed sensing, and phase
+retrieval. The proposed algorithm extends the prior art called generalized
+turbo signal recovery from a partial discrete Fourier transform matrix
+$\mathbf{A}$ to a class of general matrices. Numerical results show the
+excellent agreement of the proposed algorithm with the theoretical
+Bayesian-optimal estimator derived using the replica method.
+",1,0,1,0,0,0
+13546,Proactive Eavesdropping in Relaying Systems,"  This paper investigates the performance of a legitimate surveillance system,
+where a legitimate monitor aims to eavesdrop on a dubious decode-and-forward
+relaying communication link. In order to maximize the effective eavesdropping
+rate, two strategies are proposed, where the legitimate monitor adaptively acts
+as an eavesdropper, a jammer or a helper. In addition, the corresponding
+optimal jamming beamformer and jamming power are presented. Numerical results
+demonstrate that the proposed strategies attain better performance compared
+with intuitive benchmark schemes. Moreover, it is revealed that the position of
+the legitimate monitor plays an important role on the eavesdropping performance
+for the two strategies.
+",1,0,0,0,0,0
+13547,Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching,"  Big data problems frequently require processing datasets in a streaming
+fashion, either because all data are available at once but collectively are
+larger than available memory or because the data intrinsically arrive one data
+point at a time and must be processed online. Here, we introduce a
+computationally efficient version of similarity matching, a framework for
+online dimensionality reduction that incrementally estimates the top
+K-dimensional principal subspace of streamed data while keeping in memory only
+the last sample and the current iterate. To assess the performance of our
+approach, we construct and make public a test suite containing both a synthetic
+data generator and the infrastructure to test online dimensionality reduction
+algorithms on real datasets, as well as performant implementations of our
+algorithm and competing algorithms with similar aims. Among the algorithms
+considered we find our approach to be competitive, performing among the best on
+both synthetic and real data.
+",0,0,0,1,0,0
+13548,Resolving API Mentions in Informal Documents,"  Developer forums contain opinions and information related to the usage of
+APIs. API names in forum posts are often not explicitly linked to their
+official resources. Automatic linking of an API mention to its official
+resources can be challenging for various reasons, such as, name overloading. We
+present a technique, ANACE, to automatically resolve API mentions in the
+textual contents of forum posts. Given a database of APIs, we first detect all
+words in a forum post that are potential references to an API. We then use a
+combination of heuristics and machine learning to eliminate false positives and
+to link true positives to the actual APIs and their resources.
+",1,0,0,0,0,0
+13549,Quantifying tidal stream disruption in a simulated Milky Way,"  Simulations of tidal streams show that close encounters with dark matter
+subhalos induce density gaps and distortions in on-sky path along the streams.
+Accordingly, observing disrupted streams in the Galactic halo would
+substantiate the hypothesis that dark matter substructure exists there, while
+in contrast, observing collimated streams with smoothly varying density
+profiles would place strong upper limits on the number density and mass
+spectrum of subhalos. Here, we examine several measures of stream ""disruption""
+and their power to distinguish between halo potentials with and without
+substructure and with different global shapes. We create and evolve a
+population of 1280 streams on a range of orbits in the Via Lactea II simulation
+of a Milky Way-like halo, replete with a full mass range of {\Lambda}CDM
+subhalos, and compare it to two control stream populations evolved in smooth
+spherical and smooth triaxial potentials, respectively. We find that the number
+of gaps observed in a stellar stream is a poor indicator of the halo potential,
+but that (i) the thinness of the stream on-sky, (ii) the symmetry of the
+leading and trailing tails, and (iii) the deviation of the tails from a
+low-order polynomial path on-sky (""path regularity"") distinguish between the
+three potentials more effectively. We find that globular cluster streams on
+low-eccentricity orbits far from the galactic center (apocentric radius ~ 30-80
+kpc) are most powerful in distinguishing between the three potentials. If they
+exist, such streams will shortly be discoverable and mapped in high dimensions
+with near-future photometric and spectroscopic surveys.
+",0,1,0,0,0,0
+13550,The dehydration of water worlds via atmospheric losses,"  We present a three-species multi-fluid MHD model (H$^+$, H$_2$O$^+$ and
+e$^-$), endowed with the requisite atmospheric chemistry, that is capable of
+accurately quantifying the magnitude of water ion losses from exoplanets. We
+apply this model to a water world with Earth-like parameters orbiting a
+Sun-like star for three cases: (i) current normal solar wind conditions, (ii)
+ancient normal solar wind conditions, and (iii) one extreme ""Carrington-type""
+space weather event. We demonstrate that the ion escape rate for (ii), with a
+value of 6.0$\times$10$^{26}$ s$^{-1}$, is about an order of magnitude higher
+than the corresponding value of 6.7$\times$10$^{25}$ s$^{-1}$ for (i). Studies
+of ion losses induced by space weather events, where the ion escape rates can
+reach $\sim$ 10$^{28}$ s$^{-1}$, are crucial for understanding how an active,
+early solar-type star (e.g., with frequent coronal mass ejections) could have
+accelerated the depletion of the exoplanet's atmosphere. We briefly explore the
+ramifications arising from the loss of water ions, especially for planets
+orbiting M-dwarfs where such effects are likely to be significant.
+",0,1,0,0,0,0
+13551,Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets,"  With the ever-growing amounts of textual data from a large variety of
+languages, domains, and genres, it has become standard to evaluate NLP
+algorithms on multiple datasets in order to ensure consistent performance
+across heterogeneous setups. However, such multiple comparisons pose
+significant challenges to traditional statistical analysis methods in NLP and
+can lead to erroneous conclusions. In this paper, we propose a Replicability
+Analysis framework for a statistically sound analysis of multiple comparisons
+between algorithms for NLP tasks. We discuss the theoretical advantages of this
+framework over the current, statistically unjustified, practice in the NLP
+literature, and demonstrate its empirical value across four applications:
+multi-domain dependency parsing, multilingual POS tagging, cross-domain
+sentiment classification and word similarity prediction.
+",1,0,0,0,0,0
+13552,Reflections on Cyberethics Education for Millennial Software Engineers,"  Software is a key component of solutions for 21st Century problems. These
+problems are often ""wicked"", complex, and unpredictable. To provide the best
+possible solution, millennial software engineers must be prepared to make
+ethical decisions, thinking critically, and acting systematically. This reality
+demands continuous changes in educational systems and curricula delivery, as
+misjudgment might have serious social impact. This study aims to investigate
+and reflect on Software Engineering (SE) Programs, proposing a conceptual
+framework for analyzing cyberethics education and a set of suggestions on how
+to integrate it into the SE undergraduate curriculum.
+",1,0,0,0,0,0
+13553,The effect of an offset polar cap dipolar magnetic field on the modeling of the Vela pulsar's $γ$-ray light curves,"  We performed geometric pulsar light curve modeling using static, retarded
+vacuum, and offset polar cap (PC) dipole $B$-fields (the latter is
+characterized by a parameter $\epsilon$), in conjunction with standard two-pole
+caustic (TPC) and outer gap (OG) emission geometries. The offset-PC dipole
+$B$-field mimics deviations from the static dipole (which corresponds to
+$\epsilon=0$). In addition to constant-emissivity geometric models, we also
+considered a slot gap (SG) $E$-field associated with the offset-PC dipole
+$B$-field and found that its inclusion leads to qualitatively different light
+curves. Solving the particle transport equation shows that the particle energy
+only becomes large enough to yield significant curvature radiation at large
+altitudes above the stellar surface, given this relatively low $E$-field.
+Therefore, particles do not always attain the radiation-reaction limit. Our
+overall optimal light curve fit is for the retarded vacuum dipole field and OG
+model, at an inclination angle $\alpha=78{_{-1}^{+1}}^{\circ}$ and observer
+angle $\zeta=69{_{-1}^{+2}}^{\circ}$. For this $B$-field, the TPC model is
+statistically disfavored compared to the OG model. For the static dipole field,
+neither model is significantly preferred. We found that smaller values of
+$\epsilon$ are favored for the offset-PC dipole field when assuming constant
+emissivity, and larger $\epsilon$ values favored for variable emissivity, but
+not significantly so. When multiplying the SG $E$-field by a factor of 100, we
+found improved light curve fits, with $\alpha$ and $\zeta$ being closer to best
+fits from independent studies, as well as curvature radiation reaction at lower
+altitudes.
+",0,1,0,0,0,0
+13554,Survey on Models and Techniques for Root-Cause Analysis,"  Automation and computer intelligence to support complex human decisions
+becomes essential to manage large and distributed systems in the Cloud and IoT
+era. Understanding the root cause of an observed symptom in a complex system
+has been a major problem for decades. As industry dives into the IoT world and
+the amount of data generated per year grows at an amazing speed, an important
+question is how to find appropriate mechanisms to determine root causes that
+can handle huge amounts of data or may provide valuable feedback in real-time.
+While many survey papers aim at summarizing the landscape of techniques for
+modelling system behavior and infering the root cause of a problem based in the
+resulting models, none of those focuses on analyzing how the different
+techniques in the literature fit growing requirements in terms of performance
+and scalability. In this survey, we provide a review of root-cause analysis,
+focusing on these particular aspects. We also provide guidance to choose the
+best root-cause analysis strategy depending on the requirements of a particular
+system and application.
+",1,0,0,0,0,0
+13555,Test Prioritization in Continuous Integration Environments,"  Two heuristics namely diversity-based (DBTP) and history-based test
+prioritization (HBTP) have been separately proposed in the literature. Yet,
+their combination has not been widely studied in continuous integration (CI)
+environments. The objective of this study is to catch regression faults
+earlier, allowing developers to integrate and verify their changes more
+frequently and continuously. To achieve this, we investigated six open-source
+projects, each of which included several builds over a large time period.
+Findings indicate that previous failure knowledge seems to have strong
+predictive power in CI environments and can be used to effectively prioritize
+tests. HBTP does not necessarily need to have large data, and its effectiveness
+improves to a certain degree with larger history interval. DBTP can be used
+effectively during the early stages, when no historical data is available, and
+also combined with HBTP to improve its effectiveness. Among the investigated
+techniques, we found that history-based diversity using NCD Multiset is
+superior in terms of effectiveness but comes with relatively higher overhead in
+terms of method execution time. Test prioritization in CI environments can be
+effectively performed with negligible investment using previous failure
+knowledge, and its effectiveness can be further improved by considering
+dissimilarities among the tests.
+",1,0,0,0,0,0
+13556,Weighted integral Hankel operators with continuous spectrum,"  Using the Kato-Rosenblum theorem, we describe the absolutely continuous
+spectrum of a class of weighted integral Hankel operators in $L^2(\mathbb
+R_+)$. These self-adjoint operators generalise the explicitly diagonalisable
+operator with the integral kernel $s^\alpha t^\alpha(s+t)^{-1-2\alpha}$, where
+$\alpha>-1/2$. Our analysis can be considered as an extension of J.Howland's
+1992 paper which dealt with the unweighted case, corresponding to $\alpha=0$.
+",0,0,1,0,0,0
+13557,Local properties of Riesz minimal energy configurations and equilibrium measures,"  We investigate separation properties of $N$-point configurations that
+minimize discrete Riesz $s$-energy on a compact set $A\subset \mathbb{R}^p$.
+When $A$ is a smooth $(p-1)$-dimensional manifold without boundary and $s\in
+[p-2, p-1)$, we prove that the order of separation (as $N\to \infty$) is the
+best possible. The same conclusions hold for the points that are a fixed
+positive distance from the boundary of $A$ whenever $A$ is any $p$-dimensional
+set. These estimates extend a result of Dahlberg for certain smooth
+$(p-1)$-dimensional surfaces when $s=p-2$ (the harmonic case). Furthermore, we
+obtain the same separation results for `greedy' $s$-energy points. We deduce
+our results from an upper regularity property of the $s$-equilibrium measure
+(i.e., the measure that solves the continuous minimal Riesz $s$-energy
+problem), and we show that this property holds under a local smoothness
+assumption on the set $A$.
+",0,0,1,0,0,0
+13558,Cascaded Segmentation-Detection Networks for Word-Level Text Spotting,"  We introduce an algorithm for word-level text spotting that is able to
+accurately and reliably determine the bounding regions of individual words of
+text ""in the wild"". Our system is formed by the cascade of two convolutional
+neural networks. The first network is fully convolutional and is in charge of
+detecting areas containing text. This results in a very reliable but possibly
+inaccurate segmentation of the input image. The second network (inspired by the
+popular YOLO architecture) analyzes each segment produced in the first stage,
+and predicts oriented rectangular regions containing individual words. No
+post-processing (e.g. text line grouping) is necessary. With execution time of
+450 ms for a 1000-by-560 image on a Titan X GPU, our system achieves the
+highest score to date among published algorithms on the ICDAR 2015 Incidental
+Scene Text dataset benchmark.
+",1,0,0,0,0,0
+13559,Hilbert $C^*$-modules over $Σ^*$-algebras II: $Σ^*$-Morita equivalence,"  In previous work, we defined and studied $\Sigma^*$-modules, a class of
+Hilbert $C^*$-modules over $\Sigma^*$-algebras (the latter are $C^*$-algebras
+that are sequentially closed in the weak operator topology). The present work
+continues this study by developing the appropriate $\Sigma^*$-algebraic
+analogue of the notion of strong Morita equivalence for $C^*$-algebras. We
+define strong $\Sigma^*$-Morita equivalence, prove a few characterizations,
+look at the relationship with equivalence of categories of a certain type of
+Hilbert space representation, study $\Sigma^*$-versions of the interior and
+exterior tensor products, and prove a $\Sigma^*$-version of the
+Brown-Green-Rieffel stable isomorphism theorem.
+",0,0,1,0,0,0
+13560,Topological degeneracy and pairing in a one-dimensional gas of spinless Fermions,"  We revisit the low energy physics of one dimensional spinless fermion
+liquids, showing that with sufficiently strong interactions the conventional
+Luttinger liquid can give way to a strong pairing phase. While the density
+fluctuations in both phases are described by a gapless Luttinger liquid, single
+fermion excitations are gapped only in the strong pairing phase. Smooth spatial
+Interfaces between the two phases lead to topological degeneracies in the
+ground state and low energy phonon spectrum. Using a concrete microscopic
+model, with both single particle and pair hopping, we show that the strong
+pairing state is established through emergence of a new low energy fermionic
+mode. We characterize the two phases with numerical calculations using the
+density matrix renormalization group. In particular we find enhancement of the
+central charge from $c=1$ in the two Luttinger liquid phases to $c=3/2$ at the
+critical point, which gives direct evidence for an emergent critical Majorana
+mode. Finally, we confirm the existence of topological degeneracies in the low
+energy phonon spectrum, associated with spatial interfaces between the two
+phases.
+",0,1,0,0,0,0
+13561,Rejecting inadmissible rules in reduced normal forms in S4,"  Several methods for checking admissibility of rules in the modal logic $S4$
+are presented in [1], [15]. These methods determine admissibility of rules in
+$S4$, but they don't determine or give substitutions rejecting inadmissible
+rules. In this paper, we investigate some relations between one of the above
+methods, based on the reduced normal form rules, and sets of substitutions
+which reject them. We also generalize the method in [1], [15] for one rule to
+admissibility of a set of rules.
+",1,0,1,0,0,0
+13562,An Executable Sequential Specification for Spark Aggregation,"  Spark is a new promising platform for scalable data-parallel computation. It
+provides several high-level application programming interfaces (APIs) to
+perform parallel data aggregation. Since execution of parallel aggregation in
+Spark is inherently non-deterministic, a natural requirement for Spark programs
+is to give the same result for any execution on the same data set. We present
+PureSpark, an executable formal Haskell specification for Spark aggregate
+combinators. Our specification allows us to deduce the precise condition for
+deterministic outcomes from Spark aggregation. We report case studies analyzing
+deterministic outcomes and correctness of Spark programs.
+",1,0,0,0,0,0
+13563,Obstructions to a small hyperbolicity in Helly graphs,"  It is known that for every graph $G$ there exists the smallest Helly graph
+$\cal H(G)$ into which $G$ isometrically embeds ($\cal H(G)$ is called the
+injective hull of $G$) such that the hyperbolicity of $\cal H(G)$ is equal to
+the hyperbolicity of $G$. Motivated by this, we investigate structural
+properties of Helly graphs that govern their hyperbolicity and identify three
+isometric subgraphs of the King-grid as structural obstructions to a small
+hyperbolicity in Helly graphs.
+",1,0,0,0,0,0
+13564,Calculation of time resolution of the J-PET tomograph using the Kernel Density Estimation,"  In this paper we estimate the time resolution of the J-PET scanner built from
+plastic scintillators. We incorporate the method of signal processing using the
+Tikhonov regularization framework and the Kernel Density Estimation method. We
+obtain simple, closed-form analytical formulas for time resolutions. The
+proposed method is validated using signals registered by means of the single
+detection unit of the J-PET tomograph built out from 30 cm long plastic
+scintillator strip. It is shown that the experimental and theoretical results,
+obtained for the J-PET scanner equipped with vacuum tube photomultipliers, are
+consistent.
+",0,1,0,0,0,0
+13565,Writer Independent Offline Signature Recognition Using Ensemble Learning,"  The area of Handwritten Signature Verification has been broadly researched in
+the last decades, but remains an open research problem. In offline (static)
+signature verification, the dynamic information of the signature writing
+process is lost, and it is difficult to design good feature extractors that can
+distinguish genuine signatures and skilled forgeries. This verification task is
+even harder in writer independent scenarios which is undeniably fiscal for
+realistic cases. In this paper, we have proposed an Ensemble model for offline
+writer, independent signature verification task with Deep learning. We have
+used two CNNs for feature extraction, after that RGBT for classification &
+Stacking to generate final prediction vector. We have done extensive
+experiments on various datasets from various sources to maintain a variance in
+the dataset. We have achieved the state of the art performance on various
+datasets.
+",1,0,0,1,0,0
+13566,Automated Directed Fairness Testing,"  Fairness is a critical trait in decision making. As machine-learning models
+are increasingly being used in sensitive application domains (e.g. education
+and employment) for decision making, it is crucial that the decisions computed
+by such models are free of unintended bias. But how can we automatically
+validate the fairness of arbitrary machine-learning models? For a given
+machine-learning model and a set of sensitive input parameters, our AEQUITAS
+approach automatically discovers discriminatory inputs that highlight fairness
+violation. At the core of AEQUITAS are three novel strategies to employ
+probabilistic search over the input space with the objective of uncovering
+fairness violation. Our AEQUITAS approach leverages inherent robustness
+property in common machine-learning models to design and implement scalable
+test generation methodologies. An appealing feature of our generated test
+inputs is that they can be systematically added to the training set of the
+underlying model and improve its fairness. To this end, we design a fully
+automated module that guarantees to improve the fairness of the underlying
+model.
+We implemented AEQUITAS and we have evaluated it on six state-of-the-art
+classifiers, including a classifier that was designed with fairness
+constraints. We show that AEQUITAS effectively generates inputs to uncover
+fairness violation in all the subject classifiers and systematically improves
+the fairness of the respective models using the generated test inputs. In our
+evaluation, AEQUITAS generates up to 70% discriminatory inputs (w.r.t. the
+total number of inputs generated) and leverages these inputs to improve the
+fairness up to 94%.
+",1,0,0,1,0,0
+13567,Intel MPX Explained: An Empirical Study of Intel MPX and Software-based Bounds Checking Approaches,"  Memory-safety violations are a prevalent cause of both reliability and
+security vulnerabilities in systems software written in unsafe languages like
+C/C++. Unfortunately, all the existing software-based solutions to this problem
+exhibit high performance overheads preventing them from wide adoption in
+production runs. To address this issue, Intel recently released a new ISA
+extension - Memory Protection Extensions (Intel MPX), a hardware-assisted
+full-stack solution to protect against memory safety violations. In this work,
+we perform an exhaustive study of the Intel MPX architecture to understand its
+advantages and caveats. We base our study along three dimensions: (a)
+performance overheads, (b) security guarantees, and (c) usability issues. To
+put our results in perspective, we compare Intel MPX with three prominent
+software-based approaches: (1) trip-wire - AddressSanitizer, (2) object-based -
+SAFECode, and (3) pointer-based - SoftBound. Our main conclusion is that Intel
+MPX is a promising technique that is not yet practical for widespread adoption.
+Intel MPX's performance overheads are still high (roughly 50% on average), and
+the supporting infrastructure has bugs which may cause compilation or runtime
+errors. Moreover, we showcase the design limitations of Intel MPX: it cannot
+detect temporal errors, may have false positives and false negatives in
+multithreaded code, and its restrictions on memory layout require substantial
+code changes for some programs.
+",1,0,0,0,0,0
+13568,Generating Long-term Trajectories Using Deep Hierarchical Networks,"  We study the problem of modeling spatiotemporal trajectories over long time
+horizons using expert demonstrations. For instance, in sports, agents often
+choose action sequences with long-term goals in mind, such as achieving a
+certain strategic position. Conventional policy learning approaches, such as
+those based on Markov decision processes, generally fail at learning cohesive
+long-term behavior in such high-dimensional state spaces, and are only
+effective when myopic modeling lead to the desired behavior. The key difficulty
+is that conventional approaches are ""shallow"" models that only learn a single
+state-action policy. We instead propose a hierarchical policy class that
+automatically reasons about both long-term and short-term goals, which we
+instantiate as a hierarchical neural network. We showcase our approach in a
+case study on learning to imitate demonstrated basketball trajectories, and
+show that it generates significantly more realistic trajectories compared to
+non-hierarchical baselines as judged by professional sports analysts.
+",1,0,0,0,0,0
+13569,The usefulness of Poynting's theorem in magnetic turbulence,"  We rewrite Poynting's theorem, already used in a previous publication
+(Treumann & Baumjohann 2017) to derive relations between the turbulent magnetic
+and electric power spectral densities, to make explicit where the mechanical
+contributions enter. We then make explicit use of the relativistic
+transformation of the turbulent electric fluctuations to obtain expressions
+which depend only on the magnetic and velocity fluctuations. Any electric
+fluctuations play just an intermediate role. Equations are constructed for the
+turbulent conductivity spectrum in Alfvénic and non-Alfvénic turbulence in
+extension of the results in the above citation. An observation-based discussion
+of their use in application to solar wind turbulence is given. The inertial
+range solar wind turbulence exhibits signs of chaos and self-organisation.
+",0,1,0,0,0,0
+13570,The {\it victory} project v1.0: an efficient parquet equations solver,"  {\it Victory}, i.e. \underline{vi}enna \underline{c}omputational
+\underline{to}ol deposito\underline{ry}, is a collection of numerical tools for
+solving the parquet equations for the Hubbard model and similar many body
+problems. The parquet formalism is a self-consistent theory at both the single-
+and two-particle levels, and can thus describe individual fermions as well as
+their collective behavior on equal footing. This is essential for the
+understanding of various emergent phases and their transitions in many-body
+systems, in particular for cases in which a single-particle description fails.
+Our implementation of {\it victory} is in modern Fortran and it fully respects
+the structure of various vertex functions in both momentum and Matsubara
+frequency space. We found the latter to be crucial for the convergence of the
+parquet equations, as well as for the correct determination of various physical
+observables. In this release, we thoroughly explain the program structure and
+the controlled approximations to efficiently solve the parquet equations, i.e.
+the two-level kernel approximation and the high-frequency regulation.
+",0,1,0,0,0,0
+13571,An In Vitro Vascularized Tumor Platform for Modeling Breast Tumor Stromal Interactions and Characterizing the Subsequent Response,"  Tumor stromal interactions have been shown to be the driving force behind the
+poor prognosis associated with aggressive breast tumors. These interactions,
+specifically between tumor and the surrounding ECM, and tumor and vascular
+endothelium, promote tumor formation, angiogenesis, and metastasis. In this
+study, we develop an in vitro vascularized tumor platform that allows for
+investigation of tumor-stromal interactions in three breast tumor derived cell
+lines of varying aggressiveness: MDA-IBC3, SUM149, and MDA-MB-231. The platform
+recreates key features of breast tumors, including increased vascular
+permeability, vessel sprouting, and ECM remodeling. Morphological and
+quantitative analysis reveals differential effects from each tumor cell type on
+endothelial coverage, permeability, expression of VEGF, and collagen
+remodeling. Triple negative tumors, SUM149 and MDA-MB-321, resulted in a
+significantly (p<0.05) higher endothelial permeability and decreased
+endothelial coverage compared to the control TIME only platform. SUM149/TIME
+platforms were 1.3 fold lower (p<0.05), and MDA-MB-231/TIME platforms were 1.5
+fold lower (p<0.01) in endothelial coverage compared to the control TIME only
+platform. HER2+ MDA-IBC3 tumor cells expressed high levels of VEGF (p<0.01) and
+induced vessel sprouting. Vessels sprouting was tracked for 3 weeks and with
+increasing time exhibited formation of multiple vessel sprouts that invaded
+into the ECM and surrounded clusters of MDA-IBC3 cells. Both IBC cell lines,
+SUM149 and MDA-IBC3, resulted in a collagen ECM with significantly greater
+porosity with 1.6 and 1.1 fold higher compared to control, p<0.01. The breast
+cancer in vitro vascularized platforms introduced in this paper are an
+adaptable, high throughout tool for unearthing tumor-stromal mechanisms and
+dynamics behind tumor progression and may prove essential in developing
+effective targeted therapeutics.
+",0,0,0,0,1,0
+13572,Testing for Principal Component Directions under Weak Identifiability,"  We consider the problem of testing, on the basis of a $p$-variate Gaussian
+random sample, the null hypothesis ${\cal H}_0: {\pmb \theta}_1= {\pmb
+\theta}_1^0$ against the alternative ${\cal H}_1: {\pmb \theta}_1 \neq {\pmb
+\theta}_1^0$, where ${\pmb \theta}_1$ is the ""first"" eigenvector of the
+underlying covariance matrix and ${\pmb \theta}_1^0$ is a fixed unit
+$p$-vector. In the classical setup where eigenvalues $\lambda_1>\lambda_2\geq
+\ldots\geq \lambda_p$ are fixed, the Anderson (1963) likelihood ratio test
+(LRT) and the Hallin, Paindaveine and Verdebout (2010) Le Cam optimal test for
+this problem are asymptotically equivalent under the null hypothesis, hence
+also under sequences of contiguous alternatives. We show that this equivalence
+does not survive asymptotic scenarios where
+$\lambda_{n1}/\lambda_{n2}=1+O(r_n)$ with $r_n=O(1/\sqrt{n})$. For such
+scenarios, the Le Cam optimal test still asymptotically meets the nominal level
+constraint, whereas the LRT severely overrejects the null hypothesis.
+Consequently, the former test should be favored over the latter one whenever
+the two largest sample eigenvalues are close to each other. By relying on the
+Le Cam's asymptotic theory of statistical experiments, we study the non-null
+and optimality properties of the Le Cam optimal test in the aforementioned
+asymptotic scenarios and show that the null robustness of this test is not
+obtained at the expense of power. Our asymptotic investigation is extensive in
+the sense that it allows $r_n$ to converge to zero at an arbitrary rate. While
+we restrict to single-spiked spectra of the form
+$\lambda_{n1}>\lambda_{n2}=\ldots=\lambda_{np}$ to make our results as striking
+as possible, we extend our results to the more general elliptical case.
+Finally, we present an illustrative real data example.
+",0,0,1,1,0,0
+13573,Locally-adaptive Bayesian nonparametric inference for phylodynamics,"  Phylodynamics is an area of population genetics that uses genetic sequence
+data to estimate past population dynamics. Modern state-of-the-art Bayesian
+nonparametric methods for phylodynamics use either change-point models or
+Gaussian process priors to recover population size trajectories of unknown
+form. Change-point models suffer from computational issues when the number of
+change-points is unknown and needs to be estimated. Gaussian process-based
+methods lack local adaptivity and cannot accurately recover trajectories that
+exhibit features such as abrupt changes in trend or varying levels of
+smoothness. We propose a novel, locally-adaptive approach to Bayesian
+nonparametric phylodynamic inference that has the flexibility to accommodate a
+large class of functional behaviors. Local adaptivity results from modeling the
+log-transformed effective population size a priori as a horseshoe Markov random
+field, a recently proposed statistical model that blends together the best
+properties of the change-point and Gaussian process modeling paradigms. We use
+simulated data to assess model performance, and find that our proposed method
+results in reduced bias and increased precision when compared to contemporary
+methods. We also use our models to reconstruct past changes in genetic
+diversity of human hepatitis C virus in Egypt and to estimate population size
+changes of ancient and modern steppe bison. These analyses show that our new
+method captures features of the population size trajectories that were missed
+by the state-of-the-art phylodynamic methods.
+",0,0,0,0,1,0
+13574,Out of sight out of mind: Perceived physical distance between the observer and someone in pain shapes observer's neural empathic reactions,"  Social and affective relations may shape empathy to others' affective states.
+Previous studies also revealed that people tend to form very different mental
+representations of stimuli on the basis of their physical distance. In this
+regard, embodied cognition proposes that different physical distances between
+individuals activate different interpersonal processing modes, such that close
+physical distance tends to activate the interpersonal processing mode typical
+of socially and affectively close relationships. In Experiment 1, two groups of
+participants were administered a pain decision task involving upright and
+inverted face stimuli painfully or neutrally stimulated, and we monitored their
+neural empathic reactions by means of event-related potentials (ERPs)
+technique. Crucially, participants were presented with face stimuli of one of
+two possible sizes in order to manipulate retinal size and perceived physical
+distance, roughly corresponding to the close and far portions of social
+distance. ERPs modulations compatible with an empathic reaction were observed
+only for the group exposed to face stimuli appearing to be at a close social
+distance from the participants. This reaction was absent in the group exposed
+to smaller stimuli corresponding to face stimuli observed from a far social
+distance. In Experiment 2, one different group of participants was engaged in a
+match-to-sample task involving the two-size upright face stimuli of Experiment
+1 to test whether the modulation of neural empathic reaction observed in
+Experiment 1 could be ascribable to differences in the ability to identify
+faces of the two different sizes. Results suggested that face stimuli of the
+two sizes could be equally identifiable. In line with the Construal Level and
+Embodied Simulation theoretical frameworks, we conclude that perceived physical
+distance may shape empathy as well as social and affective distance.
+",0,0,0,0,1,0
+13575,Dynamic Difficulty Adjustment on MOBA Games,"  This paper addresses the dynamic difficulty adjustment on MOBA games as a way
+to improve the player's entertainment. Although MOBA is currently one of the
+most played genres around the world, it is known as a game that offer less
+autonomy, more challenges and consequently more frustration. Due to these
+characteristics, the use of a mechanism that performs the difficulty balance
+dynamically seems to be an interesting alternative to minimize and/or avoid
+that players experience such frustrations. In this sense, this paper presents a
+dynamic difficulty adjustment mechanism for MOBA games. The main idea is to
+create a computer controlled opponent that adapts dynamically to the player
+performance, trying to offer to the player a better game experience. This is
+done by evaluating the performance of the player using a metric based on some
+game features and switching the difficulty of the opponent's artificial
+intelligence behavior accordingly. Quantitative and qualitative experiments
+were performed and the results showed that the system is capable of adapting
+dynamically to the opponent's skills. In spite of that, the qualitative
+experiments with users showed that the player's expertise has a greater
+influence on the perception of the difficulty level and dynamic adaptation.
+",1,0,0,0,0,0
+13576,On iteration of Cox rings,"  We characterize all varieties with a torus action of complexity one that
+admit iteration of Cox rings.
+",0,0,1,0,0,0
+13577,Stability Conditions and Lagrangian Cobordisms,"  In this paper we study the interplay between Lagrangian cobordisms and
+stability conditions. We show that any stability condition on the derived
+Fukaya category $D\mathcal{F}uk(M)$ of a symplectic manifold $(M,\omega)$
+induces a stability condition on the derived Fukaya category of Lagrangian
+cobordisms $D\mathcal{F}uk(\mathbb{C} \times M)$. In addition, using stability
+conditions, we provide general conditions under which the homomorphism $\Theta:
+\Omega_{Lag}(M)\to K_0(D\mathcal{F}uk(M))$, introduced by Biran and Cornea, is
+an isomorphism. This yields a better understanding of how stability conditions
+affect $\Theta$ and it allows us to elucidate Haug's result, that the
+Lagrangian cobordism group of $T^2$ is isomorphic to
+$K_0(D\mathcal{F}uk(T^2))$.
+",0,0,1,0,0,0
+13578,Sentiment Analysis of Citations Using Word2vec,"  Citation sentiment analysis is an important task in scientific paper
+analysis. Existing machine learning techniques for citation sentiment analysis
+are focusing on labor-intensive feature engineering, which requires large
+annotated corpus. As an automatic feature extraction tool, word2vec has been
+successfully applied to sentiment analysis of short texts. In this work, I
+conducted empirical research with the question: how well does word2vec work on
+the sentiment analysis of citations? The proposed method constructed sentence
+vectors (sent2vec) by averaging the word embeddings, which were learned from
+Anthology Collections (ACL-Embeddings). I also investigated polarity-specific
+word embeddings (PS-Embeddings) for classifying positive and negative
+citations. The sentence vectors formed a feature space, to which the examined
+citation sentence was mapped to. Those features were input into classifiers
+(support vector machines) for supervised classification. Using
+10-cross-validation scheme, evaluation was conducted on a set of annotated
+citations. The results showed that word embeddings are effective on classifying
+positive and negative citations. However, hand-crafted features performed
+better for the overall classification.
+",1,0,0,0,0,0
+13579,Hybrid Clustering based on Content and Connection Structure using Joint Nonnegative Matrix Factorization,"  We present a hybrid method for latent information discovery on the data sets
+containing both text content and connection structure based on constrained low
+rank approximation. The new method jointly optimizes the Nonnegative Matrix
+Factorization (NMF) objective function for text clustering and the Symmetric
+NMF (SymNMF) objective function for graph clustering. We propose an effective
+algorithm for the joint NMF objective function, based on a block coordinate
+descent (BCD) framework. The proposed hybrid method discovers content
+associations via latent connections found using SymNMF. The method can also be
+applied with a natural conversion of the problem when a hypergraph formulation
+is used or the content is associated with hypergraph edges.
+Experimental results show that by simultaneously utilizing both content and
+connection structure, our hybrid method produces higher quality clustering
+results compared to the other NMF clustering methods that uses content alone
+(standard NMF) or connection structure alone (SymNMF). We also present some
+interesting applications to several types of real world data such as citation
+recommendations of papers. The hybrid method proposed in this paper can also be
+applied to general data expressed with both feature space vectors and pairwise
+similarities and can be extended to the case with multiple feature spaces or
+multiple similarity measures.
+",1,0,0,1,0,0
+13580,Embedding dimension and codimension of tensor products of algebras over a field,"  Let k be a field. This paper investigates the embedding dimension and
+codimension of Noetherian local rings arising as localizations of tensor
+products of k-algebras. We use results and techniques from prime spectra and
+dimension theory to establish an analogue of the ""special chain theorem"" for
+the embedding dimension of tensor products, with effective consequence on the
+transfer or defect of regularity as exhibited by the (embedding) codimension.
+",0,0,1,0,0,0
+13581,The Cosmic Axion Spin Precession Experiment (CASPEr): a dark-matter search with nuclear magnetic resonance,"  The Cosmic Axion Spin Precession Experiment (CASPEr) is a nuclear magnetic
+resonance experiment (NMR) seeking to detect axion and axion-like particles
+which could make up the dark matter present in the universe. We review the
+predicted couplings of axions and axion-like particles with baryonic matter
+that enable their detection via NMR. We then describe two measurement schemes
+being implemented in CASPEr. The first method, presented in the original CASPEr
+proposal, consists of a resonant search via continuous-wave NMR spectroscopy.
+This method offers the highest sensitivity for frequencies ranging from a few
+Hz to hundreds of MHz, corresponding to masses $ m_{\rm a} \sim
+10^{-14}$--$10^{-6}$ eV. Sub-Hz frequencies are typically difficult to probe
+with NMR due to the diminishing sensitivity of magnetometers in this region. To
+circumvent this limitation, we suggest new detection and data processing
+modalities. We describe a non-resonant frequency-modulation detection scheme,
+enabling searches from mHz to Hz frequencies ($m_{\rm a} \sim
+10^{-17}$--$10^{-14} $ eV), extending the detection bandwidth by three decades.
+",0,1,0,0,0,0
+13582,On eccentricity version of Laplacian energy of a graph,"  The energy of a graph G is equal to the sum of absolute values of the
+eigenvalues of the adjacency matrix of G, whereas the Laplacian energy of a
+graph G is equal to the sum of the absolute value of the difference between the
+eigenvalues of the Laplacian matrix of G and average degree of the vertices of
+G. Motivated by the work from Sharafdini et al. [R. Sharafdini, H. Panahbar,
+Vertex weighted Laplacian graph energy and other topological indices. J. Math.
+Nanosci. 2016, 6, 49-57.], in this paper we investigate the eccentricity
+version of Laplacian energy of a graph G.
+",1,0,1,0,0,0
+13583,LARNN: Linear Attention Recurrent Neural Network,"  The Linear Attention Recurrent Neural Network (LARNN) is a recurrent
+attention module derived from the Long Short-Term Memory (LSTM) cell and ideas
+from the consciousness Recurrent Neural Network (RNN). Yes, it LARNNs. The
+LARNN uses attention on its past cell state values for a limited window size
+$k$. The formulas are also derived from the Batch Normalized LSTM (BN-LSTM)
+cell and the Transformer Network for its Multi-Head Attention Mechanism. The
+Multi-Head Attention Mechanism is used inside the cell such that it can query
+its own $k$ past values with the attention window. This has the effect of
+augmenting the rank of the tensor with the attention mechanism, such that the
+cell can perform complex queries to question its previous inner memories, which
+should augment the long short-term effect of the memory. With a clever trick,
+the LARNN cell with attention can be easily used inside a loop on the cell
+state, just like how any other Recurrent Neural Network (RNN) cell can be
+looped linearly through time series. This is due to the fact that its state,
+which is looped upon throughout time steps within time series, stores the inner
+states in a ""first in, first out"" queue which contains the $k$ most recent
+states and on which it is easily possible to add static positional encoding
+when the queue is represented as a tensor. This neural architecture yields
+better results than the vanilla LSTM cells. It can obtain results of 91.92% for
+the test accuracy, compared to the previously attained 91.65% using vanilla
+LSTM cells. Note that this is not to compare to other research, where up to
+93.35% is obtained, but costly using 18 LSTM cells rather than with 2 to 3
+cells as analyzed here. Finally, an interesting discovery is made, such that
+adding activation within the multi-head attention mechanism's linear layers can
+yield better results in the context researched hereto.
+",0,0,0,1,0,0
+13584,Improved nonparametric estimation of the drift in diffusion processes,"  In this paper, we consider the robust adaptive non parametric estimation
+problem for the drift coefficient in diffusion processes. An adaptive model
+selection procedure, based on the improved weighted least square estimates, is
+proposed. Sharp oracle inequalities for the robust risk have been obtained.
+",0,0,1,1,0,0
+13585,Energy-efficient Hybrid CMOS-NEMS LIF Neuron Circuit in 28 nm CMOS Process,"  Designing analog sub-threshold neuromorphic circuits in deep sub-micron
+technologies e.g. 28 nm can be a daunting task due to the problem of excessive
+leakage current. We propose novel energy-efficient hybrid CMOS-nano
+electro-mechanical switches (NEMS) Leaky Integrate and Fire (LIF) neuron and
+synapse circuits and investigate the impact of NEM switches on the leakage
+power and overall energy consumption. We analyze the performance of
+biologically-inspired neuron circuit in terms of leakage power consumption and
+present new energy-efficient neural circuits that operate with biologically
+plausible firing rates. Our results show the proposed CMOS-NEMS neuron circuit
+is, on average, 35% more energy-efficient than its CMOS counterpart with same
+complexity in 28 nm process. Moreover, we discuss how NEM switches can be
+utilized to further improve the scalability of mixed-signal neuromorphic
+circuits.
+",1,0,0,0,0,0
+13586,Quasar Rain: the Broad Emission Line Region as Condensations in the Warm Accretion Disk Wind,"  The origin of the broad emission line region (BELR) in quasars and active
+galactic nuclei is still unclear. I propose that condensations form in the
+warm, radiation pressure driven, accretion disk wind of quasars creating the
+BEL clouds and uniting them with the other two manifestations of cool, 10,000
+K, gas in quasars, the low ionization phase of the warm absorbers (WAs) and the
+clouds causing X-ray eclipses. The cool clouds will condense quickly (days to
+years), before the WA outflows reach escape velocity (which takes months to
+centuries). Cool clouds form in equilibrium with the warm phase of the wind
+because the rapidly varying X-ray quasar continuum changes the force
+multiplier, causing pressure waves to move gas into stable locations in
+pressure-temperature space. The narrow range of 2-phase equilibrium densities
+may explain the scaling of the BELR size with the square root of luminosity,
+while the scaling of cloud formation timescales could produce the Baldwin
+effect. These dense clouds have force multipliers of order unity and so cannot
+be accelerated to escape velocity. They fall back on a dynamical timescale
+(months to centuries), producing an inflow that rains down toward the central
+black hole. As they soon move at Mach ~40 with respect to the WA outflow, these
+'raindrops' will be rapidly destroyed within months. This rain of clouds may
+produce the elliptical BELR orbits implied by velocity resolved reverberation
+mapping in some objects, and can explain the opening angle and destruction
+timescale of the narrow 'cometary' tails of the clouds seen in X-ray eclipse
+observations. Some consequences and challenges of this 'quasar rain' model are
+presented along with several avenues for theoretical investigation.
+",0,1,0,0,0,0
+13587,Consistent polynomial-time unseeded graph matching for Lipschitz graphons,"  We propose a consistent polynomial-time method for the unseeded node matching
+problem for networks with smooth underlying structures. Despite widely
+conjectured by the research community that the structured graph matching
+problem to be significantly easier than its worst case counterpart, well-known
+to be NP-hard, the statistical version of the problem has stood a challenge
+that resisted any solution both provable and polynomial-time. The closest
+existing work requires quasi-polynomial time. Our method is based on the latest
+advances in graphon estimation techniques and analysis on the concentration of
+empirical Wasserstein distances. Its core is a simple yet unconventional
+sampling-and-matching scheme that reduces the problem from unseeded to seeded.
+Our method allows flexible efficiencies, is convenient to analyze and
+potentially can be extended to more general settings. Our work enables a rich
+variety of subsequent estimations and inferences.
+",0,0,0,1,0,0
+13588,Photoinduced filling of near nodal gap in Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$,"  We report time and angle resolved spectroscopic measurements in optimally
+doped Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$. The spectral function is monitored as
+a function of temperature, photoexcitation density and delay time from the pump
+pulse. According to our data, the superconducting gap becomes slightly stiffer
+when moving off the nodal direction. The nodal quasiparticles develop a faster
+dynamics when pumping the superconductor with a fluence that is large enough to
+induce the total collapse of the gap. We discuss the observed relaxation in
+terms of a dynamical reformation of Cooper pairs.
+",0,1,0,0,0,0
+13589,Globally convergent Jacobi-type algorithms for simultaneous orthogonal symmetric tensor diagonalization,"  In this paper, we consider a family of Jacobi-type algorithms for
+simultaneous orthogonal diagonalization problem of symmetric tensors. For the
+Jacobi-based algorithm of [SIAM J. Matrix Anal. Appl., 2(34):651--672, 2013],
+we prove its global convergence for simultaneous orthogonal diagonalization of
+symmetric matrices and 3rd-order tensors. We also propose a new Jacobi-based
+algorithm in the general setting and prove its global convergence for
+sufficiently smooth functions.
+",1,0,1,0,0,0
+13590,Representing the Deligne-Hinich-Getzler $\infty$-groupoid,"  The goal of the present paper is to introduce a smaller, but equivalent
+version of the Deligne-Hinich-Getzler $\infty$-groupoid associated to a
+homotopy Lie algebra. In the case of differential graded Lie algebras, we
+represent it by a universal cosimplicial object.
+",0,0,1,0,0,0
+13591,Sentence-level quality estimation by predicting HTER as a multi-component metric,"  This submission investigates alternative machine learning models for
+predicting the HTER score on the sentence level. Instead of directly predicting
+the HTER score, we suggest a model that jointly predicts the amount of the 4
+distinct post-editing operations, which are then used to calculate the HTER
+score. This also gives the possibility to correct invalid (e.g. negative)
+predicted values prior to the calculation of the HTER score. Without any
+feature exploration, a multi-layer perceptron with 4 outputs yields small but
+significant improvements over the baseline.
+",1,0,0,0,0,0
+13592,Machine Assisted Analysis of Vowel Length Contrasts in Wolof,"  Growing digital archives and improving algorithms for automatic analysis of
+text and speech create new research opportunities for fundamental research in
+phonetics. Such empirical approaches allow statistical evaluation of a much
+larger set of hypothesis about phonetic variation and its conditioning factors
+(among them geographical / dialectal variants). This paper illustrates this
+vision and proposes to challenge automatic methods for the analysis of a not
+easily observable phenomenon: vowel length contrast. We focus on Wolof, an
+under-resourced language from Sub-Saharan Africa. In particular, we propose
+multiple features to make a fine evaluation of the degree of length contrast
+under different factors such as: read vs semi spontaneous speech ; standard vs
+dialectal Wolof. Our measures made fully automatically on more than 20k vowel
+tokens show that our proposed features can highlight different degrees of
+contrast for each vowel considered. We notably show that contrast is weaker in
+semi-spontaneous speech and in a non standard semi-spontaneous dialect.
+",1,0,0,0,0,0
+13593,Precision of Evaluation Methods in White Light Interferometry: Correlogram Correlation Method,"  In this paper we promote a method for the evaluation of a surface topography
+which we call the correlogram correlation method. Employing a theoretical
+analysis as well as numerical simulations the method is proven to be the most
+accurate among available evaluation algorithms in the common case of
+uncorrelated noise. Examples illustrate the superiority of the correlogram
+correlation method over the common envelope and phase methods.
+",0,1,0,0,0,0
+13594,Non-wetting drops at liquid interfaces: From liquid marbles to Leidenfrost drops,"  We consider the flotation of deformable, non-wetting drops on a liquid
+interface. We consider the deflection of both the liquid interface and the
+droplet itself in response to the buoyancy forces, density difference and the
+various surface tensions within the system. Our results suggest new insight
+into a range of phenomena in which such drops occur, including Leidenfrost
+droplets and floating liquid marbles. In particular, we show that the floating
+state of liquid marbles is very sensitive to the tension of the
+particle-covered interface and suggest that this sensitivity may make such
+experiments a useful assay of the properties of these complex interfaces.
+",0,1,0,0,0,0
+13595,Kinetics of the Crystalline Nuclei Growth in Glassy Systems,"  In this work, we study the crystalline nuclei growth in glassy systems
+focusing primarily on the early stages of the process, at which the size of a
+growing nucleus is still comparable with the critical size. On the basis of
+molecular dynamics simulation results for two crystallizing glassy systems, we
+evaluate the growth laws of the crystalline nuclei and the parameters of the
+growth kinetics at the temperatures corresponding to deep supercoolings;
+herein, the statistical treatment of the simulation results is done within the
+mean-first-passage-time method. It is found for the considered systems at
+different temperatures that the crystal growth laws rescaled onto the waiting
+times of the critically-sized nucleus follow the unified dependence, that can
+simplify significantly theoretical description of the post-nucleation growth of
+crystalline nuclei. The evaluated size-dependent growth rates are characterized
+by transition to the steady-state growth regime, which depends on the
+temperature and occurs in the glassy systems when the size of a growing nucleus
+becomes two-three times larger than a critical size. It is suggested to
+consider the temperature dependencies of the crystal growth rate
+characteristics by using the reduced temperature scale $\widetilde{T}$. Thus,
+it is revealed that the scaled values of the crystal growth rate
+characteristics (namely, the steady-state growth rate and the attachment rate
+for the critically-sized nucleus) as functions of the reduced temperature
+$\widetilde{T}$ for glassy systems follow the unified power-law dependencies.
+This finding is supported by available simulation results; the correspondence
+with the experimental data for the crystal growth rate in glassy systems at the
+temperatures near the glass transition is also discussed.
+",0,1,0,0,0,0
+13596,Electron Cloud Trapping In Recycler Combined Function Dipole Magnets,"  Electron cloud can lead to a fast instability in intense proton and positron
+beams in circular accelerators. In the Fermilab Recycler the electron cloud is
+confined within its combined function magnets. We show that the field of
+combined function magnets traps the electron cloud, present the results of
+analytical estimates of trapping, and compare them to numerical simulations of
+electron cloud formation. The electron cloud is located at the beam center and
+up to 1% of the particles can be trapped by the magnetic field. Since the
+process of electron cloud build-up is exponential, once trapped this amount of
+electrons significantly increases the density of the cloud on the next
+revolution. In a Recycler combined function dipole this multi-turn accumulation
+allows the electron cloud reaching final intensities orders of magnitude
+greater than in a pure dipole. The multi-turn build-up can be stopped by
+injection of a clearing bunch of $10^{10}$ p at any position in the ring.
+",0,1,0,0,0,0
+13597,Causal Inference Under Network Interference: A Framework for Experiments on Social Networks,"  No man is an island, as individuals interact and influence one another daily
+in our society. When social influence takes place in experiments on a
+population of interconnected individuals, the treatment on a unit may affect
+the outcomes of other units, a phenomenon known as interference. This thesis
+develops a causal framework and inference methodology for experiments where
+interference takes place on a network of influence (i.e. network interference).
+In this framework, the network potential outcomes serve as the key quantity and
+flexible building blocks for causal estimands that represent a variety of
+primary, peer, and total treatment effects. These causal estimands are
+estimated via principled Bayesian imputation of missing outcomes. The theory on
+the unconfoundedness assumptions leading to simplified imputation highlights
+the importance of including relevant network covariates in the potential
+outcome model. Additionally, experimental designs that result in balanced
+covariates and sizes across treatment exposure groups further improve the
+causal estimate, especially by mitigating potential outcome model
+mis-specification. The true potential outcome model is not typically known in
+real-world experiments, so the best practice is to account for interference and
+confounding network covariates through both balanced designs and model-based
+imputation. A full factorial simulated experiment is formulated to demonstrate
+this principle by comparing performance across different randomization schemes
+during the design phase and estimators during the analysis phase, under varying
+network topology and true potential outcome models. Overall, this thesis
+asserts that interference is not just a nuisance for analysis but rather an
+opportunity for quantifying and leveraging peer effects in real-world
+experiments.
+",0,0,1,1,0,0
+13598,Polynomial configurations in sets of positive upper density over local fields,"  Let $F(x)=(f_1(x), \dots, f_m(x))$ be such that $1, f_1, \dots, f_m$ are
+linearly independent polynomials with real coefficients. Based on ideas of
+Bachoc, DeCorte, Oliveira and Vallentin in combination with estimating certain
+oscillatory integrals with polynomial phase we will show that the independence
+ratio of the Cayley graph of $\mathbb{R}^m$ with respect to the portion of the
+graph of $F$ defined by $a\leq \log |s| \leq T$ is at most $O(1/(T-a))$. We
+conclude that if $I \subseteq \mathbb{R}^m$ has positive upper density, then
+the difference set $I-I$ contains vectors of the form $F(s)$ for an unbounded
+set of values $s \in \mathbb{R}$. It follows that the Borel chromatic number of
+the Cayley graph of $\mathbb{R}^m$ with respect to the set $\{ \pm F(s): s \in
+\mathbb{R} \}$ is infinite. Analogous results are also proven when $\mathbb{R}$
+is replaced by the field of $p$-adic numbers $\mathbb{Q}_p$. At the end, we
+will also the existence of real analytic functions $f_1, \dots, f_m$, for which
+the analogous statements no longer hold.
+",0,0,1,0,0,0
+13599,Deterministic Genericity for Polynomial Ideals,"  We consider several notions of genericity appearing in algebraic geometry and
+commutative algebra. Special emphasis is put on various stability notions which
+are defined in a combinatorial manner and for which a number of equivalent
+algebraic characterisations are provided. It is shown that in characteristic
+zero the corresponding generic positions can be obtained with a simple
+deterministic algorithm. In positive characteristic, only adapted stable
+positions are reachable except for quasi-stability which is obtainable in any
+characteristic.
+",1,0,1,0,0,0
+13600,Precise Pointing of Cubesat Telescopes: Comparison Between Heat and Light Induced Attitude Control Methods,"  CubeSats are emerging as low-cost tools to perform astronomy, exoplanet
+searches and earth observation. These satellites can target an object for
+science observation for weeks on end. This is typically not possible on larger
+missions where usage time is shared. The problem of designing an attitude
+control system for CubeSat telescopes is very challenging because current
+choice of actuators such as reaction-wheels and magnetorquers can induce jitter
+on the spacecraft due to moving mechanical parts and due to external
+disturbances. These telescopes may contain cryo-pumps and servos that introduce
+additional vibrations. A better solution is required. In our paper, we analyze
+the feasibility of utilizing solar radiation pressure (SRP) and radiometric
+force to achieve precise attitude control. Our studies show radiometric
+actuators to be a viable method to achieve precise pointing. The device uses 8
+thin vanes of different temperatures placed in a near-vacuum chamber. These
+chambers contain trace quantities of lightweight, inert gasses like argon. The
+temperature gradient across the vanes causes the gas molecules to strike the
+vanes differently and thus inducing a force. By controlling these forces, it's
+possible to produce a torque to precisely point or spin a spacecraft. We
+present a conceptual design of a CubeSat that is equipped with these actuators.
+We then analyze the potential slew maneuver and slew rates possible with these
+actuators by simulating their performance. Our analytical and simulation
+results point towards a promising pathway for laboratory testing of this
+technology and demonstration of this technology in space.
+",0,1,0,0,0,0
+13601,Cyclicity in weighted $\ell^p$ spaces,"  We study the cyclicity in weighted $\ell^p(\mathbb{Z})$ spaces. For $p \geq
+1$ and $\beta \geq 0$, let $\ell^p\_\beta(\mathbb{Z})$ be the space of
+sequences $u=(u\_n)\_{n\in \mathbb{Z}}$ such that $(u\_n |n|^{\beta})\in
+\ell^p(\mathbb{Z}) $. We obtain both necessary conditions and sufficient
+conditions for $u$ to be cyclic in $\ell^p\_\beta(\mathbb{Z})$, in other words,
+for $ \{(u\_{n+k})\_{n \in \mathbb{Z}},~ k \in \mathbb{Z} \}$ to span a dense
+subspace of $\ell^p\_\beta(\mathbb{Z})$. The conditions are given in terms of
+the Hausdorff dimension and the capacity of the zero set of the Fourier
+transform of $u$.
+",0,0,1,0,0,0
+13602,State-Space Identification of Unmanned Helicopter Dynamics using Invasive Weed Optimization Algorithm on Flight Data,"  In order to achieve a good level of autonomy in unmanned helicopters, an
+accurate replication of vehicle dynamics is required, which is achievable
+through precise mathematical modeling. This paper aims to identify a parametric
+state-space system for an unmanned helicopter to a good level of accuracy using
+Invasive Weed Optimization (IWO) algorithm. The flight data of Align TREX 550
+flybarless helicopter is used in the identification process. The rigid-body
+dynamics of the helicopter is modeled in a state-space form that has 40
+parameters, which serve as control variables for the IWO algorithm. The results
+after 1000 iterations were compared with the traditionally used Prediction
+Error Minimization (PEM) method and also with Genetic Algorithm (GA), which
+serve as references. Results show a better level of correlation between the
+actual and estimated responses of the system identified using IWO to that of
+PEM and GA.
+",1,0,0,0,0,0
+13603,Latent Molecular Optimization for Targeted Therapeutic Design,"  We devise an approach for targeted molecular design, a problem of interest in
+computational drug discovery: given a target protein site, we wish to generate
+a chemical with both high binding affinity to the target and satisfactory
+pharmacological properties. This problem is made difficult by the enormity and
+discreteness of the space of potential therapeutics, as well as the
+graph-structured nature of biomolecular surface sites. Using a dataset of
+protein-ligand complexes, we surmount these issues by extracting a signature of
+the target site with a graph convolutional network and by encoding the discrete
+chemical into a continuous latent vector space. The latter embedding permits
+gradient-based optimization in molecular space, which we perform using learned
+differentiable models of binding affinity and other pharmacological properties.
+We show that our approach is able to efficiently optimize these multiple
+objectives and discover new molecules with potentially useful binding
+properties, validated via docking methods.
+",0,0,0,0,1,0
+13604,SlimNets: An Exploration of Deep Model Compression and Acceleration,"  Deep neural networks have achieved increasingly accurate results on a wide
+variety of complex tasks. However, much of this improvement is due to the
+growing use and availability of computational resources (e.g use of GPUs, more
+layers, more parameters, etc). Most state-of-the-art deep networks, despite
+performing well, over-parameterize approximate functions and take a significant
+amount of time to train. With increased focus on deploying deep neural networks
+on resource constrained devices like smart phones, there has been a push to
+evaluate why these models are so resource hungry and how they can be made more
+efficient. This work evaluates and compares three distinct methods for deep
+model compression and acceleration: weight pruning, low rank factorization, and
+knowledge distillation. Comparisons on VGG nets trained on CIFAR10 show that
+each of the models on their own are effective, but that the true power lies in
+combining them. We show that by combining pruning and knowledge distillation
+methods we can create a compressed network 85 times smaller than the original,
+all while retaining 96% of the original model's accuracy.
+",0,0,0,1,0,0
+13605,An alternative axiomization of $N$-pseudospaces,"  We give a new axiomatization of the N-pseudospace, studied in [2]
+(Tent(2014)) and [1] (Baudisch,Martin-Pizarro,Ziegler(2014)) based on the
+zigzags introduced in [2]. We also present a more detailed account of the
+characterization of forking given in [2].
+",0,0,1,0,0,0
+13606,Symmetry breaking in linear multipole traps,"  Radiofrequency multipole traps have been used for some decades in cold
+collision experiments, and are gaining interest for precision spectroscopy due
+to their low mi-cromotion contribution, and the predicted unusual cold-ion
+structures. However, the experimental realisation is not yet fully controlled,
+and open questions in the operation of these devices remain. We present
+experimental observations of symmetry breaking of the trapping potential in a
+macroscopic octupole trap with laser-cooled ions. Numerical simulations have
+been performed in order to explain the appearance of additional local potential
+minima, and be able to control them in a next step. We characterize these
+additional potential minima, in particular with respect to their position,
+their potential depth and their probability of population as a function of the
+radial and angular displacement of the trapping rods.
+",0,1,0,0,0,0
+13607,Deep Spatio-temporal Manifold Network for Action Recognition,"  Visual data such as videos are often sampled from complex manifold. We
+propose leveraging the manifold structure to constrain the deep action feature
+learning, thereby minimizing the intra-class variations in the feature space
+and alleviating the over-fitting problem. Considering that manifold can be
+transferred, layer by layer, from the data domain to the deep features, the
+manifold priori is posed from the top layer into the back propagation learning
+procedure of convolutional neural network (CNN). The resulting algorithm
+--Spatio-Temporal Manifold Network-- is solved with the efficient Alternating
+Direction Method of Multipliers and Backward Propagation (ADMM-BP). We
+theoretically show that STMN recasts the problem as projection over the
+manifold via an embedding method. The proposed approach is evaluated on two
+benchmark datasets, showing significant improvements to the baselines.
+",1,0,0,0,0,0
+13608,Gas dynamics in strong centrifugal fields,"  Dynamics of waves generated by scopes in gas centrifuges (GC) for isotope
+separation is considered. The centrifugal acceleration in the GC reaches values
+of the order of $10^6$g. The centrifugal and Coriolis forces modify essentially
+the conventional sound waves. Three families of the waves with different
+polarisation and dispersion exist in these conditions. Dynamics of the flow in
+the model GC Iguasu is investigated numerically. Comparison of the results of
+the numerical modelling of the wave dynamics with the analytical predictions is
+performed. New phenomena of the resonances in the GC is found. The resonances
+occur for the waves polarized along the rotational axis having the smallest
+dumping due to the viscosity.
+",0,1,0,0,0,0
+13609,Robust Stackelberg controllability for the Navier--Stokes equations,"  In this paper we deal with a robust Stackelberg strategy for the
+Navier--Stokes system. The scheme is based in considering a robust control
+problem for the ""follower control"" and its associated disturbance function.
+Afterwards, we consider the notion of Stackelberg optimization (which is
+associated to the ""leader control"") in order to deduce a local null
+controllability result for the Navier--Stokes system.
+",0,0,1,0,0,0
+13610,Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications,"  In this paper, five different approaches for reduced-order modeling of
+brittle fracture in geomaterials, specifically concrete, are presented and
+compared. Four of the five methods rely on machine learning (ML) algorithms to
+approximate important aspects of the brittle fracture problem. In addition to
+the ML algorithms, each method incorporates different physics-based assumptions
+in order to reduce the computational complexity while maintaining the physics
+as much as possible. This work specifically focuses on using the ML approaches
+to model a 2D concrete sample under low strain rate pure tensile loading
+conditions with 20 preexisting cracks present. A high-fidelity finite
+element-discrete element model is used to both produce a training dataset of
+150 simulations and an additional 35 simulations for validation. Results from
+the ML approaches are directly compared against the results from the
+high-fidelity model. Strengths and weaknesses of each approach are discussed
+and the most important conclusion is that a combination of physics-informed and
+data-driven features are necessary for emulating the physics of crack
+propagation, interaction and coalescence. All of the models presented here have
+runtimes that are orders of magnitude faster than the original high-fidelity
+model and pave the path for developing accurate reduced order models that could
+be used to inform larger length-scale models with important sub-scale physics
+that often cannot be accounted for due to computational cost.
+",0,0,0,1,0,0
+13611,A ferroelectric quantum phase transition inside the superconducting dome of Sr$_{1-x}$Ca$_{x}$TiO$_{3-δ}$,"  SrTiO$_{3}$, a quantum paraelectric, becomes a metal with a superconducting
+instability after removal of an extremely small number of oxygen atoms. It
+turns into a ferroelectric upon substitution of a tiny fraction of strontium
+atoms with calcium. The two orders may be accidental neighbors or intimately
+connected, as in the picture of quantum critical ferroelectricity. Here, we
+show that in Sr$_{1-x}$Ca$_{x}$TiO$_{3-\delta}$ ($0.002<x<0.009$,
+$\delta<0.001$) the ferroelectric order coexists with dilute metallicity and
+its superconducting instability in a finite window of doping. At a critical
+carrier density, which scales with the Ca content, a quantum phase transition
+destroys the ferroelectric order. We detect an upturn in the normal-state
+scattering and a significant modification of the superconducting dome in the
+vicinity of this quantum phase transition. The enhancement of the
+superconducting transition temperature with calcium substitution documents the
+role played by ferroelectric vicinity in the precocious emergence of
+superconductivity in this system, restricting possible theoretical scenarios
+for pairing.
+",0,1,0,0,0,0
+13612,First Detection of Equatorial Dark Dust Lane in a Protostellar Disk at Submillimeter Wavelength,"  In the earliest (so-called ""Class 0"") phase of sunlike (low-mass) star
+formation, circumstellar disks are expected to form, feeding the protostars.
+However, such disks are difficult to resolve spatially because of their small
+sizes. Moreover, there are theoretical difficulties in producing such disks in
+the earliest phase, due to the retarding effects of magnetic fields on the
+rotating, collapsing material (so-called ""magnetic braking""). With the Atacama
+Large Millimeter/submillimeter Array (ALMA), it becomes possible to uncover
+such disks and study them in detail. HH 212 is a very young protostellar
+system. With ALMA, we not only detect but also spatially resolve its disk in
+dust emission at submillimeter wavelength. The disk is nearly edge-on and has a
+radius of ~ 60 AU. Interestingly, it shows a prominent equatorial dark lane
+sandwiched between two brighter features, due to relatively low temperature and
+high optical depth near the disk midplane. For the first time, this dark lane
+is seen at submillimeter wavelength, producing a ""hamburger""-shaped appearance
+that is reminiscent of the scattered-light image of an edge-on disk in optical
+and near infrared. Our observations open up an exciting possibility of directly
+detecting and characterizing small disks around the youngest protostars through
+high-resolution imaging with ALMA, which provides strong constraints on
+theories of disk formation.
+",0,1,0,0,0,0
+13613,General dynamical properties of cosmological models with nonminimal kinetic coupling,"  We consider cosmological dynamics in the theory of gravity with the scalar
+field possessing the nonminimal kinetic coupling to curvature given as $\eta
+G^{\mu\nu}\phi_{,\mu}\phi_{,\nu}$, where $\eta$ is an arbitrary coupling
+parameter, and the scalar potential $V(\phi)$ which assumed to be as general as
+possible. With an appropriate dimensionless parametrization we represent the
+field equations as an autonomous dynamical system which contains ultimately
+only one arbitrary function $\chi (x)= 8 \pi \vert \eta \vert V(x/\sqrt{8
+\pi})$ with $x=\sqrt{8 \pi}\phi$. Then, assuming the rather general properties
+of $\chi(x)$, we analyze stationary points and their stability, as well as all
+possible asymptotical regimes of the dynamical system. It has been shown that
+for a broad class of $\chi(x)$ there exist attractors representing three
+accelerated regimes of the Universe evolution, including de Sitter expansion
+(or late-time inflation), the Little Rip scenario, and the Big Rip scenario. As
+the specific examples, we consider a power-law potential
+$V(\phi)=M^4(\phi/\phi_0)^\sigma$, Higgs-like potential
+$V(\phi)=\frac{\lambda}{4}(\phi^2-\phi_0^2)^2$, and exponential potential
+$V(\phi)=M^4 e^{-\phi/\phi_0}$.
+",0,1,0,0,0,0
+13614,Extensions of interpolation between the arithmetic-geometric mean inequality for matrices,"  In this paper, we present some extensions of interpolation between the
+arithmetic-geometric means inequality. Among other inequalities, it is shown
+that if $A, B, X$ are $n\times n$ matrices, then \begin{align*}
+\|AXB^*\|^2\leq\|f_1(A^*A)Xg_1(B^*B)\|\,\|f_2(A^*A)Xg_2(B^*B)\|, \end{align*}
+where $f_1,f_2,g_1,g_2$ are non-negative continues functions such that
+$f_1(t)f_2(t)=t$ and $g_1(t)g_2(t)=t\,\,(t\geq0)$. We also obtain the
+inequality \begin{align*}
+\left|\left|\left|AB^*\right|\right|\right|^2\nonumber&\leq
+\left|\left|\left|p(A^*A)^{\frac{m}{p}}+
+(1-p)(B^*B)^{\frac{s}{1-p}}\right|\right|\right|\,\left|\left|\left|(1-p)(A^*A)^{\frac{n}{1-p}}+
+p(B^*B)^{\frac{t}{p}}\right|\right|\right|, \end{align*} in which $m,n,s,t$ are
+real numbers such that $m+n=s+t=1$, $|||\cdot|||$ is an arbitrary unitarily
+invariant norm and $p\in[0,1]$.
+",0,0,1,0,0,0
+13615,Machine Translation in Indian Languages: Challenges and Resolution,"  English to Indian language machine translation poses the challenge of
+structural and morphological divergence. This paper describes English to Indian
+language statistical machine translation using pre-ordering and suffix
+separation. The pre-ordering uses rules to transfer the structure of the source
+sentences prior to training and translation. This syntactic restructuring helps
+statistical machine translation to tackle the structural divergence and hence
+better translation quality. The suffix separation is used to tackle the
+morphological divergence between English and highly agglutinative Indian
+languages. We demonstrate that the use of pre-ordering and suffix separation
+helps in improving the quality of English to Indian Language machine
+translation.
+",1,0,0,0,0,0
+13616,Retrieving the quantitative chemical information at nanoscale from SEM EDX measurements by Machine Learning,"  The quantitative composition of metal alloy nanowires on InSb(001)
+semiconductor surface and gold nanostructures on germanium surface is
+determined by blind source separation (BSS) machine learning (ML) method using
+non negative matrix factorization (NMF) from energy dispersive X-ray
+spectroscopy (EDX) spectrum image maps measured in a scanning electron
+microscope (SEM). The BSS method blindly decomposes the collected EDX spectrum
+image into three source components, which correspond directly to the X-ray
+signals coming from the supported metal nanostructures, bulk semiconductor
+signal and carbon background. The recovered quantitative composition is
+validated by detailed Monte Carlo simulations and is confirmed by separate
+cross-sectional TEM EDX measurements of the nanostructures. This shows that SEM
+EDX measurements together with machine learning blind source separation
+processing could be successfully used for the nanostructures quantitative
+chemical composition determination.
+",0,1,0,0,0,0
+13617,Decomposition Strategies for Constructive Preference Elicitation,"  We tackle the problem of constructive preference elicitation, that is the
+problem of learning user preferences over very large decision problems,
+involving a combinatorial space of possible outcomes. In this setting, the
+suggested configuration is synthesized on-the-fly by solving a constrained
+optimization problem, while the preferences are learned itera tively by
+interacting with the user. Previous work has shown that Coactive Learning is a
+suitable method for learning user preferences in constructive scenarios. In
+Coactive Learning the user provides feedback to the algorithm in the form of an
+improvement to a suggested configuration. When the problem involves many
+decision variables and constraints, this type of interaction poses a
+significant cognitive burden on the user. We propose a decomposition technique
+for large preference-based decision problems relying exclusively on inference
+and feedback over partial configurations. This has the clear advantage of
+drastically reducing the user cognitive load. Additionally, part-wise inference
+can be (up to exponentially) less computationally demanding than inference over
+full configurations. We discuss the theoretical implications of working with
+parts and present promising empirical results on one synthetic and two
+realistic constructive problems.
+",1,0,0,1,0,0
+13618,Vibrational surface EELS probes confined Fuchs-Kliewer modes,"  Recently, two reports have demonstrated the amazing possibility to probe
+vibrational excitations from nanoparticles with a spatial resolution much
+smaller than the corresponding free-space phonon wavelength using electron
+energy loss spectroscopy (EELS). While Lagos et al. evidenced a strong spatial
+and spectral modulation of the EELS signal over a nanoparticle, Krivanek et al.
+did not. Here, we show that discrepancies among different EELS experiments as
+well as their relation to optical near- and far-field optical experiments can
+be understood by introducing the concept of confined bright and dark
+Fuchs-Kliewer modes, whose density of states is probed by EELS. Such a concise
+formalism is the vibrational counterpart of the broadly used formalism for
+localized surface plasmons; it makes it straightforward to predict or interpret
+phenomena already known for localized surface plasmons such as
+environment-related energy shifts or the possibility of 3D mapping of the
+related surface charge densities.
+",0,1,0,0,0,0
+13619,The Dependence of the Mass-Metallicity Relation on Large Scale Environment,"  We examine the relation between gas-phase oxygen abundance and stellar
+mass---the MZ relation---as a function of the large scale galaxy environment
+parameterized by the local density. The dependence of the MZ relation on the
+environment is small. The metallicity where the MZ relation saturates and the
+slope of the MZ relation are both independent of the local density. The impact
+of the large scale environment is completely parameterized by the
+anti-correlation between local density and the turnover stellar mass where the
+MZ relation begins to saturate. Analytical modeling suggests that the
+anti-correlation between the local density and turnover stellar mass is a
+consequence of a variation in the gas content of star-forming galaxies. Across
+$\sim1$ order of magnitude in local density, the gas content at a fixed stellar
+mass varies by $\sim5\%$. Variation of the specific star formation rate with
+environment is consistent with this interpretation. At a fixed stellar mass,
+galaxies in low density environments have lower metallicities because they are
+slightly more gas-rich than galaxies in high density environments. Modeling the
+shape of the mass-metallicity relation thus provides an indirect means to probe
+subtle variations in the gas content of star-forming galaxies.
+",0,1,0,0,0,0
+13620,"Proof of Riemann hypothesis, Generalized Riemann hypothesis and Ramanujan $τ$-Dirichlet series hypothesis","  We prove Riemann hypothesis, Generalized Riemann hypothesis, and Ramanujan
+$\tau$-Dirichlet series hypothesis. Method is to show the convexity of function
+which has zeros critical strip the same as zeta function.
+",0,0,1,0,0,0
+13621,A second order primal-dual method for nonsmooth convex composite optimization,"  We develop a second order primal-dual method for optimization problems in
+which the objective function is given by the sum of a strongly convex twice
+differentiable term and a possibly nondifferentiable convex regularizer. After
+introducing an auxiliary variable, we utilize the proximal operator of the
+nonsmooth regularizer to transform the associated augmented Lagrangian into a
+function that is once, but not twice, continuously differentiable. The saddle
+point of this function corresponds to the solution of the original optimization
+problem. We employ a generalization of the Hessian to define second order
+updates on this function and prove global exponential stability of the
+corresponding differential inclusion. Furthermore, we develop a globally
+convergent customized algorithm that utilizes the primal-dual augmented
+Lagrangian as a merit function. We show that the search direction can be
+computed efficiently and prove quadratic/superlinear asymptotic convergence. We
+use the $\ell_1$-regularized least squares problem and the problem of designing
+a distributed controller for a spatially-invariant system to demonstrate the
+merits and the effectiveness of our method.
+",1,1,0,0,0,0
+13622,PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking,"  Electronic medical records contain multi-format electronic medical data that
+consist of an abundance of medical knowledge. Facing with patient's symptoms,
+experienced caregivers make right medical decisions based on their professional
+knowledge that accurately grasps relationships between symptoms, diagnosis and
+corresponding treatments. In this paper, we aim to capture these relationships
+by constructing a large and high-quality heterogenous graph linking patients,
+diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework
+to extract important medical entities from MIMIC-III (Medical Information Mart
+for Intensive Care III) and automatically link them with the existing
+biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD
+graph presented in this paper is accessible on the Web via the SPARQL endpoint,
+and provides a pathway for medical discovery and applications, such as
+effective treatment recommendations.
+",1,0,0,0,0,0
+13623,A new algorithm for fast generalized DFTs,"  We give an new arithmetic algorithm to compute the generalized Discrete
+Fourier Transform (DFT) over finite groups $G$. The new algorithm uses
+$O(|G|^{\omega/2 + o(1)})$ operations to compute the generalized DFT over
+finite groups of Lie type, including the linear, orthogonal, and symplectic
+families and their variants, as well as all finite simple groups of Lie type.
+Here $\omega$ is the exponent of matrix multiplication, so the exponent
+$\omega/2$ is optimal if $\omega = 2$. Previously, ""exponent one"" algorithms
+were known for supersolvable groups and the symmetric and alternating groups.
+No exponent one algorithms were known (even under the assumption $\omega = 2$)
+for families of linear groups of fixed dimension, and indeed the previous
+best-known algorithm for $SL_2(F_q)$ had exponent $4/3$ despite being the focus
+of significant effort. We unconditionally achieve exponent at most $1.19$ for
+this group, and exponent one if $\omega = 2$. Our algorithm also yields an
+improved exponent for computing the generalized DFT over general finite groups
+$G$, which beats the longstanding previous best upper bound, for any $\omega$.
+In particular, assuming $\omega = 2$, we achieve exponent $\sqrt{2}$, while the
+previous best was $3/2$.
+",1,0,1,0,0,0
+13624,Critical magnetic fields in a superconductor coupled to a superfluid,"  We study a superconductor that is coupled to a superfluid via density and
+derivative couplings. Starting from a Lagrangian for two complex scalar fields,
+we derive a temperature-dependent Ginzburg-Landau potential, which is then used
+to compute the phase diagram at nonzero temperature and external magnetic
+field. This includes the calculation of the critical magnetic fields for the
+transition to an array of magnetic flux tubes, based on an approximation for
+the interaction between the flux tubes. We find that the transition region
+between type-I and type-II superconductivity changes qualitatively due to the
+presence of the superfluid: the phase transitions at the upper and lower
+critical fields in the type-II regime become first order, opening the
+possibility of clustered flux tube phases. These flux tube clusters may be
+realized in the core of neutron stars, where superconducting protons are
+expected to be coupled to superfluid neutrons.
+",0,1,0,0,0,0
+13625,The bubble algebras at roots of unity,"  We introduce multi-colour partition algebras $P_{n,m}(\delta_0, ...,
+\delta_{m-1})$, which are generalization of both bubble algebras and partition
+algebras, then define the bubble algebra $T_{n,m}(\delta_0, ..., \delta_{m-1})$
+as a sub-algebra of the algebra $P_{n,m}(\delta_0, ..., \delta_{m-1})$. We
+present general techniques to determine the structure of the bubble algebra
+over the complex field in the non-semisimple case.
+",0,0,1,0,0,0
+13626,Self-Repairing Energy Materials: Sine Qua Non for a Sustainable Future,"  Materials are central to our way of life and future. Energy and materials as
+resources are connected and the obvious connections between them are the energy
+cost of materials and the materials cost of energy. For both of these
+resilience of the materials is critical; thus a major goal of future chemistry
+should be to find materials for energy that can last longer, i.e., design
+principles for self-repair in these.
+",0,1,0,0,0,0
+13627,Design and performance of dual-polarization lumped-element kinetic inductance detectors for millimeter-wave polarimetry,"  Lumped-element kinetic inductance detectors (LEKIDs) are an attractive
+technology for millimeter-wave observations that require large arrays of
+extremely low-noise detectors. We designed, fabricated and characterized
+64-element (128 LEKID) arrays of horn-coupled, dual-polarization LEKIDs
+optimized for ground-based CMB polarimetry. Our devices are sensitive to two
+orthogonal polarizations in a single spectral band centered on 150 GHz with
+$\Delta\nu/\nu=0.2$. The $65\times 65$ mm square arrays are designed to be
+tiled into the focal plane of an optical system. We demonstrate the viability
+of these dual-polarization LEKIDs with laboratory measurements. The LEKID
+modules are tested with an FPGA-based readout system in a sub-kelvin cryostat
+that uses a two-stage adiabatic demagnetization refrigerator. The devices are
+characterized using a blackbody and a millimeter-wave source. The polarization
+properties are measured with a cryogenic stepped half-wave plate. We measure
+the resonator parameters and the detector sensitivity, noise spectrum, dynamic
+range, and polarization response. The resonators have internal quality factors
+approaching $1\times 10^{6}$. The detectors have uniform response between
+orthogonal polarizations and a large dynamic range. The detectors are
+photon-noise limited above 1 pW of absorbed power. The noise-equivalent
+temperatures under a 3.4 K blackbody load are $<100~\mu\mathrm{K\sqrt{s}}$. The
+polarization fractions of detectors sensitive to orthogonal polarizations are
+>80%. The entire array is multiplexed on a single readout line, demonstrating a
+multiplexing factor of 128. The array and readout meet the requirements for 4
+arrays to be read out simultaneously for a multiplexing factor of 512. This
+laboratory study demonstrates the first dual-polarization LEKID array optimized
+for CMB polarimetry and shows the readiness of the detectors for on-sky
+observations.
+",0,1,0,0,0,0
+13628,An overview of knot Floer homology,"  Knot Floer homology is an invariant for knots discovered by the authors and,
+independently, Jacob Rasmussen. The discovery of this invariant grew naturally
+out of studying how a certain three-manifold invariant, Heegaard Floer
+homology, changes as the three-manifold undergoes Dehn surgery along a knot.
+Since its original definition, thanks to the contributions of many researchers,
+knot Floer homology has emerged as a useful tool for studying knots in its own
+right. We give here a few selected highlights of this theory, and then move on
+to some new algebraic developments in the computation of knot Floer homology.
+",0,0,1,0,0,0
+13629,Mathematical model of immune response to hepatitis B,"  A new detailed mathematical model for dynamics of immune response to
+hepatitis B is proposed, which takes into account contributions from innate and
+adaptive immune responses, as well as cytokines. Stability analysis of
+different steady states is performed to identify parameter regions where the
+model exhibits clearance of infection, maintenance of a chronic infection, or
+periodic oscillations. Effects of nucleoside analogues and interferon
+treatments are analysed, and the critical drug efficiency is determined.
+",0,0,0,0,1,0
+13630,Controlling Stray Electric Fields on an Atom Chip for Rydberg Experiments,"  Experiments handling Rydberg atoms near surfaces must necessarily deal with
+the high sensitivity of Rydberg atoms to (stray) electric fields that typically
+emanate from adsorbates on the surface. We demonstrate a method to modify and
+reduce the stray electric field by changing the adsorbates distribution. We use
+one of the Rydberg excitation lasers to locally affect the adsorbed dipole
+distribution. By adjusting the averaged exposure time we change the strength
+(with the minimal value less than $0.2\,\textrm{V/cm}$ at $78\,\mu\textrm{m}$
+from the chip) and even the sign of the perpendicular field component. This
+technique is a useful tool for experiments handling Ryberg atoms near surfaces,
+including atom chips.
+",0,1,0,0,0,0
+13631,Unseen Progenitors of Luminous High-z Quasars in the R_h=ct Universe,"  Quasars at high redshift provide direct information on the mass growth of
+supermassive black holes and, in turn, yield important clues about how the
+Universe evolved since the first (Pop III) stars started forming. Yet even
+basic questions regarding the seeds of these objects and their growth mechanism
+remain unanswered. The anticipated launch of eROSITA and ATHENA is expected to
+facilitate observations of high-redshift quasars needed to resolve these
+issues. In this paper, we compare accretion-based supermassive black hole
+growth in the concordance LCDM model with that in the alternative
+Friedmann-Robertson Walker cosmology known as the R_h=ct universe. Previous
+work has shown that the timeline predicted by the latter can account for the
+origin and growth of the > 10^9 M_sol highest redshift quasars better than that
+of the standard model. Here, we significantly advance this comparison by
+determining the soft X-ray flux that would be observed for Eddington-limited
+accretion growth as a function of redshift in both cosmologies. Our results
+indicate that a clear difference emerges between the two in terms of the number
+of detectable quasars at redshift z > 6, raising the expectation that the next
+decade will provide the observational data needed to discriminate between these
+two models based on the number of detected high-redshift quasar progenitors.
+For example, while the upcoming ATHENA mission is expected to detect ~0.16
+(i.e., essentially zero) quasars at z ~ 7 in R_h=ct, it should detect ~160 in
+LCDM---a quantitatively compelling difference.
+",0,1,0,0,0,0
+13632,Decoding the spectroscopic features and timescales of aqueous proton defects,"  Acid solutions exhibit a variety of complex structural and dynamical features
+arising from the presence of multiple interacting reactive proton defects and
+counterions. However, disentangling the transient structural motifs of proton
+defects in the water hydrogen bond network and the mechanisms for their
+interconversion remains a formidable challenge. Here, we use simulations
+treating the quantum nature of both the electrons and nuclei to show how the
+experimentally observed spectroscopic features and relaxation timescales can be
+elucidated using a physically transparent coordinate that encodes the overall
+asymmetry of the solvation environment of the proton defect. We demonstrate
+that this coordinate can be used both to discriminate the extremities of the
+features observed in the linear vibrational spectrum and to explain the
+molecular motions that give rise to the interconversion timescales observed in
+recent nonlinear experiments. This analysis provides a unified condensed-phase
+picture of proton structure and dynamics that, at its extrema, encompasses
+proton sharing and spectroscopic features resembling the limiting Eigen
+[H$_{3}$O(H$_{2}$O)$_{3}$]$^{+}$ and Zundel [H(H$_{2}$O)$_{2}$]$^{+}$ gas-phase
+structures, while also describing the rich variety of interconverting
+environments in the liquid phase.
+",0,1,0,0,0,0
+13633,MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment,"  Generating music has a few notable differences from generating images and
+videos. First, music is an art of time, necessitating a temporal model. Second,
+music is usually composed of multiple instruments/tracks with their own
+temporal dynamics, but collectively they unfold over time interdependently.
+Lastly, musical notes are often grouped into chords, arpeggios or melodies in
+polyphonic music, and thereby introducing a chronological ordering of notes is
+not naturally suitable. In this paper, we propose three models for symbolic
+multi-track music generation under the framework of generative adversarial
+networks (GANs). The three models, which differ in the underlying assumptions
+and accordingly the network architectures, are referred to as the jamming
+model, the composer model and the hybrid model. We trained the proposed models
+on a dataset of over one hundred thousand bars of rock music and applied them
+to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings.
+A few intra-track and inter-track objective metrics are also proposed to
+evaluate the generative results, in addition to a subjective user study. We
+show that our models can generate coherent music of four bars right from
+scratch (i.e. without human inputs). We also extend our models to human-AI
+cooperative music generation: given a specific track composed by human, we can
+generate four additional tracks to accompany it. All code, the dataset and the
+rendered audio samples are available at this https URL .
+",1,0,0,0,0,0
+13634,Geometric Analysis of Synchronization in Neuronal Networks with Global Inhibition and Coupling Delays,"  We study synaptically coupled neuronal networks to identify the role of
+coupling delays in network's synchronized behaviors. We consider a network of
+excitable, relaxation oscillator neurons where two distinct populations, one
+excitatory and one inhibitory, are coupled and interact with each other. The
+excitatory population is uncoupled, while the inhibitory population is tightly
+coupled. A geometric singular perturbation analysis yields existence and
+stability conditions for synchronization states under different firing patterns
+between the two populations, along with formulas for the periods of such
+synchronous solutions. Our results demonstrate that the presence of coupling
+delays in the network promotes synchronization. Numerical simulations are
+conducted to supplement and validate analytical results. We show the results
+carry over to a model for spindle sleep rhythms in thalamocortical networks,
+one of the biological systems which motivated our study. The analysis helps to
+explain how coupling delays in either excitatory or inhibitory synapses
+contribute to producing synchronized rhythms.
+",0,1,0,0,0,0
+13635,Model predictive trajectory optimization and tracking for on-road autonomous vehicles,"  Motion planning for autonomous vehicles requires spatio-temporal motion plans
+(i.e. state trajectories) to account for dynamic obstacles. This requires a
+trajectory tracking control process which faithfully tracks planned
+trajectories. In this paper, a control scheme is presented which first
+optimizes a planned trajectory and then tracks the optimized trajectory using a
+feedback-feedforward controller. The feedforward element is calculated in a
+model predictive manner with a cost function focusing on driving performance.
+Stability of the error dynamic is then guaranteed by the design of the
+feedback-feedforward controller. The tracking performance of the control system
+is tested in a realistic simulated scenario where the control system must track
+an evasive lateral maneuver. The proposed controller performs well in
+simulation and can be easily adapted to different dynamic vehicle models. The
+uniqueness of the solution to the control synthesis eliminates any
+nondeterminism that could arise with switching between numerical solvers for
+the underlying mathematical program.
+",1,0,0,0,0,0
+13636,A Toolbox For Property Checking From Simulation Using Incremental SAT (Extended Abstract),"  We present a tool that primarily supports the ability to check bounded
+properties starting from a sequence of states in a run. The target design is
+compiled into an AIGNET which is then selectively and iteratively translated
+into an incremental SAT instance in which clauses are added for new terms and
+simplified by the assignment of existing literals. Additional applications of
+the tool can be derived by the user providing alternative attachments of
+constrained functions which guide the iterations and SAT checks performed. Some
+Verilog RTL examples are included for reference.
+",1,0,0,0,0,0
+13637,Machine Learning of Linear Differential Equations using Gaussian Processes,"  This work leverages recent advances in probabilistic machine learning to
+discover conservation laws expressed by parametric linear equations. Such
+equations involve, but are not limited to, ordinary and partial differential,
+integro-differential, and fractional order operators. Here, Gaussian process
+priors are modified according to the particular form of such operators and are
+employed to infer parameters of the linear equations from scarce and possibly
+noisy observations. Such observations may come from experiments or ""black-box""
+computer simulations.
+",1,0,1,1,0,0
+13638,YouTube-8M Video Understanding Challenge Approach and Applications,"  This paper introduces the YouTube-8M Video Understanding Challenge hosted as
+a Kaggle competition and also describes my approach to experimenting with
+various models. For each of my experiments, I provide the score result as well
+as possible improvements to be made. Towards the end of the paper, I discuss
+the various ensemble learning techniques that I applied on the dataset which
+significantly boosted my overall competition score. At last, I discuss the
+exciting future of video understanding research and also the many applications
+that such research could significantly improve.
+",0,0,0,1,0,0
+13639,Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients,"  Sepsis is a condition caused by the body's overwhelming and life-threatening
+response to infection, which can lead to tissue damage, organ failure, and
+finally death. Common signs and symptoms include fever, increased heart rate,
+increased breathing rate, and confusion. Sepsis is difficult to predict,
+diagnose, and treat. Patients who develop sepsis have an increased risk of
+complications and death and face higher health care costs and longer
+hospitalization. Today, sepsis is one of the leading causes of mortality among
+populations in intensive care units (ICUs). In this paper, we look at the
+problem of early detection of sepsis by using temporal data mining. We focus on
+the use of knowledge-based temporal abstraction to create meaningful
+interval-based abstractions, and on time-interval mining to discover frequent
+interval-based patterns. We used 2,560 cases derived from the MIMIC-III
+database. We found that the distribution of the temporal patterns whose
+frequency is above 10% discovered in the records of septic patients during the
+last 6 and 12 hours before onset of sepsis is significantly different from that
+distribution within a similar period, during an equivalent time window during
+hospitalization, in the records of non-septic patients. This discovery is
+encouraging for the purpose of performing an early diagnosis of sepsis using
+the discovered patterns as constructed features.
+",1,0,0,1,0,0
+13640,On the use of the energy probability distribution zeros in the study of phase transitions,"  This contribution is devoted to cover some technical aspects related to the
+use of the recently proposed energy probability distribution zeros in the study
+of phase transitions. This method is based on the partial knowledge of the
+partition function zeros and has been shown to be extremely efficient to
+precisely locate phase transition temperatures. It is based on an iterative
+method in such a way that the transition temperature can be approached at will.
+The iterative method will be detailed and some convergence issues that has been
+observed in its application to the 2D Ising model and to an artificial spin ice
+model will be shown, together with ways to circumvent them.
+",0,1,0,0,0,0
+13641,Visibility-based Power Spectrum Estimation for Low-Frequency Radio Interferometric Observations,"  We present a visibility based estimator namely, the Tapered Gridded Estimator
+(TGE) to estimate the power spectrum of the diffuse sky signal. The TGE has
+three novel features. First, the estimator uses gridded visibilities to
+estimate the power spectrum which is computationally much faster than
+individually correlating the visibilities. Second, a positive noise bias is
+removed by subtracting the auto-correlation of the visibilities which is
+responsible for the noise bias. Third, the estimator allows us to taper the
+field of view so as to suppress the contribution from the sources in the outer
+regions and the sidelobes of the telescope's primary beam. We first consider
+the two dimensional (2D) TGE to estimate the angular power spectrum $C_{\ell}$.
+We have also extended the TGE to estimate the three dimensional (3D) power
+spectrum $P({\bf k})$ of the cosmological 21-cm signal. Analytic formulas are
+presented for predicting the variance of the binned power spectrum. Both the
+estimators and their variance predictions are validated using simulations of
+$150 \, {\rm MHz}$ GMRT observations. We have applied the 2D TGE to estimate
+$C_{\ell}$ using visibility data for two of the fields observed by TIFR GMRT
+Sky Survey (TGSS). We find that the sky signal, after subtracting the point
+sources, is likely dominated by the diffuse Galactic synchrotron radiation
+across the angular multipole range $240 \le \ell \lesssim 500$.
+",0,1,0,0,0,0
+13642,Archiving Software Surrogates on the Web for Future Reference,"  Software has long been established as an essential aspect of the scientific
+process in mathematics and other disciplines. However, reliably referencing
+software in scientific publications is still challenging for various reasons. A
+crucial factor is that software dynamics with temporal versions or states are
+difficult to capture over time. We propose to archive and reference surrogates
+instead, which can be found on the Web and reflect the actual software to a
+remarkable extent. Our study shows that about a half of the webpages of
+software are already archived with almost all of them including some kind of
+documentation.
+",1,0,0,0,0,0
+13643,Exact Formulas for the Generalized Sum-of-Divisors Functions,"  We prove new exact formulas for the generalized sum-of-divisors functions.
+The formulas for $\sigma_{\alpha}(x)$ when $\alpha \in \mathbb{C}$ is fixed and
+$x \geq 1$ involves a finite sum over all of the prime factors $n \leq x$ and
+terms involving the $r$-order harmonic number sequences. The generalized
+harmonic number sequences correspond to the partial sums of the Riemann zeta
+function when $r > 1$ and are related to the generalized Bernoulli numbers when
+$r \leq 0$ is integer-valued. A key part of our expansions of the Lambert
+series generating functions for the generalized divisor functions is formed by
+taking logarithmic derivatives of the cyclotomic polynomials, $\Phi_n(q)$,
+which completely factorize the Lambert series terms $(1-q^n)^{-1}$ into
+irreducible polynomials in $q$. We also consider applications of our new
+results to asymptotic approximations for sums over these divisor functions and
+to the forms of perfect numbers defined by the special case of the divisor
+function, $\sigma(n)$, when $\alpha := 1$.
+Keywords: divisor function; sum-of-divisors function; Lambert series; perfect
+number.
+MSC (2010): 30B50; 11N64; 11B83
+",0,0,1,0,0,0
+13644,On addition theorems related to elliptic integrals,"  This paper provides some explicit formulas related to addition theorems for
+elliptic integrals $\int_0^x dt/R(t)$, where $R(t)$ is the square root from a
+polynomial of degree 4. These integrals are related to complex elliptic genera
+and are motivated by Euler's addition theorem for elliptic integrals of the
+first kind.
+",0,0,1,0,0,0
+13645,Manuscripts in Time and Space: Experiments in Scriptometrics on an Old French Corpus,"  Witnesses of medieval literary texts, preserved in manuscript, are layered
+objects , being almost exclusively copies of copies. This results in multiple
+and hard to distinguish linguistic strata -- the author's scripta interacting
+with the scriptae of the various scribes -- in a context where literary written
+language is already a dialectal hybrid. Moreover, no single linguistic
+phenomenon allows to distinguish between different scriptae, and only the
+combination of multiple characteristics is likely to be significant [9] -- but
+which ones? The most common approach is to search for these features in a set
+of previously selected texts, that are supposed to be representative of a given
+scripta. This can induce a circularity, in which texts are used to select
+features that in turn characterise them as belonging to a linguistic area. To
+counter this issue, this paper offers an unsupervised and corpus-based
+approach, in which clustering methods are applied to an Old French corpus to
+identify main divisions and groups. Ultimately, scriptometric profiles are
+built for each of them.
+",0,0,0,1,0,0
+13646,"Vertical Bifacial Solar Farms: Physics, Design, and Global Optimization","  There have been sustained interest in bifacial solar cell technology since
+1980s, with prospects of 30-50% increase in the output power from a stand-alone
+single panel. Moreover, a vertical bifacial panel reduces dust accumulation and
+provides two output peaks during the day, with the second peak aligned to the
+peak electricity demand. Recent commercialization and anticipated growth of
+bifacial panel market have encouraged a closer scrutiny of the integrated
+power-output and economic viability of bifacial solar farms, where mutual
+shading will erode some of the anticipated energy gain associated with an
+isolated, single panel. Towards that goal, in this paper we focus on
+geography-specific optimizations of ground mounted vertical bifacial solar
+farms for the entire world. For local irradiance, we combine the measured
+meteorological data with the clear-sky model. In addition, we consider the
+detailed effects of direct, diffuse, and albedo light. We assume the panel is
+configured into sub-strings with bypass-diodes. Based on calculated light
+collection and panel output, we analyze the optimum farm design for maximum
+yearly output at any given location in the world. Our results predict that,
+regardless of the geographical location, a vertical bifacial farm will yield
+10-20% more energy than a traditional monofacial farm for a practical
+row-spacing of 2m (1.2m high panels). With the prospect of additional 5-20%
+energy gain from reduced soiling and tilt optimization, bifacial solar farm do
+offer a viable technology option for large-scale solar energy generation.
+",0,1,0,0,0,0
+13647,Maximally Correlated Principal Component Analysis,"  In the era of big data, reducing data dimensionality is critical in many
+areas of science. Widely used Principal Component Analysis (PCA) addresses this
+problem by computing a low dimensional data embedding that maximally explain
+variance of the data. However, PCA has two major weaknesses. Firstly, it only
+considers linear correlations among variables (features), and secondly it is
+not suitable for categorical data. We resolve these issues by proposing
+Maximally Correlated Principal Component Analysis (MCPCA). MCPCA computes
+transformations of variables whose covariance matrix has the largest Ky Fan
+norm. Variable transformations are unknown, can be nonlinear and are computed
+in an optimization. MCPCA can also be viewed as a multivariate extension of
+Maximal Correlation. For jointly Gaussian variables we show that the covariance
+matrix corresponding to the identity (or the negative of the identity)
+transformations majorizes covariance matrices of non-identity functions. Using
+this result we characterize global MCPCA optimizers for nonlinear functions of
+jointly Gaussian variables for every rank constraint. For categorical variables
+we characterize global MCPCA optimizers for the rank one constraint based on
+the leading eigenvector of a matrix computed using pairwise joint
+distributions. For a general rank constraint we propose a block coordinate
+descend algorithm and show its convergence to stationary points of the MCPCA
+optimization. We compare MCPCA with PCA and other state-of-the-art
+dimensionality reduction methods including Isomap, LLE, multilayer autoencoders
+(neural networks), kernel PCA, probabilistic PCA and diffusion maps on several
+synthetic and real datasets. We show that MCPCA consistently provides improved
+performance compared to other methods.
+",1,0,0,1,0,0
+13648,$\mathcal{G}$-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space,"  It is well known that neural networks with rectified linear units (ReLU)
+activation functions are positively scale-invariant. Conventional algorithms
+like stochastic gradient descent optimize the neural networks in the vector
+space of weights, which is, however, not positively scale-invariant. This
+mismatch may lead to problems during the optimization process. Then, a natural
+question is: \emph{can we construct a new vector space that is positively
+scale-invariant and sufficient to represent ReLU neural networks so as to
+better facilitate the optimization process }? In this paper, we provide our
+positive answer to this question. First, we conduct a formal study on the
+positive scaling operators which forms a transformation group, denoted as
+$\mathcal{G}$. We show that the value of a path (i.e. the product of the
+weights along the path) in the neural network is invariant to positive scaling
+and prove that the value vector of all the paths is sufficient to represent the
+neural networks under mild conditions. Second, we show that one can identify
+some basis paths out of all the paths and prove that the linear span of their
+value vectors (denoted as $\mathcal{G}$-space) is an invariant space with lower
+dimension under the positive scaling group. Finally, we design stochastic
+gradient descent algorithm in $\mathcal{G}$-space (abbreviated as
+$\mathcal{G}$-SGD) to optimize the value vector of the basis paths of neural
+networks with little extra cost by leveraging back-propagation. Our experiments
+show that $\mathcal{G}$-SGD significantly outperforms the conventional SGD
+algorithm in optimizing ReLU networks on benchmark datasets.
+",0,0,0,1,0,0
+13649,Fantastic deductive systems in probability theory on generalizations of fuzzy structures,"  The aim of this paper is to introduce the notion of fantastic deductive
+systems on generalizations of fuzzy structures, and to emphasize their role in
+the probability theory on these algebras. We give a characterization of
+commutative pseudo-BE algebras and we generalize an axiom system consisting of
+four identities to the case of commutative pseudo-BE algebras. We define the
+fantastic deductive systems of pseudo-BE algebras and we investigate their
+properties. It is proved that, if a pseudo-BE(A) algebra $A$ is commutative,
+then all deductive systems of $A$ are fantastic. Moreover, we generalize the
+notions of measures, state-measures and measure-morphisms to the case of
+pseudo-BE algebras and we also prove that there is a one-to-one correspondence
+between the set of all Bosbach states on a bounded pseudo-BE algebra and the
+set of its state-measures. The notions of internal states and state-morphism
+operators on pseudo-BCK algebras are extended to the case of pseudo-BE algebras
+and we also prove that any type II state operator on a pseudo-BE algebra is a
+state-morphism operator on it. The notions of pseudo-valuation and commutative
+pseudo-valuation on pseudo-BE algebras are defined and investigated. For the
+case of commutative pseudo-BE algebras we prove that the two kind of
+pseudo-valuations coincide. Characterizations of pseudo-valuations and
+commutative pseudo-valuations are given. We show that the kernel of a Bosbach
+state (state-morphism, measure, type II state operator, pseudo-valuation) is a
+fantastic deductive system.
+",0,0,1,0,0,0
+13650,Bayesian Learning of Consumer Preferences for Residential Demand Response,"  In coming years residential consumers will face real-time electricity tariffs
+with energy prices varying day to day, and effective energy saving will require
+automation - a recommender system, which learns consumer's preferences from her
+actions. A consumer chooses a scenario of home appliance use to balance her
+comfort level and the energy bill. We propose a Bayesian learning algorithm to
+estimate the comfort level function from the history of appliance use. In
+numeric experiments with datasets generated from a simulation model of a
+consumer interacting with small home appliances the algorithm outperforms
+popular regression analysis tools. Our approach can be extended to control an
+air heating and conditioning system, which is responsible for up to half of a
+household's energy bill.
+",1,0,0,1,0,0
+13651,Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits,"  Recent work on follow the perturbed leader (FTPL) algorithms for the
+adversarial multi-armed bandit problem has highlighted the role of the hazard
+rate of the distribution generating the perturbations. Assuming that the hazard
+rate is bounded, it is possible to provide regret analyses for a variety of
+FTPL algorithms for the multi-armed bandit problem. This paper pushes the
+inquiry into regret bounds for FTPL algorithms beyond the bounded hazard rate
+condition. There are good reasons to do so: natural distributions such as the
+uniform and Gaussian violate the condition. We give regret bounds for both
+bounded support and unbounded support distributions without assuming the hazard
+rate condition. We also disprove a conjecture that the Gaussian distribution
+cannot lead to a low-regret algorithm. In fact, it turns out that it leads to
+near optimal regret, up to logarithmic factors. A key ingredient in our
+approach is the introduction of a new notion called the generalized hazard
+rate.
+",1,0,0,1,0,0
+13652,Calculation of the bulk modulus of mixed ionic crystal NH_4Cl_{1-x}Br_x,"  The ammonium halides present an interesting system for study in view of their
+polymorphism and the possible internal rotation of the ammonium ion. The static
+properties of the mixed ionic crystal NH$_4$Cl$_{1-x}$Br$_x$ have been recently
+investigated, using three-body potential model (TDPM) by the application of
+Vegard's law. Here, by using a simple theoretical model, we estimate the bulk
+modulus of their ternary alloys NH$_4$Cl$_{1-x}$Br$_x$, in terms of the bulk
+modulus of the end members alone. The calculated values are comparable to those
+deduced from the three-body potential model (TDPM) by the application of
+Vegard's law.
+",0,1,0,0,0,0
+13653,Hyers-Ulam stability of elliptic Möbius difference equation,"  The linear fractional map $ f(z) = \frac{az+ b}{cz + d} $ on the Riemann
+sphere with complex coefficients $ ad-bc \neq 0 $ is called Möbius map. If $
+f $ satisfies $ ad-bc=1 $ and $ -2<a+d<2 $, then $ f $ is called
+$\textit{elliptic}$ Möbius map. Let $ \{ b_n \}_{n \in \mathbb{N}_0} $ be the
+solution of the elliptic Möbius difference equation $ b_{n+1} = f(b_n) $ for
+every $ n \in \mathbb{N}_0 $. Then the sequence $ \{ b_n \}_{n \in
+\mathbb{N}_0} $ has no Hyers-Ulam stability.
+",0,0,1,0,0,0
+13654,"Diffeological, Frölicher, and Differential Spaces","  Differential calculus on Euclidean spaces has many generalisations. In
+particular, on a set $X$, a diffeological structure is given by maps from open
+subsets of Euclidean spaces to $X$, a differential structure is given by maps
+from $X$ to $\mathbb{R}$, and a Frölicher structure is given by maps from
+$\mathbb{R}$ to $X$ as well as maps from $X$ to $\mathbb{R}$. We illustrate the
+relations between these structures through examples.
+",0,0,1,0,0,0
+13655,Elementary abelian subgroups in some special p-groups,"  Let $P$ be a finite $p$-group and $p$ be an odd prime. Let
+$\mathcal{A}_p(P)_{\geq2}$ be a poset consisting of elementary abelian
+subgroups of rank at least 2. If the derived subgroup $P'\cong C_p\times C_p$,
+then the spheres occurring in $\mathcal{A}_p(P)_{\geq2}$ all have the same
+dimension.
+",0,0,1,0,0,0
+13656,Learning Plannable Representations with Causal InfoGAN,"  In recent years, deep generative models have been shown to 'imagine'
+convincing high-dimensional observations such as images, audio, and even video,
+learning directly from raw data. In this work, we ask how to imagine
+goal-directed visual plans -- a plausible sequence of observations that
+transition a dynamical system from its current configuration to a desired goal
+state, which can later be used as a reference trajectory for control. We focus
+on systems with high-dimensional observations, such as images, and propose an
+approach that naturally combines representation learning and planning. Our
+framework learns a generative model of sequential observations, where the
+generative process is induced by a transition in a low-dimensional planning
+model, and an additional noise. By maximizing the mutual information between
+the generated observations and the transition in the planning model, we obtain
+a low-dimensional representation that best explains the causal nature of the
+data. We structure the planning model to be compatible with efficient planning
+algorithms, and we propose several such models based on either discrete or
+continuous states. Finally, to generate a visual plan, we project the current
+and goal observations onto their respective states in the planning model, plan
+a trajectory, and then use the generative model to transform the trajectory to
+a sequence of observations. We demonstrate our method on imagining plausible
+visual plans of rope manipulation.
+",1,0,0,1,0,0
+13657,SGD Learns the Conjugate Kernel Class of the Network,"  We show that the standard stochastic gradient decent (SGD) algorithm is
+guaranteed to learn, in polynomial time, a function that is competitive with
+the best function in the conjugate kernel space of the network, as defined in
+Daniely, Frostig and Singer. The result holds for log-depth networks from a
+rich family of architectures. To the best of our knowledge, it is the first
+polynomial-time guarantee for the standard neural network learning algorithm
+for networks of depth more that two.
+As corollaries, it follows that for neural networks of any depth between $2$
+and $\log(n)$, SGD is guaranteed to learn, in polynomial time, constant degree
+polynomials with polynomially bounded coefficients. Likewise, it follows that
+SGD on large enough networks can learn any continuous function (not in
+polynomial time), complementing classical expressivity results.
+",1,0,0,1,0,0
+13658,Towards information optimal simulation of partial differential equations,"  Most simulation schemes for partial differential equations (PDEs) focus on
+minimizing a simple error norm of a discretized version of a field. This paper
+takes a fundamentally different approach; the discretized field is interpreted
+as data providing information about a real physical field that is unknown. This
+information is sought to be conserved by the scheme as the field evolves in
+time. Such an information theoretic approach to simulation was pursued before
+by information field dynamics (IFD). In this paper we work out the theory of
+IFD for nonlinear PDEs in a noiseless Gaussian approximation. The result is an
+action that can be minimized to obtain an informationally optimal simulation
+scheme. It can be brought into a closed form using field operators to calculate
+the appearing Gaussian integrals. The resulting simulation schemes are tested
+numerically in two instances for the Burgers equation. Their accuracy surpasses
+finite-difference schemes on the same resolution. The IFD scheme, however, has
+to be correctly informed on the subgrid correlation structure. In certain
+limiting cases we recover well-known simulation schemes like spectral Fourier
+Galerkin methods. We discuss implications of the approximations made.
+",0,1,0,1,0,0
+13659,Verifying Quantum Programs: From Quipper to QPMC,"  In this paper we present a translation from the quantum programming language
+Quipper to the QPMC model checker, with the main aim of verifying Quipper
+programs. Quipper is an embedded functional programming language for quantum
+computation. It is above all a circuit description language, for this reason it
+uses the vector state formalism and its main purpose is to make circuit
+implementation easy providing high level operations for circuit manipulation.
+Quipper provides both an high-level circuit building interface and a simulator.
+QPMC is a model checker for quantum protocols based on the density matrix
+formalism. QPMC extends the probabilistic model checker IscasMC allowing to
+formally verify properties specified in the temporal logic QCTL on Quantum
+Markov Chains. We implemented and tested our translation on several quantum
+algorithms, including Grover's quantum search.
+",1,0,0,0,0,0
+13660,Topological semimetal state and field-induced Fermi surface reconstruction in antiferromagnetic monopnictide NdSb,"  We report the experimental realization of Dirac semimetal state in NdSb, a
+material with antiferromagnetic ground state. The occurrence of topological
+semimetal state has been well supported by our band structure calculations and
+the experimental observation of chiral anomaly induced negative
+magnetoresistance. A field-induced Fermi surface reconstruction is observed, in
+response to the change of spin polarization. The observation of topological
+semimetal state in a magnetic material provides an opportunity to investigate
+the magneto-topological phenomena.
+",0,1,0,0,0,0
+13661,"Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs","  Understanding the 3D structure of a scene is of vital importance, when it
+comes to developing fully autonomous robots. To this end, we present a novel
+deep learning based framework that estimates depth, surface normals and surface
+curvature by only using a single RGB image. To the best of our knowledge this
+is the first work to estimate surface curvature from colour using a machine
+learning approach. Additionally, we demonstrate that by tuning the network to
+infer well designed features, such as surface curvature, we can achieve
+improved performance at estimating depth and normals.This indicates that
+network guidance is still a useful aspect of designing and training a neural
+network. We run extensive experiments where the network is trained to infer
+different tasks while the model capacity is kept constant resulting in
+different feature maps based on the tasks at hand. We outperform the previous
+state-of-the-art benchmarks which jointly estimate depths and surface normals
+while predicting surface curvature in parallel.
+",1,0,0,0,0,0
+13662,Motif and Hypergraph Correlation Clustering,"  Motivated by applications in social and biological network analysis, we
+introduce a new form of agnostic clustering termed~\emph{motif correlation
+clustering}, which aims to minimize the cost of clustering errors associated
+with both edges and higher-order network structures. The problem may be
+succinctly described as follows: Given a complete graph $G$, partition the
+vertices of the graph so that certain predetermined `important' subgraphs
+mostly lie within the same cluster, while `less relevant' subgraphs are allowed
+to lie across clusters. Our contributions are as follows: We first introduce
+several variants of motif correlation clustering and then show that these
+clustering problems are NP-hard. We then proceed to describe polynomial-time
+clustering algorithms that provide constant approximation guarantees for the
+problems at hand. Despite following the frequently used LP relaxation and
+rounding procedure, the algorithms involve a sophisticated and carefully
+designed neighborhood growing step that combines information about both edge
+and motif structures. We conclude with several examples illustrating the
+performance of the developed algorithms on synthetic and real networks.
+",1,0,0,0,0,0
+13663,Adversarial Imitation via Variational Inverse Reinforcement Learning,"  We consider a problem of learning the reward and policy from expert examples
+under unknown dynamics in high-dimensional scenarios. Our proposed method
+builds on the framework of generative adversarial networks and introduces the
+empowerment-regularized maximum-entropy inverse reinforcement learning to learn
+near-optimal rewards and policies. Empowerment-based regularization prevents
+the policy from overfitting expert demonstration, thus leads to a generalized
+behavior which results in learning near-optimal rewards. Our method
+simultaneously learns empowerment through variational information maximization
+along with the reward and policy under the adversarial learning formulation. We
+evaluate our approach on various high-dimensional complex control tasks. We
+also test our learned rewards in challenging transfer learning problems where
+training and testing environments are made to be different from each other in
+terms of dynamics or structure. The results show that our proposed method not
+only learns near-optimal rewards and policies that are matching expert behavior
+but also performs significantly better than state-of-the-art inverse
+reinforcement learning algorithms.
+",1,0,0,1,0,0
+13664,Intrinsically motivated reinforcement learning for human-robot interaction in the real-world,"  For a natural social human-robot interaction, it is essential for a robot to
+learn the human-like social skills. However, learning such skills is
+notoriously hard due to the limited availability of direct instructions from
+people to teach a robot. In this paper, we propose an intrinsically motivated
+reinforcement learning framework in which an agent gets the intrinsic
+motivation-based rewards through the action-conditional predictive model. By
+using the proposed method, the robot learned the social skills from the
+human-robot interaction experiences gathered in the real uncontrolled
+environments. The results indicate that the robot not only acquired human-like
+social skills but also took more human-like decisions, on a test dataset, than
+a robot which received direct rewards for the task achievement.
+",1,0,0,0,0,0
+13665,Beyond Backprop: Online Alternating Minimization with Auxiliary Variables,"  We propose a novel online alternating minimization (AltMin) algorithm for
+training deep neural networks, provide theoretical convergence guarantees and
+demonstrate its advantages on several classification tasks as compared both to
+standard backpropagation with stochastic gradient descent (backprop-SGD) and to
+offline alternating minimization. The key difference from backpropagation is an
+explicit optimization over hidden activations, which eliminates gradient chain
+computation in backprop, and breaks the weight training problem into
+independent, local optimization subproblems; this allows to avoid vanishing
+gradient issues, simplify handling non-differentiable nonlinearities, and
+perform parallel weight updates across the layers. Moreover, parallel local
+synaptic weight optimization with explicit activation propagation is a step
+closer to a more biologically plausible learning model than backpropagation,
+whose biological implausibility has been frequently criticized. Finally, the
+online nature of our approach allows to handle very large datasets, as well as
+continual, lifelong learning, which is our key contribution on top of recently
+proposed offline alternating minimization schemes (e.g., (Carreira-Perpinan
+andWang 2014), (Taylor et al. 2016)).
+",0,0,0,1,0,0
+13666,Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering,"  Sensing in complex systems requires large-scale information exchange and
+on-the-go communications over heterogeneous networks and integrated processing
+platforms. Many networked cyber-physical systems exhibit hierarchical
+infrastructures of information flows, which naturally leads to a multi-level
+tree-like information structure in which each level corresponds to a particular
+scale of representation. This work focuses on the multiscale fusion of data
+collected at multiple levels of the system. We propose a multiscale state-space
+model to represent multi-resolution data over the hierarchical information
+system and formulate a multi-stage dynamic zero-sum game to design a
+multi-scale $H_{\infty}$ robust filter. We present numerical experiments for
+one and two-dimensional signals and provide a comparative analysis of the
+minimax filter with the standard Kalman filter to show the improvement in
+signal-to-noise ratio (SNR).
+",1,0,0,0,0,0
+13667,Topology of irrationally indifferent attractors,"  We study the attractors of a class of holomorphic systems with an
+irrationally indifferent fixed point. We prove a trichotomy for the topology of
+the attractor based on the arithmetic of the rotation number at the fixed
+point. That is, the attractor is either a Jordan curve, a one-sided hairy
+circle, or a Cantor bouquet. This has a number of remarkable corollaries on a
+conjecture of M. Herman about the optimal arithmetic condition for the
+existence of a critical point on the boundary of the Siegel disk, and a
+conjecture of A. Douady on the topology of the boundary of Siegel disks.
+Combined with earlier results on the topic, this completes the topological
+description of the behaviors of typical orbits near such fixed points, when the
+rotation number is of high type.
+",0,0,1,0,0,0
+13668,Crystalline Soda Can Metamaterial exhibiting Graphene-like Dispersion at subwavelength scale,"  Graphene, a honeycomb lattice of carbon atoms ruled by tight-binding
+interaction, exhibits extraordinary electronic properties due to the presence
+of Dirac cones within its band structure. These intriguing singularities have
+naturally motivated the discovery of their classical analogues. In this work,
+we present a general and direct procedure to reproduce the peculiar physics of
+graphene within a very simple acoustic metamaterial: a double lattice of soda
+cans resonant at two different frequencies. The first triangular sub-lattice
+generates a bandgap at low frequency, which induces a tight-binding coupling
+between the resonant defects of the second Honeycomb one, hence allowing us to
+obtain a graphene-like band structure. We prove the relevance of this approach
+by showing that both numerical and experimental dispersion relations exhibit
+the requested Dirac cone. We also demonstrate the straightforward monitoring of
+the coupling strength within the crystal of resonant defects. This work shows
+that crystalline metamaterials are very promising candidates to investigate
+tantalizing solid-state physics phenomena with classical waves.
+",0,1,0,0,0,0
+13669,Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines,"  The retina is a complex nervous system which encodes visual stimuli before
+higher order processing occurs in the visual cortex. In this study we evaluated
+whether information about the stimuli received by the retina can be retrieved
+from the firing rate distribution of Retinal Ganglion Cells (RGCs), exploiting
+High-Density 64x64 MEA technology. To this end, we modeled the RGC population
+activity using mean-covariance Restricted Boltzmann Machines, latent variable
+models capable of learning the joint distribution of a set of continuous
+observed random variables and a set of binary unobserved random units. The idea
+was to figure out if binary latent states encode the regularities associated to
+different visual stimuli, as modes in the joint distribution. We measured the
+goodness of mcRBM encoding by calculating the Mutual Information between the
+latent states and the stimuli shown to the retina. Results show that binary
+states can encode the regularities associated to different stimuli, using both
+gratings and natural scenes as stimuli. We also discovered that hidden
+variables encode interesting properties of retinal activity, interpreted as
+population receptive fields. We further investigated the ability of the model
+to learn different modes in population activity by comparing results associated
+to a retina in normal conditions and after pharmacologically blocking GABA
+receptors (GABAC at first, and then also GABAA and GABAB). As expected, Mutual
+Information tends to decrease if we pharmacologically block receptors. We
+finally stress that the computational method described in this work could
+potentially be applied to any kind of neural data obtained through MEA
+technology, though different techniques should be applied to interpret the
+results.
+",1,0,0,0,0,0
+13670,Stochastic partial differential fluid equations as a diffusive limit of deterministic Lagrangian multi-time dynamics,"  In {\em{Holm}, Proc. Roy. Soc. A 471 (2015)} stochastic fluid equations were
+derived by employing a variational principle with an assumed stochastic
+Lagrangian particle dynamics. Here we show that the same stochastic Lagrangian
+dynamics naturally arises in a multi-scale decomposition of the deterministic
+Lagrangian flow map into a slow large-scale mean and a rapidly fluctuating
+small scale map. We employ homogenization theory to derive effective slow
+stochastic particle dynamics for the resolved mean part, thereby justifying
+stochastic fluid partial equations in the Eulerian formulation. To justify the
+application of rigorous homogenization theory, we assume mildly chaotic fast
+small-scale dynamics, as well as a centering condition. The latter requires
+that the mean of the fluctuating deviations is small, when pulled back to the
+mean flow.
+",0,1,1,0,0,0
+13671,A general framework for solving convex optimization problems involving the sum of three convex functions,"  In this paper, we consider solving a class of convex optimization problem
+which minimizes the sum of three convex functions $f(x)+g(x)+h(Bx)$, where
+$f(x)$ is differentiable with a Lipschitz continuous gradient, $g(x)$ and
+$h(x)$ have a closed-form expression of their proximity operators and $B$ is a
+bounded linear operator. This type of optimization problem has wide application
+in signal recovery and image processing. To make full use of the
+differentiability function in the optimization problem, we take advantage of
+two operator splitting methods: the forward-backward splitting method and the
+three operator splitting method. In the iteration scheme derived from the two
+operator splitting methods, we need to compute the proximity operator of $g+h
+\circ B$ and $h \circ B$, respectively. Although these proximity operators do
+not have a closed-form solution in general, they can be solved very
+efficiently. We mainly employ two different approaches to solve these proximity
+operators: one is dual and the other is primal-dual. Following this way, we
+fortunately find that three existing iterative algorithms including Condat and
+Vu algorithm, primal-dual fixed point (PDFP) algorithm and primal-dual three
+operator (PD3O) algorithm are a special case of our proposed iterative
+algorithms. Moreover, we discover a new kind of iterative algorithm to solve
+the considered optimization problem, which is not covered by the existing ones.
+Under mild conditions, we prove the convergence of the proposed iterative
+algorithms. Numerical experiments applied on fused Lasso problem, constrained
+total variation regularization in computed tomography (CT) image reconstruction
+and low-rank total variation image super-resolution problem demonstrate the
+effectiveness and efficiency of the proposed iterative algorithms.
+",0,0,1,0,0,0
+13672,Testing isotropy in the Two Micron All-Sky redshift survey with information entropy,"  We use information entropy to test the isotropy in the nearby galaxy
+distribution mapped by the Two Micron All-Sky redshift survey (2MRS). We find
+that the galaxy distribution is highly anisotropic on small scales. The radial
+anisotropy gradually decreases with increasing length scales and the observed
+anisotropy is consistent with that expected for an isotropic Poisson
+distribution beyond a length scale of $90 \, h^{-1}\, {\rm Mpc}$. Using mock
+catalogues from N-body simulations, we find that the galaxy distribution in the
+2MRS exhibits a degree of anisotropy compatible with that of the $\Lambda$CDM
+model after accounting for the clustering bias of the 2MRS galaxies. We also
+quantify the polar and azimuthal anisotropies and identify two directions
+$(l,b)=(150^{\circ}, -15^{\circ})$, $(l,b)=(310^{\circ},-15^{\circ})$ which are
+significantly anisotropic compared to the other directions in the sky. We
+suggest that their preferential orientations on the sky may indicate a possible
+alignment of the Local Group with two nearby large scale structures. Despite
+the differences in the degree of anisotropy on small scales, we find that the
+galaxy distributions in both the 2MRS and the $\Lambda$CDM model are isotropic
+on a scale of $90 \, h^{-1}\, {\rm Mpc}$.
+",0,1,0,0,0,0
+13673,VEGAS: A VST Early-type GAlaxy Survey. II. Photometric study of giant ellipticals and their stellar halos,"  Observations of diffuse starlight in the outskirts of galaxies are thought to
+be a fundamental source of constraints on the cosmological context of galaxy
+assembly in the $\Lambda$CDM model. Such observations are not trivial because
+of the extreme faintness of such regions. In this work, we investigate the
+photometric properties of six massive early type galaxies (ETGs) in the VEGAS
+sample (NGC 1399, NGC 3923, NGC 4365, NGC 4472, NGC 5044, and NGC 5846) out to
+extremely low surface brightness levels, with the goal of characterizing the
+global structure of their light profiles for comparison to state-of-the-art
+galaxy formation models. We carry out deep and detailed photometric mapping of
+our ETG sample taking advantage of deep imaging with VST/OmegaCAM in the g and
+i bands. By fitting the light profiles, and comparing the results to
+simulations of elliptical galaxy assembly, we identify signatures of a
+transition between ""relaxed"" and ""unrelaxed"" accreted components and can
+constrain the balance between in situ and accreted stars. The very good
+agreement of our results with predictions from theoretical simulations
+demonstrates that the full VEGAS sample of $\sim 100$ ETGs will allow us to use
+the distribution of diffuse light as a robust statistical probe of the
+hierarchical assembly of massive galaxies.
+",0,1,0,0,0,0
+13674,Homological dimension formulas for trivial extension algebras,"  Let $A= \Lambda \oplus C$ be a trivial extension algebra. The aim of this
+paper is to establish formulas for the projective dimension and the injective
+dimension for a certain class of $A$-modules which is expressed by using the
+derived functors $- \otimes^{\mathbb{L}}_{\Lambda}C$ and
+$\mathbb{R}\text{Hom}_{\Lambda}(C, -)$. Consequently, we obtain formulas for
+the global dimension of $A$, which gives a modern expression of the classical
+formula for the global dimension by Palmer-Roos and Löfwall that is written
+in complicated classical derived functors.
+The main application of the formulas is to give a necessary and sufficient
+condition for $A$ to be an Iwanaga-Gorenstein algebra.
+We also give a description of the kernel $\text{Ker} \varpi$ of the canonical
+functor $\varpi: \mathsf{D}^{\mathrm{b}}(\text{mod} \Lambda) \to
+\text{Sing}^{\mathbb{Z}} A$ in the case $\text{pd} C < \infty$.
+",0,0,1,0,0,0
+13675,Spread of entanglement in a Sachdev-Ye-Kitaev chain,"  We study the spread of Rényi entropy between two halves of a
+Sachdev-Ye-Kitaev (SYK) chain of Majorana fermions, prepared in a thermofield
+double (TFD) state. The SYK chain model is a model of chaotic many-body
+systems, which describes a one-dimensional lattice of Majorana fermions, with
+spatially local random quartic interaction. We find that for integer Rényi
+index $n>1$, the Rényi entanglement entropy saturates at a parametrically
+smaller value than expected. This implies that the TFD state of the SYK chain
+does not rapidly thermalize, despite being maximally chaotic: instead, it
+rapidly approaches a prethermal state. We compare our results to the signatures
+of thermalization observed in other quenches in the SYK model, and to intuition
+from nearly-$\mathrm{AdS}_2$ gravity.
+",0,1,0,0,0,0
+13676,The weak order on integer posets,"  We explore lattice structures on integer binary relations (i.e. binary
+relations on the set $\{1, 2, \dots, n\}$ for a fixed integer $n$) and on
+integer posets (i.e. partial orders on the set $\{1, 2, \dots, n\}$ for a fixed
+integer $n$). We first observe that the weak order on the symmetric group
+naturally extends to a lattice structure on all integer binary relations. We
+then show that the subposet of this weak order induced by integer posets
+defines as well a lattice. We finally study the subposets of this weak order
+induced by specific families of integer posets corresponding to the elements,
+the intervals, and the faces of the permutahedron, the associahedron, and some
+recent generalizations of those.
+",0,0,1,0,0,0
+13677,Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization,"  Undetected overfitting can occur when there are significant redundancies
+between training and validation data. We describe AVE, a new measure of
+training-validation redundancy for ligand-based classification problems that
+accounts for the similarity amongst inactive molecules as well as active. We
+investigated seven widely-used benchmarks for virtual screening and
+classification, and show that the amount of AVE bias strongly correlates with
+the performance of ligand-based predictive methods irrespective of the
+predicted property, chemical fingerprint, similarity measure, or
+previously-applied unbiasing techniques. Therefore, it may be that the
+previously-reported performance of most ligand-based methods can be explained
+by overfitting to benchmarks rather than good prospective accuracy.
+",1,0,0,1,0,0
+13678,Inverse dispersion method for calculation of complex photonic band diagram and $\cal{PT}$-symmetry,"  We suggest an inverse dispersion method for calculating photonic band diagram
+for materials with arbitrary frequency-dependent dielectric functions. The
+method is able to calculate the complex wave vector for a given frequency by
+solving the eigenvalue problem with a non-Hermitian operator. The analogy with
+$\cal{PT}$-symmetric Hamiltonians reveals that the operator corresponds to the
+momentum as a physical quantity and the singularities at the band edges are
+related to the branch points and responses for the features on the band edges.
+The method is realized using plane wave expansion technique for two-dimensional
+periodical structure in the case of TE- and TM-polarization. We illustrate the
+applicability of the method by calculation of the photonic band diagrams of an
+infinite two-dimension square lattice composed of dielectric cylinders using
+the measured frequency dependent dielectric functions of different materials
+(amorphous hydrogenated carbon, silicon, and chalcogenide glass). We show that
+the method allows to distinguish unambiguously between Bragg and Mie gaps in
+the spectra.
+",0,1,0,0,0,0
+13679,MmWave vehicle-to-infrastructure communication: Analysis of urban microcellular networks,"  Vehicle-to-infrastructure (V2I) communication may provide high data rates to
+vehicles via millimeter-wave (mmWave) microcellular networks. This paper uses
+stochastic geometry to analyze the coverage of urban mmWave microcellular
+networks. Prior work used a pathloss model with a line-of-sight probability
+function based on randomly oriented buildings, to determine whether a link was
+line-of-sight or non-line-of-sight. In this paper, we use a pathloss model
+inspired by measurements, which uses a Manhattan distance pathloss model and
+accounts for differences in pathloss exponents and losses when turning corners.
+In our model, streets are randomly located as a Manhattan Poisson line process
+(MPLP) and the base stations (BSs) are distributed according to a Poisson point
+process. Our model is well suited for urban microcellular networks where the
+BSs are deployed at street level. Based on this new approach, we derive the
+coverage probability under certain BS association rules to obtain closed-form
+solutions without much complexity. In addition, we draw two main conclusions
+from our work. First, non-line-of-sight BSs are not a major benefit for
+association or source of interference most of the time. Second, there is an
+ultra-dense regime where deploying active BSs does not enhance coverage.
+",1,0,0,0,0,0
+13680,Resolvent estimates on asymptotically cylindrical manifolds and on the half line,"  Manifolds with infinite cylindrical ends have continuous spectrum of
+increasing multiplicity as energy grows, and in general embedded resonances and
+eigenvalues can accumulate at infinity. However, we prove that if geodesic
+trapping is sufficiently mild, then such an accumulation is ruled out, and
+moreover the cutoff resolvent is uniformly bounded at high energies. We obtain
+as a corollary the existence of resonance free regions near the continuous
+spectrum.
+We also obtain improved estimates when the resolvent is cut off away from
+part of the trapping, and along the way we prove some resolvent estimates for
+repulsive potentials on the half line which may be of independent interest.
+",0,0,1,0,0,0
+13681,On absolutely normal and continued fraction normal numbers,"  We give a construction of a real number that is normal to all integer bases
+and continued fraction normal. The computation of the first n digits of its
+continued fraction expansion performs in the order of n^4 mathematical
+operations. The construction works by defining successive refinements of
+appropriate subintervals to achieve, in the limit, simple normality to all
+integer bases and continued fraction normality. The main diffculty is to
+control the length of these subintervals. To achieve this we adapt and combine
+known metric theorems on continued fractions and on expansions in integers
+bases.
+",0,0,1,0,0,0
+13682,Combinatorial properties of the G-degree,"  A strong interaction is known to exist between edge-colored graphs (which
+encode PL pseudo-manifolds of arbitrary dimension) and random tensor models (as
+a possible approach to the study of Quantum Gravity). The key tool is the {\it
+G-degree} of the involved graphs, which drives the {\it $1/N$ expansion} in the
+tensor models context. In the present paper - by making use of combinatorial
+properties concerning Hamiltonian decompositions of the complete graph - we
+prove that, in any even dimension $d\ge 4$, the G-degree of all bipartite
+graphs, as well as of all (bipartite or non-bipartite) graphs representing
+singular manifolds, is an integer multiple of $(d-1)!$. As a consequence, in
+even dimension, the terms of the $1/N$ expansion corresponding to odd powers of
+$1/N$ are null in the complex context, and do not involve colored graphs
+representing singular manifolds in the real context.
+In particular, in the 4-dimensional case, where the G-degree is shown to
+depend only on the regular genera with respect to an arbitrary pair of
+""associated"" cyclic permutations, several results are obtained, relating the
+G-degree or the regular genus of 5-colored graphs and the Euler characteristic
+of the associated PL 4-manifolds.
+",0,0,1,0,0,0
+13683,Quasi-random Agents for Image Transition and Animation,"  Quasi-random walks show similar features as standard random walks, but with
+much less randomness. We utilize this established model from discrete
+mathematics and show how agents carrying out quasi-random walks can be used for
+image transition and animation. The key idea is to generalize the notion of
+quasi-random walks and let a set of autonomous agents perform quasi-random
+walks painting an image. Each agent has one particular target image that they
+paint when following a sequence of directions for their quasi-random walk. The
+sequence can easily be chosen by an artist and allows them to produce a wide
+range of different transition patterns and animations.
+",1,0,0,0,0,0
+13684,Deep Speaker Verification: Do We Need End to End?,"  End-to-end learning treats the entire system as a whole adaptable black box,
+which, if sufficient data are available, may learn a system that works very
+well for the target task. This principle has recently been applied to several
+prototype research on speaker verification (SV), where the feature learning and
+classifier are learned together with an objective function that is consistent
+with the evaluation metric. An opposite approach to end-to-end is feature
+learning, which firstly trains a feature learning model, and then constructs a
+back-end classifier separately to perform SV. Recently, both approaches
+achieved significant performance gains on SV, mainly attributed to the smart
+utilization of deep neural networks. However, the two approaches have not been
+carefully compared, and their respective advantages have not been well
+discussed. In this paper, we compare the end-to-end and feature learning
+approaches on a text-independent SV task. Our experiments on a dataset sampled
+from the Fisher database and involving 5,000 speakers demonstrated that the
+feature learning approach outperformed the end-to-end approach. This is a
+strong support for the feature learning approach, at least with data and
+computation resources similar to ours.
+",1,0,0,0,0,0
+13685,Neural Models for Documents with Metadata,"  Most real-world document collections involve various types of metadata, such
+as author, source, and date, and yet the most commonly-used approaches to
+modeling text corpora ignore this information. While specialized models have
+been developed for particular applications, few are widely used in practice, as
+customization typically requires derivation of a custom inference algorithm. In
+this paper, we build on recent advances in variational inference methods and
+propose a general neural framework, based on topic models, to enable flexible
+incorporation of metadata and allow for rapid exploration of alternative
+models. Our approach achieves strong performance, with a manageable tradeoff
+between perplexity, coherence, and sparsity. Finally, we demonstrate the
+potential of our framework through an exploration of a corpus of articles about
+US immigration.
+",1,0,0,1,0,0
+13686,Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network,"  Recent years have witnessed great success of convolutional neural network
+(CNN) for various problems both in low and high level visions. Especially
+noteworthy is the residual network which was originally proposed to handle
+high-level vision problems and enjoys several merits. This paper aims to extend
+the merits of residual network, such as skip connection induced fast training,
+for a typical low-level vision problem, i.e., single image super-resolution. In
+general, the two main challenges of existing deep CNN for supper-resolution lie
+in the gradient exploding/vanishing problem and large numbers of parameters or
+computational cost as CNN goes deeper. Correspondingly, the skip connections or
+identity mapping shortcuts are utilized to avoid gradient exploding/vanishing
+problem. In addition, the skip connections have naturally centered the
+activation which led to better performance. To tackle with the second problem,
+a lightweight CNN architecture which has carefully designed width, depth and
+skip connections was proposed. In particular, a strategy of gradually varying
+the shape of network has been proposed for residual network. Different residual
+architectures for image super-resolution have also been compared. Experimental
+results have demonstrated that the proposed CNN model can not only achieve
+state-of-the-art PSNR and SSIM results for single image super-resolution but
+also produce visually pleasant results. This paper has extended the mmm 2017
+oral conference paper with a considerable new analyses and more experiments
+especially from the perspective of centering activations and ensemble behaviors
+of residual network.
+",1,0,0,0,0,0
+13687,Variations of BPS structure and a large rank limit,"  We study a class of flat bundles, of finite rank $N$, which arise naturally
+from the Donaldson-Thomas theory of a Calabi-Yau threefold $X$ via the notion
+of a variation of BPS structure. We prove that in a large $N$ limit their flat
+sections converge to the solutions to certain infinite dimensional
+Riemann-Hilbert problems recently found by Bridgeland. In particular this
+implies an expression for the positive degree, genus $0$ Gopakumar-Vafa
+contribution to the Gromov-Witten partition function of $X$ in terms of
+solutions to confluent hypergeometric differential equations.
+",0,0,1,0,0,0
+13688,Strong correlations between the exponent $α$ and the particle number for a Renyi-monoatomic gas in Gibbs' statistical mechanics,"  Appealing to the 1902 Gibbs' formalism for classical statistical mechanics
+(SM), the first SM axiomatic theory ever that successfully explained
+equilibrium thermodynamics, we will here show that already at the classical
+level there is a strong correlation between the Renyi's exponent $\alpha$ and
+the number of particles for very simple systems. No reference to heat baths is
+needed for such a purpose.
+",0,1,0,0,0,0
+13689,Smooth backfitting of proportional hazards -- A new approach projecting survival data,"  Smooth backfitting has proven to have a number of theoretical and practical
+advantages in structured regression. Smooth backfitting projects the data down
+onto the structured space of interest providing a direct link between data and
+estimator. This paper introduces the ideas of smooth backfitting to survival
+analysis in a proportional hazard model, where we assume an underlying
+conditional hazard with multiplicative components. We develop asymptotic theory
+for the estimator and we use the smooth backfitter in a practical application,
+where we extend recent advances of in-sample forecasting methodology by
+allowing more information to be incorporated, while still obeying the
+structured requirements of in-sample forecasting.
+",0,0,1,1,0,0
+13690,Trajectory Tracking Using Motion Primitives for the Purcell's Swimmer,"  Locomotion at low Reynolds numbers is a topic of growing interest, spurred by
+its various engineering and medical applications. This paper presents a novel
+prototype and a locomotion algorithm for the 3-link planar Purcell's swimmer
+based on Lie algebraic notions. The kinematic model based on Cox theory of the
+prototype swimmer is a driftless control-affine system. Using the existing
+strong controllability and related results, the existence of motion primitives
+is initially shown. The Lie algebra of the control vector fields is then used
+to synthesize control profiles to generate motions along the basis of the Lie
+algebra associated with the structure group of the system. An open loop control
+system with vision-based positioning is successfully implemented which allows
+tracking any given continuous trajectory of the position and orientation of the
+swimmer's base link. Alongside, the paper also provides a theoretical
+interpretation of the symmetry arguments presented in the existing literature
+to generate the control profiles of the swimmer.
+",1,0,0,0,0,0
+13691,Data Analysis in Multimedia Quality Assessment: Revisiting the Statistical Tests,"  Assessment of multimedia quality relies heavily on subjective assessment, and
+is typically done by human subjects in the form of preferences or continuous
+ratings. Such data is crucial for analysis of different multimedia processing
+algorithms as well as validation of objective (computational) methods for the
+said purpose. To that end, statistical testing provides a theoretical framework
+towards drawing meaningful inferences, and making well grounded conclusions and
+recommendations. While parametric tests (such as t test, ANOVA, and error
+estimates like confidence intervals) are popular and widely used in the
+community, there appears to be a certain degree of confusion in the application
+of such tests. Specifically, the assumption of normality and homogeneity of
+variance is often not well understood. Therefore, the main goal of this paper
+is to revisit them from a theoretical perspective and in the process provide
+useful insights into their practical implications. Experimental results on both
+simulated and real data are presented to support the arguments made. A software
+implementing the said recommendations is also made publicly available, in order
+to achieve the goal of reproducible research.
+",1,0,0,1,0,0
+13692,Removing Isolated Zeroes by Homotopy,"  Suppose that the inverse image of the zero vector by a continuous map
+$f:{\mathbb R}^n\to{\mathbb R}^q$ has an isolated point $P$. There is a local
+obstruction to removing this isolated zero by a small perturbation,
+generalizing the notion of index for vector fields, the $q=n$ case. The
+existence of a continuous map $g$ which approximates $f$ but is nonvanishing
+near $P$ is equivalent to a topological property we call ""locally inessential,""
+and for dimensions $n$, $q$ where $\pi_{n-1}(S^{q-1})$ is trivial, every
+isolated zero is locally inessential. We consider the problem of constructing
+such an approximation $g$, and show that there exists a continuous homotopy
+from $f$ to $g$ through locally nonvanishing maps. If $f$ is a semialgebraic
+map, then there exists such a homotopy which is also semialgebraic. For $q=2$
+and $f$ real analytic with a locally inessential isolated zero, there exists a
+Hölder continuous homotopy $F(x,t)$ which, for $(x,t)\ne(P,0)$, is real
+analytic and nonvanishing. The existence of a smooth homotopy, given a smooth
+map $f$, is stated as an open question.
+",0,0,1,0,0,0
+13693,Continuous cocycle superrigidity for coinduced actions and relative ends,"  We prove that certain coinduced actions for an inclusion of finitely
+generated commensurated subgroups with relative one end are continuous cocycle
+superrigid actions. We also show the necessity for the relative end assumption.
+",0,0,1,0,0,0
+13694,Brownian ratchets: How stronger thermal noise can reduce diffusion,"  We study diffusion properties of an inertial Brownian motor moving on a
+ratchet substrate, i.e. a periodic structure with broken reflection symmetry.
+The motor is driven by an unbiased time-periodic symmetric force which takes
+the system out of thermal equilibrium. For selected parameter sets, the system
+is in a non-chaotic regime in which we can identify a non-monotonic dependence
+of the diffusion coefficient on temperature: for low temperature, it initially
+increases as temperature grows, passes through its local maximum, next starts
+to diminish reaching its local minimum and finally it monotonically increases
+in accordance with the Einstein linear relation. Particularly interesting is
+the temperature interval in which diffusion is suppressed by thermal noise and
+we explain this effect in terms of transition rates of a three-state stochastic
+model.
+",0,1,0,0,0,0
+13695,Wadge Degrees of $ω$-Languages of Petri Nets,"  We prove that $\omega$-languages of (non-deterministic) Petri nets and
+$\omega$-languages of (non-deterministic) Turing machines have the same
+topological complexity: the Borel and Wadge hierarchies of the class of
+$\omega$-languages of (non-deterministic) Petri nets are equal to the Borel and
+Wadge hierarchies of the class of $\omega$-languages of (non-deterministic)
+Turing machines which also form the class of effective analytic sets. In
+particular, for each non-null recursive ordinal $\alpha < \omega\_1^{\rm CK}
+$ there exist some ${\bf \Sigma}^0\_\alpha$-complete and some ${\bf
+\Pi}^0\_\alpha$-complete $\omega$-languages of Petri nets, and the supremum of
+the set of Borel ranks of $\omega$-languages of Petri nets is the ordinal
+$\gamma\_2^1$, which is strictly greater than the first non-recursive ordinal
+$\omega\_1^{\rm CK}$. We also prove that there are some ${\bf
+\Sigma}\_1^1$-complete, hence non-Borel, $\omega$-languages of Petri nets, and
+that it is consistent with ZFC that there exist some $\omega$-languages of
+Petri nets which are neither Borel nor ${\bf \Sigma}\_1^1$-complete. This
+answers the question of the topological complexity of $\omega$-languages of
+(non-deterministic) Petri nets which was left open in [DFR14,FS14].
+",1,0,1,0,0,0
+13696,Is there agreement on the prestige of scholarly book publishers in the Humanities? DELPHI over survey results,"  Despite having an important role supporting assessment processes, criticism
+towards evaluation systems and the categorizations used are frequent.
+Considering the acceptance by the scientific community as an essential issue
+for using rankings or categorizations in research evaluation, the aim of this
+paper is testing the results of rankings of scholarly book publishers'
+prestige, Scholarly Publishers Indicators (SPI hereafter). SPI is a public,
+survey-based ranking of scholarly publishers' prestige (among other
+indicators). The latest version of the ranking (2014) was based on an expert
+consultation with a large number of respondents. In order to validate and
+refine the results for Humanities' fields as proposed by the assessment
+agencies, a Delphi technique was applied with a panel of randomly selected
+experts over the initial rankings. The results show an equalizing effect of the
+technique over the initial rankings as well as a high degree of concordance
+between its theoretical aim (consensus among experts) and its empirical results
+(summarized with Gini Index). The resulting categorization is understood as
+more conclusive and susceptible of being accepted by those under evaluation.
+",1,0,0,1,0,0
+13697,Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths,"  Zero-shot recognition aims to accurately recognize objects of unseen classes
+by using a shared visual-semantic mapping between the image feature space and
+the semantic embedding space. This mapping is learned on training data of seen
+classes and is expected to have transfer ability to unseen classes. In this
+paper, we tackle this problem by exploiting the intrinsic relationship between
+the semantic space manifold and the transfer ability of visual-semantic
+mapping. We formalize their connection and cast zero-shot recognition as a
+joint optimization problem. Motivated by this, we propose a novel framework for
+zero-shot recognition, which contains dual visual-semantic mapping paths. Our
+analysis shows this framework can not only apply prior semantic knowledge to
+infer underlying semantic manifold in the image feature space, but also
+generate optimized semantic embedding space, which can enhance the transfer
+ability of the visual-semantic mapping to unseen classes. The proposed method
+is evaluated for zero-shot recognition on four benchmark datasets, achieving
+outstanding results.
+",1,0,0,0,0,0
+13698,Spoken Language Biomarkers for Detecting Cognitive Impairment,"  In this study we developed an automated system that evaluates speech and
+language features from audio recordings of neuropsychological examinations of
+92 subjects in the Framingham Heart Study. A total of 265 features were used in
+an elastic-net regularized binomial logistic regression model to classify the
+presence of cognitive impairment, and to select the most predictive features.
+We compared performance with a demographic model from 6,258 subjects in the
+greater study cohort (0.79 AUC), and found that a system that incorporated both
+audio and text features performed the best (0.92 AUC), with a True Positive
+Rate of 29% (at 0% False Positive Rate) and a good model fit (Hosmer-Lemeshow
+test > 0.05). We also found that decreasing pitch and jitter, shorter segments
+of speech, and responses phrased as questions were positively associated with
+cognitive impairment.
+",1,0,0,0,0,0
+13699,Performance Analysis of Low-Density Parity-Check Codes over 2D Interference Channels via Density Evolution,"  The theoretical analysis of detection and decoding of low-density
+parity-check (LDPC) codes transmitted over channels with two-dimensional (2D)
+interference and additive white Gaussian noise (AWGN) is provided in this
+paper. The detection and decoding system adopts the joint iterative detection
+and decoding scheme (JIDDS) in which the log-domain sum-product algorithm is
+adopted to decode the LDPC codes. The graph representations of the JIDDS are
+explained. Using the graph representations, we prove that the message-flow
+neighborhood of the detection and decoding system will be tree-like for a
+sufficiently long code length. We further confirm that the performance of the
+JIDDS will concentrate around the performance in which message-flow
+neighborhood is tree-like. Based on the tree-like message-flow neighborhood, we
+employ a modified density evolution algorithm to track the message densities
+during the iterations. A threshold is calculated using the density evolution
+algorithm which can be considered as the theoretical performance limit of the
+system. Simulation results demonstrate that the modified density evolution is
+effective in analyzing the performance of 2D interference systems.
+",1,0,1,0,0,0
+13700,Novel market approach for locally balancing renewable energy production and flexible demand,"  Future electricity distribution grids will host a considerable share of
+variable renewable energy sources and local storage resources. Moreover, they
+will face new load structures due for example to the growth of the electric
+vehicle market. These trends raise the need for new paradigms for distribution
+grids operation, in which Distribution System Operators will increasingly rely
+on demand side flexibility and households will progressively become prosumers
+playing an active role on smart grid energy management. However, in present
+energy management architectures, the lack of coordination among actors limits
+the capability of the grid to enable the mentioned trends. In this paper we
+tackle this problem by proposing an architecture that enables households to
+autonomously exchange energy blocks and flexibility services with neighbors,
+operators and market actors. The solution is based on a blockchain transactive
+platform. We focus on a market application, where households can trade energy
+with their neighbors, aimed to locally balancing renewable energy production.
+We propose a market mechanism and dynamic transport prices that provide an
+incentive for households to locally manage energy resources in a way that
+responds to both pro-sumer and operator needs. We evaluate the impact of such
+markets through comprehensive simulations using power flow analysis and
+realistic load profiles, providing valuable insight for the design of
+appropriate mechanisms and incentives.
+",1,0,0,0,0,0
+13701,Atomic and electronic structures of stable linear carbon chains on Ag-nanoparticles,"  In this work, we report X-ray photoelectron (XPS) and valence band (VB)
+spectroscopy measurements of surfactant-free silver nanoparticles and
+silver/linear carbon chains (Ag@LCC) structures prepared by pulse laser
+ablation (PLA) in water. Our measurements demonstrate significant oxidation
+only on the surfaces of the silver nanoparticles with many covalent
+carbon-silver bonds but only negligible traces of carbon-oxygen bonds.
+Theoretical modeling also provides evidence of the formation of robust
+carbon-silver bonds between linear carbon chains and pure and partially
+oxidized silver surfaces. A comparison of theoretical and experimental
+electronic structures also provides evidence of the presence of non-oxidized
+linear carbon chains on silver surfaces. To evaluate the chemical stability, we
+investigated the energetics of the physical adsorption of oxidative species
+(water and oxygen) and found that this adsorption is much preferrable on
+oxidized or pristine silver surfaces than the adsorption of linear carbon
+chains, which makes the initial step in the oxidation of LCC energetically
+unfavorable.
+",0,1,0,0,0,0
+13702,The unsaturated flow in porous media with dynamic capillary pressure,"  In this paper we consider a degenerate pseudoparabolic equation for the
+wetting saturation of an unsaturated two-phase flow in porous media with
+dynamic capillary pressure-saturation relationship where the relaxation
+parameter depends on the saturation. Following the approach given in [12] the
+existence of a weak solution is proved using Galerkin approximation and
+regularization techniques. A priori estimates needed for passing to the limit
+when the regularization parameter goes to zero are obtained by using
+appropriate test-functions, motivated by the fact that considered PDE allows a
+natural generalization of the classical Kullback entropy. Finally, a special
+care was given in obtaining an estimate of the mixed derivative term by
+combining the information from the capillary pressure with obtained a priori
+estimates on the saturation.
+",0,0,1,0,0,0
+13703,Dissociation of one-dimensional matter-wave breathers due to quantum many-body effects,"  We use the ab initio Bethe Ansatz dynamics to predict the dissociation of
+one-dimensional cold-atom breathers that are created by a quench from a
+fundamental soliton. We find that the dissociation is a robust quantum
+many-body effect, while in the mean-field (MF) limit the dissociation is
+forbidden by the integrability of the underlying nonlinear Schrödinger
+equation. The analysis demonstrates the possibility to observe quantum
+many-body effects without leaving the MF range of experimental parameters. We
+find that the dissociation time is of the order of a few seconds for a typical
+atomic-soliton setting.
+",0,1,0,0,0,0
+13704,Human Eye Visual Hyperacuity: A New Paradigm for Sensing?,"  The human eye appears to be using a low number of sensors for image
+capturing. Furthermore, regarding the physical dimensions of
+cones-photoreceptors responsible for the sharp central vision-, we may realize
+that these sensors are of a relatively small size and area. Nonetheless, the
+eye is capable to obtain high resolution images due to visual hyperacuity and
+presents an impressive sensitivity and dynamic range when set against
+conventional digital cameras of similar characteristics. This article is based
+on the hypothesis that the human eye may be benefiting from diffraction to
+improve both image resolution and acquisition process. The developed method
+intends to explain and simulate using MATLAB software the visual hyperacuity:
+the introduction of a controlled diffraction pattern at an initial stage,
+enables the use of a reduced number of sensors for capturing the image and
+makes possible a subsequent processing to improve the final image resolution.
+The results have been compared with the outcome of an equivalent system but in
+absence of diffraction, achieving promising results. The main conclusion of
+this work is that diffraction could be helpful for capturing images or signals
+when a small number of sensors available, which is far from being a
+resolution-limiting factor.
+",1,0,0,0,0,0
+13705,Structured Matrix Estimation and Completion,"  We study the problem of matrix estimation and matrix completion under a
+general framework. This framework includes several important models as special
+cases such as the gaussian mixture model, mixed membership model, bi-clustering
+model and dictionary learning. We consider the optimal convergence rates in a
+minimax sense for estimation of the signal matrix under the Frobenius norm and
+under the spectral norm. As a consequence of our general result we obtain
+minimax optimal rates of convergence for various special models.
+",0,0,1,1,0,0
+13706,Performance evaluation of PSD for silicon ECAL,"  We are developing position sensitive silicon detectors (PSD) which have an
+electrode at each of four corners so that the incident position of a charged
+particle can be obtained using signals from the electrodes. It is expected that
+the position resolution the electromagnetic calorimeter (ECAL) of the ILD
+detector will be improved by introducing PSD into the detection layers. In this
+study, we irradiated collimated laser beams to the surface of the PSD, varying
+the incident position. We found that the incident position can be well
+reconstructed from the signals if high resistance is implemented in the p+
+layer. We also tried to observe the signal of particles by placing a radiative
+source on the PSD sensor.
+",0,1,0,0,0,0
+13707,Online Improper Learning with an Approximation Oracle,"  We revisit the question of reducing online learning to approximate
+optimization of the offline problem. In this setting, we give two algorithms
+with near-optimal performance in the full information setting: they guarantee
+optimal regret and require only poly-logarithmically many calls to the
+approximation oracle per iteration. Furthermore, these algorithms apply to the
+more general improper learning problems. In the bandit setting, our algorithm
+also significantly improves the best previously known oracle complexity while
+maintaining the same regret.
+",0,0,0,1,0,0
+13708,TC^0 circuits for algorithmic problems in nilpotent groups,"  Recently, Macdonald et. al. showed that many algorithmic problems for
+finitely generated nilpotent groups including computation of normal forms, the
+subgroup membership problem, the conjugacy problem, and computation of subgroup
+presentations can be done in Logspace. Here we follow their approach and show
+that all these problems are complete for the uniform circuit class TC^0 -
+uniformly for all r-generated nilpotent groups of class at most c for fixed r
+and c. In order to solve these problems in TC^0, we show that the unary version
+of the extended gcd problem (compute greatest common divisors and express them
+as linear combinations) is in TC^0. Moreover, if we allow a certain binary
+representation of the inputs, then the word problem and computation of normal
+forms is still in uniform TC^0, while all the other problems we examine are
+shown to be TC^0-Turing reducible to the binary extended gcd problem.
+",1,0,1,0,0,0
+13709,Alperin-McKay natural correspondences in solvable and symmetric groups for the prime $p=2$,"  Let $G$ be a finite solvable or symmetric group and let $B$ be a $2$-block of
+$G$. We construct a canonical correspondence between the irreducible characters
+of height zero in $B$ and those in its Brauer first main correspondent. For
+symmetric groups our bijection is compatible with restriction of characters.
+",0,0,1,0,0,0
+13710,Reversible temperature exchange upon thermal contact,"  According to a well-known principle of thermodynamics, the transfer of heat
+between two bodies is reversible when their temperatures are infinitesimally
+close. As we demonstrate, a little-known alternative exists: two bodies with
+temperatures different by an arbitrary amount can completely exchange their
+temperatures in a reversible way if split into infinitesimal parts that are
+brought into thermal contact sequentially.
+",0,1,0,0,0,0
+13711,On consequences of measurements of turbulent Lewis number from observations,"  Almost all parameterizations of turbulence in NWP models and GCM make the
+assumption of equality of exchange coefficients for heat $K_h$ and water $K_w$.
+However, large uncertainties exists in old papers published in the 1950s, 1960s
+and 1970s, where the turbulent Lewis number Le_t $= K_h / K_w$ have been
+evaluated from observations and then set to Le_t$=1$.
+The aim of this note is: 1) to trust the recommendations of Richardson
+(1919), who suggested to use the moist-air entropy as a variable on which the
+turbulence is acting; 2) to compute a new exchange coefficients $K_s$ for the
+moist-air entropy; 3) to determine the values of the new entropy-Lewis number
+Le_ts $= K_s / K_w$ from observations (Météopole-Flux and Cabauw masts) and
+from LES and SCM outputs for the IHOP case (Couvreux et al., 2005).
+It is shown that values of Le_ts significantly different from $1$ are
+frequently observed and may have large consequences on the way the turbulence
+fluxes are computed in NWP models and GCMs.
+",0,1,0,0,0,0
+13712,Complex Networks Analysis for Software Architecture: an Hibernate Call Graph Study,"  Recent advancements in complex network analysis are encouraging and may
+provide useful insights when applied in software engineering domain, revealing
+properties and structures that cannot be captured by traditional metrics. In
+this paper, we analyzed the topological properties of Hibernate library, a
+well-known Java-based software through the extraction of its static call graph.
+The results reveal a complex network with small-world and scale-free
+characteristics while displaying a strong propensity on forming communities.
+",1,0,0,0,0,0
+13713,The Impact of Local Geometry and Batch Size on Stochastic Gradient Descent for Nonconvex Problems,"  In several experimental reports on nonconvex optimization problems in machine
+learning, stochastic gradient descent (SGD) was observed to prefer minimizers
+with flat basins in comparison to more deterministic methods, yet there is very
+little rigorous understanding of this phenomenon. In fact, the lack of such
+work has led to an unverified, but widely-accepted stochastic mechanism
+describing why SGD prefers flatter minimizers to sharper minimizers. However,
+as we demonstrate, the stochastic mechanism fails to explain this phenomenon.
+Here, we propose an alternative deterministic mechanism that can accurately
+explain why SGD prefers flatter minimizers to sharper minimizers. We derive
+this mechanism based on a detailed analysis of a generic stochastic quadratic
+problem, which generalizes known results for classical gradient descent.
+Finally, we verify the predictions of our deterministic mechanism on two
+nonconvex problems.
+",0,0,0,1,0,0
+13714,Cosmology and the Origin of the Universe: Historical and Conceptual Perspectives,"  From a modern perspective cosmology is a historical science in so far that it
+deals with the development of the universe since its origin some 14 billion
+years ago. The origin itself may not be subject to scientific analysis and
+explanation. Nonetheless, there are theories that claim to explain the ultimate
+origin or ""creation"" of the universe. As shown by the history of cosmological
+thought, the very concept of ""origin"" is problematic and can be understood in
+different ways. While it is normally understood as a temporal concept, cosmic
+origin is not temporal by necessity. The universe can be assigned an origin
+even though it has no definite age. In order to clarify the question a view of
+earlier ideas will be helpful, these ideas coming not only from astronomy but
+also from philosophy and theology.
+",0,1,0,0,0,0
+13715,Semisimple and separable algebras in multi-fusion categories,"  We give a classification of semisimple and separable algebras in a
+multi-fusion category over an arbitrary field in analogy to Wedderben-Artin
+theorem in classical algebras. It turns out that, if the multi-fusion category
+admits a semisimple Drinfeld center, the only obstruction to the separability
+of a semisimple algebra arises from inseparable field extensions as in
+classical algebras. Among others, we show that a division algebra is separable
+if and only if it has a nonvanishing dimension.
+",0,0,1,0,0,0
+13716,Ensemble dependence of fluctuations and the canonical/micro-canonical equivalence of ensembles,"  We study the equivalence of microcanonical and canonical ensembles in
+continuous systems, in the sense of the convergence of the corresponding Gibbs
+measures. This is obtained by proving a local central limit theorem and a local
+large deviations principle. As an application we prove a formula due to
+Lebowitz-Percus-Verlet. It gives mean square fluctuations of an extensive
+observable, like the kinetic energy, in a classical micro canonical ensemble at
+fixed energy.
+",0,1,1,0,0,0
+13717,Algorithms for Positive Semidefinite Factorization,"  This paper considers the problem of positive semidefinite factorization (PSD
+factorization), a generalization of exact nonnegative matrix factorization.
+Given an $m$-by-$n$ nonnegative matrix $X$ and an integer $k$, the PSD
+factorization problem consists in finding, if possible, symmetric $k$-by-$k$
+positive semidefinite matrices $\{A^1,...,A^m\}$ and $\{B^1,...,B^n\}$ such
+that $X_{i,j}=\text{trace}(A^iB^j)$ for $i=1,...,m$, and $j=1,...n$. PSD
+factorization is NP-hard. In this work, we introduce several local optimization
+schemes to tackle this problem: a fast projected gradient method and two
+algorithms based on the coordinate descent framework. The main application of
+PSD factorization is the computation of semidefinite extensions, that is, the
+representations of polyhedrons as projections of spectrahedra, for which the
+matrix to be factorized is the slack matrix of the polyhedron. We compare the
+performance of our algorithms on this class of problems. In particular, we
+compute the PSD extensions of size $k=1+ \lceil \log_2(n) \rceil$ for the
+regular $n$-gons when $n=5$, $8$ and $10$. We also show how to generalize our
+algorithms to compute the square root rank (which is the size of the factors in
+a PSD factorization where all factor matrices $A^i$ and $B^j$ have rank one)
+and completely PSD factorizations (which is the special case where the input
+matrix is symmetric and equality $A^i=B^i$ is required for all $i$).
+",1,0,1,0,0,0
+13718,Remarks about Synthetic Upper Ricci Bounds for Metric Measure Spaces,"  We discuss various characterizations of synthetic upper Ricci bounds for
+metric measure spaces in terms of heat flow, entropy and optimal transport. In
+particular, we present a characterization in terms of semiconcavity of the
+entropy along certain Wasserstein geodesics which is stable under convergence
+of mm-spaces. And we prove that a related characterization is equivalent to an
+asymptotic lower bound on the growth of the Wasseretein distance between heat
+flows. For weighted Riemannian manifolds, the crucial result will be a precise
+uniform two-sided bound for \begin{eqnarray*}\frac{d}{dt}\Big|_{t=0}W\big(\hat
+P_t\delta_x,\hat P_t\delta_y\big)\end{eqnarray*} in terms of the mean value of
+the Bakry-Emery Ricci tensor $\mathrm{Ric}+\mathrm{Hess}\, f$ along the
+minimizing geodesic from $x$ to $y$ and an explicit correction term depending
+on the bound for the curvature along this curve.
+",0,0,1,0,0,0
+13719,Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification,"  Given a pedestrian image as a query, the purpose of person re-identification
+is to identify the correct match from a large collection of gallery images
+depicting the same person captured by disjoint camera views. The critical
+challenge is how to construct a robust yet discriminative feature
+representation to capture the compounded variations in pedestrian appearance.
+To this end, deep learning methods have been proposed to extract hierarchical
+features against extreme variability of appearance. However, existing methods
+in this category generally neglect the efficiency in the matching stage whereas
+the searching speed of a re-identification system is crucial in real-world
+applications. In this paper, we present a novel deep hashing framework with
+Convolutional Neural Networks (CNNs) for fast person re-identification.
+Technically, we simultaneously learn both CNN features and hash functions/codes
+to get robust yet discriminative features and similarity-preserving hash codes.
+Thereby, person re-identification can be resolved by efficiently computing and
+ranking the Hamming distances between images. A structured loss function
+defined over positive pairs and hard negatives is proposed to formulate a novel
+optimization problem so that fast convergence and more stable optimized
+solution can be obtained. Extensive experiments on two benchmarks CUHK03
+\cite{FPNN} and Market-1501 \cite{Market1501} show that the proposed deep
+architecture is efficacy over state-of-the-arts.
+",1,0,0,0,0,0
+13720,Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses,"  Recently, methods have been proposed that perform texture synthesis and style
+transfer by using convolutional neural networks (e.g. Gatys et al.
+[2015,2016]). These methods are exciting because they can in some cases create
+results with state-of-the-art quality. However, in this paper, we show these
+methods also have limitations in texture quality, stability, requisite
+parameter tuning, and lack of user controls. This paper presents a multiscale
+synthesis pipeline based on convolutional neural networks that ameliorates
+these issues. We first give a mathematical explanation of the source of
+instabilities in many previous approaches. We then improve these instabilities
+by using histogram losses to synthesize textures that better statistically
+match the exemplar. We also show how to integrate localized style losses in our
+multiscale framework. These losses can improve the quality of large features,
+improve the separation of content and style, and offer artistic controls such
+as paint by numbers. We demonstrate that our approach offers improved quality,
+convergence in fewer iterations, and more stability over the optimization.
+",1,0,0,0,0,0
+13721,Dynamical and Topological Aspects of Consensus Formation in Complex Networks,"  The present work analyses a particular scenario of consensus formation, where
+the individuals navigate across an underlying network defining the topology of
+the walks. The consensus, associated to a given opinion coded as a simple
+messages, is generated by interactions during the agent's walk and manifest
+itself in the collapse of the various opinions into a single one. We analyze
+how the topology of the underlying networks and the rules of interaction
+between the agents promote or inhibit the emergence of this consensus. We find
+that non-linear interaction rules are required to form consensus and that
+consensus is more easily achieved in networks whose degree distribution is
+narrower.
+",1,1,0,0,0,0
+13722,Measurement of authorship by publications: a normative approach,"  Administrators in all academic organizations across the world have to deal
+with the unenviable task of comparing researchers on the basis of their
+academic contributions. This job is further complicated by the need for
+comparing single author publication with joint author publications.
+Unfortunately, however, there is no reasonably established consensus on the
+method of arriving at such comparisons, which typically involve trading off
+accomplishments in teaching, grant writing and academic publication. In this
+paper, we focus on the particular dimension of academic publication, and
+analyze this issue from a more fundamental perspective than addressed by the
+popular $h$-index (which may lead to unfair and counter-intuitive comparisons
+in certain situations). In particular, we undertake an axiomatic analysis of
+{\it all} possible ways to measure academic authorship for a given dataset of
+research articles and find that an egalitarian $e$-index is the \textit{only}
+method which satisfies the axioms of anonymity, monotonicity, and efficiency.
+This index divides authorship of joint projects equally and sums across all
+publications of an author. Thus, our index provides a method to prorate
+authorship for multi-author projects, and thereby, delivers more balanced
+author comparisons.
+",1,1,0,0,0,0
+13723,Ordering Garside groups,"  We introduce a condition on Garside groups that we call Dehornoy structure.
+An iteration of such a structure leads to a left order on the group. We show
+conditions for a Garside group to admit a Dehornoy structure, and we apply
+these criteria to prove that the Artin groups of type A and I 2 (m), m $\ge$ 4,
+have Dehornoy structures. We show that the left orders on the Artin groups of
+type A obtained from their Dehornoy structures are the Dehornoy orders. In the
+case of the Artin groups of type I 2 (m), m $\ge$ 4, we show that the left
+orders derived from their Dehornoy structures coincide with the orders obtained
+from embeddings of the groups into braid groups. 20F36
+",0,0,1,0,0,0
+13724,Self-Taught Support Vector Machine,"  In this paper, a new approach for classification of target task using limited
+labeled target data as well as enormous unlabeled source data is proposed which
+is called self-taught learning. The target and source data can be drawn from
+different distributions. In the previous approaches, covariate shift assumption
+is considered where the marginal distributions p(x) change over domains and the
+conditional distributions p(y|x) remain the same. In our approach, we propose a
+new objective function which simultaneously learns a common space T(.) where
+the conditional distributions over domains p(T(x)|y) remain the same and learns
+robust SVM classifiers for target task using both source and target data in the
+new representation. Hence, in the proposed objective function, the hidden label
+of the source data is also incorporated. We applied the proposed approach on
+Caltech-256, MSRC+LMO datasets and compared the performance of our algorithm to
+the available competing methods. Our method has a superior performance to the
+successful existing algorithms.
+",1,0,0,1,0,0
+13725,Continuum of classical-field ensembles from canonical to grand canonical and the onset of their equivalence,"  The canonical and grand-canonical ensembles are two usual marginal cases for
+ultracold Bose gases, but real collections of experimental runs commonly have
+intermediate properties. Here we study the continuum of intermediate cases, and
+look into the appearance of ensemble equivalence as interaction rises for
+mesoscopic 1d systems. We demonstrate how at sufficient interaction strength
+the distributions of condensate and excited atoms become practically identical
+regardless of the ensemble used. Importantly, we find that features that are
+fragile in the ideal gas and appear only in a strict canonical ensemble can
+become robust in all ensembles when interactions become strong. As evidence,
+the steep cliff in the distribution of the number of excited atoms is
+preserved. To make this study, a straightforward approach for generating
+canonical and intermediate classical field ensembles using a modified
+stochastic Gross-Pitaevskii equation (SGPE) is developed.
+",0,1,0,0,0,0
+13726,Extending Partial Representations of Unit Circular-arc Graphs,"  The partial representation extension problem, introduced by Klavík et al.
+(2011), generalizes the recognition problem. In this short note we show that
+this problem is NP-complete for unit circular-arc graphs.
+",1,0,0,0,0,0
+13727,Lipschitz continuity of quasiconformal mappings and of the solutions to second order elliptic PDE with respect to the distance ratio metric,"  The main aim of this paper is to study the Lipschitz continuity of certain
+$(K, K')$-quasiconformal mappings with respect to the distance ratio metric,
+and the Lipschitz continuity of the solution of a quasilinear differential
+equation with respect to the distance ratio metric.
+",0,0,1,0,0,0
+13728,On Estimation of Conditional Modes Using Multiple Quantile Regressions,"  We propose an estimation method for the conditional mode when the
+conditioning variable is high-dimensional. In the proposed method, we first
+estimate the conditional density by solving quantile regressions multiple
+times. We then estimate the conditional mode by finding the maximum of the
+estimated conditional density. The proposed method has two advantages in that
+it is computationally stable because it has no initial parameter dependencies,
+and it is statistically efficient with a fast convergence rate. Synthetic and
+real-world data experiments demonstrate the better performance of the proposed
+method compared to other existing ones.
+",0,0,1,1,0,0
+13729,Kondo destruction in a quantum paramagnet with magnetic frustration,"  We report results of isothermal magnetotransport and susceptibility
+measurements at elevated magnetic fields B down to very low temperatures T on
+high-quality single crystals of the frustrated Kondo-lattice system CePdAl.
+They reveal a B*(T) line within the paramagnetic part of the phase diagram.
+This line denotes a thermally broadened 'small'-to-'large' Fermi surface
+crossover which substantially narrows upon cooling. At B_0* = B*(T=0) = (4.6
++/- 0.1) T, this B*(T) line merges with two other crossover lines, viz. Tp(B)
+below and T_FL(B) above B_0*. Tp characterizes a frustration-dominated
+spin-liquid state, while T_FL is the Fermi-liquid temperature associated with
+the lattice Kondo effect. Non-Fermi-liquid phenomena which are commonly
+observed near a 'Kondo destruction' quantum critical point cannot be resolved
+in CePdAl. Our observations reveal a rare case where Kondo coupling,
+frustration and quantum criticality are closely intertwined.
+",0,1,0,0,0,0
+13730,Deep supervised learning using local errors,"  Error backpropagation is a highly effective mechanism for learning
+high-quality hierarchical features in deep networks. Updating the features or
+weights in one layer, however, requires waiting for the propagation of error
+signals from higher layers. Learning using delayed and non-local errors makes
+it hard to reconcile backpropagation with the learning mechanisms observed in
+biological neural networks as it requires the neurons to maintain a memory of
+the input long enough until the higher-layer errors arrive. In this paper, we
+propose an alternative learning mechanism where errors are generated locally in
+each layer using fixed, random auxiliary classifiers. Lower layers could thus
+be trained independently of higher layers and training could either proceed
+layer by layer, or simultaneously in all layers using local error information.
+We address biological plausibility concerns such as weight symmetry
+requirements and show that the proposed learning mechanism based on fixed,
+broad, and random tuning of each neuron to the classification categories
+outperforms the biologically-motivated feedback alignment learning technique on
+the MNIST, CIFAR10, and SVHN datasets, approaching the performance of standard
+backpropagation. Our approach highlights a potential biological mechanism for
+the supervised, or task-dependent, learning of feature hierarchies. In
+addition, we show that it is well suited for learning deep networks in custom
+hardware where it can drastically reduce memory traffic and data communication
+overheads.
+",1,0,0,1,0,0
+13731,Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection,"  The mean field variational Bayes method is becoming increasingly popular in
+statistics and machine learning. Its iterative Coordinate Ascent Variational
+Inference algorithm has been widely applied to large scale Bayesian inference.
+See Blei et al. (2017) for a recent comprehensive review. Despite the
+popularity of the mean field method there exist remarkably little fundamental
+theoretical justifications. To the best of our knowledge, the iterative
+algorithm has never been investigated for any high dimensional and complex
+model. In this paper, we study the mean field method for community detection
+under the Stochastic Block Model. For an iterative Batch Coordinate Ascent
+Variational Inference algorithm, we show that it has a linear convergence rate
+and converges to the minimax rate within $\log n$ iterations. This complements
+the results of Bickel et al. (2013) which studied the global minimum of the
+mean field variational Bayes and obtained asymptotic normal estimation of
+global model parameters. In addition, we obtain similar optimality results for
+Gibbs sampling and an iterative procedure to calculate maximum likelihood
+estimation, which can be of independent interest.
+",0,0,1,1,0,0
+13732,The Chandra Deep Field South as a test case for Global Multi Conjugate Adaptive Optics,"  The era of the next generation of giant telescopes requires not only the
+advent of new technologies but also the development of novel methods, in order
+to exploit fully the extraordinary potential they are built for. Global Multi
+Conjugate Adaptive Optics (GMCAO) pursues this approach, with the goal of
+achieving good performance over a field of view of a few arcmin and an increase
+in sky coverage. In this article, we show the gain offered by this technique to
+an astrophysical application, such as the photometric survey strategy applied
+to the Chandra Deep Field South as a case study. We simulated a close-to-real
+observation of a 500 x 500 arcsec^2 extragalactic deep field with a 40-m class
+telescope that implements GMCAO. We analysed mock K-band images of 6000
+high-redshift (up to z = 2.75) galaxies therein as if they were real to recover
+the initial input parameters. We attained 94.5 per cent completeness for source
+detection with SEXTRACTOR. We also measured the morphological parameters of all
+the sources with the two-dimensional fitting tools GALFIT. The agreement we
+found between recovered and intrinsic parameters demonstrates GMCAO as a
+reliable approach to assist extremely large telescope (ELT) observations of
+extragalactic interest.
+",0,1,0,0,0,0
+13733,Probabilistic Causal Analysis of Social Influence,"  Mastering the dynamics of social influence requires separating, in a database
+of information propagation traces, the genuine causal processes from temporal
+correlation, i.e., homophily and other spurious causes. However, most studies
+to characterize social influence, and, in general, most data-science analyses
+focus on correlations, statistical independence, or conditional independence.
+Only recently, there has been a resurgence of interest in ""causal data
+science"", e.g., grounded on causality theories. In this paper we adopt a
+principled causal approach to the analysis of social influence from
+information-propagation data, rooted in the theory of probabilistic causation.
+Our approach consists of two phases. In the first one, in order to avoid the
+pitfalls of misinterpreting causation when the data spans a mixture of several
+subtypes (""Simpson's paradox""), we partition the set of propagation traces into
+groups, in such a way that each group is as less contradictory as possible in
+terms of the hierarchical structure of information propagation. To achieve this
+goal, we borrow the notion of ""agony"" and define the Agony-bounded Partitioning
+problem, which we prove being hard, and for which we develop two efficient
+algorithms with approximation guarantees. In the second phase, for each group
+from the first phase, we apply a constrained MLE approach to ultimately learn a
+minimal causal topology. Experiments on synthetic data show that our method is
+able to retrieve the genuine causal arcs w.r.t. a ground-truth generative
+model. Experiments on real data show that, by focusing only on the extracted
+causal structures instead of the whole social graph, the effectiveness of
+predicting influence spread is significantly improved.
+",1,0,0,1,0,0
+13734,Advanced reduced-order models for moisture diffusion in porous media,"  It is of great concern to produce numerically efficient methods for moisture
+diffusion through porous media, capable of accurately calculate moisture
+distribution with a reduced computational effort. In this way, model reduction
+methods are promising approaches to bring a solution to this issue since they
+do not degrade the physical model and provide a significant reduction of
+computational cost. Therefore, this article explores in details the
+capabilities of two model-reduction techniques - the Spectral Reduced-Order
+Model (Spectral-ROM) and the Proper Generalised Decomposition (PGD) - to
+numerically solve moisture diffusive transfer through porous materials. Both
+approaches are applied to three different problems to provide clear examples of
+the construction and use of these reduced-order models. The methodology of both
+approaches is explained extensively so that the article can be used as a
+numerical benchmark by anyone interested in building a reduced-order model for
+diffusion problems in porous materials. Linear and non-linear unsteady
+behaviors of unidimensional moisture diffusion are investigated. The last case
+focuses on solving a parametric problem in which the solution depends on space,
+time and the diffusivity properties. Results have highlighted that both methods
+provide accurate solutions and enable to reduce significantly the order of the
+model around ten times lower than the large original model. It also allows an
+efficient computation of the physical phenomena with an error lower than
+10^{-2} when compared to a reference solution.
+",1,1,1,0,0,0
+13735,Gapless quantum spin chains: multiple dynamics and conformal wavefunctions,"  We study gapless quantum spin chains with spin 1/2 and 1: the Fredkin and
+Motzkin models. Their entangled groundstates are known exactly but not their
+excitation spectra. We first express the groundstates in the continuum which
+allows for the calculation of spin and entanglement properties in a unified
+fashion. Doing so, we uncover an emergent conformal-type symmetry, thus
+consolidating the connection to a widely studied family of Lifshitz quantum
+critical points in 2d. We then obtain the low lying excited states via
+large-scale DMRG simulations and find that the dynamical exponent is z = 3.2 in
+both cases. Other excited states show a different z, indicating that these
+models have multiple dynamics. Moreover, we modify the spin-1/2 model by adding
+a ferromagnetic Heisenberg term, which changes the entire spectrum. We track
+the resulting non-trivial evolution of the dynamical exponents using DMRG.
+Finally, we exploit an exact map from the quantum Hamiltonian to the
+non-equilibrium dynamics of a classical spin chain to shed light on the quantum
+dynamics.
+",0,1,0,0,0,0
+13736,Real-time public transport service-level monitoring using passive WiFi: a spectral clustering approach for train timetable estimation,"  A new area in which passive WiFi analytics have promise for delivering value
+is the real-time monitoring of public transport systems. One example is
+determining the true (as opposed to the published) timetable of a public
+transport system in real-time. In most cases, there are no other
+publicly-available sources for this information. Yet, it is indispensable for
+the real-time monitoring of public transport service levels. Furthermore, this
+information, if accurate and temporally fine-grained, can be used for very
+low-latency incident detection. In this work, we propose using spectral
+clustering based on trajectories derived from passive WiFi traces of users of a
+public transport system to infer the true timetable and two key performance
+indicators of the transport service, namely public transport vehicle headway
+and in-station dwell time. By detecting anomalous dwell times or headways, we
+demonstrate that a fast and accurate real-time incident-detection procedure can
+be obtained. The method we introduce makes use of the advantages of the
+high-frequency WiFi data, which provides very low-latency,
+universally-accessible information, while minimizing the impact of the noise in
+the data.
+",1,0,0,0,0,0
+13737,Finding Network Motifs in Large Graphs using Compression as a Measure of Relevance,"  We introduce a new method for finding network motifs: interesting or
+informative subgraph patterns in a network. Current methods for finding motifs
+rely on the frequency of the motif: specifically, subgraphs are motifs when
+their frequency in the data is high compared to the expected frequency under a
+null model. To compute this expectation, the search for motifs is normally
+repeated on as many as 1000 random graphs sampled from the null model; a
+prohibitively expensive step. We use ideas from the Minimum Description Length
+(MDL) literature to define a new measure of motif relevance, and a new
+algorithm for detecting motifs. Our method allows motif analysis to scale to
+networks with billions of links, while still resulting in informative motifs.
+",1,0,0,0,0,0
+13738,A Bag-of-Words Equivalent Recurrent Neural Network for Action Recognition,"  The traditional bag-of-words approach has found a wide range of applications
+in computer vision. The standard pipeline consists of a generation of a visual
+vocabulary, a quantization of the features into histograms of visual words, and
+a classification step for which usually a support vector machine in combination
+with a non-linear kernel is used. Given large amounts of data, however, the
+model suffers from a lack of discriminative power. This applies particularly
+for action recognition, where the vast amount of video features needs to be
+subsampled for unsupervised visual vocabulary generation. Moreover, the kernel
+computation can be very expensive on large datasets. In this work, we propose a
+recurrent neural network that is equivalent to the traditional bag-of-words
+approach but enables for the application of discriminative training. The model
+further allows to incorporate the kernel computation into the neural network
+directly, solving the complexity issue and allowing to represent the complete
+classification system within a single network. We evaluate our method on four
+recent action recognition benchmarks and show that the conventional model as
+well as sparse coding methods are outperformed.
+",1,0,0,0,0,0
+13739,Limit on graviton mass from galaxy cluster Abell 1689,"  To date, the only limit on graviton mass using galaxy clusters was obtained
+by Goldhaber and Nieto in 1974, using the fact that the orbits of galaxy
+clusters are bound and closed, and extend up to 580 kpc. From positing that
+only a Newtonian potential gives rise to such stable bound orbits, a limit on
+the graviton mass $m_g<10^{-29}$ eV was obtained (PRD 9,1119, 1974). Recently,
+it has been shown that one can obtain closed bound orbits for Yukawa potential
+(arXiv:1705.02444), thus invalidating the main \emph{ansatz} used in Goldhaber
+and Nieto to obtain the graviton mass bound. In order to obtain a revised
+estimate using galaxy clusters, we use dynamical mass models of the Abell 1689
+(A1689) galaxy cluster to check their compatibility with a Yukawa gravitational
+potential. We assume mass models for the gas, dark matter, and galaxies for
+A1689 from arXiv:1703.10219 and arXiv:1610.01543, who used this cluster to test
+various alternate gravity theories, which dispense with the need for dark
+matter. We quantify the deviations in the acceleration profile using these mass
+models assuming a Yukawa potential and that obtained assuming a Newtonian
+potential by calculating the $\chi^2$ residuals between the two profiles. Our
+estimated bound on the graviton mass ($m_g$) is thereby given by, $m_g < 1.37
+\times 10^{-29}$ eV or in terms of the graviton Compton wavelength of,
+$\lambda_g>9.1 \times 10^{19}$ km at 90\% confidence level.
+",0,1,0,0,0,0
+13740,Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks,"  Artificial neural networks (ANNs) may not be worth their computational/memory
+costs when used in mobile phones or embedded devices. Parameter-pruning
+algorithms combat these costs, with some algorithms capable of removing over
+90% of an ANN's weights without harming the ANN's performance. Removing weights
+from an ANN is a form of regularization, but existing pruning algorithms do not
+significantly improve generalization error. We show that pruning ANNs can
+improve generalization if pruning targets large weights instead of small
+weights. Applying our pruning algorithm to an ANN leads to a higher image
+classification accuracy on CIFAR-10 data than applying the popular regularizer
+dropout. The pruning couples this higher accuracy with an 85% reduction of the
+ANN's parameter count.
+",0,0,0,1,0,0
+13741,The Augustin Center and The Sphere Packing Bound For Memoryless Channels,"  For any channel with a convex constraint set and finite Augustin capacity,
+existence of a unique Augustin center and associated Erven-Harremoes bound are
+established. Augustin-Legendre capacity, center, and radius are introduced and
+proved to be equal to the corresponding Renyi-Gallager entities. Sphere packing
+bounds with polynomial prefactors are derived for codes on two families of
+channels: (possibly non-stationary) memoryless channels with multiple additive
+cost constraints and stationary memoryless channels with convex constraints on
+the empirical distribution of the input codewords.
+",1,0,0,0,0,0
+13742,SuperMinHash - A New Minwise Hashing Algorithm for Jaccard Similarity Estimation,"  This paper presents a new algorithm for calculating hash signatures of sets
+which can be directly used for Jaccard similarity estimation. The new approach
+is an improvement over the MinHash algorithm, because it has a better runtime
+behavior and the resulting signatures allow a more precise estimation of the
+Jaccard index.
+",1,0,0,0,0,0
+13743,On catastrophic forgetting and mode collapse in Generative Adversarial Networks,"  Generative Adversarial Networks (GAN) are one of the most prominent tools for
+learning complicated distributions. However, problems such as mode collapse and
+catastrophic forgetting, prevent GAN from learning the target distribution.
+These problems are usually studied independently from each other. In this
+paper, we show that both problems are present in GAN and their combined effect
+makes the training of GAN unstable. We also show that methods such as gradient
+penalties and momentum based optimizers can improve the stability of GAN by
+effectively preventing these problems from happening. Finally, we study a
+mechanism for mode collapse to occur and propagate in feedforward neural
+networks.
+",0,0,0,1,0,0
+13744,Metamodel Construction for Sensitivity Analysis,"  We propose to estimate a metamodel and the sensitivity indices of a complex
+model m in the Gaussian regression framework. Our approach combines methods for
+sensitivity analysis of complex models and statistical tools for sparse
+non-parametric estimation in multivariate Gaussian regression model. It rests
+on the construction of a metamodel for aproximating the Hoeffding-Sobol
+decomposition of m. This metamodel belongs to a reproducing kernel Hilbert
+space constructed as a direct sum of Hilbert spaces leading to a functional
+ANOVA decomposition. The estimation of the metamodel is carried out via a
+penalized least-squares minimization allowing to select the subsets of
+variables that contribute to predict the output. It allows to estimate the
+sensitivity indices of m. We establish an oracle-type inequality for the risk
+of the estimator, describe the procedure for estimating the metamodel and the
+sensitivity indices, and assess the performances of the procedure via a
+simulation study.
+",0,0,1,1,0,0
+13745,Dynamics of the brain extracellular matrix governed by interactions with neural cells,"  Neuronal and glial cells release diverse proteoglycans and glycoproteins,
+which aggregate in the extracellular space and form the extracellular matrix
+(ECM) that may in turn regulate major cellular functions. Brain cells also
+release extracellular proteases that may degrade the ECM, and both synthesis
+and degradation of ECM are activity-dependent. In this study we introduce a
+mathematical model describing population dynamics of neurons interacting with
+ECM molecules over extended timescales. It is demonstrated that depending on
+the prevalent biophysical mechanism of ECM-neuronal interactions, different
+dynamical regimes of ECM activity can be observed, including bistable states
+with stable stationary levels of ECM molecule concentration, spontaneous ECM
+oscillations, and coexistence of ECM oscillations and a stationary state,
+allowing dynamical switches between activity regimes.
+",0,0,0,0,1,0
+13746,AutoPerf: A Generalized Zero-Positive Learning System to Detect Software Performance Anomalies,"  We present AutoPerf, a generalized software performance regression diagnosis
+system. AutoPerf uses autoencoders, an unsupervised learning technique, and
+hardware performance counters to learn the performance signatures of a program.
+It then uses this knowledge to identify when newer versions of the program
+suffer from performance regressions, while simultaneously providing root cause
+analysis to help programmers debug the program's performance.
+AutoPerf is the first zero-positive learning performance regression diagnosis
+system. It trains entirely in the negative (non-anomalous) space to learn
+positive (anomalous) behaviors. We demonstrate AutoPerf's generality against
+three different types of performance regressions: (i) true sharing cache
+contention, (ii) false sharing cache contention, and (iii) NUMA latencies
+across 15 real world performance regressions and 7 open source programs. On
+average, AutoPerf exhibits only 3.7% profiling overhead and diagnoses more
+regressions than prior state-of-the-art approaches.
+",1,0,0,0,0,0
+13747,Building a bridge between Classical and Quantum Mechanics,"  The way Quantum Mechanics (QM) is introduced to people used to Classical
+Mechanics (CM) is by a complete change of the general methodology) despite QM
+historically stemming from CM as a means to explain experimental results.
+Therefore, it is desirable to build a bridge from CM to QM.
+This paper presents a generalization of CM to QM. It starts from the
+generalization of a point-like object and naturally arrives at the quantum
+state vector of quantum systems in the complex valued Hilbert space, its time
+evolution and quantum representation of a measurement apparatus of any size.
+Each time, when generalization is performed, there is a possibility to develop
+new theory giving up most simple generalizations. It is shown that a
+measurement apparatus is a special case of a general quantum object. An example
+of a measurement apparatus of an intermediate size is considered in the end.
+",0,1,0,0,0,0
+13748,Size-Independent Sample Complexity of Neural Networks,"  We study the sample complexity of learning neural networks, by providing new
+bounds on their Rademacher complexity assuming norm constraints on the
+parameter matrix of each layer. Compared to previous work, these complexity
+bounds have improved dependence on the network depth, and under some additional
+assumptions, are fully independent of the network size (both depth and width).
+These results are derived using some novel techniques, which may be of
+independent interest.
+",1,0,0,1,0,0
+13749,Copula Variational Bayes inference via information geometry,"  Variational Bayes (VB), also known as independent mean-field approximation,
+has become a popular method for Bayesian network inference in recent years. Its
+application is vast, e.g. in neural network, compressed sensing, clustering,
+etc. to name just a few. In this paper, the independence constraint in VB will
+be relaxed to a conditional constraint class, called copula in statistics.
+Since a joint probability distribution always belongs to a copula class, the
+novel copula VB (CVB) approximation is a generalized form of VB. Via
+information geometry, we will see that CVB algorithm iteratively projects the
+original joint distribution to a copula constraint space until it reaches a
+local minimum Kullback-Leibler (KL) divergence. By this way, all mean-field
+approximations, e.g. iterative VB, Expectation-Maximization (EM), Iterated
+Conditional Mode (ICM) and k-means algorithms, are special cases of CVB
+approximation.
+For a generic Bayesian network, an augmented hierarchy form of CVB will also
+be designed. While mean-field algorithms can only return a locally optimal
+approximation for a correlated network, the augmented CVB network, which is an
+optimally weighted average of a mixture of simpler network structures, can
+potentially achieve the globally optimal approximation for the first time. Via
+simulations of Gaussian mixture clustering, the classification's accuracy of
+CVB will be shown to be far superior to that of state-of-the-art VB, EM and
+k-means algorithms.
+",0,0,0,1,0,0
+13750,Effect of increasing disorder on domains of the two-dimensional Coulomb glass,"  We have studied a two dimensional lattice model of Coulomb glass for a wide
+range of disorders at $T\sim 0$. The system was first annealed using Monte
+Carlo simulation. Further minimization of the total energy of the system was
+done using Baranovskii et al algorithm followed by cluster flipping to obtain
+the pseudo ground states. We have shown that the energy required to create a
+domain of linear size L in d dimensions is proportional to $L^{d-1}$. Using
+Imry-Ma arguments given for random field Ising model, one gets critical
+dimension $d_{c}\geq 2$ for Coulomb glass. The investigations of domains in the
+transition region shows a discontinuity in staggered magnetization which is an
+indication of a first-order type transition from charge-ordered phase to
+disordered phase. The structure and nature of Random field fluctuations of the
+second largest domain in Coulomb glass are inconsistent with the assumptions of
+Imry and Ma as was also reported for random field Ising model. The study of
+domains showed that in the transition region there were mostly two large
+domains and as disorder was increased, the two large domains remained but there
+were a large number of small domains. We have also studied the properties of
+the second largest domain as a function of disorder. We furthermore analysed
+the effect of disorder on the density of states and showed a transition from
+hard gap at low disorders to a soft gap at higher disorders. At $W=2$, we have
+analysed the soft gap in detail and found that the density of states deviates
+slightly ($\delta\approx 1.293 \pm 0.027$) from the linear behaviour in two
+dimensions. Analysis of local minima show that the pseudo ground states have
+similar structure.
+",0,1,0,0,0,0
+13751,"A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market","  We propose a dynamic network model where two mechanisms control the
+probability of a link between two nodes: (i) the existence or absence of this
+link in the past, and (ii) node-specific latent variables (dynamic fitnesses)
+describing the propensity of each node to create links. Assuming a Markov
+dynamics for both mechanisms, we propose an Expectation-Maximization algorithm
+for model estimation and inference of the latent variables. The estimated
+parameters and fitnesses can be used to forecast the presence of a link in the
+future. We apply our methodology to the e-MID interbank network for which the
+two linkage mechanisms are associated with two different trading behaviors in
+the process of network formation, namely preferential trading and trading
+driven by node-specific characteristics. The empirical results allow to
+recognise preferential lending in the interbank market and indicate how a
+method that does not account for time-varying network topologies tends to
+overestimate preferential linkage.
+",1,0,0,1,0,1
+13752,Predicting stock market movements using network science: An information theoretic approach,"  A stock market is considered as one of the highly complex systems, which
+consists of many components whose prices move up and down without having a
+clear pattern. The complex nature of a stock market challenges us on making a
+reliable prediction of its future movements. In this paper, we aim at building
+a new method to forecast the future movements of Standard & Poor's 500 Index
+(S&P 500) by constructing time-series complex networks of S&P 500 underlying
+companies by connecting them with links whose weights are given by the mutual
+information of 60-minute price movements of the pairs of the companies with the
+consecutive 5,340 minutes price records. We showed that the changes in the
+strength distributions of the networks provide an important information on the
+network's future movements. We built several metrics using the strength
+distributions and network measurements such as centrality, and we combined the
+best two predictors by performing a linear combination. We found that the
+combined predictor and the changes in S&P 500 show a quadratic relationship,
+and it allows us to predict the amplitude of the one step future change in S&P
+500. The result showed significant fluctuations in S&P 500 Index when the
+combined predictor was high. In terms of making the actual index predictions,
+we built ARIMA models. We found that adding the network measurements into the
+ARIMA models improves the model accuracy. These findings are useful for
+financial market policy makers as an indicator based on which they can
+interfere with the markets before the markets make a drastic change, and for
+quantitative investors to improve their forecasting models.
+",1,1,0,0,0,0
+13753,Bi-monotonic independence for pairs of algebras,"  In this article, the notion of bi-monotonic independence is introduced as an
+extension of monotonic independence to the two-faced framework for a family of
+pairs of algebras in a non-commutative space. The associated cumulants are
+defined and a moment-cumulant formula is derived in the bi-monotonic setting.
+In general the bi-monotonic product of states is not a state and the
+bi-monotonic convolution of probability measures on the plane is not a
+probability measure. This provides an additional example of how positivity need
+not be preserved under conditional bi-free convolutions.
+",0,0,1,0,0,0
+13754,Evidence of Significant Energy Input in the Late Phase of a Solar Flare from NuSTAR X-Ray Observations,"  We present observations of the occulted active region AR12222 during the
+third {\em NuSTAR} solar campaign on 2014 December 11, with concurrent {\em
+SDO/}AIA and {\em FOXSI-2} sounding rocket observations. The active region
+produced a medium size solar flare one day before the observations, at
+$\sim18$UT on 2014 December 10, with the post-flare loops still visible at the
+time of {\em NuSTAR} observations. The time evolution of the source emission in
+the {\em SDO/}AIA $335\textrm{\AA}$ channel reveals the characteristics of an
+extreme-ultraviolet late phase event, caused by the continuous formation of new
+post-flare loops that arch higher and higher in the solar corona. The spectral
+fitting of {\em NuSTAR} observations yields an isothermal source, with
+temperature $3.8-4.6$ MK, emission measure $0.3-1.8 \times 10^{46}\textrm{
+cm}^{-3}$, and density estimated at $2.5-6.0 \times 10^8 \textrm{ cm}^{-3}$.
+The observed AIA fluxes are consistent with the derived {\em NuSTAR}
+temperature range, favoring temperature values in the range $4.0-4.3$ MK. By
+examining the post-flare loops' cooling times and energy content, we estimate
+that at least 12 sets of post-flare loops were formed and subsequently cooled
+between the onset of the flare and {\em NuSTAR} observations, with their total
+thermal energy content an order of magnitude larger than the energy content at
+flare peak time. This indicates that the standard approach of using only the
+flare peak time to derive the total thermal energy content of a flare can lead
+to a large underestimation of its value.
+",0,1,0,0,0,0
+13755,Gender Bias in Sharenting: Both Men and Women Mention Sons More Often Than Daughters on Social Media,"  Gender inequality starts before birth. Parents tend to prefer boys over
+girls, which is manifested in reproductive behavior, marital life, and parents'
+pastimes and investments in their children. While social media and sharing
+information about children (so-called ""sharenting"") have become an integral
+part of parenthood, it is not well-known if and how gender preference shapes
+online behavior of users. In this paper, we investigate public mentions of
+daughters and sons on social media. We use data from a popular social
+networking site on public posts from 635,665 users. We find that both men and
+women mention sons more often than daughters in their posts. We also find that
+posts featuring sons get more ""likes"" on average. Our results indicate that
+girls are underrepresented in parents' digital narratives about their children.
+This gender imbalance may send a message that girls are less important than
+boys, or that they deserve less attention, thus reinforcing gender inequality.
+",1,0,0,0,0,0
+13756,Spatial disease mapping using Directed Acyclic Graph Auto-Regressive (DAGAR) models,"  Hierarchical models for regionally aggregated disease incidence data commonly
+involve region specific latent random effects that are modelled jointly as
+having a multivariate Gaussian distribution. The covariance or precision matrix
+incorporates the spatial dependence between the regions. Common choices for the
+precision matrix include the widely used intrinsic conditional autoregressive
+model, which is singular, and its nonsingular extension which lacks
+interpretability. We propose a new parametric model for the precision matrix
+based on a directed acyclic graph representation of the spatial dependence. Our
+model guarantees positive definiteness and, hence, in addition to being a valid
+prior for regional spatially correlated random effects, can also directly model
+the outcome from dependent data like images and networks. Theoretical and
+empirical results demonstrate the interpretability of parameters in our model.
+Our precision matrix is sparse and the model is highly scalable for large
+datasets. We also derive a novel order-free version which remedies the
+dependence of directed acyclic graphs on the ordering of the regions by
+averaging over all possible orderings. The resulting precision matrix is
+available in closed form. We demonstrate the superior performance of our models
+over competing models using simulation experiments and a public health
+application.
+",0,0,0,1,0,0
+13757,Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation,"  Accelerometer measurements are the prime type of sensor information most
+think of when seeking to measure physical activity. On the market, there are
+many fitness measuring devices which aim to track calories burned and steps
+counted through the use of accelerometers. These measurements, though good
+enough for the average consumer, are noisy and unreliable in terms of the
+precision of measurement needed in a scientific setting. The contribution of
+this paper is an innovative and highly accurate regression method which uses an
+intermediary two-stage classification step to better direct the regression of
+energy expenditure values from accelerometer counts.
+We show that through an additional unsupervised layer of intermediate feature
+construction, we can leverage latent patterns within accelerometer counts to
+provide better grounds for activity classification than expert-constructed
+timeseries features. For this, our approach utilizes a mathematical model
+originating in natural language processing, the bag-of-words model, that has in
+the past years been appearing in diverse disciplines outside of the natural
+language processing field such as image processing. Further emphasizing the
+natural language connection to stochastics, we use a gaussian mixture model to
+learn the dictionary upon which the bag-of-words model is built. Moreover, we
+show that with the addition of these features, we're able to improve regression
+root mean-squared error of energy expenditure by approximately 1.4 units over
+existing state-of-the-art methods.
+",1,0,0,1,0,0
+13758,Investigation of the commensurate magnetic structure in heavy fermion CePt2In7 using magnetic resonant X-ray diffraction,"  We investigated the magnetic structure of the heavy fermion compound
+CePt$_2$In$_7$ below $T_N~=5.34(2)$ K using magnetic resonant X-ray diffraction
+at ambient pressure. The magnetic order is characterized by a commensurate
+propagation vector ${k}_{1/2}~=~\left( \frac{1}{2} , \frac{1}{2},
+\frac{1}{2}\right)$ with spins lying in the basal plane. Our measurements did
+not reveal the presence of an incommensurate order propagating along the high
+symmetry directions in reciprocal space but cannot exclude other incommensurate
+modulations or weak scattering intensities. The observed commensurate order can
+be described equivalently by either a single-${k}$ structure or by a
+multi-${k}$ structure. Furthermore we explain how a commensurate-only ordering
+may explain the broad distribution of internal fields observed in nuclear
+quadrupolar resonance experiments (Sakai et al. 2011, Phys. Rev. B 83 140408)
+that was previously attributed to an incommensurate order. We also report
+powder X-ray diffraction showing that the crystallographic structure of
+CePt$_2$In$_7$ changes monotonically with pressure up to $P~=~7.3$ GPa at room
+temperature. The determined bulk modulus $B_0~=~81.1(3)$ GPa is similar to the
+ones of the Ce-115 family. Broad diffraction peaks confirm the presence of
+pronounced strain in polycrystalline samples of CePt$_2$In$_7$. We discuss how
+strain effects can lead to different electronic and magnetic properties between
+polycrystalline and single crystal samples.
+",0,1,0,0,0,0
+13759,Constant-Time Predictive Distributions for Gaussian Processes,"  One of the most compelling features of Gaussian process (GP) regression is
+its ability to provide well-calibrated posterior distributions. Recent advances
+in inducing point methods have sped up GP marginal likelihood and posterior
+mean computations, leaving posterior covariance estimation and sampling as the
+remaining computational bottlenecks. In this paper we address these
+shortcomings by using the Lanczos algorithm to rapidly approximate the
+predictive covariance matrix. Our approach, which we refer to as LOVE (LanczOs
+Variance Estimates), substantially improves time and space complexity. In our
+experiments, LOVE computes covariances up to 2,000 times faster and draws
+samples 18,000 times faster than existing methods, all without sacrificing
+accuracy.
+",0,0,0,1,0,0
+13760,Analysis of Thompson Sampling for Gaussian Process Optimization in the Bandit Setting,"  We consider the global optimization of a function over a continuous domain.
+At every evaluation attempt, we can observe the function at a chosen point in
+the domain and we reap the reward of the value observed. We assume that drawing
+these observations are expensive and noisy. We frame it as a continuum-armed
+bandit problem with a Gaussian Process prior on the function. In this regime,
+most algorithms have been developed to minimize some form of regret. Contrary
+to this popular norm, in this paper, we study the convergence of the sequential
+point $\boldsymbol{x}^t$ to the global optimizer $\boldsymbol{x}^*$ for the
+Thompson Sampling approach. Under some assumptions and regularity conditions,
+we show an exponential rate of convergence to the true optimal.
+",0,0,0,1,0,0
+13761,Dependency Graph Approach for Multiprocessor Real-Time Synchronization,"  Over the years, many multiprocessor locking protocols have been designed and
+analyzed. However, the performance of these protocols highly depends on how the
+tasks are partitioned and prioritized and how the resources are shared locally
+and globally. This paper answers a few fundamental questions when real-time
+tasks share resources in multiprocessor systems. We explore the fundamental
+difficulty of the multiprocessor synchronization problem and show that a very
+simplified version of this problem is ${\mathcal NP}$-hard in the strong sense
+regardless of the number of processors and the underlying scheduling paradigm.
+Therefore, the allowance of preemption or migration does not reduce the
+computational complexity. For the positive side, we develop a dependency-graph
+approach, that is specifically useful for frame-based real-time tasks, in which
+all tasks have the same period and release their jobs always at the same time.
+We present a series of algorithms with speedup factors between $2$ and $3$
+under semi-partitioned scheduling. We further explore methodologies and
+tradeoffs of preemptive against non-preemptive scheduling algorithms and
+partitioned against semi-partitioned scheduling algorithms. The approach is
+extended to periodic tasks under certain conditions.
+",1,0,0,0,0,0
+13762,On topological cyclic homology,"  Topological cyclic homology is a refinement of Connes--Tsygan's cyclic
+homology which was introduced by Bökstedt--Hsiang--Madsen in 1993 as an
+approximation to algebraic $K$-theory. There is a trace map from algebraic
+$K$-theory to topological cyclic homology, and a theorem of
+Dundas--Goodwillie--McCarthy asserts that this induces an equivalence of
+relative theories for nilpotent immersions, which gives a way for computing
+$K$-theory in various situations. The construction of topological cyclic
+homology is based on genuine equivariant homotopy theory, the use of explicit
+point-set models, and the elaborate notion of a cyclotomic spectrum.
+The goal of this paper is to revisit this theory using only
+homotopy-invariant notions. In particular, we give a new construction of
+topological cyclic homology. This is based on a new definition of the
+$\infty$-category of cyclotomic spectra: We define a cyclotomic spectrum to be
+a spectrum $X$ with $S^1$-action (in the most naive sense) together with
+$S^1$-equivariant maps $\varphi_p: X\to X^{tC_p}$ for all primes $p$. Here
+$X^{tC_p}=\mathrm{cofib}(\mathrm{Nm}: X_{hC_p}\to X^{hC_p})$ is the Tate
+construction. On bounded below spectra, we prove that this agrees with previous
+definitions. As a consequence, we obtain a new and simple formula for
+topological cyclic homology.
+In order to construct the maps $\varphi_p: X\to X^{tC_p}$ in the example of
+topological Hochschild homology we introduce and study Tate diagonals for
+spectra and Frobenius homomorphisms of commutative ring spectra. In particular
+we prove a version of the Segal conjecture for the Tate diagonals and relate
+these Frobenius homomorphisms to power operations.
+",0,0,1,0,0,0
+13763,Local Partition in Rich Graphs,"  Local graph partitioning is a key graph mining tool that allows researchers
+to identify small groups of interrelated nodes (e.g. people) and their
+connective edges (e.g. interactions). Because local graph partitioning is
+primarily focused on the network structure of the graph (vertices and edges),
+it often fails to consider the additional information contained in the
+attributes. In this paper we propose---(i) a scalable algorithm to improve
+local graph partitioning by taking into account both the network structure of
+the graph and the attribute data and (ii) an application of the proposed local
+graph partitioning algorithm (AttriPart) to predict the evolution of local
+communities (LocalForecasting). Experimental results show that our proposed
+AttriPart algorithm finds up to 1.6$\times$ denser local partitions, while
+running approximately 43$\times$ faster than traditional local partitioning
+techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm shows
+a significant improvement in the number of nodes and edges correctly predicted
+over baseline methods.
+",1,0,0,0,0,0
+13764,A Transient Queueing Analysis under Time-varying Arrival and Service Rates for Enabling Low-Latency Services,"  Understanding the detailed queueing behavior of a networking session is
+critical in enabling low-latency services over the Internet. Especially when
+the packet arrival and service rates at the queue of a link vary over time and
+moreover when the session is short-lived, analyzing the corresponding queue
+behavior as a function of time, which involves a transient analysis, becomes
+extremely challenging. In this paper, we propose and develop a new analytical
+framework that anatomizes the transient queue behavior under time-varying
+arrival and service rates even under unstable conditions. Our framework is
+capable of answering key questions in designing low-latency services such as
+the time-dependent probability distribution of the queue length; the
+instantaneous or time-averaged violation probability that the queue length
+exceeds a certain threshold; and the fraction of time during an interval $[0,
+t]$ at which the queue length exceeds a certain threshold. We validate our
+framework by comparing its prediction results over time with the statistical
+simulation results and confirm that our analysis is accurate enough. Our
+extensive demonstrations on the efficacy of the analytical framework in
+designing low-latency services reveal that its prediction ability for the
+transient queue behavior in diverse time-varying packet arrival and service
+patterns can be of a high practical value.
+",1,0,0,0,0,0
+13765,SalientDSO: Bringing Attention to Direct Sparse Odometry,"  Although cluttered indoor scenes have a lot of useful high-level semantic
+information which can be used for mapping and localization, most Visual
+Odometry (VO) algorithms rely on the usage of geometric features such as
+points, lines and planes. Lately, driven by this idea, the joint optimization
+of semantic labels and obtaining odometry has gained popularity in the robotics
+community. The joint optimization is good for accurate results but is generally
+very slow. At the same time, in the vision community, direct and sparse
+approaches for VO have stricken the right balance between speed and accuracy.
+We merge the successes of these two communities and present a way to
+incorporate semantic information in the form of visual saliency to Direct
+Sparse Odometry - a highly successful direct sparse VO algorithm. We also
+present a framework to filter the visual saliency based on scene parsing. Our
+framework, SalientDSO, relies on the widely successful deep learning based
+approaches for visual saliency and scene parsing which drives the feature
+selection for obtaining highly-accurate and robust VO even in the presence of
+as few as 40 point features per frame. We provide extensive quantitative
+evaluation of SalientDSO on the ICL-NUIM and TUM monoVO datasets and show that
+we outperform DSO and ORB-SLAM - two very popular state-of-the-art approaches
+in the literature. We also collect and publicly release a CVL-UMD dataset which
+contains two indoor cluttered sequences on which we show qualitative
+evaluations. To our knowledge this is the first paper to use visual saliency
+and scene parsing to drive the feature selection in direct VO.
+",1,0,0,0,0,0
+13766,Asynchronous Decentralized Parallel Stochastic Gradient Descent,"  Most commonly used distributed machine learning systems are either
+synchronous or centralized asynchronous. Synchronous algorithms like
+AllReduce-SGD perform poorly in a heterogeneous environment, while asynchronous
+algorithms using a parameter server suffer from 1) communication bottleneck at
+parameter servers when workers are many, and 2) significantly worse convergence
+when the traffic to parameter server is congested. Can we design an algorithm
+that is robust in a heterogeneous environment, while being communication
+efficient and maintaining the best-possible convergence rate? In this paper, we
+propose an asynchronous decentralized stochastic gradient decent algorithm
+(AD-PSGD) satisfying all above expectations. Our theoretical analysis shows
+AD-PSGD converges at the optimal $O(1/\sqrt{K})$ rate as SGD and has linear
+speedup w.r.t. number of workers. Empirically, AD-PSGD outperforms the best of
+decentralized parallel SGD (D-PSGD), asynchronous parallel SGD (A-PSGD), and
+standard data parallel SGD (AllReduce-SGD), often by orders of magnitude in a
+heterogeneous environment. When training ResNet-50 on ImageNet with up to 128
+GPUs, AD-PSGD converges (w.r.t epochs) similarly to the AllReduce-SGD, but each
+epoch can be up to 4-8X faster than its synchronous counterparts in a
+network-sharing HPC environment. To the best of our knowledge, AD-PSGD is the
+first asynchronous algorithm that achieves a similar epoch-wise convergence
+rate as AllReduce-SGD, at an over 100-GPU scale.
+",1,0,0,1,0,0
+13767,Seamless Resources Sharing in Wearable Networks by Application Function Virtualization,"  The prevalence of smart wearable devices is increasing exponentially and we
+are witnessing a wide variety of fascinating new services that leverage the
+capabilities of these wearables. Wearables are truly changing the way mobile
+computing is deployed and mobile applications are being developed. It is
+possible to leverage the capabilities such as connectivity, processing, and
+sensing of wearable devices in an adaptive manner for efficient resource usage
+and information accuracy within the personal area network. We show that
+application developers are not yet taking advantage of these cross-device
+capabilities, however, instead using wearables as passive sensors or simple end
+displays to provide notifications to the user. We thus design AFV (Application
+Function Virtualization), an architecture enabling automated dynamic function
+virtualization and scheduling across devices in a personal area network,
+simplifying the development of the apps that are adaptive to context changes.
+AFV provides a simple set of APIs hiding complex architectural tasks from app
+developers whilst continuously monitoring the user, device and network context,
+to enable the adaptive invocation of functions across devices. We show the
+feasibility of our design by implementing AFV on Android, and the benefits for
+the user in terms of resource efficiency, especially in saving energy
+consumption, and quality of experience with multiple use cases.
+",1,0,0,0,0,0
+13768,Combining Model-Free Q-Ensembles and Model-Based Approaches for Informed Exploration,"  Q-Ensembles are a model-free approach where input images are fed into
+different Q-networks and exploration is driven by the assumption that
+uncertainty is proportional to the variance of the output Q-values obtained.
+They have been shown to perform relatively well compared to other exploration
+strategies. Further, model-based approaches, such as encoder-decoder models
+have been used successfully for next frame prediction given previous frames.
+This paper proposes to integrate the model-free Q-ensembles and model-based
+approaches with the hope of compounding the benefits of both and achieving
+superior exploration as a result. Results show that a model-based trajectory
+memory approach when combined with Q-ensembles produces superior performance
+when compared to only using Q-ensembles.
+",0,0,0,1,0,0
+13769,Creating a Cybersecurity Concept Inventory: A Status Report on the CATS Project,"  We report on the status of our Cybersecurity Assessment Tools (CATS) project
+that is creating and validating a concept inventory for cybersecurity, which
+assesses the quality of instruction of any first course in cybersecurity. In
+fall 2014, we carried out a Delphi process that identified core concepts of
+cybersecurity. In spring 2016, we interviewed twenty-six students to uncover
+their understandings and misconceptions about these concepts. In fall 2016, we
+generated our first assessment tool--a draft Cybersecurity Concept Inventory
+(CCI), comprising approximately thirty multiple-choice questions. Each question
+targets a concept; incorrect answers are based on observed misconceptions from
+the interviews. This year we are validating the draft CCI using cognitive
+interviews, expert reviews, and psychometric testing. In this paper, we
+highlight our progress to date in developing the CCI.
+The CATS project provides infrastructure for a rigorous evidence-based
+improvement of cybersecurity education. The CCI permits comparisons of
+different instructional methods by assessing how well students learned the core
+concepts of the field (especially adversarial thinking), where instructional
+methods refer to how material is taught (e.g., lab-based, case-studies,
+collaborative, competitions, gaming). Specifically, the CCI is a tool that will
+enable researchers to scientifically quantify and measure the effect of their
+approaches to, and interventions in, cybersecurity education.
+",1,0,0,0,0,0
+13770,Critical Learning Periods in Deep Neural Networks,"  Critical periods are phases in the early development of humans and animals
+during which experience can irreversibly affect the architecture of neuronal
+networks. In this work, we study the effects of visual stimulus deficits on the
+training of artificial neural networks (ANNs). Introducing well-characterized
+visual deficits, such as cataract-like blurring, in the early training phase of
+a standard deep neural network causes a permanent performance loss that closely
+mimics critical period behavior in humans and animal models. Deficits that do
+not affect low-level image statistics, such as vertical flipping of the images,
+have no lasting effect on the ANNs' performance and can be rapidly overcome
+with further training. In addition, the deeper the ANN is, the more pronounced
+the critical period. To better understand this phenomenon, we use Fisher
+Information as a measure of the strength of the network's connections during
+the training. Our information-theoretic analysis suggests that the first few
+epochs are critical for the creation of strong connections across different
+layers, optimal for processing the input data distribution. Once such strong
+connections are created, they do not appear to change during additional
+training. These findings suggest that the initial rapid learning phase of ANN
+training, under-scrutinized compared to its asymptotic behavior, plays a key
+role in defining the final performance of networks. Our results also show how
+critical periods are not restricted to biological systems, but can emerge
+naturally in learning systems, whether biological or artificial, due to
+fundamental constrains arising from learning dynamics and information
+processing.
+",1,0,0,1,0,0
+13771,Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds,"  In this work we present a whole-body Nonlinear Model Predictive Control
+approach for Rigid Body Systems subject to contacts. We use a full dynamic
+system model which also includes explicit contact dynamics. Therefore, contact
+locations, sequences and timings are not prespecified but optimized by the
+solver. Yet, thorough numerical and software engineering allows for running the
+nonlinear Optimal Control solver at rates up to 190 Hz on a quadruped for a
+time horizon of half a second. This outperforms the state of the art by at
+least one order of magnitude. Hardware experiments in form of periodic and
+non-periodic tasks are applied to two quadrupeds with different actuation
+systems. The obtained results underline the performance, transferability and
+robustness of the approach.
+",1,0,0,0,0,0
+13772,An informative path planning framework for UAV-based terrain monitoring,"  Unmanned aerial vehicles (UAVs) represent a new frontier in a wide range of
+monitoring and research applications. To fully leverage their potential, a key
+challenge is planning missions for efficient data acquisition in complex
+environments. To address this issue, this article introduces a general
+informative path planning (IPP) framework for monitoring scenarios using an
+aerial robot. The approach is capable of mapping either discrete or continuous
+target variables on a terrain using variable-resolution data received from
+probabilistic sensors. During a mission, the terrain maps built online are used
+to plan information-rich trajectories in continuous 3-D space by optimizing
+initial solutions obtained by a course grid search. Extensive simulations show
+that our approach is more efficient than existing methods. We also demonstrate
+its real-time application on a photorealistic mapping scenario using a publicly
+available dataset.
+",1,0,0,0,0,0
+13773,Machine Learning on Sequential Data Using a Recurrent Weighted Average,"  Recurrent Neural Networks (RNN) are a type of statistical model designed to
+handle sequential data. The model reads a sequence one symbol at a time. Each
+symbol is processed based on information collected from the previous symbols.
+With existing RNN architectures, each symbol is processed using only
+information from the previous processing step. To overcome this limitation, we
+propose a new kind of RNN model that computes a recurrent weighted average
+(RWA) over every past processing step. Because the RWA can be computed as a
+running average, the computational overhead scales like that of any other RNN
+architecture. The approach essentially reformulates the attention mechanism
+into a stand-alone model. The performance of the RWA model is assessed on the
+variable copy problem, the adding problem, classification of artificial
+grammar, classification of sequences by length, and classification of the MNIST
+images (where the pixels are read sequentially one at a time). On almost every
+task, the RWA model is found to outperform a standard LSTM model.
+",1,0,0,1,0,0
+13774,Feynman-Kac equation for anomalous processes with space- and time-dependent forces,"  Functionals of a stochastic process Y(t) model many physical time-extensive
+observables, e.g. particle positions, local and occupation times or accumulated
+mechanical work. When Y(t) is a normal diffusive process, their statistics are
+obtained as the solution of the Feynman-Kac equation. This equation provides
+the crucial link between the expected values of diffusion processes and the
+solutions of deterministic second-order partial differential equations. When
+Y(t) is an anomalous diffusive process, generalizations of the Feynman-Kac
+equation that incorporate power-law or more general waiting time distributions
+of the underlying random walk have recently been derived. A general
+representation of such waiting times is provided in terms of a Lévy process
+whose Laplace exponent is related to the memory kernel appearing in the
+generalized Feynman-Kac equation. The corresponding anomalous processes have
+been shown to capture nonlinear mean square displacements exhibiting crossovers
+between different scaling regimes, which have been observed in biological
+systems like migrating cells or diffusing macromolecules in intracellular
+environments. However, the case where both space- and time-dependent forces
+drive the dynamics of the generalized anomalous process has not been solved
+yet. Here, we present the missing derivation of the Feynman-Kac equation in
+such general case by using the subordination technique. Furthermore, we discuss
+its extension to functionals explicitly depending on time, which are relevant
+for the stochastic thermodynamics of anomalous diffusive systems. Exact results
+on the work fluctuations of a simple non-equilibrium model are obtained. In
+this paper we also provide a pedagogical introduction to Lévy processes,
+semimartingales and their associated stochastic calculus, which underlie the
+mathematical formulation of anomalous diffusion as a subordinated process.
+",0,1,1,0,0,0
+13775,An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems,"  As efficient traffic-management platforms, public vehicle (PV) systems are
+envisioned to be a promising approach to solving traffic congestions and
+pollutions for future smart cities. PV systems provide online/dynamic
+peer-to-peer ride-sharing services with the goal of serving sufficient number
+of customers with minimum number of vehicles and lowest possible cost. A key
+component of the PV system is the online ride-sharing scheduling strategy. In
+this paper, we propose an efficient path planning strategy that focuses on a
+limited potential search area for each vehicle by filtering out the requests
+that violate passenger service quality level, so that the global search is
+reduced to local search. We analyze the performance of the proposed solution
+such as reduction ratio of computational complexity. Simulations based on the
+Manhattan taxi data set show that, the computing time is reduced by 22%
+compared with the exhaustive search method under the same service quality
+performance.
+",1,0,0,0,0,0
+13776,Ask the Right Questions: Active Question Reformulation with Reinforcement Learning,"  We frame Question Answering (QA) as a Reinforcement Learning task, an
+approach that we call Active Question Answering. We propose an agent that sits
+between the user and a black box QA system and learns to reformulate questions
+to elicit the best possible answers. The agent probes the system with,
+potentially many, natural language reformulations of an initial question and
+aggregates the returned evidence to yield the best answer. The reformulation
+system is trained end-to-end to maximize answer quality using policy gradient.
+We evaluate on SearchQA, a dataset of complex questions extracted from
+Jeopardy!. The agent outperforms a state-of-the-art base model, playing the
+role of the environment, and other benchmarks. We also analyze the language
+that the agent has learned while interacting with the question answering
+system. We find that successful question reformulations look quite different
+from natural language paraphrases. The agent is able to discover non-trivial
+reformulation strategies that resemble classic information retrieval techniques
+such as term re-weighting (tf-idf) and stemming.
+",1,0,0,0,0,0
+13777,Time consistency for scalar multivariate risk measures,"  In this paper we present results on dynamic multivariate scalar risk
+measures, which arise in markets with transaction costs and systemic risk. Dual
+representations of such risk measures are presented. These are then used to
+obtain the main results of this paper on time consistency; namely, an
+equivalent recursive formulation of multivariate scalar risk measures to
+multiportfolio time consistency. We are motivated to study time consistency of
+multivariate scalar risk measures as the superhedging risk measure in markets
+with transaction costs (with a single eligible asset) (Jouini and Kallal
+(1995), Roux and Zastawniak (2016), Loehne and Rudloff (2014)) does not satisfy
+the usual scalar concept of time consistency. In fact, as demonstrated in
+(Feinstein and Rudloff (2018)), scalar risk measures with the same
+scalarization weight at all times would not be time consistent in general. The
+deduced recursive relation for the scalarizations of multiportfolio time
+consistent set-valued risk measures provided in this paper requires
+consideration of the entire family of scalarizations. In this way we develop a
+direct notion of a ""moving scalarization"" for scalar time consistency that
+corroborates recent research on scalarizations of dynamic multi-objective
+problems (Karnam, Ma, and Zhang (2017), Kovacova and Rudloff (2018)).
+",0,0,0,0,0,1
+13778,A Continuous Beam Steering Slotted Waveguide Antenna Using Rotating Dielectric Slabs,"  The design, simulation and measurement of a beam steerable slotted waveguide
+antenna operating in X band are presented. The proposed beam steerable antenna
+consists of a standard rectangular waveguide (RWG) section with longitudinal
+slots in the broad wall. The beam steering in this configuration is achieved by
+rotating two dielectric slabs inside the waveguide and consequently changing
+the phase of the slots excitations. In order to confirm the usefulness of this
+concept, a non-resonant 20-slot waveguide array antenna with an element spacing
+of d = 0.58{\lambda}0 has been designed, built and measured. A 14 deg beam
+scanning from near broadside ({\theta} = 4 deg) toward end-fire ({\theta} = 18
+deg) direction is observed. The gain varies from 18.33 dB to 19.11 dB which
+corresponds to the radiation efficiencies between 95% and 79%. The side-lobe
+level is -14 dB at the design frequency of 9.35 GHz. The simulated co-polarized
+realized gain closely matches the fabricated prototype patterns.
+",0,1,0,0,0,0
+13779,Developing an edge computing platform for real-time descriptive analytics,"  The Internet of Mobile Things encompasses stream data being generated by
+sensors, network communications that pull and push these data streams, as well
+as running processing and analytics that can effectively leverage actionable
+information for transportation planning, management, and business advantage.
+Edge computing emerges as a new paradigm that decentralizes the communication,
+computation, control and storage resources from the cloud to the edge of the
+network. This paper proposes an edge computing platform where mobile edge nodes
+are physical devices deployed on a transit bus where descriptive analytics is
+used to uncover meaningful patterns from real-time transit data streams. An
+application experiment is used to evaluate the advantages and disadvantages of
+our proposed platform to support descriptive analytics at a mobile edge node
+and generate actionable information to transit managers.
+",1,0,0,0,0,0
+13780,Space Telescope and Optical Reverberation Mapping Project. VII. Understanding the UV anomaly in NGC 5548 with X-Ray Spectroscopy,"  During the Space Telescope and Optical Reverberation Mapping Project (STORM)
+observations of NGC 5548, the continuum and emission-line variability became
+de-correlated during the second half of the 6-month long observing campaign.
+Here we present Swift and Chandra X-ray spectra of NGC 5548 obtained as a part
+of the campaign. The Swift spectra show that excess flux (relative to a
+power-law continuum) in the soft X-ray band appears before the start of the
+anomalous emission-line behavior, peaks during the period of the anomaly, and
+then declines. This is a model-independent result suggesting that the soft
+excess is related to the anomaly. We divide the Swift data into on- and
+off-anomaly spectra to characterize the soft excess via spectral fitting. The
+cause of the spectral differences is likely due to a change in the intrinsic
+spectrum rather than being due to variable obscuration or partial covering. The
+Chandra spectra have lower signal-to-noise ratios, but are consistent with
+Swift data. Our preferred model of the soft excess is emission from an
+optically thick, warm Comptonizing corona, the effective optical depth of which
+increases during the anomaly. This model simultaneously explains all the three
+observations: the UV emission line flux decrease, the soft-excess increase, and
+the emission line anomaly.
+",0,1,0,0,0,0
+13781,Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge,"  We present the grasping system and design approach behind Cartman, the
+winning entrant in the 2017 Amazon Robotics Challenge. We investigate the
+design processes leading up to the final iteration of the system and describe
+the emergent solution by comparing it with key robotics design aspects.
+Following our experience, we propose a new design aspect, precision vs.
+redundancy, that should be considered alongside the previously proposed design
+aspects of modularity vs. integration, generality vs. assumptions, computation
+vs. embodiment and planning vs. feedback. We present the grasping system behind
+Cartman, the winning robot in the 2017 Amazon Robotics Challenge. The system
+makes strong use of redundancy in design by implementing complimentary tools, a
+suction gripper and a parallel gripper. This multi-modal end-effector is
+combined with three grasp synthesis algorithms to accommodate the range of
+objects provided by Amazon during the challenge. We provide a detailed system
+description and an evaluation of its performance before discussing the broader
+nature of the system with respect to the key aspects of robotic design as
+initially proposed by the winners of the first Amazon Picking Challenge. To
+address the principal nature of our grasping system and the reason for its
+success, we propose an additional robotic design aspect `precision vs.
+redundancy'. The full design of our robotic system, including the end-effector,
+is open sourced and available at
+this http URL
+",1,0,0,0,0,0
+13782,Coping with Construals in Broad-Coverage Semantic Annotation of Adpositions,"  We consider the semantics of prepositions, revisiting a broad-coverage
+annotation scheme used for annotating all 4,250 preposition tokens in a 55,000
+word corpus of English. Attempts to apply the scheme to adpositions and case
+markers in other languages, as well as some problematic cases in English, have
+led us to reconsider the assumption that a preposition's lexical contribution
+is equivalent to the role/relation that it mediates. Our proposal is to embrace
+the potential for construal in adposition use, expressing such phenomena
+directly at the token level to manage complexity and avoid sense proliferation.
+We suggest a framework to represent both the scene role and the adposition's
+lexical function so they can be annotated at scale---supporting automatic,
+statistical processing of domain-general language---and sketch how this
+representation would inform a constructional analysis.
+",1,0,0,0,0,0
+13783,A Framework for Generalizing Graph-based Representation Learning Methods,"  Random walks are at the heart of many existing deep learning algorithms for
+graph data. However, such algorithms have many limitations that arise from the
+use of random walks, e.g., the features resulting from these methods are unable
+to transfer to new nodes and graphs as they are tied to node identity. In this
+work, we introduce the notion of attributed random walks which serves as a
+basis for generalizing existing methods such as DeepWalk, node2vec, and many
+others that leverage random walks. Our proposed framework enables these methods
+to be more widely applicable for both transductive and inductive learning as
+well as for use on graphs with attributes (if available). This is achieved by
+learning functions that generalize to new nodes and graphs. We show that our
+proposed framework is effective with an average AUC improvement of 16.1% while
+requiring on average 853 times less space than existing methods on a variety of
+graphs from several domains.
+",1,0,0,1,0,0
+13784,A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data,"  Gated Recurrent Unit (GRU) is a recently-developed variation of the long
+short-term memory (LSTM) unit, both of which are types of recurrent neural
+network (RNN). Through empirical evidence, both models have been proven to be
+effective in a wide variety of machine learning tasks such as natural language
+processing (Wen et al., 2015), speech recognition (Chorowski et al., 2015), and
+text classification (Yang et al., 2016). Conventionally, like most neural
+networks, both of the aforementioned RNN variants employ the Softmax function
+as its final output layer for its prediction, and the cross-entropy function
+for computing its loss. In this paper, we present an amendment to this norm by
+introducing linear support vector machine (SVM) as the replacement for Softmax
+in the final output layer of a GRU model. Furthermore, the cross-entropy
+function shall be replaced with a margin-based function. While there have been
+similar studies (Alalshekmubarak & Smith, 2013; Tang, 2013), this proposal is
+primarily intended for binary classification on intrusion detection using the
+2013 network traffic data from the honeypot systems of Kyoto University.
+Results show that the GRU-SVM model performs relatively higher than the
+conventional GRU-Softmax model. The proposed model reached a training accuracy
+of ~81.54% and a testing accuracy of ~84.15%, while the latter was able to
+reach a training accuracy of ~63.07% and a testing accuracy of ~70.75%. In
+addition, the juxtaposition of these two final output layers indicate that the
+SVM would outperform Softmax in prediction time - a theoretical implication
+which was supported by the actual training and testing time in the study.
+",1,0,0,1,0,0
+13785,Spelling Correction as a Foreign Language,"  In this paper, we reformulated the spell correction problem as a machine
+translation task under the encoder-decoder framework. This reformulation
+enabled us to use a single model for solving the problem that is traditionally
+formulated as learning a language model and an error model. This model employs
+multi-layer recurrent neural networks as an encoder and a decoder. We
+demonstrate the effectiveness of this model using an internal dataset, where
+the training data is automatically obtained from user logs. The model offers
+competitive performance as compared to the state of the art methods but does
+not require any feature engineering nor hand tuning between models.
+",1,0,0,0,0,0
+13786,Intrinsic p-type W-based transition metal dichalcogenide by substitutional Ta-doping,"  Two-dimensional (2D) transition metal dichalcogenides (TMDs) have recently
+emerged as promising candidates for future electronics and optoelectronics.
+While most of TMDs are intrinsic n-type semiconductors due to electron donating
+which originates from chalcogen vacancies, obtaining intrinsic high-quality
+p-type semiconducting TMDs has been challenging. Here, we report an
+experimental approach to obtain intrinsic p-type Tungsten (W)-based TMDs by
+substitutional Ta-doping. The obtained few-layer Ta-doped WSe2 (Ta0.01W0.99Se2)
+field-effect transistor (FET) devices exhibit competitive p-type performances,
+including ~10^6 current on/off at room temperature. We also demonstrate high
+quality van der Waals (vdW) p-n heterojunctions based on Ta0.01W0.99Se2/MoS2
+structure, which exhibit nearly ideal diode characteristics (with an ideality
+factor approaching 1 and a rectification ratio up to 10^5) and excellent
+photodetecting performance. Our study suggests that substitutional Ta-doping
+holds great promise to realize intrinsic p-type W-based TMDs for future
+electronic and photonic applications.
+",0,1,0,0,0,0
+13787,Synchronisation of Partial Multi-Matchings via Non-negative Factorisations,"  In this work we study permutation synchronisation for the challenging case of
+partial permutations, which plays an important role for the problem of matching
+multiple objects (e.g. images or shapes). The term synchronisation refers to
+the property that the set of pairwise matchings is cycle-consistent, i.e. in
+the full matching case all compositions of pairwise matchings over cycles must
+be equal to the identity. Motivated by clustering and matrix factorisation
+perspectives of cycle-consistency, we derive an algorithm to tackle the
+permutation synchronisation problem based on non-negative factorisations. In
+order to deal with the inherent non-convexity of the permutation
+synchronisation problem, we use an initialisation procedure based on a novel
+rotation scheme applied to the solution of the spectral relaxation. Moreover,
+this rotation scheme facilitates a convenient Euclidean projection to obtain a
+binary solution after solving our relaxed problem. In contrast to
+state-of-the-art methods, our approach is guaranteed to produce
+cycle-consistent results. We experimentally demonstrate the efficacy of our
+method and show that it achieves better results compared to existing methods.
+",0,0,0,1,0,0
+13788,Associated varieties and Higgs branches (a survey),"  Associated varieties of vertex algebras are analogue of the associated
+varieties of primitive ideals of the universal enveloping algebras of
+semisimple Lie algebras. They not only capture some of the important properties
+of vertex algebras but also have interesting relationship with the Higgs
+branches of four-dimensional $N=2$ superconformal field theories (SCFTs). As a
+consequence, one can deduce the modular invariance of Schur indices of 4d $N=2$
+SCFTs from the theory of vertex algebras.
+",0,0,1,0,0,0
+13789,Optimization Design of Decentralized Control for Complex Decentralized Systems,"  A new method is developed to deal with the problem that a complex
+decentralized control system needs to keep centralized control performance. The
+systematic procedure emphasizes quickly finding the decentralized
+subcontrollers that matching the closed-loop performance and robustness
+characteristics of the centralized controller, which is featured by the fact
+that GA is used to optimize the design of centralized H-infinity controller
+K(s) and decentralized engine subcontroller KT(s), and that only one interface
+variable needs to satisfy decentralized control system requirement according to
+the proposed selection principle. The optimization design is motivated by the
+implementation issues where it is desirable to reduce the time in trial and
+error process and accurately find the best decentralized subcontrollers. The
+method is applied to decentralized control system design for a short takeoff
+and landing fighter. By comparing the simulation results of the decentralized
+control system with those of the centralized control system, the target of the
+decentralized control attains the performance and robustness of centralized
+control is validated.
+",1,0,0,0,0,0
+13790,SAM: Semantic Attribute Modulation for Language Modeling and Style Variation,"  This paper presents a Semantic Attribute Modulation (SAM) for language
+modeling and style variation. The semantic attribute modulation includes
+various document attributes, such as titles, authors, and document categories.
+We consider two types of attributes, (title attributes and category
+attributes), and a flexible attribute selection scheme by automatically scoring
+them via an attribute attention mechanism. The semantic attributes are embedded
+into the hidden semantic space as the generation inputs. With the attributes
+properly harnessed, our proposed SAM can generate interpretable texts with
+regard to the input attributes. Qualitative analysis, including word semantic
+analysis and attention values, shows the interpretability of SAM. On several
+typical text datasets, we empirically demonstrate the superiority of the
+Semantic Attribute Modulated language model with different combinations of
+document attributes. Moreover, we present a style variation for the lyric
+generation using SAM, which shows a strong connection between the style
+variation and the semantic attributes.
+",1,0,0,1,0,0
+13791,A Flexible Procedure for Mixture Proportion Estimation in Positive--Unlabeled Learning,"  Positive--unlabeled (PU) learning considers two samples, a positive set P
+with observations from only one class and an unlabeled set U with observations
+from two classes. The goal is to classify observations in U. Class mixture
+proportion estimation (MPE) in U is a key step in PU learning. Blanchard et al.
+[2010] showed that MPE in PU learning is a generalization of the problem of
+estimating the proportion of true null hypotheses in multiple testing problems.
+Motivated by this idea, we propose reducing the problem to one dimension via
+construction of a probabilistic classifier trained on the P and U data sets
+followed by application of a one--dimensional mixture proportion method from
+the multiple testing literature to the observation class probabilities. The
+flexibility of this framework lies in the freedom to choose the classifier and
+the one--dimensional MPE method. We prove consistency of two mixture proportion
+estimators using bounds from empirical process theory, develop tuning parameter
+free implementations, and demonstrate that they have competitive performance on
+simulated waveform data and a protein signaling problem.
+",0,0,0,1,0,0
+13792,The Application of SNiPER to the JUNO Simulation,"  JUNO is a multipurpose neutrino experiment which is designed to determine
+neutrino mass hierarchy and precisely measure oscillation parameters. As one of
+the important systems, the JUNO offline software is being developed using the
+SNiPER software. In this proceeding, we focus on the requirements of JUNO
+simulation and present the working solution based on the SNiPER.
+The JUNO simulation framework is in charge of managing event data, detector
+geometries and materials, physics processes, simulation truth information etc.
+It glues physics generator, detector simulation and electronics simulation
+modules together to achieve a full simulation chain. In the implementation of
+the framework, many attractive characteristics of the SNiPER have been used,
+such as dynamic loading, flexible flow control, multiple event management and
+Python binding. Furthermore, additional efforts have been made to make both
+detector and electronics simulation flexible enough to accommodate and optimize
+different detector designs.
+For the Geant4-based detector simulation, each sub-detector component is
+implemented as a SNiPER tool which is a dynamically loadable and configurable
+plugin. So it is possible to select the detector configuration at runtime. The
+framework provides the event loop to drive the detector simulation and
+interacts with the Geant4 which is implemented as a passive service. All levels
+of user actions are wrapped into different customizable tools, so that user
+functions can be easily extended by just adding new tools. The electronics
+simulation has been implemented by following an event driven scheme. The SNiPER
+task component is used to simulate data processing steps in the electronics
+modules. The electronics and trigger are synchronized by triggered events
+containing possible physics signals.
+",0,1,0,0,0,0
+13793,A Modified Sigma-Pi-Sigma Neural Network with Adaptive Choice of Multinomials,"  Sigma-Pi-Sigma neural networks (SPSNNs) as a kind of high-order neural
+networks can provide more powerful mapping capability than the traditional
+feedforward neural networks (Sigma-Sigma neural networks). In the existing
+literature, in order to reduce the number of the Pi nodes in the Pi layer, a
+special multinomial P_s is used in SPSNNs. Each monomial in P_s is linear with
+respect to each particular variable sigma_i when the other variables are taken
+as constants. Therefore, the monomials like sigma_i^n or sigma_i^n sigma_j with
+n>1 are not included. This choice may be somehow intuitive, but is not
+necessarily the best. We propose in this paper a modified Sigma-Pi-Sigma neural
+network (MSPSNN) with an adaptive approach to find a better multinomial for a
+given problem. To elaborate, we start from a complete multinomial with a given
+order. Then we employ a regularization technique in the learning process for
+the given problem to reduce the number of monomials used in the multinomial,
+and end up with a new SPSNN involving the same number of monomials (= the
+number of nodes in the Pi-layer) as in P_s. Numerical experiments on some
+benchmark problems show that our MSPSNN behaves better than the traditional
+SPSNN with P_s.
+",0,0,0,1,0,0
+13794,Proceedings of the 2017 AdKDD & TargetAd Workshop,"  Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with
+the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax,
+Nova Scotia, Canada.
+",1,0,0,0,0,0
+13795,On the R-superlinear convergence of the KKT residues generated by the augmented Lagrangian method for convex composite conic programming,"  Due to the possible lack of primal-dual-type error bounds, the superlinear
+convergence for the Karush-Kuhn-Tucker (KKT) residues of the sequence generated
+by augmented Lagrangian method (ALM) for solving convex composite conic
+programming (CCCP) has long been an outstanding open question. In this paper,
+we aim to resolve this issue by first conducting convergence rate analysis for
+the ALM with Rockafellar's stopping criteria under only a mild quadratic growth
+condition on the dual of CCCP. More importantly, by further assuming that the
+Robinson constraint qualification holds, we establish the R-superlinear
+convergence of the KKT residues of the iterative sequence under
+easy-to-implement stopping criteria {for} the augmented Lagrangian subproblems.
+Equipped with this discovery, we gain insightful interpretations on the
+impressive numerical performance of several recently developed semismooth
+Newton-CG based ALM solvers for solving linear and convex quadratic
+semidefinite programming.
+",0,0,1,0,0,0
+13796,It's Like Python But: Towards Supporting Transfer of Programming Language Knowledge,"  Expertise in programming traditionally assumes a binary novice-expert divide.
+Learning resources typically target programmers who are learning programming
+for the first time, or expert programmers for that language. An
+underrepresented, yet important group of programmers are those that are
+experienced in one programming language, but desire to author code in a
+different language. For this scenario, we postulate that an effective form of
+feedback is presented as a transfer from concepts in the first language to the
+second. Current programming environments do not support this form of feedback.
+In this study, we apply the theory of learning transfer to teach a language
+that programmers are less familiar with--such as R--in terms of a programming
+language they already know--such as Python. We investigate learning transfer
+using a new tool called Transfer Tutor that presents explanations for R code in
+terms of the equivalent Python code. Our study found that participants
+leveraged learning transfer as a cognitive strategy, even when unprompted.
+Participants found Transfer Tutor to be useful across a number of affordances
+like stepping through and highlighting facts that may have been missed or
+misunderstood. However, participants were reluctant to accept facts without
+code execution or sometimes had difficulty reading explanations that are
+verbose or complex. These results provide guidance for future designs and
+research directions that can support learning transfer when learning new
+programming languages.
+",1,0,0,0,0,0
+13797,How do Mixture Density RNNs Predict the Future?,"  Gaining a better understanding of how and what machine learning systems learn
+is important to increase confidence in their decisions and catalyze further
+research. In this paper, we analyze the predictions made by a specific type of
+recurrent neural network, mixture density RNNs (MD-RNNs). These networks learn
+to model predictions as a combination of multiple Gaussian distributions,
+making them particularly interesting for problems where a sequence of inputs
+may lead to several distinct future possibilities. An example is learning
+internal models of an environment, where different events may or may not occur,
+but where the average over different events is not meaningful. By analyzing the
+predictions made by trained MD-RNNs, we find that their different Gaussian
+components have two complementary roles: 1) Separately modeling different
+stochastic events and 2) Separately modeling scenarios governed by different
+rules. These findings increase our understanding of what is learned by
+predictive MD-RNNs, and open up new research directions for further
+understanding how we can benefit from their self-organizing model
+decomposition.
+",1,0,0,1,0,0
+13798,Robust Recovery of Missing Data in Electricity Distribution Systems,"  The advanced operation of future electricity distribution systems is likely
+to require significant observability of the different parameters of interest
+(e.g., demand, voltages, currents, etc.). Ensuring completeness of data is,
+therefore, paramount. In this context, an algorithm for recovering missing
+state variable observations in electricity distribution systems is presented.
+The proposed method exploits the low rank structure of the state variables via
+a matrix completion approach while incorporating prior knowledge in the form of
+second order statistics. Specifically, the recovery method combines nuclear
+norm minimization with Bayesian estimation. The performance of the new
+algorithm is compared to the information-theoretic limits and tested trough
+simulations using real data of an urban low voltage distribution system. The
+impact of the prior knowledge is analyzed when a mismatched covariance is used
+and for a Markovian sampling that introduces structure in the observation
+pattern. Numerical results demonstrate that the proposed algorithm is robust
+and outperforms existing state of the art algorithms.
+",1,0,0,0,0,0
+13799,Numerical analysis of a nonlinear free-energy diminishing Discrete Duality Finite Volume scheme for convection diffusion equations,"  We propose a nonlinear Discrete Duality Finite Volume scheme to approximate
+the solutions of drift diffusion equations. The scheme is built to preserve at
+the discrete level even on severely distorted meshes the energy / energy
+dissipation relation. This relation is of paramount importance to capture the
+long-time behavior of the problem in an accurate way. To enforce it, the linear
+convection diffusion equation is rewritten in a nonlinear form before being
+discretized. We establish the existence of positive solutions to the scheme.
+Based on compactness arguments, the convergence of the approximate solution
+towards a weak solution is established. Finally, we provide numerical evidences
+of the good behavior of the scheme when the discretization parameters tend to 0
+and when time goes to infinity.
+",0,0,1,0,0,0
+13800,A momentum conserving $N$-body scheme with individual timesteps,"  $N$-body simulations study the dynamics of $N$ particles under the influence
+of mutual long-distant forces such as gravity. In practice, $N$-body codes will
+violate Newton's third law if they use either an approximate Poisson solver or
+individual timesteps. In this study, we construct a novel $N$-body scheme by
+combining a fast multipole method (FMM) based Poisson solver and a time
+integrator using a hierarchical Hamiltonian splitting (HHS) technique. We test
+our implementation for collision-less systems using several problems in
+galactic dynamics. As a result of the momentum conserving nature of these two
+key components, the new $N$-body scheme is also momentum conserving. Moreover,
+we can fully utilize the $\mathcal O(\textit N)$ complexity of FMM with the
+integrator. With the restored force symmetry, we can improve both angular
+momentum conservation and energy conservation substantially. The new scheme
+will be suitable for many applications in galactic dynamics and structure
+formation. Our implementation, in the code Taichi, is publicly available at
+this https URL.
+",0,1,0,0,0,0
+13801,Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning,"  Deep learning has started to revolutionize several different industries, and
+the applications of these methods in medicine are now becoming more
+commonplace. This study focuses on investigating the feasibility of tracking
+patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a
+radiation oncology clinic using artificial neural networks (ANNs) and
+convolutional neural networks (CNNs). The performance of these networks was
+compared to relative received signal strength indicator (RSSI) thresholding and
+triangulation. By utilizing temporal information, a combined CNN+ANN network
+was capable of correctly identifying the location of the BLE tag with an
+accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding
+model employing majority voting (accuracy = 95%), and a triangulation
+classifier utilizing majority voting (accuracy = 95%). Future studies will seek
+to deploy this affordable real time location system in hospitals to improve
+clinical workflow, efficiency, and patient safety.
+",1,1,0,0,0,0
+13802,Metropolis Sampling,"  Monte Carlo (MC) sampling methods are widely applied in Bayesian inference,
+system simulation and optimization problems. The Markov Chain Monte Carlo
+(MCMC) algorithms are a well-known class of MC methods which generate a Markov
+chain with the desired invariant distribution. In this document, we focus on
+the Metropolis-Hastings (MH) sampler, which can be considered as the atom of
+the MCMC techniques, introducing the basic notions and different properties. We
+describe in details all the elements involved in the MH algorithm and the most
+relevant variants. Several improvements and recent extensions proposed in the
+literature are also briefly discussed, providing a quick but exhaustive
+overview of the current Metropolis-based sampling's world.
+",0,0,0,1,0,0
+13803,Approximate Structure Construction Using Large Statistical Swarms,"  In this paper we describe a novel local algorithm for large statistical
+swarms using ""harmonic attractor dynamics"", by means of which a swarm can
+construct harmonics of the environment. This in turn allows the swarm to
+approximately reconstruct desired structures in the environment. The robots
+navigate in a discrete environment, completely free of localization, being able
+to communicate with other robots in its own discrete cell only, and being able
+to sense or take reliable action within a disk of radius $r$ around itself. We
+present the mathematics that underlie such dynamics and present initial results
+demonstrating the proposed algorithm.
+",1,0,0,1,0,0
+13804,"Search for Food of Birds, Fish and Insects","  This book chapter introduces to the problem to which extent search strategies
+of foraging biological organisms can be identified by statistical data analysis
+and mathematical modeling. A famous paradigm in this field is the Levy Flight
+Hypothesis: It states that under certain mathematical conditions Levy flights,
+which are a key concept in the theory of anomalous stochastic processes,
+provide an optimal search strategy. This hypothesis may be understood
+biologically as the claim that Levy flights represent an evolutionary adaptive
+optimal search strategy for foraging organisms. Another interpretation,
+however, is that Levy flights emerge from the interaction between a forager and
+a given (scale-free) distribution of food sources. These hypotheses are
+discussed controversially in the current literature. We give examples and
+counterexamples of experimental data and their analyses supporting and
+challenging them.
+",0,0,0,0,1,0
+13805,Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning,"  Obtaining detailed and reliable data about local economic livelihoods in
+developing countries is expensive, and data are consequently scarce. Previous
+work has shown that it is possible to measure local-level economic livelihoods
+using high-resolution satellite imagery. However, such imagery is relatively
+expensive to acquire, often not updated frequently, and is mainly available for
+recent years. We train CNN models on free and publicly available multispectral
+daytime satellite images of the African continent from the Landsat 7 satellite,
+which has collected imagery with global coverage for almost two decades. We
+show that despite these images' lower resolution, we can achieve accuracies
+that exceed previous benchmarks.
+",1,0,0,1,0,0
+13806,Implicit Regularization in Matrix Factorization,"  We study implicit regularization when optimizing an underdetermined quadratic
+objective over a matrix $X$ with gradient descent on a factorization of $X$. We
+conjecture and provide empirical and theoretical evidence that with small
+enough step sizes and initialization close enough to the origin, gradient
+descent on a full dimensional factorization converges to the minimum nuclear
+norm solution.
+",1,0,0,1,0,0
+13807,A general theory of singular values with applications to signal denoising,"  We study the Pareto frontier for two competing norms $\|\cdot\|_X$ and
+$\|\cdot\|_Y$ on a vector space. For a given vector $c$, the pareto frontier
+describes the possible values of $(\|a\|_X,\|b\|_Y)$ for a decomposition
+$c=a+b$. The singular value decomposition of a matrix is closely related to the
+Pareto frontier for the spectral and nuclear norm. We will develop a general
+theory that extends the notion of singular values of a matrix to arbitrary
+finite dimensional euclidean vector spaces equipped with dual norms. This also
+generalizes the diagonal singular value decompositions for tensors introduced
+by the author in previous work. We can apply the results to denoising, where
+$c$ is a noisy signal, $a$ is a sparse signal and $b$ is noise. Applications
+include 1D total variation denoising, 2D total variation Rudin-Osher-Fatemi
+image denoising, LASSO, basis pursuit denoising and tensor decompositions.
+",1,0,1,1,0,0
+13808,A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics,"  Deep Neural Networks are increasingly being used in a variety of machine
+learning applications applied to user data on the cloud. However, this approach
+introduces a number of privacy and efficiency challenges, as the cloud operator
+can perform secondary inferences on the available data. Recently, advances in
+edge processing have paved the way for more efficient, and private, data
+processing at the source for simple tasks and lighter models, though they
+remain a challenge for larger, and more complicated models. In this paper, we
+present a hybrid approach for breaking down large, complex deep models for
+cooperative, privacy-preserving analytics. We do this by breaking down the
+popular deep architectures and fine-tune them in a suitable way. We then
+evaluate the privacy benefits of this approach based on the information exposed
+to the cloud service. We also assess the local inference cost of different
+layers on a modern handset for mobile applications. Our evaluations show that
+by using certain kind of fine-tuning and embedding techniques and at a small
+processing cost, we can greatly reduce the level of information available to
+unintended tasks applied to the data features on the cloud, and hence achieving
+the desired tradeoff between privacy and performance.
+",1,0,0,0,0,0
+13809,Exploiting Apache Spark platform for CMS computing analytics,"  The CERN IT provides a set of Hadoop clusters featuring more than 5 PBytes of
+raw storage with different open-source, user-level tools available for
+analytical purposes. The CMS experiment started collecting a large set of
+computing meta-data, e.g. dataset, file access logs, since 2015. These records
+represent a valuable, yet scarcely investigated, set of information that needs
+to be cleaned, categorized and analyzed. CMS can use this information to
+discover useful patterns and enhance the overall efficiency of the distributed
+data, improve CPU and site utilization as well as tasks completion time. Here
+we present evaluation of Apache Spark platform for CMS needs. We discuss two
+main use-cases CMS analytics and ML studies where efficient process billions of
+records stored on HDFS plays an important role. We demonstrate that both Scala
+and Python (PySpark) APIs can be successfully used to execute extremely I/O
+intensive queries and provide valuable data insight from collected meta-data.
+",0,1,0,0,0,0
+13810,Scalable Co-Optimization of Morphology and Control in Embodied Machines,"  Evolution sculpts both the body plans and nervous systems of agents together
+over time. In contrast, in AI and robotics, a robot's body plan is usually
+designed by hand, and control policies are then optimized for that fixed
+design. The task of simultaneously co-optimizing the morphology and controller
+of an embodied robot has remained a challenge. In psychology, the theory of
+embodied cognition posits that behavior arises from a close coupling between
+body plan and sensorimotor control, which suggests why co-optimizing these two
+subsystems is so difficult: most evolutionary changes to morphology tend to
+adversely impact sensorimotor control, leading to an overall decrease in
+behavioral performance. Here, we further examine this hypothesis and
+demonstrate a technique for ""morphological innovation protection"", which
+temporarily reduces selection pressure on recently morphologically-changed
+individuals, thus enabling evolution some time to ""readapt"" to the new
+morphology with subsequent control policy mutations. We show the potential for
+this method to avoid local optima and converge to similar highly fit
+morphologies across widely varying initial conditions, while sustaining fitness
+improvements further into optimization. While this technique is admittedly only
+the first of many steps that must be taken to achieve scalable optimization of
+embodied machines, we hope that theoretical insight into the cause of
+evolutionary stagnation in current methods will help to enable the automation
+of robot design and behavioral training -- while simultaneously providing a
+testbed to investigate the theory of embodied cognition.
+",1,0,0,0,0,0
+13811,"Normalizing the Taylor expansion of non-deterministic λ-terms, via parallel reduction of resource vectors","  It has been known since Ehrhard and Regnier's seminal work on the Taylor
+expansion of {\lambda}-terms that this operation commutes with normalization:
+the expansion of a {\lambda}-term is always normalizable and its normal form is
+the expansion of the Böhm tree of the term. We generalize this result to the
+non-uniform setting of the algebraic {\lambda}-calculus, i.e.
+{\lambda}-calculus extended with linear combinations of terms. This requires us
+to tackle two difficulties: foremost is the fact that Ehrhard and Regnier's
+techniques rely heavily on the uniform, deterministic nature of the ordinary
+{\lambda}-calculus, and thus cannot be adapted; second is the absence of any
+satisfactory generic extension of the notion of Böhm tree in presence of
+quantitative non-determinism, which is reflected by the fact that the Taylor
+expansion of an algebraic {\lambda}-term is not always normalizable. Our
+solution is to provide a fine grained study of the dynamics of
+{\beta}-reduction under Taylor expansion, by introducing a notion of reduction
+on resource vectors, i.e. infinite linear combinations of resource
+{\lambda}-terms. The latter form the multilinear fragment of the differential
+{\lambda}-calculus, and resource vectors are the target of the Taylor expansion
+of {\lambda}-terms. We show the reduction of resource vectors contains the
+image of any {\beta}-reduction step, from which we deduce that Taylor expansion
+and normalization commute on the nose. We moreover identify a class of
+algebraic {\lambda}-terms, encompassing both normalizable algebraic
+{\lambda}-terms and arbitrary ordinary {\lambda}-terms: the expansion of these
+is always normalizable, which guides the definition of a generalization of
+Böhm trees to this setting.
+",1,0,0,0,0,0
+13812,Quantifying telescope phase discontinuities external to AO-systems by use of Phase Diversity and Focal Plane Sharpening,"  We propose and apply two methods to estimate pupil plane phase
+discontinuities for two realistic scenarios on VLT and Keck. The methods use
+both Phase Diversity and a form of image sharpening. For the case of VLT, we
+simulate the `low wind effect' (LWE) which is responsible for focal plane
+errors in the SPHERE system in low wind and good seeing conditions. We
+successfully estimate the simulated LWE using both methods, and show that they
+are complimentary to one another. We also demonstrate that single image Phase
+Diversity (also known as Phase Retrieval with diversity) is also capable of
+estimating the simulated LWE when using the natural de-focus on the SPHERE/DTTS
+imager. We demonstrate that Phase Diversity can estimate the LWE to within 30
+nm RMS WFE, which is within the allowable tolerances to achieve a target SPHERE
+contrast of 10$^{-6}$. Finally, we simulate 153 nm RMS of piston errors on the
+mirror segments of Keck and produce NIRC2 images subject to these effects. We
+show that a single, diverse image with 1.5 waves (PV) of focus can be used to
+estimate this error to within 29 nm RMS WFE, and a perfect correction of our
+estimation would increase the Strehl ratio of a NIRC2 image by 12\%
+",0,1,0,0,0,0
+13813,Adaptive Multilevel Monte Carlo Approximation of Distribution Functions,"  We analyse a multilevel Monte Carlo method for the approximation of
+distribution functions of univariate random variables. Since, by assumption,
+the target distribution is not known explicitly, approximations have to be
+used. We provide an asymptotic analysis of the error and the cost of the
+algorithm. Furthermore we construct an adaptive version of the algorithm that
+does not require any a priori knowledge on weak or strong convergence rates. We
+apply the adaptive algorithm to smooth path-independent and path-dependent
+functionals and to stopped exit times of SDEs.
+",0,0,1,1,0,0
+13814,Constraints on the pre-impact orbits of Solar System giant impactors,"  We provide a fast method for computing constraints on impactor pre-impact
+orbits, applying this to the late giant impacts in the Solar System. These
+constraints can be used to make quick, broad comparisons of different collision
+scenarios, identifying some immediately as low-probability events, and
+narrowing the parameter space in which to target follow-up studies with
+expensive N-body simulations. We benchmark our parameter space predictions,
+finding good agreement with existing N-body studies for the Moon. We suggest
+that high-velocity impact scenarios in the inner Solar System, including all
+currently proposed single impact scenarios for the formation of Mercury, should
+be disfavoured. This leaves a multiple hit-and-run scenario as the most
+probable currently proposed for the formation of Mercury.
+",0,1,0,0,0,0
+13815,Dark matter in the Reticulum II dSph: a radio search,"  We present a deep radio search in the Reticulum II dwarf spheroidal (dSph)
+galaxy performed with the Australia Telescope Compact Array. Observations were
+conducted at 16 cm wavelength, with an rms sensitivity of 0.01 mJy/beam, and
+with the goal of searching for synchrotron emission induced by annihilation or
+decay of weakly interacting massive particles (WIMPs). Data were complemented
+with observations on large angular scales taken with the KAT-7 telescope. We
+find no evidence for a diffuse emission from the dSph and we derive competitive
+bounds on the WIMP properties. In addition, we detect more than 200 new
+background radio sources. Among them, we show there are two compelling
+candidates for being the radio counterpart of the possible gamma-ray emission
+reported by other groups using Fermi-LAT data.
+",0,1,0,0,0,0
+13816,Relevant change points in high dimensional time series,"  This paper investigates the problem of detecting relevant change points in
+the mean vector, say $\mu_t =(\mu_{1,t},\ldots ,\mu_{d,t})^T$ of a high
+dimensional time series $(Z_t)_{t\in \mathbb{Z}}$.
+While the recent literature on testing for change points in this context
+considers hypotheses for the equality of the means $\mu_h^{(1)}$ and
+$\mu_h^{(2)}$ before and after the change points in the different components,
+we are interested in a null hypothesis of the form $$ H_0: |\mu^{(1)}_{h} -
+\mu^{(2)}_{h} | \leq \Delta_h ~~~\mbox{ for all } ~~h=1,\ldots ,d $$ where
+$\Delta_1, \ldots , \Delta_d$ are given thresholds for which a smaller
+difference of the means in the $h$-th component is considered to be
+non-relevant.
+We propose a new test for this problem based on the maximum of squared and
+integrated CUSUM statistics and investigate its properties as the sample size
+$n$ and the dimension $d$ both converge to infinity. In particular, using
+Gaussian approximations for the maximum of a large number of dependent random
+variables, we show that on certain points of the boundary of the null
+hypothesis a standardised version of the maximum converges weakly to a Gumbel
+distribution.
+",0,0,1,1,0,0
+13817,Disentangling in Variational Autoencoders with Natural Clustering,"  Learning representations that disentangle the underlying factors of
+variability in data is an intuitive precursor to AI with human-like reasoning.
+Consequently, it has been the object of many efforts of the machine learning
+community. This work takes a step further in this direction by addressing the
+scenario where generative factors present a multimodal distribution due to the
+existence of class distinction in the data. We formulate a lower bound on the
+joint distribution of inputs and class labels and present N-VAE, a model which
+is capable of separating factors of variation which are exclusive to certain
+classes from factors that are shared among classes. This model implements the
+natural clustering prior through the use of a class-conditioned latent space
+and a shared latent space. We show its usefulness for detecting and
+disentangling class-dependent generative factors as well as for generating rich
+artificial samples.
+",1,0,0,1,0,0
+13818,Calibrated Boosting-Forest,"  Excellent ranking power along with well calibrated probability estimates are
+needed in many classification tasks. In this paper, we introduce a technique,
+Calibrated Boosting-Forest that captures both. This novel technique is an
+ensemble of gradient boosting machines that can support both continuous and
+binary labels. While offering superior ranking power over any individual
+regression or classification model, Calibrated Boosting-Forest is able to
+preserve well calibrated posterior probabilities. Along with these benefits, we
+provide an alternative to the tedious step of tuning gradient boosting
+machines. We demonstrate that tuning Calibrated Boosting-Forest can be reduced
+to a simple hyper-parameter selection. We further establish that increasing
+this hyper-parameter improves the ranking performance under a diminishing
+return. We examine the effectiveness of Calibrated Boosting-Forest on
+ligand-based virtual screening where both continuous and binary labels are
+available and compare the performance of Calibrated Boosting-Forest with
+logistic regression, gradient boosting machine and deep learning. Calibrated
+Boosting-Forest achieved an approximately 48% improvement compared to a
+state-of-art deep learning model. Moreover, it achieved around 95% improvement
+on probability quality measurement compared to the best individual gradient
+boosting machine. Calibrated Boosting-Forest offers a benchmark demonstration
+that in the field of ligand-based virtual screening, deep learning is not the
+universally dominant machine learning model and good calibrated probabilities
+can better facilitate virtual screening process.
+",1,0,0,1,0,0
+13819,Phase Diagram of $α$-RuCl$_3$ in an in-plane Magnetic Field,"  The low-temperature magnetic phases in the layered honeycomb lattice material
+$\alpha$-RuCl$_3$ have been studied as a function of in-plane magnetic field.
+In zero field this material orders magnetically below 7 K with so-called zigzag
+order within the honeycomb planes. Neutron diffraction data show that a
+relatively small applied field of 2 T is sufficient to suppress the population
+of the magnetic domain in which the zigzag chains run along the field
+direction. We found that the intensity of the magnetic peaks due to zigzag
+order is continuously suppressed with increasing field until their
+disappearance at $\mu_o$H$_c$=8 T. At still higher fields (above 8 T) the
+zigzag order is destroyed, while bulk magnetization and heat capacity
+measurements suggest that the material enters a state with gapped magnetic
+excitations. We discuss the magnetic phase diagram obtained in our study in the
+context of a quantum phase transition.
+",0,1,0,0,0,0
+13820,Post hoc inference via joint family-wise error rate control,"  We introduce a general methodology for post hoc inference in a large-scale
+multiple testing framework. The approach is called ""user-agnostic"" in the sense
+that the statistical guarantee on the number of correct rejections holds for
+any set of candidate items selected by the user (after having seen the data).
+This task is investigated by defining a suitable criterion, named the
+joint-family-wise-error rate (JER for short). We propose several procedures for
+controlling the JER, with a special focus on incorporating dependencies while
+adapting to the unknown quantity of signal (via a step-down approach). We show
+that our proposed setting incorporates as particular cases a version of the
+higher criticism as well as the closed testing based approach of Goeman and
+Solari (2011). Our theoretical statements are supported by numerical
+experiments.
+",0,0,1,1,0,0
+13821,Jamming Resistant Receivers for Massive MIMO,"  We design jamming resistant receivers to enhance the robustness of a massive
+MIMO uplink channel against jamming. In the pilot phase, we estimate not only
+the desired channel, but also the jamming channel by exploiting purposely
+unused pilot sequences. The jamming channel estimate is used to construct the
+linear receive filter to reduce impact that jamming has on the achievable
+rates. The performance of the proposed scheme is analytically and numerically
+evaluated. These results show that the proposed scheme greatly improves the
+rates, as compared to conventional receivers. Moreover, the proposed schemes
+still work well with stronger jamming power.
+",1,0,0,0,0,0
+13822,Thickening and sickening the SYK model,"  We discuss higher dimensional generalizations of the 0+1-dimensional
+Sachdev-Ye-Kitaev (SYK) model that has recently become the focus of intensive
+interdisciplinary studies by, both, the condensed matter and field-theoretical
+communities. Unlike the previous constructions where multiple SYK copies would
+be coupled to each other and/or hybridized with itinerant fermions via
+spatially short-ranged random hopping processes, we study algebraically varying
+long-range (spatially and/or temporally) correlated random couplings in the
+general d+1 dimensions. Such pertinent topics as translationally-invariant
+strong-coupling solutions, emergent reparametrization symmetry, effective
+action for fluctuations, chaotic behavior, and diffusive transport (or a lack
+thereof) are all addressed. We find that the most appealing properties of the
+original SYK model that suggest the existence of its 1+1-dimensional
+holographic gravity dual do not survive the aforementioned generalizations,
+thus lending no additional support to the hypothetical broad (including
+'non-AdS/non-CFT') holographic correspondence.
+",0,1,0,0,0,0
+13823,Hidden chiral symmetries in BDI multichannel Kitaev chains,"  Realistic implementations of the Kitaev chain require, in general, the
+introduction of extra internal degrees of freedom. In the present work, we
+discuss the presence of hidden BDI symmetries for free Hamiltonians describing
+systems with an arbitrary number of internal degrees of freedom. We generalize
+results of a spinfull Kitaev chain to construct a Hamiltonian with $n$ internal
+degrees of freedom and obtain the corresponding hidden chiral symmetry. As an
+explicit application of this generalized result, we exploit by analytical and
+numerical calculations the case of a spinful 2-band Kitaev chain, which can
+host up to 4 Majorana bound states. We also observe the appearence of minigap
+states, when chiral symmetry is broken.
+",0,1,0,0,0,0
+13824,Charge Berezinskii-Kosterlitz-Thouless transition in superconducting NbTiN films,"  A half-century after the discovery of the superconductor-insulator transition
+(SIT), one of the fundamental predictions of the theory, the charge
+Berezinskii-Kosterlitz-Thouless (BKT) transition that is expected to occur at
+the insulating side of the SIT, has remained unobserved. The charge BKT
+transition is a phenomenon dual to the vortex BKT transition, which is at the
+heart of the very existence of two-dimensional superconductivity as a
+zero-resistance state appearing at finite temperatures. The dual picture points
+to the possibility of the existence of a superinsulating state endowed with
+zero conductance at finite temperature. Here, we report the observation of the
+charge BKT transition on the insulating side of the SIT, identified by the
+critical behavior of the resistance. We find that the critical temperature of
+the charge BKT transition depends on the magnetic field exhibiting first the
+fast growth and then passing through the maximum at fields much less than the
+upper critical field. Finally, we ascertain the effects of the finite
+electrostatic screening length and its divergence at the magnetic field-tuned
+approach to the superconductor-insulator transition.
+",0,1,0,0,0,0
+13825,Seed-Driven Geo-Social Data Extraction - Full Version,"  Geo-social data has been an attractive source for a variety of problems such
+as mining mobility patterns, link prediction, location recommendation, and
+influence maximization. However, new geo-social data is increasingly
+unavailable and suffers several limitations. In this paper, we aim to remedy
+the problem of effective data extraction from geo-social data sources. We first
+identify and categorize the limitations of extracting geo-social data. In order
+to overcome the limitations, we propose a novel seed-driven approach that uses
+the points of one source as the seed to feed as queries for the others. We
+additionally handle differences between, and dynamics within the sources by
+proposing three variants for optimizing search radius. Furthermore, we provide
+an optimization based on recursive clustering to minimize the number of
+requests and an adaptive procedure to learn the specific data distribution of
+each source. Our comprehensive experiments with six popular sources show that
+our seed-driven approach yields 14.3 times more data overall, while our
+request-optimized algorithm retrieves up to 95% of the data with less than 16%
+of the requests. Thus, our proposed seed-driven approach set new standards for
+effective and efficient extraction of geo-social data.
+",1,0,0,0,0,0
+13826,Bayesian random-effects meta-analysis using the bayesmeta R package,"  The random-effects or normal-normal hierarchical model is commonly utilized
+in a wide range of meta-analysis applications. A Bayesian approach to inference
+is very attractive in this context, especially when a meta-analysis is based
+only on few studies. The bayesmeta R package provides readily accessible tools
+to perform Bayesian meta-analyses and generate plots and summaries, without
+having to worry about computational details. It allows for flexible prior
+specification and instant access to the resulting posterior distributions,
+including prediction and shrinkage estimation, and facilitating for example
+quick sensitivity checks. The present paper introduces the underlying theory
+and showcases its usage.
+",0,0,0,1,0,0
+13827,Dirichlet Mixture Model based VQ Performance Prediction for Line Spectral Frequency,"  In this paper, we continue our previous work on the Dirichlet mixture model
+(DMM)-based VQ to derive the performance bound of the LSF VQ. The LSF
+parameters are transformed into the $\Delta$LSF domain and the underlying
+distribution of the $\Delta$LSF parameters are modelled by a DMM with finite
+number of mixture components. The quantization distortion, in terms of the mean
+squared error (MSE), is calculated with the high rate theory. The mapping
+relation between the perceptually motivated log spectral distortion (LSD) and
+the MSE is empirically approximated by a polynomial. With this mapping
+function, the minimum required bit rate for transparent coding of the LSF is
+estimated.
+",1,0,0,1,0,0
+13828,Query K-means Clustering and the Double Dixie Cup Problem,"  We consider the problem of approximate $K$-means clustering with outliers and
+side information provided by same-cluster queries and possibly noisy answers.
+Our solution shows that, under some mild assumptions on the smallest cluster
+size, one can obtain an $(1+\epsilon)$-approximation for the optimal potential
+with probability at least $1-\delta$, where $\epsilon>0$ and $\delta\in(0,1)$,
+using an expected number of $O(\frac{K^3}{\epsilon \delta})$ noiseless
+same-cluster queries and comparison-based clustering of complexity $O(ndK +
+\frac{K^3}{\epsilon \delta})$, here, $n$ denotes the number of points and $d$
+the dimension of space. Compared to a handful of other known approaches that
+perform importance sampling to account for small cluster sizes, the proposed
+query technique reduces the number of queries by a factor of roughly
+$O(\frac{K^6}{\epsilon^3})$, at the cost of possibly missing very small
+clusters. We extend this settings to the case where some queries to the oracle
+produce erroneous information, and where certain points, termed outliers, do
+not belong to any clusters. Our proof techniques differ from previous methods
+used for $K$-means clustering analysis, as they rely on estimating the sizes of
+the clusters and the number of points needed for accurate centroid estimation
+and subsequent nontrivial generalizations of the double Dixie cup problem. We
+illustrate the performance of the proposed algorithm both on synthetic and real
+datasets, including MNIST and CIFAR $10$.
+",0,0,0,1,0,0
+13829,On the spectrum of directed uniform and non-uniform hypergraphs,"  Here, we suggest a method to represent general directed uniform and
+non-uniform hypergraphs by different connectivity tensors. We show many results
+on spectral properties of undirected hypergraphs also hold for general directed
+uniform hypergraphs. Our representation of a connectivity tensor will be very
+useful for the further development in spectral theory of directed hypergraphs.
+At the end, we have also introduced the concept of weak* irreducible
+hypermatrix to better explain connectivity of a directed hypergraph.
+",0,0,1,0,0,0
+13830,Inference for partial correlation when data are missing not at random,"  We introduce uncertainty regions to perform inference on partial correlations
+when data are missing not at random. These uncertainty regions are shown to
+have a desired asymptotic coverage. Their finite sample performance is
+illustrated via simulations and real data example.
+",0,0,1,1,0,0
+13831,Interstitial Content Detection,"  Interstitial content is online content which grays out, or otherwise obscures
+the main page content. In this technical report, we discuss exploratory
+research into detecting the presence of interstitial content in web pages. We
+discuss the use of computer vision techniques to detect interstitials, and the
+potential use of these techniques to provide a labelled dataset for machine
+learning.
+",1,0,0,0,0,0
+13832,Perfect spike detection via time reversal,"  Spiking neuronal networks are usually simulated with three main simulation
+schemes: the classical time-driven and event-driven schemes, and the more
+recent hybrid scheme. All three schemes evolve the state of a neuron through a
+series of checkpoints: equally spaced in the first scheme and determined
+neuron-wise by spike events in the latter two. The time-driven and the hybrid
+scheme determine whether the membrane potential of a neuron crosses a threshold
+at the end of of the time interval between consecutive checkpoints. Threshold
+crossing can, however, occur within the interval even if this test is negative.
+Spikes can therefore be missed. The present work derives, implements, and
+benchmarks a method for perfect retrospective spike detection. This method can
+be applied to neuron models with affine or linear subthreshold dynamics. The
+idea behind the method is to propagate the threshold with a time-inverted
+dynamics, testing whether the threshold crosses the neuron state to be evolved,
+rather than vice versa. Algebraically this translates into a set of
+inequalities necessary and sufficient for threshold crossing. This test is
+slower than the imperfect one, but faster than an alternative perfect tests
+based on bisection or root-finding methods. Comparison confirms earlier results
+that the imperfect test rarely misses spikes (less than a fraction $1/10^8$ of
+missed spikes) in biologically relevant settings. This study offers an
+alternative geometric point of view on neuronal dynamics.
+",0,1,1,0,0,0
+13833,"A response to: ""NIST experts urge caution in use of courtroom evidence presentation method""","  A press release from the National Institute of Standards and Technology
+(NIST)could potentially impede progress toward improving the analysis of
+forensic evidence and the presentation of forensic analysis results in courts
+in the United States and around the world. ""NIST experts urge caution in use of
+courtroom evidence presentation method"" was released on October 12, 2017, and
+was picked up by the phys.org news service. It argues that, except in
+exceptional cases, the results of forensic analyses should not be reported as
+""likelihood ratios"". The press release, and the journal article by NIST
+researchers Steven P. Lund & Harri Iyer on which it is based, identifies some
+legitimate points of concern, but makes a strawman argument and reaches an
+unjustified conclusion that throws the baby out with the bathwater.
+",0,0,0,1,0,0
+13834,Safe Model-based Reinforcement Learning with Stability Guarantees,"  Reinforcement learning is a powerful paradigm for learning optimal policies
+from experimental data. However, to find optimal policies, most reinforcement
+learning algorithms explore all possible actions, which may be harmful for
+real-world systems. As a consequence, learning algorithms are rarely applied on
+safety-critical systems in the real world. In this paper, we present a learning
+algorithm that explicitly considers safety, defined in terms of stability
+guarantees. Specifically, we extend control-theoretic results on Lyapunov
+stability verification and show how to use statistical models of the dynamics
+to obtain high-performance control policies with provable stability
+certificates. Moreover, under additional regularity assumptions in terms of a
+Gaussian process prior, we prove that one can effectively and safely collect
+data in order to learn about the dynamics and thus both improve control
+performance and expand the safe region of the state space. In our experiments,
+we show how the resulting algorithm can safely optimize a neural network policy
+on a simulated inverted pendulum, without the pendulum ever falling down.
+",1,0,0,1,0,0
+13835,Refining Trace Abstraction using Abstract Interpretation,"  The CEGAR loop in software model checking notoriously diverges when the
+abstraction refinement procedure does not derive a loop invariant. An
+abstraction refinement procedure based on an SMT solver is applied to a trace,
+i.e., a restricted form of a program (without loops). In this paper, we present
+a new abstraction refinement procedure that aims at circumventing this
+restriction whenever possible. We apply abstract interpretation to a program
+that we derive from the given trace. If the program contains a loop, we are
+guaranteed to obtain a loop invariant. We call an SMT solver only in the case
+where the abstract interpretation returns an indefinite answer. That is, the
+idea is to use abstract interpretation and an SMT solver in tandem. An
+experimental evaluation in the setting of trace abstraction indicates the
+practical potential of this idea.
+",1,0,0,0,0,0
+13836,A partial inverse problem for the Sturm-Liouville operator on the graph with a loop,"  The Sturm-Liouville operator with singular potentials on the lasso graph is
+considered. We suppose that the potential is known a priori on the boundary
+edge, and recover the potential on the loop from a part of the spectrum and
+some additional data. We prove the uniqueness theorem and provide a
+constructive algorithm for the solution of this partial inverse problem.
+",0,0,1,0,0,0
+13837,BPjs --- a framework for modeling reactive systems using a scripting language and BP,"  We describe some progress towards a new common framework for model driven
+engineering, based on behavioral programming. The tool we have developed
+unifies almost all of the work done in behavioral programming so far, under a
+common set of interfaces. Its architecture supports pluggable event selection
+strategies, which can make models more intuitive and compact. Program state
+space can be traversed using various algorithms, such as DFS and A*.
+Furthermore, program state is represented in a way that enables scanning a
+state space using parallel and distributed algorithms. Executable models
+created with this tool can be directly embedded in Java applications, enabling
+a model-first approach to system engineering, where initially a model is
+created and verified, and then a working application is gradually built around
+the model. The model itself consists of a collection of small scripts written
+in JavaScript (hence ""BPjs""). Using a variety of case-studies, this paper shows
+how the combination of a lenient programming language with formal model
+analysis tools creates an efficient way of developing robust complex systems.
+Additionally, as we learned from an experimental course we ran, the usage of
+JavaScript make practitioners more amenable to using this system and, thus,
+model checking and model driven engineering. In addition to providing
+infrastructure for development and case-studies in behavioral programming, the
+tool is designed to serve as a common platform for research and innovation in
+behavioral programming and in model driven engineering in general.
+",1,0,0,0,0,0
+13838,Design of Quantum Circuits for Galois Field Squaring and Exponentiation,"  This work presents an algorithm to generate depth, quantum gate and qubit
+optimized circuits for $GF(2^m)$ squaring in the polynomial basis. Further, to
+the best of our knowledge the proposed quantum squaring circuit algorithm is
+the only work that considers depth as a metric to be optimized. We compared
+circuits generated by our proposed algorithm against the state of the art and
+determine that they require $50 \%$ fewer qubits and offer gates savings that
+range from $37 \%$ to $68 \%$. Further, existing quantum exponentiation are
+based on either modular or integer arithmetic. However, Galois arithmetic is a
+useful tool to design resource efficient quantum exponentiation circuit
+applicable in quantum cryptanalysis. Therefore, we present the quantum circuit
+implementation of Galois field exponentiation based on the proposed quantum
+Galois field squaring circuit. We calculated a qubit savings ranging between
+$44\%$ to $50\%$ and quantum gate savings ranging between $37 \%$ to $68 \%$
+compared to identical quantum exponentiation circuit based on existing squaring
+circuits.
+",1,0,0,0,0,0
+13839,"Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions","  We examine the Bayes-consistency of a recently proposed
+1-nearest-neighbor-based multiclass learning algorithm. This algorithm is
+derived from sample compression bounds and enjoys the statistical advantages of
+tight, fully empirical generalization bounds, as well as the algorithmic
+advantages of a faster runtime and memory savings. We prove that this algorithm
+is strongly Bayes-consistent in metric spaces with finite doubling dimension
+--- the first consistency result for an efficient nearest-neighbor sample
+compression scheme. Rather surprisingly, we discover that this algorithm
+continues to be Bayes-consistent even in a certain infinite-dimensional
+setting, in which the basic measure-theoretic conditions on which classic
+consistency proofs hinge are violated. This is all the more surprising, since
+it is known that $k$-NN is not Bayes-consistent in this setting. We pose
+several challenging open problems for future research.
+",1,0,1,1,0,0
+13840,"AdaGrad stepsizes: Sharp convergence over nonconvex landscapes, from any initialization","  Adaptive gradient methods such as AdaGrad and its variants update the
+stepsize in stochastic gradient descent on the fly according to the gradients
+received along the way; such methods have gained widespread use in large-scale
+optimization for their ability to converge robustly, without the need to fine
+tune parameters such as the stepsize schedule. Yet, the theoretical guarantees
+to date for AdaGrad are for online and convex optimization, which is quite
+different from the offline and nonconvex setting where adaptive gradient
+methods shine in practice. We bridge this gap by providing strong theoretical
+guarantees in batch and stochastic setting, for the convergence of AdaGrad over
+smooth, nonconvex landscapes, from any initialization of the stepsize, without
+knowledge of Lipschitz constant of the gradient. We show in the stochastic
+setting that AdaGrad converges to a stationary point at the optimal
+$O(1/\sqrt{N})$ rate (up to a $\log(N)$ factor), and in the batch setting, at
+the optimal $O(1/N)$ rate. Moreover, in both settings, the constant in the rate
+matches the constant obtained as if the variance of the gradient noise and
+Lipschitz constant of the gradient were known in advance and used to tune the
+stepsize, up to a logarithmic factor of the mismatch between the optimal
+stepsize and the stepsize used to initialize AdaGrad. In particular, our
+results imply that AdaGrad is robust to both the unknown Lipschitz constant and
+level of stochastic noise on the gradient, in a near-optimal sense. When there
+is noise, AdaGrad converges at the rate of $O(1/\sqrt{N})$ with well-tuned
+stepsize, and when there is not noise, the same algorithm converges at the rate
+of $O(1/N)$ like well-tuned batch gradient descent.
+",0,0,0,1,0,0
+13841,On polynomially integrable convex bodies,"  An infinitely smooth convex body in $\mathbb R^n$ is called polynomially
+integrable of degree $N$ if its parallel section functions are polynomials of
+degree $N$. We prove that the only smooth convex bodies with this property in
+odd dimensions are ellipsoids, if $N\ge n-1$. This is in contrast with the case
+of even dimensions and the case of odd dimensions with $N<n-1$, where such
+bodies do not exist, as it was recently shown by Agranovsky.
+",0,0,1,0,0,0
+13842,Percentile Policies for Tracking of Markovian Random Processes with Asymmetric Cost and Observation,"  Motivated by wide-ranging applications such as video delivery over networks
+using Multiple Description Codes, congestion control, and inventory management,
+we study the state-tracking of a Markovian random process with a known
+transition matrix and a finite ordered state set. The decision-maker must
+select a state as an action at each time step to minimize the total expected
+cost. The decision-maker is faced with asymmetries both in cost and
+observation: in case the selected state is less than the actual state of the
+Markovian process, an under-utilization cost occurs and only partial
+observation about the actual state is revealed; otherwise, the decision incurs
+an over-utilization cost and reveals full information about the actual state.
+We can formulate this problem as a Partially Observable Markov Decision Process
+which can be expressed as a dynamic program based on the last full observed
+state and the time of full observation. This formulation determines the
+sequence of actions to be taken between any two consecutive full observations
+of the actual state. However, this DP grows exponentially in the number of
+states, with little hope for a computationally feasible solution. We present an
+interesting class of computationally tractable policies with a percentile
+structure. A generalization of binary search, this class of policies attempt at
+any given time to reduce the uncertainty by a given percentage. Among all
+percentile policies, we search for the one with the minimum expected cost. The
+result of this search is a heuristic policy which we evaluate through numerical
+simulations. We show that it outperforms the myopic policies and under some
+conditions performs close to the optimal policies. Furthermore, we derive a
+lower bound on the cost of the optimal policy which can be computed with low
+complexity and give a measure for how close our heuristic policy is to the
+optimal policy.
+",1,0,0,0,0,0
+13843,On Information Transfer Based Characterization of Power System Stability,"  In this paper, we present a novel approach to identify the generators and
+states responsible for the small-signal stability of power networks. To this
+end, the newly developed notion of information transfer between the states of a
+dynamical system is used. In particular, using the concept of information
+transfer, which characterizes influence between the various states and a linear
+combination of states of a dynamical system, we identify the generators and
+states which are responsible for causing instability of the power network.
+While characterizing influence from state to state, information transfer can
+also describe influence from state to modes thereby generalizing the well-known
+notion of participation factor while at the same time overcoming some of the
+limitations of the participation factor. The developed framework is applied to
+study the three bus system identifying various cause of instabilities in the
+system. The simulation study is extended to IEEE 39 bus system.
+",1,0,0,0,0,0
+13844,Using Session Types for Reasoning About Boundedness in the Pi-Calculus,"  The classes of depth-bounded and name-bounded processes are fragments of the
+pi-calculus for which some of the decision problems that are undecidable for
+the full calculus become decidable. P is depth-bounded at level k if every
+reduction sequence for P contains successor processes with at most k active
+nested restrictions. P is name-bounded at level k if every reduction sequence
+for P contains successor processes with at most k active bound names.
+Membership of these classes of processes is undecidable. In this paper we use
+binary session types to decise two type systems that give a sound
+characterization of the properties: If a process is well-typed in our first
+system, it is depth-bounded. If a process is well-typed in our second, more
+restrictive type system, it will also be name-bounded.
+",1,0,0,0,0,0
+13845,Combinatorial Secretary Problems with Ordinal Information,"  The secretary problem is a classic model for online decision making.
+Recently, combinatorial extensions such as matroid or matching secretary
+problems have become an important tool to study algorithmic problems in dynamic
+markets. Here the decision maker must know the numerical value of each arriving
+element, which can be a demanding informational assumption. In this paper, we
+initiate the study of combinatorial secretary problems with ordinal
+information, in which the decision maker only needs to be aware of a preference
+order consistent with the values of arrived elements. The goal is to design
+online algorithms with small competitive ratios.
+For a variety of combinatorial problems, such as bipartite matching, general
+packing LPs, and independent set with bounded local independence number, we
+design new algorithms that obtain constant competitive ratios.
+For the matroid secretary problem, we observe that many existing algorithms
+for special matroid structures maintain their competitive ratios even in the
+ordinal model. In these cases, the restriction to ordinal information does not
+represent any additional obstacle. Moreover, we show that ordinal variants of
+the submodular matroid secretary problems can be solved using algorithms for
+the linear versions by extending [Feldman and Zenklusen, 2015]. In contrast, we
+provide a lower bound of $\Omega(\sqrt{n}/(\log n))$ for algorithms that are
+oblivious to the matroid structure, where $n$ is the total number of elements.
+This contrasts an upper bound of $O(\log n)$ in the cardinal model, and it
+shows that the technique of thresholding is not sufficient for good algorithms
+in the ordinal model.
+",1,0,0,0,0,0
+13846,Modeling the Formation of Social Conventions in Multi-Agent Populations,"  In order to understand the formation of social conventions we need to know
+the specific role of control and learning in multi-agent systems. To advance in
+this direction, we propose, within the framework of the Distributed Adaptive
+Control (DAC) theory, a novel Control-based Reinforcement Learning architecture
+(CRL) that can account for the acquisition of social conventions in multi-agent
+populations that are solving a benchmark social decision-making problem. Our
+new CRL architecture, as a concrete realization of DAC multi-agent theory,
+implements a low-level sensorimotor control loop handling the agent's reactive
+behaviors (pre-wired reflexes), along with a layer based on model-free
+reinforcement learning that maximizes long-term reward. We apply CRL in a
+multi-agent game-theoretic task in which coordination must be achieved in order
+to find an optimal solution. We show that our CRL architecture is able to both
+find optimal solutions in discrete and continuous time and reproduce human
+experimental data on standard game-theoretic metrics such as efficiency in
+acquiring rewards, fairness in reward distribution and stability of convention
+formation.
+",0,0,0,1,1,0
+13847,A class of C*-algebraic locally compact quantum groupoids Part I. Motivation and definition,"  In this series of papers, we develop the theory of a class of locally compact
+quantum groupoids, which is motivated by the purely algebraic notion of weak
+multiplier Hopf algebras. In this Part I, we provide motivation and formulate
+the definition in the C*-algebra framework. Existence of a certain canonical
+idempotent element is required and it plays a fundamental role, including the
+establishment of the coassociativity of the comultiplication. This class
+contains locally compact quantum groups as a subclass.
+",0,0,1,0,0,0
+13848,Symmetry-enforced quantum spin Hall insulators in $π$-flux models,"  We prove a Lieb-Schultz-Mattis theorem for the quantum spin Hall effect
+(QSHE) in two-dimensional $\pi$-flux models. In the presence of time reversal,
+$U(1)$ charge conservation and magnetic translation (with $\pi$-flux per unit
+cell) symmetries, if a generic interacting Hamiltonian has a unique gapped
+symmetric ground state at half filling (i.e. an odd number of electrons per
+unit cell), it can only be a QSH insulator. In other words, a trivial Mott
+insulator is forbidden by symmetries at half filling. We further show that such
+a symmetry-enforced QSHE can be realized in cold atoms, by shaking an optical
+lattice and applying a time-dependent Zeeman field.
+",0,1,0,0,0,0
+13849,Stabilized microwave-frequency transfer using optical phase sensing and actuation,"  We present a stabilized microwave-frequency transfer technique that is based
+on optical phase-sensing and optical phase-actuation. This technique shares
+several attributes with optical-frequency transfer and therefore exhibits
+several advantages over other microwave-frequency transfer techniques. We
+demonstrated stabilized transfer of an 8,000 MHz microwave-frequency signal
+over a 166 km metropolitan optical fiber network, achieving a fractional
+frequency stability of 6.8x10^-14 Hz/Hz at 1 s integration, and 5.0x10^-16
+Hz/Hz at 1.6x10^4 s. This technique is being considered for use on the Square
+Kilometre Array SKA1-mid radio telescope.
+",0,1,0,0,0,0
+13850,Spectral sets for numerical range,"  We define and study a numerical-range analogue of the notion of spectral set.
+Among the results obtained are a positivity criterion and a dilation theorem,
+analogous to those already known for spectral sets. An important difference
+from the classical definition is the role played in the new definition by the
+base point. We present some examples to illustrate this aspect.
+",0,0,1,0,0,0
+13851,Learning of Gaussian Processes in Distributed and Communication Limited Systems,"  It is of fundamental importance to find algorithms obtaining optimal
+performance for learning of statistical models in distributed and communication
+limited systems. Aiming at characterizing the optimal strategies, we consider
+learning of Gaussian Processes (GPs) in distributed systems as a pivotal
+example. We first address a very basic problem: how many bits are required to
+estimate the inner-products of Gaussian vectors across distributed machines?
+Using information theoretic bounds, we obtain an optimal solution for the
+problem which is based on vector quantization. Two suboptimal and more
+practical schemes are also presented as substitute for the vector quantization
+scheme. In particular, it is shown that the performance of one of the practical
+schemes which is called per-symbol quantization is very close to the optimal
+one. Schemes provided for the inner-product calculations are incorporated into
+our proposed distributed learning methods for GPs. Experimental results show
+that with spending few bits per symbol in our communication scheme, our
+proposed methods outperform previous zero rate distributed GP learning schemes
+such as Bayesian Committee Model (BCM) and Product of experts (PoE).
+",1,0,0,1,0,0
+13852,Deep Learning: A Critical Appraisal,"  Although deep learning has historical roots going back decades, neither the
+term ""deep learning"" nor the approach was popular just over five years ago,
+when the field was reignited by papers such as Krizhevsky, Sutskever and
+Hinton's now classic (2012) deep network model of Imagenet. What has the field
+discovered in the five subsequent years? Against a background of considerable
+progress in areas such as speech recognition, image recognition, and game
+playing, and considerable enthusiasm in the popular press, I present ten
+concerns for deep learning, and suggest that deep learning must be supplemented
+by other techniques if we are to reach artificial general intelligence.
+",0,0,0,1,0,0
+13853,Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction,"  Social media is an useful platform to share health-related information due to
+its vast reach. This makes it a good candidate for public-health monitoring
+tasks, specifically for pharmacovigilance. We study the problem of extraction
+of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from
+twitter. Medical information extraction from social media is challenging,
+mainly due to short and highly information nature of text, as compared to more
+technical and formal medical reports.
+Current methods in ADR mention extraction relies on supervised learning
+methods, which suffers from labeled data scarcity problem. The State-of-the-art
+method uses deep neural networks, specifically a class of Recurrent Neural
+Network (RNN) which are Long-Short-Term-Memory networks (LSTMs)
+\cite{hochreiter1997long}. Deep neural networks, due to their large number of
+free parameters relies heavily on large annotated corpora for learning the end
+task. But in real-world, it is hard to get large labeled data, mainly due to
+heavy cost associated with manual annotation. Towards this end, we propose a
+novel semi-supervised learning based RNN model, which can leverage unlabeled
+data also present in abundance on social media. Through experiments we
+demonstrate the effectiveness of our method, achieving state-of-the-art
+performance in ADR mention extraction.
+",1,0,0,0,0,0
+13854,A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking,"  This paper introduces a novel deep learning framework including a
+lexicon-based approach for sentence-level prediction of sentiment label
+distribution. We propose to first apply semantic rules and then use a Deep
+Convolutional Neural Network (DeepCNN) for character-level embeddings in order
+to increase information for word-level embedding. After that, a Bidirectional
+Long Short-Term Memory Network (Bi-LSTM) produces a sentence-wide feature
+representation from the word-level embedding. We evaluate our approach on three
+Twitter sentiment classification datasets. Experimental results show that our
+model can improve the classification accuracy of sentence-level sentiment
+analysis in Twitter social networking.
+",1,0,0,0,0,0
+13855,A Tree-based Approach for Detecting Redundant Business Rules in very Large Financial Datasets,"  Net Asset Value (NAV) calculation and validation is the principle task of a
+fund administrator. If the NAV of a fund is calculated incorrectly then there
+is huge impact on the fund administrator; such as monetary compensation,
+reputational loss, or loss of business. In general, these companies use the
+same methodology to calculate the NAV of a fund, however the type of fund in
+question dictates the set of business rules used to validate this. Today, most
+Fund Administrators depend heavily on human resources due to the lack of an
+automated standardized solutions, however due to economic climate and the need
+for efficiency and costs reduction many banks are now looking for an automated
+solution with minimal human interaction; i.e., straight through processing
+(STP). Within the scope of a collaboration project that focuses on building an
+optimal solution for NAV validation, in this paper, we will present a new
+approach for detecting correlated business rules. We also show how we evaluate
+this approach using real-world financial data.
+",1,0,0,0,0,0
+13856,"Dynamics of the nonlinear Klein-Gordon equation in the nonrelativistic limit, I","  The nonlinear Klein-Gordon (NLKG) equation on a manifold $M$ in the
+nonrelativistic limit, namely as the speed of light $c$ tends to infinity, is
+considered. In particular, a higher-order normalized approximation of NLKG
+(which corresponds to the NLS at order $r=1$) is constructed, and when $M$ is a
+smooth compact manifold or $\mathbb{R}^d$ it is proved that the solution of the
+approximating equation approximates the solution of the NLKG locally uniformly
+in time. When $M=\mathbb{R}^d$, $d \geq 3$, it is proved that solutions of the
+linearized order $r$ normalized equation approximate solutions of linear
+Klein-Gordon equation up to times of order $\mathcal{O}(c^{2(r-1)})$ for any
+$r>1$.
+",0,0,1,0,0,0
+13857,Surface plasmons in superintense laser-solid interactions,"  We review studies of superintense laser interaction with solid targets where
+the generation of propagating surface plasmons (or surface waves) plays a key
+role. These studies include the onset of plasma instabilities at the irradiated
+surface, the enhancement of secondary emissions (protons, electrons, and
+photons as high harmonics in the XUV range) in femtosecond interactions with
+grating targets, and the generation of unipolar current pulses with picosecond
+duration. The experimental results give evidence of the existence of surface
+plasmons in the nonlinear regime of relativistic electron dynamics. These
+findings open up a route to the improvement of ultrashort laser-driven sources
+of energetic radiation and, more in general, to the extension of plasmonics in
+a high field regime.
+",0,1,0,0,0,0
+13858,Jacquard: A Large Scale Dataset for Robotic Grasp Detection,"  Grasping skill is a major ability that a wide number of real-life
+applications require for robotisation. State-of-the-art robotic grasping
+methods perform prediction of object grasp locations based on deep neural
+networks. However, such networks require huge amount of labeled data for
+training making this approach often impracticable in robotics. In this paper,
+we propose a method to generate a large scale synthetic dataset with ground
+truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on
+a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D
+images and annotations of successful grasping positions based on grasp attempts
+performed in a simulated environment. We carried out experiments using an
+off-the-shelf CNN, with three different evaluation metrics, including real
+grasping robot trials. The results show that Jacquard enables much better
+generalization skills than a human labeled dataset thanks to its diversity of
+objects and grasping positions. For the purpose of reproducible research in
+robotics, we are releasing along with the Jacquard dataset a web interface for
+researchers to evaluate the successfulness of their grasping position
+detections using our dataset.
+",1,0,0,0,0,0
+13859,Hochschild cohomology of some quantum complete intersections,"  We compute the Hochschild cohomology ring of the algebras $A= k\langle X,
+Y\rangle/ (X^a, XY-qYX, Y^a)$ over a field $k$ where $a\geq 2$ and where $q\in
+k$ is a primitive $a$-th root of unity. We find the the dimension of
+$\mathrm{HH}^n(A)$ and show that it is independent of $a$. We compute
+explicitly the ring structure of the even part of the Hochschild cohomology
+modulo homogeneous nilpotent elements.
+",0,0,1,0,0,0
+13860,Stock Market Visualization,"  We provide complete source code for a front-end GUI and its back-end
+counterpart for a stock market visualization tool. It is built based on the
+""functional visualization"" concept we discuss, whereby functionality is not
+sacrificed for fancy graphics. The GUI, among other things, displays a
+color-coded signal (computed by the back-end code) based on how ""out-of-whack""
+each stock is trading compared with its peers (""mean-reversion""), and the most
+sizable changes in the signal (""momentum""). The GUI also allows to efficiently
+filter/tier stocks by various parameters (e.g., sector, exchange, signal,
+liquidity, market cap) and functionally display them. The tool can be run as a
+web-based or local application.
+",0,0,0,0,0,1
+13861,Sharp estimates for solutions of mean field equation with collapsing singularity,"  The pioneering work of Brezis-Merle [7], Li-Shafrir [27], Li [26] and
+Bartolucci-Tarantello [4] showed that any sequence of blow up solutions for
+(singular) mean field equations of Liouville type must exhibit a ""mass
+concentration"" property. A typical situation of blow-up occurs when we let the
+singular (vortex) points involved in the equation (see (1.1) below) collapse
+together. However in this case Lin-Tarantello in [30] pointed out that the
+phenomenon: ""bubbling implies mass concentration"" might not occur and new
+scenarios open for investigation. In this paper, we present two explicit
+examples which illustrate (with mathematical rigor) how a ""non-concentration""
+situation does happen and its new features. Among other facts, we show that in
+certain situations, the collapsing rate of the singularities can be used as
+blow up parameter to describe the bubbling properties of the solution-sequence.
+In this way we are able to establish accurate estimates around the blow-up
+points which we hope to use towards a degree counting formula for the shadow
+system (1.34) below.
+",0,0,1,0,0,0
+13862,Probabilistic Forwarding of Coded Packets on Networks,"  We consider a scenario of broadcasting information over a network of nodes
+connected by noiseless communication links. A source node in the network has
+$k$ data packets to broadcast, and it suffices that a large fraction of the
+network nodes receives the broadcast. The source encodes the $k$ data packets
+into $n \ge k$ coded packets using a maximum distance separable (MDS) code, and
+transmits them to its one-hop neighbours. Every other node in the network
+follows a probabilistic forwarding protocol, in which it forwards a previously
+unreceived packet to all its neighbours with a certain probability $p$. A
+""near-broadcast"" is when the expected fraction of nodes that receive at least
+$k$ of the $n$ coded packets is close to $1$. The forwarding probability $p$ is
+chosen so as to minimize the expected total number of transmissions needed for
+a near-broadcast. In this paper, we analyze the probabilistic forwarding of
+coded packets on two specific network topologies: binary trees and square
+grids. For trees, our analysis shows that for fixed $k$, the expected total
+number of transmissions increases with $n$. On the other hand, on grids, we use
+ideas from percolation theory to show that a judicious choice of $n$ will
+significantly reduce the expected total number of transmissions needed for a
+near-broadcast.
+",1,0,0,0,0,0
+13863,Quantifiers on languages and codensity monads,"  This paper contributes to the techniques of topo-algebraic recognition for
+languages beyond the regular setting as they relate to logic on words. In
+particular, we provide a general construction on recognisers corresponding to
+adding one layer of various kinds of quantifiers and prove a related
+Reutenauer-type theorem. Our main tools are codensity monads and duality
+theory. Our construction hinges, in particular, on a measure-theoretic
+characterisation of the profinite monad of the free S-semimodule monad for
+finite and commutative semirings S, which generalises our earlier insight that
+the Vietoris monad on Boolean spaces is the codensity monad of the finite
+powerset functor.
+",1,0,1,0,0,0
+13864,Conservative Exploration using Interleaving,"  In many practical problems, a learning agent may want to learn the best
+action in hindsight without ever taking a bad action, which is significantly
+worse than the default production action. In general, this is impossible
+because the agent has to explore unknown actions, some of which can be bad, to
+learn better actions. However, when the actions are combinatorial, this may be
+possible if the unknown action can be evaluated by interleaving it with the
+production action. We formalize this concept as learning in stochastic
+combinatorial semi-bandits with exchangeable actions. We design efficient
+learning algorithms for this problem, bound their n-step regret, and evaluate
+them on both synthetic and real-world problems. Our real-world experiments show
+that our algorithms can learn to recommend K most attractive movies without
+ever violating a strict production constraint, both overall and subject to a
+diversity constraint.
+",0,0,0,1,0,0
+13865,Power Allocation for Full-Duplex Relay Selection in Underlay Cognitive Radio Networks: Coherent versus Non-Coherent Scenarios,"  This paper investigates power control and relay selection in Full Duplex
+Cognitive Relay Networks (FDCRNs), where the secondary-user (SU) relays can
+simultaneously receive data from the SU source and forward them to the SU
+destination. We study both non-coherent and coherent scenarios. In the
+non-coherent case, the SU relay forwards the signal from the SU source without
+regulating the phase; while in the coherent scenario, the SU relay regulates
+the phase when forwarding the signal to minimize the interference at the
+primary-user (PU) receiver. We consider the problem of maximizing the
+transmission rate from the SU source to the SU destination subject to the
+interference constraint at the PU receiver and power constraints at both the SU
+source and SU relay. We then develop a mathematical model to analyze the data
+rate performance of the FDCRN considering the self-interference effects at the
+FD relay. We develop low-complexity and high-performance joint power control
+and relay selection algorithms. Extensive numerical results are presented to
+illustrate the impacts of power level parameters and the self-interference
+cancellation quality on the rate performance. Moreover, we demonstrate the
+significant gain of phase regulation at the SU relay.
+",1,0,1,1,0,0
+13866,Carina: Interactive Million-Node Graph Visualization using Web Browser Technologies,"  We are working on a scalable, interactive visualization system, called
+Carina, for people to explore million-node graphs. By using latest web browser
+technologies, Carina offers fast graph rendering via WebGL, and works across
+desktop (via Electron) and mobile platforms. Different from most existing graph
+visualization tools, Carina does not store the full graph in RAM, enabling it
+to work with graphs with up to 69M edges. We are working to improve and
+open-source Carina, to offer researchers and practitioners a new, scalable way
+to explore and visualize large graph datasets.
+",1,0,0,0,0,0
+13867,Domination between different products and finiteness of associated semi-norms,"  In this note we determine all possible dominations between different products
+of manifolds, when none of the factors of the codomain is dominated by
+products. As a consequence, we determine the finiteness of every
+product-associated functorial semi-norm on the fundamental classes of the
+aforementioned products. These results give partial answers to questions of M.
+Gromov.
+",0,0,1,0,0,0
+13868,Discriminant of the ordinary transversal singularity type. The local aspects,"  Consider a space X with the singular locus, Z=Sing(X), of positive dimension.
+Suppose both Z and X are locally complete intersections. The transversal type
+of X along Z is generically constant but at some points of Z it degenerates. We
+introduce (under certain conditions) the discriminant of the transversal type,
+a subscheme of Z, that reflects these degenerations whenever the generic
+transversal type is `ordinary'.
+The scheme structure of this discriminant is imposed by various compatibility
+properties and is often non-reduced. We establish the basic properties of this
+discriminant: it is a Cartier divisor in Z, functorial under base change, flat
+under some deformations of (X,Z), and compatible with pullback under some
+morphisms, etc.
+Furthermore, we study the local geometry of this discriminant, e.g. we
+compute its multiplicity at a point, and we obtain the resolution of its
+structure sheaf (as module on Z) and study the locally defining equation.
+",0,0,1,0,0,0
+13869,Possible spin excitation structure in monolayer FeSe grown on SrTiO$_{3}$,"  Based on recent high-resolution angle-resolved photoemission spectroscopy
+measurement in monolayer FeSe grown on SrTiO$_{3}$, we constructed a
+tight-binding model and proposed a superconducting (SC) pairing function which
+can well fit the observed band structure and SC gap anisotropy. Then we
+investigated the spin excitation spectra in order to determine the possible
+sign structure of the SC order parameter. We found that a resonance-like spin
+excitation may occur if the SC order parameter changes sign along the Fermi
+surfaces. However, this resonance is located at different locations in momentum
+space compared to other FeSe-based superconductors, suggesting that the Fermi
+surface shape and pairing symmetry in monolayer FeSe grown on SrTiO$_{3}$ may
+be different from other FeSe-based superconductors.
+",0,1,0,0,0,0
+13870,Transient behavior of the solutions to the second order difference equations by the renormalization method based on Newton-Maclaurin expansion,"  The renormalization method based on the Newton-Maclaurin expansion is applied
+to study the transient behavior of the solutions to the difference equations as
+they tend to the steady-states. The key and also natural step is to make the
+renormalization equations to be continuous such that the elementary functions
+can be used to describe the transient behavior of the solutions to difference
+equations. As the concrete examples, we deal with the important second order
+nonlinear difference equations with a small parameter. The result shows that
+the method is more natural than the multi-scale method.
+",0,1,1,0,0,0
+13871,Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting,"  We present a large-scale study of gender bias in occupation classification, a
+task where the use of machine learning may lead to negative outcomes on
+peoples' lives. We analyze the potential allocation harms that can result from
+semantic representation bias. To do so, we study the impact on occupation
+classification of including explicit gender indicators---such as first names
+and pronouns---in different semantic representations of online biographies.
+Additionally, we quantify the bias that remains when these indicators are
+""scrubbed,"" and describe proxy behavior that occurs in the absence of explicit
+gender indicators. As we demonstrate, differences in true positive rates
+between genders are correlated with existing gender imbalances in occupations,
+which may compound these imbalances.
+",1,0,0,1,0,0
+13872,SphereFace: Deep Hypersphere Embedding for Face Recognition,"  This paper addresses deep face recognition (FR) problem under open-set
+protocol, where ideal face features are expected to have smaller maximal
+intra-class distance than minimal inter-class distance under a suitably chosen
+metric space. However, few existing algorithms can effectively achieve this
+criterion. To this end, we propose the angular softmax (A-Softmax) loss that
+enables convolutional neural networks (CNNs) to learn angularly discriminative
+features. Geometrically, A-Softmax loss can be viewed as imposing
+discriminative constraints on a hypersphere manifold, which intrinsically
+matches the prior that faces also lie on a manifold. Moreover, the size of
+angular margin can be quantitatively adjusted by a parameter $m$. We further
+derive specific $m$ to approximate the ideal feature criterion. Extensive
+analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF)
+and MegaFace Challenge show the superiority of A-Softmax loss in FR tasks. The
+code has also been made publicly available.
+",1,0,0,0,0,0
+13873,Speed-of-light pulses in the massless nonlinear Dirac equation with a potential,"  We consider the massless nonlinear Dirac (NLD) equation in $1+1$ dimension
+with scalar-scalar self-interaction $\frac{g^2}{2} (\bar{\Psi} \Psi)^2$ in the
+presence of three external electromagnetic potentials $V(x)$, a potential
+barrier, a constant potential, and a potential well. By solving numerically the
+NLD equation, we find that, for all three cases, after a short transit time,
+the initial pulse breaks into two pulses which are solutions of the massless
+linear Dirac equation traveling in opposite directions with the speed of light.
+During this splitting the charge and the energy are conserved, whereas the
+momentum is conserved when the solutions possess specific symmetries. For the
+case of the constant potential, we derive exact analytical solutions of the
+massless NLD equation that are also solutions of the massless linearized Dirac
+equation.
+",0,1,0,0,0,0
+13874,A Hybrid Feasibility Constraints-Guided Search to the Two-Dimensional Bin Packing Problem with Due Dates,"  The two-dimensional non-oriented bin packing problem with due dates packs a
+set of rectangular items, which may be rotated by 90 degrees, into identical
+rectangular bins. The bins have equal processing times. An item's lateness is
+the difference between its due date and the completion time of its bin. The
+problem packs all items without overlap as to minimize maximum lateness Lmax.
+The paper proposes a tight lower bound that enhances an existing bound on
+Lmax for 24.07% of the benchmark instances and matches it in 30.87% cases. In
+addition, it models the problem using mixed integer programming (MIP), and
+solves small-sized instances exactly using CPLEX. It approximately solves
+larger-sized instances using a two-stage heuristic. The first stage constructs
+an initial solution via a first-fit heuristic that applies an iterative
+constraint programming (CP)-based neighborhood search. The second stage, which
+is iterative too, approximately solves a series of assignment low-level MIPs
+that are guided by feasibility constraints. It then enhances the solution via a
+high-level random local search. The approximate approach improves existing
+upper bounds by 27.45% on average, and obtains the optimum for 33.93% of the
+instances. Overall, the exact and approximate approaches identify the optimum
+for 39.07% cases.
+The proposed approach is applicable to complex problems. It applies CP and
+MIP sequentially, while exploring their advantages, and hybridizes heuristic
+search with MIP. It embeds a new lookahead strategy that guards against
+infeasible search directions and constrains the search to improving directions
+only; thus, differs from traditional lookahead beam searches.
+",1,0,0,0,0,0
+13875,"The earliest phases of high-mass star formation, as seen in NGC 6334 by \emph{Herschel}","  To constrain models of high-mass star formation, the Herschel/HOBYS KP aims
+at discovering massive dense cores (MDCs) able to host the high-mass analogs of
+low-mass prestellar cores, which have been searched for over the past decade.
+We here focus on NGC6334, one of the best-studied HOBYS molecular cloud
+complexes.
+We used Herschel PACS and SPIRE 70-500mu images of the NGC6334 complex
+complemented with (sub)millimeter and mid-infrared data. We built a complete
+procedure to extract ~0.1 pc dense cores with the getsources software, which
+simultaneously measures their far-infrared to millimeter fluxes. We carefully
+estimated the temperatures and masses of these dense cores from their SEDs.
+A cross-correlation with high-mass star formation signposts suggests a mass
+threshold of 75Msun for MDCs in NGC6334. MDCs have temperatures of 9.5-40K,
+masses of 75-1000Msun, and densities of 10^5-10^8cm-3. Their mid-IR emission is
+used to separate 6 IR-bright and 10 IR-quiet protostellar MDCs while their 70mu
+emission strength, with respect to fitted SEDs, helps identify 16 starless MDC
+candidates. The ability of the latter to host high-mass prestellar cores is
+investigated here and remains questionable. An increase in mass and density
+from the starless to the IR-quiet and IR-bright phases suggests that the
+protostars and MDCs simultaneously grow in mass. The statistical lifetimes of
+the high-mass prestellar and protostellar core phases, estimated to be
+1-7x10^4yr and at most 3x10^5yr respectively, suggest a dynamical scenario of
+high-mass star formation.
+The present study provides good mass estimates for a statistically
+significant sample, covering the earliest phases of high-mass star formation.
+High-mass prestellar cores may not exist in NGC6334, favoring a scenario
+presented here, which simultaneously forms clouds and high-mass protostars.
+",0,1,0,0,0,0
+13876,Robust consistent a posteriori error majorants for approximate solutions of diffusion-reaction equations,"  Efficiency of the error control of numerical solutions of partial
+differential equations entirely depends on the two factors: accuracy of an a
+posteriori error majorant and the computational cost of its evaluation for some
+test function/vector-function plus the cost of the latter. In the paper,
+consistency of an a posteriori bound implies that it is the same in the order
+with the respective unimprovable a priori bound. Therefore, it is the basic
+characteristic related to the first factor. The paper is dedicated to the
+elliptic diffusion-reaction equations. We present a guaranteed robust a
+posteriori error majorant effective at any nonnegative constant reaction
+coefficient (r.c.). For a wide range of finite element solutions on a
+quasiuniform meshes the majorant is consistent. For big values of r.c. the
+majorant coincides with the majorant of Aubin (1972), which, as it is known,
+for not big r.c. ($<ch^{-2}$) is inconsistent and loses its sense at r.c.
+approaching zero. Our majorant improves also some other majorants derived for
+the Poisson and reaction-diffusion equations.
+",0,0,1,0,0,0
+13877,Extended opportunity cost model to find near equilibrium electricity prices under non-convexities,"  This paper finds near equilibrium prices for electricity markets with
+nonconvexities due to binary variables, in order to reduce the market
+participants' opportunity costs, such as generators' unrecovered costs. The
+opportunity cost is defined as the difference between the profit when the
+instructions of the market operator are followed and when the market
+participants can freely make their own decisions based on the market prices. We
+use the minimum complementarity approximation to the minimum total opportunity
+cost (MTOC) model, from previous research, with tests on a much more realistic
+unit commitment (UC) model than in previous research, including features such
+as reserve requirements, ramping constraints, and minimum up and down times.
+The developed model incorporates flexible price responsive demand, as in
+previous research, but since not all demand is price responsive, we consider
+the more realistic case that total demand is a mixture of fixed and flexible.
+Another improvement over previous MTOC research is computational: whereas the
+previous research had nonconvex terms among the objective function's continuous
+variables, we convert the objective to an equivalent form that contains only
+linear and convex quadratic terms in the continuous variables. We compare the
+unit commitment model with the standard social welfare optimization version of
+UC, in a series of sensitivity analyses, varying flexible demand to represent
+varying degrees of future penetration of electric vehicles and smart
+appliances, different ratios of generation availability, and different values
+of transmission line capacities to consider possible congestion. The minimum
+total opportunity cost and social welfare solutions are mostly very close in
+different scenarios, except in some extreme cases.
+",0,0,0,0,0,1
+13878,Kernel Two-Sample Hypothesis Testing Using Kernel Set Classification,"  The two-sample hypothesis testing problem is studied for the challenging
+scenario of high dimensional data sets with small sample sizes. We show that
+the two-sample hypothesis testing problem can be posed as a one-class set
+classification problem. In the set classification problem the goal is to
+classify a set of data points that are assumed to have a common class. We prove
+that the average probability of error given a set is less than or equal to the
+Bayes error and decreases as a power of $n$ number of sample data points in the
+set. We use the positive definite Set Kernel for directly mapping sets of data
+to an associated Reproducing Kernel Hilbert Space, without the need to learn a
+probability distribution. We specifically solve the two-sample hypothesis
+testing problem using a one-class SVM in conjunction with the proposed Set
+Kernel. We compare the proposed method with the Maximum Mean Discrepancy,
+F-Test and T-Test methods on a number of challenging simulated high dimensional
+and small sample size data. We also perform two-sample hypothesis testing
+experiments on six cancer gene expression data sets and achieve zero type-I and
+type-II error results on all data sets.
+",0,0,0,1,0,0
+13879,An Efficient Approach for Removing Look-ahead Bias in the Least Square Monte Carlo Algorithm: Leave-One-Out,"  The least square Monte Carlo (LSM) algorithm proposed by Longstaff and
+Schwartz [2001] is the most widely used method for pricing options with early
+exercise features. The LSM estimator contains look-ahead bias, and the
+conventional technique of removing it necessitates an independent set of
+simulations. This study proposes a new approach for efficiently eliminating
+look-ahead bias by using the leave-one-out method, a well-known
+cross-validation technique for machine learning applications. The leave-one-out
+LSM (LOOLSM) method is illustrated with examples, including multi-asset options
+whose LSM price is biased high. The asymptotic behavior of look-ahead bias is
+also discussed with the LOOLSM approach.
+",0,0,0,0,0,1
+13880,Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities,"  Humans can ground natural language commands to tasks at both abstract and
+fine-grained levels of specificity. For instance, a human forklift operator can
+be instructed to perform a high-level action, like ""grab a pallet"" or a
+low-level action like ""tilt back a little bit."" While robots are also capable
+of grounding language commands to tasks, previous methods implicitly assume
+that all commands and tasks reside at a single, fixed level of abstraction.
+Additionally, methods that do not use multiple levels of abstraction encounter
+inefficient planning and execution times as they solve tasks at a single level
+of abstraction with large, intractable state-action spaces closely resembling
+real world complexity. In this work, by grounding commands to all the tasks or
+subtasks available in a hierarchical planning framework, we arrive at a model
+capable of interpreting language at multiple levels of specificity ranging from
+coarse to more granular. We show that the accuracy of the grounding procedure
+is improved when simultaneously inferring the degree of abstraction in language
+used to communicate the task. Leveraging hierarchy also improves efficiency:
+our proposed approach enables a robot to respond to a command within one second
+on 90% of our tasks, while baselines take over twenty seconds on half the
+tasks. Finally, we demonstrate that a real, physical robot can ground commands
+at multiple levels of abstraction allowing it to efficiently plan different
+subtasks within the same planning hierarchy.
+",1,0,0,0,0,0
+13881,Nonlocal Venttsel' diffusion in fractal-type domains: regularity results and numerical approximation,"  We study a nonlocal Venttsel' problem in a non-convex bounded domain with a
+Koch-type boundary. Regularity results of the strict solution are proved in
+weighted Sobolev spaces. The numerical approximation of the problem is carried
+out and optimal a priori error estimates are obtained.
+",0,0,1,0,0,0
+13882,Magnon Spin-Momentum Locking: Various Spin Vortices and Dirac Magnons in Noncollinear Antiferromagnets,"  We generalize the concept of the spin-momentum locking to magnonic systems
+and derive the formula to calculate the spin expectation value for one-magnon
+states of general two-body spin Hamiltonians. We give no-go conditions for
+magnon spin to be independent of momentum. As examples of the magnon
+spin-momentum locking, we analyze a one-dimensional antiferromagnet with the
+Néel order and two-dimensional kagome lattice antiferromagnets with the
+120$^\circ$ structure. We find that the magnon spin depends on its momentum
+even when the Hamiltonian has the $z$-axis spin rotational symmetry, which can
+be explained in the context of a singular band point or a $U(1)$ symmetry
+breaking. A spin vortex in momentum space generated in a kagome lattice
+antiferromagnet has the winding number $Q=-2$, while the typical one observed
+in topological insulator surface states is characterized by $Q=+1$. A magnonic
+analogue of the surface states, the Dirac magnon with $Q=+1$, is found in
+another kagome lattice antiferromagnet. We also derive the sum rule for $Q$ by
+using the Poincaré-Hopf index theorem.
+",0,1,0,0,0,0
+13883,Analytic evaluation of some three- and four- electron atomic integrals involving s STO's and exponential correlation with unlinked $r_{ij}$'s,"  The method of evaluation outlined in a previous work has been utilized here
+to evaluate certain other three- electron and four- electron atomic integrals
+involving s Slater-type orbitals and exponential correlation with unlinked
+$r_{ij}$'s. Limiting expressions for various such integrals have been derived,
+which has not been done earlier. Closed-form expressions for $<r_{12} r_{13} /
+r_{14}>$, $<r_{12}r_{34}/r_{23}>$, $<r_{12}r_{23}/r_{34}>$,
+$<r_{12}r_{13}/r_{34}>$ and $<r_{12}r_{34}/r_{13}>$ have been obtained.
+",0,1,0,0,0,0
+13884,GIANT: Globally Improved Approximate Newton Method for Distributed Optimization,"  For distributed computing environment, we consider the empirical risk
+minimization problem and propose a distributed and communication-efficient
+Newton-type optimization method. At every iteration, each worker locally finds
+an Approximate NewTon (ANT) direction, which is sent to the main driver. The
+main driver, then, averages all the ANT directions received from workers to
+form a {\it Globally Improved ANT} (GIANT) direction. GIANT is highly
+communication efficient and naturally exploits the trade-offs between local
+computations and global communications in that more local computations result
+in fewer overall rounds of communications. Theoretically, we show that GIANT
+enjoys an improved convergence rate as compared with first-order methods and
+existing distributed Newton-type methods. Further, and in sharp contrast with
+many existing distributed Newton-type methods, as well as popular first-order
+methods, a highly advantageous practical feature of GIANT is that it only
+involves one tuning parameter. We conduct large-scale experiments on a computer
+cluster and, empirically, demonstrate the superior performance of GIANT.
+",1,0,0,1,0,0
+13885,Stability and performance analysis of linear positive systems with delays using input-output methods,"  It is known that input-output approaches based on scaled small-gain theorems
+with constant $D$-scalings and integral linear constraints are non-conservative
+for the analysis of some classes of linear positive systems interconnected with
+uncertain linear operators. This dramatically contrasts with the case of
+general linear systems with delays where input-output approaches provide, in
+general, sufficient conditions only. Using these results we provide simple
+alternative proofs for many of the existing results on the stability of linear
+positive systems with discrete/distributed/neutral time-invariant/-varying
+delays and linear difference equations. In particular, we give a simple proof
+for the characterization of diagonal Riccati stability for systems with
+discrete-delays and generalize this equation to other types of delay systems.
+The fact that all those results can be reproved in a very simple way
+demonstrates the importance and the efficiency of the input-output framework
+for the analysis of linear positive systems. The approach is also used to
+derive performance results evaluated in terms of the $L_1$-, $L_2$- and
+$L_\infty$-gains. It is also flexible enough to be used for design purposes.
+",1,0,1,0,0,0
+13886,Joint Inference of User Community and Interest Patterns in Social Interaction Networks,"  Online social media have become an integral part of our social beings.
+Analyzing conversations in social media platforms can lead to complex
+probabilistic models to understand social interaction networks. In this paper,
+we present a modeling approach for characterizing social interaction networks
+by jointly inferring user communities and interests based on social media
+interactions. We present several pattern inference models: i) Interest pattern
+model (IPM) captures population level interaction topics, ii) User interest
+pattern model (UIPM) captures user specific interaction topics, and iii)
+Community interest pattern model (CIPM) captures both community structures and
+user interests. We test our methods on Twitter data collected from Purdue
+University community. From our model results, we observe the interaction topics
+and communities related to two big events within Purdue University community,
+namely Purdue Day of Giving and Senator Bernie Sanders' visit to Purdue
+University as part of Indiana Primary Election 2016. Constructing social
+interaction networks based on user interactions accounts for the similarity of
+users' interactions on various topics of interest and indicates their community
+belonging further beyond connectivity. We observed that the
+degree-distributions of such networks follow power-law that is indicative of
+the existence of fewer nodes in the network with higher levels of interactions,
+and many other nodes with less interactions. We also discuss the application of
+such networks as a useful tool to effectively disseminate specific information
+to the target audience towards planning any large-scale events and demonstrate
+how to single out specific nodes in a given community by running network
+algorithms.
+",1,1,0,0,0,0
+13887,A mode theory for the electoweak interaction and its application to neutrino masses,"  A theory is proposed, in which the basic elements of reality are assumed to
+be something called modes. Particles are interpreted as composites of modes,
+corresponding to eigenstates of the interaction Hamiltonian of modes. At the
+fundamental level of the proposed theory, there are two basic modes only,whose
+spinor spaces are the two smallest nontrivial representation spaces of the
+SL(2,C) group, one being the complex conjugate of the other. All other modes
+are constructed from the two basic modes, making use of the operations of
+direct sum and direct product for related spinor spaces. Accompanying the
+construction of direct-product modes, interactions among modes are introduced
+in a natural way, with the interaction Hamiltonian given from mappings between
+the corresponding state spaces. The interaction Hamiltonian thus obtained turn
+out to possess a form, which is similar to a major part of the interaction
+Hamiltonian in the Glashow-Weinberg-Salam electroweak theory. In the proposed
+theory, it is possible for the second-order perturbation expansion of energy to
+be free from ultraviolet divergence. This expansion is used to derive some
+approximate relations for neutrino masses; in particular, a rough estimate is
+obtained for the ratio of mass differences of neutrinos, which gives the
+correct order of magnitude compared with the experimental result.
+",0,1,0,0,0,0
+13888,Guided Machine Learning for power grid segmentation,"  The segmentation of large scale power grids into zones is crucial for control
+room operators when managing the grid complexity near real time. In this paper
+we propose a new method in two steps which is able to automatically do this
+segmentation, while taking into account the real time context, in order to help
+them handle shifting dynamics. Our method relies on a ""guided"" machine learning
+approach. As a first step, we define and compute a task specific ""Influence
+Graph"" in a guided manner. We indeed simulate on a grid state chosen
+interventions, representative of our task of interest (managing active power
+flows in our case). For visualization and interpretation, we then build a
+higher representation of the grid relevant to this task by applying the graph
+community detection algorithm \textit{Infomap} on this Influence Graph. To
+illustrate our method and demonstrate its practical interest, we apply it on
+commonly used systems, the IEEE-14 and IEEE-118. We show promising and original
+interpretable results, especially on the previously well studied RTS-96 system
+for grid segmentation. We eventually share initial investigation and results on
+a large-scale system, the French power grid, whose segmentation had a
+surprising resemblance with RTE's historical partitioning.
+",0,0,0,1,0,0
+13889,"121,123Sb NQR as a microscopic probe in Te doped correlated semimetal FeSb2 : emergence of electronic Griffith phase, magnetism and metallic behavior %","  $^{121,123}Sb$ nuclear quadrupole resonance (NQR) was applied to
+$Fe(Sb_{1-x}Te_x)_2$ in the low doping regime (\emph{x = 0, 0.01} and
+\emph{0.05}) as a microscopic zero field probe to study the evolution of
+\emph{3d} magnetism and the emergence of metallic behavior. Whereas the NQR
+spectra itself reflects the degree of local disorder via the width of the
+individual NQR lines, the spin lattice relaxation rate (SLRR) $1/T_1(T)$ probes
+the fluctuations at the $Sb$ - site. The fluctuations originate either from
+conduction electrons or from magnetic moments. In contrast to the semi metal
+$FeSb_2$ with a clear signature of the charge and spin gap formation in
+$1/T_1(T)T ( \sim exp/ (\Delta k_BT) ) $, the 1\% $Te$ doped system exhibits
+almost metallic conductivity and a almost filled gap. A weak divergence of the
+SLRR coefficient $1/T_1(T)T \sim T^{-n} \sim T^{-0.2}$ points towards the
+presence of electronic correlations towards low temperatures wheras the
+\textit{5\%} $Te$ doped sample exhibits a much larger divergence in the SLRR
+coefficient showing $1/T_1(T)T \sim T^{-0.72} $. According to the specific heat
+divergence a power law with $n\ =\ 2\ m\ =\ 0.56$ is expected for the SLRR.
+Furthermore $Te$-doped $FeSb_2$ as a disordered paramagnetic metal might be a
+platform for the electronic Griffith phase scenario. NQR evidences a
+substantial asymmetric broadening of the $^{121,123}Sb$ NQR spectrum for the
+\emph{5\%} sample. This has purely electronic origin in agreement with the
+electronic Griffith phase and stems probably from an enhanced $Sb$-$Te$ bond
+polarization and electronic density shift towards the $Te$ atom inside
+$Sb$-$Te$ dumbbell.
+",0,1,0,0,0,0
+13890,The Merging Path Plot: adaptive fusing of k-groups with likelihood-based model selection,"  There are many statistical tests that verify the null hypothesis: the
+variable of interest has the same distribution among k-groups. But once the
+null hypothesis is rejected, how to present the structure of dissimilarity
+between groups? In this article, we introduce The Merging Path Plot - a
+methodology, and factorMerger - an R package, for exploration and visualization
+of k-group dissimilarities. Comparison of k-groups is one of the most important
+issues in exploratory analyses and it has zillions of applications. The
+classical solution is to test a~null hypothesis that observations from all
+groups come from the same distribution. If the global null hypothesis is
+rejected, a~more detailed analysis of differences among pairs of groups is
+performed. The traditional approach is to use pairwise post hoc tests in order
+to verify which groups differ significantly. However, this approach fails with
+a large number of groups in both interpretation and visualization layer.
+The~Merging Path Plot methodology solves this problem by using an
+easy-to-understand description of dissimilarity among groups based on
+Likelihood Ratio Test (LRT) statistic.
+",1,0,0,1,0,0
+13891,Constraint on cosmological parameters by Hubble parameter from gravitational wave standard sirens of neutron star binary system,"  In this paper, we present a new method of measuring Hubble parameter($H(z)$),
+making use of the anisotropy of luminosity distance($d_{L}$), and the analysis
+of gravitational wave(GW) of neutron star(NS) binary system. The method has
+never been put into practice before due to the lack of the ability of detecting
+GW. LIGO's success in detecting GW of black hole(BH) binary system merger
+announced the possibility of this new method. We apply this method to several
+GW detecting projects, including Advanced LIGO(Adv-LIGO), Einstein
+Telescope(ET) and DECIGO, finding that the $H(z)$ by Adv-LIGO and ET is of bad
+accuracy, while the $H(z)$ by DECIGO shows a good accuracy. We use the error
+information of $H(z)$ by DECIGO to simulate $H(z)$ data at every 0.1 redshift
+span, and put the mock data into the forecasting of cosmological parameters.
+Compared with the available 38 observed $H(z)$ data(OHD), mock data shows an
+obviously tighter constraint on cosmological parameters, and a concomitantly
+higher value of Figure of Merit(FoM). For a 3-year-observation by standard
+sirens of DECIGO, the FoM value is as high as 834.9. If a 10-year-observation
+is launched, the FoM could reach 2783.1. For comparison, the FoM of 38 actual
+observed $H(z)$ data is 9.3. These improvement indicates that the new method
+has great potential in further cosmological constraints.
+",0,1,0,0,0,0
+13892,Semidefinite tests for latent causal structures,"  Testing whether a probability distribution is compatible with a given
+Bayesian network is a fundamental task in the field of causal inference, where
+Bayesian networks model causal relations. Here we consider the class of causal
+structures where all correlations between observed quantities are solely due to
+the influence from latent variables. We show that each model of this type
+imposes a certain signature on the observable covariance matrix in terms of a
+particular decomposition into positive semidefinite components. This signature,
+and thus the underlying hypothetical latent structure, can be tested in a
+computationally efficient manner via semidefinite programming. This stands in
+stark contrast with the algebraic geometric tools required if the full
+observable probability distribution is taken into account. The semidefinite
+test is compared with tests based on entropic inequalities.
+",0,0,1,1,0,0
+13893,From atomistic model to the Peierls-Nabarro model with $γ$-surface for dislocations,"  The Peierls-Nabarro (PN) model for dislocations is a hybrid model that
+incorporates the atomistic information of the dislocation core structure into
+the continuum theory. In this paper, we study the convergence from a full
+atomistic model to the PN model with $\gamma$-surface for the dislocation in a
+bilayer system (e.g. bilayer graphene). We prove that the displacement field of
+and the total energy of the dislocation solution of the PN model are
+asymptotically close to those of the full atomistic model. Our work can be
+considered as a generalization of the analysis of the convergence from
+atomistic model to Cauchy-Born rule for crystals without defects in the
+literature.
+",0,0,1,0,0,0
+13894,A family of monogenic $S_4$ quartic fields arising from elliptic curves,"  We consider partial torsion fields (fields generated by a root of a division
+polynomial) for elliptic curves. By analysing the reduction properties of
+elliptic curves, and applying the Montes Algorithm, we obtain information about
+the ring of integers. In particular, for the partial $3$-torsion fields for a
+certain one-parameter family of non-CM elliptic curves, we describe a power
+basis. As a result, we show that the one-parameter family of quartic $S_4$
+fields given by $T^4 - 6T^2 - \alpha T - 3$ for $\alpha \in \mathbb{Z}$ such
+that $\alpha \pm 8$ are squarefree, are monogenic.
+",0,0,1,0,0,0
+13895,Aggregation and Resource Scheduling in Machine-type Communication Networks: A Stochastic Geometry Approach,"  Data aggregation is a promising approach to enable massive machine-type
+communication (mMTC). This paper focuses on the aggregation phase where a
+massive number of machine-type devices (MTDs) transmit to aggregators. By using
+non-orthogonal multiple access (NOMA) principles, we allow several MTDs to
+share the same orthogonal channel in our proposed hybrid access scheme. We
+develop an analytical framework based on stochastic geometry to investigate the
+system performance in terms of average success probability and average number
+of simultaneously served MTDs, under imperfect successive interference
+cancellation (SIC) at the aggregators, for two scheduling schemes: random
+resource scheduling (RRS) and channel-aware resource scheduling (CRS). We
+identify the power constraints on the MTDs sharing the same channel to attain a
+fair coexistence with purely orthogonal multiple access (OMA) setups, then
+power control coefficients are found so that these MTDs perform with similar
+reliability. We show that under high access demand, the hybrid scheme with CRS
+outperforms the OMA setup by simultaneously serving more MTDs with reduced
+power consumption.
+",1,0,0,1,0,0
+13896,Setting Players' Behaviors in World of Warcraft through Semi-Supervised Learning,"  Digital games are one of the major and most important fields on the
+entertainment domain, which also involves cinema and music. Numerous attempts
+have been done to improve the quality of the games including more realistic
+artistic production and computer science. Assessing the player's behavior, a
+task known as player modeling, is currently the need of the hour which leads to
+possible improvements in terms of: (i) better game interaction experience, (ii)
+better exploitation of the relationship between players, and (iii)
+increasing/maintaining the number of players interested in the game. In this
+paper we model players using the basic four behaviors proposed in
+\cite{BartleArtigo}, namely: achiever, explorer, socializer and killer. Our
+analysis is carried out using data obtained from the game ""World of Warcraft""
+over 3 years (2006 $-$ 2009). We employ a semi-supervised learning technique in
+order to find out characteristics that possibly impact player's behavior.
+",1,0,0,0,0,0
+13897,Boundary Hamiltonian theory for gapped topological orders,"  In this letter, we report our systematic construction of the lattice
+Hamiltonian model of topological orders on open surfaces, with explicit
+boundary terms. We do this mainly for the Levin-Wen stringnet model. The full
+Hamiltonian in our approach yields a topologically protected, gapped energy
+spectrum, with the corresponding wave functions robust under
+topology-preserving transformations of the lattice of the system. We explicitly
+present the wavefunctions of the ground states and boundary elementary
+excitations. We construct the creation and hopping operators of boundary
+quasi-particles. We find that given a bulk topological order, the gapped
+boundary conditions are classified by Frobenius algebras in its input data.
+Emergent topological properties of the ground states and boundary excitations
+are characterized by (bi-) modules over Frobenius algebras.
+",0,1,1,0,0,0
+13898,Euler characteristics of cominuscule quantum K-theory,"  We prove an identity relating the product of two opposite Schubert varieties
+in the (equivariant) quantum K-theory ring of a cominuscule flag variety to the
+minimal degree of a rational curve connecting the Schubert varieties. We deduce
+that the sum of the structure constants associated to any product of Schubert
+classes is equal to one. Equivalently, the sheaf Euler characteristic map
+extends to a ring homomorphism defined on the quantum K-theory ring.
+",0,0,1,0,0,0
+13899,Dialogue Act Sequence Labeling using Hierarchical encoder with CRF,"  Dialogue Act recognition associate dialogue acts (i.e., semantic labels) to
+utterances in a conversation. The problem of associating semantic labels to
+utterances can be treated as a sequence labeling problem. In this work, we
+build a hierarchical recurrent neural network using bidirectional LSTM as a
+base unit and the conditional random field (CRF) as the top layer to classify
+each utterance into its corresponding dialogue act. The hierarchical network
+learns representations at multiple levels, i.e., word level, utterance level,
+and conversation level. The conversation level representations are input to the
+CRF layer, which takes into account not only all previous utterances but also
+their dialogue acts, thus modeling the dependency among both, labels and
+utterances, an important consideration of natural dialogue. We validate our
+approach on two different benchmark data sets, Switchboard and Meeting Recorder
+Dialogue Act, and show performance improvement over the state-of-the-art
+methods by $2.2\%$ and $4.1\%$ absolute points, respectively. It is worth
+noting that the inter-annotator agreement on Switchboard data set is $84\%$,
+and our method is able to achieve the accuracy of about $79\%$ despite being
+trained on the noisy data.
+",1,0,0,0,0,0
+13900,Noise Stability is computable and low dimensional,"  Questions of noise stability play an important role in hardness of
+approximation in computer science as well as in the theory of voting. In many
+applications, the goal is to find an optimizer of noise stability among all
+possible partitions of $\mathbb{R}^n$ for $n \geq 1$ to $k$ parts with given
+Gaussian measures $\mu_1,\ldots,\mu_k$. We call a partition $\epsilon$-optimal,
+if its noise stability is optimal up to an additive $\epsilon$. In this paper,
+we give an explicit, computable function $n(\epsilon)$ such that an
+$\epsilon$-optimal partition exists in $\mathbb{R}^{n(\epsilon)}$. This result
+has implications for the computability of certain problems in non-interactive
+simulation, which are addressed in a subsequent work.
+",1,0,1,0,0,0
+13901,Differentially Private ANOVA Testing,"  Modern society generates an incredible amount of data about individuals, and
+releasing summary statistics about this data in a manner that provably protects
+individual privacy would offer a valuable resource for researchers in many
+fields. We present the first algorithm for analysis of variance (ANOVA) that
+preserves differential privacy, allowing this important statistical test to be
+conducted (and the results released) on databases of sensitive information. In
+addition to our private algorithm for the F test statistic, we show a rigorous
+way to compute p-values that accounts for the added noise needed to preserve
+privacy. Finally, we present experimental results quantifying the statistical
+power of this differentially private version of the test, finding that a sample
+of several thousand observations is frequently enough to detect variation
+between groups. The differentially private ANOVA algorithm is a promising
+approach for releasing a common test statistic that is valuable in fields in
+the sciences and social sciences.
+",1,0,0,1,0,0
+13902,"The $E$-cohomological Conley Index, Cup-Lengths and the Arnold Conjecture on $T^{2n}$","  We give a new proof of the strong Arnold conjecture for $1$-periodic
+solutions of Hamiltonian systems on tori, that was first shown by C. Conley and
+E. Zehnder in 1983. Our proof uses other methods and is shorter than the
+previous one. We first show that the $E$-cohomological Conley index, that was
+introduced by the first author recently, has a natural module structure. This
+yields a new cup-length and a lower bound for the number of critical points of
+functionals. Then an existence result for the $E$-cohomological Conley index,
+which applies to the setting of the Arnold conjecture, paves the way to a new
+proof of it on tori.
+",0,0,1,0,0,0
+13903,Compiling Diderot: From Tensor Calculus to C,"  Diderot is a parallel domain-specific language for analysis and visualization
+of multidimensional scientific images, such as those produced by CT and MRI
+scanners. In particular, it supports algorithms where tensor fields (i.e.,
+functions from 3D points to tensor values) are used to represent the underlying
+physical objects that were scanned by the imaging device. Diderot supports
+higher-order programming where tensor fields are first-class values and where
+differential operators and lifted linear-algebra operators can be used to
+express mathematical reasoning directly in the language. While such lifted
+field operations are central to the definition and computation of many
+scientific visualization algorithms, to date they have required extensive
+manual derivations and laborious implementation.
+The challenge for the Diderot compiler is to effectively translate the
+high-level mathematical concepts that are expressible in the surface language
+to a low-level and efficient implementation in C. This paper describes our
+approach to this challenge, which is based around the careful design of an
+intermediate representation (IR), called EIN, and a number of compiler
+transformations that lower the program from tensor calculus to C while avoiding
+combinatorial explosion in the size of the IR. We describe the challenges in
+compiling a language like Diderot, the design of EIN, and the transformation
+used by the compiler. We also present an evaluation of EIN with respect to both
+compiler efficiency and quality of generated code.
+",1,0,0,0,0,0
+13904,Semi-Supervised and Active Few-Shot Learning with Prototypical Networks,"  We consider the problem of semi-supervised few-shot classification where a
+classifier needs to adapt to new tasks using a few labeled examples and
+(potentially many) unlabeled examples. We propose a clustering approach to the
+problem. The features extracted with Prototypical Networks are clustered using
+$K$-means with the few labeled examples guiding the clustering process. We note
+that in many real-world applications the adaptation performance can be
+significantly improved by requesting the few labels through user feedback. We
+demonstrate good performance of the active adaptation strategy using image
+data.
+",1,0,0,1,0,0
+13905,Inhomogeneous exponential jump model,"  We introduce and study the inhomogeneous exponential jump model - an
+integrable stochastic interacting particle system on the continuous half line
+evolving in continuous time. An important feature of the system is the presence
+of arbitrary spatial inhomogeneity on the half line which does not break the
+integrability. We completely characterize the macroscopic limit shape and
+asymptotic fluctuations of the height function (= integrated current) in the
+model. In particular, we explain how the presence of inhomogeneity may lead to
+macroscopic phase transitions in the limit shape such as shocks or traffic
+jams. Away from these singularities the asymptotic fluctuations of the height
+function around its macroscopic limit shape are governed by the GUE Tracy-Widom
+distribution. A surprising result is that while the limit shape is
+discontinuous at a traffic jam caused by a macroscopic slowdown in the
+inhomogeneity, fluctuations on both sides of such a traffic jam still have the
+GUE Tracy-Widom distribution (but with different non-universal normalizations).
+The integrability of the model comes from the fact that it is a degeneration
+of the inhomogeneous stochastic higher spin six vertex models studied earlier
+in arXiv:1601.05770 [math.PR]. Our results on fluctuations are obtained via an
+asymptotic analysis of Fredholm determinantal formulas arising from contour
+integral expressions for the q-moments in the stochastic higher spin six vertex
+model. We also discuss ""product-form"" translation invariant stationary
+distributions of the exponential jump model which lead to an alternative
+hydrodynamic-type heuristic derivation of the macroscopic limit shape.
+",0,0,1,0,0,0
+13906,Evidence accumulation in a Laplace domain decision space,"  Evidence accumulation models of simple decision-making have long assumed that
+the brain estimates a scalar decision variable corresponding to the
+log-likelihood ratio of the two alternatives. Typical neural implementations of
+this algorithmic cognitive model assume that large numbers of neurons are each
+noisy exemplars of the scalar decision variable. Here we propose a neural
+implementation of the diffusion model in which many neurons construct and
+maintain the Laplace transform of the distance to each of the decision bounds.
+As in classic findings from brain regions including LIP, the firing rate of
+neurons coding for the Laplace transform of net accumulated evidence grows to a
+bound during random dot motion tasks. However, rather than noisy exemplars of a
+single mean value, this approach makes the novel prediction that firing rates
+grow to the bound exponentially, across neurons there should be a distribution
+of different rates. A second set of neurons records an approximate inversion of
+the Laplace transform, these neurons directly estimate net accumulated
+evidence. In analogy to time cells and place cells observed in the hippocampus
+and other brain regions, the neurons in this second set have receptive fields
+along a ""decision axis."" This finding is consistent with recent findings from
+rodent recordings. This theoretical approach places simple evidence
+accumulation models in the same mathematical language as recent proposals for
+representing time and space in cognitive models for memory.
+",0,0,0,0,1,0
+13907,Improved Energy Pooling Efficiency Through Inhibited Spontaneous Emission,"  The radiative lifetime of molecules or atoms can be increased by placing them
+within a tuned conductive cavity that inhibits spontaneous emission. This was
+examined as a possible means of enhancing three-body, singlet-based
+upconversion, known as energy pooling. Achieving efficient upconversion of
+light has potential applications in the fields of photovoltaics, biofuels, and
+medicine. The affect of the photonically constrained environment on pooling
+efficiency was quantified using a kinetic model populated with data from
+molecular quantum electrodynamics, perturbation theory, and ab initio
+calculations. This model was applied to a system with fluorescein donors and a
+hexabenzocoronene acceptor. Placing the molecules within a conducting cavity
+was found to increase the efficiency of energy pooling by increasing both the
+donor lifetime and the acceptor emission rate--i.e. a combination of inhibited
+spontaneous emission and the Purcell effect. A model system with a free-space
+pooling efficiency of 23% was found to have an efficiency of 47% in a
+rectangular cavity.
+",0,1,0,0,0,0
+13908,"A unified, mechanistic framework for developmental and evolutionary change","  The two most fundamental processes describing change in biology -development
+and evolution- occur over drastically different timescales, difficult to
+reconcile within a unified framework. Development involves a temporal sequence
+of cell states controlled by a hierarchy of regulatory structures. It occurs
+over the lifetime of a single individual, and is associated to the gene
+expression level change of a given genotype. Evolution, by contrast entails
+genotypic change through the acquisition or loss of genes, and involves the
+emergence of new, environmentally selected phenotypes over the lifetimes of
+many individ- uals. Here we present a model of regulatory network evolution
+that accounts for both timescales. We extend the framework of boolean models of
+gene regulatory network (GRN)-currently only applicable to describing
+development-to include evolutionary processes. As opposed to one-to-one maps to
+specific attractors, we identify the phenotypes of the cells as the relevant
+macrostates of the GRN. A pheno- type may now correspond to multiple
+attractors, and its formal definition no longer require a fixed size for the
+genotype. This opens the possibility for a quantitative study of the phenotypic
+change of a genotype, which is itself changing over evolutionary timescales. We
+show how the realization of specific phenotypes can be controlled by gene
+duplication events, and how successive events of gene duplication lead to new
+regulatory structures via selection. It is these structures that enable control
+of macroscale patterning, as in development. The proposed framework therefore
+provides a mechanistic explanation for the emergence of regulatory structures
+controlling development over evolutionary time.
+",0,0,0,0,1,0
+13909,Coinfection in a stochastic model for bacteriophage systems,"  A system modeling bacteriophage treatments with coinfections in a noisy
+context is analyzed. We prove that in a small noise regime, the system
+converges in the long term to a bacteria free equilibrium. Moreover, we compare
+the treatment with coinfection with the treatment without coinfection, showing
+how the coinfection affects the dose of bacteriophages that is needed to
+eliminate the bacteria and the velocity of convergence to the free bacteria
+equilibrium.
+",0,0,1,0,0,0
+13910,Field dependence of non-reciprocal magnons in chiral MnSi,"  Spin waves in chiral magnetic materials are strongly influenced by the
+Dzyaloshinskii-Moriya interaction resulting in intriguing phenomena like
+non-reciprocal magnon propagation and magnetochiral dichroism. Here, we study
+the non-reciprocal magnon spectrum of the archetypical chiral magnet MnSi and
+its evolution as a function of magnetic field covering the field-polarized and
+conical helix phase. Using inelastic neutron scattering, the magnon energies
+and their spectral weights are determined quantitatively after deconvolution
+with the instrumental resolution. In the field-polarized phase the imaginary
+part of the dynamical susceptibility $\chi''(\varepsilon, {\bf q})$ is shown to
+be asymmetric with respect to wavevectors ${\bf q}$ longitudinal to the applied
+magnetic field ${\bf H}$, which is a hallmark of chiral magnetism. In the
+helimagnetic phase, $\chi''(\varepsilon, {\bf q})$ becomes increasingly
+symmetric with decreasing ${\bf H}$ due to the formation of helimagnon bands
+and the activation of additional spinflip and non-spinflip scattering channels.
+The neutron spectra are in excellent quantitative agreement with the low-energy
+theory of cubic chiral magnets with a single fitting parameter being the
+damping rate of spin waves.
+",0,1,0,0,0,0
+13911,On the Simpson index for the Moran process with random selection and immigration,"  Moran or Wright-Fisher processes are probably the most well known model to
+study the evolution of a population under various effects. Our object of study
+will be the Simpson index which measures the level of diversity of the
+population, one of the key parameter for ecologists who study for example
+forest dynamics. Following ecological motivations, we will consider here the
+case where there are various species with fitness and immigration parameters
+being random processes (and thus time evolving). To measure biodiversity,
+ecologists generally use the Simpson index, who has no closed formula, except
+in the neutral (no selection) case via a backward approach, and which is
+difficult to evaluate even numerically when the population size is large. Our
+approach relies on the large population limit in the ""weak"" selection case, and
+thus to give a procedure which enable us to approximate, with controlled rate,
+the expectation of the Simpson index at fixed time. Our approach will be
+forward and valid for all time, which is the main difference with the
+historical approach of Kingman, or Krone-Neuhauser. We will also study the long
+time behaviour of the Wright-Fisher process in a simplified setting, allowing
+us to get a full picture for the approximation of the expectation of the
+Simpson index.
+",0,0,0,0,1,0
+13912,Small animal whole body imaging with metamaterial-inspired RF coil,"  Preclinical magnetic resonance imaging often requires the entire body of an
+animal to be imaged with sufficient quality. This is usually performed by
+combining regions scanned with small coils with high sensitivity or long scans
+using large coils with low sensitivity. Here, a metamaterial-inspired design
+employing of a parallel array of wires operating on the principle of eigenmode
+hybridization is used to produce a small animal whole-body imaging coil. The
+coil field distribution responsible for the coil field of view and sensitivity
+is simulated in an electromagnetic simulation package and the coil geometrical
+parameters are optimized for the chosen application. A prototype coil is then
+manufactured and assembled using brass telescopic tubes and copper plates as
+distributed capacitance, its field distribution is measured experimentally
+using B1+ mapping technique and found to be in close correspondence with
+simulated results. The coil field distribution is found to be suitable for
+whole-body small animal imaging and coil image quality is compared with a
+number of commercially available coils by whole-body living mice scanning.
+Signal to noise measurements in living mice show outstanding coil performance
+compared to commercially available coils with large receptive fields, and
+rivaling performance compared to small receptive field and high-sensitivity
+coils. The coil is deemed suitable for whole-body small animal preclinical
+applications.
+",0,1,0,0,0,0
+13913,Entropic Trace Estimates for Log Determinants,"  The scalable calculation of matrix determinants has been a bottleneck to the
+widespread application of many machine learning methods such as determinantal
+point processes, Gaussian processes, generalised Markov random fields, graph
+models and many others. In this work, we estimate log determinants under the
+framework of maximum entropy, given information in the form of moment
+constraints from stochastic trace estimation. The estimates demonstrate a
+significant improvement on state-of-the-art alternative methods, as shown on a
+wide variety of UFL sparse matrices. By taking the example of a general Markov
+random field, we also demonstrate how this approach can significantly
+accelerate inference in large-scale learning methods involving the log
+determinant.
+",1,0,0,1,0,0
+13914,A Mixture of Matrix Variate Bilinear Factor Analyzers,"  Over the years data has become increasingly higher dimensional, which has
+prompted an increased need for dimension reduction techniques. This is perhaps
+especially true for clustering (unsupervised classification) as well as
+semi-supervised and supervised classification. Although dimension reduction in
+the area of clustering for multivariate data has been quite thoroughly
+discussed within the literature, there is relatively little work in the area of
+three-way, or matrix variate, data. Herein, we develop a mixture of matrix
+variate bilinear factor analyzers (MMVBFA) model for use in clustering
+high-dimensional matrix variate data. This work can be considered both the
+first matrix variate bilinear factor analysis model as well as the first MMVBFA
+model. Parameter estimation is discussed, and the MMVBFA model is illustrated
+using simulated and real data.
+",0,0,0,1,0,0
+13915,The application of the competency-based approach to assess the training and employment adequacy problem,"  This review paper fits in the context of the adequate matching of training to
+employment, which is one of the main challenges that universities around the
+world strive to meet. In higher education, the revision of curricula
+necessitates a return to the skills required by the labor market to train
+skilled labors.
+In this research, we started with the presentation of the conceptual
+framework. Then we quoted different currents that discussed the problematic of
+the job training match from various perspectives. We proceeded to choose some
+studies that have attempted to remedy this problem by adopting the
+competency-based approach that involves the referential line. This approach has
+as a main characteristic the attainment of the match between training and
+employment. Therefore, it is a relevant solution for this problem. We
+scrutinized the selected studies, presenting their objectives, methodologies
+and results, and we provided our own analysis. Then, we focused on the Moroccan
+context through observations and studies already conducted. And finally, we
+introduced the problematic of our future project.
+",1,0,0,0,0,0
+13916,Prediction and Generation of Binary Markov Processes: Can a Finite-State Fox Catch a Markov Mouse?,"  Understanding the generative mechanism of a natural system is a vital
+component of the scientific method. Here, we investigate one of the fundamental
+steps toward this goal by presenting the minimal generator of an arbitrary
+binary Markov process. This is a class of processes whose predictive model is
+well known. Surprisingly, the generative model requires three distinct
+topologies for different regions of parameter space. We show that a previously
+proposed generator for a particular set of binary Markov processes is, in fact,
+not minimal. Our results shed the first quantitative light on the relative
+(minimal) costs of prediction and generation. We find, for instance, that the
+difference between prediction and generation is maximized when the process is
+approximately independently, identically distributed.
+",1,1,0,0,0,0
+13917,Topological orders of strongly interacting particles,"  We investigate the self-organization of strongly interacting particles
+confined in 1D and 2D. We consider hardcore bosons in spinless Hubbard lattice
+models with short range interactions. We show that, many-body orders with
+topological characteristics emerge, at different energy bands separated by
+large gaps. These topological orders manifest in the way the particles organize
+in real space to form states with different energy. Each of these states
+contains topological defects/condensations whose Euler characteristic can be
+used as a topological number to categorize states belonging to the same energy
+band. We provide analytical formulas for this topological number and the full
+energy spectrum of the system for both sparsely and densely filled systems.
+Furthermore, we discuss the connection with the Gauss-Bonnet theorem of
+differential geometry, by using the curvature generated in real space by the
+particle structures. Our result is a demonstration of how topological orders
+can arise in strongly interacting many-body systems with simple underlying
+rules, without considering the spin, long-range microscopic interactions, or
+external fields.
+",0,1,0,0,0,0
+13918,Virtual unknotting numbers of certain virtual torus knots,"  The virtual unknotting number of a virtual knot is the minimal number of
+crossing changes that makes the virtual knot to be the unknot, which is defined
+only for virtual knots virtually homotopic to the unknot. We focus on the
+virtual knot obtained from the standard (p,q)-torus knot diagram by replacing
+all crossings on one overstrand into virtual crossings and prove that its
+virtual unknotting number is equal to the unknotting number of the
+$(p,q)$-torus knot, i.e. it is (p-1)(q-1)/2.
+",0,0,1,0,0,0
+13919,Computing the Lambert W function in arbitrary-precision complex interval arithmetic,"  We describe an algorithm to evaluate all the complex branches of the Lambert
+W function with rigorous error bounds in interval arithmetic, which has been
+implemented in the Arb library. The classic 1996 paper on the Lambert W
+function by Corless et al. provides a thorough but partly heuristic numerical
+analysis which needs to be complemented with some explicit inequalities and
+practical observations about managing precision and branch cuts.
+",1,0,0,0,0,0
+13920,An Architecture for Embedded Systems Supporting Assisted Living,"  The rise in life expectancy is one of the great achievements of the twentieth
+century. This phenomenon originates a still increasing interest in Ambient
+Assisted Living (AAL) technological solutions that may support people in their
+daily routines allowing an independent and safe lifestyle as long as possible.
+AAL systems generally acquire data from the field and reason on them and the
+context to accomplish their tasks. Very often, AAL systems are vertical
+solutions, thus making hard their reuse and adaptation to different domains
+with respect to the ones for which they have been developed. In this paper we
+propose an architectural solution that allows the acquisition level of an ALL
+system to be easily built, configured, and extended without affecting the
+reasoning level of the system. We experienced our proposal in a fall detection
+system.
+",1,0,0,0,0,0
+13921,Hunting Rabbits on the Hypercube,"  We explore the Hunters and Rabbits game on the hypercube. In the process, we
+find the solution for all classes of graphs with an isoperimetric nesting
+property and find the exact hunter number of $Q^n$ to be
+$1+\sum\limits_{i=0}^{n-2} \binom{i}{\lfloor i/2 \rfloor}$. In addition, we
+extend results to the situation where we allow the rabbit to not move between
+shots.
+",0,0,1,0,0,0
+13922,Mixed Effect Dirichlet-Tree Multinomial for Longitudinal Microbiome Data and Weight Prediction,"  Quantifying the relation between gut microbiome and body weight can provide
+insights into personalized strategies for improving digestive health. In this
+paper, we present an algorithm that predicts weight fluctuations using gut
+microbiome in a healthy cohort of newborns from a previously published dataset.
+Microbial data has been known to present unique statistical challenges that
+defy most conventional models. We propose a mixed effect Dirichlet-tree
+multinomial (DTM) model to untangle these difficulties as well as incorporate
+covariate information and account for species relatedness. The DTM setup allows
+one to easily invoke empirical Bayes shrinkage on each node for enhanced
+inference of microbial proportions. Using these estimates, we subsequently
+apply random forest for weight prediction and obtain a microbiome-inferred
+weight metric. Our result demonstrates that microbiome-inferred weight is
+significantly associated with weight changes in the future and its non-trivial
+effect size makes it a viable candidate to forecast weight progression.
+",0,0,0,1,0,0
+13923,Approximating Throughput and Packet Decoding Delay in Linear Network Coded Wireless Broadcast,"  In this paper, we study a wireless packet broadcast system that uses linear
+network coding (LNC) to help receivers recover data packets that are missing
+due to packet erasures. We study two intertwined performance metrics, namely
+throughput and average packet decoding delay (APDD) and establish strong/weak
+approximation relations based on whether the approximation holds for the
+performance of every receiver (strong) or for the average performance across
+all receivers (weak). We prove an equivalence between strong throughput
+approximation and strong APDD approximation. We prove that throughput-optimal
+LNC techniques can strongly approximate APDD, and partition-based LNC
+techniques may weakly approximate throughput. We also prove that memoryless LNC
+techniques, including instantly decodable network coding techniques, are not
+strong throughput and APDD approximation nor weak throughput approximation
+techniques.
+",1,0,1,0,0,0
+13924,Search for electromagnetic super-preshowers using gamma-ray telescopes,"  Any considerations on propagation of particles through the Universe must
+involve particle interactions: processes leading to production of particle
+cascades. While one expects existence of such cascades, the state of the art
+cosmic-ray research is oriented purely on a detection of single particles,
+gamma rays or associated extensive air showers. The natural extension of the
+cosmic-ray research with the studies on ensembles of particles and air showers
+is being proposed by the CREDO Collaboration. Within the CREDO strategy the
+focus is put on generalized super-preshowers (SPS): spatially and/or temporally
+extended cascades of particles originated above the Earth atmosphere, possibly
+even at astrophysical distances. With CREDO we want to find out whether SPS can
+be at least partially observed by a network of terrestrial and/or satellite
+detectors receiving primary or secondary cosmic-ray signal. This paper
+addresses electromagnetic SPS, e.g. initiated by VHE photons interacting with
+the cosmic microwave background, and the SPS signatures that can be seen by
+gamma-ray telescopes, exploring the exampleof Cherenkov Telescope Array. The
+energy spectrum of secondary electrons and photons in an electromagnetic
+super-preshower might be extended over awide range of energy, down to TeV or
+even lower, as it is evident from the simulation results. This means that
+electromagnetic showers induced by such particles in the Earth atmosphere could
+be observed by imaging atmospheric Cherenkov telescopes. We present preliminary
+results from the study of response of the Cherenkov Telescope Array to SPS
+events, including the analysis of the simulated shower images on the camera
+focal plane and implementedgeneric reconstruction chains based on the Hillas
+parameters.
+",0,1,0,0,0,0
+13925,Gromov-Hausdorff limit of Wasserstein spaces on point clouds,"  We consider a point cloud $X_n := \{ x_1, \dots, x_n \}$ uniformly
+distributed on the flat torus $\mathbb{T}^d : = \mathbb{R}^d / \mathbb{Z}^d $,
+and construct a geometric graph on the cloud by connecting points that are
+within distance $\epsilon$ of each other. We let $\mathcal{P}(X_n)$ be the
+space of probability measures on $X_n$ and endow it with a discrete Wasserstein
+distance $W_n$ as introduced independently by Maas and Zhou et al. for general
+finite Markov chains. We show that as long as $\epsilon= \epsilon_n$ decays
+towards zero slower than an explicit rate depending on the level of uniformity
+of $X_n$, then the space $(\mathcal{P}(X_n), W_n)$ converges in the
+Gromov-Hausdorff sense towards the space of probability measures on
+$\mathbb{T}^d$ endowed with the Wasserstein distance.
+",0,0,1,1,0,0
+13926,Minimal axiomatic frameworks for definable hyperreals with transfer,"  We modify the definable ultrapower construction of Kanovei and Shelah (2004)
+to develop a ZF-definable extension of the continuum with transfer provable
+using countable choice only, with an additional mild hypothesis on
+well-ordering implying properness. Under the same assumptions, we also prove
+the existence of a definable, proper elementary extension of the standard
+superstructure over the reals.
+Keywords: definability; hyperreal; superstructure; elementary embedding.
+",0,0,1,0,0,0
+13927,A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity,"  We present a novel notion of complexity that interpolates between and
+generalizes some classic existing complexity notions in learning theory: for
+estimators like empirical risk minimization (ERM) with arbitrary bounded
+losses, it is upper bounded in terms of data-independent Rademacher complexity;
+for generalized Bayesian estimators, it is upper bounded by the data-dependent
+information complexity (also known as stochastic or PAC-Bayesian,
+$\mathrm{KL}(\text{posterior} \operatorname{\|} \text{prior})$ complexity. For
+(penalized) ERM, the new complexity reduces to (generalized) normalized maximum
+likelihood (NML) complexity, i.e. a minimax log-loss individual-sequence
+regret. Our first main result bounds excess risk in terms of the new
+complexity. Our second main result links the new complexity via Rademacher
+complexity to $L_2(P)$ entropy, thereby generalizing earlier results of Opper,
+Haussler, Lugosi, and Cesa-Bianchi who did the log-loss case with $L_\infty$.
+Together, these results recover optimal bounds for VC- and large (polynomial
+entropy) classes, replacing localized Rademacher complexity by a simpler
+analysis which almost completely separates the two aspects that determine the
+achievable rates: 'easiness' (Bernstein) conditions and model complexity.
+",1,0,0,1,0,0
+13928,A Naive Algorithm for Feedback Vertex Set,"  Given a graph on $n$ vertices and an integer $k$, the feedback vertex set
+problem asks for the deletion of at most $k$ vertices to make the graph
+acyclic. We show that a greedy branching algorithm, which always branches on an
+undecided vertex with the largest degree, runs in single-exponential time,
+i.e., $O(c^k\cdot n^2)$ for some constant $c$.
+",1,0,0,0,0,0
+13929,Query Complexity of Clustering with Side Information,"  Suppose, we are given a set of $n$ elements to be clustered into $k$
+(unknown) clusters, and an oracle/expert labeler that can interactively answer
+pair-wise queries of the form, ""do two elements $u$ and $v$ belong to the same
+cluster?"". The goal is to recover the optimum clustering by asking the minimum
+number of queries. In this paper, we initiate a rigorous theoretical study of
+this basic problem of query complexity of interactive clustering, and provide
+strong information theoretic lower bounds, as well as nearly matching upper
+bounds. Most clustering problems come with a similarity matrix, which is used
+by an automated process to cluster similar points together. Our main
+contribution in this paper is to show the dramatic power of side information
+aka similarity matrix on reducing the query complexity of clustering. A
+similarity matrix represents noisy pair-wise relationships such as one computed
+by some function on attributes of the elements. A natural noisy model is where
+similarity values are drawn independently from some arbitrary probability
+distribution $f_+$ when the underlying pair of elements belong to the same
+cluster, and from some $f_-$ otherwise. We show that given such a similarity
+matrix, the query complexity reduces drastically from $\Theta(nk)$ (no
+similarity matrix) to $O(\frac{k^2\log{n}}{\cH^2(f_+\|f_-)})$ where $\cH^2$
+denotes the squared Hellinger divergence. Moreover, this is also
+information-theoretic optimal within an $O(\log{n})$ factor. Our algorithms are
+all efficient, and parameter free, i.e., they work without any knowledge of $k,
+f_+$ and $f_-$, and only depend logarithmically with $n$. Along the way, our
+work also reveals intriguing connection to popular community detection models
+such as the {\em stochastic block model}, significantly generalizes them, and
+opens up many venues for interesting future research.
+",1,0,0,1,0,0
+13930,On Prediction Properties of Kriging: Uniform Error Bounds and Robustness,"  Kriging based on Gaussian random fields is widely used in reconstructing
+unknown functions. The kriging method has pointwise predictive distributions
+which are computationally simple. However, in many applications one would like
+to predict for a range of untried points simultaneously. In this work we obtain
+some error bounds for the (simple) kriging predictor under the uniform metric.
+It works for a scattered set of input points in an arbitrary dimension, and
+also covers the case where the covariance function of the Gaussian process is
+misspecified. These results lead to a better understanding of the rate of
+convergence of kriging under the Gaussian or the Matérn correlation
+functions, the relationship between space-filling designs and kriging models,
+and the robustness of the Matérn correlation functions.
+",0,0,1,1,0,0
+13931,Robust Submodular Maximization: A Non-Uniform Partitioning Approach,"  We study the problem of maximizing a monotone submodular function subject to
+a cardinality constraint $k$, with the added twist that a number of items
+$\tau$ from the returned set may be removed. We focus on the worst-case setting
+considered in (Orlin et al., 2016), in which a constant-factor approximation
+guarantee was given for $\tau = o(\sqrt{k})$. In this paper, we solve a key
+open problem raised therein, presenting a new Partitioned Robust (PRo)
+submodular maximization algorithm that achieves the same guarantee for more
+general $\tau = o(k)$. Our algorithm constructs partitions consisting of
+buckets with exponentially increasing sizes, and applies standard submodular
+optimization subroutines on the buckets in order to construct the robust
+solution. We numerically demonstrate the performance of PRo in data
+summarization and influence maximization, demonstrating gains over both the
+greedy algorithm and the algorithm of (Orlin et al., 2016).
+",1,0,0,1,0,0
+13932,Value Directed Exploration in Multi-Armed Bandits with Structured Priors,"  Multi-armed bandits are a quintessential machine learning problem requiring
+the balancing of exploration and exploitation. While there has been progress in
+developing algorithms with strong theoretical guarantees, there has been less
+focus on practical near-optimal finite-time performance. In this paper, we
+propose an algorithm for Bayesian multi-armed bandits that utilizes
+value-function-driven online planning techniques. Building on previous work on
+UCB and Gittins index, we introduce linearly-separable value functions that
+take both the expected return and the benefit of exploration into consideration
+to perform n-step lookahead. The algorithm enjoys a sub-linear performance
+guarantee and we present simulation results that confirm its strength in
+problems with structured priors. The simplicity and generality of our approach
+makes it a strong candidate for analyzing more complex multi-armed bandit
+problems.
+",1,0,0,1,0,0
+13933,Adaptive Mantel Test for AssociationTesting in Imaging Genetics Data,"  Mantel's test (MT) for association is conducted by testing the linear
+relationship of similarity of all pairs of subjects between two observational
+domains. Motivated by applications to neuroimaging and genetics data, and
+following the succes of shrinkage and kernel methods for prediction with
+high-dimensional data, we here introduce the adaptive Mantel test as an
+extension of the MT. By utilizing kernels and penalized similarity measures,
+the adaptive Mantel test is able to achieve higher statistical power relative
+to the classical MT in many settings. Furthermore, the adaptive Mantel test is
+designed to simultaneously test over multiple similarity measures such that the
+correct type I error rate under the null hypothesis is maintained without the
+need to directly adjust the significance threshold for multiple testing. The
+performance of the adaptive Mantel test is evaluated on simulated data, and is
+used to investigate associations between genetics markers related to
+Alzheimer's Disease and heatlhy brain physiology with data from a working
+memory study of 350 college students from Beijing Normal University.
+",0,0,0,1,0,0
+13934,Approximation of Functions over Manifolds: A Moving Least-Squares Approach,"  We present an algorithm for approximating a function defined over a
+$d$-dimensional manifold utilizing only noisy function values at locations
+sampled from the manifold with noise. To produce the approximation we do not
+require any knowledge regarding the manifold other than its dimension $d$. The
+approximation scheme is based upon the Manifold Moving Least-Squares (MMLS).
+The proposed algorithm is resistant to noise in both the domain and function
+values. Furthermore, the approximant is shown to be smooth and of approximation
+order of $\mathcal{O}(h^{m+1})$ for non-noisy data, where $h$ is the mesh size
+with respect to the manifold domain, and $m$ is the degree of a local
+polynomial approximation utilized in our algorithm. In addition, the proposed
+algorithm is linear in time with respect to the ambient-space's dimension.
+Thus, in case of extremely large ambient space dimension, we are able to avoid
+the curse of dimensionality without having to perform non-linear dimension
+reduction, which introduces distortions to the manifold data. Using numerical
+experiments, we compare the presented method to state-of-the-art algorithms for
+regression over manifolds and show its potential.
+",1,0,0,1,0,0
+13935,Delay sober up drunkers: Control of diffusion in random walkers,"  Time delay in general leads to instability in some systems, while a specific
+feedback with delay can control fluctuated motion in nonlinear deterministic
+systems to a stable state. In this paper, we consider a non-stationary
+stochastic process, i.e., a random walk and observe its diffusion phenomenon
+with time delayed feedback. Surprisingly, the diffusion coefficient decreases
+with increasing the delay time. We analytically illustrate this suppression of
+diffusion by using stochastic delay differential equations and justify the
+feasibility of this suppression by applying the time-delay feedback to a
+molecular dynamics model.
+",0,1,0,0,0,0
+13936,Gravitational Wave signatures of inflationary models from Primordial Black Hole Dark Matter,"  Primordial Black Holes (PBH) could be the cold dark matter of the universe.
+They could have arisen from large (order one) curvature fluctuations produced
+during inflation that reentered the horizon in the radiation era. At reentry,
+these fluctuations source gravitational waves (GW) via second order anisotropic
+stresses. These GW, together with those (possibly) sourced during inflation by
+the same mechanism responsible for the large curvature fluctuations, constitute
+a primordial stochastic GW background (SGWB) that unavoidably accompanies the
+PBH formation. We study how the amplitude and the range of frequencies of this
+signal depend on the statistics (Gaussian versus $\chi^2$) of the primordial
+curvature fluctuations, and on the evolution of the PBH mass function due to
+accretion and merging. We then compare this signal with the sensitivity of
+present and future detectors, at PTA and LISA scales. We find that this SGWB
+will help to probe, or strongly constrain, the early universe mechanism of PBH
+production. The comparison between the peak mass of the PBH distribution and
+the peak frequency of this SGWB will provide important information on the
+merging and accretion evolution of the PBH mass distribution from their
+formation to the present era. Different assumptions on the statistics and on
+the PBH evolution also result in different amounts of CMB $\mu$-distortions.
+Therefore the above results can be complemented by the detection (or the
+absence) of $\mu$-distortions with an experiment such as PIXIE.
+",0,1,0,0,0,0
+13937,Linear Parsing Expression Grammars,"  PEGs were formalized by Ford in 2004, and have several pragmatic operators
+(such as ordered choice and unlimited lookahead) for better expressing modern
+programming language syntax. Since these operators are not explicitly defined
+in the classic formal language theory, it is significant and still challenging
+to argue PEGs' expressiveness in the context of formal language theory.Since
+PEGs are relatively new, there are several unsolved problems.One of the
+problems is revealing a subclass of PEGs that is equivalent to DFAs. This
+allows application of some techniques from the theory of regular grammar to
+PEGs. In this paper, we define Linear PEGs (LPEGs), a subclass of PEGs that is
+equivalent to DFAs. Surprisingly, LPEGs are formalized by only excluding some
+patterns of recursive nonterminal in PEGs, and include the full set of ordered
+choice, unlimited lookahead, and greedy repetition, which are characteristic of
+PEGs. Although the conversion judgement of parsing expressions into DFAs is
+undecidable in general, the formalism of LPEGs allows for a syntactical
+judgement of parsing expressions.
+",1,0,0,0,0,0
+13938,Pre-Synaptic Pool Modification (PSPM): A Supervised Learning Procedure for Spiking Neural Networks,"  A central question in neuroscience is how to develop realistic models that
+predict output firing behavior based on provided external stimulus. Given a set
+of external inputs and a set of output spike trains, the objective is to
+discover a network structure which can accomplish the transformation as
+accurately as possible. Due to the difficulty of this problem in its most
+general form, approximations have been made in previous work. Past
+approximations have sacrificed network size, recurrence, allowed spiked count,
+or have imposed layered network structure. Here we present a learning rule
+without these sacrifices, which produces a weight matrix of a leaky
+integrate-and-fire (LIF) network to match the output activity of both
+deterministic LIF networks as well as probabilistic integrate-and-fire (PIF)
+networks. Inspired by synaptic scaling, our pre-synaptic pool modification
+(PSPM) algorithm outputs deterministic, fully recurrent spiking neural networks
+that can provide a novel generative model for given spike trains. Similarity in
+output spike trains is evaluated with a variety of metrics including a
+van-Rossum like measure and a numerical comparison of inter-spike interval
+distributions. Application of our algorithm to randomly generated networks
+improves similarity to the reference spike trains on both of these stated
+measures. In addition, we generated LIF networks that operate near criticality
+when trained on critical PIF outputs. Our results establish that learning rules
+based on synaptic homeostasis can be used to represent input-output
+relationships in fully recurrent spiking neural networks.
+",0,0,0,0,1,0
+13939,The Bright and Dark Sides of High-Redshift starburst galaxies from {\it Herschel} and {\it Subaru} observations,"  We present rest-frame optical spectra from the FMOS-COSMOS survey of twelve
+$z \sim 1.6$ \textit{Herschel} starburst galaxies, with Star Formation Rate
+(SFR) elevated by $\times$8, on average, above the star-forming Main Sequence
+(MS). Comparing the H$\alpha$ to IR luminosity ratio and the Balmer Decrement
+we find that the optically-thin regions of the sources contain on average only
+$\sim 10$ percent of the total SFR whereas $\sim90$ percent comes from an
+extremely obscured component which is revealed only by far-IR observations and
+is optically-thick even in H$\alpha$. We measure the [NII]$_{6583}$/H$\alpha$
+ratio, suggesting that the less obscured regions have a metal content similar
+to that of the MS population at the same stellar masses and redshifts. However,
+our objects appear to be metal-rich outliers from the metallicity-SFR
+anticorrelation observed at fixed stellar mass for the MS population. The
+[SII]$_{6732}$/[SII]$_{6717}$ ratio from the average spectrum indicates an
+electron density $n_{\rm e} \sim 1,100\ \mathrm{cm}^{-3}$, larger than what
+estimated for MS galaxies but only at the 1.5$\sigma$ level. Our results
+provide supporting evidence that high-$z$ MS outliers are the analogous of
+local ULIRGs, and are consistent with a major merger origin for the starburst
+event.
+",0,1,0,0,0,0
+13940,Edge Estimation with Independent Set Oracles,"  We study the task of estimating the number of edges in a graph with access to
+only an independent set oracle. Independent set queries draw motivation from
+group testing and have applications to the complexity of decision versus
+counting problems. We give two algorithms to estimate the number of edges in an
+$n$-vertex graph, using (i) $\mathrm{polylog}(n)$ bipartite independent set
+queries, or (ii) ${n}^{2/3} \cdot\mathrm{polylog}(n)$ independent set queries.
+",1,0,0,0,0,0
+13941,Threshold-activated transport stabilizes chaotic populations to steady states,"  We explore Random Scale-Free networks of populations, modelled by chaotic
+Ricker maps, connected by transport that is triggered when population density
+in a patch is in excess of a critical threshold level. Our central result is
+that threshold-activated dispersal leads to stable fixed populations, for a
+wide range of threshold levels. Further, suppression of chaos is facilitated
+when the threshold-activated migration is more rapid than the intrinsic
+population dynamics of a patch. Additionally, networks with large number of
+nodes open to the environment, readily yield stable steady states. Lastly we
+demonstrate that in networks with very few open nodes, the degree and
+betweeness centrality of the node open to the environment has a pronounced
+influence on control. All qualitative trends are corroborated by quantitative
+measures, reflecting the efficiency of control, and the width of the steady
+state window.
+",0,1,0,0,0,0
+13942,Bounding and Counting Linear Regions of Deep Neural Networks,"  We investigate the complexity of deep neural networks (DNN) that represent
+piecewise linear (PWL) functions. In particular, we study the number of linear
+regions, i.e. pieces, that a PWL function represented by a DNN can attain, both
+theoretically and empirically. We present (i) tighter upper and lower bounds
+for the maximum number of linear regions on rectifier networks, which are exact
+for inputs of dimension one; (ii) a first upper bound for multi-layer maxout
+networks; and (iii) a first method to perform exact enumeration or counting of
+the number of regions by modeling the DNN with a mixed-integer linear
+formulation. These bounds come from leveraging the dimension of the space
+defining each linear region. The results also indicate that a deep rectifier
+network can only have more linear regions than every shallow counterpart with
+same number of neurons if that number exceeds the dimension of the input.
+",1,0,0,1,0,0
+13943,The reactive-telegraph equation and a related kinetic model,"  We study the long-range, long-time behavior of the reactive-telegraph
+equation and a related reactive-kinetic model. The two problems are equivalent
+in one spatial dimension. We point out that the reactive-telegraph equation,
+meant to model a population density, does not preserve positivity in higher
+dimensions. In view of this, in dimensions larger than one, we consider a
+reactive-kinetic model and investigate the long-range, long-time limit of the
+solutions. We provide a general characterization of the speed of propagation
+and we compute it explicitly in one and two dimensions. We show that a phase
+transition between parabolic and hyperbolic behavior takes place only in one
+dimension. Finally, we investigate the hydrodynamic limit of the limiting
+problem.
+",0,0,1,0,0,0
+13944,Finiteness theorems for K3 surfaces and abelian varieties of CM type,"  We study abelian varieties and K3 surfaces with complex multiplication
+defined over number fields of fixed degree. We show that these varieties fall
+into finitely many isomorphism classes over an algebraic closure of the field
+of rational numbers. As an application we confirm finiteness conjectures of
+Shafarevich and Coleman in the CM case. In addition we prove the uniform
+boundedness of the Galois invariant subgroup of the geometric Brauer group for
+forms of a smooth projective variety satisfying the integral Mumford--Tate
+conjecture. When applied to K3 surfaces, this affirms a conjecture of
+Várilly-Alvarado in the CM case.
+",0,0,1,0,0,0
+13945,Rheology of inelastic hard spheres at finite density and shear rate,"  Considering a granular fluid of inelastic smooth hard spheres we discuss the
+conditions delineating the rheological regimes comprising Newtonian,
+Bagnoldian, shear thinning, and shear thickening behavior. Developing a kinetic
+theory, valid at finite shear rates and densities around the glass transition
+density, we predict the viscosity and Bagnold coefficient at practically
+relevant values of the control parameters. The determination of full flow
+curves relating the shear stress $\sigma$ to the shear rate $\dot\gamma$, and
+predictions of the yield stress complete our discussion of granular rheology
+derived from first principles.
+",0,1,0,0,0,0
+13946,The Flash ADC system and PMT waveform reconstruction for the Daya Bay Experiment,"  To better understand the energy response of the Antineutrino Detector (AD),
+the Daya Bay Reactor Neutrino Experiment installed a full Flash ADC readout
+system on one AD that allowed for simultaneous data taking with the current
+readout system. This paper presents the design, data acquisition, and
+simulation of the Flash ADC system, and focuses on the PMT waveform
+reconstruction algorithms. For liquid scintillator calorimetry, the most
+critical requirement to waveform reconstruction is linearity. Several common
+reconstruction methods were tested but the linearity performance was not
+satisfactory. A new method based on the deconvolution technique was developed
+with 1% residual non-linearity, which fulfills the requirement. The performance
+was validated with both data and Monte Carlo (MC) simulations, and 1%
+consistency between them has been achieved.
+",0,1,0,0,0,0
+13947,Quasitoric totally normally split representatives in unitary cobordism ring,"  The present paper generalises the results of Ray and Buchstaber-Ray,
+Buchstaber-Panov-Ray in unitary cobordism theory. I prove that any class $x\in
+\Omega^{*}_{U}$ of the unitary cobordism ring contains a quasitoric totally
+normally and tangentially split manifold.
+",0,0,1,0,0,0
+13948,Interpretable Feature Recommendation for Signal Analytics,"  This paper presents an automated approach for interpretable feature
+recommendation for solving signal data analytics problems. The method has been
+tested by performing experiments on datasets in the domain of prognostics where
+interpretation of features is considered very important. The proposed approach
+is based on Wide Learning architecture and provides means for interpretation of
+the recommended features. It is to be noted that such an interpretation is not
+available with feature learning approaches like Deep Learning (such as
+Convolutional Neural Network) or feature transformation approaches like
+Principal Component Analysis. Results show that the feature recommendation and
+interpretation techniques are quite effective for the problems at hand in terms
+of performance and drastic reduction in time to develop a solution. It is
+further shown by an example, how this human-in-loop interpretation system can
+be used as a prescriptive system.
+",1,0,0,1,0,0
+13949,Learning One-hidden-layer ReLU Networks via Gradient Descent,"  We study the problem of learning one-hidden-layer neural networks with
+Rectified Linear Unit (ReLU) activation function, where the inputs are sampled
+from standard Gaussian distribution and the outputs are generated from a noisy
+teacher network. We analyze the performance of gradient descent for training
+such kind of neural networks based on empirical risk minimization, and provide
+algorithm-dependent guarantees. In particular, we prove that tensor
+initialization followed by gradient descent can converge to the ground-truth
+parameters at a linear rate up to some statistical error. To the best of our
+knowledge, this is the first work characterizing the recovery guarantee for
+practical learning of one-hidden-layer ReLU networks with multiple neurons.
+Numerical experiments verify our theoretical findings.
+",0,0,0,1,0,0
+13950,Exclusion of GNSS NLOS Receptions Caused by Dynamic Objects in Heavy Traffic Urban Scenarios Using Real-Time 3D Point Cloud: An Approach without 3D Maps,"  Absolute positioning is an essential factor for the arrival of autonomous
+driving. Global Navigation Satellites System (GNSS) receiver provides absolute
+localization for it. GNSS solution can provide satisfactory positioning in open
+or sub-urban areas, however, its performance suffered in super-urbanized area
+due to the phenomenon which are well-known as multipath effects and NLOS
+receptions. The effects dominate GNSS positioning performance in the area. The
+recent proposed 3D map aided (3DMA) GNSS can mitigate most of the multipath
+effects and NLOS receptions caused by buildings based on 3D city models.
+However, the same phenomenon caused by moving objects in urban area is
+currently not modelled in the 3D geographic information system (GIS). Moving
+objects with tall height, such as the double-decker bus, can also cause NLOS
+receptions because of the blockage of GNSS signals by surface of objects.
+Therefore, we present a novel method to exclude the NLOS receptions caused by
+double-decker bus in highly urbanized area, Hong Kong. To estimate the geometry
+dimension and orientation relative to GPS receiver, a Euclidean cluster
+algorithm and a classification method are used to detect the double-decker
+buses and calculate their relative locations. To increase the accuracy and
+reliability of the proposed NLOS exclusion method, an NLOS exclusion criterion
+is proposed to exclude the blocked satellites considering the elevation, signal
+noise ratio (SNR) and horizontal dilution of precision (HDOP). Finally, GNSS
+positioning is estimated by weighted least square (WLS) method using the
+remaining satellites after the NLOS exclusion. A static experiment was
+performed near a double-decker bus stop in Hong Kong, which verified the
+effectiveness of the proposed method.
+",1,0,0,0,0,0
+13951,Energy Distribution in Intrinsically Coupled Systems: The Spring Pendulum Paradigm,"  Intrinsically nonlinear coupled systems present different oscillating
+components that exchange energy among themselves. We present a new approach to
+deal with such energy exchanges and to investigate how it depends on the system
+control parameters. The method consists in writing the total energy of the
+system, and properly identifying the energy terms for each component and,
+especially, their coupling. To illustrate the proposed approach, we work with
+the bi-dimensional spring pendulum, which is a paradigm to study nonlinear
+coupled systems, and is used as a model for several systems. For the spring
+pendulum, we identify three energy components, resembling the spring and
+pendulum like motions, and the coupling between them. With these analytical
+expressions, we analyze the energy exchange for individual trajectories, and we
+also obtain global characteristics of the spring pendulum energy distribution
+by calculating spatial and time average energy components for a great number of
+trajectories (periodic, quasi-periodic and chaotic) throughout the phase space.
+Considering an energy term due to the nonlinear coupling, we identify regions
+in the parameter space that correspond to strong and weak coupling. The
+presented procedure can be applied to nonlinear coupled systems to reveal how
+the coupling mediates internal energy exchanges, and how the energy
+distribution varies according to the system parameters.
+",0,1,0,0,0,0
+13952,Cayley deformations of compact complex surfaces,"  In this article, we consider Cayley deformations of a compact complex surface
+in a Calabi--Yau four-fold. We will study complex deformations of compact
+complex submanifolds of Calabi--Yau manifolds with a view to explaining why
+complex and Cayley deformations of a compact complex surface are the same. We
+in fact prove that the moduli space of complex deformations of any compact
+complex embedded submanifold of a Calabi--Yau manifold is a smooth manifold.
+",0,0,1,0,0,0
+13953,Ensemble Inhibition and Excitation in the Human Cortex: an Ising Model Analysis with Uncertainties,"  The pairwise maximum entropy model, also known as the Ising model, has been
+widely used to analyze the collective activity of neurons. However, controversy
+persists in the literature about seemingly inconsistent findings, whose
+significance is unclear due to lack of reliable error estimates. We therefore
+develop a method for accurately estimating parameter uncertainty based on
+random walks in parameter space using adaptive Markov Chain Monte Carlo after
+the convergence of the main optimization algorithm. We apply our method to the
+spiking patterns of excitatory and inhibitory neurons recorded with
+multielectrode arrays in the human temporal cortex during the wake-sleep cycle.
+Our analysis shows that the Ising model captures neuronal collective behavior
+much better than the independent model during wakefulness, light sleep, and
+deep sleep when both excitatory (E) and inhibitory (I) neurons are modeled;
+ignoring the inhibitory effects of I-neurons dramatically overestimates
+synchrony among E-neurons. Furthermore, information-theoretic measures reveal
+that the Ising model explains about 80%-95% of the correlations, depending on
+sleep state and neuron type. Thermodynamic measures show signatures of
+criticality, although we take this with a grain of salt as it may be merely a
+reflection of long-range neural correlations.
+",0,0,0,0,1,0
+13954,Generalized Self-Concordant Functions: A Recipe for Newton-Type Methods,"  We study the smooth structure of convex functions by generalizing a powerful
+concept so-called self-concordance introduced by Nesterov and Nemirovskii in
+the early 1990s to a broader class of convex functions, which we call
+generalized self-concordant functions. This notion allows us to develop a
+unified framework for designing Newton-type methods to solve convex optimiza-
+tion problems. The proposed theory provides a mathematical tool to analyze both
+local and global convergence of Newton-type methods without imposing
+unverifiable assumptions as long as the un- derlying functionals fall into our
+generalized self-concordant function class. First, we introduce the class of
+generalized self-concordant functions, which covers standard self-concordant
+functions as a special case. Next, we establish several properties and key
+estimates of this function class, which can be used to design numerical
+methods. Then, we apply this theory to develop several Newton-type methods for
+solving a class of smooth convex optimization problems involving the
+generalized self- concordant functions. We provide an explicit step-size for
+the damped-step Newton-type scheme which can guarantee a global convergence
+without performing any globalization strategy. We also prove a local quadratic
+convergence of this method and its full-step variant without requiring the
+Lipschitz continuity of the objective Hessian. Then, we extend our result to
+develop proximal Newton-type methods for a class of composite convex
+minimization problems involving generalized self-concordant functions. We also
+achieve both global and local convergence without additional assumption.
+Finally, we verify our theoretical results via several numerical examples, and
+compare them with existing methods.
+",0,0,1,1,0,0
+13955,On Joint Functional Calculus For Ritt Operators,"  In this paper, we study joint functional calculus for commuting $n$-tuple of
+Ritt operators. We provide an equivalent characterisation of boundedness for
+joint functional calculus for Ritt operators on $L^p$-spaces, $1< p<\infty$. We
+also investigate joint similarity problem and joint bounded functional calculus
+on non-commutative $L^p$-spaces for $n$-tuple of Ritt operators. We get our
+results by proving a suitable multivariable transfer principle between
+sectorial and Ritt operators as well as an appropriate joint dilation result in
+a general setting.
+",0,0,1,0,0,0
+13956,Controlling plasmon modes and damping in buckled two-dimensional material open systems,"  Full ranges of both hybrid plasmon-mode dispersions and their damping are
+studied systematically by our recently developed mean-field theory in open
+systems involving a conducting substrate and a two-dimensional (2D) material
+with a buckled honeycomb lattice, such as silicene, germanene, and a group
+\rom{4} dichalcogenide as well. In this hybrid system, the single plasmon mode
+for a free-standing 2D layer is split into one acoustic-like and one
+optical-like mode, leading to a dramatic change in the damping of plasmon
+modes. In comparison with gapped graphene, critical features associated with
+plasmon modes and damping in silicene and molybdenum disulfide are found with
+various spin-orbit and lattice asymmetry energy bandgaps, doping types and
+levels, and coupling strengths between 2D materials and the conducting
+substrate. The obtained damping dependence on both spin and valley degrees of
+freedom is expected to facilitate measuring the open-system dielectric property
+and the spin-orbit coupling strength of individual 2D materials. The unique
+linear dispersion of the acoustic-like plasmon mode introduces additional
+damping from the intraband particle-hole modes which is absent for a
+free-standing 2D material layer, and the use of molybdenum disulfide with a
+large bandgap simultaneously suppresses the strong damping from the interband
+particle-hole modes.
+",0,1,0,0,0,0
+13957,A high-order nonconservative approach for hyperbolic equations in fluid dynamics,"  It is well known, thanks to Lax-Wendroff theorem, that the local conservation
+of a numerical scheme for a conservative hyperbolic system is a simple and
+systematic way to guarantee that, if stable, a scheme will provide a sequence
+of solutions that will converge to a weak solution of the continuous problem.
+In [1], it is shown that a nonconservative scheme will not provide a good
+solution. The question of using, nevertheless, a nonconservative formulation of
+the system and getting the correct solution has been a long-standing debate. In
+this paper, we show how get a relevant weak solution from a pressure-based
+formulation of the Euler equations of fluid mechanics. This is useful when
+dealing with nonlinear equations of state because it is easier to compute the
+internal energy from the pressure than the opposite. This makes it possible to
+get oscillation free solutions, contrarily to classical conservative methods.
+An extension to multiphase flows is also discussed, as well as a
+multidimensional extension.
+",0,0,1,0,0,0
+13958,Improving Resilience of Autonomous Moving Platforms by Real Time Analysis of Their Cooperation,"  Environmental changes, failures, collisions or even terrorist attacks can
+cause serious malfunctions of the delivery systems. We have presented a novel
+approach improving resilience of Autonomous Moving Platforms AMPs. The approach
+is based on multi-level state diagrams describing environmental trigger
+specifications, movement actions and synchronization primitives. The upper
+level diagrams allowed us to model advanced interactions between autonomous
+AMPs and detect irregularities such as deadlocks live-locks etc. The techniques
+were presented to verify and analyze combined AMPs' behaviors using model
+checking technique. The described system, Dedan verifier, is still under
+development. In the near future, a graphical form of verified system
+representation is planned.
+",1,0,0,0,0,0
+13959,Power series expansions for the planar monomer-dimer problem,"  We compute the free energy of the planar monomer-dimer model. Unlike the
+classical planar dimer model, an exact solution is not known in this case. Even
+the computation of the low-density power series expansion requires heavy and
+nontrivial computations. Despite of the exponential computational complexity,
+we compute almost three times more terms than were previously known. Such an
+expansion provides both lower and upper bound for the free energy, and allows
+to obtain more accurate numerical values than previously possible. We expect
+that our methods can be applied to other similar problems.
+",1,0,0,0,0,0
+13960,Neural Networks Compression for Language Modeling,"  In this paper, we consider several compression techniques for the language
+modeling problem based on recurrent neural networks (RNNs). It is known that
+conventional RNNs, e.g, LSTM-based networks in language modeling, are
+characterized with either high space complexity or substantial inference time.
+This problem is especially crucial for mobile applications, in which the
+constant interaction with the remote server is inappropriate. By using the Penn
+Treebank (PTB) dataset we compare pruning, quantization, low-rank
+factorization, tensor train decomposition for LSTM networks in terms of model
+size and suitability for fast inference.
+",1,0,0,1,0,0
+13961,Relativistic effects in the non-resonant two-photon K-shell ionization of neutral atoms,"  Relativistic effects in the non-resonant two-photon K-shell ionization of
+neutral atoms are studied theoretically within the framework of second-order
+perturbation theory. The non-relativistic results are compared with the
+relativistic calculations in the dipole and no-pair approximations as well as
+with the complete relativistic approach. The calculations are performed in both
+velocity and length gauges. Our results show a significant decrease of the
+total cross section for heavy atoms as compared to the non-relativistic
+treatment, which is mainly due to the relativistic wavefunction contraction.
+The effects of higher multipoles and negative continuum energy states
+counteract the relativistic contraction contribution, but are generally much
+weaker. While the effects beyond the dipole approximation are equally important
+in both gauges, the inclusion of negative continuum energy states visibly
+contributes to the total cross section only in the velocity gauge.
+",0,1,0,0,0,0
+13962,Local Okounkov bodies and limits in prime characteristic,"  This article is concerned with the asymptotic behavior of certain sequences
+of ideals in rings of prime characteristic. These sequences, which we call
+$p$-families of ideals, are ubiquitous in prime characteristic commutative
+algebra (e.g., they occur naturally in the theories of tight closure,
+Hilbert-Kunz multiplicity, and $F$-signature). We associate to each $p$-family
+of ideals an object in Euclidean space that is analogous to the Newton-Okounkov
+body of a graded family of ideals, which we call a $p$-body. Generalizing the
+methods used to establish volume formulas for the Hilbert-Kunz multiplicity and
+$F$-signature of semigroup rings, we relate the volume of a $p$-body to a
+certain asymptotic invariant determined by the corresponding $p$-family of
+ideals. We apply these methods to obtain new existence results for limits in
+positive characteristic, an analogue of the Brunn-Minkowski theorem for
+Hilbert-Kunz multiplicity, and a uniformity result concerning the positivity of
+a $p$-family.
+",0,0,1,0,0,0
+13963,Two-Dimensional Systolic Complexes Satisfy Property A,"  We show that 2-dimensional systolic complexes are quasi-isometric to quadric
+complexes with flat intervals. We use this fact along with the weight function
+of Brodzki, Campbell, Guentner, Niblo and Wright to prove that 2-dimensional
+systolic complexes satisfy Property A.
+",0,0,1,0,0,0
+13964,Application of the Computer Capacity to the Analysis of Processors Evolution,"  The notion of computer capacity was proposed in 2012, and this quantity has
+been estimated for computers of different kinds.
+In this paper we show that, when designing new processors, the manufacturers
+change the parameters that affect the computer capacity. This allows us to
+predict the values of parameters of future processors. As the main example we
+use Intel processors, due to the accessibility of detailed description of all
+their technical characteristics.
+",1,0,0,0,0,0
+13965,On absolutely normal numbers and their discrepancy estimate,"  We construct the base $2$ expansion of an absolutely normal real number $x$
+so that, for every integer $b$ greater than or equal to $2$, the discrepancy
+modulo $1$ of the sequence $(b^0 x, b^1 x, b^2 x , \ldots)$ is essentially the
+same as that realized by almost all real numbers.
+",1,0,1,0,0,0
+13966,Multispectral computational ghost imaging with multiplexed illumination,"  Computational ghost imaging is a robust and compact system that has drawn
+wide attentions over the last two decades. Multispectral imaging possesses
+spatial and spectral resolving abilities, is very useful for surveying scenes
+and extracting detailed information. Existing multispectral imagers mostly
+utilize narrow band filters or dispersive optical devices to separate lights of
+different wavelengths, and then use multiple bucket detectors or an array
+detector to record them separately. Here, we propose a novel multispectral
+ghost imaging method that uses one single bucket detector with multiplexed
+illumination to produce colored image. The multiplexed illumination patterns
+are produced by three binary encoded matrices (corresponding to red, green,
+blue colored information, respectively) and random patterns. The results of
+simulation and experiment have verified that our method can be effective to
+recover the colored object. Our method has two major advantages: one is that
+the binary encoded matrices as cipher keys can protect the security of private
+contents; the other is that multispectral images are produced simultaneously by
+one single-pixel detector, which significantly reduces the amount of the data
+acquisition.
+",0,1,0,0,0,0
+13967,Combined MEG and fMRI Exponential Random Graph Modeling for inferring functional Brain Connectivity,"  Estimated connectomes by the means of neuroimaging techniques have enriched
+our knowledge of the organizational properties of the brain leading to the
+development of network-based clinical diagnostics. Unfortunately, to date, many
+of those network-based clinical diagnostics tools, based on the mere
+description of isolated instances of observed connectomes are noisy estimates
+of the true connectivity network. Modeling brain connectivity networks is
+therefore important to better explain the functional organization of the brain
+and allow inference of specific brain properties. In this report, we present
+pilot results on the modeling of combined MEG and fMRI neuroimaging data
+acquired during an n-back memory task experiment. We adopted a pooled
+Exponential Random Graph Model (ERGM) as a network statistical model to capture
+the underlying process in functional brain networks of 9 subjects MEG and fMRI
+data out of 32 during a 0-back vs 2-back memory task experiment. Our results
+suggested strong evidence that all the functional connectomes of the 9 subjects
+have small world properties. A group level comparison using comparing the
+conditions pairwise showed no significant difference in the functional
+connectomes across the subjects. Our pooled ERGMs successfully reproduced
+important brain properties such as functional segregation and functional
+integration. However, the ERGMs reproducing the functional segregation of the
+brain networks discriminated between the 0-back and 2-back conditions while the
+models reproducing both properties failed to successfully discriminate between
+both conditions. Our results are promising and would improve in robustness with
+a larger sample size. Nevertheless, our pilot results tend to support previous
+findings that functional segregation and integration are sufficient to
+statistically reproduce the main properties of brain network.
+",0,0,0,0,1,0
+13968,An updated Type II supernova Hubble diagram,"  We present photometry and spectroscopy of nine Type II-P/L supernovae (SNe)
+with redshifts in the 0.045 < z < 0.335 range, with a view to re-examining
+their utility as distance indicators. Specifically, we apply the expanding
+photosphere method (EPM) and the standardized candle method (SCM) to each
+target, and find that both methods yield distances that are in reasonable
+agreement with each other. The current record-holder for the highest-redshift
+spectroscopically confirmed SN II-P is PS1-13bni (z = 0.335 +0.009 -0.012), and
+illustrates the promise of Type II SNe as cosmological tools. We updated
+existing EPM and SCM Hubble diagrams by adding our sample to those previously
+published. Within the context of Type II SN distance measuring techniques, we
+investigated two related questions. First, we explored the possibility of
+utilising spectral lines other than the traditionally used Fe II 5169 to infer
+the photospheric velocity of SN ejecta. Using local well-observed objects, we
+derive an epoch-dependent relation between the strong Balmer line and Fe II
+5169 velocities that is applicable 30 to 40 days post-explosion. Motivated in
+part by the continuum of key observables such as rise time and decline rates
+exhibited from II-P to II-L SNe, we assessed the possibility of using
+Hubble-flow Type II-L SNe as distance indicators. These yield similar distances
+as the Type II-P SNe. Although these initial results are encouraging, a
+significantly larger sample of SNe II-L would be required to draw definitive
+conclusions.
+",0,1,0,0,0,0
+13969,Digital Identity: The Effect of Trust and Reputation Information on User Judgement in the Sharing Economy,"  The Sharing Economy (SE) is a growing ecosystem focusing on peer-to-peer
+enterprise. In the SE the information available to assist individuals (users)
+in making decisions focuses predominantly on community generated trust and
+reputation information. However, how such information impacts user judgement is
+still being understood. To explore such effects, we constructed an artificial
+SE accommodation platform where we varied the elements related to hosts'
+digital identity, measuring users' perceptions and decisions to interact.
+Across three studies, we find that trust and reputation information increases
+not only the users' perceived trustworthiness, credibility, and sociability of
+hosts, but also the propensity to rent a private room in their home. This
+effect is seen when providing users both with complete profiles and profiles
+with partial user-selected information. Closer investigations reveal that three
+elements relating to the host's digital identity are sufficient to produce such
+positive perceptions and increased rental decisions, regardless of which three
+elements are presented. Our findings have relevant implications for human
+judgment and privacy in the SE, and question its current culture of ever
+increasing information-sharing.
+",1,0,0,0,0,0
+13970,Can Deep Clinical Models Handle Real-World Domain Shifts?,"  The hypothesis that computational models can be reliable enough to be adopted
+in prognosis and patient care is revolutionizing healthcare. Deep learning, in
+particular, has been a game changer in building predictive models, thereby
+leading to community-wide data curation efforts. However, due to the inherent
+variabilities in population characteristics and biological systems, these
+models are often biased to the training datasets. This can be limiting when
+models are deployed in new environments, particularly when there are systematic
+domain shifts not known a priori. In this paper, we formalize these challenges
+by emulating a large class of domain shifts that can occur in clinical
+settings, and argue that evaluating the behavior of predictive models in light
+of those shifts is an effective way of quantifying the reliability of clinical
+models. More specifically, we develop an approach for building challenging
+scenarios, based on analysis of \textit{disease landscapes}, and utilize
+unsupervised domain adaptation to compensate for the domain shifts. Using the
+openly available MIMIC-III EHR dataset for phenotyping, we generate a large
+class of scenarios and evaluate the ability of deep clinical models in those
+cases. For the first time, our work sheds light into data regimes where deep
+clinical models can fail to generalize, due to significant changes in the
+disease landscapes between the source and target landscapes. This study
+emphasizes the need for sophisticated evaluation mechanisms driven by
+real-world domain shifts to build effective AI solutions for healthcare.
+",0,0,0,1,0,0
+13971,"Fabrication of grain boundary junctions using NdFeAs(O,F) superconducting thin films","  We report on the growth of NdFeAs(O,F) thin films on [001]-tilt MgO bicrystal
+substrates with misorientation angle theta_GB=6°, 12°, 24° and
+45°, and their inter- and intra-grain transport properties. X-ray
+diffraction study confirmed that all our NdFeAs(O,F) films are epitaxially
+grown on the MgO bicrystals. The theta_GB dependence of the inter-grain
+critical current density Jc shows that, unlike Co-doped BaFe2As2 and Fe(Se,Te),
+its decay with theta_GB is rather significant. As a possible reason of this
+result, fluorine may have diffused preferentially to the grain boundary region
+and eroded the crystal structure.
+",0,1,0,0,0,0
+13972,Panchromatic Hubble Andromeda Treasury XVIII. The High-mass Truncation of the Star Cluster Mass Function,"  We measure the mass function for a sample of 840 young star clusters with
+ages between 10-300 Myr observed by the Panchromatic Hubble Andromeda Treasury
+(PHAT) survey in M31. The data show clear evidence of a high-mass truncation:
+only 15 clusters more massive than $10^4$ $M_{\odot}$ are observed, compared to
+$\sim$100 expected for a canonical $M^{-2}$ pure power-law mass function with
+the same total number of clusters above the catalog completeness limit.
+Adopting a Schechter function parameterization, we fit a characteristic
+truncation mass of $M_c = 8.5^{+2.8}_{-1.8} \times 10^3$ $M_{\odot}$. While
+previous studies have measured cluster mass function truncations, the
+characteristic truncation mass we measure is the lowest ever reported.
+Combining this M31 measurement with previous results, we find that the cluster
+mass function truncation correlates strongly with the characteristic star
+formation rate surface density of the host galaxy, where $M_c \propto$ $\langle
+\Sigma_{\mathrm{SFR}} \rangle^{\sim1.1}$. We also find evidence that suggests
+the observed $M_c$-$\Sigma_{\mathrm{SFR}}$ relation also applies to globular
+clusters, linking the two populations via a common formation pathway. If so,
+globular cluster mass functions could be useful tools for constraining the star
+formation properties of their progenitor host galaxies in the early Universe.
+",0,1,0,0,0,0
+13973,Cohomology and overconvergence for representations of powers of Galois groups,"  We show that the Galois cohomology groups of $p$-adic representations of a
+direct power of $\operatorname{Gal}(\overline{\mathbb{Q}_p}/\mathbb{Q}_p)$ can
+be computed via the generalization of Herr's complex to multivariable
+$(\varphi,\Gamma)$-modules. Using Tate duality and a pairing for multivariable
+$(\varphi,\Gamma)$-modules we extend this to analogues of the Iwasawa
+cohomology. We show that all $p$-adic representations of a direct power of
+$\operatorname{Gal}(\overline{\mathbb{Q}_p}/\mathbb{Q}_p)$ are overconvergent
+and, moreover, passing to overconvergent multivariable
+$(\varphi,\Gamma)$-modules is an equivalence of categories. Finally, we prove
+that the overconvergent Herr complex also computes the Galois cohomology
+groups.
+",0,0,1,0,0,0
+13974,Global algorithms for maximal eigenpair,"  This paper is a continuation of \ct{cmf16} where an efficient algorithm for
+computing the maximal eigenpair was introduced first for tridiagonal matrices
+and then extended to the irreducible matrices with nonnegative off-diagonal
+elements. This paper introduces two global algorithms for computing the maximal
+eigenpair in a rather general setup, including even a class of real (with some
+negative off-diagonal elements) or complex matrices.
+",0,0,1,1,0,0
+13975,Entanglement and entropy production in coupled single-mode Bose-Einstein condensates,"  We investigate the time evolution of the entanglement entropy of coupled
+single-mode Bose-Einstein condensates in a double well potential at $T=0$
+temperature, by combining numerical results with analytical approximations. We
+find that the coherent oscillations of the condensates result in entropy
+oscillations on the top of a linear entropy generation at short time scales.
+Due to dephasing, the entropy eventually saturates to a stationary value, in
+spite of the lack of equilibration. We show that this long time limit of the
+entropy reflects the semiclassical dynamics of the system, revealing the
+self-trapping phase transition of the condensates at large interaction strength
+by a sudden entropy jump. We compare the stationary limit of the entropy to the
+prediction of a classical microcanonical ensemble, and find surprisingly good
+agreement in spite of the non-equilibrium state of the system. Our predictions
+should be experimentally observable on a Bose-Einstein condensate in a double
+well potential or on a two-component condensate with inter-state coupling.
+",0,1,0,0,0,0
+13976,Sparse Poisson Regression with Penalized Weighted Score Function,"  We proposed a new penalized method in this paper to solve sparse Poisson
+Regression problems. Being different from $\ell_1$ penalized log-likelihood
+estimation, our new method can be viewed as penalized weighted score function
+method. We show that under mild conditions, our estimator is $\ell_1$
+consistent and the tuning parameter can be pre-specified, which shares the same
+good property of the square-root Lasso.
+",0,0,1,1,0,0
+13977,Bubble size statistics during reionization from 21-cm tomography,"  The upcoming SKA1-Low radio interferometer will be sensitive enough to
+produce tomographic imaging data of the redshifted 21-cm signal from the Epoch
+of Reionization. Due to the non-Gaussian distribution of the signal, a power
+spectrum analysis alone will not provide a complete description of its
+properties. Here, we consider an additional metric which could be derived from
+tomographic imaging data, namely the bubble size distribution of ionized
+regions. We study three methods that have previously been used to characterize
+bubble size distributions in simulation data for the hydrogen ionization
+fraction - the spherical-average, mean-free-path and friends-of-friends methods
+- and apply them to simulated 21-cm data cubes. Our simulated data cubes have
+the (sensitivity-dictated) resolution expected for the SKA1-Low reionization
+experiment and we study the impact of both the light-cone and redshift space
+distortion effects. To identify ionized regions in the 21-cm data we introduce
+a new, self-adjusting thresholding approach based on the K-Means algorithm. We
+find that the fraction of ionized cells identified in this way consistently
+falls below the mean volume-averaged ionized fraction. From a comparison of the
+three bubble size methods, we conclude that all three methods are useful, but
+that the mean-free-path method performs best in terms of tracking the progress
+of reionization and separating different reionization scenarios. The light-cone
+effect is found to affect data spanning more than about 10~MHz in frequency
+($\Delta z\sim0.5$). We find that redshift space distortions only marginally
+affect the bubble size distributions.
+",0,1,0,0,0,0
+13978,Robust Stochastic Configuration Networks with Kernel Density Estimation,"  Neural networks have been widely used as predictive models to fit data
+distribution, and they could be implemented through learning a collection of
+samples. In many applications, however, the given dataset may contain noisy
+samples or outliers which may result in a poor learner model in terms of
+generalization. This paper contributes to a development of robust stochastic
+configuration networks (RSCNs) for resolving uncertain data regression
+problems. RSCNs are built on original stochastic configuration networks with
+weighted least squares method for evaluating the output weights, and the input
+weights and biases are incrementally and randomly generated by satisfying with
+a set of inequality constrains. The kernel density estimation (KDE) method is
+employed to set the penalty weights for each training samples, so that some
+negative impacts, caused by noisy data or outliers, on the resulting learner
+model can be reduced. The alternating optimization technique is applied for
+updating a RSCN model with improved penalty weights computed from the kernel
+density estimation function. Performance evaluation is carried out by a
+function approximation, four benchmark datasets and a case study on engineering
+application. Comparisons to other robust randomised neural modelling
+techniques, including the probabilistic robust learning algorithm for neural
+networks with random weights and improved RVFL networks, indicate that the
+proposed RSCNs with KDE perform favourably and demonstrate good potential for
+real-world applications.
+",1,0,0,1,0,0
+13979,Density of the spectrum of Jacobi matrices with power asymptotics,"  We consider Jacobi matrices $J$ whose parameters have the power asymptotics
+$\rho_n=n^{\beta_1} \left( x_0 + \frac{x_1}{n} + {\rm
+O}(n^{-1-\epsilon})\right)$ and $q_n=n^{\beta_2} \left( y_0 + \frac{y_1}{n} +
+{\rm O}(n^{-1-\epsilon})\right)$ for the off-diagonal and diagonal,
+respectively. We show that for $\beta_1 > \beta_2$, or $\beta_1=\beta_2$ and
+$2x_0 > |y_0|$, the matrix $J$ is in the limit circle case and the convergence
+exponent of its spectrum is $1/\beta_1$. Moreover, we obtain upper and lower
+bounds for the upper density of the spectrum. When the parameters of the matrix
+$J$ have a power asymptotic with one more term, we characterise the occurrence
+of the limit circle case completely (including the exceptional case $\lim_{n\to
+\infty} |q_n|\big/ \rho_n = 2$) and determine the convergence exponent in
+almost all cases.
+",0,0,1,0,0,0
+13980,Modeling of a self-sustaining ignition in a solid energetic material,"  In the present work we analyze some necessary conditions for ignition of
+solid energetic materials by low velocity impact ignition mechanism. Basing on
+reported results of {\it ab initio} computations we assume that the energetic
+activation barriers for the primary endothermic dissociation in some energetic
+materials may be locally lowered due to the effect of shear strain caused by
+the impact. We show that the ignition may be initiated in regions with the
+reduced activation barriers, even at moderately low exothermicity of the
+subsequent exothermic reactions thus suggesting that the above regions may
+serve as ""hot spots"" for the ignition. We apply our results to analyze initial
+steps of ignition in DADNE and TATB molecular crystals.
+",0,1,0,0,0,0
+13981,Election Bias: Comparing Polls and Twitter in the 2016 U.S. Election,"  While the polls have been the most trusted source for election predictions
+for decades, in the recent presidential election they were called inaccurate
+and biased. How inaccurate were the polls in this election and can social media
+beat the polls as an accurate election predictor? Polls from several news
+outlets and sentiment analysis on Twitter data were used, in conjunction with
+the results of the election, to answer this question and outline further
+research on the best method for predicting the outcome of future elections.
+",1,0,0,0,0,0
+13982,Versality of the relative Fukaya category,"  Seidel introduced the notion of a Fukaya category `relative to an ample
+divisor', explained that it is a deformation of the Fukaya category of the
+affine variety that is the complement of the divisor, and showed how the
+relevant deformation theory is controlled by the symplectic cohomology of the
+complement. We elaborate on Seidel's definition of the relative Fukaya
+category, and give a criterion under which the deformation is versal.
+",0,0,1,0,0,0
+13983,Hot Phonon and Carrier Relaxation in Si(100) Determined by Transient Extreme Ultraviolet Spectroscopy,"  The thermalization of hot carriers and phonons gives direct insight into the
+scattering processes that mediate electrical and thermal transport. Obtaining
+the scattering rates for both hot carriers and phonons currently requires
+multiple measurements with incommensurate timescales. Here, transient
+extreme-ultraviolet (XUV) spectroscopy on the silicon 2p core level at 100 eV
+is used to measure hot carrier and phonon thermalization in Si(100) from tens
+of femtoseconds to 200 ps following photoexcitation of the indirect transition
+to the {\Delta} valley at 800 nm. The ground state XUV spectrum is first
+theoretically predicted using a combination of a single plasmon pole model and
+the Bethe-Salpeter equation (BSE) with density functional theory (DFT). The
+excited state spectrum is predicted by incorporating the electronic effects of
+photo-induced state-filling, broadening, and band-gap renormalization into the
+ground state XUV spectrum. A time-dependent lattice deformation and expansion
+is also required to describe the excited state spectrum. The kinetics of these
+structural components match the kinetics of phonons excited from the
+electron-phonon and phonon-phonon scattering processes following
+photoexcitation. Separating the contributions of electronic and structural
+effects on the transient XUV spectra allows the carrier population, the
+population of phonons involved in inter- and intra-valley electron-phonon
+scattering, and the population of phonons involved in phonon-phonon scattering
+to be quantified as a function of delay time.
+",0,1,0,0,0,0
+13984,Counting Quasi-Idempotent Irreducible Integral Matrices,"  Given any polynomial $p$ in $C[X]$, we show that the set of irreducible
+matrices satisfying $p(A)=0$ is finite. In the specific case $p(X)=X^2-nX$, we
+count the number of irreducible matrices in this set and analyze the arising
+sequences and their asymptotics. Such matrices turn out to be related to
+generalized compositions and generalized partitions.
+",0,0,1,0,0,0
+13985,On sound-based interpretation of neonatal EEG,"  Significant training is required to visually interpret neonatal EEG signals.
+This study explores alternative sound-based methods for EEG interpretation
+which are designed to allow for intuitive and quick differentiation between
+healthy background activity and abnormal activity such as seizures. A novel
+method based on frequency and amplitude modulation (FM/AM) is presented. The
+algorithm is tuned to facilitate the audio domain perception of rhythmic
+activity which is specific to neonatal seizures. The method is compared with
+the previously developed phase vocoder algorithm for different time compressing
+factors. A survey is conducted amongst a cohort of non-EEG experts to
+quantitatively and qualitatively examine the performance of sound-based methods
+in comparison with the visual interpretation. It is shown that both
+sonification methods perform similarly well, with a smaller inter-observer
+variability in comparison with visual. A post-survey analysis of results is
+performed by examining the sensitivity of the ear to frequency evolution in
+audio.
+",0,0,0,1,1,0
+13986,OAuthGuard: Protecting User Security and Privacy with OAuth 2.0 and OpenID Connect,"  Millions of users routinely use Google to log in to websites supporting OAuth
+2.0 or OpenID Connect; the security of OAuth 2.0 and OpenID Connect is
+therefore of critical importance. As revealed in previous studies, in practice
+RPs often implement OAuth 2.0 incorrectly, and so many real-world OAuth 2.0 and
+OpenID Connect systems are vulnerable to attack. However, users of such flawed
+systems are typically unaware of these issues, and so are at risk of attacks
+which could result in unauthorised access to the victim user's account at an
+RP. In order to address this threat, we have developed OAuthGuard, an OAuth 2.0
+and OpenID Connect vulnerability scanner and protector, that works with RPs
+using Google OAuth 2.0 and OpenID Connect services. It protects user security
+and privacy even when RPs do not implement OAuth 2.0 or OpenID Connect
+correctly. We used OAuthGuard to survey the 1000 top-ranked websites supporting
+Google sign-in for the possible presence of five OAuth 2.0 or OpenID Connect
+security and privacy vulnerabilities, of which one has not previously been
+described in the literature. Of the 137 sites in our study that employ Google
+Sign-in, 69 were found to suffer from at least one serious vulnerability.
+OAuthGuard was able to protect user security and privacy for 56 of these 69
+RPs, and for the other 13 was able to warn users that they were using an
+insecure implementation.
+",1,0,0,0,0,0
+13987,Aspiration dynamics generate robust predictions in structured populations,"  Evolutionary game dynamics in structured populations are strongly affected by
+updating rules. Previous studies usually focus on imitation-based rules, which
+rely on payoff information of social peers. Recent behavioral experiments
+suggest that whether individuals use such social information for strategy
+updating may be crucial to the outcomes of social interactions. This hints at
+the importance of considering updating rules without dependence on social
+peers' payoff information, which, however, is rarely investigated. Here, we
+study aspiration-based self-evaluation rules, with which individuals
+self-assess the performance of strategies by comparing own payoffs with an
+imaginary value they aspire, called the aspiration level. We explore the fate
+of strategies on population structures represented by graphs or networks. Under
+weak selection, we analytically derive the condition for strategy dominance,
+which is found to coincide with the classical condition of risk-dominance. This
+condition holds for all networks and all distributions of aspiration levels,
+and for individualized ways of self-evaluation. Our condition can be
+intuitively interpreted: one strategy prevails over the other if the strategy
+brings more satisfaction to individuals than the other does. Our work thus
+sheds light on the intrinsic difference between evolutionary dynamics induced
+by aspiration-based and imitation-based rules.
+",0,0,0,0,1,0
+13988,Memory Efficient Experience Replay for Streaming Learning,"  In supervised machine learning, an agent is typically trained once and then
+deployed. While this works well for static settings, robots often operate in
+changing environments and must quickly learn new things from data streams. In
+this paradigm, known as streaming learning, a learner is trained online, in a
+single pass, from a data stream that cannot be assumed to be independent and
+identically distributed (iid). Streaming learning will cause conventional deep
+neural networks (DNNs) to fail for two reasons: 1) they need multiple passes
+through the entire dataset; and 2) non-iid data will cause catastrophic
+forgetting. An old fix to both of these issues is rehearsal. To learn a new
+example, rehearsal mixes it with previous examples, and then this mixture is
+used to update the DNN. Full rehearsal is slow and memory intensive because it
+stores all previously observed examples, and its effectiveness for preventing
+catastrophic forgetting has not been studied in modern DNNs. Here, we describe
+the ExStream algorithm for memory efficient rehearsal and compare it to
+alternatives. We find that full rehearsal can eliminate catastrophic forgetting
+in a variety of streaming learning settings, with ExStream performing well
+using far less memory and computation.
+",0,0,0,1,0,0
+13989,New integrable semi-discretizations of the coupled nonlinear Schrodinger equations,"  We have undertaken an algorithmic search for new integrable
+semi-discretizations of physically relevant nonlinear partial differential
+equations. The search is performed by using a compatibility condition for the
+discrete Lax operators and symbolic computations. We have discovered a new
+integrable system of coupled nonlinear Schrodinger equations which combines
+elements of the Ablowitz-Ladik lattice and the triangular-lattice ribbon
+studied by Vakhnenko. We show that the continuum limit of the new integrable
+system is given by uncoupled complex modified Korteweg-de Vries equations and
+uncoupled nonlinear Schrodinger equations.
+",0,1,1,0,0,0
+13990,Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations,"  Non-negative matrix factorization is a basic tool for decomposing data into
+the feature and weight matrices under non-negativity constraints, and in
+practice is often solved in the alternating minimization framework. However, it
+is unclear whether such algorithms can recover the ground-truth feature matrix
+when the weights for different features are highly correlated, which is common
+in applications. This paper proposes a simple and natural alternating gradient
+descent based algorithm, and shows that with a mild initialization it provably
+recovers the ground-truth in the presence of strong correlations. In most
+interesting cases, the correlation can be in the same order as the highest
+possible. Our analysis also reveals its several favorable features including
+robustness to noise. We complement our theoretical results with empirical
+studies on semi-synthetic datasets, demonstrating its advantage over several
+popular methods in recovering the ground-truth.
+",1,0,0,1,0,0
+13991,Bayesian mean-variance analysis: Optimal portfolio selection under parameter uncertainty,"  The paper solves the problem of optimal portfolio choice when the parameters
+of the asset returns distribution, like the mean vector and the covariance
+matrix are unknown and have to be estimated by using historical data of the
+asset returns. The new approach employs the Bayesian posterior predictive
+distribution which is the distribution of the future realization of the asset
+returns given the observable sample. The parameters of the posterior predictive
+distributions are functions of the observed data values and, consequently, the
+solution of the optimization problem is expressed in terms of data only and
+does not depend on unknown quantities. In contrast, the optimization problem of
+the traditional approach is based on unknown quantities which are estimated in
+the second step leading to a suboptimal solution. We also derive a very useful
+stochastic representation of the posterior predictive distribution whose
+application leads not only to the solution of the considered optimization
+problem, but provides the posterior predictive distribution of the optimal
+portfolio return used to construct a prediction interval. A Bayesian efficient
+frontier, a set of optimal portfolios obtained by employing the posterior
+predictive distribution, is constructed as well. Theoretically and using real
+data we show that the Bayesian efficient frontier outperforms the sample
+efficient frontier, a common estimator of the set of optimal portfolios known
+to be overoptimistic.
+",0,0,0,0,0,1
+13992,Selective inference after likelihood- or test-based model selection in linear models,"  Statistical inference after model selection requires an inference framework
+that takes the selection into account in order to be valid. Following recent
+work on selective inference, we derive analytical expressions for inference
+after likelihood- or test-based model selection for linear models.
+",0,0,0,1,0,0
+13993,Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation,"  We study the problem of sampling a bandlimited graph signal in the presence
+of noise, where the objective is to select a node subset of prescribed
+cardinality that minimizes the signal reconstruction mean squared error (MSE).
+To that end, we formulate the task at hand as the minimization of MSE subject
+to binary constraints, and approximate the resulting NP-hard problem via
+semidefinite programming (SDP) relaxation. Moreover, we provide an alternative
+formulation based on maximizing a monotone weak submodular function and propose
+a randomized-greedy algorithm to find a sub-optimal subset. We then derive a
+worst-case performance guarantee on the MSE returned by the randomized greedy
+algorithm for general non-stationary graph signals. The efficacy of the
+proposed methods is illustrated through numerical simulations on synthetic and
+real-world graphs. Notably, the randomized greedy algorithm yields an
+order-of-magnitude speedup over state-of-the-art greedy sampling schemes, while
+incurring only a marginal MSE performance loss.
+",1,0,0,1,0,0
+13994,DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images,"  We present DLTK, a toolkit providing baseline implementations for efficient
+experimentation with deep learning methods on biomedical images. It builds on
+top of TensorFlow and its high modularity and easy-to-use examples allow for a
+low-threshold access to state-of-the-art implementations for typical medical
+imaging problems. A comparison of DLTK's reference implementations of popular
+network architectures for image segmentation demonstrates new top performance
+on the publicly available challenge data ""Multi-Atlas Labeling Beyond the
+Cranial Vault"". The average test Dice similarity coefficient of $81.5$ exceeds
+the previously best performing CNN ($75.7$) and the accuracy of the challenge
+winning method ($79.0$).
+",1,0,0,0,0,0
+13995,A homotopy theory of Nakaoka twin cotorsion pairs,"  We show that the Verdier quotients can be realized as subfactors by the
+homotopy theory of additive categories with suspensions developed in
+\cite{ZWLi2, ZWLi3}. As applications, we develop the homotopy theory of Nakaoka
+twin cotorsion pairs of triangulated categories and prove that Iyama-Yoshino
+triangulated subfactors are Verdier quotients under suitable conditions.
+",0,0,1,0,0,0
+13996,Categorical relations between Langlands dual quantum affine algebras: Doubly laced types,"  We prove that the Grothendieck rings of category $\mathcal{C}^{(t)}_Q$ over
+quantum affine algebras $U_q'(\g^{(t)})$ $(t=1,2)$ associated to each Dynkin
+quiver $Q$ of finite type $A_{2n-1}$ (resp. $D_{n+1}$) is isomorphic to one of
+category $\mathcal{C}_{\mQ}$ over the Langlands dual $U_q'({^L}\g^{(2)})$ of
+$U_q'(\g^{(2)})$ associated to any twisted adapted class $[\mQ]$ of $A_{2n-1}$
+(resp. $D_{n+1}$). This results provide partial answers of conjectures of
+Frenkel-Hernandez on Langlands duality for finite-dimensional representation of
+quantum affine algebras.
+",0,0,1,0,0,0
+13997,Strategic Dynamic Pricing with Network Effects,"  We study the optimal pricing strategy of a monopolist selling homogeneous
+goods to customers over multiple periods. The customers choose their time of
+purchase to maximize their payoff that depends on their valuation of the
+product, the purchase price, and the utility they derive from past purchases of
+others, termed the network effect. We first show that the optimal price
+sequence is non-decreasing. Therefore, by postponing purchase to future rounds,
+customers trade-off a higher utility from the network effects with a higher
+price. We then show that a customer's equilibrium strategy can be characterized
+by a threshold rule in which at each round a customer purchases the product if
+and only if her valuation exceeds a certain threshold. This implies that
+customers face an inference problem regarding the valuations of others, i.e.,
+observing that a customer has not yet purchased the product, signals that her
+valuation is below a threshold. We consider a block model of network
+interactions, where there are blocks of buyers subject to the same network
+effect. A natural benchmark, this model allows us to provide an explicit
+characterization of the optimal price sequence asymptotically as the number of
+agents goes to infinity, which notably is linearly increasing in time with a
+slope that depends on the network effect through a scalar given by the sum of
+entries of the inverse of the network weight matrix. Our characterization shows
+that increasing the ""imbalance"" in the network defined as the difference
+between the in and out degree of the nodes increases the revenue of the
+monopolist. We further study the effects of price discrimination and show that
+in earlier periods monopolist offers lower prices to blocks with higher
+Bonacich centrality to encourage them to purchase, which in turn further
+incentivizes other customers to buy in subsequent periods.
+",1,0,0,0,0,0
+13998,Semisimple Leibniz algebras and their derivations and automorphisms,"  The present paper is devoted to the description of finite-dimensional
+semisimple Leibniz algebras over complex numbers, their derivations and
+automorphisms.
+",0,0,1,0,0,0
+13999,Vandermonde Matrices with Nodes in the Unit Disk and the Large Sieve,"  We derive bounds on the extremal singular values and the condition number of
+NxK, with N>=K, Vandermonde matrices with nodes in the unit disk. The
+mathematical techniques we develop to prove our main results are inspired by a
+link---first established by by Selberg [1] and later extended by Moitra
+[2]---between the extremal singular values of Vandermonde matrices with nodes
+on the unit circle and large sieve inequalities. Our main conceptual
+contribution lies in establishing a connection between the extremal singular
+values of Vandermonde matrices with nodes in the unit disk and a novel large
+sieve inequality involving polynomials in z \in C with |z|<=1. Compared to
+Bazán's upper bound on the condition number [3], which, to the best of our
+knowledge, constitutes the only analytical result---available in the
+literature---on the condition number of Vandermonde matrices with nodes in the
+unit disk, our bound not only takes a much simpler form, but is also sharper
+for certain node configurations. Moreover, the bound we obtain can be evaluated
+consistently in a numerically stable fashion, whereas the evaluation of
+Bazán's bound requires the solution of a linear system of equations which has
+the same condition number as the Vandermonde matrix under consideration and can
+therefore lead to numerical instability in practice. As a byproduct, our
+result---when particularized to the case of nodes on the unit circle---slightly
+improves upon the Selberg-Moitra bound.
+",1,0,1,0,0,0
+14000,High-buckled R3 stanene with topologically nontrivial energy gap,"  Stanene has been predicted to be a two-dimensional topological insulator
+(2DTI). Its low-buckled atomic geometry and the enhanced spin-orbit coupling
+are expected to cause a prominent quantum spin hall (QSH) effect. However, most
+of the experimentally grown stanene to date displays a metallic state without a
+real gap, possibly due to the chemical coupling with the substrate and the
+stress applied by the substrate. Here,we demonstrate an efficient way of tuning
+the atomic buckling in stanene to open a topologically nontrivial energy gap.
+Via tuning the growth kinetics, we obtain not only the low-buckled 1x1 stanene
+but also an unexpected high-buckled R3xR3 stanene on the Bi(111) substrate.
+Scanning tunneling microscopy (STM) study combined with density functional
+theory (DFT) calculation confirms that the R3xR3 stanene is a distorted 1x1
+structure with a high-buckled Sn in every three 1x1 unit cells. The
+high-buckled R3xR3 stanene favors a large band inversion at the {\Gamma} point,
+and the spin orbital coupling open a topologically nontrivial energy gap. The
+existence of edge states as verified in both STM measurement and DFT
+calculation further confirms the topology of the R3xR3 stanene. This study
+provides an alternate way to tune the topology of monolayer 2DTI materials.
+",0,1,0,0,0,0
+14001,Informed Sub-Sampling MCMC: Approximate Bayesian Inference for Large Datasets,"  This paper introduces a framework for speeding up Bayesian inference
+conducted in presence of large datasets. We design a Markov chain whose
+transition kernel uses an (unknown) fraction of (fixed size) of the available
+data that is randomly refreshed throughout the algorithm. Inspired by the
+Approximate Bayesian Computation (ABC) literature, the subsampling process is
+guided by the fidelity to the observed data, as measured by summary statistics.
+The resulting algorithm, Informed Sub-Sampling MCMC (ISS-MCMC), is a generic
+and flexible approach which, contrary to existing scalable methodologies,
+preserves the simplicity of the Metropolis-Hastings algorithm. Even though
+exactness is lost, i.e. the chain distribution approximates the posterior, we
+study and quantify theoretically this bias and show on a diverse set of
+examples that it yields excellent performances when the computational budget is
+limited. If available and cheap to compute, we show that setting the summary
+statistics as the maximum likelihood estimator is supported by theoretical
+arguments.
+",0,0,0,1,0,0
+14002,Mathematical Analysis of Anthropogenic Signatures: The Great Deceleration,"  Distributions of anthropogenic signatures (impacts and activities) are
+mathematically analysed. The aim is to understand the Anthropocene and to see
+whether anthropogenic signatures could be used to determine its beginning. A
+total of 23 signatures were analysed and results are presented in 31 diagrams.
+Some of these signatures contain undistinguishable natural components but most
+of them are of purely anthropogenic origin. Great care was taken to identify
+abrupt accelerations, which could be used to determine the beginning of the
+Anthropocene. Results of the analysis can be summarised in three conclusions.
+1. Anthropogenic signatures cannot be used to determine the beginning of the
+Anthropocene. 2. There was no abrupt Great Acceleration around 1950 or around
+any other time. 3. Anthropogenic signatures are characterised by the Great
+Deceleration in the second half of the 20th century. The second half of the
+20th century does not mark the beginning of the Anthropocene but most likely
+the beginning of the end of the strong anthropogenic impacts, maybe even the
+beginning of a transition to a sustainable future. The Anthropocene is a unique
+stage in human experience but it has no clearly marked beginning and it is
+probably not a new geological epoch.
+",0,0,0,0,1,0
+14003,Efficient Regret Minimization in Non-Convex Games,"  We consider regret minimization in repeated games with non-convex loss
+functions. Minimizing the standard notion of regret is computationally
+intractable. Thus, we define a natural notion of regret which permits efficient
+optimization and generalizes offline guarantees for convergence to an
+approximate local optimum. We give gradient-based methods that achieve optimal
+regret, which in turn guarantee convergence to equilibrium in this framework.
+",1,0,0,1,0,0
+14004,Spectral Methods for Immunization of Large Networks,"  Given a network of nodes, minimizing the spread of a contagion using a
+limited budget is a well-studied problem with applications in network security,
+viral marketing, social networks, and public health. In real graphs, virus may
+infect a node which in turn infects its neighbor nodes and this may trigger an
+epidemic in the whole graph. The goal thus is to select the best k nodes
+(budget constraint) that are immunized (vaccinated, screened, filtered) so as
+the remaining graph is less prone to the epidemic. It is known that the problem
+is, in all practical models, computationally intractable even for moderate
+sized graphs. In this paper we employ ideas from spectral graph theory to
+define relevance and importance of nodes. Using novel graph theoretic
+techniques, we then design an efficient approximation algorithm to immunize the
+graph. Theoretical guarantees on the running time of our algorithm show that it
+is more efficient than any other known solution in the literature. We test the
+performance of our algorithm on several real world graphs. Experiments show
+that our algorithm scales well for large graphs and outperforms state of the
+art algorithms both in quality (containment of epidemic) and efficiency
+(runtime and space complexity).
+",1,0,0,0,0,0
+14005,Long-time existence of nonlinear inhomogeneous compressible elastic waves,"  In this paper, we consider the nonlinear inhomogeneous compressible elastic
+waves in three spatial dimensions when the density is a small disturbance
+around a constant state. In homogeneous case, the almost global existence was
+established by Klainerman-Sideris [1996_CPAM], and global existence was built
+by Agemi [2000_Invent. Math.] and Sideris [1996_Invent. Math., 2000_Ann. Math.]
+independently. Here we establish the corresponding almost global and global
+existence theory in the inhomogeneous case.
+",0,0,1,0,0,0
+14006,Deep Convolutional Denoising of Low-Light Images,"  Poisson distribution is used for modeling noise in photon-limited imaging.
+While canonical examples include relatively exotic types of sensing like
+spectral imaging or astronomy, the problem is relevant to regular photography
+now more than ever due to the booming market for mobile cameras. Restricted
+form factor limits the amount of absorbed light, thus computational
+post-processing is called for. In this paper, we make use of the powerful
+framework of deep convolutional neural networks for Poisson denoising. We
+demonstrate how by training the same network with images having a specific peak
+value, our denoiser outperforms previous state-of-the-art by a large margin
+both visually and quantitatively. Being flexible and data-driven, our solution
+resolves the heavy ad hoc engineering used in previous methods and is an order
+of magnitude faster. We further show that by adding a reasonable prior on the
+class of the image being processed, another significant boost in performance is
+achieved.
+",1,0,0,0,0,0
+14007,"Towards a fractal cohomology: Spectra of Polya--Hilbert operators, regularized determinants and Riemann zeros","  Emil Artin defined a zeta function for algebraic curves over finite fields
+and made a conjecture about them analogous to the famous Riemann hypothesis.
+This and other conjectures about these zeta functions would come to be called
+the Weil conjectures, which were proved by Weil for curves and later, by
+Deligne for varieties over finite fields. Much work was done in the search for
+a proof of these conjectures, including the development in algebraic geometry
+of a Weil cohomology theory for these varieties, which uses the Frobenius
+operator on a finite field. The zeta function is then expressed as a
+determinant, allowing the properties of the function to relate to those of the
+operator. The search for a suitable cohomology theory and associated operator
+to prove the Riemann hypothesis is still on. In this paper, we study the
+properties of the derivative operator $D = \frac{d}{dz}$ on a particular
+weighted Bergman space of entire functions. The operator $D$ can be naturally
+viewed as the `infinitesimal shift of the complex plane'. Furthermore, this
+operator is meant to be the replacement for the Frobenius operator in the
+general case and is used to construct an operator associated to any suitable
+meromorphic function. We then show that the meromorphic function can be
+recovered by using a regularized determinant involving the above operator. This
+is illustrated in some important special cases: rational functions, zeta
+functions of curves over finite fields, the Riemann zeta function, and
+culminating in a quantized version of the Hadamard factorization theorem that
+applies to any entire function of finite order. Our construction is motivated
+in part by [23] on the infinitesimal shift of the real line, as well as by
+earlier work of Deninger [10] on cohomology in number theory and a conjectural
+`fractal cohomology theory' envisioned in [25] and [28].
+",0,0,1,0,0,0
+14008,F-index of graphs based on four operations related to the lexicographic product,"  The forgotten topological index or F-index of a graph is defined as the sum
+of cubes of the degree of all the vertices of the graph. In this paper we study
+the F-index of four operations related to the lexicographic product on graphs
+which were introduced by Sarala et al. [D. Sarala, H. Deng, S.K. Ayyaswamya and
+S. Balachandrana, The Zagreb indices of graphs based on four new operations
+related to the lexicographic product, \textit{Applied Mathematics and
+Computation}, 309 (2017) 156--169.].
+",1,0,0,0,0,0
+14009,Deleting vertices to graphs of bounded genus,"  We show that a problem of deleting a minimum number of vertices from a graph
+to obtain a graph embeddable on a surface of a given Euler genus is solvable in
+time $2^{C_g \cdot k^2 \log k} n^{O(1)}$, where $k$ is the size of the deletion
+set, $C_g$ is a constant depending on the Euler genus $g$ of the target
+surface, and $n$ is the size of the input graph. On the way to this result, we
+develop an algorithm solving the problem in question in time $2^{O((t+g) \log
+(t+g))} n$, given a tree decomposition of the input graph of width $t$. The
+results generalize previous algorithms for the surface being a sphere by Marx
+and Schlotter [Algorithmica 2012], Kawarabayashi [FOCS 2009], and Jansen,
+Lokshtanov, and Saurabh [SODA 2014].
+",1,0,0,0,0,0
+14010,Axion dark matter search using the storage ring EDM method,"  We propose using the storage ring EDM method to search for the axion dark
+matter induced EDM oscillation in nucleons. The method uses a combination of B
+and E-fields to produce a resonance between the $g-2$ spin precession frequency
+and the background axion field oscillation to greatly enhance sensitivity to
+it. An axion frequency range from $10^{-9}$ Hz to 100 MHz can in principle be
+scanned with high sensitivity, corresponding to an $f_a$ range of $10^{13} $
+GeV $\leq f_a \leq 10^{30}$ GeV, the breakdown scale of the global symmetry
+generating the axion or axion like particles (ALPs).
+",0,1,0,0,0,0
+14011,Time crystal platform: from quasi-crystal structures in time to systems with exotic interactions,"  Time crystals are quantum many-body systems which, due to interactions
+between particles, are able to spontaneously self-organize their motion in a
+periodic way in time by analogy with the formation of crystalline structures in
+space in condensed matter physics. In solid state physics properties of space
+crystals are often investigated with the help of external potentials that are
+spatially periodic and reflect various crystalline structures. A similar
+approach can be applied for time crystals, as periodically driven systems
+constitute counterparts of spatially periodic systems, but in the time domain.
+Here we show that condensed matter problems ranging from single particles in
+potentials of quasi-crystal structure to many-body systems with exotic
+long-range interactions can be realized in the time domain with an appropriate
+periodic driving. Moreover, it is possible to create molecules where atoms are
+bound together due to destructive interference if the atomic scattering length
+is modulated in time.
+",0,1,0,0,0,0
+14012,Fast and accurate classification of echocardiograms using deep learning,"  Echocardiography is essential to modern cardiology. However, human
+interpretation limits high throughput analysis, limiting echocardiography from
+reaching its full clinical and research potential for precision medicine. Deep
+learning is a cutting-edge machine-learning technique that has been useful in
+analyzing medical images but has not yet been widely applied to
+echocardiography, partly due to the complexity of echocardiograms' multi view,
+multi modality format. The essential first step toward comprehensive computer
+assisted echocardiographic interpretation is determining whether computers can
+learn to recognize standard views. To this end, we anonymized 834,267
+transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51
+percent female, 26 percent obese) seen between 2000 and 2017 and labeled them
+according to standard views. Images covered a range of real world clinical
+variation. We built a multilayer convolutional neural network and used
+supervised learning to simultaneously classify 15 standard views. Eighty
+percent of data used was randomly chosen for training and 20 percent reserved
+for validation and testing on never seen echocardiograms. Using multiple images
+from each clip, the model classified among 12 video views with 97.8 percent
+overall test accuracy without overfitting. Even on single low resolution
+images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5
+percent for board-certified echocardiographers. Confusional matrices, occlusion
+experiments, and saliency mapping showed that the model finds recognizable
+similarities among related views and classifies using clinically relevant image
+features. In conclusion, deep neural networks can classify essential
+echocardiographic views simultaneously and with high accuracy. Our results
+provide a foundation for more complex deep learning assisted echocardiographic
+interpretation.
+",1,0,0,0,0,0
+14013,Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis,"  Anomaly detection in database management systems (DBMSs) is difficult because
+of increasing number of statistics (stat) and event metrics in big data system.
+In this paper, I propose an automatic DBMS diagnosis system that detects
+anomaly periods with abnormal DB stat metrics and finds causal events in the
+periods. Reconstruction error from deep autoencoder and statistical process
+control approach are applied to detect time period with anomalies. Related
+events are found using time series similarity measures between events and
+abnormal stat metrics. After training deep autoencoder with DBMS metric data,
+efficacy of anomaly detection is investigated from other DBMSs containing
+anomalies. Experiment results show effectiveness of proposed model, especially,
+batch temporal normalization layer. Proposed model is used for publishing
+automatic DBMS diagnosis reports in order to determine DBMS configuration and
+SQL tuning.
+",1,0,0,1,0,0
+14014,On the complexity of non-orientable Seifert fibre spaces,"  In this paper we deal with Seifert fibre spaces, which are compact
+3-manifolds admitting a foliation by circles. We give a combinatorial
+description for these manifolds in all the possible cases: orientable,
+non-orientable, closed, with boundary. Moreover, we compute a potentially sharp
+upper bound for their complexity in terms of the invariants of the
+combinatorial description, extending to the non-orientable case results by
+Fominykh and Wiest for the orientable case with boundary and by Martelli and
+Petronio for the closed orientable case.
+",0,0,1,0,0,0
+14015,MIMO-UFMC Transceiver Schemes for Millimeter Wave Wireless Communications,"  The UFMC modulation is among the most considered solutions for the
+realization of beyond-OFDM air interfaces for future wireless networks. This
+paper focuses on the design and analysis of an UFMC transceiver equipped with
+multiple antennas and operating at millimeter wave carrier frequencies. The
+paper provides the full mathematical model of a MIMO-UFMC transceiver, taking
+into account the presence of hybrid analog/digital beamformers at both ends of
+the communication links. Then, several detection structures are proposed, both
+for the case of single-packet isolated transmission, and for the case of
+multiple-packet continuous transmission. In the latter situation, the paper
+also considers the case in which no guard time among adjacent packets is
+inserted, trading off an increased level of interference with higher values of
+spectral efficiency. At the analysis stage, the several considered detection
+structures and transmission schemes are compared in terms of bit-error-rate,
+root-mean-square-error, and system throughput. The numerical results show that
+the proposed transceiver algorithms are effective and that the linear MMSE data
+detector is capable of well managing the increased interference brought by the
+removal of guard times among consecutive packets, thus yielding throughput
+gains of about 10 - 13 $\%$. The effect of phase noise at the receiver is also
+numerically assessed, and it is shown that the recursive implementation of the
+linear MMSE exhibits some degree of robustness against this disturbance.
+",1,0,0,0,0,0
+14016,Modeling human intuitions about liquid flow with particle-based simulation,"  Humans can easily describe, imagine, and, crucially, predict a wide variety
+of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking,
+dripping, draining, trickling, pooling, and pouring--despite tremendous
+variability in their material and dynamical properties. Here we propose and
+test a computational model of how people perceive and predict these liquid
+dynamics, based on coarse approximate simulations of fluids as collections of
+interacting particles. Our model is analogous to a ""game engine in the head"",
+drawing on techniques for interactive simulations (as in video games) that
+optimize for efficiency and natural appearance rather than physical accuracy.
+In two behavioral experiments, we found that the model accurately captured
+people's predictions about how liquids flow among complex solid obstacles, and
+was significantly better than two alternatives based on simple heuristics and
+deep neural networks. Our model was also able to explain how people's
+predictions varied as a function of the liquids' properties (e.g., viscosity
+and stickiness). Together, the model and empirical results extend the recent
+proposal that human physical scene understanding for the dynamics of rigid,
+solid objects can be supported by approximate probabilistic simulation, to the
+more complex and unexplored domain of fluid dynamics.
+",0,0,0,0,1,0
+14017,"Comment on ""Spin-Orbit Coupling Induced Gap in Graphene on Pt(111) with Intercalated Pb Monolayer""","  Recently a paper of Klimovskikh et al. was published presenting experimental
+and theoretical analysis of the graphene/Pb/Pt(111) system. The authors
+investigate the crystallographic and electronic structure of this
+graphene-based system by means of LEED, ARPES, and spin-resolved PES of the
+graphene $\pi$ states in the vicinity of the Dirac point of graphene. The
+authors of this paper demonstrate that an energy gap of approx. 200 meV is
+opened in the spectral function of graphene directly at the Dirac point of
+graphene and spin-splitting of 100 meV is detected for the upper part of the
+Dirac cone. On the basis of the spin-resolved photoelectron spectroscopy
+measurements of the region around the gap the authors claim that these
+splittings are of a spin-orbit nature and that the observed spin structure
+confirms the observation of the quantum spin Hall state in graphene, proposed
+in earlier theoretical works. Here we will show that careful systematic
+analysis of the experimental data presented in this manuscript is needed and
+their interpretation require more critical consideration for making such
+conclusions. Our analysis demonstrates that the proposed effects and
+interpretations are questionable and require further more careful experiments.
+",0,1,0,0,0,0
+14018,Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size,"  Learning representation from relative similarity comparisons, often called
+ordinal embedding, gains rising attention in recent years. Most of the existing
+methods are batch methods designed mainly based on the convex optimization,
+say, the projected gradient descent method. However, they are generally
+time-consuming due to that the singular value decomposition (SVD) is commonly
+adopted during the update, especially when the data size is very large. To
+overcome this challenge, we propose a stochastic algorithm called SVRG-SBB,
+which has the following features: (a) SVD-free via dropping convexity, with
+good scalability by the use of stochastic algorithm, i.e., stochastic variance
+reduced gradient (SVRG), and (b) adaptive step size choice via introducing a
+new stabilized Barzilai-Borwein (SBB) method as the original version for convex
+problems might fail for the considered stochastic \textit{non-convex}
+optimization problem. Moreover, we show that the proposed algorithm converges
+to a stationary point at a rate $\mathcal{O}(\frac{1}{T})$ in our setting,
+where $T$ is the number of total iterations. Numerous simulations and
+real-world data experiments are conducted to show the effectiveness of the
+proposed algorithm via comparing with the state-of-the-art methods,
+particularly, much lower computational cost with good prediction performance.
+",1,0,0,1,0,0
+14019,The Mass-Metallicity Relation revisited with CALIFA,"  We present an updated version of the mass--metallicity relation (MZR) using
+integral field spectroscopy data obtained from 734 galaxies observed by the
+CALIFA survey. These unparalleled spatially resolved spectroscopic data allow
+us to determine the metallicity at the same physical scale ($\mathrm{R_{e}}$)
+for different calibrators. We obtain MZ relations with similar shapes for all
+calibrators, once the scale factors among them are taken into account. We do
+not find any significant secondary relation of the MZR with either the star
+formation rate (SFR) or the specific SFR for any of the calibrators used in
+this study, based on the analysis of the residuals of the best fitted relation.
+However we do see a hint for a (s)SFR-dependent deviation of the MZ-relation at
+low masses (M$<$10$^{9.5}$M$_\odot$), where our sample is not complete. We are
+thus unable to confirm the results by Mannucci et al. (2010), although we
+cannot exclude that this result is due to the differences in the analysed
+datasets. In contrast, our results are inconsistent with the results by
+Lara-Lopez et al. (2010), and we can exclude the presence of a SFR-Mass-Oxygen
+abundance Fundamental Plane. These results agree with previous findings
+suggesting that either (1) the secondary relation with the SFR could be induced
+by an aperture effect in single fiber/aperture spectroscopic surveys, (2) it
+could be related to a local effect confined to the central regions of galaxies,
+or (3) it is just restricted to the low-mass regime, or a combination of the
+three effects.
+",0,1,0,0,0,0
+14020,"(p,q)-webs of DIM representations, 5d N=1 instanton partition functions and qq-characters","  Instanton partition functions of $\mathcal{N}=1$ 5d Super Yang-Mills reduced
+on $S^1$ can be engineered in type IIB string theory from the $(p,q)$-branes
+web diagram. To this diagram is superimposed a web of representations of the
+Ding-Iohara-Miki (DIM) algebra that acts on the partition function. In this
+correspondence, each segment is associated to a representation, and the
+(topological string) vertex is identified with the intertwiner operator
+constructed by Awata, Feigin and Shiraishi. We define a new intertwiner acting
+on the representation spaces of levels $(1,n)\otimes(0,m)\to(1,n+m)$, thereby
+generalizing to higher rank $m$ the original construction. It allows us to use
+a folded version of the usual $(p,q)$-web diagram, bringing great
+simplifications to actual computations. As a result, the characterization of
+Gaiotto states and vertical intertwiners, previously obtained by some of the
+authors, is uplifted to operator relations acting in the Fock space of
+horizontal representations. We further develop a method to build qq-characters
+of linear quivers based on the horizontal action of DIM elements. While
+fundamental qq-characters can be built using the coproduct, higher ones require
+the introduction of a (quantum) Weyl reflection acting on tensor products of
+DIM generators.
+",0,0,1,0,0,0
+14021,Self-Motion of the 3-PPPS Parallel Robot with Delta-Shaped Base,"  This paper presents the kinematic analysis of the 3-PPPS parallel robot with
+an equi-lateral mobile platform and an equilateral-shaped base. Like the other
+3-PPPS robots studied in the literature, it is proved that the parallel
+singularities depend only on the orientation of the end-effector. The
+quaternion parameters are used to represent the singularity surfaces. The study
+of the direct kinematic model shows that this robot admits a self-motion of the
+Cardanic type. This explains why the direct kinematic model admits an infinite
+number of solutions in the center of the workspace at the ""home"" position but
+has never been studied until now.
+",1,0,0,0,0,0
+14022,Scaling-Up Reasoning and Advanced Analytics on BigData,"  BigDatalog is an extension of Datalog that achieves performance and
+scalability on both Apache Spark and multicore systems to the point that its
+graph analytics outperform those written in GraphX. Looking back, we see how
+this realizes the ambitious goal pursued by deductive database researchers
+beginning forty years ago: this is the goal of combining the rigor and power of
+logic in expressing queries and reasoning with the performance and scalability
+by which relational databases managed Big Data. This goal led to Datalog which
+is based on Horn Clauses like Prolog but employs implementation techniques,
+such as Semi-naive Fixpoint and Magic Sets, that extend the bottom-up
+computation model of relational systems, and thus obtain the performance and
+scalability that relational systems had achieved, as far back as the 80s, using
+data-parallelization on shared-nothing architectures. But this goal proved
+difficult to achieve because of major issues at (i) the language level and (ii)
+at the system level. The paper describes how (i) was addressed by simple rules
+under which the fixpoint semantics extends to programs using count, sum and
+extrema in recursion, and (ii) was tamed by parallel compilation techniques
+that achieve scalability on multicore systems and Apache Spark. This paper is
+under consideration for acceptance in Theory and Practice of Logic Programming
+(TPLP).
+",1,0,0,0,0,0
+14023,ALMA Observations of the Gravitational Lens SDP.9,"  We present long-baseline ALMA observations of the strong gravitational lens
+H-ATLAS J090740.0-004200 (SDP.9), which consists of an elliptical galaxy at
+$z_{\mathrm{L}}=0.6129$ lensing a background submillimeter galaxy into two
+extended arcs. The data include Band 6 continuum observations, as well as CO
+$J$=6$-$5 molecular line observations, from which we measure an updated source
+redshift of $z_{\mathrm{S}}=1.5747$. The image morphology in the ALMA data is
+different from that of the HST data, indicating a spatial offset between the
+stellar, gas, and dust component of the source galaxy. We model the lens as an
+elliptical power law density profile with external shear using a combination of
+archival HST data and conjugate points identified in the ALMA data. Our best
+model has an Einstein radius of $\theta_{\mathrm{E}}=0.66\pm0.01$ and a
+slightly steeper than isothermal mass profile slope. We search for the central
+image of the lens, which can be used constrain the inner mass distribution of
+the lens galaxy including the central supermassive black hole, but do not
+detect it in the integrated CO image at a 3$\sigma$ rms level of 0.0471 Jy km
+s$^{-1}$.
+",0,1,0,0,0,0
+14024,The Noether numbers and the Davenport constants of the groups of order less than 32,"  The computation of the Noether numbers of all groups of order less than
+thirty-two is completed. It turns out that for these groups in non-modular
+characteristic the Noether number is attained on a multiplicity free
+representation, it is strictly monotone on subgroups and factor groups, and it
+does not depend on the characteristic. Algorithms are developed and used to
+determine the small and large Davenport constants of these groups. For each of
+these groups the Noether number is greater than the small Davenport constant,
+whereas the first example of a group whose Noether number exceeds the large
+Davenport constant is found, answering partially a question posed by
+Geroldinger and Grynkiewicz.
+",0,0,1,0,0,0
+14025,Multistability and coexisting soliton combs in ring resonators: the Lugiato-Lefever approach,"  We are reporting that the Lugiato-Lefever equation describing the frequency
+comb generation in ring resonators with the localized pump and loss terms also
+describes the simultaneous nonlinear resonances leading to the multistability
+of nonlinear modes and coexisting solitons that are associated with the
+spectrally distinct frequency combs.
+",0,1,0,0,0,0
+14026,Numerical assessment of the percolation threshold using complement networks,"  Models of percolation processes on networks currently assume locally
+tree-like structures at low densities, and are derived exactly only in the
+thermodynamic limit. Finite size effects and the presence of short loops in
+real systems however cause a deviation between the empirical percolation
+threshold $p_c$ and its model-predicted value $\pi_c$. Here we show the
+existence of an empirical linear relation between $p_c$ and $\pi_c$ across a
+large number of real and model networks. Such a putatively universal relation
+can then be used to correct the estimated value of $\pi_c$. We further show how
+to obtain a more precise relation using the concept of the complement graph, by
+investigating on the connection between the percolation threshold of a network,
+$p_c$, and that of its complement, $\bar{p}_c$.
+",1,0,0,0,0,0
+14027,Planar graphs as L-intersection or L-contact graphs,"  The L-intersection graphs are the graphs that have a representation as
+intersection graphs of axis parallel shapes in the plane. A subfamily of these
+graphs are {L, |, --}-contact graphs which are the contact graphs of axis
+parallel L, |, and -- shapes in the plane. We prove here two results that were
+conjectured by Chaplick and Ueckerdt in 2013. We show that planar graphs are
+L-intersection graphs, and that triangle-free planar graphs are {L, |,
+--}-contact graphs. These results are obtained by a new and simple
+decomposition technique for 4-connected triangulations. Our results also
+provide a much simpler proof of the known fact that planar graphs are segment
+intersection graphs.
+",1,0,0,0,0,0
+14028,An EPTAS for Scheduling on Unrelated Machines of Few Different Types,"  In the classical problem of scheduling on unrelated parallel machines, a set
+of jobs has to be assigned to a set of machines. The jobs have a processing
+time depending on the machine and the goal is to minimize the makespan, that is
+the maximum machine load. It is well known that this problem is NP-hard and
+does not allow polynomial time approximation algorithms with approximation
+guarantees smaller than $1.5$ unless P$=$NP. We consider the case that there
+are only a constant number $K$ of machine types. Two machines have the same
+type if all jobs have the same processing time for them. This variant of the
+problem is strongly NP-hard already for $K=1$. We present an efficient
+polynomial time approximation scheme (EPTAS) for the problem, that is, for any
+$\varepsilon > 0$ an assignment with makespan of length at most
+$(1+\varepsilon)$ times the optimum can be found in polynomial time in the
+input length and the exponent is independent of $1/\varepsilon$. In particular
+we achieve a running time of $2^{\mathcal{O}(K\log(K)
+\frac{1}{\varepsilon}\log^4 \frac{1}{\varepsilon})}+\mathrm{poly}(|I|)$, where
+$|I|$ denotes the input length. Furthermore, we study three other problem
+variants and present an EPTAS for each of them: The Santa Claus problem, where
+the minimum machine load has to be maximized; the case of scheduling on
+unrelated parallel machines with a constant number of uniform types, where
+machines of the same type behave like uniformly related machines; and the
+multidimensional vector scheduling variant of the problem where both the
+dimension and the number of machine types are constant. For the Santa Claus
+problem we achieve the same running time. The results are achieved, using mixed
+integer linear programming and rounding techniques.
+",1,0,0,0,0,0
+14029,An extensive impurity-scattering study on the pairing symmetry of monolayer FeSe films on SrTiO3,"  Determination of the pairing symmetry in monolayer FeSe films on SrTiO3 is a
+requisite for understanding the high superconducting transition temperature in
+this system, which has attracted intense theoretical and experimental studies
+but remains controversial. Here, by introducing several types of point defects
+in FeSe monolayer films, we conduct a systematic investigation on the
+impurity-induced electronic states by spatially resolved scanning tunneling
+spectroscopy. Ranging from surface adsorption, chemical substitution to
+intrinsic structural modification, these defects generate a variety of
+scattering strength, which renders new insights on the pairing symmetry.
+",0,1,0,0,0,0
+14030,Fast Depth Imaging Denoising with the Temporal Correlation of Photons,"  This paper proposes a novel method to filter out the false alarm of LiDAR
+system by using the temporal correlation of target reflected photons. Because
+of the inevitable noise, which is due to background light and dark counts of
+the detector, the depth imaging of LiDAR system exists a large estimation
+error. Our method combines the Poisson statistical model with the different
+distribution feature of signal and noise in the time axis. Due to selecting a
+proper threshold, our method can effectively filter out the false alarm of
+system and use the ToFs of detected signal photons to rebuild the depth image
+of the scene. The experimental results reveal that by our method it can fast
+distinguish the distance between two close objects, which is confused due to
+the high background noise, and acquire the accurate depth image of the scene.
+Our method need not increase the complexity of the system and is useful in
+power-limited depth imaging.
+",0,1,0,0,0,0
+14031,From Propositional Logic to Plausible Reasoning: A Uniqueness Theorem,"  We consider the question of extending propositional logic to a logic of
+plausible reasoning, and posit four requirements that any such extension should
+satisfy. Each is a requirement that some property of classical propositional
+logic be preserved in the extended logic; as such, the requirements are simpler
+and less problematic than those used in Cox's Theorem and its variants. As with
+Cox's Theorem, our requirements imply that the extended logic must be
+isomorphic to (finite-set) probability theory. We also obtain specific
+numerical values for the probabilities, recovering the classical definition of
+probability as a theorem, with truth assignments that satisfy the premise
+playing the role of the ""possible cases.""
+",1,0,0,0,0,0
+14032,Discovering objects and their relations from entangled scene representations,"  Our world can be succinctly and compactly described as structured scenes of
+objects and relations. A typical room, for example, contains salient objects
+such as tables, chairs and books, and these objects typically relate to each
+other by their underlying causes and semantics. This gives rise to correlated
+features, such as position, function and shape. Humans exploit knowledge of
+objects and their relations for learning a wide spectrum of tasks, and more
+generally when learning the structure underlying observed data. In this work,
+we introduce relation networks (RNs) - a general purpose neural network
+architecture for object-relation reasoning. We show that RNs are capable of
+learning object relations from scene description data. Furthermore, we show
+that RNs can act as a bottleneck that induces the factorization of objects from
+entangled scene description inputs, and from distributed deep representations
+of scene images provided by a variational autoencoder. The model can also be
+used in conjunction with differentiable memory mechanisms for implicit relation
+discovery in one-shot learning tasks. Our results suggest that relation
+networks are a potentially powerful architecture for solving a variety of
+problems that require object relation reasoning.
+",1,0,0,0,0,0
+14033,Hall effect spintronics for gas detection,"  We present the concept of magnetic gas detection by the Extraordinary Hall
+effect (EHE). The technique is compatible with the existing conductometric gas
+detection technologies and allows simultaneous measurement of two independent
+parameters: resistivity and magnetization affected by the target gas.
+Feasibility of the approach is demonstrated by detecting low concentration
+hydrogen using thin CoPd films as the sensor material. The Hall effect
+sensitivity of the optimized samples exceeds 240% per 104 ppm at hydrogen
+concentrations below 0.5% in the hydrogen/nitrogen atmosphere, which is more
+than two orders of magnitude higher than the sensitivity of the conductance
+detection.
+",0,1,0,0,0,0
+14034,An evolutionary strategy for DeltaE - E identification,"  In this article we present an automatic method for charge and mass
+identification of charged nuclear fragments produced in heavy ion collisions at
+intermediate energies. The algorithm combines a generative model of DeltaE - E
+relation and a Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The
+CMA-ES is a stochastic and derivative-free method employed to search parameter
+space of the model by means of a fitness function. The article describes
+details of the method along with results of an application on simulated labeled
+data.
+",1,1,0,0,0,0
+14035,Contrastive Training for Models of Information Cascades,"  This paper proposes a model of information cascades as directed spanning
+trees (DSTs) over observed documents. In addition, we propose a contrastive
+training procedure that exploits partial temporal ordering of node infections
+in lieu of labeled training links. This combination of model and unsupervised
+training makes it possible to improve on models that use infection times alone
+and to exploit arbitrary features of the nodes and of the text content of
+messages in information cascades. With only basic node and time lag features
+similar to previous models, the DST model achieves performance with
+unsupervised training comparable to strong baselines on a blog network
+inference task. Unsupervised training with additional content features achieves
+significantly better results, reaching half the accuracy of a fully supervised
+model.
+",1,0,0,0,0,0
+14036,The Structure of the Broad-Line Region In Active Galactic Nuclei. II. Dynamical Modeling of Data from the AGN10 Reverberation Mapping Campaign,"  We present inferences on the geometry and kinematics of the broad-Hbeta
+line-emitting region in four active galactic nuclei monitored as a part of the
+fall 2010 reverberation mapping campaign at MDM Observatory led by the Ohio
+State University. From modeling the continuum variability and response in
+emission-line profile changes as a function of time, we infer the geometry of
+the Hbeta- emitting broad line regions to be thick disks that are close to
+face-on to the observer with kinematics that are well-described by either
+elliptical orbits or inflowing gas. We measure the black hole mass to be log
+(MBH) = 7.25 (+/-0.10) for Mrk 335, 7.86 (+0.20, -0.17) for Mrk 1501, 7.84
+(+0.14, -0.19) for 3C 120, and 6.92 (+0.24, -0.23) for PG 2130+099. These black
+hole mass measurements are not based on a particular assumed value of the
+virial scale factor f, allowing us to compute individual f factors for each
+target. Our results nearly double the number of targets that have been modeled
+in this manner, and investigate the properties of a more diverse sample by
+including previously modeled objects. We measure an average scale factor f in
+the entire sample to be log10(f) = 0.54 +/- 0.17 when the line dispersion is
+used to characterize the line width, which is consistent with values derived
+using the normalization of the MBH-sigma relation. We find that the scale
+factor f for individual targets is likely correlated with the black hole mass,
+inclination angle, and opening angle of the broad line region but we do not
+find any correlation with the luminosity.
+",0,1,0,0,0,0
+14037,A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series,"  Providing long-range forecasts is a fundamental challenge in time series
+modeling, which is only compounded by the challenge of having to form such
+forecasts when a time series has never previously been observed. The latter
+challenge is the time series version of the cold-start problem seen in
+recommender systems which, to our knowledge, has not been addressed in previous
+work. A similar problem occurs when a long range forecast is required after
+only observing a small number of time points --- a warm start forecast. With
+these aims in mind, we focus on forecasting seasonal profiles---or baseline
+demand---for periods on the order of a year in three cases: the long range case
+with multiple previously observed seasonal profiles, the cold start case with
+no previous observed seasonal profiles, and the warm start case with only a
+single partially observed profile. Classical time series approaches that
+perform iterated step-ahead forecasts based on previous observations struggle
+to provide accurate long range predictions; in settings with little to no
+observed data, such approaches are simply not applicable. Instead, we present a
+straightforward framework which combines ideas from high-dimensional regression
+and matrix factorization on a carefully constructed data matrix. Key to our
+formulation and resulting performance is leveraging (1) repeated patterns over
+fixed periods of time and across series, and (2) metadata associated with the
+individual series; without this additional data, the cold-start/warm-start
+problems are nearly impossible to solve. We demonstrate that our framework can
+accurately forecast an array of seasonal profiles on multiple large scale
+datasets.
+",1,0,0,1,0,0
+14038,"A Multi-Ringed, Modestly-Inclined Protoplanetary Disk around AA Tau","  AA Tau is the archetype for a class of stars with a peculiar periodic
+photometric variability thought to be related to a warped inner disk structure
+with a nearly edge-on viewing geometry. We present high resolution ($\sim$0.2"")
+ALMA observations of the 0.87 and 1.3~mm dust continuum emission from the disk
+around AA Tau. These data reveal an evenly spaced three-ringed emission
+structure, with distinct peaks at 0.34"", 0.66"", and 0.99"", all viewed at a
+modest inclination of 59.1$^{\circ}\pm$0.3$^{\circ}$ (decidedly not edge-on).
+In addition to this ringed substructure, we find non-axisymmetric features
+including a `bridge' of emission that connects opposite sides of the innermost
+ring. We speculate on the nature of this `bridge' in light of accompanying
+observations of HCO$^+$ and $^{13}$CO (J=3--2) line emission. The HCO$^+$
+emission is bright interior to the innermost dust ring, with a projected
+velocity field that appears rotated with respect to the resolved disk geometry,
+indicating the presence of a warp or inward radial flow. We suggest that the
+continuum bridge and HCO$^+$ line kinematics could originate from gap-crossing
+accretion streams, which may be responsible for the long-duration dimming of
+optical light from AA Tau.
+",0,1,0,0,0,0
+14039,Verifying the Medical Specialty from User Profile of Online Community for Health-Related Advices,"  The paper describes the verifying methods of medical specialty from user
+profile of online community for health-related advices. To avoid critical
+situations with the proliferation of unverified and inaccurate information in
+medical online community, it is necessary to develop a comprehensive software
+solution for verifying the user medical specialty of online community for
+health-related advices. The algorithm for forming the information profile of a
+medical online community user is designed. The scheme systems of formation of
+indicators of user specialization in the profession based on a training sample
+is presented. The method of forming the user information profile of online
+community for healthrelated advices by computer-linguistic analysis of the
+information content is suggested. The system of indicators based on a training
+sample of users in medical online communities is formed. The matrix of medical
+specialties indicators and method of determining weight coefficients these
+indicators is investigated. The proposed method of verifying the medical
+specialty from user profile is tested in online medical community.
+",1,0,0,0,0,0
+14040,Stress-Based Navigation for Microscopic Robots in Viscous Fluids,"  Objects moving in fluids experience patterns of stress on their surfaces
+determined by their motion and the geometry of nearby boundaries. Fish and
+underwater robots can use these patterns for navigation. This paper extends
+this stress-based navigation to microscopic robots in tiny vessels, where
+robots can exploit the physics of fluids at low Reynolds number. This applies,
+for instance, in vessels with sizes and flow speeds comparable to those of
+capillaries in biological tissues. We describe how a robot can use simple
+computations to estimate its motion, orientation and distance to nearby vessel
+walls from fluid-induced stresses on its surface. Numerically evaluating these
+estimates for a variety of vessel sizes and robot positions shows they are most
+accurate when robots are close to vessel walls.
+",1,0,0,0,0,0
+14041,The $(-β)$-shift and associated Zeta Function,"  Given a real number $ \beta > 1$, we study the associated $ (-\beta)$-shift
+introduced by S. Ito and T. Sadahiro. We compares some aspects of the
+$(-\beta)$-shift to the $\beta$-shift. When the expansion in base $ -\beta $ of
+$ -\frac{\beta}{\beta+1} $ is periodic with odd period or when $ \beta $ is
+strictly less than the golden ratio, the $ (-\beta)$-shift, as defined by S.
+Ito and T. Sadahiro cannot be coded because its language is not transitive.
+This intransitivity of words explains the existence of gaps in the interval. We
+observe that an intransitive word appears in the $(-\beta)$-expansion of a real
+number taken in the gap. Furthermore, we determine the Zeta function
+$\zeta_{-\beta}$ of the $(-\beta)$-transformation and the associated
+lap-counting function $L_{T_{-\beta}}$. These two functions are related by $
+\zeta_{-\beta}=(1-z^2)L_{T_{-\beta}}$. We observe some similarities with the
+zeta function of the $\beta$-transformation. The function $\zeta_{-\beta}$ is
+meromorphic in the unit disk, is holomorphic in the open disk $ \{z |z| <
+\frac{1}{\beta} \}$, has a simple pole at $ \frac{1}{\beta}$ and no other
+singularities $ z $ such that $\|z| = \frac{1}{\beta}$. We also note an
+influence of gaps ($\beta$ less than the golden ratio) on the zeta function. In
+factors of the denominator of $\zeta_{-\beta}$, the coefficients count the
+words generating gaps.
+",0,0,1,0,0,0
+14042,Configurational forces in electronic structure calculations using Kohn-Sham density functional theory,"  We derive the expressions for configurational forces in Kohn-Sham density
+functional theory, which correspond to the generalized variational force
+computed as the derivative of the Kohn-Sham energy functional with respect to
+the position of a material point $\textbf{x}$. These configurational forces
+that result from the inner variations of the Kohn-Sham energy functional
+provide a unified framework to compute atomic forces as well as stress tensor
+for geometry optimization. Importantly, owing to the variational nature of the
+formulation, these configurational forces inherently account for the Pulay
+corrections. The formulation presented in this work treats both pseudopotential
+and all-electron calculations in single framework, and employs a local
+variational real-space formulation of Kohn-Sham DFT expressed in terms of the
+non-orthogonal wavefunctions that is amenable to reduced-order scaling
+techniques. We demonstrate the accuracy and performance of the proposed
+configurational force approach on benchmark all-electron and pseudopotential
+calculations conducted using higher-order finite-element discretization. To
+this end, we examine the rates of convergence of the finite-element
+discretization in the computed forces and stresses for various materials
+systems, and, further, verify the accuracy from finite-differencing the energy.
+Wherever applicable, we also compare the forces and stresses with those
+obtained from Kohn-Sham DFT calculations employing plane-wave basis
+(pseudopotential calculations) and Gaussian basis (all-electron calculations).
+Finally, we verify the accuracy of the forces on large materials systems
+involving a metallic aluminum nanocluster containing 666 atoms and an alkane
+chain containing 902 atoms, where the Kohn-Sham electronic ground state is
+computed using a reduced-order scaling subspace projection technique (P.
+Motamarri and V. Gavini, Phys. Rev. B 90, 115127).
+",0,1,0,0,0,0
+14043,Utilizing Bluetooth and Adaptive Signal Control Data for Urban Arterials Safety Analysis,"  Real-time safety analysis has become a hot research topic as it can more
+accurately reveal the relationships between real-time traffic characteristics
+and crash occurrence, and these results could be applied to improve active
+traffic management systems and enhance safety performance. Most of the previous
+studies have been applied to freeways and seldom to arterials. This study
+attempts to examine the relationship between crash occurrence and real-time
+traffic and weather characteristics based on four urban arterials in Central
+Florida. Considering the substantial difference between the interrupted urban
+arterials and the access controlled freeways, the adaptive signal phasing data
+was introduced in addition to the traditional traffic data. Bayesian
+conditional logistic models were developed by incorporating the Bluetooth,
+adaptive signal control, and weather data, which were extracted for a period of
+20 minutes (four 5-minute intervals) before the time of crash occurrence. Model
+comparison results indicated that the model based on 5-10 minute interval
+dataset performs the best. It revealed that the average speed, upstream
+left-turn volume, downstream green ratio, and rainy indicator were found to
+have significant effects on crash occurrence. Furthermore, both Bayesian random
+parameters logistic and Bayesian random parameters conditional logistic models
+were developed to compare with the Bayesian conditional logistic model, and the
+Bayesian random parameters conditional logistic model was found to have the
+best model performance in terms of the AUC and DIC values. These results are
+important in real-time safety applications in the context of Integrated Active
+Traffic Management.
+",0,0,0,1,0,0
+14044,A Nernst current from the conformal anomaly in Dirac and Weyl semimetals,"  We show that a conformal anomaly in Weyl/Dirac semimetals generates a bulk
+electric current perpendicular to a temperature gradient and the direction of a
+background magnetic field. The associated conductivity of this novel
+contribution to the Nernst effect is fixed by a beta function associated with
+the electric charge renormalization in the material.
+",0,1,0,0,0,0
+14045,Employing both Gender and Emotion Cues to Enhance Speaker Identification Performance in Emotional Talking Environments,"  Speaker recognition performance in emotional talking environments is not as
+high as it is in neutral talking environments. This work focuses on proposing,
+implementing, and evaluating a new approach to enhance the performance in
+emotional talking environments. The new proposed approach is based on
+identifying the unknown speaker using both his/her gender and emotion cues.
+Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models
+(SPHMMs) have been used as classifiers in this work. This approach has been
+tested on our collected emotional speech database which is composed of six
+emotions. The results of this work show that speaker identification performance
+based on using both gender and emotion cues is higher than that based on using
+gender cues only, emotion cues only, and neither gender nor emotion cues by
+7.22%, 4.45%, and 19.56%, respectively. This work also shows that the optimum
+speaker identification performance takes place when the classifiers are
+completely biased towards suprasegmental models and no impact of acoustic
+models in the emotional talking environments. The achieved average speaker
+identification performance based on the new proposed approach falls within
+2.35% of that obtained in subjective evaluation by human judges.
+",1,0,0,0,0,0
+14046,Thermal Pressure in Diffuse H2 Gas Measured by Herschel [C II] Emission and FUSE UV H2 Absorption,"  UV absorption studies with FUSE have observed H2 molecular gas in translucent
+and diffuse clouds. Observations of the 158 micron [C II] fine structure line
+with Herschel also trace the same H2 molecular gas in emission. We present [C
+II] observations along 27 lines of sight (LOSs) towards target stars of which
+25 have FUSE H2 UV absorption. We detect [C II] emission features in all but
+one target LOS. For three Target LOSs, which are close to the Galactic plane,
+we also present position-velocity maps of [C II] emission observed by HIFI in
+on-the-fly spectral line mapping. We use the velocity resolved [C II] spectra
+towards the target LOSs observed by FUSE to identify C II] velocity components
+associated with the H2 clouds. We analyze the observed velocity integrated [C
+II] spectral line intensities in terms of the densities and thermal pressures
+in the H2 gas using the H2 column densities and temperatures measured by the UV
+absorption data. We present the H2 gas densities and thermal pressures for 26
+target LOSs and from the [C II] intensities derive a mean thermal pressure in
+the range 6100 to 7700 K cm^-3 in diffuse H2 clouds. We discuss the thermal
+pressures and densities towards 14 targets, comparing them to results obtained
+using the UV absorption data for two other tracers CI and CO.
+",0,1,0,0,0,0
+14047,A Dictionary Approach to Identifying Transient RFI,"  As radio telescopes become more sensitive, the damaging effects of radio
+frequency interference (RFI) become more apparent. Near radio telescope arrays,
+RFI sources are often easily removed or replaced; the challenge lies in
+identifying them. Transient (impulsive) RFI is particularly difficult to
+identify. We propose a novel dictionary-based approach to transient RFI
+identification. RFI events are treated as sequences of sub-events, drawn from
+particular labelled classes. We demonstrate an automated method of extracting
+and labelling sub-events using a dataset of transient RFI. A dictionary of
+labels may be used in conjunction with hidden Markov models to identify the
+sources of RFI events reliably. We attain improved classification accuracy over
+traditional approaches such as SVMs or a naïve kNN classifier. Finally, we
+investigate why transient RFI is difficult to classify. We show that cluster
+separation in the principal components domain is influenced by the mains supply
+phase for certain sources.
+",0,1,0,0,0,0
+14048,Eternal inflation and the quantum birth of cosmic structure,"  We consider the eternal inflation scenario of the slow-roll/chaotic type with
+the additional element of an objective collapse of the wave function. The
+incorporation of this new agent to the traditional inflationary setting might
+represent a possible solution to the quantum measurement problem during
+inflation, a subject that has not reached a consensus among the community.
+Specifically, it could provide an explanation for the generation of the
+primordial anisotropies and inhomogeneities, starting from a perfectly
+symmetric background and invoking symmetric dynamics. We adopt the continuous
+spontaneous localization model, in the context of inflation, as the dynamical
+reduction mechanism that generates the primordial inhomogeneities. Furthermore,
+when enforcing the objective reduction mechanism, the condition for eternal
+inflation can be bypassed. In particular, the collapse mechanism incites the
+wave function, corresponding to the inflaton, to localize itself around the
+zero mode of the field. Then the zero mode will evolve essentially unperturbed,
+driving inflation to an end in any region of the Universe where inflation
+occurred. Also, our approach achieves a primordial spectrum with an amplitude
+and shape consistent with the one that best fits the observational data.
+",0,1,0,0,0,0
+14049,Exact time dependence of causal correlations and nonequilibrium density matrices in holographic systems,"  We present the first exact calculations of the time dependence of causal
+correlations in driven nonequilibrium states in (2+1)-dimensional systems using
+holography. Comparing exact results with those obtained from simple prototype
+geometries that are parametrized only by a time dependent temperature, we find
+that the universal slowly varying features are controlled just by the pump
+duration and the initial and final temperatures only. We provide numerical
+evidence that the locations of the event and apparent horizons in the dual
+geometries can be deduced from the nonequilibrium causal correlations without
+any prior knowledge of the dual gravity theory.
+",0,1,0,0,0,0
+14050,Discrete-time construction of nonequilibrium path integrals on the Kostantinov-Perel' time contour,"  Rigorous nonequilibrium actions for the many-body problem are usually derived
+by means of path integrals combined with a discrete temporal mesh on the
+Schwinger-Keldysh time contour. The latter suffers from a fundamental
+limitation: the initial state on this contour cannot be arbitrary, but
+necessarily needs to be described by a non-interacting density matrix, while
+interactions are switched on adiabatically. The Kostantinov-Perel' contour
+overcomes these and other limitations, allowing generic initial-state
+preparations. In this Article, we apply the technique of the discrete temporal
+mesh to rigorously build the nonequilibrium path integral on the
+Kostantinov-Perel' time contour.
+",0,1,0,0,0,0
+14051,Estimation of Component Reliability in Coherent Systems,"  The first step in statistical reliability studies of coherent systems is the
+estimation of the reliability of each system component. For the cases of
+parallel and series systems the literature is abundant. It seems that the
+present paper is the first that presents the general case of component
+inferences in coherent systems. The failure time model considered here is the
+three-parameter Weibull distribution. Furthermore, neither independence nor
+identically distributed failure times are required restrictions. The proposed
+model is general in the sense that it can be used for any coherent system, from
+the simplest to the more complex structures. It can be considered for all kinds
+of censored data; including interval-censored data. An important property
+obtained for the Weibull model is the fact that the posterior distributions are
+proper, even for non-informative priors. Using several simulations, the
+excellent performance of the model is illustrated. As a real example, boys
+first use of marijuana is considered to show the efficiency of the solution
+even when censored data occurs.
+",0,0,0,1,0,0
+14052,Cell-Probe Lower Bounds from Online Communication Complexity,"  In this work, we introduce an online model for communication complexity.
+Analogous to how online algorithms receive their input piece-by-piece, our
+model presents one of the players, Bob, his input piece-by-piece, and has the
+players Alice and Bob cooperate to compute a result each time before the next
+piece is revealed to Bob. This model has a closer and more natural
+correspondence to dynamic data structures than classic communication models do,
+and hence presents a new perspective on data structures.
+We first present a tight lower bound for the online set intersection problem
+in the online communication model, demonstrating a general approach for proving
+online communication lower bounds. The online communication model prevents a
+batching trick that classic communication complexity allows, and yields a
+stronger lower bound. We then apply the online communication model to prove
+data structure lower bounds for two dynamic data structure problems: the Group
+Range problem and the Dynamic Connectivity problem for forests. Both of the
+problems admit a worst case $O(\log n)$-time data structure. Using online
+communication complexity, we prove a tight cell-probe lower bound for each:
+spending $o(\log n)$ (even amortized) time per operation results in at best an
+$\exp(-\delta^2 n)$ probability of correctly answering a
+$(1/2+\delta)$-fraction of the $n$ queries.
+",1,0,0,0,0,0
+14053,On Machine Learning and Structure for Mobile Robots,"  Due to recent advances - compute, data, models - the role of learning in
+autonomous systems has expanded significantly, rendering new applications
+possible for the first time. While some of the most significant benefits are
+obtained in the perception modules of the software stack, other aspects
+continue to rely on known manual procedures based on prior knowledge on
+geometry, dynamics, kinematics etc. Nonetheless, learning gains relevance in
+these modules when data collection and curation become easier than manual rule
+design. Building on this coarse and broad survey of current research, the final
+sections aim to provide insights into future potentials and challenges as well
+as the necessity of structure in current practical applications.
+",1,0,0,1,0,0
+14054,On the degree of incompleteness of an incomplete financial market,"  In order to find a way of measuring the degree of incompleteness of an
+incomplete financial market, the rank of the vector price process of the traded
+assets and the dimension of the associated acceptance set are introduced. We
+show that they are equal and state a variety of consequences.
+",0,0,0,0,0,1
+14055,Sparsity information and regularization in the horseshoe and other shrinkage priors,"  The horseshoe prior has proven to be a noteworthy alternative for sparse
+Bayesian estimation, but has previously suffered from two problems. First,
+there has been no systematic way of specifying a prior for the global shrinkage
+hyperparameter based on the prior information about the degree of sparsity in
+the parameter vector. Second, the horseshoe prior has the undesired property
+that there is no possibility of specifying separately information about
+sparsity and the amount of regularization for the largest coefficients, which
+can be problematic with weakly identified parameters, such as the logistic
+regression coefficients in the case of data separation. This paper proposes
+solutions to both of these problems. We introduce a concept of effective number
+of nonzero parameters, show an intuitive way of formulating the prior for the
+global hyperparameter based on the sparsity assumptions, and argue that the
+previous default choices are dubious based on their tendency to favor solutions
+with more unshrunk parameters than we typically expect a priori. Moreover, we
+introduce a generalization to the horseshoe prior, called the regularized
+horseshoe, that allows us to specify a minimum level of regularization to the
+largest values. We show that the new prior can be considered as the continuous
+counterpart of the spike-and-slab prior with a finite slab width, whereas the
+original horseshoe resembles the spike-and-slab with an infinitely wide slab.
+Numerical experiments on synthetic and real world data illustrate the benefit
+of both of these theoretical advances.
+",0,0,0,1,0,0
+14056,Learning an internal representation of the end-effector configuration space,"  Current machine learning techniques proposed to automatically discover a
+robot kinematics usually rely on a priori information about the robot's
+structure, sensors properties or end-effector position. This paper proposes a
+method to estimate a certain aspect of the forward kinematics model with no
+such information. An internal representation of the end-effector configuration
+is generated from unstructured proprioceptive and exteroceptive data flow under
+very limited assumptions. A mapping from the proprioceptive space to this
+representational space can then be used to control the robot.
+",1,0,0,0,0,0
+14057,Correlation plots of the Siberian radioheliograph,"  The Siberian Solar Radio Telescope is now being upgraded. The upgrading is
+aimed at providing the aperture synthesis imaging in the 4-8 GHz frequency
+range, instead of the single-frequency direct imaging due to the Earth
+rotation. The first phase of the upgrading is a 48-antenna array - the Siberian
+Radioheliograph. One type of radioheliograph data represents correlation plots.
+In evaluating the covariance of two-level signals, these plots are sums of
+complex correlations, obtained for different antenna pairs. Bearing in mind
+that correlation of signals from an antenna pair is related to a spatial
+frequency, we can say that each value of the plot is an integral over a spatial
+spectrum. Limits of the integration are defined by the task. Only high spatial
+frequencies are integrated to obtain dynamics of compact sources. The whole
+spectrum is integrated to reach maximum sensitivity. We show that the
+covariance of two-level variables up to Van Vleck correction is a correlation
+coefficient of these variables.
+",0,1,0,0,0,0
+14058,"Anomalous Thermal Expansion, Negative Linear Compressibility and High-Pressure Phase Transition in ZnAu2(CN)4: Neutron Inelastic Scattering and Lattice Dynamics Studies","  We present temperature dependent inelastic neutron scattering measurments,
+accompanied byab-initio calculations of phonon spectra and elastic properties
+as a function of pressure to understand anharmonicity of phonons and to study
+the mechanism of negative thermal expansion and negative linear compressibility
+behaviour of ZnAu2(CN)4. The mechanism is identified in terms of specific
+anharmonic modes that involve bending of the Zn(CN)4-Au- Zn(CN)4 linkage. The
+high-pressure phase transition at about 2 GPa is also investigated and found to
+be related to softening of a phonon mode at the L-point at the Brillouin zone
+boundary and its coupling with a zone-centre phonon and an M-point phonon in
+the ambient pressure phase. Although the phase transition is primarily driven
+by a L-point soft phonon mode, which usually leads to a second order transition
+with a 2 x 2 x 2 supercell, in the present case the structure is close to an
+elastic instability that leads to a weakly first order transition.
+",0,1,0,0,0,0
+14059,Comparing Computing Platforms for Deep Learning on a Humanoid Robot,"  The goal of this study is to test two different computing platforms with
+respect to their suitability for running deep networks as part of a humanoid
+robot software system. One of the platforms is the CPU-centered Intel NUC7i7BNH
+and the other is a NVIDIA Jetson TX2 system that puts more emphasis on GPU
+processing. The experiments addressed a number of benchmarking tasks including
+pedestrian detection using deep neural networks. Some of the results were
+unexpected but demonstrate that platforms exhibit both advantages and
+disadvantages when taking computational performance and electrical power
+requirements of such a system into account.
+",1,0,0,0,0,0
+14060,DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy,"  Super-resolution fluorescence microscopy, with a resolution beyond the
+diffraction limit of light, has become an indispensable tool to directly
+visualize biological structures in living cells at a nanometer-scale
+resolution. Despite advances in high-density super-resolution fluorescent
+techniques, existing methods still have bottlenecks, including extremely long
+execution time, artificial thinning and thickening of structures, and lack of
+ability to capture latent structures. Here we propose a novel deep learning
+guided Bayesian inference approach, DLBI, for the time-series analysis of
+high-density fluorescent images. Our method combines the strength of deep
+learning and statistical inference, where deep learning captures the underlying
+distribution of the fluorophores that are consistent with the observed
+time-series fluorescent images by exploring local features and correlation
+along time-axis, and statistical inference further refines the ultrastructure
+extracted by deep learning and endues physical meaning to the final image.
+Comprehensive experimental results on both real and simulated datasets
+demonstrate that our method provides more accurate and realistic local patch
+and large-field reconstruction than the state-of-the-art method, the 3B
+analysis, while our method is more than two orders of magnitude faster. The
+main program is available at this https URL
+",0,0,0,1,0,0
+14061,A Universal Ordinary Differential Equation,"  An astonishing fact was established by Lee A. Rubel (1981): there exists a
+fixed non-trivial fourth-order polynomial differential algebraic equation (DAE)
+such that for any positive continuous function $\varphi$ on the reals, and for
+any positive continuous function $\epsilon(t)$, it has a $\mathcal{C}^\infty$
+solution with $| y(t) - \varphi(t) | < \epsilon(t)$ for all $t$. Lee A. Rubel
+provided an explicit example of such a polynomial DAE. Other examples of
+universal DAE have later been proposed by other authors. However, Rubel's DAE
+\emph{never} has a unique solution, even with a finite number of conditions of
+the form $y^{(k_i)}(a_i)=b_i$.
+The question whether one can require the solution that approximates $\varphi$
+to be the unique solution for a given initial data is a well known open problem
+[Rubel 1981, page 2], [Boshernitzan 1986, Conjecture 6.2]. In this article, we
+solve it and show that Rubel's statement holds for polynomial ordinary
+differential equations (ODEs), and since polynomial ODEs have a unique solution
+given an initial data, this positively answers Rubel's open problem. More
+precisely, we show that there exists a \textbf{fixed} polynomial ODE such that
+for any $\varphi$ and $\epsilon(t)$ there exists some initial condition that
+yields a solution that is $\epsilon$-close to $\varphi$ at all times.
+In particular, the solution to the ODE is necessarily analytic, and we show
+that the initial condition is computable from the target function and error
+function.
+",1,0,1,0,0,0
+14062,Sun/Moon photometer for the Cherenkov Telescope Array - first results,"  Determination of the energy and flux of the gamma photons by Imaging
+Atmospheric Cherenkov Technique is strongly dependent on optical properties of
+the atmosphere. Therefore, atmospheric monitoring during the future
+observations of the Cherenkov Telescope Array (CTA) as well as anticipated
+long-term monitoring in order to characterize overal properties and annual
+variation of atmospheric conditions are very important. Several instruments are
+already installed at the CTA sites in order to monitor atmospheric conditions
+on long-term. One of them is a Sun/Moon photometer CE318-T, installed at the
+Southern CTA site. Since the photometer is installed at a place with very
+stable atmospheric conditions, it can be also used for characterization of its
+performance and testing of new methods of aerosol optical depth (AOD)
+retrieval, cloud-screening and calibration. In this work, we describe our
+calibration method for nocturnal measurements and the modification of
+cloud-screening for purposes of nocturnal AOD retrieval. We applied these
+methods on two months of observations and present the distribution of AODs in
+four photometric passbands together with their uncertainties.
+",0,1,0,0,0,0
+14063,A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System,"  What can we learn from a connectome? We constructed a simplified model of the
+first two stages of the fly visual system, the lamina and medulla. The
+resulting hexagonal lattice convolutional network was trained using
+backpropagation through time to perform object tracking in natural scene
+videos. Networks initialized with weights from connectome reconstructions
+automatically discovered well-known orientation and direction selectivity
+properties in T4 neurons and their inputs, while networks initialized at random
+did not. Our work is the first demonstration, that knowledge of the connectome
+can enable in silico predictions of the functional properties of individual
+neurons in a circuit, leading to an understanding of circuit function from
+structure alone.
+",0,0,0,0,1,0
+14064,Improved Kernels and Algorithms for Claw and Diamond Free Edge Deletion Based on Refined Observations,"  In the {claw, diamond}-free edge deletion problem, we are given a graph $G$
+and an integer $k>0$, the question is whether there are at most $k$ edges whose
+deletion results in a graph without claws and diamonds as induced graphs. Based
+on some refined observations, we propose a kernel of $O(k^3)$ vertices and
+$O(k^4)$ edges, significantly improving the previous kernel of $O(k^{12})$
+vertices and $O(k^{24})$ edges. In addition, we derive an $O^*(3.792^k)$-time
+algorithm for the {claw, diamond}-free edge deletion problem.
+",1,0,0,0,0,0
+14065,Distributed Edge Caching Scheme Considering the Tradeoff Between the Diversity and Redundancy of Cached Content,"  Caching popular contents at the edge of cellular networks has been proposed
+to reduce the load, and hence the cost of backhaul links. It is significant to
+decide which files should be cached and where to cache them. In this paper, we
+propose a distributed caching scheme considering the tradeoff between the
+diversity and redundancy of base stations' cached contents. Whether it is
+better to cache the same or different contents in different base stations? To
+find out this, we formulate an optimal redundancy caching problem. Our goal is
+to minimize the total transmission cost of the network, including cost within
+the radio access network (RAN) and cost incurred by transmission to the core
+network via backhaul links. The optimal redundancy ratio under given system
+configuration is obtained with adapted particle swarm optimization (PSO)
+algorithm. We analyze the impact of important system parameters through
+Monte-Carlo simulation. Results show that the optimal redundancy ratio is
+mainly influenced by two parameters, which are the backhaul to RAN unit cost
+ratio and the steepness of file popularity distribution. The total cost can be
+reduced by up to 54% at given unit cost ratio of backhaul to RAN when the
+optimal redundancy ratio is selected. Under typical file request pattern, the
+reduction amount can be up to 57%.
+",1,0,0,0,0,0
+14066,Orbital Graphs,"  We introduce orbital graphs and discuss some of their basic properties. Then
+we focus on their usefulness for search algorithms for permutation groups,
+including finding the intersection of groups and the stabilizer of sets in a
+group.
+",1,0,1,0,0,0
+14067,On the existence of harmonic $\mathbf{Z}_2$ spinors,"  We prove the existence of singular harmonic ${\bf Z}_2$ spinors on
+$3$-manifolds with $b_1 > 1$. The proof relies on a wall-crossing formula for
+solutions to the Seiberg-Witten equation with two spinors. The existence of
+singular harmonic ${\bf Z}_2$ spinors and the shape of our wall-crossing
+formula shed new light on recent observations made by Joyce regarding Donaldson
+and Segal's proposal for counting $G_2$-instantons.
+",0,0,1,0,0,0
+14068,Penalty-based spatial smoothing and outlier detection for childhood obesity surveillance from electronic health records,"  Childhood obesity is associated with increased morbidity and mortality in
+adulthood, leading to substantial healthcare cost. There is an urgent need to
+promote early prevention and develop an accompanying surveillance system. In
+this paper, we make use of electronic health records (EHRs) and construct a
+penalized multi-level generalized linear model. The model provides regular
+trend and outlier information simultaneously, both of which may be useful to
+raise public awareness and facilitate targeted intervention. Our strategy is to
+decompose the regional contribution in the model into smooth and sparse
+signals, where the characteristics of the signals are encouraged by the
+combination of fusion and sparse penalties imposed on the likelihood function.
+In addition, we introduce a weighting scheme to account for the missingness and
+potential non-representativeness arising from the EHRs data. We propose a novel
+alternating minimization algorithm, which is computationally efficient, easy to
+implement, and guarantees convergence. Simulation shows that the proposed
+method has a superior performance compared with traditional counterparts.
+Finally, we apply our method to the University of Wisconsin Population Health
+Information Exchange database.
+",0,0,0,1,0,0
+14069,Some simple rules for estimating reproduction numbers in the presence of reservoir exposure or imported cases,"  The basic reproduction number ($R_0$) is a threshold parameter for disease
+extinction or survival in isolated populations. However no human population is
+fully isolated from other human or animal populations. We use compartmental
+models to derive simple rules for the basic reproduction number for populations
+with local person-to-person transmission and exposure from some other source:
+either a reservoir exposure or imported cases. We introduce the idea of a
+reservoir-driven or importation-driven disease: diseases that would become
+extinct in the population of interest without reservoir exposure or imported
+cases (since $R_0<1$), but nevertheless may be sufficiently transmissible that
+many or most infections are acquired from humans in that population. We show
+that in the simplest case, $R_0<1$ if and only if the proportion of infections
+acquired from the external source exceeds the disease prevalence and explore
+how population heterogeneity and the interactions of multiple strains affect
+this rule. We apply these rules in two cases studies of Clostridium difficile
+infection and colonisation: C. difficile in the hospital setting accounting for
+imported cases, and C. difficile in the general human population accounting for
+exposure to animal reservoirs. We demonstrate that even the hospital-adapted,
+highly-transmissible NAP1/RT027 strain of C. difficile had a reproduction
+number <1 in a landmark study of hospitalised patients and therefore was
+sustained by colonised and infected admissions to the study hospital. We argue
+that C. difficile should be considered reservoir-driven if as little as 13.0%
+of transmission can be attributed to animal reservoirs.
+",0,0,0,0,1,0
+14070,Self-Assembled Monolayer Piezoelectrics: Electric-Field Driven Conformational Changes,"  We demonstrate that an applied electric field causes piezoelectric distortion
+across single molecular monolayers of oligopeptides. We deposited
+self-assembled monolayers ~1.5 nm high onto smooth gold surfaces. These
+monolayers exhibit strong piezoelectric response that varies linearly with
+applied bias (1-3V), measured using piezoresponse force microscopy (PFM). The
+response is markedly greater than control experiments with rigid alkanethiols
+and correlates with surface spectroscopy and theoretical predictions of
+conformational change from applied electric fields. Unlike existing
+piezoelectric oxides, our peptide monolayers are intrinsically flexible, easily
+fabricated, aligned and patterned without poling.
+",0,1,0,0,0,0
+14071,Matrix divisors on Riemann surfaces and Lax operator algebras,"  Matrix divisors are introduced in the work by A.Weil (1938) which is
+considered as a starting point of the theory of holomorphic vector bundles on
+Riemann surfaces. In this theory matrix divisors play the role similar to the
+role of usual divisors in the theory of line bundles. Moreover, they provide
+explicit coordinates (Tyurin parameters) in an open subset of the moduli space
+of stable vector bundles. These coordinates turned out to be helpful in
+integration of soliton equations.
+We would like to gain attention to one more relationship between matrix
+divisors of vector G-bundles (where G is a complex semi-simple Lie group) and
+the theory of integrable systems, namely to the relationship with Lax operator
+algebras. The result we obtain can be briefly formulated as follows: the moduli
+space of matrix divisors with certain discrete invariants and fixed support is
+a homogeneous space. Its tangent space at the unit is naturally isomorphic to
+the quotient space of M-operators by L-operators, both spaces essentially
+defined by the same invariants (the result goes back to Krichever, 2001). We
+give one more description of the same space in terms of root systems.
+",0,0,1,0,0,0
+14072,Observation of pseudogap in MgB2,"  Pseudogap phase in superconductors continues to be an outstanding puzzle that
+differentiates unconventional superconductors from the conventional ones
+(BCS-superconductors). Employing high resolution photoemission spectroscopy on
+a highly dense conventional superconductor, MgB2, we discover an interesting
+scenario. While the spectral evolution close to the Fermi energy is
+commensurate to BCS descriptions as expected, the spectra in the wider energy
+range reveal emergence of a pseudogap much above the superconducting transition
+temperature indicating apparent departure from the BCS scenario. The energy
+scale of the pseudogap is comparable to the energy of E2g phonon mode
+responsible for superconductivity in MgB2 and the pseudogap can be attributed
+to the effect of electron-phonon coupling on the electronic structure. These
+results reveal a scenario of the emergence of the superconducting gap within an
+electron-phonon coupling induced pseudogap.
+",0,1,0,0,0,0
+14073,"Critical fields and fluctuations determined from specific heat and magnetoresistance in the same nanogram SmFeAs(O,F) single crystal","  Through a direct comparison of specific heat and magneto-resistance we
+critically asses the nature of superconducting fluctuations in the same
+nano-gram crystal of SmFeAs(O, F). We show that although the superconducting
+fluctuation contribution to conductivity scales well within the 2D-LLL scheme
+its predictions contrast the inherently 3D nature of SmFeAs(O, F) in the
+vicinity T_{c}. Furthermore the transition seen in specific heat cannot be
+satisfactory described either by the LLL or the XY scaling. Additionally we
+have validated, through comparing Hc2 values obtained from the entropy
+conservation construction (Hab=-19.5 T/K and Hab=-2.9 T/K), the analysis of
+fluctuation contribution to conductivity as a reasonable method for estimating
+the Hc2 slope.
+",0,1,0,0,0,0
+14074,Determining the vortex tilt relative to a superconductor surface,"  It is of interest to determine the exit angle of a vortex from a
+superconducting surface, since this affects the intervortex interactions and
+their consequences. Two ways to determine this angle are to image the vortex
+magnetic fields above the surface, or the vortex core shape at the surface. In
+this work we evaluate the field h(x, y, z) above a flat superconducting surface
+x, y and the currents J(x,y) at that surface for a straight vortex tilted
+relative to the normal to the surface, for both the isotropic and anisotropic
+cases. In principle, these results can be used to determine the vortex exit
+tilt angle from analyses of magnetic field imaging or density of states data.
+",0,1,0,0,0,0
+14075,Onset of nonlinear structures due to eigenmode destabilization in tokamak plasmas,"  A general methodology is proposed to differentiate the likelihood of
+energetic-particle-driven instabilities to produce frequency chirping or
+fixed-frequency oscillations. The method employs numerically calculated
+eigenstructures and multiple resonance surfaces of a given mode in the presence
+of energetic ion drag and stochasticity (due to collisions and
+micro-turbulence). Toroidicity-induced, reversed-shear and beta-induced
+Alfven-acoustic eigenmodes are used as examples. Waves measured in experiments
+are characterized and compatibility is found between the proposed criterion
+predictions and the experimental observation or lack of observation of chirping
+behavior of Alfvenic modes in different tokamaks. It is found that the
+stochastic diffusion due to micro-turbulence can be the dominant energetic
+particle detuning mechanism near the resonances in many plasma experiments, and
+its strength is the key as to whether chirping solutions are likely to arise.
+The proposed criterion constitutes a useful predictive tool in assessing
+whether the nature of the transport for fast ion losses in fusion devices will
+be dominated by convective or diffusive processes.
+",0,1,0,0,0,0
+14076,Self-consistent semi-analytic models of the first stars,"  We have developed a semi-analytic framework to model the large-scale
+evolution of the first Population III (Pop III) stars and the transition to
+metal-enriched star formation. Our model follows dark matter halos from
+cosmological N-body simulations, utilizing their individual merger histories
+and three-dimensional positions, and applies physically motivated prescriptions
+for star formation and feedback from Lyman-Werner (LW) radiation, hydrogen
+ionizing radiation, and external metal enrichment due to supernovae winds. This
+method is intended to complement analytic studies, which do not include
+clustering or individual merger histories, and hydrodynamical cosmological
+simulations, which include detailed physics, but are computationally expensive
+and have limited dynamic range. Utilizing this technique, we compute the
+cumulative Pop III and metal-enriched star formation rate density (SFRD) as a
+function of redshift at $z \geq 20$. We find that varying the model parameters
+leads to significant qualitative changes in the global star formation history.
+The Pop III star formation efficiency and the delay time between Pop III and
+subsequent metal-enriched star formation are found to have the largest impact.
+The effect of clustering (i.e. including the three-dimensional positions of
+individual halos) on various feedback mechanisms is also investigated. The
+impact of clustering on LW and ionization feedback is found to be relatively
+mild in our fiducial model, but can be larger if external metal enrichment can
+promote metal-enriched star formation over large distances.
+",0,1,0,0,0,0
+14077,SoK: Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses,"  Research on how hardware imperfections impact security has primarily focused
+on side-channel leakage mechanisms produced by power consumption,
+electromagnetic emanations, acoustic vibrations, and optical emissions.
+However, with the proliferation of sensors in security-critical devices, the
+impact of attacks on sensor-to-microcontroller and microcontroller-to-actuator
+interfaces using the same channels is starting to become more than an academic
+curiosity. These out-of-band signal injection attacks target connections which
+transform physical quantities to analog properties and fundamentally cannot be
+authenticated, posing previously unexplored security risks. This paper contains
+the first survey of such out-of-band signal injection attacks, with a focus on
+unifying their terminology, and identifying commonalities in their causes and
+effects. The taxonomy presented contains a chronological, evolutionary, and
+thematic view of out-of-band signal injection attacks which highlights the
+cross-influences that exist and underscores the need for a common language
+irrespective of the method of injection. By placing attack and defense
+mechanisms in the wider context of their dual counterparts of side-channel
+leakage and electromagnetic interference, our paper identifies common threads
+and gaps that can help guide and inform future research. Overall, the
+ever-increasing reliance on sensors embedded in everyday commodity devices
+necessitates that a stronger focus be placed on improving the security of such
+systems against out-of-band signal injection attacks.
+",1,0,0,0,0,0
+14078,Identifying Harm Events in Clinical Care through Medical Narratives,"  Preventable medical errors are estimated to be among the leading causes of
+injury and death in the United States. To prevent such errors, healthcare
+systems have implemented patient safety and incident reporting systems. These
+systems enable clinicians to report unsafe conditions and cases where patients
+have been harmed due to errors in medical care. These reports are narratives in
+natural language and while they provide detailed information about the
+situation, it is non-trivial to perform large scale analysis for identifying
+common causes of errors and harm to the patients. In this work, we present a
+method based on attentive convolutional and recurrent networks for identifying
+harm events in patient care and categorize the harm based on its severity
+level. We demonstrate that our methods can significantly improve the
+performance over existing methods in identifying harm in clinical care.
+",1,0,0,0,0,0
+14079,Deep Self-Paced Learning for Person Re-Identification,"  Person re-identification (Re-ID) usually suffers from noisy samples with
+background clutter and mutual occlusion, which makes it extremely difficult to
+distinguish different individuals across the disjoint camera views. In this
+paper, we propose a novel deep self-paced learning (DSPL) algorithm to
+alleviate this problem, in which we apply a self-paced constraint and symmetric
+regularization to help the relative distance metric training the deep neural
+network, so as to learn the stable and discriminative features for person
+Re-ID. Firstly, we propose a soft polynomial regularizer term which can derive
+the adaptive weights to samples based on both the training loss and model age.
+As a result, the high-confidence fidelity samples will be emphasized and the
+low-confidence noisy samples will be suppressed at early stage of the whole
+training process. Such a learning regime is naturally implemented under a
+self-paced learning (SPL) framework, in which samples weights are adaptively
+updated based on both model age and sample loss using an alternative
+optimization method. Secondly, we introduce a symmetric regularizer term to
+revise the asymmetric gradient back-propagation derived by the relative
+distance metric, so as to simultaneously minimize the intra-class distance and
+maximize the inter-class distance in each triplet unit. Finally, we build a
+part-based deep neural network, in which the features of different body parts
+are first discriminately learned in the lower convolutional layers and then
+fused in the higher fully connected layers. Experiments on several benchmark
+datasets have demonstrated the superior performance of our method as compared
+with the state-of-the-art approaches.
+",1,0,0,0,0,0
+14080,Moments and Cumulants of The Two-Stage Mann-Whitney Statistic,"  This paper illustrates how to calculate the moments and cumulants of the
+two-stage Mann-Whitney statistic. These results may be used to calculate the
+asymptotic critical values of the two-stage Mann-Whitney test. In this paper, a
+large amount of deductions will be showed.
+",0,0,0,1,0,0
+14081,OGLE Cepheids and RR Lyrae Stars in the Milky Way,"  We present new large samples of Galactic Cepheids and RR Lyrae stars from the
+OGLE Galaxy Variability Survey.
+",0,1,0,0,0,0
+14082,Closed-form approximations in derivatives pricing: The Kristensen-Mele approach,"  Kristensen and Mele (2011) developed a new approach to obtain closed-form
+approximations to continuous-time derivatives pricing models. The approach uses
+a power series expansion of the pricing bias between an intractable model and
+some known auxiliary model. Since the resulting approximation formula has
+closed-form it is straightforward to obtain approximations of greeks. In this
+thesis I will introduce Kristensen and Mele's methods and apply it to a variety
+of stochastic volatility models of European style options as well as a model
+for commodity futures. The focus of this thesis is the effect of different
+model choices and different model parameter values on the numerical stability
+of Kristensen and Mele's approximation.
+",0,0,0,0,0,1
+14083,On the magnitude function of domains in Euclidean space,"  We study Leinster's notion of magnitude for a compact metric space. For a
+smooth, compact domain $X\subset \mathbb{R}^{2m-1}$, we find geometric
+significance in the function $\mathcal{M}_X(R) = \mathrm{mag}(R\cdot X)$. The
+function $\mathcal{M}_X$ extends from the positive half-line to a meromorphic
+function in the complex plane. Its poles are generalized scattering resonances.
+In the semiclassical limit $R \to \infty$, $\mathcal{M}_X$ admits an asymptotic
+expansion. The three leading terms of $\mathcal{M}_X$ at $R=+\infty$ are
+proportional to the volume, surface area and integral of the mean curvature. In
+particular, for convex $X$ the leading terms are proportional to the intrinsic
+volumes, and we obtain an asymptotic variant of the convex magnitude conjecture
+by Leinster and Willerton, with corrected coefficients.
+",0,0,1,0,0,0
+14084,Minimum edge cuts of distance-regular and strongly regular digraphs,"  In this paper, we show that the edge connectivity of a distance-regular
+digraph $\Gamma$ with valency $k$ is $k$ and for $k>2$, any minimum edge cut of
+$\Gamma$ is the set of all edges going into (or coming out of) a single vertex.
+Moreover we show that the same result holds for strongly regular digraphs.
+These results extend the same known results for undirected case with quite
+different proofs.
+",0,0,1,0,0,0
+14085,Quality Enhancement by Weighted Rank Aggregation of Crowd Opinion,"  Expertise of annotators has a major role in crowdsourcing based opinion
+aggregation models. In such frameworks, accuracy and biasness of annotators are
+occasionally taken as important features and based on them priority of the
+annotators are assigned. But instead of relying on a single feature, multiple
+features can be considered and separate rankings can be produced to judge the
+annotators properly. Finally, the aggregation of those rankings with perfect
+weightage can be done with an aim to produce better ground truth prediction.
+Here, we propose a novel weighted rank aggregation method and its efficacy with
+respect to other existing approaches is shown on artificial dataset. The
+effectiveness of weighted rank aggregation to enhance quality prediction is
+also shown by applying it on an Amazon Mechanical Turk (AMT) dataset.
+",1,0,0,0,0,0
+14086,Heterogeneous elastic plates with in-plane modulation of the target curvature and applications to thin gel sheets,"  We rigorously derive a Kirchhoff plate theory, via $\Gamma$-convergence, from
+a three-di\-men\-sio\-nal model that describes the finite elasticity of an
+elastically heterogeneous, thin sheet. The heterogeneity in the elastic
+properties of the material results in a spontaneous strain that depends on both
+the thickness and the plane variables $x'$. At the same time, the spontaneous
+strain is $h$-close to the identity, where $h$ is the small parameter
+quantifying the thickness. The 2D Kirchhoff limiting model is constrained to
+the set of isometric immersions of the mid-plane of the plate into
+$\mathbb{R}^3$, with a corresponding energy that penalizes deviations of the
+curvature tensor associated with a deformation from a $x'$-dependent target
+curvature tensor. A discussion on the 2D minimizers is provided in the case
+where the target curvature tensor is piecewise constant. Finally, we apply the
+derived plate theory to the modeling of swelling-induced shape changes in
+heterogeneous thin gel sheets.
+",0,1,1,0,0,0
+14087,An educational distributed Cosmic Ray detector network based on ArduSiPM,"  The advent of microcontrollers with enough CPU power and with analog and
+digital peripherals makes possible to design a complete particle detector with
+relative acquisition system around one microcontroller chip. The existence of a
+world wide data infrastructure as internet allows for devising a distributed
+network of cheap detectors capable to elaborate and send data or respond to
+settings commands. The internet infrastructure enables to distribute the
+absolute time (with precision of few milliseconds), to the simple devices far
+apart, with few milliseconds precision, from a few meters to thousands of
+kilometres. So it is possible to create a crowdsourcing experiment of citizen
+science that use small scintillation-based particle detectors to monitor the
+high energetic cosmic ray and the radiation environment.
+",0,1,0,0,0,0
+14088,Irreducible characters with bounded root Artin conductor,"  In this work, we prove that the growth of the Artin conductor is at most,
+exponential in the degree of the character.
+",0,0,1,0,0,0
+14089,Mapping Web Pages by Internet Protocol (IP) addresses: Analyzing Spatial and Temporal Characteristics of Web Search Engine Results,"  Internet Protocol (IP) addresses are frequently used as a method of locating
+web users by researchers in several different fields. However, there are
+competing reports concerning the accuracy of those locations, and little
+research has been done in manually comparing the IP geolocation databases and
+web page geographic information. This paper categorized web page from the Yahoo
+search engine into twelve categories, ranging from 'Blog' and 'News' to
+'Education' and 'Governmental'. Then we manually compared the mailing or street
+address of the web page's content creator with the geolocation results by the
+given IP address. We introduced a cartographic design method by creating kernel
+density maps for visualizing the information landscape of web pages associated
+with specific keywords.
+",1,0,0,0,0,0
+14090,A generalisation of Kani-Rosen decomposition theorem for Jacobian varieties,"  In this short paper we generalise a theorem due to Kani and Rosen on
+decomposition of Jacobian varieties of Riemann surfaces with group action. This
+generalisation extends the set of Jacobians for which it is possible to obtain
+an isogeny decomposition where all the factors are Jacobians.
+",0,0,1,0,0,0
+14091,Optimal group testing designs for estimating prevalence with uncertain testing errors,"  We construct optimal designs for group testing experiments where the goal is
+to estimate the prevalence of a trait by using a test with uncertain
+sensitivity and specificity. Using optimal design theory for approximate
+designs, we show that the most efficient design for simultaneously estimating
+the prevalence, sensitivity and specificity requires three different group
+sizes with equal frequencies. However, if estimating prevalence as accurately
+as possible is the only focus, the optimal strategy is to have three group
+sizes with unequal frequencies. On the basis of a chlamydia study in the
+U.S.A., we compare performances of competing designs and provide insights into
+how the unknown sensitivity and specificity of the test affect the performance
+of the prevalence estimator. We demonstrate that the locally D- and Ds-optimal
+designs proposed have high efficiencies even when the prespecified values of
+the parameters are moderately misspecified.
+",0,0,1,1,0,0
+14092,Charge transfer driven emergent phenomena in oxide heterostructures,"  Complex oxides exhibit many intriguing phenomena, including metal-insulator
+transition, ferroelectricity/multiferroicity, colossal magnetoresistance and
+high transition temperature superconductivity. Advances in epitaxial thin film
+growth techniques enable us to combine different complex oxides with atomic
+precision and form an oxide heterostructure. Recent theoretical and
+experimental work has shown that charge transfer across oxide interfaces
+generally occurs and leads to a great diversity of emergent interfacial
+properties which are not exhibited by bulk constituents. In this report, we
+review mechanisms and physical consequence of charge transfer across interfaces
+in oxide heterostructures. Both theoretical proposals and experimental
+measurements of various oxide heterostructures are discussed and compared. We
+also review the theoretical methods that are used to calculate charge transfer
+across oxide interfaces and discuss the success and challenges in theory.
+Finally, we present a summary and perspectives for future research.
+",0,1,0,0,0,0
+14093,Duality of Graphical Models and Tensor Networks,"  In this article we show the duality between tensor networks and undirected
+graphical models with discrete variables. We study tensor networks on
+hypergraphs, which we call tensor hypernetworks. We show that the tensor
+hypernetwork on a hypergraph exactly corresponds to the graphical model given
+by the dual hypergraph. We translate various notions under duality. For
+example, marginalization in a graphical model is dual to contraction in the
+tensor network. Algorithms also translate under duality. We show that belief
+propagation corresponds to a known algorithm for tensor network contraction.
+This article is a reminder that the research areas of graphical models and
+tensor networks can benefit from interaction.
+",1,0,1,1,0,0
+14094,A further generalization of the Emden-Fowler equation,"  A generalization of the Emden-Fowler equation is presented and its solutions
+are investigated. This paper is devoted to asymptotic behavior of its
+solutions. The procedure is entirely based on a previous paper by the author.
+",0,0,1,0,0,0
+14095,Data Interpolations in Deep Generative Models under Non-Simply-Connected Manifold Topology,"  Exploiting the deep generative model's remarkable ability of learning the
+data-manifold structure, some recent researches proposed a geometric data
+interpolation method based on the geodesic curves on the learned data-manifold.
+However, this interpolation method often gives poor results due to a
+topological difference between the model and the dataset. The model defines a
+family of simply-connected manifolds, whereas the dataset generally contains
+disconnected regions or holes that make them non-simply-connected. To
+compensate this difference, we propose a novel density regularizer that make
+the interpolation path circumvent the holes denoted by low probability density.
+We confirm that our method gives consistently better interpolation results from
+the experiments with real-world image datasets.
+",1,0,0,1,0,0
+14096,Effect of Surfaces on Amyloid Fibril Formation,"  Using atomic force microscopy (AFM) we investigated the interaction of
+amyloid beta (Ab) (1 42) peptide with chemically modified surfaces in order to
+better understand the mechanism of amyloid toxicity, which involves interaction
+of amyloid with cell membrane surfaces. We compared the structure and density
+of Ab fibrils on positively and negatively charged as well as hydrophobic
+chemically modified surfaces at physiologically relevant conditions.
+",0,1,0,0,0,0
+14097,Lie Transform Based Polynomial Neural Networks for Dynamical Systems Simulation and Identification,"  In the article, we discuss the architecture of the polynomial neural network
+that corresponds to the matrix representation of Lie transform. The matrix form
+of Lie transform is an approximation of general solution for the nonlinear
+system of ordinary differential equations. Thus, it can be used for simulation
+and modeling task. On the other hand, one can identify dynamical system from
+time series data simply by optimization of the coefficient matrices of the Lie
+transform. Representation of the approach by polynomial neural networks
+integrates the strength of both neural networks and traditional model-based
+methods for dynamical systems investigation. We provide a theoretical
+explanation of learning dynamical systems from time series for the proposed
+method, as well as demonstrate it in several applications. Namely, we show
+results of modeling and identification for both well-known systems like
+Lotka-Volterra equation and more complicated examples from retail,
+biochemistry, and accelerator physics.
+",1,0,0,0,0,0
+14098,Large deformations of the Tracy-Widom distribution I. Non-oscillatory asymptotics,"  We analyze the left-tail asymptotics of deformed Tracy-Widom distribution
+functions describing the fluctuations of the largest eigenvalue in invariant
+random matrix ensembles after removing each soft edge eigenvalue independently
+with probability $1-\gamma\in[0,1]$. As $\gamma$ varies, a transition from
+Tracy-Widom statistics ($\gamma=1$) to classical Weibull statistics
+($\gamma=0$) was observed in the physics literature by Bohigas, de Carvalho,
+and Pato \cite{BohigasCP:2009}. We provide a description of this transition by
+rigorously computing the leading-order left-tail asymptotics of the thinned
+GOE, GUE and GSE Tracy-Widom distributions. In this paper, we obtain the
+asymptotic behavior in the non-oscillatory region with $\gamma\in[0,1)$ fixed
+(for the GOE, GUE, and GSE distributions) and $\gamma\uparrow 1$ at a
+controlled rate (for the GUE distribution). This is the first step in an
+ongoing program to completely describe the transition between Tracy-Widom and
+Weibull statistics. As a corollary to our results, we obtain a new
+total-integral formula involving the Ablowitz-Segur solution to the second
+Painlevé equation.
+",0,1,1,0,0,0
+14099,Too Trivial To Test? An Inverse View on Defect Prediction to Identify Methods with Low Fault Risk,"  Background. Test resources are usually limited and therefore it is often not
+possible to completely test an application before a release. To cope with the
+problem of scarce resources, development teams can apply defect prediction to
+identify fault-prone code regions. However, defect prediction tends to low
+precision in cross-project prediction scenarios.
+Aims. We take an inverse view on defect prediction and aim to identify
+methods that can be deferred when testing because they contain hardly any
+faults due to their code being ""trivial"". We expect that characteristics of
+such methods might be project-independent, so that our approach could improve
+cross-project predictions.
+Method. We compute code metrics and apply association rule mining to create
+rules for identifying methods with low fault risk. We conduct an empirical
+study to assess our approach with six Java open-source projects containing
+precise fault data at the method level.
+Results. Our results show that inverse defect prediction can identify approx.
+32-44% of the methods of a project to have a low fault risk; on average, they
+are about six times less likely to contain a fault than other methods. In
+cross-project predictions with larger, more diversified training sets,
+identified methods are even eleven times less likely to contain a fault.
+Conclusions. Inverse defect prediction supports the efficient allocation of
+test resources by identifying methods that can be treated with less priority in
+testing activities and is well applicable in cross-project prediction
+scenarios.
+",1,0,0,0,0,0
+14100,Dynamical phase transitions in sampling complexity,"  We make the case for studying the complexity of approximately simulating
+(sampling) quantum systems for reasons beyond that of quantum computational
+supremacy, such as diagnosing phase transitions. We consider the sampling
+complexity as a function of time $t$ due to evolution generated by spatially
+local quadratic bosonic Hamiltonians. We obtain an upper bound on the scaling
+of $t$ with the number of bosons $n$ for which approximate sampling is
+classically efficient. We also obtain a lower bound on the scaling of $t$ with
+$n$ for which any instance of the boson sampling problem reduces to this
+problem and hence implies that the problem is hard, assuming the conjectures of
+Aaronson and Arkhipov [Proc. 43rd Annu. ACM Symp. Theory Comput. STOC '11].
+This establishes a dynamical phase transition in sampling complexity. Further,
+we show that systems in the Anderson-localized phase are always easy to sample
+from at arbitrarily long times. We view these results in the light of
+classifying phases of physical systems based on parameters in the Hamiltonian.
+In doing so, we combine ideas from mathematical physics and computational
+complexity to gain insight into the behavior of condensed matter, atomic,
+molecular and optical systems.
+",1,1,0,0,0,0
+14101,Local asymptotic equivalence of pure quantum states ensembles and quantum Gaussian white noise,"  Quantum technology is increasingly relying on specialised statistical
+inference methods for analysing quantum measurement data. This motivates the
+development of ""quantum statistics"", a field that is shaping up at the overlap
+of quantum physics and ""classical"" statistics. One of the less investigated
+topics to date is that of statistical inference for infinite dimensional
+quantum systems, which can be seen as quantum counterpart of non-parametric
+statistics. In this paper we analyse the asymptotic theory of quantum
+statistical models consisting of ensembles of quantum systems which are
+identically prepared in a pure state. In the limit of large ensembles we
+establish the local asymptotic equivalence (LAE) of this i.i.d. model to a
+quantum Gaussian white noise model. We use the LAE result in order to establish
+minimax rates for the estimation of pure states belonging to Hermite-Sobolev
+classes of wave functions. Moreover, for quadratic functional estimation of the
+same states we note an elbow effect in the rates, whereas for testing a pure
+state a sharp parametric rate is attained over the nonparametric
+Hermite-Sobolev class.
+",0,0,1,1,0,0
+14102,Training DNNs with Hybrid Block Floating Point,"  The wide adoption of DNNs has given birth to unrelenting computing
+requirements, forcing datacenter operators to adopt domain-specific
+accelerators to train them. These accelerators typically employ densely packed
+full precision floating-point arithmetic to maximize performance per area.
+Ongoing research efforts seek to further increase that performance density by
+replacing floating-point with fixed-point arithmetic. However, a significant
+roadblock for these attempts has been fixed point's narrow dynamic range, which
+is insufficient for DNN training convergence. We identify block floating point
+(BFP) as a promising alternative representation since it exhibits wide dynamic
+range and enables the majority of DNN operations to be performed with
+fixed-point logic. Unfortunately, BFP alone introduces several limitations that
+preclude its direct applicability. In this work, we introduce HBFP, a hybrid
+BFP-FP approach, which performs all dot products in BFP and other operations in
+floating point. HBFP delivers the best of both worlds: the high accuracy of
+floating point at the superior hardware density of fixed point. For a wide
+variety of models, we show that HBFP matches floating point's accuracy while
+enabling hardware implementations that deliver up to 8.5x higher throughput.
+",1,0,0,1,0,0
+14103,Unified Spectral Clustering with Optimal Graph,"  Spectral clustering has found extensive use in many areas. Most traditional
+spectral clustering algorithms work in three separate steps: similarity graph
+construction; continuous labels learning; discretizing the learned labels by
+k-means clustering. Such common practice has two potential flaws, which may
+lead to severe information loss and performance degradation. First, predefined
+similarity graph might not be optimal for subsequent clustering. It is
+well-accepted that similarity graph highly affects the clustering results. To
+this end, we propose to automatically learn similarity information from data
+and simultaneously consider the constraint that the similarity matrix has exact
+c connected components if there are c clusters. Second, the discrete solution
+may deviate from the spectral solution since k-means method is well-known as
+sensitive to the initialization of cluster centers. In this work, we transform
+the candidate solution into a new one that better approximates the discrete
+one. Finally, those three subtasks are integrated into a unified framework,
+with each subtask iteratively boosted by using the results of the others
+towards an overall optimal solution. It is known that the performance of a
+kernel method is largely determined by the choice of kernels. To tackle this
+practical problem of how to select the most suitable kernel for a particular
+data set, we further extend our model to incorporate multiple kernel learning
+ability. Extensive experiments demonstrate the superiority of our proposed
+method as compared to existing clustering approaches.
+",1,0,0,1,0,0
+14104,The Effect of Mixing on the Observed Metallicity of the Smith Cloud,"  Measurements of high-velocity clouds' metallicities provide important clues
+about their origins, and hence on whether they play a role in fueling ongoing
+star formation in the Galaxy. However, accurate interpretation of these
+measurements requires compensating for the galactic material that has been
+mixed into the clouds. In order to determine how much the metallicity changes
+as a result of this mixing, we have carried out three-dimensional
+wind-tunnel-like hydrodynamical simulations of an example cloud. Our model
+cloud is patterned after the Smith Cloud, a particularly well-studied cloud of
+mass $\sim 5 \times 10^6~M_\odot$. We calculated the fraction of the
+high-velocity material that had originated in the galactic halo,
+$F_\mathrm{h}$, for various sight lines passing through our model cloud. We
+find that $F_\mathrm{h}$ generally increases with distance from the head of the
+cloud, reaching $\sim$0.5 in the tail of the cloud. Models in which the
+metallicities (relative to solar) of the original cloud, $Z_\mathrm{cl}$, and
+of the halo, $Z_\mathrm{h}$, are in the approximate ranges $0.1 \lesssim
+Z_\mathrm{cl} \lesssim 0.3$ and $0.7 \lesssim Z_\mathrm{h} \lesssim 1.0$,
+respectively, are in rough agreement with the observations. Models with
+$Z_\mathrm{h} \sim 0.1$ and $Z_\mathrm{cl} \gtrsim 0.5$ are also in rough
+agreement with the observations, but such a low halo metallicity is
+inconsistent with recent independent measurements. We conclude that the Smith
+Cloud's observed metallicity may not be a true reflection of its original
+metallicity and that the cloud's ultimate origin remains uncertain.
+",0,1,0,0,0,0
+14105,Semi-Supervised Generation with Cluster-aware Generative Models,"  Deep generative models trained with large amounts of unlabelled data have
+proven to be powerful within the domain of unsupervised learning. Many real
+life data sets contain a small amount of labelled data points, that are
+typically disregarded when training generative models. We propose the
+Cluster-aware Generative Model, that uses unlabelled information to infer a
+latent representation that models the natural clustering of the data, and
+additional labelled data points to refine this clustering. The generative
+performances of the model significantly improve when labelled information is
+exploited, obtaining a log-likelihood of -79.38 nats on permutation invariant
+MNIST, while also achieving competitive semi-supervised classification
+accuracies. The model can also be trained fully unsupervised, and still improve
+the log-likelihood performance with respect to related methods.
+",1,0,0,1,0,0
+14106,A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks,"  We present a generalization bound for feedforward neural networks in terms of
+the product of the spectral norm of the layers and the Frobenius norm of the
+weights. The generalization bound is derived using a PAC-Bayes analysis.
+",1,0,0,0,0,0
+14107,Tidal viscosity of Enceladus,"  In the preceding paper (Efroimsky 2017), we derived an expression for the
+tidal dissipation rate in a homogeneous near-spherical Maxwell body librating
+in longitude. Now, by equating this expression to the outgoing energy flux due
+to the vapour plumes, we estimate the mean tidal viscosity of Enceladus, under
+the assumption that the Enceladean mantle behaviour is Maxwell. This method
+yields a value of $\,0.24\times 10^{14}\;\mbox{Pa~s}\,$ for the mean tidal
+viscosity, which is very close to the viscosity of ice near the melting point.
+",0,1,0,0,0,0
+14108,MOROCO: The Moldavian and Romanian Dialectal Corpus,"  In this work, we introduce the MOldavian and ROmanian Dialectal COrpus
+(MOROCO), which is freely available for download at
+this https URL. The corpus contains 33564 samples of
+text (with over 10 million tokens) collected from the news domain. The samples
+belong to one of the following six topics: culture, finance, politics, science,
+sports and tech. The data set is divided into 21719 samples for training, 5921
+samples for validation and another 5924 samples for testing. For each sample,
+we provide corresponding dialectal and category labels. This allows us to
+perform empirical studies on several classification tasks such as (i) binary
+discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect
+multi-class categorization by topic and (iii) cross-dialect multi-class
+categorization by topic. We perform experiments using a shallow approach based
+on string kernels, as well as a novel deep approach based on character-level
+convolutional neural networks containing Squeeze-and-Excitation blocks. We also
+present and analyze the most discriminative features of our best performing
+model, before and after named entity removal.
+",1,0,0,0,0,0
+14109,Linearized Einstein's field equations,"  From the Einstein field equations, in a weak-field approximation and for
+speeds small compared to the speed of light in vacuum, the following system is
+obtained \begin{align*}
+\nabla \times \overrightarrow{E_g} & =
+-\frac{1}{c} \frac{\partial \overrightarrow{B_g}}{\partial t},
+\nabla \cdot \overrightarrow{E_g} \;\; & \approx -4\pi G\rho_g,
+\nabla \times \overrightarrow{B_g} & \approx
+-\frac{4\pi G}{c^{2}}\overrightarrow{J_g}+
+\frac{1}{c}\frac{\partial \overrightarrow{E_g}}{\partial t},
+\nabla \cdot \overrightarrow{B_g} \;\; & = 0, \end{align*} where
+$\overrightarrow{E_g}$ is the gravitoelectric field, $\overrightarrow{B_g}$ is
+the gravitomagnetic field, $\overrightarrow{J_g}$ is the space-time-mass
+current density and $\rho_g$ is the space-time-mass density. This last
+gravitoelectromagnetic field system is similar to the Maxwell equations, thus
+showing an analogy between the electromagnetic theory and gravitation.
+",0,1,0,0,0,0
+14110,Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations,"  We propose a novel tree classification system called Treelogy, that fuses
+deep representations with hand-crafted features obtained from leaf images to
+perform leaf-based plant classification. Key to this system are segmentation of
+the leaf from an untextured background, using convolutional neural networks
+(CNNs) for learning deep representations, extracting hand-crafted features with
+a number of image processing techniques, training a linear SVM with feature
+vectors, merging SVM and CNN results, and identifying the species from a
+dataset of 57 trees. Our classification results show that fusion of deep
+representations with hand-crafted features leads to the highest accuracy. The
+proposed algorithm is embedded in a smart-phone application, which is publicly
+available. Furthermore, our novel dataset comprised of 5408 leaf images is also
+made public for use of other researchers.
+",1,0,0,0,0,0
+14111,Simulation and stability analysis of oblique shock wave/boundary layer interactions at Mach 5.92,"  We investigate flow instability created by an oblique shock wave impinging on
+a Mach 5.92 laminar boundary layer at a transitional Reynolds number. The
+adverse pressure gradient of the oblique shock causes the boundary layer to
+separate from the wall, resulting in the formation of a recirculation bubble.
+For sufficiently large oblique shock angles, the recirculation bubble is
+unstable to three-dimensional perturbations and the flow bifurcates from its
+original laminar state. We utilize Direct Numerical Simulation (DNS) and Global
+Stability Analysis (GSA) to show that this first occurs at a critical shock
+angle of $\theta = 12.9^o$. At bifurcation, the least stable global mode is
+non-oscillatory, and it takes place at a spanwise wavenumber $\beta=0.25$, in
+good agreement with DNS results. Examination of the critical global mode
+reveals that it originates from an interaction between small spanwise
+corrugations at the base of the incident shock, streamwise vortices inside the
+recirculation bubble, and spanwise modulation of the bubble strength. The
+global mode drives the formation of long streamwise streaks downstream of the
+bubble. While the streaks may be amplified by either the lift-up effect or by
+Görtler instability, we show that centrifugal instability plays no role in
+the upstream self-sustaining mechanism of the global mode. We employ an adjoint
+solver to corroborate our physical interpretation by showing that the critical
+global mode is most sensitive to base flow modifications that are entirely
+contained inside the recirculation bubble.
+",0,1,0,0,0,0
+14112,Notes on rate equations in nonlinear continuum mechanics,"  The paper gives an introduction to rate equations in nonlinear continuum
+mechanics which should obey specific transformation rules. Emphasis is placed
+on the geometrical nature of the operations involved in order to clarify the
+different concepts. The paper is particularly concerned with common classes of
+constitutive equations based on corotational stress rates and their proper
+implementation in time for solving initial boundary value problems. Hypoelastic
+simple shear is considered as an example application for the derived theory and
+algorithms.
+",1,1,0,0,0,0
+14113,Streaming PCA and Subspace Tracking: The Missing Data Case,"  For many modern applications in science and engineering, data are collected
+in a streaming fashion carrying time-varying information, and practitioners
+need to process them with a limited amount of memory and computational
+resources in a timely manner for decision making. This often is coupled with
+the missing data problem, such that only a small fraction of data attributes
+are observed. These complications impose significant, and unconventional,
+constraints on the problem of streaming Principal Component Analysis (PCA) and
+subspace tracking, which is an essential building block for many inference
+tasks in signal processing and machine learning. This survey article reviews a
+variety of classical and recent algorithms for solving this problem with low
+computational and memory complexities, particularly those applicable in the big
+data regime with missing data. We illustrate that streaming PCA and subspace
+tracking algorithms can be understood through algebraic and geometric
+perspectives, and they need to be adjusted carefully to handle missing data.
+Both asymptotic and non-asymptotic convergence guarantees are reviewed.
+Finally, we benchmark the performance of several competitive algorithms in the
+presence of missing data for both well-conditioned and ill-conditioned systems.
+",0,0,0,1,0,0
+14114,Do altmetrics correlate with the quality of papers? A large-scale empirical study based on F1000Prime data,"  In this study, we address the question whether (and to what extent,
+respectively) altmetrics are related to the scientific quality of papers (as
+measured by peer assessments). Only a few studies have previously investigated
+the relationship between altmetrics and assessments by peers. In the first
+step, we analyse the underlying dimensions of measurement for traditional
+metrics (citation counts) and altmetrics - by using principal component
+analysis (PCA) and factor analysis (FA). In the second step, we test the
+relationship between the dimensions and quality of papers (as measured by the
+post-publication peer-review system of F1000Prime assessments) - using
+regression analysis. The results of the PCA and FA show that altmetrics operate
+along different dimensions, whereas Mendeley counts are related to citation
+counts, and tweets form a separate dimension. The results of the regression
+analysis indicate that citation-based metrics and readership counts are
+significantly more related to quality, than tweets. This result on the one hand
+questions the use of Twitter counts for research evaluation purposes and on the
+other hand indicates potential use of Mendeley reader counts.
+",1,0,0,0,0,0
+14115,Introduction to the declination function for gerrymanders,"  The declination is a quantitative method for identifying possible partisan
+gerrymanders by analyzing vote distributions. In this expository note we
+explain and motivate the definition of the declination. The minimal computer
+code required for computing the declination is included. We end by computing
+its value on several recent elections.
+",0,0,0,1,0,0
+14116,"TFDASH: A Fairness, Stability, and Efficiency Aware Rate Control Approach for Multiple Clients over DASH","  Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed
+in the Internet and adopted in the industry. It, however, does not impose any
+adaptation logic for selecting the quality of video fragments requested by
+clients and suffers from lackluster performance with respect to a number of
+desirable properties: efficiency, stability, and fairness when multiple players
+compete for a bottleneck link. In this paper, we propose a throughput-friendly
+DASH (TFDASH) rate control scheme for video streaming with multiple clients
+over DASH to well balance the trade-offs among efficiency, stability, and
+fairness. The core idea behind guaranteeing fairness and high efficiency
+(bandwidth utilization) is to avoid OFF periods during the downloading process
+for all clients, i.e., the bandwidth is in perfect-subscription or
+over-subscription with bandwidth utilization approach to 100\%. We also propose
+a dual-threshold buffer model to solve the instability problem caused by the
+above idea. As a result, by integrating these novel components, we also propose
+a probability-driven rate adaption logic taking into account several key
+factors that most influence visual quality, including buffer occupancy, video
+playback quality, video bit-rate switching frequency and amplitude, to
+guarantee high-quality video streaming. Our experiments evidently demonstrate
+the superior performance of the proposed method.
+",1,0,0,0,0,0
+14117,Predicting language diversity with complex network,"  Evolution and propagation of the world's languages is a complex phenomenon,
+driven, to a large extent, by social interactions. Multilingual society can be
+seen as a system of interacting agents, where the interaction leads to a
+modification of the language spoken by the individuals. Two people can reach
+the state of full linguistic compatibility due to the positive interactions,
+like transfer of loanwords. But, on the other hand, if they speak entirely
+different languages, they will separate from each other. These simple
+observations make the network science the most suitable framework to describe
+and analyze dynamics of language change. Although many mechanisms have been
+explained, we lack a qualitative description of the scaling behavior for
+different sizes of a population. Here we address the issue of the language
+diversity in societies of different sizes, and we show that local interactions
+are crucial to capture characteristics of the empirical data. We propose a
+model of social interactions, extending the idea from, that explains the growth
+of the language diversity with the size of a population of country or society.
+We argue that high clustering and network disintegration are the most important
+characteristics of models properly describing empirical data. Furthermore, we
+cancel the contradiction between previous models and the Solomon Islands case.
+Our results demonstrate the importance of the topology of the network, and the
+rewiring mechanism in the process of language change.
+",1,1,0,0,0,0
+14118,The paradox of Vito Volterra's predator-prey model,"  This article is dedicated to the late Giorgio Israel. R{é}sum{é}. The aim
+of this article is to propose on the one hand a brief history of modeling
+starting from the works of Fibonacci, Robert Malthus, Pierre Francis Verhulst
+and then Vito Volterra and, on the other hand, to present the main hypotheses
+of the very famous but very little known predator-prey model elaborated in the
+1920s by Volterra in order to solve a problem posed by his son-in-law, Umberto
+D'Ancona. It is thus shown that, contrary to a widely-held notion, Volterra's
+model is realistic and his seminal work laid the groundwork for modern
+population dynamics and mathematical ecology, including seasonality, migration,
+pollution and more. 1. A short history of modeling 1.1. The Malthusian model.
+If the rst scientic view of population growth seems to be that of Leonardo
+Fibonacci [2], also called Leonardo of Pisa, whose famous sequence of numbers
+was presented in his Liber abaci (1202) as a solution to a population growth
+problem, the modern foundations of population dynamics clearly date from Thomas
+Robert Malthus [20]. Considering an ideal population consisting of a single
+homogeneous animal species, that is, neglecting the variations in age, size and
+any periodicity for birth or mortality, and which lives alone in an invariable
+environment or coexists with other species without any direct or indirect
+inuence, he founded in 1798, with his celebrated claim Population, when
+unchecked, increases in a geometrical ratio, the paradigm of exponential
+growth. This consists in assuming that the increase of the number N (t) of
+individuals of this population, during a short interval of time, is
+proportional to N (t). This translates to the following dierential equation :
+(1) dN (t) dt = $\epsilon$N (t) where $\epsilon$ is a constant factor of
+proportionality that represents the growth coe-cient or growth rate. By
+integrating (1) we obtain the law of exponential growth or law of Malthusian
+growth (see Fig. 1). This law, which does not take into account the limits
+imposed by the environment on growth and which is in disagreement with the
+actual facts, had a profound inuence on Charles Darwin's work on natural
+selection. Indeed, Darwin [1] founded the idea of survival of the ttest on the
+1. According to Frontier and Pichod-Viale [3] the correct terminology should be
+population kinetics, since the interaction between species cannot be
+represented by forces. 2. A population is dened as the set of individuals of
+the same species living on the same territory and able to reproduce among
+themselves.
+",0,0,0,0,1,0
+14119,Distributive Minimization Comprehensions and the Polynomial Hierarchy,"  A categorical point of view about minimization in subrecursive classes is
+presented by extending the concept of Symmetric Monoidal Comprehension to that
+of Distributive Minimization Comprehension. This is achieved by endowing the
+former with coproducts and a finality condition for coalgebras over the
+endofunctor sending X to ${1}\oplus{X}$ to perform a safe minimization
+operator. By relying on the characterization given by Bellantoni, a tiered
+structure is presented from which one can obtain the levels of the Polytime
+Hierarchy as those classes of partial functions obtained after a certain number
+of minimizations.
+",1,0,1,0,0,0
+14120,On the differentiability of hairs for Zorich maps,"  Devaney and Krych showed that for the exponential family $\lambda e^z$, where
+$0<\lambda <1/e$, the Julia set consists of uncountably many pairwise disjoint
+simple curves tending to $\infty$. Viana proved that these curves are smooth.
+In this article we consider a quasiregular counterpart of the exponential map,
+the so-called Zorich maps, and generalize Viana's result to these maps.
+",0,0,1,0,0,0
+14121,On the Real-time Vehicle Placement Problem,"  Motivated by ride-sharing platforms' efforts to reduce their riders' wait
+times for a vehicle, this paper introduces a novel problem of placing vehicles
+to fulfill real-time pickup requests in a spatially and temporally changing
+environment. The real-time nature of this problem makes it fundamentally
+different from other placement and scheduling problems, as it requires not only
+real-time placement decisions but also handling real-time request dynamics,
+which are influenced by human mobility patterns. We use a dataset of ten
+million ride requests from four major U.S. cities to show that the requests
+exhibit significant self-similarity. We then propose distributed online
+learning algorithms for the real-time vehicle placement problem and bound their
+expected performance under this observed self-similarity.
+",1,0,0,0,0,0
+14122,Energy Harvesting Communication Using Finite-Capacity Batteries with Internal Resistance,"  Modern systems will increasingly rely on energy harvested from their
+environment. Such systems utilize batteries to smoothen out the random
+fluctuations in harvested energy. These fluctuations induce highly variable
+battery charge and discharge rates, which affect the efficiencies of practical
+batteries that typically have non-zero internal resistances. In this paper, we
+study an energy harvesting communication system using a finite battery with
+non-zero internal resistance. We adopt a dual-path architecture, in which
+harvested energy can be directly used, or stored and then used. In a frame,
+both time and power can be split between energy storage and data transmission.
+For a single frame, we derive an analytical expression for the rate optimal
+time and power splitting ratios between harvesting energy and transmitting
+data. We then optimize the time and power splitting ratios for a group of
+frames, assuming non-causal knowledge of harvested power and fading channel
+gains, by giving an approximate solution. When only the statistics of the
+energy arrivals and channel gains are known, we derive a dynamic programming
+based policy and, propose three sub-optimal policies, which are shown to
+perform competitively. In summary, our study suggests that battery internal
+resistance significantly impacts the design and performance of energy
+harvesting communication systems and must be taken into account.
+",1,0,1,0,0,0
+14123,A Novel Algorithm for Optimal Electricity Pricing in a Smart Microgrid Network,"  The evolution of smart microgrid and its demand-response characteristics not
+only will change the paradigms of the century-old electric grid but also will
+shape the electricity market. In this new market scenario, once always energy
+consumers, now may act as sellers due to the excess of energy generated from
+newly deployed distributed generators (DG). The smart microgrid will use the
+existing electrical transmission network and a pay per use transportation cost
+without implementing new transmission lines which involve a massive capital
+investment. In this paper, we propose a novel algorithm to minimize the
+electricity price with the optimal trading of energy between sellers and buyers
+of the smart microgrid network. The algorithm is capable of solving the optimal
+power allocation problem (with optimal transmission cost) for a microgrid
+network in a polynomial time without modifying the actual marginal costs of
+power generation. We mathematically formulate the problem as a nonlinear
+non-convex and decompose the problem to separate the optimal marginal cost
+model from the electricity allocation model. Then, we develop a
+divide-and-conquer method to minimize the electricity price by jointly solving
+the optimal marginal cost model and electricity allocation problems. To
+evaluate the performance of the solution method, we develop and simulate the
+model with different marginal cost functions and compare it with a first come
+first serve electricity allocation method.
+",1,0,0,0,0,0
+14124,A Reinforcement Learning Approach to Jointly Adapt Vehicular Communications and Planning for Optimized Driving,"  Our premise is that autonomous vehicles must optimize communications and
+motion planning jointly. Specifically, a vehicle must adapt its motion plan
+staying cognizant of communications rate related constraints and adapt the use
+of communications while being cognizant of motion planning related restrictions
+that may be imposed by the on-road environment. To this end, we formulate a
+reinforcement learning problem wherein an autonomous vehicle jointly chooses
+(a) a motion planning action that executes on-road and (b) a communications
+action of querying sensed information from the infrastructure. The goal is to
+optimize the driving utility of the autonomous vehicle. We apply the Q-learning
+algorithm to make the vehicle learn the optimal policy, which makes the optimal
+choice of planning and communications actions at any given time. We demonstrate
+the ability of the optimal policy to smartly adapt communications and planning
+actions, while achieving large driving utilities, using simulations.
+",1,0,0,0,0,0
+14125,Differentially Private High Dimensional Sparse Covariance Matrix Estimation,"  In this paper, we study the problem of estimating the covariance matrix under
+differential privacy, where the underlying covariance matrix is assumed to be
+sparse and of high dimensions. We propose a new method, called DP-Thresholding,
+to achieve a non-trivial $\ell_2$-norm based error bound, which is
+significantly better than the existing ones from adding noise directly to the
+empirical covariance matrix. We also extend the $\ell_2$-norm based error bound
+to a general $\ell_w$-norm based one for any $1\leq w\leq \infty$, and show
+that they share the same upper bound asymptotically. Our approach can be easily
+extended to local differential privacy. Experiments on the synthetic datasets
+show consistent results with our theoretical claims.
+",1,0,0,1,0,0
+14126,Stochastic Functional Gradient Path Planning in Occupancy Maps,"  Planning safe paths is a major building block in robot autonomy. It has been
+an active field of research for several decades, with a plethora of planning
+methods. Planners can be generally categorised as either trajectory optimisers
+or sampling-based planners. The latter is the predominant planning paradigm for
+occupancy maps. Trajectory optimisation entails major algorithmic changes to
+tackle contextual information gaps caused by incomplete sensor coverage of the
+map. However, the benefits are substantial, as trajectory optimisers can reason
+on the trade-off between path safety and efficiency.
+In this work, we improve our previous work on stochastic functional gradient
+planners. We introduce a novel expressive path representation based on kernel
+approximation, that allows cost effective model updates based on stochastic
+samples. The main drawback of the previous stochastic functional gradient
+planner was the cubic cost, stemming from its non-parametric path
+representation. Our novel approximate kernel based model, on the other hand,
+has a fixed linear cost that depends solely on the number of features used to
+represent the path. We show that the stochasticity of the samples is crucial
+for the planner and present comparisons to other state-of-the-art planning
+methods in both simulation and with real occupancy data. The experiments
+demonstrate the advantages of the stochastic approximate kernel method for path
+planning in occupancy maps.
+",1,0,0,0,0,0
+14127,MM2RTB: Bringing Multimedia Metrics to Real-Time Bidding,"  In display advertising, users' online ad experiences are important for the
+advertising effectiveness. However, users have not been well accommodated in
+real-time bidding (RTB). This further influences their site visits and
+perception of the displayed banner ads. In this paper, we propose a novel
+computational framework which brings multimedia metrics, like the contextual
+relevance, the visual saliency and the ad memorability into RTB to improve the
+users' ad experiences as well as maintain the benefits of the publisher and the
+advertiser. We aim at developing a vigorous ecosystem by optimizing the
+trade-offs among all stakeholders. The framework considers the scenario of a
+webpage with multiple ad slots. Our experimental results show that the benefits
+of the advertiser and the user can be significantly improved if the publisher
+would slightly sacrifice his short-term revenue. The improved benefits will
+increase the advertising requests (demand) and the site visits (supply), which
+can further boost the publisher's revenue in the long run.
+",1,0,0,0,0,0
+14128,Generic coexistence of Fermi arcs and Dirac cones on the surface of time-reversal invariant Weyl semimetals,"  The hallmark of Weyl semimetals is the existence of open constant-energy
+contours on their surface -- the so-called Fermi arcs -- connecting Weyl
+points. Here, we show that for time-reversal symmetric realizations of Weyl
+semimetals these Fermi arcs in many cases coexist with closed Fermi pockets
+originating from surface Dirac cones pinned to time-reversal invariant momenta.
+The existence of Fermi pockets is required for certain Fermi-arc connectivities
+due to additional restrictions imposed by the six $\mathbb{Z}_2$ topological
+invariants characterizing a generic time-reversal invariant Weyl semimetal. We
+show that a change of the Fermi-arc connectivity generally leads to a different
+topology of the surface Fermi surface, and identify the half-Heusler compound
+LaPtBi under in-plane compressive strain as a material that realizes this
+surface Lifshitz transition. We also discuss universal features of this
+coexistence in quasi-particle interference spectra.
+",0,1,0,0,0,0
+14129,Superfluid Field response to Edge dislocation motion,"  We study the dynamic response of a superfluid field to a moving edge
+dislocation line to which the field is minimally coupled. We use a dissipative
+Gross-Pitaevskii equation, and determine the initial conditions by solving the
+equilibrium version of the model. We consider the subsequent time evolution of
+the field for both glide and climb dislocation motion and analyze the results
+for a range of values of the constant speed $V_D$ of the moving dislocation. We
+find that the type of motion of the dislocation line is very important in
+determining the time evolution of the superfluid field distribution associated
+with it. Climb motion of the dislocation line induces increasing asymmetry, as
+function of time, in the field profile, with part of the probability being, as
+it were, left behind. On the other hand, glide motion has no effect on the
+symmetry properties of the superfluid field distribution. Damping of the
+superfluid field due to excitations associated with the moving dislocation line
+occurs in both cases.
+",0,1,0,0,0,0
+14130,Koopman Operator Spectrum and Data Analysis,"  We examine spectral operator-theoretic properties of linear and nonlinear
+dynamical systems with equilibrium and quasi-periodic attractors and use such
+properties to characterize a class of datasets and introduce a new notion of
+the principal dimension of the data.
+",0,1,1,0,0,0
+14131,Exploiting Physical Dynamics to Detect Actuator and Sensor Attacks in Mobile Robots,"  Mobile robots are cyber-physical systems where the cyberspace and the
+physical world are strongly coupled. Attacks against mobile robots can
+transcend cyber defenses and escalate into disastrous consequences in the
+physical world. In this paper, we focus on the detection of active attacks that
+are capable of directly influencing robot mission operation. Through leveraging
+physical dynamics of mobile robots, we develop RIDS, a novel robot intrusion
+detection system that can detect actuator attacks as well as sensor attacks for
+nonlinear mobile robots subject to stochastic noises. We implement and evaluate
+a RIDS on Khepera mobile robot against concrete attack scenarios via various
+attack channels including signal interference, sensor spoofing, logic bomb, and
+physical damage. Evaluation of 20 experiments shows that the averages of false
+positive rates and false negative rates are both below 1%. Average detection
+delay for each attack remains within 0.40s.
+",1,0,0,0,0,0
+14132,Unsupervised Neural Machine Translation,"  In spite of the recent success of neural machine translation (NMT) in
+standard benchmarks, the lack of large parallel corpora poses a major practical
+problem for many language pairs. There have been several proposals to alleviate
+this issue with, for instance, triangulation and semi-supervised learning
+techniques, but they still require a strong cross-lingual signal. In this work,
+we completely remove the need of parallel data and propose a novel method to
+train an NMT system in a completely unsupervised manner, relying on nothing but
+monolingual corpora. Our model builds upon the recent work on unsupervised
+embedding mappings, and consists of a slightly modified attentional
+encoder-decoder model that can be trained on monolingual corpora alone using a
+combination of denoising and backtranslation. Despite the simplicity of the
+approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014
+French-to-English and German-to-English translation. The model can also profit
+from small parallel corpora, and attains 21.81 and 15.24 points when combined
+with 100,000 parallel sentences, respectively. Our implementation is released
+as an open source project.
+",1,0,0,0,0,0
+14133,Geometric Methods for Robust Data Analysis in High Dimension,"  Machine learning and data analysis now finds both scientific and industrial
+application in biology, chemistry, geology, medicine, and physics. These
+applications rely on large quantities of data gathered from automated sensors
+and user input. Furthermore, the dimensionality of many datasets is extreme:
+more details are being gathered about single user interactions or sensor
+readings. All of these applications encounter problems with a common theme: use
+observed data to make inferences about the world. Our work obtains the first
+provably efficient algorithms for Independent Component Analysis (ICA) in the
+presence of heavy-tailed data. The main tool in this result is the centroid
+body (a well-known topic in convex geometry), along with optimization and
+random walks for sampling from a convex body. This is the first algorithmic use
+of the centroid body and it is of independent theoretical interest, since it
+effectively replaces the estimation of covariance from samples, and is more
+generally accessible.
+This reduction relies on a non-linear transformation of samples from such an
+intersection of halfspaces (i.e. a simplex) to samples which are approximately
+from a linearly transformed product distribution. Through this transformation
+of samples, which can be done efficiently, one can then use an ICA algorithm to
+recover the vertices of the intersection of halfspaces.
+Finally, we again use ICA as an algorithmic primitive to construct an
+efficient solution to the widely-studied problem of learning the parameters of
+a Gaussian mixture model. Our algorithm again transforms samples from a
+Gaussian mixture model into samples which fit into the ICA model and, when
+processed by an ICA algorithm, result in recovery of the mixture parameters.
+Our algorithm is effective even when the number of Gaussians in the mixture
+grows polynomially with the ambient dimension
+",1,0,0,0,0,0
+14134,Modules Over the Ring of Ponderation functions with Applications to a Class of Integral Operators,"  In this paper we introduce new modules over the ring of ponderation
+functions, so we recover old results in harmonic analysis from the side of ring
+theory.
+Moreover, we prove that Laplace transform, Fourier transform and Hankel
+transform generate some kind of modules over the ring of ponderation functions.
+",0,0,1,0,0,0
+14135,Strict monotonicity of principal eigenvalues of elliptic operators in $\mathbb{R}^d$ and risk-sensitive control,"  This paper studies the eigenvalue problem on $\mathbb{R}^d$ for a class of
+second order, elliptic operators of the form $\mathscr{L} =
+a^{ij}\partial_{x_i}\partial_{x_j} + b^{i}\partial_{x_i} + f$, associated with
+non-degenerate diffusions. We show that strict monotonicity of the principal
+eigenvalue of the operator with respect to the potential function $f$ fully
+characterizes the ergodic properties of the associated ground state diffusion,
+and the unicity of the ground state, and we present a comprehensive study of
+the eigenvalue problem from this point of view. This allows us to extend or
+strengthen various results in the literature for a class of viscous
+Hamilton-Jacobi equations of ergodic type with smooth coefficients to equations
+with measurable drift and potential. In addition, we establish the strong
+duality for the equivalent infinite dimensional linear programming formulation
+of these ergodic control problems. We also apply these results to the study of
+the infinite horizon risk-sensitive control problem for diffusions, and
+establish existence of optimal Markov controls, verification of optimality
+results, and the continuity of the controlled principal eigenvalue with respect
+to stationary Markov controls.
+",0,0,1,0,0,0
+14136,Best Practices for Applying Deep Learning to Novel Applications,"  This report is targeted to groups who are subject matter experts in their
+application but deep learning novices. It contains practical advice for those
+interested in testing the use of deep neural networks on applications that are
+novel for deep learning. We suggest making your project more manageable by
+dividing it into phases. For each phase this report contains numerous
+recommendations and insights to assist novice practitioners.
+",1,0,0,0,0,0
+14137,Precision Interfaces,"  Building interactive tools to support data analysis is hard because it is not
+always clear what to build and how to build it. To address this problem, we
+present Precision Interfaces, a semi-automatic system to generate task-specific
+data analytics interfaces. Precision Interface can turn a log of executed
+programs into an interface, by identifying micro-variations between the
+programs and mapping them to interface components. This paper focuses on SQL
+query logs, but we can generalize the approach to other languages. Our system
+operates in two steps: it first build an interaction graph, which describes how
+the queries can be transformed into each other. Then, it finds a set of UI
+components that covers a maximal number of transformations. To restrict the
+domain of changes to be detected, our system uses a domain-specific language,
+PILang. We give a full description of Precision Interface's components,
+showcase an early prototype on real program logs and discuss future research
+opportunities.
+",1,0,0,0,0,0
+14138,Gaussian Approximation of a Risk Model with Stationary Hawkes Arrivals of Claims,"  We consider a classical risk process with arrival of claims following a
+stationary Hawkes process. We study the asymptotic regime when the premium rate
+and the baseline intensity of the claims arrival process are large, and claim
+size is small. The main goal of this article is to establish a diffusion
+approximation by verifying a functional central limit theorem of this model and
+to compute both the finite-time and infinite-time horizon ruin probabilities.
+Numerical results will also be given.
+",0,0,0,0,0,1
+14139,Data Modelling for the Evaluation of Virtualized Network Functions Resource Allocation Algorithms,"  To conduct a more realistic evaluation on Virtualized Network Functions
+resource allocation algorithms, researches needed data on: (1) potential NFs
+chains (policies), (2) traffic flows passing through these NFs chains, (3) how
+the dynamic traffic changes affect the NFs (scale out/in) and (4) different
+data center architectures for the NFC. However, there are no publicly available
+real data sets on NF chains and traffic that pass through NF chains. Therefore
+we have used data from previous empirical analyses and made some assumptions to
+derive the required data to evaluate resource allocation algorithms for VNFs.
+We developed four programs to model the gathered data and generate the required
+data. All gathered data and data modelling programs are publicly available at
+github repository.
+",1,0,0,0,0,0
+14140,Understanding Negations in Information Processing: Learning from Replicating Human Behavior,"  Information systems experience an ever-growing volume of unstructured data,
+particularly in the form of textual materials. This represents a rich source of
+information from which one can create value for people, organizations and
+businesses. For instance, recommender systems can benefit from automatically
+understanding preferences based on user reviews or social media. However, it is
+difficult for computer programs to correctly infer meaning from narrative
+content. One major challenge is negations that invert the interpretation of
+words and sentences. As a remedy, this paper proposes a novel learning strategy
+to detect negations: we apply reinforcement learning to find a policy that
+replicates the human perception of negations based on an exogenous response,
+such as a user rating for reviews. Our method yields several benefits, as it
+eliminates the former need for expensive and subjective manual labeling in an
+intermediate stage. Moreover, the inferred policy can be used to derive
+statistical inferences and implications regarding how humans process and act on
+negations.
+",1,0,0,1,0,0
+14141,A study of sliding motion of a solid body on a rough surface with asymmetric friction,"  Recent studies show interest in materials with asymmetric friction forces. We
+investigate terminal motion of a solid body with circular contact area. We
+assume that friction forces are asymmetric orthotropic. Two cases of pressure
+distribution are analyzed: Hertz and Boussinesq laws. Equations for friction
+force and moment are formulated and solved for these cases. Numer- ical results
+show significant impact of the asymmetry of friction on the motion. Our results
+can be used for more accurate prediction of contact behavior of bodies made
+from new materials with asymmetric surface textures.
+",0,1,0,0,0,0
+14142,Fisher information matrix of binary time series,"  A common approach to analyzing categorical correlated time series data is to
+fit a generalized linear model (GLM) with past data as covariate inputs. There
+remain challenges to conducting inference for short time series length. By
+treating the historical data as covariate inputs, standard errors of estimates
+of GLM parameters computed using the empirical Fisher information do not fully
+account the auto-correlation in the data. To overcome this serious limitation,
+we derive the exact conditional Fisher information matrix of a general logistic
+autoregressive model with endogenous covariates for any series length $T$.
+Moreover, we also develop an iterative computational formula that allows for
+relatively easy implementation of the proposed estimator. Our simulation
+studies show that confidence intervals derived using the exact Fisher
+information matrix tend to be narrower than those utilizing the empirical
+Fisher information matrix while maintaining type I error rates at or below
+nominal levels. Further, we establish that the exact Fisher information matrix
+approaches, as T tends to infinity, the asymptotic Fisher information matrix
+previously derived for binary time series data. The developed exact conditional
+Fisher information matrix is applied to time-series data on respiratory rate
+among a cohort of expectant mothers where it is found to provide narrower
+confidence intervals for functionals of scientific interest and lead to greater
+statistical power when compared to the empirical Fisher information matrix.
+",0,0,1,1,0,0
+14143,A Fourier analytic approach to inhomogeneous Diophantine approximation,"  In this paper, we study inhomogeneous Diophantine approximation with rational
+numbers of reduced form. The central object to study is the set $W(f,\theta)$
+as follows, \begin{eqnarray*} \left\{x\in [0,1]:\left
+|x-\frac{m+\theta(n)}{n}\right|<\frac{f(n)}{n}\text{ for infinitely many
+coprime pairs of numbers } m,n\right\}, \end{eqnarray*} where
+$\{f(n)\}_{n\in\mathbb{N}}$ and $\{\theta(n)\}_{n\in\mathbb{N}}$ are sequences
+of real numbers in $[0,1/2]$. We will completely determine the Hausdorff
+dimension of $W(f,\theta)$ in terms of $f$ and $\theta$. As a by-product, we
+also obtain a new sufficient condition for $W(f,\theta)$ to have full Lebesgue
+measure and this result is closely related to the study of \ds with extra
+conditions.
+",0,0,1,0,0,0
+14144,Screening in perturbative approaches to LSS,"  A specific value for the cosmological constant, \Lambda, can account for
+late-time cosmic acceleration. However, motivated by the so-called cosmological
+constant problem(s), several alternative mechanisms have been explored. To
+date, a host of well-studied dynamical dark energy and modified gravity models
+exists. Going beyond \Lambda CDM often comes with additional degrees of freedom
+(dofs). For these to pass existing observational tests, an efficient screening
+mechanism must be in place. The linear and quasi-linear regimes of structure
+formation are ideal probes of such dofs and can capture the onset of screening.
+We propose here a semi-phenomenological treatment to account for screening
+dynamics on LSS observables, with special emphasis on Vainshtein-type
+screening.
+",0,1,0,0,0,0
+14145,Thin films with precisely engineered nanostructures,"  Synthesis of rationally designed nanostructured materials with optimized
+mechanical properties, e.g., high strength with considerable ductility,
+requires rigorous control of diverse microstructural parameters including the
+mean size, size dispersion and spatial distribution of grains. However,
+currently available synthesis techniques can seldom satisfy these requirements.
+Here, we report a new methodology to synthesize thin films with unprecedented
+microstructural control via systematic, in-situ seeding of nanocrystals into
+amorphous precursor films. When the amorphous films are subsequently
+crystallized by thermal annealing, the nanocrystals serve as preferential grain
+nucleation sites and control their microstructure. We demonstrate the
+capability of this approach by precisely tailoring the size, geometry and
+spatial distribution of nanostructured grains in structural (TiAl) as well as
+functional (TiNi) thin films. The approach, which is applicable to a broad
+class of metallic alloys and ceramics, enables explicit microstructural control
+of thin film materials for a wide spectrum of applications.
+",0,1,0,0,0,0
+14146,End-to-End Network Delay Guarantees for Real-Time Systems using SDN,"  We propose a novel framework that reduces the management and integration
+overheads for real-time network flows by leveraging the capabilities
+(especially global visibility and management) of software-defined networking
+(SDN) architectures. Given the specifications of flows that must meet hard
+real-time requirements, our framework synthesizes paths through the network and
+associated switch configurations - to guarantee that these flows meet their
+end-to-end timing requirements. In doing so, our framework makes SDN
+architectures ""delay-aware"" - remember that SDN is otherwise not able to reason
+about delays. Hence, it is easier to use such architectures in safety-critical
+and other latency-sensitive applications. We demonstrate our principles as well
+as the feasibility of our approach using both - exhaustive simulations as well
+as experiments using real hardware switches.
+",1,0,0,0,0,0
+14147,Efficient cold outflows driven by cosmic rays in high redshift galaxies and their global effects on the IGM,"  We present semi-analytical models of galactic outflows in high redshift
+galaxies driven by both hot thermal gas and non-thermal cosmic rays. Thermal
+pressure alone may not sustain a large scale outflow in low mass galaxies (i.e
+$M\sim 10^8$~M$_\odot$), in the presence of supernovae (SNe) feedback with
+large mass loading. We show that inclusion of cosmic ray pressure allows
+outflow solutions even in these galaxies. In massive galaxies for the same
+energy efficiency, cosmic ray driven winds can propagate to larger distances
+compared to pure thermally driven winds. On an average gas in the cosmic ray
+driven winds has a lower temperature which could aid detecting it through
+absorption lines in the spectra of background sources. Using our constrained
+semi-analytical models of galaxy formation (that explains the observed UV
+luminosity functions of galaxies) we study the influence of cosmic ray driven
+winds on the properties of the intergalactic medium (IGM) at different
+redshifts. In particular, we study the volume filling factor, average
+metallicity, cosmic ray and magnetic field energy densities for models invoking
+atomic cooled and molecular cooled halos. We show that the cosmic rays in the
+IGM could have enough energy that can be transferred to the thermal gas in
+presence of magnetic fields to influence the thermal history of the
+intergalactic medium. The significant volume filling and resulting strength of
+IGM magnetic fields can also account for recent $\gamma$-ray observations of
+blazars.
+",0,1,0,0,0,0
+14148,On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers,"  Learning algorithms that learn linear models often have high representation
+bias on real-world problems. In this paper, we show that this representation
+bias can be greatly reduced by discretization. Discretization is a common
+procedure in machine learning that is used to convert a quantitative attribute
+into a qualitative one. It is often motivated by the limitation of some
+learners to qualitative data. Discretization loses information, as fewer
+distinctions between instances are possible using discretized data relative to
+undiscretized data. In consequence, where discretization is not essential, it
+might appear desirable to avoid it. However, it has been shown that
+discretization often substantially reduces the error of the linear generative
+Bayesian classifier naive Bayes. This motivates a systematic study of the
+effectiveness of discretizing quantitative attributes for other linear
+classifiers. In this work, we study the effect of discretization on the
+performance of linear classifiers optimizing three distinct discriminative
+objective functions --- logistic regression (optimizing negative
+log-likelihood), support vector classifiers (optimizing hinge loss) and a
+zero-hidden layer artificial neural network (optimizing mean-square-error). We
+show that discretization can greatly increase the accuracy of these linear
+discriminative learners by reducing their representation bias, especially on
+big datasets. We substantiate our claims with an empirical study on $42$
+benchmark datasets.
+",1,0,0,0,0,0
+14149,Kustaanheimo-Stiefel transformation with an arbitrary defining vector,"  Kustaanheimo-Stiefel (KS) transformation depends on the choice of some
+preferred direction in the Cartesian 3D space. This choice, seldom explicitly
+mentioned, amounts typically to the direction of the first or the third
+coordinate axis in celestial mechanics and atomic physics, respectively. The
+present work develops a canonical KS transformation with an arbitrary preferred
+direction, indicated by what we call a defining vector. Using a mix of vector
+and quaternion algebra, we formulate the transformation in a reference frame
+independent manner. The link between the oscillator and Keplerian first
+integrals is given. As an example of the present formulation, the Keplerian
+motion in a rotating frame is re-investigated.
+",0,0,1,0,0,0
+14150,A dynamic game approach to distributionally robust safety specifications for stochastic systems,"  This paper presents a new safety specification method that is robust against
+errors in the probability distribution of disturbances. Our proposed
+distributionally robust safe policy maximizes the probability of a system
+remaining in a desired set for all times, subject to the worst possible
+disturbance distribution in an ambiguity set. We propose a dynamic game
+formulation of constructing such policies and identify conditions under which a
+non-randomized Markov policy is optimal. Based on this existence result, we
+develop a practical design approach to safety-oriented stochastic controllers
+with limited information about disturbance distributions. This control method
+can be used to minimize another cost function while ensuring safety in a
+probabilistic way. However, an associated Bellman equation involves
+infinite-dimensional minimax optimization problems since the disturbance
+distribution may have a continuous density. To resolve computational issues, we
+propose a duality-based reformulation method that converts the
+infinite-dimensional minimax problem into a semi-infinite program that can be
+solved using existing convergent algorithms. We prove that there is no duality
+gap, and that this approach thus preserves optimality. The results of numerical
+tests confirm that the proposed method is robust against distributional errors
+in disturbances, while a standard stochastic safety specification tool is not.
+",1,0,1,0,0,0
+14151,Entity Linking for Queries by Searching Wikipedia Sentences,"  We present a simple yet effective approach for linking entities in queries.
+The key idea is to search sentences similar to a query from Wikipedia articles
+and directly use the human-annotated entities in the similar sentences as
+candidate entities for the query. Then, we employ a rich set of features, such
+as link-probability, context-matching, word embeddings, and relatedness among
+candidate entities as well as their related entities, to rank the candidates
+under a regression based framework. The advantages of our approach lie in two
+aspects, which contribute to the ranking process and final linking result.
+First, it can greatly reduce the number of candidate entities by filtering out
+irrelevant entities with the words in the query. Second, we can obtain the
+query sensitive prior probability in addition to the static link-probability
+derived from all Wikipedia articles. We conduct experiments on two benchmark
+datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ
+dataset. Experimental results show that our method outperforms state-of-the-art
+systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ
+dataset.
+",1,0,0,0,0,0
+14152,Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest,"  Reinforcement Learning AI commonly uses reward/penalty signals that are
+objective and explicit in an environment -- e.g. game score, completion time,
+etc. -- in order to learn the optimal strategy for task performance. However,
+Human-AI interaction for such AI agents should include additional reinforcement
+that is implicit and subjective -- e.g. human preferences for certain AI
+behavior -- in order to adapt the AI behavior to idiosyncratic human
+preferences. Such adaptations would mirror naturally occurring processes that
+increase trust and comfort during social interactions. Here, we show how a
+hybrid brain-computer-interface (hBCI), which detects an individual's level of
+interest in objects/events in a virtual environment, can be used to adapt the
+behavior of a Deep Reinforcement Learning AI agent that is controlling a
+virtual autonomous vehicle. Specifically, we show that the AI learns a driving
+strategy that maintains a safe distance from a lead vehicle, and most novelly,
+preferentially slows the vehicle when the human passengers of the vehicle
+encounter objects of interest. This adaptation affords an additional 20\%
+viewing time for subjectively interesting objects. This is the first
+demonstration of how an hBCI can be used to provide implicit reinforcement to
+an AI agent in a way that incorporates user preferences into the control
+system.
+",1,0,0,1,0,0
+14153,Relational recurrent neural networks,"  Memory-based neural networks model temporal data by leveraging an ability to
+remember information for long periods. It is unclear, however, whether they
+also have an ability to perform complex relational reasoning with the
+information they remember. Here, we first confirm our intuitions that standard
+memory architectures may struggle at tasks that heavily involve an
+understanding of the ways in which entities are connected -- i.e., tasks
+involving relational reasoning. We then improve upon these deficits by using a
+new memory module -- a \textit{Relational Memory Core} (RMC) -- which employs
+multi-head dot product attention to allow memories to interact. Finally, we
+test the RMC on a suite of tasks that may profit from more capable relational
+reasoning across sequential information, and show large gains in RL domains
+(e.g. Mini PacMan), program evaluation, and language modeling, achieving
+state-of-the-art results on the WikiText-103, Project Gutenberg, and GigaWord
+datasets.
+",0,0,0,1,0,0
+14154,A Scalable Discrete-Time Survival Model for Neural Networks,"  There is currently great interest in applying neural networks to prediction
+tasks in medicine. It is important for predictive models to be able to use
+survival data, where each patient has a known follow-up time and
+event/censoring indicator. This avoids information loss when training the model
+and enables generation of predicted survival curves. In this paper, we describe
+a discrete-time survival model that is designed to be used with neural
+networks, which we refer to as Nnet-survival. The model is trained with the
+maximum likelihood method using minibatch stochastic gradient descent (SGD).
+The use of SGD enables rapid convergence and application to large datasets that
+do not fit in memory. The model is flexible, so that the baseline hazard rate
+and the effect of the input data on hazard probability can vary with follow-up
+time. It has been implemented in the Keras deep learning framework, and source
+code for the model and several examples is available online. We demonstrate the
+performance of the model on both simulated and real data and compare it to
+existing models Cox-nnet and Deepsurv.
+",0,0,0,1,0,0
+14155,A driven-dissipative spin chain model based on exciton-polariton condensates,"  An infinite chain of driven-dissipative condensate spins with uniform
+nearest-neighbor coherent coupling is solved analytically and investigated
+numerically. Above a critical occupation threshold the condensates undergo
+spontaneous spin bifurcation (becoming magnetized) forming a binary chain of
+spin-up or spin-down states. Minimization of the bifurcation threshold
+determines the magnetic order as a function of the coupling strength. This
+allows control of multiple magnetic orders via adiabatic (slow ramping of)
+pumping. In addition to ferromagnetic and anti-ferromagnetic ordered states we
+show the formation of a paired-spin ordered state $\left|\dots \uparrow
+\uparrow \downarrow \downarrow \dots \right. \rangle$ as a consequence of the
+phase degree of freedom between condensates.
+",0,1,0,0,0,0
+14156,Projected Primal-Dual Gradient Flow of Augmented Lagrangian with Application to Distributed Maximization of the Algebraic Connectivity of a Network,"  In this paper, a projected primal-dual gradient flow of augmented Lagrangian
+is presented to solve convex optimization problems that are not necessarily
+strictly convex. The optimization variables are restricted by a convex set with
+computable projection operation on its tangent cone as well as equality
+constraints. As a supplement of the analysis in
+\cite{niederlander2016distributed}, we show that the projected dynamical system
+converges to one of the saddle points and hence finding an optimal solution.
+Moreover, the problem of distributedly maximizing the algebraic connectivity of
+an undirected network by optimizing the port gains of each nodes (base
+stations) is considered. The original semi-definite programming (SDP) problem
+is relaxed into a nonlinear programming (NP) problem that will be solved by the
+aforementioned projected dynamical system. Numerical examples show the
+convergence of the aforementioned algorithm to one of the optimal solutions.
+The effect of the relaxation is illustrated empirically with numerical
+examples. A methodology is presented so that the number of iterations needed to
+reach the equilibrium is suppressed. Complexity per iteration of the algorithm
+is illustrated with numerical examples.
+",0,0,1,0,0,0
+14157,A Short Note on Almost Sure Convergence of Bayes Factors in the General Set-Up,"  Although there is a significant literature on the asymptotic theory of Bayes
+factor, the set-ups considered are usually specialized and often involves
+independent and identically distributed data. Even in such specialized cases,
+mostly weak consistency results are available. In this article, for the first
+time ever, we derive the almost sure convergence theory of Bayes factor in the
+general set-up that includes even dependent data and misspecified models.
+Somewhat surprisingly, the key to the proof of such a general theory is a
+simple application of a result of Shalizi (2009) to a well-known identity
+satisfied by the Bayes factor.
+",0,0,1,1,0,0
+14158,Rainbow matchings in properly-coloured multigraphs,"  Aharoni and Berger conjectured that in any bipartite multigraph that is
+properly edge-coloured by $n$ colours with at least $n + 1$ edges of each
+colour there must be a matching that uses each colour exactly once. In this
+paper we consider the same question without the bipartiteness assumption. We
+show that in any multigraph with edge multiplicities $o(n)$ that is properly
+edge-coloured by $n$ colours with at least $n + o(n)$ edges of each colour
+there must be a matching of size $n-O(1)$ that uses each colour at most once.
+",1,0,0,0,0,0
+14159,Recursive computation of the invariant distribution of Markov and Feller processes,"  This paper provides a general and abstract approach to approximate ergodic
+regimes of Markov and Feller processes. More precisely, we show that the
+recursive algorithm presented in Lamberton & Pages (2002) and based on
+simulation algorithms of stochastic schemes with decreasing step can be used to
+build invariant measures for general Markov and Feller processes. We also
+propose applications in three different configurations: Approximation of Markov
+switching Brownian diffusion ergodic regimes using Euler scheme, approximation
+of Markov Brownian diffusion ergodic regimes with Milstein scheme and
+approximation of general diffusions with jump components ergodic regimes.
+",0,0,1,0,0,0
+14160,Secondary resonances and the boundary of effective stability of Trojan motions,"  One of the most interesting features in the libration domain of co-orbital
+motions is the existence of secondary resonances. For some combinations of
+physical parameters, these resonances occupy a large fraction of the domain of
+stability and rule the dynamics within the stable tadpole region. In this work,
+we present an application of a recently introduced `basic Hamiltonian model' Hb
+for Trojan dynamics, in Paez and Efthymiopoulos (2015), Paez, Locatelli and
+Efthymiopoulos (2016): we show that the inner border of the secondary resonance
+of lowermost order, as defined by Hb, provides a good estimation of the region
+in phase-space for which the orbits remain regular regardless the orbital
+parameters of the system. The computation of this boundary is straightforward
+by combining a resonant normal form calculation in conjunction with an
+`asymmetric expansion' of the Hamiltonian around the libration points, which
+speeds up convergence. Applications to the determination of the effective
+stability domain for exoplanetary Trojans (planet-sized objects or asteroids)
+which may accompany giant exoplanets are discussed.
+",0,1,0,0,0,0
+14161,Qualitative Measurements of Policy Discrepancy for Return-based Deep Q-Network,"  The deep Q-network (DQN) and return-based reinforcement learning are two
+promising algorithms proposed in recent years. DQN brings advances to complex
+sequential decision problems, while return-based algorithms have advantages in
+making use of sample trajectories. In this paper, we propose a general
+framework to combine DQN and most of the return-based reinforcement learning
+algorithms, named R-DQN. We show the performance of traditional DQN can be
+improved effectively by introducing return-based reinforcement learning. In
+order to further improve the R-DQN, we design a strategy with two measurements
+which can qualitatively measure the policy discrepancy. Moreover, we give the
+two measurements' bounds in the proposed R-DQN framework. We show that
+algorithms with our strategy can accurately express the trace coefficient and
+achieve a better approximation to return. The experiments, conducted on several
+representative tasks from the OpenAI Gym library, validate the effectiveness of
+the proposed measurements. The results also show that the algorithms with our
+strategy outperform the state-of-the-art methods.
+",0,0,0,1,0,0
+14162,Probabilistic Constraints on the Mass and Composition of Proxima b,"  Recent studies regarding the habitability, observability, and possible
+orbital evolution of the indirectly detected exoplanet Proxima b have mostly
+assumed a planet with $M \sim 1.3$ $M_\oplus$, a rocky composition, and an
+Earth-like atmosphere or none at all. In order to assess these assumptions, we
+use previous studies of the radii, masses, and compositions of super-Earth
+exoplanets to probabilistically constrain the mass and radius of Proxima b,
+assuming an isotropic inclination probability distribution. We find it is ~90%
+likely that the planet's density is consistent with a rocky composition;
+conversely, it is at least 10% likely that the planet has a significant amount
+of ice or an H/He envelope. If the planet does have a rocky composition, then
+we find expectation values and 95% confidence intervals of
+$\left<M\right>_\text{rocky} = 1.63_{-0.72}^{+1.66}$ $M_\oplus$ for its mass
+and $\left<R\right>_\text{rocky} = 1.07_{-0.31}^{+0.38}$ $R_\oplus$ for its
+radius.
+",0,1,0,0,0,0
+14163,"Explicit Salem sets, Fourier restriction, and metric Diophantine approximation in the $p$-adic numbers","  We exhibit the first explicit examples of Salem sets in $\mathbb{Q}_p$ of
+every dimension $0 < \alpha < 1$ by showing that certain sets of
+well-approximable $p$-adic numbers are Salem sets. We construct measures
+supported on these sets that satisfy essentially optimal Fourier decay and
+upper regularity conditions, and we observe that these conditions imply that
+the measures satisfy strong Fourier restriction inequalities. We also partially
+generalize our results to higher dimensions. Our results extend theorems of
+Kaufman, Papadimitropoulos, and Hambrook from the real to the $p$-adic setting.
+",0,0,1,0,0,0
+14164,Tunneling of the hard-core model on finite triangular lattices,"  We consider the hard-core model on finite triangular lattices with Metropolis
+dynamics. Under suitable conditions on the triangular lattice dimensions, this
+interacting particle system has three maximum-occupancy configurations and we
+investigate its high-fugacity behavior by studying tunneling times, i.e., the
+first hitting times between between these maximum-occupancy configurations, and
+the mixing time. The proof method relies on the analysis of the corresponding
+state space using geometrical and combinatorial properties of the hard-core
+configurations on finite triangular lattices, in combination with known results
+for first hitting times of Metropolis Markov chains in the equivalent
+zero-temperature limit. In particular, we show how the order of magnitude of
+the expected tunneling times depends on the triangular lattice dimensions in
+the low-temperature regime and prove the asymptotic exponentiality of the
+rescaled tunneling time leveraging the intrinsic symmetry of the state space.
+",0,1,1,0,0,0
+14165,Spectral State Compression of Markov Processes,"  Model reduction of the Markov process is a basic problem in modeling
+state-transition systems. Motivated by the state aggregation approach rooted in
+control theory, we study the statistical state compression of a finite-state
+Markov chain from empirical trajectories. Through the lens of spectral
+decomposition, we study the rank and features of Markov processes, as well as
+properties like representability, aggregatability, and lumpability. We develop
+a class of spectral state compression methods for three tasks: (1) estimate the
+transition matrix of a low-rank Markov model, (2) estimate the leading subspace
+spanned by Markov features, and (3) recover latent structures of the state
+space like state aggregation and lumpable partition. The proposed methods
+provide an unsupervised learning framework for identifying Markov features and
+clustering states. We provide upper bounds for the estimation errors and nearly
+matching minimax lower bounds. Numerical studies are performed on synthetic
+data and a dataset of New York City taxi trips.
+",0,0,0,1,0,0
+14166,"Multi-band characterization of the hot Jupiters: WASP-5b, WASP-44b and WASP-46b","  We have carried out a campaign to characterize the hot Jupiters WASP-5b,
+WASP-44b and WASP-46b using multiband photometry collected at the
+Observatório do Pico Dos Dias in Brazil. We have determined the planetary
+physical properties and new transit ephemerides for these systems. The new
+orbital parameters and physical properties of WASP-5b and WASP-44b are
+consistent with previous estimates. In the case of WASP-46b, there is some
+quota of disagreement between previous results. We provide a new determination
+of the radius of this planet and help clarify the previous differences. We also
+studied the transit time variations including our new measurements. No clear
+variation from a linear trend was found for the systems WASP-5b and WASP-44b.
+In the case of WASP-46b, we found evidence of deviations indicating the
+presence of a companion but statistical analysis of the existing times points
+to a signal due to the sampling rather than a new planet. Finally, we studied
+the fractional radius variation as a function of wavelength for these systems.
+The broad-band spectrums of WASP-5b and WASP-44b are mostly flat. In the case
+of WASP-46b we found a trend, but further measurements are necessary to confirm
+this finding.
+",0,1,0,0,0,0
+14167,Non-Fermi liquid at the FFLO quantum critical point,"  When a 2D superconductor is subjected to a strong in-plane magnetic field,
+Zeeman polarization of the Fermi surface can give rise to inhomogeneous FFLO
+order with a spatially modulated gap. Further increase of the magnetic field
+eventually drives the system into a normal metal state. Here, we perform a
+renormalization group analysis of this quantum phase transition, starting from
+an appropriate low-energy theory recently introduced by Piazza et al. (Ref.1).
+We compute one-loop flow equations within the controlled dimensional
+regularization scheme with fixed dimension of Fermi surface, expanding in
+$\epsilon = 5/2 - d$. We find a new stable non-Fermi liquid fixed point and
+discuss its critical properties. One of the most interesting aspects of the
+FFLO non-Fermi liquid scenario is that the quantum critical point is
+potentially naked, with the scaling regime observable down to arbitrary low
+temperatures. In order to study this possibility, we perform a general analysis
+of competing instabilities, which suggests that only charge density wave order
+is enhanced in the vicinity of the quantum critical point.
+",0,1,0,0,0,0
+14168,Unsupervised Ensemble Regression,"  Consider a regression problem where there is no labeled data and the only
+observations are the predictions $f_i(x_j)$ of $m$ experts $f_{i}$ over many
+samples $x_j$. With no knowledge on the accuracy of the experts, is it still
+possible to accurately estimate the unknown responses $y_{j}$? Can one still
+detect the least or most accurate experts? In this work we propose a framework
+to study these questions, based on the assumption that the $m$ experts have
+uncorrelated deviations from the optimal predictor. Assuming the first two
+moments of the response are known, we develop methods to detect the best and
+worst regressors, and derive U-PCR, a novel principal components approach for
+unsupervised ensemble regression. We provide theoretical support for U-PCR and
+illustrate its improved accuracy over the ensemble mean and median on a variety
+of regression problems.
+",1,0,0,1,0,0
+14169,Accurate approximation of the distributions of the 3D Poisson-Voronoi typical cell geometrical features,"  Although Poisson-Voronoi diagrams have interesting mathematical properties,
+there is still much to discover about the geometrical properties of its grains.
+Through simulations, many authors were able to obtain numerical approximations
+of the moments of the distributions of more or less all geometrical
+characteristics of the grain. Furthermore, many proposals on how to get close
+parametric approximations to the real distributions were put forward by several
+authors. In this paper we show that exploiting the scaling property of the
+underlying Poisson process, we are able to derive the distribution of the main
+geometrical features of the grain for every value of the intensity parameter.
+Moreover, we use a sophisticated simulation program to construct a close Monte
+Carlo based approximation for the distributions of interest. Using this, we
+also determine the closest approximating distributions within the mentioned
+frequently used parametric classes of distributions and conclude that these
+approximations can be quite accurate.
+",0,0,0,1,0,0
+14170,Flux-Stabilized Majorana Zero Modes in Coupled One-Dimensional Fermi Wires,"  One promising avenue to study one-dimensional ($1$D) topological phases is to
+realize them in synthetic materials such as cold atomic gases. Intriguingly, it
+is possible to realize Majorana boundary modes in a $1$D number-conserving
+system consisting of two fermionic chains coupled only by pair-hopping
+processes. It is commonly believed that significant interchain single-particle
+tunneling necessarily destroys these Majorana modes, as it spoils the
+$\mathbb{Z}_2$ fermion parity symmetry that protects them. In this Letter, we
+present a new mechanism to overcome this obstacle, by piercing a (synthetic)
+magnetic $\pi$-flux through each plaquette of the Fermi ladder. Using
+bosonization, we show that in this case there exists an exact leg-interchange
+symmetry that is robust to interchain hopping, and acts as fermion parity at
+long wavelengths. We utilize density matrix renormalization group and exact
+diagonalization to verify that the resulting model exhibits Majorana boundary
+modes up to large single-particle tunnelings, comparable to the intrachain
+hopping strength. Our work highlights the unusual impacts of different
+topologically trivial band structures on these interaction-driven topological
+phases, and identifies a distinct route to stabilizing Majorana boundary modes
+in $1$D fermionic ladders.
+",0,1,0,0,0,0
+14171,Nonlinear Kalman Filtering for Censored Observations,"  The use of Kalman filtering, as well as its nonlinear extensions, for the
+estimation of system variables and parameters has played a pivotal role in many
+fields of scientific inquiry where observations of the system are restricted to
+a subset of variables. However in the case of censored observations, where
+measurements of the system beyond a certain detection point are impossible, the
+estimation problem is complicated. Without appropriate consideration, censored
+observations can lead to inaccurate estimates. Motivated by the work of [1], we
+develop a modified version of the extended Kalman filter to handle the case of
+censored observations in nonlinear systems. We validate this methodology in a
+simple oscillator system first, showing its ability to accurately reconstruct
+state variables and track system parameters when observations are censored.
+Finally, we utilize the nonlinear censored filter to analyze censored datasets
+from patients with hepatitis C and human immunodeficiency virus.
+",0,0,1,1,0,0
+14172,Advances in Atomic Resolution In Situ Environmental Transmission Electron Microscopy and 1 Angstrom Aberration Corrected In Situ Electron Microscopy,"  Advances in atomic resolution in situ environmental transmission electron
+microscopy for direct probing of gas-solid reactions, including at very high
+temperatures are described. In addition, recent developments of dynamic real
+time in situ studies at the Angstrom level using a hot stage in an aberration
+corrected environment are presented. In situ data from Pt and Pd nanoparticles
+on carbon with the corresponding FFT (optical diffractogram) illustrate an
+achieved resolution of 0.11 nm at 500 C and higher in a double aberration
+corrected TEM and STEM instrument employing a wider gap objective pole piece.
+The new results open up opportunities for dynamic studies of materials in an
+aberration corrected environment.
+",0,1,0,0,0,0
+14173,An improved high order finite difference method for non-conforming grid interfaces for the wave equation,"  This paper presents an extension of a recently developed high order finite
+difference method for the wave equation on a grid with non-conforming
+interfaces. The stability proof of the existing methods relies on the
+interpolation operators being norm-contracting, which is satisfied by the
+second and fourth order operators, but not by the sixth order operator. We
+construct new penalty terms to impose interface conditions such that the
+stability proof does not require the norm-contracting condition. As a
+consequence, the sixth order accurate scheme is also provably stable. Numerical
+experiments demonstrate the improved stability and accuracy property.
+",0,0,1,0,0,0
+14174,Refined estimates for simple blow-ups of the scalar curvature equation on S^n,"  In their work on a sharp compactness theorem for the Yamabe problem, Khuri,
+Marques and Schoen apply a refined blow-up analysis (what we call `second order
+blow-up argument' in this article) to obtain highly accurate approximate
+solutions for the Yamabe equation. As for the conformal scalar curvature
+equation on S^n with n > 3, we examine the second order blow-up argument and
+obtain refined estimate for a blow-up sequence near a simple blow-up point. The
+estimate involves local effect from the Taylor expansion of the scalar
+curvature function, global effect from other blow-up points, and the balance
+formula as expressed in the Pohozaev identity in an essential way.
+",0,0,1,0,0,0
+14175,"The discrete logarithm problem over prime fields: the safe prime case. The Smart attack, non-canonical lifts and logarithmic derivatives","  In this brief note we connect the discrete logarithm problem over prime
+fields in the safe prime case to the logarithmic derivative.
+",1,0,1,0,0,0
+14176,DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model,"  Convolutional neural networks (CNNs) can be applied to graph similarity
+matching, in which case they are called graph CNNs. Graph CNNs are attracting
+increasing attention due to their effectiveness and efficiency. However, the
+existing convolution approaches focus only on regular data forms and require
+the transfer of the graph or key node neighborhoods of the graph into the same
+fixed form. During this transfer process, structural information of the graph
+can be lost, and some redundant information can be incorporated. To overcome
+this problem, we propose the disordered graph convolutional neural network
+(DGCNN) based on the mixed Gaussian model, which extends the CNN by adding a
+preprocessing layer called the disordered graph convolutional layer (DGCL). The
+DGCL uses a mixed Gaussian function to realize the mapping between the
+convolution kernel and the nodes in the neighborhood of the graph. The output
+of the DGCL is the input of the CNN. We further implement a
+backward-propagation optimization process of the convolutional layer by which
+we incorporate the feature-learning model of the irregular node neighborhood
+structure into the network. Thereafter, the optimization of the convolution
+kernel becomes part of the neural network learning process. The DGCNN can
+accept arbitrary scaled and disordered neighborhood graph structures as the
+receptive fields of CNNs, which reduces information loss during graph
+transformation. Finally, we perform experiments on multiple standard graph
+datasets. The results show that the proposed method outperforms the
+state-of-the-art methods in graph classification and retrieval.
+",1,0,0,1,0,0
+14177,Profile Estimation for Partial Functional Partially Linear Single-Index Model,"  This paper studies a \textit{partial functional partially linear single-index
+model} that consists of a functional linear component as well as a linear
+single-index component. This model generalizes many well-known existing models
+and is suitable for more complicated data structures. However, its estimation
+inherits the difficulties and complexities from both components and makes it a
+challenging problem, which calls for new methodology. We propose a novel
+profile B-spline method to estimate the parameters by approximating the unknown
+nonparametric link function in the single-index component part with B-spline,
+while the linear slope function in the functional component part is estimated
+by the functional principal component basis. The consistency and asymptotic
+normality of the parametric estimators are derived, and the global convergence
+of the proposed estimator of the linear slope function is also established.
+More excitingly, the latter convergence is optimal in the minimax sense. A
+two-stage procedure is implemented to estimate the nonparametric link function,
+and the resulting estimator possesses the optimal global rate of convergence.
+Furthermore, the convergence rate of the mean squared prediction error for a
+predictor is also obtained. Empirical properties of the proposed procedures are
+studied through Monte Carlo simulations. A real data example is also analyzed
+to illustrate the power and flexibility of the proposed methodology.
+",0,0,1,1,0,0
+14178,Optimal $k$-Coverage Charging Problem,"  Wireless rechargeable sensor networks, consisting of sensor nodes with
+rechargeable batteries and mobile chargers to replenish their batteries, have
+gradually become a promising solution to the bottleneck of energy limitation
+that hinders the wide deployment of wireless sensor networks (WSN). In this
+paper, we focus on the mobile charger scheduling and path optimization scenario
+in which the $k$-coverage ability of a network system needs to be maintained.
+We formulate the optimal $k$-coverage charging problem of finding a feasible
+path for a mobile charger to charge a set of sensor nodes within their
+estimated charging time windows under the constraint of maintaining the
+$k$-coverage ability of the network system, with an objective of minimizing the
+energy consumption on traveling per tour. We show the hardness of the problem
+that even finding a feasible path for the trivial case of the problem is an
+NP-complete one with no polytime constant-factor approximation algorithm.
+",1,0,0,0,0,0
+14179,Facebook's gender divide,"  Online social media are information resources that can have a transformative
+power in society. While the Web was envisioned as an equalizing force that
+allows everyone to access information, the digital divide prevents large
+amounts of people from being present online. Online social media in particular
+are prone to gender inequality, an important issue given the link between
+social media use and employment. Understanding gender inequality in social
+media is a challenging task due to the necessity of data sources that can
+provide unbiased measurements across multiple countries. Here we show how the
+Facebook Gender Divide (FGD), a metric based on a dataset including more than
+1.4 Billion users in 217 countries, explains various aspects of worldwide
+gender inequality. Our analysis shows that the FGD encodes gender equality
+indices in education, health, and economic opportunity. We find network effects
+that suggest that using social media has an added value for women. Furthermore,
+we find that low values of the FGD precede the approach of countries towards
+economic gender equality. Our results suggest that online social networks,
+while suffering evident gender imbalance, may lower the barriers that women
+have to access informational resources and help to narrow the economic gender
+gap.
+",1,0,0,0,0,0
+14180,Knowing the past improves cooperation in the future,"  Cooperation is the cornerstone of human evolutionary success. Like no other
+species, we champion the sacrifice of personal benefits for the common good,
+and we work together to achieve what we are unable to achieve alone. Knowledge
+and information from past generations is thereby often instrumental in ensuring
+we keep cooperating rather than deteriorating to less productive ways of
+coexistence. Here we present a mathematical model based on evolutionary game
+theory that shows how using the past as the benchmark for evolutionary success,
+rather than just current performance, significantly improves cooperation in the
+future. Interestingly, the details of just how the past is taken into account
+play only second-order importance, whether it be a weighted average of past
+payoffs or just a single payoff value from the past. Cooperation is promoted
+because information from the past disables fast invasions of defectors, thus
+enhancing the long-term benefits of cooperative behavior.
+",1,0,0,0,1,0
+14181,BMO estimate of lacunary Fourier series on nonabelian discrete groups,"  We show that the classical equivalence between the BMO norm and the $L^2$
+norm of a lacunary Fourier series has an analogue on any discrete group $G$
+equipped with a conditionally negative function.
+",0,0,1,0,0,0
+14182,Multi-objective training of Generative Adversarial Networks with multiple discriminators,"  Recent literature has demonstrated promising results for training Generative
+Adversarial Networks by employing a set of discriminators, in contrast to the
+traditional game involving one generator against a single adversary. Such
+methods perform single-objective optimization on some simple consolidation of
+the losses, e.g. an arithmetic average. In this work, we revisit the
+multiple-discriminator setting by framing the simultaneous minimization of
+losses provided by different models as a multi-objective optimization problem.
+Specifically, we evaluate the performance of multiple gradient descent and the
+hypervolume maximization algorithm on a number of different datasets. Moreover,
+we argue that the previously proposed methods and hypervolume maximization can
+all be seen as variations of multiple gradient descent in which the update
+direction can be computed efficiently. Our results indicate that hypervolume
+maximization presents a better compromise between sample quality and
+computational cost than previous methods.
+",1,0,0,1,0,0
+14183,Application of Surface Coil for Nuclear Magnetic Resonance Studies of Semi-conducting Thin Films,"  We conduct a comprehensive set of tests of performance of surface coils used
+for nuclear magnetic resonance (NMR) study of quasi 2-dimensional samples. We
+report ${^{115} \rm{In}}$ and ${^{31} \rm{P}}$ NMR measurements on InP,
+semi-conducting thin substrate samples. Surface coils of both zig-zag
+meander-line and concentric spiral geometries were used. We compare reception
+sensitivity and signal-to-noise ratio (SNR) of NMR signal obtained by using
+surface-type coils to that obtained by standard solenoid-type coils. As
+expected, we find that surface-type coils provide better sensitivity for NMR
+study of thin films samples. Moreover, we compare the reception sensitivity of
+different types of the surface coils. We identify the optimal geometry of the
+surface coils for a given application and/or direction of the applied magnetic
+field.
+",0,1,0,0,0,0
+14184,A universal coarse K-theory,"  In this paper, we construct an equivariant coarse homology theory with values
+in the category of non-commutative motives of Blumberg, Gepner and Tabuada,
+with coefficients in any small additive category. Equivariant coarse K-theory
+is obtained from the latter by passing to global sections. The present
+construction extends joint work of the first named author with Engel,
+Kasprowski and Winges by promoting codomain of the equivariant coarse
+K-homology functor to non-commutative motives.
+",0,0,1,0,0,0
+14185,Parameters of Three Selected Model Galactic Potentials Based on the Velocities of Objects at Distances up to 200 kpc,"  This paper is a continuation of our recent paper devoted to refining the
+parameters of three component (bulge, disk, halo) axisymmetric model Galactic
+gravitational potentials differing by the expression for the dark matter halo
+using the velocities of distant objects. In all models the bulge and disk
+potentials are described by the Miyamoto-Nagai expressions. In our previous
+paper we used the Allen-Santill'an (I), Wilkinson--Evans (II), and
+Navarro-Frenk-White (III) models to describe the halo. In this paper we use a
+spherical logarithmic Binney potential (model IV), a Plummer sphere (model V),
+and a Hernquist potential (model VI) to describe the halo. A set of present-day
+observational data in the range of Galactocentric distances R from 0 to 200 kpc
+is used to refine the parameters of the listed models, which are employed most
+commonly at present. The model rotation curves are fitted to the observed
+velocities by taking into account the constraints on the local matter density
+and the vertical force . Model VI looks best among the three models considered
+here from the viewpoint of the achieved accuracy of fitting the model rotation
+curves to the measurements. This model is close to the Navarro-Frenk-White
+model III refined and considered best in our previous paper, which is shown
+using the integration of the orbits of two globular clusters, Lynga 7 and NGC
+5053, as an example.
+",0,1,0,0,0,0
+14186,Giant Thermal Conductivity Enhancement in Multilayer MoS2 under Highly Compressive Strain,"  Multilayer MoS2 possesses highly anisotropic thermal conductivities along
+in-plane and cross-plane directions that could hamper heat dissipation in
+electronics. With about 9% cross-plane compressive strain created by
+hydrostatic pressure in a diamond anvil cell, we observed about 12 times
+increase in the cross-plane thermal conductivity of multilayer MoS2. Our
+experimental and theoretical studies reveal that this drastic change arises
+from the greatly strengthened interlayer interaction and heavily modified
+phonon dispersions along cross-plane direction, with negligible contribution
+from electronic thermal conductivity, despite its enhancement of 4 orders of
+magnitude. The anisotropic thermal conductivity in the multilayer MoS2 at
+ambient environment becomes almost isotropic under highly compressive strain,
+effectively transitioning from 2D to 3D heat dissipation. This strain tuning
+approach also makes possible parallel tuning of structural, thermal and
+electrical properties, and can be extended to the whole family of 2D Van der
+Waals solids, down to two layer systems.
+",0,1,0,0,0,0
+14187,Interrogation of spline surfaces with application to isogeometric design and analysis of lattice-skin structures,"  A novel surface interrogation technique is proposed to compute the
+intersection of curves with spline surfaces in isogeometric analysis. The
+intersection points are determined in one-shot without resorting to a
+Newton-Raphson iteration or successive refinement. Surface-curve intersection
+requires usually the solution of a system of nonlinear equations. It is assumed
+that the surface is given in form of a spline, such as a NURBS, T-spline or
+Catmull-Clark subdivision surface, and is convertible into a collection of
+Bézier patches. First, a hierarchical bounding volume tree is used to
+efficiently identify the Bézier patches with a convex-hull intersecting the
+convex-hull of a given curve segment. For ease of implementation convex-hulls
+are approximated with k-dops (discrete orientation polytopes). Subsequently,
+the intersections of the identified Bézier patches with the curve segment are
+determined with a matrix-based implicit representation leading to the
+computation of a sequence of small singular value decompositions (SVDs). As an
+application of the developed interrogation technique the isogeometric design
+and analysis of lattice-skin structures is investigated. Current additive
+manufacturing technologies make it possible to produce up to metre size parts
+with designed geometric features reaching down to submillimetre scale. The skin
+is a spline surface that is usually created in a computer-aided design (CAD)
+system and the periodic lattice to be fitted consists of unit cells, each
+containing a small number of struts. The lattice-skin structure is generated by
+projecting selected lattice nodes onto the surface after determining the
+intersection of unit cell edges with the surface. For mechanical analysis, the
+skin is modelled as a Kirchhoff-Love thin-shell and the lattice as a
+pin-jointed truss. The two types of structures are coupled with a standard
+Lagrange multiplier approach.
+",1,0,0,0,0,0
+14188,On recursive computation of coprime factorizations of rational matrices,"  We propose general computational procedures based on descriptor state-space
+realizations to compute coprime factorizations of rational matrices with
+minimum degree denominators. Enhanced recursive pole dislocation techniques are
+developed, which allow to successively place all poles of the factors into a
+given ""good"" domain of the complex plane. The resulting McMillan degree of the
+denominator factor is equal to the number of poles lying in the complementary
+""bad"" region and therefore is minimal. The new pole dislocation techniques are
+employed to compute coprime factorizations with proper and stable factors of
+arbitrary improper rational matrices and coprime factorizations with inner
+denominators. The proposed algorithms work for arbitrary descriptor
+representations, regardless they are stabilizable or detectable.
+",1,0,1,0,0,0
+14189,On Dark Matter Interactions with the Standard Model through an Anomalous $Z'$,"  We study electroweak scale Dark Matter (DM) whose interactions with baryonic
+matter are mediated by a heavy anomalous $Z'$. We emphasize that when the DM is
+a Majorana particle, its low-velocity annihilations are dominated by loop
+suppressed annihilations into the gauge bosons, rather than by p-wave or
+chirally suppressed annihilations into the SM fermions. Because the $Z'$ is
+anomalous, these kinds of DM models can be realized only as effective field
+theories (EFTs) with a well-defined cutoff, where heavy spectator fermions
+restore gauge invariance at high energies. We formulate these EFTs, estimate
+their cutoff and properly take into account the effect of the Chern-Simons
+terms one obtains after the spectator fermions are integrated out. We find
+that, while for light DM collider and direct detection experiments usually
+provide the strongest bounds, the bounds at higher masses are heavily dominated
+by indirect detection experiments, due to strong annihilation into $W^+W^-$,
+$ZZ$, $Z\gamma$ and possibly into $gg$ and $\gamma\gamma$. We emphasize that
+these annihilation channels are generically significant because of the
+structure of the EFT, and therefore these models are prone to strong indirect
+detection constraints. Even though we focus on selected $Z'$ models for
+illustrative purposes, our setup is completely generic and can be used for
+analyzing the predictions of any anomalous $Z'$-mediated DM model with
+arbitrary charges.
+",0,1,0,0,0,0
+14190,Spin diffusion from an inhomogeneous quench in an integrable system,"  Generalised hydrodynamics predicts universal ballistic transport in
+integrable lattice systems when prepared in generic inhomogeneous initial
+states. However, the ballistic contribution to transport can vanish in systems
+with additional discrete symmetries. Here we perform large scale numerical
+simulations of spin dynamics in the anisotropic Heisenberg $XXZ$ spin $1/2$
+chain starting from an inhomogeneous mixed initial state which is symmetric
+with respect to a combination of spin-reversal and spatial reflection. In the
+isotropic and easy-axis regimes we find non-ballistic spin transport which we
+analyse in detail in terms of scaling exponents of the transported
+magnetisation and scaling profiles of the spin density. While in the easy-axis
+regime we find accurate evidence of normal diffusion, the spin transport in the
+isotropic case is clearly super-diffusive, with the scaling exponent very close
+to $2/3$, but with universal scaling dynamics which obeys the diffusion
+equation in nonlinearly scaled time.
+",0,1,0,0,0,0
+14191,Morphological Simplification of Archaeological Fracture Surfaces,"  We propose to employ scale spaces of mathematical morphology to
+hierarchically simplify fracture surfaces of complementarily fitting
+archaeological fragments. This representation preserves contact and is
+insensitive to different kinds of abrasion affecting the exact complementarity
+of the original fragments. We present a pipeline for morphologically
+simplifying fracture surfaces, based on their Lipschitz nature; its core is a
+new embedding of fracture surfaces to simultaneously compute both closing and
+opening morphological operations, using distance transforms.
+",1,0,0,0,0,0
+14192,A note on a separating system of rational invariants for finite dimensional generic algebras,"  The paper deals with a construction of a separating system of rational
+invariants for finite dimensional generic algebras. In the process of dealing
+an approach to a rough classification of finite dimensional algebras is offered
+by attaching them some quadratic forms.
+",0,0,1,0,0,0
+14193,A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning,"  In this paper we present a method for simultaneously segmenting brain tumors
+and an extensive set of organs-at-risk for radiation therapy planning of
+glioblastomas. The method combines a contrast-adaptive generative model for
+whole-brain segmentation with a new spatial regularization model of tumor shape
+using convolutional restricted Boltzmann machines. We demonstrate
+experimentally that the method is able to adapt to image acquisitions that
+differ substantially from any available training data, ensuring its
+applicability across treatment sites; that its tumor segmentation accuracy is
+comparable to that of the current state of the art; and that it captures most
+organs-at-risk sufficiently well for radiation therapy planning purposes. The
+proposed method may be a valuable step towards automating the delineation of
+brain tumors and organs-at-risk in glioblastoma patients undergoing radiation
+therapy.
+",0,0,0,1,0,0
+14194,Protein Pattern Formation,"  Protein pattern formation is essential for the spatial organization of many
+intracellular processes like cell division, flagellum positioning, and
+chemotaxis. A prominent example of intracellular patterns are the oscillatory
+pole-to-pole oscillations of Min proteins in \textit{E. coli} whose biological
+function is to ensure precise cell division. Cell polarization, a prerequisite
+for processes such as stem cell differentiation and cell polarity in yeast, is
+also mediated by a diffusion-reaction process. More generally, these functional
+modules of cells serve as model systems for self-organization, one of the core
+principles of life. Under which conditions spatio-temporal patterns emerge, and
+how these patterns are regulated by biochemical and geometrical factors are
+major aspects of current research. Here we review recent theoretical and
+experimental advances in the field of intracellular pattern formation, focusing
+on general design principles and fundamental physical mechanisms.
+",0,0,0,0,1,0
+14195,What do we need to build explainable AI systems for the medical domain?,"  Artificial intelligence (AI) generally and machine learning (ML) specifically
+demonstrate impressive practical success in many different application domains,
+e.g. in autonomous driving, speech recognition, or recommender systems. Deep
+learning approaches, trained on extremely large data sets or using
+reinforcement learning methods have even exceeded human performance in visual
+tasks, particularly on playing games such as Atari, or mastering the game of
+Go. Even in the medical domain there are remarkable results. The central
+problem of such models is that they are regarded as black-box models and even
+if we understand the underlying mathematical principles, they lack an explicit
+declarative knowledge representation, hence have difficulty in generating the
+underlying explanatory structures. This calls for systems enabling to make
+decisions transparent, understandable and explainable. A huge motivation for
+our approach are rising legal and privacy aspects. The new European General
+Data Protection Regulation entering into force on May 25th 2018, will make
+black-box approaches difficult to use in business. This does not imply a ban on
+automatic learning approaches or an obligation to explain everything all the
+time, however, there must be a possibility to make the results re-traceable on
+demand. In this paper we outline some of our research topics in the context of
+the relatively new area of explainable-AI with a focus on the application in
+medicine, which is a very special domain. This is due to the fact that medical
+professionals are working mostly with distributed heterogeneous and complex
+sources of data. In this paper we concentrate on three sources: images, *omics
+data and text. We argue that research in explainable-AI would generally help to
+facilitate the implementation of AI/ML in the medical domain, and specifically
+help to facilitate transparency and trust.
+",1,0,0,1,0,0
+14196,Targeted matrix completion,"  Matrix completion is a problem that arises in many data-analysis settings
+where the input consists of a partially-observed matrix (e.g., recommender
+systems, traffic matrix analysis etc.). Classical approaches to matrix
+completion assume that the input partially-observed matrix is low rank. The
+success of these methods depends on the number of observed entries and the rank
+of the matrix; the larger the rank, the more entries need to be observed in
+order to accurately complete the matrix. In this paper, we deal with matrices
+that are not necessarily low rank themselves, but rather they contain low-rank
+submatrices. We propose Targeted, which is a general framework for completing
+such matrices. In this framework, we first extract the low-rank submatrices and
+then apply a matrix-completion algorithm to these low-rank submatrices as well
+as the remainder matrix separately. Although for the completion itself we use
+state-of-the-art completion methods, our results demonstrate that Targeted
+achieves significantly smaller reconstruction errors than other classical
+matrix-completion methods. One of the key technical contributions of the paper
+lies in the identification of the low-rank submatrices from the input
+partially-observed matrices.
+",1,0,0,1,0,0
+14197,Coresets for Dependency Networks,"  Many applications infer the structure of a probabilistic graphical model from
+data to elucidate the relationships between variables. But how can we train
+graphical models on a massive data set? In this paper, we show how to construct
+coresets -compressed data sets which can be used as proxy for the original data
+and have provably bounded worst case error- for Gaussian dependency networks
+(DNs), i.e., cyclic directed graphical models over Gaussians, where the parents
+of each variable are its Markov blanket. Specifically, we prove that Gaussian
+DNs admit coresets of size independent of the size of the data set.
+Unfortunately, this does not extend to DNs over members of the exponential
+family in general. As we will prove, Poisson DNs do not admit small coresets.
+Despite this worst-case result, we will provide an argument why our coreset
+construction for DNs can still work well in practice on count data. To
+corroborate our theoretical results, we empirically evaluated the resulting
+Core DNs on real data sets. The results
+",1,0,0,1,0,0
+14198,Relative stability of a ferroelectric state in (Na0.5Bi0.5)TiO3-based compounds under substitutions: Role of a tolerance factor in expansion of the temperature interval of stable ferroelectric state,"  The influence of the B-site ion substitutions in
+(1-x)(Bi1/2Na1/2)TiO3-xBaTiO3 system of solid solutions on the relative
+stability of the ferroelectric and antiferroelectric phases has been studied.
+The ions of zirconium, tin, along with (In0.5Nb0.5), (Fe0.5Nb0.5), (Al0.5V0.5)
+ion complexes have been used as substituting elements. An increase in the
+concentration of the substituting ion results in a near linear variation in the
+size of the crystal lattice cell. Along with the cell size variation a change
+in the relative stability of the ferroelectric and antiferroelectric phases
+takes place according to the changes of the tolerance factor of the solid
+solution. An increase in the tolerance factor leads to the increase in the
+temperature of the ferroelectric-antiferroelectric phase transition, and vice
+versa. All obtained results demonstrate the predominant influence of the ion
+size factor on the relative stability of the ferroelectric and
+antiferroelectric states in the (Na0.5Bi0.5)TiO3-based solid solutions and
+indicate the way for raising the temperature of the
+ferroelectric-antiferroelectric phase transition.
+",0,1,0,0,0,0
+14199,Data-driven modelling and validation of aircraft inbound-stream at some major European airports,"  This paper presents an exhaustive study on the arrivals process at eight
+important European airports. Using inbound traffic data, we define, compare,
+and contrast a data-driven Poisson and PSRA point process. Although, there is
+sufficient evidence that the interarrivals might follow an exponential
+distribution, this finding does not directly translate to evidence that the
+arrivals stream is Poisson. The main reason is that finite-capacity constraints
+impose a correlation structure to the arrivals stream, which a Poisson model
+cannot capture. We show the weaknesses and somehow the difficulties of using a
+Poisson process to model with good approximation the arrivals stream. On the
+other hand, our innovative non-parametric, data-driven PSRA model, predicts
+quite well and captures important properties of the typical arrivals stream.
+",0,0,0,1,0,0
+14200,Exact evolution equation for the effective potential,"  We derive a new exact evolution equation for the scale dependence of an
+effective action. The corresponding equation for the effective potential
+permits a useful truncation. This allows one to deal with the infrared problems
+of theories with massless modes in less than four dimensions which are relevant
+for the high temperature phase transition in particle physics or the
+computation of critical exponents in statistical mechanics.
+",0,1,0,0,0,0
+14201,Properties of Quasi-Assouad dimension,"  It is shown that for controlled Moran constructions in $\mathbb{R}$,
+including the (sub) self-similar and more generally, (sub) self-conformal sets,
+the quasi-Assouad dimension coincides with the upper box dimension. This can be
+extended to some special classes of self-similar sets in higher dimensions. The
+connections between quasi-Assouad dimension and tangents are studied. We show
+that sets with decreasing gaps have quasi-Assouad dimension $0$ or $1$ and we
+exhibit an example of a set in the plane whose quasi-Assouad dimension is
+smaller than that of its projection onto the $x$-axis, showing that
+quasi-Assouad dimension may increase under Lipschitz mappings.
+",0,0,1,0,0,0
+14202,PFAx: Predictable Feature Analysis to Perform Control,"  Predictable Feature Analysis (PFA) (Richthofer, Wiskott, ICMLA 2015) is an
+algorithm that performs dimensionality reduction on high dimensional input
+signal. It extracts those subsignals that are most predictable according to a
+certain prediction model. We refer to these extracted signals as predictable
+features.
+In this work we extend the notion of PFA to take supplementary information
+into account for improving its predictions. Such information can be a
+multidimensional signal like the main input to PFA, but is regarded external.
+That means it won't participate in the feature extraction - no features get
+extracted or composed of it. Features will be exclusively extracted from the
+main input such that they are most predictable based on themselves and the
+supplementary information. We refer to this enhanced PFA as PFAx (PFA
+extended).
+Even more important than improving prediction quality is to observe the
+effect of supplementary information on feature selection. PFAx transparently
+provides insight how the supplementary information adds to prediction quality
+and whether it is valuable at all. Finally we show how to invert that relation
+and can generate the supplementary information such that it would yield a
+certain desired outcome of the main signal.
+We apply this to a setting inspired by reinforcement learning and let the
+algorithm learn how to control an agent in an environment. With this method it
+is feasible to locally optimize the agent's state, i.e. reach a certain goal
+that is near enough. We are preparing a follow-up paper that extends this
+method such that also global optimization is feasible.
+",1,0,0,0,0,0
+14203,Multiplex decomposition of non-Markovian dynamics and the hidden layer reconstruction problem,"  Elements composing complex systems usually interact in several different ways
+and as such the interaction architecture is well modelled by a multiplex
+network. However often this architecture is hidden, as one usually only has
+experimental access to an aggregated projection. A fundamental challenge is
+thus to determine whether the hidden underlying architecture of complex systems
+is better modelled as a single interaction layer or results from the
+aggregation and interplay of multiple layers. Here we show that using local
+information provided by a random walker navigating the aggregated network one
+can decide in a robust way if the underlying structure is a multiplex or not
+and, in the former case, to determine the most probable number of hidden
+layers. As a byproduct, we show that the mathematical formalism also provides a
+principled solution for the optimal decomposition and projection of complex,
+non-Markovian dynamics into a Markov switching combination of diffusive modes.
+We validate the proposed methodology with numerical simulations of both (i)
+random walks navigating hidden multiplex networks (thereby reconstructing the
+true hidden architecture) and (ii) Markovian and non-Markovian continuous
+stochastic processes (thereby reconstructing an effective multiplex
+decomposition where each layer accounts for a different diffusive mode). We
+also state and prove two existence theorems guaranteeing that an exact
+reconstruction of the dynamics in terms of these hidden jump-Markov models is
+always possible for arbitrary finite-order Markovian and fully non-Markovian
+processes. Finally, we showcase the applicability of the method to experimental
+recordings from (i) the mobility dynamics of human players in an online
+multiplayer game and (ii) the dynamics of RNA polymerases at the
+single-molecule level.
+",0,1,0,1,0,0
+14204,Model of knowledge transfer within an organisation,"  Many studies show that the acquisition of knowledge is the key to build
+competitive advantage of companies. We propose a simple model of knowledge
+transfer within the organization and we implement the proposed model using
+cellular automata technique. In this paper the organisation is considered in
+the context of complex systems. In this perspective, the main role in
+organisation is played by the network of informal contacts and the distributed
+leadership. The goal of this paper is to check which factors influence the
+efficiency and effectiveness of knowledge transfer. Our studies indicate a
+significant role of initial concentration of chunks of knowledge for knowledge
+transfer process, and the results suggest taking action in the organisation to
+shorten the distance (social distance) between people with different levels of
+knowledge, or working out incentives to share knowledge.
+",1,1,0,0,0,0
+14205,"Efficient Propagation of Uncertainties in Manufacturing Supply Chains: Time Buckets, L-leap and Multilevel Monte Carlo","  Uncertainty propagation of large scale discrete supply chains can be
+prohibitive when a large number of events occur during the simulated period and
+discrete event simulations (DES) are costly. We present a time bucket method to
+approximate and accelerate the DES of supply chains. Its stochastic version,
+which we call the L(logistic)-leap method, can be viewed as an extension of the
+leap methods, e.g., tau-leap, D-leap, developed in the chemical engineering
+community for the acceleration of stochastic DES of chemical reactions. The
+L-leap method instantaneously updates the system state vector at discrete time
+points and the production rates and policies of a supply chain are assumed to
+be stationary during each time bucket. We propose to use Multilevel Monte Carlo
+(MLMC) to efficiently propagate the uncertainties in a supply chain network,
+where the levels are naturally defined by the sizes of the time buckets of the
+simulations. We demonstrate the efficiency and accuracy of our methods using
+four numerical examples derived from a real world manufacturing material flow.
+In these examples, our multilevel L-leap approach can be faster than the
+standard Monte Carlo (MC) method by one or two orders of magnitudes without
+compromising the accuracy.
+",0,0,0,1,0,0
+14206,Semi-automated Signal Surveying Using Smartphones and Floorplans,"  Location fingerprinting locates devices based on pattern matching signal
+observations to a pre-defined signal map. This paper introduces a technique to
+enable fast signal map creation given a dedicated surveyor with a smartphone
+and floorplan. Our technique (PFSurvey) uses accelerometer, gyroscope and
+magnetometer data to estimate the surveyor's trajectory post-hoc using
+Simultaneous Localisation and Mapping and particle filtering to incorporate a
+building floorplan. We demonstrate conventional methods can fail to recover the
+survey path robustly and determine the room unambiguously. To counter this we
+use a novel loop closure detection method based on magnetic field signals and
+propose to incorporate the magnetic loop closures and straight-line constraints
+into the filtering process to ensure robust trajectory recovery. We show this
+allows room ambiguities to be resolved.
+An entire building can be surveyed by the proposed system in minutes rather
+than days. We evaluate in a large office space and compare to state-of-the-art
+approaches. We achieve trajectories within 1.1 m of the ground truth 90% of the
+time. Output signal maps well approximate those built from conventional,
+laborious manual survey. We also demonstrate that the signal maps built by
+PFSurvey provide similar or even better positioning performance than the manual
+signal maps.
+",1,0,0,0,0,0
+14207,On the n-th row of the graded Betti table of an n-dimensional toric variety,"  We prove an explicit formula for the first non-zero entry in the n-th row of
+the graded Betti table of an n-dimensional projective toric variety associated
+to a normal polytope with at least one interior lattice point. This applies to
+Veronese embeddings of projective space where we prove a special case of a
+conjecture of Ein and Lazarsfeld. We also prove an explicit formula for the
+entire n-th row when the interior of the polytope is one-dimensional. All
+results are valid over an arbitrary field k.
+",0,0,1,0,0,0
+14208,Topological conjugacy of topological Markov shifts and Ruelle algebras,"  We will characterize topologically conjugate two-sided topological Markov
+shifts $(\bar{X}_A,\bar{\sigma}_A)$ in terms of the associated asymptotic
+Ruelle $C^*$-algebras ${\mathcal{R}}_A$ with its commutative $C^*$-subalgebras
+$C(\bar{X}_A)$ and the canonical circle actions. We will also show that
+extended Ruelle algebras ${\widetilde{\mathcal{R}}}_A$, which are purely
+infinite version of the asymptotic Ruelle algebras, with its commutative
+$C^*$-subalgebras $C(\bar{X}_A)$ and the canonical torus actions $\gamma^A$ are
+complete invariants for topological conjugacy of two-sided topological Markov
+shifts. We then have a computable topological conjugacy invariant, written in
+terms of the underlying matrix, of a two-sided topological Markov shift by
+using K-theory of the extended Ruelle algebra. The diagonal action of
+$\gamma^A$ has a unique KMS-state on ${\widetilde{\mathcal{R}}}_A$, which is an
+extension of the Parry measure on $\bar{X}_A$.
+",0,0,1,0,0,0
+14209,StealthDB: a Scalable Encrypted Database with Full SQL Query Support,"  Encrypted database systems provide a great method for protecting sensitive
+data in untrusted infrastructures. These systems are built using either
+special-purpose cryptographic algorithms that support operations over encrypted
+data, or by leveraging trusted computing co-processors. Strong cryptographic
+algorithms usually result in high performance overheads (e.g., public-key
+encryptions, garbled circuits), while weaker algorithms (e.g., order-preserving
+encryption) result in large leakage profiles. On the other hand, some encrypted
+database systems (e.g., Cipherbase, TrustedDB) leverage non-standard trusted
+computing devices, and are designed to work around their specific architectural
+limitations.
+In this work we build StealthDB -- an encrypted database system from Intel
+SGX. Our system can run on any newer generation Intel CPU. StealthDB has a very
+small trusted computing base, scales to large datasets, requires no DBMS
+changes, and provides strong security guarantees at steady state and during
+query execution.
+",1,0,0,0,0,0
+14210,"On the conformal duality between constant mean curvature surfaces in $\mathbb{E}(κ,τ)$ and $\mathbb{L}(κ,τ)$","  The main aim of this survey paper is to gather together some results
+concerning the Calabi type duality discovered by Hojoo Lee between certain
+families of (spacelike) graphs with constant mean curvature in Riemannian and
+Lorentzian homogeneous 3-manifolds with isometry group of dimension 4. The
+duality is conformal and swaps mean curvature and bundle curvature, and we will
+revisit it by giving a more general statement in terms of conformal immersions.
+This will show that some features in the theory of surfaces with mean curvature
+$\frac{1}{2}$ in $\mathbb{H}^2\times\mathbb{R}$ or minimal surfaces in the
+Heisenberg space have nice geometric interpretations in terms of their dual
+Lorentzian counterparts. We will briefly discuss some applications such as
+gradient estimates for entire minimal graphs in Heisenberg space or the
+existence of complete spacelike surfaces, and we will also give an uniform
+treatment to the behavior of the duality with respect to ambient isometries.
+Finally, some open questions are posed in the last section.
+",0,0,1,0,0,0
+14211,Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks,"  Contact-rich manipulation tasks in unstructured environments often require
+both haptic and visual feedback. However, it is non-trivial to manually design
+a robot controller that combines modalities with very different
+characteristics. While deep reinforcement learning has shown success in
+learning control policies for high-dimensional inputs, these algorithms are
+generally intractable to deploy on real robots due to sample complexity. We use
+self-supervision to learn a compact and multimodal representation of our
+sensory inputs, which can then be used to improve the sample efficiency of our
+policy learning. We evaluate our method on a peg insertion task, generalizing
+over different geometry, configurations, and clearances, while being robust to
+external perturbations. Results for simulated and real robot experiments are
+presented.
+",1,0,0,0,0,0
+14212,Positive solutions for nonlinear problems involving the one-dimensional ϕ-Laplacian,"  Let $\Omega:=\left( a,b\right) \subset\mathbb{R}$, $m\in L^{1}\left(
+\Omega\right) $ and $\lambda>0$ be a real parameter. Let $\mathcal{L}$ be the
+differential operator given by $\mathcal{L}u:=-\phi\left( u^{\prime}\right)
+^{\prime}+r\left( x\right) \phi\left( u\right) $, where $\phi
+:\mathbb{R\rightarrow R}$ is an odd increasing homeomorphism and $0\leq r\in
+L^{1}\left( \Omega\right) $. We study the existence of positive solutions for
+problems of the form $\mathcal{L}u=\lambda m\left( x\right) f\left( u\right)$
+in $\Omega,$ $u=0$ on $\partial\Omega$, where $f:\left[ 0,\infty\right)
+\rightarrow\left[ 0,\infty\right) $ is a continuos function which is, roughly
+speaking, sublinear with respect to $\phi$. Our approach combines the sub and
+supersolution method with some estimates on related nonlinear problems. We
+point out that our results are new even in the cases $r\equiv0$ and/or
+$m\geq0$.
+",0,0,1,0,0,0
+14213,Vortex lattices in binary Bose-Einstein condensates with dipole-dipole interactions,"  We study the structure and stability of vortex lattices in two-component
+rotating Bose-Einstein condensates with intrinsic dipole-dipole interactions
+(DDIs) and contact interactions. To address experimentally accessible coupled
+systems, we consider $^{164}$Dy-$^{162}$Dy and $^{168}$Er-$^{164}$Dy mixtures,
+which feature different miscibilities. The corresponding dipole moments are
+$\mu_{\mathrm{Dy}}=10\mu_{\mathrm{B}}$ and $\mu_{\mathrm{Er}}=
+7\mu_{\mathrm{B}}$, where $\mu_{\mathrm{B}}$ is the Bohr magneton. For
+comparison, we also discuss a case where one of the species is non dipolar.
+Under a large aspect ratio of the trap, we consider mixtures in the
+pancake-shaped format, which are modeled by effective two-dimensional coupled
+Gross-Pitaevskii equations, with a fixed polarization of the magnetic dipoles.
+Then, the miscibility and vortex-lattice structures are studied, by varying the
+coefficients of the contact interactions (assuming the use of the
+Feshbach-resonance mechanism) and the rotation frequency. We present phase
+diagrams for several types of lattices in the parameter plane of the rotation
+frequency and ratio of inter- and intra-species scattering lengths. The vortex
+structures are found to be diverse for the more miscible $^{164}$Dy-$^{162}$Dy
+mixture, with a variety of shapes, whereas, for the less miscible case of
+$^{168}$Er-$^{164}$Dy, the lattice patterns mainly feature circular or square
+formats.
+",0,1,0,0,0,0
+14214,Regression estimator for the tail index,"  Estimating the tail index parameter is one of the primal objectives in
+extreme value theory. For heavy-tailed distributions the Hill estimator is the
+most popular way to estimate the tail index parameter. Improving the Hill
+estimator was aimed by recent works with different methods, for example by
+using bootstrap, or Kolmogorov-Smirnov metric. These methods are asymptotically
+consistent, but for tail index $\xi >1$ and smaller sample sizes the estimation
+fails to approach the theoretical value for realistic sample sizes. In this
+paper, we introduce a new empirical method, which can estimate high tail index
+parameters well and might also be useful for relatively small sample sizes.
+",0,0,1,1,0,0
+14215,Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials,"  Metamaterial analogues of electromagnetically induced transparency (EIT) have
+been intensively studied and widely employed for slow light and enhanced
+nonlinear effects. In particular, the active modulation of the EIT analogue and
+well-controlled group delay in metamaterials have shown great prospects in
+optical communication networks. Previous studies have focused on the optical
+control of the EIT analogue by integrating the photoactive materials into the
+unit cell, however, the response time is limited by the recovery time of the
+excited carriers in these bulk materials. Graphene has recently emerged as an
+exceptional optoelectronic material. It shows an ultrafast relaxation time on
+the order of picosecond and its conductivity can be tuned via manipulating the
+Fermi energy. Here we integrate a monolayer graphene into metal-based terahertz
+(THz) metamaterials, and realize a complete modulation in the resonance
+strength of the EIT analogue at the accessible Fermi energy. The physical
+mechanism lies in the active tuning the damping rate of the dark mode resonator
+through the recombination effect of the conductive graphene. Note that the
+monolayer morphology in our work is easier to fabricate and manipulate than
+isolated fashion. This work presents a novel modulation strategy of the EIT
+analogue in the hybrid metamaterials, and pave the way towards designing very
+compact slow light devices to meet future demand of ultrafast optical signal
+processing.
+",0,1,0,0,0,0
+14216,The continuity equation with cusp singularities,"  In this paper we study a special case of the completion of cusp
+Kähler-Einstein metric on the regular part of varieties by taking the
+continuity method proposed by La Nave and Tian. The differential geometric and
+algebro-geometric properties of the noncollapsing limit in the continuity
+method with cusp singularities will be investigated.
+",0,0,1,0,0,0
+14217,Simplicial Structures for Higher Order Hochschild Homology over the $d$-Sphere,"  For $d\geq1$, we study the simplicial structure of the chain complex
+associated to the higher order Hochschild homology over the $d$-sphere. We
+discuss $H_\bullet^{S^d}(A,M)$ by way of a bar-like resolution
+$\mathcal{B}^d(A)$ in the context of simplicial modules. Besides the general
+case, we give explicit detail corresponding to $S^3$. We also present a
+description of what can replace these bar-like resolutions in order to aid with
+computation. The cohomology version can be done following a similar
+construction, of which we make mention.
+",0,0,1,0,0,0
+14218,Destination-Directed Trajectory Modeling and Prediction Using Conditionally Markov Sequences,"  In some problems there is information about the destination of a moving
+object. An example is an airliner flying from an origin to a destination. Such
+problems have three main components: an origin, a destination, and motion in
+between. To emphasize that the motion trajectories end up at the destination,
+we call them \textit{destination-directed trajectories}. The Markov sequence is
+not flexible enough to model such trajectories. Given an initial density and an
+evolution law, the future of a Markov sequence is determined probabilistically.
+One class of conditionally Markov (CM) sequences, called the $CM_L$ sequence
+(including the Markov sequence as a special case), has the following main
+components: a joint endpoint density (i.e., an initial density and a final
+density conditioned on the initial) and a Markov-like evolution law. This paper
+proposes using the $CM_L$ sequence for modeling destination-directed
+trajectories. It is demonstrated how the $CM_L$ sequence enjoys several
+desirable properties for destination-directed trajectory modeling. Some
+simulations of trajectory modeling and prediction are presented for
+illustration.
+",1,0,0,0,0,0
+14219,Monadic Second Order Logic with Measure and Category Quantifiers,"  We investigate the extension of Monadic Second Order logic, interpreted over
+infinite words and trees, with generalized ""for almost all"" quantifiers
+interpreted using the notions of Baire category and Lebesgue measure.
+",1,0,1,0,0,0
+14220,Two-point correlation in wall turbulence according to the attached-eddy hypothesis,"  For the constant-stress layer of wall turbulence, two-point correlations of
+velocity fluctuations are studied theoretically by using the attached-eddy
+hypothesis, i.e., a phenomenological model of a random superposition of
+energy-containing eddies that are attached to the wall. While the previous
+studies had invoked additional assumptions, we focus on the minimum assumptions
+of the hypothesis to derive its most general forms of the correlation
+functions. They would allow us to use or assess the hypothesis without any
+effect of those additional assumptions. We also study the energy spectra and
+the two-point correlations of the rate of momentum transfer and of the rate of
+energy dissipation.
+",0,1,0,0,0,0
+14221,Variational problems with long-range interaction,"  We consider a class of variational problems for densities that repel each
+other at distance. Typical examples are given by the Dirichlet functional and
+the Rayleigh functional \[
+D(\mathbf{u}) = \sum_{i=1}^k \int_{\Omega} |\nabla u_i|^2 \quad \text{or}
+\quad R(\mathbf{u}) = \sum_{i=1}^k \frac{\int_{\Omega} |\nabla
+u_i|^2}{\int_{\Omega} u_i^2} \] minimized in the class of
+$H^1(\Omega,\mathbb{R}^k)$ functions attaining some boundary conditions on
+$\partial \Omega$, and subjected to the constraint \[
+\mathrm{dist} (\{u_i > 0\}, \{u_j > 0\}) \ge 1 \qquad \forall i \neq j. \]
+For these problems, we investigate the optimal regularity of the solutions,
+prove a free-boundary condition, and derive some preliminary results
+characterizing the free boundary $\partial \{\sum_{i=1}^k u_i > 0\}$.
+",0,0,1,0,0,0
+14222,On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset,"  This paper presents a comparison of six machine learning (ML) algorithms:
+GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest
+Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on
+the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &
+Mangasarian, 1992) by measuring their classification test accuracy and their
+sensitivity and specificity values. The said dataset consists of features which
+were computed from digitized images of FNA tests on a breast mass (Wolberg,
+Street, & Mangasarian, 1992). For the implementation of the ML algorithms, the
+dataset was partitioned in the following fashion: 70% for training phase, and
+30% for the testing phase. The hyper-parameters used for all the classifiers
+were manually assigned. Results show that all the presented ML algorithms
+performed well (all exceeded 90% test accuracy) on the classification task. The
+MLP algorithm stands out among the implemented algorithms with a test accuracy
+of ~99.04%.
+",1,0,0,1,0,0
+14223,Loss Landscapes of Regularized Linear Autoencoders,"  Autoencoders are a deep learning model for representation learning. When
+trained to minimize the Euclidean distance between the data and its
+reconstruction, linear autoencoders (LAEs) learn the subspace spanned by the
+top principal directions but cannot learn the principal directions themselves.
+In this paper, we prove that $L_2$-regularized LAEs learn the principal
+directions as the left singular vectors of the decoder, providing an extremely
+simple and scalable algorithm for rank-$k$ SVD. More generally, we consider
+LAEs with (i) no regularization, (ii) regularization of the composition of the
+encoder and decoder, and (iii) regularization of the encoder and decoder
+separately. We relate the minimum of (iii) to the MAP estimate of probabilistic
+PCA and show that for all critical points the encoder and decoder are
+transposes. Building on topological intuition, we smoothly parameterize the
+critical manifolds for all three losses via a novel unified framework and
+illustrate these results empirically. Overall, this work clarifies the
+relationship between autoencoders and Bayesian models and between
+regularization and orthogonality.
+",1,0,0,1,0,0
+14224,Enduring Lagrangian coherence of a Loop Current ring assessed using independent observations,"  Ocean flows are routinely inferred from low-resolution satellite altimetry
+measurements of sea surface height assuming a geostrophic balance. Recent
+nonlinear dynamical systems techniques have revealed that surface currents
+derived from altimetry can support mesoscale eddies with material boundaries
+that do not filament for many months, thereby representing effective transport
+mechanisms. However, the long-range Lagrangian coherence assessed for mesoscale
+eddy boundaries detected from altimetry is constrained by the impossibility of
+current altimeters to resolve ageostrophic submesoscale motions. These may act
+to prevent Lagrangian coherence from manifesting in the rigorous form described
+by the nonlinear dynamical systems theories. Here we use a combination of
+satellite ocean color and surface drifter trajectory data, rarely available
+simultaneously over an extended period of time, to provide observational
+evidence for the enduring Lagrangian coherence of a Loop Current ring detected
+from altimetry. We also seek indications of this behavior in the flow produced
+by a data-assimilative system which demonstrated ability to reproduce observed
+relative dispersion statistics down into the marginally submesoscale range.
+However, the simulated flow, total surface and subsurface or subsampled
+emulating altimetry, is not found to support the long-lasting Lagrangian
+coherence that characterizes the observed ring. This highlights the importance
+of the Lagrangian metrics produced by the nonlinear dynamical systems tools
+employed here in assessing model performance.
+",0,1,0,0,0,0
+14225,Nonparametric inference for continuous-time event counting and link-based dynamic network models,"  A flexible approach for modeling both dynamic event counting and dynamic
+link-based networks based on counting processes is proposed, and estimation in
+these models is studied. We consider nonparametric likelihood based estimation
+of parameter functions via kernel smoothing. The asymptotic behavior of these
+estimators is rigorously analyzed by allowing the number of nodes to tend to
+infinity. The finite sample performance of the estimators is illustrated
+through an empirical analysis of bike share data.
+",0,0,1,1,0,0
+14226,Optimal Pricing-Based Edge Computing Resource Management in Mobile Blockchain,"  As the core issue of blockchain, the mining requires solving a proof-of-work
+puzzle, which is resource expensive to implement in mobile devices due to high
+computing power needed. Thus, the development of blockchain in mobile
+applications is restricted. In this paper, we consider the edge computing as
+the network enabler for mobile blockchain. In particular, we study optimal
+pricing-based edge computing resource management to support mobile blockchain
+applications where the mining process can be offloaded to an Edge computing
+Service Provider (ESP). We adopt a two-stage Stackelberg game to jointly
+maximize the profit of the ESP and the individual utilities of different
+miners. In Stage I, the ESP sets the price of edge computing services. In Stage
+II, the miners decide on the service demand to purchase based on the observed
+prices. We apply the backward induction to analyze the sub-game perfect
+equilibrium in each stage for uniform and discriminatory pricing schemes.
+Further, the existence and uniqueness of Stackelberg game are validated for
+both pricing schemes. At last, the performance evaluation shows that the ESP
+intends to set the maximum possible value as the optimal price for profit
+maximization under uniform pricing. In addition, the discriminatory pricing
+helps the ESP encourage higher total service demand from miners and achieve
+greater profit correspondingly.
+",1,0,0,0,0,0
+14227,Automorphic vector bundles with global sections on $G$-${\tt Zip}^{\mathcal Z}$-schemes,"  A general conjecture is stated on the cone of automorphic vector bundles
+admitting nonzero global sections on schemes endowed with a smooth, surjective
+morphism to a stack of $G$-zips of connected-Hodge-type; such schemes should
+include all Hodge-type Shimura varieties with hyperspecial level. We prove our
+conjecture for groups of type $A_1^n$, $C_2$ and $\mathbf F_p$-split groups of
+type $A_2$ (this includes all Hilbert-Blumenthal varieties and should also
+apply to Siegel modular threefolds and Picard modular surfaces). An example is
+given to show that our conjecture can fail for zip data not of
+connected-Hodge-type.
+",0,0,1,0,0,0
+14228,Composition and decomposition of GANs,"  In this work, we propose a composition/decomposition framework for
+adversarially training generative models on composed data - data where each
+sample can be thought of as being constructed from a fixed number of
+components. In our framework, samples are generated by sampling components from
+component generators and feeding these components to a composition function
+which combines them into a ""composed sample"". This compositional training
+approach improves the modularity, extensibility and interpretability of
+Generative Adversarial Networks (GANs) - providing a principled way to
+incrementally construct complex models out of simpler component models, and
+allowing for explicit ""division of responsibility"" between these components.
+Using this framework, we define a family of learning tasks and evaluate their
+feasibility on two datasets in two different data modalities (image and text).
+Lastly, we derive sufficient conditions such that these compositional
+generative models are identifiable. Our work provides a principled approach to
+building on pre-trained generative models or for exploiting the compositional
+nature of data distributions to train extensible and interpretable models.
+",1,0,0,1,0,0
+14229,Quasiconformal mappings and Hölder continuity,"  We establish that every $K$-quasiconformal mapping $w$ of the unit ball $\IB$
+onto a $C^2$-Jordan domain $\Omega$ is Hölder continuous with constant
+$\alpha= 2-\frac{n}{p}$, provided that its weak Laplacean $\Delta w$ is in $
+L^p(\IB)$ for some $n/2<p<n$. In particular it is Hölder continuous for every
+$0<\alpha<1$ provided that $\Delta w\in L^n(\IB)$.
+",0,0,1,0,0,0
+14230,Boundedness of averaging operators on geometrically doubling metric spaces,"  We prove that averaging operators are uniformly bounded on $L^1$ for all
+geometrically doubling metric measure spaces, with bounds independent of the
+measure. From this result, the $L^1$ convergence of averages as $r \to 0$
+immediately follows.
+",0,0,1,0,0,0
+14231,Efficient Low-Order Approximation of First-Passage Time Distributions,"  We consider the problem of computing first-passage time distributions for
+reaction processes modelled by master equations. We show that this generally
+intractable class of problems is equivalent to a sequential Bayesian inference
+problem for an auxiliary observation process. The solution can be approximated
+efficiently by solving a closed set of coupled ordinary differential equations
+(for the low-order moments of the process) whose size scales with the number of
+species. We apply it to an epidemic model and a trimerisation process, and show
+good agreement with stochastic simulations.
+",0,1,0,1,0,0
+14232,Delayed avalanches in Multi-Pixel Photon Counters,"  Hamamatsu Photonics introduced a new generation of their Multi-Pixel Photon
+Counters in 2013 with significantly reduced after-pulsing rate. In this paper,
+we investigate the causes of after-pulsing by testing pre-2013 and post-2013
+devices using laser light ranging from 405 to 820nm. Doing so we investigate
+the possibility that afterpulsing is also due to optical photons produced in
+the avalanche rather than to impurities trapping charged carriers produced in
+the avalanches and releasing them at a later time. For pre-2013 devices, we
+observe avalanches delayed by ns to several 100~ns at 637, 777nm and 820 nm
+demonstrating that holes created in the zero field region of the silicon bulk
+can diffuse back to the high field region triggering delayed avalanches. On the
+other hand post-2013 exhibit no delayed avalanches beyond 100~ns at 777nm. We
+also confirm that post-2013 devices exhibit about 25 times lower after-pulsing.
+Taken together, our measurements show that the absorption of photons from the
+avalanche in the bulk of the silicon and the subsequent hole diffusion back to
+the junction was a significant source of after-pulse for the pre-2013 devices.
+Hamamatsu appears to have fixed this problem in 2013 following the preliminary
+release of our results. We also show that even at short wavelength the timing
+distribution exhibit tails in the sub-nanosecond range that may impair the MPPC
+timing performances.
+",0,1,0,0,0,0
+14233,Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution,"  We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward
+deep neural network that allows selective execution. Given an input, only a
+subset of D2NN neurons are executed, and the particular subset is determined by
+the D2NN itself. By pruning unnecessary computation depending on input, D2NNs
+provide a way to improve computational efficiency. To achieve dynamic selective
+execution, a D2NN augments a feed-forward deep neural network (directed acyclic
+graph of differentiable modules) with controller modules. Each controller
+module is a sub-network whose output is a decision that controls whether other
+modules can execute. A D2NN is trained end to end. Both regular and controller
+modules in a D2NN are learnable and are jointly trained to optimize both
+accuracy and efficiency. Such training is achieved by integrating
+backpropagation with reinforcement learning. With extensive experiments of
+various D2NN architectures on image classification tasks, we demonstrate that
+D2NNs are general and flexible, and can effectively optimize
+accuracy-efficiency trade-offs.
+",1,0,0,1,0,0
+14234,Linear-Cost Covariance Functions for Gaussian Random Fields,"  Gaussian random fields (GRF) are a fundamental stochastic model for
+spatiotemporal data analysis. An essential ingredient of GRF is the covariance
+function that characterizes the joint Gaussian distribution of the field.
+Commonly used covariance functions give rise to fully dense and unstructured
+covariance matrices, for which required calculations are notoriously expensive
+to carry out for large data. In this work, we propose a construction of
+covariance functions that result in matrices with a hierarchical structure.
+Empowered by matrix algorithms that scale linearly with the matrix dimension,
+the hierarchical structure is proved to be efficient for a variety of random
+field computations, including sampling, kriging, and likelihood evaluation.
+Specifically, with $n$ scattered sites, sampling and likelihood evaluation has
+an $O(n)$ cost and kriging has an $O(\log n)$ cost after preprocessing,
+particularly favorable for the kriging of an extremely large number of sites
+(e.g., predicting on more sites than observed). We demonstrate comprehensive
+numerical experiments to show the use of the constructed covariance functions
+and their appealing computation time. Numerical examples on a laptop include
+simulated data of size up to one million, as well as a climate data product
+with over two million observations.
+",0,0,0,1,0,0
+14235,Models of fault-tolerant distributed computation via dynamic epistemic logic,"  The computability power of a distributed computing model is determined by the
+communication media available to the processes, the timing assumptions about
+processes and communication, and the nature of failures that processes can
+suffer. In a companion paper we showed how dynamic epistemic logic can be used
+to give a formal semantics to a given distributed computing model, to capture
+precisely the knowledge needed to solve a distributed task, such as consensus.
+Furthermore, by moving to a dual model of epistemic logic defined by simplicial
+complexes, topological invariants are exposed, which determine task
+solvability. In this paper we show how to extend the setting above to include
+in the knowledge of the processes, knowledge about the model of computation
+itself. The extension describes the knowledge processes gain about the current
+execution, in problems where processes have no input values at all.
+",1,0,1,0,0,0
+14236,Erratum: Higher Order Elicitability and Osband's Principle,"  This note corrects conditions in Proposition 3.4 and Theorem 5.2(ii) and
+comments on imprecisions in Propositions 4.2 and 4.4 in Fissler and Ziegel
+(2016).
+",0,0,1,1,0,1
+14237,Staging Human-computer Dialogs: An Application of the Futamura Projections,"  We demonstrate an application of the Futamura Projections to human-computer
+interaction, and particularly to staging human-computer dialogs. Specifically,
+by providing staging analogs to the classical Futamura Projections, we
+demonstrate that the Futamura Projections can be applied to the staging of
+human-computer dialogs in addition to the execution of programs.
+",1,0,0,0,0,0
+14238,Enhancing synchronization in chaotic oscillators by induced heterogeneity,"  We report enhancing of complete synchronization in identical chaotic
+oscillators when their interaction is mediated by a mismatched oscillator. The
+identical oscillators now interact indirectly through the intermediate relay
+oscillator. The induced heterogeneity in the intermediate oscillator plays a
+constructive role in reducing the critical coupling for a transition to
+complete synchronization. A common lag synchronization emerges between the
+mismatched relay oscillator and its neighboring identical oscillators that
+leads to this enhancing effect. We present examples of one-dimensional open
+array, a ring, a star network and a two-dimensional lattice of dynamical
+systems to demonstrate how this enhancing effect occurs. The paradigmatic
+Rössler oscillator is used as a dynamical unit, in our numerical experiment,
+for different networks to reveal the enhancing phenomenon.
+",0,1,0,0,0,0
+14239,Compressed Sensing with Deep Image Prior and Learned Regularization,"  We propose a novel method for compressed sensing recovery using untrained
+deep generative models. Our method is based on the recently proposed Deep Image
+Prior (DIP), wherein the convolutional weights of the network are optimized to
+match the observed measurements. We show that this approach can be applied to
+solve any differentiable inverse problem. We also introduce a novel learned
+regularization technique which incorporates a small amount of prior
+information, further reducing the number of measurements required for a given
+reconstruction error. Our algorithm requires approximately 4-6x fewer
+measurements than classical Lasso methods. Unlike previous approaches based on
+generative models, our method does not require the model to be pre-trained. As
+such, we can apply our method to various medical imaging datasets for which
+data acquisition is expensive and no known generative models exist.
+",0,0,0,1,0,0
+14240,Beamforming and Power Splitting Designs for AN-aided Secure Multi-user MIMO SWIPT Systems,"  In this paper, an energy harvesting scheme for a multi-user
+multiple-input-multiple-output (MIMO) secrecy channel with artificial noise
+(AN) transmission is investigated. Joint optimization of the transmit
+beamforming matrix, the AN covariance matrix, and the power splitting ratio is
+conducted to minimize the transmit power under the target secrecy rate, the
+total transmit power, and the harvested energy constraints. The original
+problem is shown to be non-convex, which is tackled by a two-layer
+decomposition approach. The inner layer problem is solved through semi-definite
+relaxation, and the outer problem, on the other hand, is shown to be a single-
+variable optimization that can be solved by one-dimensional (1- D) line search.
+To reduce computational complexity, a sequential parametric convex
+approximation (SPCA) method is proposed to find a near-optimal solution. The
+work is then extended to the imperfect channel state information case with
+norm-bounded channel errors. Furthermore, tightness of the relaxation for the
+proposed schemes are validated by showing that the optimal solution of the
+relaxed problem is rank-one. Simulation results demonstrate that the proposed
+SPCA method achieves the same performance as the scheme based on 1-D but with
+much lower complexity.
+",1,0,0,0,0,0
+14241,Spectral Properties of Tensor Products of Channels,"  We investigate spectral properties of the tensor products of two quantum
+channels defined on matrix algebras. This leads to the important question of
+when an arbitrary subalgebra can split into the tensor product of two
+subalgebras. We show that for two unital quantum channels $\mathcal{E}_1$ and
+$\mathcal{E}_2$ the multiplicative domain of
+$\mathcal{E}_1\otimes\mathcal{E}_2$ splits into the tensor product of the
+individual multiplicative domains. Consequently, we fully describe the fixed
+points and peripheral eigen operators of the tensor product of channels.
+Through a structure theorem of maximal unital proper $^*$-subalgebras (MUPSA)
+of a matrix algebra we provide a non-trivial upper bound of the 'multiplicative
+index' of a unital channel which was recently introduced. This bound gives a
+criteria on when a channel cannot be factored into a product of two different
+channels. We construct examples of channels which can not be realized as a
+tensor product of two channels in any way. With these techniques and results,
+we found some applications in quantum error correction.
+",0,0,1,0,0,0
+14242,Integral Chow motives of threefolds with $K$-motives of unit type,"  We prove that if a smooth projective algebraic variety of dimension less or
+equal to three has a unit type integral $K$-motive, then its integral Chow
+motive is of Lefschetz type. As a consequence, the integral Chow motive is of
+Lefschetz type for a smooth projective variety of dimension less or equal to
+three that admits a full exceptional collection.
+",0,0,1,0,0,0
+14243,Efficient Recurrent Neural Networks using Structured Matrices in FPGAs,"  Recurrent Neural Networks (RNNs) are becoming increasingly important for time
+series-related applications which require efficient and real-time
+implementations. The recent pruning based work ESE suffers from degradation of
+performance/energy efficiency due to the irregular network structure after
+pruning. We propose block-circulant matrices for weight matrix representation
+in RNNs, thereby achieving simultaneous model compression and acceleration. We
+aim to implement RNNs in FPGA with highest performance and energy efficiency,
+with certain accuracy requirement (negligible accuracy degradation).
+Experimental results on actual FPGA deployments shows that the proposed
+framework achieves a maximum energy efficiency improvement of 35.7$\times$
+compared with ESE.
+",1,0,0,1,0,0
+14244,Neural Architecture Search: A Survey,"  Deep Learning has enabled remarkable progress over the last years on a
+variety of tasks, such as image recognition, speech recognition, and machine
+translation. One crucial aspect for this progress are novel neural
+architectures. Currently employed architectures have mostly been developed
+manually by human experts, which is a time-consuming and error-prone process.
+Because of this, there is growing interest in automated neural architecture
+search methods. We provide an overview of existing work in this field of
+research and categorize them according to three dimensions: search space,
+search strategy, and performance estimation strategy.
+",0,0,0,1,0,0
+14245,High Efficiency Power Side-Channel Attack Immunity using Noise Injection in Attenuated Signature Domain,"  With the advancement of technology in the last few decades, leading to the
+widespread availability of miniaturized sensors and internet-connected things
+(IoT), security of electronic devices has become a top priority. Side-channel
+attack (SCA) is one of the prominent methods to break the security of an
+encryption system by exploiting the information leaked from the physical
+devices. Correlational power attack (CPA) is an efficient power side-channel
+attack technique, which analyses the correlation between the estimated and
+measured supply current traces to extract the secret key. The existing
+countermeasures to the power attacks are mainly based on reducing the SNR of
+the leaked data, or introducing large overhead using techniques like power
+balancing. This paper presents an attenuated signature AES (AS-AES), which
+resists SCA with minimal noise current overhead. AS-AES uses a shunt
+low-drop-out (LDO) regulator to suppress the AES current signature by 400x in
+the supply current traces. The shunt LDO has been fabricated and validated in
+130 nm CMOS technology. System-level implementation of the AS-AES along with
+noise injection, shows that the system remains secure even after 50K
+encryptions, with 10x reduction in power overhead compared to that of noise
+addition alone.
+",1,0,0,0,0,0
+14246,Real-time monitoring of the structure of ultra thin Fe$_3$O$_4$ films during growth on Nb-doped SrTiO$_3$(001),"  In this work thin magnetite films were deposited on SrTiO$_3$ via reactive
+molecular beam epitaxy at different substrate temperatures. The growth process
+was monitored in-situ during deposition by means of x-ray diffraction. While
+the magnetite film grown at 400$^\circ$C shows a fully relaxed vertical lattice
+constant already in the early growth stages, the film deposited at 270$^\circ$C
+exhibits a strong vertical compressive strain and relaxes towards the bulk
+value with increasing film thickness. Furthermore, a lateral tensile strain was
+observed under these growth conditions although the inverse behavior is
+expected due to the lattice mismatch of -7.5%. Additionally, the occupancy of
+the A and B sublattices of magnetite with tetrahedral and octahedral sites was
+investigated showing a lower occupancy of the A sites compared to an ideal
+inverse spinel structure. The occupation of A sites decreases for a higher
+growth temperature. Thus, we assume a relocation of the iron ions from
+tetrahedral sites to octahedral vacancies forming a deficient rock salt
+lattice.
+",0,1,0,0,0,0
+14247,Harnessing Flexible and Reliable Demand Response Under Customer Uncertainties,"  Demand response (DR) is a cost-effective and environmentally friendly
+approach for mitigating the uncertainties in renewable energy integration by
+taking advantage of the flexibility of customers' demands. However, existing DR
+programs suffer from either low participation due to strict commitment
+requirements or not being reliable in voluntary programs. In addition, the
+capacity planning for energy storage/reserves is traditionally done separately
+from the demand response program design, which incurs inefficiencies. Moreover,
+customers often face high uncertainties in their costs in providing demand
+response, which is not well studied in literature.
+This paper first models the problem of joint capacity planning and demand
+response program design by a stochastic optimization problem, which
+incorporates the uncertainties from renewable energy generation, customer power
+demands, as well as the customers' costs in providing DR. We propose online DR
+control policies based on the optimal structures of the offline solution. A
+distributed algorithm is then developed for implementing the control policies
+without efficiency loss. We further offer enhanced policy design by allowing
+flexibilities into the commitment level. We perform real world trace based
+numerical simulations. Results demonstrate that the proposed algorithms can
+achieve near optimal social costs, and significant social cost savings compared
+to baseline methods.
+",0,0,1,0,0,0
+14248,GraphGAN: Graph Representation Learning with Generative Adversarial Nets,"  The goal of graph representation learning is to embed each vertex in a graph
+into a low-dimensional vector space. Existing graph representation learning
+methods can be classified into two categories: generative models that learn the
+underlying connectivity distribution in the graph, and discriminative models
+that predict the probability of edge existence between a pair of vertices. In
+this paper, we propose GraphGAN, an innovative graph representation learning
+framework unifying above two classes of methods, in which the generative model
+and discriminative model play a game-theoretical minimax game. Specifically,
+for a given vertex, the generative model tries to fit its underlying true
+connectivity distribution over all other vertices and produces ""fake"" samples
+to fool the discriminative model, while the discriminative model tries to
+detect whether the sampled vertex is from ground truth or generated by the
+generative model. With the competition between these two models, both of them
+can alternately and iteratively boost their performance. Moreover, when
+considering the implementation of generative model, we propose a novel graph
+softmax to overcome the limitations of traditional softmax function, which can
+be proven satisfying desirable properties of normalization, graph structure
+awareness, and computational efficiency. Through extensive experiments on
+real-world datasets, we demonstrate that GraphGAN achieves substantial gains in
+a variety of applications, including link prediction, node classification, and
+recommendation, over state-of-the-art baselines.
+",1,0,0,1,0,0
+14249,Accelerating Stochastic Gradient Descent For Least Squares Regression,"  There is widespread sentiment that it is not possible to effectively utilize
+fast gradient methods (e.g. Nesterov's acceleration, conjugate gradient, heavy
+ball) for the purposes of stochastic optimization due to their instability and
+error accumulation, a notion made precise in d'Aspremont 2008 and Devolder,
+Glineur, and Nesterov 2014. This work considers these issues for the special
+case of stochastic approximation for the least squares regression problem, and
+our main result refutes the conventional wisdom by showing that acceleration
+can be made robust to statistical errors. In particular, this work introduces
+an accelerated stochastic gradient method that provably achieves the minimax
+optimal statistical risk faster than stochastic gradient descent. Critical to
+the analysis is a sharp characterization of accelerated stochastic gradient
+descent as a stochastic process. We hope this characterization gives insights
+towards the broader question of designing simple and effective accelerated
+stochastic methods for more general convex and non-convex optimization
+problems.
+",0,0,1,1,0,0
+14250,Topological Kondo insulators in one dimension: Continuous Haldane-type ground-state evolution from the strongly-interacting to the non-interacting limit,"  We study, by means of the density-matrix renormalization group (DMRG)
+technique, the evolution of the ground state in a one-dimensional topological
+insulator, from the non-interacting to the strongly-interacting limit, where
+the system can be mapped onto a topological Kondo-insulator model. We focus on
+a toy model Hamiltonian (i.e., the interacting ""$sp$-ladder"" model), which
+could be experimentally realized in optical lattices with higher orbitals
+loaded with ultra-cold fermionic atoms. Our goal is to shed light on the
+emergence of the strongly-interacting ground state and its topological
+classification as the Hubbard-$U$ interaction parameter of the model is
+increased. Our numerical results show that the ground state can be generically
+classified as a symmetry-protected topological phase of the Haldane-type, even
+in the non-interacting case $U=0$ where the system can be additionally
+classified as a time-reversal $\mathbb{Z}_{2}$-topological insulator, and
+evolves adiabatically between the non-interacting and strongly interacting
+limits.
+",0,1,0,0,0,0
+14251,A novel improved fuzzy support vector machine based stock price trend forecast model,"  Application of fuzzy support vector machine in stock price forecast. Support
+vector machine is a new type of machine learning method proposed in 1990s. It
+can deal with classification and regression problems very successfully. Due to
+the excellent learning performance of support vector machine, the technology
+has become a hot research topic in the field of machine learning, and it has
+been successfully applied in many fields. However, as a new technology, there
+are many limitations to support vector machines. There is a large amount of
+fuzzy information in the objective world. If the training of support vector
+machine contains noise and fuzzy information, the performance of the support
+vector machine will become very weak and powerless. As the complexity of many
+factors influence the stock price prediction, the prediction results of
+traditional support vector machine cannot meet people with precision, this
+study improved the traditional support vector machine fuzzy prediction
+algorithm is proposed to improve the new model precision. NASDAQ Stock Market,
+Standard & Poor's (S&P) Stock market are considered. Novel advanced- fuzzy
+support vector machine (NA-FSVM) is the proposed methodology.
+",0,0,0,1,0,1
+14252,Deformed Heisenberg Algebra with a minimal length: Application to some molecular potentials,"  We review the essentials of the formalism of quantum mechanics based on a
+deformed Heisenbeg algebra, leading to the existence of a minimal length scale.
+We compute in this context, the energy spectra of the pseudoharmonic oscillator
+and Kratzer potentials by using a perturbative approach. We derive the
+molecular constants, which characterize the vibration--rotation energy levels
+of diatomic molecules, and investigate the effect of the minimal length on each
+of these parameters for both potentials. We confront our result to experimental
+data for the hydrogen molecule to estimate an order of magnitude of this
+fundamental scale in molecular physics.
+",0,1,0,0,0,0
+14253,Protonation induced high-Tc phases in iron-based superconductors evidenced by NMR and magnetization measurements,"  Chemical substitution during growth is a well-established method to
+manipulate electronic states of quantum materials, and leads to rich spectra of
+phase diagrams in cuprate and iron-based superconductors. Here we report a
+novel and generic strategy to achieve nonvolatile electron doping in series of
+(i.e. 11 and 122 structures) Fe-based superconductors by ionic liquid gating
+induced protonation at room temperature. Accumulation of protons in bulk
+compounds induces superconductivity in the parent compounds, and enhances the
+Tc largely in some superconducting ones. Furthermore, the existence of proton
+in the lattice enables the first proton nuclear magnetic resonance (NMR) study
+to probe directly superconductivity. Using FeS as a model system, our NMR study
+reveals an emergent high-Tc phase with no coherence peak which is hard to
+measure by NMR with other isotopes. This novel electric-field-induced proton
+evolution opens up an avenue for manipulation of competing electronic states
+(e.g. Mott insulators), and may provide an innovative way for a broad
+perspective of NMR measurements with greatly enhanced detecting resolution.
+",0,1,0,0,0,0
+14254,Spectral Estimation of Plasma Fluctuations I: Comparison of Methods,"  The relative root mean squared errors (RMSE) of nonparametric methods for
+spectral estimation is compared for microwave scattering data of plasma
+fluctuations. These methods reduce the variance of the periodogram estimate by
+averaging the spectrum over a frequency bandwidth. As the bandwidth increases,
+the variance decreases, but the bias error increases. The plasma spectra vary
+by over four orders of magnitude, and therefore, using a spectral window is
+necessary. We compare the smoothed tapered periodogram with the adaptive
+multiple taper methods and hybrid methods. We find that a hybrid method, which
+uses four orthogonal tapers and then applies a kernel smoother, performs best.
+For 300 point data segments, even an optimized smoothed tapered periodogram has
+a 24 \% larger relative RMSE than the hybrid method. We present two new
+adaptive multi-taper weightings which outperform Thomson's original adaptive
+weighting.
+",0,0,0,1,0,0
+14255,Angles between curves in metric measure spaces,"  The goal of the paper is to study the angle between two curves in the
+framework of metric (and metric measure) spaces. More precisely, we give a new
+notion of angle between two curves in a metric space. Such a notion has a
+natural interplay with optimal transportation and is particularly well suited
+for metric measure spaces satisfying the curvature-dimension condition. Indeed
+one of the main results is the validity of the cosine formula on $RCD^{*}(K,N)$
+metric measure spaces. As a consequence, the new introduced notions are
+compatible with the corresponding classical ones for Riemannian manifolds,
+Ricci limit spaces and Alexandrov spaces.
+",0,0,1,0,0,0
+14256,A Hybrid Multiscale Model for Cancer Invasion of the Extracellular Matrix,"  The ability to locally degrade the extracellular matrix (ECM) and interact
+with the tumour microenvironment is a key process distinguishing cancer from
+normal cells, and is a critical step in the metastatic spread of the tumour.
+The invasion of the surrounding tissue involves the coordinated action between
+cancer cells, the ECM, the matrix degrading enzymes, and the
+epithelial-to-mesenchymal transition (EMT). This is a regulatory process
+through which epithelial cells (ECs) acquire mesenchymal characteristics and
+transform to mesenchymal-like cells (MCs). In this paper, we present a new
+mathematical model which describes the transition from a collective invasion
+strategy for the ECs to an individual invasion strategy for the MCs. We achieve
+this by formulating a coupled hybrid system consisting of partial and
+stochastic differential equations that describe the evolution of the ECs and
+the MCs, respectively. This approach allows one to reproduce in a very natural
+way fundamental qualitative features of the current biomedical understanding of
+cancer invasion that are not easily captured by classical modelling approaches,
+for example, the invasion of the ECM by self-generated gradients and the
+appearance of EC invasion islands outside of the main body of the tumour.
+",0,0,0,0,1,0
+14257,Differential quadrature method for space-fractional diffusion equations on 2D irregular domains,"  In mathematical physics, the space-fractional diffusion equations are of
+particular interest in the studies of physical phenomena modelled by Lévy
+processes, which are sometimes called super-diffusion equations. In this
+article, we develop the differential quadrature (DQ) methods for solving the 2D
+space-fractional diffusion equations on irregular domains. The methods in
+presence reduce the original equation into a set of ordinary differential
+equations (ODEs) by introducing valid DQ formulations to fractional directional
+derivatives based on the functional values at scattered nodal points on problem
+domain. The required weighted coefficients are calculated by using radial basis
+functions (RBFs) as trial functions, and the resultant ODEs are discretized by
+the Crank-Nicolson scheme. The main advantages of our methods lie in their
+flexibility and applicability to arbitrary domains. A series of illustrated
+examples are finally provided to support these points.
+",0,0,1,0,0,0
+14258,Positivstellensatzë for noncommutative rational expressions,"  We derive some Positivstellensatzë for noncommutative rational expressions
+from the Positivstellensatzë for noncommutative polynomials. Specifically, we
+show that if a noncommutative rational expression is positive on a polynomially
+convex set, then there is an algebraic certificate witnessing that fact. As in
+the case of noncommutative polynomials, our results are nicer when we
+additionally assume positivity on a convex set-- that is, we obtain a so-called
+""perfect Positivstellensatz"" on convex sets.
+",0,0,1,0,0,0
+14259,Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention,"  In the wake of the vast population of smart device users worldwide, mobile
+health (mHealth) technologies are hopeful to generate positive and wide
+influence on people's health. They are able to provide flexible, affordable and
+portable health guides to device users. Current online decision-making methods
+for mHealth assume that the users are completely heterogeneous. They share no
+information among users and learn a separate policy for each user. However,
+data for each user is very limited in size to support the separate online
+learning, leading to unstable policies that contain lots of variances. Besides,
+we find the truth that a user may be similar with some, but not all, users, and
+connected users tend to have similar behaviors. In this paper, we propose a
+network cohesion constrained (actor-critic) Reinforcement Learning (RL) method
+for mHealth. The goal is to explore how to share information among similar
+users to better convert the limited user information into sharper learned
+policies. To the best of our knowledge, this is the first online actor-critic
+RL for mHealth and first network cohesion constrained (actor-critic) RL method
+in all applications. The network cohesion is important to derive effective
+policies. We come up with a novel method to learn the network by using the warm
+start trajectory, which directly reflects the users' property. The optimization
+of our model is difficult and very different from the general supervised
+learning due to the indirect observation of values. As a contribution, we
+propose two algorithms for the proposed online RLs. Apart from mHealth, the
+proposed methods can be easily applied or adapted to other health-related
+tasks. Extensive experiment results on the HeartSteps dataset demonstrates that
+in a variety of parameter settings, the proposed two methods obtain obvious
+improvements over the state-of-the-art methods.
+",1,0,0,0,0,0
+14260,The minus order and range additivity,"  We study the minus order on the algebra of bounded linear operators on a
+Hilbert space. By giving a characterization in terms of range additivity, we
+show that the intrinsic nature of the minus order is algebraic. Applications to
+generalized inverses of the sum of two operators, to systems of operator
+equations and to optimization problems are also presented.
+",0,0,1,0,0,0
+14261,Rigidity-induced scale invariance in polymer ejection from capsid,"  While the dynamics of a fully flexible polymer ejecting a capsid through a
+nanopore has been extensively studied, the ejection dynamics of semiflexible
+polymers has not been properly characterized. Here we report results from
+simulations of ejection dynamics of semiflexible polymers ejecting from
+spherical capsids. Ejections start from strongly confined polymer conformations
+of constant initial monomer density. We find that, unlike for fully flexible
+polymers, for semiflexible polymers the force measured at the pore does not
+show a direct relation to the instantaneous ejection velocity. The cumulative
+waiting time $t(s)$, that is, the time at which a monomer $s$ exits the capsid
+the last time, shows a clear change when increasing the polymer rigidity
+$\kappa$. Major part of an ejecting polymer is driven out of the capsid by
+internal pressure. At the final stage the polymer escapes the capsid by
+diffusion. For the driven part there is a cross-over from essentially
+exponential growth of $t$ with $s$ of the fully flexible polymers to a
+scale-invariant form. In addition, a clear dependence of $t$ on $N_0$ was
+found. These findings combined give the dependence $t(s) \propto N_0^{0.55}
+s^{1.33}$ for the strongly rigid polymers. This cross-over in dynamics where
+$\kappa$ acts as a control parameter is reminiscent of a phase transition. This
+analogy is further enhanced by our finding a perfect data collapse of $t$ for
+polymers of different $N_0$ and any constant $\kappa$.
+",0,1,0,0,0,0
+14262,A Cloud-based Service for Real-Time Performance Evaluation of NoSQL Databases,"  We have created a cloud-based service that allows the end users to run tests
+on multiple different databases to find which databases are most suitable for
+their project. From our research, we could not find another application that
+enables the user to test several databases to gauge the difference between
+them. This application allows the user to choose which type of test to perform
+and which databases to target. The application also displays the results of
+different tests that were run by other users previously. There is also a map to
+show the location where all the tests are run to give the user an estimate of
+the location. Unlike the orthodox static tests and reports conducted to
+evaluate NoSQL databases, we have created a web application to run and analyze
+these tests in real time. This web application evaluates the performance of
+several NoSQL databases. The databases covered are MongoDB, DynamoDB, CouchDB,
+and Firebase. The web service is accessible from: nosqldb.nextproject.ca.
+",1,0,0,0,0,0
+14263,On hyperballeans of bounded geometry,"  A ballean (or coarse structure) is a set endowed with some family of subsets,
+the balls, is such a way that balleans with corresponding morphisms can be
+considered as asymptotic counterparts of uniform topological spaces. For a
+ballean $\mathcal{B}$ on a set $X$, the hyperballean $\mathcal{B}^{\flat}$ is a
+ballean naturally defined on the set $X^{\flat}$ of all bounded subsets of $X$.
+We describe all balleans with hyperballeans of bounded geometry and analyze the
+structure of these hyperballeans.
+",0,0,1,0,0,0
+14264,Bayesian model selection consistency and oracle inequality with intractable marginal likelihood,"  In this article, we investigate large sample properties of model selection
+procedures in a general Bayesian framework when a closed form expression of the
+marginal likelihood function is not available or a local asymptotic quadratic
+approximation of the log-likelihood function does not exist. Under appropriate
+identifiability assumptions on the true model, we provide sufficient conditions
+for a Bayesian model selection procedure to be consistent and obey the Occam's
+razor phenomenon, i.e., the probability of selecting the ""smallest"" model that
+contains the truth tends to one as the sample size goes to infinity. In order
+to show that a Bayesian model selection procedure selects the smallest model
+containing the truth, we impose a prior anti-concentration condition, requiring
+the prior mass assigned by large models to a neighborhood of the truth to be
+sufficiently small. In a more general setting where the strong model
+identifiability assumption may not hold, we introduce the notion of local
+Bayesian complexity and develop oracle inequalities for Bayesian model
+selection procedures. Our Bayesian oracle inequality characterizes a trade-off
+between the approximation error and a Bayesian characterization of the local
+complexity of the model, illustrating the adaptive nature of averaging-based
+Bayesian procedures towards achieving an optimal rate of posterior convergence.
+Specific applications of the model selection theory are discussed in the
+context of high-dimensional nonparametric regression and density regression
+where the regression function or the conditional density is assumed to depend
+on a fixed subset of predictors. As a result of independent interest, we
+propose a general technique for obtaining upper bounds of certain small ball
+probability of stationary Gaussian processes.
+",0,0,1,1,0,0
+14265,ChimpCheck: Property-Based Randomized Test Generation for Interactive Apps,"  We consider the problem of generating relevant execution traces to test rich
+interactive applications. Rich interactive applications, such as apps on mobile
+platforms, are complex stateful and often distributed systems where
+sufficiently exercising the app with user-interaction (UI) event sequences to
+expose defects is both hard and time-consuming. In particular, there is a
+fundamental tension between brute-force random UI exercising tools, which are
+fully-automated but offer low relevance, and UI test scripts, which are manual
+but offer high relevance. In this paper, we consider a middle way---enabling a
+seamless fusion of scripted and randomized UI testing. This fusion is
+prototyped in a testing tool called ChimpCheck for programming, generating, and
+executing property-based randomized test cases for Android apps. Our approach
+realizes this fusion by offering a high-level, embedded domain-specific
+language for defining custom generators of simulated user-interaction event
+sequences. What follows is a combinator library built on industrial strength
+frameworks for property-based testing (ScalaCheck) and Android testing (Android
+JUnit and Espresso) to implement property-based randomized testing for Android
+development. Driven by real, reported issues in open source Android apps, we
+show, through case studies, how ChimpCheck enables expressing effective testing
+patterns in a compact manner.
+",1,0,0,0,0,0
+14266,"Mathematical analysis of pulsatile flow, vortex breakdown and instantaneous blow-up for the axisymmetric Euler equations","  The dynamics along the particle trajectories for the 3D axisymmetric Euler
+equations are considered. It is shown that if the inflow is rapidly increasing
+(pushy) in time, the corresponding laminar profile of the incompressible Euler
+flow is not (in some sense) stable provided that the swirling component is not
+zero. It is also shown that if the vorticity on the axis is not zero (with some
+extra assumptions), then there is no steady flow. We can rephrase these
+instability to an instantaneous blow-up. In the proof, Frenet-Serret formulas
+and orthonormal moving frame are essentially used.
+",0,0,1,0,0,0
+14267,Resilient Feedback Controller Design For Linear Model of Power Grids,"  In this paper, a resilient controller is designed for the linear
+time-invariant (LTI) systems subject to attacks on the sensors and the
+actuators. A novel probabilistic attack model is proposed to capture
+vulnerabilities of the communication links from sensors to the controller and
+from the controller to actuators. The observer and the controller formulation
+under the attack are derived. Thereafter, By leveraging Lyapunov functional
+methods, it is shown that exponential mean-square stability of the system under
+the output feedback controller is guaranteed if a certain LMI is feasible. The
+simulation results show the effectiveness and applicability of the proposed
+controller design approach.
+",1,0,0,0,0,0
+14268,Integrability of dispersionless Hirota type equations in 4D and the symplectic Monge-Ampere property,"  We prove that integrability of a dispersionless Hirota type equation implies
+the symplectic Monge-Ampere property in any dimension $\geq 4$. In 4D this
+yields a complete classification of integrable dispersionless PDEs of Hirota
+type through a list of heavenly type equations arising in self-dual gravity. As
+a by-product of our approach we derive an involutive system of relations
+characterising symplectic Monge-Ampere equations in any dimension.
+Moreover, we demonstrate that in 4D the requirement of integrability is
+equivalent to self-duality of the conformal structure defined by the
+characteristic variety of the equation on every solution, which is in turn
+equivalent to the existence of a dispersionless Lax pair. We also give a
+criterion of linerisability of a Hirota type equation via flatness of the
+corresponding conformal structure, and study symmetry properties of integrable
+equations.
+",0,1,1,0,0,0
+14269,Optimal stopping via reinforced regression,"  In this note we propose a new approach towards solving numerically optimal
+stopping problems via reinforced regression based Monte Carlo algorithms. The
+main idea of the method is to reinforce standard linear regression algorithms
+in each backward induction step by adding new basis functions based on
+previously estimated continuation values. The proposed methodology is
+illustrated by a numerical example from mathematical finance.
+",0,0,0,1,0,0
+14270,Least informative distributions in Maximum q-log-likelihood estimation,"  We use the Maximum $q$-log-likelihood estimation for Least informative
+distributions (LID) in order to estimate the parameters in probability density
+functions (PDFs) efficiently and robustly when data include outlier(s). LIDs
+are derived by using convex combinations of two PDFs,
+$f_\epsilon=(1-\epsilon)f_0+\epsilon f_1$. A convex combination of two PDFs is
+considered as a contamination $f_1$ as outlier(s) to underlying $f_0$
+distributions and $f_\epsilon$ is a contaminated distribution. The optimal
+criterion is obtained by minimizing the change of Maximum q-log-likelihood
+function when the data have slightly more contamination. In this paper, we make
+a comparison among ordinary Maximum likelihood, Maximum q-likelihood
+estimations, LIDs based on $\log_q$ and Huber M-estimation. Akaike and Bayesian
+information criterions (AIC and BIC) based on $\log_q$ and LID are proposed to
+assess the fitting performance of functions. Real data sets are applied to test
+the fitting performance of estimating functions that include shape, scale and
+location parameters.
+",0,0,1,1,0,0
+14271,Visual analytics for loan guarantee network risk management,"  Groups of enterprises guarantee each other and form complex guarantee
+networks when they try to obtain loans from banks. Such secured loan can
+enhance the solvency and promote the rapid growth in the economic upturn
+period. However, potential systemic risk may happen within the risk binding
+community. Especially, during the economic down period, the crisis may spread
+in the guarantee network like a domino. Monitoring the financial status,
+preventing or reducing systematic risk when crisis happens is highly concerned
+by the regulatory commission and banks. We propose visual analytics approach
+for loan guarantee network risk management, and consolidate the five analysis
+tasks with financial experts: i) visual analytics for enterprises default risk,
+whereby a hybrid representation is devised to predict the default risk and
+developed an interface to visualize key indicators; ii) visual analytics for
+high default groups, whereby a community detection based interactive approach
+is presented; iii) visual analytics for high defaults pattern, whereby a motif
+detection based interactive approach is described, and we adopt a Shneiderman
+Mantra strategy to reduce the computation complexity. iv) visual analytics for
+evolving guarantee network, whereby animation is used to help understanding the
+guarantee dynamic; v) visual analytics approach and interface for default
+diffusion path. The temporal diffusion path analysis can be useful for the
+government and bank to monitor the default spread status. It also provides
+insight for taking precautionary measures to prevent and dissolve systemic
+financial risk. We implement the system with case studies on a real-world
+guarantee network. Two financial experts are consulted with endorsement on the
+developed tool. To the best of our knowledge, this is the first visual
+analytics tool to explore the guarantee network risks in a systematic manner.
+",1,0,0,0,0,0
+14272,"Pronunciation recognition of English phonemes /\textipa{@}/, /æ/, /\textipa{A}:/ and /\textipa{2}/ using Formants and Mel Frequency Cepstral Coefficients","  The Vocal Joystick Vowel Corpus, by Washington University, was used to study
+monophthongs pronounced by native English speakers. The objective of this study
+was to quantitatively measure the extent at which speech recognition methods
+can distinguish between similar sounding vowels. In particular, the phonemes
+/\textipa{@}/, /{\ae}/, /\textipa{A}:/ and /\textipa{2}/ were analysed. 748
+sound files from the corpus were used and subjected to Linear Predictive Coding
+(LPC) to compute their formants, and to Mel Frequency Cepstral Coefficients
+(MFCC) algorithm, to compute the cepstral coefficients. A Decision Tree
+Classifier was used to build a predictive model that learnt the patterns of the
+two first formants measured in the data set, as well as the patterns of the 13
+cepstral coefficients. An accuracy of 70\% was achieved using formants for the
+mentioned phonemes. For the MFCC analysis an accuracy of 52 \% was achieved and
+an accuracy of 71\% when /\textipa{@}/ was ignored. The results obtained show
+that the studied algorithms are far from mimicking the ability of
+distinguishing subtle differences in sounds like human hearing does.
+",1,0,0,0,0,0
+14273,Vulnerability to pandemics in a rapidly urbanizing society,"  We examine salient trends of influenza pandemics in Australia, a rapidly
+urbanizing nation. To do so, we implement state-of-the-art influenza
+transmission and progression models within a large-scale stochastic computer
+simulation, generated using comprehensive Australian census datasets from 2006,
+2011, and 2016. Our results offer the first simulation-based investigation of a
+population's sensitivity to pandemics across multiple historical time points,
+and highlight three significant trends in pandemic patterns over the years:
+increased peak prevalence, faster spreading rates, and decreasing
+spatiotemporal bimodality. We attribute these pandemic trends to increases in
+two key quantities indicative of urbanization: population fraction residing in
+major cities, and international air traffic. In addition, we identify features
+of the pandemic's geographic spread that can only be attributed to changes in
+the commuter mobility network. The generic nature of our model and the ubiquity
+of urbanization trends around the world make it likely for our results to be
+applicable in other rapidly urbanizing nations.
+",0,0,0,0,1,0
+14274,Theoretical aspects of microscale acoustofluidics,"  Henrik Bruus is professor of lab-chip systems and theoretical physics at the
+Technical University of Denmark. In this contribution, he summarizes some of
+the recent results within theory and simulation of microscale acoustofluidic
+systems that he has obtained in collaboration with his students and
+international colleagues. The main emphasis is on three dynamical effects
+induced by external ultrasound fields acting on aqueous solutions and particle
+suspensions: The acoustic radiation force acting on suspended micro- and
+nanoparticles, the acoustic streaming appearing in the fluid, and the newly
+discovered acoustic body force acting on inhomogeneous solutions.
+",0,0,0,0,1,0
+14275,Context Generation from Formal Specifications for C Analysis Tools,"  Analysis tools like abstract interpreters, symbolic execution tools and
+testing tools usually require a proper context to give useful results when
+analyzing a particular function. Such a context initializes the function
+parameters and global variables to comply with function requirements. However
+it may be error-prone to write it by hand: the handwritten context might
+contain bugs or not match the intended specification. A more robust approach is
+to specify the context in a dedicated specification language, and hold the
+analysis tools to support it properly. This may mean to put significant
+development efforts for enhancing the tools, something that is often not
+feasible if ever possible.
+This paper presents a way to systematically generate such a context from a
+formal specification of a C function. This is applied to a subset of the ACSL
+specification language in order to generate suitable contexts for the abstract
+interpretation-based value analysis plug-ins of Frama-C, a framework for
+analysis of code written in C. The idea here presented has been implemented in
+a new Frama-C plug-in which is currently in use in an operational industrial
+setting.
+",1,0,0,0,0,0
+14276,Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval,"  This paper considers the problem of solving systems of quadratic equations,
+namely, recovering an object of interest
+$\mathbf{x}^{\natural}\in\mathbb{R}^{n}$ from $m$ quadratic equations/samples
+$y_{i}=(\mathbf{a}_{i}^{\top}\mathbf{x}^{\natural})^{2}$, $1\leq i\leq m$. This
+problem, also dubbed as phase retrieval, spans multiple domains including
+physical sciences and machine learning.
+We investigate the efficiency of gradient descent (or Wirtinger flow)
+designed for the nonconvex least squares problem. We prove that under Gaussian
+designs, gradient descent --- when randomly initialized --- yields an
+$\epsilon$-accurate solution in $O\big(\log n+\log(1/\epsilon)\big)$ iterations
+given nearly minimal samples, thus achieving near-optimal computational and
+sample complexities at once. This provides the first global convergence
+guarantee concerning vanilla gradient descent for phase retrieval, without the
+need of (i) carefully-designed initialization, (ii) sample splitting, or (iii)
+sophisticated saddle-point escaping schemes. All of these are achieved by
+exploiting the statistical models in analyzing optimization algorithms, via a
+leave-one-out approach that enables the decoupling of certain statistical
+dependency between the gradient descent iterates and the data.
+",0,0,0,1,0,0
+14277,Unifying and Generalizing Methods for Removing Unwanted Variation Based on Negative Controls,"  Unwanted variation, including hidden confounding, is a well-known problem in
+many fields, particularly large-scale gene expression studies. Recent proposals
+to use control genes --- genes assumed to be unassociated with the covariates
+of interest --- have led to new methods to deal with this problem. Going by the
+moniker Removing Unwanted Variation (RUV), there are many versions --- RUV1,
+RUV2, RUV4, RUVinv, RUVrinv, RUVfun. In this paper, we introduce a general
+framework, RUV*, that both unites and generalizes these approaches. This
+unifying framework helps clarify connections between existing methods. In
+particular we provide conditions under which RUV2 and RUV4 are equivalent. The
+RUV* framework also preserves an advantage of RUV approaches --- their
+modularity --- which facilitates the development of novel methods based on
+existing matrix imputation algorithms. We illustrate this by implementing RUVB,
+a version of RUV* based on Bayesian factor analysis. In realistic simulations
+based on real data we found that RUVB is competitive with existing methods in
+terms of both power and calibration, although we also highlight the challenges
+of providing consistently reliable calibration among data sets.
+",0,0,1,1,0,0
+14278,Quantum-Accurate Molecular Dynamics Potential for Tungsten,"  The purpose of this short contribution is to report on the development of a
+Spectral Neighbor Analysis Potential (SNAP) for tungsten. We have focused on
+the characterization of elastic and defect properties of the pure material in
+order to support molecular dynamics simulations of plasma-facing materials in
+fusion reactors. A parallel genetic algorithm approach was used to efficiently
+search for fitting parameters optimized against a large number of objective
+functions. In addition, we have shown that this many-body tungsten potential
+can be used in conjunction with a simple helium pair potential to produce
+accurate defect formation energies for the W-He binary system.
+",0,1,0,0,0,0
+14279,An empirical evaluation of alternative methods of estimation for Permutation Entropy in time series with tied values,"  Bandt and Pompe introduced Permutation Entropy in 2002 for Time Series where
+equal values, xt1 = xt2, t1 = t2, were neglected and only inequalities between
+the xt were considered. Since then, this measure has been modified and
+extended, in particular in cases when the amount of equal values in the series
+can not be neglected, (i.e. heart rate variability (HRV) time series). We
+review the different existing methodologies that treats this subject by
+classifying them according to their different strategies. In addition, a novel
+Bayesian Missing Data Imputation is presented that proves to outperform the
+existing methodologies that deals with type of time series. All this facts are
+illustrated by simulations and also by distinguishing patients suffering from
+Congestive Heart Failure from a (healthy) control group using HRV time series
+",0,0,0,1,0,0
+14280,MIMIC-CXR: A large publicly available database of labeled chest radiographs,"  Chest radiography is an extremely powerful imaging modality, allowing for a
+detailed inspection of a patient's thorax, but requiring specialized training
+for proper interpretation. With the advent of high performance general purpose
+computer vision algorithms, the accurate automated analysis of chest
+radiographs is becoming increasingly of interest to researchers. However, a key
+challenge in the development of these techniques is the lack of sufficient
+data. Here we describe MIMIC-CXR, a large dataset of 371,920 chest x-rays
+associated with 227,943 imaging studies sourced from the Beth Israel Deaconess
+Medical Center between 2011 - 2016. Each imaging study can pertain to one or
+more images, but most often are associated with two images: a frontal view and
+a lateral view. Images are provided with 14 labels derived from a natural
+language processing tool applied to the corresponding free-text radiology
+reports. All images have been de-identified to protect patient privacy. The
+dataset is made freely available to facilitate and encourage a wide range of
+research in medical computer vision.
+",1,0,0,0,0,0
+14281,Convolutional Neural Networks for Page Segmentation of Historical Document Images,"  This paper presents a Convolutional Neural Network (CNN) based page
+segmentation method for handwritten historical document images. We consider
+page segmentation as a pixel labeling problem, i.e., each pixel is classified
+as one of the predefined classes. Traditional methods in this area rely on
+carefully hand-crafted features or large amounts of prior knowledge. In
+contrast, we propose to learn features from raw image pixels using a CNN. While
+many researchers focus on developing deep CNN architectures to solve different
+problems, we train a simple CNN with only one convolution layer. We show that
+the simple architecture achieves competitive results against other deep
+architectures on different public datasets. Experiments also demonstrate the
+effectiveness and superiority of the proposed method compared to previous
+methods.
+",1,0,0,1,0,0
+14282,Localized Quantitative Criteria for Equidistribution,"  Let $(x_n)_{n=1}^{\infty}$ be a sequence on the torus $\mathbb{T}$
+(normalized to length 1). We show that if there exists a sequence of positive
+real numbers $(t_n)_{n=1}^{\infty}$ converging to 0 such that $$ \lim_{N
+\rightarrow \infty}{\frac{1}{N^2} \sum_{m,n = 1}^{N}{\frac{1}{\sqrt{t_N}}
+\exp{\left(- \frac{1}{t_N} (x_m - x_n)^2 \right)}}}
+= \sqrt{\pi},$$ then $(x_n)_{n=1}^{\infty}$ is uniformly distributed. This is
+especially interesting when $t_N \sim N^{-2}$ since the size of the sum is then
+essentially determined exclusively by local gaps at scale $\sim N^{-1}$. This
+can be used to show equidistribution of sequences with Poissonian pair
+correlation, which recovers a recent result of Aistleitner, Lachmann &
+Pausinger and Grepstad & Larcher. The general form of the result is proven on
+arbitrary compact manifolds $(M,g)$ where the role of the exponential function
+is played by the heat kernel $e^{t\Delta}$: for all $x_1, \dots, x_N \in M$ and
+all $t>0$ $$ \frac{1}{N^2}\sum_{m,n=1}^{N}{[e^{t\Delta}\delta_{x_m}](x_n)} \geq
+\frac{1}{vol(M)}$$ and equality is attained as $N \rightarrow \infty$ if and
+only if $(x_n)_{n=1}^{\infty}$ equidistributes.
+",0,0,1,0,0,0
+14283,"Disturbance propagation, inertia location and slow modes in large-scale high voltage power grids","  Conventional generators in power grids are steadily substituted with new
+renewable sources of electric power. The latter are connected to the grid via
+inverters and as such have little, if any rotational inertia. The resulting
+reduction of total inertia raises important issues of power grid stability,
+especially over short-time scales. We have constructed a model of the
+synchronous grid of continental Europe with which we numerically investigate
+frequency deviations as well as rates of change of frequency (RoCoF) following
+abrupt power losses. The magnitude of RoCoF's and frequency deviations strongly
+depend on the fault location, and we find the largest effects for faults
+located on the slowest mode - the Fiedler mode - of the network Laplacian
+matrix. This mode essentially vanishes over Belgium, Eastern France, Western
+Germany, northern Italy and Switzerland. Buses inside these regions are only
+weakly affected by faults occuring outside. Conversely, faults inside these
+regions have only a local effect and disturb only weakly outside buses.
+Following this observation, we reduce rotational inertia through three
+different procedures by either (i) reducing inertia on the Fiedler mode, (ii)
+reducing inertia homogeneously and (iii) reducing inertia outside the Fiedler
+mode. We find that procedure (iii) has little effect on disturbance
+propagation, while procedure (i) leads to the strongest increase of RoCoF and
+frequency deviations. These results for our model of the European transmission
+grid are corroborated by numerical investigations on the ERCOT transmission
+grid.
+",1,0,0,0,0,0
+14284,Investigating the Characteristics of One-Sided Matching Mechanisms Under Various Preferences and Risk Attitudes,"  One-sided matching mechanisms are fundamental for assigning a set of
+indivisible objects to a set of self-interested agents when monetary transfers
+are not allowed. Two widely-studied randomized mechanisms in multiagent
+settings are the Random Serial Dictatorship (RSD) and the Probabilistic Serial
+Rule (PS). Both mechanisms require only that agents specify ordinal preferences
+and have a number of desirable economic and computational properties. However,
+the induced outcomes of the mechanisms are often incomparable and thus there
+are challenges when it comes to deciding which mechanism to adopt in practice.
+In this paper, we first consider the space of general ordinal preferences and
+provide empirical results on the (in)comparability of RSD and PS. We analyze
+their respective economic properties under general and lexicographic
+preferences. We then instantiate utility functions with the goal of gaining
+insights on the manipulability, efficiency, and envyfreeness of the mechanisms
+under different risk-attitude models. Our results hold under various preference
+distribution models, which further confirm the broad use of RSD in most
+practical applications.
+",1,0,0,0,0,0
+14285,MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer,"  We introduce MIDI-VAE, a neural network model based on Variational
+Autoencoders that is capable of handling polyphonic music with multiple
+instrument tracks, as well as modeling the dynamics of music by incorporating
+note durations and velocities. We show that MIDI-VAE can perform style transfer
+on symbolic music by automatically changing pitches, dynamics and instruments
+of a music piece from, e.g., a Classical to a Jazz style. We evaluate the
+efficacy of the style transfer by training separate style validation
+classifiers. Our model can also interpolate between short pieces of music,
+produce medleys and create mixtures of entire songs. The interpolations
+smoothly change pitches, dynamics and instrumentation to create a harmonic
+bridge between two music pieces. To the best of our knowledge, this work
+represents the first successful attempt at applying neural style transfer to
+complete musical compositions.
+",1,0,0,0,0,0
+14286,A General Deep Learning Framework for Structure and Dynamics Reconstruction from Time Series Data,"  In this work, we present Gumbel Graph Network, a model-free deep learning
+framework for dynamics learning and network reconstruction from the observed
+time series data. Our method requires no prior knowledge about underlying
+dynamics and has shown the state-of-the-art performance in three typical
+dynamical systems on complex networks.
+",1,0,0,0,0,0
+14287,Disorder robustness and protection of Majorana bound states in ferromagnetic chains on conventional superconductors,"  Majorana bound states (MBS) are well-established in the clean limit in chains
+of ferromagnetically aligned impurities deposited on conventional
+superconductors with finite spin-orbit coupling. Here we show that these MBS
+are very robust against disorder. By performing self-consistent calculations we
+find that the MBS are protected as long as the surrounding superconductor show
+no large signs of inhomogeneity. We find that longer chains offer more
+stability against disorder for the MBS, albeit the minigap decreases, as do
+increasing strengths of spin-orbit coupling and superconductivity.
+",0,1,0,0,0,0
+14288,Characterisation of novel prototypes of monolithic HV-CMOS pixel detectors for high energy physics experiments,"  An upgrade of the ATLAS experiment for the High Luminosity phase of LHC is
+planned for 2024 and foresees the replacement of the present Inner Detector
+(ID) with a new Inner Tracker (ITk) completely made of silicon devices.
+Depleted active pixel sensors built with the High Voltage CMOS (HV-CMOS)
+technology are investigated as an option to cover large areas in the outermost
+layers of the pixel detector and are especially interesting for the development
+of monolithic devices which will reduce the production costs and the material
+budget with respect to the present hybrid assemblies. For this purpose the
+H35DEMO, a large area HV-CMOS demonstrator chip, was designed by KIT, IFAE and
+University of Liverpool, and produced in AMS 350 nm CMOS technology. It
+consists of four pixel matrices and additional test structures. Two of the
+matrices include amplifiers and discriminator stages and are thus designed to
+be operated as monolithic detectors. In these devices the signal is mainly
+produced by charge drift in a small depleted volume obtained by applying a bias
+voltage of the order of 100 V. Moreover, to enhance the radiation hardness of
+the chip, this technology allows to enclose the electronics in the same deep
+N-WELLs which are also used as collecting electrodes. In this contribution the
+characterisation of H35DEMO chips and results of the very first beam test
+measurements of the monolithic CMOS matrices with high energetic pions at CERN
+SPS will be presented.
+",0,1,0,0,0,0
+14289,End-to-end DNN Based Speaker Recognition Inspired by i-vector and PLDA,"  Recently several end-to-end speaker verification systems based on deep neural
+networks (DNNs) have been proposed. These systems have been proven to be
+competitive for text-dependent tasks as well as for text-independent tasks with
+short utterances. However, for text-independent tasks with longer utterances,
+end-to-end systems are still outperformed by standard i-vector + PLDA systems.
+In this work, we develop an end-to-end speaker verification system that is
+initialized to mimic an i-vector + PLDA baseline. The system is then further
+trained in an end-to-end manner but regularized so that it does not deviate too
+far from the initial system. In this way we mitigate overfitting which normally
+limits the performance of end-to-end systems. The proposed system outperforms
+the i-vector + PLDA baseline on both long and short duration utterances.
+",1,0,0,0,0,0
+14290,Motivic infinite loop spaces,"  We prove a recognition principle for motivic infinite P1-loop spaces over a
+perfect field. This is achieved by developing a theory of framed motivic
+spaces, which is a motivic analogue of the theory of E-infinity-spaces. A
+framed motivic space is a motivic space equipped with transfers along finite
+syntomic morphisms with trivialized cotangent complex in K-theory. Our main
+result is that grouplike framed motivic spaces are equivalent to the full
+subcategory of motivic spectra generated under colimits by suspension spectra.
+As a consequence, we deduce some representability results for suspension
+spectra of smooth varieties, and in particular for the motivic sphere spectrum,
+in terms of Hilbert schemes of points in affine spaces.
+",0,0,1,0,0,0
+14291,Dimer correlation amplitudes and dimer excitation gap in spin-1/2 XXZ and Heisenberg chains,"  Correlation functions of dimer operators, the product operators of spins on
+two adjacent sites, are studied in the spin-$\frac{1}{2}$ XXZ chain in the
+critical regime. The amplitudes of the leading oscillating terms in the dimer
+correlation functions are determined with high accuracy as functions of the
+exchange anisotropy parameter and the external magnetic field, through the
+combined use of bosonization and density-matrix renormalization group methods.
+In particular, for the antiferromagnetic Heisenberg model with SU(2) symmetry,
+logarithmic corrections to the dimer correlations due to the
+marginally-irrelevant operator are studied, and the asymptotic form of the
+dimer correlation function is obtained. The asymptotic form of the spin-Peierls
+excitation gap including logarithmic corrections is also derived.
+",0,1,0,0,0,0
+14292,Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach,"  With the advent of the 5th generation of wireless standards and an increasing
+demand for higher throughput, methods to improve the spectral efficiency of
+wireless systems have become very important. In the context of cognitive radio,
+a substantial increase in throughput is possible if the secondary user can make
+smart decisions regarding which channel to sense and when or how often to
+sense. Here, we propose an algorithm to not only select a channel for data
+transmission but also to predict how long the channel will remain unoccupied so
+that the time spent on channel sensing can be minimized. Our algorithm learns
+in two stages - a reinforcement learning approach for channel selection and a
+Bayesian approach to determine the optimal duration for which sensing can be
+skipped. Comparisons with other learning methods are provided through extensive
+simulations. We show that the number of sensing is minimized with negligible
+increase in primary interference; this implies that lesser energy is spent by
+the secondary user in sensing and also higher throughput is achieved by saving
+on sensing.
+",1,0,0,0,0,0
+14293,Charge transport through a single molecule of trans-1-bis-diazofluorene [60]fullerene,"  Fullerenes have attracted interest for their possible applications in various
+electronic, biological, and optoelectronic devices. However, for efficient use
+in such devices, a suitable anchoring group has to be employed that forms
+well-defined and stable contacts with the electrodes. In this work, we propose
+a novel fullerene tetramalonate derivate functionalized with trans-1
+4,5-diazafluorene anchoring groups. The conductance of single-molecule
+junctions, investigated in two different setups with the mechanically
+controlled break junction technique, reveals the formation of molecular
+junctions at three conductance levels. We attribute the conductance peaks to
+three binding modes of the anchoring groups to the gold electrodes. Density
+functional theory calculations confirm the existence of multiple binding
+configurations and calculated transmission functions are consistent with
+experimentally determined conductance values.
+",0,1,0,0,0,0
+14294,Blind Spots for Direct Detection with Simplified DM Models and the LHC,"  Using the existing simplified model framework, we build several dark matter
+models which have suppressed spin-independent scattering cross section. We show
+that the scattering cross section can vanish due to interference effects with
+models obtained by simple combinations of simplified models. For weakly
+interacting massive particle (WIMP) masses $\gtrsim$10 GeV, collider limits are
+usually much weaker than the direct detection limits coming from LUX or
+XENON100. However, for our model combinations, LHC analyses are more
+competitive for some parts of the parameter space. The regions with direct
+detection blind spots can be strongly constrained from the complementary use of
+several Large Hadron Collider (LHC) searches like mono-jet, jets + missing
+transverse energy, heavy vector resonance searches, etc. We evaluate the
+strongest limits for combinations of scalar + vector, ""squark"" + vector, and
+scalar + ""squark"" mediator, and present the LHC 14 TeV projections.
+",0,1,0,0,0,0
+14295,Experiments of posture estimation on vehicles using wearable acceleration sensors,"  In this paper, we study methods to estimate drivers' posture in vehicles
+using acceleration data of wearable sensor and conduct a field test. Recently,
+sensor technologies have been progressed. Solutions of safety management to
+analyze vital data acquired from wearable sensor and judge work status are
+proposed. To prevent huge accidents, demands for safety management of bus and
+taxi are high. However, acceleration of vehicles is added to wearable sensor in
+vehicles, and there is no guarantee to estimate drivers' posture accurately.
+Therefore, in this paper, we study methods to estimate driving posture using
+acceleration data acquired from T-shirt type wearable sensor hitoe, conduct
+field tests and implement a sample application.
+",1,0,0,0,0,0
+14296,Matrix Completion Based Localization in the Internet of Things Network,"  In order to make a proper reaction to the collected information from internet
+of things (IoT) devices, location information of things should be available at
+the data center. One challenge for the massive IoT networks is to identify the
+location map of whole sensor nodes from partially observed distance
+information. In this paper, we propose a matrix completion based localization
+algorithm to reconstruct the location map of sensors using partially observed
+distance information. From the numerical experiments, we show that the proposed
+method based on the modified conjugate gradient is effective in recovering the
+Euclidean distance matrix.
+",1,0,0,0,0,0
+14297,Hypersurfaces with nonnegative Ricci curvature in hyperbolic space,"  Based on properties of n-subharmonic functions we show that a complete,
+noncompact, properly embedded hypersurface with nonnegative Ricci curvature in
+hyperbolic space has an asymptotic boundary at infinity of at most two points.
+Moreover, the presence of two points in the asymptotic boundary is a rigidity
+condition that forces the hypersurface to be an equidistant hypersurface about
+a geodesic line in hyperbolic space. This gives an affirmative answer to the
+question raised by Alexander and Currier in 1990.
+",0,0,1,0,0,0
+14298,"Achieving the time of $1$-NN, but the accuracy of $k$-NN","  We propose a simple approach which, given distributed computing resources,
+can nearly achieve the accuracy of $k$-NN prediction, while matching (or
+improving) the faster prediction time of $1$-NN. The approach consists of
+aggregating denoised $1$-NN predictors over a small number of distributed
+subsamples. We show, both theoretically and experimentally, that small
+subsample sizes suffice to attain similar performance as $k$-NN, without
+sacrificing the computational efficiency of $1$-NN.
+",0,0,1,1,0,0
+14299,A note on unitizations of generalized effect algebras,"  There is a forgetful functor from the category of generalized effect algebras
+to the category of effect algebras. We prove that this functor is a right
+adjoint and that the corresponding left adjoint is the well-known unitization
+construction by Hedlíková and Pulmannová. Moreover, this adjunction is
+monadic.
+",0,0,1,0,0,0
+14300,Real and Complex Integrals on Spheres and Balls,"  We evaluate integrals of certain polynomials over spheres and balls in real
+or complex spaces. We also promote the use of the Pochhammer symbol which gives
+the values of our integrals in compact forms.
+",0,0,1,0,0,0
+14301,Spin dynamics of FeGa$_{3-x}$Ge$_x$ studied by Electron Spin Resonance,"  The intermetallic semiconductor FeGa$_{3}$ acquires itinerant ferromagnetism
+upon electron doping by a partial replacement of Ga with Ge. We studied the
+electron spin resonance (ESR) of high-quality single crystals of
+FeGa$_{3-x}$Ge$_x$ for $x$ from 0 up to 0.162 where ferromagnetic order is
+observed. For $x = 0$ we observed a well-defined ESR signal, indicating the
+presence of pre-formed magnetic moments in the semiconducting phase. Upon Ge
+doping the occurrence of itinerant magnetism clearly affects the ESR properties
+below $\approx 40$~K whereas at higher temperatures an ESR signal as seen in
+FeGa$_{3}$ prevails independent on the Ge-content. The present results show
+that the ESR of FeGa$_{3-x}$Ge$_x$ is an appropriate and direct tool to
+investigate the evolution of 3d-based itinerant magnetism.
+",0,1,0,0,0,0
+14302,Topology and edge modes in quantum critical chains,"  We show that topology can protect exponentially localized, zero energy edge
+modes at critical points between one-dimensional symmetry protected topological
+phases. This is possible even without gapped degrees of freedom in the bulk
+---in contrast to recent work on edge modes in gapless chains. We present an
+intuitive picture for the existence of these edge modes in the case of
+non-interacting spinless fermions with time reversal symmetry (BDI class of the
+tenfold way). The stability of this phenomenon relies on a topological
+invariant defined in terms of a complex function, counting its zeros and poles
+inside the unit circle. This invariant can prevent two models described by the
+\emph{same} conformal field theory (CFT) from being smoothly connected. A full
+classification of critical phases in the non-interacting BDI class is obtained:
+each phase is labeled by the central charge of the CFT, $c \in
+\frac{1}{2}\mathbb N$, and the topological invariant, $\omega \in \mathbb Z$.
+Moreover, $c$ is determined by the difference in the number of edge modes
+between the phases neighboring the transition. Numerical simulations show that
+the topological edge modes of critical chains can be stable in the presence of
+interactions and disorder.
+",0,1,0,0,0,0
+14303,Incompressible fillings of manifolds,"  We find boundaries of Borel-Serre compactifications of locally symmetric
+spaces, for which any filling is incompressible. We prove this result by
+showing that these boundaries have small singular models and using these models
+to obstruct compressions. We also show that small singular models of boundaries
+obstruct $S^1$-actions (and more generally homotopically trivial $\mathbb
+Z/p$-actions) on interiors of aspherical fillings. We use this to bound the
+symmetry of complete Riemannian metrics on such interiors in terms of the
+fundamental group. We also use small singular models to simplify the proofs of
+some already known theorems about moduli spaces (the minimal orbifold theorem
+and a topological analogue of Royden's theorem).
+",0,0,1,0,0,0
+14304,Multichannel Attention Network for Analyzing Visual Behavior in Public Speaking,"  Public speaking is an important aspect of human communication and
+interaction. The majority of computational work on public speaking concentrates
+on analyzing the spoken content, and the verbal behavior of the speakers. While
+the success of public speaking largely depends on the content of the talk, and
+the verbal behavior, non-verbal (visual) cues, such as gestures and physical
+appearance also play a significant role. This paper investigates the importance
+of visual cues by estimating their contribution towards predicting the
+popularity of a public lecture. For this purpose, we constructed a large
+database of more than $1800$ TED talk videos. As a measure of popularity of the
+TED talks, we leverage the corresponding (online) viewers' ratings from
+YouTube. Visual cues related to facial and physical appearance, facial
+expressions, and pose variations are extracted from the video frames using
+convolutional neural network (CNN) models. Thereafter, an attention-based long
+short-term memory (LSTM) network is proposed to predict the video popularity
+from the sequence of visual features. The proposed network achieves
+state-of-the-art prediction accuracy indicating that visual cues alone contain
+highly predictive information about the popularity of a talk. Furthermore, our
+network learns a human-like attention mechanism, which is particularly useful
+for interpretability, i.e. how attention varies with time, and across different
+visual cues by indicating their relative importance.
+",1,0,0,0,0,0
+14305,The Łojasiewicz Exponent via The Valuative Hamburger-Noether Process,"  Let $k$ be an algebraically closed field of any characteristic. We apply the
+Hamburger-Noether process of successive quadratic transformations to show the
+equivalence of two definitions of the {\L}ojasiewicz exponent
+$\mathfrak{L}(\mathfrak{a})$ of an ideal $\mathfrak{a}\subset k[[x,y]]$.
+",0,0,1,0,0,0
+14306,Science and its significant other: Representing the humanities in bibliometric scholarship,"  Bibliometrics offers a particular representation of science. Through
+bibliometric methods a bibliometrician will always highlight particular
+elements of publications, and through these elements operationalize particular
+representations of science, while obscuring other possible representations from
+view. Understanding bibliometrics as representation implies that a bibliometric
+analysis is always performative: a bibliometric analysis brings a particular
+representation of science into being that potentially influences the science
+system itself. In this review we analyze the ways the humanities have been
+represented throughout the history of bibliometrics, often in comparison to
+other scientific domains or to a general notion of the sciences. Our review
+discusses bibliometric scholarship between 1965 and 2016 that studies the
+humanities empirically. We distinguish between two periods of bibliometric
+scholarship. The first period, between 1965 and 1989, is characterized by a
+sociological theoretical framework, the development and use of the Price index,
+and small samples of journal publications as data sources. The second period,
+from the mid-1980s up until the present day, is characterized by a new
+hinterland, that of science policy and research evaluation, in which
+bibliometric methods become embedded.
+",1,0,0,0,0,0
+14307,Referenceless Quality Estimation for Natural Language Generation,"  Traditional automatic evaluation measures for natural language generation
+(NLG) use costly human-authored references to estimate the quality of a system
+output. In this paper, we propose a referenceless quality estimation (QE)
+approach based on recurrent neural networks, which predicts a quality score for
+a NLG system output by comparing it to the source meaning representation only.
+Our method outperforms traditional metrics and a constant baseline in most
+respects; we also show that synthetic data helps to increase correlation
+results by 21% compared to the base system. Our results are comparable to
+results obtained in similar QE tasks despite the more challenging setting.
+",1,0,0,0,0,0
+14308,Regularisation of Neural Networks by Enforcing Lipschitz Continuity,"  We investigate the effect of explicitly enforcing the Lipschitz continuity of
+neural networks with respect to their inputs. To this end, we provide a simple
+technique for computing an upper bound to the Lipschitz constant of a feed
+forward neural network composed of commonly used layer types and demonstrate
+inaccuracies in previous work on this topic. Our technique is then used to
+formulate training a neural network with a bounded Lipschitz constant as a
+constrained optimisation problem that can be solved using projected stochastic
+gradient methods. Our evaluation study shows that, in isolation, our method
+performs comparatively to state-of-the-art regularisation techniques. Moreover,
+when combined with existing approaches to regularising neural networks the
+performance gains are cumulative. We also provide evidence that the
+hyperparameters are intuitive to tune and demonstrate how the choice of norm
+for computing the Lipschitz constant impacts the resulting model.
+",0,0,0,1,0,0
+14309,HiNet: Hierarchical Classification with Neural Network,"  Traditionally, classifying large hierarchical labels with more than 10000
+distinct traces can only be achieved with flatten labels. Although flatten
+labels is feasible, it misses the hierarchical information in the labels.
+Hierarchical models like HSVM by \cite{vural2004hierarchical} becomes
+impossible to train because of the sheer number of SVMs in the whole
+architecture. We developed a hierarchical architecture based on neural networks
+that is simple to train. Also, we derived an inference algorithm that can
+efficiently infer the MAP (maximum a posteriori) trace guaranteed by our
+theorems. Furthermore, the complexity of the model is only $O(n^2)$ compared to
+$O(n^h)$ in a flatten model, where $h$ is the height of the hierarchy.
+",1,0,0,0,0,0
+14310,Multiscale permutation entropy analysis of laser beam wandering in isotropic turbulence,"  We have experimentally quantified the temporal structural diversity from the
+coordinate fluctuations of a laser beam propagating through isotropic optical
+turbulence. The main focus here is on the characterization of the long-range
+correlations in the wandering of a thin Gaussian laser beam over a screen after
+propagating through a turbulent medium. To fulfill this goal, a
+laboratory-controlled experiment was conducted in which coordinate fluctuations
+of the laser beam were recorded at a sufficiently high sampling rate for a wide
+range of turbulent conditions. Horizontal and vertical displacements of the
+laser beam centroid were subsequently analyzed by implementing the symbolic
+technique based on ordinal patterns to estimate the well-known permutation
+entropy. We show that the permutation entropy estimations at multiple time
+scales evidence an interplay between different dynamical behaviors. More
+specifically, a crossover between two different scaling regimes is observed. We
+confirm a transition from an integrated stochastic process contaminated with
+electronic noise to a fractional Brownian motion with a Hurst exponent H = 5/6
+as the sampling time increases. Besides, we are able to quantify, from the
+estimated entropy, the amount of electronic noise as a function of the
+turbulence strength. We have also demonstrated that these experimental
+observations are in very good agreement with numerical simulations of noisy
+fractional Brownian motions with a well-defined crossover between two different
+scaling regimes.
+",0,1,0,0,0,0
+14311,Improving OpenCL Performance by Specializing Compiler Phase Selection and Ordering,"  Automatic compiler phase selection/ordering has traditionally been focused on
+CPUs and, to a lesser extent, FPGAs. We present experiments regarding compiler
+phase ordering specialization of OpenCL kernels targeting a GPU. We use
+iterative exploration to specialize LLVM phase orders on 15 OpenCL benchmarks
+to an NVIDIA GPU. We analyze the generated NVIDIA PTX code for the various
+versions to identify the main causes of the most significant improvements and
+present results of a set of experiments that demonstrate the importance of
+using specific phase orders. Using specialized compiler phase orders, we were
+able to achieve geometric mean improvements of 1.54x (up to 5.48x) and 1.65x
+(up to 5.7x) over PTX generated by the NVIDIA CUDA compiler from CUDA versions
+of the same kernels, and over execution of the OpenCL kernels compiled from
+source with the NVIDIA OpenCL driver, respectively. We also evaluate the use of
+code-features in the OpenCL kernels. More specifically, we evaluate an approach
+that achieves geometric mean improvements of 1.49x and 1.56x over the same
+OpenCL baseline, by using the compiler sequences of the 1 or 3 most similar
+benchmarks, respectively.
+",1,0,0,0,0,0
+14312,Shot noise and biased tracers: a new look at the halo model,"  Shot noise is an important ingredient to any measurement or theoretical
+modeling of discrete tracers of the large scale structure. Recent work has
+shown that the shot noise in the halo power spectrum becomes increasingly
+sub-Poissonian at high mass. Interestingly, while the halo model predicts a
+shot noise power spectrum in qualitative agreement with the data, it leads to
+an unphysical white noise in the cross halo-matter and matter power spectrum.
+In this work, we show that absorbing all the halo model sources of shot noise
+into the halo fluctuation field leads to meaningful predictions for the shot
+noise contributions to halo clustering statistics and remove the unphysical
+white noise from the cross halo-matter statistics. Our prescription
+straightforwardly maps onto the general bias expansion, so that the
+renormalized shot noise terms can be expressed as combinations of the halo
+model shot noises. Furthermore, we demonstrate that non-Poissonian
+contributions are related to volume integrals over correlation functions and
+their response to long-wavelength density perturbations. This leads to a new
+class of consistency relations for discrete tracers, which appear to be
+satisfied by our reformulation of the halo model. We test our theoretical
+predictions against measurements of halo shot noise bispectra extracted from a
+large suite of numerical simulations. Our model reproduces qualitatively the
+observed sub-Poissonian noise, although it underestimates the magnitude of this
+effect.
+",0,1,0,0,0,0
+14313,On the maximal directional Hilbert transform in three dimensions,"  We establish the sharp growth rate, in terms of cardinality, of the $L^p$
+norms of the maximal Hilbert transform $H_\Omega$ along finite subsets of a
+finite order lacunary set of directions $\Omega \subset \mathbb R^3$, answering
+a question of Parcet and Rogers in dimension $n=3$. Our result is the first
+sharp estimate for maximal directional singular integrals in dimensions greater
+than 2.
+The proof relies on a representation of the maximal directional Hilbert
+transform in terms of a model maximal operator associated to compositions of
+two-dimensional angular multipliers, as well as on the usage of weighted norm
+inequalities, and their extrapolation, in the directional setting.
+",0,0,1,0,0,0
+14314,MOLIERE: Automatic Biomedical Hypothesis Generation System,"  Hypothesis generation is becoming a crucial time-saving technique which
+allows biomedical researchers to quickly discover implicit connections between
+important concepts. Typically, these systems operate on domain-specific
+fractions of public medical data. MOLIERE, in contrast, utilizes information
+from over 24.5 million documents. At the heart of our approach lies a
+multi-modal and multi-relational network of biomedical objects extracted from
+several heterogeneous datasets from the National Center for Biotechnology
+Information (NCBI). These objects include but are not limited to scientific
+papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses
+using Latent Dirichlet Allocation applied on abstracts found near shortest
+paths discovered within this network, and demonstrate the effectiveness of
+MOLIERE by performing hypothesis generation on historical data. Our network,
+implementation, and resulting data are all publicly available for the broad
+scientific community.
+",1,0,0,1,0,0
+14315,Lower bounds for several online variants of bin packing,"  We consider several previously studied online variants of bin packing and
+prove new and improved lower bounds on the asymptotic competitive ratios for
+them. For that, we use a method of fully adaptive constructions. In particular,
+we improve the lower bound for the asymptotic competitive ratio of online
+square packing significantly, raising it from roughly 1.68 to above 1.75.
+",1,0,0,0,0,0
+14316,Query-limited Black-box Attacks to Classifiers,"  We study black-box attacks on machine learning classifiers where each query
+to the model incurs some cost or risk of detection to the adversary. We focus
+explicitly on minimizing the number of queries as a major objective.
+Specifically, we consider the problem of attacking machine learning classifiers
+subject to a budget of feature modification cost while minimizing the number of
+queries, where each query returns only a class and confidence score. We
+describe an approach that uses Bayesian optimization to minimize the number of
+queries, and find that the number of queries can be reduced to approximately
+one tenth of the number needed through a random strategy for scenarios where
+the feature modification cost budget is low.
+",1,0,0,1,0,0
+14317,Efficient Kinematic Planning for Mobile Manipulators with Non-holonomic Constraints Using Optimal Control,"  This work addresses the problem of kinematic trajectory planning for mobile
+manipulators with non-holonomic constraints, and holonomic operational-space
+tracking constraints. We obtain whole-body trajectories and time-varying
+kinematic feedback controllers by solving a Constrained Sequential Linear
+Quadratic Optimal Control problem. The employed algorithm features high
+efficiency through a continuous-time formulation that benefits from adaptive
+step-size integrators and through linear complexity in the number of
+integration steps. In a first application example, we solve kinematic
+trajectory planning problems for a 26 DoF wheeled robot. In a second example,
+we apply Constrained SLQ to a real-world mobile manipulator in a
+receding-horizon optimal control fashion, where we obtain optimal controllers
+and plans at rates up to 100 Hz.
+",1,0,0,0,0,0
+14318,Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation,"  Integrated Information Theory (IIT) is a prominent theory of consciousness
+that has at its centre measures that quantify the extent to which a system
+generates more information than the sum of its parts. While several candidate
+measures of integrated information (`$\Phi$') now exist, little is known about
+how they compare, especially in terms of their behaviour on non-trivial network
+models. In this article we provide clear and intuitive descriptions of six
+distinct candidate measures. We then explore the properties of each of these
+measures in simulation on networks consisting of eight interacting nodes,
+animated with Gaussian linear autoregressive dynamics. We find a striking
+diversity in the behaviour of these measures -- no two measures show consistent
+agreement across all analyses. Further, only a subset of the measures appear to
+genuinely reflect some form of dynamical complexity, in the sense of
+simultaneous segregation and integration between system components. Our results
+help guide the operationalisation of IIT and advance the development of
+measures of integrated information that may have more general applicability.
+",0,0,0,0,1,0
+14319,Volumes of $\mathrm{SL}_n\mathbb{C}$-representations of hyperbolic 3-manifolds,"  Let $M$ be a compact oriented three-manifold whose interior is hyperbolic of
+finite volume. We prove a variation formula for the volume on the variety of
+representations of $M$ in $\operatorname{SL}_n(\mathbb C)$. Our proof follows
+the strategy of Reznikov's rigidity when $M$ is closed, in particular we use
+Fuks' approach to variations by means of Lie algebra cohomology. When $n=2$, we
+get back Hodgson's formula for variation of volume on the space of hyperbolic
+Dehn fillings. Our formula also yields the variation of volume on the space of
+decorated triangulations obtained by Bergeron-Falbel-Guillou and
+Dimofte-Gabella-Goncharov.
+",0,0,1,0,0,0
+14320,Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds,"  In this paper we propose two novel bounds for the log-likelihood based on
+Kullback-Leibler and the Rényi divergences, which can be used for
+variational inference and in particular for the training of Variational
+AutoEncoders. Our proposal is motivated by the difficulties encountered in
+training VAEs on continuous datasets with high contrast images, such as those
+with handwritten digits and characters, where numerical issues often appear
+unless noise is added, either to the dataset during training or to the
+generative model given by the decoder. The new bounds we propose, which are
+obtained from the maximization of the likelihood of an interval for the
+observations, allow numerically stable training procedures without the
+necessity of adding any extra source of noise to the data.
+",0,0,0,1,0,0
+14321,Design and demonstration of an acoustic right-angle bend,"  In this paper, we design, fabricate and experimentally characterize a
+broadband acoustic right-angle bend device in air. Perforated panels with
+various hole-sizes are used to construct the bend structure. Both the simulated
+and the experimental results verify that acoustic beam can be rotated
+effectively through the acoustic bend in a wide frequency range. This model may
+have potential applications in some areas such as sound absorption and acoustic
+detection in pipeline.
+",0,1,0,0,0,0
+14322,FBG-Based Position Estimation of Highly Deformable Continuum Manipulators: Model-Dependent vs. Data-Driven Approaches,"  Conventional shape sensing techniques using Fiber Bragg Grating (FBG) involve
+finding the curvature at discrete FBG active areas and integrating curvature
+over the length of the continuum dexterous manipulator (CDM) for tip position
+estimation (TPE). However, due to limited number of sensing locations and many
+geometrical assumptions, these methods are prone to large error propagation
+especially when the CDM undergoes large deflections. In this paper, we study
+the complications of using the conventional TPE methods that are dependent on
+sensor model and propose a new data-driven method that overcomes these
+challenges. The proposed method consists of a regression model that takes FBG
+wavelength raw data as input and directly estimates the CDM's tip position.
+This model is pre-operatively (off-line) trained on position information from
+optical trackers/cameras (as the ground truth) and it intra-operatively
+(on-line) estimates CDM tip position using only the FBG wavelength data. The
+method's performance is evaluated on a CDM developed for orthopedic
+applications, and the results are compared to conventional model-dependent
+methods during large deflection bendings. Mean absolute TPE error (and standard
+deviation) of 1.52 (0.67) mm and 0.11 (0.1) mm with maximum absolute errors of
+3.63 mm and 0.62 mm for the conventional and the proposed data-driven
+techniques were obtained, respectively. These results demonstrate a significant
+out-performance of the proposed data-driven approach versus the conventional
+estimation technique.
+",1,0,0,0,0,0
+14323,Small-signal Stability Analysis and Performance Evaluation of Microgrids under Distributed Control,"  Distributed control, as a potential solution to decreasing communication
+demands in microgrids, has drawn much attention in recent years. Advantages of
+distributed control have been extensively discussed, while its impacts on
+microgrid performance and stability, especially in the case of communication
+latency, have not been explicitly studied or fully understood yet. This paper
+addresses this gap by proposing a generalized theoretical framework for
+small-signal stability analysis and performance evaluation for microgrids using
+distributed control. The proposed framework synthesizes generator and load
+frequency-domain characteristics, primary and secondary control loops, as well
+as the communication latency into a frequency-domain representation which is
+further evaluated by the generalized Nyquist theorem. In addition, various
+parameters and their impacts on microgrid dynamic performance are investigated
+and summarized into guidelines to help better design the system. Case studies
+demonstrate the effectiveness of the proposed approach.
+",1,0,0,0,0,0
+14324,Agent Failures in All-Pay Auctions,"  All-pay auctions, a common mechanism for various human and agent
+interactions, suffers, like many other mechanisms, from the possibility of
+players' failure to participate in the auction. We model such failures, and
+fully characterize equilibrium for this class of games, we present a symmetric
+equilibrium and show that under some conditions the equilibrium is unique. We
+reveal various properties of the equilibrium, such as the lack of influence of
+the most-likely-to-participate player on the behavior of the other players. We
+perform this analysis with two scenarios: the sum-profit model, where the
+auctioneer obtains the sum of all submitted bids, and the max-profit model of
+crowdsourcing contests, where the auctioneer can only use the best submissions
+and thus obtains only the winning bid.
+Furthermore, we examine various methods of influencing the probability of
+participation such as the effects of misreporting one's own probability of
+participating, and how influencing another player's participation chances
+changes the player's strategy.
+",1,0,0,0,0,0
+14325,"Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises","  We examine volume computation of general-dimensional polytopes and more
+general convex bodies, defined as the intersection of a simplex by a family of
+parallel hyperplanes, and another family of parallel hyperplanes or a family of
+concentric ellipsoids. Such convex bodies appear in modeling and predicting
+financial crises. The impact of crises on the economy (labor, income, etc.)
+makes its detection of prime interest. Certain features of dependencies in the
+markets clearly identify times of turmoil. We describe the relationship between
+asset characteristics by means of a copula; each characteristic is either a
+linear or quadratic form of the portfolio components, hence the copula can be
+constructed by computing volumes of convex bodies. We design and implement
+practical algorithms in the exact and approximate setting, we experimentally
+juxtapose them and study the tradeoff of exactness and accuracy for speed. We
+analyze the following methods in order of increasing generality: rejection
+sampling relying on uniformly sampling the simplex, which is the fastest
+approach, but inaccurate for small volumes; exact formulae based on the
+computation of integrals of probability distribution functions; an optimized
+Lawrence sign decomposition method, since the polytopes at hand are shown to be
+simple; Markov chain Monte Carlo algorithms using random walks based on the
+hit-and-run paradigm generalized to nonlinear convex bodies and relying on new
+methods for computing a ball enclosed; the latter is experimentally extended to
+non-convex bodies with very encouraging results. Our C++ software, based on
+CGAL and Eigen and available on github, is shown to be very effective in up to
+100 dimensions. Our results offer novel, effective means of computing portfolio
+dependencies and an indicator of financial crises, which is shown to correctly
+identify past crises.
+",0,0,0,0,0,1
+14326,Charting the replica symmetric phase,"  Diluted mean-field models are spin systems whose geometry of interactions is
+induced by a sparse random graph or hypergraph. Such models play an eminent
+role in the statistical mechanics of disordered systems as well as in
+combinatorics and computer science. In a path-breaking paper based on the
+non-rigorous `cavity method', physicists predicted not only the existence of a
+replica symmetry breaking phase transition in such models but also sketched a
+detailed picture of the evolution of the Gibbs measure within the replica
+symmetric phase and its impact on important problems in combinatorics, computer
+science and physics [Krzakala et al.: PNAS 2007]. In this paper we rigorise
+this picture completely for a broad class of models, encompassing the Potts
+antiferromagnet on the random graph, the $k$-XORSAT model and the diluted
+$k$-spin model for even $k$. We also prove a conjecture about the detection
+problem in the stochastic block model that has received considerable attention
+[Decelle et al.: Phys. Rev. E 2011].
+",1,1,0,0,0,0
+14327,Adaptive Clustering through Semidefinite Programming,"  We analyze the clustering problem through a flexible probabilistic model that
+aims to identify an optimal partition on the sample X 1 , ..., X n. We perform
+exact clustering with high probability using a convex semidefinite estimator
+that interprets as a corrected, relaxed version of K-means. The estimator is
+analyzed through a non-asymptotic framework and showed to be optimal or
+near-optimal in recovering the partition. Furthermore, its performances are
+shown to be adaptive to the problem's effective dimension, as well as to K the
+unknown number of groups in this partition. We illustrate the method's
+performances in comparison to other classical clustering algorithms with
+numerical experiments on simulated data.
+",0,0,1,1,0,0
+14328,Improving pairwise comparison models using Empirical Bayes shrinkage,"  Comparison data arises in many important contexts, e.g. shopping, web clicks,
+or sports competitions. Typically we are given a dataset of comparisons and
+wish to train a model to make predictions about the outcome of unseen
+comparisons. In many cases available datasets have relatively few comparisons
+(e.g. there are only so many NFL games per year) or efficiency is important
+(e.g. we want to quickly estimate the relative appeal of a product). In such
+settings it is well known that shrinkage estimators outperform maximum
+likelihood estimators. A complicating matter is that standard comparison models
+such as the conditional multinomial logit model are only models of conditional
+outcomes (who wins) and not of comparisons themselves (who competes). As such,
+different models of the comparison process lead to different shrinkage
+estimators. In this work we derive a collection of methods for estimating the
+pairwise uncertainty of pairwise predictions based on different assumptions
+about the comparison process. These uncertainty estimates allow us both to
+examine model uncertainty as well as perform Empirical Bayes shrinkage
+estimation of the model parameters. We demonstrate that our shrunk estimators
+outperform standard maximum likelihood methods on real comparison data from
+online comparison surveys as well as from several sports contexts.
+",1,0,0,1,0,0
+14329,Single Element Nonlinear Chimney Model,"  We generalize the chimney model by introducing nonlinear restoring and
+gravitational forces for the purpose of modeling swaying of trees at high wind
+speeds. Here we have restricted to the simplest case of a single element and
+the governing equation we arrive at has not been studied so far. We study the
+onset of fractal basin boundary of the two fixed points and also observe the
+chaotic solutions. We also examine the need for considering the full sine term
+in the gravitational force.
+",0,1,0,0,0,0
+14330,VOEvent Standard for Fast Radio Bursts,"  Fast radio bursts are a new class of transient radio phenomena currently
+detected as millisecond radio pulses with very high dispersion measures. As new
+radio surveys begin searching for FRBs a large population is expected to be
+detected in real-time, triggering a range of multi-wavelength and
+multi-messenger telescopes to search for repeating bursts and/or associated
+emission. Here we propose a method for disseminating FRB triggers using Virtual
+Observatory Events (VOEvents). This format was developed and is used
+successfully for transient alerts across the electromagnetic spectrum and for
+multi-messenger signals such as gravitational waves. In this paper we outline a
+proposed VOEvent standard for FRBs that includes the essential parameters of
+the event and where these parameters should be specified within the structure
+of the event. An additional advantage to the use of VOEvents for FRBs is that
+the events can automatically be ingested into the FRB Catalogue (FRBCAT)
+enabling real-time updates for public use. We welcome feedback from the
+community on the proposed standard outlined below and encourage those
+interested to join the nascent working group forming around this topic.
+",0,1,0,0,0,0
+14331,Fine-tuning deep CNN models on specific MS COCO categories,"  Fine-tuning of a deep convolutional neural network (CNN) is often desired.
+This paper provides an overview of our publicly available py-faster-rcnn-ft
+software library that can be used to fine-tune the VGG_CNN_M_1024 model on
+custom subsets of the Microsoft Common Objects in Context (MS COCO) dataset.
+For example, we improved the procedure so that the user does not have to look
+for suitable image files in the dataset by hand which can then be used in the
+demo program. Our implementation randomly selects images that contain at least
+one object of the categories on which the model is fine-tuned.
+",1,0,0,0,0,0
+14332,On the Throughput of Channels that Wear Out,"  This work investigates the fundamental limits of communication over a noisy
+discrete memoryless channel that wears out, in the sense of signal-dependent
+catastrophic failure. In particular, we consider a channel that starts as a
+memoryless binary-input channel and when the number of transmitted ones causes
+a sufficient amount of damage, the channel ceases to convey signals. Constant
+composition codes are adopted to obtain an achievability bound and the
+left-concave right-convex inequality is then refined to obtain a converse bound
+on the log-volume throughput for channels that wear out. Since infinite
+blocklength codes will always wear out the channel for any finite threshold of
+failure and therefore cannot convey information at positive rates, we analyze
+the performance of finite blocklength codes to determine the maximum expected
+transmission volume at a given level of average error probability. We show that
+this maximization problem has a recursive form and can be solved by dynamic
+programming. Numerical results demonstrate that a sequence of block codes is
+preferred to a single block code for streaming sources.
+",1,0,1,0,0,0
+14333,Reply to Marchildon: absorption and non-unitarity remain well-defined in the Relativistic Transactional Interpretation,"  I rebut some erroneous statements and attempt to clear up some
+misunderstandings in a recent set of critical remarks by Marchildon regarding
+the Relativistic Transactional Interpretation (RTI), showing that his negative
+conclusions regarding the transactional model are ill-founded.
+",0,1,0,0,0,0
+14334,Interaction-induced transition in the quantum chaotic dynamics of a disordered metal,"  We demonstrate that a weakly disordered metal with short-range interactions
+exhibits a transition in the quantum chaotic dynamics when changing the
+temperature or the interaction strength. For weak interactions, the system
+displays exponential growth of the out-of-time-ordered correlator (OTOC) of the
+current operator. The Lyapunov exponent of this growth is
+temperature-independent in the limit of vanishing interaction. With increasing
+the temperature or the interaction strength, the system undergoes a transition
+to a non-chaotic behaviour, for which the exponential growth of the OTOC is
+absent. We conjecture that the transition manifests itself in the quasiparticle
+energy-level statistics and also discuss ways of its explicit observation in
+cold-atom setups.
+",0,1,0,0,0,0
+14335,Epidemiological impact of waning immunization on a vaccinated population,"  This is an epidemiological SIRV model based study that is designed to analyze
+the impact of vaccination in containing infection spread, in a 4-tiered
+population compartment comprised of susceptible, infected, recovered and
+vaccinated agents. While many models assume a lifelong protection through
+vaccination, we focus on the impact of waning immunization due to conversion of
+vaccinated and recovered agents back to susceptible ones. Two asymptotic states
+exist, the ""disease-free equilibrium"" and the ""endemic equilibrium""; we express
+the transitions between these states as function of the vaccination and
+conversion rates using the basic reproduction number as a descriptor. We find
+that the vaccination of newborns and adults have different consequences in
+controlling epidemics. We also find that a decaying disease protection within
+the recovered sub-population is not sufficient to trigger an epidemic at the
+linear level. Our simulations focus on parameter sets that could model a
+disease with waning immunization like pertussis. For a diffusively coupled
+population, a transition to the endemic state can be initiated via the
+propagation of a traveling infection wave, described successfully within a
+Fisher-Kolmogorov framework.
+",0,0,0,0,1,0
+14336,Waves of seed propagation induced by delayed animal dispersion,"  We study a model of seed dispersal that considers the inclusion of an animal
+disperser moving diffusively, feeding on fruits and transporting the seeds,
+which are later deposited and capable of germination. The dynamics depends on
+several population parameters of growth, decay, harvesting, transport,
+digestion and germination. In particular, the deposition of transported seeds
+at places away from their collection sites produces a delay in the dynamics,
+whose effects are the focus of this work. Analytical and numerical solutions of
+different simplified scenarios show the existence of travelling waves. The
+effect of zoochory is apparent in the increase of the velocity of these waves.
+The results support the hypothesis of the relevance of animal mediated seed
+dispersion when trying to understand the origin of the high rates of vegetable
+invasion observed in real systems.
+",0,1,0,0,0,0
+14337,Sobolev GAN,"  We propose a new Integral Probability Metric (IPM) between distributions: the
+Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions
+for functions (critic) restricted to a Sobolev ball defined with respect to a
+dominant measure $\mu$. We show that the Sobolev IPM compares two distributions
+in high dimensions based on weighted conditional Cumulative Distribution
+Functions (CDF) of each coordinate on a leave one out basis. The Dominant
+measure $\mu$ plays a crucial role as it defines the support on which
+conditional CDFs are compared. Sobolev IPM can be seen as an extension of the
+one dimensional Von-Mises Cramér statistics to high dimensional
+distributions. We show how Sobolev IPM can be used to train Generative
+Adversarial Networks (GANs). We then exploit the intrinsic conditioning implied
+by Sobolev IPM in text generation. Finally we show that a variant of Sobolev
+GAN achieves competitive results in semi-supervised learning on CIFAR-10,
+thanks to the smoothness enforced on the critic by Sobolev GAN which relates to
+Laplacian regularization.
+",1,0,0,1,0,0
+14338,Understanding Human Motion and Gestures for Underwater Human-Robot Collaboration,"  In this paper, we present a number of robust methodologies for an underwater
+robot to visually detect, follow, and interact with a diver for collaborative
+task execution. We design and develop two autonomous diver-following
+algorithms, the first of which utilizes both spatial- and frequency-domain
+features pertaining to human swimming patterns in order to visually track a
+diver. The second algorithm uses a convolutional neural network-based model for
+robust tracking-by-detection. In addition, we propose a hand gesture-based
+human-robot communication framework that is syntactically simpler and
+computationally more efficient than the existing grammar-based frameworks. In
+the proposed interaction framework, deep visual detectors are used to provide
+accurate hand gesture recognition; subsequently, a finite-state machine
+performs robust and efficient gesture-to-instruction mapping. The
+distinguishing feature of this framework is that it can be easily adopted by
+divers for communicating with underwater robots without using artificial
+markers or requiring memorization of complex language rules. Furthermore, we
+validate the performance and effectiveness of the proposed methodologies
+through extensive field experiments in closed- and open-water environments.
+Finally, we perform a user interaction study to demonstrate the usability
+benefits of our proposed interaction framework compared to existing methods.
+",1,0,0,0,0,0
+14339,One-loop binding corrections to the electron $g$ factor,"  We calculate the one-loop electron self-energy correction of order
+$\alpha\,(Z\,\alpha)^5$ to the bound electron $g$ factor. Our result is in
+agreement with the extrapolated numerical value and paves the way for the
+calculation of the analogous, but as yet unknown two-loop correction.
+",0,1,0,0,0,0
+14340,Periodic solutions of Euler-Lagrange equations in an anisotropic Orlicz-Sobolev space setting,"  In this paper we consider the problem of finding periodic solutions of
+certain Euler-Lagrange equations, which include, among others, equations
+involving the $p$-Laplace and, more generality, the $(p,q)$-Laplace operator.
+We employ the direct method of the calculus of variations in the framework of
+anisotropic Orlicz-Sobolev spaces. These spaces appear to be useful in
+formulating a unified theory of existence for the type of problem considered.
+",0,0,1,0,0,0
+14341,Dehn functions of subgroups of right-angled Artin groups,"  We show that for each positive integer $k$ there exist right-angled Artin
+groups containing free-by-cyclic subgroups whose monodromy automorphisms grow
+as $n^k$. As a consequence we produce examples of right-angled Artin groups
+containing finitely presented subgroups whose Dehn functions grow as $n^{k+2}$.
+",0,0,1,0,0,0
+14342,Recovering Sparse Nonnegative Signals via Non-convex Fraction Function Penalty,"  Many real world practical problems can be formulated as
+$\ell_{0}$-minimization problems with nonnegativity constraints, which seek the
+sparsest nonnegative signals to underdetermined linear systems. They have been
+widely applied in signal and image processing, machine learning, pattern
+recognition and computer vision. Unfortunately, this $\ell_{0}$-minimization
+problem with nonnegativity constraint is computational and NP-hard because of
+the discrete and discontinuous nature of the $\ell_{0}$-norm. In this paper, we
+replace the $\ell_{0}$-norm with a non-convex fraction function, and study the
+minimization problem of this non-convex fraction function in recovering the
+sparse nonnegative signals from an underdetermined linear system. Firstly, we
+discuss the equivalence between $(P_{0}^{\geq})$ and $(FP_{a}^{\geq})$, and the
+equivalence between $(FP_{a}^{\geq})$ and $(FP_{a,\lambda}^{\geq})$. It is
+proved that the optimal solution of the problem $(P_{0}^{\geq})$ could be
+approximately obtained by solving the regularization problem
+$(FP_{a,\lambda}^{\geq})$ if some specific conditions satisfied. Secondly, we
+propose a nonnegative iterative thresholding algorithm to solve the
+regularization problem $(FP_{a,\lambda}^{\geq})$ for all $a>0$. Finally, some
+numerical experiments on sparse nonnegative siganl recovery problems show that
+our method performs effective in finding sparse nonnegative signals compared
+with the linear programming.
+",0,0,1,0,0,0
+14343,Estimating Local Interactions Among Many Agents Who Observe Their Neighbors,"  In various economic environments, people observe those with whom they
+strategically interact. We can model such information-sharing relations as an
+information network, and the strategic interactions as a game on the network.
+When any two agents in the network are connected either directly or indirectly,
+empirical modeling using an equilibrium approach is cumbersome, since the
+testable implications from an equilibrium generally involve all the players of
+the game, whereas a researcher's data set may contain only a fraction of these
+players in practice. This paper develops a tractable empirical model of linear
+interactions where each agent, after observing part of his neighbors' types,
+not knowing the full information network, uses best responses that are linear
+in his and other players' types that he observes, based on simple beliefs about
+other players' strategies. We provide conditions on information networks and
+beliefs such that best responses take an explicit form with multiple intuitive
+features. Furthermore, the best responses reveal how local payoff
+interdependence among agents is translated into local stochastic dependence of
+their actions, allowing the econometrician to perform asymptotic inference
+without having to observe all the players in the game or having to know
+precisely the sampling process.
+",0,0,0,1,0,0
+14344,Multimodal speech synthesis architecture for unsupervised speaker adaptation,"  This paper proposes a new architecture for speaker adaptation of
+multi-speaker neural-network speech synthesis systems, in which an unseen
+speaker's voice can be built using a relatively small amount of speech data
+without transcriptions. This is sometimes called ""unsupervised speaker
+adaptation"". More specifically, we concatenate the layers to the audio inputs
+when performing unsupervised speaker adaptation while we concatenate them to
+the text inputs when synthesizing speech from text. Two new training schemes
+for the new architecture are also proposed in this paper. These training
+schemes are not limited to speech synthesis, other applications are suggested.
+Experimental results show that the proposed model not only enables adaptation
+to unseen speakers using untranscribed speech but it also improves the
+performance of multi-speaker modeling and speaker adaptation using transcribed
+audio files.
+",1,0,0,1,0,0
+14345,Analytical history,"  The purpose of this note is to explain what is ""analytical history"", a
+modular and testable analysis of historical events introduced in a book
+published in 2002 (Roehner and Syme 2002). Broadly speaking, it is a
+comparative methodology for the analysis of historical events. Comparison is
+the keystone and hallmark of science. For instance, the extrasolar planets are
+crucial for understanding our own solar system. Until their discovery,
+astronomers could observe only one instance. Single instances can be described
+but they cannot be understood in a testable way. In other words, if one accepts
+that, as many historians say, ""historical events are unique"", then no testable
+understanding can be developed.
+",0,1,0,0,0,0
+14346,Control Synthesis for Multi-Agent Systems under Metric Interval Temporal Logic Specifications,"  This paper presents a framework for automatic synthesis of a control sequence
+for multi-agent systems governed by continuous linear dynamics under timed
+constraints. First, the motion of the agents in the workspace is abstracted
+into individual Transition Systems (TS). Second, each agent is assigned with an
+individual formula given in Metric Interval Temporal Logic (MITL) and in
+parallel, the team of agents is assigned with a collaborative team formula. The
+proposed method is based on a correct-by-construction control synthesis method,
+and hence guarantees that the resulting closed-loop system will satisfy the
+specifications. The specifications considers boolean-valued properties under
+real-time. Extended simulations has been performed in order to demonstrate the
+efficiency of the proposed controllers.
+",1,0,0,0,0,0
+14347,Eulerian and Lagrangian solutions to the continuity and Euler equations with $L^1$ vorticity,"  In the first part of this paper we establish a uniqueness result for
+continuity equations with velocity field whose derivative can be represented by
+a singular integral operator of an $L^1$ function, extending the Lagrangian
+theory in \cite{BouchutCrippa13}. The proof is based on a combination of a
+stability estimate via optimal transport techniques developed in \cite{Seis16a}
+and some tools from harmonic analysis introduced in \cite{BouchutCrippa13}. In
+the second part of the paper, we address a question that arose in
+\cite{FilhoMazzucatoNussenzveig06}, namely whether 2D Euler solutions obtained
+via vanishing viscosity are renormalized (in the sense of DiPerna and Lions)
+when the initial data has low integrability. We show that this is the case even
+when the initial vorticity is only in~$L^1$, extending the proof for the $L^p$
+case in \cite{CrippaSpirito15}.
+",0,1,1,0,0,0
+14348,"Star formation, supernovae, iron, and alpha: consistent cosmic and Galactic histories","  Recent versions of the observed cosmic star-formation history (SFH) have
+resolved an inconsistency with the stellar mass density history. We show that
+the revised SFH also scales up the delay-time distribution (DTD) of Type Ia
+supernovae (SNe Ia), as determined from the observed volumetric SN Ia rate
+history, aligning it with other field-galaxy SN Ia DTD measurements. The
+revised-SFH-based DTD has a $t^{-1.1 \pm 0.1}$ form and a
+Hubble-time-integrated production efficiency of $N/M_\star=1.3\pm0.1$ SNe Ia
+per $1000~{\rm M_\odot}$ of formed stellar mass. Using these revised histories
+and updated empirical iron yields of the various SN types, we re-derive the
+cosmic iron accumulation history. Core-collapse SNe and SNe Ia have contributed
+about equally to the total mass of iron in the Universe today. We find the
+track of the average cosmic gas element in the [$\alpha$/Fe] vs. [Fe/H]
+abundance-ratio plane. The track is broadly similar to the observed main locus
+of Galactic stars in this plane, indicating a Milky Way (MW) SFH similar in
+form to the cosmic one. We easily find a simple MW SFH that makes the track
+closely match this stellar locus. Galaxy clusters appear to have a
+higher-normalization DTD. This cluster DTD, combined with a short-burst MW SFH
+peaked at $z=3$, produces a track that matches remarkably well the observed
+""high-$\alpha$"" locus of MW stars, suggesting the halo/thick-disk population
+has had a galaxy-cluster-like formation mode. Thus, a simple two-component SFH,
+combined with empirical DTDs and SN iron yields, suffices to closely reproduce
+the MW's stellar abundance patterns.
+",0,1,0,0,0,0
+14349,ALFABURST: A commensal search for Fast Radio Bursts with Arecibo,"  ALFABURST has been searching for Fast Radio Bursts (FRBs) commensally with
+other projects using the Arecibo L-band Feed Array (ALFA) receiver at the
+Arecibo Observatory since July 2015. We describe the observing system and
+report on the non-detection of any FRBs from that time until August 2017 for a
+total observing time of 518 hours. With current FRB rate models, along with
+measurements of telescope sensitivity and beam size, we estimate that this
+survey probed redshifts out to about 3.4 with an effective survey volume of
+around 600,000 Mpc$^3$. Based on this, we would expect, at the 99% confidence
+level, to see at most two FRBs. We discuss the implications of this
+non-detection in the context of results from other telescopes and the
+limitation of our search pipeline. During the survey, single pulses from 17
+known pulsars were detected. We also report the discovery of a Galactic radio
+transient with a pulse width of 3 ms and dispersion measure of 281 pc
+cm$^{-3}$, which was detected while the telescope was slewing between fields.
+",0,1,0,0,0,0
+14350,Mechanical properties and thermal conductivity of graphitic carbon nitride: A molecular dynamics study,"  Graphitic carbon nitride nanosheets are among 2D attractive materials due to
+presenting unusual physicochemical properties.Nevertheless, no adequate
+information exists about their mechanical and thermal properties. Therefore, we
+used classical molecular dynamics simulations to explore the thermal
+conductivity and mechanical response of two main structures of single-layer
+triazine-basedg-C3N4 films.By performing uniaxial tensile modeling, we found
+remarkable elastic modulus of 320 and 210 GPa, and tensile strength of 47 GPa
+and 30 GPa for two different structures of g-C3N4sheets. Using equilibrium
+molecular dynamics simulations, the thermal conductivity of free-standing
+g-C3N4 structures were also predicted to be around 7.6 W/mK and 3.5 W/mK. Our
+study suggests the g-C3N4films as exciting candidate for reinforcement of
+polymeric materials mechanical properties.
+",0,1,0,0,0,0
+14351,Green's Functions of Partial Differential Equations with Involutions,"  In this paper we develop a way of obtaining Green's functions for partial
+differential equations with linear involutions by reducing the equation to a
+higher-order PDE without involutions. The developed theory is applied to a
+model of heat transfer in a conducting plate which is bent in half.
+",0,0,1,0,0,0
+14352,Zero-Modified Poisson-Lindley distribution with applications in zero-inflated and zero-deflated count data,"  The main object of this article is to present an extension of the
+zero-inflated Poisson-Lindley distribution, called of zero-modified
+Poisson-Lindley. The additional parameter $\pi$ of the zero-modified
+Poisson-Lindley has a natural interpretation in terms of either
+zero-deflated/inflated proportion. Inference is dealt with by using the
+likelihood approach. In particular the maximum likelihood estimators of the
+distribution's parameter are compared in small and large samples. We also
+consider an alternative bias-correction mechanism based on Efron's bootstrap
+resampling. The model is applied to real data sets and found to perform better
+than other competing models.
+",0,0,0,1,0,0
+14353,Phase-Retrieval as a Regularization Problem,"  It was recently shown that the phase retrieval imaging of a sample can be
+modeled as a simple convolution process. Sometimes, such a convolution depends
+on physical parameters of the sample which are difficult to estimate a priori.
+In this case, a blind choice for those parameters usually lead to wrong
+results, e.g., in posterior image segmentation processing. In this manuscript,
+we propose a simple connection between phase-retrieval algorithms and
+optimization strategies, which lead us to ways of numerically determining the
+physical parameters
+",0,0,1,0,0,0
+14354,Comparing simulations and test data of a radiation damaged charge-couple device for the Euclid mission,"  The VIS instrument on board the Euclid mission is a weak-lensing experiment
+that depends on very precise shape measurements of distant galaxies obtained by
+a large CCD array. Due to the harsh radiative environment outside the Earth's
+atmosphere, it is anticipated that the CCDs over the mission lifetime will be
+degraded to an extent that these measurements will only be possible through the
+correction of radiation damage effects. We have therefore created a Monte Carlo
+model that simulates the physical processes taking place when transferring
+signal through a radiation-damaged CCD. The software is based on
+Shockley-Read-Hall theory, and is made to mimic the physical properties in the
+CCD as closely as possible. The code runs on a single electrode level and takes
+three dimensional trap position, potential structure of the pixel, and
+multi-level clocking into account. A key element of the model is that it also
+takes device specific simulations of electron density as a direct input,
+thereby avoiding to make any analytical assumptions about the size and density
+of the charge cloud. This paper illustrates how test data and simulated data
+can be compared in order to further our understanding of the positions and
+properties of the individual radiation-induced traps.
+",0,1,0,0,0,0
+14355,A generalization of crossing families,"  For a set of points in the plane, a \emph{crossing family} is a set of line
+segments, each joining two of the points, such that any two line segments
+cross. We investigate the following generalization of crossing families: a
+\emph{spoke set} is a set of lines drawn through a point set such that each
+unbounded region of the induced line arrangement contains at least one point of
+the point set. We show that every point set has a spoke set of size
+$\sqrt{\frac{n}{8}}$. We also characterize the matchings obtained by selecting
+exactly one point in each unbounded region and connecting every such point to
+the point in the antipodal unbounded region.
+",1,0,0,0,0,0
+14356,Uncovering Offshore Financial Centers: Conduits and Sinks in the Global Corporate Ownership Network,"  Multinational corporations use highly complex structures of parents and
+subsidiaries to organize their operations and ownership. Offshore Financial
+Centers (OFCs) facilitate these structures through low taxation and lenient
+regulation, but are increasingly under scrutiny, for instance for enabling tax
+avoidance. Therefore, the identification of OFC jurisdictions has become a
+politicized and contested issue. We introduce a novel data-driven approach for
+identifying OFCs based on the global corporate ownership network, in which over
+98 million firms (nodes) are connected through 71 million ownership relations.
+This granular firm-level network data uniquely allows identifying both
+sink-OFCs and conduit-OFCs. Sink-OFCs attract and retain foreign capital while
+conduit-OFCs are attractive intermediate destinations in the routing of
+international investments and enable the transfer of capital without taxation.
+We identify 24 sink-OFCs. In addition, a small set of five countries -- the
+Netherlands, the United Kingdom, Ireland, Singapore and Switzerland -- canalize
+the majority of corporate offshore investment as conduit-OFCs. Each conduit
+jurisdiction is specialized in a geographical area and there is significant
+specialization based on industrial sectors. Against the idea of OFCs as exotic
+small islands that cannot be regulated, we show that many sink and conduit-OFCs
+are highly developed countries.
+",0,1,0,0,0,0
+14357,Predicting radio emission from the newborn hot Jupiter V830 Tau and its host star,"  Magnetised exoplanets are expected to emit at radio frequencies analogously
+to the radio auroral emission of Earth and Jupiter. We predict the radio
+emission from V830 Tau b, the youngest (2 Myr) detected exoplanet to date. We
+model the host star wind using 3DMHD simulations that take into account its
+surface magnetism. With this, we constrain the local conditions around V830 Tau
+b that we use to then compute its radio emission. We estimate average radio
+flux densities of 6 to 24mJy, depending on the assumed radius of the planet
+(one or two Rjupiter). These radio fluxes are present peaks that are up to
+twice the average values. We show here that these fluxes are weakly dependent
+(a factor of 1.8) on the assumed polar planetary magnetic field (10 to 100G),
+opposed to the maximum frequency of the emission, which ranges from 18 to
+240MHz. We also estimate the thermal radio emission from the stellar wind. By
+comparing our results with VLA and VLBA observations of the system, we
+constrain the stellar mass-loss rate to be <3e-9 Msun/yr, with likely values
+between ~1e-12 and 1e-10 Msun/yr. The frequency-dependent extension of the
+radio-emitting wind is around ~ 3 to 30 Rstar for frequencies in the range of
+275 to 50MHz, implying that V830 Tau b, at an orbital distance of 6.1 Rstar,
+could be embedded in the regions of the host star's wind that are optically
+thick to radio wavelengths, but not deeply so. Planetary emission can only
+propagate in the stellar wind plasma if the frequency of the cyclotron emission
+exceeds the stellar wind plasma frequency. For that, we find that for planetary
+radio emission to propagate through the host star wind, planetary magnetic
+field strengths larger than ~1.3 to 13 G are required. The V830 Tau system is a
+very interesting system for conducting radio observations from both the
+perspective of radio emission from the planet as well as from the host star's
+wind.
+",0,1,0,0,0,0
+14358,Redshift determination through weighted phase correlation: a linearithmic implementation,"  We present a new algorithm having a time complexity of O(N log N) and
+designed to retrieve the phase at which an input signal and a set of not
+necessarily orthogonal templates match best in a weighted chi-squared sense.
+The proposed implementation is based on an orthogonalization algorithm and thus
+also benefits from high numerical stability. We apply this method successfully
+to the redshift determination of quasars from the twelfth Sloan Digital Sky
+Survey (SDSS) quasar catalogue and derive the proper spectral reduction and
+redshift selection methods. Derivations of the redshift uncertainty and the
+associated confidence are also provided. The results of this application are
+comparable to the performance of the SDSS pipeline, while not having a
+quadratic time dependence.
+",0,1,0,0,0,0
+14359,Nonautonomous Dynamics of Acute Cell Injury,"  Clinically-relevant forms of acute cell injury, which include stroke and
+myocardial infarction, have been of long-lasting challenge in terms of
+successful intervention and treatments. Although laboratory studies have shown
+it is possible to decrease cell death after such injuries, human clinical
+trials based on laboratory therapies have generally failed. We suggested these
+failures are due, at least partially, to the lack of a quantitative theoretical
+framework for acute cell injury. Here we provide a systematic study on a
+nonlinear dynamical model of acute cell injury and characterize the global
+dynamics of a nonautonomous version of the theory. The nonautonomous model
+gives rise to four qualitative types of dynamical patterns that can be mapped
+to the behavior of cells after clinical acute injuries. In addition, the
+concept of a maximum total intrinsic stress response, $S_{max}^*$, emerges from
+the nonautonomous theory. A continuous transition across the four qualitative
+patterns has been observed, which sets a natural range for initial conditions.
+Under these initial conditions in the parameter space tested, the total induced
+stress response can be increased to 2.5-11 folds of $S_{max}^*$. This result
+indicates that cells possess a reserve stress response capacity which provides
+a theoretical explanation of how therapies can prevent cell death after lethal
+injuries. This nonautonomous theory of acute cell injury thus provides a
+quantitative framework for understanding cell death and recovery and developing
+effective therapeutics for acute injury.
+",0,0,0,0,1,0
+14360,Cavity-enhanced photoionization of an ultracold rubidium beam for application in focused ion beams,"  A two-step photoionization strategy of an ultracold rubidium beam for
+application in a focused ion beam instrument is analyzed and implemented. In
+this strategy the atomic beam is partly selected with an aperture after which
+the transmitted atoms are ionized in the overlap of a tightly cylindrically
+focused excitation laser beam and an ionization laser beam whose power is
+enhanced in a build-up cavity. The advantage of this strategy, as compared to
+without the use of a build-up cavity, is that higher ionization degrees can be
+reached at higher currents. Optical Bloch equations including the
+photoionization process are used to calculate what ionization degree and
+ionization position distribution can be reached. Furthermore, the ionization
+strategy is tested on an ultracold beam of $^{85}$Rb atoms. The beam current is
+measured as a function of the excitation and ionization laser beam intensity
+and the selection aperture size. Although details are different, the global
+trends of the measurements agree well with the calculation. With a selection
+aperture diameter of 52 $\mu$m, a current of $\left(170\pm4\right)$ pA is
+measured, which according to calculations is 63% of the current equivalent of
+the transmitted atomic flux. Taking into account the ionization degree the ion
+beam peak reduced brightness is estimated at $1\times10^7$ A/(m$^2\,$sr$\,$eV).
+",0,1,0,0,0,0
+14361,Fusion rule algebras related to a pair of compact groups,"  The purpose of the present paper is to investigate a fusion rule algebra
+arising from irreducible characters of a compact group $G$ and a closed
+subgroup $G_0$ of $G$ with finite index. The convolution of this fusion rule
+algebra is introduced by inducing irreducible representations of $G_0$ to $G$
+and by restricting irreducible representations of $G$ to $G_0$.
+",0,0,1,0,0,0
+14362,Matricial Canonical Moments and Parametrization of Matricial Hausdorff Moment Sequences,"  In this paper we study moment sequences of matrix-valued measures on compact
+intervals. A complete parametrization of such sequences is obtained via a
+symmetric version of matricial canonical moments. Furthermore, distinguished
+extensions of finite moment sequences are characterized in this framework. The
+results are applied to the underlying matrix-valued measures, generalizing some
+results from the scalar theory of canonical moments.
+",0,0,1,0,0,0
+14363,Embedding Deep Networks into Visual Explanations,"  In this paper, we propose a novel explanation module to explain the
+predictions made by a deep network. The explanation module works by embedding a
+high-dimensional deep network layer nonlinearly into a low-dimensional
+explanation space while retaining faithfulness, so that the original deep
+learning predictions can be constructed from the few concepts extracted by the
+explanation module. We then visualize such concepts for human to learn about
+the high-level concepts that deep learning is using to make decisions. We
+propose an algorithm called Sparse Reconstruction Autoencoder (SRAE) for
+learning the embedding to the explanation space. SRAE aims to reconstruct part
+of the original feature space while retaining faithfulness. A pull-away term is
+applied to SRAE to make the explanation space more orthogonal. A visualization
+system is then introduced for human understanding of the features in the
+explanation space. The proposed method is applied to explain CNN models in
+image classification tasks, and several novel metrics are introduced to
+evaluate the performance of explanations quantitatively without human
+involvement. Experiments show that the proposed approach generates interesting
+explanations of the mechanisms CNN use for making predictions.
+",1,0,0,0,0,0
+14364,Information Diffusion in Social Networks: Friendship Paradox based Models and Statistical Inference,"  Dynamic models and statistical inference for the diffusion of information in
+social networks is an area which has witnessed remarkable progress in the last
+decade due to the proliferation of social networks. Modeling and inference of
+diffusion of information has applications in targeted advertising and
+marketing, forecasting elections, predicting investor sentiment and identifying
+epidemic outbreaks. This chapter discusses three important aspects related to
+information diffusion in social networks: (i) How does observation bias named
+friendship paradox (a graph theoretic consequence) and monophilic contagion
+(influence of friends of friends) affect information diffusion dynamics. (ii)
+How can social networks adapt their structural connectivity depending on the
+state of information diffusion. (iii) How one can estimate the state of the
+network induced by information diffusion. The motivation for all three topics
+considered in this chapter stems from recent findings in network science and
+social sensing. Further, several directions for future research that arise from
+these topics are also discussed.
+",1,0,0,0,0,0
+14365,Really should we pruning after model be totally trained? Pruning based on a small amount of training,"  Pre-training of models in pruning algorithms plays an important role in
+pruning decision-making. We find that excessive pre-training is not necessary
+for pruning algorithms. According to this idea, we propose a pruning
+algorithm---Incremental pruning based on less training (IPLT). Compared with
+the traditional pruning algorithm based on a large number of pre-training, IPLT
+has competitive compression effect than the traditional pruning algorithm under
+the same simple pruning strategy. On the premise of ensuring accuracy, IPLT can
+achieve 8x-9x compression for VGG-19 on CIFAR-10 and only needs to pre-train
+few epochs. For VGG-19 on CIFAR-10, we can not only achieve 10 times test
+acceleration, but also about 10 times training acceleration. At present, the
+research mainly focuses on the compression and acceleration in the application
+stage of the model, while the compression and acceleration in the training
+stage are few. We newly proposed a pruning algorithm that can compress and
+accelerate in the training stage. It is novel to consider the amount of
+pre-training required by pruning algorithm. Our results have implications: Too
+much pre-training may be not necessary for pruning algorithms.
+",1,0,0,0,0,0
+14366,Adversarial Networks for the Detection of Aggressive Prostate Cancer,"  Semantic segmentation constitutes an integral part of medical image analyses
+for which breakthroughs in the field of deep learning were of high relevance.
+The large number of trainable parameters of deep neural networks however
+renders them inherently data hungry, a characteristic that heavily challenges
+the medical imaging community. Though interestingly, with the de facto standard
+training of fully convolutional networks (FCNs) for semantic segmentation being
+agnostic towards the `structure' of the predicted label maps, valuable
+complementary information about the global quality of the segmentation lies
+idle. In order to tap into this potential, we propose utilizing an adversarial
+network which discriminates between expert and generated annotations in order
+to train FCNs for semantic segmentation. Because the adversary constitutes a
+learned parametrization of what makes a good segmentation at a global level, we
+hypothesize that the method holds particular advantages for segmentation tasks
+on complex structured, small datasets. This holds true in our experiments: We
+learn to segment aggressive prostate cancer utilizing MRI images of 152
+patients and show that the proposed scheme is superior over the de facto
+standard in terms of the detection sensitivity and the dice-score for
+aggressive prostate cancer. The achieved relative gains are shown to be
+particularly pronounced in the small dataset limit.
+",1,0,0,0,0,0
+14367,A class of singular integrals associated with Zygmund dilations,"  The main purpose of this paper is to study multi-parameter singular integral
+operators which commute with Zygmund dilations. We introduce a class of
+singular integral operators associated with Zygmund dilations and show the
+boundedness for these operators on $L^p, 1<p<\infty$, which covers those
+studied by Ricci--Stein \cite{RS} and Nagel--Wainger \cite{NW}
+",0,0,1,0,0,0
+14368,Surge-like oscillations above sunspot light bridges driven by magnetoacoustic shocks,"  High-resolution observations of the solar chromosphere and transition region
+often reveal surge-like oscillatory activities above sunspot light bridges.
+These oscillations are often interpreted as intermittent plasma jets produced
+by quasi-periodic magnetic reconnection. We have analyzed the oscillations
+above a light bridge in a sunspot using data taken by the Interface Region
+Imaging Spectrograph (IRIS). The chromospheric 2796\AA{}~images show surge-like
+activities above the entire light bridge at any time, forming an oscillating
+wall. Within the wall we often see that the Mg~{\sc{ii}}~k 2796.35\AA{}~line
+core first experiences a large blueshift, and then gradually decreases to zero
+shift before increasing to a red shift of comparable magnitude. Such a behavior
+suggests that the oscillations are highly nonlinear and likely related to
+shocks. In the 1400\AA{}~passband which samples emission mainly from the
+Si~{\sc{iv}}~ion, the most prominent feature is a bright oscillatory front
+ahead of the surges. We find a positive correlation between the acceleration
+and maximum velocity of the moving front, which is consistent with numerical
+simulations of upward propagating slow-mode shock waves. The Si~{\sc{iv}}
+1402.77\AA{}~line profile is generally enhanced and broadened in the bright
+front, which might be caused by turbulence generated through compression or by
+the shocks. These results, together with the fact that the oscillation period
+stays almost unchanged over a long duration, lead us to propose that the
+surge-like oscillations above light bridges are caused by shocked p-mode waves
+leaked from the underlying photosphere.
+",0,1,0,0,0,0
+14369,A new Composition-Diamond lemma for dialgebras,"  Let $Di\langle X\rangle$ be the free dialgebra over a field generated by a
+set $X$. Let $S$ be a monic subset of $Di\langle X\rangle$. A
+Composition-Diamond lemma for dialgebras is firstly established by Bokut, Chen
+and Liu in 2010 \cite{Di} which claims that if (i) $S$ is a
+Gröbner-Shirshov basis in $Di\langle X\rangle$, then (ii) the set of
+$S$-irreducible words is a linear basis of the quotient dialgebra $Di\langle X
+\mid S \rangle$, but not conversely. Such a lemma based on a fixed ordering on
+normal diwords of $Di\langle X\rangle$ and special definition of composition
+trivial modulo $S$. In this paper, by introducing an arbitrary monomial-center
+ordering and the usual definition of composition trivial modulo $S$, we give a
+new Composition-Diamond lemma for dialgebras which makes the conditions (i) and
+(ii) equivalent. We show that every ideal of $Di\langle X\rangle$ has a unique
+reduced Gröbner-Shirshov basis. The new lemma is more useful and convenient
+than the one in \cite{Di}. As applications, we give a method to find normal
+forms of elements of an arbitrary disemigroup, in particular, A.V. Zhuchok's
+(2010) and Y.V. Zhuchok's (2015) normal forms of the free commutative
+disemigroups and the free abelian disemigroups, and normal forms of the free
+left (right) commutative disemigroups.
+",0,0,1,0,0,0
+14370,A Remote Interface for Live Interaction with OMNeT++ Simulations,"  Discrete event simulators, such as OMNeT++, provide fast and convenient
+methods for the assessment of algorithms and protocols, especially in the
+context of wired and wireless networks. Usually, simulation parameters such as
+topology and traffic patterns are predefined to observe the behaviour
+reproducibly. However, for learning about the dynamic behaviour of a system, a
+live interaction that allows changing parameters on the fly is very helpful.
+This is especially interesting for providing interactive demonstrations at
+conferences and fairs. In this paper, we present a remote interface to OMNeT++
+simulations that can be used to control the simulations while visualising
+real-time data merged from multiple OMNeT++ instances. We explain the software
+architecture behind our framework and how it can be used to build
+demonstrations on the foundation of OMNeT++.
+",1,0,0,0,0,0
+14371,Dynamic time warping distance for message propagation classification in Twitter,"  Social messages classification is a research domain that has attracted the
+attention of many researchers in these last years. Indeed, the social message
+is different from ordinary text because it has some special characteristics
+like its shortness. Then the development of new approaches for the processing
+of the social message is now essential to make its classification more
+efficient. In this paper, we are mainly interested in the classification of
+social messages based on their spreading on online social networks (OSN). We
+proposed a new distance metric based on the Dynamic Time Warping distance and
+we use it with the probabilistic and the evidential k Nearest Neighbors (k-NN)
+classifiers to classify propagation networks (PrNets) of messages. The
+propagation network is a directed acyclic graph (DAG) that is used to record
+propagation traces of the message, the traversed links and their types. We
+tested the proposed metric with the chosen k-NN classifiers on real world
+propagation traces that were collected from Twitter social network and we got
+good classification accuracies.
+",1,0,0,1,0,0
+14372,Proceedings of the 3rd International Workshop on Overlay Architectures for FPGAs (OLAF 2017),"  The 3rd International Workshop on Overlay Architectures for FPGAs (OLAF 2017)
+was held on 22 Feb, 2017 as a co-located workshop at the 25th ACM/SIGDA
+International Symposium on Field-Programmable Gate Arrays (FPGA 2017). This
+year, the program committee selected 3 papers and 3 extended abstracts to be
+presented at the workshop, which are subsequently collected in this online
+volume.
+",1,0,0,0,0,0
+14373,Simultaneous Modeling of Multiple Complications for Risk Profiling in Diabetes Care,"  Type 2 diabetes mellitus (T2DM) is a chronic disease that often results in
+multiple complications. Risk prediction and profiling of T2DM complications is
+critical for healthcare professionals to design personalized treatment plans
+for patients in diabetes care for improved outcomes. In this paper, we study
+the risk of developing complications after the initial T2DM diagnosis from
+longitudinal patient records. We propose a novel multi-task learning approach
+to simultaneously model multiple complications where each task corresponds to
+the risk modeling of one complication. Specifically, the proposed method
+strategically captures the relationships (1) between the risks of multiple T2DM
+complications, (2) between the different risk factors, and (3) between the risk
+factor selection patterns. The method uses coefficient shrinkage to identify an
+informative subset of risk factors from high-dimensional data, and uses a
+hierarchical Bayesian framework to allow domain knowledge to be incorporated as
+priors. The proposed method is favorable for healthcare applications because in
+additional to improved prediction performance, relationships among the
+different risks and risk factors are also identified. Extensive experimental
+results on a large electronic medical claims database show that the proposed
+method outperforms state-of-the-art models by a significant margin.
+Furthermore, we show that the risk associations learned and the risk factors
+identified lead to meaningful clinical insights.
+",0,0,0,1,0,0
+14374,On a topology property for moduli space of Kapustin-Witten equations,"  In this article, we study the Kapustin-Witten equations on a closed,
+simply-connected, four-manifold. We using a compactness theorem due to Taubes
+to prove that if $(A,\phi)$ is a solution of Kapustin-Witten equations and the
+connection $A$ is closed to a $generic$ ASD connection $A_{\infty}$, then
+$(A,\phi)$ must be a trivial solution. We also prove that the moduli space of
+the solutions of Kapustin-Witten equations is non-connected if the connections
+on the compactification of moduli space of ASD connections are all $generic$.
+As one application, we extend the ideas of Kapustin-Witten equations to other
+equations on gauge theory-- Hitchin-Simpson equations and Vafa-Witten on
+compact Kähler surface with a Kähler metric $g$.
+",0,0,1,0,0,0
+14375,Neural Semantic Parsing over Multiple Knowledge-bases,"  A fundamental challenge in developing semantic parsers is the paucity of
+strong supervision in the form of language utterances annotated with logical
+form. In this paper, we propose to exploit structural regularities in language
+in different domains, and train semantic parsers over multiple knowledge-bases
+(KBs), while sharing information across datasets. We find that we can
+substantially improve parsing accuracy by training a single
+sequence-to-sequence model over multiple KBs, when providing an encoding of the
+domain at decoding time. Our model achieves state-of-the-art performance on the
+Overnight dataset (containing eight domains), improves performance over a
+single KB baseline from 75.6% to 79.6%, while obtaining a 7x reduction in the
+number of model parameters.
+",1,0,0,0,0,0
+14376,Self-Committee Approach for Image Restoration Problems using Convolutional Neural Network,"  There have been many discriminative learning methods using convolutional
+neural networks (CNN) for several image restoration problems, which learn the
+mapping function from a degraded input to the clean output. In this letter, we
+propose a self-committee method that can find enhanced restoration results from
+the multiple trial of a trained CNN with different but related inputs.
+Specifically, it is noted that the CNN sometimes finds different mapping
+functions when the input is transformed by a reversible transform and thus
+produces different but related outputs with the original. Hence averaging the
+outputs for several different transformed inputs can enhance the results as
+evidenced by the network committee methods. Unlike the conventional committee
+approaches that require several networks, the proposed method needs only a
+single network. Experimental results show that adding an additional transform
+as a committee always brings additional gain on image denoising and single
+image supre-resolution problems.
+",1,0,0,0,0,0
+14377,Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations,"  Differential Privacy (DP) has received increasing attention as a rigorous
+privacy framework. Many existing studies employ traditional DP mechanisms
+(e.g., the Laplace mechanism) as primitives to continuously release private
+data for protecting privacy at each time point (i.e., event-level privacy),
+which assume that the data at different time points are independent, or that
+adversaries do not have knowledge of correlation between data. However,
+continuously generated data tend to be temporally correlated, and such
+correlations can be acquired by adversaries. In this paper, we investigate the
+potential privacy loss of a traditional DP mechanism under temporal
+correlations. First, we analyze the privacy leakage of a DP mechanism under
+temporal correlation that can be modeled using Markov Chain. Our analysis
+reveals that, the event-level privacy loss of a DP mechanism may
+\textit{increase over time}. We call the unexpected privacy loss
+\textit{temporal privacy leakage} (TPL). Although TPL may increase over time,
+we find that its supremum may exist in some cases. Second, we design efficient
+algorithms for calculating TPL. Third, we propose data releasing mechanisms
+that convert any existing DP mechanism into one against TPL. Experiments
+confirm that our approach is efficient and effective.
+",1,0,0,0,0,0
+14378,A Sufficient Condition for Nilpotency of the Nilpotent Residual of a Finite Group,"  Let $G$ be a finite group with the property that if $a,b$ are powers of
+$\delta_1^*$-commutators such that $(|a|,|b|)=1$, then $|ab|=|a||b|$. We show
+that $\gamma_{\infty}(G)$ is nilpotent.
+",0,0,1,0,0,0
+14379,Monte Carlo Estimation of the Density of the Sum of Dependent Random Variables,"  We study an unbiased estimator for the density of a sum of random variables
+that are simulated from a computer model. A numerical study on examples with
+copula dependence is conducted where the proposed estimator performs favourably
+in terms of variance compared to other unbiased estimators. We provide
+applications and extensions to the estimation of marginal densities in Bayesian
+statistics and to the estimation of the density of sums of random variables
+under Gaussian copula dependence.
+",0,0,1,1,0,0
+14380,Transferable neural networks for enhanced sampling of protein dynamics,"  Variational auto-encoder frameworks have demonstrated success in reducing
+complex nonlinear dynamics in molecular simulation to a single non-linear
+embedding. In this work, we illustrate how this non-linear latent embedding can
+be used as a collective variable for enhanced sampling, and present a simple
+modification that allows us to rapidly perform sampling in multiple related
+systems. We first demonstrate our method is able to describe the effects of
+force field changes in capped alanine dipeptide after learning a model using
+AMBER99. We further provide a simple extension to variational dynamics encoders
+that allows the model to be trained in a more efficient manner on larger
+systems by encoding the outputs of a linear transformation using time-structure
+based independent component analysis (tICA). Using this technique, we show how
+such a model trained for one protein, the WW domain, can efficiently be
+transferred to perform enhanced sampling on a related mutant protein, the GTT
+mutation. This method shows promise for its ability to rapidly sample related
+systems using a single transferable collective variable and is generally
+applicable to sets of related simulations, enabling us to probe the effects of
+variation in increasingly large systems of biophysical interest.
+",0,0,0,1,1,0
+14381,Collisional stripping of planetary crusts,"  Geochemical studies of planetary accretion and evolution have invoked various
+degrees of collisional erosion to explain differences in bulk composition
+between planets and chondrites. Here we undertake a full, dynamical evaluation
+of 'crustal stripping' during accretion and its key geochemical consequences.
+We present smoothed particle hydrodynamics simulations of collisions between
+differentiated rocky planetesimals and planetary embryos. We find that the
+crust is preferentially lost relative to the mantle during impacts, and we have
+developed a scaling law that approximates the mass of crust that remains in the
+largest remnant. Using this scaling law and a recent set of N-body simulations,
+we have estimated the maximum effect of crustal stripping on incompatible
+element abundances during the accretion of planetary embryos. We find that on
+average one third of the initial crust is stripped from embryos as they
+accrete, which leads to a reduction of ~20% in the budgets of the heat
+producing elements if the stripped crust does not reaccrete. Erosion of crusts
+can lead to non-chondritic ratios of incompatible elements, but the magnitude
+of this effect depends sensitively on the details of the crust-forming melting
+process. The Lu/Hf system is fractionated for a wide range of crustal formation
+scenarios. Using eucrites (the products of planetesimal silicate melting,
+thought to represent the crust of Vesta) as a guide to the Lu/Hf of
+planetesimal crust partially lost during accretion, we predict the Earth could
+evolve to a superchondritic 176-Hf/177-Hf (3-5 parts per ten thousand) at
+present day. Such values are in keeping with compositional estimates of the
+bulk Earth. Stripping of planetary crusts during accretion can lead to
+detectable changes in bulk composition of lithophile elements, but the
+fractionation is relatively subtle, and sensitive to the efficiency of
+reaccretion.
+",0,1,0,0,0,0
+14382,N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification,"  Graph Convolutional Networks (GCNs) have shown significant improvements in
+semi-supervised learning on graph-structured data. Concurrently, unsupervised
+learning of graph embeddings has benefited from the information contained in
+random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which
+marries these two lines of work. At its core, N-GCN trains multiple instances
+of GCNs over node pairs discovered at different distances in random walks, and
+learns a combination of the instance outputs which optimizes the classification
+objective. Our experiments show that our proposed N-GCN model improves
+state-of-the-art baselines on all of the challenging node classification tasks
+we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method
+has other desirable properties, including generalization to recently proposed
+semi-supervised learning methods such as GraphSAGE, allowing us to propose
+N-SAGE, and resilience to adversarial input perturbations.
+",1,0,0,1,0,0
+14383,Sparse Kneser graphs are Hamiltonian,"  For integers $k\geq 1$ and $n\geq 2k+1$, the Kneser graph $K(n,k)$ is the
+graph whose vertices are the $k$-element subsets of $\{1,\ldots,n\}$ and whose
+edges connect pairs of subsets that are disjoint. The Kneser graphs of the form
+$K(2k+1,k)$ are also known as the odd graphs. We settle an old problem due to
+Meredith, Lloyd, and Biggs from the 1970s, proving that for every $k\geq 3$,
+the odd graph $K(2k+1,k)$ has a Hamilton cycle. This and a known conditional
+result due to Johnson imply that all Kneser graphs of the form $K(2k+2^a,k)$
+with $k\geq 3$ and $a\geq 0$ have a Hamilton cycle. We also prove that
+$K(2k+1,k)$ has at least $2^{2^{k-6}}$ distinct Hamilton cycles for $k\geq 6$.
+Our proofs are based on a reduction of the Hamiltonicity problem in the odd
+graph to the problem of finding a spanning tree in a suitably defined
+hypergraph on Dyck words.
+",1,0,0,0,0,0
+14384,Fixed points of morphisms among binary generalized pseudostandard words,"  We introduce a class of fixed points of primitive morphisms among aperiodic
+binary generalized pseudostandard words. We conjecture that this class contains
+all fixed points of primitive morphisms among aperiodic binary generalized
+pseudostandard words that are not standard Sturmian words.
+",0,0,1,0,0,0
+14385,Dimension of the space of conics on Fano hypersurfaces,"  R. Beheshti showed that, for a smooth Fano hypersurface $X$ of degree $\leq
+8$ over the complex number field $\mathbb{C}$, the dimension of the space of
+lines lying in $X$ is equal to the expected dimension. We study the space of
+conics on $X$. In this case, if $X$ contains some linear subvariety, then the
+dimension of the space can be larger than the expected dimension. In this
+paper, we show that, for a smooth Fano hypersurface $X$ of degree $\leq 6$ over
+$\mathbb{C}$, and for an irreducible component $R$ of the space of conics lying
+in $X$, if the $2$-plane spanned by a general conic of $R$ is not contained in
+$X$, then the dimension of $R$ is equal to the expected dimension.
+",0,0,1,0,0,0
+14386,Automorphisms of Partially Commutative Groups III: Inversions and Transvections,"  The structure of a certain subgroup $S$ of the automorphism group of a
+partially commutative group (RAAG) $G$ is described in detail: namely the
+subgroup generated by inversions and elementary transvections. We define
+admissible subsets of the generators of $G$, and show that $S$ is the subgroup
+of automorphisms which fix all subgroups $\langle Y\rangle$ of $G$, for all
+admissible subsets $Y$. A decomposition of $S$ as an iterated tower of
+semi-direct products in given and the structure of the factors of this
+decomposition described. The construction allows a presentation of $S$ to be
+computed, from the commutation graph of $G$.
+",0,0,1,0,0,0
+14387,Testing Global Constraints,"  Every Constraint Programming (CP) solver exposes a library of constraints for
+solving combinatorial problems. In order to be useful, CP solvers need to be
+bug-free. Therefore the testing of the solver is crucial to make developers and
+users confident. We present a Java library allowing any JVM based solver to
+test that the implementations of the individual constraints are correct. The
+library can be used in a test suite executed in a continuous integration tool
+or it can also be used to discover minimalist instances violating some
+properties (arc-consistency, etc) in order to help the developer to identify
+the origin of the problem using standard debuggers.
+",1,0,0,0,0,0
+14388,Restricted Boltzmann Machines: Introduction and Review,"  The restricted Boltzmann machine is a network of stochastic units with
+undirected interactions between pairs of visible and hidden units. This model
+was popularized as a building block of deep learning architectures and has
+continued to play an important role in applied and theoretical machine
+learning. Restricted Boltzmann machines carry a rich structure, with
+connections to geometry, applied algebra, probability, statistics, machine
+learning, and other areas. The analysis of these models is attractive in its
+own right and also as a platform to combine and generalize mathematical tools
+for graphical models with hidden variables. This article gives an introduction
+to the mathematical analysis of restricted Boltzmann machines, reviews recent
+results on the geometry of the sets of probability distributions representable
+by these models, and suggests a few directions for further investigation.
+",0,0,0,1,0,0
+14389,Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes,"  3D Convolutional Neural Networks (3D-CNN) have been used for object
+recognition based on the voxelized shape of an object. However, interpreting
+the decision making process of these 3D-CNNs is still an infeasible task. In
+this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation
+Mapping method (3D-GradCAM) for visual explanations of the distinct local
+geometric features of interest within an object. To enable efficient learning
+of 3D geometries, we augment the voxel data with surface normals of the object
+boundary. We then train a 3D-CNN with this augmented data and identify the
+local features critical for decision-making using 3D GradCAM. An application of
+this feature identification framework is to recognize difficult-to-manufacture
+drilled hole features in a complex CAD geometry. The framework can be extended
+to identify difficult-to-manufacture features at multiple spatial scales
+leading to a real-time design for manufacturability decision support system.
+",1,0,0,1,0,0
+14390,Note on regions containing eigenvalues of a matrix,"  By excluding some regions, in which each eigenvalue of a matrix is not
+contained, from the \alpha\beta-type eigenvalue inclusion region provided by
+Huang et al.(Electronic Journal of Linear Algebra, 15 (2006) 215-224), a new
+eigenvalue inclusion region is given. And it is proved that the new region is
+contained in the \alpha\beta-type eigenvalue inclusion region.
+",0,0,1,0,0,0
+14391,Abstract Family-based Model Checking using Modal Featured Transition Systems: Preservation of CTL* (Extended Version),"  Variational systems allow effective building of many custom variants by using
+features (configuration options) to mark the variable functionality. In many of
+the applications, their quality assurance and formal verification are of
+paramount importance. Family-based model checking allows simultaneous
+verification of all variants of a variational system in a single run by
+exploiting the commonalities between the variants. Yet, its computational cost
+still greatly depends on the number of variants (often huge).
+In this work, we show how to achieve efficient family-based model checking of
+CTL* temporal properties using variability abstractions and off-the-shelf
+(single-system) tools. We use variability abstractions for deriving abstract
+family-based model checking, where the variability model of a variational
+system is replaced with an abstract (smaller) version of it, called modal
+featured transition system, which preserves the satisfaction of both universal
+and existential temporal properties, as expressible in CTL*. Modal featured
+transition systems contain two kinds of transitions, termed may and must
+transitions, which are defined by the conservative (over-approximating)
+abstractions and their dual (under-approximating) abstractions, respectively.
+The variability abstractions can be combined with different partitionings of
+the set of variants to infer suitable divide-and-conquer verification plans for
+the variational system. We illustrate the practicality of this approach for
+several variational systems.
+",1,0,0,0,0,0
+14392,Nonparametric Shape-restricted Regression,"  We consider the problem of nonparametric regression under shape constraints.
+The main examples include isotonic regression (with respect to any partial
+order), unimodal/convex regression, additive shape-restricted regression, and
+constrained single index model. We review some of the theoretical properties of
+the least squares estimator (LSE) in these problems, emphasizing on the
+adaptive nature of the LSE. In particular, we study the behavior of the risk of
+the LSE, and its pointwise limiting distribution theory, with special emphasis
+to isotonic regression. We survey various methods for constructing pointwise
+confidence intervals around these shape-restricted functions. We also briefly
+discuss the computation of the LSE and indicate some open research problems and
+future directions.
+",0,0,1,1,0,0
+14393,Emergence of Invariance and Disentanglement in Deep Representations,"  Using established principles from Statistics and Information Theory, we show
+that invariance to nuisance factors in a deep neural network is equivalent to
+information minimality of the learned representation, and that stacking layers
+and injecting noise during training naturally bias the network towards learning
+invariant representations. We then decompose the cross-entropy loss used during
+training and highlight the presence of an inherent overfitting term. We propose
+regularizing the loss by bounding such a term in two equivalent ways: One with
+a Kullbach-Leibler term, which relates to a PAC-Bayes perspective; the other
+using the information in the weights as a measure of complexity of a learned
+model, yielding a novel Information Bottleneck for the weights. Finally, we
+show that invariance and independence of the components of the representation
+learned by the network are bounded above and below by the information in the
+weights, and therefore are implicitly optimized during training. The theory
+enables us to quantify and predict sharp phase transitions between underfitting
+and overfitting of random labels when using our regularized loss, which we
+verify in experiments, and sheds light on the relation between the geometry of
+the loss function, invariance properties of the learned representation, and
+generalization error.
+",1,0,0,1,0,0
+14394,Fracton topological order via coupled layers,"  In this work, we develop a coupled layer construction of fracton topological
+orders in $d=3$ spatial dimensions. These topological phases have sub-extensive
+topological ground-state degeneracy and possess excitations whose movement is
+restricted in interesting ways. Our coupled layer approach is used to construct
+several different fracton topological phases, both from stacked layers of
+simple $d=2$ topological phases and from stacks of $d=3$ fracton topological
+phases. This perspective allows us to shed light on the physics of the X-cube
+model recently introduced by Vijay, Haah, and Fu, which we demonstrate can be
+obtained as the strong-coupling limit of a coupled three-dimensional stack of
+toric codes. We also construct two new models of fracton topological order: a
+semionic generalization of the X-cube model, and a model obtained by coupling
+together four interpenetrating X-cube models, which we dub the ""Four Color Cube
+model."" The couplings considered lead to fracton topological orders via
+mechanisms we dub ""p-string condensation"" and ""p-membrane condensation,"" in
+which strings or membranes built from particle excitations are driven to
+condense. This allows the fusion properties, braiding statistics, and
+ground-state degeneracy of the phases we construct to be easily studied in
+terms of more familiar degrees of freedom. Our work raises the possibility of
+studying fracton topological phases from within the framework of topological
+quantum field theory, which may be useful for obtaining a more complete
+understanding of such phases.
+",0,1,0,0,0,0
+14395,Local Asymptotic Normality of Infinite-Dimensional Concave Extended Linear Models,"  We study local asymptotic normality of M-estimates of convex minimization in
+an infinite dimensional parameter space. The objective function of M-estimates
+is not necessary differentiable and is possibly subject to convex constraints.
+In the above circumstance, narrow convergence with respect to uniform
+convergence fails to hold, because of the strength of it's topology. A new
+approach we propose to the lack-of-uniform-convergence is based on
+Mosco-convergence that is weaker topology than uniform convergence. By applying
+narrow convergence with respect to Mosco topology, we develop an
+infinite-dimensional version of the convexity argument and provide a proof of a
+local asymptotic normality. Our new technique also provides a proof of an
+asymptotic distribution of the likelihood ratio test statistic defined on real
+separable Hilbert spaces.
+",0,0,1,1,0,0
+14396,Polarization exchange of optical eigenmode pair in twisted-nematic Fabry-Pérot resonator,"  The polarization exchange effect in a twisted-nematic Fabry-Pérot resonator
+is experimentally confirmed in the regimes of both uniform and
+electric-field-deformed twisted structures. The polarization of output light in
+the transmission peaks is shown to be linear rather than elliptical. The
+polarization deflection from the nematic director grows from $0^\circ$ to
+$90^\circ$ angle and exchanges the longitudinal and transverse directions.
+Untwisting of a nematic by a voltage leads to the rotation of the polarization
+plane of light passing through the resonator. The polarization exchange effect
+allows using the investigated resonator as a spectral-selective linear
+polarizer with the voltage-controlled rotation of the polarization plane.
+",0,1,0,0,0,0
+14397,Galaxy Protoclusters as Drivers of Cosmic Star-Formation History in the First 2 Gyr,"  Present-day clusters are massive halos containing mostly quiescent galaxies,
+while distant protoclusters are extended structures containing numerous
+star-forming galaxies. We investigate the implications of this fundamental
+change in a cosmological context using a set of N-body simulations and
+semi-analytic models. We find that the fraction of the cosmic volume occupied
+by all (proto)clusters increases by nearly three orders of magnitude from z=0
+to z=7. We show that (proto)cluster galaxies are an important, and even
+dominant population at high redshift, as their expected contribution to the
+cosmic star-formation rate density rises (from 1% at z=0) to 20% at z=2 and 50%
+at z=10. Protoclusters thus provide a significant fraction of the cosmic
+ionizing photons, and may have been crucial in driving the timing and topology
+of cosmic reionization. Internally, the average history of cluster formation
+can be described by three distinct phases: at z~10-5, galaxy growth in
+protoclusters proceeded in an inside-out manner, with centrally dominant halos
+that are among the most active regions in the Universe; at z~5-1.5, rapid star
+formation occurred within the entire 10-20 Mpc structures, forming most of
+their present-day stellar mass; at z<~1.5, violent gravitational collapse drove
+these stellar contents into single cluster halos, largely erasing the details
+of cluster galaxy formation due to relaxation and virialization. Our results
+motivate observations of distant protoclusters in order to understand the
+rapid, extended stellar growth during Cosmic Noon, and their connection to
+reionization during Cosmic Dawn.
+",0,1,0,0,0,0
+14398,Universality and scaling laws in the cascading failure model with healing,"  Cascading failures may lead to dramatic collapse in interdependent networks,
+where the breakdown takes place as a discontinuity of the order parameter. In
+the cascading failure (CF) model with healing there is a control parameter
+which at some value suppresses the discontinuity of the order parameter.
+However, up to this value of the healing parameter the breakdown is a hybrid
+transition, meaning that, besides this first order character, the transition
+shows scaling too. In this paper we investigate the question of universality
+related to the scaling behavior. Recently we showed that the hybrid phase
+transition in the original CF model has two sets of exponents describing
+respectively the order parameter and the cascade statistics, which are
+connected by a scaling law. In the CF model with healing we measure these
+exponents as a function of the healing parameter. We find two universality
+classes: In the wide range below the critical healing value the exponents agree
+with those of the original model, while above this value the model displays
+trivial scaling meaning that fluctuations follow the central limit theorem.
+",1,1,0,0,0,0
+14399,Vector-valued Jack Polynomials and Wavefunctions on the Torus,"  The Hamiltonian of the quantum Calogero-Sutherland model of $N$ identical
+particles on the circle with $1/r^{2}$ interactions has eigenfunctions
+consisting of Jack polynomials times the base state. By use of the generalized
+Jack polynomials taking values in modules of the symmetric group and the matrix
+solution of a system of linear differential equations one constructs novel
+eigenfunctions of the Hamiltonian. Like the usual wavefunctions each
+eigenfunction determines a symmetric probability density on the $N$-torus. The
+construction applies to any irreducible representation of the symmetric group.
+The methods depend on the theory of generalized Jack polynomials due to
+Griffeth, and the Yang-Baxter graph approach of Luque and the author.
+",0,0,1,0,0,0
+14400,Wirtinger systems of generators of knot groups,"  We define the {\it Wirtinger number} of a link, an invariant closely related
+to the meridional rank. The Wirtinger number is the minimum number of
+generators of the fundamental group of the link complement over all meridional
+presentations in which every relation is an iterated Wirtinger relation arising
+in a diagram. We prove that the Wirtinger number of a link equals its bridge
+number. This equality can be viewed as establishing a weak version of Cappell
+and Shaneson's Meridional Rank Conjecture, and suggests a new approach to this
+conjecture. Our result also leads to a combinatorial technique for obtaining
+strong upper bounds on bridge numbers. This technique has so far allowed us to
+add the bridge numbers of approximately 50,000 prime knots of up to 14
+crossings to the knot table. As another application, we use the Wirtinger
+number to show there exists a universal constant $C$ with the property that the
+hyperbolic volume of a prime alternating link $L$ is bounded below by $C$ times
+the bridge number of $L$.
+",0,0,1,0,0,0
+14401,Labeled Memory Networks for Online Model Adaptation,"  Augmenting a neural network with memory that can grow without growing the
+number of trained parameters is a recent powerful concept with many exciting
+applications. We propose a design of memory augmented neural networks (MANNs)
+called Labeled Memory Networks (LMNs) suited for tasks requiring online
+adaptation in classification models. LMNs organize the memory with classes as
+the primary key.The memory acts as a second boosted stage following a regular
+neural network thereby allowing the memory and the primary network to play
+complementary roles. Unlike existing MANNs that write to memory for every
+instance and use LRU based memory replacement, LMNs write only for instances
+with non-zero loss and use label-based memory replacement. We demonstrate
+significant accuracy gains on various tasks including word-modelling and
+few-shot learning. In this paper, we establish their potential in online
+adapting a batch trained neural network to domain-relevant labeled data at
+deployment time. We show that LMNs are better than other MANNs designed for
+meta-learning. We also found them to be more accurate and faster than
+state-of-the-art methods of retuning model parameters for adapting to
+domain-specific labeled data.
+",1,0,0,1,0,0
+14402,Perturbative approach to weakly driven many-particle systems in the presence of approximate conservation laws,"  We develop a Liouville perturbation theory for weakly driven and weakly open
+quantum systems in situations when the unperturbed system has a number of
+conservations laws. If the perturbation violates the conservation laws, it
+drives the system to a new steady state which can be approximately but
+efficiently described by a (generalized) Gibbs ensemble characterized by one
+Lagrange parameter for each conservation law. The value of those has to be
+determined from rate equations for conserved quantities. Remarkably, even weak
+perturbations can lead to large responses of conserved quantities. We present a
+perturbative expansion of the steady state density matrix; first we give the
+condition that fixes the zeroth order expression (Lagrange parameters) and then
+determine the higher order corrections via projections of the Liouvillian. The
+formalism can be applied to a wide range of problems including two-temperature
+models for electron-phonon systems, Bose condensates of excitons or photons or
+weakly perturbed integrable models. We test our formalism by studying
+interacting fermions coupled to non-thermal reservoirs, approximately described
+by a Boltzmann equation.
+",0,1,0,0,0,0
+14403,Smart Mining for Deep Metric Learning,"  To solve deep metric learning problems and producing feature embeddings,
+current methodologies will commonly use a triplet model to minimise the
+relative distance between samples from the same class and maximise the relative
+distance between samples from different classes. Though successful, the
+training convergence of this triplet model can be compromised by the fact that
+the vast majority of the training samples will produce gradients with
+magnitudes that are close to zero. This issue has motivated the development of
+methods that explore the global structure of the embedding and other methods
+that explore hard negative/positive mining. The effectiveness of such mining
+methods is often associated with intractable computational requirements. In
+this paper, we propose a novel deep metric learning method that combines the
+triplet model and the global structure of the embedding space. We rely on a
+smart mining procedure that produces effective training samples for a low
+computational cost. In addition, we propose an adaptive controller that
+automatically adjusts the smart mining hyper-parameters and speeds up the
+convergence of the training process. We show empirically that our proposed
+method allows for fast and more accurate training of triplet ConvNets than
+other competing mining methods. Additionally, we show that our method achieves
+new state-of-the-art embedding results for CUB-200-2011 and Cars196 datasets.
+",1,0,0,0,0,0
+14404,Quantum phase transitions of a generalized compass chain with staggered Dzyaloshinskii-Moriya interaction,"  We consider a class of one-dimensional compass models with staggered
+Dzyaloshinskii-Moriya exchange interactions in an external transverse magnetic
+field. Based on the exact solution derived from Jordan-Wigner approach, we
+study the excitation gap, energy spectra, spin correlations and critical
+properties at phase transitions. We explore mutual effects of the staggered
+Dzyaloshinskii-Moriya interaction and the magnetic field on the energy spectra
+and the ground-state phase diagram. Thermodynamic quantities including the
+entropy and the specific heat are discussed, and their universal scalings at
+low temperature are demonstrated.
+",0,1,0,0,0,0
+14405,A blowup algebra of hyperplane arrangements,"  It is shown that the Orlik-Terao algebra is graded isomorphic to the special
+fiber of the ideal $I$ generated by the $(n-1)$-fold products of the members of
+a central arrangement of size $n$. This momentum is carried over to the Rees
+algebra (blowup) of $I$ and it is shown that this algebra is of fiber-type and
+Cohen-Macaulay. It follows by a result of Simis-Vasconcelos that the special
+fiber of $I$ is Cohen-Macaulay, thus giving another proof of a result of
+Proudfoot-Speyer about the Cohen-Macauleyness of the Orlik-Terao algebra.
+",0,0,1,0,0,0
+14406,Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach,"  We investigate a principle way to progressively mine discriminative object
+regions using classification networks to address the weakly-supervised semantic
+segmentation problems. Classification networks are only responsive to small and
+sparse discriminative regions from the object of interest, which deviates from
+the requirement of the segmentation task that needs to localize dense, interior
+and integral regions for pixel-wise inference. To mitigate this gap, we propose
+a new adversarial erasing approach for localizing and expanding object regions
+progressively. Starting with a single small object region, our proposed
+approach drives the classification network to sequentially discover new and
+complement object regions by erasing the current mined regions in an
+adversarial manner. These localized regions eventually constitute a dense and
+complete object region for learning semantic segmentation. To further enhance
+the quality of the discovered regions by adversarial erasing, an online
+prohibitive segmentation learning approach is developed to collaborate with
+adversarial erasing by providing auxiliary segmentation supervision modulated
+by the more reliable classification scores. Despite its apparent simplicity,
+the proposed approach achieves 55.0% and 55.7% mean Intersection-over-Union
+(mIoU) scores on PASCAL VOC 2012 val and test sets, which are the new
+state-of-the-arts.
+",1,0,0,0,0,0
+14407,Streaming Binary Sketching based on Subspace Tracking and Diagonal Uniformization,"  In this paper, we address the problem of learning compact
+similarity-preserving embeddings for massive high-dimensional streams of data
+in order to perform efficient similarity search. We present a new online method
+for computing binary compressed representations -sketches- of high-dimensional
+real feature vectors. Given an expected code length $c$ and high-dimensional
+input data points, our algorithm provides a $c$-bits binary code for preserving
+the distance between the points from the original high-dimensional space. Our
+algorithm does not require neither the storage of the whole dataset nor a
+chunk, thus it is fully adaptable to the streaming setting. It also provides
+low time complexity and convergence guarantees. We demonstrate the quality of
+our binary sketches through experiments on real data for the nearest neighbors
+search task in the online setting.
+",1,0,0,0,0,0
+14408,Improving hot-spot pressure for ignition in high-adiabat Inertial Confinement Fusion implosion,"  A novel capsule target design to improve the hot-spot pressure in the
+high-adiabat implosion for inertial confinement fusion is proposed, where a
+layer of comparatively high-density material is used as a pusher between the
+fuel and the ablator. This design is based on our theoretical finding of the
+stagnation scaling laws, which indicates that the hot spot pressure can be
+improved by increasing the kinetic energy density $\rho_d V_{imp}^2/2$
+($\rho_d$ is the shell density when the maximum shell velocity is reached,
+$V_{imp}$ is the implosion velocity.) of the shell. The proposed design uses
+the high density pusher to enhance the shell density $\rho_d$ so that the hot
+spot pressure is improved. Radio-hydrodynamic simulations show that the hot
+spot pressure of the design reaches the requirement for ignition even driven by
+a very high-adiabat short-duration two-shock pulse. The design is hopeful to
+simultaneously overcome the two major obstacles to achieving ignition--ablative
+instability and laser-plasma instability.
+",0,1,0,0,0,0
+14409,Cell-to-cell variation sets a tissue-rheology-dependent bound on collective gradient sensing,"  When a single cell senses a chemical gradient and chemotaxes, stochastic
+receptor-ligand binding can be a fundamental limit to the cell's accuracy. For
+clusters of cells responding to gradients, however, there is a critical
+difference: even genetically identical cells have differing responses to
+chemical signals. With theory and simulation, we show collective chemotaxis is
+limited by cell-to-cell variation in signaling. We find that when different
+cells cooperate the resulting bias can be much larger than the effects of
+ligand-receptor binding. Specifically, when a strongly-responding cell is at
+one end of a cell cluster, cluster motion is biased toward that cell. These
+errors are mitigated if clusters average measurements over times long enough
+for cells to rearrange. In consequence, fluid clusters are better able to sense
+gradients: we derive a link between cluster accuracy, cell-to-cell variation,
+and the cluster rheology. Because of this connection, increasing the noisiness
+of individual cell motion can actually increase the collective accuracy of a
+cluster by improving fluidity.
+",0,1,0,0,0,0
+14410,Languages of Play: Towards semantic foundations for game interfaces,"  Formal models of games help us account for and predict behavior, leading to
+more robust and innovative designs. While the games research community has
+proposed many formalisms for both the ""game half"" (game models, game
+description languages) and the ""human half"" (player modeling) of a game
+experience, little attention has been paid to the interface between the two,
+particularly where it concerns the player expressing her intent toward the
+game. We describe an analytical and computational toolbox based on programming
+language theory to examine the phenomenon sitting between control schemes and
+game rules, which we identify as a distinct player intent language for each
+game.
+",1,0,0,0,0,0
+14411,Hybrid Normed Ideal Perturbations of n-tuples of Operators I,"  In hybrid normed ideal perturbations of $n$-tuples of operators, the normed
+ideal is allowed to vary with the component operators. We begin extending to
+this setting the machinery we developed for normed ideal perturbations based on
+the modulus of quasicentral approximation and an adaptation of our
+non-commutative generalization of the Weyl--von~Neumann theorem. For commuting
+$n$-tuples of hermitian operators, the modulus of quasicentral approximation
+remains essentially the same when $\cC_n^-$ is replaced by a hybrid $n$-tuple
+$\cC_{p_1,\dots}^-,\dots,\cC^-_{p_n}$, $p_1^{-1} + \dots + p_n^{-1} = 1$. The
+proof involves singular integrals of mixed homogeneity.
+",0,0,1,0,0,0
+14412,Global behaviour of radially symmetric solutions stable at infinity for gradient systems,"  This paper is concerned with radially symmetric solutions of systems of the
+form \[ u_t = -\nabla V(u) + \Delta_x u \] where space variable $x$ and and
+state-parameter $u$ are multidimensional, and the potential $V$ is coercive at
+infinity. For such systems, under generic assumptions on the potential, the
+asymptotic behaviour of solutions ""stable at infinity"", that is approaching a
+spatially homogeneous equilibrium when $|x|$ approaches $+\infty$, is
+investigated. It is proved that every such solutions approaches a stacked
+family of radially symmetric bistable fronts travelling to infinity. This
+behaviour is similar to the one of bistable solutions for gradient systems in
+one unbounded spatial dimension, described in a companion paper. It is expected
+(but unfortunately not proved at this stage) that behind these travelling
+fronts the solution again behaves as in the one-dimensional case (that is, the
+time derivative approaches zero and the solution approaches a pattern of
+stationary solutions).
+",0,0,1,0,0,0
+14413,On the Total Forcing Number of a Graph,"  Let $G$ be a simple and finite graph without isolated vertices. In this paper
+we study forcing sets (zero forcing sets) which induce a subgraph of $G$
+without isolated vertices. Such a set is called a total forcing set, introduced
+and first studied by Davila \cite{Davila}. The minimum cardinality of a total
+forcing set in $G$ is the total forcing number of $G$, denoted $F_t(G)$. We
+study basic properties of $F_t(G)$, relate $F_t(G)$ to various domination
+parameters, and establish $NP$-completeness of the associated decision problem
+for $F_t(G)$. We also prove that if $G$ is a connected graph of order $n \ge 3$
+and maximum degree $\Delta$, then $F_t(G) \le ( \frac{\Delta}{\Delta +1} ) n$,
+with equality if and only if $G$ is a complete graph $K_{\Delta + 1}$.
+",0,0,1,0,0,0
+14414,"Brief Notes on Hard Takeoff, Value Alignment, and Coherent Extrapolated Volition","  I make some basic observations about hard takeoff, value alignment, and
+coherent extrapolated volition, concepts which have been central in analyses of
+superintelligent AI systems.
+",1,0,0,0,0,0
+14415,Convolutional Neural Networks In Classifying Cancer Through DNA Methylation,"  DNA Methylation has been the most extensively studied epigenetic mark.
+Usually a change in the genotype, DNA sequence, leads to a change in the
+phenotype, observable characteristics of the individual. But DNA methylation,
+which happens in the context of CpG (cytosine and guanine bases linked by
+phosphate backbone) dinucleotides, does not lead to a change in the original
+DNA sequence but has the potential to change the phenotype. DNA methylation is
+implicated in various biological processes and diseases including cancer. Hence
+there is a strong interest in understanding the DNA methylation patterns across
+various epigenetic related ailments in order to distinguish and diagnose the
+type of disease in its early stages. In this work, the relationship between
+methylated versus unmethylated CpG regions and cancer types is explored using
+Convolutional Neural Networks (CNNs). A CNN based Deep Learning model that can
+classify the cancer of a new DNA methylation profile based on the learning from
+publicly available DNA methylation datasets is then proposed.
+",0,0,0,1,1,0
+14416,Persistent Monitoring of Dynamically Changing Environments Using an Unmanned Vehicle,"  We consider the problem of planning a closed walk $\mathcal W$ for a UAV to
+persistently monitor a finite number of stationary targets with equal
+priorities and dynamically changing properties. A UAV must physically visit the
+targets in order to monitor them and collect information therein. The frequency
+of monitoring any given target is specified by a target revisit time, $i.e.$,
+the maximum allowable time between any two successive visits to the target. The
+problem considered in this paper is the following: Given $n$ targets and $k
+\geq n$ allowed visits to them, find an optimal closed walk $\mathcal W^*(k)$
+so that every target is visited at least once and the maximum revisit time over
+all the targets, $\mathcal R(\mathcal W(k))$, is minimized. We prove the
+following: If $k \geq n^2-n$, $\mathcal R(\mathcal W^*(k))$ (or simply,
+$\mathcal R^*(k)$) takes only two values: $\mathcal R^*(n)$ when $k$ is an
+integral multiple of $n$, and $\mathcal R^*(n+1)$ otherwise. This result
+suggests significant computational savings - one only needs to determine
+$\mathcal W^*(n)$ and $\mathcal W^*(n+1)$ to construct an optimal solution
+$\mathcal W^*(k)$. We provide MILP formulations for computing $\mathcal W^*(n)$
+and $\mathcal W^*(n+1)$. Furthermore, for {\it any} given $k$, we prove that
+$\mathcal R^*(k) \geq \mathcal R^*(k+n)$.
+",1,0,0,0,0,0
+14417,Almost sharp nonlinear scattering in one-dimensional Born-Infeld equations arising in nonlinear Electrodynamics,"  We study decay of small solutions of the Born-Infeld equation in 1+1
+dimensions, a quasilinear scalar field equation modeling nonlinear
+electromagnetism, as well as branes in String theory and minimal surfaces in
+Minkowski space-times. From the work of Whitham, it is well-known that there is
+no decay because of arbitrary solutions traveling to the speed of light just as
+linear wave equation. However, even if there is no global decay in 1+1
+dimensions, we are able to show that all globally small $H^{s+1}\times H^s$,
+$s>\frac12$ solutions do decay to the zero background state in space, inside a
+strictly proper subset of the light cone. We prove this result by constructing
+a Virial identity related to a momentum law, in the spirit of works
+\cite{KMM,KMM1}, as well as a Lyapunov functional that controls the $\dot H^1
+\times L^2$ energy.
+",0,0,1,0,0,0
+14418,Life efficiency does not always increase with the dissipation rate,"  There does not exist a general positive correlation between important
+life-supporting properties and the entropy production rate. The simple reason
+is that nondissipative and time-symmetric kinetic aspects are also relevant for
+establishing optimal functioning. In fact those aspects are even crucial in the
+nonlinear regimes around equilibrium where we find biological processing on
+mesoscopic scales. We make these claims specific via examples of molecular
+motors, of circadian cycles and of sensory adaptation, whose performance in
+some regimes is indeed spoiled by increasing the dissipated power. We use the
+relation between dissipation and the amount of time-reversal breaking to keep
+the discussion quantitative also in effective models where the physical entropy
+production is not clearly identifiable.
+",0,1,0,0,0,0
+14419,Molecular simulations of entangled defect structures around nanoparticles in nematic liquid crystals,"  We investigate the defect structures forming around two nanoparticles in a
+Gay-Berne nematic liquid crystal using molecular simulations. For small
+separations, disclinations entangle both particles forming the figure of eight,
+the figure of omega and the figure of theta. These defect structures are
+similar in shape and occur with a comparable frequency to micron-sized
+particles studied in experiments. The simulations reveal fast transitions from
+one defect structure to another suggesting that particles of nanometre size
+cannot be bound together effectively. We identify the 'three-ring' structure
+observed in previous molecular simulations as a superposition of the different
+entangled and non-entangled states over time and conclude that it is not itself
+a stable defect structure.
+",0,1,0,0,0,0
+14420,Quasi Maximum-Likelihood Estimation of Dynamic Panel Data Models,"  This paper establishes the almost sure convergence and asymptotic normality
+of levels and differenced quasi maximum-likelihood (QML) estimators of dynamic
+panel data models. The QML estimators are robust with respect to initial
+conditions, conditional and time-series heteroskedasticity, and
+misspecification of the log-likelihood. The paper also provides an ECME
+algorithm for calculating levels QML estimates. Finally, it uses Monte Carlo
+experiments to compare the finite sample performance of levels and differenced
+QML estimators, the differenced GMM estimator, and the system GMM estimator. In
+these experiments the QML estimators usually have smaller --- typically
+substantially smaller --- bias and root mean squared errors than the panel data
+GMM estimators.
+",0,0,1,1,0,0
+14421,Disunited Nations? A Multiplex Network Approach to Detecting Preference Affinity Blocs using Texts and Votes,"  This paper contributes to an emerging literature that models votes and text
+in tandem to better understand polarization of expressed preferences. It
+introduces a new approach to estimate preference polarization in
+multidimensional settings, such as international relations, based on
+developments in the natural language processing and network science literatures
+-- namely word embeddings, which retain valuable syntactical qualities of human
+language, and community detection in multilayer networks, which locates densely
+connected actors across multiple, complex networks. We find that the employment
+of these tools in tandem helps to better estimate states' foreign policy
+preferences expressed in UN votes and speeches beyond that permitted by votes
+alone. The utility of these located affinity blocs is demonstrated through an
+application to conflict onset in International Relations, though these tools
+will be of interest to all scholars faced with the measurement of preferences
+and polarization in multidimensional settings.
+",1,0,0,0,0,0
+14422,Magnetic phase diagram of the iron pnictides in the presence of spin-orbit coupling: Frustration between $C_2$ and $C_4$ magnetic phases,"  We investigate the impact of spin anisotropic interactions, promoted by
+spin-orbit coupling, on the magnetic phase diagram of the iron-based
+superconductors. Three distinct magnetic phases with Bragg peaks at $(\pi,0)$
+and $(0,\pi)$ are possible in these systems: one $C_2$ (i.e. orthorhombic)
+symmetric stripe magnetic phase and two $C_4$ (i.e. tetragonal) symmetric
+magnetic phases. While the spin anisotropic interactions allow the magnetic
+moments to point in any direction in the $C_2$ phase, they restrict the
+possible moment orientations in the $C_4$ phases. As a result, an interesting
+scenario arises in which the spin anisotropic interactions favor a $C_2$ phase,
+but the other spin isotropic interactions favor a $C_4$ phase. We study this
+frustration via both mean-field and renormalization-group approaches. We find
+that, to lift this frustration, a rich magnetic landscape emerges well below
+the magnetic transition temperature, with novel $C_2$, $C_4$, and mixed
+$C_2$-$C_4$ phases. Near the putative magnetic quantum critical point, spin
+anisotropies promote a stable Gaussian fixed point in the renormalization-group
+flow, which is absent in the spin isotropic case, and is associated with a
+near-degeneracy between $C_2$ and $C_4$ phases. We argue that this frustration
+is the reason why most $C_4$ phases in the iron pnictides only appear inside
+the $C_2$ phase, and discuss additional manifestations of this frustration in
+the phase diagrams of these materials.
+",0,1,0,0,0,0
+14423,Algorithms and Bounds for Very Strong Rainbow Coloring,"  A well-studied coloring problem is to assign colors to the edges of a graph
+$G$ so that, for every pair of vertices, all edges of at least one shortest
+path between them receive different colors. The minimum number of colors
+necessary in such a coloring is the strong rainbow connection number
+($\src(G)$) of the graph. When proving upper bounds on $\src(G)$, it is natural
+to prove that a coloring exists where, for \emph{every} shortest path between
+every pair of vertices in the graph, all edges of the path receive different
+colors. Therefore, we introduce and formally define this more restricted edge
+coloring number, which we call \emph{very strong rainbow connection number}
+($\vsrc(G)$).
+In this paper, we give upper bounds on $\vsrc(G)$ for several graph classes,
+some of which are tight. These immediately imply new upper bounds on $\src(G)$
+for these classes, showing that the study of $\vsrc(G)$ enables meaningful
+progress on bounding $\src(G)$. Then we study the complexity of the problem to
+compute $\vsrc(G)$, particularly for graphs of bounded treewidth, and show this
+is an interesting problem in its own right. We prove that $\vsrc(G)$ can be
+computed in polynomial time on cactus graphs; in contrast, this question is
+still open for $\src(G)$. We also observe that deciding whether $\vsrc(G) = k$
+is fixed-parameter tractable in $k$ and the treewidth of $G$. Finally, on
+general graphs, we prove that there is no polynomial-time algorithm to decide
+whether $\vsrc(G) \leq 3$ nor to approximate $\vsrc(G)$ within a factor
+$n^{1-\varepsilon}$, unless P$=$NP.
+",1,0,0,0,0,0
+14424,Quantile function expansion using regularly varying functions,"  We present a simple result that allows us to evaluate the asymptotic order of
+the remainder of a partial asymptotic expansion of the quantile function $h(u)$
+as $u\to 0^+$ or $1^-$. This is focussed on important univariate distributions
+when $h(\cdot)$ has no simple closed form, with a view to assessing asymptotic
+rate of decay to zero of tail dependence in the context of bivariate copulas.
+The Introduction motivates the study in terms of the standard Normal. The
+Normal, Skew-Normal and Gamma are used as initial examples. Finally, we discuss
+approximation to the lower quantile of the Variance-Gamma and Skew-Slash
+distributions.
+",0,0,1,1,0,0
+14425,Identification of Key Proteins Involved in Axon Guidance Related Disorders: A Systems Biology Approach,"  Axon guidance is a crucial process for growth of the central and peripheral
+nervous systems. In this study, 3 axon guidance related disorders, namely-
+Duane Retraction Syndrome (DRS) , Horizontal Gaze Palsy with Progressive
+Scoliosis (HGPPS) and Congenital fibrosis of the extraocular muscles type 3
+(CFEOM3) were studied using various Systems Biology tools to identify the genes
+and proteins involved with them to get a better idea about the underlying
+molecular mechanisms including the regulatory mechanisms. Based on the analyses
+carried out, 7 significant modules have been identified from the PPI network.
+Five pathways/processes have been found to be significantly associated with
+DRS, HGPPS and CFEOM3 associated genes. From the PPI network, 3 have been
+identified as hub proteins- DRD2, UBC and CUL3.
+",0,0,0,0,1,0
+14426,Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization,"  Motivated by applications in Game Theory, Optimization, and Generative
+Adversarial Networks, recent work of Daskalakis et al~\cite{DISZ17} and
+follow-up work of Liang and Stokes~\cite{LiangS18} have established that a
+variant of the widely used Gradient Descent/Ascent procedure, called
+""Optimistic Gradient Descent/Ascent (OGDA)"", exhibits last-iterate convergence
+to saddle points in {\em unconstrained} convex-concave min-max optimization
+problems. We show that the same holds true in the more general problem of {\em
+constrained} min-max optimization under a variant of the no-regret
+Multiplicative-Weights-Update method called ""Optimistic Multiplicative-Weights
+Update (OMWU)"". This answers an open question of Syrgkanis et al~\cite{SALS15}.
+The proof of our result requires fundamentally different techniques from
+those that exist in no-regret learning literature and the aforementioned
+papers. We show that OMWU monotonically improves the Kullback-Leibler
+divergence of the current iterate to the (appropriately normalized) min-max
+solution until it enters a neighborhood of the solution. Inside that
+neighborhood we show that OMWU becomes a contracting map converging to the
+exact solution. We believe that our techniques will be useful in the analysis
+of the last iterate of other learning algorithms.
+",0,0,0,1,0,0
+14427,The Lifetimes of Phases in High-Mass Star-Forming Regions,"  High-mass stars form within star clusters from dense, molecular regions, but
+is the process of cluster formation slow and hydrostatic or quick and dynamic?
+We link the physical properties of high-mass star-forming regions with their
+evolutionary stage in a systematic way, using Herschel and Spitzer data. In
+order to produce a robust estimate of the relative lifetimes of these regions,
+we compare the fraction of dense, molecular regions above a column density
+associated with high-mass star formation, N(H2) > 0.4-2.5 x 10^22 cm^-2, in the
+'starless (no signature of stars > 10 Msun forming) and star-forming phases in
+a 2x2 degree region of the Galactic Plane centered at l=30deg. Of regions
+capable of forming high-mass stars on ~1 pc scales, the starless (or embedded
+beyond detection) phase occupies about 60-70% of the dense, molecular region
+lifetime and the star-forming phase occupies about 30-40%. These relative
+lifetimes are robust over a wide range of thresholds. We outline a method by
+which relative lifetimes can be anchored to absolute lifetimes from large-scale
+surveys of methanol masers and UCHII regions. A simplistic application of this
+method estimates the absolute lifetimes of the starless phase to be 0.2-1.7 Myr
+(about 0.6-4.1 fiducial cloud free-fall times) and the star-forming phase to be
+0.1-0.7 Myr (about 0.4-2.4 free-fall times), but these are highly uncertain.
+This work uniquely investigates the star-forming nature of high-column density
+gas pixel-by-pixel and our results demonstrate that the majority of high-column
+density gas is in a starless or embedded phase.
+",0,1,0,0,0,0
+14428,Null controllability of a population dynamics with interior degeneracy,"  In this paper, we deal with the null controllability of a population dynamics
+model with an interior degenerate diffusion. To this end, we proved first a new
+Carleman estimate for the full adjoint system and afterwards we deduce a
+suitable observability inequality which will be needed to establish the
+existence of a control acting on a subset of the space which lead the
+population to extinction in a finite time.
+",0,0,1,0,0,0
+14429,ICLR Reproducibility Challenge Report (Padam : Closing The Generalization Gap Of Adaptive Gradient Methods in Training Deep Neural Networks),"  This work is a part of ICLR Reproducibility Challenge 2019, we try to
+reproduce the results in the conference submission PADAM: Closing The
+Generalization Gap of Adaptive Gradient Methods In Training Deep Neural
+Networks. Adaptive gradient methods proposed in past demonstrate a degraded
+generalization performance than the stochastic gradient descent (SGD) with
+momentum. The authors try to address this problem by designing a new
+optimization algorithm that bridges the gap between the space of Adaptive
+Gradient algorithms and SGD with momentum. With this method a new tunable
+hyperparameter called partially adaptive parameter p is introduced that varies
+between [0, 0.5]. We build the proposed optimizer and use it to mirror the
+experiments performed by the authors. We review and comment on the empirical
+analysis performed by the authors. Finally, we also propose a future direction
+for further study of Padam. Our code is available at:
+this https URL
+",1,0,0,1,0,0
+14430,Robust Bayes-Like Estimation: Rho-Bayes estimation,"  We consider the problem of estimating the joint distribution $P$ of $n$
+independent random variables within the Bayes paradigm from a non-asymptotic
+point of view. Assuming that $P$ admits some density $s$ with respect to a
+given reference measure, we consider a density model $\overline S$ for $s$ that
+we endow with a prior distribution $\pi$ (with support $\overline S$) and we
+build a robust alternative to the classical Bayes posterior distribution which
+possesses similar concentration properties around $s$ whenever it belongs to
+the model $\overline S$. Furthermore, in density estimation, the Hellinger
+distance between the classical and the robust posterior distributions tends to
+0, as the number of observations tends to infinity, under suitable assumptions
+on the model and the prior, provided that the model $\overline S$ contains the
+true density $s$. However, unlike what happens with the classical Bayes
+posterior distribution, we show that the concentration properties of this new
+posterior distribution are still preserved in the case of a misspecification of
+the model, that is when $s$ does not belong to $\overline S$ but is close
+enough to it with respect to the Hellinger distance.
+",0,0,1,1,0,0
+14431,Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problem,"  Here I introduce an extension to demixed principal component analysis (dPCA),
+a linear dimensionality reduction technique for analyzing the activity of
+neural populations, to the case of nonlinear dimensions. This is accomplished
+using kernel methods, resulting in kernel demixed principal component analysis
+(kdPCA). This extension resembles kernel-based extensions to standard principal
+component analysis and canonical correlation analysis. kdPCA includes dPCA as a
+special case when the kernel is linear. I present examples of simulated neural
+activity that follows different low dimensional configurations and compare the
+results of kdPCA to dPCA. These simulations demonstrate that nonlinear
+interactions can impede the ability of dPCA to demix neural activity
+corresponding to experimental parameters, but kdPCA can still recover
+interpretable components. Additionally, I compare kdPCA and dPCA to a neural
+population from rat orbitofrontal cortex during an odor classification task in
+recovering decision-related activity.
+",0,0,0,0,1,0
+14432,Stability of semi-wavefronts for delayed reaction-diffusion equations,"  This paper deals with the asymptotic behavior of solutions to the delayed
+monostable equation: $(*)$ $u_{t}(t,x) = u_{xx}(t,x) - u(t,x) + g(u(t-h,x)),$
+$x \in \mathbb{R},\ t >0,$ where $h>0$ and the reaction term $g: \mathbb{R}_+
+\to \mathbb{R}_+$ has exactly two fixed points (zero and $\kappa >0$). Under
+certain condition on the derivative of $g$ at $\kappa$, the global stability of
+fast wavefronts is proved. Also, the stability of the $leading \ edge$ of
+semi-wavefronts for $(*)$ with $g$ satisfying $g(u)\leq g'(0)u, u\in\R_+,$ is
+established
+",0,0,1,0,0,0
+14433,An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning,"  We propose an end-to-end approach to the natural language object retrieval
+task, which localizes an object within an image according to a natural language
+description, i.e., referring expression. Previous works divide this problem
+into two independent stages: first, compute region proposals from the image
+without the exploration of the language description; second, score the object
+proposals with regard to the referring expression and choose the top-ranked
+proposals. The object proposals are generated independently from the referring
+expression, which makes the proposal generation redundant and even irrelevant
+to the referred object. In this work, we train an agent with deep reinforcement
+learning, which learns to move and reshape a bounding box to localize the
+object according to the referring expression. We incorporate both the spatial
+and temporal context information into the training procedure. By simultaneously
+exploiting local visual information, the spatial and temporal context and the
+referring language a priori, the agent selects an appropriate action to take at
+each time. A special action is defined to indicate when the agent finds the
+referred object, and terminate the procedure. We evaluate our model on various
+datasets, and our algorithm significantly outperforms the compared algorithms.
+Notably, the accuracy improvement of our method over the recent method GroundeR
+and SCRC on the ReferItGame dataset are 7.67% and 18.25%, respectively.
+",1,0,0,0,0,0
+14434,Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction,"  With access to large datasets, deep neural networks (DNN) have achieved
+human-level accuracy in image and speech recognition tasks. However, in
+chemistry, data is inherently small and fragmented. In this work, we develop an
+approach of using rule-based knowledge for training ChemNet, a transferable and
+generalizable deep neural network for chemical property prediction that learns
+in a weak-supervised manner from large unlabeled chemical databases. When
+coupled with transfer learning approaches to predict other smaller datasets for
+chemical properties that it was not originally trained on, we show that
+ChemNet's accuracy outperforms contemporary DNN models that were trained using
+conventional supervised learning. Furthermore, we demonstrate that the ChemNet
+pre-training approach is equally effective on both CNN (Chemception) and RNN
+(SMILES2vec) models, indicating that this approach is network architecture
+agnostic and is effective across multiple data modalities. Our results indicate
+a pre-trained ChemNet that incorporates chemistry domain knowledge, enables the
+development of generalizable neural networks for more accurate prediction of
+novel chemical properties.
+",1,0,0,1,0,0
+14435,Intelligent Sensor Based Bayesian Neural Network for Combined Parameters and States Estimation of a Brushed DC Motor,"  The objective of this paper is to develop an Artificial Neural Network (ANN)
+model to estimate simultaneously, parameters and state of a brushed DC machine.
+The proposed ANN estimator is novel in the sense that his estimates
+simultaneously temperature, speed and rotor resistance based only on the
+measurement of the voltage and current inputs. Many types of ANN estimators
+have been designed by a lot of researchers during the last two decades. Each
+type is designed for a specific application. The thermal behavior of the motor
+is very slow, which leads to large amounts of data sets. The standard ANN use
+often Multi-Layer Perceptron (MLP) with Levenberg-Marquardt Backpropagation
+(LMBP), among the limits of LMBP in the case of large number of data, so the
+use of MLP based on LMBP is no longer valid in our case. As solution, we
+propose the use of Cascade-Forward Neural Network (CFNN) based Bayesian
+Regulation backpropagation (BRBP). To test our estimator robustness a random
+white-Gaussian noise has been added to the sets. The proposed estimator is in
+our viewpoint accurate and robust.
+",1,0,0,0,0,0
+14436,The infinite Fibonacci groups and relative asphericity,"  We prove that the generalised Fibonacci group F(r,n) is infinite for (r,n) in
+{(7 + 5k,5), (8 + 5k,5)} where k is greater than or equal to 0. This together
+with previously known results yields a complete classification of the finite
+F(r,n), a problem that has its origins in a question by J H Conway in 1965. The
+method is to show that a related relative presentation is aspherical from which
+it can be deduced that the groups are infinite.
+",0,0,1,0,0,0
+14437,Representations of Polynomial Rota-Baxter Algebras,"  A Rota--Baxter operator is an algebraic abstraction of integration, which is
+the typical example of a weight zero Rota-Baxter operator. We show that
+studying the modules over the polynomial Rota--Baxter algebra $(k[x],P)$ is
+equivalent to studying the modules over the Jordan plane, and we generalize the
+direct decomposability results for the $(k[x],P)$-modules in [Iy] from
+algebraically closed fields of characteristic zero to fields of characteristic
+zero. Furthermore, we provide a classification of Rota--Baxter modules up to
+isomorphism based on indecomposable $k[x]$-modules.
+",0,0,1,0,0,0
+14438,The extended ROSAT-ESO Flux-Limited X-ray Galaxy Cluster Survey (REFLEX II) VII The Mass Function of Galaxy Clusters,"  The mass function of galaxy clusters is a sensitive tracer of the
+gravitational evolution of the cosmic large-scale structure and serves as an
+important census of the fraction of matter bound in large structures. We obtain
+the mass function by fitting the observed cluster X-ray luminosity distribution
+from the REFLEX galaxy cluster survey to models of cosmological structure
+formation. We marginalise over uncertainties in the cosmological parameters as
+well as those of the relevant galaxy cluster scaling relations. The mass
+function is determined with an uncertainty less than 10% in the mass range 3 x
+10^12 to 5 x 10^14 M$_\odot$. For the cumulative mass function we find a slope
+at the low mass end consistent with a value of -1, while the mass rich end
+cut-off is milder than a Schechter function with an exponential term exp($-
+M^\delta$) with $\delta$ smaller than 1. Changing the Hubble parameter in the
+range $H_0 = 67 - 73 km s^-1 Mpc^{-1}$ or allowing the total neutrino mass to
+have a value between 0 - 0.4 eV causes variations less than the uncertainties.
+We estimate the fraction of mass locked up in galaxy clusters: about 4.4% of
+the matter in the Universe is bound in clusters (inside $r_200$) with a mass
+larger than 10^14 M$_\odot$ and 14% to clusters and groups with a mass larger
+than 10^13 M$_\odot$ at the present Universe. We also discuss the evolution of
+the galaxy cluster population with redshift. Our results imply that there is
+hardly any clusters with a mass > 10^15 M$_\odot$ above a redshift of z = 1.
+",0,1,0,0,0,0
+14439,An Optimized Pattern Recognition Algorithm for Anomaly Detection in IoT Environment,"  With the advent of large-scale heterogeneous search engines comes the problem
+of unified search control resulting in mismatches that could have otherwise
+avoided. A mechanism is needed to determine exact patterns in web mining and
+ubiquitous device searching. In this paper we demonstrate the use of an
+optimized string searching algorithm to recognize exact patterns from a large
+database. The underlying principle in designing the algorithm is that each
+letter that maps to a fixed real values and some arithmetic operations which
+are applied to compute corresponding pattern and substring values. We have
+implemented this algorithm in C. We have tested the algorithm using a large
+dataset. We created our own dataset using DNA sequences. The experimental
+result shows the number of mismatch occurred in string search from a large
+database. Furthermore, some of the inherent weaknesses in the use of this
+algorithm are highlighted.
+",1,0,0,0,0,0
+14440,Uniform asymptotics as a stationary point approaches an endpoint,"  We obtain the rigorous uniform asymptotics of a particular integral where a
+stationary point is close to an endpoint. There exists a general method
+introduced by Bleistein for obtaining uniform asymptotics in this situation.
+However, this method does not provide rigorous estimates for the error. Indeed,
+the method of Bleistein starts with a change of variables, which implies that
+the parameter governing how close the stationary point is to the endpoint
+appears in several parts of the integrand, and this means that one cannot
+obtain general error bounds. By adapting the above method to our particular
+integral, we obtain rigorous uniform leading-order asymptotics. We also give a
+rigorous derivation of the asymptotics to all orders of the same integral; the
+novelty of this second approach is that it does not involve a global change of
+variables.
+",0,0,1,0,0,0
+14441,An SDP-Based Algorithm for Linear-Sized Spectral Sparsification,"  For any undirected and weighted graph $G=(V,E,w)$ with $n$ vertices and $m$
+edges, we call a sparse subgraph $H$ of $G$, with proper reweighting of the
+edges, a $(1+\varepsilon)$-spectral sparsifier if \[
+(1-\varepsilon)x^{\intercal}L_Gx\leq x^{\intercal} L_{H} x\leq (1+\varepsilon)
+x^{\intercal} L_Gx \] holds for any $x\in\mathbb{R}^n$, where $L_G$ and $L_{H}$
+are the respective Laplacian matrices of $G$ and $H$. Noticing that $\Omega(m)$
+time is needed for any algorithm to construct a spectral sparsifier and a
+spectral sparsifier of $G$ requires $\Omega(n)$ edges, a natural question is to
+investigate, for any constant $\varepsilon$, if a $(1+\varepsilon)$-spectral
+sparsifier of $G$ with $O(n)$ edges can be constructed in $\tilde{O}(m)$ time,
+where the $\tilde{O}$ notation suppresses polylogarithmic factors. All previous
+constructions on spectral sparsification require either super-linear number of
+edges or $m^{1+\Omega(1)}$ time.
+In this work we answer this question affirmatively by presenting an algorithm
+that, for any undirected graph $G$ and $\varepsilon>0$, outputs a
+$(1+\varepsilon)$-spectral sparsifier of $G$ with $O(n/\varepsilon^2)$ edges in
+$\tilde{O}(m/\varepsilon^{O(1)})$ time. Our algorithm is based on three novel
+techniques: (1) a new potential function which is much easier to compute yet
+has similar guarantees as the potential functions used in previous references;
+(2) an efficient reduction from a two-sided spectral sparsifier to a one-sided
+spectral sparsifier; (3) constructing a one-sided spectral sparsifier by a
+semi-definite program.
+",1,0,0,0,0,0
+14442,Self-Adjusting Threshold Mechanism for Pixel Detectors,"  Readout chips of hybrid pixel detectors use a low power amplifier and
+threshold discrimination to process charge deposited in semiconductor sensors.
+Due to transistor mismatch each pixel circuit needs to be calibrated
+individually to achieve response uniformity. Traditionally this is addressed by
+programmable threshold trimming in each pixel, but requires robustness against
+radiation effects, temperature, and time. In this paper a self-adjusting
+threshold mechanism is presented, which corrects the threshold for both spatial
+inequality and time variation and maintains a constant response. It exploits
+the electrical noise as relative measure for the threshold and automatically
+adjust the threshold of each pixel to always achieve a uniform frequency of
+noise hits. A digital implementation of the method in the form of an up/down
+counter and combinatorial logic filter is presented. The behavior of this
+circuit has been simulated to evaluate its performance and compare it to
+traditional calibration results. The simulation results show that this
+mechanism can perform equally well, but eliminates instability over time and is
+immune to single event upsets.
+",0,1,0,0,0,0
+14443,Rotational inertia interface in a dynamic lattice of flexural beams,"  The paper presents a novel analysis of a transmission problem for a network
+of flexural beams incorporating conventional Euler-Bernoulli beams as well as
+Rayleigh beams with the enhanced rotational inertia. Although, in the
+low-frequency regime, these beams have a similar dynamic response, we have
+demonstrated novel features which occur in the transmission at higher
+frequencies across the layer of the Rayleigh beams.
+",0,1,0,0,0,0
+14444,Low-frequency wide band-gap elastic/acoustic meta-materials using the K-damping concept,"  The terms ""acoustic/elastic meta-materials"" describe a class of periodic
+structures with unit cells exhibiting local resonance. This localized resonant
+structure has been shown to result in negative effective stiffness and/or mass
+at frequency ranges close to these local resonances. As a result, these
+structures present unusual wave propagation properties at wavelengths well
+below the regime corresponding to band-gap generation based on spatial
+periodicity, (i.e. ""Bragg scattering""). Therefore, acoustic/elastic
+meta-materials can lead to applications, especially suitable in the
+low-frequency range. However, low frequency range applications of such
+meta-materials require very heavy internal moving masses, as well as additional
+constraints at the amplitudes of the internally oscillating locally resonating
+structures, which may prohibit their practical implementation. In order to
+resolve this disadvantage, the K-Damping concept will be analyzed. According to
+this concept, the acoustic/elastic meta-materials are designed to include
+negative stiffness elements instead or in addition to the internally resonating
+added masses. This concept removes the need for the heavy locally added heavy
+masses, while it simultaneously exploits the negative stiffness damping
+phenomenon. Application of both Bloch's theory and the classical modal analysis
+at the one-dimensional mass-in-mass lattice is analyzed and corresponding
+dispersion relations are derived. The results indicate significant advantages
+over the conventional mass-in-a mass lattice, such as broader band-gaps and
+increased damping ratio and reveal significant potential in the proposed
+solution. Preliminary feasibility analysis for seismic meta-structures and low
+frequency acoustic isolation-damping confirm the strong potential and
+applicability of this concept.
+",0,1,0,0,0,0
+14445,Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability,"  Blind source separation is a common processing tool to analyse the
+constitution of pixels of hyperspectral images. Such methods usually suppose
+that pure pixel spectra (endmembers) are the same in all the image for each
+class of materials. In the framework of remote sensing, such an assumption is
+no more valid in the presence of intra-class variabilities due to illumination
+conditions, weathering, slight variations of the pure materials, etc... In this
+paper, we first describe the results of investigations highlighting intra-class
+variability measured in real images. Considering these results, a new
+formulation of the linear mixing model is presented leading to two new methods.
+Unconstrained Pixel-by-pixel NMF (UP-NMF) is a new blind source separation
+method based on the assumption of a linear mixing model, which can deal with
+intra-class variability. To overcome UP-NMF limitations an extended method is
+proposed, named Inertia-constrained Pixel-by-pixel NMF (IP-NMF). For each
+sensed spectrum, these extended versions of NMF extract a corresponding set of
+source spectra. A constraint is set to limit the spreading of each source's
+estimates in IP-NMF. The methods are tested on a semi-synthetic data set built
+with spectra extracted from a real hyperspectral image and then numerically
+mixed. We thus demonstrate the interest of our methods for realistic source
+variabilities. Finally, IP-NMF is tested on a real data set and it is shown to
+yield better performance than state of the art methods.
+",1,1,0,1,0,0
+14446,Warped Product Pointwise Semi-slant Submanifolds of Sasakian Manifolds,"  Recently, B.-Y. Chen and O. J. Garay studied pointwise slant submanifolds of
+almost Hermitian manifolds. By using this notion, we investigate pointwise
+semi-slant submanifolds and their warped products in Sasakian manifolds. We
+give non-trivial examples of such submanifolds and obtain several fundamental
+results, including a characterization for warped product pointwise semi-slant
+submanifolds of Sasakian manifolds.
+",0,0,1,0,0,0
+14447,Bayesian Semi-supervised Learning with Graph Gaussian Processes,"  We propose a data-efficient Gaussian process-based Bayesian approach to the
+semi-supervised learning problem on graphs. The proposed model shows extremely
+competitive performance when compared to the state-of-the-art graph neural
+networks on semi-supervised learning benchmark experiments, and outperforms the
+neural networks in active learning experiments where labels are scarce.
+Furthermore, the model does not require a validation data set for early
+stopping to control over-fitting. Our model can be viewed as an instance of
+empirical distribution regression weighted locally by network connectivity. We
+further motivate the intuitive construction of the model with a Bayesian linear
+model interpretation where the node features are filtered by an operator
+related to the graph Laplacian. The method can be easily implemented by
+adapting off-the-shelf scalable variational inference algorithms for Gaussian
+processes.
+",1,0,0,1,0,0
+14448,Knowledge Engineering for Hybrid Deductive Databases,"  Modern knowledge base systems frequently need to combine a collection of
+databases in different formats: e.g., relational databases, XML databases, rule
+bases, ontologies, etc. In the deductive database system DDBASE, we can manage
+these different formats of knowledge and reason about them. Even the file
+systems on different computers can be part of the knowledge base. Often, it is
+necessary to handle different versions of a knowledge base. E.g., we might want
+to find out common parts or differences of two versions of a relational
+database.
+We will examine the use of abstractions of rule bases by predicate dependency
+and rule predicate graphs. Also the proof trees of derived atoms can help to
+compare different versions of a rule base. Moreover, it might be possible to
+have derivations joining rules with other formalisms of knowledge
+representation.
+Ontologies have shown their benefits in many applications of intelligent
+systems, and there have been many proposals for rule languages compatible with
+the semantic web stack, e.g., SWRL, the semantic web rule language. Recently,
+ontologies are used in hybrid systems for specifying the provenance of the
+different components.
+",1,0,0,0,0,0
+14449,Satisfiability Bounds for ω-regular Properties in Interval-valued Markov Chains,"  We derive an algorithm to compute satisfiability bounds for arbitrary
+{\omega}-regular properties in an Interval-valued Markov Chain (IMC)
+interpreted in the adversarial sense. IMCs generalize regular Markov Chains by
+assigning a range of possible values to the transition probabilities between
+states. In particular, we expand the automata-based theory of {\omega}-regular
+property verification in Markov Chains to apply it to IMCs. Any
+{\omega}-regular property can be represented by a Deterministic Rabin Automata
+(DRA) with acceptance conditions expressed by Rabin pairs. Previous works on
+Markov Chains have shown that computing the probability of satisfying a given
+{\omega}-regular property reduces to a reachability problem in the product
+between the Markov Chain and the corresponding DRA. We similarly define the
+notion of a product between an IMC and a DRA. Then, we show that in a product
+IMC, there exists a particular assignment of the transition values that
+generates a largest set of non-accepting states. Subsequently, we prove that a
+lower bound is found by solving a reachability problem in that refined version
+of the original product IMC. We derive a similar approach for computing a
+satisfiability upper bound in a product IMC with one Rabin pair. For product
+IMCs with more than one Rabin pair, we establish that computing a
+satisfiability upper bound is equivalent to lower-bounding the satisfiability
+of the complement of the original property. A search algorithm for finding the
+largest accepting and non-accepting sets of states in a product IMC is
+proposed. Finally, we demonstrate our findings in a case study.
+",1,0,0,0,0,0
+14450,On the relaxed mean-field stochastic control problem,"  This paper is concerned with optimal control problems for systems governed by
+mean-field stochastic differential equation, in which the control enters both
+the drift and the diffusion coefficient. We prove that the relaxed state
+process, associated with measure valued controls, is governed by an orthogonal
+martingale measure rather that a Brownian motion. In particular, we show by a
+counter example that replacing the drift and diffusion coefficient by their
+relaxed counterparts does not define a true relaxed control problem. We
+establish the existence of an optimal relaxed control, which can be
+approximated by a sequence of strict controls. Moreover under some convexity
+conditions, we show that the optimal control is realized by a strict control.
+",0,0,1,0,0,0
+14451,A New Point-set Registration Algorithm for Fingerprint Matching,"  A novel minutia-based fingerprint matching algorithm is proposed that employs
+iterative global alignment on two minutia sets. The matcher considers all
+possible minutia pairings and iteratively aligns the two sets until the number
+of minutia pairs does not exceed the maximum number of allowable one-to-one
+pairings. The optimal alignment parameters are derived analytically via linear
+least squares. The first alignment establishes a region of overlap between the
+two minutia sets, which is then (iteratively) refined by each successive
+alignment. After each alignment, minutia pairs that exhibit weak correspondence
+are discarded. The process is repeated until the number of remaining pairs no
+longer exceeds the maximum number of allowable one-to-one pairings. The
+proposed algorithm is tested on both the FVC2000 and FVC2002 databases, and the
+results indicate that the proposed matcher is both effective and efficient for
+fingerprint authentication; it is fast and does not utilize any computationally
+expensive mathematical functions (e.g. trigonometric, exponential). In addition
+to the proposed matcher, another contribution of the paper is the analytical
+derivation of the least squares solution for the optimal alignment parameters
+for two point-sets lacking exact correspondence.
+",1,0,0,0,0,0
+14452,A Faster Solution to Smale's 17th Problem I: Real Binomial Systems,"  Suppose $F:=(f_1,\ldots,f_n)$ is a system of random $n$-variate polynomials
+with $f_i$ having degree $\leq\!d_i$ and the coefficient of $x^{a_1}_1\cdots
+x^{a_n}_n$ in $f_i$ being an independent complex Gaussian of mean $0$ and
+variance $\frac{d_i!}{a_1!\cdots a_n!\left(d_i-\sum^n_{j=1}a_j \right)!}$.
+Recent progress on Smale's 17th Problem by Lairez --- building upon seminal
+work of Shub, Beltran, Pardo, Bürgisser, and Cucker --- has resulted in a
+deterministic algorithm that finds a single (complex) approximate root of $F$
+using just $N^{O(1)}$ arithmetic operations on average, where
+$N\!:=\!\sum^n_{i=1}\frac{(n+d_i)!}{n!d_i!}$ ($=n(n+\max_i
+d_i)^{O(\min\{n,\max_i d_i)\}}$) is the maximum possible total number of
+monomial terms for such an $F$. However, can one go faster when the number of
+terms is smaller, and we restrict to real coefficient and real roots? And can
+one still maintain average-case polynomial-time with more general probability
+measures?
+We show the answer is yes when $F$ is instead a binomial system --- a case
+whose numerical solution is a key step in polyhedral homotopy algorithms for
+solving arbitrary polynomial systems. We give a deterministic algorithm that
+finds a real approximate root (or correctly decides there are none) using just
+$O(n^2(\log(n)+\log\max_i d_i))$ arithmetic operations on average. Furthermore,
+our approach allows Gaussians with arbitrary variance. We also discuss briefly
+the obstructions to maintaining average-case time polynomial in $n\log \max_i
+d_i$ when $F$ has more terms.
+",1,0,0,0,0,0
+14453,Making compression algorithms for Unicode text,"  The majority of online content is written in languages other than English,
+and is most commonly encoded in UTF-8, the world's dominant Unicode character
+encoding. Traditional compression algorithms typically operate on individual
+bytes. While this approach works well for the single-byte ASCII encoding, it
+works poorly for UTF-8, where characters often span multiple bytes. Previous
+research has focused on developing Unicode compressors from scratch, which
+often failed to outperform established algorithms such as bzip2. We develop a
+technique to modify byte-based compressors to operate directly on Unicode
+characters, and implement variants of LZW and PPM that apply this technique. We
+find that our method substantially improves compression effectiveness on a
+UTF-8 corpus, with our PPM variant outperforming the state-of-the-art PPMII
+compressor. On ASCII and binary files, our variants perform similarly to the
+original unmodified compressors.
+",1,0,1,0,0,0
+14454,Adaptive Exact Learning of Decision Trees from Membership Queries,"  In this paper we study the adaptive learnability of decision trees of depth
+at most $d$ from membership queries. This has many applications in automated
+scientific discovery such as drugs development and software update problem.
+Feldman solves the problem in a randomized polynomial time algorithm that asks
+$\tilde O(2^{2d})\log n$ queries and Kushilevitz-Mansour in a deterministic
+polynomial time algorithm that asks $ 2^{18d+o(d)}\log n$ queries. We improve
+the query complexity of both algorithms. We give a randomized polynomial time
+algorithm that asks $\tilde O(2^{2d}) + 2^{d}\log n$ queries and a
+deterministic polynomial time algorithm that asks $2^{5.83d}+2^{2d+o(d)}\log n$
+queries.
+",1,0,0,1,0,0
+14455,Image denoising by median filter in wavelet domain,"  The details of an image with noise may be restored by removing noise through
+a suitable image de-noising method. In this research, a new method of image
+de-noising based on using median filter (MF) in the wavelet domain is proposed
+and tested. Various types of wavelet transform filters are used in conjunction
+with median filter in experimenting with the proposed approach in order to
+obtain better results for image de-noising process, and, consequently to select
+the best suited filter. Wavelet transform working on the frequencies of
+sub-bands split from an image is a powerful method for analysis of images.
+According to this experimental work, the proposed method presents better
+results than using only wavelet transform or median filter alone. The MSE and
+PSNR values are used for measuring the improvement in de-noised images.
+",1,0,0,0,0,0
+14456,Restoration of Images with Wavefront Aberrations,"  This contribution deals with image restoration in optical systems with
+coherent illumination, which is an important topic in astronomy, coherent
+microscopy and radar imaging. Such optical systems suffer from wavefront
+distortions, which are caused by imperfect imaging components and conditions.
+Known image restoration algorithms work well for incoherent imaging, they fail
+in case of coherent images. In this paper a novel wavefront correction
+algorithm is presented, which allows image restoration under coherent
+conditions. In most coherent imaging systems, especially in astronomy, the
+wavefront deformation is known. Using this information, the proposed algorithm
+allows a high quality restoration even in case of severe wavefront distortions.
+We present two versions of this algorithm, which are an evolution of the
+Gerchberg-Saxton and the Hybrid-Input-Output algorithm. The algorithm is
+verified on simulated and real microscopic images.
+",1,1,0,0,0,0
+14457,Controlling the shape of membrane protein polyhedra,"  Membrane proteins and lipids can self-assemble into membrane protein
+polyhedral nanoparticles (MPPNs). MPPNs have a closed spherical surface and a
+polyhedral protein arrangement, and may offer a new route for structure
+determination of membrane proteins and targeted drug delivery. We develop here
+a general analytic model of how MPPN self-assembly depends on bilayer-protein
+interactions and lipid bilayer mechanical properties. We find that the
+bilayer-protein hydrophobic thickness mismatch is a key molecular control
+parameter for MPPN shape that can be used to bias MPPN self-assembly towards
+highly symmetric and uniform MPPN shapes. Our results suggest strategies for
+optimizing MPPN shape for structural studies of membrane proteins and targeted
+drug delivery.
+",0,1,0,0,0,0
+14458,Realizing uniformly recurrent subgroups,"  We show that every uniformly recurrent subgroup of a locally compact group is
+the family of stabilizers of a minimal action on a compact space. More
+generally, every closed invariant subset of the Chabauty space is the family of
+stabilizers of an action on a compact space on which the stabilizer map is
+continuous everywhere. This answers a question of Glasner and Weiss. We also
+introduce the notion of a universal minimal flow relative to a uniformly
+recurrent subgroup and prove its existence and uniqueness.
+",0,0,1,0,0,0
+14459,A New Approximation Guarantee for Monotone Submodular Function Maximization via Discrete Convexity,"  In monotone submodular function maximization, approximation guarantees based
+on the curvature of the objective function have been extensively studied in the
+literature. However, the notion of curvature is often pessimistic, and we
+rarely obtain improved approximation guarantees, even for very simple objective
+functions.
+In this paper, we provide a novel approximation guarantee by extracting an
+M$^\natural$-concave function $h:2^E \to \mathbb R_+$, a notion in discrete
+convex analysis, from the objective function $f:2^E \to \mathbb R_+$. We
+introduce the notion of $h$-curvature, which measures how much $f$ deviates
+from $h$, and show that we can obtain a $(1-\gamma/e-\epsilon)$-approximation
+to the problem of maximizing $f$ under a cardinality constraint in polynomial
+time for any constant $\epsilon > 0$. Then, we show that we can obtain
+nontrivial approximation guarantees for various problems by applying the
+proposed algorithm.
+",1,0,0,0,0,0
+14460,Electrode Reactions in Slowly Relaxing Media,"  Standard models of reaction kinetics in condensed materials rely on the
+Boltzmann-Gibbs distribution for the population of reactants at the top of the
+free energy barrier separating them from the products. While energy dissipation
+and quantum effects at the barrier top can potentially affect the transmission
+coefficient entering the rate preexponential factor, much stronger dynamical
+effects on the reaction barrier are caused by the breakdown of ergodicity for
+populating the reaction barrier (violation of the Boltzmann-Gibbs statistics).
+When the spectrum of medium modes coupled to the reaction coordinate includes
+fluctuations slower than the reaction rate, such nuclear motions dynamically
+freeze on the reaction time-scale and do not contribute to the activation
+barrier. Here we consider the consequences of this scenario for electrode
+reactions in slowly relaxing media. Changing electrode overpotential speeds
+electrode electron transfer up, potentially cutting through the spectrum of
+nuclear modes coupled to the reaction coordinate. The reorganization energy of
+electrochemical electron transfer becomes a function of the electrode
+overpotential, switching between the thermodynamic value at low rates to the
+nonergodic limit at higher rates. The sharpness of this transition depends of
+the relaxation spectrum of the medium. The reorganization energy experiences a
+sudden drop with increasing overpotential for a medium with a Debye relaxation,
+but becomes a much shallower function of the overpotential for media with
+stretched exponential dynamics. The latter scenario characterizes electron
+transfer in ionic liquids. The analysis of electrode reactions in
+room-temperature ionic liquids shows that the magnitude of the free energy of
+nuclear solvation is significantly below its thermodynamic limit.
+",0,1,0,0,0,0
+14461,End-to-end 3D face reconstruction with deep neural networks,"  Monocular 3D facial shape reconstruction from a single 2D facial image has
+been an active research area due to its wide applications. Inspired by the
+success of deep neural networks (DNN), we propose a DNN-based approach for
+End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different
+from recent works that reconstruct and refine the 3D face in an iterative
+manner using both an RGB image and an initial 3D facial shape rendering, our
+DNN model is end-to-end, and thus the complicated 3D rendering process can be
+avoided. Moreover, we integrate in the DNN architecture two components, namely
+a multi-task loss function and a fusion convolutional neural network (CNN) to
+improve facial expression reconstruction. With the multi-task loss function, 3D
+face reconstruction is divided into neutral 3D facial shape reconstruction and
+expressive 3D facial shape reconstruction. The neutral 3D facial shape is
+class-specific. Therefore, higher layer features are useful. In comparison, the
+expressive 3D facial shape favors lower or intermediate layer features. With
+the fusion-CNN, features from different intermediate layers are fused and
+transformed for predicting the 3D expressive facial shape. Through extensive
+experiments, we demonstrate the superiority of our end-to-end framework in
+improving the accuracy of 3D face reconstruction.
+",1,0,0,0,0,0
+14462,Entanglement of photons in their dual wave-particle nature,"  Wave-particle duality is the most fundamental description of the nature of a
+quantum object which behaves like a classical particle or wave depending on the
+measurement apparatus. On the other hand, entanglement represents nonclassical
+correlations of composite quantum systems, being also a key resource in quantum
+information. Despite the very recent observations of wave-particle
+superposition and entanglement, whether these two fundamental traits of quantum
+mechanics can emerge simultaneously remains an open issue. Here we introduce
+and experimentally realize a scheme that deterministically generates
+wave-particle entanglement of two photons. The elementary tool allowing this
+achievement is a scalable single-photon setup which can be in principle
+extended to generate multiphoton wave-particle entanglement. Our study reveals
+that photons can be entangled in their dual wave-particle nature and opens the
+way to potential applications in quantum information protocols exploiting the
+wave-particle degrees of freedom to encode qubits.
+",0,1,0,0,0,0
+14463,On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm,"  Tensor train (TT) decomposition provides a space-efficient representation for
+higher-order tensors. Despite its advantage, we face two crucial limitations
+when we apply the TT decomposition to machine learning problems: the lack of
+statistical theory and of scalable algorithms. In this paper, we address the
+limitations. First, we introduce a convex relaxation of the TT decomposition
+problem and derive its error bound for the tensor completion task. Next, we
+develop an alternating optimization method with a randomization technique, in
+which the time complexity is as efficient as the space complexity is. In
+experiments, we numerically confirm the derived bounds and empirically
+demonstrate the performance of our method with a real higher-order tensor.
+",0,0,0,1,0,0
+14464,Threshold fluctuations in a superconducting current-carrying bridge,"  We calculate the energy of threshold fluctuation $\delta F_{thr}$ which
+triggers the transition of superconducting current-carrying bridge to resistive
+state. We show that the dependence $\delta F_{thr}(I)\propto
+I_{dep}\hbar(1-I/I_{dep})^{5/4}/e$, found by Langer and Ambegaokar for a long
+bridge with length $L \gg \xi$, holds far below the critical temperature both
+in dirty and clean limits (here $I_{dep}$ is the depairing current of the
+bridge and $\xi$ is a coherence length). We also find that even 'weak' local
+defect (leading to the small suppression of the critical current of the bridge
+$I_c \lesssim I_{dep}$) provides $\delta F_{thr}\propto
+I_c\hbar(1-I/I_c)^{3/2}/e$, typical for a short bridge with $L \ll \xi$ or a
+Josephson junction.
+",0,1,0,0,0,0
+14465,Transient phenomena in a three-layer waveguide and the analytical structure of the dispersion diagram,"  Excitation of waves in a three-layer acoustic wavegide is studied. The wave
+field is presented as a sum of integrals. The summation is held over all
+waveguide modes. The integration is performed over the temporal frequency axis.
+The dispersion diagram of the waveguide is analytically continued, and the
+integral is transformed by deformation of the integration contour into the
+domain of complex frequencies. As the result, the expression for the fast
+components of the signal (i.e. for the transient fields) is simplified.
+The structure of the Riemann surface of the dispersion diagram of the
+waveguide is studied. For this, a family of auxiliary problems indexed by the
+parameters describing the links between layers is introduced. The family
+depends on the linking parameters analytically, and the limiting case of weak
+links can be solved analytically.
+",0,0,1,0,0,0
+14466,Introducing symplectic billiards,"  In this article we introduce a simple dynamical system called symplectic
+billiards. As opposed to usual/Birkhoff billiards, where length is the
+generating function, for symplectic billiards symplectic area is the generating
+function. We explore basic properties and exhibit several similarities, but
+also differences of symplectic billiards to Birkhoff billiards.
+",0,0,1,0,0,0
+14467,Banchoff's sphere and branched covers over the trefoil,"  A filling Dehn surface in a $3$-manifold $M$ is a generically immersed
+surface in $M$ that induces a cellular decomposition of $M$. Given a tame link
+$L$ in $M$ there is a filling Dehn sphere of $M$ that ""trivializes""
+(\emph{diametrically splits}) it. This allows to construct filling Dehn
+surfaces in the coverings of $M$ branched over $L$. It is shown that one of the
+simplest filling Dehn spheres of $S^3$ (Banchoff's sphere) diametrically splits
+the trefoil knot. Filling Dehn spheres, and their Johansson diagrams, are
+constructed for the coverings of $S^3$ branched over the trefoil. The
+construction is explained in detail. Johansson diagrams for generic cyclic
+coverings and for the simplest locally cyclic and irregular ones are
+constructed explicitly, providing new proofs of known results about cyclic
+coverings and the $3$-fold irregular covering over the trefoil.
+",0,0,1,0,0,0
+14468,The Impact of Social Curiosity on Information Spreading on Networks,"  Most information spreading models consider that all individuals are identical
+psychologically. They ignore, for instance, the curiosity level of people,
+which may indicate that they can be influenced to seek for information given
+their interest. For example, the game Pokémon GO spread rapidly because of
+the aroused curiosity among users. This paper proposes an information
+propagation model considering the curiosity level of each individual, which is
+a dynamical parameter that evolves over time. We evaluate the efficiency of our
+model in contrast to traditional information propagation models, like SIR or
+IC, and perform analysis on different types of artificial and real-world
+networks, like Google+, Facebook, and the United States roads map. We present a
+mean-field approach that reproduces with a good accuracy the evolution of
+macroscopic quantities, such as the density of stiflers, for the system's
+behavior with the curiosity. We also obtain an analytical solution of the
+mean-field equations that allows to predicts a transition from a phase where
+the information remains confined to a small number of users to a phase where it
+spreads over a large fraction of the population. The results indicate that the
+curiosity increases the information spreading in all networks as compared with
+the spreading without curiosity, and that this increase is larger in spatial
+networks than in social networks. When the curiosity is taken into account, the
+maximum number of informed individuals is reached close to the transition
+point. Since curious people are more open to a new product, concepts, and
+ideas, this is an important factor to be considered in propagation modeling.
+Our results contribute to the understanding of the interplay between diffusion
+process and dynamical heterogeneous transmission in social networks.
+",1,1,0,0,0,0
+14469,Decomposition of mean-field Gibbs distributions into product measures,"  We show that under a low complexity condition on the gradient of a
+Hamiltonian, Gibbs distributions on the Boolean hypercube are approximate
+mixtures of product measures whose probability vectors are critical points of
+an associated mean-field functional. This extends a previous work by the first
+author. As an application, we demonstrate how this framework helps characterize
+both Ising models satisfying a mean-field condition and the conditional
+distributions which arise in the emerging theory of nonlinear large deviations,
+both in the dense case and in the polynomially-sparse case.
+",0,0,1,1,0,0
+14470,Analytic Discs and Uniform Algebras on Real-Analytic Varieties,"  Under very general conditions it is shown that if $A$ is a uniform algebra
+generated by real-analytic functions, then either $A$ consists of all
+continuous functions or else there exists a disc on which every function in $A$
+is holomorphic. This strengthens several earlier results concerning uniform
+algebras generated by real-analytic functions.
+",0,0,1,0,0,0
+14471,Novel solid state vacuum quartz encapsulated growth of p-Terphenyl: the parent High Tc Oraganic Superconductor (HTOS),"  We report an easy and versatile route for the synthesis of the parent phase
+of newest superconducting wonder material i.e. p-Terphenyl. Doped p-terphenyl
+has recently shown superconductivity with transition temperature as high as
+120K. For crystal growth, the commercially available p-Terphenyl powder is
+pelletized, encapsulated in evacuated (10-4 Torr) quartz tube and subjected to
+high temperature (260C) melt followed by slow cooling at 5C/hour. Simple
+temperature controlled heating furnace is used during the process. The obtained
+crystal is one piece, shiny and plate like. Single crystal surface XRD (X-ray
+Diffraction) showed unidirectional (00l) lines, indicating that the crystal is
+grown along c-direction. Powder XRD of the specimen showed that as grown
+p-Terphenyl is crystallized in monoclinic structure with space group P21/a
+space group, having lattice parameters a = 8.08(2) A, b = 5.62(5) A and c=
+13.58(3) A. Scanning electron microscopy (SEM) pictures of the crystal showed
+clear layered slab like growth without any visible contamination from oxygen.
+Characteristic reported Raman active modes related to C-C-C bending, C-H
+bending, C-C stretching and C-H stretching vibrations are seen clearly for the
+studied p-Terphenyl crystal. The physical properties of crystal are yet
+underway. The short letter reports an easy and versatile crystal growth method
+for obtaining quality p-terphenyl. The same growth method may probably be
+applied to doped p-terphenyl and to subsequently achieve superconductivity to
+the tune of as high 120K for the newest superconductivity wonder i.e., High Tc
+Oraganic Superconductor (HTOS).
+",0,1,0,0,0,0
+14472,Twistors from Killing Spinors alias Radiation from Pair Annihilation I: Theoretical Considerations,"  This paper is intended to be a further step through our Killing spinor
+programme started with Class. Quantum Grav. \textbf{32}, 175007 (2015), and we
+will advance our programme in accordance with the road map recently given in
+arXiv:1611.04424v2. In the latter reference many open problems were declared,
+one of which contained the uncovered relations between specific spinors in
+spacetime represented by an arrow diagram built upon them. This work deals with
+one of the arrows with almost all of its details and ends up with an important
+physical interpretation of this setup in terms of the quantum electrodynamical
+pair annihilation process. This method will shed light on the classification of
+pseudo-Riemannian manifolds admitting twistors in connection with the
+classification problem related to Killing spinors. Many physical
+interpretations are given during the text some of which include dynamics of
+brane immersions, quantum field theoretical considerations and black hole
+evaporation.
+",0,0,1,0,0,0
+14473,Table Space Designs For Implicit and Explicit Concurrent Tabled Evaluation,"  One of the main advantages of Prolog is its potential for the implicit
+exploitation of parallelism and, as a high-level language, Prolog is also often
+used as a means to explicitly control concurrent tasks. Tabling is a powerful
+implementation technique that overcomes some limitations of traditional Prolog
+systems in dealing with recursion and redundant sub-computations. Given these
+advantages, the question that arises is if tabling has also the potential for
+the exploitation of concurrency/parallelism. On one hand, tabling still
+exploits a search space as traditional Prolog but, on the other hand, the
+concurrent model of tabling is necessarily far more complex since it also
+introduces concurrency on the access to the tables. In this paper, we summarize
+Yap's main contributions to concurrent tabled evaluation and we describe the
+design and implementation challenges of several alternative table space designs
+for implicit and explicit concurrent tabled evaluation which represent
+different trade-offs between concurrency and memory usage. We also motivate for
+the advantages of using fixed-size and lock-free data structures, elaborate on
+the key role that the engine's memory allocator plays on such environments, and
+discuss how Yap's mode-directed tabling support can be extended to concurrent
+evaluation. Finally, we present our future perspectives towards an efficient
+and novel concurrent framework which integrates both implicit and explicit
+concurrent tabled evaluation in a single Prolog engine. Under consideration in
+Theory and Practice of Logic Programming (TPLP).
+",1,0,0,0,0,0
+14474,Evidence for triplet superconductivity near an antiferromagnetic instability in CrAs,"  Superconductivity was recently observed in CrAs as the helimagnetic order is
+suppressed by applying pressure, suggesting possible unconventional
+superconductivity. To reveal the nature of the superconducting order parameter
+of CrAs, here we report the angular dependence of the upper critical field
+under pressure. Upon rotating the field by 360 degrees in the $bc$-plane, six
+maxima are observed in the upper critical field, where the oscillations have
+both six-fold and two-fold symmetric components. Our analysis suggests the
+presence of an unconventional odd-parity spin triplet state.
+",0,1,0,0,0,0
+14475,"Singlet ground state in the spin-$1/2$ weakly coupled dimer compound NH$_4$[(V$_2$O$_3$)$_2$(4,4$^\prime$-$bpy$)$_2$(H$_2$PO$_4$)(PO$_4$)$_2$]$\cdot$0.5H$_2$O","  We present the synthesis and a detailed investigation of structural and
+magnetic properties of polycrystalline
+NH$_4$[(V$_2$O$_3$)$_2$(4,4$^\prime$-$bpy$)$_2$(H$_2$PO$_4$)(PO$_4$)$_2$]$\cdot$0.5H$_2$O
+by means of x-ray diffraction, magnetic susceptibility, electron spin
+resonance, and $^{31}$P nuclear magnetic resonance measurements. Temperature
+dependent magnetic susceptibility could be described well using a weakly
+coupled spin-$1/2$ dimer model with an excitation gap $\Delta/k_{\rm B}\simeq
+26.1$ K between the singlet ground state and triplet excited states and a weak
+inter-dimer exchange coupling $J^\prime/k_{\rm B} \simeq 4.6$ K. A gapped chain
+model also describes the data well with a gap of about 20 K. The ESR intensity
+as a function of temperature traces the bulk susceptibility nicely. The
+isotropic Land$\acute{\rm e}$ $g$-factor is estimated to be about $g \simeq
+1.97$, at room temperature. We are able to resolve the $^{31}$P NMR signal as
+coming from two inequivalent P-sites in the crystal structure. The hyperfine
+coupling constant between $^{31}$P nucleus and V$^{4+}$ spins is calculated to
+be $A_{\rm hf}(1) \simeq 2963$ Oe/$\mu_{\rm B}$ and $A_{\rm hf}(2) \simeq 1466$
+Oe/$\mu_{\rm B}$ for the P(1) and P(2) sites, respectively. Our NMR shift and
+spin-lattice relaxation rate for both the $^{31}$P sites show an activated
+behaviour at low temperatures, further confirming the singlet ground state. The
+estimated value of the spin gap from the NMR data measured in an applied field
+of $H = 9.394$ T is consistent with the gap obtained from the magnetic
+susceptibility analysis using the dimer model. Because of a relatively small
+spin gap,
+NH$_4$[(V$_2$O$_3$)$_2$(4,4$^\prime$-$bpy$)$_2$(H$_2$PO$_4$)(PO$_4$)$_2$]$\cdot$0.5H$_2$O
+is a promising compound for further experimental studies under high magnetic
+fields.
+",0,1,0,0,0,0
+14476,Deep learning bank distress from news and numerical financial data,"  In this paper we focus our attention on the exploitation of the information
+contained in financial news to enhance the performance of a classifier of bank
+distress. Such information should be analyzed and inserted into the predictive
+model in the most efficient way and this task deals with all the issues related
+to text analysis and specifically analysis of news media. Among the different
+models proposed for such purpose, we investigate one of the possible deep
+learning approaches, based on a doc2vec representation of the textual data, a
+kind of neural network able to map the sequential and symbolic text input onto
+a reduced latent semantic space. Afterwards, a second supervised neural network
+is trained combining news data with standard financial figures to classify
+banks whether in distressed or tranquil states, based on a small set of known
+distress events. Then the final aim is not only the improvement of the
+predictive performance of the classifier but also to assess the importance of
+news data in the classification process. Does news data really bring more
+useful information not contained in standard financial variables? Our results
+seem to confirm such hypothesis.
+",1,0,0,1,0,0
+14477,Theoretical studies of superconductivity in doped BaCoSO,"  We investigate superconductivity that may exist in the doped BaCoSO, a
+multi-orbital Mott insulator with a strong antiferromagnetic ground state. The
+superconductivity is studied in both t-J type and Hubbard type multi-orbital
+models by mean field approach and random phase approximation (RPA) analysis.
+Even if there is no C4 rotational symmetry, it is found that the system still
+carries a d-wave like pairing symmetry state with gapless nodes and sign
+changed superconducting order parameters on Fermi surfaces. The results are
+largely doping insensitive. In this superconducting state, the three t2g
+orbitals have very different superconducting form factors in momentum space. In
+particular, the intra-orbital pairing of the dx2-y2 orbital has a s-wave like
+pairing form factor. The two methods also predict very different pairing
+strength on different parts of Fermi surfaces.These results suggest that BaCoSO
+and related materials can be a new ground to test and establish fundamental
+principles for unconventional high temperature superconductivity.
+",0,1,0,0,0,0
+14478,On low for speed oracles,"  Relativizing computations of Turing machines to an oracle is a central
+concept in the theory of computation, both in complexity theory and in
+computability theory(!). Inspired by lowness notions from computability theory,
+Allender introduced the concept of ""low for speed"" oracles. An oracle A is low
+for speed if relativizing to A has essentially no effect on computational
+complexity, meaning that if a decidable language can be decided in time $f(n)$
+with access to oracle A, then it can be decided in time poly(f(n)) without any
+oracle. The existence of non-computable such A's was later proven by Bayer and
+Slaman, who even constructed a computably enumerable one, and exhibited a
+number of properties of these oracles as well as interesting connections with
+computability theory. In this paper, we pursue this line of research, answering
+the questions left by Bayer and Slaman and give further evidence that the
+structure of the class of low for speed oracles is a very rich one.
+",1,0,1,0,0,0
+14479,$\mbox{Rb}_{2}\mbox{Ti}_2\mbox{O}_{5-δ}$: A superionic conductor with colossal dielectric constant,"  Electrical conductivity and high dielectric constant are in principle
+self-excluding, which makes the terms insulator and dielectric usually
+synonymous. This is certainly true when the electrical carriers are electrons,
+but not necessarily in a material where ions are extremely mobile, electronic
+conduction is negligible and the charge transfer at the interface is
+immaterial. Here we demonstrate in a perovskite-derived structure containing
+five-coordinated Ti atoms, a colossal dielectric constant (up to $\mbox{10}^9$)
+together with very high ionic conduction $\mbox{10}^{-3}\mbox{S.cm}^{-1}$ at
+room temperature. Coupled investigations of I-V and dielectric constant
+behavior allow to demonstrate that, due to ion migration and accumulation, this
+material behaves like a giant dipole, exhibiting colossal electrical
+polarization (of the order of $\mbox{0.1\,C.cm}^{-2}$). Therefore, it may be
+considered as a ""ferro-ionet"" and is extremely promising in terms of
+applications.
+",0,1,0,0,0,0
+14480,LinNet: Probabilistic Lineup Evaluation Through Network Embedding,"  Which of your team's possible lineups has the best chances against each of
+your opponents possible lineups? In order to answer this question we develop
+LinNet. LinNet exploits the dynamics of a directed network that captures the
+performance of lineups at their matchups. The nodes of this network represent
+the different lineups, while an edge from node j to node i exists if lineup i
+has outperformed lineup j. We further annotate each edge with the corresponding
+performance margin (point margin per minute). We then utilize this structure to
+learn a set of latent features for each node (i.e., lineup) using the node2vec
+framework. Consequently, LinNet builds a model on this latent space for the
+probability of lineup A beating lineup B. We evaluate LinNet using NBA lineup
+data from the five seasons between 2007-08 and 2011-12. Our results indicate
+that our method has an out-of-sample accuracy of 69%. In comparison, utilizing
+the adjusted plus-minus of the players within a lineup for the same prediction
+problem provides an accuracy of 56%. More importantly, the probabilities are
+well-calibrated as shown by the probability validation curves. One of the
+benefits of LinNet - apart from its accuracy - is that it is generic and can be
+applied in different sports since the only input required is the lineups'
+matchup performances, i.e., not sport-specific features are needed.
+",0,0,0,1,0,0
+14481,Observational Equivalence in System Estimation: Contractions in Complex Networks,"  Observability of complex systems/networks is the focus of this paper, which
+is shown to be closely related to the concept of contraction. Indeed, for
+observable network tracking it is necessary/sufficient to have one node in each
+contraction measured. Therefore, nodes in a contraction are equivalent to
+recover for loss of observability, implying that contraction size is a key
+factor for observability recovery. Here, using a polynomial order contraction
+detection algorithm, we analyze the distribution of contractions, studying its
+relation with key network properties. Our results show that contraction size is
+related to network clustering coefficient and degree heterogeneity.
+Particularly, in networks with power-law degree distribution, if the clustering
+coefficient is high there are less contractions with smaller size on average.
+The implication is that estimation/tracking of such systems requires less
+number of measurements, while their observational recovery is more restrictive
+in case of sensor failure. Further, in Small-World networks higher degree
+heterogeneity implies that there are more contractions with smaller size on
+average. Therefore, the estimation of representing system requires more
+measurements, and also the recovery of measurement failure is more limited.
+These results imply that one can tune the properties of synthetic networks to
+alleviate their estimation/observability recovery.
+",1,0,0,0,0,0
+14482,Incremental Adversarial Domain Adaptation for Continually Changing Environments,"  Continuous appearance shifts such as changes in weather and lighting
+conditions can impact the performance of deployed machine learning models.
+While unsupervised domain adaptation aims to address this challenge, current
+approaches do not utilise the continuity of the occurring shifts. In
+particular, many robotics applications exhibit these conditions and thus
+facilitate the potential to incrementally adapt a learnt model over minor
+shifts which integrate to massive differences over time. Our work presents an
+adversarial approach for lifelong, incremental domain adaptation which benefits
+from unsupervised alignment to a series of intermediate domains which
+successively diverge from the labelled source domain. We empirically
+demonstrate that our incremental approach improves handling of large appearance
+changes, e.g. day to night, on a traversable-path segmentation task compared
+with a direct, single alignment step approach. Furthermore, by approximating
+the feature distribution for the source domain with a generative adversarial
+network, the deployment module can be rendered fully independent of retaining
+potentially large amounts of the related source training data for only a minor
+reduction in performance.
+",1,0,0,1,0,0
+14483,SAND: An automated VLBI imaging and analysing pipeline - I. Stripping component trajectories,"  We present our implementation of an automated VLBI data reduction pipeline
+dedicated to interferometric data imaging and analysis. The pipeline can handle
+massive VLBI data efficiently which makes it an appropriate tool to investigate
+multi-epoch multiband VLBI data. Compared to traditional manual data reduction,
+our pipeline provides more objective results since less human interference is
+involved. Source extraction is done in the image plane, while deconvolution and
+model fitting are done in both the image plane and the uv plane for parallel
+comparison. The output from the pipeline includes catalogues of CLEANed images
+and reconstructed models, polarisation maps, proper motion estimates, core
+light curves and multi-band spectra. We have developed a regression strip
+algorithm to automatically detect linear or non-linear patterns in the jet
+component trajectories. This algorithm offers an objective method to match jet
+components at different epochs and determine their proper motions.
+",0,1,0,0,0,0
+14484,Quench-induced entanglement and relaxation dynamics in Luttinger liquids,"  We investigate the time evolution towards the asymptotic steady state of a
+one dimensional interacting system after a quantum quench. We show that at
+finite time the latter induces entanglement between right- and left- moving
+density excitations, encoded in their cross-correlators, which vanishes in the
+long-time limit. This behavior results in a universal time-decay in system
+spectral properties $ \propto t^{-2} $, in addition to non-universal power-law
+contributions typical of Luttinger liquids. Importantly, we argue that the
+presence of quench-induced entanglement clearly emerges in transport
+properties, such as charge and energy currents injected in the system from a
+biased probe, and determines their long-time dynamics. In particular, energy
+fractionalization phenomenon turns out to be a promising platform to observe
+the universal power-law decay $ \propto t^{-2} $ induced by entanglement and
+represents a novel way to study the corresponding relaxation mechanism.
+",0,1,0,0,0,0
+14485,Coded Caching Schemes with Low Rate and Subpacketizations,"  Coded caching scheme, which is an effective technique to increase the
+transmission efficiency during peak traffic times, has recently become quite
+popular among the coding community. Generally rate can be measured to the
+transmission in the peak traffic times, i.e., this efficiency increases with
+the decreasing of rate. In order to implement a coded caching scheme, each file
+in the library must be split in a certain number of packets. And this number
+directly reflects the complexity of a coded caching scheme, i.e., the
+complexity increases with the increasing of the packet number. However there
+exists a tradeoff between the rate and packet number. So it is meaningful to
+characterize this tradeoff and design the related Pareto-optimal coded caching
+schemes with respect to both parameters.
+Recently, a new concept called placement delivery array (PDA) was proposed to
+characterize the coded caching scheme. However as far as we know no one has yet
+proved that one of the previously known PDAs is Pareto-optimal. In this paper,
+we first derive two lower bounds on the rate under the framework of PDA.
+Consequently, the PDA proposed by Maddah-Ali and Niesen is Pareto-optimal, and
+a tradeoff between rate and packet number is obtained for some parameters.
+Then, from the above observations and the view point of combinatorial design,
+two new classes of Pareto-optimal PDAs are obtained. Based on these PDAs, the
+schemes with low rate and packet number are obtained. Finally the performance
+of some previously known PDAs are estimated by comparing with these two classes
+of schemes.
+",1,0,0,0,0,0
+14486,Multitask diffusion adaptation over networks with common latent representations,"  Online learning with streaming data in a distributed and collaborative manner
+can be useful in a wide range of applications. This topic has been receiving
+considerable attention in recent years with emphasis on both single-task and
+multitask scenarios. In single-task adaptation, agents cooperate to track an
+objective of common interest, while in multitask adaptation agents track
+multiple objectives simultaneously. Regularization is one useful technique to
+promote and exploit similarity among tasks in the latter scenario. This work
+examines an alternative way to model relations among tasks by assuming that
+they all share a common latent feature representation. As a result, a new
+multitask learning formulation is presented and algorithms are developed for
+its solution in a distributed online manner. We present a unified framework to
+analyze the mean-square-error performance of the adaptive strategies, and
+conduct simulations to illustrate the theoretical findings and potential
+applications.
+",1,0,0,1,0,0
+14487,Strong perpendicular magnetic anisotropy energy density at Fe alloy/HfO2 interfaces,"  We report on the perpendicular magnetic anisotropy (PMA) behavior of heavy
+metal (HM)/ Fe alloy/MgO thin film heterostructures after an ultrathin HfO2
+passivation layer is inserted between the Fe alloy and the MgO. This is
+accomplished by depositing one to two atomic layers of Hf onto the Fe alloy
+before the subsequent rf sputter deposition of the MgO layer. This Hf layer is
+fully oxidized during the subsequent deposition of the MgO layer, as confirmed
+by X-ray photoelectron spectroscopy measurements. As the result a strong
+interfacial perpendicular anisotropy energy density can be achieved without any
+post-fabrication annealing treatment, for example 1.7 erg/cm^2 for the
+Ta/Fe60Co20B20/HfO2/MgO heterostructure. Depending on the HM, further
+enhancements of the PMA can be realized by thermal annealing to at least 400C.
+We show that ultra-thin HfO2 layers offer a range of options for enhancing the
+magnetic properties of magnetic heterostructures for spintronics applications.
+",0,1,0,0,0,0
+14488,Covering Groups of Nonconnected Topological Groups and 2-Groups,"  We investigate the universal cover of a topological group that is not
+necessarily connected. Its existence as a topological group is governed by a
+Taylor cocycle, an obstruction in 3-cohomology. Alternatively, it always exists
+as a topological 2-group. The splitness of this 2-group is also governed by an
+obstruction in 3-cohomology, a Sinh cocycle. We give explicit formulas for both
+obstructions and show that they are inverse of each other.
+",0,0,1,0,0,0
+14489,Polarization properties of turbulent synchrotron bubbles: an approach based on Chandrasekhar-Kendall functions,"  Synchrotron emitting bubbles arise when the outflow from a compact
+relativistic engine, either a Black Hole or a Neutron Star, impacts on the
+environment. The emission properties of synchrotron radiation are widely used
+to infer the dynamical properties of these bubbles, and from them the injection
+conditions of the engine. Radio polarization offers an important tool to
+investigate the level and spectrum of turbulence, the magnetic field
+configuration, and possibly the degree of mixing. Here we introduce a formalism
+based on Chandrasekhar-Kendall functions that allows us to properly take into
+account the geometry of the bubble, going beyond standard analysis based on
+periodic cartesian domains. We investigate how different turbulent spectra,
+magnetic helicity and particle distribution function, impact on global
+properties that are easily accessible to observations, even at low resolution,
+and we provide fitting formulae to relate observed quantities to the underlying
+magnetic field structure.
+",0,1,0,0,0,0
+14490,Efimov Effect in the Dirac Semi-metals,"  Efimov effect refers to quantum states with discrete scaling symmetry and a
+universal scaling factor, and has attracted considerable interests from nuclear
+to atomic physics communities. In a Dirac semi-metal, when an electron
+interacts with a static impurity though a Coulomb interaction, the same scaling
+of the kinetic and interaction energies also gives rise to such a Efimov
+effect. However, even when the Fermi energy exactly lies at the Dirac point,
+the vacuum polarization of electron-hole pair fluctuation can still screen the
+Coulomb interaction, which leads to derivation from this scaling symmetry and
+eventually breakdown of the Efimov effect. This distortion of the Efimov bound
+state energy due to vacuum polarization is a relativistic electron analogy of
+the Lamb shift for the hydrogen atom. Motivated by recent experimental
+observations in two- and three-dimensional Dirac semi-metals, in this paper we
+investigate this many-body correction to the Efimov effect, and answer the
+question that under what condition a good number of Efimov-like bound states
+can still be observed in these condensed matter experiments.
+",0,1,0,0,0,0
+14491,Logic Lectures: Gödel's Basic Logic Course at Notre Dame,"  An edited version is given of the text of Gödel's unpublished manuscript of
+the notes for a course in basic logic he delivered at the University of Notre
+Dame in 1939. Gödel's notes deal with what is today considered as important
+logical problems par excellence, completeness, decidability, independence of
+axioms, and with natural deduction too, which was all still a novelty at the
+time the course was delivered. Full of regards towards beginners, the notes are
+not excessively formalistic. Gödel presumably intended them just for himself,
+and they are full of abbreviations. This together with some other matters (like
+two versions of the same topic, and guessing the right order of the pages)
+required additional effort to obtain a readable edited version. Because of the
+quality of the material provided by Gödel, including also important
+philosophical points, this effort should however be worthwhile. The edited
+version of the text is accompanied by another version, called the source
+version, which is quite close to Gödel's manuscript. It is meant to be a
+record of the editorial interventions involved in producing the edited version
+(in particular, how the abbreviations were disabridged), and a justification of
+that later version.
+",0,0,1,0,0,0
+14492,Field dependent neutron diffraction study in Ni50Mn38Sb12 Heusler alloy,"  In this paper, we present temperature and field dependent neutron diffraction
+(ND) study to unravel the structural and the magnetic properties in
+Ni50Mn38Sb12 Heusler system. This alloy shows martensitic transition from high
+temperature austenite cubic phase to low temperature martensite orthorhombic
+phase on cooling. At 3 K, the lattice parameters and magnetic moments are found
+to be almost insensitive to field. Just below the martensitic transition
+temperature, the martensite phase fraction is found to be 85%. Upon applying
+the field, the austenite phase becomes dominant, and the field induced reverse
+martensitic transition is clearly observed in the ND data. Therefore, the
+present study gives an estimate of the strength of the martensite phase or the
+sharpness of the martensitic transition. Variation of individual moments and
+the change in the phase fraction obtained from the analysis of the ND data
+vividly show the change in the magneto-structural state of the material across
+the transition.
+",0,1,0,0,0,0
+14493,Optimistic lower bounds for convex regularized least-squares,"  Minimax lower bounds are pessimistic in nature: for any given estimator,
+minimax lower bounds yield the existence of a worst-case target vector
+$\beta^*_{worst}$ for which the prediction error of the given estimator is
+bounded from below. However, minimax lower bounds shed no light on the
+prediction error of the given estimator for target vectors different than
+$\beta^*_{worst}$. A characterization of the prediction error of any convex
+regularized least-squares is given. This characterization provide both a lower
+bound and an upper bound on the prediction error. This produces lower bounds
+that are applicable for any target vector and not only for a single, worst-case
+$\beta^*_{worst}$. Finally, these lower and upper bounds on the prediction
+error are applied to the Lasso is sparse linear regression. We obtain a lower
+bound involving the compatibility constant for any tuning parameter, matching
+upper and lower bounds for the universal choice of the tuning parameter, and a
+lower bound for the Lasso with small tuning parameter.
+",0,0,1,1,0,0
+14494,Continual Lifelong Learning with Neural Networks: A Review,"  Humans and animals have the ability to continually acquire, fine-tune, and
+transfer knowledge and skills throughout their lifespan. This ability, referred
+to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms
+that together contribute to the development and specialization of our
+sensorimotor skills as well as to the long-term memory consolidation and
+retrieval without catastrophic forgetting. Consequently, lifelong learning
+capabilities are crucial for autonomous agents interacting in the real world
+and processing continuous streams of information. However, lifelong learning
+remains a long-standing challenge for machine learning and neural network
+models since the continual acquisition of incrementally available information
+from non-stationary data distributions generally leads to catastrophic
+forgetting or interference. This limitation represents a major drawback for
+state-of-the-art deep neural network models that typically learn
+representations from stationary batches of training data, thus without
+accounting for situations in which information becomes incrementally available
+over time. In this review, we critically summarize the main challenges linked
+to lifelong learning for artificial learning systems and compare existing
+neural network approaches that alleviate, to different extents, catastrophic
+forgetting. We discuss well-established and emerging research motivated by
+lifelong learning factors in biological systems such as structural plasticity,
+memory replay, curriculum and transfer learning, intrinsic motivation, and
+multisensory integration.
+",0,0,0,1,1,0
+14495,"Potential kernel, hitting probabilities and distributional asymptotics","  Z^d-extensions of probability-preserving dynamical systems are themselves
+dynamical systems preserving an infinite measure, and generalize random walks.
+Using the method of moments, we prove a generalized central limit theorem for
+additive functionals of the extension of integral zero, under spectral
+assumptions. As a corollary, we get the fact that Green-Kubo's formula is
+invariant under induction. This allows us to relate the hitting probability of
+sites with the symmetrized potential kernel, giving an alternative proof and
+generalizing a theorem of Spitzer. Finally, this relation is used to improve in
+turn the asumptions of the generalized central limit theorem. Applications to
+Lorentz gases in finite horizon and to the geodesic flow on abelian covers of
+compact manifolds of negative curvature are discussed.
+",0,0,1,0,0,0
+14496,On the economics of electrical storage for variable renewable energy sources,"  The use of renewable energy sources is a major strategy to mitigate climate
+change. Yet Sinn (2017) argues that excessive electrical storage requirements
+limit the further expansion of variable wind and solar energy. We question, and
+alter, strong implicit assumptions of Sinn's approach and find that storage
+needs are considerably lower, up to two orders of magnitude. First, we move
+away from corner solutions by allowing for combinations of storage and
+renewable curtailment. Second, we specify a parsimonious optimization model
+that explicitly considers an economic efficiency perspective. We conclude that
+electrical storage is unlikely to limit the transition to renewable energy.
+",1,0,0,0,0,0
+14497,Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors,"  As companies increase their efforts in retaining customers, being able to
+predict accurately ahead of time, whether a customer will churn in the
+foreseeable future is an extremely powerful tool for any marketing team. The
+paper describes in depth the application of Deep Learning in the problem of
+churn prediction. Using abstract feature vectors, that can generated on any
+subscription based company's user event logs, the paper proves that through the
+use of the intrinsic property of Deep Neural Networks (learning secondary
+features in an unsupervised manner), the complete pipeline can be applied to
+any subscription based company with extremely good churn predictive
+performance. Furthermore the research documented in the paper was performed for
+Framed Data (a company that sells churn prediction as a service for other
+companies) in conjunction with the Data Science Institute at Lancaster
+University, UK. This paper is the intellectual property of Framed Data.
+",1,0,0,1,0,0
+14498,Predicting and Discovering True Muonium,"  The recent observation of discrepancies in the muonic sector motivates
+searches for the yet undiscovered atom true muonium $(\mu^+\mu^-)$. To leverage
+potential experimental signals, precise theoretical calculations are required.
+I will present the on-going work to compute higher-order corrections to the
+hyperfine splitting and the Lamb shift. Further, possible detection in rare
+meson decay experiments like REDTOP and using true muonium production to
+constrain mesonic form factors will be discussed.
+",0,1,0,0,0,0
+14499,Prediction of half-metallic properties in TlCrS2 and TlCrSe2 based on density functional theory,"  Half-metallic properties of TlCrS2, TlCrSe2 and hypothetical TlCrSSe have
+been investigated by first-principles all-electron full-potential linearized
+augmented plane wave plus local orbital (FP-LAPW+lo) method based on density
+functional theory (DFT). The results of calculations show that TlCrS2 and
+TlCrSSe are half-metals with energy gap (Eg ) ~0.12 ev for spin-down channel.
+Strong hybridization of p-state of chalchogen and d-state of Cr leads to
+bonding and antibonding states and subsequently to the appearance of a gap in
+spin-down channel of TlCrS2 and TlCrSSe. In the case of TlCrSe2, there is a
+partial hybridization and p-state is partially present in the DOS at Fermi
+level making this compound nearly half- metallic. The present calculations
+revealed that total magnetic moment keeps its integer value on a relatively
+wide range of changes in volume (-10% 10%) for TlCrS2 and TlCrSSe, while total
+magnetic moment of TlCrSe2 decreases with increasing volume approaching to
+integer value 3{\mu}B.
+",0,1,0,0,0,0
+14500,Giant interfacial perpendicular magnetic anisotropy in Fe/CuIn$_{1-x}$Ga$_x$Se$_2$ beyond Fe/MgO,"  We study interfacial magnetocrystalline anisotropies in various
+Fe/semiconductor heterostructures by means of first-principles calculations. We
+find that many of those systems show perpendicular magnetic anisotropy (PMA)
+with a positive value of the interfacial anisotropy constant $K_{\rm i}$. In
+particular, the Fe/CuInSe$_2$ interface has a large $K_{\rm i}$ of $\sim
+2.3\,{\rm mJ/m^2}$, which is about 1.6 times larger than that of Fe/MgO known
+as a typical system with relatively large PMA. We also find that the values of
+$K_{\rm i}$ in almost all the systems studied in this work follow the
+well-known Bruno's relation, which indicates that minority-spin states around
+the Fermi level provide dominant contributions to the interfacial
+magnetocrystalline anisotropies. Detailed analyses of the local density of
+states and wave-vector-resolved anisotropy energy clarify that the large
+$K_{\rm i}$ in Fe/CuInSe$_2$ is attributed to the preferable $3d$-orbital
+configurations around the Fermi level in the minority-spin states of the
+interfacial Fe atoms. Moreover, we have shown that the locations of interfacial
+Se atoms are the key for such orbital configurations of the interfacial Fe
+atoms.
+",0,1,0,0,0,0
+14501,Stable Self-Assembled Atomic-Switch Networks for Neuromorphic Applications,"  Nature inspired neuromorphic architectures are being explored as an
+alternative to imminent limitations of conventional complementary metal-oxide
+semiconductor (CMOS) architectures. Utilization of such architectures for
+practical applications like advanced pattern recognition tasks will require
+synaptic connections that are both reconfigurable and stable. Here, we report
+realization of stable atomic-switch networks (ASN), with inherent complex
+connectivity, self-assembled from percolating metal nanoparticles (NPs). The
+device conductance reflects the configuration of synapses which can be
+modulated via voltage stimulus. By controlling Relative Humidity (RH) and
+oxygen partial-pressure during NP deposition we obtain stochastic conductance
+switching that is stable over several months. Detailed characterization reveals
+signatures of electric-field induced atomic-wire formation within the
+tunnel-gaps of the oxidized percolating network. Finally we show that the
+synaptic structure can be reconfigured by stimulating at different repetition
+rates, which can be utilized as short-term to long-term memory conversion. This
+demonstration of stable stochastic switching in ASNs provides a promising route
+to hardware implementation of biological neuronal models and, as an example, we
+highlight possible applications in Reservoir Computing (RC).
+",1,1,0,0,0,0
+14502,On the arithmetic of simple singularities of type E,"  An ADE Dynkin diagram gives rise to a family of algebraic curves. In this
+paper, we use arithmetic invariant theory to study the integral points of the
+curves associated to the exceptional diagrams $E_6, E_7$, $E_8$. These curves
+are non-hyperelliptic of genus 3 or 4. We prove that a positive proportion of
+each family consists of curves with integral points everywhere locally but no
+integral points globally.
+",0,0,1,0,0,0
+14503,Centralized Network Utility Maximization over Aggregate Flows,"  We study a network utility maximization (NUM) decomposition in which the set
+of flow rates is grouped by source-destination pairs. We develop theorems for
+both single-path and multipath cases, which relate an arbitrary NUM problem
+involving all flow rates to a simpler problem involving only the aggregate
+rates for each source-destination pair. The optimal aggregate flows are then
+apportioned among the constituent flows of each pair. This apportionment is
+simple for the case of $\alpha$-fair utility functions. We also show how the
+decomposition can be implemented with the alternating direction method of
+multipliers (ADMM) algorithm.
+",1,0,1,0,0,0
+14504,Bottom-up Object Detection by Grouping Extreme and Center Points,"  With the advent of deep learning, object detection drifted from a bottom-up
+to a top-down recognition problem. State of the art algorithms enumerate a
+near-exhaustive list of object locations and classify each into: object or not.
+In this paper, we show that bottom-up approaches still perform competitively.
+We detect four extreme points (top-most, left-most, bottom-most, right-most)
+and one center point of objects using a standard keypoint estimation network.
+We group the five keypoints into a bounding box if they are geometrically
+aligned. Object detection is then a purely appearance-based keypoint estimation
+problem, without region classification or implicit feature learning. The
+proposed method performs on-par with the state-of-the-art region based
+detection methods, with a bounding box AP of 43.2% on COCO test-dev. In
+addition, our estimated extreme points directly span a coarse octagonal mask,
+with a COCO Mask AP of 18.9%, much better than the Mask AP of vanilla bounding
+boxes. Extreme point guided segmentation further improves this to 34.6% Mask
+AP.
+",1,0,0,0,0,0
+14505,The Different Shapes of the LIS Energy Spectra of Cosmic Ray He and C Nuclei Below ~1 GeV/nuc and The Cosmic Ray He/C Nuclei Ratio vs. Energy -V1 Measurements and LBM Propagation Predictions,"  This paper examines the cosmic ray He and C nuclei spectra below ~1 GeV/nuc,
+as well as the very rapid increase in the He/C ratio below ~100 MeV/nuc,
+measured by Voyager 1 beyond the heliopause. Using a simple Leaky Box Model
+(LBM) for galactic propagation we have not been able to simultaneously
+reproduce the individual He and C nuclei spectra and the large increase in He/C
+ratio that is observed at low energies. However, using a truncated LBM with
+different truncation parameters for each nucleus that are related to their rate
+of energy loss by ionization which is ~Z2/A, these different features can be
+matched. This suggests that we are observing the effects of the source
+distribution of cosmic rays in the galaxy on the low energy spectra of cosmic
+ray nuclei and that there may be a paucity of nearby sources. In this
+propagation model we start very specific source spectra for He and C which are
+~dj/dP = P-2.24, the same for each nucleus and also for all rigidities. These
+source spectra become spectra with spectral indices ~-2.69 at high rigidities
+for both charges as a result of a rigidity dependence of the diffusion
+coefficient governing the propagation which is taken to be ~P-0.45. This
+exponent is determined directly from the B/C ratio measured by AMS-2. These
+propagated P-2.69 spectra, when extended to high energies, predict He and C
+intensities and a He/C ratio that are within +3-5% of the intensities and ratio
+recently measured by AMS-2 in the energy range from 10 to 1000 GeV/nuc.
+",0,1,0,0,0,0
+14506,A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality,"  Symmetric nonnegative matrix factorization (SymNMF) has important
+applications in data analytics problems such as document clustering, community
+detection and image segmentation. In this paper, we propose a novel nonconvex
+variable splitting method for solving SymNMF. The proposed algorithm is
+guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) points of the
+nonconvex SymNMF problem. Furthermore, it achieves a global sublinear
+convergence rate. We also show that the algorithm can be efficiently
+implemented in parallel. Further, sufficient conditions are provided which
+guarantee the global and local optimality of the obtained solutions. Extensive
+numerical results performed on both synthetic and real data sets suggest that
+the proposed algorithm converges quickly to a local minimum solution.
+",0,0,1,1,0,0
+14507,Galactic Dark Matter Halos and Globular Cluster Populations. III: Extension to Extreme Environments,"  The total mass M_GCS in the globular cluster (GC) system of a galaxy is
+empirically a near-constant fraction of the total mass M_h = M_bary + M_dark of
+the galaxy, across a range of 10^5 in galaxy mass. This trend is radically
+unlike the strongly nonlinear behavior of total stellar mass M_star versus M_h.
+We discuss extensions of this trend to two more extreme situations: (a) entire
+clusters of galaxies, and (b) the Ultra-Diffuse Galaxies (UDGs) recently
+discovered in Coma and elsewhere. Our calibration of the ratio \eta_M = M_GCS /
+M_h from normal galaxies, accounting for new revisions in the adopted
+mass-to-light ratio for GCs, now gives \eta_M = 2.9 \times 10^{-5} as the mean
+absolute mass fraction. We find that the same ratio appears valid for galaxy
+clusters and UDGs. Estimates of \eta_M in the four clusters we examine tend to
+be slightly higher than for individual galaxies, butmore data and better
+constraints on the mean GC mass in such systems are needed to determine if this
+difference is significant. We use the constancy of \eta_M to estimate total
+masses for several individual cases; for example, the total mass of the Milky
+Way is calculated to be M_h = 1.1 \times 10^{12} M_sun. Physical explanations
+for the uniformity of \eta_M are still descriptive, but point to a picture in
+which massive, dense star clusters in their formation stages were relatively
+immune to the feedback that more strongly influenced lower-density regions
+where most stars form.
+",0,1,0,0,0,0
+14508,"Database of Parliamentary Speeches in Ireland, 1919-2013","  We present a database of parliamentary debates that contains the complete
+record of parliamentary speeches from Dáil Éireann, the lower house and
+principal chamber of the Irish parliament, from 1919 to 2013. In addition, the
+database contains background information on all TDs (Teachta Dála, members of
+parliament), such as their party affiliations, constituencies and office
+positions. The current version of the database includes close to 4.5 million
+speeches from 1,178 TDs. The speeches were downloaded from the official
+parliament website and further processed and parsed with a Python script.
+Background information on TDs was collected from the member database of the
+parliament website. Data on cabinet positions (ministers and junior ministers)
+was collected from the official website of the government. A record linkage
+algorithm and human coders were used to match TDs and ministers.
+",1,0,0,1,0,0
+14509,Reduced Modeling of Unknown Trajectories,"  This paper deals with model order reduction of parametrical dynamical
+systems. We consider the specific setup where the distribution of the system's
+trajectories is unknown but the following two sources of information are
+available: \textit{(i)} some ""rough"" prior knowledge on the system's
+realisations; \textit{(ii)} a set of ""incomplete"" observations of the system's
+trajectories. We propose a Bayesian methodological framework to build
+reduced-order models (ROMs) by exploiting these two sources of information. We
+emphasise that complementing the prior knowledge with the collected data
+provably enhances the knowledge of the distribution of the system's
+trajectories. We then propose an implementation of the proposed methodology
+based on Monte-Carlo methods. In this context, we show that standard ROM
+learning techniques, such e.g. Proper Orthogonal Decomposition or Dynamic Mode
+Decomposition, can be revisited and recast within the probabilistic framework
+considered in this paper.~We illustrate the performance of the proposed
+approach by numerical results obtained for a standard geophysical model.
+",0,0,0,1,0,0
+14510,Pump-Enhanced Continuous-Wave Magnetometry using Nitrogen-Vacancy Ensembles,"  Ensembles of nitrogen-vacancy centers in diamond are a highly promising
+platform for high-sensitivity magnetometry, whose efficacy is often based on
+efficiently generating and monitoring magnetic-field dependent infrared
+fluorescence. Here we report on an increased sensing efficiency with the use of
+a 532-nm resonant confocal cavity and a microwave resonator antenna for
+measuring the local magnetic noise density using the intrinsic nitrogen-vacancy
+concentration of a chemical-vapor deposited single-crystal diamond. We measure
+a near-shot-noise-limited magnetic noise floor of 200 pT/$\sqrt{\text{Hz}}$
+spanning a bandwidth up to 159 Hz, and an extracted sensitivity of
+approximately 3 nT/$\sqrt{\text{Hz}}$, with further enhancement limited by the
+noise floor of the lock-in amplifier and the laser damage threshold of the
+optical components. Exploration of the microwave and optical pump-rate
+parameter space demonstrates a linewidth-narrowing regime reached by virtue of
+using the optical cavity, allowing an enhanced sensitivity to be achieved,
+despite an unoptimized collection efficiency of <2 %, and a low
+nitrogen-vacancy concentration of about 0.2 ppb.
+",0,1,0,0,0,0
+14511,Topological Spin Liquid with Symmetry-Protected Edge States,"  Topological spin liquids are robust quantum states of matter with long-range
+entanglement and possess many exotic properties such as the fractional
+statistics of the elementary excitations. Yet these states, short of local
+parameters like all topological states, are elusive for conventional
+experimental probes. In this work, we combine theoretical analysis and quantum
+Monte Carlo numerics on a frustrated spin model which hosts a $\mathbb Z_2$
+topological spin liquid ground state, and demonstrate that the presence of
+symmetry-protected gapless edge modes is a characteristic feature of the state,
+originating from the nontrivial symmetry fractionalization of the elementary
+excitations. Experimental observation of these modes on the edge would directly
+indicate the existence of the topological spin liquids in the bulk, analogous
+to the fact that the observation of Dirac edge states confirmed the existence
+of topological insulators.
+",0,1,0,0,0,0
+14512,SMT Queries Decomposition and Caching in Semi-Symbolic Model Checking,"  In semi-symbolic (control-explicit data-symbolic) model checking the
+state-space explosion problem is fought by representing sets of states by
+first-order formulas over the bit-vector theory. In this model checking
+approach, most of the verification time is spent in an SMT solver on deciding
+satisfiability of quantified queries, which represent equality of symbolic
+states. In this paper, we introduce a new scheme for decomposition of symbolic
+states, which can be used to significantly improve the performance of any
+semi-symbolic model checker. Using the decomposition, a model checker can issue
+much simpler and smaller queries to the solver when compared to the original
+case. Some SMT calls may be even avoided completely, as the satisfaction of
+some of the simplified formulas can be decided syntactically. Moreover, the
+decomposition allows for an efficient caching scheme for quantified formulas.
+To support our theoretical contribution, we show the performance gain of our
+model checker SymDIVINE on a set of examples from the Software Verification
+Competition.
+",1,0,0,0,0,0
+14513,Homotopy dimer algebras and cyclic contractions,"  Dimer algebras arise from a particular type of quiver gauge theory. However,
+part of the input to such a theory is the gauge group, and this choice may
+impose additional constraints on the algebra. If the gauge group of a dimer
+theory is abelian, then the algebra that arises is not actually the dimer
+algebra itself, but a particular quotient we introduce called the 'homotopy
+algebra'. We show that a homotopy algebra $\Lambda$ on a torus is a dimer
+algebra if and only if it is noetherian, and otherwise $\Lambda$ is the
+quotient of a dimer algebra by homotopy relations. Stated in physics terms, a
+dimer theory is superconformal if and only if the corresponding dimer and
+homotopy algebras coincide. We also give an explicit description of the center
+of a homotopy algebra in terms of a special subset of its perfect matchings. In
+our proofs we introduce formalized notions of Higgsing and the mesonic chiral
+ring from quiver gauge theory.
+",0,0,1,0,0,0
+14514,On the trace problem for Triebel--Lizorkin spaces with mixed norms,"  The subject is traces of Sobolev spaces with mixed Lebesgue norms on
+Euclidean space. Specifically, restrictions to the hyperplanes given by the
+first and last coordinates are applied to functions belonging to
+quasi-homogeneous, mixed-norm Lizorkin--Triebel spaces; Sobolev spaces are
+obtained from these as special cases. Spaces admitting traces in the
+distribution sense are characterised except for the borderline cases; these are
+also covered in case of the first variable. With respect to the first variable
+the trace spaces are proved to be mixed-norm Lizorkin--Triebel spaces with a
+specific sum exponent. For the last variable they are similarly defined Besov
+spaces. The treatment includes continuous right-inverses and higher order
+traces. The results rely on a sequence version of Nikolskij's inequality,
+Marschall's inequality for pseudo-differential operators (and Fourier
+multiplier assertions), as well as dyadic ball criteria.
+",0,0,1,0,0,0
+14515,Breaking the curse of dimensionality in regression,"  Models with many signals, high-dimensional models, often impose structures on
+the signal strengths. The common assumption is that only a few signals are
+strong and most of the signals are zero or close (collectively) to zero.
+However, such a requirement might not be valid in many real-life applications.
+In this article, we are interested in conducting large-scale inference in
+models that might have signals of mixed strengths. The key challenge is that
+the signals that are not under testing might be collectively non-negligible
+(although individually small) and cannot be accurately learned. This article
+develops a new class of tests that arise from a moment matching formulation. A
+virtue of these moment-matching statistics is their ability to borrow strength
+across features, adapt to the sparsity size and exert adjustment for testing
+growing number of hypothesis. GRoup-level Inference of Parameter, GRIP, test
+harvests effective sparsity structures with hypothesis formulation for an
+efficient multiple testing procedure. Simulated data showcase that GRIPs error
+control is far better than the alternative methods. We develop a minimax
+theory, demonstrating optimality of GRIP for a broad range of models, including
+those where the model is a mixture of a sparse and high-dimensional dense
+signals.
+",0,0,1,1,0,0
+14516,DVAE++: Discrete Variational Autoencoders with Overlapping Transformations,"  Training of discrete latent variable models remains challenging because
+passing gradient information through discrete units is difficult. We propose a
+new class of smoothing transformations based on a mixture of two overlapping
+distributions, and show that the proposed transformation can be used for
+training binary latent models with either directed or undirected priors. We
+derive a new variational bound to efficiently train with Boltzmann machine
+priors. Using this bound, we develop DVAE++, a generative model with a global
+discrete prior and a hierarchy of convolutional continuous variables.
+Experiments on several benchmarks show that overlapping transformations
+outperform other recent continuous relaxations of discrete latent variables
+including Gumbel-Softmax (Maddison et al., 2016; Jang et al., 2016), and
+discrete variational autoencoders (Rolfe 2016).
+",0,0,0,1,0,0
+14517,Bet-hedging against demographic fluctuations,"  Biological organisms have to cope with stochastic variations in both the
+external environment and the internal population dynamics. Theoretical studies
+and laboratory experiments suggest that population diversification could be an
+effective bet-hedging strategy for adaptation to varying environments. Here we
+show that bet-hedging can also be effective against demographic fluctuations
+that pose a trade-off between growth and survival for populations even in a
+constant environment. A species can maximize its overall abundance in the long
+term by diversifying into coexisting subpopulations of both ""fast-growing"" and
+""better-surviving"" individuals. Our model generalizes statistical physics
+models of birth-death processes to incorporate dispersal, during which new
+populations are founded, and can further incorporate variations of local
+environments. In this way we unify different bet-hedging strategies against
+demographic and environmental variations as a general means of adaptation to
+both types of uncertainties in population growth.
+",0,1,0,0,0,0
+14518,ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU,"  To improve the performance of Intensive Care Units (ICUs), the field of
+bio-statistics has developed scores which try to predict the likelihood of
+negative outcomes. These help evaluate the effectiveness of treatments and
+clinical practice, and also help to identify patients with unexpected outcomes.
+However, they have been shown by several studies to offer sub-optimal
+performance. Alternatively, Deep Learning offers state of the art capabilities
+in certain prediction tasks and research suggests deep neural networks are able
+to outperform traditional techniques. Nevertheless, a main impediment for the
+adoption of Deep Learning in healthcare is its reduced interpretability, for in
+this field it is crucial to gain insight on the why of predictions, to assure
+that models are actually learning relevant features instead of spurious
+correlations. To address this, we propose a deep multi-scale convolutional
+architecture trained on the Medical Information Mart for Intensive Care III
+(MIMIC-III) for mortality prediction, and the use of concepts from coalitional
+game theory to construct visual explanations aimed to show how important these
+inputs are deemed by the network. Our results show our model attains state of
+the art performance while remaining interpretable. Supporting code can be found
+at this https URL.
+",1,0,0,1,0,0
+14519,Low-complexity Approaches for MIMO Capacity with Per-antenna Power Constraint,"  This paper proposes two low-complexity iterative algorithms to compute the
+capacity of a single-user multiple-input multiple-output channel with
+per-antenna power constraint. The first method results from manipulating the
+optimality conditions of the considered problem and applying fixed-point
+iteration. In the second approach, we transform the considered problem into a
+minimax optimization program using the well-known MAC- BC duality, and then
+solve it by a novel alternating optimization method. In both proposed iterative
+methods, each iteration involves an optimization problem which can be
+efficiently solved by the water-filling algorithm. The proposed iterative
+methods are provably convergent. Complexity analysis and extensive numerical
+experiments are carried out to demonstrate the superior performance of the
+proposed algorithms over an existing approach known as the mode-dropping
+algorithm.
+",1,0,0,0,0,0
+14520,The VISTA ZYJHKs Photometric System: Calibration from 2MASS,"  In this paper we describe the routine photometric calibration of data taken
+with the VIRCAM instrument on the ESO VISTA telescope. The broadband ZYJHKs
+data are directly calibrated from 2MASS point sources visible in every VISTA
+image. We present the empirical transformations between the 2MASS and VISTA,
+and WFCAM and VISTA, photometric systems for regions of low reddening. We
+investigate the long-term performance of VISTA+VIRCAM. An investigation of the
+dependence of the photometric calibration on interstellar reddening leads to
+these conclusions: (1) For all broadband filters, a linear colour-dependent
+correction compensates the gross effects of reddening where $E(B-V)<5.0$. (2)
+For $Z$ and $Y$, there is a significantly larger scatter above E(B-V)=5.0, and
+insufficient measurements to adequately constrain the relation beyond this
+value. (3) The $JHK\!s$ filters can be corrected to a few percent up to
+E(B-V)=10.0. We analyse spatial systematics over month-long timescales, both
+inter- and intra-detector and show that these are present only at very low
+levels in VISTA. We monitor and remove residual detector-to-detector offsets.
+We compare the calibration of the main pipeline products: pawprints and tiles.
+We show how variable seeing and transparency affect the final calibration
+accuracy of VISTA tiles, and discuss a technique, {\it grouting}, for
+mitigating these effects. Comparison between repeated reference fields is used
+to demonstrate that the VISTA photometry is precise to better than $\simeq2\%$
+for the $Y$$J$$H$$Ks$ bands and $3\%$ for the $Z$ bands. Finally we present
+empirically determined offsets to transform VISTA magnitudes into a true Vega
+system.
+",0,1,0,0,0,0
+14521,Spin-filtering in superconducting junction with the manganite interlayer,"  We report on the electronic transport and the impact of spin-filtering in
+mesa-structures made of epitaxial thin films of cuprate superconductor
+YBa2Cu3Ox(YBCO) and the manganite LaMnO3 (LMO) interlayer with the Au/Nb
+counterelectrode. Ferromagnetic resonance measurements of heterostructure
+Au/LMO/YBCO shows ferromagnetic state at temperatures below 150 K as in the
+case of reference LMO film grown on the neodymium gallate substrate. The
+heights of the tunneling barrier evaluated from resistive characteristics of
+mesa-structures at different thickness of interlayer showed an exponential
+decrease from 30 mV down to 5 mV with the increase of manganite interlayer
+thickness. Temperature dependence of the conductivity of mesa-structures could
+be described taking into account the d-wave superconductivity in YBCO and a
+spin filtering of the electron transport. Spin filtering is supported also by
+measurements of magneto-resistance and the high sensitivity of mesa-structure
+conductivity to weak magnetic fields.
+",0,1,0,0,0,0
+14522,Intersection of conjugate solvable subgroups in symmetric groups,"  It is shown that for a solvable subgroup $G$ of an almost simple group $S$
+which socle is isomorphic to $A_n$ $ (n\ge5)$ there are $x,y,z,t \in S$ such
+that $G \cap G^x \cap G^y \cap G^z \cap G^t =1.$
+",0,0,1,0,0,0
+14523,Improving inference of the dynamic biological network underlying aging via network propagation,"  Gene expression (GE) data capture valuable condition-specific information
+(""condition"" can mean a biological process, disease stage, age, patient, etc.)
+However, GE analyses ignore physical interactions between gene products, i.e.,
+proteins. Since proteins function by interacting with each other, and since
+biological networks (BNs) capture these interactions, BN analyses are
+promising. However, current BN data fail to capture condition-specific
+information. Recently, GE and BN data have been integrated using network
+propagation (NP) to infer condition-specific BNs. However, existing NP-based
+studies result in a static condition-specific network, even though cellular
+processes are dynamic. A dynamic process of our interest is aging. We use
+prominent existing NP methods in a new task of inferring a dynamic rather than
+static condition-specific (aging-related) network. Then, we study evolution of
+network structure with age - we identify proteins whose network positions
+significantly change with age and predict them as new aging-related candidates.
+We validate the predictions via e.g., functional enrichment analyses and
+literature search. Dynamic network inference via NP yields higher prediction
+quality than the only existing method for inferring a dynamic aging-related BN,
+which does not use NP.
+",0,0,0,0,1,0
+14524,Optimal Weighting for Exam Composition,"  A problem faced by many instructors is that of designing exams that
+accurately assess the abilities of the students. Typically these exams are
+prepared several days in advance, and generic question scores are used based on
+rough approximation of the question difficulty and length. For example, for a
+recent class taught by the author, there were 30 multiple choice questions
+worth 3 points, 15 true/false with explanation questions worth 4 points, and 5
+analytical exercises worth 10 points. We describe a novel framework where
+algorithms from machine learning are used to modify the exam question weights
+in order to optimize the exam scores, using the overall class grade as a proxy
+for a student's true ability. We show that significant error reduction can be
+obtained by our approach over standard weighting schemes, and we make several
+new observations regarding the properties of the ""good"" and ""bad"" exam
+questions that can have impact on the design of improved future evaluation
+methods.
+",0,0,0,1,0,0
+14525,Generalized orderless pooling performs implicit salient matching,"  Most recent CNN architectures use average pooling as a final feature encoding
+step. In the field of fine-grained recognition, however, recent global
+representations like bilinear pooling offer improved performance. In this
+paper, we generalize average and bilinear pooling to ""alpha-pooling"", allowing
+for learning the pooling strategy during training. In addition, we present a
+novel way to visualize decisions made by these approaches. We identify parts of
+training images having the highest influence on the prediction of a given test
+image. It allows for justifying decisions to users and also for analyzing the
+influence of semantic parts. For example, we can show that the higher capacity
+VGG16 model focuses much more on the bird's head than, e.g., the lower-capacity
+VGG-M model when recognizing fine-grained bird categories. Both contributions
+allow us to analyze the difference when moving between average and bilinear
+pooling. In addition, experiments show that our generalized approach can
+outperform both across a variety of standard datasets.
+",1,0,0,0,0,0
+14526,The Gaia-ESO Survey: Exploring the complex nature and origins of the Galactic bulge populations,"  Abridged: We used the fourth internal data release of the Gaia-ESO survey to
+characterize the bulge chemistry, spatial distribution, kinematics, and to
+compare it chemically with the thin and thick disks. The sample consist on
+~2500 red clump stars in 11 bulge fields ($-10^\circ\leq l\leq+8^\circ$ and
+$-10^\circ\leq b\leq-4^\circ$), and a set of ~6300 disk stars selected for
+comparison. The bulge MDF is confirmed to be bimodal across the whole sampled
+area, with metal-poor stars dominating at high latitudes. The metal-rich stars
+exhibit bar-like kinematics and display a bimodality in their magnitude
+distribution, a feature which is tightly associated with the X-shape bulge.
+They overlap with the metal-rich end of the thin disk sequence in the [Mg/Fe]
+vs. [Fe/H] plane. Metal-poor bulge stars have a more isotropic hot kinematics
+and do not participate in the X-shape bulge. With similar Mg-enhancement
+levels, the position of the metal-poor bulge sequence ""knee"" is observed at
+[Fe/H]$_{knee}=-0.37\pm0.09$, being 0.06 dex higher than that of the thick
+disk. It suggests a higher SFR for the bulge than for the thick disk. Finally,
+we present a chemical evolution model that suitably fits the whole bulge
+sequence by assuming a fast ($<1$ Gyr) intense burst of stellar formation at
+early epochs. We associate metal-rich stars with the B/P bulge formed from the
+secular evolution of the early thin disk. On the other hand, the metal-poor
+subpopulation might be the product of an early prompt dissipative collapse
+dominated by massive stars. Nevertheless, our results do not allow us to firmly
+rule out the possibility that these stars come from the secular evolution of
+the early thick disk. This is the first time that an analysis of the bulge MDF
+and $\alpha$-abundances has been performed in a large area on the basis of a
+homogeneous, fully spectroscopic analysis of high-resolution, high S/N data.
+",0,1,0,0,0,0
+14527,On the Complexity of Robust Stable Marriage,"  Robust Stable Marriage (RSM) is a variant of the classical Stable Marriage
+problem, where the robustness of a given stable matching is measured by the
+number of modifications required for repairing it in case an unforeseen event
+occurs. We focus on the complexity of finding an (a,b)-supermatch. An
+(a,b)-supermatch is defined as a stable matching in which if any 'a'
+(non-fixed) men/women break up it is possible to find another stable matching
+by changing the partners of those 'a' men/women and also the partners of at
+most 'b' other couples. In order to show deciding if there exists an
+(a,b)-supermatch is NP-Complete, we first introduce a SAT formulation that is
+NP-Complete by using Schaefer's Dichotomy Theorem. Then, we show the
+equivalence between the SAT formulation and finding a (1,1)-supermatch on a
+specific family of instances.
+",1,0,0,0,0,0
+14528,Vacuum Friction,"  We know that in empty space there is no preferred state of rest. This is true
+both in special relativity but also in Newtonian mechanics with its associated
+Galilean relativity. It comes as something of a surprise, therefore, to
+discover the existence a friction force associated with spontaneous emission.
+he resolution of this paradox relies on a central idea from special relativity
+even though our derivation of it is non-relativistic. We examine the
+possibility that the physics underlying this effect might be explored in an ion
+trap, via the observation of a superposition of different mass states.
+",0,1,0,0,0,0
+14529,Intelligent Personal Assistant with Knowledge Navigation,"  An Intelligent Personal Agent (IPA) is an agent that has the purpose of
+helping the user to gain information through reliable resources with the help
+of knowledge navigation techniques and saving time to search the best content.
+The agent is also responsible for responding to the chat-based queries with the
+help of Conversation Corpus. We will be testing different methods for optimal
+query generation. To felicitate the ease of usage of the application, the agent
+will be able to accept the input through Text (Keyboard), Voice (Speech
+Recognition) and Server (Facebook) and output responses using the same method.
+Existing chat bots reply by making changes in the input, but we will give
+responses based on multiple SRT files. The model will learn using the human
+dialogs dataset and will be able respond human-like. Responses to queries about
+famous things (places, people, and words) can be provided using web scraping
+which will enable the bot to have knowledge navigation features. The agent will
+even learn from its past experiences supporting semi-supervised learning.
+",1,0,0,0,0,0
+14530,Private Data System Enabling Self-Sovereign Storage Managed by Executable Choreographies,"  With the increased use of Internet, governments and large companies store and
+share massive amounts of personal data in such a way that leaves no space for
+transparency. When a user needs to achieve a simple task like applying for
+college or a driving license, he needs to visit a lot of institutions and
+organizations, thus leaving a lot of private data in many places. The same
+happens when using the Internet. These privacy issues raised by the centralized
+architectures along with the recent developments in the area of serverless
+applications demand a decentralized private data layer under user control. We
+introduce the Private Data System (PDS), a distributed approach which enables
+self-sovereign storage and sharing of private data. The system is composed of
+nodes spread across the entire Internet managing local key-value databases. The
+communication between nodes is achieved through executable choreographies,
+which are capable of preventing information leakage when executing across
+different organizations with different regulations in place. The user has full
+control over his private data and is able to share and revoke access to
+organizations at any time. Even more, the updates are propagated instantly to
+all the parties which have access to the data thanks to the system design.
+Specifically, the processing organizations may retrieve and process the shared
+information, but are not allowed under any circumstances to store it on long
+term. PDS offers an alternative to systems that aim to ensure self-sovereignty
+of specific types of data through blockchain inspired techniques but face
+various problems, such as low performance. Both approaches propose a
+distributed database, but with different characteristics. While the
+blockchain-based systems are built to solve consensus problems, PDS's purpose
+is to solve the self-sovereignty aspects raised by the privacy laws, rules and
+principles.
+",1,0,0,0,0,0
+14531,A Curious Family of Binomial Determinants That Count Rhombus Tilings of a Holey Hexagon,"  We evaluate a curious determinant, first mentioned by George Andrews in 1980
+in the context of descending plane partitions. Our strategy is to combine the
+famous Desnanot-Jacobi-Dodgson identity with automated proof techniques. More
+precisely, we follow the holonomic ansatz that was proposed by Doron Zeilberger
+in 2007. We derive a compact and nice formula for Andrews's determinant, and
+use it to solve a challenge problem that we posed in a previous paper. By
+noting that Andrews's determinant is a special case of a two-parameter family
+of determinants, we find closed forms for several one-parameter subfamilies.
+The interest in these determinants arises because they count cyclically
+symmetric rhombus tilings of a hexagon with several triangular holes inside.
+",1,0,0,0,0,0
+14532,Community Structure Characterization,"  This entry discusses the problem of describing some communities identified in
+a complex network of interest, in a way allowing to interpret them. We suppose
+the community structure has already been detected through one of the many
+methods proposed in the literature. The question is then to know how to extract
+valuable information from this first result, in order to allow human
+interpretation. This requires subsequent processing, which we describe in the
+rest of this entry.
+",1,1,0,0,0,0
+14533,Design of Deep Neural Networks as Add-on Blocks for Improving Impromptu Trajectory Tracking,"  This paper introduces deep neural networks (DNNs) as add-on blocks to
+baseline feedback control systems to enhance tracking performance of arbitrary
+desired trajectories. The DNNs are trained to adapt the reference signals to
+the feedback control loop. The goal is to achieve a unity map between the
+desired and the actual outputs. In previous work, the efficacy of this approach
+was demonstrated on quadrotors; on 30 unseen test trajectories, the proposed
+DNN approach achieved an average impromptu tracking error reduction of 43% as
+compared to the baseline feedback controller. Motivated by these results, this
+work aims to provide platform-independent design guidelines for the proposed
+DNN-enhanced control architecture. In particular, we provide specific
+guidelines for the DNN feature selection, derive conditions for when the
+proposed approach is effective, and show in which cases the training efficiency
+can be further increased.
+",1,0,0,0,0,0
+14534,Application of data science techniques to disentangle X-ray spectral variation of super-massive black holes,"  We apply three data science techniques, Nonnegative Matrix Factorization
+(NMF), Principal Component Analysis (PCA) and Independent Component Analysis
+(ICA), to simulated X-ray energy spectra of a particular class of super-massive
+black holes. Two competing physical models, one whose variable components are
+additive and the other whose variable components are multiplicative, are known
+to successfully describe X-ray spectral variation of these super-massive black
+holes, within accuracy of the contemporary observation. We hope to utilize
+these techniques to compare the viability of the models by probing the
+mathematical structure of the observed spectra, while comparing advantages and
+disadvantages of each technique. We find that PCA is best to determine the
+dimensionality of a dataset, while NMF is better suited for interpreting
+spectral components and comparing them in terms of the physical models in
+question. ICA is able to reconstruct the parameters responsible for spectral
+variation. In addition, we find that the results of these techniques are
+sufficiently different that applying them to observed data may be a useful test
+in comparing the accuracy of the two spectral models.
+",0,1,0,0,0,0
+14535,Strong convergence rates of modified truncated EM method for stochastic differential equations,"  Motivated by truncated EM method introduced by Mao (2015), a new explicit
+numerical method named modified truncated Euler-Maruyama method is developed in
+this paper. Strong convergence rates of the given numerical scheme to the exact
+solutions to stochastic differential equations are investigated under given
+conditions in this paper. Compared with truncated EM method, the given
+numerical simulation strongly converges to the exact solution at fixed time $T$
+and over a time interval $[0,T]$ under weaker sufficient conditions. Meanwhile,
+the convergence rates are also obtained for both cases. Two examples are
+provided to support our conclusions.
+",0,0,1,0,0,0
+14536,Some aspects of holomorphic mappings: a survey,"  This expository paper is concerned with the properties of proper holomorphic
+mappings between domains in complex affine spaces. We discuss some of the main
+geometric methods of this theory, such as the Reflection Principle, the scaling
+method, and the Kobayashi-Royden metric. We sketch the proofs of certain
+principal results and discuss some recent achievements. Several open problems
+are also stated.
+",0,0,1,0,0,0
+14537,Local Linear Constraint based Optimization Model for Dual Spectral CT,"  Dual spectral computed tomography (DSCT) can achieve energy- and
+material-selective images, and has a superior distinguishability of some
+materials than conventional single spectral computed tomography (SSCT).
+However, the decomposition process is illposed, which is sensitive with noise,
+thus the quality of decomposed images are usually degraded, especially the
+signal-to-noise ratio (SNR) is much lower than single spectra based directly
+reconstructions. In this work, we first establish a local linear relationship
+between dual spectra based decomposed results and single spectra based directly
+reconstructed images. Then, based on this constraint, we propose an
+optimization model for DSCT and develop a guided image filtering based
+iterative solution method. Both simulated and real experiments are provided to
+validate the effectiveness of the proposed approach.
+",0,0,1,0,0,0
+14538,Efficient Online Timed Pattern Matching by Automata-Based Skipping,"  The timed pattern matching problem is an actively studied topic because of
+its relevance in monitoring of real-time systems. There one is given a log $w$
+and a specification $\mathcal{A}$ (given by a timed word and a timed automaton
+in this paper), and one wishes to return the set of intervals for which the log
+$w$, when restricted to the interval, satisfies the specification
+$\mathcal{A}$. In our previous work we presented an efficient timed pattern
+matching algorithm: it adopts a skipping mechanism inspired by the classic
+Boyer--Moore (BM) string matching algorithm. In this work we tackle the problem
+of online timed pattern matching, towards embedded applications where it is
+vital to process a vast amount of incoming data in a timely manner.
+Specifically, we start with the Franek-Jennings-Smyth (FJS) string matching
+algorithm---a recent variant of the BM algorithm---and extend it to timed
+pattern matching. Our experiments indicate the efficiency of our FJS-type
+algorithm in online and offline timed pattern matching.
+",1,0,0,0,0,0
+14539,APO Time Resolved Color Photometry of Highly-Elongated Interstellar Object 1I/'Oumuamua,"  We report on $g$, $r$ and $i$ band observations of the Interstellar Object
+'Oumuamua (1I) taken on 2017 October 29 from 04:28 to 08:40 UTC by the Apache
+Point Observatory (APO) 3.5m telescope's ARCTIC camera. We find that 1I's
+colors are $g-r=0.41\pm0.24$ and $r-i=0.23\pm0.25$, consistent with the visible
+spectra of Masiero (2017), Ye et al. (2017) and Fitzsimmons et al. (2017), and
+most comparable to the population of Solar System C/D asteroids, Trojans, or
+comets. We find no evidence of any cometary activity at a heliocentric distance
+of 1.46 au, approximately 1.5 months after 1I's closest approach distance to
+the Sun. Significant brightness variability was seen in the $r$ observations,
+with the object becoming notably brighter towards the end of the run. By
+combining our APO photometric time series data with the Discovery Channel
+Telescope (DCT) data of Knight et al. (2017), taken 20 h later on 2017 October
+30, we construct an almost complete light curve with a most probable lightcurve
+period of $P \simeq 4~{\rm h}$. Our results imply a double peaked rotation
+period of 8.1 $\pm$ 0.02 h, with a peak-to-peak amplitude of 1.5 - 2.1 mags.
+Assuming that 1I's shape can be approximated by an ellipsoid, the amplitude
+constraint implies that 1I has an axial ratio of 3.5 to 10.3, which is
+strikingly elongated. Assuming that 1I is rotating above its critical break up
+limit, our results are compatible with 1I having having modest cohesive
+strength and may have obtained its elongated shape during a tidal disruption
+event before being ejected from its home system. Astrometry useful for
+constraining 1I's orbit was also obtained and published in Weaver et al.
+(2017).
+",0,1,0,0,0,0
+14540,Variable selection in discriminant analysis for mixed variables and several groups,"  We propose a method for variable selection in discriminant analysis with
+mixed categorical and continuous variables. This method is based on a criterion
+that permits to reduce the variable selection problem to a problem of
+estimating suitable permutation and dimensionality. Then, estimators for these
+parameters are proposed and the resulting method for selecting variables is
+shown to be consistent. A simulation study that permits to study several
+poperties of the proposed approach and to compare it with an existing method is
+given.
+",0,0,1,1,0,0
+14541,AdaGAN: Boosting Generative Models,"  Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an
+effective method for training generative models of complex data such as natural
+images. However, they are notoriously hard to train and can suffer from the
+problem of missing modes where the model is not able to produce examples in
+certain regions of the space. We propose an iterative procedure, called AdaGAN,
+where at every step we add a new component into a mixture model by running a
+GAN algorithm on a reweighted sample. This is inspired by boosting algorithms,
+where many potentially weak individual predictors are greedily aggregated to
+form a strong composite predictor. We prove that such an incremental procedure
+leads to convergence to the true distribution in a finite number of steps if
+each step is optimal, and convergence at an exponential rate otherwise. We also
+illustrate experimentally that this procedure addresses the problem of missing
+modes.
+",1,0,0,1,0,0
+14542,A Classification-Based Study of Covariate Shift in GAN Distributions,"  A basic, and still largely unanswered, question in the context of Generative
+Adversarial Networks (GANs) is whether they are truly able to capture all the
+fundamental characteristics of the distributions they are trained on. In
+particular, evaluating the diversity of GAN distributions is challenging and
+existing methods provide only a partial understanding of this issue. In this
+paper, we develop quantitative and scalable tools for assessing the diversity
+of GAN distributions. Specifically, we take a classification-based perspective
+and view loss of diversity as a form of covariate shift introduced by GANs. We
+examine two specific forms of such shift: mode collapse and boundary
+distortion. In contrast to prior work, our methods need only minimal human
+supervision and can be readily applied to state-of-the-art GANs on large,
+canonical datasets. Examining popular GANs using our tools indicates that these
+GANs have significant problems in reproducing the more distributional
+properties of their training dataset.
+",1,0,0,1,0,0
+14543,Sequence-to-Sequence Models Can Directly Translate Foreign Speech,"  We present a recurrent encoder-decoder deep neural network architecture that
+directly translates speech in one language into text in another. The model does
+not explicitly transcribe the speech into text in the source language, nor does
+it require supervision from the ground truth source language transcription
+during training. We apply a slightly modified sequence-to-sequence with
+attention architecture that has previously been used for speech recognition and
+show that it can be repurposed for this more complex task, illustrating the
+power of attention-based models. A single model trained end-to-end obtains
+state-of-the-art performance on the Fisher Callhome Spanish-English speech
+translation task, outperforming a cascade of independently trained
+sequence-to-sequence speech recognition and machine translation models by 1.8
+BLEU points on the Fisher test set. In addition, we find that making use of the
+training data in both languages by multi-task training sequence-to-sequence
+speech translation and recognition models with a shared encoder network can
+improve performance by a further 1.4 BLEU points.
+",1,0,0,1,0,0
+14544,Ensemble Methods for Personalized E-Commerce Search Challenge at CIKM Cup 2016,"  Personalized search has been a hot research topic for many years and has been
+widely used in e-commerce. This paper describes our solution to tackle the
+challenge of personalized e-commerce search at CIKM Cup 2016. The goal of this
+competition is to predict search relevance and re-rank the result items in SERP
+according to the personalized search, browsing and purchasing preferences.
+Based on a detailed analysis of the provided data, we extract three different
+types of features, i.e., statistic features, query-item features and session
+features. Different models are used on these features, including logistic
+regression, gradient boosted decision trees, rank svm and a novel deep match
+model. With the blending of multiple models, a stacking ensemble model is built
+to integrate the output of individual models and produce a more accurate
+prediction result. Based on these efforts, our solution won the champion of the
+competition on all the evaluation metrics.
+",1,0,0,0,0,0
+14545,Comparison of Sobol' sequences in financial applications,"  Sobol' sequences are widely used for quasi-Monte Carlo methods that arise in
+financial applications. Sobol' sequences have parameter values called direction
+numbers, which are freely chosen by the user, so there are several
+implementations of Sobol' sequence generators. The aim of this paper is to
+provide a comparative study of (non-commercial) high-dimensional Sobol'
+sequences by calculating financial models. Additionally, we implement the
+Niederreiter sequence (in base 2) with a slight modification, that is, we
+reorder the rows of the generating matrices, and analyze and compare it with
+the Sobol' sequences.
+",1,0,0,1,0,0
+14546,A wavelet integral collocation method for nonlinear boundary value problems in Physics,"  A high order wavelet integral collocation method (WICM) is developed for
+general nonlinear boundary value problems in physics. This method is
+established based on Coiflet approximation of multiple integrals of interval
+bounded functions combined with an accurate and adjustable boundary extension
+technique. The convergence order of this approximation has been proven to be N
+as long as the Coiflet with N-1 vanishing moment is adopted, which can be any
+positive even integers. Before the conventional collocation method is applied
+to the general problems, the original differential equation is changed into its
+equivalent form by denoting derivatives of the unknown function as new
+functions and constructing relations between the low and high order
+derivatives. For the linear cases, error analysis has proven that the proposed
+WICM is order N, and condition numbers of relevant matrices are almost
+independent of the number of collocation points. Numerical examples of a wide
+range of nonlinear differential equations in physics demonstrate that accuracy
+of the proposed WICM is even greater than N, and most interestingly, such
+accuracy is independent of the order of the differential equation to be solved.
+Comparison to existing numerical methods further justifies the accuracy and
+efficiency of the proposed method.
+",0,0,1,0,0,0
+14547,Recommendations of the LHC Dark Matter Working Group: Comparing LHC searches for heavy mediators of dark matter production in visible and invisible decay channels,"  Weakly-coupled TeV-scale particles may mediate the interactions between
+normal matter and dark matter. If so, the LHC would produce dark matter through
+these mediators, leading to the familiar ""mono-X"" search signatures, but the
+mediators would also produce signals without missing momentum via the same
+vertices involved in their production. This document from the LHC Dark Matter
+Working Group suggests how to compare searches for these two types of signals
+in case of vector and axial-vector mediators, based on a workshop that took
+place on September 19/20, 2016 and subsequent discussions. These suggestions
+include how to extend the spin-1 mediated simplified models already in
+widespread use to include lepton couplings. This document also provides
+analytic calculations of the relic density in the simplified models and reports
+an issue that arose when ATLAS and CMS first began to use preliminary numerical
+calculations of the dark matter relic density in these models.
+",0,1,0,0,0,0
+14548,Generalization of the concepts of seniority number and ionicity,"  We present generalized versions of the concepts of seniority number and
+ionicity. These generalized numbers count respectively the partially occupied
+and fully occupied shells for any partition of the orbital space into shells.
+The Hermitian operators whose eigenspaces correspond to wave functions of
+definite generalized seniority or ionicity values are introduced. The
+generalized seniority numbers (GSNs) afford to establish refined hierarchies of
+configuration interaction (CI) spaces within those of fixed ordinary seniority.
+Such a hierarchy is illustrated on the buckminsterfullerene molecule.
+",0,1,0,0,0,0
+14549,Rise of the HaCRS: Augmenting Autonomous Cyber Reasoning Systems with Human Assistance,"  As the size and complexity of software systems increase, the number and
+sophistication of software security flaws increase as well. The analysis of
+these flaws began as a manual approach, but it soon became apparent that tools
+were necessary to assist human experts in this task, resulting in a number of
+techniques and approaches that automated aspects of the vulnerability analysis
+process.
+Recently, DARPA carried out the Cyber Grand Challenge, a competition among
+autonomous vulnerability analysis systems designed to push the tool-assisted
+human-centered paradigm into the territory of complete automation. However,
+when the autonomous systems were pitted against human experts it became clear
+that certain tasks, albeit simple, could not be carried out by an autonomous
+system, as they require an understanding of the logic of the application under
+analysis.
+Based on this observation, we propose a shift in the vulnerability analysis
+paradigm, from tool-assisted human-centered to human-assisted tool-centered. In
+this paradigm, the automated system orchestrates the vulnerability analysis
+process, and leverages humans (with different levels of expertise) to perform
+well-defined sub-tasks, whose results are integrated in the analysis. As a
+result, it is possible to scale the analysis to a larger number of programs,
+and, at the same time, optimize the use of expensive human resources.
+In this paper, we detail our design for a human-assisted automated
+vulnerability analysis system, describe its implementation atop an open-sourced
+autonomous vulnerability analysis system that participated in the Cyber Grand
+Challenge, and evaluate and discuss the significant improvements that
+non-expert human assistance can offer to automated analysis approaches.
+",1,0,0,0,0,0
+14550,Block-Diagonal and LT Codes for Distributed Computing With Straggling Servers,"  We propose two coded schemes for the distributed computing problem of
+multiplying a matrix by a set of vectors. The first scheme is based on
+partitioning the matrix into submatrices and applying maximum distance
+separable (MDS) codes to each submatrix. For this scheme, we prove that up to a
+given number of partitions the communication load and the computational delay
+(not including the encoding and decoding delay) are identical to those of the
+scheme recently proposed by Li et al., based on a single, long MDS code.
+However, due to the use of shorter MDS codes, our scheme yields a significantly
+lower overall computational delay when the delay incurred by encoding and
+decoding is also considered. We further propose a second coded scheme based on
+Luby Transform (LT) codes under inactivation decoding. Interestingly, LT codes
+may reduce the delay over the partitioned scheme at the expense of an increased
+communication load. We also consider distributed computing under a deadline and
+show numerically that the proposed schemes outperform other schemes in the
+literature, with the LT code-based scheme yielding the best performance for the
+scenarios considered.
+",1,0,0,0,0,0
+14551,Gevrey estimates for one dimensional parabolic invariant manifolds of non-hyperbolic fixed points,"  We study the Gevrey character of a natural parameterization of one
+dimensional invariant manifolds associated to a parabolic direction of fixed
+points of analytic maps, that is, a direction associated with an eigenvalue
+equal to $1$. We show that, under general hypotheses, these invariant manifolds
+are Gevrey with type related to some explicit constants. We provide examples of
+the optimality of our results as well as some applications to celestial
+mechanics, namely, the Sitnikov problem and the restricted planar three body
+problem.
+",0,0,1,0,0,0
+14552,Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals,"  On a periodic basis, publicly traded companies are required to report
+fundamentals: financial data such as revenue, operating income, debt, among
+others. These data points provide some insight into the financial health of a
+company. Academic research has identified some factors, i.e. computed features
+of the reported data, that are known through retrospective analysis to
+outperform the market average. Two popular factors are the book value
+normalized by market capitalization (book-to-market) and the operating income
+normalized by the enterprise value (EBIT/EV). In this paper: we first show
+through simulation that if we could (clairvoyantly) select stocks using factors
+calculated on future fundamentals (via oracle), then our portfolios would far
+outperform a standard factor approach. Motivated by this analysis, we train
+deep neural networks to forecast future fundamentals based on a trailing
+5-years window. Quantitative analysis demonstrates a significant improvement in
+MSE over a naive strategy. Moreover, in retrospective analysis using an
+industry-grade stock portfolio simulator (backtester), we show an improvement
+in compounded annual return to 17.1% (MLP) vs 14.4% for a standard factor
+model.
+",1,0,0,1,0,0
+14553,Magnetic Flux Tailoring through Lenz Lenses in Toroidal Diamond Indenter Cells: A New Pathway to High Pressure Nuclear Magnetic Resonance,"  A new pathway to nuclear magnetic resonance spectroscopy in high pressure
+diamond anvil cells is introduced, using inductively coupled broadband passive
+electro-magnetic lenses to locally amplify the magnetic flux at the isolated
+sample, leading to an increase in sensitivity. The lenses are adopted for the
+geometrical restrictions imposed by a toroidal diamond indenter cell, and yield
+high signal-to-noise ratios at pressures as high as 72 GPa, at initial sample
+volumes of only 230 pl. The corresponding levels of detection, LODt, are found
+to be up to four orders of magnitude lower compared to formerly used solenoidal
+micro-coils in diamond anvil cells, as shown by Proton-NMR measurements on
+paraffin oil. This approach opens up the field of ultra-high pressure sciences
+for one of the most versatile spectroscopic methods available in a pressure
+range unprecedended up to now.
+",0,1,0,0,0,0
+14554,Spatial risk measures induced by powers of max-stable random fields,"  A meticulous assessment of the risk of extreme environmental events is of
+great necessity for populations, civil authorities as well as the
+insurance/reinsurance industry. Koch (2017, 2018) introduced a concept of
+spatial risk measure and a related set of axioms which are well-suited to
+analyse and quantify the risk due to events having a spatial extent, precisely
+such as natural disasters. In this paper, we first carry out a detailed study
+of the correlation (and covariance) structure of powers of the Smith and
+Brown-Resnick max-stable random fields. Then, using the latter results, we
+thoroughly investigate spatial risk measures associated with variance and
+induced by powers of max-stable random fields. In addition, we show that
+spatial risk measures associated with several classical risk measures and
+induced by such cost fields satisfy (at least) part of the previously mentioned
+axioms under appropriate conditions on the max-stable fields. Considering such
+cost fields is particularly relevant when studying the impact of extreme wind
+speeds on buildings and infrastructure.
+",0,0,0,0,0,1
+14555,Fluctuations in 1D stochastic homogenization of pseudo-elliptic equations with long-range dependent potentials,"  This paper deals with the homogenization problem of one-dimensional
+pseudo-elliptic equations with a rapidly varying random potential. The main
+purpose is to characterize the homogenization error (random fluctuations),
+i.e., the difference between the random solution and the homogenized solution,
+which strongly depends on the autocovariance property of the underlying random
+potential. It is well known that when the random potential has short-range
+dependence, the rescaled homogenization error converges in distribution to a
+stochastic integral with respect to standard Brownian motion. Here, we are
+interested in potentials with long-range dependence and we prove convergence to
+stochastic integrals with respect to Hermite process.
+",0,0,1,0,0,0
+14556,Runge-Kutta-Gegenbauer methods for advection-diffusion problems,"  In this paper, Runge-Kutta-Gegenbauer (RKG) stability polynomials of
+arbitrarily high order of accuracy are introduced in closed form. The stability
+domain of RKG polynomials extends in the the real direction with the square of
+polynomial degree, and in the imaginary direction as an increasing function of
+Gegenbauer parameter. Consequently, the polynomials are naturally suited to the
+construction of high order stabilized Runge-Kutta (SRK) methods for systems of
+PDEs of mixed hyperbolic-parabolic type.
+We present SRK methods composed of $L$ ordered forward Euler stages, with
+complex-valued stepsizes derived from the roots of RKG stability polynomials of
+degree $L$. Internal stability is maintained at large stage number through an
+ordering algorithm which limits internal amplification factors to $10 L^2$.
+Test results for mildly stiff nonlinear advection-diffusion-reaction problems
+with moderate ($\lesssim 1$) mesh Péclet numbers are provided at second,
+fourth, and sixth orders, with nonlinear reaction terms treated by complex
+splitting techniques above second order.
+",0,1,0,0,0,0
+14557,Tensor network method for reversible classical computation,"  We develop a tensor network technique that can solve universal reversible
+classical computational problems, formulated as vertex models on a square
+lattice [Nat. Commun. 8, 15303 (2017)]. By encoding the truth table of each
+vertex constraint in a tensor, the total number of solutions compatible with
+partial inputs/outputs at the boundary can be represented as the full
+contraction of a tensor network. We introduce an iterative
+compression-decimation (ICD) scheme that performs this contraction efficiently.
+The ICD algorithm first propagates local constraints to longer ranges via
+repeated contraction-decomposition sweeps over all lattice bonds, thus
+achieving compression on a given length scale. It then decimates the lattice
+via coarse-graining tensor contractions. Repeated iterations of these two steps
+gradually collapse the tensor network and ultimately yield the exact tensor
+trace for large systems, without the need for manual control of tensor
+dimensions. Our protocol allows us to obtain the exact number of solutions for
+computations where a naive enumeration would take astronomically long times.
+",1,1,0,0,0,0
+14558,Model Predictions for Time-Resolved Transport Measurements Made near the Superfluid Critical Points of Cold Atoms and $K_3C_{60}$ Films,"  Recent advances in ultrafast measurement in cold atoms, as well as pump-probe
+spectroscopy of $K_3 C_{60}$ films, have opened the possibility of rapidly
+quenching systems of interacting fermions to, and across, a finite temperature
+superfluid transition. However, determining that a transient state has
+approached a second-order critical point is difficult, as standard equilibrium
+techniques are inapplicable. We show that the approach to the superfluid
+critical point in a transient state may be detected via time-resolved transport
+measurements, such as the optical conductivity. We leverage the fact that
+quenching to the vicinity of the critical point produces a highly time
+dependent density of superfluid fluctuations, which affect the conductivity in
+two ways. First, by inelastic scattering between the fermions and the
+fluctuations, and second by direct conduction through the fluctuations, with
+the latter providing a lower resistance current carrying channel. The
+competition between these two effects leads to nonmonotonic behavior in the
+time- resolved optical conductivity, providing a signature of the critical
+transient state.
+",0,1,0,0,0,0
+14559,A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks,"  Phonetic segmentation is the process of splitting speech into distinct
+phonetic units. Human experts routinely perform this task manually by analyzing
+auditory and visual cues using analysis software, which is an extremely
+time-consuming process. Methods exist for automatic segmentation, but these are
+not always accurate enough. In order to improve automatic segmentation, we need
+to model it as close to the manual segmentation as possible. This corpus is an
+effort to capture the human segmentation behavior by recording experts
+performing a segmentation task. We believe that this data will enable us to
+highlight the important aspects of manual segmentation, which can be used in
+automatic segmentation to improve its accuracy.
+",1,0,0,0,0,0
+14560,Gradient Estimators for Implicit Models,"  Implicit models, which allow for the generation of samples but not for
+point-wise evaluation of probabilities, are omnipresent in real-world problems
+tackled by machine learning and a hot topic of current research. Some examples
+include data simulators that are widely used in engineering and scientific
+research, generative adversarial networks (GANs) for image synthesis, and
+hot-off-the-press approximate inference techniques relying on implicit
+distributions. The majority of existing approaches to learning implicit models
+rely on approximating the intractable distribution or optimisation objective
+for gradient-based optimisation, which is liable to produce inaccurate updates
+and thus poor models. This paper alleviates the need for such approximations by
+proposing the Stein gradient estimator, which directly estimates the score
+function of the implicitly defined distribution. The efficacy of the proposed
+estimator is empirically demonstrated by examples that include meta-learning
+for approximate inference, and entropy regularised GANs that provide improved
+sample diversity.
+",1,0,0,1,0,0
+14561,"Token Economics in Energy Systems: Concept, Functionality and Applications","  Traditional centralized energy systems have the disadvantages of difficult
+management and insufficient incentives. Blockchain is an emerging technology,
+which can be utilized in energy systems to enhance their management and
+control. Integrating token economy and blockchain technology, token economic
+systems in energy possess the characteristics of strong incentives and low
+cost, facilitating integrating renewable energy and demand side management, and
+providing guarantees for improving energy efficiency and reducing emission.
+This article describes the concept and functionality of token economics, and
+then analyzes the feasibility of applying token economics in the energy
+systems, and finally discuss the applications of token economics with an
+example in integrated energy systems.
+",0,0,0,0,0,1
+14562,ACtuAL: Actor-Critic Under Adversarial Learning,"  Generative Adversarial Networks (GANs) are a powerful framework for deep
+generative modeling. Posed as a two-player minimax problem, GANs are typically
+trained end-to-end on real-valued data and can be used to train a generator of
+high-dimensional and realistic images. However, a major limitation of GANs is
+that training relies on passing gradients from the discriminator through the
+generator via back-propagation. This makes it fundamentally difficult to train
+GANs with discrete data, as generation in this case typically involves a
+non-differentiable function. These difficulties extend to the reinforcement
+learning setting when the action space is composed of discrete decisions. We
+address these issues by reframing the GAN framework so that the generator is no
+longer trained using gradients through the discriminator, but is instead
+trained using a learned critic in the actor-critic framework with a Temporal
+Difference (TD) objective. This is a natural fit for sequence modeling and we
+use it to achieve improvements on language modeling tasks over the standard
+Teacher-Forcing methods.
+",1,0,0,1,0,0
+14563,Influence des mécanismes dissociés de ludifications sur l'apprentissage en support numérique de la lecture en classe primaire,"  The introduction of serious games as pedagogical supports in the field of
+education is a process gaining in popularity amongst the teaching community.
+This article creates a link between the integration of new pedagogical
+solutions in first-year primary class and the fundamental research on the
+motivation of the players/learners, detailing an experiment based on a game
+specifically developed, named QCM. QCM considers the learning worksheets issued
+from the Freinet pedagogy using various gameplay mechanisms. The main
+contribution of QCM in relation to more traditional games is the dissociation
+of immersion mechanisms, in order to improve the understanding of the user
+experience. This game also contains a system of gameplay metrics, the analysis
+of which shows a relative increase in the motivation of students using QCM
+instead of paper worksheets, while revealing large differences in students
+behavior in conjunction with the mechanisms of gamification employed. Keywords
+: Serious games, learning analytics, gamification, flow.
+",1,0,0,0,0,0
+14564,On the Fine-grained Complexity of One-Dimensional Dynamic Programming,"  In this paper, we investigate the complexity of one-dimensional dynamic
+programming, or more specifically, of the Least-Weight Subsequence (LWS)
+problem: Given a sequence of $n$ data items together with weights for every
+pair of the items, the task is to determine a subsequence $S$ minimizing the
+total weight of the pairs adjacent in $S$. A large number of natural problems
+can be formulated as LWS problems, yielding obvious $O(n^2)$-time solutions.
+In many interesting instances, the $O(n^2)$-many weights can be succinctly
+represented. Yet except for near-linear time algorithms for some specific
+special cases, little is known about when an LWS instantiation admits a
+subquadratic-time algorithm and when it does not. In particular, no lower
+bounds for LWS instantiations have been known before. In an attempt to remedy
+this situation, we provide a general approach to study the fine-grained
+complexity of succinct instantiations of the LWS problem. In particular, given
+an LWS instantiation we identify a highly parallel core problem that is
+subquadratically equivalent. This provides either an explanation for the
+apparent hardness of the problem or an avenue to find improved algorithms as
+the case may be.
+More specifically, we prove subquadratic equivalences between the following
+pairs (an LWS instantiation and the corresponding core problem) of problems: a
+low-rank version of LWS and minimum inner product, finding the longest chain of
+nested boxes and vector domination, and a coin change problem which is closely
+related to the knapsack problem and (min,+)-convolution. Using these
+equivalences and known SETH-hardness results for some of the core problems, we
+deduce tight conditional lower bounds for the corresponding LWS instantiations.
+We also establish the (min,+)-convolution-hardness of the knapsack problem.
+",1,0,0,0,0,0
+14565,Topological quantum paramagnet in a quantum spin ladder,"  It has recently been found that bosonic excitations of ordered media, such as
+phonons or spinons, can exhibit topologically nontrivial band structures. Of
+particular interest are magnon and triplon excitations in quantum magnets, as
+they can easily be manipulated by an applied field. Here we study triplon
+excitations in an S=1/2 quantum spin ladder and show that they exhibit
+nontrivial topology, even in the quantum-disordered paramagnetic phase. Our
+analysis reveals that the paramagnetic phase actually consists of two separate
+regions with topologically distinct triplon excitations. We demonstrate that
+the topological transition between these two regions can be tuned by an
+external magnetic field. The winding number that characterizes the topology of
+the triplons is derived and evaluated. By the bulk-boundary correspondence, we
+find that the non-zero winding number implies the presence of localized triplon
+end states. Experimental signatures and possible physical realizations of the
+topological paramagnetic phase are discussed.
+",0,1,0,0,0,0
+14566,Magic wavelengths of Ca$^{+}$ ion for linearly and circularly polarized light,"  The dynamic dipole polarizabilities of the low-lying states of Ca$^{+}$ for
+linearly and circularly polarized light are calculated by using relativistic
+configuration interaction plus core polarization (RCICP) approach. The magic
+wavelengths, at which the two levels of the transitions have the same ac Stark
+shifts, for $4s$-$4p_{j,m}$ and $4s$-$3d_{j,m}$ magnetic sublevels transitions
+are determined. The present magic wavelengths for linearly polarized light
+agree with the available results excellently. The polarizability for the
+circularly polarized light has the scalar, vector and tensor components. The
+dynamic polarizability is different for each of magnetic sublevels of the
+atomic state. Additional magic wavelengths have been found for the circularly
+polarized light. We recommend that the measurement of the magic wavelength near
+850 nm for $4s-4p_{\frac32,m=\pm\frac32,\pm\frac12}$ could be able to determine
+the oscillator strength ratio of $f_{4p_{\frac32} \to 3d_{\frac32}}$ and
+$f_{4p_{\frac32} \to 3d_{\frac52}}$.
+",0,1,0,0,0,0
+14567,"Network Slicing for 5G with SDN/NFV: Concepts, Architectures and Challenges","  The fifth generation of mobile communications is anticipated to open up
+innovation opportunities for new industries such as vertical markets. However,
+these verticals originate myriad use cases with diverging requirements that
+future 5G networks have to efficiently support. Network slicing may be a
+natural solution to simultaneously accommodate over a common network
+infrastructure the wide range of services that vertical-specific use cases will
+demand. In this article, we present the network slicing concept, with a
+particular focus on its application to 5G systems. We start by summarizing the
+key aspects that enable the realization of so-called network slices. Then, we
+give a brief overview on the SDN architecture proposed by the ONF and show that
+it provides tools to support slicing. We argue that although such architecture
+paves the way for network slicing implementation, it lacks some essential
+capabilities that can be supplied by NFV. Hence, we analyze a proposal from the
+ETSI to incorporate the capabilities of SDN into the NFV architecture.
+Additionally, we present an example scenario that combines SDN and NFV
+technologies to address the realization of network slices. Finally, we
+summarize the open research issues with the purpose of motivating new advances
+in this field.
+",1,0,0,0,0,0
+14568,Design of $n$- and $p$-type oxide thermoelectrics in LaNiO$_3$/SrTiO$_3(001)$ superlattices exploiting interface polarity,"  We investigate the structural, electronic, transport, and thermoelectric
+properties of LaNiO$_3$/SrTiO$_3(001)$ superlattices containing either
+exclusively $n$- or $p$-type interfaces or coupled interfaces of opposite
+polarity by using density functional theory calculations with an on-site
+Coulomb repulsion term. The results show that significant octahedral tilts are
+induced in the SrTiO$_3$ part of the superlattice. Moreover, the La-Sr
+distances and Ni-O out-of-plane bond lengths at the interfaces exhibit a
+distinct variation by about $7\,\%$ with the sign of the electrostatic doping.
+In contrast to the much studied LaAlO$_3$/SrTiO$_3$ system, the charge mismatch
+at the interfaces is exclusively accommodated within the LaNiO$_3$ layers,
+whereas the interface polarity leads to a band offset and to the formation of
+an electric field within the coupled superlattice. Features of the electronic
+structure indicate an orbital-selective quantization of quantum well states.
+The potential- and confinement-induced multiband splitting results in complex
+cylindrical Fermi surfaces with a tendency towards nesting that depends on the
+interface polarity. The analysis of the thermoelectric response reveals a
+particularly large positive Seebeck coefficient ($135~\mu$V/K) and a high
+figure of merit ($0.35$) for room-temperature cross-plane transport in the
+$p$-type superlattice that is attributed to the participation of the SrTiO$_3$
+valence band. Superlattices with either $n$- or $p$-type interfaces show
+cross-plane Seebeck coefficients of opposite sign and thus emerge as a platform
+to construct an oxide-based thermoelectric generator with structurally and
+electronically compatible $n$- and $p$-type oxide thermoelectrics.
+",0,1,0,0,0,0
+14569,Localized heat perturbation in harmonic 1D crystals. Solutions for an equation of anomalous heat conduction,"  In this work exact solutions for the equation that describes anomalous heat
+propagation in 1D harmonic lattices are obtained. Rectangular, triangular, and
+sawtooth initial perturbations of the temperature field are considered. The
+solution for an initially rectangular temperature profile is investigated in
+detail. It is shown that the decay of the solution near the wavefront is
+proportional to $1/ \sqrt{t}$. In the center of the perturbation zone the decay
+is proportional to $1/t$. Thus the solution decays slower near the wavefront,
+leaving clearly visible peaks that can be detected experimentally.
+",0,1,0,0,0,0
+14570,Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction,"  Recent research in psycholinguistics has provided increasing evidence that
+humans predict upcoming content. Prediction also affects perception and might
+be a key to robustness in human language processing. In this paper, we
+investigate the factors that affect human prediction by building a
+computational model that can predict upcoming discourse referents based on
+linguistic knowledge alone vs. linguistic knowledge jointly with common-sense
+knowledge in the form of scripts. We find that script knowledge significantly
+improves model estimates of human predictions. In a second study, we test the
+highly controversial hypothesis that predictability influences referring
+expression type but do not find evidence for such an effect.
+",1,0,0,1,0,0
+14571,Constraints on kinematic parameters at $z\ne0$,"  The standard cosmographic approach consists in performing a series expansion
+of a cosmological observable around $z=0$ and then using the data to constrain
+the cosmographic (or kinematic) parameters at present time. Such a procedure
+works well if applied to redshift ranges inside the $z$-series convergence
+radius ($z<1$), but can be problematic if we want to cover redshift intervals
+that fall outside the $z-$series convergence radius. This problem can be
+circumvented if we work with the $y-$redshift, $y=z/(1+z)$, or the scale
+factor, $a=1/(1+z)=1-y$, for example. In this paper, we use the scale factor
+$a$ as the variable of expansion. We expand the luminosity distance and the
+Hubble parameter around an arbitrary $\tilde{a}$ and use the Supernovae Ia (SNe
+Ia) and the Hubble parameter data to estimate $H$, $q$, $j$ and $s$ at $z\ne0$
+($\tilde{a}\neq1$). We show that the last relevant term for both expansions is
+the third. Since the third order expansion of $d_L(z)$ has one parameter less
+than the third order expansion of $H(z)$, we also consider, for completeness, a
+fourth order expansion of $d_L(z)$. For the third order expansions, the results
+obtained from both SNe Ia and $H(z)$ data are incompatible with the
+$\Lambda$CDM model at $2\sigma$ confidence level, but also incompatible with
+each other. When the fourth order expansion of $d_L(z)$ is taken into account,
+the results obtained from SNe Ia data are compatible with the $\Lambda$CDM
+model at $2\sigma$ confidence level, but still remains incompatible with
+results obtained from $H(z)$ data. These conflicting results may indicate a
+tension between the current SNe Ia and $H(z)$ data sets.
+",0,1,0,0,0,0
+14572,Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank,"  Discourse parsing has long been treated as a stand-alone problem independent
+from constituency or dependency parsing. Most attempts at this problem are
+pipelined rather than end-to-end, sophisticated, and not self-contained: they
+assume gold-standard text segmentations (Elementary Discourse Units), and use
+external parsers for syntactic features. In this paper we propose the first
+end-to-end discourse parser that jointly parses in both syntax and discourse
+levels, as well as the first syntacto-discourse treebank by integrating the
+Penn Treebank with the RST Treebank. Built upon our recent span-based
+constituency parser, this joint syntacto-discourse parser requires no
+preprocessing whatsoever (such as segmentation or feature extraction), achieves
+the state-of-the-art end-to-end discourse parsing accuracy.
+",1,0,0,0,0,0
+14573,Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret,"  In this paper, we study the combinatorial multi-armed bandit problem (CMAB)
+with probabilistically triggered arms (PTAs). Under the assumption that the arm
+triggering probabilities (ATPs) are positive for all arms, we prove that a
+class of upper confidence bound (UCB) policies, named Combinatorial UCB with
+exploration rate $\kappa$ (CUCB-$\kappa$), and Combinatorial Thompson Sampling
+(CTS), which estimates the expected states of the arms via Thompson sampling,
+achieve bounded regret. In addition, we prove that CUCB-$0$ and CTS incur
+$O(\sqrt{T})$ gap-independent regret. These results improve the results in
+previous works, which show $O(\log T)$ gap-dependent and $O(\sqrt{T\log T})$
+gap-independent regrets, respectively, under no assumptions on the ATPs. Then,
+we numerically evaluate the performance of CUCB-$\kappa$ and CTS in a
+real-world movie recommendation problem, where the actions correspond to
+recommending a set of movies, the arms correspond to the edges between the
+movies and the users, and the goal is to maximize the total number of users
+that are attracted by at least one movie. Our numerical results complement our
+theoretical findings on bounded regret. Apart from this problem, our results
+also directly apply to the online influence maximization (OIM) problem studied
+in numerous prior works.
+",1,0,0,1,0,0
+14574,Generative Adversarial Residual Pairwise Networks for One Shot Learning,"  Deep neural networks achieve unprecedented performance levels over many tasks
+and scale well with large quantities of data, but performance in the low-data
+regime and tasks like one shot learning still lags behind. While recent work
+suggests many hypotheses from better optimization to more complicated network
+structures, in this work we hypothesize that having a learnable and more
+expressive similarity objective is an essential missing component. Towards
+overcoming that, we propose a network design inspired by deep residual networks
+that allows the efficient computation of this more expressive pairwise
+similarity objective. Further, we argue that regularization is key in learning
+with small amounts of data, and propose an additional generator network based
+on the Generative Adversarial Networks where the discriminator is our residual
+pairwise network. This provides a strong regularizer by leveraging the
+generated data samples. The proposed model can generate plausible variations of
+exemplars over unseen classes and outperforms strong discriminative baselines
+for few shot classification tasks. Notably, our residual pairwise network
+design outperforms previous state-of-theart on the challenging mini-Imagenet
+dataset for one shot learning by getting over 55% accuracy for the 5-way
+classification task over unseen classes.
+",1,0,0,0,0,0
+14575,Evaporation of dilute droplets in a turbulent jet: clustering and entrainment effects,"  Droplet evaporation in turbulent sprays involves unsteady, multiscale and
+multiphase processes which make its comprehension and model capabilities still
+limited. The present work aims to investigate droplet vaporization dynamics
+within a turbulent spatial developing jet in dilute, non-reacting conditions.
+We address the problem using a Direct Numerical Simulation of jet laden with
+acetone droplets using an hybrid Eulerian/Lagrangian approach based on the
+point droplet approximation. A detailed statistical analysis of both phases is
+presented. In particular, we show how crucial is the preferential sampling of
+the vapour phase induced by the inhomogeneous localization of the droplets
+through the flow. The preferential segregation of droplets develops suddenly
+downstream the inlet both within the turbulent core and in the mixing layer.
+Two distinct mechanisms have been found to drive these phenomena, the inertial
+small-scale clustering in the jet core and the intermittent dynamics of
+droplets across the turbulent/non-turbulent interface in the mixing layer where
+dry air entrainment occurs. These phenomenologies strongly affect the overall
+vaporization process and lead to a spectacular widening of droplets size and
+vaporization rate distributions in the downstream evolution of the turbulent
+spray.
+",0,1,0,0,0,0
+14576,A priori Hölder and Lipschitz regularity for generalized $p$-harmonious functions in metric measure spaces,"  Let $(\mathbb{X} , d, \mu )$ be a proper metric measure space and let $\Omega
+\subset \mathbb{X}$ be a bounded domain. For each $x\in \Omega$, we choose a
+radius $0< \varrho (x) \leq \mathrm{dist}(x, \partial \Omega ) $ and let $B_x$
+be the closed ball centered at $x$ with radius $\varrho (x)$. If $\alpha \in
+\mathbb{R}$, consider the following operator in $C( \overline{\Omega} )$, $$
+\mathcal{T}_{\alpha}u(x)=\frac{\alpha}{2}\left(\sup_{B_x } u+\inf_{B_x }
+u\right)+(1-\alpha)\,\frac{1}{\mu(B_x)}\int_{B_x}\hspace{-0.1cm} u\ d\mu. $$
+Under appropriate assumptions on $\alpha$, $\mathbb{X}$, $\mu$ and the radius
+function $\varrho$ we show that solutions $u\in C( \overline{\Omega} )$ of the
+functional equation $\mathcal{T}_{\alpha}u = u$ satisfy a local Hölder or
+Lipschitz condition in $\Omega$. The motivation comes from the so called
+$p$-harmonious functions in euclidean domains.
+",0,0,1,0,0,0
+14577,Simultaneous tracking of spin angle and amplitude beyond classical limits,"  We show how simultaneous, back-action evading tracking of non-commuting
+observables can be achieved in a widely-used sensing technology, atomic
+interferometry. Using high-dynamic-range dynamically-decoupled quantum
+non-demolition (QND) measurements on a precessing atomic spin ensemble, we
+track the collective spin angle and amplitude with negligible effects from back
+action, giving steady-state tracking sensitivity 2.9 dB beyond the standard
+quantum limit and 7.0 dB beyond Poisson statistics.
+",0,1,0,0,0,0
+14578,Charge exchange in galaxy clusters,"  Though theoretically expected, the charge exchange emission from galaxy
+clusters has not yet been confidently detected. Accumulating hints were
+reported recently, including a rather marginal detection with the Hitomi data
+of the Perseus cluster. As suggested in Gu et al. (2015), a detection of charge
+exchange line emission from galaxy clusters would not only impact the
+interpretation of the newly-discovered 3.5 keV line, but also open up a new
+research topic on the interaction between hot and cold matter in clusters. We
+aim to perform the most systematic search for the O VIII charge exchange line
+in cluster spectra using the RGS on board XMM. We introduce a sample of 21
+clusters observed with the RGS. The dominating thermal plasma emission is
+modeled and subtracted with a two-temperature CIE component, and the residuals
+are stacked for the line search. The systematic uncertainties in the fits are
+quantified by refitting the spectra with a varying continuum and line
+broadening. By the residual stacking, we do find a hint of a line-like feature
+at 14.82 A, the characteristic wavelength expected for oxygen charge exchange.
+This feature has a marginal significance of 2.8 sigma, and the average
+equivalent width is 2.5E-4 keV. We further demonstrate that the putative
+feature can be hardly affected by the systematic errors from continuum
+modelling and instrumental effects, or the atomic uncertainties of the
+neighbouring thermal lines. Assuming a realistic temperature and abundance
+pattern, the physical model implied by the possible oxygen line agrees well
+with the theoretical model proposed previously to explain the reported 3.5 keV
+line. If the charge exchange source indeed exists, we would expect that the
+oxygen abundance is potentially overestimated by 8-22% in previous X-ray
+measurements which assumed pure thermal lines.
+",0,1,0,0,0,0
+14579,A Grazing Gaussian Beam,"  We consider Friedlander's wave equation in two space dimensions in the
+half-space x > 0 with the boundary condition u(x,y,t)=0 when x=0. For a
+Gaussian beam w(x,y,t;k) concentrated on a ray path that is tangent to x=0 at
+(x,y,t)=(0,0,0) we calculate the ""reflected"" wave z(x,y,t;k) in t > 0 such that
+w(x,y,t;k)+z(x,y,t;k) satisfies Friedlander's wave equation and vanishes on
+x=0. These computations are done to leading order in k on the ray path. The
+interaction of beams with boundaries has been studied for non-tangential beams
+and for beams gliding along the boundary. We find that the amplitude of the
+solution on the central ray for large k after leaving the boundary is very
+nearly one half of that of the incoming beam.
+",0,0,1,0,0,0
+14580,Reconfigurable Manipulator Simulation for Robotics and Multimodal Machine Learning Application: Aaria,"  This paper represents a systematic way for generation of Aaria, a simulated
+model for serial manipulators for the purpose of kinematic or dynamic analysis
+with a vast variety of structures based on Simulink SimMechanics. The proposed
+model can receive configuration parameters, for instance in accordance with
+modified Denavit-Hartenberg convention, or trajectories for its base or joints
+for structures with 1 to 6 degrees of freedom (DOF). The manipulator is
+equipped with artificial joint sensors as well as simulated Inertial
+Measurement Units (IMUs) on each link. The simulation output can be positions,
+velocities, torques, in the joint space or IMU outputs; angular velocity,
+linear acceleration, tool coordinates with respect to the inertial frame. This
+simulation model is a source of a dataset for virtual multimodal sensory data
+for automation of robot modeling and control designed for machine learning and
+deep learning approaches based on big data.
+",1,0,0,0,0,0
+14581,Group actions and a multi-parameter Falconer distance problem,"  In this paper we study the following multi-parameter variant of the
+celebrated Falconer distance problem. Given ${\textbf{d}}=(d_1,d_2, \dots,
+d_{\ell})\in \mathbb{N}^{\ell}$ with $d_1+d_2+\dots+d_{\ell}=d$ and $E
+\subseteq \mathbb{R}^d$, we define $$ \Delta_{\textbf{d}}(E) = \left\{
+\left(|x^{(1)}-y^{(1)}|,\ldots,|x^{(\ell)}-y^{(\ell)}|\right) : x,y \in E
+\right\} \subseteq \mathbb{R}^{\ell}, $$ where for $x\in \mathbb{R}^d$ we write
+$x=\left( x^{(1)},\dots, x^{(\ell)} \right)$ with $x^{(i)} \in
+\mathbb{R}^{d_i}$.
+We ask how large does the Hausdorff dimension of $E$ need to be to ensure
+that the $\ell$-dimensional Lebesgue measure of $\Delta_{\textbf{d}}(E)$ is
+positive? We prove that if $2 \leq d_i$ for $1 \leq i \leq \ell$, then the
+conclusion holds provided $$ \dim(E)>d-\frac{\min d_i}{2}+\frac{1}{3}.$$ We
+also note that, by previous constructions, the conclusion does not in general
+hold if $$\dim(E)<d-\frac{\min d_i}{2}.$$ A group action derivation of a
+suitable Mattila integral plays an important role in the argument.
+",0,0,1,0,0,0
+14582,Interacting fermions on the half-line: boundary counterterms and boundary corrections,"  Recent years witnessed an extensive development of the theory of the critical
+point in two-dimensional statistical systems, which allowed to prove {\it
+existence} and {\it conformal invariance} of the {\it scaling limit} for
+two-dimensional Ising model and dimers in planar graphs. Unfortunately, we are
+still far from a full understanding of the subject: so far, exact solutions at
+the lattice level, in particular determinant structure and exact discrete
+holomorphicity, play a cucial role in the rigorous control of the scaling
+limit. The few results about not-integrable (interacting) systems at
+criticality are still unable to deal with {\it finite domains} and {\it
+boundary corrections}, which are of course crucial for getting informations
+about conformal covariance. In this thesis, we address the question of adapting
+constructive Renormalization Group methods to non-integrable critical systems
+in $d= 1+1$ dimensions. We study a system of interacting spinless fermions on a
+one-dimensional semi-infinite lattice, which can be considered as a prototype
+of the Luttinger universality class with Dirichlet Boundary Conditions. We
+develop a convergent renormalized expression for the thermodynamic observables
+in the presence of a quadratic {\it boundary defect} counterterm, polynomially
+localized at the boundary. In particular, we get explicit bounds on the
+boundary corrections to the specific ground state energy.
+",0,1,1,0,0,0
+14583,Online and Distributed Robust Regressions under Adversarial Data Corruption,"  In today's era of big data, robust least-squares regression becomes a more
+challenging problem when considering the adversarial corruption along with
+explosive growth of datasets. Traditional robust methods can handle the noise
+but suffer from several challenges when applied in huge dataset including 1)
+computational infeasibility of handling an entire dataset at once, 2) existence
+of heterogeneously distributed corruption, and 3) difficulty in corruption
+estimation when data cannot be entirely loaded. This paper proposes online and
+distributed robust regression approaches, both of which can concurrently
+address all the above challenges. Specifically, the distributed algorithm
+optimizes the regression coefficients of each data block via heuristic hard
+thresholding and combines all the estimates in a distributed robust
+consolidation. Furthermore, an online version of the distributed algorithm is
+proposed to incrementally update the existing estimates with new incoming data.
+We also prove that our algorithms benefit from strong robustness guarantees in
+terms of regression coefficient recovery with a constant upper bound on the
+error of state-of-the-art batch methods. Extensive experiments on synthetic and
+real datasets demonstrate that our approaches are superior to those of existing
+methods in effectiveness, with competitive efficiency.
+",1,0,0,1,0,0
+14584,Global well-posedness for the Schrödinger map problem with small Besov norm,"  In this paper we prove a global result for the Schrödinger map problem with
+initial data with small Besov norm at critical regularity.
+",0,0,1,0,0,0
+14585,Stable explicit schemes for simulation of nonlinear moisture transfer in porous materials,"  Implicit schemes have been extensively used in building physics to compute
+the solution of moisture diffusion problems in porous materials for improving
+stability conditions. Nevertheless, these schemes require important
+sub-iterations when treating non-linear problems. To overcome this
+disadvantage, this paper explores the use of improved explicit schemes, such as
+Dufort-Frankel, Crank-Nicolson and hyperbolisation approaches. A first case
+study has been considered with the hypothesis of linear transfer. The
+Dufort-Frankel, Crank-Nicolson and hyperbolisation schemes were compared to the
+classical Euler explicit scheme and to a reference solution. Results have shown
+that the hyperbolisation scheme has a stability condition higher than the
+standard Courant-Friedrichs-Lewy (CFL) condition. The error of this schemes
+depends on the parameter \tau representing the hyperbolicity magnitude added
+into the equation. The Dufort-Frankel scheme has the advantages of being
+unconditionally stable and is preferable for non-linear transfer, which is the
+second case study. Results have shown the error is proportional to O(\Delta t).
+A modified Crank-Nicolson scheme has been proposed in order to avoid
+sub-iterations to treat the non-linearities at each time step. The main
+advantages of the Dufort-Frankel scheme are (i) to be twice faster than the
+Crank-Nicolson approach; (ii) to compute explicitly the solution at each time
+step; (iii) to be unconditionally stable and (iv) easier to parallelise on
+high-performance computer systems. Although the approach is unconditionally
+stable, the choice of the time discretisation $\Delta t$ remains an important
+issue to accurately represent the physical phenomena.
+",1,1,0,0,0,0
+14586,Congruence lattices of finite diagram monoids,"  We give a complete description of the congruence lattices of the following
+finite diagram monoids: the partition monoid, the planar partition monoid, the
+Brauer monoid, the Jones monoid (also known as the Temperley-Lieb monoid), the
+Motzkin monoid, and the partial Brauer monoid. All the congruences under
+discussion arise as special instances of a new construction, involving an ideal
+I, a retraction I->M onto the minimal ideal, a congruence on M, and a normal
+subgroup of a maximal subgroup outside I.
+",0,0,1,0,0,0
+14587,Weighted blowup correspondence of orbifold Gromov--Witten invariants and applications,"  Let $\sf X$ be a symplectic orbifold groupoid with $\sf S$ being a symplectic
+sub-orbifold groupoid, and $\sf X_{\mathfrak a}$ be the weight-$\mathfrak a$
+blowup of $\sf X$ along $\sf S$ with $\sf Z$ being the corresponding
+exceptional divisor. We show that there is a weighted blowup correspondence
+between some certain absolute orbifold Gromov--Witten invariants of $\sf X$
+relative to $\sf S$ and some certain relative orbifold Gromov--Witten
+invariants of the pair $(\sf X_{\mathfrak a}|Z)$. As an application, we prove
+that the symplectic uniruledness of symplectic orbifold groupoids is a weighted
+blowup invariant.
+",0,0,1,0,0,0
+14588,Constraints on Quenching of $z\lesssim2$ Massive Galaxies from the Evolution of the average Sizes of Star-Forming and Quenched Populations in COSMOS,"  We use $>$9400 $\log(m/M_{\odot})>10$ quiescent and star-forming galaxies at
+$z\lesssim2$ in COSMOS/UltraVISTA to study the average size evolution of these
+systems, with focus on the rare, ultra-massive population at
+$\log(m/M_{\odot})>11.4$. The large 2-square degree survey area delivers a
+sample of $\sim400$ such ultra-massive systems. Accurate sizes are derived
+using a calibration based on high-resolution images from the Hubble Space
+Telescope. We find that, at these very high masses, the size evolution of
+star-forming and quiescent galaxies is almost indistinguishable in terms of
+normalization and power-law slope. We use this result to investigate possible
+pathways of quenching massive $m>M^*$ galaxies at $z<2$. We consistently model
+the size evolution of quiescent galaxies from the star-forming population by
+assuming different simple models for the suppression of star-formation. These
+models include an instantaneous and delayed quenching without altering the
+structure of galaxies and a central starburst followed by compaction. We find
+that instantaneous quenching reproduces well the observed mass-size relation of
+massive galaxies at $z>1$. Our starburst$+$compaction model followed by
+individual growth of the galaxies by minor mergers is preferred over other
+models without structural change for $\log(m/M_{\odot})>11.0$ galaxies at
+$z>0.5$. None of our models is able to meet the observations at $m>M^*$ and
+$z<1$ with out significant contribution of post-quenching growth of individual
+galaxies via mergers. We conclude that quenching is a fast process in galaxies
+with $ m \ge 10^{11} M_\odot$, and that major mergers likely play a major role
+in the final steps of their evolution.
+",0,1,0,0,0,0
+14589,Improving LBP and its variants using anisotropic diffusion,"  The main purpose of this paper is to propose a new preprocessing step in
+order to improve local feature descriptors and texture classification.
+Preprocessing is implemented by using transformations which help highlight
+salient features that play a significant role in texture recognition. We
+evaluate and compare four different competing methods: three different
+anisotropic diffusion methods including the classical anisotropic Perona-Malik
+diffusion and two subsequent regularizations of it and the application of a
+Gaussian kernel, which is the classical multiscale approach in texture
+analysis. The combination of the transformed images and the original ones are
+analyzed. The results show that the use of the preprocessing step does lead to
+improved texture recognition.
+",1,0,0,0,0,0
+14590,"Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression","  This paper is focused on dimension-free PAC-Bayesian bounds, under weak
+polynomial moment assumptions, allowing for heavy tailed sample distributions.
+It covers the estimation of the mean of a vector or a matrix, with applications
+to least squares linear regression. Special efforts are devoted to the
+estimation of Gram matrices, due to their prominent role in high-dimension data
+analysis.
+",0,0,1,1,0,0
+14591,Configuration Spaces and Robot Motion Planning Algorithms,"  The paper surveys topological problems relevant to the motion planning
+problem of robotics and includes some new results and constructions. First we
+analyse the notion of topological complexity of configuration spaces which is
+responsible for discontinuities in algorithms for robot navigation. Then we
+present explicit motion planning algorithms for coordinated collision free
+control of many particles moving in Euclidean spaces or on graphs. These
+algorithms are optimal in the sense that they have minimal number of regions of
+continuity. Moreover, we describe in full detail the topology of configuration
+spaces of two particles on a tree and use it to construct some top-dimensional
+cohomology classes in configuration spaces of n particles on a tree.
+",0,0,1,0,0,0
+14592,Effect Summaries for Thread-Modular Analysis,"  We propose a novel guess-and-check principle to increase the efficiency of
+thread-modular verification of lock-free data structures. We build on a
+heuristic that guesses candidates for stateless effect summaries of programs by
+searching the code for instances of a copy-and-check programming idiom common
+in lock-free data structures. These candidate summaries are used to compute the
+interference among threads in linear time. Since a candidate summary need not
+be a sound effect summary, we show how to fully automatically check whether the
+precision of candidate summaries is sufficient. We can thus perform sound
+verification despite relying on an unsound heuristic. We have implemented our
+approach and found it up to two orders of magnitude faster than existing ones.
+",1,0,0,0,0,0
+14593,Theory of Correlated Pairs of Electrons Oscillating in Resonant Quantum States to Reach the Critical Temperature in a Metal,"  The formation of Correlated Electron Pairs Oscillating around the Fermi level
+in Resonant Quantum States (CEPO-RQS), when a metal is cooled to its critical
+temperature T=Tc, is studied. The necessary conditions for the existence of
+CEPO-RQS are analyzed. The participation of electron-electron interaction
+screened by an electron dielectric constant of the form proposed by Thomas
+Fermi is considered and a physical meaning for the electron-phonon-electron
+interaction in the formation of the CEPO-RQS is given. The internal state of
+the CEPO-RQS is characterized by taking into account the internal Hamiltonian,
+obtaining a general equation that represents its binding energy and depends
+only on temperature and critical temperature. A parameter is also defined that
+contains the properties that qualitatively characterizes the nature of a
+material to form the CEPO-RQS.
+",0,1,0,0,0,0
+14594,Feldman-Katok pseudometric and the GIKN construction of nonhyperbolic ergodic measures,"  The GIKN construction was introduced by Gorodetski, Ilyashenko, Kleptsyn, and
+Nalsky in [Functional Analysis and its Applications, 39 (2005), 21--30]. It
+gives a nonhyperbolic ergodic measure which is a weak$^*$ limit of a special
+sequence of measures supported on periodic orbits. This method was later
+adapted by numerous authors and provided examples of nonhyperbolic invariant
+measures in various settings. We prove that the result of the GIKN construction
+is always a loosely Kronecker measure in the sense of Ornstein, Rudolph, and
+Weiss (equivalently, standard measure in the sense of Katok, another name is
+loosely Bernoulli measure with zero entropy). For a proof we introduce and
+study the Feldman-Katok pseudometric $\bar{F_{K}}$. The pseudodistance
+$\bar{F_{K}}$ is a topological counterpart of the $\bar f$ metric for
+finite-state stationary stochastic processes introduced by Feldman and,
+independently, by Katok, later developed by Ornstein, Rudolph, and Weiss. We
+show that every measure given by the GIKN construction is the
+$\bar{F_{K}}$-limit of a sequence of periodic measures. On the other hand we
+prove that a measure which is the $\bar{F_{K}}$-limit of a sequence of ergodic
+measures is ergodic and its entropy is smaller or equal than the lower limit of
+entropies of measures in the sequence. Furthermore we demonstrate that
+$\bar{F_{K}}$-Cauchy sequence of periodic measures tends in the weak$^*$
+topology either to a periodic measure or to a loosely Kronecker measure.
+",0,0,1,0,0,0
+14595,Hamiltonian Monte-Carlo for Orthogonal Matrices,"  We consider the problem of sampling from posterior distributions for Bayesian
+models where some parameters are restricted to be orthogonal matrices. Such
+matrices are sometimes used in neural networks models for reasons of
+regularization and stabilization of training procedures, and also can
+parameterize matrices of bounded rank, positive-definite matrices and others.
+In \citet{byrne2013geodesic} authors have already considered sampling from
+distributions over manifolds using exact geodesic flows in a scheme similar to
+Hamiltonian Monte Carlo (HMC). We propose new sampling scheme for a set of
+orthogonal matrices that is based on the same approach, uses ideas of
+Riemannian optimization and does not require exact computation of geodesic
+flows. The method is theoretically justified by proof of symplecticity for the
+proposed iteration. In experiments we show that the new scheme is comparable or
+faster in time per iteration and more sample-efficient comparing to
+conventional HMC with explicit orthogonal parameterization and Geodesic
+Monte-Carlo. We also provide promising results of Bayesian ensembling for
+orthogonal neural networks and low-rank matrix factorization.
+",1,0,0,1,0,0
+14596,Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models,"  The Gibbs sampler is a particularly popular Markov chain used for learning
+and inference problems in Graphical Models (GMs). These tasks are
+computationally intractable in general, and the Gibbs sampler often suffers
+from slow mixing. In this paper, we study the Swendsen-Wang dynamics which is a
+more sophisticated Markov chain designed to overcome bottlenecks that impede
+the Gibbs sampler. We prove O(\log n) mixing time for attractive binary
+pairwise GMs (i.e., ferromagnetic Ising models) on stochastic partitioned
+graphs having n vertices, under some mild conditions, including low temperature
+regions where the Gibbs sampler provably mixes exponentially slow. Our
+experiments also confirm that the Swendsen-Wang sampler significantly
+outperforms the Gibbs sampler when they are used for learning parameters of
+attractive GMs.
+",1,0,0,1,0,0
+14597,Towards effective research recommender systems for repositories,"  In this paper, we argue why and how the integration of recommender systems
+for research can enhance the functionality and user experience in repositories.
+We present the latest technical innovations in the CORE Recommender, which
+provides research article recommendations across the global network of
+repositories and journals. The CORE Recommender has been recently redeveloped
+and released into production in the CORE system and has also been deployed in
+several third-party repositories. We explain the design choices of this unique
+system and the evaluation processes we have in place to continue raising the
+quality of the provided recommendations. By drawing on our experience, we
+discuss the main challenges in offering a state-of-the-art recommender solution
+for repositories. We highlight two of the key limitations of the current
+repository infrastructure with respect to developing research recommender
+systems: 1) the lack of a standardised protocol and capabilities for exposing
+anonymised user-interaction logs, which represent critically important input
+data for recommender systems based on collaborative filtering and 2) the lack
+of a voluntary global sign-on capability in repositories, which would enable
+the creation of personalised recommendation and notification solutions based on
+past user interactions.
+",1,0,0,0,0,0
+14598,Existence and a priori estimates of solutions for quasilinear singular elliptic systems with variable exponents,"  This article sets forth results on the existence, a priori estimates and
+boundedness of positive solutions of a singular quasilinear systems of elliptic
+equations involving variable exponents. The approach is based on Schauder's
+fixed point Theorem. A Moser iteration procedure is also obtained for singular
+cooperative systems involving variable exponents establishing a priori
+estimates and boundedness of solutions.
+",0,0,1,0,0,0
+14599,On the sectional curvature along central configurations,"  In this paper we characterize planar central configurations in terms of a
+sectional curvature value of the Jacobi-Maupertuis metric. This
+characterization works for the $N$-body problem with general masses and any
+$1/r^{\alpha}$ potential with $\alpha> 0$. We also observe dynamical
+consequences of these curvature values for relative equilibrium solutions.
+These curvature methods work well for strong forces ($\alpha \ge 2$).
+",0,0,1,0,0,0
+14600,"Interferometric confirmation of ""water fountain"" candidates","  Water fountain stars (WFs) are evolved objects with water masers tracing
+high-velocity jets (up to several hundreds of km s$^{-1}$). They could
+represent one of the first manifestations of collimated mass-loss in evolved
+objects and thus, be a key to understanding the shaping mechanisms of planetary
+nebulae. Only 13 objects had been confirmed so far as WFs with interferometer
+observations. We present new observations with the Australia Telescope Compact
+Array and archival observations with the Very Large Array of four objects that
+are considered to be WF candidates, mainly based on single-dish observations.
+We confirm IRAS 17291-2147 and IRAS 18596+0315 (OH 37.1-0.8) as bona fide
+members of the WF class, with high-velocity water maser emission consistent
+with tracing bipolar jets. We argue that IRAS 15544-5332 has been wrongly
+considered as a WF in previous works, since we see no evidence in our data nor
+in the literature that this object harbours high-velocity water maser emission.
+In the case of IRAS 19067+0811, we did not detect any water maser emission, so
+its confirmation as a WF is still pending. With the result of this work, there
+are 15 objects that can be considered confirmed WFs. We speculate that there is
+no significant physical difference between WFs and obscured post-AGB stars in
+general. The absence of high-velocity water maser emission in some obscured
+post-AGB stars could be attributed to a variability or orientation effect.
+",0,1,0,0,0,0
+14601,Parametric Inference for Discretely Observed Subordinate Diffusions,"  Subordinate diffusions are constructed by time changing diffusion processes
+with an independent Lévy subordinator. This is a rich family of Markovian
+jump processes which exhibit a variety of jump behavior and have found many
+applications. This paper studies parametric inference of discretely observed
+ergodic subordinate diffusions. We solve the identifiability problem for these
+processes using spectral theory and propose a two-step estimation procedure
+based on estimating functions. In the first step, we use an estimating function
+that only involves diffusion parameters. In the second step, a martingale
+estimating function based on eigenvalues and eigenfunctions of the subordinate
+diffusion is used to estimate the parameters of the Lévy subordinator and
+the problem of how to choose the weighting matrix is solved. When the
+eigenpairs do not have analytical expressions, we apply the constant
+perturbation method with high order corrections to calculate them numerically
+and the martingale estimating function can be computed efficiently. Consistency
+and asymptotic normality of our estimator are established considering the
+effect of numerical approximation. Through numerical examples, we show that our
+method is both computationally and statistically efficient. A subordinate
+diffusion model for VIX (CBOE volatility index) is developed which provides
+good fit to the data.
+",0,0,1,1,0,0
+14602,Adversarially Regularized Graph Autoencoder for Graph Embedding,"  Graph embedding is an effective method to represent graph data in a low
+dimensional space for graph analytics. Most existing embedding algorithms
+typically focus on preserving the topological structure or minimizing the
+reconstruction errors of graph data, but they have mostly ignored the data
+distribution of the latent codes from the graphs, which often results in
+inferior embedding in real-world graph data. In this paper, we propose a novel
+adversarial graph embedding framework for graph data. The framework encodes the
+topological structure and node content in a graph to a compact representation,
+on which a decoder is trained to reconstruct the graph structure. Furthermore,
+the latent representation is enforced to match a prior distribution via an
+adversarial training scheme. To learn a robust embedding, two variants of
+adversarial approaches, adversarially regularized graph autoencoder (ARGA) and
+adversarially regularized variational graph autoencoder (ARVGA), are developed.
+Experimental studies on real-world graphs validate our design and demonstrate
+that our algorithms outperform baselines by a wide margin in link prediction,
+graph clustering, and graph visualization tasks.
+",0,0,0,1,0,0
+14603,Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group,"  We introduce a novel approach to perform first-order optimization with
+orthogonal and unitary constraints. This approach is based on a parametrization
+stemming from Lie group theory through the exponential map. The parametrization
+transforms the constrained optimization problem into an unconstrained one over
+a Euclidean space, for which common first-order optimization methods can be
+used. The theoretical results presented are general enough to cover the special
+orthogonal group, the unitary group and, in general, any connected compact Lie
+group. We discuss how this and other parametrizations can be computed
+efficiently through an implementation trick, making numerically complex
+parametrizations usable at a negligible runtime cost in neural networks. In
+particular, we apply our results to RNNs with orthogonal recurrent weights,
+yielding a new architecture called expRNN. We demonstrate how our method
+constitutes a more robust approach to optimization with orthogonal constraints,
+showing faster, accurate, and more stable convergence in several tasks designed
+to test RNNs.
+",1,0,0,1,0,0
+14604,Training Big Random Forests with Little Resources,"  Without access to large compute clusters, building random forests on large
+datasets is still a challenging problem. This is, in particular, the case if
+fully-grown trees are desired. We propose a simple yet effective framework that
+allows to efficiently construct ensembles of huge trees for hundreds of
+millions or even billions of training instances using a cheap desktop computer
+with commodity hardware. The basic idea is to consider a multi-level
+construction scheme, which builds top trees for small random subsets of the
+available data and which subsequently distributes all training instances to the
+top trees' leaves for further processing. While being conceptually simple, the
+overall efficiency crucially depends on the particular implementation of the
+different phases. The practical merits of our approach are demonstrated using
+dense datasets with hundreds of millions of training instances.
+",0,0,0,1,0,0
+14605,Epitaxy of Advanced Nanowire Quantum Devices,"  Semiconductor nanowires provide an ideal platform for various low-dimensional
+quantum devices. In particular, topological phases of matter hosting
+non-Abelian quasi-particles can emerge when a semiconductor nanowire with
+strong spin-orbit coupling is brought in contact with a superconductor. To
+fully exploit the potential of non-Abelian anyons for topological quantum
+computing, they need to be exchanged in a well-controlled braiding operation.
+Essential hardware for braiding is a network of single-crystalline nanowires
+coupled to superconducting islands. Here, we demonstrate a technique for
+generic bottom-up synthesis of complex quantum devices with a special focus on
+nanowire networks having a predefined number of superconducting islands.
+Structural analysis confirms the high crystalline quality of the nanowire
+junctions, as well as an epitaxial superconductor-semiconductor interface.
+Quantum transport measurements of nanowire ""hashtags"" reveal Aharonov-Bohm and
+weak-antilocalization effects, indicating a phase coherent system with strong
+spin-orbit coupling. In addition, a proximity-induced hard superconducting gap
+is demonstrated in these hybrid superconductor-semiconductor nanowires,
+highlighting the successful materials development necessary for a first
+braiding experiment. Our approach opens new avenues for the realization of
+epitaxial 3-dimensional quantum device architectures.
+",0,1,0,0,0,0
+14606,Toward universality in degree 2 of the Kricker lift of the Kontsevich integral and the Lescop equivariant invariant,"  In the setting of finite type invariants for null-homologous knots in
+rational homology 3-spheres with respect to null Lagrangian-preserving
+surgeries, there are two candidates to be universal invariants, defined
+respectively by Kricker and Lescop. In a previous paper, the second author
+defined maps between spaces of Jacobi diagrams. Injectivity for these maps
+would imply that Kricker and Lescop invariants are indeed universal invariants;
+this would prove in particular that these two invariants are equivalent. In the
+present paper, we investigate the injectivity status of these maps for degree 2
+invariants, in the case of knots whose Blanchfield modules are direct sums of
+isomorphic Blanchfield modules of Q-- dimension two. We prove that they are
+always injective except in one case, for which we determine explicitly the
+kernel.
+",0,0,1,0,0,0
+14607,Automatic segmentation of MR brain images with a convolutional neural network,"  Automatic segmentation in MR brain images is important for quantitative
+analysis in large-scale studies with images acquired at all ages.
+This paper presents a method for the automatic segmentation of MR brain
+images into a number of tissue classes using a convolutional neural network. To
+ensure that the method obtains accurate segmentation details as well as spatial
+consistency, the network uses multiple patch sizes and multiple convolution
+kernel sizes to acquire multi-scale information about each voxel. The method is
+not dependent on explicit features, but learns to recognise the information
+that is important for the classification based on training data. The method
+requires a single anatomical MR image only.
+The segmentation method is applied to five different data sets: coronal
+T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age
+(PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired
+at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an
+average age of 70 years, and T1-weighted images of young adults acquired at an
+average age of 23 years. The method obtained the following average Dice
+coefficients over all segmented tissue classes for each data set, respectively:
+0.87, 0.82, 0.84, 0.86 and 0.91.
+The results demonstrate that the method obtains accurate segmentations in all
+five sets, and hence demonstrates its robustness to differences in age and
+acquisition protocol.
+",1,0,0,0,0,0
+14608,Integrable Structure of Multispecies Zero Range Process,"  We present a brief review on integrability of multispecies zero range process
+in one dimension introduced recently. The topics range over stochastic $R$
+matrices of quantum affine algebra $U_q (A^{(1)}_n)$, matrix product
+construction of stationary states for periodic systems, $q$-boson
+representation of Zamolodchikov-Faddeev algebra, etc. We also introduce new
+commuting Markov transfer matrices having a mixed boundary condition and prove
+the factorization of a family of $R$ matrices associated with the tetrahedron
+equation and generalized quantum groups at a special point of the spectral
+parameter.
+",0,1,1,0,0,0
+14609,A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates,"  Clinical trial registries can be used to monitor the production of trial
+evidence and signal when systematic reviews become out of date. However, this
+use has been limited to date due to the extensive manual review required to
+search for and screen relevant trial registrations. Our aim was to evaluate a
+new method that could partially automate the identification of trial
+registrations that may be relevant for systematic review updates. We identified
+179 systematic reviews of drug interventions for type 2 diabetes, which
+included 537 clinical trials that had registrations in ClinicalTrials.gov. We
+tested a matrix factorisation approach that uses a shared latent space to learn
+how to rank relevant trial registrations for each systematic review, comparing
+the performance to document similarity to rank relevant trial registrations.
+The two approaches were tested on a holdout set of the newest trials from the
+set of type 2 diabetes systematic reviews and an unseen set of 141 clinical
+trial registrations from 17 updated systematic reviews published in the
+Cochrane Database of Systematic Reviews. The matrix factorisation approach
+outperformed the document similarity approach with a median rank of 59 and
+recall@100 of 60.9%, compared to a median rank of 138 and recall@100 of 42.8%
+in the document similarity baseline. In the second set of systematic reviews
+and their updates, the highest performing approach used document similarity and
+gave a median rank of 67 (recall@100 of 62.9%). The proposed method was useful
+for ranking trial registrations to reduce the manual workload associated with
+finding relevant trials for systematic review updates. The results suggest that
+the approach could be used as part of a semi-automated pipeline for monitoring
+potentially new evidence for inclusion in a review update.
+",1,0,0,0,0,0
+14610,Spectra of Magnetic Operators on the Diamond Lattice Fractal,"  We adapt the well-known spectral decimation technique for computing spectra
+of Laplacians on certain symmetric self-similar sets to the case of magnetic
+Schrodinger operators and work through this method completely for the diamond
+lattice fractal. This connects results of physicists from the 1980's, who used
+similar techniques to compute spectra of sequences of magnetic operators on
+graph approximations to fractals but did not verify existence of a limiting
+fractal operator, to recent work describing magnetic operators on fractals via
+functional analytic techniques.
+",0,0,1,0,0,0
+14611,Arrow calculus for welded and classical links,"  We develop a calculus for diagrams of knotted objects. We define Arrow
+presentations, which encode the crossing informations of a diagram into arrows
+in a way somewhat similar to Gauss diagrams, and more generally w-tree
+presentations, which can be seen as `higher order Gauss diagrams'. This Arrow
+calculus is used to develop an analogue of Habiro's clasper theory for welded
+knotted objects, which contain classical link diagrams as a subset. This
+provides a 'realization' of Polyak's algebra of arrow diagrams at the welded
+level, and leads to a characterization of finite type invariants of welded
+knots and long knots. As a corollary, we recover several topological results
+due to K. Habiro and A. Shima and to T. Watanabe on knotted surfaces in
+4-space. We also classify welded string links up to homotopy, thus recovering a
+result of the first author with B. Audoux, P. Bellingeri and E. Wagner.
+",0,0,1,0,0,0
+14612,Determinacy of Schmidt's Game and Other Intersection Games,"  Schmidt's game, and other similar intersection games have played an important
+role in recent years in applications to number theory, dynamics, and
+Diophantine approximation theory. These games are real games, that is, games in
+which the players make moves from a complete separable metric space. The
+determinacy of these games trivially follows from the axiom of determinacy for
+real games, $\mathsf{AD}_\mathbb{R}$, which is a much stronger axiom than that
+asserting all integer games are determined, $\mathsf{AD}$. One of our main
+results is a general theorem which under the hypothesis $\mathsf{AD}$ implies
+the determinacy of intersection games which have a property allowing strategies
+to be simplified. In particular, we show that Schmidt's $(\alpha,\beta,\rho)$
+game on $\mathbb{R}$ is determined from $\mathsf{AD}$ alone, but on
+$\mathbb{R}^n$ for $n \geq 3$ we show that $\mathsf{AD}$ does not imply the
+determinacy of this game. We also prove several other results specifically
+related to the determinacy of Schmidt's game. These results highlight the
+obstacles in obtaining the determinacy of Schmidt's game from $\mathsf{AD}$.
+",0,0,1,0,0,0
+14613,Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds,"  Kernel $k$-means clustering can correctly identify and extract a far more
+varied collection of cluster structures than the linear $k$-means clustering
+algorithm. However, kernel $k$-means clustering is computationally expensive
+when the non-linear feature map is high-dimensional and there are many input
+points. Kernel approximation, e.g., the Nyström method, has been applied in
+previous works to approximately solve kernel learning problems when both of the
+above conditions are present. This work analyzes the application of this
+paradigm to kernel $k$-means clustering, and shows that applying the linear
+$k$-means clustering algorithm to $\frac{k}{\epsilon} (1 + o(1))$ features
+constructed using a so-called rank-restricted Nyström approximation results
+in cluster assignments that satisfy a $1 + \epsilon$ approximation ratio in
+terms of the kernel $k$-means cost function, relative to the guarantee provided
+by the same algorithm without the use of the Nyström method. As part of the
+analysis, this work establishes a novel $1 + \epsilon$ relative-error trace
+norm guarantee for low-rank approximation using the rank-restricted Nyström
+approximation. Empirical evaluations on the $8.1$ million instance MNIST8M
+dataset demonstrate the scalability and usefulness of kernel $k$-means
+clustering with Nyström approximation. This work argues that spectral
+clustering using Nyström approximation---a popular and computationally
+efficient, but theoretically unsound approach to non-linear clustering---should
+be replaced with the efficient and theoretically sound combination of kernel
+$k$-means clustering with Nyström approximation. The superior performance of
+the latter approach is empirically verified.
+",1,0,0,1,0,0
+14614,Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data,"  The classification of time series data is a challenge common to all
+data-driven fields. However, there is no agreement about which are the most
+efficient techniques to group unlabeled time-ordered data. This is because a
+successful classification of time series patterns depends on the goal and the
+domain of interest, i.e. it is application-dependent.
+In this article, we study free-to-play game data. In this domain, clustering
+similar time series information is increasingly important due to the large
+amount of data collected by current mobile and web applications. We evaluate
+which methods cluster accurately time series of mobile games, focusing on
+player behavior data. We identify and validate several aspects of the
+clustering: the similarity measures and the representation techniques to reduce
+the high dimensionality of time series. As a robustness test, we compare
+various temporal datasets of player activity from two free-to-play video-games.
+With these techniques we extract temporal patterns of player behavior
+relevant for the evaluation of game events and game-business diagnosis. Our
+experiments provide intuitive visualizations to validate the results of the
+clustering and to determine the optimal number of clusters. Additionally, we
+assess the common characteristics of the players belonging to the same group.
+This study allows us to improve the understanding of player dynamics and churn
+behavior.
+",1,0,0,1,0,0
+14615,On the bound states of magnetic Laplacians on wedges,"  This paper is mainly inspired by the conjecture about the existence of bound
+states for magnetic Neumann Laplacians on planar wedges of any aperture
+$\phi\in (0,\pi)$. So far, a proof was only obtained for apertures
+$\phi\lesssim 0.511\pi$. The conviction in the validity of this conjecture for
+apertures $\phi\gtrsim 0.511\pi$ mainly relied on numerical computations. In
+this paper we succeed to prove the existence of bound states for any aperture
+$\phi \lesssim 0.583\pi$ using a variational argument with suitably chosen test
+functions. Employing some more involved test functions and combining a
+variational argument with computer-assistance, we extend this interval up to
+any aperture $\phi \lesssim 0.595\pi$. Moreover, we analyse the same question
+for closely related problems concerning magnetic Robin Laplacians on wedges and
+for magnetic Schrödinger operators in the plane with $\delta$-interactions
+supported on broken lines.
+",0,0,1,0,0,0
+14616,Finite-Time Distributed Linear Equation Solver for Minimum $l_1$ Norm Solutions,"  This paper proposes distributed algorithms for multi-agent networks to
+achieve a solution in finite time to a linear equation $Ax=b$ where $A$ has
+full row rank, and with the minimum $l_1$-norm in the underdetermined case
+(where $A$ has more columns than rows). The underlying network is assumed to be
+undirected and fixed, and an analytical proof is provided for the proposed
+algorithm to drive all agents' individual states to converge to a common value,
+viz a solution of $Ax=b$, which is the minimum $l_1$-norm solution in the
+underdetermined case. Numerical simulations are also provided as validation of
+the proposed algorithms.
+",1,0,0,0,0,0
+14617,High Throughput Probabilistic Shaping with Product Distribution Matching,"  Product distribution matching (PDM) is proposed to generate target
+distributions over large alphabets by combining the output of several parallel
+distribution matchers (DMs) with smaller output alphabets. The parallel
+architecture of PDM enables low-complexity and high-throughput implementation.
+PDM is used as a shaping device for probabilistic amplitude shaping (PAS). For
+64-ASK and a spectral efficiency of 4.5 bits per channel use (bpcu), PDM is as
+power efficient as a single full-fledged DM. It is shown how PDM enables PAS
+for parallel channels present in multi-carrier systems like digital subscriber
+line (DSL) and orthogonal frequency-division multiplexing (OFDM). The key
+feature is that PDM shares the DMs for lower bit-levels among different
+sub-carriers, which improves the power efficiency significantly. A
+representative parallel channel example shows that PAS with PDM is 0.93 dB more
+power efficient than conventional uniform signaling and PDM is 0.35 dB more
+power efficient than individual per channel DMs.
+",1,0,0,0,0,0
+14618,Minimax Estimation of Large Precision Matrices with Bandable Cholesky Factor,"  Last decade witnesses significant methodological and theoretical advances in
+estimating large precision matrices. In particular, there are scientific
+applications such as longitudinal data, meteorology and spectroscopy in which
+the ordering of the variables can be interpreted through a bandable structure
+on the Cholesky factor of the precision matrix. However, the minimax theory has
+still been largely unknown, as opposed to the well established minimax results
+over the corresponding bandable covariance matrices. In this paper, we focus on
+two commonly used types of parameter spaces, and develop the optimal rates of
+convergence under both the operator norm and the Frobenius norm. A striking
+phenomenon is found: two types of parameter spaces are fundamentally different
+under the operator norm but enjoy the same rate optimality under the Frobenius
+norm, which is in sharp contrast to the equivalence of corresponding two types
+of bandable covariance matrices under both norms. This fundamental difference
+is established by carefully constructing the corresponding minimax lower
+bounds. Two new estimation procedures are developed: for the operator norm, our
+optimal procedure is based on a novel local cropping estimator targeting on all
+principle submatrices of the precision matrix while for the Frobenius norm, our
+optimal procedure relies on a delicate regression-based block-thresholding
+rule. We further establish rate optimality in the nonparanormal model.
+Numerical studies are carried out to confirm our theoretical findings.
+",0,0,1,1,0,0
+14619,Machine learning in protein engineering,"  Machine learning-guided protein engineering is a new paradigm that enables
+the optimization of complex protein functions. Machine-learning methods use
+data to predict protein function without requiring a detailed model of the
+underlying physics or biological pathways. They accelerate protein engineering
+by learning from information contained in all measured variants and using it to
+select variants that are likely to be improved. In this review, we introduce
+the steps required to collect protein data, train machine-learning models, and
+use trained models to guide engineering. We make recommendations at each stage
+and look to future opportunities for machine learning to enable the discovery
+of new protein functions and uncover the relationship between protein sequence
+and function.
+",0,0,0,0,1,0
+14620,Advanced Steel Microstructural Classification by Deep Learning Methods,"  The inner structure of a material is called microstructure. It stores the
+genesis of a material and determines all its physical and chemical properties.
+While microstructural characterization is widely spread and well known, the
+microstructural classification is mostly done manually by human experts, which
+gives rise to uncertainties due to subjectivity. Since the microstructure could
+be a combination of different phases or constituents with complex substructures
+its automatic classification is very challenging and only a few prior studies
+exist. Prior works focused on designed and engineered features by experts and
+classified microstructures separately from the feature extraction step.
+Recently, Deep Learning methods have shown strong performance in vision
+applications by learning the features from data together with the
+classification step. In this work, we propose a Deep Learning method for
+microstructural classification in the examples of certain microstructural
+constituents of low carbon steel. This novel method employs pixel-wise
+segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by a
+max-voting scheme. Our system achieves 93.94% classification accuracy,
+drastically outperforming the state-of-the-art method of 48.89% accuracy.
+Beyond the strong performance of our method, this line of research offers a
+more robust and first of all objective way for the difficult task of steel
+quality appreciation.
+",1,1,0,0,0,0
+14621,Categorizing Hirsch Index Variants,"  Utilizing the Hirsch index h and some of its variants for an exploratory
+factor analysis we discuss whether one of the most important Hirsch-type
+indices, namely the g-index comprises information about not only the size of
+the productive core but also the impact of the papers in the core. We also
+study the effect of logarithmic and square-root transformation of the data
+utilized in the factor analysis. To demonstrate our approach we use a real data
+example analysing the citation records of 26 physicists compiled from the Web
+of Science.
+",1,0,0,1,0,0
+14622,Why a Population Genetics Framework is Inappropriate for Cultural Evolution,"  Although Darwinian models are rampant in the social sciences, social
+scientists do not face the problem that motivated Darwin's theory of natural
+selection: the problem of explaining how lineages evolve despite that any
+traits they acquire are regularly discarded at the end of the lifetime of the
+individuals that acquired them. While the rationale for framing culture as an
+evolutionary process is correct, it does not follow that culture is a Darwinian
+or selectionist process, or that population genetics and phylogenetics provide
+viable starting points for modeling cultural change. This paper lays out
+step-by-step arguments as to why this approach is ill-conceived, focusing on
+the lack of randomness and lack of a self-assembly code in cultural evolution,
+and summarizes an alternative approach.
+",0,0,0,0,1,0
+14623,"Is together better? Examining scientific collaborations across multiple authors, institutions, and departments","  Collaborations are an integral part of scientific research and publishing. In
+the past, access to large-scale corpora has limited the ways in which questions
+about collaborations could be investigated. However, with improvements in
+data/metadata quality and access, it is possible to explore the idea of
+research collaboration in ways beyond the traditional definition of multiple
+authorship. In this paper, we examine scientific works through three different
+lenses of collaboration: across multiple authors, multiple institutions, and
+multiple departments. We believe this to be a first look at multiple
+departmental collaborations as we employ extensive data curation to
+disambiguate authors' departmental affiliations for nearly 70,000 scientific
+papers. We then compare citation metrics across the different definitions of
+collaboration and find that papers defined as being collaborative were more
+frequently cited than their non-collaborative counterparts, regardless of the
+definition of collaboration used. We also share preliminary results from
+examining the relationship between co-citation and co-authorship by analyzing
+the extent to which similar fields (as determined by co-citation) are
+collaborating on works (as determined by co-authorship). These preliminary
+results reveal trends of compartmentalization with respect to
+intra-institutional collaboration and show promise in being expanded.
+",1,0,0,0,0,0
+14624,A Lichnerowicz estimate for the spectral gap of the sub-Laplacian,"  For a second order operator on a compact manifold satisfying the strong
+Hörmander condition, we give a bound for the spectral gap analogous to the
+Lichnerowicz estimate for the Laplacian of a Riemannian manifold. We consider a
+wide class of such operators which includes horizontal lifts of the Laplacian
+on Riemannian submersions with minimal leaves.
+",0,0,1,0,0,0
+14625,Quasiclassical theory of spin dynamics in superfluid $^3$He: kinetic equations in the bulk and spin response of surface Majorana states,"  We develop a theory based on the formalism of quasiclassical Green's
+functions to study the spin dynamics in superfluid $^3$He. First, we derive
+kinetic equations for the spin-dependent distribution function in the bulk
+superfluid reproducing the results obtained earlier without quasiclassical
+approximation. Then we consider a spin dynamics near the surface of fully
+gapped $^3$He-B phase taking into account spin relaxation due to the
+transitions in the spectrum of localized fermionic states. The lifetime of
+longitudinal and transverse spin waves is calculate taking into account the
+Fermi-liquid corrections which lead to the crucial modification of fermionic
+spectrum and spin responses.
+",0,1,0,0,0,0
+14626,The RBO Dataset of Articulated Objects and Interactions,"  We present a dataset with models of 14 articulated objects commonly found in
+human environments and with RGB-D video sequences and wrenches recorded of
+human interactions with them. The 358 interaction sequences total 67 minutes of
+human manipulation under varying experimental conditions (type of interaction,
+lighting, perspective, and background). Each interaction with an object is
+annotated with the ground truth poses of its rigid parts and the kinematic
+state obtained by a motion capture system. For a subset of 78 sequences (25
+minutes), we also measured the interaction wrenches. The object models contain
+textured three-dimensional triangle meshes of each link and their motion
+constraints. We provide Python scripts to download and visualize the data. The
+data is available at this https URL and hosted
+at this https URL.
+",1,0,0,0,0,0
+14627,Training Generative Adversarial Networks via Primal-Dual Subgradient Methods: A Lagrangian Perspective on GAN,"  We relate the minimax game of generative adversarial networks (GANs) to
+finding the saddle points of the Lagrangian function for a convex optimization
+problem, where the discriminator outputs and the distribution of generator
+outputs play the roles of primal variables and dual variables, respectively.
+This formulation shows the connection between the standard GAN training process
+and the primal-dual subgradient methods for convex optimization. The inherent
+connection does not only provide a theoretical convergence proof for training
+GANs in the function space, but also inspires a novel objective function for
+training. The modified objective function forces the distribution of generator
+outputs to be updated along the direction according to the primal-dual
+subgradient methods. A toy example shows that the proposed method is able to
+resolve mode collapse, which in this case cannot be avoided by the standard GAN
+or Wasserstein GAN. Experiments on both Gaussian mixture synthetic data and
+real-world image datasets demonstrate the performance of the proposed method on
+generating diverse samples.
+",0,0,0,1,0,0
+14628,"A Detailed Observational Analysis of V1324 Sco, the Most Gamma-Ray Luminous Classical Nova to Date","  It has recently been discovered that some, if not all, classical novae emit
+GeV gamma rays during outburst, but the mechanisms involved in the production
+of the gamma rays are still not well understood. We present here a
+comprehensive multi-wavelength dataset---from radio to X-rays---for the most
+gamma-ray luminous classical nova to-date, V1324 Sco. Using this dataset, we
+show that V1324 Sco is a canonical dusty Fe-II type nova, with a maximum ejecta
+velocity of 2600 km s$^{-1}$ and an ejecta mass of few $\times 10^{-5}$
+M$_{\odot}$. There is also evidence for complex shock interactions, including a
+double-peaked radio light curve which shows high brightness temperatures at
+early times. To explore why V1324~Sco was so gamma-ray luminous, we present a
+model of the nova ejecta featuring strong internal shocks, and find that higher
+gamma-ray luminosities result from higher ejecta velocities and/or mass-loss
+rates. Comparison of V1324~Sco with other gamma-ray detected novae does not
+show clear signatures of either, and we conclude that a larger sample of
+similarly well-observed novae is needed to understand the origin and variation
+of gamma rays in novae.
+",0,1,0,0,0,0
+14629,The California-Kepler Survey. III. A Gap in the Radius Distribution of Small Planets,"  The size of a planet is an observable property directly connected to the
+physics of its formation and evolution. We used precise radius measurements
+from the California-Kepler Survey (CKS) to study the size distribution of 2025
+$\textit{Kepler}$ planets in fine detail. We detect a factor of $\geq$2 deficit
+in the occurrence rate distribution at 1.5-2.0 R$_{\oplus}$. This gap splits
+the population of close-in ($P$ < 100 d) small planets into two size regimes:
+R$_P$ < 1.5 R$_{\oplus}$ and R$_P$ = 2.0-3.0 R$_{\oplus}$, with few planets in
+between. Planets in these two regimes have nearly the same intrinsic frequency
+based on occurrence measurements that account for planet detection
+efficiencies. The paucity of planets between 1.5 and 2.0 R$_{\oplus}$ supports
+the emerging picture that close-in planets smaller than Neptune are composed of
+rocky cores measuring 1.5 R$_{\oplus}$ or smaller with varying amounts of
+low-density gas that determine their total sizes.
+",0,1,0,0,0,0
+14630,Large sets avoiding linear patterns,"  We prove that for any dimension function $h$ with $h \prec x^d$ and for any
+countable set of linear patterns, there exists a compact set $E$ with
+$\mathcal{H}^h(E)>0$ avoiding all the given patterns. We also give several
+applications and recover results of Keleti, Maga, and Máthé.
+",0,0,1,0,0,0
+14631,Faster Algorithms for Weighted Recursive State Machines,"  Pushdown systems (PDSs) and recursive state machines (RSMs), which are
+linearly equivalent, are standard models for interprocedural analysis. Yet RSMs
+are more convenient as they (a) explicitly model function calls and returns,
+and (b) specify many natural parameters for algorithmic analysis, e.g., the
+number of entries and exits. We consider a general framework where RSM
+transitions are labeled from a semiring and path properties are algebraic with
+semiring operations, which can model, e.g., interprocedural reachability and
+dataflow analysis problems.
+Our main contributions are new algorithms for several fundamental problems.
+As compared to a direct translation of RSMs to PDSs and the best-known existing
+bounds of PDSs, our analysis algorithm improves the complexity for
+finite-height semirings (that subsumes reachability and standard dataflow
+properties). We further consider the problem of extracting distance values from
+the representation structures computed by our algorithm, and give efficient
+algorithms that distinguish the complexity of a one-time preprocessing from the
+complexity of each individual query. Another advantage of our algorithm is that
+our improvements carry over to the concurrent setting, where we improve the
+best-known complexity for the context-bounded analysis of concurrent RSMs.
+Finally, we provide a prototype implementation that gives a significant
+speed-up on several benchmarks from the SLAM/SDV project.
+",1,0,0,0,0,0
+14632,Local Density Approximation for Almost-Bosonic Anyons,"  We discuss the average-field approximation for a trapped gas of
+non-interacting anyons in the quasi-bosonic regime. In the homogeneous case,
+i.e., for a confinement to a bounded region, we prove that the energy in the
+regime of large statistics parameter, i.e., for ""less-bosonic"" anyons, is
+independent of boundary conditions and of the shape of the domain. When a
+non-trivial trapping potential is present, we derive a local density
+approximation in terms of a Thomas-Fermi-like model.
+",0,1,1,0,0,0
+14633,Chemical dynamics between wells across a time-dependent barrier: Self-similarity in the Lagrangian descriptor and reactive basins,"  In chemical or physical reaction dynamics, it is essential to distinguish
+precisely between reactants and products for all time. This task is especially
+demanding in time-dependent or driven systems because therein the dividing
+surface (DS) between these states often exhibits a nontrivial time-dependence.
+The so-called transition state (TS) trajectory has been seen to define a DS
+which is free of recrossings in a large number of one-dimensional reactions
+across time-dependent barriers, and, thus, allows one to determine exact
+reaction rates. A fundamental challenge to applying this method is the
+construction of the TS trajectory itself. The minimization of Lagrangian
+descriptors (LDs) provides a general and powerful scheme to obtain that
+trajectory even when perturbation theory fails. Both approaches encounter
+possible breakdowns when the overall potential is bounded, admitting the
+possibility of returns to the barrier long after trajectories have reached the
+product or reactant wells. Such global dynamics cannot be captured by
+perturbation theory. Meanwhile, in the LD-DS approach, it leads to the
+emergence of additional local minima which make it difficult to extract the
+optimal branch associated with the desired TS trajectory. In this work, we
+illustrate this behavior for a time-dependent double-well potential revealing a
+self-similar structure of the LD, and we demonstrate how the reflections and
+side-minima can be addressed by an appropriate modification of the LD
+associated with the direct rate across the barrier.
+",0,1,0,0,0,0
+14634,Dust-trapping vortices and a potentially planet-triggered spiral wake in the pre-transitional disk of V1247 Orionis,"  The radial drift problem constitutes one of the most fundamental problems in
+planet formation theory, as it predicts particles to drift into the star before
+they are able to grow to planetesimal size. Dust-trapping vortices have been
+proposed as a possible solution to this problem, as they might be able to trap
+particles over millions of years, allowing them to grow beyond the radial drift
+barrier. Here, we present ALMA 0.04""-resolution imaging of the pre-transitional
+disk of V1247 Orionis that reveals an asymmetric ring as well as a
+sharply-confined crescent structure, resembling morphologies seen in
+theoretical models of vortex formation. The asymmetric ring (at 0.17""=54 au
+separation from the star) and the crescent (at 0.38""=120 au) seem smoothly
+connected through a one-armed spiral arm structure that has been found
+previously in scattered light. We propose a physical scenario with a planet
+orbiting at $\sim0.3$""$\approx$100 au, where the one-armed spiral arm detected
+in polarised light traces the accretion stream feeding the protoplanet. The
+dynamical influence of the planet clears the gap between the ring and the
+crescent and triggers two vortices that trap mm-sized particles, namely the
+crescent and the bright asymmetry seen in the ring. We conducted dedicated
+hydrodynamics simulations of a disk with an embedded planet, which results in
+similar spiral-arm morphologies as seen in our scattered light images. At the
+position of the spiral wake and the crescent we also observe $^{12}$CO (3-2)
+and H$^{12}$CO$^{+}$ (4-3) excess line emission, likely tracing the increased
+scale-height in these disk regions.
+",0,1,0,0,0,0
+14635,Weak and smooth solutions for a fractional Yamabe flow: the case of general compact and locally conformally flat manifolds,"  As a counterpart of the classical Yamabe problem, a fractional Yamabe flow
+has been introduced by Jin and Xiong (2014) on the sphere. Here we pursue its
+study in the context of general compact smooth manifolds with positive
+fractional curvature. First, we prove that the flow is locally well posed in
+the weak sense on any compact manifold. If the manifold is locally conformally
+flat with positive Yamabe invariant, we also prove that the flow is smooth and
+converges to a constant scalar curvature metric. We provide different proofs
+using extension properties introduced by Chang and González (2011) for the
+conformally covariant fractional order operators.
+",0,0,1,0,0,0
+14636,Lipschitz Properties for Deep Convolutional Networks,"  In this paper we discuss the stability properties of convolutional neural
+networks. Convolutional neural networks are widely used in machine learning. In
+classification they are mainly used as feature extractors. Ideally, we expect
+similar features when the inputs are from the same class. That is, we hope to
+see a small change in the feature vector with respect to a deformation on the
+input signal. This can be established mathematically, and the key step is to
+derive the Lipschitz properties. Further, we establish that the stability
+results can be extended for more general networks. We give a formula for
+computing the Lipschitz bound, and compare it with other methods to show it is
+closer to the optimal value.
+",1,0,1,0,0,0
+14637,Quadrature Compound: An approximating family of distributions,"  Compound distributions allow construction of a rich set of distributions.
+Typically they involve an intractable integral. Here we use a quadrature
+approximation to that integral to define the quadrature compound family.
+Special care is taken that this approximation is suitable for computation of
+gradients with respect to distribution parameters. This technique is applied to
+discrete (Poisson LogNormal) and continuous distributions. In the continuous
+case, quadrature compound family naturally makes use of parameterized
+transformations of unparameterized distributions (a.k.a ""reparameterization""),
+allowing for gradients of expectations to be estimated as the gradient of a
+sample mean. This is demonstrated in a novel distribution, the diffeomixture,
+which is is a reparameterizable approximation to a mixture distribution.
+",0,0,0,1,0,0
+14638,Centrality in Modular Networks,"  Identifying influential nodes in a network is a fundamental issue due to its
+wide applications, such as accelerating information diffusion or halting virus
+spreading. Many measures based on the network topology have emerged over the
+years to identify influential nodes such as Betweenness, Closeness, and
+Eigenvalue centrality. However, although most real-world networks are modular,
+few measures exploit this property. Recent works have shown that it has a
+significant effect on the dynamics on networks. In a modular network, a node
+has two types of influence: a local influence (on the nodes of its community)
+through its intra-community links and a global influence (on the nodes in other
+communities) through its inter-community links. Depending of the strength of
+the community structure, these two components are more or less influential.
+Based on this idea, we propose to extend all the standard centrality measures
+defined for networks with no community structure to modular networks. The
+so-called ""Modular centrality"" is a two dimensional vector. Its first component
+quantifies the local influence of a node in its community while the second
+component quantifies its global influence on the other communities of the
+network. In order to illustrate the effectiveness of the Modular centrality
+extensions, comparison with their scalar counterpart are performed in an
+epidemic process setting. Simulation results using the
+Susceptible-Infected-Recovered (SIR) model on synthetic networks with
+controlled community structure allows getting a clear idea about the relation
+between the strength of the community structure and the major type of influence
+(global/local). Furthermore, experiments on real-world networks demonstrate the
+merit of this approach.
+",1,0,0,0,0,0
+14639,Yield Trajectory Tracking for Hyperbolic Age-Structured Population Systems,"  For population systems modeled by age-structured hyperbolic partial
+differential equations (PDEs) that are bilinear in the input and evolve with a
+positive-valued infinite-dimensional state, global stabilization of constant
+yield set points was achieved in prior work. Seasonal demands in
+biotechnological production processes give rise to time-varying yield
+references. For the proposed control objective aiming at a global attractivity
+of desired yield trajectories, multiple non-standard features have to be
+considered: a non-local boundary condition, a PDE state restricted to the
+positive orthant of the function space and arbitrary restrictive but physically
+meaningful input constraints. Moreover, we provide Control Lyapunov Functionals
+ensuring an exponentially fast attraction of adequate reference trajectories.
+To achieve this goal, we make use of the relation between first-order
+hyperbolic PDEs and integral delay equations leading to a decoupling of the
+input-dependent dynamics and the infinite-dimensional internal one.
+Furthermore, the dynamic control structure does not necessitate exact knowledge
+of the model parameters or online measurements of the age-profile. With a
+Galerkin-based numerical simulation scheme using the key ideas of the
+Karhunen-Loève-decomposition, we demonstrate the controller's performance.
+",1,0,1,0,0,0
+14640,A sixteen-relator presentation of an infinite hyperbolic Kazhdan group,"  We provide an explicit presentation of an infinite hyperbolic Kazhdan group
+with $4$ generators and $16$ relators of length at most $73$. That group acts
+properly and cocompactly on a hyperbolic triangle building of type $(3,4,4)$.
+We also point out a variation of the construction that yields examples of
+lattices in $\tilde A_2$-buildings admitting non-Desarguesian residues of
+arbitrary prime power order.
+",0,0,1,0,0,0
+14641,Fast and accurate Bayesian model criticism and conflict diagnostics using R-INLA,"  Bayesian hierarchical models are increasingly popular for realistic modelling
+and analysis of complex data. This trend is accompanied by the need for
+flexible, general, and computationally efficient methods for model criticism
+and conflict detection. Usually, a Bayesian hierarchical model incorporates a
+grouping of the individual data points, for example individuals in repeated
+measurement data. In such cases, the following question arises: Are any of the
+groups ""outliers"", or in conflict with the remaining groups? Existing general
+approaches aiming to answer such questions tend to be extremely computationally
+demanding when model fitting is based on MCMC. We show how group-level model
+criticism and conflict detection can be done quickly and accurately through
+integrated nested Laplace approximations (INLA). The new method is implemented
+as a part of the open source R-INLA package for Bayesian computing
+(this http URL).
+",0,0,1,1,0,0
+14642,Optimizing deep video representation to match brain activity,"  The comparison of observed brain activity with the statistics generated by
+artificial intelligence systems is useful to probe brain functional
+organization under ecological conditions. Here we study fMRI activity in ten
+subjects watching color natural movies and compute deep representations of
+these movies with an architecture that relies on optical flow and image
+content. The association of activity in visual areas with the different layers
+of the deep architecture displays complexity-related contrasts across visual
+areas and reveals a striking foveal/peripheral dichotomy.
+",0,0,0,0,1,0
+14643,Non-Abelian Fermionization and Fractional Quantum Hall Transitions,"  There has been a recent surge of interest in dualities relating theories of
+Chern-Simons gauge fields coupled to either bosons or fermions within the
+condensed matter community, particularly in the context of topological
+insulators and the half-filled Landau level. Here, we study the application of
+one such duality to the long-standing problem of quantum Hall inter-plateaux
+transitions. The key motivating experimental observations are the anomalously
+large value of the correlation length exponent $\nu \approx 2.3$ and that $\nu$
+is observed to be super-universal, i.e., the same in the vicinity of distinct
+critical points. Duality motivates effective descriptions for a fractional
+quantum Hall plateau transition involving a Chern-Simons field with $U(N_c)$
+gauge group coupled to $N_f = 1$ fermion. We study one class of theories in a
+controlled limit where $N_f \gg N_c$ and calculate $\nu$ to leading non-trivial
+order in the absence of disorder. Although these theories do not yield an
+anomalously large exponent $\nu$ within the large $N_f \gg N_c$ expansion, they
+do offer a new parameter space of theories that is apparently different from
+prior works involving abelian Chern-Simons gauge fields.
+",0,1,0,0,0,0
+14644,Periodic solutions and regularization of a Kepler problem with time-dependent perturbation,"  We consider a Kepler problem in dimension two or three, with a time-dependent
+$T$-periodic perturbation. We prove that for any prescribed positive integer
+$N$, there exist at least $N$ periodic solutions (with period $T$) as long as
+the perturbation is small enough. Here the solutions are understood in a
+general sense as they can have collisions. The concept of generalized solutions
+is defined intrinsically and it coincides with the notion obtained in Celestial
+Mechanics via the theory of regularization of collisions.
+",0,0,1,0,0,0
+14645,Ultra-wide-band slow light in photonic crystal coupled-cavity waveguides,"  Slow light propagation in structured materials is a highly promising approach
+for realizing on-chip integrated photonic devices based on enhanced optical
+nonlinearities. One of the most successful research avenues consists in
+engineering the band dispersion of light-guiding photonic crystal (PC)
+structures. The primary goal of such devices is to achieve slow-light operation
+over the largest possible bandwidth, with large group index, minimal index
+dispersion, and constant transmission spectrum. Here, we report on the
+experimental demonstration of to date record high GBP in silicon-based
+coupled-cavity waveguides (CCWs) operating at telecom wavelengths. Our results
+rely on novel CCW designs, optimized using a genetic algorithm, and refined
+nanofabrication processes.
+",0,1,0,0,0,0
+14646,Sketch Layer Separation in Multi-Spectral Historical Document Images,"  High-resolution imaging has delivered new prospects for detecting the
+material composition and structure of cultural treasures. Despite the various
+techniques for analysis, a significant diagnostic gap remained in the range of
+available research capabilities for works on paper. Old master drawings were
+mostly composed in a multi-step manner with various materials. This resulted in
+the overlapping of different layers which made the subjacent strata difficult
+to differentiate. The separation of stratified layers using imaging methods
+could provide insights into the artistic work processes and help answer
+questions about the object, its attribution, or in identifying forgeries. The
+pattern recognition procedure was tested with mock replicas to achieve the
+separation and the capability of displaying concealed red chalk under ink. In
+contrast to RGB-sensor based imaging, the multi- or hyperspectral technology
+allows accurate layer separation by recording the characteristic signatures of
+the material's reflectance. The risk of damage to the artworks as a result of
+the examination can be reduced by using combinations of defined spectra for
+lightning and image capturing. By guaranteeing the maximum level of
+readability, our results suggest that the technique can be applied to a broader
+range of objects and assist in diagnostic research into cultural treasures in
+the future.
+",1,0,0,0,0,0
+14647,"Comment on ""Spatial optical solitons in highly nonlocal media"" and related papers","  In a recent paper [A. Alberucci, C. Jisha, N. Smyth, and G. Assanto, Phys.
+Rev. A 91, 013841 (2015)], Alberucci et al. have studied the propagation of
+bright spatial solitary waves in highly nonlocal media. We find that the main
+results in that and related papers, concerning soliton shape and dynamics,
+based on the accessible soliton (AS) approximation, are incorrect; the correct
+results have already been published by others. These and other inconsistencies
+in the paper follow from the problems in applying the AS approximation in
+earlier papers by the group that propagated to the later papers. The accessible
+soliton theory cannot describe accurately the features and dynamics of solitons
+in highly nonlocal media.
+",0,1,0,0,0,0
+14648,Local Descriptor for Robust Place Recognition using LiDAR Intensity,"  Place recognition is a challenging problem in mobile robotics, especially in
+unstructured environments or under viewpoint and illumination changes. Most
+LiDAR-based methods rely on geometrical features to overcome such challenges,
+as generally scene geometry is invariant to these changes, but tend to affect
+camera-based solutions significantly. Compared to cameras, however, LiDARs lack
+the strong and descriptive appearance information that imaging can provide.
+To combine the benefits of geometry and appearance, we propose coupling the
+conventional geometric information from the LiDAR with its calibrated intensity
+return. This strategy extracts extremely useful information in the form of a
+new descriptor design, coined ISHOT, outperforming popular state-of-art
+geometric-only descriptors by significant margin in our local descriptor
+evaluation. To complete the framework, we furthermore develop a probabilistic
+keypoint voting place recognition algorithm, leveraging the new descriptor and
+yielding sublinear place recognition performance. The efficacy of our approach
+is validated in challenging global localization experiments in large-scale
+built-up and unstructured environments.
+",1,0,0,0,0,0
+14649,Assessing the performance of self-consistent hybrid functional for band gap calculation in oxide semiconductors,"  In this paper we assess the predictive power of the self-consistent hybrid
+functional scPBE0 in calculating the band gap of oxide semiconductors. The
+computational procedure is based on the self-consistent evaluation of the
+mixing parameter $\alpha$ by means of an iterative calculation of the static
+dielectric constant using the perturbation expansion after discretization
+(PEAD) method and making use of the relation $\alpha = 1/\epsilon_{\infty}$.
+Our materials dataset is formed by 30 compounds covering a wide range of band
+gaps and dielectric properties, and includes materials with a wide spectrum of
+application as thermoelectrics, photocatalysis, photovoltaics, transparent
+conducting oxides, and refractory materials. Our results show that the scPBE0
+functional provides better band gaps than the non self-consistent hybrids PBE0
+and HSE06, but scPBE0 does not show significant improvement on the description
+of the static dielectric constants. Overall, the scPBE0 data exhibit a mean
+absolute percentage error of 14 \% (band gaps) and 10 \% ($\epsilon_\infty$).
+For materials with weak dielectric screening and large excitonic biding
+energies scPBE0, unlike PBE0 and HSE06, overestimates the band gaps, but the
+value of the gap become very close to the experimental value when excitonic
+effects are included (e.g. for SiO$_2$). However, special caution must be given
+to the compounds with small band gaps due to the tendency of scPBE0 to
+overestimate the dielectric constant in proximity of the metallic limit.
+",0,1,0,0,0,0
+14650,Local Synchronization of Sampled-Data Systems on Lie Groups,"  We present a smooth distributed nonlinear control law for local
+synchronization of identical driftless kinematic agents on a Cartesian product
+of matrix Lie groups with a connected communication graph. If the agents are
+initialized sufficiently close to one another, then synchronization is achieved
+exponentially fast. We first analyze the special case of commutative Lie groups
+and show that in exponential coordinates, the closed-loop dynamics are linear.
+We characterize all equilibria of the network and, in the case of an
+unweighted, complete graph, characterize the settling time and conditions for
+deadbeat performance. Using the Baker-Campbell-Hausdorff theorem, we show that,
+in a neighbourhood of the identity element, all results generalize to arbitrary
+matrix Lie groups.
+",1,0,1,0,0,0
+14651,On the orbits that generate the X-shape in the Milky Way bulge,"  The Milky Way bulge shows a box/peanut or X-shaped bulge (hereafter BP/X)
+when viewed in infrared or microwave bands. We examine orbits in an N-body
+model of a barred disk galaxy that is scaled to match the kinematics of the
+Milky Way (MW) bulge. We generate maps of projected stellar surface density,
+unsharp masked images, 3D excess-mass distributions (showing mass outside
+ellipsoids), line-of-sight number count distributions, and 2D line-of-sight
+kinematics for the simulation as well as co-added orbit families, in order to
+identify the orbits primarily responsible for the BP/X shape. We estimate that
+between 19-23\% of the mass of the bar is associated with the BP/X shape and
+that most bar orbits contribute to this shape which is clearly seen in
+projected surface density maps and 3D excess mass for non-resonant box orbits,
+""banana"" orbits, ""fish/pretzel"" orbits and ""brezel"" orbits. {We find that
+nearly all bar orbit families contribute some mass to the 3D BP/X-shape. All
+co-added orbit families show a bifurcation in stellar number count distribution
+with heliocentric distance that resembles the bifurcation observed in red clump
+stars in the MW. However, only the box orbit family shows an increasing
+separation of peaks with increasing galactic latitude $|b|$, similar to that
+observed.} Our analysis shows that no single orbit family fully explains all
+the observed features associated with the MW's BP/X shaped bulge, but
+collectively the non-resonant boxes and various resonant boxlet orbits
+contribute at different distances from the center to produce this feature. We
+propose that since box orbits have three incommensurable orbital fundamental
+frequencies, their 3-dimensional shapes are highly flexible and, like Lissajous
+figures, this family of orbits is most easily able to adapt to evolution in the
+shape of the underlying potential.
+",0,1,0,0,0,0
+14652,How Peer Effects Influence Energy Consumption,"  This paper analyzes the impact of peer effects on electricity consumption of
+a network of rational, utility-maximizing users. Users derive utility from
+consuming electricity as well as consuming less energy than their neighbors.
+However, a disutility is incurred for consuming more than their neighbors. To
+maximize the profit of the load-serving entity that provides electricity to
+such users, we develop a two-stage game-theoretic model, where the entity sets
+the prices in the first stage. In the second stage, consumers decide on their
+demand in response to the observed price set in the first stage so as to
+maximize their utility. To this end, we derive theoretical statements under
+which such peer effects reduce aggregate user consumption. Further, we obtain
+expressions for the resulting electricity consumption and profit of the load
+serving entity for the case of perfect price discrimination and a single price
+under complete information, and approximations under incomplete information.
+Simulations suggest that exposing only a selected subset of all users to peer
+effects maximizes the entity's profit.
+",1,0,0,0,0,0
+14653,Physical Origins of Gas Motions in Galaxy Cluster Cores: Interpreting Hitomi Observations of the Perseus Cluster,"  The Hitomi X-ray satellite has provided the first direct measurements of the
+plasma velocity dispersion in a galaxy cluster. It finds a relatively
+""quiescent"" gas with a line-of-sight velocity dispersion ~ 160 km/s, at 30 kpc
+to 60 kpc from the cluster center. This is surprising given the presence of
+jets and X-ray cavities that indicates on-going activity and feedback from the
+active galactic nucleus (AGN) at the cluster center. Using a set of mock Hitomi
+observations generated from a suite of state-of-the-art cosmological cluster
+simulations, and an isolated but higher resolution simulation of gas physics in
+the cluster core, including the effects of cooling and AGN feedback, we examine
+the likelihood of Hitomi detecting a cluster with the observed velocities. As
+long as the Perseus has not experienced a major merger in the last few
+gigayears, and AGN feedback is operating in a ""gentle"" mode, we reproduce the
+level of gas motions observed by Hitomi. The frequent mechanical AGN feedback
+generates net line-of-sight velocity dispersions ~100-200 km/s, bracketing the
+values measured in the Perseus core. The large-scale velocity shear observed
+across the core, on the other hand, is generated mainly by cosmic accretion
+such as mergers. We discuss the implications of these results for AGN feedback
+physics and cluster cosmology and progress that needs to be made in both
+simulations and observations, including a Hitomi re-flight and
+calorimeter-based instruments with higher spatial resolution.
+",0,1,0,0,0,0
+14654,Variational Approaches for Auto-Encoding Generative Adversarial Networks,"  Auto-encoding generative adversarial networks (GANs) combine the standard GAN
+algorithm, which discriminates between real and model-generated data, with a
+reconstruction loss given by an auto-encoder. Such models aim to prevent mode
+collapse in the learned generative model by ensuring that it is grounded in all
+the available training data. In this paper, we develop a principle upon which
+auto-encoders can be combined with generative adversarial networks by
+exploiting the hierarchical structure of the generative model. The underlying
+principle shows that variational inference can be used a basic tool for
+learning, but with the in- tractable likelihood replaced by a synthetic
+likelihood, and the unknown posterior distribution replaced by an implicit
+distribution; both synthetic likelihoods and implicit posterior distributions
+can be learned using discriminators. This allows us to develop a natural fusion
+of variational auto-encoders and generative adversarial networks, combining the
+best of both these methods. We describe a unified objective for optimization,
+discuss the constraints needed to guide learning, connect to the wide range of
+existing work, and use a battery of tests to systematically and quantitatively
+assess the performance of our method.
+",1,0,0,1,0,0
+14655,Verification of the anecdote about Edwin Hubble and the Nobel Prize,"  Edwin Powel Hubble is regarded as one of the most important astronomers of
+20th century. In despite of his great contributions to the field of astronomy,
+he never received the Nobel Prize because astronomy was not considered as the
+field of the Nobel Prize in Physics at that era. There is an anecdote about the
+relation between Hubble and the Nobel Prize. According to this anecdote, the
+Nobel Committee decided to award the Nobel Prize in Physics in 1953 to Hubble
+as the first Nobel laureate as an astronomer (Christianson 1995). However,
+Hubble was died just before its announcement, and the Nobel prize is not
+awarded posthumously. Documents of the Nobel selection committee are open after
+50 years, thus this anecdote can be verified. I confirmed that the Nobel
+selection committee endorsed Frederik Zernike as the Nobel laureate in Physics
+in 1953 on September 15th, 1953, which is 13 days before the Hubble's death in
+September 28th, 1953. I also confirmed that Hubble and Henry Norris Russell
+were nominated but they are not endorsed because the Committee concluded their
+astronomical works were not appropriate for the Nobel Prize in Physics.
+",0,1,0,0,0,0
+14656,LSTM Fully Convolutional Networks for Time Series Classification,"  Fully convolutional neural networks (FCN) have been shown to achieve
+state-of-the-art performance on the task of classifying time series sequences.
+We propose the augmentation of fully convolutional networks with long short
+term memory recurrent neural network (LSTM RNN) sub-modules for time series
+classification. Our proposed models significantly enhance the performance of
+fully convolutional networks with a nominal increase in model size and require
+minimal preprocessing of the dataset. The proposed Long Short Term Memory Fully
+Convolutional Network (LSTM-FCN) achieves state-of-the-art performance compared
+to others. We also explore the usage of attention mechanism to improve time
+series classification with the Attention Long Short Term Memory Fully
+Convolutional Network (ALSTM-FCN). Utilization of the attention mechanism
+allows one to visualize the decision process of the LSTM cell. Furthermore, we
+propose fine-tuning as a method to enhance the performance of trained models.
+An overall analysis of the performance of our model is provided and compared to
+other techniques.
+",1,0,0,1,0,0
+14657,"Flat $F$-manifolds, Miura invariants and integrable systems of conservation laws","  We extend some of the results proved for scalar equations in [3,4], to the
+case of systems of integrable conservation laws. In particular, for such
+systems we prove that the eigenvalues of a matrix obtained from the quasilinear
+part of the system are invariants under Miura transformations and we show how
+these invariants are related to dispersion relations. Furthermore, focusing on
+one-parameter families of dispersionless systems of integrable conservation
+laws associated to the Coxeter groups of rank $2$ found in [1], we study the
+corresponding integrable deformations up to order $2$ in the deformation
+parameter $\epsilon$. Each family contains both bi-Hamiltonian and
+non-Hamiltonian systems of conservation laws and therefore we use it to probe
+to which extent the properties of the dispersionless limit impact the nature
+and the existence of integrable deformations. It turns out that a part two
+values of the parameter all deformations of order one in $\epsilon$ are
+Miura-trivial, while all those of order two in $\epsilon$ are essentially
+parameterized by two arbitrary functions of single variables (the Riemann
+invariants) both in the bi-Hamiltonian and in the non-Hamiltonian case. In the
+two remaining cases, due to the existence of non-trivial first order
+deformations, there is an additional functional parameter.
+",0,1,0,0,0,0
+14658,"Analysis and Design of Cost-Effective, High-Throughput LDPC Decoders","  This paper introduces a new approach to cost-effective, high-throughput
+hardware designs for Low Density Parity Check (LDPC) decoders. The proposed
+approach, called Non-Surjective Finite Alphabet Iterative Decoders (NS-FAIDs),
+exploits the robustness of message-passing LDPC decoders to inaccuracies in the
+calculation of exchanged messages, and it is shown to provide a unified
+framework for several designs previously proposed in the literature. NS-FAIDs
+are optimized by density evolution for regular and irregular LDPC codes, and
+are shown to provide different trade-offs between hardware complexity and
+decoding performance. Two hardware architectures targeting high-throughput
+applications are also proposed, integrating both Min-Sum (MS) and NS-FAID
+decoding kernels. ASIC post synthesis implementation results on 65nm CMOS
+technology show that NS-FAIDs yield significant improvements in the throughput
+to area ratio, by up to 58.75% with respect to the MS decoder, with even better
+or only slightly degraded error correction performance.
+",1,0,0,0,0,0
+14659,TensorFlow Distributions,"  The TensorFlow Distributions library implements a vision of probability
+theory adapted to the modern deep-learning paradigm of end-to-end
+differentiable computation. Building on two basic abstractions, it offers
+flexible building blocks for probabilistic computation. Distributions provide
+fast, numerically stable methods for generating samples and computing
+statistics, e.g., log density. Bijectors provide composable volume-tracking
+transformations with automatic caching. Together these enable modular
+construction of high dimensional distributions and transformations not possible
+with previous libraries (e.g., pixelCNNs, autoregressive flows, and reversible
+residual networks). They are the workhorse behind deep probabilistic
+programming systems like Edward and empower fast black-box inference in
+probabilistic models built on deep-network components. TensorFlow Distributions
+has proven an important part of the TensorFlow toolkit within Google and in the
+broader deep learning community.
+",1,0,0,1,0,0
+14660,A generalised Davydov-Scott model for polarons in linear peptide chains,"  We present a one-parameter family of mathematical models describing the
+dynamics of polarons in linear periodic structures such as polypeptides. By
+tuning the parameter, we are able to recover the Davydov and the Scott models.
+We describe the physical significance of this parameter. In the continuum
+limit, we derive analytical solutions which represent stationary polarons. On a
+discrete lattice, we compute stationary polaron solutions numerically. We
+investigate polaron propagation induced by several external forcing mechanisms.
+We show that an electric field consisting of a constant and a periodic
+component can induce polaron motion with minimal energy loss. We also show that
+thermal fluctuations can facilitate the onset of polaron motion. Finally, we
+discuss the bio-physical implications of our results.
+",0,1,0,0,0,0
+14661,Wide Binaries in Tycho-{\it Gaia}: Search Method and the Distribution of Orbital Separations,"  We mine the Tycho-{\it Gaia} astrometric solution (TGAS) catalog for wide
+stellar binaries by matching positions, proper motions, and astrometric
+parallaxes. We separate genuine binaries from unassociated stellar pairs
+through a Bayesian formulation that includes correlated uncertainties in the
+proper motions and parallaxes. Rather than relying on assumptions about the
+structure of the Galaxy, we calculate Bayesian priors and likelihoods based on
+the nature of Keplerian orbits and the TGAS catalog itself. We calibrate our
+method using radial velocity measurements and obtain 6196 high-confidence
+candidate wide binaries with projected separations $s\lesssim1$ pc. The
+normalization of this distribution suggests that at least 0.6\% of TGAS stars
+have an associated, distant TGAS companion in a wide binary. We demonstrate
+that {\it Gaia}'s astrometry is precise enough that it can detect projected
+orbital velocities in wide binaries with orbital periods as large as 10$^6$ yr.
+For pairs with $s\ \lesssim\ 4\times10^4$~AU, characterization of random
+alignments indicate our contamination to be $\approx$5\%. For $s \lesssim
+5\times10^3$~AU, our distribution is consistent with Öpik's Law. At larger
+separations, the distribution is steeper and consistent with a power-law
+$P(s)\propto s^{-1.6}$; there is no evidence in our data of any bimodality in
+this distribution for $s \lesssim$ 1 pc. Using radial velocities, we
+demonstrate that at large separations, i.e., of order $s \sim$ 1 pc and beyond,
+any potential sample of genuine wide binaries in TGAS cannot be easily
+distinguished from ionized former wide binaries, moving groups, or
+contamination from randomly aligned stars.
+",0,1,0,0,0,0
+14662,Classification on Large Networks: A Quantitative Bound via Motifs and Graphons,"  When each data point is a large graph, graph statistics such as densities of
+certain subgraphs (motifs) can be used as feature vectors for machine learning.
+While intuitive, motif counts are expensive to compute and difficult to work
+with theoretically. Via graphon theory, we give an explicit quantitative bound
+for the ability of motif homomorphisms to distinguish large networks under both
+generative and sampling noise. Furthermore, we give similar bounds for the
+graph spectrum and connect it to homomorphism densities of cycles. This results
+in an easily computable classifier on graph data with theoretical performance
+guarantee. Our method yields competitive results on classification tasks for
+the autoimmune disease Lupus Erythematosus.
+",1,0,0,1,0,0
+14663,Predicting Future Machine Failure from Machine State Using Logistic Regression,"  Accurately predicting machine failures in advance can decrease maintenance
+cost and help allocate maintenance resources more efficiently. Logistic
+regression was applied to predict machine state 24 hours in the future given
+the current machine state.
+",0,0,0,1,0,0
+14664,Design and Optimisation of the FlyFast Front-end for Attribute-based Coordination,"  Collective Adaptive Systems (CAS) consist of a large number of interacting
+objects. The design of such systems requires scalable analysis tools and
+methods, which have necessarily to rely on some form of approximation of the
+system's actual behaviour. Promising techniques are those based on mean-field
+approximation. The FlyFast model-checker uses an on-the-fly algorithm for
+bounded PCTL model-checking of selected individual(s) in the context of very
+large populations whose global behaviour is approximated using deterministic
+limit mean-field techniques. Recently, a front-end for FlyFast has been
+proposed which provides a modelling language, PiFF in the sequel, for the
+Predicate-based Interaction for FlyFast. In this paper we present details of
+PiFF design and an approach to state-space reduction based on probabilistic
+bisimulation for inhomogeneous DTMCs.
+",1,0,0,0,0,0
+14665,Distributed rank-1 dictionary learning: Towards fast and scalable solutions for fMRI big data analytics,"  The use of functional brain imaging for research and diagnosis has benefitted
+greatly from the recent advancements in neuroimaging technologies, as well as
+the explosive growth in size and availability of fMRI data. While it has been
+shown in literature that using multiple and large scale fMRI datasets can
+improve reproducibility and lead to new discoveries, the computational and
+informatics systems supporting the analysis and visualization of such fMRI big
+data are extremely limited and largely under-discussed. We propose to address
+these shortcomings in this work, based on previous success in using dictionary
+learning method for functional network decomposition studies on fMRI data. We
+presented a distributed dictionary learning framework based on rank-1 matrix
+decomposition with sparseness constraint (D-r1DL framework). The framework was
+implemented using the Spark distributed computing engine and deployed on three
+different processing units: an in-house server, in-house high performance
+clusters, and the Amazon Elastic Compute Cloud (EC2) service. The whole
+analysis pipeline was integrated with our neuroinformatics system for data
+management, user input/output, and real-time visualization. Performance and
+accuracy of D-r1DL on both individual and group-wise fMRI Human Connectome
+Project (HCP) dataset shows that the proposed framework is highly scalable. The
+resulting group-wise functional network decompositions are highly accurate, and
+the fast processing time confirm this claim. In addition, D-r1DL can provide
+real-time user feedback and results visualization which are vital for
+large-scale data analysis.
+",1,0,0,0,0,0
+14666,"HARPO: 1.7 - 74 MeV gamma-ray beam validation of a high angular resolutio n, high linear polarisation dilution, gas time projection chamber telescope and polarimeter","  A presentation at the SciNeGHE conference of the past achievements, of the
+present activities and of the perspectives for the future of the HARPO project,
+the development of a time projection chamber as a high-performance gamma-ray
+telescope and linear polarimeter in the e+e- pair creation regime.
+",0,1,0,0,0,0
+14667,"One-to-one composant mappings of $[0,\infty)$ and $(-\infty,\infty)$","  Knaster continua and solenoids are well-known examples of indecomposable
+continua whose composants (maximal arcwise-connected subsets) are one-to-one
+images of lines. We show that essentially all non-trivial one-to-one composant
+images of (half-)lines are indecomposable. And if $f$ is a one-to-one mapping
+of $[0,\infty)$ or $(-\infty,\infty)$, then there is an indecomposable
+continuum of which $X:=$ran$(f)$ is a composant if and only if $f$ maps all
+final or initial segments densely and every non-closed sequence of arcs in $X$
+has a convergent subsequence in the hyperspace $K(X)\cup \{X\}$. We also prove
+the existence of composant-preserving embeddings in Euclidean $3$-space.
+Accompanying the proofs are illustrations and examples.
+",0,0,1,0,0,0
+14668,"Valued fields, Metastable groups","  We introduce a class of theories called metastable, including the theory of
+algebraically closed valued fields (ACVF) as a motivating example. The key
+local notion is that of definable types dominated by their stable part. A
+theory is metastable (over a sort $\Gamma$) if every type over a sufficiently
+rich base structure can be viewed as part of a $\Gamma$-parametrized family of
+stably dominated types. We initiate a study of definable groups in metastable
+theories of finite rank. Groups with a stably dominated generic type are shown
+to have a canonical stable quotient. Abelian groups are shown to be
+decomposable into a part coming from $\Gamma$, and a definable direct limit
+system of groups with stably dominated generic. In the case of ACVF, among
+definable subgroups of affine algebraic groups, we characterize the groups with
+stably dominated generics in terms of group schemes over the valuation ring.
+Finally, we classify all fields definable in ACVF.
+",0,0,1,0,0,0
+14669,Radio Galaxy Zoo: Cosmological Alignment of Radio Sources,"  We study the mutual alignment of radio sources within two surveys, FIRST and
+TGSS. This is done by producing two position angle catalogues containing the
+preferential directions of respectively $30\,059$ and $11\,674$ extended
+sources distributed over more than $7\,000$ and $17\,000$ square degrees. The
+identification of the sources in the FIRST sample was performed in advance by
+volunteers of the Radio Galaxy Zoo project, while for the TGSS sample it is the
+result of an automated process presented here. After taking into account
+systematic effects, marginal evidence of a local alignment on scales smaller
+than $2.5°$ is found in the FIRST sample. The probability of this happening
+by chance is found to be less than $2$ per cent. Further study suggests that on
+scales up to $1.5°$ the alignment is maximal. For one third of the sources,
+the Radio Galaxy Zoo volunteers identified an optical counterpart. Assuming a
+flat $\Lambda$CDM cosmology with $\Omega_m = 0.31, \Omega_\Lambda = 0.69$, we
+convert the maximum angular scale on which alignment is seen into a physical
+scale in the range $[19, 38]$ Mpc $h_{70}^{-1}$. This result supports recent
+evidence reported by Taylor and Jagannathan of radio jet alignment in the $1.4$
+deg$^2$ ELAIS N1 field observed with the Giant Metrewave Radio Telescope. The
+TGSS sample is found to be too sparsely populated to manifest a similar signal.
+",0,1,0,0,0,0
+14670,Non-iterative Label Propagation in Optimal Leading Forest,"  Graph based semi-supervised learning (GSSL) has intuitive representation and
+can be improved by exploiting the matrix calculation. However, it has to
+perform iterative optimization to achieve a preset objective, which usually
+leads to low efficiency. Another inconvenience lying in GSSL is that when new
+data come, the graph construction and the optimization have to be conducted all
+over again. We propose a sound assumption, arguing that: the neighboring data
+points are not in peer-to-peer relation, but in a partial-ordered relation
+induced by the local density and distance between the data; and the label of a
+center can be regarded as the contribution of its followers. Starting from the
+assumption, we develop a highly efficient non-iterative label propagation
+algorithm based on a novel data structure named as optimal leading forest
+(LaPOLeaF). The major weaknesses of the traditional GSSL are addressed by this
+study. We further scale LaPOLeaF to accommodate big data by utilizing block
+distance matrix technique, parallel computing, and Locality-Sensitive Hashing
+(LSH). Experiments on large datasets have shown the promising results of the
+proposed methods.
+",1,0,0,0,0,0
+14671,PeerHunter: Detecting Peer-to-Peer Botnets through Community Behavior Analysis,"  Peer-to-peer (P2P) botnets have become one of the major threats in network
+security for serving as the infrastructure that responsible for various of
+cyber-crimes. Though a few existing work claimed to detect traditional botnets
+effectively, the problem of detecting P2P botnets involves more challenges. In
+this paper, we present PeerHunter, a community behavior analysis based method,
+which is capable of detecting botnets that communicate via a P2P structure.
+PeerHunter starts from a P2P hosts detection component. Then, it uses mutual
+contacts as the main feature to cluster bots into communities. Finally, it uses
+community behavior analysis to detect potential botnet communities and further
+identify bot candidates. Through extensive experiments with real and simulated
+network traces, PeerHunter can achieve very high detection rate and low false
+positives.
+",1,0,0,0,0,0
+14672,An X-ray/SDSS sample (II): outflowing gas plasma properties,"  Galaxy-scale outflows are nowadays observed in many active galactic nuclei
+(AGNs); however, their characterisation in terms of (multi-) phase nature,
+amount of flowing material, effects on the host galaxy, is still unsettled. In
+particular, ionized gas mass outflow rate and related energetics are still
+affected by many sources of uncertainties. In this respect, outflowing gas
+plasma conditions, being largely unknown, play a crucial role.
+Taking advantage of the spectroscopic analysis results we obtained studying
+the X-ray/SDSS sample of 563 AGNs at z $<0.8$ presented in our companion paper,
+we analyse stacked spectra and sub-samples of sources with high signal-to-noise
+temperature- and density-sensitive emission lines to derive the plasma
+properties of the outflowing ionized gas component. For these sources, we also
+study in detail various diagnostic diagrams to infer information about
+outflowing gas ionization mechanisms. We derive, for the first time, median
+values for electron temperature and density of outflowing gas from medium-size
+samples ($\sim 30$ targets) and stacked spectra of AGNs. Evidences of shock
+excitation are found for outflowing gas.
+We measure electron temperatures of the order of $\sim 1.7\times10^4$ K and
+densities of $\sim 1200$ cm$^{-3}$ for faint and moderately luminous AGNs
+(intrinsic X-ray luminosity $40.5<log(L_X)<44$ in the 2-10 keV band). We
+caution that the usually assumed electron density ($N_e=100$ cm$^{-3}$) in
+ejected material might result in relevant overestimates of flow mass rates and
+energetics and, as a consequence, of the effects of AGN-driven outflows on the
+host galaxy.
+",0,1,0,0,0,0
+14673,ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models,"  Deep neural networks (DNNs) are one of the most prominent technologies of our
+time, as they achieve state-of-the-art performance in many machine learning
+tasks, including but not limited to image classification, text mining, and
+speech processing. However, recent research on DNNs has indicated
+ever-increasing concern on the robustness to adversarial examples, especially
+for security-critical tasks such as traffic sign identification for autonomous
+driving. Studies have unveiled the vulnerability of a well-trained DNN by
+demonstrating the ability of generating barely noticeable (to both human and
+machines) adversarial images that lead to misclassification. Furthermore,
+researchers have shown that these adversarial images are highly transferable by
+simply training and attacking a substitute model built upon the target model,
+known as a black-box attack to DNNs.
+Similar to the setting of training substitute models, in this paper we
+propose an effective black-box attack that also only has access to the input
+(images) and the output (confidence scores) of a targeted DNN. However,
+different from leveraging attack transferability from substitute models, we
+propose zeroth order optimization (ZOO) based attacks to directly estimate the
+gradients of the targeted DNN for generating adversarial examples. We use
+zeroth order stochastic coordinate descent along with dimension reduction,
+hierarchical attack and importance sampling techniques to efficiently attack
+black-box models. By exploiting zeroth order optimization, improved attacks to
+the targeted DNN can be accomplished, sparing the need for training substitute
+models and avoiding the loss in attack transferability. Experimental results on
+MNIST, CIFAR10 and ImageNet show that the proposed ZOO attack is as effective
+as the state-of-the-art white-box attack and significantly outperforms existing
+black-box attacks via substitute models.
+",1,0,0,1,0,0
+14674,"Existence of Noise Induced Order, a Computer Aided Proof","  We prove, by a computer aided proof, the existence of noise induced order in
+the model of chaotic chemical reactions where it was first discovered
+numerically by Matsumoto and Tsuda in 1983. We prove that in this random
+dynamical system the increase in amplitude of the noise causes the Lyapunov
+exponent to decrease from positive to negative, stabilizing the system. The
+method used is based on a certified approximation of the stationary measure in
+the $L^{1}$ norm. This is done by an efficient algorithm which is general
+enough to be adapted to any piecewise differentiable dynamical system on the
+interval with additive noise. We also prove that the stationary measure of the
+system and its Lyapunov exponent have a Lipschitz stability under several kinds
+of perturbation of the noise and of the system itself. The Lipschitz constants
+of this stability result are also estimated explicitly.
+",0,0,1,0,0,0
+14675,Residual Unfairness in Fair Machine Learning from Prejudiced Data,"  Recent work in fairness in machine learning has proposed adjusting for
+fairness by equalizing accuracy metrics across groups and has also studied how
+datasets affected by historical prejudices may lead to unfair decision
+policies. We connect these lines of work and study the residual unfairness that
+arises when a fairness-adjusted predictor is not actually fair on the target
+population due to systematic censoring of training data by existing biased
+policies. This scenario is particularly common in the same applications where
+fairness is a concern. We characterize theoretically the impact of such
+censoring on standard fairness metrics for binary classifiers and provide
+criteria for when residual unfairness may or may not appear. We prove that,
+under certain conditions, fairness-adjusted classifiers will in fact induce
+residual unfairness that perpetuates the same injustices, against the same
+groups, that biased the data to begin with, thus showing that even
+state-of-the-art fair machine learning can have a ""bias in, bias out"" property.
+When certain benchmark data is available, we show how sample reweighting can
+estimate and adjust fairness metrics while accounting for censoring. We use
+this to study the case of Stop, Question, and Frisk (SQF) and demonstrate that
+attempting to adjust for fairness perpetuates the same injustices that the
+policy is infamous for.
+",0,0,0,1,0,0
+14676,Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems,"  Predicting the future location of vehicles is essential for safety-critical
+applications such as advanced driver assistance systems (ADAS) and autonomous
+driving. This paper introduces a novel approach to simultaneously predict both
+the location and scale of target vehicles in the first-person (egocentric) view
+of an ego-vehicle. We present a multi-stream recurrent neural network (RNN)
+encoder-decoder model that separately captures both object location and scale
+and pixel-level observations for future vehicle localization. We show that
+incorporating dense optical flow improves prediction results significantly
+since it captures information about motion as well as appearance change. We
+also find that explicitly modeling future motion of the ego-vehicle improves
+the prediction accuracy, which could be especially beneficial in intelligent
+and automated vehicles that have motion planning capability. To evaluate the
+performance of our approach, we present a new dataset of first-person videos
+collected from a variety of scenarios at road intersections, which are
+particularly challenging moments for prediction because vehicle trajectories
+are diverse and dynamic.
+",1,0,0,0,0,0
+14677,Metriplectic formalism: friction and much more,"  The metriplectic formalism couples Poisson brackets of the Hamiltonian
+description with metric brackets for describing systems with both Hamiltonian
+and dissipative components. The construction builds in asymptotic convergence
+to a preselected equilibrium state. Phenomena such as friction, electric
+resistivity, thermal conductivity and collisions in kinetic theories are well
+represented in this framework. In this paper we present an application of the
+metriplectic formalism of interest for the theory of control: a suitable torque
+is applied to a free rigid body, which is expressed through a metriplectic
+extension of its ""natural"" Poisson algebra. On practical grounds, the effect is
+to drive the body to align its angular velocity to rotation about a stable
+principal axis of inertia, while conserving its kinetic energy in the process.
+On theoretical grounds, this example shows how the non-Hamiltonian part of a
+metriplectic system may include convergence to a limit cycle, the first example
+of a non-zero dimensional attractor in this formalism. The method suggests a
+way to extend metriplectic dynamics to systems with general attractors, e.g.
+chaotic ones, with the hope of representing bio-physical, geophysical and
+ecological models.
+",0,1,0,0,0,0
+14678,Hierarchical Video Understanding,"  We introduce a hierarchical architecture for video understanding that
+exploits the structure of real world actions by capturing targets at different
+levels of granularity. We design the model such that it first learns simpler
+coarse-grained tasks, and then moves on to learn more fine-grained targets. The
+model is trained with a joint loss on different granularity levels. We
+demonstrate empirical results on the recent release of Something-Something
+dataset, which provides a hierarchy of targets, namely coarse-grained action
+groups, fine-grained action categories, and captions. Experiments suggest that
+models that exploit targets at different levels of granularity achieve better
+performance on all levels.
+",0,0,0,1,0,0
+14679,When Hashes Met Wedges: A Distributed Algorithm for Finding High Similarity Vectors,"  Finding similar user pairs is a fundamental task in social networks, with
+numerous applications in ranking and personalization tasks such as link
+prediction and tie strength detection. A common manifestation of user
+similarity is based upon network structure: each user is represented by a
+vector that represents the user's network connections, where pairwise cosine
+similarity among these vectors defines user similarity. The predominant task
+for user similarity applications is to discover all similar pairs that have a
+pairwise cosine similarity value larger than a given threshold $\tau$. In
+contrast to previous work where $\tau$ is assumed to be quite close to 1, we
+focus on recommendation applications where $\tau$ is small, but still
+meaningful. The all pairs cosine similarity problem is computationally
+challenging on networks with billions of edges, and especially so for settings
+with small $\tau$. To the best of our knowledge, there is no practical solution
+for computing all user pairs with, say $\tau = 0.2$ on large social networks,
+even using the power of distributed algorithms.
+Our work directly addresses this challenge by introducing a new algorithm ---
+WHIMP --- that solves this problem efficiently in the MapReduce model. The key
+insight in WHIMP is to combine the ""wedge-sampling"" approach of Cohen-Lewis for
+approximate matrix multiplication with the SimHash random projection techniques
+of Charikar. We provide a theoretical analysis of WHIMP, proving that it has
+near optimal communication costs while maintaining computation cost comparable
+with the state of the art. We also empirically demonstrate WHIMP's scalability
+by computing all highly similar pairs on four massive data sets, and show that
+it accurately finds high similarity pairs. In particular, we note that WHIMP
+successfully processes the entire Twitter network, which has tens of billions
+of edges.
+",1,0,0,0,0,0
+14680,Hysteretic vortex matching effects in high-$T_c$ superconductors with nanoscale periodic pinning landscapes fabricated by He ion beam projection technique,"  Square arrays of sub-micrometer columnar defects in thin
+YBa$_{2}$Cu$_{3}$O$_{7-\delta}$ (YBCO) films with spacings down to 300 nm have
+been fabricated by a He ion beam projection technique. Pronounced peaks in the
+critical current and corresponding minima in the resistance demonstrate the
+commensurate arrangement of flux quanta with the artificial pinning landscape,
+despite the strong intrinsic pinning in epitaxial YBCO films. Whereas these
+vortex matching signatures are exactly at predicted values in field-cooled
+experiments, they are displaced in zero-field cooled, magnetic-field ramped
+experiments, conserving the equidistance of the matching peaks and minima.
+These observations reveal an unconventional critical state in a cuprate
+superconductor with an artificial, periodic pinning array. The long-term
+stability of such out-of-equilibrium vortex arrangements paves the way for
+electronic applications employing fluxons.
+",0,1,0,0,0,0
+14681,Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping,"  Gathering information about forest variables is an expensive and arduous
+activity. As such, directly collecting the data required to produce
+high-resolution maps over large spatial domains is infeasible. Next generation
+collection initiatives of remotely sensed Light Detection and Ranging (LiDAR)
+data are specifically aimed at producing complete-coverage maps over large
+spatial domains. Given that LiDAR data and forest characteristics are often
+strongly correlated, it is possible to make use of the former to model,
+predict, and map forest variables over regions of interest. This entails
+dealing with the high-dimensional ($\sim$$10^2$) spatially dependent LiDAR
+outcomes over a large number of locations (~10^5-10^6). With this in mind, we
+develop the Spatial Factor Nearest Neighbor Gaussian Process (SF-NNGP) model,
+and embed it in a two-stage approach that connects the spatial structure found
+in LiDAR signals with forest variables. We provide a simulation experiment that
+demonstrates inferential and predictive performance of the SF-NNGP, and use the
+two-stage modeling strategy to generate complete-coverage maps of forest
+variables with associated uncertainty over a large region of boreal forests in
+interior Alaska.
+",0,0,0,1,0,0
+14682,Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction,"  Recently, many studies have demonstrated deep neural network (DNN)
+classifiers can be fooled by the adversarial example, which is crafted via
+introducing some perturbations into an original sample. Accordingly, some
+powerful defense techniques were proposed. However, existing defense techniques
+often require modifying the target model or depend on the prior knowledge of
+attacks. In this paper, we propose a straightforward method for detecting
+adversarial image examples, which can be directly deployed into unmodified
+off-the-shelf DNN models. We consider the perturbation to images as a kind of
+noise and introduce two classic image processing techniques, scalar
+quantization and smoothing spatial filter, to reduce its effect. The image
+entropy is employed as a metric to implement an adaptive noise reduction for
+different kinds of images. Consequently, the adversarial example can be
+effectively detected by comparing the classification results of a given sample
+and its denoised version, without referring to any prior knowledge of attacks.
+More than 20,000 adversarial examples against some state-of-the-art DNN models
+are used to evaluate the proposed method, which are crafted with different
+attack techniques. The experiments show that our detection method can achieve a
+high overall F1 score of 96.39% and certainly raises the bar for defense-aware
+attacks.
+",1,0,0,0,0,0
+14683,Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion Lexicon,"  Sentiment analysis aims to uncover emotions conveyed through information. In
+its simplest form, it is performed on a polarity basis, where the goal is to
+classify information with positive or negative emotion. Recent research has
+explored more nuanced ways to capture emotions that go beyond polarity. For
+these methods to work, they require a critical resource: a lexicon that is
+appropriate for the task at hand, in terms of the range of emotions it captures
+diversity. In the past, sentiment analysis lexicons have been created by
+experts, such as linguists and behavioural scientists, with strict rules.
+Lexicon evaluation was also performed by experts or gold standards. In our
+paper, we propose a crowdsourcing method for lexicon acquisition, which is
+scalable, cost-effective, and doesn't require experts or gold standards. We
+also compare crowd and expert evaluations of the lexicon, to assess the overall
+lexicon quality, and the evaluation capabilities of the crowd.
+",1,0,0,0,0,0
+14684,"Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging","  We address the statistical and optimization impacts of the classical sketch
+and Hessian sketch used to approximately solve the Matrix Ridge Regression
+(MRR) problem. Prior research has quantified the effects of classical sketch on
+the strictly simpler least squares regression (LSR) problem. We establish that
+classical sketch has a similar effect upon the optimization properties of MRR
+as it does on those of LSR: namely, it recovers nearly optimal solutions. By
+contrast, Hessian sketch does not have this guarantee, instead, the
+approximation error is governed by a subtle interplay between the ""mass"" in the
+responses and the optimal objective value.
+For both types of approximation, the regularization in the sketched MRR
+problem results in significantly different statistical properties from those of
+the sketched LSR problem. In particular, there is a bias-variance trade-off in
+sketched MRR that is not present in sketched LSR. We provide upper and lower
+bounds on the bias and variance of sketched MRR, these bounds show that
+classical sketch significantly increases the variance, while Hessian sketch
+significantly increases the bias. Empirically, sketched MRR solutions can have
+risks that are higher by an order-of-magnitude than those of the optimal MRR
+solutions.
+We establish theoretically and empirically that model averaging greatly
+decreases the gap between the risks of the true and sketched solutions to the
+MRR problem. Thus, in parallel or distributed settings, sketching combined with
+model averaging is a powerful technique that quickly obtains near-optimal
+solutions to the MRR problem while greatly mitigating the increased statistical
+risk incurred by sketching.
+",1,0,0,1,0,0
+14685,Ballistic magnon heat conduction and possible Poiseuille flow in the helimagnetic insulator Cu$_2$OSeO$_3$,"  We report on the observation of magnon thermal conductivity $\kappa_m\sim$ 70
+W/mK near 5 K in the helimagnetic insulator Cu$_2$OSeO$_3$, exceeding that
+measured in any other ferromagnet by almost two orders of magnitude. Ballistic,
+boundary-limited transport for both magnons and phonons is established below 1
+K, and Poiseuille flow of magnons is proposed to explain a magnon mean-free
+path substantially exceeding the specimen width for the least defective
+specimens in the range 2 K $<T<$ 10 K. These observations establish
+Cu$_2$OSeO$_3$ as a model system for studying long-wavelength magnon dynamics.
+",0,1,0,0,0,0
+14686,On Alzer's inequality,"  Extensions and generalizations of Alzer's inequality; which is of Wirtinger
+type are proved. As applications, sharp trapezoid type inequality and sharp
+bound for the geometric mean are deduced.
+",0,0,1,0,0,0
+14687,Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs,"  We present Oncilla robot, a novel mobile, quadruped legged locomotion
+machine. This large-cat sized, 5.1 robot is one of a kind of a recent,
+bioinspired legged robot class designed with the capability of model-free
+locomotion control. Animal legged locomotion in rough terrain is clearly shaped
+by sensor feedback systems. Results with Oncilla robot show that agile and
+versatile locomotion is possible without sensory signals to some extend, and
+tracking becomes robust when feedback control is added (Ajaoolleian 2015). By
+incorporating mechanical and control blueprints inspired from animals, and by
+observing the resulting robot locomotion characteristics, we aim to understand
+the contribution of individual components. Legged robots have a wide mechanical
+and control design parameter space, and a unique potential as research tools to
+investigate principles of biomechanics and legged locomotion control. But the
+hardware and controller design can be a steep initial hurdle for academic
+research. To facilitate the easy start and development of legged robots,
+Oncilla-robot's blueprints are available through open-source. [...]
+",1,0,0,0,0,0
+14688,"A generative model for sparse, evolving digraphs","  Generating graphs that are similar to real ones is an open problem, while the
+similarity notion is quite elusive and hard to formalize. In this paper, we
+focus on sparse digraphs and propose SDG, an algorithm that aims at generating
+graphs similar to real ones. Since real graphs are evolving and this evolution
+is important to study in order to understand the underlying dynamical system,
+we tackle the problem of generating series of graphs. We propose SEDGE, an
+algorithm meant to generate series of graphs similar to a real series. SEDGE is
+an extension of SDG. We consider graphs that are representations of software
+programs and show experimentally that our approach outperforms other existing
+approaches. Experiments show the performance of both algorithms.
+",1,0,0,0,0,0
+14689,Penalized Interaction Estimation for Ultrahigh Dimensional Quadratic Regression,"  Quadratic regression goes beyond the linear model by simultaneously including
+main effects and interactions between the covariates. The problem of
+interaction estimation in high dimensional quadratic regression has received
+extensive attention in the past decade. In this article we introduce a novel
+method which allows us to estimate the main effects and interactions
+separately. Unlike existing methods for ultrahigh dimensional quadratic
+regressions, our proposal does not require the widely used heredity assumption.
+In addition, our proposed estimates have explicit formulas and obey the
+invariance principle at the population level. We estimate the interactions of
+matrix form under penalized convex loss function. The resulting estimates are
+shown to be consistent even when the covariate dimension is an exponential
+order of the sample size. We develop an efficient ADMM algorithm to implement
+the penalized estimation. This ADMM algorithm fully explores the cheap
+computational cost of matrix multiplication and is much more efficient than
+existing penalized methods such as all pairs LASSO. We demonstrate the
+promising performance of our proposal through extensive numerical studies.
+",0,0,0,1,0,0
+14690,4D limit of melting crystal model and its integrable structure,"  This paper addresses the problems of quantum spectral curves and 4D limit for
+the melting crystal model of 5D SUSY $U(1)$ Yang-Mills theory on
+$\mathbb{R}^4\times S^1$. The partition function $Z(\mathbf{t})$ deformed by an
+infinite number of external potentials is a tau function of the KP hierarchy
+with respect to the coupling constants $\mathbf{t} = (t_1,t_2,\ldots)$. A
+single-variate specialization $Z(x)$ of $Z(\mathbf{t})$ satisfies a
+$q$-difference equation representing the quantum spectral curve of the melting
+crystal model. In the limit as the radius $R$ of $S^1$ in $\mathbb{R}^4\times
+S^1$ tends to $0$, it turns into a difference equation for a 4D counterpart
+$Z_{\mathrm{4D}}(X)$ of $Z(x)$. This difference equation reproduces the quantum
+spectral curve of Gromov-Witten theory of $\mathbb{CP}^1$. $Z_{\mathrm{4D}}(X)$
+is obtained from $Z(x)$ by letting $R \to 0$ under an $R$-dependent
+transformation $x = x(X,R)$ of $x$ to $X$. A similar prescription of 4D limit
+can be formulated for $Z(\mathbf{t})$ with an $R$-dependent transformation
+$\mathbf{t} = \mathbf{t}(\mathbf{T},R)$ of $\mathbf{t}$ to $\mathbf{T} =
+(T_1,T_2,\ldots)$. This yields a 4D counterpart $Z_{\mathrm{4D}}(\mathbf{T})$
+of $Z(\mathbf{t})$. $Z_{\mathrm{4D}}(\mathbf{T})$ agrees with a generating
+function of all-genus Gromov-Witten invariants of $\mathbb{CP}^1$. Fay-type
+bilinear equations for $Z_{\mathrm{4D}}(\mathbf{T})$ can be derived from
+similar equations satisfied by $Z(\mathbf{t})$. The bilinear equations imply
+that $Z_{\mathrm{4D}}(\mathbf{T})$, too, is a tau function of the KP hierarchy.
+These results are further extended to deformations $Z(\mathbf{t},s)$ and
+$Z_{\mathrm{4D}}(\mathbf{T},s)$ by a discrete variable $s \in \mathbb{Z}$,
+which are shown to be tau functions of the 1D Toda hierarchy.
+",0,1,1,0,0,0
+14691,On constraints and dividing in ternary homogeneous structures,"  Let M be ternary, homogeneous and simple. We prove that if M is finitely
+constrained, then it is supersimple with finite SU-rank and dependence is
+$k$-trivial for some $k < \omega$ and for finite sets of real elements. Now
+suppose that, in addition, M is supersimple with SU-rank 1. If M is finitely
+constrained then algebraic closure in M is trivial. We also find connections
+between the nature of the constraints of M, the nature of the amalgamations
+allowed by the age of M, and the nature of definable equivalence relations. A
+key method of proof is to ""extract"" constraints (of M) from instances of
+dividing and from definable equivalence relations. Finally, we give new
+examples, including an uncountable family, of ternary homogeneous supersimple
+structures of SU-rank 1.
+",0,0,1,0,0,0
+14692,Changes in lipid membranes may trigger amyloid toxicity in Alzheimer's disease,"  Amyloid beta peptides (A\b{eta}), implicated in Alzheimers disease (AD),
+interact with the cellular membrane and induce amyloid toxicity. The
+composition of cellular membranes changes in aging and AD. We designed multi
+component lipid models to mimic healthy and diseased states of the neuronal
+membrane. Using atomic force microscopy (AFM), Kelvin probe force microscopy
+(KPFM) and black lipid membrane (BLM) techniques, we demonstrated that these
+model membranes differ in their nanoscale structure and physical properties,
+and interact differently with A\b{eta}. Based on our data, we propose a new
+hypothesis that changes in lipid membrane due to aging and AD may trigger
+amyloid toxicity through electrostatic mechanisms, similar to the accepted
+mechanism of antimicrobial peptide action. Understanding the role of the
+membrane changes as a key activating amyloid toxicity may aid in the
+development of a new avenue for the prevention and treatment of AD.
+",0,1,0,0,0,0
+14693,Transcendency Degree One Function Fields Over a Finite Field with Many Automorphisms,"  Let $\mathbb{K}$ be the algebraic closure of a finite field $\mathbb{F}_q$ of
+odd characteristic $p$. For a positive integer $m$ prime to $p$, let
+$F=\mathbb{K}(x,y)$ be the transcendency degree $1$ function field defined by
+$y^q+y=x^m+x^{-m}$. Let $t=x^{m(q-1)}$ and $H=\mathbb{K}(t)$. The extension
+$F|H$ is a non-Galois extension. Let $K$ be the Galois closure of $F$ with
+respect to $H$. By a result of Stichtenoth, $K$ has genus $g(K)=(qm-1)(q-1)$,
+$p$-rank (Hasse-Witt invariant) $\gamma(K)=(q-1)^2$ and a
+$\mathbb{K}$-automorphism group of order at least $2q^2m(q-1)$. In this paper
+we prove that this subgroup is the full $\mathbb{K}$-automorphism group of $K$;
+more precisely $Aut_{\mathbb {K}}(K)=Q\rtimes D$ where $Q$ is an elementary
+abelian $p$-group of order $q^2$ and $D$ has a index $2$ cyclic subgroup of
+order $m(q-1)$. In particular, $\sqrt{m}|Aut_{\mathbb{K}}(K)|> g(K)^{3/2}$, and
+if $K$ is ordinary (i.e. $g(K)=\gamma(K)$) then
+$|Aut_{\mathbb{K}}(K)|>g^{3/2}$. On the other hand, if $G$ is a solvable
+subgroup of the $\mathbb{K}$-automorphism group of an ordinary, transcendency
+degree $1$ function field $L$ of genus $g(L)\geq 2$ defined over $\mathbb{K}$,
+then by a result due to Korchmáros and Montanucci, $|Aut_{\mathbb{K}}(K)|\le
+34 (g(L)+1)^{3/2}<68\sqrt{2}g(L)^{3/2}$. This shows that $K$ hits this bound up
+to the constant $68\sqrt{2}$.
+Since $Aut_{\mathbb{K}}(K)$ has several subgroups, the fixed subfield $F^N$
+of such a subgroup $N$ may happen to have many automorphisms provided that the
+normalizer of $N$ in $Aut_{\mathbb{K}}(K)$ is large enough. This possibility is
+worked out for subgroups of $Q$.
+",0,0,1,0,0,0
+14694,"The phase transitions between $Z_n\times Z_n$ bosonic topological phases in 1+1 D, and a constraint on the central charge for the critical points between bosonic symmetry protected topological phases","  The study of continuous phase transitions triggered by spontaneous symmetry
+breaking has brought revolutionary ideas to physics. Recently, through the
+discovery of symmetry protected topological phases, it is realized that
+continuous quantum phase transition can also occur between states with the same
+symmetry but different topology. Here we study a specific class of such phase
+transitions in 1+1 dimensions -- the phase transition between bosonic
+topological phases protected by $Z_n\times Z_n$. We find in all cases the
+critical point possesses two gap opening relevant operators: one leads to a
+Landau-forbidden symmetry breaking phase transition and the other to the
+topological phase transition. We also obtained a constraint on the central
+charge for general phase transitions between symmetry protected bosonic
+topological phases in 1+1D.
+",0,1,0,0,0,0
+14695,Universal Reinforcement Learning Algorithms: Survey and Experiments,"  Many state-of-the-art reinforcement learning (RL) algorithms typically assume
+that the environment is an ergodic Markov Decision Process (MDP). In contrast,
+the field of universal reinforcement learning (URL) is concerned with
+algorithms that make as few assumptions as possible about the environment. The
+universal Bayesian agent AIXI and a family of related URL algorithms have been
+developed in this setting. While numerous theoretical optimality results have
+been proven for these agents, there has been no empirical investigation of
+their behavior to date. We present a short and accessible survey of these URL
+algorithms under a unified notation and framework, along with results of some
+experiments that qualitatively illustrate some properties of the resulting
+policies, and their relative performance on partially-observable gridworld
+environments. We also present an open-source reference implementation of the
+algorithms which we hope will facilitate further understanding of, and
+experimentation with, these ideas.
+",1,0,0,0,0,0
+14696,Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks,"  Understanding cell identity is an important task in many biomedical areas.
+Expression patterns of specific marker genes have been used to characterize
+some limited cell types, but exclusive markers are not available for many cell
+types. A second approach is to use machine learning to discriminate cell types
+based on the whole gene expression profiles (GEPs). The accuracies of simple
+classification algorithms such as linear discriminators or support vector
+machines are limited due to the complexity of biological systems. We used deep
+neural networks to analyze 1040 GEPs from 16 different human tissues and cell
+types. After comparing different architectures, we identified a specific
+structure of deep autoencoders that can encode a GEP into a vector of 30
+numeric values, which we call the cell identity code (CIC). The original GEP
+can be reproduced from the CIC with an accuracy comparable to technical
+replicates of the same experiment. Although we use an unsupervised approach to
+train the autoencoder, we show different values of the CIC are connected to
+different biological aspects of the cell, such as different pathways or
+biological processes. This network can use CIC to reproduce the GEP of the cell
+types it has never seen during the training. It also can resist some noise in
+the measurement of the GEP. Furthermore, we introduce classifier autoencoder,
+an architecture that can accurately identify cell type based on the GEP or the
+CIC.
+",0,0,0,1,1,0
+14697,Full replica symmetry breaking in p-spin-glass-like systems,"  It is shown that continuously changing the effective number of interacting
+particles in p-spin-glass-like model allows to describe the transition from the
+full replica symmetry breaking glass solution to stable first replica symmetry
+breaking glass solution in the case of non-reflective symmetry diagonal
+operators used instead of Ising spins. As an example, axial quadrupole moments
+in place of Ising spins are considered and the boundary value $p_{c_{1}}\cong
+2.5$ is found.
+",0,1,0,0,0,0
+14698,Formation and condensation of excitonic bound states in the generalized Falicov-Kimball model,"  The density-matrix-renormalization-group (DMRG) method and the Hartree-Fock
+(HF) approximation with the charge-density-wave (CDW) instability are used to
+study a formation and condensation of excitonic bound states in the generalized
+Falicov-Kimball model. In particular, we examine effects of various factors,
+like the $f$-electron hopping, the local and nonlocal hybridization, as well as
+the increasing dimension of the system on the excitonic momentum distribution
+$N(q)$ and especially on the number of zero momentum excitons $N_0=N(q=0)$ in
+the condensate. It is found that the negative values of the $f$-electron
+hopping integrals $t_f$ support the formation of zero-momentum condensate,
+while the positive values of $t_f$ have the fully opposite effect. The opposite
+effects on the formation of condensate exhibit also the local and nonlocal
+hybridization. The first one strongly supports the formation of condensate,
+while the second one destroys it completely. Moreover, it was shown that the
+zero-momentum condensate remains robust with increasing dimension of the
+system.
+",0,1,0,0,0,0
+14699,Tunable Emergent Heterostructures in a Prototypical Correlated Metal,"  At the interface between two distinct materials desirable properties, such as
+superconductivity, can be greatly enhanced, or entirely new functionalities may
+emerge. Similar to in artificially engineered heterostructures, clean
+functional interfaces alternatively exist in electronically textured bulk
+materials. Electronic textures emerge spontaneously due to competing
+atomic-scale interactions, the control of which, would enable a top-down
+approach for designing tunable intrinsic heterostructures. This is particularly
+attractive for correlated electron materials, where spontaneous
+heterostructures strongly affect the interplay between charge and spin degrees
+of freedom. Here we report high-resolution neutron spectroscopy on the
+prototypical strongly-correlated metal CeRhIn5, revealing competition between
+magnetic frustration and easy-axis anisotropy -- a well-established mechanism
+for generating spontaneous superstructures. Because the observed easy-axis
+anisotropy is field-induced and anomalously large, it can be controlled
+efficiently with small magnetic fields. The resulting field-controlled magnetic
+superstructure is closely tied to the formation of superconducting and
+electronic nematic textures in CeRhIn5, suggesting that in-situ tunable
+heterostructures can be realized in correlated electron materials.
+",0,1,0,0,0,0
+14700,Weak Label Supervision for Monaural Source Separation Using Non-negative Denoising Variational Autoencoders,"  Deep learning models are very effective in source separation when there are
+large amounts of labeled data available. However it is not always possible to
+have carefully labeled datasets. In this paper, we propose a weak supervision
+method that only uses class information rather than source signals for learning
+to separate short utterance mixtures. We associate a variational autoencoder
+(VAE) with each class within a non-negative model. We demonstrate that deep
+convolutional VAEs provide a prior model to identify complex signals in a sound
+mixture without having access to any source signal. We show that the separation
+results are on par with source signal supervision.
+",1,0,0,0,0,0
+14701,Mixed Bohr radius in several variables,"  Let $K(B_{\ell_p^n},B_{\ell_q^n}) $ be the $n$-dimensional $(p,q)$-Bohr
+radius for holomorphic functions on $\mathbb C^n$. That is,
+$K(B_{\ell_p^n},B_{\ell_q^n}) $ denotes the greatest constant $r\geq 0$ such
+that for every entire function $f(z)=\sum_{\alpha} c_{\alpha} z^{\alpha}$ in
+$n$-complex variables, we have the following (mixed) Bohr-type inequality
+$$\sup_{z \in r \cdot B_{\ell_q^n}} \sum_{\alpha} | c_{\alpha} z^{\alpha} |
+\leq \sup_{z \in B_{\ell_p^n}} | f(z) |,$$ where $B_{\ell_r^n}$ denotes the
+closed unit ball of the $n$-dimensional sequence space $\ell_r^n$.
+For every $1 \leq p, q \leq \infty$, we exhibit the exact asymptotic growth
+of the $(p,q)$-Bohr radius as $n$ (the number of variables) goes to infinity.
+",0,0,1,0,0,0
+14702,Adversarial Examples: Attacks and Defenses for Deep Learning,"  With rapid progress and significant successes in a wide spectrum of
+applications, deep learning is being applied in many safety-critical
+environments. However, deep neural networks have been recently found vulnerable
+to well-designed input samples, called adversarial examples. Adversarial
+examples are imperceptible to human but can easily fool deep neural networks in
+the testing/deploying stage. The vulnerability to adversarial examples becomes
+one of the major risks for applying deep neural networks in safety-critical
+environments. Therefore, attacks and defenses on adversarial examples draw
+great attention. In this paper, we review recent findings on adversarial
+examples for deep neural networks, summarize the methods for generating
+adversarial examples, and propose a taxonomy of these methods. Under the
+taxonomy, applications for adversarial examples are investigated. We further
+elaborate on countermeasures for adversarial examples and explore the
+challenges and the potential solutions.
+",1,0,0,1,0,0
+14703,Auto-Keras: Efficient Neural Architecture Search with Network Morphism,"  Neural architecture search (NAS) has been proposed to automatically tune deep
+neural networks, but existing search algorithms usually suffer from expensive
+computational cost. Network morphism, which keeps the functionality of a neural
+network while changing its neural architecture, could be helpful for NAS by
+enabling a more efficient training during the search. In this paper, we propose
+a novel framework enabling Bayesian optimization to guide the network morphism
+for efficient neural architecture search by introducing a neural network kernel
+and a tree-structured acquisition function optimization algorithm, which more
+efficiently explores the search space. Intensive experiments have been done to
+demonstrate the superior performance of the developed framework over the
+state-of-the-art methods. Moreover, we build an open-source AutoML system on
+our method, namely Auto-Keras. The system runs in parallel on CPU and GPU, with
+an adaptive search strategy for different GPU memory limits.
+",0,0,0,1,0,0
+14704,Distributed Representation of Subgraphs,"  Network embeddings have become very popular in learning effective feature
+representations of networks. Motivated by the recent successes of embeddings in
+natural language processing, researchers have tried to find network embeddings
+in order to exploit machine learning algorithms for mining tasks like node
+classification and edge prediction. However, most of the work focuses on
+finding distributed representations of nodes, which are inherently ill-suited
+to tasks such as community detection which are intuitively dependent on
+subgraphs.
+Here, we propose sub2vec, an unsupervised scalable algorithm to learn feature
+representations of arbitrary subgraphs. We provide means to characterize
+similarties between subgraphs and provide theoretical analysis of sub2vec and
+demonstrate that it preserves the so-called local proximity. We also highlight
+the usability of sub2vec by leveraging it for network mining tasks, like
+community detection. We show that sub2vec gets significant gains over
+state-of-the-art methods and node-embedding methods. In particular, sub2vec
+offers an approach to generate a richer vocabulary of features of subgraphs to
+support representation and reasoning.
+",1,0,0,1,0,0
+14705,The right tool for the right question --- beyond the encoding versus decoding dichotomy,"  There are two major questions that neuroimaging studies attempt to answer:
+First, how are sensory stimuli represented in the brain (which we term the
+stimulus-based setting)? And, second, how does the brain generate cognition
+(termed the response-based setting)? There has been a lively debate in the
+neuroimaging community whether encoding and decoding models can provide
+insights into these questions. In this commentary, we construct two simple and
+analytically tractable examples to demonstrate that while an encoding model
+analysis helps with the former, neither model is appropriate to satisfactorily
+answer the latter question. Consequently, we argue that if we want to
+understand how the brain generates cognition, we need to move beyond the
+encoding versus decoding dichotomy and instead discuss and develop tools that
+are specifically tailored to our endeavour.
+",0,0,0,1,0,0
+14706,Recognising Axionic Dark Matter by Compton and de-Broglie Scale Modulation of Pulsar Timing,"  Light Axionic Dark Matter, motivated by string theory, is increasingly
+favored for the ""no-WIMP era"". Galaxy formation is suppressed below a Jeans
+scale, of $\simeq 10^8 M_\odot$ by setting the axion mass to, $m_B \sim
+10^{-22}$eV, and the large dark cores of dwarf galaxies are explained as
+solitons on the de-Broglie scale. This is persuasive, but detection of the
+inherent scalar field oscillation at the Compton frequency, $\omega_B= (2.5{\rm
+\, months})^{-1}(m_B/10^{-22}eV)$, would be definitive. By evolving the coupled
+Schrödinger-Poisson equation for a Bose-Einstein condensate, we predict the
+dark matter is fully modulated by de-Broglie interference, with a dense soliton
+core of size $\simeq 150pc$, at the Galactic center. The oscillating field
+pressure induces General Relativistic time dilation in proportion to the local
+dark matter density and pulsars within this dense core have detectably large
+timing residuals, of $\simeq 400nsec/(m_B/10^{-22}eV)$. This is encouraging as
+many new pulsars should be discovered near the Galactic center with planned
+radio surveys. More generally, over the whole Galaxy, differences in dark
+matter density between pairs of pulsars imprints a pairwise Galactocentric
+signature that can be distinguished from an isotropic gravitational wave
+background.
+",0,1,0,0,0,0
+14707,Core of communities in bipartite networks,"  We use the information present in a bipartite network to detect cores of
+communities of each set of the bipartite system. Cores of communities are found
+by investigating statistically validated projected networks obtained using
+information present in the bipartite network. Cores of communities are highly
+informative and robust with respect to the presence of errors or missing
+entries in the bipartite network. We assess the statistical robustness of cores
+by investigating an artificial benchmark network, the co-authorship network,
+and the actor-movie network. The accuracy and precision of the partition
+obtained with respect to the reference partition are measured in terms of the
+adjusted Rand index and of the adjusted Wallace index respectively. The
+detection of cores is highly precise although the accuracy of the methodology
+can be limited in some cases.
+",1,1,0,0,0,0
+14708,Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling,"  In textual information extraction and other sequence labeling tasks it is now
+common to use recurrent neural networks (such as LSTM) to form rich embedded
+representations of long-term input co-occurrence patterns. Representation of
+output co-occurrence patterns is typically limited to a hand-designed graphical
+model, such as a linear-chain CRF representing short-term Markov dependencies
+among successive labels. This paper presents a method that learns embedded
+representations of latent output structure in sequence data. Our model takes
+the form of a finite-state machine with a large number of latent states per
+label (a latent variable CRF), where the state-transition matrix is
+factorized---effectively forming an embedded representation of
+state-transitions capable of enforcing long-term label dependencies, while
+supporting exact Viterbi inference over output labels. We demonstrate accuracy
+improvements and interpretable latent structure in a synthetic but complex task
+based on CoNLL named entity recognition.
+",1,0,0,0,0,0
+14709,Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications,"  In this paper, we design and analyze a new zeroth-order online algorithm,
+namely, the zeroth-order online alternating direction method of multipliers
+(ZOO-ADMM), which enjoys dual advantages of being gradient-free operation and
+employing the ADMM to accommodate complex structured regularizers. Compared to
+the first-order gradient-based online algorithm, we show that ZOO-ADMM requires
+$\sqrt{m}$ times more iterations, leading to a convergence rate of
+$O(\sqrt{m}/\sqrt{T})$, where $m$ is the number of optimization variables, and
+$T$ is the number of iterations. To accelerate ZOO-ADMM, we propose two
+minibatch strategies: gradient sample averaging and observation averaging,
+resulting in an improved convergence rate of $O(\sqrt{1+q^{-1}m}/\sqrt{T})$,
+where $q$ is the minibatch size. In addition to convergence analysis, we also
+demonstrate ZOO-ADMM to applications in signal processing, statistics, and
+machine learning.
+",1,0,0,1,0,0
+14710,Stationary solutions for stochastic damped Navier-Stokes equations in $\mathbb R^d$,"  We consider the stochastic damped Navier-Stokes equations in $\mathbb R^d$
+($d=2,3$), assuming as in our previous work [4] that the covariance of the
+noise is not too regular, so Itô calculus cannot be applied in the space of
+finite energy vector fields. We prove the existence of an invariant measure
+when $d=2$ and of a stationary solution when $d=3$.
+",0,0,1,0,0,0
+14711,On compact packings of the plane with circles of three radii,"  A compact circle-packing $P$ of the Euclidean plane is a set of circles which
+bound mutually disjoint open discs with the property that, for every circle
+$S\in P$, there exists a maximal indexed set $\{A_{0},\ldots,A_{n-1}\}\subseteq
+P$ so that, for every $i\in\{0,\ldots,n-1\}$, the circle $A_{i}$ is tangent to
+both circles $S$ and $A_{i+1\mod n}$ .
+We show that there exist at most $11462$ pairs $(r,s)$ with $0<s<r<1$ for
+which there exist a compact circle-packing of the plane consisting of circles
+with radii $s$, $r$ and $1$.
+We discuss computing the exact values of such $0<s<r<1$ as roots of
+polynomials and exhibit a selection of compact circle-packings consisting of
+circles of three radii. We also discuss the apparent infeasibility of computing
+all these values on contemporary consumer hardware.
+",0,0,1,0,0,0
+14712,Next Basket Prediction using Recurring Sequential Patterns,"  Nowadays, a hot challenge for supermarket chains is to offer personalized
+services for their customers. Next basket prediction, i.e., supplying the
+customer a shopping list for the next purchase according to her current needs,
+is one of these services. Current approaches are not capable to capture at the
+same time the different factors influencing the customer's decision process:
+co-occurrency, sequentuality, periodicity and recurrency of the purchased
+items. To this aim, we define a pattern Temporal Annotated Recurring Sequence
+(TARS) able to capture simultaneously and adaptively all these factors. We
+define the method to extract TARS and develop a predictor for next basket named
+TBP (TARS Based Predictor) that, on top of TARS, is able to to understand the
+level of the customer's stocks and recommend the set of most necessary items.
+By adopting the TBP the supermarket chains could crop tailored suggestions for
+each individual customer which in turn could effectively speed up their
+shopping sessions. A deep experimentation shows that TARS are able to explain
+the customer purchase behavior, and that TBP outperforms the state-of-the-art
+competitors.
+",1,0,0,0,0,0
+14713,Solving nonlinear circuits with pulsed excitation by multirate partial differential equations,"  In this paper the concept of Multirate Partial Differential Equations (MPDEs)
+is applied to obtain an efficient solution for nonlinear low-frequency
+electrical circuits with pulsed excitation. The MPDEs are solved by a Galerkin
+approach and a conventional time discretization. Nonlinearities are efficiently
+accounted for by neglecting the high-frequency components (ripples) of the
+state variables and using only their envelope for the evaluation. It is shown
+that the impact of this approximation on the solution becomes increasingly
+negligible for rising frequency and leads to significant performance gains.
+",1,0,0,0,0,0
+14714,Optimal modification of the LRT for the equality of two high-dimensional covariance matrices,"  This paper considers the optimal modification of the likelihood ratio test
+(LRT) for the equality of two high-dimensional covariance matrices. The
+classical LRT is not well defined when the dimensions are larger than or equal
+to one of the sample sizes. In this paper, an optimally modified test that
+works well in cases where the dimensions may be larger than the sample sizes is
+proposed. In addition, the test is established under the weakest conditions on
+the moments and the dimensions of the samples. We also present weakly
+consistent estimators of the fourth moments, which are necessary for the
+proposed test, when they are not equal to 3. From the simulation results and
+real data analysis, we find that the performances of the proposed statistics
+are robust against affine transformations.
+",0,0,1,1,0,0
+14715,Feature uncertainty bounding schemes for large robust nonlinear SVM classifiers,"  We consider the binary classification problem when data are large and subject
+to unknown but bounded uncertainties. We address the problem by formulating the
+nonlinear support vector machine training problem with robust optimization. To
+do so, we analyze and propose two bounding schemes for uncertainties associated
+to random approximate features in low dimensional spaces. The proposed
+techniques are based on Random Fourier Features and the Nyström methods. The
+resulting formulations can be solved with efficient stochastic approximation
+techniques such as stochastic (sub)-gradient, stochastic proximal gradient
+techniques or their variants.
+",1,0,0,1,0,0
+14716,Impact of carrier localization on recombination in InGaN quantum wells and the efficiency of nitride light-emitting diodes: insights from theory and numerical simulations,"  We examine the effect of carrier localization due to random alloy
+fluctuations on the radiative and Auger recombination rates in InGaN quantum
+wells as a function of alloy composition, crystal orientation, carrier density,
+and temperature. Our results show that alloy fluctuations reduce individual
+transition matrix elements by the separate localization of electrons and holes,
+but this effect is overcompensated by the additional transitions enabled by
+translational symmetry breaking and the resulting lack of momentum
+conservation. Hence, we find that localization increases both radiative and
+Auger recombination rates, but that Auger recombination rates increase by one
+order of magnitude more than radiative rates. Furthermore, we demonstrate that
+localization has an overall detrimental effect on the efficiency-droop and
+green-gap problems of InGaN LEDs.
+",0,1,0,0,0,0
+14717,A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines,"  Information in neural networks is represented as weighted connections, or
+synapses, between neurons. This poses a problem as the primary computational
+bottleneck for neural networks is the vector-matrix multiply when inputs are
+multiplied by the neural network weights. Conventional processing architectures
+are not well suited for simulating neural networks, often requiring large
+amounts of energy and time. Additionally, synapses in biological neural
+networks are not binary connections, but exhibit a nonlinear response function
+as neurotransmitters are emitted and diffuse between neurons. Inspired by
+neuroscience principles, we present a digital neuromorphic architecture, the
+Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex
+synaptic response functions without requiring additional hardware components.
+We consider the paradigm of spiking neurons with temporally coded information
+as opposed to non-spiking rate coded neurons used in most neural networks. In
+this paradigm we examine liquid state machines applied to speech recognition
+and show how a liquid state machine with temporal dynamics maps onto the
+STPU-demonstrating the flexibility and efficiency of the STPU for instantiating
+neural algorithms.
+",1,0,0,1,0,0
+14718,Volume of representations and mapping degree,"  Given a connected real Lie group and a contractible homogeneous proper
+$G$--space $X$ furnished with a $G$--invariant volume form, a real valued
+volume can be assigned to any representation $\rho\colon \pi_1(M)\to G$ for any
+oriented closed smooth manifold $M$ of the same dimension as $X$. Suppose that
+$G$ contains a closed and cocompact semisimple subgroup, it is shown in this
+paper that the set of volumes is finite for any given $M$. From a perspective
+of model geometries, examples are investigated and applications with mapping
+degrees are discussed.
+",0,0,1,0,0,0
+14719,Transiting Planets with LSST III: Detection Rate per Year of Operation,"  The Large Synoptic Survey Telescope (LSST) will generate light curves for
+approximately 1 billion stars. Our previous work has demonstrated that, by the
+end of the LSST 10 year mission, large numbers of transiting exoplanetary
+systems could be recovered using the LSST ""deep drilling"" cadence. Here we
+extend our previous work to examine how the recoverability of transiting
+planets over a range of orbital periods and radii evolves per year of LSST
+operation. As specific example systems we consider hot Jupiters orbiting
+solar-type stars and hot Neptunes orbiting K-Dwarfs at distances from Earth of
+several kpc, as well as super-Earths orbiting nearby low-mass M-dwarfs. The
+detection of transiting planets increases steadily with the accumulation of
+data over time, generally becoming large (greater than 10 percent) after 4 - 6
+years of operation. However, we also find that short-period (less than 2 day)
+hot Jupiters orbiting G-dwarfs and hot Neptunes orbiting K-dwarfs can already
+be discovered within the first 1 - 2 years of LSST operation.
+",0,1,0,0,0,0
+14720,Summing coincidence in rare event gamma-ray measurements under an ultra-low background environment,"  A Monte Carlo method based on the GEANT4 toolkit has been developed to
+correct the full-energy peak (FEP) efficiencies of a high purity germanium
+(HPGe) detector equipped with a low background shielding system, and moreover
+evaluated using summing peaks in a numerical way. It is found that the FEP
+efficiencies of $^{60}$Co, $^{133}$Ba and $^{152}$Eu can be improved up to 18\%
+by taking the calculated true summing \mbox{coincidence} factors (TSCFs)
+correction into account. Counts of summing coincidence $\gamma$ peaks in the
+spectrum of $^{152}$Eu can be well reproduced using the corrected efficiency
+curve within an accuracy of 3\%.
+",0,1,0,0,0,0
+14721,Free constructions and coproducts of d-frames,"  A general theory of presentations for d-frames does not yet exist. We review
+the difficulties and give sufficient conditions for when they can be overcome.
+As an application we prove that the category of d-frames is closed under
+coproducts.
+",1,0,1,0,0,0
+14722,Trace Expressiveness of Timed and Probabilistic Automata,"  Automata expressiveness is an essential feature in understanding which of the
+formalisms available should be chosen for modelling a particular problem.
+Probabilistic and stochastic automata are suitable for modelling systems
+exhibiting probabilistic behavior and their expressiveness has been studied
+relative to non-probabilistic transition systems and Markov chains. In this
+paper, we consider previous formalisms of Timed, Probabilistic and Stochastic
+Timed Automata, we present our new model of Timed Automata with Polynomial
+Delay, we introduce a measure of expressiveness for automata we call trace
+expressiveness and we characterize the expressiveness of these models relative
+to each other under this new measure.
+",1,0,0,0,0,0
+14723,"Open data, open review and open dialogue in making social sciences plausible","  Nowadays, protecting trust in social sciences also means engaging in open
+community dialogue, which helps to safeguard robustness and improve efficiency
+of research methods. The combination of open data, open review and open
+dialogue may sound simple but implementation in the real world will not be
+straightforward. However, in view of Begley and Ellis's (2012) statement that,
+""the scientific process demands the highest standards of quality, ethics and
+rigour,"" they are worth implementing. More importantly, they are feasible to
+work on and likely will help to restore plausibility to social sciences
+research. Therefore, I feel it likely that the triplet of open data, open
+review and open dialogue will gradually emerge to become policy requirements
+regardless of the research funding source.
+",0,0,0,1,0,0
+14724,The isoperimetric problem in the 2-dimensional Finsler space forms with k = 0. II,"  This paper is a continuation of the second author's previous work. We
+investigate the isoperimetric problem in the 2-dimensional Finsler space form
+$(F_B, B^2(1))$ with $k=0$ by using the Holmes-Thompson area and prove that the
+circle centered the origin achieves the local maximum area of the isoperimetric
+problem.
+",0,0,1,0,0,0
+14725,Estimating Buildings' Parameters over Time Including Prior Knowledge,"  Modeling buildings' heat dynamics is a complex process which depends on
+various factors including weather, building thermal capacity, insulation
+preservation, and residents' behavior. Gray-box models offer a causal inference
+of those dynamics expressed in few parameters specific to built environments.
+These parameters can provide compelling insights into the characteristics of
+building artifacts and have various applications such as forecasting HVAC
+usage, indoor temperature control monitoring of built environments, etc. In
+this paper, we present a systematic study of modeling buildings' thermal
+characteristics and thus derive the parameters of built conditions with a
+Bayesian approach. We build a Bayesian state-space model that can adapt and
+incorporate buildings' thermal equations and propose a generalized solution
+that can easily adapt prior knowledge regarding the parameters. We show that a
+faster approximate approach using variational inference for parameter
+estimation can provide similar parameters as that of a more time-consuming
+Markov Chain Monte Carlo (MCMC) approach. We perform extensive evaluations on
+two datasets to understand the generative process and show that the Bayesian
+approach is more interpretable. We further study the effects of prior selection
+for the model parameters and transfer learning, where we learn parameters from
+one season and use them to fit the model in the other. We perform extensive
+evaluations on controlled and real data traces to enumerate buildings'
+parameter within a 95% credible interval.
+",1,0,0,1,0,0
+14726,Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms,"  We present a decentralized and scalable approach for deployment of a robot
+swarm. Our approach tackles scenarios in which the swarm must reach multiple
+spatially distributed targets, and enforce the constraint that the robot
+network cannot be split. The basic idea behind our work is to construct a
+logical tree topology over the physical network formed by the robots. The
+logical tree acts as a backbone used by robots to enforce connectivity
+constraints. We study and compare two algorithms to form the logical tree:
+outwards and inwards. These algorithms differ in the order in which the robots
+join the tree: the outwards algorithm starts at the tree root and grows towards
+the targets, while the inwards algorithm proceeds in the opposite manner. Both
+algorithms perform periodic reconfiguration, to prevent suboptimal topologies
+from halting the growth of the tree. Our contributions are (i) The formulation
+of the two algorithms; (ii) A comparison of the algorithms in extensive
+physics-based simulations; (iii) A validation of our findings through
+real-robot experiments.
+",1,0,0,0,0,0
+14727,Bell's Inequality and Entanglement in Qubits,"  We propose an alternative evaluation of quantum entanglement by measuring the
+maximum violation of the Bell's inequality without performing a partial trace
+operation. This proposal is demonstrated by bridging the maximum violation of
+the Bell's inequality and the concurrence of a pure state in an $n$-qubit
+system, in which one subsystem only contains one qubit and the state is a
+linear combination of two product states. We apply this relation to the ground
+states of four qubits in the Wen-Plaquette model and show that they are
+maximally entangled. A topological entanglement entropy of the Wen-Plaquette
+model could be obtained by relating the upper bound of the maximum violation of
+the Bell's inequality to the concurrences of a pure state with respect to
+different bipartitions.
+",0,1,0,0,0,0
+14728,Stochastic Conjugate Gradient Algorithm with Variance Reduction,"  Conjugate gradient (CG) methods are a class of important methods for solving
+linear equations and nonlinear optimization problems. In this paper, we propose
+a new stochastic CG algorithm with variance reduction and we prove its linear
+convergence with the Fletcher and Reeves method for strongly convex and smooth
+functions. We experimentally demonstrate that the CG with variance reduction
+algorithm converges faster than its counterparts for four learning models,
+which may be convex, nonconvex or nonsmooth. In addition, its area under the
+curve performance on six large-scale data sets is comparable to that of the
+LIBLINEAR solver for the L2-regularized L2-loss but with a significant
+improvement in computational efficiency
+",1,0,0,1,0,0
+14729,Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization,"  Kalman filters are routinely used for many data fusion applications including
+navigation, tracking, and simultaneous localization and mapping problems.
+However, significant time and effort is frequently required to tune various
+Kalman filter model parameters, e.g. process noise covariance, pre-whitening
+filter models for non-white noise, etc. Conventional optimization techniques
+for tuning can get stuck in poor local minima and can be expensive to implement
+with real sensor data. To address these issues, a new ""black box"" Bayesian
+optimization strategy is developed for automatically tuning Kalman filters. In
+this approach, performance is characterized by one of two stochastic objective
+functions: normalized estimation error squared (NEES) when ground truth state
+models are available, or the normalized innovation error squared (NIS) when
+only sensor data is available. By intelligently sampling the parameter space to
+both learn and exploit a nonparametric Gaussian process surrogate function for
+the NEES/NIS costs, Bayesian optimization can efficiently identify multiple
+local minima and provide uncertainty quantification on its results.
+",1,0,0,1,0,0
+14730,Ultra-wide plasmonic tuning of semiconductor metasurface resonators on epsilon near zero media,"  Fully reconfigurable metasurfaces would enable new classes of optical devices
+that provide unprecedented control of electromagnetic beamforms. The principal
+challenge for achieving reconfigurability is the need to generate large
+tunability of subwavelength, low-Q metasurface resonators. Here, we demonstrate
+large refractive index tuning can be efficiently facilitated at mid-infrared
+wavelengths using novel temperature-dependent control over free-carrier
+refraction. In doped InSb we demonstrate nearly two-fold increase in the
+electron effective mass leading to a positive refractive index shift
+({\Delta}n>1.5) far greater than conventional thermo-optic effects. In undoped
+films we demonstrate more than 10-fold change in the thermal free-carrier
+concentration producing a near-unity negative refractive index shift.
+Exploiting both effects within a single resonator system, intrinsic InSb wires
+on a heavily doped (epsilon near zero) InSb substrate, we demonstrate
+dynamically tunable Mie resonances. The observed larger than line-width
+resonance shifts ({\Delta}{\lambda}>1.5{\mu}m) suggest new avenues for highly
+tunable and reconfigurable mid-infrared semiconductor metasurfaces.
+",0,1,0,0,0,0
+14731,A Method for Analysis of Patient Speech in Dialogue for Dementia Detection,"  We present an approach to automatic detection of Alzheimer's type dementia
+based on characteristics of spontaneous spoken language dialogue consisting of
+interviews recorded in natural settings. The proposed method employs additive
+logistic regression (a machine learning boosting method) on content-free
+features extracted from dialogical interaction to build a predictive model. The
+model training data consisted of 21 dialogues between patients with Alzheimer's
+and interviewers, and 17 dialogues between patients with other health
+conditions and interviewers. Features analysed included speech rate,
+turn-taking patterns and other speech parameters. Despite relying solely on
+content-free features, our method obtains overall accuracy of 86.5\%, a result
+comparable to those of state-of-the-art methods that employ more complex
+lexical, syntactic and semantic features. While further investigation is
+needed, the fact that we were able to obtain promising results using only
+features that can be easily extracted from spontaneous dialogues suggests the
+possibility of designing non-invasive and low-cost mental health monitoring
+tools for use at scale.
+",1,0,0,0,0,0
+14732,Resilient Transmission Grid Design: AC Relaxation vs. DC approximation,"  As illustrated in recent years (Superstorm Sandy, the Northeast Ice Storm of
+1998, etc.), extreme weather events pose an enormous threat to the electric
+power transmission systems and the associated socio-economic systems that
+depend on reliable delivery of electric power. Besides inevitable malfunction
+of power grid components, deliberate malicious attacks can cause high risks to
+the service. These threats motivate the need for approaches and methods that
+improve the resilience of power systems. In this paper, we develop a model and
+tractable methods for optimizing the upgrade of transmission systems through a
+combination of hardening existing components, adding redundant lines, switches,
+generators, and FACTS and phase-shifting devices. While many of these
+controllable components are included in traditional design (expansion planning)
+problems, we uniquely assess their benefits from a resiliency point of view.
+More importantly, perhaps, we evaluate the suitability of using
+state-of-the-art AC power flow relaxations versus the common DC approximation
+in resilience improvement studies. The resiliency model and algorithms are
+tested on a modified version of the RTS-96 (single area) system.
+",1,0,1,0,0,0
+14733,VSE++: Improving Visual-Semantic Embeddings with Hard Negatives,"  We present a new technique for learning visual-semantic embeddings for
+cross-modal retrieval. Inspired by hard negative mining, the use of hard
+negatives in structured prediction, and ranking loss functions, we introduce a
+simple change to common loss functions used for multi-modal embeddings. That,
+combined with fine-tuning and use of augmented data, yields significant gains
+in retrieval performance. We showcase our approach, VSE++, on MS-COCO and
+Flickr30K datasets, using ablation studies and comparisons with existing
+methods. On MS-COCO our approach outperforms state-of-the-art methods by 8.8%
+in caption retrieval and 11.3% in image retrieval (at R@1).
+",1,0,0,0,0,0
+14734,The cooling-off effect of price limits in the Chinese stock markets,"  In this paper, we investigate the cooling-off effect (opposite to the magnet
+effect) from two aspects. Firstly, from the viewpoint of dynamics, we study the
+existence of the cooling-off effect by following the dynamical evolution of
+some financial variables over a period of time before the stock price hits its
+limit. Secondly, from the probability perspective, we investigate, with the
+logit model, the existence of the cooling-off effect through analyzing the
+high-frequency data of all A-share common stocks traded on the Shanghai Stock
+Exchange and the Shenzhen Stock Exchange from 2000 to 2011 and inspecting the
+trading period from the opening phase prior to the moment that the stock price
+hits its limits. A comparison is made of the properties between up-limit hits
+and down-limit hits, and the possible difference will also be compared between
+bullish and bearish market state by dividing the whole period into three
+alternating bullish periods and three bearish periods. We find that the
+cooling-off effect emerges for both up-limit hits and down-limit hits, and the
+cooling-off effect of the down-limit hits is stronger than that of the up-limit
+hits. The difference of the cooling-off effect between bullish period and
+bearish period is quite modest. Moreover, we examine the sub-optimal orders
+effect, and infer that the professional individual investors and institutional
+investors play a positive role in the cooling-off effects. All these findings
+indicate that the price limit trading rule exerts a positive effect on
+maintaining the stability of the Chinese stock markets.
+",0,0,0,0,0,1
+14735,When Anderson localization makes quantum particles move backward,"  We unveil a novel and unexpected manifestation of Anderson localization of
+matter wave packets that carry a finite average velocity: after an initial
+ballistic motion, the packet center-of-mass experiences a retroreflection and
+slowly returns to its initial position. We describe this effect both
+numerically and analytically in dimension 1, and show that it is destroyed by
+weak particle interactions which act as a decoherence process. The
+retroreflection is also present in higher dimensions, provided the dynamics is
+Anderson localized.
+",0,1,0,0,0,0
+14736,On the origin of super-diffusive behavior in a class of non-equilibrium systems,"  Experiments and simulations have established that dynamics in a class of
+living and abiotic systems that are far from equilibrium exhibit super
+diffusive behavior at long times, which in some cases (for example evolving
+tumor) is preceded by slow glass-like dynamics. By using the evolution of a
+collection of tumor cells, driven by mechanical forces and subject to cell
+birth and apoptosis, as a case study we show theoretically that on short time
+scales the mean square displacement is sub-diffusive due to jamming, whereas at
+long times it is super diffusive. The results obtained using stochastic
+quantization method, which is needed because of the absence of
+fluctuation-dissipation theorem (FDT), show that the super-diffusive behavior
+is universal and impervious to the nature of cell-cell interactions.
+Surprisingly, the theory also quantitatively accounts for the non-trivial
+dynamics observed in simulations of a model soap foam characterized by creation
+and destruction of spherical bubbles, which suggests that the two
+non-equilibrium systems belong to the same universality class. The theoretical
+prediction for the super diffusion exponent is in excellent agreement with
+simulations for collective motion of tumor cells and dynamics associated with
+soap bubbles.
+",0,0,0,0,1,0
+14737,Statistical analysis of the first passage path ensemble of jump processes,"  The transition mechanism of jump processes between two different subsets in
+state space reveals important dynamical information of the processes and
+therefore has attracted considerable attention in the past years. In this
+paper, we study the first passage path ensemble of both discrete-time and
+continuous-time jump processes on a finite state space. The main approach is to
+divide each first passage path into nonreactive and reactive segments and to
+study them separately. The analysis can be applied to jump processes which are
+non-ergodic, as well as continuous-time jump processes where the waiting time
+distributions are non-exponential. In the particular case that the jump
+processes are both Markovian and ergodic, our analysis elucidates the relations
+between the study of the first passage paths and the study of the transition
+paths in transition path theory. We provide algorithms to numerically compute
+statistics of the first passage path ensemble. The computational complexity of
+these algorithms scales with the complexity of solving a linear system, for
+which efficient methods are available. Several examples demonstrate the wide
+applicability of the derived results across research areas.
+",0,0,1,0,0,0
+14738,The SLUGGS Survey: Dark matter fractions at large radii and assembly epochs of early-type galaxies from globular cluster kinematics,"  We use globular cluster kinematics data, primarily from the SLUGGS survey, to
+measure the dark matter fraction ($f_{\rm DM}$) and the average dark matter
+density ($\left< \rho_{\rm DM} \right>$) within the inner 5 effective radii
+($R_{\rm e}$) for 32 nearby early--type galaxies (ETGs) with stellar mass log
+$(M_*/\rm M_\odot)$ ranging from $10.1$ to $11.8$. We compare our results with
+a simple galaxy model based on scaling relations as well as with cosmological
+hydrodynamical simulations where the dark matter profile has been modified
+through various physical processes.
+We find a high $f_{\rm DM}$ ($\geq0.6$) within 5~$R_{\rm e}$ in most of our
+sample, which we interpret as a signature of a late mass assembly history that
+is largely devoid of gas-rich major mergers. However, around log $(M_*/M_\odot)
+\sim 11$, there is a wide range of $f_{\rm DM}$ which may be challenging to
+explain with any single cosmological model. We find tentative evidence that
+lenticulars (S0s), unlike ellipticals, have mass distributions that are similar
+to spiral galaxies, with decreasing $f_{\rm DM}$ within 5~$R_{\rm e}$ as galaxy
+luminosity increases. However, we do not find any difference between the
+$\left< \rho_{\rm DM} \right>$ of S0s and ellipticals in our sample, despite
+the differences in their stellar populations. We have also used $\left<
+\rho_{\rm DM} \right>$ to infer the epoch of halo assembly ($z{\sim}2-4$). By
+comparing the age of their central stars with the inferred epoch of halo
+formation, we are able to gain more insight into their mass assembly histories.
+Our results suggest a fundamental difference in the dominant late-phase mass
+assembly channel between lenticulars and elliptical galaxies.
+",0,1,0,0,0,0
+14739,Wave packet dynamics of Bogoliubov quasiparticles: quantum metric effects,"  We study the dynamics of the Bogoliubov wave packet in superconductors and
+calculate the supercurrent carried by the wave packet. We discover an anomalous
+contribution to the supercurrent, related to the quantum metric of the Bloch
+wave function. This anomalous contribution is most important for flat or
+quasiflat bands, as exemplified by the attractive Hubbard models on the Creutz
+ladder and sawtooth lattice. Our theoretical framework is general and can be
+used to study a wide variety of phenomena, such as spin transport and exciton
+transport.
+",0,1,0,0,0,0
+14740,The search for superheavy elements: Historical and philosophical perspectives,"  The heaviest of the transuranic elements known as superheavy elements (SHE)
+are produced in nuclear reactions in a few specialized laboratories located in
+Germany, the U.S., Russia, and Japan. The history of this branch of physical
+science provides several case studies of interest to the philosophy and
+sociology of modern science. The story of SHE illuminates the crucial notion of
+what constitutes a chemical element, what the criteria are for discovering a
+new element, and how an element is assigned a name. The story also cast light
+on the sometimes uneasy relationship between physics and chemistry. It is far
+from obvious that elements with Z > 110 exist in the same sense as oxygen or
+sodium exists. The answers are not given by nature but by international
+commissions responsible for the criteria and evaluation of discovery claims.
+The works of these commissions and of SHE research in general have often been
+controversial.
+",0,1,0,0,0,0
+14741,"MuLoG, or How to apply Gaussian denoisers to multi-channel SAR speckle reduction?","  Speckle reduction is a longstanding topic in synthetic aperture radar (SAR)
+imaging. Since most current and planned SAR imaging satellites operate in
+polarimetric, interferometric or tomographic modes, SAR images are
+multi-channel and speckle reduction techniques must jointly process all
+channels to recover polarimetric and interferometric information. The
+distinctive nature of SAR signal (complex-valued, corrupted by multiplicative
+fluctuations) calls for the development of specialized methods for speckle
+reduction. Image denoising is a very active topic in image processing with a
+wide variety of approaches and many denoising algorithms available, almost
+always designed for additive Gaussian noise suppression. This paper proposes a
+general scheme, called MuLoG (MUlti-channel LOgarithm with Gaussian denoising),
+to include such Gaussian denoisers within a multi-channel SAR speckle reduction
+technique. A new family of speckle reduction algorithms can thus be obtained,
+benefiting from the ongoing progress in Gaussian denoising, and offering
+several speckle reduction results often displaying method-specific artifacts
+that can be dismissed by comparison between results.
+",0,0,1,1,0,0
+14742,Radon Transform for Sheaves,"  We define the Radon transform functor for sheaves and prove that it is an
+equivalence after suitable microlocal localizations. As a result, the sheaf
+category associated to a Legendrian is invariant under the Radon transform. We
+also manage to place the Radon transform and other transforms in microlocal
+sheaf theory altogether in a diagram.
+",0,0,1,0,0,0
+14743,A type theory for synthetic $\infty$-categories,"  We propose foundations for a synthetic theory of $(\infty,1)$-categories
+within homotopy type theory. We axiomatize a directed interval type, then
+define higher simplices from it and use them to probe the internal categorical
+structures of arbitrary types. We define Segal types, in which binary
+composites exist uniquely up to homotopy; this automatically ensures
+composition is coherently associative and unital at all dimensions. We define
+Rezk types, in which the categorical isomorphisms are additionally equivalent
+to the type-theoretic identities - a ""local univalence"" condition. And we
+define covariant fibrations, which are type families varying functorially over
+a Segal type, and prove a ""dependent Yoneda lemma"" that can be viewed as a
+directed form of the usual elimination rule for identity types. We conclude by
+studying homotopically correct adjunctions between Segal types, and showing
+that for a functor between Rezk types to have an adjoint is a mere proposition.
+To make the bookkeeping in such proofs manageable, we use a three-layered
+type theory with shapes, whose contexts are extended by polytopes within
+directed cubes, which can be abstracted over using ""extension types"" that
+generalize the path-types of cubical type theory. In an appendix, we describe
+the motivating semantics in the Reedy model structure on bisimplicial sets, in
+which our Segal and Rezk types correspond to Segal spaces and complete Segal
+spaces.
+",0,0,1,0,0,0
+14744,Strongly Hierarchical Factorization Machines and ANOVA Kernel Regression,"  High-order parametric models that include terms for feature interactions are
+applied to various data mining tasks, where ground truth depends on
+interactions of features. However, with sparse data, the high- dimensional
+parameters for feature interactions often face three issues: expensive
+computation, difficulty in parameter estimation and lack of structure. Previous
+work has proposed approaches which can partially re- solve the three issues. In
+particular, models with factorized parameters (e.g. Factorization Machines) and
+sparse learning algorithms (e.g. FTRL-Proximal) can tackle the first two issues
+but fail to address the third. Regarding to unstructured parameters,
+constraints or complicated regularization terms are applied such that
+hierarchical structures can be imposed. However, these methods make the
+optimization problem more challenging. In this work, we propose Strongly
+Hierarchical Factorization Machines and ANOVA kernel regression where all the
+three issues can be addressed without making the optimization problem more
+difficult. Experimental results show the proposed models significantly
+outperform the state-of-the-art in two data mining tasks: cold-start user
+response time prediction and stock volatility prediction.
+",1,0,0,0,0,0
+14745,On the relationship of Mathematics to the real world,"  In this article, I discuss the relationship of mathematics to the physical
+world, and to other spheres of human knowledge. In particular, I argue that
+Mathematics is created by human beings, and the number $\pi$ can not be said to
+have existed $100,000$ years ago, using the conventional meaning of the word
+`exist'.
+",0,1,1,0,0,0
+14746,"$\mathsf{LLF}_{\cal P}$: a logical framework for modeling external evidence, side conditions, and proof irrelevance using monads","  We extend the constructive dependent type theory of the Logical Framework
+$\mathsf{LF}$ with monadic, dependent type constructors indexed with predicates
+over judgements, called Locks. These monads capture various possible proof
+attitudes in establishing the judgment of the object logic encoded by an
+$\mathsf{LF}$ type. Standard examples are factoring-out the verification of a
+constraint or delegating it to an external oracle, or supplying some
+non-apodictic epistemic evidence, or simply discarding the proof witness of a
+precondition deeming it irrelevant. This new framework, called Lax Logical
+Framework, $\mathsf{LLF}_{\cal P}$, is a conservative extension of
+$\mathsf{LF}$, and hence it is the appropriate metalanguage for dealing
+formally with side-conditions in rules or external evidence in logical systems.
+$\mathsf{LLF}_{\cal P}$ arises once the monadic nature of the lock
+type-constructor, ${\cal L}^{\cal P}_{M,\sigma}[\cdot]$, introduced by the
+authors in a series of papers, together with Marina Lenisa, is fully exploited.
+The nature of the lock monads permits to utilize the very Lock destructor,
+${\cal U}^{\cal P}_{M,\sigma}[\cdot]$, in place of Moggi's monadic $let_T$,
+thus simplifying the equational theory. The rules for ${\cal U}^{\cal
+P}_{M,\sigma}[\cdot]$ permit also the removal of the monad once the constraint
+is satisfied. We derive the meta-theory of $\mathsf{LLF}_{\cal P}$ by a novel
+indirect method based on the encoding of $\mathsf{LLF}_{\cal P}$ in
+$\mathsf{LF}$. We discuss encodings in $\mathsf{LLF}_{\cal P}$ of call-by-value
+$\lambda$-calculi, Hoare's Logic, and Fitch-Prawitz Naive Set Theory.
+",1,0,0,0,0,0
+14747,"Laplace equation for the Dirac, Euler and the harmonic oscillator","  In this article, we give the explicit solutions to the Laplace equations
+associated to the Dirac operator, Euler operator and the harmonic oscillator in
+R.
+",0,0,1,0,0,0
+14748,Quantifying the Reality Gap in Robotic Manipulation Tasks,"  We quantify the accuracy of various simulators compared to a real world
+robotic reaching and interaction task. Simulators are used in robotics to
+design solutions for real world hardware without the need for physical access.
+The `reality gap' prevents solutions developed or learnt in simulation from
+performing well, or at at all, when transferred to real-world hardware. Making
+use of a Kinova robotic manipulator and a motion capture system, we record a
+ground truth enabling comparisons with various simulators, and present
+quantitative data for various manipulation-oriented robotic tasks. We show the
+relative strengths and weaknesses of numerous contemporary simulators,
+highlighting areas of significant discrepancy, and assisting researchers in the
+field in their selection of appropriate simulators for their use cases. All
+code and parameter listings are publicly available from:
+this https URL .
+",1,0,0,0,0,0
+14749,On the character degrees of a Sylow $p$-subgroup of a finite Chevalley group $G(p^f)$ over a bad prime,"  Let $q$ be a power of a prime $p$ and let $U(q)$ be a Sylow $p$-subgroup of a
+finite Chevalley group $G(q)$ defined over the field with $q$ elements. We
+first give a parametrization of the set $\text{Irr}(U(q))$ of irreducible
+characters of $U(q)$ when $G(q)$ is of type $\mathrm{G}_2$. This is uniform for
+primes $p \ge 5$, while the bad primes $p=2$ and $p=3$ have to be considered
+separately. We then use this result and the contribution of several authors to
+show a general result, namely that if $G(q)$ is any finite Chevalley group with
+$p$ a bad prime, then there exists a character $\chi \in \text{Irr}(U(q))$ such
+that $\chi(1)=q^n/p$ for some $n \in \mathbb{Z}_{\ge_0}$. In particular, for
+each $G(q)$ and every bad prime $p$, we construct a family of characters of
+such degree as inflation followed by an induction of linear characters of an
+abelian subquotient $V(q)$ of $U(q)$.
+",0,0,1,0,0,0
+14750,Zero-Delay Rate Distortion via Filtering for Vector-Valued Gaussian Sources,"  We deal with zero-delay source coding of a vector-valued Gauss-Markov source
+subject to a mean-squared error (MSE) fidelity criterion characterized by the
+operational zero-delay vector-valued Gaussian rate distortion function (RDF).
+We address this problem by considering the nonanticipative RDF (NRDF) which is
+a lower bound to the causal optimal performance theoretically attainable (OPTA)
+function and operational zero-delay RDF. We recall the realization that
+corresponds to the optimal ""test-channel"" of the Gaussian NRDF, when
+considering a vector Gauss-Markov source subject to a MSE distortion in the
+finite time horizon. Then, we introduce sufficient conditions to show existence
+of solution for this problem in the infinite time horizon. For the asymptotic
+regime, we use the asymptotic characterization of the Gaussian NRDF to provide
+a new equivalent realization scheme with feedback which is characterized by a
+resource allocation (reverse-waterfilling) problem across the dimension of the
+vector source. We leverage the new realization to derive a predictive coding
+scheme via lattice quantization with subtractive dither and joint memoryless
+entropy coding. This coding scheme offers an upper bound to the operational
+zero-delay vector-valued Gaussian RDF. When we use scalar quantization, then
+for ""r"" active dimensions of the vector Gauss-Markov source the gap between the
+obtained lower and theoretical upper bounds is less than or equal to 0.254r + 1
+bits/vector. We further show that it is possible when we use vector
+quantization, and assume infinite dimensional Gauss-Markov sources to make the
+previous gap to be negligible, i.e., Gaussian NRDF approximates the operational
+zero-delay Gaussian RDF. We also extend our results to vector-valued Gaussian
+sources of any finite memory under mild conditions. Our theoretical framework
+is demonstrated with illustrative numerical experiments.
+",1,0,0,0,0,0
+14751,Damping of gravitational waves by matter,"  We develop a unified description, via the Boltzmann equation, of damping of
+gravitational waves by matter, incorporating collisions. We identify two
+physically distinct damping mechanisms -- collisional and Landau damping. We
+first consider damping in flat spacetime, and then generalize the results to
+allow for cosmological expansion. In the first regime, maximal collisional
+damping of a gravitational wave, independent of the details of the collisions
+in the matter is, as we show, significant only when its wavelength is
+comparable to the size of the horizon. Thus damping by intergalactic or
+interstellar matter for all but primordial gravitational radiation can be
+neglected. Although collisions in matter lead to a shear viscosity, they also
+act to erase anisotropic stresses, thus suppressing the damping of
+gravitational waves. Damping of primordial gravitational waves remains
+possible. We generalize Weinberg's calculation of gravitational wave damping,
+now including collisions and particles of finite mass, and interpret the
+collisionless limit in terms of Landau damping. While Landau damping of
+gravitational waves cannot occur in flat spacetime, the expansion of the
+universe allows such damping by spreading the frequency of a gravitational wave
+of given wavevector.
+",0,1,0,0,0,0
+14752,Calibrations for minimal networks in a covering space setting,"  In this paper we define a notion of calibration for an equivalent approach to
+the classical Steiner problem in a covering space setting and we give some
+explicit examples. Moreover we introduce the notion of calibration in families:
+the idea is to divide the set of competitors in a suitable way, defining an
+appropriate (and weaker) notion of calibration. Then, calibrating the candidate
+minimizers in each family and comparing their perimeter, it is possible to find
+the minimizers of the minimization problem. Thanks to this procedure we prove
+the minimality of the Steiner configurations spanning the vertices of a regular
+hexagon and of a regular pentagon.
+",0,0,1,0,0,0
+14753,Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations,"  We present the design and manufacturing of high fidelity universal 3D
+fingerprint targets, which can be imaged on a variety of fingerprint sensing
+technologies, namely capacitive, contact-optical, and contactless-optical.
+Universal 3D fingerprint targets enable, for the first time, not only a
+repeatable and controlled evaluation of fingerprint readers, but also the
+ability to conduct fingerprint reader interoperability studies. Fingerprint
+reader interoperability refers to how robust fingerprint recognition systems
+are to variations in the images acquired by different types of fingerprint
+readers. To build universal 3D fingerprint targets, we adopt a molding and
+casting framework consisting of (i) digital mapping of fingerprint images to a
+negative mold, (ii) CAD modeling a scaffolding system to hold the negative
+mold, (iii) fabricating the mold and scaffolding system with a high resolution
+3D printer, (iv) producing or mixing a material with similar electrical,
+optical, and mechanical properties to that of the human finger, and (v)
+fabricating a 3D fingerprint target using controlled casting. Our experiments
+conducted with PIV and Appendix F certified optical (contact and contactless)
+and capacitive fingerprint readers demonstrate the usefulness of universal 3D
+fingerprint targets for controlled and repeatable fingerprint reader
+evaluations and also fingerprint reader interoperability studies.
+",1,0,0,0,0,0
+14754,The Velocity of the Propagating Wave for Spatially Coupled Systems with Applications to LDPC Codes,"  We consider the dynamics of message passing for spatially coupled codes and,
+in particular, the set of density evolution equations that tracks the profile
+of decoding errors along the spatial direction of coupling. It is known that,
+for suitable boundary conditions and after a transient phase, the error profile
+exhibits a ""solitonic behavior"". Namely, a uniquely-shaped wavelike solution
+develops, that propagates with constant velocity. Under this assumption we
+derive an analytical formula for the velocity in the framework of a continuum
+limit of the spatially coupled system. The general formalism is developed for
+spatially coupled low-density parity-check codes on general binary memoryless
+symmetric channels which form the main system of interest in this work. We
+apply the formula for special channels and illustrate that it matches the
+direct numerical evaluation of the velocity for a wide range of noise values. A
+possible application of the velocity formula to the evaluation of finite size
+scaling law parameters is also discussed. We conduct a similar analysis for
+general scalar systems and illustrate the findings with applications to
+compressive sensing and generalized low-density parity-check codes on the
+binary erasure or binary symmetric channels.
+",1,1,1,0,0,0
+14755,Voevodsky's conjecture for cubic fourfolds and Gushel-Mukai fourfolds via noncommutative K3 surfaces,"  In the first part of this paper we will prove the Voevodsky's nilpotence
+conjecture for smooth cubic fourfolds and ordinary generic Gushel-Mukai
+fourfolds. Then, making use of noncommutative motives, we will prove the
+Voevodsky's nilpotence conjecture for generic Gushel-Mukai fourfolds containing
+a $\tau$-plane $\G(2,3)$ and for ordinary Gushel-Mukai fourfolds containing a
+quintic del Pezzo surface.
+",0,0,1,0,0,0
+14756,Projection-Free Bandit Convex Optimization,"  In this paper, we propose the first computationally efficient projection-free
+algorithm for bandit convex optimization (BCO). We show that our algorithm
+achieves a sublinear regret of $O(nT^{4/5})$ (where $T$ is the horizon and $n$
+is the dimension) for any bounded convex functions with uniformly bounded
+gradients. We also evaluate the performance of our algorithm against baselines
+on both synthetic and real data sets for quadratic programming, portfolio
+selection and matrix completion problems.
+",0,0,0,1,0,0
+14757,The Recommendation System to SNS Community for Tourists by Using Altruistic Behaviors,"  We have already developed the recommendation system of sightseeing
+information on SNS by using smartphone based user participatory sensing system.
+The system can post the attractive information for tourists to the specified
+Facebook page by our developed smartphone application. The users in Facebook,
+who are interested in sightseeing, can come flocking through information space
+from far and near. However, the activities in the community on SNS are only
+supported by the specified people called a hub. We proposed the method of
+vitalization of tourist behaviors to give a stimulus to the people. We
+developed the simulation system for multi agent system with altruistic
+behaviors inspired by the Army Ants. The army ant takes feeding action with
+altruistic behaviors to suppress selfish behavior to a common object used by a
+plurality of users in common. In this paper, we introduced the altruism
+behavior determined by some simulation to vitalize the SNS community. The
+efficiency of the revitalization process of the community was investigated by
+some experimental simulation results.
+",1,0,0,0,0,0
+14758,Constructive Stabilization and Pole Placement by Arbitrary Decentralized Architectures,"  A seminal result in decentralized control is the development of fixed modes
+by Wang and Davison in 1973 - that plant modes which cannot be moved with a
+static decentralized controller cannot be moved by a dynamic one either, and
+that the other modes which can be moved can be shifted to any chosen location
+with arbitrary precision. These results were developed for perfectly
+decentralized, or block diagonal, information structure, where each control
+input may only depend on a single corresponding measurement. Furthermore, the
+results were claimed after a preliminary step was demonstrated, omitting a
+rigorous induction for each of these results, and the remaining task is
+nontrivial.
+In this paper, we consider fixed modes for arbitrary information structures,
+where certain control inputs may depend on some measurements but not others. We
+provide a comprehensive proof that the modes which cannot be altered by a
+static controller with the given structure cannot be moved by a dynamic one
+either, and that the modes which can be altered by a static controller with the
+given structure can be moved by a dynamic one to any chosen location with
+arbitrary precision, thus generalizing and solidifying Wang and Davison's
+results.
+This shows that a system can be stabilized by a linear time-invariant
+controller with the given information structure as long as all of the modes
+which are fixed with respect to that structure are in the left half-plane; an
+algorithm for synthesizing such a stabilizing decentralized controller is then
+distilled from the proof.
+",1,0,0,0,0,0
+14759,Hyperpolarizability and operational magic wavelength in an optical lattice clock,"  Optical clocks benefit from tight atomic confinement enabling extended
+interrogation times as well as Doppler- and recoil-free operation. However,
+these benefits come at the cost of frequency shifts that, if not properly
+controlled, may degrade clock accuracy. Numerous theoretical studies have
+predicted optical lattice clock frequency shifts that scale nonlinearly with
+trap depth. To experimentally observe and constrain these shifts in an
+$^{171}$Yb optical lattice clock, we construct a lattice enhancement cavity
+that exaggerates the light shifts. We observe an atomic temperature that is
+proportional to the optical trap depth, fundamentally altering the scaling of
+trap-induced light shifts and simplifying their parametrization. We identify an
+""operational"" magic wavelength where frequency shifts are insensitive to
+changes in trap depth. These measurements and scaling analysis constitute an
+essential systematic characterization for clock operation at the $10^{-18}$
+level and beyond.
+",0,1,0,0,0,0
+14760,A Unified Approach to Configuration-based Dynamic Analysis of Quadcopters for Optimal Stability,"  A special type of rotary-wing Unmanned Aerial Vehicles (UAV), called
+Quadcopter have prevailed to the civilian use for the past decade. They have
+gained significant amount of attention within the UAV community for their
+redundancy and ease of control, despite the fact that they fall under an
+under-actuated system category. They come in a variety of configurations. The
+""+"" and ""x"" configurations were introduced first. Literature pertinent to these
+two configurations is vast. However, in this paper, we define 6 additional
+possible configurations for a Quadcopter that can be built under either ""+"" or
+""x"" setup. These configurations can be achieved by changing the angle that the
+axis of rotation for rotors make with the main body, i.e., fuselage. This would
+also change the location of the COM with respect to the propellers which can
+add to the overall stability. A comprehensive dynamic model for all these
+configurations is developed for the first time. The overall stability for these
+configurations are addressed. In particular, it is shown that one configuration
+can lead to the most statically-stable platform by adopting damping motion in
+Roll/Pitch/Yaw, which is described for the first time to the best of our
+knowledge.
+",1,0,0,0,0,0
+14761,Improving Vision-based Self-positioning in Intelligent Transportation Systems via Integrated Lane and Vehicle Detection,"  Traffic congestion is a widespread problem. Dynamic traffic routing systems
+and congestion pricing are getting importance in recent research. Lane
+prediction and vehicle density estimation is an important component of such
+systems. We introduce a novel problem of vehicle self-positioning which
+involves predicting the number of lanes on the road and vehicle's position in
+those lanes using videos captured by a dashboard camera. We propose an
+integrated closed-loop approach where we use the presence of vehicles to aid
+the task of self-positioning and vice-versa. To incorporate multiple factors
+and high-level semantic knowledge into the solution, we formulate this problem
+as a Bayesian framework. In the framework, the number of lanes, the vehicle's
+position in those lanes and the presence of other vehicles are considered as
+parameters. We also propose a bounding box selection scheme to reduce the
+number of false detections and increase the computational efficiency. We show
+that the number of box proposals decreases by a factor of 6 using the selection
+approach. It also results in large reduction in the number of false detections.
+The entire approach is tested on real-world videos and is found to give
+acceptable results.
+",1,0,0,0,0,0
+14762,"BPS Algebras, Genus Zero, and the Heterotic Monster","  In this note, we expand on some technical issues raised in \cite{PPV} by the
+authors, as well as providing a friendly introduction to and summary of our
+previous work. We construct a set of heterotic string compactifications to 0+1
+dimensions intimately related to the Monstrous moonshine module of Frenkel,
+Lepowsky, and Meurman (and orbifolds thereof). Using this model, we review our
+physical interpretation of the genus zero property of Monstrous moonshine.
+Furthermore, we show that the space of (second-quantized) BPS-states forms a
+module over the Monstrous Lie algebras $\mathfrak{m}_g$---some of the first and
+most prominent examples of Generalized Kac-Moody algebras---constructed by
+Borcherds and Carnahan. In particular, we clarify the structure of the module
+present in the second-quantized string theory. We also sketch a proof of our
+methods in the language of vertex operator algebras, for the interested
+mathematician.
+",0,0,1,0,0,0
+14763,Exploiting Nontrivial Connectivity for Automatic Speech Recognition,"  Nontrivial connectivity has allowed the training of very deep networks by
+addressing the problem of vanishing gradients and offering a more efficient
+method of reusing parameters. In this paper we make a comparison between
+residual networks, densely-connected networks and highway networks on an image
+classification task. Next, we show that these methodologies can easily be
+deployed into automatic speech recognition and provide significant improvements
+to existing models.
+",1,0,0,1,0,0
+14764,A unified treatment of multiple testing with prior knowledge using the p-filter,"  There is a significant literature on methods for incorporating knowledge into
+multiple testing procedures so as to improve their power and precision. Some
+common forms of prior knowledge include (a) beliefs about which hypotheses are
+null, modeled by non-uniform prior weights; (b) differing importances of
+hypotheses, modeled by differing penalties for false discoveries; (c) multiple
+arbitrary partitions of the hypotheses into (possibly overlapping) groups; and
+(d) knowledge of independence, positive or arbitrary dependence between
+hypotheses or groups, suggesting the use of more aggressive or conservative
+procedures. We present a unified algorithmic framework called p-filter for
+global null testing and false discovery rate (FDR) control that allows the
+scientist to incorporate all four types of prior knowledge (a)-(d)
+simultaneously, recovering a variety of known algorithms as special cases.
+",0,0,1,1,0,0
+14765,The flip Markov chain for connected regular graphs,"  Mahlmann and Schindelhauer (2005) defined a Markov chain which they called
+$k$-Flipper, and showed that it is irreducible on the set of all connected
+regular graphs of a given degree (at least 3). We study the 1-Flipper chain,
+which we call the flip chain, and prove that the flip chain converges rapidly
+to the uniform distribution over connected $2r$-regular graphs with $n$
+vertices, where $n\geq 8$ and $r = r(n)\geq 2$. Formally, we prove that the
+distribution of the flip chain will be within $\varepsilon$ of uniform in total
+variation distance after $\text{poly}(n,r,\log(\varepsilon^{-1}))$ steps. This
+polynomial upper bound on the mixing time is given explicitly, and improves
+markedly on a previous bound given by Feder et al.(2006). We achieve this
+improvement by using a direct two-stage canonical path construction, which we
+define in a general setting.
+This work has applications to decentralised networks based on random regular
+connected graphs of even degree, as a self-stabilising protocol in which nodes
+spontaneously perform random flips in order to repair the network.
+",1,0,1,0,0,0
+14766,FPGA-based ORB Feature Extraction for Real-Time Visual SLAM,"  Simultaneous Localization And Mapping (SLAM) is the problem of constructing
+or updating a map of an unknown environment while simultaneously keeping track
+of an agent's location within it. How to enable SLAM robustly and durably on
+mobile, or even IoT grade devices, is the main challenge faced by the industry
+today. The main problems we need to address are: 1.) how to accelerate the SLAM
+pipeline to meet real-time requirements; and 2.) how to reduce SLAM energy
+consumption to extend battery life. After delving into the problem, we found
+out that feature extraction is indeed the bottleneck of performance and energy
+consumption. Hence, in this paper, we design, implement, and evaluate a
+hardware ORB feature extractor and prove that our design is a great balance
+between performance and energy consumption compared with ARM Krait and Intel
+Core i5.
+",1,0,0,0,0,0
+14767,On the Transformation of Latent Space in Autoencoders,"  Noting the importance of the latent variables in inference and learning, we
+propose a novel framework for autoencoders based on the homeomorphic
+transformation of latent variables --- which could reduce the distance between
+vectors in the transformed space, while preserving the topological properties
+of the original space --- and investigate the effect of the transformation in
+both learning generative models and denoising corrupted data. The results of
+our experiments show that the proposed model can work as both a generative
+model and a denoising model with improved performance due to the transformation
+compared to conventional variational and denoising autoencoders.
+",1,0,0,1,0,0
+14768,Log-Convexity of Weighted Area Integral Means of $H^p$ Functions on the Upper Half-plan,"  In the present work weighted area integral means $M_{p,\varphi}(f;{\mathrm
+{Im}}z)$ are studied and it is proved that the function $y\to \log
+M_{p,\varphi}(f;y)$ is convex in the case when $f$ belongs to a Hardy space on
+the upper half-plane.
+",0,0,1,0,0,0
+14769,From Nodal Chain Semimetal To Weyl Semimetal in HfC,"  Based on first-principles calculations and effective model analysis, we
+propose that the WC-type HfC, in the absence of spin-orbit coupling (SOC), can
+host a three-dimensional nodal chain semimetal state. Distinguished from the
+previous material IrF4 [T. Bzdusek et al., Nature 538, 75 (2016)], the nodal
+chain here is protected by mirror reflection symmetries of a simple space
+group, while in IrF4 the nonsymmorphic space group with a glide plane is a
+necessity. Moreover, in the presence of SOC, the nodal chain in WC type HfC
+evolves into Weyl points. In the Brillouin zone, a total of 30 pairs of Weyl
+points in three types are obtained through the first-principles calculations.
+Besides, the surface states and the pattern of the surface Fermi arcs
+connecting these Weyl points are studied, which may be measured by future
+experiments.
+",0,1,0,0,0,0
+14770,Protein and hydration-water dynamics are decoupled: A new model connecting dynamics and biochemical function is required,"  Water plays a major role in bio-systems, greatly contributing to determine
+their structure, stability and even function. It is well know, for instance,
+that proteins require a minimum amount of water to be functionally active.
+Since the biological functions of proteins involve changes of conformation, and
+sometimes chemical reactions, it is natural to expect a connection of these
+functions with dynamical properties of the coupled system of proteins and their
+hydration water. However, despite many years of intensive research, the
+detailed nature of protein - hydration water interactions, and their effect on
+the biochemical activity of proteins through peculiar dynamical effects, is
+still partly unknown. In particular, models proposed so far, fail to explain
+the full set of experimental data. The well-accepted 'protein dynamical
+transition' scenario is based on perfect coupling between the dynamics of
+proteins and of their hydration water, which has never been confuted
+experimentally. We present high-energy resolution elastic neutron scattering
+measurements of the atomistic dynamics of the model protein, lysozyme, in water
+that were carried out on the IN16B spectrometer at the Institut Laue-Langevin
+in Grenoble, France. These show for the first time that the dynamics of
+proteins and of their hydration water are actually de-coupled. This important
+result militates against the well-accepted scenario, and requires a new model
+to link protein dynamics to the dynamics of its hydration water, and, in turn,
+to biochemical function.
+",0,1,0,0,0,0
+14771,Lakshmibai-Seshadri paths for hyperbolic Kac-Moody algebras of rank $2$,"  Let $\mathfrak{g}$ be a hyperbolic Kac-Moody algebra of rank $2$, and set
+$\lambda: = \Lambda_1 - \Lambda_2$, where $\Lambda_1, \Lambda_2$ are the
+fundamental weights for $\mathfrak{g}$; note that $\lambda$ is neither dominant
+nor antidominant. Let $\mathbb{B}(\lambda)$ be the crystal of all
+Lakshmibai-Seshadri paths of shape $\lambda$. We prove that (the crystal graph
+of) $\mathbb{B}(\lambda)$ is connected. Furthermore, we give an explicit
+description of Lakshmibai-Seshadri paths of shape $\lambda$.
+",0,0,1,0,0,0
+14772,Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization,"  Affine policies (or control) are widely used as a solution approach in
+dynamic optimization where computing an optimal adjustable solution is usually
+intractable. While the worst case performance of affine policies can be
+significantly bad, the empirical performance is observed to be near-optimal for
+a large class of problem instances. For instance, in the two-stage dynamic
+robust optimization problem with linear covering constraints and uncertain
+right hand side, the worst-case approximation bound for affine policies is
+$O(\sqrt m)$ that is also tight (see Bertsimas and Goyal (2012)), whereas
+observed empirical performance is near-optimal. In this paper, we aim to
+address this stark-contrast between the worst-case and the empirical
+performance of affine policies. In particular, we show that affine policies
+give a good approximation for the two-stage adjustable robust optimization
+problem with high probability on random instances where the constraint
+coefficients are generated i.i.d. from a large class of distributions; thereby,
+providing a theoretical justification of the observed empirical performance. On
+the other hand, we also present a distribution such that the performance bound
+for affine policies on instances generated according to that distribution is
+$\Omega(\sqrt m)$ with high probability; however, the constraint coefficients
+are not i.i.d.. This demonstrates that the empirical performance of affine
+policies can depend on the generative model for instances.
+",0,0,1,0,0,0
+14773,Hausdorff operators on modulation and Wiener amalgam spaces,"  We give the sharp conditions for boundedness of Hausdorff operators on
+certain modulation and Wiener amalgam spaces.
+",0,0,1,0,0,0
+14774,From Deep to Shallow: Transformations of Deep Rectifier Networks,"  In this paper, we introduce transformations of deep rectifier networks,
+enabling the conversion of deep rectifier networks into shallow rectifier
+networks. We subsequently prove that any rectifier net of any depth can be
+represented by a maximum of a number of functions that can be realized by a
+shallow network with a single hidden layer. The transformations of both deep
+rectifier nets and deep residual nets are conducted to demonstrate the
+advantages of the residual nets over the conventional neural nets and the
+advantages of the deep neural nets over the shallow neural nets. In summary,
+for two rectifier nets with different depths but with same total number of
+hidden units, the corresponding single hidden layer representation of the
+deeper net is much more complex than the corresponding single hidden
+representation of the shallower net. Similarly, for a residual net and a
+conventional rectifier net with the same structure except for the skip
+connections in the residual net, the corresponding single hidden layer
+representation of the residual net is much more complex than the corresponding
+single hidden layer representation of the conventional net.
+",1,0,0,1,0,0
+14775,Second-order Convolutional Neural Networks,"  Convolutional Neural Networks (CNNs) have been successfully applied to many
+computer vision tasks, such as image classification. By performing linear
+combinations and element-wise nonlinear operations, these networks can be
+thought of as extracting solely first-order information from an input image. In
+the past, however, second-order statistics computed from handcrafted features,
+e.g., covariances, have proven highly effective in diverse recognition tasks.
+In this paper, we introduce a novel class of CNNs that exploit second-order
+statistics. To this end, we design a series of new layers that (i) extract a
+covariance matrix from convolutional activations, (ii) compute a parametric,
+second-order transformation of a matrix, and (iii) perform a parametric
+vectorization of a matrix. These operations can be assembled to form a
+Covariance Descriptor Unit (CDU), which replaces the fully-connected layers of
+standard CNNs. Our experiments demonstrate the benefits of our new
+architecture, which outperform the first-order CNNs, while relying on up to 90%
+fewer parameters.
+",1,0,0,0,0,0
+14776,On the quasi-sure superhedging duality with frictions,"  We prove the superhedging duality for a discrete-time financial market with
+proportional transaction costs under portfolio constraints and model
+uncertainty. Frictions are modeled through solvency cones as in the original
+model of [Kabanov, Y., Hedging and liquidation under transaction costs in
+currency markets. Fin. Stoch., 3(2):237-248, 1999] adapted to the quasi-sure
+setup of [Bouchard, B. and Nutz, M., Arbitrage and duality in nondominated
+discrete-time models. Ann. Appl. Probab., 25(2):823-859, 2015]. Our results
+hold under the condition of No Strict Arbitrage and under the efficient
+friction hypothesis.
+",0,0,0,0,0,1
+14777,The Gaia-ESO Survey: low-alpha element stars in the Galactic Bulge,"  We take advantage of the Gaia-ESO Survey iDR4 bulge data to search for
+abundance anomalies that could shed light on the composite nature of the Milky
+Way bulge. The alpha-elements (Mg, Si, and whenever available, Ca) abundances,
+and their trends with Fe abundances have been analysed for a total of 776 bulge
+stars. In addition, the aluminum abundances and their ratio to Fe and Mg have
+also been examined. Our analysis reveals the existence of low-alpha element
+abundance stars with respect to the standard bulge sequence in the [alpha/Fe]
+vs. [Fe/H] plane. 18 objects present deviations in [alpha/Fe] ranging from 2.1
+to 5.3 sigma with respect to the median standard value. Those stars do not show
+Mg-Al anti-correlation patterns. Incidentally, this sign of the existence of
+multiple stellar populations is reported firmly for the first time for the
+bulge globular cluster NGC 6522. The identified low-alpha abundance stars have
+chemical patterns compatible with those of the thin disc. Their link with
+massive dwarf galaxies accretion seems unlikely, as larger deviations in alpha
+abundance and Al would be expected. The vision of a bulge composite nature and
+a complex formation process is reinforced by our results. The used approach, a
+multi-method and model-driven analysis of high resolution data seems crucial to
+reveal this complexity.
+",0,1,0,0,0,0
+14778,On Kedlaya type inequalities for weighted means,"  In 2016 we proved that for every symmetric, repetition invariant and Jensen
+concave mean $\mathscr{M}$ the Kedlaya-type inequality $$
+\mathscr{A}\big(x_1,\mathscr{M}(x_1,x_2),\ldots,\mathscr{M}(x_1,\ldots,x_n)\big)\le
+\mathscr{M} \big(x_1,
+\mathscr{A}(x_1,x_2),\ldots,\mathscr{A}(x_1,\ldots,x_n)\big) $$ holds for an
+arbitrary $(x_n)$ ($\mathscr{A}$ stands for the arithmetic mean). We are going
+to prove the weighted counterpart of this inequality. More precisely, if
+$(x_n)$ is a vector with corresponding (non-normalized) weights $(\lambda_n)$
+and $\mathscr{M}_{i=1}^n(x_i,\lambda_i)$ denotes the weighted mean then, under
+analogous conditions on $\mathscr{M}$, the inequality $$ \mathscr{A}_{i=1}^n
+\big(\mathscr{M}_{j=1}^i (x_j,\lambda_j),\:\lambda_i\big) \le
+\mathscr{M}_{i=1}^n \big(\mathscr{A}_{j=1}^i (x_j,\lambda_j),\:\lambda_i\big)
+$$ holds for every $(x_n)$ and $(\lambda_n)$ such that the sequence
+$(\frac{\lambda_k}{\lambda_1+\cdots+\lambda_k})$ is decreasing.
+",0,0,1,0,0,0
+14779,Mesh-free Semi-Lagrangian Methods for Transport on a Sphere Using Radial Basis Functions,"  We present three new semi-Lagrangian methods based on radial basis function
+(RBF) interpolation for numerically simulating transport on a sphere. The
+methods are mesh-free and are formulated entirely in Cartesian coordinates,
+thus avoiding any irregular clustering of nodes at artificial boundaries on the
+sphere and naturally bypassing any apparent artificial singularities associated
+with surface-based coordinate systems. For problems involving tracer transport
+in a given velocity field, the semi-Lagrangian framework allows these new
+methods to avoid the use of any stabilization terms (such as hyperviscosity)
+during time-integration, thus reducing the number of parameters that have to be
+tuned. The three new methods are based on interpolation using 1) global RBFs,
+2) local RBF stencils, and 3) RBF partition of unity. For the latter two of
+these methods, we find that it is crucial to include some low degree spherical
+harmonics in the interpolants. Standard test cases consisting of solid body
+rotation and deformational flow are used to compare and contrast the methods in
+terms of their accuracy, efficiency, conservation properties, and
+dissipation/dispersion errors. For global RBFs, spectral spatial convergence is
+observed for smooth solutions on quasi-uniform nodes, while high-order accuracy
+is observed for the local RBF stencil and partition of unity approaches.
+",1,0,0,0,0,0
+14780,Spatial Models with the Integrated Nested Laplace Approximation within Markov Chain Monte Carlo,"  The Integrated Nested Laplace Approximation (INLA) is a convenient way to
+obtain approximations to the posterior marginals for parameters in Bayesian
+hierarchical models when the latent effects can be expressed as a Gaussian
+Markov Random Field (GMRF). In addition, its implementation in the R-INLA
+package for the R statistical software provides an easy way to fit models using
+INLA in practice. R-INLA implements a number of widely used latent models,
+including several spatial models. In addition, R-INLA can fit models in a
+fraction of the time than other computer intensive methods (e.g. Markov Chain
+Monte Carlo) take to fit the same model.
+Although INLA provides a fast approximation to the marginals of the model
+parameters, it is difficult to use it with models not implemented in R-INLA. It
+is also difficult to make multivariate posterior inference on the parameters of
+the model as INLA focuses on the posterior marginals and not the joint
+posterior distribution.
+In this paper we describe how to use INLA within the Metropolis-Hastings
+algorithm to fit spatial models and estimate the joint posterior distribution
+of a reduced number of parameters. We will illustrate the benefits of this new
+method with two examples on spatial econometrics and disease mapping where
+complex spatial models with several spatial structures need to be fitted.
+",0,0,0,1,0,0
+14781,Prediction-Constrained Topic Models for Antidepressant Recommendation,"  Supervisory signals can help topic models discover low-dimensional data
+representations that are more interpretable for clinical tasks. We propose a
+framework for training supervised latent Dirichlet allocation that balances two
+goals: faithful generative explanations of high-dimensional data and accurate
+prediction of associated class labels. Existing approaches fail to balance
+these goals by not properly handling a fundamental asymmetry: the intended task
+is always predicting labels from data, not data from labels. Our new
+prediction-constrained objective trains models that predict labels from heldout
+data well while also producing good generative likelihoods and interpretable
+topic-word parameters. In a case study on predicting depression medications
+from electronic health records, we demonstrate improved recommendations
+compared to previous supervised topic models and high- dimensional logistic
+regression from words alone.
+",1,0,0,1,0,0
+14782,Casimir-Polder force fluctuations as spatial probes of dissipation in metals,"  We study the spatial fluctuations of the Casimir-Polder force experienced by
+an atom or a small sphere moved above a metallic plate at fixed separation
+distance. We demonstrate that unlike the mean force, the magnitude of these
+fluctuations crucially relies on the relaxation of conduction electron in the
+metallic bulk, and even achieves values that differ by orders of magnitude
+depending on the amount of dissipation. We also discover that fluctuations
+suffer a spectacular decrease at large distances in the case of nonzero
+temperature.
+",0,1,0,0,0,0
+14783,A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels,"  Non-linear kernel methods can be approximated by fast linear ones using
+suitable explicit feature maps allowing their application to large scale
+problems. To this end, explicit feature maps of kernels for vectorial data have
+been extensively studied. As many real-world data is structured, various
+kernels for complex data like graphs have been proposed. Indeed, many of them
+directly compute feature maps. However, the kernel trick is employed when the
+number of features is very large or the individual vertices of graphs are
+annotated by real-valued attributes.
+Can we still compute explicit feature maps efficiently under these
+circumstances? Triggered by this question, we investigate how general
+convolution kernels are composed from base kernels and construct corresponding
+feature maps. We apply our results to widely used graph kernels and analyze for
+which kernels and graph properties computation by explicit feature maps is
+feasible and actually more efficient. In particular, we derive feature maps for
+random walk and subgraph matching kernels and apply them to real-world graphs
+with discrete labels. Thereby, our theoretical results are confirmed
+experimentally by observing a phase transition when comparing running time with
+respect to label diversity, walk lengths and subgraph size, respectively.
+Moreover, we derive approximative, explicit feature maps for state-of-the-art
+kernels supporting real-valued attributes including the GraphHopper and Graph
+Invariant kernels. In extensive experiments we show that our approaches often
+achieve a classification accuracy close to the exact methods based on the
+kernel trick, but require only a fraction of their running time.
+",1,0,0,1,0,0
+14784,Predicting Adolescent Suicide Attempts with Neural Networks,"  Though suicide is a major public health problem in the US, machine learning
+methods are not commonly used to predict an individual's risk of
+attempting/committing suicide. In the present work, starting with an anonymized
+collection of electronic health records for 522,056 unique, California-resident
+adolescents, we develop neural network models to predict suicide attempts. We
+frame the problem as a binary classification problem in which we use a
+patient's data from 2006-2009 to predict either the presence (1) or absence (0)
+of a suicide attempt in 2010. After addressing issues such as severely
+imbalanced classes and the variable length of a patient's history, we build
+neural networks with depths varying from two to eight hidden layers. For test
+set observations where we have at least five ED/hospital visits' worth of data
+on a patient, our depth-4 model achieves a sensitivity of 0.703, specificity of
+0.980, and AUC of 0.958.
+",1,0,0,1,0,0
+14785,Designing RNA Secondary Structures is Hard,"  An RNA sequence is a word over an alphabet on four elements $\{A,C,G,U\}$
+called bases. RNA sequences fold into secondary structures where some bases
+match one another while others remain unpaired. Pseudoknot-free secondary
+structures can be represented as well-parenthesized expressions with additional
+dots, where pairs of matching parentheses symbolize paired bases and dots,
+unpaired bases. The two fundamental problems in RNA algorithmic are to predict
+how sequences fold within some model of energy and to design sequences of bases
+which will fold into targeted secondary structures. Predicting how a given RNA
+sequence folds into a pseudoknot-free secondary structure is known to be
+solvable in cubic time since the eighties and in truly subcubic time by a
+recent result of Bringmann et al. (FOCS 2016). As a stark contrast, it is
+unknown whether or not designing a given RNA secondary structure is a tractable
+task; this has been raised as a challenging open question by Anne Condon (ICALP
+2003). Because of its crucial importance in a number of fields such as
+pharmaceutical research and biochemistry, there are dozens of heuristics and
+software libraries dedicated to RNA secondary structure design. It is therefore
+rather surprising that the computational complexity of this central problem in
+bioinformatics has been unsettled for decades.
+In this paper we show that, in the simplest model of energy which is the
+Watson-Crick model the design of secondary structures is NP-complete if one
+adds natural constraints of the form: index $i$ of the sequence has to be
+labeled by base $b$. This negative result suggests that the same lower bound
+holds for more realistic models of energy. It is noteworthy that the additional
+constraints are by no means artificial: they are provided by all the RNA design
+pieces of software and they do correspond to the actual practice.
+",1,0,0,0,0,0
+14786,Towards Understanding the Impact of Human Mobility on Police Allocation,"  Motivated by recent findings that human mobility is proxy for crime behavior
+in big cities and that there is a superlinear relationship between the people's
+movement and crime, this article aims to evaluate the impact of how these
+findings influence police allocation. More precisely, we shed light on the
+differences between an allocation strategy, in which the resources are
+distributed by clusters of floating population, and conventional allocation
+strategies, in which the police resources are distributed by an Administrative
+Area (typically based on resident population). We observed a substantial
+difference in the distributions of police resources allocated following these
+strategies, what evidences the imprecision of conventional police allocation
+methods.
+",1,1,0,0,0,0
+14787,Normality and Related Properties of Forcing Algebras,"  We present a sufficient condition for irreducibility of forcing algebras and
+study the (non)-reducedness phenomenon. Furthermore, we prove a criterion for
+normality for forcing algebras over a polynomial base ring with coefficients in
+a perfect field. This gives a geometrical normality criterion for algebraic
+(forcing) varieties over algebraically closed fields. Besides, we examine in
+detail an specific (enlightening) example with several forcing equations.
+Finally, we compute explicitly the normalization of a particular forcing
+algebra by means of finding explicitly the generators of the ideal defining it
+as an affine ring.
+",0,0,1,0,0,0
+14788,Training Triplet Networks with GAN,"  Triplet networks are widely used models that are characterized by good
+performance in classification and retrieval tasks. In this work we propose to
+train a triplet network by putting it as the discriminator in Generative
+Adversarial Nets (GANs). We make use of the good capability of representation
+learning of the discriminator to increase the predictive quality of the model.
+We evaluated our approach on Cifar10 and MNIST datasets and observed
+significant improvement on the classification performance using the simple k-nn
+method.
+",1,0,0,1,0,0
+14789,Recovering Pairwise Interactions Using Neural Networks,"  Recovering pairwise interactions, i.e. pairs of input features whose joint
+effect on an output is different from the sum of their marginal effects, is
+central in many scientific applications. We conceptualize a solution to this
+problem as a two-stage procedure: first, we model the relationship between the
+features and the output using a flexible hybrid neural network; second, we
+detect feature interactions from the trained model. For the second step we
+propose a simple and intuitive interaction measure (IM), which has no specific
+requirements on the machine learning model used in the first step, only that it
+defines a mapping from an input to an output. And in a special case it reduces
+to the averaged Hessian of the input-output mapping. Importantly, our method
+upper bounds the interaction recovery error with the error of the learning
+model, which ensures that we can improve the recovered interactions by training
+a more accurate model. We present analyses of simulated and real-world data
+which demonstrate the benefits of our method compared to available
+alternatives, and theoretically analyse its properties and relation to other
+methods.
+",1,0,0,1,0,0
+14790,Boundary Algebraic Bethe Ansatz for a nineteen vertex model with $U_{q}[\mathrm{osp}(2|2)^{(2)}] symmetry$,"  The boundary algebraic Bethe Ansatz for a supersymmetric nineteen
+vertex-model constructed from a three-dimensional representation of the twisted
+quantum affine Lie superalgebra $U_{q}[\mathrm{osp}(2|2)^{(2)}]$ is presented.
+The eigenvalues and eigenvectors of Sklyanin's transfer matrix, with diagonal
+reflection $K$-matrices, are calculated and the corresponding Bethe Ansatz
+equations are obtained.
+",0,1,0,0,0,0
+14791,Optimal cost for strengthening or destroying a given network,"  Strengthening or destroying a network is a very important issue in designing
+resilient networks or in planning attacks against networks including planning
+strategies to immunize a network against diseases, viruses etc.. Here we
+develop a method for strengthening or destroying a random network with a
+minimum cost. We assume a correlation between the cost required to strengthen
+or destroy a node and the degree of the node. Accordingly, we define a cost
+function c(k), which is the cost of strengthening or destroying a node with
+degree k. Using the degrees $k$ in a network and the cost function c(k), we
+develop a method for defining a list of priorities of degrees, and for choosing
+the right group of degrees to be strengthened or destroyed that minimizes the
+total price of strengthening or destroying the entire network. We find that the
+list of priorities of degrees is universal and independent of the network's
+degree distribution, for all kinds of random networks. The list of priorities
+is the same for both strengthening a network and for destroying a network with
+minimum cost. However, in spite of this similarity there is a difference
+between their p_c - the critical fraction of nodes that has to be functional,
+to guarantee the existence of a giant component in the network.
+",0,1,0,0,0,0
+14792,Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder,"  Reparameterization of variational auto-encoders with continuous latent spaces
+is an effective method for reducing the variance of their gradient estimates.
+However, using the same approach when latent variables are discrete is
+problematic, due to the resulting non-differentiable objective. In this work,
+we present a direct optimization method that propagates gradients through a
+non-differentiable $\arg \max$ prediction operation. We apply this method to
+discrete variational auto-encoders, by modeling a discrete random variable by
+the $\arg \max$ function of the Gumbel-Max perturbation model.
+",0,0,0,1,0,0
+14793,Orbit classification in the Hill problem: I. The classical case,"  The case of the classical Hill problem is numerically investigated by
+performing a thorough and systematic classification of the initial conditions
+of the orbits. More precisely, the initial conditions of the orbits are
+classified into four categories: (i) non-escaping regular orbits; (ii) trapped
+chaotic orbits; (iii) escaping orbits; and (iv) collision orbits. In order to
+obtain a more general and complete view of the orbital structure of the
+dynamical system our exploration takes place in both planar (2D) and the
+spatial (3D) version of the Hill problem. For the 2D system we numerically
+integrate large sets of initial conditions in several types of planes, while
+for the system with three degrees of freedom, three-dimensional distributions
+of initial conditions of orbits are examined. For distinguishing between
+ordered and chaotic bounded motion the Smaller ALingment Index (SALI) method is
+used. We managed to locate the several bounded basins, as well as the basins of
+escape and collision and also to relate them with the corresponding escape and
+collision time of the orbits. Our numerical calculations indicate that the
+overall orbital dynamics of the Hamiltonian system is a complicated but highly
+interested problem. We hope our contribution to be useful for a further
+understanding of the orbital properties of the classical Hill problem.
+",0,1,0,0,0,0
+14794,Bio-Inspired Local Information-Based Control for Probabilistic Swarm Distribution Guidance,"  This paper addresses a task allocation problem for a large-scale robotic
+swarm, namely swarm distribution guidance problem. Unlike most of the existing
+frameworks handling this problem, the proposed framework suggests utilising
+local information available to generate its time-varying stochastic policies.
+As each agent requires only local consistency on information with neighbouring
+agents, rather than the global consistency, the proposed framework offers
+various advantages, e.g., a shorter timescale for using new information and
+potential to incorporate an asynchronous decision-making process. We perform
+theoretical analysis on the properties of the proposed framework. From the
+analysis, it is proved that the framework can guarantee the convergence to the
+desired density distribution even using local information while maintaining
+advantages of global-information-based approaches. The design requirements for
+these advantages are explicitly listed in this paper. This paper also provides
+specific examples of how to implement the framework developed. The results of
+numerical experiments confirm the effectiveness and comparability of the
+proposed framework, compared with the global-information-based framework.
+",0,0,1,1,0,0
+14795,Simultaneous Confidence Band for Partially Linear Panel Data Models with Fixed Effects,"  In this paper, we construct the simultaneous confidence band (SCB) for the
+nonparametric component in partially linear panel data models with fixed
+effects. We remove the fixed effects, and further obtain the estimators of
+parametric and nonparametric components, which do not depend on the fixed
+effects. We establish the asymptotic distribution of their maximum absolute
+deviation between the estimated nonparametric component and the true
+nonparametric component under some suitable conditions, and hence the result
+can be used to construct the simultaneous confidence band of the nonparametric
+component. Based on the asymptotic distribution, it becomes difficult for the
+construction of the simultaneous confidence band. The reason is that the
+asymptotic distribution involves the estimators of the asymptotic bias and
+conditional variance, and the choice of the bandwidth for estimating the second
+derivative of nonparametric function. Clearly, these will cause computational
+burden and accumulative errors. To overcome these problems, we propose a
+Bootstrap method to construct simultaneous confidence band. Simulation studies
+indicate that the proposed Bootstrap method exhibits better performance under
+the limited samples.
+",0,0,0,1,0,0
+14796,Tent--Shaped Surface Morphologies of Silicon: Texturization by Metal Induced Etching,"  Nano--metal/semiconductor junction dependent porosification of silicon (Si)
+has been studied here. The silicon (Si) nanostructures (NS) have been textured
+on n-- and p-- type silicon wafers using Ag and Au metal nano particles induced
+chemical etching. The combinations of n--Si/Ag and p--Si/Au form ohmic contact
+and result in the same texturization on the Si surface on porosification where
+tent--shaped morphology has been observed consistently with n-- and p--type Si.
+Whereas, porosification result in different surface texturization for other two
+combinations (p--Si/Ag and n--Si/Au) where Schottkey contacts are formed.
+Quantitative analysis have been done using ImageJ to process the SEM images of
+SiNS, which confirms that the tent like SiNS are formed when etching of silicon
+wafer is done by AgNPs and AuNPs on n and p type Si wafer respectively. These
+easily prepared sharp tent--shaped Si NSs can be used for enhanced field
+emission applications.
+",0,1,0,0,0,0
+14797,Attention networks for image-to-text,"  The paper approaches the problem of image-to-text with attention-based
+encoder-decoder networks that are trained to handle sequences of characters
+rather than words. We experiment on lines of text from a popular handwriting
+database with different attention mechanisms for the decoder. The model trained
+with softmax attention achieves the lowest test error, outperforming several
+other RNN-based models. Our results show that softmax attention is able to
+learn a linear alignment whereas the alignment generated by sigmoid attention
+is linear but much less precise.
+",0,0,0,1,0,0
+14798,A Monocular Vision System for Playing Soccer in Low Color Information Environments,"  Humanoid soccer robots perceive their environment exclusively through
+cameras. This paper presents a monocular vision system that was originally
+developed for use in the RoboCup Humanoid League, but is expected to be
+transferable to other soccer leagues. Recent changes in the Humanoid League
+rules resulted in a soccer environment with less color coding than in previous
+years, which makes perception of the game situation more challenging. The
+proposed vision system addresses these challenges by using brightness and
+texture for the detection of the required field features and objects. Our
+system is robust to changes in lighting conditions, and is designed for
+real-time use on a humanoid soccer robot. This paper describes the main
+components of the detection algorithms in use, and presents experimental
+results from the soccer field, using ROS and the igus Humanoid Open Platform as
+a testbed. The proposed vision system was used successfully at RoboCup 2015.
+",1,0,0,0,0,0
+14799,Low-shot learning with large-scale diffusion,"  This paper considers the problem of inferring image labels from images when
+only a few annotated examples are available at training time. This setup is
+often referred to as low-shot learning, where a standard approach is to
+re-train the last few layers of a convolutional neural network learned on
+separate classes for which training examples are abundant. We consider a
+semi-supervised setting based on a large collection of images to support label
+propagation. This is possible by leveraging the recent advances on large-scale
+similarity graph construction.
+We show that despite its conceptual simplicity, scaling label propagation up
+to hundred millions of images leads to state of the art accuracy in the
+low-shot learning regime.
+",1,0,0,1,0,0
+14800,Instrumentation and its Interaction with the Secondary Beam for the Fermilab Muon Campus,"  The Fermilab Muon Campus will host the Muon g-2 experiment - a world class
+experiment dedicated to the search for signals of new physics. Strict demands
+are placed on beam diagnostics in order to ensure delivery of high quality
+beams to the storage ring with minimal losses. In this study, we briefly
+describe the available secondary beam diagnostics for the Fermilab Muon Campus.
+Then, with the aid of numerical simulations we detail their interaction with
+the secondary beam. Finally, we compare our results against theoretical
+findings.
+",0,1,0,0,0,0
+14801,On the wave propagation analysis and supratransmission prediction of a metastable modular metastructure for adaptive non-reciprocal energy transmission,"  In this research, we investigate the nonlinear energy transmission phenomenon
+in a reconfigurable and adaptable metastable modular metastructure. Numerical
+studies on a 1D metastable chain uncover that when the driving frequency is
+within the stopband of the periodic structure, there exists a threshold input
+amplitude, beyond which sudden increase in the energy transmission can be
+observed. This onset of transmission is due to nonlinear instability and is
+known as supratransmission. We show that due to spatial asymmetry of
+strategically configured constituents, such transmission thresholds could shift
+considerably when the structure is excited from different ends and therefore
+enabling the non-reciprocal energy transmission. We discover that the critical
+threshold amplitude can be predicted analytically using a localized
+nonlinear-linear model combining harmonic balancing and transfer matrix
+analyses. Additionally, influences of important parameters on the change of
+threshold amplitude are investigated to provide insight on synthesizing systems
+with desired non-reciprocal characteristics. These investigations elucidate the
+rich and intricate dynamics achievable by nonlinearity, asymmetry, and
+metastability, and provide new insights and opportunities to accomplish
+adaptable non-reciprocal wave energy transmission.
+",0,1,0,0,0,0
+14802,Advances in Variational Inference,"  Many modern unsupervised or semi-supervised machine learning algorithms rely
+on Bayesian probabilistic models. These models are usually intractable and thus
+require approximate inference. Variational inference (VI) lets us approximate a
+high-dimensional Bayesian posterior with a simpler variational distribution by
+solving an optimization problem. This approach has been successfully used in
+various models and large-scale applications. In this review, we give an
+overview of recent trends in variational inference. We first introduce standard
+mean field variational inference, then review recent advances focusing on the
+following aspects: (a) scalable VI, which includes stochastic approximations,
+(b) generic VI, which extends the applicability of VI to a large class of
+otherwise intractable models, such as non-conjugate models, (c) accurate VI,
+which includes variational models beyond the mean field approximation or with
+atypical divergences, and (d) amortized VI, which implements the inference over
+local latent variables with inference networks. Finally, we provide a summary
+of promising future research directions.
+",1,0,0,1,0,0
+14803,Security Against Impersonation Attacks in Distributed Systems,"  In a multi-agent system, transitioning from a centralized to a distributed
+decision-making strategy can introduce vulnerability to adversarial
+manipulation. We study the potential for adversarial manipulation in a class of
+graphical coordination games where the adversary can pose as a friendly agent
+in the game, thereby influencing the decision-making rules of a subset of
+agents. The adversary's influence can cascade throughout the system, indirectly
+influencing other agents' behavior and significantly impacting the emergent
+collective behavior. The main results in this paper focus on characterizing
+conditions under which the adversary's local influence can dramatically impact
+the emergent global behavior, e.g., destabilize efficient Nash equilibria.
+",1,0,0,0,0,0
+14804,Large-Batch Training for LSTM and Beyond,"  Large-batch training approaches have enabled researchers to utilize
+large-scale distributed processing and greatly accelerate deep-neural net (DNN)
+training. For example, by scaling the batch size from 256 to 32K, researchers
+have been able to reduce the training time of ResNet50 on ImageNet from 29
+hours to 2.2 minutes (Ying et al., 2018). In this paper, we propose a new
+approach called linear-epoch gradual-warmup (LEGW) for better large-batch
+training. With LEGW, we are able to conduct large-batch training for both CNNs
+and RNNs with the Sqrt Scaling scheme. LEGW enables Sqrt Scaling scheme to be
+useful in practice and as a result we achieve much better results than the
+Linear Scaling learning rate scheme. For LSTM applications, we are able to
+scale the batch size by a factor of 64 without losing accuracy and without
+tuning the hyper-parameters. For CNN applications, LEGW is able to achieve the
+same accuracy even as we scale the batch size to 32K. LEGW works better than
+previous large-batch auto-tuning techniques. LEGW achieves a 5.3X average
+speedup over the baselines for four LSTM-based applications on the same
+hardware. We also provide some theoretical explanations for LEGW.
+",1,0,0,1,0,0
+14805,One-shot and few-shot learning of word embeddings,"  Standard deep learning systems require thousands or millions of examples to
+learn a concept, and cannot integrate new concepts easily. By contrast, humans
+have an incredible ability to do one-shot or few-shot learning. For instance,
+from just hearing a word used in a sentence, humans can infer a great deal
+about it, by leveraging what the syntax and semantics of the surrounding words
+tells us. Here, we draw inspiration from this to highlight a simple technique
+by which deep recurrent networks can similarly exploit their prior knowledge to
+learn a useful representation for a new word from little data. This could make
+natural language processing systems much more flexible, by allowing them to
+learn continually from the new words they encounter.
+",1,0,0,1,0,0
+14806,Exactly Robust Kernel Principal Component Analysis,"  We propose a novel method called robust kernel principal component analysis
+(RKPCA) to decompose a partially corrupted matrix as a sparse matrix plus a
+high or full-rank matrix whose columns are drawn from a nonlinear
+low-dimensional latent variable model. RKPCA can be applied to many problems
+such as noise removal and subspace clustering and is so far the only
+unsupervised nonlinear method robust to sparse noises. We also provide
+theoretical guarantees for RKPCA. The optimization of RKPCA is challenging
+because it involves nonconvex and indifferentiable problems simultaneously. We
+propose two nonconvex optimization algorithms for RKPCA: alternating direction
+method of multipliers with backtracking line search and proximal linearized
+minimization with adaptive step size. Comparative studies on synthetic data and
+nature images corroborate the effectiveness and superiority of RKPCA in noise
+removal and robust subspace clustering.
+",0,0,0,1,0,0
+14807,Moderate deviation analysis for classical communication over quantum channels,"  We analyse families of codes for classical data transmission over quantum
+channels that have both a vanishing probability of error and a code rate
+approaching capacity as the code length increases. To characterise the
+fundamental tradeoff between decoding error, code rate and code length for such
+codes we introduce a quantum generalisation of the moderate deviation analysis
+proposed by Altug and Wagner as well as Polyanskiy and Verdu. We derive such a
+tradeoff for classical-quantum (as well as image-additive) channels in terms of
+the channel capacity and the channel dispersion, giving further evidence that
+the latter quantity characterises the necessary backoff from capacity when
+transmitting finite blocks of classical data. To derive these results we also
+study asymmetric binary quantum hypothesis testing in the moderate deviations
+regime. Due to the central importance of the latter task, we expect that our
+techniques will find further applications in the analysis of other quantum
+information processing tasks.
+",1,0,1,0,0,0
+14808,Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks,"  We study the training process of Deep Neural Networks (DNNs) from the Fourier
+analysis perspective. Our starting point is a Frequency Principle (F-Principle)
+--- DNNs initialized with small parameters often fit target functions from low
+to high frequencies --- which was first proposed by Xu et al. (2018) and
+Rahaman et al. (2018) on synthetic datasets. In this work, we first show the
+universality of the F-Principle by demonstrating this phenomenon on
+high-dimensional benchmark datasets, such as MNIST and CIFAR10. Then, based on
+experiments, we show that the F-Principle provides insight into both the
+success and failure of DNNs in different types of problems. Based on the
+F-Principle, we further propose that DNN can be adopted to accelerate the
+convergence of low frequencies for scientific computing problems, in which most
+of the conventional methods (e.g., Jacobi method) exhibit the opposite
+convergence behavior --- faster convergence for higher frequencies. Finally, we
+prove a theorem for DNNs of one hidden layer as a first step towards a
+mathematical explanation of the F-Principle. Our work indicates that the
+F-Principle with Fourier analysis is a promising approach to the study of DNNs
+because it seems ubiquitous, applicable, and explainable.
+",1,0,0,1,0,0
+14809,"Efficient, Safe, and Probably Approximately Complete Learning of Action Models","  In this paper we explore the theoretical boundaries of planning in a setting
+where no model of the agent's actions is given. Instead of an action model, a
+set of successfully executed plans are given and the task is to generate a plan
+that is safe, i.e., guaranteed to achieve the goal without failing. To this
+end, we show how to learn a conservative model of the world in which actions
+are guaranteed to be applicable. This conservative model is then given to an
+off-the-shelf classical planner, resulting in a plan that is guaranteed to
+achieve the goal. However, this reduction from a model-free planning to a
+model-based planning is not complete: in some cases a plan will not be found
+even when such exists. We analyze the relation between the number of observed
+plans and the likelihood that our conservative approach will indeed fail to
+solve a solvable problem. Our analysis show that the number of trajectories
+needed scales gracefully.
+",1,0,0,0,0,0
+14810,Uniform cohomological expansion of uniformly quasiregular mappings,"  Let $f\colon M \to M$ be a uniformly quasiregular self-mapping of a compact,
+connected, and oriented Riemannian $n$-manifold $M$ without boundary, $n\ge 2$.
+We show that, for $k \in \{0,\ldots, n\}$, the induced homomorphism $f^* \colon
+H^k(M;\mathbb{R}) \to H^k(M;\mathbb{R})$, where $H^k(M;\mathbb{R})$ is the
+$k$:th singular cohomology of $M$, is complex diagonalizable and the
+eigenvalues of $f^*$ have modulus $(\mathrm{deg}\ f)^{k/n}$. As an application,
+we obtain a degree restriction for uniformly quasiregular self-mappings of
+closed manifolds. In the proof of the main theorem, we use a Sobolev--de Rham
+cohomology based on conformally invariant differential forms and an induced
+push-forward operator.
+",0,0,1,0,0,0
+14811,The logic of pseudo-uninorms and their residua,"  Our method of density elimination is generalized to the non-commutative
+substructural logic GpsUL*. Then the standard completeness of GpsUL* follows as
+a lemma by virtue of previous work by Metcalfe and Montagna. This result shows
+that GpsUL* is the logic of pseudo-uninorms and their residua and answered the
+question posed by Prof. Metcalfe, Olivetti, Gabbay and Tsinakis.
+",0,0,1,0,0,0
+14812,Computation of Green's functions through algebraic decomposition of operators,"  In this article we use linear algebra to improve the computational time for
+the obtaining of Green's functions of linear differential equations with
+reflection (DER). This is achieved by decomposing both the `reduced' equation
+(the ODE associated to a given DER) and the corresponding two-point boundary
+conditions.
+",0,0,1,0,0,0
+14813,Methods for finding leader--follower equilibria with multiple followers,"  The concept of leader--follower (or Stackelberg) equilibrium plays a central
+role in a number of real--world applications of game theory. While the case
+with a single follower has been thoroughly investigated, results with multiple
+followers are only sporadic and the problem of designing and evaluating
+computationally tractable equilibrium-finding algorithms is still largely open.
+In this work, we focus on the fundamental case where multiple followers play a
+Nash equilibrium once the leader has committed to a strategy---as we
+illustrate, the corresponding equilibrium finding problem can be easily shown
+to be $\mathcal{FNP}$--hard and not in Poly--$\mathcal{APX}$ unless
+$\mathcal{P} = \mathcal{NP}$ and therefore it is one among the hardest problems
+to solve and approximate. We propose nonconvex mathematical programming
+formulations and global optimization methods to find both exact and approximate
+equilibria, as well as a heuristic black box algorithm. All the methods and
+formulations that we introduce are thoroughly evaluated computationally.
+",1,0,0,0,0,0
+14814,Pressure Induced Superconductivity in the New Compound ScZrCo1-$δ$,"  It is widely perceived that the correlation effect may play an important role
+in several unconventional superconducting families, such as cuprate, iron-based
+and heavy-fermion superconductors. The application of high pressure can tune
+the ground state properties and balance the localization and itineracy of
+electrons in correlated systems, which may trigger unconventional
+superconductivity. Moreover, non-centrosymmetric structure may induce the spin
+triplet pairing which is very rare in nature. Here, we report a new compound
+ScZrCo1-${\delta}$ crystallizing in the Ti2Ni structure with the space group of
+FD3-MS without a spatial inversion center. The resistivity of the material at
+ambient pressure shows a bad metal and weak semiconducting behavior.
+Furthermore, specific heat and magnetic susceptibility measurements yield a
+rather large value of Wilson ratio ~4.47. Both suggest a ground state with
+correlation effect. By applying pressure, the up-going behavior of resistivity
+in lowering temperature at ambient pressure is suppressed and gradually it
+becomes metallic. At a pressure of about 19.5 GPa superconductivity emerges. Up
+to 36.05 GPa, a superconducting transition at about 3.6 K with a quite high
+upper critical field is observed. Our discovery here provides a new platform
+for investigating the relationship between correlation effect and
+superconductivity.
+",0,1,0,0,0,0
+14815,Universal Adversarial Perturbations Against Semantic Image Segmentation,"  While deep learning is remarkably successful on perceptual tasks, it was also
+shown to be vulnerable to adversarial perturbations of the input. These
+perturbations denote noise added to the input that was generated specifically
+to fool the system while being quasi-imperceptible for humans. More severely,
+there even exist universal perturbations that are input-agnostic but fool the
+network on the majority of inputs. While recent work has focused on image
+classification, this work proposes attacks against semantic image segmentation:
+we present an approach for generating (universal) adversarial perturbations
+that make the network yield a desired target segmentation as output. We show
+empirically that there exist barely perceptible universal noise patterns which
+result in nearly the same predicted segmentation for arbitrary inputs.
+Furthermore, we also show the existence of universal noise which removes a
+target class (e.g., all pedestrians) from the segmentation while leaving the
+segmentation mostly unchanged otherwise.
+",1,0,0,1,0,0
+14816,Marginal sequential Monte Carlo for doubly intractable models,"  Bayesian inference for models that have an intractable partition function is
+known as a doubly intractable problem, where standard Monte Carlo methods are
+not applicable. The past decade has seen the development of auxiliary variable
+Monte Carlo techniques (M{\o}ller et al., 2006; Murray et al., 2006) for
+tackling this problem; these approaches being members of the more general class
+of pseudo-marginal, or exact-approximate, Monte Carlo algorithms (Andrieu and
+Roberts, 2009), which make use of unbiased estimates of intractable posteriors.
+Everitt et al. (2017) investigated the use of exact-approximate importance
+sampling (IS) and sequential Monte Carlo (SMC) in doubly intractable problems,
+but focussed only on SMC algorithms that used data-point tempering. This paper
+describes SMC samplers that may use alternative sequences of distributions, and
+describes ways in which likelihood estimates may be improved adaptively as the
+algorithm progresses, building on ideas from Moores et al. (2015). This
+approach is compared with a number of alternative algorithms for doubly
+intractable problems, including approximate Bayesian computation (ABC), which
+we show is closely related to the method of M{\o}ller et al. (2006).
+",1,0,0,1,0,0
+14817,The impact of neutral impurity concentration on charge drift mobility in n-type germanium,"  The impact of neutral impurity scattering of electrons on the charge drift
+mobility in high purity n-type germanium crystals at 77 Kelvin is investigated.
+We calculated the contributions from ionized impurity scattering, lattice
+scattering, and neutral impurity scattering to the total charge drift mobility
+using theoretical models. The experimental data such as charge carrier
+concentration, mobility and resistivity are measured by Hall Effect system at
+77 Kelvin. The neutral impurity concentration is derived from the Matthiessen's
+rule using the measured Hall mobility and ionized impurity concentration. The
+radial distribution of the neutral impurity concentration in the self-grown
+crystals is determined. Consequently, we demonstrated that neutral impurity
+scattering is a significant contribution to the charge drift mobility, which
+has a dependence on the concentration of neutral impurities in high purity
+n-type germanium crystal.
+",0,1,0,0,0,0
+14818,Calabi-Yau hypersurfaces and SU-bordism,"  Batyrev constructed a family of Calabi-Yau hypersurfaces dual to the first
+Chern class in toric Fano varieties. Using this construction, we introduce a
+family of Calabi-Yau manifolds whose SU-bordism classes generate the special
+unitary bordism ring
+$\varOmega^{SU}\otimes\mathbb{Z}[\frac{1}{2}]\cong\mathbb{Z}[\frac{1}{2}][y_{i}\colon
+i\ge 2]$. We also describe explicit Calabi-Yau representatives for
+multiplicative generators of the SU-bordism ring in low dimensions.
+",0,0,1,0,0,0
+14819,Distribution-Based Categorization of Classifier Transfer Learning,"  Transfer Learning (TL) aims to transfer knowledge acquired in one problem,
+the source problem, onto another problem, the target problem, dispensing with
+the bottom-up construction of the target model. Due to its relevance, TL has
+gained significant interest in the Machine Learning community since it paves
+the way to devise intelligent learning models that can easily be tailored to
+many different applications. As it is natural in a fast evolving area, a wide
+variety of TL methods, settings and nomenclature have been proposed so far.
+However, a wide range of works have been reporting different names for the same
+concepts. This concept and terminology mixture contribute however to obscure
+the TL field, hindering its proper consideration. In this paper we present a
+review of the literature on the majority of classification TL methods, and also
+a distribution-based categorization of TL with a common nomenclature suitable
+to classification problems. Under this perspective three main TL categories are
+presented, discussed and illustrated with examples.
+",1,0,0,0,0,0
+14820,Alexander invariants of periodic virtual knots,"  We show that every periodic virtual knot can be realized as the closure of a
+periodic virtual braid and use this to study the Alexander invariants of
+periodic virtual knots. If $K$ is a $q$-periodic and almost classical knot, we
+show that its quotient knot $K_*$ is also almost classical, and in the case
+$q=p^r$ is a prime power, we establish an analogue of Murasugi's congruence
+relating the Alexander polynomials of $K$ and $K_*$ over the integers modulo
+$p$. This result is applied to the problem of determining the possible periods
+of a virtual knot $K$. One consequence is that if $K$ is an almost classical
+knot with a nontrivial Alexander polynomial, then it is $p$-periodic for only
+finitely many primes $p$. Combined with parity and Manturov projection, our
+methods provide conditions that a general virtual knot must satisfy in order to
+be $q$-periodic.
+",0,0,1,0,0,0
+14821,Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration,"  Computing optimal transport distances such as the earth mover's distance is a
+fundamental problem in machine learning, statistics, and computer vision.
+Despite the recent introduction of several algorithms with good empirical
+performance, it is unknown whether general optimal transport distances can be
+approximated in near-linear time. This paper demonstrates that this ambitious
+goal is in fact achieved by Cuturi's Sinkhorn Distances. This result relies on
+a new analysis of Sinkhorn iteration, which also directly suggests a new greedy
+coordinate descent algorithm, Greenkhorn, with the same theoretical guarantees.
+Numerical simulations illustrate that Greenkhorn significantly outperforms the
+classical Sinkhorn algorithm in practice.
+",1,0,0,1,0,0
+14822,Mathematical model of gender bias and homophily in professional hierarchies,"  Women have become better represented in business, academia, and government
+over time, yet a dearth of women at the highest levels of leadership remains.
+Sociologists have attributed the leaky progression of women through
+professional hierarchies to various cultural and psychological factors, such as
+self-segregation and bias. Here, we present a minimal mathematical model that
+reveals the relative role that bias and homophily (self-seeking) may play in
+the ascension of women through professional hierarchies. Unlike previous
+models, our novel model predicts that gender parity is not inevitable, and
+deliberate intervention may be required to achieve gender balance in several
+fields. To validate the model, we analyze a new database of gender
+fractionation over time for 16 professional hierarchies. We quantify the degree
+of homophily and bias in each professional hierarchy, and we propose specific
+interventions to achieve gender parity more quickly.
+",1,0,0,0,0,0
+14823,Monotonicity of non-pluripolar products and complex Monge-Ampère equations with prescribed singularity,"  We establish the monotonicity property for the mass of non-pluripolar
+products on compact Kahler manifolds, and we initiate the study of complex
+Monge-Ampere type equations with prescribed singularity type. Using the
+variational method of Berman-Boucksom-Guedj-Zeriahi we prove existence and
+uniqueness of solutions with small unbounded locus. We give applications to
+Kahler-Einstein metrics with prescribed singularity, and we show that the
+log-concavity property holds for non-pluripolar products with small unbounded
+locus.
+",0,0,1,0,0,0
+14824,Fast and high-quality tetrahedral mesh generation from neuroanatomical scans,"  Creating tetrahedral meshes with anatomically accurate surfaces is critically
+important for a wide range of model-based neuroimaging modalities. However,
+computationally efficient brain meshing algorithms and software are greatly
+lacking. Here, we report a fully automated open-source software to rapidly
+create high-quality tetrahedral meshes from brain segmentations. Built upon
+various open-source meshing utilities, the proposed meshing workflow allows
+robust generation of complex head and brain mesh models from multi-label
+volumes, tissue probability maps, surface meshes and their combinations. The
+quality of the complex tissue boundaries is preserved through a surface-based
+approach, allowing fine-grained control over the sizes and quality of the mesh
+elements through explicit user-defined meshing criteria. The proposed meshing
+pipeline is highly versatile and compatible with many commonly used brain
+analysis tools, including SPM, FSL, FreeSurfer, and BrainSuite. With this
+mesh-generation pipeline, we demonstrate that one can generate 3D full-head
+meshes that combine scalp, skull, cerebrospinal fluid, gray matter, white
+matter, and air cavities with a typical processing time of less than 40
+seconds. This approach can also incorporate highly detailed cortical and white
+matter surface meshes derived from FSL and FreeSurfer with tissue segmentation
+data. Finally, a high-quality brain atlas mesh library for different age
+groups, ranging from infants to elderlies, was built to demonstrate the
+robustness of the proposed workflow, as well as to serve as a common platform
+for simulation-based brain studies. Our open-source meshing software
+""brain2mesh"" and the human brain atlas mesh library can be downloaded at
+this http URL.
+",0,1,0,0,0,0
+14825,Curriculum Dropout,"  Dropout is a very effective way of regularizing neural networks.
+Stochastically ""dropping out"" units with a certain probability discourages
+over-specific co-adaptations of feature detectors, preventing overfitting and
+improving network generalization. Besides, Dropout can be interpreted as an
+approximate model aggregation technique, where an exponential number of smaller
+networks are averaged in order to get a more powerful ensemble. In this paper,
+we show that using a fixed dropout probability during training is a suboptimal
+choice. We thus propose a time scheduling for the probability of retaining
+neurons in the network. This induces an adaptive regularization scheme that
+smoothly increases the difficulty of the optimization problem. This idea of
+""starting easy"" and adaptively increasing the difficulty of the learning
+problem has its roots in curriculum learning and allows one to train better
+models. Indeed, we prove that our optimization strategy implements a very
+general curriculum scheme, by gradually adding noise to both the input and
+intermediate feature representations within the network architecture.
+Experiments on seven image classification datasets and different network
+architectures show that our method, named Curriculum Dropout, frequently yields
+to better generalization and, at worst, performs just as well as the standard
+Dropout method.
+",1,0,0,1,0,0
+14826,Self-dual and logarithmic representations of the twisted Heisenberg--Virasoro algebra at level zero,"  This paper is a continuation of arXiv:1405.1707. We present certain new
+applications and generalizations of the free field realization of the twisted
+Heisenberg-Virasoro algebra ${\mathcal H}$ at level zero.
+We find explicit formulas for singular vectors in certain Verma modules. A
+free field realization of self-dual modules for ${\mathcal H}$ is presented by
+combining a bosonic construction of Whittaker modules from arXiv:1409.5354 with
+a construction of logarithmic modules for vertex algebras. As an application,
+we prove that there exists a non-split self-extension of irreducible self-dual
+module which is a logarithmic module of rank two.
+We construct a large family of logarithmic modules containing different types
+of highest weight modules as subquotients. We believe that these logarithmic
+modules are related with projective covers of irreducible modules in a suitable
+category of ${\mathcal H}$-modules.
+",0,0,1,0,0,0
+14827,Distance weighted discrimination of face images for gender classification,"  We illustrate the advantages of distance weighted discrimination for
+classification and feature extraction in a High Dimension Low Sample Size
+(HDLSS) situation. The HDLSS context is a gender classification problem of face
+images in which the dimension of the data is several orders of magnitude larger
+than the sample size. We compare distance weighted discrimination with Fisher's
+linear discriminant, support vector machines, and principal component analysis
+by exploring their classification interpretation through insightful
+visuanimations and by examining the classifiers' discriminant errors. This
+analysis enables us to make new contributions to the understanding of the
+drivers of human discrimination between males and females.
+",1,0,0,1,0,0
+14828,Partition-based Unscented Kalman Filter for Reconfigurable Battery Pack State Estimation using an Electrochemical Model,"  Accurate state estimation of large-scale lithium-ion battery packs is
+necessary for the advanced control of batteries, which could potentially
+increase their lifetime through e.g. reconfiguration. To tackle this problem,
+an enhanced reduced-order electrochemical model is used here. This model allows
+considering a wider operating range and thermal coupling between cells, the
+latter turning out to be significant. The resulting nonlinear model is
+exploited for state estimation through unscented Kalman filters (UKF). A sensor
+network composed of one sensor node per battery cell is deployed. Each sensor
+node is equipped with a local UKF, which uses available local measurements
+together with additional information coming from neighboring sensor nodes. Such
+state estimation scheme gives rise to a partition-based unscented Kalman filter
+(PUKF). The method is validated on data from a detailed simulator for a battery
+pack comprised of six cells, with reconfiguration capabilities. The results
+show that the distributed approach outperforms the centralized one in terms of
+computation time at the expense of a very low increase of mean-square
+estimation error.
+",1,0,0,0,0,0
+14829,Towards Principled Methods for Training Generative Adversarial Networks,"  The goal of this paper is not to introduce a single algorithm or method, but
+to make theoretical steps towards fully understanding the training dynamics of
+generative adversarial networks. In order to substantiate our theoretical
+analysis, we perform targeted experiments to verify our assumptions, illustrate
+our claims, and quantify the phenomena. This paper is divided into three
+sections. The first section introduces the problem at hand. The second section
+is dedicated to studying and proving rigorously the problems including
+instability and saturation that arize when training generative adversarial
+networks. The third section examines a practical and theoretically grounded
+direction towards solving these problems, while introducing new tools to study
+them.
+",1,0,0,1,0,0
+14830,Co-Clustering for Multitask Learning,"  This paper presents a new multitask learning framework that learns a shared
+representation among the tasks, incorporating both task and feature clusters.
+The jointly-induced clusters yield a shared latent subspace where task
+relationships are learned more effectively and more generally than in
+state-of-the-art multitask learning methods. The proposed general framework
+enables the derivation of more specific or restricted state-of-the-art
+multitask methods. The paper also proposes a highly-scalable multitask learning
+algorithm, based on the new framework, using conjugate gradient descent and
+generalized \textit{Sylvester equations}. Experimental results on synthetic and
+benchmark datasets show that the proposed method systematically outperforms
+several state-of-the-art multitask learning methods.
+",1,0,0,1,0,0
+14831,Avoiding Communication in Proximal Methods for Convex Optimization Problems,"  The fast iterative soft thresholding algorithm (FISTA) is used to solve
+convex regularized optimization problems in machine learning. Distributed
+implementations of the algorithm have become popular since they enable the
+analysis of large datasets. However, existing formulations of FISTA communicate
+data at every iteration which reduces its performance on modern distributed
+architectures. The communication costs of FISTA, including bandwidth and
+latency costs, is closely tied to the mathematical formulation of the
+algorithm. This work reformulates FISTA to communicate data at every k
+iterations and reduce data communication when operating on large data sets. We
+formulate the algorithm for two different optimization methods on the Lasso
+problem and show that the latency cost is reduced by a factor of k while
+bandwidth and floating-point operation costs remain the same. The convergence
+rates and stability properties of the reformulated algorithms are similar to
+the standard formulations. The performance of communication-avoiding FISTA and
+Proximal Newton methods is evaluated on 1 to 1024 nodes for multiple benchmarks
+and demonstrate average speedups of 3-10x with scaling properties that
+outperform the classical algorithms.
+",1,0,0,0,0,0
+14832,Stochastic Development Regression on Non-Linear Manifolds,"  We introduce a regression model for data on non-linear manifolds. The model
+describes the relation between a set of manifold valued observations, such as
+shapes of anatomical objects, and Euclidean explanatory variables. The approach
+is based on stochastic development of Euclidean diffusion processes to the
+manifold. Defining the data distribution as the transition distribution of the
+mapped stochastic process, parameters of the model, the non-linear analogue of
+design matrix and intercept, are found via maximum likelihood. The model is
+intrinsically related to the geometry encoded in the connection of the
+manifold. We propose an estimation procedure which applies the Laplace
+approximation of the likelihood function. A simulation study of the performance
+of the model is performed and the model is applied to a real dataset of Corpus
+Callosum shapes.
+",1,0,0,0,0,0
+14833,Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks,"  A Convolutional Neural Network was used to predict kidney function in
+patients with chronic kidney disease from high-resolution digital pathology
+scans of their kidney biopsies. Kidney biopsies were taken from participants of
+the NEPTUNE study, a longitudinal cohort study whose goal is to set up
+infrastructure for observing the evolution of 3 forms of idiopathic nephrotic
+syndrome, including developing predictors for progression of kidney disease.
+The knowledge of future kidney function is desirable as it can identify
+high-risk patients and influence treatment decisions, reducing the likelihood
+of irreversible kidney decline.
+",0,0,0,1,0,0
+14834,Weighing neutrinos in dynamical dark energy models,"  We briefly review the recent results of constraining neutrino mass in
+dynamical dark energy models using cosmological observations and summarize the
+findings. (i) In dynamical dark energy models, compared to $\Lambda$CDM, the
+upper limit of $\sum m_\nu$ can become larger and can also become smaller. In
+the cases of phantom and early phantom (i.e., the quintom evolving from $w<-1$
+to $w>-1$), the constraint on $\sum m_\nu$ becomes looser; but in the cases of
+quintessence and early quintessence (i.e., the quintom evolving from $w>-1$ to
+$w<-1$), the constraint on $\sum m_\nu$ becomes tighter. (ii) In the
+holographic dark energy (HDE) model, the tightest constraint on $\sum m_\nu$,
+i.e., $\sum m_\nu<0.105$ eV, is obtained, which is almost equal to the lower
+limit of $\sum m_\nu$ of IH case. Thus, it is of great interest to find that
+the future neutrino oscillation experiments would potentially offer a possible
+falsifying scheme for the HDE model. (iii) The mass splitting of neutrinos can
+influence the cosmological fits. We find that the NH case fits the current
+observations slightly better than the IH case, although the difference of
+$\chi^2$ of the two cases is still not significant enough to definitely
+distinguish the neutrino mass hierarchy.
+",0,1,0,0,0,0
+14835,Can supersymmetry emerge at a quantum critical point?,"  Supersymmetry plays an important role in superstring theory and particle
+physics, but has never been observed in experiments. At certain quantum
+critical points of condensed matter systems, the fermionic excitations are
+gapless due to the special electronic structure whereas the bosonic order
+parameter is automatically gapless, offering a promising platform to realize
+emergent supersymmetry by tuning a single parameter. Here, we study under what
+circumstances can supersymmetry emerge in a quantum critical system. We
+demonstrate that the Yukawa-type coupling between the gapless fermion and boson
+may induce a number of highly nonlocal self-interacting terms in the effective
+field theory of the boson. Only when such terms do not exist or are irrelevant,
+could supersymmetry have the chance to be dynamically generated at low
+energies. This strong constraint provides an important guidance for the
+exploration of emergent supersymmetry in various condensed matter systems, and
+also should be carefully considered in the study of quantum critical behaviors.
+",0,1,0,0,0,0
+14836,Five-parameter potential box with inverse square singular boundaries,"  Using the Tridiagonal Representation Approach, we obtain solutions (energy
+spectrum and corresponding wavefunctions) for a new five-parameter potential
+box with inverse square singularity at the boundaries.
+",0,1,0,0,0,0
+14837,Succinctness in subsystems of the spatial mu-calculus,"  In this paper we systematically explore questions of succinctness in modal
+logics employed in spatial reasoning. We show that the closure operator,
+despite being less expressive, is exponentially more succinct than the
+limit-point operator, and that the $\mu$-calculus is exponentially more
+succinct than the equally-expressive tangled limit operator. These results hold
+for any class of spaces containing at least one crowded metric space or
+containing all spaces based on ordinals below $\omega^\omega$, with the usual
+limit operator. We also show that these results continue to hold even if we
+enrich the less succinct language with the universal modality.
+",0,0,1,0,0,0
+14838,Evidence for electronically-driven ferroelectricity in the family of strongly correlated dimerized BEDT-TTF molecular conductors,"  By applying measurements of the dielectric constants and relative length
+changes to the dimerized molecular conductor
+$\kappa$-(BEDT-TTF)$_2$Hg(SCN)$_2$Cl, we provide evidence for order-disorder
+type electronic ferroelectricity which is driven by charge order within the
+(BEDT-TTF)$_2$ dimers and stabilized by a coupling to the anions. According to
+our density functional theory calculations, this material is characterized by a
+moderate strength of dimerization. This system thus bridges the gap between
+strongly dimerized materials, often approximated as dimer-Mott systems at 1/2
+filling, and non- or weakly dimerized systems at 1/4 filling exhibiting charge
+order. Our results indicate that intra-dimer charge degrees of freedom are of
+particular importance in correlated $\kappa$-(BEDT-TTF)$_2$X salts and can
+create novel states, such as electronically-driven multiferroicity or
+charge-order-induced quasi-1D spin liquids.
+",0,1,0,0,0,0
+14839,Testing FLUKA on neutron activation of Si and Ge at nuclear research reactor using gamma spectroscopy,"  Samples of two characteristic semiconductor sensor materials, silicon and
+germanium, have been irradiated with neutrons produced at the RP-10 Nuclear
+Research Reactor at 4.5 MW. Their radionuclides photon spectra have been
+measured with high resolution gamma spectroscopy, quantifying four
+radioisotopes ($^{28}$Al, $^{29}$Al for Si and $^{75}$Ge and $^{77}$Ge for Ge).
+We have compared the radionuclides production and their emission spectrum data
+with Monte Carlo simulation results from FLUKA. Thus we have tested FLUKA's low
+energy neutron library (ENDF/B-VIIR) and decay photon scoring with respect to
+the activation of these semiconductors. We conclude that FLUKA is capable of
+predicting relative photon peak amplitudes, with gamma intensities greater than
+1%, of produced radionuclides with an average uncertainty of 13%. This work
+allows us to estimate the corresponding systematic error on neutron activation
+simulation studies of these sensor materials.
+",0,1,0,0,0,0
+14840,Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes,"  Databases are widespread, yet extracting relevant data can be difficult.
+Without substantial domain knowledge, multivariate search queries often return
+sparse or uninformative results. This paper introduces an approach for
+searching structured data based on probabilistic programming and nonparametric
+Bayes. Users specify queries in a probabilistic language that combines standard
+SQL database search operators with an information theoretic ranking function
+called predictive relevance. Predictive relevance can be calculated by a fast
+sparse matrix algorithm based on posterior samples from CrossCat, a
+nonparametric Bayesian model for high-dimensional, heterogeneously-typed data
+tables. The result is a flexible search technique that applies to a broad class
+of information retrieval problems, which we integrate into BayesDB, a
+probabilistic programming platform for probabilistic data analysis. This paper
+demonstrates applications to databases of US colleges, global macroeconomic
+indicators of public health, and classic cars. We found that human evaluators
+often prefer the results from probabilistic search to results from a standard
+baseline.
+",1,0,0,1,0,0
+14841,Superposition of p-superharmonic functions,"  The Dominative $p$-Laplace Operator is introduced. This operator is a
+relative to the $p$-Laplacian, but with the distinguishing property of being
+sublinear. It explains the superposition principle in the $p$-Laplace Equation.
+",0,0,1,0,0,0
+14842,Ramsey properties and extending partial automorphisms for classes of finite structures,"  We show that every free amalgamation class of finite structures with
+relations and (symmetric) partial functions is a Ramsey class when enriched by
+a free linear ordering of vertices. This is a common strengthening of the
+Nešetřil-Rödl Theorem and the second and third authors' Ramsey
+theorem for finite models (that is, structures with both relations and
+functions). We also find subclasses with the ordering property. For languages
+with relational symbols and unary functions we also show the extension property
+for partial automorphisms (EPPA) of free amalgamation classes. These general
+results solve several conjectures and provide an easy Ramseyness test for many
+classes of structures.
+",1,0,1,0,0,0
+14843,Estimation of the shape of the density contours of star-shaped distributions,"  Elliptically contoured distributions generalize the multivariate normal
+distributions in such a way that the density generators need not be
+exponential. However, as the name suggests, elliptically contoured
+distributions remain to be restricted in that the similar density contours
+ought to be elliptical. Kamiya, Takemura and Kuriki [Star-shaped distributions
+and their generalizations, Journal of Statistical Planning and Inference 138
+(2008), 3429--3447] proposed star-shaped distributions, for which the density
+contours are allowed to be boundaries of arbitrary similar star-shaped sets. In
+the present paper, we propose a nonparametric estimator of the shape of the
+density contours of star-shaped distributions, and prove its strong consistency
+with respect to the Hausdorff distance. We illustrate our estimator by
+simulation.
+",0,0,1,1,0,0
+14844,Aggregated Pairwise Classification of Statistical Shapes,"  The classification of shapes is of great interest in diverse areas ranging
+from medical imaging to computer vision and beyond. While many statistical
+frameworks have been developed for the classification problem, most are
+strongly tied to early formulations of the problem - with an object to be
+classified described as a vector in a relatively low-dimensional Euclidean
+space. Statistical shape data have two main properties that suggest a need for
+a novel approach: (i) shapes are inherently infinite dimensional with strong
+dependence among the positions of nearby points, and (ii) shape space is not
+Euclidean, but is fundamentally curved. To accommodate these features of the
+data, we work with the square-root velocity function of the curves to provide a
+useful formal description of the shape, pass to tangent spaces of the manifold
+of shapes at different projection points which effectively separate shapes for
+pairwise classification in the training data, and use principal components
+within these tangent spaces to reduce dimensionality. We illustrate the impact
+of the projection point and choice of subspace on the misclassification rate
+with a novel method of combining pairwise classifiers.
+",1,0,0,1,0,0
+14845,The MUSE-Wide survey: Detection of a clustering signal from Lyman-α-emitters at 3<z<6,"  We present a clustering analysis of a sample of 238 Ly{$\alpha$}-emitters at
+redshift 3<z<6 from the MUSE-Wide survey. This survey mosaics extragalactic
+legacy fields with 1h MUSE pointings to detect statistically relevant samples
+of emission line galaxies. We analysed the first year observations from
+MUSE-Wide making use of the clustering signal in the line-of-sight direction.
+This method relies on comparing pair-counts at close redshifts for a fixed
+transverse distance and thus exploits the full potential of the redshift range
+covered by our sample. A clear clustering signal with a correlation length of
+r0 = 2.9(+1.0/-1.1) Mpc (comoving) is detected. Whilst this result is based on
+only about a quarter of the full survey size, it already shows the immense
+potential of MUSE for efficiently observing and studying the clustering of
+Ly{$\alpha$}-emitters.
+",0,1,0,0,0,0
+14846,On Polymorphic Sessions and Functions: A Tale of Two (Fully Abstract) Encodings,"  This work exploits the logical foundation of session types to determine what
+kind of type discipline for the pi-calculus can exactly capture, and is
+captured by, lambda-calculus behaviours. Leveraging the proof theoretic content
+of the soundness and completeness of sequent calculus and natural deduction
+presentations of linear logic, we develop the first mutually inverse and fully
+abstract processes-as-functions and functions-as-processes encodings between a
+polymorphic session pi-calculus and a linear formulation of System F. We are
+then able to derive results of the session calculus from the theory of the
+lambda-calculus: (1) we obtain a characterisation of inductive and coinductive
+session types via their algebraic representations in System F; and (2) we
+extend our results to account for value and process passing, entailing strong
+normalisation.
+",1,0,0,0,0,0
+14847,Effects of initial spatial phase in radiative neutrino pair emission,"  We study radiative neutrino pair emission in deexcitation process of atoms
+taking into account coherence effect in a macroscopic target system. In the
+course of preparing the coherent initial state to enhance the rate, a spatial
+phase factor is imprinted in the macroscopic target. It is shown that this
+initial spatial phase changes the kinematics of the radiative neutrino pair
+emission. We investigate effects of the initial spatial phase in the photon
+spectrum of the process. It turns out that the initial spatial phase provides
+us significant improvements in exploring neutrino physics such as the
+Dirac-Majorana distinction and the cosmic neutrino background.
+",0,1,0,0,0,0
+14848,Finite-sample risk bounds for maximum likelihood estimation with arbitrary penalties,"  The MDL two-part coding $ \textit{index of resolvability} $ provides a
+finite-sample upper bound on the statistical risk of penalized likelihood
+estimators over countable models. However, the bound does not apply to
+unpenalized maximum likelihood estimation or procedures with exceedingly small
+penalties. In this paper, we point out a more general inequality that holds for
+arbitrary penalties. In addition, this approach makes it possible to derive
+exact risk bounds of order $1/n$ for iid parametric models, which improves on
+the order $(\log n)/n$ resolvability bounds. We conclude by discussing
+implications for adaptive estimation.
+",0,0,1,1,0,0
+14849,Integrated Modeling of Second Phase Precipitation in Cold-Worked 316 Stainless Steels under Irradiation,"  The current work combines the Cluster Dynamics (CD) technique and
+CALPHAD-based precipitation modeling to address the second phase precipitation
+in cold-worked (CW) 316 stainless steels (SS) under irradiation at 300-400 C.
+CD provides the radiation enhanced diffusion and dislocation evolution as
+inputs for the precipitation model. The CALPHAD-based precipitation model
+treats the nucleation, growth and coarsening of precipitation processes based
+on classical nucleation theory and evolution equations, and simulates the
+composition, size and size distribution of precipitate phases. We benchmark the
+model against available experimental data at fast reactor conditions (9.4 x
+10^-7 dpa/s and 390 C) and then use the model to predict the phase instability
+of CW 316 SS under light water reactor (LWR) extended life conditions (7 x
+10^-8 dpa/s and 275 C). The model accurately predicts the gamma-prime (Ni3Si)
+precipitation evolution under fast reactor conditions and that the formation of
+this phase is dominated by radiation enhanced segregation. The model also
+predicts a carbide volume fraction that agrees well with available experimental
+data from a PWR reactor but is much higher than the volume fraction observed in
+fast reactors. We propose that radiation enhanced dissolution and/or carbon
+depletion at sinks that occurs at high flux could be the main sources of this
+inconsistency. The integrated model predicts ~1.2% volume fraction for carbide
+and ~3.0% volume fraction for gamma-prime for typical CW 316 SS (with 0.054
+wt.% carbon) under LWR extended life conditions. This work provides valuable
+insights into the magnitudes and mechanisms of precipitation in irradiated CW
+316 SS for nuclear applications.
+",0,1,0,0,0,0
+14850,Emergent topological superconductivity at nematic domain wall of FeSe,"  One dimensional hybrid systems play an important role in the search for
+topological superconductivity. Nevertheless, all one dimensional hybrid systems
+so far have been externally defined. Here we show that one-dimensional domain
+wall in a nematic superconductor can serve as an emergent hybrid system in the
+presence of spin-orbit coupling. As a concrete setting we study the domain wall
+between nematic domains in FeSe, which is well established to be a nematic
+superconductor. We first show on the symmetry grounds that spin-triplet pairing
+can be induced at the domain wall by constructing a Ginzburg-Landau theory. We
+then demonstrate using Bogoliubov-de Gennes approach that such nematic domain
+wall supports zero energy bound states which would satisfy Majorana condition.
+Well-known existence of these domain walls at relatively high temperatures,
+which can in principle be located and investigated with scanning tunneling
+microscopy, presents new opportunities for a search for realization of Majorana
+bound states.
+",0,1,0,0,0,0
+14851,Linear and nonlinear market correlations: characterizing financial crises and portfolio optimization,"  Pearson correlation and mutual information based complex networks of the
+day-to-day returns of US S&P500 stocks between 1985 and 2015 have been
+constructed in order to investigate the mutual dependencies of the stocks and
+their nature. We show that both networks detect qualitative differences
+especially during (recent) turbulent market periods thus indicating strongly
+fluctuating interconnections between the stocks of different companies in
+changing economic environments. A measure for the strength of nonlinear
+dependencies is derived using surrogate data and leads to interesting
+observations during periods of financial market crises. In contrast to the
+expectation that dependencies reduce mainly to linear correlations during
+crises we show that (at least in the 2008 crisis) nonlinear effects are
+significantly increasing. It turns out that the concept of centrality within a
+network could potentially be used as some kind of an early warning indicator
+for abnormal market behavior as we demonstrate with the example of the 2008
+subprime mortgage crisis. Finally, we apply a Markowitz mean variance portfolio
+optimization and integrate the measure of nonlinear dependencies to scale the
+investment exposure. This leads to significant outperformance as compared to a
+fully invested portfolio.
+",0,1,0,0,0,0
+14852,An explicit projective bimodule resolution of a Leavitt path algebra,"  We construct an explicit projective bimodule resolution for the Leavitt path
+algebra of a row-finite quiver. We prove that the Leavitt path algebra of a
+row-countable quiver has Hochschild cohomolgical dimension at most one, that
+is, it is quasi-free in the sense of Cuntz-Quillen. The construction of the
+resolution relies on an explicit derivation of the Leavitt path algebra.
+",0,0,1,0,0,0
+14853,Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks,"  Modern deep neural networks (DNNs) spend a large amount of their execution
+time computing convolutions. Winograd's minimal algorithm for small
+convolutions can greatly reduce the number of arithmetic operations. However, a
+large reduction in floating point (FP) operations in these algorithms can
+result in poor numeric accuracy. In this paper we analyse the FP error and
+prove boundaries on the error. We show that the ""modified"" algorithm gives a
+significantly better accuracy of the result. We propose several methods for
+reducing FP error of these algorithms. Minimal convolution algorithms depend on
+the selection of several numeric \textit{points} that have a large impact on
+the accuracy of the result. We propose a canonical evaluation ordering that
+both reduces FP error and the size of the search space based on Huffman coding.
+We study point selection experimentally, and find empirically good points. We
+also identify the main factors that associated with sets of points that result
+in a low error. In addition, we explore other methods to reduce FP error,
+including mixed-precision convolution, and pairwise addition across DNN
+channels. Using our methods we can significantly reduce FP error for a given
+block size, which allows larger block sizes and reduced computation.
+",1,0,0,1,0,0
+14854,Active Learning amidst Logical Knowledge,"  Structured prediction is ubiquitous in applications of machine learning such
+as knowledge extraction and natural language processing. Structure often can be
+formulated in terms of logical constraints. We consider the question of how to
+perform efficient active learning in the presence of logical constraints among
+variables inferred by different classifiers. We propose several methods and
+provide theoretical results that demonstrate the inappropriateness of employing
+uncertainty guided sampling, a commonly used active learning method.
+Furthermore, experiments on ten different datasets demonstrate that the methods
+significantly outperform alternatives in practice. The results are of practical
+significance in situations where labeled data is scarce.
+",1,0,0,0,0,0
+14855,On the Limiting Stokes' Wave of Extreme Height in Arbitrary Water Depth,"  As mentioned by Schwartz (1974) and Cokelet (1977), it was failed to gain
+convergent results of limiting Stokes' waves in extremely shallow water by
+means of perturbation methods even with the aid of extrapolation techniques
+such as Padé approximant. Especially, it is extremely difficult for
+traditional analytic/numerical approaches to present the wave profile of
+limiting waves with a sharp crest of $120^\circ$ included angle first mentioned
+by Stokes in 1880s. Thus, traditionally, different wave models are used for
+waves in different water depths. In this paper, by means of the homotopy
+analysis method (HAM), an analytic approximation method for highly nonlinear
+equations, we successfully gain convergent results (and especially the wave
+profiles) of the limiting Stokes' waves with this kind of sharp crest in
+arbitrary water depth, even including solitary waves of extreme form in
+extremely shallow water, without using any extrapolation techniques. Therefore,
+in the frame of the HAM, the Stokes' wave can be used as a unified theory for
+all kinds of waves, including periodic waves in deep and intermediate depth,
+cnoidal waves in shallow water and solitary waves in extremely shallow water.
+",0,1,0,0,0,0
+14856,Pressure impact on the stability and distortion of the crystal structure of CeScO3,"  The effects of high pressure on the crystal structure of orthorhombic (Pnma)
+perovskite type cerium scandate have been studied in situ under high pressure
+by means of synchrotron x-ray powder diffraction, using a diamond anvil cell.
+We have found that the perovskite type crystal structure remains stable up to
+40 GPa, the highest pressure reached in the experiments. The evolution of
+unit-cell parameters with pressure has indicated an anisotropic compression.
+The room-temperature pressure-volume equation of state is obtained from the
+experiments. From the evolution of microscopic structural parameters like bond
+distances and coordination polyhedra of cerium and scandium, the macroscopic
+behavior of CeScO3 under compression has been explained and reasoned for its
+large pressure stability. The reported results are discussed in comparison with
+high-pressure results from other perovskites.
+",0,1,0,0,0,0
+14857,The system of cloud oriented learning tools as an element of educational and scientific environment of high school,"  The aim of this research is to design and implementation of cloud based
+learning environment for separate division of the university. The analysis of
+existing approaches to the construction of cloud based learning environments,
+the formation of requirements cloud based learning tools, the selection on the
+basis of these requirements, cloud ICT training and pilot their use for
+building cloud based learning environment for separate division of the
+university with the use of open source software and resources its own IT
+infrastructure of the institution. Results of the study is planned to
+generalize to develop recommendations for the design of cloud based environment
+of high school.
+",1,0,0,0,0,0
+14858,Chemical abundances of two extragalactic young massive clusters,"  We use integrated-light spectroscopic observations to measure metallicities
+and chemical abundances for two extragalactic young massive star clusters
+(NGC1313-379 and NGC1705-1). The spectra were obtained with the X-Shooter
+spectrograph on the ESO Very Large Telescope. We compute synthetic
+integrated-light spectra, based on colour-magnitude diagrams for the brightest
+stars in the clusters from Hubble Space Telescope photometry and theoretical
+isochrones. Furthermore, we test the uncertainties arising from the use of
+Colour Magnitude Diagram (CMD) +Isochrone method compared to an Isochrone-Only
+method. The abundances of the model spectra are iteratively adjusted until the
+best fit to the observations is obtained. In this work we mainly focus on the
+optical part of the spectra. We find metallicities of [Fe/H] = $-$0.84 $\pm$
+0.07 and [Fe/H] = $-$0.78 $\pm$ 0.10 for NGC1313-379 and NGC1705-1,
+respectively. We measure [$\alpha$/Fe]=$+$0.06 $\pm$ 0.11 for NGC1313-379 and a
+super-solar [$\alpha$/Fe]=$+$0.32 $\pm$ 0.12 for NGC1705-1. The roughly solar
+[$\alpha$/Fe] ratio in NGC1313-379 resembles those for young stellar
+populations in the Milky Way (MW) and the Magellanic Clouds, whereas the
+enhanced [$\alpha$/Fe] ratio in NGC1705-1 is similar to that found for the
+cluster NGC1569-B by previous studies. Such super-solar [$\alpha$/Fe] ratios
+are also predicted by chemical evolution models that incorporate the bursty
+star formation histories of these dwarf galaxies. Furthermore, our
+$\alpha$-element abundances agree with abundance measurements from H II regions
+in both galaxies. In general we derive Fe-peak abundances similar to those
+observed in the MW and Large Magellanic Cloud (LMC) for both young massive
+clusters. For these elements, however, we recommend higher-resolution
+observations to improve the Fe-peak abundance measurements.
+",0,1,0,0,0,0
+14859,Clingo goes Linear Constraints over Reals and Integers,"  The recent series 5 of the ASP system clingo provides generic means to
+enhance basic Answer Set Programming (ASP) with theory reasoning capabilities.
+We instantiate this framework with different forms of linear constraints,
+discuss the respective implementations, and present techniques of how to use
+these constraints in a reactive context. More precisely, we introduce
+extensions to clingo with difference and linear constraints over integers and
+reals, respectively, and realize them in complementary ways. Finally, we
+empirically evaluate the resulting clingo derivatives clingo[dl] and clingo[lp]
+on common fragments and contrast them to related ASP systems.
+This paper is under consideration for acceptance in TPLP.
+",1,0,0,0,0,0
+14860,Dynamic Uplink/Downlink Resource Management in Flexible Duplex-Enabled Wireless Networks,"  Flexible duplex is proposed to adapt to the channel and traffic asymmetry for
+future wireless networks. In this paper, we propose two novel algorithms within
+the flexible duplex framework for joint uplink and downlink resource allocation
+in multi-cell scenario, named SAFP and RMDI, based on the awareness of
+interference coupling among wireless links. Numerical results show significant
+performance gain over the baseline system with fixed uplink/downlink resource
+configuration, and over the dynamic TDD scheme that independently adapts the
+configuration to time-varying traffic volume in each cell. The proposed
+algorithms achieve two-fold increase when compared with the baseline scheme,
+measured by the worst-case quality of service satisfaction level, under a low
+level of traffic asymmetry. The gain is more significant when the traffic is
+highly asymmetric, as it achieves three-fold increase.
+",1,0,0,0,0,0
+14861,Carleman estimates for forward and backward stochastic fourth order Schrödinger equations and their applications,"  In this paper, we establish the Carleman estimates for forward and backward
+stochastic fourth order Schrödinger equations, on basis of which, we can
+obtain the observability, unique continuation property and the exact
+controllability for the forward and backward stochastic fourth order
+Schrödinger equations.
+",0,0,1,0,0,0
+14862,Reply to comment on `Poynting flux in the neighbourhood of a point charge in arbitrary motion and the radiative power losses',"  Doubts have been expressed in a comment (Eur. J. Phys., 39, 018001, 2018),
+about the tenability of the formulation for radiative losses in our recent
+published work (Eur. J. Phys., 37, 045210, 2016). We provide our reply to the
+comment. In particular, it is pointed out that one need to clearly distinguish
+between the rate of the energy-momentum being carried by the electromagnetic
+radiation to far-off space, and that of the mechanical energy-momentum losses
+being incurred by the radiating charge. It is also demonstrated that while the
+Poynting flux is always positive through a spherical surface centred on the
+retarded position of the charge, it could surprisingly be negative through a
+surface centred on the ""present"" position of the charge. It is further shown
+that the mysterious Schott term, hitherto thought in literature to arise from
+some acceleration-dependent energy in fields, is actually nothing but the
+difference in rate of change of energy in self-fields of the charge between the
+retarded and present times.
+",0,1,0,0,0,0
+14863,Delegated Causality of Complex Systems,"  A notion of delegated causality is introduced. This subtle kind of causality
+is dual to interventional causality. Delegated causality elucidates the causal
+role of dynamical systems at the ""edge of chaos"", explicates evident cases of
+downward causation, and relates emergent phenomena to Godel's incompleteness
+theorem. Apparently rich implications are noticed in biology and Chinese
+philosophy.
+",0,1,0,0,0,0
+14864,Resonating Valence Bond Theory of Superconductivity: Beyond Cuprates,"  Resonating valence bond (RVB) theory of high Tc superconductivity, an
+electron correlation based mechanism, began as an insightful response by
+Anderson, to Bednorz and Muller's discovery of high Tc superconductivity in
+cuprates in late 1986. Shortly a theoretical framework for quantum spin liquids
+and superconductivity was developed. This theory adresses a formidable strong
+coupling quantum manybody problem, in modern times. It is built on certain key
+experimental facts: i) survival of a dynamical Mott localization in a metallic
+state, ii) proliferation of bond singlets and iii) absence of fermi liquid
+quasi particles. After summarising RVB theory I will provide an aerial view of,
+mostly, new superconductors where I believe that, to a large degree RVB
+mechanism is at work and indicate prospects for even higher Tc's.
+",0,1,0,0,0,0
+14865,Efficient and accurate numerical schemes for a hydrodynamically coupled phase field diblock copolymer model,"  In this paper, we consider numerical approximations of a hydrodynamically
+coupled phase field diblock copolymer model, in which the free energy contains
+a kinetic potential, a gradient entropy, a Ginzburg-Landau double well
+potential, and a long range nonlocal type potential. We develop a set of second
+order time marching schemes for this system using the ""Invariant Energy
+Quadratization"" approach for the double well potential, the projection method
+for the Navier-Stokes equation, and a subtle implicit-explicit treatment for
+the stress and convective term. The resulting schemes are linear and lead to
+symmetric positive definite systems at each time step, thus they can be
+efficiently solved. We further prove that these schemes are unconditionally
+energy stable. Various numerical experiments are performed to validate the
+accuracy and energy stability of the proposed schemes.
+",0,0,1,0,0,0
+14866,Brownian Motion of a Classical Particle in Quantum Environment,"  The Klein-Kramers equation, governing the Brownian motion of a classical
+particle in quantum environment under the action of an arbitrary external
+potential, is derived. Quantum temperature and friction operators are
+introduced and at large friction the corresponding Smoluchowski equation is
+obtained. Introducing the Bohm quantum potential, this Smoluchowski equation is
+extended to describe the Brownian motion of a quantum particle in quantum
+environment.
+",0,1,0,0,0,0
+14867,Hierarchical Policy Search via Return-Weighted Density Estimation,"  Learning an optimal policy from a multi-modal reward function is a
+challenging problem in reinforcement learning (RL). Hierarchical RL (HRL)
+tackles this problem by learning a hierarchical policy, where multiple option
+policies are in charge of different strategies corresponding to modes of a
+reward function and a gating policy selects the best option for a given
+context. Although HRL has been demonstrated to be promising, current
+state-of-the-art methods cannot still perform well in complex real-world
+problems due to the difficulty of identifying modes of the reward function. In
+this paper, we propose a novel method called hierarchical policy search via
+return-weighted density estimation (HPSDE), which can efficiently identify the
+modes through density estimation with return-weighted importance sampling. Our
+proposed method finds option policies corresponding to the modes of the return
+function and automatically determines the number and the location of option
+policies, which significantly reduces the burden of hyper-parameters tuning.
+Through experiments, we demonstrate that the proposed HPSDE successfully learns
+option policies corresponding to modes of the return function and that it can
+be successfully applied to a challenging motion planning problem of a redundant
+robotic manipulator.
+",1,0,0,1,0,0
+14868,Two-dimensional Fourier transformations and Mordell integrals,"  Several Fourier transformations of functions of one and two variables are
+evaluated and then used to derive some integral and series identities. It is
+shown that certain two- dimensional Mordell integrals factorize into product of
+two integrals and that the square of the absolute value of the Mordell integral
+can be reduced to a single one-dimensional integral. Some connections to
+elliptic functions and lattice sums are discussed.
+",0,0,1,0,0,0
+14869,Optimal Allocation of Static Var Compensator via Mixed Integer Conic Programming,"  Shunt FACTS devices, such as, a Static Var Compensator (SVC), are capable of
+providing local reactive power compensation. They are widely used in the
+network to reduce the real power loss and improve the voltage profile. This
+paper proposes a planning model based on mixed integer conic programming (MICP)
+to optimally allocate SVCs in the transmission network considering load
+uncertainty. The load uncertainties are represented by a number of scenarios.
+Reformulation and linearization techniques are utilized to transform the
+original non-convex model into a convex second order cone programming (SOCP)
+model. Numerical case studies based on the IEEE 30-bus system demonstrate the
+effectiveness of the proposed planning model.
+",0,0,1,0,0,0
+14870,Polarity tuning of spin-orbit-induced spin splitting in two-dimensional transition metal dichalcogenides semiconductors,"  The established spin splitting in monolayer (ML) of transition metal
+dichalcogenides (TMDs) that is caused by inversion symmetry breaking is
+dictated by mirror symmetry operations to exhibit fully out-of-plane direction
+of spin polarization. Through first-principles density functional theory
+calculations, we show that polarity-induced mirror symmetry breaking leads to
+new sizable spin splitting having in-plane spin polarization. These splittings
+are effectively controlled by tuning the polarity using biaxial strain.
+Furthermore, the admixtures of the out-of-plane and in-plane spin-polarized
+states in the strained polar systems are identified, which is expected to
+influence the spin relaxation through the Dyakonov-Perel mechanism. Our study
+clarified that the polarity-induced mirror symmetry breaking plays an important
+role in controlling the spin splitting and spin relaxation in the TMDs ML,
+which is useful for designing future spintronic devices.
+",0,1,0,0,0,0
+14871,Some parametrized dynamic priority policies for 2-class M/G/1 queues: completeness and applications,"  Completeness of a dynamic priority scheduling scheme is of fundamental
+importance for the optimal control of queues in areas as diverse as computer
+communications, communication networks, supply chains and manufacturing
+systems. Our first main contribution is to identify the mean waiting time
+completeness as a unifying aspect for four different dynamic priority
+scheduling schemes by proving their completeness and equivalence in 2-class
+M/G/1 queue. These dynamic priority schemes are earliest due date based, head
+of line priority jump, relative priority, and probabilistic priority.
+In our second main contribution, we characterize the optimal scheduling
+policies for the case studies in different domains by exploiting the
+completeness of above dynamic priority schemes. The major theme of second main
+contribution is resource allocation/optimal control in revenue management
+problems for contemporary systems such as cloud computing, high-performance
+computing, etc., where congestion is inherent. Using completeness and
+theoretically tractable nature of relative priority policy, we study the impact
+of approximation in a fairly generic data network utility framework. We
+introduce the notion of min-max fairness in multi-class queues and show that a
+simple global FCFS policy is min-max fair. Next, we re-derive the celebrated
+$c/\rho$ rule for 2-class M/G/1 queues by an elegant argument and also simplify
+a complex joint pricing and scheduling problem for a wider class of scheduling
+policies.
+",1,0,0,0,0,0
+14872,Vector bundles over classifying spaces of p-local finite groups and Benson-Carlson duality,"  In this paper we obtain a description of the Grothendieck group of complex
+vector bundles over the classifying space of a p-local finite group in terms of
+representation rings of subgroups of its Sylow. We also prove a stable elements
+formula for generalized cohomological invariants of p-local finite groups,
+which is used to show the existence of unitary embeddings of p-local finite
+groups. Finally, we show that the augmentation map for the cochains of the
+classifying space of a p-local finite group is Gorenstein in the sense of
+Dwyer-Greenlees-Iyengar and obtain some consequences about the cohomology ring
+of these classifying spaces.
+",0,0,1,0,0,0
+14873,Identification of a complete YPT1 Rab GTPase sequence from the fungal pathogen Colletotrichum incanum,"  Colletotrichum represent a genus of fungal species primarily known as plant
+pathogens with severe economic impacts in temperate, subtropical and tropical
+climates Consensus taxonomy and classification systems for Colletotrichum
+species have been undergoing revision as high resolution genomic data becomes
+available. Here we propose an alternative annotation that provides a complete
+sequence for a Colletotrichum YPT1 gene homolog using the whole genome shotgun
+sequence of Colletotrichum incanum isolated from soybean crops in Illinois,
+USA.
+",0,0,0,0,1,0
+14874,Multiplicative Structure in the Stable Splitting of $ΩSL_n(\mathbb{C})$,"  The space of based loops in $SL_n(\mathbb{C})$, also known as the affine
+Grassmannian of $SL_n(\mathbb{C})$, admits an $\mathbb{E}_2$ or fusion product.
+Work of Mitchell and Richter proves that this based loop space stably splits as
+an infinite wedge sum. We prove that the Mitchell--Richter splitting is
+coherently multiplicative, but not $\mathbb{E}_2$. Nonetheless, we show that
+the splitting becomes $\mathbb{E}_2$ after base-change to complex cobordism.
+Our proof of the $\mathbb{A}_\infty$ splitting involves on the one hand an
+analysis of the multiplicative properties of Weiss calculus, and on the other a
+use of Beilinson--Drinfeld Grassmannians to verify a conjecture of Mahowald and
+Richter. Other results are obtained by explicit, obstruction-theoretic
+computations.
+",0,0,1,0,0,0
+14875,Strange duality on rational surfaces II: higher rank cases,"  We study Le Potier's strange duality conjecture on a rational surface. We
+focus on the strange duality map $SD_{c_n^r,L}$ which involves the moduli space
+of rank $r$ sheaves with trivial first Chern class and second Chern class $n$,
+and the moduli space of 1-dimensional sheaves with determinant $L$ and Euler
+characteristic 0. We show there is an exact sequence relating the map
+$SD_{c_r^r,L}$ to $SD_{c^{r-1}_{r},L}$ and $SD_{c_r^r,L\otimes K_X}$ for all
+$r\geq1$ under some conditions on $X$ and $L$ which applies to a large number
+of cases on $\p^2$ or Hirzebruch surfaces . Also on $\mathbb{P}^2$ we show that
+for any $r>0$, $SD_{c^r_r,dH}$ is an isomorphism for $d=1,2$, injective for
+$d=3$ and moreover $SD_{c_3^3,rH}$ and $SD_{c_3^2,rH}$ are injective. At the
+end we prove that the map $SD_{c_n^2,L}$ ($n\geq2$) is an isomorphism for
+$X=\mathbb{P}^2$ or Fano rational ruled surfaces and $g_L=3$, and hence so is
+$SD_{c_3^3,L}$ as a corollary of our main result.
+",0,0,1,0,0,0
+14876,Graph learning under sparsity priors,"  Graph signals offer a very generic and natural representation for data that
+lives on networks or irregular structures. The actual data structure is however
+often unknown a priori but can sometimes be estimated from the knowledge of the
+application domain. If this is not possible, the data structure has to be
+inferred from the mere signal observations. This is exactly the problem that we
+address in this paper, under the assumption that the graph signals can be
+represented as a sparse linear combination of a few atoms of a structured graph
+dictionary. The dictionary is constructed on polynomials of the graph
+Laplacian, which can sparsely represent a general class of graph signals
+composed of localized patterns on the graph. We formulate a graph learning
+problem, whose solution provides an ideal fit between the signal observations
+and the sparse graph signal model. As the problem is non-convex, we propose to
+solve it by alternating between a signal sparse coding and a graph update step.
+We provide experimental results that outline the good graph recovery
+performance of our method, which generally compares favourably to other recent
+network inference algorithms.
+",1,0,0,1,0,0
+14877,Improving Trajectory Optimization using a Roadmap Framework,"  We present an evaluation of several representative sampling-based and
+optimization-based motion planners, and then introduce an integrated motion
+planning system which incorporates recent advances in trajectory optimization
+into a sparse roadmap framework. Through experiments in 4 common application
+scenarios with 5000 test cases each, we show that optimization-based or
+sampling-based planners alone are not effective for realistic problems where
+fast planning times are required. To the best of our knowledge, this is the
+first work that presents such a systematic and comprehensive evaluation of
+state-of-the-art motion planners, which are based on a significant amount of
+experiments. We then combine different stand-alone planners with trajectory
+optimization. The results show that the combination of our sparse roadmap and
+trajectory optimization provides superior performance over other standard
+sampling-based planners combinations. By using a multi-query roadmap instead of
+generating completely new trajectories for each planning problem, our approach
+allows for extensions such as persistent control policy information associated
+with a trajectory across planning problems. Also, the sub-optimality resulting
+from the sparsity of roadmap, as well as the unexpected disturbances from the
+environment, can both be overcome by the real-time trajectory optimization
+process.
+",1,0,0,0,0,0
+14878,Smooth and Efficient Policy Exploration for Robot Trajectory Learning,"  Many policy search algorithms have been proposed for robot learning and
+proved to be practical in real robot applications. However, there are still
+hyperparameters in the algorithms, such as the exploration rate, which requires
+manual tuning. The existing methods to design the exploration rate manually or
+automatically may not be general enough or hard to apply in the real robot. In
+this paper, we propose a learning model to update the exploration rate
+adaptively. The overall algorithm is a combination of methods proposed by other
+researchers. Smooth trajectories for the robot can be produced by the algorithm
+and the updated exploration rate maximizes the lower bound of the expected
+return. Our method is tested in the ball-in-cup problem. The results show that
+our method can receive the same learning outcome as the previous methods but
+with fewer iterations.
+",1,0,0,0,0,0
+14879,"Social Innovation and the Evolution of Creative, Sustainable Worldviews","  The ideas that we forge creatively as individuals and groups build on one
+another in a manner that is cumulative and adaptive, forming open-ended
+lineages across space and time. Thus, human culture is believed to evolve. The
+pervasiveness of cross-domain creativity--as when a song inspires a
+painting--would appear indicative of discontinuities in cultural lineages.
+However, if what evolves through culture is our worldviews--the webs of
+thoughts, ideas, and attitudes that constitutes our way of seeing being in the
+world--then the problem of discontinuities is solved. The state of a worldview
+can be affected by information assimilated in one domain, and this
+change-of-state can be expressed in another domain. In this view, the gesture,
+narrative, or artifact that constitutes a specific creative act is not what is
+evolving; it is merely the external manifestation of the state of an evolving
+worldview. Like any evolutionary process, cultural evolution requires a balance
+between novelty, via the generation of variation, and continuity, via the
+preservation of variants that are adaptive. In cultural evolution, novelty is
+generated through creativity, and continuity is provided by social learning
+processes, e.g., imitation. Both the generative and imitative aspects of
+cultural evolution are affected by social media. We discuss the trajectory from
+social ideation to social innovation, focusing on the role of
+self-organization, renewal, and perspective-taking at the individual and social
+group level.
+",0,0,0,0,1,0
+14880,Interaction between magnetic moments and itinerant carriers in d0 ferromagnetic SiC,"  Elucidating the interaction between magnetic moments and itinerant carriers
+is an important step to spintronic applications. Here, we investigate magnetic
+and transport properties in d0 ferromagnetic SiC single crystals prepared by
+postimplantation pulsed laser annealing. Magnetic moments are contributed by
+the p states of carbon atoms, but their magnetic circular dichroism is
+different from that in semi-insulating SiC samples. The anomalous Hall effect
+and negative magnetoresistance indicate the influence of d0 spin order on free
+carriers. The ferromagnetism is relatively weak in N-implanted SiC compared
+with that in Al-implanted SiC after annealing. The results suggest that d0
+magnetic moments and itinerant carriers can interact with each other, which
+will facilitate the development of SiC spintronic devices with d0
+ferromagnetism.
+",0,1,0,0,0,0
+14881,Il Fattore di Sylvester,"  Sylvester factor, an essential part of the asymptotic formula of Hardy and
+Littlewood which is the extended Goldbach conjecture, regarded as strongly
+multiplicative arithmetic function, has several remarkable properties.
+",0,0,1,0,0,0
+14882,Bright and Gap Solitons in Membrane-Type Acoustic Metamaterials,"  We study analytically and numerically envelope solitons (bright and gap
+solitons) in a one-dimensional, nonlinear acoustic metamaterial, composed of an
+air-filled waveguide periodically loaded by clamped elastic plates. Based on
+the transmission line approach, we derive a nonlinear dynamical lattice model
+which, in the continuum approximation, leads to a nonlinear, dispersive and
+dissipative wave equation. Applying the multiple scales perturbation method, we
+derive an effective lossy nonlinear Schrödinger equation and obtain
+analytical expressions for bright and gap solitons. We also perform direct
+numerical simulations to study the dissipation-induced dynamics of the bright
+and gap solitons. Numerical and analytical results, relying on the analytical
+approximations and perturbation theory for solions, are found to be in good
+agreement.
+",0,1,0,0,0,0
+14883,Local reservoir model for choice-based learning,"  Decision making based on behavioral and neural observations of living systems
+has been extensively studied in brain science, psychology, and other
+disciplines. Decision-making mechanisms have also been experimentally
+implemented in physical processes, such as single photons and chaotic lasers.
+The findings of these experiments suggest that there is a certain common basis
+in describing decision making, regardless of its physical realizations. In this
+study, we propose a local reservoir model to account for choice-based learning
+(CBL). CBL describes decision consistency as a phenomenon where making a
+certain decision increases the possibility of making that same decision again
+later, which has been intensively investigated in neuroscience, psychology,
+etc. Our proposed model is inspired by the viewpoint that a decision is
+affected by its local environment, which is referred to as a local reservoir.
+If the size of the local reservoir is large enough, consecutive decision making
+will not be affected by previous decisions, thus showing lower degrees of
+decision consistency in CBL. In contrast, if the size of the local reservoir
+decreases, a biased distribution occurs within it, which leads to higher
+degrees of decision consistency in CBL. In this study, an analytical approach
+on local reservoirs is presented, as well as several numerical demonstrations.
+Furthermore, a physical architecture for CBL based on single photons is
+discussed, and the effects of local reservoirs is numerically demonstrated.
+Decision consistency in human decision-making tasks and in recruiting empirical
+data are evaluated based on local reservoir. In summary, the proposed local
+reservoir model paves a path toward establishing a foundation for computational
+mechanisms and the systematic analysis of decision making on different levels.
+",0,0,0,1,1,0
+14884,Weil-Petersson geometry on the space of Bridgeland stability conditions,"  Inspired by mirror symmetry, we investigate some differential geometric
+aspects of the space of Bridgeland stability conditions on a Calabi-Yau
+triangulated category. The aim is to develop theory of Weil-Petersson geometry
+on the stringy Kähler moduli space. A few basic examples are studied. In
+particular, we identify our Weil-Petersson metric with the Bergman metric on a
+Siegel modular variety in the case of the self-product of an elliptic curve.
+",0,0,1,0,0,0
+14885,Analysis of a Sputtered Si Surface for Ar Sputter Gas Supply Purity Monitoring,"  For sputter depth profiling often sample erosion by Ar+ ions is used. Only a
+high purity of the sputter gas and a low contamination level of the ion gun
+avoids misleading depth profile measurements results. Here a new measurement
+procedure is presented, which monitors these parameters. A Si sample is
+sputtered inside the instrument and then the surface concentration of the
+elements Ar, C, N and O is measured. Results of such measurements of an XPS
+microprobe PHI Quantum 2000, which were recorded over a period of 10 years, are
+presented.
+",0,1,0,0,0,0
+14886,Extracting spectroscopic molecular parameters from short pulse photo-electron angular distributions,"  Using a quantum wave packet simulation including the nuclear and electronic
+degrees of freedom, we investigate the femtosecond and picosecond energy- and
+angle-resolved photoelectron spectra of the E($^1\Sigma_g^+$) electronic state
+of Li$_2$. We find that the angular distributions of the emitted photoelectrons
+depend strongly on the pulse duration in the regime of ultrashort laser pulses.
+This effect is illustrated by the extraction of a time-dependent asymmetry
+parameter whose variation with pulse duration can be explained by an incoherent
+average over different ion rotational quantum numbers. We then derive for the
+variation of the asymmetry parameter a simple analytical formula, which can be
+used to extract the asymptotic CW asymmetry parameters of individual
+transitions from measurements performed with ultra-short pulses.
+",0,1,0,0,0,0
+14887,A Generative Model for Score Normalization in Speaker Recognition,"  We propose a theoretical framework for thinking about score normalization,
+which confirms that normalization is not needed under (admittedly fragile)
+ideal conditions. If, however, these conditions are not met, e.g. under
+data-set shift between training and runtime, our theory reveals dependencies
+between scores that could be exploited by strategies such as score
+normalization. Indeed, it has been demonstrated over and over experimentally,
+that various ad-hoc score normalization recipes do work. We present a first
+attempt at using probability theory to design a generative score-space
+normalization model which gives similar improvements to ZT-norm on the
+text-dependent RSR 2015 database.
+",1,0,0,1,0,0
+14888,Deep Networks tag the location of bird vocalisations on audio spectrograms,"  This work focuses on reliable detection and segmentation of bird
+vocalizations as recorded in the open field. Acoustic detection of avian sounds
+can be used for the automatized monitoring of multiple bird taxa and querying
+in long-term recordings for species of interest. These tasks are tackled in
+this work, by suggesting two approaches: A) First, DenseNets are applied to
+weekly labeled data to infer the attention map of the dataset (i.e. Salience
+and CAM). We push further this idea by directing attention maps to the YOLO v2
+Deepnet-based, detection framework to localize bird vocalizations. B) A deep
+autoencoder, namely the U-net, maps the audio spectrogram of bird vocalizations
+to its corresponding binary mask that encircles the spectral blobs of
+vocalizations while suppressing other audio sources. We focus solely on
+procedures requiring minimum human attendance, suitable to scan massive volumes
+of data, in order to analyze them, evaluate insights and hypotheses and
+identify patterns of bird activity. Hopefully, this approach will be valuable
+to researchers, conservation practitioners, and decision makers that need to
+design policies on biodiversity issues.
+",1,0,0,0,0,0
+14889,To Wait or Not to Wait: Two-way Functional Hazards Model for Understanding Waiting in Call Centers,"  Telephone call centers offer a convenient communication channel between
+businesses and their customers. Efficient management of call centers needs
+accurate modeling of customer waiting behavior, which contains important
+information about customer patience (how long a customer is willing to wait)
+and service quality (how long a customer needs to wait to get served). Hazard
+functions offer dynamic characterization of customer waiting behavior, and
+provide critical inputs for agent scheduling. Motivated by this application, we
+develop a two-way functional hazards (tF-Hazards) model to study customer
+waiting behavior as a function of two timescales, waiting duration and the time
+of day that a customer calls in. The model stems from a two-way piecewise
+constant hazard function, and imposes low-rank structure and smoothness on the
+hazard rates to enhance interpretability. We exploit an alternating direction
+method of multipliers (ADMM) algorithm to optimize a penalized likelihood
+function of the model. We carefully analyze the data from a US bank call
+center, and provide informative insights about customer patience and service
+quality patterns along waiting time and across different times of a day. The
+findings provide primitive inputs for call center agent staffing and
+scheduling, as well as for call center practitioners to understand the effect
+of system protocols on customer waiting behavior.
+",0,0,0,1,0,0
+14890,Bayesian Gaussian models for interpolating large-dimensional data at misaligned areal units,"  Areal level spatial data are often large, sparse and may appear with
+geographical shapes that are regular or irregular (e.g., postcode). Moreover,
+sometimes it is important to obtain predictive inference in regular or
+irregular areal shapes that is misaligned with the observed spatial areal
+geographical boundary. For example, in a survey the respondents were asked
+about their postcode, however for policy making purposes, researchers are often
+interested to obtain information at the SA2. The statistical challenge is to
+obtain spatial prediction at the SA2s, where the SA2s may have overlapped
+geographical boundaries with postcodes. The study is motivated by a practical
+survey data obtained from the Australian National University (ANU) Poll. Here
+the main research question is to understand respondents' satisfaction level
+with the way Australia is heading. The data are observed at 1,944 postcodes
+among the 2,516 available postcodes across Australia, and prediction is
+obtained at the 2,196 SA2s. The proposed method also explored through a
+grid-based simulation study, where data have been observed in a regular grid
+and spatial prediction has been done in a regular grid that has a misaligned
+geographical boundary with the first regular grid-set. The real-life example
+with ANU Poll data addresses the situation of irregular geographical boundaries
+that are misaligned, i.e., model fitted with postcode data and hence obtained
+prediction at the SA2. A comparison study is also performed to validate the
+proposed method. In this paper, a Gaussian model is constructed under Bayesian
+hierarchy. The novelty lies in the development of the basis function that can
+address spatial sparsity and localised spatial structure. It can also address
+the large-dimensional spatial data modelling problem by constructing knot based
+reduced-dimensional basis functions.
+",0,0,0,1,0,0
+14891,Report on TBAS 2012: Workshop on Task-Based and Aggregated Search,"  The ECIR half-day workshop on Task-Based and Aggregated Search (TBAS) was
+held in Barcelona, Spain on 1 April 2012. The program included a keynote talk
+by Professor Jarvelin, six full paper presentations, two poster presentations,
+and an interactive discussion among the approximately 25 participants. This
+report overviews the aims and contents of the workshop and outlines the major
+outcomes.
+",1,0,0,0,0,0
+14892,Reliability of the measured velocity anisotropy of the Milky Way stellar halo,"  Determining the velocity distribution of halo stars is essential for
+estimating the mass of the Milky Way and for inferring its formation history.
+Since the stellar halo is a dynamically hot system, the velocity distribution
+of halo stars is well described by the 3-dimensional velocity dispersions
+$(\sigma_r, \sigma_\theta, \sigma_\phi)$, or by the velocity anisotropy
+parameter $\beta=1-(\sigma_\theta^2+\sigma_\phi^2)/(2\sigma_r^2)$. Direct
+measurements of $(\sigma_r, \sigma_\theta, \sigma_\phi)$ consistently suggest
+$\beta =0.5$-$0.7$ for nearby halo stars. In contrast, the value of $\beta$ at
+large Galactocentric radius $r$ is still controversial, since reliable proper
+motion data are available for only a handful of stars. In the last decade,
+several authors have tried to estimate $\beta$ for distant halo stars by
+fitting the observed line-of-sight velocities at each radius with simple
+velocity distribution models (local fitting methods). Some results of local
+fitting methods imply $\beta<0$ at $r \gtrsim 20 \;\rm{kpc}$, which is
+inconsistent with recent predictions from cosmological simulations. Here we
+perform mock-catalogue analyses to show that the estimates of $\beta$ based on
+local fitting methods are reliable only at $r \leq 15 \;\rm{kpc}$ with the
+current sample size ($\sim10^3$ stars at a given radius). As $r$ increases, the
+line-of-sight velocity (corrected for the Solar reflex motion) becomes
+increasingly closer to the Galactocentric radial velocity, so that it becomes
+increasingly more difficult to estimate tangential velocity dispersion
+$(\sigma_\theta, \sigma_\phi)$ from line-of-sight velocity distribution. Our
+results suggest that the forthcoming Gaia data will be crucial for
+understanding the velocity distribution of halo stars at $r \geq 20\;\rm{kpc}$.
+",0,1,0,0,0,0
+14893,$H$-compactness of elliptic operators on weighted Riemannian Manifolds,"  In this paper we study the asymptotic behavior of second-order uniformly
+elliptic operators on weighted Riemannian manifolds. We appeal to the notion of
+\mbox{$H$-convergence} introduced by Murat and Tartar. In our main result we
+establish an \mbox{$H$-compactness} result that applies to elliptic operators
+with measurable, uniformly elliptic coefficients on weighted Riemannian
+manifolds. We further discuss the special case of ""locally periodic""
+coefficients and study the asymptotic behavior of the Laplace-Beltrami operator
+on families of weighted manifolds obtained from a reference manifold by a
+conformal (rapidly oscillating) change of metrics.
+",0,0,1,0,0,0
+14894,$b$-symbol distance distribution of repeated-root cyclic codes,"  Symbol-pair codes, introduced by Cassuto and Blaum [1], have been raised for
+symbol-pair read channels. This new idea is motivated by the limitation of the
+reading process in high-density data storage technologies. Yaakobi et al. [8]
+introduced codes for $b$-symbol read channels, where the read operation is
+performed as a consecutive sequence of $b>2$ symbols. In this paper, we come up
+with a method to compute the $b$-symbol-pair distance of two $n$-tuples, where
+$n$ is a positive integer. Also, we deal with the $b$-symbol-pair distances of
+some kind of cyclic codes of length $p^e$ over $\mathbb{F}_{p^m}$.
+",1,0,0,0,0,0
+14895,"Knowledge Fusion via Embeddings from Text, Knowledge Graphs, and Images","  We present a baseline approach for cross-modal knowledge fusion. Different
+basic fusion methods are evaluated on existing embedding approaches to show the
+potential of joining knowledge about certain concepts across modalities in a
+fused concept representation.
+",1,0,0,1,0,0
+14896,Observation of spin superfluidity: YIG magnetic films and beyond,"  From topology of the order parameter of the magnon condensate observed in
+yttrium-iron-garnet (YIG) magnetic films one must not expect energetic barriers
+making spin supercurrents metastable. But we show that some barriers of
+dynamical origin are possible nevertheless until the gradient of the phase
+(angle of spin precession) does not exceed the critical value (analog of the
+Landau critical velocity in superfluids). On the other hand, recently published
+claims of experimental detection of spin superfluidity in YIG films and
+antiferromagnets are not justified, and spin superfluidity in magnetically
+ordered solids has not yet been experimentally confirmed.
+",0,1,0,0,0,0
+14897,Tuning of Interlayer Coupling in Large-Area Graphene/WSe2 van der Waals Heterostructure via Ion Irradiation: Optical Evidences and Photonic Applications,"  Van der Waals (vdW) heterostructures are receiving great attentions due to
+their intriguing properties and potentials in many research fields. The flow of
+charge carriers in vdW heterostructures can be efficiently rectified by the
+inter-layer coupling between neighboring layers, offering a rich collection of
+functionalities and a mechanism for designing atomically thin devices.
+Nevertheless, non-uniform contact in larger-area heterostructures reduces the
+device efficiency. In this work, ion irradiation had been verified as an
+efficient technique to enhance the contact and interlayer coupling in the newly
+developed graphene/WSe2 hetero-structure with a large area of 10 mm x 10 mm.
+During the ion irradiation process, the morphology of monolayer graphene had
+been modified, promoting the contact with WSe2. Experimental evidences of the
+tunable interlayer electron transfer are displayed by investigation of
+photoluminescence and ultrafast absorption of the irradiated heterostructure.
+Besides, we have found that in graphene/WSe2 heterostructure, graphene serves
+as a fast channel for the photo-excited carriers to relax in WSe2, and the
+nonlinear absorption of WSe2 could be effectively tuned by the carrier transfer
+process in graphene, enabling specific optical absorption of the
+heterostructure in comparison with separated graphene or WSe2. On the basis of
+these new findings, by applying the ion beam modified graphene/WSe2
+heterostructure as a saturable absorber, Q-switched pulsed lasing with
+optimized performance has been realized in a Nd:YAG waveguide cavity. This work
+paves the way towards developing novel devices based on large-area
+heterostructures by using ion beam irradiation.
+",0,1,0,0,0,0
+14898,Learning to Multi-Task by Active Sampling,"  One of the long-standing challenges in Artificial Intelligence for learning
+goal-directed behavior is to build a single agent which can solve multiple
+tasks. Recent progress in multi-task learning for goal-directed sequential
+problems has been in the form of distillation based learning wherein a student
+network learns from multiple task-specific expert networks by mimicking the
+task-specific policies of the expert networks. While such approaches offer a
+promising solution to the multi-task learning problem, they require supervision
+from large expert networks which require extensive data and computation time
+for training. In this work, we propose an efficient multi-task learning
+framework which solves multiple goal-directed tasks in an on-line setup without
+the need for expert supervision. Our work uses active learning principles to
+achieve multi-task learning by sampling the harder tasks more than the easier
+ones. We propose three distinct models under our active sampling framework. An
+adaptive method with extremely competitive multi-tasking performance. A
+UCB-based meta-learner which casts the problem of picking the next task to
+train on as a multi-armed bandit problem. A meta-learning method that casts the
+next-task picking problem as a full Reinforcement Learning problem and uses
+actor critic methods for optimizing the multi-tasking performance directly. We
+demonstrate results in the Atari 2600 domain on seven multi-tasking instances:
+three 6-task instances, one 8-task instance, two 12-task instances and one
+21-task instance.
+",1,0,0,0,0,0
+14899,Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks,"  We prove, under two sufficient conditions, that idealised models can have no
+adversarial examples. We discuss which idealised models satisfy our conditions,
+and show that idealised Bayesian neural networks (BNNs) satisfy these. We
+continue by studying near-idealised BNNs using HMC inference, demonstrating the
+theoretical ideas in practice. We experiment with HMC on synthetic data derived
+from MNIST for which we know the ground-truth image density, showing that
+near-perfect epistemic uncertainty correlates to density under image manifold,
+and that adversarial images lie off the manifold in our setting. This suggests
+why MC dropout, which can be seen as performing approximate inference, has been
+observed to be an effective defence against adversarial examples in practice;
+We highlight failure-cases of non-idealised BNNs relying on dropout, suggesting
+a new attack for dropout models and a new defence as well. Lastly, we
+demonstrate the defence on a cats-vs-dogs image classification task with a
+VGG13 variant.
+",0,0,0,1,0,0
+14900,Long term availability of raw experimental data in experimental fracture mechanics,"  Experimental data availability is a cornerstone for reproducibility in
+experimental fracture mechanics, which is crucial to the scientific method.
+This short communication focuses on the accessibility and long term
+availability of raw experimental data. The corresponding authors of the eleven
+most cited papers, related to experimental fracture mechanics, for every year
+from 2000 up to 2016, were kindly asked about the availability of the raw
+experimental data associated with each publication. For the 187 e-mails sent:
+22.46% resulted in outdated contact information, 57.75% of the authors did
+received our request and did not reply, and 19.79 replied to our request. The
+availability of data is generally low with only $11$ available data sets
+(5.9%). The authors identified two main issues for the lacking availability of
+raw experimental data. First, the ability to retrieve data is strongly attached
+to the the possibility to contact the corresponding author. This study suggests
+that institutional e-mail addresses are insufficient means for obtaining
+experimental data sets. Second, lack of experimental data is also due that
+submission and publication does not require to make the raw experimental data
+available. The following solutions are proposed: (1) Requirement of unique
+identifiers, like ORCID or ResearcherID, to detach the author(s) from their
+institutional e-mail address, (2) Provide DOIs, like Zenodo or Dataverse, to
+make raw experimental data citable, and (3) grant providing organizations
+should ensure that experimental data by public funded projects is available to
+the public.
+",0,0,0,1,0,0
+14901,MRA - Proof of Concept of a Multilingual Report Annotator Web Application,"  MRA (Multilingual Report Annotator) is a web application that translates
+Radiology text and annotates it with RadLex terms. Its goal is to explore the
+solution of translating non-English Radiology reports as a way to solve the
+problem of most of the Text Mining tools being developed for English. In this
+brief paper we explain the language barrier problem and shortly describe the
+application. MRA can be found at this https URL .
+",1,0,0,0,0,0
+14902,"Contagions in Social Networks: Effects of Monophilic Contagion, Friendship Paradox and Reactive Networks","  We consider SIS contagion processes over networks where, a classical
+assumption is that individuals' decisions to adopt a contagion are based on
+their immediate neighbors. However, recent literature shows that some
+attributes are more correlated between two-hop neighbors, a concept referred to
+as monophily. This motivates us to explore monophilic contagion, the case where
+a contagion (e.g. a product, disease) is adopted by considering two-hop
+neighbors instead of immediate neighbors (e.g. you ask your friend about the
+new iPhone and she recommends you the opinion of one of her friends). We show
+that the phenomenon called friendship paradox makes it easier for the
+monophilic contagion to spread widely. We also consider the case where the
+underlying network stochastically evolves in response to the state of the
+contagion (e.g. depending on the severity of a flu virus, people restrict their
+interactions with others to avoid getting infected) and show that the dynamics
+of such a process can be approximated by a differential equation whose
+trajectory satisfies an algebraic constraint restricting it to a manifold. Our
+results shed light on how graph theoretic consequences affect contagions and,
+provide simple deterministic models to approximate the collective dynamics of
+contagions over stochastic graph processes.
+",1,0,0,0,0,0
+14903,Derivation of a multilayer approach to model suspended sediment transport: application to hyperpycnal and hypopycnal plumes,"  We propose a multi-layer approach to simulate hyperpycnal and hypopycnal
+plumes in flows with free surface. The model allows to compute the vertical
+profile of the horizontal and the vertical components of the velocity of the
+fluid flow. The model can describe as well the vertical profile of the sediment
+concentration and the velocity components of each one of the sediment species
+that form the turbidity current. To do so, it takes into account the settling
+velocity of the particles and their interaction with the fluid. This allows to
+better describe the phenomena than a single layer approach. It is in better
+agreement with the physics of the problem and gives promising results. The
+numerical simulation is carried out by rewriting the multi-layer approach in a
+compact formulation, which corresponds to a system with non-conservative
+products, and using path-conservative numerical scheme. Numerical results are
+presented in order to show the potential of the model.
+",0,1,0,0,0,0
+14904,Continued fractions and conformal mappings for domains with angle points,"  Here we construct the conformal mappings with the help of continuous
+fractions approximations. These approximations converge to the algebraic roots
+$\sqrt[N]{z}$ for $N \in \mathbb{N}$ and $z$ from the right half-plane of the
+complex plane. We estimate both the convergence rate and the compact set of
+convergence. Also we give the examples that illustrate the introduced technique
+of a conformal mapping construction.
+",0,0,1,0,0,0
+14905,Stochastic Activation Pruning for Robust Adversarial Defense,"  Neural networks are known to be vulnerable to adversarial examples. Carefully
+chosen perturbations to real images, while imperceptible to humans, induce
+misclassification and threaten the reliability of deep learning systems in the
+wild. To guard against adversarial examples, we take inspiration from game
+theory and cast the problem as a minimax zero-sum game between the adversary
+and the model. In general, for such games, the optimal strategy for both
+players requires a stochastic policy, also known as a mixed strategy. In this
+light, we propose Stochastic Activation Pruning (SAP), a mixed strategy for
+adversarial defense. SAP prunes a random subset of activations (preferentially
+pruning those with smaller magnitude) and scales up the survivors to
+compensate. We can apply SAP to pretrained networks, including adversarially
+trained models, without fine-tuning, providing robustness against adversarial
+examples. Experiments demonstrate that SAP confers robustness against attacks,
+increasing accuracy and preserving calibration.
+",0,0,0,1,0,0
+14906,Canonical bases of modules over one dimensional k-algebras,"  Let K be a field and denote by K[t], the polynomial ring with coefficients in
+K. Set A = K[f1,. .. , fs], with f1,. .. , fs $\in$ K[t]. We give a procedure
+to calculate the monoid of degrees of the K algebra M = F1A + $\times$ $\times$
+$\times$ + FrA with F1,. .. , Fr $\in$ K[t]. We show some applications to the
+problem of the classification of plane polynomial curves (that is, plane
+algebraic curves parametrized by polynomials) with respect to some oh their
+invariants, using the module of K{ä}hler differentials.
+",0,0,1,0,0,0
+14907,Detecting Adversarial Samples from Artifacts,"  Deep neural networks (DNNs) are powerful nonlinear architectures that are
+known to be robust to random perturbations of the input. However, these models
+are vulnerable to adversarial perturbations--small input changes crafted
+explicitly to fool the model. In this paper, we ask whether a DNN can
+distinguish adversarial samples from their normal and noisy counterparts. We
+investigate model confidence on adversarial samples by looking at Bayesian
+uncertainty estimates, available in dropout neural networks, and by performing
+density estimation in the subspace of deep features learned by the model. The
+result is a method for implicit adversarial detection that is oblivious to the
+attack algorithm. We evaluate this method on a variety of standard datasets
+including MNIST and CIFAR-10 and show that it generalizes well across different
+architectures and attacks. Our findings report that 85-93% ROC-AUC can be
+achieved on a number of standard classification tasks with a negative class
+that consists of both normal and noisy samples.
+",1,0,0,1,0,0
+14908,Bots sustain and inflate striking opposition in online social systems,"  Societies are complex systems which tend to polarize into sub-groups of
+individuals with dramatically opposite perspectives. This phenomenon is
+reflected -- and often amplified -- in online social networks where, however,
+humans are no more the only players, and co-exist alongside with social bots,
+i.e. software-controlled accounts. Analyzing large-scale social data collected
+during the Catalan referendum for independence on October 1 2017, consisting of
+nearly 4 millions Twitter posts generated by almost 1 million users, we
+identify the two polarized groups of Independentists and Constitutionalists and
+quantify the structural and emotional roles played by social bots. We show that
+bots act from peripheral areas of the social system to target influential
+humans of both groups, mostly bombarding Independentists with negative and
+violent contents, sustaining and inflating instability in this online society.
+These results quantify the potential dangerous influence of political bots
+during voting processes.
+",1,0,0,0,0,0
+14909,Approximation of full-boundary data from partial-boundary electrode measurements,"  Measurements on a subset of the boundary are common in electrical impedance
+tomography, especially any electrode model can be interpreted as a partial
+boundary problem. The information obtained is different to full-boundary
+measurements as modeled by the ideal continuum model. In this study we discuss
+an approach to approximate full-boundary data from partial-boundary
+measurements that is based on the knowledge of the involved projections. The
+approximate full-boundary data can then be obtained as the solution of a
+suitable optimization problem on the coefficients of the Neumann-to-Dirichlet
+map. By this procedure we are able to improve the reconstruction quality of
+continuum model based algorithms, in particular we present the effectiveness
+with a D-bar method. Reconstructions are presented for noisy simulated and real
+measurement data.
+",0,0,1,0,0,0
+14910,A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression,"  In this paper, we propose a new optimization algorithm for sparse logistic
+regression based on a stochastic version of the Douglas-Rachford splitting
+method. Our algorithm sweeps the training set by randomly selecting a
+mini-batch of data at each iteration, and it allows us to update the variables
+in a block coordinate manner. Our approach leverages the proximity operator of
+the logistic loss, which is expressed with the generalized Lambert W function.
+Experiments carried out on standard datasets demonstrate the efficiency of our
+approach w.r.t. stochastic gradient-like methods.
+",1,0,0,0,0,0
+14911,Corrective Re-gridding Techniques for Non-Uniform Sampling in Time Domain Terahertz Spectroscopy,"  Time domain terahertz spectroscopy typically uses mechanical delay stages
+that inherently suffer from non-uniform sampling positions. We review,
+simulate, and experimentally test the ability of corrective cubic spline and
+Shannon re-gridding algorithms to mitigate the inherent sampling position
+noise. We present simulations and experimental results that show re-gridding
+algorithms can increase the signal to noise ratio within the frequency range of
+100 GHz to 2 THz. We also predict that re-gridding corrections will become
+increasingly important to both spectroscopy and imaging as THz technology
+continues to improve and higher frequencies become experimentally accessible.
+",0,1,0,0,0,0
+14912,Optimistic Robust Optimization With Applications To Machine Learning,"  Robust Optimization has traditionally taken a pessimistic, or worst-case
+viewpoint of uncertainty which is motivated by a desire to find sets of optimal
+policies that maintain feasibility under a variety of operating conditions. In
+this paper, we explore an optimistic, or best-case view of uncertainty and show
+that it can be a fruitful approach. We show that these techniques can be used
+to address a wide variety of problems. First, we apply our methods in the
+context of robust linear programming, providing a method for reducing
+conservatism in intuitive ways that encode economically realistic modeling
+assumptions. Second, we look at problems in machine learning and find that this
+approach is strongly connected to the existing literature. Specifically, we
+provide a new interpretation for popular sparsity inducing non-convex
+regularization schemes. Additionally, we show that successful approaches for
+dealing with outliers and noise can be interpreted as optimistic robust
+optimization problems. Although many of the problems resulting from our
+approach are non-convex, we find that DCA or DCA-like optimization approaches
+can be intuitive and efficient.
+",1,0,0,1,0,0
+14913,High Dimensional Time Series Generators,"  Multidimensional time series are sequences of real valued vectors. They occur
+in different areas, for example handwritten characters, GPS tracking, and
+gestures of modern virtual reality motion controllers. Within these areas, a
+common task is to search for similar time series. Dynamic Time Warping (DTW) is
+a common distance function to compare two time series. The Edit Distance with
+Real Penalty (ERP) and the Dog Keeper Distance (DK) are two more distance
+functions on time series. Their behaviour has been analyzed on 1-dimensional
+time series. However, it is not easy to evaluate their behaviour in relation to
+growing dimensionality. For this reason we propose two new data synthesizers
+generating multidimensional time series. The first synthesizer extends the well
+known cylinder-bell-funnel (CBF) dataset to multidimensional time series. Here,
+each time series has an arbitrary type (cylinder, bell, or funnel) in each
+dimension, thus for $d$-dimensional time series there are $3^{d}$ different
+classes. The second synthesizer (RAM) creates time series with ideas adapted
+from Brownian motions which is a common model of movement in physics. Finally,
+we evaluate the applicability of a 1-nearest neighbor classifier using DTW on
+datasets generated by our synthesizers.
+",0,0,0,1,0,0
+14914,An isoperimetric inequality for Laplace eigenvalues on the sphere,"  We show that for any positive integer k, the k-th nonzero eigenvalue of the
+Laplace-Beltrami operator on the two-dimensional sphere endowed with a
+Riemannian metric of unit area, is maximized in the limit by a sequence of
+metrics converging to a union of k touching identical round spheres. This
+proves a conjecture posed by the second author in 2002 and yields a sharp
+isoperimetric inequality for all nonzero eigenvalues of the Laplacian on a
+sphere. Earlier, the result was known only for k=1 (J.Hersch, 1970), k=2
+(N.Nadirashvili, 2002; R.Petrides, 2014) and k=3 (N.Nadirashvili and Y.Sire,
+2017). In particular, we argue that for any k>=2, the supremum of the k-th
+nonzero eigenvalue on a sphere of unit area is not attained in the class of
+Riemannin metrics which are smooth outsitde a finite set of conical
+singularities. The proof uses certain properties of harmonic maps between
+spheres, the key new ingredient being a bound on the harmonic degree of a
+harmonic map into a sphere obtained by N. Ejiri.
+",0,0,1,0,0,0
+14915,"Numerical solutions of an unsteady 2-D incompressible flow with heat and mass transfer at low, moderate, and high Reynolds numbers","  In this paper, we have proposed a modified Marker-And-Cell (MAC) method to
+investigate the problem of an unsteady 2-D incompressible flow with heat and
+mass transfer at low, moderate, and high Reynolds numbers with no-slip and slip
+boundary conditions. We have used this method to solve the governing equations
+along with the boundary conditions and thereby to compute the flow variables,
+viz. $u$-velocity, $v$-velocity, $P$, $T$, and $C$. We have used the staggered
+grid approach of this method to discretize the governing equations of the
+problem. A modified MAC algorithm was proposed and used to compute the
+numerical solutions of the flow variables for Reynolds numbers $Re = 10$, 500,
+and 50,000 in consonance with low, moderate, and high Reynolds numbers. We have
+also used appropriate Prandtl $(Pr)$ and Schmidt $(Sc)$ numbers in consistence
+with relevancy of the physical problem considered. We have executed this
+modified MAC algorithm with the aid of a computer program developed and run in
+C compiler. We have also computed numerical solutions of local Nusselt $(Nu)$
+and Sherwood $(Sh)$ numbers along the horizontal line through the geometric
+center at low, moderate, and high Reynolds numbers for fixed $Pr = 6.62$ and
+$Sc = 340$ for two grid systems at time $t = 0.0001s$. Our numerical solutions
+for u and v velocities along the vertical and horizontal line through the
+geometric center of the square cavity for $Re = 100$ has been compared with
+benchmark solutions available in the literature and it has been found that they
+are in good agreement. The present numerical results indicate that, as we move
+along the horizontal line through the geometric center of the domain, we
+observed that, the heat and mass transfer decreases up to the geometric center.
+It, then, increases symmetrically.
+",0,1,0,0,0,0
+14916,A Dynamic Programming Solution to Bounded Dejittering Problems,"  We propose a dynamic programming solution to image dejittering problems with
+bounded displacements and obtain efficient algorithms for the removal of line
+jitter, line pixel jitter, and pixel jitter.
+",1,0,1,0,0,0
+14917,Delayed pull-in transitions in overdamped MEMS devices,"  We consider the dynamics of overdamped MEMS devices undergoing the pull-in
+instability. Numerous previous experiments and numerical simulations have shown
+a significant increase in the pull-in time under DC voltages close to the
+pull-in voltage. Here the transient dynamics slow down as the device passes
+through a meta-stable or bottleneck phase, but this slowing down is not well
+understood quantitatively. Using a lumped parallel-plate model, we perform a
+detailed analysis of the pull-in dynamics in this regime. We show that the
+bottleneck phenomenon is a type of critical slowing down arising from the
+pull-in transition. This allows us to show that the pull-in time obeys an
+inverse square-root scaling law as the transition is approached; moreover we
+determine an analytical expression for this pull-in time. We then compare our
+prediction to a wide range of pull-in time data reported in the literature,
+showing that the observed slowing down is well captured by our scaling law,
+which appears to be generic for overdamped pull-in under DC loads. This
+realization provides a useful design rule with which to tune dynamic response
+in applications, including state-of-the-art accelerometers and pressure sensors
+that use pull-in time as a sensing mechanism. We also propose a method to
+estimate the pull-in voltage based only on data of the pull-in times.
+",0,1,0,0,0,0
+14918,How ConvNets model Non-linear Transformations,"  In this paper, we theoretically address three fundamental problems involving
+deep convolutional networks regarding invariance, depth and hierarchy. We
+introduce the paradigm of Transformation Networks (TN) which are a direct
+generalization of Convolutional Networks (ConvNets). Theoretically, we show
+that TNs (and thereby ConvNets) are can be invariant to non-linear
+transformations of the input despite pooling over mere local translations. Our
+analysis provides clear insights into the increase in invariance with depth in
+these networks. Deeper networks are able to model much richer classes of
+transformations. We also find that a hierarchical architecture allows the
+network to generate invariance much more efficiently than a non-hierarchical
+network. Our results provide useful insight into these three fundamental
+problems in deep learning using ConvNets.
+",1,0,0,0,0,0
+14919,Volcano transition in a solvable model of oscillator glass,"  In 1992 a puzzling transition was discovered in simulations of randomly
+coupled limit-cycle oscillators. This so-called volcano transition has resisted
+analysis ever since. It was originally conjectured to mark the emergence of an
+oscillator glass, but here we show it need not. We introduce and solve a
+simpler model with a qualitatively identical volcano transition and find,
+unexpectedly, that its supercritical state is not glassy. We discuss the
+implications for the original model and suggest experimental systems in which a
+volcano transition and oscillator glass may appear.
+",0,1,0,0,0,0
+14920,Recent advances and open questions on the susy structure of the chiral de Rham Complex,"  We review different constructions of the supersymmetry subalgebras of the
+chiral de Rham complex on special holonomy manifolds. We describe the
+difference between the holomorphic-anti-holomorphic sectors based on a local
+free ghost system vs the decomposition in left-right sectors from a local
+Boson-Fermion system. We describe the topological twist in the case of $G_2$
+and $Spin_7$ manifolds. We describe the construction of these algebras as
+quantum Hamiltonian reduction of Lie superalgebras at the minimal or
+superprincipal nilpotent.
+",0,0,1,0,0,0
+14921,"Toda maps, cocycles, and canonical systems","  I present a discussion of the hierarchy of Toda flows that gives center stage
+to the associated cocycles and the maps they induce on the $m$ functions. In
+the second part, these ideas are then applied to canonical systems; an
+important feature of this discussion will be my proposal that the role of the
+shift on Jacobi matrices should now be taken over by the more general class of
+twisted shifts.
+",0,0,1,0,0,0
+14922,"The Gibbs paradox, the Landauer principle and the irreversibility associated with tilted observers","  It is well known that, in the context of General Relativity, some spacetimes,
+when described by a congruence of comoving observers, may consist in a
+distribution of a perfect (non-dissipative) fluid, whereas the same spacetime
+as seen by a ""tilted""' (Lorentz-boosted) congruence of observers, may exhibit
+the presence of dissipative processes. As we shall see, the appearence of
+entropy producing processes are related to the tight dependence of entropy on
+the specific congruence of observers. This fact is well illustrated by the
+Gibbs paradox. The appearance of such dissipative processes, as required by the
+Landauer principle, are necessary, in order to erase the different amount of
+information stored by comoving observers, with respect to tilted ones.
+",0,1,0,0,0,0
+14923,PlumX As a Potential Tool to Assess the Macroscopic Multidimensional Impact of Books,"  The main purpose of this macro-study is to shed light on the broad impact of
+books. For this purpose, the impact of a very large collection of books has
+been analyzed by using PlumX, an analytical tool providing a great number of
+different metrics provided by various tools.
+",1,0,0,0,0,0
+14924,Action Robust Reinforcement Learning and Applications in Continuous Control,"  A policy is said to be robust if it maximizes the reward while considering a
+bad, or even adversarial, model. In this work we formalize two new criteria of
+robustness to action uncertainty. Specifically, we consider two scenarios in
+which the agent attempts to perform an action $\mathbf{a}$, and (i) with
+probability $\alpha$, an alternative adversarial action $\bar{\mathbf{a}}$ is
+taken, or (ii) an adversary adds a perturbation to the selected action in the
+case of continuous action space. We show that our criteria are related to
+common forms of uncertainty in robotics domains, such as the occurrence of
+abrupt forces, and suggest algorithms in the tabular case. Building on the
+suggested algorithms, we generalize our approach to deep reinforcement learning
+(DRL) and provide extensive experiments in the various MuJoCo domains. Our
+experiments show that not only does our approach produce robust policies, but
+it also improves the performance in the absence of perturbations. This
+generalization indicates that action-robustness can be thought of as implicit
+regularization in RL problems.
+",1,0,0,1,0,0
+14925,A characterization of finite vector bundles on Gauduchon astheno-Kahler manifolds,"  A vector bundle E on a projective variety X is called finite if it satisfies
+a nontrivial polynomial equation with integral coefficients. A theorem of Nori
+implies that E is finite if and only if the pullback of E to some finite etale
+Galois covering of X is trivial. We prove the same statement when X is a
+compact complex manifold admitting a Gauduchon astheno-Kahler metric.
+",0,0,1,0,0,0
+14926,APSYNSIM: An Interactive Tool To Learn Interferometry,"  The APerture SYNthesis SIMulator is a simple interactive tool to help the
+students visualize and understand the basics of the Aperture Synthesis
+technique, applied to astronomical interferometers. The users can load many
+different interferometers and source models (and also create their own), change
+the observing parameters (e.g., source coordinates, observing wavelength,
+antenna location, integration time, etc.), and even deconvolve the
+interferometric images and corrupt the data with gain errors (amplitude and
+phase). The program is fully interactive and all the figures are updated in
+real time. APSYNSIM has already been used in several interferometry schools and
+has got very positive feedback from the students.
+",0,1,0,0,0,0
+14927,The dimensionless dissipation rate and the Kolmogorov (1941) hypothesis of local stationarity in freely decaying isotropic turbulence,"  An expression for the dimensionless dissipation rate was derived from the
+Karman-Howarth equation by asymptotic expansion of the second- and third- order
+structure functions in powers of the inverse Reynolds number. The implications
+of the time-derivative term for the assumption of local stationarity (or local
+equilibrium) which underpins the derivation of the Kolmogorov `4/5' law for the
+third-order structure function were studied. It was concluded that neglect of
+the time-derivative cannot be justified by reason of restriction to certain
+scales (the inertial range) nor to large Reynolds numbers. In principle,
+therefore, the hypothesis cannot be correct, although it may be a good
+approximation. It follows, at least in principle, that the quantitative aspects
+of the hypothesis of local stationarity could be tested by a comparison of the
+asymptotic dimensionless dissipation rate for free decay with that for the
+stationary case. But in practice this is complicated by the absence of an
+agreed evolution time for making the measurements during the decay. However, we
+can assess the quantitative error involved in using the hypothesis by comparing
+the exact asymptotic value of the dimensionless dissipation in free decay
+calculated on the assumption of local stationarity to the experimentally
+determined value (e.g. by means of direct numerical simulation), as this
+relationship holds for all measuring times. Should the assumption of local
+stationarity lead to significant error, then the `4/5' law needs to be
+corrected. Despite this, scale invariance in wavenumber space appears to hold
+in the formal limit of infinite Reynolds numbers, which implies that the `-5/3'
+energy spectrum does not require correction in this limit.
+",0,1,0,0,0,0
+14928,Consistent estimation of the spectrum of trace class data augmentation algorithms,"  Markov chain Monte Carlo is widely used in a variety of scientific
+applications to generate approximate samples from intractable distributions. A
+thorough understanding of the convergence and mixing properties of these Markov
+chains can be obtained by studying the spectrum of the associated Markov
+operator. While several methods to bound/estimate the second largest eigenvalue
+are available in the literature, very few general techniques for consistent
+estimation of the entire spectrum have been proposed. Existing methods for this
+purpose require the Markov transition density to be available in closed form,
+which is often not true in practice, especially in modern statistical
+applications. In this paper, we propose a novel method to consistently estimate
+the entire spectrum of a general class of Markov chains arising from a popular
+and widely used statistical approach known as Data Augmentation. The transition
+densities of these Markov chains can often only be expressed as intractable
+integrals. We illustrate the applicability of our method using real and
+simulated data.
+",0,0,0,1,0,0
+14929,"Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior","  Previous research using evolutionary computation in Multi-Agent Systems
+indicates that assigning fitness based on team vs.\ individual behavior has a
+strong impact on the ability of evolved teams of artificial agents to exhibit
+teamwork in challenging tasks. However, such research only made use of
+single-objective evolution. In contrast, when a multiobjective evolutionary
+algorithm is used, populations can be subject to individual-level objectives,
+team-level objectives, or combinations of the two. This paper explores the
+performance of cooperatively coevolved teams of agents controlled by artificial
+neural networks subject to these types of objectives. Specifically, predator
+agents are evolved to capture scripted prey agents in a torus-shaped grid
+world. Because of the tension between individual and team behaviors, multiple
+modes of behavior can be useful, and thus the effect of modular neural networks
+is also explored. Results demonstrate that fitness rewarding individual
+behavior is superior to fitness rewarding team behavior, despite being applied
+to a cooperative task. However, the use of networks with multiple modules
+allows predators to discover intelligent behavior, regardless of which type of
+objectives are used.
+",1,0,0,0,0,0
+14930,High-throughput nanofluidic device for one-dimensional confined detection of single fluorophores,"  Ensemble averaging experiments may conceal many fundamental molecular
+interactions. To overcome that, a high-throughput detection of single molecules
+or colloidal nanodots is crucial for biomedical, nanoelectronic, and
+solid-state applications. One-dimensional (1D) discrete flow of nanoscale
+objects is an efficient approach in this direction. The development of simple
+and cost-effective nanofluidic devices is a critical step to realise 1D flow.
+This letter presents a nanofabrication technique using
+shadow-angle-electron-beam-deposition for a high-throughput preparation of
+parallel nanofluidic channels. These were used to flow and detect DNA,
+carbon-nanodots, and organic fluorophores. The 1D molecular mass transport was
+performed using electro-osmotic flow. The 1D flow behaviour was identified and
+analysed using two-focus fluorescence correlation spectroscopy (2fFCS). A range
+of flow velocities of single molecules was achieved. The transitions of single
+molecules or nanodots through the two foci were quantitatively analysed using
+confocal scanning imaging, correlative photon detection, and burst size
+distribution analysis. The results suggest an efficient nanofabrication
+technique is developed to prepare nanofluidic devices. This first demonstration
+of high-throughput nanochannel fabrication process and using 2fFCS-based single
+molecule flow detection should have a potential impact on ultra-sensitive
+biomedical diagnostics and studying biomolecular interactions as well as
+nanomaterials.
+",0,1,0,0,0,0
+14931,Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection,"  Joint analysis of data from multiple information repositories facilitates
+uncovering the underlying structure in heterogeneous datasets. Single and
+coupled matrix-tensor factorization (CMTF) has been widely used in this context
+for imputation-based recommendation from ratings, social network, and other
+user-item data. When this side information is in the form of item-item
+correlation matrices or graphs, existing CMTF algorithms may fall short.
+Alleviating current limitations, we introduce a novel model coined coupled
+graph-tensor factorization (CGTF) that judiciously accounts for graph-related
+side information. The CGTF model has the potential to overcome practical
+challenges, such as missing slabs from the tensor and/or missing rows/columns
+from the correlation matrices. A novel alternating direction method of
+multipliers (ADMM) is also developed that recovers the nonnegative factors of
+CGTF. Our algorithm enjoys closed-form updates that result in reduced
+computational complexity and allow for convergence claims. A novel direction is
+further explored by employing the interpretable factors to detect graph
+communities having the tensor as side information. The resulting community
+detection approach is successful even when some links in the graphs are
+missing. Results with real data sets corroborate the merits of the proposed
+methods relative to state-of-the-art competing factorization techniques in
+providing recommendations and detecting communities.
+",1,0,0,1,0,0
+14932,A computer algebra system for R: Macaulay2 and the m2r package,"  Algebraic methods have a long history in statistics. The most prominent
+manifestation of modern algebra in statistics can be seen in the field of
+algebraic statistics, which brings tools from commutative algebra and algebraic
+geometry to bear on statistical problems. Now over two decades old, algebraic
+statistics has applications in a wide range of theoretical and applied
+statistical domains. Nevertheless, algebraic statistical methods are still not
+mainstream, mostly due to a lack of easy off-the-shelf implementations. In this
+article we debut m2r, an R package that connects R to Macaulay2 through a
+persistent back-end socket connection running locally or on a cloud server.
+Topics range from basic use of m2r to applications and design philosophy.
+",0,0,0,1,0,0
+14933,Transversal magnetoresistance and Shubnikov-de Haas oscillations in Weyl semimetals,"  We explore theoretically the magnetoresistance of Weyl semimetals in
+transversal magnetic fields away from charge neutrality. The analysis within
+the self-consistent Born approximation is done for the two different models of
+disorder: (i) short-range impurties and (ii) charged (Coulomb) impurities. For
+these models of disorder, we calculate the conductivity away from charge
+neutrality point as well as the Hall conductivity, and analyze the transversal
+magnetoresistance (TMR) and Shubnikov-de Haas oscillations for both types of
+disorder. We further consider a model with Weyl nodes shifted in energy with
+respect to each other (as found in various materials) with the chemical
+potential corresponding to the total charge neutrality. In the experimentally
+most relevant case of Coulomb impurities, we find in this model a large TMR in
+a broad range of quantizing magnetic fields. More specifically, in the
+ultra-quantum limit, where only the zeroth Landau level is effective, the TMR
+is linear in magnetic field. In the regime of moderate (but still quantizing)
+magnetic fields, where the higher Landau levels are relevant, the rapidly
+growing TMR is supplemented by strong Shubnikov-de Haas oscillations,
+consistent with experimental observations.
+",0,1,0,0,0,0
+14934,In Search of Lost (Mixing) Time: Adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p,"  The availability of data sets with large numbers of variables is rapidly
+increasing. The effective application of Bayesian variable selection methods
+for regression with these data sets has proved difficult since available Markov
+chain Monte Carlo methods do not perform well in typical problem sizes of
+interest. The current paper proposes new adaptive Markov chain Monte Carlo
+algorithms to address this shortcoming. The adaptive design of these algorithms
+exploits the observation that in large $p$ small $n$ settings, the majority of
+the $p$ variables will be approximately uncorrelated a posteriori. The
+algorithms adaptively build suitable non-local proposals that result in moves
+with squared jumping distance significantly larger than standard methods. Their
+performance is studied empirically in high-dimension problems (with both
+simulated and actual data) and speedups of up to 4 orders of magnitude are
+observed. The proposed algorithms are easily implementable on multi-core
+architectures and are well suited for parallel tempering or sequential Monte
+Carlo implementations.
+",0,0,0,1,0,0
+14935,Network growth models: A behavioural basis for attachment proportional to fitness,"  Several growth models have been proposed in the literature for scale-free
+complex networks, with a range of fitness-based attachment models gaining
+prominence recently. However, the processes by which such fitness-based
+attachment behaviour can arise are less well understood, making it difficult to
+compare the relative merits of such models. This paper analyses an evolutionary
+mechanism that would give rise to a fitness-based attachment process. In
+particular, it is proven by analytical and numerical methods that in
+homogeneous networks, the minimisation of maximum exposure to node unfitness
+leads to attachment probabilities that are proportional to node fitness. This
+result is then extended to heterogeneous networks, with supply chain networks
+being used as an example.
+",1,1,0,0,0,0
+14936,On Convergence Property of Implicit Self-paced Objective,"  Self-paced learning (SPL) is a new methodology that simulates the learning
+principle of humans/animals to start learning easier aspects of a learning
+task, and then gradually take more complex examples into training. This
+new-coming learning regime has been empirically substantiated to be effective
+in various computer vision and pattern recognition tasks. Recently, it has been
+proved that the SPL regime has a close relationship to a implicit self-paced
+objective function. While this implicit objective could provide helpful
+interpretations to the effectiveness, especially the robustness, insights under
+the SPL paradigms, there are still no theoretical results strictly proved to
+verify such relationship. To this issue, in this paper, we provide some
+convergence results on this implicit objective of SPL. Specifically, we prove
+that the learning process of SPL always converges to critical points of this
+implicit objective under some mild conditions. This result verifies the
+intrinsic relationship between SPL and this implicit objective, and makes the
+previous robustness analysis on SPL complete and theoretically rational.
+",1,0,0,0,0,0
+14937,Graphene quantum dots prevent alpha-synucleinopathy in Parkinson's disease,"  While the emerging evidence indicates that the pathogenesis of Parkinson's
+disease (PD) is strongly correlated to the accumulation of alpha-synuclein
+({\alpha}-syn) aggregates, there has been no clinical success in
+anti-aggregation agents for the disease to date. Here we show that graphene
+quantum dots (GQDs) exhibit anti-amyloid activity via direct interaction with
+{\alpha}-syn. Employing biophysical, biochemical, and cell-based assays as well
+as molecular dynamics (MD) simulation, we find that GQDs have notable potency
+in not only inhibiting fibrillization of {\alpha}-syn but also disaggregating
+mature fibrils in a time-dependent manner. Remarkably, GQDs rescue neuronal
+death and synaptic loss, reduce Lewy body (LB)/Lewy neurite (LN) formation,
+ameliorate mitochondrial dysfunctions, and prevent neuron-to-neuron
+transmission of {\alpha}-syn pathology induced by {\alpha}-syn preformed
+fibrils (PFFs) in neurons. In addition, in vivo administration of GQDs protects
+against {\alpha}-syn PFFs-induced loss of dopamine neurons, LB/LN pathology,
+and behavioural deficits through the penetration of the blood-brain barrier
+(BBB). The finding that GQDs function as an anti-aggregation agent provides a
+promising novel therapeutic target for the treatment of PD and related
+{\alpha}-synucleinopathies.
+",0,1,0,0,0,0
+14938,Liu-Nagel phase diagrams in infinite dimension,"  We study Harmonic Soft Spheres as a model of thermal structural glasses in
+the limit of infinite dimensions. We show that cooling, compressing and
+shearing a glass lead to a Gardner transition and, hence, to a marginally
+stable amorphous solid as found for Hard Spheres systems. A general outcome of
+our results is that a reduced stability of the glass favors the appearance of
+the Gardner transition. Therefore using strong perturbations, e.g. shear and
+compression, on standard glasses or using weak perturbations on weakly stable
+glasses, e.g. the ones prepared close to the jamming point, are the generic
+ways to induce a Gardner transition. The formalism that we discuss allows to
+study general perturbations, including strain deformations that are important
+to study soft glassy rheology at the mean field level.
+",0,1,0,0,0,0
+14939,Networks of planar Hamiltonian systems,"  We introduce diffusively coupled networks where the dynamical system at each
+vertex is planar Hamiltonian. The problems we address are synchronisation and
+an analogue of diffusion-driven Turing instability for time-dependent
+homogeneous states. As a consequence of the underlying Hamiltonian structure
+there exist unusual behaviours compared with networks of coupled limit cycle
+oscillators or activator-inhibitor systems.
+",0,1,1,0,0,0
+14940,Robust Tracking Using Region Proposal Networks,"  Recent advances in visual tracking showed that deep Convolutional Neural
+Networks (CNN) trained for image classification can be strong feature
+extractors for discriminative trackers. However, due to the drastic difference
+between image classification and tracking, extra treatments such as model
+ensemble and feature engineering must be carried out to bridge the two domains.
+Such procedures are either time consuming or hard to generalize well across
+datasets. In this paper we discovered that the internal structure of Region
+Proposal Network (RPN)'s top layer feature can be utilized for robust visual
+tracking. We showed that such property has to be unleashed by a novel loss
+function which simultaneously considers classification accuracy and bounding
+box quality. Without ensemble and any extra treatment on feature maps, our
+proposed method achieved state-of-the-art results on several large scale
+benchmarks including OTB50, OTB100 and VOT2016. We will make our code publicly
+available.
+",1,0,0,0,0,0
+14941,Recovering sparse graphs,"  We construct a fixed parameter algorithm parameterized by d and k that takes
+as an input a graph G' obtained from a d-degenerate graph G by complementing on
+at most k arbitrary subsets of the vertex set of G and outputs a graph H such
+that G and H agree on all but f(d,k) vertices.
+Our work is motivated by the first order model checking in graph classes that
+are first order interpretable in classes of sparse graphs. We derive as a
+corollary that if G_0 is a graph class with bounded expansion, then the first
+order model checking is fixed parameter tractable in the class of all graphs
+that can obtained from a graph G from G_0 by complementing on at most k
+arbitrary subsets of the vertex set of G; this implies an earlier result that
+the first order model checking is fixed parameter tractable in graph classes
+interpretable in classes of graphs with bounded maximum degree.
+",1,0,0,0,0,0
+14942,Remark on arithmetic topology,"  We formalize the arithmetic topology, i.e. a relationship between knots and
+primes. Namely, using the notion of a cluster C*-algebra we construct a functor
+from the category of 3-dimensional manifolds M to a category of algebraic
+number fields K, such that the prime ideals (ideals, resp.) in the ring of
+integers of K correspond to knots (links, resp.) in M. It is proved that the
+functor realizes all axioms of the arithmetic topology conjectured in the
+1960's by Manin, Mazur and Mumford.
+",0,0,1,0,0,0
+14943,Stable Architectures for Deep Neural Networks,"  Deep neural networks have become invaluable tools for supervised machine
+learning, e.g., classification of text or images. While often offering superior
+results over traditional techniques and successfully expressing complicated
+patterns in data, deep architectures are known to be challenging to design and
+train such that they generalize well to new data. Important issues with deep
+architectures are numerical instabilities in derivative-based learning
+algorithms commonly called exploding or vanishing gradients. In this paper we
+propose new forward propagation techniques inspired by systems of Ordinary
+Differential Equations (ODE) that overcome this challenge and lead to
+well-posed learning problems for arbitrarily deep networks.
+The backbone of our approach is our interpretation of deep learning as a
+parameter estimation problem of nonlinear dynamical systems. Given this
+formulation, we analyze stability and well-posedness of deep learning and use
+this new understanding to develop new network architectures. We relate the
+exploding and vanishing gradient phenomenon to the stability of the discrete
+ODE and present several strategies for stabilizing deep learning for very deep
+networks. While our new architectures restrict the solution space, several
+numerical experiments show their competitiveness with state-of-the-art
+networks.
+",1,0,1,0,0,0
+14944,Geometry and Arithmetic of Crystallographic Sphere Packings,"  We introduce the notion of a ""crystallographic sphere packing,"" defined to be
+one whose limit set is that of a geometrically finite hyperbolic reflection
+group in one higher dimension. We exhibit for the first time an infinite family
+of conformally-inequivalent such with all radii being reciprocals of integers.
+We then prove a result in the opposite direction: the ""superintegral"" ones
+exist only in finitely many ""commensurability classes,"" all in dimensions below
+30.
+",0,0,1,0,0,0
+14945,On the global convergence of the Jacobi method for symmetric matrices of order 4 under parallel strategies,"  The paper analyzes special cyclic Jacobi methods for symmetric matrices of
+order $4$. Only those cyclic pivot strategies that enable full parallelization
+of the method are considered. These strategies, unlike the serial pivot
+strategies, can force the method to be very slow or very fast within one cycle,
+depending on the underlying matrix. Hence, for the global convergence proof one
+has to consider two or three adjacent cycles. It is proved that for any
+symmetric matrix $A$ of order~$4$ the inequality
+$S(A^{[2]})\leq(1-10^{-5})S(A)$ holds, where $A^{[2]}$ results from $A$ by
+applying two cycles of a particular parallel method. Here $S(A)$ stands for the
+Frobenius norm of the strictly upper-triangular part of $A$. The result holds
+for two special parallel strategies and implies the global convergence of the
+method under all possible fully parallel strategies. It is also proved that for
+every $\epsilon>0$ and $n\geq4$ there exist a symmetric matrix $A(\epsilon)$ of
+order $n$ and a cyclic strategy, such that upon completion of the first cycle
+of the appropriate Jacobi method the inequality $S(A^{[1]})>
+(1-\epsilon)S(A(\epsilon))$ holds.
+",0,0,1,0,0,0
+14946,Counting Motifs with Graph Sampling,"  Applied researchers often construct a network from a random sample of nodes
+in order to infer properties of the parent network. Two of the most widely used
+sampling schemes are subgraph sampling, where we sample each vertex
+independently with probability $p$ and observe the subgraph induced by the
+sampled vertices, and neighborhood sampling, where we additionally observe the
+edges between the sampled vertices and their neighbors.
+In this paper, we study the problem of estimating the number of motifs as
+induced subgraphs under both models from a statistical perspective. We show
+that: for any connected $h$ on $k$ vertices, to estimate $s=\mathsf{s}(h,G)$,
+the number of copies of $h$ in the parent graph $G$ of maximum degree $d$, with
+a multiplicative error of $\epsilon$, (a) For subgraph sampling, the optimal
+sampling ratio $p$ is $\Theta_{k}(\max\{ (s\epsilon^2)^{-\frac{1}{k}}, \;
+\frac{d^{k-1}}{s\epsilon^{2}} \})$, achieved by Horvitz-Thompson type of
+estimators. (b) For neighborhood sampling, we propose a family of estimators,
+encompassing and outperforming the Horvitz-Thompson estimator and achieving the
+sampling ratio $O_{k}(\min\{ (\frac{d}{s\epsilon^2})^{\frac{1}{k-1}}, \;
+\sqrt{\frac{d^{k-2}}{s\epsilon^2}}\})$. This is shown to be optimal for all
+motifs with at most $4$ vertices and cliques of all sizes.
+The matching minimax lower bounds are established using certain algebraic
+properties of subgraph counts. These results quantify how much more informative
+neighborhood sampling is than subgraph sampling, as empirically verified by
+experiments on both synthetic and real-world data. We also address the issue of
+adaptation to the unknown maximum degree, and study specific problems for
+parent graphs with additional structures, e.g., trees or planar graphs.
+",0,0,0,1,0,0
+14947,Optimizing noise level for perturbing geo-location data,"  With the tremendous increase in the number of smart phones, app stores have
+been overwhelmed with applications requiring geo-location access in order to
+provide their users better services through personalization. Revealing a user's
+location to these third party apps, no matter at what frequency, is a severe
+privacy breach which can have unpleasant social consequences. In order to
+prevent inference attacks derived from geo-location data, a number of location
+obfuscation techniques have been proposed in the literature. However, none of
+them provides any objective measure of privacy guarantee. Some work has been
+done to define differential privacy for geo-location data in the form of
+geo-indistinguishability with l privacy guarantee. These techniques do not
+utilize any prior background information about the Points of Interest (PoIs) of
+a user and apply Laplacian noise to perturb all the location coordinates.
+Intuitively, the utility of such a mechanism can be improved if the noise
+distribution is derived after considering some prior information about PoIs.
+In this paper, we apply the standard definition of differential privacy on
+geo-location data. We use first principles to model various privacy and utility
+constraints, prior background information available about the PoIs
+(distribution of PoI locations in a 1D plane) and the granularity of the input
+required by different types of apps, in order to produce a more accurate and a
+utility maximizing differentially private algorithm for geo-location data at
+the OS level. We investigate this for a particular category of apps and for
+some specific scenarios. This will also help us to verify that whether
+Laplacian noise is still the optimal perturbation when we have such prior
+information.
+",1,0,0,0,0,0
+14948,Hochschild Cohomology and Deformation Quantization of Affine Toric Varieties,"  For an affine toric variety $\mathrm{Spec}(A)$, we give a convex geometric
+description of the Hodge decomposition of its Hochschild cohomology. Under
+certain assumptions we compute the dimensions of the Hodge summands
+$T^1_{(i)}(A)$, generalizing the existing results about the Andre-Quillen
+cohomology group $T^1_{(1)}(A)$. We prove that every Poisson structure on a
+possibly singular affine toric variety can be quantized in the sense of
+deformation quantization.
+",0,0,1,0,0,0
+14949,Adversarial Examples for Semantic Image Segmentation,"  Machine learning methods in general and Deep Neural Networks in particular
+have shown to be vulnerable to adversarial perturbations. So far this
+phenomenon has mainly been studied in the context of whole-image
+classification. In this contribution, we analyse how adversarial perturbations
+can affect the task of semantic segmentation. We show how existing adversarial
+attackers can be transferred to this task and that it is possible to create
+imperceptible adversarial perturbations that lead a deep network to misclassify
+almost all pixels of a chosen class while leaving network prediction nearly
+unchanged outside this class.
+",1,0,0,1,0,0
+14950,Graphical virtual links and a polynomial of signed cyclic graphs,"  For a signed cyclic graph G, we can construct a unique virtual link L by
+taking the medial construction and convert 4-valent vertices of the medial
+graph to crossings according to the signs. If a virtual link can occur in this
+way then we say that the virtual link is graphical. In the article we shall
+prove that a virtual link L is graphical if and only if it is checkerboard
+colorable. On the other hand, we introduce a polynomial F[G] for signed cyclic
+graphs, which is defined via a deletion-marking recursion. We shall establish
+the relationship between F[G] of a signed cyclic graph G and the bracket
+polynomial of one of the virtual link diagrams associated with G. Finally we
+give a spanning subgraph expansion for F[G].
+",0,0,1,0,0,0
+14951,Exploring the Single-Particle Mobility Edge in a One-Dimensional Quasiperiodic Optical Lattice,"  A single-particle mobility edge (SPME) marks a critical energy separating
+extended from localized states in a quantum system. In one-dimensional systems
+with uncorrelated disorder, a SPME cannot exist, since all single-particle
+states localize for arbitrarily weak disorder strengths. However, if
+correlations are present in the disorder potential, the localization transition
+can occur at a finite disorder strength and SPMEs become possible. In this
+work, we find experimental evidence for the existence of such a SPME in a
+one-dimensional quasi-periodic optical lattice. Specifically, we find a regime
+where extended and localized single-particle states coexist, in good agreement
+with theoretical simulations, which predict a SPME in this regime.
+",0,1,0,0,0,0
+14952,Integral field observations of the blue compact galaxy Haro14. Star formation and feedback in dwarf galaxies,"  (Abridged) Low-luminosity, gas-rich blue compact galaxies (BCG) are ideal
+laboratories to investigate many aspects of the star formation in galaxies. We
+study the morphology, stellar content, kinematics, and the nebular excitation
+and ionization mechanism in the BCG Haro 14 by means of integral field
+observations with VIMOS in the VLT. From these data we build maps in continuum
+and in the brighter emission lines, produce line-ratio maps, and obtain the
+velocity and velocity dispersion fields. We also generate the integrated
+spectrum of the major HII regions and young stellar clusters identified in the
+maps to determine reliable physical parameters and oxygen abundances. We find
+as follows: i) the current star formation in Haro 14 is spatially extended with
+the major HII regions placed along a linear structure, elongated in the
+north-south direction, and in a horseshoe-like curvilinear feature that extends
+about 760 pc eastward; the continuum emission is more concentrated and peaks
+close to the galaxy center; ii) two different episodes of star formation are
+present: the recent starburst, with ages $\leq$ 6 Myrs and the intermediate-age
+clusters, with ages between 10 and 30 Myrs; these stellar components rest on a
+several Gyr old underlying host galaxy; iii) the H$\alpha$/H$\beta$ pattern is
+inhomogeneous, with excess color values varying from E(B-V)=0.04 up to
+E(B-V)=1.09; iv) shocks play a significant role in the galaxy; and v) the
+velocity field displays a complicated pattern with regions of material moving
+toward us in the east and north galaxy areas. The morphology of Haro 14, its
+irregular velocity field, and the presence of shocks speak in favor of a
+scenario of triggered star formation. Ages of the knots are consistent with the
+ongoing burst being triggered by the collective action of stellar winds and
+supernovae originated in the central clusters.
+",0,1,0,0,0,0
+14953,A Framework for Automated Cellular Network Tuning with Reinforcement Learning,"  Tuning cellular network performance against always occurring wireless
+impairments can dramatically improve reliability to end users. In this paper,
+we formulate cellular network performance tuning as a reinforcement learning
+(RL) problem and provide a solution to improve the signal to
+interference-plus-noise ratio (SINR) for indoor and outdoor environments. By
+leveraging the ability of Q-learning to estimate future SINR improvement
+rewards, we propose two algorithms: (1) voice over LTE (VoLTE) downlink closed
+loop power control (PC) and (2) self-organizing network (SON) fault management.
+The VoLTE PC algorithm uses RL to adjust the indoor base station transmit power
+so that the effective SINR meets the target SINR. The SON fault management
+algorithm uses RL to improve the performance of an outdoor cluster by resolving
+faults in the network through configuration management. Both algorithms exploit
+measurements from the connected users, wireless impairments, and relevant
+configuration parameters to solve a non-convex SINR optimization problem using
+RL. Simulation results show that our proposed RL based algorithms outperform
+the industry standards today in realistic cellular communication environments.
+",0,0,0,1,0,0
+14954,A Survey of Augmented Reality Navigation,"  Navigation has been a popular area of research in both academia and industry.
+Combined with maps, and different localization technologies, navigation systems
+have become robust and more usable. By combining navigation with augmented
+reality, it can be improved further to become realistic and user friendly. This
+paper surveys existing researches carried out in this area, describes existing
+techniques for building augmented reality navigation systems, and the problems
+faced.
+",1,0,0,0,0,0
+14955,Strong Landau-quantization effects in high-magnetic-field superconductivity of a two-dimensional multiple-band metal near the Lifshitz transition,"  We investigate the onset of superconductivity in magnetic field for a clean
+two-dimensional multiple-band superconductor in the vicinity of the Lifshitz
+transition when one of the bands is very shallow. Due to small number of
+carriers in this band, the quasiclassical Werthamer-Helfand approximation
+breaks down and Landau quantization has to be taken into account. We found that
+the transition temperature TC2(H) has giant oscillations and is resonantly
+enhanced at the magnetic fields corresponding to full occupancy of the Landau
+levels in the shallow band. This enhancement is especially pronounced for the
+lowest Landau level. As a consequence, the reentrant superconducting regions in
+the temperature-field phase diagram emerge at low temperatures near the
+magnetic fields at which the chemical potential matches the Landau levels. The
+specific behavior depends on the relative strength of the intraband and
+interband pairing interactions and the reentrance is most pronounced in the
+purely interband coupling scenario. The reentrant behavior is suppressed by the
+Zeeman spin splitting in the shallow band, the separated regions disappear
+already for very small spin-splitting factors. On the other hand, the
+reentrance is restored in the resonance cases when the spin-splitting energy
+exactly matches the separation between the Landau levels. The predicted
+behavior may realize in the gate-tuned FeSe monolayer.
+",0,1,0,0,0,0
+14956,On boundary extension of mappings in metric spaces in terms of prime ends,"  We study the boundary behavior of the so-called ring $Q$-mappings obtained as
+a natural generalization of mappings with bounded distortion. We establish a
+series of conditions imposed on a function $Q(x)$ for the continuous extension
+of given mappings with respect to prime ends in domains with regular boundaries
+in metric spaces.
+",0,0,1,0,0,0
+14957,Temporally Evolving Community Detection and Prediction in Content-Centric Networks,"  In this work, we consider the problem of combining link, content and temporal
+analysis for community detection and prediction in evolving networks. Such
+temporal and content-rich networks occur in many real-life settings, such as
+bibliographic networks and question answering forums. Most of the work in the
+literature (that uses both content and structure) deals with static snapshots
+of networks, and they do not reflect the dynamic changes occurring over
+multiple snapshots. Incorporating dynamic changes in the communities into the
+analysis can also provide useful insights about the changes in the network such
+as the migration of authors across communities. In this work, we propose
+Chimera, a shared factorization model that can simultaneously account for graph
+links, content, and temporal analysis. This approach works by extracting the
+latent semantic structure of the network in multidimensional form, but in a way
+that takes into account the temporal continuity of these embeddings. Such an
+approach simplifies temporal analysis of the underlying network by using the
+embedding as a surrogate. A consequence of this simplification is that it is
+also possible to use this temporal sequence of embeddings to predict future
+communities. We present experimental results illustrating the effectiveness of
+the approach.
+",1,0,0,1,0,0
+14958,A Robust Utility Learning Framework via Inverse Optimization,"  In many smart infrastructure applications flexibility in achieving
+sustainability goals can be gained by engaging end-users. However, these users
+often have heterogeneous preferences that are unknown to the decision-maker
+tasked with improving operational efficiency. Modeling user interaction as a
+continuous game between non-cooperative players, we propose a robust parametric
+utility learning framework that employs constrained feasible generalized least
+squares estimation with heteroskedastic inference. To improve forecasting
+performance, we extend the robust utility learning scheme by employing
+bootstrapping with bagging, bumping, and gradient boosting ensemble methods.
+Moreover, we estimate the noise covariance which provides approximated
+correlations between players which we leverage to develop a novel correlated
+utility learning framework. We apply the proposed methods both to a toy example
+arising from Bertrand-Nash competition between two firms as well as to data
+from a social game experiment designed to encourage energy efficient behavior
+amongst smart building occupants. Using occupant voting data for shared
+resources such as lighting, we simulate the game defined by the estimated
+utility functions to demonstrate the performance of the proposed methods.
+",1,0,1,0,0,0
+14959,Evaluating stochastic seeding strategies in networks,"  When trying to maximize the adoption of a behavior in a population connected
+by a social network, it is common to strategize about where in the network to
+seed the behavior, often with an element of randomness. Selecting seeds
+uniformly at random is a basic but compelling strategy in that it distributes
+seeds broadly throughout the network. A more sophisticated stochastic strategy,
+one-hop targeting, is to select random network neighbors of random individuals;
+this exploits a version of the friendship paradox, whereby the friend of a
+random individual is expected to have more friends than a random individual,
+with the hope that seeding a behavior at more connected individuals leads to
+more adoption. Many seeding strategies have been proposed, but empirical
+evaluations have demanded large field experiments designed specifically for
+this purpose and have yielded relatively imprecise comparisons of strategies.
+Here we show how stochastic seeding strategies can be evaluated more
+efficiently in such experiments, how they can be evaluated ""off-policy"" using
+existing data arising from experiments designed for other purposes, and how to
+design more efficient experiments. In particular, we consider contrasts between
+stochastic seeding strategies and analyze nonparametric estimators adapted from
+policy evaluation and importance sampling. We use simulations on real networks
+to show that the proposed estimators and designs can dramatically increase
+precision while yielding valid inference. We then apply our proposed estimators
+to two field experiments, one that assigned households to an intensive
+marketing intervention and one that assigned students to an anti-bullying
+intervention.
+",1,0,0,0,0,0
+14960,Flux noise in a superconducting transmission line,"  We study a superconducting transmission line (TL) formed by distributed LC
+oscillators and excited by external magnetic fluxes which are aroused from
+random magnetization (A) placed in substrate or (B) distributed at interfaces
+of a two-wire TL. Low-frequency dynamics of a random magnetic field is
+described based on the diffusion Langevin equation with a short-range source
+caused by (a) random amplitude or (b) gradient of magnetization. For a TL
+modeled as a two-port network with open and shorted ends, the effective
+magnetic flux at the open end has non-local dependency on noise distribution
+along the TL. The flux-flux correlation function is evaluated and analyzed for
+the regimes (Aa), (Ab). (Ba), and (Bb). Essential frequency dispersion takes
+place around the inverse diffusion time of random flux along the TL. Typically,
+noise effect increases with size faster than the area of TL. The flux-flux
+correlator can be verified both via the population relaxation rate of the
+qubit, which is formed by the Josephson junction shunted by the TL with flux
+noises, and via random voltage at the open end of the TL.
+",0,1,0,0,0,0
+14961,Relativistic Newtonian Dynamics for Objects and Particles,"  Relativistic Newtonian Dynamics (RND) was introduced in a series of recent
+papers by the author, in partial cooperation with J. M. Steiner. RND was
+capable of describing non-classical behavior of motion under a central
+attracting force. RND incorporates the influence of potential energy on
+spacetime in Newtonian dynamics, treating gravity as a force in flat spacetime.
+It was shown that this dynamics predicts accurately gravitational time
+dilation, the anomalous precession of Mercury and the periastron advance of any
+binary.
+In this paper the model is further refined and extended to describe also the
+motion of both objects with non-zero mass and massless particles, under a
+conservative attracting force. It is shown that for any conservative force a
+properly defined energy is conserved on the trajectories and if this force is
+central, the angular momentum is also preserved. An RND equation of motion is
+derived for motion under a conservative force. As an application, it is shown
+that RND predicts accurately also the Shapiro time delay - the fourth test of
+GR.
+",0,1,0,0,0,0
+14962,Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors,"  This paper introduces a new sparse spatio-temporal structured Gaussian
+process regression framework for online and offline Bayesian inference. This is
+the first framework that gives a time-evolving representation of the
+interdependencies between the components of the sparse signal of interest. A
+hierarchical Gaussian process describes such structure and the
+interdependencies are represented via the covariance matrices of the prior
+distributions. The inference is based on the expectation propagation method and
+the theoretical derivation of the posterior distribution is provided in the
+paper. The inference framework is thoroughly evaluated over synthetic, real
+video and electroencephalography (EEG) data where the spatio-temporal evolving
+patterns need to be reconstructed with high accuracy. It is shown that it
+achieves 15% improvement of the F-measure compared with the alternating
+direction method of multipliers, spatio-temporal sparse Bayesian learning
+method and one-level Gaussian process model. Additionally, the required memory
+for the proposed algorithm is less than in the one-level Gaussian process
+model. This structured sparse regression framework is of broad applicability to
+source localisation and object detection problems with sparse signals.
+",0,0,0,1,0,0
+14963,Answer Set Solving with Bounded Treewidth Revisited,"  Parameterized algorithms are a way to solve hard problems more efficiently,
+given that a specific parameter of the input is small. In this paper, we apply
+this idea to the field of answer set programming (ASP). To this end, we propose
+two kinds of graph representations of programs to exploit their treewidth as a
+parameter. Treewidth roughly measures to which extent the internal structure of
+a program resembles a tree. Our main contribution is the design of
+parameterized dynamic programming algorithms, which run in linear time if the
+treewidth and weights of the given program are bounded. Compared to previous
+work, our algorithms handle the full syntax of ASP. Finally, we report on an
+empirical evaluation that shows good runtime behaviour for benchmark instances
+of low treewidth, especially for counting answer sets.
+",1,0,0,0,0,0
+14964,Community structure: A comparative evaluation of community detection methods,"  Discovering community structure in complex networks is a mature field since a
+tremendous number of community detection methods have been introduced in the
+literature. Nevertheless, it is still very challenging for practioners to
+determine which method would be suitable to get insights into the structural
+information of the networks they study. Many recent efforts have been devoted
+to investigating various quality scores of the community structure, but the
+problem of distinguishing between different types of communities is still open.
+In this paper, we propose a comparative, extensive and empirical study to
+investigate what types of communities many state-of-the-art and well-known
+community detection methods are producing. Specifically, we provide
+comprehensive analyses on computation time, community size distribution, a
+comparative evaluation of methods according to their optimisation schemes as
+well as a comparison of their partioning strategy through validation metrics.
+We process our analyses on a very large corpus of hundreds of networks from
+five different network categories and propose ways to classify community
+detection methods, helping a potential user to navigate the complex landscape
+of community detection.
+",1,0,0,0,0,0
+14965,Classification of isoparametric submanifolds admitting a reflective focal submanifold in symmetric spaces of non-compact type,"  In this paper, we assume that all isoparametric submanifolds have flat
+section. The main purpose of this paper is to prove that, if a full irreducible
+complete isoparametric submanifold of codimension greater than one in a
+symmetric space of non-compact type admits a reflective focal submanifold and
+if it of real analytic, then it is a principal orbit of a Hermann type action
+on the symmetric space. A hyperpolar action on a symmetric space of non-compact
+type admits a reflective singular orbit if and only if it is a Hermann type
+action. Hence is not extra the assumption that the isoparametric submanifold
+admits a reflective focal submanifold. Also, we prove that, if a full
+irreducible complete isoparametric submanifold of codimension greater than one
+in a symmetric space of non-compact type satisfies some additional conditions,
+then it is a principal orbit of the isotropy action of the symmetric space,
+where we need not impose that the submanifold is of real analytic. We use the
+building theory in the proof.
+",0,0,1,0,0,0
+14966,Training Group Orthogonal Neural Networks with Privileged Information,"  Learning rich and diverse representations is critical for the performance of
+deep convolutional neural networks (CNNs). In this paper, we consider how to
+use privileged information to promote inherent diversity of a single CNN model
+such that the model can learn better representations and offer stronger
+generalization ability. To this end, we propose a novel group orthogonal
+convolutional neural network (GoCNN) that learns untangled representations
+within each layer by exploiting provided privileged information and enhances
+representation diversity effectively. We take image classification as an
+example where image segmentation annotations are used as privileged information
+during the training process. Experiments on two benchmark datasets -- ImageNet
+and PASCAL VOC -- clearly demonstrate the strong generalization ability of our
+proposed GoCNN model. On the ImageNet dataset, GoCNN improves the performance
+of state-of-the-art ResNet-152 model by absolute value of 1.2% while only uses
+privileged information of 10% of the training images, confirming effectiveness
+of GoCNN on utilizing available privileged knowledge to train better CNNs.
+",1,0,0,0,0,0
+14967,Antiunitary representations and modular theory,"  Antiunitary representations of Lie groups take values in the group of unitary
+and antiunitary operators on a Hilbert space H. In quantum physics, antiunitary
+operators implement time inversion or a PCT symmetry, and in the modular theory
+of operator algebras they arise as modular conjugations from cyclic separating
+vectors of von Neumann algebras. We survey some of the key concepts at the
+borderline between the theory of local observables (Quantum Field Theory (QFT)
+in the sense of Araki--Haag--Kastler) and modular theory of operator algebras
+from the perspective of antiunitary group representations. Here a central point
+is to encode modular objects in standard subspaces V in H which in turn are in
+one-to-one correspondence with antiunitary representations of the
+multiplicative group R^x. Half-sided modular inclusions and modular
+intersections of standard subspaces correspond to antiunitary representations
+of Aff(R), and these provide the basic building blocks for a general theory
+started in the 90s with the ground breaking work of Borchers and Wiesbrock and
+developed in various directions in the QFT context. The emphasis of these notes
+lies on the translation between configurations of standard subspaces as they
+arise in the context of modular localization developed by Brunetti, Guido and
+Longo, and the more classical context of von Neumann algebras with cyclic
+separating vectors. Our main point is that configurations of standard subspaces
+can be studied from the perspective of antiunitary Lie group representations
+and the geometry of the corresponding spaces, which are often fiber bundles
+over ordered symmetric spaces. We expect this perspective to provide new and
+systematic insight into the much richer configurations of nets of local
+observables in QFT.
+",0,0,1,0,0,0
+14968,ARTENOLIS: Automated Reproducibility and Testing Environment for Licensed Software,"  Motivation:
+Automatically testing changes to code is an essential feature of continuous
+integration. For open-source code, without licensed dependencies, a variety of
+continuous integration services exist. The COnstraint-Based Reconstruction and
+Analysis (COBRA) Toolbox is a suite of open-source code for computational
+modelling with dependencies on licensed software. A novel automated framework
+of continuous integration in a semi-licensed environment is required for the
+development of the COBRA Toolbox and related tools of the COBRA community.
+Results:
+ARTENOLIS is a general-purpose infrastructure software application that
+implements continuous integration for open-source software with licensed
+dependencies. It uses a master-slave framework, tests code on multiple
+operating systems, and multiple versions of licensed software dependencies.
+ARTENOLIS ensures the stability, integrity, and cross-platform compatibility of
+code in the COBRA Toolbox and related tools.
+Availability and Implementation:
+The continuous integration server, core of the reproducibility and testing
+infrastructure, can be freely accessed under artenolis.lcsb.uni.lu. The
+continuous integration framework code is located in the /.ci directory and at
+the root of the repository freely available under
+github.com/opencobra/cobratoolbox.
+",1,0,0,0,0,0
+14969,Droplet states in quantum XXZ spin systems on general graphs,"  We study XXZ spin systems on general graphs. In particular, we describe the
+formation of droplet states near the bottom of the spectrum in the Ising phase
+of the model, where the Z-term dominates the XX-term. As key tools we use
+particle number conservation of XXZ systems and symmetric products of graphs
+with their associated adjacency matrices and Laplacians. Of particular interest
+to us are strips and multi-dimensional Euclidean lattices, for which we discuss
+the existence of spectral gaps above the droplet regime. We also prove a
+Combes-Thomas bound which shows that the eigenstates in the droplet regime are
+exponentially small perturbations of strict (classical) droplets.
+",0,0,1,0,0,0
+14970,Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning,"  There is growing interest in estimating and analyzing heterogeneous treatment
+effects in experimental and observational studies. We describe a number of
+meta-algorithms that can take advantage of any supervised learning or
+regression method in machine learning and statistics to estimate the
+Conditional Average Treatment Effect (CATE) function. Meta-algorithms build on
+base algorithms---such as Random Forests (RF), Bayesian Additive Regression
+Trees (BART) or neural networks---to estimate the CATE, a function that the
+base algorithms are not designed to estimate directly. We introduce a new
+meta-algorithm, the X-learner, that is provably efficient when the number of
+units in one treatment group is much larger than in the other, and can exploit
+structural properties of the CATE function. For example, if the CATE function
+is linear and the response functions in treatment and control are Lipschitz
+continuous, the X-learner can still achieve the parametric rate under
+regularity conditions. We then introduce versions of the X-learner that use RF
+and BART as base learners. In extensive simulation studies, the X-learner
+performs favorably, although none of the meta-learners is uniformly the best.
+In two persuasion field experiments from political science, we demonstrate how
+our new X-learner can be used to target treatment regimes and to shed light on
+underlying mechanisms. A software package is provided that implements our
+methods.
+",0,0,1,1,0,0
+14971,Counting triangles formula for the first Chern class of a circle bundle,"  We consider the problem of the combinatorial computation of the first Chern
+class of a circle bundle. N.Mnev found such a formula in terms of canonical
+shellings. It represents certain invariant of a triangulation computed by
+analyzing cyclic word in 3-character alphabet associated to the bundle. This
+curvature is a kind of discretization of Konstevich's curvature differential
+2-form.
+We find a new expression of Mnev's curvature by counting triangles in a
+cyclic word. Our formula is different from that of Mnev. In particular, it is
+cyclically invariant by its very form. We present also some sample computations
+of this invariant and also provide a small Mathematica code for the computation
+of this invariant.
+",0,0,1,0,0,0
+14972,Probing homogeneity with standard candles,"  We show that standard candles can provide some valuable information about the
+density contrast, which could be particularly important at redshifts where
+other observations are not available. We use an inversion method to reconstruct
+the local radial density profile from luminosity distance observations assuming
+background cosmological parameters obtained from large scale observations.
+Using type Ia Supernovae% (SNe) , Cepheids and the cosmological parameters from
+the Planck mission we reconstruct the radial density profiles along two
+different directions of the sky. We compare these profiles to other density
+maps obtained from luminosity density, in particular Keenan et al. 2013 and the
+2M++ galaxy catalogue. The method independently confirms the existence of
+inhomogeneities, could be particularly useful to correctly normalize density
+maps from galaxy surveys with respect to the average density of the Universe,
+and could clarify the apparent discrepancy between local and large scale
+estimations of the Hubble constant. When better observational supernovae data
+will be available, the accuracy of the reconstructed density profiles will
+improve and will allow to further investigate the existence of structures whose
+size is beyond the reach of galaxy surveys.
+",0,1,0,0,0,0
+14973,Fabrication tolerant chalcogenide mid-infrared multimode interference coupler design with application for Bracewell nulling interferometry,"  Understanding exoplanet formation and finding potentially habitable
+exoplanets is vital to an enhanced understanding of the universe. The use of
+nulling interferometry to strongly attenuate the central starlight provides the
+opportunity to see objects closer to the star than ever before. Given that
+exoplanets are usually warm, the 4 microns Mid-Infrared region is advantageous
+for such observations. The key performance parameters for a nulling
+interferometer are the extinction ratio it can attain and how well that is
+maintained across the operational bandwidth. Both parameters depend on the
+design and fabrication accuracy of the subcomponents and their wavelength
+dependence. Via detailed simulation it is shown in this paper that a planar
+chalcogenide photonic chip, consisting of three highly fabrication tolerant
+multimode interference couplers, can exceed an extinction ratio of 60 dB in
+double nulling operation and up to 40 dB for a single nulling operation across
+a wavelength window of 3.9 to 4.2 microns. This provides a beam combiner with
+sufficient performance, in theory, to image exoplanets.
+",0,1,0,0,0,0
+14974,Accurate Bayesian Data Classification without Hyperparameter Cross-validation,"  We extend the standard Bayesian multivariate Gaussian generative data
+classifier by considering a generalization of the conjugate, normal-Wishart
+prior distribution and by deriving the hyperparameters analytically via
+evidence maximization. The behaviour of the optimal hyperparameters is explored
+in the high-dimensional data regime. The classification accuracy of the
+resulting generalized model is competitive with state-of-the art Bayesian
+discriminant analysis methods, but without the usual computational burden of
+cross-validation.
+",1,0,1,1,0,0
+14975,Offline Biases in Online Platforms: a Study of Diversity and Homophily in Airbnb,"  How diverse are sharing economy platforms? Are they fair marketplaces, where
+all participants operate on a level playing field, or are they large-scale
+online aggregators of offline human biases? Often portrayed as easy-to-access
+digital spaces whose participants receive equal opportunities, such platforms
+have recently come under fire due to reports of discriminatory behaviours among
+their users, and have been associated with gentrification phenomena that
+exacerbate preexisting inequalities along racial lines. In this paper, we focus
+on the Airbnb sharing economy platform, and analyse the diversity of its user
+base across five large cities. We find it to be predominantly young, female,
+and white. Notably, we find this to be true even in cities with a diverse
+racial composition. We then introduce a method based on the statistical
+analysis of networks to quantify behaviours of homophily, heterophily and
+avoidance between Airbnb hosts and guests. Depending on cities and property
+types, we do find signals of such behaviours relating both to race and gender.
+We use these findings to provide platform design recommendations, aimed at
+exposing and possibly reducing the biases we detect, in support of a more
+inclusive growth of sharing economy platforms.
+",1,0,0,0,0,0
+14976,Parameter Estimation for Thurstone Choice Models,"  We consider the estimation accuracy of individual strength parameters of a
+Thurstone choice model when each input observation consists of a choice of one
+item from a set of two or more items (so called top-1 lists). This model
+accommodates the well-known choice models such as the Luce choice model for
+comparison sets of two or more items and the Bradley-Terry model for pair
+comparisons.
+We provide a tight characterization of the mean squared error of the maximum
+likelihood parameter estimator. We also provide similar characterizations for
+parameter estimators defined by a rank-breaking method, which amounts to
+deducing one or more pair comparisons from a comparison of two or more items,
+assuming independence of these pair comparisons, and maximizing a likelihood
+function derived under these assumptions. We also consider a related binary
+classification problem where each individual parameter takes value from a set
+of two possible values and the goal is to correctly classify all items within a
+prescribed classification error.
+",0,0,1,1,0,0
+14977,Quantitative modeling and analysis of bifurcation-induced bursting,"  Modeling and parameter estimation for neuronal dynamics are often challenging
+because many parameters can range over orders of magnitude and are difficult to
+measure experimentally. Moreover, selecting a suitable model complexity
+requires a sufficient understanding of the model's potential use, such as
+highlighting essential mechanisms underlying qualitative behavior or precisely
+quantifying realistic dynamics. We present a novel approach that can guide
+model development and tuning to achieve desired qualitative and quantitative
+solution properties. Our approach relies on the presence of disparate time
+scales and employs techniques of separating the dynamics of fast and slow
+variables, which are well known in the analysis of qualitative solution
+features. We build on these methods to show how it is also possible to obtain
+quantitative solution features by imposing designed dynamics for the slow
+variables in the form of specified two-dimensional paths in a
+bifurcation-parameter landscape.
+",0,1,1,0,0,0
+14978,Uniqueness and radial symmetry of minimizers for a nonlocal variational problem,"  In this paper we prove the uniqueness and radial symmetry of minimizers for
+variational problems that model several phenomena. The uniqueness is a
+consequence of the convexity of the functional. The main technique is Fourier
+transform of tempered distributions.
+",0,0,1,0,0,0
+14979,Improved Accounting for Differentially Private Learning,"  We consider the problem of differential privacy accounting, i.e. estimation
+of privacy loss bounds, in machine learning in a broad sense. We propose two
+versions of a generic privacy accountant suitable for a wide range of learning
+algorithms. Both versions are derived in a simple and principled way using
+well-known tools from probability theory, such as concentration inequalities.
+We demonstrate that our privacy accountant is able to achieve state-of-the-art
+estimates of DP guarantees and can be applied to new areas like variational
+inference. Moreover, we show that the latter enjoys differential privacy at
+minor cost.
+",1,0,0,1,0,0
+14980,Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ,"  Scattertext is an open source tool for visualizing linguistic variation
+between document categories in a language-independent way. The tool presents a
+scatterplot, where each axis corresponds to the rank-frequency a term occurs in
+a category of documents. Through a tie-breaking strategy, the tool is able to
+display thousands of visible term-representing points and find space to legibly
+label hundreds of them. Scattertext also lends itself to a query-based
+visualization of how the use of terms with similar embeddings differs between
+document categories, as well as a visualization for comparing the importance
+scores of bag-of-words features to univariate metrics.
+",1,0,0,0,0,0
+14981,Tight contact structures on Seifert surface complements,"  We consider complements of standard Seifert surfaces of special alternating
+links. On these handlebodies, we use Honda's method to enumerate those tight
+contact structures whose dividing sets are isotopic to the link, and find their
+number to be the leading coefficient of the Alexander polynomial. The Euler
+classes of the contact structures are identified with hypertrees in a certain
+hypergraph. Using earlier work, this establishes a connection between contact
+topology and the Homfly polynomial. We also show that the contact invariants of
+our tight contact structures form a basis for sutured Floer homology. Finally,
+we relate our methods and results to Kauffman's formal knot theory.
+",0,0,1,0,0,0
+14982,Modular invariant representations of the $\mathcal{N}=2$ superconformal algebra,"  We compute the modular transformation formula of the characters for a certain
+family of (finitely or uncountably many) simple modules over the simple
+$\mathcal{N}=2$ vertex operator superalgebra of central charge
+$c_{p,p'}=3\left(1-\frac{2p'}{p}\right),$ where $(p,p')$ is a pair of coprime
+positive integers such that $p\geq2$. When $p'=1$, the formula coincides with
+that of the $\mathcal{N}=2$ unitary minimal series found by F. Ravanini and
+S.-K. Yang. In addition, we study the properties of the corresponding ""modular
+$S$-matrix"", which is no longer a matrix if $p'\geq2$.
+",0,0,1,0,0,0
+14983,The CODALEMA/EXTASIS experiment: Contributions to the 35th International Cosmic Ray Conference (ICRC 2017),"  Contributions of the CODALEMA/EXTASIS experiment to the 35th International
+Cosmic Ray Conference, 12-20 July 2017, Busan, South Korea.
+",0,1,0,0,0,0
+14984,Distributed Kernel K-Means for Large Scale Clustering,"  Clustering samples according to an effective metric and/or vector space
+representation is a challenging unsupervised learning task with a wide spectrum
+of applications. Among several clustering algorithms, k-means and its
+kernelized version have still a wide audience because of their conceptual
+simplicity and efficacy. However, the systematic application of the kernelized
+version of k-means is hampered by its inherent square scaling in memory with
+the number of samples. In this contribution, we devise an approximate strategy
+to minimize the kernel k-means cost function in which the trade-off between
+accuracy and velocity is automatically ruled by the available system memory.
+Moreover, we define an ad-hoc parallelization scheme well suited for hybrid
+cpu-gpu state-of-the-art parallel architectures. We proved the effectiveness
+both of the approximation scheme and of the parallelization method on standard
+UCI datasets and on molecular dynamics (MD) data in the realm of computational
+chemistry. In this applicative domain, clustering can play a key role for both
+quantitively estimating kinetics rates via Markov State Models or to give
+qualitatively a human compatible summarization of the underlying chemical
+phenomenon under study. For these reasons, we selected it as a valuable
+real-world application scenario.
+",0,0,0,1,0,0
+14985,The Origin of Solar Filament Plasma Inferred from in situ Observations of Elemental Abundances,"  Solar filaments/prominences are one of the most common features in the
+corona, which may lead to energetic coronal mass ejections (CMEs) and flares
+when they erupt. Filaments are about one hundred times cooler and denser than
+the coronal material, and physical understanding of their material origin
+remains controversial. Two types of scenarios have been proposed: one argues
+that the filament plasma is brought into the corona from photosphere or
+chromosphere through a siphon or evaporation/injection process, while the other
+suggests that the material condenses from the surrounding coronal plasma due to
+thermal instability. The elemental abundance analysis is a reasonable clue to
+constrain the models, as the siphon or evaporation/injection model would
+predict that the filament material abundances are close to the photospheric or
+chromospheric ones, while the condensation model should have coronal
+abundances. In this letter, we analyze the elemental abundances of a magnetic
+cloud that contains the ejected filament material. The corresponding filament
+eruption occurred on 1998 April 29, accompanying an M6.8 class soft X-ray flare
+located at the heliographic coordinates S18E20 (NOAA 08210) and a fast halo CME
+with the linear velocity of 1374 km s$^{-1}$ near the Sun. We find that the
+abundance ratios of elements with low and high First Ionization Potential such
+as Fe/O, Mg/O, and Si/O are 0.150, 0.050, and 0.070, respectively, approaching
+their corresponding photospheric values 0.065, 0.081, and 0.066, which does not
+support the coronal origin of the filament plasma.
+",0,1,0,0,0,0
+14986,Distributed Stochastic Optimization via Adaptive SGD,"  Stochastic convex optimization algorithms are the most popular way to train
+machine learning models on large-scale data. Scaling up the training process of
+these models is crucial, but the most popular algorithm, Stochastic Gradient
+Descent (SGD), is a serial method that is surprisingly hard to parallelize. In
+this paper, we propose an efficient distributed stochastic optimization method
+by combining adaptivity with variance reduction techniques. Our analysis yields
+a linear speedup in the number of machines, constant memory footprint, and only
+a logarithmic number of communication rounds. Critically, our approach is a
+black-box reduction that parallelizes any serial online learning algorithm,
+streamlining prior analysis and allowing us to leverage the significant
+progress that has been made in designing adaptive algorithms. In particular, we
+achieve optimal convergence rates without any prior knowledge of smoothness
+parameters, yielding a more robust algorithm that reduces the need for
+hyperparameter tuning. We implement our algorithm in the Spark distributed
+framework and exhibit dramatic performance gains on large-scale logistic
+regression problems.
+",0,0,0,1,0,0
+14987,Bilipschitz Equivalence of Trees and Hyperbolic Fillings,"  We combine conditions found in [Wh] with results from [MPR] to show that
+quasi-isometries between uniformly discrete bounded geometry spaces that
+satisfy linear isoperimetric inequalities are within bounded distance to
+bilipschitz equivalences. We apply this result to regularly branching trees and
+hyperbolic fillings of metric spaces.
+",0,0,1,0,0,0
+14988,On the contribution of thermal excitation to the total 630.0 nm emissions in the northern cusp ionosphere,"  Direct impact excitation by precipitating electrons is believed to be the
+main source of 630.0 nm emissions in the cusp ionosphere. However, this paper
+investigates a different source, 630.0 emissions caused by thermally excited
+atomic oxygen O$(^{1}$D) when high electron temperature prevail in the cusp. On
+22 January 2012 and 14 January 2013, the European Incoherent Scatter Scientific
+Association (EISCAT) radar on Svalbard measured electron temperature
+enhancements exceeding 3000 K near magnetic noon in the cusp ionosphere over
+Svalbard. The electron temperature enhancements corresponded to electron
+density enhancements exceeding $10^{11}$m$^{-3}$ accompanied by intense 630.0
+nm emissions in a field of view common to both the EISCAT Svalbard radar and a
+meridian scanning photometer. This offered an excellent opportunity to
+investigate the role of thermally excited O$(^{1}$D) 630.0 nm emissions in the
+cusp ionosphere. The thermal component was derived from the EISCAT Radar
+measurements and compared with optical data. For both events the calculated
+thermal component had a correlation coefficient greater than 0.8 to the total
+observed 630.0 nm intensity which contains both thermal and particle impact
+components. Despite fairly constant solar wind, the calculated thermal
+component intensity fluctuated possibly due to dayside transients in the
+aurora.
+",0,1,0,0,0,0
+14989,Dose finding for new vaccines: the role for immunostimulation/immunodynamic modelling,"  Current methods to optimize vaccine dose are purely empirically based,
+whereas in the drug development field, dosing determinations use far more
+advanced quantitative methodology to accelerate decision-making. Applying these
+established methods in the field of vaccine development may reduce the
+currently large clinical trial sample sizes, long time frames, high costs, and
+ultimately have a better potential to save lives. We propose the field of
+immunostimulation/immunodynamic (IS/ID) modelling, which aims to translate
+mathematical frameworks used for drug dosing towards optimizing vaccine dose
+decision-making. Analogous to PK/PD modelling, IS/ID modelling approaches apply
+mathematical models to describe the underlying mechanisms by which the immune
+response is stimulated by vaccination (IS) and the resulting measured immune
+response dynamics (ID). To move IS/ID modelling forward, existing datasets and
+further data on vaccine allometry and dose-dependent dynamics need to be
+generated and collate, requiring a collaborative environment with input from
+academia, industry, regulators, governmental and non-governmental agencies to
+share modelling expertise, and connect modellers to vaccine data.
+",0,0,0,0,1,0
+14990,On Learning Mixtures of Well-Separated Gaussians,"  We consider the problem of efficiently learning mixtures of a large number of
+spherical Gaussians, when the components of the mixture are well separated. In
+the most basic form of this problem, we are given samples from a uniform
+mixture of $k$ standard spherical Gaussians, and the goal is to estimate the
+means up to accuracy $\delta$ using $poly(k,d, 1/\delta)$ samples.
+In this work, we study the following question: what is the minimum separation
+needed between the means for solving this task? The best known algorithm due to
+Vempala and Wang [JCSS 2004] requires a separation of roughly
+$\min\{k,d\}^{1/4}$. On the other hand, Moitra and Valiant [FOCS 2010] showed
+that with separation $o(1)$, exponentially many samples are required. We
+address the significant gap between these two bounds, by showing the following
+results.
+1. We show that with separation $o(\sqrt{\log k})$, super-polynomially many
+samples are required. In fact, this holds even when the $k$ means of the
+Gaussians are picked at random in $d=O(\log k)$ dimensions.
+2. We show that with separation $\Omega(\sqrt{\log k})$, $poly(k,d,1/\delta)$
+samples suffice. Note that the bound on the separation is independent of
+$\delta$. This result is based on a new and efficient ""accuracy boosting""
+algorithm that takes as input coarse estimates of the true means and in time
+$poly(k,d, 1/\delta)$ outputs estimates of the means up to arbitrary accuracy
+$\delta$ assuming the separation between the means is $\Omega(\min\{\sqrt{\log
+k},\sqrt{d}\})$ (independently of $\delta$).
+We also present a computationally efficient algorithm in $d=O(1)$ dimensions
+with only $\Omega(\sqrt{d})$ separation. These results together essentially
+characterize the optimal order of separation between components that is needed
+to learn a mixture of $k$ spherical Gaussians with polynomial samples.
+",1,0,1,0,0,0
+14991,Sylvester's Problem and Mock Heegner Points,"  We prove that if $p \equiv 4,7 \pmod{9}$ is prime and $3$ is not a cube
+modulo $p$, then both of the equations $x^3+y^3=p$ and $x^3+y^3=p^2$ have a
+solution with $x,y \in \mathbb{Q}$.
+",0,0,1,0,0,0
+14992,Community Detection in the Network of German Princes in 1225: a Case Study,"  Many social networks exhibit some underlying community structure. In
+particular, in the context of historical research, clustering of different
+groups into warring or friendly factions can lead to a better understanding of
+how conflicts may arise, and whether they could be avoided or not. In this work
+we study the crisis that started in 1225 when the Emperor of the Holy Roman
+Empire, Frederick II and his son Henry VII got into a conflict which almost led
+to the rupture and dissolution of the Empire. We use a spin-glass-based
+community detection algorithm to see how good this method is in detecting this
+rift and compare the results with an analysis performed by one of the authors
+(Gramsch) using standard social balance theory applied to History.
+",1,1,0,0,0,0
+14993,Coupon Advertising in Online Social Systems: Algorithms and Sampling Techniques,"  Online social systems have become important platforms for viral marketing
+where the advertising of products is carried out with the communication of
+users. After adopting the product, the seed buyers may spread the information
+to their friends via online messages e.g. posts and tweets. In another issue,
+electronic coupon system is one of the relevant promotion vehicles that help
+manufacturers and retailers attract more potential customers. By offering
+coupons to seed buyers, there is a chance to convince the influential users who
+are, however, at first not very interested in the product. In this paper, we
+propose a coupon based online influence model and consider the problem that how
+to maximize the profit by selecting appropriate seed buyers. The considered
+problem herein is markedly different from other influence related problems as
+its objective function is not monotone. We provide an algorithmic analysis and
+give several algorithms designed with different sampling techniques. In
+particular, we propose the RA-T and RA-S algorithms which are not only provably
+effective but also scalable on large datasets. The proposed theoretical results
+are evaluated by extensive experiments done on large-scale real-world social
+networks. The analysis of this paper also provides an algorithmic framework for
+non-monotone submodular maximization problems in social networks.
+",1,0,0,0,0,0
+14994,Statistical study of auroral omega bands,"  The presence of very few statistical studies on auroral omega bands motivated
+us to test-use a semi-automatic method for identifying large-scale undulations
+of the diffuse aurora boundary and to investigate their occurrence. Five
+identical all-sky cameras with overlapping fields of view provided data for 438
+auroral omega-like structures over Fennoscandian Lapland from 1996 to 2007. The
+results from this set of omega band events agree remarkably well with previous
+observations of omega band occurrence in magnetic local time (MLT), lifetime,
+location between the region 1 and 2 field-aligned currents, as well as current
+density estimates. The average peak emission height of omega forms corresponds
+to the estimated precipitation energies of a few keV, which experienced no
+significant change during the events. Analysis of both local and global
+magnetic indices demonstrates that omega bands are observed during substorm
+expansion and recovery phases that are more intense than average substorm
+expansion and recovery phases in the same region. The omega occurrence with
+respect to the substorm expansion and recovery phases is in a very good
+agreement with an earlier observed distribution of fast earthward flows in the
+plasma sheet during expansion and recovery phases. These findings support the
+theory that omegas are produced by fast earthward flows and auroral streamers,
+despite the rarity of good conjugate observations.
+",0,1,0,0,0,0
+14995,Efficient Lightweight Encryption Algorithm for Smart Video Applications,"  The future generation networks: Internet of things (IoT), in combination with
+the advanced computer vision techniques poses new challenges for securing
+videos for end-users. The visual devices generally have constrained resources
+in respects to their low computation power, small memory with limited power
+supply. Therefore, to facilitate the video security in smart environment,
+lightweight security schemes are required instead of inefficient existing
+traditional cryptography algorithms. This research paper provides the solution
+to overcome such problems. A novel lightweight cipher algorithm is proposed
+here which targets multimedia in IoT with an in-house name EXPer i.e. Extended
+permutation with eXclusive OR (XOR). EXPer is a symmetric stream cipher that
+consists of simple XOR and left shift operations with three keys of 128 bits.
+The proposed cipher algorithm has been tested on various sample videos.
+Comparison of proposed algorithm has been made with the traditional cipher
+algorithms XOR and Advanced Encryption Standard (AES). Visual results confirm
+that EXPer provides security level equivalent to the AES algorithm with less
+computational cost than AES. Therefore, it can easily be perceived that the
+EXPer is a better replacement of AES for securing real-time video applications
+in IoT.
+",1,0,0,0,0,0
+14996,Inter-site pair superconductivity: origins and recent validation experiments,"  The challenge of understanding high-temperature superconductivity has led to
+a plethora of ideas, but 30 years after its discovery in cuprates, very few
+have achieved convincing experimental validation. While Hubbard and t-J models
+were given a lot of attention, a number of recent experiments appear to give
+decisive support to the model of real-space inter-site pairing and percolative
+superconductivity in cuprates. Systematic measurements of the doping dependence
+of the superfluid density show a linear dependence on superfluid density -
+rather than doping - over the entire phase diagram, in accordance with the
+model's predictions. The doping-dependence of the anomalous lattice dynamics of
+in-plane Cu-O mode vibrations observed by inelastic neutron scattering, gives
+remarkable reciprocal space signature of the inter-site pairing interaction
+whose doping dependence closely follows the predicted pair density.
+Symmetry-specific time-domain spectroscopy shows carrier localization, polaron
+formation, pairing and superconductivity to be distinct processes occurring on
+distinct timescales throughout the entire superconducting phase diagram. The
+three diverse experimental results confirm non-trivial predictions made more
+than a decade ago by the inter-site pairing model in the cuprates, remarkably
+also confirming some of the fundamental notions mentioned in the seminal paper
+on the discovery of high-temperature superconductivity in cuprates.
+",0,1,0,0,0,0
+14997,Non-standard FDTD implementation of the Schrödinger equation,"  In this work, we apply the Cole's non-standard form of the FDTD to solve the
+time dependent Schrödinger equation. We deduce the equations for the
+non-standard FDTD considering an electronic wave function in the presence of
+potentials which can be higher or lower in comparison with the energy of the
+electron. The non-standard term is found to be almost the same, except for a
+sine functions which is transformed to a hyperbolic sine function,as the
+argument is imaginary when the potential has higher energy than the electron.
+Perfectly Matched Layers using this methodology are also presented.
+",0,1,0,0,0,0
+14998,The magnetic and electronic properties of Oxyselenides - influence of transition metal ions and lanthanides,"  Magnetic oxyselenides have been the topic of research for several decades
+being first of interest in the context of photoconductivity and
+thermoelectricity owing to their intrinsic semiconducting properties and
+ability to tune the energy gap through metal ion substitution. More recently,
+interest in the oxyselenides has experienced a resurgence owing to the possible
+relation to strongly correlated phenomena given the fact that many oxyslenides
+share a similar structure to unconventional superconducting pnictides and
+chalcogenides. The two dimensional nature of many oxyselenide systems also
+draws an analogy to cuprate physics where a strong interplay between
+unconventional electronic phases and localised magnetism has been studied for
+several decades. It is therefore timely to review the physics of the
+oxyselenides in the context of the broader field of strongly correlated
+magnetism and electronic phenomena. Here we review the current status and
+progress in this area of research with the focus on the influence of
+lanthanides and transition metal ions on the intertwined magnetic and
+electronic properties of oxyselenides. The emphasis of the review is on the
+magnetic properties and comparisons are made with iron based pnictide and
+chalcogenide systems.
+",0,1,0,0,0,0
+14999,Localizing the Object Contact through Matching Tactile Features with Visual Map,"  This paper presents a novel framework for integration of vision and tactile
+sensing by localizing tactile readings in a visual object map. Intuitively,
+there are some correspondences, e.g., prominent features, between visual and
+tactile object identification. To apply it in robotics, we propose to localize
+tactile readings in visual images by sharing same sets of feature descriptors
+through two sensing modalities. It is then treated as a probabilistic
+estimation problem solved in a framework of recursive Bayesian filtering.
+Feature-based measurement model and Gaussian based motion model are thus built.
+In our tests, a tactile array sensor is utilized to generate tactile images
+during interaction with objects and the results have proven the feasibility of
+our proposed framework.
+",1,0,0,0,0,0
+15000,Scattering Cross Section in a Cylindrical anisotropic layered metamaterial,"  To design a uniaxial anisotropic metamaterial a layered cylindrical
+metamaterial is introduced for TE polarization. Unlike to the previous work,
+which the layers were in radial direction, here the layers are in azimuthal
+direction. Scattering efficiency for this metamaterial in different frequency
+is analyzed with solving Maxwell's wave equation. It is observed that in some
+frequencies when the effective permittivity of the structure goes to zero the
+scattering efficiency would be negligible. This result approves the previous
+predictions. It is also found out that the scattering cancellation depends on
+the relative permittivity of the environmental medium for the cylinder. The
+finite element simulations are also confirmed the results.
+",0,1,0,0,0,0
+15001,Stability analysis of a system coupled to a heat equation,"  As a first approach to the study of systems coupling finite and infinite
+dimensional natures, this article addresses the stability of a system of
+ordinary differential equations coupled with a classic heat equation using a
+Lyapunov functional technique. Inspired from recent developments in the area of
+time delay systems, a new methodology to study the stability of such a class of
+distributed parameter systems is presented here. The idea is to use a
+polynomial approximation of the infinite dimensional state of the heat equation
+in order to build an enriched energy functional. A well known efficient
+integral inequality (Bessel inequality) will allow to obtain stability
+conditions expressed in terms of linear matrix inequalities. We will eventually
+test our approach on academic examples in order to illustrate the efficiency of
+our theoretical results.
+",0,0,1,0,0,0
+15002,Corral Framework: Trustworthy and Fully Functional Data Intensive Parallel Astronomical Pipelines,"  Data processing pipelines represent an important slice of the astronomical
+software library that include chains of processes that transform raw data into
+valuable information via data reduction and analysis. In this work we present
+Corral, a Python framework for astronomical pipeline generation. Corral
+features a Model-View-Controller design pattern on top of an SQL Relational
+Database capable of handling: custom data models; processing stages; and
+communication alerts, and also provides automatic quality and structural
+metrics based on unit testing. The Model-View-Controller provides concept
+separation between the user logic and the data models, delivering at the same
+time multi-processing and distributed computing capabilities. Corral represents
+an improvement over commonly found data processing pipelines in Astronomy since
+the design pattern eases the programmer from dealing with processing flow and
+parallelization issues, allowing them to focus on the specific algorithms
+needed for the successive data transformations and at the same time provides a
+broad measure of quality over the created pipeline. Corral and working examples
+of pipelines that use it are available to the community at
+this https URL.
+",1,1,0,0,0,0
+15003,Radial orbit instability in systems of highly eccentric orbits: Antonov problem reviewed,"  Stationary stellar systems with radially elongated orbits are subject to
+radial orbit instability -- an important phenomenon that structures galaxies.
+Antonov (1973) presented a formal proof of the instability for spherical
+systems in the limit of purely radial orbits. However, such spheres have highly
+inhomogeneous density distributions with singularity $\sim 1/r^2$, resulting in
+an inconsistency in the proof. The proof can be refined, if one considers an
+orbital distribution close to purely radial, but not entirely radial, which
+allows to avoid the central singularity. For this purpose we employ
+non-singular analogs of generalised polytropes elaborated recently in our work
+in order to derive and solve new integral equations adopted for calculation of
+unstable eigenmodes in systems with nearly radial orbits. In addition, we
+establish a link between our and Antonov's approaches and uncover the meaning
+of infinite entities in the purely radial case. Maximum growth rates tend to
+infinity as the system becomes more and more radially anisotropic. The
+instability takes place both for even and odd spherical harmonics, with all
+unstable modes developing rapidly, i.e. having eigenfrequencies comparable to
+or greater than typical orbital frequencies. This invalidates orbital
+approximation in the case of systems with all orbits very close to purely
+radial.
+",0,1,0,0,0,0
+15004,Short-range wakefields generated in the blowout regime of plasma-wakefield acceleration,"  In the past, calculation of wakefields generated by an electron bunch
+propagating in a plasma has been carried out in linear approximation, where the
+plasma perturbation can be assumed small and plasma equations of motion
+linearized. This approximation breaks down in the blowout regime where a
+high-density electron driver expels plasma electrons from its path and creates
+a cavity void of electrons in its wake. In this paper, we develop a technique
+that allows to calculate short-range longitudinal and transverse wakes
+generated by a witness bunch being accelerated inside the cavity. Our results
+can be used for studies of the beam loading and the hosing instability of the
+witness bunch in PWFA and LWFA.
+",0,1,0,0,0,0
+15005,Scaled Nuclear Norm Minimization for Low-Rank Tensor Completion,"  Minimizing the nuclear norm of a matrix has been shown to be very efficient
+in reconstructing a low-rank sampled matrix. Furthermore, minimizing the sum of
+nuclear norms of matricizations of a tensor has been shown to be very efficient
+in recovering a low-Tucker-rank sampled tensor. In this paper, we propose to
+recover a low-TT-rank sampled tensor by minimizing a weighted sum of nuclear
+norms of unfoldings of the tensor. We provide numerical results to show that
+our proposed method requires significantly less number of samples to recover to
+the original tensor in comparison with simply minimizing the sum of nuclear
+norms since the structure of the unfoldings in the TT tensor model is
+fundamentally different from that of matricizations in the Tucker tensor model.
+",1,0,0,1,0,0
+15006,Stability and Transparency Analysis of a Bilateral Teleoperation in Presence of Data Loss,"  This paper presents a novel approach for stability and transparency analysis
+for bilateral teleoperation in the presence of data loss in communication
+media. A new model for data loss is proposed based on a set of periodic
+continuous pulses and its finite series representation. The passivity of the
+overall system is shown using wave variable approach including the newly
+defined model for data loss. Simulation results are presented to show the
+effectiveness of the proposed approach.
+",1,0,0,0,0,0
+15007,Quantum eigenstate tomography with qubit tunneling spectroscopy,"  Measurement of the energy eigenvalues (spectrum) of a multi-qubit system has
+recently become possible by qubit tunneling spectroscopy (QTS). In the standard
+QTS experiments, an incoherent probe qubit is strongly coupled to one of the
+qubits of the system in such a way that its incoherent tunneling rate provides
+information about the energy eigenvalues of the original (source) system. In
+this paper, we generalize QTS by coupling the probe qubit to many source
+qubits. We show that by properly choosing the couplings, one can perform
+projective measurements of the source system energy eigenstates in an arbitrary
+basis, thus performing quantum eigenstate tomography. As a practical example of
+a limited tomography, we apply our scheme to probe the eigenstates of a kink in
+a frustrated transverse Ising chain.
+",0,1,0,0,0,0
+15008,Binary hermitian forms and optimal embeddings,"  Fix a quadratic order over the ring of integers. An embedding of the
+quadratic order into a quaternionic order naturally gives an integral binary
+hermitian form over the quadratic order. We show that, in certain cases, this
+correspondence is a discriminant preserving bijection between the isomorphism
+classes of embeddings and integral binary hermitian forms.
+",0,0,1,0,0,0
+15009,An Improved Training Procedure for Neural Autoregressive Data Completion,"  Neural autoregressive models are explicit density estimators that achieve
+state-of-the-art likelihoods for generative modeling. The D-dimensional data
+distribution is factorized into an autoregressive product of one-dimensional
+conditional distributions according to the chain rule. Data completion is a
+more involved task than data generation: the model must infer missing variables
+for any partially observed input vector. Previous work introduced an
+order-agnostic training procedure for data completion with autoregressive
+models. Missing variables in any partially observed input vector can be imputed
+efficiently by choosing an ordering where observed dimensions precede
+unobserved ones and by computing the autoregressive product in this order. In
+this paper, we provide evidence that the order-agnostic (OA) training procedure
+is suboptimal for data completion. We propose an alternative procedure (OA++)
+that reaches better performance in fewer computations. It can handle all data
+completion queries while training fewer one-dimensional conditional
+distributions than the OA procedure. In addition, these one-dimensional
+conditional distributions are trained proportionally to their expected usage at
+inference time, reducing overfitting. Finally, our OA++ procedure can exploit
+prior knowledge about the distribution of inference completion queries, as
+opposed to OA. We support these claims with quantitative experiments on
+standard datasets used to evaluate autoregressive generative models.
+",1,0,0,1,0,0
+15010,A Stochastic Formulation of the Resolution of Identity: Application to Second Order Møller-Plesset Perturbation Theory,"  A stochastic orbital approach to the resolution of identity (RI)
+approximation for 4-index 2-electron electron repulsion integrals (ERIs) is
+presented. The stochastic RI-ERIs are then applied to M\o ller-Plesset
+perturbation theory (MP2) utilizing a \textit{multiple stochastic orbital
+approach}. The introduction of multiple stochastic orbitals results in an $N^3$
+scaling for both the stochastic RI-ERIs and stochastic RI-MP2. We demonstrate
+that this method exhibits a small prefactor and an observed scaling of
+$N^{2.4}$ for a range of water clusters, already outperforming MP2 for clusters
+with as few as 21 water molecules.
+",0,1,0,0,0,0
+15011,Automatic Mapping of NES Games with Mappy,"  Game maps are useful for human players, general-game-playing agents, and
+data-driven procedural content generation. These maps are generally made by
+hand-assembling manually-created screenshots of game levels. Besides being
+tedious and error-prone, this approach requires additional effort for each new
+game and level to be mapped. The results can still be hard for humans or
+computational systems to make use of, privileging visual appearance over
+semantic information. We describe a software system, Mappy, that produces a
+good approximation of a linked map of rooms given a Nintendo Entertainment
+System game program and a sequence of button inputs exploring its world. In
+addition to visual maps, Mappy outputs grids of tiles (and how they change over
+time), positions of non-tile objects, clusters of similar rooms that might in
+fact be the same room, and a set of links between these rooms. We believe this
+is a necessary step towards developing larger corpora of high-quality
+semantically-annotated maps for PCG via machine learning and other
+applications.
+",1,0,0,0,0,0
+15012,Proportional Closeness Estimation of Probability of Contamination Under Group Testing,"  The paper is focused on the problem of estimating the probability $p$ of
+individual contaminated sample, under group testing. The precision of the
+estimator is given by the probability of proportional closeness, a concept
+defined in the Introduction. Two-stage and sequential sampling procedures are
+characterized. An adaptive procedure is examined.
+",0,0,1,1,0,0
+15013,Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research,"  Conventional text classification models make a bag-of-words assumption
+reducing text into word occurrence counts per document. Recent algorithms such
+as word2vec are capable of learning semantic meaning and similarity between
+words in an entirely unsupervised manner using a contextual window and doing so
+much faster than previous methods. Each word is projected into vector space
+such that similar meaning words such as ""strong"" and ""powerful"" are projected
+into the same general Euclidean space. Open questions about these embeddings
+include their utility across classification tasks and the optimal properties
+and source of documents to construct broadly functional embeddings. In this
+work, we demonstrate the usefulness of pre-trained embeddings for
+classification in our task and demonstrate that custom word embeddings, built
+in the domain and for the tasks, can improve performance over word embeddings
+learnt on more general data including news articles or Wikipedia.
+",1,0,0,1,0,0
+15014,The AKARI IRC asteroid flux catalogue: updated diameters and albedos,"  The AKARI IRC All-sky survey provided more than twenty thousand thermal
+infrared observations of over five thousand asteroids. Diameters and albedos
+were obtained by fitting an empirically calibrated version of the standard
+thermal model to these data. After the publication of the flux catalogue in
+October 2016, our aim here is to present the AKARI IRC all-sky survey data and
+discuss valuable scientific applications in the field of small-body physical
+properties studies. As an example, we update the catalogue of asteroid
+diameters and albedos based on AKARI using the near-Earth asteroid thermal
+model (NEATM). We fit the NEATM to derive asteroid diameters and, whenever
+possible, infrared beaming parameters. We obtained a total of 8097 diameters
+and albedos for 5170 asteroids, and we fitted the beaming parameter for almost
+two thousand of them. When it was not possible to fit the beaming parameter, we
+used a straight line fit to our sample's beaming parameter-versus-phase angle
+plot to set the default value for each fit individually instead of using a
+single average value. Our diameters agree with stellar-occultation-based
+diameters well within the accuracy expected for the model. They also match the
+previous AKARI-based catalogue at phase angles lower than 50 degrees, but we
+find a systematic deviation at higher phase angles, at which near-Earth and
+Mars-crossing asteroids were observed. The AKARI IRC All-sky survey provides
+observations at different observation geometries, rotational coverages and
+aspect angles. For example, by comparing in more detail a few asteroids for
+which dimensions were derived from occultations, we discuss how the multiple
+observations per object may already provide three-dimensional information about
+elongated objects even based on an idealised model like the NEATM.
+",0,1,0,0,0,0
+15015,Finite sample Bernstein - von Mises theorems for functionals and spectral projectors of covariance matrix,"  We demonstrate that a prior influence on the posterior distribution of
+covariance matrix vanishes as sample size grows. The assumptions on a prior are
+explicit and mild. The results are valid for a finite sample and admit the
+dimension $p$ growing with the sample size $n$. We exploit the described fact
+to derive the finite sample Bernstein - von Mises theorem for functionals of
+covariance matrix (e.g. eigenvalues) and to find the posterior distribution of
+the Frobenius distance between spectral projector and empirical spectral
+projector. This can be useful for constructing sharp confidence sets for the
+true value of the functional or for the true spectral projector.
+",0,0,1,1,0,0
+15016,Low-temperature behavior of the multicomponent Widom-Rowlison model on finite square lattices,"  We consider the multicomponent Widom-Rowlison with Metropolis dynamics, which
+describes the evolution of a particle system where $M$ different types of
+particles interact subject to certain hard-core constraints. Focusing on the
+scenario where the spatial structure is modeled by finite square lattices, we
+study the asymptotic behavior of this interacting particle system in the
+low-temperature regime, analyzing the tunneling times between its $M$
+maximum-occupancy configurations, and the mixing time of the corresponding
+Markov chain. In particular, we develop a novel combinatorial method that,
+exploiting geometrical properties of the Widom-Rowlinson configurations on
+finite square lattices, leads to the identification of the timescale at which
+transitions between maximum-occupancy configurations occur and shows how this
+depends on the chosen boundary conditions and the square lattice dimensions.
+",0,1,1,0,0,0
+15017,Stream Graphs and Link Streams for the Modeling of Interactions over Time,"  Graph theory provides a language for studying the structure of relations, and
+it is often used to study interactions over time too. However, it poorly
+captures the both temporal and structural nature of interactions, that calls
+for a dedicated formalism. In this paper, we generalize graph concepts in order
+to cope with both aspects in a consistent way. We start with elementary
+concepts like density, clusters, or paths, and derive from them more advanced
+concepts like cliques, degrees, clustering coefficients, or connected
+components. We obtain a language to directly deal with interactions over time,
+similar to the language provided by graphs to deal with relations. This
+formalism is self-consistent: usual relations between different concepts are
+preserved. It is also consistent with graph theory: graph concepts are special
+cases of the ones we introduce. This makes it easy to generalize higher-level
+objects such as quotient graphs, line graphs, k-cores, and centralities. This
+paper also considers discrete versus continuous time assumptions, instantaneous
+links, and extensions to more complex cases.
+",1,0,0,1,0,0
+15018,Supermetric Search,"  Metric search is concerned with the efficient evaluation of queries in metric
+spaces. In general,a large space of objects is arranged in such a way that,
+when a further object is presented as a query, those objects most similar to
+the query can be efficiently found. Most mechanisms rely upon the triangle
+inequality property of the metric governing the space. The triangle inequality
+property is equivalent to a finite embedding property, which states that any
+three points of the space can be isometrically embedded in two-dimensional
+Euclidean space. In this paper, we examine a class of semimetric space which is
+finitely four-embeddable in three-dimensional Euclidean space. In mathematics
+this property has been extensively studied and is generally known as the
+four-point property. All spaces with the four-point property are metric spaces,
+but they also have some stronger geometric guarantees. We coin the term
+supermetric space as, in terms of metric search, they are significantly more
+tractable. Supermetric spaces include all those governed by Euclidean, Cosine,
+Jensen-Shannon and Triangular distances, and are thus commonly used within many
+domains. In previous work we have given a generic mathematical basis for the
+supermetric property and shown how it can improve indexing performance for a
+given exact search structure. Here we present a full investigation into its use
+within a variety of different hyperplane partition indexing structures, and go
+on to show some more of its flexibility by examining a search structure whose
+partition and exclusion conditions are tailored, at each node, to suit the
+individual reference points and data set present there. Among the results
+given, we show a new best performance for exact search using a well-known
+benchmark.
+",1,0,0,0,0,0
+15019,Liouville-type theorems with finite Morse index for Δ_λ-Laplace operator,"  In this paper we study solutions, possibly unbounded and sign-changing, of
+the following problem:
+-\D_{\lambda} u=|x|_{\lambda}^a |u|^{p-1}u, in R^n,\;n\geq 1,\; p>1, and a
+\geq 0, where \D_{\lambda} is a strongly degenerate elliptic operator, the
+functions \lambda=(\lambda_1, ..., \lambda_k) : R^n \rightarrow R^k, satisfies
+some certain conditions, and |.|_{\lambda} the homogeneous norm associated to
+the \D_{\lambda}-Laplacian.
+We prove various Liouville-type theorems for smooth solutions under the
+assumption that they are stable or stable outside a compact set of R^n. First,
+we establish the standard integralestimates via stability property to derive
+the nonexistence results for stable solutions. Next, by mean of the Pohozaev
+identity, we deduce the Liouville-type theorem for solutions stable outside a
+compact set.
+",0,0,1,0,0,0
+15020,Performance Impact of Base Station Antenna Heights in Dense Cellular Networks,"  In this paper, we present a new and significant theoretical discovery. If the
+absolute height difference between base station (BS) antenna and user equipment
+(UE) antenna is larger than zero, then the network performance in terms of both
+the coverage probability and the area spectral efficiency (ASE) will
+continuously decrease toward zero as the BS density increases for ultra-dense
+(UD) small cell networks (SCNs). Such findings are completely different from
+the conclusions in existing works, both quantitatively and qualitatively. In
+particular, this performance behavior has a tremendous impact on the deployment
+of UD SCNs in the 5th-generation (5G) era. Network operators may invest large
+amounts of money in deploying more network infrastructure to only obtain an
+even less network capacity. Our study results reveal that one way to address
+this issue is to lower the SCN BS antenna height to the UE antenna height.
+However, this requires a revolutionized approach of BS architecture and
+deployment, which is explored in this paper too.
+",1,0,0,0,0,0
+15021,An information-theoretic approach for selecting arms in clinical trials,"  The question of selecting the ""best"" amongst different choices is a common
+problem in statistics. In drug development, our motivating setting, the
+question becomes, for example: what is the dose that gives me a pre-specified
+risk of toxicity or which treatment gives the best response rate. Motivated by
+a recent development in the weighted information measures theory, we propose an
+experimental design based on a simple and intuitive criterion which governs arm
+selection in the experiment with multinomial outcomes. The criterion leads to
+accurate arm selection without any parametric or monotonicity assumption. The
+asymptotic properties of the design are studied for different allocation rules
+and the small sample size behaviour is evaluated in simulations in the context
+of Phase I and Phase II clinical trials with binary endpoints. We compare the
+proposed design to currently used alternatives and discuss its practical
+implementation.
+",0,0,1,1,0,0
+15022,A watershed-based algorithm to segment and classify cells in fluorescence microscopy images,"  Imaging assays of cellular function, especially those using fluorescent
+stains, are ubiquitous in the biological and medical sciences. Despite advances
+in computer vision, such images are often analyzed using only manual or
+rudimentary automated processes. Watershed-based segmentation is an effective
+technique for identifying objects in images; it outperforms commonly used image
+analysis methods, but requires familiarity with computer-vision techniques to
+be applied successfully. In this report, we present and implement a
+watershed-based image analysis and classification algorithm in a GUI, enabling
+a broad set of users to easily understand the algorithm and adjust the
+parameters to their specific needs. As an example, we implement this algorithm
+to find and classify cells in a complex imaging assay for mitochondrial
+function. In a second example, we demonstrate a workflow using manual
+comparisons and receiver operator characteristics to optimize the algorithm
+parameters for finding live and dead cells in a standard viability assay.
+Overall, this watershed-based algorithm is more advanced than traditional
+thresholding and can produce optimized, automated results. By incorporating
+associated pre-processing steps in the GUI, the algorithm is also easily
+adjusted, rendering it user-friendly.
+",1,0,0,0,0,0
+15023,On the restricted Chebyshev-Boubaker polynomials,"  Using the language of Riordan arrays, we study a one-parameter family of
+orthogonal polynomials that we call the restricted Chebyshev-Boubaker
+polynomials. We characterize these polynomials in terms of the three term
+recurrences that they satisfy, and we study certain central sequences defined
+by their coefficient arrays. We give an integral representation for their
+moments, and we show that the Hankel transforms of these moments have a simple
+form. We show that the (sequence) Hankel transform of the row sums of the
+corresponding moment matrix is defined by a family of polynomials closely
+related to the Chebyshev polynomials of the second kind, and that these row
+sums are in fact the moments of another family of orthogonal polynomials.
+",0,0,1,0,0,0
+15024,Band filling control of the Dzyaloshinskii-Moriya interaction in weakly ferromagnetic insulators,"  We observe and explain theoretically a dramatic evolution of the
+Dzyaloshinskii-Moriya interaction in the series of isostructural weak
+ferromagnets, MnCO$_3$, FeBO$_3$, CoCO$_3$ and NiCO$_3$. The sign of the
+interaction is encoded in the phase of x-ray magnetic diffraction amplitude,
+observed through interference with resonant quadrupole scattering. We find very
+good quantitative agreement with first-principles electronic structure
+calculations, reproducing both sign and magnitude through the series, and
+propose a simplified `toy model' to explain the change in sign with 3 d shell
+filling. The model gives a clue for qualitative understanding of the evolution
+of the DMI in Mott and charge transfer insulators.
+",0,1,0,0,0,0
+15025,Reducing Estimation Risk in Mean-Variance Portfolios with Machine Learning,"  In portfolio analysis, the traditional approach of replacing population
+moments with sample counterparts may lead to suboptimal portfolio choices. I
+show that optimal portfolio weights can be estimated using a machine learning
+(ML) framework, where the outcome to be predicted is a constant and the vector
+of explanatory variables is the asset returns. It follows that ML specifically
+targets estimation risk when estimating portfolio weights, and that
+""off-the-shelf"" ML algorithms can be used to estimate the optimal portfolio in
+the presence of parameter uncertainty. The framework nests the traditional
+approach and recently proposed shrinkage approaches as special cases. By
+relying on results from the ML literature, I derive new insights for existing
+approaches and propose new estimation methods. Based on simulation studies and
+several datasets, I find that ML significantly reduces estimation risk compared
+to both the traditional approach and the equal weight strategy.
+",0,0,0,0,0,1
+15026,CutFEM topology optimization of 3D laminar incompressible flow problems,"  This paper studies the characteristics and applicability of the CutFEM
+approach as the core of a robust topology optimization framework for 3D laminar
+incompressible flow and species transport problems at low Reynolds number (Re <
+200). CutFEM is a methodology for discretizing partial differential equations
+on complex geometries by immersed boundary techniques. In this study, the
+geometry of the fluid domain is described by an explicit level set method,
+where the parameters of a level set function are defined as functions of the
+optimization variables. The fluid behavior is modeled by the incompressible
+Navier-Stokes equations. Species transport is modeled by an advection-diffusion
+equation. The governing equations are discretized in space by a generalized
+extended finite element method. Face-oriented ghost-penalty terms are added for
+stability reasons and to improve the conditioning of the system. The boundary
+conditions are enforced weakly via Nit\-sc\-he's method. The emergence of
+isolated volumes of fluid surrounded by solid during the optimization process
+leads to a singular analysis problem. An auxiliary indicator field is modeled
+to identify these volumes and to impose a constraint on the average pressure.
+Numerical results for 3D, steady-state and transient problems demonstrate that
+the CutFEM analyses are sufficiently accurate, and the optimized designs agree
+well with results from prior studies solved in 2D or by density approaches.
+",1,0,1,0,0,0
+15027,Network topology of neural systems supporting avalanche dynamics predicts stimulus propagation and recovery,"  Many neural systems display avalanche behavior characterized by uninterrupted
+sequences of neuronal firing whose distributions of size and durations are
+heavy-tailed. Theoretical models of such systems suggest that these dynamics
+support optimal information transmission and storage. However, the unknown role
+of network structure precludes an understanding of how variations in network
+topology manifest in neural dynamics and either support or impinge upon
+information processing. Here, using a generalized spiking model, we develop a
+mechanistic understanding of how network topology supports information
+processing through network dynamics. First, we show how network topology
+determines network dynamics by analytically and numerically demonstrating that
+network topology can be designed to propagate stimulus patterns for long
+durations. We then identify strongly connected cycles as empirically observable
+network motifs that are prevalent in such networks. Next, we show that within a
+network, mathematical intuitions from network control theory are tightly linked
+with dynamics initiated by node-specific stimulation and can identify stimuli
+that promote long-lasting cascades. Finally, we use these network-based metrics
+and control-based stimuli to demonstrate that long-lasting cascade dynamics
+facilitate delayed recovery of stimulus patterns from network activity, as
+measured by mutual information. Collectively, our results provide evidence that
+cortical networks are structured with architectural motifs that support
+long-lasting propagation and recovery of a few crucial patterns of stimulation,
+especially those consisting of activity in highly controllable neurons.
+Broadly, our results imply that avalanching neural networks could contribute to
+cognitive faculties that require persistent activation of neuronal patterns,
+such as working memory or attention.
+",0,0,0,0,1,0
+15028,Comparison of ontology alignment systems across single matching task via the McNemar's test,"  Ontology alignment is widely-used to find the correspondences between
+different ontologies in diverse fields.After discovering the alignments,several
+performance scores are available to evaluate them.The scores typically require
+the identified alignment and a reference containing the underlying actual
+correspondences of the given ontologies.The current trend in the alignment
+evaluation is to put forward a new score(e.g., precision, weighted precision,
+etc.)and to compare various alignments by juxtaposing the obtained scores.
+However,it is substantially provocative to select one measure among others for
+comparison.On top of that, claiming if one system has a better performance than
+one another cannot be substantiated solely by comparing two scalars.In this
+paper,we propose the statistical procedures which enable us to theoretically
+favor one system over one another.The McNemar's test is the statistical means
+by which the comparison of two ontology alignment systems over one matching
+task is drawn.The test applies to a 2x2 contingency table which can be
+constructed in two different ways based on the alignments,each of which has
+their own merits/pitfalls.The ways of the contingency table construction and
+various apposite statistics from the McNemar's test are elaborated in minute
+detail.In the case of having more than two alignment systems for comparison,
+the family-wise error rate is expected to happen. Thus, the ways of preventing
+such an error are also discussed.A directed graph visualizes the outcome of the
+McNemar's test in the presence of multiple alignment systems.From this graph,
+it is readily understood if one system is better than one another or if their
+differences are imperceptible.The proposed statistical methodologies are
+applied to the systems participated in the OAEI 2016 anatomy track, and also
+compares several well-known similarity metrics for the same matching problem.
+",1,0,0,0,0,0
+15029,Polarizability Extraction for Waveguide-Fed Metasurfaces,"  We consider the design and modeling of metasurfaces that couple energy from
+guided waves to propagating wavefronts. This is a first step towards a
+comprehensive, multiscale modeling platform for metasurface antennas-large
+arrays of metamaterial elements embedded in a waveguide structure that radiates
+into free-space--in which the detailed electromagnetic responses of
+metamaterial elements are replaced by polarizable dipoles. We present two
+methods to extract the effective polarizability of a metamaterial element
+embedded in a one- or two-dimensional waveguide. The first method invokes
+surface equivalence principles, averaging over the effective surface currents
+and charges within an element to obtain the effective dipole moments; the
+second method is based on computing the coefficients of the scattered waves
+within the waveguide, from which the effective polarizability can be inferred.
+We demonstrate these methods on several variants of waveguide-fed metasurface
+elements, finding excellent agreement between the two, as well as with
+analytical expressions derived for irises with simpler geometries. Extending
+the polarizability extraction technique to higher order multipoles, we confirm
+the validity of the dipole approximation for common metamaterial elements. With
+the effective polarizabilities of the metamaterial elements accurately
+determined, the radiated fields generated by a metasurface antenna (inside and
+outside the antenna) can be found self-consistently by including the
+interactions between polarizable dipoles. The dipole description provides an
+alternative language and computational framework for engineering metasurface
+antennas, holograms, lenses, beam-forming arrays, and other electrically large,
+waveguide-fed metasurface structures.
+",0,1,0,0,0,0
+15030,Connection Scan Algorithm,"  We introduce the Connection Scan Algorithm (CSA) to efficiently answer
+queries to timetable information systems. The input consists, in the simplest
+setting, of a source position and a desired target position. The output consist
+is a sequence of vehicles such as trains or buses that a traveler should take
+to get from the source to the target. We study several problem variations such
+as the earliest arrival and profile problems. We present algorithm variants
+that only optimize the arrival time or additionally optimize the number of
+transfers in the Pareto sense. An advantage of CSA is that is can easily adjust
+to changes in the timetable, allowing the easy incorporation of known vehicle
+delays. We additionally introduce the Minimum Expected Arrival Time (MEAT)
+problem to handle possible, uncertain, future vehicle delays. We present a
+solution to the MEAT problem that is based upon CSA. Finally, we extend CSA
+using the multilevel overlay paradigm to answer complex queries on nation-wide
+integrated timetables with trains and buses.
+",1,0,0,0,0,0
+15031,A Proof of Orthogonal Double Machine Learning with $Z$-Estimators,"  We consider two stage estimation with a non-parametric first stage and a
+generalized method of moments second stage, in a simpler setting than
+(Chernozhukov et al. 2016). We give an alternative proof of the theorem given
+in (Chernozhukov et al. 2016) that orthogonal second stage moments, sample
+splitting and $n^{1/4}$-consistency of the first stage, imply
+$\sqrt{n}$-consistency and asymptotic normality of second stage estimates. Our
+proof is for a variant of their estimator, which is based on the empirical
+version of the moment condition (Z-estimator), rather than a minimization of a
+norm of the empirical vector of moments (M-estimator). This note is meant
+primarily for expository purposes, rather than as a new technical contribution.
+",0,0,1,1,0,0
+15032,Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with only Two Time Periods,"  This paper shows that the Conditional Quantile Treatment Effect on the
+Treated can be identified using a combination of (i) a conditional
+Distributional Difference in Differences assumption and (ii) an assumption on
+the conditional dependence between the change in untreated potential outcomes
+and the initial level of untreated potential outcomes for the treated group.
+The second assumption recovers the unknown dependence from the observed
+dependence for the untreated group. We also consider estimation and inference
+in the case where all of the covariates are discrete. We propose a uniform
+inference procedure based on the exchangeable bootstrap and show its validity.
+We conclude the paper by estimating the effect of state-level changes in the
+minimum wage on the distribution of earnings for subgroups defined by race,
+gender, and education.
+",0,0,1,1,0,0
+15033,"Coppersmith's lattices and ""focus groups"": an attack on small-exponent RSA","  We present a principled technique for reducing the matrix size in some
+applications of Coppersmith's lattice method for finding roots of modular
+polynomial equations. It relies on an analysis of the actual performance of
+Coppersmith's attack for smaller parameter sizes, which can be thought of as
+""focus group"" testing. When applied to the small-exponent RSA problem, it
+reduces lattice dimensions and consequently running times (sometimes by factors
+of two or more). We also argue that existing metrics (such as enabling
+condition bounds) are not as important as often thought for measuring the true
+performance of attacks based on Coppersmith's method. Finally, experiments are
+given to indicate that certain lattice reductive algorithms (such as
+Nguyen-Stehlé's L2) may be particularly well-suited for Coppersmith's method.
+",1,0,1,0,0,0
+15034,Gas vs. solid phase deuterated chemistry: HDCO and D$_2$CO in massive star-forming regions,"  The formation of deuterated molecules is favoured at low temperatures and
+high densities. Therefore, the deuteration fraction D$_{frac}$ is expected to
+be enhanced in cold, dense prestellar cores and to decrease after protostellar
+birth. Previous studies have shown that the deuterated forms of species such as
+N2H+ (formed in the gas phase) and CH3OH (formed on grain surfaces) can be used
+as evolutionary indicators and to constrain their dominant formation processes
+and time-scales. Formaldehyde (H2CO) and its deuterated forms can be produced
+both in the gas phase and on grain surfaces. However, the relative importance
+of these two chemical pathways is unclear. Comparison of the deuteration
+fraction of H2CO with respect to that of N2H+, NH3 and CH3OH can help us to
+understand its formation processes and time-scales. With the new SEPIA Band 5
+receiver on APEX, we have observed the J=3-2 rotational lines of HDCO and D2CO
+at 193 GHz and 175 GHz toward three massive star forming regions hosting
+objects at different evolutionary stages: two High-mass Starless Cores (HMSC),
+two High-mass Protostellar Objects (HMPOs), and one Ultracompact HII region
+(UCHII). By using previously obtained H2CO J=3-2 data, the deuteration
+fractions HDCO/H2CO and D2CO/HDCO are estimated. Our observations show that
+singly-deuterated H2CO is detected toward all sources and that the deuteration
+fraction of H2CO increases from the HMSC to the HMPO phase and then sharply
+decreases in the latest evolutionary stage (UCHII). The doubly-deuterated form
+of H2CO is detected only in the earlier evolutionary stages with D2CO/H2CO
+showing a pattern that is qualitatively consistent with that of HDCO/H2CO,
+within current uncertainties. Our initial results show that H2CO may display a
+similar D$_{frac}$ pattern as that of CH3OH in massive young stellar objects.
+This finding suggests that solid state reactions dominate its formation.
+",0,1,0,0,0,0
+15035,Improved stability of optimal traffic paths,"  Models involving branched structures are employed to describe several
+supply-demand systems such as the structure of the nerves of a leaf, the system
+of roots of a tree and the nervous or cardiovascular systems. Given a flow
+(traffic path) that transports a given measure $\mu^-$ onto a target measure
+$\mu^+$, along a 1-dimensional network, the transportation cost per unit length
+is supposed in these models to be proportional to a concave power $\alpha \in
+(0,1)$ of the intensity of the flow.
+In this paper we address an open problem in the book ""Optimal transportation
+networks"" by Bernot, Caselles and Morel and we improve the stability for
+optimal traffic paths in the Euclidean space $\mathbb{R}^d$, with respect to
+variations of the given measures $(\mu^-,\mu^+)$, which was known up to now
+only for $\alpha>1-\frac1d$. We prove it for exponents $\alpha>1-\frac1{d-1}$
+(in particular, for every $\alpha \in (0,1)$ when $d=2$), for a fairly large
+class of measures $\mu^+$ and $\mu^-$.
+",0,0,1,0,0,0
+15036,Neural Code Comprehension: A Learnable Representation of Code Semantics,"  With the recent success of embeddings in natural language processing,
+research has been conducted into applying similar methods to code analysis.
+Most works attempt to process the code directly or use a syntactic tree
+representation, treating it like sentences written in a natural language.
+However, none of the existing methods are sufficient to comprehend program
+semantics robustly, due to structural features such as function calls,
+branching, and interchangeable order of statements. In this paper, we propose a
+novel processing technique to learn code semantics, and apply it to a variety
+of program analysis tasks. In particular, we stipulate that a robust
+distributional hypothesis of code applies to both human- and machine-generated
+programs. Following this hypothesis, we define an embedding space, inst2vec,
+based on an Intermediate Representation (IR) of the code that is independent of
+the source programming language. We provide a novel definition of contextual
+flow for this IR, leveraging both the underlying data- and control-flow of the
+program. We then analyze the embeddings qualitatively using analogies and
+clustering, and evaluate the learned representation on three different
+high-level tasks. We show that even without fine-tuning, a single RNN
+architecture and fixed inst2vec embeddings outperform specialized approaches
+for performance prediction (compute device mapping, optimal thread coarsening);
+and algorithm classification from raw code (104 classes), where we set a new
+state-of-the-art.
+",1,0,0,1,0,0
+15037,Electron conduction in solid state via time varying wavevectors,"  In this paper, we study electron wavepacket dynamics in electric and magnetic
+fields. We rigorously derive the semiclassical equations of electron dynamics
+in electric and magnetic fields. We do it both for free electron and electron
+in a periodic potential. We do this by introducing time varying wavevectors
+$k(t)$. In the presence of magnetic field, our wavepacket reproduces the
+classical cyclotron orbits once the origin of the Schröedinger equation is
+correctly chosen to be center of cyclotron orbit. In the presence of both
+electric and magnetic fields, our equations for wavepacket dynamics differ from
+classical Lorentz force equations. We show that in a periodic potential, on
+application of electric field, the electron wave function adiabatically follows
+the wavefunction of a time varying Bloch wavevector $k(t)$, with its energies
+suitably shifted with time. We derive the effective mass equation and discuss
+conduction in conductors and insulators.
+",0,1,0,0,0,0
+15038,On blowup of co-rotational wave maps in odd space dimensions,"  We consider co-rotational wave maps from the $(1+d)$-dimensional Minkowski
+space into the $d$-sphere for $d\geq 3$ odd. This is an energy-supercritical
+model which is known to exhibit finite-time blowup via self-similar solutions.
+Based on a method developed by the second author and Schörkhuber, we prove
+the asymptotic nonlinear stability of the ""ground-state"" self-similar solution.
+",0,0,1,0,0,0
+15039,"The Young Substellar Companion ROXs 12 B: Near-Infrared Spectrum, System Architecture, and Spin-Orbit Misalignment","  ROXs 12 (2MASS J16262803-2526477) is a young star hosting a directly imaged
+companion near the deuterium-burning limit. We present a suite of
+spectroscopic, imaging, and time-series observations to characterize the
+physical and environmental properties of this system. Moderate-resolution
+near-infrared spectroscopy of ROXs 12 B from Gemini-North/NIFS and Keck/OSIRIS
+reveals signatures of low surface gravity including weak alkali absorption
+lines and a triangular $H$-band pseudo-continuum shape. No signs of Pa$\beta$
+emission are evident. As a population, however, we find that about half (46
+$\pm$ 14\%) of young ($\lesssim$15 Myr) companions with masses $\lesssim$20
+$M_\mathrm{Jup}$ possess actively accreting subdisks detected via Pa$\beta$
+line emission, which represents a lower limit on the prevalence of
+circumplanetary disks in general as some are expected to be in a quiescent
+phase of accretion. The bolometric luminosity of the companion and age of the
+host star (6$^{+4}_{-2}$ Myr) imply a mass of 17.5 $\pm$ 1.5 $M_\mathrm{Jup}$
+for ROXs 12 B based on hot-start evolutionary models. We identify a wide (5100
+AU) tertiary companion to this system, 2MASS J16262774-2527247, which is
+heavily accreting and exhibits stochastic variability in its $K2$ light curve.
+By combining $v$sin$i_*$ measurements with rotation periods from $K2$, we
+constrain the line-of-sight inclinations of ROXs 12 A and 2MASS
+J16262774-2527247 and find that they are misaligned by
+60$^{+7}_{-11}$$^{\circ}$. In addition, the orbital axis of ROXs 12 B is likely
+misaligned from the spin axis of its host star ROXs 12 A, suggesting that ROXs
+12 B formed akin to fragmenting binary stars or in an equatorial disk that was
+torqued by the wide stellar tertiary.
+",0,1,0,0,0,0
+15040,Connecting dissipation and phase slips in a Josephson junction between fermionic superfluids,"  We study the emergence of dissipation in an atomic Josephson junction between
+weakly-coupled superfluid Fermi gases. We find that vortex-induced phase
+slippage is the dominant microscopic source of dissipation across the BEC-BCS
+crossover. We explore different dynamical regimes by tuning the bias chemical
+potential between the two superfluid reservoirs. For small excitations, we
+observe dissipation and phase coherence to coexist, with a resistive current
+followed by well-defined Josephson oscillations. We link the junction transport
+properties to the phase-slippage mechanism, finding that vortex nucleation is
+primarily responsible for the observed trends of conductance and critical
+current. For large excitations, we observe the irreversible loss of coherence
+between the two superfluids, and transport cannot be described only within an
+uncorrelated phase-slip picture. Our findings open new directions for
+investigating the interplay between dissipative and superfluid transport in
+strongly correlated Fermi systems, and general concepts in out-of-equlibrium
+quantum systems.
+",0,1,0,0,0,0
+15041,Satellite conjunction analysis and the false confidence theorem,"  Satellite conjunction analysis is the assessment of collision risk during a
+close encounter between a satellite and another object in orbit. A
+counterintuitive phenomenon has emerged in the conjunction analysis literature:
+probability dilution, in which lower quality data paradoxically appear to
+reduce the risk of collision. We show that probability dilution is a symptom of
+a fundamental deficiency in epistemic probability distributions. In
+probabilistic representations of statistical inference, there are always false
+propositions that have a high probability of being assigned a high degree of
+belief. We call this deficiency false confidence. In satellite conjunction
+analysis, it results in a severe and persistent underestimation of collision
+risk exposure.
+We introduce the Martin--Liu validity criterion as a benchmark by which to
+identify statistical methods that are free from false confidence. If expressed
+using belief functions, such inferences will necessarily be non-additive. In
+satellite conjunction analysis, we show that $K \sigma$ uncertainty ellipsoids
+satisfy the validity criterion. Performing collision avoidance maneuvers based
+on ellipsoid overlap will ensure that collision risk is capped at the
+user-specified level. Further, this investigation into satellite conjunction
+analysis provides a template for recognizing and resolving false confidence
+issues as they occur in other problems of statistical inference.
+",0,0,1,1,0,0
+15042,A formula goes to court: Partisan gerrymandering and the efficiency gap,"  Recently, a proposal has been advanced to detect unconstitutional partisan
+gerrymandering with a simple formula called the efficiency gap. The efficiency
+gap is now working its way towards a possible landmark case in the Supreme
+Court. This note explores some of its mathematical properties in light of the
+fact that it reduces to a straight proportional comparison of votes to seats.
+Though we offer several critiques, we assess that EG can still be a useful
+component of a courtroom analysis. But a famous formula can take on a life of
+its own and this one will need to be watched closely.
+",0,1,0,0,0,0
+15043,Quantitative aspects of linear and affine closed lambda terms,"  Affine $\lambda$-terms are $\lambda$-terms in which each bound variable
+occurs at most once and linear $\lambda$-terms are $\lambda$-terms in which
+each bound variables occurs once. and only once. In this paper we count the
+number of closed affine $\lambda$-terms of size $n$, closed linear
+$\lambda$-terms of size $n$, affine $\beta$-normal forms of size $n$ and linear
+$\beta$-normal forms of ise $n$, for different ways of measuring the size of
+$\lambda$-terms. From these formulas, we show how we can derive programs for
+generating all the terms of size $n$ for each class. For this we use a specific
+data structure, which are contexts taking into account all the holes at levels
+of abstractions.
+",1,0,1,0,0,0
+15044,Drug response prediction by ensemble learning and drug-induced gene expression signatures,"  Chemotherapeutic response of cancer cells to a given compound is one of the
+most fundamental information one requires to design anti-cancer drugs. Recent
+advances in producing large drug screens against cancer cell lines provided an
+opportunity to apply machine learning methods for this purpose. In addition to
+cytotoxicity databases, considerable amount of drug-induced gene expression
+data has also become publicly available. Following this, several methods that
+exploit omics data were proposed to predict drug activity on cancer cells.
+However, due to the complexity of cancer drug mechanisms, none of the existing
+methods are perfect. One possible direction, therefore, is to combine the
+strengths of both the methods and the databases for improved performance. We
+demonstrate that integrating a large number of predictions by the proposed
+method improves the performance for this task. The predictors in the ensemble
+differ in several aspects such as the method itself, the number of tasks method
+considers (multi-task vs. single-task) and the subset of data considered
+(sub-sampling). We show that all these different aspects contribute to the
+success of the final ensemble. In addition, we attempt to use the drug screen
+data together with two novel signatures produced from the drug-induced gene
+expression profiles of cancer cell lines. Finally, we evaluate the method
+predictions by in vitro experiments in addition to the tests on data sets.The
+predictions of the methods, the signatures and the software are available from
+\url{this http URL}.
+",0,0,0,1,1,0
+15045,Eigenvalue approximation of sums of Hermitian matrices from eigenvector localization/delocalization,"  We propose a technique for calculating and understanding the eigenvalue
+distribution of sums of random matrices from the known distribution of the
+summands. The exact problem is formidably hard. One extreme approximation to
+the true density amounts to classical probability, in which the matrices are
+assumed to commute; the other extreme is related to free probability, in which
+the eigenvectors are assumed to be in generic positions and sufficiently large.
+In practice, free probability theory can give a good approximation of the
+density.
+We develop a technique based on eigenvector localization/delocalization that
+works very well for important problems of interest where free probability is
+not sufficient, but certain uniformity properties apply. The
+localization/delocalization property appears in a convex combination parameter
+that notably, is independent of any eigenvalue properties and yields accurate
+eigenvalue density approximations.
+We demonstrate this technique on a number of examples as well as discuss a
+more general technique when the uniformity properties fail to apply.
+",0,1,1,0,0,0
+15046,Some Ultraspheroidal Monogenic Clifford Gegenbauer Jacobi Polynomials and Associated Wavelets,"  In the present paper, new classes of wavelet functions are presented in the
+framework of Clifford analysis. Firstly, some classes of orthogonal polynomials
+are provided based on 2-parameters weight functions. Such classes englobe the
+well known ones of Jacobi and Gegenbauer polynomials when relaxing one of the
+parameters. The discovered polynomial sets are next applied to introduce new
+wavelet functions. Reconstruction formula as well as Fourier-Plancherel rules
+have been proved.
+",0,0,1,0,0,0
+15047,Gait learning for soft microrobots controlled by light fields,"  Soft microrobots based on photoresponsive materials and controlled by light
+fields can generate a variety of different gaits. This inherent flexibility can
+be exploited to maximize their locomotion performance in a given environment
+and used to adapt them to changing conditions. Albeit, because of the lack of
+accurate locomotion models, and given the intrinsic variability among
+microrobots, analytical control design is not possible. Common data-driven
+approaches, on the other hand, require running prohibitive numbers of
+experiments and lead to very sample-specific results. Here we propose a
+probabilistic learning approach for light-controlled soft microrobots based on
+Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach
+results in a learning scheme that is data-efficient, enabling gait optimization
+with a limited experimental budget, and robust against differences among
+microrobot samples. These features are obtained by designing the learning
+scheme through the comparison of different GP priors and BO settings on a
+semi-synthetic data set. The developed learning scheme is validated in
+microrobot experiments, resulting in a 115% improvement in a microrobot's
+locomotion performance with an experimental budget of only 20 tests. These
+encouraging results lead the way toward self-adaptive microrobotic systems
+based on light-controlled soft microrobots and probabilistic learning control.
+",1,0,0,0,0,0
+15048,Evolution of Nagaoka phase with kinetic energy frustrating hoppings,"  We investigate, using the density matrix renormalization group, the evolution
+of the Nagaoka state with $t'$ hoppings that frustrate the hole kinetic energy
+in the $U=\infty$ Hubbard model on the anisotropic triangular lattice and the
+square lattice with second-nearest neighbor hoppings. We find that the Nagaoka
+ferromagnet survives up to a rather small $t'_c/t \sim 0.2.$ At this critical
+value, there is a transition to an antiferromagnetic phase, that depends on the
+lattice: a ${\bf Q}=(Q,0)$ spiral order, that continuously evolves with $t'$,
+for the triangular lattice, and the usual ${\bf Q}=(\pi,\pi)$ Néel order for
+the square lattice. Remarkably, the local magnetization takes its classical
+value for all considered $t'$ ($t'/t \le 1$). Our results show that the
+recently found classical kinetic antiferromagnetism, a perfect counterpart of
+Nagaoka ferromagnetism, is a generic phenomenon in these kinetically frustrated
+electronic systems.
+",0,1,0,0,0,0
+15049,Blocking Transferability of Adversarial Examples in Black-Box Learning Systems,"  Advances in Machine Learning (ML) have led to its adoption as an integral
+component in many applications, including banking, medical diagnosis, and
+driverless cars. To further broaden the use of ML models, cloud-based services
+offered by Microsoft, Amazon, Google, and others have developed ML-as-a-service
+tools as black-box systems. However, ML classifiers are vulnerable to
+adversarial examples: inputs that are maliciously modified can cause the
+classifier to provide adversary-desired outputs. Moreover, it is known that
+adversarial examples generated on one classifier are likely to cause another
+classifier to make the same mistake, even if the classifiers have different
+architectures or are trained on disjoint datasets. This property, which is
+known as transferability, opens up the possibility of attacking black-box
+systems by generating adversarial examples on a substitute classifier and
+transferring the examples to the target classifier. Therefore, the key to
+protect black-box learning systems against the adversarial examples is to block
+their transferability. To this end, we propose a training method that, as the
+input is more perturbed, the classifier smoothly outputs lower confidence on
+the original label and instead predicts that the input is ""invalid"". In
+essence, we augment the output class set with a NULL label and train the
+classifier to reject the adversarial examples by classifying them as NULL. In
+experiments, we apply a wide range of attacks based on adversarial examples on
+the black-box systems. We show that a classifier trained with the proposed
+method effectively resists against the adversarial examples, while maintaining
+the accuracy on clean data.
+",1,0,0,0,0,0
+15050,Analytic continuation of Wolynes theory into the Marcus inverted regime,"  The Wolynes theory of electronically nonadiabatic reaction rates [P. G.
+Wolynes, J. Chem. Phys. 87, 6559 (1987)] is based on a saddle point
+approximation to the time integral of a reactive flux autocorrelation function
+in the nonadiabatic (golden rule) limit. The dominant saddle point is on the
+imaginary time axis at $t_{\rm sp}=i\lambda_{\rm sp}\hbar$, and provided
+$\lambda_{\rm sp}$ lies in the range $-\beta/2\le\lambda_{\rm sp}\le\beta/2$,
+it is straightforward to evaluate the rate constant using information obtained
+from an imaginary time path integral calculation. However, if $\lambda_{\rm
+sp}$ lies outside this range, as it does in the Marcus inverted regime, the
+path integral diverges. This has led to claims in the literature that Wolynes
+theory cannot describe the correct behaviour in the inverted regime. Here we
+show how the imaginary time correlation function obtained from a path integral
+calculation can be analytically continued to $\lambda_{\rm sp}<-\beta/2$, and
+the continuation used to evaluate the rate in the inverted regime. Comparisons
+with exact golden rule results for a spin-boson model and a more demanding
+(asymmetric and anharmonic) model of electronic predissociation show that the
+theory it is just as accurate in the inverted regime as it is in the normal
+regime.
+",0,1,0,0,0,0
+15051,JDFTx: software for joint density-functional theory,"  Density-functional theory (DFT) has revolutionized computational prediction
+of atomic-scale properties from first principles in physics, chemistry and
+materials science. Continuing development of new methods is necessary for
+accurate predictions of new classes of materials and properties, and for
+connecting to nano- and mesoscale properties using coarse-grained theories.
+JDFTx is a fully-featured open-source electronic DFT software designed
+specifically to facilitate rapid development of new theories, models and
+algorithms. Using an algebraic formulation as an abstraction layer, compact
+C++11 code automatically performs well on diverse hardware including GPUs. This
+code hosts the development of joint density-functional theory (JDFT) that
+combines electronic DFT with classical DFT and continuum models of liquids for
+first-principles calculations of solvated and electrochemical systems. In
+addition, the modular nature of the code makes it easy to extend and interface
+with, facilitating the development of multi-scale toolkits that connect to ab
+initio calculations, e.g. photo-excited carrier dynamics combining electron and
+phonon calculations with electromagnetic simulations.
+",0,1,0,0,0,0
+15052,Experimental realization of purely excitonic lasing in ZnO microcrystals at room temperature: transition from exciton-exciton to exciton-electron scattering,"  Since the seminal observation of room-temperature laser emission from ZnO
+thin films and nanowires, numerous attempts have been carried out for detailed
+understanding of the lasing mechanism in ZnO. In spite of the extensive efforts
+performed over the last decades, the origin of optical gain at room temperature
+is still a matter of considerable discussion,. We show that ZnO microcrystals
+with a size of a few micrometers exhibit purely excitonic lasing at room
+temperature without showing any symptoms of electron-hole plasma emission. We
+then present the distinct experimental evidence that the room-temperature
+excitonic lasing is achieved not by exciton-exciton scattering, as has been
+generally believed, but by exciton-electron scattering. As the temperature is
+lowered below ~150 K, the lasing mechanism is shifted from the exciton-electron
+scattering to the exciton-exciton scattering. We also argue that the ease of
+carrier diffusion plays a significant role in showing room-temperature
+excitonic lasing.
+",0,1,0,0,0,0
+15053,Selective probing of hidden spin-polarized states in inversion-symmetric bulk MoS2,"  Spin- and angle-resolved photoemission spectroscopy is used to reveal that a
+large spin polarization is observable in the bulk centrosymmetric transition
+metal dichalcogenide MoS2. It is found that the measured spin polarization can
+be reversed by changing the handedness of incident circularly-polarized light.
+Calculations based on a three-step model of photoemission show that the valley
+and layer-locked spin-polarized electronic states can be selectively addressed
+by circularly-polarized light, therefore providing a novel route to probe these
+hidden spin-polarized states in inversion-symmetric systems as predicted by
+Zhang et al. [Nature Physics 10, 387 (2014)].
+",0,1,0,0,0,0
+15054,Classification without labels: Learning from mixed samples in high energy physics,"  Modern machine learning techniques can be used to construct powerful models
+for difficult collider physics problems. In many applications, however, these
+models are trained on imperfect simulations due to a lack of truth-level
+information in the data, which risks the model learning artifacts of the
+simulation. In this paper, we introduce the paradigm of classification without
+labels (CWoLa) in which a classifier is trained to distinguish statistical
+mixtures of classes, which are common in collider physics. Crucially, neither
+individual labels nor class proportions are required, yet we prove that the
+optimal classifier in the CWoLa paradigm is also the optimal classifier in the
+traditional fully-supervised case where all label information is available.
+After demonstrating the power of this method in an analytical toy example, we
+consider a realistic benchmark for collider physics: distinguishing quark-
+versus gluon-initiated jets using mixed quark/gluon training samples. More
+generally, CWoLa can be applied to any classification problem where labels or
+class proportions are unknown or simulations are unreliable, but statistical
+mixtures of the classes are available.
+",0,0,0,1,0,0
+15055,Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information,"  Trending topics in microblogs such as Twitter are valuable resources to
+understand social aspects of real-world events. To enable deep analyses of such
+trends, semantic annotation is an effective approach; yet the problem of
+annotating microblog trending topics is largely unexplored by the research
+community. In this work, we tackle the problem of mapping trending Twitter
+topics to entities from Wikipedia. We propose a novel model that complements
+traditional text-based approaches by rewarding entities that exhibit a high
+temporal correlation with topics during their burst time period. By exploiting
+temporal information from the Wikipedia edit history and page view logs, we
+have improved the annotation performance by 17-28\%, as compared to the
+competitive baselines.
+",1,0,0,0,0,0
+15056,Twin-beam real-time position estimation of micro-objects in 3D,"  Various optical methods for measuring positions of micro-objects in 3D have
+been reported in the literature. Nevertheless, majority of them are not
+suitable for real-time operation, which is needed, for example, for feedback
+position control. In this paper, we present a method for real-time estimation
+of the position of micro-objects in 3D; the method is based on twin-beam
+illumination and it requires only a very simple hardware setup whose essential
+part is a standard image sensor without any lens. Performance of the proposed
+method is tested during a micro-manipulation task in which the estimated
+position served as a feedback for the controller. The experiments show that the
+estimate is accurate to within ~3 um in the lateral position and ~7 um in the
+axial distance with the refresh rate of 10 Hz. Although the experiments are
+done using spherical objects, the presented method could be modified to handle
+non-spherical objects as well.
+",1,1,0,0,0,0
+15057,Asymmetric metallicity patterns in the stellar velocity space with RAVE,"  We explore the correlations between velocity and metallicity and the possible
+distinct chemical signatures of the velocity over-densities of the local
+Galactic neighbourhood. We use the large spectroscopic survey RAVE and the
+Geneva Copenhagen Survey. We compare the metallicity distribution of regions in
+the velocity plane ($v_R,v_\phi$) with that of their symmetric counterparts
+($-v_R,v_\phi$). We expect similar metallicity distributions if there are no
+tracers of a sub-population (e.g., a dispersed cluster, accreted stars), if the
+disk of the Galaxy is axisymmetric, and if the orbital effects of the spiral
+arms and the bar are weak. We find that the metallicity-velocity space of the
+solar neighbourhood is highly patterned. A large fraction of the velocity plane
+shows differences in the metallicity distribution when comparing symmetric
+$v_R$ regions. The typical differences in the median metallicity are of $0.05$
+dex with a statistical significance of at least $95\%$, and with values up to
+$0.6$ dex. For low azimuthal velocity $v_\phi$, stars moving outwards in the
+Galaxy have on average higher metallicity than those moving inwards. These
+include stars in the Hercules and Hyades moving groups and other velocity
+branch-like structures. For higher $v_\phi$, the stars moving inwards have
+higher metallicity than those moving outwards. The most likely interpretation
+of the metallicity asymmetry is that it is due to the orbital effects of the
+bar and the radial metallicity gradient of the disk. We present a simulation
+that supports this idea. We have also discovered a positive gradient in
+$v_\phi$ with respect to metallicity at high metallicities, apart from the two
+known positive and negative gradients for the thick and thin disks,
+respectively.
+",0,1,0,0,0,0
+15058,End-to-End Attention based Text-Dependent Speaker Verification,"  A new type of End-to-End system for text-dependent speaker verification is
+presented in this paper. Previously, using the phonetically
+discriminative/speaker discriminative DNNs as feature extractors for speaker
+verification has shown promising results. The extracted frame-level (DNN
+bottleneck, posterior or d-vector) features are equally weighted and aggregated
+to compute an utterance-level speaker representation (d-vector or i-vector). In
+this work we use speaker discriminative CNNs to extract the noise-robust
+frame-level features. These features are smartly combined to form an
+utterance-level speaker vector through an attention mechanism. The proposed
+attention model takes the speaker discriminative information and the phonetic
+information to learn the weights. The whole system, including the CNN and
+attention model, is joint optimized using an end-to-end criterion. The training
+algorithm imitates exactly the evaluation process --- directly mapping a test
+utterance and a few target speaker utterances into a single verification score.
+The algorithm can automatically select the most similar impostor for each
+target speaker to train the network. We demonstrated the effectiveness of the
+proposed end-to-end system on Windows $10$ ""Hey Cortana"" speaker verification
+task.
+",1,0,0,1,0,0
+15059,Automated Discovery of Process Models from Event Logs: Review and Benchmark,"  Process mining allows analysts to exploit logs of historical executions of
+business processes to extract insights regarding the actual performance of
+these processes. One of the most widely studied process mining operations is
+automated process discovery. An automated process discovery method takes as
+input an event log, and produces as output a business process model that
+captures the control-flow relations between tasks that are observed in or
+implied by the event log. Various automated process discovery methods have been
+proposed in the past two decades, striking different tradeoffs between
+scalability, accuracy and complexity of the resulting models. However, these
+methods have been evaluated in an ad-hoc manner, employing different datasets,
+experimental setups, evaluation measures and baselines, often leading to
+incomparable conclusions and sometimes unreproducible results due to the use of
+closed datasets. This article provides a systematic review and comparative
+evaluation of automated process discovery methods, using an open-source
+benchmark and covering twelve publicly-available real-life event logs, twelve
+proprietary real-life event logs, and nine quality metrics. The results
+highlight gaps and unexplored tradeoffs in the field, including the lack of
+scalability of some methods and a strong divergence in their performance with
+respect to the different quality metrics used.
+",1,0,0,0,0,0
+15060,Criteria for strict monotonicity of the mixed volume of convex polytopes,"  Let $P_1,\dots, P_n$ and $Q_1,\dots, Q_n$ be convex polytopes in
+$\mathbb{R}^n$ such that $P_i\subset Q_i$. It is well-known that the mixed
+volume has the monotonicity property: $V(P_1,\dots,P_n)\leq V(Q_1,\dots,Q_n)$.
+We give two criteria for when this inequality is strict in terms of essential
+collections of faces as well as mixed polyhedral subdivisions. This geometric
+result allows us to characterize sparse polynomial systems with Newton
+polytopes $P_1,\dots,P_n$ whose number of isolated solutions equals the
+normalized volume of the convex hull of $P_1\cup\dots\cup P_n$. In addition, we
+obtain an analog of Cramer's rule for sparse polynomial systems.
+",0,0,1,0,0,0
+15061,Colorings with Fractional Defect,"  Consider a coloring of a graph such that each vertex is assigned a fraction
+of each color, with the total amount of colors at each vertex summing to $1$.
+We define the fractional defect of a vertex $v$ to be the sum of the overlaps
+with each neighbor of $v$, and the fractional defect of the graph to be the
+maximum of the defects over all vertices. Note that this coincides with the
+usual definition of defect if every vertex is monochromatic. We provide results
+on the minimum fractional defect of $2$-colorings of some graphs.
+",0,0,1,0,0,0
+15062,The new concepts of measurement error's regularities and effect characteristics,"  In several literatures, the authors give a new thinking of measurement theory
+system based on error non-classification philosophy, which completely
+overthrows the existing measurement concept system of precision, trueness and
+accuracy. In this paper, by focusing on the issues of error's regularities and
+effect characteristics, the authors will do a thematic interpretation, and
+prove that the error's regularities actually come from different cognitive
+perspectives, are also unable to be used for classifying errors, and that the
+error's effect characteristics actually depend on artificial condition rules of
+repeated measurement, and are still unable to be used for classifying errors.
+Thus, from the perspectives of error's regularities and effect characteristics,
+the existing error classification philosophy is still incorrect; and an
+uncertainty concept system, which must be interpreted by the error
+non-classification philosophy, naturally becomes the only way out of
+measurement theory.
+",0,0,1,1,0,0
+15063,The cavity approach for Steiner trees packing problems,"  The Belief Propagation approximation, or cavity method, has been recently
+applied to several combinatorial optimization problems in its zero-temperature
+implementation, the max-sum algorithm. In particular, recent developments to
+solve the edge-disjoint paths problem and the prize-collecting Steiner tree
+problem on graphs have shown remarkable results for several classes of graphs
+and for benchmark instances. Here we propose a generalization of these
+techniques for two variants of the Steiner trees packing problem where multiple
+""interacting"" trees have to be sought within a given graph. Depending on the
+interaction among trees we distinguish the vertex-disjoint Steiner trees
+problem, where trees cannot share nodes, from the edge-disjoint Steiner trees
+problem, where edges cannot be shared by trees but nodes can be members of
+multiple trees. Several practical problems of huge interest in network design
+can be mapped into these two variants, for instance, the physical design of
+Very Large Scale Integration (VLSI) chips. The formalism described here relies
+on two components edge-variables that allows us to formulate a massage-passing
+algorithm for the V-DStP and two algorithms for the E-DStP differing in the
+scaling of the computational time with respect to some relevant parameters. We
+will show that one of the two formalisms used for the edge-disjoint variant
+allow us to map the max-sum update equations into a weighted maximum matching
+problem over proper bipartite graphs. We developed a heuristic procedure based
+on the max-sum equations that shows excellent performance in synthetic networks
+(in particular outperforming standard multi-step greedy procedures by large
+margins) and on large benchmark instances of VLSI for which the optimal
+solution is known, on which the algorithm found the optimum in two cases and
+the gap to optimality was never larger than 4 %.
+",1,0,0,0,0,0
+15064,Observational Learning by Reinforcement Learning,"  Observational learning is a type of learning that occurs as a function of
+observing, retaining and possibly replicating or imitating the behaviour of
+another agent. It is a core mechanism appearing in various instances of social
+learning and has been found to be employed in several intelligent species,
+including humans. In this paper, we investigate to what extent the explicit
+modelling of other agents is necessary to achieve observational learning
+through machine learning. Especially, we argue that observational learning can
+emerge from pure Reinforcement Learning (RL), potentially coupled with memory.
+Through simple scenarios, we demonstrate that an RL agent can leverage the
+information provided by the observations of an other agent performing a task in
+a shared environment. The other agent is only observed through the effect of
+its actions on the environment and never explicitly modeled. Two key aspects
+are borrowed from observational learning: i) the observer behaviour needs to
+change as a result of viewing a 'teacher' (another agent) and ii) the observer
+needs to be motivated somehow to engage in making use of the other agent's
+behaviour. The later is naturally modeled by RL, by correlating the learning
+agent's reward with the teacher agent's behaviour.
+",1,0,0,1,0,0
+15065,P4-compatible High-level Synthesis of Low Latency 100 Gb/s Streaming Packet Parsers in FPGAs,"  Packet parsing is a key step in SDN-aware devices. Packet parsers in SDN
+networks need to be both reconfigurable and fast, to support the evolving
+network protocols and the increasing multi-gigabit data rates. The combination
+of packet processing languages with FPGAs seems to be the perfect match for
+these requirements. In this work, we develop an open-source FPGA-based
+configurable architecture for arbitrary packet parsing to be used in SDN
+networks. We generate low latency and high-speed streaming packet parsers
+directly from a packet processing program. Our architecture is pipelined and
+entirely modeled using templated C++ classes. The pipeline layout is derived
+from a parser graph that corresponds a P4 code after a series of graph
+transformation rounds. The RTL code is generated from the C++ description using
+Xilinx Vivado HLS and synthesized with Xilinx Vivado. Our architecture achieves
+100 Gb/s data rate in a Xilinx Virtex-7 FPGA while reducing the latency by 45%
+and the LUT usage by 40% compared to the state-of-the-art.
+",1,0,0,0,0,0
+15066,Uniruledness of Strata of Holomorphic Differentials in Small Genus,"  We address the question concerning the birational geometry of the strata of
+holomorphic and quadratic differentials. We show strata of holomorphic and
+quadratic differentials to be uniruled in small genus by constructing rational
+curves via pencils on K3 and del Pezzo surfaces respectively. Restricting to
+genus $3\leq g\leq6$, we construct projective bundles over a rational varieties
+that dominate the holomorphic strata with length at most $g-1$, hence showing
+in addition that these strata are unirational.
+",0,0,1,0,0,0
+15067,"Conservation laws, vertex corrections, and screening in Raman spectroscopy","  We present a microscopic theory for the Raman response of a clean multiband
+superconductor accounting for the effects of vertex corrections and long-range
+Coulomb interaction. The measured Raman intensity, $R(\Omega)$, is proportional
+to the imaginary part of the fully renormalized particle-hole correlator with
+Raman form-factors $\gamma(\vec k)$. In a BCS superconductor, a bare Raman
+bubble is non-zero for any $\gamma(\vec k)$ and diverges at $\Omega = 2\Delta
++0$, where $\Delta$ is the largest gap along the Fermi surface. However, for
+$\gamma(\vec k) =$ const, the full $R(\Omega)$ is expected to vanish due to
+particle number conservation. It was long thought that this vanishing is due to
+the singular screening by long-range Coulomb interaction. We argue that this
+vanishing actually holds due to vertex corrections from the same short-range
+interaction that gives rise to superconductivity. We further argue that
+long-range Coulomb interaction does not affect the Raman signal for $any$
+$\gamma(\vec k)$. We argue that vertex corrections eliminate the divergence at
+$2\Delta$ and replace it with a maximum at a somewhat larger frequency. We also
+argue that vertex corrections give rise to sharp peaks in $R(\Omega)$ at
+$\Omega < 2\Delta$, when $\Omega$ coincides with the frequency of one of
+collective modes in a superconductor, e.g, Leggett mode, Bardasis-Schrieffer
+mode, or an excitonic mode.
+",0,1,0,0,0,0
+15068,The distribution of symmetry of a naturally reductive nilpotent Lie group,"  We show that the distribution of symmetry of a naturally reductive nilpotent
+Lie group coincides with the invariant distribution induced by the set of fixed
+vectors of the isotropy. This extends a known result on compact naturally
+reductive spaces. We also address the study of the quotient by the foliation of
+symmetry.
+",0,0,1,0,0,0
+15069,Odd-integer quantum Hall states and giant spin susceptibility in p-type few-layer WSe2,"  We fabricate high-mobility p-type few-layer WSe2 field-effect transistors and
+surprisingly observe a series of quantum Hall (QH) states following an
+unconventional sequence predominated by odd-integer states under a moderate
+strength magnetic field. By tilting the magnetic field, we discover Landau
+level (LL) crossing effects at ultra-low coincident angles, revealing that the
+Zeeman energy is about three times as large as the cyclotron energy near the
+valence band top at {\Gamma} valley. This result implies the significant roles
+played by the exchange interactions in p-type few-layer WSe2, in which
+itinerant or QH ferromagnetism likely occurs. Evidently, the {\Gamma} valley of
+few-layer WSe2 offers a unique platform with unusually heavy hole-carriers and
+a substantially enhanced g-factor for exploring strongly correlated phenomena.
+",0,1,0,0,0,0
+15070,Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning,"  In the field of reinforcement learning there has been recent progress towards
+safety and high-confidence bounds on policy performance. However, to our
+knowledge, no practical methods exist for determining high-confidence policy
+performance bounds in the inverse reinforcement learning setting---where the
+true reward function is unknown and only samples of expert behavior are given.
+We propose a sampling method based on Bayesian inverse reinforcement learning
+that uses demonstrations to determine practical high-confidence upper bounds on
+the $\alpha$-worst-case difference in expected return between any evaluation
+policy and the optimal policy under the expert's unknown reward function. We
+evaluate our proposed bound on both a standard grid navigation task and a
+simulated driving task and achieve tighter and more accurate bounds than a
+feature count-based baseline. We also give examples of how our proposed bound
+can be utilized to perform risk-aware policy selection and risk-aware policy
+improvement. Because our proposed bound requires several orders of magnitude
+fewer demonstrations than existing high-confidence bounds, it is the first
+practical method that allows agents that learn from demonstration to express
+confidence in the quality of their learned policy.
+",1,0,0,1,0,0
+15071,Face Identification and Clustering,"  In this thesis, we study two problems based on clustering algorithms. In the
+first problem, we study the role of visual attributes using an agglomerative
+clustering algorithm to whittle down the search area where the number of
+classes is high to improve the performance of clustering. We observe that as we
+add more attributes, the clustering performance increases overall. In the
+second problem, we study the role of clustering in aggregating templates in a
+1:N open set protocol using multi-shot video as a probe. We observe that by
+increasing the number of clusters, the performance increases with respect to
+the baseline and reaches a peak, after which increasing the number of clusters
+causes the performance to degrade. Experiments are conducted using recently
+introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition
+datasets.
+",1,0,0,0,0,0
+15072,Properties of Hydrogen Bonds in the Protic Ionic Liquid Ethylammonium Nitrate. DFT versus DFTB Molecular Dynamics,"  Comparative molecular dynamics simulations of a hexamer cluster of the protic
+ionic liquid ethylammonium nitrate are performed using density functional
+theory (DFT) and density functional-based tight binding (DFTB) methods. The
+focus is on assessing the performance of the DFTB approach to describe the
+dynamics and infrared spectroscopic signatures of hydrogen bonding between the
+ions. Average geometries and geometric correlations are found to be rather
+similar. The same holds true for the far-infrared spectral region. Differences
+are more pronounced for the NH- and CH-stretching band, where DFTB predicts a
+broader intensity distribution. DFTB completely fails to describe the
+fingerprint range shaped by nitrate anion vibrations. Finally, charge
+fluctuations within the H-bonds are characterized yielding moderate
+dependencies on geometry. On the basis of these results, DFTB is recommend for
+the simulation of H-bond properties of this type of ionic liquids.
+",0,1,0,0,0,0
+15073,On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems,"  We study the non-stationary stochastic multiarmed bandit (MAB) problem and
+propose two generic algorithms, namely, the limited memory deterministic
+sequencing of exploration and exploitation (LM-DSEE) and the Sliding-Window
+Upper Confidence Bound# (SW-UCB#). We rigorously analyze these algorithms in
+abruptly-changing and slowly-varying environments and characterize their
+performance. We show that the expected cumulative regret for these algorithms
+under either of the environments is upper bounded by sublinear functions of
+time, i.e., the time average of the regret asymptotically converges to zero. We
+complement our analytic results with numerical illustrations.
+",0,0,0,1,0,0
+15074,Validation of small Kepler transiting planet candidates in or near the habitable zone,"  A main goal of NASA's Kepler Mission is to establish the frequency of
+potentially habitable Earth-size planets (eta Earth). Relatively few such
+candidates identified by the mission can be confirmed to be rocky via dynamical
+measurement of their mass. Here we report an effort to validate 18 of them
+statistically using the BLENDER technique, by showing that the likelihood they
+are true planets is far greater than that of a false positive. Our analysis
+incorporates follow-up observations including high-resolution optical and
+near-infrared spectroscopy, high-resolution imaging, and information from the
+analysis of the flux centroids of the Kepler observations themselves. While
+many of these candidates have been previously validated by others, the
+confidence levels reported typically ignore the possibility that the planet may
+transit a different star than the target along the same line of sight. If that
+were the case, a planet that appears small enough to be rocky may actually be
+considerably larger and therefore less interesting from the point of view of
+habitability. We take this into consideration here, and are able to validate 15
+of our candidates at a 99.73% (3 sigma) significance level or higher, and the
+other three at slightly lower confidence. We characterize the GKM host stars
+using available ground-based observations and provide updated parameters for
+the planets, with sizes between 0.8 and 2.9 Earth radii. Seven of them
+(KOI-0438.02, 0463.01, 2418.01, 2626.01, 3282.01, 4036.01, and 5856.01) have a
+better than 50% chance of being smaller than 2 Earth radii and being in the
+habitable zone of their host stars.
+",0,1,0,0,0,0
+15075,CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compression,"  Convolutional Neural Networks (CNNs) has shown a great success in many areas
+including complex image classification tasks. However, they need a lot of
+memory and computational cost, which hinders them from running in relatively
+low-end smart devices such as smart phones. We propose a CNN compression method
+based on CP-decomposition and Tensor Power Method. We also propose an iterative
+fine tuning, with which we fine-tune the whole network after decomposing each
+layer, but before decomposing the next layer. Significant reduction in memory
+and computation cost is achieved compared to state-of-the-art previous work
+with no more accuracy loss.
+",1,0,0,0,0,0
+15076,Stoic Ethics for Artificial Agents,"  We present a position paper advocating the notion that Stoic philosophy and
+ethics can inform the development of ethical A.I. systems. This is in sharp
+contrast to most work on building ethical A.I., which has focused on
+Utilitarian or Deontological ethical theories. We relate ethical A.I. to
+several core Stoic notions, including the dichotomy of control, the four
+cardinal virtues, the ideal Sage, Stoic practices, and Stoic perspectives on
+emotion or affect. More generally, we put forward an ethical view of A.I. that
+focuses more on internal states of the artificial agent rather than on external
+actions of the agent. We provide examples relating to near-term A.I. systems as
+well as hypothetical superintelligent agents.
+",1,0,0,0,0,0
+15077,From quarks to nucleons in dark matter direct detection,"  We provide expressions for the nonperturbative matching of the effective
+field theory describing dark matter interactions with quarks and gluons to the
+effective theory of nonrelativistic dark matter interacting with
+nonrelativistic nucleons. We give the leading and subleading order expressions
+in chiral counting. In general, a single partonic operator already matches onto
+several nonrelativistic operators at leading order in chiral counting. Thus,
+keeping only one operator at the time in the nonrelativistic effective theory
+does not properly describe the scattering in direct detection. Moreover, the
+matching of the axial--axial partonic level operator, as well as the matching
+of the operators coupling DM to the QCD anomaly term, naively include momentum
+suppressed terms. However, these are still of leading chiral order due to pion
+poles and can be numerically important. We illustrate the impact of these
+effects with several examples.
+",0,1,0,0,0,0
+15078,Sharpened Strichartz estimates and bilinear restriction for the mass-critical quantum harmonic oscillator,"  We develop refined Strichartz estimates at $L^2$ regularity for a class of
+time-dependent Schrödinger operators. Such refinements begin to
+characterize the near-optimizers of the Strichartz estimate, and play a pivotal
+part in the global theory of mass-critical NLS. On one hand, the harmonic
+analysis is quite subtle in the $L^2$-critical setting due to an enormous group
+of symmetries, while on the other hand, the spacetime Fourier analysis employed
+by the existing approaches to the constant-coefficient equation are not adapted
+to nontranslation-invariant situations, especially with potentials as large as
+those considered in this article.
+Using phase space techniques, we reduce to proving certain analogues of
+(adjoint) bilinear Fourier restriction estimates. Then we extend Tao's bilinear
+restriction estimate for paraboloids to more general Schrödinger operators.
+As a particular application, the resulting inverse Strichartz theorem and
+profile decompositions constitute a key harmonic analysis input for studying
+large data solutions to the $L^2$-critical NLS with a harmonic oscillator
+potential in dimensions $\ge 2$. This article builds on recent work of Killip,
+Visan, and the author in one space dimension.
+",0,0,1,0,0,0
+15079,A fast and stable test to check if a weakly diagonally dominant matrix is a nonsingular M-matrix,"  We present a test for determining if a substochastic matrix is convergent. By
+establishing a duality between weakly chained diagonally dominant (w.c.d.d.)
+L-matrices and convergent substochastic matrices, we show that this test can be
+trivially extended to determine whether a weakly diagonally dominant (w.d.d.)
+matrix is a nonsingular M-matrix. The test's runtime is linear in the order of
+the input matrix if it is sparse and quadratic if it is dense. This is a
+partial strengthening of the cubic test in [J. M. Peña., A stable test to
+check if a matrix is a nonsingular M-matrix, Math. Comp., 247, 1385-1392,
+2004]. As a by-product of our analysis, we prove that a nonsingular w.d.d.
+M-matrix is a w.c.d.d. L-matrix, a fact whose converse has been known since at
+least 1964. We point out that this strengthens some recent results on
+M-matrices in the literature.
+",1,0,1,0,0,0
+15080,Task-Oriented Query Reformulation with Reinforcement Learning,"  Search engines play an important role in our everyday lives by assisting us
+in finding the information we need. When we input a complex query, however,
+results are often far from satisfactory. In this work, we introduce a query
+reformulation system based on a neural network that rewrites a query to
+maximize the number of relevant documents returned. We train this neural
+network with reinforcement learning. The actions correspond to selecting terms
+to build a reformulated query, and the reward is the document recall. We
+evaluate our approach on three datasets against strong baselines and show a
+relative improvement of 5-20% in terms of recall. Furthermore, we present a
+simple method to estimate a conservative upper-bound performance of a model in
+a particular environment and verify that there is still large room for
+improvements.
+",1,0,0,0,0,0
+15081,"Inverse scattering transform for the nonlocal reverse space-time Sine-Gordon, Sinh-Gordon and nonlinear Schrödinger equations with nonzero boundary conditions","  The reverse space-time (RST) Sine-Gordon, Sinh-Gordon and nonlinear
+Schrödinger equations were recently introduced and shown to be integrable
+infinite-dimensional dynamical systems. The inverse scattering transform (IST)
+for rapidly decaying data was also constructed. In this paper, IST for these
+equations with nonzero boundary conditions (NZBCs) at infinity is presented.
+The NZBC problem is more complicated due to the associated branching structure
+of the associated linear eigenfunctions. With constant amplitude at infinity,
+four cases are analyzed; they correspond to two different signs of nonlinearity
+and two different values of the phase at infinity. Special soliton solutions
+are discussed and explicit 1-soliton and 2-soliton solutions are found. In
+terms of IST, the difference between the RST Sine-Gordon/Sinh-Gordon equations
+and the RST NLS equation is the time dependence of the scattering data.
+Spatially dependent boundary conditions are also briefly considered.
+",0,1,0,0,0,0
+15082,Relieving the frustration through Mn$^{3+}$ substitution in Holmium Gallium Garnet,"  We present a study on the impact of Mn$^{3+}$ substitution in the
+geometrically frustrated Ising garnet Ho$_3$Ga$_5$O$_{12}$ using bulk magnetic
+measurements and low temperature powder neutron diffraction. We find that the
+transition temperature, $T_N$ = 5.8 K, for Ho$_3$MnGa$_4$O$_{12}$ is raised by
+almost 20 when compared to Ho$_3$Ga$_5$O$_{12}$. Powder neutron diffraction on
+Ho$_3$Mn$_x$Ga$_{5-x}$O$_{12}$ ($x$ = 0.5, 1) below $T_N$ shows the formation
+of a long range ordered ordered state with $\mathbf{k}$ = (0,0,0). Ho$^{3+}$
+spins are aligned antiferromagnetically along the six crystallographic axes
+with no resultant moment while the Mn$^{3+}$ spins are oriented along the body
+diagonals, such that there is a net moment along [111]. The magnetic structure
+can be visualised as ten-membered rings of corner-sharing triangles of
+Ho$^{3+}$ spins with the Mn$^{3+}$ spins ferromagnetically coupled to each
+individual Ho$^{3+}$ spin in the triangle. Substitution of Mn$^{3+}$ completely
+relieves the magnetic frustration with $f = \theta_{CW}/T_N \approx 1.1$ for
+Ho$_3$MnGa$_4$O$_{12}$.
+",0,1,0,0,0,0
+15083,Inference on Auctions with Weak Assumptions on Information,"  Given a sample of bids from independent auctions, this paper examines the
+question of inference on auction fundamentals (e.g. valuation distributions,
+welfare measures) under weak assumptions on information structure. The question
+is important as it allows us to learn about the valuation distribution in a
+robust way, i.e., without assuming that a particular information structure
+holds across observations. We leverage the recent contributions of
+\cite{Bergemann2013} in the robust mechanism design literature that exploit the
+link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in
+incomplete information games to construct an econometrics framework for
+learning about auction fundamentals using observed data on bids. We showcase
+our construction of identified sets in private value and common value auctions.
+Our approach for constructing these sets inherits the computational simplicity
+of solving for correlated equilibria: checking whether a particular valuation
+distribution belongs to the identified set is as simple as determining whether
+a {\it linear} program is feasible. A similar linear program can be used to
+construct the identified set on various welfare measures and counterfactual
+objects. For inference and to summarize statistical uncertainty, we propose
+novel finite sample methods using tail inequalities that are used to construct
+confidence regions on sets. We also highlight methods based on Bayesian
+bootstrap and subsampling. A set of Monte Carlo experiments show adequate
+finite sample properties of our inference procedures. We illustrate our methods
+using data from OCS auctions.
+",1,0,1,0,0,0
+15084,Introduction to OXPath,"  Contemporary web pages with increasingly sophisticated interfaces rival
+traditional desktop applications for interface complexity and are often called
+web applications or RIA (Rich Internet Applications). They often require the
+execution of JavaScript in a web browser and can call AJAX requests to
+dynamically generate the content, reacting to user interaction. From the
+automatic data acquisition point of view, thus, it is essential to be able to
+correctly render web pages and mimic user actions to obtain relevant data from
+the web page content. Briefly, to obtain data through existing Web interfaces
+and transform it into structured form, contemporary wrappers should be able to:
+1) interact with sophisticated interfaces of web applications; 2) precisely
+acquire relevant data; 3) scale with the number of crawled web pages or states
+of web application; 4) have an embeddable programming API for integration with
+existing web technologies. OXPath is a state-of-the-art technology, which is
+compliant with these requirements and demonstrated its efficiency in
+comprehensive experiments. OXPath integrates Firefox for correct rendering of
+web pages and extends XPath 1.0 for the DOM node selection, interaction, and
+extraction. It provides means for converting extracted data into different
+formats, such as XML, JSON, CSV, and saving data into relational databases.
+This tutorial explains main features of the OXPath language and the setup of
+a suitable working environment. The guidelines for using OXPath are provided in
+the form of prototypical examples.
+",1,0,0,0,0,0
+15085,Image Registration for the Alignment of Digitized Historical Documents,"  In this work, we conducted a survey on different registration algorithms and
+investigated their suitability for hyperspectral historical image registration
+applications. After the evaluation of different algorithms, we choose an
+intensity based registration algorithm with a curved transformation model. For
+the transformation model, we select cubic B-splines since they should be
+capable to cope with all non-rigid deformations in our hyperspectral images.
+From a number of similarity measures, we found that residual complexity and
+localized mutual information are well suited for the task at hand. In our
+evaluation, both measures show an acceptable performance in handling all
+difficulties, e.g., capture range, non-stationary and spatially varying
+intensity distortions or multi-modality that occur in our application.
+",1,0,0,0,0,0
+15086,Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification,"  Person Re-Identification (person re-id) is a crucial task as its applications
+in visual surveillance and human-computer interaction. In this work, we present
+a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for
+video-based person re-identification, which enables the feature extractor to be
+aware of the current input video sequences, in a way that interdependency from
+the matching items can directly influence the computation of each other's
+representation. Specifically, the spatial pooling layer is able to select
+regions from each frame, while the attention temporal pooling performed can
+select informative frames over the sequence, both pooling guided by the
+information from distance matching. Experiments are conduced on the iLIDS-VID,
+PRID-2011 and MARS datasets and the results demonstrate that this approach
+outperforms existing state-of-art methods. We also analyze how the joint
+pooling in both dimensions can boost the person re-id performance more
+effectively than using either of them separately.
+",1,0,0,1,0,0
+15087,"Inertia, positive definiteness and $\ell_p$ norm of GCD and LCM matrices and their unitary analogs","  Let $S=\{x_1,x_2,\dots,x_n\}$ be a set of distinct positive integers, and let
+$f$ be an arithmetical function. The GCD matrix $(S)_f$ on $S$ associated with
+$f$ is defined as the $n\times n$ matrix having $f$ evaluated at the greatest
+common divisor of $x_i$ and $x_j$ as its $ij$ entry. The LCM matrix $[S]_f$ is
+defined similarly. We consider inertia, positive definiteness and $\ell_p$ norm
+of GCD and LCM matrices and their unitary analogs. Proofs are based on matrix
+factorizations and convolutions of arithmetical functions.
+",0,0,1,0,0,0
+15088,Low-Latency Millimeter-Wave Communications: Traffic Dispersion or Network Densification?,"  This paper investigates two strategies to reduce the communication delay in
+future wireless networks: traffic dispersion and network densification. A
+hybrid scheme that combines these two strategies is also considered. The
+probabilistic delay and effective capacity are used to evaluate performance.
+For probabilistic delay, the violation probability of delay, i.e., the
+probability that the delay exceeds a given tolerance level, is characterized in
+terms of upper bounds, which are derived by applying stochastic network
+calculus theory. In addition, to characterize the maximum affordable arrival
+traffic for mmWave systems, the effective capacity, i.e., the service
+capability with a given quality-of-service (QoS) requirement, is studied. The
+derived bounds on the probabilistic delay and effective capacity are validated
+through simulations. These numerical results show that, for a given average
+system gain, traffic dispersion, network densification, and the hybrid scheme
+exhibit different potentials to reduce the end-to-end communication delay. For
+instance, traffic dispersion outperforms network densification, given high
+average system gain and arrival rate, while it could be the worst option,
+otherwise. Furthermore, it is revealed that, increasing the number of
+independent paths and/or relay density is always beneficial, while the
+performance gain is related to the arrival rate and average system gain,
+jointly. Therefore, a proper transmission scheme should be selected to optimize
+the delay performance, according to the given conditions on arrival traffic and
+system service capability.
+",1,0,0,0,0,0
+15089,The structure of multiplicative tilings of the real line,"  Suppose $\Omega, A \subseteq \RR\setminus\Set{0}$ are two sets, both of mixed
+sign, that $\Omega$ is Lebesgue measurable and $A$ is a discrete set. We study
+the problem of when $A \cdot \Omega$ is a (multiplicative) tiling of the real
+line, that is when almost every real number can be uniquely written as a
+product $a\cdot \omega$, with $a \in A$, $\omega \in \Omega$. We study both the
+structure of the set of multiples $A$ and the structure of the tile $\Omega$.
+We prove strong results in both cases. These results are somewhat analogous to
+the known results about the structure of translational tiling of the real line.
+There is, however, an extra layer of complexity due to the presence of sign in
+the sets $A$ and $\Omega$, which makes multiplicative tiling roughly equivalent
+to translational tiling on the larger group $\ZZ_2 \times \RR$.
+",0,0,1,0,0,0
+15090,"Supporting Crowd-Powered Science in Economics: FRACTI, a Conceptual Framework for Large-Scale Collaboration and Transparent Investigation in Financial Markets","  Modern investigation in economics and in other sciences requires the ability
+to store, share, and replicate results and methods of experiments that are
+often multidisciplinary and yield a massive amount of data. Given the
+increasing complexity and growing interaction across diverse bodies of
+knowledge it is becoming imperative to define a platform to properly support
+collaborative research and track origin, accuracy and use of data. This paper
+starts by defining a set of methods leveraging scientific principles and
+advocating the importance of those methods in multidisciplinary, computer
+intensive fields like computational finance. The next part of this paper
+defines a class of systems called scientific support systems, vis-a-vis usages
+in other research fields such as bioinformatics, physics and engineering. We
+outline a basic set of fundamental concepts, and list our goals and motivation
+for leveraging such systems to enable large-scale investigation, ""crowd powered
+science"", in economics. The core of this paper provides an outline of FRACTI in
+five steps. First we present definitions related to scientific support systems
+intrinsic to finance and describe common characteristics of financial use
+cases. The second step concentrates on what can be exchanged through the
+definition of shareable entities called contributions. The third step is the
+description of a classification system for building blocks of the conceptual
+framework, called facets. The fourth step introduces the meta-model that will
+enable provenance tracking and representation of data fragments and simulation.
+Finally we describe intended cases of use to highlight main strengths of
+FRACTI: application of the scientific method for investigation in computational
+finance, large-scale collaboration and simulation.
+",0,0,0,0,0,1
+15091,The effect of temperature on generic stable periodic structures in the parameter space of dissipative relativistic standard map,"  In this work, we have characterized changes in the dynamics of a
+two-dimensional relativistic standard map in the presence of dissipation and
+specially when it is submitted to thermal effects modeled by a Gaussian noise
+reservoir. By the addition of thermal noise in the dissipative relativistic
+standard map (DRSM) it is possible to suppress typical stable periodic
+structures (SPSs) embedded in the chaotic domains of parameter space for large
+enough temperature strengths. Smaller SPSs are first affected by thermal
+effects, starting from their borders, as a function of temperature. To estimate
+the necessary temperature strength capable to destroy those SPSs we use the
+largest Lyapunov exponent to obtain the critical temperature ($T_C$) diagrams.
+For critical temperatures the chaotic behavior takes place with the suppression
+of periodic motion, although, the temperature strengths considered in this work
+are not so large to convert the deterministic features of the underlying system
+into a stochastic ones.
+",0,1,0,0,0,0
+15092,The $u^n$-invariant and the Symbol Length of $H_2^n(F)$,"  Given a field $F$ of $\operatorname{char}(F)=2$, we define $u^n(F)$ to be the
+maximal dimension of an anisotropic form in $I_q^n F$. For $n=1$ it recaptures
+the definition of $u(F)$. We study the relations between this value and the
+symbol length of $H_2^n(F)$, denoted by $sl_2^n(F)$. We show for any $n \geq 2$
+that if $2^n \leq u^n(F) \leq u^2(F) < \infty$ then $sl_2^n(F) \leq
+\prod_{i=2}^n (\frac{u^i(F)}{2}+1-2^{i-1})$. As a result, if $u(F)$ is finite
+then $sl_2^n(F)$ is finite for any $n$, a fact which was previously proven when
+$\operatorname{char}(F) \neq 2$ by Saltman and Krashen. We also show that if
+$sl_2^n(F)=1$ then $u^n(F)$ is either $2^n$ or $2^{n+1}$.
+",0,0,1,0,0,0
+15093,An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario,"  Being able to recognize emotions in human users is considered a highly
+desirable trait in Human-Robot Interaction (HRI) scenarios. However, most
+contemporary approaches rarely attempt to apply recognized emotional features
+in an active manner to modulate robot decision-making and dialogue for the
+benefit of the user. In this position paper, we propose a method of
+incorporating recognized emotions into a Reinforcement Learning (RL) based
+dialogue management module that adapts its dialogue responses in order to
+attempt to make cognitive training tasks, like the 2048 Puzzle Game, more
+enjoyable for the users.
+",1,0,0,0,0,0
+15094,Rao-Blackwellization to give Improved Estimates in Multi-List Studies,"  Sufficient statistics are derived for the population size and parameters of
+commonly used closed population mark-recapture models. Rao-Blackwellization
+details for improving estimators that are not functions of the statistics are
+presented. As Rao-Blackwellization entails enumerating all sample reorderings
+consistent with the sufficient statistic, Markov chain Monte Carlo resampling
+procedures are provided to approximate the computationally intensive
+estimators. Simulation studies demonstrate that significant improvements can be
+made with the strategy. Supplementary materials for this article are available
+online.
+",0,0,0,1,0,0
+15095,Bernstein Polynomial Model for Nonparametric Multivariate Density,"  In this paper, we study the Bernstein polynomial model for estimating the
+multivariate distribution functions and densities with bounded support. As a
+mixture model of multivariate beta distributions, the maximum (approximate)
+likelihood estimate can be obtained using EM algorithm. A change-point method
+of choosing optimal degrees of the proposed Bernstein polynomial model is
+presented. Under some conditions the optimal rate of convergence in the mean
+$\chi^2$-divergence of new density estimator is shown to be nearly parametric.
+The method is illustrated by an application to a real data set. Finite sample
+performance of the proposed method is also investigated by simulation study and
+is shown to be much better than the kernel density estimate but close to the
+parametric ones.
+",0,0,0,1,0,0
+15096,Small-space encoding LCE data structure with constant-time queries,"  The \emph{longest common extension} (\emph{LCE}) problem is to preprocess a
+given string $w$ of length $n$ so that the length of the longest common prefix
+between suffixes of $w$ that start at any two given positions is answered
+quickly. In this paper, we present a data structure of $O(z \tau^2 +
+\frac{n}{\tau})$ words of space which answers LCE queries in $O(1)$ time and
+can be built in $O(n \log \sigma)$ time, where $1 \leq \tau \leq \sqrt{n}$ is a
+parameter, $z$ is the size of the Lempel-Ziv 77 factorization of $w$ and
+$\sigma$ is the alphabet size. This is an \emph{encoding} data structure, i.e.,
+it does not access the input string $w$ when answering queries and thus $w$ can
+be deleted after preprocessing. On top of this main result, we obtain further
+results using (variants of) our LCE data structure, which include the
+following:
+- For highly repetitive strings where the $z\tau^2$ term is dominated by
+$\frac{n}{\tau}$, we obtain a \emph{constant-time and sub-linear space} LCE
+query data structure.
+- Even when the input string is not well compressible via Lempel-Ziv 77
+factorization, we still can obtain a \emph{constant-time and sub-linear space}
+LCE data structure for suitable $\tau$ and for $\sigma \leq 2^{o(\log n)}$.
+- The time-space trade-off lower bounds for the LCE problem by Bille et al.
+[J. Discrete Algorithms, 25:42-50, 2014] and by Kosolobov [CoRR,
+abs/1611.02891, 2016] can be ""surpassed"" in some cases with our LCE data
+structure.
+",1,0,0,0,0,0
+15097,Machine learning application in the life time of materials,"  Materials design and development typically takes several decades from the
+initial discovery to commercialization with the traditional trial and error
+development approach. With the accumulation of data from both experimental and
+computational results, data based machine learning becomes an emerging field in
+materials discovery, design and property prediction. This manuscript reviews
+the history of materials science as a disciplinary the most common machine
+learning method used in materials science, and specifically how they are used
+in materials discovery, design, synthesis and even failure detection and
+analysis after materials are deployed in real application. Finally, the
+limitations of machine learning for application in materials science and
+challenges in this emerging field is discussed.
+",1,1,0,0,0,0
+15098,A Unified Framework for Stochastic Matrix Factorization via Variance Reduction,"  We propose a unified framework to speed up the existing stochastic matrix
+factorization (SMF) algorithms via variance reduction. Our framework is general
+and it subsumes several well-known SMF formulations in the literature. We
+perform a non-asymptotic convergence analysis of our framework and derive
+computational and sample complexities for our algorithm to converge to an
+$\epsilon$-stationary point in expectation. In addition, extensive experiments
+for a wide class of SMF formulations demonstrate that our framework
+consistently yields faster convergence and a more accurate output dictionary
+vis-à-vis state-of-the-art frameworks.
+",1,0,1,1,0,0
+15099,Short Term Power Demand Prediction Using Stochastic Gradient Boosting,"  Power prediction demand is vital in power system and delivery engineering
+fields. By efficiently predicting the power demand, we can forecast the total
+energy to be consumed in a certain city or district. Thus, exact resources
+required to produce the demand power can be allocated. In this paper, a
+Stochastic Gradient Boosting (aka Treeboost) model is used to predict the short
+term power demand for the Emirate of Sharjah in the United Arab Emirates (UAE).
+Results show that the proposed model gives promising results in comparison to
+the model used by Sharjah Electricity and Water Authority (SEWA).
+",0,0,0,1,0,0
+15100,Learning Qualitatively Diverse and Interpretable Rules for Classification,"  There has been growing interest in developing accurate models that can also
+be explained to humans. Unfortunately, if there exist multiple distinct but
+accurate models for some dataset, current machine learning methods are unlikely
+to find them: standard techniques will likely recover a complex model that
+combines them. In this work, we introduce a way to identify a maximal set of
+distinct but accurate models for a dataset. We demonstrate empirically that, in
+situations where the data supports multiple accurate classifiers, we tend to
+recover simpler, more interpretable classifiers rather than more complex ones.
+",0,0,0,1,0,0
+15101,Propagation of self-localised Q-ball solitons in the $^3$He universe,"  In relativistic quantum field theories, compact objects of interacting bosons
+can become stable owing to conservation of an additive quantum number $Q$.
+Discovering such $Q$-balls propagating in the Universe would confirm
+supersymmetric extensions of the standard model and may shed light on the
+mysteries of dark matter, but no unambiguous experimental evidence exists. We
+report observation of a propagating long-lived $Q$-ball in superfluid $^3$He,
+where the role of $Q$-ball is played by a Bose-Einstein condensate of magnon
+quasiparticles. We achieve accurate representation of the $Q$-ball Hamiltonian
+using the influence of the number of magnons, corresponding to the charge $Q$,
+on the orbital structure of the superfluid $^3$He order parameter. This
+realisation supports multiple coexisting $Q$-balls which in future allows
+studies of $Q$-ball dynamics, interactions, and collisions.
+",0,1,0,0,0,0
+15102,Improving the staggered grid Lagrangian hydrodynamics for modeling multi-material flows,"  In this work, we make two improvements on the staggered grid hydrodynamics
+(SGH) Lagrangian scheme for modeling 2-dimensional compressible multi-material
+flows on triangular mesh. The first improvement is the construction of a
+dynamic local remeshing scheme for preventing mesh distortion. The remeshing
+scheme is similar to many published algorithms except that it introduces some
+special operations for treating grids around multi-material interfaces. This
+makes the simulation of extremely deforming and topology-variable
+multi-material processes possible, such as the complete process of a heavy
+fluid dipping into a light fluid. The second improvement is the construction of
+an Euler-like flow on each edge of the mesh to count for the ""edge-bending""
+effect, so as to mitigate the ""checkerboard"" oscillation that commonly exists
+in Lagrangian simulations, especially the triangular mesh based simulations.
+Several typical hydrodynamic problems are simulated by the improved staggered
+grid Lagrangian hydrodynamic method to test its performance.
+",0,1,0,0,0,0
+15103,Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins,"  Vehicle climate control systems aim to keep passengers thermally comfortable.
+However, current systems control temperature rather than thermal comfort and
+tend to be energy hungry, which is of particular concern when considering
+electric vehicles. This paper poses energy-efficient vehicle comfort control as
+a Markov Decision Process, which is then solved numerically using
+Sarsa({\lambda}) and an empirically validated, single-zone, 1D thermal model of
+the cabin. The resulting controller was tested in simulation using 200 randomly
+selected scenarios and found to exceed the performance of bang-bang,
+proportional, simple fuzzy logic, and commercial controllers with 23%, 43%,
+40%, 56% increase, respectively. Compared to the next best performing
+controller, energy consumption is reduced by 13% while the proportion of time
+spent thermally comfortable is increased by 23%. These results indicate that
+this is a viable approach that promises to translate into substantial comfort
+and energy improvements in the car.
+",1,0,0,0,0,0
+15104,A functional model for the Fourier--Plancherel operator truncated on the positive half-axis,"  The truncated Fourier operator $\mathscr{F}_{\mathbb{R^{+}}}$, $$
+(\mathscr{F}_{\mathbb{R^{+}}}x)(t)=\frac{1}{\sqrt{2\pi}}
+\int\limits_{\mathbb{R^{+}}}x(\xi)e^{it\xi}\,d\xi\,,\ \ \
+t\in{}{\mathbb{R^{+}}}, $$ is studied. The operator
+$\mathscr{F}_{\mathbb{R^{+}}}$ is considered as an operator acting in the space
+$L^2(\mathbb{R^{+}})$. The functional model for the operator
+$\mathscr{F}_{\mathbb{R^{+}}}$ is constructed. This functional model is the
+multiplication operator on the appropriate $2\times2$ matrix function acting in
+the space $L^2(\mathbb{R^{+}})\oplus{}L^2(\mathbb{R^{+}})$. Using this
+functional model, the spectrum of the operator $\mathscr{F}_{\mathbb{R^{+}}}$
+is found. The resolvent of the operator $\mathscr{F}_{\mathbb{R^{+}}}$ is
+estimated near its spectrum.
+",0,0,1,0,0,0
+15105,Mapping $n$ grid points onto a square forces an arbitrarily large Lipschitz constant,"  We prove that the regular $n\times n$ square grid of points in the integer
+lattice $\mathbb{Z}^{2}$ cannot be recovered from an arbitrary $n^{2}$-element
+subset of $\mathbb{Z}^{2}$ via a mapping with prescribed Lipschitz constant
+(independent of $n$). This answers negatively a question of Feige from 2002.
+Our resolution of Feige's question takes place largely in a continuous setting
+and is based on some new results for Lipschitz mappings falling into two broad
+areas of interest, which we study independently. Firstly the present work
+contains a detailed investigation of Lipschitz regular mappings on Euclidean
+spaces, with emphasis on their bilipschitz decomposability in a sense
+comparable to that of the well known result of Jones. Secondly, we build on
+work of Burago and Kleiner and McMullen on non-realisable densities. We verify
+the existence, and further prevalence, of strongly non-realisable densities
+inside spaces of continuous functions.
+",1,0,1,0,0,0
+15106,An FPT algorithm for planar multicuts with sources and sinks on the outer face,"  Given a list of k source-sink pairs in an edge-weighted graph G, the minimum
+multicut problem consists in selecting a set of edges of minimum total weight
+in G, such that removing these edges leaves no path from each source to its
+corresponding sink. To the best of our knowledge, no non-trivial FPT result for
+special cases of this problem, which is APX-hard in general graphs for any
+fixed k>2, is known with respect to k only. When the graph G is planar, this
+problem is known to be polynomial-time solvable if k=O(1), but cannot be FPT
+with respect to k under the Exponential Time Hypothesis.
+In this paper, we show that, if G is planar and in addition all sources and
+sinks lie on the outer face, then this problem does admit an FPT algorithm when
+parameterized by k (although it remains APX-hard when k is part of the input,
+even in stars). To do this, we provide a new characterization of optimal
+solutions in this case, and then use it to design a ""divide-and-conquer""
+approach: namely, some edges that are part of any such solution actually define
+an optimal solution for a polynomial-time solvable multiterminal variant of the
+problem on some of the sources and sinks (which can be identified thanks to a
+reduced enumeration phase). Removing these edges from the graph cuts it into
+several smaller instances, which can then be solved recursively.
+",1,0,0,0,0,0
+15107,Calibration with Bias-Corrected Temperature Scaling Improves Domain Adaptation Under Label Shift in Modern Neural Networks,"  Label shift refers to the phenomenon where the marginal probability p(y) of
+observing a particular class changes between the training and test
+distributions while the conditional probability p(x|y) stays fixed. This is
+relevant in settings such as medical diagnosis, where a classifier trained to
+predict disease based on observed symptoms may need to be adapted to a
+different distribution where the baseline frequency of the disease is higher.
+Given calibrated estimates of p(y|x), one can apply an EM algorithm to correct
+for the shift in class imbalance between the training and test distributions
+without ever needing to calculate p(x|y). Unfortunately, modern neural networks
+typically fail to produce well-calibrated probabilities, compromising the
+effectiveness of this approach. Although Temperature Scaling can greatly reduce
+miscalibration in these networks, it can leave behind a systematic bias in the
+probabilities that still poses a problem. To address this, we extend
+Temperature Scaling with class-specific bias parameters, which largely
+eliminates systematic bias in the calibrated probabilities and allows for
+effective domain adaptation under label shift. We term our calibration approach
+""Bias-Corrected Temperature Scaling"". On experiments with CIFAR10, we find that
+EM with Bias-Corrected Temperature Scaling significantly outperforms both EM
+with Temperature Scaling and the recently-proposed Black-Box Shift Estimation.
+",1,0,0,1,0,0
+15108,Propagation Networks for Model-Based Control Under Partial Observation,"  There has been an increasing interest in learning dynamics simulators for
+model-based control. Compared with off-the-shelf physics engines, a learnable
+simulator can quickly adapt to unseen objects, scenes, and tasks. However,
+existing models like interaction networks only work for fully observable
+systems; they also only consider pairwise interactions within a single time
+step, both restricting their use in practical systems. We introduce Propagation
+Networks (PropNet), a differentiable, learnable dynamics model that handles
+partially observable scenarios and enables instantaneous propagation of signals
+beyond pairwise interactions. With these innovations, our propagation networks
+not only outperform current learnable physics engines in forward simulation,
+but also achieves superior performance on various control tasks. Compared with
+existing deep reinforcement learning algorithms, model-based control with
+propagation networks is more accurate, efficient, and generalizable to novel,
+partially observable scenes and tasks.
+",1,0,0,0,0,0
+15109,High-redshift galaxies and black holes in the eyes of JWST: a population synthesis model from infrared to X-rays,"  The first billion years of the Universe is a pivotal time: stars, black holes
+(BHs) and galaxies form and assemble, sowing the seeds of galaxies as we know
+them today. Detecting, identifying and understand- ing the first galaxies and
+BHs is one of the current observational and theoretical challenges in galaxy
+formation. In this paper we present a population synthesis model aimed at
+galaxies, BHs and Active Galactic Nuclei (AGNs) at high redshift. The model
+builds a population based on empirical relations. Galaxies are characterized by
+a spectral energy distribution determined by age and metallicity, and AGNs by a
+spectral energy distribution determined by BH mass and accretion rate. We
+validate the model against observational constraints, and then predict
+properties of galaxies and AGN in other wavelength and/or luminosity ranges,
+estimating the contamination of stellar populations (normal stars and high-mass
+X-ray binaries) for AGN searches from the infrared to X-rays, and vice-versa
+for galaxy searches. For high-redshift galaxies, with stellar ages < 1 Gyr, we
+find that disentangling stellar and AGN emission is challenging at restframe
+UV/optical wavelengths, while high-mass X-ray binaries become more important
+sources of confusion in X-rays. We propose a color-color selection in JWST
+bands to separate AGN vs star-dominated galaxies in photometric observations.
+We also esti- mate the AGN contribution, with respect to massive, hot,
+metal-poor stars, at driving high ionization lines, such as C IV and He II.
+Finally, we test the influence of the minimum BH mass and occupa- tion fraction
+of BHs in low mass galaxies on the restframe UV/near-IR and X-ray AGN
+luminosity function.
+",0,1,0,0,0,0
+15110,Soft Label Memorization-Generalization for Natural Language Inference,"  Often when multiple labels are obtained for a training example it is assumed
+that there is an element of noise that must be accounted for. It has been shown
+that this disagreement can be considered signal instead of noise. In this work
+we investigate using soft labels for training data to improve generalization in
+machine learning models. However, using soft labels for training Deep Neural
+Networks (DNNs) is not practical due to the costs involved in obtaining
+multiple labels for large data sets. We propose soft label
+memorization-generalization (SLMG), a fine-tuning approach to using soft labels
+for training DNNs. We assume that differences in labels provided by human
+annotators represent ambiguity about the true label instead of noise.
+Experiments with SLMG demonstrate improved generalization performance on the
+Natural Language Inference (NLI) task. Our experiments show that by injecting a
+small percentage of soft label training data (0.03% of training set size) we
+can improve generalization performance over several baselines.
+",1,0,0,0,0,0
+15111,Floquet Topological Magnons,"  We introduce the concept of Floquet topological magnons --- a mechanism by
+which a synthetic tunable Dzyaloshinskii-Moriya interaction (DMI) can be
+generated in quantum magnets using circularly polarized electric (laser) field.
+The resulting effect is that Dirac magnons and nodal magnons in two-dimensional
+(2D) and three-dimensional (3D) quantum magnets can be tuned to magnon Chern
+insulators and Weyl magnons respectively under circularly polarized laser
+field. The Floquet formalism also yields a tunable intrinsic DMI in insulating
+quantum magnets without an inversion center. We demonstrate that the Floquet
+topological magnons possess a finite thermal Hall conductivity tunable by the
+laser field.
+",0,1,0,0,0,0
+15112,Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds,"  Inspired by recent interests of developing machine learning and data mining
+algorithms on hypergraphs, we investigate in this paper the semi-supervised
+learning algorithm of propagating ""soft labels"" (e.g. probability
+distributions, class membership scores) over hypergraphs, by means of optimal
+transportation. Borrowing insights from Wasserstein propagation on graphs
+[Solomon et al. 2014], we re-formulate the label propagation procedure as a
+message-passing algorithm, which renders itself naturally to a generalization
+applicable to hypergraphs through Wasserstein barycenters. Furthermore, in a
+PAC learning framework, we provide generalization error bounds for propagating
+one-dimensional distributions on graphs and hypergraphs using 2-Wasserstein
+distance, by establishing the \textit{algorithmic stability} of the proposed
+semi-supervised learning algorithm. These theoretical results also shed new
+lights upon deeper understandings of the Wasserstein propagation on graphs.
+",0,0,0,1,0,0
+15113,Psychological and Personality Profiles of Political Extremists,"  Global recruitment into radical Islamic movements has spurred renewed
+interest in the appeal of political extremism. Is the appeal a rational
+response to material conditions or is it the expression of psychological and
+personality disorders associated with aggressive behavior, intolerance,
+conspiratorial imagination, and paranoia? Empirical answers using surveys have
+been limited by lack of access to extremist groups, while field studies have
+lacked psychological measures and failed to compare extremists with contrast
+groups. We revisit the debate over the appeal of extremism in the U.S. context
+by comparing publicly available Twitter messages written by over 355,000
+political extremist followers with messages written by non-extremist U.S.
+users. Analysis of text-based psychological indicators supports the moral
+foundation theory which identifies emotion as a critical factor in determining
+political orientation of individuals. Extremist followers also differ from
+others in four of the Big Five personality traits.
+",1,1,0,0,0,0
+15114,Techniques for Interpretable Machine Learning,"  Interpretable machine learning tackles the important problem that humans
+cannot understand the behaviors of complex machine learning models and how
+these models arrive at a particular decision. Although many approaches have
+been proposed, a comprehensive understanding of the achievements and challenges
+is still lacking. We provide a survey covering existing techniques to increase
+the interpretability of machine learning models. We also discuss crucial issues
+that the community should consider in future work such as designing
+user-friendly explanations and developing comprehensive evaluation metrics to
+further push forward the area of interpretable machine learning.
+",0,0,0,1,0,0
+15115,New Models and Methods for Formation and Analysis of Social Networks,"  This doctoral work focuses on three main problems related to social networks:
+(1) Orchestrating Network Formation: We consider the problem of orchestrating
+formation of a social network having a certain given topology that may be
+desirable for the intended usecases. Assuming the social network nodes to be
+strategic in forming relationships, we derive conditions under which a given
+topology can be uniquely obtained. We also study the efficiency and robustness
+of the derived conditions. (2) Multi-phase Influence Maximization: We propose
+that information diffusion be carried out in multiple phases rather than in a
+single instalment. With the objective of achieving better diffusion, we
+discover optimal ways of splitting the available budget among the phases,
+determining the time delay between consecutive phases, and also finding the
+individuals to be targeted for initiating the diffusion process. (3) Scalable
+Preference Aggregation: It is extremely useful to determine a small number of
+representatives of a social network such that the individual preferences of
+these nodes, when aggregated, reflect the aggregate preference of the entire
+network. Using real-world data collected from Facebook with human subjects, we
+discover a model that faithfully captures the spread of preferences in a social
+network. We hence propose fast and reliable ways of computing a truly
+representative aggregate preference of the entire network. In particular, we
+develop models and methods for solving the above problems, which primarily deal
+with formation and analysis of social networks.
+",1,1,0,0,0,0
+15116,Identifying networks with common organizational principles,"  Many complex systems can be represented as networks, and the problem of
+network comparison is becoming increasingly relevant. There are many techniques
+for network comparison, from simply comparing network summary statistics to
+sophisticated but computationally costly alignment-based approaches. Yet it
+remains challenging to accurately cluster networks that are of a different size
+and density, but hypothesized to be structurally similar. In this paper, we
+address this problem by introducing a new network comparison methodology that
+is aimed at identifying common organizational principles in networks. The
+methodology is simple, intuitive and applicable in a wide variety of settings
+ranging from the functional classification of proteins to tracking the
+evolution of a world trade network.
+",1,1,0,1,0,0
+15117,Image-based Proof of Work Algorithm for the Incentivization of Blockchain Archival of Interesting Images,"  A new variation of blockchain proof of work algorithm is proposed to
+incentivize the timely execution of image processing algorithms. A sample image
+processing algorithm is proposed to determine interesting images using analysis
+of the entropy of pixel subsets within images. The efficacy of the image
+processing algorithm is examined using two small sets of training and test
+data. The interesting image algorithm is then integrated into a simplified
+blockchain mining proof of work algorithm based on Bitcoin. The incentive of
+cryptocurrency mining is theorized to incentivize the execution of the
+algorithm and thus the retrieval of images that satisfy a minimum requirement
+set forth by the interesting image algorithm. The digital storage implications
+of running an image- based blockchain are then examined mathematically.
+",1,0,0,0,0,0
+15118,Multi-Lane Perception Using Feature Fusion Based on GraphSLAM,"  An extensive, precise and robust recognition and modeling of the environment
+is a key factor for next generations of Advanced Driver Assistance Systems and
+development of autonomous vehicles. In this paper, a real-time approach for the
+perception of multiple lanes on highways is proposed. Lane markings detected by
+camera systems and observations of other traffic participants provide the input
+data for the algorithm. The information is accumulated and fused using
+GraphSLAM and the result constitutes the basis for a multilane clothoid model.
+To allow incorporation of additional information sources, input data is
+processed in a generic format. Evaluation of the method is performed by
+comparing real data, collected with an experimental vehicle on highways, to a
+ground truth map. The results show that ego and adjacent lanes are robustly
+detected with high quality up to a distance of 120 m. In comparison to serial
+lane detection, an increase in the detection range of the ego lane and a
+continuous perception of neighboring lanes is achieved. The method can
+potentially be utilized for the longitudinal and lateral control of
+self-driving vehicles.
+",1,0,0,0,0,0
+15119,Stability analysis and stabilization of LPV systems with jumps and (piecewise) differentiable parameters using continuous and sampled-data controllers,"  Linear Parameter-Varying (LPV) systems with jumps and piecewise
+differentiable parameters is a class of hybrid LPV systems for which no
+tailored stability analysis and stabilization conditions have been obtained so
+far. We fill this gap here by proposing an approach relying on the
+reformulation of the considered LPV system as an extended equivalent hybrid
+system that will incorporate, through a suitable state augmentation,
+information on both the dynamics of the state of the system and the considered
+class of parameter trajectories. Two stability conditions are established using
+a result pertaining on the stability of hybrid systems and shown to naturally
+generalize and unify the well-known quadratic and robust stability criteria
+together. The obtained conditions being infinite-dimensional semidefinite
+programming problems, a relaxation approach based on sum of squares programming
+is used in order to obtain tractable finite-dimensional conditions. The
+conditions are then losslessly extended to solve two control problems, namely,
+the stabilization by continuous and sampled-data gain-scheduled state-feedback
+controllers. The approach is finally illustrated on several examples from the
+literature.
+",1,0,1,0,0,0
+15120,Identity Testing and Interpolation from High Powers of Polynomials of Large Degree over Finite Fields,"  We consider the problem of identity testing and recovering (that is,
+interpolating) of a ""hidden"" monic polynomials $f$, given an oracle access to
+$f(x)^e$ for $x\in\mathbb F_q$, where $\mathbb F_q$ is the finite field of $q$
+elements and an extension fields access is not permitted.
+The naive interpolation algorithm needs $de+1$ queries, where $d =\max\{{\rm
+deg}\ f, {\rm deg }\ g\}$ and thus requires $ de<q$. For a prime $q = p$, we
+design an algorithm that is asymptotically better in certain cases, especially
+when $d$ is large. The algorithm is based on a result of independent interest
+in spirit of additive combinatorics. It gives an upper bound on the number of
+values of a rational function of large degree, evaluated on a short sequence of
+consecutive integers, that belong to a small subgroup of $\mathbb F_p^*$.
+",1,0,1,0,0,0
+15121,A bird's eye view on the flat and conic band world of the honeycomb and Kagome lattices: Towards an understanding of 2D Metal-Organic Frameworks electronic structure,"  We present a thorough tight-binding analysis of the band structure of a wide
+variety of lattices belonging to the class of honeycomb and Kagome systems
+including several mixed forms combining both lattices. The band structure of
+these systems are made of a combination of dispersive and flat bands. The
+dispersive bands possess Dirac cones (linear dispersion) at the six corners (K
+points) of the Brillouin zone although in peculiar cases Dirac cones at the
+center of the zone $(\Gamma$ point) appear. The flat bands can be of different
+nature. Most of them are tangent to the dispersive bands at the center of the
+zone but some, for symmetry reasons, do not hybridize with other states. The
+objective of our work is to provide an analysis of a wide class of so-called
+ligand-decorated honeycomb Kagome lattices that are observed in 2D
+metal-organic framework (MOF) where the ligand occupy honeycomb sites and the
+metallic atoms the Kagome sites. We show that the $p_x$-$p_y$ graphene model is
+relevant in these systems and there exists four types of flat bands: Kagome
+flat (singly degenerate) bands, two kinds of ligand-centered flat bands (A$_2$
+like and E like, respectively doubly and singly degenerate) and metal-centered
+(three fold degenerate) flat bands.
+",0,1,0,0,0,0
+15122,Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis,"  An increasing number of sensors on mobile, Internet of things (IoT), and
+wearable devices generate time-series measurements of physical activities.
+Though access to the sensory data is critical to the success of many beneficial
+applications such as health monitoring or activity recognition, a wide range of
+potentially sensitive information about the individuals can also be discovered
+through access to sensory data and this cannot easily be protected using
+traditional privacy approaches.
+In this paper, we propose a privacy-preserving sensing framework for managing
+access to time-series data in order to provide utility while protecting
+individuals' privacy. We introduce Replacement AutoEncoder, a novel algorithm
+which learns how to transform discriminative features of data that correspond
+to sensitive inferences, into some features that have been more observed in
+non-sensitive inferences, to protect users' privacy. This efficiency is
+achieved by defining a user-customized objective function for deep
+autoencoders. Our replacement method will not only eliminate the possibility of
+recognizing sensitive inferences, it also eliminates the possibility of
+detecting the occurrence of them. That is the main weakness of other approaches
+such as filtering or randomization. We evaluate the efficacy of the algorithm
+with an activity recognition task in a multi-sensing environment using
+extensive experiments on three benchmark datasets. We show that it can retain
+the recognition accuracy of state-of-the-art techniques while simultaneously
+preserving the privacy of sensitive information. Finally, we utilize the GANs
+for detecting the occurrence of replacement, after releasing data, and show
+that this can be done only if the adversarial network is trained on the users'
+original data.
+",1,0,0,1,0,0
+15123,Application of the Mixed Time-averaging Semiclassical Initial Value Representation method to Complex Molecular Spectra,"  The recently introduced mixed time-averaging semiclassical initial value
+representation molecular dynamics method for spectroscopic calculations [M.
+Buchholz, F. Grossmann, and M. Ceotto, J. Chem. Phys. 144, 094102 (2016)] is
+applied to systems with up to 61 dimensions, ruled by a condensed phase
+Caldeira-Leggett model potential. By calculating the ground state as well as
+the first few excited states of the system Morse oscillator, changes of both
+the harmonic frequency and the anharmonicity are determined. The method
+faithfully reproduces blueshift and redshift effects and the importance of the
+counter term, as previously suggested by other methods. Differently from
+previous methods, the present semiclassical method does not take advantage of
+the specific form of the potential and it can represent a practical tool that
+opens the route to direct ab initio semiclassical simulation of condensed phase
+systems.
+",0,1,0,0,0,0
+15124,Bounds on layer potentials with rough inputs for higher order elliptic equations,"  In this paper we establish square-function estimates on the double and single
+layer potentials with rough inputs for divergence form elliptic operators, of
+arbitrary even order 2m, with variable t-independent coefficients in the upper
+half-space.
+",0,0,1,0,0,0
+15125,From support $τ$-tilting posets to algebras,"  The aim of this paper is to study a poset isomorphism between two support
+$\tau$-tilting posets. We take several algebraic information from combinatorial
+properties of support $\tau$-tilting posets. As an application, we treat a
+certain class of basic algebras which contains preprojective algebras of type
+$A$, Nakayama algebras, and generalized Brauer tree algebras. We provide a
+necessary condition for that an algebra $\Lambda$ share the same support
+$\tau$-tilting poset with a given algebra $\Gamma$ in this class. Furthermore,
+we see that this necessary condition is also a sufficient condition if $\Gamma$
+is either a preprojective algebra of type $A$, a Nakayama algebra, or a
+generalized Brauer tree algebra.
+",0,0,1,0,0,0
+15126,Measuring the reionization 21 cm fluctuations using clustering wedges,"  One of the main challenges in probing the reionization epoch using the
+redshifted 21 cm line is that the magnitude of the signal is several orders
+smaller than the astrophysical foregrounds. One of the methods to deal with the
+problem is to avoid a wedge-shaped region in the Fourier $k_{\perp} -
+k_{\parallel}$ space which contains the signal from the spectrally smooth
+foregrounds. However, measuring the spherically averaged power spectrum using
+only modes outside this wedge (i.e., in the reionization window), leads to a
+bias. We provide a prescription, based on expanding the power spectrum in terms
+of the shifted Legendre polynomials, which can be used to compute the angular
+moments of the power spectrum in the reionization window. The prescription
+requires computation of the monopole, quadrupole and hexadecapole moments of
+the power spectrum using the theoretical model under consideration and also the
+knowledge of the effective extent of the foreground wedge in the $k_{\perp} -
+k_{\parallel}$ plane. One can then calculate the theoretical power spectrum in
+the window which can be directly compared with observations. The analysis
+should have implications for avoiding any bias in the parameter constraints
+using 21 cm power spectrum data.
+",0,1,0,0,0,0
+15127,Sensing-Constrained LQG Control,"  Linear-Quadratic-Gaussian (LQG) control is concerned with the design of an
+optimal controller and estimator for linear Gaussian systems with imperfect
+state information. Standard LQG assumes the set of sensor measurements, to be
+fed to the estimator, to be given. However, in many problems, arising in
+networked systems and robotics, one may not be able to use all the available
+sensors, due to power or payload constraints, or may be interested in using the
+smallest subset of sensors that guarantees the attainment of a desired control
+goal. In this paper, we introduce the sensing-constrained LQG control problem,
+in which one has to jointly design sensing, estimation, and control, under
+given constraints on the resources spent for sensing. We focus on the realistic
+case in which the sensing strategy has to be selected among a finite set of
+possible sensing modalities. While the computation of the optimal sensing
+strategy is intractable, we present the first scalable algorithm that computes
+a near-optimal sensing strategy with provable sub-optimality guarantees. To
+this end, we show that a separation principle holds, which allows the design of
+sensing, estimation, and control policies in isolation. We conclude the paper
+by discussing two applications of sensing-constrained LQG control, namely,
+sensing-constrained formation control and resource-constrained robot
+navigation.
+",1,0,0,0,0,0
+15128,Deep Residual Learning for Instrument Segmentation in Robotic Surgery,"  Detection, tracking, and pose estimation of surgical instruments are crucial
+tasks for computer assistance during minimally invasive robotic surgery. In the
+majority of cases, the first step is the automatic segmentation of surgical
+tools. Prior work has focused on binary segmentation, where the objective is to
+label every pixel in an image as tool or background. We improve upon previous
+work in two major ways. First, we leverage recent techniques such as deep
+residual learning and dilated convolutions to advance binary-segmentation
+performance. Second, we extend the approach to multi-class segmentation, which
+lets us segment different parts of the tool, in addition to background. We
+demonstrate the performance of this method on the MICCAI Endoscopic Vision
+Challenge Robotic Instruments dataset.
+",1,0,0,0,0,0
+15129,Computing Constrained Approximate Equilibria in Polymatrix Games,"  This paper is about computing constrained approximate Nash equilibria in
+polymatrix games, which are succinctly represented many-player games defined by
+an interaction graph between the players. In a recent breakthrough, Rubinstein
+showed that there exists a small constant $\epsilon$, such that it is
+PPAD-complete to find an (unconstrained) $\epsilon$-Nash equilibrium of a
+polymatrix game. In the first part of the paper, we show that is NP-hard to
+decide if a polymatrix game has a constrained approximate equilibrium for 9
+natural constraints and any non-trivial approximation guarantee. These results
+hold even for planar bipartite polymatrix games with degree 3 and at most 7
+strategies per player, and all non-trivial approximation guarantees. These
+results stand in contrast to similar results for bimatrix games, which
+obviously need a non-constant number of actions, and which rely on stronger
+complexity-theoretic conjectures such as the exponential time hypothesis. In
+the second part, we provide a deterministic QPTAS for interaction graphs with
+bounded treewidth and with logarithmically many actions per player that can
+compute constrained approximate equilibria for a wide family of constraints
+that cover many of the constraints dealt with in the first part.
+",1,0,0,0,0,0
+15130,Evolution in Groups: A deeper look at synaptic cluster driven evolution of deep neural networks,"  A promising paradigm for achieving highly efficient deep neural networks is
+the idea of evolutionary deep intelligence, which mimics biological evolution
+processes to progressively synthesize more efficient networks. A crucial design
+factor in evolutionary deep intelligence is the genetic encoding scheme used to
+simulate heredity and determine the architectures of offspring networks. In
+this study, we take a deeper look at the notion of synaptic cluster-driven
+evolution of deep neural networks which guides the evolution process towards
+the formation of a highly sparse set of synaptic clusters in offspring
+networks. Utilizing a synaptic cluster-driven genetic encoding, the
+probabilistic encoding of synaptic traits considers not only individual
+synaptic properties but also inter-synaptic relationships within a deep neural
+network. This process results in highly sparse offspring networks which are
+particularly tailored for parallel computational devices such as GPUs and deep
+neural network accelerator chips. Comprehensive experimental results using four
+well-known deep neural network architectures (LeNet-5, AlexNet, ResNet-56, and
+DetectNet) on two different tasks (object categorization and object detection)
+demonstrate the efficiency of the proposed method. Cluster-driven genetic
+encoding scheme synthesizes networks that can achieve state-of-the-art
+performance with significantly smaller number of synapses than that of the
+original ancestor network. ($\sim$125-fold decrease in synapses for MNIST).
+Furthermore, the improved cluster efficiency in the generated offspring
+networks ($\sim$9.71-fold decrease in clusters for MNIST and a $\sim$8.16-fold
+decrease in clusters for KITTI) is particularly useful for accelerated
+performance on parallel computing hardware architectures such as those in GPUs
+and deep neural network accelerator chips.
+",1,0,0,1,0,0
+15131,Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods,"  The problem of Non-Gaussian Component Analysis (NGCA) is about finding a
+maximal low-dimensional subspace $E$ in $\mathbb{R}^n$ so that data points
+projected onto $E$ follow a non-gaussian distribution. Although this is an
+appropriate model for some real world data analysis problems, there has been
+little progress on this problem over the last decade.
+In this paper, we attempt to address this state of affairs in two ways.
+First, we give a new characterization of standard gaussian distributions in
+high-dimensions, which lead to effective tests for non-gaussianness. Second, we
+propose a simple algorithm, \emph{Reweighted PCA}, as a method for solving the
+NGCA problem. We prove that for a general unknown non-gaussian distribution,
+this algorithm recovers at least one direction in $E$, with sample and time
+complexity depending polynomially on the dimension of the ambient space. We
+conjecture that the algorithm actually recovers the entire $E$.
+",1,0,0,1,0,0
+15132,ARABIS: an Asynchronous Acoustic Indoor Positioning System for Mobile Devices,"  Acoustic ranging based indoor positioning solutions have the advantage of
+higher ranging accuracy and better compatibility with commercial-off-the-self
+consumer devices. However, similar to other time-domain based approaches using
+Time-of-Arrival and Time-Difference-of-Arrival, they suffer from performance
+degradation in presence of multi-path propagation and low received
+signal-to-noise ratio (SNR) in indoor environments. In this paper, we improve
+upon our previous work on asynchronous acoustic indoor positioning and develop
+ARABIS, a robust and low-cost acoustic positioning system (IPS) for mobile
+devices. We develop a low-cost acoustic board custom-designed to support large
+operational ranges and extensibility. To mitigate the effects of low SNR and
+multi-path propagation, we devise a robust algorithm that iteratively removes
+possible outliers by taking advantage of redundant TDoA estimates. Experiments
+have been carried in two testbeds of sizes 10.67m*7.76m and 15m*15m, one in an
+academic building and one in a convention center. The proposed system achieves
+average and 95% quantile localization errors of 7.4cm and 16.0cm in the first
+testbed with 8 anchor nodes and average and 95% quantile localization errors of
+20.4cm and 40.0cm in the second testbed with 4 anchor nodes only.
+",1,0,0,0,0,0
+15133,Realistic Evaluation of Deep Semi-Supervised Learning Algorithms,"  Semi-supervised learning (SSL) provides a powerful framework for leveraging
+unlabeled data when labels are limited or expensive to obtain. SSL algorithms
+based on deep neural networks have recently proven successful on standard
+benchmark tasks. However, we argue that these benchmarks fail to address many
+issues that these algorithms would face in real-world applications. After
+creating a unified reimplementation of various widely-used SSL techniques, we
+test them in a suite of experiments designed to address these issues. We find
+that the performance of simple baselines which do not use unlabeled data is
+often underreported, that SSL methods differ in sensitivity to the amount of
+labeled and unlabeled data, and that performance can degrade substantially when
+the unlabeled dataset contains out-of-class examples. To help guide SSL
+research towards real-world applicability, we make our unified reimplemention
+and evaluation platform publicly available.
+",0,0,0,1,0,0
+15134,Recovering piecewise constant refractive indices by a single far-field pattern,"  We are concerned with the inverse scattering problem of recovering an
+inhomogeneous medium by the associated acoustic wave measurement. We prove that
+under certain assumptions, a single far-field pattern determines the values of
+a perturbation to the refractive index on the corners of its support. These
+assumptions are satisfied for example in the low acoustic frequency regime. As
+a consequence if the perturbation is piecewise constant with either a
+polyhedral nest geometry or a known polyhedral cell geometry, such as a pixel
+or voxel array, we establish the injectivity of the perturbation to far-field
+map given a fixed incident wave. This is the first unique determinancy result
+of its type in the literature, and all of the existing results essentially make
+use of infinitely many measurements.
+",0,0,1,0,0,0
+15135,On Bezout Inequalities for non-homogeneous Polynomial Ideals,"  We introduce a ""workable"" notion of degree for non-homogeneous polynomial
+ideals and formulate and prove ideal theoretic Bézout Inequalities for the
+sum of two ideals in terms of this notion of degree and the degree of
+generators. We compute probabilistically the degree of an equidimensional
+ideal.
+",1,0,1,0,0,0
+15136,Joint Mixability of Elliptical Distributions and Related Families,"  In this paper, we further develop the theory of complete mixability and joint
+mixability for some distribution families. We generalize a result of
+Rüschendorf and Uckelmann (2002) related to complete mixability of continuous
+distribution function having a symmetric and unimodal density. Two different
+proofs to a result of Wang and Wang (2016) which related to the joint
+mixability of elliptical distributions with the same characteristic generator
+are present. We solve the Open Problem 7 in Wang (2015) by constructing a
+bimodal-symmetric distribution. The joint mixability of slash-elliptical
+distributions and skew-elliptical distributions is studied and the extension to
+multivariate distributions is also investigated.
+",0,0,1,1,0,0
+15137,Secure uniform random number extraction via incoherent strategies,"  To guarantee the security of uniform random numbers generated by a quantum
+random number generator, we study secure extraction of uniform random numbers
+when the environment of a given quantum state is controlled by the third party,
+the eavesdropper. Here we restrict our operations to incoherent strategies that
+are composed of the measurement on the computational basis and incoherent
+operations (or incoherence-preserving operations). We show that the maximum
+secure extraction rate is equal to the relative entropy of coherence. By
+contrast, the coherence of formation gives the extraction rate when a certain
+constraint is imposed on eavesdropper's operations. The condition under which
+the two extraction rates coincide is then determined. Furthermore, we find that
+the exponential decreasing rate of the leaked information is characterized by
+Rényi relative entropies of coherence. These results clarify the power of
+incoherent strategies in random number generation, and can be applied to
+guarantee the quality of random numbers generated by a quantum random number
+generator.
+",1,0,0,0,0,0
+15138,Mobile Encryption Gateway (MEG) for Email Encryption,"  Email cryptography applications often suffer from major problems that prevent
+their widespread implementation. MEG, or the Mobile Encryption Gateway aims to
+fix the issues associated with email encryption by ensuring that encryption is
+easy to perform while still maintaining data security. MEG performs automatic
+decryption and encryption of all emails using PGP. Users do not need to
+understand the internal workings of the encryption process to use the
+application. MEG is meant to be email-client-agnostic, enabling users to employ
+virtually any email service to send messages. Encryption actions are performed
+on the user's mobile device, which means their keys and data remain personal.
+MEG can also tackle network effect problems by inviting non-users to join. Most
+importantly, MEG uses end-to-end encryption, which ensures that all aspects of
+the encrypted information remains private. As a result, we are hopeful that MEG
+will finally solve the problem of practical email encryption.
+",1,0,0,0,0,0
+15139,Nematic Skyrmions in Odd-Parity Superconductors,"  We study topological excitations in two-component nematic superconductors,
+with a particular focus on Cu$_x$Bi$_2$Se$_3$ as a candidate material. We find
+that the lowest-energy topological excitations are coreless vortices: a bound
+state of two spatially separated half-quantum vortices. These objects are
+nematic Skyrmions, since they are characterized by an additional topological
+charge. The inter-Skyrmion forces are dipolar in this model, i.e. attractive
+for certain relative orientations of the Skyrmions, hence forming
+multi-Skyrmion bound states.
+",0,1,0,0,0,0
+15140,Concentration and consistency results for canonical and curved exponential-family models of random graphs,"  Statistical inference for exponential-family models of random graphs with
+dependent edges is challenging. We stress the importance of additional
+structure and show that additional structure facilitates statistical inference.
+A simple example of a random graph with additional structure is a random graph
+with neighborhoods and local dependence within neighborhoods. We develop the
+first concentration and consistency results for maximum likelihood and
+$M$-estimators of a wide range of canonical and curved exponential-family
+models of random graphs with local dependence. All results are non-asymptotic
+and applicable to random graphs with finite populations of nodes, although
+asymptotic consistency results can be obtained as well. In addition, we show
+that additional structure can facilitate subgraph-to-graph estimation, and
+present concentration results for subgraph-to-graph estimators. As an
+application, we consider popular curved exponential-family models of random
+graphs, with local dependence induced by transitivity and parameter vectors
+whose dimensions depend on the number of nodes.
+",0,0,1,1,0,0
+15141,Bayesian Network Learning via Topological Order,"  We propose a mixed integer programming (MIP) model and iterative algorithms
+based on topological orders to solve optimization problems with acyclic
+constraints on a directed graph. The proposed MIP model has a significantly
+lower number of constraints compared to popular MIP models based on cycle
+elimination constraints and triangular inequalities. The proposed iterative
+algorithms use gradient descent and iterative reordering approaches,
+respectively, for searching topological orders. A computational experiment is
+presented for the Gaussian Bayesian network learning problem, an optimization
+problem minimizing the sum of squared errors of regression models with L1
+penalty over a feature network with application of gene network inference in
+bioinformatics.
+",1,0,0,1,0,0
+15142,The nature of the giant exomoon candidate Kepler-1625 b-i,"  The recent announcement of a Neptune-sized exomoon candidate around the
+transiting Jupiter-sized object Kepler-1625 b could indicate the presence of a
+hitherto unknown kind of gas giant moons, if confirmed. Three transits have
+been observed, allowing radius estimates of both objects. Here we investigate
+possible mass regimes of the transiting system that could produce the observed
+signatures and study them in the context of moon formation in the solar system,
+i.e. via impacts, capture, or in-situ accretion. The radius of Kepler-1625 b
+suggests it could be anything from a gas giant planet somewhat more massive
+than Saturn (0.4 M_Jup) to a brown dwarf (BD) (up to 75 M_Jup) or even a
+very-low-mass star (VLMS) (112 M_Jup ~ 0.11 M_sun). The proposed companion
+would certainly have a planetary mass. Possible extreme scenarios range from a
+highly inflated Earth-mass gas satellite to an atmosphere-free water-rock
+companion of about 180 M_Ear. Furthermore, the planet-moon dynamics during the
+transits suggest a total system mass of 17.6_{-12.6}^{+19.2} M_Jup. A
+Neptune-mass exomoon around a giant planet or low-mass BD would not be
+compatible with the common mass scaling relation of the solar system moons
+about gas giants. The case of a mini-Neptune around a high-mass BD or a VLMS,
+however, would be located in a similar region of the satellite-to-host mass
+ratio diagram as Proxima b, the TRAPPIST-1 system, and LHS 1140 b. The capture
+of a Neptune-mass object around a 10 M_Jup planet during a close binary
+encounter is possible in principle. The ejected object, however, would have had
+to be a super-Earth object, raising further questions of how such a system
+could have formed. In summary, this exomoon candidate is barely compatible with
+established moon formation theories. If it can be validated as orbiting a
+super-Jovian planet, then it would pose an exquisite riddle for formation
+theorists to solve.
+",0,1,0,0,0,0
+15143,Generative Adversarial Privacy,"  We present a data-driven framework called generative adversarial privacy
+(GAP). Inspired by recent advancements in generative adversarial networks
+(GANs), GAP allows the data holder to learn the privatization mechanism
+directly from the data. Under GAP, finding the optimal privacy mechanism is
+formulated as a constrained minimax game between a privatizer and an adversary.
+We show that for appropriately chosen adversarial loss functions, GAP provides
+privacy guarantees against strong information-theoretic adversaries. We also
+evaluate the performance of GAP on multi-dimensional Gaussian mixture models
+and the GENKI face database.
+",0,0,0,1,0,0
+15144,Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans,"  Eradicating hunger and malnutrition is a key development goal of the 21st
+century. We address the problem of optimally identifying seed varieties to
+reliably increase crop yield within a risk-sensitive decision-making framework.
+Specifically, we introduce a novel hierarchical machine learning mechanism for
+predicting crop yield (the yield of different seed varieties of the same crop).
+We integrate this prediction mechanism with a weather forecasting model, and
+propose three different approaches for decision making under uncertainty to
+select seed varieties for planting so as to balance yield maximization and
+risk.We apply our model to the problem of soybean variety selection given in
+the 2016 Syngenta Crop Challenge. Our prediction model achieves a median
+absolute error of 3.74 bushels per acre and thus provides good estimates for
+input into the decision models.Our decision models identify the selection of
+soybean varieties that appropriately balance yield and risk as a function of
+the farmer's risk aversion level. More generally, our models support farmers in
+decision making about which seed varieties to plant.
+",1,0,0,1,0,0
+15145,A Clinical and Finite Elements Study of Stress Urinary Incontinence in Women Using Fluid-Structure Interactions,"  Stress Urinary Incontinence (SUI) or urine leakage from urethra occurs due to
+an increase in abdominal pressure resulting from stress like a cough or jumping
+height. SUI is more frequent among post-menopausal women. In the absence of
+bladder contraction, vesical pressure exceeds from urethral pressure leading to
+urine leakage. Despite a large number of patients diagnosed with this problem,
+few studies have investigated its function and mechanics. The main goal of this
+study is to model bladder and urethra computationally under an external
+pressure like sneezing. Finite Element Method and Fluid-Structure Interactions
+are utilized for simulation. Linear mechanical properties assigned to the
+bladder and urethra and pressure boundary conditions are indispensable in this
+model. The results show good accordance between the clinical data and predicted
+values of the computational models, such as the pressure at the center of the
+bladder. This indicates that numerical methods and simplified physics of
+biological systems like inferior urinary tract are helpful to achieve the
+results similar to clinical results, in order to investigate pathological
+conditions.
+",1,0,0,0,0,0
+15146,The n-term Approximation of Periodic Generalized Lévy Processes,"  In this paper, we study the compressibility of random processes and fields,
+called generalized Lévy processes, that are solutions of stochastic
+differential equations driven by $d$-dimensional periodic Lévy white noises.
+Our results are based on the estimation of the Besov regularity of Lévy white
+noises and generalized Lévy processes. We show in particular that
+non-Gaussian generalized Lévy processes are more compressible in a wavelet
+basis than the corresponding Gaussian processes, in the sense that their
+$n$-term approximation error decays faster. We quantify this compressibility in
+terms of the Blumenthal-Getoor index of the underlying Lévy white noise.
+",0,0,1,0,0,0
+15147,Sliced rotated sphere packing designs,"  Space-filling designs are popular choices for computer experiments. A sliced
+design is a design that can be partitioned into several subdesigns. We propose
+a new type of sliced space-filling design called sliced rotated sphere packing
+designs. Their full designs and subdesigns are rotated sphere packing designs.
+They are constructed by rescaling, rotating, translating and extracting the
+points from a sliced lattice. We provide two fast algorithms to generate such
+designs. Furthermore, we propose a strategy to use sliced rotated sphere
+packing designs adaptively. Under this strategy, initial runs are uniformly
+distributed in the design space, follow-up runs are added by incorporating
+information gained from initial runs, and the combined design is space-filling
+for any local region. Examples are given to illustrate its potential
+application.
+",0,0,1,1,0,0
+15148,On the Convergence of Weighted AdaGrad with Momentum for Training Deep Neural Networks,"  Adaptive stochastic gradient descent methods, such as AdaGrad, RMSProp, Adam,
+AMSGrad, etc., have been demonstrated efficacious in solving non-convex
+stochastic optimization, such as training deep neural networks. However, their
+convergence rates have not been touched under the non-convex stochastic
+circumstance except recent breakthrough results on AdaGrad, perturbed AdaGrad
+and AMSGrad. In this paper, we propose two new adaptive stochastic gradient
+methods called AdaHB and AdaNAG which integrate a novel weighted
+coordinate-wise AdaGrad with heavy ball momentum and Nesterov accelerated
+gradient momentum, respectively. The $\mathcal{O}(\frac{\log{T}}{\sqrt{T}})$
+non-asymptotic convergence rates of AdaHB and AdaNAG in non-convex stochastic
+setting are also jointly established by leveraging a newly developed unified
+formulation of these two momentum mechanisms. Moreover, comparisons have been
+made between AdaHB, AdaNAG, Adam and RMSProp, which, to a certain extent,
+explains the reasons why Adam and RMSProp are divergent. In particular, when
+momentum term vanishes we obtain convergence rate of coordinate-wise AdaGrad in
+non-convex stochastic setting as a byproduct.
+",1,0,0,1,0,0
+15149,Cyclic Dominance in the Spatial Coevolutionary Optional Prisoner's Dilemma Game,"  This paper studies scenarios of cyclic dominance in a coevolutionary spatial
+model in which game strategies and links between agents adaptively evolve over
+time. The Optional Prisoner's Dilemma (OPD) game is employed. The OPD is an
+extended version of the traditional Prisoner's Dilemma where players have a
+third option to abstain from playing the game. We adopt an agent-based
+simulation approach and use Monte Carlo methods to perform the OPD with
+coevolutionary rules. The necessary conditions to break the scenarios of cyclic
+dominance are also investigated. This work highlights that cyclic dominance is
+essential in the sustenance of biodiversity. Moreover, we also discuss the
+importance of a spatial coevolutionary model in maintaining cyclic dominance in
+adverse conditions.
+",1,1,1,0,0,0
+15150,Analysis of universal adversarial perturbations,"  Deep networks have recently been shown to be vulnerable to universal
+perturbations: there exist very small image-agnostic perturbations that cause
+most natural images to be misclassified by such classifiers. In this paper, we
+propose the first quantitative analysis of the robustness of classifiers to
+universal perturbations, and draw a formal link between the robustness to
+universal perturbations, and the geometry of the decision boundary.
+Specifically, we establish theoretical bounds on the robustness of classifiers
+under two decision boundary models (flat and curved models). We show in
+particular that the robustness of deep networks to universal perturbations is
+driven by a key property of their curvature: there exists shared directions
+along which the decision boundary of deep networks is systematically positively
+curved. Under such conditions, we prove the existence of small universal
+perturbations. Our analysis further provides a novel geometric method for
+computing universal perturbations, in addition to explaining their properties.
+",1,0,0,1,0,0
+15151,Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks,"  We investigate anomaly detection in an unsupervised framework and introduce
+Long Short Term Memory (LSTM) neural network based algorithms. In particular,
+given variable length data sequences, we first pass these sequences through our
+LSTM based structure and obtain fixed length sequences. We then find a decision
+function for our anomaly detectors based on the One Class Support Vector
+Machines (OC-SVM) and Support Vector Data Description (SVDD) algorithms. As the
+first time in the literature, we jointly train and optimize the parameters of
+the LSTM architecture and the OC-SVM (or SVDD) algorithm using highly effective
+gradient and quadratic programming based training methods. To apply the
+gradient based training method, we modify the original objective criteria of
+the OC-SVM and SVDD algorithms, where we prove the convergence of the modified
+objective criteria to the original criteria. We also provide extensions of our
+unsupervised formulation to the semi-supervised and fully supervised
+frameworks. Thus, we obtain anomaly detection algorithms that can process
+variable length data sequences while providing high performance, especially for
+time series data. Our approach is generic so that we also apply this approach
+to the Gated Recurrent Unit (GRU) architecture by directly replacing our LSTM
+based structure with the GRU based structure. In our experiments, we illustrate
+significant performance gains achieved by our algorithms with respect to the
+conventional methods.
+",1,0,0,1,0,0
+15152,Wearable Health Monitoring Using Capacitive Voltage-Mode Human Body Communication,"  Rapid miniaturization and cost reduction of computing, along with the
+availability of wearable and implantable physiological sensors have led to the
+growth of human Body Area Network (BAN) formed by a network of such sensors and
+computing devices. One promising application of such a network is wearable
+health monitoring where the collected data from the sensors would be
+transmitted and analyzed to assess the health of a person. Typically, the
+devices in a BAN are connected through wireless (WBAN), which suffers from
+energy inefficiency due to the high-energy consumption of wireless
+transmission. Human Body Communication (HBC) uses the relatively low loss human
+body as the communication medium to connect these devices, promising order(s)
+of magnitude better energy-efficiency and built-in security compared to WBAN.
+In this paper, we demonstrate a health monitoring device and system built using
+Commercial-Off-The- Shelf (COTS) sensors and components, that can collect data
+from physiological sensors and transmit it through a) intra-body HBC to another
+device (hub) worn on the body or b) upload health data through HBC-based
+human-machine interaction to an HBC capable machine. The system design
+constraints and signal transfer characteristics for the implemented HBC-based
+wearable health monitoring system are measured and analyzed, showing reliable
+connectivity with >8x power savings compared to Bluetooth lowenergy (BTLE).
+",1,0,0,0,0,0
+15153,Electromagnetically Induced Transparency (EIT) and Autler-Townes (AT) splitting in the Presence of Band-Limited White Gaussian Noise,"  We investigate the effect of band-limited white Gaussian noise (BLWGN) on
+electromagnetically induced transparency (EIT) and Autler-Townes (AT)
+splitting, when performing atom-based continuous-wave (CW) radio-frequency (RF)
+electric (E) field strength measurements with Rydberg atoms in an atomic vapor.
+This EIT/AT-based E-field measurement approach is currently being investigated
+by several groups around the world as a means to develop a new SI traceable RF
+E-field measurement technique. For this to be a useful technique, it is
+important to understand the influence of BLWGN. We perform EIT/AT based E-field
+experiments with BLWGN centered on the RF transition frequency and for the
+BLWGN blue-shifted and red-shifted relative to the RF transition frequency. The
+EIT signal can be severely distorted for certain noise conditions (band-width,
+center-frequency, and noise power), hence altering the ability to accurately
+measure a CW RF E-field strength. We present a model to predict the changes in
+the EIT signal in the presence of noise. This model includes AC Stark shifts
+and on resonance transitions associated with the noise source. The results of
+this model are compared to the experimental data and we find very good
+agreement between the two.
+",0,1,0,0,0,0
+15154,SINR Outage Evaluation in Cellular Networks: Saddle Point Approximation (SPA) Using Normal Inverse Gaussian (NIG) Distribution,"  Signal-to-noise-plus-interference ratio (SINR) outage probability is among
+one of the key performance metrics of a wireless cellular network. In this
+paper, we propose a semi-analytical method based on saddle point approximation
+(SPA) technique to calculate the SINR outage of a wireless system whose SINR
+can be modeled in the form $\frac{\sum_{i=1}^M X_i}{\sum_{i=1}^N Y_i +1}$ where
+$X_i$ denotes the useful signal power, $Y_i$ denotes the power of the
+interference signal, and $\sum_{i=1}^M X_i$, $\sum_{i=1}^N Y_i$ are independent
+random variables. Both $M$ and $N$ can also be random variables. The proposed
+approach is based on the saddle point approximation to cumulative distribution
+function (CDF) as given by \tit{Wood-Booth-Butler formula}. The approach is
+applicable whenever the cumulant generating function (CGF) of the received
+signal and interference exists, and it allows us to tackle distributions with
+large skewness and kurtosis with higher accuracy. In this regard, we exploit a
+four parameter \tit{normal-inverse Gaussian} (NIG) distribution as a base
+distribution. Given that the skewness and kurtosis satisfy a specific
+condition, NIG-based SPA works reliably. When this condition is violated, we
+recommend SPA based on normal or symmetric NIG distribution, both special cases
+of NIG distribution, at the expense of reduced accuracy. For the purpose of
+demonstration, we apply SPA for the SINR outage evaluation of a typical user
+experiencing a downlink coordinated multi-point transmission (CoMP) from the
+base stations (BSs) that are modeled by homogeneous Poisson point process. We
+characterize the outage of the typical user in scenarios such as (a)~when the
+number and locations of interferers are random, and (b)~when the fading
+channels and number of interferers are random. Numerical results are presented
+to illustrate the accuracy of the proposed set of approximations.
+",1,0,1,0,0,0
+15155,Smart grid modeling and simulation - Comparing GridLAB-D and RAPSim via two Case studies,"  One of the most important tools for the development of the smart grid is
+simulation. Therefore, analyzing, designing, modeling, and simulating the smart
+grid will allow to explore future scenarios and support decision making for the
+grid's development. In this paper, we compare two open source simulation tools
+for the smart grid, GridLAB-Distribution (GridLAB-D) and Renewable Alternative
+Power systems Simulation (RAPSim). The comparison is based on the
+implementation of two case studies related to a power flow problem and the
+integration of renewable energy resources to the grid. Results show that even
+for very simple case studies, specific properties such as weather simulation or
+load modeling are influencing the results in a way that they are not
+reproducible with a different simulator.
+",1,0,0,0,0,0
+15156,A Large-Scale CNN Ensemble for Medication Safety Analysis,"  Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing
+drug surveillance, and data from health-related forums and medical communities
+can be of a great significance for estimating such effects. In this paper, we
+propose an end-to-end CNN-based method for predicting drug safety on user
+comments from healthcare discussion forums. We present an architecture that is
+based on a vast ensemble of CNNs with varied structural parameters, where the
+prediction is determined by the majority vote. To evaluate the performance of
+the proposed solution, we present a large-scale dataset collected from a
+medical website that consists of over 50 thousand reviews for more than 4000
+drugs. The results demonstrate that our model significantly outperforms
+conventional approaches and predicts medicine safety with an accuracy of 87.17%
+for binary and 62.88% for multi-classification tasks.
+",1,0,0,0,0,0
+15157,Solutions to twisted word equations and equations in virtually free groups,"  It is well-known that the problem to solve equations in virtually free groups
+can be reduced to the problem to solve twisted word equations with regular
+constraints over free monoids with involution.
+In a first part of the paper we prove that the set of all solutions of such a
+twisted word equation is an EDT0L language and that the specification of that
+EDT0L language can be computed in PSPACE. (We give a more precise bound in the
+paper.) Within the same complexity bound we can decide whether the solution set
+is empty, finite, or infinite. No PSPACE-algorithm, actually no concrete
+complexity bound was known for deciding emptiness before. Decidability of
+finiteness was considered to be an open problem.
+In the second part we apply the results to the solution set of equations with
+rational constraints in finitely generated virtually free groups. For each such
+group we obtain the same results as above for the set of solutions in standard
+normal forms with respect to some natural set of generators. In particular, for
+a fixed group we can decide in PSPACE whether the solution set is empty,
+finite, or infinite.
+Our results generalize the work by Lohrey and Sénizergues (ICALP 2006) and
+Dahmani and Guirardel (J. of Topology 2010) with respect to both complexity and
+expressive power. Neither paper gave any concrete complexity bound and the
+results in these papers are stated subsets of solutions only, whereas our
+results concern all solutions. Moreover, we give a formal language
+characterization of the full solution set as an EDT0L language.
+",1,0,1,0,0,0
+15158,DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting,"  Predicting traffic conditions has been recently explored as a way to relieve
+traffic congestion. Several pioneering approaches have been proposed based on
+traffic observations of the target location as well as its adjacent regions,
+but they obtain somewhat limited accuracy due to lack of mining road topology.
+To address the effect attenuation problem, we propose to take account of the
+traffic of surrounding locations(wider than adjacent range). We propose an
+end-to-end framework called DeepTransport, in which Convolutional Neural
+Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain
+spatial-temporal traffic information within a transport network topology. In
+addition, attention mechanism is introduced to align spatial and temporal
+information. Moreover, we constructed and released a real-world large traffic
+condition dataset with 5-minute resolution. Our experiments on this dataset
+demonstrate our method captures the complex relationship in temporal and
+spatial domain. It significantly outperforms traditional statistical methods
+and a state-of-the-art deep learning method.
+",1,0,0,0,0,0
+15159,On Statistical Optimality of Variational Bayes,"  The article addresses a long-standing open problem on the justification of
+using variational Bayes methods for parameter estimation. We provide general
+conditions for obtaining optimal risk bounds for point estimates acquired from
+mean-field variational Bayesian inference. The conditions pertain to the
+existence of certain test functions for the distance metric on the parameter
+space and minimal assumptions on the prior. A general recipe for verification
+of the conditions is outlined which is broadly applicable to existing Bayesian
+models with or without latent variables. As illustrations, specific
+applications to Latent Dirichlet Allocation and Gaussian mixture models are
+discussed.
+",0,0,1,1,0,0
+15160,The Teichmüller Stack,"  This paper is a comprehensive introduction to the results of [7]. It grew as
+an expanded version of a talk given at INdAM Meeting Complex and Symplectic
+Geometry, held at Cortona in June 12-18, 2016. It deals with the construction
+of the Teichmüller space of a smooth compact manifold M (that is the space of
+isomorphism classes of complex structures on M) in arbitrary dimension. The
+main problem is that, whenever we leave the world of surfaces, the
+Teichmüller space is no more a complex manifold or an analytic space but an
+analytic Artin stack. We explain how to construct explicitly an atlas for this
+stack using ideas coming from foliation theory. Throughout the article, we use
+the case of $\mathbb{S}^3\times\mathbb{S}^1$ as a recurrent example.
+",0,0,1,0,0,0
+15161,On the Impact of Micro-Packages: An Empirical Study of the npm JavaScript Ecosystem,"  The rise of user-contributed Open Source Software (OSS) ecosystems
+demonstrate their prevalence in the software engineering discipline. Libraries
+work together by depending on each other across the ecosystem. From these
+ecosystems emerges a minimized library called a micro-package. Micro- packages
+become problematic when breaks in a critical ecosystem dependency ripples its
+effects to unsuspecting users. In this paper, we investigate the impact of
+micro-packages in the npm JavaScript ecosystem. Specifically, we conducted an
+empirical in- vestigation with 169,964 JavaScript npm packages to understand
+(i) the widespread phenomena of micro-packages, (ii) the size dependencies
+inherited by a micro-package and (iii) the developer usage cost (ie., fetch,
+install, load times) of using a micro-package. Results of the study find that
+micro-packages form a significant portion of the npm ecosystem. Apart from the
+ease of readability and comprehension, we show that some micro-packages have
+long dependency chains and incur just as much usage costs as other npm
+packages. We envision that this work motivates the need for developers to be
+aware of how sensitive their third-party dependencies are to critical changes
+in the software ecosystem.
+",1,0,0,0,0,0
+15162,Location and Orientation Optimisation for Spatially Stretched Tripole Arrays Based on Compressive Sensing,"  The design of sparse spatially stretched tripole arrays is an important but
+also challenging task and this paper proposes for the very first time efficient
+solutions to this problem. Unlike for the design of traditional sparse antenna
+arrays, the developed approaches optimise both the dipole locations and
+orientations. The novelty of the paper consists in formulating these
+optimisation problems into a form that can be solved by the proposed
+compressive sensing and Bayesian compressive sensing based approaches. The
+performance of the developed approaches is validated and it is shown that
+accurate approximation of a reference response can be achieved with a 67%
+reduction in the number of dipoles required as compared to an equivalent
+uniform spatially stretched tripole array, leading to a significant reduction
+in the cost associated with the resulting arrays.
+",1,0,0,0,0,0
+15163,Hausdorff dimension of limsup sets of random rectangles in products of regular spaces,"  The almost sure Hausdorff dimension of the limsup set of randomly distributed
+rectangles in a product of Ahlfors regular metric spaces is computed in terms
+of the singular value function of the rectangles.
+",0,0,1,0,0,0
+15164,Thermal Characterization of Microscale Heat Convection under Rare Gas Condition by a Modified Hot Wire Method,"  As power electronics shrinks down to sub-micron scale, the thermal transport
+from a solid surface to environment becomes significant. Under circumstances
+when the device works in rare gas environment, the scale for thermal transport
+is comparable to the mean free path of molecules, and is difficult to
+characterize. In this work, we present an experimental study about thermal
+transport around a microwire in rare gas environment by using a steady state
+hot wire method. Unlike conventional hot wire technique of using transient heat
+transfer process, this method considers both the heat conduction along the wire
+and convection effect from wire surface to surroundings. Convection heat
+transfer coefficient from a platinum wire in diameter 25 um to air is
+characterized under different heating power and air pressures to comprehend the
+effect of temperature and density of gas molecules. It is observed that
+convection heat transfer coefficient varies from 14 Wm-2K-1 at 7 Pa to 629
+Wm-2K-1 at atmosphere pressure. In free molecule regime, Nusselt number has a
+linear relationship with inverse Knudsen number and the slope of 0.274 is
+employed to determined equivalent thermal dissipation boundary as 7.03E10-4 m.
+In transition regime, the equivalent thermal dissipation boundary is obtained
+as 5.02E10-4 m. Under a constant pressure, convection heat transfer coefficient
+decreases with increasing temperature, and this correlation is more sensitive
+to larger pressure. This work provides a pathway for studying both heat
+conduction and heat convection effect at micro/nanoscale under rare gas
+environment, the knowledge of which is essential for regulating heat
+dissipation in various industrial applications.
+",0,1,0,0,0,0
+15165,A summation formula for triples of quadratic spaces,"  Let $V_1,V_2,V_3$ be a triple of even dimensional vector spaces over a number
+field $F$ equipped with nondegenerate quadratic forms
+$\mathcal{Q}_1,\mathcal{Q}_2,\mathcal{Q}_3$, respectively. Let \begin{align*} Y
+\subset \prod_{i=1}V_i \end{align*} be the closed subscheme consisting of
+$(v_1,v_2,v_3)$ on which
+$\mathcal{Q}_1(v_1)=\mathcal{Q}_2(v_2)=\mathcal{Q}_3(v_3)$. Motivated by
+conjectures of Braverman and Kazhdan and related work of Lafforgue, Ngô, and
+Sakellaridis we prove an analogue of the Poisson summation formula for certain
+functions on this space.
+",0,0,1,0,0,0
+15166,Designing Strassen's algorithm,"  In 1969, Strassen shocked the world by showing that two n x n matrices could
+be multiplied in time asymptotically less than $O(n^3)$. While the recursive
+construction in his algorithm is very clear, the key gain was made by showing
+that 2 x 2 matrix multiplication could be performed with only 7 multiplications
+instead of 8. The latter construction was arrived at by a process of
+elimination and appears to come out of thin air. Here, we give the simplest and
+most transparent proof of Strassen's algorithm that we are aware of, using only
+a simple unitary 2-design and a few easy lines of calculation. Moreover, using
+basic facts from the representation theory of finite groups, we use 2-designs
+coming from group orbits to generalize our construction to all n (although the
+resulting algorithms aren't optimal for n at least 3).
+",1,0,1,0,0,0
+15167,A Software-equivalent SNN Hardware using RRAM-array for Asynchronous Real-time Learning,"  Spiking Neural Network (SNN) naturally inspires hardware implementation as it
+is based on biology. For learning, spike time dependent plasticity (STDP) may
+be implemented using an energy efficient waveform superposition on memristor
+based synapse. However, system level implementation has three challenges.
+First, a classic dilemma is that recognition requires current reading for short
+voltage$-$spikes which is disturbed by large voltage$-$waveforms that are
+simultaneously applied on the same memristor for real$-$time learning i.e. the
+simultaneous read$-$write dilemma. Second, the hardware needs to exactly
+replicate software implementation for easy adaptation of algorithm to hardware.
+Third, the devices used in hardware simulations must be realistic. In this
+paper, we present an approach to address the above concerns. First, the
+learning and recognition occurs in separate arrays simultaneously in
+real$-$time, asynchronously $-$ avoiding non$-$biomimetic clocking based
+complex signal management. Second, we show that the hardware emulates software
+at every stage by comparison of SPICE (circuit$-$simulator) with MATLAB
+(mathematical SNN algorithm implementation in software) implementations. As an
+example, the hardware shows 97.5 per cent accuracy in classification which is
+equivalent to software for a Fisher$-$Iris dataset. Third, the STDP is
+implemented using a model of synaptic device implemented using HfO2 memristor.
+We show that an increasingly realistic memristor model slightly reduces the
+hardware performance (85 per cent), which highlights the need to engineer RRAM
+characteristics specifically for SNN.
+",1,0,0,0,0,0
+15168,Max flow vitality in general and $st$-planar graphs,"  The \emph{vitality} of an arc/node of a graph with respect to the maximum
+flow between two fixed nodes $s$ and $t$ is defined as the reduction of the
+maximum flow caused by the removal of that arc/node. In this paper we address
+the issue of determining the vitality of arcs and/or nodes for the maximum flow
+problem. We show how to compute the vitality of all arcs in a general
+undirected graph by solving only $2(n-1)$ max flow instances and, In
+$st$-planar graphs (directed or undirected) we show how to compute the vitality
+of all arcs and all nodes in $O(n)$ worst-case time. Moreover, after
+determining the vitality of arcs and/or nodes, and given a planar embedding of
+the graph, we can determine the vitality of a `contiguous' set of arcs/nodes in
+time proportional to the size of the set.
+",1,0,0,0,0,0
+15169,Between Homomorphic Signal Processing and Deep Neural Networks: Constructing Deep Algorithms for Polyphonic Music Transcription,"  This paper presents a new approach in understanding how deep neural networks
+(DNNs) work by applying homomorphic signal processing techniques. Focusing on
+the task of multi-pitch estimation (MPE), this paper demonstrates the
+equivalence relation between a generalized cepstrum and a DNN in terms of their
+structures and functionality. Such an equivalence relation, together with pitch
+perception theories and the recently established
+rectified-correlations-on-a-sphere (RECOS) filter analysis, provide an
+alternative way in explaining the role of the nonlinear activation function and
+the multi-layer structure, both of which exist in a cepstrum and a DNN. To
+validate the efficacy of this new approach, a new feature designed in the same
+fashion is proposed for pitch salience function. The new feature outperforms
+the one-layer spectrum in the MPE task and, as predicted, it addresses the
+issue of the missing fundamental effect and also achieves better robustness to
+noise.
+",1,0,0,0,0,0
+15170,Negative membrane capacitance of outer hair cells: electromechanical coupling near resonance,"  The ability of the mammalian ear in processing high frequency sounds, up to
+$\sim$100 kHz, is based on the capability of outer hair cells (OHCs) responding
+to stimulation at high frequencies. These cells show a unique motility in their
+cell body coupled with charge movement. With this motile element, voltage
+changes generated by stimuli at their hair bundles drives the cell body and
+that, in turn, amplifies the stimuli. In vitro experiments show that the
+movement of these charges significantly increases the membrane capacitance,
+limiting the motile activity by additionally attenuating voltage changes. It
+was found, however, that such an effect is due to the absence of mechanical
+load. In the presence of mechanical resonance, such as in vivo conditions, the
+movement of motile charges is expected to create negative capacitance near the
+resonance frequency. Therefore this motile mechanism is effective at high
+frequencies.
+",0,1,0,0,0,0
+15171,Resonant inelastic x-ray scattering operators for $t_{2g}$ orbital systems,"  We derive general expressions for resonant inelastic x-ray scattering (RIXS)
+operators for $t_{2g}$ orbital systems, which exhibit a rich array of
+unconventional magnetism arising from unquenched orbital moments. Within the
+fast collision approximation, which is valid especially for 4$d$ and 5$d$
+transition metal compounds with short core-hole lifetimes, the RIXS operators
+are expressed in terms of total spin and orbital angular momenta of the
+constituent ions. We then map these operators onto pseudospins that represent
+spin-orbit entangled magnetic moments in systems with strong spin-orbit
+coupling. Applications of our theory to such systems as iridates and ruthenates
+are discussed, with a particular focus on compounds based on $d^4$ ions with
+Van Vleck-type nonmagnetic ground state.
+",0,1,0,0,0,0
+15172,Hamiltonian Path in Split Graphs- a Dichotomy,"  In this paper, we investigate Hamiltonian path problem in the context of
+split graphs, and produce a dichotomy result on the complexity of the problem.
+Our main result is a deep investigation of the structure of $K_{1,4}$-free
+split graphs in the context of Hamiltonian path problem, and as a consequence,
+we obtain a polynomial-time algorithm to the Hamiltonian path problem in
+$K_{1,4}$-free split graphs. We close this paper with the hardness result: we
+show that, unless P=NP, Hamiltonian path problem is NP-complete in
+$K_{1,5}$-free split graphs by reducing from Hamiltonian cycle problem in
+$K_{1,5}$-free split graphs. Thus this paper establishes a ""thin complexity
+line"" separating NP-complete instances and polynomial-time solvable instances.
+",1,0,0,0,0,0
+15173,Design Patterns for Fusion-Based Object Retrieval,"  We address the task of ranking objects (such as people, blogs, or verticals)
+that, unlike documents, do not have direct term-based representations. To be
+able to match them against keyword queries, evidence needs to be amassed from
+documents that are associated with the given object. We present two design
+patterns, i.e., general reusable retrieval strategies, which are able to
+encompass most existing approaches from the past. One strategy combines
+evidence on the term level (early fusion), while the other does it on the
+document level (late fusion). We demonstrate the generality of these patterns
+by applying them to three different object retrieval tasks: expert finding,
+blog distillation, and vertical ranking.
+",1,0,0,0,0,0
+15174,Early Routability Assessment in VLSI Floorplans: A Generalized Routing Model,"  Multiple design iterations are inevitable in nanometer Integrated Circuit
+(IC) design flow until desired printability and performance metrics are
+achieved. This starts with placement optimization aimed at improving
+routability, wirelength, congestion and timing in the design. Contrarily, no
+such practice exists on a floorplanned layout, during the early stage of the
+design flow. Recently, STAIRoute \cite{karb2} aimed to address that by
+identifying the shortest routing path of a net through a set of routing regions
+in the floorplan in multiple metal layers. Since the blocks in hierarchical
+ASIC/SoC designs do not use all the permissible routing layers for the internal
+routing corresponding to standard cell connectivity, the proposed STAIRoute
+framework is not an effective for early global routability assessment. This
+leads to improper utilization of routing area, specifically in higher routing
+layers with fewer routing blockages, as the lack of placement of standard cells
+does not facilitates any routing of their interconnections.
+This paper presents a generalized model for early global routability
+assessment, HGR, by utilizing the free regions over the blocks beyond certain
+metal layers. The proposed (hybrid) routing model comprises of (a) the junction
+graph model in STAIRoute routing through the block boundary regions in lower
+routing layers, and (ii) the grid graph model for routing in higher layers over
+the free regions of the blocks.
+Experiment with the latest floorplanning benchmarks exhibit an average
+reduction of $4\%$, $54\%$ and $70\%$ in netlength, via count, and congestion
+respectively when HGR is used over STAIRoute. Further, we conducted another
+experiment on an industrial design flow targeted for $45nm$ process, and the
+results are encouraging with $~3$X runtime boost when early global routing is
+used in conjunction with the existing physical design flow.
+",1,0,0,0,0,0
+15175,Inverse Ising problem in continuous time: A latent variable approach,"  We consider the inverse Ising problem, i.e. the inference of network
+couplings from observed spin trajectories for a model with continuous time
+Glauber dynamics. By introducing two sets of auxiliary latent random variables
+we render the likelihood into a form, which allows for simple iterative
+inference algorithms with analytical updates. The variables are: (1) Poisson
+variables to linearise an exponential term which is typical for point process
+likelihoods and (2) Pólya-Gamma variables, which make the likelihood
+quadratic in the coupling parameters. Using the augmented likelihood, we derive
+an expectation-maximization (EM) algorithm to obtain the maximum likelihood
+estimate of network parameters. Using a third set of latent variables we extend
+the EM algorithm to sparse couplings via L1 regularization. Finally, we develop
+an efficient approximate Bayesian inference algorithm using a variational
+approach. We demonstrate the performance of our algorithms on data simulated
+from an Ising model. For data which are simulated from a more biologically
+plausible network with spiking neurons, we show that the Ising model captures
+well the low order statistics of the data and how the Ising couplings are
+related to the underlying synaptic structure of the simulated network.
+",0,0,0,1,0,0
+15176,Analysis of the flux growth rate in emerging active regions on the Sun,"  We studied the emergence process of 42 active region (ARs) by analyzing the
+time derivative, R(t), of the total unsigned flux. Line-of-sight magnetograms
+acquired by the Helioseismic and Magnetic Imager (HMI) onboard the Solar
+Dynamics Observatory (SDO) were used. A continuous piecewise linear fitting to
+the R(t)-profile was applied to detect an interval, dt_2, of nearly-constant
+R(t) covering one or several local maxima. The averaged over dt_2 magnitude of
+R(t) was accepted as an estimate of the maximal value of the flux growth rate,
+R_MAX, which varies in a range of (0.5-5)x10^20 Mx hour^-1 for active regions
+with the maximal total unsigned flux of (0.5-3)x10^22 Mx. The normalized flux
+growth rate, R_N, was defined under an assumption that the saturated total
+unsigned flux, F_MAX, equals unity. Out of 42 ARs in our initial list, 36 event
+were successfully fitted and they form two subsets (with a small overlap of 8
+events): the ARs with a short (<13 hours) interval dt_2 and a high (>0.024
+hour^-1) normalized flux emergence rate, R_N, form the ""rapid"" emergence event
+subset. The second subset consists of ""gradual"" emergence events and it is
+characterized by a long (>13 hours) interval dt_2 and a low R_N (<0.024
+hour^-1). In diagrams of R_MAX plotted versus F_MAX, the events from different
+subsets are not overlapped and each subset displays an individual power law.
+The power law index derived from the entire ensemble of 36 events is
+0.69+-0.10. The ""rapid"" emergence is consistent with a ""two-step"" emergence
+process of a single twisted flux tube. The ""gradual"" emergence is possibly
+related to a consecutive rising of several flux tubes emerging at nearly the
+same location in the photosphere.
+",0,1,0,0,0,0
+15177,Porosity and Differentiability of Lipschitz Maps from Stratified Groups to Banach Homogeneous Groups,"  Let $f$ be a Lipschitz map from a subset $A$ of a stratified group to a
+Banach homogeneous group. We show that directional derivatives of $f$ act as
+homogeneous homomorphisms at density points of $A$ outside a $\sigma$-porous
+set. At density points of $A$ we establish a pointwise characterization of
+differentiability in terms of directional derivatives. We use these new results
+to obtain an alternate proof of almost everywhere differentiability of
+Lipschitz maps from subsets of stratified groups to Banach homogeneous groups
+satisfying a suitably weakened Radon-Nikodym property. As a consequence we also
+get an alternative proof of Pansu's Theorem.
+",0,0,1,0,0,0
+15178,A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging,"  Sleep plays a vital role in human health, both mental and physical. Sleep
+disorders like sleep apnea are increasing in prevalence, with the rapid
+increase in factors like obesity. Sleep apnea is most commonly treated with
+Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is
+no mechanism to monitor a patient's progress with CPAP. Accurate detection of
+sleep stages from CPAP flow signal is crucial for such a mechanism. We propose,
+for the first time, an automated sleep staging model based only on the flow
+signal. Deep neural networks have recently shown high accuracy on sleep staging
+by eliminating handcrafted features. However, these methods focus exclusively
+on extracting informative features from the input signal, without paying much
+attention to the dynamics of sleep stages in the output sequence. We propose an
+end-to-end framework that uses a combination of deep convolution and recurrent
+neural networks to extract high-level features from raw flow signal with a
+structured output layer based on a conditional random field to model the
+temporal transition structure of the sleep stages. We improve upon the previous
+methods by 10% using our model, that can be augmented to the previous sleep
+staging deep learning methods. We also show that our method can be used to
+accurately track sleep metrics like sleep efficiency calculated from sleep
+stages that can be deployed for monitoring the response of CPAP therapy on
+sleep apnea patients. Apart from the technical contributions, we expect this
+study to motivate new research questions in sleep science.
+",0,0,0,1,0,0
+15179,Dominant dimension and tilting modules,"  We study which algebras have tilting modules that are both generated and
+cogenerated by projective-injective modules. Crawley-Boevey and Sauter have
+shown that Auslander algebras have such tilting modules; and for algebras of
+global dimension $2$, Auslander algebras are classified by the existence of
+such tilting modules.
+In this paper, we show that the existence of such a tilting module is
+equivalent to the algebra having dominant dimension at least $2$, independent
+of its global dimension. In general such a tilting module is not necessarily
+cotilting. Here, we show that the algebras which have a tilting-cotilting
+module generated-cogenerated by projective-injective modules are precisely
+$1$-Auslander-Gorenstein algebras.
+When considering such a tilting module, without the assumption that it is
+cotilting, we study the global dimension of its endomorphism algebra, and
+discuss a connection with the Finitistic Dimension Conjecture. Furthermore, as
+special cases, we show that triangular matrix algebras obtained from Auslander
+algebras and certain injective modules, have such a tilting module. We also
+give a description of which Nakayama algebras have such a tilting module.
+",0,0,1,0,0,0
+15180,Fast and Accurate 3D Medical Image Segmentation with Data-swapping Method,"  Deep neural network models used for medical image segmentation are large
+because they are trained with high-resolution three-dimensional (3D) images.
+Graphics processing units (GPUs) are widely used to accelerate the trainings.
+However, the memory on a GPU is not large enough to train the models. A popular
+approach to tackling this problem is patch-based method, which divides a large
+image into small patches and trains the models with these small patches.
+However, this method would degrade the segmentation quality if a target object
+spans multiple patches. In this paper, we propose a novel approach for 3D
+medical image segmentation that utilizes the data-swapping, which swaps out
+intermediate data from GPU memory to CPU memory to enlarge the effective GPU
+memory size, for training high-resolution 3D medical images without patching.
+We carefully tuned parameters in the data-swapping method to obtain the best
+training performance for 3D U-Net, a widely used deep neural network model for
+medical image segmentation. We applied our tuning to train 3D U-Net with
+full-size images of 192 x 192 x 192 voxels in brain tumor dataset. As a result,
+communication overhead, which is the most important issue, was reduced by
+17.1%. Compared with the patch-based method for patches of 128 x 128 x 128
+voxels, our training for full-size images achieved improvement on the mean Dice
+score by 4.48% and 5.32 % for detecting whole tumor sub-region and tumor core
+sub-region, respectively. The total training time was reduced from 164 hours to
+47 hours, resulting in 3.53 times of acceleration.
+",1,0,0,0,0,0
+15181,Direct Optical Visualization of Water Transport across Polymer Nano-films,"  Gaining a detailed understanding of water transport behavior through
+ultra-thin polymer membranes is increasingly becoming necessary due to the
+recent interest in exploring applications such as water desalination using
+nanoporous membranes. Current techniques only measure bulk water transport
+rates and do not offer direct visualization of water transport which can
+provide insights into the microscopic mechanisms affecting bulk behavior such
+as the role of defects. We describe the use of a technique, referred here as
+Bright-Field Nanoscopy (BFN) to directly image the transport of water across
+thin polymer films using a regular bright-field microscope. The technique
+exploits the strong thickness dependent color response of an optical stack
+consisting of a thin (~25 nm) germanium film deposited over a gold substrate.
+Using this technique, we were able to observe the strong influence of the
+terminal layer and ambient conditions on the bulk water transport rates in thin
+(~ 20 nm) layer-by-layer deposited multilayer films of weak polyelectrolytes
+(PEMs).
+",0,1,0,0,0,0
+15182,Hyperbolicity cones and imaginary projections,"  Recently, the authors and de Wolff introduced the imaginary projection of a
+polynomial $f\in\mathbb{C}[\mathbf{z}]$ as the projection of the variety of $f$
+onto its imaginary part, $\mathcal{I}(f) \ = \ \{\text{Im}(\mathbf{z}) \, : \,
+\mathbf{z} \in \mathcal{V}(f) \}$. Since a polynomial $f$ is stable if and only
+if $\mathcal{I}(f) \cap \mathbb{R}_{>0}^n \ = \ \emptyset$, the notion offers a
+novel geometric view underlying stability questions of polynomials. In this
+article, we study the relation between the imaginary projections and
+hyperbolicity cones, where the latter ones are only defined for homogeneous
+polynomials. Building upon this, for homogeneous polynomials we provide a tight
+upper bound for the number of components in the complement $\mathcal{I}(f)^{c}$
+and thus for the number of hyperbolicity cones of $f$. And we show that for $n
+\ge 2$, a polynomial $f$ in $n$ variables can have an arbitrarily high number
+of strictly convex and bounded components in $\mathcal{I}(f)^{c}$.
+",0,0,1,0,0,0
+15183,"Fairness-aware Classification: Criterion, Convexity, and Bounds","  Fairness-aware classification is receiving increasing attention in the
+machine learning fields. Recently research proposes to formulate the
+fairness-aware classification as constrained optimization problems. However,
+several limitations exist in previous works due to the lack of a theoretical
+framework for guiding the formulation. In this paper, we propose a general
+framework for learning fair classifiers which addresses previous limitations.
+The framework formulates various commonly-used fairness metrics as convex
+constraints that can be directly incorporated into classic classification
+models. Within the framework, we propose a constraint-free criterion on the
+training data which ensures that any classifier learned from the data is fair.
+We also derive the constraints which ensure that the real fairness metric is
+satisfied when surrogate functions are used to achieve convexity. Our framework
+can be used to for formulating fairness-aware classification with fairness
+guarantee and computational efficiency. The experiments using real-world
+datasets demonstrate our theoretical results and show the effectiveness of
+proposed framework and methods.
+",0,0,0,1,0,0
+15184,Optimal Rates for Community Estimation in the Weighted Stochastic Block Model,"  Community identification in a network is an important problem in fields such
+as social science, neuroscience, and genetics. Over the past decade, stochastic
+block models (SBMs) have emerged as a popular statistical framework for this
+problem. However, SBMs have an important limitation in that they are suited
+only for networks with unweighted edges; in various scientific applications,
+disregarding the edge weights may result in a loss of valuable information. We
+study a weighted generalization of the SBM, in which observations are collected
+in the form of a weighted adjacency matrix and the weight of each edge is
+generated independently from an unknown probability density determined by the
+community membership of its endpoints. We characterize the optimal rate of
+misclustering error of the weighted SBM in terms of the Renyi divergence of
+order 1/2 between the weight distributions of within-community and
+between-community edges, substantially generalizing existing results for
+unweighted SBMs. Furthermore, we present a computationally tractable algorithm
+based on discretization that achieves the optimal error rate. Our method is
+adaptive in the sense that the algorithm, without assuming knowledge of the
+weight densities, performs as well as the best algorithm that knows the weight
+densities.
+",0,0,1,1,0,0
+15185,On Sound Relative Error Bounds for Floating-Point Arithmetic,"  State-of-the-art static analysis tools for verifying finite-precision code
+compute worst-case absolute error bounds on numerical errors. These are,
+however, often not a good estimate of accuracy as they do not take into account
+the magnitude of the computed values. Relative errors, which compute errors
+relative to the value's magnitude, are thus preferable. While today's tools do
+report relative error bounds, these are merely computed via absolute errors and
+thus not necessarily tight or more informative. Furthermore, whenever the
+computed value is close to zero on part of the domain, the tools do not report
+any relative error estimate at all. Surprisingly, the quality of relative error
+bounds computed by today's tools has not been systematically studied or
+reported to date. In this paper, we investigate how state-of-the-art static
+techniques for computing sound absolute error bounds can be used, extended and
+combined for the computation of relative errors. Our experiments on a standard
+benchmark set show that computing relative errors directly, as opposed to via
+absolute errors, is often beneficial and can provide error estimates up to six
+orders of magnitude tighter, i.e. more accurate. We also show that interval
+subdivision, another commonly used technique to reduce over-approximations, has
+less benefit when computing relative errors directly, but it can help to
+alleviate the effects of the inherent issue of relative error estimates close
+to zero.
+",1,0,0,0,0,0
+15186,Light yield determination in large sodium iodide detectors applied in the search for dark matter,"  Application of NaI(Tl) detectors in the search for galactic dark matter
+particles through their elastic scattering off the target nuclei is well
+motivated because of the long standing DAMA/LIBRA highly significant positive
+result on annual modulation, still requiring confirmation. For such a goal, it
+is mandatory to reach very low threshold in energy (at or below the keV level),
+very low radioactive background (at a few counts/keV/kg/day), and high
+detection mass (at or above the 100 kg scale). One of the most relevant
+technical issues is the optimization of the crystal intrinsic scintillation
+light yield and the efficiency of the light collecting system for large mass
+crystals. In the frame of the ANAIS (Annual modulation with NaI Scintillators)
+dark matter search project large NaI(Tl) crystals from different providers
+coupled to two photomultiplier tubes (PMTs) have been tested at the Canfranc
+Underground Laboratory. In this paper we present the estimates of the NaI(Tl)
+scintillation light collected using full-absorption peaks at very low energy
+from external and internal sources emitting gammas/electrons, and
+single-photoelectron events populations selected by using very low energy
+pulses tails. Outstanding scintillation light collection at the level of
+15~photoelectrons/keV can be reported for the final design and provider chosen
+for ANAIS detectors. Taking into account the Quantum Efficiency of the PMT
+units used, the intrinsic scintillation light yield in these NaI(Tl) crystals
+is above 40~photoelectrons/keV for energy depositions in the range from 3 up to
+25~keV. This very high light output of ANAIS crystals allows triggering below
+1~keV, which is very important in order to increase the sensitivity in the
+direct detection of dark matter.
+",0,1,0,0,0,0
+15187,A Systematic Approach to Numerical Dispersion in Maxwell Solvers,"  The finite-difference time-domain (FDTD) method is a well established method
+for solving the time evolution of Maxwell's equations. Unfortunately the scheme
+introduces numerical dispersion and therefore phase and group velocities which
+deviate from the correct values. The solution to Maxwell's equations in more
+than one dimension results in non-physical predictions such as numerical
+dispersion or numerical Cherenkov radiation emitted by a relativistic electron
+beam propagating in vacuum.
+Improved solvers, which keep the staggered Yee-type grid for electric and
+magnetic fields, generally modify the spatial derivative operator in the
+Maxwell-Faraday equation by increasing the computational stencil. These
+modified solvers can be characterized by different sets of coefficients,
+leading to different dispersion properties. In this work we introduce a norm
+function to rewrite the choice of coefficients into a minimization problem. We
+solve this problem numerically and show that the minimization procedure leads
+to phase and group velocities that are considerably closer to $c$ as compared
+to schemes with manually set coefficients available in the literature.
+Depending on a specific problem at hand (e.g. electron beam propagation in
+plasma, high-order harmonic generation from plasma surfaces, etc), the norm
+function can be chosen accordingly, for example, to minimize the numerical
+dispersion in a certain given propagation direction. Particle-in-cell
+simulations of an electron beam propagating in vacuum using our solver are
+provided.
+",0,1,0,0,0,0
+15188,"On Synchronous, Asynchronous, and Randomized Best-Response schemes for computing equilibria in Stochastic Nash games","  This work considers a stochastic Nash game in which each player solves a
+parameterized stochastic optimization problem. In deterministic regimes,
+best-response schemes have been shown to be convergent under a suitable
+spectral property associated with the proximal best-response map. However, a
+direct application of this scheme to stochastic settings requires obtaining
+exact solutions to stochastic optimization at each iteration. Instead, we
+propose an inexact generalization in which an inexact solution is computed via
+an increasing number of projected stochastic gradient steps. Based on this
+framework, we present three inexact best-response schemes: (i) First, we
+propose a synchronous scheme where all players simultaneously update their
+strategies; (ii) Subsequently, we extend this to a randomized setting where a
+subset of players is randomly chosen to their update strategies while the
+others keep their strategies invariant; (iii) Finally, we propose an
+asynchronous scheme, where each player determines its own update frequency and
+may use outdated rival-specific data in updating its strategy. Under a suitable
+contractive property of the proximal best-response map, we derive a.s.
+convergence of the iterates for (i) and (ii) and mean-convergence for (i) --
+(iii). In addition, we show that for (i) -- (iii), the iterates converge to the
+unique equilibrium in mean at a prescribed linear rate. Finally, we establish
+the overall iteration complexity in terms of projected stochastic gradient
+steps for computing an $\epsilon-$Nash equilibrium and in all settings, the
+iteration complexity is ${\cal O}(1/\epsilon^{2(1+c) + \delta})$ where $c = 0$
+in the context of (i) and represents the positive cost of randomization (in
+(ii)) and asynchronicity and delay (in (iii)). The schemes are further extended
+to linear and quadratic recourse-based stochastic Nash games.
+",0,0,1,0,0,0
+15189,On the insertion of n-powers,"  In algebraic terms, the insertion of $n$-powers in words may be modelled at
+the language level by considering the pseudovariety of ordered monoids defined
+by the inequality $1\le x^n$. We compare this pseudovariety with several other
+natural pseudovarieties of ordered monoids and of monoids associated with the
+Burnside pseudovariety of groups defined by the identity $x^n=1$. In
+particular, we are interested in determining the pseudovariety of monoids that
+it generates, which can be viewed as the problem of determining the Boolean
+closure of the class of regular languages closed under $n$-power insertions. We
+exhibit a simple upper bound and show that it satisfies all pseudoidentities
+which are provable from $1\le x^n$ in which both sides are regular elements
+with respect to the upper bound.
+",1,0,1,0,0,0
+15190,The TUS detector of extreme energy cosmic rays on board the Lomonosov satellite,"  The origin and nature of extreme energy cosmic rays (EECRs), which have
+energies above the 50 EeV, the Greisen-Zatsepin-Kuzmin (GZK) energy limit, is
+one of the most interesting and complicated problems in modern cosmic-ray
+physics. Existing ground-based detectors have helped to obtain remarkable
+results in studying cosmic rays before and after the GZK limit, but have also
+produced some contradictions in our understanding of cosmic ray mass
+composition. Moreover, each of these detectors covers only a part of the
+celestial sphere, which poses problems for studying the arrival directions of
+EECRs and identifying their sources. As a new generation of EECR space
+detectors, TUS (Tracking Ultraviolet Set-up), KLYPVE and JEM-EUSO, are intended
+to study the most energetic cosmic-ray particles, providing larger, uniform
+exposures of the entire celestial sphere. The TUS detector, launched on board
+the Lomonosov satellite on April 28, 2016, from Vostochny Cosmodrome in Russia,
+is the first of these. It employs a single-mirror optical system and a
+photomultiplier tube matrix as a photo-detector and will test the fluorescent
+method of measuring EECRs from space. Utilizing the Earth's atmosphere as a
+huge calorimeter, it is expected to detect EECRs with energies above 100 EeV.
+It will also be able to register slower atmospheric transient events:
+atmospheric fluorescence in electrical discharges of various types including
+precipitating electrons escaping the magnetosphere and from the radiation of
+meteors passing through the atmosphere. We describe the design of the TUS
+detector and present results of different ground-based tests and simulations.
+",0,1,0,0,0,0
+15191,New ellipsometric approach for determining small light ellipticities,"  We propose a precise ellipsometric method for the investigation of coherent
+light with a small ellipticity. The main feature of this method is the use of
+compensators with phase delays providing the maximum accuracy of measurements
+for the selected range of ellipticities and taking into account the
+interference of multiple reflections of coherent light. The relative error of
+the ellipticity measurement in the range of mesurement does not exceed 0.02.
+",0,1,0,0,0,0
+15192,Maximizing the Mutual Information of Multi-Antenna Links Under an Interfered Receiver Power Constraint,"  Single-user multiple-input / multiple-output (SU-MIMO) communication systems
+have been successfully used over the years and have provided a significant
+increase on a wireless link's capacity by enabling the transmission of multiple
+data streams. Assuming channel knowledge at the transmitter, the maximization
+of the mutual information of a MIMO link is achieved by finding the optimal
+power allocation under a given sum-power constraint, which is in turn obtained
+by the water-filling (WF) algorithm. However, in spectrum sharing setups, such
+as Licensed Shared Access (LSA), where a primary link (PL) and a secondary link
+(SL) coexist, the power transmitted by the SL transmitter may induce harmful
+interference to the PL receiver. While such co-existing links have been
+considered extensively in various spectrum sharing setups, the mutual
+information of the SL under a constraint on the interference it may cause to
+the PL receiver has, quite astonishingly, not been evaluated so far. In this
+paper, we solve this problem, find its unique optimal solution and provide the
+power allocation policy and corresponding precoding solution that achieves the
+optimal capacity under the imposed constraint. The performance of the optimal
+solution and the penalty due to the interference constraint are evaluated over
+some indicative Rayleigh fading channel conditions and interference thresholds.
+We believe that the obtained results are of general nature and that they may
+apply, beyond spectrum sharing, to a variety of applications that admit a
+similar setup.
+",1,0,0,0,0,0
+15193,Multistationarity and Bistability for Fewnomial Chemical Reaction Networks,"  Bistability and multistationarity are properties of reaction networks linked
+to switch-like responses and connected to cell memory and cell decision making.
+Determining whether and when a network exhibits bistability is a hard and open
+mathematical problem. One successful strategy consists of analyzing small
+networks and deducing that some of the properties are preserved upon passage to
+the full network. Motivated by this we study chemical reaction networks with
+few chemical complexes. Under mass-action kinetics the steady states of these
+networks are described by fewnomial systems, that is polynomial systems having
+few distinct monomials. Such systems of polynomials are often studied in real
+algebraic geometry by the use of Gale dual systems. Using this Gale duality we
+give precise conditions in terms of the reaction rate constants for the number
+and stability of the steady states of families of reaction networks with one
+non-flow reaction.
+",1,0,0,0,1,0
+15194,Erratum: Link prediction in drug-target interactions network using similarity indices,"  Background: In silico drug-target interaction (DTI) prediction plays an
+integral role in drug repositioning: the discovery of new uses for existing
+drugs. One popular method of drug repositioning is network-based DTI
+prediction, which uses complex network theory to predict DTIs from a
+drug-target network. Currently, most network-based DTI prediction is based on
+machine learning methods such as Restricted Boltzmann Machines (RBM) or Support
+Vector Machines (SVM). These methods require additional information about the
+characteristics of drugs, targets and DTIs, such as chemical structure, genome
+sequence, binding types, causes of interactions, etc., and do not perform
+satisfactorily when such information is unavailable. We propose a new,
+alternative method for DTI prediction that makes use of only network topology
+information attempting to solve this problem.
+Results: We compare our method for DTI prediction against the well-known RBM
+approach. We show that when applied to the MATADOR database, our approach based
+on node neighborhoods yield higher precision for high-ranking predictions than
+RBM when no information regarding DTI types is available.
+Conclusion: This demonstrates that approaches purely based on network
+topology provide a more suitable approach to DTI prediction in the many
+real-life situations where little or no prior knowledge is available about the
+characteristics of drugs, targets, or their interactions.
+",1,0,0,0,0,0
+15195,Variable Selection for Highly Correlated Predictors,"  Penalty-based variable selection methods are powerful in selecting relevant
+covariates and estimating coefficients simultaneously. However, variable
+selection could fail to be consistent when covariates are highly correlated.
+The partial correlation approach has been adopted to solve the problem with
+correlated covariates. Nevertheless, the restrictive range of partial
+correlation is not effective for capturing signal strength for relevant
+covariates. In this paper, we propose a new Semi-standard PArtial Covariance
+(SPAC) which is able to reduce correlation effects from other predictors while
+incorporating the magnitude of coefficients. The proposed SPAC variable
+selection facilitates choosing covariates which have direct association with
+the response variable, via utilizing dependency among covariates. We show that
+the proposed method with the Lasso penalty (SPAC-Lasso) enjoys strong sign
+consistency in both finite-dimensional and high-dimensional settings under
+regularity conditions. Simulation studies and the `HapMap' gene data
+application show that the proposed method outperforms the traditional Lasso,
+adaptive Lasso, SCAD, and Peter-Clark-simple (PC-simple) methods for highly
+correlated predictors.
+",0,0,1,1,0,0
+15196,Scraping and Preprocessing Commercial Auction Data for Fraud Classification,"  In the last three decades, we have seen a significant increase in trading
+goods and services through online auctions. However, this business created an
+attractive environment for malicious moneymakers who can commit different types
+of fraud activities, such as Shill Bidding (SB). The latter is predominant
+across many auctions but this type of fraud is difficult to detect due to its
+similarity to normal bidding behaviour. The unavailability of SB datasets makes
+the development of SB detection and classification models burdensome.
+Furthermore, to implement efficient SB detection models, we should produce SB
+data from actual auctions of commercial sites. In this study, we first scraped
+a large number of eBay auctions of a popular product. After preprocessing the
+raw auction data, we build a high-quality SB dataset based on the most reliable
+SB strategies. The aim of our research is to share the preprocessed auction
+dataset as well as the SB training (unlabelled) dataset, thereby researchers
+can apply various machine learning techniques by using authentic data of
+auctions and fraud.
+",0,0,0,1,0,0
+15197,Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases,"  The impact of the maximally possible batch size (for the better runtime) on
+performance of graphic processing units (GPU) and tensor processing units (TPU)
+during training and inference phases is investigated. The numerous runs of the
+selected deep neural network (DNN) were performed on the standard MNIST and
+Fashion-MNIST datasets. The significant speedup was obtained even for extremely
+low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite
+powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training
+stage (without taking into account the overheads) and speedup up to 2x for
+prediction stage (with and without taking into account overheads). The precise
+speedup values depend on the utilization level of TPUv2 units and increase with
+the increase of the data volume under processing, but for the datasets used in
+this work (MNIST and Fashion-MNIST with images of sizes 28x28) the speedup was
+observed for batch sizes >512 images for training phase and >40 000 images for
+prediction phase. It should be noted that these results were obtained without
+detriment to the prediction accuracy and loss that were equal for both GPU and
+TPU runs up to the 3rd significant digit for MNIST dataset, and up to the 2nd
+significant digit for Fashion-MNIST dataset.
+",1,0,0,0,0,0
+15198,Minimax Euclidean Separation Rates for Testing Convex Hypotheses in $\mathbb{R}^d$,"  We consider composite-composite testing problems for the expectation in the
+Gaussian sequence model where the null hypothesis corresponds to a convex
+subset $\mathcal{C}$ of $\mathbb{R}^d$. We adopt a minimax point of view and
+our primary objective is to describe the smallest Euclidean distance between
+the null and alternative hypotheses such that there is a test with small total
+error probability. In particular, we focus on the dependence of this distance
+on the dimension $d$ and the sample size/variance parameter $n$ giving rise to
+the minimax separation rate. In this paper we discuss lower and upper bounds on
+this rate for different smooth and non- smooth choices for $\mathcal{C}$.
+",0,0,1,1,0,0
+15199,Simple Policy Evaluation for Data-Rich Iterative Tasks,"  A data-based policy for iterative control task is presented. The proposed
+strategy is model-free and can be applied whenever safe input and state
+trajectories of a system performing an iterative task are available. These
+trajectories, together with a user-defined cost function, are exploited to
+construct a piecewise affine approximation to the value function. Approximated
+value functions are then used to evaluate the control policy by solving a
+linear program. We show that for linear system subject to convex cost and
+constraints, the proposed strategy guarantees closed-loop constraint
+satisfaction and performance bounds on the closed-loop trajectory. We evaluate
+the proposed strategy in simulations and experiments, the latter carried out on
+the Berkeley Autonomous Race Car (BARC) platform. We show that the proposed
+strategy is able to reduce the computation time by one order of magnitude while
+achieving the same performance as our model-based control algorithm.
+",1,0,0,0,0,0
+15200,Proper quadrics in the Euclidean $n$-space,"  In this paper we investigate the metric properties of quadrics and cones of
+the $n$-dimensional Euclidean space. As applications of our formulas we give a
+more detailed description of the construction of Chasles and the wire model of
+Staude, respectively.
+",0,0,1,0,0,0
+15201,Mapping of the dark exciton landscape in transition metal dichalcogenides,"  Transition metal dichalcogenides (TMDs) exhibit a remarkable exciton physics
+including optically accessible (bright) as well as spin- and momentum-forbidden
+(dark) excitonic states. So far the dark exciton landscape has not been
+revealed leaving in particular the spectral position of momentum-forbidden dark
+states completely unclear. This has a significant impact on the technological
+application potential of TMDs, since the nature of the energetically lowest
+state determines, if the material is a direct-gap semiconductor. Here, we show
+how dark states can be experimentally revealed by probing the intra-excitonic
+1s-2p transition. Distinguishing the optical response shortly after the
+excitation (< 100$\,$fs) and after the exciton thermalization (> 1$\,$ps)
+allows us to demonstrate the relative position of bright and dark excitons. We
+find both in theory and experiment a clear blue-shift in the optical response
+demonstrating for the first time the transition of bright exciton populations
+into lower lying momentum- and spin-forbidden dark excitonic states in
+monolayer WSe$_2$.
+",0,1,0,0,0,0
+15202,Active set algorithms for estimating shape-constrained density ratios,"  We review and modify the active set algorithm by Duembgen et al. (2011) for
+nonparametric maximum-likelihood estimation of a log-concave density. This
+particular estimation problem is embedded into a more general framework
+including also the estimation of a log-convex tail inflation function as
+proposed by McCullagh and Polson (2012).
+",0,0,0,1,0,0
+15203,Asymptotic orthogonalization of subalgebras in II$_1$ factors,"  Let $M$ be a II$_1$ factor with a von Neumann subalgebra $Q\subset M$ that
+has infinite index under any projection in $Q'\cap M$ (e.g., $Q$ abelian; or
+$Q$ an irreducible subfactor with infinite Jones index). We prove that given
+any separable subalgebra $B$ of the ultrapower II$_1$ factor $M^\omega$, for a
+non-principal ultrafilter $\omega$ on $\Bbb N$, there exists a unitary element
+$u\in M^\omega$ such that $uBu^*$ is orthogonal to $Q^\omega$.
+",0,0,1,0,0,0
+15204,Neural Control Variates for Variance Reduction,"  In statistics and machine learning, approximation of an intractable
+integration is often achieved by using the unbiased Monte Carlo estimator, but
+the variances of the estimation are generally high in many applications.
+Control variates approaches are well-known to reduce the variance of the
+estimation. These control variates are typically constructed by employing
+predefined parametric functions or polynomials, determined by using those
+samples drawn from the relevant distributions. Instead, we propose to construct
+those control variates by learning neural networks to handle the cases when
+test functions are complex. In many applications, obtaining a large number of
+samples for Monte Carlo estimation is expensive, which may result in
+overfitting when training a neural network. We thus further propose to employ
+auxiliary random variables induced by the original ones to extend data samples
+for training the neural networks. We apply the proposed control variates with
+augmented variables to thermodynamic integration and reinforcement learning.
+Experimental results demonstrate that our method can achieve significant
+variance reduction compared with other alternatives.
+",0,0,0,1,0,0
+15205,On tidal energy in Newtonian two-body motion,"  In this work, which is based on an essential linear analysis carried out by
+Christodoulou, we study the evolution of tidal energy for the motion of two
+gravitating incompressible fluid balls with free boundaries obeying the
+Euler-Poisson equations. The orbital energy is defined as the mechanical energy
+of the two bodies' center of mass. According to the classical analysis of
+Kepler and Newton, when the fluids are replaced by point masses, the conic
+curve describing the trajectories of the masses is a hyperbola when the orbital
+energy is positive and an ellipse when the orbital energy is negative. The
+orbital energy is conserved in the case of point masses. If the point masses
+are initially very far, then the orbital energy is positive, corresponding to
+hyperbolic motion. However, in the motion of fluid bodies the orbital energy is
+no longer conserved because part of the conserved energy is used in deforming
+the boundaries of the bodies. In this case the total energy
+$\tilde{\mathcal{E}}$ can be decomposed into a sum
+$\tilde{\mathcal{E}}:=\widetilde{\mathcal{E}_{\mathrm{orbital}}}+\widetilde{\mathcal{E}_{\mathrm{tidal}}}$,
+with $\widetilde{\mathcal{E}_{\mathrm{tidal}}}$ measuring the energy used in
+deforming the boundaries, such that if
+$\widetilde{\mathcal{E}_{\mathrm{orbital}}}<-c<0$ for some absolute constant
+$c>0$, then the orbit of the bodies must be bounded. In this work we prove that
+under appropriate conditions on the initial configuration of the system, the
+fluid boundaries and velocity remain regular up to the point of the first
+closest approach in the orbit, and that the tidal energy
+$\widetilde{\mathcal{E}_{\mathrm{tidal}}}$ can be made arbitrarily large
+relative to the total energy $\tilde{\mathcal{E}}$. In particular under these
+conditions $\widetilde{\mathcal{E}_{\mathrm{orbital}}}$, which is initially
+positive, becomes negative before the point of the first closest approach.
+",0,1,1,0,0,0
+15206,Markov-Modulated Linear Regression,"  Classical linear regression is considered for a case when regression
+parameters depend on the external random environment. The last is described as
+a continuous time Markov chain with finite state space. Here the expected
+sojourn times in various states are additional regressors. Necessary formulas
+for an estimation of regression parameters have been derived. The numerical
+example illustrates the results obtained.
+",0,0,0,1,0,0
+15207,Compactness of the resolvent for the Witten Laplacian,"  In this paper we consider the Witten Laplacian on 0-forms and give sufficient
+conditions under which the Witten Laplacian admits a compact resolvent. These
+conditions are imposed on the potential itself, involving the control of high
+order derivatives by lower ones, as well as the control of the positive
+eigenvalues of the Hessian matrix. This compactness criterion for resolvent is
+inspired by the one for the Fokker-Planck operator. Our method relies on the
+nilpotent group techniques developed by Helffer-Nourrigat [Hypoellipticité
+maximale pour des opérateurs polynômes de champs de vecteurs, 1985].
+",0,0,1,0,0,0
+15208,On the Azuma inequality in spaces of subgaussian of rank $p$ random variables,"  For $p > 1$ let a function $\varphi_p(x) = x^2/2$ if $|x|\le 1$ and
+$\varphi_p(x) = 1/p|x|^p -1/p + 1/2$ if $|x| > 1$. For a random variable $\xi$
+let $\tau_{\varphi_p}(\xi)$ denote $\inf\{c\ge 0 :\;
+\forall_{\lambda\in\mathbb{R}}\;
+\ln\mathbb{E}\exp(\lambda\xi)\le\varphi_p(c\lambda)\}$; $\tau_{\varphi_p}$ is a
+norm in a space $Sub_{\varphi_p}(\Omega) =\{\xi:
+\; \tau_{\varphi_p}(\xi) <\infty\}$ of $\varphi_p$-subgaussian random
+variables which we call {\it subgaussian of rank $p$ random variables}. For $p
+= 2$ we have the classic subgaussian random variables. The Azuma inequality
+gives an estimate on the probability of the deviations of a zero-mean
+martingale $(\xi_n)_{n\ge 0}$ with bounded increments from zero. In its classic
+form is assumed that $\xi_0 = 0$. In this paper it is shown a version of the
+Azuma inequality under assumption that $\xi_0$ is any subgaussian of rank $p$
+random variable.
+",0,0,1,0,0,0
+15209,Realization of the Axial Next-Nearest-Neighbor Ising model in U$_3$Al$_2$Ge$_3$,"  Here we report small-angle neutron scattering (SANS) measurements and
+theoretical modeling of U$_3$Al$_2$Ge$_3$. Analysis of the SANS data reveals a
+phase transition to sinusoidally modulated magnetic order, at
+$T_{\mathrm{N}}=63$~K to be second order, and a first order phase transition to
+ferromagnetic order at $T_{\mathrm{c}}=48$~K. Within the sinusoidally modulated
+magnetic phase ($T_{\mathrm{c}} < T < T_{\mathrm{N}}$), we uncover a dramatic
+change, by a factor of three, in the ordering wave-vector as a function of
+temperature. These observations all indicate that U$_3$Al$_2$Ge$_3$ is a close
+realization of the three-dimensional Axial Next-Nearest-Neighbor Ising model, a
+prototypical framework for describing commensurate to incommensurate phase
+transitions in frustrated magnets.
+",0,1,0,0,0,0
+15210,Long-time asymptotics for the derivative nonlinear Schrödinger equation on the half-line,"  We derive asymptotic formulas for the solution of the derivative nonlinear
+Schrödinger equation on the half-line under the assumption that the initial
+and boundary values lie in the Schwartz class. The formulas clearly show the
+effect of the boundary on the solution. The approach is based on a nonlinear
+steepest descent analysis of an associated Riemann-Hilbert problem.
+",0,1,1,0,0,0
+15211,Non-Semisimple Extended Topological Quantum Field Theories,"  We develop the general theory for the construction of Extended Topological
+Quantum Field Theories (ETQFTs) associated with the Costantino-Geer-Patureau
+quantum invariants of closed 3-manifolds. In order to do so, we introduce
+relative modular categories, a class of ribbon categories which are modeled on
+representations of unrolled quantum groups, and which can be thought of as a
+non-semisimple analogue to modular categories. Our approach exploits a
+2-categorical version of the universal construction introduced by Blanchet,
+Habegger, Masbaum, and Vogel. The 1+1+1-EQFTs thus obtained are realized by
+symmetric monoidal 2-functors which are defined over non-rigid 2-categories of
+admissible cobordisms decorated with colored ribbon graphs and cohomology
+classes, and which take values in 2-categories of complete graded linear
+categories. In particular, our construction extends the family of graded
+2+1-TQFTs defined for the unrolled version of quantum $\mathfrak{sl}_2$ by
+Blanchet, Costantino, Geer, and Patureau to a new family of graded ETQFTs. The
+non-semisimplicity of the theory is witnessed by the presence of non-semisimple
+graded linear categories associated with critical 1-manifolds.
+",0,0,1,0,0,0
+15212,Community Aware Random Walk for Network Embedding,"  Social network analysis provides meaningful information about behavior of
+network members that can be used for diverse applications such as
+classification, link prediction. However, network analysis is computationally
+expensive because of feature learning for different applications. In recent
+years, many researches have focused on feature learning methods in social
+networks. Network embedding represents the network in a lower dimensional
+representation space with the same properties which presents a compressed
+representation of the network. In this paper, we introduce a novel algorithm
+named ""CARE"" for network embedding that can be used for different types of
+networks including weighted, directed and complex. Current methods try to
+preserve local neighborhood information of nodes, whereas the proposed method
+utilizes local neighborhood and community information of network nodes to cover
+both local and global structure of social networks. CARE builds customized
+paths, which are consisted of local and global structure of network nodes, as a
+basis for network embedding and uses the Skip-gram model to learn
+representation vector of nodes. Subsequently, stochastic gradient descent is
+applied to optimize our objective function and learn the final representation
+of nodes. Our method can be scalable when new nodes are appended to network
+without information loss. Parallelize generation of customized random walks is
+also used for speeding up CARE. We evaluate the performance of CARE on multi
+label classification and link prediction tasks. Experimental results on various
+networks indicate that the proposed method outperforms others in both Micro and
+Macro-f1 measures for different size of training data.
+",1,0,0,0,0,0
+15213,A combined photometric and kinematic recipe for evaluating the nature of bulges using the CALIFA sample,"  Understanding the nature of bulges in disc galaxies can provide important
+insights into the formation and evolution of galaxies. For instance, the
+presence of a classical bulge suggests a relatively violent history, in
+contrast, the presence of simply an inner disc (also referred to as a
+""pseudobulge"") indicates the occurrence of secular evolution processes in the
+main disc. However, we still lack criteria to effectively categorise bulges,
+limiting our ability to study their impact on the evolution of the host
+galaxies. Here we present a recipe to separate inner discs from classical
+bulges by combining four different parameters from photometric and kinematic
+analyses: The bulge Sérsic index $n_\mathrm{b}$, the concentration index
+$C_{20,50}$, the Kormendy (1977) relation and the inner slope of the radial
+velocity dispersion profile $\nabla\sigma$. With that recipe we provide a
+detailed bulge classification for a sample of 45 galaxies from the
+integral-field spectroscopic survey CALIFA. To aid in categorising bulges
+within these galaxies, we perform 2D image decomposition to determine bulge
+Sérsic index, bulge-to-total light ratio, surface brightness and effective
+radius of the bulge and use growth curve analysis to derive a new concentration
+index, $C_{20,50}$. We further extract the stellar kinematics from CALIFA data
+cubes and analyse the radial velocity dispersion profile. The results of the
+different approaches are in good agreement and allow a safe classification for
+approximately $95\%$ of the galaxies. In particular, we show that our new
+""inner"" concentration index performs considerably better than the traditionally
+used $C_{50,90}$ when yielding the nature of bulges. We also found that a
+combined use of this index and the Kormendy (1977) relation gives a very robust
+indication of the physical nature of the bulge.
+",0,1,0,0,0,0
+15214,Fast construction of efficient composite likelihood equations,"  Growth in both size and complexity of modern data challenges the
+applicability of traditional likelihood-based inference. Composite likelihood
+(CL) methods address the difficulties related to model selection and
+computational intractability of the full likelihood by combining a number of
+low-dimensional likelihood objects into a single objective function used for
+inference. This paper introduces a procedure to combine partial likelihood
+objects from a large set of feasible candidates and simultaneously carry out
+parameter estimation. The new method constructs estimating equations balancing
+statistical efficiency and computing cost by minimizing an approximate distance
+from the full likelihood score subject to a L1-norm penalty representing the
+available computing resources. This results in truncated CL equations
+containing only the most informative partial likelihood score terms. An
+asymptotic theory within a framework where both sample size and data dimension
+grow is developed and finite-sample properties are illustrated through
+numerical examples.
+",0,0,1,1,0,0
+15215,State-selective influence of the Breit interaction on the angular distribution of emitted photons following dielectronic recombination,"  We report a measurement of $KLL$ dielectronic recombination in charge states
+from Kr$^{+34}$ through Kr$^{+28}$, in order to investigate the contribution of
+Breit interaction for a wide range of resonant states. Highly charged Kr ions
+were produced in an electron beam ion trap, while the electron-ion collision
+energy was scanned over a range of dielectronic recombination resonances. The
+subsequent $K\alpha$ x rays were recorded both along and perpendicular to the
+electron beam axis, which allowed the observation of the influence of Breit
+interaction on the angular distribution of the x rays. Experimental results are
+in good agreement with distorted-wave calculations. We demonstrate, both
+theoretically and experimentally, that there is a strong state-selective
+influence of the Breit interaction that can be traced back to the angular and
+radial properties of the wavefunctions in the dielectronic capture.
+",0,1,0,0,0,0
+15216,Anti-spoofing Methods for Automatic SpeakerVerification System,"  Growing interest in automatic speaker verification (ASV)systems has lead to
+significant quality improvement of spoofing attackson them. Many research works
+confirm that despite the low equal er-ror rate (EER) ASV systems are still
+vulnerable to spoofing attacks. Inthis work we overview different acoustic
+feature spaces and classifiersto determine reliable and robust countermeasures
+against spoofing at-tacks. We compared several spoofing detection systems,
+presented so far,on the development and evaluation datasets of the Automatic
+SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge
+2015.Experimental results presented in this paper demonstrate that the useof
+magnitude and phase information combination provides a substantialinput into
+the efficiency of the spoofing detection systems. Also wavelet-based features
+show impressive results in terms of equal error rate. Inour overview we compare
+spoofing performance for systems based on dif-ferent classifiers. Comparison
+results demonstrate that the linear SVMclassifier outperforms the conventional
+GMM approach. However, manyresearchers inspired by the great success of deep
+neural networks (DNN)approaches in the automatic speech recognition, applied
+DNN in thespoofing detection task and obtained quite low EER for known and
+un-known type of spoofing attacks.
+",1,0,0,1,0,0
+15217,Face Deidentification with Generative Deep Neural Networks,"  Face deidentification is an active topic amongst privacy and security
+researchers. Early deidentification methods relying on image blurring or
+pixelization were replaced in recent years with techniques based on formal
+anonymity models that provide privacy guaranties and at the same time aim at
+retaining certain characteristics of the data even after deidentification. The
+latter aspect is particularly important, as it allows to exploit the
+deidentified data in applications for which identity information is irrelevant.
+In this work we present a novel face deidentification pipeline, which ensures
+anonymity by synthesizing artificial surrogate faces using generative neural
+networks (GNNs). The generated faces are used to deidentify subjects in images
+or video, while preserving non-identity-related aspects of the data and
+consequently enabling data utilization. Since generative networks are very
+adaptive and can utilize a diverse set of parameters (pertaining to the
+appearance of the generated output in terms of facial expressions, gender,
+race, etc.), they represent a natural choice for the problem of face
+deidentification. To demonstrate the feasibility of our approach, we perform
+experiments using automated recognition tools and human annotators. Our results
+show that the recognition performance on deidentified images is close to
+chance, suggesting that the deidentification process based on GNNs is highly
+effective.
+",1,0,0,0,0,0
+15218,"Towards a theory of word order. Comment on ""Dependency distance: a new perspective on syntactic patterns in natural language"" by Haitao Liu et al","  Comment on ""Dependency distance: a new perspective on syntactic patterns in
+natural language"" by Haitao Liu et al
+",1,1,0,0,0,0
+15219,Optimal Frequency Ranges for Sub-Microsecond Precision Pulsar Timing,"  Precision pulsar timing requires optimization against measurement errors and
+astrophysical variance from the neutron stars themselves and the interstellar
+medium. We investigate optimization of arrival time precision as a function of
+radio frequency and bandwidth. We find that increases in bandwidth that reduce
+the contribution from receiver noise are countered by the strong chromatic
+dependence of interstellar effects and intrinsic pulse-profile evolution. The
+resulting optimal frequency range is therefore telescope and pulsar dependent.
+We demonstrate the results for five pulsars included in current pulsar timing
+arrays and determine that they are not optimally observed at current center
+frequencies. For those objects, we find that better choices of total bandwidth
+as well as center frequency can improve the arrival-time precision. Wideband
+receivers centered at somewhat higher frequencies with respect to the currently
+adopted receivers can reduce required overall integration times and provide
+significant improvements in arrival time uncertainty by a factor of ~sqrt(2) in
+most cases, assuming a fixed integration time. We also discuss how timing
+programs can be extended to pulsars with larger dispersion measures through the
+use of higher-frequency observations.
+",0,1,0,0,0,0
+15220,Submodular Mini-Batch Training in Generative Moment Matching Networks,"  This article was withdrawn because (1) it was uploaded without the
+co-authors' knowledge or consent, and (2) there are allegations of plagiarism.
+",1,0,0,0,0,0
+15221,Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction,"  Traffic speed is a key indicator for the efficiency of an urban
+transportation system. Accurate modeling of the spatiotemporally varying
+traffic speed thus plays a crucial role in urban planning and development. This
+paper addresses the problem of efficient fine-grained traffic speed prediction
+using big traffic data obtained from static sensors. Gaussian processes (GPs)
+have been previously used to model various traffic phenomena, including flow
+and speed. However, GPs do not scale with big traffic data due to their cubic
+time complexity. In this work, we address their efficiency issues by proposing
+local GPs to learn from and make predictions for correlated subsets of data.
+The main idea is to quickly group speed variables in both spatial and temporal
+dimensions into a finite number of clusters, so that future and unobserved
+traffic speed queries can be heuristically mapped to one of such clusters. A
+local GP corresponding to that cluster can then be trained on the fly to make
+predictions in real-time. We call this method localization. We use non-negative
+matrix factorization for localization and propose simple heuristics for cluster
+mapping. We additionally leverage on the expressiveness of GP kernel functions
+to model road network topology and incorporate side information. Extensive
+experiments using real-world traffic data collected in the two U.S. cities of
+Pittsburgh and Washington, D.C., show that our proposed local GPs significantly
+improve both runtime performances and prediction accuracies compared to the
+baseline global and local GPs.
+",1,0,0,0,0,0
+15222,Vertex algebras associated with hypertoric varieties,"  We construct a family of vertex algebras associated with a family of
+symplectic singularity/resolution, called hypertoric varieties. While the
+hypertoric varieties are constructed by a certain Hamiltonian reduction
+associated with a torus action, our vertex algebras are constructed by
+(semi-infinite) BRST reduction. The construction works algebro-geometrically
+and we construct sheaves of $\hbar$-adic vertex algebras over hypertoric
+varieties which localize the vertex algebras. We show when the vertex algebras
+are vertex operator algebras by giving explicit conformal vectors. We also show
+that the Zhu algebras of the vertex algebras, associative algebras associated
+with non-negatively graded vertex algebras, gives a certain family of filtered
+quantizations of the coordinate rings of the hypertoric varieties.
+",0,0,1,0,0,0
+15223,Bit-Vector Model Counting using Statistical Estimation,"  Approximate model counting for bit-vector SMT formulas (generalizing \#SAT)
+has many applications such as probabilistic inference and quantitative
+information-flow security, but it is computationally difficult. Adding random
+parity constraints (XOR streamlining) and then checking satisfiability is an
+effective approximation technique, but it requires a prior hypothesis about the
+model count to produce useful results. We propose an approach inspired by
+statistical estimation to continually refine a probabilistic estimate of the
+model count for a formula, so that each XOR-streamlined query yields as much
+information as possible. We implement this approach, with an approximate
+probability model, as a wrapper around an off-the-shelf SMT solver or SAT
+solver. Experimental results show that the implementation is faster than the
+most similar previous approaches which used simpler refinement strategies. The
+technique also lets us model count formulas over floating-point constraints,
+which we demonstrate with an application to a vulnerability in differential
+privacy mechanisms.
+",1,0,0,0,0,0
+15224,Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory,"  We develop a family of reformulations of an arbitrary consistent linear
+system into a stochastic problem. The reformulations are governed by two
+user-defined parameters: a positive definite matrix defining a norm, and an
+arbitrary discrete or continuous distribution over random matrices. Our
+reformulation has several equivalent interpretations, allowing for researchers
+from various communities to leverage their domain specific insights. In
+particular, our reformulation can be equivalently seen as a stochastic
+optimization problem, stochastic linear system, stochastic fixed point problem
+and a probabilistic intersection problem. We prove sufficient, and necessary
+and sufficient conditions for the reformulation to be exact.
+Further, we propose and analyze three stochastic algorithms for solving the
+reformulated problem---basic, parallel and accelerated methods---with global
+linear convergence rates. The rates can be interpreted as condition numbers of
+a matrix which depends on the system matrix and on the reformulation
+parameters. This gives rise to a new phenomenon which we call stochastic
+preconditioning, and which refers to the problem of finding parameters (matrix
+and distribution) leading to a sufficiently small condition number. Our basic
+method can be equivalently interpreted as stochastic gradient descent,
+stochastic Newton method, stochastic proximal point method, stochastic fixed
+point method, and stochastic projection method, with fixed stepsize (relaxation
+parameter), applied to the reformulations.
+",1,0,0,1,0,0
+15225,Isotropic-Nematic Phase Transitions in Gravitational Systems,"  We examine dense self-gravitating stellar systems dominated by a central
+potential, such as nuclear star clusters hosting a central supermassive black
+hole. Different dynamical properties of these systems evolve on vastly
+different timescales. In particular, the orbital-plane orientations are
+typically driven into internal thermodynamic equilibrium by vector resonant
+relaxation before the orbital eccentricities or semimajor axes relax. We show
+that the statistical mechanics of such systems exhibit a striking resemblance
+to liquid crystals, with analogous ordered-nematic and disordered-isotropic
+phases. The ordered phase consists of bodies orbiting in a disk in both
+directions, with the disk thickness depending on temperature, while the
+disordered phase corresponds to a nearly isotropic distribution of the orbit
+normals. We show that below a critical value of the total angular momentum, the
+system undergoes a first-order phase transition between the ordered and
+disordered phases. At the critical point the phase transition becomes
+second-order while for higher angular momenta there is a smooth crossover. We
+also find metastable equilibria containing two identical disks with mutual
+inclinations between $90^{\circ}$ and $180^\circ$.
+",0,1,0,0,0,0
+15226,"Topological Semimetals carrying Arbitrary Hopf Numbers: Hopf-Link, Solomon's-Knot, Trefoil-Knot and Other Semimetals","  We propose a new type of Hopf semimetals indexed by a pair of numbers
+$(p,q)$, where the Hopf number is given by $pq$. The Fermi surface is given by
+the preimage of the Hopf map, which is nontrivially linked for a nonzero Hopf
+number. The Fermi surface forms a torus link, whose examples are the Hopf link
+indexed by $(1,1)$, the Solomon's knot $(2,1)$, the double Hopf-link $(2,2)$
+and the double trefoil-knot $(3,2)$. We may choose $p$ or $q$ as a half
+integer, where torus-knot Fermi surfaces such as the trefoil knot $(3/2,1)$ are
+realized. It is even possible to make the Hopf number an arbitrary rational
+number, where a semimetal whose Fermi surface forms open strings is generated.
+",0,1,0,0,0,0
+15227,Online Learning with Abstention,"  We present an extensive study of the key problem of online learning where
+algorithms are allowed to abstain from making predictions. In the adversarial
+setting, we show how existing online algorithms and guarantees can be adapted
+to this problem. In the stochastic setting, we first point out a bias problem
+that limits the straightforward extension of algorithms such as UCB-N to
+time-varying feedback graphs, as needed in this context. Next, we give a new
+algorithm, UCB-GT, that exploits historical data and is adapted to time-varying
+feedback graphs. We show that this algorithm benefits from more favorable
+regret guarantees than a possible, but limited, extension of UCB-N. We further
+report the results of a series of experiments demonstrating that UCB-GT largely
+outperforms that extension of UCB-N, as well as more standard baselines.
+",1,0,0,0,0,0
+15228,Stabilization of self-mode-locked quantum dash lasers by symmetric dual-loop optical feedback,"  We report experimental studies of the influence of symmetric dual-loop
+optical feedback on the RF linewidth and timing jitter of self-mode-locked
+two-section quantum dash lasers emitting at 1550 nm. Various feedback schemes
+were investigated and optimum levels determined for narrowest RF linewidth and
+low timing jitter, for single-loop and symmetric dual-loop feedback. Two
+symmetric dual-loop configurations, with balanced and unbalanced feedback
+ratios, were studied. We demonstrate that unbalanced symmetric dual loop
+feedback, with the inner cavity resonant and fine delay tuning of the outer
+loop, gives narrowest RF linewidth and reduced timing jitter over a wide range
+of delay, unlike single and balanced symmetric dual-loop configurations. This
+configuration with feedback lengths 80 and 140 m narrows the RF linewidth by
+4-67x and 10-100x, respectively, across the widest delay range, compared to
+free-running. For symmetric dual-loop feedback, the influence of different
+power split ratios through the feedback loops was determined. Our results show
+that symmetric dual-loop feedback is markedly more effective than single-loop
+feedback in reducing RF linewidth and timing jitter, and is much less sensitive
+to delay phase, making this technique ideal for applications where robustness
+and alignment tolerance are essential.
+",0,1,0,0,0,0
+15229,Sensitivity Analysis for Mirror-Stratifiable Convex Functions,"  This paper provides a set of sensitivity analysis and activity identification
+results for a class of convex functions with a strong geometric structure, that
+we coined ""mirror-stratifiable"". These functions are such that there is a
+bijection between a primal and a dual stratification of the space into
+partitioning sets, called strata. This pairing is crucial to track the strata
+that are identifiable by solutions of parametrized optimization problems or by
+iterates of optimization algorithms. This class of functions encompasses all
+regularizers routinely used in signal and image processing, machine learning,
+and statistics. We show that this ""mirror-stratifiable"" structure enjoys a nice
+sensitivity theory, allowing us to study stability of solutions of optimization
+problems to small perturbations, as well as activity identification of
+first-order proximal splitting-type algorithms. Existing results in the
+literature typically assume that, under a non-degeneracy condition, the active
+set associated to a minimizer is stable to small perturbations and is
+identified in finite time by optimization schemes. In contrast, our results do
+not require any non-degeneracy assumption: in consequence, the optimal active
+set is not necessarily stable anymore, but we are able to track precisely the
+set of identifiable strata.We show that these results have crucial implications
+when solving challenging ill-posed inverse problems via regularization, a
+typical scenario where the non-degeneracy condition is not fulfilled. Our
+theoretical results, illustrated by numerical simulations, allow to
+characterize the instability behaviour of the regularized solutions, by
+locating the set of all low-dimensional strata that can be potentially
+identified by these solutions.
+",0,0,1,1,0,0
+15230,Coupling Story to Visualization: Using Textual Analysis as a Bridge Between Data and Interpretation,"  Online writers and journalism media are increasingly combining visualization
+(and other multimedia content) with narrative text to create narrative
+visualizations. Often, however, the two elements are presented independently of
+one another. We propose an approach to automatically integrate text and
+visualization elements. We begin with a writer's narrative that presumably can
+be supported with visual data evidence. We leverage natural language
+processing, quantitative narrative analysis, and information visualization to
+(1) automatically extract narrative components (who, what, when, where) from
+data-rich stories, and (2) integrate the supporting data evidence with the text
+to develop a narrative visualization. We also employ bidirectional interaction
+from text to visualization and visualization to text to support reader
+exploration in both directions. We demonstrate the approach with a case study
+in the data-rich field of sports journalism.
+",1,0,0,0,0,0
+15231,Automatic Prediction of Discourse Connectives,"  Accurate prediction of suitable discourse connectives (however, furthermore,
+etc.) is a key component of any system aimed at building coherent and fluent
+discourses from shorter sentences and passages. As an example, a dialog system
+might assemble a long and informative answer by sampling passages extracted
+from different documents retrieved from the Web. We formulate the task of
+discourse connective prediction and release a dataset of 2.9M sentence pairs
+separated by discourse connectives for this task. Then, we evaluate the
+hardness of the task for human raters, apply a recently proposed decomposable
+attention (DA) model to this task and observe that the automatic predictor has
+a higher F1 than human raters (32 vs. 30). Nevertheless, under specific
+conditions the raters still outperform the DA model, suggesting that there is
+headroom for future improvements.
+",1,0,0,0,0,0
+15232,"Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning","  Although reinforcement learning methods can achieve impressive results in
+simulation, the real world presents two major challenges: generating samples is
+exceedingly expensive, and unexpected perturbations or unseen situations cause
+proficient but specialized policies to fail at test time. Given that it is
+impractical to train separate policies to accommodate all situations the agent
+may see in the real world, this work proposes to learn how to quickly and
+effectively adapt online to new tasks. To enable sample-efficient learning, we
+consider learning online adaptation in the context of model-based reinforcement
+learning. Our approach uses meta-learning to train a dynamics model prior such
+that, when combined with recent data, this prior can be rapidly adapted to the
+local context. Our experiments demonstrate online adaptation for continuous
+control tasks on both simulated and real-world agents. We first show simulated
+agents adapting their behavior online to novel terrains, crippled body parts,
+and highly-dynamic environments. We also illustrate the importance of
+incorporating online adaptation into autonomous agents that operate in the real
+world by applying our method to a real dynamic legged millirobot. We
+demonstrate the agent's learned ability to quickly adapt online to a missing
+leg, adjust to novel terrains and slopes, account for miscalibration or errors
+in pose estimation, and compensate for pulling payloads.
+",1,0,0,1,0,0
+15233,A Novel Receiver Design with Joint Coherent and Non-Coherent Processing,"  In this paper, we propose a novel splitting receiver, which involves joint
+processing of coherently and non-coherently received signals. Using a passive
+RF power splitter, the received signal at each receiver antenna is split into
+two streams which are then processed by a conventional coherent detection (CD)
+circuit and a power-detection (PD) circuit, respectively. The streams of the
+signals from all the receiver antennas are then jointly used for information
+detection. We show that the splitting receiver creates a three-dimensional
+received signal space, due to the joint coherent and non-coherent processing.
+We analyze the achievable rate of a splitting receiver, which shows that the
+splitting receiver provides a rate gain of $3/2$ compared to either the
+conventional (CD-based) coherent receiver or the PD-based non-coherent receiver
+in the high SNR regime. We also analyze the symbol error rate (SER) for
+practical modulation schemes, which shows that the splitting receiver achieves
+asymptotic SER reduction by a factor of at least $\sqrt{M}-1$ for $M$-QAM
+compared to either the conventional (CD-based) coherent receiver or the
+PD-based non-coherent receiver.
+",1,0,0,0,0,0
+15234,Comment on the Equality Condition for the I-MMSE Proof of Entropy Power Inequality,"  The paper establishes the equality condition in the I-MMSE proof of the
+entropy power inequality (EPI). This is done by establishing an exact
+expression for the deficit between the two sides of the EPI. Interestingly, a
+necessary condition for the equality is established by making a connection to
+the famous Cauchy functional equation.
+",1,0,0,0,0,0
+15235,Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models,"  The gap between our ability to collect interesting data and our ability to
+analyze these data is growing at an unprecedented rate. Recent algorithmic
+attempts to fill this gap have employed unsupervised tools to discover
+structure in data. Some of the most successful approaches have used
+probabilistic models to uncover latent thematic structure in discrete data.
+Despite the success of these models on textual data, they have not generalized
+as well to image data, in part because of the spatial and temporal structure
+that may exist in an image stream.
+We introduce a novel unsupervised machine learning framework that
+incorporates the ability of convolutional autoencoders to discover features
+from images that directly encode spatial information, within a Bayesian
+nonparametric topic model that discovers meaningful latent patterns within
+discrete data. By using this hybrid framework, we overcome the fundamental
+dependency of traditional topic models on rigidly hand-coded data
+representations, while simultaneously encoding spatial dependency in our topics
+without adding model complexity. We apply this model to the motivating
+application of high-level scene understanding and mission summarization for
+exploratory marine robots. Our experiments on a seafloor dataset collected by a
+marine robot show that the proposed hybrid framework outperforms current
+state-of-the-art approaches on the task of unsupervised seafloor terrain
+characterization.
+",1,0,0,1,0,0
+15236,"Multiplicative models for frequency data, estimation and testing","  This paper is about models for a vector of probabilities whose elements must
+have a multiplicative structure and sum to 1 at the same time; in certain
+applications, as basket analysis, these models may be seen as a constrained
+version of quasi-independence. After reviewing the basic properties of these
+models, their geometric features as a curved exponential family are
+investigated. A new algorithm for computing maximum likelihood estimates is
+presented and new insights are provided on the underlying geometry. The
+asymptotic distribution of three statistics for hypothesis testing are derived
+and a small simulation study is presented to investigate the accuracy of
+asymptotic approximations.
+",0,0,1,1,0,0
+15237,An Efficient Keyless Fragmentation Algorithm for Data Protection,"  The family of Information Dispersal Algorithms is applied to distributed
+systems for secure and reliable storage and transmission. In comparison with
+perfect secret sharing it achieves a significantly smaller memory overhead and
+better performance, but provides only incremental confidentiality. Therefore,
+even if it is not possible to explicitly reconstruct data from less than the
+required amount of fragments, it is still possible to deduce some information
+about the nature of data by looking at preserved data patterns inside a
+fragment. The idea behind this paper is to provide a lightweight data
+fragmentation scheme, that would combine the space efficiency and simplicity
+that could be find in Information Dispersal Algorithms with a computational
+level of data confidentiality.
+",1,0,0,0,0,0
+15238,Schramm--Loewner-evolution-type growth processes corresponding to Wess--Zumino--Witten theories,"  A group theoretical formulation of Schramm--Loewner-evolution-type growth
+processes corresponding to Wess--Zumino--Witten theories is developed that
+makes it possible to construct stochastic differential equations associated
+with more general null vectors than the ones considered in the most fundamental
+example in [Alekseev et al., Lett. Math. Phys. 97, 243-261 (2011)]. Also given
+are examples of Schramm--Loewner-evolution-type growth processes associated
+with null vectors of conformal weight $4$ in the basic representations of
+$\widehat{\mathfrak{sl}}_{2}$ and $\widehat{\mathfrak{sl}}_{3}$.
+",0,0,1,0,0,0
+15239,Explicit Commutativity Conditions for Second-order Linear Time-Varying Systems with Non-Zero Initial Conditions,"  Although the explicit commutativitiy conditions for second-order linear
+time-varying systems have been appeared in some literature, these are all for
+initially relaxed systems. This paper presents explicit necessary and
+sufficient commutativity conditions for commutativity of second-order linear
+time-varying systems with non-zero initial conditions. It has appeared
+interesting that the second requirement for the commutativity of non-relaxed
+systems plays an important role on the commutativity conditions when non-zero
+initial conditions exist. Another highlight is that the commutativity of
+switched systems is considered and spoiling of commutativity at the switching
+instants is illustrated for the first time. The simulation results support the
+theory developed in the paper.
+",1,0,0,0,0,0
+15240,Energy Acceptance of the St. George Recoil Separator,"  Radiative alpha-capture, ($\alpha,\gamma$), reactions play a critical role in
+nucleosynthesis and nuclear energy generation in a variety of astrophysical
+environments. The St. George recoil separator at the University of Notre Dame's
+Nuclear Science Laboratory was developed to measure ($\alpha,\gamma$) reactions
+in inverse kinematics via recoil detection in order to obtain nuclear reaction
+cross sections at the low energies of astrophysical interest, while avoiding
+the $\gamma$-background that plagues traditional measurement techniques. Due to
+the $\gamma$-ray produced by the nuclear reaction at the target location,
+recoil nuclei are produced with a variety of energies and angles, all of which
+must be accepted by St. George in order to accurately determine the reaction
+cross section. We demonstrate the energy acceptance of the St. George recoil
+separator using primary beams of helium, hydrogen, neon, and oxygen, spanning
+the magnetic and electric rigidity phase space populated by recoils of
+anticipated ($\alpha,\gamma$) reaction measurements. We found the performance
+of St. George meets the design specifications, demonstrating its suitability
+for ($\alpha,\gamma$) reaction measurements of astrophysical interest.
+",0,1,0,0,0,0
+15241,Topological and Algebraic Characterizations of Gallai-Simplicial Complexes,"  We recall first Gallai-simplicial complex $\Delta_{\Gamma}(G)$ associated to
+Gallai graph $\Gamma(G)$ of a planar graph $G$. The Euler characteristic is a
+very useful topological and homotopic invariant to classify surfaces. In
+Theorems 3.2 and 3.4, we compute Euler characteristics of Gallai-simplicial
+complexes associated to triangular ladder and prism graphs, respectively.
+Let $G$ be a finite simple graph on $n$ vertices of the form $n=3l+2$ or
+$3l+3$. In Theorem 4.4, we prove that $G$ will be $f$-Gallai graph for the
+following types of constructions of $G$.
+Type 1. When $n=3l+2$. $G=\mathbb{S}_{4l}$ is a graph consisting of two
+copies of star graphs $S_{2l}$ and $S'_{2l}$ with $l\geq 2$ having $l$ common
+vertices.
+Type 2. When $n=3l+3$. $G=\mathbb{S}_{4l+1}$ is a graph consisting of two
+star graphs $S_{2l}$ and $S_{2l+1}$ with $l\geq 2$ having $l$ common vertices.
+",0,0,1,0,0,0
+15242,Fairness risk measures,"  Ensuring that classifiers are non-discriminatory or fair with respect to a
+sensitive feature (e.g., race or gender) is a topical problem. Progress in this
+task requires fixing a definition of fairness, and there have been several
+proposals in this regard over the past few years. Several of these, however,
+assume either binary sensitive features (thus precluding categorical or
+real-valued sensitive groups), or result in non-convex objectives (thus
+adversely affecting the optimisation landscape). In this paper, we propose a
+new definition of fairness that generalises some existing proposals, while
+allowing for generic sensitive features and resulting in a convex objective.
+The key idea is to enforce that the expected losses (or risks) across each
+subgroup induced by the sensitive feature are commensurate. We show how this
+relates to the rich literature on risk measures from mathematical finance. As a
+special case, this leads to a new convex fairness-aware objective based on
+minimising the conditional value at risk (CVaR).
+",1,0,0,1,0,0
+15243,On-line tracing of XACML-based policy coverage criteria,"  Currently, eXtensible Access Control Markup Language (XACML) has becoming the
+standard for implementing access control policies and consequently more
+attention is dedicated to testing the correctness of XACML policies. In
+particular, coverage measures can be adopted for assessing test strategy
+effectiveness in exercising the policy elements. This study introduces a set of
+XACML coverage criteria and describes the access control infrastructure, based
+on a monitor engine, enabling the coverage criterion selection and the on-line
+tracing of the testing activity. Examples of infrastructure usage and of
+assessment of different test strategies are provided.
+",1,0,0,0,0,0
+15244,The square lattice Ising model on the rectangle II: Finite-size scaling limit,"  Based on the results published recently [J. Phys. A: Math. Theor. 50, 065201
+(2017)], the universal finite-size contributions to the free energy of the
+square lattice Ising model on the $L\times M$ rectangle, with open boundary
+conditions in both directions, are calculated exactly in the finite-size
+scaling limit $L,M\to\infty$, $T\to T_\mathrm{c}$, with fixed temperature
+scaling variable $x\propto(T/T_\mathrm{c}-1)M$ and fixed aspect ratio
+$\rho\propto L/M$. We derive exponentially fast converging series for the
+related Casimir potential and Casimir force scaling functions. At the critical
+point $T=T_\mathrm{c}$ we confirm predictions from conformal field theory by
+Cardy & Peschel [Nucl. Phys. B 300, 377 (1988)] and by Kleban & Vassileva [J.
+Phys. A: Math. Gen. 24, 3407 (1991)]. The presence of corners and the related
+corner free energy has dramatic impact on the Casimir scaling functions and
+leads to a logarithmic divergence of the Casimir potential scaling function at
+criticality.
+",0,1,1,0,0,0
+15245,Using MRI Cell Tracking to Monitor Immune Cell Recruitment in Response to a Peptide-Based Cancer Vaccine,"  Purpose: MRI cell tracking can be used to monitor immune cells involved in
+the immunotherapy response, providing insight into the mechanism of action,
+temporal progression of tumour growth and individual potency of therapies. To
+evaluate whether MRI could be used to track immune cell populations in response
+to immunotherapy, CD8+ cytotoxic T cells (CTLs), CD4+CD25+FoxP3+ regulatory T
+cells (Tregs) and myeloid derived suppressor cells (MDSCs) were labelled with
+superparamagnetic iron oxide (SPIO) particles.
+Methods: SPIO-labelled cells were injected into mice (one cell type/mouse)
+implanted with an HPV-based cervical cancer model. Half of these mice were also
+vaccinated with DepoVaxTM, a lipid-based vaccine platform that was developed to
+enhance the potency of peptide-based vaccines.
+Results: MRI visualization of CTLs, Tregs and MDSCs was apparent 24 hours
+post-injection, with hypointensities due to iron labelled cells clearing
+approximately 72 hours post-injection. Vaccination resulted in increased
+recruitment of CTLs and decreased recruitment of MDSCs and Tregs to the tumour.
+We also found that MDSC and Treg recruitment was positively correlated with
+final tumour volume.
+Conclusion: This type of analysis can be used to non-invasively study changes
+in immune cell recruitment in individual mice over time, potentially allowing
+improved application and combination of immunotherapies.
+",0,1,0,0,0,0
+15246,Entropic Spectral Learning in Large Scale Networks,"  We present a novel algorithm for learning the spectral density of large scale
+networks using stochastic trace estimation and the method of maximum entropy.
+The complexity of the algorithm is linear in the number of non-zero elements of
+the matrix, offering a computational advantage over other algorithms. We apply
+our algorithm to the problem of community detection in large networks. We show
+state-of-the-art performance on both synthetic and real datasets.
+",0,0,0,1,0,0
+15247,On Green's proof of infinitesimal Torelli theorem for hypersurfaces,"  We prove an equivalence between the infinitesimal Torelli theorem for top
+forms on a hypersurface contained inside a Grassmannian $\mathbb G$ and the
+theory of adjoint volume forms presented in L. Rizzi, F. Zucconi, ""Generalized
+adjoint forms on algebraic varieties"", Ann. Mat. Pura e Applicata, in press.
+More precisely, via this theory and a suitable generalization of Macaulay's
+theorem we show that the differential of the period map vanishes on an
+infinitesimal deformation if and only if certain explicitly given twisted
+volume forms go in the generalized Jacobi ideal of $X$ via the cup product
+homomorphism.
+",0,0,1,0,0,0
+15248,Gentle heating by mixing in cooling flow clusters,"  We analyze three-dimensional hydrodynamical simulations of the interaction of
+jets and the bubbles they inflate with the intra-cluster medium (ICM), and show
+that the heating of the ICM by mixing hot bubble gas with the ICM operates over
+tens of millions of years, and hence can smooth the sporadic activity of the
+jets. The inflation process of hot bubbles by propagating jets forms many
+vortices, and these vortices mix the hot bubble gas with the ICM. The mixing,
+hence the heating of the ICM, starts immediately after the jets are launched,
+but continues for tens of millions of years. We suggest that the smoothing of
+the active galactic nucleus (AGN) sporadic activity by the long-lived vortices
+accounts for the recent finding of a gentle energy coupling between AGN heating
+and the ICM.
+",0,1,0,0,0,0
+15249,Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization,"  This paper tackles the reduction of redundant repeating generation that is
+often observed in RNN-based encoder-decoder models. Our basic idea is to
+jointly estimate the upper-bound frequency of each target vocabulary in the
+encoder and control the output words based on the estimation in the decoder.
+Our method shows significant improvement over a strong RNN-based
+encoder-decoder baseline and achieved its best results on an abstractive
+summarization benchmark.
+",1,0,0,1,0,0
+15250,Some exercises with the Lasso and its compatibility constant,"  We consider the Lasso for a noiseless experiment where one has observations
+$X \beta^0$ and uses the penalized version of basis pursuit. We compute for
+some special designs the compatibility constant, a quantity closely related to
+the restricted eigenvalue. We moreover show the dependence of the (penalized)
+prediction error on this compatibility constant. This exercise illustrates that
+compatibility is necessarily entering into the bounds for the (penalized)
+prediction error and that the bounds in the literature therefore are - up to
+constants - tight. We also give conditions that show that in the noisy case the
+dominating term for the prediction error is given by the prediction error of
+the noiseless case.
+",0,0,1,1,0,0
+15251,Leveraging Sensory Data in Estimating Transformer Lifetime,"  Transformer lifetime assessments plays a vital role in reliable operation of
+power systems. In this paper, leveraging sensory data, an approach in
+estimating transformer lifetime is presented. The winding hottest-spot
+temperature, which is the pivotal driver that impacts transformer aging, is
+measured hourly via a temperature sensor, then transformer loss of life is
+calculated based on the IEEE Std. C57.91-2011. A Cumulative Moving Average
+(CMA) model is subsequently applied to the data stream of the transformer loss
+of life to provide hourly estimates until convergence. Numerical examples
+demonstrate the effectiveness of the proposed approach for the transformer
+lifetime estimation, and explores its efficiency and practical merits.
+",1,0,0,0,0,0
+15252,Spatial Projection of Multiple Climate Variables using Hierarchical Multitask Learning,"  Future projection of climate is typically obtained by combining outputs from
+multiple Earth System Models (ESMs) for several climate variables such as
+temperature and precipitation. While IPCC has traditionally used a simple model
+output average, recent work has illustrated potential advantages of using a
+multitask learning (MTL) framework for projections of individual climate
+variables. In this paper we introduce a framework for hierarchical multitask
+learning (HMTL) with two levels of tasks such that each super-task, i.e., task
+at the top level, is itself a multitask learning problem over sub-tasks. For
+climate projections, each super-task focuses on projections of specific climate
+variables spatially using an MTL formulation. For the proposed HMTL approach, a
+group lasso regularization is added to couple parameters across the
+super-tasks, which in the climate context helps exploit relationships among the
+behavior of different climate variables at a given spatial location. We show
+that some recent works on MTL based on learning task dependency structures can
+be viewed as special cases of HMTL. Experiments on synthetic and real climate
+data show that HMTL produces better results than decoupled MTL methods applied
+separately on the super-tasks and HMTL significantly outperforms baselines for
+climate projection.
+",1,0,0,1,0,0
+15253,Two-dimensional plasmons in the random impedance network model of disordered thin-film nanocomposites,"  Random impedance networks are widely used as a model to describe plasmon
+resonances in disordered metal-dielectric nanocomposites. In order to study
+thin films, two-dimensional networks are often used despite the fact that such
+networks correspond to a two-dimensional electrodynamics [J.P. Clerc et al, J.
+Phys. A 29, 4781 (1996)]. In the present work, we propose a model of
+two-dimensional systems with three-dimensional Coulomb interaction and show
+that this model is equivalent to a planar network with long-range capacitive
+connections between sites. In a case of a metal film, we get a known dispersion
+$\omega \propto \sqrt{k}$ of plane-wave two-dimensional plasmons. In the
+framework of the proposed model, we study the evolution of resonances with
+decreasing of metal filling factor. In the subcritical region with metal
+filling $p$ lower than the percolation threshold $p_c$, we observe a gap with
+Lifshitz tails in the spectral density of states (DOS). In the supercritical
+region $p>p_c$, the DOS demonstrates a crossover between plane-wave
+two-dimensional plasmons and resonances associated with small clusters.
+",0,1,0,0,0,0
+15254,K-edge subtraction vs. A-space processing for x-ray imaging of contrast agents: SNR,"  Purpose: To compare two methods that use x-ray spectral information to image
+externally administered contrast agents: K-edge subtraction and basis-function
+decomposition (the A-space method), Methods: The K-edge method uses narrow band
+x-ray spectra with energies infinitesimally below and above the contrast
+material K-edge energy. The A-space method uses a broad spectrum x-ray tube
+source and measures the transmitted spectrum with photon counting detectors
+with pulse height analysis. The methods are compared by their signal to noise
+ratio (SNR) divided by the patient dose for an imaging task to decide whether
+contrast material is present in a soft tissue background. The performance with
+iodine or gadolinium containing contrast material is evaluated as a function of
+object thickness and the x-ray tube voltage of the A-space method. Results: For
+a tube voltages above 60 kV and soft tissue thicknesses from 5 to 25 g/cm^2,
+the A-space method has a larger SNR per dose than the K-edge subtraction method
+for either iodine or gadolinium containing contrast agent. Conclusion: Even
+with the unrealistic spectra assumed for the K-edge method, the A-space method
+has a substantially larger SNR per patient dose.
+",0,1,0,0,0,0
+15255,Recall Traces: Backtracking Models for Efficient Reinforcement Learning,"  In many environments only a tiny subset of all states yield high reward. In
+these cases, few of the interactions with the environment provide a relevant
+learning signal. Hence, we may want to preferentially train on those
+high-reward states and the probable trajectories leading to them. To this end,
+we advocate for the use of a backtracking model that predicts the preceding
+states that terminate at a given high-reward state. We can train a model which,
+starting from a high value state (or one that is estimated to have high value),
+predicts and sample for which the (state, action)-tuples may have led to that
+high value state. These traces of (state, action) pairs, which we refer to as
+Recall Traces, sampled from this backtracking model starting from a high value
+state, are informative as they terminate in good states, and hence we can use
+these traces to improve a policy. We provide a variational interpretation for
+this idea and a practical algorithm in which the backtracking model samples
+from an approximate posterior distribution over trajectories which lead to
+large rewards. Our method improves the sample efficiency of both on- and
+off-policy RL algorithms across several environments and tasks.
+",0,0,0,1,0,0
+15256,Path-integral formalism for stochastic resetting: Exactly solved examples and shortcuts to confinement,"  We study the dynamics of overdamped Brownian particles diffusing in
+conservative force fields and undergoing stochastic resetting to a given
+location with a generic space-dependent rate of resetting. We present a
+systematic approach involving path integrals and elements of renewal theory
+that allows to derive analytical expressions for a variety of statistics of the
+dynamics such as (i) the propagator prior to first reset; (ii) the distribution
+of the first-reset time, and (iii) the spatial distribution of the particle at
+long times. We apply our approach to several representative and hitherto
+unexplored examples of resetting dynamics. A particularly interesting example
+for which we find analytical expressions for the statistics of resetting is
+that of a Brownian particle trapped in a harmonic potential with a rate of
+resetting that depends on the instantaneous energy of the particle. We find
+that using energy-dependent resetting processes is more effective in achieving
+spatial confinement of Brownian particles on a faster timescale than by
+performing quenches of parameters of the harmonic potential.
+",0,1,0,0,0,0
+15257,A strongly convergent numerical scheme from Ensemble Kalman inversion,"  The Ensemble Kalman methodology in an inverse problems setting can be viewed
+as an iterative scheme, which is a weakly tamed discretization scheme for a
+certain stochastic differential equation (SDE). Assuming a suitable
+approximation result, dynamical properties of the SDE can be rigorously pulled
+back via the discrete scheme to the original Ensemble Kalman inversion.
+The results of this paper make a step towards closing the gap of the missing
+approximation result by proving a strong convergence result in a simplified
+model of a scalar stochastic differential equation. We focus here on a toy
+model with similar properties than the one arising in the context of Ensemble
+Kalman filter. The proposed model can be interpreted as a single particle
+filter for a linear map and thus forms the basis for further analysis. The
+difficulty in the analysis arises from the formally derived limiting SDE with
+non-globally Lipschitz continuous nonlinearities both in the drift and in the
+diffusion. Here the standard Euler-Maruyama scheme might fail to provide a
+strongly convergent numerical scheme and taming is necessary. In contrast to
+the strong taming usually used, the method presented here provides a weaker
+form of taming.
+We present a strong convergence analysis by first proving convergence on a
+domain of high probability by using a cut-off or localisation, which then
+leads, combined with bounds on moments for both the SDE and the numerical
+scheme, by a bootstrapping argument to strong convergence.
+",0,0,1,0,0,0
+15258,Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images,"  Positron Emission Tomography (PET) is a functional imaging modality widely
+used in neuroscience studies. To obtain meaningful quantitative results from
+PET images, attenuation correction is necessary during image reconstruction.
+For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance
+(MR) images do not reflect attenuation coefficients directly. To address this
+issue, we present deep neural network methods to derive the continuous
+attenuation coefficients for brain PET imaging from MR images. With only Dixon
+MR images as the network input, the existing U-net structure was adopted and
+analysis using forty patient data sets shows it is superior than other Dixon
+based methods. When both Dixon and zero echo time (ZTE) images are available,
+we have proposed a modified U-net structure, named GroupU-net, to efficiently
+make use of both Dixon and ZTE information through group convolution modules
+when the network goes deeper. Quantitative analysis based on fourteen real
+patient data sets demonstrates that both network approaches can perform better
+than the standard methods, and the proposed network structure can further
+reduce the PET quantification error compared to the U-net structure.
+",0,1,0,1,0,0
+15259,Resolving Local Electrochemistry at the Nanoscale via Electrochemical Strain Microscopy: Modeling and Experiments,"  Electrochemistry is the underlying mechanism in a variety of energy
+conversion and storage systems, and it is well known that the composition,
+structure, and properties of electrochemical materials near active interfaces
+often deviates substantially and inhomogeneously from the bulk properties. A
+universal challenge facing the development of electrochemical systems is our
+lack of understanding of physical and chemical rates at local length scales,
+and the recently developed electrochemical strain microscopy (ESM) provides a
+promising method to probe crucial local information regarding the underlying
+electrochemical mechanisms. Here we develop a computational model that couples
+mechanics and electrochemistry relevant for ESM experiments, with the goal to
+enable quantitative analysis of electrochemical processes underneath a charged
+scanning probe. We show that the model captures the essence of a number of
+different ESM experiments, making it possible to de-convolute local ionic
+concentration and diffusivity via combined ESM mapping, spectroscopy, and
+relaxation studies. Through the combination of ESM experiments and
+computations, it is thus possible to obtain deep insight into the local
+electrochemistry at the nanoscale.
+",0,1,0,0,0,0
+15260,Genuine equivariant operads,"  We build new algebraic structures, which we call genuine equivariant operads,
+which can be thought of as a hybrid between equivariant operads and coefficient
+systems. We then prove an Elmendorf-Piacenza type theorem stating that
+equivariant operads, with their graph model structure, are equivalent to
+genuine equivariant operads, with their projective model structure.
+As an application, we build explicit models for the $N_{\infty}$-operads of
+Blumberg and Hill.
+",0,0,1,0,0,0
+15261,A linear-time algorithm for the maximum-area inscribed triangle in a convex polygon,"  Given the n vertices of a convex polygon in cyclic order, can the triangle of
+maximum area inscribed in P be determined by an algorithm with O(n) time
+complexity? A purported linear-time algorithm by Dobkin and Snyder from 1979
+has recently been shown to be incorrect by Keikha, Löffler, Urhausen, and van
+der Hoog. These authors give an alternative algorithm with O(n log n) time
+complexity. Here we give an algorithm with linear time complexity.
+",1,0,1,0,0,0
+15262,Estimation of the infinitesimal generator by square-root approximation,"  For the analysis of molecular processes, the estimation of time-scales, i.e.,
+transition rates, is very important. Estimating the transition rates between
+molecular conformations is -- from a mathematical point of view -- an invariant
+subspace projection problem. A certain infinitesimal generator acting on
+function space is projected to a low-dimensional rate matrix. This projection
+can be performed in two steps. First, the infinitesimal generator is
+discretized, then the invariant subspace is approxi-mated and used for the
+subspace projection. In our approach, the discretization will be based on a
+Voronoi tessellation of the conformational space. We will show that the
+discretized infinitesimal generator can simply be approximated by the geometric
+average of the Boltzmann weights of the Voronoi cells. Thus, there is a direct
+correla-tion between the potential energy surface of molecular structures and
+the transition rates of conformational changes. We present results for a
+2d-diffusion process and Alanine dipeptide.
+",0,1,0,0,0,0
+15263,"Korea Microlensing Telescope Network Microlensing Events from 2015: Event-Finding Algorithm, Vetting, and Photometry","  We present microlensing events in the 2015 Korea Microlensing Telescope
+Network (KMTNet) data and our procedure for identifying these events. In
+particular, candidates were detected with a novel ""completed event""
+microlensing event-finder algorithm. The algorithm works by making linear fits
+to a (t0,teff,u0) grid of point-lens microlensing models. This approach is
+rendered computationally efficient by restricting u0 to just two values (0 and
+1), which we show is quite adequate. The implementation presented here is
+specifically tailored to the commission-year character of the 2015 data, but
+the algorithm is quite general and has already been applied to a completely
+different (non-KMTNet) data set. We outline expected improvements for 2016 and
+future KMTNet data. The light curves of the 660 ""clear microlensing"" and 182
+""possible microlensing"" events that were found in 2015 are presented along with
+our policy for their public release.
+",0,1,0,0,0,0
+15264,"Caveat Emptor, Computational Social Science: Large-Scale Missing Data in a Widely-Published Reddit Corpus","  As researchers use computational methods to study complex social behaviors at
+scale, the validity of this computational social science depends on the
+integrity of the data. On July 2, 2015, Jason Baumgartner published a dataset
+advertised to include ``every publicly available Reddit comment'' which was
+quickly shared on Bittorrent and the Internet Archive. This data quickly became
+the basis of many academic papers on topics including machine learning, social
+behavior, politics, breaking news, and hate speech. We have discovered
+substantial gaps and limitations in this dataset which may contribute to bias
+in the findings of that research. In this paper, we document the dataset,
+substantial missing observations in the dataset, and the risks to research
+validity from those gaps. In summary, we identify strong risks to research that
+considers user histories or network analysis, moderate risks to research that
+compares counts of participation, and lesser risk to machine learning research
+that avoids making representative claims about behavior and participation on
+Reddit.
+",1,0,0,0,0,0
+15265,Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates,"  Skeleton-based human action recognition has attracted a lot of research
+attention during the past few years. Recent works attempted to utilize
+recurrent neural networks to model the temporal dependencies between the 3D
+positional configurations of human body joints for better analysis of human
+activities in the skeletal data. The proposed work extends this idea to spatial
+domain as well as temporal domain to better analyze the hidden sources of
+action-related information within the human skeleton sequences in both of these
+domains simultaneously. Based on the pictorial structure of Kinect's skeletal
+data, an effective tree-structure based traversal framework is also proposed.
+In order to deal with the noise in the skeletal data, a new gating mechanism
+within LSTM module is introduced, with which the network can learn the
+reliability of the sequential data and accordingly adjust the effect of the
+input data on the updating procedure of the long-term context representation
+stored in the unit's memory cell. Moreover, we introduce a novel multi-modal
+feature fusion strategy within the LSTM unit in this paper. The comprehensive
+experimental results on seven challenging benchmark datasets for human action
+recognition demonstrate the effectiveness of the proposed method.
+",1,0,0,0,0,0
+15266,Interpolation and Extrapolation of Toeplitz Matrices via Optimal Mass Transport,"  In this work, we propose a novel method for quantifying distances between
+Toeplitz structured covariance matrices. By exploiting the spectral
+representation of Toeplitz matrices, the proposed distance measure is defined
+based on an optimal mass transport problem in the spectral domain. This may
+then be interpreted in the covariance domain, suggesting a natural way of
+interpolating and extrapolating Toeplitz matrices, such that the positive
+semi-definiteness and the Toeplitz structure of these matrices are preserved.
+The proposed distance measure is also shown to be contractive with respect to
+both additive and multiplicative noise, and thereby allows for a quantification
+of the decreased distance between signals when these are corrupted by noise.
+Finally, we illustrate how this approach can be used for several applications
+in signal processing. In particular, we consider interpolation and
+extrapolation of Toeplitz matrices, as well as clustering problems and tracking
+of slowly varying stochastic processes.
+",0,0,1,1,0,0
+15267,Performance of time delay estimation in a cognitive radar,"  A cognitive radar adapts the transmit waveform in response to changes in the
+radar and target environment. In this work, we analyze the recently proposed
+sub-Nyquist cognitive radar wherein the total transmit power in a multi-band
+cognitive waveform remains the same as its full-band conventional counterpart.
+For such a system, we derive lower bounds on the mean-squared-error (MSE) of a
+single-target time delay estimate. We formulate a procedure to select the
+optimal bands, and recommend distribution of the total power in different bands
+to enhance the accuracy of delay estimation. In particular, using Cramér-Rao
+bounds, we show that equi-width subbands in cognitive radar always have better
+delay estimation than the conventional radar. Further analysis using Ziv-Zakai
+bound reveals that cognitive radar performs well in low signal-to-noise (SNR)
+regions.
+",1,0,1,0,0,0
+15268,RAIL: Risk-Averse Imitation Learning,"  Imitation learning algorithms learn viable policies by imitating an expert's
+behavior when reward signals are not available. Generative Adversarial
+Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies
+when the expert's behavior is available as a fixed set of trajectories. We
+evaluate in terms of the expert's cost function and observe that the
+distribution of trajectory-costs is often more heavy-tailed for GAIL-agents
+than the expert at a number of benchmark continuous-control tasks. Thus,
+high-cost trajectories, corresponding to tail-end events of catastrophic
+failure, are more likely to be encountered by the GAIL-agents than the expert.
+This makes the reliability of GAIL-agents questionable when it comes to
+deployment in risk-sensitive applications like robotic surgery and autonomous
+driving. In this work, we aim to minimize the occurrence of tail-end events by
+minimizing tail risk within the GAIL framework. We quantify tail risk by the
+Conditional-Value-at-Risk (CVaR) of trajectories and develop the Risk-Averse
+Imitation Learning (RAIL) algorithm. We observe that the policies learned with
+RAIL show lower tail-end risk than those of vanilla GAIL. Thus the proposed
+RAIL algorithm appears as a potent alternative to GAIL for improved reliability
+in risk-sensitive applications.
+",1,0,0,0,0,0
+15269,Nucleation and growth of hierarchical martensite in epitaxial shape memory films,"  Shape memory alloys often show a complex hierarchical morphology in the
+martensitic state. To understand the formation of this twin-within-twins
+microstructure, we examine epitaxial Ni-Mn-Ga films as a model system. In-situ
+scanning electron microscopy experiments show beautiful complex twinning
+patterns with a number of different mesoscopic twin boundaries and macroscopic
+twin boundaries between already twinned regions. We explain the appearance and
+geometry of these patterns by constructing an internally twinned martensitic
+nucleus, which can take the shape of a diamond or a parallelogram, within the
+basic phenomenological theory of martensite. These nucleus contains already the
+seeds of different possible mesoscopic twin boundaries. Nucleation and growth
+of these nuclei determines the creation of the hierarchical space-filling
+martensitic microstructure. This is in contrast to previous approaches to
+explain a hierarchical martensitic microstructure. This new picture of creation
+and anisotropic, well-oriented growth of twinned martensitic nuclei explains
+the morphology and exact geometrical features of our experimentally observed
+twins-within-twins microstructure on the meso- and macroscopic scale.
+",0,1,0,0,0,0
+15270,Towards Sparse Hierarchical Graph Classifiers,"  Recent advances in representation learning on graphs, mainly leveraging graph
+convolutional networks, have brought a substantial improvement on many
+graph-based benchmark tasks. While novel approaches to learning node embeddings
+are highly suitable for node classification and link prediction, their
+application to graph classification (predicting a single label for the entire
+graph) remains mostly rudimentary, typically using a single global pooling step
+to aggregate node features or a hand-designed, fixed heuristic for hierarchical
+coarsening of the graph structure. An important step towards ameliorating this
+is differentiable graph coarsening---the ability to reduce the size of the
+graph in an adaptive, data-dependent manner within a graph neural network
+pipeline, analogous to image downsampling within CNNs. However, the previous
+prominent approach to pooling has quadratic memory requirements during training
+and is therefore not scalable to large graphs. Here we combine several recent
+advances in graph neural network design to demonstrate that competitive
+hierarchical graph classification results are possible without sacrificing
+sparsity. Our results are verified on several established graph classification
+benchmarks, and highlight an important direction for future research in
+graph-based neural networks.
+",1,0,0,0,0,0
+15271,Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations,"  Deep convolutional neural networks (CNNs) based approaches are the
+state-of-the-art in various computer vision tasks, including face recognition.
+Considerable research effort is currently being directed towards further
+improving deep CNNs by focusing on more powerful model architectures and better
+learning techniques. However, studies systematically exploring the strengths
+and weaknesses of existing deep models for face recognition are still
+relatively scarce in the literature. In this paper, we try to fill this gap and
+study the effects of different covariates on the verification performance of
+four recent deep CNN models using the Labeled Faces in the Wild (LFW) dataset.
+Specifically, we investigate the influence of covariates related to: image
+quality -- blur, JPEG compression, occlusion, noise, image brightness,
+contrast, missing pixels; and model characteristics -- CNN architecture, color
+information, descriptor computation; and analyze their impact on the face
+verification performance of AlexNet, VGG-Face, GoogLeNet, and SqueezeNet. Based
+on comprehensive and rigorous experimentation, we identify the strengths and
+weaknesses of the deep learning models, and present key areas for potential
+future research. Our results indicate that high levels of noise, blur, missing
+pixels, and brightness have a detrimental effect on the verification
+performance of all models, whereas the impact of contrast changes and
+compression artifacts is limited. It has been found that the descriptor
+computation strategy and color information does not have a significant
+influence on performance.
+",0,0,0,1,0,0
+15272,BayesVP: a Bayesian Voigt profile fitting package,"  We introduce a Bayesian approach for modeling Voigt profiles in absorption
+spectroscopy and its implementation in the python package, BayesVP, publicly
+available at this https URL. The code fits the
+absorption line profiles within specified wavelength ranges and generates
+posterior distributions for the column density, Doppler parameter, and
+redshifts of the corresponding absorbers. The code uses publicly available
+efficient parallel sampling packages to sample posterior and thus can be run on
+parallel platforms. BayesVP supports simultaneous fitting for multiple
+absorption components in high-dimensional parameter space. We provide other
+useful utilities in the package, such as explicit specification of priors of
+model parameters, continuum model, Bayesian model comparison criteria, and
+posterior sampling convergence check.
+",0,1,0,0,0,0
+15273,Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization,"  We consider the minimization of submodular functions subject to ordering
+constraints. We show that this optimization problem can be cast as a convex
+optimization problem on a space of uni-dimensional measures, with ordering
+constraints corresponding to first-order stochastic dominance. We propose new
+discretization schemes that lead to simple and efficient algorithms based on
+zero-th, first, or higher order oracles; these algorithms also lead to
+improvements without isotonic constraints. Finally, our experiments show that
+non-convex loss functions can be much more robust to outliers for isotonic
+regression, while still leading to an efficient optimization problem.
+",1,0,0,1,0,0
+15274,The effects of oxygen in spinel oxide Li1+xTi2-xO4-delta thin films,"  The evolution from superconducting LiTi2O4-delta to insulating Li4Ti5O12 thin
+films has been studied by precisely adjusting the oxygen pressure during the
+sample fabrication process. In the superconducting LiTi2O4-delta films, with
+the increase of oxygen pressure, the oxygen vacancies are filled, and the
+c-axis lattice constant decreases gradually. With the increase of the oxygen
+pressure to a certain critical value, the c-axis lattice constant becomes
+stable, which implies that the Li4Ti5O12 phase comes into being. The process of
+oxygen filling is manifested by the angular bright-field images of the scanning
+transmission electron microscopy techniques. The temperature of
+magnetoresistance changed from positive and negative shows a non-monotonous
+behavior with the increase of oxygen pressure. The theoretical explanation of
+the oxygen effects on the structure and superconductivity of LiTi2O4-delta has
+also been discussed in this work.
+",0,1,0,0,0,0
+15275,Qualitative uncertainty principle for Gabor transform on certain locally compact groups,"  Classes of locally compact groups having qualitative uncertainty principle
+for Gabor transform have been investigated. These include Moore groups,
+Heisenberg Group $\mathbb{H}_n, \mathbb{H}_{n} \times D,$ where $D$ is discrete
+group and other low dimensional nilpotent Lie groups.
+",0,0,1,0,0,0
+15276,Strategyproof Mechanisms for Additively Separable Hedonic Games and Fractional Hedonic Games,"  Additively separable hedonic games and fractional hedonic games have received
+considerable attention. They are coalition forming games of selfish agents
+based on their mutual preferences. Most of the work in the literature
+characterizes the existence and structure of stable outcomes (i.e., partitions
+in coalitions), assuming that preferences are given. However, there is little
+discussion on this assumption. In fact, agents receive different utilities if
+they belong to different partitions, and thus it is natural for them to declare
+their preferences strategically in order to maximize their benefit. In this
+paper we consider strategyproof mechanisms for additively separable hedonic
+games and fractional hedonic games, that is, partitioning methods without
+payments such that utility maximizing agents have no incentive to lie about
+their true preferences. We focus on social welfare maximization and provide
+several lower and upper bounds on the performance achievable by strategyproof
+mechanisms for general and specific additive functions. In most of the cases we
+provide tight or asymptotically tight results. All our mechanisms are simple
+and can be computed in polynomial time. Moreover, all the lower bounds are
+unconditional, that is, they do not rely on any computational or complexity
+assumptions.
+",1,0,0,0,0,0
+15277,The Conditional Analogy GAN: Swapping Fashion Articles on People Images,"  We present a novel method to solve image analogy problems : it allows to
+learn the relation between paired images present in training data, and then
+generalize and generate images that correspond to the relation, but were never
+seen in the training set. Therefore, we call the method Conditional Analogy
+Generative Adversarial Network (CAGAN), as it is based on adversarial training
+and employs deep convolutional neural networks. An especially interesting
+application of that technique is automatic swapping of clothing on fashion
+model photos. Our work has the following contributions. First, the definition
+of the end-to-end trainable CAGAN architecture, which implicitly learns
+segmentation masks without expensive supervised labeling data. Second,
+experimental results show plausible segmentation masks and often convincing
+swapped images, given the target article. Finally, we discuss the next steps
+for that technique: neural network architecture improvements and more advanced
+applications.
+",1,0,0,1,0,0
+15278,Inverse antiplane problem on $n$ uniformly stressed inclusions,"  The inverse problem of antiplane elasticity on determination of the profiles
+of $n$ uniformly stressed inclusions is studied. The inclusions are in ideal
+contact with the surrounding matrix, the stress field inside the inclusions is
+uniform, and at infinity the body is subjected to antiplane uniform shear. The
+exterior of the inclusions, an $n$-connected domain, is treated as the image by
+a conformal map of an $n$-connected slit domain with the slits lying in the
+same line. The inverse problem is solved by quadratures by reducing it to two
+Riemann-Hilbert problems on a Riemann surface of genus $n-1$. Samples of two
+and three symmetric and non-symmetric uniformly stressed inclusions are
+reported.
+",0,0,1,0,0,0
+15279,Categorical Structures on Bundle Gerbes and Higher Geometric Prequantisation,"  We present a construction of a 2-Hilbert space of sections of a bundle gerbe,
+a suitable candidate for a prequantum 2-Hilbert space in higher geometric
+quantisation. We introduce a direct sum on the morphism categories in the
+2-category of bundle gerbes and show that these categories are cartesian
+monoidal and abelian. Endomorphisms of the trivial bundle gerbe, or higher
+functions, carry the structure of a rig-category, which acts on generic
+morphism categories of bundle gerbes. We continue by presenting a
+categorification of the hermitean metric on a hermitean line bundle. This is
+achieved by introducing a functorial dual that extends the dual of vector
+bundles to morphisms of bundle gerbes, and constructing a two-variable
+adjunction for the aforementioned rig-module category structure on morphism
+categories. Its right internal hom is the module action, composed by taking the
+dual of higher functions, while the left internal hom is interpreted as a
+bundle gerbe metric. Sections of bundle gerbes are defined as morphisms from
+the trivial bundle gerbe to a given bundle gerbe. The resulting categories of
+sections carry a rig-module structure over the category of finite-dimensional
+Hilbert spaces. A suitable definition of 2-Hilbert spaces is given, modifying
+previous definitions by the use of two-variable adjunctions. We prove that the
+category of sections of a bundle gerbe fits into this framework, thus obtaining
+a 2-Hilbert space of sections. In particular, this can be constructed for
+prequantum bundle gerbes in problems of higher geometric quantisation. We
+define a dimensional reduction functor and show that the categorical structures
+introduced on bundle gerbes naturally reduce to their counterparts on hermitean
+line bundles with connections. In several places in this thesis, we provide
+examples, making 2-Hilbert spaces of sections and dimensional reduction very
+explicit.
+",0,0,1,0,0,0
+15280,The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge,"  This article describes the final solution of team monkeytyping, who finished
+in second place in the YouTube-8M video understanding challenge. The dataset
+used in this challenge is a large-scale benchmark for multi-label video
+classification. We extend the work in [1] and propose several improvements for
+frame sequence modeling. We propose a network structure called Chaining that
+can better capture the interactions between labels. Also, we report our
+approaches in dealing with multi-scale information and attention pooling. In
+addition, We find that using the output of model ensemble as a side target in
+training can boost single model performance. We report our experiments in
+bagging, boosting, cascade, and stacking, and propose a stacking algorithm
+called attention weighted stacking. Our final submission is an ensemble that
+consists of 74 sub models, all of which are listed in the appendix.
+",1,0,0,0,0,0
+15281,Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification,"  Brain Electroencephalography (EEG) classification is widely applied to
+analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs
+degrade the diagnosis performance and most previously developed methods ignore
+the necessity of EEG selection for classification. To this end, this paper
+proposes a novel maximum weight clique-based EEG selection approach, named
+mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques
+from an improved Fréchet distance-weighted undirected EEG graph
+simultaneously considering edge weights and vertex weights. Our mwcEEGs
+improves the classification performance by selecting intra-clique pairwise
+similar and inter-clique discriminative EEGs with similarity threshold
+$\delta$. Experimental results demonstrate the algorithm effectiveness compared
+with the state-of-the-art time series selection algorithms on real-world EEG
+datasets.
+",0,0,0,1,1,0
+15282,Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses,"  We present a case-study demonstrating the usefulness of Bayesian hierarchical
+mixture modelling for investigating cognitive processes. In sentence
+comprehension, it is widely assumed that the distance between linguistic
+co-dependents affects the latency of dependency resolution: the longer the
+distance, the longer the retrieval time (the distance-based account). An
+alternative theory, direct-access, assumes that retrieval times are a mixture
+of two distributions: one distribution represents successful retrievals (these
+are independent of dependency distance) and the other represents an initial
+failure to retrieve the correct dependent, followed by a reanalysis that leads
+to successful retrieval. We implement both models as Bayesian hierarchical
+models and show that the direct-access model explains Chinese relative clause
+reading time data better than the distance account.
+",1,0,0,1,0,0
+15283,Performance analysis of local ensemble Kalman filter,"  Ensemble Kalman filter (EnKF) is an important data assimilation method for
+high dimensional geophysical systems. Efficient implementation of EnKF in
+practice often involves the localization technique, which updates each
+component using only information within a local radius. This paper rigorously
+analyzes the local EnKF (LEnKF) for linear systems, and shows that the filter
+error can be dominated by the ensemble covariance, as long as 1) the sample
+size exceeds the logarithmic of state dimension and a constant that depends
+only on the local radius; 2) the forecast covariance matrix admits a stable
+localized structure. In particular, this indicates that with small system and
+observation noises, the filter error will be accurate in long time even if the
+initialization is not. The analysis also reveals an intrinsic inconsistency
+caused by the localization technique, and a stable localized structure is
+necessary to control this inconsistency. While this structure is usually taken
+for granted for the operation of LEnKF, it can also be rigorously proved for
+linear systems with sparse local observations and weak local interactions.
+These theoretical results are also validated by numerical implementation of
+LEnKF on a simple stochastic turbulence in two dynamical regimes.
+",0,0,1,1,0,0
+15284,The homology class of a Poisson transversal,"  This note is devoted to the study of the homology class of a compact Poisson
+transversal in a Poisson manifold. For specific classes of Poisson structures,
+such as unimodular Poisson structures and Poisson manifolds with closed leaves,
+we prove that all their compact Poisson transversals represent non-trivial
+homology classes, generalizing the symplectic case. We discuss several examples
+in which this property does not hold, as well as a weaker version of this
+property, which holds for log-symplectic structures. Finally, we extend our
+results to Dirac geometry.
+",0,0,1,0,0,0
+15285,Discrete Time Dynamic Programming with Recursive Preferences: Optimality and Applications,"  This paper provides an alternative approach to the theory of dynamic
+programming, designed to accommodate the kinds of recursive preference
+specifications that have become popular in economic and financial analysis,
+while still supporting traditional additively separable rewards. The approach
+exploits the theory of monotone convex operators, which turns out to be well
+suited to dynamic maximization. The intuition is that convexity is preserved
+under maximization, so convexity properties found in preferences extend
+naturally to the Bellman operator.
+",0,0,0,0,0,1
+15286,Anisotropic triangulations via discrete Riemannian Voronoi diagrams,"  The construction of anisotropic triangulations is desirable for various
+applications, such as the numerical solving of partial differential equations
+and the representation of surfaces in graphics. To solve this notoriously
+difficult problem in a practical way, we introduce the discrete Riemannian
+Voronoi diagram, a discrete structure that approximates the Riemannian Voronoi
+diagram. This structure has been implemented and was shown to lead to good
+triangulations in $\mathbb{R}^2$ and on surfaces embedded in $\mathbb{R}^3$ as
+detailed in our experimental companion paper.
+In this paper, we study theoretical aspects of our structure. Given a finite
+set of points $\cal P$ in a domain $\Omega$ equipped with a Riemannian metric,
+we compare the discrete Riemannian Voronoi diagram of $\cal P$ to its
+Riemannian Voronoi diagram. Both diagrams have dual structures called the
+discrete Riemannian Delaunay and the Riemannian Delaunay complex. We provide
+conditions that guarantee that these dual structures are identical. It then
+follows from previous results that the discrete Riemannian Delaunay complex can
+be embedded in $\Omega$ under sufficient conditions, leading to an anisotropic
+triangulation with curved simplices. Furthermore, we show that, under similar
+conditions, the simplices of this triangulation can be straightened.
+",1,0,0,0,0,0
+15287,"Heteroskedastic PCA: Algorithm, Optimality, and Applications","  Principal component analysis (PCA) and singular value decomposition (SVD) are
+widely used in statistics, machine learning, and applied mathematics. It has
+been well studied in the case of homoskedastic noise, where the noise levels of
+the contamination are homogeneous.
+In this paper, we consider PCA and SVD in the presence of heteroskedastic
+noise, which arises naturally in a range of applications. We introduce a
+general framework for heteroskedastic PCA and propose an algorithm called
+HeteroPCA, which involves iteratively imputing the diagonal entries to remove
+the bias due to heteroskedasticity. This procedure is computationally efficient
+and provably optimal under the generalized spiked covariance model. A key
+technical step is a deterministic robust perturbation analysis on the singular
+subspace, which can be of independent interest. The effectiveness of the
+proposed algorithm is demonstrated in a suite of applications, including
+heteroskedastic low-rank matrix denoising, Poisson PCA, and SVD based on
+heteroskedastic and incomplete data.
+",0,0,0,1,0,0
+15288,A Manifesto for Web Science @ 10,"  Twenty-seven years ago, one of the biggest societal changes in human history
+began slowly when the technical foundations for the World Wide Web were defined
+by Tim Berners-Lee. Ever since, the Web has grown exponentially, reaching far
+beyond its original technical foundations and deeply affecting the world today
+- and even more so the society of the future. We have seen that the Web can
+influence the realization of human rights and even the pursuit of happiness.
+The Web provides an infrastructure to help us to learn, to work, to communicate
+with loved ones, and to provide entertainment. However, it also creates an
+environment affected by the digital divide between those who have and those who
+do not have access. Additionally, the Web provides challenges we must
+understand if we are to find a viable balance between data ownership and
+privacy protection, between over-whelming surveillance and the prevention of
+terrorism. For the Web to succeed, we need to understand its societal
+challenges including increased crime, the impact of social platforms and
+socio-economic discrimination, and we must work towards fairness, social
+inclusion, and open governance.
+Ten Yars ago, the field of Web Science was created to explore the science
+underlying the Web from a socio-technical perspective including its
+mathematical properties, engineering principles, and social impacts. Ten years
+later, we are learning much as the interdisciplinary endeavor to understand the
+Web's global information space continues to grow.
+In this article we want to elicit the major lessons we have learned through
+Web Science and make some cautious predictions of what to expect next.
+",1,0,0,0,0,0
+15289,LAMOST Spectroscopic Survey of the Galactic Anticentre (LSS-GAC): the second release of value-added catalogues,"  We present the second release of value-added catalogues of the LAMOST
+Spectroscopic Survey of the Galactic Anticentre (LSS-GAC DR2). The catalogues
+present values of radial velocity $V_{\rm r}$, atmospheric parameters ---
+effective temperature $T_{\rm eff}$, surface gravity log$g$, metallicity
+[Fe/H], $\alpha$-element to iron (metal) abundance ratio [$\alpha$/Fe]
+([$\alpha$/M]), elemental abundances [C/H] and [N/H], and absolute magnitudes
+${\rm M}_V$ and ${\rm M}_{K_{\rm s}}$ deduced from 1.8 million spectra of 1.4
+million unique stars targeted by the LSS-GAC since September 2011 until June
+2014. The catalogues also give values of interstellar reddening, distance and
+orbital parameters determined with a variety of techniques, as well as proper
+motions and multi-band photometry from the far-UV to the mid-IR collected from
+the literature and various surveys. Accuracies of radial velocities reach
+5kms$^{-1}$ for late-type stars, and those of distance estimates range between
+10 -- 30 per cent, depending on the spectral signal-to-noise ratios. Precisions
+of [Fe/H], [C/H] and [N/H] estimates reach 0.1dex, and those of [$\alpha$/Fe]
+and [$\alpha$/M] reach 0.05dex. The large number of stars, the contiguous sky
+coverage, the simple yet non-trivial target selection function and the robust
+estimates of stellar radial velocities and atmospheric parameters, distances
+and elemental abundances, make the catalogues a valuable data set to study the
+structure and evolution of the Galaxy, especially the solar-neighbourhood and
+the outer disk.
+",0,1,0,0,0,0
+15290,Counterfactual Fairness,"  Machine learning can impact people with legal or ethical consequences when it
+is used to automate decisions in areas such as insurance, lending, hiring, and
+predictive policing. In many of these scenarios, previous decisions have been
+made that are unfairly biased against certain subpopulations, for example those
+of a particular race, gender, or sexual orientation. Since this past data may
+be biased, machine learning predictors must account for this to avoid
+perpetuating or creating discriminatory practices. In this paper, we develop a
+framework for modeling fairness using tools from causal inference. Our
+definition of counterfactual fairness captures the intuition that a decision is
+fair towards an individual if it is the same in (a) the actual world and (b) a
+counterfactual world where the individual belonged to a different demographic
+group. We demonstrate our framework on a real-world problem of fair prediction
+of success in law school.
+",1,0,0,1,0,0
+15291,A Frame Tracking Model for Memory-Enhanced Dialogue Systems,"  Recently, resources and tasks were proposed to go beyond state tracking in
+dialogue systems. An example is the frame tracking task, which requires
+recording multiple frames, one for each user goal set during the dialogue. This
+allows a user, for instance, to compare items corresponding to different goals.
+This paper proposes a model which takes as input the list of frames created so
+far during the dialogue, the current user utterance as well as the dialogue
+acts, slot types, and slot values associated with this utterance. The model
+then outputs the frame being referenced by each triple of dialogue act, slot
+type, and slot value. We show that on the recently published Frames dataset,
+this model significantly outperforms a previously proposed rule-based baseline.
+In addition, we propose an extensive analysis of the frame tracking task by
+dividing it into sub-tasks and assessing their difficulty with respect to our
+model.
+",1,0,0,0,0,0
+15292,How to place an obstacle having a dihedral symmetry centered at a given point inside a disk so as to optimize the fundamental Dirichlet eigenvalue,"  A generic model for the shape optimization problems we consider in this paper
+is the optimization of the Dirichlet eigenvalues of the Laplace operator with a
+volume constraint. We deal with an obstacle placement problem which can be
+formulated as the following eigenvalue optimization problem: Fix two positive
+real numbers $r_1$ and $A$. We consider a disk $B\subset \mathbb{R}^2$ having
+radius $r_1$. We want to place an obstacle $P$ of area $A$ within $B$ so as to
+maximize or minimize the fundamental Dirichlet eigenvalue $\lambda_1$ for the
+Laplacian on $B\setminus P$. That is, we want to study the behavior of the
+function $\rho \mapsto \lambda_1(B\setminus\rho(P))$, where $\rho$ runs over
+the set of all rigid motions of the plane fixing the center of mass for $P$
+such that $\rho(P)\subset B$. In this paper, we consider a non-concentric
+obstacle placement problem. The extremal configurations correspond to the cases
+where an axis of symmetry of $P$ coincide with an axis of symmetry of $B$. We
+also characterize the maximizing and the minimizing configurations in our main
+result, viz., Theorem 4.1. Equation (6), Propositions 5.1 and 5.2 imply Theorem
+4.1. We give many different generalizations of our result. At the end, we
+provide some numerical evidence to validate our main theorem for the case where
+the obstacle $P$ has $\mathbb{D}_4$ symmetry. For the $n$ odd case, we identify
+some of the extremal configuration for $\lambda_1$. We prove that equation (6)
+and Proposition 5.1 hold true for $n$ odd too. We highlight some of the
+difficulties faced in proving Proposition 5.2 for this case. We provide
+numerical evidence for $n=5$ and conjecture that Theorem 4.1 holds true for $n$
+odd too.
+",0,0,1,0,0,0
+15293,The mapping class groups of reducible Heegaard splittings of genus two,"  The manifold which admits a genus-$2$ reducible Heegaard splitting is one of
+the $3$-sphere, $\mathbb{S}^2 \times \mathbb{S}^1$, lens spaces and their
+connected sums. For each of those manifolds except most lens spaces, the
+mapping class group of the genus-$2$ splitting was shown to be finitely
+presented. In this work, we study the remaining generic lens spaces, and show
+that the mapping class group of the genus-$2$ Heegaard splitting is finitely
+presented for any lens space by giving its explicit presentation. As an
+application, we show that the fundamental groups of the spaces of the genus-$2$
+Heegaard splittings of lens spaces are all finitely presented.
+",0,0,1,0,0,0
+15294,Linguistic Relativity and Programming Languages,"  The use of programming languages can wax and wane across the decades. We
+examine the split-apply- combine pattern that is common in statistical
+computing, and consider how its invocation or implementation in languages like
+MATLAB and APL differ from R/dplyr. The differences in spelling illustrate how
+the concept of linguistic relativity applies to programming languages in ways
+that are analogous to human languages. Finally, we discuss how Julia, by being
+a high performance yet general purpose dynamic language, allows its users to
+express different abstractions to suit individual preferences.
+",1,0,0,1,0,0
+15295,Double-sided probing by map of Asplund's distances using Logarithmic Image Processing in the framework of Mathematical Morphology,"  We establish the link between Mathematical Morphology and the map of
+Asplund's distances between a probe and a grey scale function, using the
+Logarithmic Image Processing scalar multiplication. We demonstrate that the map
+is the logarithm of the ratio between a dilation and an erosion of the function
+by a structuring function: the probe. The dilations and erosions are mappings
+from the lattice of the images into the lattice of the positive functions.
+Using a flat structuring element, the expression of the map of Asplund's
+distances can be simplified with a dilation and an erosion of the image; these
+mappings stays in the lattice of the images. We illustrate our approach by an
+example of pattern matching with a non-flat structuring function.
+",1,0,1,0,0,0
+15296,Regression approaches for Approximate Bayesian Computation,"  This book chapter introduces regression approaches and regression adjustment
+for Approximate Bayesian Computation (ABC). Regression adjustment adjusts
+parameter values after rejection sampling in order to account for the imperfect
+match between simulations and observations. Imperfect match between simulations
+and observations can be more pronounced when there are many summary statistics,
+a phenomenon coined as the curse of dimensionality. Because of this imperfect
+match, credibility intervals obtained with regression approaches can be
+inflated compared to true credibility intervals. The chapter presents the main
+concepts underlying regression adjustment. A theorem that compares theoretical
+properties of posterior distributions obtained with and without regression
+adjustment is presented. Last, a practical application of regression adjustment
+in population genetics shows that regression adjustment shrinks posterior
+distributions compared to rejection approaches, which is a solution to avoid
+inflated credibility intervals.
+",0,0,0,1,0,0
+15297,Feature learning in feature-sample networks using multi-objective optimization,"  Data and knowledge representation are fundamental concepts in machine
+learning. The quality of the representation impacts the performance of the
+learning model directly. Feature learning transforms or enhances raw data to
+structures that are effectively exploited by those models. In recent years,
+several works have been using complex networks for data representation and
+analysis. However, no feature learning method has been proposed for such
+category of techniques. Here, we present an unsupervised feature learning
+mechanism that works on datasets with binary features. First, the dataset is
+mapped into a feature--sample network. Then, a multi-objective optimization
+process selects a set of new vertices to produce an enhanced version of the
+network. The new features depend on a nonlinear function of a combination of
+preexisting features. Effectively, the process projects the input data into a
+higher-dimensional space. To solve the optimization problem, we design two
+metaheuristics based on the lexicographic genetic algorithm and the improved
+strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced
+network contains more information and can be exploited to improve the
+performance of machine learning methods. The advantages and disadvantages of
+each optimization strategy are discussed.
+",1,0,0,0,0,0
+15298,Analog control with two Artificial Axons,"  The artificial axon is a recently introduced synthetic assembly of supported
+lipid bilayers and voltage gated ion channels, displaying the basic
+electrophysiology of nerve cells. Here we demonstrate the use of two artificial
+axons as control elements to achieve a simple task. Namely, we steer a remote
+control car towards a light source, using the sensory input dependent firing
+rate of the axons as the control signal for turning left or right. We present
+the result in the form of the analysis of a movie of the car approaching the
+light source. In general terms, with this work we pursue a constructivist
+approach to exploring the nexus between machine language at the nerve cell
+level and behavior.
+",0,0,0,0,1,0
+15299,Designing a cost-time-quality-efficient grinding process using MODM methods,"  In this paper a multi-objective mathematical model has been used to optimize
+grinding parameters include workpiece speed, depth of cut and wheel speed which
+highly affect the final surface quality. The mathematical model of the
+optimization problem consists of three conflict objective functions subject to
+wheel wear and production rate constraints. Exact methods can solve the NLP
+model in few seconds, therefore using Meta-heuristic algorithms which provide
+near optimal solutions in not suitable. Considering this, five Multi-Objective
+Decision Making methods have been used to solve the multi-objective
+mathematical model using GAMS software to achieve the optimal parameters of the
+grinding process. The Multi-Objective Decision Making methods provide different
+effective solutions where the decision maker can choose each solution in
+different situations. Different criteria have been considered to evaluate the
+performance of the five Multi-Objective Decision Making methods. Also,
+Technique for Order of Preference by Similarity to Ideal Solution method has
+been used to obtain the priority of each method and determine which
+Multi-Objective Decision Making method performs better considering all criteria
+simultaneously. The results indicated that Weighted Sum Method and Goal
+programming method are the best Multi-Objective Decision Making methods. The
+Weighted Sum Method and Goal programming provided solutions which are
+competitive to each other. In addition, these methods obtained solutions which
+have minimum grinding time, cost and surface roughness among other
+Multi-Objective Decision Making methods.
+",1,0,0,0,0,0
+15300,Treewidth distance on phylogenetic trees,"  In this article we study the treewidth of the \emph{display graph}, an
+auxiliary graph structure obtained from the fusion of phylogenetic (i.e.,
+evolutionary) trees at their leaves. Earlier work has shown that the treewidth
+of the display graph is bounded if the trees are in some formal sense
+topologically similar. Here we further expand upon this relationship. We
+analyse a number of reduction rules which are commonly used in the
+phylogenetics literature to obtain fixed parameter tractable algorithms. In
+some cases (the \emph{subtree} reduction) the reduction rules behave similarly
+with respect to treewidth, while others (the \emph{cluster} reduction) behave
+very differently, and the behaviour of the \emph{chain reduction} is
+particularly intriguing because of its link with graph separators and forbidden
+minors. We also show that the gap between treewidth and Tree Bisection and
+Reconnect (TBR) distance can be infinitely large, and that unlike, for example,
+planar graphs the treewidth of the display graph can be as much as linear in
+its number of vertices. On a slightly different note we show that if a display
+graph is formed from the fusion of a phylogenetic network and a tree, rather
+than from two trees, the treewidth of the display graph is bounded whenever the
+tree can be topologically embedded (""displayed"") within the network. This opens
+the door to the formulation of the display problem in Monadic Second Order
+Logic (MSOL). A number of other auxiliary results are given. We conclude with a
+discussion and list a number of open problems.
+",1,0,0,0,0,0
+15301,What is the definition of two meromorphic functions sharing a small function?,"  Two meromorphic functions $f(z)$ and $g(z)$ sharing a small function
+$\alpha(z)$ usually is defined in terms of vanishing of the functions
+$f-\alpha$ and $g-\alpha$. We argue that it would be better to modify this
+definition at the points where $\alpha$ has poles. Related to this issue we
+also point out some possible gaps in proofs in the published literature.
+",0,0,1,0,0,0
+15302,Nb3Sn wire shape and cross sectional area inhomogeneity in Rutherford cables,"  During Rutherford cable production the wires are plastically deformed and
+their initially round shape is distorted. Using X-ray absorption tomography we
+have determined the 3D shape of an unreacted Nb3Sn 11 T dipole Rutherford
+cable, and of a reacted and impregnated Nb3Sn cable double stack.
+State-of-the-art image processing was applied to correct for tomographic
+artefacts caused by the large cable aspect ratio, for the segmentation of the
+individual wires and subelement bundles inside the wires, and for the
+calculation of the wire cross sectional area and shape variations. The 11 T
+dipole cable cross section oscillates by 2% with a frequency of 1.24 mm (1/80
+of the transposition pitch length of the 40 wire cable). A comparatively
+stronger cross sectional area variation is observed in the individual wires at
+the thin edge of the keystoned cable where the wire aspect ratio is largest.
+",0,1,0,0,0,0
+15303,Towards Accurate Modelling of Galaxy Clustering on Small Scales: Testing the Standard $Λ\mathrm{CDM}$ + Halo Model,"  Interpreting the small-scale clustering of galaxies with halo models can
+elucidate the connection between galaxies and dark matter halos. Unfortunately,
+the modelling is typically not sufficiently accurate for ruling out models
+statistically. It is thus difficult to use the information encoded in small
+scales to test cosmological models or probe subtle features of the galaxy-halo
+connection. In this paper, we attempt to push halo modelling into the
+""accurate"" regime with a fully numerical mock-based methodology and careful
+treatment of statistical and systematic errors. With our forward-modelling
+approach, we can incorporate clustering statistics beyond the traditional
+two-point statistics. We use this modelling methodology to test the standard
+$\Lambda\mathrm{CDM}$ + halo model against the clustering of SDSS DR7 galaxies.
+Specifically, we use the projected correlation function, group multiplicity
+function and galaxy number density as constraints. We find that while the model
+fits each statistic separately, it struggles to fit them simultaneously. Adding
+group statistics leads to a more stringent test of the model and significantly
+tighter constraints on model parameters. We explore the impact of varying the
+adopted halo definition and cosmological model and find that changing the
+cosmology makes a significant difference. The most successful model we tried
+(Planck cosmology with Mvir halos) matches the clustering of low luminosity
+galaxies, but exhibits a 2.3$\sigma$ tension with the clustering of luminous
+galaxies, thus providing evidence that the ""standard"" halo model needs to be
+extended. This work opens the door to adding interesting freedom to the halo
+model and including additional clustering statistics as constraints.
+",0,1,0,0,0,0
+15304,Graphene nanoplatelets induced tailoring in photocatalytic activity and antibacterial characteristics of MgO/graphene nanoplatelets nanocomposites,"  The synthesis, physical, photocatalytic, and antibacterial properties of MgO
+and graphene nanoplatelets (GNPs) nanocomposites are reported. The
+crystallinity, phase, morphology, chemical bonding, and vibrational modes of
+prepared nanomaterials are studied. The conductive nature of GNPs is tailored
+via photocatalysis and enhanced antibacterial activity. It is interestingly
+observed that the MgO/GNPs nanocomposite with optimized GNPs content show a
+significant photocatalytic activity (97.23% degradation) as compared to bare
+MgO (43%) which makes it the potential photocatalyst for purification of
+industrial waste water. In addition, the effect of increased amount of GNPs on
+antibacterial performance of nanocomposites against pathogenic micro-organisms
+is researched, suggesting them toxic. MgO/GNPs 25% nanocomposite may have
+potential applications in waste water treatment and nanomedicine due its
+multifunctionality.
+",0,1,0,0,0,0
+15305,"Lower Bounds for Searching Robots, some Faulty","  Suppose we are sending out $k$ robots from $0$ to search the real line at
+constant speed (with turns) to find a target at an unknown location; $f$ of the
+robots are faulty, meaning that they fail to report the target although
+visiting its location (called crash type). The goal is to find the target in
+time at most $\lambda |d|$, if the target is located at $d$, $|d| \ge 1$, for
+$\lambda$ as small as possible. We show that this cannot be achieved for
+$$\lambda < 2\frac{\rho^\rho}{(\rho-1)^{\rho-1}}+1,~~ \rho :=
+\frac{2(f+1)}{k}~, $$ which is tight due to earlier work (see J. Czyzowitz, E.
+Kranakis, D. Krizanc, L. Narayanan, J. Opatrny, PODC'16, where this problem was
+introduced). This also gives some better than previously known lower bounds for
+so-called Byzantine-type faulty robots that may actually wrongly report a
+target.
+In the second part of the paper, we deal with the $m$-rays generalization of
+the problem, where the hidden target is to be detected on $m$ rays all
+emanating at the same point. Using a generalization of our methods, along with
+a useful relaxation of the original problem, we establish a tight lower for
+this setting as well (as above, with $\rho := m(f+1)/k$). When specialized to
+the case $f=0$, this resolves the question on parallel search on $m$ rays,
+posed by three groups of scientists some 15 to 30 years ago: by Baeza-Yates,
+Culberson, and Rawlins; by Kao, Ma, Sipser, and Yin; and by Bernstein,
+Finkelstein, and Zilberstein. The $m$-rays generalization is known to have
+connections to other, seemingly unrelated, problems, including hybrid
+algorithms for on-line problems, and so-called contract algorithms.
+",1,0,0,0,0,0
+15306,An efficient relativistic density-matrix renormalization group implementation in a matrix-product formulation,"  We present an implementation of the relativistic quantum-chemical density
+matrix renormalization group (DMRG) approach based on a matrix-product
+formalism. Our approach allows us to optimize matrix product state (MPS) wave
+functions including a variational description of scalar-relativistic effects
+and spin-orbit coupling from which we can calculate, for example, first-order
+electric and magnetic properties in a relativistic framework. While
+complementing our pilot implementation (S. Knecht et al., J. Chem. Phys., 140,
+041101 (2014)) this work exploits all features provided by its underlying
+non-relativistic DMRG implementation based on an matrix product state and
+operator formalism. We illustrate the capabilities of our relativistic DMRG
+approach by studying the ground-state magnetization as well as current density
+of a paramagnetic $f^9$ dysprosium complex as a function of the active orbital
+space employed in the MPS wave function optimization.
+",0,1,0,0,0,0
+15307,Cyclic Hypergraph Degree Sequences,"  The problem of efficiently characterizing degree sequences of simple
+hypergraphs is a fundamental long-standing open problem in Graph Theory.
+Several results are known for restricted versions of this problem. This paper
+adds to the list of sufficient conditions for a degree sequence to be {\em
+hypergraphic}. This paper proves a combinatorial lemma about cyclically
+permuting the columns of a binary table with length $n$ binary sequences as
+rows. We prove that for any set of cyclic permutations acting on its columns,
+the resulting table has all of its $2^n$ rows distinct. Using this property, we
+first define a subset {\em cyclic hyper degrees} of hypergraphic sequences and
+show that they admit a polynomial time recognition algorithm. Next, we prove
+that there are at least $2^{\frac{(n-1)(n-2)}{2}}$ {\em cyclic hyper degrees},
+which also serves as a lower bound on the number of {\em hypergraphic}
+sequences. The {\em cyclic hyper degrees} also enjoy a structural
+characterization, they are the integral points contained in the union of some
+$n$-dimensional rectangles.
+",1,0,0,0,0,0
+15308,First and Second Order Methods for Online Convolutional Dictionary Learning,"  Convolutional sparse representations are a form of sparse representation with
+a structured, translation invariant dictionary. Most convolutional dictionary
+learning algorithms to date operate in batch mode, requiring simultaneous
+access to all training images during the learning process, which results in
+very high memory usage and severely limits the training data that can be used.
+Very recently, however, a number of authors have considered the design of
+online convolutional dictionary learning algorithms that offer far better
+scaling of memory and computational cost with training set size than batch
+methods. This paper extends our prior work, improving a number of aspects of
+our previous algorithm; proposing an entirely new one, with better performance,
+and that supports the inclusion of a spatial mask for learning from incomplete
+data; and providing a rigorous theoretical analysis of these methods.
+",1,0,0,1,0,0
+15309,DPCA: Dimensionality Reduction for Discriminative Analytics of Multiple Large-Scale Datasets,"  Principal component analysis (PCA) has well-documented merits for data
+extraction and dimensionality reduction. PCA deals with a single dataset at a
+time, and it is challenged when it comes to analyzing multiple datasets. Yet in
+certain setups, one wishes to extract the most significant information of one
+dataset relative to other datasets. Specifically, the interest may be on
+identifying, namely extracting features that are specific to a single target
+dataset but not the others. This paper develops a novel approach for such
+so-termed discriminative data analysis, and establishes its optimality in the
+least-squares (LS) sense under suitable data modeling assumptions. The
+criterion reveals linear combinations of variables by maximizing the ratio of
+the variance of the target data to that of the remainders. The novel approach
+solves a generalized eigenvalue problem by performing SVD just once. Numerical
+tests using synthetic and real datasets showcase the merits of the proposed
+approach relative to its competing alternatives.
+",1,0,0,1,0,0
+15310,Modeling Magnetic Anisotropy of Single Chain Magnets in $|d/J| \geq 1$ Regime,"  Single molecule magnets (SMMs) with single-ion anisotropies $\mathbf d$,
+comparable to exchange interactions J, between spins have recently been
+synthesized. In this paper, we provide theoretical insights into the magnetism
+of such systems. We study spin chains with site spins, s=1, 3/2 and 2 and
+on-site anisotropy $\mathbf d$ comparable to the exchange constants between the
+spins. We find that large $\mathbf d$ leads to crossing of the states with
+different $M_S$ values in the same spin manifold of the $\mathbf d = 0$ limit.
+For very large $\mathbf d$'s we also find that the $M_S$ states of the higher
+energy spin states descend below the $M_S$ states of the ground state spin
+manifold. Total spin in this limit is no longer conserved and describing the
+molecular anisotropy by the constants $D_M$ and $E_M$ is not possible. However,
+the total spin of the low-lying large $M_S$ states is very nearly an integer
+and using this spin value it is possible to construct an effective spin
+Hamiltonian and compute the molecular magnetic anisotropy constants $D_M$ and
+$E_M$. We report effect of finite sizes, rotations of site anisotropies and
+chain dimerization on the effective anisotropy of the spin chains.
+",0,1,0,0,0,0
+15311,Compressive optical interferometry,"  Compressive sensing (CS) combines data acquisition with compression coding to
+reduce the number of measurements required to reconstruct a sparse signal. In
+optics, this usually takes the form of projecting the field onto sequences of
+random spatial patterns that are selected from an appropriate random ensemble.
+We show here that CS can be exploited in `native' optics hardware without
+introducing added components. Specifically, we show that random sub-Nyquist
+sampling of an interferogram helps reconstruct the field modal structure. The
+distribution of reduced sensing matrices corresponding to random measurements
+is provably incoherent and isotropic, which helps us carry out CS successfully.
+",0,1,0,0,0,0
+15312,Automated labeling of bugs and tickets using attention-based mechanisms in recurrent neural networks,"  We explore solutions for automated labeling of content in bug trackers and
+customer support systems. In order to do that, we classify content in terms of
+several criteria, such as priority or product area. In the first part of the
+paper, we provide an overview of existing methods used for text classification.
+These methods fall into two categories - the ones that rely on neural networks
+and the ones that don't. We evaluate results of several solutions of both
+kinds. In the second part of the paper we present our own recurrent neural
+network solution based on hierarchical attention paradigm. It consists of
+several Hierarchical Attention network blocks with varying Gated Recurrent Unit
+cell sizes and a complementary shallow network that goes alongside. Lastly, we
+evaluate above-mentioned methods when predicting fields from two datasets -
+Arch Linux bug tracker and Chromium bug tracker. Our contributions include a
+comprehensive benchmark between a variety of methods on relevant datasets; a
+novel solution that outperforms previous generation methods; and two new
+datasets that are made public for further research.
+",0,0,0,1,0,0
+15313,Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning,"  For robots to coexist with humans in a social world like ours, it is crucial
+that they possess human-like social interaction skills. Programming a robot to
+possess such skills is a challenging task. In this paper, we propose a
+Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like
+interaction skills through a trial and error method. This paper aims to develop
+a robot that gathers data during its interaction with a human and learns human
+interaction behaviour from the high-dimensional sensory information using
+end-to-end reinforcement learning. This paper demonstrates that the robot was
+able to learn basic interaction skills successfully, after 14 days of
+interacting with people.
+",1,0,0,1,0,0
+15314,Preprint Déjà Vu: an FAQ,"  I give a brief overview of arXiv history, and describe the current state of
+arXiv practice, both technical and sociological. This commentary originally
+appeared in the EMBO Journal, 19 Oct 2016. It was intended as an update on
+comments from the late 1990s regarding use of preprints by biologists (or lack
+thereof), but may be of interest to practitioners of other disciplines. It is
+based largely on a keynote presentation I gave to the ASAPbio inaugural meeting
+in Feb 2016, and responds as well to some follow-up questions.
+",1,1,0,0,0,0
+15315,Implications of hydrodynamical simulations for the interpretation of direct dark matter searches,"  In recent years, realistic hydrodynamical simulations of galaxies like the
+Milky Way have become available, enabling a reliable estimate of the dark
+matter density and velocity distribution in the Solar neighborhood. We review
+here the status of hydrodynamical simulations and their implications for the
+interpretation of direct dark matter searches. We focus in particular on: the
+criteria to identify Milky Way-like galaxies; the impact of baryonic physics on
+the dark matter velocity distribution; the possible presence of substructures
+like clumps, streams, or dark disks; and on the implications for the direct
+detection of dark matter with standard and non-standard interactions.
+",0,1,0,0,0,0
+15316,Stable Charged Antiparallel Domain Walls in Hyperferroelectrics,"  Charge-neutral 180$^\circ$ domain walls that separate domains of antiparallel
+polarization directions are common structural topological defects in
+ferroelectrics. In normal ferroelectrics, charged 180$^\circ$ domain walls
+running perpendicular to the polarization directions are highly energetically
+unfavorable because of the depolarization field and are difficult to stabilize.
+We explore both neutral and charged 180$^\circ$ domain walls in
+hyperferroelectrics, a class of proper ferroelectrics with persistent
+polarization in the presence of a depolarization field, using density
+functional theory. We obtain zero temperature equilibrium structures of
+head-to-head and tail-to-tail walls in recently discovered $ABC$-type hexagonal
+hyperferroelectrics. Charged domain walls can also be stabilized in canonical
+ferroelectrics represented by LiNbO$_3$ without any dopants, defects or
+mechanical clamping. First-principles electronic structure calculations show
+that charged domain walls can reduce and even close the band gap of host
+materials and support quasi-two-dimensional electron(hole) gas with enhanced
+electrical conductivity.
+",0,1,0,0,0,0
+15317,Healing Data Loss Problems in Android Apps,"  Android apps should be designed to cope with stop-start events, which are the
+events that require stopping and restoring the execution of an app while
+leaving its state unaltered. These events can be caused by run-time
+configuration changes, such as a screen rotation, and by context-switches, such
+as a switch from one app to another. When a stop-start event occurs, Android
+saves the state of the app, handles the event, and finally restores the saved
+state. To let Android save and restore the state correctly, apps must provide
+the appropriate support. Unfortunately, Android developers often implement this
+support incorrectly, or do not implement it at all. This bad practice makes
+apps to incorrectly react to stop-start events, thus generating what we defined
+data loss problems, that is Android apps that lose user data, behave
+unexpectedly, and crash due to program variables that lost their values. Data
+loss problems are difficult to detect because they might be observed only when
+apps are in specific states and with specific inputs. Covering all the possible
+cases with testing may require a large number of test cases whose execution
+must be checked manually to discover whether the app under test has been
+correctly restored after each stop-start event. It is thus important to
+complement traditional in-house testing activities with mechanisms that can
+protect apps as soon as a data loss problem occurs in the field. In this paper
+we present DataLossHealer, a technique for automatically identifying and
+healing data loss problems in the field as soon as they occur. DataLossHealer
+is a technique that checks at run-time whether states are recovered correctly,
+and heals the app when needed. DataLossHealer can learn from experience,
+incrementally reducing the overhead that is introduced avoiding to monitor
+interactions that have been managed correctly by the app in the past.
+",1,0,0,0,0,0
+15318,Emergent SU(N) symmetry in disordered SO(N) spin chains,"  Strongly disordered spin chains invariant under the SO(N) group are shown to
+display antiferromagnetic phases with emergent SU(N) symmetry without fine
+tuning. The phases with emergent SU(N) symmetry are of two kinds: one has a
+ground state formed of randomly distributed singlets of strongly bound pairs of
+SO(N) spins (the `mesonic' phase), while the other has a ground state composed
+of singlets made out of strongly bound integer multiples of N SO(N) spins (the
+`baryonic' phase). Although the mechanism is general, we argue that the cases
+N=2,3,4 and 6 can in principle be realized with the usual spin and orbital
+degrees of freedom.
+",0,1,0,0,0,0
+15319,Imaging structural transitions in organometallic molecules on Ag(100) for solar thermal energy storage,"  The use of opto-thermal molecular energy storage at the nanoscale creates new
+opportunities for powering future microdevices with flexible synthetic
+tailorability. Practical application of these molecular materials, however,
+requires a deeper microscopic understanding of how their behavior is altered by
+the presence of different types of substrates. Here we present single-molecule
+resolved scanning tunneling microscopy imaging of thermally- and
+optically-induced structural transitions in (fulvalene)tetracarbonyldiruthenium
+molecules adsorbed onto a Ag(100) surface as a prototype system. Both the
+parent complex and the photoisomer display distinct thermally-driven phase
+transformations when they are in contact with a Ag(100) surface. This behavior
+is consistent with the loss of carbonyl ligands due to strong molecule-surface
+coupling. Ultraviolet radiation induces marked structural changes only in the
+intact parent complex, thus indicating a photoisomerization reaction. These
+results demonstrate how stimuli-induced structural transitions in this class of
+molecule depend on the nature of the underlying substrate.
+",0,1,0,0,0,0
+15320,Spontaneous antiferromagnetic order and strain effect on electronic properties of $α$-graphyne,"  Using hybrid exchange-correlation functional in ab initio density functional
+theory calculations, we study magnetic properties and strain effect on the
+electronic properties of $\alpha$-graphyne monolayer. We find that a
+spontaneous antiferromagnetic (AF) ordering occurs with energy band gap ($\sim$
+0.5 eV) in the equilibrated $\alpha$-graphyne. Bi-axial tensile strain enhances
+the stability of AF state as well as the staggered spin moment and value of the
+energy gap. The antiferromagnetic semiconductor phase is quite robust against
+moderate carrier filling with threshold carrier density up to
+1.7$\times$10$^{14}$ electrons/cm$^2$ to destabilize the phase. The spontaneous
+AF ordering and strain effect in $\alpha$-graphyne can be well described by the
+framework of the Hubbard model. Our study shows that it is essential to
+consider the electronic correlation effect properly in $\alpha$-graphyne and
+may pave an avenue for exploring magnetic ordering in other carbon allotropes
+with mixed hybridization of s and p orbitals.
+",0,1,0,0,0,0
+15321,Big Data Technology Accelerate Genomics Precision Medicine,"  During genomics life science research, the data volume of whole genomics and
+life science algorithm is going bigger and bigger, which is calculated as TB,
+PB or EB etc. The key problem will be how to store and analyze the data with
+optimized way. This paper demonstrates how Intel Big Data Technology and
+Architecture help to facilitate and accelerate the genomics life science
+research in data store and utilization. Intel defines high performance
+GenomicsDB for variant call data query and Lustre filesystem with Hierarchal
+Storage Management for genomics data store. Based on these great technology,
+Intel defines genomics knowledge share and exchange architecture, which is
+landed and validated in BGI China and Shanghai Children Hospital with very
+positive feedback. And these big data technology can definitely be scaled to
+much more genomics life science partners in the world.
+",1,0,0,0,0,0
+15322,Dirichlet-to-Neumann or Poincaré-Steklov operator on fractals described by d -sets,"  In the framework of the Laplacian transport, described by a Robin boundary
+value problem in an exterior domain in $\mathbb{R}^n$, we generalize the
+definition of the Poincaré-Steklov operator to $d$-set boundaries, $n-2<
+d<n$, and give its spectral properties to compare to the spectra of the
+interior domain and also of a truncated domain, considered as an approximation
+of the exterior case. The well-posedness of the Robin boundary value problems
+for the truncated and exterior domains is given in the general framework of
+$n$-sets. The results are obtained thanks to a generalization of the continuity
+and compactness properties of the trace and extension operators in Sobolev,
+Lebesgue and Besov spaces, in particular, by a generalization of the classical
+Rellich-Kondrachov Theorem of compact embeddings for $n$ and $d$-sets.
+",0,0,1,0,0,0
+15323,Grammar Variational Autoencoder,"  Deep generative models have been wildly successful at learning coherent
+latent representations for continuous data such as video and audio. However,
+generative modeling of discrete data such as arithmetic expressions and
+molecular structures still poses significant challenges. Crucially,
+state-of-the-art methods often produce outputs that are not valid. We make the
+key observation that frequently, discrete data can be represented as a parse
+tree from a context-free grammar. We propose a variational autoencoder which
+encodes and decodes directly to and from these parse trees, ensuring the
+generated outputs are always valid. Surprisingly, we show that not only does
+our model more often generate valid outputs, it also learns a more coherent
+latent space in which nearby points decode to similar discrete outputs. We
+demonstrate the effectiveness of our learned models by showing their improved
+performance in Bayesian optimization for symbolic regression and molecular
+synthesis.
+",0,0,0,1,0,0
+15324,Optimal Bayesian Minimax Rates for Unconstrained Large Covariance Matrices,"  We obtain the optimal Bayesian minimax rate for the unconstrained large
+covariance matrix of multivariate normal sample with mean zero, when both the
+sample size, n, and the dimension, p, of the covariance matrix tend to
+infinity. Traditionally the posterior convergence rate is used to compare the
+frequentist asymptotic performance of priors, but defining the optimality with
+it is elusive. We propose a new decision theoretic framework for prior
+selection and define Bayesian minimax rate. Under the proposed framework, we
+obtain the optimal Bayesian minimax rate for the spectral norm for all rates of
+p. We also considered Frobenius norm, Bregman divergence and squared
+log-determinant loss and obtain the optimal Bayesian minimax rate under certain
+rate conditions on p. A simulation study is conducted to support the
+theoretical results.
+",0,0,1,1,0,0
+15325,Retirement spending and biological age,"  We solve a lifecycle model in which the consumer's chronological age does not
+move in lockstep with calendar time. Instead, biological age increases at a
+stochastic non-linear rate in time like a broken clock that might occasionally
+move backwards. In other words, biological age could actually decline. Our
+paper is inspired by the growing body of medical literature that has identified
+biomarkers which indicate how people age at different rates. This offers better
+estimates of expected remaining lifetime and future mortality rates. It isn't
+farfetched to argue that in the not-too-distant future personal age will be
+more closely associated with biological vs. calendar age. Thus, after
+introducing our stochastic mortality model we derive optimal consumption rates
+in a classic Yaari (1965) framework adjusted to our proper clock time. In
+addition to the normative implications of having access to biological age, our
+positive objective is to partially explain the cross-sectional heterogeneity in
+retirement spending rates at any given chronological age. In sum, we argue that
+neither biological nor chronological age alone is a sufficient statistic for
+making economic decisions. Rather, both ages are required to behave rationally.
+",0,0,0,0,0,1
+15326,Quantum X Waves with Orbital Angular Momentum in Nonlinear Dispersive Media,"  We present a complete and consistent quantum theory of generalised X waves
+with orbital angular momentum (OAM) in dispersive media. We show that the
+resulting quantised light pulses are affected by neither dispersion nor
+diffraction and are therefore resilient against external perturbations. The
+nonlinear interaction of quantised X waves in quadratic and Kerr nonlinear
+media is also presented and studied in detail.
+",0,1,0,0,0,0
+15327,Role of Skin Friction Drag during Flow-Induced Reconfiguration of a Flexible Thin Plate,"  We investigate drag reduction due to the flow-induced reconfiguration of a
+flexible thin plate in presence of skin friction drag at low Reynolds Number.
+The plate is subjected to a uniform free stream and is tethered at one end. We
+extend existing models in the literature to account for the skin friction drag.
+The total drag on the plate with respect to a rigid upright plate decreases due
+to flow-induced reconfiguration and further reconfiguration increases the total
+drag due to increase in skin friction drag. A critical value of Cauchy number
+($Ca$) exists at which the total drag on the plate with respect to a rigid
+upright plate is minimum at a given Reynolds number. The reconfigured shape of
+the plate for this condition is unique, beyond which the total drag increases
+on the plate even with reconfiguration. The ratio of the form drag coefficient
+for an upright rigid plate and skin drag coefficient for a horizontal rigid
+plate ($\lambda$) determines the critical Cauchy number ($Ca_{cr}$). We propose
+modification in the drag scaling with free stream velocity ($F_{x}$ ${\propto}$
+$U^{n}$) in presence of the skin friction drag. The following expressions of
+$n$ are found for $0.01 \leq Re \leq 1$, $n = 4/5 + {\lambda}/5$ for 1 $\leq$
+$Ca$ $<$ $Ca_{cr}$ and $n = 1 + {\lambda}/5$ for $Ca_{cr} \leq Ca \leq 300$,
+where $Re$ is Reynolds number. We briefly discuss the combined effect of the
+skin friction drag and buoyancy on the drag reduction. An assessment of the
+feasibility of experiments is presented in order to translate the present model
+to physical systems.
+",0,1,0,0,0,0
+15328,Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization,"  The success of deep learning has led to a rising interest in the
+generalization property of the stochastic gradient descent (SGD) method, and
+stability is one popular approach to study it. Existing works based on
+stability have studied nonconvex loss functions, but only considered the
+generalization error of the SGD in expectation. In this paper, we establish
+various generalization error bounds with probabilistic guarantee for the SGD.
+Specifically, for both general nonconvex loss functions and gradient dominant
+loss functions, we characterize the on-average stability of the iterates
+generated by SGD in terms of the on-average variance of the stochastic
+gradients. Such characterization leads to improved bounds for the
+generalization error for SGD. We then study the regularized risk minimization
+problem with strongly convex regularizers, and obtain improved generalization
+error bounds for proximal SGD. With strongly convex regularizers, we further
+establish the generalization error bounds for nonconvex loss functions under
+proximal SGD with high-probability guarantee, i.e., exponential concentration
+in probability.
+",0,0,0,1,0,0
+15329,Distributed algorithm for empty vehicles management in personal rapid transit (PRT) network,"  In this paper, an original heuristic algorithm of empty vehicles management
+in personal rapid transit network is presented. The algorithm is used for the
+delivery of empty vehicles for waiting passengers, for balancing the
+distribution of empty vehicles within the network, and for providing an empty
+space for vehicles approaching a station. Each of these tasks involves a
+decision on the trip that has to be done by a selected empty vehicle from its
+actual location to some determined destination. The decisions are based on a
+multi-parameter function involving a set of factors and thresholds. An
+important feature of the algorithm is that it does not use any central database
+of passenger input (demand) and locations of free vehicles. Instead, it is
+based on the local exchange of data between stations: on their states and on
+the vehicles they expect. Therefore, it seems well-tailored for a distributed
+implementation. The algorithm is uniform, meaning that the same basic procedure
+is used for multiple tasks using a task-specific set of parameters.
+",1,0,0,0,0,0
+15330,Controllability of the 1D Schrödinger equation using flatness,"  We derive in a direct way the exact controllability of the 1D free
+Schrödinger equation with Dirichlet boundary control. We use the so-called
+flatness approach, which consists in parametrizing the solution and the control
+by the derivatives of a ""flat output"". This provides an explicit and very
+regular control achieving the exact controllability in the energy space.
+",0,0,1,0,0,0
+15331,ICT Green Governance: new generation model based on Corporate Social Responsibility and Green IT,"  The strategy of sustainable development in the governance of information and
+communication technology (ICT) is a sector of advanced research that leads to
+rising challenges posed by social and environmental requirements in the
+implementation and establishment of the governance strategy. This paper offers
+new generation governance model that we call ""ICT Green Governance"". The
+proposed framework provides an original model based on the Corporate Social
+Responsibility (CSR) concept and Green IT strategy. Facing increasing pressure
+from stakeholders, the model offers a new vision of ICT governance to ensure
+effective and efficient use of ICT in enabling an enterprise to achieve its
+goals. We present here the relevance of our model, on the basis of a literature
+review, and provide guidelines and principles for effective ICT governance in
+the way of sustainable development, in order to improve the economic, social
+and environmental performance of companies.
+",1,0,0,0,0,0
+15332,Speaker Selective Beamformer with Keyword Mask Estimation,"  This paper addresses the problem of automatic speech recognition (ASR) of a
+target speaker in background speech. The novelty of our approach is that we
+focus on a wakeup keyword, which is usually used for activating ASR systems
+like smart speakers. The proposed method firstly utilizes a DNN-based mask
+estimator to separate the mixture signal into the keyword signal uttered by the
+target speaker and the remaining background speech. Then the separated signals
+are used for calculating a beamforming filter to enhance the subsequent
+utterances from the target speaker. Experimental evaluations show that the
+trained DNN-based mask can selectively separate the keyword and background
+speech from the mixture signal. The effectiveness of the proposed method is
+also verified with Japanese ASR experiments, and we confirm that the character
+error rates are significantly improved by the proposed method for both
+simulated and real recorded test sets.
+",1,0,0,0,0,0
+15333,Proof of an entropy conjecture of Leighton and Moitra,"  We prove the following conjecture of Leighton and Moitra. Let $T$ be a
+tournament on $[n]$ and $S_n$ the set of permutations of $[n]$. For an arc $uv$
+of $T$, let $A_{uv}=\{\sigma \in S_n \, : \, \sigma(u)<\sigma(v) \}$.
+$\textbf{Theorem.}$ For a fixed $\varepsilon>0$, if $\mathbb{P}$ is a
+probability distribution on $S_n$ such that
+$\mathbb{P}(A_{uv})>1/2+\varepsilon$ for every arc $uv$ of $T$, then the binary
+entropy of $\mathbb{P}$ is at most $(1-\vartheta_{\varepsilon})\log_2 n!$ for
+some (fixed) positive $\vartheta_\varepsilon$.
+When $T$ is transitive the theorem is due to Leighton and Moitra; for this
+case we give a short proof with a better $\vartheta_\varepsilon$.
+",0,0,1,0,0,0
+15334,What drives transient behaviour in complex systems?,"  We study transient behaviour in the dynamics of complex systems described by
+a set of non-linear ODE's. Destabilizing nature of transient trajectories is
+discussed and its connection with the eigenvalue-based linearization procedure.
+The complexity is realized as a random matrix drawn from a modified May-Wigner
+model. Based on the initial response of the system, we identify a novel
+stable-transient regime. We calculate exact abundances of typical and extreme
+transient trajectories finding both Gaussian and Tracy-Widom distributions
+known in extreme value statistics. We identify degrees of freedom driving
+transient behaviour as connected to the eigenvectors and encoded in a
+non-orthogonality matrix $T_0$. We accordingly extend the May-Wigner model to
+contain a phase with typical transient trajectories present. An exact norm of
+the trajectory is obtained in the vanishing $T_0$ limit where it describes a
+normal matrix.
+",0,1,0,0,0,0
+15335,Complexity of strong approximation on the sphere,"  By assuming some widely-believed arithmetic conjectures, we show that the
+task of accepting a number that is representable as a sum of $d\geq2$ squares
+subjected to given congruence conditions is NP-complete. On the other hand, we
+develop and implement a deterministic polynomial-time algorithm that represents
+a number as a sum of 4 squares with some restricted congruence conditions, by
+assuming a polynomial-time algorithm for factoring integers and
+Conjecture~\ref{cc}. As an application, we develop and implement a
+deterministic polynomial-time algorithm for navigating LPS Ramanujan graphs,
+under the same assumptions.
+",0,0,1,0,0,0
+15336,Correlation between Foam-Bubble Size and Drag Coefficient in Hurricane Conditions,"  Recently proposed model of foam impact on the air sea drag coefficient Cd has
+been employed for the estimation of the efficient foam-bubble radius Rb
+variation with wind speed U10 in hurricane conditions. The model relates Cd
+(U10) with the efficient roughness length Zeff (U10) represented as a sum of
+aerodynamic roughness lengths of the foam free and foam covered sea surfaces Zw
+(U10 ), and Zf (U10) weighted with the foam coverage coefficient. This relation
+is treated for known phenomenological distributions Cd (U10), Zw (U10) at
+strong wind speeds as an inverse problem for the efficient roughness parameter
+of foam-covered sea surface Zf (U10).
+",0,1,0,0,0,0
+15337,On the Structure of Superconducting Order Parameter in High-Temperature Fe-Based Superconductors,"  This paper discusses the synthesis, characterization, and comprehensive study
+of Ba-122 single crystals with various substitutions and various $T_c$. The
+paper uses five complementary techniques to obtain a self-consistent set of
+data on the superconducting properties of Ba-122. A major conclusion of the
+authors work is the coexistence of two superconducting condensates differing in
+the electron-boson coupling strength. The two gaps that develop in distinct
+Fermi surface sheets are nodeless in the $k_xk_y$-plane and exhibit s-wave
+symmetry, the two-band model represents a sufficient data description tool. A
+moderate interband coupling and a considerable Coulomb repulsion in the
+description of the two-gap superconducting state of barium pnictides favor the
+$s^{++}$-model.
+",0,1,0,0,0,0
+15338,Rigorous statistical analysis of HTTPS reachability,"  The use of secure connections using HTTPS as the default means, or even the
+only means, to connect to web servers is increasing. It is being pushed from
+both sides: from the bottom up by client distributions and plugins, and from
+the top down by organisations such as Google. However, there are potential
+technical hurdles that might lock some clients out of the modern web. This
+paper seeks to measure and precisely quantify those hurdles in the wild. More
+than three million measurements provide statistically significant evidence of
+degradation. We show this through a variety of statistical techniques. Various
+factors are shown to influence the problem, ranging from the client's browser,
+to the locale from which they connect.
+",1,0,0,1,0,0
+15339,Exploiting Negative Curvature in Deterministic and Stochastic Optimization,"  This paper addresses the question of whether it can be beneficial for an
+optimization algorithm to follow directions of negative curvature. Although
+prior work has established convergence results for algorithms that integrate
+both descent and negative curvature steps, there has not yet been extensive
+numerical evidence showing that such methods offer consistent performance
+improvements. In this paper, we present new frameworks for combining descent
+and negative curvature directions: alternating two-step approaches and dynamic
+step approaches. The aspect that distinguishes our approaches from ones
+previously proposed is that they make algorithmic decisions based on
+(estimated) upper-bounding models of the objective function. A consequence of
+this aspect is that our frameworks can, in theory, employ fixed stepsizes,
+which makes the methods readily translatable from deterministic to stochastic
+settings. For deterministic problems, we show that instances of our dynamic
+framework yield gains in performance compared to related methods that only
+follow descent steps. We also show that gains can be made in a stochastic
+setting in cases when a standard stochastic-gradient-type method might make
+slow progress.
+",0,0,1,0,0,0
+15340,Outer automorphism groups of right-angled Coxeter groups are either large or virtually abelian,"  We generalise the notion of a separating intersection of links (SIL) to give
+necessary and sufficient criteria on the defining graph $\Gamma$ of a
+right-angled Coxeter group $W_\Gamma$ so that its outer automorphism group is
+large: that is, it contains a finite index subgroup that admits the free group
+$F_2$ as a quotient. When $Out(W_\Gamma)$ is not large, we show it is virtually
+abelian. We also show that the same dichotomy holds for the outer automorphism
+groups of graph products of finite abelian groups. As a consequence, these
+groups have property (T) if and only if they are finite, or equivalently
+$\Gamma$ contains no SIL.
+",0,0,1,0,0,0
+15341,Retrieving Instantaneous Field of View and Geophysical Information for Atmospheric Limb Sounding with USGNC Near Real-Time Orbit Data,"  The Limb-imaging Ionospheric and Thermospheric Extreme-ultraviolet
+Spectrograph (LITES) experiment is one of thirteen instruments aboard the Space
+Test Program Houston 5 (STP-H5) payload on the International Space Station.
+Along with the complementary GPS Radio Occultation and Ultraviolet Photometry
+-- Colocated (GROUP-C) experiment, LITES will investigate ionospheric
+structures and variability relevant to the global ionosphere. The ISS has an
+orbital inclination of 51.6° which combined with its altitude of about 410
+km enables middle- and low-latitude measurements from slightly above the peak
+region of the ionosphere. The LITES instrument features a 10° by 10°
+field of view which is collapsed horizontally, combining all information from a
+given altitude. The instrument is installed such it looks in the wake of the
+ISS and about 14.5° downwards in order to image altitudes ranging from
+about 350 km to 150 km. The actual viewing altitude and geometry is directly
+dependent on the pitch of the ISS, affecting the geophysical information
+captured by the instrument.
+",0,1,0,0,0,0
+15342,Better than counting: Density profiles from force sampling,"  Calculating one-body density profiles in equilibrium via particle-based
+simulation methods involves counting of events of particle occurrences at
+(histogram-resolved) space points. Here we investigate an alternative method
+based on a histogram of the local force density. Via an exact sum rule the
+density profile is obtained with a simple spatial integration. The method
+circumvents the inherent ideal gas fluctuations. We have tested the method in
+Monte Carlo, Brownian Dynamics and Molecular Dynamics simulations. The results
+carry a statistical uncertainty smaller than that of the standard, counting,
+method, reducing therefore the computation time.
+",0,1,0,0,0,0
+15343,Integrating self-efficacy into a gamified approach to thwart phishing attacks,"  Security exploits can include cyber threats such as computer programs that
+can disturb the normal behavior of computer systems (viruses), unsolicited
+e-mail (spam), malicious software (malware), monitoring software (spyware),
+attempting to make computer resources unavailable to their intended users
+(Distributed Denial-of-Service or DDoS attack), the social engineering, and
+online identity theft (phishing). One such cyber threat, which is particularly
+dangerous to computer users is phishing. Phishing is well known as online
+identity theft, which targets to steal victims' sensitive information such as
+username, password and online banking details. This paper focuses on designing
+an innovative and gamified approach to educate individuals about phishing
+attacks. The study asks how one can integrate self-efficacy, which has a
+co-relation with the user's knowledge, into an anti-phishing educational game
+to thwart phishing attacks? One of the main reasons would appear to be a lack
+of user knowledge to prevent from phishing attacks. Therefore, this research
+investigates the elements that influence (in this case, either conceptual or
+procedural knowledge or their interaction effect) and then integrate them into
+an anti-phishing educational game to enhance people's phishing prevention
+behaviour through their motivation.
+",1,0,0,0,0,0
+15344,"Quantum dynamics of bosons in a two-ring ladder: dynamical algebra, vortex-like excitations and currents","  We study the quantum dynamics of the Bose-Hubbard model on a ladder formed by
+two rings coupled by tunneling effect. By implementing the Bogoliubov
+approximation scheme, we prove that, despite the presence of the inter-ring
+coupling term, the Hamiltonian decouples in many independent sub-Hamiltonians
+$\hat{H}_k$ associated to momentum-mode pairs $\pm k$. Each sub-Hamiltonian
+$\hat{H}_k$ is then shown to be part of a specific dynamical algebra. The
+properties of the latter allow us to perform the diagonalization process, to
+find energy spectrum, the conserved quantities of the model, and to derive the
+time evolution of important physical observables. We then apply this solution
+scheme to the simplest possible closed ladder, the double trimer. After
+observing that the excitations of the system are weakly-populated vortices, we
+explore the corresponding dynamics by varying the initial conditions and the
+model parameters. Finally, we show that the inter-ring tunneling determines a
+spectral collapse when approaching the border of the dynamical-stability
+region.
+",0,1,0,0,0,0
+15345,"A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction","  The inception network has been shown to provide good performance on image
+classification problems, but there are not much evidences that it is also
+effective for the image restoration or pixel-wise labeling problems. For image
+restoration problems, the pooling is generally not used because the decimated
+features are not helpful for the reconstruction of an image as the output.
+Moreover, most deep learning architectures for the restoration problems do not
+use dense prediction that need lots of training parameters. From these
+observations, for enjoying the performance of inception-like structure on the
+image based problems we propose a new convolutional network-in-network
+structure. The proposed network can be considered a modification of inception
+structure where pool projection and pooling layer are removed for maintaining
+the entire feature map size, and a larger kernel filter is added instead.
+Proposed network greatly reduces the number of parameters on account of removed
+dense prediction and pooling, which is an advantage, but may also reduce the
+receptive field in each layer. Hence, we add a larger kernel than the original
+inception structure for not increasing the depth of layers. The proposed
+structure is applied to typical image-to-image learning problems, i.e., the
+problems where the size of input and output are same such as skin detection,
+semantic segmentation, and compression artifacts reduction. Extensive
+experiments show that the proposed network brings comparable or better results
+than the state-of-the-art convolutional neural networks for these problems.
+",1,0,0,0,0,0
+15346,Rapid rotators revisited: absolute dimensions of KOI-13,"  We analyse Kepler light-curves of the exoplanet KOI-13b transiting its
+moderately rapidly rotating (gravity-darkened) parent star. A physical model,
+with minimal ad hoc free parameters, reproduces the time-averaged light-curve
+at the ca. 10 parts per million level. We demonstrate that this Roche-model
+solution allows the absolute dimensions of the system to be determined from the
+star's projected equatorial rotation speed, v(e)sin(i), without any additional
+assumptions; we find a planetary radius 1.33+/-0.05 R(Jup), stellar polar
+radius 1.55+/-0.06 R(sun), combined mass M(*) + M(P) (\simeq M*) = 1.47 +/-
+0.17 M(sun), and distance d \simeq 370+/-25 pc, where the errors are dominated
+by uncertainties in relative flux contribution of the visual-binary companion
+KOI-13B. The implied stellar rotation period is within ca. 5% of the
+non-orbital, 25.43-hr signal found in the Kepler photometry. We show that the
+model accurately reproduces independent tomographic observations, and yields an
+offset between orbital and stellar-rotation angular-momentum vectors of
+60.25+/-0.05 degrees.
+",0,1,0,0,0,0
+15347,Learning Linear Dynamical Systems via Spectral Filtering,"  We present an efficient and practical algorithm for the online prediction of
+discrete-time linear dynamical systems with a symmetric transition matrix. We
+circumvent the non-convex optimization problem using improper learning:
+carefully overparameterize the class of LDSs by a polylogarithmic factor, in
+exchange for convexity of the loss functions. From this arises a
+polynomial-time algorithm with a near-optimal regret guarantee, with an
+analogous sample complexity bound for agnostic learning. Our algorithm is based
+on a novel filtering technique, which may be of independent interest: we
+convolve the time series with the eigenvectors of a certain Hankel matrix.
+",1,0,0,1,0,0
+15348,Time pressure and honesty in a deception game,"  Previous experiments have found mixed results on whether honesty is intuitive
+or requires deliberation. Here we add to this literature by building on prior
+work of Capraro (2017). We report a large study (N=1,389) manipulating time
+pressure vs time delay in a deception game. We find that, in this setting,
+people are more honest under time pressure, and that this result is not driven
+by confounds present in earlier work.
+",0,0,0,0,1,0
+15349,A deep learning approach to real-time parking occupancy prediction in spatio-termporal networks incorporating multiple spatio-temporal data sources,"  A deep learning model is proposed for predicting block-level parking
+occupancy in real time. The model leverages Graph-Convolutional Neural Networks
+(GCNN) to extract the spatial relations of traffic flow in large-scale
+networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term
+Memory (LSTM) to capture the temporal features. In addition, the model is
+capable of taking multiple heterogeneously structured traffic data sources as
+input, such as parking meter transactions, traffic speed, and weather
+conditions. The model performance is evaluated through a case study in
+Pittsburgh downtown area. The proposed model outperforms other baseline methods
+including multi-layer LSTM and Lasso with an average testing MAPE of 12.0\%
+when predicting block-level parking occupancies 30 minutes in advance. The case
+study also shows that, in generally, the prediction model works better for
+business areas than for recreational locations. We found that incorporating
+traffic speed and weather information can significantly improve the prediction
+performance. Weather data is particularly useful for improving predicting
+accuracy in recreational areas.
+",1,0,0,1,0,0
+15350,Time series experiments and causal estimands: exact randomization tests and trading,"  We define causal estimands for experiments on single time series, extending
+the potential outcome framework to dealing with temporal data. Our approach
+allows the estimation of some of these estimands and exact randomization based
+p-values for testing causal effects, without imposing stringent assumptions. We
+test our methodology on simulated ""potential autoregressions,""which have a
+causal interpretation. Our methodology is partially inspired by data from a
+large number of experiments carried out by a financial company who compared the
+impact of two different ways of trading equity futures contracts. We use our
+methodology to make causal statements about their trading methods.
+",0,0,1,1,0,0
+15351,Sparse phase retrieval of one-dimensional signals by Prony's method,"  In this paper, we show that sparse signals f representable as a linear
+combination of a finite number N of spikes at arbitrary real locations or as a
+finite linear combination of B-splines of order m with arbitrary real knots can
+be almost surely recovered from O(N^2) Fourier intensity measurements up to
+trivial ambiguities. The constructive proof consists of two steps, where in the
+first step the Prony method is applied to recover all parameters of the
+autocorrelation function and in the second step the parameters of f are
+derived. Moreover, we present an algorithm to evaluate f from its Fourier
+intensities and illustrate it at different numerical examples.
+",1,0,1,0,0,0
+15352,Atomic and electronic structure of a copper/graphene interface as prepared and 1.5 years after,"  We report the results of X-ray spectroscopy and Raman measurements of
+as-prepared graphene on a high quality copper surface and the same materials
+after 1.5 years under different conditions (ambient and low humidity). The
+obtained results were compared with density functional theory calculations of
+the formation energies and electronic structures of various structural defects
+in graphene/Cu interfaces. For evaluation of the stability of the carbon cover,
+we propose a two-step model. The first step is oxidation of the graphene, and
+the second is perforation of graphene with the removal of carbon atoms as part
+of the carbon dioxide molecule. Results of the modeling and experimental
+measurements provide evidence that graphene grown on high-quality copper
+substrate becomes robust and stable in time (1.5 years). However, the stability
+of this interface depends on the quality of the graphene and the number of
+native defects in the graphene and substrate. The effect of the presence of a
+metallic substrate with defects on the stability and electronic structure of
+graphene is also discussed.
+",0,1,0,0,0,0
+15353,Modeling and Control of Humanoid Robots in Dynamic Environments: iCub Balancing on a Seesaw,"  Forthcoming applications concerning humanoid robots may involve physical
+interaction between the robot and a dynamic environment. In such scenario,
+classical balancing and walking controllers that neglect the environment
+dynamics may not be sufficient for achieving a stable robot behavior. This
+paper presents a modeling and control framework for balancing humanoid robots
+in contact with a dynamic environment. We first model the robot and environment
+dynamics, together with the contact constraints. Then, a control strategy for
+stabilizing the full system is proposed. Theoretical results are verified in
+simulation with robot iCub balancing on a seesaw.
+",1,0,0,0,0,0
+15354,Forward-Backward Selection with Early Dropping,"  Forward-backward selection is one of the most basic and commonly-used feature
+selection algorithms available. It is also general and conceptually applicable
+to many different types of data. In this paper, we propose a heuristic that
+significantly improves its running time, while preserving predictive accuracy.
+The idea is to temporarily discard the variables that are conditionally
+independent with the outcome given the selected variable set. Depending on how
+those variables are reconsidered and reintroduced, this heuristic gives rise to
+a family of algorithms with increasingly stronger theoretical guarantees. In
+distributions that can be faithfully represented by Bayesian networks or
+maximal ancestral graphs, members of this algorithmic family are able to
+correctly identify the Markov blanket in the sample limit. In experiments we
+show that the proposed heuristic increases computational efficiency by about
+two orders of magnitude in high-dimensional problems, while selecting fewer
+variables and retaining predictive performance. Furthermore, we show that the
+proposed algorithm and feature selection with LASSO perform similarly when
+restricted to select the same number of variables, making the proposed
+algorithm an attractive alternative for problems where no (efficient) algorithm
+for LASSO exists.
+",1,0,0,1,0,0
+15355,Ultracold bosonic scattering dynamics off a repulsive barrier: coherence loss at the dimensional crossover,"  We explore the impact of dimensionality on the scattering of a small bosonic
+ensemble in an elongated harmonic trap off a centered repulsive barrier,
+thereby taking particle correlations into account. The loss of coherence as
+well as the oscillation of the center of mass are studied and we analyze the
+influence of both particle and spatial correlations. Two different mechanisms
+of coherence losses in dependence of the aspect ratio are found. For small
+aspect ratios, loss of coherence between the region close to the barrier and
+outer regions occurs, due to spatial correlations, and for large aspect ratios,
+incoherence between the two density fragments of the left and right side of the
+barrier arises, due to particle correlations. Apart form the decay of the
+center of mass motion induced by the reflection and transmission, further
+effects due to the particle and spatial correlations are explored. For tight
+transversal traps, the amplitude of the center of mass oscillation experiences
+a weaker damping, which can be traced back to the population of a second
+natural orbital, and for a weaker transversal confinement, we detect a strong
+decay, due to the possibility of transferring energy to transversal excited
+modes. These effects are enhanced if the aspect ratio is integer valued.
+",0,1,0,0,0,0
+15356,On Lebesgue Integral Quadrature,"  A new type of quadrature is developed. The Gauss quadrature, for a given
+measure, finds optimal values of a function's argument (nodes) and the
+corresponding weights. In contrast, the Lebesgue quadrature developed in this
+paper, finds optimal values of function (value-nodes) and the corresponding
+weights. The Gauss quadrature groups sums by function argument, it can be
+viewed as a $n$-point discrete measure, producing the Riemann integral. The
+Lebesgue quadrature groups sums by function value, it can be viewed as a
+$n$-point discrete distribution, producing the Lebesgue integral.
+Mathematically, the problem is reduced to a generalized eigenvalue problem:
+Lebesgue quadrature value-nodes are the eigenvalues and the corresponding
+weights are the square of the averaged eigenvectors. A numerical estimation of
+an integral as the Lebesgue integral is especially advantageous when analyzing
+irregular and stochastic processes. The approach separates the outcome
+(value-nodes) and the probability of the outcome (weight). For this reason, it
+is especially well-suited for the study of non--Gaussian processes. The
+software implementing the theory is available from the authors.
+",0,0,0,1,0,0
+15357,Protection Number in Plane Trees,"  The protection number of a plane tree is the minimal distance of the root to
+a leaf; this definition carries over to an arbitrary node in a plane tree by
+considering the maximal subtree having this node as a root. We study the the
+protection number of a uniformly chosen random tree of size $n$ and also the
+protection number of a uniformly chosen node in a uniformly chosen random tree
+of size $n$. The method is to apply singularity analysis to appropriate
+generating functions. Additional results are provided as well.
+",0,0,1,0,0,0
+15358,Improved bounds for restricted families of projections to planes in $\mathbb{R}^{3}$,"  For $e \in S^{2}$, the unit sphere in $\mathbb{R}^3$, let $\pi_{e}$ be the
+orthogonal projection to $e^{\perp} \subset \mathbb{R}^{3}$, and let $W \subset
+\mathbb{R}^{3}$ be any $2$-plane, which is not a subspace. We prove that if $K
+\subset \mathbb{R}^{3}$ is a Borel set with $\dim_{\mathrm{H}} K \leq
+\tfrac{3}{2}$, then $\dim_{\mathrm{H}} \pi_{e}(K) = \dim_{\mathrm{H}} K$ for
+$\mathcal{H}^{1}$ almost every $e \in S^{2} \cap W$, where $\mathcal{H}^{1}$
+denotes the $1$-dimensional Hausdorff measure and $\dim_{\mathrm{H}}$ the
+Hausdorff dimension. This was known earlier, due to Järvenpää,
+Järvenpää, Ledrappier and Leikas, for Borel sets $K \subset
+\mathbb{R}^{3}$ with $\dim_{\mathrm{H}} K \leq 1$. We also prove a partial
+result for sets with dimension exceeding $3/2$, improving earlier bounds by D.
+Oberlin and R. Oberlin.
+",0,0,1,0,0,0
+15359,Differential galois theory and mechanics,"  The classical Galois theory deals with certain finite algebraic extensions
+and establishes a bijective order reversing correspondence between the
+intermediate fields and the subgroups of a group of permutations called the
+Galois group of the extension. It has been the dream of many mathematicians at
+the end of the nineteenth century to generalize these results to systems of
+algebraic partial differential (PD) equations and the corresponding finitely
+generated differential extensions, in order to be able to add the word
+differential in front of any classical statement. The achievement of the
+Picard-Vessiot theory by E. Kolchin between 1950 and 1970 is now well known.
+The purpose of this paper is to sketch the general theory for such differential
+extensions and algebraic pseudogroups by means of new methods mixing
+differential algebra, differential geometry and algebraic geometry. As already
+discovered by E. Vessiot in 1904 through the use of automorphic systems, a
+concept never acknowledged, the main point is to notice that the Galois theory
+(old and new) is mainly a study of principal homogeneous spaces (PHS) for
+algebraic groups or pseudogroups. Hence, all the formal theory of PD equations
+developped by D.C. Spencer around 1970 must be used together with modern
+algebraic geometry, in particular tensor products of rings and fields. However,
+the combination of these new tools is not sufficient and we have to create the
+analogue for Lie pseudogroups of the so-called invariant derivations introduced
+by A. Bialynicki-Birula after 1960 in the study of algebraic groups and fields
+with derivations. We shall finally prove the usefulness of the resulting
+differential Galois theory through striking applications to mechanics,
+revisiting shell theory, chain theory, the Frenet-Serret formulas and the
+integration of Hamilton-Jacobi equations.
+",0,1,1,0,0,0
+15360,"Note on ""Average resistance of toroidal graphs"" by Rossi, Frasca and Fagnani","  In our recent paper W.S. Rossi, P. Frasca and F. Fagnani, ""Average resistance
+of toroidal graphs"", SIAM Journal on Control and Optimization,
+53(4):2541--2557, 2015, we studied how the average resistances of
+$d$-dimensional toroidal grids depend on the graph topology and on the
+dimension of the graph. Our results were based on the connection between
+resistance and Laplacian eigenvalues. In this note, we contextualize our work
+in the body of literature about random walks on graphs. Indeed, the average
+effective resistance of the $d$-dimensional toroidal grid is proportional to
+the mean hitting time of the simple random walk on that grid. If $d\geq3 $,
+then the average resistance can be bounded uniformly in the number of nodes and
+its value is of order $1/d$ for large $d$.
+",1,0,1,0,0,0
+15361,A Survey of Runtime Monitoring Instrumentation Techniques,"  Runtime Monitoring is a lightweight and dynamic verification technique that
+involves observing the internal operations of a software system and/or its
+interactions with other external entities, with the aim of determining whether
+the system satisfies or violates a correctness specification. Compilation
+techniques employed in Runtime Monitoring tools allow monitors to be
+automatically derived from high-level correctness specifications (aka.
+properties). This allows the same property to be converted into different types
+of monitors, which may apply different instrumentation techniques for checking
+whether the property was satisfied or not. In this paper we compare and
+contrast the various types of monitoring methodologies found in the current
+literature, and classify them into a spectrum of monitoring instrumentation
+techniques, ranging from completely asynchronous monitoring on the one end and
+completely synchronous monitoring on the other.
+",1,0,0,0,0,0
+15362,Interpretable Counting for Visual Question Answering,"  Questions that require counting a variety of objects in images remain a major
+challenge in visual question answering (VQA). The most common approaches to VQA
+involve either classifying answers based on fixed length representations of
+both the image and question or summing fractional counts estimated from each
+section of the image. In contrast, we treat counting as a sequential decision
+process and force our model to make discrete choices of what to count.
+Specifically, the model sequentially selects from detected objects and learns
+interactions between objects that influence subsequent selections. A
+distinction of our approach is its intuitive and interpretable output, as
+discrete counts are automatically grounded in the image. Furthermore, our
+method outperforms the state of the art architecture for VQA on multiple
+metrics that evaluate counting.
+",1,0,0,0,0,0
+15363,How consistent is my model with the data? Information-Theoretic Model Check,"  The choice of model class is fundamental in statistical learning and system
+identification, no matter whether the class is derived from physical principles
+or is a generic black-box. We develop a method to evaluate the specified model
+class by assessing its capability of reproducing data that is similar to the
+observed data record. This model check is based on the information-theoretic
+properties of models viewed as data generators and is applicable to e.g.
+sequential data and nonlinear dynamical models. The method can be understood as
+a specific two-sided posterior predictive test. We apply the
+information-theoretic model check to both synthetic and real data and compare
+it with a classical whiteness test.
+",1,0,0,1,0,0
+15364,Stable recovery of deep linear networks under sparsity constraints,"  We study a deep linear network expressed under the form of a matrix
+factorization problem. It takes as input a matrix $X$ obtained by multiplying
+$K$ matrices (called factors and corresponding to the action of a layer). Each
+factor is obtained by applying a fixed linear operator to a vector of
+parameters satisfying a sparsity constraint. In machine learning, the error
+between the product of the estimated factors and $X$ (i.e. the reconstruction
+error) relates to the statistical risk. The stable recovery of the parameters
+defining the factors is required in order to interpret the factors and the
+intermediate layers of the network. In this paper, we provide sharp conditions
+on the network topology under which the error on the parameters defining the
+factors (i.e. the stability of the recovered parameters) scales linearly with
+the reconstruction error (i.e. the risk). Therefore, under these conditions on
+the network topology, any successful learning tasks leads to robust and
+therefore interpretable layers. The analysis is based on the recently proposed
+Tensorial Lifting. The particularity of this paper is to consider a sparse
+prior. As an illustration, we detail the analysis and provide sharp guarantees
+for the stable recovery of convolutional linear network under sparsity prior.
+As expected, the condition are rather strong.
+",1,0,1,1,0,0
+15365,Revisiting Parametricity: Inductives and Uniformity of Propositions,"  Reynold's parametricity theory captures the property that parametrically
+polymorphic functions behave uniformly: they produce related results on related
+instantiations. In dependently-typed programming languages, such relations and
+uniformity proofs can be expressed internally, and generated as a program
+translation.
+We present a new parametricity translation for a significant fragment of Coq.
+Previous translations of parametrically polymorphic propositions allowed
+non-uniformity. For example, on related instantiations, a function may return
+propositions that are logically inequivalent (e.g. True and False). We show
+that uniformity of polymorphic propositions is not achievable in general.
+Nevertheless, our translation produces proofs that the two propositions are
+logically equivalent and also that any two proofs of those propositions are
+related. This is achieved at the cost of potentially requiring more assumptions
+on the instantiations, requiring them to be isomorphic in the worst case.
+Our translation augments the previous one for Coq by carrying and
+compositionally building extra proofs about parametricity relations. It is made
+easier by a new method for translating inductive types and pattern matching.
+The new method builds upon and generalizes previous such translations for
+dependently-typed programming languages.
+Using reification and reflection, we have implemented our translation as Coq
+programs. We obtain several stronger free theorems applicable to an ongoing
+compiler-correctness project. Previously, proofs of some of these theorems took
+several hours to finish.
+",1,0,0,0,0,0
+15366,"Characterizing Minimal Semantics-preserving Slices of predicate-linear, Free, Liberal Program Schemas","  A program schema defines a class of programs, all of which have identical
+statement structure, but whose functions and predicates may differ. A schema
+thus defines an entire class of programs according to how its symbols are
+interpreted. A subschema of a schema is obtained from a schema by deleting some
+of its statements. We prove that given a schema $S$ which is predicate-linear,
+free and liberal, such that the true and false parts of every if predicate
+satisfy a simple additional condition, and a slicing criterion defined by the
+final value of a given variable after execution of any program defined by $S$,
+the minimal subschema of $S$ which respects this slicing criterion contains all
+the function and predicate symbols `needed' by the variable according to the
+data dependence and control dependence relations used in program slicing, which
+is the symbol set given by Weiser's static slicing algorithm. Thus this
+algorithm gives predicate-minimal slices for classes of programs represented by
+schemas satisfying our set of conditions. We also give an example to show that
+the corresponding result with respect to the slicing criterion defined by
+termination behaviour is incorrect. This complements a result by the authors in
+which $S$ was required to be function-linear, instead of predicate-linear.
+",1,0,0,0,0,0
+15367,Exposing Twitter Users to Contrarian News,"  Polarized topics often spark discussion and debate on social media. Recent
+studies have shown that polarized debates have a specific clustered structure
+in the endorsement net- work, which indicates that users direct their
+endorsements mostly to ideas they already agree with. Understanding these
+polarized discussions and exposing social media users to content that broadens
+their views is of paramount importance.
+The contribution of this demonstration is two-fold. (i) A tool to visualize
+retweet networks about controversial issues on Twitter. By using our
+visualization, users can understand how polarized discussions are shaped on
+Twitter, and explore the positions of the various actors. (ii) A solution to
+reduce polarization of such discussions. We do so by exposing users to
+information which presents a contrarian point of view. Users can visually
+inspect our recommendations and understand why and how these would play out in
+terms of the retweet network.
+Our demo (this https URL homepage)
+provides one of the first steps in developing automated tools that help users
+explore, and possibly escape, their echo chambers. The ideas in the demo can
+also help content providers design tools to broaden their reach to people with
+different political and ideological backgrounds.
+",1,0,0,0,0,0
+15368,Value-Decomposition Networks For Cooperative Multi-Agent Learning,"  We study the problem of cooperative multi-agent reinforcement learning with a
+single joint reward signal. This class of learning problems is difficult
+because of the often large combined action and observation spaces. In the fully
+centralized and decentralized approaches, we find the problem of spurious
+rewards and a phenomenon we call the ""lazy agent"" problem, which arises due to
+partial observability. We address these problems by training individual agents
+with a novel value decomposition network architecture, which learns to
+decompose the team value function into agent-wise value functions. We perform
+an experimental evaluation across a range of partially-observable multi-agent
+domains and show that learning such value-decompositions leads to superior
+results, in particular when combined with weight sharing, role information and
+information channels.
+",1,0,0,0,0,0
+15369,Application of a Shallow Neural Network to Short-Term Stock Trading,"  Machine learning is increasingly prevalent in stock market trading. Though
+neural networks have seen success in computer vision and natural language
+processing, they have not been as useful in stock market trading. To
+demonstrate the applicability of a neural network in stock trading, we made a
+single-layer neural network that recommends buying or selling shares of a stock
+by comparing the highest high of 10 consecutive days with that of the next 10
+days, a process repeated for the stock's year-long historical data. A
+chi-squared analysis found that the neural network can accurately and
+appropriately decide whether to buy or sell shares for a given stock, showing
+that a neural network can make simple decisions about the stock market.
+",1,0,0,0,0,0
+15370,A characterization of round spheres in space forms,"  Let $\mathbb Q^{n+1}_c$ be the complete simply-connected $(n+1)$-dimensional
+space form of curvature $c$. In this paper we obtain a new characterization of
+geodesic spheres in $\mathbb Q^{n+1}_c$ in terms of the higher order mean
+curvatures. In particular, we prove that the geodesic sphere is the only
+complete bounded immersed hypersurface in $\mathbb Q^{n+1}_c,\;c\leq 0,$ with
+constant mean curvature and constant scalar curvature. The proof relies on the
+well known Omori-Yau maximum principle, a formula of Walter for the Laplacian
+of the $r$-th mean curvature of a hypersurface in a space form, and a classical
+inequality of G\aa rding for hyperbolic polynomials.
+",0,0,1,0,0,0
+15371,Oxygen - Dislocation interaction in zirconium from first principles,"  Plasticity in zirconium alloys is mainly controlled by the interaction of 1/3
+1210 screw dislocations with oxygen atoms in interstitial octahedral sites of
+the hexagonal close-packed lattice. This process is studied here using ab
+initio calculations based on the density functional theory. The atomic
+simulations show that a strong repulsion exists only when the O atoms lie in
+the dislocation core and belong to the prismatic dislocation habit plane. This
+is a consequence of the destruction of the octahedral sites by the stacking
+fault arising from the dislocation dissociation. Because of the repulsion, the
+dislocation partially cross-slips to an adjacent prismatic plane, in agreement
+with experiments where the lattice friction on screw dislocations in Zr-O
+alloys has been attributed to the presence of jogs on the dislocations due to
+local cross-slip.
+",0,1,0,0,0,0
+15372,Generating Spatial Spectrum with Metasurfaces,"  Fourier optics, the principle of using Fourier Transformation to understand
+the functionalities of optical elements, lies at the heart of modern optics,
+and has been widely applied to optical information processing, imaging,
+holography etc. While a simple thin lens is capable of resolving Fourier
+components of an arbitrary optical wavefront, its operation is limited to near
+normal light incidence, i.e. the paraxial approximation, which put a severe
+constraint on the resolvable Fourier domain. As a result, high-order Fourier
+components are lost, resulting in extinction of high-resolution information of
+an image. Here, we experimentally demonstrate a dielectric metasurface
+consisting of high-aspect-ratio silicon waveguide array, which is capable of
+performing Fourier transform for a large incident angle range and a broad
+operating bandwidth. Thus our device significantly expands the operational
+Fourier space, benefitting from the large numerical aperture (NA), and
+negligible angular dispersion at large incident angles. Our Fourier metasurface
+will not only facilitate efficient manipulation of spatial spectrum of
+free-space optical wavefront, but also be readily integrated into micro-optical
+platforms due to its compact size.
+",0,1,0,0,0,0
+15373,Modal clustering asymptotics with applications to bandwidth selection,"  Density-based clustering relies on the idea of linking groups to some
+specific features of the probability distribution underlying the data. The
+reference to a true, yet unknown, population structure allows to frame the
+clustering problem in a standard inferential setting, where the concept of
+ideal population clustering is defined as the partition induced by the true
+density function. The nonparametric formulation of this approach, known as
+modal clustering, draws a correspondence between the groups and the domains of
+attraction of the density modes. Operationally, a nonparametric density
+estimate is required and a proper selection of the amount of smoothing,
+governing the shape of the density and hence possibly the modal structure, is
+crucial to identify the final partition. In this work, we address the issue of
+density estimation for modal clustering from an asymptotic perspective. A
+natural and easy to interpret metric to measure the distance between
+density-based partitions is discussed, its asymptotic approximation explored,
+and employed to study the problem of bandwidth selection for nonparametric
+modal clustering.
+",0,0,0,1,0,0
+15374,"Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow","  This report provides an introduction to some Machine Learning tools within
+the most common development environments. It mainly focuses on practical
+problems, skipping any theoretical introduction. It is oriented to both
+students trying to approach Machine Learning and experts looking for new
+frameworks.
+",1,0,0,1,0,0
+15375,Spectral determination of semi-regular polygons,"  Let us say that an $n$-sided polygon is semi-regular if it is
+circumscriptible and its angles are all equal but possibly one, which is then
+larger than the rest. Regular polygons, in particular, are semi-regular. We
+prove that semi-regular polygons are spectrally determined in the class of
+convex piecewise smooth domains. Specifically, we show that if $\Omega$ is a
+convex piecewise smooth planar domain, possibly with straight corners, whose
+Dirichlet or Neumann spectrum coincides with that of an $n$-sided semi-regular
+polygon $P_n$, then $\Omega$ is congruent to $P_n$.
+",0,0,1,0,0,0
+15376,Accuracy and validity of posterior distributions using the Cressie-Read empirical likelihoods,"  The class of Cressie-Read empirical likelihoods are constructed with weights
+derived at a minimum distance from the empirical distribution in the
+Cressie-Read family of divergences indexed by $\gamma$ under the constraint of
+an unbiased set of $M$-estimating equations. At first order, they provide valid
+posterior probability statements for any given prior, but the bias in coverage
+of the resulting empirical quantile is inversely proportional to the asymptotic
+efficiency of the corresponding $M$-estimator. The Cressie-Read empirical
+likelihoods based on the maximum likelihood estimating equations bring about
+quantiles covering with $O(n^{-1})$ accuracy at the underlying posterior
+distribution. The choice of $\gamma$ has an impact on the variance in small
+samples of the posterior quantile function. Examples are given for the $M$-type
+estimating equations of location and for the quasi-likelihood functions in the
+generalized linear models.
+",0,0,1,1,0,0
+15377,A Quillen's Theorem A for strict $\infty$-categories I: the simplicial proof,"  The aim of this paper is to prove a generalization of the famous Theorem A of
+Quillen for strict $\infty$-categories. This result is central to the homotopy
+theory of strict $\infty$-categories developed by the authors. The proof
+presented here is of a simplicial nature and uses Steiner's theory of augmented
+directed complexes. In a subsequent paper, we will prove the same result by
+purely $\infty$-categorical methods.
+",0,0,1,0,0,0
+15378,Assessing Excited State Energy Gaps with Time-Dependent Density Functional Theory on Ru(II) Complexes,"  A set of density functionals coming from different rungs on Jacob's ladder
+are employed to evaluate the electronic excited states of three Ru(II)
+complexes. While most studies on the performance of density functionals compare
+the vertical excitation energies, in this work we focus on the energy gaps
+between the electronic excited states, of the same and different multiplicity.
+Excited state energy gaps are important for example to determine radiationless
+transition probabilities. Besides energies, a functional should deliver the
+correct state character and state ordering. Therefore, wavefunction overlaps
+are introduced to systematically evaluate the effect of different functionals
+on the character of the excited states. As a reference, the energies and state
+characters from multi-state second-order perturbation theory complete active
+space (MS-CASPT2) are used. In comparison to MS-CASPT2, it is found that while
+hybrid functionals provide better vertical excitation energies, pure
+functionals typically give more accurate excited state energy gaps. Pure
+functionals are also found to reproduce the state character and ordering in
+closer agreement to MS-CASPT2 than the hybrid functionals.
+",0,1,0,0,0,0
+15379,Description of the evolution of inhomogeneities on a Dark Matter halo with the Vlasov equation,"  We use a direct numerical integration of the Vlasov equation in spherical
+symmetry with a background gravitational potential to determine the evolution
+of a collection of particles in different models of a galactic halo. Such a
+collection is assumed to represent a dark matter inhomogeneity which reaches a
+stationary state determined by the virialization of the system. We describe
+some features of the stationary states and, by using several halo models,
+obtain distinctive signatures for the evolution of the inhomogeneities in each
+of the models.
+",0,1,0,0,0,0
+15380,Fast generation of isotropic Gaussian random fields on the sphere,"  The efficient simulation of isotropic Gaussian random fields on the unit
+sphere is a task encountered frequently in numerical applications. A fast
+algorithm based on Markov properties and fast Fourier Transforms in 1d is
+presented that generates samples on an n x n grid in O(n^2 log n). Furthermore,
+an efficient method to set up the necessary conditional covariance matrices is
+derived and simulations demonstrate the performance of the algorithm. An open
+source implementation of the code has been made available at
+this https URL .
+",0,0,1,1,0,0
+15381,Initial-boundary value problem to 2D Boussinesq equations for MHD convection with stratification effects,"  This paper is concerned with the initial-boundary value problem to 2D
+magnetohydrodynamics-Boussinesq system with the temperature-dependent
+viscosity, thermal diffusivity and electrical conductivity. First, we establish
+the global weak solutions under the minimal initial assumption. Then by
+imposing higher regularity assumption on the initial data, we obtain the global
+strong solution with uniqueness. Moreover, the exponential decay estimate of
+the solution is obtained.
+",0,0,1,0,0,0
+15382,Profile of a coherent vortex in two-dimensional turbulence at static pumping,"  We examine the velocity profile of coherent vortices appearing as a
+consequence of the inverse cascade of two-dimensional turbulence in a finite
+box in the case of static pumping. We demonstrate that in the passive regime
+the flat velocity profile is realized, as in the case of pumping short
+correlated in time. However, in the static case the energy flux to large scales
+is dependent on the system parameters. We demonstrate that it is proportional
+to $f^{4/3}$ where $f$ is the characteristic force exciting turbulence.
+",0,1,0,0,0,0
+15383,Computation of life expectancy from incomplete data,"  Estimating the human longevity and computing of life expectancy are central
+to the population dynamics. These aspects were studied seriously by scientists
+since fifteenth century, including renowned astronomer Edmund Halley. From
+basic principles of population dynamics, we propose a method to compute life
+expectancy from incomplete data.
+",0,0,0,0,1,0
+15384,Residual Squeeze VGG16,"  Deep learning has given way to a new era of machine learning, apart from
+computer vision. Convolutional neural networks have been implemented in image
+classification, segmentation and object detection. Despite recent advancements,
+we are still in the very early stages and have yet to settle on best practices
+for network architecture in terms of deep design, small in size and a short
+training time. In this work, we propose a very deep neural network comprised of
+16 Convolutional layers compressed with the Fire Module adapted from the
+SQUEEZENET model. We also call for the addition of residual connections to help
+suppress degradation. This model can be implemented on almost every neural
+network model with fully incorporated residual learning. This proposed model
+Residual-Squeeze-VGG16 (ResSquVGG16) trained on the large-scale MIT
+Places365-Standard scene dataset. In our tests, the model performed with
+accuracy similar to the pre-trained VGG16 model in Top-1 and Top-5 validation
+accuracy while also enjoying a 23.86% reduction in training time and an 88.4%
+reduction in size. In our tests, this model was trained from scratch.
+",1,0,0,0,0,0
+15385,Generic partiality for $\frac{3}{2}$-institutions,"  $\frac{3}{2}$-institutions have been introduced as an extension of
+institution theory that accommodates implicitly partiality of the signature
+morphisms together with its syntactic and semantic effects. In this paper we
+show that ordinary institutions that are equipped with an inclusion system for
+their categories of signatures generate naturally $\frac{3}{2}$ -institutions
+with explicit partiality for their signature morphisms. This provides a general
+uniform way to build 3 -institutions for the foundations of conceptual blending
+and software evolution. Moreover our general construction allows for an uniform
+derivation of some useful technical properties.
+",0,0,1,0,0,0
+15386,Independently Controllable Factors,"  It has been postulated that a good representation is one that disentangles
+the underlying explanatory factors of variation. However, it remains an open
+question what kind of training framework could potentially achieve that.
+Whereas most previous work focuses on the static setting (e.g., with images),
+we postulate that some of the causal factors could be discovered if the learner
+is allowed to interact with its environment. The agent can experiment with
+different actions and observe their effects. More specifically, we hypothesize
+that some of these factors correspond to aspects of the environment which are
+independently controllable, i.e., that there exists a policy and a learnable
+feature for each such aspect of the environment, such that this policy can
+yield changes in that feature with minimal changes to other features that
+explain the statistical variations in the observed data. We propose a specific
+objective function to find such factors and verify experimentally that it can
+indeed disentangle independently controllable aspects of the environment
+without any extrinsic reward signal.
+",1,0,0,1,0,0
+15387,Determination and biological application of a time dependent thermal parameter and sensitivity analysis for a conduction problem with superficial evaporation,"  A boundary value problem, which could represent a transcendent temperature
+conduction problem with evaporation in a part of the boundary, was studied to
+determine unknown thermophysical parameters, which can be constants or time
+dependent functions. The goal of this paper was elucidate which parameters may
+be determined using only the measured superficial temperature in part of the
+boundary of the domain. We formulated a nonlinear inverse problem to determine
+the unknown parameters and a sensitivity analysis was also performed. In
+particular, we introduced a new way of computing a sensitivity analysis of a
+parameter which is variable in time. We applied the proposed method to model
+tissue temperature changes under transient conditions in a biological problem:
+the hamster cheek pouch. In this case, the time dependent unknown parameter can
+be associated to the loss of heat due to water evaporation at the superficial
+layer of the pouch. Finally, we performed the sensitivity analysis to determine
+the most sensible parameters to variations of the superficial experimental data
+in the hamster cheek pouch.
+",0,1,0,0,0,0
+15388,Setting Boundaries with Memory: Generation of Topological Boundary States in Floquet-Induced Synthetic Crystals,"  When a d-dimensional quantum system is subjected to a periodic drive, it may
+be treated as a (d+1)-dimensional system, where the extra dimension is a
+synthetic one. In this work, we take these ideas to the next level by showing
+that non-uniform potentials, and particularly edges, in the synthetic dimension
+are created whenever the dynamics of system has a memory component. We
+demonstrate that topological states appear on the edges of these synthetic
+dimensions and can be used as a basis for a wave packet construction. Such
+systems may act as an optical isolator which allows transmission of light in a
+directional way. We supplement our ideas by an example of a physical system
+that shows this type of physics.
+",0,1,0,0,0,0
+15389,Collaborative Nested Sampling: Big Data vs. complex physical models,"  The data torrent unleashed by current and upcoming astronomical surveys
+demands scalable analysis methods. Many machine learning approaches scale well,
+but separating the instrument measurement from the physical effects of
+interest, dealing with variable errors, and deriving parameter uncertainties is
+often an after-thought. Classic forward-folding analyses with Markov Chain
+Monte Carlo or Nested Sampling enable parameter estimation and model
+comparison, even for complex and slow-to-evaluate physical models. However,
+these approaches require independent runs for each data set, implying an
+unfeasible number of model evaluations in the Big Data regime. Here I present a
+new algorithm, collaborative nested sampling, for deriving parameter
+probability distributions for each observation. Importantly, the number of
+physical model evaluations scales sub-linearly with the number of data sets,
+and no assumptions about homogeneous errors, Gaussianity, the form of the model
+or heterogeneity/completeness of the observations need to be made.
+Collaborative nested sampling has immediate application in speeding up analyses
+of large surveys, integral-field-unit observations, and Monte Carlo
+simulations.
+",0,1,0,1,0,0
+15390,Latent Room-Temperature T$_c$ in Cuprate Superconductors,"  The ancient phrase, ""All roads lead to Rome"" applies to Chemistry and
+Physics. Both are highly evolved sciences, with their own history, traditions,
+language, and approaches to problems. Despite all these differences, these two
+roads generally lead to the same place. For high temperature cuprate
+superconductors however, the Chemistry and Physics roads do not meet or even
+come close to each other. In this paper, we analyze the physics and chemistry
+approaches to the doped electronic structure of cuprates and find the chemistry
+doped hole (out-of-the-CuO$\mathrm{_2}$-planes) leads to explanations of a vast
+array of normal state cuprate phenomenology using simple counting arguments.
+The chemistry picture suggests that phonons are responsible for
+superconductivity in cuprates. We identify the important phonon modes, and show
+that the observed T$\mathrm{_c} \sim 100$ K, the T$\mathrm{_c}$-dome as a
+function of hole doping, the change in T$\mathrm{_c}$ as a function of the
+number of CuO$\mathrm{_2}$ layers per unit cell, the lack of an isotope effect
+at optimal T$\mathrm{_c}$ doping, and the D-wave symmetry of the
+superconducting Cooper pair wavefunction are all explained by the chemistry
+picture. Finally, we show that ""crowding"" the dopants in cuprates leads to a
+pair wavefunction with S-wave symmetry and T$\mathrm{_c}\approx280-390$ K.
+Hence, we believe there is enormous ""latent"" T$\mathrm{_c}$ remaining in the
+cuprate class of superconductors.
+",0,1,0,0,0,0
+15391,Characterization of minimizers of an anisotropic variant of the Rudin-Osher-Fatemi functional with $L^1$ fidelity term,"  In this paper we study an anisotropic variant of the Rudin-Osher-Fatemi
+functional with $L^1$ fidelity term of the form \[ E(u) = \int_{\mathbb{R}^n}
+\phi(\nabla u) + \lambda \| u -f \|_{L^1(\mathbb{R}^n)}. \] We will
+characterize the minimizers of $E$ in terms of the Wulff shape of $\phi$ and
+the dual anisotropy. In particular we will calculate the subdifferential of
+$E$. We will apply this characterization to the special case $\phi = |\cdot|_1$
+and $n=2$, which has been used in the denoising of 2D bar codes. In this case,
+we determine the shape of a minimizer $u$ when $f$ is the characteristic
+function of a circle.
+",0,0,1,0,0,0
+15392,Partial regularity of weak solutions and life-span of smooth solutions to a biological network formulation model,"  In this paper we first study partial regularity of weak solutions to the
+initial boundary value problem for the system
+$-\mbox{div}\left[(I+\mathbf{m}\otimes \mathbf{m})\nabla p\right]=S(x),\ \
+\partial_t\mathbf{m}-D^2\Delta \mathbf{m}-E^2(\mathbf{m}\cdot\nabla p)\nabla
+p+|\mathbf{m}|^{2(\gamma-1)}\mathbf{m}=0$, where $S(x)$ is a given function and
+$D, E, \gamma$ are given numbers. This problem has been proposed as a PDE model
+for biological transportation networks. Mathematically, it seems to have a
+connection to a conjecture by De Giorgi \cite{DE}. Then we investigate the
+life-span of classical solutions. Our results show that local existence of a
+classical solution can always be obtained and the life-span of such a solution
+can be extended as far away as one wishes as long as the term $\|{\bf
+m}(x,0)\|_{\infty, \Omega}+\|S(x)\|_{\frac{2N}{3}, \Omega}$ is made suitably
+small, where $N$ is the space dimension and $\|\cdot\|_{q,\Omega}$ denotes the
+norm in $L^q(\Omega)$.
+",0,0,1,0,0,0
+15393,Distributed control of vehicle strings under finite-time and safety specifications,"  This paper studies an optimal control problem for a string of vehicles with
+safety requirements and finite-time specifications on the approach time to a
+target region. Our problem formulation is motivated by scenarios involving
+autonomous vehicles circulating on arterial roads with intelligent management
+at traffic intersections. We propose a provably correct distributed control
+algorithm that ensures that the vehicles satisfy the finite-time specifications
+under speed limits, acceleration saturation, and safety requirements. The
+safety specifications are such that collisions can be avoided even in cases of
+communication failure. We also discuss how the proposed distributed algorithm
+can be integrated with an intelligent intersection manager to provide
+information about the feasible approach times of the vehicle string and a
+guaranteed bound of its time of occupancy of the intersection. Our simulation
+study illustrates the algorithm and its properties regarding approach time,
+occupancy time, and fuel and time cost.
+",1,0,1,0,0,0
+15394,Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics,"  Kernel-based methods exhibit well-documented performance in various nonlinear
+learning tasks. Most of them rely on a preselected kernel, whose prudent choice
+presumes task-specific prior information. Especially when the latter is not
+available, multi-kernel learning has gained popularity thanks to its
+flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging
+the random feature approximation and its recent orthogonality-promoting
+variant, the present contribution develops a scalable multi-kernel learning
+scheme (termed Raker) to obtain the sought nonlinear learning function `on the
+fly,' first for static environments. To further boost performance in dynamic
+environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is
+developed. AdaRaker accounts not only for data-driven learning of kernel
+combination, but also for the unknown dynamics. Performance is analyzed in
+terms of both static and dynamic regrets. AdaRaker is uniquely capable of
+tracking nonlinear learning functions in environments with unknown dynamics,
+and with with analytic performance guarantees. Tests with synthetic and real
+datasets are carried out to showcase the effectiveness of the novel algorithms.
+",1,0,0,1,0,0
+15395,Faster Convergence & Generalization in DNNs,"  Deep neural networks have gained tremendous popularity in last few years.
+They have been applied for the task of classification in almost every domain.
+Despite the success, deep networks can be incredibly slow to train for even
+moderate sized models on sufficiently large datasets. Additionally, these
+networks require large amounts of data to be able to generalize. The importance
+of speeding up convergence, and generalization in deep networks can not be
+overstated. In this work, we develop an optimization algorithm based on
+generalized-optimal updates derived from minibatches that lead to faster
+convergence. Towards the end, we demonstrate on two benchmark datasets that the
+proposed method achieves two orders of magnitude speed up over traditional
+back-propagation, and is more robust to noise/over-fitting.
+",0,0,0,1,0,0
+15396,Regular Sequences from Determinantal Conditions,"  In this paper we construct some regular sequences which arise naturally from
+determinantal conditions.
+",0,0,1,0,0,0
+15397,"Nonlinear Zeeman effect, line shapes and optical pumping in electromagnetically induced transparency","  We perform Zeeman spectroscopy on a Rydberg electromagnetically induced
+transparency (EIT) system in a room-temperature Cs vapor cell, in magnetic
+fields up to 50~Gauss and for several polarization configurations. The magnetic
+interactions of the $\vert 6S_{1/2}, F_g=4 \rangle$ ground, $\vert 6P_{3/2},
+F_e=5 \rangle$ intermediate, and $\vert 33S_{1/2} \rangle$ Rydberg states that
+form the ladder-type EIT system are in the linear Zeeman, quadratic Zeeman, and
+the deep hyperfine Paschen-Back regimes, respectively. Starting in magnetic
+fields of about 5~Gauss, the spectra develop an asymmetry that becomes
+paramount in fields $\gtrsim40$~Gauss. We use a quantum Monte Carlo
+wave-function approach to quantitatively model the spectra. Simulated spectra
+are in good agreement with experimental data. The asymmetry in the spectra is,
+in part, due to level shifts caused by the quadratic Zeeman effect, but it also
+reflects the complicated interplay between optical pumping and EIT in the
+magnetic field. Relevance to measurement applications is discussed. %The
+simulations are also used to study optical pumping in the magnetic field and to
+investigate the interplay between optical pumping and EIT, which reduces photon
+scattering and optical pumping.
+",0,1,0,0,0,0
+15398,On gradient regularizers for MMD GANs,"  We propose a principled method for gradient-based regularization of the
+critic of GAN-like models trained by adversarially optimizing the kernel of a
+Maximum Mean Discrepancy (MMD). We show that controlling the gradient of the
+critic is vital to having a sensible loss function, and devise a method to
+enforce exact, analytical gradient constraints at no additional cost compared
+to existing approximate techniques based on additive regularizers. The new loss
+function is provably continuous, and experiments show that it stabilizes and
+accelerates training, giving image generation models that outperform
+state-of-the art methods on $160 \times 160$ CelebA and $64 \times 64$
+unconditional ImageNet.
+",0,0,0,1,0,0
+15399,Backprop with Approximate Activations for Memory-efficient Network Training,"  Larger and deeper neural network architectures deliver improved accuracy on a
+variety of tasks, but also require a large amount of memory for training to
+store intermediate activations for back-propagation. We introduce an
+approximation strategy to significantly reduce this memory footprint, with
+minimal effect on training performance and negligible computational cost. Our
+method replaces intermediate activations with lower-precision approximations to
+free up memory, after the full-precision versions have been used for
+computation in subsequent layers in the forward pass. Only these approximate
+activations are retained for use in the backward pass. Compared to naive
+low-precision computation, our approach limits the accumulation of errors
+across layers and allows the use of much lower-precision approximations without
+affecting training accuracy. Experiments on CIFAR and ImageNet show that our
+method yields performance comparable to full-precision training, while storing
+activations at a fraction of the memory cost with 8- and even 4-bit fixed-point
+precision.
+",1,0,0,1,0,0
+15400,"Testing SPARUS II AUV, an open platform for industrial, scientific and academic applications","  This paper describes the experience of preparing and testing the SPARUS II
+AUV in different applications. The AUV was designed as a lightweight vehicle
+combining the classical torpedo-shape features with the hovering capability.
+The robot has a payload area to allow the integration of different equipment
+depending on the application. The software architecture is based on ROS, an
+open framework that allows an easy integration of many devices and systems. Its
+flexibility, easy operation and openness makes the SPARUS II AUV a multipurpose
+platform that can adapt to industrial, scientific and academic applications.
+Five units were developed in 2014, and different teams used and adapted the
+platform for different applications. The paper describes some of the
+experiences in preparing and testing this open platform to different
+applications.
+",1,0,0,0,0,0
+15401,The Urban Last Mile Problem: Autonomous Drone Delivery to Your Balcony,"  Drone delivery has been a hot topic in the industry in the past few years.
+However, existing approaches either focus on rural areas or rely on centralized
+drop-off locations from where the last mile delivery is performed. In this
+paper we tackle the problem of autonomous last mile delivery in urban
+environments using an off-the-shelf drone. We build a prototype system that is
+able to fly to the approximate delivery location using GPS and then find the
+exact drop-off location using visual navigation. The drop-off location could,
+e.g., be on a balcony or porch, and simply needs to be indicated by a visual
+marker on the wall or window. We test our system components in simulated
+environments, including the visual navigation and collision avoidance. Finally,
+we deploy our drone in a real-world environment and show how it can find the
+drop-off point on a balcony. To stimulate future research in this topic we open
+source our code.
+",1,0,0,0,0,0
+15402,Extragalactic VLBI surveys in the MeerKAT era,"  The past decade has seen significant advances in cm-wave VLBI extragalactic
+observations due to a wide range of technical successes, including the increase
+in processed field-of-view and bandwidth. The future inclusion of MeerKAT into
+global VLBI networks would provide further enhancement, particularly the
+dramatic sensitivity boost to >7000 km baselines. This will not be without its
+limitations, however, considering incomplete MeerKAT band overlap with current
+VLBI arrays and the small (real-time) field-of-view afforded by the phased up
+MeerKAT array. We provide a brief overview of the significant contributions
+MeerKAT-VLBI could make, with an emphasis on the scientific output of several
+MeerKAT extragalactic Large Survey Projects.
+",0,1,0,0,0,0
+15403,"Equicontinuity, orbit closures and invariant compact open sets for group actions on zero-dimensional spaces","  Let $X$ be a locally compact zero-dimensional space, let $S$ be an
+equicontinuous set of homeomorphisms such that $1 \in S = S^{-1}$, and suppose
+that $\overline{Gx}$ is compact for each $x \in X$, where $G = \langle S
+\rangle$. We show in this setting that a number of conditions are equivalent:
+(a) $G$ acts minimally on the closure of each orbit; (b) the orbit closure
+relation is closed; (c) for every compact open subset $U$ of $X$, there is $F
+\subseteq G$ finite such that $\bigcap_{g \in F}g(U)$ is $G$-invariant. All of
+these are equivalent to a notion of recurrence, which is a variation on a
+concept of Auslander-Glasner-Weiss. It follows in particular that the action is
+distal if and only if it is equicontinuous.
+",0,0,1,0,0,0
+15404,"The Ramsey property for Banach spaces, Choquet simplices, and their noncommutative analogs","  We show that the Gurarij space $\mathbb{G}$ and its noncommutative analog
+$\mathbb{NG}$ both have extremely amenable automorphism group. We also compute
+the universal minimal flows of the automorphism groups of the Poulsen simplex
+$\mathbb{P}$ and its noncommutative analogue $\mathbb{NP}$. The former is
+$\mathbb{P}$ itself, and the latter is the state space of the operator system
+associated with $\mathbb{NP}$. This answers a question of Conley and
+Törnquist. We also show that the pointwise stabilizer of any closed proper
+face of $\mathbb{P}$ is extremely amenable. Similarly, the pointwise stabilizer
+of any closed proper biface of the unit ball of the dual of the Gurarij space
+(the Lusky simplex) is extremely amenable.
+These results are obtained via the Kechris--Pestov--Todorcevic
+correspondence, by establishing the approximate Ramsey property for several
+classes of finite-dimensional operator spaces and operator systems (with
+distinguished linear functionals), including: Banach spaces, exact operator
+spaces, function systems with a distinguished state, and exact operator systems
+with a distinguished state. This is the first direct application of the
+Kechris--Pestov--Todorcevic correspondence in the setting of metric structures.
+The fundamental combinatorial principle that underpins the proofs is the Dual
+Ramsey Theorem of Graham and Rothschild.
+In the second part of the paper, we obtain factorization theorems for
+colorings of matrices and Grassmannians over $\mathbb{R}$ and ${\mathbb{C}}$,
+which can be considered as continuous versions of the Dual Ramsey Theorem for
+Boolean matrices and of the Graham-Leeb-Rothschild Theorem for Grassmannians
+over a finite field.
+",0,0,1,0,0,0
+15405,Evasion Attacks against Machine Learning at Test Time,"  In security-sensitive applications, the success of machine learning depends
+on a thorough vetting of their resistance to adversarial data. In one
+pertinent, well-motivated attack scenario, an adversary may attempt to evade a
+deployed system at test time by carefully manipulating attack samples. In this
+work, we present a simple but effective gradient-based approach that can be
+exploited to systematically assess the security of several, widely-used
+classification algorithms against evasion attacks. Following a recently
+proposed framework for security evaluation, we simulate attack scenarios that
+exhibit different risk levels for the classifier by increasing the attacker's
+knowledge of the system and her ability to manipulate attack samples. This
+gives the classifier designer a better picture of the classifier performance
+under evasion attacks, and allows him to perform a more informed model
+selection (or parameter setting). We evaluate our approach on the relevant
+security task of malware detection in PDF files, and show that such systems can
+be easily evaded. We also sketch some countermeasures suggested by our
+analysis.
+",1,0,0,0,0,0
+15406,Evaluation of Lightweight Block Ciphers in Hardware Implementation: A Comprehensive Survey,"  The conventional cryptography solutions are ill-suited to strict memory, size
+and power limitations of resource-constrained devices, so lightweight
+cryptography solutions have been specifically developed for this type of
+applications. In this domain of cryptography, the term lightweight never refers
+to inadequately low security, but rather to establishing the best balance to
+maintain sufficient security. This paper presents the first comprehensive
+survey evaluation of lightweight block ciphers in terms of their speed, cost,
+performance, and balanced efficiency in hardware implementation, and
+facilitates the comparison of studied ciphers in these respects. The cost of
+lightweight block ciphers is evaluated with the metric of Gate Equivalent
+(Fig.1), their speed with the metric of clock-cycle-per-block (Fig.2), their
+performance with the metric of throughput (Fig.3) and their balanced efficiency
+with the metric of Figure of Merit (Fig.4). The results of these evaluations
+show that SIMON, SPECK, and Piccolo are the best lightweight block ciphers in
+hardware implementation.(Abstract)
+",1,0,0,0,0,0
+15407,The Sample Complexity of Online One-Class Collaborative Filtering,"  We consider the online one-class collaborative filtering (CF) problem that
+consists of recommending items to users over time in an online fashion based on
+positive ratings only. This problem arises when users respond only occasionally
+to a recommendation with a positive rating, and never with a negative one. We
+study the impact of the probability of a user responding to a recommendation,
+p_f, on the sample complexity, i.e., the number of ratings required to make
+`good' recommendations, and ask whether receiving positive and negative
+ratings, instead of positive ratings only, improves the sample complexity. Both
+questions arise in the design of recommender systems. We introduce a simple
+probabilistic user model, and analyze the performance of an online user-based
+CF algorithm. We prove that after an initial cold start phase, where
+recommendations are invested in exploring the user's preferences, this
+algorithm makes---up to a fraction of the recommendations required for updating
+the user's preferences---perfect recommendations. The number of ratings
+required for the cold start phase is nearly proportional to 1/p_f, and that for
+updating the user's preferences is essentially independent of p_f. As a
+consequence we find that, receiving positive and negative ratings instead of
+only positive ones improves the number of ratings required for initial
+exploration by a factor of 1/p_f, which can be significant.
+",1,0,0,1,0,0
+15408,Coherent long-distance displacement of individual electron spins,"  Controlling nanocircuits at the single electron spin level is a possible
+route for large-scale quantum information processing. In this context,
+individual electron spins have been identified as versatile quantum information
+carriers to interconnect different nodes of a spin-based semiconductor quantum
+circuit. Despite important experimental efforts to control the electron
+displacement over long distances, keeping the electron spin coherence after
+transfer remained up to now elusive. Here we demonstrate that individual
+electron spins can be displaced coherently over a distance of 5 micrometers.
+This displacement is realized on a closed path made of three tunnel-coupled
+lateral quantum dots. Using fast quantum dot control, the electrons tunnel from
+one dot to another at a speed approaching 100 m/s. We find that the spin
+coherence length is 8 times longer than expected from the electron spin
+coherence without displacement. Such an enhanced spin coherence points at a
+process similar to motional narrowing observed in nuclear magnetic resonance
+experiments6. The demonstrated coherent displacement will enable long-range
+interaction between distant spin-qubits and will open the route towards
+non-abelian and holonomic manipulation of a single electron spin.
+",0,1,0,0,0,0
+15409,Transparency and Explanation in Deep Reinforcement Learning Neural Networks,"  Autonomous AI systems will be entering human society in the near future to
+provide services and work alongside humans. For those systems to be accepted
+and trusted, the users should be able to understand the reasoning process of
+the system, i.e. the system should be transparent. System transparency enables
+humans to form coherent explanations of the system's decisions and actions.
+Transparency is important not only for user trust, but also for software
+debugging and certification. In recent years, Deep Neural Networks have made
+great advances in multiple application areas. However, deep neural networks are
+opaque. In this paper, we report on work in transparency in Deep Reinforcement
+Learning Networks (DRLN). Such networks have been extremely successful in
+accurately learning action control in image input domains, such as Atari games.
+In this paper, we propose a novel and general method that (a) incorporates
+explicit object recognition processing into deep reinforcement learning models,
+(b) forms the basis for the development of ""object saliency maps"", to provide
+visualization of internal states of DRLNs, thus enabling the formation of
+explanations and (c) can be incorporated in any existing deep reinforcement
+learning framework. We present computational results and human experiments to
+evaluate our approach.
+",0,0,0,1,0,0
+15410,Refactoring Software Packages via Community Detection from Stability Point of View,"  As the complexity and size of software projects increases in real-world
+environments, maintaining and creating maintainable and dependable code becomes
+harder and more costly. Refactoring is considered as a method for enhancing the
+internal structure of code for improving many software properties such as
+maintainability.
+In this thesis, the subject of refactoring software packages using community
+detection algorithms is discussed, with a focus on the notion of package
+stability. The proposed algorithm starts by extracting a package dependency
+network from Java byte code and a community detection algorithm is used to find
+possible changes in package structures. In this work, the reasons for the
+importance of considering dependency directions while modeling package
+dependencies with graphs are also discussed, and a proof for the relationship
+between package stability and the modularity of package dependency graphs is
+presented that shows how modularity is in favor of package stability.
+For evaluating the proposed algorithm, a tool for live analysis of software
+packages is implemented, and two software systems are tested. Results show that
+modeling package dependencies with directed graphs and applying the presented
+refactoring method, leads to a higher increase in package stability than
+undirected graph modeling approaches that have been studied in the literature.
+",1,0,0,0,0,0
+15411,Mechanism Design in Social Networks,"  This paper studies an auction design problem for a seller to sell a commodity
+in a social network, where each individual (the seller or a buyer) can only
+communicate with her neighbors. The challenge to the seller is to design a
+mechanism to incentivize the buyers, who are aware of the auction, to further
+propagate the information to their neighbors so that more buyers will
+participate in the auction and hence, the seller will be able to make a higher
+revenue. We propose a novel auction mechanism, called information diffusion
+mechanism (IDM), which incentivizes the buyers to not only truthfully report
+their valuations on the commodity to the seller, but also further propagate the
+auction information to all their neighbors. In comparison, the direct extension
+of the well-known Vickrey-Clarke-Groves (VCG) mechanism in social networks can
+also incentivize the information diffusion, but it will decrease the seller's
+revenue or even lead to a deficit sometimes. The formalization of the problem
+has not yet been addressed in the literature of mechanism design and our
+solution is very significant in the presence of large-scale online social
+networks.
+",1,0,0,0,0,0
+15412,Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification,"  One of the fundamental tasks in understanding genomics is the problem of
+predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds
+of Transcription Factors (TFs) as labels, genomic-sequence based TFBS
+prediction is a challenging multi-label classification task. There are two
+major biological mechanisms for TF binding: (1) sequence-specific binding
+patterns on genomes known as ""motifs"" and (2) interactions among TFs known as
+co-binding effects. In this paper, we propose a novel deep architecture, the
+Prototype Matching Network (PMN) to mimic the TF binding mechanisms. Our PMN
+model automatically extracts prototypes (""motif""-like features) for each TF
+through a novel prototype-matching loss. Borrowing ideas from few-shot matching
+models, we use the notion of support set of prototypes and an LSTM to learn how
+TFs interact and bind to genomic sequences. On a reference TFBS dataset with
+$2.1$ $million$ genomic sequences, PMN significantly outperforms baselines and
+validates our design choices empirically. To our knowledge, this is the first
+deep learning architecture that introduces prototype learning and considers
+TF-TF interactions for large-scale TFBS prediction. Not only is the proposed
+architecture accurate, but it also models the underlying biology.
+",1,0,0,1,0,0
+15413,Quantum Multicriticality near the Dirac-Semimetal to Band-Insulator Critical Point in Two Dimensions: A Controlled Ascent from One Dimension,"  We compute the effects of generic short-range interactions on gapless
+electrons residing at the quantum critical point separating a two-dimensional
+Dirac semimetal (DSM) and a symmetry-preserving band insulator (BI). The
+electronic dispersion at this critical point is anisotropic ($E_{\mathbf k}=\pm
+\sqrt{v^2 k^2_x + b^2 k^{2n}_y}$ with $n=2$), which results in unconventional
+scaling of physical observables. Due to the vanishing density of states
+($\varrho(E) \sim |E|^{1/n}$), this anisotropic semimetal (ASM) is stable
+against weak short-range interactions. However, for stronger interactions the
+direct DSM-BI transition can either $(i)$ become a first-order transition, or
+$(ii)$ get avoided by an intervening broken-symmetry phase (BSP). We perform a
+renormalization group analysis by perturbing away from the one-dimensional
+limit with the small parameter $\epsilon = 1/n$, augmented with a $1/n$
+expansion (parametrically suppressing quantum fluctuations in higher
+dimension). We identify charge density wave (CDW), antiferromagnet (AFM) and
+singlet s-wave superconductor as the three dominant candidates for the BSP. The
+onset of any such order at strong coupling $(\sim \epsilon)$ takes place
+through a continuous quantum phase transition across multicritical point. We
+also present the phase diagram of an extended Hubbard model for the ASM,
+obtained via the controlled deformation of its counterpart in one dimension.
+The latter displays spin-charge separation and instabilities to CDW, spin
+density wave, and Luther-Emery liquid phases at arbitrarily weak coupling. The
+spin density wave and Luther-Emery liquid phases deform into pseudospin
+SU(2)-symmetric quantum critical points separating the ASM from the AFM and
+superconducting orders, respectively. Our results can be germane for a
+uniaxially strained honeycomb lattice or organic compound
+$\alpha$-(BEDT-TTF)$_2\text{I}_3$.
+",0,1,0,0,0,0
+15414,Wider frequency domain for negative refraction index in a quantized composite right-left handed transmission line,"  The refraction index of the quantized lossy composite right-left handed
+transmission line (CRLH-TL) is deduced in the thermal coherence state. The
+results show that the negative refraction index (herein the left-handedness)
+can be implemented by the electric circuit dissipative factors(i.e., the
+resistances \(R\) and conductances \( G\)) in a higher frequency band
+(1.446GHz\(\leq\omega\leq \) 15GHz), and flexibly adjusted by the left-handed
+circuit components (\(C_l\), \(L_l\)) and the right-handed circuit components
+(\(C_r\), \(L_r\)) at a lower frequency (\(\omega\)=0.995GHz) . The flexible
+adjustment for left-handedness in a wider bandwidth will be significant for the
+microscale circuit design of the CRLH-TL and may make the theoretical
+preparation for its compact applications.
+",0,1,0,0,0,0
+15415,Model Averaging and its Use in Economics,"  The method of model averaging has become an important tool to deal with model
+uncertainty, for example in situations where a large amount of different
+theories exist, as are common in economics. Model averaging is a natural and
+formal response to model uncertainty in a Bayesian framework, and most of the
+paper deals with Bayesian model averaging. The important role of the prior
+assumptions in these Bayesian procedures is highlighted. In addition,
+frequentist model averaging methods are also discussed. Numerical methods to
+implement these methods are explained, and I point the reader to some freely
+available computational resources. The main focus is on uncertainty regarding
+the choice of covariates in normal linear regression models, but the paper also
+covers other, more challenging, settings, with particular emphasis on sampling
+models commonly used in economics. Applications of model averaging in economics
+are reviewed and discussed in a wide range of areas, among which growth
+economics, production modelling, finance and forecasting macroeconomic
+quantities.
+",0,0,0,1,0,0
+15416,Dropout Feature Ranking for Deep Learning Models,"  Deep neural networks (DNNs) achieve state-of-the-art results in a variety of
+domains. Unfortunately, DNNs are notorious for their non-interpretability, and
+thus limit their applicability in hypothesis-driven domains such as biology and
+healthcare. Moreover, in the resource-constraint setting, it is critical to
+design tests relying on fewer more informative features leading to high
+accuracy performance within reasonable budget. We aim to close this gap by
+proposing a new general feature ranking method for deep learning. We show that
+our simple yet effective method performs on par or compares favorably to eight
+strawman, classical and deep-learning feature ranking methods in two
+simulations and five very different datasets on tasks ranging from
+classification to regression, in both static and time series scenarios. We also
+illustrate the use of our method on a drug response dataset and show that it
+identifies genes relevant to the drug-response.
+",1,0,0,1,0,0
+15417,Per-instance Differential Privacy,"  We consider a refinement of differential privacy --- per instance
+differential privacy (pDP), which captures the privacy of a specific individual
+with respect to a fixed data set. We show that this is a strict generalization
+of the standard DP and inherits all its desirable properties, e.g.,
+composition, invariance to side information and closedness to postprocessing,
+except that they all hold for every instance separately. When the data is drawn
+from a distribution, we show that per-instance DP implies generalization.
+Moreover, we provide explicit calculations of the per-instance DP for the
+output perturbation on a class of smooth learning problems. The result reveals
+an interesting and intuitive fact that an individual has stronger privacy if
+he/she has small ""leverage score"" with respect to the data set and if he/she
+can be predicted more accurately using the leave-one-out data set. Our
+simulation shows several orders-of-magnitude more favorable privacy and utility
+trade-off when we consider the privacy of only the users in the data set. In a
+case study on differentially private linear regression, provide a novel
+analysis of the One-Posterior-Sample (OPS) estimator and show that when the
+data set is well-conditioned it provides $(\epsilon,\delta)$-pDP for any target
+individuals and matches the exact lower bound up to a
+$1+\tilde{O}(n^{-1}\epsilon^{-2})$ multiplicative factor. We also demonstrate
+how we can use a ""pDP to DP conversion"" step to design AdaOPS which uses
+adaptive regularization to achieve the same results with
+$(\epsilon,\delta)$-DP.
+",1,0,0,1,0,0
+15418,Dual quadratic differentials and entire minimal graphs in Heisenberg space,"  We define holomorphic quadratic differentials for spacelike surfaces with
+constant mean curvature in the Lorentzian homogeneous spaces
+$\mathbb{L}(\kappa,\tau)$ with isometry group of dimension 4, which are dual to
+the Abresch-Rosenberg differentials in the Riemannian counterparts
+$\mathbb{E}(\kappa,\tau)$, and obtain some consequences. On the one hand, we
+give a very short proof of the Bernstein problem in Heisenberg space, and
+provide a geometric description of the family of entire graphs sharing the same
+differential in terms of a 2-parameter conformal deformation. On the other
+hand, we prove that entire minimal graphs in Heisenberg space have negative
+Gauss curvature.
+",0,0,1,0,0,0
+15419,Adaptive Modular Exponentiation Methods v.s. Python's Power Function,"  In this paper we use Python to implement two efficient modular exponentiation
+methods: the adaptive m-ary method and the adaptive sliding-window method of
+window size k, where both m's are adaptively chosen based on the length of
+exponent. We also conduct the benchmark for both methods. Evaluation results
+show that compared to the industry-standard efficient implementations of
+modular power function in CPython and Pypy, our algorithms can reduce 1-5%
+computing time for exponents with more than 3072 bits.
+",1,0,0,0,0,0
+15420,Bayesian radiocarbon modelling for beginners,"  Due to freely available, tailored software, Bayesian statistics is fast
+becoming the dominant paradigm in archaeological chronology construction. Such
+software provides users with powerful tools for Bayesian inference for
+chronological models with little need to undertake formal study of statistical
+modelling or computer programming. This runs the risk that it is reduced to the
+status of a black-box which is not sensible given the power and complexity of
+the modelling tools it implements. In this paper we seek to offer intuitive
+insight to ensure that readers from the archaeological research community who
+use Bayesian chronological modelling software will be better able to make well
+educated choices about the tools and techniques they adopt. Our hope is that
+they will then be both better informed about their own research designs and
+better prepared to offer constructively critical assessments of the modelling
+undertaken by others.
+",0,0,0,1,0,0
+15421,Suspended Load Path Tracking Control Using a Tilt-rotor UAV Based on Zonotopic State Estimation,"  This work addresses the problem of path tracking control of a suspended load
+using a tilt-rotor UAV. The main challenge in controlling this kind of system
+arises from the dynamic behavior imposed by the load, which is usually coupled
+to the UAV by means of a rope, adding unactuated degrees of freedom to the
+whole system. Furthermore, to perform the load transportation it is often
+needed the knowledge of the load position to accomplish the task. Since
+available sensors are commonly embedded in the mobile platform, information on
+the load position may not be directly available. To solve this problem in this
+work, initially, the kinematics of the multi-body mechanical system are
+formulated from the load's perspective, from which a detailed dynamic model is
+derived using the Euler-Lagrange approach, yielding a highly coupled, nonlinear
+state-space representation of the system, affine in the inputs, with the load's
+position and orientation directly represented by state variables. A zonotopic
+state estimator is proposed to solve the problem of estimating the load
+position and orientation, which is formulated based on sensors located at the
+aircraft, with different sampling times, and unknown-but-bounded measurement
+noise. To solve the path tracking problem, a discrete-time mixed
+$\mathcal{H}_2/\mathcal{H}_\infty$ controller with pole-placement constraints
+is designed with guaranteed time-response properties and robust to unmodeled
+dynamics, parametric uncertainties, and external disturbances. Results from
+numerical experiments, performed in a platform based on the Gazebo simulator
+and on a Computer Aided Design (CAD) model of the system, are presented to
+corroborate the performance of the zonotopic state estimator along with the
+designed controller.
+",1,0,0,0,0,0
+15422,Decay Estimates and Strichartz Estimates of Fourth-order Schrödinger Operator,"  We study time decay estimates of the fourth-order Schrödinger operator
+$H=(-\Delta)^{2}+V(x)$ in $\mathbb{R}^{d}$ for $d=3$ and $d\geq5$. We analyze
+the low energy and high energy behaviour of resolvent $R(H; z)$, and then
+derive the Jensen-Kato dispersion decay estimate and local decay estimate for
+$e^{-itH}P_{ac}$ under suitable spectrum assumptions of $H$. Based on
+Jensen-Kato decay estimate and local decay estimate, we obtain the
+$L^1\rightarrow L^{\infty}$ estimate of $e^{-itH}P_{ac}$ in $3$-dimension by
+Ginibre argument, and also establish the endpoint global Strichartz estimates
+of $e^{-itH}P_{ac}$ for $d\geq5$. Furthermore, using the local decay estimate
+and the Georgescu-Larenas-Soffer conjugate operator method, we prove the
+Jensen-Kato type decay estimates for some functions of $H$.
+",0,0,1,0,0,0
+15423,On the Uniqueness of FROG Methods,"  The problem of recovering a signal from its power spectrum, called phase
+retrieval, arises in many scientific fields. One of many examples is
+ultra-short laser pulse characterization in which the electromagnetic field is
+oscillating with ~10^15 Hz and phase information cannot be measured directly
+due to limitations of the electronic sensors. Phase retrieval is ill-posed in
+most cases as there are many different signals with the same Fourier transform
+magnitude. To overcome this fundamental ill-posedness, several measurement
+techniques are used in practice. One of the most popular methods for complete
+characterization of ultra-short laser pulses is the Frequency-Resolved Optical
+Gating (FROG). In FROG, the acquired data is the power spectrum of the product
+of the unknown pulse with its delayed replica. Therefore the measured signal is
+a quartic function of the unknown pulse. A generalized version of FROG, where
+the delayed replica is replaced by a second unknown pulse, is called blind
+FROG. In this case, the measured signal is quadratic with respect to both
+pulses. In this letter we introduce and formulate FROG-type techniques. We then
+show that almost all band-limited signals are determined uniquely, up to
+trivial ambiguities, by blind FROG measurements (and thus also by FROG), if in
+addition we have access to the signals power spectrum.
+",1,0,1,0,0,0
+15424,Riesz sequences and generalized arithmetic progressions,"  The purpose of this note is to verify that the results attained in [6] admit
+an extension to the multidimensional setting. Namely, for subsets of the two
+dimensional torus we find the sharp growth rate of the step(s) of a generalized
+arithmetic progression in terms of its size which may be found in an
+exponential systems satisfying the Riesz sequence property.
+",0,0,1,0,0,0
+15425,Positive Scalar Curvature and Minimal Hypersurface Singularities,"  In this paper we develop methods to extend the minimal hypersurface approach
+to positive scalar curvature problems to all dimensions. This includes a proof
+of the positive mass theorem in all dimensions without a spin assumption. It
+also includes statements about the structure of compact manifolds of positive
+scalar curvature extending the work of \cite{sy1} to all dimensions. The
+technical work in this paper is to construct minimal slicings and associated
+weight functions in the presence of small singular sets and to show that the
+singular sets do not become too large in the lower dimensional slices. It is
+shown that the singular set in any slice is a closed set with Hausdorff
+codimension at least three. In particular for arguments which involve slicing
+down to dimension $1$ or $2$ the method is successful. The arguments can be
+viewed as an extension of the minimal hypersurface regularity theory to this
+setting of minimal slicings.
+",0,0,1,0,0,0
+15426,Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression,"  Concentration inequalities form an essential toolkit in the study of
+high-dimensional statistical methods. Most of the relevant statistics
+literature is based on the assumptions of sub-Gaussian/sub-exponential random
+vectors. In this paper, we bring together various probability inequalities for
+sums of independent random variables under much weaker exponential type
+(sub-Weibull) tail assumptions. These results extract a part sub-Gaussian tail
+behavior in finite samples, matching the asymptotics governed by the central
+limit theorem, and are compactly represented in terms of a new Orlicz
+quasi-norm - the Generalized Bernstein-Orlicz norm - that typifies such tail
+behaviors.
+We illustrate the usefulness of these inequalities through the analysis of
+four fundamental problems in high-dimensional statistics. In the first two
+problems, we study the rate of convergence of the sample covariance matrix in
+terms of the maximum elementwise norm and the maximum k-sub-matrix operator
+norm which are key quantities of interest in bootstrap procedures and
+high-dimensional structured covariance matrix estimation. The third example
+concerns the restricted eigenvalue condition, required in high dimensional
+linear regression, which we verify for all sub-Weibull random vectors under
+only marginal (not joint) tail assumptions on the covariates. To our knowledge,
+this is the first unified result obtained in such generality. In the final
+example, we consider the Lasso estimator for linear regression and establish
+its rate of convergence under much weaker tail assumptions (on the errors as
+well as the covariates) than those in the existing literature. The common
+feature in all our results is that the convergence rates under most exponential
+tails match the usual ones under sub-Gaussian assumptions. Finally, we also
+establish a high-dimensional CLT and tail bounds for empirical processes for
+sub-Weibulls.
+",0,0,0,1,0,0
+15427,The additive groups of $\mathbb{Z}$ and $\mathbb{Q}$ with predicates for being square-free,"  We consider the four structures $(\mathbb{Z}; \mathrm{Sqf}^\mathbb{Z})$,
+$(\mathbb{Z}; <, \mathrm{Sqf}^\mathbb{Z})$, $(\mathbb{Q};
+\mathrm{Sqf}^\mathbb{Q})$, and $(\mathbb{Q}; <, \mathrm{Sqf}^\mathbb{Q})$ where
+$\mathbb{Z}$ is the additive group of integers, $\mathrm{Sqf}^\mathbb{Z}$ is
+the set of $a \in \mathbb{Z}$ such that $v_{p}(a) < 2$ for every prime $p$ and
+corresponding $p$-adic valuation $v_{p}$, $\mathbb{Q}$ and
+$\mathrm{Sqf}^\mathbb{Q}$ are defined likewise for rational numbers, and $<$
+denotes the natural ordering on each of these domains. We prove that the second
+structure is model-theoretically wild while the other three structures are
+model-theoretically tame. Moreover, all these results can be seen as examples
+where number-theoretic randomness yields model-theoretic consequences.
+",0,0,1,0,0,0
+15428,Particle-hole symmetry of charge excitation spectra in the paramagnetic phase of the Hubbard model,"  The Kotliar and Ruckenstein slave-boson representation of the Hubbard model
+allows to obtain an approximation of the charge dynamical response function
+resulting from the Gaussian fluctuations around the paramagnetic saddle-point
+in analytical form. Numerical evaluation in the thermodynamical limit yields
+charge excitation spectra consisting of a continuum, a gapless collective mode
+with anisotropic zero-sound velocity, and a correlation induced high-frequency
+mode at $\omega\approx U$. In this work we show that this analytical expression
+obeys the particle-hole symmetry of the model on any bipartite lattice with one
+atom in the unit cell. Other formal aspects of the approach are also addressed.
+",0,1,0,0,0,0
+15429,"OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning","  Deep neural networks trained using a softmax layer at the top and the
+cross-entropy loss are ubiquitous tools for image classification. Yet, this
+does not naturally enforce intra-class similarity nor inter-class margin of the
+learned deep representations. To simultaneously achieve these two goals,
+different solutions have been proposed in the literature, such as the pairwise
+or triplet losses. However, such solutions carry the extra task of selecting
+pairs or triplets, and the extra computational burden of computing and learning
+for many combinations of them. In this paper, we propose a plug-and-play loss
+term for deep networks that explicitly reduces intra-class variance and
+enforces inter-class margin simultaneously, in a simple and elegant geometric
+manner. For each class, the deep features are collapsed into a learned linear
+subspace, or union of them, and inter-class subspaces are pushed to be as
+orthogonal as possible. Our proposed Orthogonal Low-rank Embedding (OLÉ) does
+not require carefully crafting pairs or triplets of samples for training, and
+works standalone as a classification loss, being the first reported deep metric
+learning framework of its kind. Because of the improved margin between features
+of different classes, the resulting deep networks generalize better, are more
+discriminative, and more robust. We demonstrate improved classification
+performance in general object recognition, plugging the proposed loss term into
+existing off-the-shelf architectures. In particular, we show the advantage of
+the proposed loss in the small data/model scenario, and we significantly
+advance the state-of-the-art on the Stanford STL-10 benchmark.
+",1,0,0,1,0,0
+15430,Decidability problems in automaton semigroups,"  We consider decidability problems in self-similar semigroups, and in
+particular in semigroups of automatic transformations of $X^*$. We describe
+algorithms answering the word problem, and bound its complexity under some
+additional assumptions. We give a partial algorithm that decides in a group
+generated by an automaton, given $x,y$, whether an Engel identity
+($[\cdots[[x,y],y],\dots,y]=1$ for a long enough commutator sequence) is
+satisfied. This algorithm succeeds, importantly, in proving that Grigorchuk's
+$2$-group is not Engel. We consider next the problem of recognizing Engel
+elements, namely elements $y$ such that the map $x\mapsto[x,y]$ attracts to
+$\{1\}$. Although this problem seems intractable in general, we prove that it
+is decidable for Grigorchuk's group: Engel elements are precisely those of
+order at most $2$. We include, in the text, a large number of open problems.
+Our computations were implemented using the package ""Fr"" within the computer
+algebra system ""Gap"".
+",1,0,1,0,0,0
+15431,Migration of a Carbon Adatom on a Charged Single-Walled Carbon Nanotube,"  We find that negative charges on an armchair single-walled carbon nanotube
+(SWCNT) can significantly enhance the migration of a carbon adatom on the
+external surfaces of SWCNTs, along the direction of the tube axis. Nanotube
+charging results in stronger binding of adatoms to SWCNTs and consequent longer
+lifetimes of adatoms before desorption, which in turn increases their migration
+distance several orders of magnitude. These results support the hypothesis of
+diffusion enhanced SWCNT growth in the volume of arc plasma. This process could
+enhance effective carbon flux to the metal catalyst.
+",0,1,0,0,0,0
+15432,Function Norms and Regularization in Deep Networks,"  Deep neural networks (DNNs) have become increasingly important due to their
+excellent empirical performance on a wide range of problems. However,
+regularization is generally achieved by indirect means, largely due to the
+complex set of functions defined by a network and the difficulty in measuring
+function complexity. There exists no method in the literature for additive
+regularization based on a norm of the function, as is classically considered in
+statistical learning theory. In this work, we propose sampling-based
+approximations to weighted function norms as regularizers for deep neural
+networks. We provide, to the best of our knowledge, the first proof in the
+literature of the NP-hardness of computing function norms of DNNs, motivating
+the necessity of an approximate approach. We then derive a generalization bound
+for functions trained with weighted norms and prove that a natural stochastic
+optimization strategy minimizes the bound. Finally, we empirically validate the
+improved performance of the proposed regularization strategies for both convex
+function sets as well as DNNs on real-world classification and image
+segmentation tasks demonstrating improved performance over weight decay,
+dropout, and batch normalization. Source code will be released at the time of
+publication.
+",1,0,0,1,0,0
+15433,Efficiently Clustering Very Large Attributed Graphs,"  Attributed graphs model real networks by enriching their nodes with
+attributes accounting for properties. Several techniques have been proposed for
+partitioning these graphs into clusters that are homogeneous with respect to
+both semantic attributes and to the structure of the graph. However, time and
+space complexities of state of the art algorithms limit their scalability to
+medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a
+fast and scalable algorithm for partitioning large attributed graphs. The
+approach is robust, being compatible both with categorical and with
+quantitative attributes, and it is tailorable, allowing the user to weight the
+semantic and topological components. Further, the approach does not require the
+user to guess in advance the number of clusters. SToC relies on well known
+approximation techniques such as bottom-k sketches, traditional graph-theoretic
+concepts, and a new perspective on the composition of heterogeneous distance
+measures. Experimental results demonstrate its ability to efficiently compute
+high-quality partitions of large scale attributed graphs.
+",1,1,0,0,0,0
+15434,Thermochemistry and vertical mixing in the tropospheres of Uranus and Neptune: How convection inhibition can affect the derivation of deep oxygen abundances,"  Thermochemical models have been used in the past to constrain the deep oxygen
+abundance in the gas and ice giant planets from tropospheric CO spectroscopic
+measurements. Knowing the oxygen abundance of these planets is a key to better
+understand their formation. These models have widely used dry and/or moist
+adiabats to extrapolate temperatures from the measured values in the upper
+troposphere down to the level where the thermochemical equilibrium between
+H$_2$O and CO is established. The mean molecular mass gradient produced by the
+condensation of H$_2$O stabilizes the atmosphere against convection and results
+in a vertical thermal profile and H$_2$O distribution that departs
+significantly from previous estimates. We revisit O/H estimates using an
+atmospheric structure that accounts for the inhibition of the convection by
+condensation. We use a thermochemical network and the latest observations of CO
+in Uranus and Neptune to calculate the internal oxygen enrichment required to
+satisfy both these new estimates of the thermal profile and the observations.
+We also present the current limitations of such modeling.
+",0,1,0,0,0,0
+15435,Attentive cross-modal paratope prediction,"  Antibodies are a critical part of the immune system, having the function of
+directly neutralising or tagging undesirable objects (the antigens) for future
+destruction. Being able to predict which amino acids belong to the paratope,
+the region on the antibody which binds to the antigen, can facilitate antibody
+design and contribute to the development of personalised medicine. The
+suitability of deep neural networks has recently been confirmed for this task,
+with Parapred outperforming all prior physical models. Our contribution is
+twofold: first, we significantly outperform the computational efficiency of
+Parapred by leveraging à trous convolutions and self-attention. Secondly, we
+implement cross-modal attention by allowing the antibody residues to attend
+over antigen residues. This leads to new state-of-the-art results on this task,
+along with insightful interpretations.
+",0,0,0,1,1,0
+15436,Tomonaga-Luttinger spin liquid in the spin-1/2 inequilateral diamond-chain compound K$_3$Cu$_3$AlO$_2$(SO$_4$)$_4$,"  K$_3$Cu$_3$AlO$_2$(SO$_4$)$_4$ is a highly one-dimensional spin-1/2
+inequilateral diamond-chain antiferromagnet. Spinon continuum and spin-singlet
+dimer excitations are observed in the inelastic neutron scattering spectra,
+which is in excellent agreement with a theoretical prediction: a dimer-monomer
+composite structure, where the dimer is caused by strong antiferromagnetic
+(AFM) coupling and the monomer forms an almost isolated quantum AFM chain
+controlling low-energy excitations. Moreover, muon spin rotation/relaxation
+spectroscopy shows no long-range ordering down to 90~mK, which is roughly three
+orders of magnitude lower than the exchange interaction of the quantum AFM
+chain. K$_3$Cu$_3$AlO$_2$(SO$_4$)$_4$ is, thus, regarded as a compound that
+exhibits a Tomonaga-Luttinger spin liquid behavior at low temperatures close to
+the ground state.
+",0,1,0,0,0,0
+15437,Weak Convergence of Stationary Empirical Processes,"  We offer an umbrella type result which extends weak convergence of the
+classical empirical process on the line to that of more general processes
+indexed by functions of bounded variation. This extension is not contingent on
+the type of dependence of the underlying sequence of random variables. As a
+consequence we establish weak convergence for stationary empirical processes
+indexed by general classes of functions under alpha mixing conditions.
+",0,0,1,1,0,0
+15438,Syntax Error Recovery in Parsing Expression Grammars,"  Parsing Expression Grammars (PEGs) are a formalism used to describe top-down
+parsers with backtracking. As PEGs do not provide a good error recovery
+mechanism, PEG-based parsers usually do not recover from syntax errors in the
+input, or recover from syntax errors using ad-hoc, implementation-specific
+features. The lack of proper error recovery makes PEG parsers unsuitable for
+using with Integrated Development Environments (IDEs), which need to build
+syntactic trees even for incomplete, syntactically invalid programs.
+We propose a conservative extension, based on PEGs with labeled failures,
+that adds a syntax error recovery mechanism for PEGs. This extension associates
+recovery expressions to labels, where a label now not only reports a syntax
+error but also uses this recovery expression to reach a synchronization point
+in the input and resume parsing. We give an operational semantics of PEGs with
+this recovery mechanism, and use an implementation based on such semantics to
+build a robust parser for the Lua language. We evaluate the effectiveness of
+this parser, alone and in comparison with a Lua parser with automatic error
+recovery generated by ANTLR, a popular parser generator.
+",1,0,0,0,0,0
+15439,"Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers","  This paper describes the Pressure Ulcers Online Website, which is a first
+step solution towards a new and innovative platform for helping people to
+detect, understand and manage pressure ulcers. It outlines the reasons why the
+project has been developed and provides a central point of contact for pressure
+ulcer analysis and ongoing research. Using state-of-the-art technologies in
+convolutional neural networks and transfer learning along with end-to-end web
+technologies, this platform allows pressure ulcers to be analysed and findings
+to be reported. As the system evolves through collaborative partnerships,
+future versions will provide decision support functions to describe the complex
+characteristics of pressure ulcers along with information on wound care across
+multiple user boundaries. This project is therefore intended to raise awareness
+and support for people suffering with or providing care for pressure ulcers.
+",0,0,0,1,0,0
+15440,Brain networks reveal the effects of antipsychotic drugs on schizophrenia patients and controls,"  The study of brain networks, including derived from functional neuroimaging
+data, attracts broad interest and represents a rapidly growing
+interdisciplinary field. Comparing networks of healthy volunteers with those of
+patients can potentially offer new, quantitative diagnostic methods, and a
+framework for better understanding brain and mind disorders. We explore resting
+state fMRI data through network measures, and demonstrate that not only is
+there a distinctive network architecture in the healthy brain that is disrupted
+in schizophrenia, but also that both networks respond to medication. We
+construct networks representing 15 healthy individuals and 12 schizophrenia
+patients (males and females), all of whom are administered three drug
+treatments: (i) a placebo; and two antipsychotic medications (ii) aripiprazole
+and; (iii) sulpiride. We first reproduce the established finding that brain
+networks of schizophrenia patients exhibit increased efficiency and reduced
+clustering compared to controls. Our data then reveals that the antipsychotic
+medications mitigate this effect, shifting the metrics towards those observed
+in healthy volunteers, with a marked difference in efficacy between the two
+drugs. Additionally, we find that aripiprazole considerably alters the network
+statistics of healthy controls. Using a test of cognitive ability, we establish
+that aripiprazole also adversely affects their performance. This provides
+evidence that changes to macroscopic brain network architecture result in
+measurable behavioural differences. This is the first time different
+medications have been assessed in this way. Our results lay the groundwork for
+an objective methodology with which to calculate and compare the efficacy of
+different treatments of mind and brain disorders.
+",0,0,0,0,1,0
+15441,New approach to Minkowski fractional inequalities using generalized k-fractional integral operator,"  In this paper, we obtain new results related to Minkowski fractional integral
+inequality using generalized k-fractional integral operator which is in terms
+of the Gauss hypergeometric function.
+",0,0,1,0,0,0
+15442,Segmentation of nearly isotropic overlapped tracks in photomicrographs using successive erosions as watershed markers,"  The major challenges of automatic track counting are distinguishing tracks
+and material defects, identifying small tracks and defects of similar size, and
+detecting overlapping tracks. Here we address the latter issue using WUSEM, an
+algorithm which combines the watershed transform, morphological erosions and
+labeling to separate regions in photomicrographs. WUSEM shows reliable results
+when used in photomicrographs presenting almost isotropic objects. We tested
+this method in two datasets of diallyl phthalate (DAP) photomicrographs and
+compared the results when counting manually and using the classic watershed.
+The mean automatic/manual efficiency ratio when using WUSEM in the test
+datasets is 0.97 +/- 0.11.
+",1,1,0,0,0,0
+15443,"A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging","  Recently, impressive denoising results have been achieved by Bayesian
+approaches which assume Gaussian models for the image patches. This improvement
+in performance can be attributed to the use of per-patch models. Unfortunately
+such an approach is particularly unstable for most inverse problems beyond
+denoising. In this work, we propose the use of a hyperprior to model image
+patches, in order to stabilize the estimation procedure. There are two main
+advantages to the proposed restoration scheme: Firstly it is adapted to
+diagonal degradation matrices, and in particular to missing data problems (e.g.
+inpainting of missing pixels or zooming). Secondly it can deal with signal
+dependent noise models, particularly suited to digital cameras. As such, the
+scheme is especially adapted to computational photography. In order to
+illustrate this point, we provide an application to high dynamic range imaging
+from a single image taken with a modified sensor, which shows the effectiveness
+of the proposed scheme.
+",1,0,0,1,0,0
+15444,Development and evaluation of a deep learning model for protein-ligand binding affinity prediction,"  Structure based ligand discovery is one of the most successful approaches for
+augmenting the drug discovery process. Currently, there is a notable shift
+towards machine learning (ML) methodologies to aid such procedures. Deep
+learning has recently gained considerable attention as it allows the model to
+""learn"" to extract features that are relevant for the task at hand. We have
+developed a novel deep neural network estimating the binding affinity of
+ligand-receptor complexes. The complex is represented with a 3D grid, and the
+model utilizes a 3D convolution to produce a feature map of this
+representation, treating the atoms of both proteins and ligands in the same
+manner. Our network was tested on the CASF ""scoring power"" benchmark and Astex
+Diverse Set and outperformed classical scoring functions. The model, together
+with usage instructions and examples, is available as a git repository at
+this http URL
+",1,0,0,1,0,0
+15445,The Bayesian update: variational formulations and gradient flows,"  The Bayesian update can be viewed as a variational problem by characterizing
+the posterior as the minimizer of a functional. The variational viewpoint is
+far from new and is at the heart of popular methods for posterior
+approximation. However, some of its consequences seem largely unexplored. We
+focus on the following one: defining the posterior as the minimizer of a
+functional gives a natural path towards the posterior by moving in the
+direction of steepest descent of the functional. This idea is made precise
+through the theory of gradient flows, allowing to bring new tools to the study
+of Bayesian models and algorithms. Since the posterior may be characterized as
+the minimizer of different functionals, several variational formulations may be
+considered. We study three of them and their three associated gradient flows.
+We show that, in all cases, the rate of convergence of the flows to the
+posterior can be bounded by the geodesic convexity of the functional to be
+minimized. Each gradient flow naturally suggests a nonlinear diffusion with the
+posterior as invariant distribution. These diffusions may be discretized to
+build proposals for Markov chain Monte Carlo (MCMC) algorithms. By
+construction, the diffusions are guaranteed to satisfy a certain optimality
+condition, and rates of convergence are given by the convexity of the
+functionals. We use this observation to propose a criterion for the choice of
+metric in Riemannian MCMC methods.
+",0,0,1,1,0,0
+15446,Magnetic diode at $T$ = 300 K,"  We report the finding of unidirectional electronic properties, analogous to a
+semiconductor diode, in two-dimensional artificial permalloy honeycomb lattice
+of ultra-small bond, with a typical length of ~ 12 nm. The unidirectional
+transport behavior, characterized by the asymmetric colossal enhancement in
+differential conductivity at a modest current application of ~ 10-15 $\mu$A,
+persists to T = 300 K in honeycomb lattice of thickness ~ 6 nm. The asymmetric
+behavior arises without the application of magnetic field. A qualitative
+analysis of experimental data suggests the role of magnetic charge or monopoles
+in the unusual observations with strong implication for spintronics.
+",0,1,0,0,0,0
+15447,Efficient mixture model for clustering of sparse high dimensional binary data,"  In this paper we propose a mixture model, SparseMix, for clustering of sparse
+high dimensional binary data, which connects model-based with centroid-based
+clustering. Every group is described by a representative and a probability
+distribution modeling dispersion from this representative. In contrast to
+classical mixture models based on EM algorithm, SparseMix:
+-is especially designed for the processing of sparse data,
+-can be efficiently realized by an on-line Hartigan optimization algorithm,
+-is able to automatically reduce unnecessary clusters.
+We perform extensive experimental studies on various types of data, which
+confirm that SparseMix builds partitions with higher compatibility with
+reference grouping than related methods. Moreover, constructed representatives
+often better reveal the internal structure of data.
+",1,0,0,1,0,0
+15448,Normalized Total Gradient: A New Measure for Multispectral Image Registration,"  Image registration is a fundamental issue in multispectral image processing.
+In filter wheel based multispectral imaging systems, the non-coplanar placement
+of the filters always causes the misalignment of multiple channel images. The
+selective characteristic of spectral response in multispectral imaging raises
+two challenges to image registration. First, the intensity levels of a local
+region may be different in individual channel images. Second, the local
+intensity may vary rapidly in some channel images while keeps stationary in
+others. Conventional multimodal measures, such as mutual information,
+correlation coefficient, and correlation ratio, can register images with
+different regional intensity levels, but will fail in the circumstance of
+severe local intensity variation. In this paper, a new measure, namely
+normalized total gradient (NTG), is proposed for multispectral image
+registration. The NTG is applied on the difference between two channel images.
+This measure is based on the key assumption (observation) that the gradient of
+difference image between two aligned channel images is sparser than that
+between two misaligned ones. A registration framework, which incorporates image
+pyramid and global/local optimization, is further introduced for rigid
+transform. Experimental results validate that the proposed method is effective
+for multispectral image registration and performs better than conventional
+methods.
+",1,0,0,0,0,0
+15449,Graphical-model based estimation and inference for differential privacy,"  Many privacy mechanisms reveal high-level information about a data
+distribution through noisy measurements. It is common to use this information
+to estimate the answers to new queries. In this work, we provide an approach to
+solve this estimation problem efficiently using graphical models, which is
+particularly effective when the distribution is high-dimensional but the
+measurements are over low-dimensional marginals. We show that our approach is
+far more efficient than existing estimation techniques from the privacy
+literature and that it can improve the accuracy and scalability of many
+state-of-the-art mechanisms.
+",1,0,0,1,0,0
+15450,Hierarchical Kriging for multi-fidelity aero-servo-elastic simulators - Application to extreme loads on wind turbines,"  In the present work, we consider multi-fidelity surrogate modelling to fuse
+the output of multiple aero-servo-elastic computer simulators of varying
+complexity. In many instances, predictions from multiple simulators for the
+same quantity of interest on a wind turbine are available. In this type of
+situation, there is strong evidence that fusing the output from multiple
+aero-servo-elastic simulators yields better predictive ability and lower model
+uncertainty than using any single simulator. Hierarchical Kriging is a
+multi-fidelity surrogate modelling method in which the Kriging surrogate model
+of the cheap (low-fidelity) simulator is used as a trend of the Kriging
+surrogate model of the higher fidelity simulator. We propose a parametric
+approach to Hierarchical Kriging where the best surrogate models are selected
+based on evaluating all possible combinations of the available Kriging
+parameters candidates. The parametric Hierarchical Kriging approach is
+illustrated by fusing the extreme flapwise bending moment at the blade root of
+a large multi-megawatt wind turbine as a function of wind velocity, turbulence
+and wind shear exponent in the presence of model uncertainty and
+heterogeneously noisy output. The extreme responses are obtained by two widely
+accepted wind turbine specific aero-servo-elastic computer simulators, FAST and
+Bladed. With limited high-fidelity simulations, Hierarchical Kriging produces
+more accurate predictions of validation data compared to conventional Kriging.
+In addition, contrary to conventional Kriging, Hierarchical Kriging is shown to
+be a robust surrogate modelling technique because it is less sensitive to the
+choice of the Kriging parameters and the choice of the estimation error.
+",0,0,0,1,0,0
+15451,Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning,"  Statistical performance bounds for reinforcement learning (RL) algorithms can
+be critical for high-stakes applications like healthcare. This paper introduces
+a new framework for theoretically measuring the performance of such algorithms
+called Uniform-PAC, which is a strengthening of the classical Probably
+Approximately Correct (PAC) framework. In contrast to the PAC framework, the
+uniform version may be used to derive high probability regret guarantees and so
+forms a bridge between the two setups that has been missing in the literature.
+We demonstrate the benefits of the new framework for finite-state episodic MDPs
+with a new algorithm that is Uniform-PAC and simultaneously achieves optimal
+regret and PAC guarantees except for a factor of the horizon.
+",1,0,0,1,0,0
+15452,Alternative Semantic Representations for Zero-Shot Human Action Recognition,"  A proper semantic representation for encoding side information is key to the
+success of zero-shot learning. In this paper, we explore two alternative
+semantic representations especially for zero-shot human action recognition:
+textual descriptions of human actions and deep features extracted from still
+images relevant to human actions. Such side information are accessible on Web
+with little cost, which paves a new way in gaining side information for
+large-scale zero-shot human action recognition. We investigate different
+encoding methods to generate semantic representations for human actions from
+such side information. Based on our zero-shot visual recognition method, we
+conducted experiments on UCF101 and HMDB51 to evaluate two proposed semantic
+representations . The results suggest that our proposed text- and image-based
+semantic representations outperform traditional attributes and word vectors
+considerably for zero-shot human action recognition. In particular, the
+image-based semantic representations yield the favourable performance even
+though the representation is extracted from a small number of images per class.
+",1,0,0,0,0,0
+15453,"Being Corrupt Requires Being Clever, But Detecting Corruption Doesn't","  We consider a variation of the problem of corruption detection on networks
+posed by Alon, Mossel, and Pemantle '15. In this model, each vertex of a graph
+can be either truthful or corrupt. Each vertex reports about the types
+(truthful or corrupt) of all its neighbors to a central agency, where truthful
+nodes report the true types they see and corrupt nodes report adversarially.
+The central agency aggregates these reports and attempts to find a single
+truthful node. Inspired by real auditing networks, we pose our problem for
+arbitrary graphs and consider corruption through a computational lens. We
+identify a key combinatorial parameter of the graph $m(G)$, which is the
+minimal number of corrupted agents needed to prevent the central agency from
+identifying a single corrupt node. We give an efficient (in fact, linear time)
+algorithm for the central agency to identify a truthful node that is successful
+whenever the number of corrupt nodes is less than $m(G)/2$. On the other hand,
+we prove that for any constant $\alpha > 1$, it is NP-hard to find a subset of
+nodes $S$ in $G$ such that corrupting $S$ prevents the central agency from
+finding one truthful node and $|S| \leq \alpha m(G)$, assuming the Small Set
+Expansion Hypothesis (Raghavendra and Steurer, STOC '10). We conclude that
+being corrupt requires being clever, while detecting corruption does not.
+Our main technical insight is a relation between the minimum number of
+corrupt nodes required to hide all truthful nodes and a certain notion of
+vertex separability for the underlying graph. Additionally, this insight lets
+us design an efficient algorithm for a corrupt party to decide which graphs
+require the fewest corrupted nodes, up to a multiplicative factor of $O(\log
+n)$.
+",1,0,0,0,0,0
+15454,Computing the homology of basic semialgebraic sets in weak exponential time,"  We describe and analyze an algorithm for computing the homology (Betti
+numbers and torsion coefficients) of basic semialgebraic sets which works in
+weak exponential time. That is, out of a set of exponentially small measure in
+the space of data the cost of the algorithm is exponential in the size of the
+data. All algorithms previously proposed for this problem have a complexity
+which is doubly exponential (and this is so for almost all data).
+",1,0,1,0,0,0
+15455,Deterministic and Randomized Diffusion based Iterative Generalized Hard Thresholding (DiFIGHT) for Distributed Sparse Signal Recovery,"  In this paper, we propose a distributed iterated hard thresholding algorithm
+termed DiFIGHT over a network that is built on the diffusion mechanism and also
+propose a modification of the proposed algorithm termed MoDiFIGHT, that has low
+complexity in terms of communication in the network. We additionally propose
+four different strategies termed RP, RNP, RGPr, and RGNPr that are used to
+randomly select a subset of nodes that are subsequently activated to take part
+in the distributed algorithm, so as to reduce the mean number of communications
+during the run of the distributed algorithm. We present theoretical estimates
+of the long run communication per unit time for these different strategies,
+when used by the two proposed algorithms. Also, we present an analysis of the
+two proposed algorithms and provide provable bounds on their recovery
+performance with or without using the random node selection strategies.
+Finally, we use numerical studies to show that both when the random strategies
+are used as well as when they are not used, the proposed algorithms display
+performances far superior to distributed IHT algorithm using consensus
+mechanism.
+",1,0,0,0,0,0
+15456,Iterated doubles of the Joker and their realisability,"  Let $\mathcal{A}(1)^*$ be the subHopf algebra of the mod~$2$ Steenrod algebra
+$\mathcal{A}^*$ generated by $\mathrm{Sq}^1$ and $\mathrm{Sq}^2$. The
+\emph{Joker} is the cyclic $\mathcal{A}(1)^*$-module
+$\mathcal{A}(1)^*/\mathcal{A}(1)^*\{\mathrm{Sq}^3\}$ which plays a special
+rôle in the study of $\mathcal{A}(1)^*$-modules. We discuss realisations of
+the Joker both as an $\mathcal{A}^*$-module and as the cohomology of a
+spectrum. We also consider analogous $\mathcal{A}(n)^*$-modules for $n\geq2$
+and prove realisability results (both stable and unstable) for $n=2,3$ and
+non-realisability results for $n\geq4$.
+",0,0,1,0,0,0
+15457,On subtrees of the representation tree in rational base numeration systems,"  Every rational number p/q defines a rational base numeration system in which
+every integer has a unique finite representation, up to leading zeroes. This
+work is a contribution to the study of the set of the representations of
+integers. This prefix-closed subset of the free monoid is naturally represented
+as a highly non-regular tree. Its nodes are the integers, its edges bear labels
+taken in {0,1,...,p-1}, and its subtrees are all distinct.
+We associate with each subtree (or with its root n) three infinite words. The
+bottom word of n is the lexicographically smallest word that is the label of a
+branch of the subtree. The top word of n is defined similarly. The span-word of
+n is the digitwise difference between the latter and the former.
+First, we show that the set of all the span-words is accepted by an infinite
+automaton whose underlying graph is essentially the same as the tree itself.
+Second, we study the function that computes for all n the bottom word
+associated with n+1 from the one associated with n, and show that it is
+realised by an infinite sequential transducer whose underlying graph is once
+again essentially the same as the tree itself.
+An infinite word may be interpreted as an expansion in base p/q after the
+radix point, hence evaluated to a real number. If T is a subtree whose root is
+n, then the evaluations of the labels of the branches of T form an interval of
+$\mathbb{R}$. The length of this interval is called the span of n and is equal
+to the evaluation of the span-word of n. The set of all spans is then a subset
+of R and we use the preceding construction to study its topological closure. We
+show that it is an interval when p is greater than or equal to 2q-1, and a
+Cantor set of measure zero otherwise.
+",1,0,0,0,0,0
+15458,A Practical Method for Solving Contextual Bandit Problems Using Decision Trees,"  Many efficient algorithms with strong theoretical guarantees have been
+proposed for the contextual multi-armed bandit problem. However, applying these
+algorithms in practice can be difficult because they require domain expertise
+to build appropriate features and to tune their parameters. We propose a new
+method for the contextual bandit problem that is simple, practical, and can be
+applied with little or no domain expertise. Our algorithm relies on decision
+trees to model the context-reward relationship. Decision trees are
+non-parametric, interpretable, and work well without hand-crafted features. To
+guide the exploration-exploitation trade-off, we use a bootstrapping approach
+which abstracts Thompson sampling to non-Bayesian settings. We also discuss
+several computational heuristics and demonstrate the performance of our method
+on several datasets.
+",1,0,0,1,0,0
+15459,Trainable back-propagated functional transfer matrices,"  Connections between nodes of fully connected neural networks are usually
+represented by weight matrices. In this article, functional transfer matrices
+are introduced as alternatives to the weight matrices: Instead of using real
+weights, a functional transfer matrix uses real functions with trainable
+parameters to represent connections between nodes. Multiple functional transfer
+matrices are then stacked together with bias vectors and activations to form
+deep functional transfer neural networks. These neural networks can be trained
+within the framework of back-propagation, based on a revision of the delta
+rules and the error transmission rule for functional connections. In
+experiments, it is demonstrated that the revised rules can be used to train a
+range of functional connections: 20 different functions are applied to neural
+networks with up to 10 hidden layers, and most of them gain high test
+accuracies on the MNIST database. It is also demonstrated that a functional
+transfer matrix with a memory function can roughly memorise a non-cyclical
+sequence of 400 digits.
+",1,0,0,1,0,0
+15460,Multiple Exciton Generation in Chiral Carbon Nanotubes: Density Functional Theory Based Computation,"  We use Boltzmann transport equation (BE) to study time evolution of a
+photo-excited state in a nanoparticle including phonon-mediated exciton
+relaxation and the multiple exciton generation (MEG) processes, such as
+exciton-to-biexciton multiplication and biexciton-to-exciton recombination. BE
+collision integrals are computed using Kadanoff-Baym-Keldysh many-body
+perturbation theory (MBPT) based on density functional theory (DFT)
+simulations, including exciton effects. We compute internal quantum efficiency
+(QE), which is the number of excitons generated from an absorbed photon in the
+course of the relaxation. We apply this approach to chiral single-wall carbon
+nanotubes (SWCNTs), such as (6,2), and (6,5). We predict efficient MEG in the
+(6,2) and (6,5) SWCNTs within the solar spectrum range starting at the $2 E_g$
+energy threshold and with QE reaching $\sim 1.6$ at about $3 E_g,$ where $E_g$
+is the electronic gap.
+",0,1,0,0,0,0
+15461,"Turaev-Viro invariants, colored Jones polynomials and volume","  We obtain a formula for the Turaev-Viro invariants of a link complement in
+terms of values of the colored Jones polynomial of the link. As an application
+we give the first examples for which the volume conjecture of Chen and the
+third named author\,\cite{Chen-Yang} is verified. Namely, we show that the
+asymptotics of the Turaev-Viro invariants of the Figure-eight knot and the
+Borromean rings complement determine the corresponding hyperbolic volumes. Our
+calculations also exhibit new phenomena of asymptotic behavior of values of the
+colored Jones polynomials that seem not to be predicted by neither the
+Kashaev-Murakami-Murakami volume conjecture and various of its generalizations
+nor by Zagier's quantum modularity conjecture. We conjecture that the
+asymptotics of the Turaev-Viro invariants of any link complement determine the
+simplicial volume of the link, and verify it for all knots with zero simplicial
+volume. Finally we observe that our simplicial volume conjecture is stable
+under connect sum and split unions of links.
+",0,0,1,0,0,0
+15462,Credit card fraud detection through parenclitic network analysis,"  The detection of frauds in credit card transactions is a major topic in
+financial research, of profound economic implications. While this has hitherto
+been tackled through data analysis techniques, the resemblances between this
+and other problems, like the design of recommendation systems and of diagnostic
+/ prognostic medical tools, suggest that a complex network approach may yield
+important benefits. In this contribution we present a first hybrid data mining
+/ complex network classification algorithm, able to detect illegal instances in
+a real card transaction data set. It is based on a recently proposed network
+reconstruction algorithm that allows creating representations of the deviation
+of one instance from a reference group. We show how the inclusion of features
+extracted from the network data representation improves the score obtained by a
+standard, neural network-based classification algorithm; and additionally how
+this combined approach can outperform a commercial fraud detection system in
+specific operation niches. Beyond these specific results, this contribution
+represents a new example on how complex networks and data mining can be
+integrated as complementary tools, with the former providing a view to data
+beyond the capabilities of the latter.
+",1,1,0,0,0,0
+15463,On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms,"  A popular approach to semi-supervised learning proceeds by endowing the input
+data with a graph structure in order to extract geometric information and
+incorporate it into a Bayesian framework. We introduce new theory that gives
+appropriate scalings of graph parameters that provably lead to a well-defined
+limiting posterior as the size of the unlabeled data set grows. Furthermore, we
+show that these consistency results have profound algorithmic implications.
+When consistency holds, carefully designed graph-based Markov chain Monte Carlo
+algorithms are proved to have a uniform spectral gap, independent of the number
+of unlabeled inputs. Several numerical experiments corroborate both the
+statistical consistency and the algorithmic scalability established by the
+theory.
+",1,0,0,1,0,0
+15464,Bayesian Optimization for Parameter Tuning of the XOR Neural Network,"  When applying Machine Learning techniques to problems, one must select model
+parameters to ensure that the system converges but also does not become stuck
+at the objective function's local minimum. Tuning these parameters becomes a
+non-trivial task for large models and it is not always apparent if the user has
+found the optimal parameters. We aim to automate the process of tuning a Neural
+Network, (where only a limited number of parameter search attempts are
+available) by implementing Bayesian Optimization. In particular, by assigning
+Gaussian Process Priors to the parameter space, we utilize Bayesian
+Optimization to tune an Artificial Neural Network used to learn the XOR
+function, with the result of achieving higher prediction accuracy.
+",1,0,0,1,0,0
+15465,A differential model for growing sandpiles on networks,"  We consider a system of differential equations of Monge-Kantorovich type
+which describes the equilibrium configurations of granular material poured by a
+constant source on a network. Relying on the definition of viscosity solution
+for Hamilton-Jacobi equations on networks, recently introduced by P.-L. Lions
+and P. E. Souganidis, we prove existence and uniqueness of the solution of the
+system and we discuss its numerical approximation. Some numerical experiments
+are carried out.
+",0,0,1,0,0,0
+15466,DCT-like Transform for Image Compression Requires 14 Additions Only,"  A low-complexity 8-point orthogonal approximate DCT is introduced. The
+proposed transform requires no multiplications or bit-shift operations. The
+derived fast algorithm requires only 14 additions, less than any existing DCT
+approximation. Moreover, in several image compression scenarios, the proposed
+transform could outperform the well-known signed DCT, as well as
+state-of-the-art algorithms.
+",1,0,0,1,0,0
+15467,On realizability of sign patterns by real polynomials,"  The classical Descartes' rule of signs limits the number of positive roots of
+a real polynomial in one variable by the number of sign changes in the sequence
+of its coefficients. One can ask the question which pairs of nonnegative
+integers $(p,n)$, chosen in accordance with this rule and with some other
+natural conditions, can be the pairs of numbers of positive and negative roots
+of a real polynomial with prescribed signs of the coefficients. The paper
+solves this problem for degree $8$ polynomials.
+",0,0,1,0,0,0
+15468,HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing,"  Multiple sequence alignment (MSA) plays a key role in biological sequence
+analyses, especially in phylogenetic tree construction. Extreme increase in
+next-generation sequencing results in shortage of efficient ultra-large
+biological sequence alignment approaches for coping with different sequence
+types. Distributed and parallel computing represents a crucial technique for
+accelerating ultra-large sequence analyses. Based on HAlign and Spark
+distributed computing system, we implement a highly cost-efficient and
+time-efficient HAlign-II tool to address ultra-large multiple biological
+sequence alignment and phylogenetic tree construction. After comparing with
+most available state-of-the-art methods, our experimental results indicate the
+following: 1) HAlign-II can efficiently carry out MSA and construct
+phylogenetic trees with ultra-large biological sequences; 2) HAlign-II shows
+extremely high memory efficiency and scales well with increases in computing
+resource; 3) HAlign-II provides a user-friendly web server based on our
+distributed computing infrastructure. HAlign-II with open-source codes and
+datasets was established at this http URL.
+",1,0,0,0,0,0
+15469,Search for Evergreens in Science: A Functional Data Analysis,"  Evergreens in science are papers that display a continual rise in annual
+citations without decline, at least within a sufficiently long time period.
+Aiming to better understand evergreens in particular and patterns of citation
+trajectory in general, this paper develops a functional data analysis method to
+cluster citation trajectories of a sample of 1699 research papers published in
+1980 in the American Physical Society (APS) journals. We propose a functional
+Poisson regression model for individual papers' citation trajectories, and fit
+the model to the observed 30-year citations of individual papers by functional
+principal component analysis and maximum likelihood estimation. Based on the
+estimated paper-specific coefficients, we apply the K-means clustering
+algorithm to cluster papers into different groups, for uncovering general types
+of citation trajectories. The result demonstrates the existence of an evergreen
+cluster of papers that do not exhibit any decline in annual citations over 30
+years.
+",1,0,0,1,0,0
+15470,Tied Hidden Factors in Neural Networks for End-to-End Speaker Recognition,"  In this paper we propose a method to model speaker and session variability
+and able to generate likelihood ratios using neural networks in an end-to-end
+phrase dependent speaker verification system. As in Joint Factor Analysis, the
+model uses tied hidden variables to model speaker and session variability and a
+MAP adaptation of some of the parameters of the model. In the training
+procedure our method jointly estimates the network parameters and the values of
+the speaker and channel hidden variables. This is done in a two-step
+backpropagation algorithm, first the network weights and factor loading
+matrices are updated and then the hidden variables, whose gradients are
+calculated by aggregating the corresponding speaker or session frames, since
+these hidden variables are tied. The last layer of the network is defined as a
+linear regression probabilistic model whose inputs are the previous layer
+outputs. This choice has the advantage that it produces likelihoods and
+additionally it can be adapted during the enrolment using MAP without the need
+of a gradient optimization. The decisions are made based on the ratio of the
+output likelihoods of two neural network models, speaker adapted and universal
+background model. The method was evaluated on the RSR2015 database.
+",1,0,0,0,0,0
+15471,An Optimal Control Formulation of Pulse-Based Control Using Koopman Operator,"  In many applications, and in systems/synthetic biology, in particular, it is
+desirable to compute control policies that force the trajectory of a bistable
+system from one equilibrium (the initial point) to another equilibrium (the
+target point), or in other words to solve the switching problem. It was
+recently shown that, for monotone bistable systems, this problem admits
+easy-to-implement open-loop solutions in terms of temporal pulses (i.e., step
+functions of fixed length and fixed magnitude). In this paper, we develop this
+idea further and formulate a problem of convergence to an equilibrium from an
+arbitrary initial point. We show that this problem can be solved using a static
+optimization problem in the case of monotone systems. Changing the initial
+point to an arbitrary state allows to build closed-loop, event-based or
+open-loop policies for the switching/convergence problems. In our derivations
+we exploit the Koopman operator, which offers a linear infinite-dimensional
+representation of an autonomous nonlinear system. One of the main advantages of
+using the Koopman operator is the powerful computational tools developed for
+this framework. Besides the presence of numerical solutions, the
+switching/convergence problem can also serve as a building block for solving
+more complicated control problems and can potentially be applied to
+non-monotone systems. We illustrate this argument on the problem of
+synchronizing cardiac cells by defibrillation. Potentially, our approach can be
+extended to problems with different parametrizations of control signals since
+the only fundamental limitation is the finite time application of the control
+signal.
+",1,0,1,0,0,0
+15472,A Converse to Banach's Fixed Point Theorem and its CLS Completeness,"  Banach's fixed point theorem for contraction maps has been widely used to
+analyze the convergence of iterative methods in non-convex problems. It is a
+common experience, however, that iterative maps fail to be globally contracting
+under the natural metric in their domain, making the applicability of Banach's
+theorem limited. We explore how generally we can apply Banach's fixed point
+theorem to establish the convergence of iterative methods when pairing it with
+carefully designed metrics.
+Our first result is a strong converse of Banach's theorem, showing that it is
+a universal analysis tool for establishing global convergence of iterative
+methods to unique fixed points, and for bounding their convergence rate. In
+other words, we show that, whenever an iterative map globally converges to a
+unique fixed point, there exists a metric under which the iterative map is
+contracting and which can be used to bound the number of iterations until
+convergence. We illustrate our approach in the widely used power method,
+providing a new way of bounding its convergence rate through contraction
+arguments.
+We next consider the computational complexity of Banach's fixed point
+theorem. Making the proof of our converse theorem constructive, we show that
+computing a fixed point whose existence is guaranteed by Banach's fixed point
+theorem is CLS-complete. We thus provide the first natural complete problem for
+the class CLS, which was defined in [Daskalakis, Papadimitriou 2011] to capture
+the complexity of problems such as P-matrix LCP, computing KKT-points, and
+finding mixed Nash equilibria in congestion and network coordination games.
+",1,0,1,1,0,0
+15473,Improved Discrete RRT for Coordinated Multi-robot Planning,"  This paper addresses the problem of coordination of a fleet of mobile robots
+- the problem of finding an optimal set of collision-free trajectories for
+individual robots in the fleet. Many approaches have been introduced during the
+last decades, but a minority of them is practically applicable, i.e. fast,
+producing near-optimal solutions, and complete. We propose a novel
+probabilistic approach based on the Rapidly Exploring Random Tree algorithm
+(RRT) by significantly improving its multi-robot variant for discrete
+environments. The presented experimental results show that the proposed
+approach is fast enough to solve problems with tens of robots in seconds.
+Although the solutions generated by the approach are slightly worse than one of
+the best state-of-the-art algorithms presented in (ter Mors et al., 2010), it
+solves problems where ter Mors's algorithm fails.
+",1,0,0,0,0,0
+15474,A note on the stratification by automorphisms of smooth plane curves of genus 6,"  In this note, we give a so-called representative classification for the
+strata by automorphism group of smooth $\bar{k}$-plane curves of genus $6$,
+where $\bar{k}$ is a fixed separable closure of a field $k$ of characteristic
+$p = 0$ or $p > 13$. We start with a classification already obtained by the
+first author and we use standard techniques.
+Interestingly, in the way to get these families for the different strata, we
+find two remarkable phenomenons that did not appear before. One is the
+existence of a non $0$-dimensional final stratum of plane curves. At a first
+sight it may sound odd, but we will see that this is a normal situation for
+higher degrees and we will give a explanation for it.
+We explicitly describe representative families for all strata, except for the
+stratum with automorphism group $\mathbb{Z}/5\mathbb{Z}$. Here we find the
+second difference with the lower genus cases where the previous techniques do
+not fully work. Fortunately, we are still able to prove the existence of such
+family by applying a version of Luroth's theorem in dimension $2$.
+",0,0,1,0,0,0
+15475,Relativistic verifiable delegation of quantum computation,"  The importance of being able to verify quantum computation delegated to
+remote servers increases with recent development of quantum technologies. In
+some of the proposed protocols for this task, a client delegates her quantum
+computation to non-communicating servers. The fact that the servers do not
+communicate is not physically justified and it is essential for the proof of
+security of such protocols. For the best of our knowledge, we present in this
+work the first verifiable delegation scheme where a classical client delegates
+her quantum computation to two entangled servers that are allowed to
+communicate, but respecting the plausible assumption that information cannot be
+propagated faster than speed of light. We achieve this result by proposing the
+first one-round two-prover game for the Local Hamiltonian problem where provers
+only need polynomial time quantum computation and access to copies of the
+groundstate of the Hamiltonian.
+",1,0,0,0,0,0
+15476,Finiteness of étale fundamental groups by reduction modulo $p$,"  We introduce a spreading out technique to deduce finiteness results for
+étale fundamental groups of complex varieties by characteristic $p$ methods,
+and apply this to recover a finiteness result proven recently for local
+fundamental groups in characteristic $0$ using birational geometry.
+",0,0,1,0,0,0
+15477,Morphological Error Detection in 3D Segmentations,"  Deep learning algorithms for connectomics rely upon localized classification,
+rather than overall morphology. This leads to a high incidence of erroneously
+merged objects. Humans, by contrast, can easily detect such errors by acquiring
+intuition for the correct morphology of objects. Biological neurons have
+complicated and variable shapes, which are challenging to learn, and merge
+errors take a multitude of different forms. We present an algorithm, MergeNet,
+that shows 3D ConvNets can, in fact, detect merge errors from high-level
+neuronal morphology. MergeNet follows unsupervised training and operates across
+datasets. We demonstrate the performance of MergeNet both on a variety of
+connectomics data and on a dataset created from merged MNIST images.
+",1,0,0,1,0,0
+15478,Structural Feature Selection for Event Logs,"  We consider the problem of classifying business process instances based on
+structural features derived from event logs. The main motivation is to provide
+machine learning based techniques with quick response times for interactive
+computer assisted root cause analysis. In particular, we create structural
+features from process mining such as activity and transition occurrence counts,
+and ordering of activities to be evaluated as potential features for
+classification. We show that adding such structural features increases the
+amount of information thus potentially increasing classification accuracy.
+However, there is an inherent trade-off as using too many features leads to too
+long run-times for machine learning classification models. One way to improve
+the machine learning algorithms' run-time is to only select a small number of
+features by a feature selection algorithm. However, the run-time required by
+the feature selection algorithm must also be taken into account. Also, the
+classification accuracy should not suffer too much from the feature selection.
+The main contributions of this paper are as follows: First, we propose and
+compare six different feature selection algorithms by means of an experimental
+setup comparing their classification accuracy and achievable response times.
+Second, we discuss the potential use of feature selection results for computer
+assisted root cause analysis as well as the properties of different types of
+structural features in the context of feature selection.
+",1,0,0,1,0,0
+15479,Counting $G$-Extensions by Discriminant,"  The problem of analyzing the number of number field extensions $L/K$ with
+bounded (relative) discriminant has been the subject of renewed interest in
+recent years, with significant advances made by Schmidt, Ellenberg-Venkatesh,
+Bhargava, Bhargava-Shankar-Wang, and others. In this paper, we use the geometry
+of numbers and invariant theory of finite groups, in a manner similar to
+Ellenberg and Venkatesh, to give an upper bound on the number of extensions
+$L/K$ with fixed degree, bounded relative discriminant, and specified Galois
+closure.
+",0,0,1,0,0,0
+15480,Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes,"  We introduce a new formulation of the Hidden Parameter Markov Decision
+Process (HiP-MDP), a framework for modeling families of related tasks using
+low-dimensional latent embeddings. Our new framework correctly models the joint
+uncertainty in the latent parameters and the state space. We also replace the
+original Gaussian Process-based model with a Bayesian Neural Network, enabling
+more scalable inference. Thus, we expand the scope of the HiP-MDP to
+applications with higher dimensions and more complex dynamics.
+",1,0,0,1,0,0
+15481,Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling,"  Sampling from the lattice Gaussian distribution plays an important role in
+various research fields. In this paper, the Markov chain Monte Carlo
+(MCMC)-based sampling technique is advanced in several fronts. Firstly, the
+spectral gap for the independent Metropolis-Hastings-Klein (MHK) algorithm is
+derived, which is then extended to Peikert's algorithm and rejection sampling;
+we show that independent MHK exhibits faster convergence. Then, the performance
+of bounded distance decoding using MCMC is analyzed, revealing a flexible
+trade-off between the decoding radius and complexity. MCMC is further applied
+to trapdoor sampling, again offering a trade-off between security and
+complexity. Finally, the independent multiple-try Metropolis-Klein (MTMK)
+algorithm is proposed to enhance the convergence rate. The proposed algorithms
+allow parallel implementation, which is beneficial for practical applications.
+",1,0,0,0,0,0
+15482,"$S$-Leaping: An adaptive, accelerated stochastic simulation algorithm, bridging $τ$-leaping and $R$-leaping","  We propose the $S$-leaping algorithm for the acceleration of Gillespie's
+stochastic simulation algorithm that combines the advantages of the two main
+accelerated methods; the $\tau$-leaping and $R$-leaping algorithms. These
+algorithms are known to be efficient under different conditions; the
+$\tau$-leaping is efficient for non-stiff systems or systems with partial
+equilibrium, while the $R$-leaping performs better in stiff system thanks to an
+efficient sampling procedure. However, even a small change in a system's set up
+can critically affect the nature of the simulated system and thus reduce the
+efficiency of an accelerated algorithm. The proposed algorithm combines the
+efficient time step selection from the $\tau$-leaping with the effective
+sampling procedure from the $R$-leaping algorithm. The $S$-leaping is shown to
+maintain its efficiency under different conditions and in the case of large and
+stiff systems or systems with fast dynamics, the $S$-leaping outperforms both
+methods. We demonstrate the performance and the accuracy of the $S$-leaping in
+comparison with the $\tau$-leaping and $R$-leaping on a number of benchmark
+systems involving biological reaction networks.
+",0,0,0,0,1,0
+15483,Diclofenac sodium ion exchange resin complex loaded melt cast films for sustained release ocular delivery,"  The goal of the present study is to develop polymeric matrix films loaded
+with a combination of free diclofenac sodium (DFSfree) and DFS:Ion exchange
+resin complexes (DFS:IR) for immediate and sustained release profiles,
+respectively. Effect of ratio of DFS and IR on the DFS:IR complexation
+efficiency was studied using batch processing. DFS:IR complex, DFSfree, or a
+combination of DFSfree+DFS:IR loaded matrix films were prepared by melt-cast
+technology. DFS content was 20% w/w in these matrix films. In vitro
+transcorneal permeability from the film formulations were compared against DFS
+solution, using a side-by-side diffusion apparatus, over a 6 h period. Ocular
+disposition of DFS from the solution, films and corresponding suspensions were
+evaluated in conscious New Zealand albino rabbits, 4 h and 8 h post-topical
+administration. All in vivo studies were carried out as per the University of
+Mississippi IACUC approved protocol. Complexation efficiency of DFS:IR was
+found to be 99% with a 1:1 ratio of DFS:IR. DFS release from DFS:IR suspension
+and the film were best-fit to a Higuchi model. In vitro transcorneal flux with
+the DFSfree+DFS:IR(1:1)(1 + 1) was twice that of only DFS:IR(1:1) film. In
+vivo, DFS solution and DFS:IR(1:1) suspension formulations were not able to
+maintain therapeutic DFS levels in the aqueous humor (AH). Both DFSfree and
+DFSfree+DFS:IR(1:1)(3 + 1) loaded matrix films were able to achieve and
+maintain high DFS concentrations in the AH, but elimination of DFS from the
+ocular tissues was much faster with the DFSfree formulation. DFSfree+DFS:IR
+combination loaded matrix films were able to deliver and maintain therapeutic
+DFS concentrations in the anterior ocular chamber for up to 8 h. Thus, free
+drug/IR complex loaded matrix films could be a potential topical ocular
+delivery platform for achieving immediate and sustained release
+characteristics.
+",0,1,0,0,0,0
+15484,Method of precision increase by averaging with application to numerical differentiation,"  If several independent algorithms for a computer-calculated quantity exist,
+then one can expect their results (which differ because of numerical errors) to
+follow approximately Gaussian distribution. The mean of this distribution,
+interpreted as the value of the quantity of interest, can be determined with
+better precision than what is the precision provided by a single algorithm.
+Often, with lack of enough independent algorithms, one can proceed differently:
+many practical algorithms introduce a bias using a parameter, e.g. a small but
+finite number to compute a limit or a large but finite number (cutoff) to
+approximate infinity. One may vary such parameter of a single algorithm and
+interpret the resulting numbers as generated by several algorithms. A numerical
+evidence for the validity of this approach is shown for differentiation.
+",0,0,1,0,0,0
+15485,On-line Building Energy Optimization using Deep Reinforcement Learning,"  Unprecedented high volumes of data are becoming available with the growth of
+the advanced metering infrastructure. These are expected to benefit planning
+and operation of the future power system, and to help the customers transition
+from a passive to an active role. In this paper, we explore for the first time
+in the smart grid context the benefits of using Deep Reinforcement Learning, a
+hybrid type of methods that combines Reinforcement Learning with Deep Learning,
+to perform on-line optimization of schedules for building energy management
+systems. The learning procedure was explored using two methods, Deep Q-learning
+and Deep Policy Gradient, both of them being extended to perform multiple
+actions simultaneously. The proposed approach was validated on the large-scale
+Pecan Street Inc. database. This highly-dimensional database includes
+information about photovoltaic power generation, electric vehicles as well as
+buildings appliances. Moreover, these on-line energy scheduling strategies
+could be used to provide real-time feedback to consumers to encourage more
+efficient use of electricity.
+",1,0,1,0,0,0
+15486,A KL-LUCB Bandit Algorithm for Large-Scale Crowdsourcing,"  This paper focuses on best-arm identification in multi-armed bandits with
+bounded rewards. We develop an algorithm that is a fusion of lil-UCB and
+KL-LUCB, offering the best qualities of the two algorithms in one method. This
+is achieved by proving a novel anytime confidence bound for the mean of bounded
+distributions, which is the analogue of the LIL-type bounds recently developed
+for sub-Gaussian distributions. We corroborate our theoretical results with
+numerical experiments based on the New Yorker Cartoon Caption Contest.
+",0,0,1,1,0,0
+15487,Refractive index tomography with structured illumination,"  This work introduces a novel reinterpretation of structured illumination (SI)
+microscopy for coherent imaging that allows three-dimensional imaging of
+complex refractive index (RI). To do so, we show that coherent SI is
+mathematically equivalent to a superposition of angled illuminations. It
+follows that raw acquisitions for standard SI-enhanced quantitative-phase
+images can be processed into complex electric-field maps describing sample
+diffraction under angled illuminations. Standard diffraction tomography (DT)
+computation can then be used to reconstruct the sample 3D RI distribution at
+sub-diffraction resolutions. We demonstrate this concept by using a
+SI-quantitative-phase imaging system to computationally reconstruct 3D RI
+distributions of human breast (MCF-7) and colorectal (HT-29) adenocarcinoma
+cells. Our experimental setup uses a spatial light modulator to generate
+structured patterns at the sample and collects angle-dependent sample
+diffraction using a common-path, off-axis interference configuration with no
+moving components. Furthermore, this technique holds promise for easy pairing
+with SI fluorescence microscopy, and important future extensions may include
+multimodal, sub-diffraction resolution, 3D RI and fluorescent visualizations.
+",0,1,0,0,0,0
+15488,Foreign English Accent Adjustment by Learning Phonetic Patterns,"  State-of-the-art automatic speech recognition (ASR) systems struggle with the
+lack of data for rare accents. For sufficiently large datasets, neural engines
+tend to outshine statistical models in most natural language processing
+problems. However, a speech accent remains a challenge for both approaches.
+Phonologists manually create general rules describing a speaker's accent, but
+their results remain underutilized. In this paper, we propose a model that
+automatically retrieves phonological generalizations from a small dataset. This
+method leverages the difference in pronunciation between a particular dialect
+and General American English (GAE) and creates new accented samples of words.
+The proposed model is able to learn all generalizations that previously were
+manually obtained by phonologists. We use this statistical method to generate a
+million phonological variations of words from the CMU Pronouncing Dictionary
+and train a sequence-to-sequence RNN to recognize accented words with 59%
+accuracy.
+",1,0,0,1,0,0
+15489,The geometry of hypothesis testing over convex cones: Generalized likelihood tests and minimax radii,"  We consider a compound testing problem within the Gaussian sequence model in
+which the null and alternative are specified by a pair of closed, convex cones.
+Such cone testing problem arise in various applications, including detection of
+treatment effects, trend detection in econometrics, signal detection in radar
+processing, and shape-constrained inference in non-parametric statistics. We
+provide a sharp characterization of the GLRT testing radius up to a universal
+multiplicative constant in terms of the geometric structure of the underlying
+convex cones. When applied to concrete examples, this result reveals some
+interesting phenomena that do not arise in the analogous problems of estimation
+under convex constraints. In particular, in contrast to estimation error, the
+testing error no longer depends purely on the problem complexity via a
+volume-based measure (such as metric entropy or Gaussian complexity), other
+geometric properties of the cones also play an important role. To address the
+issue of optimality, we prove information-theoretic lower bounds for minimax
+testing radius again in terms of geometric quantities. Our general theorems are
+illustrated by examples including the cases of monotone and orthant cones, and
+involve some results of independent interest.
+",1,0,1,1,0,0
+15490,Permutation methods for factor analysis and PCA,"  Researchers often have datasets measuring features $x_{ij}$ of samples, such
+as test scores of students. In factor analysis and PCA, these features are
+thought to be influenced by unobserved factors, such as skills. Can we
+determine how many components affect the data? This is an important problem,
+because it has a large impact on all downstream data analysis. Consequently,
+many approaches have been developed to address it. Parallel Analysis is a
+popular permutation method. It works by randomly scrambling each feature of the
+data. It selects components if their singular values are larger than those of
+the permuted data. Despite widespread use in leading textbooks and scientific
+publications, as well as empirical evidence for its accuracy, it currently has
+no theoretical justification.
+In this paper, we show that the parallel analysis permutation method
+consistently selects the large components in certain high-dimensional factor
+models. However, it does not select the smaller components. The intuition is
+that permutations keep the noise invariant, while ""destroying"" the low-rank
+signal. This provides justification for permutation methods in PCA and factor
+models under some conditions. Our work uncovers drawbacks of permutation
+methods, and paves the way to improvements.
+",0,0,1,1,0,0
+15491,Adapting the CVA model to Leland's framework,"  We consider the framework proposed by Burgard and Kjaer (2011) that derives
+the PDE which governs the price of an option including bilateral counterparty
+risk and funding. We extend this work by relaxing the assumption of absence of
+transaction costs in the hedging portfolio by proposing a cost proportional to
+the amount of assets traded and the traded price. After deriving the nonlinear
+PDE, we prove the existence of a solution for the corresponding
+initial-boundary value problem. Moreover, we develop a numerical scheme that
+allows to find the solution of the PDE by setting different values for each
+parameter of the model. To understand the impact of each variable within the
+model, we analyze the Greeks of the option and the sensitivity of the price to
+changes in all the risk factors.
+",0,0,0,0,0,1
+15492,Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program,"  The 7th Symposium on Educational Advances in Artificial Intelligence
+(EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and
+Future AI Educator Program to support the training of early-career university
+faculty, secondary school faculty, and future educators (PhD candidates or
+postdocs who intend a career in academia). As part of the program, awardees
+were asked to address one of the following ""blue sky"" questions:
+* How could/should Artificial Intelligence (AI) courses incorporate ethics
+into the curriculum?
+* How could we teach AI topics at an early undergraduate or a secondary
+school level?
+* AI has the potential for broad impact to numerous disciplines. How could we
+make AI education more interdisciplinary, specifically to benefit
+non-engineering fields?
+This paper is a collection of their responses, intended to help motivate
+discussion around these issues in AI education.
+",1,0,0,0,0,0
+15493,Network Analysis of Particles and Grains,"  The arrangements of particles and forces in granular materials have a complex
+organization on multiple spatial scales that ranges from local structures to
+mesoscale and system-wide ones. This multiscale organization can affect how a
+material responds or reconfigures when exposed to external perturbations or
+loading. The theoretical study of particle-level, force-chain, domain, and bulk
+properties requires the development and application of appropriate physical,
+mathematical, statistical, and computational frameworks. Traditionally,
+granular materials have been investigated using particulate or continuum
+models, each of which tends to be implicitly agnostic to multiscale
+organization. Recently, tools from network science have emerged as powerful
+approaches for probing and characterizing heterogeneous architectures across
+different scales in complex systems, and a diverse set of methods have yielded
+fascinating insights into granular materials. In this paper, we review work on
+network-based approaches to studying granular matter and explore the potential
+of such frameworks to provide a useful description of these systems and to
+enhance understanding of their underlying physics. We also outline a few open
+questions and highlight particularly promising future directions in the
+analysis and design of granular matter and other kinds of material networks.
+",0,1,1,0,0,0
+15494,Perfect Sequences and Arrays over the Unit Quaternions,"  We introduce several new constructions for perfect periodic autocorrelation
+sequences and arrays over the unit quaternions. This paper uses both
+mathematical proofs and com- puter experiments to prove the (bounded) array
+constructions have perfect periodic auto- correlation. Furthermore, the first
+sequence construction generates odd-perfect sequences of unbounded lengths,
+with good ZCZ.
+",1,0,1,0,0,0
+15495,Schwarzian conditions for linear differential operators with selected differential Galois groups (unabridged version),"  We show that non-linear Schwarzian differential equations emerging from
+covariance symmetry conditions imposed on linear differential operators with
+hypergeometric function solutions, can be generalized to arbitrary order linear
+differential operators with polynomial coefficients having selected
+differential Galois groups. For order three and order four linear differential
+operators we show that this pullback invariance up to conjugation eventually
+reduces to symmetric powers of an underlying order-two operator. We give,
+precisely, the conditions to have modular correspondences solutions for such
+Schwarzian differential equations, which was an open question in a previous
+paper. We analyze in detail a pullbacked hypergeometric example generalizing
+modular forms, that ushers a pullback invariance up to operator homomorphisms.
+We expect this new concept to be well-suited in physics and enumerative
+combinatorics. We finally consider the more general problem of the equivalence
+of two different order-four linear differential
+Calabi-Yau operators up to pullbacks and conjugation, and clarify the cases
+where they have the same Yukawa couplings.
+",0,1,0,0,0,0
+15496,Robust Bayesian Filtering and Smoothing Using Student's t Distribution,"  State estimation in heavy-tailed process and measurement noise is an
+important challenge that must be addressed in, e.g., tracking scenarios with
+agile targets and outlier-corrupted measurements. The performance of the Kalman
+filter (KF) can deteriorate in such applications because of the close relation
+to the Gaussian distribution. Therefore, this paper describes the use of
+Student's t distribution to develop robust, scalable, and simple filtering and
+smoothing algorithms.
+After a discussion of Student's t distribution, exact filtering in linear
+state-space models with t noise is analyzed. Intermediate approximation steps
+are used to arrive at filtering and smoothing algorithms that closely resemble
+the KF and the Rauch-Tung-Striebel (RTS) smoother except for a nonlinear
+measurement-dependent matrix update. The required approximations are discussed
+and an undesirable behavior of moment matching for t densities is revealed. A
+favorable approximation based on minimization of the Kullback-Leibler
+divergence is presented. Because of its relation to the KF, some properties and
+algorithmic extensions are inherited by the t filter. Instructive simulation
+examples demonstrate the performance and robustness of the novel algorithms.
+",1,0,0,1,0,0
+15497,Multi-pass configuration for Improved Squeezed Vacuum Generation in Hot Rb Vapor,"  We study a squeezed vacuum field generated in hot Rb vapor via the
+polarization self-rotation effect. Our previous experiments showed that the
+amount of observed squeezing may be limited by the contamination of the
+squeezed vacuum output with higher-order spatial modes, also generated inside
+the cell. Here, we demonstrate that the squeezing can be improved by making the
+light interact several times with a less dense atomic ensemble. With
+optimization of some parameters we can achieve up to -2.6 dB of squeezing in
+the multi-pass case, which is 0.6 dB improvement compared to the single-pass
+experimental configuration. Our results show that other than the optical depth
+of the medium, the spatial mode structure and cell configuration also affect
+the squeezing level.
+",0,1,0,0,0,0
+15498,Improved Computation of Involutive Bases,"  In this paper, we describe improved algorithms to compute Janet and Pommaret
+bases. To this end, based on the method proposed by Moller et al., we present a
+more efficient variant of Gerdt's algorithm (than the algorithm presented by
+Gerdt-Hashemi-M.Alizadeh) to compute minimal involutive bases. Further, by
+using the involutive version of Hilbert driven technique, along with the new
+variant of Gerdt's algorithm, we modify the algorithm, given by Seiler, to
+compute a linear change of coordinates for a given homogeneous ideal so that
+the new ideal (after performing this change) possesses a finite Pommaret basis.
+All the proposed algorithms have been implemented in Maple and their efficiency
+is discussed via a set of benchmark polynomials.
+",1,0,1,0,0,0
+15499,Dynamic Stochastic Approximation for Multi-stage Stochastic Optimization,"  In this paper, we consider multi-stage stochastic optimization problems with
+convex objectives and conic constraints at each stage. We present a new
+stochastic first-order method, namely the dynamic stochastic approximation
+(DSA) algorithm, for solving these types of stochastic optimization problems.
+We show that DSA can achieve an optimal ${\cal O}(1/\epsilon^4)$ rate of
+convergence in terms of the total number of required scenarios when applied to
+a three-stage stochastic optimization problem. We further show that this rate
+of convergence can be improved to ${\cal O}(1/\epsilon^2)$ when the objective
+function is strongly convex. We also discuss variants of DSA for solving more
+general multi-stage stochastic optimization problems with the number of stages
+$T > 3$. The developed DSA algorithms only need to go through the scenario tree
+once in order to compute an $\epsilon$-solution of the multi-stage stochastic
+optimization problem. To the best of our knowledge, this is the first time that
+stochastic approximation type methods are generalized for multi-stage
+stochastic optimization with $T \ge 3$.
+",1,0,1,1,0,0
+15500,Stability Analysis of Piecewise Affine Systems with Multi-model Model Predictive Control,"  Constrained model predictive control (MPC) is a widely used control strategy,
+which employs moving horizon-based on-line optimisation to compute the optimum
+path of the manipulated variables. Nonlinear MPC can utilize detailed models
+but it is computationally expensive; on the other hand linear MPC may not be
+adequate. Piecewise affine (PWA) models can describe the underlying nonlinear
+dynamics more accurately, therefore they can provide a viable trade-off through
+their use in multi-model linear MPC configurations, which avoid integer
+programming. However, such schemes may introduce uncertainty affecting the
+closed loop stability. In this work, we propose an input to output stability
+analysis for closed loop systems, consisting of PWA models, where an observer
+and multi-model linear MPC are applied together, under unstructured
+uncertainty. Integral quadratic constraints (IQCs) are employed to assess the
+robustness of MPC under uncertainty. We create a model pool, by performing
+linearisation on selected transient points. All the possible uncertainties and
+nonlinearities (including the controller) can be introduced in the framework,
+assuming that they admit the appropriate IQCs, whilst the dissipation
+inequality can provide necessary conditions incorporating IQCs. We demonstrate
+the existence of static multipliers, which can reduce the conservatism of the
+stability analysis significantly. The proposed methodology is demonstrated
+through two engineering case studies.
+",1,0,0,0,0,0
+15501,Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning,"  Traditional recommendation systems rely on past usage data in order to
+generate new recommendations. Those approaches fail to generate sensible
+recommendations for new users and items into the system due to missing
+information about their past interactions. In this paper, we propose a solution
+for successfully addressing item-cold start problem which uses model-based
+approach and recent advances in deep learning. In particular, we use latent
+factor model for recommendation, and predict the latent factors from item's
+descriptions using convolutional neural network when they cannot be obtained
+from usage data. Latent factors obtained by applying matrix factorization to
+the available usage data are used as ground truth to train the convolutional
+neural network. To create latent factor representations for the new items, the
+convolutional neural network uses their textual description. The results from
+the experiments reveal that the proposed approach significantly outperforms
+several baseline estimators.
+",1,0,0,1,0,0
+15502,End-to-End Monaural Multi-speaker ASR System without Pretraining,"  Recently, end-to-end models have become a popular approach as an alternative
+to traditional hybrid models in automatic speech recognition (ASR). The
+multi-speaker speech separation and recognition task is a central task in
+cocktail party problem. In this paper, we present a state-of-the-art monaural
+multi-speaker end-to-end automatic speech recognition model. In contrast to
+previous studies on the monaural multi-speaker speech recognition, this
+end-to-end framework is trained to recognize multiple label sequences
+completely from scratch. The system only requires the speech mixture and
+corresponding label sequences, without needing any indeterminate supervisions
+obtained from non-mixture speech or corresponding labels/alignments. Moreover,
+we exploited using the individual attention module for each separated speaker
+and the scheduled sampling to further improve the performance. Finally, we
+evaluate the proposed model on the 2-speaker mixed speech generated from the
+WSJ corpus and the wsj0-2mix dataset, which is a speech separation and
+recognition benchmark. The experiments demonstrate that the proposed methods
+can improve the performance of the end-to-end model in separating the
+overlapping speech and recognizing the separated streams. From the results, the
+proposed model leads to ~10.0% relative performance gains in terms of CER and
+WER respectively.
+",1,0,0,0,0,0
+15503,Characterization of Calabi--Yau variations of Hodge structure over tube domains by characteristic forms,"  Sheng and Zuo's characteristic forms are invariants of a variation of Hodge
+structure. We show that they characterize Gross's canonical variations of Hodge
+structure of Calabi-Yau type over (Hermitian symmetric) tube domains.
+",0,0,1,0,0,0
+15504,Spectral Theory of Infinite Quantum Graphs,"  We investigate quantum graphs with infinitely many vertices and edges without
+the common restriction on the geometry of the underlying metric graph that
+there is a positive lower bound on the lengths of its edges. Our central result
+is a close connection between spectral properties of a quantum graph and the
+corresponding properties of a certain weighted discrete Laplacian on the
+underlying discrete graph. Using this connection together with spectral theory
+of (unbounded) discrete Laplacians on infinite graphs, we prove a number of new
+results on spectral properties of quantum graphs. Namely, we prove several
+self-adjointness results including a Gaffney type theorem. We investigate the
+problem of lower semiboundedness, prove several spectral estimates (bounds for
+the bottom of spectra and essential spectra of quantum graphs, CLR-type
+estimates) and study spectral types.
+",0,0,1,0,0,0
+15505,"A comment on ""A test of general relativity using the LARES and LAGEOS satellites and a GRACE Earth gravity model"", by I. Ciufolini et al","  Recently, Ciufolini et al. reported on a test of the general relativistic
+gravitomagnetic Lense-Thirring effect by analyzing about 3.5 years of laser
+ranging data to the LAGEOS, LAGEOS II, LARES geodetic satellites orbiting the
+Earth. By using the GRACE-based GGM05S Earth's global gravity model and a
+linear combination of the nodes $\Omega$ of the three satellites designed to
+remove the impact of errors in the first two even zonal harmonic coefficients
+$J_2,~J_4$ of the multipolar expansion of the Newtonian part of the Earth's
+gravitational potential, they claimed an overall accuracy of $5\%$ for the
+Lense-Thirring caused node motion. We show that the scatter in the nominal
+values of the uncancelled even zonals of degree $\ell = 6,~8,~10$ from some of
+the most recent global gravity models does not yet allow to reach unambiguously
+and univocally the expected $\approx 1\%$ level, being large up to $\lesssim
+15\%~(\ell=6),~6\%~(\ell=8),~36\%~(\ell=10)$ for some pairs of models.
+",0,1,0,0,0,0
+15506,SWIFT Detection of a 65-Day X-ray Period from the Ultraluminous Pulsar NGC 7793 P13,"  NGC 7793 P13 is an ultraluminous X-ray source harboring an accreting pulsar.
+We report on the detection of a ~65 d period X-ray modulation with Swift
+observations in this system. The modulation period found in the X-ray band is
+P=65.05+/-0.10 d and the profile is asymmetric with a fast rise and a slower
+decay. On the other hand, the u-band light curve collected by Swift UVOT
+confirmed an optical modulation with a period of P=64.24+/-0.13 d. We explored
+the phase evolution of the X-ray and optical periodicities and propose two
+solutions. A superorbital modulation with a period of ~2,700-4,700 d probably
+caused by the precession of a warped accretion disk is necessary to interpret
+the phase drift of the optical data. We further discuss the implication if this
+~65d periodicity is caused by the superorbital modulation. Estimated from the
+relationship between the spin-orbital and orbital-superorbital periods of known
+disk-fed high-mass X-ray binaries, the orbital period of P13 is roughly
+estimated as 3-7 d. In this case, an unknown mechanism with a much longer time
+scale is needed to interpret the phase drift. Further studies on the stability
+of these two periodicities with a long-term monitoring could help us to probe
+their physical origins.
+",0,1,0,0,0,0
+15507,Pushing Configuration-Interaction to the Limit: Towards Massively Parallel MCSCF Calculations,"  A new large-scale parallel multiconfigurational self-consistent field (MCSCF)
+implementation in the open-source NWChem computational chemistry code is
+presented. The generalized active space (GAS) approach is used to partition
+large configuration interaction (CI) vectors and generate a sufficient number
+of batches that can be distributed to the available nodes. Massively parallel
+CI calculations with large active spaces can be treated. The performance of the
+new parallel MCSCF implementation is presented for the chromium trimer and for
+an active space of 20 electrons in 20 orbitals. Unprecedented CI calculations
+with an active space of 22 electrons in 22 orbitals for the pentacene systems
+were performed and a single CI iteration calculation with an active space of 24
+electrons in 24 orbitals for the chromium tetramer was possible. The chromium
+tetramer corresponds to a CI expansion of one trillion SDs (914 058 513 424)
+and is largest conventional CI calculation attempted up to date.
+",0,1,0,0,0,0
+15508,Outer Regions of the Milky Way,"  With the start of the Gaia era, the time has come to address the major
+challenge of deriving the star formation history and evolution of the disk of
+our MilkyWay. Here we review our present knowledge of the outer regions of the
+Milky Way disk population. Its stellar content, its structure and its dynamical
+and chemical evolution are summarized, focussing on our lack of understanding
+both from an observational and a theoretical viewpoint. We describe the
+unprecedented data that Gaia and the upcoming ground-based spectroscopic
+surveys will provide in the next decade. More in detail, we quantify the expect
+accuracy in position, velocity and astrophysical parameters of some of the key
+tracers of the stellar populations in the outer Galactic disk. Some insights on
+the future capability of these surveys to answer crucial and fundamental issues
+are discussed, such as the mechanisms driving the spiral arms and the warp
+formation. Our Galaxy, theMilkyWay, is our cosmological laboratory for
+understanding the process of formation and evolution of disk galaxies. What we
+learn in the next decades will be naturally transferred to the extragalactic
+domain.
+",0,1,0,0,0,0
+15509,Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement,"  We propose a multi-objective framework to learn both secondary targets not
+directly related to the intended task of speech enhancement (SE) and the
+primary target of the clean log-power spectra (LPS) features to be used
+directly for constructing the enhanced speech signals. In deep neural network
+(DNN) based SE we introduce an auxiliary structure to learn secondary
+continuous features, such as mel-frequency cepstral coefficients (MFCCs), and
+categorical information, such as the ideal binary mask (IBM), and integrate it
+into the original DNN architecture for joint optimization of all the
+parameters. This joint estimation scheme imposes additional constraints not
+available in the direct prediction of LPS, and potentially improves the
+learning of the primary target. Furthermore, the learned secondary information
+as a byproduct can be used for other purposes, e.g., the IBM-based
+post-processing in this work. A series of experiments show that joint LPS and
+MFCC learning improves the SE performance, and IBM-based post-processing
+further enhances listening quality of the reconstructed speech.
+",1,0,0,0,0,0
+15510,Latent variable approach to diarization of audio recordings using ad-hoc randomly placed mobile devices,"  Diarization of audio recordings from ad-hoc mobile devices using spatial
+information is considered in this paper. A two-channel synchronous recording is
+assumed for each mobile device, which is used to compute directional statistics
+separately at each device in a frame-wise manner. The recordings across the
+mobile devices are asynchronous, but a coarse synchronization is performed by
+aligning the signals using acoustic events, or real-time clock. Direction
+statistics computed for all the devices, are then modeled jointly using a
+Dirichlet mixture model, and the posterior probability over the mixture
+components is used to derive the diarization information. Experiments on real
+life recordings using mobile phones show a diarization error rate of less than
+14%.
+",1,0,0,0,0,0
+15511,A multiple attribute model resolves a conflict between additive and multiplicative models of incentive salience,"  A model of incentive salience as a function of stimulus value and
+interoceptive state has been previously proposed. In that model, the function
+differs depending on whether the stimulus is appetitive or aversive; it is
+multiplicative for appetitive stimuli and additive for aversive stimuli. The
+authors argued it was necessary to capture data on how extreme changes in salt
+appetite could move evaluation of an extreme salt solution from negative to
+positive. We demonstrate that arbitrarily varying this function is unnecessary,
+and that a multiplicative function is sufficient if one assumes the incentive
+salience function for an incentive (such as salt) is comprised of multiple
+stimulus features and multiple interoceptive signals. We show that it is also
+unnecessary considering the dual-structure approach-aversive nature of the
+reward system, which results in separate weighting of appetitive and aversive
+stimulus features.
+",0,0,0,0,1,0
+15512,Perturbation theory approaches to Anderson and Many-Body Localization: some lecture notes,"  These are lecture notes based on three lectures given by Antonello
+Scardicchio at the December 2016 Topical School on Many-Body-Localization
+organized by the Statistical Physics Group of the Institute Jean Lamour in
+Nancy. They were compiled and put in a coherent logical form by Thimothée
+Thiery.
+",0,1,0,0,0,0
+15513,A measurement of the z = 0 UV background from H$α$ fluorescence,"  We report the detection of extended Halpha emission from the tip of the HI
+disk of the nearby edge-on galaxy UGC 7321, observed with the Multi Unit
+Spectroscopic Explorer (MUSE) instrument at the Very Large Telescope. The
+Halpha surface brightness fades rapidly where the HI column density drops below
+N(HI) = 10^19 cm^-2 , consistent with fluorescence arising at the ionisation
+front from gas that is photoionized by the extragalactic ultraviolet background
+(UVB). The surface brightness measured at this location is (1.2 +/- 0.5)x10^-19
+erg/s/cm^2/arcsec^2, where the error is mostly systematic and results from the
+proximity of the signal to the edge of the MUSE field of view, and from the
+presence of a sky line next to the redshifted Halpha wavelength. By combining
+the Halpha and the HI 21 cm maps with a radiative transfer calculation of an
+exponential disk illuminated by the UVB, we derive a value for the HI
+photoionization rate of Gamma ~ (6-8)x10^-14 1/s . This value is consistent
+with transmission statistics of the Lyalpha forest and with recent models of a
+UVB which is dominated by quasars.
+",0,1,0,0,0,0
+15514,The landscape of the spiked tensor model,"  We consider the problem of estimating a large rank-one tensor ${\boldsymbol
+u}^{\otimes k}\in({\mathbb R}^{n})^{\otimes k}$, $k\ge 3$ in Gaussian noise.
+Earlier work characterized a critical signal-to-noise ratio $\lambda_{Bayes}=
+O(1)$ above which an ideal estimator achieves strictly positive correlation
+with the unknown vector of interest. Remarkably no polynomial-time algorithm is
+known that achieved this goal unless $\lambda\ge C n^{(k-2)/4}$ and even
+powerful semidefinite programming relaxations appear to fail for $1\ll
+\lambda\ll n^{(k-2)/4}$.
+In order to elucidate this behavior, we consider the maximum likelihood
+estimator, which requires maximizing a degree-$k$ homogeneous polynomial over
+the unit sphere in $n$ dimensions. We compute the expected number of critical
+points and local maxima of this objective function and show that it is
+exponential in the dimensions $n$, and give exact formulas for the exponential
+growth rate. We show that (for $\lambda$ larger than a constant) critical
+points are either very close to the unknown vector ${\boldsymbol u}$, or are
+confined in a band of width $\Theta(\lambda^{-1/(k-1)})$ around the maximum
+circle that is orthogonal to ${\boldsymbol u}$. For local maxima, this band
+shrinks to be of size $\Theta(\lambda^{-1/(k-2)})$. These `uninformative' local
+maxima are likely to cause the failure of optimization algorithms.
+",0,0,1,1,0,0
+15515,System Identification of a Multi-timescale Adaptive Threshold Neuronal Model,"  In this paper, the parameter estimation problem for a multi-timescale
+adaptive threshold (MAT) neuronal model is investigated. By manipulating the
+system dynamics, which comprise of a non-resetting leaky integrator coupled
+with an adaptive threshold, the threshold voltage can be obtained as a
+realizable model that is linear in the unknown parameters. This linearly
+parametrized realizable model is then utilized inside a prediction error based
+framework to identify the threshold parameters with the purpose of predicting
+single neuron precise firing times. The iterative linear least squares
+estimation scheme is evaluated using both synthetic data obtained from an exact
+model as well as experimental data obtained from in vitro rat somatosensory
+cortical neurons. Results show the ability of this approach to fit the MAT
+model to different types of fluctuating reference data. The performance of the
+proposed approach is seen to be superior when comparing with existing
+identification approaches used by the neuronal community.
+",0,0,0,0,1,0
+15516,Free deterministic equivalent Z-scores of compound Wishart models: A goodness of fit test of 2DARMA models,"  We introduce a new method to qualify the goodness of fit parameter estimation
+of compound Wishart models. Our method based on the free deterministic
+equivalent Z-score, which we introduce in this paper. Furthermore, an
+application to two dimensional autoregressive moving-average model is provided.
+Our proposal method is a generalization of statistical hypothesis testing to
+one dimensional moving average model based on fluctuations of real compound
+Wishart matrices, which is a recent result by Hasegawa, Sakuma and Yoshida.
+",0,0,1,1,0,0
+15517,Numerical algorithms for mean exit time and escape probability of stochastic systems with asymmetric Lévy motion,"  For non-Gaussian stochastic dynamical systems, mean exit time and escape
+probability are important deterministic quantities, which can be obtained from
+integro-differential (nonlocal) equations. We develop an efficient and
+convergent numerical method for the mean first exit time and escape probability
+for stochastic systems with an asymmetric Lévy motion, and analyze the
+properties of the solutions of the nonlocal equations. We also investigate the
+effects of different system factors on the mean exit time and escape
+probability, including the skewness parameter, the size of the domain, the
+drift term and the intensity of Gaussian and non-Gaussian noises. We find that
+the behavior of the mean exit time and the escape probability has dramatic
+difference at the boundary of the domain when the index of stability crosses
+the critical value of one.
+",0,0,1,0,0,0
+15518,Canonical Models and the Complexity of Modal Team Logic,"  We study modal team logic MTL, the team-semantical extension of modal logic
+ML closed under Boolean negation. Its fragments, such as modal dependence,
+independence, and inclusion logic, are well-understood. However, due to the
+unrestricted Boolean negation, the satisfiability problem of full MTL has been
+notoriously resistant to a complexity theoretical classification. In our
+approach, we introduce the notion of canonical models into the team-semantical
+setting. By construction of such a model, we reduce the satisfiability problem
+of MTL to simple model checking. Afterwards, we show that this approach is
+optimal in the sense that MTL-formulas can efficiently enforce canonicity.
+Furthermore, to capture these results in terms of complexity, we introduce a
+non-elementary complexity class, TOWER(poly), and prove that it contains
+satisfiability and validity of MTL as complete problems. We also prove that the
+fragments of MTL with bounded modal depth are complete for the levels of the
+elementary hierarchy (with polynomially many alternations). The respective
+hardness results hold for both strict or lax semantics of the modal operators
+and the splitting disjunction, and also over the class of reflexive and
+transitive frames.
+",1,0,0,0,0,0
+15519,CoT: Cooperative Training for Generative Modeling of Discrete Data,"  We propose Cooperative Training (CoT) for training generative models that
+measure a tractable density for discrete data. CoT coordinately trains a
+generator $G$ and an auxiliary predictive mediator $M$. The training target of
+$M$ is to estimate a mixture density of the learned distribution $G$ and the
+target distribution $P$, and that of $G$ is to minimize the Jensen-Shannon
+divergence estimated through $M$. CoT achieves independent success without the
+necessity of pre-training via Maximum Likelihood Estimation or involving
+high-variance algorithms like REINFORCE. This low-variance algorithm is
+theoretically proved to be unbiased for both generative and predictive tasks.
+We also theoretically and empirically show the superiority of CoT over most
+previous algorithms in terms of generative quality and diversity, predictive
+generalization ability and computational cost.
+",0,0,0,1,0,0
+15520,Iterated function systems consisting of phi-max-contractions have attractor,"  We associate to each iterated function system consisting of
+phi-max-contractions an operator (on the space of continuous functions from the
+shift space on the metric space corresponding to the system) having a unique
+fixed point whose image turns out to be the attractor of the system. Moreover,
+we prove that the unique fixed point of the operator associated to an iterated
+function system consisting of convex contractions is the canonical projection
+from the shift space on the attractor of the system.
+",0,0,1,0,0,0
+15521,Decay Rates of the Solutions to the Thermoelastic Bresse System of Types I and III,"  In this paper, we study the energy decay for the thermoelastic Bresse system
+in the whole line with two different dissipative mechanism, given by heat
+conduction (Types I and III). We prove that the decay rate of the solutions are
+very slow. More precisely, we show that the solutions decay with the rate of
+$(1+t)^{-\frac{1}{8}}$ in the $L^2$-norm, whenever the initial data belongs to
+$L^1(R) \cap H^{s}(R)$ for a suitable $s$. The wave speeds of propagation have
+influence on the decay rate with respect to the regularity of the initial data.
+This phenomenon is known as \textit{regularity-loss}. The main tool used to
+prove our results is the energy method in the Fourier space.
+",0,0,1,0,0,0
+15522,Baryogenesis at a Lepton-Number-Breaking Phase Transition,"  We study a scenario in which the baryon asymmetry of the universe arises from
+a cosmological phase transition where lepton-number is spontaneously broken. If
+the phase transition is first order, a lepton-number asymmetry can arise at the
+bubble wall, through dynamics similar to electroweak baryogenesis, but
+involving right-handed neutrinos. In addition to the usual neutrinoless double
+beta decay in nuclear experiments, the model may be probed through a variety of
+""baryogenesis by-products,"" which include a stochastic background of
+gravitational waves created by the colliding bubbles. Depending on the model,
+other aspects may include a network of topological defects that produce their
+own gravitational waves, additional contribution to dark radiation, and a light
+pseudo-Goldstone boson (majoron) as dark matter candidate.
+",0,1,0,0,0,0
+15523,Contact resistance between two REBCO tapes under load and load-cycles,"  No-insulation (NI) REBCO magnets have many advantages. They are
+self-protecting, therefore do not need quench detection and protection which
+can be very challenging in a high Tc superconducting magnet. Moreover, by
+removing insulation and allowing thinner copper stabilizer, NI REBCO magnets
+have significantly higher engineering current density and higher mechanical
+strength. On the other hand, NI REBCO magnets have drawbacks of long magnet
+charging time and high field-ramp-loss. In principle, these drawbacks can be
+mitigated by managing the turn-to-turn contact resistivity (Rc). Evidently the
+first step toward managing Rc is to establish a reliable method of accurate Rc
+measurement. In this paper, we present experimental Rc measurements of REBCO
+tapes as a function of mechanical load up to 144 MPa and load cycles up to 14
+times. We found that Rc is in the range of 26-100 uOhm-cm2; it decreases with
+increasing pressure, and gradually increases with number of load cycles. The
+results are discussed in the framework of Holm's electric contact theory.
+",0,1,0,0,0,0
+15524,Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for $k$-means Clustering,"  In this paper, we propose an implicit gradient descent algorithm for the
+classic $k$-means problem. The implicit gradient step or backward Euler is
+solved via stochastic fixed-point iteration, in which we randomly sample a
+mini-batch gradient in every iteration. It is the average of the fixed-point
+trajectory that is carried over to the next gradient step. We draw connections
+between the proposed stochastic backward Euler and the recent entropy
+stochastic gradient descent (Entropy-SGD) for improving the training of deep
+neural networks. Numerical experiments on various synthetic and real datasets
+show that the proposed algorithm provides better clustering results compared to
+$k$-means algorithms in the sense that it decreased the objective function (the
+cluster) and is much more robust to initialization.
+",1,0,0,1,0,0
+15525,The Frobenius number for sequences of triangular and tetrahedral numbers,"  We compute the Frobenius number for sequences of triangular and tetrahedral
+numbers. In addition, we study some properties of the numerical semigroups
+associated to those sequences.
+",0,0,1,0,0,0
+15526,Information-entropic analysis of Korteweg--de Vries solitons in the quark-gluon plasma,"  Solitary waves propagation of baryonic density perturbations, ruled by the
+Korteweg--de Vries equation in a mean-field quark-gluon plasma model, are
+investigated from the point of view of the theory of information. A recently
+proposed continuous logarithmic measure of information, called configurational
+entropy, is used to derive the soliton width, defining the pulse, for which the
+informational content of the soliton spatial profile is more compressed, in the
+Shannon's sense.
+",0,1,0,0,0,0
+15527,The photon identification loophole in EPRB experiments: computer models with single-wing selection,"  Recent Einstein-Podolsky-Rosen-Bohm experiments [M. Giustina et al. Phys.
+Rev. Lett. 115, 250401 (2015); L. K. Shalm et al. Phys. Rev. Lett. 115, 250402
+(2015)] that claim to be loophole free are scrutinized and are shown to suffer
+a photon identification loophole. The combination of a digital computer and
+discrete-event simulation is used to construct a minimal but faithful model of
+the most perfected realization of these laboratory experiments. In contrast to
+prior simulations, all photon selections are strictly made, as they are in the
+actual experiments, at the local station and no other ""post-selection"" is
+involved. The simulation results demonstrate that a manifestly non-quantum
+model that identifies photons in the same local manner as in these experiments
+can produce correlations that are in excellent agreement with those of the
+quantum theoretical description of the corresponding thought experiment, in
+conflict with Bell's theorem. The failure of Bell's theorem is possible because
+of our recognition of the photon identification loophole. Such identification
+measurement-procedures are necessarily included in all actual experiments but
+are not included in the theory of Bell and his followers.
+",0,1,0,0,0,0
+15528,Tilings of the plane with unit area triangles of bounded diameter,"  There exist tilings of the plane with pairwise noncongruent triangles of
+equal area and bounded perimeter. Analogously, there exist tilings with
+triangles of equal perimeter, the areas of which are bounded from below by a
+positive constant. This solves a problem of Nandakumar.
+",1,0,1,0,0,0
+15529,The cauchy problem for radially symmetric homogeneous boltzmann equation with shubin class initial datum and gelfand-shilov smoothing effect,"  In this paper, we study the Cauchy problem for radially symmetric homogeneous
+non-cutoff Boltzmann equation with Maxwellian molecules, the initial datum
+belongs to Shubin space of the negative index which can be characterized by
+spectral decomposition of the harmonic oscillators. The Shubin space of the
+negative index contains the measure functions. Based on this spectral
+decomposition, we construct the weak solution with Shubin class initial datum,
+we also prove that the Cauchy problem enjoys Gelfand-Shilov smoothing effect,
+meaning that the smoothing properties are the same as the Cauchy problem
+defined by the evolution equation associated to a fractional harmonic
+oscillator.
+",0,0,1,0,0,0
+15530,Network Dissection: Quantifying Interpretability of Deep Visual Representations,"  We propose a general framework called Network Dissection for quantifying the
+interpretability of latent representations of CNNs by evaluating the alignment
+between individual hidden units and a set of semantic concepts. Given any CNN
+model, the proposed method draws on a broad data set of visual concepts to
+score the semantics of hidden units at each intermediate convolutional layer.
+The units with semantics are given labels across a range of objects, parts,
+scenes, textures, materials, and colors. We use the proposed method to test the
+hypothesis that interpretability of units is equivalent to random linear
+combinations of units, then we apply our method to compare the latent
+representations of various networks when trained to solve different supervised
+and self-supervised training tasks. We further analyze the effect of training
+iterations, compare networks trained with different initializations, examine
+the impact of network depth and width, and measure the effect of dropout and
+batch normalization on the interpretability of deep visual representations. We
+demonstrate that the proposed method can shed light on characteristics of CNN
+models and training methods that go beyond measurements of their discriminative
+power.
+",1,0,0,0,0,0
+15531,Systole inequalities for arithmetic locally symmetric spaces,"  In this paper we study the systole growth of arithmetic locally symmetric
+spaces up congruence covers and show that this growth is at least logarithmic
+in volume. This generalizes previous work of Buser and Sarnak as well as Katz,
+Schaps and Vishne where the case of compact hyperbolic 2- and 3-manifolds was
+considered.
+",0,0,1,0,0,0
+15532,Featured Weighted Automata,"  A featured transition system is a transition system in which the transitions
+are annotated with feature expressions: Boolean expressions on a finite number
+of given features. Depending on its feature expression, each individual
+transition can be enabled when some features are present, and disabled for
+other sets of features. The behavior of a featured transition system hence
+depends on a given set of features. There are algorithms for featured
+transition systems which can check their properties for all sets of features at
+once, for example for LTL or CTL properties.
+Here we introduce a model of featured weighted automata which combines
+featured transition systems and (semiring-) weighted automata. We show that
+methods and techniques from weighted automata extend to featured weighted
+automata and devise algorithms to compute quantitative properties of featured
+weighted automata for all sets of features at once. We show applications to
+minimum reachability and to energy properties.
+",1,0,0,0,0,0
+15533,Schrödinger model and Stratonovich-Weyl correspondence for Heisenberg motion groups,"  We introduce a Schrödinger model for the unitary irreducible
+representations of a Heisenberg motion group and we show that the usual Weyl
+quantization then provides a Stratonovich-Weyl correspondence.
+",0,0,1,0,0,0
+15534,Estimation in emerging epidemics: biases and remedies,"  When analysing new emerging infectious disease outbreaks one typically has
+observational data over a limited period of time and several parameters to
+estimate, such as growth rate, R0, serial or generation interval distribution,
+latent and incubation times or case fatality rates. Also parameters describing
+the temporal relations between appearance of symptoms, notification, death and
+recovery/discharge will be of interest. These parameters form the basis for
+predicting the future outbreak, planning preventive measures and monitoring the
+progress of the disease. We study the problem of making inference during the
+emerging phase of an outbreak and point out potential sources of bias related
+to contact tracing, replacing generation times by serial intervals, multiple
+potential infectors or truncation effects amplified by exponential growth.
+These biases directly affect the estimation of e.g. the generation time
+distribution and the case fatality rate, but can then propagate to other
+estimates, e.g. of R0 and growth rate. Many of the traditionally used
+estimation methods in disease epidemiology may suffer from these biases when
+applied to the emerging disease outbreak situation. We show how to avoid these
+biases based on proper statistical modelling. We illustrate the theory by
+numerical examples and simulations based on the recent 2014-15 Ebola outbreak
+to quantify possible estimation biases, which may be up to 20% underestimation
+of R0, if the epidemic growth rate is fitted to observed data or, conversely,
+up to 62% overestimation of the growth rate if the correct R0 is used in
+conjunction with the Euler-Lotka equation.
+",0,0,0,1,0,0
+15535,Long-Term Evolution of Genetic Programming Populations,"  We evolve binary mux-6 trees for up to 100000 generations evolving some
+programs with more than a hundred million nodes. Our unbounded Long-Term
+Evolution Experiment LTEE GP appears not to evolve building blocks but does
+suggests a limit to bloat. We do see periods of tens even hundreds of
+generations where the population is 100 percent functionally converged. The
+distribution of tree sizes is not as predicted by theory.
+",1,0,0,0,0,0
+15536,"A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood","  OpenStreetMap offers a valuable source of worldwide geospatial data useful to
+urban researchers. This study uses the OSMnx software to automatically download
+and analyze 27,000 US street networks from OpenStreetMap at metropolitan,
+municipal, and neighborhood scales - namely, every US city and town, census
+urbanized area, and Zillow-defined neighborhood. It presents empirical findings
+on US urban form and street network characteristics, emphasizing measures
+relevant to graph theory, transportation, urban design, and morphology such as
+structure, connectedness, density, centrality, and resilience. In the past,
+street network data acquisition and processing have been challenging and ad
+hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently
+conduct street network analysis with extremely large sample sizes, with clearly
+defined network definitions and extents for reproducibility, and using
+nonplanar, directed graphs. These street networks and measures data have been
+shared in a public repository for other researchers to use.
+",1,1,0,0,0,0
+15537,Towards Detection of Exoplanetary Rings Via Transit Photometry: Methodology and a Possible Candidate,"  Detection of a planetary ring of exoplanets remains as one of the most
+attractive but challenging goals in the field. We present a methodology of a
+systematic search for exoplanetary rings via transit photometry of long-period
+planets. The methodology relies on a precise integration scheme we develop to
+compute a transit light curve of a ringed planet. We apply the methodology to
+89 long-period planet candidates from the Kepler data so as to estimate, and/or
+set upper limits on, the parameters of possible rings. While a majority of our
+samples do not have a sufficiently good signal-to-noise ratio for meaningful
+constraints on ring parameters, we find that six systems with a higher
+signal-to-noise ratio are inconsistent with the presence of a ring larger than
+1.5 times the planetary radius assuming a grazing orbit and a tilted ring.
+Furthermore, we identify five preliminary candidate systems whose light curves
+exhibit ring-like features. After removing four false positives due to the
+contamination from nearby stars, we identify KIC 10403228 as a reasonable
+candidate for a ringed planet. A systematic parameter fit of its light curve
+with a ringed planet model indicates two possible solutions corresponding to a
+Saturn-like planet with a tilted ring. There also remain other two possible
+scenarios accounting for the data; a circumstellar disk and a hierarchical
+triple. Due to large uncertain factors, we cannot choose one specific model
+among the three.
+",0,1,0,0,0,0
+15538,SiMon: Simulation Monitor for Computational Astrophysics,"  Scientific discovery via numerical simulations is important in modern
+astrophysics. This relatively new branch of astrophysics has become possible
+due to the development of reliable numerical algorithms and the high
+performance of modern computing technologies. These enable the analysis of
+large collections of observational data and the acquisition of new data via
+simulations at unprecedented accuracy and resolution. Ideally, simulations run
+until they reach some pre-determined termination condition, but often other
+factors cause extensive numerical approaches to break down at an earlier stage.
+In those cases, processes tend to be interrupted due to unexpected events in
+the software or the hardware. In those cases, the scientist handles the
+interrupt manually, which is time-consuming and prone to errors. We present the
+Simulation Monitor (SiMon) to automatize the farming of large and extensive
+simulation processes. Our method is light-weight, it fully automates the entire
+workflow management, operates concurrently across multiple platforms and can be
+installed in user space. Inspired by the process of crop farming, we perceive
+each simulation as a crop in the field and running simulation becomes analogous
+to growing crops. With the development of SiMon we relax the technical aspects
+of simulation management. The initial package was developed for extensive
+parameter searchers in numerical simulations, but it turns out to work equally
+well for automating the computational processing and reduction of observational
+data reduction.
+",0,1,0,0,0,0
+15539,Asymptotic and bootstrap tests for the dimension of the non-Gaussian subspace,"  Dimension reduction is often a preliminary step in the analysis of large data
+sets. The so-called non-Gaussian component analysis searches for a projection
+onto the non-Gaussian part of the data, and it is then important to know the
+correct dimension of the non-Gaussian signal subspace. In this paper we develop
+asymptotic as well as bootstrap tests for the dimension based on the popular
+fourth order blind identification (FOBI) method.
+",0,0,1,1,0,0
+15540,Pseudoconcavity of flag domains: The method of supporting cycles,"  A flag domain of a real from $G_0$ of a complex semismiple Lie group $G$ is
+an open $G_0$-orbit $D$ in a (compact) $G$-flag manifold. In the usual way one
+reduces to the case where $G_0$ is simple. It is known that if $D$ possesses
+non-constant holomorphic functions, then it is the product of a compact flag
+manifold and a Hermitian symmetric bounded domain. This pseudoconvex case is
+rare in the geography of flag domains. Here it is shown that otherwise, i.e.,
+when $\mathcal{O}(D)\cong\mathbb{C}$, the flag domain $D$ is pseudoconcave. In
+a rather general setting the degree of the pseudoconcavity is estimated in
+terms of root invariants. This estimate is explicitly computed for domains in
+certain Grassmannians.
+",0,0,1,0,0,0
+15541,Rule Formats for Nominal Process Calculi,"  The nominal transition systems (NTSs) of Parrow et al. describe the
+operational semantics of nominal process calculi. We study NTSs in terms of the
+nominal residual transition systems (NRTSs) that we introduce. We provide rule
+formats for the specifications of NRTSs that ensure that the associated NRTS is
+an NTS and apply them to the operational specifications of the early and late
+pi-calculus. We also explore alternative specifications of the NTSs in which we
+allow residuals of abstraction sort, and introduce translations between the
+systems with and without residuals of abstraction sort. Our study stems from
+the Nominal SOS of Cimini et al. and from earlier works in nominal sets and
+nominal logic by Gabbay, Pitts and their collaborators.
+",1,0,0,0,0,0
+15542,Tilings with noncongruent triangles,"  We solve a problem of R. Nandakumar by proving that there is no tiling of the
+plane with pairwise noncongruent triangles of equal area and equal perimeter.
+We also show that no convex polygon with more than three sides can be tiled
+with finitely many triangles such that no pair of them share a full side.
+",1,0,1,0,0,0
+15543,Phase limitations of Zames-Falb multipliers,"  Phase limitations of both continuous-time and discrete-time Zames-Falb
+multipliers and their relation with the Kalman conjecture are analysed. A phase
+limitation for continuous-time multipliers given by Megretski is generalised
+and its applicability is clarified; its relation to the Kalman conjecture is
+illustrated with a classical example from the literature. It is demonstrated
+that there exist fourth-order plants where the existence of a suitable
+Zames-Falb multiplier can be discarded and for which simulations show unstable
+behavior. A novel phase-limitation for discrete-time Zames-Falb multipliers is
+developed. Its application is demonstrated with a second-order counterexample
+to the Kalman conjecture. Finally, the discrete-time limitation is used to show
+that there can be no direct counterpart of the off-axis circle criterion in the
+discrete-time domain.
+",1,0,1,0,0,0
+15544,Fragmentation of vertically stratified gaseous layers: monolithic or coalescence-driven collapse,"  We investigate, using 3D hydrodynamic simulations, the fragmentation of
+pressure-confined, vertically stratified, self-gravitating gaseous layers. The
+confining pressure is either thermal pressure acting on both surfaces, or
+thermal pressure acting on one surface and ram-pressure on the other. In the
+linear regime of fragmentation, the dispersion relation we obtain agrees well
+with that derived by Elmegreen & Elmegreen (1978), and consequently deviates
+from the dispersion relations based on the thin shell approximation (Vishniac
+1983) or pressure assisted gravitational instability (Wünsch et al. 2010). In
+the non-linear regime, the relative importance of the confining pressure to the
+self-gravity is a crucial parameter controlling the qualitative course of
+fragmentation. When confinement of the layer is dominated by external pressure,
+self- gravitating condensations are delivered by a two-stage process: first the
+layer fragments into gravitationally bound but stable clumps, and then these
+clumps coalesce until they assemble enough mass to collapse. In contrast, when
+external pressure makes a small contribution to confinement of the layer, the
+layer fragments monolithically into gravitationally unstable clumps and there
+is no coalescence. This dichotomy persists whether the external pressure is
+thermal or ram. We apply these results to fragments forming in a shell swept up
+by an expanding H II region, and find that, unless the swept up gas is quite
+hot or the surrounding medium has low density, the fragments have low-mass ( ~<
+3 M_Sun ), and therefore they are unlikely to spawn stars that are sufficiently
+massive to promote sequential self-propagating star formation.
+",0,1,0,0,0,0
+15545,Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs,"  We propose a simple and generic layer formulation that extends the properties
+of convolutional layers to any domain that can be described by a graph. Namely,
+we use the support of its adjacency matrix to design learnable weight sharing
+filters able to exploit the underlying structure of signals in the same fashion
+as for images. The proposed formulation makes it possible to learn the weights
+of the filter as well as a scheme that controls how they are shared across the
+graph. We perform validation experiments with image datasets and show that
+these filters offer performances comparable with convolutional ones.
+",1,0,0,0,0,0
+15546,Galaxies with Shells in the Illustris Simulation: Metallicity Signatures,"  Stellar shells are low surface brightness arcs of overdense stellar regions,
+extending to large galactocentric distances. In a companion study, we
+identified 39 shell galaxies in a sample of 220 massive ellipticals
+($\mathrm{M}_{\mathrm{200crit}}>6\times10^{12}\,\mathrm{M}_\odot$) from the
+Illustris cosmological simulation. We used stellar history catalogs to trace
+the history of each individual star particle inside the shell substructures,
+and we found that shells in high-mass galaxies form through mergers with
+massive satellites (stellar mass ratios $\mu_{\mathrm{stars}}\gtrsim1:10$).
+Using the same sample of shell galaxies, the current study extends the stellar
+history catalogs in order to investigate the metallicity of stellar shells
+around massive galaxies. Our results indicate that outer shells are often times
+more metal-rich than the surrounding stellar material in a galaxy's halo. For a
+galaxy with two different satellites forming $z=0$ shells, we find a
+significant difference in the metallicity of the shells produced by each
+progenitor. We also find that shell galaxies have higher mass-weighted
+logarithmic metallicities ([Z/H]) at $2$-$4\,\mathrm{R}_{\mathrm{eff}}$
+compared to galaxies without shells. Our results indicate that observations
+comparing the metallicities of stars in tidal features, such as shells, to the
+average metallicities in the stellar halo can provide information about the
+assembly histories of galaxies.
+",0,1,0,0,0,0
+15547,Runtime Verification of Temporal Properties over Out-of-order Data Streams,"  We present a monitoring approach for verifying systems at runtime. Our
+approach targets systems whose components communicate with the monitors over
+unreliable channels, where messages can be delayed or lost. In contrast to
+prior works, whose property specification languages are limited to
+propositional temporal logics, our approach handles an extension of the
+real-time logic MTL with freeze quantifiers for reasoning about data values. We
+present its underlying theory based on a new three-valued semantics that is
+well suited to soundly and completely reason online about event streams in the
+presence of message delay or loss. We also evaluate our approach
+experimentally. Our prototype implementation processes hundreds of events per
+second in settings where messages are received out of order.
+",1,0,0,0,0,0
+15548,Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning,"  In recent years, deep learning algorithms have become increasingly more
+prominent for their unparalleled ability to automatically learn discriminant
+features from large amounts of data. However, within the field of
+electromyography-based gesture recognition, deep learning algorithms are seldom
+employed as they require an unreasonable amount of effort from a single person,
+to generate tens of thousands of examples.
+This work's hypothesis is that general, informative features can be learned
+from the large amounts of data generated by aggregating the signals of multiple
+users, thus reducing the recording burden while enhancing gesture recognition.
+Consequently, this paper proposes applying transfer learning on aggregated data
+from multiple users, while leveraging the capacity of deep learning algorithms
+to learn discriminant features from large datasets. Two datasets comprised of
+19 and 17 able-bodied participants respectively (the first one is employed for
+pre-training) were recorded for this work, using the Myo Armband. A third Myo
+Armband dataset was taken from the NinaPro database and is comprised of 10
+able-bodied participants. Three different deep learning networks employing
+three different modalities as input (raw EMG, Spectrograms and Continuous
+Wavelet Transform (CWT)) are tested on the second and third dataset. The
+proposed transfer learning scheme is shown to systematically and significantly
+enhance the performance for all three networks on the two datasets, achieving
+an offline accuracy of 98.31% for 7 gestures over 17 participants for the
+CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw
+EMG-based ConvNet. Finally, a use-case study employing eight able-bodied
+participants suggests that real-time feedback allows users to adapt their
+muscle activation strategy which reduces the degradation in accuracy normally
+experienced over time.
+",0,0,0,1,0,0
+15549,Adaptive Non-uniform Compressive Sampling for Time-varying Signals,"  In this paper, adaptive non-uniform compressive sampling (ANCS) of
+time-varying signals, which are sparse in a proper basis, is introduced. ANCS
+employs the measurements of previous time steps to distribute the sensing
+energy among coefficients more intelligently. To this aim, a Bayesian inference
+method is proposed that does not require any prior knowledge of importance
+levels of coefficients or sparsity of the signal. Our numerical simulations
+show that ANCS is able to achieve the desired non-uniform recovery of the
+signal. Moreover, if the signal is sparse in canonical basis, ANCS can reduce
+the number of required measurements significantly.
+",1,0,0,1,0,0
+15550,Enabling Reasoning with LegalRuleML,"  In order to automate verification process, regulatory rules written in
+natural language need to be translated into a format that machines can
+understand. However, none of the existing formalisms can fully represent the
+elements that appear in legal norms. For instance, most of these formalisms do
+not provide features to capture the behavior of deontic effects, which is an
+important aspect in automated compliance checking. This paper presents an
+approach for transforming legal norms represented using LegalRuleML to a
+variant of Modal Defeasible Logic (and vice versa) such that a legal statement
+represented using LegalRuleML can be transformed into a machine-readable format
+that can be understood and reasoned about depending upon the client's
+preferences.
+",1,0,0,0,0,0
+15551,Combinatorial distance geometry in normed spaces,"  We survey problems and results from combinatorial geometry in normed spaces,
+concentrating on problems that involve distances. These include various
+properties of unit-distance graphs, minimum-distance graphs, diameter graphs,
+as well as minimum spanning trees and Steiner minimum trees. In particular, we
+discuss translative kissing (or Hadwiger) numbers, equilateral sets, and the
+Borsuk problem in normed spaces. We show how to use the angular measure of
+Peter Brass to prove various statements about Hadwiger and blocking numbers of
+convex bodies in the plane, including some new results. We also include some
+new results on thin cones and their application to distinct distances and other
+combinatorial problems for normed spaces.
+",0,0,1,0,0,0
+15552,When is Network Lasso Accurate: The Vector Case,"  A recently proposed learning algorithm for massive network-structured data
+sets (big data over networks) is the network Lasso (nLasso), which extends the
+well- known Lasso estimator from sparse models to network-structured datasets.
+Efficient implementations of the nLasso have been presented using modern convex
+optimization methods. In this paper, we provide sufficient conditions on the
+network structure and available label information such that nLasso accurately
+learns a vector-valued graph signal (representing label information) from the
+information provided by the labels of a few data points.
+",1,0,0,0,0,0
+15553,Surrogate Aided Unsupervised Recovery of Sparse Signals in Single Index Models for Binary Outcomes,"  We consider the recovery of regression coefficients, denoted by
+$\boldsymbol{\beta}_0$, for a single index model (SIM) relating a binary
+outcome $Y$ to a set of possibly high dimensional covariates $\boldsymbol{X}$,
+based on a large but 'unlabeled' dataset $\mathcal{U}$, with $Y$ never
+observed. On $\mathcal{U}$, we fully observe $\boldsymbol{X}$ and additionally,
+a surrogate $S$ which, while not being strongly predictive of $Y$ throughout
+the entirety of its support, can forecast it with high accuracy when it assumes
+extreme values. Such datasets arise naturally in modern studies involving large
+databases such as electronic medical records (EMR) where $Y$, unlike
+$(\boldsymbol{X}, S)$, is difficult and/or expensive to obtain. In EMR studies,
+an example of $Y$ and $S$ would be the true disease phenotype and the count of
+the associated diagnostic codes respectively. Assuming another SIM for $S$
+given $\boldsymbol{X}$, we show that under sparsity assumptions, we can recover
+$\boldsymbol{\beta}_0$ proportionally by simply fitting a least squares LASSO
+estimator to the subset of the observed data on $(\boldsymbol{X}, S)$
+restricted to the extreme sets of $S$, with $Y$ imputed using the surrogacy of
+$S$. We obtain sharp finite sample performance bounds for our estimator,
+including deterministic deviation bounds and probabilistic guarantees. We
+demonstrate the effectiveness of our approach through multiple simulation
+studies, as well as by application to real data from an EMR study conducted at
+the Partners HealthCare Systems.
+",0,0,1,1,0,0
+15554,Superior lattice thermal conductance of single layer borophene,"  By way of the nonequilibrium Green's function simulations and first
+principles calculations, we report that borophene, a single layer of boron
+atoms that was fabricated recently, possesses an extraordinarily high lattice
+thermal conductance in the ballistic transport regime, which even exceeds
+graphene. In addition to the obvious reasons of light mass and strong bonding
+of boron atoms, the superior thermal conductance is mainly rooted in its strong
+structural anisotropy and unusual phonon transmission. For low-frequency
+phonons, the phonon transmission within borophene is nearly isotropic, similar
+to that of graphene. For high frequency phonons, however, the transmission is
+one dimensional, that is, all the phonons travel in one direction, giving rise
+to its ultrahigh thermal conductance. The present study suggests that borophene
+is promising for applications in efficient heat dissipation and thermal
+management, and also an ideal material for revealing fundamentals of
+dimensionality effect on phonon transport in ballistic regime.
+",0,1,0,0,0,0
+15555,The liar paradox is a real problem,"  The liar paradox is widely seen as not a serious problem. I try to explain
+why this view is mistaken.
+",0,0,1,0,0,0
+15556,High-dimensional ABC,"  This Chapter, ""High-dimensional ABC"", is to appear in the forthcoming
+Handbook of Approximate Bayesian Computation (2018). It details the main ideas
+and concepts behind extending ABC methods to higher dimensions, with supporting
+examples and illustrations.
+",0,0,0,1,0,0
+15557,Automating Release of Deep Link APIs for Android Applications,"  Unlike the Web where each web page has a global URL to reach, a specific
+""content page"" inside a mobile app cannot be opened unless the user explores
+the app with several operations from the landing page. Recently, deep links
+have been advocated by major companies to enable targeting and opening a
+specific page of an app externally with an accessible uniform resource
+identifier (URI). To empirically investigate the state of the practice on
+adopting deep links, in this article, we present the largest empirical study of
+deep links over 20,000 Android apps, and find that deep links do not get wide
+adoption among current Android apps, and non-trivial manual efforts are
+required for app developers to support deep links. To address such an issue, we
+propose the Aladdin approach and supporting tool to release deep links to
+access arbitrary location of existing apps. Aladdin instantiates our novel
+cooperative framework to synergically combine static analysis and dynamic
+analysis while minimally engaging developers to provide inputs to the framework
+for automation, without requiring any coding efforts or additional deployment
+efforts. We evaluate Aladdin with popular apps and demonstrate its
+effectiveness and performance.
+",1,0,0,0,0,0
+15558,"Refractive index measurements of single, spherical cells using digital holographic microscopy","  In this chapter, we introduce digital holographic microscopy (DHM) as a
+marker-free method to determine the refractive index of single, spherical cells
+in suspension. The refractive index is a conclusive measure in a biological
+context. Cell conditions, such as differentiation or infection, are known to
+yield significant changes in the refractive index. Furthermore, the refractive
+index of biological tissue determines the way it interacts with light. Besides
+the biological relevance of this interaction in the retina, a lot of methods
+used in biology, including microscopy, rely on light-tissue or light-cell
+interactions. Hence, determining the refractive index of cells using DHM is
+valuable in many biological applications. This chapter covers the main topics
+which are important for the implementation of DHM: setup, sample preparation
+and analysis. First, the optical setup is described in detail including notes
+and suggestions for the implementation. Following that, a protocol for the
+sample and measurement preparation is explained. In the analysis section, an
+algorithm for the determination of the quantitative phase map is described.
+Subsequently, all intermediate steps for the calculation of the refractive
+index of suspended cells are presented, exploiting their spherical shape. In
+the last section, a discussion of possible extensions to the setup, further
+measurement configurations and additional analysis methods are given.
+Throughout this chapter, we describe a simple, robust, and thus easily
+reproducible implementation of DHM. The different possibilities for extensions
+show the diverse fields of application for this technique.
+",0,0,0,0,1,0
+15559,Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent,"  Data compression is a popular technique for improving the efficiency of data
+processing workloads such as SQL queries and more recently, machine learning
+(ML) with classical batch gradient methods. But the efficacy of such ideas for
+mini-batch stochastic gradient descent (MGD), arguably the workhorse algorithm
+of modern ML, is an open question. MGD's unique data access pattern renders
+prior art, including those designed for batch gradient methods, less effective.
+We fill this crucial research gap by proposing a new lossless compression
+scheme we call tuple-oriented compression (TOC) that is inspired by an unlikely
+source, the string/text compression scheme Lempel-Ziv-Welch, but tailored to
+MGD in a way that preserves tuple boundaries within mini-batches. We then
+present a suite of novel compressed matrix operation execution techniques
+tailored to the TOC compression scheme that operate directly over the
+compressed data representation and avoid decompression overheads. An extensive
+empirical evaluation with real-world datasets shows that TOC consistently
+achieves substantial compression ratios by up to 51x and reduces runtimes for
+MGD workloads by up to 10.2x in popular ML systems.
+",1,0,0,1,0,0
+15560,Program Language Translation Using a Grammar-Driven Tree-to-Tree Model,"  The task of translating between programming languages differs from the
+challenge of translating natural languages in that programming languages are
+designed with a far more rigid set of structural and grammatical rules.
+Previous work has used a tree-to-tree encoder/decoder model to take advantage
+of the inherent tree structure of programs during translation. Neural decoders,
+however, by default do not exploit known grammar rules of the target language.
+In this paper, we describe a tree decoder that leverages knowledge of a
+language's grammar rules to exclusively generate syntactically correct
+programs. We find that this grammar-based tree-to-tree model outperforms the
+state of the art tree-to-tree model in translating between two programming
+languages on a previously used synthetic task.
+",1,0,0,1,0,0
+15561,In-Place Initializable Arrays,"  Initializing all elements of an array to a specified value is a basic
+operation that frequently appears in numerous algorithms and programs.
+Initializable arrays are abstract arrays that support initialization as well as
+reading and writing of any element of the array in less than linear time
+proportional to the length of the array. On the word RAM model with $w$ bits
+word size, we propose an in-place algorithm using only 1 extra bit which
+implements an initializable array of length $N$ each of whose elements can
+store $\ell \in O(w)$ bits value, and supports all operations in constant worst
+case time. We also show that our algorithm is not only time optimal but also
+space optimal. Our algorithm significantly improves upon the previous best
+algorithm [Navarro, CSUR 2014] using $N + \ell + o(N)$ extra bits supporting
+all operations in constant worst case time.
+Moreover, for a special cast that $\ell \ge 2 \lceil \log N \rceil$ and $\ell
+\in O(w)$, we also propose an algorithm so that each element of initializable
+array can store $2^\ell$ normal states and a one optional state, which uses
+$\ell + \lceil \log N \rceil + 1$ extra bits and supports all operations in
+constant worst case time.
+",1,0,0,0,0,0
+15562,A Simple Exponential Family Framework for Zero-Shot Learning,"  We present a simple generative framework for learning to predict previously
+unseen classes, based on estimating class-attribute-gated class-conditional
+distributions. We model each class-conditional distribution as an exponential
+family distribution and the parameters of the distribution of each seen/unseen
+class are defined as functions of the respective observed class attributes.
+These functions can be learned using only the seen class data and can be used
+to predict the parameters of the class-conditional distribution of each unseen
+class. Unlike most existing methods for zero-shot learning that represent
+classes as fixed embeddings in some vector space, our generative model
+naturally represents each class as a probability distribution. It is simple to
+implement and also allows leveraging additional unlabeled data from unseen
+classes to improve the estimates of their class-conditional distributions using
+transductive/semi-supervised learning. Moreover, it extends seamlessly to
+few-shot learning by easily updating these distributions when provided with a
+small number of additional labelled examples from unseen classes. Through a
+comprehensive set of experiments on several benchmark data sets, we demonstrate
+the efficacy of our framework.
+",1,0,0,1,0,0
+15563,Modeling Hormesis Using a Non-Monotonic Copula Method,"  This paper presents a probabilistic method for capturing non-monotonic
+behavior under the biphasic dose-response regime observed in many biological
+systems experiencing different types of stress. The proposed method is based on
+the rolling-pin method introduced earlier to estimate highly nonlinear and
+non-monotonic joint probability distributions from continuous domain data. We
+show that the proposed method outperforms the conventional parametric methods
+in terms of the error (namely RMSE) and it needs fewer parameters to be
+estimated a priori, while offering high flexibility. The application and
+performance of the proposed method are shown through an example.
+",0,0,0,1,0,0
+15564,Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks,"  Mobile-Edge Computing (MEC) is an emerging paradigm that provides a capillary
+distribution of cloud computing capabilities to the edge of the wireless access
+network, enabling rich services and applications in close proximity to the end
+users. In this article, a MEC enabled multi-cell wireless network is considered
+where each Base Station (BS) is equipped with a MEC server that can assist
+mobile users in executing computation-intensive tasks via task offloading. The
+problem of Joint Task Offloading and Resource Allocation (JTORA) is studied in
+order to maximize the users' task offloading gains, which is measured by the
+reduction in task completion time and energy consumption. The considered
+problem is formulated as a Mixed Integer Non-linear Program (MINLP) that
+involves jointly optimizing the task offloading decision, uplink transmission
+power of mobile users, and computing resource allocation at the MEC servers.
+Due to the NP-hardness of this problem, solving for optimal solution is
+difficult and impractical for a large-scale network. To overcome this drawback,
+our approach is to decompose the original problem into (i) a Resource
+Allocation (RA) problem with fixed task offloading decision and (ii) a Task
+Offloading (TO) problem that optimizes the optimal-value function corresponding
+to the RA problem. We address the RA problem using convex and quasi-convex
+optimization techniques, and propose a novel heuristic algorithm to the TO
+problem that achieves a suboptimal solution in polynomial time. Numerical
+simulation results show that our algorithm performs closely to the optimal
+solution and that it significantly improves the users' offloading utility over
+traditional approaches.
+",1,0,0,0,0,0
+15565,The Sum Over Topological Sectors and $θ$ in the 2+1-Dimensional $\mathbb{C}\mathbb{P}^1$ $σ$-Model,"  We discuss the three spacetime dimensional $\mathbb{C}\mathbb{P}^N$ model and
+specialize to the $\mathbb{C}\mathbb{P}^1$ model. Because of the Hopf map
+$\pi_3(\mathbb{C}\mathbb{P}^1)=\mathbb{Z}$ one might try to couple the model to
+a periodic $\theta$ parameter. However, we argue that only the values
+$\theta=0$ and $\theta=\pi$ are consistent. For these values the Skyrmions in
+the model are bosons and fermions respectively, rather than being anyons. We
+also extend the model by coupling it to a topological quantum field theory,
+such that the Skyrmions are anyons. We use techniques from geometry and
+topology to construct the $\theta =\pi $ theory on arbitrary 3-manifolds, and
+use recent results about invertible field theories to prove that no other
+values of $\theta $ satisfy the necessary locality.
+",0,1,1,0,0,0
+15566,Continued Kinematic and Photometric Investigations of Hierarchical Solar-Type Multiple Star Systems,"  We observed 15 of the solar-type binaries within 67 pc of the Sun previously
+observed by the Robo-AO system in the visible, with the PHARO near-IR camera
+and the PALM-3000 adaptive optics system on the 5 m Hale telescope. The
+physical status of the binaries is confirmed through common proper motion and
+detection of orbital motion. In the process we detected a new candidate
+companion to HIP 95309. We also resolved the primary of HIP 110626 into a close
+binary making that system a triple. These detections increase the completeness
+of the multiplicity survey of the solar-type stars within 67 pc of the Sun.
+Combining our observations of HIP 103455 with archival astrometric measurements
+and RV measurements, we are able to compute the first orbit of HIP 103455
+showing that the binary has a 68 yr period. We place the components on a
+color-magnitude diagram and discuss each multiple system individually.
+",0,1,0,0,0,0
+15567,A Note on Multiparty Communication Complexity and the Hales-Jewett Theorem,"  For integers $n$ and $k$, the density Hales-Jewett number $c_{n,k}$ is
+defined as the maximal size of a subset of $[k]^n$ that contains no
+combinatorial line. We show that for $k \ge 3$ the density Hales-Jewett number
+$c_{n,k}$ is equal to the maximal size of a cylinder intersection in the
+problem $Part_{n,k}$ of testing whether $k$ subsets of $[n]$ form a partition.
+It follows that the communication complexity, in the Number On the Forehead
+(NOF) model, of $Part_{n,k}$, is equal to the minimal size of a partition of
+$[k]^n$ into subsets that do not contain a combinatorial line. Thus, the bound
+in \cite{chattopadhyay2007languages} on $Part_{n,k}$ using the Hales-Jewett
+theorem is in fact tight, and the density Hales-Jewett number can be thought of
+as a quantity in communication complexity. This gives a new angle to this well
+studied quantity.
+As a simple application we prove a lower bound on $c_{n,k}$, similar to the
+lower bound in \cite{polymath2010moser} which is roughly $c_{n,k}/k^n \ge
+\exp(-O(\log n)^{1/\lceil \log_2 k\rceil})$. This lower bound follows from a
+protocol for $Part_{n,k}$. It is interesting to better understand the
+communication complexity of $Part_{n,k}$ as this will also lead to the better
+understanding of the Hales-Jewett number. The main purpose of this note is to
+motivate this study.
+",1,0,0,0,0,0
+15568,First Results from Using Game Refinement Measure and Learning Coefficient in Scrabble,"  This paper explores the entertainment experience and learning experience in
+Scrabble. It proposes a new measure from the educational point of view, which
+we call learning coefficient, based on the balance between the learner's skill
+and the challenge in Scrabble. Scrabble variants, generated using different
+size of board and dictionary, are analyzed with two measures of game refinement
+and learning coefficient. The results show that 13x13 Scrabble yields the best
+entertainment experience and 15x15 (standard) Scrabble with 4% of original
+dictionary size yields the most effective environment for language learners.
+Moreover, 15x15 Scrabble with 10% of original dictionary size has a good
+balance between entertainment and learning experience.
+",1,0,0,0,0,0
+15569,Structured Local Optima in Sparse Blind Deconvolution,"  Blind deconvolution is a ubiquitous problem of recovering two unknown signals
+from their convolution. Unfortunately, this is an ill-posed problem in general.
+This paper focuses on the {\em short and sparse} blind deconvolution problem,
+where the one unknown signal is short and the other one is sparsely and
+randomly supported. This variant captures the structure of the unknown signals
+in several important applications. We assume the short signal to have unit
+$\ell^2$ norm and cast the blind deconvolution problem as a nonconvex
+optimization problem over the sphere. We demonstrate that (i) in a certain
+region of the sphere, every local optimum is close to some shift truncation of
+the ground truth, and (ii) for a generic short signal of length $k$, when the
+sparsity of activation signal $\theta\lesssim k^{-2/3}$ and number of
+measurements $m\gtrsim poly(k)$, a simple initialization method together with a
+descent algorithm which escapes strict saddle points recovers a near shift
+truncation of the ground truth kernel.
+",0,0,0,1,0,0
+15570,The empirical Christoffel function with applications in data analysis,"  We illustrate the potential applications in machine learning of the
+Christoffel function, or more precisely, its empirical counterpart associated
+with a counting measure uniformly supported on a finite set of points. Firstly,
+we provide a thresholding scheme which allows to approximate the support of a
+measure from a finite subset of its moments with strong asymptotic guaranties.
+Secondly, we provide a consistency result which relates the empirical
+Christoffel function and its population counterpart in the limit of large
+samples. Finally, we illustrate the relevance of our results on simulated and
+real world datasets for several applications in statistics and machine
+learning: (a) density and support estimation from finite samples, (b) outlier
+and novelty detection and (c) affine matching.
+",1,0,0,0,0,0
+15571,Around power law for PageRank components in Buckley-Osthus model of web graph,"  In the paper we investigate power law for PageRank components for the
+Buckley-Osthus model for web graph. We compare different numerical methods for
+PageRank calculation. With the best method we do a lot of numerical
+experiments. These experiments confirm the hypothesis about power law. At the
+end we discuss real model of web-ranking based on the classical PageRank
+approach.
+",1,0,1,0,0,0
+15572,Cash-settled options for wholesale electricity markets,"  Wholesale electricity market designs in practice do not provide the market
+participants with adequate mechanisms to hedge their financial risks. Demanders
+and suppliers will likely face even greater risks with the deepening
+penetration of variable renewable resources like wind and solar. This paper
+explores the design of a centralized cash-settled call option market to
+mitigate such risks. A cash-settled call option is a financial instrument that
+allows its holder the right to claim a monetary reward equal to the positive
+difference between the real-time price of an underlying commodity and a
+pre-negotiated strike price for an upfront fee. Through an example, we
+illustrate that a bilateral call option can reduce the payment volatility of
+market participants. Then, we design a centralized clearing mechanism for call
+options that generalizes the bilateral trade. We illustrate through an example
+how the centralized clearing mechanism generalizes the bilateral trade.
+Finally, the effect of risk preference of the market participants, as well as
+some generalizations are discussed.
+",1,0,1,0,0,0
+15573,Power in High-Dimensional Testing Problems,"  Fan et al. (2015) recently introduced a remarkable method for increasing
+asymptotic power of tests in high-dimensional testing problems. If applicable
+to a given test, their power enhancement principle leads to an improved test
+that has the same asymptotic size, uniformly non-inferior asymptotic power, and
+is consistent against a strictly broader range of alternatives than the
+initially given test. We study under which conditions this method can be
+applied and show the following: In asymptotic regimes where the dimensionality
+of the parameter space is fixed as sample size increases, there often exist
+tests that can not be further improved with the power enhancement principle.
+However, when the dimensionality of the parameter space increases sufficiently
+slowly with sample size and a marginal local asymptotic normality (LAN)
+condition is satisfied, every test with asymptotic size smaller than one can be
+improved with the power enhancement principle. While the marginal LAN condition
+alone does not allow one to extend the latter statement to all rates at which
+the dimensionality increases with sample size, we give sufficient conditions
+under which this is the case.
+",0,0,1,1,0,0
+15574,Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model,"  With the goal of making high-resolution forecasts of regional rainfall,
+precipitation nowcasting has become an important and fundamental technology
+underlying various public services ranging from rainstorm warnings to flight
+safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to
+outperform traditional optical flow based methods for precipitation nowcasting,
+suggesting that deep learning models have a huge potential for solving the
+problem. However, the convolutional recurrence structure in ConvLSTM-based
+models is location-invariant while natural motion and transformation (e.g.,
+rotation) are location-variant in general. Furthermore, since
+deep-learning-based precipitation nowcasting is a newly emerging area, clear
+evaluation protocols have not yet been established. To address these problems,
+we propose both a new model and a benchmark for precipitation nowcasting.
+Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU)
+model that can actively learn the location-variant structure for recurrent
+connections. Besides, we provide a benchmark that includes a real-world
+large-scale dataset from the Hong Kong Observatory, a new training loss, and a
+comprehensive evaluation protocol to facilitate future research and gauge the
+state of the art.
+",1,0,0,0,0,0
+15575,PAWS: A Tool for the Analysis of Weighted Systems,"  PAWS is a tool to analyse the behaviour of weighted automata and conditional
+transition systems. At its core PAWS is based on a generic implementation of
+algorithms for checking language equivalence in weighted automata and
+bisimulation in conditional transition systems. This architecture allows for
+the use of arbitrary user-defined semirings. New semirings can be generated
+during run-time and the user can rely on numerous automatisation techniques to
+create new semiring structures for PAWS' algorithms. Basic semirings such as
+distributive complete lattices and fields of fractions can be defined by
+specifying few parameters, more exotic semirings can be generated from other
+semirings or defined from scratch using a built-in semiring generator. In the
+most general case, users can define new semirings by programming (in C#) the
+base operations of the semiring and a procedure to solve linear equations and
+use their newly generated semiring in the analysis tools that PAWS offers.
+",1,0,0,0,0,0
+15576,Quantum-optical spectroscopy for plasma electric field measurements and diagnostics,"  Measurements of plasma electric fields are essential to the advancement of
+plasma science and applications. Methods for non-invasive in situ measurements
+of plasma fields on sub-millimeter length scales with high sensitivity over a
+large field range remain an outstanding challenge. Here, we introduce and
+demonstrate a new method for plasma electric field measurement that employs
+electromagnetically induced transparency as a high-resolution quantum-optical
+probe for the Stark energy level shifts of plasma-embedded Rydberg atoms, which
+serve as highly-sensitive field sensors with a large dynamic range. The method
+is applied in diagnostics of plasmas photo-excited out of a cesium vapor. The
+plasma electric fields are extracted from spatially-resolved measurements of
+field-induced shape changes and shifts of Rydberg resonances in rubidium tracer
+atoms. Measurement capabilities over a range of plasma densities and
+temperatures are exploited to characterize plasmas in applied magnetic fields
+and to image electric-field distributions in cyclotron-heated plasmas.
+",0,1,0,0,0,0
+15577,ROSA: R Optimizations with Static Analysis,"  R is a popular language and programming environment for data scientists. It
+is increasingly co-packaged with both relational and Hadoop-based data
+platforms and can often be the most dominant computational component in data
+analytics pipelines. Recent work has highlighted inefficiencies in executing R
+programs, both in terms of execution time and memory requirements, which in
+practice limit the size of data that can be analyzed by R. This paper presents
+ROSA, a static analysis framework to improve the performance and space
+efficiency of R programs. ROSA analyzes input programs to determine program
+properties such as reaching definitions, live variables, aliased variables, and
+types of variables. These inferred properties enable program transformations
+such as C++ code translation, strength reduction, vectorization, code motion,
+in addition to interpretive optimizations such as avoiding redundant object
+copies and performing in-place evaluations. An empirical evaluation shows
+substantial reductions by ROSA in execution time and memory consumption over
+both CRAN R and Microsoft R Open.
+",1,0,0,0,0,0
+15578,Human Perception of Performance,"  Humans are routinely asked to evaluate the performance of other individuals,
+separating success from failure and affecting outcomes from science to
+education and sports. Yet, in many contexts, the metrics driving the human
+evaluation process remain unclear. Here we analyse a massive dataset capturing
+players' evaluations by human judges to explore human perception of performance
+in soccer, the world's most popular sport. We use machine learning to design an
+artificial judge which accurately reproduces human evaluation, allowing us to
+demonstrate how human observers are biased towards diverse contextual features.
+By investigating the structure of the artificial judge, we uncover the aspects
+of the players' behavior which attract the attention of human judges,
+demonstrating that human evaluation is based on a noticeability heuristic where
+only feature values far from the norm are considered to rate an individual's
+performance.
+",1,0,0,1,0,0
+15579,Practical Distance Functions for Path-Planning in Planar Domains,"  Path planning is an important problem in robotics. One way to plan a path
+between two points $x,y$ within a (not necessarily simply-connected) planar
+domain $\Omega$, is to define a non-negative distance function $d(x,y)$ on
+$\Omega\times\Omega$ such that following the (descending) gradient of this
+distance function traces such a path. This presents two equally important
+challenges: A mathematical challenge -- to define $d$ such that $d(x,y)$ has a
+single minimum for any fixed $y$ (and this is when $x=y$), since a local
+minimum is in effect a ""dead end"", A computational challenge -- to define $d$
+such that it may be computed efficiently. In this paper, given a description of
+$\Omega$, we show how to assign coordinates to each point of $\Omega$ and
+define a family of distance functions between points using these coordinates,
+such that both the mathematical and the computational challenges are met. This
+is done using the concepts of \emph{harmonic measure} and
+\emph{$f$-divergences}.
+In practice, path planning is done on a discrete network defined on a finite
+set of \emph{sites} sampled from $\Omega$, so any method that works well on the
+continuous domain must be adapted so that it still works well on the discrete
+domain. Given a set of sites sampled from $\Omega$, we show how to define a
+network connecting these sites such that a \emph{greedy routing} algorithm
+(which is the discrete equivalent of continuous gradient descent) based on the
+distance function mentioned above is guaranteed to generate a path in the
+network between any two such sites. In many cases, this network is close to a
+(desirable) planar graph, especially if the set of sites is dense.
+",1,0,0,0,0,0
+15580,FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software,"  A deep-learning inference accelerator is synthesized from a C-language
+software program parallelized with Pthreads. The software implementation uses
+the well-known producer/consumer model with parallel threads interconnected by
+FIFO queues. The LegUp high-level synthesis (HLS) tool synthesizes threads into
+parallel FPGA hardware, translating software parallelism into spatial
+parallelism. A complete system is generated where convolution, pooling and
+padding are realized in the synthesized accelerator, with remaining tasks
+executing on an embedded ARM processor. The accelerator incorporates reduced
+precision, and a novel approach for zero-weight-skipping in convolution. On a
+mid-sized Intel Arria 10 SoC FPGA, peak performance on VGG-16 is 138 effective
+GOPS.
+",1,0,0,1,0,0
+15581,"The set of forces that ideal trusses, or wire webs, under tension can support","  The problem of determining those multiplets of forces, or sets of force
+multiplets, acting at a set of points, such that there exists a truss
+structure, or wire web, that can support these force multiplets with all the
+elements of the truss or wire web being under tension, is considered. The
+two-dimensional problem where the points are at the vertices of a convex
+polygon is essentially solved: each multiplet of forces must be such that the
+net anticlockwise torque around any vertex of the forces summed over any number
+of consecutive points clockwise past the vertex must be non-negative; and one
+can find a truss structure that supports under tension, and only supports,
+those force multiplets in a convex polyhedron of force multiplets that is
+generated by a finite number of force multiplets each satisfying the torque
+condition. Progress is also made on the problem where only a subset of the
+points are at the vertices of a convex polygon, and the other points are
+inside. In particular, in the case where only one point is inside, an explicit
+procedure is described for constructing a suitable truss, if one exists. An
+alternative recipe to that provided by Guevara-Vasquez, Milton, and Onofrei
+(2011), based on earlier work of Camar Eddine and Seppecher (2003), is given
+for constructing a truss structure, with elements under either compression or
+tension, that supports an arbitrary collection of balanced forces at the
+vertices of a convex polygon. Finally some constraints are given on the forces
+that a three-dimension truss, or wire web, under tension must satisfy.
+",0,1,0,0,0,0
+15582,A Thematic Study of Requirements Modeling and Analysis for Self-Adaptive Systems,"  Over the last decade, researchers and engineers have developed a vast body of
+methodologies and technologies in requirements engineering for self-adaptive
+systems. Although existing studies have explored various aspects of this topic,
+few of them have categorized and summarized these areas of research in
+require-ments modeling and analysis. This study aims to investigate the
+research themes based on the utilized modeling methods and RE activities. We
+conduct a thematic study in the systematic literature review. The results are
+derived by synthesizing the extracted data with statistical methods. This paper
+provides an updated review of the research literature, enabling researchers and
+practitioners to better understand the research themes in these areas and
+identify research gaps which need to be further studied.
+",1,0,0,0,0,0
+15583,On predictive density estimation with additional information,"  Based on independently distributed $X_1 \sim N_p(\theta_1, \sigma^2_1 I_p)$
+and $X_2 \sim N_p(\theta_2, \sigma^2_2 I_p)$, we consider the efficiency of
+various predictive density estimators for $Y_1 \sim N_p(\theta_1, \sigma^2_Y
+I_p)$, with the additional information $\theta_1 - \theta_2 \in A$ and known
+$\sigma^2_1, \sigma^2_2, \sigma^2_Y$. We provide improvements on benchmark
+predictive densities such as plug-in, the maximum likelihood, and the minimum
+risk equivariant predictive densities. Dominance results are obtained for
+$\alpha-$divergence losses and include Bayesian improvements for reverse
+Kullback-Leibler loss, and Kullback-Leibler (KL) loss in the univariate case
+($p=1$). An ensemble of techniques are exploited, including variance expansion
+(for KL loss), point estimation duality, and concave inequalities.
+Representations for Bayesian predictive densities, and in particular for
+$\hat{q}_{\pi_{U,A}}$ associated with a uniform prior for $\theta=(\theta_1,
+\theta_2)$ truncated to $\{\theta \in \mathbb{R}^{2p}: \theta_1 - \theta_2 \in
+A \}$, are established and are used for the Bayesian dominance findings.
+Finally and interestingly, these Bayesian predictive densities also relate to
+skew-normal distributions, as well as new forms of such distributions.
+",0,0,1,1,0,0
+15584,Optimizing Channel Selection for Seizure Detection,"  Interpretation of electroencephalogram (EEG) signals can be complicated by
+obfuscating artifacts. Artifact detection plays an important role in the
+observation and analysis of EEG signals. Spatial information contained in the
+placement of the electrodes can be exploited to accurately detect artifacts.
+However, when fewer electrodes are used, less spatial information is available,
+making it harder to detect artifacts. In this study, we investigate the
+performance of a deep learning algorithm, CNN-LSTM, on several channel
+configurations. Each configuration was designed to minimize the amount of
+spatial information lost compared to a standard 22-channel EEG. Systems using a
+reduced number of channels ranging from 8 to 20 achieved sensitivities between
+33% and 37% with false alarms in the range of [38, 50] per 24 hours. False
+alarms increased dramatically (e.g., over 300 per 24 hours) when the number of
+channels was further reduced. Baseline performance of a system that used all 22
+channels was 39% sensitivity with 23 false alarms. Since the 22-channel system
+was the only system that included referential channels, the rapid increase in
+the false alarm rate as the number of channels was reduced underscores the
+importance of retaining referential channels for artifact reduction. This
+cautionary result is important because one of the biggest differences between
+various types of EEGs administered is the type of referential channel used.
+",0,0,0,1,1,0
+15585,Bases of standard modules for affine Lie algebras of type $C_\ell^{(1)}$,"  Feigin-Stoyanovsky's type subspaces for affine Lie algebras of type
+$C_\ell^{(1)}$ have monomial bases with a nice combinatorial description. We
+describe bases of whole standard modules in terms of semi-infinite monomials
+obtained as ""a limit of translations"" of bases for Feigin-Stoyanovsky's type
+subspaces.
+",0,0,1,0,0,0
+15586,Connectivity-Driven Brain Parcellation via Consensus Clustering,"  We present two related methods for deriving connectivity-based brain atlases
+from individual connectomes. The proposed methods exploit a previously proposed
+dense connectivity representation, termed continuous connectivity, by first
+performing graph-based hierarchical clustering of individual brains, and
+subsequently aggregating the individual parcellations into a consensus
+parcellation. The search for consensus minimizes the sum of cluster membership
+distances, effectively estimating a pseudo-Karcher mean of individual
+parcellations. We assess the quality of our parcellations using (1)
+Kullback-Liebler and Jensen-Shannon divergence with respect to the dense
+connectome representation, (2) inter-hemispheric symmetry, and (3) performance
+of the simplified connectome in a biological sex classification task. We find
+that the parcellation based-atlas computed using a greedy search at a
+hierarchical depth 3 outperforms all other parcellation-based atlases as well
+as the standard Dessikan-Killiany anatomical atlas in all three assessments.
+",0,0,0,1,1,0
+15587,Baryon acoustic oscillations from the complete SDSS-III Ly$α$-quasar cross-correlation function at $z=2.4$,"  We present a measurement of baryon acoustic oscillations (BAO) in the
+cross-correlation of quasars with the Ly$\alpha$-forest flux-transmission at a
+mean redshift $z=2.40$. The measurement uses the complete SDSS-III data sample:
+168,889 forests and 234,367 quasars from the SDSS Data Release DR12. In
+addition to the statistical improvement on our previous study using DR11, we
+have implemented numerous improvements at the analysis level allowing a more
+accurate measurement of this cross-correlation. We also developed the first
+simulations of the cross-correlation allowing us to test different aspects of
+our data analysis and to search for potential systematic errors in the
+determination of the BAO peak position. We measure the two ratios
+$D_{H}(z=2.40)/r_{d} = 9.01 \pm 0.36$ and $D_{M}(z=2.40)/r_{d} = 35.7 \pm 1.7$,
+where the errors include marginalization over the non-linear velocity of
+quasars and the metal - quasar cross-correlation contribution, among other
+effects. These results are within $1.8\sigma$ of the prediction of the
+flat-$\Lambda$CDM model describing the observed CMB anisotropies. We combine
+this study with the Ly$\alpha$-forest auto-correlation function
+[2017A&A...603A..12B], yielding $D_{H}(z=2.40)/r_{d} = 8.94 \pm 0.22$ and
+$D_{M}(z=2.40)/r_{d} = 36.6 \pm 1.2$, within $2.3\sigma$ of the same
+flat-$\Lambda$CDM model.
+",0,1,0,0,0,0
+15588,"Gamma-ray and Optical Oscillations of 0716+714, MRK 421, and BL Lac","  We examine the 2008-2016 $\gamma$-ray and optical light curves of three
+bright BL Lac objects, 0716+714, MRK 421, BL Lac, which exhibit large
+structured variability. We searched for periodicities by using a fully Bayesian
+approach. For two out of three sources investigated no significant periodic
+variability was found. In the case of BL Lac we detected a periodicity of ~ 680
+days. Although the signal related to this is modest, the coincidence of the
+periods in both gamma and optical bands is indicative of a physical relevance.
+Considering previous literature results, possibly related $\gamma$-ray and
+optical periodicities of about one year time scale are proposed in 4 bright
+$\gamma$-ray blazars out of the 10 examined in detail. Comparing with results
+from periodicity search of optical archives of quasars, the presence of
+quasi-periodicities in blazars might be more frequent by a large factor. This
+suggests the intriguing possibility that the basic conditions for their
+observability are related to the relativistic jet in the observer direction,
+but the overall picture remains uncertain.
+",0,1,0,0,0,0
+15589,Antibonding Ground state of Adatom Molecules in Bulk Dirac Semimetals,"  The ground state of the diatomic molecules in nature is inevitably bonding,
+and its first excited state is antibonding. We demonstrate theoretically that,
+for a pair of distant adatoms placed buried in three-dimensional-Dirac
+semimetals, this natural order of the states can be reversed and an antibonding
+ground state occurs at the lowest energy of the so-called bound states in the
+continuum. We propose an experimental protocol with the use of a scanning
+tunneling microscope tip to visualize the topographic map of the local density
+of states on the surface of the system to reveal the emerging physics.
+",0,1,0,0,0,0
+15590,Baby MIND: A magnetized segmented neutrino detector for the WAGASCI experiment,"  T2K (Tokai-to-Kamioka) is a long-baseline neutrino experiment in Japan
+designed to study various parameters of neutrino oscillations. A near detector
+complex (ND280) is located 280~m downstream of the production target and
+measures neutrino beam parameters before any oscillations occur. ND280's
+measurements are used to predict the number and spectra of neutrinos in the
+Super-Kamiokande detector at the distance of 295~km. The difference in the
+target material between the far (water) and near (scintillator, hydrocarbon)
+detectors leads to the main non-cancelling systematic uncertainty for the
+oscillation analysis. In order to reduce this uncertainty a new
+WAter-Grid-And-SCintillator detector (WAGASCI) has been developed. A magnetized
+iron neutrino detector (Baby MIND) will be used to measure momentum and charge
+identification of the outgoing muons from charged current interactions. The
+Baby MIND modules are composed of magnetized iron plates and long plastic
+scintillator bars read out at the both ends with wavelength shifting fibers and
+silicon photomultipliers. The front-end electronics board has been developed to
+perform the readout and digitization of the signals from the scintillator bars.
+Detector elements were tested with cosmic rays and in the PS beam at CERN. The
+obtained results are presented in this paper.
+",0,1,0,0,0,0
+15591,Applications of noncommutative deformations,"  For a general class of contractions of a variety X to a base Y, I discuss
+recent joint work with M. Wemyss defining a noncommutative enhancement of the
+locus in Y over which the contraction is not an isomorphism, along with
+applications to the derived symmetries of X. This note is based on a talk given
+at the Kinosaki Symposium in 2016.
+",0,0,1,0,0,0
+15592,Photometric and radial-velocity time-series of RR Lyrae stars in M3: analysis of single-mode variables,"  We present the first simultaneous photometric and spectroscopic investigation
+of a large set of RR Lyrae variables in a globular cluster. The radial-velocity
+data presented comprise the largest sample of RVs of RR Lyrae stars ever
+obtained. The target is M3; $BVI_{\mathrm{C}}$ time-series of 111 and $b$ flux
+data of further 64 RRab stars, and RV data of 79 RR Lyrae stars are published.
+Blazhko modulation of the light curves of 47 percent of the RRab stars are
+detected. The mean value of the center-of-mass velocities of RR Lyrae stars is
+$-146.8$ km s$^{-1}$ with 4.52 km s$^{-1}$ standard deviation, which is in good
+agreement with the results obtained for the red giants of the cluster. The
+${\Phi_{21}}^{\mathrm RV}$ phase difference of the RV curves of RRab stars is
+found to be uniformly constant both for the M3 and for Galactic field RRab
+stars; no period or metallicity dependence of the ${\Phi_{21}}^{\mathrm RV}$ is
+detected. The Baade-Wesselink distances of 26 non-Blazhko variables with the
+best phase-coverage radial-velocity curves are determined; the corresponding
+distance of the cluster, $10480\pm210$ pc, agrees with the previous literature
+information. A quadratic formula for the $A_{\mathrm{puls}}-A_V$ relation of
+RRab stars is given, which is valid for both OoI and OoII variables. We also
+show that the $(V-I)_0$ of RRab stars measured at light minimum is period
+dependent, there is at least 0.1 mag difference between the colours at minimum
+light of the shortest- and longest-period variables.
+",0,1,0,0,0,0
+15593,Particlelike scattering states in a microwave cavity,"  We realize scattering states in a lossy and chaotic two-dimensional microwave
+cavity which follow bundles of classical particle trajectories. To generate
+such particlelike scattering states we measure the system's transmission matrix
+and apply an adapted Wigner-Smith time-delay formalism to it. The necessary
+shaping of the incident wave is achieved in situ using phase and amplitude
+regulated microwave antennas. Our experimental findings pave the way for
+establishing spatially confined communication channels that avoid possible
+intruders or obstacles in wave-based communication systems.
+",0,1,0,0,0,0
+15594,"Market Self-Learning of Signals, Impact and Optimal Trading: Invisible Hand Inference with Free Energy","  We present a simple model of a non-equilibrium self-organizing market where
+asset prices are partially driven by investment decisions of a bounded-rational
+agent. The agent acts in a stochastic market environment driven by various
+exogenous ""alpha"" signals, agent's own actions (via market impact), and noise.
+Unlike traditional agent-based models, our agent aggregates all traders in the
+market, rather than being a representative agent. Therefore, it can be
+identified with a bounded-rational component of the market itself, providing a
+particular implementation of an Invisible Hand market mechanism. In such
+setting, market dynamics are modeled as a fictitious self-play of such
+bounded-rational market-agent in its adversarial stochastic environment. As
+rewards obtained by such self-playing market agent are not observed from market
+data, we formulate and solve a simple model of such market dynamics based on a
+neuroscience-inspired Bounded Rational Information Theoretic Inverse
+Reinforcement Learning (BRIT-IRL). This results in effective asset price
+dynamics with a non-linear mean reversion - which in our model is generated
+dynamically, rather than being postulated. We argue that our model can be used
+in a similar way to the Black-Litterman model. In particular, it represents, in
+a simple modeling framework, market views of common predictive signals, market
+impacts and implied optimal dynamic portfolio allocations, and can be used to
+assess values of private signals. Moreover, it allows one to quantify a
+""market-implied"" optimal investment strategy, along with a measure of market
+rationality. Our approach is numerically light, and can be implemented using
+standard off-the-shelf software such as TensorFlow.
+",0,0,0,0,0,1
+15595,Current-driven skyrmion dynamics in disordered films,"  A theoretical study of the current-driven dynamics of magnetic skyrmions in
+disordered perpendicularly-magnetized ultrathin films is presented. The
+disorder is simulated as a granular structure in which the local anisotropy
+varies randomly from grain to grain. The skyrmion velocity is computed for
+different disorder parameters and ensembles. Similar behavior is seen for
+spin-torques due to in-plane currents and the spin Hall effect, where a pinning
+regime can be identified at low currents with a transition towards the
+disorder-free case at higher currents, similar to domain wall motion in
+disordered films. Moreover, a current-dependent skyrmion Hall effect and
+fluctuations in the core radius are found, which result from the interaction
+with the pinning potential.
+",0,1,0,0,0,0
+15596,On the Rigidity of Riemannian-Penrose Inequality for Asymptotically Flat 3-manifolds with Corners,"  In this paper we prove a rigidity result for the equality case of the Penrose
+inequality on $3$-dimensional asymptotically flat manifolds with nonnegative
+scalar curvature and corners. Our result also has deep connections with the
+equality cases of Theorem 1 in \cite{Miao2} and Theorem 1.1 in \cite{LM}.
+",0,0,1,0,0,0
+15597,An Algebra Model for the Higher Order Sum Rules,"  We introduce an algebra model to study higher order sum rules for orthogonal
+polynomials on the unit circle. We build the relation between the algebra model
+and sum rules, and prove an equivalent expression on the algebra side for the
+sum rules, involving a Hall-Littlewood type polynomial. By this expression, we
+recover an earlier result by Golinskii and Zlatǒs, and prove a new case -
+half of the Lukic conjecture in the case of a single critical point with
+arbitrary order.
+",0,0,1,0,0,0
+15598,On maxispaces of nonparametric tests,"  For the problems of nonparametric hypothesis testing we introduce the notion
+of maxisets and maxispace. We point out the maxisets of $\chi^2-$tests,
+Cramer-von Mises tests, tests generated $\mathbb{L}_2$- norms of kernel
+estimators and tests generated quadratic forms of estimators of Fourier
+coefficients. For these tests we show that, if sequence of alternatives having
+given rates of convergence to hypothesis is consistent, then each altehrnative
+can be broken down into the sum of two parts: a function belonging to maxiset
+and orthogonal function. Sequence of functions belonging to maxiset is
+consistent sequence of alternatives.
+We point out asymptotically minimax tests if sets of alternatives are maxiset
+with deleted ""small"" $\mathbb{L}_2$-balls.
+",0,0,1,1,0,0
+15599,Learning Unsupervised Learning Rules,"  A major goal of unsupervised learning is to discover data representations
+that are useful for subsequent tasks, without access to supervised labels
+during training. Typically, this goal is approached by minimizing a surrogate
+objective, such as the negative log likelihood of a generative model, with the
+hope that representations useful for subsequent tasks will arise incidentally.
+In this work, we propose instead to directly target a later desired task by
+meta-learning an unsupervised learning rule, which leads to representations
+useful for that task. Here, our desired task (meta-objective) is the
+performance of the representation on semi-supervised classification, and we
+meta-learn an algorithm -- an unsupervised weight update rule -- that produces
+representations that perform well under this meta-objective. Additionally, we
+constrain our unsupervised update rule to a be a biologically-motivated,
+neuron-local function, which enables it to generalize to novel neural network
+architectures. We show that the meta-learned update rule produces useful
+features and sometimes outperforms existing unsupervised learning techniques.
+We further show that the meta-learned unsupervised update rule generalizes to
+train networks with different widths, depths, and nonlinearities. It also
+generalizes to train on data with randomly permuted input dimensions and even
+generalizes from image datasets to a text task.
+",0,0,0,1,0,0
+15600,Conformal Nanocarbon Coating of Alumina Nanocrystals for Biosensing and Bioimaging,"  A conformal coating technique with nanocarbon was developed to enhance the
+surface properties of alumina nanoparticles for bio-applications. The
+ultra-thin carbon layer induces new surface properties such as water
+dispersion, cytocompatibility and tuneable surface chemistry, while maintaining
+the optical properties of the core particle. The possibility of using these
+particles as agents for DNA sensing was demonstrated in a competitive assay.
+Additionally, the inherent fluorescence of the core alumina particles provided
+a unique platform for localization and monitoring of living organisms, allowing
+simultaneous cell monitoring and intra-cellular sensing. Nanoparticles were
+able to carry genes to the cells and release them in an environment where
+specific biomarkers were present.
+",0,1,0,0,0,0
+15601,Characterizing spectral continuity in SDSS u'g'r'i'z' asteroid photometry,"  Context. The 4th release of the SDSS Moving Object Catalog (SDSSMOC) is
+presently the largest photometric dataset of asteroids. Up to this point, the
+release of large asteroid datasets has always been followed by a redefinition
+of asteroid taxonomy. In the years that followed the release of the first
+SDSSMOC, several classification schemes using its data were proposed, all using
+the taxonomic classes from previous taxonomies. However, no successful attempt
+has been made to derive a new taxonomic system directly from the SDSS dataset.
+Aims. The scope of the work is to propose a different interpretation scheme for
+gauging u0g0r0i0z0 asteroid observations based on the continuity of spectral
+features. The scheme is integrated into previous taxonomic labeling, but is not
+dependent on them. Methods. We analyzed the behavior of asteroid sampling
+through principal components analysis to understand the role of uncertainties
+in the SDSSMOC. We identified that asteroids in this space follow two separate
+linear trends using reflectances in the visible, which is characteristic of
+their spectrophotometric features. Results. Introducing taxonomic classes, we
+are able to interpret both trends as representative of featured and featureless
+spectra. The evolution within the trend is connected mainly to the band depth
+for featured asteroids and to the spectral slope for featureless ones. We
+defined a different taxonomic system that allowed us to only classify asteroids
+by two labels. Conclusions. We have classified 69% of all SDSSMOC sample, which
+is a robustness higher than reached by previous SDSS classifications.
+Furthermore, as an example, we present the behavior of asteroid (5129) Groom,
+whose taxonomic labeling changes according to one of the trends owing to phase
+reddening. Now, such behavior can be characterized by the variation of one
+single parameter, its position in the trend.
+",0,1,0,0,0,0
+15602,Transverse-spin correlations of the random transverse-field Ising model,"  The critical behavior of the random transverse-field Ising model in finite
+dimensional lattices is governed by infinite disorder fixed points, several
+properties of which have already been calculated by the use of the strong
+disorder renormalization group (SDRG) method. Here we extend these studies and
+calculate the connected transverse-spin correlation function by a numerical
+implementation of the SDRG method in $d=1,2$ and $3$ dimensions. At the
+critical point an algebraic decay of the form $\sim r^{-\eta_t}$ is found, with
+a decay exponent being approximately $\eta_t \approx 2+2d$. In $d=1$ the
+results are related to dimer-dimer correlations in the random AF XX-chain and
+have been tested by numerical calculations using free-fermionic techniques.
+",0,1,0,0,0,0
+15603,Multivariate stable distributions and their applications for modelling cryptocurrency-returns,"  In this paper we extend the known methodology for fitting stable
+distributions to the multivariate case and apply the suggested method to the
+modelling of daily cryptocurrency-return data. The investigated time period is
+cut into 10 non-overlapping sections, thus the changes can also be observed. We
+apply bootstrap tests for checking the models and compare our approach to the
+more traditional extreme-value and copula models.
+",0,0,0,0,0,1
+15604,Beautiful and damned. Combined effect of content quality and social ties on user engagement,"  User participation in online communities is driven by the intertwinement of
+the social network structure with the crowd-generated content that flows along
+its links. These aspects are rarely explored jointly and at scale. By looking
+at how users generate and access pictures of varying beauty on Flickr, we
+investigate how the production of quality impacts the dynamics of online social
+systems. We develop a deep learning computer vision model to score images
+according to their aesthetic value and we validate its output through
+crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
+time how image beauty is distributed over a large-scale social system.
+Beautiful images are evenly distributed in the network, although only a small
+core of people get social recognition for them. To study the impact of exposure
+to quality on user engagement, we set up matching experiments aimed at
+detecting causality from observational data. Exposure to beauty is
+double-edged: following people who produce high-quality content increases one's
+probability of uploading better photos; however, an excessive imbalance between
+the quality generated by a user and the user's neighbors leads to a decline in
+engagement. Our analysis has practical implications for improving link
+recommender systems.
+",1,0,0,0,0,0
+15605,SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities,"  The detection of software vulnerabilities (or vulnerabilities for short) is
+an important problem that has yet to be tackled, as manifested by many
+vulnerabilities reported on a daily basis. This calls for machine learning
+methods to automate vulnerability detection. Deep learning is attractive for
+this purpose because it does not require human experts to manually define
+features. Despite the tremendous success of deep learning in other domains, its
+applicability to vulnerability detection is not systematically understood. In
+order to fill this void, we propose the first systematic framework for using
+deep learning to detect vulnerabilities. The framework, dubbed Syntax-based,
+Semantics-based, and Vector Representations (SySeVR), focuses on obtaining
+program representations that can accommodate syntax and semantic information
+pertinent to vulnerabilities. Our experiments with 4 software products
+demonstrate the usefulness of the framework: we detect 15 vulnerabilities that
+are not reported in the National Vulnerability Database. Among these 15
+vulnerabilities, 7 are unknown and have been reported to the vendors, and the
+other 8 have been ""silently"" patched by the vendors when releasing newer
+versions of the products.
+",0,0,0,1,0,0
+15606,Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models,"  We investigate the problem of learning discrete, undirected graphical models
+in a differentially private way. We show that the approach of releasing noisy
+sufficient statistics using the Laplace mechanism achieves a good trade-off
+between privacy, utility, and practicality. A naive learning algorithm that
+uses the noisy sufficient statistics ""as is"" outperforms general-purpose
+differentially private learning algorithms. However, it has three limitations:
+it ignores knowledge about the data generating process, rests on uncertain
+theoretical foundations, and exhibits certain pathologies. We develop a more
+principled approach that applies the formalism of collective graphical models
+to perform inference over the true sufficient statistics within an
+expectation-maximization framework. We show that this learns better models than
+competing approaches on both synthetic data and on real human mobility data
+used as a case study.
+",1,0,0,1,0,0
+15607,"Phase unwinding, or invariant subspace decompositions of Hardy spaces","  We consider orthogonal decompositions of invariant subspaces of Hardy spaces,
+these relate to the Blaschke based phase unwinding decompositions. We prove
+convergence in Lp. In particular we build an explicit multiscale wavelet basis.
+We also obtain an explicit unwindinig decomposition for the singular inner
+function, exp 2i\pi/x.
+",0,0,1,0,0,0
+15608,SalProp: Salient object proposals via aggregated edge cues,"  In this paper, we propose a novel object proposal generation scheme by
+formulating a graph-based salient edge classification framework that utilizes
+the edge context. In the proposed method, we construct a Bayesian probabilistic
+edge map to assign a saliency value to the edgelets by exploiting low level
+edge features. A Conditional Random Field is then learned to effectively
+combine these features for edge classification with object/non-object label. We
+propose an objectness score for the generated windows by analyzing the salient
+edge density inside the bounding box. Extensive experiments on PASCAL VOC 2007
+dataset demonstrate that the proposed method gives competitive performance
+against 10 popular generic object detection techniques while using fewer number
+of proposals.
+",1,0,0,0,0,0
+15609,Quantum gap and spin-wave excitations in the Kitaev model on a triangular lattice,"  We study the effects of quantum fluctuations on the dynamical generation of a
+gap and on the evolution of the spin-wave spectra of a frustrated magnet on a
+triangular lattice with bond-dependent Ising couplings, analog of the Kitaev
+honeycomb model. The quantum fluctuations lift the subextensive degeneracy of
+the classical ground-state manifold by a quantum order-by-disorder mechanism.
+Nearest-neighbor chains remain decoupled and the surviving discrete degeneracy
+of the ground state is protected by a hidden model symmetry. We show how the
+four-spin interaction, emergent from the fluctuations, generates a spin gap
+shifting the nodal lines of the linear spin-wave spectrum to finite energies.
+",0,1,0,0,0,0
+15610,Sparse Gaussian ICA,"  Independent component analysis (ICA) is a cornerstone of modern data
+analysis. Its goal is to recover a latent random vector S with independent
+components from samples of X=AS where A is an unknown mixing matrix.
+Critically, all existing methods for ICA rely on and exploit strongly the
+assumption that S is not Gaussian as otherwise A becomes unidentifiable. In
+this paper, we show that in fact one can handle the case of Gaussian components
+by imposing structure on the matrix A. Specifically, we assume that A is sparse
+and generic in the sense that it is generated from a sparse Bernoulli-Gaussian
+ensemble. Under this condition, we give an efficient algorithm to recover the
+columns of A given only the covariance matrix of X as input even when S has
+several Gaussian components.
+",0,0,0,1,0,0
+15611,Supervisor Synthesis of POMDP based on Automata Learning,"  As a general and thus popular model for autonomous systems, partially
+observable Markov decision process (POMDP) can capture uncertainties from
+different sources like sensing noises, actuation errors, and uncertain
+environments. However, its comprehensiveness makes the planning and control in
+POMDP difficult. Traditional POMDP planning problems target to find the optimal
+policy to maximize the expectation of accumulated rewards. But for safety
+critical applications, guarantees of system performance described by formal
+specifications are desired, which motivates us to consider formal methods to
+synthesize supervisor for POMDP. With system specifications given by
+Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control
+framework with a type of deterministic finite automata (DFA), za-DFA, as the
+controller form. While the existing work mainly relies on optimization
+techniques to learn fixed-size finite state controllers (FSCs), we develop an
+$L^*$ learning based algorithm to determine both space and transitions of
+za-DFA. Membership queries and different oracles for conjectures are defined.
+The learning algorithm is sound and complete. An example is given in detailed
+steps to illustrate the supervisor synthesis algorithm.
+",1,0,0,0,0,0
+15612,A Pseudo Knockoff Filter for Correlated Features,"  In 2015, Barber and Candes introduced a new variable selection procedure
+called the knockoff filter to control the false discovery rate (FDR) and prove
+that this method achieves exact FDR control. Inspired by the work of Barber and
+Candes (2015), we propose and analyze a pseudo-knockoff filter that inherits
+some advantages of the original knockoff filter and has more flexibility in
+constructing its knockoff matrix. Although we have not been able to obtain
+exact FDR control of the pseudo knockoff filter, we show that it satisfies an
+expectation inequality that offers some insight into FDR control. Moreover, we
+provide some partial analysis of the pseudo knockoff filter for the half Lasso
+and the least squares statistics. Our analysis indicates that the inverse of
+the covariance matrix of the feature matrix plays an important role in
+designing and analyzing the pseudo knockoff filter. Our preliminary numerical
+experiments show that the pseudo knockoff filter with the half Lasso statistic
+has FDR control. Moreover, our numerical experiments show that the
+pseudo-knockoff filter could offer more power than the original knockoff filter
+with the OMP or Lasso Path statistic when the features are correlated and
+non-sparse.
+",0,0,1,1,0,0
+15613,$μ$-constant monodromy groups and Torelli results for the quadrangle singularities and the bimodal series,"  This paper is a sequel to [He11] and [GH17]. In [He11] a notion of marking of
+isolated hypersurface singularities was defined, and a moduli space
+$M_\mu^{mar}$ for marked singularities in one $\mu$-homotopy class of isolated
+hypersurface singularities was established. It is an analogue of a
+Teichmüller space. It comes together with a $\mu$-constant monodromy group
+$G^{mar}\subset G_{\mathbb{Z}}$. Here $G_{\mathbb{Z}}$ is the group of
+automorphisms of a Milnor lattice which respect the Seifert form. It was
+conjectured that $M_\mu^{mar}$ is connected. This is equivalent to $G^{mar}=
+G_{\mathbb{Z}}$. Also Torelli type conjectures were formulated. In [He11] and
+[GH17] $M_\mu^{mar}, G_{\mathbb{Z}}$ and $G^{mar}$ were determined and all
+conjectures were proved for the simple, the unimodal and the exceptional
+bimodal singularities. In this paper the quadrangle singularities and the
+bimodal series are treated. The Torelli type conjectures are true. But the
+conjecture $G^{mar}= G_{\mathbb{Z}}$ and $M_\mu^{mar}$ connected does not hold
+for certain subseries of the bimodal series.
+",0,0,1,0,0,0
+15614,SoaAlloc: Accelerating Single-Method Multiple-Objects Applications on GPUs,"  We propose SoaAlloc, a dynamic object allocator for Single-Method
+Multiple-Objects applications in CUDA. SoaAlloc is the first allocator for GPUs
+that (a) arranges allocations in a SIMD-friendly Structure of Arrays (SOA) data
+layout, (b) provides a do-all operation for maximizing the benefit of SOA, and
+(c) is on par with state-of-the-art memory allocators for raw (de)allocation
+time. Our benchmarks show that the SOA layout leads to significantly better
+memory bandwidth utilization, resulting in a 2x speedup of application code.
+",1,0,0,0,0,0
+15615,Probabilistic risk bounds for the characterization of radiological contamination,"  The radiological characterization of contaminated elements (walls, grounds,
+objects) from nuclear facilities often suffers from a too small number of
+measurements. In order to determine risk prediction bounds on the level of
+contamination, some classic statistical methods may then reveal unsuited as
+they rely upon strong assumptions (e.g. that the underlying distribution is
+Gaussian) which cannot be checked. Considering that a set of measurements or
+their average value arise from a Gaussian distribution can sometimes lead to
+erroneous conclusion, possibly underconservative. This paper presents several
+alternative statistical approaches which are based on much weaker hypotheses
+than Gaussianity. They result from general probabilistic inequalities and
+order-statistics based formula. Given a data sample, these inequalities make it
+possible to derive prediction intervals for a random variable, which can be
+directly interpreted as probabilistic risk bounds. For the sake of validation,
+they are first applied to synthetic data samples generated from several known
+theoretical distributions. In a second time, the proposed methods are applied
+to two data sets obtained from real radiological contamination measurements.
+",0,0,1,1,0,0
+15616,Automatic Backward Differentiation for American Monte-Carlo Algorithms (Conditional Expectation),"  In this note we derive the backward (automatic) differentiation (adjoint
+[automatic] differentiation) for an algorithm containing a conditional
+expectation operator. As an example we consider the backward algorithm as it is
+used in Bermudan product valuation, but the method is applicable in full
+generality.
+The method relies on three simple properties: 1) a forward or backward
+(automatic) differentiation of an algorithm containing a conditional
+expectation operator results in a linear combination of the conditional
+expectation operators; 2) the differential of an expectation is the expectation
+of the differential $\frac{d}{dx} E(Y) = E(\frac{d}{dx}Y)$; 3) if we are only
+interested in the expectation of the final result (as we are in all valuation
+problems), we may use $E(A \cdot E(B\vert\mathcal{F})) = E(E(A\vert\mathcal{F})
+\cdot B)$, i.e., instead of applying the (conditional) expectation operator to
+a function of the underlying random variable (continuation values), it may be
+applied to the adjoint differential. \end{enumerate}
+The methodology not only allows for a very clean and simple implementation,
+but also offers the ability to use different conditional expectation estimators
+in the valuation and the differentiation.
+",1,0,0,0,0,0
+15617,The Belgian repository of fundamental atomic data and stellar spectra (BRASS). I. Cross-matching atomic databases of astrophysical interest,"  Fundamental atomic parameters, such as oscillator strengths, play a key role
+in modelling and understanding the chemical composition of stars in the
+universe. Despite the significant work underway to produce these parameters for
+many astrophysically important ions, uncertainties in these parameters remain
+large and can propagate throughout the entire field of astronomy. The Belgian
+repository of fundamental atomic data and stellar spectra (BRASS) aims to
+provide the largest systematic and homogeneous quality assessment of atomic
+data to date in terms of wavelength, atomic and stellar parameter coverage. To
+prepare for it, we first compiled multiple literature occurrences of many
+individual atomic transitions, from several atomic databases of astrophysical
+interest, and assessed their agreement. Several atomic repositories were
+searched and their data retrieved and formatted in a consistent manner. Data
+entries from all repositories were cross-matched against our initial BRASS
+atomic line list to find multiple occurrences of the same transition. Where
+possible we used a non-parametric cross-match depending only on electronic
+configurations and total angular momentum values. We also checked for duplicate
+entries of the same physical transition, within each retrieved repository,
+using the non-parametric cross-match. We report the cross-matched transitions
+for each repository and compare their fundamental atomic parameters. We find
+differences in log(gf) values of up to 2 dex or more. We also find and report
+that ~2% of our line list and Vienna Atomic Line Database retrievals are
+composed of duplicate transitions. Finally we provide a number of examples of
+atomic spectral lines with different log(gf) values, and discuss the impact of
+these uncertain log(gf) values on quantitative spectroscopy. All cross-matched
+atomic data and duplicate transitions are available to download at
+brass.sdf.org.
+",0,1,0,0,0,0
+15618,High-Precision Calculations in Strongly Coupled Quantum Field Theory with Next-to-Leading-Order Renormalized Hamiltonian Truncation,"  Hamiltonian Truncation (a.k.a. Truncated Spectrum Approach) is an efficient
+numerical technique to solve strongly coupled QFTs in d=2 spacetime dimensions.
+Further theoretical developments are needed to increase its accuracy and the
+range of applicability. With this goal in mind, here we present a new variant
+of Hamiltonian Truncation which exhibits smaller dependence on the UV cutoff
+than other existing implementations, and yields more accurate spectra. The key
+idea for achieving this consists in integrating out exactly a certain class of
+high energy states, which corresponds to performing renormalization at the
+cubic order in the interaction strength. We test the new method on the strongly
+coupled two-dimensional quartic scalar theory. Our work will also be useful for
+the future goal of extending Hamiltonian Truncation to higher dimensions d >=
+3.
+",0,1,0,0,0,0
+15619,State Distribution-aware Sampling for Deep Q-learning,"  A critical and challenging problem in reinforcement learning is how to learn
+the state-action value function from the experience replay buffer and
+simultaneously keep sample efficiency and faster convergence to a high quality
+solution. In prior works, transitions are uniformly sampled at random from the
+replay buffer or sampled based on their priority measured by
+temporal-difference (TD) error. However, these approaches do not fully take
+into consideration the intrinsic characteristics of transition distribution in
+the state space and could result in redundant and unnecessary TD updates,
+slowing down the convergence of the learning procedure. To overcome this
+problem, we propose a novel state distribution-aware sampling method to balance
+the replay times for transitions with skew distribution, which takes into
+account both the occurrence frequencies of transitions and the uncertainty of
+state-action values. Consequently, our approach could reduce the unnecessary TD
+updates and increase the TD updates for state-action value with more
+uncertainty, making the experience replay more effective and efficient.
+Extensive experiments are conducted on both classic control tasks and Atari
+2600 games based on OpenAI gym platform and the experimental results
+demonstrate the effectiveness of our approach in comparison with the standard
+DQN approach.
+",0,0,0,1,0,0
+15620,Energy Trading between microgrids Individual Cost Minimization and Social Welfare Maximization,"  High penetration of renewable energy source makes microgrid (MGs) be
+environment friendly. However, the stochastic input from renewable energy
+resource brings difficulty in balancing the energy supply and demand.
+Purchasing extra energy from macrogrid to deal with energy shortage will
+increase MG energy cost. To mitigate intermittent nature of renewable energy,
+energy trading and energy storage which can exploit diversity of renewable
+energy generation across space and time are efficient and cost-effective
+methods. But current energy storage control action will impact the future
+control action which brings challenge to energy management. In addition, due to
+MG participating energy trading as prosumer, it calls for an efficient trading
+mechanism. Therefore, this paper focuses on the problem of MG energy management
+and trading. Energy trading problem is formulated as a stochastic optimization
+one with both individual profit and social welfare maximization. Firstly a
+Lyapunov optimization based algorithm is developed to solve the stochastic
+problem. Secondly the double-auction based mechanism is provided to attract MG
+truthful bidding for buying and selling energy. Through theoretical analysis,
+we demonstrate that individual MG can achieve a time average energy cost close
+to offline optimum with tradeoff between storage capacity and energy trading
+cost. Meanwhile the social welfare is also asymptotically maximized under
+double auction. Simulation results based on real world data show the
+effectiveness of our algorithm.
+",1,0,0,0,0,0
+15621,Learning Kolmogorov Models for Binary Random Variables,"  We summarize our recent findings, where we proposed a framework for learning
+a Kolmogorov model, for a collection of binary random variables. More
+specifically, we derive conditions that link outcomes of specific random
+variables, and extract valuable relations from the data. We also propose an
+algorithm for computing the model and show its first-order optimality, despite
+the combinatorial nature of the learning problem. We apply the proposed
+algorithm to recommendation systems, although it is applicable to other
+scenarios. We believe that the work is a significant step toward interpretable
+machine learning.
+",0,0,0,1,0,0
+15622,Möbius topological superconductivity in UPt$_3$,"  Intensive studies for more than three decades have elucidated multiple
+superconducting phases and odd-parity Cooper pairs in a heavy fermion
+superconductor UPt$_3$. We identify a time-reversal invariant superconducting
+phase of UPt$_3$ as a recently proposed topological nonsymmorphic
+superconductivity. Combining the band structure of UPt$_3$, order parameter of
+$E_{\rm 2u}$ representation allowed by $P6_3/mmc$ space group symmetry, and
+topological classification by $K$-theory, we demonstrate the nontrivial
+$Z_2$-invariant of three-dimensional DIII class enriched by glide symmetry.
+Correspondingly, double Majorana cone surface states appear at the surface
+Brillouin zone boundary. Furthermore, we show a variety of surface states and
+clarify the topological protection by crystal symmetry. Majorana arcs
+corresponding to tunable Weyl points appear in the time-reversal symmetry
+broken B-phase. Majorana cone protected by mirror Chern number and Majorana
+flat band by glide-winding number are also revealed.
+",0,1,0,0,0,0
+15623,Sparse Identification and Estimation of High-Dimensional Vector AutoRegressive Moving Averages,"  The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the
+study of multivariate time series. However, estimation becomes challenging in
+even relatively low-dimensional VARMA models. With growing interest in the
+simultaneous modeling of large numbers of marginal time series, many authors
+have abandoned the VARMA model in favor of the Vector AutoRegressive (VAR)
+model, which is seen as a simpler alternative, both in theory and practice, in
+this high-dimensional context. However, even very simple VARMA models can be
+very complicated to represent using only VAR modeling. In this paper, we
+develop a new approach to VARMA identification and propose a two-phase method
+for estimation. Our identification and estimation strategies are linked in
+their use of sparsity-inducing convex regularizers, which favor VARMA models
+that have only a small number of nonzero parameters. We establish sufficient
+conditions for consistency of sparse infinite-order VAR estimates in high
+dimensions, a key ingredient for our two-phase sparse VARMA estimation
+strategy. The proposed framework has good estimation and forecast accuracy
+under numerous simulation settings. We illustrate the forecast performance of
+the sparse VARMA models for several application domains, including
+macro-economic forecasting, demand forecasting, and volatility forecasting. The
+proposed sparse VARMA estimator gives parsimonious forecast models that lead to
+important gains in relative forecast accuracy.
+",0,0,0,1,0,0
+15624,Information Retrieval and Criticality in Parity-Time-Symmetric Systems,"  By investigating information flow between a general parity-time (PT)
+-symmetric non-Hermitian system and an environment, we find that the complete
+information retrieval from the environment can be achieved in the PT-unbroken
+phase, whereas no information can be retrieved in the PT-broken phase. The
+PT-transition point thus marks the reversible-irreversible criticality of
+information flow, around which many physical quantities such as the recurrence
+time and the distinguishability between quantum states exhibit power-law
+behavior. Moreover, by embedding a PT-symmetric system into a larger Hilbert
+space so that the entire system obeys unitary dynamics, we reveal that behind
+the information retrieval lies a hidden entangled partner protected by PT
+symmetry. Possible experimental situations are also discussed.
+",0,1,0,0,0,0
+15625,A practical guide and software for analysing pairwise comparison experiments,"  Most popular strategies to capture subjective judgments from humans involve
+the construction of a unidimensional relative measurement scale, representing
+order preferences or judgments about a set of objects or conditions. This
+information is generally captured by means of direct scoring, either in the
+form of a Likert or cardinal scale, or by comparative judgments in pairs or
+sets. In this sense, the use of pairwise comparisons is becoming increasingly
+popular because of the simplicity of this experimental procedure. However, this
+strategy requires non-trivial data analysis to aggregate the comparison ranks
+into a quality scale and analyse the results, in order to take full advantage
+of the collected data. This paper explains the process of translating pairwise
+comparison data into a measurement scale, discusses the benefits and
+limitations of such scaling methods and introduces a publicly available
+software in Matlab. We improve on existing scaling methods by introducing
+outlier analysis, providing methods for computing confidence intervals and
+statistical testing and introducing a prior, which reduces estimation error
+when the number of observers is low. Most of our examples focus on image
+quality assessment.
+",1,0,0,1,0,0
+15626,Web-Based Implementation of Travelling Salesperson Problem Using Genetic Algorithm,"  The world is connected through the Internet. As the abundance of Internet
+users connected into the Web and the popularity of cloud computing research,
+the need of Artificial Intelligence (AI) is demanding. In this research,
+Genetic Algorithm (GA) as AI optimization method through natural selection and
+genetic evolution is utilized. There are many applications of GA such as web
+mining, load balancing, routing, and scheduling or web service selection.
+Hence, it is a challenging task to discover whether the code mainly server side
+and web based language technology affects the performance of GA. Travelling
+Salesperson Problem (TSP) as Non Polynomial-hard (NP-hard) problem is provided
+to be a problem domain to be solved by GA. While many scientists prefer Python
+in GA implementation, another popular high-level interpreter programming
+language such as PHP (PHP Hypertext Preprocessor) and Ruby were benchmarked.
+Line of codes, file sizes, and performances based on GA implementation and
+runtime were found varies among these programming languages. Based on the
+result, the use of Ruby in GA implementation is recommended.
+",1,0,0,0,0,0
+15627,"Random Spatial Networks: Small Worlds without Clustering, Traveling Waves, and Hop-and-Spread Disease Dynamics","  Random network models play a prominent role in modeling, analyzing and
+understanding complex phenomena on real-life networks. However, a key property
+of networks is often neglected: many real-world networks exhibit spatial
+structure, the tendency of a node to select neighbors with a probability
+depending on physical distance. Here, we introduce a class of random spatial
+networks (RSNs) which generalizes many existing random network models but adds
+spatial structure. In these networks, nodes are placed randomly in space and
+joined in edges with a probability depending on their distance and their
+individual expected degrees, in a manner that crucially remains analytically
+tractable. We use this network class to propose a new generalization of
+small-world networks, where the average shortest path lengths in the graph are
+small, as in classical Watts-Strogatz small-world networks, but with close
+spatial proximity of nodes that are neighbors in the network playing the role
+of large clustering. Small-world effects are demonstrated on these spatial
+small-world networks without clustering. We are able to derive partial
+integro-differential equations governing susceptible-infectious-recovered
+disease spreading through an RSN, and we demonstrate the existence of traveling
+wave solutions. If the distance kernel governing edge placement decays slower
+than exponential, the population-scale dynamics are dominated by long-range
+hops followed by local spread of traveling waves. This provides a theoretical
+modeling framework for recent observations of how epidemics like Ebola evolve
+in modern connected societies, with long-range connections seeding new focal
+points from which the epidemic locally spreads in a wavelike manner.
+",0,1,0,0,0,0
+15628,The evolution of magnetic hot massive stars: Implementation of the quantitative influence of surface magnetic fields in modern models of stellar evolution,"  Large-scale dipolar surface magnetic fields have been detected in a fraction
+of OB stars, however only few stellar evolution models of massive stars have
+considered the impact of these fossil fields. We are performing 1D
+hydrodynamical model calculations taking into account evolutionary consequences
+of the magnetospheric-wind interactions in a simplified parametric way. Two
+effects are considered: i) the global mass-loss rates are reduced due to
+mass-loss quenching, and ii) the surface angular momentum loss is enhanced due
+to magnetic braking. As a result of the magnetic mass-loss quenching, the mass
+of magnetic massive stars remains close to their initial masses. Thus magnetic
+massive stars - even at Galactic metallicity - have the potential to be
+progenitors of `heavy' stellar mass black holes. Similarly, at Galactic
+metallicity, the formation of pair instability supernovae is plausible with a
+magnetic progenitor.
+",0,1,0,0,0,0
+15629,The finite gap method and the analytic description of the exact rogue wave recurrence in the periodic NLS Cauchy problem. 1,"  The focusing NLS equation is the simplest universal model describing the
+modulation instability (MI) of quasi monochromatic waves in weakly nonlinear
+media, considered the main physical mechanism for the appearance of rogue
+(anomalous) waves (RWs) in Nature. In this paper we study, using the finite gap
+method, the NLS Cauchy problem for periodic initial perturbations of the
+unstable background solution of NLS exciting just one of the unstable modes. We
+distinguish two cases. In the case in which only the corresponding unstable gap
+is theoretically open, the solution describes an exact deterministic alternate
+recurrence of linear and nonlinear stages of MI, and the nonlinear RW stages
+are described by the 1-breather Akhmediev solution, whose parameters, different
+at each RW appearance, are always given in terms of the initial data through
+elementary functions. If the number of unstable modes is >1, this uniform in t
+dynamics is sensibly affected by perturbations due to numerics and/or real
+experiments, provoking O(1) corrections to the result. In the second case in
+which more than one unstable gap is open, a detailed investigation of all these
+gaps is necessary to get a uniform in $t$ dynamics, and this study is postponed
+to a subsequent paper. It is however possible to obtain the elementary
+description of the first nonlinear stage of MI, given again by the Akhmediev
+1-breather solution, and how perturbations due to numerics and/or real
+experiments can affect this result.
+",0,1,0,0,0,0
+15630,First Discoveries of z>6 Quasars with the DECam Legacy Survey and UKIRT Hemisphere Survey,"  We present the first discoveries from a survey of $z\gtrsim6$ quasars using
+imaging data from the DECam Legacy Survey (DECaLS) in the optical, the UKIRT
+Deep Infrared Sky Survey (UKIDSS) and a preliminary version of the UKIRT
+Hemisphere Survey (UHS) in the near-IR, and ALLWISE in the mid-IR. DECaLS will
+image 9000 deg$^2$ of sky down to $z_{\rm AB}\sim23.0$, and UKIDSS and UHS,
+which will map the northern sky at $0<DEC<+60^{\circ}$, reaching $J_{\rm
+VEGA}\sim19.6$ (5-$\sigma$). The combination of these datasets allows us to
+discover quasars at redshift $z\gtrsim7$ and to conduct a complete census of
+the faint quasar population at $z\gtrsim6$. In this paper, we report on the
+selection method of our search, and on the initial discoveries of two new,
+faint $z\gtrsim6$ quasars and one new $z=6.63$ quasar in our pilot
+spectroscopic observations. The two new $z\sim6$ quasars are at $z=6.07$ and
+$z=6.17$ with absolute magnitudes at rest-frame wavelength 1450 \AA\ being
+$M_{1450}=-25.83$ and $M_{1450}=-25.76$, respectively. These discoveries
+suggest that we can find quasars close to or fainter than the break magnitude
+of the Quasar Luminosity Function (QLF) at $z\gtrsim6$. The new $z=6.63$ quasar
+has an absolute magnitude of $M_{1450}=-25.95$. This demonstrates the potential
+of using the combined DECaLS and UKIDSS/UHS datasets to find $z\gtrsim7$
+quasars. Extrapolating from previous QLF measurements, we predict that these
+combined datasets will yield $\sim200$ $z\sim6$ quasars to $z_{\rm AB} < 21.5$,
+$\sim1{,}000$ $z\sim6$ quasars to $z_{\rm AB}<23$, and $\sim 30$ quasars at
+$z>6.5$ to $J_{\rm VEGA}<19.5$.
+",0,1,0,0,0,0
+15631,On unique continuation for solutions of the Schr{ö}dinger equation on trees,"  We prove that if a solution of the time-dependent Schr{ö}dinger equation on
+an homogeneous tree with bounded potential decays fast at two distinct times
+then the solution is trivial. For the free Schr{ö}dinger operator, we use the
+spectral theory of the Laplacian and complex analysis and obtain a
+characterization of the initial conditions that lead to a sharp decay at any
+time. We then use the recent spectral decomposition of the Schr{ö}dinger
+operator with compactly supported potential due to Colin de Verdi{è}rre and
+Turc to extend our results in the presence of such potentials. Finally, we use
+real variable methods first introduced by Escauriaza, Kenig, Ponce and Vega to
+establish a general sharp result in the case of bounded potentials.
+",0,0,1,0,0,0
+15632,Particle-hole Asymmetry in the Cuprate Pseudogap Measured with Time-Resolved Spectroscopy,"  One of the most puzzling features of high-temperature cuprate superconductors
+is the pseudogap state, which appears above the temperature at which
+superconductivity is destroyed. There remain fundamental questions regarding
+its nature and its relation to superconductivity. But to address these
+questions, we must first determine whether the pseudogap and superconducting
+states share a common property: particle-hole symmetry. We introduce a new
+technique to test particle-hole symmetry by using laser pulses to manipulate
+and measure the chemical potential on picosecond time scales. The results
+strongly suggest that the asymmetry in the density of states is inverted in the
+pseudogap state, implying a particle-hole asymmetric gap. Independent of
+interpretation, these results can test theoretical predictions of the density
+of states in cuprates.
+",0,1,0,0,0,0
+15633,Numerically modeling Brownian thermal noise in amorphous and crystalline thin coatings,"  Thermal noise is expected to be one of the noise sources limiting the
+astrophysical reach of Advanced LIGO (once commissioning is complete) and
+third-generation detectors. Adopting crystalline materials for thin, reflecting
+mirror coatings, rather than the amorphous coatings used in current-generation
+detectors, could potentially reduce thermal noise. Understanding and reducing
+thermal noise requires accurate theoretical models, but modeling thermal noise
+analytically is especially challenging with crystalline materials. Thermal
+noise models typically rely on the fluctuation-dissipation theorem, which
+relates the power spectral density of the thermal noise to an auxiliary elastic
+problem. In this paper, we present results from a new, open-source tool that
+numerically solves the auxiliary elastic problem to compute the Brownian
+thermal noise for both amorphous and crystalline coatings. We employ
+open-source frameworks to solve the auxiliary elastic problem using a
+finite-element method, adaptive mesh refinement, and parallel processing that
+enables us to use high resolutions capable of resolving the thin reflective
+coating. We compare with approximate analytic solutions for amorphous
+materials, and we verify that our solutions scale as expected. Finally, we
+model the crystalline coating thermal noise in an experiment reported by Cole
+and collaborators (2013), comparing our results to a simpler numerical
+calculation that treats the coating as an ""effectively amorphous"" material. We
+find that treating the coating as a cubic crystal instead of as an effectively
+amorphous material increases the thermal noise by about 3%. Our results are a
+step toward better understanding and reducing thermal noise to increase the
+reach of future gravitational-wave detectors. (Abstract abbreviated.)
+",0,1,0,0,0,0
+15634,Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks,"  In an effort to understand the meaning of the intermediate representations
+captured by deep networks, recent papers have tried to associate specific
+semantic concepts to individual neural network filter responses, where
+interesting correlations are often found, largely by focusing on extremal
+filter responses. In this paper, we show that this approach can favor
+easy-to-interpret cases that are not necessarily representative of the average
+behavior of a representation.
+A more realistic but harder-to-study hypothesis is that semantic
+representations are distributed, and thus filters must be studied in
+conjunction. In order to investigate this idea while enabling systematic
+visualization and quantification of multiple filter responses, we introduce the
+Net2Vec framework, in which semantic concepts are mapped to vectorial
+embeddings based on corresponding filter responses. By studying such
+embeddings, we are able to show that 1., in most cases, multiple filters are
+required to code for a concept, that 2., often filters are not concept specific
+and help encode multiple concepts, and that 3., compared to single filter
+activations, filter embeddings are able to better characterize the meaning of a
+representation and its relationship to other concepts.
+",0,0,0,1,0,0
+15635,Can the removal of molecular cloud envelopes by external feedback affect the efficiency of star formation?,"  We investigate how star formation efficiency can be significantly decreased
+by the removal of a molecular cloud's envelope by feedback from an external
+source. Feedback from star formation has difficulties halting the process in
+dense gas but can easily remove the less dense and warmer envelopes where star
+formation does not occur. However, the envelopes can play an important role
+keeping their host clouds bound by deepening the gravitational potential and
+providing a constraining pressure boundary. We use numerical simulations to
+show that removal of the cloud envelopes results in all cases in a fall in the
+star formation efficiency (SFE). At 1.38 free-fall times our 4 pc cloud
+simulation experienced a drop in the SFE from 16 to six percent, while our 5 pc
+cloud fell from 27 to 16 per cent. At the same time, our 3 pc cloud (the least
+bound) fell from an SFE of 5.67 per cent to zero when the envelope was lost.
+The star formation efficiency per free-fall time varied from zero to $\approx$
+0.25 according to $\alpha$, defined to be the ratio of the kinetic plus thermal
+to gravitational energy, and irrespective of the absolute star forming mass
+available. Furthermore the fall in SFE associated with the loss of the envelope
+is found to even occur at later times. We conclude that the SFE will always
+fall should a star forming cloud lose its envelope due to stellar feedback,
+with less bound clouds suffering the greatest decrease.
+",0,1,0,0,0,0
+15636,Joint Rate and Resource Allocation in Hybrid Digital-Analog Transmission over Fading Channels,"  In hybrid digital-analog (HDA) systems, resource allocation has been utilized
+to achieve desired distortion performance. However, existing studies on this
+issue assume error-free digital transmission, which is not valid for fading
+channels. With time-varying channel fading, the exact channel state information
+is not available at the transmitter. Thus, random outage and resulting digital
+distortion cannot be ignored. Moreover, rate allocation should be considered in
+resource allocation, since it not only determines the amount of information for
+digital transmission and that for analog transmission, but also affects the
+outage probability. Based on above observations, in this paper, we attempt to
+perform joint rate and resource allocation strategies to optimize system
+distortion in HDA systems over fading channels. Consider a bandwidth expansion
+scenario where a memoryless Gaussian source is transmitted in an HDA system
+with the entropy-constrained scalar quantizer (ECSQ). Firstly, we formulate the
+joint allocation problem as an expected system distortion minimization problem
+where both analog and digital distortion are considered. Then, in the limit of
+low outage probability, we decompose the problem into two coupled sub-problems
+based on the block coordinate descent method, and propose an iterative gradient
+algorithm to approach the optimal solution. Furthermore, we extend our work to
+the multivariate Gaussian source scenario where a two-stage fast algorithm
+integrating rounding and greedy strategies is proposed to optimize the joint
+rate and resource allocation problem. Finally, simulation results demonstrate
+that the proposed algorithms can achieve up to 2.3dB gains in terms of
+signal-to-distortion ratio over existing schemes under the single Gaussian
+source scenario, and up to 3.5dB gains under the multivariate Gaussian source
+scenario.
+",1,0,0,0,0,0
+15637,Relaxation to a Phase-locked Equilibrium State in a One-dimensional Bosonic Josephson Junction,"  We present an experimental study on the non-equilibrium tunnel dynamics of
+two coupled one-dimensional Bose-Einstein quasi-condensates deep in the
+Josephson regime. Josephson oscillations are initiated by splitting a single
+one-dimensional condensate and imprinting a relative phase between the
+superfluids. Regardless of the initial state and experimental parameters, the
+dynamics of the relative phase and atom number imbalance shows a relaxation to
+a phase-locked steady state. The latter is characterized by a high phase
+coherence and reduced fluctuations with respect to the initial state. We
+propose an empirical model based on the analogy with the anharmonic oscillator
+to describe the effect of various experimental parameters. A microscopic theory
+compatible with our observations is still missing.
+",0,1,0,0,0,0
+15638,A Hybrid Model for Role-related User Classification on Twitter,"  To aid a variety of research studies, we propose TWIROLE, a hybrid model for
+role-related user classification on Twitter, which detects male-related,
+female-related, and brand-related (i.e., organization or institution) users.
+TWIROLE leverages features from tweet contents, user profiles, and profile
+images, and then applies our hybrid model to identify a user's role. To
+evaluate it, we used two existing large datasets about Twitter users, and
+conducted both intra- and inter-comparison experiments. TWIROLE outperforms
+existing methods and obtains more balanced results over the several roles. We
+also confirm that user names and profile images are good indicators for this
+task. Our research extends prior work that does not consider brand-related
+users, and is an aid to future evaluation efforts relative to investigations
+that rely upon self-labeled datasets.
+",1,0,0,0,0,0
+15639,SecureBoost: A Lossless Federated Learning Framework,"  The protection of user privacy is an important concern in machine learning,
+as evidenced by the rolling out of the General Data Protection Regulation
+(GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give
+users more control over their personal data, which motivates us to explore
+machine learning frameworks with data sharing without violating user privacy.
+To meet this goal, in this paper, we propose a novel lossless
+privacy-preserving tree-boosting system known as SecureBoost in the setting of
+federated learning. This federated-learning system allows a learning process to
+be jointly conducted over multiple parties with partially common user samples
+but different feature sets, which corresponds to a vertically partitioned
+virtual data set. An advantage of SecureBoost is that it provides the same
+level of accuracy as the non-privacy-preserving approach while at the same
+time, reveal no information of each private data provider. We theoretically
+prove that the SecureBoost framework is as accurate as other non-federated
+gradient tree-boosting algorithms that bring the data into one place. In
+addition, along with a proof of security, we discuss what would be required to
+make the protocols completely secure.
+",1,0,0,1,0,0
+15640,Selective reflection from Rb layer with thickness below $λ$/12 and applications,"  We have studied the peculiarities of selective reflection from Rb vapor cell
+with thickness $L <$ 70 nm, which is over an order of magnitude smaller than
+the resonant wavelength for Rb atomic D$_1$ line $\lambda$ = 795 nm. A huge
+($\approx$ 240 MHz) red shift and spectral broadening of reflection signal is
+recorded for $L =$ 40 nm caused by the atom-surface interaction. Also
+completely frequency resolved hyperfine Paschen-Back splitting of atomic
+transitions to four components for $^{87}$Rb and six components for $^{85}$Rb
+is recorded in strong magnetic field ($B >$ 2 kG).
+",0,1,0,0,0,0
+15641,The Complexity of Abstract Machines,"  The lambda-calculus is a peculiar computational model whose definition does
+not come with a notion of machine. Unsurprisingly, implementations of the
+lambda-calculus have been studied for decades. Abstract machines are
+implementations schema for fixed evaluation strategies that are a compromise
+between theory and practice: they are concrete enough to provide a notion of
+machine and abstract enough to avoid the many intricacies of actual
+implementations. There is an extensive literature about abstract machines for
+the lambda-calculus, and yet-quite mysteriously-the efficiency of these
+machines with respect to the strategy that they implement has almost never been
+studied.
+This paper provides an unusual introduction to abstract machines, based on
+the complexity of their overhead with respect to the length of the implemented
+strategies. It is conceived to be a tutorial, focusing on the case study of
+implementing the weak head (call-by-name) strategy, and yet it is an original
+re-elaboration of known results. Moreover, some of the observation contained
+here never appeared in print before.
+",1,0,0,0,0,0
+15642,Recent progress in the Zimmer program,"  This paper can be viewed as a sequel to the author's long survey on the
+Zimmer program \cite{F11} published in 2011. The sequel focuses on recent rapid
+progress on certain aspects of the program particularly concerning rigidity of
+Anosov actions and Zimmer's conjecture that there are no actions in low
+dimensions. Some emphasis is put on the surprising connections between these
+two different sets of developments and also on the key connections and ideas
+for future research that arise from these works taken together.
+",0,0,1,0,0,0
+15643,Restricted Causal Inference Algorithm,"  This paper proposes a new algorithm for recovery of belief network structure
+from data handling hidden variables. It consists essentially in an extension of
+the CI algorithm of Spirtes et al. by restricting the number of conditional
+dependencies checked up to k variables and in an extension of the original CI
+by additional steps transforming so called partial including path graph into a
+belief network. Its correctness is demonstrated.
+",1,0,0,0,0,0
+15644,Fiber plucking by molecular motors yields large emergent contractility in stiff biopolymer networks,"  The mechanical properties of the cell depend crucially on the tension of its
+cytoskeleton, a biopolymer network that is put under stress by active motor
+proteins. While the fibrous nature of the network is known to strongly affect
+the transmission of these forces to the cellular scale, our understanding of
+this process remains incomplete. Here we investigate the transmission of forces
+through the network at the individual filament level, and show that active
+forces can be geometrically amplified as a transverse motor-generated force
+force ""plucks"" the fiber and induces a nonlinear tension. In stiff and densely
+connnected networks, this tension results in large network-wide tensile
+stresses that far exceed the expectation drawn from a linear elastic theory.
+This amplification mechanism competes with a recently characterized
+network-level amplification due to fiber buckling, suggesting that that fiber
+networks provide several distinct pathways for living systems to amplify their
+molecular forces.
+",0,0,0,0,1,0
+15645,Demographics of News Sharing in the U.S. Twittersphere,"  The widespread adoption and dissemination of online news through social media
+systems have been revolutionizing many segments of our society and ultimately
+our daily lives. In these systems, users can play a central role as they share
+content to their friends. Despite that, little is known about news spreaders in
+social media. In this paper, we provide the first of its kind in-depth
+characterization of news spreaders in social media. In particular, we
+investigate their demographics, what kind of content they share, and the
+audience they reach. Among our main findings, we show that males and white
+users tend to be more active in terms of sharing news, biasing the news
+audience to the interests of these demographic groups. Our results also
+quantify differences in interests of news sharing across demographics, which
+has implications for personalized news digests.
+",1,0,0,0,0,0
+15646,Dynamics and fragmentation mechanism of (CH3-C5H4)Pt(CH3)3 on SiO2 Surfaces,"  The interaction of (CH3-C5H4)Pt(CH3)3
+((methylcyclopentadienyl)trimethylplatinum)) molecules on fully and partially
+hydroxylated SiO2 surfaces, as well as the dynamics of this interaction were
+investigated using density functional theory (DFT) and finite temperature
+DFT-based molecular dynamics simulations. Fully and partially hydroxylated
+surfaces represent substrates before and after electron beam treatment and this
+study examines the role of electron beam pretreatment on the substrates in the
+initial stages of precursor dissociation and formation of Pt deposits. Our
+simulations show that on fully hydroxylated surfaces or untreated surfaces, the
+precursor molecules remain inactivated while we observe fragmentation of
+(CH3-C5H4)Pt(CH3)3 on partially hydroxylated surfaces. The behavior of
+precursor molecules on the partially hydroxylated surfaces has been found to
+depend on the initial orientation of the molecule and the distribution of
+surface active sites. Based on the observations from the simulations and
+available experiments, we discuss possible dissociation channels of the
+precursor.
+",0,1,0,0,0,0
+15647,On the robustness of the H$β$ Lick index as a cosmic clock in passive early-type galaxies,"  We examine the H$\beta$ Lick index in a sample of $\sim 24000$ massive ($\rm
+log(M/M_{\odot})>10.75$) and passive early-type galaxies extracted from SDSS at
+z<0.3, in order to assess the reliability of this index to constrain the epoch
+of formation and age evolution of these systems. We further investigate the
+possibility of exploiting this index as ""cosmic chronometer"", i.e. to derive
+the Hubble parameter from its differential evolution with redshift, hence
+constraining cosmological models independently of other probes. We find that
+the H$\beta$ strength increases with redshift as expected in passive evolution
+models, and shows at each redshift weaker values in more massive galaxies.
+However, a detailed comparison of the observed index with the predictions of
+stellar population synthesis models highlights a significant tension, with the
+observed index being systematically lower than expected. By analyzing the
+stacked spectra, we find a weak [NII]$\lambda6584$ emission line (not
+detectable in the single spectra) which anti-correlates with the mass, that can
+be interpreted as a hint of the presence of ionized gas. We estimated the
+correction of the H$\beta$ index by the residual emission component exploiting
+different approaches, but find it very uncertain and model-dependent. We
+conclude that, while the qualitative trends of the observed H$\beta$-z
+relations are consistent with the expected passive and downsizing scenario, the
+possible presence of ionized gas even in the most massive and passive galaxies
+prevents to use this index for a quantitative estimate of the age evolution and
+for cosmological applications.
+",0,1,0,0,0,0
+15648,Finite-size effects in a stochastic Kuramoto model,"  We present a collective coordinate approach to study the collective behaviour
+of a finite ensemble of $N$ stochastic Kuramoto oscillators using two degrees
+of freedom; one describing the shape dynamics of the oscillators and one
+describing their mean phase. Contrary to the thermodynamic limit $N\to\infty$
+in which the mean phase of the cluster of globally synchronized oscillators is
+constant in time, the mean phase of a finite-size cluster experiences Brownian
+diffusion with a variance proportional to $1/N$. This finite-size effect is
+quantitatively well captured by our collective coordinate approach.
+",0,1,0,0,0,0
+15649,The normal distribution is freely selfdecomposable,"  The class of selfdecomposable distributions in free probability theory was
+introduced by Barndorff-Nielsen and the third named author. It constitutes a
+fairly large subclass of the freely infinitely divisible distributions, but so
+far specific examples have been limited to Wigner's semicircle distributions,
+the free stable distributions, two kinds of free gamma distributions and a few
+other examples. In this paper, we prove that the (classical) normal
+distributions are freely selfdecomposable. More generally it is established
+that the Askey-Wimp-Kerov distribution $\mu_c$ is freely selfdecomposable for
+any $c$ in $[-1,0]$. The main ingredient in the proof is a general
+characterization of the freely selfdecomposable distributions in terms of the
+derivative of their free cumulant transform.
+",0,0,1,0,0,0
+15650,Simplex Queues for Hot-Data Download,"  In cloud storage systems, hot data is usually replicated over multiple nodes
+in order to accommodate simultaneous access by multiple users as well as
+increase the fault tolerance of the system. Recent cloud storage research has
+proposed using availability codes, which is a special class of erasure codes,
+as a more storage efficient way to store hot data. These codes enable data
+recovery from multiple, small disjoint groups of servers. The number of the
+recovery groups is referred to as the availability and the size of each group
+as the locality of the code. Until now, we have very limited knowledge on how
+code locality and availability affect data access time. Data download from
+these systems involves multiple fork-join queues operating in-parallel, making
+the analysis of access time a very challenging problem. In this paper, we
+present an approximate analysis of data access time in storage systems that
+employ simplex codes, which are an important and in certain sense optimal class
+of availability codes. We consider and compare three strategies in assigning
+download requests to servers; first one aggressively exploits the storage
+availability for faster download, second one implements only load balancing,
+and the last one employs storage availability only for hot data download
+without incurring any negative impact on the cold data download.
+",1,0,0,0,0,0
+15651,Voting power of political parties in the Senate of Chile during the whole binomial system period: 1990-2017,"  The binomial system is an electoral system unique in the world. It was used
+to elect the senators and deputies of Chile during 27 years, from the return of
+democracy in 1990 until 2017. In this paper we study the real voting power of
+the different political parties in the Senate of Chile during the whole
+binomial period. We not only consider the different legislative periods, but
+also any party changes between one period and the next. The real voting power
+is measured by considering power indices from cooperative game theory, which
+are based on the capability of the political parties to form winning
+coalitions. With this approach, we can do an analysis that goes beyond the
+simple count of parliamentary seats.
+",0,0,0,0,0,1
+15652,A uniform stability principle for dual lattices,"  We prove a highly uniform stability or ""almost-near"" theorem for dual
+lattices of lattices $L \subseteq \Bbb R^n$. More precisely, we show that, for
+a vector $x$ from the linear span of a lattice $L \subseteq \Bbb R^n$, subject
+to $\lambda_1(L) \ge \lambda > 0$, to be $\varepsilon$-close to some vector
+from the dual lattice $L'$ of $L$, it is enough that the inner products $u\,x$
+are $\delta$-close (with $\delta < 1/3$) to some integers for all vectors $u
+\in L$ satisfying $\| u \| \le r$, where $r > 0$ depends on $n$, $\lambda$,
+$\delta$ and $\varepsilon$, only. This generalizes an earlier analogous result
+proved for integral vector lattices by M. Mačaj and the second author. The
+proof is nonconstructive, using the ultraproduct construction and a slight
+portion of nonstandard analysis.
+",0,0,1,0,0,0
+15653,"pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems","  pandapower is a Python based, BSD-licensed power system analysis tool aimed
+at automation of static and quasi-static analysis and optimization of balanced
+power systems. It provides power flow, optimal power flow, state estimation,
+topological graph searches and short circuit calculations according to IEC
+60909. pandapower includes a Newton-Raphson power flow solver formerly based on
+PYPOWER, which has been accelerated with just-in-time compilation. Additional
+enhancements to the solver include the capability to model constant current
+loads, grids with multiple reference nodes and a connectivity check. The
+pandapower network model is based on electric elements, such as lines, two and
+three-winding transformers or ideal switches. All elements can be defined with
+nameplate parameters and are internally processed with equivalent circuit
+models, which have been validated against industry standard software tools. The
+tabular data structure used to define networks is based on the Python library
+pandas, which allows comfortable handling of input and output parameters. The
+implementation in Python makes pandapower easy to use and allows comfortable
+extension with third-party libraries. pandapower has been successfully applied
+in several grid studies as well as for educational purposes. A comprehensive,
+publicly available case-study demonstrates a possible application of pandapower
+in an automated time series calculation.
+",1,0,0,0,0,0
+15654,Temporal oscillations of light transmission through dielectric microparticles subjected to optically induced motion,"  We consider light-induced binding and motion of dielectric microparticles in
+an optical waveguide that gives rise to a back-action effect such as light
+transmission oscillating with time. Modeling the particles by dielectric slabs
+allows us to solve the problem analytically and obtain a rich variety of
+dynamical regimes both for Newtonian and damped motion. This variety is clearly
+reflected in temporal oscillations of the light transmission. The
+characteristic frequencies of the oscillations are within the ultrasound range
+of the order of $10^{5}$ Hz for micron size particles and injected power of the
+order of 100 mW. In addition, we consider driven by propagating light dynamics
+of a dielectric particle inside a Fabry-Perot resonator. These phenomena pave a
+way for optical driving and monitoring of motion of particles in waveguides and
+resonators.
+",0,1,0,0,0,0
+15655,Berry-Esséen bounds for parameter estimation of general Gaussian processes,"  We study rates of convergence in central limit theorems for the partial sum
+of squares of general Gaussian sequences, using tools from analysis on Wiener
+space. No assumption of stationarity, asymptotically or otherwise, is made. The
+main theoretical tool is the so-called Optimal Fourth Moment Theorem
+\cite{NP2015}, which provides a sharp quantitative estimate of the total
+variation distance on Wiener chaos to the normal law. The only assumptions made
+on the sequence are the existence of an asymptotic variance, that a
+least-squares-type estimator for this variance parameter has a bias and a
+variance which can be controlled, and that the sequence's auto-correlation
+function, which may exhibit long memory, has a no-worse memory than that of
+fractional Brownian motion with Hurst parameter }$H<3/4$.{\ \ Our main result
+is explicit, exhibiting the trade-off between bias, variance, and memory. We
+apply our result to study drift parameter estimation problems for subfractional
+Ornstein-Uhlenbeck and bifractional Ornstein-Uhlenbeck processes with
+fixed-time-step observations. These are processes which fail to be stationary
+or self-similar, but for which detailed calculations result in explicit
+formulas for the estimators' asymptotic normality.
+",0,0,1,1,0,0
+15656,Anomaly Detection via Minimum Likelihood Generative Adversarial Networks,"  Anomaly detection aims to detect abnormal events by a model of normality. It
+plays an important role in many domains such as network intrusion detection,
+criminal activity identity and so on. With the rapidly growing size of
+accessible training data and high computation capacities, deep learning based
+anomaly detection has become more and more popular. In this paper, a new
+domain-based anomaly detection method based on generative adversarial networks
+(GAN) is proposed. Minimum likelihood regularization is proposed to make the
+generator produce more anomalies and prevent it from converging to normal data
+distribution. Proper ensemble of anomaly scores is shown to improve the
+stability of discriminator effectively. The proposed method has achieved
+significant improvement than other anomaly detection methods on Cifar10 and UCI
+datasets.
+",0,0,0,1,0,0
+15657,Supercurrent as a Probe for Topological Superconductivity in Magnetic Adatom Chains,"  A magnetic adatom chain, proximity coupled to a conventional superconductor
+with spin-orbit coupling, exhibits locally an odd-parity, spin-triplet pairing
+amplitude. We show that the singlet-triplet junction, thus formed, leads to a
+net spin accumulation in the near vicinity of the chain. The accumulated spins
+are polarized along the direction of the local $\mathbf{d}$-vector for triplet
+pairing and generate an enhanced persistent current flowing around the chain.
+The spin polarization and the ""supercurrent"" reverse their directions beyond a
+critical exchange coupling strength at which the singlet superconducting order
+changes its sign on the chain. The current is strongly enhanced in the
+topological superconducting regime where Majorana bound states appear at the
+chain ends. The current and the spin profile offer alternative routes to
+characterize the topological superconducting state in adatom chains and
+islands.
+",0,1,0,0,0,0
+15658,Experimental statistics of veering triangulations,"  Certain fibered hyperbolic 3-manifolds admit a $\mathit{\text{layered veering
+triangulation}}$, which can be constructed algorithmically given the stable
+lamination of the monodromy. These triangulations were introduced by Agol in
+2011, and have been further studied by several others in the years since. We
+obtain experimental results which shed light on the combinatorial structure of
+veering triangulations, and its relation to certain topological invariants of
+the underlying manifold.
+",0,0,1,0,0,0
+15659,Concept Drift and Anomaly Detection in Graph Streams,"  Graph representations offer powerful and intuitive ways to describe data in a
+multitude of application domains. Here, we consider stochastic processes
+generating graphs and propose a methodology for detecting changes in
+stationarity of such processes. The methodology is general and considers a
+process generating attributed graphs with a variable number of vertices/edges,
+without the need to assume one-to-one correspondence between vertices at
+different time steps. The methodology acts by embedding every graph of the
+stream into a vector domain, where a conventional multivariate change detection
+procedure can be easily applied. We ground the soundness of our proposal by
+proving several theoretical results. In addition, we provide a specific
+implementation of the methodology and evaluate its effectiveness on several
+detection problems involving attributed graphs representing biological
+molecules and drawings. Experimental results are contrasted with respect to
+suitable baseline methods, demonstrating the effectiveness of our approach.
+",1,0,0,1,0,0
+15660,Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks,"  Computational Fluid Dynamics (CFD) is a hugely important subject with
+applications in almost every engineering field, however, fluid simulations are
+extremely computationally and memory demanding. Towards this end, we present
+Lat-Net, a method for compressing both the computation time and memory usage of
+Lattice Boltzmann flow simulations using deep neural networks. Lat-Net employs
+convolutional autoencoders and residual connections in a fully differentiable
+scheme to compress the state size of a simulation and learn the dynamics on
+this compressed form. The result is a computationally and memory efficient
+neural network that can be iterated and queried to reproduce a fluid
+simulation. We show that once Lat-Net is trained, it can generalize to large
+grid sizes and complex geometries while maintaining accuracy. We also show that
+Lat-Net is a general method for compressing other Lattice Boltzmann based
+simulations such as Electromagnetism.
+",0,1,0,1,0,0
+15661,Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study,"  While first-order optimization methods such as stochastic gradient descent
+(SGD) are popular in machine learning (ML), they come with well-known
+deficiencies, including relatively-slow convergence, sensitivity to the
+settings of hyper-parameters such as learning rate, stagnation at high training
+errors, and difficulty in escaping flat regions and saddle points. These issues
+are particularly acute in highly non-convex settings such as those arising in
+neural networks. Motivated by this, there has been recent interest in
+second-order methods that aim to alleviate these shortcomings by capturing
+curvature information. In this paper, we report detailed empirical evaluations
+of a class of Newton-type methods, namely sub-sampled variants of trust region
+(TR) and adaptive regularization with cubics (ARC) algorithms, for non-convex
+ML problems. In doing so, we demonstrate that these methods not only can be
+computationally competitive with hand-tuned SGD with momentum, obtaining
+comparable or better generalization performance, but also they are highly
+robust to hyper-parameter settings. Further, in contrast to SGD with momentum,
+we show that the manner in which these Newton-type methods employ curvature
+information allows them to seamlessly escape flat regions and saddle points.
+",1,0,0,1,0,0
+15662,Computational Approaches for Stochastic Shortest Path on Succinct MDPs,"  We consider the stochastic shortest path (SSP) problem for succinct Markov
+decision processes (MDPs), where the MDP consists of a set of variables, and a
+set of nondeterministic rules that update the variables. First, we show that
+several examples from the AI literature can be modeled as succinct MDPs. Then
+we present computational approaches for upper and lower bounds for the SSP
+problem: (a)~for computing upper bounds, our method is polynomial-time in the
+implicit description of the MDP; (b)~for lower bounds, we present a
+polynomial-time (in the size of the implicit description) reduction to
+quadratic programming. Our approach is applicable even to infinite-state MDPs.
+Finally, we present experimental results to demonstrate the effectiveness of
+our approach on several classical examples from the AI literature.
+",1,0,0,0,0,0
+15663,One-dimensional in-plane edge domain walls in ultrathin ferromagnetic films,"  We study existence and properties of one-dimensional edge domain walls in
+ultrathin ferromagnetic films with uniaxial in-plane magnetic anisotropy. In
+these materials, the magnetization vector is constrained to lie entirely in the
+film plane, with the preferred directions dictated by the magnetocrystalline
+easy axis. We consider magnetization profiles in the vicinity of a straight
+film edge oriented at an arbitrary angle with respect to the easy axis. To
+minimize the micromagnetic energy, these profiles form transition layers in
+which the magnetization vector rotates away from the direction of the easy axis
+to align with the film edge. We prove existence of edge domain walls as
+minimizers of the appropriate one-dimensional micromagnetic energy functional
+and show that they are classical solutions of the associated Euler-Lagrange
+equation with Dirichlet boundary condition at the edge. We also perform a
+numerical study of these one-dimensional domain walls and uncover further
+properties of these domain wall profiles.
+",0,1,1,0,0,0
+15664,Nontrivial Turmites are Turing-universal,"  A Turmit is a Turing machine that works over a two-dimensional grid, that is,
+an agent that moves, reads and writes symbols over the cells of the grid. Its
+state is an arrow and, depending on the symbol that it reads, it turns to the
+left or to the right, switching the symbol at the same time. Several symbols
+are admitted, and the rule is specified by the turning sense that the machine
+has over each symbol. Turmites are a generalization of Langtons ant, and they
+present very complex and diverse behaviors. We prove that any Turmite, except
+for those whose rule does not depend on the symbol, can simulate any Turing
+Machine. We also prove the P-completeness of prediction their future behavior
+by explicitly giving a log-space reduction from the Topological Circuit Value
+Problem. A similar result was already established for Langtons ant; here we use
+a similar technique but prove a stronger notion of simulation, and for a more
+general family.
+",1,1,0,0,0,0
+15665,Dandelion: Redesigning the Bitcoin Network for Anonymity,"  Bitcoin and other cryptocurrencies have surged in popularity over the last
+decade. Although Bitcoin does not claim to provide anonymity for its users, it
+enjoys a public perception of being a `privacy-preserving' financial system. In
+reality, cryptocurrencies publish users' entire transaction histories in
+plaintext, albeit under a pseudonym; this is required for transaction
+validation. Therefore, if a user's pseudonym can be linked to their human
+identity, the privacy fallout can be significant. Recently, researchers have
+demonstrated deanonymization attacks that exploit weaknesses in the Bitcoin
+network's peer-to-peer (P2P) networking protocols. In particular, the P2P
+network currently forwards content in a structured way that allows observers to
+deanonymize users. In this work, we redesign the P2P network from first
+principles with the goal of providing strong, provable anonymity guarantees. We
+propose a simple networking policy called Dandelion, which achieves
+nearly-optimal anonymity guarantees at minimal cost to the network's utility.
+We also provide a practical implementation of Dandelion.
+",1,0,0,0,0,0
+15666,A characterisation of Lie algebras amongst anti-commutative algebras,"  Let $\mathbb{K}$ be an infinite field. We prove that if a variety of
+anti-commutative $\mathbb{K}$-algebras - not necessarily associative, where
+$xx=0$ is an identity - is locally algebraically cartesian closed, then it must
+be a variety of Lie algebras over $\mathbb{K}$. In particular,
+$\mathsf{Lie}_{\mathbb{K}}$ is the largest such. Thus, for a given variety of
+anti-commutative $\mathbb{K}$-algebras, the Jacobi identity becomes equivalent
+to a categorical condition: it is an identity in~$\mathcal{V}$ if and only if
+$\mathcal{V}$ is a subvariety of a locally algebraically cartesian closed
+variety of anti-commutative $\mathbb{K}$-algebras. This is based on a result
+saying that an algebraically coherent variety of anti-commutative
+$\mathbb{K}$-algebras is either a variety of Lie algebras or a variety of
+anti-associative algebras over $\mathbb{K}$.
+",0,0,1,0,0,0
+15667,Characteristics of a magneto-optical trap of molecules,"  We present the properties of a magneto-optical trap (MOT) of CaF molecules.
+We study the process of loading the MOT from a decelerated buffer-gas-cooled
+beam, and how best to slow this molecular beam in order to capture the most
+molecules. We determine how the number of molecules, the photon scattering
+rate, the oscillation frequency, damping constant, temperature, cloud size and
+lifetime depend on the key parameters of the MOT, especially the intensity and
+detuning of the main cooling laser. We compare our results to analytical and
+numerical models, to the properties of standard atomic MOTs, and to MOTs of SrF
+molecules. We load up to $2 \times 10^4$ molecules, and measure a maximum
+scattering rate of $2.5 \times 10^6$ s$^{-1}$ per molecule, a maximum
+oscillation frequency of 100 Hz, a maximum damping constant of 500 s$^{-1}$,
+and a minimum MOT rms radius of 1.5 mm. A minimum temperature of 730 $\mu$K is
+obtained by ramping down the laser intensity to low values. The lifetime,
+typically about 100 ms, is consistent with a leak out of the cooling cycle with
+a branching ratio of about $6 \times 10^{-6}$. The MOT has a capture velocity
+of about 11 m/s.
+",0,1,0,0,0,0
+15668,Reconstructing a Lattice Equation: a Non-Autonomous Approach to the Hietarinta Equation,"  In this paper we construct a non-autonomous version of the Hietarinta
+equation [Hietarinta J., J. Phys. A: Math. Gen. 37 (2004), L67-L73] and study
+its integrability properties. We show that this equation possess linear growth
+of the degrees of iterates, generalized symmetries depending on arbitrary
+functions, and that it is Darboux integrable. We use the first integrals to
+provide a general solution of this equation. In particular we show that this
+equation is a sub-case of the non-autonomous $Q_{\rm V}$ equation, and we
+provide a non-autonomous Möbius transformation to another equation found in
+[Hietarinta J., J. Nonlinear Math. Phys. 12 (2005), suppl. 2, 223-230] and
+appearing also in Boll's classification [Boll R., Ph.D. Thesis, Technische
+Universität Berlin, 2012].
+",0,1,0,0,0,0
+15669,Adversarial Training Versus Weight Decay,"  Performance-critical machine learning models should be robust to input
+perturbations not seen during training. Adversarial training is a method for
+improving a model's robustness to some perturbations by including them in the
+training process, but this tends to exacerbate other vulnerabilities of the
+model. The adversarial training framework has the effect of translating the
+data with respect to the cost function, while weight decay has a scaling
+effect. Although weight decay could be considered a crude regularization
+technique, it appears superior to adversarial training as it remains stable
+over a broader range of regimes and reduces all generalization errors. Equipped
+with these abstractions, we provide key baseline results and methodology for
+characterizing robustness. The two approaches can be combined to yield one
+small model that demonstrates good robustness to several white-box attacks
+associated with different metrics.
+",0,0,0,1,0,0
+15670,Ray tracing method for stereo image synthesis using CUDA,"  This paper presents a realization of the approach to spatial 3D stereo of
+visualization of 3D images with use parallel Graphics processing unit (GPU).
+The experiments of realization of synthesis of images of a 3D stage by a method
+of trace of beams on GPU with Compute Unified Device Architecture (CUDA) have
+shown that 60 % of the time is spent for the decision of a computing problem
+approximately, the major part of time (40 %) is spent for transfer of data
+between the central processing unit and GPU for computations and the
+organization process of visualization. The study of the influence of increase
+in the size of the GPU network at the speed of computations showed importance
+of the correct task of structure of formation of the parallel computer network
+and general mechanism of parallelization. Keywords: Volumetric 3D
+visualization, stereo 3D visualization, ray tracing, parallel computing on GPU,
+CUDA
+",1,0,0,0,0,0
+15671,Numerical investigation of gapped edge states in fractional quantum Hall-superconductor heterostructures,"  Fractional quantum Hall-superconductor heterostructures may provide a
+platform towards non-abelian topological modes beyond Majoranas. However their
+quantitative theoretical study remains extremely challenging. We propose and
+implement a numerical setup for studying edge states of fractional quantum Hall
+droplets with a superconducting instability. The fully gapped edges carry a
+topological degree of freedom that can encode quantum information protected
+against local perturbations. We simulate such a system numerically using exact
+diagonalization by restricting the calculation to the quasihole-subspace of a
+(time-reversal symmetric) bilayer fractional quantum Hall system of Laughlin
+$\nu=1/3$ states. We show that the edge ground states are permuted by
+spin-dependent flux insertion and demonstrate their fractional $6\pi$ Josephson
+effect, evidencing their topological nature and the Cooper pairing of
+fractionalized quasiparticles.
+",0,1,0,0,0,0
+15672,Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving,"  In this paper we present the first results of a pilot experiment in the
+capture and interpretation of multimodal signals of human experts engaged in
+solving challenging chess problems. Our goal is to investigate the extent to
+which observations of eye-gaze, posture, emotion and other physiological
+signals can be used to model the cognitive state of subjects, and to explore
+the integration of multiple sensor modalities to improve the reliability of
+detection of human displays of awareness and emotion. We observed chess players
+engaged in problems of increasing difficulty while recording their behavior.
+Such recordings can be used to estimate a participant's awareness of the
+current situation and to predict ability to respond effectively to challenging
+situations. Results show that a multimodal approach is more accurate than a
+unimodal one. By combining body posture, visual attention and emotion, the
+multimodal approach can reach up to 93% of accuracy when determining player's
+chess expertise while unimodal approach reaches 86%. Finally this experiment
+validates the use of our equipment as a general and reproducible tool for the
+study of participants engaged in screen-based interaction and/or problem
+solving.
+",1,0,0,1,0,0
+15673,Theoretical limitations of Encoder-Decoder GAN architectures,"  Encoder-decoder GANs architectures (e.g., BiGAN and ALI) seek to add an
+inference mechanism to the GANs setup, consisting of a small encoder deep net
+that maps data-points to their succinct encodings. The intuition is that being
+forced to train an encoder alongside the usual generator forces the system to
+learn meaningful mappings from the code to the data-point and vice-versa, which
+should improve the learning of the target distribution and ameliorate
+mode-collapse. It should also yield meaningful codes that are useful as
+features for downstream tasks. The current paper shows rigorously that even on
+real-life distributions of images, the encode-decoder GAN training objectives
+(a) cannot prevent mode collapse; i.e. the objective can be near-optimal even
+when the generated distribution has low and finite support (b) cannot prevent
+learning meaningless codes for data -- essentially white noise. Thus if
+encoder-decoder GANs do indeed work then it must be due to reasons as yet not
+understood, since the training objective can be low even for meaningless
+solutions.
+",1,0,0,1,0,0
+15674,Ground state sign-changing solutions for a class of nonlinear fractional Schrödinger-Poisson system in $\mathbb{R}^{3}$,"  In this paper, we are concerned with the existence of the least energy
+sign-changing solutions for the following fractional Schrödinger-Poisson
+system: \begin{align*}
+\left\{ \begin{aligned} &(-\Delta)^{s} u+V(x)u+\lambda\phi(x)u=f(x, u),\quad
+&\text{in}\, \ \mathbb{R}^{3},\\ &(-\Delta)^{t}\phi=u^{2},& \text{in}\,\
+\mathbb{R}^{3}, \end{aligned} \right. \end{align*} where $\lambda\in
+\mathbb{R}^{+}$ is a parameter, $s, t\in (0, 1)$ and $4s+2t>3$, $(-\Delta)^{s}$
+stands for the fractional Laplacian. By constraint variational method and
+quantitative deformation lemma, we prove that the above problem has one least
+energy sign-changing solution. Moreover, for any $\lambda>0$, we show that the
+energy of the least energy sign-changing solutions is strictly larger than two
+times the ground state energy.
+Finally, we consider $\lambda$ as a parameter and study the convergence
+property of the least energy sign-changing solutions as $\lambda\searrow 0$.
+",0,0,1,0,0,0
+15675,Monads on higher monoidal categories,"  We study the action of monads on categories equipped with several monoidal
+structures. We identify the structure and conditions that guarantee that the
+higher monoidal structure is inherited by the category of algebras over the
+monad. Monoidal monads and comonoidal monads appear as the base cases in this
+hierarchy. Monads acting on duoidal categories constitute the next case. We
+cover the general case of $n$-monoidal categories and discuss several naturally
+occurring examples in which $n\leq 3$.
+",0,0,1,0,0,0
+15676,A Review of Augmented Reality Applications for Building Evacuation,"  Evacuation is one of the main disaster management solutions to reduce the
+impact of man-made and natural threats on building occupants. To date, several
+modern technologies and gamification concepts, e.g. immersive virtual reality
+and serious games, have been used to enhance building evacuation preparedness
+and effectiveness. Those tools have been used both to investigate human
+behavior during building emergencies and to train building occupants on how to
+cope with building evacuations.
+Augmented Reality (AR) is novel technology that can enhance this process
+providing building occupants with virtual contents to improve their evacuation
+performance. This work aims at reviewing existing AR applications developed for
+building evacuation. This review identifies the disasters and types of building
+those tools have been applied for. Moreover, the application goals, hardware
+and evacuation stages affected by AR are also investigated in the review.
+Finally, this review aims at identifying the challenges to face for further
+development of AR evacuation tools.
+",1,0,0,0,0,0
+15677,Topology in time-reversal symmetric crystals,"  The discovery of topological insulators has reformed modern materials
+science, promising to be a platform for tabletop relativistic physics,
+electronic transport without scattering, and stable quantum computation.
+Topological invariants are used to label distinct types of topological
+insulators. But it is not generally known how many or which invariants can
+exist in any given crystalline material. Using a new and efficient counting
+algorithm, we study the topological invariants that arise in time-reversal
+symmetric crystals. This results in a unified picture that explains the
+relations between all known topological invariants in these systems. It also
+predicts new topological phases and one entirely new topological invariant. We
+present explicitly the classification of all two-dimensional crystalline
+fermionic materials, and give a straightforward procedure for finding the
+analogous result in any three-dimensional structure. Our study represents a
+single, intuitive physical picture applicable to all topological invariants in
+real materials, with crystal symmetries.
+",0,1,0,0,0,0
+15678,Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations,"  Deep convolutional neural network (CNN) inference requires significant amount
+of memory and computation, which limits its deployment on embedded devices. To
+alleviate these problems to some extent, prior research utilize low precision
+fixed-point numbers to represent the CNN weights and activations. However, the
+minimum required data precision of fixed-point weights varies across different
+networks and also across different layers of the same network. In this work, we
+propose using floating-point numbers for representing the weights and
+fixed-point numbers for representing the activations. We show that using
+floating-point representation for weights is more efficient than fixed-point
+representation for the same bit-width and demonstrate it on popular large-scale
+CNNs such as AlexNet, SqueezeNet, GoogLeNet and VGG-16. We also show that such
+a representation scheme enables compact hardware multiply-and-accumulate (MAC)
+unit design. Experimental results show that the proposed scheme reduces the
+weight storage by up to 36% and power consumption of the hardware multiplier by
+up to 50%.
+",1,0,0,0,0,0
+15679,Voids in the Cosmic Web as a probe of dark energy,"  The formation of large voids in the Cosmic Web from the initial adiabatic
+cosmological perturbations of space-time metric, density and velocity of matter
+is investigated in cosmological model with the dynamical dark energy
+accelerating expansion of the Universe. It is shown that the negative density
+perturbations with the initial radius of about 50 Mpc in comoving to the
+cosmological background coordinates and the amplitude corresponding to the
+r.m.s. temperature fluctuations of the cosmic microwave background lead to the
+formation of voids with the density contrast up to $-$0.9, maximal peculiar
+velocity about 400 km/s and the radius close to the initial one. An important
+feature of voids formation from the analyzed initial amplitudes and profiles is
+establishing the surrounding overdensity shell. We have shown that the ratio of
+the peculiar velocity in units of the Hubble flow to the density contrast in
+the central part of a void does not depend or weakly depends on the distance
+from the center of the void. It is also shown that this ratio is sensitive to
+the values of dark energy parameters and can be used to find them based on the
+observational data on mass density and peculiar velocities of galaxies in the
+voids.
+",0,1,0,0,0,0
+15680,Templated ligation can create a hypercycle replication network,"  The stability of sequence replication was crucial for the emergence of
+molecular evolution and early life. Exponential replication with a first-order
+growth dynamics show inherent instabilities such as the error catastrophe and
+the dominance by the fastest replicators. This favors less structured and short
+sequences. The theoretical concept of hypercycles has been proposed to solve
+these problems. Their higher-order growth kinetics leads to frequency-dependent
+selection and stabilizes the replication of majority molecules. However, many
+implementations of hypercycles are unstable or require special sequences with
+catalytic activity. Here, we demonstrate the spontaneous emergence of
+higher-order cooperative replication from a network of simple ligation chain
+reactions (LCR). We performed long-term LCR experiments from a mixture of
+sequences under molecule degrading conditions with a ligase protein. At the
+chosen temperature cycling, a network of positive feedback loops arose from
+both the cooperative ligation of matching sequences and the emerging increase
+in sequence length. It generated higher-order replication with
+frequency-dependent selection. The experiments matched a complete simulation
+using experimentally determined ligation rates and the hypercycle mechanism was
+also confirmed by abstracted modeling. Since templated ligation is a most basic
+reaction of oligonucleotides, the described mechanism could have been
+implemented under microthermal convection on early Earth.
+",0,0,0,0,1,0
+15681,Variational characterization of H^p,"  In this paper we obtain the variational characterization of Hardy space $H^p$
+for $p\in(\frac n{n+1},1]$ and get estimates for the oscillation operator and
+the $\lambda$-jump operator associated with approximate identities acting on
+$H^p$ for $p\in(\frac n{n+1},1]$. Moreover, we give counterexamples to show
+that the oscillation and $\lambda$-jump associated with some approximate
+identity can not be used to characterize $H^p$ for $p\in(\frac n{n+1},1]$.
+",0,0,1,0,0,0
+15682,Towards Deep Learning Models Resistant to Adversarial Attacks,"  Recent work has demonstrated that neural networks are vulnerable to
+adversarial examples, i.e., inputs that are almost indistinguishable from
+natural data and yet classified incorrectly by the network. In fact, some of
+the latest findings suggest that the existence of adversarial attacks may be an
+inherent weakness of deep learning models. To address this problem, we study
+the adversarial robustness of neural networks through the lens of robust
+optimization. This approach provides us with a broad and unifying view on much
+of the prior work on this topic. Its principled nature also enables us to
+identify methods for both training and attacking neural networks that are
+reliable and, in a certain sense, universal. In particular, they specify a
+concrete security guarantee that would protect against any adversary. These
+methods let us train networks with significantly improved resistance to a wide
+range of adversarial attacks. They also suggest the notion of security against
+a first-order adversary as a natural and broad security guarantee. We believe
+that robustness against such well-defined classes of adversaries is an
+important stepping stone towards fully resistant deep learning models.
+",1,0,0,1,0,0
+15683,Internal delensing of Planck CMB temperature and polarization,"  We present a first internal delensing of CMB maps, both in temperature and
+polarization, using the public foreground-cleaned (SMICA) Planck 2015 maps.
+After forming quadratic estimates of the lensing potential, we use the
+corresponding displacement field to undo the lensing on the same data. We build
+differences of the delensed spectra to the original data spectra specifically
+to look for delensing signatures. After taking into account reconstruction
+noise biases in the delensed spectra, we find an expected sharpening of the
+power spectrum acoustic peaks with a delensing efficiency of $29\,\%$ ($TT$)
+$25\,\%$ ($TE$) and $22\,\%$ ($EE$). The detection significance of the
+delensing effects is very high in all spectra: $12\,\sigma$ in $EE$
+polarization; $18\,\sigma$ in $TE$; and $20\,\sigma$ in $TT$. The null
+hypothesis of no lensing in the maps is rejected at $26\,\sigma$. While direct
+detection of the power in lensing $B$-modes themselves is not possible at high
+significance at Planck noise levels, we do detect (at $4.5\,\sigma$ under the
+null hypothesis) delensing effects in the $B$-mode map, with $7\,\%$ reduction
+in lensing power. Our results provide a first demonstration of polarization
+delensing, and generally of internal CMB delensing, and stand in agreement with
+the baseline $\Lambda$CDM Planck 2015 cosmology expectations.
+",0,1,0,0,0,0
+15684,Renaissance: Self-Stabilizing Distributed SDN Control Plane,"  By introducing programmability, automated verification, and innovative
+debugging tools, Software-Defined Networks (SDNs) are poised to meet the
+increasingly stringent dependability requirements of today's communication
+networks. However, the design of fault-tolerant SDNs remains an open challenge.
+This paper considers the design of dependable SDNs through the lenses of
+self-stabilization - a very strong notion of fault-tolerance. In particular, we
+develop algorithms for an in-band and distributed control plane for SDNs,
+called Renaissance, which tolerate a wide range of (concurrent) controller,
+link, and communication failures. Our self-stabilizing algorithms ensure that
+after the occurrence of an arbitrary combination of failures, (i) every
+non-faulty SDN controller can eventually reach any switch in the network within
+a bounded communication delay (in the presence of a bounded number of
+concurrent failures) and (ii) every switch is managed by at least one
+non-faulty controller. We evaluate Renaissance through a rigorous worst-case
+analysis as well as a prototype implementation (based on OVS and Floodlight),
+and we report on our experiments using Mininet.
+",1,0,0,0,0,0
+15685,Robust and Scalable Power System State Estimation via Composite Optimization,"  In today's cyber-enabled smart grids, high penetration of uncertain
+renewables, purposeful manipulation of meter readings, and the need for
+wide-area situational awareness, call for fast, accurate, and robust power
+system state estimation. The least-absolute-value (LAV) estimator is known for
+its robustness relative to the weighted least-squares (WLS) one. However, due
+to nonconvexity and nonsmoothness, existing LAV solvers based on linear
+programming are typically slow, hence inadequate for real-time system
+monitoring. This paper develops two novel algorithms for efficient LAV
+estimation, which draw from recent advances in composite optimization. The
+first is a deterministic linear proximal scheme that handles a sequence of
+convex quadratic problems, each efficiently solvable either via off-the-shelf
+algorithms or through the alternating direction method of multipliers.
+Leveraging the sparse connectivity inherent to power networks, the second
+scheme is stochastic, and updates only \emph{a few} entries of the complex
+voltage state vector per iteration. In particular, when voltage magnitude and
+(re)active power flow measurements are used only, this number reduces to one or
+two, \emph{regardless of} the number of buses in the network. This
+computational complexity evidently scales well to large-size power systems.
+Furthermore, by carefully \emph{mini-batching} the voltage and power flow
+measurements, accelerated implementation of the stochastic iterations becomes
+possible. The developed algorithms are numerically evaluated using a variety of
+benchmark power networks. Simulated tests corroborate that improved robustness
+can be attained at comparable or markedly reduced computation times for medium-
+or large-size networks relative to the ""workhorse"" WLS-based Gauss-Newton
+iterations.
+",1,0,0,0,0,0
+15686,A new algorithm for constraint satisfaction problems with few subpowers templates,"  In this article, we provide a new algorithm for solving constraint
+satisfaction problems over templates with few subpowers, by reducing the
+problem to the combination of solvability of a polynomial number of systems of
+linear equations over finite fields and reductions via absorbing subuniverses.
+",0,0,1,0,0,0
+15687,Testing of General Relativity with Geodetic VLBI,"  The geodetic VLBI technique is capable of measuring the Sun's gravity light
+deflection from distant radio sources around the whole sky. This light
+deflection is equivalent to the conventional gravitational delay used for the
+reduction of geodetic VLBI data. While numerous tests based on a global set of
+VLBI data have shown that the parameter 'gamma' of the post-Newtonian
+approximation is equal to unity with a precision of about 0.02 percent, more
+detailed analysis reveals some systematic deviations depending on the angular
+elongation from the Sun. In this paper a limited set of VLBI observations near
+the Sun were adjusted to obtain the estimate of the parameter 'gamma' free of
+the elongation angle impact. The parameter 'gamma' is still found to be close
+to unity with precision of 0.06 percent, two subsets of VLBI data measured at
+short and long baselines produce some statistical inconsistency.
+",0,1,0,0,0,0
+15688,An unbiased estimator for the ellipticity from image moments,"  An unbiased estimator for the ellipticity of an object in a noisy image is
+given in terms of the image moments. Three assumptions are made: i) the pixel
+noise is normally distributed, although with arbitrary covariance matrix, ii)
+the image moments are taken about a fixed centre, and iii) the point-spread
+function is known. The relevant combinations of image moments are then jointly
+normal and their covariance matrix can be computed. A particular estimator for
+the ratio of the means of jointly normal variates is constructed and used to
+provide the unbiased estimator for the ellipticity. Furthermore, an unbiased
+estimate of the covariance of the new estimator is also given.
+",0,1,1,1,0,0
+15689,Spectral estimation of the percolation transition in clustered networks,"  There have been several spectral bounds for the percolation transition in
+networks, using spectrum of matrices associated with the network such as the
+adjacency matrix and the non-backtracking matrix. However they are far from
+being tight when the network is sparse and displays clustering or transitivity,
+which is represented by existence of short loops e.g. triangles. In this work,
+for the bond percolation, we first propose a message passing algorithm for
+calculating size of percolating clusters considering effects of triangles, then
+relate the percolation transition to the leading eigenvalue of a matrix that we
+name the triangle-non-backtracking matrix, by analyzing stability of the
+message passing equations. We establish that our method gives a tighter
+lower-bound to the bond percolation transition than previous spectral bounds,
+and it becomes exact for an infinite network with no loops longer than 3. We
+evaluate numerically our methods on synthetic and real-world networks, and
+discuss further generalizations of our approach to include higher-order
+sub-structures.
+",1,1,0,1,0,0
+15690,Collapsibility to a subcomplex of a given dimension is NP-complete,"  In this paper we extend the works of Tancer and of Malgouyres and Francés,
+showing that $(d,k)$-collapsibility is NP-complete for $d\geq k+2$ except
+$(2,0)$. By $(d,k)$-collapsibility we mean the following problem: determine
+whether a given $d$-dimensional simplicial complex can be collapsed to some
+$k$-dimensional subcomplex. The question of establishing the complexity status
+of $(d,k)$-collapsibility was asked by Tancer, who proved NP-completeness of
+$(d,0)$ and $(d,1)$-collapsibility (for $d\geq 3$). Our extended result,
+together with the known polynomial-time algorithms for $(2,0)$ and $d=k+1$,
+answers the question completely.
+",1,0,1,0,0,0
+15691,"Run, skeleton, run: skeletal model in a physics-based simulation","  In this paper, we present our approach to solve a physics-based reinforcement
+learning challenge ""Learning to Run"" with objective to train
+physiologically-based human model to navigate a complex obstacle course as
+quickly as possible. The environment is computationally expensive, has a
+high-dimensional continuous action space and is stochastic. We benchmark state
+of the art policy-gradient methods and test several improvements, such as layer
+normalization, parameter noise, action and state reflecting, to stabilize
+training and improve its sample-efficiency. We found that the Deep
+Deterministic Policy Gradient method is the most efficient method for this
+environment and the improvements we have introduced help to stabilize training.
+Learned models are able to generalize to new physical scenarios, e.g. different
+obstacle courses.
+",1,0,0,1,0,0
+15692,Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression,"  Acquisition of labeled training samples for affective computing is usually
+costly and time-consuming, as affects are intrinsically subjective, subtle and
+uncertain, and hence multiple human assessors are needed to evaluate each
+affective sample. Particularly, for affect estimation in the 3D space of
+valence, arousal and dominance, each assessor has to perform the evaluations in
+three dimensions, which makes the labeling problem even more challenging. Many
+sophisticated machine learning approaches have been proposed to reduce the data
+labeling requirement in various other domains, but so far few have considered
+affective computing. This paper proposes two multi-task active learning for
+regression approaches, which select the most beneficial samples to label, by
+considering the three affect primitives simultaneously. Experimental results on
+the VAM corpus demonstrated that our optimal sample selection approaches can
+result in better estimation performance than random selection and several
+traditional single-task active learning approaches. Thus, they can help
+alleviate the data labeling problem in affective computing, i.e., better
+estimation performance can be obtained from fewer labeling queries.
+",1,0,0,1,0,0
+15693,Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning,"  In this paper, a sparse Markov decision process (MDP) with novel causal
+sparse Tsallis entropy regularization is proposed.The proposed policy
+regularization induces a sparse and multi-modal optimal policy distribution of
+a sparse MDP. The full mathematical analysis of the proposed sparse MDP is
+provided.We first analyze the optimality condition of a sparse MDP. Then, we
+propose a sparse value iteration method which solves a sparse MDP and then
+prove the convergence and optimality of sparse value iteration using the Banach
+fixed point theorem. The proposed sparse MDP is compared to soft MDPs which
+utilize causal entropy regularization. We show that the performance error of a
+sparse MDP has a constant bound, while the error of a soft MDP increases
+logarithmically with respect to the number of actions, where this performance
+error is caused by the introduced regularization term. In experiments, we apply
+sparse MDPs to reinforcement learning problems. The proposed method outperforms
+existing methods in terms of the convergence speed and performance.
+",1,0,0,1,0,0
+15694,Mid-infrared Spectroscopic Observations of the Dust-forming Classical Nova V2676 Oph,"  The dust-forming nova V2676 Oph is unique in that it was the first nova to
+provide evidence of C_2 and CN molecules during its near-maximum phase and
+evidence of CO molecules during its early decline phase. Observations of this
+nova have revealed the slow evolution of its lightcurves and have also shown
+low isotopic ratios of carbon (12C/13C) and nitrogen (14N/15N) in its nova
+envelope. These behaviors indicate that the white dwarf (WD) star hosting V2676
+Oph is a CO-rich WD rather than an ONe-rich WD (typically larger in mass than
+the former). We performed mid-infrared spectroscopic and photometric
+observations of V2676 Oph in 2013 and 2014 (respectively 452 and 782 days after
+its discovery). No significant [Ne II] emission at 12.8 micron was detected at
+either epoch. These provided evidence for a CO-rich WD star hosting V2676 Oph.
+Both carbon-rich and oxygen-rich grains were detected in addition to an
+unidentified infrared feature at 11.4 micron originating from polycyclic
+aromatic hydrocarbon molecules or hydrogenated amorphous carbon grains in the
+envelope of V2676 Oph.
+",0,1,0,0,0,0
+15695,An invitation to 2D TQFT and quantization of Hitchin spectral curves,"  This article consists of two parts. In Part 1, we present a formulation of
+two-dimensional topological quantum field theories in terms of a functor from a
+category of Ribbon graphs to the endofuntor category of a monoidal category.
+The key point is that the category of ribbon graphs produces all Frobenius
+objects. Necessary backgrounds from Frobenius algebras, topological quantum
+field theories, and cohomological field theories are reviewed. A result on
+Frobenius algebra twisted topological recursion is included at the end of Part
+1.
+In Part 2, we explain a geometric theory of quantum curves. The focus is
+placed on the process of quantization as a passage from families of Hitchin
+spectral curves to families of opers. To make the presentation simpler, we
+unfold the story using SL_2(\mathbb{C})-opers and rank 2 Higgs bundles defined
+on a compact Riemann surface $C$ of genus greater than $1$. In this case,
+quantum curves, opers, and projective structures in $C$ all become the same
+notion. Background materials on projective coordinate systems, Higgs bundles,
+opers, and non-Abelian Hodge correspondence are explained.
+",0,0,1,0,0,0
+15696,An Ensemble Classification Algorithm Based on Information Entropy for Data Streams,"  Data stream mining problem has caused widely concerns in the area of machine
+learning and data mining. In some recent studies, ensemble classification has
+been widely used in concept drift detection, however, most of them regard
+classification accuracy as a criterion for judging whether concept drift
+happening or not. Information entropy is an important and effective method for
+measuring uncertainty. Based on the information entropy theory, a new algorithm
+using information entropy to evaluate a classification result is developed. It
+uses ensemble classification techniques, and the weight of each classifier is
+decided through the entropy of the result produced by an ensemble classifiers
+system. When the concept in data streams changing, the classifiers' weight
+below a threshold value will be abandoned to adapt to a new concept in one
+time. In the experimental analysis section, six databases and four proposed
+algorithms are executed. The results show that the proposed method can not only
+handle concept drift effectively, but also have a better classification
+accuracy and time performance than the contrastive algorithms.
+",1,0,0,0,0,0
+15697,Competition between disorder and interaction effects in 3D Weyl semimetals,"  We investigate the low-energy scaling behavior of an interacting 3D Weyl
+semimetal in the presence of disorder. In order to achieve a renormalization
+group analysis of the theory, we focus on the effects of a
+short-ranged-correlated disorder potential, checking nevertheless that this
+choice is not essential to locate the different phases of the Weyl semimetal.
+We show that there is a line of fixed-points in the renormalization group flow
+of the interacting theory, corresponding to the disorder-driven transition to a
+diffusive metal phase. Along that boundary, the critical disorder strength
+undergoes a strong increase with respect to the noninteracting theory, as a
+consequence of the unconventional screening of the Coulomb and disorder-induced
+interactions. A complementary resolution of the Schwinger-Dyson equations
+allows us to determine the full phase diagram of the system, showing the
+prevalence of a renormalized semimetallic phase in the regime of intermediate
+interaction strength, and adjacent to the non-Fermi liquid phase characteristic
+of the strong interaction regime of 3D Weyl semimetals.
+",0,1,0,0,0,0
+15698,Mixing properties and central limit theorem for associated point processes,"  Positively (resp. negatively) associated point processes are a class of point
+processes that induce attraction (resp. inhibition) between the points. As an
+important example, determinantal point processes (DPPs) are negatively
+associated. We prove $\alpha$-mixing properties for associated spatial point
+processes by controlling their $\alpha$-coefficients in terms of the first two
+intensity functions. A central limit theorem for functionals of associated
+point processes is deduced, using both the association and the $\alpha$-mixing
+properties. We discuss in detail the case of DPPs, for which we obtain the
+limiting distribution of sums, over subsets of close enough points of the
+process, of any bounded function of the DPP. As an application, we get the
+asymptotic properties of the parametric two-step estimator of some
+inhomogeneous DPPs.
+",0,0,1,1,0,0
+15699,Computational landscape of user behavior on social media,"  With the increasing abundance of 'digital footprints' left by human
+interactions in online environments, e.g., social media and app use, the
+ability to model complex human behavior has become increasingly possible. Many
+approaches have been proposed, however, most previous model frameworks are
+fairly restrictive. We introduce a new social modeling approach that enables
+the creation of models directly from data with minimal a priori restrictions on
+the model class. In particular, we infer the minimally complex, maximally
+predictive representation of an individual's behavior when viewed in isolation
+and as driven by a social input. We then apply this framework to a
+heterogeneous catalog of human behavior collected from fifteen thousand users
+on the microblogging platform Twitter. The models allow us to describe how a
+user processes their past behavior and their social inputs. Despite the
+diversity of observed user behavior, most models inferred fall into a small
+subclass of all possible finite-state processes. Thus, our work demonstrates
+that user behavior, while quite complex, belies simple underlying computational
+structures.
+",1,0,0,1,0,0
+15700,Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model,"  We present a finite difference time domain (FDTD) model for computation of A
+line scans in time domain optical coherence tomography (OCT). By simulating
+only the end of the two arms of the interferometer and computing the
+interference signal in post processing, it is possible to reduce the
+computational time required by the simulations and, thus, to simulate much
+bigger environments. Moreover, it is possible to simulate successive A lines
+and thus obtaining a cross section of the sample considered. In this paper we
+present the model applied to two different samples: a glass rod filled with
+water-sucrose solution at different concentrations and a peripheral nerve. This
+work demonstrates the feasibility of using OCT for non-invasive, direct optical
+monitoring of peripheral nerve activity, which is a long-sought goal of
+neuroscience.
+",0,1,0,0,0,0
+15701,On Achievable Rates of AWGN Energy-Harvesting Channels with Block Energy Arrival and Non-Vanishing Error Probabilities,"  This paper investigates the achievable rates of an additive white Gaussian
+noise (AWGN) energy-harvesting (EH) channel with an infinite battery. The EH
+process is characterized by a sequence of blocks of harvested energy, which is
+known causally at the source. The harvested energy remains constant within a
+block while the harvested energy across different blocks is characterized by a
+sequence of independent and identically distributed (i.i.d.) random variables.
+The blocks have length $L$, which can be interpreted as the coherence time of
+the energy arrival process. If $L$ is a constant or grows sublinearly in the
+blocklength $n$, we fully characterize the first-order term in the asymptotic
+expansion of the maximum transmission rate subject to a fixed tolerable error
+probability $\varepsilon$. The first-order term is known as the
+$\varepsilon$-capacity. In addition, we obtain lower and upper bounds on the
+second-order term in the asymptotic expansion, which reveal that the second
+order term scales as $\sqrt{\frac{L}{n}}$ for any $\varepsilon$ less than
+$1/2$. The lower bound is obtained through analyzing the save-and-transmit
+strategy. If $L$ grows linearly in $n$, we obtain lower and upper bounds on the
+$\varepsilon$-capacity, which coincide whenever the cumulative distribution
+function (cdf) of the EH random variable is continuous and strictly increasing.
+In order to achieve the lower bound, we have proposed a novel adaptive
+save-and-transmit strategy, which chooses different save-and-transmit codes
+across different blocks according to the energy variation across the blocks.
+",1,0,1,0,0,0
+15702,Probabilistic Projection of Subnational Total Fertility Rates,"  We consider the problem of probabilistic projection of the total fertility
+rate (TFR) for subnational regions. We seek a method that is consistent with
+the UN's recently adopted Bayesian method for probabilistic TFR projections for
+all countries, and works well for all countries. We assess various possible
+methods using subnational TFR data for 47 countries. We find that the method
+that performs best in terms of out-of-sample predictive performance and also in
+terms of reproducing the within-country correlation in TFR is a method that
+scales the national trajectory by a region-specific scale factor that is
+allowed to vary slowly over time. This supports the hypothesis of Watkins
+(1990, 1991) that within-country TFR converges over time in response to
+country-specific factors, and extends the Watkins hypothesis to the last 50
+years and to a much wider range of countries around the world.
+",0,0,0,1,0,0
+15703,Optimising the topological information of the $A_\infty$-persistence groups,"  Persistent homology typically studies the evolution of homology groups
+$H_p(X)$ (with coefficients in a field) along a filtration of topological
+spaces. $A_\infty$-persistence extends this theory by analysing the evolution
+of subspaces such as $V := \text{Ker}\, {\Delta_n}_{| H_p(X)} \subseteq
+H_p(X)$, where $\{\Delta_m\}_{m\geq1}$ denotes a structure of
+$A_\infty$-coalgebra on $H_*(X)$. In this paper we illustrate how
+$A_\infty$-persistence can be useful beyond persistent homology by discussing
+the topological meaning of $V$, which is the most basic form of
+$A_\infty$-persistence group. In addition, we explore how to choose
+$A_\infty$-coalgebras along a filtration to make the $A_\infty$-persistence
+groups carry more faithful information.
+",1,0,1,0,0,0
+15704,Transmission XMCD-PEEM imaging of an engineered vertical FEBID cobalt nanowire with a domain wall,"  Using focused electron-beam-induced deposition (FEBID), we fabricate
+vertical, platinum-coated cobalt nanowires with a controlled three-dimensional
+structure. The latter is engineered to feature bends along the height: these
+are used as pinning sites for domain walls, the presence of which we
+investigate using X-ray Magnetic Circular Dichroism (XMCD) coupled to
+PhotoEmission Electron Microscopy (PEEM). The vertical geometry of our sample
+combined with the low incidence of the X-ray beam produce an extended wire
+shadow which we use to recover the wire's magnetic configuration. In this
+transmission configuration, the whole sample volume is probed, thus
+circumventing the limitation of PEEM to surfaces. This article reports on the
+first study of magnetic nanostructures standing perpendicular to the substrate
+with XMCD-PEEM. The use of this technique in shadow mode enabled us to confirm
+the presence of a domain wall (DW) without direct imaging of the nanowire.
+",0,1,0,0,0,0
+15705,A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations,"  Developing efficient numerical algorithms for the solution of high
+dimensional random Partial Differential Equations (PDEs) has been a challenging
+task due to the well-known curse of dimensionality. We present a new solution
+framework for these problems based on a deep learning approach. Specifically,
+the random PDE is approximated by a feed-forward fully-connected deep residual
+network, with either strong or weak enforcement of initial and boundary
+constraints. The framework is mesh-free, and can handle irregular computational
+domains. Parameters of the approximating deep neural network are determined
+iteratively using variants of the Stochastic Gradient Descent (SGD) algorithm.
+The satisfactory accuracy of the proposed frameworks is numerically
+demonstrated on diffusion and heat conduction problems, in comparison with the
+converged Monte Carlo-based finite element results.
+",1,0,0,1,0,0
+15706,Fluid photonic crystal from colloidal quantum dots,"  We study optical forces acting upon semiconductor quantum dots and the force
+driven motion of the dots in a colloid. In the spectral range of exciton
+transitions in uantum dots, when the photon energy is close to the exciton
+energy, the polarizability of the dots is drastically increased. It leads to a
+resonant increase of both the gradient and the scattering contributions to the
+optical force, which enables the efficient manipulation with the dots. We
+reveal that the optical grating of the colloid leads to the formation of a
+fluid photonic crystal with spatially periodic circulating fluxes and density
+of the dots. Pronounced resonant dielectric response of semiconductor quantum
+dots enables a separation of the quantum dots with different exciton
+frequencies.
+",0,1,0,0,0,0
+15707,Subject Selection on a Riemannian Manifold for Unsupervised Cross-subject Seizure Detection,"  Inter-subject variability between individuals poses a challenge in
+inter-subject brain signal analysis problems. A new algorithm for
+subject-selection based on clustering covariance matrices on a Riemannian
+manifold is proposed. After unsupervised selection of the subsets of relevant
+subjects, data in a cluster is mapped to a tangent space at the mean point of
+covariance matrices in that cluster and an SVM classifier on labeled data from
+relevant subjects is trained. Experiment on an EEG seizure database shows that
+the proposed method increases the accuracy over state-of-the-art from 86.83% to
+89.84% and specificity from 87.38% to 89.64% while reducing the false positive
+rate/hour from 0.8/hour to 0.77/hour.
+",1,0,0,1,0,0
+15708,On a Surprising Oversight by John S. Bell in the Proof of his Famous Theorem,"  Bell inequalities are usually derived by assuming locality and realism, and
+therefore experimental violations of Bell inequalities are usually taken to
+imply violations of either locality or realism, or both. But, after reviewing
+an oversight by Bell, here we derive the Bell-CHSH inequality by assuming only
+that Bob can measure along the directions b and b' simultaneously while Alice
+measures along either a or a', and likewise Alice can measure along the
+directions a and a' simultaneously while Bob measures along either b or b',
+without assuming locality. The observed violations of the Bell-CHSH inequality
+therefore simply verify the manifest impossibility of measuring along the
+directions b and b' (or along the directions a and a') simultaneously, in any
+realizable EPR-Bohm type experiment.
+",0,1,0,0,0,0
+15709,On the higher derivatives of the inverse tangent function,"  In this paper, we find explicit formulas for higher order derivatives of the
+inverse tangent function. More precisely, we study polynomials which are
+induced from the higher-order derivatives of arctan(x). Successively, we give
+generating functions, recurrence relations and some particular properties for
+these polynomials. Connections to Chebyshev, Fibonacci, Lucas and Matching
+polynomials are established.
+",0,0,1,0,0,0
+15710,Science with e-ASTROGAM (A space mission for MeV-GeV gamma-ray astrophysics),"  e-ASTROGAM (enhanced ASTROGAM) is a breakthrough Observatory space mission,
+with a detector composed by a Silicon tracker, a calorimeter, and an
+anticoincidence system, dedicated to the study of the non-thermal Universe in
+the photon energy range from 0.3 MeV to 3 GeV - the lower energy limit can be
+pushed to energies as low as 150 keV for the tracker, and to 30 keV for
+calorimetric detection. The mission is based on an advanced space-proven
+detector technology, with unprecedented sensitivity, angular and energy
+resolution, combined with polarimetric capability. Thanks to its performance in
+the MeV-GeV domain, substantially improving its predecessors, e-ASTROGAM will
+open a new window on the non-thermal Universe, making pioneering observations
+of the most powerful Galactic and extragalactic sources, elucidating the nature
+of their relativistic outflows and their effects on the surroundings. With a
+line sensitivity in the MeV energy range one to two orders of magnitude better
+than previous generation instruments, e-ASTROGAM will determine the origin of
+key isotopes fundamental for the understanding of supernova explosion and the
+chemical evolution of our Galaxy. The mission will provide unique data of
+significant interest to a broad astronomical community, complementary to
+powerful observatories such as LIGO-Virgo-GEO600-KAGRA, SKA, ALMA, E-ELT, TMT,
+LSST, JWST, Athena, CTA, IceCube, KM3NeT, and LISA.
+",0,1,0,0,0,0
+15711,$J_1$-$J_2$ square lattice antiferromagnetism in the orbitally quenched insulator MoOPO$_4$,"  We report magnetic and thermodynamic properties of a $4d^1$ (Mo$^{5+}$)
+magnetic insulator MoOPO$_4$ single crystal, which realizes a $J_1$-$J_2$
+Heisenberg spin-$1/2$ model on a stacked square lattice. The specific-heat
+measurements show a magnetic transition at 16 K which is also confirmed by
+magnetic susceptibility, ESR, and neutron diffraction measurements. Magnetic
+entropy deduced from the specific heat corresponds to a two-level degree of
+freedom per Mo$^{5+}$ ion, and the effective moment from the susceptibility
+corresponds to the spin-only value. Using {\it ab initio} quantum chemistry
+calculations we demonstrate that the Mo$^{5+}$ ion hosts a purely spin-$1/2$
+magnetic moment, indicating negligible effects of spin-orbit interaction. The
+quenched orbital moments originate from the large displacement of Mo ions
+inside the MoO$_6$ octahedra along the apical direction. The ground state is
+shown by neutron diffraction to support a collinear Néel-type magnetic order,
+and a spin-flop transition is observed around an applied magnetic field of 3.5
+T. The magnetic phase diagram is reproduced by a mean-field calculation
+assuming a small easy-axis anisotropy in the exchange interactions. Our results
+suggest $4d$ molybdates as an alternative playground to search for model
+quantum magnets.
+",0,1,0,0,0,0
+15712,Eventness: Object Detection on Spectrograms for Temporal Localization of Audio Events,"  In this paper, we introduce the concept of Eventness for audio event
+detection, which can, in part, be thought of as an analogue to Objectness from
+computer vision. The key observation behind the eventness concept is that audio
+events reveal themselves as 2-dimensional time-frequency patterns with specific
+textures and geometric structures in spectrograms. These time-frequency
+patterns can then be viewed analogously to objects occurring in natural images
+(with the exception that scaling and rotation invariance properties do not
+apply). With this key observation in mind, we pose the problem of detecting
+monophonic or polyphonic audio events as an equivalent visual object(s)
+detection problem under partial occlusion and clutter in spectrograms. We adapt
+a state-of-the-art visual object detection model to evaluate the audio event
+detection task on publicly available datasets. The proposed network has
+comparable results with a state-of-the-art baseline and is more robust on
+minority events. Provided large-scale datasets, we hope that our proposed
+conceptual model of eventness will be beneficial to the audio signal processing
+community towards improving performance of audio event detection.
+",1,0,0,0,0,0
+15713,Bayesian Scale Estimation for Monocular SLAM Based on Generic Object Detection for Correcting Scale Drift,"  This work proposes a new, online algorithm for estimating the local scale
+correction to apply to the output of a monocular SLAM system and obtain an as
+faithful as possible metric reconstruction of the 3D map and of the camera
+trajectory. Within a Bayesian framework, it integrates observations from a
+deep-learning based generic object detector and a prior on the evolution of the
+scale drift. For each observation class, a predefined prior on the heights of
+the class objects is used. This allows to define the observations likelihood.
+Due to the scale drift inherent to monocular SLAM systems, we integrate a rough
+model on the dynamics of scale drift. Quantitative evaluations of the system
+are presented on the KITTI dataset, and compared with different approaches. The
+results show a superior performance of our proposal in terms of relative
+translational error when compared to other monocular systems.
+",1,0,0,0,0,0
+15714,A Linear-time Algorithm for Orthogonal Watchman Route Problem with Minimum Bends,"  Given an orthogonal polygon $ P $ with $ n $ vertices, the goal of the
+watchman route problem is finding a path $ S $ of the minimum length in $ P $
+such that every point of the polygon $ P $ is visible from at least one of the
+point of $ S $. In the other words, in the watchman route problem we must
+compute a shortest watchman route inside a simple polygon of $ n $ vertices
+such that all the points interior to the polygon and on its boundary are
+visible to at least one point on the route. If route and polygon be orthogonal,
+it is called orthogonal watchman route problem. One of the targets of this
+problem is finding the orthogonal path with the minimum number of bends as
+possible. We present a linear-time algorithm for the orthogonal watchman route
+problem, in which the given polygon is monotone. Our algorithm can be used also
+for the problem on simple orthogonal polygons $ P $ for which the dual graph
+induced by the vertical decomposition of $ P $ is a path, which is called path
+polygon.
+",1,0,0,0,0,0
+15715,A note on surjectivity of piecewise affine mappings,"  A standard theorem in nonsmooth analysis states that a piecewise affine
+function $F:\mathbb R^n\rightarrow\mathbb R^n$ is surjective if it is
+coherently oriented in that the linear parts of its selection functions all
+have the same nonzero determinant sign. In this note we prove that surjectivity
+already follows from coherent orientation of the selection functions which are
+active on the unbounded sets of a polyhedral subdivision of the domain
+corresponding to $F$. A side bonus of the argumentation is a short proof of the
+classical statement that an injective piecewise affine function is coherently
+oriented.
+",0,0,1,0,0,0
+15716,Intrinsic Gaussian processes on complex constrained domains,"  We propose a class of intrinsic Gaussian processes (in-GPs) for
+interpolation, regression and classification on manifolds with a primary focus
+on complex constrained domains or irregular shaped spaces arising as subsets or
+submanifolds of R, R2, R3 and beyond. For example, in-GPs can accommodate
+spatial domains arising as complex subsets of Euclidean space. in-GPs respect
+the potentially complex boundary or interior conditions as well as the
+intrinsic geometry of the spaces. The key novelty of the proposed approach is
+to utilise the relationship between heat kernels and the transition density of
+Brownian motion on manifolds for constructing and approximating valid and
+computationally feasible covariance kernels. This enables in-GPs to be
+practically applied in great generality, while existing approaches for
+smoothing on constrained domains are limited to simple special cases. The broad
+utilities of the in-GP approach is illustrated through simulation studies and
+data examples.
+",0,0,0,1,0,0
+15717,Adaptive Interference Removal for Un-coordinated Radar/Communication Co-existence,"  Most existing approaches to co-existing communication/radar systems assume
+that the radar and communication systems are coordinated, i.e., they share
+information, such as relative position, transmitted waveforms and channel
+state. In this paper, we consider an un-coordinated scenario where a
+communication receiver is to operate in the presence of a number of radars, of
+which only a sub-set may be active, which poses the problem of estimating the
+active waveforms and the relevant parameters thereof, so as to cancel them
+prior to demodulation. Two algorithms are proposed for such a joint waveform
+estimation/data demodulation problem, both exploiting sparsity of a proper
+representation of the interference and of the vector containing the errors of
+the data block, so as to implement an iterative joint interference removal/data
+demodulation process. The former algorithm is based on classical on-grid
+compressed sensing (CS), while the latter forces an atomic norm (AN)
+constraint: in both cases the radar parameters and the communication
+demodulation errors can be estimated by solving a convex problem. We also
+propose a way to improve the efficiency of the AN-based algorithm. The
+performance of these algorithms are demonstrated through extensive simulations,
+taking into account a variety of conditions concerning both the interferers and
+the respective channel states.
+",1,0,0,1,0,0
+15718,Learning Credible Models,"  In many settings, it is important that a model be capable of providing
+reasons for its predictions (i.e., the model must be interpretable). However,
+the model's reasoning may not conform with well-established knowledge. In such
+cases, while interpretable, the model lacks \textit{credibility}. In this work,
+we formally define credibility in the linear setting and focus on techniques
+for learning models that are both accurate and credible. In particular, we
+propose a regularization penalty, expert yielded estimates (EYE), that
+incorporates expert knowledge about well-known relationships among covariates
+and the outcome of interest. We give both theoretical and empirical results
+comparing our proposed method to several other regularization techniques.
+Across a range of settings, experiments on both synthetic and real data show
+that models learned using the EYE penalty are significantly more credible than
+those learned using other penalties. Applied to a large-scale patient risk
+stratification task, our proposed technique results in a model whose top
+features overlap significantly with known clinical risk factors, while still
+achieving good predictive performance.
+",1,0,0,1,0,0
+15719,The common patterns of abundance: the log series and Zipf's law,"  In a language corpus, the probability that a word occurs $n$ times is often
+proportional to $1/n^2$. Assigning rank, $s$, to words according to their
+abundance, $\log s$ vs $\log n$ typically has a slope of minus one. That simple
+Zipf's law pattern also arises in the population sizes of cities, the sizes of
+corporations, and other patterns of abundance. By contrast, for the abundances
+of different biological species, the probability of a population of size $n$ is
+typically proportional to $1/n$, declining exponentially for larger $n$, the
+log series pattern. This article shows that the differing patterns of Zipf's
+law and the log series arise as the opposing endpoints of a more general
+theory. The general theory follows from the generic form of all probability
+patterns as a consequence of conserved average values and the associated
+invariances of scale. To understand the common patterns of abundance, the
+generic form of probability distributions plus the conserved average abundance
+is sufficient. The general theory includes cases that are between the Zipf and
+log series endpoints, providing a broad framework for analyzing widely observed
+abundance patterns.
+",0,0,0,0,1,0
+15720,Modular meta-learning,"  Many prediction problems, such as those that arise in the context of
+robotics, have a simplifying underlying structure that could accelerate
+learning. In this paper, we present a strategy for learning a set of neural
+network modules that can be combined in different ways. We train different
+modular structures on a set of related tasks and generalize to new tasks by
+composing the learned modules in new ways. We show this improves performance in
+two robotics-related problems.
+",1,0,0,1,0,0
+15721,Universal experimental test for the role of free charge carriers in thermal Casimir effect within a micrometer separation range,"  We propose a universal experiment to measure the differential Casimir force
+between a Au-coated sphere and two halves of a structured plate covered with a
+P-doped Si overlayer. The concentration of free charge carriers in the
+overlayer is chosen slightly below the critical one, f or which the phase
+transition from dielectric to metal occurs. One ha f of the structured plate is
+insulating, while its second half is made of gold. For the former we consider
+two different structures, one consisting of bulk high-resistivity Si and the
+other of a layer of silica followed by bulk high-resistivity Si. The
+differential Casimir force is computed within the Lifshitz theory using four
+approaches that have been proposed in the literature to account for the role of
+free charge carriers in metallic and dielectric materials interacting with
+quantum fluctuations. According to these approaches, Au at low frequencies is
+described by either the Drude or the plasma model, whereas the free charge
+carriers in dielectric materials at room temperature are either taken into
+account or disregarded. It is shown that the values of differential Casimir
+forces, computed in the micrometer separation range using these four
+approaches, are widely distinct from each other and can be easily discriminated
+experimentally. It is shown that for all approaches the thermal component of
+the differential Casimir force is sufficiently large for direct observation.
+The possible errors and uncertainties in the proposed experiment are estimated
+and its importance for the theory of quantum fluctuations is discussed.
+",0,1,0,0,0,0
+15722,Gauss-Bonnet for matrix conformally rescaled Dirac,"  We derive an explicit formula for the scalar curvature over a two-torus with
+a Dirac operator conformally rescaled by a globally diagonalizable matrix. We
+show that the Gauss-Bonnet theorem holds and extend the result to all Riemann
+surfaces with Dirac operators modified in the same way.
+",0,0,1,0,0,0
+15723,Multi-Pose Face Recognition Using Hybrid Face Features Descriptor,"  This paper presents a multi-pose face recognition approach using hybrid face
+features descriptors (HFFD). The HFFD is a face descriptor containing of rich
+discriminant information that is created by fusing some frequency-based
+features extracted using both wavelet and DCT analysis of several different
+poses of 2D face images. The main aim of this method is to represent the
+multi-pose face images using a dominant frequency component with still having
+reasonable achievement compared to the recent multi-pose face recognition
+methods. The HFFD based face recognition tends to achieve better performance
+than that of the recent 2D-based face recognition method. In addition, the
+HFFD-based face recognition also is sufficiently to handle large face
+variability due to face pose variations .
+",1,0,0,0,0,0
+15724,Freed-Moore K-theory,"  The twisted equivariant K-theory given by Freed and Moore is a K-theory which
+unifies twisted equivariant complex K-theory, Atiyah's `Real' K-theory, and
+their variants. In a general setting, we formulate this K-theory by using
+Fredholm operators, and establish basic properties such as the Bott periodicity
+and the Thom isomorphism. We also provide formulations of the K-theory based on
+Karoubi's gradations in both infinite and finite dimensions, clarifying their
+relationship with the Fredholm formulation.
+",0,0,1,0,0,0
+15725,Dirac dispersion and non-trivial Berry's phase in three-dimensional semimetal RhSb3,"  We report observations of magnetoresistance, quantum oscillations and
+angle-resolved photoemission in RhSb$_3$, a unfilled skutterudite semimetal
+with low carrier density. The calculated electronic band structure of RhSb$_3$
+entails a $Z_2$ quantum number $\nu_0=0,\nu_1=\nu_2=\nu_3=1$ in analogy to
+strong topological insulators, and inverted linear valence/conduction bands
+that touch at discrete points close to the Fermi level, in agreement with
+angle-resolved photoemission results. Transport experiments reveal an
+unsaturated linear magnetoresistance that approaches a factor of 200 at 60 T
+magnetic fields, and quantum oscillations observable up to 150~K that are
+consistent with a large Fermi velocity ($\sim 1.3\times 10^6$ ms$^{-1}$), high
+carrier mobility ($\sim 14$ $m^2$/Vs), and small three dimensional hole pockets
+with nontrivial Berry phase. A very small, sample-dependent effective mass that
+falls as low as $0.015(7)$ bare masses scales with Fermi velocity, suggesting
+RhSb$_3$ is a new class of zero-gap three-dimensional Dirac semimetal.
+",0,1,0,0,0,0
+15726,Fréchet Analysis Of Variance For Random Objects,"  Fréchet mean and variance provide a way of obtaining mean and variance for
+general metric space valued random variables and can be used for statistical
+analysis of data objects that lie in abstract spaces devoid of algebraic
+structure and operations. Examples of such spaces include covariance matrices,
+graph Laplacians of networks and univariate probability distribution functions.
+We derive a central limit theorem for Fréchet variance under mild regularity
+conditions, utilizing empirical process theory, and also provide a consistent
+estimator of the asymptotic variance. These results lead to a test to compare k
+populations based on Fréchet variance for general metric space valued data
+objects, with emphasis on comparing means and variances. We examine the finite
+sample performance of this inference procedure through simulation studies for
+several special cases that include probability distributions and graph
+Laplacians, which leads to tests to compare populations of networks. The
+proposed methodology has good finite sample performance in simulations for
+different kinds of random objects. We illustrate the proposed methods with data
+on mortality profiles of various countries and resting state Functional
+Magnetic Resonance Imaging data.
+",0,0,1,1,0,0
+15727,Backward Simulation of Stochastic Process using a Time Reverse Monte Carlo method,"  The ""backward simulation"" of a stochastic process is defined as the
+stochastic dynamics that trace a time-reversed path from the target region to
+the initial configuration. If the probabilities calculated by the original
+simulation are easily restored from those obtained by backward dynamics, we can
+use it as a computational tool. It is shown that the naive approach to backward
+simulation does not work as expected. As a remedy, the Time Reverse Monte Carlo
+method (TRMC) based on the ideas of Sequential Importance Sampling (SIS) and
+Sequential Monte Carlo (SMC) is proposed and successfully tested with a
+stochastic typhoon model and the Lorenz 96 model. TRMC with SMC, which contains
+resampling steps, is shown to be more efficient for simulations with a larger
+number of time steps. A limitation of TRMC and its relation to the Bayes
+formula are also discussed.
+",0,1,0,1,0,0
+15728,Spectral Inference Networks: Unifying Spectral Methods With Deep Learning,"  We present Spectral Inference Networks, a framework for learning
+eigenfunctions of linear operators by stochastic optimization. Spectral
+Inference Networks generalize Slow Feature Analysis to generic symmetric
+operators, and are closely related to Variational Monte Carlo methods from
+computational physics. As such, they can be a powerful tool for unsupervised
+representation learning from video or pairs of data. We derive a training
+algorithm for Spectral Inference Networks that addresses the bias in the
+gradients due to finite batch size and allows for online learning of multiple
+eigenfunctions. We show results of training Spectral Inference Networks on
+problems in quantum mechanics and feature learning for videos on synthetic
+datasets as well as the Arcade Learning Environment. Our results demonstrate
+that Spectral Inference Networks accurately recover eigenfunctions of linear
+operators, can discover interpretable representations from video and find
+meaningful subgoals in reinforcement learning environments.
+",0,0,0,1,0,0
+15729,Heavy tailed spatial autocorrelation models,"  Appropriate models for spatially autocorrelated data account for the fact
+that observations are not independent. A popular model in this context is the
+simultaneous autoregressive (SAR) model that allows to model the spatial
+dependency structure of a response variable and the influence of covariates on
+this variable. This spatial regression model assumes that the error follows a
+normal distribution. Since this assumption cannot always be met, it is
+necessary to extend this model to other error distributions. We propose the
+extension to the $t$-distribution, the tSAR model, which can be used if we
+observe heavy tails in the fitted residuals of the SAR model. In addition, we
+provide a variance estimate that considers the spatial structure of a variable
+which helps us to specify inputs for our models. An extended simulation study
+shows that the proposed estimators of the tSAR model are performing well and in
+an application to fire danger we see that the tSAR model is a notable
+improvement compared to the SAR model.
+",0,0,0,1,0,0
+15730,Only Bayes should learn a manifold (on the estimation of differential geometric structure from data),"  We investigate learning of the differential geometric structure of a data
+manifold embedded in a high-dimensional Euclidean space. We first analyze
+kernel-based algorithms and show that under the usual regularizations,
+non-probabilistic methods cannot recover the differential geometric structure,
+but instead find mostly linear manifolds or spaces equipped with teleports. To
+properly learn the differential geometric structure, non-probabilistic methods
+must apply regularizations that enforce large gradients, which go against
+common wisdom. We repeat the analysis for probabilistic methods and find that
+under reasonable priors, the geometric structure can be recovered. Fully
+exploiting the recovered structure, however, requires the development of
+stochastic extensions to classic Riemannian geometry. We take early steps in
+that regard. Finally, we partly extend the analysis to modern models based on
+neural networks, thereby highlighting geometric and probabilistic shortcomings
+of current deep generative models.
+",0,0,0,1,0,0
+15731,Predictive modelling of training loads and injury in Australian football,"  To investigate whether training load monitoring data could be used to predict
+injuries in elite Australian football players, data were collected from elite
+athletes over 3 seasons at an Australian football club. Loads were quantified
+using GPS devices, accelerometers and player perceived exertion ratings.
+Absolute and relative training load metrics were calculated for each player
+each day (rolling average, exponentially weighted moving average, acute:chronic
+workload ratio, monotony and strain). Injury prediction models (regularised
+logistic regression, generalised estimating equations, random forests and
+support vector machines) were built for non-contact, non-contact time-loss and
+hamstring specific injuries using the first two seasons of data. Injury
+predictions were generated for the third season and evaluated using the area
+under the receiver operator characteristic (AUC). Predictive performance was
+only marginally better than chance for models of non-contact and non-contact
+time-loss injuries (AUC$<$0.65). The best performing model was a multivariate
+logistic regression for hamstring injuries (best AUC=0.76). Learning curves
+suggested logistic regression was underfitting the load-injury relationship and
+that using a more complex model or increasing the amount of model building data
+may lead to future improvements. Injury prediction models built using training
+load data from a single club showed poor ability to predict injuries when
+tested on previously unseen data, suggesting they are limited as a daily
+decision tool for practitioners. Focusing the modelling approach on specific
+injury types and increasing the amount of training data may lead to the
+development of improved predictive models for injury prevention.
+",0,0,0,1,0,0
+15732,Level structure of deeply bound levels of the $c^3Σ_g^+$ state of $^{87}\text{Rb}_2$,"  We spectroscopically investigate the hyperfine, rotational and Zeeman
+structure of the vibrational levels $\text{v}'=0$, $7$, $13$ within the
+electronically excited $c^3\Sigma_g^+$ state of $^{87}\text{Rb}_2$ for magnetic
+fields of up to $1000\,\text{G}$. As spectroscopic methods we use short-range
+photoassociation of ultracold Rb atoms as well as photoexcitation of ultracold
+molecules which have been previously prepared in several well-defined quantum
+states of the $a^3\Sigma_u^+$ potential. As a byproduct, we present optical
+two-photon transfer of weakly bound Feshbach molecules into $a^3\Sigma_u^+$,
+$\text{v}=0$ levels featuring different nuclear spin quantum numbers. A simple
+model reproduces well the molecular level structures of the $c^3\Sigma_g^+$
+vibrational states and provides a consistent assignment of the measured
+resonance lines. Furthermore, the model can be used to predict the relative
+transition strengths of the lines. From fits to the data we extract for each
+vibrational level the rotational constant, the effective spin-spin interaction
+constant, as well as the Fermi contact parameter and (for the first time) the
+anisotropic hyperfine constant. In an alternative approach, we perform
+coupled-channel calculations where we fit the relevant potential energy curves,
+spin-orbit interactions and hyperfine functions. The calculations reproduce the
+measured hyperfine level term frequencies with an average uncertainty of
+$\pm9\:$MHz, similar as for the simple model. From these fits we obtain a
+section of the potential energy curve for the $c^3\Sigma_g^+$ state which can
+be used for predicting the level structure for the vibrational manifold
+$\text{v}'=0$ to $13$ of this electronic state.
+",0,1,0,0,0,0
+15733,The mod 2 cohomology of the infinite families of Coxeter groups of type B and D as almost Hopf rings,"  We describe a Hopf ring structure on the direct sum of the cohomology groups
+$\bigoplus_{n \geq 0} H^* \left( B_n; \mathbb{Z}_2 \right)$ of the Coxeter
+groups of type $B_n$, and an almost-Hopf ring structure on the direct sum of
+the cohomology groups $\bigoplus_{n \geq 0} H^* \left( D_n; \mathbb{Z}_2
+\right)$ of the Coxeter groups of type $D_n$, with coefficient in the field
+with two elements $\mathbb{Z}_2$. We give presentations with generators and
+relations, determine additive bases and compute the Steenrod algebra action.
+The generators are described both in terms of a geometric construction by De
+Concini and Salvetti and in terms of their restriction to elementary abelian
+2-subgroups.
+",0,0,1,0,0,0
+15734,Geometry-Based Optimization of One-Way Quantum Computation Measurement Patterns,"  In one-way quantum computation (1WQC) model, an initial highly entangled
+state called a graph state is used to perform universal quantum computations by
+a sequence of adaptive single-qubit measurements and post-measurement Pauli-X
+and Pauli-Z corrections. The needed computations are organized as measurement
+patterns, or simply patterns, in the 1WQC model. The entanglement operations in
+a pattern can be shown by a graph which together with the set of its input and
+output qubits is called the geometry of the pattern. Since a one-way quantum
+computation pattern is based on quantum measurements, which are fundamentally
+nondeterministic evolutions, there must be conditions over geometries to
+guarantee determinism. Causal flow is a sufficient and generalized flow (gflow)
+is a necessary and sufficient condition over geometries to identify a
+dependency structure for the measurement sequences in order to achieve
+determinism. Previously, three optimization methods have been proposed to
+simplify 1WQC patterns which are called standardization, signal shifting and
+Pauli simplification. These optimizations can be performed using measurement
+calculus formalism by rewriting rules. However, maintaining and searching these
+rules in the library can be complicated with respect to implementation.
+Moreover, serial execution of these rules is time consuming due to executing
+many ineffective commutation rules. To overcome this problem, in this paper, a
+new scheme is proposed to perform optimization techniques on patterns with flow
+or gflow only based on their geometries instead of using rewriting rules.
+Furthermore, the proposed scheme obtains the maximally delayed gflow order for
+geometries with flow. It is shown that the time complexity of the proposed
+approach is improved over the previous ones.
+",1,0,0,0,0,0
+15735,Categorical entropy for Fourier-Mukai transforms on generic abelian surfaces,"  In this note, we shall compute the categorical entropy of an autoequivalence
+on a generic abelian surface.
+",0,0,1,0,0,0
+15736,Machine Learning Approach to RF Transmitter Identification,"  With the development and widespread use of wireless devices in recent years
+(mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has
+become extremely crowded. In order to counter security threats posed by rogue
+or unknown transmitters, it is important to identify RF transmitters not by the
+data content of the transmissions but based on the intrinsic physical
+characteristics of the transmitters. RF waveforms represent a particular
+challenge because of the extremely high data rates involved and the potentially
+large number of transmitters present in a given location. These factors outline
+the need for rapid fingerprinting and identification methods that go beyond the
+traditional hand-engineered approaches. In this study, we investigate the use
+of machine learning (ML) strategies to the classification and identification
+problems, and the use of wavelets to reduce the amount of data required. Four
+different ML strategies are evaluated: deep neural nets (DNN), convolutional
+neural nets (CNN), support vector machines (SVM), and multi-stage training
+(MST) using accelerated Levenberg-Marquardt (A-LM) updates. The A-LM MST method
+preconditioned by wavelets was by far the most accurate, achieving 100%
+classification accuracy of transmitters, as tested using data originating from
+12 different transmitters. We discuss strategies for extension of MST to a much
+larger number of transmitters.
+",1,0,0,1,0,0
+15737,Analyzing Chaos in Higher Order Disordered Quartic-Sextic Klein-Gordon Lattices Using $q$-Statistics,"  In the study of subdiffusive wave-packet spreading in disordered Klein-Gordon
+(KG) nonlinear lattices, a central open question is whether the motion
+continues to be chaotic despite decreasing densities, or tends to become
+quasi-periodic as nonlinear terms become negligible. In a recent study of such
+KG particle chains with quartic (4th order) anharmonicity in the on-site
+potential, it was shown that $q-$Gaussian probability distribution functions of
+sums of position observables with $q > 1$ always approach pure Gaussians
+($q=1$) in the long time limit and hence the motion of the full system is
+ultimately ""strongly chaotic"". In the present paper, we show that these results
+continue to hold even when a sextic (6th order) term is gradually added to the
+potential and ultimately prevails over the 4th order anharmonicity, despite
+expectations that the dynamics is more ""regular"", at least in the regime of
+small oscillations. Analyzing this system in the subdiffusive energy domain
+using $q$-statistics, we demonstrate that groups of oscillators centered around
+the initially excited one (as well as the full chain) possess strongly chaotic
+dynamics and are thus far from any quasi-periodic torus, for times as long as
+$t=10^9$.
+",0,1,0,0,0,0
+15738,UV/EUV High-Throughput Spectroscopic Telescope: A Next Generation Solar Physics Mission white paper,"  The origin of the activity in the solar corona is a long-standing problem in
+solar physics. Recent satellite observations, such as Hinode, Solar Dynamics
+Observatory (SDO), Interface Region Imaging Spectrograph (IRIS), show the
+detail characteristics of the solar atmosphere and try to reveal the energy
+transfer from the photosphere to the corona through the magnetic fields and its
+energy conversion by various processes. However, quantitative estimation of
+energy transfer along the magnetic field is not enough. There are mainly two
+reason why it is difficult to observe the energy transfer from photosphere to
+corona; 1) spatial resolution gap between photosphere (a few 0.1 arcsec) and
+corona (a few arcsec), 2) lack in temperature coverage. Furthermore, there is
+not enough observational knowledge of the physical parameters in the energy
+dissipation region. There are mainly three reason why it is difficult to
+observe in the vicinity of the energy dissipation region; 1) small spatial
+scale, 2) short time scale, 3) low emission. It is generally believed that the
+energy dissipation occurs in the very small scale and its duration is very
+short (10 second). Further, the density in the dissipation region might be very
+low. Therefore, the high spatial and temporal resolution UV/EUV spectroscopic
+observation with wide temperature coverage is crucial to estimate the energy
+transport from photosphere to corona quantitatively and diagnose the plasma
+dynamics in the vicinity of the energy dissipation region. Main Science Target
+for the telescope is quantitative estimation for the energy transfer from the
+photosphere to the corona, and clarification of the plasma dynamics in the
+vicinity of the energy dissipation region, where is the key region for coronal
+heating, solar wind acceleration, and/or solar flare, by the high spatial and
+temporal resolution UV/EUV spectroscopy.
+",0,1,0,0,0,0
+15739,Investigating Collaboration Within Online Communities: Software Development Vs. Artistic Creation,"  Online creative communities have been able to develop large, open source
+software (OSS) projects like Linux and Firefox throughout the successful
+collaborations carried out over the Internet. These communities have also
+expanded to creative arts domains such as animation, video games, and music.
+Despite their growing popularity, the factors that lead to successful
+collaborations in these communities are not entirely understood. In the
+following, I describe my PhD research project aimed at improving communication,
+collaboration, and retention in creative arts communities, starting from the
+experience gained from the literature about OSS communities.
+",1,0,0,0,0,0
+15740,Safe Non-blocking Synchronization in Ada 202x,"  The mutual-exclusion property of locks stands in the way to scalability of
+parallel programs on many-core architectures. Locks do not allow progress
+guarantees, because a task may fail inside a critical section and keep holding
+a lock that blocks other tasks from accessing shared data. With non-blocking
+synchronization, the drawbacks of locks are avoided by synchronizing access to
+shared data by atomic read-modify-write operations. To incorporate non-blocking
+synchronization in Ada~202x, programmers must be able to reason about the
+behavior and performance of tasks in the absence of protected objects and
+rendezvous. We therefore extend Ada's memory model by synchronized types, which
+support the expression of memory ordering operations at a sufficient level of
+detail. To mitigate the complexity associated with non-blocking
+synchronization, we propose concurrent objects as a novel high-level language
+construct. Entities of a concurrent object execute in parallel, due to a
+fine-grained, optimistic synchronization mechanism. Synchronization is framed
+by the semantics of concurrent entry execution. The programmer is only required
+to label shared data accesses in the code of concurrent entries. Labels
+constitute memory-ordering operations expressed through attributes. To the best
+of our knowledge, this is the first approach to provide a non-blocking
+synchronization construct as a first-class citizen of a high-level programming
+language. We illustrate the use of concurrent objects by several examples.
+",1,0,0,0,0,0
+15741,Rotational Unit of Memory,"  The concepts of unitary evolution matrices and associative memory have
+boosted the field of Recurrent Neural Networks (RNN) to state-of-the-art
+performance in a variety of sequential tasks. However, RNN still have a limited
+capacity to manipulate long-term memory. To bypass this weakness the most
+successful applications of RNN use external techniques such as attention
+mechanisms. In this paper we propose a novel RNN model that unifies the
+state-of-the-art approaches: Rotational Unit of Memory (RUM). The core of RUM
+is its rotational operation, which is, naturally, a unitary matrix, providing
+architectures with the power to learn long-term dependencies by overcoming the
+vanishing and exploding gradients problem. Moreover, the rotational unit also
+serves as associative memory. We evaluate our model on synthetic memorization,
+question answering and language modeling tasks. RUM learns the Copying Memory
+task completely and improves the state-of-the-art result in the Recall task.
+RUM's performance in the bAbI Question Answering task is comparable to that of
+models with attention mechanism. We also improve the state-of-the-art result to
+1.189 bits-per-character (BPC) loss in the Character Level Penn Treebank (PTB)
+task, which is to signify the applications of RUM to real-world sequential
+data. The universality of our construction, at the core of RNN, establishes RUM
+as a promising approach to language modeling, speech recognition and machine
+translation.
+",1,0,0,1,0,0
+15742,A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks,"  Gene regulatory networks play a crucial role in controlling an organism's
+biological processes, which is why there is significant interest in developing
+computational methods that are able to extract their structure from
+high-throughput genetic data. A typical approach consists of a series of
+conditional independence tests on the covariance structure meant to
+progressively reduce the space of possible causal models. We propose a novel
+efficient Bayesian method for discovering the local causal relationships among
+triplets of (normally distributed) variables. In our approach, we score the
+patterns in the covariance matrix in one go and we incorporate the available
+background knowledge in the form of priors over causal structures. Our method
+is flexible in the sense that it allows for different types of causal
+structures and assumptions. We apply the approach to the task of inferring gene
+regulatory networks by learning regulatory relationships between gene
+expression levels. We show that our algorithm produces stable and conservative
+posterior probability estimates over local causal structures that can be used
+to derive an honest ranking of the most meaningful regulatory relationships. We
+demonstrate the stability and efficacy of our method both on simulated data and
+on real-world data from an experiment on yeast.
+",0,0,0,1,1,0
+15743,$η$-metric structures,"  In this paper, we discuss recent results about generalized metric spaces and
+fixed point theory. We introduce the notion of $\eta$-cone metric spaces, give
+some topological properties and prove some fixed point theorems for contractive
+type maps on these spaces. In particular we show that theses $\eta$-cone metric
+spaces are natural generalizations of both cone metric spaces and metric type
+spaces.
+",0,0,1,0,0,0
+15744,Statistical Analysis of Precipitation Events,"  In the present paper we demonstrate the results of a statistical analysis of
+some characteristics of precipitation events and propose a kind of a
+theoretical explanation of the proposed models in terms of mixed Poisson and
+mixed exponential distributions based on the information-theoretical entropy
+reasoning. The proposed models can be also treated as the result of following
+the popular Bayesian approach.
+",0,0,0,1,0,0
+15745,Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation,"  Vision-language navigation (VLN) is the task of navigating an embodied agent
+to carry out natural language instructions inside real 3D environments. In this
+paper, we study how to address three critical challenges for this task: the
+cross-modal grounding, the ill-posed feedback, and the generalization problems.
+First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that
+enforces cross-modal grounding both locally and globally via reinforcement
+learning (RL). Particularly, a matching critic is used to provide an intrinsic
+reward to encourage global matching between instructions and trajectories, and
+a reasoning navigator is employed to perform cross-modal grounding in the local
+visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model
+significantly outperforms existing methods by 10% on SPL and achieves the new
+state-of-the-art performance. To improve the generalizability of the learned
+policy, we further introduce a Self-Supervised Imitation Learning (SIL) method
+to explore unseen environments by imitating its own past, good decisions. We
+demonstrate that SIL can approximate a better and more efficient policy, which
+tremendously minimizes the success rate performance gap between seen and unseen
+environments (from 30.7% to 11.7%).
+",1,0,0,0,0,0
+15746,Proton Beam Intensity Upgrades for the Neutrino Program at Fermilab,"  Fermilab is committed to upgrading its accelerator complex towards the
+intensity frontier to pursue HEP research in the neutrino sector and beyond.
+The upgrade has two steps: 1) the Proton Improvement Plan (PIP), which is
+underway, has its primary goal to start providing 700 kW beam power on NOvA
+target by the end of 2017 and 2) the foreseen PIP-II will replace the existing
+LINAC, a 400 MeV injector to the Booster, by an 800 MeV superconducting LINAC
+by the middle of next decade, with output beam intensity from the Booster
+increased significantly and the beam power on the NOvA target increased to <1.2
+MW. In any case, the Fermilab Booster is going to play a very significant role
+for the next two decades. In this context, we have recently developed and
+commissioned an innovative beam injection scheme for the Booster called ""early
+injection scheme."" This scheme is already in operation and has a potential to
+increase the Booster beam intensity from the PIP design goal by a considerable
+amount with a reduced beam emittance and beam loss. In this paper, we will
+present results from our experience from the new scheme in operation, current
+status and future plans.
+",0,1,0,0,0,0
+15747,On indecomposable $τ$-rigid modules over cluster-tilted algebras of tame type,"  For a given cluster-tilted algebra $A$ of tame type, it is proved that
+different indecomposable $\tau$-rigid $A$-modules have different dimension
+vectors. This is motivated by Fomin-Zelevinsky's denominator conjecture for
+cluster algebras. As an application, we establish a weak version of the
+denominator conjecture for cluster algebras of tame type. Namely, we show that
+different cluster variables have different denominators with respect to a given
+cluster for a cluster algebra of tame type. Our approach involves
+Iyama-Yoshino's construction of subfactors of triangulated categories. In
+particular,we obtain a description of the subfactors of cluster categories of
+tame type with respect to an indecomposable rigid object, which is of
+independent interest.
+",0,0,1,0,0,0
+15748,Tensors Come of Age: Why the AI Revolution will help HPC,"  This article discusses how the automation of tensor algorithms, based on A
+Mathematics of Arrays and Psi Calculus, and a new way to represent numbers,
+Unum Arithmetic, enables mechanically provable, scalable, portable, and more
+numerically accurate software.
+",1,0,0,0,0,0
+15749,Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling,"  Conventional methods of estimating latent behaviour generally use attitudinal
+questions which are subjective and these survey questions may not always be
+available. We hypothesize that an alternative approach can be used for latent
+variable estimation through an undirected graphical models. For instance,
+non-parametric artificial neural networks. In this study, we explore the use of
+generative non-parametric modelling methods to estimate latent variables from
+prior choice distribution without the conventional use of measurement
+indicators. A restricted Boltzmann machine is used to represent latent
+behaviour factors by analyzing the relationship information between the
+observed choices and explanatory variables. The algorithm is adapted for latent
+behaviour analysis in discrete choice scenario and we use a graphical approach
+to evaluate and understand the semantic meaning from estimated parameter vector
+values. We illustrate our methodology on a financial instrument choice dataset
+and perform statistical analysis on parameter sensitivity and stability. Our
+findings show that through non-parametric statistical tests, we can extract
+useful latent information on the behaviour of latent constructs through machine
+learning methods and present strong and significant influence on the choice
+process. Furthermore, our modelling framework shows robustness in input
+variability through sampling and validation.
+",1,0,0,0,0,0
+15750,LLASSO: A linear unified LASSO for multicollinear situations,"  We propose a rescaled LASSO, by premultipying the LASSO with a matrix term,
+namely linear unified LASSO (LLASSO) for multicollinear situations. Our
+numerical study has shown that the LLASSO is comparable with other sparse
+modeling techniques and often outperforms the LASSO and elastic net. Our
+findings open new visions about using the LASSO still for sparse modeling and
+variable selection. We conclude our study by pointing that the LLASSO can be
+solved by the same efficient algorithm for solving the LASSO and suggest to
+follow the same construction technique for other penalized estimators.
+",0,0,0,1,0,0
+15751,Filamentary fragmentation in a turbulent medium,"  We present the results of smoothed particle hydrodynamic simulations
+investigating the evolution and fragmentation of filaments that are accreting
+from a turbulent medium. We show that the presence of turbulence, and the
+resulting inhomogeneities in the accretion flow, play a significant role in the
+fragmentation process. Filaments which experience a weakly turbulent accretion
+flow fragment in a two-tier hierarchical fashion, similar to the fragmentation
+pattern seen in the Orion Integral Shaped Filament. Increasing the energy in
+the turbulent velocity field results in more sub-structure within the
+filaments, and one sees a shift from gravity-dominated fragmentation to
+turbulence-dominated fragmentation. The sub-structure formed in the filaments
+is elongated and roughly parallel to the longitudinal axis of the filament,
+similar to the fibres seen in observations of Taurus, and suggests that the
+fray and fragment scenario is a possible mechanism for the production of
+fibres. We show that the formation of these fibre-like structures is linked to
+the vorticity of the velocity field inside the filament and the filament's
+accretion from an inhomogeneous medium. Moreover, we find that accretion is
+able to drive and sustain roughly sonic levels of turbulence inside the
+filaments, but is not able to prevent radial collapse once the filaments become
+supercritical. However, the supercritical filaments which contain fibre-like
+structures do not collapse radially, suggesting that fibrous filaments may not
+necessarily become radially unstable once they reach the critical line-density.
+",0,1,0,0,0,0
+15752,Uncovering the role of flow rate in redox-active polymer flow batteries: simulation of reaction distributions with simultaneous mixing in tanks,"  Redox flow batteries (RFBs) are potential solutions for grid-scale energy
+storage, and deeper understanding of the effect of flow rate on RFB performance
+is needed to develop efficient, low-cost designs. In this study we highlight
+the importance of modeling tanks, which can limit the charge/discharge capacity
+of redox-active polymer (RAP) based RFBs. The losses due to tank mixing
+dominate over the polarization-induced capacity losses that arise due to
+resistive processes in the reactor. A porous electrode model is used to
+separate these effects by predicting the time variation of active species
+concentration in electrodes and tanks. A simple transient model based on
+species conservation laws developed in this study reveals that charge
+utilization and polarization are affected by two dimensionless numbers
+quantifying (1) flow rate relative to stoichiometric flow and (2) size of flow
+battery tanks relative to the reactor. The RFB's utilization is shown to
+increase monotonically with flow rate, reaching 90% of the theoretical value
+only when flow rate exceeds twenty-fold of the stoichiometric value. We also
+identify polarization due to irreversibilities inherent to RFB architecture as
+a result of tank mixing and current distribution internal to the reactor, and
+this polarization dominates over that resulting from ohmic resistances
+particularly when cycling RFBs at low flow rates and currents. These findings
+are summarized in a map of utilization and polarization that can be used to
+select adequate flow rate for a given tank size.
+",0,1,0,0,0,0
+15753,The magnetocaloric effect from the point of view of Tsallis non-extensive thermostatistics,"  In this work we have analyzed the magnetocaloric effect (MCE) from the
+Tsallis thermostatistics formalism (TTF) point of view. The problem discussed
+here is a two level system MCE. We have calculated, both analytically and
+numerically, the entropy of this system as a function of the Tsallis' parameter
+(the well known q-parameter) which value depends on the extensivity (q<1) or
+non-extensivity (q>1) of the system. Since we consider this MCE not depending
+on the initial conditions, which classify our system as a non-extensive one, we
+used several greater than one q-parameters to understand the effect of the
+nonextensive formalism in the entropy as well as the magnetocaloric potential,
+$\Delta S$. We have plotted several curves that shows precisely the behavior of
+this effect when dealt with non-extensive statistics.
+",0,1,0,0,0,0
+15754,"Reheating, thermalization and non-thermal gravitino production in MSSM inflation","  In the framework of MSSM inflation, matter and gravitino production are here
+investigated through the decay of the fields which are coupled to the udd
+inflaton, a gauge invariant combination of squarks. After the end of inflation,
+the flat direction oscillates about the minimum of its potential, losing at
+each oscillation about 56% of its energy into bursts of gauge/gaugino and
+scalar quanta when crossing the origin. These particles then acquire a large
+inflaton VEV-induced mass and decay perturbatively into the MSSM quanta and
+gravitinos, transferring the inflaton energy very efficiently via instant
+preheating. Regarding thermalization, we show that the MSSM degrees of freedom
+thermalize very quickly, yet not immediately by virtue of the large vacuum
+expectation value of the inflaton, which breaks the $SU(3)_C\times U(1)_Y$
+symmetry into a residual $U(1)$. The energy transfer to the MSSM quanta is very
+efficient, since full thermalization is achieved after only $\mathcal{O}(40)$
+complete oscillations. The udd inflaton thus provides an extremely efficient
+reheating of the Universe, with a temperature
+$T_{reh}=\mathcal{O}(10^8\mathrm{GeV})$ that allows for instance several
+mechanisms of baryogenesis. We also compute the gravitino number density from
+the perturbative decay of the flat direction and of the SUSY multiplet. We find
+that the gravitinos are produced in negligible amount and satisfy cosmological
+bounds such as the Big Bang Nucleosynthesis (BBN) and Dark Matter (DM)
+constraints.
+",0,1,0,0,0,0
+15755,Emotion Controlled Spectrum Mobility Scheme for Efficient Syntactic Interoperability In Cognitive Radio Based Internet of Vehicles,"  Blind spots are one of the causes of road accidents in the hilly and flat
+areas. These blind spot accidents can be decreased by establishing an Internet
+of Vehicles (IoV) using Vehicle-2-Vehicle (V2V) and Vehicle-2-Infrastrtructure
+(V2I) communication systems. But the problem with these IoV is that most of
+them are using DSRC or single Radio Access Technology (RAT) as a wireless
+technology, which has been proven to be failed for efficient communication
+between vehicles. Recently, Cognitive Radio (CR) based IoV have to be proven
+best wireless communication systems for vehicular networks. However, the
+spectrum mobility is a challenging task to keep CR based vehicular networks
+interoperable and has not been addressed sufficiently in existing research. In
+our previous research work, the Cognitive Radio Site (CR-Site) has been
+proposed as in-vehicle CR-device, which can be utilized to establish efficient
+IoV systems. H In this paper, we have introduced the Emotions Inspired
+Cognitive Agent (EIC_Agent) based spectrum mobility mechanism in CR-Site and
+proposed a novel emotions controlled spectrum mobility scheme for efficient
+syntactic interoperability between vehicles. For this purpose, a probabilistic
+deterministic finite automaton using fear factor is proposed to perform
+efficient spectrum mobility using fuzzy logic. In addition, the quantitative
+computation of different fear intensity levels has been performed with the help
+of fuzzy logic. The system has been tested using active data from different GSM
+service providers on Mangla-Mirpur road. This is supplemented by extensive
+simulation experiments which validate the proposed scheme for CR based
+high-speed vehicular networks. The qualitative comparison with the
+existing-state-of the-art has proven the superiority of the proposed emotions
+controlled syntactic interoperable spectrum mobility scheme within cognitive
+radio based IoV systems.
+",1,0,0,0,0,0
+15756,Rota-Baxter modules toward derived functors,"  In this paper we study Rota-Baxter modules with emphasis on the role played
+by the Rota-Baxter operators and resulting difference between Rota-Baxter
+modules and the usual modules over an algebra. We introduce the concepts of
+free, projective, injective and flat Rota-Baxter modules. We give the
+construction of free modules and show that there are enough projective,
+injective and flat Rota-Baxter modules to provide the corresponding resolutions
+for derived functor.
+",0,0,1,0,0,0
+15757,Video Pandemics: Worldwide Viral Spreading of Psy's Gangnam Style Video,"  Viral videos can reach global penetration traveling through international
+channels of communication similarly to real diseases starting from a
+well-localized source. In past centuries, disease fronts propagated in a
+concentric spatial fashion from the the source of the outbreak via the short
+range human contact network. The emergence of long-distance air-travel changed
+these ancient patterns. However, recently, Brockmann and Helbing have shown
+that concentric propagation waves can be reinstated if propagation time and
+distance is measured in the flight-time and travel volume weighted underlying
+air-travel network. Here, we adopt this method for the analysis of viral meme
+propagation in Twitter messages, and define a similar weighted network distance
+in the communication network connecting countries and states of the World. We
+recover a wave-like behavior on average and assess the randomizing effect of
+non-locality of spreading. We show that similar result can be recovered from
+Google Trends data as well.
+",1,0,0,0,0,0
+15758,Orthogonal foliations on riemannian manifolds,"  In this work, we find an equation that relates the Ricci curvature of a
+riemannian manifold $M$ and the second fundamental forms of two orthogonal
+foliations of complementary dimensions, $\mathcal{F}$ and $\mathcal{F}^{\bot}$,
+defined on $M$. Using this equation, we show a sufficient condition for the
+manifold M to be locally a riemannian product of the leaves of $\mathcal{F}$
+and $\mathcal{F}^{\bot}$, if one of the foliations is totally umbilical. We
+also prove an integral formula for such foliations.
+",0,0,1,0,0,0
+15759,Mapping Objects to Persistent Predicates,"  The Logic Programming through Prolog has been widely used for supply
+persistence in many systems that need store knowledge. Some implementations of
+Prolog Programming Language used for supply persistence have bidirectional
+interfaces with other programming languages over all with Object Oriented
+Programing Languages. In present days is missing tools and frameworks for the
+systems development that use logic predicate persistence in easy and agile
+form. More specifically an object oriented and logic persistence provider is
+need in present days that allow the object manipulation in main memory and the
+persistence for this objects have a Logic Programming predicates aspect. The
+present work introduce an object-prolog declarative mappings alternative to
+support by an object oriented and logic persistence provider. The proposed
+alternative consists in a correspondence of the Logic Programming predicates
+with an Object Oriented approach, where for each element of the Logic
+Programming one Object Oriented element makes to reciprocate. The Object
+Oriented representation of Logic Programming predicates offers facility of
+manipulation on the elements that compose a knowledge.
+",1,0,0,0,0,0
+15760,Efficient modified Jacobi-Bernstein basis transformations,"  In the paper, we show that the transformations between modified Jacobi and
+Bernstein bases of the constrained space of polynomials of degree at most $n$
+can be performed with the complexity $O(n^2)$. As a result, the algorithm of
+degree reduction of Bézier curves that was first presented in (Bhrawy et al.,
+J. Comput. Appl. Math. 302 (2016), 369--384), and then corrected in (Lu and
+Xiang, J. Comput. Appl. Math. 315 (2017), 65--69), can be significantly
+improved, since the necessary transformations are done in those papers with the
+complexity $O(n^3)$. The comparison of running times shows that our
+transformations are also faster in practice.
+",0,0,1,0,0,0
+15761,Cospectral mates for the union of some classes in the Johnson association scheme,"  Let $n\geq k\geq 2$ be two integers and $S$ a subset of $\{0,1,\dots,k-1\}$.
+The graph $J_{S}(n,k)$ has as vertices the $k$-subsets of the $n$-set
+$[n]=\{1,\dots,n\}$ and two $k$-subsets $A$ and $B$ are adjacent if $|A\cap
+B|\in S$. In this paper, we use Godsil-McKay switching to prove that for $m\geq
+0$, $k\geq \max(m+2,3)$ and $S = \{0, 1, ..., m\}$, the graphs $J_S(3k-2m-1,k)$
+are not determined by spectrum and for $m\geq 2$, $n\geq 4m+2$ and $S =
+\{0,1,...,m\}$ the graphs $J_{S}(n,2m+1)$ are not determined by spectrum. We
+also report some computational searches for Godsil-McKay switching sets in the
+union of classes in the Johnson scheme for $k\leq 5$.
+",1,0,1,0,0,0
+15762,Electromagnetic properties of terbium gallium garnet at millikelvin temperatures and single photon energy,"  Electromagnetic properties of single crystal terbium gallium garnet (TGG) are
+characterised from room down to millikelvin temperatures using the whispering
+gallery mode method. Microwave spectroscopy is performed at low powers
+equivalent to a few photons in energy and conducted as functions of the
+magnetic field and temperature. A phase transition is detected close to the
+temperature of 3.5 K. This is observed for multiple whispering gallery modes
+causing an abrupt negative frequency shift and a change in transmission due to
+extra losses in the new phase caused by a change in complex magnetic
+susceptibility.
+",0,1,0,0,0,0
+15763,The abundance of compact quiescent galaxies since z ~ 0.6,"  We set out to quantify the number density of quiescent massive compact
+galaxies at intermediate redshifts. We determine structural parameters based on
+i-band imaging using the CFHT equatorial SDSS Stripe 82 (CS82) survey (~170 sq.
+degrees) taking advantage of an exquisite median seeing of ~0.6''. We select
+compact massive (M > 5x10^10 M_sun) galaxies within the redshift range of
+0.2<z<0.6. The large volume sampled allows to decrease the effect of cosmic
+variance that has hampered the calculation of the number density for this
+enigmatic population in many previous studies. We undertake an exhaustive
+analysis in an effort to untangle the various findings inherent to the diverse
+definition of compactness present in the literature. We find that the absolute
+number of compact galaxies is very dependent on the adopted definition and can
+change up to a factor of >10. We systematically measure a factor of ~5 more
+compacts at the same redshift than what was previously reported on smaller
+fields with HST imaging, which are more affected by cosmic variance. This means
+that the decrease in number density from z ~ 1.5 to z ~ 0.2 might be only of a
+factor of ~2-5, significantly smaller than what previously reported. This
+supports progenitor bias as the main contributor to the size evolution. This
+milder decrease is roughly compatible with the predictions from recent
+numerical simulations. Only the most extreme compact galaxies, with Reff <
+1.5x( M/10^11 M_sun)^0.75 and M > 10^10.7 M_sun, appear to drop in number by a
+factor of ~20 and hence likely experience a noticeable size evolution.
+",0,1,0,0,0,0
+15764,Combining Probabilistic Load Forecasts,"  Probabilistic load forecasts provide comprehensive information about future
+load uncertainties. In recent years, many methodologies and techniques have
+been proposed for probabilistic load forecasting. Forecast combination, a
+widely recognized best practice in point forecasting literature, has never been
+formally adopted to combine probabilistic load forecasts. This paper proposes a
+constrained quantile regression averaging (CQRA) method to create an improved
+ensemble from several individual probabilistic forecasts. We formulate the CQRA
+parameter estimation problem as a linear program with the objective of
+minimizing the pinball loss, with the constraints that the parameters are
+nonnegative and summing up to one. We demonstrate the effectiveness of the
+proposed method using two publicly available datasets, the ISO New England data
+and Irish smart meter data. Comparing with the best individual probabilistic
+forecast, the ensemble can reduce the pinball score by 4.39% on average. The
+proposed ensemble also demonstrates superior performance over nine other
+benchmark ensembles.
+",0,0,0,1,0,0
+15765,Stability and instability of the sub-extremal Reissner-Nordström black hole interior for the Einstein-Maxwell-Klein-Gordon equations in spherical symmetry,"  We show non-linear stability and instability results in spherical symmetry
+for the interior of a charged black hole -approaching a sub-extremal
+Reissner-Nordström background fast enough at infinity- in presence of a
+massive and charged scalar field, motivated by the strong cosmic censorship
+conjecture in that setting :
+1. Stability : We prove that spherically symmetric characteristic initial
+data to the Einstein-Maxwell- Klein-Gordon equations approaching a
+Reissner-Nordström background with a sufficiently decaying polynomial decay
+rate on the event horizon gives rise to a space-time possessing a Cauchy
+horizon in a neighbourhood of time-like infinity. Moreover if the decay is even
+stronger, we prove that the spacetime metric admits a continuous extension to
+the Cauchy horizon. This generalizes the celebrated stability result of
+Dafermos for Einstein-Maxwell-real-scalar-field in spherical symmetry.
+2. Instability : We prove that for the class of space-times considered in the
+stability part, whose scalar field in addition obeys a polynomial averaged-L^2
+(consistent) lower bound on the event horizon, the scalar field obeys an
+integrated lower bound transversally to the Cauchy horizon. As a consequence we
+prove that the non-degenerate energy is infinite on any null surface crossing
+the Cauchy horizon and the curvature of a geodesic vector field blows up at the
+Cauchy horizon near time-like infinity. This generalizes an instability result
+due to Luk and Oh for Einstein-Maxwell-real-scalar-field in spherical symmetry.
+This instability of the black hole interior can also be viewed as a step
+towards the resolution of the C^2 strong cosmic censorship conjecture for
+one-ended asymptotically initial data.
+",0,0,1,0,0,0
+15766,Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial,"  In this article, the JAGS software program is systematically introduced to
+fit common Bayesian cognitive diagnosis models (CDMs), including the
+deterministic inputs, noisy ""and"" gate (DINA) model, the deterministic inputs,
+noisy ""or"" gate (DINO) model, the linear logistic model, the reduced
+reparameterized unified model (rRUM), and the log-linear CDM (LCDM). The
+unstructured latent structural model and the higher-order latent structural
+model are both introduced. We also show how to extend those models to consider
+the polytomous attributes, the testlet effect, and the longitudinal diagnosis.
+Finally, an empirical example is presented as a tutorial to illustrate how to
+use the JAGS codes in R.
+",0,0,0,1,0,0
+15767,Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network,"  Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT
+are computationally expensive. To address this problem, we recently proposed a
+deep convolutional neural network (CNN) for low-dose X-ray CT and won the
+second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the
+texture were not fully recovered. To address this problem, here we propose a
+novel framelet-based denoising algorithm using wavelet residual network which
+synergistically combines the expressive power of deep learning and the
+performance guarantee from the framelet-based denoising algorithms. The new
+algorithms were inspired by the recent interpretation of the deep convolutional
+neural network (CNN) as a cascaded convolution framelet signal representation.
+Extensive experimental results confirm that the proposed networks have
+significantly improved performance and preserves the detail texture of the
+original images.
+",1,0,0,1,0,0
+15768,Static Analysis of Deterministic Negotiations,"  Negotiation diagrams are a model of concurrent computation akin to workflow
+Petri nets. Deterministic negotiation diagrams, equivalent to the much studied
+and used free-choice workflow Petri nets, are surprisingly amenable to
+verification. Soundness (a property close to deadlock-freedom) can be decided
+in PTIME. Further, other fundamental questions like computing summaries or the
+expected cost, can also be solved in PTIME for sound deterministic negotiation
+diagrams, while they are PSPACE-complete in the general case.
+In this paper we generalize and explain these results. We extend the
+classical ""meet-over-all-paths"" (MOP) formulation of static analysis problems
+to our concurrent setting, and introduce Mazurkiewicz-invariant analysis
+problems, which encompass the questions above and new ones. We show that any
+Mazurkiewicz-invariant analysis problem can be solved in PTIME for sound
+deterministic negotiations whenever it is in PTIME for sequential
+flow-graphs---even though the flow-graph of a deterministic negotiation diagram
+can be exponentially larger than the diagram itself. This gives a common
+explanation to the low-complexity of all the analysis questions studied so far.
+Finally, we show that classical gen/kill analyses are also an instance of our
+framework, and obtain a PTIME algorithm for detecting anti-patterns in
+free-choice workflow Petri nets.
+Our result is based on a novel decomposition theorem, of independent
+interest, showing that sound deterministic negotiation diagrams can be
+hierarchically decomposed into (possibly overlapping) smaller sound diagrams.
+",1,0,0,0,0,0
+15769,"Wild character varieties, meromorphic Hitchin systems and Dynkin diagrams","  The theory of Hitchin systems is something like a ""global theory of Lie
+groups"", where one works over a Riemann surface rather than just at a point.
+We'll describe how one can take this analogy a few steps further by attempting
+to make precise the class of rich geometric objects that appear in this story
+(including the non-compact case), and discuss their classification, outlining a
+theory of ""Dynkin diagrams"" as a step towards classifying some examples of such
+objects.
+",0,1,1,0,0,0
+15770,A mathematical characterization of confidence as valid belief,"  Confidence is a fundamental concept in statistics, but there is a tendency to
+misinterpret it as probability. In this paper, I argue that an intuitively and
+mathematically more appropriate interpretation of confidence is through
+belief/plausibility functions, in particular, those that satisfy a certain
+validity property. Given their close connection with confidence, it is natural
+to ask how a valid belief/plausibility function can be constructed directly.
+The inferential model (IM) framework provides such a construction, and here I
+prove a complete-class theorem stating that, for every nominal confidence
+region, there exists a valid IM whose plausibility regions are contained by the
+given confidence region. This characterization has implications for statistics
+understanding and communication, and highlights the importance of belief
+functions and the IM framework.
+",0,0,1,1,0,0
+15771,Optimal Control Problems with Symmetry Breaking Cost Functions,"  We investigate symmetry reduction of optimal control problems for
+left-invariant control systems on Lie groups, with partial symmetry breaking
+cost functions. Our approach emphasizes the role of variational principles and
+considers a discrete-time setting as well as the standard continuous-time
+formulation. Specifically, we recast the optimal control problem as a
+constrained variational problem with a partial symmetry breaking Lagrangian and
+obtain the Euler--Poincaré equations from a variational principle. By
+applying a Legendre transformation to it, we recover the Lie-Poisson equations
+obtained by A. D. Borum [Master's Thesis, University of Illinois at
+Urbana-Champaign, 2015] in the same context. We also discretize the variational
+principle in time and obtain the discrete-time Lie-Poisson equations. We
+illustrate the theory with some practical examples including a motion planning
+problem in the presence of an obstacle.
+",1,0,1,0,0,0
+15772,Multiplying a Gaussian Matrix by a Gaussian Vector,"  We provide a new and simple characterization of the multivariate generalized
+Laplace distribution. In particular, this result implies that the product of a
+Gaussian matrix with independent and identically distributed columns by an
+independent isotropic Gaussian vector follows a symmetric multivariate
+generalized Laplace distribution.
+",0,0,1,1,0,0
+15773,NEON+: Accelerated Gradient Methods for Extracting Negative Curvature for Non-Convex Optimization,"  Accelerated gradient (AG) methods are breakthroughs in convex optimization,
+improving the convergence rate of the gradient descent method for optimization
+with smooth functions. However, the analysis of AG methods for non-convex
+optimization is still limited. It remains an open question whether AG methods
+from convex optimization can accelerate the convergence of the gradient descent
+method for finding local minimum of non-convex optimization problems. This
+paper provides an affirmative answer to this question. In particular, we
+analyze two renowned variants of AG methods (namely Polyak's Heavy Ball method
+and Nesterov's Accelerated Gradient method) for extracting the negative
+curvature from random noise, which is central to escaping from saddle points.
+By leveraging the proposed AG methods for extracting the negative curvature, we
+present a new AG algorithm with double loops for non-convex
+optimization~\footnote{this is in contrast to a single-loop AG algorithm
+proposed in a recent manuscript~\citep{AGNON}, which directly analyzed the
+Nesterov's AG method for non-convex optimization and appeared online on
+November 29, 2017. However, we emphasize that our work is an independent work,
+which is inspired by our earlier work~\citep{NEON17} and is based on a
+different novel analysis.}, which converges to second-order stationary point
+$\x$ such that $\|\nabla f(\x)\|\leq \epsilon$ and $\nabla^2 f(\x)\geq
+-\sqrt{\epsilon} I$ with $\widetilde O(1/\epsilon^{1.75})$ iteration
+complexity, improving that of gradient descent method by a factor of
+$\epsilon^{-0.25}$ and matching the best iteration complexity of second-order
+Hessian-free methods for non-convex optimization.
+",0,0,0,1,0,0
+15774,Optimal Energy Distribution with Energy Packet Networks,"  We use Energy Packet Network paradigms to investigate energy distribution
+problems in a computer system with energy harvesting and storages units. Our
+goal is to minimize both the overall average response time of jobs at
+workstations and the total rate of energy lost in the network. Energy is lost
+when it arrives at idle workstations which are empty. Energy is also lost in
+storage leakages. We assume that the total rate of energy harvesting and the
+rate of jobs arriving at workstations are known. We also consider a special
+case in which the total rate of energy harvesting is sufficiently large so that
+workstations are less busy. In this case, energy is more likely to be sent to
+an idle workstation. Optimal solutions are obtained which minimize both the
+overall response time and energy loss under the constraint of a fixed energy
+harvesting rate.
+",1,0,0,0,0,0
+15775,A refined version of Grothendieck's anabelian conjecture for hyperbolic curves over finite fields,"  In this paper we prove a refined version of a theorem by Tamagawa and
+Mochizuki on isomorphisms between (tame) arithmetic fundamental groups of
+hyperbolic curves over finite fields, where one ""ignores"" the information
+provided by a ""small"" set of primes.
+",0,0,1,0,0,0
+15776,A discussion about LNG Experiment: Irreversible or Reversible Generation of the OR Logic Gate?,"  In a recent paper M. Lopez-Suarez, I. Neri, and L. Gammaitoni (LNG) present a
+concrete realization of the Boolean OR irreversible gate, but contrary to the
+standard Landauer principle, with an arbitrary small dissipation of energy. A
+Popperian good falsification! In this paper we discuss a theoretical
+description of the LNG device which is indeed a 3in/3out self--reversible
+realization of the involved OR gate, satisfying in this way the Landauer
+principle of no dispersion of energy, contrary to the LNG conclusions. The
+different point of view is due to a different interpretation of the two outputs
+corresponding to the inputs 10 and 01, which are considered by LNG
+indistinguishable so producing a non reversible realization of the standard
+2in/1out gate. On the contrary, always considering these two outputs
+indistinguishable, by a suitable normalization function of the cantilever
+angles, the experimental results obtained by the LNG device coincide with the
+OR connective obtained from the third output of the self-reversible 3in/3out CL
+gate by the Inputs-Ancilla->Garbage-Output procedure. Thus, by the
+self-reversibility this realization is without dissipation of energy according
+to the Landauer principle. Furthermore, using the self-reversible Toffoli gate
+it is possible to obtain from the LNG device the realization of the connective
+AND adopting another normalization function on the cantilever angles. Finally,
+by other suitable normalization procedures on cantilever angles it is possible
+to obtain also the other logic NOR and NAND connectives, and in a more
+sophisticated way the XOR and NXOR connectives in a self-reversible way. All
+this leads to introduce a universal logic machine consisting of the LNG device
+plus a memory containing all the necessary angle normalization functions to
+produce in a self-reversible way, by choosing one of these latter, the logic
+connectives now listed.
+",1,0,0,0,0,0
+15777,"On $(σ,δ)$-skew McCoy modules","  Let $(\sigma,\delta)$ be a quasi derivation of a ring $R$ and $M_R$ a right
+$R$-module. In this paper, we introduce the notion of $(\sigma,\delta)$-skew
+McCoy modules which extends the notion of McCoy modules and $\sigma$-skew McCoy
+modules. This concept can be regarded also as a generalization of
+$(\sigma,\delta)$-skew Armendariz modules. Some properties of this concept are
+established and some connections between $(\sigma,\delta)$-skew McCoyness and
+$(\sigma,\delta)$-compatible reduced modules are examined. Also, we study the
+property $(\sigma,\delta)$-skew McCoy of some skew triangular matrix extensions
+$V_n(M,\sigma)$, for any nonnegative integer $n\geq 2$. As a consequence, we
+obtain: (1) $M_R$ is $(\sigma,\delta)$-skew McCoy if and only if
+$M[x]/M[x](x^n)$ is $(\overline{\sigma},\overline{\delta})$-skew McCoy, and (2)
+$M_R$ is $\sigma$-skew McCoy if and only if $M[x;\sigma]/M[x;\sigma](x^n)$ is
+$\overline{\sigma}$-skew McCoy.
+",0,0,1,0,0,0
+15778,Bivariate Exponentiated Generalized Linear Exponential Distribution with Applications in Reliability Analysis,"  The aim of this paper, is to define a bivariate exponentiated generalized
+linear exponential distribution based on Marshall-Olkin shock model.
+Statistical and reliability properties of this distribution are discussed. This
+includes quantiles, moments, stress-strength reliability, joint reliability
+function, joint reversed (hazard) rates functions and joint mean waiting time
+function. Moreover, the hazard rate, the availability and the mean residual
+lifetime functions for a parallel system, are established. One data set is
+analyzed, and it is observed that, the proposed distribution provides a better
+fit than Marshall-Olkin bivariate exponential, bivariate generalized
+exponential and bivariate generalized linear failure rate distributions.
+Simulation studies are presented to estimate both the relative absolute bias,
+and the relative mean square error for the distribution parameters based on
+complete data.
+",0,0,1,1,0,0
+15779,Soft-proton exchange on Magnesium-oxide-doped substrates a route toward efficient and power-resistant nonlinear converters,"  Despite its attractive features, Congruent-melted Lithium Niobate (CLN)
+suffers from Photo-Refractive Damage (PRD). This light-induced refractive-index
+change hampers the use of CLN when high-power densities are in play, a typical
+regime in integrated optics. The resistance to PRD can be largely improved by
+doping the lithium-niobate substrates with magnesium oxide. However, the
+fabrication of waveguides on MgO-doped substrates is not as effective as for
+CLN: either the resistance to PRD is strongly reduced by the waveguide
+fabrication process (as it happens in Ti-indiffused waveguides) or the
+nonlinear conversion efficiency is lowered (as it occurs in annealed-proton
+exchange). Here we fabricate, for the first time, waveguides starting from
+MgO-doped substrates using the Soft-Proton Exchange (SPE) technique and we show
+that this third way represents a promising alternative. We demonstrate that SPE
+allows to produce refractive-index profiles almost identical to those produced
+on CLN without reducing the nonlinearity in the substrate. We also prove that
+the SPE does not affect substantially the resistance to PRD. Since the
+fabrication recipe is identical between CLN and MgO-doped substrates, we
+believe that SPE might outperform standard techniques to fabricate robust and
+efficient waveguides for high-intensity-beam confinement.
+",0,1,0,0,0,0
+15780,Optimized Cost per Click in Taobao Display Advertising,"  Taobao, as the largest online retail platform in the world, provides billions
+of online display advertising impressions for millions of advertisers every
+day. For commercial purposes, the advertisers bid for specific spots and target
+crowds to compete for business traffic. The platform chooses the most suitable
+ads to display in tens of milliseconds. Common pricing methods include cost per
+mille (CPM) and cost per click (CPC). Traditional advertising systems target
+certain traits of users and ad placements with fixed bids, essentially regarded
+as coarse-grained matching of bid and traffic quality. However, the fixed bids
+set by the advertisers competing for different quality requests cannot fully
+optimize the advertisers' key requirements. Moreover, the platform has to be
+responsible for the business revenue and user experience. Thus, we proposed a
+bid optimizing strategy called optimized cost per click (OCPC) which
+automatically adjusts the bid to achieve finer matching of bid and traffic
+quality of page view (PV) request granularity. Our approach optimizes
+advertisers' demands, platform business revenue and user experience and as a
+whole improves traffic allocation efficiency. We have validated our approach in
+Taobao display advertising system in production. The online A/B test shows our
+algorithm yields substantially better results than previous fixed bid manner.
+",1,0,0,1,0,0
+15781,Recursive constructions and their maximum likelihood decoding,"  We consider recursive decoding techniques for RM codes, their subcodes, and
+newly designed codes. For moderate lengths up to 512, we obtain near-optimum
+decoding with feasible complexity.
+",1,0,0,0,0,0
+15782,Development of a 32-channel ASIC for an X-ray APD Detector onboard the ISS,"  We report on the design and performance of a mixed-signal application
+specific integrated circuit (ASIC) dedicated to avalanche photodiodes (APDs) in
+order to detect hard X-ray emissions in a wide energy band onboard the
+International Space Station. To realize wide-band detection from 20 keV to 1
+MeV, we use Ce:GAGG scintillators, each coupled to an APD, with low-noise
+front-end electronics capable of achieving a minimum energy detection threshold
+of 20 keV. The developed ASIC has the ability to read out 32-channel APD
+signals using 0.35 $\mu$m CMOS technology, and an analog amplifier at the input
+stage is designed to suppress the capacitive noise primarily arising from the
+large detector capacitance of the APDs. The ASIC achieves a performance of 2099
+e$^{-}$ + 1.5 e$^{-}$/pF at root mean square (RMS) with a wide 300 fC dynamic
+range. Coupling a reverse-type APD with a Ce:GAGG scintillator, we obtain an
+energy resolution of 6.7% (FWHM) at 662 keV and a minimum detectable energy of
+20 keV at room temperature (20 $^{\circ}$C). Furthermore, we examine the
+radiation tolerance for space applications by using a 90 MeV proton beam,
+confirming that the ASIC is free of single-event effects and can operate
+properly without serious degradation in analog and digital processing.
+",0,1,0,0,0,0
+15783,Impact of Detour-Aware Policies on Maximizing Profit in Ridesharing,"  This paper provides efficient solutions to maximize profit for commercial
+ridesharing services, under a pricing model with detour-based discounts for
+passengers. We propose greedy heuristics for real-time ride matching that offer
+different trade-offs between optimality and speed. Simulations on New York City
+(NYC) taxi trip data show that our heuristics are up to 90% optimal and 10^5
+times faster than the (necessarily) exponential-time optimal algorithm.
+Commercial ridesharing service providers generate significant savings by
+matching multiple ride requests using heuristic methods. The resulting savings
+are typically shared between the service provider (in the form of increased
+profit) and the ridesharing passengers (in the form of discounts). It is not
+clear a priori how this split should be effected, since higher discounts would
+encourage more ridesharing, thereby increasing total savings, but the fraction
+of savings taken as profit is reduced. We simulate a scenario where the
+decisions of the passengers to opt for ridesharing depend on the discount
+offered by the service provider. We provide an adaptive learning algorithm
+IDFLA that learns the optimal profit-maximizing discount factor for the
+provider. An evaluation over NYC data shows that IDFLA, on average, learns the
+optimal discount factor in under 16 iterations.
+Finally, we investigate the impact of imposing a detour-aware routing policy
+based on sequential individual rationality, a recently proposed concept. Such
+restricted policies offer a better ride experience, increasing the provider's
+market share, but at the cost of decreased average per-ride profit due to the
+reduced number of matched rides. We construct a model that captures these
+opposing effects, wherein simulations based on NYC data show that a 7% increase
+in market share would suffice to offset the decreased average per-ride profit.
+",1,0,1,0,0,0
+15784,Optimal lower exponent for the higher gradient integrability of solutions to two-phase elliptic equations in two dimensions,"  We study the higher gradient integrability of distributional solutions $u$ to
+the equation $div(\sigma \nabla u) = 0$ in dimension two, in the case when the
+essential range of $\sigma$ consists of only two elliptic matrices, i.e.,
+$\sigma\in\{\sigma_1, \sigma_2\}$ a.e. in $\Omega$.
+In [4], for every pair of elliptic matrices $\sigma_1$ and $\sigma_2$,
+exponents $p_{\sigma_1,\sigma_2}\in(2,+\infty)$ and $q_{\sigma_1,\sigma_2}\in
+(1,2)$ have been characterised so that if $u\in
+W^{1,q_{\sigma_1,\sigma_2}}(\Omega)$ is solution to the elliptic equation then
+$\nabla u\in L^{p_{\sigma_1,\sigma_2}}_{\rm weak}(\Omega)$ and the optimality
+of the upper exponent $p_{\sigma_1,\sigma_2}$ has been proved. In this paper we
+complement the above result by proving the optimality of the lower exponent
+$q_{\sigma_1,\sigma_2}$. Precisely, we show that for every arbitrarily small
+$\delta$, one can find a particular microgeometry, i.e., an arrangement of the
+sets $\sigma^{-1}(\sigma_1)$ and $\sigma^{-1}(\sigma_2)$, for which there
+exists a solution $u$ to the corresponding elliptic equation such that $\nabla
+u \in L^{q_{\sigma_1,\sigma_2}-\delta}$, but $\nabla u \notin
+L^{q_{\sigma_1,\sigma_2}}.$ The existence of such optimal microgeometries is
+achieved by convex integration methods, adapting to the present setting the
+geometric constructions provided in [2] for the isotropic case.
+",0,0,1,0,0,0
+15785,On the existence of young embedded clusters at high Galactic latitude,"  Careful analyses of photometric and star count data available for the nine
+putative young clusters identified by Camargo et al. (2015, 2016) at high
+Galactic latitudes reveal that none of the groups contain early-type stars, and
+most are not significant density enhancements above field level. 2MASS colours
+for stars in the groups match those of unreddened late-type dwarfs and giants,
+as expected for contamination by (mostly) thin disk objects. A simulation of
+one such field using only typical high latitude foreground stars yields a
+colour-magnitude diagram that is very similar to those constructed by Camargo
+et al. (2015, 2016) as evidence for their young groups as well as the means of
+deriving their reddenings and distances. Although some of the fields are
+coincident with clusters of galaxies, one must conclude that there is no
+evidence that the putative clusters are extremely young stellar groups.
+",0,1,0,0,0,0
+15786,The risk of contagion spreading and its optimal control in the economy,"  The global crisis of 2008 provoked a heightened interest among scientists to
+study the phenomenon, its propagation and negative consequences. The process of
+modelling the spread of a virus is commonly used in epidemiology. Conceptually,
+the spread of a disease among a population is similar to the contagion process
+in economy. This similarity allows considering the contagion in the world
+financial system using the same mathematical model of infection spread that is
+often used in epidemiology. Our research focuses on the dynamic behaviour of
+contagion spreading in the global financial network. The effect of infection by
+a systemic spread of risks in the network of national banking systems of
+countries is tested. An optimal control problem is then formulated to simulate
+a control that may avoid significant financial losses. The results show that
+the proposed approach describes well the reality of the world economy, and
+emphasizes the importance of international relations between countries on the
+financial stability.
+",0,0,0,0,0,1
+15787,A Learning-to-Infer Method for Real-Time Power Grid Topology Identification,"  Identifying arbitrary topologies of power networks in real time is a
+computationally hard problem due to the number of hypotheses that grows
+exponentially with the network size. A new ""Learning-to-Infer"" variational
+inference method is developed for efficient inference of every line status in
+the network. Optimizing the variational model is transformed to and solved as a
+discriminative learning problem based on Monte Carlo samples generated with
+power flow simulations. A major advantage of the developed Learning-to-Infer
+method is that the labeled data used for training can be generated in an
+arbitrarily large amount fast and at very little cost. As a result, the power
+of offline training is fully exploited to learn very complex classifiers for
+effective real-time topology identification. The proposed methods are evaluated
+in the IEEE 30, 118 and 300 bus systems. Excellent performance in identifying
+arbitrary power network topologies in real time is achieved even with
+relatively simple variational models and a reasonably small amount of data.
+",1,0,0,1,0,0
+15788,Convergence analysis of belief propagation for pairwise linear Gaussian models,"  Gaussian belief propagation (BP) has been widely used for distributed
+inference in large-scale networks such as the smart grid, sensor networks, and
+social networks, where local measurements/observations are scattered over a
+wide geographical area. One particular case is when two neighboring agents
+share a common observation. For example, to estimate voltage in the direct
+current (DC) power flow model, the current measurement over a power line is
+proportional to the voltage difference between two neighboring buses. When
+applying the Gaussian BP algorithm to this type of problem, the convergence
+condition remains an open issue. In this paper, we analyze the convergence
+properties of Gaussian BP for this pairwise linear Gaussian model. We show
+analytically that the updating information matrix converges at a geometric rate
+to a unique positive definite matrix with arbitrary positive semidefinite
+initial value and further provide the necessary and sufficient convergence
+condition for the belief mean vector to the optimal estimate.
+",1,0,0,1,0,0
+15789,Banach-Alaoglu theorem for Hilbert $H^*$-module,"  We provided an analogue Banach-Alaoglu theorem for Hilbert $H^*$-module. We
+construct a $\Lambda$-weak$^*$ topology on a Hilbert $H^*$-module over a proper
+$H^*$-algebra $\Lambda$, such that the unit ball is compact with respect to
+$\Lambda$-weak$^*$ topology.
+",0,0,1,0,0,0
+15790,An Overflow Free Fixed-point Eigenvalue Decomposition Algorithm: Case Study of Dimensionality Reduction in Hyperspectral Images,"  We consider the problem of enabling robust range estimation of eigenvalue
+decomposition (EVD) algorithm for a reliable fixed-point design. The simplicity
+of fixed-point circuitry has always been so tempting to implement EVD algo-
+rithms in fixed-point arithmetic. Working towards an effective fixed-point
+design, integer bit-width allocation is a significant step which has a crucial
+impact on accuracy and hardware efficiency. This paper investigates the
+shortcomings of the existing range estimation methods while deriving bounds for
+the variables of the EVD algorithm. In light of the circumstances, we introduce
+a range estimation approach based on vector and matrix norm properties together
+with a scaling procedure that maintains all the assets of an analytical method.
+The method could derive robust and tight bounds for the variables of EVD
+algorithm. The bounds derived using the proposed approach remain same for any
+input matrix and are also independent of the number of iterations or size of
+the problem. Some benchmark hyperspectral data sets have been used to evaluate
+the efficiency of the proposed technique. It was found that by the proposed
+range estimation approach, all the variables generated during the computation
+of Jacobi EVD is bounded within $\pm1$.
+",1,0,0,0,0,0
+15791,Incentivized Advertising: Treatment Effect and Adverse Selection,"  Incentivized advertising is a new ad format that is gaining popularity in
+digital mobile advertising. In incentivized advertising, the publisher rewards
+users for watching an ad. An endemic issue here is adverse selection, where
+reward-seeking users select into incentivized ad placements to obtain rewards.
+Adverse selection reduces the publisher's ad profit as well as poses a
+difficulty to causal inference of the effectiveness of incentivized
+advertising. To this end, we develop a treatment effect model that allows and
+controls for unobserved adverse selection, and estimate the model using data
+from a mobile gaming app that offers both incentivized and non-incentivized
+ads. We find that rewarding users to watch an ad has an overall positive effect
+on the ad conversion rate. A user is 27% more likely to convert when being
+rewarded to watch an ad. However there is a negative offsetting effect that
+reduces the effectiveness of incentivized ads. Some users are averse to delayed
+rewards, they prefer to collect their rewards immediately after watching the
+incentivized ads, instead of pursuing the content of the ads further. For the
+subset of users who are averse to delayed rewards, the treatment effect is only
+13%, while it can be as high as 47% for other users.
+",0,0,0,1,0,0
+15792,Prospects for indirect MeV Dark Matter detection with Gamma Rays in light of Cosmic Microwave Background Constraints,"  The self-annihilation of dark matter particles with mass in the MeV range can
+produce gamma rays via prompt or secondary radiation. The annihilation rate for
+such light dark matter particles is however tightly constrained by cosmic
+microwave background (CMB) data. Here we explore the possibility of discovering
+MeV dark matter annihilation with future MeV gamma-ray telescopes taking into
+account the latest and future CMB constraints. We study the optimal energy
+window as a function of the dominant annihilation final state. We consider both
+the (conservative) case of the dwarf spheroidal galaxy Draco and the (more
+optimistic) case of the Galactic center. We find that for certain channels,
+including those with one or two monochromatic photon(s) and one or two neutral
+pion(s), a detectable gamma-ray signal is possible for both targets under
+consideration, and compatible with CMB constraints. For other annihilation
+channels, however, including all leptonic annihilation channels and two charged
+pions, CMB data rule out any significant signal of dark matter annihilation at
+future MeV gamma-ray telescopes from dwarf galaxies, but possibly not for the
+Galactic center.
+",0,1,0,0,0,0
+15793,Special cases of the orbifold version of Zvonkine's $r$-ELSV formula,"  We prove the orbifold version of Zvonkine's $r$-ELSV formula in two special
+cases: the case of $r=2$ (complete $3$-cycles) for any genus $g\geq 0$ and the
+case of any $r\geq 1$ for genus $g=0$.
+",0,0,1,0,0,0
+15794,From homogeneous metric spaces to Lie groups,"  We study connected, locally compact metric spaces with transitive isometry
+groups. For all $\epsilon \in \mathbb{R}^+$, each such space is
+$(1,\epsilon)$-quasi-isometric to a Lie group equipped with a left-invariant
+metric. Further, every metric Lie group is $(1, C)$-quasi-isometric to a
+solvable Lie group, and every simply connected metric Lie group is $(1,
+C)$-quasi-isometrically homeomorphic to a solvable-by-compact metric Lie group.
+While any contractible Lie group may be made isometric to a solvable group,
+only those that are solvable and of type (R) may be made isometric to a
+nilpotent Lie group, in which case the nilpotent group is the nilshadow of the
+group. Finally, we give a complete metric characterisation of metric Lie groups
+for which there exists an automorphic dilation. These coincide with the metric
+spaces that are locally compact, connected, homogeneous, and admit a metric
+dilation.
+",0,0,1,0,0,0
+15795,Growth of Sobolev norms for abstract linear Schrödinger Equations,"  We prove an abstract theorem giving a $\langle t\rangle^\epsilon$ bound
+($\forall \epsilon>0$) on the growth of the Sobolev norms in linear
+Schrödinger equations of the form $i \dot \psi = H_0 \psi + V(t) \psi $ when
+the time $t \to \infty$. The abstract theorem is applied to several cases,
+including the cases where (i) $H_0$ is the Laplace operator on a Zoll manifold
+and $V(t)$ a pseudodifferential operator of order smaller then 2; (ii) $H_0$ is
+the (resonant or nonresonant) Harmonic oscillator in $R^d$ and $V(t)$ a
+pseudodifferential operator of order smaller then $H_0$ depending in a
+quasiperiodic way on time. The proof is obtained by first conjugating the
+system to some normal form in which the perturbation is a smoothing operator
+and then applying the results of \cite{MaRo}.
+",0,0,1,0,0,0
+15796,Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence,"  User-based Collaborative Filtering (CF) is one of the most popular approaches
+to create recommender systems. This approach is based on finding the most
+relevant k users from whose rating history we can extract items to recommend.
+CF, however, suffers from data sparsity and the cold-start problem since users
+often rate only a small fraction of available items. One solution is to
+incorporate additional information into the recommendation process such as
+explicit trust scores that are assigned by users to others or implicit trust
+relationships that result from social connections between users. Such
+relationships typically form a very sparse trust network, which can be utilized
+to generate recommendations for users based on people they trust. In our work,
+we explore the use of a measure from network science, i.e. regular equivalence,
+applied to a trust network to generate a similarity matrix that is used to
+select the k-nearest neighbors for recommending items. We evaluate our approach
+on Epinions and we find that we can outperform related methods for tackling
+cold-start users in terms of recommendation accuracy.
+",1,0,0,0,0,0
+15797,Experimental parametric study of the self-coherent camera,"  Direct imaging of exoplanets requires the detection of very faint objects
+orbiting close to very bright stars. In this context, the SPICES mission was
+proposed to the European Space Agency for planet characterization at visible
+wavelength. SPICES is a 1.5m space telescope which uses a coronagraph to
+strongly attenuate the central source. However, small optical aberrations,
+which appear even in space telescopes, dramatically decrease coronagraph
+performance. To reduce these aberrations, we want to estimate, directly on the
+coronagraphic image, the electric field, and, with the help of a deformable
+mirror, correct the wavefront upstream of the coronagraph. We propose an
+instrument, the Self-Coherent Camera (SCC) for this purpose. By adding a small
+""reference hole"" into the Lyot stop, located after the coronagraph, we can
+produce interferences in the focal plane, using the coherence of the stellar
+light. We developed algorithms to decode the information contained in these
+Fizeau fringes and retrieve an estimation of the field in the focal plane.
+After briefly recalling the SCC principle, we will present the results of a
+study, based on both experiment and numerical simulation, analyzing the impact
+of the size of the reference hole.
+",0,1,0,0,0,0
+15798,A Comparison of Parallel Graph Processing Implementations,"  The rapidly growing number of large network analysis problems has led to the
+emergence of many parallel and distributed graph processing systems---one
+survey in 2014 identified over 80. Since then, the landscape has evolved; some
+packages have become inactive while more are being developed. Determining the
+best approach for a given problem is infeasible for most developers. To enable
+easy, rigorous, and repeatable comparison of the capabilities of such systems,
+we present an approach and associated software for analyzing the performance
+and scalability of parallel, open-source graph libraries. We demonstrate our
+approach on five graph processing packages: GraphMat, the Graph500, the Graph
+Algorithm Platform Benchmark Suite, GraphBIG, and PowerGraph using synthetic
+and real-world datasets. We examine previously overlooked aspects of parallel
+graph processing performance such as phases of execution and energy usage for
+three algorithms: breadth first search, single source shortest paths, and
+PageRank and compare our results to Graphalytics.
+",1,0,0,0,0,0
+15799,A note on optimization with Morse polynomials,"  In this paper we prove that the gradient ideal of a Morse polynomial is
+radical. This gives a generic class of polynomials whose gradient ideals are
+radical. As a consequence we reclaim a previous result that the unconstrained
+polynomial optimization problem for Morse polynomials has a finite convergence.
+",0,0,1,0,0,0
+15800,Compression of Deep Neural Networks for Image Instance Retrieval,"  Image instance retrieval is the problem of retrieving images from a database
+which contain the same object. Convolutional Neural Network (CNN) based
+descriptors are becoming the dominant approach for generating {\it global image
+descriptors} for the instance retrieval problem. One major drawback of
+CNN-based {\it global descriptors} is that uncompressed deep neural network
+models require hundreds of megabytes of storage making them inconvenient to
+deploy in mobile applications or in custom hardware. In this work, we study the
+problem of neural network model compression focusing on the image instance
+retrieval task. We study quantization, coding, pruning and weight sharing
+techniques for reducing model size for the instance retrieval problem. We
+provide extensive experimental results on the trade-off between retrieval
+performance and model size for different types of networks on several data sets
+providing the most comprehensive study on this topic. We compress models to the
+order of a few MBs: two orders of magnitude smaller than the uncompressed
+models while achieving negligible loss in retrieval performance.
+",1,0,0,0,0,0
+15801,Finite temperature disordered bosons in two dimensions,"  We study phase transitions in a two dimensional weakly interacting Bose gas
+in a random potential at finite temperatures. We identify superfluid, normal
+fluid, and insulator phases and construct the phase diagram. At T=0 one has a
+tricritical point where the three phases coexist. The truncation of the energy
+distribution at the trap barrier, which is a generic phenomenon in cold atom
+systems, limits the growth of the localization length and in contrast to the
+thermodynamic limit the insulator phase is present at any temperature.
+",0,1,0,0,0,0
+15802,Morphology and properties evolution upon ring-opening polymerization during extrusion of cyclic butylene terephthalate and graphene-related-materials into thermally conductive nanocomposites,"  In this work, the study of thermal conductivity before and after in-situ
+ring-opening polymerization of cyclic butylene terephthalate into poly
+(butylene terephthalate) in presence of graphene-related materials (GRM) is
+addressed, to gain insight in the modification of nanocomposites morphology
+upon polymerization. Five types of GRM were used: one type of graphite
+nanoplatelets, two different grades of reduced graphene oxide (rGO) and the
+same rGO grades after thermal annealing for 1 hour at 1700°C under vacuum
+to reduce their defectiveness. Polymerization of CBT into pCBT, morphology and
+nanoparticle organization were investigated by means of differential scanning
+calorimetry, electron microscopy and rheology. Electrical and thermal
+properties were investigated by means of volumetric resistivity and bulk
+thermal conductivity measurement. In particular, the reduction of nanoflake
+aspect ratio during ring-opening polymerization was found to have a detrimental
+effect on both electrical and thermal conductivities in nanocomposites.
+",0,1,0,0,0,0
+15803,Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes,"  We investigate the forecasting ability of the most commonly used benchmarks
+in financial economics. We approach the usual caveats of probabilistic
+forecasts studies -small samples, limited models and non-holistic validations-
+by performing a comprehensive comparison of 15 predictive schemes during a time
+period of over 21 years. All densities are evaluated in terms of their
+statistical consistency, local accuracy and forecasting errors. Using a new
+composite indicator, the Integrated Forecast Score (IFS), we show that
+risk-neutral densities outperform historical-based predictions in terms of
+information content. We find that the Variance Gamma model generates the
+highest out-of-sample likelihood of observed prices and the lowest predictive
+errors, whereas the ARCH-based GJR-FHS delivers the most consistent forecasts
+across the entire density range. In contrast, lognormal densities, the Heston
+model or the Breeden-Litzenberger formula yield biased predictions and are
+rejected in statistical tests.
+",0,0,0,1,0,1
+15804,Deep Room Recognition Using Inaudible Echos,"  Recent years have seen the increasing need of location awareness by mobile
+applications. This paper presents a room-level indoor localization approach
+based on the measured room's echos in response to a two-millisecond single-tone
+inaudible chirp emitted by a smartphone's loudspeaker. Different from other
+acoustics-based room recognition systems that record full-spectrum audio for up
+to ten seconds, our approach records audio in a narrow inaudible band for 0.1
+seconds only to preserve the user's privacy. However, the short-time and
+narrowband audio signal carries limited information about the room's
+characteristics, presenting challenges to accurate room recognition. This paper
+applies deep learning to effectively capture the subtle fingerprints in the
+rooms' acoustic responses. Our extensive experiments show that a two-layer
+convolutional neural network fed with the spectrogram of the inaudible echos
+achieve the best performance, compared with alternative designs using other raw
+data formats and deep models. Based on this result, we design a RoomRecognize
+cloud service and its mobile client library that enable the mobile application
+developers to readily implement the room recognition functionality without
+resorting to any existing infrastructures and add-on hardware.
+Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and
+89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in
+a quiet museum, and 15 spots in a crowded museum, respectively. Compared with
+the state-of-the-art approaches based on support vector machine, RoomRecognize
+significantly improves the Pareto frontier of recognition accuracy versus
+robustness against interfering sounds (e.g., ambient music).
+",1,0,0,0,0,0
+15805,Linguistic Diversities of Demographic Groups in Twitter,"  The massive popularity of online social media provides a unique opportunity
+for researchers to study the linguistic characteristics and patterns of user's
+interactions. In this paper, we provide an in-depth characterization of
+language usage across demographic groups in Twitter. In particular, we extract
+the gender and race of Twitter users located in the U.S. using advanced image
+processing algorithms from Face++. Then, we investigate how demographic groups
+(i.e. male/female, Asian/Black/White) differ in terms of linguistic styles and
+also their interests. We extract linguistic features from 6 categories
+(affective attributes, cognitive attributes, lexical density and awareness,
+temporal references, social and personal concerns, and interpersonal focus), in
+order to identify the similarities and differences in particular writing set of
+attributes. In addition, we extract the absolute ranking difference of top
+phrases between demographic groups. As a dimension of diversity, we also use
+the topics of interest that we retrieve from each user. Our analysis unveils
+clear differences in the writing styles (and the topics of interest) of
+different demographic groups, with variation seen across both gender and race
+lines. We hope our effort can stimulate the development of new studies related
+to demographic information in the online space.
+",1,0,0,0,0,0
+15806,And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue,"  We investigate the characteristics of factual and emotional argumentation
+styles observed in online debates. Using an annotated set of ""factual"" and
+""feeling"" debate forum posts, we extract patterns that are highly correlated
+with factual and emotional arguments, and then apply a bootstrapping
+methodology to find new patterns in a larger pool of unannotated forum posts.
+This process automatically produces a large set of patterns representing
+linguistic expressions that are highly correlated with factual and emotional
+language. Finally, we analyze the most discriminating patterns to better
+understand the defining characteristics of factual and emotional arguments.
+",1,0,0,0,0,0
+15807,A conservative sharp-interface method for compressible multi-material flows,"  In this paper we develop a conservative sharp-interface method dedicated to
+simulating multiple compressible fluids. Numerical treatments for a cut cell
+shared by more than two materials are proposed. First, we simplify the
+interface interaction inside such a cell with a reduced model to avoid explicit
+interface reconstruction and complex flux calculation. Second, conservation is
+strictly preserved by an efficient conservation correction procedure for the
+cut cell. To improve the robustness, a multi-material scale separation model is
+developed to consistently remove non-resolved interface scales. In addition,
+the multi-resolution method and local time-stepping scheme are incorporated
+into the proposed multi-material method to speed up the high-resolution
+simulations. Various numerical test cases, including the multi-material shock
+tube problem, inertial confinement fusion implosion, triple-point shock
+interaction and shock interaction with multi-material bubbles, show that the
+method is suitable for a wide range of complex compressible multi-material
+flows.
+",0,1,0,0,0,0
+15808,Complexity of products: the effect of data regularisation,"  Among several developments, the field of Economic Complexity (EC) has notably
+seen the introduction of two new techniques. One is the Bootstrapped Selective
+Predictability Scheme (SPSb), which can provide quantitative forecasts of the
+Gross Domestic Product of countries. The other, Hidden Markov Model (HMM)
+regularisation, denoises the datasets typically employed in the literature. We
+contribute to EC along three different directions. First, we prove the
+convergence of the SPSb algorithm to a well-known statistical learning
+technique known as Nadaraya-Watson Kernel regression. The latter has
+significantly lower time complexity, produces deterministic results, and it is
+interchangeable with SPSb for the purpose of making predictions. Second, we
+study the effects of HMM regularization on the Product Complexity and logPRODY
+metrics, for which a model of time evolution has been recently proposed. We
+find confirmation for the original interpretation of the logPRODY model as
+describing the change in the global market structure of products with new
+insights allowing a new interpretation of the Complexity measure, for which we
+propose a modification. Third, we explore new effects of regularisation on the
+data. We find that it reduces noise, and observe for the first time that it
+increases nestedness in the export network adjacency matrix.
+",0,0,0,0,0,1
+15809,A review on applications of two-dimensional materials in surface enhanced Raman spectroscopy,"  Two-dimensional (2D) materials, such as graphene and MoS2, have been
+attracting wide interest in surface enhancement Raman spectroscopy. This
+perspective gives an overview of recent developments in 2D materials'
+application in surface enhanced Raman spectroscopy. This review focuses on the
+applications of using bare 2D materials and metal/2D material hybrid substrate
+for Raman enhancement. The Raman enhancing mechanism of 2D materials will also
+be discussed. The progress covered herein shows great promise for widespread
+adoption of 2D materials in SERS application.
+",0,1,0,0,0,0
+15810,A variety of elastic anomalies in orbital-active nearly-itinerant cobalt vanadate spinel,"  We perform ultrasound velocity measurements on a single crystal of
+nearly-metallic spinel Co$_{1.21}$V$_{1.79}$O$_4$ which exhibits a
+ferrimagnetic phase transition at $T_C \sim$ 165 K. The experiments reveal a
+variety of elastic anomalies in not only the paramagnetic phase above $T_C$ but
+also the ferrimagnetic phase below $T_C$, which should be driven by the
+nearly-itinerant character of the orbitally-degenerate V 3$d$ electrons. In the
+paramagnetic phase above $T_C$, the elastic moduli exhibit
+elastic-mode-dependent unusual temperature variations, suggesting the existence
+of a dynamic spin-cluster state. Furthermore, above $T_C$, the sensitive
+magnetic-field response of the elastic moduli suggests that, with the negative
+magnetoresistance, the magnetic-field-enhanced nearly-itinerant character of
+the V 3$d$ electrons emerges from the spin-cluster state. This should be
+triggered by the inter-V-site interactions acting on the orbitally-degenerate
+3$d$ electrons. In the ferrimagnetic phase below $T_C$, the elastic moduli
+exhibit distinct anomalies at $T_1\sim$ 95 K and $T_2\sim$ 50 K, with a sign
+change of the magnetoresistance at $T_1$ (positive below $T_1$) and an
+enhancement of the positive magnetoresistance below $T_2$, respectively. These
+observations below $T_C$ suggest the successive occurrence of an orbital glassy
+order at $T_1$ and a structural phase transition at $T_2$, where the rather
+localized character of the V 3$d$ electrons evolves below $T_1$ and is further
+enhanced below $T_2$.
+",0,1,0,0,0,0
+15811,Anomaly Detection using One-Class Neural Networks,"  We propose a one-class neural network (OC-NN) model to detect anomalies in
+complex data sets. OC-NN combines the ability of deep networks to extract a
+progressively rich representation of data with the one-class objective of
+creating a tight envelope around normal data. The OC-NN approach breaks new
+ground for the following crucial reason: data representation in the hidden
+layer is driven by the OC-NN objective and is thus customized for anomaly
+detection. This is a departure from other approaches which use a hybrid
+approach of learning deep features using an autoencoder and then feeding the
+features into a separate anomaly detection method like one-class SVM (OC-SVM).
+The hybrid OC-SVM approach is sub-optimal because it is unable to influence
+representational learning in the hidden layers. A comprehensive set of
+experiments demonstrate that on complex data sets (like CIFAR and GTSRB), OC-NN
+performs on par with state-of-the-art methods and outperformed conventional
+shallow methods in some scenarios.
+",0,0,0,1,0,0
+15812,"Low Cost, Open-Source Testbed to Enable Full-Sized Automated Vehicle Research","  An open-source vehicle testbed to enable the exploration of automation
+technologies for road vehicles is presented. The platform hardware and
+software, based on the Robot Operating System (ROS), are detailed. Two methods
+are discussed for enabling the remote control of a vehicle (in this case, an
+electric 2013 Ford Focus). The first approach used digital filtering of
+Controller Area Network (CAN) messages. In the case of the test vehicle, this
+approach allowed for the control of acceleration from a tap-point on the CAN
+bus and the OBD-II port. The second approach, based on the emulation of the
+analog output(s) of a vehicle's accelerator pedal, brake pedal, and steering
+torque sensors, is more generally applicable and, in the test vehicle, allowed
+for the full control vehicle acceleration, braking, and steering. To
+demonstrate the utility of the testbed for vehicle automation research, system
+identification was performed on the test vehicle and speed and steering
+controllers were designed to allow the vehicle to follow a predetermined path.
+The resulting system was shown to be differentially flat, and a high level path
+following algorithm was developed using the differentially flat properties and
+state feedback. The path following algorithm is experimentally validated on the
+automation testbed developed in the paper.
+",1,0,0,0,0,0
+15813,Sub-sampled Cubic Regularization for Non-convex Optimization,"  We consider the minimization of non-convex functions that typically arise in
+machine learning. Specifically, we focus our attention on a variant of trust
+region methods known as cubic regularization. This approach is particularly
+attractive because it escapes strict saddle points and it provides stronger
+convergence guarantees than first- and second-order as well as classical trust
+region methods. However, it suffers from a high computational complexity that
+makes it impractical for large-scale learning. Here, we propose a novel method
+that uses sub-sampling to lower this computational cost. By the use of
+concentration inequalities we provide a sampling scheme that gives sufficiently
+accurate gradient and Hessian approximations to retain the strong global and
+local convergence guarantees of cubically regularized methods. To the best of
+our knowledge this is the first work that gives global convergence guarantees
+for a sub-sampled variant of cubic regularization on non-convex functions.
+Furthermore, we provide experimental results supporting our theory.
+",1,0,1,1,0,0
+15814,Emergence of Seismic Metamaterials: Current State and Future Perspectives,"  Following the advent of electromagnetic metamaterials at the turn of the
+century, researchers working in other areas of wave physics have translated
+concepts of electromagnetic metamaterials to acoustics, elastodynamics, as well
+as to heat, mass and light diffusion processes. In elastodynamics, seismic
+metamaterials have emerged in the last decade for soft soils structured at the
+meter scale, and have been tested thanks to full-scale experiments on holey
+soils five years ago. Born in the soil, seismic metamaterials grow
+simultaneously on the field of tuned-resonators buried in the soil, around
+building's foundations or near the soil-structure's interface, and on the field
+of above-surface resonators. In this perspective article, we quickly recall
+some research advances made in all these types of seismic metamaterials and we
+further dress an inventory of which material parameters can be achieved and
+which cannot, notably from the effective medium theory perspective. We finally
+envision perspectives on future developments of large scale auxetic
+metamaterials for building's foundations, forests of trees for seismic
+protection and metamaterial-like transformed urbanism at the city scale.
+",0,1,0,0,0,0
+15815,Case studies in network community detection,"  Community structure describes the organization of a network into subgraphs
+that contain a prevalence of edges within each subgraph and relatively few
+edges across boundaries between subgraphs. The development of
+community-detection methods has occurred across disciplines, with numerous and
+varied algorithms proposed to find communities. As we present in this Chapter
+via several case studies, community detection is not just an ""end game"" unto
+itself, but rather a step in the analysis of network data which is then useful
+for furthering research in the disciplinary domain of interest. These
+case-study examples arise from diverse applications, ranging from social and
+political science to neuroscience and genetics, and we have chosen them to
+demonstrate key aspects of community detection and to highlight that community
+detection, in practice, should be directed by the application at hand.
+",1,1,0,0,0,0
+15816,Multi-particle instability in a spin-imbalanced Fermi gas,"  Weak attractive interactions in a spin-imbalanced Fermi gas induce a
+multi-particle instability, binding multiple fermions together. The maximum
+binding energy per particle is achieved when the ratio of the number of up- and
+down-spin particles in the instability is equal to the ratio of the up- and
+down-spin densities of states in momentum at the Fermi surfaces, to utilize the
+variational freedom of all available momentum states. We derive this result
+using an analytical approach, and verify it using exact diagonalization. The
+multi-particle instability extends the Cooper pairing instability of balanced
+Fermi gases to the imbalanced case, and could form the basis of a many-body
+state, analogously to the construction of the Bardeen-Cooper-Schrieffer theory
+of superconductivity out of Cooper pairs.
+",0,1,0,0,0,0
+15817,Weighted boundedness of maximal functions and fractional Bergman operators,"  The aim of this paper is to study two-weight norm inequalities for fractional
+maximal functions and fractional Bergman operator defined on the upper-half
+space. Namely, we characterize those pairs of weights for which these maximal
+operators satisfy strong and weak type inequalities. Our characterizations are
+in terms of Sawyer and Békollé-Bonami type conditions. We also obtain a
+$\Phi$-bump characterization for these maximal functions, where $\Phi$ is a
+Orlicz function. As a consequence, we obtain two-weight norm inequalities for
+fractional Bergman operators. Finally, we provide some sharp weighted
+inequalities for the fractional maximal functions.
+",0,0,1,0,0,0
+15818,A Framework for Algorithm Stability,"  We say that an algorithm is stable if small changes in the input result in
+small changes in the output. This kind of algorithm stability is particularly
+relevant when analyzing and visualizing time-varying data. Stability in general
+plays an important role in a wide variety of areas, such as numerical analysis,
+machine learning, and topology, but is poorly understood in the context of
+(combinatorial) algorithms. In this paper we present a framework for analyzing
+the stability of algorithms. We focus in particular on the tradeoff between the
+stability of an algorithm and the quality of the solution it computes. Our
+framework allows for three types of stability analysis with increasing degrees
+of complexity: event stability, topological stability, and Lipschitz stability.
+We demonstrate the use of our stability framework by applying it to kinetic
+Euclidean minimum spanning trees.
+",1,0,0,0,0,0
+15819,A Survey of Question Answering for Math and Science Problem,"  Turing test was long considered the measure for artificial intelligence. But
+with the advances in AI, it has proved to be insufficient measure. We can now
+aim to mea- sure machine intelligence like we measure human intelligence. One
+of the widely accepted measure of intelligence is standardized math and science
+test. In this paper, we explore the progress we have made towards the goal of
+making a machine smart enough to pass the standardized test. We see the
+challenges and opportunities posed by the domain, and note that we are quite
+some ways from actually making a system as smart as a even a middle school
+scholar.
+",1,0,0,0,0,0
+15820,Thermal and structural properties of iron at high pressure by molecular dynamics,"  We investigate the basic thermal, mechanical and structural properties of
+body centred cubic iron ($\alpha$-Fe) at several temperatures and positive
+loading by means of Molecular Dynamics simulations in conjunction with the
+embedded-atom method potential and its modified counterpart one. Computations
+of its thermal properties like average energy and density of atoms, transport
+sound velocities at finite temperatures and pressures are detailed studied as
+well. Moreover, there are suggestions to obtain hexagonal close- packed
+structure ($\varepsilon$-phase) of this metal under positive loading. To
+demonstrate that, one can increase sufficiently the pressure of simulated
+system at several temperature's ranges; these structural changes depend only on
+potential type used. The ensuring structures are studied via the pair radial
+distribution functions (PRDF) and precise common- neighbour analysis method
+(CNA) as well.
+",0,1,0,0,0,0
+15821,Interacting Fields and Flows: Magnetic Hot Jupiters,"  We present Magnetohydrodynamic (MHD) simulations of the magnetic interactions
+between a solar type star and short period hot Jupiter exoplanets, using the
+publicly available MHD code PLUTO. It has been predicted that emission due to
+magnetic interactions such as the electron cyclotron maser instability (ECMI)
+will be observable. In our simulations, a planetary outflow, due to UV
+evaporation of the exoplanets atmosphere, results in the build-up of
+circumplanetary material. We predict the ECMI emission and determine that the
+emission is prevented from escaping from the system. This is due to the
+evaporated material leading to a high plasma frequency in the vicinity of the
+planet, which inhibits the ECMI process.
+",0,1,0,0,0,0
+15822,From which world is your graph?,"  Discovering statistical structure from links is a fundamental problem in the
+analysis of social networks. Choosing a misspecified model, or equivalently, an
+incorrect inference algorithm will result in an invalid analysis or even
+falsely uncover patterns that are in fact artifacts of the model. This work
+focuses on unifying two of the most widely used link-formation models: the
+stochastic blockmodel (SBM) and the small world (or latent space) model (SWM).
+Integrating techniques from kernel learning, spectral graph theory, and
+nonlinear dimensionality reduction, we develop the first statistically sound
+polynomial-time algorithm to discover latent patterns in sparse graphs for both
+models. When the network comes from an SBM, the algorithm outputs a block
+structure. When it is from an SWM, the algorithm outputs estimates of each
+node's latent position.
+",1,0,0,1,0,0
+15823,Topological and Hodge L-Classes of Singular Covering Spaces and Varieties with Trivial Canonical Class,"  The signature of closed oriented manifolds is well-known to be multiplicative
+under finite covers. This fails for Poincaré complexes as examples of C. T.
+C. Wall show. We establish the multiplicativity of the signature, and more
+generally, the topological L-class, for closed oriented stratified
+pseudomanifolds that can be equipped with a middle-perverse Verdier self-dual
+complex of sheaves, determined by Lagrangian sheaves along strata of odd
+codimension (so-called L-pseudomanifolds). This class of spaces contains all
+Witt spaces and thus all pure-dimensional complex algebraic varieties. We apply
+this result in proving the Brasselet-Schürmann-Yokura conjecture for normal
+complex projective 3-folds with at most canonical singularities, trivial
+canonical class and positive irregularity. The conjecture asserts the equality
+of topological and Hodge L-class for compact complex algebraic rational
+homology manifolds.
+",0,0,1,0,0,0
+15824,Identities for the shifted harmonic numbers and binomial coefficients,"  We develop new closed form representations of sums of (n + {\alpha})th
+shifted harmonic numbers and reciprocal binomial coefficients in terms of
+{\alpha}th shifted harmonic numbers. Some interesting new consequences and
+illustrative examples are considered.
+",0,0,1,0,0,0
+15825,Mechanism of light energy transport in the avian retina,"  We studied intermediate filaments (IFs) in the retina of the Pied flycatcher
+(Ficedula hypoleuca) in the foveolar zone. Single IFs span Müller cells (MC)
+lengthwise; cylindrical bundles of IFs (IFBs) appear inside the cone inner
+segment (CIS) at the outer limiting membrane (OLM) level. IFBs adjoin the cone
+cytoplasmatic membrane, following lengthwise regularly spaced, forming a
+skeleton of the CIS, located above the OLM. IFBs follow along the cone outer
+segment (COS), with single IFs separating from the IFB, touching and entering
+in-between the light-sensitive disks of the cone membrane. We propose a
+mechanism of exciton transfer from the inner retinal surface to the visual
+pigments in the photoreceptor cells. This includes excitation transfer in
+donor-acceptor systems, from the IF donors to the rhodopsin acceptors, with
+theoretic efficiency over 80%. This explains high image contrast in fovea and
+foveola in daylight, while the classical mechanism that describes Müller
+cells as optical lightguides operates in night vision, with loss of resolution
+traded for sensitivity. Our theory receives strong confirmation in morphology
+and function of the cones and pigment cells. In daylight the lateral surface of
+the photosensor disks is blocked from the (scattered or oblique) light by the
+pigment cells. Thus the light energy can only get to the cone via intermediate
+filaments that absorb photons in the Müller cell endfeet and conduct excitons
+to the cone. Thus, the disks are consumed at their lateral surfaces, moving to
+the apex of the cone, with new disks produced below. An alternative hypothesis
+of direct light passing through the cone with its organelles and hitting the
+lowest disk contradicts morphological evidence, as thus all of the other disks
+would have no useful function in daylight vision.
+",0,1,0,0,0,0
+15826,"The Molecular Structures of Local Arm and Perseus Arm in the Galactic Region of l=[139.75,149.75]$^\circ$, b=[-5.25,5.25]$^\circ$","  Using the Purple Mountain Observatory Delingha (PMODLH) 13.7 m telescope, we
+report a 96-square-degree 12CO/13CO/C18O mapping observation toward the
+Galactic region of l = [139.75, 149.75]$^\circ$, b = [-5.25, 5.25]$^\circ$. The
+molecular structure of the Local Arm and Perseus Arm are presented. Combining
+HI data and part of the Outer Arm results, we obtain that the warp structure of
+both atomic and molecular gas is obvious, while the flare structure only exists
+in atomic gas in this observing region. In addition, five filamentary giant
+molecular clouds on the Perseus Arm are identified. Among them, four are newly
+identified. Their relations with the Milky Way large-scale structure are
+discussed.
+",0,1,0,0,0,0
+15827,"The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness","  When we test a theory using data, it is common to focus on correctness: do
+the predictions of the theory match what we see in the data? But we also care
+about completeness: how much of the predictable variation in the data is
+captured by the theory? This question is difficult to answer, because in
+general we do not know how much ""predictable variation"" there is in the
+problem. In this paper, we consider approaches motivated by machine learning
+algorithms as a means of constructing a benchmark for the best attainable level
+of prediction.
+We illustrate our methods on the task of predicting human-generated random
+sequences. Relative to an atheoretical machine learning algorithm benchmark, we
+find that existing behavioral models explain roughly 15 percent of the
+predictable variation in this problem. This fraction is robust across several
+variations on the problem. We also consider a version of this approach for
+analyzing field data from domains in which human perception and generation of
+randomness has been used as a conceptual framework; these include sequential
+decision-making and repeated zero-sum games. In these domains, our framework
+for testing the completeness of theories provides a way of assessing their
+effectiveness over different contexts; we find that despite some differences,
+the existing theories are fairly stable across our field domains in their
+performance relative to the benchmark. Overall, our results indicate that (i)
+there is a significant amount of structure in this problem that existing models
+have yet to capture and (ii) there are rich domains in which machine learning
+may provide a viable approach to testing completeness.
+",1,0,0,1,0,0
+15828,Clustering with Statistical Error Control,"  This paper presents a clustering approach that allows for rigorous
+statistical error control similar to a statistical test. We develop estimators
+for both the unknown number of clusters and the clusters themselves. The
+estimators depend on a tuning parameter alpha which is similar to the
+significance level of a statistical hypothesis test. By choosing alpha, one can
+control the probability of overestimating the true number of clusters, while
+the probability of underestimation is asymptotically negligible. In addition,
+the probability that the estimated clusters differ from the true ones is
+controlled. In the theoretical part of the paper, formal versions of these
+statements on statistical error control are derived in a standard model setting
+with convex clusters. A simulation study and two applications to temperature
+and gene expression microarray data complement the theoretical analysis.
+",0,0,1,1,0,0
+15829,DNA Base Pair Mismatches Induce Structural Changes and Alter the Free Energy Landscape of Base Flip,"  Double-stranded DNA may contain mismatched base pairs beyond the Watson-Crick
+pairs guanine-cytosine and adenine-thymine. Such mismatches bear adverse
+consequences for human health. We utilize molecular dynamics and metadynamics
+computer simulations to study the equilibrium structure and dynamics for both
+matched and mismatched base pairs. We discover significant differences between
+matched and mismatched pairs in structure, hydrogen bonding, and base flip work
+profiles. Mismatched pairs shift further in the plane normal to the DNA strand
+and are more likely to exhibit non-canonical structures, including the e-motif.
+We discuss potential implications on mismatch repair enzymes' detection of DNA
+mismatches.
+",0,0,0,0,1,0
+15830,Average sampling and average splines on combinatorial graphs,"  In the setting of a weighted combinatorial finite or infinite countable graph
+$G$ we introduce functional Paley-Wiener spaces $PW_{\omega}(L),\>\omega>0,$
+defined in terms of the spectral resolution of the combinatorial Laplace
+operator $L$ in the space $L_{2}(G)$. It is shown that functions in certain
+$PW_{\omega}(L),\>\omega>0,$ are uniquely defined by their averages over some
+families of ""small"" subgraphs which form a cover of $G$. Reconstruction methods
+for reconstruction of an $f\in PW_{\omega}(L)$ from appropriate set of its
+averages are introduced. One method is using language of Hilbert frames.
+Another one is using average variational interpolating splines which are
+constructed in the setting of combinatorial graphs.
+",1,0,0,0,0,0
+15831,Hilbert isometries and maximal deviation preserving maps on JB-algebras,"  In this paper we characterize the surjective linear variation norm isometries
+on JB-algebras. Variation norm isometries are precisely the maps that preserve
+the maximal deviation, the quantum analogue of the standard deviation, which
+plays an important role in quantum statistics. Consequently, we characterize
+the Hilbert's metric isometries on cones in JB-algebras.
+",0,0,1,0,0,0
+15832,Visual Entailment: A Novel Task for Fine-Grained Image Understanding,"  Existing visual reasoning datasets such as Visual Question Answering (VQA),
+often suffer from biases conditioned on the question, image or answer
+distributions. The recently proposed CLEVR dataset addresses these limitations
+and requires fine-grained reasoning but the dataset is synthetic and consists
+of similar objects and sentence structures across the dataset.
+In this paper, we introduce a new inference task, Visual Entailment (VE) -
+consisting of image-sentence pairs whereby a premise is defined by an image,
+rather than a natural language sentence as in traditional Textual Entailment
+tasks. The goal of a trained VE model is to predict whether the image
+semantically entails the text. To realize this task, we build a dataset SNLI-VE
+based on the Stanford Natural Language Inference corpus and Flickr30k dataset.
+We evaluate various existing VQA baselines and build a model called Explainable
+Visual Entailment (EVE) system to address the VE task. EVE achieves up to 71%
+accuracy and outperforms several other state-of-the-art VQA based models.
+Finally, we demonstrate the explainability of EVE through cross-modal attention
+visualizations. The SNLI-VE dataset is publicly available at
+this https URL necla-ml/SNLI-VE.
+",1,0,0,0,0,0
+15833,On relations between weak and strong type inequalities for maximal operators on non-doubling metric measure spaces,"  In this article we characterize all possible cases that may occur in the
+relations between the sets of $p$ for which weak type $(p,p)$ and strong type
+$(p,p)$ inequalities for the Hardy--Littlewood maximal operators, both centered
+and non-centered, hold in the context of general metric measure spaces.
+",0,0,1,0,0,0
+15834,Random Caching Based Cooperative Transmission in Heterogeneous Wireless Networks,"  Base station cooperation in heterogeneous wireless networks (HetNets) is a
+promising approach to improve the network performance, but it also imposes a
+significant challenge on backhaul. On the other hand, caching at small base
+stations (SBSs) is considered as an efficient way to reduce backhaul load in
+HetNets. In this paper, we jointly consider SBS caching and cooperation in a
+downlink largescale HetNet. We propose two SBS cooperative transmission schemes
+under random caching at SBSs with the caching distribution as a design
+parameter. Using tools from stochastic geometry and adopting appropriate
+integral transformations, we first derive a tractable expression for the
+successful transmission probability under each scheme. Then, under each scheme,
+we consider the successful transmission probability maximization by optimizing
+the caching distribution, which is a challenging optimization problem with a
+non-convex objective function. By exploring optimality properties and using
+optimization techniques, under each scheme, we obtain a local optimal solution
+in the general case and global optimal solutions in some special cases.
+Compared with some existing caching designs in the literature, e.g., the most
+popular caching, the i.i.d. caching and the uniform caching, the optimal random
+caching under each scheme achieves better successful transmission probability
+performance. The analysis and optimization results provide valuable design
+insights for practical HetNets.
+",1,0,0,0,0,0
+15835,Electronic characteristics of ultrathin SrRuO$_3$ films and their relationship with the metal$-$insulator transition,"  SrRuO$_3$ (SRO) films are known to exhibit insulating behavior as their
+thickness approaches four unit cells. We employ electron energy$-$loss (EEL)
+spectroscopy to probe the spatially resolved electronic structures of both
+insulating and conducting SRO to correlate them with the metal$-$insulator
+transition (MIT). Importantly, the central layer of the ultrathin insulating
+film exhibits distinct features from the metallic SRO. Moreover, EEL near edge
+spectra adjacent to the SrTiO$_3$ (STO) substrate or to the capping layer are
+remarkably similar to those of STO. The site$-$projected density of states
+based on density functional theory (DFT) partially reflects the characteristics
+of the spectra of these layers. These results may provide important information
+on the possible influence of STO on the electronic states of ultrathin SRO.
+",0,1,0,0,0,0
+15836,Solution properties of a 3D stochastic Euler fluid equation,"  We prove local well-posedness in regular spaces and a Beale-Kato-Majda
+blow-up criterion for a recently derived stochastic model of the 3D Euler fluid
+equation for incompressible flow. This model describes incompressible fluid
+motions whose Lagrangian particle paths follow a stochastic process with
+cylindrical noise and also satisfy Newton's 2nd Law in every Lagrangian domain.
+",0,1,1,0,0,0
+15837,Existence of locally maximally entangled quantum states via geometric invariant theory,"  We study a question which has natural interpretations in both quantum
+mechanics and in geometry. Let $V_1,..., V_n$ be complex vector spaces of
+dimension $d_1,...,d_n$ and let $G= SL_{d_1} \times \dots \times SL_{d_n}$.
+Geometrically, we ask given $(d_1,...,d_n)$, when is the geometric invariant
+theory quotient $\mathbb{P}(V_1 \otimes \dots \otimes V_n)// G$ non-empty? This
+is equivalent to the quantum mechanical question of whether the multipart
+quantum system with Hilbert space $V_1\otimes \dots \otimes V_n$ has a locally
+maximally entangled state, i.e. a state such that the density matrix for each
+elementary subsystem is a multiple of the identity. We show that the answer to
+this question is yes if and only if $R(d_1,...,d_n)\geqslant 0$ where \[
+R(d_1,...,d_n) = \prod_i d_i +\sum_{k=1}^n (-1)^k \sum_{1\leq i_1<\dotsb
+<i_k\leq n} (\gcd(d_{i_1},\dotsc ,d_{i_k}) )^{2}. \] We also provide a simple
+recursive algorithm which determines the answer to the question, and we compute
+the dimension of the resulting quotient in the non-empty cases.
+",0,0,1,0,0,0
+15838,Convergence of the Expectation-Maximization Algorithm Through Discrete-Time Lyapunov Stability Theory,"  In this paper, we propose a dynamical systems perspective of the
+Expectation-Maximization (EM) algorithm. More precisely, we can analyze the EM
+algorithm as a nonlinear state-space dynamical system. The EM algorithm is
+widely adopted for data clustering and density estimation in statistics,
+control systems, and machine learning. This algorithm belongs to a large class
+of iterative algorithms known as proximal point methods. In particular, we
+re-interpret limit points of the EM algorithm and other local maximizers of the
+likelihood function it seeks to optimize as equilibria in its dynamical system
+representation. Furthermore, we propose to assess its convergence as asymptotic
+stability in the sense of Lyapunov. As a consequence, we proceed by leveraging
+recent results regarding discrete-time Lyapunov stability theory in order to
+establish asymptotic stability (and thus, convergence) in the dynamical system
+representation of the EM algorithm.
+",1,0,0,0,0,0
+15839,A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems,"  In this paper we consider a general matrix factorization model which covers a
+large class of existing models with many applications in areas such as machine
+learning and imaging sciences. To solve this possibly nonconvex, nonsmooth and
+non-Lipschitz problem, we develop a non-monotone alternating updating method
+based on a potential function. Our method essentially updates two blocks of
+variables in turn by inexactly minimizing this potential function, and updates
+another auxiliary block of variables using an explicit formula. The special
+structure of our potential function allows us to take advantage of efficient
+computational strategies for non-negative matrix factorization to perform the
+alternating minimization over the two blocks of variables. A suitable line
+search criterion is also incorporated to improve the numerical performance.
+Under some mild conditions, we show that the line search criterion is well
+defined, and establish that the sequence generated is bounded and any cluster
+point of the sequence is a stationary point. Finally, we conduct some numerical
+experiments using real datasets to compare our method with some existing
+efficient methods for non-negative matrix factorization and matrix completion.
+The numerical results show that our method can outperform these methods for
+these specific applications.
+",0,0,1,1,0,0
+15840,Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions,"  Error bound conditions (EBC) are properties that characterize the growth of
+an objective function when a point is moved away from the optimal set. They
+have recently received increasing attention in the field of optimization for
+developing optimization algorithms with fast convergence. However, the studies
+of EBC in statistical learning are hitherto still limited. The main
+contributions of this paper are two-fold. First, we develop fast and
+intermediate rates of empirical risk minimization (ERM) under EBC for risk
+minimization with Lipschitz continuous, and smooth convex random functions.
+Second, we establish fast and intermediate rates of an efficient stochastic
+approximation (SA) algorithm for risk minimization with Lipschitz continuous
+random functions, which requires only one pass of $n$ samples and adapts to
+EBC. For both approaches, the convergence rates span a full spectrum between
+$\widetilde O(1/\sqrt{n})$ and $\widetilde O(1/n)$ depending on the power
+constant in EBC, and could be even faster than $O(1/n)$ in special cases for
+ERM. Moreover, these convergence rates are automatically adaptive without using
+any knowledge of EBC. Overall, this work not only strengthens the understanding
+of ERM for statistical learning but also brings new fast stochastic algorithms
+for solving a broad range of statistical learning problems.
+",0,0,0,1,0,0
+15841,Combining the $k$-CNF and XOR Phase-Transitions,"  The runtime performance of modern SAT solvers on random $k$-CNF formulas is
+deeply connected with the 'phase-transition' phenomenon seen empirically in the
+satisfiability of random $k$-CNF formulas. Recent universal hashing-based
+approaches to sampling and counting crucially depend on the runtime performance
+of SAT solvers on formulas expressed as the conjunction of both $k$-CNF and XOR
+constraints (known as $k$-CNF-XOR formulas), but the behavior of random
+$k$-CNF-XOR formulas is unexplored in prior work. In this paper, we present the
+first study of the satisfiability of random $k$-CNF-XOR formulas. We show
+empirical evidence of a surprising phase-transition that follows a linear
+trade-off between $k$-CNF and XOR constraints. Furthermore, we prove that a
+phase-transition for $k$-CNF-XOR formulas exists for $k = 2$ and (when the
+number of $k$-CNF constraints is small) for $k > 2$.
+",1,0,0,0,0,0
+15842,Advertising and Brand Attitudes: Evidence from 575 Brands over Five Years,"  Little is known about how different types of advertising affect brand
+attitudes. We investigate the relationships between three brand attitude
+variables (perceived quality, perceived value and recent satisfaction) and
+three types of advertising (national traditional, local traditional and
+digital). The data represent ten million brand attitude surveys and $264
+billion spent on ads by 575 regular advertisers over a five-year period,
+approximately 37% of all ad spend measured between 2008 and 2012. Inclusion of
+brand/quarter fixed effects and industry/week fixed effects brings parameter
+estimates closer to expectations without major reductions in estimation
+precision. The findings indicate that (i) national traditional ads increase
+perceived quality, perceived value, and recent satisfaction; (ii) local
+traditional ads increase perceived quality and perceived value; (iii) digital
+ads increase perceived value; and (iv) competitor ad effects are generally
+negative.
+",0,0,0,0,0,1
+15843,NMR studies of the topological insulator Bi2Te3,"  Te NMR studies were carried out for the bismuth telluride topological
+insulator in a wide range from room temperature down to 12.5 K. The
+measurements were made on a Bruker Avance 400 pulse spectrometer. The NMR
+spectra were collected for the mortar and pestle powder sample and for single
+crystalline stacks with orientations c parallel and perpendicular to field. The
+activation energy responsible for thermal activation. The spectra for the stack
+with c parallel to field showed some particular behavior below 91 K.
+",0,1,0,0,0,0
+15844,The Gibbons-Hawking ansatz over a wedge,"  We discuss the Ricci-flat `model metrics' on $\mathbb{C}^2$ with cone
+singularities along the conic $\{zw=1\}$ constructed by Donaldson using the
+Gibbons-Hawking ansatz over wedges in $\mathbb{R}^3$. In particular we describe
+their asymptotic behavior at infinity and compute their energies.
+",0,0,1,0,0,0
+15845,Beacon-referenced Mutual Pursuit in Three Dimensions,"  Motivated by station-keeping applications in various unmanned settings, this
+paper introduces a steering control law for a pair of agents operating in the
+vicinity of a fixed beacon in a three-dimensional environment. This feedback
+law is a modification of the previously studied three-dimensional constant
+bearing (CB) pursuit law, in the sense that it incorporates an additional term
+to allocate attention to the beacon. We investigate the behavior of the
+closed-loop dynamics for a two agent mutual pursuit system in which each agent
+employs the beacon-referenced CB pursuit law with regards to the other agent
+and a stationary beacon. Under certain assumptions on the associated control
+parameters, we demonstrate that this problem admits circling equilibria wherein
+the agents move on circular orbits with a common radius, in planes
+perpendicular to a common axis passing through the beacon. As the common radius
+and distances from the beacon are determined by choice of parameters in the
+feedback law, this approach provides a means to engineer desired formations in
+a three-dimensional setting.
+",1,0,0,0,0,0
+15846,Increased stability of CuZrAl metallic glasses prepared by physical vapor deposition,"  We carried out molecular dynamics simulations (MD) using realistic empirical
+potentials for the vapor deposition (VD) of CuZrAl glasses. VD glasses have
+higher densities and lower potential and inherent structure energies than the
+melt-quenched glasses for the same alloys. The optimal substrate temperature
+for the deposition process is 0.625$\times T_\mathrm{g}$. In VD metallic
+glasses (MGs), the total number of icosahedral like clusters is higher than in
+the melt-quenched MGs. Surprisingly, the VD glasses have a lower degree of
+chemical mixing than the melt-quenched glasses. The reason for it is that the
+melt-quenched MGs can be viewed as frozen liquids, which means that their
+chemical order is the same as in the liquid state. In contrast, during the
+formation of the VD MGs, the absence of the liquid state results in the
+creation of a different chemical order with more Zr-Zr homonuclear bonds
+compared with the melt-quenched MGs. In order to obtain MGs from melt-quench
+technique with similarly low energies as in the VD process, the cooling rate
+during quenching would have to be many orders of magnitude lower than currently
+accessible to MD simulations. The method proposed in this manuscript is a more
+efficient way to create MGs by using MD simulations.
+",0,1,0,0,0,0
+15847,A geometric realization of the $m$-cluster categories of type $\tilde{D_n}$,"  We show that a subcategory of the $m$-cluster category of type $\tilde{D_n}$
+is isomorphic to a category consisting of arcs in an $(n-2)m$-gon with two
+central $(m-1)$-gons inside of it. We show that the mutation of colored quivers
+and $m$-cluster-tilting objects is compatible with the flip of an
+$(m+2)$-angulation. In the final part of this paper, we detail an example of a
+quiver of type $\tilde{D_7}$.
+",0,0,1,0,0,0
+15848,The SoLid anti-neutrino detector's readout system,"  The SoLid collaboration have developed an intelligent readout system to
+reduce their 3200 silicon photomultiplier detector's data rate by a factor of
+10000 whilst maintaining high efficiency for storing data from anti-neutrino
+interactions. The system employs an FPGA-level waveform characterisation to
+trigger on neutron signals. Following a trigger, data from a space time region
+of interest around the neutron will be read out using the IPbus protocol. In
+these proceedings the design of the readout system is explained and results
+showing the performance of a prototype version of the system are presented.
+",0,1,0,0,0,0
+15849,Data hiding in Fingerprint Minutiae Template for Privacy Protection,"  In this paper, we propose a novel scheme for data hiding in the fingerprint
+minutiae template, which is the most popular in fingerprint recognition
+systems. Various strategies are proposed in data embedding in order to maintain
+the accuracy of fingerprint recognition as well as the undetectability of data
+hiding. In bits replacement based data embedding, we replace the last few bits
+of each element of the original minutiae template with the data to be hidden.
+This strategy can be further improved using an optimized bits replacement based
+data embedding, which is able to minimize the impact of data hiding on the
+performance of fingerprint recognition. The third strategy is an order
+preserving mechanism which is proposed to reduce the detectability of data
+hiding. By using such a mechanism, it would be difficult for the attacker to
+differentiate the minutiae template with hidden data from the original minutiae
+templates. The experimental results show that the proposed data hiding scheme
+achieves sufficient capacity for hiding common personal data, where the
+accuracy of fingerprint recognition is acceptable after the data hiding.
+",1,0,0,0,0,0
+15850,On asymptotic normality of certain linear rank statistics,"  We consider asymptotic normality of linear rank statistics under various
+randomization rules met in clinical trials and designed for patients'
+allocation into treatment and placebo arms. Exposition relies on some general
+limit theorem due to McLeish (1974) which appears to be well suited for the
+problem considered and may be employed for other similar rules undis- cussed in
+the paper. Examples of applications include well known results as well as
+several new ones.
+",0,0,1,1,0,0
+15851,Computations with p-adic numbers,"  This document contains the notes of a lecture I gave at the ""Journées
+Nationales du Calcul Formel"" (JNCF) on January 2017. The aim of the lecture was
+to discuss low-level algorithmics for p-adic numbers. It is divided into two
+main parts: first, we present various implementations of p-adic numbers and
+compare them and second, we introduce a general framework for studying
+precision issues and apply it in several concrete situations.
+",1,0,1,0,0,0
+15852,A novel procedure for the identification of chaos in complex biological systems,"  We demonstrate the presence of chaos in stochastic simulations that are
+widely used to study biodiversity in nature. The investigation deals with a set
+of three distinct species that evolve according to the standard rules of
+mobility, reproduction and predation, with predation following the cyclic rules
+of the popular rock, paper and scissors game. The study uncovers the
+possibility to distinguish between time evolutions that start from slightly
+different initial states, guided by the Hamming distance which heuristically
+unveils the chaotic behavior. The finding opens up a quantitative approach that
+relates the correlation length to the average density of maxima of a typical
+species, and an ensemble of stochastic simulations is implemented to support
+the procedure. The main result of the work shows how a single and simple
+experimental realization that counts the density of maxima associated with the
+chaotic evolution of the species serves to infer its correlation length. We use
+the result to investigate others distinct complex systems, one dealing with a
+set of differential equations that can be used to model a diversity of natural
+and artificial chaotic systems, and another one, focusing on the ocean water
+level.
+",0,1,0,0,0,0
+15853,DeepStory: Video Story QA by Deep Embedded Memory Networks,"  Question-answering (QA) on video contents is a significant challenge for
+achieving human-level intelligence as it involves both vision and language in
+real-world settings. Here we demonstrate the possibility of an AI agent
+performing video story QA by learning from a large amount of cartoon videos. We
+develop a video-story learning model, i.e. Deep Embedded Memory Networks
+(DEMN), to reconstruct stories from a joint scene-dialogue video stream using a
+latent embedding space of observed data. The video stories are stored in a
+long-term memory component. For a given question, an LSTM-based attention model
+uses the long-term memory to recall the best question-story-answer triplet by
+focusing on specific words containing key information. We trained the DEMN on a
+novel QA dataset of children's cartoon video series, Pororo. The dataset
+contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained
+sentences for scene description, and 8,913 story-related QA pairs. Our
+experimental results show that the DEMN outperforms other QA models. This is
+mainly due to 1) the reconstruction of video stories in a scene-dialogue
+combined form that utilize the latent embedding and 2) attention. DEMN also
+achieved state-of-the-art results on the MovieQA benchmark.
+",1,0,0,0,0,0
+15854,Optimisation de la QoS dans un r{é}seau de radio cognitive en utilisant la m{é}taheuristique SFLA (Shuffled Frog Leaping Algorithm),"  This work proposes a study of quality of service (QoS) in cognitive radio
+networks. This study is based on a stochastic optimization method called
+shuffled frog leaping algorithm (SFLA). The interest of the SFLA algorithm is
+to guarantee a better solution in a multi-carrier context in order to satisfy
+the requirements of the secondary user (SU).
+",1,0,0,0,0,0
+15855,Some remarks on Kuratowski partitions,"  We introduce the notion of $K$-ideals associated with Kuratowski partitions
+and we prove that each $\kappa$-complete ideal on a measurable cardinal
+$\kappa$ can be represented as a $K$-ideal. Moreover, we show some results
+concerning precipitous and Fréchet ideals.
+",0,0,1,0,0,0
+15856,Boosted nonparametric hazards with time-dependent covariates,"  Given functional data samples from a survival process with time dependent
+covariates, we propose a practical boosting procedure for estimating its hazard
+function nonparametrically. The estimator is consistent if the model is
+correctly specified; alternatively an oracle inequality can be demonstrated for
+tree-based models. To avoid overfitting, boosting employs several
+regularization devices. One of them is step-size restriction, but the rationale
+for this is somewhat mysterious from the viewpoint of consistency. Our
+convergence bounds bring some clarity to this issue by revealing that step-size
+restriction is a mechanism for preventing the curvature of the risk from
+derailing convergence. We use our boosting procedure to shed new light on a
+question from the operations literature concerning the effect of workload on
+service rates in an emergency department.
+",0,0,0,1,0,0
+15857,$J^+$-like invariants of periodic orbits of the second kind in the restricted three body problem,"  We determine three invariants: Arnold's $J^+$-invariant as well as
+$\mathcal{J}_1$ and $\mathcal{J}_2$ invariants, which were introduced by
+Cieliebak-Frauenfelder-van Koert, of periodic orbits of the second kind near
+the heavier primary in the restricted three-body problem, provided that the
+mass ratio is sufficiently small.
+",0,0,1,0,0,0
+15858,Nonlinear Calderón-Zygmund inequalities for maps,"  Being motivated by the problem of deducing $L^p$-bounds on the second
+fundamental form of an isometric immersion from $L^p$-bounds on its mean
+curvature vector field, we prove a (nonlinear) Calderón-Zygmund inequality
+for maps between complete (possibly noncompact) Riemannian manifolds.
+",0,0,1,0,0,0
+15859,Comparing multiple networks using the Co-expression Differential Network Analysis (CoDiNA),"  Biomedical sciences are increasingly recognising the relevance of gene
+co-expression-networks for analysing complex-systems, phenotypes or diseases.
+When the goal is investigating complex-phenotypes under varying conditions, it
+comes naturally to employ comparative network methods. While approaches for
+comparing two networks exist, this is not the case for multiple networks. Here
+we present a method for the systematic comparison of an unlimited number of
+networks: Co-expression Differential Network Analysis (CoDiNA) for detecting
+links and nodes that are common, specific or different to the networks.
+Applying CoDiNA to a neurogenesis study identified genes for neuron
+differentiation. Experimentally overexpressing one candidate resulted in
+significant disturbance in the underlying neurogenesis' gene regulatory
+network. We compared data from adults and children with active tuberculosis to
+test for signatures of HIV. We also identified common and distinct network
+features for particular cancer types with CoDiNA. These studies show that
+CoDiNA successfully detects genes associated with the diseases.
+",0,0,0,1,1,0
+15860,Classical and Quantum Factors of Channels,"  Given a classical channel, a stochastic map from inputs to outputs, can we
+replace the input with a simple intermediate variable that still yields the
+correct conditional output distribution? We examine two cases: first, when the
+intermediate variable is classical; second, when the intermediate variable is
+quantum. We show that the quantum variable's size is generically smaller than
+the classical, according to two different measures---cardinality and entropy.
+We demonstrate optimality conditions for a special case. We end with several
+related results: a proposal for extending the special case, a demonstration of
+the impact of quantum phases, and a case study concerning pure versus mixed
+states.
+",0,0,0,1,0,0
+15861,Learning Intrinsic Sparse Structures within Long Short-Term Memory,"  Model compression is significant for the wide adoption of Recurrent Neural
+Networks (RNNs) in both user devices possessing limited resources and business
+clusters requiring quick responses to large-scale service requests. This work
+aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the
+sizes of basic structures within LSTM units, including input updates, gates,
+hidden states, cell states and outputs. Independently reducing the sizes of
+basic structures can result in inconsistent dimensions among them, and
+consequently, end up with invalid LSTM units. To overcome the problem, we
+propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS
+will simultaneously decrease the sizes of all basic structures by one and
+thereby always maintain the dimension consistency. By learning ISS within LSTM
+units, the obtained LSTMs remain regular while having much smaller basic
+structures. Based on group Lasso regularization, our method achieves 10.59x
+speedup without losing any perplexity of a language modeling of Penn TreeBank
+dataset. It is also successfully evaluated through a compact model with only
+2.69M weights for machine Question Answering of SQuAD dataset. Our approach is
+successfully extended to non- LSTM RNNs, like Recurrent Highway Networks
+(RHNs). Our source code is publicly available at
+this https URL
+",1,0,0,0,0,0
+15862,Electron Acceleration Mechanisms in Thunderstorms,"  Thunderstorms produce strong electric fields over regions on the order of
+kilometer. The corresponding electric potential differences are on the order of
+100 MV. Secondary cosmic rays reaching these regions may be significantly
+accelerated and even amplified in relativistic runaway avalanche processes.
+These phenomena lead to enhancements of the high-energy background radiation
+observed by detectors on the ground and on board aircraft. Moreover, intense
+submillisecond gamma-ray bursts named terrestrial gamma-ray flashes (TGFs)
+produced in thunderstorms are detected from low Earth orbit satellites. When
+passing through the atmosphere, these gamma-rays are recognized to produce
+secondary relativistic electrons and positrons rapidly trapped in the
+geomagnetic field and injected into the near-Earth space environment. In the
+present work, we attempt to give an overview of the current state of research
+on high-energy phenomena associated with thunderstorms.
+",0,1,0,0,0,0
+15863,Consistent estimation in Cox proportional hazards model with measurement errors and unbounded parameter set,"  Cox proportional hazards model with measurement error is investigated. In
+Kukush et al. (2011) [Journal of Statistical Research 45, 77-94] and Chimisov
+and Kukush (2014) [Modern Stochastics: Theory and Applications 1, 13-32]
+asymptotic properties of simultaneous estimator $\lambda_n(\cdot)$, $\beta_n$
+were studied for baseline hazard rate $\lambda(\cdot)$ and regression parameter
+$\beta$, at that the parameter set $\Theta=\Theta_{\lambda}\times
+\Theta_{\beta}$ was assumed bounded. In the present paper, the set
+$\Theta_{\lambda}$ is unbounded from above and not separated away from $0$. We
+construct the estimator in two steps: first we derive a strongly consistent
+estimator and then modify it to provide its asymptotic normality.
+",0,0,1,1,0,0
+15864,Comparative Efficiency of Altruism and Egoism as Voting Strategies in Stochastic Environment,"  In this paper, we study the efficiency of egoistic and altruistic strategies
+within the model of social dynamics determined by voting in a stochastic
+environment (the ViSE model) using two criteria: maximizing the average capital
+increment and minimizing the number of bankrupt participants. The proposals are
+generated stochastically; three families of the corresponding distributions are
+considered: normal distributions, symmetrized Pareto distributions, and
+Student's $t$-distributions. It is found that the ""pit of losses"" paradox
+described earlier does not occur in the case of heavy-tailed distributions. The
+egoistic strategy better protects agents from extinction in aggressive
+environments than the altruistic ones, however, the efficiency of altruism is
+higher in more favorable environments. A comparison of altruistic strategies
+with each other shows that in aggressive environments, everyone should be
+supported to minimize extinction, while under more favorable conditions, it is
+more efficient to support the weakest participants. Studying the dynamics of
+participants' capitals we identify situations where the two considered criteria
+contradict each other. At the next stage of the study, combined voting
+strategies and societies involving participants with selfish and altruistic
+strategies will be explored.
+",1,0,0,0,0,0
+15865,Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision,"  Learning to drive faithfully in highly stochastic urban settings remains an
+open problem. To that end, we propose a Multi-task Learning from Demonstration
+(MT-LfD) framework which uses supervised auxiliary task prediction to guide the
+main task of predicting the driving commands. Our framework involves an
+end-to-end trainable network for imitating the expert demonstrator's driving
+commands. The network intermediately predicts visual affordances and action
+primitives through direct supervision which provide the aforementioned
+auxiliary supervised guidance. We demonstrate that such joint learning and
+supervised guidance facilitates hierarchical task decomposition, assisting the
+agent to learn faster, achieve better driving performance and increases
+transparency of the otherwise black-box end-to-end network. We run our
+experiments to validate the MT-LfD framework in CARLA, an open-source urban
+driving simulator. We introduce multiple non-player agents in CARLA and induce
+temporal noise in them for realistic stochasticity.
+",1,0,0,1,0,0
+15866,"Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability","  Anomaly detection (AD) task corresponds to identifying the true anomalies
+from a given set of data instances. AD algorithms score the data instances and
+produce a ranked list of candidate anomalies, which are then analyzed by a
+human to discover the true anomalies. However, this process can be laborious
+for the human analyst when the number of false-positives is very high.
+Therefore, in many real-world AD applications including computer security and
+fraud prevention, the anomaly detector must be configurable by the human
+analyst to minimize the effort on false positives.
+In this paper, we study the problem of active learning to automatically tune
+ensemble of anomaly detectors to maximize the number of true anomalies
+discovered. We make four main contributions towards this goal. First, we
+present an important insight that explains the practical successes of AD
+ensembles and how ensembles are naturally suited for active learning. Second,
+we present several algorithms for active learning with tree-based AD ensembles.
+These algorithms help us to improve the diversity of discovered anomalies,
+generate rule sets for improved interpretability of anomalous instances, and
+adapt to streaming data settings in a principled manner. Third, we present a
+novel algorithm called GLocalized Anomaly Detection (GLAD) for active learning
+with generic AD ensembles. GLAD allows end-users to retain the use of simple
+and understandable global anomaly detectors by automatically learning their
+local relevance to specific data instances using label feedback. Fourth, we
+present extensive experiments to evaluate our insights and algorithms. Our
+results show that in addition to discovering significantly more anomalies than
+state-of-the-art unsupervised baselines, our active learning algorithms under
+the streaming-data setup are competitive with the batch setup.
+",1,0,0,1,0,0
+15867,Accuracy of parameterized proton range models; a comparison,"  An accurate calculation of proton ranges in phantoms or detector geometries
+is crucial for decision making in proton therapy and proton imaging. To this
+end, several parameterizations of the range-energy relationship exist, with
+different levels of complexity and accuracy. In this study we compare the
+accuracy four different parameterizations models: Two analytical models derived
+from the Bethe equation, and two different interpolation schemes applied to
+range-energy tables. In conclusion, a spline interpolation scheme yields the
+highest reproduction accuracy, while the shape of the energy loss-curve is best
+reproduced with the differentiated Bragg-Kleeman equation.
+",0,1,0,0,0,0
+15868,"On the connectivity of level sets of automorphisms of free groups, with applications to decision problems","  We show that the level sets of automorphisms of free groups with respect to
+the Lipschitz metric are connected as subsets of Culler-Vogtmann space. In fact
+we prove our result in a more general setting of deformation spaces. As
+applications, we give metric solutions of the conjugacy problem for irreducible
+automorphisms and the detection of reducibility. We additionally prove
+technical results that may be of independent interest --- such as the fact that
+the set of displacements is well ordered.
+",0,0,1,0,0,0
+15869,Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation,"  Machine learning algorithms are typically run on large scale, distributed
+compute infrastructure that routinely face a number of unavailabilities such as
+failures and temporary slowdowns. Adding redundant computations using
+coding-theoretic tools called ""codes"" is an emerging technique to alleviate the
+adverse effects of such unavailabilities. A code consists of an encoding
+function that proactively introduces redundant computation and a decoding
+function that reconstructs unavailable outputs using the available ones. Past
+work focuses on using codes to provide resilience for linear computations and
+specific iterative optimization algorithms. However, computations performed for
+a variety of applications including inference on state-of-the-art machine
+learning algorithms, such as neural networks, typically fall outside this
+realm. In this paper, we propose taking a learning-based approach to designing
+codes that can handle non-linear computations. We present carefully designed
+neural network architectures and a training methodology for learning encoding
+and decoding functions that produce approximate reconstructions of unavailable
+computation results. We present extensive experimental results demonstrating
+the effectiveness of the proposed approach: we show that the our learned codes
+can accurately reconstruct $64 - 98\%$ of the unavailable predictions from
+neural-network based image classifiers on the MNIST, Fashion-MNIST, and
+CIFAR-10 datasets. To the best of our knowledge, this work proposes the first
+learning-based approach for designing codes, and also presents the first
+coding-theoretic solution that can provide resilience for any non-linear
+(differentiable) computation. Our results show that learning can be an
+effective technique for designing codes, and that learned codes are a highly
+promising approach for bringing the benefits of coding to non-linear
+computations.
+",0,0,0,1,0,0
+15870,Detection via simultaneous trajectory estimation and long time integration,"  In this work, we consider the detection of manoeuvring small objects with
+radars. Such objects induce low signal to noise ratio (SNR) reflections in the
+received signal. We consider both co-located and separated transmitter/receiver
+pairs, i.e., mono-static and bi-static configurations, respectively, as well as
+multi-static settings involving both types. We propose a detection approach
+which is capable of coherently integrating these reflections within a coherent
+processing interval (CPI) in all these configurations and continuing
+integration for an arbitrarily long time across consecutive CPIs. We estimate
+the complex value of the reflection coefficients for integration while
+simultaneously estimating the object trajectory. Compounded with this is the
+estimation of the unknown time reference shift of the separated transmitters
+necessary for coherent processing. Detection is made by using the resulting
+integration value in a Neyman-Pearson test against a constant false alarm rate
+threshold. We demonstrate the efficacy of our approach in a simulation example
+with a very low SNR object which cannot be detected with conventional
+techniques.
+",1,0,0,1,0,0
+15871,Dust Growth and Magnetic Fields: from Cores to Disks (even down to Planets),"  The recent rapid progress in observations of circumstellar disks and
+extrasolar planets has reinforced the importance of understanding an intimate
+coupling between star and planet formation. Under such a circumstance, it may
+be invaluable to attempt to specify when and how planet formation begins in
+star-forming regions and to identify what physical processes/quantities are the
+most significant to make a link between star and planet formation. To this end,
+we have recently developed a couple of projects. These include an observational
+project about dust growth in Class 0 YSOs and a theoretical modeling project of
+the HL Tauri disk. For the first project, we utilize the archive data of radio
+interferometric observations, and examine whether dust growth, a first step of
+planet formation, occurs in Class 0 YSOs. We find that while our observational
+results can be reproduced by the presence of large ($\sim$ mm) dust grains for
+some of YSOs under the single-component modified blackbody formalism, an
+interpretation of no dust growth would be possible when a more detailed model
+is used. For the second project, we consider an origin of the disk
+configuration around HL Tauri, focusing on magnetic fields. We find that
+magnetically induced disk winds may play an important role in the HL Tauri
+disk. The combination of these attempts may enable us to move towards a
+comprehensive understanding of how star and planet formation are intimately
+coupled with each other.
+",0,1,0,0,0,0
+15872,Economic Design of Memory-Type Control Charts: The Fallacy of the Formula Proposed by Lorenzen and Vance (1986),"  The memory-type control charts, such as EWMA and CUSUM, are powerful tools
+for detecting small quality changes in univariate and multivariate processes.
+Many papers on economic design of these control charts use the formula proposed
+by Lorenzen and Vance (1986) [Lorenzen, T. J., & Vance, L. C. (1986). The
+economic design of control charts: A unified approach. Technometrics, 28(1),
+3-10, DOI: 10.2307/1269598]. This paper shows that this formula is not correct
+for memory-type control charts and its values can significantly deviate from
+the original values even if the ARL values used in this formula are accurately
+computed. Consequently, the use of this formula can result in charts that are
+not economically optimal. The formula is corrected for memory-type control
+charts, but unfortunately the modified formula is not a helpful tool from a
+computational perspective. We show that simulation-based optimization is a
+possible alternative method.
+",1,0,0,1,0,0
+15873,Error estimates for Riemann sums of some singular functions,"  In this short note, we obtain error estimates for Riemann sums of some
+singular functions.
+",0,0,1,0,0,0
+15874,The Relation Between Fundamental Constants and Particle Physics Parameters,"  The observed constraints on the variability of the proton to electron mass
+ratio $\mu$ and the fine structure constant $\alpha$ are used to establish
+constraints on the variability of the Quantum Chromodynamic Scale and a
+combination of the Higgs Vacuum Expectation Value and the Yukawa couplings.
+Further model dependent assumptions provide constraints on the Higgs VEV and
+the Yukawa couplings separately. A primary conclusion is that limits on the
+variability of dimensionless fundamental constants such as $\mu$ and $\alpha$
+provide important constraints on the parameter space of new physics and
+cosmologies.
+",0,1,0,0,0,0
+15875,Real-Time Object Pose Estimation with Pose Interpreter Networks,"  In this work, we introduce pose interpreter networks for 6-DoF object pose
+estimation. In contrast to other CNN-based approaches to pose estimation that
+require expensively annotated object pose data, our pose interpreter network is
+trained entirely on synthetic pose data. We use object masks as an intermediate
+representation to bridge real and synthetic. We show that when combined with a
+segmentation model trained on RGB images, our synthetically trained pose
+interpreter network is able to generalize to real data. Our end-to-end system
+for object pose estimation runs in real-time (20 Hz) on live RGB data, without
+using depth information or ICP refinement.
+",1,0,0,0,0,0
+15876,"Regularization, sparse recovery, and median-of-means tournaments","  A regularized risk minimization procedure for regression function estimation
+is introduced that achieves near optimal accuracy and confidence under general
+conditions, including heavy-tailed predictor and response variables. The
+procedure is based on median-of-means tournaments, introduced by the authors in
+[8]. It is shown that the new procedure outperforms standard regularized
+empirical risk minimization procedures such as lasso or slope in heavy-tailed
+problems.
+",0,0,1,1,0,0
+15877,A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex,"  One of the primary objectives of human brain mapping is the division of the
+cortical surface into functionally distinct regions, i.e. parcellation. While
+it is generally agreed that at macro-scale different regions of the cortex have
+different functions, the exact number and configuration of these regions is not
+known. Methods for the discovery of these regions are thus important,
+particularly as the volume of available information grows. Towards this end, we
+present a parcellation method based on a Bayesian non-parametric mixture model
+of cortical connectivity.
+",1,0,0,1,0,0
+15878,On Unifying Deep Generative Models,"  Deep generative models have achieved impressive success in recent years.
+Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as
+emerging families for generative model learning, have largely been considered
+as two distinct paradigms and received extensive independent studies
+respectively. This paper aims to establish formal connections between GANs and
+VAEs through a new formulation of them. We interpret sample generation in GANs
+as performing posterior inference, and show that GANs and VAEs involve
+minimizing KL divergences of respective posterior and inference distributions
+with opposite directions, extending the two learning phases of classic
+wake-sleep algorithm, respectively. The unified view provides a powerful tool
+to analyze a diverse set of existing model variants, and enables to transfer
+techniques across research lines in a principled way. For example, we apply the
+importance weighting method in VAE literatures for improved GAN learning, and
+enhance VAEs with an adversarial mechanism that leverages generated samples.
+Experiments show generality and effectiveness of the transferred techniques.
+",1,0,0,1,0,0
+15879,DeepVisage: Making face recognition simple yet with powerful generalization skills,"  Face recognition (FR) methods report significant performance by adopting the
+convolutional neural network (CNN) based learning methods. Although CNNs are
+mostly trained by optimizing the softmax loss, the recent trend shows an
+improvement of accuracy with different strategies, such as task-specific CNN
+learning with different loss functions, fine-tuning on target dataset, metric
+learning and concatenating features from multiple CNNs. Incorporating these
+tasks obviously requires additional efforts. Moreover, it demotivates the
+discovery of efficient CNN models for FR which are trained only with identity
+labels. We focus on this fact and propose an easily trainable and single CNN
+based FR method. Our CNN model exploits the residual learning framework.
+Additionally, it uses normalized features to compute the loss. Our extensive
+experiments show excellent generalization on different datasets. We obtain very
+competitive and state-of-the-art results on the LFW, IJB-A, YouTube faces and
+CACD datasets.
+",1,0,0,0,0,0
+15880,The Erdős-Ginzburg-Ziv constant and progression-free subsets,"  Ellenberg and Gijswijt gave recently a new exponential upper bound for the
+size of three-term arithmetic progression free sets in $({\mathbb Z_p})^n$,
+where $p$ is a prime. Petrov summarized their method and generalized their
+result to linear forms.
+In this short note we use Petrov's result to give new exponential upper
+bounds for the Erdős-Ginzburg-Ziv constant of finite Abelian groups of high
+rank. Our main results depend on a conjecture about Property D.
+",0,0,1,0,0,0
+15881,"The Differing Relationships Between Size, Mass, Metallicity and Core Velocity Dispersion of Central and Satellite Galaxies","  We study the role of environment in the evolution of central and satellite
+galaxies with the Sloan Digital Sky Survey. We begin by studying the size-mass
+relation, replicating previous studies, which showed no difference between the
+sizes of centrals and satellites at fixed stellar mass, before turning our
+attention to the size-core velocity dispersion ($\sigma_0$) and mass-$\sigma_0$
+relations. By comparing the median size and mass of the galaxies at fixed
+velocity dispersion we find that the central galaxies are consistently larger
+and more massive than their satellite counterparts in the quiescent population.
+In the star forming population we find there is no difference in size and only
+a small difference in mass. To analyse why these difference may be present we
+investigate the radial mass profiles and stellar metallicity of the galaxies.
+We find that in the cores of the galaxies there is no difference in mass
+surface density between centrals and satellites, but there is a large
+difference at larger radii. We also find almost no difference between the
+stellar metallicity of centrals and satellites when they are separated into
+star forming and quiescent groups. Under the assumption that $\sigma_0$ is
+invariant to environmental processes, our results imply that central galaxies
+are likely being increased in mass and size by processes such as minor mergers,
+particularly at high $\sigma_0$, while satellites are being slightly reduced in
+mass and size by tidal stripping and harassment, particularly at low
+$\sigma_0$, all of which predominantly affect the outer regions of the
+galaxies.
+",0,1,0,0,0,0
+15882,Configuration Space Singularities of The Delta Manipulator,"  We investigate the configuration space of the Delta-Manipulator, identify 24
+points in the configuration space, where the Jacobian of the Constraint
+Equations looses rank and show, that these are not manifold points of the Real
+Algebraic Set, which is defined by the Constraint Equations.
+",1,0,0,0,0,0
+15883,Constrained Optimisation of Rational Functions for Accelerating Subspace Iteration,"  Earlier this decade, the so-called FEAST algorithm was released for computing
+the eigenvalues of a matrix in a given interval. Previously, rational filter
+functions have been examined as a parameter of FEAST. In this thesis, we expand
+on existing work with the following contributions: (i) Obtaining
+well-performing rational filter functions via standard minimisation algorithms,
+(ii) Obtaining constrained rational filter functions efficiently, and (iii)
+Improving existing rational filter functions algorithmically. Using our new
+rational filter functions, FEAST requires up to one quarter fewer iterations on
+average compared to state-of-art rational filter functions.
+",1,0,0,0,0,0
+15884,Sets of lengths in atomic unit-cancellative finitely presented monoids,"  For an element $a$ of a monoid $H$, its set of lengths $\mathsf L (a) \subset
+\mathbb N$ is the set of all positive integers $k$ for which there is a
+factorization $a=u_1 \cdot \ldots \cdot u_k$ into $k$ atoms. We study the
+system $\mathcal L (H) = \{\mathsf L (a) \mid a \in H \}$ with a focus on the
+unions $\mathcal U_k (H) \subset \mathbb N$ which are the unions of all sets of
+lengths containing a given $k \in \mathbb N$. The Structure Theorem for Unions
+-- stating that for all sufficiently large $k$, the sets $\mathcal U_k (H)$ are
+almost arithmetical progressions with the same difference and global bound --
+has found much attention for commutative monoids and domains. We show that it
+holds true for the not necessarily commutative monoids in the title satisfying
+suitable algebraic finiteness conditions. Furthermore, we give an explicit
+description of the system of sets of lengths of monoids $B_{n} = \langle a,b
+\mid ba=b^{n} \rangle$ for $n \in \N_{\ge 2}$. Based on this description, we
+show that the monoids $B_n$ are not transfer Krull, which implies that their
+systems $\mathcal L (B_n)$ are distinct from systems of sets of lengths of
+commutative Krull monoids and others.
+",0,0,1,0,0,0
+15885,Spin-orbit effective fields in Pt/GdFeCo bilayers,"  In the increasing interests on spin-orbit torque (SOT) with various magnetic
+materials, we investigated SOT in rare earth-transition metal ferrimagnetic
+alloys. The harmonic Hall measurements were performed in Pt/GdFeCo bilayers to
+quantify the effective fields resulting from the SOT. It is found that the
+damping-like torque rapidly increases near the magnetization compensation
+temperature TM of the GdFeCo, which is attributed to the reduction of the net
+magnetic moment.
+",0,1,0,0,0,0
+15886,Bridging Static and Dynamic Program Analysis using Fuzzy Logic,"  Static program analysis is used to summarize properties over all dynamic
+executions. In a unifying approach based on 3-valued logic properties are
+either assigned a definite value or unknown. But in summarizing a set of
+executions, a property is more accurately represented as being biased towards
+true, or towards false. Compilers use program analysis to determine benefit of
+an optimization. Since benefit (e.g., performance) is justified based on the
+common case understanding bias is essential in guiding the compiler.
+Furthermore, successful optimization also relies on understanding the quality
+of the information, i.e. the plausibility of the bias. If the quality of the
+static information is too low to form a decision we would like a mechanism that
+improves dynamically.
+We consider the problem of building such a reasoning framework and present
+the fuzzy data-flow analysis. Our approach generalize previous work that use
+3-valued logic. We derive fuzzy extensions of data-flow analyses used by the
+lazy code motion optimization and unveil opportunities previous work would not
+detect due to limited expressiveness. Furthermore we show how the results of
+our analysis can be used in an adaptive classifier that improve as the
+application executes.
+",1,0,0,0,0,0
+15887,The Formation of Heliospheric Arcs of Slow Solar Wind,"  A major challenge in solar and heliospheric physics is understanding how
+highly localized regions, far smaller than 1 degree at the Sun, are the source
+of solar-wind structures spanning more than 20 degrees near Earth. The Sun's
+atmosphere is divided into magnetically open regions, coronal holes, where
+solar-wind plasma streams out freely and fills the solar system, and closed
+regions, where the plasma is confined to coronal loops. The boundary between
+these regions extends outward as the heliospheric current sheet (HCS).
+Measurements of plasma composition imply that the solar wind near the HCS, the
+so-called slow solar wind, originates in closed regions, presumably by the
+processes of field-line opening or interchange reconnection. Mysteriously,
+however, slow wind is also often seen far from the HCS. We use high-resolution,
+three-dimensional magnetohydrodynamic simulations to calculate the dynamics of
+a coronal hole whose geometry includes a narrow corridor flanked by closed
+field and which is driven by supergranule-like flows at the coronal-hole
+boundary. We find that these dynamics result in the formation of giant arcs of
+closed-field plasma that extend far from the HCS and span tens of degrees in
+latitude and longitude at Earth, accounting for the slow solar wind
+observations.
+",0,1,0,0,0,0
+15888,A Construction of Infinitely Many Solutions to the Strominger System,"  In this paper we construct explicit smooth solutions to the Strominger system
+on generalized Calabi-Gray manifolds, which are compact non-Kähler Calabi-Yau
+3-folds with infinitely many distinct topological types and sets of Hodge
+numbers.
+",0,0,1,0,0,0
+15889,A clustering algorithm for multivariate data streams with correlated components,"  Common clustering algorithms require multiple scans of all the data to
+achieve convergence, and this is prohibitive when large databases, with data
+arriving in streams, must be processed. Some algorithms to extend the popular
+K-means method to the analysis of streaming data are present in literature
+since 1998 (Bradley et al. in Scaling clustering algorithms to large databases.
+In: KDD. p. 9-15, 1998; O'Callaghan et al. in Streaming-data algorithms for
+high-quality clustering. In: Proceedings of IEEE international conference on
+data engineering. p. 685, 2001), based on the memorization and recursive update
+of a small number of summary statistics, but they either don't take into
+account the specific variability of the clusters, or assume that the random
+vectors which are processed and grouped have uncorrelated components.
+Unfortunately this is not the case in many practical situations. We here
+propose a new algorithm to process data streams, with data having correlated
+components and coming from clusters with different covariance matrices. Such
+covariance matrices are estimated via an optimal double shrinkage method, which
+provides positive definite estimates even in presence of a few data points, or
+of data having components with small variance. This is needed to invert the
+matrices and compute the Mahalanobis distances that we use for the data
+assignment to the clusters. We also estimate the total number of clusters from
+the data.
+",0,0,1,1,0,0
+15890,Semantic Interpolation in Implicit Models,"  In implicit models, one often interpolates between sampled points in latent
+space. As we show in this paper, care needs to be taken to match-up the
+distributional assumptions on code vectors with the geometry of the
+interpolating paths. Otherwise, typical assumptions about the quality and
+semantics of in-between points may not be justified. Based on our analysis we
+propose to modify the prior code distribution to put significantly more
+probability mass closer to the origin. As a result, linear interpolation paths
+are not only shortest paths, but they are also guaranteed to pass through
+high-density regions, irrespective of the dimensionality of the latent space.
+Experiments on standard benchmark image datasets demonstrate clear visual
+improvements in the quality of the generated samples and exhibit more
+meaningful interpolation paths.
+",1,0,0,1,0,0
+15891,Error analysis for global minima of semilinear optimal control problems,"  In [1] we consider an optimal control problem subject to a semilinear
+elliptic PDE together with its variational discretization, where we provide a
+condition which allows to decide whether a solution of the necessary first
+order conditions is a global minimum. This condition can be explicitly
+evaluated at the discrete level. Furthermore, we prove that if the above
+condition holds uniformly with respect to the discretization parameter the
+sequence of discrete solutions converges to a global solution of the
+corresponding limit problem. With the present work we complement our
+investigations of [1] in that we prove an error estimate for those discrete
+global solutions. Numerical experiments confirm our analytical findings.
+",0,0,1,0,0,0
+15892,Random dynamics of two-dimensional stochastic second grade fluids,"  In this paper, we consider a stochastic model of incompressible non-Newtonian
+fluids of second grade on a bounded domain of $\mathbb{R}^2$ with
+multiplicative noise. We first show that the solutions to the stochastic
+equations of second grade fluids generate a continuous random dynamical system.
+Second, we investigate the Fréchet differentiability of the random
+dynamical system. Finally, we establish the asymptotic compactness of the
+random dynamical system, and the existence of random attractors for the random
+dynamical system, we also obtain the upper semi-continuity of the perturbed
+random attractors when the noise intensity approaches zero.
+",0,0,1,0,0,0
+15893,Lago Distributed Network Of Data Repositories,"  We describe a set of tools, services and strategies of the Latin American
+Giant Observatory (LAGO) data repository network, to implement Data
+Accessibility, Reproducibility and Trustworthiness.
+",1,0,0,0,0,0
+15894,"Hyperfunctions, the Duistermaat-Heckman theorem, and Loop Groups","  In this article we investigate the Duistermaat-Heckman theorem using the
+theory of hyperfunctions. In applications involving Hamiltonian torus actions
+on infinite dimensional manifolds, this more general theory seems to be
+necessary in order to accomodate the existence of the infinite order
+differential operators which arise from the isotropy representations on the
+tangent spaces to fixed points. We will quickly review of the theory of
+hyperfunctions and their Fourier transforms. We will then apply this theory to
+construct a hyperfunction analogue of the Duistermaat-Heckman distribution. Our
+main goal will be to study the Duistermaat-Heckman hyperfunction of $\Omega
+SU(2)$, but in getting to this goal we will also characterize the singular
+locus of the moment map for the Hamiltonian action of $T\times S^1$ on $\Omega
+G$. The main goal of this paper is to present a Duistermaat-Heckman
+hyperfunction arising from a Hamiltonian action on an infinite dimensional
+manifold.
+",0,0,1,0,0,0
+15895,When is selfish routing bad? The price of anarchy in light and heavy traffic,"  This paper examines the behavior of the price of anarchy as a function of the
+traffic inflow in nonatomic congestion games with multiple origin-destination
+(O/D) pairs. Empirical studies in real-world networks show that the price of
+anarchy is close to 1 in both light and heavy traffic, thus raising the
+question: can these observations be justified theoretically? We first show that
+this is not always the case: the price of anarchy may remain a positive
+distance away from 1 for all values of the traffic inflow, even in simple
+three-link networks with a single O/D pair and smooth, convex costs. On the
+other hand, for a large class of cost functions (including all polynomials),
+the price of anarchy does converge to 1 in both heavy and light traffic,
+irrespective of the network topology and the number of O/D pairs in the
+network. We also examine the rate of convergence of the price of anarchy, and
+we show that it follows a power law whose degree can be computed explicitly
+when the network's cost functions are polynomials.
+",1,0,1,0,0,0
+15896,"Roadmap for the international, accelerator-based neutrino programme","  In line with its terms of reference the ICFA Neutrino Panel has developed a
+roadmapfor the international, accelerator-based neutrino programme. A ""roadmap
+discussion document"" was presented in May 2016 taking into account the
+peer-group-consultation described in the Panel's initial report. The ""roadmap
+discussion document"" was used to solicit feedback from the neutrino
+community---and more broadly, the particle- and astroparticle-physics
+communities---and the various stakeholders in the programme. The roadmap, the
+conclusions and recommendations presented in this document take into account
+the comments received following the publication of the roadmap discussion
+document.
+With its roadmap the Panel documents the approved objectives and milestones
+of the experiments that are presently in operation or under construction.
+Approval, construction and exploitation milestones are presented for
+experiments that are being considered for approval. The timetable proposed by
+the proponents is presented for experiments that are not yet being considered
+formally for approval. Based on this information, the evolution of the
+precision with which the critical parameters governinger the neutrino are known
+has been evaluated. Branch or decision points have been identified based on the
+anticipated evolution in precision. The branch or decision points have in turn
+been used to identify desirable timelines for the neutrino-nucleus cross
+section and hadro-production measurements that are required to maximise the
+integrated scientific output of the programme. The branch points have also been
+used to identify the timeline for the R&D required to take the programme beyond
+the horizon of the next generation of experiments. The theory and phenomenology
+programme, including nuclear theory, required to ensure that maximum benefit is
+derived from the experimental programme is also discussed.
+",0,1,0,0,0,0
+15897,Parallel Implementation of Lossy Data Compression for Temporal Data Sets,"  Many scientific data sets contain temporal dimensions. These are the data
+storing information at the same spatial location but different time stamps.
+Some of the biggest temporal datasets are produced by parallel computing
+applications such as simulations of climate change and fluid dynamics. Temporal
+datasets can be very large and cost a huge amount of time to transfer among
+storage locations. Using data compression techniques, files can be transferred
+faster and save storage space. NUMARCK is a lossy data compression algorithm
+for temporal data sets that can learn emerging distributions of element-wise
+change ratios along the temporal dimension and encodes them into an index table
+to be concisely represented. This paper presents a parallel implementation of
+NUMARCK. Evaluated with six data sets obtained from climate and astrophysics
+simulations, parallel NUMARCK achieved scalable speedups of up to 8788 when
+running 12800 MPI processes on a parallel computer. We also compare the
+compression ratios against two lossy data compression algorithms, ISABELA and
+ZFP. The results show that NUMARCK achieved higher compression ratio than
+ISABELA and ZFP.
+",1,0,0,0,0,0
+15898,What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State,"  As the first step to model emotional state of a person, we build sentiment
+analysis models with existing deep neural network algorithms and compare the
+models with psychological measurements to enlighten the relationship. In the
+experiments, we first examined psychological state of 64 participants and asked
+them to summarize the story of a book, Chronicle of a Death Foretold (Marquez,
+1981). Secondly, we trained models using crawled 365,802 movie review data;
+then we evaluated participants' summaries using the pretrained model as a
+concept of transfer learning. With the background that emotion affects on
+memories, we investigated the relationship between the evaluation score of the
+summaries from computational models and the examined psychological
+measurements. The result shows that although CNN performed the best among other
+deep neural network algorithms (LSTM, GRU), its results are not related to the
+psychological state. Rather, GRU shows more explainable results depending on
+the psychological state. The contribution of this paper can be summarized as
+follows: (1) we enlighten the relationship between computational models and
+psychological measurements. (2) we suggest this framework as objective methods
+to evaluate the emotion; the real sentiment analysis of a person.
+",1,0,0,0,0,0
+15899,"Survey of multifidelity methods in uncertainty propagation, inference, and optimization","  In many situations across computational science and engineering, multiple
+computational models are available that describe a system of interest. These
+different models have varying evaluation costs and varying fidelities.
+Typically, a computationally expensive high-fidelity model describes the system
+with the accuracy required by the current application at hand, while
+lower-fidelity models are less accurate but computationally cheaper than the
+high-fidelity model. Outer-loop applications, such as optimization, inference,
+and uncertainty quantification, require multiple model evaluations at many
+different inputs, which often leads to computational demands that exceed
+available resources if only the high-fidelity model is used. This work surveys
+multifidelity methods that accelerate the solution of outer-loop applications
+by combining high-fidelity and low-fidelity model evaluations, where the
+low-fidelity evaluations arise from an explicit low-fidelity model (e.g., a
+simplified physics approximation, a reduced model, a data-fit surrogate, etc.)
+that approximates the same output quantity as the high-fidelity model. The
+overall premise of these multifidelity methods is that low-fidelity models are
+leveraged for speedup while the high-fidelity model is kept in the loop to
+establish accuracy and/or convergence guarantees. We categorize multifidelity
+methods according to three classes of strategies: adaptation, fusion, and
+filtering. The paper reviews multifidelity methods in the outer-loop contexts
+of uncertainty propagation, inference, and optimization.
+",1,0,0,1,0,0
+15900,Nebular spectroscopy: A guide on H II regions and planetary nebulae,"  We present a tutorial on the determination of the physical conditions and
+chemical abundances in gaseous nebulae. We also include a brief review of
+recent results on the study of gaseous nebulae, their relevance for the study
+of stellar evolution, galactic chemical evolution, and the evolution of the
+universe. One of the most important problems in abundance determinations is the
+existence of a discrepancy between the abundances determined with collisionally
+excited lines and those determined by recombination lines, this is called the
+ADF (abundance discrepancy factor) problem; we review results related to this
+problem. Finally, we discuss possible reasons for the large t$^2$ values
+observed in gaseous nebulae.
+",0,1,0,0,0,0
+15901,Global Model Interpretation via Recursive Partitioning,"  In this work, we propose a simple but effective method to interpret black-box
+machine learning models globally. That is, we use a compact binary tree, the
+interpretation tree, to explicitly represent the most important decision rules
+that are implicitly contained in the black-box machine learning models. This
+tree is learned from the contribution matrix which consists of the
+contributions of input variables to predicted scores for each single
+prediction. To generate the interpretation tree, a unified process recursively
+partitions the input variable space by maximizing the difference in the average
+contribution of the split variable between the divided spaces. We demonstrate
+the effectiveness of our method in diagnosing machine learning models on
+multiple tasks. Also, it is useful for new knowledge discovery as such insights
+are not easily identifiable when only looking at single predictions. In
+general, our work makes it easier and more efficient for human beings to
+understand machine learning models.
+",0,0,0,1,0,0
+15902,Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models,"  Adversarial learning of probabilistic models has recently emerged as a
+promising alternative to maximum likelihood. Implicit models such as generative
+adversarial networks (GAN) often generate better samples compared to explicit
+models trained by maximum likelihood. Yet, GANs sidestep the characterization
+of an explicit density which makes quantitative evaluations challenging. To
+bridge this gap, we propose Flow-GANs, a generative adversarial network for
+which we can perform exact likelihood evaluation, thus supporting both
+adversarial and maximum likelihood training. When trained adversarially,
+Flow-GANs generate high-quality samples but attain extremely poor
+log-likelihood scores, inferior even to a mixture model memorizing the training
+data; the opposite is true when trained by maximum likelihood. Results on MNIST
+and CIFAR-10 demonstrate that hybrid training can attain high held-out
+likelihoods while retaining visual fidelity in the generated samples.
+",1,0,0,1,0,0
+15903,SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud,"  Inference using deep neural networks is often outsourced to the cloud since
+it is a computationally demanding task. However, this raises a fundamental
+issue of trust. How can a client be sure that the cloud has performed inference
+correctly? A lazy cloud provider might use a simpler but less accurate model to
+reduce its own computational load, or worse, maliciously modify the inference
+results sent to the client. We propose SafetyNets, a framework that enables an
+untrusted server (the cloud) to provide a client with a short mathematical
+proof of the correctness of inference tasks that they perform on behalf of the
+client. Specifically, SafetyNets develops and implements a specialized
+interactive proof (IP) protocol for verifiable execution of a class of deep
+neural networks, i.e., those that can be represented as arithmetic circuits.
+Our empirical results on three- and four-layer deep neural networks demonstrate
+the run-time costs of SafetyNets for both the client and server are low.
+SafetyNets detects any incorrect computations of the neural network by the
+untrusted server with high probability, while achieving state-of-the-art
+accuracy on the MNIST digit recognition (99.4%) and TIMIT speech recognition
+tasks (75.22%).
+",1,0,0,0,0,0
+15904,The Malgrange Form and Fredholm Determinants,"  We consider the factorization problem of matrix symbols relative to a closed
+contour, i.e., a Riemann-Hilbert problem, where the symbol depends analytically
+on parameters. We show how to define a function $\tau$ which is locally
+analytic on the space of deformations and that is expressed as a Fredholm
+determinant of an operator of ""integrable"" type in the sense of
+Its-Izergin-Korepin-Slavnov. The construction is not unique and the
+non-uniqueness highlights the fact that the tau function is really the section
+of a line bundle.
+",0,1,0,0,0,0
+15905,Programming from Metaphorisms,"  This paper presents a study of the metaphorism pattern of relational
+specification, showing how it can be refined into recursive programs.
+Metaphorisms express input-output relationships which preserve relevant
+information while at the same time some intended optimization takes place. Text
+processing, sorting, representation changers, etc., are examples of
+metaphorisms. The kind of metaphorism refinement studied in this paper is a
+strategy known as change of virtual data structure. By framing metaphorisms in
+the class of (inductive) regular relations, sufficient conditions are given for
+such implementations to be calculated using relation algebra. The strategy is
+illustrated with examples including the derivation of the quicksort and
+mergesort algorithms, showing what they have in common and what makes them
+different from the very start of development.
+",1,0,0,0,0,0
+15906,On the Conditional Distribution of a Multivariate Normal given a Transformation - the Linear Case,"  We show that the orthogonal projection operator onto the range of the adjoint
+of a linear operator $T$ can be represented as $UT,$ where $U$ is an invertible
+linear operator. Using this representation we obtain a decomposition of a
+Normal random vector $Y$ as the sum of a linear transformation of $Y$ that is
+independent of $TY$ and an affine transformation of $TY$. We then use this
+decomposition to prove that the conditional distribution of a Normal random
+vector $Y$ given a linear transformation $\mathcal{T}Y$ is again a multivariate
+Normal distribution. This result is equivalent to the well-known result that
+given a $k$-dimensional component of a $n$-dimensional Normal random vector,
+where $k<n$, the conditional distribution of the remaining
+$\left(n-k\right)$-dimensional component is a $\left(n-k\right)$-dimensional
+multivariate Normal distribution, and sets the stage for approximating the
+conditional distribution of $Y$ given $g\left(Y\right)$, where $g$ is a
+continuously differentiable vector field.
+",0,0,1,1,0,0
+15907,Spacings Around An Order Statistic,"  We determine the joint limiting distribution of adjacent spacings around a
+central, intermediate, or an extreme order statistic $X_{k:n}$ of a random
+sample of size $n$ from a continuous distribution $F$. For central and
+intermediate cases, normalized spacings in the left and right neighborhoods are
+asymptotically i.i.d. exponential random variables. The associated independent
+Poisson arrival processes are independent of $X_{k:n}$. For an extreme
+$X_{k:n}$, the asymptotic independence property of spacings fails for $F$ in
+the domain of attraction of Fréchet and Weibull ($\alpha \neq 1$)
+distributions. This work also provides additional insight into the limiting
+distribution for the number of observations around $X_{k:n}$ for all three
+cases.
+",0,0,1,1,0,0
+15908,On Nevanlinna - Cartan theory for holomorphic curves with Tsuji characteristics,"  In this paper, we prove some fundamental theorems for holomorphic curves on
+angular domain intersecting a hypersurface, finite set of fixed hyperplanes in
+general position and finite set of fixed hypersurfaces in general position on
+complex projective variety with the level of truncation. As applications of the
+second main theorems for an angle, we will discuss the uniqueness problem of
+holomorphic curves in an angle instead of the whole complex plane. Detail, we
+establish a result for uniqueness problem of holomorphic curve by inverse image
+of a hypersurface. In my knowledge, this is the first result for uniqueness
+problem of holomorphic curve by inverse image of hypersurface on angular
+domain. On complex plane, we obtain a uniqueness result for holomorphic curves,
+it is improvement of some results before [5, 10] in this trend.
+",0,0,1,0,0,0
+15909,Theoretical investigation of excitonic magnetism in LaSrCoO$_{4}$,"  We use the LDA+U approach to search for possible ordered ground states of
+LaSrCoO$_4$. We find a staggered arrangement of magnetic multipoles to be
+stable over a broad range of Co $3d$ interaction parameters. This ordered state
+can be described as a spin-denity-wave-type condensate of $d_{xy} \otimes
+d_{x^2-y^2}$ excitons carrying spin $S=1$. Further, we construct an effective
+strong-coupling model, calculate the exciton dispersion and investigate closing
+of the exciton gap, which marks the exciton condensation instability. Comparing
+the layered LaSrCoO$_4$ with its pseudo cubic analog LaCoO$_3$, we find that
+for the same interaction parameters the excitonic gap is smaller (possibly
+vanishing) in the layered cobaltite.
+",0,1,0,0,0,0
+15910,The Dark Matter Programme of the Cherenkov Telescope Array,"  In the last decades a vaste amount of evidence for the existence of dark
+matter has been accumulated. At the same time, many efforts have been
+undertaken to try to identify what dark matter is. Indirect searches look at
+places in the Universe where dark matter is believed to be abundant and seek
+for possible annihilation or decay signatures. The Cherenkov Telescope Array
+(CTA) represents the next generation of imaging Cherenkov telescopes and, with
+one site in the Southern hemisphere and one in the Northern hemisphere, will be
+able to observe all the sky with unprecedented sensitivity and angular
+resolution above a few tens of GeV. The CTA Consortium will undertake an
+ambitious program of indirect dark matter searches for which we report here the
+brightest prospects.
+",0,1,0,0,0,0
+15911,Modeling the Vertical Structure of Nuclear Starburst Discs: A Possible Source of AGN Obscuration at $z\sim 1$,"  Nuclear starburst discs (NSDs) are star-forming discs that may be residing in
+the nuclear regions of active galaxies at intermediate redshifts. One
+dimensional (1D) analytical models developed by Thompson et al. (2005) show
+that these discs can possess an inflationary atmosphere when dust is sublimated
+on parsec scales. This make NSDs a viable source for AGN obscuration. We model
+the two dimensional (2D) structure of NSDs using an iterative method in order
+to compute the explicit vertical solutions for a given annulus. These solutions
+satisfy energy and hydrostatic balance, as well as the radiative transfer
+equation. In comparison to the 1D model, the 2D calculation predicts a less
+extensive expansion of the atmosphere by orders of magnitude at the
+parsec/sub-parsec scale, but the new scale-height $h$ may still exceed the
+radial distance $R$ for various physical conditions. A total of 192 NSD models
+are computed across the input parameter space in order to predict distributions
+of a line of sight column density $N_H$. Assuming a random distribution of
+input parameters, the statistics yield 56% of Type 1, 23% of Compton-thin Type
+2s (CN), and 21% of Compton-thick (CK) AGNs. Depending on a viewing angle
+($\theta$) of a particular NSD (fixed physical conditions), any central AGN can
+appear to be Type 1, CN, or CK which is consistent with the basic unification
+theory of AGNs. Our results show that $\log[N_H(\text{cm}^{-2})]\in$ [23,25.5]
+can be oriented at any $\theta$ from 0$^\circ$ to $\approx$80$^\circ$ due to
+the degeneracy in the input parameters.
+",0,1,0,0,0,0
+15912,Rapid behavioral transitions produce chaotic mixing by a planktonic microswimmer,"  Despite their vast morphological diversity, many invertebrates have similar
+larval forms characterized by ciliary bands, innervated arrays of beating cilia
+that facilitate swimming and feeding. Hydrodynamics suggests that these bands
+should tightly constrain the behavioral strategies available to the larvae;
+however, their apparent ubiquity suggests that these bands also confer
+substantial adaptive advantages. Here, we use hydrodynamic techniques to
+investigate ""blinking,"" an unusual behavioral phenomenon observed in many
+invertebrate larvae in which ciliary bands across the body rapidly change
+beating direction and produce transient rearrangement of the local flow field.
+Using a general theoretical model combined with quantitative experiments on
+starfish larvae, we find that the natural rhythm of larval blinking is
+hydrodynamically optimal for inducing strong mixing of the local fluid
+environment due to transient streamline crossing, thereby maximizing the
+larvae's overall feeding rate. Our results are consistent with previous
+hypotheses that filter feeding organisms may use chaotic mixing dynamics to
+overcome circulation constraints in viscous environments, and it suggests
+physical underpinnings for complex neurally-driven behaviors in early-divergent
+animals.
+",0,0,0,0,1,0
+15913,Testing for long memory in panel random-coefficient AR(1) data,"  It is well-known that random-coefficient AR(1) process can have long memory
+depending on the index $\beta$ of the tail distribution function of the random
+coefficient, if it is a regularly varying function at unity. We discuss
+estimation of $\beta$ from panel data comprising N random-coefficient AR(1)
+series, each of length T. The estimator of $\beta$ is constructed as a version
+of the tail index estimator of Goldie and Smith (1987) applied to sample lag 1
+autocorrelations of individual time series. Its asymptotic normality is derived
+under certain conditions on N, T and some parameters of our statistical model.
+Based on this result, we construct a statistical procedure to test if the panel
+random-coefficient AR(1) data exhibit long memory. A simulation study
+illustrates finite-sample performance of the introduced estimator and testing
+procedure.
+",0,0,1,1,0,0
+15914,Hilsum-Skandalis maps as Frobenius adjunctions with application to geometric morphisms,"  Hilsum-Skandalis maps, from differential geometry, are studied in the context
+of a cartesian category. It is shown that Hilsum-Skandalis maps can be
+represented as stably Frobenius adjunctions. This leads to a new and more
+general proof that Hilsum-Skandalis maps represent a universal way of inverting
+essential equivalences between internal groupoids. To prove the representation
+theorem, a new characterisation of the con- nected components adjunction of any
+internal groupoid is given. The charaterisation is that the adjunction is
+covered by a stable Frobenius adjunction that is a slice and whose right
+adjoint is monadic. Geometric morphisms can be represented as stably Frobenius
+adjunctions. As applications of the study we show how it is easy to recover
+properties of geometric morphisms, seeing them as aspects of properties of
+stably Frobenius adjunctions.
+",0,0,1,0,0,0
+15915,Pairs of commuting isometries - I,"  We present an explicit version of Berger, Coburn and Lebow's classification
+result for pure pairs of commuting isometries in the sense of an explicit
+recipe for constructing pairs of commuting isometric multipliers with precise
+coefficients. We describe a complete set of (joint) unitary invariants and
+compare the Berger, Coburn and Lebow's representations with other natural
+analytic representations of pure pairs of commuting isometries. Finally, we
+study the defect operators of pairs of commuting isometries.
+",0,0,1,0,0,0
+15916,Sampled-Data Boundary Feedback Control of 1-D Parabolic PDEs,"  The paper provides results for the application of boundary feedback control
+with Zero-Order-Hold (ZOH) to 1-D linear parabolic systems on bounded domains.
+It is shown that the continuous-time boundary feedback applied in a
+sample-and-hold fashion guarantees closed-loop exponential stability, provided
+that the sampling period is sufficiently small. Two different continuous-time
+feedback designs are considered: the reduced model design and the backstepping
+design. The obtained results provide stability estimates for weighted 2-norms
+of the state and robustness with respect to perturbations of the sampling
+schedule is guaranteed.
+",1,0,1,0,0,0
+15917,PEN as self-vetoing structural Material,"  Polyethylene Naphtalate (PEN) is a mechanically very favorable polymer.
+Earlier it was found that thin foils made from PEN can have very high
+radio-purity compared to other commercially available foils. In fact, PEN is
+already in use for low background signal transmission applications (cables).
+Recently it has been realized that PEN also has favorable scintillating
+properties. In combination, this makes PEN a very promising candidate as a
+self-vetoing structural material in low background experiments. Components
+instrumented with light detectors could be built from PEN. This includes
+detector holders, detector containments, signal transmission links, etc. The
+current R\&D towards qualification of PEN as a self-vetoing low background
+structural material is be presented.
+",0,1,0,0,0,0
+15918,Quantum Cohomology under Birational Maps and Transitions,"  This is an expanded version of the third author's lecture in String-Math 2015
+at Sanya. It summarizes some of our works in quantum cohomology.
+After reviewing the quantum Lefschetz and quantum Leray--Hirsch, we discuss
+their applications to the functoriality properties under special smooth flops,
+flips and blow-ups. Finally, for conifold transitions of Calabi--Yau 3-folds,
+formulations for small resolutions (blow-ups along Weil divisors) are sketched.
+",0,0,1,0,0,0
+15919,A conjecture on the zeta functions of pairs of ternary quadratic forms,"  We consider the prehomogeneous vector space of pairs of ternary quadratic
+forms. For the lattice of pairs of integral ternary quadratic forms and its
+dual lattice, there are six zeta functions associated with the the
+prehomogeneous vector space. We present a conjecture which states that there
+are simple relations among the six zeta functions. We prove that the
+coefficients coincide on fundamental discriminants.
+",0,0,1,0,0,0
+15920,Abstract Syntax Networks for Code Generation and Semantic Parsing,"  Tasks like code generation and semantic parsing require mapping unstructured
+(or partially structured) inputs to well-formed, executable outputs. We
+introduce abstract syntax networks, a modeling framework for these problems.
+The outputs are represented as abstract syntax trees (ASTs) and constructed by
+a decoder with a dynamically-determined modular structure paralleling the
+structure of the output tree. On the benchmark Hearthstone dataset for code
+generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy,
+compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we
+perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with
+no task-specific engineering.
+",1,0,0,1,0,0
+15921,Theoretical derivation of laser-dressed atomic states by using a fractal space,"  The derivation of approximate wave functions for an electron submitted to
+both a coulomb and a time-dependent laser electric fields, the so-called
+Coulomb-Volkov (CV) state, is addressed. Despite its derivation for continuum
+states does not exhibit any particular problem within the framework of the
+standard theory of quantum mechanics (QM), difficulties arise when considering
+an initially bound atomic state. Indeed the natural way of translating the
+unperturbed momentum by the laser vector potential is no longer possible since
+a bound state does not exhibit a plane wave form including explicitely a
+momentum. The use of a fractal space permits to naturally define a momentum for
+a bound wave function. Within this framework, it is shown how the derivation of
+laser-dressed bound states can be performed. Based on a generalized eikonal
+approach, a new expression for the laser-dressed states is also derived, fully
+symmetric relative to the continuum or bound nature of the initial unperturbed
+wave function. It includes an additional crossed term in the Volkov phase which
+was not obtained within the standard theory of quantum mechanics. The
+derivations within this fractal framework have highlighted other possible ways
+to derive approximate laser-dressed states in QM. After comparing the various
+obtained wave functions, an application to the prediction of the ionization
+probability of hydrogen targets by attosecond XUV pulses within the sudden
+approximation is provided. This approach allows to make predictions in various
+regimes depending on the laser intensity, going from the non-resonant
+multiphoton absorption to tunneling and barrier-suppression ionization.
+",0,1,0,0,0,0
+15922,Implications of the interstellar object 1I/'Oumuamua for planetary dynamics and planetesimal formation,"  'Oumuamua, the first bona-fide interstellar planetesimal, was discovered
+passing through our Solar System on a hyperbolic orbit. This object was likely
+dynamically ejected from an extrasolar planetary system after a series of close
+encounters with gas giant planets. To account for 'Oumuamua's detection, simple
+arguments suggest that ~1 Earth mass of planetesimals are ejected per Solar
+mass of Galactic stars. However, that value assumes mono-sized planetesimals.
+If the planetesimal mass distribution is instead top-heavy the inferred mass in
+interstellar planetesimals increases to an implausibly high value. The tension
+between theoretical expectations for the planetesimal mass function and the
+observation of 'Oumuamua can be relieved if a small fraction (~0.1-1%) of
+planetesimals are tidally disrupted on the pathway to ejection into
+'Oumuamua-sized fragments. Using a large suite of simulations of giant planet
+dynamics including planetesimals, we confirm that 0.1-1% of planetesimals pass
+within the tidal disruption radius of a gas giant on their pathway to ejection.
+'Oumuamua may thus represent a surviving fragment of a disrupted planetesimal.
+Finally, we argue that an asteroidal composition is dynamically disfavoured for
+'Oumuamua, as asteroidal planetesimals are both less abundant and ejected at a
+lower efficiency than cometary planetesimals.
+",0,1,0,0,0,0
+15923,Automatic symbolic computation for discontinuous Galerkin finite element methods,"  The implementation of discontinuous Galerkin finite element methods (DGFEMs)
+represents a very challenging computational task, particularly for systems of
+coupled nonlinear PDEs, including multiphysics problems, whose parameters may
+consist of power series or functionals of the solution variables. Thereby, the
+exploitation of symbolic algebra to express a given DGFEM approximation of a
+PDE problem within a high level language, whose syntax closely resembles the
+mathematical definition, is an invaluable tool. Indeed, this then facilitates
+the automatic assembly of the resulting system of (nonlinear) equations, as
+well as the computation of Fréchet derivative(s) of the DGFEM scheme, needed,
+for example, within a Newton-type solver. However, even exploiting symbolic
+algebra, the discretisation of coupled systems of PDEs can still be extremely
+verbose and hard to debug. Thereby, in this article we develop a further layer
+of abstraction by designing a class structure for the automatic computation of
+DGFEM formulations. This work has been implemented within the FEniCS package,
+based on exploiting the Unified Form Language. Numerical examples are presented
+which highlight the simplicity of implementation of DGFEMs for the numerical
+approximation of a range of PDE problems.
+",1,0,0,0,0,0
+15924,Methodological Framework for Determining the Land Eligibility of Renewable Energy Sources,"  The quantity and distribution of land which is eligible for renewable energy
+sources is fundamental to the role these technologies will play in future
+energy systems. As it stands, however, the current state of land eligibility
+investigation is found to be insufficient to meet the demands of the future
+energy modelling community. Three key areas are identified as the predominate
+causes of this; inconsistent criteria definitions, inconsistent or unclear
+methodologies, and inconsistent dataset usage. To combat these issues, a land
+eligibility framework is developed and described in detail. The validity of
+this framework is then shown via the recreation of land eligibility results
+found in the literature, showing strong agreement in the majority of cases.
+Following this, the framework is used to perform an evaluation of land
+eligibility criteria within the European context whereby the relative
+importance of commonly considered criteria are compared.
+",1,0,0,0,0,0
+15925,Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks,"  We conduct an extensive empirical study on short-term electricity price
+forecasting (EPF) to address the long-standing question if the optimal model
+structure for EPF is univariate or multivariate. We provide evidence that
+despite a minor edge in predictive performance overall, the multivariate
+modeling framework does not uniformly outperform the univariate one across all
+12 considered datasets, seasons of the year or hours of the day, and at times
+is outperformed by the latter. This is an indication that combining advanced
+structures or the corresponding forecasts from both modeling approaches can
+bring a further improvement in forecasting accuracy. We show that this indeed
+can be the case, even for a simple averaging scheme involving only two models.
+Finally, we also analyze variable selection for the best performing
+high-dimensional lasso-type models, thus provide guidelines to structuring
+better performing forecasting model designs.
+",0,0,0,1,0,1
+15926,On Strong Small Loop Transfer Spaces Relative to Subgroups of Fundamental Groups,"  Let $H$ be a subgroup of the fundamental group $\pi_{1}(X,x_{0})$. By
+extending the concept of strong SLT space to a relative version with respect to
+$H$, strong $H$-SLT space, first, we investigate the existence of a covering
+map for strong $H$-SLT spaces. Moreover, we show that a semicovering map is a
+covering map in the presence of strong $H$-SLT property. Second, we present
+conditions under which the whisker topology agrees with the lasso topology on
+$\widetilde{X}_{H}$. Also, we study the relationship between open subsets of
+$\pi_{1}^{wh}(X,x_{0})$ and $\pi_{1}^{l}(X,x_{0})$. Finally, we give some
+examples to justify the definition and study of strong $H$-SLT spaces.
+",0,0,1,0,0,0
+15927,Headphones on the wire,"  We analyze a dataset providing the complete information on the effective
+plays of thousands of music listeners during several months. Our analysis
+confirms a number of properties previously highlighted by research based on
+interviews and questionnaires, but also uncover new statistical patterns, both
+at the individual and collective levels. In particular, we show that
+individuals follow common listening rhythms characterized by the same
+fluctuations, alternating heavy and light listening periods, and can be
+classified in four groups of similar sizes according to their temporal habits
+--- 'early birds', 'working hours listeners', 'evening listeners' and 'night
+owls'. We provide a detailed radioscopy of the listeners' interplay between
+repeated listening and discovery of new content. We show that different genres
+encourage different listening habits, from Classical or Jazz music with a more
+balanced listening among different songs, to Hip Hop and Dance with a more
+heterogeneous distribution of plays. Finally, we provide measures of how
+distant people are from each other in terms of common songs. In particular, we
+show that the number of songs $S$ a DJ should play to a random audience of size
+$N$ such that everyone hears at least one song he/she currently listens to, is
+of the form $S\sim N^\alpha$ where the exponent depends on the music genre and
+is in the range $[0.5,0.8]$. More generally, our results show that the recent
+access to virtually infinite catalogs of songs does not promote exploration for
+novelty, but that most users favor repetition of the same songs.
+",1,1,0,0,0,0
+15928,Li-intercalated Graphene on SiC(0001): an STM study,"  We present a systematical study via scanning tunneling microscopy (STM) and
+low-energy electron diffraction (LEED) on the effect of the exposure of Lithium
+(Li) on graphene on silicon carbide (SiC). We have investigated Li deposition
+both on epitaxial monolayer graphene and on buffer layer surfaces on the
+Si-face of SiC. At room temperature, Li immediately intercalates at the
+interface between the SiC substrate and the buffer layer and transforms the
+buffer layer into a quasi-free-standing graphene. This conclusion is
+substantiated by LEED and STM evidence. We show that intercalation occurs
+through the SiC step sites or graphene defects. We obtain a good quantitative
+agreement between the number of Li atoms deposited and the number of available
+Si bonds at the surface of the SiC crystal. Through STM analysis, we are able
+to determine the interlayer distance induced by Li-intercalation at the
+interface between the SiC substrate and the buffer layer.
+",0,1,0,0,0,0
+15929,Generative Adversarial Perturbations,"  In this paper, we propose novel generative models for creating adversarial
+examples, slightly perturbed images resembling natural images but maliciously
+crafted to fool pre-trained models. We present trainable deep neural networks
+for transforming images to adversarial perturbations. Our proposed models can
+produce image-agnostic and image-dependent perturbations for both targeted and
+non-targeted attacks. We also demonstrate that similar architectures can
+achieve impressive results in fooling classification and semantic segmentation
+models, obviating the need for hand-crafting attack methods for each task.
+Using extensive experiments on challenging high-resolution datasets such as
+ImageNet and Cityscapes, we show that our perturbations achieve high fooling
+rates with small perturbation norms. Moreover, our attacks are considerably
+faster than current iterative methods at inference time.
+",1,0,0,1,0,0
+15930,Lifted Polymatroid Inequalities for Mean-Risk Optimization with Indicator Variables,"  We investigate a mixed 0-1 conic quadratic optimization problem with
+indicator variables arising in mean-risk optimization. The indicator variables
+are often used to model non-convexities such as fixed charges or cardinality
+constraints. Observing that the problem reduces to a submodular function
+minimization for its binary restriction, we derive three classes of strong
+convex valid inequalities by lifting the polymatroid inequalities on the binary
+variables. Computational experiments demonstrate the effectiveness of the
+inequalities in strengthening the convex relaxations and, thereby, improving
+the solution times for mean-risk problems with fixed charges and cardinality
+constraints significantly.
+",0,0,1,0,0,0
+15931,Illusion and Reality in the Atmospheres of Exoplanets,"  The atmospheres of exoplanets reveal all their properties beyond mass,
+radius, and orbit. Based on bulk densities, we know that exoplanets larger than
+1.5 Earth radii must have gaseous envelopes, hence atmospheres. We discuss
+contemporary techniques for characterization of exoplanetary atmospheres. The
+measurements are difficult, because - even in current favorable cases - the
+signals can be as small as 0.001-percent of the host star's flux. Consequently,
+some early results have been illusory, and not confirmed by subsequent
+investigations. Prominent illusions to date include polarized scattered light,
+temperature inversions, and the existence of carbon planets. The field moves
+from the first tentative and often incorrect conclusions, converging to the
+reality of exoplanetary atmospheres. That reality is revealed using transits
+for close-in exoplanets, and direct imaging for young or massive exoplanets in
+distant orbits. Several atomic and molecular constituents have now been
+robustly detected in exoplanets as small as Neptune. In our current
+observations, the effects of clouds and haze appear ubiquitous. Topics at the
+current frontier include the measurement of heavy element abundances in giant
+planets, detection of carbon-based molecules, measurement of atmospheric
+temperature profiles, definition of heat circulation efficiencies for tidally
+locked planets, and the push to detect and characterize the atmospheres of
+super-Earths. Future observatories for this quest include the James Webb Space
+Telescope, and the new generation of Extremely Large Telescopes on the ground.
+On a more distant horizon, NASA's concepts for the HabEx and LUVOIR missions
+could extend the study of exoplanetary atmospheres to true twins of Earth.
+",0,1,0,0,0,0
+15932,Actors without Borders: Amnesty for Imprisoned State,"  In concurrent systems, some form of synchronisation is typically needed to
+achieve data-race freedom, which is important for correctness and safety. In
+actor-based systems, messages are exchanged concurrently but executed
+sequentially by the receiving actor. By relying on isolation and non-sharing,
+an actor can access its own state without fear of data-races, and the internal
+behavior of an actor can be reasoned about sequentially.
+However, actor isolation is sometimes too strong to express useful patterns.
+For example, letting the iterator of a data-collection alias the internal
+structure of the collection allows a more efficient implementation than if each
+access requires going through the interface of the collection. With full
+isolation, in order to maintain sequential reasoning the iterator must be made
+part of the collection, which bloats the interface of the collection and means
+that a client must have access to the whole data-collection in order to use the
+iterator.
+In this paper, we propose a programming language construct that enables a
+relaxation of isolation but without sacrificing sequential reasoning. We
+formalise the mechanism in a simple lambda calculus with actors and passive
+objects, and show how an actor may leak parts of its internal state while
+ensuring that any interaction with this data is still synchronised.
+",1,0,0,0,0,0
+15933,Bayesian Estimation of Gaussian Graphical Models with Predictive Covariance Selection,"  Gaussian graphical models are used for determining conditional relationships
+between variables. This is accomplished by identifying off-diagonal elements in
+the inverse-covariance matrix that are non-zero. When the ratio of variables
+(p) to observations (n) approaches one, the maximum likelihood estimator of the
+covariance matrix becomes unstable and requires shrinkage estimation. Whereas
+several classical (frequentist) methods have been introduced to address this
+issue, fully Bayesian methods remain relatively uncommon in practice and
+methodological literatures. Here we introduce a Bayesian method for estimating
+sparse matrices, in which conditional relationships are determined with
+projection predictive selection. With this method, that uses Kullback-Leibler
+divergence and cross-validation for neighborhood selection, we reconstruct the
+inverse-covariance matrix in both low and high-dimensional settings. Through
+simulation and applied examples, we characterized performance compared to
+several Bayesian methods and the graphical lasso, in addition to TIGER that
+similarly estimates the inverse-covariance matrix with regression. Our results
+demonstrate that projection predictive selection not only has superior
+performance compared to selecting the most probable model and Bayesian model
+averaging, particularly for high-dimensional data, but also compared to the the
+Bayesian and classical glasso methods. Further, we show that estimating the
+inverse-covariance matrix with multiple regression is often more accurate, with
+respect to various loss functions, and efficient than direct estimation. In
+low-dimensional settings, we demonstrate that projection predictive selection
+also provides competitive performance. We have implemented the projection
+predictive method for covariance selection in the R package GGMprojpred
+",0,0,0,1,0,0
+15934,ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models,"  While deep learning models have achieved state-of-the-art accuracies for many
+prediction tasks, understanding these models remains a challenge. Despite the
+recent interest in developing visual tools to help users interpret deep
+learning models, the complexity and wide variety of models deployed in
+industry, and the large-scale datasets that they used, pose unique design
+challenges that are inadequately addressed by existing work. Through
+participatory design sessions with over 15 researchers and engineers at
+Facebook, we have developed, deployed, and iteratively improved ActiVis, an
+interactive visualization system for interpreting large-scale deep learning
+models and results. By tightly integrating multiple coordinated views, such as
+a computation graph overview of the model architecture, and a neuron activation
+view for pattern discovery and comparison, users can explore complex deep
+neural network models at both the instance- and subset-level. ActiVis has been
+deployed on Facebook's machine learning platform. We present case studies with
+Facebook researchers and engineers, and usage scenarios of how ActiVis may work
+with different models.
+",1,0,0,1,0,0
+15935,A topological lower bound for the energy of a unit vector field on a closed Euclidean hypersurface,"  For a unit vector field on a closed immersed Euclidean hypersurface
+$M^{2n+1}$, $n\geq 1$, we exhibit a nontrivial lower bound for its energy which
+depends on the degree of the Gauss map of the immersion. When the hypersurface
+is the unit sphere $\mathbb{S}^{2n+1}$, immersed with degree one, this lower
+bound corresponds to a well established value from the literature. We introduce
+a list of functionals $\mathcal{B}_k$ on a compact Riemannian manifold $M^{m}$,
+$1\leq k\leq m$, and show that, when the underlying manifold is a closed
+hypersurface, these functionals possess similar properties regarding the degree
+of the immersion. In addition, we prove that Hopf flows minimize
+$\mathcal{B}_n$ on $\mathbb{S}^{2n+1}$.
+",0,0,1,0,0,0
+15936,Permutation invariant proper polyhedral cones and their Lyapunov rank,"  The Lyapunov rank of a proper cone $K$ in a finite dimensional real Hilbert
+space is defined as the dimension of the space of all Lyapunov-like
+transformations on $K$, or equivalently, the dimension of the Lie algebra of
+the automorphism group of $K$. This (rank) measures the number of linearly
+independent bilinear relations needed to express a complementarity system on
+$K$ (that arises, for example, from a linear program or a complementarity
+problem on the cone). Motivated by the problem of describing spectral/proper
+cones where the complementarity system can be expressed as a square system
+(that is, where the Lyapunov rank is greater than equal to the dimension of the
+ambient space), we consider proper polyhedral cones in $\mathbb{R}^n$ that are
+permutation invariant. For such cones we show that the Lyapunov rank is either
+1 (in which case, the cone is irreducible) or n (in which case, the cone is
+isomorphic to the nonnegative orthart in $\mathbb{R}^n$). In the latter case,
+we show that the corresponding spectral cone is isomorphic to a symmetric cone.
+",0,0,1,0,0,0
+15937,On the Relation of External and Internal Feature Interactions: A Case Study,"  Detecting feature interactions is imperative for accurately predicting
+performance of highly-configurable systems. State-of-the-art performance
+prediction techniques rely on supervised machine learning for detecting feature
+interactions, which, in turn, relies on time consuming performance measurements
+to obtain training data. By providing information about potentially interacting
+features, we can reduce the number of required performance measurements and
+make the overall performance prediction process more time efficient. We expect
+that the information about potentially interacting features can be obtained by
+statically analyzing the source code of a highly-configurable system, which is
+computationally cheaper than performing multiple performance measurements. To
+this end, we conducted a qualitative case study in which we explored the
+relation between control-flow feature interactions (detected through static
+program analysis) and performance feature interactions (detected by performance
+prediction techniques using performance measurements). We found that a relation
+exists, which can potentially be exploited to predict performance interactions.
+",1,0,0,0,0,0
+15938,Properties of the water to boron nitride interaction: from zero to two dimensions with benchmark accuracy,"  Molecular adsorption on surfaces plays an important part in catalysis,
+corrosion, desalination, and various other processes that are relevant to
+industry and in nature. As a complement to experiments, accurate adsorption
+energies can be obtained using various sophisticated electronic structure
+methods that can now be applied to periodic systems. The adsorption energy of
+water on boron nitride substrates, going from zero to 2-dimensional
+periodicity, is particularly interesting as it calls for an accurate treatment
+of polarizable electrostatics and dispersion interactions, as well as posing a
+practical challenge to experiments and electronic structure methods. Here, we
+present reference adsorption energies, static polarizabilities, and dynamic
+polarizabilities, for water on BN substrates of varying size and dimension.
+Adsorption energies are computed with coupled cluster theory, fixed-node
+quantum Monte Carlo (FNQMC), the random phase approximation (RPA), and second
+order M{\o}ller-Plesset (MP2) theory. These explicitly correlated methods are
+found to agree in molecular as well as periodic systems. The best estimate of
+the water/h-BN adsorption energy is $-107\pm7$ meV from FNQMC. In addition, the
+water adsorption energy on the BN substrates could be expected to grow
+monotonically with the size of the substrate due to increased dispersion
+interactions but interestingly, this is not the case here. This peculiar
+finding is explained using the static polarizabilities and molecular dispersion
+coefficients of the systems, as computed from time-dependent density functional
+theory (DFT). Dynamic as well as static polarizabilities are found to be highly
+anisotropic in these systems. In addition, the many-body dispersion method in
+DFT emerges as a particularly useful estimation of finite size effects for
+other expensive, many-body wavefunction based methods.
+",0,1,0,0,0,0
+15939,Theory of Large Intrinsic Spin Hall Effect in Iridate Semimetals,"  We theoretically investigate the mechanism to generate large intrinsic spin
+Hall effect in iridates or more broadly in 5d transition metal oxides with
+strong spin-orbit coupling. We demonstrate such a possibility by taking the
+example of orthorhombic perovskite iridate with nonsymmorphic lattice symmetry,
+SrIrO$_3$, which is a three-dimensional semimetal with nodal line spectrum. It
+is shown that large intrinsic spin Hall effect arises in this system via the
+spin-Berry curvature originating from the nearly degenerate electronic spectra
+surrounding the nodal line. This effect exists even when the nodal line is
+gently gapped out, due to the persistent nearly degenerate electronic
+structure, suggesting a distinct robustness. The magnitude of the spin Hall
+conductivity is shown to be comparable to the best known example such as doped
+topological insulators and the biggest in any transition metal oxides. To gain
+further insight, we compute the intrinsic spin Hall conductivity in both of the
+bulk and thin film systems. We find that the geometric confinement in thin
+films leads to significant modifications of the electronic states, leading to
+even bigger spin Hall conductivity in certain cases. We compare our findings
+with the recent experimental report on the discovery of large spin Hall effect
+in SrIrO$_3$ thin films.
+",0,1,0,0,0,0
+15940,A pathway-based kernel boosting method for sample classification using genomic data,"  The analysis of cancer genomic data has long suffered ""the curse of
+dimensionality"". Sample sizes for most cancer genomic studies are a few
+hundreds at most while there are tens of thousands of genomic features studied.
+Various methods have been proposed to leverage prior biological knowledge, such
+as pathways, to more effectively analyze cancer genomic data. Most of the
+methods focus on testing marginal significance of the associations between
+pathways and clinical phenotypes. They can identify relevant pathways, but do
+not involve predictive modeling. In this article, we propose a Pathway-based
+Kernel Boosting (PKB) method for integrating gene pathway information for
+sample classification, where we use kernel functions calculated from each
+pathway as base learners and learn the weights through iterative optimization
+of the classification loss function. We apply PKB and several competing methods
+to three cancer studies with pathological and clinical information, including
+tumor grade, stage, tumor sites, and metastasis status. Our results show that
+PKB outperforms other methods, and identifies pathways relevant to the outcome
+variables.
+",0,0,0,1,1,0
+15941,Analyzing the Approximation Error of the Fast Graph Fourier Transform,"  The graph Fourier transform (GFT) is in general dense and requires O(n^2)
+time to compute and O(n^2) memory space to store. In this paper, we pursue our
+previous work on the approximate fast graph Fourier transform (FGFT). The FGFT
+is computed via a truncated Jacobi algorithm, and is defined as the product of
+J Givens rotations (very sparse orthogonal matrices). The truncation parameter,
+J, represents a trade-off between precision of the transform and time of
+computation (and storage space). We explore further this trade-off and study,
+on different types of graphs, how is the approximation error distributed along
+the spectrum.
+",1,0,0,0,0,0
+15942,Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer Survival Curves in Iowa,"  Neural networks are capable of learning rich, nonlinear feature
+representations shown to be beneficial in many predictive tasks. In this work,
+we use such models to explore different geographical feature representations in
+the context of predicting colorectal cancer survival curves for patients in the
+state of Iowa, spanning the years 1989 to 2013. Specifically, we compare model
+performance using ""area between the curves"" (ABC) to assess (a) whether
+survival curves can be reasonably predicted for colorectal cancer patients in
+the state of Iowa, (b) whether geographical features improve predictive
+performance, (c) whether a simple binary representation, or a richer, spectral
+analysis-elicited representation perform better, and (d) whether spectral
+analysis-based representations can be improved upon by leveraging
+geographically-descriptive features. In exploring (d), we devise a
+similarity-based spectral analysis procedure, which allows for the combination
+of geographically relational and geographically descriptive features. Our
+findings suggest that survival curves can be reasonably estimated on average,
+with predictive performance deviating at the five-year survival mark among all
+models. We also find that geographical features improve predictive performance,
+and that better performance is obtained using richer, spectral
+analysis-elicited features. Furthermore, we find that similarity-based spectral
+analysis-elicited representations improve upon the original spectral analysis
+results by approximately 40%.
+",0,0,0,1,0,0
+15943,On the Integrality Gap of the Prize-Collecting Steiner Forest LP,"  In the prize-collecting Steiner forest (PCSF) problem, we are given an
+undirected graph $G=(V,E)$, edge costs $\{c_e\geq 0\}_{e\in E}$, terminal pairs
+$\{(s_i,t_i)\}_{i=1}^k$, and penalties $\{\pi_i\}_{i=1}^k$ for each terminal
+pair; the goal is to find a forest $F$ to minimize $c(F)+\sum_{i:
+(s_i,t_i)\text{ not connected in }F}\pi_i$. The Steiner forest problem can be
+viewed as the special case where $\pi_i=\infty$ for all $i$. It was widely
+believed that the integrality gap of the natural (and well-studied)
+linear-programming (LP) relaxation for PCSF is at most 2. We dispel this belief
+by showing that the integrality gap of this LP is at least $9/4$. This holds
+even for planar graphs. We also show that using this LP, one cannot devise a
+Lagrangian-multiplier-preserving (LMP) algorithm with approximation guarantee
+better than $4$. Our results thus show a separation between the integrality
+gaps of the LP-relaxations for prize-collecting and non-prize-collecting (i.e.,
+standard) Steiner forest, as well as the approximation ratios achievable
+relative to the optimal LP solution by LMP- and non-LMP- approximation
+algorithms for PCSF. For the special case of prize-collecting Steiner tree
+(PCST), we prove that the natural LP relaxation admits basic feasible solutions
+with all coordinates of value at most $1/3$ and all edge variables positive.
+Thus, we rule out the possibility of approximating PCST with guarantee better
+than $3$ using a direct iterative rounding method.
+",1,0,1,0,0,0
+15944,Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges,"  There is a large literature on semiparametric estimation of average treatment
+effects under unconfounded treatment assignment in settings with a fixed number
+of covariates. More recently attention has focused on settings with a large
+number of covariates. In this paper we extend lessons from the earlier
+literature to this new setting. We propose that in addition to reporting point
+estimates and standard errors, researchers report results from a number of
+supplementary analyses to assist in assessing the credibility of their
+estimates.
+",0,0,0,1,0,0
+15945,Markov cubature rules for polynomial processes,"  We study discretizations of polynomial processes using finite state Markov
+processes satisfying suitable moment matching conditions. The states of these
+Markov processes together with their transition probabilities can be
+interpreted as Markov cubature rules. The polynomial property allows us to
+study such rules using algebraic techniques. Markov cubature rules aid the
+tractability of path-dependent tasks such as American option pricing in models
+where the underlying factors are polynomial processes.
+",0,0,0,0,0,1
+15946,Statistical inference for high dimensional regression via Constrained Lasso,"  In this paper, we propose a new method for estimation and constructing
+confidence intervals for low-dimensional components in a high-dimensional
+model. The proposed estimator, called Constrained Lasso (CLasso) estimator, is
+obtained by simultaneously solving two estimating equations---one imposing a
+zero-bias constraint for the low-dimensional parameter and the other forming an
+$\ell_1$-penalized procedure for the high-dimensional nuisance parameter. By
+carefully choosing the zero-bias constraint, the resulting estimator of the low
+dimensional parameter is shown to admit an asymptotically normal limit
+attaining the Cramér-Rao lower bound in a semiparametric sense. We propose
+a tuning-free iterative algorithm for implementing the CLasso. We show that
+when the algorithm is initialized at the Lasso estimator, the de-sparsified
+estimator proposed in van de Geer et al. [\emph{Ann. Statist.} {\bf 42} (2014)
+1166--1202] is asymptotically equivalent to the first iterate of the algorithm.
+We analyse the asymptotic properties of the CLasso estimator and show the
+globally linear convergence of the algorithm. We also demonstrate encouraging
+empirical performance of the CLasso through numerical studies.
+",0,0,1,1,0,0
+15947,VALES: I. The molecular gas content in star-forming dusty H-ATLAS galaxies up to z=0.35,"  We present an extragalactic survey using observations from the Atacama Large
+Millimeter/submillimeter Array (ALMA) to characterise galaxy populations up to
+$z=0.35$: the Valparaíso ALMA Line Emission Survey (VALES). We use ALMA
+Band-3 CO(1--0) observations to study the molecular gas content in a sample of
+67 dusty normal star-forming galaxies selected from the $Herschel$
+Astrophysical Terahertz Large Area Survey ($H$-ATLAS). We have spectrally
+detected 49 galaxies at $>5\sigma$ significance and 12 others are seen at low
+significance in stacked spectra. CO luminosities are in the range of
+$(0.03-1.31)\times10^{10}$ K km s$^{-1}$ pc$^2$, equivalent to $\log({\rm
+M_{gas}/M_{\odot}}) =8.9-10.9$ assuming an $\alpha_{\rm CO}$=4.6(K km s$^{-1}$
+pc$^{2}$)$^{-1}$, which perfectly complements the parameter space previously
+explored with local and high-z normal galaxies. We compute the optical to CO
+size ratio for 21 galaxies resolved by ALMA at $\sim 3$.""$5$ resolution (6.5
+kpc), finding that the molecular gas is on average $\sim$ 0.6 times more
+compact than the stellar component. We obtain a global Schmidt-Kennicutt
+relation, given by $\log [\Sigma_{\rm SFR}/({\rm M_{\odot}
+yr^{-1}kpc^{-2}})]=(1.26 \pm 0.02) \times \log [\Sigma_{\rm M_{H2}}/({\rm
+M_{\odot}\,pc^{-2}})]-(3.6 \pm 0.2)$. We find a significant fraction of
+galaxies lying at `intermediate efficiencies' between a long-standing mode of
+star-formation activity and a starburst, specially at $\rm L_{IR}=10^{11-12}
+L_{\odot}$. Combining our observations with data taken from the literature, we
+propose that star formation efficiencies can be parameterised by $\log [{\rm
+SFR/M_{H2}}]=0.19 \times {\rm (\log {L_{IR}}-11.45)}-8.26-0.41 \times
+\arctan[-4.84 (\log {\rm L_{IR}}-11.45) ]$. Within the redshift range we
+explore ($z<0.35$), we identify a rapid increase of the gas content as a
+function of redshift.
+",0,1,0,0,0,0
+15948,Variational methods for degenerate Kirchhoff equations,"  For a degenerate autonomous Kirchhoff equation which is set on $\mathbb{R}^N$
+and involves the Berestycki-Lions type nonlinearity, we cope with the cases
+$N=2,3$ and $N\geq5$ by using mountain pass and symmetric mountain pass
+approaches and by using Clark theorem respectively.
+",0,0,1,0,0,0
+15949,Chang'e 3 lunar mission and upper limit on stochastic background of gravitational wave around the 0.01 Hz band,"  The Doppler tracking data of the Chang'e 3 lunar mission is used to constrain
+the stochastic background of gravitational wave in cosmology within the 1 mHz
+to 0.05 Hz frequency band. Our result improves on the upper bound on the energy
+density of the stochastic background of gravitational wave in the 0.02 Hz to
+0.05 Hz band obtained by the Apollo missions, with the improvement reaching
+almost one order of magnitude at around 0.05 Hz. Detailed noise analysis of the
+Doppler tracking data is also presented, with the prospect that these noise
+sources will be mitigated in future Chinese deep space missions. A feasibility
+study is also undertaken to understand the scientific capability of the Chang'e
+4 mission, due to be launched in 2018, in relation to the stochastic
+gravitational wave background around 0.01 Hz. The study indicates that the
+upper bound on the energy density may be further improved by another order of
+magnitude from the Chang'e 3 mission, which will fill the gap in the frequency
+band from 0.02 Hz to 0.1 Hz in the foreseeable future.
+",0,1,0,0,0,0
+15950,On the complexity of range searching among curves,"  Modern tracking technology has made the collection of large numbers of
+densely sampled trajectories of moving objects widely available. We consider a
+fundamental problem encountered when analysing such data: Given $n$ polygonal
+curves $S$ in $\mathbb{R}^d$, preprocess $S$ into a data structure that answers
+queries with a query curve $q$ and radius $\rho$ for the curves of $S$ that
+have \Frechet distance at most $\rho$ to $q$.
+We initiate a comprehensive analysis of the space/query-time trade-off for
+this data structuring problem. Our lower bounds imply that any data structure
+in the pointer model model that achieves $Q(n) + O(k)$ query time, where $k$ is
+the output size, has to use roughly $\Omega\left((n/Q(n))^2\right)$ space in
+the worst case, even if queries are mere points (for the discrete \Frechet
+distance) or line segments (for the continuous \Frechet distance). More
+importantly, we show that more complex queries and input curves lead to
+additional logarithmic factors in the lower bound. Roughly speaking, the number
+of logarithmic factors added is linear in the number of edges added to the
+query and input curve complexity. This means that the space/query time
+trade-off worsens by an exponential factor of input and query complexity. This
+behaviour addresses an open question in the range searching literature: whether
+it is possible to avoid the additional logarithmic factors in the space and
+query time of a multilevel partition tree. We answer this question negatively.
+On the positive side, we show we can build data structures for the \Frechet
+distance by using semialgebraic range searching. Our solution for the discrete
+\Frechet distance is in line with the lower bound, as the number of levels in
+the data structure is $O(t)$, where $t$ denotes the maximal number of vertices
+of a curve. For the continuous \Frechet distance, the number of levels
+increases to $O(t^2)$.
+",1,0,0,0,0,0
+15951,Topic Compositional Neural Language Model,"  We propose a Topic Compositional Neural Language Model (TCNLM), a novel
+method designed to simultaneously capture both the global semantic meaning and
+the local word ordering structure in a document. The TCNLM learns the global
+semantic coherence of a document via a neural topic model, and the probability
+of each learned latent topic is further used to build a Mixture-of-Experts
+(MoE) language model, where each expert (corresponding to one topic) is a
+recurrent neural network (RNN) that accounts for learning the local structure
+of a word sequence. In order to train the MoE model efficiently, a matrix
+factorization method is applied, by extending each weight matrix of the RNN to
+be an ensemble of topic-dependent weight matrices. The degree to which each
+member of the ensemble is used is tied to the document-dependent probability of
+the corresponding topics. Experimental results on several corpora show that the
+proposed approach outperforms both a pure RNN-based model and other
+topic-guided language models. Further, our model yields sensible topics, and
+also has the capacity to generate meaningful sentences conditioned on given
+topics.
+",1,0,0,0,0,0
+15952,Transmission spectra and valley processing of graphene and carbon nanotube superlattices with inter-valley coupling,"  We numerically investigate the electronic transport properties of graphene
+nanoribbons and carbon nanotubes with inter-valley coupling, e.g., in \sqrt{3}N
+\times \sqrt{3}N and 3N \times 3N superlattices. By taking the \sqrt{3} \times
+\sqrt{3} graphene superlattice as an example, we show that tailoring the bulk
+graphene superlattice results in rich structural configurations of nanoribbons
+and nanotubes. After studying the electronic characteristics of the
+corresponding armchair and zigzag nanoribbon geometries, we find that the
+linear bands of carbon nanotubes can lead to the Klein tunnelling-like
+phenomenon, i.e., electrons propagate along tubes without backscattering even
+in the presence of a barrier. Due to the coupling between K and K' valleys of
+pristine graphene by \sqrt{3} \times \sqrt{3} supercells,we propose a
+valley-field-effect transistor based on the armchair carbon nanotube, where the
+valley polarization of the current can be tuned by applying a gate voltage or
+varying the length of the armchair carbon nanotubes.
+",0,1,0,0,0,0
+15953,Parametric Adversarial Divergences are Good Task Losses for Generative Modeling,"  Generative modeling of high dimensional data like images is a notoriously
+difficult and ill-defined problem. In particular, how to evaluate a learned
+generative model is unclear. In this position paper, we argue that adversarial
+learning, pioneered with generative adversarial networks (GANs), provides an
+interesting framework to implicitly define more meaningful task losses for
+generative modeling tasks, such as for generating ""visually realistic"" images.
+We refer to those task losses as parametric adversarial divergences and we give
+two main reasons why we think parametric divergences are good learning
+objectives for generative modeling. Additionally, we unify the processes of
+choosing a good structured loss (in structured prediction) and choosing a
+discriminator architecture (in generative modeling) using statistical decision
+theory; we are then able to formalize and quantify the intuition that ""weaker""
+losses are easier to learn from, in a specific setting. Finally, we propose two
+new challenging tasks to evaluate parametric and nonparametric divergences: a
+qualitative task of generating very high-resolution digits, and a quantitative
+task of learning data that satisfies high-level algebraic constraints. We use
+two common divergences to train a generator and show that the parametric
+divergence outperforms the nonparametric divergence on both the qualitative and
+the quantitative task.
+",1,0,0,1,0,0
+15954,Boundedness and homogeneous asymptotics for a fractional logistic Keller-Segel equations,"  In this paper we consider a $d$-dimensional ($d=1,2$) parabolic-elliptic
+Keller-Segel equation with a logistic forcing and a fractional diffusion of
+order $\alpha \in (0,2)$. We prove uniform in time boundedness of its solution
+in the supercritical range $\alpha>d\left(1-c\right)$, where $c$ is an explicit
+constant depending on parameters of our problem. Furthermore, we establish
+sufficient conditions for $\|u(t)-u_\infty\|_{L^\infty}\rightarrow0$, where
+$u_\infty\equiv 1$ is the only nontrivial homogeneous solution. Finally, we
+provide a uniqueness result.
+",0,0,1,0,0,0
+15955,"Explicit formulas, symmetry and symmetry breaking for Willmore surfaces of revolution","  In this paper we prove explicit formulas for all Willmore surfaces of
+revolution and demonstrate their use in the discussion of the associated
+Dirichlet boundary value problems. It is shown by an explicit example that
+symmetric Dirichlet boundary conditions do in general not entail the symmetry
+of the surface. In addition we prove a symmetry result for a subclass of
+Willmore surfaces satisfying symmetric Dirichlet boundary data.
+",0,0,1,0,0,0
+15956,Dynamic Task Allocation for Crowdsourcing Settings,"  We consider the problem of optimal budget allocation for crowdsourcing
+problems, allocating users to tasks to maximize our final confidence in the
+crowdsourced answers. Such an optimized worker assignment method allows us to
+boost the efficacy of any popular crowdsourcing estimation algorithm. We
+consider a mutual information interpretation of the crowdsourcing problem,
+which leads to a stochastic subset selection problem with a submodular
+objective function. We present experimental simulation results which
+demonstrate the effectiveness of our dynamic task allocation method for
+achieving higher accuracy, possibly requiring fewer labels, as well as
+improving upon a previous method which is sensitive to the proportion of users
+to questions.
+",1,0,0,1,0,0
+15957,From optimal transport to generative modeling: the VEGAN cookbook,"  We study unsupervised generative modeling in terms of the optimal transport
+(OT) problem between true (but unknown) data distribution $P_X$ and the latent
+variable model distribution $P_G$. We show that the OT problem can be
+equivalently written in terms of probabilistic encoders, which are constrained
+to match the posterior and prior distributions over the latent space. When
+relaxed, this constrained optimization problem leads to a penalized optimal
+transport (POT) objective, which can be efficiently minimized using stochastic
+gradient descent by sampling from $P_X$ and $P_G$. We show that POT for the
+2-Wasserstein distance coincides with the objective heuristically employed in
+adversarial auto-encoders (AAE) (Makhzani et al., 2016), which provides the
+first theoretical justification for AAEs known to the authors. We also compare
+POT to other popular techniques like variational auto-encoders (VAE) (Kingma
+and Welling, 2014). Our theoretical results include (a) a better understanding
+of the commonly observed blurriness of images generated by VAEs, and (b)
+establishing duality between Wasserstein GAN (Arjovsky and Bottou, 2017) and
+POT for the 1-Wasserstein distance.
+",0,0,0,1,0,0
+15958,The Gauss map of a free boundary minimal surface,"  In this paper, we study the Gauss map of a free boundary minimal surface. The
+main theorem asserts that if components of the Gauss map are eigenfunctions of
+the Jacobi-Steklov operator, then the surface must be rotationally symmetric.
+",0,0,1,0,0,0
+15959,Computational Experiments on $a^4+b^4+c^4+d^4=(a+b+c+d)^4$,"  Computational approaches to finding non-trivial integer solutions of the
+equation in the title are discussed. We summarize previous work and provide
+several new solutions.
+",0,0,1,0,0,0
+15960,An information theoretic approach to the autoencoder,"  We present a variation of the Autoencoder (AE) that explicitly maximizes the
+mutual information between the input data and the hidden representation. The
+proposed model, the InfoMax Autoencoder (IMAE), by construction is able to
+learn a robust representation and good prototypes of the data. IMAE is compared
+both theoretically and then computationally with the state of the art models:
+the Denoising and Contractive Autoencoders in the one-hidden layer setting and
+the Variational Autoencoder in the multi-layer case. Computational experiments
+are performed with the MNIST and Fashion-MNIST datasets and demonstrate
+particularly the strong clusterization performance of IMAE.
+",1,0,0,1,0,0
+15961,A Certified-Complete Bimanual Manipulation Planner,"  Planning motions for two robot arms to move an object collaboratively is a
+difficult problem, mainly because of the closed-chain constraint, which arises
+whenever two robot hands simultaneously grasp a single rigid object. In this
+paper, we propose a manipulation planning algorithm to bring an object from an
+initial stable placement (position and orientation of the object on the support
+surface) towards a goal stable placement. The key specificity of our algorithm
+is that it is certified-complete: for a given object and a given environment,
+we provide a certificate that the algorithm will find a solution to any
+bimanual manipulation query in that environment whenever one exists. Moreover,
+the certificate is constructive: at run-time, it can be used to quickly find a
+solution to a given query. The algorithm is tested in software and hardware on
+a number of large pieces of furniture.
+",1,0,0,0,0,0
+15962,The Short Baseline Neutrino Oscillation Program at Fermilab,"  The Short-Baseline Neutrino (SBN) Program is a short-baseline neutrino
+oscillation experiment in the Booster Neutrino Beam-line (BNB) at Fermilab. It
+consists of three Liquid Argon Time Projection Chambers (LArTPCs) from the
+Short-Baseline Near Detector (SBND), Micro Booster Neutrino Experiment
+(MicroBooNE), and Imaging Cosmic And Rare Underground Signals (ICARUS)
+experiments. The SBN Program will definitively search for short-baseline
+neutrino oscillations in the 1 eV mass range, make precision neutrino-argon
+interaction measurements, and further develop the LArTPC technology. The
+physics program and current status of the program, and its constituent
+experiments, are presented.
+",0,1,0,0,0,0
+15963,Best Rank-One Tensor Approximation and Parallel Update Algorithm for CPD,"  A novel algorithm is proposed for CANDECOMP/PARAFAC tensor decomposition to
+exploit best rank-1 tensor approximation. Different from the existing
+algorithms, our algorithm updates rank-1 tensors simultaneously in parallel. In
+order to achieve this, we develop new all-at-once algorithms for best rank-1
+tensor approximation based on the Levenberg-Marquardt method and the rotational
+update. We show that the LM algorithm has the same complexity of first-order
+optimisation algorithms, while the rotational method leads to solving the best
+rank-1 approximation of tensors of size $2 \times 2 \times \cdots \times 2$. We
+derive a closed-form expression of the best rank-1 tensor of $2\times 2 \times
+2$ tensors and present an ALS algorithm which updates 3 component at a time for
+higher order tensors. The proposed algorithm is illustrated in decomposition of
+difficult tensors which are associated with multiplication of two matrices.
+",1,0,0,0,0,0
+15964,"Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling","  Testing (conditional) independence of multivariate random variables is a task
+central to statistical inference and modelling in general - though
+unfortunately one for which to date there does not exist a practicable
+workflow. State-of-art workflows suffer from the need for heuristic or
+subjective manual choices, high computational complexity, or strong parametric
+assumptions.
+We address these problems by establishing a theoretical link between
+multivariate/conditional independence testing, and model comparison in the
+multivariate predictive modelling aka supervised learning task. This link
+allows advances in the extensively studied supervised learning workflow to be
+directly transferred to independence testing workflows - including automated
+tuning of machine learning type which addresses the need for a heuristic
+choice, the ability to quantitatively trade-off computational demand with
+accuracy, and the modern black-box philosophy for checking and interfacing.
+As a practical implementation of this link between the two workflows, we
+present a python package 'pcit', which implements our novel multivariate and
+conditional independence tests, interfacing the supervised learning API of the
+scikit-learn package. Theory and package also allow for straightforward
+independence test based learning of graphical model structure.
+We empirically show that our proposed predictive independence test outperform
+or are on par to current practice, and the derived graphical model structure
+learning algorithms asymptotically recover the 'true' graph. This paper, and
+the 'pcit' package accompanying it, thus provide powerful, scalable,
+generalizable, and easy-to-use methods for multivariate and conditional
+independence testing, as well as for graphical model structure learning.
+",1,0,1,1,0,0
+15965,Sharp Minima Can Generalize For Deep Nets,"  Despite their overwhelming capacity to overfit, deep learning architectures
+tend to generalize relatively well to unseen data, allowing them to be deployed
+in practice. However, explaining why this is the case is still an open area of
+research. One standing hypothesis that is gaining popularity, e.g. Hochreiter &
+Schmidhuber (1997); Keskar et al. (2017), is that the flatness of minima of the
+loss function found by stochastic gradient based methods results in good
+generalization. This paper argues that most notions of flatness are problematic
+for deep models and can not be directly applied to explain generalization.
+Specifically, when focusing on deep networks with rectifier units, we can
+exploit the particular geometry of parameter space induced by the inherent
+symmetries that these architectures exhibit to build equivalent models
+corresponding to arbitrarily sharper minima. Furthermore, if we allow to
+reparametrize a function, the geometry of its parameters can change drastically
+without affecting its generalization properties.
+",1,0,0,0,0,0
+15966,Coarse-grained model of the J-integral of carbon nanotube reinforced polymer composites,"  The J-integral is recognized as a fundamental parameter in fracture mechanics
+that characterizes the inherent resistance of materials to crack growth.
+However, the conventional methods to calculate the J-integral, which require
+knowledge of the exact position of a crack tip and the continuum fields around
+it, are unable to precisely measure the J-integral of polymer composites at the
+nanoscale. This work aims to propose an effective calculation method based on
+coarse-grained (CG) simulations for predicting the J-integral of carbon
+nanotube (CNT)/polymer composites. In the proposed approach, the J-integral is
+determined from the load displacement curve of a single specimen. The
+distinguishing feature of the method is the calculation of J-integral without
+need of information about the crack tip, which makes it applicable to complex
+polymer systems. The effects of the CNT weight fraction and covalent
+cross-links between the polymer matrix and nanotubes, and polymer chains on the
+fracture behavior of the composites are studied in detail. The dependence of
+the J-integral on the crack length and the size of representative volume
+element (RVE) is also explored.
+",0,1,0,0,0,0
+15967,On Estimating Multi-Attribute Choice Preferences using Private Signals and Matrix Factorization,"  Revealed preference theory studies the possibility of modeling an agent's
+revealed preferences and the construction of a consistent utility function.
+However, modeling agent's choices over preference orderings is not always
+practical and demands strong assumptions on human rationality and
+data-acquisition abilities. Therefore, we propose a simple generative choice
+model where agents are assumed to generate the choice probabilities based on
+latent factor matrices that capture their choice evaluation across multiple
+attributes. Since the multi-attribute evaluation is typically hidden within the
+agent's psyche, we consider a signaling mechanism where agents are provided
+with choice information through private signals, so that the agent's choices
+provide more insight about his/her latent evaluation across multiple
+attributes. We estimate the choice model via a novel multi-stage matrix
+factorization algorithm that minimizes the average deviation of the factor
+estimates from choice data. Simulation results are presented to validate the
+estimation performance of our proposed algorithm.
+",0,0,0,1,0,0
+15968,"Analysis of evolutionary origins of genomic loci harboring 59,732 candidate human-specific regulatory sequences identifies genetic divergence patterns during evolution of Great Apes","  Our view of the universe of genomic regions harboring various types of
+candidate human-specific regulatory sequences (HSRS) has been markedly expanded
+in recent years. To infer the evolutionary origins of loci harboring HSRS,
+analyses of conservations patterns of 59,732 loci in Modern Humans, Chimpanzee,
+Bonobo, Gorilla, Orangutan, Gibbon, and Rhesus genomes have been performed. Two
+major evolutionary pathways have been identified comprising thousands of
+sequences that were either inherited from extinct common ancestors (ECAs) or
+created de novo in humans after human/chimpanzee split. Thousands of HSRS
+appear inherited from ECAs yet bypassed genomes of our closest evolutionary
+relatives, presumably due to the incomplete lineage sorting and/or
+species-specific loss or regulatory DNA. The bypassing pattern is prominent for
+HSRS associated with development and functions of human brain. Common genomic
+loci that may contributed to speciation during evolution of Great Apes comprise
+248 insertions sites of African Great Ape-specific retrovirus PtERV1 (45.9%; p
+= 1.03E-44) intersecting regions harboring 442 HSRS, which are enriched for
+HSRS associated with human-specific (HS) changes of gene expression in cerebral
+organoids. Among non-human primates (NHP), most significant fractions of
+candidate HSRS associated with HS expression changes in both excitatory neurons
+(347 loci; 67%) and radial glia (683 loci; 72%) are highly conserved in Gorilla
+genome. Modern Humans acquired unique combinations of regulatory sequences
+highly conserved in distinct species of six NHP separated by 30 million years
+of evolution. Concurrently, this unique mosaic of regulatory sequences
+inherited from ECAs was supplemented with 12,486 created de novo HSRS. These
+observations support the model of complex continuous speciation process during
+evolution of Great Apes that is not likely to occur as an instantaneous event.
+",0,0,0,0,1,0
+15969,Verification of operational solar flare forecast: Case of Regional Warning Center Japan,"  In this article, we discuss a verification study of an operational solar
+flare forecast in the Regional Warning Center (RWC) Japan. The RWC Japan has
+been issuing four-categorical deterministic solar flare forecasts for a long
+time. In this forecast verification study, we used solar flare forecast data
+accumulated over 16 years (from 2000 to 2015). We compiled the forecast data
+together with solar flare data obtained with the Geostationary Operational
+Environmental Satellites (GOES). Using the compiled data sets, we estimated
+some conventional scalar verification measures with 95% confidence intervals.
+We also estimated a multi-categorical scalar verification measure. These scalar
+verification measures were compared with those obtained by the persistence
+method and recurrence method. As solar activity varied during the 16 years, we
+also applied verification analyses to four subsets of forecast-observation pair
+data with different solar activity levels. We cannot conclude definitely that
+there are significant performance difference between the forecasts of RWC Japan
+and the persistence method, although a slightly significant difference is found
+for some event definitions. We propose to use a scalar verification measure to
+assess the judgment skill of the operational solar flare forecast. Finally, we
+propose a verification strategy for deterministic operational solar flare
+forecasting.
+",0,1,0,1,0,0
+15970,Conformational dynamics of a single protein monitored for 24 hours at video rate,"  We use plasmon rulers to follow the conformational dynamics of a single
+protein for up to 24 h at a video rate. The plasmon ruler consists of two gold
+nanospheres connected by a single protein linker. In our experiment, we follow
+the dynamics of the molecular chaperone heat shock protein 90, which is known
+to show open and closed conformations. Our measurements confirm the previously
+known conformational dynamics with transition times in the second to minute
+time scale and reveals new dynamics on the time scale of minutes to hours.
+Plasmon rulers thus extend the observation bandwidth 3/4 orders of magnitude
+with respect to single-molecule fluorescence resonance energy transfer and
+enable the study of molecular dynamics with unprecedented precision.
+",0,0,0,0,1,0
+15971,Dynamics of domain walls in weak ferromagnets,"  It is shown that the total set of equations, which determines the dynamics of
+the domain bounds (DB) in a weak ferromagnet, has the same type of specific
+solution as the well-known Walker's solution for ferromagnets. We calculated
+the functional dependence of the velocity of the DB on the magnetic field,
+which is described by the obtained solution. This function has a maximum at a
+finite field and a section of the negative differential mobility of the DB.
+According to the calculation, the maximum velocity $ c \approx 2 \times 10^6$
+cm/sec in YFeO$_3$ is reached at $H_m \approx 4 \times 10^3$ Oe.
+",0,1,0,0,0,0
+15972,Generalized Short Circuit Ratio for Multi Power Electronic based Devices Infeed Systems: Defi-nition and Theoretical Analysis,"  Short circuit ratio (SCR) is widely applied to analyze the strength of AC
+system and the small signal stability for single power elec-tronic based
+devices infeed systems (SPEISs). However, there still lacking the theory of
+short circuit ratio applicable for multi power electronic based devices infeed
+systems (MPEIS), as the complex coupling among multi power electronic devices
+(PEDs) leads to difficulties in stability analysis. In this regard, this paper
+firstly proposes a concept named generalized short circuit ratio (gSCR) to
+measure the strength of connected AC grid in a multi-infeed system from the
+small signal stability point of view. Generally, the gSCR is physically and
+mathematically extended from conven-tional SCR by decomposing the multi-infeed
+system into n inde-pendent single infeed systems. Then the operation gSCR
+(OgSCR) is proposed based on gSCR in order to take the variation of op-eration
+point into consideration. The participation factors and sensitivity are
+analyzed as well. Finally, simulations are conducted to demonstrate the
+rationality and effectiveness of the defined gSCR and OgSCR.
+",1,0,0,0,0,0
+15973,A Correction Method of a Binary Classifier Applied to Multi-label Pairwise Models,"  In this work, we addressed the issue of applying a stochastic classifier and
+a local, fuzzy confusion matrix under the framework of multi-label
+classification. We proposed a novel solution to the problem of correcting label
+pairwise ensembles. The main step of the correction procedure is to compute
+classifier- specific competence and cross-competence measures, which estimates
+error pattern of the underlying classifier. We considered two improvements of
+the method of obtaining confusion matrices. The first one is aimed to deal with
+imbalanced labels. The other utilizes double labelled instances which are
+usually removed during the pairwise transformation. The proposed methods were
+evaluated using 29 benchmark datasets. In order to assess the efficiency of the
+introduced models, they were compared against 1 state-of-the-art approach and
+the correction scheme based on the original method of confusion matrix
+estimation. The comparison was performed using four different multi-label
+evaluation measures: macro and micro-averaged F1 loss, zero-one loss and
+Hamming loss. Additionally, we investigated relations between classification
+quality, which is expressed in terms of different quality criteria, and
+characteristics of multi-label datasets such as average imbalance ratio or
+label density. The experimental study reveals that the correction approaches
+significantly outperforms the reference method only in terms of zero-one loss.
+",1,0,0,1,0,0
+15974,Augment your batch: better training with larger batches,"  Large-batch SGD is important for scaling training of deep neural networks.
+However, without fine-tuning hyperparameter schedules, the generalization of
+the model may be hampered. We propose to use batch augmentation: replicating
+instances of samples within the same batch with different data augmentations.
+Batch augmentation acts as a regularizer and an accelerator, increasing both
+generalization and performance scaling. We analyze the effect of batch
+augmentation on gradient variance and show that it empirically improves
+convergence for a wide variety of deep neural networks and datasets. Our
+results show that batch augmentation reduces the number of necessary SGD
+updates to achieve the same accuracy as the state-of-the-art. Overall, this
+simple yet effective method enables faster training and better generalization
+by allowing more computational resources to be used concurrently.
+",1,0,0,1,0,0
+15975,"Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion and Blind Deconvolution","  Recent years have seen a flurry of activities in designing provably efficient
+nonconvex procedures for solving statistical estimation problems. Due to the
+highly nonconvex nature of the empirical loss, state-of-the-art procedures
+often require proper regularization (e.g. trimming, regularized cost,
+projection) in order to guarantee fast convergence. For vanilla procedures such
+as gradient descent, however, prior theory either recommends highly
+conservative learning rates to avoid overshooting, or completely lacks
+performance guarantees.
+This paper uncovers a striking phenomenon in nonconvex optimization: even in
+the absence of explicit regularization, gradient descent enforces proper
+regularization implicitly under various statistical models. In fact, gradient
+descent follows a trajectory staying within a basin that enjoys nice geometry,
+consisting of points incoherent with the sampling mechanism. This ""implicit
+regularization"" feature allows gradient descent to proceed in a far more
+aggressive fashion without overshooting, which in turn results in substantial
+computational savings. Focusing on three fundamental statistical estimation
+problems, i.e. phase retrieval, low-rank matrix completion, and blind
+deconvolution, we establish that gradient descent achieves near-optimal
+statistical and computational guarantees without explicit regularization. In
+particular, by marrying statistical modeling with generic optimization theory,
+we develop a general recipe for analyzing the trajectories of iterative
+algorithms via a leave-one-out perturbation argument. As a byproduct, for noisy
+matrix completion, we demonstrate that gradient descent achieves near-optimal
+error control --- measured entrywise and by the spectral norm --- which might
+be of independent interest.
+",1,0,1,1,0,0
+15976,The effect of surface tension on steadily translating bubbles in an unbounded Hele-Shaw cell,"  New numerical solutions to the so-called selection problem for one and two
+steadily translating bubbles in an unbounded Hele-Shaw cell are presented. Our
+approach relies on conformal mapping which, for the two-bubble problem,
+involves the Schottky-Klein prime function associated with an annulus. We show
+that a countably infinite number of solutions exist for each fixed value of
+dimensionless surface tension, with the bubble shapes becoming more exotic as
+the solution branch number increases. Our numerical results suggest that a
+single solution is selected in the limit that surface tension vanishes, with
+the scaling between the bubble velocity and surface tension being different to
+the well-studied problems for a bubble or a finger propagating in a channel
+geometry.
+",0,1,1,0,0,0
+15977,Core or cusps: The central dark matter profile of a redshift one strong lensing cluster with a bright central image,"  We report on SPT-CLJ2011-5228, a giant system of arcs created by a cluster at
+$z=1.06$. The arc system is notable for the presence of a bright central image.
+The source is a Lyman Break galaxy at $z_s=2.39$ and the mass enclosed within
+the 14 arc second radius Einstein ring is $10^{14.2}$ solar masses. We perform
+a full light profile reconstruction of the lensed images to precisely infer the
+parameters of the mass distribution. The brightness of the central image
+demands that the central total density profile of the lens be shallow. By
+fitting the dark matter as a generalized Navarro-Frenk-White profile---with a
+free parameter for the inner density slope---we find that the break radius is
+$270^{+48}_{-76}$ kpc, and that the inner density falls with radius to the
+power $-0.38\pm0.04$ at 68 percent confidence. Such a shallow profile is in
+strong tension with our understanding of relaxed cold dark matter halos; dark
+matter only simulations predict the inner density should fall as $r^{-1}$. The
+tension can be alleviated if this cluster is in fact a merger; a two halo model
+can also reconstruct the data, with both clumps (density going as $r^{-0.8}$
+and $r^{-1.0}$) much more consistent with predictions from dark matter only
+simulations. At the resolution of our Dark Energy Survey imaging, we are unable
+to choose between these two models, but we make predictions for forthcoming
+Hubble Space Telescope imaging that will decisively distinguish between them.
+",0,1,0,0,0,0
+15978,Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search,"  One of the most interesting features of Bayesian optimization for direct
+policy search is that it can leverage priors (e.g., from simulation or from
+previous tasks) to accelerate learning on a robot. In this paper, we are
+interested in situations for which several priors exist but we do not know in
+advance which one fits best the current situation. We tackle this problem by
+introducing a novel acquisition function, called Most Likely Expected
+Improvement (MLEI), that combines the likelihood of the priors and the expected
+improvement. We evaluate this new acquisition function on a transfer learning
+task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has
+to learn to walk on flat ground and on stairs, with priors corresponding to
+different stairs and different kinds of damages. Our results show that MLEI
+effectively identifies and exploits the priors, even when there is no obvious
+match between the current situations and the priors.
+",1,0,0,1,0,0
+15979,Coupled elliptic systems involving the square root of the Laplacian and Trudinger-Moser critical growth,"  In this paper we prove the existence of a nonnegative ground state solution
+to the following class of coupled systems involving Schrödinger equations
+with square root of the Laplacian
+$$
+\left\{
+\begin{array}{lr}
+(-\Delta)^{1/2}u+V_{1}(x)u=f_{1}(u)+\lambda(x)v, & x\in\mathbb{R},
+(-\Delta)^{1/2}v+V_{2}(x)v=f_{2}(v)+\lambda(x)u, & x\in\mathbb{R},
+\end{array}
+\right.
+$$
+where the nonlinearities $f_{1}(s)$ and $f_{2}(s)$ have exponential critical
+growth of the Trudinger-Moser type, the potentials $V_{1}(x)$ and $V_{2}(x)$
+are nonnegative and periodic. Moreover, we assume that there exists $\delta\in
+(0,1)$ such that $\lambda(x)\leq\delta\sqrt{V_{1}(x)V_{2}(x)}$. We are also
+concerned with the existence of ground states when the potentials are
+asymptotically periodic. Our approach is variational and based on minimization
+technique over the Nehari manifold.
+",0,0,1,0,0,0
+15980,Gravitational mass and energy gradient in the ultra-strong magnetic fields,"  The paper aims to apply the complex octonion to explore the influence of the
+energy gradient on the Eotvos experiment, impacting the gravitational mass in
+the ultra-strong magnetic fields. Until now the Eotvos experiment has never
+been validated under the ultra-strong magnetic field. It is aggravating the
+existing serious qualms about the Eotvos experiment. According to the
+electromagnetic and gravitational theory described with the complex octonions,
+the ultra-strong magnetic field must result in a tiny variation of the
+gravitational mass. The magnetic field with the gradient distribution will
+generate the energy gradient. These influencing factors will exert an influence
+on the state of equilibrium in the Eotvos experiment. That is, the
+gravitational mass will depart from the inertial mass to a certain extent, in
+the ultra-strong magnetic fields. Only under exceptional circumstances,
+especially in the case of the weak field strength, the gravitational mass may
+be equal to the inertial mass approximately. The paper appeals intensely to
+validate the Eotvos experiment in the ultra-strong electromagnetic strengths.
+It is predicted that the physical property of gravitational mass will be
+distinct from that of inertial mass.
+",0,1,0,0,0,0
+15981,Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations,"  Knowledge graphs are a versatile framework to encode richly structured data
+relationships, but it can be challenging to combine these graphs with
+unstructured data. Methods for retrofitting pre-trained entity representations
+to the structure of a knowledge graph typically assume that entities are
+embedded in a connected space and that relations imply similarity. However,
+useful knowledge graphs often contain diverse entities and relations (with
+potentially disjoint underlying corpora) which do not accord with these
+assumptions. To overcome these limitations, we present Functional Retrofitting,
+a framework that generalizes current retrofitting methods by explicitly
+modeling pairwise relations. Our framework can directly incorporate a variety
+of pairwise penalty functions previously developed for knowledge graph
+completion. Further, it allows users to encode, learn, and extract information
+about relation semantics. We present both linear and neural instantiations of
+the framework. Functional Retrofitting significantly outperforms existing
+retrofitting methods on complex knowledge graphs and loses no accuracy on
+simpler graphs (in which relations do imply similarity). Finally, we
+demonstrate the utility of the framework by predicting new drug--disease
+treatment pairs in a large, complex health knowledge graph.
+",1,0,0,1,0,0
+15982,Privacy-Preserving Economic Dispatch in Competitive Electricity Market,"  With the emerging of smart grid techniques, cyber attackers may be able to
+gain access to critical energy infrastructure data and strategic market
+participants may be able to identify offer prices of their rivals. This paper
+discusses a privacy-preserving economic dispatch approach in competitive
+electricity market, in which individual generation companies (GENCOs) and load
+serving entities (LSEs) can mask their actual bidding information and physical
+data by multiplying with random numbers before submitting to Independent System
+Operators (ISOs) and Regional Transmission Owners (RTOs). This would avoid
+potential information leakage of critical energy infrastructure and financial
+data of market participants. The optimal solution to the original ED problem,
+including optimal dispatches of generators and loads and locational marginal
+prices (LMPs), can be retrieved from the optimal solution of the proposed
+privacy-preserving ED approach. Numerical case studies show the effectiveness
+of the proposed approach for protecting private information of individual
+market participants while guaranteeing the same optimal ED solution.
+Computation and communication costs of the proposed privacy-preserving ED
+approach and the original ED are also compared in case studies.
+",0,0,1,0,0,0
+15983,On the estimation of the current density in space plasmas: multi versus single-point techniques,"  Thanks to multi-spacecraft mission, it has recently been possible to directly
+estimate the current density in space plasmas, by using magnetic field time
+series from four satellites flying in a quasi perfect tetrahedron
+configuration. The technique developed, commonly called 'curlometer' permits a
+good estimation of the current density when the magnetic field time series vary
+linearly in space. This approximation is generally valid for small spacecraft
+separation. The recent space missions Cluster and Magnetospheric Multiscale
+(MMS) have provided high resolution measurements with inter-spacecraft
+separation up to 100 km and 10 km, respectively. The former scale corresponds
+to the proton gyroradius/ion skin depth in 'typical' solar wind conditions,
+while the latter to sub-proton scale. However, some works have highlighted an
+underestimation of the current density via the curlometer technique with
+respect to the current computed directly from the velocity distribution
+functions, measured at sub-proton scales resolution with MMS. In this paper we
+explore the limit of the curlometer technique studying synthetic data sets
+associated to a cluster of four artificial satellites allowed to fly in a
+static turbulent field, spanning a wide range of relative separation. This
+study tries to address the relative importance of measuring plasma moments at
+very high resolution from a single spacecraft with respect to the
+multi-spacecraft missions in the current density evaluation.
+",0,1,0,0,0,0
+15984,Homological vanishing for the Steinberg representation,"  For a field $k$, we prove that the $i$th homology of the groups $GL_n(k)$,
+$SL_n(k)$, $Sp_{2n}(k)$, $SO_{n,n}(k)$, and $SO_{n,n+1}(k)$ with coefficients
+in their Steinberg representations vanish for $n \geq 2i+2$.
+",0,0,1,0,0,0
+15985,A tale of seven narrow spikes and a long trough: constraining the timing of the percolation of HII bubbles at the tail-end of reionization with ULAS J1120+0641,"  High-signal to noise observations of the Ly$\alpha$ forest transmissivity in
+the z = 7.085 QSO ULAS J1120+0641 show seven narrow transmission spikes
+followed by a long 240 cMpc/h trough. Here we use radiative transfer
+simulations of cosmic reionization previously calibrated to match a wider range
+of Ly$\alpha$ forest data to show that the occurrence of seven transmission
+spikes in the narrow redshift range z = 5.85 - 6.1 is very sensitive to the
+exact timing of reionization. Occurrence of the spikes requires the most under
+dense regions of the IGM to be already fully ionised. The rapid onset of a long
+trough at z = 6.12 requires a strong decrease of the photo-ionisation rate at
+z$\sim$6.1 in this line-of-sight, consistent with the end of percolation at
+this redshift. The narrow range of reionisation histories that we previously
+found to be consistent with a wider range of Ly$\alpha$ forest data have a
+reasonable probability of showing seven spikes and the mock absorption spectra
+provide an excellent match to the spikes and the trough in the observed
+spectrum of ULAS J1120+0641. Despite the large overall opacity of Ly$\alpha$ at
+z > 5.8, larger samples of high signal-to-noise observations of rare
+transmission spikes should therefore provide important further insights into
+the exact timing of the percolation of HII bubbles at the tail-end of
+reionization
+",0,1,0,0,0,0
+15986,Quantum spin fluctuations in the bulk insulating state of pure and Fe-doped SmB6,"  The intermediate-valence compound SmB6 is a well-known Kondo insulator, in
+which hybridization of itinerant 5d electrons with localized 4f electrons leads
+to a transition from metallic to insulating behavior at low temperatures.
+Recent studies suggest that SmB6 is a topological insulator, with topological
+metallic surface states emerging from a fully insulating hybridized bulk band
+structure. Here we locally probe the bulk magnetic properties of pure and 0.5 %
+Fe-doped SmB6 by muon spin rotation/relaxation methods. Below 6 K the Fe
+impurity induces simultaneous changes in the bulk local magnetism and the
+electrical conductivity. In the low-temperature insulating bulk state we
+observe a temperature-independent dynamic relaxation rate indicative of
+low-lying magnetic excitations driven primarily by quantum fluctuations.
+",0,1,0,0,0,0
+15987,Device-Aware Routing and Scheduling in Multi-Hop Device-to-Device Networks,"  The dramatic increase in data and connectivity demand, in addition to
+heterogeneous device capabilities, poses a challenge for future wireless
+networks. One of the promising solutions is Device-to-Device (D2D) networking.
+D2D networking, advocating the idea of connecting two or more devices directly
+without traversing the core network, is promising to address the increasing
+data and connectivity demand. In this paper, we consider D2D networks, where
+devices with heterogeneous capabilities including computing power, energy
+limitations, and incentives participate in D2D activities heterogeneously. We
+develop (i) a device-aware routing and scheduling algorithm (DARS) by taking
+into account device capabilities, and (ii) a multi-hop D2D testbed using
+Android-based smartphones and tablets by exploiting Wi-Fi Direct and legacy
+Wi-Fi connections. We show that DARS significantly improves throughput in our
+testbed as compared to state-of-the-art.
+",1,0,0,0,0,0
+15988,Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control,"  Techniques known as Nonlinear Set Membership prediction, Lipschitz
+Interpolation or Kinky Inference are approaches to machine learning that
+utilise presupposed Lipschitz properties to compute inferences over unobserved
+function values. Provided a bound on the true best Lipschitz constant of the
+target function is known a priori they offer convergence guarantees as well as
+bounds around the predictions. Considering a more general setting that builds
+on Hoelder continuity relative to pseudo-metrics, we propose an online method
+for estimating the Hoelder constant online from function value observations
+that possibly are corrupted by bounded observational errors. Utilising this to
+compute adaptive parameters within a kinky inference rule gives rise to a
+nonparametric machine learning method, for which we establish strong universal
+approximation guarantees. That is, we show that our prediction rule can learn
+any continuous function in the limit of increasingly dense data to within a
+worst-case error bound that depends on the level of observational uncertainty.
+We apply our method in the context of nonparametric model-reference adaptive
+control (MRAC). Across a range of simulated aircraft roll-dynamics and
+performance metrics our approach outperforms recently proposed alternatives
+that were based on Gaussian processes and RBF-neural networks. For
+discrete-time systems, we provide guarantees on the tracking success of our
+learning-based controllers both for the batch and the online learning setting.
+",1,0,1,1,0,0
+15989,Information Pursuit: A Bayesian Framework for Sequential Scene Parsing,"  Despite enormous progress in object detection and classification, the problem
+of incorporating expected contextual relationships among object instances into
+modern recognition systems remains a key challenge. In this work we propose
+Information Pursuit, a Bayesian framework for scene parsing that combines prior
+models for the geometry of the scene and the spatial arrangement of objects
+instances with a data model for the output of high-level image classifiers
+trained to answer specific questions about the scene. In the proposed
+framework, the scene interpretation is progressively refined as evidence
+accumulates from the answers to a sequence of questions. At each step, we
+choose the question to maximize the mutual information between the new answer
+and the full interpretation given the current evidence obtained from previous
+inquiries. We also propose a method for learning the parameters of the model
+from synthesized, annotated scenes obtained by top-down sampling from an
+easy-to-learn generative scene model. Finally, we introduce a database of
+annotated indoor scenes of dining room tables, which we use to evaluate the
+proposed approach.
+",1,0,0,1,0,0
+15990,Decentralized Clustering based on Robust Estimation and Hypothesis Testing,"  This paper considers a network of sensors without fusion center that may be
+difficult to set up in applications involving sensors embedded on autonomous
+drones or robots. In this context, this paper considers that the sensors must
+perform a given clustering task in a fully decentralized setup. Standard
+clustering algorithms usually need to know the number of clusters and are very
+sensitive to initialization, which makes them difficult to use in a fully
+decentralized setup. In this respect, this paper proposes a decentralized
+model-based clustering algorithm that overcomes these issues. The proposed
+algorithm is based on a novel theoretical framework that relies on hypothesis
+testing and robust M-estimation. More particularly, the problem of deciding
+whether two data belong to the same cluster can be optimally solved via Wald's
+hypothesis test on the mean of a Gaussian random vector. The p-value of this
+test makes it possible to define a new type of score function, particularly
+suitable for devising an M-estimation of the centroids. The resulting
+decentralized algorithm efficiently performs clustering without prior knowledge
+of the number of clusters. It also turns out to be less sensitive to
+initialization than the already existing clustering algorithms, which makes it
+appropriate for use in a network of sensors without fusion center.
+",0,0,1,1,0,0
+15991,Modeling open nanophotonic systems using the Fourier modal method: Generalization to 3D Cartesian coordinates,"  Recently, an open geometry Fourier modal method based on a new combination of
+an open boundary condition and a non-uniform $k$-space discretization was
+introduced for rotationally symmetric structures providing a more efficient
+approach for modeling nanowires and micropillar cavities [J. Opt. Soc. Am. A
+33, 1298 (2016)]. Here, we generalize the approach to three-dimensional (3D)
+Cartesian coordinates allowing for the modeling of rectangular geometries in
+open space. The open boundary condition is a consequence of having an infinite
+computational domain described using basis functions that expand the whole
+space. The strength of the method lies in discretizing the Fourier integrals
+using a non-uniform circular ""dartboard"" sampling of the Fourier $k$ space. We
+show that our sampling technique leads to a more accurate description of the
+continuum of the radiation modes that leak out from the structure. We also
+compare our approach to conventional discretization with direct and inverse
+factorization rules commonly used in established Fourier modal methods. We
+apply our method to a variety of optical waveguide structures and demonstrate
+that the method leads to a significantly improved convergence enabling more
+accurate and efficient modeling of open 3D nanophotonic structures.
+",0,1,0,0,0,0
+15992,Microscopic Conductivity of Lattice Fermions at Equilibrium - Part II: Interacting Particles,"  We apply Lieb-Robinson bounds for multi-commutators we recently derived to
+study the (possibly non-linear) response of interacting fermions at thermal
+equilibrium to perturbations of the external electromagnetic field. This
+analysis leads to an extension of the results for quasi-free fermions of
+\cite{OhmI,OhmII} to fermion systems on the lattice with short-range
+interactions. More precisely, we investigate entropy production and charge
+transport properties of non-autonomous $C^{\ast }$-dynamical systems associated
+with interacting lattice fermions within bounded static potentials and in
+presence of an electric field that is time- and space-dependent. We verify the
+1st law of thermodynamics for the heat production of the system under
+consideration. In linear response theory, the latter is related with Ohm and
+Joule's laws. These laws are proven here to hold at the microscopic scale,
+uniformly with respect to the size of the (microscopic) region where the
+electric field is applied. An important outcome is the extension of the notion
+of conductivity measures to interacting fermions.
+",0,0,1,0,0,0
+15993,Revealing the Unseen: How to Expose Cloud Usage While Protecting User Privacy,"  Cloud users have little visibility into the performance characteristics and
+utilization of the physical machines underpinning the virtualized cloud
+resources they use. This uncertainty forces users and researchers to reverse
+engineer the inner workings of cloud systems in order to understand and
+optimize the conditions their applications operate. At Massachusetts Open Cloud
+(MOC), as a public cloud operator, we'd like to expose the utilization of our
+physical infrastructure to stop this wasteful effort. Mindful that such
+exposure can be used maliciously for gaining insight into other users
+workloads, in this position paper we argue for the need for an approach that
+balances openness of the cloud overall with privacy for each tenant inside of
+it. We believe that this approach can be instantiated via a novel combination
+of several security and privacy technologies. We discuss the potential
+benefits, implications of transparency for cloud systems and users, and
+technical challenges/possibilities.
+",1,0,0,0,0,0
+15994,An hp-adaptive strategy for elliptic problems,"  In this paper a new hp-adaptive strategy for elliptic problems based on
+refinement history is proposed, which chooses h-, p- or hp-refinement on
+individual elements according to a posteriori error estimate, as well as
+smoothness estimate of the solution obtained by comparing the actual and
+expected error reduction rate. Numerical experiments show that exponential
+convergence can be achieved with this strategy.
+",1,0,1,0,0,0
+15995,High Radiation Pressure on Interstellar Dust Computed by Light-Scattering Simulation on Fluffy Agglomerates of Magnesium-silicate Grains with Metallic-iron Inclusions,"  Recent space missions have provided information on the physical and chemical
+properties of interstellar grains such as the ratio $\beta$ of radiation
+pressure to gravity acting on the grains in addition to the composition,
+structure, and size distribution of the grains. Numerical simulation on the
+trajectories of interstellar grains captured by Stardust and returned to Earth
+constrained the $\beta$ ratio for the Stardust samples of interstellar origin.
+However, recent accurate calculations of radiation pressure cross sections for
+model dust grains have given conflicting stories in the $\beta$ ratio of
+interstellar grains. The $\beta$ ratio for model dust grains of so-called
+""astronomical silicate"" in the femto-kilogram range lies below unity, in
+conflict with $\beta \sim 1$ for the Stardust interstellar grains. Here, I
+tackle this conundrum by re-evaluating the $\beta$ ratio of interstellar grains
+on the assumption that the grains are aggregated particles grown by coagulation
+and composed of amorphous MgSiO$_{3}$ with the inclusion of metallic iron. My
+model is entirely consistent with the depletion and the correlation of major
+rock-forming elements in the Local Interstellar Cloud surrounding the Sun and
+the mineralogical identification of interstellar grains in the Stardust and
+Cassini missions. I find that my model dust particles fulfill the constraints
+on the $\beta$ ratio derived from not only the Stardust mission but also the
+Ulysses and Cassini missions. My results suggest that iron is not incorporated
+into silicates but exists as metal, contrary to the majority of interstellar
+dust models available to date.
+",0,1,0,0,0,0
+15996,Unexpected Robustness of the Band Gaps of TiO2 under High Pressures,"  Titanium dioxide (TiO2) is a wide band gap semiconducting material which is
+promising for photocatalysis. Here we present first-principles calculations to
+study the pressure dependence of structural and electronic properties of two
+TiO2 phases: the cotunnite-type and the Fe2P-type structure. The band gaps are
+calculated using density functional theory (DFT) with the generalized gradient
+approximation (GGA), as well as the many-body perturbation theory with the GW
+approximation. The band gaps of both phases are found to be unexpectedly robust
+across a broad range pressures. The corresponding pressure coefficients are
+significantly smaller than that of diamond and silicon carbide (SiC), whose
+pressure coefficient is the smallest value ever measured by experiment. The
+robustness originates from the synchronous change of valence band maximum (VBM)
+and conduction band minimum (CBM) with nearly identical rates of changes. A
+step-like jump of band gaps around the phase transition pressure point is
+expected and understood in light of the difference in crystal structures.
+",0,1,0,0,0,0
+15997,Geometric GAN,"  Generative Adversarial Nets (GANs) represent an important milestone for
+effective generative models, which has inspired numerous variants seemingly
+different from each other. One of the main contributions of this paper is to
+reveal a unified geometric structure in GAN and its variants. Specifically, we
+show that the adversarial generative model training can be decomposed into
+three geometric steps: separating hyperplane search, discriminator parameter
+update away from the separating hyperplane, and the generator update along the
+normal vector direction of the separating hyperplane. This geometric intuition
+reveals the limitations of the existing approaches and leads us to propose a
+new formulation called geometric GAN using SVM separating hyperplane that
+maximizes the margin. Our theoretical analysis shows that the geometric GAN
+converges to a Nash equilibrium between the discriminator and generator. In
+addition, extensive numerical results show that the superior performance of
+geometric GAN.
+",1,1,0,1,0,0
+15998,A Searchable Symmetric Encryption Scheme using BlockChain,"  At present, the cloud storage used in searchable symmetric encryption schemes
+(SSE) is provided in a private way, which cannot be seen as a true cloud.
+Moreover, the cloud server is thought to be credible, because it always returns
+the search result to the user, even they are not correct. In order to really
+resist this malicious adversary and accelerate the usage of the data, it is
+necessary to store the data on a public chain, which can be seen as a
+decentralized system. As the increasing amount of the data, the search problem
+becomes more and more intractable, because there does not exist any effective
+solution at present.
+In this paper, we begin by pointing out the importance of storing the data in
+a public chain. We then innovatively construct a model of SSE using
+blockchain(SSE-using-BC) and give its security definition to ensure the privacy
+of the data and improve the search efficiency. According to the size of data,
+we consider two different cases and propose two corresponding schemes. Lastly,
+the security and performance analyses show that our scheme is feasible and
+secure.
+",1,0,0,0,0,0
+15999,Operando imaging of all-electric spin texture manipulation in ferroelectric and multiferroic Rashba semiconductors,"  The control of the electron spin by external means is a key issue for
+spintronic devices. Using spin- and angle-resolved photoemission spectroscopy
+(SARPES) with three-dimensional spin detection, we demonstrate operando
+electrostatic spin manipulation in ferroelectric GeTe and multiferroic
+Ge1-xMnxTe. We not only demonstrate for the first time electrostatic spin
+manipulation in Rashba semiconductors due to ferroelectric polarization
+reversal, but are also able to follow the switching pathway in detail, and show
+a gain of the Rashba-splitting strength under external fields. In multiferroic
+Ge1-xMnxTe operando SARPES reveals switching of the perpendicular spin
+component due to electric field induced magnetization reversal. This provides
+firm evidence of effective multiferroic coupling which opens up magnetoelectric
+functionality with a multitude of spin-switching paths in which the magnetic
+and electric order parameters are coupled through ferroelastic relaxation
+paths. This work thus provides a new type of magnetoelectric switching
+entangled with Rashba-Zeeman splitting in a multiferroic system.
+",0,1,0,0,0,0
+16000,Experimental Evidence for Selection Rules in Multiphoton Double Ionization of Helium,"  We report on the observation of phase space modulations in the correlated
+electron emission after strong field double ionization of helium using laser
+pulses with a wavelength of 394~nm and an intensity of $3\cdot10^{14}$W/cm$^2$.
+Those modulations are identified as direct results of quantum mechanical
+selection rules predicted by many theoretical calculations. They only occur for
+an odd number of absorbed photons. By that we attribute this effect to the
+parity of the continuum wave function.
+",0,1,0,0,0,0
+16001,Conformal growth rates and spectral geometry on distributional limits of graphs,"  For a unimodular random graph $(G,\rho)$, we consider deformations of its
+intrinsic path metric by a (random) weighting of its vertices. This leads to
+the notion of the {\em conformal growth exponent of $(G,\rho)$}, which is the
+best asymptotic degree of volume growth of balls that can be achieved by such a
+reweighting. Under moment conditions on the degree of the root, we show that
+the conformal growth exponent of a unimodular random graph bounds the almost
+sure spectral dimension.
+In two dimensions, one obtains more precise information. If $(G,\rho)$ has a
+property we call {\em quadratic conformal growth}, then the following holds: If
+the degree of the root is uniformly bounded almost surely, then $G$ is almost
+surely recurrent. Since limits of finite $H$-minor-free graphs have gauged
+quadratic conformal growth, such limits are almost surely recurrent; this
+affirms a conjecture of Benjamini and Schramm (2001). For the special case of
+planar graphs, this gives a proof of the Benjamini-Schramm Recurrence Theorem
+that does not proceed via the analysis of circle packings.
+Gurel-Gurevich and Nachmias (2013) resolved a central open problem by showing
+that the uniform infinite planar triangulation (UIPT) and quadrangulation
+(UIPQ) are almost surely recurrent. They proved that this holds for any
+distributional limit of planar graphs in which the degree of the root has
+exponential tails (which is known to hold for UIPT and UIPQ). We use the
+quadratic conformal growth property to give a new proof of this result that
+holds for distributional limits of finite $H$-minor-free graphs. Moreover, our
+arguments yield quantitative bounds on the heat kernel in terms of the degree
+distribution at the root. This also yields a new approach to subdiffusivity of
+the random walk on UIPT/UIPQ, using only the volume growth profile of balls in
+the intrinsic metric.
+",0,0,1,0,0,0
+16002,Self-Supervised Vision-Based Detection of the Active Speaker as a Prerequisite for Socially-Aware Language Acquisition,"  This paper presents a self-supervised method for detecting the active speaker
+in a multi-person spoken interaction scenario. We argue that this capability is
+a fundamental prerequisite for any artificial cognitive system attempting to
+acquire language in social settings. Our methods are able to detect an
+arbitrary number of possibly overlapping active speakers based exclusively on
+visual information about their face. Our methods do not rely on external
+annotations, thus complying with cognitive development. Instead, they use
+information from the auditory modality to support learning in the visual
+domain. The methods have been extensively evaluated on a large multi-person
+face-to-face interaction dataset. The results reach an accuracy of 80% on a
+multi-speaker setting. We believe this system represents an essential component
+of any artificial cognitive system or robotic platform engaging in social
+interaction.
+",1,0,0,1,0,0
+16003,Monocular Imaging-based Autonomous Tracking for Low-cost Quad-rotor Design - TraQuad,"  TraQuad is an autonomous tracking quadcopter capable of tracking any moving
+(or static) object like cars, humans, other drones or any other object
+on-the-go. This article describes the applications and advantages of TraQuad
+and the reduction in cost (to about 250$) that has been achieved so far using
+the hardware and software capabilities and our custom algorithms wherever
+needed. This description is backed by strong data and the research analyses
+which have been drawn out of extant information or conducted on own when
+necessary. This also describes the development of completely autonomous (even
+GPS is optional) low-cost drone which can act as a major platform for further
+developments in automation, transportation, reconnaissance and more. We
+describe our ROS Gazebo simulator and our STATUS algorithms which form the core
+of our development of our object tracking drone for generic purposes.
+",1,0,0,0,0,0
+16004,Persistence Diagrams with Linear Machine Learning Models,"  Persistence diagrams have been widely recognized as a compact descriptor for
+characterizing multiscale topological features in data. When many datasets are
+available, statistical features embedded in those persistence diagrams can be
+extracted by applying machine learnings. In particular, the ability for
+explicitly analyzing the inverse in the original data space from those
+statistical features of persistence diagrams is significantly important for
+practical applications. In this paper, we propose a unified method for the
+inverse analysis by combining linear machine learning models with persistence
+images. The method is applied to point clouds and cubical sets, showing the
+ability of the statistical inverse analysis and its advantages.
+",1,0,1,0,0,0
+16005,"Characterizing the number of coloured $m$-ary partitions modulo $m$, with and without gaps","  In a pair of recent papers, Andrews, Fraenkel and Sellers provide a complete
+characterization for the number of $m$-ary partitions modulo $m$, with and
+without gaps. In this paper we extend these results to the case of coloured
+$m$-ary partitions, with and without gaps. Our method of proof is different,
+giving explicit expansions for the generating functions modulo $m$
+",0,0,1,0,0,0
+16006,Towards Approximate Mobile Computing,"  Mobile computing is one of the main drivers of innovation, yet the future
+growth of mobile computing capabilities remains critically threatened by
+hardware constraints, such as the already extremely dense transistor packing
+and limited battery capacity. The breakdown of Dennard scaling and stagnating
+energy storage improvements further amplify these threats. However, the
+computational burden we put on our mobile devices is not always justified. In a
+myriad of situations the result of a computation is further manipulated,
+interpreted, and finally acted upon. This allows for the computation to be
+relaxed, so that the result is calculated with ""good enough"", not perfect
+accuracy. For example, results of a Web search may be perfectly acceptable even
+if the order of the last few listed items is shuffled, as an end user decides
+which of the available links to follow. Similarly, the quality of a
+voice-over-IP call may be acceptable, despite being imperfect, as long as the
+two involved parties can clearly understand each other. This novel way of
+thinking about computation is termed Approximate Computing (AC) and promises to
+reduce resource usage, while ensuring that satisfactory performance is
+delivered to end-users. AC is already experimented with on various levels of
+desktop computer architecture, from the hardware level where incorrect adders
+have been designed to sacrifice result correctness for reduced energy
+consumption, to compiler-level optimisations that omit certain lines of code to
+speed up video encoding. AC is yet to be attempted on mobile devices and in
+this article we examine the potential benefits of mobile AC and present an
+overview of AC techniques applicable in the mobile domain.
+",1,0,0,0,0,0
+16007,Room-temperature high detectivity mid-infrared photodetectors based on black arsenic phosphorus,"  The mid-infrared (MIR) spectral range, pertaining to important applications
+such as molecular 'fingerprint' imaging, remote sensing, free space
+telecommunication and optical radar, is of particular scientific interest and
+technological importance. However, state-of-the-art materials for MIR detection
+are limited by intrinsic noise and inconvenient fabrication processes,
+resulting in high cost photodetectors requiring cryogenic operation. We report
+black arsenic-phosphorus-based long wavelength infrared photodetectors with
+room temperature operation up to 8.2 um, entering the second MIR atmospheric
+transmission window. Combined with a van der Waals heterojunction, room
+temperature specific detectivity higher than 4.9*10^9 Jones was obtained in the
+3-5 um range. The photodetector works in a zero-bias photovoltaic mode,
+enabling fast photoresponse and low dark noise. Our van der Waals
+heterojunction photodector not only exemplify black arsenic-phosphorus as a
+promising candidate for MIR opto-electronic applications, but also pave the way
+for a general strategy to suppress 1/f noise in photonic devices.
+",0,1,0,0,0,0
+16008,Topology determines force distributions in one-dimensional random spring networks,"  Networks of elastic fibers are ubiquitous in biological systems and often
+provide mechanical stability to cells and tissues. Fiber reinforced materials
+are also common in technology. An important characteristic of such materials is
+their resistance to failure under load. Rupture occurs when fibers break under
+excessive force and when that failure propagates. Therefore it is crucial to
+understand force distributions. Force distributions within such networks are
+typically highly inhomogeneous and are not well understood. Here we construct a
+simple one-dimensional model system with periodic boundary conditions by
+randomly placing linear springs on a circle. We consider ensembles of such
+networks that consist of $N$ nodes and have an average degree of connectivity
+$z$, but vary in topology. Using a graph-theoretical approach that accounts for
+the full topology of each network in the ensemble, we show that, surprisingly,
+the force distributions can be fully characterized in terms of the parameters
+$(N,z)$. Despite the universal properties of such $(N,z)$-ensembles, our
+analysis further reveals that a classical mean-field approach fails to capture
+force distributions correctly. We demonstrate that network topology is a
+crucial determinant of force distributions in elastic spring networks.
+",0,1,0,0,0,0
+16009,On a local invariant of elliptic curves with a p-isogeny,"  An elliptic curve $E$ defined over a $p$-adic field $K$ with a $p$-isogeny
+$\phi:E\rightarrow E^\prime$ comes equipped with an invariant $\alpha_{\phi/K}$
+that measures the valuation of the leading term of the formal group
+homomorphism $\Phi:\hat E \rightarrow \hat E^\prime$. We prove that if
+$K/\mathbb{Q}_p$ is unramified and $E$ has additive, potentially supersingular
+reduction, then $\alpha_{\phi/K}$ is determined by the number of distinct
+geometric components on the special fibers of the minimal proper regular models
+of $E$ and $E^\prime$.
+",0,0,1,0,0,0
+16010,IIFA: Modular Inter-app Intent Information Flow Analysis of Android Applications,"  Android apps cooperate through message passing via intents. However, when
+apps do not have identical sets of privileges inter-app communication (IAC) can
+accidentally or maliciously be misused, e.g., to leak sensitive information
+contrary to users expectations. Recent research considered static program
+analysis to detect dangerous data leaks due to inter-component communication
+(ICC) or IAC, but suffers from shortcomings with respect to precision,
+soundness, and scalability. To solve these issues we propose a novel approach
+for static ICC/IAC analysis. We perform a fixed-point iteration of ICC/IAC
+summary information to precisely resolve intent communication with more than
+two apps involved. We integrate these results with information flows generated
+by a baseline (i.e. not considering intents) information flow analysis, and
+resolve if sensitive data is flowing (transitively) through components/apps in
+order to be ultimately leaked. Our main contribution is the first fully
+automatic sound and precise ICC/IAC information flow analysis that is scalable
+for realistic apps due to modularity, avoiding combinatorial explosion: Our
+approach determines communicating apps using short summaries rather than
+inlining intent calls, which often requires simultaneously analyzing all tuples
+of apps. We evaluated our tool IIFA in terms of scalability, precision, and
+recall. Using benchmarks we establish that precision and recall of our
+algorithm are considerably better than prominent state-of-the-art analyses for
+IAC. But foremost, applied to the 90 most popular applications from the Google
+Playstore, IIFA demonstrated its scalability to a large corpus of real-world
+apps. IIFA reports 62 problematic ICC-/IAC-related information flows via two or
+more apps/components.
+",1,0,0,0,0,0
+16011,Glass+Skin: An Empirical Evaluation of the Added Value of Finger Identification to Basic Single-Touch Interaction on Touch Screens,"  The usability of small devices such as smartphones or interactive watches is
+often hampered by the limited size of command vocabularies. This paper is an
+attempt at better understanding how finger identification may help users invoke
+commands on touch screens, even without recourse to multi-touch input. We
+describe how finger identification can increase the size of input vocabularies
+under the constraint of limited real estate, and we discuss some visual cues to
+communicate this novel modality to novice users. We report a controlled
+experiment that evaluated, over a large range of input-vocabulary sizes, the
+efficiency of single-touch command selections with vs. without finger
+identification. We analyzed the data not only in terms of traditional time and
+error metrics, but also in terms of a throughput measure based on Shannon's
+theory, which we show offers a synthetic and parsimonious account of users'
+performance. The results show that the larger the input vocabulary needed by
+the designer, the more promising the identification of individual fingers.
+",1,0,0,0,0,0
+16012,The Morphospace of Consciousness,"  We construct a complexity-based morphospace to study systems-level properties
+of conscious & intelligent systems. The axes of this space label 3 complexity
+types: autonomous, cognitive & social. Given recent proposals to synthesize
+consciousness, a generic complexity-based conceptualization provides a useful
+framework for identifying defining features of conscious & synthetic systems.
+Based on current clinical scales of consciousness that measure cognitive
+awareness and wakefulness, we take a perspective on how contemporary
+artificially intelligent machines & synthetically engineered life forms measure
+on these scales. It turns out that awareness & wakefulness can be associated to
+computational & autonomous complexity respectively. Subsequently, building on
+insights from cognitive robotics, we examine the function that consciousness
+serves, & argue the role of consciousness as an evolutionary game-theoretic
+strategy. This makes the case for a third type of complexity for describing
+consciousness: social complexity. Having identified these complexity types,
+allows for a representation of both, biological & synthetic systems in a common
+morphospace. A consequence of this classification is a taxonomy of possible
+conscious machines. We identify four types of consciousness, based on
+embodiment: (i) biological consciousness, (ii) synthetic consciousness, (iii)
+group consciousness (resulting from group interactions), & (iv) simulated
+consciousness (embodied by virtual agents within a simulated reality). This
+taxonomy helps in the investigation of comparative signatures of consciousness
+across domains, in order to highlight design principles necessary to engineer
+conscious machines. This is particularly relevant in the light of recent
+developments at the crossroads of cognitive neuroscience, biomedical
+engineering, artificial intelligence & biomimetics.
+",1,1,0,0,0,0
+16013,Transverse Magnetic Susceptibility of a Frustrated Spin-$\frac{1}{2}$ $J_{1}$--$J_{2}$--$J_{1}^{\perp}$ Heisenberg Antiferromagnet on a Bilayer Honeycomb Lattice,"  We use the coupled cluster method (CCM) to study a frustrated
+spin-$\frac{1}{2}$ $J_{1}$--$J_{2}$--$J_{1}^{\perp}$ Heisenberg antiferromagnet
+on a bilayer honeycomb lattice with $AA$ stacking. Both nearest-neighbor (NN)
+and frustrating next-nearest-neighbor antiferromagnetic (AFM) exchange
+interactions are present in each layer, with respective exchange coupling
+constants $J_{1}>0$ and $J_{2} \equiv \kappa J_{1} > 0$. The two layers are
+coupled with NN AFM exchanges with coupling strength $J_{1}^{\perp}\equiv
+\delta J_{1}>0$. We calculate to high orders of approximation within the CCM
+the zero-field transverse magnetic susceptibility $\chi$ in the Néel phase.
+We thus obtain an accurate estimate of the full boundary of the Néel phase in
+the $\kappa\delta$ plane for the zero-temperature quantum phase diagram. We
+demonstrate explicitly that the phase boundary derived from $\chi$ is fully
+consistent with that obtained from the vanishing of the Néel magnetic order
+parameter. We thus conclude that at all points along the Néel phase boundary
+quasiclassical magnetic order gives way to a nonclassical paramagnetic phase
+with a nonzero energy gap. The Néel phase boundary exhibits a marked
+reentrant behavior, which we discuss in detail.
+",0,1,0,0,0,0
+16014,Minimum polyhedron with $n$ vertices,"  We study a polyhedron with $n$ vertices of fixed volume having minimum
+surface area. Completing the proof of Toth, we show that all faces of a minimum
+polyhedron are triangles, and further prove that a minimum polyhedron does not
+allow deformation of a single vertex. We also present possible minimum shapes
+for $n\le 12$, some of them are quite unexpected, in particular $n=8$.
+",0,0,1,0,0,0
+16015,Theory of interacting fermions in shaken square optical lattice,"  We develop a theory of weakly interacting fermionic atoms in shaken optical
+lattices based on the orbital mixing in the presence of time-periodic
+modulations. Specifically, we focus on fermionic atoms in circularly shaken
+square lattice with near resonance frequencies, i.e., tuned close to the energy
+separation between $s$-band and the $p$-bands. First, we derive a
+time-independent four-band effective Hamiltonian in the non-interacting limit.
+Diagonalization of the effective Hamiltonian yields a quasi-energy spectrum
+consistent with the full numerical Floquet solution that includes all higher
+bands. In particular, we find that the hybridized $s$-band develops multiple
+minima and therefore non-trivial Fermi surfaces at different fillings. We then
+obtain the effective interactions for atoms in the hybridized $s$-band
+analytically and show that they acquire momentum dependence on the Fermi
+surface even though the bare interaction is contact-like. We apply the theory
+to find the phase diagram of fermions with weak attractive interactions and
+demonstrate that the pairing symmetry is $s+d$-wave. Our theory is valid for a
+range of shaking frequencies near resonance, and it can be generalized to other
+phases of interacting fermions in shaken lattices.
+",0,1,0,0,0,0
+16016,Simple to Complex Cross-modal Learning to Rank,"  The heterogeneity-gap between different modalities brings a significant
+challenge to multimedia information retrieval. Some studies formalize the
+cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
+embedding space to measure the cross-modality similarity. However, previous
+methods often establish the shared embedding space based on linear mapping
+functions which might not be sophisticated enough to reveal more complicated
+inter-modal correspondences. Additionally, current studies assume that the
+rankings are of equal importance, and thus all rankings are used
+simultaneously, or a small number of rankings are selected randomly to train
+the embedding space at each iteration. Such strategies, however, always suffer
+from outliers as well as reduced generalization capability due to their lack of
+insightful understanding of procedure of human cognition. In this paper, we
+involve the self-paced learning theory with diversity into the cross-modal
+learning to rank and learn an optimal multi-modal embedding space based on
+non-linear mapping functions. This strategy enhances the model's robustness to
+outliers and achieves better generalization via training the model gradually
+from easy rankings by diverse queries to more complex ones. An efficient
+alternative algorithm is exploited to solve the proposed challenging problem
+with fast convergence in practice. Extensive experimental results on several
+benchmark datasets indicate that the proposed method achieves significant
+improvements over the state-of-the-arts in this literature.
+",1,0,0,1,0,0
+16017,Iteration-complexity analysis of a generalized alternating direction method of multipliers,"  This paper analyzes the iteration-complexity of a generalized alternating
+direction method of multipliers (G-ADMM) for solving linearly constrained
+convex problems. This ADMM variant, which was first proposed by Bertsekas and
+Eckstein, introduces a relaxation parameter $\alpha \in (0,2)$ into the second
+ADMM subproblem. Our approach is to show that the G-ADMM is an instance of a
+hybrid proximal extragradient framework with some special properties, and, as a
+by product, we obtain ergodic iteration-complexity for the G-ADMM with
+$\alpha\in (0,2]$, improving and complementing related results in the
+literature. Additionally, we also present pointwise iteration-complexity for
+the G-ADMM.
+",0,0,1,0,0,0
+16018,A Game Theoretic Macroscopic Model of Bypassing at Traffic Diverges with Applications to Mixed Autonomy Networks,"  Vehicle bypassing is known to negatively affect delays at traffic diverges.
+However, due to the complexities of this phenomenon, accurate and yet simple
+models of such lane change maneuvers are hard to develop. In this work, we
+present a macroscopic model for predicting the number of vehicles that bypass
+at a traffic diverge. We take into account the selfishness of vehicles in
+selecting their lanes; every vehicle selects lanes such that its own cost is
+minimized. We discuss how we model the costs experienced by the vehicles. Then,
+taking into account the selfish behavior of the vehicles, we model the lane
+choice of vehicles at a traffic diverge as a Wardrop equilibrium. We state and
+prove the properties of Wardrop equilibrium in our model. We show that there
+always exists an equilibrium for our model. Moreover, unlike most nonlinear
+asymmetrical routing games, we prove that the equilibrium is unique under mild
+assumptions. We discuss how our model can be easily calibrated by running a
+simple optimization problem. Using our calibrated model, we validate it through
+simulation studies and demonstrate that our model successfully predicts the
+aggregate lane change maneuvers that are performed by vehicles for bypassing at
+a traffic diverge. We further discuss how our model can be employed to obtain
+the optimal lane choice behavior of the vehicles, where the social or total
+cost of vehicles is minimized. Finally, we demonstrate how our model can be
+utilized in scenarios where a central authority can dictate the lane choice and
+trajectory of certain vehicles so as to increase the overall vehicle mobility
+at a traffic diverge. Examples of such scenarios include the case when both
+human driven and autonomous vehicles coexist in the network. We show how
+certain decisions of the central authority can affect the total delays in such
+scenarios via an example.
+",1,0,0,0,0,0
+16019,Non-resonant secular dynamics of trans-Neptunian objects perturbed by a distant super-Earth,"  We use a secular model to describe the non-resonant dynamics of
+trans-Neptunian objects in the presence of an external ten-earth-mass
+perturber. The secular dynamics is analogous to an ""eccentric Kozai mechanism""
+but with both an inner component (the four giant planets) and an outer one (the
+eccentric distant perturber). By the means of Poincaré sections, the cases of
+a non-inclined or inclined outer planet are successively studied, making the
+connection with previous works. In the inclined case, the problem is reduced to
+two degrees of freedom by assuming a non-precessing argument of perihelion for
+the perturbing body.
+The size of the perturbation is typically ruled by the semi-major axis of the
+small body: we show that the classic integrable picture is still valid below
+about 70 AU, but it is progressively destroyed when we get closer to the
+external perturber. In particular, for a>150 AU, large-amplitude orbital flips
+become possible, and for a>200 AU, the Kozai libration islands are totally
+submerged by the chaotic sea. Numerous resonance relations are highlighted. The
+most large and persistent ones are associated to apsidal alignments or
+anti-alignments with the orbit of the distant perturber.
+",0,1,0,0,0,0
+16020,Stability Analysis for Switched Systems with Sequence-based Average Dwell Time,"  This note investigates the stability of both linear and nonlinear switched
+systems with average dwell time. Two new analysis methods are proposed.
+Different from existing approaches, the proposed methods take into account the
+sequence in which the subsystems are switched. Depending on the predecessor or
+successor subsystems to be considered, sequence-based average preceding dwell
+time (SBAPDT) and sequence-based average subsequence dwell time (SBASDT)
+approaches are proposed and discussed for both continuous and discrete time
+systems. These proposed methods, when considering the switch sequence, have the
+potential to further reduce the conservativeness of the existing approaches. A
+comparative numerical example is also given to demonstrate the advantages of
+the proposed approaches.
+",1,0,0,0,0,0
+16021,Interpreting and using CPDAGs with background knowledge,"  We develop terminology and methods for working with maximally oriented
+partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from
+imposing restrictions on a Markov equivalence class of directed acyclic graphs,
+or equivalently on its graphical representation as a completed partially
+directed acyclic graph (CPDAG), for example when adding background knowledge
+about certain edge orientations. Although maximal PDAGs often arise in
+practice, causal methods have been mostly developed for CPDAGs. In this paper,
+we extend such methodology to maximal PDAGs. In particular, we develop
+methodology to read off possible ancestral relationships, we introduce a
+graphical criterion for covariate adjustment to estimate total causal effects,
+and we adapt the IDA and joint-IDA frameworks to estimate multi-sets of
+possible causal effects. We also present a simulation study that illustrates
+the gain in identifiability of total causal effects as the background knowledge
+increases. All methods are implemented in the R package pcalg.
+",0,0,1,1,0,0
+16022,A cable-driven parallel manipulator with force sensing capabilities for high-accuracy tissue endomicroscopy,"  This paper introduces a new surgical end-effector probe, which allows to
+accurately apply a contact force on a tissue, while at the same time allowing
+for high resolution and highly repeatable probe movement. These are achieved by
+implementing a cable-driven parallel manipulator arrangement, which is deployed
+at the distal-end of a robotic instrument. The combination of the offered
+qualities can be advantageous in several ways, with possible applications
+including: large area endomicroscopy and multi-spectral imaging, micro-surgery,
+tissue palpation, safe energy-based and conventional tissue resection. To
+demonstrate the concept and its adaptability, the probe is integrated with a
+modified da Vinci robot instrument.
+",1,0,0,0,0,0
+16023,Overfitting Mechanism and Avoidance in Deep Neural Networks,"  Assisted by the availability of data and high performance computing, deep
+learning techniques have achieved breakthroughs and surpassed human performance
+empirically in difficult tasks, including object recognition, speech
+recognition, and natural language processing. As they are being used in
+critical applications, understanding underlying mechanisms for their successes
+and limitations is imperative. In this paper, we show that overfitting, one of
+the fundamental issues in deep neural networks, is due to continuous gradient
+updating and scale sensitiveness of cross entropy loss. By separating samples
+into correctly and incorrectly classified ones, we show that they behave very
+differently, where the loss decreases in the correct ones and increases in the
+incorrect ones. Furthermore, by analyzing dynamics during training, we propose
+a consensus-based classification algorithm that enables us to avoid overfitting
+and significantly improve the classification accuracy especially when the
+number of training samples is limited. As each trained neural network depends
+on extrinsic factors such as initial values as well as training data, requiring
+consensus among multiple models reduces extrinsic factors substantially; for
+statistically independent models, the reduction is exponential. Compared to
+ensemble algorithms, the proposed algorithm avoids overgeneralization by not
+classifying ambiguous inputs. Systematic experimental results demonstrate the
+effectiveness of the proposed algorithm. For example, using only 1000 training
+samples from MNIST dataset, the proposed algorithm achieves 95% accuracy,
+significantly higher than any of the individual models, with 90% of the test
+samples classified.
+",1,0,0,1,0,0
+16024,What deep learning can tell us about higher cognitive functions like mindreading?,"  Can deep learning (DL) guide our understanding of computations happening in
+biological brain? We will first briefly consider how DL has contributed to the
+research on visual object recognition. In the main part we will assess whether
+DL could also help us to clarify the computations underlying higher cognitive
+functions such as Theory of Mind. In addition, we will compare the objectives
+and learning signals of brains and machines, leading us to conclude that simply
+scaling up the current DL algorithms will not lead to human level mindreading
+skills. We then provide some insights about how to fairly compare human and DL
+performance. In the end we find that DL can contribute to our understanding of
+biological computations by providing an example of an end-to-end algorithm that
+solves the same problems the biological agents face.
+",0,0,0,0,1,0
+16025,Concentration of quantum states from quantum functional and transportation cost inequalities,"  Quantum functional inequalities (e.g. the logarithmic Sobolev- and Poincaré
+inequalities) have found widespread application in the study of the behavior of
+primitive quantum Markov semigroups. The classical counterparts of these
+inequalities are related to each other via a so-called transportation cost
+inequality of order 2 (TC2). The latter inequality relies on the notion of a
+metric on the set of probability distributions called the Wasserstein distance
+of order 2. (TC2) in turn implies a transportation cost inequality of order 1
+(TC1). In this paper, we introduce quantum generalizations of the inequalities
+(TC1) and (TC2), making use of appropriate quantum versions of the Wasserstein
+distances, one recently defined by Carlen and Maas and the other defined by us.
+We establish that these inequalities are related to each other, and to the
+quantum modified logarithmic Sobolev- and Poincaré inequalities, as in the
+classical case. We also show that these inequalities imply certain
+concentration-type results for the invariant state of the underlying semigroup.
+We consider the example of the depolarizing semigroup to derive concentration
+inequalities for any finite dimensional full-rank quantum state. These
+inequalities are then applied to derive upper bounds on the error probabilities
+occurring in the setting of finite blocklength quantum parameter estimation.
+",0,0,1,0,0,0
+16026,Stochastic Training of Neural Networks via Successive Convex Approximations,"  This paper proposes a new family of algorithms for training neural networks
+(NNs). These are based on recent developments in the field of non-convex
+optimization, going under the general name of successive convex approximation
+(SCA) techniques. The basic idea is to iteratively replace the original
+(non-convex, highly dimensional) learning problem with a sequence of (strongly
+convex) approximations, which are both accurate and simple to optimize.
+Differently from similar ideas (e.g., quasi-Newton algorithms), the
+approximations can be constructed using only first-order information of the
+neural network function, in a stochastic fashion, while exploiting the overall
+structure of the learning problem for a faster convergence. We discuss several
+use cases, based on different choices for the loss function (e.g., squared loss
+and cross-entropy loss), and for the regularization of the NN's weights. We
+experiment on several medium-sized benchmark problems, and on a large-scale
+dataset involving simulated physical data. The results show how the algorithm
+outperforms state-of-the-art techniques, providing faster convergence to a
+better minimum. Additionally, we show how the algorithm can be easily
+parallelized over multiple computational units without hindering its
+performance. In particular, each computational unit can optimize a tailored
+surrogate function defined on a randomly assigned subset of the input
+variables, whose dimension can be selected depending entirely on the available
+computational power.
+",1,0,0,1,0,0
+16027,Best arm identification in multi-armed bandits with delayed feedback,"  We propose a generalization of the best arm identification problem in
+stochastic multi-armed bandits (MAB) to the setting where every pull of an arm
+is associated with delayed feedback. The delay in feedback increases the
+effective sample complexity of standard algorithms, but can be offset if we
+have access to partial feedback received before a pull is completed. We propose
+a general framework to model the relationship between partial and delayed
+feedback, and as a special case we introduce efficient algorithms for settings
+where the partial feedback are biased or unbiased estimators of the delayed
+feedback. Additionally, we propose a novel extension of the algorithms to the
+parallel MAB setting where an agent can control a batch of arms. Our
+experiments in real-world settings, involving policy search and hyperparameter
+optimization in computational sustainability domains for fast charging of
+batteries and wildlife corridor construction, demonstrate that exploiting the
+structure of partial feedback can lead to significant improvements over
+baselines in both sequential and parallel MAB.
+",0,0,0,1,0,0
+16028,"General $(α, β)$ metrics with relatively isotroic mean Landsberg curvature","  In this paper, we study a new class of Finsler metrics, F=\alpha\phi(b^2,s),
+s:=\beta/\alpha, defined by a Riemannian metric \alpha and 1-form \beta. It is
+called general (\alpha, \beta) metric. In this paper, we assume \phi be
+coefficient by s and \beta be closed and conformal. We find a nessecary and
+sufficient condition for the metric of relatively isotropic mean Landsberg
+curvature to be Berwald.
+",0,0,1,0,0,0
+16029,Structured Optimal Transport,"  Optimal Transport has recently gained interest in machine learning for
+applications ranging from domain adaptation, sentence similarities to deep
+learning. Yet, its ability to capture frequently occurring structure beyond the
+""ground metric"" is limited. In this work, we develop a nonlinear generalization
+of (discrete) optimal transport that is able to reflect much additional
+structure. We demonstrate how to leverage the geometry of this new model for
+fast algorithms, and explore connections and properties. Illustrative
+experiments highlight the benefit of the induced structured couplings for tasks
+in domain adaptation and natural language processing.
+",1,0,0,1,0,0
+16030,Very metal-poor stars observed by the RAVE survey,"  We present a novel analysis of the metal-poor star sample in the complete
+Radial Velocity Experiment (RAVE) Data Release 5 catalog with the goal of
+identifying and characterizing all very metal-poor stars observed by the
+survey. Using a three-stage method, we first identified the candidate stars
+using only their spectra as input information. We employed an algorithm called
+t-SNE to construct a low-dimensional projection of the spectrum space and
+isolate the region containing metal-poor stars. Following this step, we
+measured the equivalent widths of the near-infrared CaII triplet lines with a
+method based on flexible Gaussian processes to model the correlated noise
+present in the spectra. In the last step, we constructed a calibration relation
+that converts the measured equivalent widths and the color information coming
+from the 2MASS and WISE surveys into metallicity and temperature estimates. We
+identified 877 stars with at least a 50% probability of being very metal-poor
+$(\rm [Fe/H] < -2\,\rm dex)$, out of which 43 are likely extremely metal-poor
+$(\rm [Fe/H] < -3\,\rm dex )$. The comparison of the derived values to a small
+subsample of stars with literature metallicity values shows that our method
+works reliably and correctly estimates the uncertainties, which typically have
+values $\sigma_{\rm [Fe/H]} \approx 0.2\,\mathrm{dex}$. In addition, when
+compared to the metallicity results derived using the RAVE DR5 pipeline, it is
+evident that we achieve better accuracy than the pipeline and therefore more
+reliably evaluate the very metal-poor subsample. Based on the repeated
+observations of the same stars, our method gives very consistent results. The
+method used in this work can also easily be extended to other large-scale data
+sets, including to the data from the Gaia mission and the upcoming 4MOST
+survey.
+",0,1,0,0,0,0
+16031,Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting,"  Recently, the intervention calculus when the DAG is absent (IDA) method was
+developed to estimate lower bounds of causal effects from observational
+high-dimensional data. Originally it was introduced to assess the effect of
+baseline biomarkers which do not vary over time. However, in many clinical
+settings, measurements of biomarkers are repeated at fixed time points during
+treatment exposure and, therefore, this method need to be extended. The purpose
+of this paper is then to extend the first step of the IDA, the Peter Clarks
+(PC)-algorithm, to a time-dependent exposure in the context of a binary
+outcome. We generalised the PC-algorithm for taking into account the
+chronological order of repeated measurements of the exposure and propose to
+apply the IDA with our new version, the chronologically ordered PC-algorithm
+(COPC-algorithm). A simulation study has been performed before applying the
+method for estimating causal effects of time-dependent immunological biomarkers
+on toxicity, death and progression in patients with metastatic melanoma. The
+simulation study showed that the completed partially directed acyclic graphs
+(CPDAGs) obtained using COPC-algorithm were structurally closer to the true
+CPDAG than CPDAGs obtained using PC-algorithm. Also, causal effects were more
+accurate when they were estimated based on CPDAGs obtained using
+COPC-algorithm. Moreover, CPDAGs obtained by COPC-algorithm allowed removing
+non-chronologic arrows with a variable measured at a time t pointing to a
+variable measured at a time t' where t'< t. Bidirected edges were less present
+in CPDAGs obtained with the COPC-algorithm, supporting the fact that there was
+less variability in causal effects estimated from these CPDAGs. The
+COPC-algorithm provided CPDAGs that keep the chronological structure present in
+the data, thus allowed to estimate lower bounds of the causal effect of
+time-dependent biomarkers.
+",0,0,0,1,0,0
+16032,A Note on Cyclotomic Integers,"  In this note, we present a new proof that the cyclotomic integers constitute
+the full ring of integers in the cyclotomic field.
+",0,0,1,0,0,0
+16033,Stochastic homogenization for functionals with anisotropic rescaling and non-coercive Hamilton-Jacobi equations,"  We study the stochastic homogenization for a Cauchy problem for a first-order
+Hamilton-Jacobi equation whose operator is not coercive w.r.t. the gradient
+variable. We look at Hamiltonians like $H(x,\sigma(x)p,\omega)$ where
+$\sigma(x)$ is a matrix associated to a Carnot group. The rescaling considered
+is consistent with the underlying Carnot group structure, thus anisotropic. We
+will prove that under suitable assumptions for the Hamiltonian, the solutions
+of the $\varepsilon$-problem converge to a deterministic function which can be
+characterized as the unique (viscosity) solution of a suitable deterministic
+Hamilton-Jacobi problem.
+",0,0,1,0,0,0
+16034,Automatic Software Repair: a Bibliography,"  This article presents a survey on automatic software repair. Automatic
+software repair consists of automatically finding a solution to software bugs
+without human intervention. This article considers all kinds of repairs. First,
+it discusses behavioral repair where test suites, contracts, models, and
+crashing inputs are taken as oracle. Second, it discusses state repair, also
+known as runtime repair or runtime recovery, with techniques such as checkpoint
+and restart, reconfiguration, and invariant restoration. The uniqueness of this
+article is that it spans the research communities that contribute to this body
+of knowledge: software engineering, dependability, operating systems,
+programming languages, and security. It provides a novel and structured
+overview of the diversity of bug oracles and repair operators used in the
+literature.
+",1,0,0,0,0,0
+16035,The design and the performance of stratospheric mission in the search for the Schumann resonances,"  The technical details of a balloon stratospheric mission that is aimed at
+measuring the Schumann resonances are described. The gondola is designed
+specifically for the measuring of faint effects of ELF (Extremely Low Frequency
+electromagnetic waves) phenomena. The prototype met the design requirements.
+The ELF measuring system worked properly for entire mission; however, the level
+of signal amplification that was chosen taking into account ground-level
+measurements was too high. Movement of the gondola in the Earth magnetic field
+induced the signal in the antenna that saturated the measuring system. This
+effect will be taken into account in the planning of future missions. A large
+telemetry dataset was gathered during the experiment and is currently under
+processing. The payload consists also of biological material as well as
+electronic equipment that was tested under extreme conditions.
+",0,1,0,0,0,0
+16036,A Systematic Comparison of Deep Learning Architectures in an Autonomous Vehicle,"  Self-driving technology is advancing rapidly --- albeit with significant
+challenges and limitations. This progress is largely due to recent developments
+in deep learning algorithms. To date, however, there has been no systematic
+comparison of how different deep learning architectures perform at such tasks,
+or an attempt to determine a correlation between classification performance and
+performance in an actual vehicle, a potentially critical factor in developing
+self-driving systems. Here, we introduce the first controlled comparison of
+multiple deep-learning architectures in an end-to-end autonomous driving task
+across multiple testing conditions. We compared performance, under identical
+driving conditions, across seven architectures including a fully-connected
+network, a simple 2 layer CNN, AlexNet, VGG-16, Inception-V3, ResNet, and an
+LSTM by assessing the number of laps each model was able to successfully
+complete without crashing while traversing an indoor racetrack. We compared
+performance across models when the conditions exactly matched those in training
+as well as when the local environment and track were configured differently and
+objects that were not included in the training dataset were placed on the track
+in various positions. In addition, we considered performance using several
+different data types for training and testing including single grayscale and
+color frames, and multiple grayscale frames stacked together in sequence. With
+the exception of a fully-connected network, all models performed reasonably
+well (around or above 80\%) and most very well (~95\%) on at least one input
+type but with considerable variation across models and inputs. Overall,
+AlexNet, operating on single color frames as input, achieved the best level of
+performance (100\% success rate in phase one and 55\% in phase two) while
+VGG-16 performed well most consistently across image types.
+",1,0,0,1,0,0
+16037,On the Theory of Light Propagation in Crystalline Dielectrics,"  A synoptic view on the long-established theory of light propagation in
+crystalline dielectrics is presented, providing a new exact solution for the
+microscopic local electromagnetic field thus disclosing the role of the
+divergence-free (transversal) and curl-free (longitudinal) parts of the
+electromagnetic field inside a material as a function of the density of
+polarizable atoms. Our results enable fast and efficient calculation of the
+photonic bandstructure and also the (non-local) dielectric tensor, solely with
+the crystalline symmetry and atom-individual polarizabilities as input.
+",0,1,0,0,0,0
+16038,Microfluidic control of nucleation and growth of calcite,"  The nucleation and growth of calcite is an important research in scientific
+and industrial field. Both the macroscopic and microscopic observation of
+calcite growth have been reported. Now, with the development of microfluidic
+device, we could focus the nucleation and growth of one single calcite. By
+changing the flow rate of fluid, the concentration of fluid is controlled. We
+introduced a new method to study calcite growth in situ and measured the growth
+rate of calcite in microfluidic channel.
+",0,1,0,0,0,0
+16039,Using low-frequency pulsar observations to study the 3-D structure of the Galactic magnetic field,"  The Galactic magnetic field (GMF) plays a role in many astrophysical
+processes and is a significant foreground to cosmological signals, such as the
+Epoch of Reionization (EoR), but is not yet well understood. Dispersion and
+Faraday rotation measurements (DMs and RMs, respectively) towards a large
+number of pulsars provide an efficient method to probe the three-dimensional
+structure of the GMF. Low-frequency polarisation observations with large
+fractional bandwidth can be used to measure precise DMs and RMs. This is
+demonstrated by a catalogue of RMs (corrected for ionospheric Faraday rotation)
+from the Low Frequency Array (LOFAR), with a growing complementary catalogue in
+the southern hemisphere from the Murchison Widefield Array (MWA). These data
+further our knowledge of the three-dimensional GMF, particularly towards the
+Galactic halo. Recently constructed or upgraded pathfinder and precursor
+telescopes, such as LOFAR and the MWA, have reinvigorated low-frequency science
+and represent progress towards the construction of the Square Kilometre Array
+(SKA), which will make significant advancements in studies of astrophysical
+magnetic fields in the future. A key science driver for the SKA-Low is to study
+the EoR, for which pulsar and polarisation data can provide valuable insights
+in terms of Galactic foreground conditions.
+",0,1,0,0,0,0
+16040,Simple mechanical cues could explain adipose tissue morphology,"  The mechanisms by which organs acquire their functional structure and realize
+its maintenance (or homeostasis) over time are still largely unknown. In this
+paper, we investigate this question on adipose tissue. Adipose tissue can
+represent 20 to 50% of the body weight. Its investigation is key to overcome a
+large array of metabolic disorders that heavily strike populations worldwide.
+Adipose tissue consists of lobular clusters of adipocytes surrounded by an
+organized collagen fiber network. By supplying substrates needed for
+adipogenesis, vasculature was believed to induce the regroupment of adipocytes
+near capillary extremities. This paper shows that the emergence of these
+structures could be explained by simple mechanical interactions between the
+adipocytes and the collagen fibers. Our assumption is that the fiber network
+resists the pressure induced by the growing adipocytes and forces them to
+regroup into clusters. Reciprocally, cell clusters force the fibers to merge
+into a well-organized network. We validate this hypothesis by means of a
+two-dimensional Individual Based Model (IBM) of interacting adipocytes and
+extra-cellular-matrix fiber elements. The model produces structures that
+compare quantitatively well to the experimental observations. Our model seems
+to indicate that cell clusters could spontaneously emerge as a result of simple
+mechanical interactions between cells and fibers and surprisingly, vasculature
+is not directly needed for these structures to emerge.
+",0,1,0,0,0,0
+16041,"Comments on the National Toxicology Program Report on Cancer, Rats and Cell Phone Radiation","  With the National Toxicology Program issuing its final report on cancer, rats
+and cell phone radiation, one can draw the following conclusions from their
+data. There is a roughly linear relationship between gliomas (brain cancers)
+and schwannomas (cancers of the nerve sheaths around the heart) with increased
+absorption of 900 MHz radiofrequency radiation for male rats. The rate of these
+cancers in female rats is about one third the rate in male rats; the rate of
+gliomas in female humans is about two thirds the rate in male humans. Both of
+these observations can be explained by a decrease in sensitivity to chemical
+carcinogenesis in both female rats and female humans. The increase in male rat
+life spans with increased radiofrequency absorption is due to a reduction in
+kidney failure from a decrease in food intake. No such similar increase in the
+life span of humans who use cell phones is expected.
+",0,0,0,0,1,0
+16042,Estimates for the coefficients of differential dimension polynomials,"  We answer the following long-standing question of Kolchin: given a system of
+algebraic-differential equations $\Sigma(x_1,\dots,x_n)=0$ in $m$ derivatives
+over a differential field of characteristic zero, is there a computable bound,
+that only depends on the order of the system (and on the fixed data $m$ and
+$n$), for the typical differential dimension of any prime component of
+$\Sigma$? We give a positive answer in a strong form; that is, we compute a
+(lower and upper) bound for all the coefficients of the Kolchin polynomial of
+every such prime component. We then show that, if we look at those components
+of a specified differential type, we can compute a significantly better bound
+for the typical differential dimension. This latter improvement comes from new
+combinatorial results on characteristic sets, in combination with the classical
+theorems of Macaulay and Gotzmann on the growth of Hilbert-Samuel functions.
+",0,0,1,0,0,0
+16043,Giant perpendicular exchange bias with antiferromagnetic MnN,"  We investigated an out-of-plane exchange bias system that is based on the
+antiferromagnet MnN. Polycrystalline, highly textured film stacks of Ta / MnN /
+CoFeB / MgO / Ta were grown on SiO$_x$ by (reactive) magnetron sputtering and
+studied by x-ray diffraction and Kerr magnetometry. Nontrivial modifications of
+the exchange bias and the perpendicular magnetic anisotropy were observed both
+as functions of film thicknesses as well as field cooling temperatures. In
+optimized film stacks, a giant perpendicular exchange bias of 3600 Oe and a
+coercive field of 350 Oe were observed at room temperature. The effective
+interfacial exchange energy is estimated to be $J_\mathrm{eff} = 0.24$ mJ/m$^2$
+and the effective uniaxial anisotropy constant of the antiferromagnet is
+$K_\mathrm{eff} = 24$ kJ/m$^3$. The maximum effective perpendicular anisotropy
+field of the CoFeB layer is $H_\mathrm{ani} = 3400$ Oe. These values are larger
+than any previously reported values. These results possibly open a route to
+magnetically stable, exchange biased perpendicularly magnetized spin valves.
+",0,1,0,0,0,0
+16044,A Fuzzy Community-Based Recommender System Using PageRank,"  Recommendation systems are widely used by different user service providers
+specially those who have interactions with the large community of users. This
+paper introduces a recommender system based on community detection. The
+recommendation is provided using the local and global similarities between
+users. The local information is obtained from communities, and the global ones
+are based on the ratings. Here, a new fuzzy community detection using the
+personalized PageRank metaphor is introduced. The fuzzy membership values of
+the users to the communities are utilized to define a similarity measure. The
+method is evaluated by using two well-known datasets: MovieLens and FilmTrust.
+The results show that our method outperforms recent recommender systems.
+",1,0,0,0,0,0
+16045,Foundations of Complex Event Processing,"  Complex Event Processing (CEP) has emerged as the unifying field for
+technologies that require processing and correlating distributed data sources
+in real-time. CEP finds applications in diverse domains, which has resulted in
+a large number of proposals for expressing and processing complex events.
+However, existing CEP languages lack from a clear semantics, making them hard
+to understand and generalize. Moreover, there are no general techniques for
+evaluating CEP query languages with clear performance guarantees.
+In this paper we embark on the task of giving a rigorous and efficient
+framework to CEP. We propose a formal language for specifying complex events,
+called CEL, that contains the main features used in the literature and has a
+denotational and compositional semantics. We also formalize the so-called
+selection strategies, which had only been presented as by-design extensions to
+existing frameworks. With a well-defined semantics at hand, we study how to
+efficiently evaluate CEL for processing complex events in the case of unary
+filters. We start by studying the syntactical properties of CEL and propose
+rewriting optimization techniques for simplifying the evaluation of formulas.
+Then, we introduce a formal computational model for CEP, called complex event
+automata (CEA), and study how to compile CEL formulas into CEA. Furthermore, we
+provide efficient algorithms for evaluating CEA over event streams using
+constant time per event followed by constant-delay enumeration of the results.
+By gathering these results together, we propose a framework for efficiently
+evaluating CEL with unary filters. Finally, we show experimentally that this
+framework consistently outperforms the competition, and even over trivial
+queries can be orders of magnitude more efficient.
+",1,0,0,0,0,0
+16046,Prior Variances and Depth Un-Biased Estimators in EEG Focal Source Imaging,"  In electroencephalography (EEG) source imaging, the inverse source estimates
+are depth biased in such a way that their maxima are often close to the
+sensors. This depth bias can be quantified by inspecting the statistics (mean
+and co-variance) of these estimates. In this paper, we find weighting factors
+within a Bayesian framework for the used L1/L2 sparsity prior that the
+resulting maximum a posterior (MAP) estimates do not favor any particular
+source location. Due to the lack of an analytical expression for the MAP
+estimate when this sparsity prior is used, we solve the weights indirectly.
+First, we calculate the Gaussian prior variances that lead to depth un-biased
+maximum a posterior (MAP) estimates. Subsequently, we approximate the
+corresponding weight factors in the sparsity prior based on the solved Gaussian
+prior variances. Finally, we reconstruct focal source configurations using the
+sparsity prior with the proposed weights and two other commonly used choices of
+weights that can be found in literature.
+",0,1,0,0,0,0
+16047,Standard Galactic Field RR Lyrae. I. Optical to Mid-infrared Phased Photometry,"  We present a multi-wavelength compilation of new and previously-published
+photometry for 55 Galactic field RR Lyrae variables. Individual studies,
+spanning a time baseline of up to 30 years, are self-consistently phased to
+produce light curves in 10 photometric bands covering the wavelength range from
+0.4 to 4.5 microns. Data smoothing via the GLOESS technique is described and
+applied to generate high-fidelity light curves, from which mean magnitudes,
+amplitudes, rise-times, and times of minimum and maximum light are derived.
+60,000 observations were acquired using the new robotic Three-hundred
+MilliMeter Telescope (TMMT), which was first deployed at the Carnegie
+Observatories in Pasadena, CA, and is now permanently installed and operating
+at Las Campanas Observatory in Chile. We provide a full description of the TMMT
+hardware, software, and data reduction pipeline. Archival photometry
+contributed approximately 31,000 observations. Photometric data are given in
+the standard Johnson UBV, Kron-Cousins RI, 2MASS JHK, and Spitzer [3.6] & [4.5]
+bandpasses.
+",0,1,0,0,0,0
+16048,An Updated Literature Review of Distance Correlation and its Applications to Time Series,"  The concept of distance covariance/correlation was introduced recently to
+characterize dependence among vectors of random variables. We review some
+statistical aspects of distance covariance/correlation function and we
+demonstrate its applicability to time series analysis. We will see that the
+auto-distance covariance/correlation function is able to identify nonlinear
+relationships and can be employed for testing the i.i.d.\ hypothesis.
+Comparisons with other measures of dependence are included.
+",0,0,0,1,0,0
+16049,A Novel Method for Extrinsic Calibration of Multiple RGB-D Cameras Using Descriptor-Based Patterns,"  This letter presents a novel method to estimate the relative poses between
+RGB-D cameras with minimal overlapping fields of view in a panoramic RGB-D
+camera system. This calibration problem is relevant to applications such as
+indoor 3D mapping and robot navigation that can benefit from a 360$^\circ$
+field of view using RGB-D cameras. The proposed approach relies on
+descriptor-based patterns to provide well-matched 2D keypoints in the case of a
+minimal overlapping field of view between cameras. Integrating the matched 2D
+keypoints with corresponding depth values, a set of 3D matched keypoints are
+constructed to calibrate multiple RGB-D cameras. Experiments validated the
+accuracy and efficiency of the proposed calibration approach, both superior to
+those of existing methods (800 ms vs. 5 seconds; rotation error of 0.56 degrees
+vs. 1.6 degrees; and translation error of 1.80 cm vs. 2.5 cm.
+",1,0,0,0,0,0
+16050,Particular type of gap in the spectrum of multiband superconductors,"  We show, that in contrast to the free electron model (standard BCS model), a
+particular gap in the spectrum of multiband superconductors opens at some
+distance from the Fermi energy, if conduction band is composed of hybridized
+atomic orbitals of different symmetries. This gap has composite
+superconducting-hybridization origin, because it exists only if both the
+superconductivity and the hybridization between different kinds of orbitals are
+present. So for many classes of superconductors with multiorbital structure
+such spectrum changes should take place. These particular changes in the
+spectrum at some distance from the Fermi level result in slow convergence of
+the spectral weight of the optical conductivity even in quite conventional
+superconductors with isotropic s-wave pairing mechanism.
+",0,1,0,0,0,0
+16051,Reliable Decision Support using Counterfactual Models,"  Decision-makers are faced with the challenge of estimating what is likely to
+happen when they take an action. For instance, if I choose not to treat this
+patient, are they likely to die? Practitioners commonly use supervised learning
+algorithms to fit predictive models that help decision-makers reason about
+likely future outcomes, but we show that this approach is unreliable, and
+sometimes even dangerous. The key issue is that supervised learning algorithms
+are highly sensitive to the policy used to choose actions in the training data,
+which causes the model to capture relationships that do not generalize. We
+propose using a different learning objective that predicts counterfactuals
+instead of predicting outcomes under an existing action policy as in supervised
+learning. To support decision-making in temporal settings, we introduce the
+Counterfactual Gaussian Process (CGP) to predict the counterfactual future
+progression of continuous-time trajectories under sequences of future actions.
+We demonstrate the benefits of the CGP on two important decision-support tasks:
+risk prediction and ""what if?"" reasoning for individualized treatment planning.
+",1,0,0,1,0,0
+16052,Robust temporal difference learning for critical domains,"  We present a new Q-function operator for temporal difference (TD) learning
+methods that explicitly encodes robustness against significant rare events
+(SRE) in critical domains. The operator, which we call the $\kappa$-operator,
+allows to learn a safe policy in a model-based fashion without actually
+observing the SRE. We introduce single- and multi-agent robust TD methods using
+the operator $\kappa$. We prove convergence of the operator to the optimal safe
+Q-function with respect to the model using the theory of Generalized Markov
+Decision Processes. In addition we prove convergence to the optimal Q-function
+of the original MDP given that the probability of SREs vanishes. Empirical
+evaluations demonstrate the superior performance of $\kappa$-based TD methods
+both in the early learning phase as well as in the final converged stage. In
+addition we show robustness of the proposed method to small model errors, as
+well as its applicability in a multi-agent context.
+",1,0,0,1,0,0
+16053,Structure of the Entanglement Entropy of (3+1)D Gapped Phases of Matter,"  We study the entanglement entropy of gapped phases of matter in three spatial
+dimensions. We focus in particular on size-independent contributions to the
+entropy across entanglement surfaces of arbitrary topologies. We show that for
+low energy fixed-point theories, the constant part of the entanglement entropy
+across any surface can be reduced to a linear combination of the entropies
+across a sphere and a torus. We first derive our results using strong
+sub-additivity inequalities along with assumptions about the entanglement
+entropy of fixed-point models, and identify the topological contribution by
+considering the renormalization group flow; in this way we give an explicit
+definition of topological entanglement entropy $S_{\mathrm{topo}}$ in (3+1)D,
+which sharpens previous results. We illustrate our results using several
+concrete examples and independent calculations, and show adding ""twist"" terms
+to the Lagrangian can change $S_{\mathrm{topo}}$ in (3+1)D. For the generalized
+Walker-Wang models, we find that the ground state degeneracy on a 3-torus is
+given by $\exp(-3S_{\mathrm{topo}}[T^2])$ in terms of the topological
+entanglement entropy across a 2-torus. We conjecture that a similar
+relationship holds for Abelian theories in $(d+1)$ dimensional spacetime, with
+the ground state degeneracy on the $d$-torus given by
+$\exp(-dS_{\mathrm{topo}}[T^{d-1}])$.
+",0,1,0,0,0,0
+16054,Dynamics and asymptotic profiles of endemic equilibrium for two frequency-dependent SIS epidemic models with cross-diffusion,"  This paper is concerned with two frequency-dependent SIS epidemic
+reaction-diffusion models in heterogeneous environment, with a cross-diffusion
+term modeling the effect that susceptible individuals tend to move away from
+higher concentration of infected individuals. It is first shown that the
+corresponding Neumann initial-boundary value problem in an $n$-dimensional
+bounded smooth domain possesses a unique global classical solution which is
+uniformly-in-time bounded regardless of the strength of the cross-diffusion and
+the spatial dimension $n$. It is further shown that, even in the presence of
+cross-diffusion, the models still admit threshold-type dynamics in terms of the
+basic reproduction number $\mathcal R_0$; that is, the unique disease free
+equilibrium is globally stable if $\mathcal R_0<1$, while if $\mathcal R_0>1$,
+the disease is uniformly persistent and there is an endemic equilibrium, which
+is globally stable in some special cases with weak chemotactic sensitivity. Our
+results on the asymptotic profiles of endemic equilibrium illustrate that
+restricting the motility of susceptible population may eliminate the infectious
+disease entirely for the first model with constant total population but fails
+for the second model with varying total population. In particular, this implies
+that such cross-diffusion does not contribute to the elimination of the
+infectious disease modelled by the second one.
+",0,0,1,0,0,0
+16055,"Stable Clustering Ansatz, Consistency Relations and Gravity Dual of Large-Scale Structure","  Gravitational clustering in the nonlinear regime remains poorly understood.
+Gravity dual of gravitational clustering has recently been proposed as a means
+to study the nonlinear regime. The stable clustering ansatz remains a key
+ingredient to our understanding of gravitational clustering in the highly
+nonlinear regime. We study certain aspects of violation of the stable
+clustering ansatz in the gravity dual of Large Scale Structure (LSS). We extend
+the recent studies of gravitational clustering using AdS gravity dual to take
+into account possible departure from the stable clustering ansatz and to
+arbitrary dimensions. Next, we extend the recently introduced consistency
+relations to arbitrary dimensions. We use the consistency relations to test the
+commonly used models of gravitational clustering including the halo models and
+hierarchical ansätze. In particular we establish a tower of consistency
+relations for the hierarchical amplitudes: $Q, R_a, R_b, S_a,S_b,S_c$ etc. as a
+functions of the scaled peculiar velocity $h$. We also study the variants of
+popular halo models in this context. In contrast to recent claims, none of
+these models, in their simplest incarnation, seem to satisfy the consistency
+relations in the soft limit.
+",0,1,0,0,0,0
+16056,Heavy-Tailed Analogues of the Covariance Matrix for ICA,"  Independent Component Analysis (ICA) is the problem of learning a square
+matrix $A$, given samples of $X=AS$, where $S$ is a random vector with
+independent coordinates. Most existing algorithms are provably efficient only
+when each $S_i$ has finite and moderately valued fourth moment. However, there
+are practical applications where this assumption need not be true, such as
+speech and finance. Algorithms have been proposed for heavy-tailed ICA, but
+they are not practical, using random walks and the full power of the ellipsoid
+algorithm multiple times. The main contributions of this paper are:
+(1) A practical algorithm for heavy-tailed ICA that we call HTICA. We provide
+theoretical guarantees and show that it outperforms other algorithms in some
+heavy-tailed regimes, both on real and synthetic data. Like the current
+state-of-the-art, the new algorithm is based on the centroid body (a first
+moment analogue of the covariance matrix). Unlike the state-of-the-art, our
+algorithm is practically efficient. To achieve this, we use explicit analytic
+representations of the centroid body, which bypasses the use of the ellipsoid
+method and random walks.
+(2) We study how heavy tails affect different ICA algorithms, including
+HTICA. Somewhat surprisingly, we show that some algorithms that use the
+covariance matrix or higher moments can successfully solve a range of ICA
+instances with infinite second moment. We study this theoretically and
+experimentally, with both synthetic and real-world heavy-tailed data.
+",0,0,0,1,0,0
+16057,Breaking through the bandwidth barrier in distributed fiber vibration sensing by sub-Nyquist randomized sampling,"  The round trip time of the light pulse limits the maximum detectable
+frequency response range of vibration in phase-sensitive optical time domain
+reflectometry ({\phi}-OTDR). We propose a method to break the frequency
+response range restriction of {\phi}-OTDR system by modulating the light pulse
+interval randomly which enables a random sampling for every vibration point in
+a long sensing fiber. This sub-Nyquist randomized sampling method is suits for
+detecting sparse-wideband-frequency vibration signals. Up to MHz resonance
+vibration signal with over dozens of frequency components and 1.153MHz single
+frequency vibration signal are clearly identified for a sensing range of 9.6km
+with 10kHz maximum sampling rate.
+",0,1,0,0,0,0
+16058,The Continuity of the Gauge Fixing Condition $n\cdot\partial n\cdot A=0$ for $SU(2)$ Gauge Theory,"  The continuity of the gauge fixing condition $n\cdot\partial n\cdot A=0$ for
+$SU(2)$ gauge theory on the manifold $R\bigotimes S^{1}\bigotimes
+S^{1}\bigotimes S^{1}$ is studied here, where $n^{\mu}$ stands for directional
+vector along $x_{i}$-axis($i=1,2,3$). It is proved that the gauge fixing
+condition is continuous given that gauge potentials are differentiable with
+continuous derivatives on the manifold $R\bigotimes S^{1}\bigotimes
+S^{1}\bigotimes S^{1}$ which is compact.
+",0,1,0,0,0,0
+16059,Estimating functions for jump-diffusions,"  Asymptotic theory for approximate martingale estimating functions is
+generalised to diffusions with finite-activity jumps, when the sampling
+frequency and terminal sampling time go to infinity. Rate optimality and
+efficiency are of particular concern. Under mild assumptions, it is shown that
+estimators of drift, diffusion, and jump parameters are consistent and
+asymptotically normal, as well as rate-optimal for the drift and jump
+parameters. Additional conditions are derived, which ensure rate-optimality for
+the diffusion parameter as well as efficiency for all parameters. The findings
+indicate a potentially fruitful direction for the further development of
+estimation for jump-diffusions.
+",0,0,1,1,0,0
+16060,Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks,"  Malignant melanoma has one of the most rapidly increasing incidences in the
+world and has a considerable mortality rate. Early diagnosis is particularly
+important since melanoma can be cured with prompt excision. Dermoscopy images
+play an important role in the non-invasive early detection of melanoma [1].
+However, melanoma detection using human vision alone can be subjective,
+inaccurate and poorly reproducible even among experienced dermatologists. This
+is attributed to the challenges in interpreting images with diverse
+characteristics including lesions of varying sizes and shapes, lesions that may
+have fuzzy boundaries, different skin colors and the presence of hair [2].
+Therefore, the automatic analysis of dermoscopy images is a valuable aid for
+clinical decision making and for image-based diagnosis to identify diseases
+such as melanoma [1-4]. Deep residual networks (ResNets) has achieved
+state-of-the-art results in image classification and detection related problems
+[5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to
+exploit the deep ResNets for robust visual features learning and
+representations.
+",1,0,0,0,0,0
+16061,Quadrature-based features for kernel approximation,"  We consider the problem of improving kernel approximation via randomized
+feature maps. These maps arise as Monte Carlo approximation to integral
+representations of kernel functions and scale up kernel methods for larger
+datasets. Based on an efficient numerical integration technique, we propose a
+unifying approach that reinterprets the previous random features methods and
+extends to better estimates of the kernel approximation. We derive the
+convergence behaviour and conduct an extensive empirical study that supports
+our hypothesis.
+",0,0,0,1,0,0
+16062,"The impossibility of ""fairness"": a generalized impossibility result for decisions","  Various measures can be used to estimate bias or unfairness in a predictor.
+Previous work has already established that some of these measures are
+incompatible with each other. Here we show that, when groups differ in
+prevalence of the predicted event, several intuitive, reasonable measures of
+fairness (probability of positive prediction given occurrence or
+non-occurrence; probability of occurrence given prediction or non-prediction;
+and ratio of predictions over occurrences for each group) are all mutually
+exclusive: if one of them is equal among groups, the other two must differ. The
+only exceptions are for perfect, or trivial (always-positive or
+always-negative) predictors. As a consequence, any non-perfect, non-trivial
+predictor must necessarily be ""unfair"" under two out of three reasonable sets
+of criteria. This result readily generalizes to a wide range of well-known
+statistical quantities (sensitivity, specificity, false positive rate,
+precision, etc.), all of which can be divided into three mutually exclusive
+groups. Importantly, The results applies to all predictors, whether algorithmic
+or human. We conclude with possible ways to handle this effect when assessing
+and designing prediction methods.
+",1,0,0,1,0,0
+16063,Measuring Quantum Entropy,"  The entropy of a quantum system is a measure of its randomness, and has
+applications in measuring quantum entanglement. We study the problem of
+measuring the von Neumann entropy, $S(\rho)$, and Rényi entropy,
+$S_\alpha(\rho)$ of an unknown mixed quantum state $\rho$ in $d$ dimensions,
+given access to independent copies of $\rho$.
+We provide an algorithm with copy complexity $O(d^{2/\alpha})$ for estimating
+$S_\alpha(\rho)$ for $\alpha<1$, and copy complexity $O(d^{2})$ for estimating
+$S(\rho)$, and $S_\alpha(\rho)$ for non-integral $\alpha>1$. These bounds are
+at least quadratic in $d$, which is the order dependence on the number of
+copies required for learning the entire state $\rho$. For integral $\alpha>1$,
+on the other hand, we provide an algorithm for estimating $S_\alpha(\rho)$ with
+a sub-quadratic copy complexity of $O(d^{2-2/\alpha})$. We characterize the
+copy complexity for integral $\alpha>1$ up to constant factors by providing
+matching lower bounds. For other values of $\alpha$, and the von Neumann
+entropy, we show lower bounds on the algorithm that achieves the upper bound.
+This shows that we either need new algorithms for better upper bounds, or
+better lower bounds to tighten the results.
+For non-integral $\alpha$, and the von Neumann entropy, we consider the well
+known Empirical Young Diagram (EYD) algorithm, which is the analogue of
+empirical plug-in estimator in classical distribution estimation. As a
+corollary, we strengthen a lower bound on the copy complexity of the EYD
+algorithm for learning the maximally mixed state by showing that the lower
+bound holds with exponential probability (which was previously known to hold
+with a constant probability). For integral $\alpha>1$, we provide new
+concentration results of certain polynomials that arise in Kerov algebra of
+Young diagrams.
+",1,0,0,0,0,0
+16064,Elliptic Transverse Circulation Equations for Balanced Models in a Generalized Vertical Coordinate,"  When studying tropical cyclones using the $f$-plane, axisymmetric, gradient
+balanced model, there arises a second-order elliptic equation for the
+transverse circulation. Similarly, when studying zonally symmetric meridional
+circulations near the equator (the tropical Hadley cells) or the katabatically
+forced meridional circulation over Antarctica, there also arises a second order
+elliptic equation. These elliptic equations are usually derived in the pressure
+coordinate or the potential temperature coordinate, since the thermal wind
+equation has simple non-Jacobian forms in these two vertical coordinates.
+Because of the large variations in surface pressure that can occur in tropical
+cyclones and over the Antarctic ice sheet, there is interest in using other
+vertical coordinates, e.g., the height coordinate, the classical
+$\sigma$-coordinate, or some type of hybrid coordinate typically used in global
+numerical weather prediction or climate models. Because the thermal wind
+equation in these coordinates takes a Jacobian form, the derivation of the
+elliptic transverse circulation equation is not as simple. Here we present a
+method for deriving the elliptic transverse circulation equation in a
+generalized vertical coordinate, which allows for many particular vertical
+coordinates, such as height, pressure, log-pressure, potential temperature,
+classical $\sigma$, and most hybrid cases. Advantages and disadvantages of the
+various coordinates are discussed.
+",0,1,0,0,0,0
+16065,The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods,"  We present the Voice Conversion Challenge 2018, designed as a follow up to
+the 2016 edition with the aim of providing a common framework for evaluating
+and comparing different state-of-the-art voice conversion (VC) systems. The
+objective of the challenge was to perform speaker conversion (i.e. transform
+the vocal identity) of a source speaker to a target speaker while maintaining
+linguistic information. As an update to the previous challenge, we considered
+both parallel and non-parallel data to form the Hub and Spoke tasks,
+respectively. A total of 23 teams from around the world submitted their
+systems, 11 of them additionally participated in the optional Spoke task. A
+large-scale crowdsourced perceptual evaluation was then carried out to rate the
+submitted converted speech in terms of naturalness and similarity to the target
+speaker identity. In this paper, we present a brief summary of the
+state-of-the-art techniques for VC, followed by a detailed explanation of the
+challenge tasks and the results that were obtained.
+",0,0,0,1,0,0
+16066,Levels of distribution for sieve problems in prehomogeneous vector spaces,"  In a companion paper, we developed an efficient algebraic method for
+computing the Fourier transforms of certain functions defined on prehomogeneous
+vector spaces over finite fields, and we carried out these computations in a
+variety of cases.
+Here we develop a method, based on Fourier analysis and algebraic geometry,
+which exploits these Fourier transform formulas to yield level of distribution
+results, in the sense of analytic number theory. Such results are of the shape
+typically required for a variety of sieve methods. As an example of such an
+application we prove that there are $\gg$ X/log(X) quartic fields whose
+discriminant is squarefree, bounded above by X, and has at most eight prime
+factors.
+",0,0,1,0,0,0
+16067,Imagination-Augmented Agents for Deep Reinforcement Learning,"  We introduce Imagination-Augmented Agents (I2As), a novel architecture for
+deep reinforcement learning combining model-free and model-based aspects. In
+contrast to most existing model-based reinforcement learning and planning
+methods, which prescribe how a model should be used to arrive at a policy, I2As
+learn to interpret predictions from a learned environment model to construct
+implicit plans in arbitrary ways, by using the predictions as additional
+context in deep policy networks. I2As show improved data efficiency,
+performance, and robustness to model misspecification compared to several
+baselines.
+",1,0,0,1,0,0
+16068,Cluster-glass phase in pyrochlore XY antiferromagnets with quenched disorder,"  We study the impact of quenched disorder (random exchange couplings or site
+dilution) on easy-plane pyrochlore antiferromagnets. In the clean system,
+order-by-disorder selects a magnetically ordered state from a classically
+degenerate manifold. In the presence of randomness, however, different orders
+can be chosen locally depending on details of the disorder configuration. Using
+a combination of analytical considerations and classical Monte-Carlo
+simulations, we argue that any long-range-ordered magnetic state is destroyed
+beyond a critical level of randomness where the system breaks into magnetic
+domains due to random exchange anisotropies, becoming, therefore, a glass of
+spin clusters, in accordance with the available experimental data. These random
+anisotropies originate from off-diagonal exchange couplings in the microscopic
+Hamiltonian, establishing their relevance to other magnets with strong
+spin-orbit coupling.
+",0,1,0,0,0,0
+16069,Non-line-of-sight tracking of people at long range,"  A remote-sensing system that can determine the position of hidden objects has
+applications in many critical real-life scenarios, such as search and rescue
+missions and safe autonomous driving. Previous work has shown the ability to
+range and image objects hidden from the direct line of sight, employing
+advanced optical imaging technologies aimed at small objects at short range. In
+this work we demonstrate a long-range tracking system based on single laser
+illumination and single-pixel single-photon detection. This enables us to track
+one or more people hidden from view at a stand-off distance of over 50~m. These
+results pave the way towards next generation LiDAR systems that will
+reconstruct not only the direct-view scene but also the main elements hidden
+behind walls or corners.
+",1,1,0,0,0,0
+16070,DeSIGN: Design Inspiration from Generative Networks,"  Can an algorithm create original and compelling fashion designs to serve as
+an inspirational assistant? To help answer this question, we design and
+investigate different image generation models associated with different loss
+functions to boost creativity in fashion generation. The dimensions of our
+explorations include: (i) different Generative Adversarial Networks
+architectures that start from noise vectors to generate fashion items, (ii)
+novel loss functions that encourage novelty, inspired from Sharma-Mittal
+divergence, a generalized mutual information measure for the widely used
+relative entropies such as Kullback-Leibler, and (iii) a generation process
+following the key elements of fashion design (disentangling shape and texture
+components). A key challenge of this study is the evaluation of generated
+designs and the retrieval of best ones, hence we put together an evaluation
+protocol associating automatic metrics and human experimental studies that we
+hope will help ease future research. We show that our proposed creativity
+criterion yield better overall appreciation than the one employed in Creative
+Adversarial Networks. In the end, about 61% of our images are thought to be
+created by human designers rather than by a computer while also being
+considered original per our human subject experiments, and our proposed loss
+scores the highest compared to existing losses in both novelty and likability.
+",0,0,0,1,0,0
+16071,Challenges of facet analysis and concept placement in universal classifications: the example of architecture in UDC,"  The paper discusses the challenges of faceted vocabulary organization in
+universal classifications which treat the universe of knowledge as a coherent
+whole and in which the concepts and subjects in different disciplines are
+shared, related and combined. The authors illustrate the challenges of the
+facet analytical approach using, as an example, the revision of class 72 in
+UDC. The paper reports on the research undertaken in 2013 as preparation for
+the revision. This consisted of analysis of concept organization in the UDC
+schedules in comparison with the Art & Architecture Thesaurus and class W of
+the Bliss Bibliographic Classification. The paper illustrates how such research
+can contribute to a better understanding of the field and may lead to
+improvements in the facet structure of this segment of the UDC vocabulary.
+",1,0,0,0,0,0
+16072,Coalescent-based species tree estimation: a stochastic Farris transform,"  The reconstruction of a species phylogeny from genomic data faces two
+significant hurdles: 1) the trees describing the evolution of each individual
+gene--i.e., the gene trees--may differ from the species phylogeny and 2) the
+molecular sequences corresponding to each gene often provide limited
+information about the gene trees themselves. In this paper we consider an
+approach to species tree reconstruction that addresses both these hurdles.
+Specifically, we propose an algorithm for phylogeny reconstruction under the
+multispecies coalescent model with a standard model of site substitution. The
+multispecies coalescent is commonly used to model gene tree discordance due to
+incomplete lineage sorting, a well-studied population-genetic effect.
+In previous work, an information-theoretic trade-off was derived in this
+context between the number of loci, $m$, needed for an accurate reconstruction
+and the length of the locus sequences, $k$. It was shown that to reconstruct an
+internal branch of length $f$, one needs $m$ to be of the order of $1/[f^{2}
+\sqrt{k}]$. That previous result was obtained under the molecular clock
+assumption, i.e., under the assumption that mutation rates (as well as
+population sizes) are constant across the species phylogeny.
+Here we generalize this result beyond the restrictive molecular clock
+assumption, and obtain a new reconstruction algorithm that has the same data
+requirement (up to log factors). Our main contribution is a novel reduction to
+the molecular clock case under the multispecies coalescent. As a corollary, we
+also obtain a new identifiability result of independent interest: for any
+species tree with $n \geq 3$ species, the rooted species tree can be identified
+from the distribution of its unrooted weighted gene trees even in the absence
+of a molecular clock.
+",1,0,1,1,0,0
+16073,Variance-Reduced Stochastic Learning by Networked Agents under Random Reshuffling,"  A new amortized variance-reduced gradient (AVRG) algorithm was developed in
+\cite{ying2017convergence}, which has constant storage requirement in
+comparison to SAGA and balanced gradient computations in comparison to SVRG.
+One key advantage of the AVRG strategy is its amenability to decentralized
+implementations. In this work, we show how AVRG can be extended to the network
+case where multiple learning agents are assumed to be connected by a graph
+topology. In this scenario, each agent observes data that is spatially
+distributed and all agents are only allowed to communicate with direct
+neighbors. Moreover, the amount of data observed by the individual agents may
+differ drastically. For such situations, the balanced gradient computation
+property of AVRG becomes a real advantage in reducing idle time caused by
+unbalanced local data storage requirements, which is characteristic of other
+reduced-variance gradient algorithms. The resulting diffusion-AVRG algorithm is
+shown to have linear convergence to the exact solution, and is much more memory
+efficient than other alternative algorithms. In addition, we propose a
+mini-batch strategy to balance the communication and computation efficiency for
+diffusion-AVRG. When a proper batch size is employed, it is observed in
+simulations that diffusion-AVRG is more computationally efficient than exact
+diffusion or EXTRA while maintaining almost the same communication efficiency.
+",1,0,0,1,0,0
+16074,Blind Community Detection from Low-rank Excitations of a Graph Filter,"  This paper considers a novel framework to detect communities in a graph from
+the observation of signals at its nodes. We model the observed signals as noisy
+outputs of an unknown network process -- represented as a graph filter -- that
+is excited by a set of low-rank inputs. Rather than learning the precise
+parameters of the graph itself, the proposed method retrieves the community
+structure directly; Furthermore, as in blind system identification methods, it
+does not require knowledge of the system excitation. The paper shows that
+communities can be detected by applying spectral clustering to the low-rank
+output covariance matrix obtained from the graph signals. The performance
+analysis indicates that the community detection accuracy depends on the
+spectral properties of the graph filter considered. Furthermore, we show that
+the accuracy can be improved via a low-rank matrix decomposition method when
+the excitation signals are known. Numerical experiments demonstrate that our
+approach is effective for analyzing network data from diffusion, consumers, and
+social dynamics.
+",1,0,0,0,0,0
+16075,Realistic theory of electronic correlations in nanoscopic systems,"  Nanostructures with open shell transition metal or molecular constituents
+host often strong electronic correlations and are highly sensitive to atomistic
+material details. This tutorial review discusses method developments and
+applications of theoretical approaches for the realistic description of the
+electronic and magnetic properties of nanostructures with correlated electrons.
+First, the implementation of a flexible interface between density functional
+theory and a variant of dynamical mean field theory (DMFT) highly suitable for
+the simulation of complex correlated structures is explained and illustrated.
+On the DMFT side, this interface is largely based on recent developments of
+quantum Monte Carlo and exact diagonalization techniques allowing for efficient
+descriptions of general four fermion Coulomb interactions, reduced symmetries
+and spin-orbit coupling, which are explained here. With the examples of the Cr
+(001) surfaces, magnetic adatoms, and molecular systems it is shown how the
+interplay of Hubbard U and Hund's J determines charge and spin fluctuations and
+how these interactions drive different sorts of correlation effects in
+nanosystems. Non-local interactions and correlations present a particular
+challenge for the theory of low dimensional systems. We present our method
+developments addressing these two challenges, i.e., advancements of the
+dynamical vertex approximation and a combination of the constrained random
+phase approximation with continuum medium theories. We demonstrate how
+non-local interaction and correlation phenomena are controlled not only by
+dimensionality but also by coupling to the environment which is typically
+important for determining the physics of nanosystems.
+",0,1,0,0,0,0
+16076,The Multiplier Problem of the Calculus of Variations for Scalar Ordinary Differential Equations,"  In the inverse problem of the calculus of variations one is asked to find a
+Lagrangian and a multiplier so that a given differential equation, after
+multiplying with the multiplier, becomes the Euler--Lagrange equation for the
+Lagrangian. An answer to this problem for the case of a scalar ordinary
+differential equation of order $2n, n\geq 2,$ is proposed.
+",0,0,1,0,0,0
+16077,Representation categories of Mackey Lie algebras as universal monoidal categories,"  Let $\mathbb{K}$ be an algebraically closed field of characteristic $0$. We
+study a monoidal category $\mathbb{T}_\alpha$ which is universal among all
+symmetric $\mathbb{K}$-linear monoidal categories generated by two objects $A$
+and $B$ such that $A$ has a, possibly transfinite, filtration. We construct
+$\mathbb{T}_\alpha$ as a category of representations of the Lie algebra
+$\mathfrak{gl}^M(V_*,V)$ consisting of endomorphisms of a fixed diagonalizable
+pairing $V_*\otimes V\to \mathbb{K}$ of vector spaces $V_*$ and $V$ of
+dimension $\alpha$. Here $\alpha$ is an arbitrary cardinal number. We describe
+explicitly the simple and the injective objects of $\mathbb{T}_\alpha$ and
+prove that the category $\mathbb{T}_\alpha$ is Koszul. We pay special attention
+to the case where the filtration on $A$ is finite. In this case
+$\alpha=\aleph_t$ for $t\in\mathbb{Z}_{\geq 0}$.
+",0,0,1,0,0,0
+16078,Dielectric media considered as vacuum with sources,"  Conventional textbook treatments on electromagnetic wave propagation consider
+the induced charge and current densities as ""bound"", and therefore absorb them
+into a refractive index. In principle it must also be possible to treat the
+medium as vacuum, but with explicit charge and current densities. This gives a
+more direct, physical description. However, since the induced waves propagate
+in vacuum in this picture, it is not straightforward to realize that the
+wavelength becomes different compared to that in vacuum. We provide an
+explanation, and also associated time-domain simulations. As an extra bonus the
+results turn out to illuminate the behavior of metamaterials.
+",0,1,0,0,0,0
+16079,Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversaries,"  With a large number of sensors and control units in networked systems,
+distributed support vector machines (DSVMs) play a fundamental role in scalable
+and efficient multi-sensor classification and prediction tasks. However, DSVMs
+are vulnerable to adversaries who can modify and generate data to deceive the
+system to misclassification and misprediction. This work aims to design defense
+strategies for DSVM learner against a potential adversary. We establish a
+game-theoretic framework to capture the conflicting interests between the DSVM
+learner and the attacker. The Nash equilibrium of the game allows predicting
+the outcome of learning algorithms in adversarial environments, and enhancing
+the resilience of the machine learning through dynamic distributed learning
+algorithms. We show that the DSVM learner is less vulnerable when he uses a
+balanced network with fewer nodes and higher degree. We also show that adding
+more training samples is an efficient defense strategy against an attacker. We
+present secure and resilient DSVM algorithms with verification method and
+rejection method, and show their resiliency against adversary with numerical
+experiments.
+",1,0,0,1,0,0
+16080,Rank conditional coverage and confidence intervals in high dimensional problems,"  Confidence interval procedures used in low dimensional settings are often
+inappropriate for high dimensional applications. When a large number of
+parameters are estimated, marginal confidence intervals associated with the
+most significant estimates have very low coverage rates: They are too small and
+centered at biased estimates. The problem of forming confidence intervals in
+high dimensional settings has previously been studied through the lens of
+selection adjustment. In this framework, the goal is to control the proportion
+of non-covering intervals formed for selected parameters.
+In this paper we approach the problem by considering the relationship between
+rank and coverage probability. Marginal confidence intervals have very low
+coverage rates for significant parameters and high rates for parameters with
+more boring estimates. Many selection adjusted intervals display the same
+pattern. This connection motivates us to propose a new coverage criterion for
+confidence intervals in multiple testing/covering problems --- the rank
+conditional coverage (RCC). This is the expected coverage rate of an interval
+given the significance ranking for the associated estimator. We propose
+interval construction via bootstrapping which produces small intervals and have
+a rank conditional coverage close to the nominal level. These methods are
+implemented in the R package rcc.
+",0,0,0,1,0,0
+16081,Phase-Encoded Hyperpolarized Nanodiamond for Magnetic Resonance Imaging,"  Surface-functionalized nanomaterials can act as theranostic agents that
+detect disease and track biological processes using hyperpolarized magnetic
+resonance imaging (MRI). Candidate materials are sparse however, requiring
+spinful nuclei with long spin-lattice relaxation (T1) and spin-dephasing times
+(T2), together with a reservoir of electrons to impart hyperpolarization. Here,
+we demonstrate the versatility of the nanodiamond material system for
+hyperpolarized 13C MRI, making use of its intrinsic paramagnetic defect
+centers, hours-long nuclear T1 times, and T2 times suitable for spatially
+resolving millimeter-scale structures. Combining these properties, we enable a
+new imaging modality that exploits the phase-contrast between spins encoded
+with a hyperpolarization that is aligned, or anti-aligned with the external
+magnetic field. The use of phase-encoded hyperpolarization allows nanodiamonds
+to be tagged and distinguished in an MRI based on their spin-orientation alone,
+and could permit the action of specific bio-functionalized complexes to be
+directly compared and imaged.
+",0,1,0,0,0,0
+16082,k-Space Deep Learning for Reference-free EPI Ghost Correction,"  Nyquist ghost artifacts in EPI images are originated from phase mismatch
+between the even and odd echoes. However, conventional correction methods using
+reference scans often produce erroneous results especially in high-field MRI
+due to the non-linear and time-varying local magnetic field changes. Recently,
+it was shown that the problem of ghost correction can be transformed into
+k-space data interpolation problem that can be solved using the annihilating
+filter-based low-rank Hankel structured matrix completion approach (ALOHA).
+Another recent discovery has shown that the deep convolutional neural network
+is closely related to the data-driven Hankel matrix decomposition. By
+synergistically combining these findings, here we propose a k-space deep
+learning approach that immediately corrects the k-space phase mismatch without
+a reference scan. Reconstruction results using 7T in vivo data showed that the
+proposed reference-free k-space deep learning approach for EPI ghost correction
+significantly improves the image quality compared to the existing methods, and
+the computing time is several orders of magnitude faster.
+",0,0,0,1,0,0
+16083,Binary Search in Graphs Revisited,"  In the classical binary search in a path the aim is to detect an unknown
+target by asking as few queries as possible, where each query reveals the
+direction to the target. This binary search algorithm has been recently
+extended by [Emamjomeh-Zadeh et al., STOC, 2016] to the problem of detecting a
+target in an arbitrary graph. Similarly to the classical case in the path, the
+algorithm of Emamjomeh-Zadeh et al. maintains a candidates' set for the target,
+while each query asks an appropriately chosen vertex-- the ""median""--which
+minimises a potential $\Phi$ among the vertices of the candidates' set. In this
+paper we address three open questions posed by Emamjomeh-Zadeh et al., namely
+(a) detecting a target when the query response is a direction to an
+approximately shortest path to the target, (b) detecting a target when querying
+a vertex that is an approximate median of the current candidates' set (instead
+of an exact one), and (c) detecting multiple targets, for which to the best of
+our knowledge no progress has been made so far. We resolve questions (a) and
+(b) by providing appropriate upper and lower bounds, as well as a new potential
+$\Gamma$ that guarantees efficient target detection even by querying an
+approximate median each time. With respect to (c), we initiate a systematic
+study for detecting two targets in graphs and we identify sufficient conditions
+on the queries that allow for strong (linear) lower bounds and strong
+(polylogarithmic) upper bounds for the number of queries. All of our positive
+results can be derived using our new potential $\Gamma$ that allows querying
+approximate medians.
+",1,0,0,0,0,0
+16084,"Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients","  The ADAM optimizer is exceedingly popular in the deep learning community.
+Often it works very well, sometimes it doesn't. Why? We interpret ADAM as a
+combination of two aspects: for each weight, the update direction is determined
+by the sign of stochastic gradients, whereas the update magnitude is determined
+by an estimate of their relative variance. We disentangle these two aspects and
+analyze them in isolation, gaining insight into the mechanisms underlying ADAM.
+This analysis also extends recent results on adverse effects of ADAM on
+generalization, isolating the sign aspect as the problematic one. Transferring
+the variance adaptation to SGD gives rise to a novel method, completing the
+practitioner's toolbox for problems where ADAM fails.
+",1,0,0,1,0,0
+16085,Econometric Modeling of Regional Electricity Spot Prices in the Australian Market,"  Wholesale electricity markets are increasingly integrated via high voltage
+interconnectors, and inter-regional trade in electricity is growing. To model
+this, we consider a spatial equilibrium model of price formation, where
+constraints on inter-regional flows result in three distinct equilibria in
+prices. We use this to motivate an econometric model for the distribution of
+observed electricity spot prices that captures many of their unique empirical
+characteristics. The econometric model features supply and inter-regional trade
+cost functions, which are estimated using Bayesian monotonic regression
+smoothing methodology. A copula multivariate time series model is employed to
+capture additional dependence -- both cross-sectional and serial-- in regional
+prices. The marginal distributions are nonparametric, with means given by the
+regression means. The model has the advantage of preserving the heavy
+right-hand tail in the predictive densities of price. We fit the model to
+half-hourly spot price data in the five interconnected regions of the
+Australian national electricity market. The fitted model is then used to
+measure how both supply and price shocks in one region are transmitted to the
+distribution of prices in all regions in subsequent periods. Finally, to
+validate our econometric model, we show that prices forecast using the proposed
+model compare favorably with those from some benchmark alternatives.
+",0,0,0,1,0,0
+16086,Accurate Effective Medium Theory for the Analysis of Spoof Localized Surface Plasmons in Textured Metallic Cylinders,"  It has been recently demonstrated that textured closed surfaces which are
+made out of perfect electric conductors (PECs) can mimic highly localized
+surface plasmons (LSPs). Here, we propose an effective medium which can
+accurately model LSP resonances in a two-dimensional periodically decorated PEC
+cylinder. The accuracy of previous models is limited to structures with
+deep-subwavelength and high number of grooves. However, we show that our model
+can successfully predict the ultra-sharp LSP resonances which exist in
+structures with relatively lower number of grooves. Such resonances are not
+correctly predictable with previous models that give some spurious resonances.
+The success of the proposed model is indebted to the incorporation of an
+effective surface conductivity which is created at the interface of the
+cylinder and the homogeneous medium surrounding the structure. This surface
+conductivity models the effect of higher diffracted orders which are excited in
+the periodic structure. The validity of the proposed model is verified by
+full-wave simulations.
+",0,1,0,0,0,0
+16087,Joint Regression and Ranking for Image Enhancement,"  Research on automated image enhancement has gained momentum in recent years,
+partially due to the need for easy-to-use tools for enhancing pictures captured
+by ubiquitous cameras on mobile devices. Many of the existing leading methods
+employ machine-learning-based techniques, by which some enhancement parameters
+for a given image are found by relating the image to the training images with
+known enhancement parameters. While knowing the structure of the parameter
+space can facilitate search for the optimal solution, none of the existing
+methods has explicitly modeled and learned that structure. This paper presents
+an end-to-end, novel joint regression and ranking approach to model the
+interaction between desired enhancement parameters and images to be processed,
+employing a Gaussian process (GP). GP allows searching for ideal parameters
+using only the image features. The model naturally leads to a ranking technique
+for comparing images in the induced feature space. Comparative evaluation using
+the ground-truth based on the MIT-Adobe FiveK dataset plus subjective tests on
+an additional data-set were used to demonstrate the effectiveness of the
+proposed approach.
+",1,0,0,0,0,0
+16088,From arteries to boreholes: Transient response of a poroelastic cylinder to fluid injection,"  The radially outward flow of fluid through a porous medium occurs in many
+practical problems, from transport across vascular walls to the pressurisation
+of boreholes in the subsurface. When the driving pressure is non-negligible
+relative to the stiffness of the solid structure, the poromechanical coupling
+between the fluid and the solid can control both the steady-state and the
+transient mechanics of the system. Very large pressures or very soft materials
+lead to large deformations of the solid skeleton, which introduce kinematic and
+constitutive nonlinearity that can have a nontrivial impact on these mechanics.
+Here, we study the transient response of a poroelastic cylinder to sudden fluid
+injection. We consider the impacts of kinematic and constitutive nonlinearity,
+both separately and in combination, and we highlight the central role of
+driving method in the evolution of the response. We show that the various
+facets of nonlinearity may either accelerate or decelerate the transient
+response relative to linear poroelasticity, depending on the boundary
+conditions and the initial geometry, and that an imposed fluid pressure leads
+to a much faster response than an imposed fluid flux.
+",0,1,0,0,0,0
+16089,Social Clustering in Epidemic Spread on Coevolving Networks,"  Even though transitivity is a central structural feature of social networks,
+its influence on epidemic spread on coevolving networks has remained relatively
+unexplored. Here we introduce and study an adaptive SIS epidemic model wherein
+the infection and network coevolve with non-trivial probability to close
+triangles during edge rewiring, leading to substantial reinforcement of network
+transitivity. This new model provides a unique opportunity to study the role of
+transitivity in altering the SIS dynamics on a coevolving network. Using
+numerical simulations and Approximate Master Equations (AME), we identify and
+examine a rich set of dynamical features in the new model. In many cases, the
+AME including transitivity reinforcement provide accurate predictions of
+stationary-state disease prevalence and network degree distributions.
+Furthermore, for some parameter settings, the AME accurately trace the temporal
+evolution of the system. We show that higher transitivity reinforcement in the
+model leads to lower levels of infective individuals in the population; when
+closing a triangle is the only rewiring mechanism. These methods and results
+may be useful in developing ideas and modeling strategies for controlling SIS
+type epidemics.
+",1,0,0,0,0,0
+16090,A Mathematical Aspect of Hohenberg-Kohn Theorem,"  The Hohenberg-Kohn theorem plays a fundamental role in density functional
+theory, which has become a basic tool for the study of electronic structure of
+matter. In this article, we study the Hohenberg-Kohn theorem for a class of
+external potentials based on a unique continuation principle.
+",0,1,0,0,0,0
+16091,Autocomplete 3D Sculpting,"  Digital sculpting is a popular means to create 3D models but remains a
+challenging task for many users. This can be alleviated by recent advances in
+data-driven and procedural modeling, albeit bounded by the underlying data and
+procedures. We propose a 3D sculpting system that assists users in freely
+creating models without predefined scope. With a brushing interface similar to
+common sculpting tools, our system silently records and analyzes users'
+workflows, and predicts what they might or should do in the future to reduce
+input labor or enhance output quality. Users can accept, ignore, or modify the
+suggestions and thus maintain full control and individual style. They can also
+explicitly select and clone past workflows over output model regions. Our key
+idea is to consider how a model is authored via dynamic workflows in addition
+to what it is shaped in static geometry, for more accurate analysis of user
+intentions and more general synthesis of shape structures. The workflows
+contain potential repetitions for analysis and synthesis, including user inputs
+(e.g. pen strokes on a pressure sensing tablet), model outputs (e.g. extrusions
+on an object surface), and camera viewpoints. We evaluate our method via user
+feedbacks and authored models.
+",1,0,0,0,0,0
+16092,KeyXtract Twitter Model - An Essential Keywords Extraction Model for Twitter Designed using NLP Tools,"  Since a tweet is limited to 140 characters, it is ambiguous and difficult for
+traditional Natural Language Processing (NLP) tools to analyse. This research
+presents KeyXtract which enhances the machine learning based Stanford CoreNLP
+Part-of-Speech (POS) tagger with the Twitter model to extract essential
+keywords from a tweet. The system was developed using rule-based parsers and
+two corpora. The data for the research was obtained from a Twitter profile of a
+telecommunication company. The system development consisted of two stages. At
+the initial stage, a domain specific corpus was compiled after analysing the
+tweets. The POS tagger extracted the Noun Phrases and Verb Phrases while the
+parsers removed noise and extracted any other keywords missed by the POS
+tagger. The system was evaluated using the Turing Test. After it was tested and
+compared against Stanford CoreNLP, the second stage of the system was developed
+addressing the shortcomings of the first stage. It was enhanced using Named
+Entity Recognition and Lemmatization. The second stage was also tested using
+the Turing test and its pass rate increased from 50.00% to 83.33%. The
+performance of the final system output was measured using the F1 score.
+Stanford CoreNLP with the Twitter model had an average F1 of 0.69 while the
+improved system had a F1 of 0.77. The accuracy of the system could be improved
+by using a complete domain specific corpus. Since the system used linguistic
+features of a sentence, it could be applied to other NLP tools.
+",1,0,0,0,0,0
+16093,A Generalized Zero-Forcing Precoder with Successive Dirty-Paper Coding in MISO Broadcast Channels,"  In this paper, we consider precoder designs for multiuser
+multiple-input-single-output (MISO) broadcasting channels. Instead of using a
+traditional linear zero-forcing (ZF) precoder, we propose a generalized ZF
+(GZF) precoder in conjunction with successive dirty-paper coding (DPC) for
+data-transmissions, namely, the GZF-DP precoder, where the suffix \lq{}DP\rq{}
+stands for \lq{}dirty-paper\rq{}. The GZF-DP precoder is designed to generate a
+band-shaped and lower-triangular effective channel $\vec{F}$ such that only the
+entries along the main diagonal and the $\nu$ first lower-diagonals can take
+non-zero values. Utilizing the successive DPC, the known non-causal inter-user
+interferences from the other (up to) $\nu$ users are canceled through
+successive encoding. We analyze optimal GZF-DP precoder designs both for
+sum-rate and minimum user-rate maximizations. Utilizing Lagrange multipliers,
+the optimal precoders for both cases are solved in closed-forms in relation to
+optimal power allocations. For the sum-rate maximization, the optimal power
+allocation can be found through water-filling, but with modified water-levels
+depending on the parameter $\nu$. While for the minimum user-rate maximization
+that measures the quality of the service (QoS), the optimal power allocation is
+directly solved in closed-form which also depends on $\nu$. Moreover, we
+propose two low-complexity user-ordering algorithms for the GZF-DP precoder
+designs for both maximizations, respectively. We show through numerical results
+that, the proposed GZF-DP precoder with a small $\nu$ ($\leq\!3$) renders
+significant rate increments compared to the previous precoder designs such as
+the linear ZF and user-grouping based DPC (UG-DP) precoders.
+",1,0,0,0,0,0
+16094,Generating Connected Random Graphs,"  Sampling random graphs is essential in many applications, and often
+algorithms use Markov chain Monte Carlo methods to sample uniformly from the
+space of graphs. However, often there is a need to sample graphs with some
+property that we are unable, or it is too inefficient, to sample using standard
+approaches. In this paper, we are interested in sampling graphs from a
+conditional ensemble of the underlying graph model. We present an algorithm to
+generate samples from an ensemble of connected random graphs using a
+Metropolis-Hastings framework. The algorithm extends to a general framework for
+sampling from a known distribution of graphs, conditioned on a desired
+property. We demonstrate the method to generate connected spatially embedded
+random graphs, specifically the well known Waxman network, and illustrate the
+convergence and practicalities of the algorithm.
+",1,0,0,1,0,0
+16095,Machine Learning Predicts Laboratory Earthquakes,"  Forecasting fault failure is a fundamental but elusive goal in earthquake
+science. Here we show that by listening to the acoustic signal emitted by a
+laboratory fault, machine learning can predict the time remaining before it
+fails with great accuracy. These predictions are based solely on the
+instantaneous physical characteristics of the acoustical signal, and do not
+make use of its history. Surprisingly, machine learning identifies a signal
+emitted from the fault zone previously thought to be low-amplitude noise that
+enables failure forecasting throughout the laboratory quake cycle. We
+hypothesize that applying this approach to continuous seismic data may lead to
+significant advances in identifying currently unknown signals, in providing new
+insights into fault physics, and in placing bounds on fault failure times.
+",0,1,0,0,0,0
+16096,New results of the search for hidden photons by means of a multicathode counter,"  New upper limit on a mixing parameter for hidden photons with a mass from 5
+eV till 10 keV has been obtained from the results of measurements during 78
+days in two configurations R1 and R2 of a multicathode counter. For a region of
+a maximal sensitivity from 10 eV till 30 eV the upper limit obtained is less
+than 4 x 10-11. The measurements have been performed at three temperatures:
+26C, 31C and 36C. A positive effect for the spontaneous emission of single
+electrons has been obtained at the level of more than 7{\sigma}. A falling
+tendency of a temperature dependence of the spontaneous emission rate indicates
+that the effect of thermal emission from a copper cathode can be neglected.
+",0,1,0,0,0,0
+16097,Higher dimensional Steinhaus and Slater problems via homogeneous dynamics,"  The three gap theorem, also known as the Steinhaus conjecture or three
+distance theorem, states that the gaps in the fractional parts of
+$\alpha,2\alpha,\ldots, N\alpha$ take at most three distinct values. Motivated
+by a question of Erdős, Geelen and Simpson, we explore a higher-dimensional
+variant, which asks for the number of gaps between the fractional parts of a
+linear form. Using the ergodic properties of the diagonal action on the space
+of lattices, we prove that for almost all parameter values the number of
+distinct gaps in the higher dimensional problem is unbounded. Our results in
+particular improve earlier work by Boshernitzan, Dyson and Bleher et al. We
+furthermore discuss a close link with the Littlewood conjecture in
+multiplicative Diophantine approximation. Finally, we also demonstrate how our
+methods can be adapted to obtain similar results for gaps between return times
+of translations to shrinking regions on higher dimensional tori.
+",0,0,1,0,0,0
+16098,Bayesian Unbiasing of the Gaia Space Mission Time Series Database,"  21 st century astrophysicists are confronted with the herculean task of
+distilling the maximum scientific return from extremely expensive and complex
+space- or ground-based instrumental projects. This paper concentrates in the
+mining of the time series catalog produced by the European Space Agency Gaia
+mission, launched in December 2013. We tackle in particular the problem of
+inferring the true distribution of the variability properties of Cepheid stars
+in the Milky Way satellite galaxy known as the Large Magellanic Cloud (LMC).
+Classical Cepheid stars are the first step in the so-called distance ladder: a
+series of techniques to measure cosmological distances and decipher the
+structure and evolution of our Universe. In this work we attempt to unbias the
+catalog by modelling the aliasing phenomenon that distorts the true
+distribution of periods. We have represented the problem by a 2-level
+generative Bayesian graphical model and used a Markov chain Monte Carlo (MCMC)
+algorithm for inference (classification and regression). Our results with
+synthetic data show that the system successfully removes systematic biases and
+is able to infer the true hyperparameters of the frequency and magnitude
+distributions.
+",0,1,0,0,0,0
+16099,On the connectivity of the hyperbolicity region of irreducible polynomials,"  We give an elementary proof for the fact that an irreducible hyperbolic
+polynomial has only one pair of hyperbolicity cones.
+",0,0,1,0,0,0
+16100,Matrix Completion via Factorizing Polynomials,"  Predicting unobserved entries of a partially observed matrix has found wide
+applicability in several areas, such as recommender systems, computational
+biology, and computer vision. Many scalable methods with rigorous theoretical
+guarantees have been developed for algorithms where the matrix is factored into
+low-rank components, and embeddings are learned for the row and column
+entities. While there has been recent research on incorporating explicit side
+information in the low-rank matrix factorization setting, often implicit
+information can be gleaned from the data, via higher-order interactions among
+entities. Such implicit information is especially useful in cases where the
+data is very sparse, as is often the case in real-world datasets. In this
+paper, we design a method to learn embeddings in the context of recommendation
+systems, using the observation that higher powers of a graph transition
+probability matrix encode the probability that a random walker will hit that
+node in a given number of steps. We develop a coordinate descent algorithm to
+solve the resulting optimization, that makes explicit computation of the higher
+order powers of the matrix redundant, preserving sparsity and making
+computations efficient. Experiments on several datasets show that our method,
+that can use higher order information, outperforms methods that only use
+explicitly available side information, those that use only second-order
+implicit information and in some cases, methods based on deep neural networks
+as well.
+",1,0,0,1,0,0
+16101,A visual search engine for Bangladeshi laws,"  Browsing and finding relevant information for Bangladeshi laws is a challenge
+faced by all law students and researchers in Bangladesh, and by citizens who
+want to learn about any legal procedure. Some law archives in Bangladesh are
+digitized, but lack proper tools to organize the data meaningfully. We present
+a text visualization tool that utilizes machine learning techniques to make the
+searching of laws quicker and easier. Using Doc2Vec to layout law article
+nodes, link mining techniques to visualize relevant citation networks, and
+named entity recognition to quickly find relevant sections in long law
+articles, our tool provides a faster and better search experience to the users.
+Qualitative feedback from law researchers, students, and government officials
+show promise for visually intuitive search tools in the context of
+governmental, legal, and constitutional data in developing countries, where
+digitized data does not necessarily pave the way towards an easy access to
+information.
+",1,0,0,1,0,0
+16102,Minors of two-connected graphs of large path-width,"  Let $P$ be a graph with a vertex $v$ such that $P\backslash v$ is a forest,
+and let $Q$ be an outerplanar graph. We prove that there exists a number
+$p=p(P,Q)$ such that every 2-connected graph of path-width at least $p$ has a
+minor isomorphic to $P$ or $Q$. This result answers a question of Seymour and
+implies a conjecture of Marshall and Wood. The proof is based on a new property
+of tree-decompositions.
+",1,0,0,0,0,0
+16103,Computable Operations on Compact Subsets of Metric Spaces with Applications to Fréchet Distance and Shape Optimization,"  We extend the Theory of Computation on real numbers, continuous real
+functions, and bounded closed Euclidean subsets, to compact metric spaces
+$(X,d)$: thereby generically including computational and optimization problems
+over higher types, such as the compact 'hyper' spaces of (i) nonempty closed
+subsets of $X$ w.r.t. Hausdorff metric, and of (ii) equicontinuous functions on
+$X$. The thus obtained Cartesian closure is shown to exhibit the same
+structural properties as in the Euclidean case, particularly regarding function
+pre/image. This allows us to assert the computability of (iii) Fréchet
+Distances between curves and between loops, as well as of (iv)
+constrained/Shape Optimization.
+",1,0,1,0,0,0
+16104,Siamese Capsule Networks,"  Capsule Networks have shown encouraging results on \textit{defacto} benchmark
+computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are
+yet to be tested on tasks where (1) the entities detected inherently have more
+complex internal representations and (2) there are very few instances per class
+to learn from and (3) where point-wise classification is not suitable. Hence,
+this paper carries out experiments on face verification in both controlled and
+uncontrolled settings that together address these points. In doing so we
+introduce \textit{Siamese Capsule Networks}, a new variant that can be used for
+pairwise learning tasks. The model is trained using contrastive loss with
+$\ell_2$-normalized capsule encoded pose features. We find that \textit{Siamese
+Capsule Networks} perform well against strong baselines on both pairwise
+learning datasets, yielding best results in the few-shot learning setting where
+image pairs in the test set contain unseen subjects.
+",0,0,0,1,0,0
+16105,Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition,"  An adversarial attack is an exploitative process in which minute alterations
+are made to natural inputs, causing the inputs to be misclassified by neural
+models. In the field of speech recognition, this has become an issue of
+increasing significance. Although adversarial attacks were originally
+introduced in computer vision, they have since infiltrated the realm of speech
+recognition. In 2017, a genetic attack was shown to be quite potent against the
+Speech Commands Model. Limited-vocabulary speech classifiers, such as the
+Speech Commands Model, are used in a variety of applications, particularly in
+telephony; as such, adversarial examples produced by this attack pose as a
+major security threat. This paper explores various methods of detecting these
+adversarial examples with combinations of audio preprocessing. One particular
+combined defense incorporating compressions, speech coding, filtering, and
+audio panning was shown to be quite effective against the attack on the Speech
+Commands Model, detecting audio adversarial examples with 93.5% precision and
+91.2% recall.
+",1,0,0,0,0,0
+16106,Revisiting the cavity-method threshold for random 3-SAT,"  A detailed Monte Carlo-study of the satisfiability threshold for random 3-SAT
+has been undertaken. In combination with a monotonicity assumption we find that
+the threshold for random 3-SAT satisfies $\alpha_3 \leq 4.262$. If the
+assumption is correct, this means that the actual threshold value for $k=3$ is
+lower than that given by the cavity method. In contrast the latter has recently
+been shown to give the correct value for large $k$. Our result thus indicate
+that there are distinct behaviors for $k$ above and below some critical $k_c$,
+and the cavity method may provide a correct mean-field picture for the range
+above $k_c$.
+",0,1,0,0,0,0
+16107,An improved Krylov eigenvalue strategy using the FEAST algorithm with inexact system solves,"  The FEAST eigenvalue algorithm is a subspace iteration algorithm that uses
+contour integration in the complex plane to obtain the eigenvectors of a matrix
+for the eigenvalues that are located in any user-defined search interval. By
+computing small numbers of eigenvalues in specific regions of the complex
+plane, FEAST is able to naturally parallelize the solution of eigenvalue
+problems by solving for multiple eigenpairs simultaneously. The traditional
+FEAST algorithm is implemented by directly solving collections of shifted
+linear systems of equations; in this paper, we describe a variation of the
+FEAST algorithm that uses iterative Krylov subspace algorithms for solving the
+shifted linear systems inexactly. We show that this iterative FEAST algorithm
+(which we call IFEAST) is mathematically equivalent to a block Krylov subspace
+method for solving eigenvalue problems. By using Krylov subspaces indirectly
+through solving shifted linear systems, rather than directly for projecting the
+eigenvalue problem, IFEAST is able to solve eigenvalue problems using very
+large dimension Krylov subspaces, without ever having to store a basis for
+those subspaces. IFEAST thus combines the flexibility and power of Krylov
+methods, requiring only matrix-vector multiplication for solving eigenvalue
+problems, with the natural parallelism of the traditional FEAST algorithm. We
+discuss the relationship between IFEAST and more traditional Krylov methods,
+and provide numerical examples illustrating its behavior.
+",1,0,0,0,0,0
+16108,Planar magnetic structures in coronal mass ejection-driven sheath regions,"  Planar magnetic structures (PMSs) are periods in the solar wind during which
+interplanetary magnetic field vectors are nearly parallel to a single plane.
+One of the specific regions where PMSs have been reported are coronal mass
+ejection (CME)-driven sheaths. We use here an automated method to identify PMSs
+in 95 CME sheath regions observed in-situ by the Wind and ACE spacecraft
+between 1997 and 2015. The occurrence and location of the PMSs are related to
+various shock, sheath and CME properties. We find that PMSs are ubiquitous in
+CME sheaths; 85% of the studied sheath regions had PMSs with the mean duration
+of 6.0 hours. In about one-third of the cases the magnetic field vectors
+followed a single PMS plane that covered a significant part (at least 67%) of
+the sheath region. Our analysis gives strong support for two suggested PMS
+formation mechanisms: the amplification and alignment of solar wind
+discontinuities near the CME-driven shock and the draping of the magnetic field
+lines around the CME ejecta. For example, we found that the shock and PMS plane
+normals generally coincided for the events where the PMSs occurred near the
+shock (68% of the PMS plane normals near the shock were separated by less than
+20° from the shock normal), while deviations were clearly larger when PMSs
+occurred close to the ejecta leading edge. In addition, PMSs near the shock
+were generally associated with lower upstream plasma beta than the cases where
+PMSs occurred near the leading edge of the CME. We also demonstrate that the
+planar parts of the sheath contain a higher amount of strongly southward
+magnetic field than the non-planar parts, suggesting that planar sheaths are
+more likely to drive magnetospheric activity.
+",0,1,0,0,0,0
+16109,Transportation analysis of denoising autoencoders: a novel method for analyzing deep neural networks,"  The feature map obtained from the denoising autoencoder (DAE) is investigated
+by determining transportation dynamics of the DAE, which is a cornerstone for
+deep learning. Despite the rapid development in its application, deep neural
+networks remain analytically unexplained, because the feature maps are nested
+and parameters are not faithful. In this paper, we address the problem of the
+formulation of nested complex of parameters by regarding the feature map as a
+transport map. Even when a feature map has different dimensions between input
+and output, we can regard it as a transportation map by considering that both
+the input and output spaces are embedded in a common high-dimensional space. In
+addition, the trajectory is a geometric object and thus, is independent of
+parameterization. In this manner, transportation can be regarded as a universal
+character of deep neural networks. By determining and analyzing the
+transportation dynamics, we can understand the behavior of a deep neural
+network. In this paper, we investigate a fundamental case of deep neural
+networks: the DAE. We derive the transport map of the DAE, and reveal that the
+infinitely deep DAE transports mass to decrease a certain quantity, such as
+entropy, of the data distribution. These results though analytically simple,
+shed light on the correspondence between deep neural networks and the
+Wasserstein gradient flows.
+",1,0,0,1,0,0
+16110,Weighted Contrastive Divergence,"  Learning algorithms for energy based Boltzmann architectures that rely on
+gradient descent are in general computationally prohibitive, typically due to
+the exponential number of terms involved in computing the partition function.
+In this way one has to resort to approximation schemes for the evaluation of
+the gradient. This is the case of Restricted Boltzmann Machines (RBM) and its
+learning algorithm Contrastive Divergence (CD). It is well-known that CD has a
+number of shortcomings, and its approximation to the gradient has several
+drawbacks. Overcoming these defects has been the basis of much research and new
+algorithms have been devised, such as persistent CD. In this manuscript we
+propose a new algorithm that we call Weighted CD (WCD), built from small
+modifications of the negative phase in standard CD. However small these
+modifications may be, experimental work reported in this paper suggest that WCD
+provides a significant improvement over standard CD and persistent CD at a
+small additional computational cost.
+",0,0,0,1,0,0
+16111,Small-Scale Challenges to the $Λ$CDM Paradigm,"  The dark energy plus cold dark matter ($\Lambda$CDM) cosmological model has
+been a demonstrably successful framework for predicting and explaining the
+large-scale structure of Universe and its evolution with time. Yet on length
+scales smaller than $\sim 1$ Mpc and mass scales smaller than $\sim 10^{11}
+M_{\odot}$, the theory faces a number of challenges. For example, the observed
+cores of many dark-matter dominated galaxies are both less dense and less cuspy
+than naively predicted in $\Lambda$CDM. The number of small galaxies and dwarf
+satellites in the Local Group is also far below the predicted count of low-mass
+dark matter halos and subhalos within similar volumes. These issues underlie
+the most well-documented problems with $\Lambda$CDM: Cusp/Core, Missing
+Satellites, and Too-Big-to-Fail. The key question is whether a better
+understanding of baryon physics, dark matter physics, or both will be required
+to meet these challenges. Other anomalies, including the observed planar and
+orbital configurations of Local Group satellites and the tight baryonic/dark
+matter scaling relations obeyed by the galaxy population, have been less
+thoroughly explored in the context of $\Lambda$CDM theory. Future surveys to
+discover faint, distant dwarf galaxies and to precisely measure their masses
+and density structure hold promising avenues for testing possible solutions to
+the small-scale challenges going forward. Observational programs to constrain
+or discover and characterize the number of truly dark low-mass halos are among
+the most important, and achievable, goals in this field over then next decade.
+These efforts will either further verify the $\Lambda$CDM paradigm or demand a
+substantial revision in our understanding of the nature of dark matter.
+",0,1,0,0,0,0
+16112,"Alternating minimization, scaling algorithms, and the null-cone problem from invariant theory","  Alternating minimization heuristics seek to solve a (difficult) global
+optimization task through iteratively solving a sequence of (much easier) local
+optimization tasks on different parts (or blocks) of the input parameters.
+While popular and widely applicable, very few examples of this heuristic are
+rigorously shown to converge to optimality, and even fewer to do so
+efficiently.
+In this paper we present a general framework which is amenable to rigorous
+analysis, and expose its applicability. Its main feature is that the local
+optimization domains are each a group of invertible matrices, together
+naturally acting on tensors, and the optimization problem is minimizing the
+norm of an input tensor under this joint action. The solution of this
+optimization problem captures a basic problem in Invariant Theory, called the
+null-cone problem.
+This algebraic framework turns out to encompass natural computational
+problems in combinatorial optimization, algebra, analysis, quantum information
+theory, and geometric complexity theory. It includes and extends to high
+dimensions the recent advances on (2-dimensional) operator scaling.
+Our main result is a fully polynomial time approximation scheme for this
+general problem, which may be viewed as a multi-dimensional scaling algorithm.
+This directly leads to progress on some of the problems in the areas above, and
+a unified view of others. We explain how faster convergence of an algorithm for
+the same problem will allow resolving central open problems.
+Our main techniques come from Invariant Theory, and include its rich
+non-commutative duality theory, and new bounds on the bitsizes of coefficients
+of invariant polynomials. They enrich the algorithmic toolbox of this very
+computational field of mathematics, and are directly related to some challenges
+in geometric complexity theory (GCT).
+",1,0,0,0,0,0
+16113,Positivity of denominator vectors of cluster algebras,"  In this paper, we prove that positivity of denominator vectors holds for any
+skew-symmetric cluster algebra.
+",0,0,1,0,0,0
+16114,Primordial Black Holes and Slow-Roll Violation,"  For primordial black holes (PBH) to be the dark matter in single-field
+inflation, the slow-roll approximation must be violated by at least ${\cal
+O}(1)$ in order to enhance the curvature power spectrum within the required
+number of efolds between CMB scales and PBH mass scales. Power spectrum
+predictions which rely on the inflaton remaining on the slow-roll attractor can
+fail dramatically leading to qualitatively incorrect conclusions in models like
+an inflection potential and misestimate the mass scale in a running mass model.
+We show that an optimized temporal evaluation of the Hubble slow-roll
+parameters to second order remains a good description for a wide range of PBH
+formation models where up to a $10^7$ amplification of power occurs in $10$
+efolds or more.
+",0,1,0,0,0,0
+16115,Using Optimal Ratio Mask as Training Target for Supervised Speech Separation,"  Supervised speech separation uses supervised learning algorithms to learn a
+mapping from an input noisy signal to an output target. With the fast
+development of deep learning, supervised separation has become the most
+important direction in speech separation area in recent years. For the
+supervised algorithm, training target has a significant impact on the
+performance. Ideal ratio mask is a commonly used training target, which can
+improve the speech intelligibility and quality of the separated speech.
+However, it does not take into account the correlation between noise and clean
+speech. In this paper, we use the optimal ratio mask as the training target of
+the deep neural network (DNN) for speech separation. The experiments are
+carried out under various noise environments and signal to noise ratio (SNR)
+conditions. The results show that the optimal ratio mask outperforms other
+training targets in general.
+",1,0,0,0,0,0
+16116,Volumetric parametrization from a level set boundary representation with PHT Splines,"  A challenge in isogeometric analysis is constructing analysis-suitable
+volumetric meshes which can accurately represent the geometry of a given
+physical domain. In this paper, we propose a method to derive a spline-based
+representation of a domain of interest from voxel-based data. We show an
+efficient way to obtain a boundary representation of the domain by a level-set
+function. Then, we use the geometric information from the boundary (the normal
+vectors and curvature) to construct a matching C1 representation with
+hierarchical cubic splines. The approximation is done by a single template and
+linear transformations (scaling, translations and rotations) without the need
+for solving an optimization problem. We illustrate our method with several
+examples in two and three dimensions, and show good performance on some
+standard benchmark test problems.
+",1,0,0,0,0,0
+16117,On the Compressive Power of Deep Rectifier Networks for High Resolution Representation of Class Boundaries,"  This paper provides a theoretical justification of the superior
+classification performance of deep rectifier networks over shallow rectifier
+networks from the geometrical perspective of piecewise linear (PWL) classifier
+boundaries. We show that, for a given threshold on the approximation error, the
+required number of boundary facets to approximate a general smooth boundary
+grows exponentially with the dimension of the data, and thus the number of
+boundary facets, referred to as boundary resolution, of a PWL classifier is an
+important quality measure that can be used to estimate a lower bound on the
+classification errors. However, learning naively an exponentially large number
+of boundary facets requires the determination of an exponentially large number
+of parameters and also requires an exponentially large number of training
+patterns. To overcome this issue of ""curse of dimensionality"", compressive
+representations of high resolution classifier boundaries are required. To show
+the superior compressive power of deep rectifier networks over shallow
+rectifier networks, we prove that the maximum boundary resolution of a single
+hidden layer rectifier network classifier grows exponentially with the number
+of units when this number is smaller than the dimension of the patterns. When
+the number of units is larger than the dimension of the patterns, the growth
+rate is reduced to a polynomial order. Consequently, the capacity of generating
+a high resolution boundary will increase if the same large number of units are
+arranged in multiple layers instead of a single hidden layer. Taking high
+dimensional spherical boundaries as examples, we show how deep rectifier
+networks can utilize geometric symmetries to approximate a boundary with the
+same accuracy but with a significantly fewer number of parameters than single
+hidden layer nets.
+",1,0,0,1,0,0
+16118,Absolute spectroscopy near 7.8 μm with a comb-locked extended-cavity quantum-cascade-laser,"  We report the first experimental demonstration of frequency-locking of an
+extended-cavity quantum-cascade-laser (EC-QCL) to a near-infrared frequency
+comb. The locking scheme is applied to carry out absolute spectroscopy of N2O
+lines near 7.87 {\mu}m with an accuracy of ~60 kHz. Thanks to a single mode
+operation over more than 100 cm^{-1}, the comb-locked EC-QCL shows great
+potential for the accurate retrieval of line center frequencies in a spectral
+region that is currently outside the reach of broadly tunable cw sources,
+either based on difference frequency generation or optical parametric
+oscillation. The approach described here can be straightforwardly extended up
+to 12 {\mu}m, which is the current wavelength limit for commercial cw EC-QCLs.
+",0,1,0,0,0,0
+16119,Some remarks on upper bounds for Weierstrass primary factors and their application in spectral theory,"  We study upper bounds on Weierstrass primary factors and discuss their
+application in spectral theory. One of the main aims of this note is to draw
+attention to works of Blumenthal and Denjoy from 1910, but we also provide some
+new results and some numerical computations of our own.
+",0,0,1,0,0,0
+16120,Topology of polyhedral products over simplicial multiwedges,"  We prove that certain conditions on multigraded Betti numbers of a simplicial
+complex $K$ imply existence of a higher Massey product in cohomology of a
+moment-angle-complex $\mathcal Z_K$, which contains a unique element (a
+strictly defined product). Using the simplicial multiwedge construction, we
+find a family $\mathcal{F}$ of polyhedral products being smooth closed
+manifolds such that for any $l,r\geq 2$ there exists an $l$-connected manifold
+$M\in\mathcal F$ with a nontrivial strictly defined $r$-fold Massey product in
+$H^{*}(M)$. As an application to homological algebra, we determine a wide class
+of triangulated spheres $K$ such that a nontrivial higher Massey product of any
+order may exist in Koszul homology of their Stanley--Reisner rings. As an
+application to rational homotopy theory, we establish a combinatorial criterion
+for a simple graph $\Gamma$ to provide a (rationally) formal generalized
+moment-angle manifold $\mathcal Z_{P}^{J}=(D^{2j_{i}},S^{2j_{i}-1})^{\partial
+P^*}$, $J=(j_{1},\ldots,j_m)$ over a graph-associahedron $P=P_{\Gamma}$ and
+compute all the diffeomorphism types of formal moment-angle manifolds over
+graph-associahedra.
+",0,0,1,0,0,0
+16121,Improved Representation Learning for Predicting Commonsense Ontologies,"  Recent work in learning ontologies (hierarchical and partially-ordered
+structures) has leveraged the intrinsic geometry of spaces of learned
+representations to make predictions that automatically obey complex structural
+constraints. We explore two extensions of one such model, the order-embedding
+model for hierarchical relation learning, with an aim towards improved
+performance on text data for commonsense knowledge representation. Our first
+model jointly learns ordering relations and non-hierarchical knowledge in the
+form of raw text. Our second extension exploits the partial order structure of
+the training data to find long-distance triplet constraints among embeddings
+which are poorly enforced by the pairwise training procedure. We find that both
+incorporating free text and augmented training constraints improve over the
+original order-embedding model and other strong baselines.
+",1,0,0,1,0,0
+16122,Mesoporous Silica as a Carrier for Amorphous Solid Dispersion,"  In the past decade, the discovery of active pharmaceutical substances with
+high therapeutic value but poor aqueous solubility has increased, thus making
+it challenging to formulate these compounds as oral dosage forms. The
+bioavailability of these drugs can be increased by formulating these drugs as
+an amorphous drug delivery system. Use of porous media like mesoporous silica
+has been investigated as a potential means to increase the solubility of poorly
+soluble drugs and to stabilize the amorphous drug delivery system. These
+materials have nanosized capillaries and the large surface area which enable
+the materials to accommodate high drug loading and promote the controlled and
+fast release. Therefore, mesoporous silica has been used as a carrier in the
+solid dispersion to form an amorphous solid dispersion (ASD). Mesoporous silica
+is also being used as an adsorbent in a conventional solid dispersion, which
+has many useful aspects. This review focuses on the use of mesoporous silica in
+ASD as potential means to increase the dissolution rate and to provide or
+increase the stability of the ASD. First, an overview of mesoporous silica and
+the classification is discussed. Subsequently, methods of drug incorporation,
+the stability of dispersion and, much more are discussed.
+",0,1,0,0,0,0
+16123,A Probability Monad as the Colimit of Finite Powers,"  We define and study a probability monad on the category of complete metric
+spaces and short maps. It assigns to each space the space of Radon probability
+measures on it with finite first moment, equipped with the
+Kantorovich-Wasserstein distance. This monad is analogous to the Giry monad on
+the category of Polish spaces, and it extends a construction due to van Breugel
+for compact and for 1-bounded complete metric spaces.
+We prove that this Kantorovich monad arises from a colimit construction on
+finite powers, which formalizes the intuition that probability measures are
+limits of finite samples. The proof relies on a criterion for when an ordinary
+left Kan extension of lax monoidal functors is a monoidal Kan extension. The
+colimit characterization allows the development of integration theory and the
+treatment of measures on spaces of measures, without measure theory.
+We also show that the category of algebras of the Kantorovich monad is
+equivalent to the category of closed convex subsets of Banach spaces with short
+affine maps as morphisms.
+",1,0,0,0,0,0
+16124,Robust Optimal Design of Energy Efficient Series Elastic Actuators: Application to a Powered Prosthetic Ankle,"  Design of robotic systems that safely and efficiently operate in uncertain
+operational conditions, such as rehabilitation and physical assistance robots,
+remains an important challenge in the field. Current methods for the design of
+energy efficient series elastic actuators use an optimization formulation that
+typically assumes known operational conditions. This approach could lead to
+actuators that cannot perform in uncertain environments because elongation,
+speed, or torque requirements may be beyond actuator specifications when the
+operation deviates from its nominal conditions. Addressing this gap, we propose
+a convex optimization formulation to design the stiffness of series elastic
+actuators to minimize energy consumption and satisfy actuator constraints
+despite uncertainty due to manufacturing of the spring, unmodeled dynamics,
+efficiency of the transmission, and the kinematics and kinetics of the load. In
+our formulation, we express energy consumption as a scalar convex-quadratic
+function of compliance. In the unconstrained case, this quadratic equation
+provides an analytical solution to the optimal value of stiffness that
+minimizes energy consumption for arbitrary periodic reference trajectories. As
+actuator constraints, we consider peak motor torque, peak motor velocity,
+limitations due to the speed-torque relationship of DC motors, and peak
+elongation of the spring. As a simulation case study, we apply our formulation
+to the robust design of a series elastic actuator for a powered prosthetic
+ankle. Our simulation results indicate that a small trade-off between energy
+efficiency and robustness is justified to design actuators that can operate
+with uncertainty.
+",1,0,0,0,0,0
+16125,Subwavelength phononic bandgap opening in bubbly media,"  The aim of this paper is to show both analytically and numerically the
+existence of a subwavelength phononic bandgap in bubble phononic crystals. The
+key is an original formula for the quasi-periodic Minnaert resonance
+frequencies of an arbitrarily shaped bubble. The main findings in this paper
+are illustrated with a variety of numerical experiments.
+",0,0,1,0,0,0
+16126,Hybrid integration of solid-state quantum emitters on a silicon photonic chip,"  Scalable quantum photonic systems require efficient single photon sources
+coupled to integrated photonic devices. Solid-state quantum emitters can
+generate single photons with high efficiency, while silicon photonic circuits
+can manipulate them in an integrated device structure. Combining these two
+material platforms could, therefore, significantly increase the complexity of
+integrated quantum photonic devices. Here, we demonstrate hybrid integration of
+solid-state quantum emitters to a silicon photonic device. We develop a
+pick-and-place technique that can position epitaxially grown InAs/InP quantum
+dots emitting at telecom wavelengths on a silicon photonic chip
+deterministically with nanoscale precision. We employ an adiabatic tapering
+approach to transfer the emission from the quantum dots to the waveguide with
+high efficiency. We also incorporate an on-chip silicon-photonic beamsplitter
+to perform a Hanbury-Brown and Twiss measurement. Our approach could enable
+integration of pre-characterized III-V quantum photonic devices into
+large-scale photonic structures to enable complex devices composed of many
+emitters and photons.
+",0,1,0,0,0,0
+16127,Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks,"  Tasks such as search and recommendation have become increas- ingly important
+for E-commerce to deal with the information over- load problem. To meet the
+diverse needs of di erent users, person- alization plays an important role. In
+many large portals such as Taobao and Amazon, there are a bunch of di erent
+types of search and recommendation tasks operating simultaneously for person-
+alization. However, most of current techniques address each task separately.
+This is suboptimal as no information about users shared across di erent tasks.
+In this work, we propose to learn universal user representations across
+multiple tasks for more e ective personalization. In partic- ular, user
+behavior sequences (e.g., click, bookmark or purchase of products) are modeled
+by LSTM and attention mechanism by integrating all the corresponding content,
+behavior and temporal information. User representations are shared and learned
+in an end-to-end setting across multiple tasks. Bene ting from better
+information utilization of multiple tasks, the user representations are more e
+ective to re ect their interests and are more general to be transferred to new
+tasks. We refer this work as Deep User Perception Network (DUPN) and conduct an
+extensive set of o ine and online experiments. Across all tested ve di erent
+tasks, our DUPN consistently achieves better results by giving more e ective
+user representations. Moreover, we deploy DUPN in large scale operational tasks
+in Taobao. Detailed implementations, e.g., incre- mental model updating, are
+also provided to address the practical issues for the real world applications.
+",0,0,0,1,0,0
+16128,Phase-diagram and dynamics of Rydberg-dressed fermions in two-dimensions,"  We investigate the ground-state properties and the collective modes of a
+two-dimensional two-component Rydberg-dressed Fermi liquid in the
+dipole-blockade regime. We find instability of the homogeneous system toward
+phase separated and density ordered phases, using the Hartree-Fock and
+random-phase approximations, respectively. The spectral weight of collective
+density oscillations in the homogenous phase also signals the emergence of
+density-wave instability. We examine the effect of exchange-hole on the
+density-wave instability and on the collective mode dispersion using the
+Hubbard local-field factor.
+",0,1,0,0,0,0
+16129,Ambient noise correlation-based imaging with moving sensors,"  Waves can be used to probe and image an unknown medium. Passive imaging uses
+ambient noise sources to illuminate the medium. This paper considers passive
+imaging with moving sensors. The motivation is to generate large synthetic
+apertures, which should result in enhanced resolution. However Doppler effects
+and lack of reciprocity significantly affect the imaging process. This paper
+discusses the consequences in terms of resolution and it shows how to design
+appropriate imaging functions depending on the sensor trajectory and velocity.
+",0,1,0,0,0,0
+16130,Anderson localization in the Non-Hermitian Aubry-André-Harper model with physical gain and loss,"  We investigate the Anderson localization in non-Hermitian
+Aubry-André-Harper (AAH) models with imaginary potentials added to lattice
+sites to represent the physical gain and loss during the interacting processes
+between the system and environment. By checking the mean inverse participation
+ratio (MIPR) of the system, we find that different configurations of physical
+gain and loss have very different impacts on the localization phase transition
+in the system. In the case with balanced physical gain and loss added in an
+alternate way to the lattice sites, the critical region (in the case with
+p-wave superconducting pairing) and the critical value (both in the situations
+with and without p-wave pairing) for the Anderson localization phase transition
+will be significantly reduced, which implies an enhancement of the localization
+process. However, if the system is divided into two parts with one of them
+coupled to physical gain and the other coupled to the corresponding physical
+loss, the transition process will be impacted only in a very mild way. Besides,
+we also discuss the situations with imbalanced physical gain and loss and find
+that the existence of random imaginary potentials in the system will also
+affect the localization process while constant imaginary potentials will not.
+",0,1,0,0,0,0
+16131,Towards de Sitter from 10D,"  Using a 10D lift of non-perturbative volume stabilization in type IIB string
+theory we study the limitations for obtaining de Sitter vacua. Based on this we
+find that the simplest KKLT vacua with a single Kahler modulus stabilized by a
+gaugino condensate cannot be uplifted to de Sitter. Rather, the uplift flattens
+out due to stronger backreaction on the volume modulus than has previously been
+anticipated, resulting in vacua which are meta-stable and SUSY breaking, but
+that are always AdS. However, we also show that setups such as racetrack
+stabilization can avoid this issue. In these models it is possible to obtain
+supersymmetric AdS vacua with a cosmological constant that can be tuned to zero
+while retaining finite moduli stabilization. In this regime, it seems that de
+Sitter uplifts are possible with negligible backreaction on the internal
+volume. We exhibit this behavior also from the 10D perspective.
+",0,1,0,0,0,0
+16132,Incorporating genuine prior information about between-study heterogeneity in random effects pairwise and network meta-analyses,"  Background: Pairwise and network meta-analyses using fixed effect and random
+effects models are commonly applied to synthesise evidence from randomised
+controlled trials. The models differ in their assumptions and the
+interpretation of the results. The model choice depends on the objective of the
+analysis and knowledge of the included studies. Fixed effect models are often
+used because there are too few studies with which to estimate the between-study
+standard deviation from the data alone. Objectives: The aim is to propose a
+framework for eliciting an informative prior distribution for the between-study
+standard deviation in a Bayesian random effects meta-analysis model to
+genuinely represent heterogeneity when data are sparse. Methods: We developed
+an elicitation method using external information such as empirical evidence and
+experts' beliefs on the 'range' of treatment effects in order to infer the
+prior distribution for the between-study standard deviation. We also developed
+the method to be implemented in R. Results: The three-stage elicitation
+approach allows uncertainty to be represented by a genuine prior distribution
+to avoid making misleading inferences. It is flexible to what judgments an
+expert can provide, and is applicable to all types of outcome measure for which
+a treatment effect can be constructed on an additive scale. Conclusions: The
+choice between using a fixed effect or random effects meta-analysis model
+depends on the inferences required and not on the number of available studies.
+Our elicitation framework captures external evidence about heterogeneity and
+overcomes the often implausible assumption that studies are estimating the same
+treatment effect, thereby improving the quality of inferences in decision
+making.
+",0,0,0,1,0,0
+16133,Wasserstein Distributional Robustness and Regularization in Statistical Learning,"  A central question in statistical learning is to design algorithms that not
+only perform well on training data, but also generalize to new and unseen data.
+In this paper, we tackle this question by formulating a distributionally robust
+stochastic optimization (DRSO) problem, which seeks a solution that minimizes
+the worst-case expected loss over a family of distributions that are close to
+the empirical distribution in Wasserstein distances. We establish a connection
+between such Wasserstein DRSO and regularization. More precisely, we identify a
+broad class of loss functions, for which the Wasserstein DRSO is asymptotically
+equivalent to a regularization problem with a gradient-norm penalty. Such
+relation provides new interpretations for problems involving regularization,
+including a great number of statistical learning problems and discrete choice
+models (e.g. multinomial logit). The connection suggests a principled way to
+regularize high-dimensional, non-convex problems. This is demonstrated through
+the training of Wasserstein generative adversarial networks in deep learning.
+",1,0,0,1,0,0
+16134,InfiniteBoost: building infinite ensembles with gradient descent,"  In machine learning ensemble methods have demonstrated high accuracy for the
+variety of problems in different areas. Two notable ensemble methods widely
+used in practice are gradient boosting and random forests. In this paper we
+present InfiniteBoost - a novel algorithm, which combines important properties
+of these two approaches. The algorithm constructs the ensemble of trees for
+which two properties hold: trees of the ensemble incorporate the mistakes done
+by others; at the same time the ensemble could contain the infinite number of
+trees without the over-fitting effect. The proposed algorithm is evaluated on
+the regression, classification, and ranking tasks using large scale, publicly
+available datasets.
+",1,0,0,1,0,0
+16135,On-demand microwave generator of shaped single photons,"  We demonstrate the full functionality of a circuit that generates single
+microwave photons on demand, with a wave packet that can be modulated with a
+near-arbitrary shape. We achieve such a high tunability by coupling a
+superconducting qubit near the end of a semi-infinite transmission line. A dc
+superconducting quantum interference device shunts the line to ground and is
+employed to modify the spatial dependence of the electromagnetic mode structure
+in the transmission line. This control allows us to couple and decouple the
+qubit from the line, shaping its emission rate on fast time scales. Our
+decoupling scheme is applicable to all types of superconducting qubits and
+other solid-state systems and can be generalized to multiple qubits as well as
+to resonators.
+",0,1,0,0,0,0
+16136,Fast and Stable Pascal Matrix Algorithms,"  In this paper, we derive a family of fast and stable algorithms for
+multiplying and inverting $n \times n$ Pascal matrices that run in $O(n log^2
+n)$ time and are closely related to De Casteljau's algorithm for Bézier curve
+evaluation. These algorithms use a recursive factorization of the triangular
+Pascal matrices and improve upon the cripplingly unstable $O(n log n)$ fast
+Fourier transform-based algorithms which involve a Toeplitz matrix
+factorization. We conduct numerical experiments which establish the speed and
+stability of our algorithm, as well as the poor performance of the Toeplitz
+factorization algorithm. As an example, we show how our formulation relates to
+Bézier curve evaluation.
+",1,0,0,0,0,0
+16137,Variational Continual Learning,"  This paper develops variational continual learning (VCL), a simple but
+general framework for continual learning that fuses online variational
+inference (VI) and recent advances in Monte Carlo VI for neural networks. The
+framework can successfully train both deep discriminative models and deep
+generative models in complex continual learning settings where existing tasks
+evolve over time and entirely new tasks emerge. Experimental results show that
+VCL outperforms state-of-the-art continual learning methods on a variety of
+tasks, avoiding catastrophic forgetting in a fully automatic way.
+",1,0,0,1,0,0
+16138,Dynamic adaptive procedures that control the false discovery rate,"  In the multiple testing problem with independent tests, the classical linear
+step-up procedure controls the false discovery rate (FDR) at level
+$\pi_0\alpha$, where $\pi_0$ is the proportion of true null hypotheses and
+$\alpha$ is the target FDR level. Adaptive procedures can improve power by
+incorporating estimates of $\pi_0$, which typically rely on a tuning parameter.
+Fixed adaptive procedures set their tuning parameters before seeing the data
+and can be shown to control the FDR in finite samples. We develop theoretical
+results for dynamic adaptive procedures whose tuning parameters are determined
+by the data. We show that, if the tuning parameter is chosen according to a
+left-to-right stopping time rule, the corresponding dynamic adaptive procedure
+controls the FDR in finite samples. Examples include the recently proposed
+right-boundary procedure and the widely used lowest-slope procedure, among
+others. Simulation results show that the right-boundary procedure is more
+powerful than other dynamic adaptive procedures under independence and mild
+dependence conditions.
+",0,0,1,1,0,0
+16139,Compression-Based Regularization with an Application to Multi-Task Learning,"  This paper investigates, from information theoretic grounds, a learning
+problem based on the principle that any regularity in a given dataset can be
+exploited to extract compact features from data, i.e., using fewer bits than
+needed to fully describe the data itself, in order to build meaningful
+representations of a relevant content (multiple labels). We begin by
+introducing the noisy lossy source coding paradigm with the log-loss fidelity
+criterion which provides the fundamental tradeoffs between the
+\emph{cross-entropy loss} (average risk) and the information rate of the
+features (model complexity). Our approach allows an information theoretic
+formulation of the \emph{multi-task learning} (MTL) problem which is a
+supervised learning framework in which the prediction models for several
+related tasks are learned jointly from common representations to achieve better
+generalization performance. Then, we present an iterative algorithm for
+computing the optimal tradeoffs and its global convergence is proven provided
+that some conditions hold. An important property of this algorithm is that it
+provides a natural safeguard against overfitting, because it minimizes the
+average risk taking into account a penalization induced by the model
+complexity. Remarkably, empirical results illustrate that there exists an
+optimal information rate minimizing the \emph{excess risk} which depends on the
+nature and the amount of available training data. An application to
+hierarchical text categorization is also investigated, extending previous
+works.
+",1,0,0,1,0,0
+16140,Isomorphism between Differential and Moment Invariants under Affine Transform,"  The invariant is one of central topics in science, technology and
+engineering. The differential invariant is essential in understanding or
+describing some important phenomena or procedures in mathematics, physics,
+chemistry, biology or computer science etc. The derivation of differential
+invariants is usually difficult or complicated. This paper reports a discovery
+that under the affine transform, differential invariants have similar
+structures with moment invariants up to a scalar function of transform
+parameters. If moment invariants are known, relative differential invariants
+can be obtained by the substitution of moments by derivatives with the same
+order. Whereas moment invariants can be calculated by multiple integrals, this
+method provides a simple way to derive differential invariants without the need
+to resolve any equation system. Since the definition of moments on different
+manifolds or in different dimension of spaces is well established, differential
+invariants on or in them will also be well defined. Considering that moments
+have a strong background in mathematics and physics, this technique offers a
+new view angle to the inner structure of invariants. Projective differential
+invariants can also be found in this way with a screening process.
+",1,0,0,0,0,0
+16141,Throughput Analysis for Wavelet OFDM in Broadband Power Line Communications,"  Windowed orthogonal frequency-division multiplexing (OFDM) and wavelet OFDM
+have been proposed as medium access techniques for broadband communications
+over the power line network by the standard IEEE 1901. Windowed OFDM has been
+extensively researched and employed in different fields of communication, while
+wavelet OFDM, which has been recently recommended for the first time in a
+standard, has received less attention. This work is aimed to show that wavelet
+OFDM, which basically is an Extended Lapped Transform-based multicarrier
+modulation (ELT-MCM), is a viable and attractive alternative for data
+transmission in hostile scenarios, such as in-home PLC. To this end, we obtain
+theoretical expressions for ELT-MCM of: 1) the useful signal power, 2) the
+inter-symbol interference (ISI) power, 3) the inter-carrier interference (ICI)
+power, and 4) the noise power at the receiver side. The system capacity and the
+achievable throughput are derived from these. This study includes several
+computer simulations that show that ELT-MCM is an efficient alternative to
+improve data rates in PLC networks.
+",1,0,0,0,0,0
+16142,Simple And Efficient Architecture Search for Convolutional Neural Networks,"  Neural networks have recently had a lot of success for many tasks. However,
+neural network architectures that perform well are still typically designed
+manually by experts in a cumbersome trial-and-error process. We propose a new
+method to automatically search for well-performing CNN architectures based on a
+simple hill climbing procedure whose operators apply network morphisms,
+followed by short optimization runs by cosine annealing. Surprisingly, this
+simple method yields competitive results, despite only requiring resources in
+the same order of magnitude as training a single network. E.g., on CIFAR-10,
+our method designs and trains networks with an error rate below 6% in only 12
+hours on a single GPU; training for one day reduces this error further, to
+almost 5%.
+",1,0,0,1,0,0
+16143,Quantum estimation of detection efficiency with no-knowledge quantum feedback,"  We investigate that no-knowledge measurement-based feedback control is
+utilized to obtain the estimation precision of the detection efficiency. For
+the feedback operators that concern us, no-knowledge measurement is the optimal
+way to estimate the detection efficiency. We show that the higher precision can
+be achieved for the lower or larger detection efficiency. It is found that
+no-knowledge feedback can be used to cancel decoherence. No-knowledge feedback
+with a high detection efficiency can perform well in estimating frequency and
+detection efficiency parameters simultaneously. And simultaneous estimation is
+better than independent estimation given by the same probes.
+",1,0,0,0,0,0
+16144,Intermittent Granular Dynamics at a Seismogenic Plate Boundary,"  Earthquakes at seismogenic plate boundaries are a response to the
+differential motions of tectonic blocks embedded within a geometrically complex
+network of branching and coalescing faults. Elastic strain is accumulated at a
+slow strain rate of the order of $10^{-15}$ s$^{-1}$, and released
+intermittently at intervals $>100$ years, in the form of rapid (seconds to
+minutes) coseismic ruptures. The development of macroscopic models of
+quasi-static planar tectonic dynamics at these plate boundaries has remained
+challenging due to uncertainty with regard to the spatial and kinematic
+complexity of fault system behaviors. In particular, the characteristic length
+scale of kinematically distinct tectonic structures is poorly constrained. Here
+we analyze fluctuations in GPS recordings of interseismic velocities from the
+southern California plate boundary, identifying heavy-tailed scaling behavior.
+This suggests that the plate boundary can be understood as a densely packed
+granular medium near the jamming transition, with a characteristic length scale
+of $91 \pm 20$ km. In this picture fault and block systems may rapidly
+rearrange the distribution of forces within them, driving a mixture of
+transient and intermittent fault slip behaviors over tectonic time scales.
+",0,1,0,0,0,0
+16145,Optimal continuous-time ALM for insurers: a martingale approach,"  We study a continuous-time asset-allocation problem for a firm in the
+insurance industry that backs up the liabilities raised by the insurance
+contracts with the underwriting profits and the income resulting from investing
+in the financial market. Using the martingale approach and convex duality
+techniques we characterize strategies that maximize expected utility from
+consumption and final wealth under CRRA preferences. We present numerical
+results for some distributions of claims/liabilities with policy limits.
+",0,0,0,0,0,1
+16146,Combating Fake News: A Survey on Identification and Mitigation Techniques,"  The proliferation of fake news on social media has opened up new directions
+of research for timely identification and containment of fake news, and
+mitigation of its widespread impact on public opinion. While much of the
+earlier research was focused on identification of fake news based on its
+contents or by exploiting users' engagements with the news on social media,
+there has been a rising interest in proactive intervention strategies to
+counter the spread of misinformation and its impact on society. In this survey,
+we describe the modern-day problem of fake news and, in particular, highlight
+the technical challenges associated with it. We discuss existing methods and
+techniques applicable to both identification and mitigation, with a focus on
+the significant advances in each method and their advantages and limitations.
+In addition, research has often been limited by the quality of existing
+datasets and their specific application contexts. To alleviate this problem, we
+comprehensively compile and summarize characteristic features of available
+datasets. Furthermore, we outline new directions of research to facilitate
+future development of effective and interdisciplinary solutions.
+",1,0,0,1,0,0
+16147,A causal approach to analysis of censored medical costs in the presence of time-varying treatment,"  There has recently been a growing interest in the development of statistical
+methods to compare medical costs between treatment groups. When cumulative cost
+is the outcome of interest, right-censoring poses the challenge of informative
+missingness due to heterogeneity in the rates of cost accumulation across
+subjects. Existing approaches seeking to address the challenge of informative
+cost trajectories typically rely on inverse probability weighting and target a
+net ""intent-to-treat"" effect. However, no approaches capable of handling
+time-dependent treatment and confounding in this setting have been developed to
+date. A method to estimate the joint causal effect of a treatment regime on
+cost would be of value to inform public policy when comparing interventions. In
+this paper, we develop a nested g-computation approach to cost analysis in
+order to accommodate time-dependent treatment and repeated outcome measures. We
+demonstrate that our procedure is reasonably robust to departures from its
+distributional assumptions and can provide unique insights into fundamental
+differences in average cost across time-dependent treatment regimes.
+",0,0,0,1,0,0
+16148,Inferring the parameters of a Markov process from snapshots of the steady state,"  We seek to infer the parameters of an ergodic Markov process from samples
+taken independently from the steady state. Our focus is on non-equilibrium
+processes, where the steady state is not described by the Boltzmann measure,
+but is generally unknown and hard to compute, which prevents the application of
+established equilibrium inference methods. We propose a quantity we call
+propagator likelihood, which takes on the role of the likelihood in equilibrium
+processes. This propagator likelihood is based on fictitious transitions
+between those configurations of the system which occur in the samples. The
+propagator likelihood can be derived by minimising the relative entropy between
+the empirical distribution and a distribution generated by propagating the
+empirical distribution forward in time. Maximising the propagator likelihood
+leads to an efficient reconstruction of the parameters of the underlying model
+in different systems, both with discrete configurations and with continuous
+configurations. We apply the method to non-equilibrium models from statistical
+physics and theoretical biology, including the asymmetric simple exclusion
+process (ASEP), the kinetic Ising model, and replicator dynamics.
+",0,1,0,1,0,0
+16149,What Does a TextCNN Learn?,"  TextCNN, the convolutional neural network for text, is a useful deep learning
+algorithm for sentence classification tasks such as sentiment analysis and
+question classification. However, neural networks have long been known as black
+boxes because interpreting them is a challenging task. Researchers have
+developed several tools to understand a CNN for image classification by deep
+visualization, but research about deep TextCNNs is still insufficient. In this
+paper, we are trying to understand what a TextCNN learns on two classical NLP
+datasets. Our work focuses on functions of different convolutional kernels and
+correlations between convolutional kernels.
+",0,0,0,1,0,0
+16150,The Wisdom of Polarized Crowds,"  As political polarization in the United States continues to rise, the
+question of whether polarized individuals can fruitfully cooperate becomes
+pressing. Although diversity of individual perspectives typically leads to
+superior team performance on complex tasks, strong political perspectives have
+been associated with conflict, misinformation and a reluctance to engage with
+people and perspectives beyond one's echo chamber. It is unclear whether
+self-selected teams of politically diverse individuals will create higher or
+lower quality outcomes. In this paper, we explore the effect of team political
+composition on performance through analysis of millions of edits to Wikipedia's
+Political, Social Issues, and Science articles. We measure editors' political
+alignments by their contributions to conservative versus liberal articles. A
+survey of editors validates that those who primarily edit liberal articles
+identify more strongly with the Democratic party and those who edit
+conservative ones with the Republican party. Our analysis then reveals that
+polarized teams---those consisting of a balanced set of politically diverse
+editors---create articles of higher quality than politically homogeneous teams.
+The effect appears most strongly in Wikipedia's Political articles, but is also
+observed in Social Issues and even Science articles. Analysis of article ""talk
+pages"" reveals that politically polarized teams engage in longer, more
+constructive, competitive, and substantively focused but linguistically diverse
+debates than political moderates. More intense use of Wikipedia policies by
+politically diverse teams suggests institutional design principles to help
+unleash the power of politically polarized teams.
+",1,0,0,1,0,0
+16151,Probabilistic Database Summarization for Interactive Data Exploration,"  We present a probabilistic approach to generate a small, query-able summary
+of a dataset for interactive data exploration. Departing from traditional
+summarization techniques, we use the Principle of Maximum Entropy to generate a
+probabilistic representation of the data that can be used to give approximate
+query answers. We develop the theoretical framework and formulation of our
+probabilistic representation and show how to use it to answer queries. We then
+present solving techniques and give three critical optimizations to improve
+preprocessing time and query accuracy. Lastly, we experimentally evaluate our
+work using a 5 GB dataset of flights within the United States and a 210 GB
+dataset from an astronomy particle simulation. While our current work only
+supports linear queries, we show that our technique can successfully answer
+queries faster than sampling while introducing, on average, no more error than
+sampling and can better distinguish between rare and nonexistent values.
+",1,0,0,0,0,0
+16152,GP-GAN: Towards Realistic High-Resolution Image Blending,"  Recent advances in generative adversarial networks (GANs) have shown
+promising potentials in conditional image generation. However, how to generate
+high-resolution images remains an open problem. In this paper, we aim at
+generating high-resolution well-blended images given composited copy-and-paste
+ones, i.e. realistic high-resolution image blending. To achieve this goal, we
+propose Gaussian-Poisson GAN (GP-GAN), a framework that combines the strengths
+of classical gradient-based approaches and GANs, which is the first work that
+explores the capability of GANs in high-resolution image blending task to the
+best of our knowledge. Particularly, we propose Gaussian-Poisson Equation to
+formulate the high-resolution image blending problem, which is a joint
+optimisation constrained by the gradient and colour information. Gradient
+filters can obtain gradient information. For generating the colour information,
+we propose Blending GAN to learn the mapping between the composited image and
+the well-blended one. Compared to the alternative methods, our approach can
+deliver high-resolution, realistic images with fewer bleedings and unpleasant
+artefacts. Experiments confirm that our approach achieves the state-of-the-art
+performance on Transient Attributes dataset. A user study on Amazon Mechanical
+Turk finds that majority of workers are in favour of the proposed approach.
+",1,0,0,0,0,0
+16153,Relational Autoencoder for Feature Extraction,"  Feature extraction becomes increasingly important as data grows high
+dimensional. Autoencoder as a neural network based feature extraction method
+achieves great success in generating abstract features of high dimensional
+data. However, it fails to consider the relationships of data samples which may
+affect experimental results of using original and new features. In this paper,
+we propose a Relation Autoencoder model considering both data features and
+their relationships. We also extend it to work with other major autoencoder
+models including Sparse Autoencoder, Denoising Autoencoder and Variational
+Autoencoder. The proposed relational autoencoder models are evaluated on a set
+of benchmark datasets and the experimental results show that considering data
+relationships can generate more robust features which achieve lower
+construction loss and then lower error rate in further classification compared
+to the other variants of autoencoders.
+",0,0,0,1,0,0
+16154,Sentiment and Sarcasm Classification with Multitask Learning,"  Sentiment classification and sarcasm detection are both important NLP tasks.
+We show that these two tasks are correlated, and present a multi-task
+learning-based framework using deep neural network that models this correlation
+to improve the performance of both tasks in a multi-task learning setting.
+",1,0,0,0,0,0
+16155,Context in Neural Machine Translation: A Review of Models and Evaluations,"  This review paper discusses how context has been used in neural machine
+translation (NMT) in the past two years (2017-2018). Starting with a brief
+retrospect on the rapid evolution of NMT models, the paper then reviews studies
+that evaluate NMT output from various perspectives, with emphasis on those
+analyzing limitations of the translation of contextual phenomena. In a
+subsequent version, the paper will then present the main methods that were
+proposed to leverage context for improving translation quality, and
+distinguishes methods that aim to improve the translation of specific phenomena
+from those that consider a wider unstructured context.
+",1,0,0,0,0,0
+16156,Manipulation of type-I and type-II Dirac points in PdTe2 superconductor by external pressure,"  A pair of type-II Dirac cones in PdTe$_2$ was recently predicted by theories
+and confirmed in experiments, making PdTe$_2$ the first material that processes
+both superconductivity and type-II Dirac fermions. In this work, we study the
+evolution of Dirac cones in PdTe$_2$ under hydrostatic pressure by the
+first-principles calculations. Our results show that the pair of type-II Dirac
+points disappears at 6.1 GPa. Interestingly, a new pair of type-I Dirac points
+from the same two bands emerges at 4.7 GPa. Due to the distinctive band
+structures compared with those of PtSe$_2$ and PtTe$_2$, the two types of Dirac
+points can coexist in PdTe$_2$ under proper pressure (4.7-6.1 GPa). The
+emergence of type-I Dirac cones and the disappearance of type-II Dirac ones are
+attributed to the increase/decrease of the energy of the states at $\Gamma$ and
+$A$ point, which have the anti-bonding/bonding characters of interlayer Te-Te
+atoms. On the other hand, we find that the superconductivity of PdTe$_2$
+slightly decreases with pressure. The pressure-induced different types of Dirac
+cones combined with superconductivity may open a promising way to investigate
+the complex interactions between Dirac fermions and superconducting
+quasi-particles.
+",0,1,0,0,0,0
+16157,A fast Metropolis-Hastings method for generating random correlation matrices,"  We propose a novel Metropolis-Hastings algorithm to sample uniformly from the
+space of correlation matrices. Existing methods in the literature are based on
+elaborated representations of a correlation matrix, or on complex
+parametrizations of it. By contrast, our method is intuitive and simple, based
+the classical Cholesky factorization of a positive definite matrix and Markov
+chain Monte Carlo theory. We perform a detailed convergence analysis of the
+resulting Markov chain, and show how it benefits from fast convergence, both
+theoretically and empirically. Furthermore, in numerical experiments our
+algorithm is shown to be significantly faster than the current alternative
+approaches, thanks to its simple yet principled approach.
+",0,0,0,1,0,0
+16158,Compact Groups analysis using weak gravitational lensing,"  We present a weak lensing analysis of a sample of SDSS Compact Groups (CGs).
+Using the measured radial density contrast profile, we derive the average
+masses under the assumption of spherical symmetry, obtaining a velocity
+dispersion for the Singular Isothermal Spherical model, $\sigma_V = 270 \pm 40
+\rm ~km~s^{-1}$, and for the NFW model, $R_{200}=0.53\pm0.10\,h_{70}^{-1}\,\rm
+Mpc$. We test three different definitions of CGs centres to identify which best
+traces the true dark matter halo centre, concluding that a luminosity weighted
+centre is the most suitable choice. We also study the lensing signal dependence
+on CGs physical radius, group surface brightness, and morphological mixing. We
+find that groups with more concentrated galaxy members show steeper mass
+profiles and larger velocity dispersions. We argue that both, a possible lower
+fraction of interloper and a true steeper profile, could be playing a role in
+this effect. Straightforward velocity dispersion estimates from member
+spectroscopy yields $\sigma_V \approx 230 \rm ~km~s^{-1}$ in agreement with our
+lensing results.
+",0,1,0,0,0,0
+16159,An Applied Knowledge Framework to Study Complex Systems,"  The complexity of knowledge production on complex systems is well-known, but
+there still lacks knowledge framework that would both account for a certain
+structure of knowledge production at an epistemological level and be directly
+applicable to the study and management of complex systems. We set a basis for
+such a framework, by first analyzing in detail a case study of the construction
+of a geographical theory of complex territorial systems, through mixed methods,
+namely qualitative interview analysis and quantitative citation network
+analysis. We can therethrough inductively build a framework that considers
+knowledge entreprises as perspectives, with co-evolving components within
+complementary knowledge domains. We finally discuss potential applications and
+developments.
+",1,1,0,0,0,0
+16160,Classifying subcategories in quotients of exact categories,"  We classify certain subcategories in quotients of exact categories. In
+particular, we classify the triangulated and thick subcategories of an
+algebraic triangulated category, i.e. the stable category of a Frobenius
+category.
+",0,0,1,0,0,0
+16161,Dealing with Rational Second Order Ordinary Differential Equations where both Darboux and Lie Find It Difficult: The $S$-function Method,"  Here we present a new approach to search for first order invariants (first
+integrals) of rational second order ordinary differential equations. This
+method is an alternative to the Darbouxian and symmetry approaches. Our
+procedure can succeed in many cases where these two approaches fail. We also
+present here a Maple implementation of the theoretical results and methods,
+hereby introduced, in a computational package -- {\it InSyDE}. The package is
+designed, apart from materializing the algorithms presented, to provide a set
+of tools to allow the user to analyse the intermediary steps of the process.
+",1,0,0,0,0,0
+16162,Urban Vibrancy and Safety in Philadelphia,"  Statistical analyses of urban environments have been recently improved
+through publicly available high resolution data and mapping technologies that
+have been adopted across industries. These technologies allow us to create
+metrics to empirically investigate urban design principles of the past
+half-century. Philadelphia is an interesting case study for this work, with its
+rapid urban development and population increase in the last decade. We outline
+a data analysis pipeline for exploring the association between safety and local
+neighborhood features such as population, economic health and the built
+environment. As a particular example of our analysis pipeline, we focus on
+quantitative measures of the built environment that serve as proxies for
+vibrancy: the amount of human activity in a local area. Historically, vibrancy
+has been very challenging to measure empirically. Measures based on land use
+zoning are not an adequate description of local vibrancy and so we construct a
+database and set of measures of business activity in each neighborhood. We
+employ several matching analyses to explore the relationship between
+neighborhood vibrancy and safety, such as comparing high crime versus low crime
+locations within the same neighborhood. As additional sources of urban data
+become available, our analysis pipeline can serve as the template for further
+investigations into the relationships between safety, economic factors and the
+built environment at the local neighborhood level.
+",0,0,0,1,0,0
+16163,Exponential quadrature rules without order reduction,"  In this paper a technique is suggested to integrate linear initial boundary
+value problems with exponential quadrature rules in such a way that the order
+in time is as high as possible. A thorough error analysis is given for both the
+classical approach of integrating the problem firstly in space and then in time
+and of doing it in the reverse order in a suitable manner. Time-dependent
+boundary conditions are considered with both approaches and full discretization
+formulas are given to implement the methods once the quadrature nodes have been
+chosen for the time integration and a particular (although very general) scheme
+is selected for the space discretization. Numerical experiments are shown which
+corroborate that, for example, with the suggested technique, order $2s$ is
+obtained when choosing the $s$ nodes of Gaussian quadrature rule.
+",0,0,1,0,0,0
+16164,New Derivatives for the Functions with the Fractal Tartan Support,"  In this manuscript, we generalize F-calculus to apply it on fractal Tartan
+spaces. The generalized standard F-calculus is used to obtain the integral and
+derivative of the functions on the fractal Tartan with different dimensions.
+The generalized fractional derivatives have local properties that make it more
+useful in modelling physical problems. The illustrative examples are used to
+present the details.
+",0,0,1,0,0,0
+16165,Learning Navigation Behaviors End to End,"  A longstanding goal of behavior-based robotics is to solve high-level
+navigation tasks using end to end navigation behaviors that directly map
+sensors to actions. Navigation behaviors, such as reaching a goal or following
+a path without collisions, can be learned from exploration and interaction with
+the environment, but are constrained by the type and quality of a robot's
+sensors, dynamics, and actuators. Traditional motion planning handles varied
+robot geometry and dynamics, but typically assumes high-quality observations.
+Modern vision-based navigation typically considers imperfect or partial
+observations, but simplifies the robot action space. With both approaches, the
+transition from simulation to reality can be difficult. Here, we learn two end
+to end navigation behaviors that avoid moving obstacles: point to point and
+path following. These policies receive noisy lidar observations and output
+robot linear and angular velocities. We train these policies in small, static
+environments with Shaped-DDPG, an adaptation of the Deep Deterministic Policy
+Gradient (DDPG) reinforcement learning method which optimizes reward and
+network architecture. Over 500 meters of on-robot experiments show , these
+policies generalize to new environments and moving obstacles, are robust to
+sensor, actuator, and localization noise, and can serve as robust building
+blocks for larger navigation tasks. The path following and point and point
+policies are 83% and 56% more successful than the baseline, respectively.
+",1,0,0,0,0,0
+16166,Bounded Information Rate Variational Autoencoders,"  This paper introduces a new member of the family of Variational Autoencoders
+(VAE) that constrains the rate of information transferred by the latent layer.
+The latent layer is interpreted as a communication channel, the information
+rate of which is bound by imposing a pre-set signal-to-noise ratio. The new
+constraint subsumes the mutual information between the input and latent
+variables, combining naturally with the likelihood objective of the observed
+data as used in a conventional VAE. The resulting Bounded-Information-Rate
+Variational Autoencoder (BIR-VAE) provides a meaningful latent representation
+with an information resolution that can be specified directly in bits by the
+system designer. The rate constraint can be used to prevent overtraining, and
+the method naturally facilitates quantisation of the latent variables at the
+set rate. Our experiments confirm that the BIR-VAE has a meaningful latent
+representation and that its performance is at least as good as state-of-the-art
+competing algorithms, but with lower computational complexity.
+",0,0,0,1,0,0
+16167,Wasan geometry with the division by 0,"  Results in Wasan geometry of tangents circles can still be considered in a
+singular case by the division by 0.
+",0,0,1,0,0,0
+16168,New insights into non-central beta distributions,"  The beta family owes its privileged status within unit interval distributions
+to several relevant features such as, for example, easyness of interpretation
+and versatility in modeling different types of data. However, its flexibility
+at the unit interval endpoints is poor enough to prevent from properly modeling
+the portions of data having values next to zero and one. Such a drawback can be
+overcome by resorting to the class of the non-central beta distributions.
+Indeed, the latter allows the density to take on arbitrary positive and finite
+limits which have a really simple form. That said, new insights into such class
+are provided in this paper. In particular, new representations and moments
+expressions are derived. Moreover, its potential with respect to alternative
+models is highlighted through applications to real data.
+",0,0,1,1,0,0
+16169,Mean Curvature Flows of Closed Hypersurfaces in Warped Product Manifolds,"  We investigate the mean curvature flows in a class of warped product
+manifolds with closed hypersurfaces fibering over $\mathbb{R}$. In particular,
+we prove that under natural conditions on the warping function and Ricci
+curvature bound for the ambient space, there exists a large class of closed
+initial hypersurfaces, as geodesic graphs over the totally geodesic
+hypersurface $\Sigma$, such that the mean curvature flow starting from $S_0$
+exists for all time and converges to $\Sigma$.
+",0,0,1,0,0,0
+16170,Optimising finite-difference methods for PDEs through parameterised time-tiling in Devito,"  Finite-difference methods are widely used in solving partial differential
+equations. In a large problem set, approximations can take days or weeks to
+evaluate, yet the bulk of computation may occur within a single loop nest. The
+modelling process for researchers is not straightforward either, requiring
+models with differential equations to be translated into stencil kernels, then
+optimised separately. One tool that seeks to speed up and eliminate mistakes
+from this tedious procedure is Devito, used to efficiently employ
+finite-difference methods.
+In this work, we implement time-tiling, a loop nest optimisation, in Devito
+yielding a decrease in runtime of up to 45%, and at least 20% across stencils
+from the acoustic wave equation family, widely used in Devito's target domain
+of seismic imaging. We present an estimator for arithmetic intensity under
+time-tiling and a model to predict runtime improvements in stencil
+computations. We also consider generalisation of time-tiling to imperfect loop
+nests, a less widely studied problem.
+",1,0,0,0,0,0
+16171,An Ontology to support automated negotiation,"  In this work we propose an ontology to support automated negotiation in
+multiagent systems. The ontology can be connected with some domain-specific
+ontologies to facilitate the negotiation in different domains, such as
+Intelligent Transportation Systems (ITS), e-commerce, etc. The specific
+negotiation rules for each type of negotiation strategy can also be defined as
+part of the ontology, reducing the amount of knowledge hardcoded in the agents
+and ensuring the interoperability. The expressiveness of the ontology was
+proved in a multiagent architecture for the automatic traffic light setting
+application on ITS.
+",1,0,0,0,0,0
+16172,Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks,"  We present a technique for efficiently synthesizing images of atmospheric
+clouds using a combination of Monte Carlo integration and neural networks. The
+intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming
+aerosols make rendering of clouds---e.g. the characteristic silverlining and
+the ""whiteness"" of the inner body---challenging for methods based solely on
+Monte Carlo integration or diffusion theory. We approach the problem
+differently. Instead of simulating all light transport during rendering, we
+pre-learn the spatial and directional distribution of radiant flux from tens of
+cloud exemplars. To render a new scene, we sample visible points of the cloud
+and, for each, extract a hierarchical 3D descriptor of the cloud geometry with
+respect to the shading location and the light source. The descriptor is input
+to a deep neural network that predicts the radiance function for each shading
+configuration. We make the key observation that progressively feeding the
+hierarchical descriptor into the network enhances the network's ability to
+learn faster and predict with high accuracy while using few coefficients. We
+also employ a block design with residual connections to further improve
+performance. A GPU implementation of our method synthesizes images of clouds
+that are nearly indistinguishable from the reference solution within seconds
+interactively. Our method thus represents a viable solution for applications
+such as cloud design and, thanks to its temporal stability, also for
+high-quality production of animated content.
+",1,0,0,1,0,0
+16173,On the Upward/Downward Closures of Petri Nets,"  We study the size and the complexity of computing finite state automata (FSA)
+representing and approximating the downward and the upward closure of Petri net
+languages with coverability as the acceptance condition. We show how to
+construct an FSA recognizing the upward closure of a Petri net language in
+doubly-exponential time, and therefore the size is at most doubly exponential.
+For downward closures, we prove that the size of the minimal automata can be
+non-primitive recursive. In the case of BPP nets, a well-known subclass of
+Petri nets, we show that an FSA accepting the downward/upward closure can be
+constructed in exponential time. Furthermore, we consider the problem of
+checking whether a simple regular language is included in the downward/upward
+closure of a Petri net/BPP net language. We show that this problem is
+EXPSPACE-complete (resp. NP-complete) in the case of Petri nets (resp. BPP
+nets). Finally, we show that it is decidable whether a Petri net language is
+upward/downward closed. To this end, we prove that one can decide whether a
+given regular language is a subset of a Petri net coverability language.
+",1,0,0,0,0,0
+16174,Semantic Similarity from Natural Language and Ontology Analysis,"  Artificial Intelligence federates numerous scientific fields in the aim of
+developing machines able to assist human operators performing complex
+treatments -- most of which demand high cognitive skills (e.g. learning or
+decision processes). Central to this quest is to give machines the ability to
+estimate the likeness or similarity between things in the way human beings
+estimate the similarity between stimuli.
+In this context, this book focuses on semantic measures: approaches designed
+for comparing semantic entities such as units of language, e.g. words,
+sentences, or concepts and instances defined into knowledge bases. The aim of
+these measures is to assess the similarity or relatedness of such semantic
+entities by taking into account their semantics, i.e. their meaning --
+intuitively, the words tea and coffee, which both refer to stimulating
+beverage, will be estimated to be more semantically similar than the words
+toffee (confection) and coffee, despite that the last pair has a higher
+syntactic similarity. The two state-of-the-art approaches for estimating and
+quantifying semantic similarities/relatedness of semantic entities are
+presented in detail: the first one relies on corpora analysis and is based on
+Natural Language Processing techniques and semantic models while the second is
+based on more or less formal, computer-readable and workable forms of knowledge
+such as semantic networks, thesaurus or ontologies. (...) Beyond a simple
+inventory and categorization of existing measures, the aim of this monograph is
+to convey novices as well as researchers of these domains towards a better
+understanding of semantic similarity estimation and more generally semantic
+measures.
+",1,0,0,0,0,0
+16175,The Hardness of Synthesizing Elementary Net Systems from Highly Restricted Inputs,"  Elementary net systems (ENS) are the most fundamental class of Petri nets.
+Their synthesis problem has important applications in the design of digital
+hardware and commercial processes. Given a labeled transition system (TS) $A$,
+feasibility is the NP-complete decision problem whether $A$ can be equivalently
+synthesized into an ENS. It is well known that $A$ is feasible if and only if
+it has the event state separation property (ESSP) and the state separation
+property (SSP). Recently, these properties have also been studied individually
+for their practical implications. A fast ESSP algorithm, for instance, would
+allow applications to at least validate the language equivalence of $A$ and a
+synthesized ENS. Being able to efficiently decide SSP, on the other hand, could
+serve as a quick-fail preprocessing mechanism for synthesis. Although a few
+tractable subclasses have been found, this paper destroys much of the hope that
+many practically meaningful input restrictions make feasibility or at least one
+of ESSP and SSP efficient. We show that all three problems remain NP-complete
+even if the input is restricted to linear TSs where every event occurs at most
+three times or if the input is restricted to TSs where each event occurs at
+most twice and each state has at most two successor and two predecessor states.
+",1,0,0,0,0,0
+16176,Exceptional Lattice Green's Functions,"  The three exceptional lattices, $E_6$, $E_7$, and $E_8$, have attracted much
+attention due to their anomalously dense and symmetric structures which are of
+critical importance in modern theoretical physics. Here, we study the
+electronic band structure of a single spinless quantum particle hopping between
+their nearest-neighbor lattice points in the tight-binding limit. Using Markov
+chain Monte Carlo methods, we numerically sample their lattice Green's
+functions, densities of states, and random walk return probabilities. We find
+and tabulate a plethora of Van Hove singularities in the densities of states,
+including degenerate ones in $E_6$ and $E_7$. Finally, we use brute force
+enumeration to count the number of distinct closed walks of length up to eight,
+which gives the first eight moments of the densities of states.
+",0,1,0,0,0,0
+16177,Optimal design with EGM approach in conjugate natural convection with surface radiation in a two-dimensional enclosure,"  Analysis of conjugate natural convection with surface radiation in a
+two-dimensional enclosure is carried out in order to search the optimal
+location of the heat source with entropy generation minimization (EGM) approach
+and conventional heat transfer parameters. The air as an incompressible fluid
+and transparent media is considered the fluid filling the enclosure with the
+steady and laminar regime. The enclosure internal surfaces are also gray,
+opaque and diffuse. The governing equations with stream function and vorticity
+formulation are solved using finite difference approach. Results include the
+effect of Rayleigh number and emissivity on the dimensionless average rate of
+entropy generation and its optimum location. The optimum location search with
+conventional heat transfer parameters including maximum temperature and Nusselt
+numbers are also examined.
+",0,1,0,0,0,0
+16178,Convex cocompactness in pseudo-Riemannian hyperbolic spaces,"  Anosov representations of word hyperbolic groups into higher-rank semisimple
+Lie groups are representations with finite kernel and discrete image that have
+strong analogies with convex cocompact representations into rank-one Lie
+groups. However, the most naive analogy fails: generically, Anosov
+representations do not act properly and cocompactly on a convex set in the
+associated Riemannian symmetric space. We study representations into projective
+indefinite orthogonal groups PO(p,q) by considering their action on the
+associated pseudo-Riemannian hyperbolic space H^{p,q-1} in place of the
+Riemannian symmetric space. Following work of Barbot and Mérigot in anti-de
+Sitter geometry, we find an intimate connection between Anosov representations
+and the natural notion of convex cocompactness in this setting.
+",0,0,1,0,0,0
+16179,A Closer Look at Memorization in Deep Networks,"  We examine the role of memorization in deep learning, drawing connections to
+capacity, generalization, and adversarial robustness. While deep networks are
+capable of memorizing noise data, our results suggest that they tend to
+prioritize learning simple patterns first. In our experiments, we expose
+qualitative differences in gradient-based optimization of deep neural networks
+(DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned
+explicit regularization (e.g., dropout) we can degrade DNN training performance
+on noise datasets without compromising generalization on real data. Our
+analysis suggests that the notions of effective capacity which are dataset
+independent are unlikely to explain the generalization performance of deep
+networks when trained with gradient based methods because training data itself
+plays an important role in determining the degree of memorization.
+",1,0,0,1,0,0
+16180,Hierarchical 3D fully convolutional networks for multi-organ segmentation,"  Recent advances in 3D fully convolutional networks (FCN) have made it
+feasible to produce dense voxel-wise predictions of full volumetric images. In
+this work, we show that a multi-class 3D FCN trained on manually labeled CT
+scans of seven abdominal structures (artery, vein, liver, spleen, stomach,
+gallbladder, and pancreas) can achieve competitive segmentation results, while
+avoiding the need for handcrafting features or training organ-specific models.
+To this end, we propose a two-stage, coarse-to-fine approach that trains an FCN
+model to roughly delineate the organs of interest in the first stage (seeing
+$\sim$40% of the voxels within a simple, automatically generated binary mask of
+the patient's body). We then use these predictions of the first-stage FCN to
+define a candidate region that will be used to train a second FCN. This step
+reduces the number of voxels the FCN has to classify to $\sim$10% while
+maintaining a recall high of $>$99%. This second-stage FCN can now focus on
+more detailed segmentation of the organs. We respectively utilize training and
+validation sets consisting of 281 and 50 clinical CT images. Our hierarchical
+approach provides an improved Dice score of 7.5 percentage points per organ on
+average in our validation set. We furthermore test our models on a completely
+unseen data collection acquired at a different hospital that includes 150 CT
+scans with three anatomical labels (liver, spleen, and pancreas). In such
+challenging organs as the pancreas, our hierarchical approach improves the mean
+Dice score from 68.5 to 82.2%, achieving the highest reported average score on
+this dataset.
+",1,0,0,0,0,0
+16181,An evolutionary game model for behavioral gambit of loyalists: Global awareness and risk-aversion,"  We study the phase diagram of a minority game where three classes of agents
+are present. Two types of agents play a risk-loving game that we model by the
+standard Snowdrift Game. The behaviour of the third type of agents is coded by
+{\em indifference} w.r.t. the game at all: their dynamics is designed to
+account for risk-aversion as an innovative behavioral gambit. From this point
+of view, the choice of this solitary strategy is enhanced when innovation
+starts, while is depressed when it becomes the majority option. This implies
+that the payoff matrix of the game becomes dependent on the global awareness of
+the agents measured by the relevance of the population of the indifferent
+players. The resulting dynamics is non-trivial with different kinds of phase
+transition depending on a few model parameters. The phase diagram is studied on
+regular as well as complex networks.
+",0,0,0,0,1,0
+16182,The Minimum Euclidean-Norm Point on a Convex Polytope: Wolfe's Combinatorial Algorithm is Exponential,"  The complexity of Philip Wolfe's method for the minimum Euclidean-norm point
+problem over a convex polytope has remained unknown since he proposed the
+method in 1974. The method is important because it is used as a subroutine for
+one of the most practical algorithms for submodular function minimization. We
+present the first example that Wolfe's method takes exponential time.
+Additionally, we improve previous results to show that linear programming
+reduces in strongly-polynomial time to the minimum norm point problem over a
+simplex.
+",1,0,1,0,0,0
+16183,Constraints on a possible evolution of mass density power-law index in strong gravitational lensing from cosmological data,"  In this work, by using strong gravitational lensing (SGL) observations along
+with Type Ia Supernovae (Union2.1) and gamma ray burst data (GRBs), we propose
+a new method to study a possible redshift evolution of $\gamma(z)$, the mass
+density power-law index of strong gravitational lensing systems. In this
+analysis, we assume the validity of cosmic distance duality relation and the
+flat universe. In order to explore the $\gamma(z)$ behavior, three different
+parametrizations are considered, namely: (P1) $\gamma(z_l)=\gamma_0+\gamma_1
+z_l$, (P2) $\gamma(z_l)=\gamma_0+\gamma_1 z_l/(1+z_l)$ and (P3)
+$\gamma(z_l)=\gamma_0+\gamma_1 \ln(1+z_l)$, where $z_l$ corresponds to lens
+redshift. If $\gamma_0=2$ and $\gamma_1=0$ the singular isothermal sphere model
+is recovered. Our method is performed on SGL sub-samples defined by different
+lens redshifts and velocity dispersions. For the former case, the results are
+in full agreement with each other, while a 1$\sigma$ tension between the
+sub-samples with low ($\leq 250$ km/s) and high ($>250$ km/s) velocity
+dispersions was obtained on the ($\gamma_0$-$\gamma_1$) plane. By considering
+the complete SGL sample, we obtain $\gamma_0 \approx 2$ and $ \gamma_1 \approx
+0$ within 1$\sigma$ c.l. for all $\gamma(z)$ parametrizations. However, we find
+the following best fit values of $\gamma_1$: $-0.085$, $-0.16$ and $-0.12$ for
+P1, P2 and P3 parametrizations, respectively, suggesting a mild evolution for
+$\gamma(z)$. By repeating the analysis with Type Ia Supernovae from JLA
+compilation, GRBs and SGL systems this mild evolution is reinforced.
+",0,1,0,0,0,0
+16184,The Strong Cell-based Hydrogen Peroxide Generation Triggered by Cold Atmospheric Plasma,"  Hydrogen peroxide (H2O2) is an important signaling molecule in cancer cells.
+However, the significant secretion of H2O2 by cancer cells have been rarely
+observed. Cold atmospheric plasma (CAP) is a near room temperature ionized gas
+composed of neutral particles, charged particles, reactive species, and
+electrons. Here, we first demonstrated that breast cancer cells and pancreatic
+adenocarcinoma cells generated micromolar level H2O2 during just 1 min of
+direct CAP treatment on these cells. The cell-based H2O2 generation is affected
+by the medium volume, the cell confluence, as well as the discharge voltage.
+The application of cold atmospheric plasma (CAP) in the cancer treatment has
+been intensively investigated over the past decade. Several cellular responses
+to the CAP treatment have been observed including the consumption of the
+CAP-originated reactive species, the rise of intracellular reactive oxygen
+species, the damage on DNA and mitochondria, as well as the activation of
+apoptotic events. This is a new previously unknown cellular response to CAP,
+which provides a new prospective to understand the interaction between CAP and
+cells.
+",0,1,0,0,0,0
+16185,Theory of circadian metabolism,"  Many organisms repartition their proteome in a circadian fashion in response
+to the daily nutrient changes in their environment. A striking example is
+provided by cyanobacteria, which perform photosynthesis during the day to fix
+carbon. These organisms not only face the challenge of rewiring their proteome
+every 12 hours, but also the necessity of storing the fixed carbon in the form
+of glycogen to fuel processes during the night. In this manuscript, we extend
+the framework developed by Hwa and coworkers (Scott et al., Science 330, 1099
+(2010)) for quantifying the relatinship between growth and proteome composition
+to circadian metabolism. We then apply this framework to investigate the
+circadian metabolism of the cyanobacterium Cyanothece, which not only fixes
+carbon during the day, but also nitrogen during the night, storing it in the
+polymer cyanophycin. Our analysis reveals that the need to store carbon and
+nitrogen tends to generate an extreme growth strategy, in which the cells
+predominantly grow during the day, as observed experimentally. This strategy
+maximizes the growth rate over 24 hours, and can be quantitatively understood
+by the bacterial growth laws. Our analysis also shows that the slow relaxation
+of the proteome, arising from the slow growth rate, puts a severe constraint on
+implementing this optimal strategy. Yet, the capacity to estimate the time of
+the day, enabled by the circadian clock, makes it possible to anticipate the
+daily changes in the environment and mount a response ahead of time. This
+significantly enhances the growth rate by counteracting the detrimental effects
+of the slow proteome relaxation.
+",0,0,0,0,1,0
+16186,"Rare-earth/transition-metal magnetic interactions in pristine and (Ni,Fe)-doped YCo5 and GdCo5","  We present an investigation into the intrinsic magnetic properties of the
+compounds YCo5 and GdCo5, members of the RETM5 class of permanent magnets (RE =
+rare earth, TM = transition metal). Focusing on Y and Gd provides direct
+insight into both the TM magnetization and RE-TM interactions without the
+complication of strong crystal field effects. We synthesize single crystals of
+YCo5 and GdCo5 using the optical floating zone technique and measure the
+magnetization from liquid helium temperatures up to 800 K. These measurements
+are interpreted through calculations based on a Green's function formulation of
+density-functional theory, treating the thermal disorder of the local magnetic
+moments within the coherent potential approximation. The rise in the
+magnetization of GdCo5 with temperature is shown to arise from a faster
+disordering of the Gd magnetic moments compared to the antiferromagnetically
+aligned Co sublattice. We use the calculations to analyze the different Curie
+temperatures of the compounds and also compare the molecular (Weiss) fields at
+the RE site with previously published neutron scattering experiments. To gain
+further insight into the RE-TM interactions, we perform substitutional doping
+on the TM site, studying the compounds RECo4.5Ni0.5, RECo4Ni, and RECo4.5Fe0.5.
+Both our calculations and experiments on powdered samples find an
+increased/decreased magnetization with Fe/Ni doping, respectively. The
+calculations further reveal a pronounced dependence on the location of the
+dopant atoms of both the Curie temperatures and the Weiss field at the RE site.
+",0,1,0,0,0,0
+16187,Optimum weight chamber examples of moduli spaces of stable parabolic bundles in genus 0,"  We present an explicit construction of the moduli spaces of rank 2 stable
+parabolic bundles of parabolic degree 0 over the Riemann sphere, corresponding
+to ""optimum"" open weight chambers of parabolic weights in the weight polytope.
+The complexity of the different moduli space' weight chambers is understood in
+terms of the complexity of the actions of the corresponding groups of bundle
+automorphisms on stable parabolic structures. For the given choices of
+parabolic weights, $\mathscr{N}$ consists entirely of isomorphism classes of
+strictly stable parabolic bundles whose underlying Birkhoff-Grothendieck
+splitting coefficients are constant and minimal, is constructed as a quotient
+of a set of stable parabolic structures by a group of bundle automorphisms, and
+is a smooth, compact complex manifold biholomorphic to
+$\left(\mathbb{C}\mathbb{P}^{1}\right)^{n-3}$ for even degree, and
+$\mathbb{C}\mathbb{P}^{n-3}$ for odd degree. As an application of the
+construction of such explicit models, we provide an explicit characterization
+of the nilpotent cone locus on $T^{*}\mathscr{N}$ for Hitchin's integrable
+system.
+",0,0,1,0,0,0
+16188,Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model,"  We report the development and validation of a data-driven real-time risk
+score that provides timely assessments for the clinical acuity of ward patients
+based on their temporal lab tests and vital signs, which allows for timely
+intensive care unit (ICU) admissions. Unlike the existing risk scoring
+technologies, the proposed score is individualized; it uses the electronic
+health record (EHR) data to cluster the patients based on their static
+covariates into subcohorts of similar patients, and then learns a separate
+temporal, non-stationary multi-task Gaussian Process (GP) model that captures
+the physiology of every subcohort. Experiments conducted on data from a
+heterogeneous cohort of 6,094 patients admitted to the Ronald Reagan UCLA
+medical center show that our risk score significantly outperforms the
+state-of-the-art risk scoring technologies, such as the Rothman index and MEWS,
+in terms of timeliness, true positive rate (TPR), and positive predictive value
+(PPV). In particular, the proposed score increases the AUC with 20% and 38% as
+compared to Rothman index and MEWS respectively, and can predict ICU admissions
+8 hours before clinicians at a PPV of 35% and a TPR of 50%. Moreover, we show
+that the proposed risk score allows for better decisions on when to discharge
+clinically stable patients from the ward, thereby improving the efficiency of
+hospital resource utilization.
+",1,0,0,0,0,0
+16189,X-View: Graph-Based Semantic Multi-View Localization,"  Global registration of multi-view robot data is a challenging task.
+Appearance-based global localization approaches often fail under drastic
+view-point changes, as representations have limited view-point invariance. This
+work is based on the idea that human-made environments contain rich semantics
+which can be used to disambiguate global localization. Here, we present X-View,
+a Multi-View Semantic Global Localization system. X-View leverages semantic
+graph descriptor matching for global localization, enabling localization under
+drastically different view-points. While the approach is general in terms of
+the semantic input data, we present and evaluate an implementation on visual
+data. We demonstrate the system in experiments on the publicly available
+SYNTHIA dataset, on a realistic urban dataset recorded with a simulator, and on
+real-world StreetView data. Our findings show that X-View is able to globally
+localize aerial-to-ground, and ground-to-ground robot data of drastically
+different view-points. Our approach achieves an accuracy of up to 85 % on
+global localizations in the multi-view case, while the benchmarked baseline
+appearance-based methods reach up to 75 %.
+",1,0,0,0,0,0
+16190,Local Hardy spaces with variable exponents associated to non-negative self-adjoint operators satisfying Gaussian estimates,"  In this paper we introduce variable exponent local Hardy spaces associated
+with a non-negative self-adjoint operator L. We define them by using an area
+square integral involving the heat semigroup associated to L. A molecular
+characterization is established and as an aplication of the molecular
+characterization we prove that our local Hardy space coincides with the
+(global) variable exponent Hardy space associated to L, provided that 0 does
+not belong to the spectrum of L. Also, we show that it coincides with the
+global variable exponent Hardy space associated to L+I.
+",0,0,1,0,0,0
+16191,Provable Inductive Robust PCA via Iterative Hard Thresholding,"  The robust PCA problem, wherein, given an input data matrix that is the
+superposition of a low-rank matrix and a sparse matrix, we aim to separate out
+the low-rank and sparse components, is a well-studied problem in machine
+learning. One natural question that arises is that, as in the inductive
+setting, if features are provided as input as well, can we hope to do better?
+Answering this in the affirmative, the main goal of this paper is to study the
+robust PCA problem while incorporating feature information. In contrast to
+previous works in which recovery guarantees are based on the convex relaxation
+of the problem, we propose a simple iterative algorithm based on
+hard-thresholding of appropriate residuals. Under weaker assumptions than
+previous works, we prove the global convergence of our iterative procedure;
+moreover, it admits a much faster convergence rate and lesser computational
+complexity per iteration. In practice, through systematic synthetic and real
+data simulations, we confirm our theoretical findings regarding improvements
+obtained by using feature information.
+",1,0,0,1,0,0
+16192,The Topological Period-Index Problem over 8-Complexes,"  We study the Postnikov tower of the classifying space of a compact Lie group
+P(n,mn), which gives obstructions to lifting a topological Brauer class of
+period $n$ to a PU_{mn}-torsor, where the base space is a CW complex of
+dimension 8. Combined with the study of a twisted version of Atiyah-Hirzebruch
+spectral sequence, this solves the topological period-index problem for CW
+complexes of dimension 8.
+",0,0,1,0,0,0
+16193,The Rise of Jihadist Propaganda on Social Networks,"  Using a dataset of over 1.9 million messages posted on Twitter by about
+25,000 ISIS members, we explore how ISIS makes use of social media to spread
+its propaganda and to recruit militants from the Arab world and across the
+globe. By distinguishing between violence-driven, theological, and sectarian
+content, we trace the connection between online rhetoric and key events on the
+ground. To the best of our knowledge, ours is one of the first studies to focus
+on Arabic content, while most literature focuses on English content. Our
+findings yield new important insights about how social media is used by radical
+militant groups to target the Arab-speaking world, and reveal important
+patterns in their propaganda efforts.
+",1,1,0,0,0,0
+16194,Simulated performance of the production target for the Muon g-2 Experiment,"  The Muon g-2 Experiment plans to use the Fermilab Recycler Ring for forming
+the proton bunches that hit its production target. The proposed scheme uses one
+RF system, 80 kV of 2.5 MHz RF. In order to avoid bunch rotations in a
+mismatched bucket, the 2.5 MHz is ramped adiabatically from 3 to 80 kV in 90
+ms. In this study, the interaction of the primary proton beam with the
+production target for the Muon g-2 Experiment is numerically examined.
+",0,1,0,0,0,0
+16195,Virtual-to-Real: Learning to Control in Visual Semantic Segmentation,"  Collecting training data from the physical world is usually time-consuming
+and even dangerous for fragile robots, and thus, recent advances in robot
+learning advocate the use of simulators as the training platform.
+Unfortunately, the reality gap between synthetic and real visual data prohibits
+direct migration of the models trained in virtual worlds to the real world.
+This paper proposes a modular architecture for tackling the virtual-to-real
+problem. The proposed architecture separates the learning model into a
+perception module and a control policy module, and uses semantic image
+segmentation as the meta representation for relating these two modules. The
+perception module translates the perceived RGB image to semantic image
+segmentation. The control policy module is implemented as a deep reinforcement
+learning agent, which performs actions based on the translated image
+segmentation. Our architecture is evaluated in an obstacle avoidance task and a
+target following task. Experimental results show that our architecture
+significantly outperforms all of the baseline methods in both virtual and real
+environments, and demonstrates a faster learning curve than them. We also
+present a detailed analysis for a variety of variant configurations, and
+validate the transferability of our modular architecture.
+",1,0,0,0,0,0
+16196,On Fienup Methods for Regularized Phase Retrieval,"  Alternating minimization, or Fienup methods, have a long history in phase
+retrieval. We provide new insights related to the empirical and theoretical
+analysis of these algorithms when used with Fourier measurements and combined
+with convex priors. In particular, we show that Fienup methods can be viewed as
+performing alternating minimization on a regularized nonconvex least-squares
+problem with respect to amplitude measurements. We then prove that under mild
+additional structural assumptions on the prior (semi-algebraicity), the
+sequence of signal estimates has a smooth convergent behaviour towards a
+critical point of the nonconvex regularized least-squares objective. Finally,
+we propose an extension to Fienup techniques, based on a projected gradient
+descent interpretation and acceleration using inertial terms. We demonstrate
+experimentally that this modification combined with an $\ell_1$ prior
+constitutes a competitive approach for sparse phase retrieval.
+",1,0,1,0,0,0
+16197,Coupling the reduced-order model and the generative model for an importance sampling estimator,"  In this work, we develop an importance sampling estimator by coupling the
+reduced-order model and the generative model in a problem setting of
+uncertainty quantification. The target is to estimate the probability that the
+quantity of interest (QoI) in a complex system is beyond a given threshold. To
+avoid the prohibitive cost of sampling a large scale system, the reduced-order
+model is usually considered for a trade-off between efficiency and accuracy.
+However, the Monte Carlo estimator given by the reduced-order model is biased
+due to the error from dimension reduction. To correct the bias, we still need
+to sample the fine model. An effective technique to reduce the variance
+reduction is importance sampling, where we employ the generative model to
+estimate the distribution of the data from the reduced-order model and use it
+for the change of measure in the importance sampling estimator. To compensate
+the approximation errors of the reduced-order model, more data that induce a
+slightly smaller QoI than the threshold need to be included into the training
+set. Although the amount of these data can be controlled by a posterior error
+estimate, redundant data, which may outnumber the effective data, will be kept
+due to the epistemic uncertainty. To deal with this issue, we introduce a
+weighted empirical distribution to process the data from the reduced-order
+model. The generative model is then trained by minimizing the cross entropy
+between it and the weighted empirical distribution. We also introduce a penalty
+term into the objective function to deal with the overfitting for more
+robustness. Numerical results are presented to demonstrate the effectiveness of
+the proposed methodology.
+",1,0,0,1,0,0
+16198,Anomalous dynamical phase in quantum spin chains with long-range interactions,"  The existence or absence of non-analytic cusps in the Loschmidt-echo return
+rate is traditionally employed to distinguish between a regular dynamical phase
+(regular cusps) and a trivial phase (no cusps) in quantum spin chains after a
+global quench. However, numerical evidence in a recent study [J. C. Halimeh and
+V. Zauner-Stauber, arXiv:1610.02019] suggests that instead of the trivial phase
+a distinct anomalous dynamical phase characterized by a novel type of
+non-analytic cusps occurs in the one-dimensional transverse-field Ising model
+when interactions are sufficiently long-range. Using an analytic semiclassical
+approach and exact diagonalization, we show that this anomalous phase also
+arises in the fully-connected case of infinite-range interactions, and we
+discuss its defining signature. Our results show that the transition from the
+regular to the anomalous dynamical phase coincides with Z2-symmetry breaking in
+the infinite-time limit, thereby showing a connection between two different
+concepts of dynamical criticality. Our work further expands the dynamical phase
+diagram of long-range interacting quantum spin chains, and can be tested
+experimentally in ion-trap setups and ultracold atoms in optical cavities,
+where interactions are inherently long-range.
+",0,1,0,0,0,0
+16199,Cold keV dark matter from decays and scatterings,"  We explore ways of creating cold keV-scale dark matter by means of decays and
+scatterings. The main observation is that certain thermal freeze-in processes
+can lead to a cold dark matter distribution in regions with small available
+phase space. In this way the free-streaming length of keV particles can be
+suppressed without decoupling them too much from the Standard Model. In all
+cases, dark matter needs to be produced together with a heavy particle that
+carries away most of the initial momentum. For decays, this simply requires an
+off-diagonal DM coupling to two heavy particles; for scatterings, the coupling
+of soft DM to two heavy particles needs to be diagonal, in particular in spin
+space. Decays can thus lead to cold light DM of any spin, while scatterings
+only work for bosons with specific couplings. We explore a number of simple
+models and also comment on the connection to the tentative 3.5 keV line.
+",0,1,0,0,0,0
+16200,Solving high-dimensional partial differential equations using deep learning,"  Developing algorithms for solving high-dimensional partial differential
+equations (PDEs) has been an exceedingly difficult task for a long time, due to
+the notoriously difficult problem known as the ""curse of dimensionality"". This
+paper introduces a deep learning-based approach that can handle general
+high-dimensional parabolic PDEs. To this end, the PDEs are reformulated using
+backward stochastic differential equations and the gradient of the unknown
+solution is approximated by neural networks, very much in the spirit of deep
+reinforcement learning with the gradient acting as the policy function.
+Numerical results on examples including the nonlinear Black-Scholes equation,
+the Hamilton-Jacobi-Bellman equation, and the Allen-Cahn equation suggest that
+the proposed algorithm is quite effective in high dimensions, in terms of both
+accuracy and cost. This opens up new possibilities in economics, finance,
+operational research, and physics, by considering all participating agents,
+assets, resources, or particles together at the same time, instead of making ad
+hoc assumptions on their inter-relationships.
+",1,0,1,0,0,0
+16201,An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecasting,"  Spatio-temporal data and processes are prevalent across a wide variety of
+scientific disciplines. These processes are often characterized by nonlinear
+time dynamics that include interactions across multiple scales of spatial and
+temporal variability. The data sets associated with many of these processes are
+increasing in size due to advances in automated data measurement, management,
+and numerical simulator output. Non- linear spatio-temporal models have only
+recently seen interest in statistics, but there are many classes of such models
+in the engineering and geophysical sciences. Tradi- tionally, these models are
+more heuristic than those that have been presented in the statistics
+literature, but are often intuitive and quite efficient computationally. We
+show here that with fairly simple, but important, enhancements, the echo state
+net- work (ESN) machine learning approach can be used to generate long-lead
+forecasts of nonlinear spatio-temporal processes, with reasonable uncertainty
+quantification, and at only a fraction of the computational expense of a
+traditional parametric nonlinear spatio-temporal models.
+",0,0,0,1,0,0
+16202,Profit Maximization for Online Advertising Demand-Side Platforms,"  We develop an optimization model and corresponding algorithm for the
+management of a demand-side platform (DSP), whereby the DSP aims to maximize
+its own profit while acquiring valuable impressions for its advertiser clients.
+We formulate the problem of profit maximization for a DSP interacting with ad
+exchanges in a real-time bidding environment in a
+cost-per-click/cost-per-action pricing model. Our proposed formulation leads to
+a nonconvex optimization problem due to the joint optimization over both
+impression allocation and bid price decisions. We use Lagrangian relaxation to
+develop a tractable convex dual problem, which, due to the properties of
+second-price auctions, may be solved efficiently with subgradient methods. We
+propose a two-phase solution procedure, whereby in the first phase we solve the
+convex dual problem using a subgradient algorithm, and in the second phase we
+use the previously computed dual solution to set bid prices and then solve a
+linear optimization problem to obtain the allocation probability variables. On
+several synthetic examples, we demonstrate that our proposed solution approach
+leads to superior performance over a baseline method that is used in practice.
+",1,0,1,0,0,0
+16203,Traces of surfactants can severely limit the drag reduction of superhydrophobic surfaces,"  Superhydrophobic surfaces (SHSs) have the potential to achieve large drag
+reduction for internal and external flow applications. However, experiments
+have shown inconsistent results, with many studies reporting significantly
+reduced performance. Recently, it has been proposed that surfactants,
+ubiquitous in flow applications, could be responsible, by creating adverse
+Marangoni stresses. Yet, testing this hypothesis is challenging. Careful
+experiments with purified water show large interfacial stresses and,
+paradoxically, adding surfactants yields barely measurable drag increases. This
+suggests that other physical processes, such as thermal Marangoni stresses or
+interface deflection, could explain the lower performance. To test the
+surfactant hypothesis, we perform the first numerical simulations of flows over
+a SHS inclusive of surfactant kinetics. These simulations reveal that
+surfactant-induced stresses are significant at extremely low concentrations,
+potentially yielding a no-slip boundary condition on the air--water interface
+(the ""plastron"") for surfactant amounts below typical environmental values.
+These stresses decrease as the streamwise distance between plastron stagnation
+points increases. We perform microchannel experiments with thermally-controlled
+SHSs consisting of streamwise parallel gratings, which confirm this numerical
+prediction. We introduce a new, unsteady test of surfactant effects. When we
+rapidly remove the driving pressure following a loading phase, a backflow
+develops at the plastron, which can only be explained by surfactant gradients
+formed in the loading phase. This demonstrates the significance of surfactants
+in deteriorating drag reduction, and thus the importance of including
+surfactant stresses in SHS models. Our time-dependent protocol can assess the
+impact of surfactants in SHS testing and guide future mitigating designs.
+",0,1,0,0,0,0
+16204,Non-Convex Rank/Sparsity Regularization and Local Minima,"  This paper considers the problem of recovering either a low rank matrix or a
+sparse vector from observations of linear combinations of the vector or matrix
+elements. Recent methods replace the non-convex regularization with $\ell_1$ or
+nuclear norm relaxations. It is well known that this approach can be guaranteed
+to recover a near optimal solutions if a so called restricted isometry property
+(RIP) holds. On the other hand it is also known to perform soft thresholding
+which results in a shrinking bias which can degrade the solution.
+In this paper we study an alternative non-convex regularization term. This
+formulation does not penalize elements that are larger than a certain threshold
+making it much less prone to small solutions. Our main theoretical results show
+that if a RIP holds then the stationary points are often well separated, in the
+sense that their differences must be of high cardinality/rank. Thus, with a
+suitable initial solution the approach is unlikely to fall into a bad local
+minima. Our numerical tests show that the approach is likely to converge to a
+better solution than standard $\ell_1$/nuclear-norm relaxation even when
+starting from trivial initializations. In many cases our results can also be
+used to verify global optimality of our method.
+",0,0,1,0,0,0
+16205,Supervised Speech Separation Based on Deep Learning: An Overview,"  Speech separation is the task of separating target speech from background
+interference. Traditionally, speech separation is studied as a signal
+processing problem. A more recent approach formulates speech separation as a
+supervised learning problem, where the discriminative patterns of speech,
+speakers, and background noise are learned from training data. Over the past
+decade, many supervised separation algorithms have been put forward. In
+particular, the recent introduction of deep learning to supervised speech
+separation has dramatically accelerated progress and boosted separation
+performance. This article provides a comprehensive overview of the research on
+deep learning based supervised speech separation in the last several years. We
+first introduce the background of speech separation and the formulation of
+supervised separation. Then we discuss three main components of supervised
+separation: learning machines, training targets, and acoustic features. Much of
+the overview is on separation algorithms where we review monaural methods,
+including speech enhancement (speech-nonspeech separation), speaker separation
+(multi-talker separation), and speech dereverberation, as well as
+multi-microphone techniques. The important issue of generalization, unique to
+supervised learning, is discussed. This overview provides a historical
+perspective on how advances are made. In addition, we discuss a number of
+conceptual issues, including what constitutes the target source.
+",1,0,0,0,0,0
+16206,Sample Efficient Feature Selection for Factored MDPs,"  In reinforcement learning, the state of the real world is often represented
+by feature vectors. However, not all of the features may be pertinent for
+solving the current task. We propose Feature Selection Explore and Exploit
+(FS-EE), an algorithm that automatically selects the necessary features while
+learning a Factored Markov Decision Process, and prove that under mild
+assumptions, its sample complexity scales with the in-degree of the dynamics of
+just the necessary features, rather than the in-degree of all features. This
+can result in a much better sample complexity when the in-degree of the
+necessary features is smaller than the in-degree of all features.
+",1,0,0,1,0,0
+16207,Sgoldstino-less inflation and low energy SUSY breaking,"  We assess the range of validity of sgoldstino-less inflation in a scenario of
+low energy supersymmetry breaking. We first analyze the consistency conditions
+that an effective theory of the inflaton and goldstino superfields should
+satisfy in order to be faithfully described by a sgoldstino-less model.
+Enlarging the scope of previous studies, we investigate the case where the
+effective field theory cut-off, and hence also the sgoldstino mass, are
+inflaton-dependent. We then introduce a UV complete model where one can realize
+successfully sgoldstino-less inflation and gauge mediation of supersymmetry
+breaking, combining the alpha-attractor mechanism and a weakly coupled model of
+spontaneous breaking of supersymmetry. In this class of models we find that,
+given current limits on superpartner masses, the gravitino mass has a lower
+bound of the order of the MeV, i.e. we cannot reach very low supersymmetry
+breaking scales. On the plus side, we recognize that in this framework, one can
+derive the complete superpartner spectrum as well as compute inflation
+observables, the reheating temperature, and address the gravitino overabundance
+problem. We then show that further constraints come from collider results and
+inflation observables. Their non trivial interplay seems a staple feature of
+phenomenological studies of supersymmetric inflationary models.
+",0,1,0,0,0,0
+16208,SideEye: A Generative Neural Network Based Simulator of Human Peripheral Vision,"  Foveal vision makes up less than 1% of the visual field. The other 99% is
+peripheral vision. Precisely what human beings see in the periphery is both
+obvious and mysterious in that we see it with our own eyes but can't visualize
+what we see, except in controlled lab experiments. Degradation of information
+in the periphery is far more complex than what might be mimicked with a radial
+blur. Rather, behaviorally-validated models hypothesize that peripheral vision
+measures a large number of local texture statistics in pooling regions that
+overlap and grow with eccentricity. In this work, we develop a new method for
+peripheral vision simulation by training a generative neural network on a
+behaviorally-validated full-field synthesis model. By achieving a 21,000 fold
+reduction in running time, our approach is the first to combine realism and
+speed of peripheral vision simulation to a degree that provides a whole new way
+to approach visual design: through peripheral visualization.
+",1,0,0,0,0,0
+16209,Carbon stars in the X-Shooter Spectral Library: II. Comparison with models,"  In a previous paper, we assembled a collection of medium-resolution spectra
+of 35 carbon stars, covering optical and near-infrared wavelengths from 400 to
+2400 nm. The sample includes stars from the Milky Way and the Magellanic
+Clouds, with a variety of $(J-K_s)$ colors and pulsation properties. In the
+present paper, we compare these observations to a new set of high-resolution
+synthetic spectra, based on hydrostatic model atmospheres. We find that the
+broad-band colors and the molecular-band strengths measured by
+spectrophotometric indices match those of the models when $(J-K_s)$ is bluer
+than about 1.6, while the redder stars require either additional reddening or
+dust emission or both. Using a grid of models to fit the full observed spectra,
+we estimate the most likely atmospheric parameters $T_\mathrm{eff}$, $\log(g)$,
+$[\mathrm{Fe/H}]$ and C/O. These parameters derived independently in the
+optical and near-infrared are generally consistent when $(J-K_s)<1.6$. The
+temperatures found based on either wavelength range are typically within
+$\pm$100K of each other, and $\log(g)$ and $[\mathrm{Fe/H}]$ are consistent
+with the values expected for this sample. The reddest stars ($(J-K_s)$ $>$ 1.6)
+are divided into two families, characterized by the presence or absence of an
+absorption feature at 1.53\,$\mu$m, generally associated with HCN and
+C$_2$H$_2$. Stars from the first family begin to be more affected by
+circumstellar extinction. The parameters found using optical or near-infrared
+wavelengths are still compatible with each other, but the error bars become
+larger. In stars showing the 1.53\,$\mu$m feature, which are all
+large-amplitude variables, the effects of pulsation are strong and the spectra
+are poorly matched with hydrostatic models. For these, atmospheric parameters
+could not be derived reliably, and dynamical models are needed for proper
+interpretation.
+",0,1,0,0,0,0
+16210,Four-variable expanders over the prime fields,"  Let $\mathbb{F}_p$ be a prime field of order $p>2$, and $A$ be a set in
+$\mathbb{F}_p$ with very small size in terms of $p$. In this note, we show that
+the number of distinct cubic distances determined by points in $A\times A$
+satisfies \[|(A-A)^3+(A-A)^3|\gg |A|^{8/7},\] which improves a result due to
+Yazici, Murphy, Rudnev, and Shkredov. In addition, we investigate some new
+families of expanders in four and five variables.
+We also give an explicit exponent of a problem of Bukh and Tsimerman, namely,
+we prove that \[\max \left\lbrace |A+A|, |f(A, A)|\right\rbrace\gg |A|^{6/5},\]
+where $f(x, y)$ is a quadratic polynomial in $\mathbb{F}_p[x, y]$ that is not
+of the form $g(\alpha x+\beta y)$ for some univariate polynomial $g$.
+",0,0,1,0,0,0
+16211,Limit multiplicities for ${\rm SL}_2(\mathcal{O}_F)$ in ${\rm SL}_2(\mathbb{R}^{r_1}\oplus\mathbb{C}^{r_2})$,"  We prove that the family of lattices ${\rm SL}_2(\mathcal{O}_F)$, $F$ running
+over number fields with fixed archimedean signature $(r_1, r_2)$, in ${\rm
+SL}_2(\mathbb{R}^{r_1}\oplus\mathbb{C}^{r_2})$ has the limit multiplicity
+property.
+",0,0,1,0,0,0
+16212,On comparing clusterings: an element-centric framework unifies overlaps and hierarchy,"  Clustering is one of the most universal approaches for understanding complex
+data. A pivotal aspect of clustering analysis is quantitatively comparing
+clusterings; clustering comparison is the basis for tasks such as clustering
+evaluation, consensus clustering, and tracking the temporal evolution of
+clusters. For example, the extrinsic evaluation of clustering methods requires
+comparing the uncovered clusterings to planted clusterings or known metadata.
+Yet, as we demonstrate, existing clustering comparison measures have critical
+biases which un- dermine their usefulness, and no measure accommodates both
+overlapping and hierarchical clusterings. Here we unify the comparison of
+disjoint, overlapping, and hierarchically struc- tured clusterings by proposing
+a new element-centric framework: elements are compared based on the
+relationships induced by the cluster structure, as opposed to the traditional
+cluster-centric philosophy. We demonstrate that, in contrast to standard
+clustering simi- larity measures, our framework does not suffer from critical
+biases and naturally provides unique insights into how the clusterings differ.
+We illustrate the strengths of our framework by revealing new insights into the
+organization of clusters in two applications: the improved classification of
+schizophrenia based on the overlapping and hierarchical community struc- ture
+of fMRI brain networks, and the disentanglement of various social homophily
+factors in Facebook social networks. The universality of clustering suggests
+far-reaching impact of our framework throughout all areas of science.
+",1,0,0,1,0,0
+16213,Parametric Oscillatory Instability in a Fabry-Perot Cavity of the Einstein Telescope with different mirror's materials,"  We discuss the parametric oscillatory instability in a Fabry-Perot cavity of
+the Einstein Telescope. Unstable combinations of elastic and optical modes for
+two possible configurations of gravitational wave third-generation detector are
+deduced. The results are compared with the results for gravita- tional wave
+interferometers LIGO and LIGO Voyager.
+",0,1,0,0,0,0
+16214,Mapping properties of the Hilbert and Fubini--Study maps in Kähler geometry,"  Suppose that we have a compact Kähler manifold $X$ with a very ample line
+bundle $\mathcal{L}$. We prove that any positive definite hermitian form on the
+space $H^0 (X,\mathcal{L})$ of holomorphic sections can be written as an
+$L^2$-inner product with respect to an appropriate hermitian metric on
+$\mathcal{L}$. We apply this result to show that the Fubini--Study map, which
+associates a hermitian metric on $\mathcal{L}$ to a hermitian form on $H^0
+(X,\mathcal{L})$, is injective.
+",0,0,1,0,0,0
+16215,"Space-time Constructivism vs. Modal Provincialism: Or, How Special Relativistic Theories Needn't Show Minkowski Chronogeometry","  In 1835 Lobachevski entertained the possibility of multiple (rival)
+geometries. This idea has reappeared on occasion (e.g., Poincaré) but
+didn't become key in space-time foundations prior to Brown's \emph{Physical
+Relativity} (at the end, the interpretive key to the book). A crucial
+difference between his constructivism and orthodox ""space-time realism"" is
+modal scope. Constructivism applies to all local classical field theories,
+including those with multiple geometries. But the orthodox view provincially
+assumes a unique geometry, as familiar theories (Newton, Special Relativity,
+Nordström, and GR) have. They serve as the orthodox ""canon."" Their
+historical roles suggest a story of inevitable progress. Physics literature
+after c. 1920 is relevant to orthodoxy mostly as commentary on the canon, which
+closed in the 1910s. The orthodox view explains the behavior of matter as the
+manifestation of the real space-time geometry, which works within the canon.
+The orthodox view, Whiggish history, and the canon relate symbiotically.
+If one considers a theory outside the canon, space-time realism sheds little
+light on matter's behavior. Worse, it gives the wrong answer when applied to an
+example arguably in the canon, massive scalar gravity with universal coupling.
+Which is the true geometry---the flat metric from the Poincaré symmetry,
+the conformally flat metric exhibited by material rods and clocks, or both---or
+is the question bad? How does space-time realism explain that all matter fields
+see the same curved geometry, given so many ways to mix and match?
+Constructivist attention to dynamical details is vindicated; geometrical
+shortcuts disappoint. The more exhaustive exploration of relativistic field
+theories (especially massive) in particle physics is an underused resource for
+foundations.
+",0,1,0,0,0,0
+16216,On the representation of integers by binary quadratic forms,"  In this note we show that for a given irreducible binary quadratic form
+$f(x,y)$ with integer coefficients, whenever we have $f(x,y) = f(u,v)$ for
+integers $x,y,u,v$, there exists a rational automorphism of $f$ which sends
+$(x,y)$ to $(u,v)$.
+",0,0,1,0,0,0
+16217,The First Planetary Microlensing Event with Two Microlensed Source Stars,"  We present the analysis of microlensing event MOA-2010-BLG-117, and show that
+the light curve can only be explained by the gravitational lensing of a binary
+source star system by a star with a Jupiter mass ratio planet. It was necessary
+to modify standard microlensing modeling methods to find the correct light
+curve solution for this binary-source, binary-lens event. We are able to
+measure a strong microlensing parallax signal, which yields the masses of the
+host star, $M_* = 0.58\pm 0.11 M_\odot$, and planet $m_p = 0.54\pm 0.10 M_{\rm
+Jup}$ at a projected star-planet separation of $a_\perp = 2.42\pm 0.26\,$AU,
+corresponding to a semi-major axis of $a = 2.9{+1.6\atop -0.6}\,$AU. Thus, the
+system resembles a half-scale model of the Sun-Jupiter system with a
+half-Jupiter mass planet orbiting a half-solar mass star at very roughly half
+of Jupiter's orbital distance from the Sun. The source stars are slightly
+evolved, and by requiring them to lie on the same isochrone, we can constrain
+the source to lie in the near side of the bulge at a distance of $D_S = 6.9 \pm
+0.7\,$kpc, which implies a distance to the planetary lens system of $D_L =
+3.5\pm 0.4\,$kpc. The ability to model unusual planetary microlensing events,
+like this one, will be necessary to extract precise statistical information
+from the planned large exoplanet microlensing surveys, such as the WFIRST
+microlensing survey.
+",0,1,0,0,0,0
+16218,A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model,"  Misunderstanding of driver correction behaviors (DCB) is the primary reason
+for false warnings of lane-departure-prediction systems. We propose a
+learning-based approach to predicting unintended lane-departure behaviors (LDB)
+and the chance for drivers to bring the vehicle back to the lane. First, in
+this approach, a personalized driver model for lane-departure and lane-keeping
+behavior is established by combining the Gaussian mixture model and the hidden
+Markov model. Second, based on this model, we develop an online model-based
+prediction algorithm to predict the forthcoming vehicle trajectory and judge
+whether the driver will demonstrate an LDB or a DCB. We also develop a warning
+strategy based on the model-based prediction algorithm that allows the
+lane-departure warning system to be acceptable for drivers according to the
+predicted trajectory. In addition, the naturalistic driving data of 10 drivers
+is collected through the University of Michigan Safety Pilot Model Deployment
+program to train the personalized driver model and validate this approach. We
+compare the proposed method with a basic time-to-lane-crossing (TLC) method and
+a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. The
+results show that the proposed approach can reduce the false-warning rate to
+3.07\%.
+",1,0,0,0,0,0
+16219,Internet of Things: Survey on Security and Privacy,"  The Internet of Things (IoT) is intended for ubiquitous connectivity among
+different entities or ""things"". While its purpose is to provide effective and
+efficient solutions, security of the devices and network is a challenging
+issue. The number of devices connected along with the ad-hoc nature of the
+system further exacerbates the situation. Therefore, security and privacy has
+emerged as a significant challenge for the IoT. In this paper,we aim to provide
+a thorough survey related to the privacy and security challenges of the IoT.
+This document addresses these challenges from the perspective of technologies
+and architecture used. This work focuses also in IoT intrinsic vulnerabilities
+as well as the security challenges of various layers based on the security
+principles of data confidentiality, integrity and availability. This survey
+analyzes articles published for the IoT at the time and relates it to the
+security conjuncture of the field and its projection to the future.
+",1,0,0,0,0,0
+16220,A Connectedness Constraint for Learning Sparse Graphs,"  Graphs are naturally sparse objects that are used to study many problems
+involving networks, for example, distributed learning and graph signal
+processing. In some cases, the graph is not given, but must be learned from the
+problem and available data. Often it is desirable to learn sparse graphs.
+However, making a graph highly sparse can split the graph into several
+disconnected components, leading to several separate networks. The main
+difficulty is that connectedness is often treated as a combinatorial property,
+making it hard to enforce in e.g. convex optimization problems. In this
+article, we show how connectedness of undirected graphs can be formulated as an
+analytical property and can be enforced as a convex constraint. We especially
+show how the constraint relates to the distributed consensus problem and graph
+Laplacian learning. Using simulated and real data, we perform experiments to
+learn sparse and connected graphs from data.
+",0,0,0,1,0,0
+16221,Quotients of finite-dimensional operators by symmetry representations,"  A finite dimensional operator that commutes with some symmetry group admits
+quotient operators, which are determined by the choice of associated
+representation. Taking the quotient isolates the part of the spectrum
+supporting the chosen representation and reduces the complexity of the problem,
+however it is not uniquely defined. Here we present a computationally simple
+way of choosing a special basis for the space of intertwiners, allowing us to
+construct a quotient that reflects the structure of the original operator. This
+quotient construction generalizes previous definitions for discrete graphs,
+which either dealt with restricted group actions or only with the trivial
+representation.
+We also extend the method to quantum graphs, which simplifies previous
+constructions within this context, answers an open question regarding
+self-adjointness and offers alternative viewpoints in terms of a scattering
+approach. Applications to isospectrality are discussed, together with numerous
+examples and comparisons with previous results.
+",0,0,1,0,0,0
+16222,A probabilistic framework for the control of systems with discrete states and stochastic excitation,"  A probabilistic framework is proposed for the optimization of efficient
+switched control strategies for physical systems dominated by stochastic
+excitation. In this framework, the equation for the state trajectory is
+replaced with an equivalent equation for its probability distribution function
+in the constrained optimization setting. This allows for a large class of
+control rules to be considered, including hysteresis and a mix of continuous
+and discrete random variables. The problem of steering atmospheric balloons
+within a stratified flowfield is a motivating application; the same approach
+can be extended to a variety of mixed-variable stochastic systems and to new
+classes of control rules.
+",1,0,1,0,0,0
+16223,"Supersonic Flow onto Solid Wedges, Multidimensional Shock Waves and Free Boundary Problems","  When an upstream steady uniform supersonic flow impinges onto a symmetric
+straight-sided wedge, governed by the Euler equations, there are two possible
+steady oblique shock configurations if the wedge angle is less than the
+detachment angle -- the steady weak shock with supersonic or subsonic
+downstream flow (determined by the wedge angle that is less or larger than the
+sonic angle) and the steady strong shock with subsonic downstream flow, both of
+which satisfy the entropy condition. The fundamental issue -- whether one or
+both of the steady weak and strong shocks are physically admissible solutions
+-- has been vigorously debated over the past eight decades. In this paper, we
+survey some recent developments on the stability analysis of the steady shock
+solutions in both the steady and dynamic regimes. For the static stability, we
+first show how the stability problem can be formulated as an initial-boundary
+value type problem and then reformulate it into a free boundary problem when
+the perturbation of both the upstream steady supersonic flow and the wedge
+boundary are suitably regular and small, and we finally present some recent
+results on the static stability of the steady supersonic and transonic shocks.
+For the dynamic stability for potential flow, we first show how the stability
+problem can be formulated as an initial-boundary value problem and then use the
+self-similarity of the problem to reduce it into a boundary value problem and
+further reformulate it into a free boundary problem, and we finally survey some
+recent developments in solving this free boundary problem for the existence of
+the Prandtl-Meyer configurations that tend to the steady weak supersonic or
+transonic oblique shock solutions as time goes to infinity. Some further
+developments and mathematical challenges in this direction are also discussed.
+",0,1,1,0,0,0
+16224,A Zero-Shot Learning application in Deep Drawing process using Hyper-Process Model,"  One of the consequences of passing from mass production to mass customization
+paradigm in the nowadays industrialized world is the need to increase
+flexibility and responsiveness of manufacturing companies. The high-mix /
+low-volume production forces constant accommodations of unknown product
+variants, which ultimately leads to high periods of machine calibration. The
+difficulty related with machine calibration is that experience is required
+together with a set of experiments to meet the final product quality.
+Unfortunately, all possible combinations of machine parameters is so high that
+is difficult to build empirical knowledge. Due to this fact, normally trial and
+error approaches are taken making one-of-a-kind products not viable. Therefore,
+a Zero-Shot Learning (ZSL) based approach called hyper-process model (HPM) to
+learn the relation among multiple tasks is used as a way to shorten the
+calibration phase. Assuming each product variant is a task to solve, first, a
+shape analysis on data to learn common modes of deformation between tasks is
+made, and secondly, a mapping between these modes and task descriptions is
+performed. Ultimately, the present work has two main contributions: 1)
+Formulation of an industrial problem into a ZSL setting where new process
+models can be generated for process optimization and 2) the definition of a
+regression problem in the domain of ZSL. For that purpose, a 2-d deep drawing
+simulated process was used based on data collected from the Abaqus simulator,
+where a significant number of process models were collected to test the
+effectiveness of the approach. The obtained results show that is possible to
+learn new tasks without any available data (both labeled and unlabeled) by
+leveraging information about already existing tasks, allowing to speed up the
+calibration phase and make a quicker integration of new products into
+manufacturing systems.
+",1,0,0,1,0,0
+16225,Fracton topological phases from strongly coupled spin chains,"  We provide a new perspective on fracton topological phases, a class of
+three-dimensional topologically ordered phases with unconventional
+fractionalized excitations that are either completely immobile or only mobile
+along particular lines or planes. We demonstrate that a wide range of these
+fracton phases can be constructed by strongly coupling mutually intersecting
+spin chains and explain via a concrete example how such a coupled-spin-chain
+construction illuminates the generic properties of a fracton phase. In
+particular, we describe a systematic translation from each coupled-spin-chain
+construction into a parton construction where the partons correspond to the
+excitations that are mobile along lines. Remarkably, our construction of
+fracton phases is inherently based on spin models involving only two-spin
+interactions and thus brings us closer to their experimental realization.
+",0,1,0,0,0,0
+16226,The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations,"  With the large-scale penetration of the internet, for the first time,
+humanity has become linked by a single, open, communications platform.
+Harnessing this fact, we report insights arising from a unified internet
+activity and location dataset of an unparalleled scope and accuracy drawn from
+over a trillion (1.5$\times 10^{12}$) observations of end-user internet
+connections, with temporal resolution of just 15min over 2006-2012. We first
+apply this dataset to the expansion of the internet itself over 1,647 urban
+agglomerations globally. We find that unique IP per capita counts reach
+saturation at approximately one IP per three people, and take, on average, 16.1
+years to achieve; eclipsing the estimated 100- and 60- year saturation times
+for steam-power and electrification respectively. Next, we use intra-diurnal
+internet activity features to up-scale traditional over-night sleep
+observations, producing the first global estimate of over-night sleep duration
+in 645 cities over 7 years. We find statistically significant variation between
+continental, national and regional sleep durations including some evidence of
+global sleep duration convergence. Finally, we estimate the relationship
+between internet concentration and economic outcomes in 411 OECD regions and
+find that the internet's expansion is associated with negative or positive
+productivity gains, depending strongly on sectoral considerations. To our
+knowledge, our study is the first of its kind to use online/offline activity of
+the entire internet to infer social science insights, demonstrating the
+unparalleled potential of the internet as a social data-science platform.
+",1,1,0,1,0,0
+16227,Deep Embedding Kernel,"  In this paper, we propose a novel supervised learning method that is called
+Deep Embedding Kernel (DEK). DEK combines the advantages of deep learning and
+kernel methods in a unified framework. More specifically, DEK is a learnable
+kernel represented by a newly designed deep architecture. Compared with
+pre-defined kernels, this kernel can be explicitly trained to map data to an
+optimized high-level feature space where data may have favorable features
+toward the application. Compared with typical deep learning using SoftMax or
+logistic regression as the top layer, DEK is expected to be more generalizable
+to new data. Experimental results show that DEK has superior performance than
+typical machine learning methods in identity detection, classification,
+regression, dimension reduction, and transfer learning.
+",0,0,0,1,0,0
+16228,A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps,"  In this paper, we develop a system for the low-cost indoor localization and
+tracking problem using radio signal strength indicator, Inertial Measurement
+Unit (IMU), and magnetometer sensors. We develop a novel and simplified
+probabilistic IMU motion model as the proposal distribution of the sequential
+Monte-Carlo technique to track the robot trajectory. Our algorithm can globally
+localize and track a robot with a priori unknown location, given an informative
+prior map of the Bluetooth Low Energy (BLE) beacons. Also, we formulate the
+problem as an optimization problem that serves as the Back-end of the algorithm
+mentioned above (Front-end). Thus, by simultaneously solving for the robot
+trajectory and the map of BLE beacons, we recover a continuous and smooth
+trajectory of the robot, corrected locations of the BLE beacons, and the
+time-varying IMU bias. The evaluations achieved using hardware show that
+through the proposed closed-loop system the localization performance can be
+improved; furthermore, the system becomes robust to the error in the map of
+beacons by feeding back the optimized map to the Front-end.
+",1,0,0,0,0,0
+16229,"Simple groups, generation and probabilistic methods","  It is well known that every finite simple group can be generated by two
+elements and this leads to a wide range of problems that have been the focus of
+intensive research in recent years. In this survey article we discuss some of
+the extraordinary generation properties of simple groups, focussing on topics
+such as random generation, $(a,b)$-generation and spread, as well as
+highlighting the application of probabilistic methods in the proofs of many of
+the main results. We also present some recent work on the minimal generation of
+maximal and second maximal subgroups of simple groups, which has applications
+to the study of subgroup growth and the generation of primitive permutation
+groups.
+",0,0,1,0,0,0
+16230,Large polaron evolution in anatase TiO2 due to carrier and temperature dependence of electron-phonon coupling,"  The electronic and magneto transport properties of reduced anatase TiO2
+epitaxial thin films are analyzed considering various polaronic effects.
+Unexpectedly, with increasing carrier concentration, the mobility increases,
+which rarely happens in common metallic systems. We find that the screening of
+the electron-phonon (e-ph) coupling by excess carriers is necessary to explain
+this unusual dependence. We also find that the magnetoresistance (MR) could be
+decomposed into a linear and a quadratic component, separately characterizing
+the transport and trap behavior of carriers as a function of temperature. The
+various transport behaviors could be organized into a single phase diagram
+which clarifies the nature of large polaron in this material.
+",0,1,0,0,0,0
+16231,AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks,"  New types of machine learning hardware in development and entering the market
+hold the promise of revolutionizing deep learning in a manner as profound as
+GPUs. However, existing software frameworks and training algorithms for deep
+learning have yet to evolve to fully leverage the capability of the new wave of
+silicon. We already see the limitations of existing algorithms for models that
+exploit structured input via complex and instance-dependent control flow, which
+prohibits minibatching. We present an asynchronous model-parallel (AMP)
+training algorithm that is specifically motivated by training on networks of
+interconnected devices. Through an implementation on multi-core CPUs, we show
+that AMP training converges to the same accuracy as conventional synchronous
+training algorithms in a similar number of epochs, but utilizes the available
+hardware more efficiently even for small minibatch sizes, resulting in
+significantly shorter overall training times. Our framework opens the door for
+scaling up a new class of deep learning models that cannot be efficiently
+trained today.
+",1,0,0,1,0,0
+16232,Random data wave equations,"  Nowadays we have many methods allowing to exploit the regularising properties
+of the linear part of a nonlinear dispersive equation (such as the KdV
+equation, the nonlinear wave or the nonlinear Schroedinger equations) in order
+to prove well-posedness in low regularity Sobolev spaces. By well-posedness in
+low regularity Sobolev spaces we mean that less regularity than the one imposed
+by the energy methods is required (the energy methods do not exploit the
+dispersive properties of the linear part of the equation). In many cases these
+methods to prove well-posedness in low regularity Sobolev spaces lead to
+optimal results in terms of the regularity of the initial data. By optimal we
+mean that if one requires slightly less regularity then the corresponding
+Cauchy problem becomes ill-posed in the Hadamard sense. We call the Sobolev
+spaces in which these ill-posedness results hold spaces of supercritical
+regularity.
+More recently, methods to prove probabilistic well-posedness in Sobolev
+spaces of supercritical regularity were developed. More precisely, by
+probabilistic well-posedness we mean that one endows the corresponding Sobolev
+space of supercritical regularity with a non degenerate probability measure and
+then one shows that almost surely with respect to this measure one can define a
+(unique) global flow. However, in most of the cases when the methods to prove
+probabilistic well-posedness apply, there is no information about the measure
+transported by the flow. Very recently, a method to prove that the transported
+measure is absolutely continuous with respect to the initial measure was
+developed. In such a situation, we have a measure which is quasi-invariant
+under the corresponding flow.
+The aim of these lectures is to present all of the above described
+developments in the context of the nonlinear wave equation.
+",0,0,1,0,0,0
+16233,The Persistent Homotopy Type Distance,"  We introduce the persistent homotopy type distance dHT to compare real valued
+functions defined on possibly different homotopy equivalent topological spaces.
+The underlying idea in the definition of dHT is to measure the minimal shift
+that is necessary to apply to one of the two functions in order that the
+sublevel sets of the two functions become homotopically equivalent. This
+distance is interesting in connection with persistent homology. Indeed, our
+main result states that dHT still provides an upper bound for the bottleneck
+distance between the persistence diagrams of the intervening functions.
+Moreover, because homotopy equivalences are weaker than homeomorphisms, this
+implies a lifting of the standard stability results provided by the L-infty
+distance and the natural pseudo-distance dNP. From a different standpoint, we
+prove that dHT extends the L-infty distance and dNP in two ways. First, we show
+that, appropriately restricting the category of objects to which dHT applies,
+it can be made to coincide with the other two distances. Finally, we show that
+dHT has an interpretation in terms of interleavings that naturally places it in
+the family of distances used in persistence theory.
+",1,0,1,0,0,0
+16234,Optimal algorithms for smooth and strongly convex distributed optimization in networks,"  In this paper, we determine the optimal convergence rates for strongly convex
+and smooth distributed optimization in two settings: centralized and
+decentralized communications over a network. For centralized (i.e.
+master/slave) algorithms, we show that distributing Nesterov's accelerated
+gradient descent is optimal and achieves a precision $\varepsilon > 0$ in time
+$O(\sqrt{\kappa_g}(1+\Delta\tau)\ln(1/\varepsilon))$, where $\kappa_g$ is the
+condition number of the (global) function to optimize, $\Delta$ is the diameter
+of the network, and $\tau$ (resp. $1$) is the time needed to communicate values
+between two neighbors (resp. perform local computations). For decentralized
+algorithms based on gossip, we provide the first optimal algorithm, called the
+multi-step dual accelerated (MSDA) method, that achieves a precision
+$\varepsilon > 0$ in time
+$O(\sqrt{\kappa_l}(1+\frac{\tau}{\sqrt{\gamma}})\ln(1/\varepsilon))$, where
+$\kappa_l$ is the condition number of the local functions and $\gamma$ is the
+(normalized) eigengap of the gossip matrix used for communication between
+nodes. We then verify the efficiency of MSDA against state-of-the-art methods
+for two problems: least-squares regression and classification by logistic
+regression.
+",0,0,1,1,0,0
+16235,Superconductivity in ultra-thin carbon nanotubes and carbyne-nanotube composites: an ab-initio approach,"  The superconductivity of the 4-angstrom single-walled carbon nanotubes
+(SWCNTs) was discovered more than a decade ago, and marked the breakthrough of
+finding superconductivity in pure elemental undoped carbon compounds. The van
+Hove singularities in the electronic density of states at the Fermi level in
+combination with a large Debye temperature of the SWCNTs are expected to cause
+an impressively large superconducting gap. We have developed an innovative
+computational algorithm specially tailored for the investigation of
+superconductivity in ultrathin SWCNTs. We predict the superconducting
+transition temperature of various thin carbon nanotubes resulting from
+electron-phonon coupling by an ab-initio method, taking into account the effect
+of radial pressure, symmetry, chirality (N,M) and bond lengths. By optimizing
+the geometry of the carbon nanotubes, a maximum Tc of 60K is found. We also use
+our method to calculate the Tc of a linear carbon chain embedded in the center
+of (5,0) SWCNTs. The strong curvature in the (5,0) carbon nanotubes in the
+presence of the inner carbon chain provides an alternative path to increase the
+Tc of this carbon composite by a factor of 2.2 with respect to the empty (5,0)
+SWCNTs.
+",0,1,0,0,0,0
+16236,Exponential lower bounds for history-based simplex pivot rules on abstract cubes,"  The behavior of the simplex algorithm is a widely studied subject.
+Specifically, the question of the existence of a polynomial pivot rule for the
+simplex algorithm is of major importance. Here, we give exponential lower
+bounds for three history-based pivot rules. Those rules decide their next step
+based on memory of the past steps. In particular, we study Zadeh's least
+entered rule, Johnson's least-recently basic rule and Cunningham's
+least-recently considered (or round-robin) rule. We give exponential lower
+bounds on Acyclic Unique Sink Orientations (AUSO) of the abstract cube, for all
+of these pivot rules. For Johnson's rule our bound is the first superpolynomial
+one in any context; for Zadeh's it is the first one for AUSO. Those two are our
+main results.
+",1,0,0,0,0,0
+16237,Machine learning regression on hyperspectral data to estimate multiple water parameters,"  In this paper, we present a regression framework involving several machine
+learning models to estimate water parameters based on hyperspectral data.
+Measurements from a multi-sensor field campaign, conducted on the River Elbe,
+Germany, represent the benchmark dataset. It contains hyperspectral data and
+the five water parameters chlorophyll a, green algae, diatoms, CDOM and
+turbidity. We apply a PCA for the high-dimensional data as a possible
+preprocessing step. Then, we evaluate the performance of the regression
+framework with and without this preprocessing step. The regression results of
+the framework clearly reveal the potential of estimating water parameters based
+on hyperspectral data with machine learning. The proposed framework provides
+the basis for further investigations, such as adapting the framework to
+estimate water parameters of different inland waters.
+",0,0,0,0,1,0
+16238,Critical factors and enablers of food quality and safety compliance risk management in the Vietnamese seafood supply chain,"  Recently, along with the emergence of food scandals, food supply chains have
+to face with ever-increasing pressure from compliance with food quality and
+safety regulations and standards. This paper aims to explore critical factors
+of compliance risk in food supply chain with an illustrated case in Vietnamese
+seafood industry. To this end, this study takes advantage of both primary and
+secondary data sources through a comprehensive literature research of
+industrial and scientific papers, combined with expert interview. Findings
+showed that there are three main critical factor groups influencing on
+compliance risk including challenges originating from Vietnamese food supply
+chain itself, characteristics of regulation and standards, and business
+environment. Furthermore, author proposed enablers to eliminate compliance
+risks to food supply chain managers as well as recommendations to government
+and other influencers and supporters.
+",0,0,0,0,0,1
+16239,Asymptotic behavior of semilinear parabolic equations on the circle with time almost-periodic/recurrent dependence,"  We study topological structure of the $\omega$-limit sets of the skew-product
+semiflow generated by the following scalar reaction-diffusion equation
+\begin{equation*} u_{t}=u_{xx}+f(t,u,u_{x}),\,\,t>0,\,x\in
+S^{1}=\mathbb{R}/2\pi \mathbb{Z}, \end{equation*} where $f(t,u,u_x)$ is
+$C^2$-admissible with time-recurrent structure including almost-periodicity and
+almost-automorphy. Contrary to the time-periodic cases (for which any
+$\omega$-limit set can be imbedded into a periodically forced circle flow), it
+is shown that one cannot expect that any $\omega$-limit set can be imbedded
+into an almost-periodically forced circle flow even if $f$ is uniformly
+almost-periodic in $t$.
+More precisely, we prove that, for a given $\omega$-limit set $\Omega$, if
+${\rm dim}V^c(\Omega)\leq 1$ ($V^c(\Omega)$ is the center space associated with
+$\Omega$), then $\Omega$ is either spatially-homogeneous or
+spatially-inhomogeneous; and moreover, any spatially-inhomogeneous $\Omega$ can
+be imbedded into a time-recurrently forced circle flow (resp. imbedded into an
+almost periodically-forced circle flow if $f$ is uniformly almost-periodic in
+$t$). On the other hand, when ${\rm dim}V^c(\Omega>1$, it is pointed out that
+the above embedding property cannot hold anymore. Furthermore, we also show the
+new phenomena of the residual imbedding into a time-recurrently forced circle
+flow (resp. into an almost automorphically-forced circle flow if $f$ is
+uniformly almost-periodic in $t$) provided that $\dim V^c(\Omega)=2$ and $\dim
+V^u(\Omega)$ is odd. All these results reveal that for such system there are
+essential differences between time-periodic cases and non-periodic cases.
+",0,0,1,0,0,0
+16240,Exploring 4D Quantum Hall Physics with a 2D Topological Charge Pump,"  The discovery of topological states of matter has profoundly augmented our
+understanding of phase transitions in physical systems. Instead of local order
+parameters, topological phases are described by global topological invariants
+and are therefore robust against perturbations. A prominent example thereof is
+the two-dimensional integer quantum Hall effect. It is characterized by the
+first Chern number which manifests in the quantized Hall response induced by an
+external electric field. Generalizing the quantum Hall effect to
+four-dimensional systems leads to the appearance of a novel non-linear Hall
+response that is quantized as well, but described by a 4D topological invariant
+- the second Chern number. Here, we report on the first observation of a bulk
+response with intrinsic 4D topology and the measurement of the associated
+second Chern number. By implementing a 2D topological charge pump with
+ultracold bosonic atoms in an angled optical superlattice, we realize a
+dynamical version of the 4D integer quantum Hall effect. Using a small atom
+cloud as a local probe, we fully characterize the non-linear response of the
+system by in-situ imaging and site-resolved band mapping. Our findings pave the
+way to experimentally probe higher-dimensional quantum Hall systems, where new
+topological phases with exotic excitations are predicted.
+",0,1,0,0,0,0
+16241,Non-hermitian operator modelling of basic cancer cell dynamics,"  We propose a dynamical system of tumor cells proliferation based on
+operatorial methods. The approach we propose is quantum-like: we use ladder and
+number operators to describe healthy and tumor cells birth and death, and the
+evolution is ruled by a non-hermitian Hamiltonian which includes, in a non
+reversible way, the basic biological mechanisms we consider for the system. We
+show that this approach is rather efficient in describing some processes of the
+cells. We further add some medical treatment, described by adding a suitable
+term in the Hamiltonian, which controls and limits the growth of tumor cells,
+and we propose an optimal approach to stop, and reverse, this growth.
+",0,0,0,0,1,0
+16242,Giant ripples on comet 67P/Churyumov-Gerasimenko sculpted by sunset thermal wind,"  Explaining the unexpected presence of dune-like patterns at the surface of
+the comet 67P/Churyumov-Gerasimenko requires conceptual and quantitative
+advances in the understanding of surface and outgassing processes. We show here
+that vapor flow emitted by the comet around its perihelion spreads laterally in
+a surface layer, due to the strong pressure difference between zones
+illuminated by sunlight and those in shadow. For such thermal winds to be dense
+enough to transport grains -- ten times greater than previous estimates --
+outgassing must take place through a surface porous granular layer, and that
+layer must be composed of grains whose roughness lowers cohesion consistently
+with contact mechanics. The linear stability analysis of the problem, entirely
+tested against laboratory experiments, quantitatively predicts the emergence of
+bedforms in the observed wavelength range, and their propagation at the scale
+of a comet revolution. Although generated by a rarefied atmosphere, they are
+paradoxically analogous to ripples emerging on granular beds submitted to
+viscous shear flows. This quantitative agreement shows that our understanding
+of the coupling between hydrodynamics and sediment transport is able to account
+for bedform emergence in extreme conditions and provides a reliable tool to
+predict the erosion and accretion processes controlling the evolution of small
+solar system bodies.
+",0,1,0,0,0,0
+16243,A ROS multi-ontology references services: OWL reasoners and application prototyping issues,"  The challenge of sharing and communicating information is crucial in complex
+human-robot interaction (HRI) scenarios. Ontologies and symbolic reasoning are
+the state-of-the-art approaches for a natural representation of knowledge,
+especially within the Semantic Web domain. In such a context, scripted
+paradigms have been adopted to achieve high expressiveness. Nevertheless, since
+symbolic reasoning is a high complexity problem, optimizing its performance
+requires a careful design of the knowledge. Specifically, a robot architecture
+requires the integration of several components implementing different behaviors
+and generating a series of beliefs. Most of the components are expected to
+access, manipulate, and reason upon a run-time generated semantic
+representation of knowledge grounding robot behaviors and perceptions through
+formal axioms, with soft real-time requirements.
+",1,0,0,0,0,0
+16244,Efficient Covariance Approximations for Large Sparse Precision Matrices,"  The use of sparse precision (inverse covariance) matrices has become popular
+because they allow for efficient algorithms for joint inference in
+high-dimensional models. Many applications require the computation of certain
+elements of the covariance matrix, such as the marginal variances, which may be
+non-trivial to obtain when the dimension is large. This paper introduces a fast
+Rao-Blackwellized Monte Carlo sampling based method for efficiently
+approximating selected elements of the covariance matrix. The variance and
+confidence bounds of the approximations can be precisely estimated without
+additional computational costs. Furthermore, a method that iterates over
+subdomains is introduced, and is shown to additionally reduce the approximation
+errors to practically negligible levels in an application on functional
+magnetic resonance imaging data. Both methods have low memory requirements,
+which is typically the bottleneck for competing direct methods.
+",0,0,0,1,0,0
+16245,The Density of Numbers Represented by Diagonal Forms of Large Degree,"  Let $s \geq 3$ be a fixed positive integer and $a_1,\dots,a_s \in \mathbb{Z}$
+be arbitrary. We show that, on average over $k$, the density of numbers
+represented by the degree $k$ diagonal form \[ a_1 x_1^k + \cdots + a_s x_s^k
+\] decays rapidly with respect to $k$.
+",0,0,1,0,0,0
+16246,Meta-Learning for Resampling Recommendation Systems,"  One possible approach to tackle the class imbalance in classification tasks
+is to resample a training dataset, i.e., to drop some of its elements or to
+synthesize new ones. There exist several widely-used resampling methods. Recent
+research showed that the choice of resampling method significantly affects the
+quality of classification, which raises resampling selection problem.
+Exhaustive search for optimal resampling is time-consuming and hence it is of
+limited use. In this paper, we describe an alternative approach to the
+resampling selection. We follow the meta-learning concept to build resampling
+recommendation systems, i.e., algorithms recommending resampling for datasets
+on the basis of their properties.
+",1,0,0,1,0,0
+16247,Robustness Analysis of Systems' Safety through a New Notion of Input-to-State Safety,"  In this paper, we propose a new robustness notion that is applicable for
+certifying systems' safety with respect to external disturbance signals. The
+proposed input-to-state safety (ISSf) notion allows us to certify systems'
+safety in the presence of the disturbances which is analogous to the notion of
+input-to-state stability (ISS) for analyzing systems' stability.
+",1,0,1,0,0,0
+16248,Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach,"  Fusing satellite observations and station measurements to estimate
+ground-level PM2.5 is promising for monitoring PM2.5 pollution. A
+geo-intelligent approach, which incorporates geographical correlation into an
+intelligent deep learning architecture, is developed to estimate PM2.5.
+Specifically, it considers geographical distance and spatiotemporally
+correlated PM2.5 in a deep belief network (denoted as Geoi-DBN). Geoi-DBN can
+capture the essential features associated with PM2.5 from latent factors. It
+was trained and tested with data from China in 2015. The results show that
+Geoi-DBN performs significantly better than the traditional neural network. The
+cross-validation R increases from 0.63 to 0.94, and RMSE decreases from 29.56
+to 13.68${\mu}$g/m3. On the basis of the derived PM2.5 distribution, it is
+predicted that over 80% of the Chinese population live in areas with an annual
+mean PM2.5 of greater than 35${\mu}$g/m3. This study provides a new perspective
+for air pollution monitoring in large geographic regions.
+",0,1,0,0,0,0
+16249,An Overview of Recent Progress in Laser Wakefield Acceleration Experiments,"  The goal of this paper is to examine experimental progress in laser wakefield
+acceleration over the past decade (2004-2014), and to use trends in the data to
+understand some of the important physical processes. By examining a set of over
+50 experiments, various trends concerning the relationship between plasma
+density, accelerator length, laser power and the final electron beam en- ergy
+are revealed. The data suggest that current experiments are limited by
+dephasing and that current experiments typically require some pulse evolution
+to reach the trapping threshold.
+",0,1,0,0,0,0
+16250,MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network,"  Person identification technology recognizes individuals by exploiting their
+unique, measurable physiological and behavioral characteristics. However, the
+state-of-the-art person identification systems have been shown to be
+vulnerable, e.g., contact lenses can trick iris recognition and fingerprint
+films can deceive fingerprint sensors. EEG (Electroencephalography)-based
+identification, which utilizes the users brainwave signals for identification
+and offers a more resilient solution, draw a lot of attention recently.
+However, the accuracy still requires improvement and very little work is
+focusing on the robustness and adaptability of the identification system. We
+propose MindID, an EEG-based biometric identification approach, achieves higher
+accuracy and better characteristics. At first, the EEG data patterns are
+analyzed and the results show that the Delta pattern contains the most
+distinctive information for user identification. Then the decomposed Delta
+pattern is fed into an attention-based Encoder-Decoder RNNs (Recurrent Neural
+Networks) structure which assigns varies attention weights to different EEG
+channels based on the channels importance. The discriminative representations
+learned from the attention-based RNN are used to recognize the user
+identification through a boosting classifier. The proposed approach is
+evaluated over 3 datasets (two local and one public). One local dataset (EID-M)
+is used for performance assessment and the result illustrate that our model
+achieves the accuracy of 0.982 which outperforms the baselines and the
+state-of-the-art. Another local dataset (EID-S) and a public dataset (EEG-S)
+are utilized to demonstrate the robustness and adaptability, respectively. The
+results indicate that the proposed approach has the potential to be largely
+deployment in practice environment.
+",1,0,0,0,0,0
+16251,Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography,"  The present paper is motivated by one of the most fundamental challenges in
+inverse problems, that of quantifying model discrepancies and errors. While
+significant strides have been made in calibrating model parameters, the
+overwhelming majority of pertinent methods is based on the assumption of a
+perfect model. Motivated by problems in solid mechanics which, as all problems
+in continuum thermodynamics, are described by conservation laws and
+phenomenological constitutive closures, we argue that in order to quantify
+model uncertainty in a physically meaningful manner, one should break open the
+black-box forward model. In particular we propose formulating an undirected
+probabilistic model that explicitly accounts for the governing equations and
+their validity. This recasts the solution of both forward and inverse problems
+as probabilistic inference tasks where the problem's state variables should not
+only be compatible with the data but also with the governing equations as well.
+Even though the probability densities involved do not contain any black-box
+terms, they live in much higher-dimensional spaces. In combination with the
+intractability of the normalization constant of the undirected model employed,
+this poses significant challenges which we propose to address with a
+linearly-scaling, double-layer of Stochastic Variational Inference. We
+demonstrate the capabilities and efficacy of the proposed model in synthetic
+forward and inverse problems (with and without model error) in elastography.
+",0,0,0,1,0,0
+16252,On the Spectral Properties of Symmetric Functions,"  We characterize the approximate monomial complexity, sign monomial complexity
+, and the approximate L 1 norm of symmetric functions in terms of simple
+combinatorial measures of the functions. Our characterization of the
+approximate L 1 norm solves the main conjecture in [AFH12]. As an application
+of the characterization of the sign monomial complexity, we prove a conjecture
+in [ZS09] and provide a characterization for the unbounded-error communication
+complexity of symmetric-xor functions.
+",1,0,0,0,0,0
+16253,Strong light shifts from near-resonant and polychromatic fields: comparison of Floquet theory and experiment,"  We present a non-perturbative numerical technique for calculating strong
+light shifts in atoms under the influence of multiple optical fields with
+arbitrary polarization. We confirm our technique experimentally by performing
+spectroscopy of a cloud of cold $^{87}$Rb atoms subjected to $\sim$ kW/cm$^2$
+intensities of light at 1560.492 nm simultaneous with 1529.269 nm or 1529.282
+nm. In these conditions the excited state resonances at 1529.26 nm and 1529.36
+nm induce strong level mixing and the shifts are highly nonlinear. By
+absorption spectroscopy, we observe that the induced shifts of the 5P3/2
+hyperfine Zeeman sublevels agree well with our theoretical predictions.. We
+propose the application of our theory and experiment to accurate measurements
+of excited-state electric-dipole matrix elements.
+",0,1,0,0,0,0
+16254,Positive-rank elliptic curves arising pythagorean triples,"  In the present paper, we introduce some new families of elliptic curves with
+positive rank arrising from Pythagorean triples.
+",0,0,1,0,0,0
+16255,Modeling Relational Data with Graph Convolutional Networks,"  Knowledge graphs enable a wide variety of applications, including question
+answering and information retrieval. Despite the great effort invested in their
+creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata)
+remain incomplete. We introduce Relational Graph Convolutional Networks
+(R-GCNs) and apply them to two standard knowledge base completion tasks: Link
+prediction (recovery of missing facts, i.e. subject-predicate-object triples)
+and entity classification (recovery of missing entity attributes). R-GCNs are
+related to a recent class of neural networks operating on graphs, and are
+developed specifically to deal with the highly multi-relational data
+characteristic of realistic knowledge bases. We demonstrate the effectiveness
+of R-GCNs as a stand-alone model for entity classification. We further show
+that factorization models for link prediction such as DistMult can be
+significantly improved by enriching them with an encoder model to accumulate
+evidence over multiple inference steps in the relational graph, demonstrating a
+large improvement of 29.8% on FB15k-237 over a decoder-only baseline.
+",1,0,0,1,0,0
+16256,Application of shifted-Laplace preconditioners for heterogenous Helmholtz equation- part 1: Data modelling,"  In several geophysical applications, such as full waveform inversion and data
+modelling, we are facing the solution of inhomogeneous Helmholtz equation. The
+difficulties of solving the Helmholtz equa- tion are two fold. Firstly, in the
+case of large scale problems we cannot calculate the inverse of the Helmholtz
+operator directly. Hence, iterative algorithms should be implemented. Secondly,
+the Helmholtz operator is non-unitary and non-diagonalizable which in turn
+deteriorates the performances of the iterative algorithms (especially for high
+wavenumbers). To overcome this issue, we need to im- plement proper
+preconditioners for a Krylov subspace method to solve the problem efficiently.
+In this paper we incorporated shifted-Laplace operators to precondition the
+system of equations and then generalized minimal residual (GMRES) method used
+to solve the problem iteratively. The numerical results show the performance of
+the preconditioning operator in improving the convergence rate of the GMRES
+algorithm for data modelling case. In the companion paper we discussed the
+application of preconditioned data modelling algorithm in the context of
+frequency domain full waveform inversion. However, the analysis of the degree
+of suitability of the preconditioners in the solution of Helmholtz equation is
+an ongoing field of study.
+",0,1,0,0,0,0
+16257,Algorithms For Longest Chains In Pseudo- Transitive Graphs,"  A directed acyclic graph G = (V, E) is pseudo-transitive with respect to a
+given subset of edges E1, if for any edge ab in E1 and any edge bc in E, we
+have ac in E. We give algorithms for computing longest chains and demonstrate
+geometric applications that unify and improves some important past results.
+(For specific applications see the introduction.)
+",1,0,1,0,0,0
+16258,From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation,"  A network-based approach is presented to investigate the cerebrovascular flow
+patterns during atrial fibrillation (AF) with respect to normal sinus rhythm
+(NSR). AF, the most common cardiac arrhythmia with faster and irregular
+beating, has been recently and independently associated with the increased risk
+of dementia. However, the underlying hemodynamic mechanisms relating the two
+pathologies remain mainly undetermined so far; thus the contribution of
+modeling and refined statistical tools is valuable. Pressure and flow rate
+temporal series in NSR and AF are here evaluated along representative cerebral
+sites (from carotid arteries to capillary brain circulation), exploiting
+reliable artificially built signals recently obtained from an in silico
+approach. The complex network analysis evidences, in a synthetic and original
+way, a dramatic signal variation towards the distal/capillary cerebral regions
+during AF, which has no counterpart in NSR conditions. At the large artery
+level, networks obtained from both AF and NSR hemodynamic signals exhibit
+elongated and chained features, which are typical of pseudo-periodic series.
+These aspects are almost completely lost towards the microcirculation during
+AF, where the networks are topologically more circular and present random-like
+characteristics. As a consequence, all the physiological phenomena at
+microcerebral level ruled by periodicity - such as regular perfusion, mean
+pressure per beat, and average nutrient supply at cellular level - can be
+strongly compromised, since the AF hemodynamic signals assume irregular
+behaviour and random-like features. Through a powerful approach which is
+complementary to the classical statistical tools, the present findings further
+strengthen the potential link between AF hemodynamic and cognitive decline.
+",0,1,0,0,0,0
+16259,On the matrix $pth$ root functions and generalized Fibonacci sequences,"  This study is devoted to the polynomial representation of the matrix $p$th
+root functions. The Fibonacci-Hörner decomposition of the matrix powers and
+some techniques arisen from properties of generalized Fibonacci sequences,
+notably the Binet formula, serves as a triggering factor to provide explicit
+formulas for the matrix $p$th roots. Special cases and illustrative numerical
+examples are given.
+",0,0,1,0,0,0
+16260,Investigation and Automating Extraction of Thumbnails Produced by Image viewers,"  Today, in digital forensics, images normally provide important information
+within an investigation. However, not all images may still be available within
+a forensic digital investigation as they were all deleted for example. Data
+carving can be used in this case to retrieve deleted images but the carving
+time is normally significant and these images can be moreover overwritten by
+other data. One of the solutions is to look at thumbnails of images that are no
+longer available. These thumbnails can often be found within databases created
+by either operating systems or image viewers. In literature, most research and
+practical focus on the extraction of thumbnails from databases created by the
+operating system. There is a little research working on the thumbnails created
+by the image reviewers as these thumbnails are application-driven in terms of
+pre-defined sizes, adjustments and storage location. Eventually, thumbnail
+databases from image viewers are significant forensic artefacts for
+investigators as these programs deal with large amounts of images. However,
+investigating these databases so far is still manual or semi-automatic task
+that leads to the huge amount of forensic time. Therefore, in this paper we
+propose a new approach of automating extraction of thumbnails produced by image
+viewers. We also test our approach with popular image viewers in different
+storage structures and locations to show its robustness.
+",1,0,0,0,0,0
+16261,Weighted network estimation by the use of topological graph metrics,"  Topological metrics of graphs provide a natural way to describe the prominent
+features of various types of networks. Graph metrics describe the structure and
+interplay of graph edges and have found applications in many scientific fields.
+In this work, graph metrics are used in network estimation by developing
+optimisation methods that incorporate prior knowledge of a network's topology.
+The derivatives of graph metrics are used in gradient descent schemes for
+weighted undirected network denoising, network completion, and network
+decomposition. The successful performance of our methodology is shown in a
+number of toy examples and real-world datasets. Most notably, our work
+establishes a new link between graph theory, network science and optimisation.
+",1,0,0,0,0,0
+16262,Petrophysical property estimation from seismic data using recurrent neural networks,"  Reservoir characterization involves the estimation petrophysical properties
+from well-log data and seismic data. Estimating such properties is a
+challenging task due to the non-linearity and heterogeneity of the subsurface.
+Various attempts have been made to estimate petrophysical properties using
+machine learning techniques such as feed-forward neural networks and support
+vector regression (SVR). Recent advances in machine learning have shown
+promising results for recurrent neural networks (RNN) in modeling complex
+sequential data such as videos and speech signals. In this work, we propose an
+algorithm for property estimation from seismic data using recurrent neural
+networks. An applications of the proposed workflow to estimate density and
+p-wave impedance using seismic data shows promising results compared to
+feed-forward neural networks.
+",1,0,0,0,0,0
+16263,Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading,"  Deep learning applies hierarchical layers of hidden variables to construct
+nonlinear high dimensional predictors. Our goal is to develop and train deep
+learning architectures for spatio-temporal modeling. Training a deep
+architecture is achieved by stochastic gradient descent (SGD) and drop-out (DO)
+for parameter regularization with a goal of minimizing out-of-sample predictive
+mean squared error. To illustrate our methodology, we predict the sharp
+discontinuities in traffic flow data, and secondly, we develop a classification
+rule to predict short-term futures market prices as a function of the order
+book depth. Finally, we conclude with directions for future research.
+",0,0,0,1,0,0
+16264,Multi-objective Bandits: Optimizing the Generalized Gini Index,"  We study the multi-armed bandit (MAB) problem where the agent receives a
+vectorial feedback that encodes many possibly competing objectives to be
+optimized. The goal of the agent is to find a policy, which can optimize these
+objectives simultaneously in a fair way. This multi-objective online
+optimization problem is formalized by using the Generalized Gini Index (GGI)
+aggregation function. We propose an online gradient descent algorithm which
+exploits the convexity of the GGI aggregation function, and controls the
+exploration in a careful way achieving a distribution-free regret
+$\tilde{\bigO} (T^{-1/2} )$ with high probability. We test our algorithm on
+synthetic data as well as on an electric battery control problem where the goal
+is to trade off the use of the different cells of a battery in order to balance
+their respective degradation rates.
+",1,0,0,0,0,0
+16265,Floquet multi-Weyl points in crossing-nodal-line semimetals,"  Weyl points with monopole charge $\pm 1$ have been extensively studied,
+however, real materials of multi-Weyl points, whose monopole charges are higher
+than $1$, have yet to be found. In this Rapid Communication, we show that
+nodal-line semimetals with nontrivial line connectivity provide natural
+platforms for realizing Floquet multi-Weyl points. In particular, we show that
+driving crossing nodal lines by circularly polarized light generates
+double-Weyl points. Furthermore, we show that monopole combination and
+annihilation can be observed in crossing-nodal-line semimetals and nodal-chain
+semimetals. These proposals can be experimentally verified in pump-probe
+angle-resolved photoemission spectroscopy.
+",0,1,0,0,0,0
+16266,Impact Ionization in $β-Ga_2O_3$,"  A theoretical investigation of extremely high field transport in an emerging
+wide-bandgap material $\beta-Ga_2O_3$ is reported from first principles. The
+signature high-field effect explored here is impact ionization. Interaction
+between a valence-band electron and an excited electron is computed from the
+matrix elements of a screened Coulomb operator. Maximally localized Wannier
+functions (MLWF) are utilized in computing the impact ionization rates. A
+full-band Monte Carlo (FBMC) simulation is carried out incorporating the impact
+ionization rates, and electron-phonon scattering rates. This work brings out
+valuable insights on the impact ionization coefficient (IIC) of electrons in
+$\beta-Ga_2O_3$. The isolation of the $\Gamma$ point conduction band minimum by
+a significantly high energy from other satellite band pockets play a vital role
+in determining ionization co-efficients. IICs are calculated for electric
+fields ranging up to 8 MV/cm for different crystal directions. A Chynoweth
+fitting of the computed IICs is done to calibrate ionization models in device
+simulators.
+",0,1,0,0,0,0
+16267,Entombed: An archaeological examination of an Atari 2600 game,"  The act and experience of programming is, at its heart, a fundamentally human
+activity that results in the production of artifacts. When considering
+programming, therefore, it would be a glaring omission to not involve people
+who specialize in studying artifacts and the human activity that yields them:
+archaeologists. Here we consider this with respect to computer games, the focus
+of archaeology's nascent subarea of archaeogaming.
+One type of archaeogaming research is digital excavation, a technical
+examination of the code and techniques used in old games' implementation. We
+apply that in a case study of Entombed, an Atari 2600 game released in 1982 by
+US Games. The player in this game is, appropriately, an archaeologist who must
+make their way through a zombie-infested maze. Maze generation is a fruitful
+area for comparative retrogame archaeology, because a number of early games on
+different platforms featured mazes, and their variety of approaches can be
+compared. The maze in Entombed is particularly interesting: it is shaped in
+part by the extensive real-time constraints of the Atari 2600 platform, and
+also had to be generated efficiently and use next to no memory. We reverse
+engineered key areas of the game's code to uncover its unusual maze-generation
+algorithm, which we have also built a reconstruction of, and analyzed the
+mysterious table that drives it. In addition, we discovered what appears to be
+a 35-year-old bug in the code, as well as direct evidence of code-reuse
+practices amongst game developers.
+What further makes this game's development interesting is that, in an era
+where video games were typically solo projects, a total of five people were
+involved in various ways with Entombed. We piece together some of the backstory
+of the game's development and intoxicant-fueled design using interviews to
+complement our technical work.
+Finally, we contextualize this example in archaeology and lay the groundwork
+for a broader interdisciplinary discussion about programming, one that includes
+both computer scientists and archaeologists.
+",1,0,0,0,0,0
+16268,Particle Filters for Partially-Observed Boolean Dynamical Systems,"  Partially-observed Boolean dynamical systems (POBDS) are a general class of
+nonlinear models with application in estimation and control of Boolean
+processes based on noisy and incomplete measurements. The optimal minimum mean
+square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean
+Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the
+case of large systems, due to computational and memory requirements. To address
+this, we propose approximate MMSE filtering and smoothing algorithms based on
+the auxiliary particle filter (APF) method from sequential Monte-Carlo theory.
+These algorithms are used jointly with maximum-likelihood (ML) methods for
+simultaneous state and parameter estimation in POBDS models. In the presence of
+continuous parameters, ML estimation is performed using the
+expectation-maximization (EM) algorithm; we develop for this purpose a special
+smoother which reduces the computational complexity of the EM algorithm. The
+resulting particle-based adaptive filter is applied to a POBDS model of Boolean
+gene regulatory networks observed through noisy RNA-Seq time series data, and
+performance is assessed through a series of numerical experiments using the
+well-known cell cycle gene regulatory model.
+",0,0,1,1,0,0
+16269,Cross-National Measurement of Polarization in Political Discourse: Analyzing floor debate in the U.S. and the Japanese legislatures,"  Political polarization in public space can seriously hamper the function and
+the integrity of contemporary democratic societies. In this paper, we propose a
+novel measure of such polarization, which, by way of simple topic modelling,
+quantifies differences in collective articulation of public agendas among
+relevant political actors. Unlike most other polarization measures, our measure
+allows cross-national comparison. Analyzing a large amount of speech records of
+legislative debate in the United States Congress and the Japanese Diet over a
+long period of time, we have reached two intriguing findings. First, on
+average, Japanese political actors are far more polarized in their issue
+articulation than their counterparts in the U.S., which is somewhat surprising
+given the recent notion of U.S. politics as highly polarized. Second, the
+polarization in each country shows its own temporal dynamics in response to a
+different set of factors. In Japan, structural factors such as the roles of the
+ruling party and the opposition often dominate such dynamics, whereas the U.S.
+legislature suffers from persistent ideological differences over particular
+issues between major political parties. The analysis confirms a strong
+influence of institutional differences on legislative debate in parliamentary
+democracies.
+",1,0,0,0,0,0
+16270,Size-Change Termination as a Contract,"  Program termination is an undecidable, yet important, property relevant to
+program verification, optimization, debugging, partial evaluation, and
+dependently-typed programming, among many other topics. This has given rise to
+a large body of work on static methods for conservatively predicting or
+enforcing termination. A simple effective approach is the size-change
+termination (SCT) method, which operates in two-phases: (1) abstract programs
+into ""size-change graphs,"" and (2) check these graphs for the size-change
+property: the existence of paths that lead to infinitely decreasing value
+sequences.
+This paper explores the termination problem starting from a different vantage
+point: we propose transposing the two phases of the SCT analysis by developing
+an operational semantics that accounts for the run time checking of the
+size-change property, postponing program abstraction or avoiding it entirely.
+This choice has two important consequences: SCT can be monitored and enforced
+at run-time and termination analysis can be rephrased as a traditional safety
+property and computed using existing abstract interpretation methods.
+We formulate this run-time size-change check as a contract. This contributes
+the first run-time mechanism for checking termination in a general-purporse
+programming language. The result nicely compliments existing contracts that
+enforce partial correctness to obtain the first contracts for total
+correctness. Our approach combines the robustness of SCT with precise
+information available at run-time. To obtain a sound and computable analysis,
+it is possible to apply existing abstract interpretation techniques directly to
+the operational semantics; there is no need for an abstraction tailored to
+size-change graphs. We apply higher-order symbolic execution to obtain a novel
+termination analysis that is competitive with existing, purpose-built
+termination analyzers.
+",1,0,0,0,0,0
+16271,Disorder Dependent Valley Properties in Monolayer WSe2,"  We investigate the effect on disorder potential on exciton valley
+polarization and valley coherence in monolayer WSe2. By analyzing polarization
+properties of photoluminescence, the valley coherence (VC) and valley
+polarization (VP) is quantified across the inhomogeneously broadened exciton
+resonance. We find that disorder plays a critical role in the exciton VC, while
+minimally affecting VP. For different monolayer samples with disorder
+characterized by their Stokes Shift (SS), VC decreases in samples with higher
+SS while VP again remains unchanged. These two methods consistently demonstrate
+that VC as defined by the degree of linearly polarized photoluminescence is
+more sensitive to disorder potential, motivating further theoretical studies.
+",0,1,0,0,0,0
+16272,Results on the Hilbert coefficients and reduction numbers,"  Let $(R,\frak{m})$ be a $d$-dimensional Cohen-Macaulay local ring, $I$ an
+$\frak{m}$-primary ideal and $J$ a minimal reduction of $I$. In this paper we
+study the independence of reduction ideals and the behavior of the higher
+Hilbert coefficients. In addition, we give some examples in this regards.
+",0,0,1,0,0,0
+16273,Progress on Experiments towards LWFA-driven Transverse Gradient Undulator-Based FELs,"  Free Electron Lasers (FEL) are commonly regarded as the potential key
+application of laser wakefield accelerators (LWFA). It has been found that
+electron bunches exiting from state-of-the-art LWFAs exhibit a normalized
+6-dimensional beam brightness comparable to those in conventional linear
+accelerators. Effectively exploiting this beneficial beam property for
+LWFA-based FELs is challenging due to the extreme initial conditions
+particularly in terms of beam divergence and energy spread. Several different
+approaches for capturing, reshaping and matching LWFA beams to suited
+undulators, such as bunch decompression or transverse-gradient undulator
+schemes, are currently being explored. In this article the transverse gradient
+undulator concept will be discussed with a focus on recent experimental
+achievements.
+",0,1,0,0,0,0
+16274,Non-escaping endpoints do not explode,"  The family of exponential maps $f_a(z)= e^z+a$ is of fundamental importance
+in the study of transcendental dynamics. Here we consider the topological
+structure of certain subsets of the Julia set $J(f_a)$. When $a\in
+(-\infty,-1)$, and more generally when $a$ belongs to the Fatou set of $f_a$,
+it is known that $J(f_a)$ can be written as a union of ""hairs"" and ""endpoints""
+of these hairs. In 1990, Mayer proved for $a\in (-\infty,-1)$ that, while the
+set of endpoints is totally separated, its union with infinity is a connected
+set. Recently, Alhabib and the second author extended this result to the case
+where $a \in F(f_a)$, and showed that it holds even for the smaller set of all
+escaping endpoints.
+We show that, in contrast, the set of non-escaping endpoints together with
+infinity is totally separated. It turns out that this property is closely
+related to a topological structure known as a `spider's web'; in particular we
+give a new topological characterisation of spiders' webs that may be of
+independent interest. We also show how our results can be applied to Fatou's
+function, $z\mapsto z + 1 + e^{-z}$.
+",0,0,1,0,0,0
+16275,The Fornax Deep Survey with VST. II. Fornax A: a two-phase assembly caught on act,"  As part of the Fornax Deep Survey with the ESO VLT Survey Telescope, we
+present new $g$ and $r$ bands mosaics of the SW group of the Fornax cluster. It
+covers an area of $3 \times 2$ square degrees around the central galaxy
+NGC1316. The deep photometry, the high spatial resolution of OmegaCam and the
+large covered area allow us to study the galaxy structure, to trace stellar
+halo formation and look at the galaxy environment. We map the surface
+brightness profile out to 33arcmin ($\sim 200$kpc $\sim15R_e$) from the galaxy
+centre, down to $\mu_g \sim 31$ mag arcsec$^{-2}$ and $\mu_r \sim 29$ mag
+arcsec$^{-2}$. This allow us to estimate the scales of the main components
+dominating the light distribution, which are the central spheroid, inside 5.5
+arcmin ($\sim33$ kpc), and the outer stellar envelope. Data analysis suggests
+that we are catching in act the second phase of the mass assembly in this
+galaxy, since the accretion of smaller satellites is going on in both
+components. The outer envelope of NGC1316 still hosts the remnants of the
+accreted satellite galaxies that are forming the stellar halo. We discuss the
+possible formation scenarios for NGC1316, by comparing the observed properties
+(morphology, colors, gas content, kinematics and dynamics) with predictions
+from cosmological simulations of galaxy formation. We find that {\it i)} the
+central spheroid could result from at least one merging event, it could be a
+pre-existing early-type disk galaxy with a lower mass companion, and {\it ii)}
+the stellar envelope comes from the gradual accretion of small satellites.
+",0,1,0,0,0,0
+16276,Local decoding and testing of polynomials over grids,"  The well-known DeMillo-Lipton-Schwartz-Zippel lemma says that $n$-variate
+polynomials of total degree at most $d$ over grids, i.e. sets of the form $A_1
+\times A_2 \times \cdots \times A_n$, form error-correcting codes (of distance
+at least $2^{-d}$ provided $\min_i\{|A_i|\}\geq 2$). In this work we explore
+their local decodability and (tolerant) local testability. While these aspects
+have been studied extensively when $A_1 = \cdots = A_n = \mathbb{F}_q$ are the
+same finite field, the setting when $A_i$'s are not the full field does not
+seem to have been explored before.
+In this work we focus on the case $A_i = \{0,1\}$ for every $i$. We show that
+for every field (finite or otherwise) there is a test whose query complexity
+depends only on the degree (and not on the number of variables). In contrast we
+show that decodability is possible over fields of positive characteristic (with
+query complexity growing with the degree of the polynomial and the
+characteristic), but not over the reals, where the query complexity must grow
+with $n$. As a consequence we get a natural example of a code (one with a
+transitive group of symmetries) that is locally testable but not locally
+decodable.
+Classical results on local decoding and testing of polynomials have relied on
+the 2-transitive symmetries of the space of low-degree polynomials (under
+affine transformations). Grids do not possess this symmetry: So we introduce
+some new techniques to overcome this handicap and in particular use the
+hypercontractivity of the (constant weight) noise operator on the Hamming cube.
+",1,0,0,0,0,0
+16277,A New Backpressure Algorithm for Joint Rate Control and Routing with Vanishing Utility Optimality Gaps and Finite Queue Lengths,"  The backpressure algorithm has been widely used as a distributed solution to
+the problem of joint rate control and routing in multi-hop data networks. By
+controlling a parameter $V$ in the algorithm, the backpressure algorithm can
+achieve an arbitrarily small utility optimality gap. However, this in turn
+brings in a large queue length at each node and hence causes large network
+delay. This phenomenon is known as the fundamental utility-delay tradeoff. The
+best known utility-delay tradeoff for general networks is $[O(1/V), O(V)]$ and
+is attained by a backpressure algorithm based on a drift-plus-penalty
+technique. This may suggest that to achieve an arbitrarily small utility
+optimality gap, the existing backpressure algorithms necessarily yield an
+arbitrarily large queue length. However, this paper proposes a new backpressure
+algorithm that has a vanishing utility optimality gap, so utility converges to
+exact optimality as the algorithm keeps running, while queue lengths are
+bounded throughout by a finite constant. The technique uses backpressure and
+drift concepts with a new method for convex programming.
+",1,0,1,0,0,0
+16278,Lifshitz transition from valence fluctuations in YbAl3,"  In Kondo lattice systems with mixed valence, such as YbAl3, interactions
+between localized electrons in a partially filled f shell and delocalized
+conduction electrons can lead to fluctuations between two different valence
+configurations with changing temperature or pressure. The impact of this change
+on the momentum-space electronic structure and Fermi surface topology is
+essential for understanding their emergent properties, but has remained
+enigmatic due to a lack of appropriate experimental probes. Here by employing a
+combination of molecular beam epitaxy (MBE) and in situ angle-resolved
+photoemission spectroscopy (ARPES) we show that valence fluctuations can lead
+to dramatic changes in the Fermi surface topology, even resulting in a Lifshitz
+transition. As the temperature is lowered, a small electron pocket in YbAl3
+becomes completely unoccupied while the low-energy ytterbium (Yb) 4f states
+become increasingly itinerant, acquiring additional spectral weight, longer
+lifetimes, and well-defined dispersions. Our work presents the first unified
+picture of how local valence fluctuations connect to momentum space concepts
+including band filling and Fermi surface topology in the longstanding problem
+of mixed-valence systems.
+",0,1,0,0,0,0
+16279,Diversity from the Topology of Citation Networks,"  We study transitivity in directed acyclic graphs and its usefulness in
+capturing nodes that act as bridges between more densely interconnected parts
+in such type of network. In transitively reduced citation networks degree
+centrality could be used as a measure of interdisciplinarity or diversity. We
+study the measure's ability to capture ""diverse"" nodes in random directed
+acyclic graphs and citation networks. We show that transitively reduced degree
+centrality is capable of capturing ""diverse"" nodes, thus this measure could be
+a timely alternative to text analysis techniques for retrieving papers,
+influential in a variety of research fields.
+",1,0,0,0,0,0
+16280,Superluminal transmission of phase modulation information by a long macroscopic pulse propagating through interstellar space,"  A method of transmitting information in interstellar space at superluminal,
+or $> c$, speeds is proposed. The information is encoded as phase modulation of
+an electromagnetic wave of constant intensity, i.e. fluctuations in the rate of
+energy transport plays no role in the communication, and no energy is
+transported at speed $>$ c. Of course, such a constant wave can ultimately last
+only the duration of its enveloping wave packet. However, as a unique feature
+of this paper, we assume the source is sufficiently steady to be capable of
+emitting wave packets, or pulses, of size much larger than the separation
+between sender and receiver. Therefore, if a pre-existing and enduring wave
+envelope already connects the two sides, the subluminal nature of the
+envelope's group velocity will no longer slow down the communication, which is
+now limited by the speed at which information encoded as phase modulation
+propagates through the plasma, i.e. the phase velocity $v_p > c$. The method
+involves no sharp structure in either time or frequency. As a working example,
+we considered two spaceships separated by 1 lt-s in the local hot bubble.
+Provided the bandwidth of the extra Fourier modes generated by the phase
+modulation is much smaller than the carrier wave frequency, the radio
+communication of a message, encoded as a specific alignment between the carrier
+wave phase and the anomalous (modulated) phase, can take place at a speed in
+excess of light by a few parts in 10$^{11}$ at $\nu\approx 1$~GHz, and higher
+at smaller $\nu$.
+",0,1,0,0,0,0
+16281,Causal Regularization,"  In application domains such as healthcare, we want accurate predictive models
+that are also causally interpretable. In pursuit of such models, we propose a
+causal regularizer to steer predictive models towards causally-interpretable
+solutions and theoretically study its properties. In a large-scale analysis of
+Electronic Health Records (EHR), our causally-regularized model outperforms its
+L1-regularized counterpart in causal accuracy and is competitive in predictive
+performance. We perform non-linear causality analysis by causally regularizing
+a special neural network architecture. We also show that the proposed causal
+regularizer can be used together with neural representation learning algorithms
+to yield up to 20% improvement over multilayer perceptron in detecting
+multivariate causation, a situation common in healthcare, where many causal
+factors should occur simultaneously to have an effect on the target variable.
+",1,0,0,1,0,0
+16282,Voice Disorder Detection Using Long Short Term Memory (LSTM) Model,"  Automated detection of voice disorders with computational methods is a recent
+research area in the medical domain since it requires a rigorous endoscopy for
+the accurate diagnosis. Efficient screening methods are required for the
+diagnosis of voice disorders so as to provide timely medical facilities in
+minimal resources. Detecting Voice disorder using computational methods is a
+challenging problem since audio data is continuous due to which extracting
+relevant features and applying machine learning is hard and unreliable. This
+paper proposes a Long short term memory model (LSTM) to detect pathological
+voice disorders and evaluates its performance in a real 400 testing samples
+without any labels. Different feature extraction methods are used to provide
+the best set of features before applying LSTM model for classification. The
+paper describes the approach and experiments that show promising results with
+22% sensitivity, 97% specificity and 56% unweighted average recall.
+",1,0,0,0,0,0
+16283,Topology of two-dimensional turbulent flows of dust and gas,"  We perform direct numerical simulations (DNS) of passive heavy inertial
+particles (dust) in homogeneous and isotropic two-dimensional turbulent flows
+(gas) for a range of Stokes number, ${\rm St} < 1$, using both Lagrangian and
+Eulerian approach (with a shock-capturing scheme). We find that: The
+dust-density field in our Eulerian simulations have the same correlation
+dimension $d_2$ as obtained from the clustering of particles in the Lagrangian
+simulations for ${\rm St} < 1$; The cumulative probability distribution
+function of the dust-density coarse-grained over a scale $r$ in the inertial
+range has a left-tail with a power-law fall-off indicating presence of voids;
+The energy spectrum of the dust-velocity has a power-law range with an exponent
+that is same as the gas-velocity spectrum except at very high Fourier modes;
+The compressibility of the dust-velocity field is proportional to ${\rm St}^2$.
+We quantify the topological properties of the dust-velocity and the
+gas-velocity through their gradient matrices, called $\mathcal{A}$ and
+$\mathcal{B}$, respectively. The topological properties of $\mathcal{B}$ are
+the same in Eulerian and Lagrangian frames only if the Eulerian data are
+weighed by the dust-density -- a correspondence that we use to study Lagrangian
+properties of $\mathcal{A}$. In the Lagrangian frame, the mean value of the
+trace of $\mathcal{A} \sim - \exp(-C/{\rm St}$, with a constant $C\approx 0.1$.
+The topology of the dust-velocity fields shows that as ${\rm St} increases the
+contribution to negative divergence comes mostly from saddles and the
+contribution to positive divergence comes from both vortices and saddles.
+Compared to the Eulerian case, the density-weighed Eulerian case has less
+inward spirals and more converging saddles. Outward spirals are the least
+probable topological structures in both cases.
+",0,1,0,0,0,0
+16284,"Trimming the Independent Fat: Sufficient Statistics, Mutual Information, and Predictability from Effective Channel States","  One of the most fundamental questions one can ask about a pair of random
+variables X and Y is the value of their mutual information. Unfortunately, this
+task is often stymied by the extremely large dimension of the variables. We
+might hope to replace each variable by a lower-dimensional representation that
+preserves the relationship with the other variable. The theoretically ideal
+implementation is the use of minimal sufficient statistics, where it is
+well-known that either X or Y can be replaced by their minimal sufficient
+statistic about the other while preserving the mutual information. While
+intuitively reasonable, it is not obvious or straightforward that both
+variables can be replaced simultaneously. We demonstrate that this is in fact
+possible: the information X's minimal sufficient statistic preserves about Y is
+exactly the information that Y's minimal sufficient statistic preserves about
+X. As an important corollary, we consider the case where one variable is a
+stochastic process' past and the other its future and the present is viewed as
+a memoryful channel. In this case, the mutual information is the channel
+transmission rate between the channel's effective states. That is, the
+past-future mutual information (the excess entropy) is the amount of
+information about the future that can be predicted using the past. Translating
+our result about minimal sufficient statistics, this is equivalent to the
+mutual information between the forward- and reverse-time causal states of
+computational mechanics. We close by discussing multivariate extensions to this
+use of minimal sufficient statistics.
+",1,1,0,1,0,0
+16285,Statistical Physics of the Symmetric Group,"  Ordered chains (such as chains of amino acids) are ubiquitous in biological
+cells, and these chains perform specific functions contingent on the sequence
+of their components. Using the existence and general properties of such
+sequences as a theoretical motivation, we study the statistical physics of
+systems whose state space is defined by the possible permutations of an ordered
+list, i.e., the symmetric group, and whose energy is a function of how certain
+permutations deviate from some chosen correct ordering. Such a non-factorizable
+state space is quite different from the state spaces typically considered in
+statistical physics systems and consequently has novel behavior in systems with
+interacting and even non-interacting Hamiltonians. Various parameter choices of
+a mean-field model reveal the system to contain five different physical regimes
+defined by two transition temperatures, a triple point, and a quadruple point.
+Finally, we conclude by discussing how the general analysis can be extended to
+state spaces with more complex combinatorial properties and to other standard
+questions of statistical mechanics models.
+",0,1,0,0,0,0
+16286,A New Phosphorus Allotrope with Direct Band Gap and High Mobility,"  Based on ab initio evolutionary crystal structure search computation, we
+report a new phase of phosphorus called green phosphorus ({\lambda}-P), which
+exhibits the direct band gaps ranging from 0.7 to 2.4 eV and the strong
+anisotropy in optical and transport properties. Free energy calculations show
+that a single-layer form, termed green phosphorene, is energetically more
+stable than blue phosphorene and a phase transition from black to green
+phosphorene can occur at temperatures above 87 K. Due to its buckled structure,
+green phosphorene can be synthesized on corrugated metal surfaces rather than
+clean surfaces.
+",0,1,0,0,0,0
+16287,An open shop approach in approximating optimal data transmission duration in WDM networks,"  In the past decade Optical WDM Networks (Wavelength Division Multiplexing)
+are being used quite often and especially as far as broadband applications are
+concerned. Message packets transmitted through such networks can be interrupted
+using time slots in order to maximize network usage and minimize the time
+required for all messages to reach their destination. However, preempting a
+packet will result in time cost. The problem of scheduling message packets
+through such a network is referred to as PBS and is known to be NP-Hard. In
+this paper we have reduced PBS to Open Shop Scheduling and designed variations
+of polynomially solvable instances of Open Shop to approximate PBS. We have
+combined these variations and called the induced algorithm HSA (Hybridic
+Scheduling Algorithm). We ran experiments to establish the efficiency of HSA
+and found that in all datasets used it produces schedules very close to the
+optimal. To further establish HSAs efficiency we ran tests to compare it to
+SGA, another algorithm which when tested in the past has yielded excellent
+results.
+",1,0,0,0,0,0
+16288,Optimal prediction in the linearly transformed spiked model,"  We consider the linearly transformed spiked model, where observations $Y_i$
+are noisy linear transforms of unobserved signals of interest $X_i$:
+\begin{align*}
+Y_i = A_i X_i + \varepsilon_i, \end{align*} for $i=1,\ldots,n$. The transform
+matrices $A_i$ are also observed. We model $X_i$ as random vectors lying on an
+unknown low-dimensional space. How should we predict the unobserved signals
+(regression coefficients) $X_i$?
+The naive approach of performing regression for each observation separately
+is inaccurate due to the large noise. Instead, we develop optimal linear
+empirical Bayes methods for predicting $X_i$ by ""borrowing strength"" across the
+different samples. Our methods are applicable to large datasets and rely on
+weak moment assumptions. The analysis is based on random matrix theory.
+We discuss applications to signal processing, deconvolution, cryo-electron
+microscopy, and missing data in the high-noise regime. For missing data, we
+show in simulations that our methods are faster, more robust to noise and to
+unequal sampling than well-known matrix completion methods.
+",0,0,1,1,0,0
+16289,Self-Normalizing Neural Networks,"  Deep Learning has revolutionized vision via convolutional neural networks
+(CNNs) and natural language processing via recurrent neural networks (RNNs).
+However, success stories of Deep Learning with standard feed-forward neural
+networks (FNNs) are rare. FNNs that perform well are typically shallow and,
+therefore cannot exploit many levels of abstract representations. We introduce
+self-normalizing neural networks (SNNs) to enable high-level abstract
+representations. While batch normalization requires explicit normalization,
+neuron activations of SNNs automatically converge towards zero mean and unit
+variance. The activation function of SNNs are ""scaled exponential linear units""
+(SELUs), which induce self-normalizing properties. Using the Banach fixed-point
+theorem, we prove that activations close to zero mean and unit variance that
+are propagated through many network layers will converge towards zero mean and
+unit variance -- even under the presence of noise and perturbations. This
+convergence property of SNNs allows to (1) train deep networks with many
+layers, (2) employ strong regularization, and (3) to make learning highly
+robust. Furthermore, for activations not close to unit variance, we prove an
+upper and lower bound on the variance, thus, vanishing and exploding gradients
+are impossible. We compared SNNs on (a) 121 tasks from the UCI machine learning
+repository, on (b) drug discovery benchmarks, and on (c) astronomy tasks with
+standard FNNs and other machine learning methods such as random forests and
+support vector machines. SNNs significantly outperformed all competing FNN
+methods at 121 UCI tasks, outperformed all competing methods at the Tox21
+dataset, and set a new record at an astronomy data set. The winning SNN
+architectures are often very deep. Implementations are available at:
+github.com/bioinf-jku/SNNs.
+",1,0,0,1,0,0
+16290,Real-time Fault Localization in Power Grids With Convolutional Neural Networks,"  Diverse fault types, fast re-closures and complicated transient states after
+a fault event make real-time fault location in power grids challenging.
+Existing localization techniques in this area rely on simplistic assumptions,
+such as static loads, or require much higher sampling rates or total
+measurement availability. This paper proposes a data-driven localization method
+based on a Convolutional Neural Network (CNN) classifier using bus voltages.
+Unlike prior data-driven methods, the proposed classifier is based on features
+with physical interpretations that are described in details. The accuracy of
+our CNN based localization tool is demonstrably superior to other machine
+learning classifiers in the literature. To further improve the location
+performance, a novel phasor measurement units (PMU) placement strategy is
+proposed and validated against other methods. A significant aspect of our
+methodology is that under very low observability (7% of buses), the algorithm
+is still able to localize the faulted line to a small neighborhood with high
+probability. The performance of our scheme is validated through simulations of
+faults of various types in the IEEE 68-bus power system under varying load
+conditions, system observability and measurement quality.
+",1,0,0,0,0,0
+16291,Effective identifiability criteria for tensors and polynomials,"  A tensor $T$, in a given tensor space, is said to be $h$-identifiable if it
+admits a unique decomposition as a sum of $h$ rank one tensors. A criterion for
+$h$-identifiability is called effective if it is satisfied in a dense, open
+subset of the set of rank $h$ tensors. In this paper we give effective
+$h$-identifiability criteria for a large class of tensors. We then improve
+these criteria for some symmetric tensors. For instance, this allows us to give
+a complete set of effective identifiability criteria for ternary quintic
+polynomial. Finally, we implement our identifiability algorithms in Macaulay2.
+",1,0,1,0,0,0
+16292,Functoriality properties of the dual group,"  Let $G$ be a connected reductive group. In a previous paper,
+arXiv:1702.08264, is was shown that the dual group $G^\vee_X$ attached to a
+$G$-variety $X$ admits a natural homomorphism with finite kernel to the
+Langlands dual group $G^\vee$ of $G$. Here, we prove that the dual group is
+functorial in the following sense: if there is a dominant $G$-morphism $X\to Y$
+or an injective $G$-morphism $Y\to X$ then there is a canonical homomorphism
+$G^\vee_Y\to G^\vee_X$ which is compatible with the homomorphisms to $G^\vee$.
+",0,0,1,0,0,0
+16293,Chaotic dynamics around cometary nuclei,"  We apply a generalized Kepler map theory to describe the qualitative chaotic
+dynamics around cometary nuclei, based on accessible observational data for
+five comets whose nuclei are well-documented to resemble dumb-bells. The sizes
+of chaotic zones around the nuclei and the Lyapunov times of the motion inside
+these zones are estimated. In the case of Comet 1P/Halley, the circumnuclear
+chaotic zone seems to engulf an essential part of the Hill sphere, at least for
+orbits of moderate to high eccentricity.
+",0,1,0,0,0,0
+16294,Riemannian curvature measures,"  A famous theorem of Weyl states that if $M$ is a compact submanifold of
+euclidean space, then the volumes of small tubes about $M$ are given by a
+polynomial in the radius $r$, with coefficients that are expressible as
+integrals of certain scalar invariants of the curvature tensor of $M$ with
+respect to the induced metric. It is natural to interpret this phenomenon in
+terms of curvature measures and smooth valuations, in the sense of Alesker,
+canonically associated to the Riemannian structure of $M$. This perspective
+yields a fundamental new structure in Riemannian geometry, in the form of a
+certain abstract module over the polynomial algebra $\mathbb R[t]$ that
+reflects the behavior of Alesker multiplication. This module encodes a key
+piece of the array of kinematic formulas of any Riemannian manifold on which a
+group of isometries acts transitively on the sphere bundle. We illustrate this
+principle in precise terms in the case where $M$ is a complex space form.
+",0,0,1,0,0,0
+16295,A new lower bound for the on-line coloring of intervals with bandwidth,"  The on-line interval coloring and its variants are important combinatorial
+problems with many applications in network multiplexing, resource allocation
+and job scheduling. In this paper we present a new lower bound of $4.1626$ for
+the competitive ratio for the on-line coloring of intervals with bandwidth
+which improves the best known lower bound of $\frac{24}{7}$. For the on-line
+coloring of unit intervals with bandwidth we improve the lower bound of $1.831$
+to $2$.
+",1,0,1,0,0,0
+16296,Orbits of monomials and factorization into products of linear forms,"  This paper is devoted to the factorization of multivariate polynomials into
+products of linear forms, a problem which has applications to differential
+algebra, to the resolution of systems of polynomial equations and to Waring
+decomposition (i.e., decomposition in sums of d-th powers of linear forms; this
+problem is also known as symmetric tensor decomposition). We provide three
+black box algorithms for this problem. Our main contribution is an algorithm
+motivated by the application to Waring decomposition. This algorithm reduces
+the corresponding factorization problem to simultaenous matrix diagonalization,
+a standard task in linear algebra. The algorithm relies on ideas from invariant
+theory, and more specifically on Lie algebras. Our second algorithm
+reconstructs a factorization from several bi-variate projections. Our third
+algorithm reconstructs it from the determination of the zero set of the input
+polynomial, which is a union of hyperplanes.
+",1,0,0,0,0,0
+16297,Monodromy and Vinberg fusion for the principal degeneration of the space of G-bundles,"  We study the geometry and the singularities of the principal direction of the
+Drinfeld-Lafforgue-Vinberg degeneration of the moduli space of G-bundles Bun_G
+for an arbitrary reductive group G, and their relationship to the Langlands
+dual group of G.
+In the first part of the article we study the monodromy action on the nearby
+cycles sheaf along the principal degeneration of Bun_G. We describe the
+weight-monodromy filtration in terms of the combinatorics of the Langlands dual
+group of G and generalizations of the Picard-Lefschetz oscillators found in
+[Sch1]. Our proofs use certain local models for the principal degeneration
+whose geometry is studied in the second part.
+Our local models simultaneously provide two types of degenerations of the
+Zastava spaces, which together equip the Zastava spaces with the geometric
+analog of a Hopf algebra structure. The first degeneration corresponds to the
+usual Beilinson-Drinfeld fusion of divisors on the curve. The second
+degeneration is new and corresponds to what we call Vinberg fusion: It is
+obtained not by degenerating divisors on the curve, but by degenerating the
+group G via the Vinberg semigroup. On the level of cohomology the Vinberg
+fusion gives rise to an algebra structure, while the Beilinson-Drinfeld fusion
+gives rise to a coalgebra structure; the Hopf algebra axiom is a consequence of
+the underlying geometry.
+It is natural to conjecture that this Hopf algebra agrees with the universal
+enveloping algebra of the positive part of the Langlands dual Lie algebra. The
+above procedure would then yield a novel and highly geometric way to pass to
+the Langlands dual side: Elements of the Langlands dual Lie algebra are
+represented as cycles on the above moduli spaces, and the Lie bracket of two
+elements is obtained by deforming the cartesian product cycle along the Vinberg
+degeneration.
+",0,0,1,0,0,0
+16298,Sketched Subspace Clustering,"  The immense amount of daily generated and communicated data presents unique
+challenges in their processing. Clustering, the grouping of data without the
+presence of ground-truth labels, is an important tool for drawing inferences
+from data. Subspace clustering (SC) is a relatively recent method that is able
+to successfully classify nonlinearly separable data in a multitude of settings.
+In spite of their high clustering accuracy, SC methods incur prohibitively high
+computational complexity when processing large volumes of high-dimensional
+data. Inspired by random sketching approaches for dimensionality reduction, the
+present paper introduces a randomized scheme for SC, termed Sketch-SC, tailored
+for large volumes of high-dimensional data. Sketch-SC accelerates the
+computationally heavy parts of state-of-the-art SC approaches by compressing
+the data matrix across both dimensions using random projections, thus enabling
+fast and accurate large-scale SC. Performance analysis as well as extensive
+numerical tests on real data corroborate the potential of Sketch-SC and its
+competitive performance relative to state-of-the-art scalable SC approaches.
+",1,0,0,1,0,0
+16299,An infinite class of unsaturated rooted trees corresponding to designable RNA secondary structures,"  An RNA secondary structure is designable if there is an RNA sequence which
+can attain its maximum number of base pairs only by adopting that structure.
+The combinatorial RNA design problem, introduced by Haleš et al. in 2016,
+is to determine whether or not a given RNA secondary structure is designable.
+Haleš et al. identified certain classes of designable and non-designable
+secondary structures by reference to their corresponding rooted trees. We
+introduce an infinite class of rooted trees containing unpaired nucleotides at
+the greatest height, and prove constructively that their corresponding
+secondary structures are designable. This complements previous results for the
+combinatorial RNA design problem.
+",1,0,0,0,0,0
+16300,Any Baumslag-Solitar action on surfaces with a pseudo-Anosov element has a finite orbit,"  We consider $f, h$ homeomorphims generating a faithful $BS(1,n)$-action on a
+closed surface $S$, that is, $h f h^{-1} = f^n$, for some $ n\geq 2$. According
+to \cite{GL}, after replacing $f$ by a suitable iterate if necessary, we can
+assume that there exists a minimal set $\Lambda$ of the action, included in
+$Fix(f)$.
+Here, we suppose that $f$ and $h$ are $C^1$ in neighbourhood of $\Lambda$ and
+any point $x\in\Lambda$ admits an $h$-unstable manifold $W^u(x)$. Using
+Bonatti's techniques, we prove that either there exists an integer $N$ such
+that $W^u(x)$ is included in $Fix(f^N)$ or there is a lower bound for the norm
+of the differential of $h$ only depending on $n$ and the Riemannian metric on
+$S$.
+Combining last statement with a result of \cite{AGX}, we show that any
+faithful action of $BS(1, n)$ on $S$ with $h$ a pseudo-Anosov homeomorphism has
+a finite orbit. As a consequence, there is no faithful $C^1$-action of $BS(1,
+n)$ on the torus with $h$ an Anosov.
+",0,0,1,0,0,0
+16301,Fault Localization for Declarative Models in Alloy,"  Fault localization is a popular research topic and many techniques have been
+proposed to locate faults in imperative code, e.g. C and Java. In this paper,
+we focus on the problem of fault localization for declarative models in Alloy
+-- a first order relational logic with transitive closure. We introduce
+AlloyFL, the first set of fault localization techniques for faulty Alloy models
+which leverages multiple test formulas. AlloyFL is also the first set of fault
+localization techniques at the AST node granularity. We implements in AlloyFL
+both spectrum-based and mutation-based fault localization techniques, as well
+as techniques that are based on Alloy's built-in unsat core. We introduce new
+metrics to measure the accuracy of AlloyFL and systematically evaluate AlloyFL
+on 38 real faulty models and 9000 mutant models. The results show that the
+mutation-based fault localization techniques are significantly more accurate
+than other types of techniques.
+",1,0,0,0,0,0
+16302,Comparing the fractal basins of attraction in the Hill problem with oblateness and radiation,"  The basins of convergence, associated with the roots (attractors) of a
+complex equation, are revealed in the Hill problem with oblateness and
+radiation, using a large variety of numerical methods. Three cases are
+investigated, regarding the values of the oblateness and radiation. In all
+cases, a systematic and thorough scan of the complex plane is performed in
+order to determine the basins of attraction of the several iterative schemes.
+The correlations between the attracting domains and the corresponding required
+number of iterations are also illustrated and discussed. Our numerical analysis
+strongly suggests that the basins of convergence, with the highly fractal basin
+boundaries, produce extraordinary and beautiful formations on the complex
+plane.
+",0,1,0,0,0,0
+16303,Parameters for Generalized Hecke Algebras in Type B,"  The irreducible representations of full support in the rational Cherednik
+category $\mathcal{O}_c(W)$ attached to a Coxeter group $W$ are in bijection
+with the irreducible representations of an associated Iwahori-Hecke algebra.
+Recent work has shown that the irreducible representations in
+$\mathcal{O}_c(W)$ of arbitrary given support are similarly governed by certain
+generalized Hecke algebras. In this paper we compute the parameters for these
+generalized Hecke algebras in the remaining previously unknown cases,
+corresponding to the parabolic subgroup $B_n \times S_k$ in $B_{n+k}$ for $k
+\geq 2$ and $n \geq 0$.
+",0,0,1,0,0,0
+16304,Arbitrage-Free Interpolation in Models of Market Observable Interest Rates,"  Models which postulate lognormal dynamics for interest rates which are
+compounded according to market conventions, such as forward LIBOR or forward
+swap rates, can be constructed initially in a discrete tenor framework.
+Interpolating interest rates between maturities in the discrete tenor structure
+is equivalent to extending the model to continuous tenor. The present paper
+sets forth an alternative way of performing this extension; one which preserves
+the Markovian properties of the discrete tenor models and guarantees the
+positivity of all interpolated rates.
+",0,0,0,0,0,1
+16305,Bayesian Simultaneous Estimation for Means in $k$ Sample Problems,"  This paper is concerned with the simultaneous estimation of $k$ population
+means when one suspects that the $k$ means are nearly equal. As an alternative
+to the preliminary test estimator based on the test statistics for testing
+hypothesis of equal means, we derive Bayesian and minimax estimators which
+shrink individual sample means toward a pooled mean estimator given under the
+hypothesis. It is shown that both the preliminary test estimator and the
+Bayesian minimax shrinkage estimators are further improved by shrinking the
+pooled mean estimator. The performance of the proposed shrinkage estimators is
+investigated by simulation.
+",0,0,1,1,0,0
+16306,The Camassa--Holm Equation and The String Density Problem,"  In this paper we review the recent progress in the (indefinite) string
+density problem and its applications to the Camassa--Holm equation.
+",0,0,1,0,0,0
+16307,A simple efficient density estimator that enables fast systematic search,"  This paper introduces a simple and efficient density estimator that enables
+fast systematic search. To show its advantage over commonly used kernel density
+estimator, we apply it to outlying aspects mining. Outlying aspects mining
+discovers feature subsets (or subspaces) that describe how a query stand out
+from a given dataset. The task demands a systematic search of subspaces. We
+identify that existing outlying aspects miners are restricted to datasets with
+small data size and dimensions because they employ kernel density estimator,
+which is computationally expensive, for subspace assessments. We show that a
+recent outlying aspects miner can run orders of magnitude faster by simply
+replacing its density estimator with the proposed density estimator, enabling
+it to deal with large datasets with thousands of dimensions that would
+otherwise be impossible.
+",1,0,0,1,0,0
+16308,"Perches, Post-holes and Grids","  The ""Planning in the Early Medieval Landscape"" project (PEML)
+<this http URL>,
+funded by the Leverhulme Trust, has organized and collated a substantial
+quantity of images, and has used this as evidence to support the hypothesis
+that Anglo-Saxon building construction was based on grid-like planning
+structures based on fixed modules or quanta of measurement. We report on the
+development of some statistical contributions to the debate concerning this
+hypothesis. In practice the PEML images correspond to data arising in a wide
+variety of different forms. It does not seem feasible to produce a single
+automatic method which can be applied uniformly to all such images; even the
+initial chore of cleaning up an image (removing extraneous material such as
+legends and physical features which do not bear on the planning hypothesis)
+typically presents a separate and demanding challenge for each different image.
+Moreover care must be taken, even in the relatively straightforward cases of
+clearly defined ground-plans (for example for large ecclesiastical buildings of
+the period), to consider exactly what measurements might be relevant. We report
+on pilot statistical analyses concerning three different situations. These
+establish not only the presence of underlying structure (which indeed is often
+visually obvious), but also provide an account of the numerical evidence
+supporting the deduction that such structure is present. We contend that
+statistical methodology thus contributes to the larger historical debate and
+provides useful input to the wide and varied range of evidence that has to be
+debated.
+",0,0,0,1,0,0
+16309,Sharp bounds for population recovery,"  The population recovery problem is a basic problem in noisy unsupervised
+learning that has attracted significant research attention in recent years
+[WY12,DRWY12, MS13, BIMP13, LZ15,DST16]. A number of different variants of this
+problem have been studied, often under assumptions on the unknown distribution
+(such as that it has restricted support size). In this work we study the sample
+complexity and algorithmic complexity of the most general version of the
+problem, under both bit-flip noise and erasure noise model. We give essentially
+matching upper and lower sample complexity bounds for both noise models, and
+efficient algorithms matching these sample complexity bounds up to polynomial
+factors.
+",1,0,1,1,0,0
+16310,MultiBUGS: A parallel implementation of the BUGS modelling framework for faster Bayesian inference,"  MultiBUGS (this https URL) is a new version of the general-purpose
+Bayesian modelling software BUGS that implements a generic algorithm for
+parallelising Markov chain Monte Carlo (MCMC) algorithms to speed up posterior
+inference of Bayesian models. The algorithm parallelises evaluation of the
+product-form likelihoods formed when a parameter has many children in the
+directed acyclic graph (DAG) representation; and parallelises sampling of
+conditionally-independent sets of parameters. A heuristic algorithm is used to
+decide which approach to use for each parameter and to apportion computation
+across computational cores. This enables MultiBUGS to automatically parallelise
+the broad range of statistical models that can be fitted using BUGS-language
+software, making the dramatic speed-ups of modern multi-core computing
+accessible to applied statisticians, without requiring any experience of
+parallel programming. We demonstrate the use of MultiBUGS on simulated data
+designed to mimic a hierarchical e-health linked-data study of methadone
+prescriptions including 425,112 observations and 20,426 random effects.
+Posterior inference for the e-health model takes several hours in existing
+software, but MultiBUGS can perform inference in only 28 minutes using 48
+computational cores.
+",0,0,0,1,0,0
+16311,Road to safe autonomy with data and formal reasoning,"  We present an overview of recently developed data-driven tools for safety
+analysis of autonomous vehicles and advanced driver assist systems. The core
+algorithms combine model-based, hybrid system reachability analysis with
+sensitivity analysis of components with unknown or inaccessible models. We
+illustrate the applicability of this approach with a new case study of
+emergency braking systems in scenarios with two or three vehicles. This problem
+is representative of the most common type of rear-end crashes, which is
+relevant for safety analysis of automatic emergency braking (AEB) and forward
+collision avoidance systems. We show that our verification tool can effectively
+prove the safety of certain scenarios (specified by several parameters like
+braking profiles, initial velocities, uncertainties in position and reaction
+times), and also compute the severity of accidents for unsafe scenarios.
+Through hundreds of verification experiments, we quantified the safety envelope
+of the system across relevant parameters. These results show that the approach
+is promising for design, debugging and certification. We also show how the
+reachability analysis can be combined with statistical information about the
+parameters, to assess the risk level of the control system, which in turn is
+essential, for example, for determining Automotive Safety Integrity Levels
+(ASIL) for the ISO26262 standard.
+",1,0,0,0,0,0
+16312,Social Events in a Time-Varying Mobile Phone Graph,"  The large-scale study of human mobility has been significantly enhanced over
+the last decade by the massive use of mobile phones in urban populations.
+Studying the activity of mobile phones allows us, not only to infer social
+networks between individuals, but also to observe the movements of these
+individuals in space and time. In this work, we investigate how these two
+related sources of information can be integrated within the context of
+detecting and analyzing large social events. We show that large social events
+can be characterized not only by an anomalous increase in activity of the
+antennas in the neighborhood of the event, but also by an increase in social
+relationships of the attendants present in the event. Moreover, having detected
+a large social event via increased antenna activity, we can use the network
+connections to infer whether an unobserved user was present at the event. More
+precisely, we address the following three challenges: (i) automatically
+detecting large social events via increased antenna activity; (ii)
+characterizing the social cohesion of the detected event; and (iii) analyzing
+the feasibility of inferring whether unobserved users were in the event.
+",1,1,0,0,0,0
+16313,Range Assignment of Base-Stations Maximizing Coverage Area without Interference,"  We study the problem of assigning non-overlapping geometric objects centered
+at a given set of points such that the sum of area covered by them is
+maximized. If the points are placed on a straight-line and the objects are
+disks, then the problem is solvable in polynomial time. However, we show that
+the problem is NP-hard even for simplest objects like disks or squares in
+${\mathbb{R}}^2$. Eppstein [CCCG, pages 260--265, 2016] proposed a polynomial
+time algorithm for maximizing the sum of radii (or perimeter) of
+non-overlapping balls or disks when the points are arbitrarily placed on a
+plane. We show that Eppstein's algorithm for maximizing sum of perimeter of the
+disks in ${\mathbb{R}}^2$ gives a $2$-approximation solution for the sum of
+area maximization problem. We propose a PTAS for our problem. These
+approximation results are extendible to higher dimensions. All these
+approximation results hold for the area maximization problem by regular convex
+polygons with even number of edges centered at the given points.
+",1,0,0,0,0,0
+16314,A Polylogarithm Solution to the Epsilon--Delta Problem,"  Let $f$ be a continuous real function defined in a subset of the real line.
+The standard definition of continuity at a point $x$ allow us to correlate any
+given epsilon with a (possibly depending of $x$) delta value. This pairing is
+known as the epsilon--delta relation of $f$. In this work, we demonstrate the
+existence of a privileged choice of delta in the sense that it is continuous,
+invertible, maximal and it is the solution of a simple functional equation. We
+also introduce an algorithm that can be used to numerically calculate this map
+in polylogarithm time, proving the computability of the epsilon--delta
+relation. Finally, some examples are analyzed in order to showcase the accuracy
+and effectiveness of these methods, even when the explicit formula for the
+aforementioned privileged function is unknown due to the lack of analytical
+tools for solving the functional equation.
+",0,0,1,0,0,0
+16315,Pricing of debt and equity in a financial network with comonotonic endowments,"  In this paper we present formulas for the valuation of debt and equity of
+firms in a financial network under comonotonic endowments. We demonstrate that
+the comonotonic setting provides a lower bound to the price of debt under
+Eisenberg-Noe financial networks with consistent marginal endowments. Such
+financial networks encode the interconnection of firms through debt claims. The
+proposed pricing formulas consider the realized, endogenous, recovery rate on
+debt claims. Special consideration will be given to the setting in which firms
+only invest in a risk-free bond and a common risky asset following a geometric
+Brownian motion.
+",0,0,0,0,0,1
+16316,Resolvent expansion for the Schrödinger operator on a graph with infinite rays,"  We consider the Schrödinger operator on a combinatorial graph consisting of
+a finite graph and a finite number of discrete half-lines, all jointed
+together, and compute an asymptotic expansion of its resolvent around the
+threshold $0$. Precise expressions are obtained for the first few coefficients
+of the expansion in terms of the generalized eigenfunctions. This result
+justifies the classification of threshold types solely by growth properties of
+the generalized eigenfunctions. By choosing an appropriate free operator a
+priori possessing no zero eigenvalue or zero resonance we can simplify the
+expansion procedure as much as that on the single discrete half-line.
+",0,0,1,0,0,0
+16317,Space-Filling Fractal Description of Ion-induced Local Thermal Spikes in Molecular Solid of ZnO,"  Anions of the molecules ZnO, O2 and atomic Zn and O constitute mass spectra
+of the species sputtered from pellets of molecular solid of ZnO under Cs+
+irradiation. Their normalized yields are independent of energy of the
+irradiating Cs+. Collision cascades cannot explain the simultaneous sputtering
+of atoms and molecules. We propose that the origin of the molecular
+sublimation, dissociation and subsequent emission is the result of localized
+thermal spikes induced by individual Cs+ ions. The fractal dimension of binary
+collision cascades of atomic recoils in the irradiated ZnO solid increases with
+reduction in the energy of recoils. Upon reaching the collision diameters of
+atomic dimensions, the space-filling fractal-like transition occurs where
+cascades transform into thermal spikes. These localized thermal spikes induce
+sublimation, dissociation and sputtering from the region. The calculated rates
+of the subliming and dissociating species due to localized thermal spikes agree
+well with the experimental results.
+",0,1,0,0,0,0
+16318,Integrable modules over affine Lie superalgebras sl(1|n)^,"  We describe the category of integrable sl(1|n)^ -modules with the positive
+central charge and show that the irreducible modules provide the full set of
+irreducible representations for the corresponding simple vertex algebra.
+",0,0,1,0,0,0
+16319,LocDyn: Robust Distributed Localization for Mobile Underwater Networks,"  How to self-localize large teams of underwater nodes using only noisy range
+measurements? How to do it in a distributed way, and incorporating dynamics
+into the problem? How to reject outliers and produce trustworthy position
+estimates? The stringent acoustic communication channel and the accuracy needs
+of our geophysical survey application demand faster and more accurate
+localization methods. We approach dynamic localization as a MAP estimation
+problem where the prior encodes dynamics, and we devise a convex relaxation
+method that takes advantage of previous estimates at each measurement
+acquisition step; The algorithm converges at an optimal rate for first order
+methods. LocDyn is distributed: there is no fusion center responsible for
+processing acquired data and the same simple computations are performed for
+each node. LocDyn is accurate: experiments attest to a smaller positioning
+error than a comparable Kalman filter. LocDyn is robust: it rejects outlier
+noise, while the comparing methods succumb in terms of positioning error.
+",1,0,1,1,0,0
+16320,High-speed X-ray imaging spectroscopy system with Zynq SoC for solar observations,"  We have developed a system combining a back-illuminated
+Complementary-Metal-Oxide-Semiconductor (CMOS) imaging sensor and Xilinx Zynq
+System-on-Chip (SoC) device for a soft X-ray (0.5-10 keV) imaging spectroscopy
+observation of the Sun to investigate the dynamics of the solar corona. Because
+typical timescales of energy release phenomena in the corona span a few minutes
+at most, we aim to obtain the corresponding energy spectra and derive the
+physical parameters, i.e., temperature and emission measure, every few tens of
+seconds or less for future solar X-ray observations. An X-ray photon-counting
+technique, with a frame rate of a few hundred frames per second or more, can
+achieve such results. We used the Zynq SoC device to achieve the requirements.
+Zynq contains an ARM processor core, which is also known as the Processing
+System (PS) part, and a Programmable Logic (PL) part in a single chip. We use
+the PL and PS to control the sensor and seamless recording of data to a storage
+system, respectively. We aim to use the system for the third flight of the
+Focusing Optics Solar X-ray Imager (FOXSI-3) sounding rocket experiment for the
+first photon-counting X-ray imaging and spectroscopy of the Sun.
+",0,1,0,0,0,0
+16321,Cooperative Multi-Sender Index Coding,"  In this paper, we propose a new coding scheme and establish new bounds on the
+capacity region for the multi-sender unicast index-coding problem. We revisit
+existing partitioned Distributed Composite Coding (DCC) proposed by Sadeghi et
+al. and identify its limitations in the implementation of multi-sender
+composite coding and in the strategy of sender partitioning. We then propose
+two new coding components to overcome these limitations and develop a
+multi-sender Cooperative Composite Coding (CCC). We show that CCC can strictly
+improve upon partitioned DCC, and is the key to achieve optimality for a number
+of index-coding instances. The usefulness of CCC and its special cases is
+illuminated via non-trivial examples, and the capacity region is established
+for each example. Comparisons between CCC and other non-cooperative schemes in
+recent works are also provided to further demonstrate the advantage of CCC.
+",1,0,1,0,0,0
+16322,"Comment on ""Eshelby twist and correlation effects in diffraction from nanocrystals"" [J. Appl. Phys. 117, 164304 (2015)]","  The aim of this comment is to show that anisotropic effects and image fields
+should not be omitted as they are in the publication of A. Leonardi, S. Ryu, N.
+M. Pugno, and P. Scardi (LRPS) [J. Appl. Phys. 117, 164304 (2015)] on Pd <011>
+cylindrical nanowires containing an axial screw dislocation. Indeed, according
+to our previous study [Phys. Rev. B 88, 224101 (2013)], the axial displacement
+field along the nanowire exhibits both a radial and an azimuthal dependence
+with a twofold symmetry due the <011> orientation. As a consequence, the
+deviatoric strain term used by LRPS is not suitable to analyze the anisotropic
+strain fields that should be observed in their atomistic simulations. In this
+comment, we first illustrate the importance of anisotropy in <011> Pd nanowire
+by calculating the azimuthal dependence of the deviatoric strain term. Then the
+expression of the anisotropic elastic field is recalled in term of strain
+tensor components to show that image fields should be also considered. The
+other aspect of this comment concerns the supposedly loss of correlation along
+the nanorod caused by the twist. It is claimed for instance by LRPS that : ""As
+an effect of the dislocation strain and twist, if the cylinder is long enough,
+upper/lower regions tend to lose correlation, as if the rod were made of
+different sub-domains."". This assertion appears to us misleading since for any
+twist the position of all the atoms in the nanorod is perfectly defined and
+therefore prevents any loss of correlation. To clarify this point, it should be
+specified that this apparent loss of correlation can not be ascribed to the
+twisted state of the nanowire but is rather due to a limitation of the X-ray
+powder diffraction. Considering for instance coherent X-ray diffraction, we
+show an example of high twist where the simulated diffractogram presents a
+clear signature of the perfect correlation.
+",0,1,0,0,0,0
+16323,Variational Community Partition with Novel Network Structure Centrality Prior,"  In this paper, we proposed a novel two-stage optimization method for network
+community partition, which is based on inherent network structure information.
+The introduced optimization approach utilizes the new network centrality
+measure of both links and vertices to construct the key affinity description of
+the given network, where the direct similarities between graph nodes or nodal
+features are not available to obtain the classical affinity matrix. Indeed,
+such calculated network centrality information presents the essential structure
+of network, hence, the proper measure for detecting network communities, which
+also introduces a `confidence' criterion for referencing new labeled benchmark
+nodes. For the resulted challenging combinatorial optimization problem of graph
+clustering, the proposed optimization method iteratively employs an efficient
+convex optimization algorithm which is developed based under a new variational
+perspective of primal and dual. Experiments over both artificial and real-world
+network datasets demonstrate that the proposed optimization strategy of
+community detection significantly improves result accuracy and outperforms the
+state-of-the-art algorithms in terms of accuracy and reliability.
+",1,0,0,0,0,0
+16324,Possible formation pathways for the low density Neptune-mass planet HAT-P-26b,"  We investigate possible pathways for the formation of the low density
+Neptune-mass planet HAT-P-26b. We use two formation different models based on
+pebbles and planetesimals accretion, and includes gas accretion, disk migration
+and simple photoevaporation. The models tracks the atmospheric oxygen
+abundance, in addition to the orbital period, and mass of the forming planets,
+that we compare to HAT-P-26b. We find that pebbles accretion can explain this
+planet more naturally than planetesimals accretion that fails completely unless
+we artificially enhance the disk metallicity significantly. Pebble accretion
+models can reproduce HAT-P-26b with either a high initial core mass and low
+amount of envelope enrichment through core erosion or pebbles dissolution, or
+the opposite, with both scenarios being possible. Assuming a low envelope
+enrichment factor as expected from convection theory and comparable to the
+values we can infer from the D/H measurements in Uranus and Neptune, our most
+probable formation pathway for HAT-P-26b is through pebble accretion starting
+around 10 AU early in the disk's lifetime.
+",0,1,0,0,0,0
+16325,Shape Classification using Spectral Graph Wavelets,"  Spectral shape descriptors have been used extensively in a broad spectrum of
+geometry processing applications ranging from shape retrieval and segmentation
+to classification. In this pa- per, we propose a spectral graph wavelet
+approach for 3D shape classification using the bag-of-features paradigm. In an
+effort to capture both the local and global geometry of a 3D shape, we present
+a three-step feature description framework. First, local descriptors are
+extracted via the spectral graph wavelet transform having the Mexican hat
+wavelet as a generating ker- nel. Second, mid-level features are obtained by
+embedding lo- cal descriptors into the visual vocabulary space using the soft-
+assignment coding step of the bag-of-features model. Third, a global descriptor
+is constructed by aggregating mid-level fea- tures weighted by a geodesic
+exponential kernel, resulting in a matrix representation that describes the
+frequency of appearance of nearby codewords in the vocabulary. Experimental
+results on two standard 3D shape benchmarks demonstrate the effective- ness of
+the proposed classification approach in comparison with state-of-the-art
+methods.
+",1,0,0,0,0,0
+16326,Dynamical inverse problem for Jacobi matrices,"  We consider the inverse dynamical problem for the dynamical system with
+discrete time associated with the semi-infinite Jacobi matrix. We solve the
+inverse problem for such a system and answer a question on the characterization
+of the inverse data. As a by-product we give a necessary and sufficient
+condition for the measure on the real line line to be the spectral measure of
+semi-infinite discrete Schrodinger operator.
+",0,0,1,0,0,0
+16327,A short note on the computation of the generalised Jacobsthal function for paired progressions,"  Jacobsthal's function was recently generalised for the case of paired
+progressions. It was proven that a specific bound of this function is
+sufficient for the truth of Goldbach's conjecture and of the prime pairs
+conjecture as well. We extended and adapted algorithms described for the
+computation of the common Jacobsthal function, and computed respective function
+values of the paired Jacobsthal function for primorial numbers for primes up to
+73. All these values fulfil the conjectured specific bound. In addition to this
+note, we provide a detailed review of the algorithmic approaches and the
+complete computational results in ancillary files.
+",0,0,1,0,0,0
+16328,Improved SVD-based Initialization for Nonnegative Matrix Factorization using Low-Rank Correction,"  Due to the iterative nature of most nonnegative matrix factorization
+(\textsc{NMF}) algorithms, initialization is a key aspect as it significantly
+influences both the convergence and the final solution obtained. Many
+initialization schemes have been proposed for NMF, among which one of the most
+popular class of methods are based on the singular value decomposition (SVD).
+However, these SVD-based initializations do not satisfy a rather natural
+condition, namely that the error should decrease as the rank of factorization
+increases. In this paper, we propose a novel SVD-based \textsc{NMF}
+initialization to specifically address this shortcoming by taking into account
+the SVD factors that were discarded to obtain a nonnegative initialization.
+This method, referred to as nonnegative SVD with low-rank correction
+(NNSVD-LRC), allows us to significantly reduce the initial error at a
+negligible additional computational cost using the low-rank structure of the
+discarded SVD factors. NNSVD-LRC has two other advantages compared to previous
+SVD-based initializations: (1) it provably generates sparse initial factors,
+and (2) it is faster as it only requires to compute a truncated SVD of rank
+$\lceil r/2 + 1 \rceil$ where $r$ is the factorization rank of the sought NMF
+decomposition (as opposed to a rank-$r$ truncated SVD for other methods). We
+show on several standard dense and sparse data sets that our new method
+competes favorably with state-of-the-art SVD-based initializations for NMF.
+",1,0,0,1,0,0
+16329,Connectivity jamming game for physical layer attack in peer to peer networks,"  Because of the open access nature of wireless communications, wireless
+networks can suffer from malicious activity, such as jamming attacks, aimed at
+undermining the network's ability to sustain communication links and acceptable
+throughput. One important consideration when designing networks is to
+appropriately tune the network topology and its connectivity so as to support
+the communication needs of those participating in the network. This paper
+examines the problem of interference attacks that are intended to harm
+connectivity and throughput, and illustrates the method of mapping network
+performance parameters into the metric of topographic connectivity.
+Specifically, this paper arrives at anti-jamming strategies aimed at coping
+with interference attacks through a unified stochastic game. In such a
+framework, an entity trying to protect a network faces a dilemma: (i) the
+underlying motivations for the adversary can be quite varied, which depends
+largely on the network's characteristics such as power and distance; (ii) the
+metrics for such an attack can be incomparable (e.g., network connectivity and
+total throughput). To deal with the problem of such incomparable metrics, this
+paper proposes using the attack's expected duration as a unifying metric to
+compare distinct attack metrics because a longer-duration of unsuccessful
+attack assumes a higher cost. Based on this common metric, a mechanism of
+maxmin selection for an attack prevention strategy is suggested.
+",1,0,0,0,0,0
+16330,"Global Symmetries, Counterterms, and Duality in Chern-Simons Matter Theories with Orthogonal Gauge Groups","  We study three-dimensional gauge theories based on orthogonal groups.
+Depending on the global form of the group these theories admit discrete
+$\theta$-parameters, which control the weights in the sum over topologically
+distinct gauge bundles. We derive level-rank duality for these topological
+field theories. Our results may also be viewed as level-rank duality for
+$SO(N)_{K}$ Chern-Simons theory in the presence of background fields for
+discrete global symmetries. In particular, we include the required counterterms
+and analysis of the anomalies. We couple our theories to charged matter and
+determine how these counterterms are shifted by integrating out massive
+fermions. By gauging discrete global symmetries we derive new boson-fermion
+dualities for vector matter, and present the phase diagram of theories with
+two-index tensor fermions, thus extending previous results for $SO(N)$ to other
+global forms of the gauge group.
+",0,1,0,0,0,0
+16331,Tail sums of Wishart and GUE eigenvalues beyond the bulk edge,"  Consider the classical Gaussian unitary ensemble of size $N$ and the real
+Wishart ensemble $W_N(n,I)$. In the limits as $N \to \infty$ and $N/n \to
+\gamma > 0$, the expected number of eigenvalues that exit the upper bulk edge
+is less than one, 0.031 and 0.170 respectively, the latter number being
+independent of $\gamma$. These statements are consequences of quantitative
+bounds on tail sums of eigenvalues outside the bulk which are established here
+for applications in high dimensional covariance matrix estimation.
+",0,0,1,1,0,0
+16332,Ultrafast Energy Transfer with Competing Channels: Non-equilibrium Foerster and Modified Redfield Theories,"  We derive equations of motion for the reduced density matrix of a molecular
+system which undergoes energy transfer dynamics competing with fast internal
+conversion channels. Environmental degrees of freedom of such a system have no
+time to relax to quasi-equilibrium in the electronic excited state of the donor
+molecule, and thus the conditions of validity of Foerster and Modified Redfield
+theories in their standard formulations do not apply. We derive non-equilibrium
+versions of the two well-known rate theories and apply them to the case of
+carotenoid-chlorophyll energy transfer. Although our reduced density matrix
+approach does not account for the formation of vibronic excitons, it still
+confirms the important role of the donor ground-state vibrational states in
+establishing the resonance energy transfer conditions. We show that it is
+essential to work with a theory valid in strong system-bath interaction regime
+to obtain correct dependence of the rates on donor-acceptor energy gap.
+",0,1,0,0,0,0
+16333,Observation of non-Fermi liquid behavior in hole doped Eu2Ir2O7,"  The Weyl semimetallic compound Eu2Ir2O7 along with its hole doped derivatives
+(which is achieved by substituting trivalent Eu by divalent Sr) are
+investigated through transport, magnetic and calorimetric studies. The
+metal-insulator transition (MIT) temperature is found to get substantially
+reduced with hole doping and for 10% Sr doping the composition is metallic down
+to temperature as low as 5 K. These doped compounds are found to violate the
+Mott-Ioffe-Regel condition for minimum electrical conductivity and show
+distinct signature of non-Fermi liquid behavior at low temperature. The MIT in
+the doped compounds does not correlate with the magnetic transition point and
+Anderson-Mott type disorder induced localization may be attributed to the
+ground state insulating phase. The observed non-Fermi liquid behavior can be
+understood on the basis of disorder induced distribution of spin orbit coupling
+parameter which is markedly different in case of Ir4+ and Ir5+ ions.
+",0,1,0,0,0,0
+16334,"Galactic Outflows, Star Formation Histories, and Timescales in Starburst Dwarf Galaxies from STARBIRDS","  Winds are predicted to be ubiquitous in low-mass, actively star-forming
+galaxies. Observationally, winds have been detected in relatively few local
+dwarf galaxies, with even fewer constraints placed on their timescales. Here,
+we compare galactic outflows traced by diffuse, soft X-ray emission from
+Chandra Space Telescope archival observations to the star formation histories
+derived from Hubble Space Telescope imaging of the resolved stellar populations
+in six starburst dwarfs. We constrain the longevity of a wind to have an upper
+limit of 25 Myr based on galaxies whose starburst activity has already
+declined, although a larger sample is needed to confirm this result. We find an
+average 16% efficiency for converting the mechanical energy of stellar feedback
+to thermal, soft X-ray emission on the 25 Myr timescale, somewhat higher than
+simulations predict. The outflows have likely been sustained for timescales
+comparable to the duration of the starbursts (i.e., 100's Myr), after taking
+into account the time for the development and cessation of the wind. The wind
+timescales imply that material is driven to larger distances in the
+circumgalactic medium than estimated by assuming short, 5-10 Myr starburst
+durations, and that less material is recycled back to the host galaxy on short
+timescales. In the detected outflows, the expelled hot gas shows various
+morphologies which are not consistent with a simple biconical outflow
+structure. The sample and analysis are part of a larger program, the STARBurst
+IRregular Dwarf Survey (STARBIRDS), aimed at understanding the lifecycle and
+impact of starburst activity in low-mass systems.
+",0,1,0,0,0,0
+16335,Compact microwave kinetic inductance nanowire galvanometer for cryogenic detectors at 4.2 K,"  We present a compact current sensor based on a superconducting microwave
+lumped-element resonator with a nanowire kinetic inductor, operating at 4.2 K.
+The sensor is suitable for multiplexed readout in GHz range of large-format
+arrays of cryogenic detectors. The device consists of a lumped-element resonant
+circuit, fabricated from a single 4-nm-thick superconducting layer of niobium
+nitride. Thus, the fabrication and operation is significantly simplified in
+comparison to state-of-the-art approaches. Because the resonant circuit is
+inductively coupled to the feed line the current to be measured can directly be
+injected without having the need of an impedance matching circuit, reducing the
+system complexity. With the proof-of-concept device we measured a current noise
+floor {\delta}Imin of 10 pA/Hz1/2 at 10 kHz. Furthermore, we demonstrate the
+ability of our sensor to amplify a pulsed response of a superconducting
+nanowire single-photon detector using a GHz-range carrier for effective
+frequency-division multiplexing.
+",0,1,0,0,0,0
+16336,Evolution of dust extinction curves in galaxy simulation,"  To understand the evolution of extinction curve, we calculate the dust
+evolution in a galaxy using smoothed particle hydrodynamics simulations
+incorporating stellar dust production, dust destruction in supernova shocks,
+grain growth by accretion and coagulation, and grain disruption by shattering.
+The dust species are separated into carbonaceous dust and silicate. The
+evolution of grain size distribution is considered by dividing grain population
+into large and small gains, which allows us to estimate extinction curves. We
+examine the dependence of extinction curves on the position, gas density, and
+metallicity in the galaxy, and find that extinction curves are flat at $t
+\lesssim 0.3$ Gyr because stellar dust production dominates the total dust
+abundance. The 2175 \AA\ bump and far-ultraviolet (FUV) rise become prominent
+after dust growth by accretion. At $t \gtrsim 3$ Gyr, shattering works
+efficiently in the outer disc and low density regions, so extinction curves
+show a very strong 2175 \AA\ bump and steep FUV rise. The extinction curves at
+$t\gtrsim 3$ Gyr are consistent with the Milky Way extinction curve, which
+implies that we successfully included the necessary dust processes in the
+model. The outer disc component caused by stellar feedback has an extinction
+curves with a weaker 2175 \AA\ bump and flatter FUV slope. The strong
+contribution of carbonaceous dust tends to underproduce the FUV rise in the
+Small Magellanic Cloud extinction curve, which supports selective loss of small
+carbonaceous dust in the galaxy. The snapshot at young ages also explain the
+extinction curves in high-redshift quasars.
+",0,1,0,0,0,0
+16337,Witten Deformation And Some Topics Relating To It,"  This is a simple reading report of professor Weiping Zhang's lectures. In
+this article we will mainly introduce the basic ideas of Witten deformation,
+which were first introduced by Edward Witten on, and some applications of it.
+The first part of this article mainly focuses on deformation of Dirac operators
+and some important analytic facts about the deformed Dirac operators. In the
+second part of this article some applications of Witten deformation will be
+given, to be more specific, an analytic proof of Poincar$\acute{e}$-Hopf index
+theorem and Real Morse Inequilities will be given. Also we will use Witten
+deformation to prove that the Thom Smale complex is quasi-isomorphism to the
+de-Rham complex (Witten suggested that Thom Smale complex can be recovered from
+his deformation and his suggestion was first realized by Helffer and
+Sj$\ddot{o}$strand, the proof in this article is given by Bismut and Zhang).
+And in the last part an analytic proof of Atiyah vanishing theorem via Witten
+deformation will be given.
+",0,0,1,0,0,0
+16338,Inverse sensitivity of plasmonic nanosensors at the single-molecule limit,"  Recent work using plasmonic nanosensors in a clinically relevant detection
+assay reports extreme sensitivity based upon a mechanism termed 'inverse
+sensitivity', whereby reduction of substrate concentration increases reaction
+rate, even at the single-molecule limit. This near-homoeopathic mechanism
+contradicts the law of mass action. The assay involves deposition of silver
+atoms upon gold nanostars, changing their absorption spectrum. Multiple
+additional aspects of the assay appear to be incompatible with settled chemical
+knowledge, in particular the detection of tiny numbers of silver atoms on a
+background of the classic 'silver mirror reaction'. Finally, it is estimated
+here that the reported spectral changes require some 2.5E11 times more silver
+atoms than are likely to be produced. It is suggested that alternative
+explanations must be sought for the original observations.
+",0,1,0,0,0,0
+16339,On approximation of Ginzburg-Landau minimizers by $\mathbb S^1$-valued maps in domains with vanishingly small holes,"  We consider a two-dimensional Ginzburg-Landau problem on an arbitrary domain
+with a finite number of vanishingly small circular holes. A special choice of
+scaling relation between the material and geometric parameters (Ginzburg-Landau
+parameter vs hole radius) is motivated by a recently dsicovered phenomenon of
+vortex phase separation in superconducting composites. We show that, for each
+hole, the degrees of minimizers of the Ginzburg-Landau problems in the classes
+of $\mathbb S^1$-valued and $\mathbb C$-valued maps, respectively, are the
+same. The presence of two parameters that are widely separated on a logarithmic
+scale constitutes the principal difficulty of the analysis that is based on
+energy decomposition techniques.
+",0,0,1,0,0,0
+16340,A Theory of Solvability for Lossless Power Flow Equations -- Part II: Conditions for Radial Networks,"  This two-part paper details a theory of solvability for the power flow
+equations in lossless power networks. In Part I, we derived a new formulation
+of the lossless power flow equations, which we term the fixed-point power flow.
+The model is parameterized by several graph-theoretic matrices -- the power
+network stiffness matrices -- which quantify the internal coupling strength of
+the network. In Part II, we leverage the fixed-point power flow to study power
+flow solvability. For radial networks, we derive parametric conditions which
+guarantee the existence and uniqueness of a high-voltage power flow solution,
+and construct examples for which the conditions are also necessary. Our
+conditions (i) imply convergence of the fixed-point power flow iteration, (ii)
+unify and extend recent results on solvability of decoupled power flow, (iii)
+directly generalize the textbook two-bus system results, and (iv) provide new
+insights into how the structure and parameters of the grid influence power flow
+solvability.
+",0,0,1,0,0,0
+16341,Coordination game in bidirectional flow,"  We have introduced evolutionary game dynamics to a one-dimensional
+cellular-automaton to investigate evolution and maintenance of cooperative
+avoiding behavior of self-driven particles in bidirectional flow. In our model,
+there are two kinds of particles, which are right-going particles and
+left-going particles. They often face opponent particles, so that they swerve
+to the right or left stochastically in order to avoid conflicts. The particles
+reinforce their preferences of the swerving direction after their successful
+avoidance. The preference is also weakened by memory-loss effect.
+Result of our simulation indicates that cooperative avoiding behavior is
+achieved, i.e., swerving directions of the particles are unified, when the
+density of particles is close to 1/2 and the memory-loss rate is small.
+Furthermore, when the right-going particles occupy the majority of the system,
+we observe that their flow increases when the number of left-going particles,
+which prevent the smooth movement of right-going particles, becomes large. It
+is also investigated that the critical memory-loss rate of the cooperative
+avoiding behavior strongly depends on the size of the system. Small system can
+prolong the cooperative avoiding behavior in wider range of memory-loss rate
+than large system.
+",1,1,0,0,0,0
+16342,Leaderboard Effects on Player Performance in a Citizen Science Game,"  Quantum Moves is a citizen science game that investigates the ability of
+humans to solve complex physics challenges that are intractable for computers.
+During the launch of Quantum Moves in April 2016 the game's leaderboard
+function broke down resulting in a ""no leaderboard"" game experience for some
+players for a couple of days (though their scores were still displayed). The
+subsequent quick fix of an all-time Top 5 leaderboard, and the following
+long-term implementation of a personalized relative-position (infinite)
+leaderboard provided us with a unique opportunity to compare and investigate
+the effect of different leaderboard implementations on player performance in a
+points-driven citizen science game.
+All three conditions were live sequentially during the game's initial influx
+of more than 150.000 players that stemmed from global press attention on
+Quantum Moves due the publication of a Nature paper about the use of Quantum
+Moves in solving a specific quantum physics problem. Thus, it has been possible
+to compare the three conditions and their influence on the performance (defined
+as a player's quality of game play related to a high-score) of over 4500 new
+players. These 4500 odd players in our three leaderboard-conditions have a
+similar demographic background based upon the time-window over which the
+implementations occurred and controlled against Player ID tags. Our results
+placed Condition 1 experience over condition 3 and in some cases even over
+condition 2 which goes against the general assumption that leaderboards enhance
+gameplay and its subsequent overuse as a an oft-relied upon element that
+designers slap onto a game to enhance said appeal. Our study thus questions the
+use of leaderboards as general performance enhancers in gamification contexts
+and brings some empirical rigor to an often under-reported but overused
+phenomenon.
+",0,1,0,0,0,0
+16343,"Marchenko-based target replacement, accounting for all orders of multiple reflections","  In seismic monitoring one is usually interested in the response of a changing
+target zone, embedded in a static inhomogeneous medium. We introduce an
+efficient method which predicts reflection responses at the earth's surface for
+different target-zone scenarios, from a single reflection response at the
+surface and a model of the changing target zone. The proposed process consists
+of two main steps. In the first step, the response of the original target zone
+is removed from the reflection response, using the Marchenko method. In the
+second step, the modelled response of a new target zone is inserted between the
+overburden and underburden responses. The method fully accounts for all orders
+of multiple scattering and, in the elastodynamic case, for wave conversion. For
+monitoring purposes, only the second step needs to be repeated for each
+target-zone model. Since the target zone covers only a small part of the entire
+medium, the proposed method is much more efficient than repeated modelling of
+the entire reflection response.
+",0,1,0,0,0,0
+16344,Bayesian model checking: A comparison of tests,"  Two procedures for checking Bayesian models are compared using a simple test
+problem based on the local Hubble expansion. Over four orders of magnitude,
+p-values derived from a global goodness-of-fit criterion for posterior
+probability density functions (Lucy 2017) agree closely with posterior
+predictive p-values. The former can therefore serve as an effective proxy for
+the difficult-to-calculate posterior predictive p-values.
+",0,1,0,1,0,0
+16345,Idempotents in Intersection of the Kernel and the Image of Locally Finite Derivations and $\mathcal E$-derivations,"  Let $K$ be a field of characteristic zero, $\mathcal A$ a $K$-algebra and
+$\delta$ a $K$-derivation of $\mathcal A$ or $K$-$\mathcal E$-derivation of
+$\mathcal A$ (i.e., $\delta=\operatorname{Id}_A-\phi$ for some $K$-algebra
+endomorphism $\phi$ of $\mathcal A$). Motivated by the Idempotent conjecture
+proposed in [Z4], we first show that for every idempotent $e$ lying in both the
+kernel ${\mathcal A}^\delta$ and the image $\operatorname{Im}\delta \!:=\delta
+({\mathcal A})$ of $\delta$, the principal ideal $(e)\subseteq
+\operatorname{Im} \delta$ if $\delta$ is a locally finite $K$-derivation or a
+locally nilpotent $K$-$\mathcal E$-derivation of $\mathcal A$; and $e{\mathcal
+A}, {\mathcal A}e \subseteq \operatorname{Im} \delta$ if $\delta$ is a locally
+finite $K$-$\mathcal E$-derivation of $\mathcal A$. Consequently, the
+Idempotent conjecture holds for all locally finite $K$-derivations and all
+locally nilpotent $K$-$\mathcal E$-derivations of $\mathcal A$. We then show
+that $1_{\mathcal A} \in \operatorname{Im} \delta$, (if and) only if $\delta$
+is surjective, which generalizes the same result [GN, W] for locally nilpotent
+$K$-derivations of commutative $K$-algebras to locally finite $K$-derivations
+and $K$-$\mathcal E$-derivations $\delta$ of all $K$-algebras $\mathcal A$.
+",0,0,1,0,0,0
+16346,A connection between the good property of an Artinian Gorenstein local ring and that of its quotient modulo socle,"  Following Roos, we say that a local ring $R$ is good if all finitely
+generated $R$-modules have rational Poincaré series over $R$, sharing a
+common denominator. Rings with the Backelin-Roos property and generalised Golod
+rings are good due to results of Levin and Avramov respectively. Let $R$ be an
+Artinian Gorenstein local ring. The ring $R$ is shown to have the Backelin-Roos
+property if $R/ soc(R)$ is a Golod ring. Furthermore the ring $R$ is
+generalised Golod if and only if $R/ soc(R)$ is so.
+We explore when connected sums of Artinian Gorenstein local rings are good.
+We provide a uniform argument to show that stretched, almost stretched
+Gorenstein rings are good and show further that the Auslander-Reiten conjecture
+holds true for such rings. We prove that Gorenstein rings of multiplicity at
+most eleven are good. We recover a result of Rossi-Şega on the good
+property of compressed Gorenstein local rings in a stronger form by a shorter
+argument.
+",0,0,1,0,0,0
+16347,Towards Efficient Verification of Population Protocols,"  Population protocols are a well established model of computation by
+anonymous, identical finite state agents. A protocol is well-specified if from
+every initial configuration, all fair executions reach a common consensus. The
+central verification question for population protocols is the
+well-specification problem: deciding if a given protocol is well-specified.
+Esparza et al. have recently shown that this problem is decidable, but with
+very high complexity: it is at least as hard as the Petri net reachability
+problem, which is EXPSPACE-hard, and for which only algorithms of non-primitive
+recursive complexity are currently known.
+In this paper we introduce the class WS3 of well-specified strongly-silent
+protocols and we prove that it is suitable for automatic verification. More
+precisely, we show that WS3 has the same computational power as general
+well-specified protocols, and captures standard protocols from the literature.
+Moreover, we show that the membership problem for WS3 reduces to solving
+boolean combinations of linear constraints over N. This allowed us to develop
+the first software able to automatically prove well-specification for all of
+the infinitely many possible inputs.
+",1,0,0,0,0,0
+16348,"Simplicial Homotopy Theory, Link Homology and Khovanov Homology","  The purpose of this note is to point out that simplicial methods and the
+well-known Dold-Kan construction in simplicial homotopy theory can be
+fruitfully applied to convert link homology theories into homotopy theories.
+Dold and Kan prove that there is a functor from the category of chain complexes
+over a commutative ring with unit to the category of simplicial objects over
+that ring such that chain homotopic maps go to homotopic maps in the simplicial
+category. Furthermore, this is an equivalence of categories. In this way, given
+a link homology theory, we construct a mapping taking link diagrams to a
+category of simplicial objects such that up to looping or delooping, link
+diagrams related by Reidemeister moves will give rise to homotopy equivalent
+simplicial objects, and the homotopy groups of these objects will be equal to
+the link homology groups of the original link homology theory. The construction
+is independent of the particular link homology theory. A simplifying point in
+producing a homotopy simplicial object in relation to a chain complex occurs
+when the chain complex is itself derived (via face maps) from a simplicial
+object that satisfies the Kan extension condition. Under these circumstances
+one can use that simplicial object rather than apply the Dold-Kan functor to
+the chain complex. We will give examples of this situation in regard to
+Khovanov homology. We will investigate detailed working out of this
+correspondence in separate papers. The purpose of this note is to announce the
+basic relationships for using simplicial methods in this domain. Thus we do
+more than just quote the Dold-Kan Theorem. We give a review of simplicial
+theory and we point to specific constructions, particularly in relation to
+Khovanov homology, that can be used to make simplicial homotopy types directly.
+",0,0,1,0,0,0
+16349,Optimised Maintenance of Datalog Materialisations,"  To efficiently answer queries, datalog systems often materialise all
+consequences of a datalog program, so the materialisation must be updated
+whenever the input facts change. Several solutions to the materialisation
+update problem have been proposed. The Delete/Rederive (DRed) and the
+Backward/Forward (B/F) algorithms solve this problem for general datalog, but
+both contain steps that evaluate rules 'backwards' by matching their heads to a
+fact and evaluating the partially instantiated rule bodies as queries. We show
+that this can be a considerable source of overhead even on very small updates.
+In contrast, the Counting algorithm does not evaluate the rules 'backwards',
+but it can handle only nonrecursive rules. We present two hybrid approaches
+that combine DRed and B/F with Counting so as to reduce or even eliminate
+'backward' rule evaluation while still handling arbitrary datalog programs. We
+show empirically that our hybrid algorithms are usually significantly faster
+than existing approaches, sometimes by orders of magnitude.
+",1,0,0,0,0,0
+16350,Inversion of some curvature operators near a parallel Ricci metric II: Non-compact manifold with bounded geometry,"  Let (M,g) be a complete noncompact riemannian manifold with bounded geometry
+and parallel Ricci curvature. We show that some operators, ""affine"" relatively
+to the Ricci curvature, are locally invertible, in some classical Sobolev
+spaces, near the metric g.
+",0,0,1,0,0,0
+16351,Partisan gerrymandering with geographically compact districts,"  Bizarrely shaped voting districts are frequently lambasted as likely
+instances of gerrymandering. In order to systematically identify such
+instances, researchers have devised several tests for so-called geographic
+compactness (i.e., shape niceness). We demonstrate that under certain
+conditions, a party can gerrymander a competitive state into geographically
+compact districts to win an average of over 70% of the districts. Our results
+suggest that geometric features alone may fail to adequately combat partisan
+gerrymandering.
+",0,0,1,0,0,0
+16352,Random Scalar Fields and Hyperuniformity,"  Disordered many-particle hyperuniform systems are exotic amorphous states of
+matter that lie between crystals and liquids. Hyperuniform systems have
+attracted recent attention because they are endowed with novel transport and
+optical properties. Recently, the hyperuniformity concept has been generalized
+to characterize scalar fields, two-phase media and random vector fields. In
+this paper, we devise methods to explicitly construct hyperuniform scalar
+fields. We investigate explicitly spatial patterns generated from Gaussian
+random fields, which have been used to model the microwave background radiation
+and heterogeneous materials, the Cahn-Hilliard equation for spinodal
+decomposition, and Swift-Hohenberg equations that have been used to model
+emergent pattern formation, including Rayleigh-B{\' e}nard convection. We show
+that the Gaussian random scalar fields can be constructed to be hyperuniform.
+We also numerically study the time evolution of spinodal decomposition patterns
+and demonstrate that these patterns are hyperuniform in the scaling regime.
+Moreover, we find that labyrinth-like patterns generated by the Swift-Hohenberg
+equation are effectively hyperuniform. We show that thresholding a hyperuniform
+Gaussian random field to produce a two-phase random medium tends to destroy the
+hyperuniformity of the progenitor scalar field. We then propose guidelines to
+achieve effectively hyperuniform two-phase media derived from thresholded
+non-Gaussian fields. Our investigation paves the way for new research
+directions to characterize the large-structure spatial patterns that arise in
+physics, chemistry, biology and ecology. Moreover, our theoretical results are
+expected to guide experimentalists to synthesize new classes of hyperuniform
+materials with novel physical properties via coarsening processes and using
+state-of-the-art techniques, such as stereolithography and 3D printing.
+",0,1,0,0,0,0
+16353,Methodological Approach for the Design of a Complex Inclusive Human-Machine System,"  Modern industrial automatic machines and robotic cells are equipped with
+highly complex human-machine interfaces (HMIs) that often prevent human
+operators from an effective use of the automatic systems. In particular, this
+applies to vulnerable users, such as those with low experience or education
+level, the elderly and the disabled. To tackle this issue, it becomes necessary
+to design user-oriented HMIs, which adapt to the capabilities and skills of
+users, thus compensating their limitations and taking full advantage of their
+knowledge. In this paper, we propose a methodological approach to the design of
+complex adaptive human-machine systems that might be inclusive of all users, in
+particular the vulnerable ones. The proposed approach takes into account both
+the technical requirements and the requirements for ethical, legal and social
+implications (ELSI) for the design of automatic systems. The technical
+requirements derive from a thorough analysis of three use cases taken from the
+European project INCLUSIVE. To achieve the ELSI requirements, the MEESTAR
+approach is combined with the specific legal issues for occupational systems
+and requirements of the target users.
+",1,0,0,0,0,0
+16354,Deep learning for comprehensive forecasting of Alzheimer's Disease progression,"  Most approaches to machine learning from electronic health data can only
+predict a single endpoint. Here, we present an alternative that uses
+unsupervised deep learning to simulate detailed patient trajectories. We use
+data comprising 18-month trajectories of 44 clinical variables from 1908
+patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model
+for personalized forecasting of disease progression. We simulate synthetic
+patient data including the evolution of each sub-component of cognitive exams,
+laboratory tests, and their associations with baseline clinical
+characteristics, generating both predictions and their confidence intervals.
+Our unsupervised model predicts changes in total ADAS-Cog scores with the same
+accuracy as specifically trained supervised models and identifies
+sub-components associated with word recall as predictive of progression. The
+ability to simultaneously simulate dozens of patient characteristics is a
+crucial step towards personalized medicine for Alzheimer's Disease.
+",0,0,0,1,0,0
+16355,"Experimental constraints on the rheology, eruption and emplacement dynamics of analog lavas comparable to Mercury's northern volcanic plains","  We present new viscosity measurements of a synthetic silicate system
+considered an analogue for the lava erupted on the surface of Mercury. In
+particular, we focus on the northern volcanic plains (NVP), which correspond to
+the largest lava flows on Mercury and possibly in the Solar System.
+High-temperature viscosity measurements were performed at both superliquidus
+(up to 1736 K) and subliquidus conditions (1569-1502 K) to constrain the
+viscosity variations as a function of crystallinity (from 0 to 28\%) and shear
+rate (from 0.1 to 5 s 1). Melt viscosity shows moderate variations (4-16 Pa s)
+in the temperature range of 1736-1600 K. Experiments performed below the
+liquidus temperature show an increase in viscosity as shear rate decreases from
+5 to 0.1 s 1, resulting in a shear thinning behavior, with a decrease in
+viscosity of 1 log unit. The low viscosity of the studied composition may
+explain the ability of NVP lavas to cover long distances, on the order of
+hundreds of kilometers in a turbulent flow regime. Using our experimental data
+we estimate that lava flows with thickness of 1, 5, and 10 m are likely to have
+velocities of 4.8, 6.5, and 7.2 m/s, respectively, on a 5 degree ground slope.
+Numerical modeling incorporating both the heat loss of the lavas and its
+possible crystallization during emplacement allows us to infer that high
+effusion rates (>10,000 m3/s) are necessary to cover the large distances
+indicated by satellite data from the MErcury Surface, Space ENvironment,
+GEochemistry, and Ranging spacecraft.
+",0,1,0,0,0,0
+16356,Empirical Bayes Estimators for High-Dimensional Sparse Vectors,"  The problem of estimating a high-dimensional sparse vector
+$\boldsymbol{\theta} \in \mathbb{R}^n$ from an observation in i.i.d. Gaussian
+noise is considered. The performance is measured using squared-error loss. An
+empirical Bayes shrinkage estimator, derived using a Bernoulli-Gaussian prior,
+is analyzed and compared with the well-known soft-thresholding estimator. We
+obtain concentration inequalities for the Stein's unbiased risk estimate and
+the loss function of both estimators. The results show that for large $n$, both
+the risk estimate and the loss function concentrate on deterministic values
+close to the true risk.
+Depending on the underlying $\boldsymbol{\theta}$, either the proposed
+empirical Bayes (eBayes) estimator or soft-thresholding may have smaller loss.
+We consider a hybrid estimator that attempts to pick the better of the
+soft-thresholding estimator and the eBayes estimator by comparing their risk
+estimates. It is shown that: i) the loss of the hybrid estimator concentrates
+on the minimum of the losses of the two competing estimators, and ii) the risk
+of the hybrid estimator is within order $\frac{1}{\sqrt{n}}$ of the minimum of
+the two risks. Simulation results are provided to support the theoretical
+results. Finally, we use the eBayes and hybrid estimators as denoisers in the
+approximate message passing (AMP) algorithm for compressed sensing, and show
+that their performance is superior to the soft-thresholding denoiser in a wide
+range of settings.
+",0,0,1,1,0,0
+16357,Self-consistent DFT+U method for real-space time-dependent density functional theory calculations,"  We implemented various DFT+U schemes, including the ACBN0 self-consistent
+density-functional version of the DFT+U method [Phys. Rev. X 5, 011006 (2015)]
+within the massively parallel real-space time-dependent density functional
+theory (TDDFT) code Octopus. We further extended the method to the case of the
+calculation of response functions with real-time TDDFT+U and to the description
+of non-collinear spin systems. The implementation is tested by investigating
+the ground-state and optical properties of various transition metal oxides,
+bulk topological insulators, and molecules. Our results are found to be in good
+agreement with previously published results for both the electronic band
+structure and structural properties. The self consistent calculated values of U
+and J are also in good agreement with the values commonly used in the
+literature. We found that the time-dependent extension of the self-consistent
+DFT+U method yields improved optical properties when compared to the empirical
+TDDFT+U scheme. This work thus opens a different theoretical framework to
+address the non equilibrium properties of correlated systems.
+",0,1,0,0,0,0
+16358,The search for neutron-antineutron oscillations at the Sudbury Neutrino Observatory,"  Tests on $B-L$ symmetry breaking models are important probes to search for
+new physics. One proposed model with $\Delta(B-L)=2$ involves the oscillations
+of a neutron to an antineutron. In this paper a new limit on this process is
+derived for the data acquired from all three operational phases of the Sudbury
+Neutrino Observatory experiment. The search was concentrated in oscillations
+occurring within the deuteron, and 23 events are observed against a background
+expectation of 30.5 events. These translate to a lower limit on the nuclear
+lifetime of $1.48\times 10^{31}$ years at 90% confidence level (CL) when no
+restriction is placed on the signal likelihood space (unbounded).
+Alternatively, a lower limit on the nuclear lifetime was found to be
+$1.18\times 10^{31}$ years at 90% CL when the signal was forced into a positive
+likelihood space (bounded). Values for the free oscillation time derived from
+various models are also provided in this article. This is the first search for
+neutron-antineutron oscillation with the deuteron as a target.
+",0,1,0,0,0,0
+16359,Compact Multi-Class Boosted Trees,"  Gradient boosted decision trees are a popular machine learning technique, in
+part because of their ability to give good accuracy with small models. We
+describe two extensions to the standard tree boosting algorithm designed to
+increase this advantage. The first improvement extends the boosting formalism
+from scalar-valued trees to vector-valued trees. This allows individual trees
+to be used as multiclass classifiers, rather than requiring one tree per class,
+and drastically reduces the model size required for multiclass problems. We
+also show that some other popular vector-valued gradient boosted trees
+modifications fit into this formulation and can be easily obtained in our
+implementation. The second extension, layer-by-layer boosting, takes smaller
+steps in function space, which is empirically shown to lead to a faster
+convergence and to a more compact ensemble. We have added both improvements to
+the open-source TensorFlow Boosted trees (TFBT) package, and we demonstrate
+their efficacy on a variety of multiclass datasets. We expect these extensions
+will be of particular interest to boosted tree applications that require small
+models, such as embedded devices, applications requiring fast inference, or
+applications desiring more interpretable models.
+",1,0,0,1,0,0
+16360,Road Friction Estimation for Connected Vehicles using Supervised Machine Learning,"  In this paper, the problem of road friction prediction from a fleet of
+connected vehicles is investigated. A framework is proposed to predict the road
+friction level using both historical friction data from the connected cars and
+data from weather stations, and comparative results from different methods are
+presented. The problem is formulated as a classification task where the
+available data is used to train three machine learning models including
+logistic regression, support vector machine, and neural networks to predict the
+friction class (slippery or non-slippery) in the future for specific road
+segments. In addition to the friction values, which are measured by moving
+vehicles, additional parameters such as humidity, temperature, and rainfall are
+used to obtain a set of descriptive feature vectors as input to the
+classification methods. The proposed prediction models are evaluated for
+different prediction horizons (0 to 120 minutes in the future) where the
+evaluation shows that the neural networks method leads to more stable results
+in different conditions.
+",1,0,0,1,0,0
+16361,A Decentralized Mobile Computing Network for Multi-Robot Systems Operations,"  Collective animal behaviors are paradigmatic examples of fully decentralized
+operations involving complex collective computations such as collective turns
+in flocks of birds or collective harvesting by ants. These systems offer a
+unique source of inspiration for the development of fault-tolerant and
+self-healing multi-robot systems capable of operating in dynamic environments.
+Specifically, swarm robotics emerged and is significantly growing on these
+premises. However, to date, most swarm robotics systems reported in the
+literature involve basic computational tasks---averages and other algebraic
+operations. In this paper, we introduce a novel Collective computing framework
+based on the swarming paradigm, which exhibits the key innate features of
+swarms: robustness, scalability and flexibility. Unlike Edge computing, the
+proposed Collective computing framework is truly decentralized and does not
+require user intervention or additional servers to sustain its operations. This
+Collective computing framework is applied to the complex task of collective
+mapping, in which multiple robots aim at cooperatively map a large area. Our
+results confirm the effectiveness of the cooperative strategy, its robustness
+to the loss of multiple units, as well as its scalability. Furthermore, the
+topology of the interconnecting network is found to greatly influence the
+performance of the collective action.
+",1,0,0,0,0,0
+16362,Deep Architectures for Neural Machine Translation,"  It has been shown that increasing model depth improves the quality of neural
+machine translation. However, different architectural variants to increase
+model depth have been proposed, and so far, there has been no thorough
+comparative study.
+In this work, we describe and evaluate several existing approaches to
+introduce depth in neural machine translation. Additionally, we explore novel
+architectural variants, including deep transition RNNs, and we vary how
+attention is used in the deep decoder. We introduce a novel ""BiDeep"" RNN
+architecture that combines deep transition RNNs and stacked RNNs.
+Our evaluation is carried out on the English to German WMT news translation
+dataset, using a single-GPU machine for both training and inference. We find
+that several of our proposed architectures improve upon existing approaches in
+terms of speed and translation quality. We obtain best improvements with a
+BiDeep RNN of combined depth 8, obtaining an average improvement of 1.5 BLEU
+over a strong shallow baseline.
+We release our code for ease of adoption.
+",1,0,0,0,0,0
+16363,Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection,"  Speech recognition systems have achieved high recognition performance for
+several tasks. However, the performance of such systems is dependent on the
+tremendously costly development work of preparing vast amounts of task-matched
+transcribed speech data for supervised training. The key problem here is the
+cost of transcribing speech data. The cost is repeatedly required to support
+new languages and new tasks. Assuming broad network services for transcribing
+speech data for many users, a system would become more self-sufficient and more
+useful if it possessed the ability to learn from very light feedback from the
+users without annoying them. In this paper, we propose a general reinforcement
+learning framework for speech recognition systems based on the policy gradient
+method. As a particular instance of the framework, we also propose a hypothesis
+selection-based reinforcement learning method. The proposed framework provides
+a new view for several existing training and adaptation methods. The
+experimental results show that the proposed method improves the recognition
+performance compared to unsupervised adaptation.
+",1,0,0,1,0,0
+16364,Profile-Based Ad Hoc Social Networking Using Wi-Fi Direct on the Top of Android,"  Ad-hoc Social Networks have become popular to support novel applications
+related to location-based mobile services that are of great importance to users
+and businesses. Unlike traditional social services using a centralized server
+to fetch location, ad-hoc social network services support infrastructure less
+real-time social networking. It allows users to collaborate and share views
+anytime anywhere. However, current ad-hoc social network applications are
+either not available without rooting the mobile phones or don't filter the
+nearby users based on common interests without a centralized server. This paper
+presents an architecture and implementation of social networks on commercially
+available mobile devices that allow broadcasting name and a limited number of
+keywords representing users' interests without any connection in a nearby
+region to facilitate matching of interests. The broadcasting region creates a
+digital aura and is limited by WiFi region that is around 200 meters. The
+application connects users to form a group based on their profile or interests
+using peer-to-peer communication mode without using any centralized networking
+or profile matching infrastructure. The peer-to-peer group can be used for
+private communication when the network is not available.
+",1,0,0,0,0,0
+16365,An a posteriori error analysis for a coupled continuum pipe-flow/Darcy model in Karst aquifers: anisotropic and isotropic discretizations,"  This paper presents an a posteriori error analysis for a coupled continuum
+pipe-flow/Darcy model in karst aquifers. We consider a unified anisotropic
+finite element discretization (i.e. elements with very large aspect ratio). Our
+analysis covers two-dimensional domains, conforming and nonconforming
+discretizations as well as different elements. Many examples of finite elements
+that are covered by analysis are presented. From the finite element solution,
+the error estimators are constructed and based on the residual of model
+equations. Lower and upper error bounds form the main result with minimal
+assumptions on the elements. The lower error bound is uniform with respect to
+the mesh anisotropy in the entire domain. The upper error bound depends on a
+proper alignment of the anisotropy of the mesh which is a common feature of
+anisotropic error estimation. In the special case of isotropic meshes, the
+results simplify, and upper and lower error bounds hold unconditionally.
+",0,0,1,0,0,0
+16366,A Hybrid DOS-Tolerant PKC-Based Key Management System for WSNs,"  Security is a critical and vital task in wireless sensor networks, therefore
+different key management systems have been proposed, many of which are based on
+symmetric cryptography. Such systems are very energy efficient, but they lack
+some other desirable characteristics. On the other hand, systems based on
+public key cryptography have those desirable characteristics, but they consume
+more energy. Recently based on authenticated messages from base station a new
+PKC based key agreement protocol was proposed. We show this method is
+susceptible to a form of denial of service attack where resources of the
+network can be exhausted with bogus messages. Then, we propose two different
+improvements to solve this vulnerability. Simulation results show that these
+new protocols retain desirable characteristics of the basic method and solve
+its deficiencies.
+",1,0,0,0,0,0
+16367,Enceladus's crust as a non-uniform thin shell: I Tidal deformations,"  The geologic activity at Enceladus's south pole remains unexplained, though
+tidal deformations are probably the ultimate cause. Recent gravity and
+libration data indicate that Enceladus's icy crust floats on a global ocean, is
+rather thin, and has a strongly non-uniform thickness. Tidal effects are
+enhanced by crustal thinning at the south pole, so that realistic models of
+tidal tectonics and dissipation should take into account the lateral variations
+of shell structure. I construct here the theory of non-uniform viscoelastic
+thin shells, allowing for depth-dependent rheology and large lateral variations
+of shell thickness and rheology. Coupling to tides yields two 2D linear partial
+differential equations of the 4th order on the sphere which take into account
+self-gravity, density stratification below the shell, and core viscoelasticity.
+If the shell is laterally uniform, the solution agrees with analytical formulas
+for tidal Love numbers; errors on displacements and stresses are less than 5%
+and 15%, respectively, if the thickness is less than 10% of the radius. If the
+shell is non-uniform, the tidal thin shell equations are solved as a system of
+coupled linear equations in a spherical harmonic basis. Compared to finite
+element models, thin shell predictions are similar for the deformations due to
+Enceladus's pressurized ocean, but differ for the tides of Ganymede. If
+Enceladus's shell is conductive with isostatic thickness variations, surface
+stresses are approximately inversely proportional to the local shell thickness.
+The radial tide is only moderately enhanced at the south pole. The combination
+of crustal thinning and convection below the poles can amplify south polar
+stresses by a factor of 10, but it cannot explain the apparent time lag between
+the maximum plume brightness and the opening of tiger stripes. In a second
+paper, I will study tidal dissipation in a non-uniform crust.
+",0,1,0,0,0,0
+16368,Minimax Rates and Efficient Algorithms for Noisy Sorting,"  There has been a recent surge of interest in studying permutation-based
+models for ranking from pairwise comparison data. Despite being structurally
+richer and more robust than parametric ranking models, permutation-based models
+are less well understood statistically and generally lack efficient learning
+algorithms. In this work, we study a prototype of permutation-based ranking
+models, namely, the noisy sorting model. We establish the optimal rates of
+learning the model under two sampling procedures. Furthermore, we provide a
+fast algorithm to achieve near-optimal rates if the observations are sampled
+independently. Along the way, we discover properties of the symmetric group
+which are of theoretical interest.
+",1,0,1,1,0,0
+16369,Automated Synthesis of Secure Platform Mappings,"  System development often involves decisions about how a high-level design is
+to be implemented using primitives from a low-level platform. Certain
+decisions, however, may introduce undesirable behavior into the resulting
+implementation, possibly leading to a violation of a desired property that has
+already been established at the design level. In this paper, we introduce the
+problem of synthesizing a property-preserving platform mapping: A set of
+implementation decisions ensuring that a desired property is preserved from a
+high-level design into a low-level platform implementation. We provide a
+formalization of the synthesis problem and propose a technique for synthesizing
+a mapping based on symbolic constraint search. We describe our prototype
+implementation, and a real-world case study demonstrating the application of
+our technique to synthesizing secure mappings for the popular web authorization
+protocols OAuth 1.0 and 2.0.
+",1,0,0,0,0,0
+16370,Timely Feedback in Unstructured Cybersecurity Exercises,"  Cyber defence exercises are intensive, hands-on learning events for teams of
+professionals who gain or develop their skills to successfully prevent and
+respond to cyber attacks. The exercises mimic the real-life, routine operation
+of an organization which is being attacked by an unknown offender. Teams of
+learners receive very limited immediate feedback from the instructors during
+the exercise; they can usually see only a scoreboard showing the aggregated
+gain or loss of points for particular tasks. An in-depth analysis of learners'
+actions requires considerable human effort, which results in days or weeks of
+delay. The intensive experience is thus not followed by proper feedback
+facilitating actual learning, and this diminishes the effect of the exercise.
+In this initial work, we investigate how to provide valuable feedback to
+learners right after the exercise without any unnecessary delay. Based on the
+scoring system of a cyber defence exercise, we have developed a new feedback
+tool that presents an interactive, personalized timeline of exercise events. We
+deployed this tool during an international exercise, where we monitored
+participants' interactions and gathered their reflections. The results show
+that learners did use the new tool and rated it positively. Since this new
+feature is not bound to a particular defence exercise, it can be applied to all
+exercises that employ scoring based on the evaluation of individual exercise
+objectives. As a result, it enables the learner to immediately reflect on the
+experience gained.
+",1,0,0,0,0,0
+16371,An $ω$-Algebra for Real-Time Energy Problems,"  We develop a $^*$-continuous Kleene $\omega$-algebra of real-time energy
+functions. Together with corresponding automata, these can be used to model
+systems which can consume and regain energy (or other types of resources)
+depending on available time. Using recent results on $^*$-continuous Kleene
+$\omega$-algebras and computability of certain manipulations on real-time
+energy functions, it follows that reachability and Büchi acceptance in
+real-time energy automata can be decided in a static way which only involves
+manipulations of real-time energy functions.
+",1,0,0,0,0,0
+16372,Trajectory Normalized Gradients for Distributed Optimization,"  Recently, researchers proposed various low-precision gradient compression,
+for efficient communication in large-scale distributed optimization. Based on
+these work, we try to reduce the communication complexity from a new direction.
+We pursue an ideal bijective mapping between two spaces of gradient
+distribution, so that the mapped gradient carries greater information entropy
+after the compression. In our setting, all servers should share a reference
+gradient in advance, and they communicate via the normalized gradients, which
+are the subtraction or quotient, between current gradients and the reference.
+To obtain a reference vector that yields a stronger signal-to-noise ratio,
+dynamically in each iteration, we extract and fuse information from the past
+trajectory in hindsight, and search for an optimal reference for compression.
+We name this to be the trajectory-based normalized gradients (TNG). It bridges
+the research from different societies, like coding, optimization, systems, and
+learning. It is easy to implement and can universally combine with existing
+algorithms. Our experiments on benchmarking hard non-convex functions, convex
+problems like logistic regression demonstrate that TNG is more
+compression-efficient for communication of distributed optimization of general
+functions.
+",1,0,0,1,0,0
+16373,On the convergence of mirror descent beyond stochastic convex programming,"  In this paper, we examine the convergence of mirror descent in a class of
+stochastic optimization problems that are not necessarily convex (or even
+quasi-convex), and which we call variationally coherent. Since the standard
+technique of ""ergodic averaging"" offers no tangible benefits beyond convex
+programming, we focus directly on the algorithm's last generated sample (its
+""last iterate""), and we show that it converges with probabiility $1$ if the
+underlying problem is coherent. We further consider a localized version of
+variational coherence which ensures local convergence of stochastic mirror
+descent (SMD) with high probability. These results contribute to the landscape
+of non-convex stochastic optimization by showing that (quasi-)convexity is not
+essential for convergence to a global minimum: rather, variational coherence, a
+much weaker requirement, suffices. Finally, building on the above, we reveal an
+interesting insight regarding the convergence speed of SMD: in problems with
+sharp minima (such as generic linear programs or concave minimization
+problems), SMD reaches a minimum point in a finite number of steps (a.s.), even
+in the presence of persistent gradient noise. This result is to be contrasted
+with existing black-box convergence rate estimates that are only asymptotic.
+",1,0,1,0,0,0
+16374,Can MPTCP Secure Internet Communications from Man-in-the-Middle Attacks?,"  -Multipath communications at the Internet scale have been a myth for a long
+time, with no actual protocol being deployed so that multiple paths could be
+taken by a same connection on the way towards an Internet destination.
+Recently, the Multipath Transport Control Protocol (MPTCP) extension was
+standardized and is undergoing a quick adoption in many use-cases, from mobile
+to fixed access networks, from data-centers to core networks. Among its major
+benefits -- i.e., reliability thanks to backup path rerouting; throughput
+increase thanks to link aggregation; and confidentiality thanks to harder
+capacity to intercept a full connection -- the latter has attracted lower
+attention. How interesting would it be using MPTCP to exploit multiple
+Internet-scale paths hence decreasing the probability of man-in-the-middle
+(MITM) attacks is a question to which we try to answer. By analyzing the
+Autonomous System (AS) level graph, we identify which countries and regions
+show a higher level of robustness against MITM AS-level attacks, for example
+due to core cable tapping or route hijacking practices.
+",1,0,0,0,0,0
+16375,Network Structure of Two-Dimensional Decaying Isotropic Turbulence,"  The present paper reports on our effort to characterize vortical interactions
+in complex fluid flows through the use of network analysis. In particular, we
+examine the vortex interactions in two-dimensional decaying isotropic
+turbulence and find that the vortical interaction network can be characterized
+by a weighted scale-free network. It is found that the turbulent flow network
+retains its scale-free behavior until the characteristic value of circulation
+reaches a critical value. Furthermore, we show that the two-dimensional
+turbulence network is resilient against random perturbations but can be greatly
+influenced when forcing is focused towards the vortical structures that are
+categorized as network hubs. These findings can serve as a network-analytic
+foundation to examine complex geophysical and thin-film flows and take
+advantage of the rapidly growing field of network theory, which complements
+ongoing turbulence research based on vortex dynamics, hydrodynamic stability,
+and statistics. While additional work is essential to extend the mathematical
+tools from network analysis to extract deeper physical insights of turbulence,
+an understanding of turbulence based on the interaction-based network-theoretic
+framework presents a promising alternative in turbulence modeling and control
+efforts.
+",0,1,0,0,0,0
+16376,Pattern Generation for Walking on Slippery Terrains,"  In this paper, we extend state of the art Model Predictive Control (MPC)
+approaches to generate safe bipedal walking on slippery surfaces. In this
+setting, we formulate walking as a trade off between realizing a desired
+walking velocity and preserving robust foot-ground contact. Exploiting this
+formulation inside MPC, we show that safe walking on various flat terrains can
+be achieved by compromising three main attributes, i. e. walking velocity
+tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient
+of Friction (RCoF) regulation. Simulation results show that increasing the
+walking velocity increases the possibility of slippage, while reducing the
+slippage possibility conflicts with reducing the tip-over possibility of the
+contact and vice versa.
+",1,0,0,0,0,0
+16377,"Fisher-Rao Metric, Geometry, and Complexity of Neural Networks","  We study the relationship between geometry and capacity measures for deep
+neural networks from an invariance viewpoint. We introduce a new notion of
+capacity --- the Fisher-Rao norm --- that possesses desirable invariance
+properties and is motivated by Information Geometry. We discover an analytical
+characterization of the new capacity measure, through which we establish
+norm-comparison inequalities and further show that the new measure serves as an
+umbrella for several existing norm-based complexity measures. We discuss upper
+bounds on the generalization error induced by the proposed measure. Extensive
+numerical experiments on CIFAR-10 support our theoretical findings. Our
+theoretical analysis rests on a key structural lemma about partial derivatives
+of multi-layer rectifier networks.
+",1,0,0,1,0,0
+16378,YUI and HANA: Control and Visualization Programs for HRC in J-PARC,"  We developed control and visualization programs, YUI and HANA, for High-
+Resolution Chopper spectrometer (HRC) installed at BL12 in MLF, J-PARC. YUI is
+a comprehensive program to control DAQ-middleware, the accessories, and sample
+environment devices. HANA is a program for the data transformation and
+visualization of inelastic neutron scattering spectra. In this paper, we
+describe the basic system structures and unique functions of these programs
+from the viewpoint of users.
+",0,1,0,0,0,0
+16379,A generalization of a theorem of Hurewicz for quasi-Polish spaces,"  We identify four countable topological spaces $S_2$, $S_1$, $S_D$, and $S_0$
+which serve as canonical examples of topological spaces which fail to be
+quasi-Polish. These four spaces respectively correspond to the $T_2$, $T_1$,
+$T_D$, and $T_0$-separation axioms. $S_2$ is the space of rationals, $S_1$ is
+the natural numbers with the cofinite topology, $S_D$ is an infinite chain
+without a top element, and $S_0$ is the set of finite sequences of natural
+numbers with the lower topology induced by the prefix ordering. Our main result
+is a generalization of Hurewicz's theorem showing that a co-analytic subset of
+a quasi-Polish space is either quasi-Polish or else contains a countable
+$\Pi^0_2$-subset homeomorphic to one of these four spaces.
+",1,0,1,0,0,0
+16380,Reconstruction of stochastic 3-D signals with symmetric statistics from 2-D projection images motivated by cryo-electron microscopy,"  Cryo-electron microscopy provides 2-D projection images of the 3-D electron
+scattering intensity of many instances of the particle under study (e.g., a
+virus). Both symmetry (rotational point groups) and heterogeneity are important
+aspects of biological particles and both aspects can be combined by describing
+the electron scattering intensity of the particle as a stochastic process with
+a symmetric probability law and therefore symmetric moments. A maximum
+likelihood estimator implemented by an expectation-maximization algorithm is
+described which estimates the unknown statistics of the electron scattering
+intensity stochastic process from images of instances of the particle. The
+algorithm is demonstrated on the bacteriophage HK97 and the virus N$\omega$V.
+The results are contrasted with existing algorithms which assume that each
+instance of the particle has the symmetry rather than the less restrictive
+assumption that the probability law has the symmetry.
+",0,1,0,1,0,0
+16381,Gradient estimates for singular quasilinear elliptic equations with measure data,"  In this paper, we prove $L^q$-estimates for gradients of solutions to
+singular quasilinear elliptic equations with measure data
+$$-\operatorname{div}(A(x,\nabla u))=\mu,$$ in a bounded domain
+$\Omega\subset\mathbb{R}^{N}$, where $A(x,\nabla u)\nabla u \asymp |\nabla
+u|^p$, $p\in (1,2-\frac{1}{n}]$ and $\mu$ is a Radon measure in $\Omega$
+",0,0,1,0,0,0
+16382,Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression,"  For various applications, the relations between the dependent and independent
+variables are highly nonlinear. Consequently, for large scale complex problems,
+neural networks and regression trees are commonly preferred over linear models
+such as Lasso. This work proposes learning the feature nonlinearities by
+binning feature values and finding the best fit in each quantile using
+non-convex regularized linear regression. The algorithm first captures the
+dependence between neighboring quantiles by enforcing smoothness via
+piecewise-constant/linear approximation and then selects a sparse subset of
+good features. We prove that the proposed algorithm is statistically and
+computationally efficient. In particular, it achieves linear rate of
+convergence while requiring near-minimal number of samples. Evaluations on
+synthetic and real datasets demonstrate that algorithm is competitive with
+current state-of-the-art and accurately learns feature nonlinearities. Finally,
+we explore an interesting connection between the binning stage of our algorithm
+and sparse Johnson-Lindenstrauss matrices.
+",1,0,1,1,0,0
+16383,Empirical distributions of the robustified $t$-test statistics,"  Based on the median and the median absolute deviation estimators, and the
+Hodges-Lehmann and Shamos estimators, robustified analogues of the conventional
+$t$-test statistic are proposed. The asymptotic distributions of these
+statistics are recently provided. However, when the sample size is small, it is
+not appropriate to use the asymptotic distribution of the robustified $t$-test
+statistics for making a statistical inference including hypothesis testing,
+confidence interval, p-value, etc.
+In this article, through extensive Monte Carlo simulations, we obtain the
+empirical distributions of the robustified $t$-test statistics and their
+quantile values. Then these quantile values can be used for making a
+statistical inference.
+",0,0,0,1,0,0
+16384,Exponential error rates of SDP for block models: Beyond Grothendieck's inequality,"  In this paper we consider the cluster estimation problem under the Stochastic
+Block Model. We show that the semidefinite programming (SDP) formulation for
+this problem achieves an error rate that decays exponentially in the
+signal-to-noise ratio. The error bound implies weak recovery in the sparse
+graph regime with bounded expected degrees, as well as exact recovery in the
+dense regime. An immediate corollary of our results yields error bounds under
+the Censored Block Model. Moreover, these error bounds are robust, continuing
+to hold under heterogeneous edge probabilities and a form of the so-called
+monotone attack.
+Significantly, this error rate is achieved by the SDP solution itself without
+any further pre- or post-processing, and improves upon existing
+polynomially-decaying error bounds proved using the Grothendieck\textquoteright
+s inequality. Our analysis has two key ingredients: (i) showing that the graph
+has a well-behaved spectrum, even in the sparse regime, after discounting an
+exponentially small number of edges, and (ii) an order-statistics argument that
+governs the final error rate. Both arguments highlight the implicit
+regularization effect of the SDP formulation.
+",1,0,1,1,0,0
+16385,Large-Margin Classification in Hyperbolic Space,"  Representing data in hyperbolic space can effectively capture latent
+hierarchical relationships. With the goal of enabling accurate classification
+of points in hyperbolic space while respecting their hyperbolic geometry, we
+introduce hyperbolic SVM, a hyperbolic formulation of support vector machine
+classifiers, and elucidate through new theoretical work its connection to the
+Euclidean counterpart. We demonstrate the performance improvement of hyperbolic
+SVM for multi-class prediction tasks on real-world complex networks as well as
+simulated datasets. Our work allows analytic pipelines that take the inherent
+hyperbolic geometry of the data into account in an end-to-end fashion without
+resorting to ill-fitting tools developed for Euclidean space.
+",0,0,0,1,0,0
+16386,The airglow layer emission altitude cannot be determined unambiguously from temperature comparison with lidars,"  I investigate the nightly mean emission height and width of the OH*(3-1)
+layer by comparing nightly mean temperatures measured by the ground-based
+spectrometer GRIPS 9 and the Na lidar at ALOMAR. The data set contains 42
+coincident measurements between November 2010 and February 2014, when GRIPS 9
+was in operation at the ALOMAR observatory (69.3$^\circ$N, 16.0$^\circ$E) in
+northern Norway. To closely resemble the mean temperature measured by GRIPS 9,
+I weight each nightly mean temperature profile measured by the lidar using
+Gaussian distributions with 40 different centre altitudes and 40 different full
+widths at half maximum. In principle, one can thus determine the altitude and
+width of an airglow layer by finding the minimum temperature difference between
+the two instruments. On most nights, several combinations of centre altitude
+and width yield a temperature difference of $\pm$2 K. The generally assumed
+altitude of 87 km and width of 8 km is never an unambiguous, good solution for
+any of the measurements. Even for a fixed width of $\sim$8.4 km, one can
+sometimes find several centre altitudes that yield equally good temperature
+agreement. Weighted temperatures measured by lidar are not suitable to
+determine unambiguously the emission height and width of an airglow layer.
+However, when actual altitude and width data are lacking, a comparison with
+lidars can provide an estimate of how representative a measured rotational
+temperature is of an assumed altitude and width. I found the rotational
+temperature to represent the temperature at the commonly assumed altitude of
+87.4 km and width of 8.4 km to within $\pm$16 K, on average. This is not a
+measurement uncertainty.
+",0,1,0,0,0,0
+16387,Microscopic origin of the mobility enhancement at a spinel/perovskite oxide heterointerface revealed by photoemission spectroscopy,"  The spinel/perovskite heterointerface $\gamma$-Al$_2$O$_3$/SrTiO$_3$ hosts a
+two-dimensional electron system (2DES) with electron mobilities exceeding those
+in its all-perovskite counterpart LaAlO$_3$/SrTiO$_3$ by more than an order of
+magnitude despite the abundance of oxygen vacancies which act as electron
+donors as well as scattering sites. By means of resonant soft x-ray
+photoemission spectroscopy and \textit{ab initio} calculations we reveal the
+presence of a sharply localized type of oxygen vacancies at the very interface
+due to the local breaking of the perovskite symmetry. We explain the
+extraordinarily high mobilities by reduced scattering resulting from the
+preferential formation of interfacial oxygen vacancies and spatial separation
+of the resulting 2DES in deeper SrTiO$_3$ layers. Our findings comply with
+transport studies and pave the way towards defect engineering at interfaces of
+oxides with different crystal structures.
+",0,1,0,0,0,0
+16388,Ordered Monoids: Languages and Relations,"  We give a finite axiomatization for the variety generated by relational,
+integral ordered monoids. As a corollary we get a finite axiomatization for the
+language interpretation as well.
+",0,0,1,0,0,0
+16389,On the secrecy gain of $\ell$-modular lattices,"  We show that for every $\ell>1$, there is a counterexample to the
+$\ell$-modular secrecy function conjecture by Oggier, Solé and Belfiore.
+These counterexamples all satisfy the modified conjecture by Ernvall-Hytönen
+and Sethuraman. Furthermore, we provide a method to prove or disprove the
+modified conjecture for any given $\ell$-modular lattice rationally equivalent
+to a suitable amount of copies of $\mathbb{Z}\oplus \sqrt{\ell}\,\mathbb{Z}$
+with $\ell \in \{3,5,7,11,23\}$. We also provide a variant of the method for
+strongly $\ell$-modular lattices when $\ell\in \{6,14,15\}$.
+",0,0,1,0,0,0
+16390,Extremal invariant polynomials not satisfying the Riemann hypothesis,"  Zeta functions for linear codes were defined by Iwan Duursma in 1999. They
+were generalized to the case of some invariant polynomials by the preset
+author. One of the most important problems is whether extremal weight
+enumerators satisfy the Riemann hypothesis. In this article, we show there
+exist extremal polynomials of the weight enumerator type which are invariant
+under the MacWilliams transform and do not satisfy the Riemann hypothesis.
+",0,0,1,0,0,0
+16391,Fourier analysis of serial dependence measures,"  Classical spectral analysis is based on the discrete Fourier transform of the
+auto-covariances. In this paper we investigate the asymptotic properties of new
+frequency domain methods where the auto-covariances in the spectral density are
+replaced by alternative dependence measures which can be estimated by
+U-statistics. An interesting example is given by Kendall{'}s $\tau$ , for which
+the limiting variance exhibits a surprising behavior.
+",0,0,1,1,0,0
+16392,HVACKer: Bridging the Air-Gap by Attacking the Air Conditioning System,"  Modern corporations physically separate their sensitive computational
+infrastructure from public or other accessible networks in order to prevent
+cyber-attacks. However, attackers still manage to infect these networks, either
+by means of an insider or by infiltrating the supply chain. Therefore, an
+attacker's main challenge is to determine a way to command and control the
+compromised hosts that are isolated from an accessible network (e.g., the
+Internet).
+In this paper, we propose a new adversarial model that shows how an air
+gapped network can receive communications over a covert thermal channel.
+Concretely, we show how attackers may use a compromised air-conditioning system
+(connected to the internet) to send commands to infected hosts within an
+air-gapped network. Since thermal communication protocols are a rather
+unexplored domain, we propose a novel line-encoding and protocol suitable for
+this type of channel. Moreover, we provide experimental results to demonstrate
+the covert channel's feasibility, and to calculate the channel's bandwidth.
+Lastly, we offer a forensic analysis and propose various ways this channel can
+be detected and prevented.
+We believe that this study details a previously unseen vector of attack that
+security experts should be aware of.
+",1,0,0,0,0,0
+16393,Koszul binomial edge ideals of pairs of graphs,"  We study the Koszul property of a standard graded $K$-algebra $R$ defined by
+the binomial edge ideal of a pair of graphs $(G_1,G_2)$. We show that the
+following statements are equivalent: (i) $R$ is Koszul; (ii) the defining ideal
+$J_{G_1,G_2}$ of $R$ has a quadratic Gröbner basis; (iii) the graded maximal
+ideal of $R$ has linear quotients with respect to a suitable order of its
+generators
+",0,0,1,0,0,0
+16394,On Loss Functions for Deep Neural Networks in Classification,"  Deep neural networks are currently among the most commonly used classifiers.
+Despite easily achieving very good performance, one of the best selling points
+of these models is their modular design - one can conveniently adapt their
+architecture to specific needs, change connectivity patterns, attach
+specialised layers, experiment with a large amount of activation functions,
+normalisation schemes and many others. While one can find impressively wide
+spread of various configurations of almost every aspect of the deep nets, one
+element is, in authors' opinion, underrepresented - while solving
+classification problems, vast majority of papers and applications simply use
+log loss. In this paper we try to investigate how particular choices of loss
+functions affect deep models and their learning dynamics, as well as resulting
+classifiers robustness to various effects. We perform experiments on classical
+datasets, as well as provide some additional, theoretical insights into the
+problem. In particular we show that L1 and L2 losses are, quite surprisingly,
+justified classification objectives for deep nets, by providing probabilistic
+interpretation in terms of expected misclassification. We also introduce two
+losses which are not typically used as deep nets objectives and show that they
+are viable alternatives to the existing ones.
+",1,0,0,0,0,0
+16395,Are theoretical results 'Results'?,"  Yes.
+",0,0,0,0,1,0
+16396,Self-supervised learning of visual features through embedding images into text topic spaces,"  End-to-end training from scratch of current deep architectures for new
+computer vision problems would require Imagenet-scale datasets, and this is not
+always possible. In this paper we present a method that is able to take
+advantage of freely available multi-modal content to train computer vision
+algorithms without human supervision. We put forward the idea of performing
+self-supervised learning of visual features by mining a large scale corpus of
+multi-modal (text and image) documents. We show that discriminative visual
+features can be learnt efficiently by training a CNN to predict the semantic
+context in which a particular image is more probable to appear as an
+illustration. For this we leverage the hidden semantic structures discovered in
+the text corpus with a well-known topic modeling technique. Our experiments
+demonstrate state of the art performance in image classification, object
+detection, and multi-modal retrieval compared to recent self-supervised or
+natural-supervised approaches.
+",1,0,0,0,0,0
+16397,Detector sampling of optical/IR spectra: how many pixels per FWHM?,"  Most optical and IR spectra are now acquired using detectors with
+finite-width pixels in a square array. This paper examines the effects of such
+pixellation, using computed simulations to illustrate the effects which most
+concern the astronomer end-user. Coarse sampling increases the random noise
+errors in wavelength by typically 10 - 20% at 2 pixels/FWHM, but with wide
+variation depending on the functional form of the instrumental Line Spread
+Function (LSF) and on the pixel phase. Line widths are even more strongly
+affected at low sampling frequencies. However, the noise in fitted peak
+amplitudes is minimally affected. Pixellation has a substantial but complex
+effect on the ability to see a relative minimum between two closely-spaced
+peaks (or relative maximum between two absorption lines). The consistent scale
+of resolving power presented by Robertson (2013) is extended to cover
+pixellated spectra. The systematic bias errors in wavelength introduced by
+pixellation are examined. While they may be negligible for smooth well-sampled
+symmetric LSFs, they are very sensitive to asymmetry and high spatial frequency
+substructure. The Modulation Transfer Function for sampled data is shown to
+give a useful indication of the extent of improperly sampled signal in an LSF.
+The common maxim that 2 pixels/FWHM is the Nyquist limit is incorrect and most
+LSFs will exhibit some aliasing at this sample frequency. While 2 pixels/FWHM
+is often an acceptable minimum for moderate signal/noise work, it is preferable
+to carry out simulations for any actual or proposed LSF to find the effects of
+sampling frequency. Where end-users have a choice of sampling frequencies,
+through on-chip binning and/or spectrograph configurations, the instrument user
+manual should include an examination of their effects. (Abridged)
+",0,1,0,0,0,0
+16398,Accurate Single Stage Detector Using Recurrent Rolling Convolution,"  Most of the recent successful methods in accurate object detection and
+localization used some variants of R-CNN style two stage Convolutional Neural
+Networks (CNN) where plausible regions were proposed in the first stage then
+followed by a second stage for decision refinement. Despite the simplicity of
+training and the efficiency in deployment, the single stage detection methods
+have not been as competitive when evaluated in benchmarks consider mAP for high
+IoU thresholds. In this paper, we proposed a novel single stage end-to-end
+trainable object detection network to overcome this limitation. We achieved
+this by introducing Recurrent Rolling Convolution (RRC) architecture over
+multi-scale feature maps to construct object classifiers and bounding box
+regressors which are ""deep in context"". We evaluated our method in the
+challenging KITTI dataset which measures methods under IoU threshold of 0.7. We
+showed that with RRC, a single reduced VGG-16 based model already significantly
+outperformed all the previously published results. At the time this paper was
+written our models ranked the first in KITTI car detection (the hard level),
+the first in cyclist detection and the second in pedestrian detection. These
+results were not reached by the previous single stage methods. The code is
+publicly available.
+",1,0,0,0,0,0
+16399,On separable higher Gauss maps,"  We study the $m$-th Gauss map in the sense of F.~L.~Zak of a projective
+variety $X \subset \mathbb{P}^N$ over an algebraically closed field in any
+characteristic. For all integer $m$ with $n:=\dim(X) \leq m < N$, we show that
+the contact locus on $X$ of a general tangent $m$-plane is a linear variety if
+the $m$-th Gauss map is separable. We also show that for smooth $X$ with $n <
+N-2$, the $(n+1)$-th Gauss map is birational if it is separable, unless $X$ is
+the Segre embedding $\mathbb{P}^1 \times \mathbb{P}^n \subset
+\mathbb{P}^{2n-1}$. This is related to L. Ein's classification of varieties
+with small dual varieties in characteristic zero.
+",0,0,1,0,0,0
+16400,Diffeomorphisms of the closed unit disc converging to the identity,"  If $\mathcal{G}$ is the group (under composition) of diffeomorphisms $f :
+{\bar{D}}(0;1) \rightarrow {\bar{D}}(0;1)$ of the closed unit disc
+${\bar{D}}(0;1)$ which are the identity map $id : {\bar{D}}(0;1) \rightarrow
+{\bar{D}}(0;1)$ on the closed unit circle and satisfy the condition $det(J(f))
+> 0$, where $J(f)$ is the Jacobian matrix of $f$ or (equivalently) the
+Fréchet derivative of $f$, then $\mathcal{G}$ equipped with the metric
+$d_{\mathcal{G}}(f,g) = \Vert f-g \Vert_{\infty } + \Vert J(f) - J(g)
+\Vert_{\infty }$, where $f$, $g$ range over $\mathcal{G}$, is a metric space in
+which $d_{\mathcal{G}} \left( f_{t} , id \right) \rightarrow 0$ as $t
+\rightarrow 1^{+}$, where $f_{t}(z) = \frac{ tz }{ 1 + (t-1) \vert z \vert }$,
+whenever $z \in {\bar{D}}(0;1)$ and $t \geq 1$.
+",0,0,1,0,0,0
+16401,Spitzer Secondary Eclipses of Qatar-1b,"  Previous secondary eclipse observations of the hot Jupiter Qatar-1b in the Ks
+band suggest that it may have an unusually high day side temperature,
+indicative of minimal heat redistribution. There have also been indications
+that the orbit may be slightly eccentric, possibly forced by another planet in
+the system. We investigate the day side temperature and orbital eccentricity
+using secondary eclipse observations with Spitzer. We observed the secondary
+eclipse with Spitzer/IRAC in subarray mode, in both 3.6 and 4.5 micron
+wavelengths. We used pixel-level decorrelation to correct for Spitzer's
+intra-pixel sensitivity variations and thereby obtain accurate eclipse depths
+and central phases. Our 3.6 micron eclipse depth is 0.149 +/- 0.051% and the
+4.5 micron depth is 0.273 +/- 0.049%. Fitting a blackbody planet to our data
+and two recent Ks band eclipse depths indicates a brightness temperature of
+1506 +/- 71K. Comparison to model atmospheres for the planet indicates that its
+degree of longitudinal heat redistribution is intermediate between fully
+uniform and day side only. The day side temperature of the planet is unlikely
+to be as high (1885K) as indicated by the ground-based eclipses in the Ks band,
+unless the planet's emergent spectrum deviates strongly from model atmosphere
+predictions. The average central phase for our Spitzer eclipses is 0.4984 +/-
+0.0017, yielding e cos(omega) = -0.0028 +/- 0.0027. Our results are consistent
+with a circular orbit, and we constrain e cos(omega) much more strongly than
+has been possible with previous observations.
+",0,1,0,0,0,0
+16402,Dielectric response of Anderson and pseudogapped insulators,"  Using a combination of analytic and numerical methods, we study the
+polarizability of a (non-interacting) Anderson insulator in one, two, and three
+dimensions and demonstrate that, in a wide range of parameters, it scales
+proportionally to the square of the localization length, contrary to earlier
+claims based on the effective-medium approximation. We further analyze the
+effect of electron-electron interactions on the dielectric constant in
+quasi-1D, quasi-2D and 3D materials with large localization length, including
+both Coulomb repulsion and phonon-mediated attraction. The phonon-mediated
+attraction (in the pseudogapped state on the insulating side of the
+Superconductor-Insulator Transition) produces a correction to the dielectric
+constant, which may be detected from a linear response of a dielectric constant
+to an external magnetic field.
+",0,1,0,0,0,0
+16403,Epidemiological modeling of the 2005 French riots: a spreading wave and the role of contagion,"  As a large-scale instance of dramatic collective behaviour, the 2005 French
+riots started in a poor suburb of Paris, then spread in all of France, lasting
+about three weeks. Remarkably, although there were no displacements of rioters,
+the riot activity did travel. Access to daily national police data has allowed
+us to explore the dynamics of riot propagation. Here we show that an
+epidemic-like model, with just a few parameters and a single sociological
+variable characterizing neighbourhood deprivation, accounts quantitatively for
+the full spatio-temporal dynamics of the riots. This is the first time that
+such data-driven modelling involving contagion both within and between cities
+(through geographic proximity or media) at the scale of a country, and on a
+daily basis, is performed. Moreover, we give a precise mathematical
+characterization to the expression ""wave of riots"", and provide a visualization
+of the propagation around Paris, exhibiting the wave in a way not described
+before. The remarkable agreement between model and data demonstrates that
+geographic proximity played a major role in the propagation, even though
+information was readily available everywhere through media. Finally, we argue
+that our approach gives a general framework for the modelling of the dynamics
+of spontaneous collective uprisings.
+",1,1,0,0,0,0
+16404,Option Pricing in Illiquid Markets with Jumps,"  The classical linear Black--Scholes model for pricing derivative securities
+is a popular model in financial industry. It relies on several restrictive
+assumptions such as completeness, and frictionless of the market as well as the
+assumption on the underlying asset price dynamics following a geometric
+Brownian motion. The main purpose of this paper is to generalize the classical
+Black--Scholes model for pricing derivative securities by taking into account
+feedback effects due to an influence of a large trader on the underlying asset
+price dynamics exhibiting random jumps. The assumption that an investor can
+trade large amounts of assets without affecting the underlying asset price
+itself is usually not satisfied, especially in illiquid markets. We generalize
+the Frey--Stremme nonlinear option pricing model for the case the underlying
+asset follows a Levy stochastic process with jumps. We derive and analyze a
+fully nonlinear parabolic partial-integro differential equation for the price
+of the option contract. We propose a semi-implicit numerical discretization
+scheme and perform various numerical experiments showing influence of a large
+trader and intensity of jumps on the option price.
+",0,0,0,0,0,1
+16405,A Knowledge-Based Analysis of the Blockchain Protocol,"  At the heart of the Bitcoin is a blockchain protocol, a protocol for
+achieving consensus on a public ledger that records bitcoin transactions. To
+the extent that a blockchain protocol is used for applications such as contract
+signing and making certain transactions (such as house sales) public, we need
+to understand what guarantees the protocol gives us in terms of agents'
+knowledge. Here, we provide a complete characterization of agent's knowledge
+when running a blockchain protocol using a variant of common knowledge that
+takes into account the fact that agents can enter and leave the system, it is
+not known which agents are in fact following the protocol (some agents may want
+to deviate if they can gain by doing so), and the fact that the guarantees
+provided by blockchain protocols are probabilistic. We then consider some
+scenarios involving contracts and show that this level of knowledge suffices
+for some scenarios, but not others.
+",1,0,0,0,0,0
+16406,Convergence Analysis of Optimization Algorithms,"  The regret bound of an optimization algorithms is one of the basic criteria
+for evaluating the performance of the given algorithm. By inspecting the
+differences between the regret bounds of traditional algorithms and adaptive
+one, we provide a guide for choosing an optimizer with respect to the given
+data set and the loss function. For analysis, we assume that the loss function
+is convex and its gradient is Lipschitz continuous.
+",1,0,1,1,0,0
+16407,On the combinatorics of the 2-class classification problem,"  A set of points $X = X_B \cup X_R \subseteq \mathbb{R}^d$ is linearly
+separable if the convex hulls of $X_B$ and $X_R$ are disjoint, hence there
+exists a hyperplane separating $X_B$ from $X_R$. Such a hyperplane provides a
+method for classifying new points, according to which side of the hyperplane
+the new points lie. When such a linear separation is not possible, it may still
+be possible to partition $X_B$ and $X_R$ into prespecified numbers of groups,
+in such a way that every group from $X_B$ is linearly separable from every
+group from $X_R$. We may also discard some points as outliers, and seek to
+minimize the number of outliers necessary to find such a partition. Based on
+these ideas, Bertsimas and Shioda proposed the classification and regression by
+integer optimization (CRIO) method in 2007. In this work we explore the integer
+programming aspects of the classification part of CRIO, in particular
+theoretical properties of the associated formulation. We are able to find
+facet-inducing inequalities coming from the stable set polytope, hence showing
+that this classification problem has exploitable combinatorial properties.
+",1,0,0,0,0,0
+16408,Laboratory evidence of dynamo amplification of magnetic fields in a turbulent plasma,"  Magnetic fields are ubiquitous in the Universe. Extragalactic disks, halos
+and clusters have consistently been shown, via diffuse radio-synchrotron
+emission and Faraday rotation measurements, to exhibit magnetic field strengths
+ranging from a few nG to tens of $\mu$G. The energy density of these fields is
+typically comparable to the energy density of the fluid motions of the plasma
+in which they are embedded, making magnetic fields essential players in the
+dynamics of the luminous matter. The standard theoretical model for the origin
+of these strong magnetic fields is through the amplification of tiny seed
+fields via turbulent dynamo to the level consistent with current observations.
+Here we demonstrate, using laser-produced colliding plasma flows, that
+turbulence is indeed capable of rapidly amplifying seed fields to near
+equipartition with the turbulent fluid motions. These results support the
+notion that turbulent dynamo is a viable mechanism responsible for the observed
+present-day magnetization of the Universe.
+",0,1,0,0,0,0
+16409,"Radiating Electron Interaction with Multiple Colliding Electromagnetic Waves: Random Walk Trajectories, Levy Flights, Limit Circles, and Attractors (Survey of the Structurally Determinate Patterns)","  The multiple colliding laser pulse concept formulated in Ref. [1] is
+beneficial for achieving an extremely high amplitude of coherent
+electromagnetic field. Since the topology of electric and magnetic fields
+oscillating in time of multiple colliding laser pulses is far from trivial and
+the radiation friction effects are significant in the high field limit, the
+dynamics of charged particles interacting with the multiple colliding laser
+pulses demonstrates remarkable features corresponding to random walk
+trajectories, limit circles, attractors, regular patterns and Levy flights.
+Under extremely high intensity conditions the nonlinear dissipation mechanism
+stabilizes the particle motion resulting in the charged particle trajectory
+being located within narrow regions and in the occurrence of a new class of
+regular patterns made by the particle ensembles.
+",0,1,0,0,0,0
+16410,Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks,"  In this work, we jointly address the problem of text detection and
+recognition in natural scene images based on convolutional recurrent neural
+networks. We propose a unified network that simultaneously localizes and
+recognizes text with a single forward pass, avoiding intermediate processes
+like image cropping and feature re-calculation, word separation, or character
+grouping. In contrast to existing approaches that consider text detection and
+recognition as two distinct tasks and tackle them one by one, the proposed
+framework settles these two tasks concurrently. The whole framework can be
+trained end-to-end, requiring only images, the ground-truth bounding boxes and
+text labels. Through end-to-end training, the learned features can be more
+informative, which improves the overall performance. The convolutional features
+are calculated only once and shared by both detection and recognition, which
+saves processing time. Our proposed method has achieved competitive performance
+on several benchmark datasets.
+",1,0,0,0,0,0
+16411,Evidence for OH or H2O on the surface of 433 Eros and 1036 Ganymed,"  Water and hydroxyl, once thought to be found only in the primitive airless
+bodies that formed beyond roughly 2.5-3 AU, have recently been detected on the
+Moon and Vesta, which both have surfaces dominated by evolved, non-primitive
+compositions. In both these cases, the water/OH is thought to be exogenic,
+either brought in via impacts with comets or hydrated asteroids or created via
+solar wind interactions with silicates in the regolith or both. Such exogenic
+processes should also be occurring on other airless body surfaces. To test this
+hypothesis, we used the NASA Infrared Telescope Facility (IRTF) to measure
+reflectance spectra (2.0 to 4.1 {\mu}m) of two large near-Earth asteroids
+(NEAs) with compositions generally interpreted as anhydrous: 433 Eros and 1036
+Ganymed. OH is detected on both of these bodies in the form of absorption
+features near 3 {\mu}m. The spectra contain a component of thermal emission at
+longer wavelengths, from which we estimate thermal of 167+/- 98 J m-2s-1/2K-1
+for Eros (consistent with previous estimates) and 214+/- 80 J m-2s-1/2K-1 for
+Ganymed, the first reported measurement of thermal inertia for this object.
+These observations demonstrate that processes responsible for water/OH creation
+on large airless bodies also act on much smaller bodies.
+",0,1,0,0,0,0
+16412,Jump Locations of Jump-Diffusion Processes with State-Dependent Rates,"  We propose a general framework for studying jump-diffusion systems driven by
+both Gaussian noise and a jump process with state-dependent intensity. Of
+particular natural interest are the jump locations: the system evaluated at the
+jump times. However, the state-dependence of the jump rate provides direct
+coupling between the diffusion and jump components, making disentangling the
+two to study individually difficult. We provide an iterative map formulation of
+the sequence of distributions of jump locations. Computation of these
+distributions allows for the extraction of the interjump time statistics. These
+quantities reveal a relationship between the long-time distribution of jump
+location and the stationary density of the full process. We provide a few
+examples to demonstrate the analytical and numerical tools stemming from the
+results proposed in the paper, including an application that shows a
+non-monotonic dependence on the strength of diffusion.
+",0,1,1,0,0,0
+16413,Dust evolution with active galactic nucleus feedback in elliptical galaxies,"  We have recently suggested that dust growth in the cold gas phase dominates
+the dust abundance in elliptical galaxies while dust is efficiently destroyed
+in the hot X-ray emitting plasma (hot gas). In order to understand the dust
+evolution in elliptical galaxies, we construct a simple model that includes
+dust growth in the cold gas and dust destruction in the hot gas. We also take
+into account the effect of mass exchange between these two gas components
+induced by active galactic nucleus (AGN) feedback. We survey reasonable ranges
+of the relevant parameters in the model and find that AGN feedback cycles
+actually produce a variety in cold gas mass and dust-to-gas ratio. By comparing
+with an observational sample of nearby elliptical galaxies, we find that,
+although the dust-to-gas ratio varies by an order of magnitude in our model,
+the entire range of the observed dust-to-gas ratios is difficult to be
+reproduced under a single parameter set. Variation of the dust growth
+efficiency is the most probable solution to explain the large variety in
+dust-to-gas ratio of the observational sample. Therefore, dust growth can play
+a central role in creating the variation in dust-to-gas ratio through the AGN
+feedback cycle and through the variation in dust growth efficiency.
+",0,1,0,0,0,0
+16414,Generative-Discriminative Variational Model for Visual Recognition,"  The paradigm shift from shallow classifiers with hand-crafted features to
+end-to-end trainable deep learning models has shown significant improvements on
+supervised learning tasks. Despite the promising power of deep neural networks
+(DNN), how to alleviate overfitting during training has been a research topic
+of interest. In this paper, we present a Generative-Discriminative Variational
+Model (GDVM) for visual classification, in which we introduce a latent variable
+inferred from inputs for exhibiting generative abilities towards prediction. In
+other words, our GDVM casts the supervised learning task as a generative
+learning process, with data discrimination to be jointly exploited for improved
+classification. In our experiments, we consider the tasks of multi-class
+classification, multi-label classification, and zero-shot learning. We show
+that our GDVM performs favorably against the baselines or recent generative DNN
+models.
+",1,0,0,0,0,0
+16415,PCA-Initialized Deep Neural Networks Applied To Document Image Analysis,"  In this paper, we present a novel approach for initializing deep neural
+networks, i.e., by turning PCA into neural layers. Usually, the initialization
+of the weights of a deep neural network is done in one of the three following
+ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or
+as auto-encoder, and 3) re-use of layers from another network (transfer
+learning). Therefore, typically, many training epochs are needed before
+meaningful weights are learned, or a rather similar dataset is required for
+seeding a fine-tuning of transfer learning. In this paper, we describe how to
+turn a PCA into an auto-encoder, by generating an encoder layer of the PCA
+parameters and furthermore adding a decoding layer. We analyze the
+initialization technique on real documents. First, we show that a PCA-based
+initialization is quick and leads to a very stable initialization. Furthermore,
+for the task of layout analysis we investigate the effectiveness of PCA-based
+initialization and show that it outperforms state-of-the-art random weight
+initialization methods.
+",1,0,0,1,0,0
+16416,On a frame theoretic measure of quality of LTI systems,"  It is of practical significance to define the notion of a measure of quality
+of a control system, i.e., a quantitative extension of the classical notion of
+controllability. In this article we demonstrate that the three standard
+measures of quality involving the trace, minimum eigenvalue, and the
+determinant of the controllability grammian achieve their optimum values when
+the columns of the controllability matrix from a tight frame. Motivated by
+this, and in view of some recent developments in frame theoretic signal
+processing, we provide a measure of quality for LTI systems based on a measure
+of tightness of the columns of the reachability matrix .
+",1,0,1,0,0,0
+16417,Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing,"  A task of clustering data given in the ordinal scale under conditions of
+overlapping clusters has been considered. It's proposed to use an approach
+based on memberhsip and likelihood functions sharing. A number of performed
+experiments proved effectiveness of the proposed method. The proposed method is
+characterized by robustness to outliers due to a way of ordering values while
+constructing membership functions.
+",1,0,0,0,0,0
+16418,The ALICE O2 common driver for the C-RORC and CRU read-out cards,"  ALICE (A Large Ion Collider Experiment) is the heavy-ion detector designed to
+study the strongly interacting state of matter realized in relativistic
+heavy-ion collisions at the CERN Large Hadron Collider (LHC). A major upgrade
+of the experiment is planned during the 2019-2020 long shutdown. In order to
+cope with a data rate 100 times higher than during LHC Run 1 and with the
+continuous read-out of the Time Projection Chamber (TPC), it is necessary to
+upgrade the Online and Offline Computing to a new common system called O2 . The
+O2 read- out chain will use commodity x86 Linux servers equipped with custom
+PCIe FPGA-based read- out cards. This paper discusses the driver architecture
+for the cards that will be used in O2 : the PCIe v2 x8, Xilinx Virtex 6 based
+C-RORC (Common Readout Receiver Card) and the PCIe v3 x16, Intel Arria 10 based
+CRU (Common Readout Unit). Access to the PCIe cards is provided via three
+layers of software. Firstly, the low-level PCIe (PCI Express) layer responsible
+for the userspace interface for low-level operations such as memory mapping the
+PCIe BAR (Base Address Registers) and creating scatter-gather lists, which is
+provided by the PDA (Portable Driver Architecture) library developed by the
+Frankfurt Institute for Advanced Studies (FIAS). Above that sits our userspace
+driver which implements synchronization, controls the read-out card -- e.g.
+resetting and configuring the card, providing it with bus addresses to transfer
+data to and checking for data arrival -- and presents a uniform, high-level C++
+interface that abstracts over the differences between the C-RORC and CRU. This
+interface -- of which direct usage is principally intended for high-performance
+read-out processes -- allows users to configure and use the various aspects of
+the read-out cards, such as configuration, DMA transfers and commands to the
+front-end. [...]
+",1,1,0,0,0,0
+16419,Adapting control policies from simulation to reality using a pairwise loss,"  This paper proposes an approach to domain transfer based on a pairwise loss
+function that helps transfer control policies learned in simulation onto a real
+robot. We explore the idea in the context of a 'category level' manipulation
+task where a control policy is learned that enables a robot to perform a mating
+task involving novel objects. We explore the case where depth images are used
+as the main form of sensor input. Our experimental results demonstrate that
+proposed method consistently outperforms baseline methods that train only in
+simulation or that combine real and simulated data in a naive way.
+",1,0,0,0,0,0
+16420,Evidence against a supervoid causing the CMB Cold Spot,"  We report the results of the 2dF-VST ATLAS Cold Spot galaxy redshift survey
+(2CSz) based on imaging from VST ATLAS and spectroscopy from 2dF AAOmega over
+the core of the CMB Cold Spot. We sparsely surveyed the inner 5$^{\circ}$
+radius of the Cold Spot to a limit of $i_{AB} \le 19.2$, sampling $\sim7000$
+galaxies at $z<0.4$. We have found voids at $z=$ 0.14, 0.26 and 0.30 but they
+are interspersed with small over-densities and the scale of these voids is
+insufficient to explain the Cold Spot through the $\Lambda$CDM ISW effect.
+Combining with previous data out to $z\sim1$, we conclude that the CMB Cold
+Spot could not have been imprinted by a void confined to the inner core of the
+Cold Spot. Additionally we find that our 'control' field GAMA G23 shows a
+similarity in its galaxy redshift distribution to the Cold Spot. Since the GAMA
+G23 line-of-sight shows no evidence of a CMB temperature decrement we conclude
+that the Cold Spot may have a primordial origin rather than being due to
+line-of-sight effects.
+",0,1,0,0,0,0
+16421,How to Produce an Arbitrarily Small Tensor to Scalar Ratio,"  We construct a toy a model which demonstrates that large field single scalar
+inflation can produce an arbitrarily small scalar to tensor ratio in the window
+of e-foldings recoverable from CMB experiments. This is done by generalizing
+the $\alpha$-attractor models to allow the potential to approach a constant as
+rapidly as we desire for super-planckian field values. This implies that a
+non-detection of r alone can never rule out entirely the theory of large field
+inflation.
+",0,1,0,0,0,0
+16422,Meta Learning Shared Hierarchies,"  We develop a metalearning approach for learning hierarchically structured
+policies, improving sample efficiency on unseen tasks through the use of shared
+primitives---policies that are executed for large numbers of timesteps.
+Specifically, a set of primitives are shared within a distribution of tasks,
+and are switched between by task-specific policies. We provide a concrete
+metric for measuring the strength of such hierarchies, leading to an
+optimization problem for quickly reaching high reward on unseen tasks. We then
+present an algorithm to solve this problem end-to-end through the use of any
+off-the-shelf reinforcement learning method, by repeatedly sampling new tasks
+and resetting task-specific policies. We successfully discover meaningful motor
+primitives for the directional movement of four-legged robots, solely by
+interacting with distributions of mazes. We also demonstrate the
+transferability of primitives to solve long-timescale sparse-reward obstacle
+courses, and we enable 3D humanoid robots to robustly walk and crawl with the
+same policy.
+",1,0,0,0,0,0
+16423,The bright-star masks for the HSC-SSP survey,"  We present the procedure to build and validate the bright-star masks for the
+Hyper-Suprime-Cam Strategic Subaru Proposal (HSC-SSP) survey. To identify and
+mask the saturated stars in the full HSC-SSP footprint, we rely on the Gaia and
+Tycho-2 star catalogues. We first assemble a pure star catalogue down to
+$G_{\rm Gaia} < 18$ after removing $\sim1.5\%$ of sources that appear extended
+in the Sloan Digital Sky Survey (SDSS). We perform visual inspection on the
+early data from the S16A internal release of HSC-SSP, finding that our star
+catalogue is $99.2\%$ pure down to $G_{\rm Gaia} < 18$. Second, we build the
+mask regions in an automated way using stacked detected source measurements
+around bright stars binned per $G_{\rm Gaia}$ magnitude. Finally, we validate
+those masks from visual inspection and comparison with the literature of galaxy
+number counts and angular two-point correlation functions. This version
+(Arcturus) supersedes the previous version (Sirius) used in the S16A internal
+and DR1 public releases. We publicly release the full masks and tools to flag
+objects in the entire footprint of the planned HSC-SSP observations at this
+address: this ftp URL.
+",0,1,0,0,0,0
+16424,Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step,"  Generative adversarial networks (GANs) are a family of generative models that
+do not minimize a single training criterion. Unlike other generative models,
+the data distribution is learned via a game between a generator (the generative
+model) and a discriminator (a teacher providing training signal) that each
+minimize their own cost. GANs are designed to reach a Nash equilibrium at which
+each player cannot reduce their cost without changing the other players'
+parameters. One useful approach for the theory of GANs is to show that a
+divergence between the training distribution and the model distribution obtains
+its minimum value at equilibrium. Several recent research directions have been
+motivated by the idea that this divergence is the primary guide for the
+learning process and that every step of learning should decrease the
+divergence. We show that this view is overly restrictive. During GAN training,
+the discriminator provides learning signal in situations where the gradients of
+the divergences between distributions would not be useful. We provide empirical
+counterexamples to the view of GAN training as divergence minimization.
+Specifically, we demonstrate that GANs are able to learn distributions in
+situations where the divergence minimization point of view predicts they would
+fail. We also show that gradient penalties motivated from the divergence
+minimization perspective are equally helpful when applied in other contexts in
+which the divergence minimization perspective does not predict they would be
+helpful. This contributes to a growing body of evidence that GAN training may
+be more usefully viewed as approaching Nash equilibria via trajectories that do
+not necessarily minimize a specific divergence at each step.
+",1,0,0,1,0,0
+16425,The Tensor Memory Hypothesis,"  We discuss memory models which are based on tensor decompositions using
+latent representations of entities and events. We show how episodic memory and
+semantic memory can be realized and discuss how new memory traces can be
+generated from sensory input: Existing memories are the basis for perception
+and new memories are generated via perception. We relate our mathematical
+approach to the hippocampal memory indexing theory. We describe the first
+detailed mathematical models for the complete processing pipeline from sensory
+input and its semantic decoding, i.e., perception, to the formation of episodic
+and semantic memories and their declarative semantic decodings. Our main
+hypothesis is that perception includes an active semantic decoding process,
+which relies on latent representations of entities and predicates, and that
+episodic and semantic memories depend on the same decoding process. We
+contribute to the debate between the leading memory consolidation theories,
+i.e., the standard consolidation theory (SCT) and the multiple trace theory
+(MTT). The latter is closely related to the complementary learning systems
+(CLS) framework. In particular, we show explicitly how episodic memory can
+teach the neocortex to form a semantic memory, which is a core issue in MTT and
+CLS.
+",1,0,0,1,0,0
+16426,Data-driven Approach to Measuring the Level of Press Freedom Using Media Attention Diversity from Unfiltered News,"  Published by Reporters Without Borders every year, the Press Freedom Index
+(PFI) reflects the fear and tension in the newsroom pushed by the government
+and private sectors. While the PFI is invaluable in monitoring media
+environments worldwide, the current survey-based method has inherent
+limitations to updates in terms of cost and time. In this work, we introduce an
+alternative way to measure the level of press freedom using media attention
+diversity compiled from Unfiltered News.
+",1,0,0,0,0,0
+16427,Advanced Satellite-based Frequency Transfer at the 10^{-16} Level,"  Advanced satellite-based frequency transfers by TWCP and IPPP have been
+performed between NICT and KRISS. We confirm that the disagreement between them
+is less than 1x10^{-16} at an averaging time of several days. Additionally, an
+intercontinental frequency ratio measurement of Sr and Yb optical lattice
+clocks was directly performed by TWCP. We achieved an uncertainty at the
+mid-10^{-16} level after a total measurement time of 12 hours. The frequency
+ratio was consistent with the recently reported values within the uncertainty.
+",0,1,0,0,0,0
+16428,Are there needles in a moving haystack? Adaptive sensing for detection of dynamically evolving signals,"  In this paper we investigate the problem of detecting dynamically evolving
+signals. We model the signal as an $n$ dimensional vector that is either zero
+or has $s$ non-zero components. At each time step $t\in \mathbb{N}$ the
+non-zero components change their location independently with probability $p$.
+The statistical problem is to decide whether the signal is a zero vector or in
+fact it has non-zero components. This decision is based on $m$ noisy
+observations of individual signal components collected at times $t=1,\ldots,m$.
+We consider two different sensing paradigms, namely adaptive and non-adaptive
+sensing. For non-adaptive sensing the choice of components to measure has to be
+decided before the data collection process started, while for adaptive sensing
+one can adjust the sensing process based on observations collected earlier. We
+characterize the difficulty of this detection problem in both sensing paradigms
+in terms of the aforementioned parameters, with special interest to the speed
+of change of the active components. In addition we provide an adaptive sensing
+algorithm for this problem and contrast its performance to that of non-adaptive
+detection algorithms.
+",0,0,1,1,0,0
+16429,Image-based Localization using Hourglass Networks,"  In this paper, we propose an encoder-decoder convolutional neural network
+(CNN) architecture for estimating camera pose (orientation and location) from a
+single RGB-image. The architecture has a hourglass shape consisting of a chain
+of convolution and up-convolution layers followed by a regression part. The
+up-convolution layers are introduced to preserve the fine-grained information
+of the input image. Following the common practice, we train our model in
+end-to-end manner utilizing transfer learning from large scale classification
+data. The experiments demonstrate the performance of the approach on data
+exhibiting different lighting conditions, reflections, and motion blur. The
+results indicate a clear improvement over the previous state-of-the-art even
+when compared to methods that utilize sequence of test frames instead of a
+single frame.
+",1,0,0,0,0,0
+16430,Suppression of Decoherence of a Spin-Boson System by Time-Periodic Control,"  We consider a finite-dimensional quantum system coupled to the bosonic
+radiation field and subject to a time-periodic control operator. Assuming the
+validity of a certain dynamic decoupling condition we approximate the system's
+time evolution with respect to the non-interacting dynamics. For sufficiently
+small coupling constants $g$ and control periods $T$ we show that a certain
+deviation of coupled and uncoupled propagator may be estimated by
+$\mathcal{O}(gt \, T)$. Our approach relies on the concept of Kato stability
+and general theory on non-autonomous linear evolution equations.
+",0,0,1,0,0,0
+16431,Energy saving for building heating via a simple and efficient model-free control design: First steps with computer simulations,"  The model-based control of building heating systems for energy saving
+encounters severe physical, mathematical and calibration difficulties in the
+numerous attempts that has been published until now. This topic is addressed
+here via a new model-free control setting, where the need of any mathematical
+description disappears. Several convincing computer simulations are presented.
+Comparisons with classic PI controllers and flatness-based predictive control
+are provided.
+",1,0,0,0,0,0
+16432,Trace Properties from Separation Logic Specifications,"  We propose a formal approach for relating abstract separation logic library
+specifications with the trace properties they enforce on interactions between a
+client and a library. Separation logic with abstract predicates enforces a
+resource discipline that constrains when and how calls may be made between a
+client and a library. Intuitively, this can enforce a protocol on the
+interaction trace. This intuition is broadly used in the separation logic
+community but has not previously been formalised. We provide just such a
+formalisation. Our approach is based on using wrappers which instrument library
+code to induce execution traces for the properties under examination. By
+considering a separation logic extended with trace resources, we prove that
+when a library satisfies its separation logic specification then its wrapped
+version satisfies the same specification and, moreover, maintains the trace
+properties as an invariant. Consequently, any client and library implementation
+that are correct with respect to the separation logic specification will
+satisfy the trace properties.
+",1,0,0,0,0,0
+16433,Estimation of Local Degree Distributions via Local Weighted Averaging and Monte Carlo Cross-Validation,"  Owing to their capability of summarising interactions between elements of a
+system, networks have become a common type of data in many fields. As networks
+can be inhomogeneous, in that different regions of the network may exhibit
+different topologies, an important topic concerns their local properties. This
+paper focuses on the estimation of the local degree distribution of a vertex in
+an inhomogeneous network. The contributions are twofold: we propose an
+estimator based on local weighted averaging, and we set up a Monte Carlo
+cross-validation procedure to pick the parameters of this estimator. Under a
+specific modelling assumption we derive an oracle inequality that shows how the
+model parameters affect the precision of the estimator. We illustrate our
+method by several numerical experiments, on both real and synthetic data,
+showing in particular that the approach considerably improves upon the natural,
+empirical estimator.
+",0,0,0,1,0,0
+16434,The ALMA Phasing System: A Beamforming Capability for Ultra-High-Resolution Science at (Sub)Millimeter Wavelengths,"  The Atacama Millimeter/submillimeter Array (ALMA) Phasing Project (APP) has
+developed and deployed the hardware and software necessary to coherently sum
+the signals of individual ALMA antennas and record the aggregate sum in Very
+Long Baseline Interferometry (VLBI) Data Exchange Format. These beamforming
+capabilities allow the ALMA array to collectively function as the equivalent of
+a single large aperture and participate in global VLBI arrays. The inclusion of
+phased ALMA in current VLBI networks operating at (sub)millimeter wavelengths
+provides an order of magnitude improvement in sensitivity, as well as
+enhancements in u-v coverage and north-south angular resolution. The
+availability of a phased ALMA enables a wide range of new ultra-high angular
+resolution science applications, including the resolution of supermassive black
+holes on event horizon scales and studies of the launch and collimation of
+astrophysical jets. It also provides a high-sensitivity aperture that may be
+used for investigations such as pulsar searches at high frequencies. This paper
+provides an overview of the ALMA Phasing System design, implementation, and
+performance characteristics.
+",0,1,0,0,0,0
+16435,Signatures of two-step impurity mediated vortex lattice melting in Bose-Einstein Condensates,"  We simulate a rotating 2D BEC to study the melting of a vortex lattice in
+presence of random impurities. Impurities are introduced either through a
+protocol in which vortex lattice is produced in an impurity potential or first
+creating the vortex lattice in the absence of random pinning and then cranking
+up the (co-rotating) impurity potential. We find that for a fixed strength,
+pinning of vortices at randomly distributed impurities leads to the new states
+of vortex lattice. It is unearthed that the vortex lattice follow a two-step
+melting via loss of positional and orientational order. Also, the comparisons
+between the states obtained in two protocols show that the vortex lattice
+states are metastable states when impurities are introduced after the formation
+of an ordered vortex lattice. We also show the existence of metastable states
+which depend on the history of how the vortex lattice is created.
+",0,1,0,0,0,0
+16436,Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography,"  In coronary CT angiography, a series of CT images are taken at different
+levels of radiation dose during the examination. Although this reduces the
+total radiation dose, the image quality during the low-dose phases is
+significantly degraded. To address this problem, here we propose a novel
+semi-supervised learning technique that can remove the noises of the CT images
+obtained in the low-dose phases by learning from the CT images in the routine
+dose phases. Although a supervised learning approach is not possible due to the
+differences in the underlying heart structure in two phases, the images in the
+two phases are closely related so that we propose a cycle-consistent
+adversarial denoising network to learn the non-degenerate mapping between the
+low and high dose cardiac phases. Experimental results showed that the proposed
+method effectively reduces the noise in the low-dose CT image while the
+preserving detailed texture and edge information. Moreover, thanks to the
+cyclic consistency and identity loss, the proposed network does not create any
+artificial features that are not present in the input images. Visual grading
+and quality evaluation also confirm that the proposed method provides
+significant improvement in diagnostic quality.
+",0,0,0,1,0,0
+16437,The multidimensional truncated Moment Problem: Carathéodory Numbers,"  Let $\mathcal{A}$ be a finite-dimensional subspace of
+$C(\mathcal{X};\mathbb{R})$, where $\mathcal{X}$ is a locally compact Hausdorff
+space, and $\mathsf{A}=\{f_1,\dots,f_m\}$ a basis of $\mathcal{A}$. A sequence
+$s=(s_j)_{j=1}^m$ is called a moment sequence if $s_j=\int f_j(x) \, d\mu(x)$,
+$j=1,\dots,m$, for some positive Radon measure $\mu$ on $\mathcal{X}$. Each
+moment sequence $s$ has a finitely atomic representing measure $\mu$. The
+smallest possible number of atoms is called the Carathéodory number
+$\mathcal{C}_{\mathsf{A}}(s)$. The largest number $\mathcal{C}_{\mathsf{A}}(s)$
+among all moment sequences $s$ is the Carathéodory number
+$\mathcal{C}_{\mathsf{A}}$. In this paper the Carathéodory numbers
+$\mathcal{C}_{\mathsf{A}}(s)$ and $\mathcal{C}_{\mathsf{A}}$ are studied. In
+the case of differentiable functions methods from differential geometry are
+used. The main emphasis is on real polynomials. For a large class of spaces of
+polynomials in one variable the number $\mathcal{C}_{\mathsf{A}}$ is
+determined. In the multivariate case we obtain some lower bounds and we use
+results on zeros of positive polynomials to derive upper bounds for the
+Carathéodory numbers.
+",0,0,1,0,0,0
+16438,"Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation","  The World Wide Web (WWW) has fundamentally changed the ways billions of
+people are able to access information. Thus, understanding how people seek
+information online is an important issue of study. Wikipedia is a hugely
+important part of information provision on the web, with hundreds of millions
+of users browsing and contributing to its network of knowledge. The study of
+navigational behaviour on Wikipedia, due to the site's popularity and breadth
+of content, can reveal more general information seeking patterns that may be
+applied beyond Wikipedia and the Web. Our work addresses the relative
+shortcomings of existing literature in relating how information structure
+influences patterns of navigation online. We study aggregated clickstream data
+for articles on the English Wikipedia in the form of a weighted, directed
+navigational network. We introduce two parameters that describe how articles
+act to source and spread traffic through the network, based on their in/out
+strength and entropy. From these, we construct a navigational phase space where
+different article types occupy different, distinct regions, indicating how the
+structure of information online has differential effects on patterns of
+navigation. Finally, we go on to suggest applications for this analysis in
+identifying and correcting deficiencies in the Wikipedia page network that may
+also be adapted to more general information networks.
+",1,0,0,0,0,0
+16439,Multiphase Aluminum A356 Foam Formation Process Simulation Using Lattice Boltzmann Method,"  Shan-Chen model is a numerical scheme to simulate multiphase fluid flows
+using Lattice Boltzmann approach. The original Shan-Chen model suffers from
+inability to accurately predict behavior of air bubbles interacting in a
+non-aqueous fluid. In the present study, we extended the Shan-Chen model to
+take the effect of the attraction-repulsion barriers among bubbles in to
+account. The proposed model corrects the interaction and coalescence criterion
+of the original Shan-Chen scheme in order to have a more accurate simulation of
+bubbles morphology in a metal foam. The model is based on forming a thin film
+(narrow channel) between merging bubbles during growth. Rupturing of the film
+occurs when an oscillation in velocity and pressure arises inside the channel
+followed by merging of the bubbles. Comparing numerical results obtained from
+proposed model with mettallorgraphy images for aluminum A356 demonstrated a
+good consistency in mean bubble size and bubbles distribution
+",1,1,0,0,0,0
+16440,Deformations of coisotropic submanifolds in Jacobi manifolds,"  In this thesis, we study the deformation problem of coisotropic submanifolds
+in Jacobi manifolds. In particular we attach two algebraic invariants to any
+coisotropic submanifold $S$ in a Jacobi manifold, namely the
+$L_\infty[1]$-algebra and the BFV-complex of $S$. Our construction generalizes
+and unifies analogous constructions in symplectic, Poisson, and locally
+conformal symplectic geometry. As a new special case we also attach an
+$L_\infty[1]$-algebra and a BFV-complex to any coisotropic submanifold in a
+contact manifold. The $L_\infty[1]$-algebra of $S$ controls the formal
+coisotropic deformation problem of $S$, even under Hamiltonian equivalence. The
+BFV-complex of $S$ controls the non-formal coisotropic deformation problem of
+$S$, even under both Hamiltonian and Jacobi equivalence. In view of these
+results, we exhibit, in the contact setting, two examples of coisotropic
+submanifolds whose coisotropic deformation problem is obstructed.
+",0,0,1,0,0,0
+16441,Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets,"  The complexity of a learning task is increased by transformations in the
+input space that preserve class identity. Visual object recognition for example
+is affected by changes in viewpoint, scale, illumination or planar
+transformations. While drastically altering the visual appearance, these
+changes are orthogonal to recognition and should not be reflected in the
+representation or feature encoding used for learning. We introduce a framework
+for weakly supervised learning of image embeddings that are robust to
+transformations and selective to the class distribution, using sets of
+transforming examples (orbit sets), deep parametrizations and a novel
+orbit-based loss. The proposed loss combines a discriminative, contrastive part
+for orbits with a reconstruction error that learns to rectify orbit
+transformations. The learned embeddings are evaluated in distance metric-based
+tasks, such as one-shot classification under geometric transformations, as well
+as face verification and retrieval under more realistic visual variability. Our
+results suggest that orbit sets, suitably computed or observed, can be used for
+efficient, weakly-supervised learning of semantically relevant image
+embeddings.
+",1,0,0,1,0,0
+16442,Covering compact metric spaces greedily,"  A general greedy approach to construct coverings of compact metric spaces by
+metric balls is given and analyzed. The analysis is a continuous version of
+Chvatal's analysis of the greedy algorithm for the weighted set cover problem.
+The approach is demonstrated in an exemplary manner to construct efficient
+coverings of the n-dimensional sphere and n-dimensional Euclidean space to give
+short and transparent proofs of several best known bounds obtained from
+deterministic constructions in the literature on sphere coverings.
+",0,0,1,0,0,0
+16443,Gauge covariances and nonlinear optical responses,"  The formalism of the reduced density matrix is pursued in both length and
+velocity gauges of the perturbation to the crystal Hamiltonian. The covariant
+derivative is introduced as a convenient representation of the position
+operator. This allow us to write compact expressions for the reduced density
+matrix in any order of the perturbation which simplifies the calculations of
+nonlinear optical responses; as an example, we compute the first and third
+order contributions of the monolayer graphene. Expressions obtained in both
+gauges share the same formal structure, allowing a comparison of the effects of
+truncation to a finite set of bands. This truncation breaks the equivalence
+between the two approaches: its proper implementation can be done directly in
+the expressions derived in the length gauge, but require a revision of the
+equations of motion of the reduced density matrix in the velocity gauge.
+",0,1,0,0,0,0
+16444,Quasi-Static Internal Magnetic Field Detected in the Pseudogap Phase of Bi$_{2+x}$Sr$_{2-x}$CaCu$_2$O$_{8+δ}$ by $μ$SR,"  We report muon spin relaxation ($\mu$SR) measurements of optimally-doped and
+overdoped Bi$_{2+x}$Sr$_{2-x}$CaCu$_2$O$_{8+\delta}$ (Bi2212) single crystals
+that reveal the presence of a weak temperature-dependent quasi-static internal
+magnetic field of electronic origin in the superconducting (SC) and pseudogap
+(PG) phases. In both samples the internal magnetic field persists up to 160~K,
+but muon diffusion prevents following the evolution of the field to higher
+temperatures. We consider the evidence from our measurments in support of PG
+order parameter candidates, namely, electronic loop currents and
+magnetoelectric quadrupoles.
+",0,1,0,0,0,0
+16445,Auto-Encoding Total Correlation Explanation,"  Advances in unsupervised learning enable reconstruction and generation of
+samples from complex distributions, but this success is marred by the
+inscrutability of the representations learned. We propose an
+information-theoretic approach to characterizing disentanglement and dependence
+in representation learning using multivariate mutual information, also called
+total correlation. The principle of total Cor-relation Ex-planation (CorEx) has
+motivated successful unsupervised learning applications across a variety of
+domains, but under some restrictive assumptions. Here we relax those
+restrictions by introducing a flexible variational lower bound to CorEx.
+Surprisingly, we find that this lower bound is equivalent to the one in
+variational autoencoders (VAE) under certain conditions. This
+information-theoretic view of VAE deepens our understanding of hierarchical VAE
+and motivates a new algorithm, AnchorVAE, that makes latent codes more
+interpretable through information maximization and enables generation of richer
+and more realistic samples.
+",0,0,0,1,0,0
+16446,A Characterisation of Open Bisimilarity using an Intuitionistic Modal Logic,"  Open bisimilarity is the original notion of bisimilarity to be introduced for
+the pi-calculus that is a congruence. In open bisimilarity, free names in
+processes are treated as variables that may be instantiated lazily; in contrast
+to early and late bisimilarity where free names are constants. We build on the
+established line of work, due to Milner, Parrow, and Walker, on classical modal
+logics characterising early and late bisimilarity for the $\pi$-calculus. The
+important insight is, to characterise open bisimilarity, we move to the setting
+of intuitionistic modal logics. The intuitionistic modal logic introduced,
+called OM, is such that modalities are closed under (respectful) substitutions,
+inducing a property known as intuitionistic hereditary. Intuitionistic
+hereditary reflects the lazy instantiation of names in open bisimilarity. The
+soundness proof for open bisimilarity with respect to the modal logic is
+mechanised in Abella. The constructive content of the completeness proof
+provides an algorithm for generating distinguishing formulae, where such
+formulae are useful as a certificate explaining why two processes are not open
+bisimilar. We draw attention to the fact that open bisimilarity is not the only
+notion of bisimilarity that is a congruence: for name-passing calculi there is
+a classical/intuitionistic spectrum of bisimilarities.
+",1,0,0,0,0,0
+16447,Using data science as a community advocacy tool to promote equity in urban renewal programs: An analysis of Atlanta's Anti-Displacement Tax Fund,"  Cities across the United States are undergoing great transformation and urban
+growth. Data and data analysis has become an essential element of urban
+planning as cities use data to plan land use and development. One great
+challenge is to use the tools of data science to promote equity along with
+growth. The city of Atlanta is an example site of large-scale urban renewal
+that aims to engage in development without displacement. On the Westside of
+downtown Atlanta, the construction of the new Mercedes-Benz Stadium and the
+conversion of an underutilized rail-line into a multi-use trail may result in
+increased property values. In response to community residents' concerns and a
+commitment to development without displacement, the city and philanthropic
+partners announced an Anti-Displacement Tax Fund to subsidize future property
+tax increases of owner occupants for the next twenty years. To achieve greater
+transparency, accountability, and impact, residents expressed a desire for a
+tool that would help them determine eligibility and quantify this commitment.
+In support of this goal, we use machine learning techniques to analyze
+historical tax assessment and predict future tax assessments. We then apply
+eligibility estimates to our predictions to estimate the total cost for the
+first seven years of the program. These forecasts are also incorporated into an
+interactive tool for community residents to determine their eligibility for the
+fund and the expected increase in their home value over the next seven years.
+",1,0,0,0,0,0
+16448,General Bayesian inference schemes in infinite mixture models,"  Bayesian statistical models allow us to formalise our knowledge about the
+world and reason about our uncertainty, but there is a need for better
+procedures to accurately encode its complexity. One way to do so is through
+compositional models, which are formed by combining blocks consisting of
+simpler models. One can increase the complexity of the compositional model by
+either stacking more blocks or by using a not-so-simple model as a building
+block. This thesis is an example of the latter. One first aim is to expand the
+choice of Bayesian nonparametric (BNP) blocks for constructing tractable
+compositional models. So far, most of the models that have a Bayesian
+nonparametric component use a Dirichlet Process or a Pitman-Yor process because
+of the availability of tractable and compact representations. This thesis shows
+how to overcome certain intractabilities in order to obtain analogous compact
+representations for the class of Poisson-Kingman priors which includes the
+Dirichlet and Pitman-Yor processes.
+A major impediment to the widespread use of Bayesian nonparametric building
+blocks is that inference is often costly, intractable or difficult to carry
+out. This is an active research area since dealing with the model's infinite
+dimensional component forbids the direct use of standard simulation-based
+methods. The main contribution of this thesis is a variety of inference schemes
+that tackle this problem: Markov chain Monte Carlo and Sequential Monte Carlo
+methods, which are exact inference schemes since they target the true
+posterior. The contributions of this thesis, in a larger context, provide
+general purpose exact inference schemes in the flavour or probabilistic
+programming: the user is able to choose from a variety of models, focusing only
+on the modelling part. Indeed, if the wide enough class of Poisson-Kingman
+priors is used as one of our blocks, this objective is achieved.
+",0,0,0,1,0,0
+16449,Glow: Generative Flow with Invertible 1x1 Convolutions,"  Flow-based generative models (Dinh et al., 2014) are conceptually attractive
+due to tractability of the exact log-likelihood, tractability of exact
+latent-variable inference, and parallelizability of both training and
+synthesis. In this paper we propose Glow, a simple type of generative flow
+using an invertible 1x1 convolution. Using our method we demonstrate a
+significant improvement in log-likelihood on standard benchmarks. Perhaps most
+strikingly, we demonstrate that a generative model optimized towards the plain
+log-likelihood objective is capable of efficient realistic-looking synthesis
+and manipulation of large images. The code for our model is available at
+this https URL
+",0,0,0,1,0,0
+16450,Pohozaev identity for the fractional $p-$Laplacian on $\mathbb{R}^N$,"  By virtue of a suitable approximation argument, we prove a Pohozaev identity
+for nonlinear nonlocal problems on $\mathbb{R}^N$ involving the fractional
+$p-$Laplacian operator. Furthermore we provide an application of the identity
+to show that some relevant levels of the energy functional associated with the
+problem coincide.
+",0,0,1,0,0,0
+16451,A Second Wave of Expanders over Finite Fields,"  This is an expository survey on recent sum-product results in finite fields.
+We present a number of sum-product or ""expander"" results that say that if
+$|A| > p^{2/3}$ then some set determined by sums and product of elements of $A$
+is nearly as large as possible, and if $|A|<p^{2/3}$ then the set in question
+is significantly larger that $A$. These results are based on a point-plane
+incidence bound of Rudnev, and are quantitatively stronger than a wave of
+earlier results following Bourgain, Katz, and Tao's breakthrough sum-product
+result.
+In addition, we present two geometric results: an incidence bound due to
+Stevens and de Zeeuw, and bound on collinear triples, and an example of an
+expander that breaks the threshold of $p^{2/3}$ required by the other results.
+We have simplified proofs wherever possible, and hope that this survey may
+serve as a compact guide to recent advances in arithmetic combinatorics over
+finite fields. We do not claim originality for any of the results.
+",0,0,1,0,0,0
+16452,Homology of torus knots,"  Using the method of Elias-Hogancamp and combinatorics of toric braids we give
+an explicit formula for the triply graded Khovanov-Rozansky homology of an
+arbitrary torus knot, thereby proving some of the conjectures of
+Aganagic-Shakirov, Cherednik, Gorsky-Negut and Oblomkov-Rasmussen-Shende.
+",0,0,1,0,0,0
+16453,2-associahedra,"  For any $r\geq 1$ and $\mathbf{n} \in \mathbb{Z}_{\geq0}^r \setminus
+\{\mathbf0\}$ we construct a poset $W_{\mathbf{n}}$ called a 2-associahedron.
+The 2-associahedra arose in symplectic geometry, where they are expected to
+control maps between Fukaya categories of different symplectic manifolds. We
+prove that the completion $\widehat{W_{\mathbf{n}}}$ is an abstract polytope of
+dimension $|\mathbf{n}|+r-3$. There are forgetful maps $W_{\mathbf{n}} \to
+K_r$, where $K_r$ is the $(r-2)$-dimensional associahedron, and the
+2-associahedra specialize to the associahedra (in two ways) and to the
+multiplihedra. In an appendix, we work out the 2- and 3-dimensional
+associahedra in detail.
+",0,0,1,0,0,0
+16454,Anisotropic thermophoresis,"  Colloidal migration in temperature gradient is referred to as thermophoresis.
+In contrast to particles with spherical shape, we show that elongated colloids
+may have a thermophoretic response that varies with the colloid orientation.
+Remarkably, this can translate into a non-vanishing thermophoretic force in the
+direction perpendicular to the temperature gradient. Oppositely to the friction
+force, the thermophoretic force of a rod oriented with the temperature gradient
+can be larger or smaller than when oriented perpendicular to it. The precise
+anisotropic thermophoretic behavior clearly depends on the colloidal rod aspect
+ratio, and also on its surface details, which provides an interesting
+tunability to the devices constructed based on this principle. By means of
+mesoscale hydrodynamic simulations, we characterize this effect for different
+types of rod-like colloids.
+",0,1,0,0,0,0
+16455,Ultra-Wideband Aided Fast Localization and Mapping System,"  This paper proposes an ultra-wideband (UWB) aided localization and mapping
+system that leverages on inertial sensor and depth camera. Inspired by the fact
+that visual odometry (VO) system, regardless of its accuracy in the short term,
+still faces challenges with accumulated errors in the long run or under
+unfavourable environments, the UWB ranging measurements are fused to remove the
+visual drift and improve the robustness. A general framework is developed which
+consists of three parallel threads, two of which carry out the visual-inertial
+odometry (VIO) and UWB localization respectively. The other mapping thread
+integrates visual tracking constraints into a pose graph with the proposed
+smooth and virtual range constraints, such that an optimization is performed to
+provide robust trajectory estimation. Experiments show that the proposed system
+is able to create dense drift-free maps in real-time even running on an
+ultra-low power processor in featureless environments.
+",1,0,0,0,0,0
+16456,Modular Sensor Fusion for Semantic Segmentation,"  Sensor fusion is a fundamental process in robotic systems as it extends the
+perceptual range and increases robustness in real-world operations. Current
+multi-sensor deep learning based semantic segmentation approaches do not
+provide robustness to under-performing classes in one modality, or require a
+specific architecture with access to the full aligned multi-sensor training
+data. In this work, we analyze statistical fusion approaches for semantic
+segmentation that overcome these drawbacks while keeping a competitive
+performance. The studied approaches are modular by construction, allowing to
+have different training sets per modality and only a much smaller subset is
+needed to calibrate the statistical models. We evaluate a range of statistical
+fusion approaches and report their performance against state-of-the-art
+baselines on both real-world and simulated data. In our experiments, the
+approach improves performance in IoU over the best single modality segmentation
+results by up to 5%. We make all implementations and configurations publicly
+available.
+",1,0,0,0,0,0
+16457,Thickness-dependent electronic and magnetic properties of $γ'$-Fe$_{\mathrm 4}$N atomic layers on Cu(001),"  Growth, electronic and magnetic properties of $\gamma'$-Fe$_{4}$N atomic
+layers on Cu(001) are studied by scanning tunneling microscopy/spectroscopy and
+x-ray absorption spectroscopy/magnetic circular dichroism. A continuous film of
+ordered trilayer $\gamma'$-Fe$_{4}$N is obtained by Fe deposition under N$_{2}$
+atmosphere onto monolayer Fe$_{2}$N/Cu(001), while the repetition of a
+bombardment with 0.5 keV N$^{+}$ ions during growth cycles results in imperfect
+bilayer $\gamma'$-Fe$_{4}$N. The increase in the sample thickness causes the
+change of the surface electronic structure, as well as the enhancement in the
+spin magnetic moment of Fe atoms reaching $\sim$ 1.4 $\mu_{\mathrm B}$/atom in
+the trilayer sample. The observed thickness-dependent properties of the system
+are well interpreted by layer-resolved density of states calculated using first
+principles, which demonstrates the strongly layer-dependent electronic states
+within each surface, subsurface, and interfacial plane of the
+$\gamma'$-Fe$_{4}$N atomic layers on Cu(001).
+",0,1,0,0,0,0
+16458,"Error Characterization, Mitigation, and Recovery in Flash Memory Based Solid-State Drives","  NAND flash memory is ubiquitous in everyday life today because its capacity
+has continuously increased and cost has continuously decreased over decades.
+This positive growth is a result of two key trends: (1) effective process
+technology scaling, and (2) multi-level (e.g., MLC, TLC) cell data coding.
+Unfortunately, the reliability of raw data stored in flash memory has also
+continued to become more difficult to ensure, because these two trends lead to
+(1) fewer electrons in the flash memory cell (floating gate) to represent the
+data and (2) larger cell-to-cell interference and disturbance effects. Without
+mitigation, worsening reliability can reduce the lifetime of NAND flash memory.
+As a result, flash memory controllers in solid-state drives (SSDs) have become
+much more sophisticated: they incorporate many effective techniques to ensure
+the correct interpretation of noisy data stored in flash memory cells.
+In this article, we review recent advances in SSD error characterization,
+mitigation, and data recovery techniques for reliability and lifetime
+improvement. We provide rigorous experimental data from state-of-the-art MLC
+and TLC NAND flash devices on various types of flash memory errors, to motivate
+the need for such techniques. Based on the understanding developed by the
+experimental characterization, we describe several mitigation and recovery
+techniques, including (1) cell-to-cell interference mitigation, (2) optimal
+multi-level cell sensing, (3) error correction using state-of-the-art
+algorithms and methods, and (4) data recovery when error correction fails. We
+quantify the reliability improvement provided by each of these techniques.
+Looking forward, we briefly discuss how flash memory and these techniques could
+evolve into the future.
+",1,0,0,0,0,0
+16459,Unified Backpropagation for Multi-Objective Deep Learning,"  A common practice in most of deep convolutional neural architectures is to
+employ fully-connected layers followed by Softmax activation to minimize
+cross-entropy loss for the sake of classification. Recent studies show that
+substitution or addition of the Softmax objective to the cost functions of
+support vector machines or linear discriminant analysis is highly beneficial to
+improve the classification performance in hybrid neural networks. We propose a
+novel paradigm to link the optimization of several hybrid objectives through
+unified backpropagation. This highly alleviates the burden of extensive
+boosting for independent objective functions or complex formulation of
+multiobjective gradients. Hybrid loss functions are linked by basic probability
+assignment from evidence theory. We conduct our experiments for a variety of
+scenarios and standard datasets to evaluate the advantage of our proposed
+unification approach to deliver consistent improvements into the classification
+performance of deep convolutional neural networks.
+",1,0,0,1,0,0
+16460,Information-Theoretic Representation Learning for Positive-Unlabeled Classification,"  Recent advances in weakly supervised classification allow us to train a
+classifier only from positive and unlabeled (PU) data. However, existing PU
+classification methods typically require an accurate estimate of the
+class-prior probability, which is a critical bottleneck particularly for
+high-dimensional data. This problem has been commonly addressed by applying
+principal component analysis in advance, but such unsupervised dimension
+reduction can collapse underlying class structure. In this paper, we propose a
+novel representation learning method from PU data based on the
+information-maximization principle. Our method does not require class-prior
+estimation and thus can be used as a preprocessing method for PU
+classification. Through experiments, we demonstrate that our method combined
+with deep neural networks highly improves the accuracy of PU class-prior
+estimation, leading to state-of-the-art PU classification performance.
+",1,0,0,1,0,0
+16461,Aspects of Chaitin's Omega,"  The halting probability of a Turing machine,also known as Chaitin's Omega, is
+an algorithmically random number with many interesting properties. Since
+Chaitin's seminal work, many popular expositions have appeared, mainly focusing
+on the metamathematical or philosophical significance of Omega (or debating
+against it). At the same time, a rich mathematical theory exploring the
+properties of Chaitin's Omega has been brewing in various technical papers,
+which quietly reveals the significance of this number to many aspects of
+contemporary algorithmic information theory. The purpose of this survey is to
+expose these developments and tell a story about Omega, which outlines its
+multifaceted mathematical properties and roles in algorithmic randomness.
+",1,0,1,0,0,0
+16462,This Looks Like That: Deep Learning for Interpretable Image Recognition,"  When we are faced with challenging image classification tasks, we often
+explain our reasoning by dissecting the image, and pointing out prototypical
+aspects of one class or another. The mounting evidence for each of the classes
+helps us make our final decision. In this work, we introduce a deep network
+architecture that reasons in a similar way: the network dissects the image by
+finding prototypical parts, and combines evidence from the prototypes to make a
+final classification. The model thus reasons in a way that is qualitatively
+similar to the way ornithologists, physicians, geologists, architects, and
+others would explain to people on how to solve challenging image classification
+tasks. The network uses only image-level labels for training, meaning that
+there are no labels for parts of images. We demonstrate our method on the
+CUB-200-2011 dataset and the CBIS-DDSM dataset. Our experiments show that our
+interpretable network can achieve comparable accuracy with its analogous
+standard non-interpretable counterpart as well as other interpretable deep
+models.
+",0,0,0,1,0,0
+16463,Deep Learning in the Automotive Industry: Applications and Tools,"  Deep Learning refers to a set of machine learning techniques that utilize
+neural networks with many hidden layers for tasks, such as image
+classification, speech recognition, language understanding. Deep learning has
+been proven to be very effective in these domains and is pervasively used by
+many Internet services. In this paper, we describe different automotive uses
+cases for deep learning in particular in the domain of computer vision. We
+surveys the current state-of-the-art in libraries, tools and infrastructures
+(e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural
+networks. We particularly focus on convolutional neural networks and computer
+vision use cases, such as the visual inspection process in manufacturing plants
+and the analysis of social media data. To train neural networks, curated and
+labeled datasets are essential. In particular, both the availability and scope
+of such datasets is typically very limited. A main contribution of this paper
+is the creation of an automotive dataset, that allows us to learn and
+automatically recognize different vehicle properties. We describe an end-to-end
+deep learning application utilizing a mobile app for data collection and
+process support, and an Amazon-based cloud backend for storage and training.
+For training we evaluate the use of cloud and on-premises infrastructures
+(including multiple GPUs) in conjunction with different neural network
+architectures and frameworks. We assess both the training times as well as the
+accuracy of the classifier. Finally, we demonstrate the effectiveness of the
+trained classifier in a real world setting during manufacturing process.
+",1,0,0,0,0,0
+16464,"Design, Engineering and Optimization of a Grid-Tie Multicell Inverter for Energy Storage Applications","  Multilevel converters have found many applications within renewable energy
+systems thanks to their unique capability of generating multiple voltage
+levels. However, these converters need multiple DC sources and the voltage
+balancing over capacitors for these systems is cumbersome. In this work, a new
+grid-tie multicell inverter with high level of safety has been designed,
+engineered and optimized for integrating energy storage devices to the electric
+grid. The multilevel converter proposed in this work is capable of maintaining
+the flying capacitors voltage in the desired value. The solar cells are the
+primary energy sources for proposed inverter where the maximum power density is
+obtained. Finally, the performance of the inverter and its control method
+simulated using PSCAD/EMTDC software package and good agreement achieved with
+experimental data.
+",0,1,0,0,0,0
+16465,An infinitely differentiable function with compact support: Definition and properties,"  This is the English translation of my old paper 'Definición y estudio de
+una función indefinidamente diferenciable de soporte compacto', Rev. Real
+Acad. Ciencias 76 (1982) 21-38. In it a function (essentially Fabius function)
+is defined and given its main properties, including: unicity, interpretation as
+a probability, partition of unity with its translates, formulas for its $n$-th
+derivates, rationality of its values at dyadic points, formulas for the
+effective computation of these values, and some arithmetical properties of
+these values. Since I need it now for a reference, I have translated it.
+",0,0,1,0,0,0
+16466,Analysis of bacterial population growth using extended logistic growth model with distributed delay,"  In the present work, we develop a delayed Logistic growth model to study the
+effects of decontamination on the bacterial population in the ambient
+environment. Using the linear stability analysis, we study different case
+scenarios, where bacterial population may establish at the positive equilibrium
+or go extinct due to increased decontamination. The results are verified using
+numerical simulation of the model.
+",0,0,0,0,1,0
+16467,Reverse Quantum Annealing Approach to Portfolio Optimization Problems,"  We investigate a hybrid quantum-classical solution method to the
+mean-variance portfolio optimization problems. Starting from real financial
+data statistics and following the principles of the Modern Portfolio Theory, we
+generate parametrized samples of portfolio optimization problems that can be
+related to quadratic binary optimization forms programmable in the analog
+D-Wave Quantum Annealer 2000Q. The instances are also solvable by an
+industry-established Genetic Algorithm approach, which we use as a classical
+benchmark. We investigate several options to run the quantum computation
+optimally, ultimately discovering that the best results in terms of expected
+time-to-solution as a function of number of variables for the hardest instances
+set are obtained by seeding the quantum annealer with a solution candidate
+found by a greedy local search and then performing a reverse annealing
+protocol. The optimized reverse annealing protocol is found to be more than 100
+times faster than the corresponding forward quantum annealing on average.
+",0,0,0,0,0,1
+16468,GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations,"  Modern deep transfer learning approaches have mainly focused on learning
+generic feature vectors from one task that are transferable to other tasks,
+such as word embeddings in language and pretrained convolutional features in
+vision. However, these approaches usually transfer unary features and largely
+ignore more structured graphical representations. This work explores the
+possibility of learning generic latent relational graphs that capture
+dependencies between pairs of data units (e.g., words or pixels) from
+large-scale unlabeled data and transferring the graphs to downstream tasks. Our
+proposed transfer learning framework improves performance on various tasks
+including question answering, natural language inference, sentiment analysis,
+and image classification. We also show that the learned graphs are generic
+enough to be transferred to different embeddings on which the graphs have not
+been trained (including GloVe embeddings, ELMo embeddings, and task-specific
+RNN hidden unit), or embedding-free units such as image pixels.
+",0,0,0,1,0,0
+16469,Learning to cluster in order to transfer across domains and tasks,"  This paper introduces a novel method to perform transfer learning across
+domains and tasks, formulating it as a problem of learning to cluster. The key
+insight is that, in addition to features, we can transfer similarity
+information and this is sufficient to learn a similarity function and
+clustering network to perform both domain adaptation and cross-task transfer
+learning. We begin by reducing categorical information to pairwise constraints,
+which only considers whether two instances belong to the same class or not.
+This similarity is category-agnostic and can be learned from data in the source
+domain using a similarity network. We then present two novel approaches for
+performing transfer learning using this similarity function. First, for
+unsupervised domain adaptation, we design a new loss function to regularize
+classification with a constrained clustering loss, hence learning a clustering
+network with the transferred similarity metric generating the training inputs.
+Second, for cross-task learning (i.e., unsupervised clustering with unseen
+categories), we propose a framework to reconstruct and estimate the number of
+semantic clusters, again using the clustering network. Since the similarity
+network is noisy, the key is to use a robust clustering algorithm, and we show
+that our formulation is more robust than the alternative constrained and
+unconstrained clustering approaches. Using this method, we first show state of
+the art results for the challenging cross-task problem, applied on Omniglot and
+ImageNet. Our results show that we can reconstruct semantic clusters with high
+accuracy. We then evaluate the performance of cross-domain transfer using
+images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both
+datasets. Our approach doesn't explicitly deal with domain discrepancy. If we
+combine with a domain adaptation loss, it shows further improvement.
+",1,0,0,0,0,0
+16470,Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models,"  Perceptual aliasing is one of the main causes of failure for Simultaneous
+Localization and Mapping (SLAM) systems operating in the wild. Perceptual
+aliasing is the phenomenon where different places generate a similar visual
+(or, in general, perceptual) footprint. This causes spurious measurements to be
+fed to the SLAM estimator, which typically results in incorrect localization
+and mapping results. The problem is exacerbated by the fact that those outliers
+are highly correlated, in the sense that perceptual aliasing creates a large
+number of mutually-consistent outliers. Another issue stems from the fact that
+most state-of-the-art techniques rely on a given trajectory guess (e.g., from
+odometry) to discern between inliers and outliers and this makes the resulting
+pipeline brittle, since the accumulation of error may result in incorrect
+choices and recovery from failures is far from trivial. This work provides a
+unified framework to model perceptual aliasing in SLAM and provides practical
+algorithms that can cope with outliers without relying on any initial guess. We
+present two main contributions. The first is a Discrete-Continuous Graphical
+Model (DC-GM) for SLAM: the continuous portion of the DC-GM captures the
+standard SLAM problem, while the discrete portion describes the selection of
+the outliers and models their correlation. The second contribution is a
+semidefinite relaxation to perform inference in the DC-GM that returns
+estimates with provable sub-optimality guarantees. Experimental results on
+standard benchmarking datasets show that the proposed technique compares
+favorably with state-of-the-art methods while not relying on an initial guess
+for optimization.
+",1,0,0,0,0,0
+16471,"Sparse distributed representation, hierarchy, critical periods, metaplasticity: the keys to lifelong fixed-time learning and best-match retrieval","  Among the more important hallmarks of human intelligence, which any
+artificial general intelligence (AGI) should have, are the following. 1. It
+must be capable of on-line learning, including with single/few trials. 2.
+Memories/knowledge must be permanent over lifelong durations, safe from
+catastrophic forgetting. Some confabulation, i.e., semantically plausible
+retrieval errors, may gradually accumulate over time. 3. The time to both: a)
+learn a new item, and b) retrieve the best-matching / most relevant item(s),
+i.e., do similarity-based retrieval, must remain constant throughout the
+lifetime. 4. The system should never become full: it must remain able to store
+new information, i.e., make new permanent memories, throughout very long
+lifetimes. No artificial computational system has been shown to have all these
+properties. Here, we describe a neuromorphic associative memory model, Sparsey,
+which does, in principle, possess them all. We cite prior results supporting
+possession of hallmarks 1 and 3 and sketch an argument, hinging on strongly
+recursive, hierarchical, part-whole compositional structure of natural data,
+that Sparsey also possesses hallmarks 2 and 4.
+",0,0,0,0,1,0
+16472,Efficient implementations of the modified Gram-Schmidt orthogonalization with a non-standard inner product,"  The modified Gram-Schmidt (MGS) orthogonalization is one of the most
+well-used algorithms for computing the thin QR factorization. MGS can be
+straightforwardly extended to a non-standard inner product with respect to a
+symmetric positive definite matrix $A$. For the thin QR factorization of an $m
+\times n$ matrix with the non-standard inner product, a naive implementation of
+MGS requires $2n$ matrix-vector multiplications (MV) with respect to $A$. In
+this paper, we propose $n$-MV implementations: a high accuracy (HA) type and a
+high performance (HP) type, of MGS. We also provide error bounds of the HA-type
+implementation. Numerical experiments and analysis indicate that the proposed
+implementations have competitive advantages over the naive implementation in
+terms of both computational cost and accuracy.
+",0,0,1,0,0,0
+16473,"On the isoperimetric constant, covariance inequalities and $L_p$-Poincaré inequalities in dimension one","  Firstly, we derive in dimension one a new covariance inequality of
+$L_{1}-L_{\infty}$ type that characterizes the isoperimetric constant as the
+best constant achieving the inequality. Secondly, we generalize our result to
+$L_{p}-L_{q}$ bounds for the covariance. Consequently, we recover Cheeger's
+inequality without using the co-area formula. We also prove a generalized
+weighted Hardy type inequality that is needed to derive our covariance
+inequalities and that is of independent interest. Finally, we explore some
+consequences of our covariance inequalities for $L_{p}$-Poincaré
+inequalities and moment bounds. In particular, we obtain optimal constants in
+general $L_{p}$-Poincaré inequalities for measures with finite
+isoperimetric constant, thus generalizing in dimension one Cheeger's
+inequality, which is a $L_{p}$-Poincaré inequality for $p=2$, to any real
+$p\geq 1$.
+",0,0,1,1,0,0
+16474,Faster arbitrary-precision dot product and matrix multiplication,"  We present algorithms for real and complex dot product and matrix
+multiplication in arbitrary-precision floating-point and ball arithmetic. A
+low-overhead dot product is implemented on the level of GMP limb arrays; it is
+about twice as fast as previous code in MPFR and Arb at precision up to several
+hundred bits. Up to 128 bits, it is 3-4 times as fast, costing 20-30 cycles per
+term for floating-point evaluation and 40-50 cycles per term for balls. We
+handle large matrix multiplications even more efficiently via blocks of scaled
+integer matrices. The new methods are implemented in Arb and significantly
+speed up polynomial operations and linear algebra.
+",1,0,0,0,0,0
+16475,Improving Distributed Representations of Tweets - Present and Future,"  Unsupervised representation learning for tweets is an important research
+field which helps in solving several business applications such as sentiment
+analysis, hashtag prediction, paraphrase detection and microblog ranking. A
+good tweet representation learning model must handle the idiosyncratic nature
+of tweets which poses several challenges such as short length, informal words,
+unusual grammar and misspellings. However, there is a lack of prior work which
+surveys the representation learning models with a focus on tweets. In this
+work, we organize the models based on its objective function which aids the
+understanding of the literature. We also provide interesting future directions,
+which we believe are fruitful in advancing this field by building high-quality
+tweet representation learning models.
+",1,0,0,0,0,0
+16476,Tverberg-type theorems for matroids: A counterexample and a proof,"  Bárány, Kalai, and Meshulam recently obtained a topological Tverberg-type
+theorem for matroids, which guarantees multiple coincidences for continuous
+maps from a matroid complex to d-dimensional Euclidean space, if the matroid
+has sufficiently many disjoint bases. They make a conjecture on the
+connectivity of k-fold deleted joins of a matroid with many disjoint bases,
+which would yield a much tighter result - but we provide a counterexample
+already for the case of k=2, where a tight Tverberg-type theorem would be a
+topological Radon theorem for matroids. Nevertheless, we prove the topological
+Radon theorem for the counterexample family of matroids by an index
+calculation, despite the failure of the connectivity-based approach.
+",0,0,1,0,0,0
+16477,Near-Perfect Conversion of a Propagating Plane Wave into a Surface Wave Using Metasurfaces,"  In this paper, theoretical and numerical studies of perfect/nearly-perfect
+conversion of a plane wave into a surface wave are presented. The problem of
+determining the electromagnetic properties of an inhomogeneous lossless
+boundary which would fully transform an incident plane wave into a surface wave
+propagating along the boundary is considered. An approximate field solution
+which produces a slowly growing surface wave and satisfies the energy
+conservation law is discussed and numerically demonstrated. The results of the
+study are of great importance for the future development of such devices as
+perfect leaky-wave antennas and can potentially lead to many novel
+applications.
+",0,1,0,0,0,0
+16478,A National Research Agenda for Intelligent Infrastructure,"  Our infrastructure touches the day-to-day life of each of our fellow
+citizens, and its capabilities, integrity and sustainability are crucial to the
+overall competitiveness and prosperity of our country. Unfortunately, the
+current state of U.S. infrastructure is not good: the American Society of Civil
+Engineers' latest report on America's infrastructure ranked it at a D+ -- in
+need of $3.9 trillion in new investments. This dire situation constrains the
+growth of our economy, threatens our quality of life, and puts our global
+leadership at risk. The ASCE report called out three actions that need to be
+taken to address our infrastructure problem: 1) investment and planning in the
+system; 2) bold leadership by elected officials at the local and federal state;
+and 3) planning sustainability and resiliency in our infrastructure.
+While our immediate infrastructure needs are critical, it would be
+shortsighted to simply replicate more of what we have today. By doing so, we
+miss the opportunity to create Intelligent Infrastructure that will provide the
+foundation for increased safety and resilience, improved efficiencies and civic
+services, and broader economic opportunities and job growth. Indeed, our
+challenge is to proactively engage the declining, incumbent national
+infrastructure system and not merely repair it, but to enhance it; to create an
+internationally competitive cyber-physical system that provides an immediate
+opportunity for better services for citizens and that acts as a platform for a
+21st century, high-tech economy and beyond.
+",1,0,0,0,0,0
+16479,Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations,"  Surveying 3D scenes is a common task in robotics. Systems can do so
+autonomously by iteratively obtaining measurements. This process of planning
+observations to improve the model of a scene is called Next Best View (NBV)
+planning.
+NBV planning approaches often use either volumetric (e.g., voxel grids) or
+surface (e.g., triangulated meshes) representations. Volumetric approaches
+generalise well between scenes as they do not depend on surface geometry but do
+not scale to high-resolution models of large scenes. Surface representations
+can obtain high-resolution models at any scale but often require tuning of
+unintuitive parameters or multiple survey stages.
+This paper presents a scene-model-free NBV planning approach with a density
+representation. The Surface Edge Explorer (SEE) uses the density of current
+measurements to detect and explore observed surface boundaries. This approach
+is shown experimentally to provide better surface coverage in lower computation
+time than the evaluated state-of-the-art volumetric approaches while moving
+equivalent distances.
+",1,0,0,0,0,0
+16480,"N/O abundance ratios in gamma-ray burst and supernova host galaxies at z<4. Comparison with AGN, starburst and HII regions","  The distribution of N/O abundance ratios calculated by the detailed modelling
+of different galaxy spectra at z<4 is investigated. Supernova (SN) and long
+gamma-ray-burst (LGRB) host galaxies cover different redshift domains. N/O in
+SN hosts increases due to secondary N production towards low z (0.01)
+accompanying the growing trend of active galaxies (AGN, LINER). N/O in LGRB
+hosts decreases rapidly between z>1 and z ~0.1 following the N/H trend and
+reach the characteristic N/O ratios calculated for the HII regions in local and
+nearby galaxies. The few short period GRB (SGRB) hosts included in the galaxy
+sample show N/H <0.04 solar and O/H solar. They seem to continue the low bound
+N/H trend of SN hosts at z<0.3. The distribution of N/O as function of
+metallicity for SN and LGRB hosts is compared with star chemical evolution
+models. The results show that several LGRB hosts can be explained by star
+multi-bursting models when 12+log(O/H) <8.5, while some objects follow the
+trend of continuous star formation models. N/O in SN hosts at log(O/H)+12 <8.5
+are not well explained by stellar chemical evolution models calculated for
+starburst galaxies. At 12+log(O/H) >8.5 many different objects are nested close
+to O/H solar with N/O ranging between the maximum corresponding to starburst
+galaxies and AGN and the minimum corresponding to HII regions and SGRB.
+",0,1,0,0,0,0
+16481,Baselines and a datasheet for the Cerema AWP dataset,"  This paper presents the recently published Cerema AWP (Adverse Weather
+Pedestrian) dataset for various machine learning tasks and its exports in
+machine learning friendly format. We explain why this dataset can be
+interesting (mainly because it is a greatly controlled and fully annotated
+image dataset) and present baseline results for various tasks. Moreover, we
+decided to follow the very recent suggestions of datasheets for dataset, trying
+to standardize all the available information of the dataset, with a
+transparency objective.
+",0,0,0,1,0,0
+16482,Characterizing Directed and Undirected Networks via Multidimensional Walks with Jumps,"  Estimating distributions of node characteristics (labels) such as number of
+connections or citizenship of users in a social network via edge and node
+sampling is a vital part of the study of complex networks. Due to its low cost,
+sampling via a random walk (RW) has been proposed as an attractive solution to
+this task. Most RW methods assume either that the network is undirected or that
+walkers can traverse edges regardless of their direction. Some RW methods have
+been designed for directed networks where edges coming into a node are not
+directly observable. In this work, we propose Directed Unbiased Frontier
+Sampling (DUFS), a sampling method based on a large number of coordinated
+walkers, each starting from a node chosen uniformly at random. It is applicable
+to directed networks with invisible incoming edges because it constructs, in
+real-time, an undirected graph consistent with the walkers trajectories, and
+due to the use of random jumps which prevent walkers from being trapped. DUFS
+generalizes previous RW methods and is suited for undirected networks and to
+directed networks regardless of in-edges visibility. We also propose an
+improved estimator of node label distributions that combines information from
+the initial walker locations with subsequent RW observations. We evaluate DUFS,
+compare it to other RW methods, investigate the impact of its parameters on
+estimation accuracy and provide practical guidelines for choosing them. In
+estimating out-degree distributions, DUFS yields significantly better estimates
+of the head of the distribution than other methods, while matching or exceeding
+estimation accuracy of the tail. Last, we show that DUFS outperforms uniform
+node sampling when estimating distributions of node labels of the top 10%
+largest degree nodes, even when sampling a node uniformly has the same cost as
+RW steps.
+",1,1,0,0,0,0
+16483,Tensorial Recurrent Neural Networks for Longitudinal Data Analysis,"  Traditional Recurrent Neural Networks assume vectorized data as inputs.
+However many data from modern science and technology come in certain structures
+such as tensorial time series data. To apply the recurrent neural networks for
+this type of data, a vectorisation process is necessary, while such a
+vectorisation leads to the loss of the precise information of the spatial or
+longitudinal dimensions. In addition, such a vectorized data is not an optimum
+solution for learning the representation of the longitudinal data. In this
+paper, we propose a new variant of tensorial neural networks which directly
+take tensorial time series data as inputs. We call this new variant as
+Tensorial Recurrent Neural Network (TRNN). The proposed TRNN is based on tensor
+Tucker decomposition.
+",1,0,0,1,0,0
+16484,End-to-End Learning for the Deep Multivariate Probit Model,"  The multivariate probit model (MVP) is a popular classic model for studying
+binary responses of multiple entities. Nevertheless, the computational
+challenge of learning the MVP model, given that its likelihood involves
+integrating over a multidimensional constrained space of latent variables,
+significantly limits its application in practice. We propose a flexible deep
+generalization of the classic MVP, the Deep Multivariate Probit Model (DMVP),
+which is an end-to-end learning scheme that uses an efficient parallel sampling
+process of the multivariate probit model to exploit GPU-boosted deep neural
+networks. We present both theoretical and empirical analysis of the convergence
+behavior of DMVP's sampling process with respect to the resolution of the
+correlation structure. We provide convergence guarantees for DMVP and our
+empirical analysis demonstrates the advantages of DMVP's sampling compared with
+standard MCMC-based methods. We also show that when applied to multi-entity
+modelling problems, which are natural DMVP applications, DMVP trains faster
+than classical MVP, by at least an order of magnitude, captures rich
+correlations among entities, and further improves the joint likelihood of
+entities compared with several competitive models.
+",0,0,0,1,0,0
+16485,Purity and separation for oriented matroids,"  Leclerc and Zelevinsky, motivated by the study of quasi-commuting quantum
+flag minors, introduced the notions of strongly separated and weakly separated
+collections. These notions are closely related to the theory of cluster
+algebras, to the combinatorics of the double Bruhat cells, and to the totally
+positive Grassmannian.
+A key feature, called the purity phenomenon, is that every maximal by
+inclusion strongly (resp., weakly) separated collection of subsets in $[n]$ has
+the same cardinality.
+In this paper, we extend these notions and define $\mathcal{M}$-separated
+collections, for any oriented matroid $\mathcal{M}$.
+We show that maximal by size $\mathcal{M}$-separated collections are in
+bijection with fine zonotopal tilings (if $\mathcal{M}$ is a realizable
+oriented matroid), or with one-element liftings of $\mathcal{M}$ in general
+position (for an arbitrary oriented matroid).
+We introduce the class of pure oriented matroids for which the purity
+phenomenon holds: an oriented matroid $\mathcal{M}$ is pure if
+$\mathcal{M}$-separated collections form a pure simplicial complex, i.e., any
+maximal by inclusion $\mathcal{M}$-separated collection is also maximal by
+size.
+We pay closer attention to several special classes of oriented matroids:
+oriented matroids of rank $3$, graphical oriented matroids, and uniform
+oriented matroids. We classify pure oriented matroids in these cases. An
+oriented matroid of rank $3$ is pure if and only if it is a positroid (up to
+reorienting and relabeling its ground set). A graphical oriented matroid is
+pure if and only if its underlying graph is an outerplanar graph, that is, a
+subgraph of a triangulation of an $n$-gon.
+We give a simple conjectural characterization of pure oriented matroids by
+forbidden minors and prove it for the above classes of matroids (rank $3$,
+graphical, uniform).
+",0,0,1,0,0,0
+16486,Random Manifolds have no Totally Geodesic Submanifolds,"  For $n\geq 4$ we show that generic closed Riemannian $n$-manifolds have no
+nontrivial totally geodesic submanifolds, answering a question of Spivak. An
+immediate consequence is a severe restriction on the isometry group of a
+generic Riemannian metric. Both results are widely believed to be true, but we
+are not aware of any proofs in the literature.
+",0,0,1,0,0,0
+16487,Kinematically Redundant Octahedral Motion Platform for Virtual Reality Simulations,"  We propose a novel design of a parallel manipulator of Stewart Gough type for
+virtual reality application of single individuals; i.e. an omni-directional
+treadmill is mounted on the motion platform in order to improve VR immersion by
+giving feedback to the human body. For this purpose we modify the well-known
+octahedral manipulator in a way that it has one degree of kinematical
+redundancy; namely an equiform reconfigurability of the base. The instantaneous
+kinematics and singularities of this mechanism are studied, where especially
+""unavoidable singularities"" are characterized. These are poses of the motion
+platform, which can only be realized by singular configurations of the
+mechanism despite its kinematic redundancy.
+",1,0,0,0,0,0
+16488,Loss Max-Pooling for Semantic Image Segmentation,"  We introduce a novel loss max-pooling concept for handling imbalanced
+training data distributions, applicable as alternative loss layer in the
+context of deep neural networks for semantic image segmentation. Most
+real-world semantic segmentation datasets exhibit long tail distributions with
+few object categories comprising the majority of data and consequently biasing
+the classifiers towards them. Our method adaptively re-weights the
+contributions of each pixel based on their observed losses, targeting
+under-performing classification results as often encountered for
+under-represented object classes. Our approach goes beyond conventional
+cost-sensitive learning attempts through adaptive considerations that allow us
+to indirectly address both, inter- and intra-class imbalances. We provide a
+theoretical justification of our approach, complementary to experimental
+analyses on benchmark datasets. In our experiments on the Cityscapes and Pascal
+VOC 2012 segmentation datasets we find consistently improved results,
+demonstrating the efficacy of our approach.
+",1,0,0,1,0,0
+16489,Nonlinear Traveling Internal Waves in Depth-Varying Currents,"  In this work, we study the nonlinear traveling waves in density stratified
+fluids with depth varying shear currents. Beginning the formulation of the
+water-wave problem due to [1], we extend the work of [4] and [18] to examine
+the interface between two fluids of differing densities and varying linear
+shear. We derive as systems of equations depending only on variables at the
+interface, and numerically solve for periodic traveling wave solutions using
+numerical continuation. Here we consider only branches which bifurcate from
+solutions where there is no slip in the tangential velocity at the interface
+for the trivial flow. The spectral stability of these solutions is then
+determined using a numerical Fourier-Floquet technique. We find that the
+strength of the linear shear in each fluid impacts the stability of the
+corresponding traveling wave solutions. Specifically, opposing shears may
+amplify or suppress instabilities.
+",0,1,0,0,0,0
+16490,Instabilities of Internal Gravity Wave Beams,"  Internal gravity waves play a primary role in geophysical fluids: they
+contribute significantly to mixing in the ocean and they redistribute energy
+and momentum in the middle atmosphere. Until recently, most studies were
+focused on plane wave solutions. However, these solutions are not a
+satisfactory description of most geophysical manifestations of internal gravity
+waves, and it is now recognized that internal wave beams with a confined
+profile are ubiquitous in the geophysical context.
+We will discuss the reason for the ubiquity of wave beams in stratified
+fluids, related to the fact that they are solutions of the nonlinear governing
+equations. We will focus more specifically on situations with a constant
+buoyancy frequency. Moreover, in light of recent experimental and analytical
+studies of internal gravity beams, it is timely to discuss the two main
+mechanisms of instability for those beams. i) The Triadic Resonant Instability
+generating two secondary wave beams. ii) The streaming instability
+corresponding to the spontaneous generation of a mean flow.
+",0,1,0,0,0,0
+16491,Multiphoton-Excited Fluorescence of Silicon-Vacancy Color Centers in Diamond,"  Silicon-vacancy color centers in nanodiamonds are promising as fluorescent
+labels for biological applications, with a narrow, non-bleaching emission line
+at 738\,nm. Two-photon excitation of this fluorescence offers the possibility
+of low-background detection at significant tissue depth with high
+three-dimensional spatial resolution. We have measured the two-photon
+fluorescence cross section of a negatively-charged silicon vacancy (SiV$^-$) in
+ion-implanted bulk diamond to be $0.74(19) \times 10^{-50}{\rm cm^4\;s/photon}$
+at an excitation wavelength of 1040\,nm. In comparison to the diamond nitrogen
+vacancy (NV) center, the expected detection threshold of a two-photon excited
+SiV center is more than an order of magnitude lower, largely due to its much
+narrower linewidth. We also present measurements of two- and three-photon
+excitation spectra, finding an increase in the two-photon cross section with
+decreasing wavelength, and discuss the physical interpretation of the spectra
+in the context of existing models of the SiV energy-level structure.
+",0,1,0,0,0,0
+16492,Exploring Latent Semantic Factors to Find Useful Product Reviews,"  Online reviews provided by consumers are a valuable asset for e-Commerce
+platforms, influencing potential consumers in making purchasing decisions.
+However, these reviews are of varying quality, with the useful ones buried deep
+within a heap of non-informative reviews. In this work, we attempt to
+automatically identify review quality in terms of its helpfulness to the end
+consumers. In contrast to previous works in this domain exploiting a variety of
+syntactic and community-level features, we delve deep into the semantics of
+reviews as to what makes them useful, providing interpretable explanation for
+the same. We identify a set of consistency and semantic factors, all from the
+text, ratings, and timestamps of user-generated reviews, making our approach
+generalizable across all communities and domains. We explore review semantics
+in terms of several latent factors like the expertise of its author, his
+judgment about the fine-grained facets of the underlying product, and his
+writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet
+Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii)
+item facets, and (iii) review helpfulness. Large-scale experiments on five
+real-world datasets from Amazon show significant improvement over
+state-of-the-art baselines in predicting and ranking useful reviews.
+",1,0,0,1,0,0
+16493,Thermodynamically-consistent semi-classical $\ell$-changing rates,"  We compare the results of the semi-classical (SC) and quantum-mechanical (QM)
+formalisms for angular-momentum changing transitions in Rydberg atom collisions
+given by Vrinceanu & Flannery, J. Phys. B 34, L1 (2001), and Vrinceanu, Onofrio
+& Sadeghpour, ApJ 747, 56 (2012), with those of the SC formalism using a
+modified Monte Carlo realization. We find that this revised SC formalism agrees
+well with the QM results. This provides further evidence that the rates derived
+from the QM treatment are appropriate to be used when modelling recombination
+through Rydberg cascades, an important process in understanding the state of
+material in the early universe. The rates for $\Delta\ell=\pm1$ derived from
+the QM formalism diverge when integrated to sufficiently large impact
+parameter, $b$. Further to the empirical limits to the $b$ integration
+suggested by Pengelly & Seaton, MNRAS 127, 165 (1964), we suggest that the
+fundamental issue causing this divergence in the theory is that it does not
+fully cater for the finite time taken for such distant collisions to complete.
+",0,1,0,0,0,0
+16494,A Fourier transform for the quantum Toda lattice,"  We introduce an algebraic Fourier transform for the quantum Toda lattice.
+",0,0,1,0,0,0
+16495,Semi-supervised learning,"  Semi-supervised learning deals with the problem of how, if possible, to take
+advantage of a huge amount of not classified data, to perform classification,
+in situations when, typically, the labelled data are few. Even though this is
+not always possible (it depends on how useful is to know the distribution of
+the unlabelled data in the inference of the labels), several algorithm have
+been proposed recently. A new algorithm is proposed, that under almost
+neccesary conditions, attains asymptotically the performance of the best
+theoretical rule, when the size of unlabeled data tends to infinity. The set of
+necessary assumptions, although reasonables, show that semi-parametric
+classification only works for very well conditioned problems.
+",0,0,1,1,0,0
+16496,The Cosmic-Ray Neutron Rover - Mobile Surveys of Field Soil Moisture and the Influence of Roads,"  Measurements of root-zone soil moisture across spatial scales of tens to
+thousands of meters have been a challenge for many decades. The mobile
+application of Cosmic-Ray Neutron Sensing (CRNS) is a promising approach to
+measure field soil moisture non-invasively by surveying large regions with a
+ground-based vehicle. Recently, concerns have been raised about a potentially
+biasing influence of local structures and roads. We employed neutron transport
+simulations and dedicated experiments to quantify the influence of different
+road types on the CRNS measurement. We found that the presence of roads
+introduces a bias in the CRNS estimation of field soil moisture compared to
+non-road scenarios. However, this effect becomes insignificant at distances
+beyond a few meters from the road. Measurements from the road could
+overestimate the field value by up to 40 % depending on road material, width,
+and the surrounding field water content. The bias could be successfully removed
+with an analytical correction function that accounts for these parameters.
+Additionally, an empirical approach is proposed that can be used on-the-fly
+without prior knowledge of field soil moisture. Tests at different study sites
+demonstrated good agreement between road-effect corrected measurements and
+field soil moisture observations. However, if knowledge about the road
+characteristics is missing, any measurements on the road could substantially
+reduce the accuracy of this method. Our results constitute a practical
+advancement of the mobile CRNS methodology, which is important for providing
+unbiased estimates of field-scale soil moisture to support applications in
+hydrology, remote sensing, and agriculture.
+",0,1,0,0,0,0
+16497,Invariance in Constrained Switching,"  We study discrete time linear constrained switching systems with additive
+disturbances, in which the switching may be on the system matrices, the
+disturbance sets, the state constraint sets or a combination of the above. In
+our general setting, a switching sequence is admissible if it is accepted by an
+automaton. For this family of systems, stability does not necessarily imply the
+existence of an invariant set. Nevertheless, it does imply the existence of an
+invariant multi-set, which is a relaxation of invariance and the object of our
+work. First, we establish basic results concerning the characterization,
+approximation and computation of the minimal and the maximal admissible
+invariant multi-set. Second, by exploiting the topological properties of the
+directed graph which defines the switching constraints, we propose invariant
+multi-set constructions with several benefits. We illustrate our results in
+benchmark problems in control.
+",1,0,1,0,0,0
+16498,On the choice of the low-dimensional domain for global optimization via random embeddings,"  The challenge of taking many variables into account in optimization problems
+may be overcome under the hypothesis of low effective dimensionality. Then, the
+search of solutions can be reduced to the random embedding of a low dimensional
+space into the original one, resulting in a more manageable optimization
+problem. Specifically, in the case of time consuming black-box functions and
+when the budget of evaluations is severely limited, global optimization with
+random embeddings appears as a sound alternative to random search. Yet, in the
+case of box constraints on the native variables, defining suitable bounds on a
+low dimensional domain appears to be complex. Indeed, a small search domain
+does not guarantee to find a solution even under restrictive hypotheses about
+the function, while a larger one may slow down convergence dramatically. Here
+we tackle the issue of low-dimensional domain selection based on a detailed
+study of the properties of the random embedding, giving insight on the
+aforementioned difficulties. In particular, we describe a minimal
+low-dimensional set in correspondence with the embedded search space. We
+additionally show that an alternative equivalent embedding procedure yields
+simultaneously a simpler definition of the low-dimensional minimal set and
+better properties in practice. Finally, the performance and robustness gains of
+the proposed enhancements for Bayesian optimization are illustrated on
+numerical examples.
+",0,0,1,1,0,0
+16499,"Distributed resource allocation through utility design - Part II: applications to submodular, supermodular and set covering problems","  A fundamental component of the game theoretic approach to distributed control
+is the design of local utility functions. In Part I of this work we showed how
+to systematically design local utilities so as to maximize the induced worst
+case performance. The purpose of the present manuscript is to specialize the
+general results obtained in Part I to a class of monotone submodular,
+supermodular and set covering problems. In the case of set covering problems,
+we show how any distributed algorithm capable of computing a Nash equilibrium
+inherits a performance certificate matching the well known 1-1/e approximation
+of Nemhauser. Relative to the class of submodular maximization problems
+considered here, we show how the performance offered by the game theoretic
+approach improves on existing approximation algorithms. We briefly discuss the
+algorithmic complexity of computing (pure) Nash equilibria and show how our
+approach generalizes and subsumes previously fragmented results in the area of
+optimal utility design. Two applications and corresponding numerics are
+presented: the vehicle target assignment problem and a coverage problem arising
+in distributed caching for wireless networks.
+",1,0,0,0,0,0
+16500,Quasiparticle band structure engineering in van der Waals heterostructures via dielectric screening,"  The idea of combining different two-dimensional (2D) crystals in van der
+Waals heterostructures (vdWHs) has led to a new paradigm for band structure
+engineering with atomic precision. Due to the weak interlayer couplings, the
+band structures of the individual 2D crystals are largely preserved upon
+formation of the heterostructure. However, regardless of the details of the
+interlayer hybridisation, the size of the 2D crystal band gaps are always
+reduced due to the enhanced dielectric screening provided by the surrounding
+layers. The effect can be on the order of electron volts, but its precise
+magnitude is non-trivial to predict because of the non-local nature of the
+screening in quasi-2D materials, and it is not captured by effective
+single-particle methods such as density functional theory. Here we present an
+efficient and general method for calculating the band gap renormalization of a
+2D material embedded in an arbitrary vdWH. The method evaluates the change in
+the GW self-energy of the 2D material from the change in the screened Coulomb
+interaction. The latter is obtained using the quantum-electrostatic
+heterostructure (QEH) model. We benchmark the G$\Delta$W method against full
+first-principles GW calculations and use it to unravel the importance of
+screening-induced band structure renormalisation in various vdWHs. A main
+result is the observation that the size of the band gap reduction of a given 2D
+material when inserted into a heterostructure scales inversely with the
+polarisability of the 2D material. Our work demonstrates that dielectric
+engineering \emph{via} van der Waals heterostructuring represents a promising
+strategy for tailoring the band structure of 2D materials.
+",0,1,0,0,0,0
+16501,Special Lagrangian submanifolds and cohomogeneity one actions on the complex projective space,"  We construct examples of cohomogeneity one special Lagrangian submanifolds in
+the cotangent bundle over the complex projective space, whose Calabi-Yau
+structure was given by Stenzel. For each example, we describe the condition of
+special Lagrangian as an ordinary differential equation. Our method is based on
+a moment map technique and the classification of cohomogeneity one actions on
+the complex projective space classified by Takagi.
+",0,0,1,0,0,0
+16502,Linear Regression with Sparsely Permuted Data,"  In regression analysis of multivariate data, it is tacitly assumed that
+response and predictor variables in each observed response-predictor pair
+correspond to the same entity or unit. In this paper, we consider the situation
+of ""permuted data"" in which this basic correspondence has been lost. Several
+recent papers have considered this situation without further assumptions on the
+underlying permutation. In applications, the latter is often to known to have
+additional structure that can be leveraged. Specifically, we herein consider
+the common scenario of ""sparsely permuted data"" in which only a small fraction
+of the data is affected by a mismatch between response and predictors. However,
+an adverse effect already observed for sparsely permuted data is that the least
+squares estimator as well as other estimators not accounting for such partial
+mismatch are inconsistent. One approach studied in detail herein is to treat
+permuted data as outliers which motivates the use of robust regression
+formulations to estimate the regression parameter. The resulting estimate can
+subsequently be used to recover the permutation. A notable benefit of the
+proposed approach is its computational simplicity given the general lack of
+procedures for the above problem that are both statistically sound and
+computationally appealing.
+",0,0,1,1,0,0
+16503,Matrix Scaling and Balancing via Box Constrained Newton's Method and Interior Point Methods,"  In this paper, we study matrix scaling and balancing, which are fundamental
+problems in scientific computing, with a long line of work on them that dates
+back to the 1960s. We provide algorithms for both these problems that, ignoring
+logarithmic factors involving the dimension of the input matrix and the size of
+its entries, both run in time $\widetilde{O}\left(m\log \kappa \log^2
+(1/\epsilon)\right)$ where $\epsilon$ is the amount of error we are willing to
+tolerate. Here, $\kappa$ represents the ratio between the largest and the
+smallest entries of the optimal scalings. This implies that our algorithms run
+in nearly-linear time whenever $\kappa$ is quasi-polynomial, which includes, in
+particular, the case of strictly positive matrices. We complement our results
+by providing a separate algorithm that uses an interior-point method and runs
+in time $\widetilde{O}(m^{3/2} \log (1/\epsilon))$.
+In order to establish these results, we develop a new second-order
+optimization framework that enables us to treat both problems in a unified and
+principled manner. This framework identifies a certain generalization of linear
+system solving that we can use to efficiently minimize a broad class of
+functions, which we call second-order robust. We then show that in the context
+of the specific functions capturing matrix scaling and balancing, we can
+leverage and generalize the work on Laplacian system solving to make the
+algorithms obtained via this framework very efficient.
+",1,0,0,0,0,0
+16504,"AP17-OLR Challenge: Data, Plan, and Baseline","  We present the data profile and the evaluation plan of the second oriental
+language recognition (OLR) challenge AP17-OLR. Compared to the event last year
+(AP16-OLR), the new challenge involves more languages and focuses more on short
+utterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two
+types of baselines are constructed to assist the participants, one is based on
+the i-vector model and the other is based on various neural networks. We report
+the baseline results evaluated with various metrics defined by the AP17-OLR
+evaluation plan and demonstrate that the combined database is a reasonable data
+resource for multilingual research. All the data is free for participants, and
+the Kaldi recipes for the baselines have been published online.
+",1,0,0,0,0,0
+16505,First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies,"  The IllustrisTNG project is a new suite of cosmological
+magneto-hydrodynamical simulations of galaxy formation performed with the Arepo
+code and updated models for feedback physics. Here we introduce the first two
+simulations of the series, TNG100 and TNG300, and quantify the stellar mass
+content of about 4000 massive galaxy groups and clusters ($10^{13} \leq M_{\rm
+200c}/M_{\rm sun} \leq 10^{15}$) at recent times ($z \leq 1$). The richest
+clusters have half of their total stellar mass bound to satellite galaxies,
+with the other half being associated with the central galaxy and the diffuse
+intra-cluster light. The exact ICL fraction depends sensitively on the
+definition of a central galaxy's mass and varies in our most massive clusters
+between 20 to 40% of the total stellar mass. Haloes of $5\times 10^{14}M_{\rm
+sun}$ and above have more diffuse stellar mass outside 100 kpc than within 100
+kpc, with power-law slopes of the radial mass density distribution as shallow
+as the dark matter's ( $-3.5 < \alpha_{\rm 3D} < -3$). Total halo mass is a
+very good predictor of stellar mass, and vice versa: at $z=0$, the 3D stellar
+mass measured within 30 kpc scales as $\propto (M_{\rm 500c})^{0.49}$ with a
+$\sim 0.12$ dex scatter. This is possibly too steep in comparison to the
+available observational constraints, even though the abundance of TNG less
+massive galaxies ($< 10^{11}M_{\rm sun}$ in stars) is in good agreement with
+the measured galaxy stellar mass functions at recent epochs. The 3D sizes of
+massive galaxies fall too on a tight ($\sim$0.16 dex scatter) power-law
+relation with halo mass, with $r^{\rm stars}_{\rm 0.5} \propto (M_{\rm
+500c})^{0.53}$. Even more fundamentally, halo mass alone is a good predictor
+for the whole stellar mass profiles beyond the inner few kpc, and we show how
+on average these can be precisely recovered given a single mass measurement of
+the galaxy or its halo.
+",0,1,0,0,0,0
+16506,A Fast Implementation of Singular Value Thresholding Algorithm using Recycling Rank Revealing Randomized Singular Value Decomposition,"  In this paper, we present a fast implementation of the Singular Value
+Thresholding (SVT) algorithm for matrix completion. A rank-revealing randomized
+singular value decomposition (R3SVD) algorithm is used to adaptively carry out
+partial singular value decomposition (SVD) to fast approximate the SVT operator
+given a desired, fixed precision. We extend the R3SVD algorithm to a recycling
+rank revealing randomized singular value decomposition (R4SVD) algorithm by
+reusing the left singular vectors obtained from the previous iteration as the
+approximate basis in the current iteration, where the computational cost for
+partial SVD at each SVT iteration is significantly reduced. A simulated
+annealing style cooling mechanism is employed to adaptively adjust the low-rank
+approximation precision threshold as SVT progresses. Our fast SVT
+implementation is effective in both large and small matrices, which is
+demonstrated in matrix completion applications including image recovery and
+movie recommendation system.
+",1,0,0,0,0,0
+16507,Surface magnetism of gallium arsenide nanofilms,"  Gallium arsenide (GaAs) is the widest used second generation semiconductor
+with a direct band gap and increasingly used as nanofilms. However, the
+magnetic properties of GaAs nanofilms have never been studied. Here we find by
+comprehensive density functional theory calculations that GaAs nanofilms
+cleaved along the <111> and <100> directions become intrinsically metallic
+films with strong surface magnetism and magnetoelectric (ME) effect. The
+surface magnetism and electrical conductivity are realized via a combined
+effect of transferring charge induced by spontaneous electric-polarization
+through the film thickness and spin-polarized surface states. The surface
+magnetism of <111> nanofilms can be significantly and linearly tuned by
+vertically applied electric field, endowing the nanofilms unexpectedly high ME
+coefficients, which are tens of times higher than those of ferromagnetic metals
+and transition metal oxides.
+",0,1,0,0,0,0
+16508,"Monte Carlo study of the Coincidence Resolving Time of a liquid xenon PET scanner, using Cherenkov radiation","  In this paper we use detailed Monte Carlo simulations to demonstrate that
+liquid xenon (LXe) can be used to build a Cherenkov-based TOF-PET, with an
+intrinsic coincidence resolving time (CRT) in the vicinity of 10 ps. This
+extraordinary performance is due to three facts: a) the abundant emission of
+Cherenkov photons by liquid xenon; b) the fact that LXe is transparent to
+Cherenkov light; and c) the fact that the fastest photons in LXe have
+wavelengths higher than 300 nm, therefore making it possible to separate the
+detection of scintillation and Cherenkov light. The CRT in a Cherenkov LXe
+TOF-PET detector is, therefore, dominated by the resolution (time jitter)
+introduced by the photosensors and the electronics. However, we show that for
+sufficiently fast photosensors (e.g, an overall 40 ps jitter, which can be
+achieved by current micro-channel plate photomultipliers) the overall CRT
+varies between 30 and 55 ps, depending of the detection efficiency. This is
+still one order of magnitude better than commercial CRT devices and improves by
+a factor 3 the best CRT obtained with small laboratory prototypes.
+",0,1,0,0,0,0
+16509,Detecting Hierarchical Ties Using Link-Analysis Ranking at Different Levels of Time Granularity,"  Social networks contain implicit knowledge that can be used to infer
+hierarchical relations that are not explicitly present in the available data.
+Interaction patterns are typically affected by users' social relations. We
+present an approach to inferring such information that applies a link-analysis
+ranking algorithm at different levels of time granularity. In addition, a
+voting scheme is employed for obtaining the hierarchical relations. The
+approach is evaluated on two datasets: the Enron email data set, where the goal
+is to infer manager-subordinate relationships, and the Co-author data set,
+where the goal is to infer PhD advisor-advisee relations. The experimental
+results indicate that the proposed approach outperforms more traditional
+approaches to inferring hierarchical relations from social networks.
+",1,1,0,0,0,0
+16510,Optimal stimulation protocol in a bistable synaptic consolidation model,"  Consolidation of synaptic changes in response to neural activity is thought
+to be fundamental for memory maintenance over a timescale of hours. In
+experiments, synaptic consolidation can be induced by repeatedly stimulating
+presynaptic neurons. However, the effectiveness of such protocols depends
+crucially on the repetition frequency of the stimulations and the mechanisms
+that cause this complex dependence are unknown. Here we propose a simple
+mathematical model that allows us to systematically study the interaction
+between the stimulation protocol and synaptic consolidation. We show the
+existence of optimal stimulation protocols for our model and, similarly to LTP
+experiments, the repetition frequency of the stimulation plays a crucial role
+in achieving consolidation. Our results show that the complex dependence of LTP
+on the stimulation frequency emerges naturally from a model which satisfies
+only minimal bistability requirements.
+",0,0,0,0,1,0
+16511,Parametric Polynomial Preserving Recovery on Manifolds,"  This paper investigates gradient recovery schemes for data defined on
+discretized manifolds. The proposed method, parametric polynomial preserving
+recovery (PPPR), does not require the tangent spaces of the exact manifolds
+which have been assumed for some significant gradient recovery methods in the
+literature. Another advantage is that superconvergence is guaranteed for PPPR
+without the symmetric condition which has been asked in the existing
+techniques. There is also numerical evidence that the superconvergence by PPPR
+is high curvature stable, which distinguishes itself from the other methods. As
+an application, we show that its capability of constructing an asymptotically
+exact \textit{a posteriori} error estimator. Several numerical examples on
+two-dimensional surfaces are presented to support the theoretical results and
+make comparisons with state of the art methods.
+",0,0,1,0,0,0
+16512,Integral points on the complement of plane quartics,"  Let $Y$ be the complement of a plane quartic curve $D$ defined over a number
+field. Our main theorem confirms the Lang-Vojta conjecture for $Y$ when $D$ is
+a generic smooth quartic curve, by showing that its integral points are
+confined in a curve except for a finite number of exceptions. The required
+finiteness will be obtained by reducing it to the Shafarevich conjecture for K3
+surfaces. Some variants of our method confirm the same conjecture when $D$ is a
+reducible generic quartic curve which consists of four lines, two lines and a
+conic, or two conics.
+",0,0,1,0,0,0
+16513,Modelling collective motion based on the principle of agency,"  Collective motion is an intriguing phenomenon, especially considering that it
+arises from a set of simple rules governing local interactions between
+individuals. In theoretical models, these rules are normally \emph{assumed} to
+take a particular form, possibly constrained by heuristic arguments. We propose
+a new class of models, which describe the individuals as \emph{agents}, capable
+of deciding for themselves how to act and learning from their experiences. The
+local interaction rules do not need to be postulated in this model, since they
+\emph{emerge} from the learning process. We apply this ansatz to a concrete
+scenario involving marching locusts, in order to model the phenomenon of
+density-dependent alignment. We show that our learning agent-based model can
+account for a Fokker-Planck equation that describes the collective motion and,
+most notably, that the agents can learn the appropriate local interactions,
+requiring no strong previous assumptions on their form. These results suggest
+that learning agent-based models are a powerful tool for studying a broader
+class of problems involving collective motion and animal agency in general.
+",0,0,0,1,0,0
+16514,Cosmic Microwave Background constraints for global strings and global monopoles,"  We present the first CMB power spectra from numerical simulations of the
+global O(N) linear $\sigma$-model with N = 2,3, which have global strings and
+monopoles as topological defects. In order to compute the CMB power spectra we
+compute the unequal time correlators (UETCs) of the energy-momentum tensor,
+showing that they fall off at high wave number faster than naive estimates
+based on the geometry of the defects, indicating non-trivial
+(anti-)correlations between the defects and the surrounding Goldstone boson
+field. We obtain source functions for Einstein-Boltzmann solvers from the
+UETCs, using a recent method that improves the modelling at the radiation-
+matter transition. We show that the interpolation function that mimics the
+transition is similar to other defect models, but not identical, confirming the
+non-universality of the interpolation function. The CMB power spectra for
+global strings and monopoles have the same overall shape as those obtained
+using the non-linear $\sigma$-model approximation, which is well captured by a
+large-N calculation. However, the amplitudes are larger than the large-N
+calculation predict, and in the case of global strings much larger: a factor of
+20 at the peak. Finally we compare the CMB power spectra with the latest CMB
+data to put limits on the allowed contribution to the temperature power
+spectrum at multipole $\ell$ = 10 of 1.7% for global strings and 2.4% for
+global monopoles. These limits correspond to symmetry-breaking scales of
+2.9x1015 GeV (6.3x1014 GeV with the expected logarithmic scaling of the
+effective string tension between the simulation time and decoupling) and
+6.4x1015 GeV respectively. The bound on global strings is a significant one for
+the ultra-light axion scenario with axion masses ma 10-28 eV. These upper
+limits indicate that gravitational wave from global topological defects will
+not be observable at the GW observatory LISA.
+",0,1,0,0,0,0
+16515,A parallel implementation of the Synchronised Louvain method,"  Community detection in networks is a very actual and important field of
+research with applications in many areas. But, given that the amount of
+processed data increases more and more, existing algorithms need to be adapted
+for very large graphs. The objective of this project was to parallelise the
+Synchronised Louvain Method, a community detection algorithm developed by
+Arnaud Browet, in order to improve its performances in terms of computation
+time and thus be able to faster detect communities in very large graphs. To
+reach this goal, we used the API OpenMP to parallelise the algorithm and then
+carried out performance tests. We studied the computation time and speedup of
+the parallelised algorithm and were able to bring out some qualitative trends.
+We obtained a great speedup, compared with the theoretical prediction of Amdahl
+law. To conclude, using the parallel implementation of the algorithm of Browet
+on large graphs seems to give good results, both in terms of computation time
+and speedup. Further tests should be carried out in order to obtain more
+quantitative results.
+",1,0,0,0,0,0
+16516,Index coding with erroneous side information,"  In this paper, new index coding problems are studied, where each receiver has
+erroneous side information. Although side information is a crucial part of
+index coding, the existence of erroneous side information has not yet been
+considered. We study an index code with receivers that have erroneous side
+information symbols in the error-free broadcast channel, which is called an
+index code with side information errors (ICSIE). The encoding and decoding
+procedures of the ICSIE are proposed, based on the syndrome decoding. Then, we
+derive the bounds on the optimal codelength of the proposed index code with
+erroneous side information. Furthermore, we introduce a special graph for the
+proposed index coding problem, called a $\delta_s$-cycle whose properties are
+similar to those of the cycle in the conventional index coding problem.
+Properties of the ICSIE are also discussed in the $\delta_s$-cycle and clique.
+Finally, the proposed ICSIE is generalized to an index code for the scenario
+having both additive channel errors and side information errors, called a
+generalized error correcting index code (GECIC).
+",1,0,0,0,0,0
+16517,HATS-36b and 24 other transiting/eclipsing systems from the HATSouth - K2 Campaign 7 program,"  We report on the result of a campaign to monitor 25 HATSouth candidates using
+the K2 space telescope during Campaign 7 of the K2 mission. We discover
+HATS-36b (EPIC 215969174b), a hot Jupiter with a mass of 2.79$\pm$0.40 M$_J$
+and a radius of 1.263$\pm$0.045 R$_J$ which transits a solar-type G0V star
+(V=14.386) in a 4.1752d period. We also refine the properties of three
+previously discovered HATSouth transiting planets (HATS-9b, HATS-11b, and
+HATS-12b) and search the K2 data for TTVs and additional transiting planets in
+these systems. In addition we also report on a further three systems that
+remain as Jupiter-radius transiting exoplanet candidates. These candidates do
+not have determined masses, however pass all of our other vetting observations.
+Finally we report on the 18 candidates which we are now able to classify as
+eclipsing binary or blended eclipsing binary systems based on a combination of
+the HATSouth data, the K2 data, and follow-up ground-based photometry and
+spectroscopy. These range in periods from 0.7 days to 16.7 days, and down to
+1.5 mmag in eclipse depths. Our results show the power of combining
+ground-based imaging and spectroscopy with higher precision space-based
+photometry, and serve as an illustration as to what will be possible when
+combining ground-based observations with TESS data.
+",0,1,0,0,0,0
+16518,The Carnegie-Chicago Hubble Program: Discovery of the Most Distant Ultra-faint Dwarf Galaxy in the Local Universe,"  Ultra-faint dwarf galaxies (UFDs) are the faintest known galaxies and due to
+their incredibly low surface brightness, it is difficult to find them beyond
+the Local Group. We report a serendipitous discovery of an UFD, Fornax UFD1, in
+the outskirts of NGC 1316, a giant galaxy in the Fornax cluster. The new galaxy
+is located at a projected radius of 55 kpc in the south-east of NGC 1316. This
+UFD is found as a small group of resolved stars in the Hubble Space Telescope
+images of a halo field of NGC 1316, obtained as part of the Carnegie-Chicago
+Hubble Program. Resolved stars in this galaxy are consistent with being mostly
+metal-poor red giant branch (RGB) stars. Applying the tip of the RGB method to
+the mean magnitude of the two brightest RGB stars, we estimate the distance to
+this galaxy, 19.0 +- 1.3 Mpc. Fornax UFD1 is probably a member of the Fornax
+cluster. The color-magnitude diagram of these stars is matched by a 12 Gyr
+isochrone with low metallicity ([Fe/H] ~ -2.4). Total magnitude and effective
+radius of Fornax UFD1 are Mv ~ -7.6 +- 0.2 mag and r_eff = 146 +- 9 pc, which
+are similar to those of Virgo UFD1 that was discovered recently in the
+intracluster field of Virgo by Jang & Lee (2014).Fornax UFD1 is the most
+distant known UFD that is confirmed by resolved stars. This indicates that UFDs
+are ubiquitous and that more UFDs remain to be discovered in the Fornax
+cluster.
+",0,1,0,0,0,0
+16519,An Exploratory Study on the Implementation and Adoption of ERP Solutions for Businesses,"  Enterprise Resource Planning (ERP) systems have been covered in both
+mainstream Information Technology (IT) periodicals, and in academic literature,
+as a result of extensive adoption by organisations in the last two decades.
+Some of the past studies have reported operational efficiency and other gains,
+while other studies have pointed out the challenges. ERP systems continue to
+evolve, moving into the cloud hosted sphere, and being implemented by
+relatively smaller and regional companies. This project has carried out an
+exploratory study into the use of ERP systems, within Hawke's Bay New Zealand.
+ERP systems make up a major investment and undertaking by those companies.
+Therefore, research and lessons learned in this area are very important. In
+addition to a significant initial literature review, this project has conducted
+a survey on the local users' experience with Microsoft Dynamics NAV (a popular
+ERP brand). As a result, this study will contribute new and relevant
+information to the literature on business information systems and to ERP
+systems, in particular.
+",1,0,0,0,0,0
+16520,Selective inference for the problem of regions via multiscale bootstrap,"  A general approach to selective inference is considered for hypothesis
+testing of the null hypothesis represented as an arbitrary shaped region in the
+parameter space of multivariate normal model. This approach is useful for
+hierarchical clustering where confidence levels of clusters are calculated only
+for those appeared in the dendrogram, thus subject to heavy selection bias. Our
+computation is based on a raw confidence measure, called bootstrap probability,
+which is easily obtained by counting how many times the same cluster appears in
+bootstrap replicates of the dendrogram. We adjust the bias of the bootstrap
+probability by utilizing the scaling-law in terms of geometric quantities of
+the region in the abstract parameter space, namely, signed distance and mean
+curvature. Although this idea has been used for non-selective inference of
+hierarchical clustering, its selective inference version has not been discussed
+in the literature. Our bias-corrected $p$-values are asymptotically
+second-order accurate in the large sample theory of smooth boundary surfaces of
+regions, and they are also justified for nonsmooth surfaces such as polyhedral
+cones. The $p$-values are asymptotically equivalent to those of the iterated
+bootstrap but with less computation.
+",0,0,1,1,0,0
+16521,Arbitrary Beam Synthesis of Different Hybrid Beamforming Systems,"  For future mmWave mobile communication systems the use of analog/hybrid
+beamforming is envisioned be a key as- pect. The synthesis of beams is a key
+technology of enable the best possible operation during beamsearch, data
+transmission and MU MIMO operation. The developed method for synthesizing beams
+is based on previous work in radar technology considering only phase array
+antennas. With this technique it is possible to generate a desired beam of any
+shape with the constraints of the desired target transceiver antenna frontend.
+It is not constraint to a certain antenna array geometry, but can handle 1d, 2d
+and even 3d antenna array geometries like cylindric arrays. The numerical
+examples show that the method can synthesize beams by considering a user
+defined tradeoff between gain, transition width and passband ripples.
+",1,0,0,0,0,0
+16522,The emergence of the concept of filter in topological categories,"  In all approaches to convergence where the concept of filter is taken as
+primary, the usual motivation is the notion of neighborhood filter in a
+topological space. However, these approaches often lead to spaces more general
+than topological ones, thereby calling into question the need to use filters in
+the first place. In this note we overturn the usual view and take as primary
+the notion of convergence in the most general context of centered spaces. In
+this setting, the notion of filterbase emerges from the concept of germ of a
+function, while the concept of filter emerges from an amnestic modification of
+the subcategory of centered spaces admitting germs at each point.
+",0,0,1,0,0,0
+16523,Memory-augmented Neural Machine Translation,"  Neural machine translation (NMT) has achieved notable success in recent
+times, however it is also widely recognized that this approach has limitations
+with handling infrequent words and word pairs. This paper presents a novel
+memory-augmented NMT (M-NMT) architecture, which stores knowledge about how
+words (usually infrequently encountered ones) should be translated in a memory
+and then utilizes them to assist the neural model. We use this memory mechanism
+to combine the knowledge learned from a conventional statistical machine
+translation system and the rules learned by an NMT system, and also propose a
+solution for out-of-vocabulary (OOV) words based on this framework. Our
+experiments on two Chinese-English translation tasks demonstrated that the
+M-NMT architecture outperformed the NMT baseline by $9.0$ and $2.7$ BLEU points
+on the two tasks, respectively. Additionally, we found this architecture
+resulted in a much more effective OOV treatment compared to competitive
+methods.
+",1,0,0,0,0,0
+16524,RFID Localisation For Internet Of Things Smart Homes: A Survey,"  The Internet of Things (IoT) enables numerous business opportunities in
+fields as diverse as e-health, smart cities, smart homes, among many others.
+The IoT incorporates multiple long-range, short-range, and personal area
+wireless networks and technologies into the designs of IoT applications.
+Localisation in indoor positioning systems plays an important role in the IoT.
+Location Based IoT applications range from tracking objects and people in
+real-time, assets management, agriculture, assisted monitoring technologies for
+healthcare, and smart homes, to name a few. Radio Frequency based systems for
+indoor positioning such as Radio Frequency Identification (RFID) is a key
+enabler technology for the IoT due to its costeffective, high readability
+rates, automatic identification and, importantly, its energy efficiency
+characteristic. This paper reviews the state-of-the-art RFID technologies in
+IoT Smart Homes applications. It presents several comparable studies of RFID
+based projects in smart homes and discusses the applications, techniques,
+algorithms, and challenges of adopting RFID technologies in IoT smart home
+systems.
+",1,0,0,0,0,0
+16525,Symplectic capacities from positive S^1-equivariant symplectic homology,"  We use positive S^1-equivariant symplectic homology to define a sequence of
+symplectic capacities c_k for star-shaped domains in R^{2n}. These capacities
+are conjecturally equal to the Ekeland-Hofer capacities, but they satisfy
+axioms which allow them to be computed in many more examples. In particular, we
+give combinatorial formulas for the capacities c_k of any ""convex toric domain""
+or ""concave toric domain"". As an application, we determine optimal symplectic
+embeddings of a cube into any convex or concave toric domain. We also extend
+the capacities c_k to functions of Liouville domains which are almost but not
+quite symplectic capacities.
+",0,0,1,0,0,0
+16526,Synthesis versus analysis in patch-based image priors,"  In global models/priors (for example, using wavelet frames), there is a well
+known analysis vs synthesis dichotomy in the way signal/image priors are
+formulated. In patch-based image models/priors, this dichotomy is also present
+in the choice of how each patch is modeled. This paper shows that there is
+another analysis vs synthesis dichotomy, in terms of how the whole image is
+related to the patches, and that all existing patch-based formulations that
+provide a global image prior belong to the analysis category. We then propose a
+synthesis formulation, where the image is explicitly modeled as being
+synthesized by additively combining a collection of independent patches. We
+formally establish that these analysis and synthesis formulations are not
+equivalent in general and that both formulations are compatible with analysis
+and synthesis formulations at the patch level. Finally, we present an instance
+of the alternating direction method of multipliers (ADMM) that can be used to
+perform image denoising under the proposed synthesis formulation, showing its
+computational feasibility. Rather than showing the superiority of the synthesis
+or analysis formulations, the contributions of this paper is to establish the
+existence of both alternatives, thus closing the corresponding gap in the field
+of patch-based image processing.
+",1,0,0,0,0,0
+16527,Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks,"  We prove new upper and lower bounds on the VC-dimension of deep neural
+networks with the ReLU activation function. These bounds are tight for almost
+the entire range of parameters. Letting $W$ be the number of weights and $L$ be
+the number of layers, we prove that the VC-dimension is $O(W L \log(W))$, and
+provide examples with VC-dimension $\Omega( W L \log(W/L) )$. This improves
+both the previously known upper bounds and lower bounds. In terms of the number
+$U$ of non-linear units, we prove a tight bound $\Theta(W U)$ on the
+VC-dimension. All of these bounds generalize to arbitrary piecewise linear
+activation functions, and also hold for the pseudodimensions of these function
+classes.
+Combined with previous results, this gives an intriguing range of
+dependencies of the VC-dimension on depth for networks with different
+non-linearities: there is no dependence for piecewise-constant, linear
+dependence for piecewise-linear, and no more than quadratic dependence for
+general piecewise-polynomial.
+",1,0,0,0,0,0
+16528,Benchmarking Numerical Methods for Lattice Equations with the Toda Lattice,"  We compare performances of well-known numerical time-stepping methods that
+are widely used to compute solutions of the doubly-infinite
+Fermi-Pasta-Ulam-Tsingou (FPUT) lattice equations. The methods are benchmarked
+according to (1) their accuracy in capturing the soliton peaks and (2) in
+capturing highly-oscillatory parts of the solutions of the Toda lattice
+resulting from a variety of initial data. The numerical inverse scattering
+transform method is used to compute a reference solution with high accuracy. We
+find that benchmarking a numerical method on pure-soliton initial data can lead
+one to overestimate the accuracy of the method.
+",0,1,0,0,0,0
+16529,Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT?,"  Due to its wide field of view, cone-beam computed tomography (CBCT) is
+plagued by large amounts of scatter, where attenuated photons hit the detector,
+and corrupt the linear models used for reconstruction. Given that one can
+generate a good estimate of scatter however, then image accuracy can be
+retained. In the context of adaptive radiotherapy, one usually has a
+low-scatter planning CT image of the same patient at an earlier time.
+Correcting for scatter in the subsequent CBCT scan can either be self
+consistent with the new measurements or exploit the prior image, and there are
+several recent methods that report high accuracy with the latter. In this
+study, we will look at the accuracy of various scatter estimation methods, how
+they can be effectively incorporated into a statistical reconstruction
+algorithm, along with introducing a method for matching off-line Monte-Carlo
+(MC) prior estimates to the new measurements. Conclusions we draw from testing
+on a neck cancer patient are: statistical reconstruction that incorporates the
+scatter estimate significantly outperforms analytic and iterative methods with
+pre-correction; and although the most accurate scatter estimates can be made
+from the MC on planning image, they only offer a slight advantage over the
+measurement based scatter kernel superposition (SKS) in reconstruction error.
+",0,1,0,0,0,0
+16530,Proactive Defense Against Physical Denial of Service Attacks using Poisson Signaling Games,"  While the Internet of things (IoT) promises to improve areas such as energy
+efficiency, health care, and transportation, it is highly vulnerable to
+cyberattacks. In particular, distributed denial-of-service (DDoS) attacks
+overload the bandwidth of a server. But many IoT devices form part of
+cyber-physical systems (CPS). Therefore, they can be used to launch ""physical""
+denial-of-service attacks (PDoS) in which IoT devices overflow the ""physical
+bandwidth"" of a CPS. In this paper, we quantify the population-based risk to a
+group of IoT devices targeted by malware for a PDoS attack. In order to model
+the recruitment of bots, we develop a ""Poisson signaling game,"" a signaling
+game with an unknown number of receivers, which have varying abilities to
+detect deception. Then we use a version of this game to analyze two mechanisms
+(legal and economic) to deter botnet recruitment. Equilibrium results indicate
+that 1) defenders can bound botnet activity, and 2) legislating a minimum level
+of security has only a limited effect, while incentivizing active defense can
+decrease botnet activity arbitrarily. This work provides a quantitative
+foundation for proactive PDoS defense.
+",1,0,0,0,0,0
+16531,The weakly compact reflection principle need not imply a high order of weak compactness,"  The weakly compact reflection principle $\text{Refl}_{\text{wc}}(\kappa)$
+states that $\kappa$ is a weakly compact cardinal and every weakly compact
+subset of $\kappa$ has a weakly compact proper initial segment. The weakly
+compact reflection principle at $\kappa$ implies that $\kappa$ is an
+$\omega$-weakly compact cardinal. In this article we show that the weakly
+compact reflection principle does not imply that $\kappa$ is
+$(\omega+1)$-weakly compact. Moreover, we show that if the weakly compact
+reflection principle holds at $\kappa$ then there is a forcing extension
+preserving this in which $\kappa$ is the least $\omega$-weakly compact
+cardinal. Along the way we generalize the well-known result which states that
+if $\kappa$ is a regular cardinal then in any forcing extension by
+$\kappa$-c.c. forcing the nonstationary ideal equals the ideal generated by the
+ground model nonstationary ideal; our generalization states that if $\kappa$ is
+a weakly compact cardinal then after forcing with a `typical' Easton-support
+iteration of length $\kappa$ the weakly compact ideal equals the ideal
+generated by the ground model weakly compact ideal.
+",0,0,1,0,0,0
+16532,Large scale distributed neural network training through online distillation,"  Techniques such as ensembling and distillation promise model quality
+improvements when paired with almost any base model. However, due to increased
+test-time cost (for ensembles) and increased complexity of the training
+pipeline (for distillation), these techniques are challenging to use in
+industrial settings. In this paper we explore a variant of distillation which
+is relatively straightforward to use as it does not require a complicated
+multi-stage setup or many new hyperparameters. Our first claim is that online
+distillation enables us to use extra parallelism to fit very large datasets
+about twice as fast. Crucially, we can still speed up training even after we
+have already reached the point at which additional parallelism provides no
+benefit for synchronous or asynchronous stochastic gradient descent. Two neural
+networks trained on disjoint subsets of the data can share knowledge by
+encouraging each model to agree with the predictions the other model would have
+made. These predictions can come from a stale version of the other model so
+they can be safely computed using weights that only rarely get transmitted. Our
+second claim is that online distillation is a cost-effective way to make the
+exact predictions of a model dramatically more reproducible. We support our
+claims using experiments on the Criteo Display Ad Challenge dataset, ImageNet,
+and the largest to-date dataset used for neural language modeling, containing
+$6\times 10^{11}$ tokens and based on the Common Crawl repository of web data.
+",0,0,0,1,0,0
+16533,New method to design stellarator coils without the winding surface,"  Finding an easy-to-build coils set has been a critical issue for stellarator
+design for decades. Conventional approaches assume a toroidal ""winding""
+surface. We'll investigate if the existence of winding surface unnecessarily
+constrains the optimization, and a new method to design coils for stellarators
+is presented. Each discrete coil is represented as an arbitrary, closed,
+one-dimensional curve embedded in three-dimensional space. A target function to
+be minimized that covers both physical requirements and engineering constraints
+is constructed. The derivatives of the target function are calculated
+analytically. A numerical code, named FOCUS, has been developed. Applications
+to a simple configuration, the W7-X, and LHD plasmas are presented.
+",0,1,0,0,0,0
+16534,An Analytical Framework for Modeling a Spatially Repulsive Cellular Network,"  We propose a new cellular network model that captures both deterministic and
+random aspects of base station deployments. Namely, the base station locations
+are modeled as the superposition of two independent stationary point processes:
+a random shifted grid with intensity $\lambda_g$ and a Poisson point process
+(PPP) with intensity $\lambda_p$. Grid and PPP deployments are special cases
+with $\lambda_p \to 0$ and $\lambda_g \to 0$, with actual deployments in
+between these two extremes, as we demonstrate with deployment data. Assuming
+that each user is associated with the base station that provides the strongest
+average received signal power, we obtain the probability that a typical user is
+associated with either a grid or PPP base station. Assuming Rayleigh fading
+channels, we derive the expression for the coverage probability of the typical
+user, resulting in the following observations. First, the association and the
+coverage probability of the typical user are fully characterized as functions
+of intensity ratio $\rho_\lambda = \lambda_p/\lambda_g$. Second, the user
+association is biased towards the base stations located on a grid. Finally, the
+proposed model predicts the coverage probability of the actual deployment with
+great accuracy.
+",1,0,1,0,0,0
+16535,Glycolaldehyde in Perseus young solar analogs,"  Aims: In this paper we focus on the occurrence of glycolaldehyde (HCOCH2OH)
+in young solar analogs by performing the first homogeneous and unbiased study
+of this molecule in the Class 0 protostars of the nearby Perseus star forming
+region. Methods: We obtained sub-arcsec angular resolution maps at 1.3mm and
+1.4mm of glycolaldehyde emission lines using the IRAM Plateau de Bure (PdB)
+interferometer in the framework of the CALYPSO IRAM large program. Results:
+Glycolaldehyde has been detected towards 3 Class 0 and 1 Class I protostars out
+of the 13 continuum sources targeted in Perseus: NGC1333-IRAS2A1,
+NGC1333-IRAS4A2, NGC1333-IRAS4B1, and SVS13-A. The NGC1333 star forming region
+looks particularly glycolaldehyde rich, with a rate of occurrence up to 60%.
+The glycolaldehyde spatial distribution overlaps with the continuum one,
+tracing the inner 100 au around the protostar. A large number of lines (up to
+18), with upper-level energies Eu from 37 K up to 375 K has been detected. We
+derived column densities > 10^15 cm^-2 and rotational temperatures Trot between
+115 K and 236 K, imaging for the first time hot-corinos around NGC1333-IRAS4B1
+and SVS13-A. Conclusions: In multiple systems glycolaldehyde emission is
+detected only in one component. The case of the SVS13-A+B and IRAS4-A1+A2
+systems support that the detection of glycolaldehyde (at least in the present
+Perseus sample) indicates older protostars (i.e. SVS13-A and IRAS4-A2), evolved
+enough to develop the hot-corino region (i.e. 100 K in the inner 100 au).
+However, only two systems do not allow us to firmly conclude whether the
+primary factor leading to the detection of glycolaldehyde emission is the
+environments hosting the protostars, evolution (e.g. low value of Lsubmm/Lint),
+or accretion luminosity (high Lint).
+",0,1,0,0,0,0
+16536,Comprehension-guided referring expressions,"  We consider generation and comprehension of natural language referring
+expression for objects in an image. Unlike generic ""image captioning"" which
+lacks natural standard evaluation criteria, quality of a referring expression
+may be measured by the receiver's ability to correctly infer which object is
+being described. Following this intuition, we propose two approaches to utilize
+models trained for comprehension task to generate better expressions. First, we
+use a comprehension module trained on human-generated expressions, as a
+""critic"" of referring expression generator. The comprehension module serves as
+a differentiable proxy of human evaluation, providing training signal to the
+generation module. Second, we use the comprehension module in a
+generate-and-rerank pipeline, which chooses from candidate expressions
+generated by a model according to their performance on the comprehension task.
+We show that both approaches lead to improved referring expression generation
+on multiple benchmark datasets.
+",1,0,0,0,0,0
+16537,Modeling the Multiple Sclerosis Brain Disease Using Agents: What Works and What Doesn't?,"  The human brain is one of the most complex living structures in the known
+Universe. It consists of billions of neurons and synapses. Due to its intrinsic
+complexity, it can be a formidable task to accurately depict brain's structure
+and functionality. In the past, numerous studies have been conducted on
+modeling brain disease, structure, and functionality. Some of these studies
+have employed Agent-based approaches including multiagent-based simulation
+models as well as brain complex networks. While these models have all been
+developed using agent-based computing, however, to our best knowledge, none of
+them have employed the use of Agent-Oriented Software Engineering (AOSE)
+methodologies in developing the brain or disease model. This is a problem
+because without due process, developed models can miss out on important
+requirements. AOSE has the unique capability of merging concepts from
+multiagent systems, agent-based modeling, artificial intelligence, besides
+concepts from distributed systems. AOSE involves the various tested software
+engineering principles in various phases of the model development ranging from
+analysis, design, implementation, and testing phases. In this paper, we employ
+the use of three different AOSE methodologies for modeling the Multiple
+Sclerosis brain disease namely GAIA, TROPOS, and MASE. After developing the
+models, we further employ the use of Exploratory Agent-based Modeling (EABM) to
+develop an actual model replicating previous results as a proof of concept. The
+key objective of this study is to demonstrate and explore the viability and
+effectiveness of AOSE methodologies in the development of complex brain
+structure and cognitive process models. Our key finding include demonstration
+that AOSE methodologies can be considerably helpful in modeling various living
+complex systems, in general, and the human brain, in particular.
+",1,1,0,0,0,0
+16538,Improved NN-JPDAF for Joint Multiple Target Tracking and Feature Extraction,"  Feature aided tracking can often yield improved tracking performance over the
+standard multiple target tracking (MTT) algorithms with only kinematic
+measurements. However, in many applications, the feature signal of the targets
+consists of sparse Fourier-domain signals. It changes quickly and nonlinearly
+in the time domain, and the feature measurements are corrupted by missed
+detections and mis-associations. These two factors make it hard to extract the
+feature information to be used in MTT. In this paper, we develop a
+feature-aided nearest neighbour joint probabilistic data association filter
+(NN-JPDAF) for joint MTT and feature extraction in dense target environments.
+To estimate the rapidly varying feature signal from incomplete and corrupted
+measurements, we use the atomic norm constraint to formulate the sparsity of
+feature signal and use the $\ell_1$-norm to formulate the sparsity of the
+corruption induced by mis-associations. Based on the sparse representation, the
+feature signal are estimated by solving a semidefinite program (SDP) which is
+convex. We also provide an iterative method for solving this SDP via the
+alternating direction method of multipliers (ADMM) where each iteration
+involves closed-form computation. With the estimated feature signal,
+re-filtering is performed to estimate the kinematic states of the targets,
+where the association makes use of both kinematic and feature information.
+Simulation results are presented to illustrate the performance of the proposed
+algorithm in a radar application.
+",1,0,0,1,0,0
+16539,KINETyS: Constraining spatial variations of the stellar initial mass function in early-type galaxies,"  The heavyweight stellar initial mass function (IMF) observed in the cores of
+massive early-type galaxies (ETGs) has been linked to formation of their cores
+in an initial swiftly-quenched rapid starburst. However, the outskirts of ETGs
+are thought to be assembled via the slow accumulation of smaller systems in
+which the star formation is less extreme; this suggests the form of the IMF
+should exhibit a radial trend in ETGs. Here we report radial stellar population
+gradients out to the half-light radii of a sample of eight nearby ETGs.
+Spatially resolved spectroscopy at 0.8-1.35{\mu}m from the VLT's KMOS
+instrument was used to measure radial trends in the strengths of a variety of
+IMF-sensitive absorption features (including some which are previously
+unexplored). We find weak or no radial variation in some of these which, given
+a radial IMF trend, ought to vary measurably, e.g. for the Wing-Ford band we
+measure a gradient of +0.06$\pm$0.04 per decade in radius.
+Using stellar population models to fit stacked and individual spectra, we
+infer that the measured radial changes in absorption feature strengths are
+primarily accounted for by abundance gradients which are fairly consistent
+across our sample (e.g. we derive an average [Na/H] gradient of
+-0.53$\pm$0.07). The inferred contribution of dwarf stars to the total light
+typically corresponds to a bottom heavy IMF, but we find no evidence for radial
+IMF variations in the majority of our sample galaxies.
+",0,1,0,0,0,0
+16540,Multi-Agent Deep Reinforcement Learning with Human Strategies,"  Deep learning has enabled traditional reinforcement learning methods to deal
+with high-dimensional problems. However, one of the disadvantages of deep
+reinforcement learning methods is the limited exploration capacity of learning
+agents. In this paper, we introduce an approach that integrates human
+strategies to increase the exploration capacity of multiple deep reinforcement
+learning agents. We also report the development of our own multi-agent
+environment called Multiple Tank Defence to simulate the proposed approach. The
+results show the significant performance improvement of multiple agents that
+have learned cooperatively with human strategies. This implies that there is a
+critical need for human intellect teamed with machines to solve complex
+problems. In addition, the success of this simulation indicates that our
+developed multi-agent environment can be used as a testbed platform to develop
+and validate other multi-agent control algorithms. Details of the environment
+implementation can be referred to
+this http URL
+",0,0,0,1,0,0
+16541,Deep into the Water Fountains: The case of IRAS 18043-2116,"  (Abridged) The formation of large-scale (hundreds to few thousands of AU)
+bipolar structures in the circumstellar envelopes (CSEs) of post-Asymptotic
+Giant Branch (post-AGB) stars is poorly understood. The shape of these
+structures, traced by emission from fast molecular outflows, suggests that the
+dynamics at the innermost regions of these CSEs does not depend only on the
+energy of the radiation field of the central star. Deep into the Water
+Fountains is an observational project based on the results of programs carried
+out with three telescope facilities: The Karl G. Jansky Very Large Array
+(JVLA), The Australia Telescope Compact Array (ATCA), and the Very Large
+Telescope (SINFONI-VLT). Here we report the results of the observations towards
+the WF nebula IRAS 18043$-$2116: Detection of radio continuum emission in the
+frequency range 1.5GHz - 8.0GHz; H$_{2}$O maser spectral features and radio
+continuum emission detected at 22GHz, and H$_{2}$ ro-vibrational emission lines
+detected at the near infrared. The high-velocity H$_{2}$O maser spectral
+features, and the shock-excited H$_{2}$ emission detected could be produced in
+molecular layers which are swept up as a consequence of the propagation of a
+jet-driven wind. Using the derived H$_{2}$ column density, we estimated a
+molecular mass-loss rate of the order of $10^{-9}$M$_{\odot}$yr$^{-1}$. On the
+other hand, if the radio continuum flux detected is generated as a consequence
+of the propagation of a thermal radio jet, the mass-loss rate associated to the
+outflowing ionized material is of the order of 10$^{-5}$M$_{\odot}$yr$^{-1}$.
+The presence of a rotating disk could be a plausible explanation for the
+mass-loss rates estimated.
+",0,1,0,0,0,0
+16542,QAOA for Max-Cut requires hundreds of qubits for quantum speed-up,"  Computational quantum technologies are entering a new phase in which noisy
+intermediate-scale quantum computers are available, but are still too small to
+benefit from active error correction. Even with a finite coherence budget to
+invest in quantum information processing, noisy devices with about 50 qubits
+are expected to experimentally demonstrate quantum supremacy in the next few
+years. Defined in terms of artificial tasks, current proposals for quantum
+supremacy, even if successful, will not help to provide solutions to practical
+problems. Instead, we believe that future users of quantum computers are
+interested in actual applications and that noisy quantum devices may still
+provide value by approximately solving hard combinatorial problems via hybrid
+classical-quantum algorithms. To lower bound the size of quantum computers with
+practical utility, we perform realistic simulations of the Quantum Approximate
+Optimization Algorithm and conclude that quantum speedup will not be
+attainable, at least for a representative combinatorial problem, until several
+hundreds of qubits are available.
+",1,0,0,0,0,0
+16543,Mixed-Effect Modeling for Longitudinal Prediction of Cancer Tumor,"  In this paper, a mixed-effect modeling scheme is proposed to construct a
+predictor for different features of cancer tumor. For this purpose, a set of
+features is extracted from two groups of patients with the same type of cancer
+but with two medical outcome: 1) survived and 2) passed away. The goal is to
+build different models for the two groups, where in each group,
+patient-specified behavior of individuals can be characterized. These models
+are then used as predictors to forecast future state of patients with a given
+history or initial state. To this end, a leave-on-out cross validation method
+is used to measure the prediction accuracy of each patient-specified model.
+Experiments show that compared to fixed-effect modeling (regression),
+mixed-effect modeling has a superior performance on some of the extracted
+features and similar or worse performance on the others.
+",0,0,0,1,0,0
+16544,Distributed Exact Shortest Paths in Sublinear Time,"  The distributed single-source shortest paths problem is one of the most
+fundamental and central problems in the message-passing distributed computing.
+Classical Bellman-Ford algorithm solves it in $O(n)$ time, where $n$ is the
+number of vertices in the input graph $G$. Peleg and Rubinovich (FOCS'99)
+showed a lower bound of $\tilde{\Omega}(D + \sqrt{n})$ for this problem, where
+$D$ is the hop-diameter of $G$.
+Whether or not this problem can be solved in $o(n)$ time when $D$ is
+relatively small is a major notorious open question. Despite intensive research
+\cite{LP13,N14,HKN15,EN16,BKKL16} that yielded near-optimal algorithms for the
+approximate variant of this problem, no progress was reported for the original
+problem.
+In this paper we answer this question in the affirmative. We devise an
+algorithm that requires $O((n \log n)^{5/6})$ time, for $D = O(\sqrt{n \log
+n})$, and $O(D^{1/3} \cdot (n \log n)^{2/3})$ time, for larger $D$. This
+running time is sublinear in $n$ in almost the entire range of parameters,
+specifically, for $D = o(n/\log^2 n)$. For the all-pairs shortest paths
+problem, our algorithm requires $O(n^{5/3} \log^{2/3} n)$ time, regardless of
+the value of $D$.
+We also devise the first algorithm with non-trivial complexity guarantees for
+computing exact shortest paths in the multipass semi-streaming model of
+computation.
+From the technical viewpoint, our algorithm computes a hopset $G""$ of a
+skeleton graph $G'$ of $G$ without first computing $G'$ itself. We then conduct
+a Bellman-Ford exploration in $G' \cup G""$, while computing the required edges
+of $G'$ on the fly. As a result, our algorithm computes exactly those edges of
+$G'$ that it really needs, rather than computing approximately the entire $G'$.
+",1,0,0,0,0,0
+16545,Fingerprints of angulon instabilities in the spectra of matrix-isolated molecules,"  The formation of vortices is usually considered to be the main mechanism of
+angular momentum disposal in superfluids. Recently, it was predicted that a
+superfluid can acquire angular momentum via an alternative, microscopic route
+-- namely, through interaction with rotating impurities, forming so-called
+`angulon quasiparticles' [Phys. Rev. Lett. 114, 203001 (2015)]. The angulon
+instabilities correspond to transfer of a small number of angular momentum
+quanta from the impurity to the superfluid, as opposed to vortex instabilities,
+where angular momentum is quantized in units of $\hbar$ per atom. Furthermore,
+since conventional impurities (such as molecules) represent three-dimensional
+(3D) rotors, the angular momentum transferred is intrinsically 3D as well, as
+opposed to a merely planar rotation which is inherent to vortices. Herein we
+show that the angulon theory can explain the anomalous broadening of the
+spectroscopic lines observed for CH$_3$ and NH$_3$ molecules in superfluid
+helium nanodroplets, thereby providing a fingerprint of the emerging angulon
+instabilities in experiment.
+",0,1,0,0,0,0
+16546,Considering Multiple Uncertainties in Stochastic Security-Constrained Unit Commitment Using Point Estimation Method,"  Security-Constrained Unit Commitment (SCUC) is one of the most significant
+problems in secure and optimal operation of modern electricity markets. New
+sources of uncertainties such as wind speed volatility and price-sensitive
+loads impose additional challenges to this large-scale problem. This paper
+proposes a new Stochastic SCUC using point estimation method to model the power
+system uncertainties more efficiently. Conventional scenario-based Stochastic
+SCUC approaches consider the Mont Carlo method; which presents additional
+computational burdens to this large-scale problem. In this paper we use point
+estimation instead of scenario generating to detract computational burdens of
+the problem. The proposed approach is implemented on a six-bus system and on a
+modified IEEE 118-bus system with 94 uncertain variables. The efficacy of
+proposed algorithm is confirmed, especially in the last case with notable
+reduction in computational burden without considerable loss of precision.
+",0,1,1,0,0,0
+16547,Gluing Delaunay ends to minimal n-noids using the DPW method,"  We construct constant mean curvature surfaces in euclidean space by gluing n
+half Delaunay surfaces to a non-degenerate minimal n-noid, using the DPW
+method.
+",0,0,1,0,0,0
+16548,Some Remarks on the Hyperkähler Reduction,"  We consider a hyperkähler reduction and describe it via frame bundles.
+Tracing the connection through the various reductions, we recover the results
+of Gocho and Nakajima. In addition, we show that the fibers of such a reduction
+are necessarily totally geodesic. As an independent result, we describe
+O'Neill's submersion tensors on principal bundles.
+",0,0,1,0,0,0
+16549,Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment,"  We introduce a novel approach to Maximum A Posteriori inference based on
+discrete graphical models. By utilizing local Wasserstein distances for
+coupling assignment measures across edges of the underlying graph, a given
+discrete objective function is smoothly approximated and restricted to the
+assignment manifold. A corresponding multiplicative update scheme combines in a
+single process (i) geometric integration of the resulting Riemannian gradient
+flow and (ii) rounding to integral solutions that represent valid labelings.
+Throughout this process, local marginalization constraints known from the
+established LP relaxation are satisfied, whereas the smooth geometric setting
+results in rapidly converging iterations that can be carried out in parallel
+for every edge.
+",1,0,0,0,0,0
+16550,Operator algebraic approach to inverse and stability theorems for amenable groups,"  We prove an inverse theorem for the Gowers $U^2$-norm for maps $G\to\mathcal
+M$ from an countable, discrete, amenable group $G$ into a von Neumann algebra
+$\mathcal M$ equipped with an ultraweakly lower semi-continuous, unitarily
+invariant (semi-)norm $\Vert\cdot\Vert$. We use this result to prove a
+stability result for unitary-valued $\varepsilon$-representations $G\to\mathcal
+U(\mathcal M)$ with respect to $\Vert\cdot \Vert$.
+",0,0,1,0,0,0
+16551,Collective irregular dynamics in balanced networks of leaky integrate-and-fire neurons,"  We extensively explore networks of weakly unbalanced, leaky
+integrate-and-fire (LIF) neurons for different coupling strength, connectivity,
+and by varying the degree of refractoriness, as well as the delay in the spike
+transmission. We find that the neural network does not only exhibit a
+microscopic (single-neuron) stochastic-like evolution, but also a collective
+irregular dynamics (CID). Our analysis is based on the computation of a
+suitable order parameter, typically used to characterize synchronization
+phenomena and on a detailed scaling analysis (i.e. simulations of different
+network sizes). As a result, we can conclude that CID is a true thermodynamic
+phase, intrinsically different from the standard asynchronous regime.
+",0,0,0,0,1,0
+16552,Measurement of human activity using velocity GPS data obtained from mobile phones,"  Human movement is used as an indicator of human activity in modern society.
+The velocity of moving humans is calculated based on position information
+obtained from mobile phones. The level of human activity, as recorded by
+velocity, varies throughout the day. Therefore, velocity can be used to
+identify the intervals of highest and lowest activity. More specifically, we
+obtained mobile-phone GPS data from the people around Shibuya station in Tokyo,
+which has the highest population density in Japan. From these data, we observe
+that velocity tends to consistently increase with the changes in social
+activities. For example, during the earthquake in Kumamoto Prefecture in April
+2016, the activity on that day was much lower than usual. In this research, we
+focus on natural disasters such as earthquakes owing to their significant
+effects on human activities in developed countries like Japan. In the event of
+a natural disaster in another developed country, considering the change in
+human behavior at the time of the disaster (e.g., the 2016 Kumamoto Great
+Earthquake) from the viewpoint of velocity allows us to improve our planning
+for mitigation measures. Thus, we analyze the changes in human activity through
+velocity calculations in Shibuya, Tokyo, and compare times of disasters with
+normal times.
+",1,1,0,0,0,0
+16553,Concurrent Segmentation and Localization for Tracking of Surgical Instruments,"  Real-time instrument tracking is a crucial requirement for various
+computer-assisted interventions. In order to overcome problems such as specular
+reflections and motion blur, we propose a novel method that takes advantage of
+the interdependency between localization and segmentation of the surgical tool.
+In particular, we reformulate the 2D instrument pose estimation as heatmap
+regression and thereby enable a concurrent, robust and near real-time
+regression of both tasks via deep learning. As demonstrated by our experimental
+results, this modeling leads to a significantly improved performance than
+directly regressing the tool position and allows our method to outperform the
+state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis
+Challenge 2015.
+",1,0,0,0,0,0
+16554,Bobtail: A Proof-of-Work Target that Minimizes Blockchain Mining Variance (Draft),"  Blockchain systems are designed to produce blocks at a constant average rate.
+The most popular systems currently employ a Proof of Work (PoW) algorithm as a
+means of creating these blocks. Bitcoin produces, on average, one block every
+10 minutes. An unfortunate limitation of all deployed PoW blockchain systems is
+that the time between blocks has high variance. For example, 5% of the time,
+Bitcoin's inter-block time is at least 40 minutes. This variance impedes the
+consistent flow of validated transactions through the system. We propose an
+alternative process for PoW-based block discovery that results in an
+inter-block time with significantly lower variance. Our algorithm, called
+Bobtail, generalizes the current algorithm by comparing the mean of the k
+lowest order statistics to a target. We show that the variance of inter-block
+times decreases as k increases. If our approach were applied to Bitcoin, about
+80% of blocks would be found within 7 to 12 minutes, and nearly every block
+would be found within 5 to 18 minutes; the average inter-block time would
+remain at 10 minutes. Further, we show that low-variance mining significantly
+thwarts doublespend and selfish mining attacks. For Bitcoin and Ethereum
+currently (k=1), an attacker with 40% of the mining power will succeed with 30%
+probability when the merchant sets up an embargo of 8 blocks; however, when
+k>=20, the probability of success falls to less than 1%. Similarly, for Bitcoin
+and Ethereum currently, a selfish miner with 40% of the mining power will claim
+about 66% of blocks; however, when k>=5, the same miner will find that selfish
+mining is less successful than honest mining. The cost of our approach is a
+larger block header.
+",1,0,0,0,0,0
+16555,Mobility Edges in 1D Bichromatic Incommensurate Potentials,"  We theoretically study a one-dimensional (1D) mutually incommensurate
+bichromatic lattice system which has been implemented in ultracold atoms to
+study quantum localization. It has been universally believed that the
+tight-binding version of this bichromatic incommensurate system is represented
+by the well-known Aubry-Andre model. Here we establish that this belief is
+incorrect and that the Aubry-Andre model description, which applies only in the
+extreme tight-binding limit of very deep primary lattice potential, generically
+breaks down near the localization transition due to the unavoidable appearance
+of single-particle mobility edges (SPME). In fact, we show that the 1D
+bichromatic incommensurate potential system manifests generic mobility edges
+which disappear in the tight-binding limit, leading to the well-studied
+Aubry-Andre physics. We carry out an extensive study of the localization
+properties of the 1D incommensurate optical lattice without making any
+tight-binding approximation. We find that, for the full lattice system, an
+intermediate phase between completely localized and completely delocalized
+regions appears due to the existence of the SPME, making the system
+qualitatively distinct from the Aubry-Andre prediction. Using the Wegner flow
+approach, we show that the SPME in the real lattice system can be attributed to
+significant corrections of higher-order harmonics in the lattice potential
+which are absent in the strict tight-binding limit. We calculate the dynamical
+consequences of the intermediate phase in detail to guide future experimental
+investigations for the observation of 1D SPME and the associated intermediate
+phase. We consider effects of interaction numerically, and conjecture the
+stability of SPME to weak interaction effects, thus leading to the exciting
+possibility of an experimentally viable nonergodic extended phase in
+interacting 1D optical lattices.
+",0,1,0,0,0,0
+16556,Privacy Preserving Identification Using Sparse Approximation with Ambiguization,"  In this paper, we consider a privacy preserving encoding framework for
+identification applications covering biometrics, physical object security and
+the Internet of Things (IoT). The proposed framework is based on a sparsifying
+transform, which consists of a trained linear map, an element-wise
+nonlinearity, and privacy amplification. The sparsifying transform and privacy
+amplification are not symmetric for the data owner and data user. We
+demonstrate that the proposed approach is closely related to sparse ternary
+codes (STC), a recent information-theoretic concept proposed for fast
+approximate nearest neighbor (ANN) search in high dimensional feature spaces
+that being machine learning in nature also offers significant benefits in
+comparison to sparse approximation and binary embedding approaches. We
+demonstrate that the privacy of the database outsourced to a server as well as
+the privacy of the data user are preserved at a low computational cost, storage
+and communication burdens.
+",1,0,0,1,0,0
+16557,Manifold Learning Using Kernel Density Estimation and Local Principal Components Analysis,"  We consider the problem of recovering a $d-$dimensional manifold $\mathcal{M}
+\subset \mathbb{R}^n$ when provided with noiseless samples from $\mathcal{M}$.
+There are many algorithms (e.g., Isomap) that are used in practice to fit
+manifolds and thus reduce the dimensionality of a given data set. Ideally, the
+estimate $\mathcal{M}_\mathrm{put}$ of $\mathcal{M}$ should be an actual
+manifold of a certain smoothness; furthermore, $\mathcal{M}_\mathrm{put}$
+should be arbitrarily close to $\mathcal{M}$ in Hausdorff distance given a
+large enough sample. Generally speaking, existing manifold learning algorithms
+do not meet these criteria. Fefferman, Mitter, and Narayanan (2016) have
+developed an algorithm whose output is provably a manifold. The key idea is to
+define an approximate squared-distance function (asdf) to $\mathcal{M}$. Then,
+$\mathcal{M}_\mathrm{put}$ is given by the set of points where the gradient of
+the asdf is orthogonal to the subspace spanned by the largest $n - d$
+eigenvectors of the Hessian of the asdf. As long as the asdf meets certain
+regularity conditions, $\mathcal{M}_\mathrm{put}$ is a manifold that is
+arbitrarily close in Hausdorff distance to $\mathcal{M}$. In this paper, we
+define two asdfs that can be calculated from the data and show that they meet
+the required regularity conditions. The first asdf is based on kernel density
+estimation, and the second is based on estimation of tangent spaces using local
+principal components analysis.
+",0,0,0,1,0,0
+16558,Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach,"  The RGB-D camera maintains a limited range for working and is hard to
+accurately measure the depth information in a far distance. Besides, the RGB-D
+camera will easily be influenced by strong lighting and other external factors,
+which will lead to a poor accuracy on the acquired environmental depth
+information. Recently, deep learning technologies have achieved great success
+in the visual SLAM area, which can directly learn high-level features from the
+visual inputs and improve the estimation accuracy of the depth information.
+Therefore, deep learning technologies maintain the potential to extend the
+source of the depth information and improve the performance of the SLAM system.
+However, the existing deep learning-based methods are mainly supervised and
+require a large amount of ground-truth depth data, which is hard to acquire
+because of the realistic constraints. In this paper, we first present an
+unsupervised learning framework, which not only uses image reconstruction for
+supervising but also exploits the pose estimation method to enhance the
+supervised signal and add training constraints for the task of monocular depth
+and camera motion estimation. Furthermore, we successfully exploit our
+unsupervised learning framework to assist the traditional ORB-SLAM system when
+the initialization module of ORB-SLAM method could not match enough features.
+Qualitative and quantitative experiments have shown that our unsupervised
+learning framework performs the depth estimation task comparable to the
+supervised methods and outperforms the previous state-of-the-art approach by
+$13.5\%$ on KITTI dataset. Besides, our unsupervised learning framework could
+significantly accelerate the initialization process of ORB-SLAM system and
+effectively improve the accuracy on environmental mapping in strong lighting
+and weak texture scenes.
+",1,0,0,0,0,0
+16559,On the Properties of the Power Systems Nodal Admittance Matrix,"  This letter provides conditions determining the rank of the nodal admittance
+matrix, and arbitrary block partitions of it, for connected AC power networks
+with complex admittances. Furthermore, some implications of these properties
+concerning Kron Reduction and Hybrid Network Parameters are outlined.
+",1,0,1,0,0,0
+16560,Isolation and connectivity in random geometric graphs with self-similar intensity measures,"  Random geometric graphs consist of randomly distributed nodes (points), with
+pairs of nodes within a given mutual distance linked. In the usual model the
+distribution of nodes is uniform on a square, and in the limit of infinitely
+many nodes and shrinking linking range, the number of isolated nodes is Poisson
+distributed, and the probability of no isolated nodes is equal to the
+probability the whole graph is connected. Here we examine these properties for
+several self-similar node distributions, including smooth and fractal, uniform
+and nonuniform, and finitely ramified or otherwise. We show that nonuniformity
+can break the Poisson distribution property, but it strengthens the link
+between isolation and connectivity. It also stretches out the connectivity
+transition. Finite ramification is another mechanism for lack of connectivity.
+The same considerations apply to fractal distributions as smooth, with some
+technical differences in evaluation of the integrals and analytical arguments.
+",0,1,0,0,0,0
+16561,Multi-SpaM: a Maximum-Likelihood approach to Phylogeny reconstruction based on Multiple Spaced-Word Matches,"  Motivation: Word-based or `alignment-free' methods for phylogeny
+reconstruction are much faster than traditional approaches, but they are
+generally less accurate. Most of these methods calculate pairwise distances for
+a set of input sequences, for example from word frequencies, from so-called
+spaced-word matches or from the average length of common substrings.
+Results: In this paper, we propose the first word-based approach to tree
+reconstruction that is based on multiple sequence comparison and Maximum
+Likelihood. Our algorithm first samples small, gap-free alignments involving
+four taxa each. For each of these alignments, it then calculates a quartet tree
+and, finally, the program Quartet MaxCut is used to infer a super tree topology
+for the full set of input taxa from the calculated quartet trees. Experimental
+results show that trees calculated with our approach are of high quality.
+Availability: The source code of the program is available at
+this https URL
+Contact: thomas.dencker@stud.uni-goettingen.de
+",0,0,0,0,1,0
+16562,Zermelo deformation of Finsler metrics by Killing vector fields,"  We show how geodesics, Jacobi vector fields and flag curvature of a Finsler
+metric behave under Zermelo deformation with respect to a Killing vector field.
+We also show that Zermelo deformation with respect to a Killing vector field of
+a locally symmetric Finsler metric is also locally symmetric.
+",0,0,1,0,0,0
+16563,Robust estimation of mixing measures in finite mixture models,"  In finite mixture models, apart from underlying mixing measure, true kernel
+density function of each subpopulation in the data is, in many scenarios,
+unknown. Perhaps the most popular approach is to choose some kernel functions
+that we empirically believe our data are generated from and use these kernels
+to fit our models. Nevertheless, as long as the chosen kernel and the true
+kernel are different, statistical inference of mixing measure under this
+setting will be highly unstable. To overcome this challenge, we propose
+flexible and efficient robust estimators of the mixing measure in these models,
+which are inspired by the idea of minimum Hellinger distance estimator, model
+selection criteria, and superefficiency phenomenon. We demonstrate that our
+estimators consistently recover the true number of components and achieve the
+optimal convergence rates of parameter estimation under both the well- and
+mis-specified kernel settings for any fixed bandwidth. These desirable
+asymptotic properties are illustrated via careful simulation studies with both
+synthetic and real data.
+",0,0,1,1,0,0
+16564,Phase Noise and Jitter in Digital Electronics,"  This article explains phase noise, jitter, and some slower phenomena in
+digital integrated circuits, focusing on high-demanding, noise-critical
+applications. We introduce the concept of phase type and time type phase noise.
+The rules for scaling the noise with frequency are chiefly determined by the
+spectral properties of these two basic types, by the aliasing phenomenon, and
+by the input and output circuits. Then, we discuss the parameter extraction
+from experimental data and we report on the measured phase noise in some
+selected devices of different node size and complexity. We observed flicker
+noise between -80 and -130 dBrad^2/Hz at 1 Hz offset, and white noise down to
+-165 dBrad^2/Hz in some fortunate cases and using the appropriate tricks. It
+turns out that flicker noise is proportional to the reciprocal of the volume of
+the transistor. This unpleasant conclusion is supported by a gedanken
+experiment. Further experiments provide understanding on: (i) the interplay
+between noise sources in the internal PLL, often present in FPGAs; (ii) the
+chattering phenomenon, which consists in multiple bouncing at transitions; and
+(iii) thermal time constants, and their effect on phase wander and on the Allan
+variance.
+",0,1,0,0,0,0
+16565,Advanced Soccer Skills and Team Play of RoboCup 2017 TeenSize Winner NimbRo,"  In order to pursue the vision of the RoboCup Humanoid League of beating the
+soccer world champion by 2050, new rules and competitions are added or modified
+each year fostering novel technological advances. In 2017, the number of
+players in the TeenSize class soccer games was increase to 3 vs. 3, which
+allowed for more team play strategies. Improvements in individual skills were
+also demanded through a set of technical challenges. This paper presents the
+latest individual skills and team play developments used in RoboCup 2017 that
+lead our team Nimbro winning the 2017 TeenSize soccer tournament, the technical
+challenges, and the drop-in games.
+",1,0,0,0,0,0
+16566,Phase partitioning in a novel near equi-atomic AlCuFeMn alloy,"  A novel low cost, near equi-atomic alloy comprising of Al, Cu, Fe and Mn is
+synthesized using arc-melting technique. The cast alloy possesses a dendritic
+microstructure where the dendrites consist of disordered FCC and ordered FCC
+phases. The inter-dendritic region is comprised of ordered FCC phase and
+spinodally decomposed BCC phases. A Cu segregation is observed in the
+inter-dendritic region while dendritic region is rich in Fe. The bulk hardness
+of the alloy is ~ 380 HV, indicating significant yield strength.
+",0,1,0,0,0,0
+16567,Overview of Project 8 and Progress Towards Tritium Operation,"  Project 8 is a tritium endpoint neutrino mass experiment utilizing a phased
+program to achieve sensitivity to the range of neutrino masses allowed by the
+inverted mass hierarchy. The Cyclotron Radiation Emission Spectroscopy (CRES)
+technique is employed to measure the differential energy spectrum of decay
+electrons with high precision. We present an overview of the Project 8
+experimental program, from first demonstration of the CRES technique to
+ultimate sensitivity with an atomic tritium source. We highlight recent
+advances in preparation for the first measurement of the continuous tritium
+spectrum with CRES.
+",0,1,0,0,0,0
+16568,Proper efficiency and cone efficiency,"  In this report, two general concepts for proper efficiency in vector
+optimization are studied. Properly efficient elements can be defined as
+minimizers of functionals with certain monotonicity properties or as weakly
+efficient elements with respect to sets that contain the domination set.
+Interdependencies between both concepts are proved in topological vector spaces
+by means of Gerstewitz functionals. The investigation includes proper
+efficiency notions introduced by Henig and by Nehse and Iwanow. In contrary to
+Henig's notion, proper efficiency by Nehse and Iwanow is defined as efficiency
+with respect to certain convex sets which are not necessarily cones. For the
+finite-dimensional case, we turn to Geoffrion's proper efficiency as a special
+case of Henig's proper efficiency. It is characterized as efficiency with
+regard to subclasses of the set of polyhedral cones. Conditions for the
+existence of Geoffrion's properly efficient points are proved. For closed
+feasible point sets, Geoffrion's properly efficient point set is empty or
+coincides with that of Nehse and Iwanow. Properly efficient elements by Nehse
+and Iwanow are the minimizers of continuous convex functionals with certain
+monotonicity properties. Henig's proper efficiency can be described by means of
+minimizers of continuous sublinear functionals with certain monotonicity
+properties.
+",0,0,1,0,0,0
+16569,Sequential Skip Prediction with Few-shot in Streamed Music Contents,"  This paper provides an outline of the algorithms submitted for the WSDM Cup
+2019 Spotify Sequential Skip Prediction Challenge (team name: mimbres). In the
+challenge, complete information including acoustic features and user
+interaction logs for the first half of a listening session is provided. Our
+goal is to predict whether the individual tracks in the second half of the
+session will be skipped or not, only given acoustic features. We proposed two
+different kinds of algorithms that were based on metric learning and sequence
+learning. The experimental results showed that the sequence learning approach
+performed significantly better than the metric learning approach. Moreover, we
+conducted additional experiments to find that significant performance gain can
+be achieved using complete user log information.
+",1,0,0,0,0,0
+16570,Genetic interactions from first principles,"  We derive a general statistical model of interactions, starting from
+probabilistic principles and elementary requirements. Prevailing interaction
+models in biomedical researches diverge both mathematically and practically. In
+particular, genetic interaction inquiries are formulated without an obvious
+mathematical unity. Our model reveals theoretical properties unnoticed so far,
+particularly valuable for genetic interaction mapping, where mechanistic
+details are mostly unknown, distribution of gene variants differ between
+populations, and genetic susceptibilities are spuriously propagated by linkage
+disequilibrium. When applied to data of the largest interaction mapping
+experiment on Saccharomyces Cerevisiae to date, our results imply less aversion
+to positive interactions, detection of well-documented hubs and partial
+remapping of functional regions of the currently known genetic interaction
+landscape. Assessment of divergent annotations across functional categories
+further suggests that positive interactions have a more important role on
+ribosome biogenesis than previously realized. The unity of arguments elaborated
+here enables the analysis of dissimilar interaction models and experimental
+data with a common framework.
+",0,0,0,1,0,0
+16571,On the Log Partition Function of Ising Model on Stochastic Block Model,"  A sparse stochastic block model (SBM) with two communities is defined by the
+community probability $\pi_0,\pi_1$, and the connection probability between
+communities $a,b\in\{0,1\}$, namely $q_{ab} = \frac{\alpha_{ab}}{n}$. When
+$q_{ab}$ is constant in $a,b$, the random graph is simply the
+Erdős-Rény random graph. We evaluate the log partition function of the
+Ising model on sparse SBM with two communities.
+As an application, we give consistent parameter estimation of the sparse SBM
+with two communities in a special case. More specifically, let $d_0,d_1$ be the
+average degree of the two communities, i.e.,
+$d_0\overset{def}{=}\pi_0\alpha_{00}+\pi_1\alpha_{01},d_1\overset{def}{=}\pi_0\alpha_{10}+\pi_1\alpha_{11}$.
+We focus on the regime $d_0=d_1$ (the regime $d_0\ne d_1$ is trivial). In this
+regime, there exists $d,\lambda$ and $r\geq 0$ with $\pi_0=\frac{1}{1+r},
+\pi_1=\frac{r}{1+r}$, $\alpha_{00}=d(1+r\lambda), \alpha_{01}=\alpha_{10} =
+d(1-\lambda), \alpha_{11} = d(1+\frac{\lambda}{r})$. We give a consistent
+estimator of $r$ when $\lambda<0$. The estimator of $\lambda$ given by
+\citep{mossel2015reconstruction} is valid in the general situation. We also
+provide a random clustering algorithm which does not require knowledge of
+parameters and which is positively correlated with the true community label
+when $\lambda<0$.
+",0,0,0,1,0,0
+16572,Radial transonic shock solutions of Euler-Poisson system in convergent nozzles,"  Given constant data of density $\rho_0$, velocity $-u_0{\bf e}_r$, pressure
+$p_0$ and electric force $-E_0{\bf e}_r$ for supersonic flow at the entrance,
+and constant pressure $p_{\rm ex}$ for subsonic flow at the exit, we prove that
+Euler-Poisson system admits a unique transonic shock solution in a two
+dimensional convergent nozzle, provided that $u_0>0$, $E_0>0$, and that $E_0$
+is sufficiently large depending on $(\rho_0, u_0, p_0)$ and the length of the
+nozzle.
+",0,0,1,0,0,0
+16573,X-rays from Green Pea Analogs,"  X-ray observations of two metal-deficient luminous compact galaxies (LCG)
+(SHOC~486 and SDSS J084220.94+115000.2) with properties similar to the
+so-called Green Pea galaxies were obtained using the {\emph{Chandra X-ray
+Observatory}}. Green Pea galaxies are relatively small, compact (a few kpc
+across) galaxies that get their green color from strong [OIII]$\lambda$5007\AA\
+emission, an indicator of intense, recent star formation. These two galaxies
+were predicted to have the highest observed count rates, using the X-ray
+luminosity -- star formation rate ($L_X$--SFR) relation for X-ray binaries,
+from a statistically complete sample drawn from optical criteria. We determine
+the X-ray luminosity relative to star-formation rate and metallicity for these
+two galaxies. Neither exhibit any evidence of active galactic nuclei and we
+suspect the X-ray emission originates from unresolved populations of high mass
+X-ray binaries. We discuss the $L_X$--SFR--metallicity plane for star-forming
+galaxies and show that the two LCGs are consistent with the prediction of this
+relation. This is the first detection of Green Pea analogs in X-rays.
+",0,1,0,0,0,0
+16574,A deeper view of the CoRoT-9 planetary system. A small non-zero eccentricity for CoRoT-9b likely generated by planet-planet scattering,"  CoRoT-9b is one of the rare long-period (P=95.3 days) transiting giant
+planets with a measured mass known to date. We present a new analysis of the
+CoRoT-9 system based on five years of radial-velocity (RV) monitoring with
+HARPS and three new space-based transits observed with CoRoT and Spitzer.
+Combining our new data with already published measurements we redetermine the
+CoRoT-9 system parameters and find good agreement with the published values. We
+uncover a higher significance for the small but non-zero eccentricity of
+CoRoT-9b ($e=0.133^{+0.042}_{-0.037}$) and find no evidence for additional
+planets in the system. We use simulations of planet-planet scattering to show
+that the eccentricity of CoRoT-9b may have been generated by an instability in
+which a $\sim 50~M_\oplus$ planet was ejected from the system. This scattering
+would not have produced a spin-orbit misalignment, so we predict that CoRoT-9b
+orbit should lie within a few degrees of the initial plane of the
+protoplanetary disk. As a consequence, any significant stellar obliquity would
+indicate that the disk was primordially tilted.
+",0,1,0,0,0,0
+16575,Wave propagation characteristics of Parareal,"  The paper derives and analyses the (semi-)discrete dispersion relation of the
+Parareal parallel-in-time integration method. It investigates Parareal's wave
+propagation characteristics with the aim to better understand what causes the
+well documented stability problems for hyperbolic equations. The analysis shows
+that the instability is caused by convergence of the amplification factor to
+the exact value from above for medium to high wave numbers. Phase errors in the
+coarse propagator are identified as the culprit, which suggests that
+specifically tailored coarse level methods could provide a remedy.
+",1,0,1,0,0,0
+16576,Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets,"  Deep learning models (aka Deep Neural Networks) have revolutionized many
+fields including computer vision, natural language processing, speech
+recognition, and is being increasingly used in clinical healthcare
+applications. However, few works exist which have benchmarked the performance
+of the deep learning models with respect to the state-of-the-art machine
+learning models and prognostic scoring systems on publicly available healthcare
+datasets. In this paper, we present the benchmarking results for several
+clinical prediction tasks such as mortality prediction, length of stay
+prediction, and ICD-9 code group prediction using Deep Learning models,
+ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA
+scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III)
+(v1.4) publicly available dataset, which includes all patients admitted to an
+ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the
+benchmarking tasks. Our results show that deep learning models consistently
+outperform all the other approaches especially when the `raw' clinical time
+series data is used as input features to the models.
+",1,0,0,1,0,0
+16577,Modeling of Persistent Homology,"  Topological Data Analysis (TDA) is a novel statistical technique,
+particularly powerful for the analysis of large and high dimensional data sets.
+Much of TDA is based on the tool of persistent homology, represented visually
+via persistence diagrams. In an earlier paper we proposed a parametric
+representation for the probability distributions of persistence diagrams, and
+based on it provided a method for their replication. Since the typical
+situation for big data is that only one persistence diagram is available, these
+replications allow for conventional statistical inference, which, by its very
+nature, requires some form of replication. In the current paper we continue
+this analysis, and further develop its practical statistical methodology, by
+investigating a wider class of examples than treated previously.
+",0,0,0,1,0,0
+16578,Auto-Encoding Sequential Monte Carlo,"  We build on auto-encoding sequential Monte Carlo (AESMC): a method for model
+and proposal learning based on maximizing the lower bound to the log marginal
+likelihood in a broad family of structured probabilistic models. Our approach
+relies on the efficiency of sequential Monte Carlo (SMC) for performing
+inference in structured probabilistic models and the flexibility of deep neural
+networks to model complex conditional probability distributions. We develop
+additional theoretical insights and introduce a new training procedure which
+improves both model and proposal learning. We demonstrate that our approach
+provides a fast, easy-to-implement and scalable means for simultaneous model
+learning and proposal adaptation in deep generative models.
+",0,0,0,1,0,0
+16579,Einstein's accelerated reference systems and Fermi-Walker coordinates,"  We show that the uniformly accelerated reference systems proposed by Einstein
+when introducing acceleration in the theory of relativity are Fermi-Walker
+coordinate systems. We then consider more general accelerated motions and, on
+the one hand we obtain Thomas precession and, on the other, we prove that the
+only accelerated reference systems that at any time admit an instantaneously
+comoving inertial system belong necessarily to the Fermi-Walker class.
+",0,1,0,0,0,0
+16580,Premise Selection for Theorem Proving by Deep Graph Embedding,"  We propose a deep learning-based approach to the problem of premise
+selection: selecting mathematical statements relevant for proving a given
+conjecture. We represent a higher-order logic formula as a graph that is
+invariant to variable renaming but still fully preserves syntactic and semantic
+information. We then embed the graph into a vector via a novel embedding method
+that preserves the information of edge ordering. Our approach achieves
+state-of-the-art results on the HolStep dataset, improving the classification
+accuracy from 83% to 90.3%.
+",1,0,0,0,0,0
+16581,Goldbach Representations in Arithmetic Progressions and zeros of Dirichlet L-functions,"  Assuming a conjecture on distinct zeros of Dirichlet L-functions we get
+asymptotic results on the average number of representations of an integer as
+the sum of two primes in arithmetic progression. On the other hand the
+existence of good error terms gives information on the the location of zeros of
+L-functions and possible Siegel zeros. Similar results are obtained for an
+integer in a congruence class expressed as the sum of two primes.
+",0,0,1,0,0,0
+16582,Estimable group effects for strongly correlated variables in linear models,"  It is well known that parameters for strongly correlated predictor variables
+in a linear model cannot be accurately estimated. We look for linear
+combinations of these parameters that can be. Under a uniform model, we find
+such linear combinations in a neighborhood of a simple variability weighted
+average of these parameters. Surprisingly, this variability weighted average is
+more accurately estimated when the variables are more strongly correlated, and
+it is the only linear combination with this property. It can be easily computed
+for strongly correlated predictor variables in all linear models and has
+applications in inference and estimation concerning parameters of such
+variables.
+",0,0,1,1,0,0
+16583,Online Structure Learning for Sum-Product Networks with Gaussian Leaves,"  Sum-product networks have recently emerged as an attractive representation
+due to their dual view as a special type of deep neural network with clear
+semantics and a special type of probabilistic graphical model for which
+inference is always tractable. Those properties follow from some conditions
+(i.e., completeness and decomposability) that must be respected by the
+structure of the network. As a result, it is not easy to specify a valid
+sum-product network by hand and therefore structure learning techniques are
+typically used in practice. This paper describes the first online structure
+learning technique for continuous SPNs with Gaussian leaves. We also introduce
+an accompanying new parameter learning technique.
+",1,0,0,1,0,0
+16584,Rapid Design of Wide-Area Heterogeneous Electromagnetic Metasurfaces beyond the Unit-Cell Approximation,"  We propose a novel numerical approach for the optimal design of wide-area
+heterogeneous electromagnetic metasurfaces beyond the conventionally used
+unit-cell approximation. The proposed method exploits the combination of
+Rigorous Coupled Wave Analysis (RCWA) and global optimization techniques (two
+evolutionary algorithms namely the Genetic Algorithm (GA) and a modified form
+of the Artificial Bee Colony (ABC with memetic search phase method) are
+considered). As a specific example, we consider the design of beam deflectors
+using all-dielectric nanoantennae for operation in the visible wavelength
+region; beam deflectors can serve as building blocks for other more complicated
+devices like metalenses. Compared to previous reports using local optimization
+approaches our approach improves device efficiency; transmission efficiency is
+especially improved for wide deflection angle beam deflectors. The ABC method
+with memetic search phase is also an improvement over the more commonly used GA
+as it reaches similar efficiency levels with upto 35% reduction in computation
+time. The method described here is of interest for the rapid design of a wide
+variety of electromagnetic metasurfaces irrespective of their operational
+wavelength.
+",0,1,0,0,0,0
+16585,Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks,"  Autonomous driving presents one of the largest problems that the robotics and
+artificial intelligence communities are facing at the moment, both in terms of
+difficulty and potential societal impact. Self-driving vehicles (SDVs) are
+expected to prevent road accidents and save millions of lives while improving
+the livelihood and life quality of many more. However, despite large interest
+and a number of industry players working in the autonomous domain, there is
+still more to be done in order to develop a system capable of operating at a
+level comparable to best human drivers. One reason for this is high uncertainty
+of traffic behavior and large number of situations that an SDV may encounter on
+the roads, making it very difficult to create a fully generalizable system. To
+ensure safe and efficient operations, an autonomous vehicle is required to
+account for this uncertainty and to anticipate a multitude of possible
+behaviors of traffic actors in its surrounding. In this work, we address this
+critical problem and present a method to predict multiple possible trajectories
+of actors while also estimating their probabilities. The method encodes each
+actor's surrounding context into a raster image, used as input by deep
+convolutional networks to automatically derive relevant features for the task.
+Following extensive offline evaluation and comparison to state-of-the-art
+baselines, as well as closed course tests, the method was successfully deployed
+to a fleet of SDVs.
+",1,0,0,0,0,0
+16586,Phase-Resolved Two-Dimensional Spectroscopy of Electronic Wavepackets by Laser-Induced XUV Free Induction Decay,"  We present a novel time- and phase-resolved, background-free scheme to study
+the extreme ultraviolet dipole emission of a bound electronic wavepacket,
+without the use of any extreme ultraviolet exciting pulse. Using multiphoton
+transitions, we populate a superposition of quantum states which coherently
+emit extreme ultraviolet radiation through free induction decay. This emission
+is probed and controlled, both in amplitude and phase, by a time-delayed
+infrared femtosecond pulse. We directly measure the laser-induced dephasing of
+the emission by using a simple heterodyne detection scheme based on two-source
+interferometry. This technique provides rich information about the interplay
+between the laser field and the Coulombic potential on the excited electron
+dynamics. Its background-free nature enables us to use a large range of gas
+pressures and to reveal the influence of collisions in the relaxation process.
+",0,1,0,0,0,0
+16587,Optimized Spatial Partitioning via Minimal Swarm Intelligence,"  Optimized spatial partitioning algorithms are the corner stone of many
+successful experimental designs and statistical methods. Of these algorithms,
+the Centroidal Voronoi Tessellation (CVT) is the most widely utilized. CVT
+based methods require global knowledge of spatial boundaries, do not readily
+allow for weighted regions, have challenging implementations, and are
+inefficiently extended to high dimensional spaces. We describe two simple
+partitioning schemes based on nearest and next nearest neighbor locations which
+easily incorporate these features at the slight expense of optimal placement.
+Several novel qualitative techniques which assess these partitioning schemes
+are also included. The feasibility of autonomous uninformed sensor networks
+utilizing these algorithms are considered. Some improvements in particle swarm
+optimizer results on multimodal test functions from partitioned initial
+positions in two space are also illustrated. Pseudo code for all of the novel
+algorithms depicted here-in is available in the supplementary information of
+this manuscript.
+",1,0,0,1,0,0
+16588,Learning Correspondence Structures for Person Re-identification,"  This paper addresses the problem of handling spatial misalignments due to
+camera-view changes or human-pose variations in person re-identification. We
+first introduce a boosting-based approach to learn a correspondence structure
+which indicates the patch-wise matching probabilities between images from a
+target camera pair. The learned correspondence structure can not only capture
+the spatial correspondence pattern between cameras but also handle the
+viewpoint or human-pose variation in individual images. We further introduce a
+global constraint-based matching process. It integrates a global matching
+constraint over the learned correspondence structure to exclude cross-view
+misalignments during the image patch matching process, hence achieving a more
+reliable matching score between images. Finally, we also extend our approach by
+introducing a multi-structure scheme, which learns a set of local
+correspondence structures to capture the spatial correspondence sub-patterns
+between a camera pair, so as to handle the spatial misalignments between
+individual images in a more precise way. Experimental results on various
+datasets demonstrate the effectiveness of our approach.
+",1,0,0,0,0,0
+16589,Inner Product and Set Disjointness: Beyond Logarithmically Many Parties,"  A basic goal in complexity theory is to understand the communication
+complexity of number-on-the-forehead problems
+$f\colon(\{0,1\}^n)^{k}\to\{0,1\}$ with $k\gg\log n$ parties. We study the
+problems of inner product and set disjointness and determine their randomized
+communication complexity for every $k\geq\log n$, showing in both cases that
+$\Theta(1+\lceil\log n\rceil/\log\lceil1+k/\log n\rceil)$ bits are necessary
+and sufficient. In particular, these problems admit constant-cost protocols if
+and only if the number of parties is $k\geq n^{\epsilon}$ for some constant
+$\epsilon>0.$
+",1,0,0,0,0,0
+16590,Topological classification of time-asymmetry in unitary quantum processes,"  Effective gauge fields have allowed the emulation of matter under strong
+magnetic fields leading to the realization of Harper-Hofstadter, Haldane
+models, and led to demonstrations of one-way waveguides and topologically
+protected edge states. Central to these discoveries is the chirality induced by
+time-symmetry breaking. Due to the discovery of quantum search algorithms based
+on walks on graphs, recent work has discovered new implications the effect of
+time-reversal symmetry breaking has on the transport of quantum states and has
+brought with it a host of new experimental implementations. We provide a full
+classification of the unitary operators defining quantum processes which break
+time-reversal symmetry in their induced transition properties between basis
+elements in a preferred site-basis. Our results are furthermore proven in terms
+of the geometry of the corresponding Hamiltonian support graph and hence
+provide a topological classification. A quantum process of this type is
+necessarily time-symmetric for any choice of time-independent Hamiltonian if
+and only if the underlying support graph is bipartite. Moreover, for
+non-bipartite support, there exists a time-independent Hamiltonian with
+necessarily complex edge weights that induces time-asymmetric transition
+probabilities between edge(s). We further prove that certain bipartite graphs
+give rise to transition probability suppression, but not broken time-reversal
+symmetry. These results fill an important missing gap in understanding the role
+this omnipresent effect has in quantum physics. Furthermore, through our
+development of a general framework, along the way to our results we completely
+characterize gauge potentials on combinatorial graphs.
+",0,1,0,0,0,0
+16591,A Light Modality for Recursion,"  We investigate the interplay between a modality for controlling the behaviour
+of recursive functional programs on infinite structures which are completely
+silent in the syntax. The latter means that programs do not contain ""marks""
+showing the application of the introduction and elimination rules for the
+modality. This shifts the burden of controlling recursion from the programmer
+to the compiler. To do this, we introduce a typed lambda calculus a la Curry
+with a silent modality and guarded recursive types. The typing discipline
+guarantees normalisation and can be transformed into an algorithm which infers
+the type of a program.
+",1,0,0,0,0,0
+16592,Counting the number of metastable states in the modularity landscape: Algorithmic detectability limit of greedy algorithms in community detection,"  Modularity maximization using greedy algorithms continues to be a popular
+approach toward community detection in graphs, even after various better
+forming algorithms have been proposed. Apart from its clear mechanism and ease
+of implementation, this approach is persistently popular because, presumably,
+its risk of algorithmic failure is not well understood. This Rapid
+Communication provides insight into this issue by estimating the algorithmic
+performance limit of modularity maximization. This is achieved by counting the
+number of metastable states under a local update rule. Our results offer a
+quantitative insight into the level of sparsity at which a greedy algorithm
+typically fails.
+",1,0,0,0,0,0
+16593,Infinite rank surface cluster algebras,"  We generalise surface cluster algebras to the case of infinite surfaces where
+the surface contains finitely many accumulation points of boundary marked
+points. To connect different triangulations of an infinite surface, we consider
+infinite mutation sequences.
+We show transitivity of infinite mutation sequences on triangulations of an
+infinite surface and examine different types of mutation sequences. Moreover,
+we use a hyperbolic structure on an infinite surface to extend the notion of
+surface cluster algebras to infinite rank by giving cluster variables as lambda
+lengths of arcs. Furthermore, we study the structural properties of infinite
+rank surface cluster algebras in combinatorial terms, namely we extend ""snake
+graph combinatorics"" to give an expansion formula for cluster variables. We
+also show skein relations for infinite rank surface cluster algebras.
+",0,0,1,0,0,0
+16594,Machine learning prediction errors better than DFT accuracy,"  We investigate the impact of choosing regressors and molecular
+representations for the construction of fast machine learning (ML) models of
+thirteen electronic ground-state properties of organic molecules. The
+performance of each regressor/representation/property combination is assessed
+using learning curves which report out-of-sample errors as a function of
+training set size with up to $\sim$117k distinct molecules. Molecular
+structures and properties at hybrid density functional theory (DFT) level of
+theory used for training and testing come from the QM9 database [Ramakrishnan
+et al, {\em Scientific Data} {\bf 1} 140022 (2014)] and include dipole moment,
+polarizability, HOMO/LUMO energies and gap, electronic spatial extent, zero
+point vibrational energy, enthalpies and free energies of atomization, heat
+capacity and the highest fundamental vibrational frequency. Various
+representations from the literature have been studied (Coulomb matrix, bag of
+bonds, BAML and ECFP4, molecular graphs (MG)), as well as newly developed
+distribution based variants including histograms of distances (HD), and angles
+(HDA/MARAD), and dihedrals (HDAD). Regressors include linear models (Bayesian
+ridge regression (BR) and linear regression with elastic net regularization
+(EN)), random forest (RF), kernel ridge regression (KRR) and two types of
+neural net works, graph convolutions (GC) and gated graph networks (GG). We
+present numerical evidence that ML model predictions deviate from DFT less than
+DFT deviates from experiment for all properties. Furthermore, our out-of-sample
+prediction errors with respect to hybrid DFT reference are on par with, or
+close to, chemical accuracy. Our findings suggest that ML models could be more
+accurate than hybrid DFT if explicitly electron correlated quantum (or
+experimental) data was available.
+",0,1,0,0,0,0
+16595,Invariant tori for the Nosé Thermostat near the High-Temperature Limit,"  Let H(q,p) = p^2/2 + V(q) be a 1-degree of freedom mechanical Hamiltonian
+with a C^n periodic potential V where n>4. The Nosé-thermostated system
+associated to H is shown to have invariant tori near the infinite temperature
+limit. This is shown to be true for all thermostats similar to Nosé's. These
+results complement the result of Legoll, Luskin and Moeckel who proved the
+existence of such tori near the decoupling limit.
+",0,0,1,0,0,0
+16596,Dyadic Green's function formalism for photo-induced forces in tip-sample nanojunctions,"  A comprehensive theoretical analysis of photo-induced forces in an
+illuminated nanojunction, formed between an atomic force microscopy tip and a
+sample, is presented. The formalism is valid within the dipolar approximation
+and includes multiple scattering effects between the tip, sample and a planar
+substrate through a dyadic Green's function approach. This physically intuitive
+description allows a detailed look at the quantitative contribution of multiple
+scattering effects to the measured photo-induced force, effects that are
+typically unaccounted for in simpler analytical models. Our findings show that
+the presence of the planar substrate and anisotropy of the tip have a
+substantial effect on the magnitude and the spectral response of the
+photo-induced force exerted on the tip. Unlike previous models, our
+calculations predict photo-induced forces that are within range of
+experimentally measured values in photo-induced force microscopy (PiFM)
+experiments.
+",0,1,0,0,0,0
+16597,Semi-supervised Embedding in Attributed Networks with Outliers,"  In this paper, we propose a novel framework, called Semi-supervised Embedding
+in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
+representation that systematically captures the topological proximity,
+attribute affinity and label similarity of vertices in a partially labeled
+attributed network (PLAN). Our method is designed to work in both transductive
+and inductive settings while explicitly alleviating noise effects from
+outliers. Experimental results on various datasets drawn from the web, text and
+image domains demonstrate the advantages of SEANO over state-of-the-art methods
+in semi-supervised classification under transductive as well as inductive
+settings. We also show that a subset of parameters in SEANO is interpretable as
+outlier score and can significantly outperform baseline methods when applied
+for detecting network outliers. Finally, we present the use of SEANO in a
+challenging real-world setting -- flood mapping of satellite images and show
+that it is able to outperform modern remote sensing algorithms for this task.
+",1,0,0,0,0,0
+16598,Kernel partial least squares for stationary data,"  We consider the kernel partial least squares algorithm for non-parametric
+regression with stationary dependent data. Probabilistic convergence rates of
+the kernel partial least squares estimator to the true regression function are
+established under a source and an effective dimensionality condition. It is
+shown both theoretically and in simulations that long range dependence results
+in slower convergence rates. A protein dynamics example shows high predictive
+power of kernel partial least squares.
+",0,0,1,1,0,0
+16599,Doubled Khovanov Homology,"  We define a homology theory of virtual links built out of the direct sum of
+the standard Khovanov complex with itself, motivating the name doubled Khovanov
+homology. We demonstrate that it can be used to show that some virtual links
+are non-classical, and that it yields a condition on a virtual knot being the
+connect sum of two unknots. Further, we show that doubled Khovanov homology
+possesses a perturbation analogous to that defined by Lee in the classical case
+and define a doubled Rasmussen invariant. This invariant is used to obtain
+various cobordism obstructions; in particular it is an obstruction to
+sliceness. Finally, we show that the doubled Rasmussen invariant contains the
+odd writhe of a virtual knot, and use this to show that knots with non-zero odd
+writhe are not slice.
+",0,0,1,0,0,0
+16600,On the multi-dimensional elephant random walk,"  The purpose of this paper is to investigate the asymptotic behavior of the
+multi-dimensional elephant random walk (MERW). It is a non-Markovian random
+walk which has a complete memory of its entire history. A wide range of
+literature is available on the one-dimensional ERW. Surprisingly, no references
+are available on the MERW. The goal of this paper is to fill the gap by
+extending the results on the one-dimensional ERW to the MERW. In the diffusive
+and critical regimes, we establish the almost sure convergence, the law of
+iterated logarithm and the quadratic strong law for the MERW. The asymptotic
+normality of the MERW, properly normalized, is also provided. In the
+superdiffusive regime, we prove the almost sure convergence as well as the mean
+square convergence of the MERW. All our analysis relies on asymptotic results
+for multi-dimensional martingales.
+",0,0,1,1,0,0
+16601,Acceleration and Averaging in Stochastic Mirror Descent Dynamics,"  We formulate and study a general family of (continuous-time) stochastic
+dynamics for accelerated first-order minimization of smooth convex functions.
+Building on an averaging formulation of accelerated mirror descent, we propose
+a stochastic variant in which the gradient is contaminated by noise, and study
+the resulting stochastic differential equation. We prove a bound on the rate of
+change of an energy function associated with the problem, then use it to derive
+estimates of convergence rates of the function values, (a.s. and in
+expectation) both for persistent and asymptotically vanishing noise. We discuss
+the interaction between the parameters of the dynamics (learning rate and
+averaging weights) and the covariation of the noise process, and show, in
+particular, how the asymptotic rate of covariation affects the choice of
+parameters and, ultimately, the convergence rate.
+",0,0,1,1,0,0
+16602,Electrothermal Feedback in Kinetic Inductance Detectors,"  In Kinetic Inductance Detectors (KIDs) and other similar applications of
+superconducting microresonators, both the large and small-signal behaviour of
+the device may be affected by electrothermal feedback. Microwave power applied
+to read out the device is absorbed by and heats the superconductor
+quasiparticles, changing the superconductor conductivity and hence the readout
+power absorbed in a positive or negative feedback loop. In this work, we
+explore numerically the implications of an extensible theoretical model of a
+generic superconducting microresonator device for a typical KID, incorporating
+recent work on the power flow between superconductor quasiparticles and
+phonons. This model calculates the large-signal (changes in operating point)
+and small-signal behaviour of a device, allowing us to determine the effect of
+electrothermal feedback on device responsivity and noise characteristics under
+various operating conditions. We also investigate how thermally isolating the
+device from the bath, for example by designing the device on a membrane only
+connected to the bulk substrate by thin legs, affects device performance. We
+find that at a typical device operating point, positive electrothermal feedback
+reduces the effective thermal conductance from the superconductor
+quasiparticles to the bath, and so increases responsivity to signal
+(pair-breaking) power, increases noise from temperature fluctuations, and
+decreases the Noise Equivalent Power (NEP). Similarly, increasing the thermal
+isolation of the device while keeping the quasiparticle temperature constant
+decreases the NEP, but also decreases the device response bandwidth.
+",0,1,0,0,0,0
+16603,Comparison of Modified Kneser-Ney and Witten-Bell Smoothing Techniques in Statistical Language Model of Bahasa Indonesia,"  Smoothing is one technique to overcome data sparsity in statistical language
+model. Although in its mathematical definition there is no explicit dependency
+upon specific natural language, different natures of natural languages result
+in different effects of smoothing techniques. This is true for Russian language
+as shown by Whittaker (1998). In this paper, We compared Modified Kneser-Ney
+and Witten-Bell smoothing techniques in statistical language model of Bahasa
+Indonesia. We used train sets of totally 22M words that we extracted from
+Indonesian version of Wikipedia. As far as we know, this is the largest train
+set used to build statistical language model for Bahasa Indonesia. The
+experiments with 3-gram, 5-gram, and 7-gram showed that Modified Kneser-Ney
+consistently outperforms Witten-Bell smoothing technique in term of perplexity
+values. It is interesting to note that our experiments showed 5-gram model for
+Modified Kneser-Ney smoothing technique outperforms that of 7-gram. Meanwhile,
+Witten-Bell smoothing is consistently improving over the increase of n-gram
+order.
+",1,0,0,0,0,0
+16604,Social evolution of structural discrimination,"  Structural discrimination appears to be a persistent phenomenon in social
+systems. We here outline the hypothesis that it can result from the
+evolutionary dynamics of the social system itself. We study the evolutionary
+dynamics of agents with neutral badges in a simple social game and find that
+the badges are readily discriminated by the system although not being tied to
+the payoff matrix of the game. The sole property of being distinguishable leads
+to the subsequent discrimination, therefore providing a model for the emergence
+and freezing of social prejudice.
+",0,1,0,0,0,0
+16605,Decentralized Control of a Hexapod Robot Using a Wireless Time Synchronized Network,"  Robots and control systems rely upon precise timing of sensors and actuators
+in order to operate intelligently. We present a functioning hexapod robot that
+walks with a dual tripod gait; each tripod is actuated using its own local
+controller running on a separate wireless node. We compare and report the
+results of operating the robot using two different decentralized control
+schemes. With the first scheme, each controller relies on its own local clock
+to generate control signals for the tripod it controls. With the second scheme,
+each controller relies on a variable that is local to itself but that is
+necessarily the same across controllers as a by-product of their host nodes
+being part of a time synchronized IEEE802.15.4e network. The gait
+synchronization error (time difference between what both controllers believe is
+the start of the gait period) grows linearly when the controllers use their
+local clocks, but remains bounded to within 112 microseconds when the
+controllers use their nodes' time synchronized local variable.
+",1,0,0,0,0,0
+16606,Domain wall motion by localized temperature gradients,"  Magnetic domain wall (DW) motion induced by a localized Gaussian temperature
+profile is studied in a Permalloy nanostrip within the framework of the
+stochastic Landau-Lifshitz-Bloch equation. The different contributions to
+thermally induced DW motion, entropic torque and magnonic spin transfer torque,
+are isolated and compared. The analysis of magnonic spin transfer torque
+includes a description of thermally excited magnons in the sample. A third
+driving force due to a thermally induced dipolar field is found and described.
+Finally, thermally induced DW motion is studied under realistic conditions by
+taking into account the edge roughness. The results give quantitative insights
+into the different mechanisms responsible for domain wall motion in temperature
+gradients and allow for comparison with experimental results.
+",0,1,0,0,0,0
+16607,Generation of High Dynamic Range Illumination from a Single Image for the Enhancement of Undesirably Illuminated Images,"  This paper presents an algorithm that enhances undesirably illuminated images
+by generating and fusing multi-level illuminations from a single image.The
+input image is first decomposed into illumination and reflectance components by
+using an edge-preserving smoothing filter. Then the reflectance component is
+scaled up to improve the image details in bright areas. The illumination
+component is scaled up and down to generate several illumination images that
+correspond to certain camera exposure values different from the original. The
+virtual multi-exposure illuminations are blended into an enhanced illumination,
+where we also propose a method to generate appropriate weight maps for the tone
+fusion. Finally, an enhanced image is obtained by multiplying the equalized
+illumination and enhanced reflectance. Experiments show that the proposed
+algorithm produces visually pleasing output and also yields comparable
+objective results to the conventional enhancement methods, while requiring
+modest computational loads.
+",1,0,0,0,0,0
+16608,Replace or Retrieve Keywords In Documents at Scale,"  In this paper we introduce, the FlashText algorithm for replacing keywords or
+finding keywords in a given text. FlashText can search or replace keywords in
+one pass over a document. The time complexity of this algorithm is not
+dependent on the number of terms being searched or replaced. For a document of
+size N (characters) and a dictionary of M keywords, the time complexity will be
+O(N). This algorithm is much faster than Regex, because regex time complexity
+is O(MxN). It is also different from Aho Corasick Algorithm, as it doesn't
+match substrings. FlashText is designed to only match complete words (words
+with boundary characters on both sides). For an input dictionary of {Apple},
+this algorithm won't match it to 'I like Pineapple'. This algorithm is also
+designed to go for the longest match first. For an input dictionary {Machine,
+Learning, Machine learning} on a string 'I like Machine learning', it will only
+consider the longest match, which is Machine Learning. We have made python
+implementation of this algorithm available as open-source on GitHub, released
+under the permissive MIT License.
+",1,0,0,0,0,0
+16609,A Multi Objective Reliable Location-Inventory Capacitated Disruption Facility Problem with Penalty Cost Solve with Efficient Meta Historic Algorithms,"  Logistics network is expected that opened facilities work continuously for a
+long time horizon without any failure, but in real world problems, facilities
+may face disruptions. This paper studies a reliable joint inventory location
+problem to optimize the cost of facility locations, customers assignment, and
+inventory management decisions when facilities face failure risks and do not
+work. In our model we assume when a facility is out of work, its customers may
+be reassigned to other operational facilities otherwise they must endure high
+penalty costs associated with losing service. For defining the model closer to
+real world problems, the model is proposed based on pmedian problem and the
+facilities are considered to have limited capacities. We define a new binary
+variable for showing that customers are not assigned to any facilities. Our
+problem involves a biobjective model, the first one minimizes the sum of
+facility construction costs and expected inventory holding costs, the second
+one function that mentions for the first one is minimized maximum expected
+customer costs under normal and failure scenarios. For solving this model we
+use NSGAII and MOSS algorithms have been applied to find the Pareto archive
+solution. Also, Response Surface Methodology (RSM) is applied for optimizing
+the NSGAII Algorithm Parameters. We compare the performance of two algorithms
+with three metrics and the results show NSGAII is more suitable for our model.
+",1,0,0,1,0,0
+16610,Formation of Galactic Prominence in Galactic Central Region,"  We carried out 2.5-dimensional resistive MHD simulations to study the
+formation mechanism of molecular loops observed by Fukui et al. (2006) at
+Galactic central region. Since it is hard to form molecular loops by uplifting
+dense molecular gas, we study the formation mechanism of molecular gas in
+rising magnetic arcades. This model is based on the in-situ formation model of
+solar prominences, in which prominences are formed by cooling instability in
+helical magnetic flux ropes formed by imposing converging and shearing motion
+at footpoints of the magnetic arch anchored to the solar surface. We extended
+this model to Galactic center scale (a few hundreds pc). Numerical results
+indicate that magnetic reconnection taking place in the current sheet formed
+inside the rising magnetic arcade creates dense blobs confined by the rising
+helical magnetic flux ropes. Thermal instability taking place in the flux ropes
+forms dense molecular filaments floating at high Galactic latitude. The mass of
+the filament increases with time, and can exceed 10^5 solar mass.
+",0,1,0,0,0,0
+16611,Joint Probabilistic Linear Discriminant Analysis,"  Standard probabilistic linear discriminant analysis (PLDA) for speaker
+recognition assumes that the sample's features (usually, i-vectors) are given
+by a sum of three terms: a term that depends on the speaker identity, a term
+that models the within-speaker variability and is assumed independent across
+samples, and a final term that models any remaining variability and is also
+independent across samples. In this work, we propose a generalization of this
+model where the within-speaker variability is not necessarily assumed
+independent across samples but dependent on another discrete variable. This
+variable, which we call the channel variable as in the standard PLDA approach,
+could be, for example, a discrete category for the channel characteristics, the
+language spoken by the speaker, the type of speech in the sample
+(conversational, monologue, read), etc. The value of this variable is assumed
+to be known during training but not during testing. Scoring is performed, as in
+standard PLDA, by computing a likelihood ratio between the null hypothesis that
+the two sides of a trial belong to the same speaker versus the alternative
+hypothesis that the two sides belong to different speakers. The two likelihoods
+are computed by marginalizing over two hypothesis about the channels in both
+sides of a trial: that they are the same and that they are different. This way,
+we expect that the new model will be better at coping with same-channel versus
+different-channel trials than standard PLDA, since knowledge about the channel
+(or language, or speech style) is used during training and implicitly
+considered during scoring.
+",1,0,0,1,0,0
+16612,Adversarial Attack on Graph Structured Data,"  Deep learning on graph structures has shown exciting results in various
+applications. However, few attentions have been paid to the robustness of such
+models, in contrast to numerous research work for image or text adversarial
+attack and defense. In this paper, we focus on the adversarial attacks that
+fool the model by modifying the combinatorial structure of data. We first
+propose a reinforcement learning based attack method that learns the
+generalizable attack policy, while only requiring prediction labels from the
+target classifier. Also, variants of genetic algorithms and gradient methods
+are presented in the scenario where prediction confidence or gradients are
+available. We use both synthetic and real-world data to show that, a family of
+Graph Neural Network models are vulnerable to these attacks, in both
+graph-level and node-level classification tasks. We also show such attacks can
+be used to diagnose the learned classifiers.
+",1,0,0,1,0,0
+16613,Uncertainty quantification for kinetic models in socio-economic and life sciences,"  Kinetic equations play a major rule in modeling large systems of interacting
+particles. Recently the legacy of classical kinetic theory found novel
+applications in socio-economic and life sciences, where processes characterized
+by large groups of agents exhibit spontaneous emergence of social structures.
+Well-known examples are the formation of clusters in opinion dynamics, the
+appearance of inequalities in wealth distributions, flocking and milling
+behaviors in swarming models, synchronization phenomena in biological systems
+and lane formation in pedestrian traffic. The construction of kinetic models
+describing the above processes, however, has to face the difficulty of the lack
+of fundamental principles since physical forces are replaced by empirical
+social forces. These empirical forces are typically constructed with the aim to
+reproduce qualitatively the observed system behaviors, like the emergence of
+social structures, and are at best known in terms of statistical information of
+the modeling parameters. For this reason the presence of random inputs
+characterizing the parameters uncertainty should be considered as an essential
+feature in the modeling process. In this survey we introduce several examples
+of such kinetic models, that are mathematically described by nonlinear Vlasov
+and Fokker--Planck equations, and present different numerical approaches for
+uncertainty quantification which preserve the main features of the kinetic
+solution.
+",0,1,0,0,0,0
+16614,How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?,"  The meteoric rise of deep learning models in computer vision research, having
+achieved human-level accuracy in image recognition tasks is firm evidence of
+the impact of representation learning of deep neural networks. In the chemistry
+domain, recent advances have also led to the development of similar CNN models,
+such as Chemception, that is trained to predict chemical properties using
+images of molecular drawings. In this work, we investigate the effects of
+systematically removing and adding localized domain-specific information to the
+image channels of the training data. By augmenting images with only 3
+additional basic information, and without introducing any architectural
+changes, we demonstrate that an augmented Chemception (AugChemception)
+outperforms the original model in the prediction of toxicity, activity, and
+solvation free energy. Then, by altering the information content in the images,
+and examining the resulting model's performance, we also identify two distinct
+learning patterns in predicting toxicity/activity as compared to solvation free
+energy. These patterns suggest that Chemception is learning about its tasks in
+the manner that is consistent with established knowledge. Thus, our work
+demonstrates that advanced chemical knowledge is not a pre-requisite for deep
+learning models to accurately predict complex chemical properties.
+",1,0,0,1,0,0
+16615,Accelerated Computing in Magnetic Resonance Imaging -- Real-Time Imaging Using Non-Linear Inverse Reconstruction,"  Purpose: To develop generic optimization strategies for image reconstruction
+using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and
+to exemplarily report about our experience with a highly accelerated
+implementation of the non-linear inversion algorithm (NLINV) for dynamic MRI
+with high frame rates. Methods: The NLINV algorithm is optimized and ported to
+run on an a multi-GPU single-node server. The algorithm is mapped to multiple
+GPUs by decomposing the data domain along the channel dimension. Furthermore,
+the algorithm is decomposed along the temporal domain by relaxing a temporal
+regularization constraint, allowing the algorithm to work on multiple frames in
+parallel. Finally, an autotuning method is presented that is capable of
+combining different decomposition variants to achieve optimal algorithm
+performance in different imaging scenarios. Results: The algorithm is
+successfully ported to a multi-GPU system and allows online image
+reconstruction with high frame rates. Real-time reconstruction with low latency
+and frame rates up to 30 frames per second is demonstrated. Conclusion: Novel
+parallel decomposition methods are presented which are applicable to many
+iterative algorithms for dynamic MRI. Using these methods to parallelize the
+NLINV algorithm on multiple GPUs it is possible to achieve online image
+reconstruction with high frame rates.
+",1,1,0,0,0,0
+16616,Exploiting Color Name Space for Salient Object Detection,"  In this paper, we will investigate the contribution of color names for
+salient object detection. Each input image is first converted to the color name
+space, which is consisted of 11 probabilistic channels. By exploring the
+topological structure relationship between the figure and the ground, we obtain
+a saliency map through a linear combination of a set of sequential attention
+maps. To overcome the limitation of only exploiting the surroundedness cue, two
+global cues with respect to color names are invoked for guiding the computation
+of another weighted saliency map. Finally, we integrate the two saliency maps
+into a unified framework to infer the saliency result. In addition, an improved
+post-processing procedure is introduced to effectively suppress the background
+while uniformly highlight the salient objects. Experimental results show that
+the proposed model produces more accurate saliency maps and performs well
+against 23 saliency models in terms of three evaluation metrics on three public
+datasets.
+",1,0,0,0,0,0
+16617,Possibility to realize spin-orbit-induced correlated physics in iridium fluorides,"  Recent theoretical predictions of ""unprecedented proximity"" of the electronic
+ground state of iridium fluorides to the SU(2) symmetric $j_{\mathrm{eff}}=1/2$
+limit, relevant for superconductivity in iridates, motivated us to investigate
+their crystal and electronic structure. To this aim, we performed
+high-resolution x-ray powder diffraction, Ir L$_3$-edge resonant inelastic
+x-ray scattering, and quantum chemical calculations on Rb$_2$[IrF$_6$] and
+other iridium fluorides. Our results are consistent with the Mott insulating
+scenario predicted by Birol and Haule [Phys. Rev. Lett. 114, 096403 (2015)],
+but we observe a sizable deviation of the $j_{\mathrm{eff}}=1/2$ state from the
+SU(2) symmetric limit. Interactions beyond the first coordination shell of
+iridium are negligible, hence the iridium fluorides do not show any magnetic
+ordering down to at least 20 K. A larger spin-orbit coupling in iridium
+fluorides compared to oxides is ascribed to a reduction of the degree of
+covalency, with consequences on the possibility to realize spin-orbit-induced
+strongly correlated physics in iridium fluorides.
+",0,1,0,0,0,0
+16618,Numerical Investigation of Unsteady Aerodynamic Effects on Thick Flatback Airfoils,"  The unsteady characteristics of the flow over thick flatback airfoils have
+been investigated by means of CFD calculations. Sandia airfoils which have 35%
+maximum thickness with three different trailing edge thicknesses were selected.
+The calculations provided good results compared with available experimental
+data with regard to the lift curve and the impact of trailing edge thickness.
+Unsteady CFD simulations revealed that the Strouhal number is found to be
+independent of the lift coefficient before stall and increases with the
+trailing edge. The present work shows the dependency of the Strouhal number and
+the wake development on the trailing edge thickness. A recommendation of the
+Strouhal number definition is given for flatback airfoils by considering the
+trailing edge separation at low angle of attack. The detailed unsteady
+characteristics of thick flatback airfoils are discussed more in the present
+paper.
+",0,1,0,0,0,0
+16619,Development of a passive Rehabilitation Robot for the wrist joint through the implementation of an Arduino UNO microcontroller,"  In this research was implemented the use of an Arduino UNO R3 microcontroller
+to control the movements of a prototype robotic functional developed to perform
+rehabilitation exercises in the wrist joint; This device can be used to assist
+the physiatrist to rehabilitate the tendinitis, synovitis, rheumatoid arthritis
+and for pre-operative and post-operative therapy in this joint. During the
+design stage of the functional prototype, the methodology of the industrial
+design process was used from a concurrent engineering approach, through which
+anthropometric studies could be performed related to the dimensions and angles
+of movement of the wrist joint in the population Venezuelan from the
+information collected, the design proposal was elaborated, and the use of CAD
+programs defined the different forms, geometries and materials of the
+components of the rehabilitation device, which were later analyzed using the
+finite element method for the determination The tensional state of efforts and
+safety factors through the use of CAE programs. In addition, a software was
+developed for the acquisition, registration, reproduction and execution of the
+different movements produced during the rehabilitation therapy. Through the
+research developed, a device was designed that will help the rehabilitation of
+the wrist joint allowing the combination of dorsal-palmar flexion and
+ulnar-radial movements to recover the joint function of various pathologies
+presented in the Venezuelan population.
+",1,1,0,0,0,0
+16620,PHOEG Helps Obtaining Extremal Graphs,"  Extremal Graph Theory aims to determine bounds for graph invariants as well
+as the graphs attaining those bounds.
+We are currently developping PHOEG, an ecosystem of tools designed to help
+researchers in Extremal Graph Theory.
+It uses a big relational database of undirected graphs and works with the
+convex hull of the graphs as points in the invariants space in order to exactly
+obtain the extremal graphs and optimal bounds on the invariants for some fixed
+parameters. The results obtained on the restricted finite class of graphs can
+later be used to infer conjectures. This database also allows us to make
+queries on those graphs. Once the conjecture defined, PHOEG goes one step
+further by helping in the process of designing a proof guided by successive
+applications of transformations from any graph to an extremal graph. To this
+aim, we use a second database based on a graph data model.
+The paper presents ideas and techniques used in PHOEG to assist the study of
+Extremal Graph Theory.
+",1,0,0,0,0,0
+16621,Crime Prediction by Data-Driven Green's Function method,"  We develop an algorithm that forecasts cascading events, by employing a
+Green's function scheme on the basis of the self-exciting point process model.
+This method is applied to open data of 10 types of crimes happened in Chicago.
+It shows a good prediction accuracy superior to or comparable to the standard
+methods which are the expectation-maximization method and prospective hotspot
+maps method. We find a cascade influence of the crimes that has a long-time,
+logarithmic tail; this result is consistent with an earlier study on
+burglaries. This long-tail feature cannot be reproduced by the other standard
+methods. In addition, a merit of the Green's function method is the low
+computational cost in the case of high density of events and/or large amount of
+the training data.
+",1,1,0,1,0,0
+16622,DZ Cha: a bona fide photoevaporating disc,"  DZ Cha is a weak-lined T Tauri star (WTTS) surrounded by a bright
+protoplanetary disc with evidence of inner disc clearing. Its narrow $\Ha$ line
+and infrared spectral energy distribution suggest that DZ Cha may be a
+photoevaporating disc. We aim to analyse the DZ Cha star + disc system to
+identify the mechanism driving the evolution of this object. We have analysed
+three epochs of high resolution optical spectroscopy, photometry from the UV up
+to the sub-mm regime, infrared spectroscopy, and J-band imaging polarimetry
+observations of DZ Cha. Combining our analysis with previous studies we find no
+signatures of accretion in the $\Ha$ line profile in nine epochs covering a
+time baseline of $\sim20$ years. The optical spectra are dominated by
+chromospheric emission lines, but they also show emission from the forbidden
+lines [SII] 4068 and [OI] 6300$\,\AA$ that indicate a disc outflow. The
+polarized images reveal a dust depleted cavity of $\sim7$ au in radius and two
+spiral-like features, and we derive a disc dust mass limit of
+$M_\mathrm{dust}<3\MEarth$ from the sub-mm photometry. No stellar ($M_\star >
+80 \MJup$) companions are detected down to $0\farcs07$ ($\sim 8$ au,
+projected). The negligible accretion rate, small cavity, and forbidden line
+emission strongly suggests that DZ Cha is currently at the initial stages of
+disc clearing by photoevaporation. At this point the inner disc has drained and
+the inner wall of the truncated outer disc is directly exposed to the stellar
+radiation. We argue that other mechanisms like planet formation or binarity
+cannot explain the observed properties of DZ Cha. The scarcity of objects like
+this one is in line with the dispersal timescale ($\lesssim 10^5$ yr) predicted
+by this theory. DZ Cha is therefore an ideal target to study the initial stages
+of photoevaporation.
+",0,1,0,0,0,0
+16623,A Bayesian Filtering Algorithm for Gaussian Mixture Models,"  A Bayesian filtering algorithm is developed for a class of state-space
+systems that can be modelled via Gaussian mixtures. In general, the exact
+solution to this filtering problem involves an exponential growth in the number
+of mixture terms and this is handled here by utilising a Gaussian mixture
+reduction step after both the time and measurement updates. In addition, a
+square-root implementation of the unified algorithm is presented and this
+algorithm is profiled on several simulated systems. This includes the state
+estimation for two non-linear systems that are strictly outside the class
+considered in this paper.
+",1,0,0,1,0,0
+16624,Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems,"  We propose a generic algorithmic building block to accelerate training of
+machine learning models on heterogeneous compute systems. Our scheme allows to
+efficiently employ compute accelerators such as GPUs and FPGAs for the training
+of large-scale machine learning models, when the training data exceeds their
+memory capacity. Also, it provides adaptivity to any system's memory hierarchy
+in terms of size and processing speed. Our technique is built upon novel
+theoretical insights regarding primal-dual coordinate methods, and uses duality
+gap information to dynamically decide which part of the data should be made
+available for fast processing. To illustrate the power of our approach we
+demonstrate its performance for training of generalized linear models on a
+large-scale dataset exceeding the memory size of a modern GPU, showing an
+order-of-magnitude speedup over existing approaches.
+",1,0,0,1,0,0
+16625,Fast Autonomous Flight in Warehouses for Inventory Applications,"  The past years have shown a remarkable growth in use-cases for micro aerial
+vehicles (MAVs). Conceivable indoor applications require highly robust
+environment perception, fast reaction to changing situations, and stable
+navigation, but reliable sources of absolute positioning like GNSS or compass
+measurements are unavailable during indoor flights. We present a
+high-performance autonomous inventory MAV for operation inside warehouses. The
+MAV navigates along warehouse aisles and detects the placed stock in the
+shelves alongside its path with a multimodal sensor setup containing an RFID
+reader and two high-resolution cameras. We describe in detail the SLAM pipeline
+based on a 3D lidar, the setup for stock recognition, the mission planning and
+trajectory generation, as well as a low-level routine for avoidance of
+dynamical or previously unobserved obstacles. Experiments were performed in an
+operative warehouse of a logistics provider, in which an external warehouse
+management system provided the MAV with high-level inspection missions that are
+executed fully autonomously.
+",1,0,0,0,0,0
+16626,Cosmological solutions in generalized hybrid metric-Palatini gravity,"  We construct exact solutions representing a
+Friedmann-Lemaître-Robsertson-Walker (FLRW) universe in a generalized hybrid
+metric-Palatini theory. By writing the gravitational action in a scalar-tensor
+representation, the new solutions are obtained by either making an ansatz on
+the scale factor or on the effective potential. Among other relevant results,
+we show that it is possible to obtain exponentially expanding solutions for
+flat universes even when the cosmology is not purely vacuum. We then derive the
+classes of actions for the original theory which generate these solutions.
+",0,1,0,0,0,0
+16627,Ultimate Boundedness for Switched Systems with Multiple Equilibria Under Disturbances,"  In this paper, we investigate the robustness to external disturbances of
+switched discrete and continuous systems with multiple equilibria. It is shown
+that if each subsystem of the switched system is Input-to-State Stable (ISS),
+then under switching signals that satisfy an average dwell-time bound, the
+solutions are ultimately bounded within a compact set. Furthermore, the size of
+this set varies monotonically with the supremum norm of the disturbance signal.
+It is observed that when the subsystems share a common equilibrium, ISS is
+recovered for solutions of the corresponding switched system; hence, the
+results in this paper are a natural generalization of classical results in
+switched systems that exhibit a common equilibrium. Additionally, we provide a
+method to analytically compute the average dwell time if each subsystem
+possesses a quadratic ISS-Lyapunov function. Our motivation for studying this
+class of switched systems arises from certain motion planning problems in
+robotics, where primitive motions, each corresponding to an equilibrium point
+of a dynamical system, must be composed to realize a task. However, the results
+are relevant to a much broader class of applications, in which composition of
+different modes of behavior is required.
+",1,0,0,0,0,0
+16628,The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics,"  The singular value matrix decomposition plays a ubiquitous role throughout
+statistics and related fields. Myriad applications including clustering,
+classification, and dimensionality reduction involve studying and exploiting
+the geometric structure of singular values and singular vectors.
+This paper provides a novel collection of technical and theoretical tools for
+studying the geometry of singular subspaces using the two-to-infinity norm.
+Motivated by preliminary deterministic Procrustes analysis, we consider a
+general matrix perturbation setting in which we derive a new Procrustean matrix
+decomposition. Together with flexible machinery developed for the
+two-to-infinity norm, this allows us to conduct a refined analysis of the
+induced perturbation geometry with respect to the underlying singular vectors
+even in the presence of singular value multiplicity. Our analysis yields
+singular vector entrywise perturbation bounds for a range of popular matrix
+noise models, each of which has a meaningful associated statistical inference
+task. In addition, we demonstrate how the two-to-infinity norm is the preferred
+norm in certain statistical settings. Specific applications discussed in this
+paper include covariance estimation, singular subspace recovery, and multiple
+graph inference.
+Both our Procrustean matrix decomposition and the technical machinery
+developed for the two-to-infinity norm may be of independent interest.
+",0,0,1,1,0,0
+16629,Variational Bayesian Complex Network Reconstruction,"  Complex network reconstruction is a hot topic in many fields. A popular
+data-driven reconstruction framework is based on lasso. However, it is found
+that, in the presence of noise, it may be inefficient for lasso to determine
+the network topology. This paper builds a new framework to cope with this
+problem. The key idea is to employ a series of linear regression problems to
+model the relationship between network nodes, and then to use an efficient
+variational Bayesian method to infer the unknown coefficients. Based on the
+obtained information, the network is finally reconstructed by determining
+whether two nodes connect with each other or not. The numerical experiments
+conducted with both synthetic and real data demonstrate that the new method
+outperforms lasso with regard to both reconstruction accuracy and running
+speed.
+",1,0,0,0,0,0
+16630,Renormalization of the two-dimensional stochastic nonlinear wave equations,"  We study the two-dimensional stochastic nonlinear wave equations (SNLW) with
+an additive space-time white noise forcing. In particular, we introduce a
+time-dependent renor- malization and prove that SNLW is pathwise locally
+well-posed. As an application of the local well-posedness argument, we also
+establish a weak universality result for the renormalized SNLW.
+",0,0,1,0,0,0
+16631,Generation of High-Purity Millimeter-Wave Orbital Angular Momentum Modes Using Horn Antenna: Theory and Implementation,"  Twisted electromagnetic waves, of which the helical phase front is called
+orbital angular momentum (OAM), have been recently explored for quantum
+information, high speed communication and radar detections. In this context,
+generation of high purity waves carrying OAM is of great significance and
+challenge from low frequency band to optical area. Here, a novel strategy of
+mode combination method is proposed to generate twisted waves with arbitrary
+order of OAM index. The higher order mode of a circular horn antenna is used to
+generate the twisted waves with quite high purity. The proposed strategy is
+verified with theoretical analysis, numerical simulation and experiments. A
+circular horn antenna operating at millimeter wave band is designed,
+fabricated, and measured. Two twisted waves with OAM index of l=+1 and l=-1
+with a mode purity as high as 87% are obtained. Compared with the other OAM
+antennas, the antenna proposed here owns a high antenna gain (over 12 dBi) and
+wide operating bandwidth (over 15%). The high mode purity, high antenna gain
+and wide operating band make the antenna suitable for the twisted-wave
+applications, not only in the microwave and millimeter wave band, but also in
+the terahertz band.
+",0,1,0,0,0,0
+16632,CFT: A Cluster-based File Transfer Scheme for Highway,"  Effective file transfer between vehicles is fundamental to many emerging
+vehicular infotainment applications in the highway Vehicular Ad Hoc Networks
+(VANETs), such as content distribution and social networking. However, due to
+fast mobility, the connection between vehicles tends to be short-lived and
+lossy, which makes intact file transfer extremely challenging. To tackle this
+problem, we presents a novel Cluster-based File Transfer (CFT) scheme for
+highway VANETs in this paper. With CFT, when a vehicle requests a file, the
+transmission capacity between the resource vehicle and the destination vehicle
+is evaluated. If the requested file can be successfully transferred over the
+direct Vehicular-to-Vehicular (V2V) connection, the file transfer will be
+completed by the resource and the destination themselves. Otherwise, a cluster
+will be formed to help the file transfer. As a fully-distributed scheme that
+relies on the collaboration of cluster members, CFT does not require any
+assistance from roadside units or access points. Our experimental results
+indicate that CFT outperforms the existing file transfer schemes for highway
+VANETs.
+",1,0,0,0,0,0
+16633,Empirical priors and posterior concentration rates for a monotone density,"  In a Bayesian context, prior specification for inference on monotone
+densities is conceptually straightforward, but proving posterior convergence
+theorems is complicated by the fact that desirable prior concentration
+properties often are not satisfied. In this paper, I first develop a new prior
+designed specifically to satisfy an empirical version of the prior
+concentration property, and then I give sufficient conditions on the prior
+inputs such that the corresponding empirical Bayes posterior concentrates
+around the true monotone density at nearly the optimal minimax rate. Numerical
+illustrations also reveal the practical benefits of the proposed empirical
+Bayes approach compared to Dirichlet process mixtures.
+",0,0,1,1,0,0
+16634,PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review,"  We consider the problem of automated assignment of papers to reviewers in
+conference peer review, with a focus on fairness and statistical accuracy. Our
+fairness objective is to maximize the review quality of the most disadvantaged
+paper, in contrast to the commonly used objective of maximizing the total
+quality over all papers. We design an assignment algorithm based on an
+incremental max-flow procedure that we prove is near-optimally fair. Our
+statistical accuracy objective is to ensure correct recovery of the papers that
+should be accepted. We provide a sharp minimax analysis of the accuracy of the
+peer-review process for a popular objective-score model as well as for a novel
+subjective-score model that we propose in the paper. Our analysis proves that
+our proposed assignment algorithm also leads to a near-optimal statistical
+accuracy. Finally, we design a novel experiment that allows for an objective
+comparison of various assignment algorithms, and overcomes the inherent
+difficulty posed by the absence of a ground truth in experiments on
+peer-review. The results of this experiment corroborate the theoretical
+guarantees of our algorithm.
+",0,0,0,1,0,0
+16635,CT Image Reconstruction in a Low Dimensional Manifold,"  Regularization methods are commonly used in X-ray CT image reconstruction.
+Different regularization methods reflect the characterization of different
+prior knowledge of images. In a recent work, a new regularization method called
+a low-dimensional manifold model (LDMM) is investigated to characterize the
+low-dimensional patch manifold structure of natural images, where the manifold
+dimensionality characterizes structural information of an image. In this paper,
+we propose a CT image reconstruction method based on the prior knowledge of the
+low-dimensional manifold of CT image. Using the clinical raw projection data
+from GE clinic, we conduct comparisons for the CT image reconstruction among
+the proposed method, the simultaneous algebraic reconstruction technique (SART)
+with the total variation (TV) regularization, and the filtered back projection
+(FBP) method. Results show that the proposed method can successfully recover
+structural details of an imaging object, and achieve higher spatial and
+contrast resolution of the reconstructed image than counterparts of FBP and
+SART with TV.
+",1,1,0,0,0,0
+16636,Deep Unsupervised Clustering Using Mixture of Autoencoders,"  Unsupervised clustering is one of the most fundamental challenges in machine
+learning. A popular hypothesis is that data are generated from a union of
+low-dimensional nonlinear manifolds; thus an approach to clustering is
+identifying and separating these manifolds. In this paper, we present a novel
+approach to solve this problem by using a mixture of autoencoders. Our model
+consists of two parts: 1) a collection of autoencoders where each autoencoder
+learns the underlying manifold of a group of similar objects, and 2) a mixture
+assignment neural network, which takes the concatenated latent vectors from the
+autoencoders as input and infers the distribution over clusters. By jointly
+optimizing the two parts, we simultaneously assign data to clusters and learn
+the underlying manifolds of each cluster.
+",1,0,0,1,0,0
+16637,Enabling a Pepper Robot to provide Automated and Interactive Tours of a Robotics Laboratory,"  The Pepper robot has become a widely recognised face for the perceived
+potential of social robots to enter our homes and businesses. However, to date,
+commercial and research applications of the Pepper have been largely restricted
+to roles in which the robot is able to remain stationary. This restriction is
+the result of a number of technical limitations, including limited sensing
+capabilities, and have as a result, reduced the number of roles in which use of
+the robot can be explored. In this paper, we present our approach to solving
+these problems, with the intention of opening up new research applications for
+the robot. To demonstrate the applicability of our approach, we have framed
+this work within the context of providing interactive tours of an open-plan
+robotics laboratory.
+",1,0,0,0,0,0
+16638,The Ensemble Kalman Filter: A Signal Processing Perspective,"  The ensemble Kalman filter (EnKF) is a Monte Carlo based implementation of
+the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear and
+non-Gaussian state estimation problems. Its ability to handle state dimensions
+in the order of millions has made the EnKF a popular algorithm in different
+geoscientific disciplines. Despite a similarly vital need for scalable
+algorithms in signal processing, e.g., to make sense of the ever increasing
+amount of sensor data, the EnKF is hardly discussed in our field.
+This self-contained review paper is aimed at signal processing researchers
+and provides all the knowledge to get started with the EnKF. The algorithm is
+derived in a KF framework, without the often encountered geoscientific
+terminology. Algorithmic challenges and required extensions of the EnKF are
+provided, as well as relations to sigma-point KF and particle filters. The
+relevant EnKF literature is summarized in an extensive survey and unique
+simulation examples, including popular benchmark problems, complement the
+theory with practical insights. The signal processing perspective highlights
+new directions of research and facilitates the exchange of potentially
+beneficial ideas, both for the EnKF and high-dimensional nonlinear and
+non-Gaussian filtering in general.
+",1,0,0,1,0,0
+16639,Simultaneous Transmit and Receive Operation in Next Generation IEEE 802.11 WLANs: A MAC Protocol Design Approach,"  Full-duplex (FD) technology is likely to be adopted in various legacy
+communications standards. The IEEE 802.11ax working group has been considering
+a simultaneous transmit and receive (STR) mode for the next generation wireless
+local area networks (WLANs). Enabling STR mode (FD communication mode) in
+802.11 networks creates bi-directional FD (BFD) and uni-directional FD (UFD)
+links. The key challenge is to integrate STR mode with minimal protocol
+modifications, while considering the co-existence of FD and legacy half-duplex
+(HD) stations (STAs) and backwards compatibility. This paper proposes a simple
+and practical approach to enable STR mode in 802.11 networks with co-existing
+FD and HD STAs. The protocol explicitly accounts for the peculiarities of FD
+environments and backwards compatibility. Key aspects of the proposed solution
+include FD capability discovery, handshake mechanism for channel access, node
+selection for UFD transmission, adaptive acknowledgement (ACK) timeout for STAs
+engaged in BFD or UFD transmission, and mitigation of contention unfairness.
+Performance evaluation demonstrates the effectiveness of the proposed solution
+in realizing the gains of FD technology for next generation WLANs.
+",1,0,0,0,0,0
+16640,Transfer Learning-Based Crack Detection by Autonomous UAVs,"  Unmanned Aerial Vehicles (UAVs) have recently shown great performance
+collecting visual data through autonomous exploration and mapping in building
+inspection. Yet, the number of studies is limited considering the post
+processing of the data and its integration with autonomous UAVs. These will
+enable huge steps onward into full automation of building inspection. In this
+regard, this work presents a decision making tool for revisiting tasks in
+visual building inspection by autonomous UAVs. The tool is an implementation of
+fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack
+detection. It offers an optional mechanism for task planning of revisiting
+pinpoint locations during inspection. It is integrated to a quadrotor UAV
+system that can autonomously navigate in GPS-denied environments. The UAV is
+equipped with onboard sensors and computers for autonomous localization,
+mapping and motion planning. The integrated system is tested through
+simulations and real-world experiments. The results show that the system
+achieves crack detection and autonomous navigation in GPS-denied environments
+for building inspection.
+",1,0,0,0,0,0
+16641,A Separation Between Run-Length SLPs and LZ77,"  In this paper we give an infinite family of strings for which the length of
+the Lempel-Ziv'77 parse is a factor $\Omega(\log n/\log\log n)$ smaller than
+the smallest run-length grammar.
+",1,0,0,0,0,0
+16642,A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing,"  Tactile sensing can enable a robot to infer properties of its surroundings,
+such as the material of an object. Heat transfer based sensing can be used for
+material recognition due to differences in the thermal properties of materials.
+While data-driven methods have shown promise for this recognition problem, many
+factors can influence performance, including sensor noise, the initial
+temperatures of the sensor and the object, the thermal effusivities of the
+materials, and the duration of contact. We present a physics-based mathematical
+model that predicts material recognition performance given these factors. Our
+model uses semi-infinite solids and a statistical method to calculate an F1
+score for the binary material recognition. We evaluated our method using
+simulated contact with 69 materials and data collected by a real robot with 12
+materials. Our model predicted the material recognition performance of support
+vector machine (SVM) with 96% accuracy for the simulated data, with 92%
+accuracy for real-world data with constant initial sensor temperatures, and
+with 91% accuracy for real-world data with varied initial sensor temperatures.
+Using our model, we also provide insight into the roles of various factors on
+recognition performance, such as the temperature difference between the sensor
+and the object. Overall, our results suggest that our model could be used to
+help design better thermal sensors for robots and enable robots to use them
+more effectively.
+",1,0,0,0,0,0
+16643,The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving,"  Safe interaction with human drivers is one of the primary challenges for
+autonomous vehicles. In order to plan driving maneuvers effectively, the
+vehicle's control system must infer and predict how humans will behave based on
+their latent internal state (e.g., intentions and aggressiveness). This
+research uses a simple model for human behavior with unknown parameters that
+make up the internal states of the traffic participants and presents a method
+for quantifying the value of estimating these states and planning with their
+uncertainty explicitly modeled. An upper performance bound is established by an
+omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of
+the internal states. A baseline lower bound is established by planning with
+MCTS assuming that all drivers have the same internal state. MCTS variants are
+then used to solve a partially observable Markov decision process (POMDP) that
+models the internal state uncertainty to determine whether inferring the
+internal state offers an advantage over the baseline. Applying this method to a
+freeway lane changing scenario reveals that there is a significant performance
+gap between the upper bound and baseline. POMDP planning techniques come close
+to closing this gap, especially when important hidden model parameters are
+correlated with measurable parameters.
+",1,0,0,0,0,0
+16644,Long range scattering for nonlinear Schrödinger equations with critical homogeneous nonlinearity in three space dimensions,"  In this paper, we consider the final state problem for the nonlinear
+Schrödinger equation with a homogeneous nonlinearity of the critical order
+which is not necessarily a polynomial. In [10], the first and the second
+authors consider one- and two-dimensional cases and gave a sufficient condition
+on the nonlinearity for that the corresponding equation admits a solution that
+behaves like a free solution with or without a logarithmic phase correction.
+The present paper is devoted to the study of the three-dimensional case, in
+which it is required that a solution converges to a given asymptotic profile in
+a faster rate than in the lower dimensional cases. To obtain the necessary
+convergence rate, we employ the end-point Strichartz estimate and modify a
+time-dependent regularizing operator, introduced in [10]. Moreover, we present
+a candidate of the second asymptotic profile to the solution.
+",0,0,1,0,0,0
+16645,SegMap: 3D Segment Mapping using Data-Driven Descriptors,"  When performing localization and mapping, working at the level of structure
+can be advantageous in terms of robustness to environmental changes and
+differences in illumination. This paper presents SegMap: a map representation
+solution to the localization and mapping problem based on the extraction of
+segments in 3D point clouds. In addition to facilitating the computationally
+intensive task of processing 3D point clouds, working at the level of segments
+addresses the data compression requirements of real-time single- and
+multi-robot systems. While current methods extract descriptors for the single
+task of localization, SegMap leverages a data-driven descriptor in order to
+extract meaningful features that can also be used for reconstructing a dense 3D
+map of the environment and for extracting semantic information. This is
+particularly interesting for navigation tasks and for providing visual feedback
+to end-users such as robot operators, for example in search and rescue
+scenarios. These capabilities are demonstrated in multiple urban driving and
+search and rescue experiments. Our method leads to an increase of area under
+the ROC curve of 28.3% over current state of the art using eigenvalue based
+features. We also obtain very similar reconstruction capabilities to a model
+specifically trained for this task. The SegMap implementation will be made
+available open-source along with easy to run demonstrations at
+www.github.com/ethz-asl/segmap. A video demonstration is available at
+this https URL.
+",1,0,0,0,0,0
+16646,The Hesse curve of a Lefschtz pencil of plane curves,"  We prove that for a generic Lefschetz pencil of plane curves of degree $d\geq
+3$ there exists a curve $H$ (called the Hesse curve of the pencil) of degree
+$6(d-1)$ and genus $3(4d^2-13d+8)+1$, and such that: $(i)$ $H$ has $d^2$
+singular points of multiplicity three at the base points of the pencil and
+$3(d-1)^2$ ordinary nodes at the singular points of the degenerate members of
+the pencil; $(ii)$ for each member of the pencil the intersection of $H$ with
+this fibre consists of the inflection points of this member and the base points
+of the pencil.
+",0,0,1,0,0,0
+16647,Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation Bias,"  Many state of the art methods for the thermodynamic and kinetic
+characterization of large and complex biomolecular systems by simulation rely
+on ensemble approaches, where data from large numbers of relatively short
+trajectories are integrated. In this context, Markov state models (MSMs) are
+extremely popular because they can be used to compute stationary quantities and
+long-time kinetics from ensembles of short simulations, provided that these
+short simulations are in ""local equilibrium"" within the MSM states. However, in
+the last over 15 years since the inception of MSMs, it has been controversially
+discussed and not yet been answered how deviations from local equilibrium can
+be detected, whether these deviations induce a practical bias in MSM
+estimation, and how to correct for them. In this paper, we address these
+issues: We systematically analyze the estimation of Markov state models (MSMs)
+from short non-equilibrium simulations, and we provide an expression for the
+error between unbiased transition probabilities and the expected estimate from
+many short simulations. We show that the unbiased MSM estimate can be obtained
+even from relatively short non-equilibrium simulations in the limit of long lag
+times and good discretization. Further, we exploit observable operator model
+(OOM) theory to derive an unbiased estimator for the MSM transition matrix that
+corrects for the effect of starting out of equilibrium, even when short lag
+times are used. Finally, we show how the OOM framework can be used to estimate
+the exact eigenvalues or relaxation timescales of the system without estimating
+an MSM transition matrix, which allows us to practically assess the
+discretization quality of the MSM. Applications to model systems and molecular
+dynamics simulation data of alanine dipeptide are included for illustration.
+The improved MSM estimator is implemented in PyEMMA as of version 2.3.
+",0,1,1,0,0,0
+16648,Principal series for general linear groups over finite commutative rings,"  We construct, for any finite commutative ring $R$, a family of
+representations of the general linear group $\mathrm{GL}_n(R)$ whose
+intertwining properties mirror those of the principal series for
+$\mathrm{GL}_n$ over a finite field.
+",0,0,1,0,0,0
+16649,Constraining Radon Backgrounds in LZ,"  The LZ dark matter detector, like many other rare-event searches, will suffer
+from backgrounds due to the radioactive decay of radon daughters. In order to
+achieve its science goals, the concentration of radon within the xenon should
+not exceed $2\mu$Bq/kg, or 20 mBq total within its 10 tonnes. The LZ
+collaboration is in the midst of a program to screen all significant components
+in contact with the xenon. The four institutions involved in this effort have
+begun sharing two cross-calibration sources to ensure consistent measurement
+results across multiple distinct devices. We present here five preliminary
+screening results, some mitigation strategies that will reduce the amount of
+radon produced by the most problematic components, and a summary of the current
+estimate of radon emanation throughout the detector. This best estimate totals
+$<17.3$ mBq, sufficiently low to meet the detector's science goals.
+",0,1,0,0,0,0
+16650,Single-particle dispersion in stably stratified turbulence,"  We present models for single-particle dispersion in vertical and horizontal
+directions of stably stratified flows. The model in the vertical direction is
+based on the observed Lagrangian spectrum of the vertical velocity, while the
+model in the horizontal direction is a combination of a continuous-time
+eddy-constrained random walk process with a contribution to transport from
+horizontal winds. Transport at times larger than the Lagrangian turnover time
+is not universal and dependent on these winds. The models yield results in good
+agreement with direct numerical simulations of stratified turbulence, for which
+single-particle dispersion differs from the well studied case of homogeneous
+and isotropic turbulence.
+",0,1,0,0,0,0
+16651,Pili mediated intercellular forces shape heterogeneous bacterial microcolonies prior to multicellular differentiation,"  Microcolonies are aggregates of a few dozen to a few thousand cells exhibited
+by many bacteria. The formation of microcolonies is a crucial step towards the
+formation of more mature bacterial communities known as biofilms, but also
+marks a significant change in bacterial physiology. Within a microcolony,
+bacteria forgo a single cell lifestyle for a communal lifestyle hallmarked by
+high cell density and physical interactions between cells potentially altering
+their behaviour. It is thus crucial to understand how initially identical
+single cells start to behave differently while assembling in these tight
+communities. Here we show that cells in the microcolonies formed by the human
+pathogen Neisseria gonorrhoeae (Ng) present differential motility behaviors
+within an hour upon colony formation. Observation of merging microcolonies and
+tracking of single cells within microcolonies reveal a heterogeneous motility
+behavior: cells close to the surface of the microcolony exhibit a much higher
+motility compared to cells towards the center. Numerical simulations of a
+biophysical model for the microcolonies at the single cell level suggest that
+the emergence of differential behavior within a multicellular microcolony of
+otherwise identical cells is of mechanical origin. It could suggest a route
+toward further bacterial differentiation and ultimately mature biofilms.
+",0,1,0,0,0,0
+16652,Accurate Inference for Adaptive Linear Models,"  Estimators computed from adaptively collected data do not behave like their
+non-adaptive brethren. Rather, the sequential dependence of the collection
+policy can lead to severe distributional biases that persist even in the
+infinite data limit. We develop a general method -- $\mathbf{W}$-decorrelation
+-- for transforming the bias of adaptive linear regression estimators into
+variance. The method uses only coarse-grained information about the data
+collection policy and does not need access to propensity scores or exact
+knowledge of the policy. We bound the finite-sample bias and variance of the
+$\mathbf{W}$-estimator and develop asymptotically correct confidence intervals
+based on a novel martingale central limit theorem. We then demonstrate the
+empirical benefits of the generic $\mathbf{W}$-decorrelation procedure in two
+different adaptive data settings: the multi-armed bandit and the autoregressive
+time series.
+",1,0,0,1,0,0
+16653,Distribution on Warp Maps for Alignment of Open and Closed Curves,"  Alignment of curve data is an integral part of their statistical analysis,
+and can be achieved using model- or optimization-based approaches. The
+parameter space is usually the set of monotone, continuous warp maps of a
+domain. Infinite-dimensional nature of the parameter space encourages sampling
+based approaches, which require a distribution on the set of warp maps.
+Moreover, the distribution should also enable sampling in the presence of
+important landmark information on the curves which constrain the warp maps. For
+alignment of closed and open curves in $\mathbb{R}^d, d=1,2,3$, possibly with
+landmark information, we provide a constructive, point-process based definition
+of a distribution on the set of warp maps of $[0,1]$ and the unit circle
+$\mathbb{S}^1$ that is (1) simple to sample from, and (2) possesses the
+desiderata for decomposition of the alignment problem with landmark constraints
+into multiple unconstrained ones. For warp maps on $[0,1]$, the distribution is
+related to the Dirichlet process. We demonstrate its utility by using it as a
+prior distribution on warp maps in a Bayesian model for alignment of two
+univariate curves, and as a proposal distribution in a stochastic algorithm
+that optimizes a suitable alignment functional for higher-dimensional curves.
+Several examples from simulated and real datasets are provided.
+",0,0,1,1,0,0
+16654,Unbiased and Consistent Nested Sampling via Sequential Monte Carlo,"  We introduce a new class of sequential Monte Carlo methods called Nested
+Sampling via Sequential Monte Carlo (NS-SMC), which reframes the Nested
+Sampling method of Skilling (2006) in terms of sequential Monte Carlo
+techniques. This new framework allows convergence results to be obtained in the
+setting when Markov chain Monte Carlo (MCMC) is used to produce new samples. An
+additional benefit is that marginal likelihood estimates are unbiased. In
+contrast to NS, the analysis of NS-SMC does not require the (unrealistic)
+assumption that the simulated samples be independent. As the original NS
+algorithm is a special case of NS-SMC, this provides insights as to why NS
+seems to produce accurate estimates despite a typical violation of its
+assumptions. For applications of NS-SMC, we give advice on tuning MCMC kernels
+in an automated manner via a preliminary pilot run, and present a new method
+for appropriately choosing the number of MCMC repeats at each iteration.
+Finally, a numerical study is conducted where the performance of NS-SMC and
+temperature-annealed SMC is compared on several challenging and realistic
+problems. MATLAB code for our experiments is made available at
+this https URL .
+",0,0,0,1,0,0
+16655,Cuntz semigroups of compact-type Hopf C*-algebras,"  The classical Cuntz semigroup has an important role in the study of
+C*-algebras, being one of the main invariants used to classify recalcitrant
+C*-algebras up to isomorphism. We consider C*-algebras that have Hopf algebra
+structure, and find additional structure in their Cuntz semigroups, thus
+generalizing the equivariant Cuntz semigroup. We develop various aspects of the
+theory of such semigroups, and in particular, we give general results allowing
+classification results of the Elliott classification program to be extended to
+classification results for C*-algebraic quantum groups.
+",0,0,1,0,0,0
+16656,City-Scale Intelligent Systems and Platforms,"  As of 2014, 54% of the earth's population resides in urban areas, and it is
+steadily increasing, expecting to reach 66% by 2050. Urban areas range from
+small cities with tens of thousands of people to megacities with greater than
+10 million people. Roughly 12% of the global population today lives in 28
+megacities, and at least 40 are projected by 2030. At these scales, the urban
+infrastructure such as roads, buildings, and utility networks will cover areas
+as large as New England. This steady urbanization and the resulting expansion
+of infrastructure, combined with renewal of aging urban infrastructure,
+represent tens of trillion of dollars in new urban infrastructure investment
+over the coming decades. These investments must balance factors including
+impact on clean air and water, energy and maintenance costs, and the
+productivity and health of city dwellers. Moreover, cost-effective management
+and sustainability of these growing urban areas will be one of the most
+critical challenges to our society, motivating the concept of science- and
+data-driven urban design, retrofit, and operation-that is, ""Smart Cities"".
+",1,0,0,0,0,0
+16657,A simulated comparison between profile and areal surface parameters: $R_a$ as an estimate of $S_a$,"  Direct comparison of areal and profile roughness measurement values is not
+advisable due to fundamental differences in the measurement techniques. However
+researchers may wish to compare between laboratories with differing equipment,
+or against literature values. This paper investigates how well the profile
+arithmetic mean average roughness, $R_a$, approximates its areal equivalent
+$S_a$. Simulated rough surfaces and samples from the ETOPO1 global relief model
+were used. The mean of up to 20 $R_a$ profiles from the surface were compared
+with surface $S_a$ for 100 repeats. Differences between $\bar{R_a}$ and $S_a$
+fell as the number of $R_a$ values averaged increased. For simulated surfaces
+mean % difference between $\bar{R_a}$ and $S_a$ was in the range 16.06% to
+3.47% when only one $R_a$ profile was taken. By averaging 20 $R_a$ values mean
+% difference fell to 6.60% to 0.81%. By not considering $R_a$ profiles parallel
+to the main feature direction (identified visually), mean % difference was
+further reduced. For ETOPO1 global relief surfaces mean % difference was in the
+range 52.09% to 22.60% when only one $R_a$ value was used, and was 33.22% to
+9.90% when 20 $R_a$ values were averaged. Where a surface feature direction
+could be identified, accounting for reduced the difference between $\bar{R_a}$
+and $S_a$ by approximately 5% points. The results suggest that taking the mean
+of between 3 and 5 $R_a$ values will give a good estimate of $S_a$ on regular
+or simple surfaces. However, for some complex real world surfaces discrepancy
+between $\bar{R_a}$ and $S_a$ are high. Caveats including the use of filters
+for areal and profile measurements, and profile alignment are discussed.
+",0,1,0,0,0,0
+16658,The VLA-COSMOS 3 GHz Large Project: Continuum data and source catalog release,"  We present the VLA-COSMOS 3 GHz Large Project based on 384 hours of
+observations with the Karl G. Jansky Very Large Array (VLA) at 3 GHz (10 cm)
+toward the two square degree Cosmic Evolution Survey (COSMOS) field. The final
+mosaic reaches a median rms of 2.3 uJy/beam over the two square degrees at an
+angular resolution of 0.75"". To fully account for the spectral shape and
+resolution variations across the broad (2 GHz) band, we image all data with a
+multiscale, multifrequency synthesis algorithm. We present a catalog of 10,830
+radio sources down to 5 sigma, out of which 67 are combined from multiple
+components. Comparing the positions of our 3 GHz sources with those from the
+Very Long Baseline Array (VLBA)-COSMOS survey, we estimate that the astrometry
+is accurate to 0.01"" at the bright end (signal-to-noise ratio, S/N_3GHz > 20).
+Survival analysis on our data combined with the VLA-COSMOS 1.4~GHz Joint
+Project catalog yields an expected median radio spectral index of alpha=-0.7.
+We compute completeness corrections via Monte Carlo simulations to derive the
+corrected 3 GHz source counts. Our counts are in agreement with previously
+derived 3 GHz counts based on single-pointing (0.087 square degrees) VLA data.
+In summary, the VLA-COSMOS 3 GHz Large Project simultaneously provides the
+largest and deepest radio continuum survey at high (0.75"") angular resolution
+to date, bridging the gap between last-generation and next-generation surveys.
+",0,1,0,0,0,0
+16659,DeepSource: Point Source Detection using Deep Learning,"  Point source detection at low signal-to-noise is challenging for astronomical
+surveys, particularly in radio interferometry images where the noise is
+correlated. Machine learning is a promising solution, allowing the development
+of algorithms tailored to specific telescope arrays and science cases. We
+present DeepSource - a deep learning solution - that uses convolutional neural
+networks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio
+(SNR) of the original map and then uses dynamic blob detection to detect
+sources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT
+images with a total of 300,000 sources, DeepSource is essentially perfect in
+both purity and completeness down to SNR = 4 and outperforms PyBDSF in all
+metrics. For uniformly-weighted images it achieves a Purity x Completeness (PC)
+score at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For
+natural-weighting we find a smaller improvement of ~40% in the PC score at SNR
+= 3. If instead we ask where either of the purity or completeness first drop to
+90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the
+4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can
+learn to optimally trade off purity and completeness for any science case under
+consideration. Our results show that deep learning is a promising approach to
+point source detection in astronomical images.
+",0,0,0,1,0,0
+16660,"Co-design of aperiodic sampled-data min-jumping rules for linear impulsive, switched impulsive and sampled-data systems","  Co-design conditions for the design of a jumping-rule and a sampled-data
+control law for impulsive and impulsive switched systems subject to aperiodic
+sampled-data measurements are provided. Semi-infinite discrete-time
+Lyapunov-Metzler conditions are first obtained. As these conditions are
+difficult to check and generalize to more complex systems, an equivalent
+formulation is provided in terms of clock-dependent (infinite-dimensional)
+matrix inequalities. These conditions are then, in turn, approximated by a
+finite-dimensional optimization problem using a sum of squares based
+relaxation. It is proven that the sum of squares relaxation is non conservative
+provided that the degree of the polynomials is sufficiently large. It is
+emphasized that acceptable results are obtained for low polynomial degrees in
+the considered examples.
+",1,0,1,0,0,0
+16661,Coalescence of Two Impurities in a Trapped One-dimensional Bose Gas,"  We study the ground state of a one-dimensional (1D) trapped Bose gas with two
+mobile impurity particles. To investigate this set-up, we develop a variational
+procedure in which the coordinates of the impurity particles are slow-like
+variables. We validate our method using the exact results obtained for small
+systems. Then, we discuss energies and pair densities for systems that contain
+of the order of one hundred atoms. We show that bosonic non-interacting
+impurities cluster. To explain this clustering, we calculate and discuss
+induced impurity-impurity potentials in a harmonic trap. Further, we compute
+the force between static impurities in a ring ({\it {à} la} the Casimir
+force), and contrast the two effective potentials: the one obtained from the
+mean-field approximation, and the one due to the one-phonon exchange. Our
+formalism and findings are important for understanding (beyond the polaron
+model) the physics of modern 1D cold-atom systems with more than one impurity.
+",0,1,0,0,0,0
+16662,Rust Distilled: An Expressive Tower of Languages,"  Rust represents a major advancement in production programming languages
+because of its success in bridging the gap between high-level application
+programming and low-level systems programming. At the heart of its design lies
+a novel approach to ownership that remains highly programmable.
+In this talk, we will describe our ongoing work on designing a formal
+semantics for Rust that captures ownership and borrowing without the details of
+lifetime analysis. This semantics models a high-level understanding of
+ownership and as a result is close to source-level Rust (but with full type
+annotations) which differs from the recent RustBelt effort that essentially
+models MIR, a CPS-style IR used in the Rust compiler. Further, while RustBelt
+aims to verify the safety of unsafe code in Rust's standard library, we model
+standard library APIs as primitives, which is sufficient to reason about their
+behavior. This yields a simpler model of Rust and its type system that we think
+researchers will find easier to use as a starting point for investigating Rust
+extensions. Unlike RustBelt, we aim to prove type soundness using progress and
+preservation instead of a Kripke logical relation. Finally, our semantics is a
+family of languages of increasing expressive power, where subsequent levels
+have features that are impossible to define in previous levels. Following
+Felleisen, expressive power is defined in terms of observational equivalence.
+Separating the language into different levels of expressive power should
+provide a framework for future work on Rust verification and compiler
+optimization.
+",1,0,0,0,0,0
+16663,"Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends","  Game Theory (GT) has been used with significant success to formulate, and
+either design or optimize, the operation of many representative communications
+and networking scenarios. The games in these scenarios involve, as usual,
+diverse players with conflicting goals. This paper primarily surveys the
+literature that has applied theoretical games to wireless networks, emphasizing
+use cases of upcoming Multi-Access Edge Computing (MEC). MEC is relatively new
+and offers cloud services at the network periphery, aiming to reduce service
+latency backhaul load, and enhance relevant operational aspects such as Quality
+of Experience or security. Our presentation of GT is focused on the major
+challenges imposed by MEC services over the wireless resources. The survey is
+divided into classical and evolutionary games. Then, our discussion proceeds to
+more specific aspects which have a considerable impact on the game usefulness,
+namely: rational vs. evolving strategies, cooperation among players, available
+game information, the way the game is played (single turn, repeated), the game
+model evaluation, and how the model results can be applied for both optimizing
+resource-constrained resources and balancing diverse trade-offs in real edge
+networking scenarios. Finally, we reflect on lessons learned, highlighting
+future trends and research directions for applying theoretical model games in
+upcoming MEC services, considering both network design issues and usage
+scenarios.
+",1,0,0,0,0,0
+16664,General description of spin motion in storage rings in presence of oscillating horizontal fields,"  The general theoretical description of the influence of oscillating
+horizontal magnetic and quasimagnetic fields on the spin evolution in storage
+rings is presented. Previous results are generalized to the case when both of
+the horizontal components of the oscillating field are nonzero and the vector
+of this field circumscribes an ellipse. General equations describing a behavior
+of all components of the polarization vector are derived and the case of an
+arbitrary initial polarization is considered. The derivation is fulfilled in
+the case when the oscillation frequency is nonresonant. The general spin
+evolution in storage rings conditioned by vertical betatron oscillations is
+calculated as an example.
+",0,1,0,0,0,0
+16665,Simultaneous active parameter estimation and control using sampling-based Bayesian reinforcement learning,"  Robots performing manipulation tasks must operate under uncertainty about
+both their pose and the dynamics of the system. In order to remain robust to
+modeling error and shifts in payload dynamics, agents must simultaneously
+perform estimation and control tasks. However, the optimal estimation actions
+are often not the optimal actions for accomplishing the control tasks, and thus
+agents trade between exploration and exploitation. This work frames the problem
+as a Bayes-adaptive Markov decision process and solves it online using Monte
+Carlo tree search and an extended Kalman filter to handle Gaussian process
+noise and parameter uncertainty in a continuous space. MCTS selects control
+actions to reduce model uncertainty and reach the goal state nearly optimally.
+Certainty equivalent model predictive control is used as a benchmark to compare
+performance in simulations with varying process noise and parameter
+uncertainty.
+",1,0,0,0,0,0
+16666,An exploratory factor analysis model for slum severity index in Mexico City,"  In Mexico, 25 per cent of the urban population now lives in informal
+settlements with varying degree of depravity. Although some informal
+neighbourhoods have contributed to the upward mobility of the inhabitants, the
+majority still lack basic services. Mexico City and the conurbation around it,
+form a mega city of 21 million people that has been growing in a manner
+qualified as ""highly unproductive, (that) deepens inequality, raises pollution
+levels"" and contains the largest slum in the world, Neza-Chalco-Izta. Urban
+reforms are now aiming to better the conditions in these slums and therefore it
+is very important to have reliable measurement tools to assess the changes that
+are undergoing. In this paper, we use exploratory factor analysis to define an
+index of depravity in Mexico City, namely the Slum Severity Index (SSI), based
+on the UN-HABITATs definition of a slum. We apply this novel approach to the
+Census survey of Mexico and measure the housing deprivation levels types from
+1990 - 2010. The analysis highlights high variability in housing conditions
+within Mexico City. We find that the SSI decreased significantly between 1990 -
+2000 due to several policy reforms, but increased between 2000 - 2010. We also
+show correlations of the SSI with other social factors such as education,
+health and migration. We present a validation of the SSI using Grey Level
+Co-occurrence Matrix (GLCM) features extracted from Very-High Resolution (VHR)
+remote-sensed satellite images. Finally, we show that the SSI can present a
+cardinally meaningful assessment of the extent of the difference in depravity
+as compared to a similar index defined by CONEVAL, a government institution
+that studies poverty in Mexico.
+",0,0,0,1,0,0
+16667,An Estimate of the First Eigenvalue of a Schrödinger Operator on Closed Surfaces,"  Based on the work of Schoen-Yau, we derive an estimate of the first
+eigenvalue of a Schrödinger Operator (the Jaocbi operator of minimal surfaces
+in flat 3-spaces) on surfaces.
+",0,0,1,0,0,0
+16668,Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads,"  We present ease.ml, a declarative machine learning service platform we built
+to support more than ten research groups outside the computer science
+departments at ETH Zurich for their machine learning needs. With ease.ml, a
+user defines the high-level schema of a machine learning application and
+submits the task via a Web interface. The system automatically deals with the
+rest, such as model selection and data movement. In this paper, we describe the
+ease.ml architecture and focus on a novel technical problem introduced by
+ease.ml regarding resource allocation. We ask, as a ""service provider"" that
+manages a shared cluster of machines among all our users running machine
+learning workloads, what is the resource allocation strategy that maximizes the
+global satisfaction of all our users?
+Resource allocation is a critical yet subtle issue in this multi-tenant
+scenario, as we have to balance between efficiency and fairness. We first
+formalize the problem that we call multi-tenant model selection, aiming for
+minimizing the total regret of all users running automatic model selection
+tasks. We then develop a novel algorithm that combines multi-armed bandits with
+Bayesian optimization and prove a regret bound under the multi-tenant setting.
+Finally, we report our evaluation of ease.ml on synthetic data and on one
+service we are providing to our users, namely, image classification with deep
+neural networks. Our experimental evaluation results show that our proposed
+solution can be up to 9.8x faster in achieving the same global quality for all
+users as the two popular heuristics used by our users before ease.ml.
+",1,0,0,1,0,0
+16669,Computation on Encrypted Data using Data Flow Authentication,"  Encrypting data before sending it to the cloud protects it against hackers
+and malicious insiders, but requires the cloud to compute on encrypted data.
+Trusted (hardware) modules, e.g., secure enclaves like Intel's SGX, can very
+efficiently run entire programs in encrypted memory. However, it already has
+been demonstrated that software vulnerabilities give an attacker ample
+opportunity to insert arbitrary code into the program. This code can then
+modify the data flow of the program and leak any secret in the program to an
+observer in the cloud via SGX side-channels. Since any larger program is rife
+with software vulnerabilities, it is not a good idea to outsource entire
+programs to an SGX enclave. A secure alternative with a small trusted code base
+would be fully homomorphic encryption (FHE) -- the holy grail of encrypted
+computation. However, due to its high computational complexity it is unlikely
+to be adopted in the near future. As a result researchers have made several
+proposals for transforming programs to perform encrypted computations on less
+powerful encryption schemes. Yet, current approaches fail on programs that make
+control-flow decisions based on encrypted data. In this paper, we introduce the
+concept of data flow authentication (DFAuth). DFAuth prevents an adversary from
+arbitrarily deviating from the data flow of a program. Hence, an attacker
+cannot perform an attack as outlined before on SGX. This enables that all
+programs, even those including operations on control-flow decision variables,
+can be computed on encrypted data. We implemented DFAuth using a novel
+authenticated homomorphic encryption scheme, a Java bytecode-to-bytecode
+compiler producing fully executable programs, and SGX enclaves. A transformed
+neural network that performs machine learning on sensitive medical data can be
+evaluated on encrypted inputs and encrypted weights in 0.86 seconds.
+",1,0,0,0,0,0
+16670,Analytic and Numerical Analysis of Singular Cauchy integrals with exponential-type weights,"  Let $I=(c,d)$, $c < 0 < d$, $Q\in C^1: I\rightarrow[0,\infty)$ be a function
+with given regularity behavior on $I$. Write $w:=\exp(-Q)$ on $I$ and assume
+that $\int_I x^nw^2(x)dx<\infty$ for all $n=0,1,2,\ldots$. For $x\in I$, we
+consider the problem of the analytic and numerical approximation of the Cauchy
+principal value integral: \begin{equation*} I[f;x]:=\lim_{\varepsilon \to 0+}
+\left( \int_{c}^{x-\varepsilon} w^2(t)\frac{f(t)}{t-x}dt+
+\int_{x+\varepsilon}^{d} w^2(t)\frac{f(t)}{t-x}dt. \right) \end{equation*} for
+a class of functions $f: I\rightarrow \mathbb{R^+}$ for which $I[f;x]$ is
+finite. In [1-4], the first two authors studied this problem and some of its
+applications for even exponential weights $w$ on $(-\infty,\infty)$ of smooth
+polynomial decay at $\pm \infty$ and given regularity.
+",0,0,1,0,0,0
+16671,Observation of a new field-induced phase transition and its concomitant quantum critical fluctuations in CeCo(In$_{1-x}$Zn$_x$)$_5$,"  We demonstrate a close connection between observed field-induced
+antiferromagnetic (AFM) order and quantum critical fluctuation (QCF) in the
+Zn7%-doped heavy-fermion superconductor CeCoIn5. Magnetization, specific heat,
+and electrical resistivity at low temperatures all show the presence of new
+field-induced AFM order under the magnetic field B of 5-10 T, whose order
+parameter is clearly distinguished from the low-field AFM phase observed for B
+< 5 T and the superconducting phase for B < 3 T. The 4f electronic specific
+heat divided by the temperature, C_e/T, exhibits -lnT dependence at B~10 T (=
+B_0), and furthermore, the C_e/T data for B >= B_0 are well scaled by the
+logarithmic function of B and T: ln[(B-B_0)/T^{2.7}]. These features are quite
+similar to the scaling behavior found in pure CeCoIn5, strongly suggesting that
+the field-induced QCF in pure CeCoIn5 originates from the hidden AFM order
+parameter equivalent to high-field AFM order in Zn7%-doped CeCoIn5.
+",0,1,0,0,0,0
+16672,Two-dimensional magneto-optical trap as a source for cold strontium atoms,"  We report on the realization of a transversely loaded two-dimensional
+magneto-optical trap serving as a source for cold strontium atoms. We analyze
+the dependence of the source's properties on various parameters, in particular
+the intensity of a pushing beam accelerating the atoms out of the source. An
+atomic flux exceeding $10^9\,\mathrm{atoms/s}$ at a rather moderate oven
+temperature of $500\,^\circ\mathrm{C}$ is achieved. The longitudinal velocity
+of the atomic beam can be tuned over several tens of m/s by adjusting the power
+of the pushing laser beam. The beam divergence is around $60$ mrad, determined
+by the transverse velocity distribution of the cold atoms. The slow atom source
+is used to load a three-dimensional magneto-optical trap realizing loading
+rates up to $10^9\,\mathrm{atoms/s}$ without indication of saturation of the
+loading rate for increasing oven temperature. The compact setup avoids
+undesired effects found in alternative sources like, e.g., Zeeman slowers, such
+as vacuum contamination and black-body radiation due to the hot strontium oven.
+",0,1,0,0,0,0
+16673,Loading a linear Paul trap to saturation from a magneto-optical trap,"  We present experimental measurements of the steady-state ion number in a
+linear Paul trap (LPT) as a function of the ion-loading rate. These
+measurements, taken with (a) constant Paul trap stability parameter $q$, (b)
+constant radio-frequency (rf) amplitude, or (c) constant rf frequency, show
+nonlinear behavior. At the loading rates achieved in this experiment, a plot of
+the steady-state ion number as a function of loading rate has two regions: a
+monotonic rise (region I) followed by a plateau (region II). Also described are
+simulations and analytical theory which match the experimental results. Region
+I is caused by rf heating and is fundamentally due to the time dependence of
+the rf Paul-trap forces. We show that the time-independent pseudopotential,
+frequently used in the analytical investigation of trapping experiments, cannot
+explain region I, but explains the plateau in region II and can be used to
+predict the steady-state ion number in that region. An important feature of our
+experimental LPT is the existence of a radial cut-off $\hat R_{\rm cut}$ that
+limits the ion capacity of our LPT and features prominently in the analytical
+and numerical analysis of our LPT-loading results. We explain the dynamical
+origin of $\hat R_{\rm cut}$ and relate it to the chaos border of the fractal
+of non-escaping trajectories in our LPT. We also present an improved model of
+LPT ion-loading as a function of time.
+",0,1,0,0,0,0
+16674,Early warning signals in plant disease outbreaks,"  Summary
+1. Infectious disease outbreaks in plants threaten ecosystems, agricultural
+crops and food trade. Currently, several fungal diseases are affecting forests
+worldwide, posing a major risk to tree species, habitats and consequently
+ecosystem decay. Prediction and control of disease spread are difficult, mainly
+due to the complexity of the interaction between individual components
+involved.
+2. In this work, we introduce a lattice-based epidemic model coupled with a
+stochastic process that mimics, in a very simplified way, the interaction
+between the hosts and pathogen. We studied the disease spread by measuring the
+propagation velocity of the pathogen on the susceptible hosts. Quantitative
+results indicate the occurrence of a critical transition between two stable
+phases: local confinement and an extended epiphytotic outbreak that depends on
+the density of the susceptible individuals.
+3. Quantitative predictions of epiphytotics are performed using the framework
+early-warning indicators for impending regime shifts, widely applied on
+dynamical systems. These signals forecast successfully the outcome of the
+critical shift between the two stable phases before the system enters the
+epiphytotic regime.
+4. Synthesis: Our study demonstrates that early-warning indicators could be
+useful for the prediction of forest disease epidemics through mathematical and
+computational models suited to more specific pathogen-host-environmental
+interactions.
+",0,0,0,0,1,0
+16675,Predicting the Gender of Indonesian Names,"  We investigated a way to predict the gender of a name using character-level
+Long-Short Term Memory (char-LSTM). We compared our method with some
+conventional machine learning methods, namely Naive Bayes, logistic regression,
+and XGBoost with n-grams as the features. We evaluated the models on a dataset
+consisting of the names of Indonesian people. It is not common to use a family
+name as the surname in Indonesian culture, except in some ethnicities.
+Therefore, we inferred the gender from both full names and first names. The
+results show that we can achieve 92.25% accuracy from full names, while using
+first names only yields 90.65% accuracy. These results are better than the ones
+from applying the classical machine learning algorithms to n-grams.
+",1,0,0,0,0,0
+16676,Exploring Students Blended Learning Through Social Media,"  Information technology (IT) has been used widely in many aspects of our daily
+life. After discuss politics related aspects for some articles. In this article
+author would like to discuss social media for students learning environment.
+Social media as a leading application on the internet has changed many aspects
+of life become more globalized. This article discusses the use of social media
+to support learning activities for students in the faculty of computer science.
+The author uses Facebook and WordPress as an alternative to electronic
+learning: 1) online attendance tool, 2) media storage and dissemination of
+course materials, 3) event scheduling for the lectures. Social media succeed to
+change the way of modern learning styles and environment. The results of this
+study are some learning activities such as : 1) Preparation, 2) Weekly meeting
+activities, 3) Course Page, 4) Social Media as Online Attendance Tool, 5)
+Social Media as Learning Repository and Dissemination, and 6) Social Media as
+Online Event Scheduling.
+",1,0,0,0,0,0
+16677,Thompson Sampling for a Fatigue-aware Online Recommendation System,"  In this paper we consider an online recommendation setting, where a platform
+recommends a sequence of items to its users at every time period. The users
+respond by selecting one of the items recommended or abandon the platform due
+to fatigue from seeing less useful items. Assuming a parametric stochastic
+model of user behavior, which captures positional effects of these items as
+well as the abandoning behavior of users, the platform's goal is to recommend
+sequences of items that are competitive to the single best sequence of items in
+hindsight, without knowing the true user model a priori. Naively applying a
+stochastic bandit algorithm in this setting leads to an exponential dependence
+on the number of items. We propose a new Thompson sampling based algorithm with
+expected regret that is polynomial in the number of items in this combinatorial
+setting, and performs extremely well in practice. We also show a contextual
+version of our solution.
+",1,0,0,1,0,0
+16678,Finding Large Primes,"  In this paper we present and expand upon procedures for obtaining large d
+digit prime number to an arbitrary probability. We use a layered approach. The
+first step is to limit the pool of random number to exclude numbers that are
+obviously composite. We first remove any number ending in 1,3,7 or 9. We then
+exclude numbers whose digital root is not 3, 6, or 9. This sharply reduces the
+probability of the random number being composite. We then use the Prime Number
+Theorem to find the probability that the selected number n is prime and use
+primality tests to increase the probability to an arbitrarily high degree that
+n is prime. We apply primality tests including Euler's test based on Fermat
+Little theorem and the Miller-Rabin test. We computed these conditional
+probabilities and implemented it using the GNU GMP library.
+",0,0,1,0,0,0
+16679,D-optimal Designs for Multinomial Logistic Models,"  We consider optimal designs for general multinomial logistic models, which
+cover baseline-category, cumulative, adjacent-categories, and
+continuation-ratio logit models, with proportional odds, non-proportional odds,
+or partial proportional odds assumption. We derive the corresponding Fisher
+information matrices in three different forms to facilitate their calculations,
+determine the conditions for their positive definiteness, and search for
+optimal designs. We conclude that, unlike the designs for binary responses, a
+feasible design for a multinomial logistic model may contain less experimental
+settings than parameters, which is of practical significance. We also conclude
+that even for a minimally supported design, a uniform allocation, which is
+typically used in practice, is not optimal in general for a multinomial
+logistic model. We develop efficient algorithms for searching D-optimal
+designs. Using examples based on real experiments, we show that the efficiency
+of an experiment can be significantly improved if our designs are adopted.
+",0,0,1,1,0,0
+16680,Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces,"  Learning to optimize - the idea that we can learn from data algorithms that
+optimize a numerical criterion - has recently been at the heart of a growing
+number of research efforts. One of the most challenging issues within this
+approach is to learn a policy that is able to optimize over classes of
+functions that are fairly different from the ones that it was trained on. We
+propose a novel way of framing learning to optimize as a problem of learning a
+good navigation policy on a partially observable loss surface. To this end, we
+develop Rover Descent, a solution that allows us to learn a fairly broad
+optimization policy from training on a small set of prototypical
+two-dimensional surfaces that encompasses the classically hard cases such as
+valleys, plateaus, cliffs and saddles and by using strictly zero-order
+information. We show that, without having access to gradient or curvature
+information, we achieve state-of-the-art convergence speed on optimization
+problems not presented at training time such as the Rosenbrock function and
+other hard cases in two dimensions. We extend our framework to optimize over
+high dimensional landscapes, while still handling only two-dimensional local
+landscape information and show good preliminary results.
+",0,0,0,1,0,0
+16681,Reordering of the Logistic Map with a Nonlinear Growth Rate,"  In the well known logistic map, the parameter of interest is weighted by a
+coefficient that decreases linearly when this parameter increases. Since such a
+linear decrease forms a specific case, we consider the more general case where
+this coefficient decreases nonlinearly as in a hyperbolic tangent relaxation of
+a system toward equilibrium. We show that, in this latter case, the asymptotic
+values obtained via iteration of the logistic map are considerably modified. We
+demonstrate that both the steepness of the nonlinear decrease as well as its
+upper and lower boundaries significantly alter the bifurcation diagram. New
+period doubling features and transitions to chaos appear, possibly leading to
+regimes with small periods. Computations with a variety of parameter values
+show that the logistic map can be significantly reordered in the case of a
+nonlinear growth rate.
+",0,1,1,0,0,0
+16682,Tensor network factorizations: Relationships between brain structural connectomes and traits,"  Advanced brain imaging techniques make it possible to measure individuals'
+structural connectomes in large cohort studies non-invasively. The structural
+connectome is initially shaped by genetics and subsequently refined by the
+environment. It is extremely interesting to study relationships between
+structural connectomes and environment factors or human traits, such as
+substance use and cognition. Due to limitations in structural connectome
+recovery, previous studies largely focus on functional connectomes. Questions
+remain about how well structural connectomes can explain variance in different
+human traits. Using a state-of-the-art structural connectome processing
+pipeline and a novel dimensionality reduction technique applied to data from
+the Human Connectome Project (HCP), we show strong relationships between
+structural connectomes and various human traits. Our dimensionality reduction
+approach uses a tensor characterization of the connectome and relies on a
+generalization of principal components analysis. We analyze over 1100 scans for
+1076 subjects from the HCP and the Sherbrooke test-retest data set, as well as
+$175$ human traits that measure domains including cognition, substance use,
+motor, sensory and emotion. We find that structural connectomes are associated
+with many traits. Specifically, fluid intelligence, language comprehension, and
+motor skills are associated with increased cortical-cortical brain structural
+connectivity, while the use of alcohol, tobacco, and marijuana are associated
+with decreased cortical-cortical connectivity.
+",0,0,0,1,0,0
+16683,Model-free prediction of noisy chaotic time series by deep learning,"  We present a deep neural network for a model-free prediction of a chaotic
+dynamical system from noisy observations. The proposed deep learning model aims
+to predict the conditional probability distribution of a state variable. The
+Long Short-Term Memory network (LSTM) is employed to model the nonlinear
+dynamics and a softmax layer is used to approximate a probability distribution.
+The LSTM model is trained by minimizing a regularized cross-entropy function.
+The LSTM model is validated against delay-time chaotic dynamical systems,
+Mackey-Glass and Ikeda equations. It is shown that the present LSTM makes a
+good prediction of the nonlinear dynamics by effectively filtering out the
+noise. It is found that the prediction uncertainty of a multiple-step forecast
+of the LSTM model is not a monotonic function of time; the predicted standard
+deviation may increase or decrease dynamically in time.
+",1,1,0,0,0,0
+16684,Robust XVA,"  We introduce an arbitrage-free framework for robust valuation adjustments. An
+investor trades a credit default swap portfolio with a risky counterparty, and
+hedges credit risk by taking a position in the counterparty bond. The investor
+does not know the expected rate of return of the counterparty bond, but he is
+confident that it lies within an uncertainty interval. We derive both upper and
+lower bounds for the XVA process of the portfolio, and show that these bounds
+may be recovered as solutions of nonlinear ordinary differential equations. The
+presence of collateralization and closeout payoffs leads to fundamental
+differences with respect to classical credit risk valuation. The value of the
+super-replicating portfolio cannot be directly obtained by plugging one of the
+extremes of the uncertainty interval in the valuation equation, but rather
+depends on the relation between the XVA replicating portfolio and the close-out
+value throughout the life of the transaction.
+",0,0,0,0,0,1
+16685,Rarefaction Waves for the Toda Equation via Nonlinear Steepest Descent,"  We apply the method of nonlinear steepest descent to compute the long-time
+asymptotics of the Toda lattice with steplike initial data corresponding to a
+rarefaction wave.
+",0,1,1,0,0,0
+16686,IRA codes derived from Gruenbaum graph,"  In this paper, we consider coding of short data frames (192 bits) by IRA
+codes. A new interleaver for the IRA codes based on a Gruenbaum graph is
+proposed. The difference of the proposed algorithm from known methods consists
+in the following: permutation is performed by using a match smaller interleaver
+which is derived from the Gruenbaum graph by finding in this graph a
+Hamiltonian path, enumerating the passed vertices in ascending order and
+passing them again in the permuted order through the edges which are not
+included in the Hamiltonian path. For the IRA code the obtained interleaver
+provides 0.7-0.8 db gain over a convolutional code decoded by Viterbi
+algorithm.
+",1,0,1,0,0,0
+16687,"Three- and four-electron integrals involving Gaussian geminals: fundamental integrals, upper bounds and recurrence relations","  We report the three main ingredients to calculate three- and four-electron
+integrals over Gaussian basis functions involving Gaussian geminal operators:
+fundamental integrals, upper bounds, and recurrence relations. In particular,
+we consider the three- and four-electron integrals that may arise in
+explicitly-correlated F12 methods. A straightforward method to obtain the
+fundamental integrals is given. We derive vertical, transfer and horizontal
+recurrence relations to build up angular momentum over the centers. Strong,
+simple and scaling-consistent upper bounds are also reported. This latest
+ingredient allows to compute only the $\order{N^2}$ significant three- and
+four-electron integrals, avoiding the computation of the very large number of
+negligible integrals.
+",0,1,0,0,0,0
+16688,HARP: Hierarchical Representation Learning for Networks,"  We present HARP, a novel method for learning low dimensional embeddings of a
+graph's nodes which preserves higher-order structural features. Our proposed
+method achieves this by compressing the input graph prior to embedding it,
+effectively avoiding troublesome embedding configurations (i.e. local minima)
+which can pose problems to non-convex optimization. HARP works by finding a
+smaller graph which approximates the global structure of its input. This
+simplified graph is used to learn a set of initial representations, which serve
+as good initializations for learning representations in the original, detailed
+graph. We inductively extend this idea, by decomposing a graph in a series of
+levels, and then embed the hierarchy of graphs from the coarsest one to the
+original graph. HARP is a general meta-strategy to improve all of the
+state-of-the-art neural algorithms for embedding graphs, including DeepWalk,
+LINE, and Node2vec. Indeed, we demonstrate that applying HARP's hierarchical
+paradigm yields improved implementations for all three of these methods, as
+evaluated on both classification tasks on real-world graphs such as DBLP,
+BlogCatalog, CiteSeer, and Arxiv, where we achieve a performance gain over the
+original implementations by up to 14% Macro F1.
+",1,0,0,0,0,0
+16689,Mixed penalization in convolutive nonnegative matrix factorization for blind speech dereverberation,"  When a signal is recorded in an enclosed room, it typically gets affected by
+reverberation. This degradation represents a problem when dealing with audio
+signals, particularly in the field of speech signal processing, such as
+automatic speech recognition. Although there are some approaches to deal with
+this issue that are quite satisfactory under certain conditions, constructing a
+method that works well in a general context still poses a significant
+challenge. In this article, we propose a method based on convolutive
+nonnegative matrix factorization that mixes two penalizers in order to impose
+certain characteristics over the time-frequency components of the restored
+signal and the reverberant components. An algorithm for implementing the method
+is described and tested. Comparisons of the results against those obtained with
+state of the art methods are presented, showing significant improvement.
+",1,0,0,0,0,0
+16690,Using Contour Trees in the Analysis and Visualization of Radio Astronomy Data Cubes,"  The current generation of radio and millimeter telescopes, particularly the
+Atacama Large Millimeter Array (ALMA), offers enormous advances in observing
+capabilities. While these advances represent an unprecedented opportunity to
+advance scientific understanding, the increased complexity in the spatial and
+spectral structure of even a single spectral line is hard to interpret. The
+complexity present in current ALMA data cubes therefore challenges not only the
+existing tools for fundamental analysis of these datasets, but also users'
+ability to explore and visualize their data. We have performed a feasibility
+study for applying forms of topological data analysis and visualization never
+before tested by the ALMA community. Through contour tree-based data analysis,
+we seek to improve upon existing data cube analysis and visualization
+workflows, in the forms of improved accuracy and speed in extracting features.
+In this paper, we review our design process in building effective analysis and
+visualization capabilities for the astrophysicist users. We summarize effective
+design practices, in particular, we identify domain-specific needs of
+simplicity, integrability and reproducibility, in order to best target and
+service the large astrophysics community.
+",1,1,0,0,0,0
+16691,The Price of BitCoin: GARCH Evidence from High Frequency Data,"  This is the first paper that estimates the price determinants of BitCoin in a
+Generalised Autoregressive Conditional Heteroscedasticity framework using high
+frequency data. Derived from a theoretical model, we estimate BitCoin
+transaction demand and speculative demand equations in a GARCH framework using
+hourly data for the period 2013-2018. In line with the theoretical model, our
+empirical results confirm that both the BitCoin transaction demand and
+speculative demand have a statistically significant impact on the BitCoin price
+formation. The BitCoin price responds negatively to the BitCoin velocity,
+whereas positive shocks to the BitCoin stock, interest rate and the size of the
+BitCoin economy exercise an upward pressure on the BitCoin price.
+",0,0,0,0,0,1
+16692,Application of a unified Kenmotsu-type formula for surfaces in Euclidean or Lorentzian three-space,"  Kenmotsu's formula describes surfaces in Euclidean 3-space by their mean
+curvature functions and Gauss maps. In Lorentzian 3-space,
+Akutagawa-Nishikawa's formula and Magid's formula are Kenmotsu-type formulas
+for spacelike surfaces and for timelike surfaces, respectively. We apply them
+to a few problems concerning rotational or helicoidal surfaces with constant
+mean curvature. Before that, we show that the three formulas above can be
+written in a unified single equation.
+",0,0,1,0,0,0
+16693,A Vector Matroid-Theoretic Approach in the Study of Structural Controllability Over F(z),"  In this paper, the structural controllability of the systems over F(z) is
+studied using a new mathematical method-matroids. Firstly, a vector matroid is
+defined over F(z). Secondly, the full rank conditions of [sI-A|B] are derived
+in terms of the concept related to matroid theory, such as rank, base and
+union. Then the sufficient condition for the linear system and composite system
+over F(z) to be structurally controllable is obtained. Finally, this paper
+gives several examples to demonstrate that the married-theoretic approach is
+simpler than other existing approaches.
+",1,0,0,0,0,0
+16694,From cold Fermi fluids to the hot QGP,"  Strongly coupled quantum fluids are found in different forms, including
+ultracold Fermi gases or tiny droplets of extremely hot Quark-Gluon Plasma.
+Although the systems differ in temperature by many orders of magnitude, they
+exhibit a similar almost inviscid fluid dynamical behavior. In this work, we
+summarize some of the recent theoretical developments toward better
+understanding this property in cold Fermi gases at and near unitarity.
+",0,1,0,0,0,0
+16695,Decomposition method related to saturated hyperball packings,"  In this paper we study the problem of hyperball (hypersphere) packings in
+$3$-dimensional hyperbolic space. We introduce a new definition of the
+non-compact saturated ball packings and describe to each saturated hyperball
+packing, a new procedure to get a decomposition of 3-dimensional hyperbolic
+space $\HYP$ into truncated tetrahedra. Therefore, in order to get a density
+upper bound for hyperball packings, it is sufficient to determine the density
+upper bound of hyperball packings in truncated simplices.
+",0,0,1,0,0,0
+16696,Dynamic interdependence and competition in multilayer networks,"  From critical infrastructure, to physiology and the human brain, complex
+systems rarely occur in isolation. Instead, the functioning of nodes in one
+system often promotes or suppresses the functioning of nodes in another.
+Despite advances in structural interdependence, modeling interdependence and
+other interactions between dynamic systems has proven elusive. Here we define a
+broadly applicable dynamic dependency link and develop a general framework for
+interdependent and competitive interactions between general dynamic systems. We
+apply our framework to studying interdependent and competitive synchronization
+in multi-layer oscillator networks and cooperative/competitive contagions in an
+epidemic model. Using a mean-field theory which we verify numerically, we find
+explosive transitions and rich behavior which is absent in percolation models
+including hysteresis, multi-stability and chaos. The framework presented here
+provides a powerful new way to model and understand many of the interacting
+complex systems which surround us.
+",0,1,0,0,0,0
+16697,Applications of L systems to group theory,"  L systems generalise context-free grammars by incorporating parallel
+rewriting, and generate languages such as EDT0L and ET0L that are strictly
+contained in the class of indexed languages. In this paper we show that many of
+the languages naturally appearing in group theory, and that were known to be
+indexed or context-sensitive, are in fact ET0L and in many cases EDT0L. For
+instance, the language of primitives in the free group on two generators, the
+Bridson-Gilman normal forms for the fundamental groups of 3-manifolds or
+orbifolds, and the co-word problem of Grigorchuk's group can be generated by L
+systems. To complement the result on primitives in free groups, we show that
+the language of primitives, and primitive sets, in free groups of rank higher
+than two is context-sensitive. We also show the existence of EDT0L and ET0L
+languages of intermediate growth.
+",1,0,1,0,0,0
+16698,"'Senator, We Sell Ads': Analysis of the 2016 Russian Facebook Ads Campaign","  One of the key aspects of the United States democracy is free and fair
+elections that allow for a peaceful transfer of power from one President to the
+next. The 2016 US presidential election stands out due to suspected foreign
+influence before, during, and after the election. A significant portion of that
+suspected influence was carried out via social media. In this paper, we look
+specifically at 3,500 Facebook ads allegedly purchased by the Russian
+government. These ads were released on May 10, 2018 by the US Congress House
+Intelligence Committee. We analyzed the ads using natural language processing
+techniques to determine textual and semantic features associated with the most
+effective ones. We clustered the ads over time into the various campaigns and
+the labeled parties associated with them. We also studied the effectiveness of
+Ads on an individual, campaign and party basis. The most effective ads tend to
+have less positive sentiment, focus on past events and are more specific and
+personalized in nature. The more effective campaigns also show such similar
+characteristics. The campaigns' duration and promotion of the Ads suggest a
+desire to sow division rather than sway the election.
+",1,0,0,0,0,0
+16699,On the k-Means/Median Cost Function,"  In this work, we study the $k$-means cost function. The (Euclidean) $k$-means
+problem can be described as follows: given a dataset $X \subseteq \mathbb{R}^d$
+and a positive integer $k$, find a set of $k$ centers $C \subseteq
+\mathbb{R}^d$ such that $\Phi(C, X) \stackrel{def}{=} \sum_{x \in X} \min_{c
+\in C} ||x - c||^2$ is minimized. Let $\Delta_k(X) \stackrel{def}{=} \min_{C
+\subseteq \mathbb{R}^d} \Phi(C, X)$ denote the cost of the optimal $k$-means
+solution. It is simple to observe that for any dataset $X$, $\Delta_k(X)$
+decreases as $k$ increases. We try to understand this behaviour more precisely.
+For any dataset $X \subseteq \mathbb{R}^d$, integer $k \geq 1$, and a small
+precision parameter $\varepsilon > 0$, let $\mathcal{L}_{X}^{k, \varepsilon}$
+denote the smallest integer such that $\Delta_{\mathcal{L}_{X}^{k,
+\varepsilon}}(X) \leq \varepsilon \cdot \Delta_{k}(X)$. We show upper and lower
+bounds on this quantity. Our techniques generalize for the metric $k$-median
+problem in arbitrary metrics and we give bounds in terms of the doubling
+dimension of the metric. Finally, we observe that for any dataset $X$, we can
+compute a set $S$ of size $O \left(\mathcal{L}_{X}^{k, \frac{\varepsilon}{c}}
+\right)$ such that $\Delta_{S}(X) \leq \varepsilon \cdot \Delta_k(X)$ using the
+$D^2$-sampling algorithm which is also known as the $k$-means++ seeding
+procedure. In the previous statement, $c$ is some fixed constant. We also
+discuss some applications of our bounds.
+",1,0,0,0,0,0
+16700,Secure and Reconfigurable Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT),"  The Internet of things (IoT) is revolutionizing the management and control of
+automated systems leading to a paradigm shift in areas such as smart homes,
+smart cities, health care, transportation, etc. The IoT technology is also
+envisioned to play an important role in improving the effectiveness of military
+operations in battlefields. The interconnection of combat equipment and other
+battlefield resources for coordinated automated decisions is referred to as the
+Internet of battlefield things (IoBT). IoBT networks are significantly
+different from traditional IoT networks due to the battlefield specific
+challenges such as the absence of communication infrastructure, and the
+susceptibility of devices to cyber and physical attacks. The combat efficiency
+and coordinated decision-making in war scenarios depends highly on real-time
+data collection, which in turn relies on the connectivity of the network and
+the information dissemination in the presence of adversaries. This work aims to
+build the theoretical foundations of designing secure and reconfigurable IoBT
+networks. Leveraging the theories of stochastic geometry and mathematical
+epidemiology, we develop an integrated framework to study the communication of
+mission-critical data among different types of network devices and consequently
+design the network in a cost effective manner.
+",1,0,0,0,0,0
+16701,Symmetric Variational Autoencoder and Connections to Adversarial Learning,"  A new form of the variational autoencoder (VAE) is proposed, based on the
+symmetric Kullback-Leibler divergence. It is demonstrated that learning of the
+resulting symmetric VAE (sVAE) has close connections to previously developed
+adversarial-learning methods. This relationship helps unify the previously
+distinct techniques of VAE and adversarially learning, and provides insights
+that allow us to ameliorate shortcomings with some previously developed
+adversarial methods. In addition to an analysis that motivates and explains the
+sVAE, an extensive set of experiments validate the utility of the approach.
+",0,0,0,1,0,0
+16702,"Matrix Product Unitaries: Structure, Symmetries, and Topological Invariants","  Matrix Product Vectors form the appropriate framework to study and classify
+one-dimensional quantum systems. In this work, we develop the structure theory
+of Matrix Product Unitary operators (MPUs) which appear e.g. in the description
+of time evolutions of one-dimensional systems. We prove that all MPUs have a
+strict causal cone, making them Quantum Cellular Automata (QCAs), and derive a
+canonical form for MPUs which relates different MPU representations of the same
+unitary through a local gauge. We use this canonical form to prove an Index
+Theorem for MPUs which gives the precise conditions under which two MPUs are
+adiabatically connected, providing an alternative derivation to that of
+[Commun. Math. Phys. 310, 419 (2012), arXiv:0910.3675] for QCAs. We also
+discuss the effect of symmetries on the MPU classification. In particular, we
+characterize the tensors corresponding to MPU that are invariant under
+conjugation, time reversal, or transposition. In the first case, we give a full
+characterization of all equivalence classes. Finally, we give several examples
+of MPU possessing different symmetries.
+",0,1,0,0,0,0
+16703,Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning,"  Deep Learning has recently become hugely popular in machine learning,
+providing significant improvements in classification accuracy in the presence
+of highly-structured and large databases.
+Researchers have also considered privacy implications of deep learning.
+Models are typically trained in a centralized manner with all the data being
+processed by the same training algorithm. If the data is a collection of users'
+private data, including habits, personal pictures, geographical positions,
+interests, and more, the centralized server will have access to sensitive
+information that could potentially be mishandled. To tackle this problem,
+collaborative deep learning models have recently been proposed where parties
+locally train their deep learning structures and only share a subset of the
+parameters in the attempt to keep their respective training sets private.
+Parameters can also be obfuscated via differential privacy (DP) to make
+information extraction even more challenging, as proposed by Shokri and
+Shmatikov at CCS'15.
+Unfortunately, we show that any privacy-preserving collaborative deep
+learning is susceptible to a powerful attack that we devise in this paper. In
+particular, we show that a distributed, federated, or decentralized deep
+learning approach is fundamentally broken and does not protect the training
+sets of honest participants. The attack we developed exploits the real-time
+nature of the learning process that allows the adversary to train a Generative
+Adversarial Network (GAN) that generates prototypical samples of the targeted
+training set that was meant to be private (the samples generated by the GAN are
+intended to come from the same distribution as the training data).
+Interestingly, we show that record-level DP applied to the shared parameters of
+the model, as suggested in previous work, is ineffective (i.e., record-level DP
+is not designed to address our attack).
+",1,0,0,1,0,0
+16704,"A. G. W. Cameron 1925-2005, Biographical Memoir, National Academy of Sciences","  Alastair Graham Walker Cameron was an astrophysicist and planetary scientist
+of broad interests and exceptional originality. A founder of the field of
+nuclear astrophysics, he developed the theoretical understanding of the
+chemical elements’ origins and made pioneering connections between the
+abundances of elements in meteorites to advance the theory that the Moon
+originated from a giant impact with the young Earth by an object at least the
+size of Mars. Cameron was an early and persistent exploiter of computer
+technology in the theoretical study of complex astronomical systems—including
+nuclear reactions in supernovae, the structure of neutron stars, and planetary
+collisions.
+",0,1,0,0,0,0
+16705,"Realization of ""Time Crystal"" Lagrangians and Emergent Sisyphus Dynamics","  We demonstrate how non-convex ""time crystal"" Lagrangians arise in the
+effective description of conventional, realizable physical systems. Such
+embeddings allow for the resolution of dynamical singularities that arise in
+the reduced description. Sisyphus dynamics, featuring intervals of forward
+motion interrupted by quick resets, is a generic consequence. Near the would-be
+singularity of the time crystal, we find striking microstructure.
+",0,1,0,0,0,0
+16706,Gradient Normalization & Depth Based Decay For Deep Learning,"  In this paper we introduce a novel method of gradient normalization and decay
+with respect to depth. Our method leverages the simple concept of normalizing
+all gradients in a deep neural network, and then decaying said gradients with
+respect to their depth in the network. Our proposed normalization and decay
+techniques can be used in conjunction with most current state of the art
+optimizers and are a very simple addition to any network. This method, although
+simple, showed improvements in convergence time on state of the art networks
+such as DenseNet and ResNet on image classification tasks, as well as on an
+LSTM for natural language processing tasks.
+",1,0,0,1,0,0
+16707,CARET analysis of multithreaded programs,"  Dynamic Pushdown Networks (DPNs) are a natural model for multithreaded
+programs with (recursive) procedure calls and thread creation. On the other
+hand, CARET is a temporal logic that allows to write linear temporal formulas
+while taking into account the matching between calls and returns. We consider
+in this paper the model-checking problem of DPNs against CARET formulas. We
+show that this problem can be effectively solved by a reduction to the
+emptiness problem of Büchi Dynamic Pushdown Systems. We then show that CARET
+model checking is also decidable for DPNs communicating with locks. Our results
+can, in particular, be used for the detection of concurrent malware.
+",1,0,0,0,0,0
+16708,Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data,"  A new approach of solving the ill-conditioned inverse problem for analytical
+continuation is proposed. The root of the problem lies in the fact that even
+tiny noise of imaginary-time input data has a serious impact on the inferred
+real-frequency spectra. By means of a modern regularization technique, we
+eliminate redundant degrees of freedom that essentially carry the noise,
+leaving only relevant information unaffected by the noise. The resultant
+spectrum is represented with minimal bases and thus a stable analytical
+continuation is achieved. This framework further provides a tool for analyzing
+to what extent the Monte Carlo data need to be accurate to resolve details of
+an expected spectral function.
+",0,1,0,1,0,0
+16709,Distral: Robust Multitask Reinforcement Learning,"  Most deep reinforcement learning algorithms are data inefficient in complex
+and rich environments, limiting their applicability to many scenarios. One
+direction for improving data efficiency is multitask learning with shared
+neural network parameters, where efficiency may be improved through transfer
+across related tasks. In practice, however, this is not usually observed,
+because gradients from different tasks can interfere negatively, making
+learning unstable and sometimes even less data efficient. Another issue is the
+different reward schemes between tasks, which can easily lead to one task
+dominating the learning of a shared model. We propose a new approach for joint
+training of multiple tasks, which we refer to as Distral (Distill & transfer
+learning). Instead of sharing parameters between the different workers, we
+propose to share a ""distilled"" policy that captures common behaviour across
+tasks. Each worker is trained to solve its own task while constrained to stay
+close to the shared policy, while the shared policy is trained by distillation
+to be the centroid of all task policies. Both aspects of the learning process
+are derived by optimizing a joint objective function. We show that our approach
+supports efficient transfer on complex 3D environments, outperforming several
+related methods. Moreover, the proposed learning process is more robust and
+more stable---attributes that are critical in deep reinforcement learning.
+",1,0,0,1,0,0
+16710,Finding the number density of atomic vapor by studying its absorption profile,"  We demonstrate a technique for obtaining the density of atomic vapor, by
+doing a fit of the resonant absorption spectrum to a density-matrix model. In
+order to demonstrate the usefulness of the technique, we apply it to absorption
+in the ${\rm D_2}$ line of a Cs vapor cell at room temperature. The lineshape
+of the spectrum is asymmetric due to the role of open transitions. This
+asymmetry is explained in the model using transit-time relaxation as the atoms
+traverse the laser beam. We also obtain the latent heat of evaporation by
+studying the number density as a function of temperature close to room
+temperature.
+",0,1,0,0,0,0
+16711,Rigidity for von Neumann algebras given by locally compact groups and their crossed products,"  We prove the first rigidity and classification theorems for crossed product
+von Neumann algebras given by actions of non-discrete, locally compact groups.
+We prove that for arbitrary free probability measure preserving actions of
+connected simple Lie groups of real rank one, the crossed product has a unique
+Cartan subalgebra up to unitary conjugacy. We then deduce a W* strong rigidity
+theorem for irreducible actions of products of such groups. More generally, our
+results hold for products of locally compact groups that are nonamenable,
+weakly amenable and that belong to Ozawa's class S.
+",0,0,1,0,0,0
+16712,Quantum Singwi-Tosi-Land-Sjoelander approach for interacting inhomogeneous systems under electromagnetic fields: Comparison with exact results,"  For inhomogeneous interacting electronic systems under a time-dependent
+electromagnetic perturbation, we derive the linear equation for response
+functions in a quantum mechanical manner. It is a natural extension of the
+original semi-classical Singwi-Tosi-Land-Sjoelander (STLS) approach for an
+electron gas. The factorization ansatz for the two-particle distribution is an
+indispensable ingredient in the STLS approaches for determination of the
+response function and the pair correlation function. In this study, we choose
+an analytically solvable interacting two-electron system as the target for
+which we examine the validity of the approximation. It is demonstrated that the
+STLS response function reproduces well the exact one for low-energy
+excitations. The interaction energy contributed from the STLS response function
+is also discussed.
+",0,1,0,0,0,0
+16713,3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures,"  This paper analyzes the use of 3D Convolutional Neural Networks for brain
+tumor segmentation in MR images. We address the problem using three different
+architectures that combine fine and coarse features to obtain the final
+segmentation. We compare three different networks that use multi-resolution
+features in terms of both design and performance and we show that they improve
+their single-resolution counterparts.
+",0,0,0,1,0,0
+16714,Learning Random Fourier Features by Hybrid Constrained Optimization,"  The kernel embedding algorithm is an important component for adapting kernel
+methods to large datasets. Since the algorithm consumes a major computation
+cost in the testing phase, we propose a novel teacher-learner framework of
+learning computation-efficient kernel embeddings from specific data. In the
+framework, the high-precision embeddings (teacher) transfer the data
+information to the computation-efficient kernel embeddings (learner). We
+jointly select informative embedding functions and pursue an orthogonal
+transformation between two embeddings. We propose a novel approach of
+constrained variational expectation maximization (CVEM), where the alternate
+direction method of multiplier (ADMM) is applied over a nonconvex domain in the
+maximization step. We also propose two specific formulations based on the
+prevalent Random Fourier Feature (RFF), the masked and blocked version of
+Computation-Efficient RFF (CERF), by imposing a random binary mask or a block
+structure on the transformation matrix. By empirical studies of several
+applications on different real-world datasets, we demonstrate that the CERF
+significantly improves the performance of kernel methods upon the RFF, under
+certain arithmetic operation requirements, and suitable for structured matrix
+multiplication in Fastfood type algorithms.
+",1,0,0,1,0,0
+16715,Min-max formulas and other properties of certain classes of nonconvex effective Hamiltonians,"  This paper is the first attempt to systematically study properties of the
+effective Hamiltonian $\overline{H}$ arising in the periodic homogenization of
+some coercive but nonconvex Hamilton-Jacobi equations. Firstly, we introduce a
+new and robust decomposition method to obtain min-max formulas for a class of
+nonconvex $\overline{H}$. Secondly, we analytically and numerically investigate
+other related interesting phenomena, such as ""quasi-convexification"" and
+breakdown of symmetry, of $\overline{H}$ from other typical nonconvex
+Hamiltonians. Finally, in the appendix, we show that our new method and those a
+priori formulas from the periodic setting can be used to obtain stochastic
+homogenization for same class of nonconvex Hamilton-Jacobi equations. Some
+conjectures and problems are also proposed.
+",0,0,1,0,0,0
+16716,The Prescribed Ricci Curvature Problem on Homogeneous Spaces with Intermediate Subgroups,"  Consider a compact Lie group $G$ and a closed subgroup $H<G$. Suppose
+$\mathcal M$ is the set of $G$-invariant Riemannian metrics on the homogeneous
+space $M=G/H$. We obtain a sufficient condition for the existence of
+$g\in\mathcal M$ and $c>0$ such that the Ricci curvature of $g$ equals $cT$ for
+a given $T\in\mathcal M$. This condition is also necessary if the isotropy
+representation of $M$ splits into two inequivalent irreducible summands.
+Immediate and potential applications include new existence results for Ricci
+iterations.
+",0,0,1,0,0,0
+16717,Solving Boundary Value Problem for a Nonlinear Stationary Controllable System with Synthesizing Control,"  An algorithm for constructing a control function that transfers a wide class
+of stationary nonlinear systems of ordinary differential equations from an
+initial state to a final state under certain control restrictions is proposed.
+The algorithm is designed to be convenient for numerical implementation. A
+constructive criterion of the desired transfer possibility is presented. The
+problem of an interorbital flight is considered as a test example and it is
+simulated numerically with the presented method.
+",0,0,1,0,0,0
+16718,Modelling and prediction of financial trading networks: An application to the NYMEX natural gas futures market,"  Over the last few years there has been a growing interest in using financial
+trading networks to understand the microstructure of financial markets. Most of
+the methodologies developed so far for this purpose have been based on the
+study of descriptive summaries of the networks such as the average node degree
+and the clustering coefficient. In contrast, this paper develops novel
+statistical methods for modeling sequences of financial trading networks. Our
+approach uses a stochastic blockmodel to describe the structure of the network
+during each period, and then links multiple time periods using a hidden Markov
+model. This structure allows us to identify events that affect the structure of
+the market and make accurate short-term prediction of future transactions. The
+methodology is illustrated using data from the NYMEX natural gas futures market
+from January 2005 to December 2008.
+",0,0,0,1,0,0
+16719,A comparative study of fairness-enhancing interventions in machine learning,"  Computers are increasingly used to make decisions that have significant
+impact in people's lives. Often, these predictions can affect different
+population subgroups disproportionately. As a result, the issue of fairness has
+received much recent interest, and a number of fairness-enhanced classifiers
+and predictors have appeared in the literature. This paper seeks to study the
+following questions: how do these different techniques fundamentally compare to
+one another, and what accounts for the differences? Specifically, we seek to
+bring attention to many under-appreciated aspects of such fairness-enhancing
+interventions. Concretely, we present the results of an open benchmark we have
+developed that lets us compare a number of different algorithms under a variety
+of fairness measures, and a large number of existing datasets. We find that
+although different algorithms tend to prefer specific formulations of fairness
+preservations, many of these measures strongly correlate with one another. In
+addition, we find that fairness-preserving algorithms tend to be sensitive to
+fluctuations in dataset composition (simulated in our benchmark by varying
+training-test splits), indicating that fairness interventions might be more
+brittle than previously thought.
+",0,0,0,1,0,0
+16720,Predicting Opioid Relapse Using Social Media Data,"  Opioid addiction is a severe public health threat in the U.S, causing massive
+deaths and many social problems. Accurate relapse prediction is of practical
+importance for recovering patients since relapse prediction promotes timely
+relapse preventions that help patients stay clean. In this paper, we introduce
+a Generative Adversarial Networks (GAN) model to predict the addiction relapses
+based on sentiment images and social influences. Experimental results on real
+social media data from Reddit.com demonstrate that the GAN model delivers a
+better performance than comparable alternative techniques. The sentiment images
+generated by the model show that relapse is closely connected with two emotions
+`joy' and `negative'. This work is one of the first attempts to predict
+relapses using massive social media data and generative adversarial nets. The
+proposed method, combined with knowledge of social media mining, has the
+potential to revolutionize the practice of opioid addiction prevention and
+treatment.
+",1,0,0,0,0,0
+16721,"Self-Trapping of G-Mode Oscillations in Relativistic Thin Disks, Revisited","  We examine by a perturbation method how the self-trapping of g-mode
+oscillations in geometrically thin relativistic disks is affected by uniform
+vertical magnetic fields. Disks which we consider are isothermal in the
+vertical direction, but are truncated at a certain height by presence of hot
+coronae. We find that the characteristics of self-trapping of axisymmetric
+g-mode oscillations in non-magnetized disks is kept unchanged in magnetized
+disks at least till a strength of the fields, depending on vertical thickness
+of disks. These magnetic fields become stronger as the disk becomes thinner.
+This result suggests that trapped g-mode oscillations still remain as one of
+possible candidates of quasi-periodic oscillations observed in black-hole and
+neutron-star X-ray binaries in the cases where vertical magnetic fields in
+disks are weak.
+",0,1,0,0,0,0
+16722,Computable geometric complex analysis and complex dynamics,"  We discuss computability and computational complexity of conformal mappings
+and their boundary extensions. As applications, we review the state of the art
+regarding computability and complexity of Julia sets, their invariant measures
+and external rays impressions.
+",0,0,1,0,0,0
+16723,PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes,"  Estimating the 6D pose of known objects is important for robots to interact
+with the real world. The problem is challenging due to the variety of objects
+as well as the complexity of a scene caused by clutter and occlusions between
+objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network
+for 6D object pose estimation. PoseCNN estimates the 3D translation of an
+object by localizing its center in the image and predicting its distance from
+the camera. The 3D rotation of the object is estimated by regressing to a
+quaternion representation. We also introduce a novel loss function that enables
+PoseCNN to handle symmetric objects. In addition, we contribute a large scale
+video dataset for 6D object pose estimation named the YCB-Video dataset. Our
+dataset provides accurate 6D poses of 21 objects from the YCB dataset observed
+in 92 videos with 133,827 frames. We conduct extensive experiments on our
+YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is
+highly robust to occlusions, can handle symmetric objects, and provide accurate
+pose estimation using only color images as input. When using depth data to
+further refine the poses, our approach achieves state-of-the-art results on the
+challenging OccludedLINEMOD dataset. Our code and dataset are available at
+this https URL.
+",1,0,0,0,0,0
+16724,MIP Formulations for the Steiner Forest Problem,"  The Steiner Forest problem is among the fundamental network design problems.
+Finding tight linear programming bounds for the problem is the key for both
+fast Branch-and-Bound algorithms and good primal-dual approximations. On the
+theoretical side, the best known bound can be obtained from an integer program
+[KLSv08]. It guarantees a value that is a (2-eps)-approximation of the integer
+optimum. On the practical side, bounds from a mixed integer program by Magnanti
+and Raghavan [MR05] are very close to the integer optimum in computational
+experiments, but the size of the model limits its practical usefulness. We
+compare a number of known integer programming formulations for the problem and
+propose three new formulations. We can show that the bounds from our two new
+cut-based formulations for the problem are within a factor of 2 of the integer
+optimum. In our experiments, the formulations prove to be both tractable and
+provide better bounds than all other tractable formulations. In particular, the
+factor to the integer optimum is much better than 2 in the experiments.
+",1,0,0,0,0,0
+16725,Luminescence in germania-silica fibers in 1-2 μm region,"  We analyze the origins of the luminescence in germania-silica fibers with
+high germanium concentration (about 30 mol. % GeO2) in the region 1-2 {\mu}m
+with a laser pump at the wavelength 532 nm. We show that such fibers
+demonstrate the high level of luminescence which unlikely allows the
+observation of photon triplets, generated in a third-order spontaneous
+parametric down-conversion process in such fibers. The only efficient approach
+to the luminescence reduction is the hydrogen saturation of fiber samples,
+however, even in this case the level of residual luminescence is still too high
+for three-photon registration.
+",0,1,0,0,0,0
+16726,Boundedness in languages of infinite words,"  We define a new class of languages of $\omega$-words, strictly extending
+$\omega$-regular languages.
+One way to present this new class is by a type of regular expressions. The
+new expressions are an extension of $\omega$-regular expressions where two new
+variants of the Kleene star $L^*$ are added: $L^B$ and $L^S$. These new
+exponents are used to say that parts of the input word have bounded size, and
+that parts of the input can have arbitrarily large sizes, respectively. For
+instance, the expression $(a^Bb)^\omega$ represents the language of infinite
+words over the letters $a,b$ where there is a common bound on the number of
+consecutive letters $a$. The expression $(a^Sb)^\omega$ represents a similar
+language, but this time the distance between consecutive $b$'s is required to
+tend toward the infinite.
+We develop a theory for these languages, with a focus on decidability and
+closure. We define an equivalent automaton model, extending Büchi automata.
+The main technical result is a complementation lemma that works for languages
+where only one type of exponent---either $L^B$ or $L^S$---is used.
+We use the closure and decidability results to obtain partial decidability
+results for the logic MSOLB, a logic obtained by extending monadic second-order
+logic with new quantifiers that speak about the size of sets.
+",1,0,0,0,0,0
+16727,Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications,"  Maximum regularized likelihood estimators (MRLEs) are arguably the most
+established class of estimators in high-dimensional statistics. In this paper,
+we derive guarantees for MRLEs in Kullback-Leibler divergence, a general
+measure of prediction accuracy. We assume only that the densities have a convex
+parametrization and that the regularization is definite and positive
+homogenous. The results thus apply to a very large variety of models and
+estimators, such as tensor regression and graphical models with convex and
+non-convex regularized methods. A main conclusion is that MRLEs are broadly
+consistent in prediction - regardless of whether restricted eigenvalues or
+similar conditions hold.
+",0,0,1,1,0,0
+16728,Emergent $\mathrm{SU}(4)$ Symmetry in $α$-ZrCl$_3$ and Crystalline Spin-Orbital Liquids,"  While the enhancement of the spin-space symmetry from the usual
+$\mathrm{SU}(2)$ to $\mathrm{SU}(N)$ is promising for finding nontrivial
+quantum spin liquids, its realization in magnetic materials remains
+challenging. Here we propose a new mechanism by which the $\mathrm{SU}(4)$
+symmetry emerges in the strong spin-orbit coupling limit. In $d^1$ transition
+metal compounds with edge-sharing anion octahedra, the spin-orbit coupling
+gives rise to strongly bond-dependent and apparently $\mathrm{SU}(4)$-breaking
+hopping between the $J_\textrm{eff}=3/2$ quartets. However, in the honeycomb
+structure, a gauge transformation maps the system to an
+$\mathrm{SU}(4)$-symmetric Hubbard model. In the strong repulsion limit at
+quarter filling, as realized in $\alpha$-ZrCl$_3,$ the low-energy effective
+model is the $\mathrm{SU}(4)$ Heisenberg model on the honeycomb lattice, which
+cannot have a trivial gapped ground state and is expected to host a gapless
+spin-orbital liquid. By generalizing this model to other three-dimensional
+lattices, we also propose crystalline spin-orbital liquids protected by this
+emergent $\mathrm{SU}(4)$ symmetry and space group symmetries.
+",0,1,0,0,0,0
+16729,Central elements of the Jennings basis and certain Morita invariants,"  From Morita theoretic viewpoint, computing Morita invariants is important. We
+prove that the intersection of the center and the $n$th (right) socle $ZS^n(A)
+:= Z(A) \cap \operatorname{Soc}^n(A)$ of a finite-dimensional algebra $A$ is a
+Morita invariant; This is a generalization of important Morita invariants ---
+the center $Z(A)$ and the Reynolds ideal $ZS^1(A)$. As an example, we also
+studied $ZS^n(FG)$ for the group algebra $FG$ of a finite $p$-group $G$ over a
+field $F$ of positive characteristic $p$. Such an algebra has a basis along the
+socle filtration, known as the Jennings basis. We prove certain elements of the
+Jennings basis are central and hence form a linearly independent set of
+$ZS^n(FG)$. In fact, such elements form a basis of $ZS^n(FG)$ for every integer
+$1 \le n \le p$ if $G$ is powerful. As a corollary we have
+$\operatorname{Soc}^p(FG) \subseteq Z(FG)$ if $G$ is powerful.
+",0,0,1,0,0,0
+16730,Linear Disentangled Representation Learning for Facial Actions,"  Limited annotated data available for the recognition of facial expression and
+action units embarrasses the training of deep networks, which can learn
+disentangled invariant features. However, a linear model with just several
+parameters normally is not demanding in terms of training data. In this paper,
+we propose an elegant linear model to untangle confounding factors in
+challenging realistic multichannel signals such as 2D face videos. The simple
+yet powerful model does not rely on huge training data and is natural for
+recognizing facial actions without explicitly disentangling the identity. Base
+on well-understood intuitive linear models such as Sparse Representation based
+Classification (SRC), previous attempts require a prepossessing of explicit
+decoupling which is practically inexact. Instead, we exploit the low-rank
+property across frames to subtract the underlying neutral faces which are
+modeled jointly with sparse representation on the action components with group
+sparsity enforced. On the extended Cohn-Kanade dataset (CK+), our one-shot
+automatic method on raw face videos performs as competitive as SRC applied on
+manually prepared action components and performs even better than SRC in terms
+of true positive rate. We apply the model to the even more challenging task of
+facial action unit recognition, verified on the MPI Face Video Database
+(MPI-VDB) achieving a decent performance. All the programs and data have been
+made publicly available.
+",1,0,0,1,0,0
+16731,ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?,"  Generating molecules with desired chemical properties is important for drug
+discovery. The use of generative neural networks is promising for this task.
+However, from visual inspection, it often appears that generated samples lack
+diversity. In this paper, we quantify this internal chemical diversity, and we
+raise the following challenge: can a nontrivial AI model reproduce natural
+chemical diversity for desired molecules? To illustrate this question, we
+consider two generative models: a Reinforcement Learning model and the recently
+introduced ORGAN. Both fail at this challenge. We hope this challenge will
+stimulate research in this direction.
+",1,0,0,1,0,0
+16732,"Nodal domains, spectral minimal partitions, and their relation to Aharonov-Bohm operators","  This survey is a short version of a chapter written by the first two authors
+in the book [A. Henrot, editor. Shape optimization and spectral theory. Berlin:
+De Gruyter, 2017] (where more details and references are given) but we have
+decided here to put more emphasis on the role of the Aharonov-Bohm operators
+which appear to be a useful tool coming from physics for understanding a
+problem motivated either by spectral geometry or dynamics of population.
+Similar questions appear also in Bose-Einstein theory. Finally some open
+problems which might be of interest are mentioned.
+",0,0,1,0,0,0
+16733,Optimal control of two qubits via a single cavity drive in circuit quantum electrodynamics,"  Optimization of the fidelity of control operations is of critical importance
+in the pursuit of fault-tolerant quantum computation. We apply optimal control
+techniques to demonstrate that a single drive via the cavity in circuit quantum
+electrodynamics can implement a high-fidelity two-qubit all-microwave gate that
+directly entangles the qubits via the mutual qubit-cavity couplings. This is
+performed by driving at one of the qubits' frequencies which generates a
+conditional two-qubit gate, but will also generate other spurious interactions.
+These optimal control techniques are used to find pulse shapes that can perform
+this two-qubit gate with high fidelity, robust against errors in the system
+parameters. The simulations were all performed using experimentally relevant
+parameters and constraints.
+",0,1,0,0,0,0
+16734,Modular Representation of Layered Neural Networks,"  Layered neural networks have greatly improved the performance of various
+applications including image processing, speech recognition, natural language
+processing, and bioinformatics. However, it is still difficult to discover or
+interpret knowledge from the inference provided by a layered neural network,
+since its internal representation has many nonlinear and complex parameters
+embedded in hierarchical layers. Therefore, it becomes important to establish a
+new methodology by which layered neural networks can be understood.
+In this paper, we propose a new method for extracting a global and simplified
+structure from a layered neural network. Based on network analysis, the
+proposed method detects communities or clusters of units with similar
+connection patterns. We show its effectiveness by applying it to three use
+cases. (1) Network decomposition: it can decompose a trained neural network
+into multiple small independent networks thus dividing the problem and reducing
+the computation time. (2) Training assessment: the appropriateness of a trained
+result with a given hyperparameter or randomly chosen initial parameters can be
+evaluated by using a modularity index. And (3) data analysis: in practical data
+it reveals the community structure in the input, hidden, and output layers,
+which serves as a clue for discovering knowledge from a trained neural network.
+",1,0,0,1,0,0
+16735,A Guide to General-Purpose Approximate Bayesian Computation Software,"  This Chapter, ""A Guide to General-Purpose ABC Software"", is to appear in the
+forthcoming Handbook of Approximate Bayesian Computation (2018). We present
+general-purpose software to perform Approximate Bayesian Computation (ABC) as
+implemented in the R-packages abc and EasyABC and the c++ program ABCtoolbox.
+With simple toy models we demonstrate how to perform parameter inference, model
+selection, validation and optimal choice of summary statistics. We demonstrate
+how to combine ABC with Markov Chain Monte Carlo and describe a realistic
+population genetics application.
+",0,0,0,1,0,0
+16736,Feature Selection Facilitates Learning Mixtures of Discrete Product Distributions,"  Feature selection can facilitate the learning of mixtures of discrete random
+variables as they arise, e.g. in crowdsourcing tasks. Intuitively, not all
+workers are equally reliable but, if the less reliable ones could be
+eliminated, then learning should be more robust. By analogy with Gaussian
+mixture models, we seek a low-order statistical approach, and here introduce an
+algorithm based on the (pairwise) mutual information. This induces an order
+over workers that is well structured for the `one coin' model. More generally,
+it is justified by a goodness-of-fit measure and is validated empirically.
+Improvement in real data sets can be substantial.
+",1,0,0,1,0,0
+16737,3D Human Pose Estimation on a Configurable Bed from a Pressure Image,"  Robots have the potential to assist people in bed, such as in healthcare
+settings, yet bedding materials like sheets and blankets can make observation
+of the human body difficult for robots. A pressure-sensing mat on a bed can
+provide pressure images that are relatively insensitive to bedding materials.
+However, prior work on estimating human pose from pressure images has been
+restricted to 2D pose estimates and flat beds. In this work, we present two
+convolutional neural networks to estimate the 3D joint positions of a person in
+a configurable bed from a single pressure image. The first network directly
+outputs 3D joint positions, while the second outputs a kinematic model that
+includes estimated joint angles and limb lengths. We evaluated our networks on
+data from 17 human participants with two bed configurations: supine and seated.
+Our networks achieved a mean joint position error of 77 mm when tested with
+data from people outside the training set, outperforming several baselines. We
+also present a simple mechanical model that provides insight into ambiguity
+associated with limbs raised off of the pressure mat, and demonstrate that
+Monte Carlo dropout can be used to estimate pose confidence in these
+situations. Finally, we provide a demonstration in which a mobile manipulator
+uses our network's estimated kinematic model to reach a location on a person's
+body in spite of the person being seated in a bed and covered by a blanket.
+",1,0,0,0,0,0
+16738,Multilingual Hierarchical Attention Networks for Document Classification,"  Hierarchical attention networks have recently achieved remarkable performance
+for document classification in a given language. However, when multilingual
+document collections are considered, training such models separately for each
+language entails linear parameter growth and lack of cross-language transfer.
+Learning a single multilingual model with fewer parameters is therefore a
+challenging but potentially beneficial objective. To this end, we propose
+multilingual hierarchical attention networks for learning document structures,
+with shared encoders and/or shared attention mechanisms across languages, using
+multi-task learning and an aligned semantic space as input. We evaluate the
+proposed models on multilingual document classification with disjoint label
+sets, on a large dataset which we provide, with 600k news documents in 8
+languages, and 5k labels. The multilingual models outperform monolingual ones
+in low-resource as well as full-resource settings, and use fewer parameters,
+thus confirming their computational efficiency and the utility of
+cross-language transfer.
+",1,0,0,0,0,0
+16739,The complexity of recognizing minimally tough graphs,"  Let $t$ be a positive real number. A graph is called $t$-tough, if the
+removal of any cutset $S$ leaves at most $|S|/t$ components. The toughness of a
+graph is the largest $t$ for which the graph is $t$-tough. A graph is minimally
+$t$-tough, if the toughness of the graph is $t$ and the deletion of any edge
+from the graph decreases the toughness. The complexity class DP is the set of
+all languages that can be expressed as the intersection of a language in NP and
+a language in coNP. We prove that recognizing minimally $t$-tough graphs is
+DP-complete for any positive integer $t$ and for any positive rational number
+$t \leq 1/2$.
+",1,0,0,0,0,0
+16740,Homeostatic plasticity and external input shape neural network dynamics,"  In vitro and in vivo spiking activity clearly differ. Whereas networks in
+vitro develop strong bursts separated by periods of very little spiking
+activity, in vivo cortical networks show continuous activity. This is puzzling
+considering that both networks presumably share similar single-neuron dynamics
+and plasticity rules. We propose that the defining difference between in vitro
+and in vivo dynamics is the strength of external input. In vitro, networks are
+virtually isolated, whereas in vivo every brain area receives continuous input.
+We analyze a model of spiking neurons in which the input strength, mediated by
+spike rate homeostasis, determines the characteristics of the dynamical state.
+In more detail, our analytical and numerical results on various network
+topologies show consistently that under increasing input, homeostatic
+plasticity generates distinct dynamic states, from bursting, to
+close-to-critical, reverberating and irregular states. This implies that the
+dynamic state of a neural network is not fixed but can readily adapt to the
+input strengths. Indeed, our results match experimental spike recordings in
+vitro and in vivo: the in vitro bursting behavior is consistent with a state
+generated by very low network input (< 0.1%), whereas in vivo activity suggests
+that on the order of 1% recorded spikes are input-driven, resulting in
+reverberating dynamics. Importantly, this predicts that one can abolish the
+ubiquitous bursts of in vitro preparations, and instead impose dynamics
+comparable to in vivo activity by exposing the system to weak long-term
+stimulation, thereby opening new paths to establish an in vivo-like assay in
+vitro for basic as well as neurological studies.
+",0,0,0,0,1,0
+16741,How Sensitive are Sensitivity-Based Explanations?,"  We propose a simple objective evaluation measure for explanations of a
+complex black-box machine learning model. While most such model explanations
+have largely been evaluated via qualitative measures, such as how humans might
+qualitatively perceive the explanations, it is vital to also consider objective
+measures such as the one we propose in this paper. Our evaluation measure that
+we naturally call sensitivity is simple: it characterizes how an explanation
+changes as we vary the test input, and depending on how we measure these
+changes, and how we vary the input, we arrive at different notions of
+sensitivity. We also provide a calculus for deriving sensitivity of complex
+explanations in terms of that for simpler explanations, which thus allows an
+easy computation of sensitivities for yet to be proposed explanations. One
+advantage of an objective evaluation measure is that we can optimize the
+explanation with respect to the measure: we show that (1) any given explanation
+can be simply modified to improve its sensitivity with just a modest deviation
+from the original explanation, and (2) gradient based explanations of an
+adversarially trained network are less sensitive. Perhaps surprisingly, our
+experiments show that explanations optimized to have lower sensitivity can be
+more faithful to the model predictions.
+",1,0,0,1,0,0
+16742,Synchronization Strings: Channel Simulations and Interactive Coding for Insertions and Deletions,"  We present many new results related to reliable (interactive) communication
+over insertion-deletion channels. Synchronization errors, such as insertions
+and deletions, strictly generalize the usual symbol corruption errors and are
+much harder to protect against.
+We show how to hide the complications of synchronization errors in many
+applications by introducing very general channel simulations which efficiently
+transform an insertion-deletion channel into a regular symbol corruption
+channel with an error rate larger by a constant factor and a slightly smaller
+alphabet. We generalize synchronization string based methods which were
+recently introduced as a tool to design essentially optimal error correcting
+codes for insertion-deletion channels. Our channel simulations depend on the
+fact that, at the cost of increasing the error rate by a constant factor,
+synchronization strings can be decoded in a streaming manner that preserves
+linearity of time. We also provide a lower bound showing that this constant
+factor cannot be improved to $1+\epsilon$, in contrast to what is achievable
+for error correcting codes. Our channel simulations drastically generalize the
+applicability of synchronization strings.
+We provide new interactive coding schemes which simulate any interactive
+two-party protocol over an insertion-deletion channel. Our results improve over
+the interactive coding schemes of Braverman et al. [TransInf 2017] and Sherstov
+and Wu [FOCS 2017], which achieve a small constant rate and require exponential
+time computations, with respect to computational and communication
+complexities. We provide the first computationally efficient interactive coding
+schemes for synchronization errors, the first coding scheme with a rate
+approaching one for small noise rates, and also the first coding scheme that
+works over arbitrarily small alphabet sizes.
+",1,0,0,0,0,0
+16743,Learning to Generate Samples from Noise through Infusion Training,"  In this work, we investigate a novel training procedure to learn a generative
+model as the transition operator of a Markov chain, such that, when applied
+repeatedly on an unstructured random noise sample, it will denoise it into a
+sample that matches the target distribution from the training set. The novel
+training procedure to learn this progressive denoising operation involves
+sampling from a slightly different chain than the model chain used for
+generation in the absence of a denoising target. In the training chain we
+infuse information from the training target example that we would like the
+chains to reach with a high probability. The thus learned transition operator
+is able to produce quality and varied samples in a small number of steps.
+Experiments show competitive results compared to the samples generated with a
+basic Generative Adversarial Net
+",1,0,0,1,0,0
+16744,Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons,"  Pairwise comparison data arises in many domains, including tournament
+rankings, web search, and preference elicitation. Given noisy comparisons of a
+fixed subset of pairs of items, we study the problem of estimating the
+underlying comparison probabilities under the assumption of strong stochastic
+transitivity (SST). We also consider the noisy sorting subclass of the SST
+model. We show that when the assignment of items to the topology is arbitrary,
+these permutation-based models, unlike their parametric counterparts, do not
+admit consistent estimation for most comparison topologies used in practice. We
+then demonstrate that consistent estimation is possible when the assignment of
+items to the topology is randomized, thus establishing a dichotomy between
+worst-case and average-case designs. We propose two estimators in the
+average-case setting and analyze their risk, showing that it depends on the
+comparison topology only through the degree sequence of the topology. The rates
+achieved by these estimators are shown to be optimal for a large class of
+graphs. Our results are corroborated by simulations on multiple comparison
+topologies.
+",1,0,0,1,0,0
+16745,Decoupling multivariate polynomials: interconnections between tensorizations,"  Decoupling multivariate polynomials is useful for obtaining an insight into
+the workings of a nonlinear mapping, performing parameter reduction, or
+approximating nonlinear functions. Several different tensor-based approaches
+have been proposed independently for this task, involving different tensor
+representations of the functions, and ultimately leading to a canonical
+polyadic decomposition.
+We first show that the involved tensors are related by a linear
+transformation, and that their CP decompositions and uniqueness properties are
+closely related. This connection provides a way to better assess which of the
+methods should be favored in certain problem settings, and may be a starting
+point to unify the two approaches. Second, we show that taking into account the
+previously ignored intrinsic structure in the tensor decompositions improves
+the uniqueness properties of the decompositions and thus enlarges the
+applicability range of the methods.
+",0,0,1,0,0,0
+16746,Arcades: A deep model for adaptive decision making in voice controlled smart-home,"  In a voice-controlled smart-home, a controller must respond not only to
+user's requests but also according to the interaction context. This paper
+describes Arcades, a system which uses deep reinforcement learning to extract
+context from a graphical representation of home automation system and to update
+continuously its behavior to the user's one. This system is robust to changes
+in the environment (sensor breakdown or addition) through its graphical
+representation (scale well) and the reinforcement mechanism (adapt well). The
+experiments on realistic data demonstrate that this method promises to reach
+long life context-aware control of smart-home.
+",1,0,0,1,0,0
+16747,Evolutionary Centrality and Maximal Cliques in Mobile Social Networks,"  This paper introduces an evolutionary approach to enhance the process of
+finding central nodes in mobile networks. This can provide essential
+information and important applications in mobile and social networks. This
+evolutionary approach considers the dynamics of the network and takes into
+consideration the central nodes from previous time slots. We also study the
+applicability of maximal cliques algorithms in mobile social networks and how
+it can be used to find the central nodes based on the discovered maximal
+cliques. The experimental results are promising and show a significant
+enhancement in finding the central nodes.
+",1,0,0,0,0,0
+16748,Band structure engineered layered metals for low-loss plasmonics,"  Plasmonics currently faces the problem of seemingly inevitable optical losses
+occurring in the metallic components that challenges the implementation of
+essentially any application. In this work we show that Ohmic losses are reduced
+in certain layered metals, such as the transition metal dichalcogenide TaS$_2$,
+due to an extraordinarily small density of states for scattering in the near-IR
+originating from their special electronic band structure. Based on this
+observation we propose a new class of band structure engineered van der Waals
+layered metals composed of hexagonal transition metal chalcogenide-halide
+layers with greatly suppressed intrinsic losses. Using first-principles
+calculations we show that the suppression of optical losses lead to improved
+performance for thin film waveguiding and transformation optics.
+",0,1,0,0,0,0
+16749,Acceleration through Optimistic No-Regret Dynamics,"  We consider the problem of minimizing a smooth convex function by reducing
+the optimization to computing the Nash equilibrium of a particular zero-sum
+convex-concave game. Zero-sum games can be solved using online learning
+dynamics, where a classical technique involves simulating two no-regret
+algorithms that play against each other and, after $T$ rounds, the average
+iterate is guaranteed to solve the original optimization problem with error
+decaying as $O(\log T/T)$. In this paper we show that the technique can be
+enhanced to a rate of $O(1/T^2)$ by extending recent work \cite{RS13,SALS15}
+that leverages \textit{optimistic learning} to speed up equilibrium
+computation. The resulting optimization algorithm derived from this analysis
+coincides \textit{exactly} with the well-known \NA \cite{N83a} method, and
+indeed the same story allows us to recover several variants of the Nesterov's
+algorithm via small tweaks. We are also able to establish the accelerated
+linear rate for a function which is both strongly-convex and smooth. This
+methodology unifies a number of different iterative optimization methods: we
+show that the \HB algorithm is precisely the non-optimistic variant of \NA, and
+recent prior work already established a similar perspective on \FW
+\cite{AW17,ALLW18}.
+",0,0,0,1,0,0
+16750,A Full Bayesian Model to Handle Structural Ones and Missingness in Economic Evaluations from Individual-Level Data,"  Economic evaluations from individual-level data are an important component of
+the process of technology appraisal, with a view to informing resource
+allocation decisions. A critical problem in these analyses is that both
+effectiveness and cost data typically present some complexity (e.g. non
+normality, spikes and missingness) that should be addressed using appropriate
+methods. However, in routine analyses, simple standardised approaches are
+typically used, possibly leading to biased inferences. We present a general
+Bayesian framework that can handle the complexity. We show the benefits of
+using our approach with a motivating example, the MenSS trial, for which there
+are spikes at one in the effectiveness and missingness in both outcomes. We
+contrast a set of increasingly complex models and perform sensitivity analysis
+to assess the robustness of the conclusions to a range of plausible missingness
+assumptions. This paper highlights the importance of adopting a comprehensive
+modelling approach to economic evaluations and the strategic advantages of
+building these complex models within a Bayesian framework.
+",0,0,0,1,0,0
+16751,Misconceptions about Calorimetry,"  In the past 50 years, calorimeters have become the most important detectors
+in many particle physics experiments, especially experiments in colliding-beam
+accelerators at the energy frontier. In this paper, we describe and discuss a
+number of common misconceptions about these detectors, as well as the
+consequences of these misconceptions. We hope that it may serve as a useful
+source of information for young colleagues who want to familiarize themselves
+with these tricky instruments.
+",0,1,0,0,0,0
+16752,Exothermicity is not a necessary condition for enhanced diffusion of enzymes,"  Recent experiments have revealed that the diffusivity of exothermic and fast
+enzymes is enhanced when they are catalytically active, and different physical
+mechanisms have been explored and quantified to account for this observation.
+We perform measurements on the endothermic and relatively slow enzyme aldolase,
+which also shows substrate-induced enhanced diffusion. We propose a new
+physical paradigm, which reveals that the diffusion coefficient of a model
+enzyme hydrodynamically coupled to its environment increases significantly when
+undergoing changes in conformational fluctuations in a substrate-dependent
+manner, and is independent of the overall turnover rate of the underlying
+enzymatic reaction. Our results show that substrate-induced enhanced diffusion
+of enzyme molecules can be explained within an equilibrium picture, and that
+the exothermicity of the catalyzed reaction is not a necessary condition for
+the observation of this phenomenon.
+",0,1,0,0,0,0
+16753,Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks,"  This paper introduces a new approach to automatically quantify the severity
+of knee OA using X-ray images. Automatically quantifying knee OA severity
+involves two steps: first, automatically localizing the knee joints; next,
+classifying the localized knee joint images. We introduce a new approach to
+automatically detect the knee joints using a fully convolutional neural network
+(FCN). We train convolutional neural networks (CNN) from scratch to
+automatically quantify the knee OA severity optimizing a weighted ratio of two
+loss functions: categorical cross-entropy and mean-squared loss. This joint
+training further improves the overall quantification of knee OA severity, with
+the added benefit of naturally producing simultaneous multi-class
+classification and regression outputs. Two public datasets are used to evaluate
+our approach, the Osteoarthritis Initiative (OAI) and the Multicenter
+Osteoarthritis Study (MOST), with extremely promising results that outperform
+existing approaches.
+",1,0,0,0,0,0
+16754,Data-Driven Filtered Reduced Order Modeling Of Fluid Flows,"  We propose a data-driven filtered reduced order model (DDF-ROM) framework for
+the numerical simulation of fluid flows. The novel DDF-ROM framework consists
+of two steps: (i) In the first step, we use explicit ROM spatial filtering of
+the nonlinear PDE to construct a filtered ROM. This filtered ROM is
+low-dimensional, but is not closed (because of the nonlinearity in the given
+PDE). (ii) In the second step, we use data-driven modeling to close the
+filtered ROM, i.e., to model the interaction between the resolved and
+unresolved modes. To this end, we use a quadratic ansatz to model this
+interaction and close the filtered ROM. To find the new coefficients in the
+closed filtered ROM, we solve an optimization problem that minimizes the
+difference between the full order model data and our ansatz. We emphasize that
+the new DDF-ROM is built on general ideas of spatial filtering and optimization
+and is independent of (restrictive) phenomenological arguments.
+We investigate the DDF-ROM in the numerical simulation of a 2D channel flow
+past a circular cylinder at Reynolds number $Re=100$. The DDF-ROM is
+significantly more accurate than the standard projection ROM. Furthermore, the
+computational costs of the DDF-ROM and the standard projection ROM are similar,
+both costs being orders of magnitude lower than the computational cost of the
+full order model. We also compare the new DDF-ROM with modern ROM closure
+models in the numerical simulation of the 1D Burgers equation. The DDF-ROM is
+more accurate and significantly more efficient than these ROM closure models.
+",0,1,0,0,0,0
+16755,FEAST Eigensolver for Nonlinear Eigenvalue Problems,"  The linear FEAST algorithm is a method for solving linear eigenvalue
+problems. It uses complex contour integration to calculate the eigenvectors
+whose eigenvalues that are located inside some user-defined region in the
+complex plane. This makes it possible to parallelize the process of solving
+eigenvalue problems by simply dividing the complex plane into a collection of
+disjoint regions and calculating the eigenpairs in each region independently of
+the eigenpairs in the other regions. In this paper we present a generalization
+of the linear FEAST algorithm that can be used to solve nonlinear eigenvalue
+problems. Like its linear progenitor, the nonlinear FEAST algorithm can be used
+to solve nonlinear eigenvalue problems for the eigenpairs whose eigenvalues lie
+in a user-defined region in the complex plane, thereby allowing for the
+calculation of large numbers of eigenpairs in parallel. We describe the
+nonlinear FEAST algorithm, and use several physically-motivated examples to
+demonstrate its properties.
+",1,0,0,0,0,0
+16756,Twin Primes In Quadratic Arithmetic Progressions,"  A recent heuristic argument based on basic concepts in spectral analysis
+showed that the twin prime conjecture and a few other related primes counting
+problems are valid. A rigorous version of the spectral method, and a proof for
+the existence of infinitely many quadratic twin primes $n^{2}+1$ and $n^{2}+3$,
+$n \geq 1$, are proposed in this note.
+",0,0,1,0,0,0
+16757,Bit Complexity of Computing Solutions for Symmetric Hyperbolic Systems of PDEs with Guaranteed Precision,"  We establish upper bounds of bit complexity of computing solution operators
+for symmetric hyperbolic systems of PDEs. Here we continue the research started
+in in our revious publications where computability, in the rigorous sense of
+computable analysis, has been established for solution operators of Cauchy and
+dissipative boundary-value problems for such systems.
+",1,0,0,0,0,0
+16758,Relativistic corrections for the ground electronic state of molecular hydrogen,"  We recalculate the leading relativistic corrections for the ground electronic
+state of the hydrogen molecule using variational method with explicitly
+correlated functions which satisfy the interelectronic cusp condition. The new
+computational approach allowed for the control of the numerical precision which
+reached about 8 significant digits. More importantly, the updated theoretical
+energies became discrepant with the known experimental values and we conclude
+that the yet unknown relativistic recoil corrections might be larger than
+previously anticipated.
+",0,1,0,0,0,0
+16759,Homogenization in Perforated Domains and Interior Lipschitz Estimates,"  We establish interior Lipschitz estimates at the macroscopic scale for
+solutions to systems of linear elasticity with rapidly oscillating periodic
+coefficients and mixed boundary conditions in domains periodically perforated
+at a microscopic scale $\varepsilon$ by establishing $H^1$-convergence rates
+for such solutions. The interior estimates are derived directly without the use
+of compactness via an argument presented in [3] that was adapted for elliptic
+equations in [2] and [11]. As a consequence, we derive a Liouville type
+estimate for solutions to the systems of linear elasticity in unbounded
+periodically perforated domains.
+",0,0,1,0,0,0
+16760,Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach,"  Most people simultaneously belong to several distinct social networks, in
+which their relations can be different. They have opinions about certain
+topics, which they share and spread on these networks, and are influenced by
+the opinions of other persons. In this paper, we build upon this observation to
+propose a new nodal centrality measure for multiplex networks. Our measure,
+called Opinion centrality, is based on a stochastic model representing opinion
+propagation dynamics in such a network. We formulate an optimization problem
+consisting in maximizing the opinion of the whole network when controlling an
+external influence able to affect each node individually. We find a
+mathematical closed form of this problem, and use its solution to derive our
+centrality measure. According to the opinion centrality, the more a node is
+worth investing external influence, and the more it is central. We perform an
+empirical study of the proposed centrality over a toy network, as well as a
+collection of real-world networks. Our measure is generally negatively
+correlated with existing multiplex centrality measures, and highlights
+different types of nodes, accordingly to its definition.
+",1,1,0,0,0,0
+16761,FDTD: solving 1+1D delay PDE in parallel,"  We present a proof of concept for solving a 1+1D complex-valued, delay
+partial differential equation (PDE) that emerges in the study of waveguide
+quantum electrodynamics (QED) by adapting the finite-difference time-domain
+(FDTD) method. The delay term is spatially non-local, rendering conventional
+approaches such as the method of lines inapplicable. We show that by properly
+designing the grid and by supplying the (partial) exact solution as the
+boundary condition, the delay PDE can be numerically solved. In addition, we
+demonstrate that while the delay imposes strong data dependency, multi-thread
+parallelization can nevertheless be applied to such a problem. Our code
+provides a numerically exact solution to the time-dependent multi-photon
+scattering problem in waveguide QED.
+",1,1,0,0,0,0
+16762,Optimal Installation for Electric Vehicle Wireless Charging Lanes,"  Range anxiety, the persistent worry about not having enough battery power to
+complete a trip, remains one of the major obstacles to widespread
+electric-vehicle adoption. As cities look to attract more users to adopt
+electric vehicles, the emergence of wireless in-motion car charging technology
+presents itself as a solution to range anxiety. For a limited budget, cities
+could face the decision problem of where to install these wireless charging
+units. With a heavy price tag, an installation without a careful study can lead
+to inefficient use of limited resources. In this work, we model the
+installation of wireless charging units as an integer programming problem. We
+use our basic formulation as a building block for different realistic
+scenarios, carry out experiments using real geospatial data, and compare our
+results to different heuristics.
+",0,0,1,0,0,0
+16763,Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects,"  The Nyström method is a popular technique for computing fixed-rank
+approximations of large kernel matrices using a small number of landmark
+points. In practice, to ensure high quality approximations, the number of
+landmark points is chosen to be greater than the target rank. However, the
+standard Nyström method uses a sub-optimal procedure for rank reduction
+mainly due to its simplicity. In this paper, we highlight the drawbacks of
+standard Nyström in terms of poor performance and lack of theoretical
+guarantees. To address these issues, we present an efficient method for
+generating improved fixed-rank Nyström approximations. Theoretical analysis
+and numerical experiments are provided to demonstrate the advantages of the
+modified method over the standard Nyström method. Overall, the aim of this
+paper is to convince researchers to use the modified method, as it has nearly
+identical computational complexity, is easy to code, and has greatly improved
+accuracy in many cases.
+",1,0,0,1,0,0
+16764,Population splitting of rodlike swimmers in Couette flow,"  We present a quantitative analysis on the response of a dilute active
+suspension of self-propelled rods (swimmers) in a planar channel subjected to
+an imposed shear flow. To best capture the salient features of shear-induced
+effects, we consider the case of an imposed Couette flow, providing a constant
+shear rate across the channel. We argue that the steady-state behavior of
+swimmers can be understood in the light of a population splitting phenomenon,
+occurring as the shear rate exceeds a certain threshold, initiating the
+reversal of swimming direction for a finite fraction of swimmers from down- to
+upstream or vice versa, depending on swimmer position within the channel.
+Swimmers thus split into two distinct, statistically significant and oppositely
+swimming majority and minority populations. The onset of population splitting
+translates into a transition from a self-propulsion-dominated regime to a
+shear-dominated regime, corresponding to a unimodal-to-bimodal change in the
+probability distribution function of the swimmer orientation. We present a
+phase diagram in terms of the swim and flow Peclet numbers showing the
+separation of these two regimes by a discontinuous transition line. Our results
+shed further light on the behavior of swimmers in a shear flow and provide an
+explanation for the previously reported non-monotonic behavior of the mean,
+near-wall, parallel-to-flow orientation of swimmers with increasing shear
+strength.
+",0,1,0,0,0,0
+16765,MultiAmdahl: Optimal Resource Allocation in Heterogeneous Architectures,"  Future multiprocessor chips will integrate many different units, each
+tailored to a specific computation. When designing such a system, the chip
+architect must decide how to distribute limited system resources such as area,
+power, and energy among the computational units. We extend MultiAmdahl, an
+analytical optimization technique for resource allocation in heterogeneous
+architectures, for energy optimality under a variety of constant system power
+scenarios. We conclude that reduction in constant system power should be met by
+reallocating resources from general-purpose computing to heterogeneous
+accelerator-dominated computing, to keep the overall energy consumption at a
+minimum. We extend this conclusion to offer an intuition regarding
+energy-optimal resource allocation in data center computing.
+",1,0,0,0,0,0
+16766,Coset Vertex Operator Algebras and $\W$-Algebras,"  We give an explicit description for the weight three generator of the coset
+vertex operator algebra $C_{L_{\widehat{\sl_{n}}}(l,0)\otimes
+L_{\widehat{\sl_{n}}}(1,0)}(L_{\widehat{\sl_{n}}}(l+1,0))$, for $n\geq 2, l\geq
+1$. Furthermore, we prove that the commutant
+$C_{L_{\widehat{\sl_{3}}}(l,0)\otimes
+L_{\widehat{\sl_{3}}}(1,0)}(L_{\widehat{\sl_{3}}}(l+1,0))$ is isomorphic to the
+$\W$-algebra $\W_{-3+\frac{l+3}{l+4}}(\sl_3)$, which confirms the conjecture
+for the $\sl_3$ case that $C_{L_{\widehat{\frak g}}(l,0)\otimes
+L_{\widehat{\frak g}}(1,0)}(L_{\widehat{\frak g}}(l+1,0))$ is isomorphic to
+$\W_{-h+\frac{l+h}{l+h+1}}(\frak g)$ for simply-laced Lie algebras ${\frak g}$
+with its Coxeter number $h$ for a positive integer $l$.
+",0,0,1,0,0,0
+16767,The Moore and the Myhill Property For Strongly Irreducible Subshifts Of Finite Type Over Group Sets,"  We prove the Moore and the Myhill property for strongly irreducible subshifts
+over right amenable and finitely right generated left homogeneous spaces with
+finite stabilisers. Both properties together mean that the global transition
+function of each big-cellular automaton with finite set of states and finite
+neighbourhood over such a subshift is surjective if and only if it is
+pre-injective. This statement is known as Garden of Eden theorem.
+Pre-Injectivity means that two global configurations that differ at most on a
+finite subset and have the same image under the global transition function must
+be identical.
+",1,0,1,0,0,0
+16768,eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial,"  Objective: The Learning Health System (LHS) requires integration of research
+into routine practice. eSource or embedding clinical trial functionalities into
+routine electronic health record (EHR) systems has long been put forward as a
+solution to the rising costs of research. We aimed to create and validate an
+eSource solution that would be readily extensible as part of a LHS.
+Materials and Methods: The EU FP7 TRANSFoRm project's approach is based on
+dual modelling, using the Clinical Research Information Model (CRIM) and the
+Clinical Data Integration Model of meaning (CDIM) to bridge the gap between
+clinical and research data structures, using the CDISC Operational Data Model
+(ODM) standard. Validation against GCP requirements was conducted in a clinical
+site, and a cluster randomised evaluation by site nested into a live clinical
+trial.
+Results: Using the form definition element of ODM, we linked precisely
+modelled data queries to data elements, constrained against CDIM concepts, to
+enable automated patient identification for specific protocols and
+prepopulation of electronic case report forms (e-CRF). Both control and eSource
+sites recruited better than expected with no significant difference.
+Completeness of clinical forms was significantly improved by eSource, but
+Patient Related Outcome Measures (PROMs) were less well completed on
+smartphones than paper in this population.
+Discussion: The TRANSFoRm approach provides an ontologically-based approach
+to eSource in a low-resource, heterogeneous, highly distributed environment,
+that allows precise prospective mapping of data elements in the EHR.
+Conclusion: Further studies using this approach to CDISC should optimise the
+delivery of PROMS, whilst building a sustainable infrastructure for eSource
+with research networks, trials units and EHR vendors.
+",1,0,0,0,0,0
+16769,SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules,"  Simplified Molecular Input Line Entry System (SMILES) is a single line text
+representation of a unique molecule. One molecule can however have multiple
+SMILES strings, which is a reason that canonical SMILES have been defined,
+which ensures a one to one correspondence between SMILES string and molecule.
+Here the fact that multiple SMILES represent the same molecule is explored as a
+technique for data augmentation of a molecular QSAR dataset modeled by a long
+short term memory (LSTM) cell based neural network. The augmented dataset was
+130 times bigger than the original. The network trained with the augmented
+dataset shows better performance on a test set when compared to a model built
+with only one canonical SMILES string per molecule. The correlation coefficient
+R2 on the test set was improved from 0.56 to 0.66 when using SMILES
+enumeration, and the root mean square error (RMS) likewise fell from 0.62 to
+0.55. The technique also works in the prediction phase. By taking the average
+per molecule of the predictions for the enumerated SMILES a further improvement
+to a correlation coefficient of 0.68 and a RMS of 0.52 was found.
+",1,0,0,0,0,0
+16770,Binary Tomography Reconstructions With Few Projections,"  We approach the tomographic problem in terms of linear system of equations
+$A\mathbf{x}=\mathbf{p}$ in an $(M\times N)$-sized lattice grid $\mathcal{A}$.
+Using a finite number of directions always yields the presence of ghosts, so
+preventing uniqueness. Ghosts can be managed by increasing the number of
+directions, which implies that also the number of collected projections (also
+called bins) increases. Therefore, for a best performing outcome, a kind of
+compromise should be sought among the number of employed directions, the number
+of collected projections, and the percentage of exactly reconstructed image. In
+this paper we wish to investigate such a problem in the case of binary images.
+We move from a theoretical result that allow uniqueness in $\mathcal{A}$ with
+just four suitably selected X-ray directions. This is exploited in studying the
+structure of the allowed ghosts in the given lattice grid. The knowledge of the
+ghost sizes, combined with geometrical information concerning the real valued
+solution of $A\mathbf{x}=\mathbf{p}$ having minimal Euclidean norm, leads to an
+explicit implementation of the previously obtained uniqueness theorem. This
+provides an easy binary algorithm (BRA) that, in the grid model, quickly
+returns perfect noise-free tomographic reconstructions.
+Then we focus on the tomography-side relevant problem of reducing the number
+of collected projections and, in the meantime, preserving a good quality of
+reconstruction. It turns out that, using sets of just four suitable directions,
+a high percentage of reconstructed pixel is preserved, even when the size of
+the projection vector $\mathbf{p}$ is considerably smaller than the size of the
+image to be reconstructed.
+Results are commented and discussed, also showing applications of BRA on
+phantoms with different features.
+",1,0,0,0,0,0
+16771,On finite determinacy of complete intersection singularities,"  We give an elementary combinatorial proof of the following fact: Every real
+or complex analytic complete intersection germ X is equisingular -- in the
+sense of the Hilbert-Samuel function -- with a germ of an algebraic set defined
+by sufficiently long truncations of the defining equations of X.
+",0,0,1,0,0,0
+16772,Topological phase transformations and intrinsic size effects in ferroelectric nanoparticles,"  Composite materials comprised of ferroelectric nanoparticles in a dielectric
+matrix are being actively investigated for a variety of functional properties
+attractive for a wide range of novel electronic and energy harvesting devices.
+However, the dependence of these functionalities on shapes, sizes, orientation
+and mutual arrangement of ferroelectric particles is currently not fully
+understood. In this study, we utilize a time-dependent Ginzburg-Landau approach
+combined with coupled-physics finite-element-method based simulations to
+elucidate the behavior of polarization in isolated spherical PbTiO3 or BaTiO3
+nanoparticles embedded in a dielectric medium, including air. The equilibrium
+polarization topology is strongly affected by particle diameter, as well as the
+choice of inclusion and matrix materials, with monodomain, vortex-like and
+multidomain patterns emerging for various combinations of size and materials
+parameters. This leads to radically different polarization vs electric field
+responses, resulting in highly tunable size-dependent dielectric properties
+that should be possible to observe experimentally. Our calculations show that
+there is a critical particle size below which ferroelectricity vanishes. For
+the PbTiO3 particle, this size is 2 and 3.4 nm, respectively, for high- and
+low-permittivity media. For the BaTiO3 particle, it is ~3.6 nm regardless of
+the medium dielectric strength.
+",0,1,0,0,0,0
+16773,Honors Thesis: On the faithfulness of the Burau representation at roots of unity,"  We study the kernel of the evaluated Burau representation through the braid
+element $\sigma_i \sigma_{i+1} \sigma_i$. The element is significant as a part
+of the standard braid relation. We establish the form of this element's image
+raised to the $n^{th}$ power. Interestingly, the cyclotomic polynomials arise
+and can be used to define the expression. The main result of this paper is that
+the Burau representation of the braid group of $n$ strands for $n \geq 3$ is
+unfaithful at any primitive root of unity, excepting the first three.
+",0,0,1,0,0,0
+16774,On Interpolation and Symbol Elimination in Theory Extensions,"  In this paper we study possibilities of interpolation and symbol elimination
+in extensions of a theory $\mathcal{T}_0$ with additional function symbols
+whose properties are axiomatised using a set of clauses. We analyze situations
+in which we can perform such tasks in a hierarchical way, relying on existing
+mechanisms for symbol elimination in $\mathcal{T}_0$. This is for instance
+possible if the base theory allows quantifier elimination. We analyze
+possibilities of extending such methods to situations in which the base theory
+does not allow quantifier elimination but has a model completion which does. We
+illustrate the method on various examples.
+",1,0,0,0,0,0
+16775,Stellar Abundances for Galactic Archaeology Database IV - Compilation of Stars in Dwarf Galaxies,"  We have constructed the database of stars in the local group using the
+extended version of the SAGA (Stellar Abundances for Galactic Archaeology)
+database that contains stars in 24 dwarf spheroidal galaxies and ultra faint
+dwarfs. The new version of the database includes more than 4500 stars in the
+Milky Way, by removing the previous metallicity criterion of [Fe/H] <= -2.5,
+and more than 6000 stars in the local group galaxies. We examined a validity of
+using a combined data set for elemental abundances. We also checked a
+consistency between the derived distances to individual stars and those to
+galaxies in the literature values. Using the updated database, the
+characteristics of stars in dwarf galaxies are discussed. Our statistical
+analyses of alpha-element abundances show that the change of the slope of the
+[alpha/Fe] relative to [Fe/H] (so-called ""knee"") occurs at [Fe/H] = -1.0+-0.1
+for the Milky Way. The knee positions for selected galaxies are derived by
+applying the same method. Star formation history of individual galaxies are
+explored using the slope of the cumulative metallicity distribution function.
+Radial gradients along the four directions are inspected in six galaxies where
+we find no direction dependence of metallicity gradients along the major and
+minor axes. The compilation of all the available data shows a lack of CEMP-s
+population in dwarf galaxies, while there may be some CEMP-no stars at [Fe/H]
+<~ -3 even in the very small sample. The inspection of the relationship between
+Eu and Ba abundances confirms an anomalously Ba-rich population in Fornax,
+which indicates a pre-enrichment of interstellar gas with r-process elements.
+We do not find any evidence of anti-correlations in O-Na and Mg-Al abundances,
+which characterises the abundance trends in the Galactic globular clusters.
+",0,1,0,0,0,0
+16776,Relativistic distortions in the large-scale clustering of SDSS-III BOSS CMASS galaxies,"  General relativistic effects have long been predicted to subtly influence the
+observed large-scale structure of the universe. The current generation of
+galaxy redshift surveys have reached a size where detection of such effects is
+becoming feasible. In this paper, we report the first detection of the redshift
+asymmetry from the cross-correlation function of two galaxy populations which
+is consistent with relativistic effects. The dataset is taken from the Sloan
+Digital Sky Survey DR12 CMASS galaxy sample, and we detect the asymmetry at the
+$2.7\sigma$ level by applying a shell-averaged estimator to the
+cross-correlation function. Our measurement dominates at scales around $10$
+h$^{-1}$Mpc, larger than those over which the gravitational redshift profile
+has been recently measured in galaxy clusters, but smaller than scales for
+which linear perturbation theory is likely to be accurate. The detection
+significance varies by 0.5$\sigma$ with the details of our measurement and
+tests for systematic effects. We have also devised two null tests to check for
+various survey systematics and show that both results are consistent with the
+null hypothesis. We measure the dipole moment of the cross-correlation
+function, and from this the asymmetry is also detected, at the $2.8 \sigma$
+level. The amplitude and scale-dependence of the clustering asymmetries are
+approximately consistent with the expectations of General Relativity and a
+biased galaxy population, within large uncertainties. We explore theoretical
+predictions using numerical simulations in a companion paper.
+",0,1,0,0,0,0
+16777,Superconductivity at the vacancy disorder boundary in K$_x$Fe$_{2-y}$Se$_2$,"  The role of phase separation in the emergence of superconductivity in alkali
+metal doped iron selenides A$_{x}$Fe$_{2-y}$Se$_{2}$ (A = K, Rb, Cs) is
+revisited. High energy X-ray diffraction and Monte Carlo simulation were used
+to investigate the crystal structure of quenched superconducting (SC) and
+as-grown non-superconducting (NSC) K$_{x}$Fe$_{2-y}$Se$_{2}$ single crystals.
+The coexistence of superlattice structures with the in-plane
+$\sqrt{2}\times\sqrt{2}$ K-vacancy ordering and the $\sqrt{5}\times\sqrt{5}$
+Fe-vacancy ordering were observed in SC and NSC crystals along side the
+\textit{I4/mmm} Fe-vacancy free phase. Moreover, in the SC crystal an
+Fe-vacancy disordered phase is additionally present. It appears at the boundary
+between the \textit{I4/mmm} vacancy free phase and the \textit{I4/m} vacancy
+ordered phase ($\sqrt{5}\times\sqrt{5}$). The vacancy disordered phase is most
+likely the host of superconductivity.
+",0,1,0,0,0,0
+16778,Probing Primordial-Black-Hole Dark Matter with Gravitational Waves,"  Primordial black holes (PBHs) have long been suggested as a candidate for
+making up some or all of the dark matter in the Universe. Most of the
+theoretically possible mass range for PBH dark matter has been ruled out with
+various null observations of expected signatures of their interaction with
+standard astrophysical objects. However, current constraints are significantly
+less robust in the 20 M_sun < M_PBH < 100 M_sun mass window, which has received
+much attention recently, following the detection of merging black holes with
+estimated masses of ~30 M_sun by LIGO and the suggestion that these could be
+black holes formed in the early Universe. We consider the potential of advanced
+LIGO (aLIGO) operating at design sensitivity to probe this mass range by
+looking for peaks in the mass spectrum of detected events. To quantify the
+background, which is due to black holes that are formed from dying stars, we
+model the shape of the stellar-black-hole mass function and calibrate its
+amplitude to match the O1 results. Adopting very conservative assumptions about
+the PBH and stellar-black-hole merger rates, we show that ~5 years of aLIGO
+data can be used to detect a contribution of >20 M_sun PBHs to dark matter down
+to f_PBH<0.5 at >99.9% confidence level. Combined with other probes that
+already suggest tension with f_PBH=1, the obtainable independent limits from
+aLIGO will thus enable a firm test of the scenario that PBHs make up all of
+dark matter.
+",0,1,0,0,0,0
+16779,Fundamental limits of low-rank matrix estimation: the non-symmetric case,"  We consider the high-dimensional inference problem where the signal is a
+low-rank matrix which is corrupted by an additive Gaussian noise. Given a
+probabilistic model for the low-rank matrix, we compute the limit in the large
+dimension setting for the mutual information between the signal and the
+observations, as well as the matrix minimum mean square error, while the rank
+of the signal remains constant. This allows to locate the information-theoretic
+threshold for this estimation problem, i.e. the critical value of the signal
+intensity below which it is impossible to recover the low-rank matrix.
+",0,0,1,0,0,0
+16780,Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees,"  Gaussian processes (GPs) offer a flexible class of priors for nonparametric
+Bayesian regression, but popular GP posterior inference methods are typically
+prohibitively slow or lack desirable finite-data guarantees on quality. We
+develop an approach to scalable approximate GP regression with finite-data
+guarantees on the accuracy of pointwise posterior mean and variance estimates.
+Our main contribution is a novel objective for approximate inference in the
+nonparametric setting: the preconditioned Fisher (pF) divergence. We show that
+unlike the Kullback--Leibler divergence (used in variational inference), the pF
+divergence bounds the 2-Wasserstein distance, which in turn provides tight
+bounds the pointwise difference of the mean and variance functions. We
+demonstrate that, for sparse GP likelihood approximations, we can minimize the
+pF divergence efficiently. Our experiments show that optimizing the pF
+divergence has the same computational requirements as variational sparse GPs
+while providing comparable empirical performance--in addition to our novel
+finite-data quality guarantees.
+",0,0,0,1,0,0
+16781,Integral representations and asymptotic behaviours of Mittag-Leffler type functions of two variables,"  The paper explores various special functions which generalize the
+two-parametric Mittag-Leffler type function of two variables. Integral
+representations for these functions in different domains of variation of
+arguments for certain values of the parameters are obtained. The asymptotic
+expansions formulas and asymptotic properties of such functions are also
+established for large values of the variables. This provides statements of
+theorems for these formulas and their corresponding properties.
+",0,0,1,0,0,0
+16782,Efficient Mendler-Style Lambda-Encodings in Cedille,"  It is common to model inductive datatypes as least fixed points of functors.
+We show that within the Cedille type theory we can relax functoriality
+constraints and generically derive an induction principle for Mendler-style
+lambda-encoded inductive datatypes, which arise as least fixed points of
+covariant schemes where the morphism lifting is defined only on identities.
+Additionally, we implement a destructor for these lambda-encodings that runs in
+constant-time. As a result, we can define lambda-encoded natural numbers with
+an induction principle and a constant-time predecessor function so that the
+normal form of a numeral requires only linear space. The paper also includes
+several more advanced examples.
+",1,0,0,0,0,0
+16783,Super Jack-Laurent Polynomials,"  Let $\mathcal{D}_{n,m}$ be the algebra of the quantum integrals of the
+deformed Calogero-Moser-Sutherland problem corresponding to the root system of
+the Lie superalgebra $\frak{gl}(n,m)$. The algebra $\mathcal{D}_{n,m}$ acts
+naturally on the quasi-invariant Laurent polynomials and we investigate the
+corresponding spectral decomposition. Even for general value of the parameter
+$k$ the spectral decomposition is not simple and we prove that the image of the
+algebra $\mathcal{D}_{n,m}$ in the algebra of endomorphisms of the generalised
+eigen-space is $k[\varepsilon]^{\otimes r}$ where $k[\varepsilon]$ is the
+algebra of the dual numbers the corresponding representation is the regular
+representation of the algebra $k[\varepsilon]^{\otimes r}$.
+",0,0,1,0,0,0
+16784,A New Classification of Technologies,"  This study here suggests a classification of technologies based on taxonomic
+characteristics of interaction between technologies in complex systems that is
+not a studied research field in economics of technical change. The proposed
+taxonomy here categorizes technologies in four typologies, in a broad analogy
+with the ecology: 1) technological parasitism is a relationship between two
+technologies T1 and T2 in a complex system S where one technology T1 benefits
+from the interaction with T2, whereas T2 has a negative side from interaction
+with T1; 2) technological commensalism is a relationship between two
+technologies in S where one technology benefits from the other without
+affecting it; 3) technological mutualism is a relationship in which each
+technology benefits from the activity of the other within complex systems; 4)
+technological symbiosis is a long-term interaction between two (or more)
+technologies that evolve together in complex systems. This taxonomy
+systematizes the typologies of interactive technologies within complex systems
+and predicts their evolutionary pathways that generate stepwise coevolutionary
+processes of complex systems of technology. This study here begins the process
+of generalizing, as far as possible, critical typologies of interactive
+technologies that explain the long-run evolution of technology. The theoretical
+framework developed here opens the black box of the interaction between
+technologies that affects, with different types of technologies, the
+evolutionary pathways of complex systems of technology over time and space.
+Overall, then, this new theoretical framework may be useful for bringing a new
+perspective to categorize the gradient of benefit to technologies from
+interaction with other technologies that can be a ground work for development
+of more sophisticated concepts to clarify technological and economic change in
+human society.
+",1,0,0,0,0,0
+16785,Potential functions on Grassmannians of planes and cluster transformations,"  With a triangulation of a planar polygon with $n$ sides, one can associate an
+integrable system on the Grassmannian of 2-planes in an $n$-space. In this
+paper, we show that the potential functions of Lagrangian torus fibers of the
+integrable systems associated with different triangulations glue together by
+cluster transformations. We also prove that the cluster transformations
+coincide with the wall-crossing formula in Lagrangian intersection Floer
+theory.
+",0,0,1,0,0,0
+16786,Physical properties of the first spectroscopically confirmed red supergiant stars in the Sculptor Group galaxy NGC 55,"  We present K-band Multi-Object Spectrograph (KMOS) observations of 18 Red
+Supergiant (RSG) stars in the Sculptor Group galaxy NGC 55. Radial velocities
+are calculated and are shown to be in good agreement with previous estimates,
+confirming the supergiant nature of the targets and providing the first
+spectroscopically confirmed RSGs in NGC 55. Stellar parameters are estimated
+for 14 targets using the $J$-band analysis technique, making use of
+state-of-the-art stellar model atmospheres. The metallicities estimated confirm
+the low-metallicity nature of NGC 55, in good agreement with previous studies.
+This study provides an independent estimate of the metallicity gradient of NGC
+55, in excellent agreement with recent results published using hot massive
+stars. In addition, we calculate luminosities of our targets and compare their
+distribution of effective temperatures and luminosities to other RSGs, in
+different environments, estimated using the same technique.
+",0,1,0,0,0,0
+16787,"Gas near a wall: a shortened mean free path, reduced viscosity, and the manifestation of a turbulent Knudsen layer in the Navier-Stokes solution of a shear flow","  For the gas near a solid planar wall, we propose a scaling formula for the
+mean free path of a molecule as a function of the distance from the wall, under
+the assumption of a uniform distribution of the incident directions of the
+molecular free flight. We subsequently impose the same scaling onto the
+viscosity of the gas near the wall, and compute the Navier-Stokes solution of
+the velocity of a shear flow parallel to the wall. This solution exhibits the
+Knudsen velocity boundary layer in agreement with the corresponding Direct
+Simulation Monte Carlo computations for argon and nitrogen. We also find that
+the proposed mean free path and viscosity scaling sets the second derivative of
+the velocity to infinity at the wall boundary of the flow domain, which
+suggests that the gas flow is formally turbulent within the Knudsen boundary
+layer near the wall.
+",0,1,0,0,0,0
+16788,Greater data science at baccalaureate institutions,"  Donoho's JCGS (in press) paper is a spirited call to action for
+statisticians, who he points out are losing ground in the field of data science
+by refusing to accept that data science is its own domain. (Or, at least, a
+domain that is becoming distinctly defined.) He calls on writings by John
+Tukey, Bill Cleveland, and Leo Breiman, among others, to remind us that
+statisticians have been dealing with data science for years, and encourages
+acceptance of the direction of the field while also ensuring that statistics is
+tightly integrated.
+As faculty at baccalaureate institutions (where the growth of undergraduate
+statistics programs has been dramatic), we are keen to ensure statistics has a
+place in data science and data science education. In his paper, Donoho is
+primarily focused on graduate education. At our undergraduate institutions, we
+are considering many of the same questions.
+",0,0,0,1,0,0
+16789,Direct evidence of hierarchical assembly at low masses from isolated dwarf galaxy groups,"  The demographics of dwarf galaxy populations have long been in tension with
+predictions from the Cold Dark Matter (CDM) paradigm. If primordial density
+fluctuations were scale-free as predicted, dwarf galaxies should themselves
+host dark matter subhaloes, the most massive of which may have undergone star
+formation resulting in dwarf galaxy groups. Ensembles of dwarf galaxies are
+observed as satellites of more massive galaxies, and there is observational and
+theoretical evidence to suggest that these satellites at z=0 were captured by
+the massive host halo as a group. However, the evolution of dwarf galaxies is
+highly susceptible to environment making these satellite groups imperfect
+probes of CDM in the low mass regime. We have identified one of the clearest
+examples to date of hierarchical structure formation at low masses: seven
+isolated, spectroscopically confirmed groups with only dwarf galaxies as
+members. Each group hosts 3-5 known members, has a baryonic mass of ~4.4 x 10^9
+to 2 x 10^10 Msun, and requires a mass-to-light ratio of <100 to be
+gravitationally bound. Such groups are predicted to be rare theoretically and
+found to be rare observationally at the current epoch and thus provide a unique
+window into the possible formation mechanism of more massive, isolated
+galaxies.
+",0,1,0,0,0,0
+16790,Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data,"  Risk prediction is central to both clinical medicine and public health. While
+many machine learning models have been developed to predict mortality, they are
+rarely applied in the clinical literature, where classification tasks typically
+rely on logistic regression. One reason for this is that existing machine
+learning models often seek to optimize predictions by incorporating features
+that are not present in the databases readily available to providers and policy
+makers, limiting generalizability and implementation. Here we tested a number
+of machine learning classifiers for prediction of six-month mortality in a
+population of elderly Medicare beneficiaries, using an administrative claims
+database of the kind available to the majority of health care payers and
+providers. We show that machine learning classifiers substantially outperform
+current widely-used methods of risk prediction but only when used with an
+improved feature set incorporating insights from clinical medicine, developed
+for this study. Our work has applications to supporting patient and provider
+decision making at the end of life, as well as population health-oriented
+efforts to identify patients at high risk of poor outcomes.
+",1,0,0,1,0,0
+16791,R-C3D: Region Convolutional 3D Network for Temporal Activity Detection,"  We address the problem of activity detection in continuous, untrimmed video
+streams. This is a difficult task that requires extracting meaningful
+spatio-temporal features to capture activities, accurately localizing the start
+and end times of each activity. We introduce a new model, Region Convolutional
+3D Network (R-C3D), which encodes the video streams using a three-dimensional
+fully convolutional network, then generates candidate temporal regions
+containing activities, and finally classifies selected regions into specific
+activities. Computation is saved due to the sharing of convolutional features
+between the proposal and the classification pipelines. The entire model is
+trained end-to-end with jointly optimized localization and classification
+losses. R-C3D is faster than existing methods (569 frames per second on a
+single Titan X Maxwell GPU) and achieves state-of-the-art results on THUMOS'14.
+We further demonstrate that our model is a general activity detection framework
+that does not rely on assumptions about particular dataset properties by
+evaluating our approach on ActivityNet and Charades. Our code is available at
+this http URL.
+",1,0,0,0,0,0
+16792,Regularization for Deep Learning: A Taxonomy,"  Regularization is one of the crucial ingredients of deep learning, yet the
+term regularization has various definitions, and regularization methods are
+often studied separately from each other. In our work we present a systematic,
+unifying taxonomy to categorize existing methods. We distinguish methods that
+affect data, network architectures, error terms, regularization terms, and
+optimization procedures. We do not provide all details about the listed
+methods; instead, we present an overview of how the methods can be sorted into
+meaningful categories and sub-categories. This helps revealing links and
+fundamental similarities between them. Finally, we include practical
+recommendations both for users and for developers of new regularization
+methods.
+",1,0,0,1,0,0
+16793,Re-Evaluating the Netflix Prize - Human Uncertainty and its Impact on Reliability,"  In this paper, we examine the statistical soundness of comparative
+assessments within the field of recommender systems in terms of reliability and
+human uncertainty. From a controlled experiment, we get the insight that users
+provide different ratings on same items when repeatedly asked. This volatility
+of user ratings justifies the assumption of using probability densities instead
+of single rating scores. As a consequence, the well-known accuracy metrics
+(e.g. MAE, MSE, RMSE) yield a density themselves that emerges from convolution
+of all rating densities. When two different systems produce different RMSE
+distributions with significant intersection, then there exists a probability of
+error for each possible ranking. As an application, we examine possible ranking
+errors of the Netflix Prize. We are able to show that all top rankings are more
+or less subject to high probabilities of error and that some rankings may be
+deemed to be caused by mere chance rather than system quality.
+",1,0,0,0,0,0
+16794,Infinite monochromatic sumsets for colourings of the reals,"  N. Hindman, I. Leader and D. Strauss proved that it is consistent that there
+is a finite colouring of $\mathbb R$ so that no infinite sumset
+$X+X=\{x+y:x,y\in X\}$ is monochromatic. Our aim in this paper is to prove a
+consistency result in the opposite direction: we show that, under certain
+set-theoretic assumptions, for any $c:\mathbb R\to r$ with $r$ finite there is
+an infinite $X\subseteq \mathbb R$ so that $c$ is constant on $X+X$.
+",0,0,1,0,0,0
+16795,Mean-Field Games with Differing Beliefs for Algorithmic Trading,"  Even when confronted with the same data, agents often disagree on a model of
+the real-world. Here, we address the question of how interacting heterogenous
+agents, who disagree on what model the real-world follows, optimize their
+trading actions. The market has latent factors that drive prices, and agents
+account for the permanent impact they have on prices. This leads to a large
+stochastic game, where each agents' performance criteria is computed under a
+different probability measure. We analyse the mean-field game (MFG) limit of
+the stochastic game and show that the Nash equilibria is given by the solution
+to a non-standard vector-valued forward-backward stochastic differential
+equation. Under some mild assumptions, we construct the solution in terms of
+expectations of the filtered states. We prove the MFG strategy forms an
+\epsilon-Nash equilibrium for the finite player game. Lastly, we present a
+least-squares Monte Carlo based algorithm for computing the optimal control and
+illustrate the results through simulation in market where agents disagree on
+the model.
+",0,0,0,0,0,1
+16796,"Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors","  In addition to hardware wall-time restrictions commonly seen in
+high-performance computing systems, it is likely that future systems will also
+be constrained by energy budgets. In the present work, finite difference
+algorithms of varying computational and memory intensity are evaluated with
+respect to both energy efficiency and runtime on an Intel Ivy Bridge CPU node,
+an Intel Xeon Phi Knights Landing processor, and an NVIDIA Tesla K40c GPU. The
+conventional way of storing the discretised derivatives to global arrays for
+solution advancement is found to be inefficient in terms of energy consumption
+and runtime. In contrast, a class of algorithms in which the discretised
+derivatives are evaluated on-the-fly or stored as thread-/process-local
+variables (yielding high compute intensity) is optimal both with respect to
+energy consumption and runtime. On all three hardware architectures considered,
+a speed-up of ~2 and an energy saving of ~2 are observed for the high compute
+intensive algorithms compared to the memory intensive algorithm. The energy
+consumption is found to be proportional to runtime, irrespective of the power
+consumed and the GPU has an energy saving of ~5 compared to the same algorithm
+on a CPU node.
+",1,1,0,0,0,0
+16797,A Plane of High Velocity Galaxies Across the Local Group,"  We recently showed that several Local Group (LG) galaxies have much higher
+radial velocities (RVs) than predicted by a 3D dynamical model of the standard
+cosmological paradigm. Here, we show that 6 of these 7 galaxies define a thin
+plane with root mean square thickness of only 101 kpc despite a widest extent
+of nearly 3 Mpc, much larger than the conventional virial radius of the Milky
+Way (MW) or M31. This plane passes within ${\sim 70}$ kpc of the MW-M31
+barycentre and is oriented so the MW-M31 line is inclined by $16^\circ$ to it.
+We develop a toy model to constrain the scenario whereby a past MW-M31 flyby
+in Modified Newtonian Dynamics (MOND) forms tidal dwarf galaxies that settle
+into the recently discovered planes of satellites around the MW and M31. The
+scenario is viable only for a particular MW-M31 orbital plane. This roughly
+coincides with the plane of LG dwarfs with anomalously high RVs.
+Using a restricted $N$-body simulation of the LG in MOND, we show how the
+once fast-moving MW and M31 gravitationally slingshot test particles outwards
+at high speeds. The most distant such particles preferentially lie within the
+MW-M31 orbital plane, probably because the particles ending up with the highest
+RVs are those flung out almost parallel to the motion of the perturber. This
+suggests a dynamical reason for our finding of a similar trend in the real LG,
+something not easily explained as a chance alignment of galaxies with an
+isotropic or mildly flattened distribution (probability $= {0.0015}$).
+",0,1,0,0,0,0
+16798,Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps,"  We present a method for scalable and fully 3D magnetic field simultaneous
+localisation and mapping (SLAM) using local anomalies in the magnetic field as
+a source of position information. These anomalies are due to the presence of
+ferromagnetic material in the structure of buildings and in objects such as
+furniture. We represent the magnetic field map using a Gaussian process model
+and take well-known physical properties of the magnetic field into account. We
+build local maps using three-dimensional hexagonal block tiling. To make our
+approach computationally tractable we use reduced-rank Gaussian process
+regression in combination with a Rao-Blackwellised particle filter. We show
+that it is possible to obtain accurate position and orientation estimates using
+measurements from a smartphone, and that our approach provides a scalable
+magnetic field SLAM algorithm in terms of both computational complexity and map
+storage.
+",1,0,0,1,0,0
+16799,K-means Algorithm over Compressed Binary Data,"  We consider a network of binary-valued sensors with a fusion center. The
+fusion center has to perform K-means clustering on the binary data transmitted
+by the sensors. In order to reduce the amount of data transmitted within the
+network, the sensors compress their data with a source coding scheme based on
+binary sparse matrices. We propose to apply the K-means algorithm directly over
+the compressed data without reconstructing the original sensors measurements,
+in order to avoid potentially complex decoding operations. We provide
+approximated expressions of the error probabilities of the K-means steps in the
+compressed domain. From these expressions, we show that applying the K-means
+algorithm in the compressed domain enables to recover the clusters of the
+original domain. Monte Carlo simulations illustrate the accuracy of the
+obtained approximated error probabilities, and show that the coding rate needed
+to perform K-means clustering in the compressed domain is lower than the rate
+needed to reconstruct all the measurements.
+",1,0,1,0,0,0
+16800,Variational Inference for Gaussian Process Models with Linear Complexity,"  Large-scale Gaussian process inference has long faced practical challenges
+due to time and space complexity that is superlinear in dataset size. While
+sparse variational Gaussian process models are capable of learning from
+large-scale data, standard strategies for sparsifying the model can prevent the
+approximation of complex functions. In this work, we propose a novel
+variational Gaussian process model that decouples the representation of mean
+and covariance functions in reproducing kernel Hilbert space. We show that this
+new parametrization generalizes previous models. Furthermore, it yields a
+variational inference problem that can be solved by stochastic gradient ascent
+with time and space complexity that is only linear in the number of mean
+function parameters, regardless of the choice of kernels, likelihoods, and
+inducing points. This strategy makes the adoption of large-scale expressive
+Gaussian process models possible. We run several experiments on regression
+tasks and show that this decoupled approach greatly outperforms previous sparse
+variational Gaussian process inference procedures.
+",1,0,0,1,0,0
+16801,Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data,"  Diffusion MRI measurements using hyperpolarized gases are generally acquired
+during patient breath hold, which yields a compromise between achievable image
+resolution, lung coverage and number of b-values. In this work, we propose a
+novel method that accelerates the acquisition of MR diffusion data by
+undersampling in both spatial and b-value dimensions, thanks to incorporating
+knowledge about the signal decay into the reconstruction (SIDER). SIDER is
+compared to total variation (TV) reconstruction by assessing their effect on
+both the recovery of ventilation images and estimated mean alveolar dimensions
+(MAD). Both methods are assessed by retrospectively undersampling diffusion
+datasets of normal volunteers and COPD patients (n=8) for acceleration factors
+between x2 and x10. TV led to large errors and artefacts for acceleration
+factors equal or larger than x5. SIDER improved TV, presenting lower errors and
+histograms of MAD closer to those obtained from fully sampled data for
+accelerations factors up to x10. SIDER preserved image quality at all
+acceleration factors but images were slightly smoothed and some details were
+lost at x10. In conclusion, we have developed and validated a novel compressed
+sensing method for lung MRI imaging and achieved high acceleration factors,
+which can be used to increase the amount of data acquired during a breath-hold.
+This methodology is expected to improve the accuracy of estimated lung
+microstructure dimensions and widen the possibilities of studying lung diseases
+with MRI.
+",1,1,0,0,0,0
+16802,Rabi noise spectroscopy of individual two-level tunneling defects,"  Understanding the nature of two-level tunneling defects is important for
+minimizing their disruptive effects in various nano-devices. By exploiting the
+resonant coupling of these defects to a superconducting qubit, one can probe
+and coherently manipulate them individually. In this work we utilize a phase
+qubit to induce Rabi oscillations of single tunneling defects and measure their
+dephasing rates as a function of the defect's asymmetry energy, which is tuned
+by an applied strain. The dephasing rates scale quadratically with the external
+strain and are inversely proportional to the Rabi frequency. These results are
+analyzed and explained within a model of interacting standard defects, in which
+pure dephasing of coherent high-frequency (GHz) defects is caused by
+interaction with incoherent low-frequency thermally excited defects.
+",0,1,0,0,0,0
+16803,Learning rate adaptation for federated and differentially private learning,"  We propose an algorithm for the adaptation of the learning rate for
+stochastic gradient descent (SGD) that avoids the need for validation set use.
+The idea for the adaptiveness comes from the technique of extrapolation: to get
+an estimate for the error against the gradient flow which underlies SGD, we
+compare the result obtained by one full step and two half-steps. The algorithm
+is applied in two separate frameworks: federated and differentially private
+learning. Using examples of deep neural networks we empirically show that the
+adaptive algorithm is competitive with manually tuned commonly used
+optimisation methods for differentially privately training. We also show that
+it works robustly in the case of federated learning unlike commonly used
+optimisation methods.
+",0,0,0,1,0,0
+16804,Holomorphic Hermite polynomials in two variables,"  Generalizations of the Hermite polynomials to many variables and/or to the
+complex domain have been located in mathematical and physical literature for
+some decades. Polynomials traditionally called complex Hermite ones are mostly
+understood as polynomials in $z$ and $\bar{z}$ which in fact makes them
+polynomials in two real variables with complex coefficients. The present paper
+proposes to investigate for the first time holomorphic Hermite polynomials in
+two variables. Their algebraic and analytic properties are developed here.
+While the algebraic properties do not differ too much for those considered so
+far, their analytic features are based on a kind of non-rotational
+orthogonality invented by van Eijndhoven and Meyers. Inspired by their
+invention we merely follow the idea of Bargmann's seminal paper (1961) giving
+explicit construction of reproducing kernel Hilbert spaces based on those
+polynomials. ""Homotopic"" behavior of our new formation culminates in comparing
+it to the very classical Bargmann space of two variables on one edge and the
+aforementioned Hermite polynomials in $z$ and $\bar{z}$ on the other. Unlike in
+the case of Bargmann's basis our Hermite polynomials are not product ones but
+factorize to it when bonded together with the first case of limit properties
+leading both to the Bargmann basis and suitable form of the reproducing kernel.
+Also in the second limit we recover standard results obeyed by Hermite
+polynomials in $z$ and $\bar{z}$.
+",0,0,1,0,0,0
+16805,"Equilibria, information and frustration in heterogeneous network games with conflicting preferences","  Interactions between people are the basis on which the structure of our
+society arises as a complex system and, at the same time, are the starting
+point of any physical description of it. In the last few years, much
+theoretical research has addressed this issue by combining the physics of
+complex networks with a description of interactions in terms of evolutionary
+game theory. We here take this research a step further by introducing a most
+salient societal factor such as the individuals' preferences, a characteristic
+that is key to understand much of the social phenomenology these days. We
+consider a heterogeneous, agent-based model in which agents interact
+strategically with their neighbors but their preferences and payoffs for the
+possible actions differ. We study how such a heterogeneous network behaves
+under evolutionary dynamics and different strategic interactions, namely
+coordination games and best shot games. With this model we study the emergence
+of the equilibria predicted analytically in random graphs under best response
+dynamics, and we extend this test to unexplored contexts like proportional
+imitation and scale free networks. We show that some theoretically predicted
+equilibria do not arise in simulations with incomplete Information, and we
+demonstrate the importance of the graph topology and the payoff function
+parameters for some games. Finally, we discuss our results with available
+experimental evidence on coordination games, showing that our model agrees
+better with the experiment that standard economic theories, and draw hints as
+to how to maximize social efficiency in situations of conflicting preferences.
+",1,1,0,0,0,0
+16806,Scalable Generalized Dynamic Topic Models,"  Dynamic topic models (DTMs) model the evolution of prevalent themes in
+literature, online media, and other forms of text over time. DTMs assume that
+word co-occurrence statistics change continuously and therefore impose
+continuous stochastic process priors on their model parameters. These dynamical
+priors make inference much harder than in regular topic models, and also limit
+scalability. In this paper, we present several new results around DTMs. First,
+we extend the class of tractable priors from Wiener processes to the generic
+class of Gaussian processes (GPs). This allows us to explore topics that
+develop smoothly over time, that have a long-term memory or are temporally
+concentrated (for event detection). Second, we show how to perform scalable
+approximate inference in these models based on ideas around stochastic
+variational inference and sparse Gaussian processes. This way we can train a
+rich family of DTMs to massive data. Our experiments on several large-scale
+datasets show that our generalized model allows us to find interesting patterns
+that were not accessible by previous approaches.
+",0,0,0,1,0,0
+16807,Session Types for Orchestrated Interactions,"  In the setting of the pi-calculus with binary sessions, we aim at relaxing
+the notion of duality of session types by the concept of retractable compliance
+developed in contract theory. This leads to extending session types with a new
+type operator of ""speculative selection"" including choices not necessarily
+offered by a compliant partner. We address the problem of selecting successful
+communicating branches by means of an operational semantics based on
+orchestrators, which has been shown to be equivalent to the retractable
+semantics of contracts, but clearly more feasible. A type system, sound with
+respect to such a semantics, is hence provided.
+",1,0,0,0,0,0
+16808,An Agent-Based Approach for Optimizing Modular Vehicle Fleet Operation,"  Modularity in military vehicle designs enables on-base assembly, disassembly,
+and reconfiguration of vehicles, which can be beneficial in promoting fleet
+adaptability and life cycle cost savings. To properly manage the fleet
+operation and to control the resupply, demand prediction, and scheduling
+process, this paper illustrates an agent-based approach customized for highly
+modularized military vehicle fleets and studies the feasibility and flexibility
+of modularity for various mission scenarios. Given deterministic field demands
+with operation stochasticity, we compare the performance of a modular fleet to
+a conventional fleet in equivalent operation strategies and also compare fleet
+performance driven by heuristic rules and optimization. Several indicators are
+selected to quantify the fleet performance, including operation costs, total
+resupplied resources, and fleet readiness.
+When the model is implemented for military Joint Tactical Transport System
+(JTTS) mission, our results indicate that fleet modularity can reduce total
+resource supplies without significant losses in fleet readiness. The benefits
+of fleet modularity can also be amplified through a real-time optimized
+operation strategy. To highlight the feasibility of fleet modularity, a
+parametric study is performed to show the impacts from working capacity on
+modular fleet performance. Finally, we provide practical suggestions of modular
+vehicle designs based on the analysis and other possible usage.
+",1,0,0,0,0,0
+16809,Delta-epsilon functions and uniform continuity on metric spaces,"  Under certain general conditions, an explicit formula to compute the greatest
+delta-epsilon function of a continuous function is given. From this formula, a
+new way to analyze the uniform continuity of a continuous function is given.
+Several examples illustrating the theory are discussed.
+",0,0,1,0,0,0
+16810,Deterministic Dispersion of Mobile Robots in Dynamic Rings,"  In this work, we study the problem of dispersion of mobile robots on dynamic
+rings. The problem of dispersion of $n$ robots on an $n$ node graph, introduced
+by Augustine and Moses Jr. [1], requires robots to coordinate with each other
+and reach a configuration where exactly one robot is present on each node. This
+problem has real world applications and applies whenever we want to minimize
+the total cost of $n$ agents sharing $n$ resources, located at various places,
+subject to the constraint that the cost of an agent moving to a different
+resource is comparatively much smaller than the cost of multiple agents sharing
+a resource (e.g. smart electric cars sharing recharge stations). The study of
+this problem also provides indirect benefits to the study of scattering on
+graphs, the study of exploration by mobile robots, and the study of load
+balancing on graphs.
+We solve the problem of dispersion in the presence of two types of dynamism
+in the underlying graph: (i) vertex permutation and (ii) 1-interval
+connectivity. We introduce the notion of vertex permutation dynamism and have
+it mean that for a given set of nodes, in every round, the adversary ensures a
+ring structure is maintained, but the connections between the nodes may change.
+We use the idea of 1-interval connectivity from Di Luna et al. [10], where for
+a given ring, in each round, the adversary chooses at most one edge to remove.
+We assume robots have full visibility and present asymptotically time optimal
+algorithms to achieve dispersion in the presence of both types of dynamism when
+robots have chirality. When robots do not have chirality, we present
+asymptotically time optimal algorithms to achieve dispersion subject to certain
+constraints. Finally, we provide impossibility results for dispersion when
+robots have no visibility.
+",1,0,0,0,0,0
+16811,A brain signature highly predictive of future progression to Alzheimer's dementia,"  Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment
+(MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI
+individuals will progress to dementia, even when screened using biomarkers. We
+propose here to identify a subset of individuals who share a common brain
+signature highly predictive of oncoming dementia. This signature was composed
+of brain atrophy and functional dysconnectivity and discovered using a machine
+learning model in patients suffering from dementia. The model recognized the
+same brain signature in MCI individuals, 90% of which progressed to dementia
+within three years. This result is a marked improvement on the state-of-the-art
+in prognostic precision, while the brain signature still identified 47% of all
+MCI progressors. We thus discovered a sizable MCI subpopulation which
+represents an excellent recruitment target for clinical trials at the prodromal
+stage of Alzheimer's disease.
+",0,0,0,1,0,0
+16812,Deep scattering transform applied to note onset detection and instrument recognition,"  Automatic Music Transcription (AMT) is one of the oldest and most
+well-studied problems in the field of music information retrieval. Within this
+challenging research field, onset detection and instrument recognition take
+important places in transcription systems, as they respectively help to
+determine exact onset times of notes and to recognize the corresponding
+instrument sources. The aim of this study is to explore the usefulness of
+multiscale scattering operators for these two tasks on plucked string
+instrument and piano music. After resuming the theoretical background and
+illustrating the key features of this sound representation method, we evaluate
+its performances comparatively to other classical sound representations. Using
+both MIDI-driven datasets with real instrument samples and real musical pieces,
+scattering is proved to outperform other sound representations for these AMT
+subtasks, putting forward its richer sound representation and invariance
+properties.
+",1,0,0,1,0,0
+16813,Gaschütz Lemma for Compact Groups,"  We prove the Gaschütz Lemma holds for all metrisable compact groups.
+",0,0,1,0,0,0
+16814,Driven flow with exclusion and spin-dependent transport in graphenelike structures,"  We present a simplified description for spin-dependent electronic transport
+in honeycomb-lattice structures with spin-orbit interactions, using
+generalizations of the stochastic non-equilibrium model known as the totally
+asymmetric simple exclusion process. Mean field theory and numerical
+simulations are used to study currents, density profiles and current
+polarization in quasi- one dimensional systems with open boundaries, and
+externally-imposed particle injection ($\alpha$) and ejection ($\beta$) rates.
+We investigate the influence of allowing for double site occupancy, according
+to Pauli's exclusion principle, on the behavior of the quantities of interest.
+We find that double occupancy shows strong signatures for specific combinations
+of rates, namely high $\alpha$ and low $\beta$, but otherwise its effects are
+quantitatively suppressed. Comments are made on the possible relevance of the
+present results to experiments on suitably doped graphenelike structures.
+",0,1,0,0,0,0
+16815,MOG: Mapper on Graphs for Relationship Preserving Clustering,"  The interconnected nature of graphs often results in difficult to interpret
+clutter. Typically techniques focus on either decluttering by clustering nodes
+with similar properties or grouping edges with similar relationship. We propose
+using mapper, a powerful topological data analysis tool, to summarize the
+structure of a graph in a way that both clusters data with similar properties
+and preserves relationships. Typically, mapper operates on a given data by
+utilizing a scalar function defined on every point in the data and a cover for
+scalar function codomain. The output of mapper is a graph that summarize the
+shape of the space. In this paper, we outline how to use this mapper
+construction on an input graphs, outline three filter functions that capture
+important structures of the input graph, and provide an interface for
+interactively modifying the cover. To validate our approach, we conduct several
+case studies on synthetic and real world data sets and demonstrate how our
+method can give meaningful summaries for graphs with various complexities
+",0,0,0,1,0,0
+16816,"Variation Evolving for Optimal Control Computation, A Compact Way","  A compact version of the Variation Evolving Method (VEM) is developed for the
+optimal control computation. It follows the idea that originates from the
+continuous-time dynamics stability theory in the control field. The optimal
+solution is analogized to the equilibrium point of a dynamic system and is
+anticipated to be obtained in an asymptotically evolving way. With the
+introduction of a virtual dimension, the variation time, the Evolution Partial
+Differential Equation (EPDE), which describes the variation motion towards the
+optimal solution, is deduced from the Optimal Control Problem (OCP), and the
+equivalent optimality conditions with no employment of costates are
+established. In particular, it is found that theoretically the analytic
+feedback optimal control law does not exist for general OCPs because the
+optimal control is related to the future state. Since the derived EPDE is
+suitable to be solved with the semi-discrete method in the field of PDE
+numerical calculation, the resulting Initial-value Problems (IVPs) may be
+solved with mature Ordinary Differential Equation (ODE) numerical integration
+methods.
+",1,0,0,0,0,0
+16817,Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection,"  Convolutional Neural Networks (CNNs) have become the state-of-the-art in
+various computer vision tasks, but they are still premature for most sensor
+data, especially in pervasive and wearable computing. A major reason for this
+is the limited amount of annotated training data. In this paper, we propose the
+idea of leveraging the discriminative power of pre-trained deep CNNs on
+2-dimensional sensor data by transforming the sensor modality to the visual
+domain. By three proposed strategies, 2D sensor output is converted into
+pressure distribution imageries. Then we utilize a pre-trained CNN for transfer
+learning on the converted imagery data. We evaluate our method on a gait
+dataset of floor surface pressure mapping. We obtain a classification accuracy
+of 87.66%, which outperforms the conventional machine learning methods by over
+10%.
+",1,0,0,0,0,0
+16818,The Price of Differential Privacy For Online Learning,"  We design differentially private algorithms for the problem of online linear
+optimization in the full information and bandit settings with optimal
+$\tilde{O}(\sqrt{T})$ regret bounds. In the full-information setting, our
+results demonstrate that $\epsilon$-differential privacy may be ensured for
+free -- in particular, the regret bounds scale as
+$O(\sqrt{T})+\tilde{O}\left(\frac{1}{\epsilon}\right)$. For bandit linear
+optimization, and as a special case, for non-stochastic multi-armed bandits,
+the proposed algorithm achieves a regret of
+$\tilde{O}\left(\frac{1}{\epsilon}\sqrt{T}\right)$, while the previously known
+best regret bound was
+$\tilde{O}\left(\frac{1}{\epsilon}T^{\frac{2}{3}}\right)$.
+",1,0,0,1,0,0
+16819,Simulation chain and signal classification for acoustic neutrino detection in seawater,"  Acoustic neutrino detection is a promising approach to extend the energy
+range of neutrino telescopes to energies beyond $10^{18}$\,eV. Currently
+operational and planned water-Cherenkov neutrino telescopes, most notably
+KM3NeT, include acoustic sensors in addition to the optical ones. These
+acoustic sensors could be used as instruments for acoustic detection, while
+their main purpose is the position calibration of the detection units. In this
+article, a Monte Carlo simulation chain for acoustic detectors will be
+presented, covering the initial interaction of the neutrino up to the signal
+classification of recorded events. The ambient and transient background in the
+simulation was implemented according to data recorded by the acoustic set-up
+AMADEUS inside the ANTARES detector. The effects of refraction on the neutrino
+signature in the detector are studied, and a classification of the recorded
+events is implemented. As bipolar waveforms similar to those of the expected
+neutrino signals are also emitted from other sound sources, additional features
+like the geometrical shape of the propagation have to be considered for the
+signal classification. This leads to a large improvement of the background
+suppression by almost two orders of magnitude, since a flat cylindrical
+""pancake"" propagation pattern is a distinctive feature of neutrino signals. An
+overview of the simulation chain and the signal classification will be
+presented and preliminary studies of the performance of the classification will
+be discussed.
+",0,1,0,0,0,0
+16820,Parameter Space Noise for Exploration,"  Deep reinforcement learning (RL) methods generally engage in exploratory
+behavior through noise injection in the action space. An alternative is to add
+noise directly to the agent's parameters, which can lead to more consistent
+exploration and a richer set of behaviors. Methods such as evolutionary
+strategies use parameter perturbations, but discard all temporal structure in
+the process and require significantly more samples. Combining parameter noise
+with traditional RL methods allows to combine the best of both worlds. We
+demonstrate that both off- and on-policy methods benefit from this approach
+through experimental comparison of DQN, DDPG, and TRPO on high-dimensional
+discrete action environments as well as continuous control tasks. Our results
+show that RL with parameter noise learns more efficiently than traditional RL
+with action space noise and evolutionary strategies individually.
+",1,0,0,1,0,0
+16821,Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network,"  We present Deep Illumination, a novel machine learning technique for
+approximating global illumination (GI) in real-time applications using a
+Conditional Generative Adversarial Network. Our primary focus is on generating
+indirect illumination and soft shadows with offline rendering quality at
+interactive rates. Inspired from recent advancement in image-to-image
+translation problems using deep generative convolutional networks, we introduce
+a variant of this network that learns a mapping from Gbuffers (depth map,
+normal map, and diffuse map) and direct illumination to any global illumination
+solution. Our primary contribution is showing that a generative model can be
+used to learn a density estimation from screen space buffers to an advanced
+illumination model for a 3D environment. Once trained, our network can
+approximate global illumination for scene configurations it has never
+encountered before within the environment it was trained on. We evaluate Deep
+Illumination through a comparison with both a state of the art real-time GI
+technique (VXGI) and an offline rendering GI technique (path tracing). We show
+that our method produces effective GI approximations and is also
+computationally cheaper than existing GI techniques. Our technique has the
+potential to replace existing precomputed and screen-space techniques for
+producing global illumination effects in dynamic scenes with physically-based
+rendering quality.
+",1,0,0,0,0,0
+16822,Fraternal Dropout,"  Recurrent neural networks (RNNs) are important class of architectures among
+neural networks useful for language modeling and sequential prediction.
+However, optimizing RNNs is known to be harder compared to feed-forward neural
+networks. A number of techniques have been proposed in literature to address
+this problem. In this paper we propose a simple technique called fraternal
+dropout that takes advantage of dropout to achieve this goal. Specifically, we
+propose to train two identical copies of an RNN (that share parameters) with
+different dropout masks while minimizing the difference between their
+(pre-softmax) predictions. In this way our regularization encourages the
+representations of RNNs to be invariant to dropout mask, thus being robust. We
+show that our regularization term is upper bounded by the expectation-linear
+dropout objective which has been shown to address the gap due to the difference
+between the train and inference phases of dropout. We evaluate our model and
+achieve state-of-the-art results in sequence modeling tasks on two benchmark
+datasets - Penn Treebank and Wikitext-2. We also show that our approach leads
+to performance improvement by a significant margin in image captioning
+(Microsoft COCO) and semi-supervised (CIFAR-10) tasks.
+",1,0,0,1,0,0
+16823,Finite-sample bounds for the multivariate Behrens-Fisher distribution with proportional covariances,"  The Behrens-Fisher problem is a well-known hypothesis testing problem in
+statistics concerning two-sample mean comparison. In this article, we confirm
+one conjecture in Eaton and Olshen (1972), which provides stochastic bounds for
+the multivariate Behrens-Fisher test statistic under the null hypothesis. We
+also extend their results on the stochastic ordering of random quotients to the
+arbitrary finite dimensional case. This work can also be seen as a
+generalization of Hsu (1938) that provided the bounds for the univariate
+Behrens-Fisher problem. The results obtained in this article can be used to
+derive a testing procedure for the multivariate Behrens-Fisher problem that
+strongly controls the Type I error.
+",0,0,1,1,0,0
+16824,Evidence for mixed rationalities in preference formation,"  Understanding the mechanisms underlying the formation of cultural traits,
+such as preferences, opinions and beliefs is an open challenge. Trait formation
+is intimately connected to cultural dynamics, which has been the focus of a
+variety of quantitative models. Recently, some studies have emphasized the
+importance of connecting those models to snapshots of cultural dynamics that
+are empirically accessible. By analyzing data obtained from different sources,
+it has been suggested that culture has properties that are universally present,
+and that empirical cultural states differ systematically from randomized
+counterparts. Hence, a question about the mechanism responsible for the
+observed patterns naturally arises. This study proposes a stochastic structural
+model for generating cultural states that retain those robust, empirical
+properties. One ingredient of the model, already used in previous work, assumes
+that every individual's set of traits is partly dictated by one of several,
+universal ""rationalities"", informally postulated by several social science
+theories. The second, new ingredient taken from the same theories assumes that,
+apart from a dominant rationality, each individual also has a certain exposure
+to the other rationalities. It is shown that both ingredients are required for
+reproducing the empirical regularities. This key result suggests that the
+effects of cultural dynamics in the real world can be described as an interplay
+of multiple, mixing rationalities, and thus provides indirect evidence for the
+class of social science theories postulating such mixing. The model should be
+seen as a static, effective description of culture, while a dynamical, more
+fundamental description is left for future research.
+",1,1,0,0,0,0
+16825,A Variance Maximization Criterion for Active Learning,"  Active learning aims to train a classifier as fast as possible with as few
+labels as possible. The core element in virtually any active learning strategy
+is the criterion that measures the usefulness of the unlabeled data based on
+which new points to be labeled are picked. We propose a novel approach which we
+refer to as maximizing variance for active learning or MVAL for short. MVAL
+measures the value of unlabeled instances by evaluating the rate of change of
+output variables caused by changes in the next sample to be queried and its
+potential labelling. In a sense, this criterion measures how unstable the
+classifier's output is for the unlabeled data points under perturbations of the
+training data. MVAL maintains, what we refer to as, retraining information
+matrices to keep track of these output scores and exploits two kinds of
+variance to measure the informativeness and representativeness, respectively.
+By fusing these variances, MVAL is able to select the instances which are both
+informative and representative. We employ our technique both in combination
+with logistic regression and support vector machines and demonstrate that MVAL
+achieves state-of-the-art performance in experiments on a large number of
+standard benchmark datasets.
+",1,0,0,1,0,0
+16826,"Polarization, plasmon, and Debye screening in doped 3D ani-Weyl semimetal","  We compute the polarization function in a doped three-dimensional
+anisotropic-Weyl semimetal, in which the fermion energy dispersion is linear in
+two components of the momenta and quadratic in the third. Through detailed
+calculations, we find that the long wavelength plasmon mode depends on the
+fermion density $n_e$ in the form $\Omega_{p}^{\bot}\propto n_{e}^{3/10}$
+within the basal plane and behaves as $\Omega_{p}^{z}\propto n_{e}^{1/2}$ along
+the third direction. This unique characteristic of the plasmon mode can be
+probed by various experimental techniques, such as electron energy-loss
+spectroscopy. The Debye screening at finite chemical potential and finite
+temperature is also analyzed based on the polarization function.
+",0,1,0,0,0,0
+16827,Identifying Product Order with Restricted Boltzmann Machines,"  Unsupervised machine learning via a restricted Boltzmann machine is an useful
+tool in distinguishing an ordered phase from a disordered phase. Here we study
+its application on the two-dimensional Ashkin-Teller model, which features a
+partially ordered product phase. We train the neural network with spin
+configuration data generated by Monte Carlo simulations and show that distinct
+features of the product phase can be learned from non-ergodic samples resulting
+from symmetry breaking. Careful analysis of the weight matrices inspires us to
+define a nontrivial machine-learning motivated quantity of the product form,
+which resembles the conventional product order parameter.
+",0,1,0,0,0,0
+16828,A finite temperature study of ideal quantum gases in the presence of one dimensional quasi-periodic potential,"  We study the thermodynamics of ideal Bose gas as well as the transport
+properties of non interacting bosons and fermions in a one dimensional
+quasi-periodic potential, namely Aubry-André (AA) model at finite
+temperature. For bosons in finite size systems, the effect of quasi-periodic
+potential on the crossover phenomena corresponding to Bose-Einstein
+condensation (BEC), superfluidity and localization phenomena at finite
+temperatures are investigated. From the ground state number fluctuation we
+calculate the crossover temperature of BEC which exhibits a non monotonic
+behavior with the strength of AA potential and vanishes at the self-dual
+critical point following power law. Appropriate rescaling of the crossover
+temperatures reveals universal behavior which is studied for different
+quasi-periodicity of the AA model. Finally, we study the temperature and flux
+dependence of the persistent current of fermions in presence of a
+quasi-periodic potential to identify the localization at the Fermi energy from
+the decay of the current.
+",0,1,0,0,0,0
+16829,High-Frequency Analysis of Effective Interactions and Bandwidth for Transient States after Monocycle Pulse Excitation of Extended Hubbard Model,"  Using a high-frequency expansion in periodically driven extended Hubbard
+models, where the strengths and ranges of density-density interactions are
+arbitrary, we obtain the effective interactions and bandwidth, which depend
+sensitively on the polarization of the driving field. Then, we numerically
+calculate modulations of correlation functions in a quarter-filled extended
+Hubbard model with nearest-neighbor interactions on a triangular lattice with
+trimers after monocycle pulse excitation. We discuss how the resultant
+modulations are compatible with the effective interactions and bandwidth
+derived above on the basis of their dependence on the polarization of
+photoexcitation, which is easily accessible by experiments. Some correlation
+functions after monocycle pulse excitation are consistent with the effective
+interactions, which are weaker or stronger than the original ones. However, the
+photoinduced enhancement of anisotropic charge correlations previously
+discussed for the three-quarter-filled organic conductor
+$\alpha$-(bis[ethylenedithio]-tetrathiafulvalene)$_2$I$_3$
+[$\alpha$-(BEDT-TTF)$_2$I$_3$] in the metallic phase is not fully explained by
+the effective interactions or bandwidth, which are derived independently of the
+filling.
+",0,1,0,0,0,0
+16830,"Fast binary embeddings, and quantized compressed sensing with structured matrices","  This paper deals with two related problems, namely distance-preserving binary
+embeddings and quantization for compressed sensing . First, we propose fast
+methods to replace points from a subset $\mathcal{X} \subset \mathbb{R}^n$,
+associated with the Euclidean metric, with points in the cube $\{\pm 1\}^m$ and
+we associate the cube with a pseudo-metric that approximates Euclidean distance
+among points in $\mathcal{X}$. Our methods rely on quantizing fast
+Johnson-Lindenstrauss embeddings based on bounded orthonormal systems and
+partial circulant ensembles, both of which admit fast transforms. Our
+quantization methods utilize noise-shaping, and include Sigma-Delta schemes and
+distributed noise-shaping schemes. The resulting approximation errors decay
+polynomially and exponentially fast in $m$, depending on the embedding method.
+This dramatically outperforms the current decay rates associated with binary
+embeddings and Hamming distances. Additionally, it is the first such binary
+embedding result that applies to fast Johnson-Lindenstrauss maps while
+preserving $\ell_2$ norms.
+Second, we again consider noise-shaping schemes, albeit this time to quantize
+compressed sensing measurements arising from bounded orthonormal ensembles and
+partial circulant matrices. We show that these methods yield a reconstruction
+error that again decays with the number of measurements (and bits), when using
+convex optimization for reconstruction. Specifically, for Sigma-Delta schemes,
+the error decays polynomially in the number of measurements, and it decays
+exponentially for distributed noise-shaping schemes based on beta encoding.
+These results are near optimal and the first of their kind dealing with bounded
+orthonormal systems.
+",0,0,0,1,0,0
+16831,The Many Faces of Link Fraud,"  Most past work on social network link fraud detection tries to separate
+genuine users from fraudsters, implicitly assuming that there is only one type
+of fraudulent behavior. But is this assumption true? And, in either case, what
+are the characteristics of such fraudulent behaviors? In this work, we set up
+honeypots (""dummy"" social network accounts), and buy fake followers (after
+careful IRB approval). We report the signs of such behaviors including oddities
+in local network connectivity, account attributes, and similarities and
+differences across fraud providers. Most valuably, we discover and characterize
+several types of fraud behaviors. We discuss how to leverage our insights in
+practice by engineering strongly performing entropy-based features and
+demonstrating high classification accuracy. Our contributions are (a)
+instrumentation: we detail our experimental setup and carefully engineered data
+collection process to scrape Twitter data while respecting API rate-limits, (b)
+observations on fraud multimodality: we analyze our honeypot fraudster
+ecosystem and give surprising insights into the multifaceted behaviors of these
+fraudster types, and (c) features: we propose novel features that give strong
+(>0.95 precision/recall) discriminative power on ground-truth Twitter data.
+",1,0,0,0,0,0
+16832,Diophantine approximation by special primes,"  We show that whenever $\delta>0$, $\eta$ is real and constants $\lambda_i$
+satisfy some necessary conditions, there are infinitely many prime triples
+$p_1,\, p_2,\, p_3$ satisfying the inequality $|\lambda_1p_1 + \lambda_2p_2 +
+\lambda_3p_3+\eta|<(\max p_j)^{-1/12+\delta}$ and such that, for each
+$i\in\{1,2,3\}$, $p_i+2$ has at most $28$ prime factors.
+",0,0,1,0,0,0
+16833,A Compositional Treatment of Iterated Open Games,"  Compositional Game Theory is a new, recently introduced model of economic
+games based upon the computer science idea of compositionality. In it, complex
+and irregular games can be built up from smaller and simpler games, and the
+equilibria of these complex games can be defined recursively from the
+equilibria of their simpler subgames. This paper extends the model by providing
+a final coalgebra semantics for infinite games. In the course of this, we
+introduce a new operator on games to model the economic concept of subgame
+perfection.
+",1,0,0,0,0,0
+16834,Bayesian inference for spectral projectors of covariance matrix,"  Let $X_1, \ldots, X_n$ be i.i.d. sample in $\mathbb{R}^p$ with zero mean and
+the covariance matrix $\mathbf{\Sigma^*}$. The classic principal component
+analysis estimates the projector $\mathbf{P^*_{\mathcal{J}}}$ onto the direct
+sum of some eigenspaces of $\mathbf{\Sigma^*}$ by its empirical counterpart
+$\mathbf{\widehat{P}_{\mathcal{J}}}$. Recent papers [Koltchinskii, Lounici
+(2017)], [Naumov et al. (2017)] investigate the asymptotic distribution of the
+Frobenius distance between the projectors $\|
+\mathbf{\widehat{P}_{\mathcal{J}}} - \mathbf{P^*_{\mathcal{J}}} \|_2$. The
+problem arises when one tries to build a confidence set for the true projector
+effectively. We consider the problem from Bayesian perspective and derive an
+approximation for the posterior distribution of the Frobenius distance between
+projectors. The derived theorems hold true for non-Gaussian data: the only
+assumption that we impose is the concentration of the sample covariance
+$\mathbf{\widehat{\Sigma}}$ in a vicinity of $\mathbf{\Sigma^*}$. The obtained
+results are applied to construction of sharp confidence sets for the true
+projector. Numerical simulations illustrate good performance of the proposed
+procedure even on non-Gaussian data in quite challenging regime.
+",0,0,1,1,0,0
+16835,Handling Incomplete Heterogeneous Data using VAEs,"  Variational autoencoders (VAEs), as well as other generative models, have
+been shown to be efficient and accurate to capture the latent structure of vast
+amounts of complex high-dimensional data. However, existing VAEs can still not
+directly handle data that are heterogenous (mixed continuous and discrete) or
+incomplete (with missing data at random), which is indeed common in real-world
+applications.
+In this paper, we propose a general framework to design VAEs, suitable for
+fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood
+models for real-valued, positive real valued, interval, categorical, ordinal
+and count data, and allows to estimate (and potentially impute) missing data
+accurately. Furthermore, HI-VAE presents competitive predictive performance in
+supervised tasks, outperforming super- vised models when trained on incomplete
+data
+",0,0,0,1,0,0
+16836,Geometric mean of probability measures and geodesics of Fisher information metric,"  The space of all probability measures having positive density function on a
+connected compact smooth manifold $M$, denoted by $\mathcal{P}(M)$, carries the
+Fisher information metric $G$. We define the geometric mean of probability
+measures by the aid of which we investigate information geometry of
+$\mathcal{P}(M)$, equipped with $G$. We show that a geodesic segment joining
+arbitrary probability measures $\mu_1$ and $\mu_2$ is expressed by using the
+normalized geometric mean of its endpoints. As an application, we show that any
+two points of $\mathcal{P}(M)$ can be joined by a geodesic. Moreover, we prove
+that the function $\ell$ defined by $\ell(\mu_1, \mu_2):=2\arccos\int_M
+\sqrt{p_1\,p_2}\,d\lambda$, $\mu_i=p_i\,\lambda$, $i=1,2$ gives the distance
+function on $\mathcal{P}(M)$. It is shown that geodesics are all minimal.
+",0,0,1,0,0,0
+16837,On automorphism groups of Toeplitz subshifts,"  In this article we study automorphisms of Toeplitz subshifts. Such groups are
+abelian and any finitely generated torsion subgroup is finite and cyclic. When
+the complexity is non superlinear, we prove that the automorphism group is,
+modulo a finite cyclic group, generated by a unique root of the shift. In the
+subquadratic complexity case, we show that the automorphism group modulo the
+torsion is generated by the roots of the shift map and that the result of the
+non superlinear case is optimal. Namely, for any $\varepsilon > 0$ we construct
+examples of minimal Toeplitz subshifts with complexity bounded by $C
+n^{1+\epsilon}$ whose automorphism groups are not finitely generated. Finally,
+we observe the coalescence and the automorphism group give no restriction on
+the complexity since we provide a family of coalescent Toeplitz subshifts with
+positive entropy such that their automorphism groups are arbitrary finitely
+generated infinite abelian groups with cyclic torsion subgroup (eventually
+restricted to powers of the shift).
+",0,0,1,0,0,0
+16838,How to Generate Pseudorandom Permutations Over Other Groups,"  Recent results by Alagic and Russell have given some evidence that the
+Even-Mansour cipher may be secure against quantum adversaries with quantum
+queries, if considered over other groups than $(\mathbb{Z}/2)^n$. This prompts
+the question as to whether or not other classical schemes may be generalized to
+arbitrary groups and whether classical results still apply to those generalized
+schemes. In this thesis, we generalize the Even-Mansour cipher and the Feistel
+cipher. We show that Even and Mansour's original notions of secrecy are
+obtained on a one-key, group variant of the Even-Mansour cipher. We generalize
+the result by Kilian and Rogaway, that the Even-Mansour cipher is pseudorandom,
+to super pseudorandomness, also in the one-key, group case. Using a Slide
+Attack we match the bound found above. After generalizing the Feistel cipher to
+arbitrary groups we resolve an open problem of Patel, Ramzan, and Sundaram by
+showing that the 3-round Feistel cipher over an arbitrary group is not super
+pseudorandom. We generalize a result by Gentry and Ramzan showing that the
+Even-Mansour cipher can be implemented using the Feistel cipher as the public
+permutation. In this result, we also consider the one-key case over a group and
+generalize their bound. Finally, we consider Zhandry's result on quantum
+pseudorandom permutations, showing that his result may be generalized to hold
+for arbitrary groups. In this regard, we consider whether certain card shuffles
+may be generalized as well.
+",1,0,1,0,0,0
+16839,Measures of Tractography Convergence,"  In the present work, we use information theory to understand the empirical
+convergence rate of tractography, a widely-used approach to reconstruct
+anatomical fiber pathways in the living brain. Based on diffusion MRI data,
+tractography is the starting point for many methods to study brain
+connectivity. Of the available methods to perform tractography, most
+reconstruct a finite set of streamlines, or 3D curves, representing probable
+connections between anatomical regions, yet relatively little is known about
+how the sampling of this set of streamlines affects downstream results, and how
+exhaustive the sampling should be. Here we provide a method to measure the
+information theoretic surprise (self-cross entropy) for tract sampling schema.
+We then empirically assess four streamline methods. We demonstrate that the
+relative information gain is very low after a moderate number of streamlines
+have been generated for each tested method. The results give rise to several
+guidelines for optimal sampling in brain connectivity analyses.
+",0,0,0,1,1,0
+16840,Network Flow Based Post Processing for Sales Diversity,"  Collaborative filtering is a broad and powerful framework for building
+recommendation systems that has seen widespread adoption. Over the past decade,
+the propensity of such systems for favoring popular products and thus creating
+echo chambers have been observed. This has given rise to an active area of
+research that seeks to diversify recommendations generated by such algorithms.
+We address the problem of increasing diversity in recommendation systems that
+are based on collaborative filtering that use past ratings to predicting a
+rating quality for potential recommendations. Following our earlier work, we
+formulate recommendation system design as a subgraph selection problem from a
+candidate super-graph of potential recommendations where both diversity and
+rating quality are explicitly optimized: (1) On the modeling side, we define a
+new flexible notion of diversity that allows a system designer to prescribe the
+number of recommendations each item should receive, and smoothly penalizes
+deviations from this distribution. (2) On the algorithmic side, we show that
+minimum-cost network flow methods yield fast algorithms in theory and practice
+for designing recommendation subgraphs that optimize this notion of diversity.
+(3) On the empirical side, we show the effectiveness of our new model and
+method to increase diversity while maintaining high rating quality in standard
+rating data sets from Netflix and MovieLens.
+",1,0,0,0,0,0
+16841,Lattice Model for Production of Gas,"  We define a lattice model for rock, absorbers, and gas that makes it possible
+to examine the flow of gas to a complicated absorbing boundary over long
+periods of time. The motivation is to deduce the geometry of the boundary from
+the time history of gas absorption. We find a solution to this model using
+Green's function techniques, and apply the solution to three absorbing networks
+of increasing complexity.
+",0,1,0,0,0,0
+16842,Adaptive Representation Selection in Contextual Bandit,"  We consider an extension of the contextual bandit setting, motivated by
+several practical applications, where an unlabeled history of contexts can
+become available for pre-training before the online decision-making begins. We
+propose an approach for improving the performance of contextual bandit in such
+setting, via adaptive, dynamic representation learning, which combines offline
+pre-training on unlabeled history of contexts with online selection and
+modification of embedding functions. Our experiments on a variety of datasets
+and in different nonstationary environments demonstrate clear advantages of our
+approach over the standard contextual bandit.
+",0,0,0,1,0,0
+16843,Algebraic surfaces with zero-dimensional cohomology support locus,"  Using the theory of cohomology support locus, we give a necessary condition
+for the Albanese map of a smooth projective surface being a submersion. More
+precisely, assuming the cohomology support locus of any finite abelian cover of
+a smooth projective surface consists of finitely many points, we prove that the
+surface has trivial first Betti number, or is a ruled surface of genus one, or
+is an abelian surface.
+",0,0,1,0,0,0
+16844,Insight into the temperature dependent properties of the ferromagnetic Kondo lattice YbNiSn,"  Analyzing temperature dependent photoemission (PE) data of the ferromagnetic
+Kondo-lattice (KL) system YbNiSn in the light of the Periodic Anderson model
+(PAM) we show that the KL behavior is not limited to temperatures below a
+temperature T_K, defined empirically from resistivity and specificic heat
+measurements. As characteristic for weakly hybridized Ce and Yb systems, the PE
+spectra reveal a 4f-derived Fermi level peak, which reflects contributions from
+the Kondo resonance and its crystal electric field (CEF) satellites. In YbNiSn
+this peak has an unusual temperature dependence: With decreasing temperature a
+steady linear increase of intensity is observed which extends over a large
+interval ranging from 100 K down to 1 K without showing any peculiarities in
+the region of T_K ~ TC= 5.6 K. In the light of the single-impurity Anderson
+model (SIAM) this intensity variation reflects a linear increase of 4f
+occupancy with decreasing temperature, indicating an onset of Kondo screening
+at temperatures above 100 K. Within the PAM this phenomenon could be described
+by a non-Fermi liquid like T- linear damping of the self-energy which accounts
+phenomenologically for the feedback from the closely spaced CEF-states.
+",0,1,0,0,0,0
+16845,Some Ageing Properties of Dynamic Additive Mean Residual Life Model,"  Although proportional hazard rate model is a very popular model to analyze
+failure time data, sometimes it becomes important to study the additive hazard
+rate model. Again, sometimes the concept of the hazard rate function is
+abstract, in comparison to the concept of mean residual life function. A new
+model called `dynamic additive mean residual life model' where the covariates
+are time-dependent has been defined in the literature. Here we study the
+closure properties of the model for different positive and negative ageing
+classes under certain condition(s). Quite a few examples are presented to
+illustrate different properties of the model.
+",0,0,1,1,0,0
+16846,From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets,"  We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)
+estimators of Restricted Boltzmann Machines (RBMs). We denote our approach
+Markov Chain Las Vegas (MCLV). MCLV gives statistical guarantees in exchange
+for random running times. MCLV uses a stopping set built from the training data
+and has maximum number of Markov chain steps K (referred as MCLV-K). We present
+a MCLV-K gradient estimator (LVS-K) for RBMs and explore the correspondence and
+differences between LVS-K and Contrastive Divergence (CD-K), with LVS-K
+significantly outperforming CD-K training RBMs over the MNIST dataset,
+indicating MCLV to be a promising direction in learning generative models.
+",1,0,0,1,0,0
+16847,CD meets CAT,"  We show that if a noncollapsed $CD(K,n)$ space $X$ with $n\ge 2$ has
+curvature bounded above by $\kappa$ in the sense of Alexandrov then $K\le
+(n-1)\kappa$ and $X$ is an Alexandrov space of curvature bounded below by
+$K-\kappa (n-2)$. We also show that if a $CD(K,n)$ space $Y$ with finite $n$
+has curvature bounded above then it is infinitesimally Hilbertian.
+",0,0,1,0,0,0
+16848,Cost Models for Selecting Materialized Views in Public Clouds,"  Data warehouse performance is usually achieved through physical data
+structures such as indexes or materialized views. In this context, cost models
+can help select a relevant set ofsuch performance optimization structures.
+Nevertheless, selection becomes more complex in the cloud. The criterion to
+optimize is indeed at least two-dimensional, with monetary cost balancing
+overall query response time. This paper introduces new cost models that fit
+into the pay-as-you-go paradigm of cloud computing. Based on these cost models,
+an optimization problem is defined to discover, among candidate views, those to
+be materialized to minimize both the overall cost of using and maintaining the
+database in a public cloud and the total response time ofa given query
+workload. We experimentally show that maintaining materialized views is always
+advantageous, both in terms of performance and cost.
+",1,0,0,0,0,0
+16849,Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes,"  Bayesian optimization (BO) methods are useful for optimizing functions that
+are expensive to evaluate, lack an analytical expression and whose evaluations
+can be contaminated by noise. These methods rely on a probabilistic model of
+the objective function, typically a Gaussian process (GP), upon which an
+acquisition function is built. This function guides the optimization process
+and measures the expected utility of performing an evaluation of the objective
+at a new point. GPs assume continous input variables. When this is not the
+case, such as when some of the input variables take integer values, one has to
+introduce extra approximations. A common approach is to round the suggested
+variable value to the closest integer before doing the evaluation of the
+objective. We show that this can lead to problems in the optimization process
+and describe a more principled approach to account for input variables that are
+integer-valued. We illustrate in both synthetic and a real experiments the
+utility of our approach, which significantly improves the results of standard
+BO methods on problems involving integer-valued variables.
+",0,0,0,1,0,0
+16850,Second-order constrained variational problems on Lie algebroids: applications to optimal control,"  The aim of this work is to study, from an intrinsic and geometric point of
+view, second-order constrained variational problems on Lie algebroids, that is,
+optimization problems defined by a cost functional which depends on
+higher-order derivatives of admissible curves on a Lie algebroid. Extending the
+classical Skinner and Rusk formalism for the mechanics in the context of Lie
+algebroids, for second-order constrained mechanical systems, we derive the
+corresponding dynamical equations. We find a symplectic Lie subalgebroid where,
+under some mild regularity conditions, the second-order constrained variational
+problem, seen as a presymplectic Hamiltonian system, has a unique solution. We
+study the relationship of this formalism with the second-order constrained
+Euler-Poincaré and Lagrange-Poincaré equations, among others. Our study is
+applied to the optimal control of mechanical systems.
+",0,0,1,0,0,0
+16851,"The Galactic Cosmic Ray Electron Spectrum from 3 to 70 MeV Measured by Voyager 1 Beyond the Heliopause, What This Tells Us About the Propagation of Electrons and Nuclei In and Out of the Galaxy at Low Energies","  The cosmic ray electrons measured by Voyager 1 between 3-70 MeV beyond the
+heliopause have intensities several hundred times those measured at the Earth
+by PAMELA at nearly the same energies. This paper compares this new V1 data
+with data from the earth-orbiting PAMELA experiment up to energies greater than
+10 GeV where solar modulation effects are negligible. In this energy regime we
+assume the main parameters governing electron propagation are diffusion and
+energy loss and we use a Monte Carlo program to describe this propagation in
+the galaxy. To reproduce the new Voyager electron spectrum, which is E-1.3,
+together with that measured by PAMELA which is E-3.20 above 10 GeV, we require
+a diffusion coefficient which is P 0.45 at energies above 0.5 GeV changing to a
+P-1.00 dependence at lower rigidities. The entire electron spectrum observed at
+both V1 and PAMELA from 3 MeV to 30 GeV can then be described by a simple
+source spectrum, dj/dP P-2.25, with a spectral exponent that is independent of
+rigidity. The change in exponent of the measured electron spectrum from -1.3 at
+low energies to 3.2 at the highest energies can be explained by galactic
+propagation effects related to the changing dependence of the diffusion
+coefficient below 0.5 GeV, and the increasing importance above 0.5 GV of energy
+loss from synchrotron and inverse Compton radiation, which are both E2, and
+which are responsible for most of the changing spectral exponent above 1.0 GV.
+As a result of the P-1.00 dependence of the diffusion coefficient below 0.5
+GV that is required to fit the V1 electron spectrum, there is a rapid flow of
+these low energy electrons out of the galaxy. These electrons in local IG space
+are unobservable to us at any wave length and therefore form a dark energy
+component which is 100 times the electrons rest energy.
+",0,1,0,0,0,0
+16852,Online Scheduling of Spark Workloads with Mesos using Different Fair Allocation Algorithms,"  In the following, we present example illustrative and experimental results
+comparing fair schedulers allocating resources from multiple servers to
+distributed application frameworks. Resources are allocated so that at least
+one resource is exhausted in every server. Schedulers considered include DRF
+(DRFH) and Best-Fit DRF (BF-DRF), TSF, and PS-DSF. We also consider server
+selection under Randomized Round Robin (RRR) and based on their residual
+(unreserved) resources. In the following, we consider cases with frameworks of
+equal priority and without server-preference constraints. We first give typical
+results of a illustrative numerical study and then give typical results of a
+study involving Spark workloads on Mesos which we have modified and
+open-sourced to prototype different schedulers.
+",1,0,0,0,0,0
+16853,On the representation dimension and finitistic dimension of special multiserial algebras,"  For monomial special multiserial algebras, which in general are of wild
+representation type, we construct radical embeddings into algebras of finite
+representation type. As a consequence, we show that the representation
+dimension of monomial and self-injective special multiserial algebras is less
+or equal to three. This implies that the finitistic dimension conjecture holds
+for all special multiserial algebras.
+",0,0,1,0,0,0
+16854,Would You Like to Motivate Software Testers? Ask Them How,"  Context. Considering the importance of software testing to the development of
+high quality and reliable software systems, this paper aims to investigate how
+can work-related factors influence the motivation of software testers. Method.
+We applied a questionnaire that was developed using a previous theory of
+motivation and satisfaction of software engineers to conduct a survey-based
+study to explore and understand how professional software testers perceive and
+value work-related factors that could influence their motivation at work.
+Results. With a sample of 80 software testers we observed that software testers
+are strongly motivated by variety of work, creative tasks, recognition for
+their work, and activities that allow them to acquire new knowledge, but in
+general the social impact of this activity has low influence on their
+motivation. Conclusion. This study discusses the difference of opinions among
+software testers, regarding work-related factors that could impact their
+motivation, which can be relevant for managers and leaders in software
+engineering practice.
+",1,0,0,0,0,0
+16855,POMDP Structural Results for Controlled Sensing,"  This article provides a short review of some structural results in controlled
+sensing when the problem is formulated as a partially observed Markov decision
+process. In particular, monotone value functions, Blackwell dominance and
+quickest detection are described.
+",1,0,0,0,0,0
+16856,Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption,"  We study a data model in which the data matrix D can be expressed as D = L +
+S + C, where L is a low rank matrix, S an element-wise sparse matrix and C a
+matrix whose non-zero columns are outlying data points. To date, robust PCA
+algorithms have solely considered models with either S or C, but not both. As
+such, existing algorithms cannot account for simultaneous element-wise and
+column-wise corruptions. In this paper, a new robust PCA algorithm that is
+robust to simultaneous types of corruption is proposed. Our approach hinges on
+the sparse approximation of a sparsely corrupted column so that the sparse
+expansion of a column with respect to the other data points is used to
+distinguish a sparsely corrupted inlier column from an outlying data point. We
+also develop a randomized design which provides a scalable implementation of
+the proposed approach. The core idea of sparse approximation is analyzed
+analytically where we show that the underlying ell_1-norm minimization can
+obtain the representation of an inlier in presence of sparse corruptions.
+",1,0,0,1,0,0
+16857,Power-Sum Denominators,"  The power sum $1^n + 2^n + \cdots + x^n$ has been of interest to
+mathematicians since classical times. Johann Faulhaber, Jacob Bernoulli, and
+others who followed expressed power sums as polynomials in $x$ of degree $n+1$
+with rational coefficients. Here we consider the denominators of these
+polynomials, and prove some of their properties. A remarkable one is that such
+a denominator equals $n+1$ times the squarefree product of certain primes $p$
+obeying the condition that the sum of the base-$p$ digits of $n+1$ is at least
+$p$. As an application, we derive a squarefree product formula for the
+denominators of the Bernoulli polynomials.
+",0,0,1,0,0,0
+16858,A resource-frugal probabilistic dictionary and applications in bioinformatics,"  Indexing massive data sets is extremely expensive for large scale problems.
+In many fields, huge amounts of data are currently generated, however
+extracting meaningful information from voluminous data sets, such as computing
+similarity between elements, is far from being trivial. It remains nonetheless
+a fundamental need. This work proposes a probabilistic data structure based on
+a minimal perfect hash function for indexing large sets of keys. Our structure
+out-compete the hash table for construction, query times and for memory usage,
+in the case of the indexation of a static set. To illustrate the impact of
+algorithms performances, we provide two applications based on similarity
+computation between collections of sequences, and for which this calculation is
+an expensive but required operation. In particular, we show a practical case in
+which other bioinformatics tools fail to scale up the tested data set or
+provide lower recall quality results.
+",1,0,0,0,0,0
+16859,Fast learning rate of deep learning via a kernel perspective,"  We develop a new theoretical framework to analyze the generalization error of
+deep learning, and derive a new fast learning rate for two representative
+algorithms: empirical risk minimization and Bayesian deep learning. The series
+of theoretical analyses of deep learning has revealed its high expressive power
+and universal approximation capability. Although these analyses are highly
+nonparametric, existing generalization error analyses have been developed
+mainly in a fixed dimensional parametric model. To compensate this gap, we
+develop an infinite dimensional model that is based on an integral form as
+performed in the analysis of the universal approximation capability. This
+allows us to define a reproducing kernel Hilbert space corresponding to each
+layer. Our point of view is to deal with the ordinary finite dimensional deep
+neural network as a finite approximation of the infinite dimensional one. The
+approximation error is evaluated by the degree of freedom of the reproducing
+kernel Hilbert space in each layer. To estimate a good finite dimensional
+model, we consider both of empirical risk minimization and Bayesian deep
+learning. We derive its generalization error bound and it is shown that there
+appears bias-variance trade-off in terms of the number of parameters of the
+finite dimensional approximation. We show that the optimal width of the
+internal layers can be determined through the degree of freedom and the
+convergence rate can be faster than $O(1/\sqrt{n})$ rate which has been shown
+in the existing studies.
+",1,0,1,1,0,0
+16860,Closed-loop field development optimization with multipoint geostatistics and statistical assessment,"  Closed-loop field development (CLFD) optimization is a comprehensive
+framework for optimal development of subsurface resources. CLFD involves three
+major steps: 1) optimization of full development plan based on current set of
+models, 2) drilling new wells and collecting new spatial and temporal
+(production) data, 3) model calibration based on all data. This process is
+repeated until the optimal number of wells is drilled. This work introduces an
+efficient CLFD implementation for complex systems described by multipoint
+geostatistics (MPS). Model calibration is accomplished in two steps:
+conditioning to spatial data by a geostatistical simulation method, and
+conditioning to production data by optimization-based PCA. A statistical
+procedure is presented to assess the performance of CLFD. Methodology is
+applied to an oil reservoir example for 25 different true-model cases.
+Application of a single-step of CLFD, improved the true NPV in 64%--80% of
+cases. The full CLFD procedure (with three steps) improved the true NPV in 96%
+of cases, with an average improvement of 37%.
+",1,0,0,1,0,0
+16861,Reduction and regular $t$-balanced Cayley maps on split metacyclic 2-groups,"  A regular $t$-balanced Cayley map (RBCM$_t$ for short) on a group $\Gamma$ is
+an embedding of a Cayley graph on $\Gamma$ into a surface with some special
+symmetric properties. We propose a reduction method to study RBCM$_t$'s, and as
+a first practice, we completely classify RBCM$_t$'s for a class of split
+metacyclic 2-groups.
+",0,0,1,0,0,0
+16862,Perovskite Substrates Boost the Thermopower of Cobaltate Thin Films at High Temperatures,"  Transition metal oxides are promising candidates for thermoelectric
+applications, because they are stable at high temperature and because strong
+electronic correlations can generate large Seebeck coefficients, but their
+thermoelectric power factors are limited by the low electrical conductivity. We
+report transport measurements on Ca3Co4O9 films on various perovskite
+substrates and show that reversible incorporation of oxygen into SrTiO3 and
+LaAlO3 substrates activates a parallel conduction channel for p-type carriers,
+greatly enhancing the thermoelectric performance of the film-substrate system
+at temperatures above 450 °C. Thin-film structures that take advantage of
+both electronic correlations and the high oxygen mobility of transition metal
+oxides thus open up new perspectives for thermopower generation at high
+temperature.
+",0,1,0,0,0,0
+16863,Motion of a rod pushed at one point in a weightless environment in space,"  We analyze the motion of a rod floating in a weightless environment in space
+when a force is applied at some point on the rod in a direction perpendicular
+to its length. If the force applied is at the centre of mass, then the rod gets
+a linear motion perpendicular to its length. However, if the same force is
+applied at a point other than the centre of mass, say, near one end of the rod,
+thereby giving rise to a torque, then there will also be a rotation of the rod
+about its centre of mass, in addition to the motion of the centre of mass
+itself. If the force applied is for a very short duration, but imparting
+nevertheless a finite impulse, like in a sudden (quick) hit at one end of the
+rod, then the centre of mass will move with a constant linear speed and
+superimposed on it will be a rotation of the rod with constant angular speed
+about the centre of mass. However, if force is applied continuously, say by
+strapping a tiny rocket at one end of the rod, then the rod will spin faster
+and faster about the centre of mass, with angular speed increasing linearly
+with time. As the direction of the applied force, as seen by an external
+(inertial) observer, will be changing continuously with the rotation of the
+rod, the acceleration of the centre of mass would also be not in one fixed
+direction. However, it turns out that the locus of the velocity vector of the
+centre of mass will describe a Cornu spiral, with the velocity vector reaching
+a final constant value with time. The mean motion of the centre of mass will be
+in a straight line, with superposed initial oscillations that soon die down.
+",0,1,0,0,0,0
+16864,Dimension theory and components of algebraic stacks,"  We prove some basic results on the dimension theory of algebraic stacks, and
+on the multiplicities of their irreducible components, for which we do not know
+a reference.
+",0,0,1,0,0,0
+16865,When is a polynomial ideal binomial after an ambient automorphism?,"  Can an ideal I in a polynomial ring k[x] over a field be moved by a change of
+coordinates into a position where it is generated by binomials $x^a - cx^b$
+with c in k, or by unital binomials (i.e., with c = 0 or 1)? Can a variety be
+moved into a position where it is toric? By fibering the G-translates of I over
+an algebraic group G acting on affine space, these problems are special cases
+of questions about a family F of ideals over an arbitrary base B. The main
+results in this general setting are algorithms to find the locus of points in B
+over which the fiber of F
+- is contained in the fiber of a second family F' of ideals over B;
+- defines a variety of dimension at least d;
+- is generated by binomials; or
+- is generated by unital binomials.
+A faster containment algorithm is also presented when the fibers of F are
+prime. The big-fiber algorithm is probabilistic but likely faster than known
+deterministic ones. Applications include the setting where a second group T
+acts on affine space, in addition to G, in which case algorithms compute the
+set of G-translates of I
+- whose stabilizer subgroups in T have maximal dimension; or
+- that admit a faithful multigrading by $Z^r$ of maximal rank r.
+Even with no ambient group action given, the final application is an
+algorithm to
+- decide whether a normal projective variety is abstractly toric.
+All of these loci in B and subsets of G are constructible; in some cases they
+are closed.
+",1,0,1,0,0,0
+16866,Annihilators in $\mathbb{N}^k$-graded and $\mathbb{Z}^k$-graded rings,"  It has been shown by McCoy that a right ideal of a polynomial ring with
+several indeterminates has a non-trivial homogeneous right annihilator of
+degree 0 provided its right annihilator is non-trivial to begin with. In this
+note, it is documented that any $\mathbb{N}$-graded ring $R$ has a slightly
+weaker property: the right annihilator of a right ideal contains a homogeneous
+non-zero element, if it is non-trivial to begin with. If $R$ is a subring of a
+$\mathbb{Z}^k$ -graded ring $S$ satisfying a certain non-annihilation property
+(which is the case if $S$ is strongly graded, for example), then it is possible
+to find annihilators of degree 0.
+",0,0,1,0,0,0
+16867,q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators,"  We propose a new generic type of stochastic neurons, called $q$-neurons, that
+considers activation functions based on Jackson's $q$-derivatives with
+stochastic parameters $q$. Our generalization of neural network architectures
+with $q$-neurons is shown to be both scalable and very easy to implement. We
+demonstrate experimentally consistently improved performances over
+state-of-the-art standard activation functions, both on training and testing
+loss functions.
+",0,0,0,1,0,0
+16868,Liveness-Driven Random Program Generation,"  Randomly generated programs are popular for testing compilers and program
+analysis tools, with hundreds of bugs in real-world C compilers found by random
+testing. However, existing random program generators may generate large amounts
+of dead code (computations whose result is never used). This leaves relatively
+little code to exercise a target compiler's more complex optimizations.
+To address this shortcoming, we introduce liveness-driven random program
+generation. In this approach the random program is constructed bottom-up,
+guided by a simultaneous structural data-flow analysis to ensure that the
+generator never generates dead code.
+The algorithm is implemented as a plugin for the Frama-C framework. We
+evaluate it in comparison to Csmith, the standard random C program generator.
+Our tool generates programs that compile to more machine code with a more
+complex instruction mix.
+",1,0,0,0,0,0
+16869,Modeling and optimal control of HIV/AIDS prevention through PrEP,"  Pre-exposure prophylaxis (PrEP) consists in the use of an antiretroviral
+medication to prevent the acquisition of HIV infection by uninfected
+individuals and has recently demonstrated to be highly efficacious for HIV
+prevention. We propose a new epidemiological model for HIV/AIDS transmission
+including PrEP. Existence, uniqueness and global stability of the disease free
+and endemic equilibriums are proved. The model with no PrEP is calibrated with
+the cumulative cases of infection by HIV and AIDS reported in Cape Verde from
+1987 to 2014, showing that it predicts well such reality. An optimal control
+problem with a mixed state control constraint is then proposed and analyzed,
+where the control function represents the PrEP strategy and the mixed
+constraint models the fact that, due to PrEP costs, epidemic context and
+program coverage, the number of individuals under PrEP is limited at each
+instant of time. The objective is to determine the PrEP strategy that satisfies
+the mixed state control constraint and minimizes the number of individuals with
+pre-AIDS HIV-infection as well as the costs associated with PrEP. The optimal
+control problem is studied analytically. Through numerical simulations, we
+demonstrate that PrEP reduces HIV transmission significantly.
+",0,0,1,0,0,0
+16870,STARIMA-based Traffic Prediction with Time-varying Lags,"  Based on the observation that the correlation between observed traffic at two
+measurement points or traffic stations may be time-varying, attributable to the
+time-varying speed which subsequently causes variations in the time required to
+travel between the two points, in this paper, we develop a modified Space-Time
+Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags
+for short-term traffic flow prediction. Particularly, the temporal lags in the
+modified STARIMA change with the time-varying speed at different time of the
+day or equivalently change with the (time-varying) time required to travel
+between two measurement points. Firstly, a technique is developed to evaluate
+the temporal lag in the STARIMA model, where the temporal lag is formulated as
+a function of the spatial lag (spatial distance) and the average speed.
+Secondly, an unsupervised classification algorithm based on ISODATA algorithm
+is designed to classify different time periods of the day according to the
+variation of the speed. The classification helps to determine the appropriate
+time lag to use in the STARIMA model. Finally, a STARIMA-based model with
+time-varying lags is developed for short-term traffic prediction. Experimental
+results using real traffic data show that the developed STARIMA-based model
+with time-varying lags has superior accuracy compared with its counterpart
+developed using the traditional cross-correlation function and without
+employing time-varying lags.
+",1,0,1,0,0,0
+16871,Electric Vehicle Charging Station Placement Method for Urban Areas,"  For accommodating more electric vehicles (EVs) to battle against fossil fuel
+emission, the problem of charging station placement is inevitable and could be
+costly if done improperly. Some researches consider a general setup, using
+conditions such as driving ranges for planning. However, most of the EV growths
+in the next decades will happen in the urban area, where driving ranges is not
+the biggest concern. For such a need, we consider several practical aspects of
+urban systems, such as voltage regulation cost and protection device upgrade
+resulting from the large integration of EVs. Notably, our diversified objective
+can reveal the trade-off between different factors in different cities
+worldwide. To understand the global optimum of large-scale analysis, we add
+constraint one-by-one to see how to preserve the problem convexity. Our
+sensitivity analysis before and after convexification shows that our approach
+is not only universally applicable but also has a small approximation error for
+prioritizing the most urgent constraint in a specific setup. Finally, numerical
+results demonstrate the trade-off, the relationship between different factors
+and the global objective, and the small approximation error. A unique
+observation in this study shows the importance of incorporating the protection
+device upgrade in urban system planning on charging stations.
+",1,0,0,0,0,0
+16872,The Steinberg linkage class for a reductive algebraic group,"  Let G be a reductive algebraic group over a field of positive characteristic
+and denote by C(G) the category of rational G-modules. In this note we
+investigate the subcategory of C(G) consisting of those modules whose
+composition factors all have highest weights linked to the Steinberg weight.
+This subcategory is denoted ST and called the Steinberg component. We give an
+explicit equivalence between ST and C(G) and we derive some consequences. In
+particular, our result allows us to relate the Frobenius contracting functor to
+the projection functor from C(G) onto ST .
+",0,0,1,0,0,0
+16873,Detection and Resolution of Rumours in Social Media: A Survey,"  Despite the increasing use of social media platforms for information and news
+gathering, its unmoderated nature often leads to the emergence and spread of
+rumours, i.e. pieces of information that are unverified at the time of posting.
+At the same time, the openness of social media platforms provides opportunities
+to study how users share and discuss rumours, and to explore how natural
+language processing and data mining techniques may be used to find ways of
+determining their veracity. In this survey we introduce and discuss two types
+of rumours that circulate on social media; long-standing rumours that circulate
+for long periods of time, and newly-emerging rumours spawned during fast-paced
+events such as breaking news, where reports are released piecemeal and often
+with an unverified status in their early stages. We provide an overview of
+research into social media rumours with the ultimate goal of developing a
+rumour classification system that consists of four components: rumour
+detection, rumour tracking, rumour stance classification and rumour veracity
+classification. We delve into the approaches presented in the scientific
+literature for the development of each of these four components. We summarise
+the efforts and achievements so far towards the development of rumour
+classification systems and conclude with suggestions for avenues for future
+research in social media mining for detection and resolution of rumours.
+",1,0,0,0,0,0
+16874,On harmonic analysis of spherical convolutions on semisimple Lie groups,"  This paper contains a non-trivial generalization of the Harish-Chandra
+transforms on a connected semisimple Lie group $G,$ with finite center, into
+what we term spherical convolutions. Among other results we show that its
+integral over the collection of bounded spherical functions at the identity
+element $e \in G$ is a weighted Fourier transforms of the Abel transform at
+$0.$ Being a function on $G,$ the restriction of this integral of its spherical
+Fourier transforms to the positive-definite spherical functions is then shown
+to be (the non-zero constant multiple of) a positive-definite distribution on
+$G,$ which is tempered and invariant on $G=SL(2,\mathbb{R}).$ These results
+suggest the consideration of a calculus on the Schwartz algebras of spherical
+functions. The Plancherel measure of the spherical convolutions is also
+explicitly computed.
+",0,0,1,0,0,0
+16875,Relaxation-based viscosity mapping for magnetic particle imaging,"  Magnetic Particle Imaging (MPI) has been shown to provide remarkable contrast
+for imaging applications such as angiography, stem cell tracking, and cancer
+imaging. Recently, there is growing interest in the functional imaging
+capabilities of MPI, where color MPI techniques have explored separating
+different nanoparticles, which could potentially be used to distinguish
+nanoparticles in different states or environments. Viscosity mapping is a
+promising functional imaging application for MPI, as increased viscosity levels
+in vivo have been associated with numerous diseases such as hypertension,
+atherosclerosis, and cancer. In this work, we propose a viscosity mapping
+technique for MPI through the estimation of the relaxation time constant of the
+nanoparticles. Importantly, the proposed time constant estimation scheme does
+not require any prior information regarding the nanoparticles. We validate this
+method with extensive experiments in an in-house magnetic particle spectroscopy
+(MPS) setup at four different frequencies (between 250 Hz and 10.8 kHz) and at
+three different field strengths (between 5 mT and 15 mT) for viscosities
+ranging between 0.89 mPa.s to 15.33 mPa.s. Our results demonstrate the
+viscosity mapping ability of MPI in the biologically relevant viscosity range.
+",0,1,0,0,0,0
+16876,Detecting Statistically Significant Communities,"  Community detection is a key data analysis problem across different fields.
+During the past decades, numerous algorithms have been proposed to address this
+issue. However, most work on community detection does not address the issue of
+statistical significance. Although some research efforts have been made towards
+mining statistically significant communities, deriving an analytical solution
+of p-value for one community under the configuration model is still a
+challenging mission that remains unsolved. To partially fulfill this void, we
+present a tight upper bound on the p-value of a single community under the
+configuration model, which can be used for quantifying the statistical
+significance of each community analytically. Meanwhile, we present a local
+search method to detect statistically significant communities in an iterative
+manner. Experimental results demonstrate that our method is comparable with the
+competing methods on detecting statistically significant communities.
+",1,0,0,0,0,0
+16877,On the effectivity of spectra representing motivic cohomology theories,"  Let k be an infinite perfect field. We provide a general criterion for a
+spectrum in the stable homotopy category over k to be effective, i.e. to be in
+the localizing subcategory generated by the suspension spectra of smooth
+schemes. As a consequence, we show that two recent versions of generalized
+motivic cohomology theories coincide.
+",0,0,1,0,0,0
+16878,Aggregated Momentum: Stability Through Passive Damping,"  Momentum is a simple and widely used trick which allows gradient-based
+optimizers to pick up speed along low curvature directions. Its performance
+depends crucially on a damping coefficient $\beta$. Large $\beta$ values can
+potentially deliver much larger speedups, but are prone to oscillations and
+instability; hence one typically resorts to small values such as 0.5 or 0.9. We
+propose Aggregated Momentum (AggMo), a variant of momentum which combines
+multiple velocity vectors with different $\beta$ parameters. AggMo is trivial
+to implement, but significantly dampens oscillations, enabling it to remain
+stable even for aggressive $\beta$ values such as 0.999. We reinterpret
+Nesterov's accelerated gradient descent as a special case of AggMo and analyze
+rates of convergence for quadratic objectives. Empirically, we find that AggMo
+is a suitable drop-in replacement for other momentum methods, and frequently
+delivers faster convergence.
+",0,0,0,1,0,0
+16879,On the Number of Bins in Equilibria for Signaling Games,"  We investigate the equilibrium behavior for the decentralized quadratic cheap
+talk problem in which an encoder and a decoder, viewed as two decision makers,
+have misaligned objective functions. In prior work, we have shown that the
+number of bins under any equilibrium has to be at most countable, generalizing
+a classical result due to Crawford and Sobel who considered sources with
+density supported on $[0,1]$. In this paper, we refine this result in the
+context of exponential and Gaussian sources. For exponential sources, a
+relation between the upper bound on the number of bins and the misalignment in
+the objective functions is derived, the equilibrium costs are compared, and it
+is shown that there also exist equilibria with infinitely many bins under
+certain parametric assumptions. For Gaussian sources, it is shown that there
+exist equilibria with infinitely many bins.
+",1,0,0,0,0,0
+16880,The Dantzig selector for a linear model of diffusion processes,"  In this paper, a linear model of diffusion processes with unknown drift and
+diagonal diffusion matrices is discussed. We will consider the estimation
+problems for unknown parameters based on the discrete time observation in
+high-dimensional and sparse settings. To estimate drift matrices, the Dantzig
+selector which was proposed by Candés and Tao in 2007 will be applied. Then,
+we will prove two types of consistency of the estimator of drift matrix; one is
+the consistency in the sense of $l_q$ norm for every $q \in [1,\infty]$ and the
+other is the variable selection consistency. Moreover, we will construct an
+asymptotically normal estimator of the drift matrix by using the variable
+selection consistency of the Dantzig selector.
+",0,0,1,1,0,0
+16881,A Spatio-Temporal Multivariate Shared Component Model with an Application in Iran Cancer Data,"  Among the proposals for joint disease mapping, the shared component model has
+become more popular. Another recent advance to strengthen inference of disease
+data has been the extension of purely spatial models to include time and
+space-time interaction. Such analyses have additional benefits over purely
+spatial models. However, only a few proposed spatio-temporal models could
+address analysing multiple diseases jointly.
+In the proposed model, each component is shared by different subsets of
+diseases, spatial and temporal trends are considered for each component, and
+the relative weight of these trends for each component for each relevant
+disease can be estimated. We present an application of the proposed method on
+incidence rates of seven prevalent cancers in Iran. The effect of the shared
+components on the individual cancer types can be identified. Regional and
+temporal variation in relative risks is shown. We present a model which
+combines the benefits of shared-components with spatio-temporal techniques for
+multivariate data. We show, how the model allows to analyse geographical and
+temporal variation among diseases beyond previous approaches.
+",0,0,0,1,0,0
+16882,The dynamo effect in decaying helical turbulence,"  We show that in decaying hydromagnetic turbulence with initial kinetic
+helicity, a weak magnetic field eventually becomes fully helical. The sign of
+magnetic helicity is opposite to that of the kinetic helicity - regardless of
+whether or not the initial magnetic field was helical. The magnetic field
+undergoes inverse cascading with the magnetic energy decaying approximately
+like t^{-1/2}. This is even slower than in the fully helical case, where it
+decays like t^{-2/3}. In this parameter range, the product of magnetic energy
+and correlation length raised to a certain power slightly larger than unity, is
+approximately constant. This scaling of magnetic energy persists over long time
+scales. At very late times and for domain sizes large enough to accommodate the
+growing spatial scales, we expect a cross-over to the t^{-2/3} decay law that
+is commonly observed for fully helical magnetic fields. Regardless of the
+presence or absence of initial kinetic helicity, the magnetic field experiences
+exponential growth during the first few turnover times, which is suggestive of
+small-scale dynamo action. Our results have applications to a wide range of
+experimental dynamos and astrophysical time-dependent plasmas, including
+primordial turbulence in the early universe.
+",0,1,0,0,0,0
+16883,Geometrically finite amalgamations of hyperbolic 3-manifold groups are not LERF,"  We prove that, for any two finite volume hyperbolic $3$-manifolds, the
+amalgamation of their fundamental groups along any nontrivial geometrically
+finite subgroup is not LERF. This generalizes the author's previous work on
+nonLERFness of amalgamations of hyperbolic $3$-manifold groups along abelian
+subgroups. A consequence of this result is that closed arithmetic hyperbolic
+$4$-manifolds have nonLERF fundamental groups. Along with the author's previous
+work, we get that, for any arithmetic hyperbolic manifold with dimension at
+least $4$, with possible exceptions in $7$-dimensional manifolds defined by the
+octonion, its fundamental group is not LERF.
+",0,0,1,0,0,0
+16884,"Dining Philosophers, Leader Election and Ring Size problems, in the quantum setting","  We provide the first quantum (exact) protocol for the Dining Philosophers
+problem (DP), a central problem in distributed algorithms. It is well known
+that the problem cannot be solved exactly in the classical setting. We then use
+our DP protocol to provide a new quantum protocol for the tightly related
+problem of exact leader election (LE) on a ring, improving significantly in
+both time and memory complexity over the known LE protocol by Tani et. al. To
+do this, we show that in some sense the exact DP and exact LE problems are
+equivalent; interestingly, in the classical non-exact setting they are not.
+Hopefully, the results will lead to exact quantum protocols for other important
+distributed algorithmic questions; in particular, we discuss interesting
+connections to the ring size problem, as well as to a physically motivated
+question of breaking symmetry in 1D translationally invariant systems.
+",1,0,0,0,0,0
+16885,An Online Secretary Framework for Fog Network Formation with Minimal Latency,"  Fog computing is seen as a promising approach to perform distributed,
+low-latency computation for supporting Internet of Things applications.
+However, due to the unpredictable arrival of available neighboring fog nodes,
+the dynamic formation of a fog network can be challenging. In essence, a given
+fog node must smartly select the set of neighboring fog nodes that can provide
+low-latency computations. In this paper, this problem of fog network formation
+and task distribution is studied considering a hybrid cloud-fog architecture.
+The goal of the proposed framework is to minimize the maximum computational
+latency by enabling a given fog node to form a suitable fog network, under
+uncertainty on the arrival process of neighboring fog nodes. To solve this
+problem, a novel approach based on the online secretary framework is proposed.
+To find the desired set of neighboring fog nodes, an online algorithm is
+developed to enable a task initiating fog node to decide on which other nodes
+can be used as part of its fog network, to offload computational tasks, without
+knowing any prior information on the future arrivals of those other nodes.
+Simulation results show that the proposed online algorithm can successfully
+select an optimal set of neighboring fog nodes while achieving a latency that
+is as small as the one resulting from an ideal, offline scheme that has
+complete knowledge of the system. The results also show how, using the proposed
+approach, the computational tasks can be properly distributed between the fog
+network and a remote cloud server.
+",1,0,0,0,0,0
+16886,Computer-assisted proof of heteroclinic connections in the one-dimensional Ohta-Kawasaki model,"  We present a computer-assisted proof of heteroclinic connections in the
+one-dimensional Ohta-Kawasaki model of diblock copolymers. The model is a
+fourth-order parabolic partial differential equation subject to homogeneous
+Neumann boundary conditions, which contains as a special case the celebrated
+Cahn-Hilliard equation. While the attractor structure of the latter model is
+completely understood for one-dimensional domains, the diblock copolymer
+extension exhibits considerably richer long-term dynamical behavior, which
+includes a high level of multistability. In this paper, we establish the
+existence of certain heteroclinic connections between the homogeneous
+equilibrium state, which represents a perfect copolymer mixture, and all local
+and global energy minimizers. In this way, we show that not every solution
+originating near the homogeneous state will converge to the global energy
+minimizer, but rather is trapped by a stable state with higher energy. This
+phenomenon can not be observed in the one-dimensional Cahn-Hillard equation,
+where generic solutions are attracted by a global minimizer.
+",1,0,1,0,0,0
+16887,Dust and Gas in Star Forming Galaxies at z~3 - Extending Galaxy Uniformity to 11.5 Billion Years,"  We present millimetre dust emission measurements of two Lyman Break Galaxies
+at z~3 and construct for the first time fully sampled infrared spectral energy
+distributions (SEDs), from mid-IR to the Rayleigh-Jeans tail, of individually
+detected, unlensed, UV-selected, main sequence (MS) galaxies at $z=3$. The SED
+modelling of the two sources confirms previous findings, based on stacked
+ensembles, of an increasing mean radiation field <U> with redshift, consistent
+with a rapidly decreasing gas metallicity in z > 2 galaxies. Complementing our
+study with CO[3-2] emission line observations, we measure the molecular gas
+mass (M_H2) reservoir of the systems using three independent approaches: 1) CO
+line observations, 2) the dust to gas mass ratio vs metallicity relation and 3)
+a single band, dust emission flux on the Rayleigh-Jeans side of the SED. All
+techniques return consistent M_H2 estimates within a factor of ~2 or less,
+yielding gas depletion time-scales (tau_dep ~ 0.35 Gyrs) and gas-to-stellar
+mass ratios (M_H2/M* ~ 0.5-1) for our z~3 massive MS galaxies. The overall
+properties of our galaxies are consistent with trends and relations established
+at lower redshifts, extending the apparent uniformity of star-forming galaxies
+over the last 11.5 billion years.
+",0,1,0,0,0,0
+16888,Flow speed has little impact on propulsive characteristics of oscillating foils,"  Experiments are reported on the performance of a pitching and heaving
+two-dimensional foil in a water channel in either continuous or intermittent
+motion. We find that the thrust and power are independent of the mean
+freestream velocity for two-fold changes in the mean velocity (four-fold in the
+dynamic pressure), and for oscillations in the velocity up to 38\% of the mean,
+where the oscillations are intended to mimic those of freely swimming motions
+where the thrust varies during the flapping cycle. We demonstrate that the
+correct velocity scale is not the flow velocity but the mean velocity of the
+trailing edge. We also find little or no impact of streamwise velocity change
+on the wake characteristics such as vortex organization, vortex strength, and
+time-averaged velocity profile development---the wake is both qualitatively and
+quantitatively unchanged. Our results suggest that constant velocity studies
+can be used to make robust conclusions about swimming performance without a
+need to explore the free-swimming condition.
+",0,1,0,0,0,0
+16889,"Switching and Data Injection Attacks on Stochastic Cyber-Physical Systems: Modeling, Resilient Estimation and Attack Mitigation","  In this paper, we consider the problem of attack-resilient state estimation,
+that is to reliably estimate the true system states despite two classes of
+attacks: (i) attacks on the switching mechanisms and (ii) false data injection
+attacks on actuator and sensor signals, in the presence of unbounded stochastic
+process and measurement noise signals. We model the systems under attack as
+hidden mode stochastic switched linear systems with unknown inputs and propose
+the use of a multiple-model inference algorithm to tackle these security
+issues. Moreover, we characterize fundamental limitations to resilient
+estimation (e.g., upper bound on the number of tolerable signal attacks) and
+discuss the topics of attack detection, identification and mitigation under
+this framework. Simulation examples of switching and false data injection
+attacks on a benchmark system and an IEEE 68-bus test system show the efficacy
+of our approach to recover resilient (i.e., asymptotically unbiased) state
+estimates as well as to identify and mitigate the attacks.
+",1,0,1,0,0,0
+16890,Generating Sentence Planning Variations for Story Telling,"  There has been a recent explosion in applications for dialogue interaction
+ranging from direction-giving and tourist information to interactive story
+systems. Yet the natural language generation (NLG) component for many of these
+systems remains largely handcrafted. This limitation greatly restricts the
+range of applications; it also means that it is impossible to take advantage of
+recent work in expressive and statistical language generation that can
+dynamically and automatically produce a large number of variations of given
+content. We propose that a solution to this problem lies in new methods for
+developing language generation resources. We describe the ES-Translator, a
+computational language generator that has previously been applied only to
+fables, and quantitatively evaluate the domain independence of the EST by
+applying it to personal narratives from weblogs. We then take advantage of
+recent work on language generation to create a parameterized sentence planner
+for story generation that provides aggregation operations, variations in
+discourse and in point of view. Finally, we present a user evaluation of
+different personal narrative retellings.
+",1,0,0,0,0,0
+16891,"Detection of planet candidates around K giants, HD 40956, HD 111591, and HD 113996","  Aims. The purpose of this paper is to detect and investigate the nature of
+long-term radial velocity (RV) variations of K-type giants and to confirm
+planetary companions around the stars.
+Methods. We have conducted two planet search programs by precise RV
+measurement using the 1.8 m telescope at Bohyunsan Optical Astronomy
+Observatory (BOAO) and the 1.88 m telescope at Okayama Astrophysical
+Observatory (OAO). The BOAO program searches for planets around 55 early K
+giants. The OAO program is looking for 190 G-K type giants.
+Results. In this paper, we report the detection of long-period RV variations
+of three K giant stars, HD 40956, HD 111591, and HD 113996. We investigated the
+cause of the observed RV variations and conclude the substellar companions are
+most likely the cause of the RV variations. The orbital analyses yield P =
+578.6 $\pm$ 3.3 d, $m$ sin $i$ = 2.7 $\pm$ 0.6 $M_{\rm{J}}$, $a$ = 1.4 $\pm$
+0.1 AU for HD 40956; P = 1056.4 $\pm$ 14.3 d, $m$ sin $i$ = 4.4 $\pm$ 0.4
+$M_{\rm{J}}$, $a$ = 2.5 $\pm$ 0.1 AU for HD 111591; P = 610.2 $\pm$ 3.8 d, $m$
+sin $i$ = 6.3 $\pm$ 1.0 $M_{\rm{J}}$, $a$ = 1.6 $\pm$ 0.1 AU for HD 113996.
+",0,1,0,0,0,0
+16892,Perfect Half Space Games,"  We introduce perfect half space games, in which the goal of Player 2 is to
+make the sums of encountered multi-dimensional weights diverge in a direction
+which is consistent with a chosen sequence of perfect half spaces (chosen
+dynamically by Player 2). We establish that the bounding games of Jurdziński
+et al. (ICALP 2015) can be reduced to perfect half space games, which in turn
+can be translated to the lexicographic energy games of Colcombet and
+Niwiński, and are positionally determined in a strong sense (Player 2 can
+play without knowing the current perfect half space). We finally show how
+perfect half space games and bounding games can be employed to solve
+multi-dimensional energy parity games in pseudo-polynomial time when both the
+numbers of energy dimensions and of priorities are fixed, regardless of whether
+the initial credit is given as part of the input or existentially quantified.
+This also yields an optimal 2-EXPTIME complexity with given initial credit,
+where the best known upper bound was non-elementary.
+",1,0,0,0,0,0
+16893,"Energy-efficient Analog Sensing for Large-scale, High-density Persistent Wireless Monitoring","  The research challenge of current Wireless Sensor Networks~(WSNs) is to
+design energy-efficient, low-cost, high-accuracy, self-healing, and scalable
+systems for applications such as environmental monitoring. Traditional WSNs
+consist of low density, power-hungry digital motes that are expensive and
+cannot remain functional for long periods on a single charge. In order to
+address these challenges, a \textit{dumb-sensing and smart-processing}
+architecture that splits sensing and computation capabilities among tiers is
+proposed. Tier-1 consists of dumb sensors that only sense and transmit, while
+the nodes in Tier-2 do all the smart processing on Tier-1 sensor data. A
+low-power and low-cost solution for Tier-1 sensors has been proposed using
+Analog Joint Source Channel Coding~(AJSCC). An analog circuit that realizes the
+rectangular type of AJSCC has been proposed and realized on a Printed Circuit
+Board for feasibility analysis. A prototype consisting of three Tier-1 sensors
+(sensing temperature and humidity) communicating to a Tier-2 Cluster Head has
+been demonstrated to verify the proposed approach. Results show that our
+framework is indeed feasible to support large scale high density and persistent
+WSN deployment.
+",1,0,0,0,0,0
+16894,Computation of ground-state properties in molecular systems: back-propagation with auxiliary-field quantum Monte Carlo,"  We address the computation of ground-state properties of chemical systems and
+realistic materials within the auxiliary-field quantum Monte Carlo method. The
+phase constraint to control the fermion phase problem requires the random walks
+in Slater determinant space to be open-ended with branching. This in turn makes
+it necessary to use back-propagation (BP) to compute averages and correlation
+functions of operators that do not commute with the Hamiltonian. Several BP
+schemes are investigated and their optimization with respect to the phaseless
+constraint is considered. We propose a modified BP method for the computation
+of observables in electronic systems, discuss its numerical stability and
+computational complexity, and assess its performance by computing ground-state
+properties for several substances, including constituents of the primordial
+terrestrial atmosphere and small organic molecules.
+",0,1,0,0,0,0
+16895,Demonstration of the Relationship between Sensitivity and Identifiability for Inverse Uncertainty Quantification,"  Inverse Uncertainty Quantification (UQ), or Bayesian calibration, is the
+process to quantify the uncertainties of random input parameters based on
+experimental data. The introduction of model discrepancy term is significant
+because ""over-fitting"" can theoretically be avoided. But it also poses
+challenges in the practical applications. One of the mostly concerned and
+unresolved problem is the ""lack of identifiability"" issue. With the presence of
+model discrepancy, inverse UQ becomes ""non-identifiable"" in the sense that it
+is difficult to precisely distinguish between the parameter uncertainties and
+model discrepancy when estimating the calibration parameters. Previous research
+to alleviate the non-identifiability issue focused on using informative priors
+for the calibration parameters and the model discrepancy, which is usually not
+a viable solution because one rarely has such accurate and informative prior
+knowledge. In this work, we show that identifiability is largely related to the
+sensitivity of the calibration parameters with regards to the chosen responses.
+We adopted an improved modular Bayesian approach for inverse UQ that does not
+require priors for the model discrepancy term. The relationship between
+sensitivity and identifiability was demonstrated with a practical example in
+nuclear engineering. It was shown that, in order for a certain calibration
+parameter to be statistically identifiable, it should be significant to at
+least one of the responses whose data are used for inverse UQ. Good
+identifiability cannot be achieved for a certain calibration parameter if it is
+not significant to any of the responses. It is also demonstrated that ""fake
+identifiability"" is possible if model responses are not appropriately chosen,
+or inaccurate but informative priors are specified.
+",0,0,0,1,0,0
+16896,The GENIUS Approach to Robust Mendelian Randomization Inference,"  Mendelian randomization (MR) is a popular instrumental variable (IV)
+approach. A key IV identification condition known as the exclusion restriction
+requires no direct effect of an IV on the outcome not through the exposure
+which is unrealistic in most MR analyses. As a result, possible violation of
+the exclusion restriction can seldom be ruled out in such studies. To address
+this concern, we introduce a new class of IV estimators which are robust to
+violation of the exclusion restriction under a large collection of data
+generating mechanisms consistent with parametric models commonly assumed in the
+MR literature. Our approach named ""MR G-Estimation under No Interaction with
+Unmeasured Selection"" (MR GENIUS) may be viewed as a modification to Robins'
+G-estimation approach that is robust to both additive unmeasured confounding
+and violation of the exclusion restriction assumption. We also establish that
+estimation with MR GENIUS may also be viewed as a robust generalization of the
+well-known Lewbel estimator for a triangular system of structural equations
+with endogeneity. Specifically, we show that unlike Lewbel estimation, MR
+GENIUS is under fairly weak conditions also robust to unmeasured confounding of
+the effects of the genetic IVs, another possible violation of a key IV
+Identification condition. Furthermore, while Lewbel estimation involves
+specification of linear models both for the outcome and the exposure, MR GENIUS
+generally does not require specification of a structural model for the direct
+effect of invalid IVs on the outcome, therefore allowing the latter model to be
+unrestricted. Finally, unlike Lewbel estimation, MR GENIUS is shown to equally
+apply for binary, discrete or continuous exposure and outcome variables and can
+be used under prospective sampling, or retrospective sampling such as in a
+case-control study.
+",0,0,0,1,0,0
+16897,Evaluation of Trace Alignment Quality and its Application in Medical Process Mining,"  Trace alignment algorithms have been used in process mining for discovering
+the consensus treatment procedures and process deviations. Different alignment
+algorithms, however, may produce very different results. No widely-adopted
+method exists for evaluating the results of trace alignment. Existing
+reference-free evaluation methods cannot adequately and comprehensively assess
+the alignment quality. We analyzed and compared the existing evaluation
+methods, identifying their limitations, and introduced improvements in two
+reference-free evaluation methods. Our approach assesses the alignment result
+globally instead of locally, and therefore helps the algorithm to optimize
+overall alignment quality. We also introduced a novel metric to measure the
+alignment complexity, which can be used as a constraint on alignment algorithm
+optimization. We tested our evaluation methods on a trauma resuscitation
+dataset and provided the medical explanation of the activities and patterns
+identified as deviations using our proposed evaluation methods.
+",1,0,0,0,0,0
+16898,Size Constraints on Majorana Beamsplitter Interferometer: Majorana Coupling and Surface-Bulk Scattering,"  Topological insulator surfaces in proximity to superconductors have been
+proposed as a way to produce Majorana fermions in condensed matter physics. One
+of the simplest proposed experiments with such a system is Majorana
+interferometry. Here, we consider two possibly conflicting constraints on the
+size of such an interferometer. Coupling of a Majorana mode from the edge (the
+arms) of the interferometer to vortices in the centre of the device sets a
+lower bound on the size of the device. On the other hand, scattering to the
+usually imperfectly insulating bulk sets an upper bound. From estimates of
+experimental parameters, we find that typical samples may have no size window
+in which the Majorana interferometer can operate, implying that a new
+generation of more highly insulating samples must be explored.
+",0,1,0,0,0,0
+16899,Counting Arithmetical Structures on Paths and Cycles,"  Let $G$ be a finite, simple, connected graph. An arithmetical structure on
+$G$ is a pair of positive integer vectors $\mathbf{d},\mathbf{r}$ such that
+$(\mathrm{diag}(\mathbf{d})-A)\mathbf{r}=0$, where $A$ is the adjacency matrix
+of $G$. We investigate the combinatorics of arithmetical structures on path and
+cycle graphs, as well as the associated critical groups (the cokernels of the
+matrices $(\mathrm{diag}(\mathbf{d})-A)$). For paths, we prove that
+arithmetical structures are enumerated by the Catalan numbers, and we obtain
+refined enumeration results related to ballot sequences. For cycles, we prove
+that arithmetical structures are enumerated by the binomial coefficients
+$\binom{2n-1}{n-1}$, and we obtain refined enumeration results related to
+multisets. In addition, we determine the critical groups for all arithmetical
+structures on paths and cycles.
+",0,0,1,0,0,0
+16900,From synaptic interactions to collective dynamics in random neuronal networks models: critical role of eigenvectors and transient behavior,"  The study of neuronal interactions is currently at the center of several
+neuroscience big collaborative projects (including the Human Connectome, the
+Blue Brain, the Brainome, etc.) which attempt to obtain a detailed map of the
+entire brain matrix. Under certain constraints, mathematical theory can advance
+predictions of the expected neural dynamics based solely on the statistical
+properties of such synaptic interaction matrix. This work explores the
+application of free random variables (FRV) to the study of large synaptic
+interaction matrices. Besides recovering in a straightforward way known results
+on eigenspectra of neural networks, we extend them to heavy-tailed
+distributions of interactions. More importantly, we derive analytically the
+behavior of eigenvector overlaps, which determine stability of the spectra. We
+observe that upon imposing the neuronal excitation/inhibition balance, although
+the eigenvalues remain unchanged, their stability dramatically decreases due to
+strong non-orthogonality of associated eigenvectors. It leads us to the
+conclusion that the understanding of the temporal evolution of asymmetric
+neural networks requires considering the entangled dynamics of both
+eigenvectors and eigenvalues, which might bear consequences for learning and
+memory processes in these models. Considering the success of FRV analysis in a
+wide variety of branches disciplines, we hope that the results presented here
+foster additional application of these ideas in the area of brain sciences.
+",0,0,0,0,1,0
+16901,Exploring cosmic origins with CORE: mitigation of systematic effects,"  We present an analysis of the main systematic effects that could impact the
+measurement of CMB polarization with the proposed CORE space mission. We employ
+timeline-to-map simulations to verify that the CORE instrumental set-up and
+scanning strategy allow us to measure sky polarization to a level of accuracy
+adequate to the mission science goals. We also show how the CORE observations
+can be processed to mitigate the level of contamination by potentially worrying
+systematics, including intensity-to-polarization leakage due to bandpass
+mismatch, asymmetric main beams, pointing errors and correlated noise. We use
+analysis techniques that are well validated on data from current missions such
+as Planck to demonstrate how the residual contamination of the measurements by
+these effects can be brought to a level low enough not to hamper the scientific
+capability of the mission, nor significantly increase the overall error budget.
+We also present a prototype of the CORE photometric calibration pipeline, based
+on that used for Planck, and discuss its robustness to systematics, showing how
+CORE can achieve its calibration requirements. While a fine-grained assessment
+of the impact of systematics requires a level of knowledge of the system that
+can only be achieved in a future study phase, the analysis presented here
+strongly suggests that the main areas of concern for the CORE mission can be
+addressed using existing knowledge, techniques and algorithms.
+",0,1,0,0,0,0
+16902,A non-ordinary peridynamics implementation for anisotropic materials,"  Peridynamics (PD) represents a new approach for modelling fracture mechanics,
+where a continuum domain is modelled through particles connected via physical
+bonds. This formulation allows us to model crack initiation, propagation,
+branching and coalescence without special assumptions. Up to date, anisotropic
+materials were modelled in the PD framework as different isotropic materials
+(for instance, fibre and matrix of a composite laminate), where the stiffness
+of the bond depends on its orientation. A non-ordinary state-based formulation
+will enable the modelling of generally anisotropic materials, where the
+material properties are directly embedded in the formulation. Other material
+models include rocks, concrete and biomaterials such as bones. In this paper,
+we implemented this model and validated it for anisotropic composite materials.
+A composite damage criterion has been employed to model the crack propagation
+behaviour. Several numerical examples have been used to validate the approach,
+and compared to other benchmark solution from the finite element method (FEM)
+and experimental results when available.
+",1,1,0,0,0,0
+16903,Discrete-attractor-like Tracking in Continuous Attractor Neural Networks,"  Continuous attractor neural networks generate a set of smoothly connected
+attractor states. In memory systems of the brain, these attractor states may
+represent continuous pieces of information such as spatial locations and head
+directions of animals. However, during the replay of previous experiences,
+hippocampal neurons show a discontinuous sequence in which discrete transitions
+of neural state are phase-locked with the slow-gamma (30-40 Hz) oscillation.
+Here, we explored the underlying mechanisms of the discontinuous sequence
+generation. We found that a continuous attractor neural network has several
+phases depending on the interactions between external input and local
+inhibitory feedback. The discrete-attractor-like behavior naturally emerges in
+one of these phases without any discreteness assumption. We propose that the
+dynamics of continuous attractor neural networks is the key to generate
+discontinuous state changes phase-locked to the brain rhythm.
+",0,0,0,0,1,0
+16904,Framework for an Innovative Perceptive Mobile Network Using Joint Communication and Sensing,"  In this paper, we develop a framework for an innovative perceptive mobile
+(i.e. cellular) network that integrates sensing with communication, and
+supports new applications widely in transportation, surveillance and
+environmental sensing. Three types of sensing methods implemented in the
+base-stations are proposed, using either uplink or downlink multiuser
+communication signals. The required changes to system hardware and major
+technical challenges are briefly discussed. We also demonstrate the feasibility
+of estimating sensing parameters via developing a compressive sensing based
+scheme and providing simulation results to validate its effectiveness.
+",1,0,0,0,0,0
+16905,On the smallest non-abelian quotient of $\mathrm{Aut}(F_n)$,"  We show that the smallest non-abelian quotient of $\mathrm{Aut}(F_n)$ is
+$\mathrm{PSL}_n(\mathbb{Z}/2\mathbb{Z}) = \mathrm{L}_n(2)$, thus confirming a
+conjecture of Mecchia--Zimmermann. In the course of the proof we give an
+exponential (in $n$) lower bound for the cardinality of a set on which
+$\mathrm{SAut}(F_n)$, the unique index $2$ subgroup of $\mathrm{Aut}(F_n)$, can
+act non-trivially. We also offer new results on the representation theory of
+$\mathrm{SAut(F_n)}$ in small dimensions over small, positive characteristics,
+and on rigidity of maps from $\mathrm{SAut}(F_n)$ to finite groups of Lie type
+and algebraic groups in characteristic $2$.
+",0,0,1,0,0,0
+16906,Property Testing in High Dimensional Ising models,"  This paper explores the information-theoretic limitations of graph property
+testing in zero-field Ising models. Instead of learning the entire graph
+structure, sometimes testing a basic graph property such as connectivity, cycle
+presence or maximum clique size is a more relevant and attainable objective.
+Since property testing is more fundamental than graph recovery, any necessary
+conditions for property testing imply corresponding conditions for graph
+recovery, while custom property tests can be statistically and/or
+computationally more efficient than graph recovery based algorithms.
+Understanding the statistical complexity of property testing requires the
+distinction of ferromagnetic (i.e., positive interactions only) and general
+Ising models. Using combinatorial constructs such as graph packing and strong
+monotonicity, we characterize how target properties affect the corresponding
+minimax upper and lower bounds within the realm of ferromagnets. On the other
+hand, by studying the detection of an antiferromagnetic (i.e., negative
+interactions only) Curie-Weiss model buried in Rademacher noise, we show that
+property testing is strictly more challenging over general Ising models. In
+terms of methodological development, we propose two types of correlation based
+tests: computationally efficient screening for ferromagnets, and score type
+tests for general models, including a fast cycle presence test. Our correlation
+screening tests match the information-theoretic bounds for property testing in
+ferromagnets.
+",0,0,1,1,0,0
+16907,Stratification and duality for homotopical groups,"  In this paper, we show that the category of module spectra over
+$C^*(B\mathcal{G},\mathbb{F}_p)$ is stratified for any $p$-local compact group
+$\mathcal{G}$, thereby giving a support-theoretic classification of all
+localizing subcategories of this category. To this end, we generalize Quillen's
+$F$-isomorphism theorem, Quillen's stratification theorem, Chouinard's theorem,
+and the finite generation of cohomology rings from finite groups to homotopical
+groups. Moreover, we show that $p$-compact groups admit a homotopical form of
+Gorenstein duality.
+",0,0,1,0,0,0
+16908,"Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE","  We present a family of Python modules for the numerical integration of
+ordinary, delay, or stochastic differential equations. The key features are
+that the user enters the derivative symbolically and it is
+just-in-time-compiled, allowing the user to efficiently integrate differential
+equations from a higher-level interpreted language. The presented modules are
+particularly suited for large systems of differential equations such as used to
+describe dynamics on complex networks. Through the selected method of input,
+the presented modules also allow to almost completely automatize the process of
+estimating regular as well as transversal Lyapunov exponents for ordinary and
+delay differential equations. We conceptually discuss the modules' design,
+analyze their performance, and demonstrate their capabilities by application to
+timely problems.
+",1,1,0,0,0,0
+16909,Adaptive Diffusions for Scalable Learning over Graphs,"  Diffusion-based classifiers such as those relying on the Personalized
+PageRank and the Heat kernel, enjoy remarkable classification accuracy at
+modest computational requirements. Their performance however is affected by the
+extent to which the chosen diffusion captures a typically unknown label
+propagation mechanism, that can be specific to the underlying graph, and
+potentially different for each class. The present work introduces a
+disciplined, data-efficient approach to learning class-specific diffusion
+functions adapted to the underlying network topology. The novel learning
+approach leverages the notion of ""landing probabilities"" of class-specific
+random walks, which can be computed efficiently, thereby ensuring scalability
+to large graphs. This is supported by rigorous analysis of the properties of
+the model as well as the proposed algorithms. Furthermore, a robust version of
+the classifier facilitates learning even in noisy environments.
+Classification tests on real networks demonstrate that adapting the diffusion
+function to the given graph and observed labels, significantly improves the
+performance over fixed diffusions; reaching -- and many times surpassing -- the
+classification accuracy of computationally heavier state-of-the-art competing
+methods, that rely on node embeddings and deep neural networks.
+",0,0,0,1,0,0
+16910,On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search,"  Active Learning (AL) methods have proven cost-saving against passive
+supervised methods in many application domains. An active learner, aiming to
+find some target hypothesis, formulates sequential queries to some oracle. The
+set of hypotheses consistent with the already answered queries is called
+version space. Several query selection measures (QSMs) for determining the best
+query to ask next have been proposed. Assuming binaryoutcome queries, we
+analyze various QSMs wrt. to the discrimination power of their selected queries
+within the current version space. As a result, we derive superiority and
+equivalence relations between these QSMs and introduce improved versions of
+existing QSMs to overcome identified issues. The obtained picture gives a hint
+about which QSMs should preferably be used in pool-based AL scenarios.
+Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on
+these, we demonstrate how efficient heuristic search methods for optimal
+queries in query synthesis AL scenarios can be devised.
+",1,0,0,0,0,0
+16911,Torchbearer: A Model Fitting Library for PyTorch,"  We introduce torchbearer, a model fitting library for pytorch aimed at
+researchers working on deep learning or differentiable programming. The
+torchbearer library provides a high level metric and callback API that can be
+used for a wide range of applications. We also include a series of built in
+callbacks that can be used for: model persistence, learning rate decay,
+logging, data visualization and more. The extensive documentation includes an
+example library for deep learning and dynamic programming problems and can be
+found at this http URL. The code is licensed under the MIT
+License and available at this https URL.
+",0,0,0,1,0,0
+16912,On estimation of contamination from hydrogen cyanide in carbon monoxide line intensity mapping,"  Line-intensity mapping surveys probe large-scale structure through spatial
+variations in molecular line emission from a population of unresolved
+cosmological sources. Future such surveys of carbon monoxide line emission,
+specifically the CO(1-0) line, face potential contamination from a disjoint
+population of sources emitting in a hydrogen cyanide emission line, HCN(1-0).
+This paper explores the potential range of the strength of HCN emission and its
+effect on the CO auto power spectrum, using simulations with an empirical model
+of the CO/HCN--halo connection. We find that effects on the observed CO power
+spectrum depend on modeling assumptions but are very small for our fiducial
+model based on our understanding of the galaxy--halo connection, with the bias
+in overall CO detection significance due to HCN expected to be less than 1%.
+",0,1,0,0,0,0
+16913,Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints,"  Classifiers can be trained with data-dependent constraints to satisfy
+fairness goals, reduce churn, achieve a targeted false positive rate, or other
+policy goals. We study the generalization performance for such constrained
+optimization problems, in terms of how well the constraints are satisfied at
+evaluation time, given that they are satisfied at training time. To improve
+generalization performance, we frame the problem as a two-player game where one
+player optimizes the model parameters on a training dataset, and the other
+player enforces the constraints on an independent validation dataset. We build
+on recent work in two-player constrained optimization to show that if one uses
+this two-dataset approach, then constraint generalization can be significantly
+improved. As we illustrate experimentally, this approach works not only in
+theory, but also in practice.
+",0,0,0,1,0,0
+16914,Automated Website Fingerprinting through Deep Learning,"  Several studies have shown that the network traffic that is generated by a
+visit to a website over Tor reveals information specific to the website through
+the timing and sizes of network packets. By capturing traffic traces between
+users and their Tor entry guard, a network eavesdropper can leverage this
+meta-data to reveal which website Tor users are visiting. The success of such
+attacks heavily depends on the particular set of traffic features that are used
+to construct the fingerprint. Typically, these features are manually engineered
+and, as such, any change introduced to the Tor network can render these
+carefully constructed features ineffective. In this paper, we show that an
+adversary can automate the feature engineering process, and thus automatically
+deanonymize Tor traffic by applying our novel method based on deep learning. We
+collect a dataset comprised of more than three million network traces, which is
+the largest dataset of web traffic ever used for website fingerprinting, and
+find that the performance achieved by our deep learning approaches is
+comparable to known methods which include various research efforts spanning
+over multiple years. The obtained success rate exceeds 96% for a closed world
+of 100 websites and 94% for our biggest closed world of 900 classes. In our
+open world evaluation, the most performant deep learning model is 2% more
+accurate than the state-of-the-art attack. Furthermore, we show that the
+implicit features automatically learned by our approach are far more resilient
+to dynamic changes of web content over time. We conclude that the ability to
+automatically construct the most relevant traffic features and perform accurate
+traffic recognition makes our deep learning based approach an efficient,
+flexible and robust technique for website fingerprinting.
+",1,0,0,0,0,0
+16915,"DataCite as a novel bibliometric source: Coverage, strengths and limitations","  This paper explores the characteristics of DataCite to determine its
+possibilities and potential as a new bibliometric data source to analyze the
+scholarly production of open data. Open science and the increasing data sharing
+requirements from governments, funding bodies, institutions and scientific
+journals has led to a pressing demand for the development of data metrics. As a
+very first step towards reliable data metrics, we need to better comprehend the
+limitations and caveats of the information provided by sources of open data. In
+this paper, we critically examine records downloaded from the DataCite's OAI
+API and elaborate a series of recommendations regarding the use of this source
+for bibliometric analyses of open data. We highlight issues related to metadata
+incompleteness, lack of standardization, and ambiguous definitions of several
+fields. Despite these limitations, we emphasize DataCite's value and potential
+to become one of the main sources for data metrics development.
+",1,0,0,0,0,0
+16916,Parameter Estimation of Complex Fractional Ornstein-Uhlenbeck Processes with Fractional Noise,"  We obtain strong consistency and asymptotic normality of a least squares
+estimator of the drift coefficient for complex-valued Ornstein-Uhlenbeck
+processes disturbed by fractional noise, extending the result of Y. Hu and D.
+Nualart, [Statist. Probab. Lett., 80 (2010), 1030-1038] to a special
+2-dimensions. The strategy is to exploit the Garsia-Rodemich-Rumsey inequality
+and complex fourth moment theorems. The main ingredients of this paper are the
+sample path regularity of a multiple Wiener-Ito integral and two equivalent
+conditions of complex fourth moment theorems in terms of the contractions of
+integral kernels and complex Malliavin derivatives.
+",0,0,1,1,0,0
+16917,E-learning Information Technology Based on an Ontology Driven Learning Engine,"  In the article, proposed is a new e-learning information technology based on
+an ontology driven learning engine, which is matched with modern pedagogical
+technologies. With the help of proposed engine and developed question database
+we have conducted an experiment, where students were tested. The developed
+ontology driven system of e-learning facilitates the creation of favorable
+conditions for the development of personal qualities and creation of a holistic
+understanding of the subject area among students throughout the educational
+process.
+",1,0,0,0,0,0
+16918,Global regularity for 1D Eulerian dynamics with singular interaction forces,"  The Euler-Poisson-Alignment (EPA) system appears in mathematical biology and
+is used to model, in a hydrodynamic limit, a set agents interacting through
+mutual attraction/repulsion as well as alignment forces. We consider
+one-dimensional EPA system with a class of singular alignment terms as well as
+natural attraction/repulsion terms. The singularity of the alignment kernel
+produces an interesting effect regularizing the solutions of the equation and
+leading to global regularity for wide range of initial data. This was recently
+observed in the paper by Do, Kiselev, Ryzhik and Tan. Our goal in this paper is
+to generalize the result and to incorporate the attractive/repulsive potential.
+We prove that global regularity persists for these more general models.
+",0,0,1,0,0,0
+16919,A $q$-generalization of the para-Racah polynomials,"  New bispectral orthogonal polynomials are obtained from an unconventional
+truncation of the Askey-Wilson polynomials. In the limit $q \to 1$, they reduce
+to the para-Racah polynomials which are orthogonal with respect to a quadratic
+bi-lattice. The three term recurrence relation and q-difference equation are
+obtained through limits of those of the Askey-Wilson polynomials. An explicit
+expression in terms of hypergeometric series and the orthogonality relation are
+provided. A $q$-generalization of the para-Krawtchouk polynomials is obtained
+as a special case. Connections with the $q$-Racah and dual-Hahn polynomials are
+also presented.
+",0,0,1,0,0,0
+16920,Data Poisoning Attack against Unsupervised Node Embedding Methods,"  Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have
+attracted growing interests given their simplicity and effectiveness. However,
+although these methods have been proved effective in a variety of applications,
+none of the existing work has analyzed the robustness of them. This could be
+very risky if these methods are attacked by an adversarial party. In this
+paper, we take the task of link prediction as an example, which is one of the
+most fundamental problems for graph analysis, and introduce a data positioning
+attack to node embedding methods. We give a complete characterization of
+attacker's utilities and present efficient solutions to adversarial attacks for
+two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed
+attack model on multiple real-world graphs. Experimental results show that our
+proposed model can significantly affect the results of link prediction by
+slightly changing the graph structures (e.g., adding or removing a few edges).
+We also show that our proposed model is very general and can be transferable
+across different embedding methods. Finally, we conduct a case study on a
+coauthor network to better understand our attack method.
+",1,0,0,0,0,0
+16921,Entanglement properties of the two-dimensional SU(3) AKLT state,"  Two-dimensional (spin-$2$) Affleck-Kennedy-Lieb-Tasaki (AKLT) type valence
+bond solids on the square lattice are known to be symmetry protected
+topological (SPT) gapped spin liquids [Shintaro Takayoshi, Pierre Pujol, and
+Akihiro Tanaka Phys. Rev. B ${\bf 94}$, 235159 (2016)]. Using the projected
+entangled pair state (PEPS) framework, we extend the construction of the AKLT
+state to the case of $SU(3)$, relevant for cold atom systems. The entanglement
+spectrum is shown to be described by an alternating $SU(3)$ chain of ""quarks""
+and ""antiquarks"", subject to exponentially decaying (with distance) Heisenberg
+interactions, in close similarity with its $SU(2)$ analog. We discuss the SPT
+feature of the state.
+",0,1,0,0,0,0
+16922,Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms,"  Efficient management of low blood pressure (BP) in preterm neonates remains
+challenging with a considerable variability in clinical practice. The ability
+to assess preterm wellbeing during episodes of low BP will help to decide when
+and whether hypotension treatment should be initiated. This work aims to
+investigate the relationship between heart rate variability (HRV), BP and the
+short-term neurological outcome in preterm infants less than 32 weeks
+gestational age (GA). The predictive power of common HRV features with respect
+to the outcome is assessed and shown to improve when HRV is observed during
+episodes of low mean arterial pressure (MAP) - with a single best feature
+leading to an AUC of 0.87. Combining multiple features with a boosted decision
+tree classifier achieves an AUC of 0.97. The work presents a promising step
+towards the use of multimodal data in building an objective decision support
+tool for clinical prediction of short-term outcome in preterms who suffer
+episodes of low BP.
+",0,0,0,1,0,0
+16923,Understanding News Outlets' Audience-Targeting Patterns,"  The power of the press to shape the informational landscape of a population
+is unparalleled, even now in the era of democratic access to all information
+outlets. However, it is known that news outlets (particularly more traditional
+ones) tend to discriminate who they want to reach, and who to leave aside. In
+this work, we attempt to shed some light on the audience targeting patterns of
+newspapers, using the Chilean media ecosystem. First, we use the gravity model
+to analyze geography as a factor in explaining audience reachability. This
+shows that some newspapers are indeed driven by geographical factors (mostly
+local news outlets) but some others are not (national-distribution outlets).
+For those which are not, we use a regression model to study the influence of
+socioeconomic and political characteristics in news outlets adoption. We
+conclude that indeed larger, national-distribution news outlets target
+populations based on these factors, rather than on geography or immediacy.
+",1,0,0,0,0,0
+16924,Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings,"  Selecting a representative vector for a set of vectors is a very common
+requirement in many algorithmic tasks. Traditionally, the mean or median vector
+is selected. Ontology classes are sets of homogeneous instance objects that can
+be converted to a vector space by word vector embeddings. This study proposes a
+methodology to derive a representative vector for ontology classes whose
+instances were converted to the vector space. We start by deriving five
+candidate vectors which are then used to train a machine learning model that
+would calculate a representative vector for the class. We show that our
+methodology out-performs the traditional mean and median vector
+representations.
+",1,0,0,0,0,0
+16925,The ESA Gaia Archive: Data Release 1,"  ESA Gaia mission is producing the more accurate source catalogue in astronomy
+up to now. That represents a challenge on the archiving area to make accessible
+this information to the astronomers in an efficient way. Also, new astronomical
+missions have reinforced the change on the development of archives. Archives,
+as simple applications to access the data are being evolving into complex data
+center structures where computing power services are available for users and
+data mining tools are integrated into the server side. In the case of astronomy
+science that involves the use of big catalogues, as in Gaia (or Euclid to
+come), the common ways to work on the data need to be changed to a new paradigm
+""move code close to the data"", what implies that data mining functionalities
+are becoming a must to allow the science exploitation. To enable these
+capabilities, a TAP+ interface, crossmatch capabilities, full catalogue
+histograms, serialisation of intermediate results in cloud resources like
+VOSpace, etc have been implemented for the Gaia DR1, to enable the exploitation
+of these science resources by the community without the bottlenecks on the
+connection bandwidth. We present the architecture, infrastructure and tools
+already available in the Gaia Archive Data Release 1
+(this http URL) and we describe capabilities and
+infrastructure.
+",0,1,0,0,0,0
+16926,Efficient algorithm for large spectral partitions,"  We present an amelioration of current known algorithms for optimal spectral
+partitioning problems. The idea is to use the advantage of a representation
+using density functions while decreasing the computational time. This is done
+by restricting the computation to neighbourhoods of regions where the
+associated densities are above a certain threshold. The algorithm extends and
+improves known methods in the plane and on surfaces in dimension 3. It also
+makes possible to make some of the first computations of volumic 3D spectral
+partitions on sufficiently large discretizations.
+",0,0,1,0,0,0
+16927,A Martian Origin for the Mars Trojan Asteroids,"  Seven of the nine known Mars Trojan asteroids belong to an orbital cluster
+named after its largest member 5261 Eureka. Eureka is likely the progenitor of
+the whole cluster, which formed at least 1 Gyr ago. It was suggested that the
+thermal YORP effect spun-up Eureka resulting with fragments being ejected by
+the rotational-fission mechanism. Eureka's spectrum exhibits a broad and deep
+absorption band around 1 {\mu}m, indicating an olivine-rich composition. Here
+we show evidence that the Trojan Eureka cluster progenitor could have
+originated as impact debris excavated from the Martian mantle. We present new
+near-infrared observations of two Trojans (311999 2007 NS2 and 385250 2001
+DH47) and find that both exhibit an olivine-rich reflectance spectrum similar
+to Eureka's. These measurements confirm that the progenitor of the cluster has
+an achondritic composition. Olivine-rich reflectance spectra are rare amongst
+asteroids but are seen around the largest basins on Mars. They are also
+consistent with some Martian meteorites (e.g. Chassigny), and with the material
+comprising much of the Martian mantle. Using numerical simulations, we show
+that the Mars Trojans are more likely to be impact ejecta from Mars than
+captured olivine-rich asteroids transported from the main belt. This result
+links directly specific asteroids to debris from the forming planets.
+",0,1,0,0,0,0
+16928,Far-infrared metallicity diagnostics: Application to local ultraluminous infrared galaxies,"  The abundance of metals in galaxies is a key parameter which permits to
+distinguish between different galaxy formation and evolution models. Most of
+the metallicity determinations are based on optical line ratios. However, the
+optical spectral range is subject to dust extinction and, for high-z objects (z
+> 3), some of the lines used in optical metallicity diagnostics are shifted to
+wavelengths not accessible to ground based observatories. For this reason, we
+explore metallicity diagnostics using far-infrared (IR) line ratios which can
+provide a suitable alternative in such situations. To investigate these far-IR
+line ratios, we modeled the emission of a starburst with the photoionization
+code CLOUDY. The most sensitive far-IR ratios to measure metallicities are the
+[OIII]52$\mu$m and 88$\mu$m to [NIII]57$\mu$m ratios. We show that this ratio
+produces robust metallicities in the presence of an AGN and is insensitive to
+changes in the age of the ionizing stellar. Another metallicity sensitive ratio
+is the [OIII]88$\mu$m/[NII]122$\mu$m ratio, although it depends on the
+ionization parameter. We propose various mid- and far-IR line ratios to break
+this dependency. Finally, we apply these far-IR diagnostics to a sample of 19
+local ultraluminous IR galaxies (ULIRGs) observed with Herschel and Spitzer. We
+find that the gas-phase metallicity in these local ULIRGs is in the range 0.7 <
+Z_gas/Z_sun < 1.5, which corresponds to 8.5 < 12 + log (O/H) < 8.9. The
+inferred metallicities agree well with previous estimates for local ULIRGs and
+this confirms that they lie below the local mass-metallicity relation.
+",0,1,0,0,0,0
+16929,Quantum communication by means of collapse of the wave function,"  We show that quantum communication by means of collapse of the wave function
+is possible. In this study, quantum communication does not mean quantum
+teleportation or quantum cryptography, but transmission of information itself.
+Because of consistency with special relativity, the possibility of the quantum
+communication leads to another conclusion that the collapse of the wave
+function must propagate at the speed of light or slower.
+We show this requirement is consistent with nonlocality in quantum mechanics.
+We also demonstrate that the Einstein-Podolsky-Rosen experiment does not
+disprove our conclusion.
+",0,1,0,0,0,0
+16930,DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning,"  Terramechanics plays a critical role in the areas of ground vehicles and
+ground mobile robots since understanding and estimating the variables
+influencing the vehicle-terrain interaction may mean the success or the failure
+of an entire mission. This research applies state-of-the-art algorithms in deep
+learning to two key problems: estimating wheel slip and classifying the terrain
+being traversed by a ground robot. Three data sets collected by ground robotic
+platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot
+Fitorobot) are employed in order to compare the performance of traditional
+machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer
+Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural
+Networks (CNNs). This work also shows the impact that certain tuning parameters
+and the network architecture (MLP, DNN and CNN) play on the performance of
+those methods. This paper also contributes a deep discussion with the lessons
+learned in the implementation of DNNs and CNNs and how these methods can be
+extended to solve other problems.
+",1,0,0,0,0,0
+16931,"Characterizations of multinormality and corresponding tests of fit, including for Garch models","  We provide novel characterizations of multivariate normality that incorporate
+both the characteristic function and the moment generating function, and we
+employ these results to construct a class of affine invariant, consistent and
+easy-to-use goodness-of-fit tests for normality. The test statistics are
+suitably weighted $L^2$-statistics, and we provide their asymptotic behavior
+both for i.i.d. observations as well as in the context of testing that the
+innovation distribution of a multivariate GARCH model is Gaussian. We also
+study the finite-sample behavior of the new tests and compare the new criteria
+with alternative existing tests.
+",0,0,1,1,0,0
+16932,Grouped Gaussian Processes for Solar Power Prediction,"  We consider multi-task regression models where the observations are assumed
+to be a linear combination of several latent node functions and weight
+functions, which are both drawn from Gaussian process priors. Driven by the
+problem of developing scalable methods for forecasting distributed solar and
+other renewable power generation, we propose coupled priors over groups of
+(node or weight) processes to exploit spatial dependence between functions. We
+estimate forecast models for solar power at multiple distributed sites and
+ground wind speed at multiple proximate weather stations. Our results show that
+our approach maintains or improves point-prediction accuracy relative to
+competing solar benchmarks and improves over wind forecast benchmark models on
+all measures. Our approach consistently dominates the equivalent model without
+coupled priors, achieving faster gains in forecast accuracy. At the same time
+our approach provides better quantification of predictive uncertainties.
+",0,0,0,1,0,0
+16933,Modeling epidemics on d-cliqued graphs,"  Since social interactions have been shown to lead to symmetric clusters, we
+propose here that symmetries play a key role in epidemic modeling. Mathematical
+models on d-ary tree graphs were recently shown to be particularly effective
+for modeling epidemics in simple networks [Seibold & Callender, 2016]. To
+account for symmetric relations, we generalize this to a new type of networks
+modeled on d-cliqued tree graphs, which are obtained by adding edges to regular
+d-trees to form d-cliques. This setting gives a more realistic model for
+epidemic outbreaks originating, for example, within a family or classroom and
+which could reach a population by transmission via children in schools.
+Specifically, we quantify how an infection starting in a clique (e.g. family)
+can reach other cliques through the body of the graph (e.g. public places).
+Moreover, we propose and study the notion of a safe zone, a subset that has a
+negligible probability of infection.
+",1,0,0,0,1,0
+16934,On the K-theory stable bases of the Springer resolution,"  Cohomological and K-theoretic stable bases originated from the study of
+quantum cohomology and quantum K-theory. Restriction formula for cohomological
+stable bases played an important role in computing the quantum connection of
+cotangent bundle of partial flag varieties. In this paper we study the
+K-theoretic stable bases of cotangent bundles of flag varieties. We describe
+these bases in terms of the action of the affine Hecke algebra and the twisted
+group algebra of Kostant-Kumar. Using this algebraic description and the method
+of root polynomials, we give a restriction formula of the stable bases. We
+apply it to obtain the restriction formula for partial flag varieties. We also
+build a relation between the stable basis and the Casselman basis in the
+principal series representations of the Langlands dual group. As an
+application, we give a closed formula for the transition matrix between
+Casselman basis and the characteristic functions.
+",0,0,1,0,0,0
+16935,Recency Bias in the Era of Big Data: The Need to Strengthen the Status of History of Mathematics in Nigerian Schools,"  The amount of information available to the mathematics teacher is so enormous
+that the selection of desirable content is gradually becoming a huge task in
+itself. With respect to the inclusion of elements of history of mathematics in
+mathematics instruction, the era of Big Data introduces a high likelihood of
+Recency Bias, a hitherto unconnected challenge for stakeholders in mathematics
+education. This tendency to choose recent information at the expense of
+relevant older, composite, historical facts stands to defeat the aims and
+objectives of the epistemological and cultural approach to mathematics
+instructional delivery. This study is a didactic discourse with focus on this
+threat to the history and pedagogy of mathematics, particularly as it affects
+mathematics education in Nigeria. The implications for mathematics curriculum
+developers, teacher-training programmes, teacher lesson preparation, and
+publication of mathematics instructional materials were also deeply considered.
+",1,0,1,0,0,0
+16936,Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings,"  This paper presents a convergence analysis of kernel-based quadrature rules
+in misspecified settings, focusing on deterministic quadrature in Sobolev
+spaces. In particular, we deal with misspecified settings where a test
+integrand is less smooth than a Sobolev RKHS based on which a quadrature rule
+is constructed. We provide convergence guarantees based on two different
+assumptions on a quadrature rule: one on quadrature weights, and the other on
+design points. More precisely, we show that convergence rates can be derived
+(i) if the sum of absolute weights remains constant (or does not increase
+quickly), or (ii) if the minimum distance between design points does not
+decrease very quickly. As a consequence of the latter result, we derive a rate
+of convergence for Bayesian quadrature in misspecified settings. We reveal a
+condition on design points to make Bayesian quadrature robust to
+misspecification, and show that, under this condition, it may adaptively
+achieve the optimal rate of convergence in the Sobolev space of a lesser order
+(i.e., of the unknown smoothness of a test integrand), under a slightly
+stronger regularity condition on the integrand.
+",1,0,0,1,0,0
+16937,The toric Frobenius morphism and a conjecture of Orlov,"  We combine the Bondal-Uehara method for producing exceptional collections on
+toric varieties with a result of the first author and Favero to expand the set
+of varieties satisfying Orlov's Conjecture on derived dimension.
+",0,0,1,0,0,0
+16938,Friendship Maintenance and Prediction in Multiple Social Networks,"  Due to the proliferation of online social networks (OSNs), users find
+themselves participating in multiple OSNs. These users leave their activity
+traces as they maintain friendships and interact with other users in these
+OSNs. In this work, we analyze how users maintain friendship in multiple OSNs
+by studying users who have accounts in both Twitter and Instagram.
+Specifically, we study the similarity of a user's friendship and the evenness
+of friendship distribution in multiple OSNs. Our study shows that most users in
+Twitter and Instagram prefer to maintain different friendships in the two OSNs,
+keeping only a small clique of common friends in across the OSNs. Based upon
+our empirical study, we conduct link prediction experiments to predict missing
+friendship links in multiple OSNs using the neighborhood features, neighborhood
+friendship maintenance features and cross-link features. Our link prediction
+experiments shows that un- supervised methods can yield good accuracy in
+predicting links in one OSN using another OSN data and the link prediction
+accuracy can be further improved using supervised method with friendship
+maintenance and others measures as features.
+",1,1,0,0,0,0
+16939,Learning to Generate Music with BachProp,"  As deep learning advances, algorithms of music composition increase in
+performance. However, most of the successful models are designed for specific
+musical structures. Here, we present BachProp, an algorithmic composer that can
+generate music scores in many styles given sufficient training data. To adapt
+BachProp to a broad range of musical styles, we propose a novel representation
+of music and train a deep network to predict the note transition probabilities
+of a given music corpus. In this paper, new music scores generated by BachProp
+are compared with the original corpora as well as with different network
+architectures and other related models. We show that BachProp captures
+important features of the original datasets better than other models and invite
+the reader to a qualitative comparison on a large collection of generated
+songs.
+",1,0,0,0,0,0
+16940,How to Quantize $n$ Outputs of a Binary Symmetric Channel to $n-1$ Bits?,"  Suppose that $Y^n$ is obtained by observing a uniform Bernoulli random vector
+$X^n$ through a binary symmetric channel with crossover probability $\alpha$.
+The ""most informative Boolean function"" conjecture postulates that the maximal
+mutual information between $Y^n$ and any Boolean function $\mathrm{b}(X^n)$ is
+attained by a dictator function. In this paper, we consider the ""complementary""
+case in which the Boolean function is replaced by
+$f:\left\{0,1\right\}^n\to\left\{0,1\right\}^{n-1}$, namely, an $n-1$ bit
+quantizer, and show that $I(f(X^n);Y^n)\leq (n-1)\cdot\left(1-h(\alpha)\right)$
+for any such $f$. Thus, in this case, the optimal function is of the form
+$f(x^n)=(x_1,\ldots,x_{n-1})$.
+",1,0,1,0,0,0
+16941,Semi-Supervised Deep Learning for Monocular Depth Map Prediction,"  Supervised deep learning often suffers from the lack of sufficient training
+data. Specifically in the context of monocular depth map prediction, it is
+barely possible to determine dense ground truth depth images in realistic
+dynamic outdoor environments. When using LiDAR sensors, for instance, noise is
+present in the distance measurements, the calibration between sensors cannot be
+perfect, and the measurements are typically much sparser than the camera
+images. In this paper, we propose a novel approach to depth map prediction from
+monocular images that learns in a semi-supervised way. While we use sparse
+ground-truth depth for supervised learning, we also enforce our deep network to
+produce photoconsistent dense depth maps in a stereo setup using a direct image
+alignment loss. In experiments we demonstrate superior performance in depth map
+prediction from single images compared to the state-of-the-art methods.
+",1,0,0,0,0,0
+16942,Approximation Schemes for Clustering with Outliers,"  Clustering problems are well-studied in a variety of fields such as data
+science, operations research, and computer science. Such problems include
+variants of centre location problems, $k$-median, and $k$-means to name a few.
+In some cases, not all data points need to be clustered; some may be discarded
+for various reasons.
+We study clustering problems with outliers. More specifically, we look at
+Uncapacitated Facility Location (UFL), $k$-Median, and $k$-Means. In UFL with
+outliers, we have to open some centres, discard up to $z$ points of $\cal X$
+and assign every other point to the nearest open centre, minimizing the total
+assignment cost plus centre opening costs. In $k$-Median and $k$-Means, we have
+to open up to $k$ centres but there are no opening costs. In $k$-Means, the
+cost of assigning $j$ to $i$ is $\delta^2(j,i)$. We present several results.
+Our main focus is on cases where $\delta$ is a doubling metric or is the
+shortest path metrics of graphs from a minor-closed family of graphs. For
+uniform-cost UFL with outliers on such metrics we show that a multiswap simple
+local search heuristic yields a PTAS. With a bit more work, we extend this to
+bicriteria approximations for the $k$-Median and $k$-Means problems in the same
+metrics where, for any constant $\epsilon > 0$, we can find a solution using
+$(1+\epsilon)k$ centres whose cost is at most a $(1+\epsilon)$-factor of the
+optimum and uses at most $z$ outliers. We also show that natural local search
+heuristics that do not violate the number of clusters and outliers for
+$k$-Median (or $k$-Means) will have unbounded gap even in Euclidean metrics.
+Furthermore, we show how our analysis can be extended to general metrics for
+$k$-Means with outliers to obtain a $(25+\epsilon,1+\epsilon)$ bicriteria.
+",1,0,0,0,0,0
+16943,Order preserving pattern matching on trees and DAGs,"  The order preserving pattern matching (OPPM) problem is, given a pattern
+string $p$ and a text string $t$, find all substrings of $t$ which have the
+same relative orders as $p$. In this paper, we consider two variants of the
+OPPM problem where a set of text strings is given as a tree or a DAG. We show
+that the OPPM problem for a single pattern $p$ of length $m$ and a text tree
+$T$ of size $N$ can be solved in $O(m+N)$ time if the characters of $p$ are
+drawn from an integer alphabet of polynomial size. The time complexity becomes
+$O(m \log m + N)$ if the pattern $p$ is over a general ordered alphabet. We
+then show that the OPPM problem for a single pattern and a text DAG is
+NP-complete.
+",1,0,0,0,0,0
+16944,Categorical Probabilistic Theories,"  We present a simple categorical framework for the treatment of probabilistic
+theories, with the aim of reconciling the fields of Categorical Quantum
+Mechanics (CQM) and Operational Probabilistic Theories (OPTs). In recent years,
+both CQM and OPTs have found successful application to a number of areas in
+quantum foundations and information theory: they present many similarities,
+both in spirit and in formalism, but they remain separated by a number of
+subtle yet important differences. We attempt to bridge this gap, by adopting a
+minimal number of operationally motivated axioms which provide clean
+categorical foundations, in the style of CQM, for the treatment of the problems
+that OPTs are concerned with.
+",0,0,1,0,0,0
+16945,Maximal polynomial modulations of singular integrals,"  Let $K$ be a standard Hölder continuous Calderón--Zygmund kernel on
+$\mathbb{R}^{\mathbf{d}}$ whose truncations define $L^2$ bounded operators. We
+show that the maximal operator obtained by modulating $K$ by polynomial phases
+of a fixed degree is bounded on $L^p(\mathbb{R}^{\mathbf{d}})$ for $1 < p <
+\infty$. This extends Sjölin's multidimensional Carleson theorem and Lie's
+polynomial Carleson theorem.
+",0,0,1,0,0,0
+16946,Effects of Hubbard term correction on the structural parameters and electronic properties of wurtzite Zn,"  The effects of including the Hubbard on-site Coulombic correction to the
+structural parameters and valence energy states of wurtzite ZnO was explored.
+Due to the changes in the structural parameters caused by correction of
+hybridization between Zn d states and O p states, suitable parameters of
+Hubbard terms have to be determined for an accurate prediction of ZnO
+properties. Using the LDA+${U}$ method by applying Hubbard corrections $U_p$ to
+Zn 3d states and $U_p$ to O 2p states, the lattice constants were
+underestimated for all tested Hubbard parameters. The combination of both $U_d$
+and $U_p$ correction terms managed to widen the band gap of wurtzite ZnO to the
+experimental value. Pairs of $U_p$ and $U_p$ parameters with the correct
+positioning of d-band and accurate bandwidths were selected, in addition to
+predicting an accurate band gap value. Inspection of vibrational properties,
+however, revealed mismatches between the estimated gamma phonon frequencies and
+experimental values. The selection of Hubbard terms based on electronic band
+properties alone cannot ensure an accurate vibrational description in LDA+${U}$
+calculation.
+",0,1,0,0,0,0
+16947,A multi-scale Gaussian beam parametrix for the wave equation: the Dirichlet boundary value problem,"  We present a construction of a multi-scale Gaussian beam parametrix for the
+Dirichlet boundary value problem associated with the wave equation, and study
+its convergence rate to the true solution in the highly oscillatory regime. The
+construction elaborates on the wave-atom parametrix of Bao, Qian, Ying, and
+Zhang and extends to a multi-scale setting the technique of Gaussian beam
+propagation from a boundary of Katchalov, Kurylev and Lassas.
+",0,0,1,0,0,0
+16948,Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes,"  A functional risk curve gives the probability of an undesirable event as a
+function of the value of a critical parameter of a considered physical system.
+In several applicative situations, this curve is built using phenomenological
+numerical models which simulate complex physical phenomena. To avoid cpu-time
+expensive numerical models, we propose to use Gaussian process regression to
+build functional risk curves. An algorithm is given to provide confidence
+bounds due to this approximation. Two methods of global sensitivity analysis of
+the models' random input parameters on the functional risk curve are also
+studied. In particular, the PLI sensitivity indices allow to understand the
+effect of misjudgment on the input parameters' probability density functions.
+",0,0,1,1,0,0
+16949,Global optimization for low-dimensional switching linear regression and bounded-error estimation,"  The paper provides global optimization algorithms for two particularly
+difficult nonconvex problems raised by hybrid system identification: switching
+linear regression and bounded-error estimation. While most works focus on local
+optimization heuristics without global optimality guarantees or with guarantees
+valid only under restrictive conditions, the proposed approach always yields a
+solution with a certificate of global optimality. This approach relies on a
+branch-and-bound strategy for which we devise lower bounds that can be
+efficiently computed. In order to obtain scalable algorithms with respect to
+the number of data, we directly optimize the model parameters in a continuous
+optimization setting without involving integer variables. Numerical experiments
+show that the proposed algorithms offer a higher accuracy than convex
+relaxations with a reasonable computational burden for hybrid system
+identification. In addition, we discuss how bounded-error estimation is related
+to robust estimation in the presence of outliers and exact recovery under
+sparse noise, for which we also obtain promising numerical results.
+",1,0,0,1,0,0
+16950,An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction,"  During visuomotor tasks, robots must compensate for temporal delays inherent
+in their sensorimotor processing systems. Delay compensation becomes crucial in
+a dynamic environment where the visual input is constantly changing, e.g.,
+during the interacting with a human demonstrator. For this purpose, the robot
+must be equipped with a prediction mechanism for using the acquired perceptual
+experience to estimate possible future motor commands. In this paper, we
+present a novel neural network architecture that learns prototypical visuomotor
+representations and provides reliable predictions on the basis of the visual
+input. These predictions are used to compensate for the delayed motor behavior
+in an online manner. We investigate the performance of our method with a set of
+experiments comprising a humanoid robot that has to learn and generate visually
+perceived arm motion trajectories. We evaluate the accuracy in terms of mean
+prediction error and analyze the response of the network to novel movement
+demonstrations. Additionally, we report experiments with incomplete data
+sequences, showing the robustness of the proposed architecture in the case of a
+noisy and faulty visual sensor.
+",1,0,0,0,0,0
+16951,A CutFEM method for two-phase flow problems,"  In this article, we present a cut finite element method for two-phase
+Navier-Stokes flows. The main feature of the method is the formulation of a
+unified continuous interior penalty stabilisation approach for, on the one
+hand, stabilising advection and the pressure-velocity coupling and, on the
+other hand, stabilising the cut region. The accuracy of the algorithm is
+enhanced by the development of extended fictitious domains to guarantee a well
+defined velocity from previous time steps in the current geometry. Finally, the
+robustness of the moving-interface algorithm is further improved by the
+introduction of a curvature smoothing technique that reduces spurious
+velocities. The algorithm is shown to perform remarkably well for low capillary
+number flows, and is a first step towards flexible and robust CutFEM algorithms
+for the simulation of microfluidic devices.
+",1,0,0,0,0,0
+16952,Learning under selective labels in the presence of expert consistency,"  We explore the problem of learning under selective labels in the context of
+algorithm-assisted decision making. Selective labels is a pervasive selection
+bias problem that arises when historical decision making blinds us to the true
+outcome for certain instances. Examples of this are common in many
+applications, ranging from predicting recidivism using pre-trial release data
+to diagnosing patients. In this paper we discuss why selective labels often
+cannot be effectively tackled by standard methods for adjusting for sample
+selection bias, even if there are no unobservables. We propose a data
+augmentation approach that can be used to either leverage expert consistency to
+mitigate the partial blindness that results from selective labels, or to
+empirically validate whether learning under such framework may lead to
+unreliable models prone to systemic discrimination.
+",0,0,0,1,0,0
+16953,Opacity limit for supermassive protostars,"  We present a model for the evolution of supermassive protostars from their
+formation at $M_\star \simeq 0.1\,\text{M}_\odot$ until their growth to
+$M_\star \simeq 10^5\,\text{M}_\odot$. To calculate the initial properties of
+the object in the optically thick regime we follow two approaches: based on
+idealized thermodynamic considerations, and on a more detailed one-zone model.
+Both methods derive a similar value of $n_{\rm F} \simeq 2 \times 10^{17}
+\,\text{cm}^{-3}$ for the density of the object when opacity becomes important,
+i.e. the opacity limit. The subsequent evolution of the growing protostar is
+determined by the accretion of gas onto the object and can be described by a
+mass-radius relation of the form $R_\star \propto M_\star^{1/3}$ during the
+early stages, and of the form $R_\star \propto M_\star^{1/2}$ when internal
+luminosity becomes important. For the case of a supermassive protostar, this
+implies that the radius of the star grows from $R_\star \simeq 0.65 \,{\rm AU}$
+to $R_\star \simeq 250 \,{\rm AU}$ during its evolution. Finally, we use this
+model to construct a sub-grid recipe for accreting sink particles in numerical
+simulations. A prime ingredient thereof is a physically motivated prescription
+for the accretion radius and the effective temperature of the growing protostar
+embedded inside it. From the latter, we can conclude that photo-ionization
+feedback can be neglected until very late in the assembly process of the
+supermassive object.
+",0,1,0,0,0,0
+16954,Learning to Imagine Manipulation Goals for Robot Task Planning,"  Prospection is an important part of how humans come up with new task plans,
+but has not been explored in depth in robotics. Predicting multiple task-level
+is a challenging problem that involves capturing both task semantics and
+continuous variability over the state of the world. Ideally, we would combine
+the ability of machine learning to leverage big data for learning the semantics
+of a task, while using techniques from task planning to reliably generalize to
+new environment. In this work, we propose a method for learning a model
+encoding just such a representation for task planning. We learn a neural net
+that encodes the $k$ most likely outcomes from high level actions from a given
+world. Our approach creates comprehensible task plans that allow us to predict
+changes to the environment many time steps into the future. We demonstrate this
+approach via application to a stacking task in a cluttered environment, where
+the robot must select between different colored blocks while avoiding
+obstacles, in order to perform a task. We also show results on a simple
+navigation task. Our algorithm generates realistic image and pose predictions
+at multiple points in a given task.
+",1,0,0,0,0,0
+16955,On the Combinatorial Power of the Weisfeiler-Lehman Algorithm,"  The classical Weisfeiler-Lehman method WL[2] uses edge colors to produce a
+powerful graph invariant. It is at least as powerful in its ability to
+distinguish non-isomorphic graphs as the most prominent algebraic graph
+invariants. It determines not only the spectrum of a graph, and the angles
+between standard basis vectors and the eigenspaces, but even the angles between
+projections of standard basis vectors into the eigenspaces. Here, we
+investigate the combinatorial power of WL[2]. For sufficiently large k, WL[k]
+determines all combinatorial properties of a graph. Many traditionally used
+combinatorial invariants are determined by WL[k] for small k. We focus on two
+fundamental invariants, the num- ber of cycles Cp of length p, and the number
+of cliques Kp of size p. We show that WL[2] determines the number of cycles of
+lengths up to 6, but not those of length 8. Also, WL[2] does not determine the
+number of 4-cliques.
+",1,0,0,0,0,0
+16956,Learning to Generate Reviews and Discovering Sentiment,"  We explore the properties of byte-level recurrent language models. When given
+sufficient amounts of capacity, training data, and compute time, the
+representations learned by these models include disentangled features
+corresponding to high-level concepts. Specifically, we find a single unit which
+performs sentiment analysis. These representations, learned in an unsupervised
+manner, achieve state of the art on the binary subset of the Stanford Sentiment
+Treebank. They are also very data efficient. When using only a handful of
+labeled examples, our approach matches the performance of strong baselines
+trained on full datasets. We also demonstrate the sentiment unit has a direct
+influence on the generative process of the model. Simply fixing its value to be
+positive or negative generates samples with the corresponding positive or
+negative sentiment.
+",1,0,0,0,0,0
+16957,Designing magnetism in Fe-based Heusler alloys: a machine learning approach,"  Combining material informatics and high-throughput electronic structure
+calculations offers the possibility of a rapid characterization of complex
+magnetic materials. Here we demonstrate that datasets of electronic properties
+calculated at the ab initio level can be effectively used to identify and
+understand physical trends in magnetic materials, thus opening new avenues for
+accelerated materials discovery. Following a data-centric approach, we utilize
+a database of Heusler alloys calculated at the density functional theory level
+to identify the ideal ions neighbouring Fe in the $X_2$Fe$Z$ Heusler prototype.
+The hybridization of Fe with the nearest neighbour $X$ ion is found to cause
+redistribution of the on-site Fe charge and a net increase of its magnetic
+moment proportional to the valence of $X$. Thus, late transition metals are
+ideal Fe neighbours for producing high-moment Fe-based Heusler magnets. At the
+same time a thermodynamic stability analysis is found to restrict $Z$ to main
+group elements. Machine learning regressors, trained to predict magnetic moment
+and volume of Heusler alloys, are used to determine the magnetization for all
+materials belonging to the proposed prototype. We find that Co$_2$Fe$Z$ alloys,
+and in particular Co$_2$FeSi, maximize the magnetization, which reaches values
+up to 1.2T. This is in good agreement with both ab initio and experimental
+data. Furthermore, we identify the Cu$_2$Fe$Z$ family to be a cost-effective
+materials class, offering a magnetization of approximately 0.65T.
+",0,1,0,0,0,0
+16958,On a diffuse interface model for tumour growth with non-local interactions and degenerate mobilities,"  We study a non-local variant of a diffuse interface model proposed by
+Hawkins--Darrud et al. (2012) for tumour growth in the presence of a chemical
+species acting as nutrient. The system consists of a Cahn--Hilliard equation
+coupled to a reaction-diffusion equation. For non-degenerate mobilities and
+smooth potentials, we derive well-posedness results, which are the non-local
+analogue of those obtained in Frigeri et al. (European J. Appl. Math. 2015).
+Furthermore, we establish existence of weak solutions for the case of
+degenerate mobilities and singular potentials, which serves to confine the
+order parameter to its physically relevant interval. Due to the non-local
+nature of the equations, under additional assumptions continuous dependence on
+initial data can also be shown.
+",0,0,1,0,0,0
+16959,Gradient Descent Can Take Exponential Time to Escape Saddle Points,"  Although gradient descent (GD) almost always escapes saddle points
+asymptotically [Lee et al., 2016], this paper shows that even with fairly
+natural random initialization schemes and non-pathological functions, GD can be
+significantly slowed down by saddle points, taking exponential time to escape.
+On the other hand, gradient descent with perturbations [Ge et al., 2015, Jin et
+al., 2017] is not slowed down by saddle points - it can find an approximate
+local minimizer in polynomial time. This result implies that GD is inherently
+slower than perturbed GD, and justifies the importance of adding perturbations
+for efficient non-convex optimization. While our focus is theoretical, we also
+present experiments that illustrate our theoretical findings.
+",1,0,1,1,0,0
+16960,Spectral parameter power series for arbitrary order linear differential equations,"  Let $L$ be the $n$-th order linear differential operator $Ly = \phi_0y^{(n)}
++ \phi_1y^{(n-1)} + \cdots + \phi_ny$ with variable coefficients. A
+representation is given for $n$ linearly independent solutions of $Ly=\lambda r
+y$ as power series in $\lambda$, generalizing the SPPS (spectral parameter
+power series) solution which has been previously developed for $n=2$. The
+coefficient functions in these series are obtained by recursively iterating a
+simple integration process, begining with a solution system for $\lambda=0$. It
+is shown how to obtain such an initializing system working upwards from
+equations of lower order. The values of the successive derivatives of the power
+series solutions at the basepoint of integration are given, which provides a
+technique for numerical solution of $n$-th order initial value problems and
+spectral problems.
+",0,0,1,0,0,0
+16961,Antropologia de la Informatica Social: Teoria de la Convergencia Tecno-Social,"  The traditional humanism of the twentieth century, inspired by the culture of
+the book, systematically distanced itself from the new society of digital
+information; the Internet and tools of information processing revolutionized
+the world, society during this period developed certain adaptive
+characteristics based on coexistence (Human - Machine), this transformation
+sets based on the impact of three technology segments: devices, applications
+and infrastructure of social communication, which are involved in various
+physical, behavioural and cognitive changes of the human being; and the
+emergence of new models of influence and social control through the new
+ubiquitous communication; however in this new process of conviviality new
+models like the ""collaborative thinking"" and ""InfoSharing"" develop; managing
+social information under three Human ontological dimensions (h) - Information
+(i) - Machine (m), which is the basis of a new physical-cyber ecosystem, where
+they coexist and develop new social units called ""virtual communities "". This
+new communication infrastructure and social management of information given
+discovered areas of vulnerability ""social perspective of risk"", impacting all
+social units through massive impact vector (i); The virtual environment ""H + i
++ M""; and its components, as well as the life cycle management of social
+information allows us to understand the path of integration ""Techno - Social""
+and setting a new contribution to cybernetics, within the convergence of
+technology with society and the new challenges of coexistence, aimed at a new
+holistic and not pragmatic vision, as the human component (h) in the virtual
+environment is the precursor of the future and needs to be studied not as an
+application, but as the hub of a new society.
+",1,0,0,0,0,0
+16962,A Deterministic Approach to Avoid Saddle Points,"  Loss functions with a large number of saddle points are one of the main
+obstacles to training many modern machine learning models. Gradient descent
+(GD) is a fundamental algorithm for machine learning and converges to a saddle
+point for certain initial data. We call the region formed by these initial
+values the ""attraction region."" For quadratic functions, GD converges to a
+saddle point if the initial data is in a subspace of up to n-1 dimensions. In
+this paper, we prove that a small modification of the recently proposed
+Laplacian smoothing gradient descent (LSGD) [Osher, et al., arXiv:1806.06317]
+contributes to avoiding saddle points without sacrificing the convergence rate
+of GD. In particular, we show that the dimension of the LSGD's attraction
+region is at most floor((n-1)/2) for a class of quadratic functions which is
+significantly smaller than GD's (n-1)-dimensional attraction region.
+",1,0,0,1,0,0
+16963,Automatic Generation of Typographic Font from a Small Font Subset,"  This paper addresses the automatic generation of a typographic font from a
+subset of characters. Specifically, we use a subset of a typographic font to
+extrapolate additional characters. Consequently, we obtain a complete font
+containing a number of characters sufficient for daily use. The automated
+generation of Japanese fonts is in high demand because a Japanese font requires
+over 1,000 characters. Unfortunately, professional typographers create most
+fonts, resulting in significant financial and time investments for font
+generation. The proposed method can be a great aid for font creation because
+designers do not need to create the majority of the characters for a new font.
+The proposed method uses strokes from given samples for font generation. The
+strokes, from which we construct characters, are extracted by exploiting a
+character skeleton dataset. This study makes three main contributions: a novel
+method of extracting strokes from characters, which is applicable to both
+standard fonts and their variations; a fully automated approach for
+constructing characters; and a selection method for sample characters. We
+demonstrate our proposed method by generating 2,965 characters in 47 fonts.
+Objective and subjective evaluations verify that the generated characters are
+similar to handmade characters.
+",1,0,0,0,0,0
+16964,The Second Postulate of Euclid and the Hyperbolic Geometry,"  The article deals with the connection between the second postulate of Euclid
+and non-Euclidean geometry. It is shown that the violation of the second
+postulate of Euclid inevitably leads to hyperbolic geometry. This eliminates
+misunderstandings about the sums of some divergent series. The connection
+between hyperbolic geometry and relativistic computations is noted.
+",0,0,1,0,0,0
+16965,Transkernel: An Executor for Commodity Kernels on Peripheral Cores,"  Modern mobile and embedded platforms see a large number of ephemeral tasks
+driven by background activities. In order to execute such a task, the OS kernel
+wakes up the platform beforehand and puts it back to sleep afterwards. In doing
+so, the kernel operates various IO devices and orchestrates their power state
+transitions. Such kernel execution phases are lengthy, having high energy cost,
+and yet difficult to optimize. We advocate for relieving the CPU from these
+kernel phases by executing them on a low-power, microcontroller-like core,
+dubbed peripheral core, hence leaving the CPU off. Yet, for a peripheral core
+to execute phases in a complex commodity kernel (e.g. Linux), existing
+approaches either incur high engineering effort or high runtime overhead. We
+take a radical approach with a new executor model called transkernel. Running
+on a peripheral core, a transkernel executes the binary of the commodity kernel
+through cross-ISA, dynamic binary translation (DBT). The transkernel translates
+stateful kernel code while emulating a small set of stateless kernel services;
+it sets a narrow, stable binary interface for emulated services; it specializes
+for kernel's beaten paths; it exploits ISA similarities for low DBT cost. With
+a concrete implementation on a heterogeneous ARM SoC, we demonstrate the
+feasibility and benefit of transkernel. Our result contributes a new OS
+structure that combines cross-ISA DBT and emulation for harnessing a
+heterogeneous SoC. Our result demonstrates that while cross-ISA DBT is
+typically used under the assumption of efficiency loss, it can be used for
+efficiency gain, even atop off-the-shelf hardware.
+",1,0,0,0,0,0
+16966,No iterated identities satisfied by all finite groups,"  We show that there is no iterated identity satisfied by all finite groups.
+For $w$ being a non-trivial word of length $l$, we show that there exists a
+finite group $G$ of cardinality at most $\exp(l^C)$ which does not satisfy the
+iterated identity $w$. The proof uses the approach of Borisov and Sapir, who
+used dynamics of polynomial mappings for the proof of non residual finiteness
+of some groups.
+",0,0,1,0,0,0
+16967,Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning,"  The goal of this tutorial is to introduce key models, algorithms, and open
+questions related to the use of optimization methods for solving problems
+arising in machine learning. It is written with an INFORMS audience in mind,
+specifically those readers who are familiar with the basics of optimization
+algorithms, but less familiar with machine learning. We begin by deriving a
+formulation of a supervised learning problem and show how it leads to various
+optimization problems, depending on the context and underlying assumptions. We
+then discuss some of the distinctive features of these optimization problems,
+focusing on the examples of logistic regression and the training of deep neural
+networks. The latter half of the tutorial focuses on optimization algorithms,
+first for convex logistic regression, for which we discuss the use of
+first-order methods, the stochastic gradient method, variance reducing
+stochastic methods, and second-order methods. Finally, we discuss how these
+approaches can be employed to the training of deep neural networks, emphasizing
+the difficulties that arise from the complex, nonconvex structure of these
+models.
+",1,0,0,1,0,0
+16968,A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data,"  Partial Least Squares (PLS) methods have been heavily exploited to analyse
+the association between two blocs of data. These powerful approaches can be
+applied to data sets where the number of variables is greater than the number
+of observations and in presence of high collinearity between variables.
+Different sparse versions of PLS have been developed to integrate multiple data
+sets while simultaneously selecting the contributing variables. Sparse
+modelling is a key factor in obtaining better estimators and identifying
+associations between multiple data sets. The cornerstone of the sparsity
+version of PLS methods is the link between the SVD of a matrix (constructed
+from deflated versions of the original matrices of data) and least squares
+minimisation in linear regression. We present here an accurate description of
+the most popular PLS methods, alongside their mathematical proofs. A unified
+algorithm is proposed to perform all four types of PLS including their
+regularised versions. Various approaches to decrease the computation time are
+offered, and we show how the whole procedure can be scalable to big data sets.
+",0,0,0,1,0,0
+16969,Asymptotic behaviour of the fifth Painlevé transcendents in the space of initial values,"  We study the asymptotic behaviour of the solutions of the fifth Painlevé
+equation as the independent variable approaches zero and infinity in the space
+of initial values. We show that the limit set of each solution is compact and
+connected and, moreover, that any solution with the essential singularity at
+zero has an infinite number of poles and zeroes, and any solution with the
+essential singularity at infinity has infinite number of poles and takes value
+$1$ infinitely many times.
+",0,1,1,0,0,0
+16970,Hidden Treasures - Recycling Large-Scale Internet Measurements to Study the Internet's Control Plane,"  Internet-wide scans are a common active measurement approach to study the
+Internet, e.g., studying security properties or protocol adoption. They involve
+probing large address ranges (IPv4 or parts of IPv6) for specific ports or
+protocols. Besides their primary use for probing (e.g., studying protocol
+adoption), we show that - at the same time - they provide valuable insights
+into the Internet control plane informed by ICMP responses to these probes - a
+currently unexplored secondary use. We collect one week of ICMP responses
+(637.50M messages) to several Internet-wide ZMap scans covering multiple TCP
+and UDP ports as well as DNS-based scans covering > 50% of the domain name
+space. This perspective enables us to study the Internet's control plane as a
+by-product of Internet measurements. We receive ICMP messages from ~171M
+different IPs in roughly 53K different autonomous systems. Additionally, we
+uncover multiple control plane problems, e.g., we detect a plethora of outdated
+and misconfigured routers and uncover the presence of large-scale persistent
+routing loops in IPv4.
+",1,0,0,0,0,0
+16971,Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms,"  We present a method for metric optimization in the Large Deformation
+Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced
+Riemannian metric on the space of diffeomorphisms as a kernel in a machine
+learning context. For simplicity, we choose the kernel Fischer Linear
+Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters
+in an Expectation-Maximization framework, we define model fidelity via the
+hinge loss of the decision function. The resulting algorithm optimizes the
+parameters of the LDDMM norm-inducing differential operator as a solution to a
+group-wise registration and classification problem. In practice, this may lead
+to a biology-aware registration, focusing its attention on the predictive task
+at hand such as identifying the effects of disease. We first tested our
+algorithm on a synthetic dataset, showing that our parameter selection improves
+registration quality and classification accuracy. We then tested the algorithm
+on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our
+Schizpohrenia-Control predictive model showed significant improvement in ROC
+AUC compared to baseline parameters.
+",0,0,0,1,0,0
+16972,On a direct algorithm for constructing recursion operators and Lax pairs for integrable models,"  We suggested an algorithm for searching the recursion operators for nonlinear
+integrable equations. It was observed that the recursion operator $R$ can be
+represented as a ratio of the form $R=L_1^{-1}L_2$ where the linear
+differential operators $L_1$ and $L_2$ are chosen in such a way that the
+ordinary differential equation $(L_2-\lambda L_1)U=0$ is consistent with the
+linearization of the given nonlinear integrable equation for any value of the
+parameter $\lambda\in \textbf{C}$. For constructing the operator $L_1$ we use
+the concept of the invariant manifold which is a generalization of the
+symmetry. Then for searching $L_2$ we take an auxiliary linear equation
+connected with the linearized equation by the Darboux transformation.
+Connection of the invariant manifold with the Lax pairs and the
+Dubrovin-Weierstrass equations is discussed.
+",0,1,0,0,0,0
+16973,Network Classification and Categorization,"  To the best of our knowledge, this paper presents the first large-scale study
+that tests whether network categories (e.g., social networks vs. web graphs)
+are distinguishable from one another (using both categories of real-world
+networks and synthetic graphs). A classification accuracy of $94.2\%$ was
+achieved using a random forest classifier with both real and synthetic
+networks. This work makes two important findings. First, real-world networks
+from various domains have distinct structural properties that allow us to
+predict with high accuracy the category of an arbitrary network. Second,
+classifying synthetic networks is trivial as our models can easily distinguish
+between synthetic graphs and the real-world networks they are supposed to
+model.
+",1,0,0,1,0,0
+16974,A Polynomial-Time Algorithm for Solving the Minimal Observability Problem in Conjunctive Boolean Networks,"  Many complex systems in biology, physics, and engineering include a large
+number of state-variables, and measuring the full state of the system is often
+impossible. Typically, a set of sensors is used to measure part of the
+state-variables. A system is called observable if these measurements allow to
+reconstruct the entire state of the system. When the system is not observable,
+an important and practical problem is how to add a \emph{minimal} number of
+sensors so that the system becomes observable. This minimal observability
+problem is practically useful and theoretically interesting, as it pinpoints
+the most informative nodes in the system. We consider the minimal observability
+problem for an important special class of Boolean networks, called conjunctive
+Boolean networks (CBNs). Using a graph-theoretic approach, we provide a
+necessary and sufficient condition for observability of a CBN with $n$
+state-variables, and an efficient~$O(n^2)$-time algorithm for solving the
+minimal observability problem. We demonstrate the usefulness of these results
+by studying the properties of a class of random CBNs.
+",1,0,1,0,0,0
+16975,The Description and Scaling Behavior for the Inner Region of the Boundary Layer for 2-D Wall-bounded Flows,"  A second derivative-based moment method is proposed for describing the
+thickness and shape of the region where viscous forces are dominant in
+turbulent boundary layer flows. Rather than the fixed location sublayer model
+presently employed, the new method defines thickness and shape parameters that
+are experimentally accessible without differentiation. It is shown
+theoretically that one of the new length parameters used as a scaling parameter
+is also a similarity parameter for the velocity profile. In fact, we show that
+this new length scale parameter removes one of the theoretical inconsistencies
+present in the traditional Prandtl Plus scalings. Furthermore, the new length
+parameter and the Prandtl Plus scaling parameters perform identically when
+operating on experimental datasets. This means that many of the past successes
+ascribed to the Prandtl Plus scaling also apply to the new parameter set but
+without one of the theoretical inconsistencies. Examples are offered to show
+how the new description method is useful in exploring the actual physics of the
+boundary layer.
+",0,1,0,0,0,0
+16976,Completely Sidon sets in $C^*$-algebras (New title),"  A sequence in a $C^*$-algebra $A$ is called completely Sidon if its span in
+$A$ is completely isomorphic to the operator space version of the space
+$\ell_1$ (i.e. $\ell_1$ equipped with its maximal operator space structure).
+The latter can also be described as the span of the free unitary generators in
+the (full) $C^*$-algebra of the free group $\F_\infty$ with countably
+infinitely many generators. Our main result is a generalization to this context
+of Drury's classical theorem stating that Sidon sets are stable under finite
+unions. In the particular case when $A=C^*(G)$ the (maximal) $C^*$-algebra of a
+discrete group $G$, we recover the non-commutative (operator space) version of
+Drury's theorem that we recently proved. We also give several non-commutative
+generalizations of our recent work on uniformly bounded orthonormal systems to
+the case of von Neumann algebras equipped with normal faithful tracial states.
+",0,0,1,0,0,0
+16977,Conflict-Free Coloring of Planar Graphs,"  A conflict-free k-coloring of a graph assigns one of k different colors to
+some of the vertices such that, for every vertex v, there is a color that is
+assigned to exactly one vertex among v and v's neighbors. Such colorings have
+applications in wireless networking, robotics, and geometry, and are
+well-studied in graph theory. Here we study the natural problem of the
+conflict-free chromatic number chi_CF(G) (the smallest k for which
+conflict-free k-colorings exist). We provide results both for closed
+neighborhoods N[v], for which a vertex v is a member of its neighborhood, and
+for open neighborhoods N(v), for which vertex v is not a member of its
+neighborhood.
+For closed neighborhoods, we prove the conflict-free variant of the famous
+Hadwiger Conjecture: If an arbitrary graph G does not contain K_{k+1} as a
+minor, then chi_CF(G) <= k. For planar graphs, we obtain a tight worst-case
+bound: three colors are sometimes necessary and always sufficient. We also give
+a complete characterization of the computational complexity of conflict-free
+coloring. Deciding whether chi_CF(G)<= 1 is NP-complete for planar graphs G,
+but polynomial for outerplanar graphs. Furthermore, deciding whether
+chi_CF(G)<= 2 is NP-complete for planar graphs G, but always true for
+outerplanar graphs. For the bicriteria problem of minimizing the number of
+colored vertices subject to a given bound k on the number of colors, we give a
+full algorithmic characterization in terms of complexity and approximation for
+outerplanar and planar graphs.
+For open neighborhoods, we show that every planar bipartite graph has a
+conflict-free coloring with at most four colors; on the other hand, we prove
+that for k in {1,2,3}, it is NP-complete to decide whether a planar bipartite
+graph has a conflict-free k-coloring. Moreover, we establish that any general}
+planar graph has a conflict-free coloring with at most eight colors.
+",1,0,1,0,0,0
+16978,Explicit solutions to utility maximization problems in a regime-switching market model via Laplace transforms,"  We study the problem of utility maximization from terminal wealth in which an
+agent optimally builds her portfolio by investing in a bond and a risky asset.
+The asset price dynamics follow a diffusion process with regime-switching
+coefficients modeled by a continuous-time finite-state Markov chain. We
+consider an investor with a Constant Relative Risk Aversion (CRRA) utility
+function. We deduce the associated Hamilton-Jacobi-Bellman equation to
+construct the solution and the optimal trading strategy and verify optimality
+by showing that the value function is the unique constrained viscosity solution
+of the HJB equation. By means of a Laplace transform method, we show how to
+explicitly compute the value function and illustrate the method with the two-
+and three-states cases. This method is interesting in its own right and can be
+adapted in other applications involving hybrid systems and using other types of
+transforms with basic properties similar to the Laplace transform.
+",0,0,0,0,0,1
+16979,Spectroscopic study of the elusive globular cluster ESO452-SC11 and its surroundings,"  Globular clusters (GCs) are amongst the oldest objects in the Galaxy and play
+a pivotal role in deciphering its early history. We present the first
+spectroscopic study of the GC ESO452-SC11 using the AAOmega spectrograph at
+medium resolution. Given the sparsity of this object and high degree of
+foreground contamination due to its location toward the bulge, few details are
+known for this cluster: there is no consensus of its age, metallicity, or its
+association with the disk or bulge. We identify 5 members based on radial
+velocity, metallicity, and position within the GC. Using spectral synthesis,
+accurate abundances of Fe and several $\alpha$-, Fe-peak, neutron-capture
+elements (Si,Ca,Ti,Cr,Co,Ni,Sr,Eu) were measured. Two of the 5 cluster
+candidates are likely non-members, as they have deviant Fe abundances and
+[$\alpha$/Fe] ratios. The mean radial velocity is 19$\pm$2 km s$^{-1}$ with a
+low dispersion of 2.8$\pm$3.4 km s$^{-1}$, in line with its low mass. The mean
+Fe-abundance from spectral fitting is $-0.88\pm0.03$, with a spread driven by
+observational errors. The $\alpha$-elements of the GC candidates are marginally
+lower than expected for the bulge at similar metallicities. As spectra of
+hundreds of stars were collected in a 2 degree field around ESO452-SC11,
+detailed abundances in the surrounding field were measured. Most non-members
+have higher [$\alpha$/Fe] ratios, typical of the nearby bulge population. Stars
+with measured Fe-peak abundances show a large scatter around Solar values,
+though with large uncertainties. Our study provides the first systematic
+measurement of Sr in a Galactic bulge GC. The Eu and Sr abundances of the GC
+candidates are consistent with a disk or bulge association. Our calculations
+place ESO452 on an elliptical orbit in the central 3 kpc of the bulge. We find
+no evidence of extratidal stars in our data. (Abridged)
+",0,1,0,0,0,0
+16980,The Fundamental Infinity-Groupoid of a Parametrized Family,"  Given an infinity-category C, one can naturally construct an
+infinity-category Fam(C) of families of objects in C indexed by
+infinity-groupoids. An ordinary categorical version of this construction was
+used by Borceux and Janelidze in the study of generalized covering maps in
+categorical Galois theory. In this paper, we develop the homotopy theory of
+such ""parametrized families"" as generalization of the classical homotopy theory
+of spaces. In particular, we study homotopy-theoretical constructions that
+arise from the fundamental infinity-groupoids of families in an
+infinity-category. In the same spirit, we show that Fam(C) admits a
+Grothendieck topology which generalizes the canonical/epimorphism topology on
+the infinity-topos of infinity-groupoids in the sense of Carchedi.
+",0,0,1,0,0,0
+16981,Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation,"  Training 3D object detectors for autonomous driving has been limited to small
+datasets due to the effort required to generate annotations. Reducing both task
+complexity and the amount of task switching done by annotators is key to
+reducing the effort and time required to generate 3D bounding box annotations.
+This paper introduces a novel ground truth generation method that combines
+human supervision with pretrained neural networks to generate per-instance 3D
+point cloud segmentation, 3D bounding boxes, and class annotations. The
+annotators provide object anchor clicks which behave as a seed to generate
+instance segmentation results in 3D. The points belonging to each instance are
+then used to regress object centroids, bounding box dimensions, and object
+orientation. Our proposed annotation scheme requires 30x lower human annotation
+time. We use the KITTI 3D object detection dataset to evaluate the efficiency
+and the quality of our annotation scheme. We also test the the proposed scheme
+on previously unseen data from the Autonomoose self-driving vehicle to
+demonstrate generalization capabilities of the network.
+",0,0,0,1,0,0
+16982,Epidemic spreading in multiplex networks influenced by opinion exchanges on vaccination,"  We study the changes of opinions about vaccination together with the
+evolution of a disease. In our model we consider a multiplex network consisting
+of two layers. One of the layers corresponds to a social network where people
+share their opinions and influence others opinions. The social model that rules
+the dynamic is the M-model, which takes into account two different processes
+that occurs in a society: persuasion and compromise. This two processes are
+related through a parameter $r$, $r<1$ describes a moderate and committed
+society, for $r>1$ the society tends to have extremist opinions, while $r=1$
+represents a neutral society. This social network may be of real or virtual
+contacts. On the other hand, the second layer corresponds to a network of
+physical contacts where the disease spreading is described by the SIR-Model. In
+this model the individuals may be in one of the following four states:
+Susceptible ($S$), Infected($I$), Recovered ($R$) or Vaccinated ($V$). A
+Susceptible individual can: i) get vaccinated, if his opinion in the other
+layer is totally in favor of the vaccine, ii) get infected, with probability
+$\beta$ if he is in contact with an infected neighbor. Those $I$ individuals
+recover after a certain period $t_r=6$. Vaccinated individuals have an
+extremist positive opinion that does not change. We consider that the vaccine
+has a certain effectiveness $\omega$ and as a consequence vaccinated nodes can
+be infected with probability $\beta (1 - \omega)$ if they are in contact with
+an infected neighbor. In this case, if the infection process is successful, the
+new infected individual changes his opinion from extremist positive to totally
+against the vaccine. We find that depending on the trend in the opinion of the
+society, which depends on $r$, different behaviors in the spread of the
+epidemic occurs. An epidemic threshold was found.
+",0,1,0,0,0,0
+16983,CASP Solutions for Planning in Hybrid Domains,"  CASP is an extension of ASP that allows for numerical constraints to be added
+in the rules. PDDL+ is an extension of the PDDL standard language of automated
+planning for modeling mixed discrete-continuous dynamics.
+In this paper, we present CASP solutions for dealing with PDDL+ problems,
+i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP
+CASP solver in order to solve CASP programs arising from PDDL+ domains. An
+experimental analysis, performed on well-known linear and non-linear variants
+of PDDL+ domains, involving various configurations of the EZCSP solver, other
+CASP solvers, and PDDL+ planners, shows the viability of our solution.
+",1,0,0,0,0,0
+16984,Primordial black holes from inflaton and spectator field perturbations in a matter-dominated era,"  We study production of primordial black holes (PBHs) during an early
+matter-dominated phase. As a source of perturbations, we consider either the
+inflaton field with a running spectral index or a spectator field that has a
+blue spectrum and thus provides a significant contribution to the PBH
+production at small scales. First, we identify the region of the parameter
+space where a significant fraction of the observed dark matter can be produced,
+taking into account all current PBH constraints. Then, we present constraints
+on the amplitude and spectral index of the spectator field as a function of the
+reheating temperature. We also derive constraints on the running of the
+inflaton spectral index, ${\rm d}n/{\rm d}{\rm ln}k \lesssim -0.002$, which are
+comparable to those from the Planck satellite for a scenario where the
+spectator field is absent.
+",0,1,0,0,0,0
+16985,State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning,"  Typical reinforcement learning (RL) agents learn to complete tasks specified
+by reward functions tailored to their domain. As such, the policies they learn
+do not generalize even to similar domains. To address this issue, we develop a
+framework through which a deep RL agent learns to generalize policies from
+smaller, simpler domains to more complex ones using a recurrent attention
+mechanism. The task is presented to the agent as an image and an instruction
+specifying the goal. This meta-controller guides the agent towards its goal by
+designing a sequence of smaller subtasks on the part of the state space within
+the attention, effectively decomposing it. As a baseline, we consider a setup
+without attention as well. Our experiments show that the meta-controller learns
+to create subgoals within the attention.
+",1,0,0,1,0,0
+16986,Robust Imitation of Diverse Behaviors,"  Deep generative models have recently shown great promise in imitation
+learning for motor control. Given enough data, even supervised approaches can
+do one-shot imitation learning; however, they are vulnerable to cascading
+failures when the agent trajectory diverges from the demonstrations. Compared
+to purely supervised methods, Generative Adversarial Imitation Learning (GAIL)
+can learn more robust controllers from fewer demonstrations, but is inherently
+mode-seeking and more difficult to train. In this paper, we show how to combine
+the favourable aspects of these two approaches. The base of our model is a new
+type of variational autoencoder on demonstration trajectories that learns
+semantic policy embeddings. We show that these embeddings can be learned on a 9
+DoF Jaco robot arm in reaching tasks, and then smoothly interpolated with a
+resulting smooth interpolation of reaching behavior. Leveraging these policy
+representations, we develop a new version of GAIL that (1) is much more robust
+than the purely-supervised controller, especially with few demonstrations, and
+(2) avoids mode collapse, capturing many diverse behaviors when GAIL on its own
+does not. We demonstrate our approach on learning diverse gaits from
+demonstration on a 2D biped and a 62 DoF 3D humanoid in the MuJoCo physics
+environment.
+",1,0,0,0,0,0
+16987,Efficient Measurement of the Vibrational Rogue Waves by Compressive Sampling Based Wavelet Analysis,"  In this paper we discuss the possible usage of the compressive sampling based
+wavelet analysis for the efficient measurement and for the early detection of
+one dimensional (1D) vibrational rogue waves. We study the construction of the
+triangular (V-shaped) wavelet spectra using compressive samples of rogue waves
+that can be modeled as Peregrine and Akhmediev-Peregrine solitons. We show that
+triangular wavelet spectra can be sensed by compressive measurements at the
+early stages of the development of vibrational rogue waves. Our results may
+lead to development of the efficient vibrational rogue wave measurement and
+early sensing systems with reduced memory requirements which use the
+compressive sampling algorithms. In typical solid mechanics applications,
+compressed measurements can be acquired by randomly positioning single sensor
+and multisensors.
+",0,0,1,0,0,0
+16988,SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes,"  This paper presents SceneCut, a novel approach to jointly discover previously
+unseen objects and non-object surfaces using a single RGB-D image. SceneCut's
+joint reasoning over scene semantics and geometry allows a robot to detect and
+segment object instances in complex scenes where modern deep learning-based
+methods either fail to separate object instances, or fail to detect objects
+that were not seen during training. SceneCut automatically decomposes a scene
+into meaningful regions which either represent objects or scene surfaces. The
+decomposition is qualified by an unified energy function over objectness and
+geometric fitting. We show how this energy function can be optimized
+efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage
+a pre-trained convolutional oriented boundary network to predict accurate
+boundaries from images, which are used to construct high-quality region
+hierarchies. We evaluate SceneCut on several different indoor environments, and
+the results show that SceneCut significantly outperforms all the existing
+methods.
+",1,0,0,0,0,0
+16989,An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions,"  In this paper, we show that different body parts do not play equally
+important roles in recognizing a human action in video data. We investigate to
+what extent a body part plays a role in recognition of different actions and
+hence propose a generic method of assigning weights to different body points.
+The approach is inspired by the strong evidence in the applied perception
+community that humans perform recognition in a foveated manner, that is they
+recognize events or objects by only focusing on visually significant aspects.
+An important contribution of our method is that the computation of the weights
+assigned to body parts is invariant to viewing directions and camera parameters
+in the input data. We have performed extensive experiments to validate the
+proposed approach and demonstrate its significance. In particular, results show
+that considerable improvement in performance is gained by taking into account
+the relative importance of different body parts as defined by our approach.
+",1,0,0,0,0,0
+16990,Forecasting Crime with Deep Learning,"  The objective of this work is to take advantage of deep neural networks in
+order to make next day crime count predictions in a fine-grain city partition.
+We make predictions using Chicago and Portland crime data, which is augmented
+with additional datasets covering weather, census data, and public
+transportation. The crime counts are broken into 10 bins and our model predicts
+the most likely bin for a each spatial region at a daily level. We train this
+data using increasingly complex neural network structures, including variations
+that are suited to the spatial and temporal aspects of the crime prediction
+problem. With our best model we are able to predict the correct bin for overall
+crime count with 75.6% and 65.3% accuracy for Chicago and Portland,
+respectively. The results show the efficacy of neural networks for the
+prediction problem and the value of using external datasets in addition to
+standard crime data.
+",0,0,0,1,0,0
+16991,A family of compact semitoric systems with two focus-focus singularities,"  About 6 years ago, semitoric systems were classified by Pelayo & Vu Ngoc by
+means of five invariants. Standard examples are the coupled spin oscillator on
+$\mathbb{S}^2 \times \mathbb{R}^2$ and coupled angular momenta on $\mathbb{S}^2
+\times \mathbb{S}^2$, both having exactly one focus-focus singularity. But so
+far there were no explicit examples of systems with more than one focus-focus
+singularity which are semitoric in the sense of that classification. This paper
+introduces a 6-parameter family of integrable systems on $\mathbb{S}^2 \times
+\mathbb{S}^2$ and proves that, for certain ranges of the parameters, it is a
+compact semitoric system with precisely two focus-focus singularities. Since
+the twisting index (one of the semitoric invariants) is related to the
+relationship between different focus-focus points, this paper provides systems
+for the future study of the twisting index.
+",0,0,1,0,0,0
+16992,Mixed Threefolds Isogenous to a Product,"  In this paper we study \emph{threefolds isogenous to a product of mixed type}
+i.e. quotients of a product of three compact Riemann surfaces $C_i$ of genus at
+least two by the action of a finite group $G$, which is free, but not diagonal.
+In particular, we are interested in the systematic construction and
+classification of these varieties. Our main result is the full classification
+of threefolds isogenous to a product of mixed type with $\chi(\mathcal O_X)=-1$
+assuming that any automorphism in $G$, which restricts to the trivial element
+in $Aut(C_i)$ for some $C_i$, is the identity on the product. Since the
+holomorphic Euler-Poincaré-characteristic of a smooth threefold of general
+type with ample canonical class is always negative, these examples lie on the
+boundary, in the sense of threefold geography. To achieve our result we use
+techniques from computational group theory. Indeed, we develop a MAGMA
+algorithm to classify these threefolds for any given value of $\chi(\mathcal
+O_X)$.
+",0,0,1,0,0,0
+16993,Discriminatory Transfer,"  We observe standard transfer learning can improve prediction accuracies of
+target tasks at the cost of lowering their prediction fairness -- a phenomenon
+we named discriminatory transfer. We examine prediction fairness of a standard
+hypothesis transfer algorithm and a standard multi-task learning algorithm, and
+show they both suffer discriminatory transfer on the real-world Communities and
+Crime data set. The presented case study introduces an interaction between
+fairness and transfer learning, as an extension of existing fairness studies
+that focus on single task learning.
+",1,0,0,1,0,0
+16994,Ultrafast relaxation of hot phonons in Graphene-hBN Heterostructures,"  Fast carrier cooling is important for high power graphene based devices.
+Strongly Coupled Optical Phonons (SCOPs) play a major role in the relaxation of
+photoexcited carriers in graphene. Heterostructures of graphene and hexagonal
+boron nitride (hBN) have shown exceptional mobility and high saturation
+current, which makes them ideal for applications, but the effect of the hBN
+substrate on carrier cooling mechanisms is not understood. We track the cooling
+of hot photo-excited carriers in graphene-hBN heterostructures using ultrafast
+pump-probe spectroscopy. We find that the carriers cool down four times faster
+in the case of graphene on hBN than on a silicon oxide substrate thus
+overcoming the hot phonon (HP) bottleneck that plagues cooling in graphene
+devices.
+",0,1,0,0,0,0
+16995,Non-linear Associative-Commutative Many-to-One Pattern Matching with Sequence Variables,"  Pattern matching is a powerful tool which is part of many functional
+programming languages as well as computer algebra systems such as Mathematica.
+Among the existing systems, Mathematica offers the most expressive pattern
+matching. Unfortunately, no open source alternative has comparable pattern
+matching capabilities. Notably, these features include support for associative
+and/or commutative function symbols and sequence variables. While those
+features have individually been subject of previous research, their
+comprehensive combination has not yet been investigated. Furthermore, in many
+applications, a fixed set of patterns is matched repeatedly against different
+subjects. This many-to-one matching can be sped up by exploiting similarities
+between patterns. Discrimination nets are the state-of-the-art solution for
+many-to-one matching. In this thesis, a generalized discrimination net which
+supports the full feature set is presented. All algorithms have been
+implemented as an open-source library for Python. In experiments on real world
+examples, significant speedups of many-to-one over one-to-one matching have
+been observed.
+",1,0,0,0,0,0
+16996,Pair Background Envelopes in the SiD Detector,"  The beams at the ILC produce electron positron pairs due to beam-beam
+interactions. This note presents for the first time a study of these processes
+in a detailed simulation, which shows that these pair background particles
+appear at angles that extend to the inner layers of the detector. The full data
+set of pairs produced in one bunch crossing was used to calculate the helix
+tracks, which the particles form in the solenoid field of the SiD detector. The
+results suggest to further study the reduction of the beam pipe radius and
+therefore to either add another SiD vertex detector layer, or reduce the radius
+of the existing vertex detector layers, without increasing the detector
+occupancy significantly. This has to go along with additional studies whether
+the improvement in physics reconstruction methods, like c-tagging, is worth the
+increased background level at smaller radii.
+",0,1,0,0,0,0
+16997,Expansion of percolation critical points for Hamming graphs,"  The Hamming graph $H(d,n)$ is the Cartesian product of $d$ complete graphs on
+$n$ vertices. Let $m=d(n-1)$ be the degree and $V = n^d$ be the number of
+vertices of $H(d,n)$. Let $p_c^{(d)}$ be the critical point for bond
+percolation on $H(d,n)$. We show that, for $d \in \mathbb N$ fixed and $n \to
+\infty$,
+\begin{equation*}
+p_c^{(d)}= \dfrac{1}{m} + \dfrac{2d^2-1}{2(d-1)^2}\dfrac{1}{m^2}
++ O(m^{-3}) + O(m^{-1}V^{-1/3}),
+\end{equation*} which extends the asymptotics found in
+\cite{BorChaHofSlaSpe05b} by one order. The term $O(m^{-1}V^{-1/3})$ is the
+width of the critical window. For $d=4,5,6$ we have $m^{-3} =
+O(m^{-1}V^{-1/3})$, and so the above formula represents the full asymptotic
+expansion of $p_c^{(d)}$. In \cite{FedHofHolHul16a} \st{we show that} this
+formula is a crucial ingredient in the study of critical bond percolation on
+$H(d,n)$ for $d=2,3,4$. The proof uses a lace expansion for the upper bound and
+a novel comparison with a branching random walk for the lower bound. The proof
+of the lower bound also yields a refined asymptotics for the susceptibility of
+a subcritical Erdős-Rényi random graph.
+",0,0,1,0,0,0
+16998,Efficiently Manifesting Asynchronous Programming Errors in Android Apps,"  Android, the #1 mobile app framework, enforces the single-GUI-thread model,
+in which a single UI thread manages GUI rendering and event dispatching. Due to
+this model, it is vital to avoid blocking the UI thread for responsiveness. One
+common practice is to offload long-running tasks into async threads. To achieve
+this, Android provides various async programming constructs, and leaves
+developers themselves to obey the rules implied by the model. However, as our
+study reveals, more than 25% apps violate these rules and introduce
+hard-to-detect, fail-stop errors, which we term as aysnc programming errors
+(APEs). To this end, this paper introduces APEChecker, a technique to
+automatically and efficiently manifest APEs. The key idea is to characterize
+APEs as specific fault patterns, and synergistically combine static analysis
+and dynamic UI exploration to detect and verify such errors. Among the 40
+real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51
+are confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs
+than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces
+testing time from half an hour to a few minutes. On a specific type of APEs,
+APEChecker confirms 5X more errors than the data race detection tool,
+EventRacer, with very few false alarms.
+",1,0,0,0,0,0
+16999,AI Challenges in Human-Robot Cognitive Teaming,"  Among the many anticipated roles for robots in the future is that of being a
+human teammate. Aside from all the technological hurdles that have to be
+overcome with respect to hardware and control to make robots fit to work with
+humans, the added complication here is that humans have many conscious and
+subconscious expectations of their teammates - indeed, we argue that teaming is
+mostly a cognitive rather than physical coordination activity. This introduces
+new challenges for the AI and robotics community and requires fundamental
+changes to the traditional approach to the design of autonomy. With this in
+mind, we propose an update to the classical view of the intelligent agent
+architecture, highlighting the requirements for mental modeling of the human in
+the deliberative process of the autonomous agent. In this article, we outline
+briefly the recent efforts of ours, and others in the community, towards
+developing cognitive teammates along these guidelines.
+",1,0,0,0,0,0
+17000,"Generalizing the MVW involution, and the contragredient","  For certain quasi-split reductive groups $G$ over a general field $F$, we
+construct an automorphism $\iota_G$ of $G$ over $F$, well-defined as an element
+of ${\rm Aut}(G)(F)/jG(F)$ where $j:G(F) \rightarrow {\rm Aut}(G)(F)$ is the
+inner-conjugation action of $G(F)$ on $G$. The automorphism $\iota_G$
+generalizes (although only for quasi-split groups) an involution due to
+Moeglin-Vigneras-Waldspurger in [MVW] for classical groups which takes any
+irreducible admissible representation $\pi$ of $G(F)$ for $G$ a classical group
+and $F$ a local field, to its contragredient $\pi^\vee$. The paper also
+formulates a conjecture on the contragredient of an irreducible admissible
+representation of $G(F)$ for $G$ a reductive algebraic group over a local field
+$F$ in terms of the (enhanced) Langlands parameter of the representation.
+",0,0,1,0,0,0
+17001,Miraculous cancellations for quantum $SL_2$,"  In earlier work, Helen Wong and the author discovered certain ""miraculous
+cancellations"" for the quantum trace map connecting the Kauffman bracket skein
+algebra of a surface to its quantum Teichmueller space, occurring when the
+quantum parameter $q$ is a root of unity. The current paper is devoted to
+giving a more representation theoretic interpretation of this phenomenon, in
+terms of the quantum group $U_q(sl_2)$ and its dual Hopf algebra $SL_2^q$.
+",0,0,1,0,0,0
+17002,Energy and time measurements with high-granular silicon devices,"  This note is a short summary of the workshop on ""Energy and time measurements
+with high-granular silicon devices"" that took place on the 13/6/16 and the
+14/6/16 at DESY/Hamburg in the frame of the first AIDA-2020 Annual Meeting.
+This note tries to put forward trends that could be spotted and to emphasise in
+particular open issues that were addressed by the speakers.
+",0,1,0,0,0,0
+17003,Action Tubelet Detector for Spatio-Temporal Action Localization,"  Current state-of-the-art approaches for spatio-temporal action localization
+rely on detections at the frame level that are then linked or tracked across
+time. In this paper, we leverage the temporal continuity of videos instead of
+operating at the frame level. We propose the ACtion Tubelet detector
+(ACT-detector) that takes as input a sequence of frames and outputs tubelets,
+i.e., sequences of bounding boxes with associated scores. The same way
+state-of-the-art object detectors rely on anchor boxes, our ACT-detector is
+based on anchor cuboids. We build upon the SSD framework. Convolutional
+features are extracted for each frame, while scores and regressions are based
+on the temporal stacking of these features, thus exploiting information from a
+sequence. Our experimental results show that leveraging sequences of frames
+significantly improves detection performance over using individual frames. The
+gain of our tubelet detector can be explained by both more accurate scores and
+more precise localization. Our ACT-detector outperforms the state-of-the-art
+methods for frame-mAP and video-mAP on the J-HMDB and UCF-101 datasets, in
+particular at high overlap thresholds.
+",1,0,0,0,0,0
+17004,Significance of Side Information in the Graph Matching Problem,"  Percolation based graph matching algorithms rely on the availability of seed
+vertex pairs as side information to efficiently match users across networks.
+Although such algorithms work well in practice, there are other types of side
+information available which are potentially useful to an attacker. In this
+paper, we consider the problem of matching two correlated graphs when an
+attacker has access to side information, either in the form of community labels
+or an imperfect initial matching. In the former case, we propose a naive graph
+matching algorithm by introducing the community degree vectors which harness
+the information from community labels in an efficient manner. Furthermore, we
+analyze a variant of the basic percolation algorithm proposed in literature for
+graphs with community structure. In the latter case, we propose a novel
+percolation algorithm with two thresholds which uses an imperfect matching as
+input to match correlated graphs.
+We evaluate the proposed algorithms on synthetic as well as real world
+datasets using various experiments. The experimental results demonstrate the
+importance of communities as side information especially when the number of
+seeds is small and the networks are weakly correlated.
+",1,1,0,0,0,0
+17005,Extended Gray-Wyner System with Complementary Causal Side Information,"  We establish the rate region of an extended Gray-Wyner system for 2-DMS
+$(X,Y)$ with two additional decoders having complementary causal side
+information. This extension is interesting because in addition to the
+operationally significant extreme points of the Gray-Wyner rate region, which
+include Wyner's common information, G{á}cs-K{ö}rner common information and
+information bottleneck, the rate region for the extended system also includes
+the K{ö}rner graph entropy, the privacy funnel and excess functional
+information, as well as three new quantities of potential interest, as extreme
+points. To simplify the investigation of the 5-dimensional rate region of the
+extended Gray-Wyner system, we establish an equivalence of this region to a
+3-dimensional mutual information region that consists of the set of all triples
+of the form $(I(X;U),\,I(Y;U),\,I(X,Y;U))$ for some $p_{U|X,Y}$. We further
+show that projections of this mutual information region yield the rate regions
+for many settings involving a 2-DMS, including lossless source coding with
+causal side information, distributed channel synthesis, and lossless source
+coding with a helper.
+",1,0,1,0,0,0
+17006,Learning Powers of Poisson Binomial Distributions,"  We introduce the problem of simultaneously learning all powers of a Poisson
+Binomial Distribution (PBD). A PBD of order $n$ is the distribution of a sum of
+$n$ mutually independent Bernoulli random variables $X_i$, where
+$\mathbb{E}[X_i] = p_i$. The $k$'th power of this distribution, for $k$ in a
+range $[m]$, is the distribution of $P_k = \sum_{i=1}^n X_i^{(k)}$, where each
+Bernoulli random variable $X_i^{(k)}$ has $\mathbb{E}[X_i^{(k)}] = (p_i)^k$.
+The learning algorithm can query any power $P_k$ several times and succeeds in
+learning all powers in the range, if with probability at least $1- \delta$:
+given any $k \in [m]$, it returns a probability distribution $Q_k$ with total
+variation distance from $P_k$ at most $\epsilon$. We provide almost matching
+lower and upper bounds on query complexity for this problem. We first show a
+lower bound on the query complexity on PBD powers instances with many distinct
+parameters $p_i$ which are separated, and we almost match this lower bound by
+examining the query complexity of simultaneously learning all the powers of a
+special class of PBD's resembling the PBD's of our lower bound. We study the
+fundamental setting of a Binomial distribution, and provide an optimal
+algorithm which uses $O(1/\epsilon^2)$ samples. Diakonikolas, Kane and Stewart
+[COLT'16] showed a lower bound of $\Omega(2^{1/\epsilon})$ samples to learn the
+$p_i$'s within error $\epsilon$. The question whether sampling from powers of
+PBDs can reduce this sampling complexity, has a negative answer since we show
+that the exponential number of samples is inevitable. Having sampling access to
+the powers of a PBD we then give a nearly optimal algorithm that learns its
+$p_i$'s. To prove our two last lower bounds we extend the classical minimax
+risk definition from statistics to estimating functions of sequences of
+distributions.
+",1,0,1,1,0,0
+17007,Geometry of simplices in Minkowski spaces,"  There are many problems and configurations in Euclidean geometry that were
+never extended to the framework of (normed or) finite dimensional real Banach
+spaces, although their original versions are inspiring for this type of
+generalization, and the analogous definitions for normed spaces represent a
+promising topic. An example is the geometry of simplices in non-Euclidean
+normed spaces. We present new generalizations of well known properties of
+Euclidean simplices. These results refer to analogues of circumcenters, Euler
+lines, and Feuerbach spheres of simplices in normed spaces. Using duality, we
+also get natural theorems on angular bisectors as well as in- and exspheres of
+(dual) simplices.
+",0,0,1,0,0,0
+17008,DLR : Toward a deep learned rhythmic representation for music content analysis,"  In the use of deep neural networks, it is crucial to provide appropriate
+input representations for the network to learn from. In this paper, we propose
+an approach to learn a representation that focus on rhythmic representation
+which is named as DLR (Deep Learning Rhythmic representation). The proposed
+approach aims to learn DLR from the raw audio signal and use it for other music
+informatics tasks. A 1-dimensional convolutional network is utilised in the
+learning of DLR. In the experiment, we present the results from the source task
+and the target task as well as visualisations of DLRs. The results reveals that
+DLR provides compact rhythmic information which can be used on multi-tagging
+task.
+",1,0,0,0,0,0
+17009,Phylogeny-based tumor subclone identification using a Bayesian feature allocation model,"  Tumor cells acquire different genetic alterations during the course of
+evolution in cancer patients. As a result of competition and selection, only a
+few subgroups of cells with distinct genotypes survive. These subgroups of
+cells are often referred to as subclones. In recent years, many statistical and
+computational methods have been developed to identify tumor subclones, leading
+to biologically significant discoveries and shedding light on tumor
+progression, metastasis, drug resistance and other processes. However, most
+existing methods are either not able to infer the phylogenetic structure among
+subclones, or not able to incorporate copy number variations (CNV). In this
+article, we propose SIFA (tumor Subclone Identification by Feature Allocation),
+a Bayesian model which takes into account both CNV and tumor phylogeny
+structure to infer tumor subclones. We compare the performance of SIFA with two
+other commonly used methods using simulation studies with varying sequencing
+depth, evolutionary tree size, and tree complexity. SIFA consistently yields
+better results in terms of Rand Index and cellularity estimation accuracy. The
+usefulness of SIFA is also demonstrated through its application to whole genome
+sequencing (WGS) samples from four patients in a breast cancer study.
+",0,0,0,1,1,0
+17010,Confidence-based Graph Convolutional Networks for Semi-Supervised Learning,"  Predicting properties of nodes in a graph is an important problem with
+applications in a variety of domains. Graph-based Semi-Supervised Learning
+(SSL) methods aim to address this problem by labeling a small subset of the
+nodes as seeds and then utilizing the graph structure to predict label scores
+for the rest of the nodes in the graph. Recently, Graph Convolutional Networks
+(GCNs) have achieved impressive performance on the graph-based SSL task. In
+addition to label scores, it is also desirable to have confidence scores
+associated with them. Unfortunately, confidence estimation in the context of
+GCN has not been previously explored. We fill this important gap in this paper
+and propose ConfGCN, which estimates labels scores along with their confidences
+jointly in GCN-based setting. ConfGCN uses these estimated confidences to
+determine the influence of one node on another during neighborhood aggregation,
+thereby acquiring anisotropic capabilities. Through extensive analysis and
+experiments on standard benchmarks, we find that ConfGCN is able to outperform
+state-of-the-art baselines. We have made ConfGCN's source code available to
+encourage reproducible research.
+",1,0,0,1,0,0
+17011,Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network,"  This paper studies the daily connectivity time series of a wind
+speed-monitoring network using multifractal detrended fluctuation analysis. It
+investigates the long-range fluctuation and multifractality in the residuals of
+the connectivity time series. Our findings reveal that the daily connectivity
+of the correlation-based network is persistent for any correlation threshold.
+Further, the multifractality degree is higher for larger absolute values of the
+correlation threshold
+",0,0,0,1,0,0
+17012,The Dynamics of Norm Change in the Cultural Evolution of Language,"  What happens when a new social convention replaces an old one? While the
+possible forces favoring norm change - such as institutions or committed
+activists - have been identified since a long time, little is known about how a
+population adopts a new convention, due to the difficulties of finding
+representative data. Here we address this issue by looking at changes occurred
+to 2,541 orthographic and lexical norms in English and Spanish through the
+analysis of a large corpora of books published between the years 1800 and 2008.
+We detect three markedly distinct patterns in the data, depending on whether
+the behavioral change results from the action of a formal institution, an
+informal authority or a spontaneous process of unregulated evolution. We
+propose a simple evolutionary model able to capture all the observed behaviors
+and we show that it reproduces quantitatively the empirical data. This work
+identifies general mechanisms of norm change and we anticipate that it will be
+of interest to researchers investigating the cultural evolution of language
+and, more broadly, human collective behavior.
+",0,0,0,0,1,0
+17013,Bayesian Joint Spike-and-Slab Graphical Lasso,"  In this article, we propose a new class of priors for Bayesian inference with
+multiple Gaussian graphical models. We introduce fully Bayesian treatments of
+two popular procedures, the group graphical lasso and the fused graphical
+lasso, and extend them to a continuous spike-and-slab framework to allow
+self-adaptive shrinkage and model selection simultaneously. We develop an EM
+algorithm that performs fast and dynamic explorations of posterior modes. Our
+approach selects sparse models efficiently with substantially smaller bias than
+would be induced by alternative regularization procedures. The performance of
+the proposed methods are demonstrated through simulation and two real data
+examples.
+",0,0,0,1,0,0
+17014,Variations on the theme of the uniform boundary condition,"  The uniform boundary condition in a normed chain complex asks for a uniform
+linear bound on fillings of null-homologous cycles. For the $\ell^1$-norm on
+the singular chain complex, Matsumoto and Morita established a characterisation
+of the uniform boundary condition in terms of bounded cohomology. In
+particular, spaces with amenable fundamental group satisfy the uniform boundary
+condition in every degree. We will give an alternative proof of statements of
+this type, using geometric F{\o}lner arguments on the chain level instead of
+passing to the dual cochain complex. These geometric methods have the advantage
+that they also lead to integral refinements. In particular, we obtain
+applications in the context of integral foliated simplicial volume.
+",0,0,1,0,0,0
+17015,revisit: a Workflow Tool for Data Science,"  In recent years there has been widespread concern in the scientific community
+over a reproducibility crisis. Among the major causes that have been identified
+is statistical: In many scientific research the statistical analysis (including
+data preparation) suffers from a lack of transparency and methodological
+problems, major obstructions to reproducibility. The revisit package aims
+toward remedying this problem, by generating a ""software paper trail"" of the
+statistical operations applied to a dataset. This record can be ""replayed"" for
+verification purposes, as well as be modified to enable alternative analyses.
+The software also issues warnings of certain kinds of potential errors in
+statistical methodology, again related to the reproducibility issue.
+",1,0,0,1,0,0
+17016,Programmatically Interpretable Reinforcement Learning,"  We present a reinforcement learning framework, called Programmatically
+Interpretable Reinforcement Learning (PIRL), that is designed to generate
+interpretable and verifiable agent policies. Unlike the popular Deep
+Reinforcement Learning (DRL) paradigm, which represents policies by neural
+networks, PIRL represents policies using a high-level, domain-specific
+programming language. Such programmatic policies have the benefits of being
+more easily interpreted than neural networks, and being amenable to
+verification by symbolic methods. We propose a new method, called Neurally
+Directed Program Search (NDPS), for solving the challenging nonsmooth
+optimization problem of finding a programmatic policy with maximal reward. NDPS
+works by first learning a neural policy network using DRL, and then performing
+a local search over programmatic policies that seeks to minimize a distance
+from this neural ""oracle"". We evaluate NDPS on the task of learning to drive a
+simulated car in the TORCS car-racing environment. We demonstrate that NDPS is
+able to discover human-readable policies that pass some significant performance
+bars. We also show that PIRL policies can have smoother trajectories, and can
+be more easily transferred to environments not encountered during training,
+than corresponding policies discovered by DRL.
+",1,0,0,1,0,0
+17017,Kinetic Simulation of Collisional Magnetized Plasmas with Semi-Implicit Time Integration,"  Plasmas with varying collisionalities occur in many applications, such as
+tokamak edge regions, where the flows are characterized by significant
+variations in density and temperature. While a kinetic model is necessary for
+weakly-collisional high-temperature plasmas, high collisionality in colder
+regions render the equations numerically stiff due to disparate time scales. In
+this paper, we propose an implicit-explicit algorithm for such cases, where the
+collisional term is integrated implicitly in time, while the advective term is
+integrated explicitly in time, thus allowing time step sizes that are
+comparable to the advective time scales. This partitioning results in a more
+efficient algorithm than those using explicit time integrators, where the time
+step sizes are constrained by the stiff collisional time scales. We implement
+semi-implicit additive Runge-Kutta methods in COGENT, a finite-volume
+gyrokinetic code for mapped, multiblock grids and test the accuracy,
+convergence, and computational cost of these semi-implicit methods for test
+cases with highly-collisional plasmas.
+",1,1,0,0,0,0
+17018,VC-dimension of short Presburger formulas,"  We study VC-dimension of short formulas in Presburger Arithmetic, defined to
+have a bounded number of variables, quantifiers and atoms. We give both lower
+and upper bounds, which are tight up to a polynomial factor in the bit length
+of the formula.
+",1,0,1,0,0,0
+17019,Real-time Traffic Accident Risk Prediction based on Frequent Pattern Tree,"  Traffic accident data are usually noisy, contain missing values, and
+heterogeneous. How to select the most important variables to improve real-time
+traffic accident risk prediction has become a concern of many recent studies.
+This paper proposes a novel variable selection method based on the Frequent
+Pattern tree (FP tree) algorithm. First, all the frequent patterns in the
+traffic accident dataset are discovered. Then for each frequent pattern, a new
+criterion, called the Relative Object Purity Ratio (ROPR) which we proposed, is
+calculated. This ROPR is added to the importance score of the variables that
+differentiate one frequent pattern from the others. To test the proposed
+method, a dataset was compiled from the traffic accidents records detected by
+only one detector on interstate highway I-64 in Virginia in 2005. This dataset
+was then linked to other variables such as real-time traffic information and
+weather conditions. Both the proposed method based on the FP tree algorithm, as
+well as the widely utilized, random forest method, were then used to identify
+the important variables or the Virginia dataset. The results indicate that
+there are some differences between the variables deemed important by the FP
+tree and those selected by the random forest method. Following this, two
+baseline models (i.e. a nearest neighbor (k-NN) method and a Bayesian network)
+were developed to predict accident risk based on the variables identified by
+both the FP tree method and the random forest method. The results show that the
+models based on the variable selection using the FP tree performed better than
+those based on the random forest method for several versions of the k-NN and
+Bayesian network models.The best results were derived from a Bayesian network
+model using variables from FP tree. That model could predict 61.11% of
+accidents accurately while having a false alarm rate of 38.16%.
+",1,0,0,1,0,0
+17020,Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migration,"  Third-party library reuse has become common practice in contemporary software
+development, as it includes several benefits for developers. Library
+dependencies are constantly evolving, with newly added features and patches
+that fix bugs in older versions. To take full advantage of third-party reuse,
+developers should always keep up to date with the latest versions of their
+library dependencies. In this paper, we investigate the extent of which
+developers update their library dependencies. Specifically, we conducted an
+empirical study on library migration that covers over 4,600 GitHub software
+projects and 2,700 library dependencies. Results show that although many of
+these systems rely heavily on dependencies, 81.5% of the studied systems still
+keep their outdated dependencies. In the case of updating a vulnerable
+dependency, the study reveals that affected developers are not likely to
+respond to a security advisory. Surveying these developers, we find that 69% of
+the interviewees claim that they were unaware of their vulnerable dependencies.
+Furthermore, developers are not likely to prioritize library updates, citing it
+as extra effort and added responsibility. This study concludes that even though
+third-party reuse is commonplace, the practice of updating a dependency is not
+as common for many developers.
+",1,0,0,0,0,0
+17021,Is Smaller Better: A Proposal To Consider Bacteria For Biologically Inspired Modeling,"  Bacteria are easily characterizable model organisms with an impressively
+complicated set of capabilities. Among their capabilities is quorum sensing, a
+detailed cell-cell signaling system that may have a common origin with
+eukaryotic cell-cell signaling. Not only are the two phenomena similar, but
+quorum sensing, as is the case with any bacterial phenomenon when compared to
+eukaryotes, is also easier to study in depth than eukaryotic cell-cell
+signaling. This ease of study is a contrast to the only partially understood
+cellular dynamics of neurons. Here we review the literature on the strikingly
+neuron-like qualities of bacterial colonies and biofilms, including ion-based
+and hormonal signaling, and action potential-like behavior. This allows them to
+feasibly act as an analog for neurons that could produce more detailed and more
+accurate biologically-based computational models. Using bacteria as the basis
+for biologically feasible computational models may allow models to better
+harness the tremendous ability of biological organisms to make decisions and
+process information. Additionally, principles gleaned from bacterial function
+have the potential to influence computational efforts divorced from biology,
+just as neuronal function has in the abstract influenced countless machine
+learning efforts.
+",1,0,0,0,0,0
+17022,A Bayesian Data Augmentation Approach for Learning Deep Models,"  Data augmentation is an essential part of the training process applied to
+deep learning models. The motivation is that a robust training process for deep
+learning models depends on large annotated datasets, which are expensive to be
+acquired, stored and processed. Therefore a reasonable alternative is to be
+able to automatically generate new annotated training samples using a process
+known as data augmentation. The dominant data augmentation approach in the
+field assumes that new training samples can be obtained via random geometric or
+appearance transformations applied to annotated training samples, but this is a
+strong assumption because it is unclear if this is a reliable generative model
+for producing new training samples. In this paper, we provide a novel Bayesian
+formulation to data augmentation, where new annotated training points are
+treated as missing variables and generated based on the distribution learned
+from the training set. For learning, we introduce a theoretically sound
+algorithm --- generalised Monte Carlo expectation maximisation, and demonstrate
+one possible implementation via an extension of the Generative Adversarial
+Network (GAN). Classification results on MNIST, CIFAR-10 and CIFAR-100 show the
+better performance of our proposed method compared to the current dominant data
+augmentation approach mentioned above --- the results also show that our
+approach produces better classification results than similar GAN models.
+",1,0,0,0,0,0
+17023,Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach,"  Knowledge bases are employed in a variety of applications from natural
+language processing to semantic web search; alas, in practice their usefulness
+is hurt by their incompleteness. Embedding models attain state-of-the-art
+accuracy in knowledge base completion, but their predictions are notoriously
+hard to interpret. In this paper, we adapt ""pedagogical approaches"" (from the
+literature on neural networks) so as to interpret embedding models by
+extracting weighted Horn rules from them. We show how pedagogical approaches
+have to be adapted to take upon the large-scale relational aspects of knowledge
+bases and show experimentally their strengths and weaknesses.
+",0,0,0,1,0,0
+17024,Parameterized complexity of machine scheduling: 15 open problems,"  Machine scheduling problems are a long-time key domain of algorithms and
+complexity research. A novel approach to machine scheduling problems are
+fixed-parameter algorithms. To stimulate this thriving research direction, we
+propose 15 open questions in this area whose resolution we expect to lead to
+the discovery of new approaches and techniques both in scheduling and
+parameterized complexity theory.
+",1,0,0,0,0,0
+17025,"Potential Conditional Mutual Information: Estimators, Properties and Applications","  The conditional mutual information I(X;Y|Z) measures the average information
+that X and Y contain about each other given Z. This is an important primitive
+in many learning problems including conditional independence testing, graphical
+model inference, causal strength estimation and time-series problems. In
+several applications, it is desirable to have a functional purely of the
+conditional distribution p_{Y|X,Z} rather than of the joint distribution
+p_{X,Y,Z}. We define the potential conditional mutual information as the
+conditional mutual information calculated with a modified joint distribution
+p_{Y|X,Z} q_{X,Z}, where q_{X,Z} is a potential distribution, fixed airport. We
+develop K nearest neighbor based estimators for this functional, employing
+importance sampling, and a coupling trick, and prove the finite k consistency
+of such an estimator. We demonstrate that the estimator has excellent practical
+performance and show an application in dynamical system inference.
+",1,0,0,1,0,0
+17026,"A new approach to divergences in quantum electrodynamics, concrete examples","  An interesting attempt for solving infrared divergence problems via the
+theory of generalized wave operators was made by P. Kulish and L. Faddeev. Our
+method of using the ideas from the theory of generalized wave operators is
+essentially different. We assume that the unperturbed operator $A_0$ is known
+and that the scattering operator $S$ and the unperturbed operator $A_0$ are
+permutable. (In the Kulish-Faddeev theory this basic property is not
+fulfilled.) The permutability of $S$ and $A_0$ gives us an important
+information about the structure of the scattering operator. We show that the
+divergences appeared because the deviations of the initial and final waves from
+the free waves were not taken into account. The approach is demonstrated on
+important examples.
+",0,0,1,0,0,0
+17027,Indefinite boundary value problems on graphs,"  We consider the spectral structure of indefinite second order boundary-value
+problems on graphs. A variational formulation for such boundary-value problems
+on graphs is given and we obtain both full and half-range completeness results.
+This leads to a max-min principle and as a consequence we can formulate an
+analogue of Dirichlet-Neumann bracketing and this in turn gives rise to
+asymptotic approximations for the eigenvalues.
+",0,0,1,0,0,0
+17028,Integral curvatures of Finsler manifolds and applications,"  In this paper, we study the integral curvatures of Finsler manifolds. Some
+Bishop-Gromov relative volume comparisons and several Myers type theorems are
+obtained. We also establish a Gromov type precompactness theorem and a
+Yamaguchi type finiteness theorem. Furthermore, the isoperimetric and Sobolev
+constants of a closed Finsler manifold are estimated by integral curvature
+bounds.
+",0,0,1,0,0,0
+17029,L-functions and sharp resonances of infinite index congruence subgroups of $SL_2(\mathbb{Z})$,"  For convex co-compact subgroups of SL2(Z) we consider the ""congruence
+subgroups"" for p prime. We prove a factorization formula for the Selberg zeta
+function in term of L-functions related to irreducible representations of the
+Galois group SL2(Fp) of the covering, together with a priori bounds and
+analytic continuation. We use this factorization property combined with an
+averaging technique over representations to prove a new existence result of
+non-trivial resonances in an effective low frequency strip.
+",0,0,1,0,0,0
+17030,An Enhanced Lumped Element Electrical Model of a Double Barrier Memristive Device,"  The massive parallel approach of neuromorphic circuits leads to effective
+methods for solving complex problems. It has turned out that resistive
+switching devices with a continuous resistance range are potential candidates
+for such applications. These devices are memristive systems - nonlinear
+resistors with memory. They are fabricated in nanotechnology and hence
+parameter spread during fabrication may aggravate reproducible analyses. This
+issue makes simulation models of memristive devices worthwhile.
+Kinetic Monte-Carlo simulations based on a distributed model of the device
+can be used to understand the underlying physical and chemical phenomena.
+However, such simulations are very time-consuming and neither convenient for
+investigations of whole circuits nor for real-time applications, e.g. emulation
+purposes. Instead, a concentrated model of the device can be used for both fast
+simulations and real-time applications, respectively. We introduce an enhanced
+electrical model of a valence change mechanism (VCM) based double barrier
+memristive device (DBMD) with a continuous resistance range. This device
+consists of an ultra-thin memristive layer sandwiched between a tunnel barrier
+and a Schottky-contact. The introduced model leads to very fast simulations by
+using usual circuit simulation tools while maintaining physically meaningful
+parameters.
+Kinetic Monte-Carlo simulations based on a distributed model and experimental
+data have been utilized as references to verify the concentrated model.
+",1,1,0,0,0,0
+17031,Non-perturbative positive Lyapunov exponent of Schrödinger equations and its applications to skew-shift,"  We first study the discrete Schrödinger equations with analytic potentials
+given by a class of transformations. It is shown that if the coupling number is
+large, then its logarithm equals approximately to the Lyapunov exponents. When
+the transformation becomes the skew-shift, we prove that the Lyapunov exponent
+is week Hölder continuous, and the spectrum satisfies Anderson Localization
+and contains large intervals. Moreover, all of these conclusions are
+non-perturbative.
+",0,0,1,0,0,0
+17032,Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees,"  Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolfe
+(FW) algorithms regained popularity in recent years due to their simplicity,
+effectiveness and theoretical guarantees. MP and FW address optimization over
+the linear span and the convex hull of a set of atoms, respectively. In this
+paper, we consider the intermediate case of optimization over the convex cone,
+parametrized as the conic hull of a generic atom set, leading to the first
+principled definitions of non-negative MP algorithms for which we give explicit
+convergence rates and demonstrate excellent empirical performance. In
+particular, we derive sublinear ($\mathcal{O}(1/t)$) convergence on general
+smooth and convex objectives, and linear convergence ($\mathcal{O}(e^{-t})$) on
+strongly convex objectives, in both cases for general sets of atoms.
+Furthermore, we establish a clear correspondence of our algorithms to known
+algorithms from the MP and FW literature. Our novel algorithms and analyses
+target general atom sets and general objective functions, and hence are
+directly applicable to a large variety of learning settings.
+",1,0,0,1,0,0
+17033,Probing the accretion disc structure by the twin kHz QPOs and spins of neutron stars in LMXBs,"  We analyze the relation between the emission radii of twin kilohertz
+quasi-periodic oscillations (kHz QPOs) and the co-rotation radii of the 12
+neutron star low mass X-ray binaries (NS-LMXBs) which are simultaneously
+detected with the twin kHz QPOs and NS spins. We find that the average
+co-rotation radius of these sources is r_co about 32 km, and all the emission
+positions of twin kHz QPOs lie inside the corotation radii, indicating that the
+twin kHz QPOs are formed in the spin-up process. It is noticed that the upper
+frequency of twin kHz QPOs is higher than NS spin frequency by > 10%, which may
+account for a critical velocity difference between the Keplerian motion of
+accretion matter and NS spin that is corresponding to the production of twin
+kHz QPOs. In addition, we also find that about 83% of twin kHz QPOs cluster
+around the radius range of 15-20 km, which may be affected by the hard surface
+or the local strong magnetic field of NS. As a special case, SAX J1808.4-3658
+shows the larger emission radii of twin kHz QPOs of r about 21-24 km, which may
+be due to its low accretion rate or small measured NS mass (< 1.4 solar mass).
+",0,1,0,0,0,0
+17034,Can scientists and their institutions become their own open access publishers?,"  This article offers a personal perspective on the current state of academic
+publishing, and posits that the scientific community is beset with journals
+that contribute little valuable knowledge, overload the community's capacity
+for high-quality peer review, and waste resources. Open access publishing can
+offer solutions that benefit researchers and other information users, as well
+as institutions and funders, but commercial journal publishers have influenced
+open access policies and practices in ways that favor their economic interests
+over those of other stakeholders in knowledge creation and sharing. One way to
+free research from constraints on access is the diamond route of open access
+publishing, in which institutions and funders that produce new knowledge
+reclaim responsibility for publication via institutional journals or other open
+platforms. I argue that research journals (especially those published for
+profit) may no longer be fit for purpose, and hope that readers will consider
+whether the time has come to put responsibility for publishing back into the
+hands of researchers and their institutions. The potential advantages and
+challenges involved in a shift away from for-profit journals in favor of
+institutional open access publishing are explored.
+",1,0,0,0,0,0
+17035,Character sums for elliptic curve densities,"  If $E$ is an elliptic curve over $\mathbb{Q}$, then it follows from work of
+Serre and Hooley that, under the assumption of the Generalized Riemann
+Hypothesis, the density of primes $p$ such that the group of
+$\mathbb{F}_p$-rational points of the reduced curve $\tilde{E}(\mathbb{F}_p)$
+is cyclic can be written as an infinite product $\prod \delta_\ell$ of local
+factors $\delta_\ell$ reflecting the degree of the $\ell$-torsion fields,
+multiplied by a factor that corrects for the entanglements between the various
+torsion fields. We show that this correction factor can be interpreted as a
+character sum, and the resulting description allows us to easily determine
+non-vanishing criteria for it. We apply this method in a variety of other
+settings. Among these, we consider the aforementioned problem with the
+additional condition that the primes $p$ lie in a given arithmetic progression.
+We also study the conjectural constants appearing in Koblitz's conjecture, a
+conjecture which relates to the density of primes $p$ for which the cardinality
+of the group of $\mathbb{F}_p$-points of $E$ is prime.
+",0,0,1,0,0,0
+17036,A monolithic fluid-structure interaction formulation for solid and liquid membranes including free-surface contact,"  A unified fluid-structure interaction (FSI) formulation is presented for
+solid, liquid and mixed membranes. Nonlinear finite elements (FE) and the
+generalized-alpha scheme are used for the spatial and temporal discretization.
+The membrane discretization is based on curvilinear surface elements that can
+describe large deformations and rotations, and also provide a straightforward
+description for contact. The fluid is described by the incompressible
+Navier-Stokes equations, and its discretization is based on stabilized
+Petrov-Galerkin FE. The coupling between fluid and structure uses a conforming
+sharp interface discretization, and the resulting non-linear FE equations are
+solved monolithically within the Newton-Raphson scheme. An arbitrary
+Lagrangian-Eulerian formulation is used for the fluid in order to account for
+the mesh motion around the structure. The formulation is very general and
+admits diverse applications that include contact at free surfaces. This is
+demonstrated by two analytical and three numerical examples exhibiting strong
+coupling between fluid and structure. The examples include balloon inflation,
+droplet rolling and flapping flags. They span a Reynolds-number range from
+0.001 to 2000. One of the examples considers the extension to rotation-free
+shells using isogeometric FE.
+",1,1,0,0,0,0
+17037,Different Non-extensive Models for heavy-ion collisions,"  The transverse momentum ($p_T$) spectra from heavy-ion collisions at
+intermediate momenta are described by non-extensive statistical models.
+Assuming a fixed relative variance of the temperature fluctuating event by
+event or alternatively a fixed mean multiplicity in a negative binomial
+distribution (NBD), two different linear relations emerge between the
+temperature, $T$, and the Tsallis parameter $q-1$. Our results qualitatively
+agree with that of G.~Wilk. Furthermore we revisit the ""Soft+Hard"" model,
+proposed recently by G.~G.~Barnaföldi \textit{et.al.}, by a $T$-independent
+average $p_T^2$ assumption. Finally we compare results with those predicted by
+another deformed distribution, using Kaniadakis' $\kappa$ parametrization.
+",0,1,0,0,0,0
+17038,Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees,"  Toxicity prediction of chemical compounds is a grand challenge. Lately, it
+achieved significant progress in accuracy but using a huge set of features,
+implementing a complex blackbox technique such as a deep neural network, and
+exploiting enormous computational resources. In this paper, we strongly argue
+for the models and methods that are simple in machine learning characteristics,
+efficient in computing resource usage, and powerful to achieve very high
+accuracy levels. To demonstrate this, we develop a single task-based chemical
+toxicity prediction framework using only 2D features that are less compute
+intensive. We effectively use a decision tree to obtain an optimum number of
+features from a collection of thousands of them. We use a shallow neural
+network and jointly optimize it with decision tree taking both network
+parameters and input features into account. Our model needs only a minute on a
+single CPU for its training while existing methods using deep neural networks
+need about 10 min on NVidia Tesla K40 GPU. However, we obtain similar or better
+performance on several toxicity benchmark tasks. We also develop a cumulative
+feature ranking method which enables us to identify features that can help
+chemists perform prescreening of toxic compounds effectively.
+",1,0,0,1,0,0
+17039,Minmax Hierarchies and Minimal Surfaces in Manifolds,"  We introduce a general scheme that permits to generate successive min-max
+problems for producing critical points of higher and higher indices to
+Palais-Smale Functionals in Banach manifolds equipped with Finsler structures.
+We call the resulting tree of minmax problems a minmax hierarchy. Using the
+viscosity approach to the minmax theory of minimal surfaces introduced by the
+author in a series of recent works, we explain how this scheme can be deformed
+for producing smooth minimal surfaces of strictly increasing area in arbitrary
+codimension. We implement this scheme to the case of the $3-$dimensional
+sphere. In particular we are giving a min-max characterization of the Clifford
+Torus and conjecture what are the next minimal surfaces to come in the $S^3$
+hierarchy. Among other results we prove here the lower semi continuity of the
+Morse Index in the viscosity method below an area level.
+",0,0,1,0,0,0
+17040,Nonseparable Multinomial Choice Models in Cross-Section and Panel Data,"  Multinomial choice models are fundamental for empirical modeling of economic
+choices among discrete alternatives. We analyze identification of binary and
+multinomial choice models when the choice utilities are nonseparable in
+observed attributes and multidimensional unobserved heterogeneity with
+cross-section and panel data. We show that derivatives of choice probabilities
+with respect to continuous attributes are weighted averages of utility
+derivatives in cross-section models with exogenous heterogeneity. In the
+special case of random coefficient models with an independent additive effect,
+we further characterize that the probability derivative at zero is proportional
+to the population mean of the coefficients. We extend the identification
+results to models with endogenous heterogeneity using either a control function
+or panel data. In time stationary panel models with two periods, we find that
+differences over time of derivatives of choice probabilities identify utility
+derivatives ""on the diagonal,"" i.e. when the observed attributes take the same
+values in the two periods. We also show that time stationarity does not
+identify structural derivatives ""off the diagonal"" both in continuous and
+multinomial choice panel models.
+",0,0,0,1,0,0
+17041,Corona limits of tilings : Periodic case,"  We study the limit shape of successive coronas of a tiling, which models the
+growth of crystals. We define basic terminologies and discuss the existence and
+uniqueness of corona limits, and then prove that corona limits are completely
+characterized by directional speeds. As an application, we give another proof
+that the corona limit of a periodic tiling is a centrally symmetric convex
+polyhedron (see [Zhuravlev 2001], [Maleev-Shutov 2011]).
+",0,0,1,0,0,0
+17042,The Spatial Range of Conformity,"  Properties of galaxies like their absolute magnitude and their stellar mass
+content are correlated. These correlations are tighter for close pairs of
+galaxies, which is called galactic conformity. In hierarchical structure
+formation scenarios, galaxies form within dark matter halos. To explain the
+amplitude and the spatial range of galactic conformity two--halo terms or
+assembly bias become important. With the scale dependent correlation
+coefficients the amplitude and the spatial range of conformity are determined
+from galaxy and halo samples. The scale dependent correlation coefficients are
+introduced as a new descriptive statistic to quantify the correlations between
+properties of galaxies or halos, depending on the distances to other galaxies
+or halos. These scale dependent correlation coefficients can be applied to the
+galaxy distribution directly. Neither a splitting of the sample into
+subsamples, nor an a priori clustering is needed. This new descriptive
+statistic is applied to galaxy catalogues derived from the Sloan Digital Sky
+Survey III and to halo catalogues from the MultiDark simulations. In the galaxy
+sample the correlations between absolute Magnitude, velocity dispersion,
+ellipticity, and stellar mass content are investigated. The correlations of
+mass, spin, and ellipticity are explored in the halo samples. Both for galaxies
+and halos a scale dependent conformity is confirmed. Moreover the scale
+dependent correlation coefficients reveal a signal of conformity out to 40Mpc
+and beyond. The halo and galaxy samples show a differing amplitude and range of
+conformity.
+",0,1,0,0,0,0
+17043,Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis,"  Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of
+Stochastic Gradient Descent, where properly scaled isotropic Gaussian noise is
+added to an unbiased estimate of the gradient at each iteration. This modest
+change allows SGLD to escape local minima and suffices to guarantee asymptotic
+convergence to global minimizers for sufficiently regular non-convex objectives
+(Gelfand and Mitter, 1991). The present work provides a nonasymptotic analysis
+in the context of non-convex learning problems, giving finite-time guarantees
+for SGLD to find approximate minimizers of both empirical and population risks.
+As in the asymptotic setting, our analysis relates the discrete-time SGLD
+Markov chain to a continuous-time diffusion process. A new tool that drives the
+results is the use of weighted transportation cost inequalities to quantify the
+rate of convergence of SGLD to a stationary distribution in the Euclidean
+$2$-Wasserstein distance.
+",1,0,1,1,0,0
+17044,Multilevel preconditioner of Polynomial Chaos Method for quantifying uncertainties in a blood pump,"  More than 23 million people are suffered by Heart failure worldwide. Despite
+the modern transplant operation is well established, the lack of heart
+donations becomes a big restriction on transplantation frequency. With respect
+to this matter, ventricular assist devices (VADs) can play an important role in
+supporting patients during waiting period and after the surgery. Moreover, it
+has been shown that VADs by means of blood pump have advantages for working
+under different conditions. While a lot of work has been done on modeling the
+functionality of the blood pump, but quantifying uncertainties in a numerical
+model is a challenging task. We consider the Polynomial Chaos (PC) method,
+which is introduced by Wiener for modeling stochastic process with Gaussian
+distribution. The Galerkin projection, the intrusive version of the generalized
+Polynomial Chaos (gPC), has been densely studied and applied for various
+problems. The intrusive Galerkin approach could represent stochastic process
+directly at once with Polynomial Chaos series expansions, it would therefore
+optimize the total computing effort comparing with classical non-intrusive
+methods. We compared different preconditioning techniques for a steady state
+simulation of a blood pump configuration in our previous work, the comparison
+shows that an inexact multilevel preconditioner has a promising performance. In
+this work, we show an instationary blood flow through a FDA blood pump
+configuration with Galerkin Projection method, which is implemented in our open
+source Finite Element library Hiflow3. Three uncertainty sources are
+considered: inflow boundary condition, rotor angular speed and dynamic
+viscosity, the numerical results are demonstrated with more than 30 Million
+degrees of freedom by using supercomputer.
+",0,1,0,0,0,0
+17045,Superradiant Mott Transition,"  The combination of strong correlation and emergent lattice can be achieved
+when quantum gases are confined in a superradiant Fabry-Perot cavity. In
+addition to the discoveries of exotic phases, such as density wave ordered Mott
+insulator and superfluid, a surprising kink structure is found in the slope of
+the cavity strength as a function of the pumping strength. In this Letter, we
+show that the appearance of such a kink is a manifestation of a liquid-gas like
+transition between two superfluids with different densities. The slopes in the
+immediate neighborhood of the kink become divergent at the liquid-gas critical
+points and display a critical scaling law with a critical exponent 1 in the
+quantum critical region. Our predictions could be tested in current
+experimental set-up.
+",0,1,0,0,0,0
+17046,Communication via FRET in Nanonetworks of Mobile Proteins,"  A practical, biologically motivated case of protein complexes (immunoglobulin
+G and FcRII receptors) moving on the surface of mastcells, that are common
+parts of an immunological system, is investigated. Proteins are considered as
+nanomachines creating a nanonetwork. Accurate molecular models of the proteins
+and the fluorophores which act as their nanoantennas are used to simulate the
+communication between the nanomachines when they are close to each other. The
+theory of diffusion-based Brownian motion is applied to model movements of the
+proteins. It is assumed that fluorophore molecules send and receive signals
+using the Forster Resonance Energy Transfer. The probability of the efficient
+signal transfer and the respective bit error rate are calculated and discussed.
+",0,0,0,0,1,0
+17047,"Multivariate generalized Pareto distributions: parametrizations, representations, and properties","  Multivariate generalized Pareto distributions arise as the limit
+distributions of exceedances over multivariate thresholds of random vectors in
+the domain of attraction of a max-stable distribution. These distributions can
+be parametrized and represented in a number of different ways. Moreover,
+generalized Pareto distributions enjoy a number of interesting stability
+properties. An overview of the main features of such distributions are given,
+expressed compactly in several parametrizations, giving the potential user of
+these distributions a convenient catalogue of ways to handle and work with
+generalized Pareto distributions.
+",0,0,1,1,0,0
+17048,Invertibility of spectral x-ray data with pileup--two dimension-two spectrum case,"  In the Alvarez-Macovski method, the line integrals of the x-ray basis set
+coefficients are computed from measurements with multiple spectra. An important
+question is whether the transformation from measurements to line integrals is
+invertible. This paper presents a proof that for a system with two spectra and
+a photon counting detector, pileup does not affect the invertibility of the
+system. If the system is invertible with no pileup, it will remain invertible
+with pileup although the reduced Jacobian may lead to increased noise.
+",0,1,0,0,0,0
+17049,Steinberg representations and harmonic cochains for split adjoint quasi-simple groups,"  Let $G$ be an adjoint quasi-simple group defined and split over a
+non-archimedean local field $K$. We prove that the dual of the Steinberg
+representation of $G$ is isomorphic to a certain space of harmonic cochains on
+the Bruhat-Tits building of $G$. The Steinberg representation is considered
+with coefficients in any commutative ring.
+",0,0,1,0,0,0
+17050,Lorentzian surfaces and the curvature of the Schmidt metric,"  The b-boundary is a mathematical tool used to attach a topological boundary
+to incomplete Lorentzian manifolds using a Riemaniann metric called the Schmidt
+metric on the frame bundle. In this paper, we give the general form of the
+Schmidt metric in the case of Lorentzian surfaces. Furthermore, we write the
+Ricci scalar of the Schmidt metric in terms of the Ricci scalar of the
+Lorentzian manifold and give some examples. Finally, we discuss some
+applications to general relativity.
+",0,0,1,0,0,0
+17051,Mixed Precision Solver Scalable to 16000 MPI Processes for Lattice Quantum Chromodynamics Simulations on the Oakforest-PACS System,"  Lattice Quantum Chromodynamics (Lattice QCD) is a quantum field theory on a
+finite discretized space-time box so as to numerically compute the dynamics of
+quarks and gluons to explore the nature of subatomic world. Solving the
+equation of motion of quarks (quark solver) is the most compute-intensive part
+of the lattice QCD simulations and is one of the legacy HPC applications. We
+have developed a mixed-precision quark solver for a large Intel Xeon Phi (KNL)
+system named ""Oakforest-PACS"", employing the $O(a)$-improved Wilson quarks as
+the discretized equation of motion. The nested-BiCGSTab algorithm for the
+solver was implemented and optimized using mixed-precision,
+communication-computation overlapping with MPI-offloading, SIMD vectorization,
+and thread stealing techniques. The solver achieved 2.6 PFLOPS in the
+single-precision part on a $400^3\times 800$ lattice using 16000 MPI processes
+on 8000 nodes on the system.
+",0,1,0,0,0,0
+17052,A spectral/hp element MHD solver,"  A new MHD solver, based on the Nektar++ spectral/hp element framework, is
+presented in this paper. The velocity and electric potential quasi-static MHD
+model is used. The Hartmann flow in plane channel and its stability, the
+Hartmann flow in rectangular duct, and the stability of Hunt's flow are
+explored as examples. Exponential convergence is achieved and the resulting
+numerical values were found to have an accuracy up to $10^{-12}$ for the state
+flows compared to an exact solution, and $10^{-5}$ for the stability
+eigenvalues compared to independent numerical results.
+",0,1,0,0,0,0
+17053,"Journalists' information needs, seeking behavior, and its determinants on social media","  We describe the results of a qualitative study on journalists' information
+seeking behavior on social media. Based on interviews with eleven journalists
+along with a study of a set of university level journalism modules, we
+determined the categories of information need types that lead journalists to
+social media. We also determined the ways that social media is exploited as a
+tool to satisfy information needs and to define influential factors, which
+impacted on journalists' information seeking behavior. We find that not only is
+social media used as an information source, but it can also be a supplier of
+stories found serendipitously. We find seven information need types that expand
+the types found in previous work. We also find five categories of influential
+factors that affect the way journalists seek information.
+",1,0,0,0,0,0
+17054,Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models,"  The problem of low rank matrix completion is considered in this paper. To
+exploit the underlying low-rank structure of the data matrix, we propose a
+hierarchical Gaussian prior model, where columns of the low-rank matrix are
+assumed to follow a Gaussian distribution with zero mean and a common precision
+matrix, and a Wishart distribution is specified as a hyperprior over the
+precision matrix. We show that such a hierarchical Gaussian prior has the
+potential to encourage a low-rank solution. Based on the proposed hierarchical
+prior model, a variational Bayesian method is developed for matrix completion,
+where the generalized approximate massage passing (GAMP) technique is embedded
+into the variational Bayesian inference in order to circumvent cumbersome
+matrix inverse operations. Simulation results show that our proposed method
+demonstrates superiority over existing state-of-the-art matrix completion
+methods.
+",1,0,0,1,0,0
+17055,Emergence of superconductivity in the canonical heavy-electron metal YbRh2Si2,"  We report magnetic and calorimetric measurements down to T = 1 mK on the
+canonical heavy-electron metal YbRh2Si2. The data reveal the development of
+nuclear antiferromagnetic order slightly above 2 mK. The latter weakens the
+primary electronic antiferromagnetism, thereby paving the way for
+heavy-electron superconductivity below Tc = 2 mK. Our results demonstrate that
+superconductivity driven by quantum criticality is a general phenomenon.
+",0,1,0,0,0,0
+17056,Obtaining a Proportional Allocation by Deleting Items,"  We consider the following control problem on fair allocation of indivisible
+goods. Given a set $I$ of items and a set of agents, each having strict linear
+preference over the items, we ask for a minimum subset of the items whose
+deletion guarantees the existence of a proportional allocation in the remaining
+instance; we call this problem Proportionality by Item Deletion (PID). Our main
+result is a polynomial-time algorithm that solves PID for three agents. By
+contrast, we prove that PID is computationally intractable when the number of
+agents is unbounded, even if the number $k$ of item deletions allowed is small,
+since the problem turns out to be W[3]-hard with respect to the parameter $k$.
+Additionally, we provide some tight lower and upper bounds on the complexity of
+PID when regarded as a function of $|I|$ and $k$.
+",1,0,0,0,0,0
+17057,DeepFace: Face Generation using Deep Learning,"  We use CNNs to build a system that both classifies images of faces based on a
+variety of different facial attributes and generates new faces given a set of
+desired facial characteristics. After introducing the problem and providing
+context in the first section, we discuss recent work related to image
+generation in Section 2. In Section 3, we describe the methods used to
+fine-tune our CNN and generate new images using a novel approach inspired by a
+Gaussian mixture model. In Section 4, we discuss our working dataset and
+describe our preprocessing steps and handling of facial attributes. Finally, in
+Sections 5, 6 and 7, we explain our experiments and results and conclude in the
+following section. Our classification system has 82\% test accuracy.
+Furthermore, our generation pipeline successfully creates well-formed faces.
+",1,0,0,0,0,0
+17058,High quality mesh generation using cross and asterisk fields: Application on coastal domains,"  This paper presents a method to generate high quality triangular or
+quadrilateral meshes that uses direction fields and a frontal point insertion
+strategy. Two types of direction fields are considered: asterisk fields and
+cross fields. With asterisk fields we generate high quality triangulations,
+while with cross fields we generate right-angled triangulations that are
+optimal for transformation to quadrilateral meshes. The input of our algorithm
+is an initial triangular mesh and a direction field calculated on it. The goal
+is to compute the vertices of the final mesh by an advancing front strategy
+along the direction field. We present an algorithm that enables to efficiently
+generate the points using solely information from the base mesh. A
+multi-threaded implementation of our algorithm is presented, allowing us to
+achieve significant speedup of the point generation. Regarding the
+quadrangulation process, we develop a quality criterion for right-angled
+triangles with respect to the local cross field and an optimization process
+based on it. Thus we are able to further improve the quality of the output
+quadrilaterals. The algorithm is demonstrated on the sphere and examples of
+high quality triangular and quadrilateral meshes of coastal domains are
+presented.
+",1,0,0,0,0,0
+17059,ELFI: Engine for Likelihood-Free Inference,"  Engine for Likelihood-Free Inference (ELFI) is a Python software library for
+performing likelihood-free inference (LFI). ELFI provides a convenient syntax
+for arranging components in LFI, such as priors, simulators, summaries or
+distances, to a network called ELFI graph. The components can be implemented in
+a wide variety of languages. The stand-alone ELFI graph can be used with any of
+the available inference methods without modifications. A central method
+implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference
+(BOLFI), which has recently been shown to accelerate likelihood-free inference
+up to several orders of magnitude by surrogate-modelling the distance. ELFI
+also has an inbuilt support for output data storing for reuse and analysis, and
+supports parallelization of computation from multiple cores up to a cluster
+environment. ELFI is designed to be extensible and provides interfaces for
+widening its functionality. This makes the adding of new inference methods to
+ELFI straightforward and automatically compatible with the inbuilt features.
+",1,0,0,1,0,0
+17060,Boosting Adversarial Attacks with Momentum,"  Deep neural networks are vulnerable to adversarial examples, which poses
+security concerns on these algorithms due to the potentially severe
+consequences. Adversarial attacks serve as an important surrogate to evaluate
+the robustness of deep learning models before they are deployed. However, most
+of existing adversarial attacks can only fool a black-box model with a low
+success rate. To address this issue, we propose a broad class of momentum-based
+iterative algorithms to boost adversarial attacks. By integrating the momentum
+term into the iterative process for attacks, our methods can stabilize update
+directions and escape from poor local maxima during the iterations, resulting
+in more transferable adversarial examples. To further improve the success rates
+for black-box attacks, we apply momentum iterative algorithms to an ensemble of
+models, and show that the adversarially trained models with a strong defense
+ability are also vulnerable to our black-box attacks. We hope that the proposed
+methods will serve as a benchmark for evaluating the robustness of various deep
+models and defense methods. With this method, we won the first places in NIPS
+2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack
+competitions.
+",1,0,0,1,0,0
+17061,Information spreading during emergencies and anomalous events,"  The most critical time for information to spread is in the aftermath of a
+serious emergency, crisis, or disaster. Individuals affected by such situations
+can now turn to an array of communication channels, from mobile phone calls and
+text messages to social media posts, when alerting social ties. These channels
+drastically improve the speed of information in a time-sensitive event, and
+provide extant records of human dynamics during and afterward the event.
+Retrospective analysis of such anomalous events provides researchers with a
+class of ""found experiments"" that may be used to better understand social
+spreading. In this chapter, we study information spreading due to a number of
+emergency events, including the Boston Marathon Bombing and a plane crash at a
+western European airport. We also contrast the different information which may
+be gleaned by social media data compared with mobile phone data and we estimate
+the rate of anomalous events in a mobile phone dataset using a proposed anomaly
+detection method.
+",1,1,0,0,0,0
+17062,Large-Scale Plant Classification with Deep Neural Networks,"  This paper discusses the potential of applying deep learning techniques for
+plant classification and its usage for citizen science in large-scale
+biodiversity monitoring. We show that plant classification using near
+state-of-the-art convolutional network architectures like ResNet50 achieves
+significant improvements in accuracy compared to the most widespread plant
+classification application in test sets composed of thousands of different
+species labels. We find that the predictions can be confidently used as a
+baseline classification in citizen science communities like iNaturalist (or its
+Spanish fork, Natusfera) which in turn can share their data with biodiversity
+portals like GBIF.
+",1,0,0,1,0,0
+17063,Deep Reinforcement Learning for General Video Game AI,"  The General Video Game AI (GVGAI) competition and its associated software
+framework provides a way of benchmarking AI algorithms on a large number of
+games written in a domain-specific description language. While the competition
+has seen plenty of interest, it has so far focused on online planning,
+providing a forward model that allows the use of algorithms such as Monte Carlo
+Tree Search.
+In this paper, we describe how we interface GVGAI to the OpenAI Gym
+environment, a widely used way of connecting agents to reinforcement learning
+problems. Using this interface, we characterize how widely used implementations
+of several deep reinforcement learning algorithms fare on a number of GVGAI
+games. We further analyze the results to provide a first indication of the
+relative difficulty of these games relative to each other, and relative to
+those in the Arcade Learning Environment under similar conditions.
+",0,0,0,1,0,0
+17064,Purely infinite labeled graph $C^*$-algebras,"  In this paper, we consider pure infiniteness of generalized Cuntz-Krieger
+algebras associated to labeled spaces $(E,\mathcal{L},\mathcal{E})$. It is
+shown that a $C^*$-algebra $C^*(E,\mathcal{L},\mathcal{E})$ is purely infinite
+in the sense that every nonzero hereditary subalgebra contains an infinite
+projection (we call this property (IH)) if $(E, \mathcal{L},\mathcal{E})$ is
+disagreeable and every vertex connects to a loop. We also prove that under the
+condition analogous to (K) for usual graphs,
+$C^*(E,\mathcal{L},\mathcal{E})=C^*(p_A, s_a)$ is purely infinite in the sense
+of Kirchberg and R{\o}rdam if and only if every generating projection $p_A$,
+$A\in \mathcal{E}$, is properly infinite, and also if and only if every
+quotient of $C^*(E,\mathcal{L},\mathcal{E})$ has the property (IH).
+",0,0,1,0,0,0
+17065,From safe screening rules to working sets for faster Lasso-type solvers,"  Convex sparsity-promoting regularizations are ubiquitous in modern
+statistical learning. By construction, they yield solutions with few non-zero
+coefficients, which correspond to saturated constraints in the dual
+optimization formulation. Working set (WS) strategies are generic optimization
+techniques that consist in solving simpler problems that only consider a subset
+of constraints, whose indices form the WS. Working set methods therefore
+involve two nested iterations: the outer loop corresponds to the definition of
+the WS and the inner loop calls a solver for the subproblems. For the Lasso
+estimator a WS is a set of features, while for a Group Lasso it refers to a set
+of groups. In practice, WS are generally small in this context so the
+associated feature Gram matrix can fit in memory. Here we show that the
+Gauss-Southwell rule (a greedy strategy for block coordinate descent
+techniques) leads to fast solvers in this case. Combined with a working set
+strategy based on an aggressive use of so-called Gap Safe screening rules, we
+propose a solver achieving state-of-the-art performance on sparse learning
+problems. Results are presented on Lasso and multi-task Lasso estimators.
+",1,0,1,1,0,0
+17066,Exoplanet Radius Gap Dependence on Host Star Type,"  Exoplanets smaller than Neptune are numerous, but the nature of the planet
+populations in the 1-4 Earth radii range remains a mystery. The complete Kepler
+sample of Q1-Q17 exoplanet candidates shows a radius gap at ~ 2 Earth radii, as
+reported by us in January 2017 in LPSC conference abstract #1576 (Zeng et al.
+2017). A careful analysis of Kepler host stars spectroscopy by the CKS survey
+allowed Fulton et al. (2017) in March 2017 to unambiguously show this radius
+gap. The cause of this gap is still under discussion (Ginzburg et al. 2017;
+Lehmer & Catling 2017; Owen & Wu 2017). Here we add to our original analysis
+the dependence of the radius gap on host star type.
+",0,1,0,0,0,0
+17067,Mapping the Invocation Structure of Online Political Interaction,"  The surge in political information, discourse, and interaction has been one
+of the most important developments in social media over the past several years.
+There is rich structure in the interaction among different viewpoints on the
+ideological spectrum. However, we still have only a limited analytical
+vocabulary for expressing the ways in which these viewpoints interact.
+In this paper, we develop network-based methods that operate on the ways in
+which users share content; we construct \emph{invocation graphs} on Web domains
+showing the extent to which pages from one domain are invoked by users to reply
+to posts containing pages from other domains. When we locate the domains on a
+political spectrum induced from the data, we obtain an embedded graph showing
+how these interaction links span different distances on the spectrum. The
+structure of this embedded network, and its evolution over time, helps us
+derive macro-level insights about how political interaction unfolded through
+2016, leading up to the US Presidential election. In particular, we find that
+the domains invoked in replies spanned increasing distances on the spectrum
+over the months approaching the election, and that there was clear asymmetry
+between the left-to-right and right-to-left patterns of linkage.
+",1,0,0,0,0,0
+17068,Collective decision for open set recognition,"  In open set recognition (OSR), almost all existing methods are designed
+specially for recognizing individual instances, even these instances are
+collectively coming in batch. Recognizers in decision either reject or
+categorize them to some known class using empirically-set threshold. Thus the
+threshold plays a key role, however, the selection for it usually depends on
+the knowledge of known classes, inevitably incurring risks due to lacking
+available information from unknown classes. On the other hand, a more realistic
+OSR system should NOT just rest on a reject decision but should go further,
+especially for discovering the hidden unknown classes among the reject
+instances, whereas existing OSR methods do not pay special attention. In this
+paper, we introduce a novel collective/batch decision strategy with an aim to
+extend existing OSR for new class discovery while considering correlations
+among the testing instances. Specifically, a collective decision-based OSR
+framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet
+process (HDP). Thanks to the HDP, our CD-OSR does not need to define the
+specific threshold and can automatically reserve space for unknown classes in
+testing, naturally resulting in a new class discovery function. Finally,
+extensive experiments on benchmark datasets indicate the validity of CD-OSR.
+",0,0,0,1,0,0
+17069,HARPS-N high spectral resolution observations of Cepheids I. The Baade-Wesselink projection factor of δ Cep revisited,"  The projection factor p is the key quantity used in the Baade-Wesselink (BW)
+method for distance determination; it converts radial velocities into pulsation
+velocities. Several methods are used to determine p, such as geometrical and
+hydrodynamical models or the inverse BW approach when the distance is known. We
+analyze new HARPS-N spectra of delta Cep to measure its cycle-averaged
+atmospheric velocity gradient in order to better constrain the projection
+factor. We first apply the inverse BW method to derive p directly from
+observations. The projection factor can be divided into three subconcepts: (1)
+a geometrical effect (p0); (2) the velocity gradient within the atmosphere
+(fgrad); and (3) the relative motion of the optical pulsating photosphere with
+respect to the corresponding mass elements (fo-g). We then measure the fgrad
+value of delta Cep for the first time. When the HARPS-N mean cross-correlated
+line-profiles are fitted with a Gaussian profile, the projection factor is
+pcc-g = 1.239 +/- 0.034(stat) +/- 0.023(syst). When we consider the different
+amplitudes of the radial velocity curves that are associated with 17 selected
+spectral lines, we measure projection factors ranging from 1.273 to 1.329. We
+find a relation between fgrad and the line depth measured when the Cepheid is
+at minimum radius. This relation is consistent with that obtained from our best
+hydrodynamical model of delta Cep and with our projection factor decomposition.
+Using the observational values of p and fgrad found for the 17 spectral lines,
+we derive a semi-theoretical value of fo-g. We alternatively obtain fo-g =
+0.975+/-0.002 or 1.006+/-0.002 assuming models using radiative transfer in
+plane-parallel or spherically symmetric geometries, respectively. The new
+HARPS-N observations of delta Cep are consistent with our decomposition of the
+projection factor.
+",0,1,0,0,0,0
+17070,Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US,"  The United States spends more than $1B each year on initiatives such as the
+American Community Survey (ACS), a labor-intensive door-to-door study that
+measures statistics relating to race, gender, education, occupation,
+unemployment, and other demographic factors. Although a comprehensive source of
+data, the lag between demographic changes and their appearance in the ACS can
+exceed half a decade. As digital imagery becomes ubiquitous and machine vision
+techniques improve, automated data analysis may provide a cheaper and faster
+alternative. Here, we present a method that determines socioeconomic trends
+from 50 million images of street scenes, gathered in 200 American cities by
+Google Street View cars. Using deep learning-based computer vision techniques,
+we determined the make, model, and year of all motor vehicles encountered in
+particular neighborhoods. Data from this census of motor vehicles, which
+enumerated 22M automobiles in total (8% of all automobiles in the US), was used
+to accurately estimate income, race, education, and voting patterns, with
+single-precinct resolution. (The average US precinct contains approximately
+1000 people.) The resulting associations are surprisingly simple and powerful.
+For instance, if the number of sedans encountered during a 15-minute drive
+through a city is higher than the number of pickup trucks, the city is likely
+to vote for a Democrat during the next Presidential election (88% chance);
+otherwise, it is likely to vote Republican (82%). Our results suggest that
+automated systems for monitoring demographic trends may effectively complement
+labor-intensive approaches, with the potential to detect trends with fine
+spatial resolution, in close to real time.
+",1,0,0,0,0,0
+17071,Gaussian Process Neurons Learn Stochastic Activation Functions,"  We propose stochastic, non-parametric activation functions that are fully
+learnable and individual to each neuron. Complexity and the risk of overfitting
+are controlled by placing a Gaussian process prior over these functions. The
+result is the Gaussian process neuron, a probabilistic unit that can be used as
+the basic building block for probabilistic graphical models that resemble the
+structure of neural networks. The proposed model can intrinsically handle
+uncertainties in its inputs and self-estimate the confidence of its
+predictions. Using variational Bayesian inference and the central limit
+theorem, a fully deterministic loss function is derived, allowing it to be
+trained as efficiently as a conventional neural network using mini-batch
+gradient descent. The posterior distribution of activation functions is
+inferred from the training data alongside the weights of the network.
+The proposed model favorably compares to deep Gaussian processes, both in
+model complexity and efficiency of inference. It can be directly applied to
+recurrent or convolutional network structures, allowing its use in audio and
+image processing tasks.
+As an preliminary empirical evaluation we present experiments on regression
+and classification tasks, in which our model achieves performance comparable to
+or better than a Dropout regularized neural network with a fixed activation
+function. Experiments are ongoing and results will be added as they become
+available.
+",1,0,0,1,0,0
+17072,The short-term price impact of trades is universal,"  We analyze a proprietary dataset of trades by a single asset manager,
+comparing their price impact with that of the trades of the rest of the market.
+In the context of a linear propagator model we find no significant difference
+between the two, suggesting that both the magnitude and time dependence of
+impact are universal in anonymous, electronic markets. This result is important
+as optimal execution policies often rely on propagators calibrated on anonymous
+data. We also find evidence that in the wake of a trade the order flow of other
+market participants first adds further copy-cat trades enhancing price impact
+on very short time scales. The induced order flow then quickly inverts, thereby
+contributing to impact decay.
+",0,1,0,0,0,0
+17073,Questions on mod p representations of reductive p-adic groups,"  This is a list of questions raised by our joint work arXiv:1412.0737 and its
+sequels.
+",0,0,1,0,0,0
+17074,Filamentary superconductivity in semiconducting policrystalline ZrSe2 compound with Zr vacancies,"  ZrSe2 is a band semiconductor studied long time ago. It has interesting
+electronic properties, and because its layers structure can be intercalated
+with different atoms to change some of the physical properties. In this
+investigation we found that Zr deficiencies alter the semiconducting behavior
+and the compound can be turned into a superconductor. In this paper we report
+our studies related to this discovery. The decreasing of the number of Zr atoms
+in small proportion according to the formula ZrxSe2, where x is varied from
+about 8.1 to 8.6 K, changing the semiconducting behavior to a superconductor
+with transition temperatures ranging between 7.8 to 8.5 K, it depending of the
+deficiencies. Outside of those ranges the compound behaves as semiconducting
+with the properties already known. In our experiments we found that this new
+superconductor has only a very small fraction of superconducting material
+determined by magnetic measurements with applied magnetic field of 10 Oe. Our
+conclusions is that superconductivity is filamentary. However, in one studied
+sample the fraction was about 10.2 %, whereas in others is only about 1 % or
+less. We determined the superconducting characteristics; the critical fields
+that indicate a type two superonductor with Ginzburg-Landau ? parameter of the
+order about 2.7. The synthesis procedure is quite normal fol- lowing the
+conventional solid state reaction. In this paper are included, the electronic
+characteristics, transition temperature, and evolution with temperature of the
+critical fields.
+",0,1,0,0,0,0
+17075,Stochastic Block Model Reveals the Map of Citation Patterns and Their Evolution in Time,"  In this study we map out the large-scale structure of citation networks of
+science journals and follow their evolution in time by using stochastic block
+models (SBMs). The SBM fitting procedures are principled methods that can be
+used to find hierarchical grouping of journals into blocks that show similar
+incoming and outgoing citations patterns. These methods work directly on the
+citation network without the need to construct auxiliary networks based on
+similarity of nodes. We fit the SBMs to the networks of journals we have
+constructed from the data set of around 630 million citations and find a
+variety of different types of blocks, such as clusters, bridges, sources, and
+sinks. In addition we use a recent generalization of SBMs to determine how much
+a manually curated classification of journals into subfields of science is
+related to the block structure of the journal network and how this relationship
+changes in time. The SBM method tries to find a network of blocks that is the
+best high-level representation of the network of journals, and we illustrate
+how these block networks (at various levels of resolution) can be used as maps
+of science.
+",1,1,0,0,0,0
+17076,Limits on the anomalous speed of gravitational waves from binary pulsars,"  A large class of modified theories of gravity used as models for dark energy
+predict a propagation speed for gravitational waves which can differ from the
+speed of light. This difference of propagations speeds for photons and
+gravitons has an impact in the emission of gravitational waves by binary
+systems. Thus, we revisit the usual quadrupolar emission of binary system for
+an arbitrary propagation speed of gravitational waves and obtain the
+corresponding period decay formula. We then use timing data from the
+Hulse-Taylor binary pulsar and obtain that the speed of gravitational waves can
+only differ from the speed of light at the percentage level. This bound places
+tight constraints on dark energy models featuring an anomalous propagations
+speed for the gravitational waves.
+",0,1,0,0,0,0
+17077,Central limit theorems for entropy-regularized optimal transport on finite spaces and statistical applications,"  The notion of entropy-regularized optimal transport, also known as Sinkhorn
+divergence, has recently gained popularity in machine learning and statistics,
+as it makes feasible the use of smoothed optimal transportation distances for
+data analysis. The Sinkhorn divergence allows the fast computation of an
+entropically regularized Wasserstein distance between two probability
+distributions supported on a finite metric space of (possibly) high-dimension.
+For data sampled from one or two unknown probability distributions, we derive
+the distributional limits of the empirical Sinkhorn divergence and its centered
+version (Sinkhorn loss). We also propose a bootstrap procedure which allows to
+obtain new test statistics for measuring the discrepancies between multivariate
+probability distributions. Our work is inspired by the results of Sommerfeld
+and Munk (2016) on the asymptotic distribution of empirical Wasserstein
+distance on finite space using unregularized transportation costs. Incidentally
+we also analyze the asymptotic distribution of entropy-regularized Wasserstein
+distances when the regularization parameter tends to zero. Simulated and real
+datasets are used to illustrate our approach.
+",0,0,1,1,0,0
+17078,Inference for Stochastically Contaminated Variable Length Markov Chains,"  In this paper, we present a methodology to estimate the parameters of
+stochastically contaminated models under two contamination regimes. In both
+regimes, we assume that the original process is a variable length Markov chain
+that is contaminated by a random noise. In the first regime we consider that
+the random noise is added to the original source and in the second regime, the
+random noise is multiplied by the original source. Given a contaminated sample
+of these models, the original process is hidden. Then we propose a two steps
+estimator for the parameters of these models, that is, the probability
+transitions and the noise parameter, and prove its consistency. The first step
+is an adaptation of the Baum-Welch algorithm for Hidden Markov Models. This
+step provides an estimate of a complete order $k$ Markov chain, where $k$ is
+bigger than the order of the variable length Markov chain if it has finite
+order and is a constant depending on the sample size if the hidden process has
+infinite order. In the second estimation step, we propose a bootstrap Bayesian
+Information Criterion, given a sample of the Markov chain estimated in the
+first step, to obtain the variable length time dependence structure associated
+with the hidden process. We present a simulation study showing that our
+methodology is able to accurately recover the parameters of the models for a
+reasonable interval of random noises.
+",0,0,0,1,0,0
+17079,Variable-Length Resolvability for General Sources and Channels,"  We introduce the problem of variable-length source resolvability, where a
+given target probability distribution is approximated by encoding a
+variable-length uniform random number, and the asymptotically minimum average
+length rate of the uniform random numbers, called the (variable-length)
+resolvability, is investigated. We first analyze the variable-length
+resolvability with the variational distance as an approximation measure. Next,
+we investigate the case under the divergence as an approximation measure. When
+the asymptotically exact approximation is required, it is shown that the
+resolvability under the two kinds of approximation measures coincides. We then
+extend the analysis to the case of channel resolvability, where the target
+distribution is the output distribution via a general channel due to the fixed
+general source as an input. The obtained characterization of the channel
+resolvability is fully general in the sense that when the channel is just the
+identity mapping, the characterization reduces to the general formula for the
+source resolvability. We also analyze the second-order variable-length
+resolvability.
+",1,0,0,0,0,0
+17080,Diattenuation of Brain Tissue and its Impact on 3D Polarized Light Imaging,"  3D-Polarized Light Imaging (3D-PLI) reconstructs nerve fibers in histological
+brain sections by measuring their birefringence. This study investigates
+another effect caused by the optical anisotropy of brain tissue -
+diattenuation. Based on numerical and experimental studies and a complete
+analytical description of the optical system, the diattenuation was determined
+to be below 4 % in rat brain tissue. It was demonstrated that the diattenuation
+effect has negligible impact on the fiber orientations derived by 3D-PLI. The
+diattenuation signal, however, was found to highlight different anatomical
+structures that cannot be distinguished with current imaging techniques, which
+makes Diattenuation Imaging a promising extension to 3D-PLI.
+",0,1,0,0,0,0
+17081,Higgs Modes in the Pair Density Wave Superconducting State,"  The pair density wave (PDW) superconducting state has been proposed to
+explain the layer- decoupling effect observed in the compound
+La$_{2-x}$Ba$_x$CuO$_4$ at $x=1/8$ (Phys. Rev. Lett. 99, 127003). In this state
+the superconducting order parameter is spatially modulated, in contrast with
+the usual superconducting (SC) state where the order parameter is uniform. In
+this work, we study the properties of the amplitude (Higgs) modes in a
+unidirectional PDW state. To this end we consider a phenomenological model of
+PDW type states coupled to a Fermi surface of fermionic quasiparticles. In
+contrast to conventional superconductors that have a single Higgs mode,
+unidirectional PDW superconductors have two Higgs modes. While in the PDW state
+the Fermi surface largely remains gapless, we find that the damping of the PDW
+Higgs modes into fermionic quasiparticles requires exceeding an energy
+threshold. We show that this suppression of damping in the PDW state is due to
+kinematics. As a result, only one of the two Higgs modes is significantly
+damped. In addition, motivated by the experimental phase diagram, we discuss
+the mixing of Higgs modes in the coexistence regime of the PDW and uniform SC
+states. These results should be observable directly in a Raman spectroscopy, in
+momentum resolved electron energy loss spectroscopy, and in resonant inelastic
+X-ray scattering, thus providing evidence of the PDW states.
+",0,1,0,0,0,0
+17082,A Serverless Tool for Platform Agnostic Computational Experiment Management,"  Neuroscience has been carried into the domain of big data and high
+performance computing (HPC) on the backs of initiatives in data collection and
+an increasingly compute-intensive tools. While managing HPC experiments
+requires considerable technical acumen, platforms and standards have been
+developed to ease this burden on scientists. While web-portals make resources
+widely accessible, data organizations such as the Brain Imaging Data Structure
+and tool description languages such as Boutiques provide researchers with a
+foothold to tackle these problems using their own datasets, pipelines, and
+environments. While these standards lower the barrier to adoption of HPC and
+cloud systems for neuroscience applications, they still require the
+consolidation of disparate domain-specific knowledge. We present Clowdr, a
+lightweight tool to launch experiments on HPC systems and clouds, record rich
+execution records, and enable the accessible sharing of experimental summaries
+and results. Clowdr uniquely sits between web platforms and bare-metal
+applications for experiment management by preserving the flexibility of
+do-it-yourself solutions while providing a low barrier for developing,
+deploying and disseminating neuroscientific analysis.
+",1,0,0,0,0,0
+17083,Traveling-wave parametric amplifier based on three-wave mixing in a Josephson metamaterial,"  We have developed a recently proposed Josephson traveling-wave parametric
+amplifier with three-wave mixing [A. B. Zorin, Phys. Rev. Applied 6, 034006,
+2016]. The amplifier consists of a microwave transmission line formed by a
+serial array of nonhysteretic one-junction SQUIDs. These SQUIDs are flux-biased
+in a way that the phase drops across the Josephson junctions are equal to 90
+degrees and the persistent currents in the SQUID loops are equal to the
+Josephson critical current values. Such a one-dimensional metamaterial
+possesses a maximal quadratic nonlinearity and zero cubic (Kerr) nonlinearity.
+This property allows phase matching and exponential power gain of traveling
+microwaves to take place over a wide frequency range. We report the
+proof-of-principle experiment performed at a temperature of T = 4.2 K on Nb
+trilayer samples, which has demonstrated that our concept of a practical
+broadband Josephson parametric amplifier is valid and very promising for
+achieving quantum-limited operation.
+",0,1,0,0,0,0
+17084,Measuring LDA Topic Stability from Clusters of Replicated Runs,"  Background: Unstructured and textual data is increasing rapidly and Latent
+Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods
+for it. Past work suggests that instability of LDA topics may lead to
+systematic errors. Aim: We propose a method that relies on replicated LDA runs,
+clustering, and providing a stability metric for the topics. Method: We
+generate k LDA topics and replicate this process n times resulting in n*k
+topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k
+clusters now represent the original LDA topics and we present them like normal
+LDA topics showing the ten most probable words. For the clusters, we try
+multiple stability metrics, out of which we recommend Rank-Biased Overlap,
+showing the stability of the topics inside the clusters. Results: We provide an
+initial validation where our method is used for 270,000 Mozilla Firefox commit
+messages with k=20 and n=20. We show how our topic stability metrics are
+related to the contents of the topics. Conclusions: Advances in text mining
+enable us to analyze large masses of text in software engineering but
+non-deterministic algorithms, such as LDA, may lead to unreplicable
+conclusions. Our approach makes LDA stability transparent and is also
+complementary rather than alternative to many prior works that focus on LDA
+parameter tuning.
+",1,0,0,0,0,0
+17085,Continuum Foreground Polarization and Na~I Absorption in Type Ia SNe,"  We present a study of the continuum polarization over the 400--600 nm range
+of 19 Type Ia SNe obtained with FORS at the VLT. We separate them in those that
+show Na I D lines at the velocity of their hosts and those that do not.
+Continuum polarization of the sodium sample near maximum light displays a broad
+range of values, from extremely polarized cases like SN 2006X to almost
+unpolarized ones like SN 2011ae. The non--sodium sample shows, typically,
+smaller polarization values. The continuum polarization of the sodium sample in
+the 400--600 nm range is linear with wavelength and can be characterized by the
+mean polarization (P$_{\rm{mean}}$). Its values span a wide range and show a
+linear correlation with color, color excess, and extinction in the visual band.
+Larger dispersion correlations were found with the equivalent width of the Na I
+D and Ca II H & K lines, and also a noisy relation between P$_{\rm{mean}}$ and
+$R_{V}$, the ratio of total to selective extinction. Redder SNe show stronger
+continuum polarization, with larger color excesses and extinctions. We also
+confirm that high continuum polarization is associated with small values of
+$R_{V}$.
+The correlation between extinction and polarization -- and polarization
+angles -- suggest that the dominant fraction of dust polarization is imprinted
+in interstellar regions of the host galaxies.
+We show that Na I D lines from foreground matter in the SN host are usually
+associated with non-galactic ISM, challenging the typical assumptions in
+foreground interstellar polarization models.
+",0,1,0,0,0,0
+17086,Toward Faultless Content-Based Playlists Generation for Instrumentals,"  This study deals with content-based musical playlists generation focused on
+Songs and Instrumentals. Automatic playlist generation relies on collaborative
+filtering and autotagging algorithms. Autotagging can solve the cold start
+issue and popularity bias that are critical in music recommender systems.
+However, autotagging remains to be improved and cannot generate satisfying
+music playlists. In this paper, we suggest improvements toward better
+autotagging-generated playlists compared to state-of-the-art. To assess our
+method, we focus on the Song and Instrumental tags. Song and Instrumental are
+two objective and opposite tags that are under-studied compared to genres or
+moods, which are subjective and multi-modal tags. In this paper, we consider an
+industrial real-world musical database that is unevenly distributed between
+Songs and Instrumentals and bigger than databases used in previous studies. We
+set up three incremental experiments to enhance automatic playlist generation.
+Our suggested approach generates an Instrumental playlist with up to three
+times less false positives than cutting edge methods. Moreover, we provide a
+design of experiment framework to foster research on Songs and Instrumentals.
+We give insight on how to improve further the quality of generated playlists
+and to extend our methods to other musical tags. Furthermore, we provide the
+source code to guarantee reproducible research.
+",1,0,0,0,0,0
+17087,Direct observation of the band gap transition in atomically thin ReS$_2$,"  ReS$_2$ is considered as a promising candidate for novel electronic and
+sensor applications. The low crystal symmetry of the van der Waals compound
+ReS$_2$ leads to a highly anisotropic optical, vibrational, and transport
+behavior. However, the details of the electronic band structure of this
+fascinating material are still largely unexplored. We present a
+momentum-resolved study of the electronic structure of monolayer, bilayer, and
+bulk ReS$_2$ using k-space photoemission microscopy in combination with
+first-principles calculations. We demonstrate that the valence electrons in
+bulk ReS$_2$ are - contrary to assumptions in recent literature - significantly
+delocalized across the van der Waals gap. Furthermore, we directly observe the
+evolution of the valence band dispersion as a function of the number of layers,
+revealing a significantly increased effective electron mass in single-layer
+crystals. We also find that only bilayer ReS$_2$ has a direct band gap. Our
+results establish bilayer ReS$_2$ as a advantageous building block for
+two-dimensional devices and van der Waals heterostructures.
+",0,1,0,0,0,0
+17088,Lattice embeddings between types of fuzzy sets. Closed-valued fuzzy sets,"  In this paper we deal with the problem of extending Zadeh's operators on
+fuzzy sets (FSs) to interval-valued (IVFSs), set-valued (SVFSs) and type-2
+(T2FSs) fuzzy sets. Namely, it is known that seeing FSs as SVFSs, or T2FSs,
+whose membership degrees are singletons is not order-preserving. We then
+describe a family of lattice embeddings from FSs to SVFSs. Alternatively, if
+the former singleton viewpoint is required, we reformulate the intersection on
+hesitant fuzzy sets and introduce what we have called closed-valued fuzzy sets.
+This new type of fuzzy sets extends standard union and intersection on FSs. In
+addition, it allows handling together membership degrees of different nature
+as, for instance, closed intervals and finite sets. Finally, all these
+constructions are viewed as T2FSs forming a chain of lattices.
+",1,0,0,0,0,0
+17089,Coupling of Magneto-Thermal and Mechanical Superconducting Magnet Models by Means of Mesh-Based Interpolation,"  In this paper we present an algorithm for the coupling of magneto-thermal and
+mechanical finite element models representing superconducting accelerator
+magnets. The mechanical models are used during the design of the mechanical
+structure as well as the optimization of the magnetic field quality under
+nominal conditions. The magneto-thermal models allow for the analysis of
+transient phenomena occurring during quench initiation, propagation, and
+protection. Mechanical analysis of quenching magnets is of high importance
+considering the design of new protection systems and the study of new
+superconductor types. We use field/circuit coupling to determine temperature
+and electromagnetic force evolution during the magnet discharge. These
+quantities are provided as a load to existing mechanical models. The models are
+discretized with different meshes and, therefore, we employ a mesh-based
+interpolation method to exchange coupled quantities. The coupling algorithm is
+illustrated with a simulation of a mechanical response of a standalone
+high-field dipole magnet protected with CLIQ (Coupling-Loss Induced Quench)
+technology.
+",1,1,0,0,0,0
+17090,Converging expansions for Lipschitz self-similar perforations of a plane sector,"  In contrast with the well-known methods of matching asymptotics and
+multiscale (or compound) asymptotics, the "" functional analytic approach "" of
+Lanza de Cristoforis (Analysis 28, 2008) allows to prove convergence of
+expansions around interior small holes of size $\epsilon$ for solutions of
+elliptic boundary value problems. Using the method of layer potentials, the
+asymptotic behavior of the solution as $\epsilon$ tends to zero is described
+not only by asymptotic series in powers of $\epsilon$, but by convergent power
+series. Here we use this method to investigate the Dirichlet problem for the
+Laplace operator where holes are collapsing at a polygonal corner of opening
+$\omega$. Then in addition to the scale $\epsilon$ there appears the scale
+$\eta = \epsilon^{\pi/\omega}$. We prove that when $\pi/\omega$ is irrational,
+the solution of the Dirichlet problem is given by convergent series in powers
+of these two small parameters. Due to interference of the two scales, this
+convergence is obtained, in full generality, by grouping together integer
+powers of the two scales that are very close to each other. Nevertheless, there
+exists a dense subset of openings $\omega$ (characterized by Diophantine
+approximation properties), for which real analyticity in the two variables
+$\epsilon$ and $\eta$ holds and the power series converge unconditionally. When
+$\pi/\omega$ is rational, the series are unconditionally convergent, but
+contain terms in log $\epsilon$.
+",0,0,1,0,0,0
+17091,A Viral Timeline Branching Process to study a Social Network,"  Bio-inspired paradigms are proving to be useful in analyzing propagation and
+dissemination of information in networks. In this paper we explore the use of
+multi-type branching processes to analyse viral properties of content in a
+social network, with and without competition from other sources. We derive and
+compute various virality measures, e.g., probability of virality, expected
+number of shares, or the rate of growth of expected number of shares etc. They
+allow one to predict the emergence of global macro properties (e.g., viral
+spread of a post in the entire network) from the laws and parameters that
+determine local interactions. The local interactions, greatly depend upon the
+structure of the timelines holding the content and the number of friends (i.e.,
+connections) of users of the network. We then formulate a non-cooperative game
+problem and study the Nash equilibria as a function of the parameters. The
+branching processes modelling the social network under competition turn out to
+be decomposable, multi-type and continuous time variants. For such processes
+types belonging to different sub-classes evolve at different rates and have
+different probabilities of extinction etc. We compute content provider wise
+extinction probability, rate of growth etc. We also conjecture the
+content-provider wise growth rate of expected shares.
+",1,0,0,0,0,0
+17092,Algorithmic Bio-surveillance For Precise Spatio-temporal Prediction of Zoonotic Emergence,"  Viral zoonoses have emerged as the key drivers of recent pandemics. Human
+infection by zoonotic viruses are either spillover events -- isolated
+infections that fail to cause a widespread contagion -- or species jumps, where
+successful adaptation to the new host leads to a pandemic. Despite expensive
+bio-surveillance efforts, historically emergence response has been reactive,
+and post-hoc. Here we use machine inference to demonstrate a high accuracy
+predictive bio-surveillance capability, designed to pro-actively localize an
+impending species jump via automated interrogation of massive sequence
+databases of viral proteins. Our results suggest that a jump might not purely
+be the result of an isolated unfortunate cross-infection localized in space and
+time; there are subtle yet detectable patterns of genotypic changes
+accumulating in the global viral population leading up to emergence. Using tens
+of thousands of protein sequences simultaneously, we train models that track
+maximum achievable accuracy for disambiguating host tropism from the primary
+structure of surface proteins, and show that the inverse classification
+accuracy is a quantitative indicator of jump risk. We validate our claim in the
+context of the 2009 swine flu outbreak, and the 2004 emergence of H5N1
+subspecies of Influenza A from avian reservoirs; illustrating that
+interrogation of the global viral population can unambiguously track a near
+monotonic risk elevation over several preceding years leading to eventual
+emergence.
+",0,0,0,1,1,0
+17093,Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward,"  Operationalizing machine learning based security detections is extremely
+challenging, especially in a continuously evolving cloud environment.
+Conventional anomaly detection does not produce satisfactory results for
+analysts that are investigating security incidents in the cloud. Model
+evaluation alone presents its own set of problems due to a lack of benchmark
+datasets. When deploying these detections, we must deal with model compliance,
+localization, and data silo issues, among many others. We pose the problem of
+""attack disruption"" as a way forward in the security data science space. In
+this paper, we describe the framework, challenges, and open questions
+surrounding the successful operationalization of machine learning based
+security detections in a cloud environment and provide some insights on how we
+have addressed them.
+",1,0,0,0,0,0
+17094,SOI RF Switch for Wireless Sensor Network,"  The objective of this research was to design a 0-5 GHz RF SOI switch, with
+0.18um power Jazz SOI technology by using Cadence software, for health care
+applications. This paper introduces the design of a RF switch implemented in
+shunt-series topology. An insertion loss of 0.906 dB and an isolation of 30.95
+dB were obtained at 5 GHz. The switch also achieved a third order distortion of
+53.05 dBm and 1 dB compression point reached 50.06dBm. The RF switch
+performance meets the desired specification requirements.
+",1,0,0,0,0,0
+17095,The Pentagonal Inequality,"  Given a positive linear combination of five (respectively seven) cosines,
+where the angles are positive and sum to pi, the aim of this article is to
+express the sharp bound of the combination as a Positive Real Fraction in the
+coefficients (hence cosine-free). The method uses algebraic and arithmetic
+manipulations with judicious transformations.
+",0,0,1,0,0,0
+17096,The Landscape of Deep Learning Algorithms,"  This paper studies the landscape of empirical risk of deep neural networks by
+theoretically analyzing its convergence behavior to the population risk as well
+as its stationary points and properties. For an $l$-layer linear neural
+network, we prove its empirical risk uniformly converges to its population risk
+at the rate of $\mathcal{O}(r^{2l}\sqrt{d\log(l)}/\sqrt{n})$ with training
+sample size of $n$, the total weight dimension of $d$ and the magnitude bound
+$r$ of weight of each layer. We then derive the stability and generalization
+bounds for the empirical risk based on this result. Besides, we establish the
+uniform convergence of gradient of the empirical risk to its population
+counterpart. We prove the one-to-one correspondence of the non-degenerate
+stationary points between the empirical and population risks with convergence
+guarantees, which describes the landscape of deep neural networks. In addition,
+we analyze these properties for deep nonlinear neural networks with sigmoid
+activation functions. We prove similar results for convergence behavior of
+their empirical risks as well as the gradients and analyze properties of their
+non-degenerate stationary points.
+To our best knowledge, this work is the first one theoretically
+characterizing landscapes of deep learning algorithms. Besides, our results
+provide the sample complexity of training a good deep neural network. We also
+provide theoretical understanding on how the neural network depth $l$, the
+layer width, the network size $d$ and parameter magnitude determine the neural
+network landscapes.
+",1,0,1,1,0,0
+17097,"The effect of the environment on the structure, morphology and star-formation history of intermediate-redshift galaxies","  With the aim of understanding the effect of the environment on the star
+formation history and morphological transformation of galaxies, we present a
+detailed analysis of the colour, morphology and internal structure of cluster
+and field galaxies at $0.4 \le z \le 0.8$. We use {\em HST} data for over 500
+galaxies from the ESO Distant Cluster Survey (EDisCS) to quantify how the
+galaxies' light distribution deviate from symmetric smooth profiles. We
+visually inspect the galaxies' images to identify the likely causes for such
+deviations. We find that the residual flux fraction ($RFF$), which measures the
+fractional contribution to the galaxy light of the residuals left after
+subtracting a symmetric and smooth model, is very sensitive to the degree of
+structural disturbance but not the causes of such disturbance. On the other
+hand, the asymmetry of these residuals ($A_{\rm res}$) is more sensitive to the
+causes of the disturbance, with merging galaxies having the highest values of
+$A_{\rm res}$. Using these quantitative parameters we find that, at a fixed
+morphology, cluster and field galaxies show statistically similar degrees of
+disturbance. However, there is a higher fraction of symmetric and passive
+spirals in the cluster than in the field. These galaxies have smoother light
+distributions than their star-forming counterparts. We also find that while
+almost all field and cluster S0s appear undisturbed, there is a relatively
+small population of star-forming S0s in clusters but not in the field. These
+findings are consistent with relatively gentle environmental processes acting
+on galaxies infalling onto clusters.
+",0,1,0,0,0,0
+17098,Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning,"  Recommender systems play a crucial role in mitigating the problem of
+information overload by suggesting users' personalized items or services. The
+vast majority of traditional recommender systems consider the recommendation
+procedure as a static process and make recommendations following a fixed
+strategy. In this paper, we propose a novel recommender system with the
+capability of continuously improving its strategies during the interactions
+with users. We model the sequential interactions between users and a
+recommender system as a Markov Decision Process (MDP) and leverage
+Reinforcement Learning (RL) to automatically learn the optimal strategies via
+recommending trial-and-error items and receiving reinforcements of these items
+from users' feedback. Users' feedback can be positive and negative and both
+types of feedback have great potentials to boost recommendations. However, the
+number of negative feedback is much larger than that of positive one; thus
+incorporating them simultaneously is challenging since positive feedback could
+be buried by negative one. In this paper, we develop a novel approach to
+incorporate them into the proposed deep recommender system (DEERS) framework.
+The experimental results based on real-world e-commerce data demonstrate the
+effectiveness of the proposed framework. Further experiments have been
+conducted to understand the importance of both positive and negative feedback
+in recommendations.
+",0,0,0,1,0,0
+17099,Accumulated Gradient Normalization,"  This work addresses the instability in asynchronous data parallel
+optimization. It does so by introducing a novel distributed optimizer which is
+able to efficiently optimize a centralized model under communication
+constraints. The optimizer achieves this by pushing a normalized sequence of
+first-order gradients to a parameter server. This implies that the magnitude of
+a worker delta is smaller compared to an accumulated gradient, and provides a
+better direction towards a minimum compared to first-order gradients, which in
+turn also forces possible implicit momentum fluctuations to be more aligned
+since we make the assumption that all workers contribute towards a single
+minima. As a result, our approach mitigates the parameter staleness problem
+more effectively since staleness in asynchrony induces (implicit) momentum, and
+achieves a better convergence rate compared to other optimizers such as
+asynchronous EASGD and DynSGD, which we show empirically.
+",1,0,0,1,0,0
+17100,An Optimal Algorithm for Changing from Latitudinal to Longitudinal Formation of Autonomous Aircraft Squadrons,"  This work presents an algorithm for changing from latitudinal to longitudinal
+formation of autonomous aircraft squadrons. The maneuvers are defined
+dynamically by using a predefined set of 3D basic maneuvers. This formation
+changing is necessary when the squadron has to perform tasks which demand both
+formations, such as lift off, georeferencing, obstacle avoidance and landing.
+Simulations show that the formation changing is made without collision. The
+time complexity analysis of the transformation algorithm reveals that its
+efficiency is optimal, and the proof of correction ensures its longitudinal
+formation features.
+",1,0,0,0,0,0
+17101,Ideal Cluster Points in Topological Spaces,"  Given an ideal $\mathcal{I}$ on $\omega$, we show that a sequence in a
+topological space $X$ is $\mathcal{I}$-convergent if and only if there exists a
+""big"" $\mathcal{I}$-convergent subsequence. In addition, we study several
+properties of $\mathcal{I}$-cluster points. As a consequence, the underlying
+topology $\tau$ coincides with the topology generated by the pair
+$(\tau,\mathcal{I})$. Then, we obtain two characterizations of the set of
+$\mathcal{I}$-cluster points as classical cluster points of a filters on $X$
+and as the smallest closed set containing ""almost all"" the sequence.
+",0,0,1,0,0,0
+17102,Spin Hall effect of gravitational waves,"  Gravitons possess a Berry curvature due to their helicity. We derive the
+semiclassical equations of motion for gravitons taking into account the Berry
+curvature. We show that this quantum correction leads to the splitting of the
+trajectories of right- and left-handed gravitational waves in curved space, and
+that this correction can be understood as a topological phenomenon. This is the
+spin Hall effect (SHE) of gravitational waves. We find that the SHE of
+gravitational waves is twice as large as that of light. Possible future
+observations of the SHE of gravitational waves can potentially test the quantum
+nature of gravitons beyond the classical general relativity.
+",0,1,0,0,0,0
+17103,Many-Goals Reinforcement Learning,"  All-goals updating exploits the off-policy nature of Q-learning to update all
+possible goals an agent could have from each transition in the world, and was
+introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work
+this was mostly explored in small-state RL problems that allowed tabular
+representations and where all possible goals could be explicitly enumerated and
+learned separately. In this paper we empirically explore 3 different extensions
+of the idea of updating many (instead of all) goals in the context of RL with
+deep neural networks (or DeepRL for short). First, in a direct adaptation of
+Kaelbling's approach we explore if many-goals updating can be used to achieve
+mastery in non-tabular visual-observation domains. Second, we explore whether
+many-goals updating can be used to pre-train a network to subsequently learn
+faster and better on a single main task of interest. Third, we explore whether
+many-goals updating can be used to provide auxiliary task updates in training a
+network to learn faster and better on a single main task of interest. We
+provide comparisons to baselines for each of the 3 extensions.
+",0,0,0,1,0,0
+17104,Localized Structured Prediction,"  Key to structured prediction is exploiting the problem structure to simplify
+the learning process. A major challenge arises when data exhibit a local
+structure (e.g., are made by ""parts"") that can be leveraged to better
+approximate the relation between (parts of) the input and (parts of) the
+output. Recent literature on signal processing, and in particular computer
+vision, has shown that capturing these aspects is indeed essential to achieve
+state-of-the-art performance. While such algorithms are typically derived on a
+case-by-case basis, in this work we propose the first theoretical framework to
+deal with part-based data from a general perspective. We derive a novel
+approach to deal with these problems and study its generalization properties
+within the setting of statistical learning theory. Our analysis is novel in
+that it explicitly quantifies the benefits of leveraging the part-based
+structure of the problem with respect to the learning rates of the proposed
+estimator.
+",0,0,0,1,0,0
+17105,Routing in FRET-based Nanonetworks,"  Nanocommunications, understood as communications between nanoscale devices,
+is commonly regarded as a technology essential for cooperation of large groups
+of nanomachines and thus crucial for development of the whole area of
+nanotechnology. While solutions for point-to-point nanocommunications have been
+already proposed, larger networks cannot function properly without routing. In
+this article we focus on the nanocommunications via Forster Resonance Energy
+Transfer (FRET), which was found to be a technique with a very high signal
+propagation speed, and discuss how to route signals through nanonetworks. We
+introduce five new routing mechanisms, based on biological properties of
+specific molecules. We experimentally validate one of these mechanisms.
+Finally, we analyze open issues showing the technical challenges for signal
+transmission and routing in FRET-based nanocommunications.
+",0,0,0,0,1,0
+17106,"FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why","  Winograd-based convolution has quickly gained traction as a preferred
+approach to implement convolutional neural networks (ConvNet) on various
+hardware platforms because it requires fewer floating point operations than
+FFT-based or direct convolutions.
+This paper compares three highly optimized implementations (regular FFT--,
+Gauss--FFT--, and Winograd--based convolutions) on modern multi-- and
+many--core CPUs. Although all three implementations employed the same
+optimizations for modern CPUs, our experimental results with two popular
+ConvNets (VGG and AlexNet) show that the FFT--based implementations generally
+outperform the Winograd--based approach, contrary to the popular belief.
+To understand the results, we use a Roofline performance model to analyze the
+three implementations in detail, by looking at each of their computation phases
+and by considering not only the number of floating point operations, but also
+the memory bandwidth and the cache sizes. The performance analysis explains
+why, and under what conditions, the FFT--based implementations outperform the
+Winograd--based one, on modern CPUs.
+",1,0,0,0,0,0
+17107,The application of selection principles in the study of the properties of function spaces,"  In this paper we investigate the properties of function spaces using the
+selection principles.
+",0,0,1,0,0,0
+17108,Progressive Neural Architecture Search,"  We propose a new method for learning the structure of convolutional neural
+networks (CNNs) that is more efficient than recent state-of-the-art methods
+based on reinforcement learning and evolutionary algorithms. Our approach uses
+a sequential model-based optimization (SMBO) strategy, in which we search for
+structures in order of increasing complexity, while simultaneously learning a
+surrogate model to guide the search through structure space. Direct comparison
+under the same search space shows that our method is up to 5 times more
+efficient than the RL method of Zoph et al. (2018) in terms of number of models
+evaluated, and 8 times faster in terms of total compute. The structures we
+discover in this way achieve state of the art classification accuracies on
+CIFAR-10 and ImageNet.
+",1,0,0,1,0,0
+17109,Lower Bounds for Maximum Gap in (Inverse) Cyclotomic Polynomials,"  The maximum gap $g(f)$ of a polynomial $f$ is the maximum of the differences
+(gaps) between two consecutive exponents that appear in $f$. Let $\Phi_{n}$ and
+$\Psi_{n}$ denote the $n$-th cyclotomic and $n$-th inverse cyclotomic
+polynomial, respectively. In this paper, we give several lower bounds for
+$g(\Phi_{n})$ and $g(\Psi_{n})$, where $n$ is the product of odd primes. We
+observe that they are very often exact. We also give an exact expression for
+$g(\Psi_{n})$ under a certain condition. Finally we conjecture an exact
+expression for $g(\Phi_{n})$ under a certain condition.
+",0,0,1,0,0,0
+17110,"Dynamical Analysis of Cylindrically Symmetric Anisotropic Sources in $f(R,T)$ Gravity","  In this paper, we have analyzed the stability of cylindrically symmetric
+collapsing object filled with locally anisotropic fluid in $f(R,T)$ theory,
+where $R$ is the scalar curvature and $T$ is the trace of stress-energy tensor
+of matter. Modified field equations and dynamical equations are constructed in
+$f(R,T)$ gravity. Evolution or collapse equation is derived from dynamical
+equations by performing linear perturbation on them. Instability range is
+explored in both Newtonian and post-Newtonian regimes with the help of
+adiabetic index, which defines the impact of physical parameters on the
+instability range. Some conditions are imposed on physical quantities to secure
+the stability of the gravitating sources.
+",0,1,0,0,0,0
+17111,Trace and Kunneth formulas for singularity categories and applications,"  We present an $\ell$-adic trace formula for saturated and admissible
+dg-categories over a base monoidal dg-category. Moreover, we prove Künneth
+formulas for dg-category of singularities, and for inertia-invariant vanishing
+cycles. As an application, we prove a version of Bloch's Conductor Conjecture
+(stated by Spencer Bloch in 1985), under the additional hypothesis that the
+monodromy action of the inertia group is unipotent.
+",0,0,1,0,0,0
+17112,A Redshift Survey of the Nearby Galaxy Cluster Abell 2199: Comparison of the Spatial and Kinematic Distributions of Galaxies with the Intracluster Medium,"  We present the results from an extensive spectroscopic survey of the central
+region of the nearby galaxy cluster Abell 2199 at $z=0.03$. By combining 775
+new redshifts from the MMT/Hectospec observations with the data in the
+literature, we construct a large sample of 1624 galaxies with measured
+redshifts at $R<30^\prime$, which results in high spectroscopic completeness at
+$r_{\rm petro,0}<20.5$ (77%). We use these data to study the kinematics and
+clustering of galaxies focusing on the comparison with those of the
+intracluster medium (ICM) from Suzaku X-ray observations. We identify 406
+member galaxies of A2199 at $R<30^\prime$ using the caustic technique. The
+velocity dispersion profile of cluster members appears smoothly connected to
+the stellar velocity dispersion profile of the cD galaxy. The luminosity
+function is well fitted with a Schechter function at $M_r<-15$. The radial
+velocities of cluster galaxies generally agree well with those of the ICM, but
+there are some regions where the velocity difference between the two is about a
+few hundred kilometer per second. The cluster galaxies show a hint of global
+rotation at $R<5^\prime$ with $v_{\rm rot}=300{-}600\,\textrm{km s}^{-1}$, but
+the ICM in the same region do not show such rotation. We apply a
+friends-of-friends algorithm to the cluster galaxy sample at $R<60^\prime$ and
+identify 32 group candidates, and examine the spatial correlation between the
+galaxy groups and X-ray emission. This extensive survey in the central region
+of A2199 provides an important basis for future studies of interplay among the
+galaxies, the ICM and the dark matter in the cluster.
+",0,1,0,0,0,0
+17113,A trapped field of 13.4 T in a stack of HTS tapes with 30 μm substrate,"  Superconducting bulk (RE)Ba$_2$Cu$_3$O$_{7-x}$ materials (RE-rare earth
+elements) have been successfully used to generate magnetic flux densities in
+excess of 17 T. This work investigates an alternative approach by trapping flux
+in stacks of second generation high temperature superconducting tape from
+several manufacturers using field cooling and pulsed field magnetisation
+techniques. Flux densities of up to 13.4 T were trapped by field cooling at ~5
+K between two 12 mm square stacks, an improvement of 70% over previous value
+achieved in an HTS tape stack. The trapped flux approaches the record values in
+(RE)BCO bulks and reflects the rapid developments still being made in the HTS
+tape performance.
+",0,1,0,0,0,0
+17114,Exact Simulation of the Extrema of Stable Processes,"  We exhibit an exact simulation algorithm for the supremum of a stable process
+over a finite time interval using dominated coupling from the past (DCFTP). We
+establish a novel perpetuity equation for the supremum (via the representation
+of the concave majorants of Lévy processes) and apply it to construct a
+Markov chain in the DCFTP algorithm. We prove that the number of steps taken
+backwards in time before the coalescence is detected is finite.
+",0,0,0,1,0,0
+17115,Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient,"  We consider a nonparametric Bayesian approach to estimate the diffusion
+coefficient of a stochastic differential equation given discrete time
+observations over a fixed time interval. As a prior on the diffusion
+coefficient, we employ a histogram-type prior with piecewise constant
+realisations on bins forming a partition of the time interval. Specifically,
+these constants are realizations of independent inverse Gamma distributed
+randoma variables. We justify our approach by deriving the rate at which the
+corresponding posterior distribution asymptotically concentrates around the
+data-generating diffusion coefficient. This posterior contraction rate turns
+out to be optimal for estimation of a Hölder-continuous diffusion coefficient
+with smoothness parameter $0<\lambda\leq 1.$ Our approach is straightforward to
+implement, as the posterior distributions turn out to be inverse Gamma again,
+and leads to good practical results in a wide range of simulation examples.
+Finally, we apply our method on exchange rate data sets.
+",0,0,1,1,0,0
+17116,Dirac State in a Centrosymmetric Superconductor alpha-PdBi2,"  Topological superconductor (TSC) hosting Majorana fermions has been
+established as a milestone that may shift our scientific trajectory from
+research to applications in topological quantum computing. Recently,
+superconducting Pd-Bi binaries have attracted great attention as a possible
+medium for the TSC phase as a result of their large spin-orbit coupling
+strength. Here, we report a systematic high-resolution angle-resolved
+photoemission spectroscopy (ARPES) study on the normal state electronic
+structure of superconducting alpha-PdBi2 (Tc = 1.7 K). Our results show the
+presence of Dirac states at higher-binding energy with the location of the
+Dirac point at 1.26 eV below the chemical potential at the zone center.
+Furthermore, the ARPES data indicate multiple band crossings at the chemical
+potential, consistent with the metallic behavior of alpha-PdBi2. Our detailed
+experimental studies are complemented by first-principles calculations, which
+reveal the presence of surface Rashba states residing in the vicinity of the
+chemical potential. The obtained results provide an opportunity to investigate
+the relationship between superconductivity and topology, as well as explore
+pathways to possible future platforms for topological quantum computing.
+",0,1,0,0,0,0
+17117,Hölder and Lipschitz continuity of functions definable over Henselian rank one valued fields,"  Consider a Henselian rank one valued field $K$ of equicharacteristic zero
+with the three-sorted language $\mathcal{L}$ of Denef--Pas. Let $f: A \to K$ be
+a continuous $\mathcal{L}$-definable (with parameters) function on a closed
+bounded subset $A \subset K^{n}$. The main purpose is to prove that then $f$ is
+Hölder continuous with some exponent $s\geq 0$ and constant $c \geq 0$, a
+fortiori, $f$ is uniformly continuous. Further, if $f$ is locally Lipschitz
+continuous with a constant $c$, then $f$ is (globally) Lipschitz continuous
+with possibly some larger constant $d$. Also stated are some problems
+concerning continuous and Lipschitz continuous functions definable over
+Henselian valued fields.
+",0,0,1,0,0,0
+17118,Bistability of Cavity Magnon Polaritons,"  We report the first observation of the magnon-polariton bistability in a
+cavity magnonics system consisting of cavity photons strongly interacting with
+the magnons in a small yttrium iron garnet (YIG) sphere. The bistable behaviors
+are emerged as sharp frequency switchings of the cavity magnon-polaritons
+(CMPs) and related to the transition between states with large and small number
+of polaritons. In our experiment, we align, respectively, the [100] and [110]
+crystallographic axes of the YIG sphere parallel to the static magnetic field
+and find very different bistable behaviors (e.g., clockwise and
+counter-clockwise hysteresis loops) in these two cases. The experimental
+results are well fitted and explained as being due to the Kerr nonlinearity
+with either positive or negative coefficient. Moreover, when the magnetic field
+is tuned away from the anticrossing point of CMPs, we observe simultaneous
+bistability of both magnons and cavity photons by applying a drive field on the
+lower branch.
+",0,1,0,0,0,0
+17119,Errors and secret data in the Italian research assessment exercise. A comment to a reply,"  Italy adopted a performance-based system for funding universities that is
+centered on the results of a national research assessment exercise, realized by
+a governmental agency (ANVUR). ANVUR evaluated papers by using 'a dual system
+of evaluation', that is by informed peer review or by bibliometrics. In view of
+validating that system, ANVUR performed an experiment for estimating the
+agreement between informed review and bibliometrics. Ancaiani et al. (2015)
+presents the main results of the experiment. Baccini and De Nicolao (2017)
+documented in a letter, among other critical issues, that the statistical
+analysis was not realized on a random sample of articles. A reply to the letter
+has been published by Research Evaluation (Benedetto et al. 2017). This note
+highlights that in the reply there are (1) errors in data, (2) problems with
+'representativeness' of the sample, (3) unverifiable claims about weights used
+for calculating kappas, (4) undisclosed averaging procedures; (5) a statement
+about 'same protocol in all areas' contradicted by official reports. Last but
+not least: the data used by the authors continue to be undisclosed. A general
+warning concludes: many recently published papers use data originating from
+Italian research assessment exercise. These data are not accessible to the
+scientific community and consequently these papers are not reproducible. They
+can be hardly considered as containing sound evidence at least until authors or
+ANVUR disclose the data necessary for replication.
+",1,0,0,0,0,0
+17120,Exploring Features for Predicting Policy Citations,"  In this study we performed an initial investigation and evaluation of
+altmetrics and their relationship with public policy citation of research
+papers. We examined methods for using altmetrics and other data to predict
+whether a research paper is cited in public policy and applied receiver
+operating characteristic curve on various feature groups in order to evaluate
+their potential usefulness. From the methods we tested, classifying based on
+tweet count provided the best results, achieving an area under the ROC curve of
+0.91.
+",1,0,0,0,0,0
+17121,Recovery guarantees for compressed sensing with unknown errors,"  From a numerical analysis perspective, assessing the robustness of
+l1-minimization is a fundamental issue in compressed sensing and sparse
+regularization. Yet, the recovery guarantees available in the literature
+usually depend on a priori estimates of the noise, which can be very hard to
+obtain in practice, especially when the noise term also includes unknown
+discrepancies between the finite model and data. In this work, we study the
+performance of l1-minimization when these estimates are not available,
+providing robust recovery guarantees for quadratically constrained basis
+pursuit and random sampling in bounded orthonormal systems. Several
+applications of this work are approximation of high-dimensional functions,
+infinite-dimensional sparse regularization for inverse problems, and fast
+algorithms for non-Cartesian Magnetic Resonance Imaging.
+",0,0,1,0,0,0
+17122,Synthetic Homology in Homotopy Type Theory,"  This paper defines homology in homotopy type theory, in the process stable
+homotopy groups are also defined. Previous research in synthetic homotopy
+theory is relied on, in particular the definition of cohomology. This work lays
+the foundation for a computer checked construction of homology.
+",1,0,1,0,0,0
+17123,Spatial Models of Vector-Host Epidemics with Directed Movement of Vectors Over Long Distances,"  We investigate a time-dependent spatial vector-host epidemic model with
+non-coincident domains for the vector and host populations. The host population
+resides in small non-overlapping sub-regions, while the vector population
+resides throughout a much larger region. The dynamics of the populations are
+modeled by a reaction-diffusion-advection compartmental system of partial
+differential equations. The disease is transmitted through vector and host
+populations in criss-cross fashion. We establish global well-posedness and
+uniform a prior bounds as well as the long-term behavior. The model is applied
+to simulate the outbreak of bluetongue disease in sheep transmitted by midges
+infected with bluetongue virus. We show that the long-range directed movement
+of the midge population, due to wind-aided movement, enhances the transmission
+of the disease to sheep in distant sites.
+",0,0,0,0,1,0
+17124,Complex and Quaternionic Principal Component Pursuit and Its Application to Audio Separation,"  Recently, the principal component pursuit has received increasing attention
+in signal processing research ranging from source separation to video
+surveillance. So far, all existing formulations are real-valued and lack the
+concept of phase, which is inherent in inputs such as complex spectrograms or
+color images. Thus, in this letter, we extend principal component pursuit to
+the complex and quaternionic cases to account for the missing phase
+information. Specifically, we present both complex and quaternionic proximity
+operators for the $\ell_1$- and trace-norm regularizers. These operators can be
+used in conjunction with proximal minimization methods such as the inexact
+augmented Lagrange multiplier algorithm. The new algorithms are then applied to
+the singing voice separation problem, which aims to separate the singing voice
+from the instrumental accompaniment. Results on the iKala and MSD100 datasets
+confirmed the usefulness of phase information in principal component pursuit.
+",0,0,0,1,0,0
+17125,Light axion-like dark matter must be present during inflation,"  Axion-like particles (ALPs) might constitute the totality of the cold dark
+matter (CDM) observed. The parameter space of ALPs depends on the mass of the
+particle $m$ and on the energy scale of inflation $H_I$ , the latter being
+bound by the non-detection of primordial gravitational waves. We show that the
+bound on HI implies the existence of a mass scale $m_\chi = 10 {\rm \,neV}
+÷ 0.5 {\rm \,peV}$, depending on the ALP susceptibility $\chi$, such that
+the energy density of ALPs of mass smaller than $m_\chi$ is too low to explain
+the present CDM budget, if the ALP field has originated after the end of
+inflation. This bound affects Ultra-Light Axions (ULAs), which have recently
+regained popularity as CDM candidates. Light ($m < m_\chi$) ALPs can then be
+CDM candidates only if the ALP field has already originated during the
+inflationary period, in which case the parameter space is constrained by the
+non-detection of axion isocurvature fluctuations. We comment on the effects on
+these bounds from additional physics beyond the Standard Model, besides ALPs.
+",0,1,0,0,0,0
+17126,Boolean function analysis meets stochastic optimization: An approximation scheme for stochastic knapsack,"  The stochastic knapsack problem is the stochastic variant of the classical
+knapsack problem in which the algorithm designer is given a a knapsack with a
+given capacity and a collection of items where each item is associated with a
+profit and a probability distribution on its size. The goal is to select a
+subset of items with maximum profit and violate the capacity constraint with
+probability at most $p$ (referred to as the overflow probability). While
+several approximation algorithms have been developed for this problem, most of
+these algorithms relax the capacity constraint of the knapsack. In this paper,
+we design efficient approximation schemes for this problem without relaxing the
+capacity constraint.
+(i) Our first result is in the case when item sizes are Bernoulli random
+variables. In this case, we design a (nearly) fully polynomial time
+approximation scheme (FPTAS) which only relaxes the overflow probability. (ii)
+Our second result generalizes the first result to the case when all the item
+sizes are supported on a (common) set of constant size. (iii) Our third result
+is in the case when item sizes are so-called ""hypercontractive"" random
+variables i.e., random variables whose second and fourth moments are within
+constant factors of each other. In other words, the kurtosis of the random
+variable is upper bounded by a constant.
+Crucially, all of our algorithms meet the capacity constraint exactly, a
+result which was previously known only when the item sizes were Poisson or
+Gaussian random variables. Our results rely on new connections between Boolean
+function analysis and stochastic optimization. We believe that these ideas and
+techniques may prove to be useful in other stochastic optimization problems as
+well.
+",1,0,0,0,0,0
+17127,Nonparanormal Information Estimation,"  We study the problem of using i.i.d. samples from an unknown multivariate
+probability distribution $p$ to estimate the mutual information of $p$. This
+problem has recently received attention in two settings: (1) where $p$ is
+assumed to be Gaussian and (2) where $p$ is assumed only to lie in a large
+nonparametric smoothness class. Estimators proposed for the Gaussian case
+converge in high dimensions when the Gaussian assumption holds, but are
+brittle, failing dramatically when $p$ is not Gaussian. Estimators proposed for
+the nonparametric case fail to converge with realistic sample sizes except in
+very low dimensions. As a result, there is a lack of robust mutual information
+estimators for many realistic data. To address this, we propose estimators for
+mutual information when $p$ is assumed to be a nonparanormal (a.k.a., Gaussian
+copula) model, a semiparametric compromise between Gaussian and nonparametric
+extremes. Using theoretical bounds and experiments, we show these estimators
+strike a practical balance between robustness and scaling with dimensionality.
+",1,0,1,1,0,0
+17128,Controlling a population,"  We introduce a new setting where a population of agents, each modelled by a
+finite-state system, are controlled uniformly: the controller applies the same
+action to every agent. The framework is largely inspired by the control of a
+biological system, namely a population of yeasts, where the controller may only
+change the environment common to all cells. We study a synchronisation problem
+for such populations: no matter how individual agents react to the actions of
+the controller, the controller aims at driving all agents synchronously to a
+target state. The agents are naturally represented by a non-deterministic
+finite state automaton (NFA), the same for every agent, and the whole system is
+encoded as a 2-player game. The first player (Controller) chooses actions, and
+the second player (Agents) resolves non-determinism for each agent. The game
+with m agents is called the m -population game. This gives rise to a
+parameterized control problem (where control refers to 2 player games), namely
+the population control problem: can Controller control the m-population game
+for all m in N whatever Agents does?
+",1,0,0,0,0,0
+17129,Finite scale local Lyapunov exponents distribution in fully developed homogeneous isotropic turbulence,"  The present work analyzes the distribution function of the finite scale local
+Lyapunov exponent of a pair fluid particles trajectories in fully developed
+incompressible homogeneous isotropic turbulence. According to the hypothesis of
+fully developed chaos, this PDF is reasonably estimated by maximizing the
+entropy associated to such distribution, resulting to be an uniform
+distribution function in a proper interval of variation of the local Lyapunov
+exponents. From this PDF, we determine the relationship between the average and
+maximum Lyapunov exponents and the longitudinal velocity correlation function.
+This link, which leads to the closure of von Kàrmàn--Howarth and Corrsin
+equations, agrees with the relation obtained in the previous work, supporting
+the proposed PDF calculation, at least for the purposes of the energy cascade
+effect estimation. Furthermore, through the property that the Lyapunov vectors
+tend to align to the direction of the maximum growth rate of trajectories
+distance, we obtain the link between maximum and average Lyapunov exponents in
+line with the previous result.
+",0,1,0,0,0,0
+17130,Improved Power Decoding of One-Point Hermitian Codes,"  We propose a new partial decoding algorithm for one-point Hermitian codes
+that can decode up to the same number of errors as the Guruswami--Sudan
+decoder. Simulations suggest that it has a similar failure probability as the
+latter one. The algorithm is based on a recent generalization of the power
+decoding algorithm for Reed--Solomon codes and does not require an expensive
+root-finding step. In addition, it promises improvements for decoding
+interleaved Hermitian codes.
+",1,0,0,0,0,0
+17131,Finite numbers of initial ideals in non-Noetherian polynomial rings,"  In this article, we generalize the well-known result that ideals of
+Noetherian polynomial rings have only finitely many initial ideals to the
+situation of ascending ideal chains in non-Noetherian polynomial rings. More
+precisely, we study ideal chains in the polynomial ring $R=K[x_{i,j}\,|\,1\leq
+i\leq c,j\in N]$ that are invariant under the action of the monoid $Inc(N)$ of
+strictly increasing functions on $N$, which acts on $R$ by shifting the second
+variable index. We show that for every such ideal chain, the number of initial
+ideal chains with respect to term orders on $R$ that are compatible with the
+action of $Inc(N)$ is finite. As a consequence of this, we will see that
+$Inc(N)$-invariant ideals of $R$ have only finitely many initial ideals with
+respect to $Inc(N)$-compatible term orders. The article also addresses the
+question of how many such term orders exist. We give a complete list of the
+$Inc(N)$-compatible term orders for the case $c=1$ and show that there are
+infinitely many for $c >1$. This answers a question by Hillar, Kroner, Leykin.
+",0,0,1,0,0,0
+17132,The Diederich-Fornaess Index and Good Vector Fields,"  We consider the relationship between two sufficient conditions for regularity
+of the Bergman Projection on smooth, bounded, pseudoconvex domains. We show
+that if the set of infinite type points is reasonably well-behaved, then the
+existence of a family of good vector fields in the sense of Boas and Straube
+implies that the Diederich-Fornaess Index of the domain is equal to one.
+",0,0,1,0,0,0
+17133,Complete Minors of Self-Complementary Graphs,"  We show that any self-complementary graph with $n$ vertices contains a
+$K_{\lfloor \frac{n+1}{2}\rfloor}$ minor. We derive topological properties of
+self-complementary graphs.
+",0,0,1,0,0,0
+17134,Statistical estimation of the Oscillating Brownian Motion,"  We study the asymptotic behavior of estimators of a two-valued, discontinuous
+diffusion coefficient in a Stochastic Differential Equation, called an
+Oscillating Brownian Motion. Using the relation of the latter process with the
+Skew Brownian Motion, we propose two natural consistent estimators, which are
+variants of the integrated volatility estimator and take the occupation times
+into account. We show the stable convergence of the renormalized errors'
+estimations toward some Gaussian mixture, possibly corrected by a term that
+depends on the local time. These limits stem from the lack of ergodicity as
+well as the behavior of the local time at zero of the process. We test both
+estimators on simulated processes, finding a complete agreement with the
+theoretical predictions.
+",0,0,1,1,0,0
+17135,Competing Ferromagnetic and Anti-Ferromagnetic interactions in Iron Nitride $ζ$-Fe$_2$N,"  The paper discusses the magnetic state of zeta phase of iron nitride viz.
+$\zeta$-Fe$_2$N on the basis of spin polarized first principles electronic
+structure calculations together with a review of already published data.
+Results of our first principles study suggest that the ground state of
+$\zeta$-Fe$_2$N is ferromagnetic (FM) with a magnetic moment of 1.528
+$\mu_\text{B}$ on the Fe site. The FM ground state is lower than the
+anti-ferromagnetic (AFM) state by 8.44 meV and non-magnetic(NM) state by 191
+meV per formula unit. These results are important in view of reports which
+claim that $\zeta$-Fe$_2$N undergoes an AFM transition below 10K and others
+which do not observe any magnetic transition up to 4.2K. We argue that the
+experimental results of AFM transition below 10K are inconclusive and we
+propose the presence of competing FM and AFM superexchange interactions between
+Fe sites mediated by nitrogen atoms, which are consistent with
+Goodenough-Kanamori-Anderson rules. We find that the anti-ferromagnetically
+coupled Fe sites are outnumbered by ferromagnetically coupled Fe sites leading
+to a stable FM ground state. A Stoner analysis of the results also supports our
+claim of a FM ground state.
+",0,1,0,0,0,0
+17136,A cautionary tale: limitations of a brightness-based spectroscopic approach to chromatic exoplanet radii,"  Determining wavelength-dependent exoplanet radii measurements is an excellent
+way to probe the composition of exoplanet atmospheres. In light of this, Borsa
+et al. (2016) sought to develop a technique to obtain such measurements by
+comparing ground-based transmission spectra to the expected brightness
+variations during an exoplanet transit. However, we demonstrate herein that
+this is not possible due to the transit light curve normalisation necessary to
+remove the effects of the Earth's atmosphere on the ground-based observations.
+This is because the recoverable exoplanet radius is set by the planet-to-star
+radius ratio within the transit light curve; we demonstrate this both
+analytically and with simulated planet transits, as well as through a
+reanalysis of the HD 189733b data.
+",0,1,0,0,0,0
+17137,Generating Memorable Mnemonic Encodings of Numbers,"  The major system is a mnemonic system that can be used to memorize sequences
+of numbers. In this work, we present a method to automatically generate
+sentences that encode a given number. We propose several encoding models and
+compare the most promising ones in a password memorability study. The results
+of the study show that a model combining part-of-speech sentence templates with
+an $n$-gram language model produces the most memorable password
+representations.
+",1,0,0,0,0,0
+17138,De-excitation spectroscopy of strongly interacting Rydberg gases,"  We present experimental results on the controlled de-excitation of Rydberg
+states in a cold gas of Rb atoms. The effect of the van der Waals interactions
+between the Rydberg atoms is clearly seen in the de-excitation spectrum and
+dynamics. Our observations are confirmed by numerical simulations. In
+particular, for off-resonant (facilitated) excitation we find that the
+de-excitation spectrum reflects the spatial arrangement of the atoms in the
+quasi one-dimensional geometry of our experiment. We discuss future
+applications of this technique and implications for detection and controlled
+dissipation schemes.
+",0,1,0,0,0,0
+17139,Hyperplane Clustering Via Dual Principal Component Pursuit,"  We extend the theoretical analysis of a recently proposed single subspace
+learning algorithm, called Dual Principal Component Pursuit (DPCP), to the case
+where the data are drawn from of a union of hyperplanes. To gain insight into
+the properties of the $\ell_1$ non-convex problem associated with DPCP, we
+develop a geometric analysis of a closely related continuous optimization
+problem. Then transferring this analysis to the discrete problem, our results
+state that as long as the hyperplanes are sufficiently separated, the dominant
+hyperplane is sufficiently dominant and the points are uniformly distributed
+inside the associated hyperplanes, then the non-convex DPCP problem has a
+unique global solution, equal to the normal vector of the dominant hyperplane.
+This suggests the correctness of a sequential hyperplane learning algorithm
+based on DPCP. A thorough experimental evaluation reveals that hyperplane
+learning schemes based on DPCP dramatically improve over the state-of-the-art
+methods for the case of synthetic data, while are competitive to the
+state-of-the-art in the case of 3D plane clustering for Kinect data.
+",1,0,0,1,0,0
+17140,Utilizing Domain Knowledge in End-to-End Audio Processing,"  End-to-end neural network based approaches to audio modelling are generally
+outperformed by models trained on high-level data representations. In this
+paper we present preliminary work that shows the feasibility of training the
+first layers of a deep convolutional neural network (CNN) model to learn the
+commonly-used log-scaled mel-spectrogram transformation. Secondly, we
+demonstrate that upon initializing the first layers of an end-to-end CNN
+classifier with the learned transformation, convergence and performance on the
+ESC-50 environmental sound classification dataset are similar to a CNN-based
+model trained on the highly pre-processed log-scaled mel-spectrogram features.
+",1,0,0,1,0,0
+17141,Testing Microfluidic Fully Programmable Valve Arrays (FPVAs),"  Fully Programmable Valve Array (FPVA) has emerged as a new architecture for
+the next-generation flow-based microfluidic biochips. This 2D-array consists of
+regularly-arranged valves, which can be dynamically configured by users to
+realize microfluidic devices of different shapes and sizes as well as
+interconnections. Additionally, the regularity of the underlying structure
+renders FPVAs easier to integrate on a tiny chip. However, these arrays may
+suffer from various manufacturing defects such as blockage and leakage in
+control and flow channels. Unfortunately, no efficient method is yet known for
+testing such a general-purpose architecture. In this paper, we present a novel
+formulation using the concept of flow paths and cut-sets, and describe an
+ILP-based hierarchical strategy for generating compact test sets that can
+detect multiple faults in FPVAs. Simulation results demonstrate the efficacy of
+the proposed method in detecting manufacturing faults with only a small number
+of test vectors.
+",1,0,0,0,0,0
+17142,"Fundamental groups, slalom curves and extremal length","  We define the extremal length of elements of the fundamental group of the
+twice punctured complex plane and give upper and lower bounds for this
+invariant. The bounds differ by a multiplicative constant. The main motivation
+comes from $3$-braid invariants and their application.
+",0,0,1,0,0,0
+17143,Topology and stability of the Kondo phase in quark matter,"  We investigate properties of the ground state of a light quark matter with
+heavy quark impurities. This system exhibits the ""QCD Kondo effect"" where the
+interaction strength between a light quark near the Fermi surface and a heavy
+quark increases with decreasing energy of the light quark towards the Fermi
+energy, and diverges at some scale near the Fermi energy, called the Kondo
+scale. Around and below the Kondo scale, we must treat the dynamics
+nonperturbatively. As a typical nonperturbative method to treat the strong
+coupling regime, we adopt a mean-field approach where we introduce a
+condensate, the Kondo condensate, representing a mixing between a light quark
+and a heavy quark, and determine the ground state in the presence of the Kondo
+condensate. We show that the ground state is a topologically non-trivial state
+and the heavy quark spin forms the hedgehog configuration in the momentum
+space. We can define the Berry phase for the ground-state wavefunction in the
+momentum space which is associated with a monopole at the position of a heavy
+quark. We also investigate fluctuations around the mean field in the
+random-phase approximation, and show the existence of (exciton-like) collective
+excitations made of a hole $h$ of a light quark and a heavy quark $Q$.
+",0,1,0,0,0,0
+17144,Second order nonlinear gyrokinetic theory : From the particle to the gyrocenter,"  A gyrokinetic reduction is based on a specific ordering of the different
+small parameters characterizing the background magnetic field and the
+fluctuating electromagnetic fields. In this tutorial, we consider the following
+ordering of the small parameters: $\epsilon\_B=\epsilon\_\delta^2$ where
+$\epsilon\_B$ is the small parameter associated with spatial inhomogeneities of
+the background magnetic field and $\epsilon\_\delta$ characterizes the small
+amplitude of the fluctuating fields. In particular, we do not make any
+assumption on the amplitude of the background magnetic field. Given this choice
+of ordering, we describe a self-contained and systematic derivation which is
+particularly well suited for the gyrokinetic reduction, following a two-step
+procedure. We follow the approach developed in [Sugama, Physics of Plasmas 7,
+466 (2000)]:In a first step, using a translation in velocity, we embed the
+transformation performed on the symplectic part of the gyrocentre reduction in
+the guiding-centre one. In a second step, using a canonical Lie transform, we
+eliminate the gyroangle dependence from the Hamiltonian. As a consequence, we
+explicitly derive the fully electromagnetic gyrokinetic equations at the second
+order in $\epsilon\_\delta$.
+",0,1,0,0,0,0
+17145,On the Classification and Algorithmic Analysis of Carmichael Numbers,"  In this paper, we study the properties of Carmichael numbers, false positives
+to several primality tests. We provide a classification for Carmichael numbers
+with a proportion of Fermat witnesses of less than 50%, based on if the
+smallest prime factor is greater than a determined lower bound. In addition, we
+conduct a Monte Carlo simulation as part of a probabilistic algorithm to detect
+if a given composite number is Carmichael. We modify this highly accurate
+algorithm with a deterministic primality test to create a novel, more efficient
+algorithm that differentiates between Carmichael numbers and prime numbers.
+",0,0,1,0,0,0
+17146,Phase reduction and synchronization of a network of coupled dynamical elements exhibiting collective oscillations,"  A general phase reduction method for a network of coupled dynamical elements
+exhibiting collective oscillations, which is applicable to arbitrary networks
+of heterogeneous dynamical elements, is developed. A set of coupled adjoint
+equations for phase sensitivity functions, which characterize phase response of
+the collective oscillation to small perturbations applied to individual
+elements, is derived. Using the phase sensitivity functions, collective
+oscillation of the network under weak perturbation can be described
+approximately by a one-dimensional phase equation. As an example, mutual
+synchronization between a pair of collectively oscillating networks of
+excitable and oscillatory FitzHugh-Nagumo elements with random coupling is
+studied.
+",0,1,0,0,0,0
+17147,Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs,"  Knowledge graphs are large, useful, but incomplete knowledge repositories.
+They encode knowledge through entities and relations which define each other
+through the connective structure of the graph. This has inspired methods for
+the joint embedding of entities and relations in continuous low-dimensional
+vector spaces, that can be used to induce new edges in the graph, i.e., link
+prediction in knowledge graphs. Learning these representations relies on
+contrasting positive instances with negative ones. Knowledge graphs include
+only positive relation instances, leaving the door open for a variety of
+methods for selecting negative examples. In this paper we present an empirical
+study on the impact of negative sampling on the learned embeddings, assessed
+through the task of link prediction. We use state-of-the-art knowledge graph
+embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on
+benchmark datasets -- FB15k and WN18. We compare well known methods for
+negative sampling and additionally propose embedding based sampling methods. We
+note a marked difference in the impact of these sampling methods on the two
+datasets, with the ""traditional"" corrupting positives method leading to best
+results on WN18, while embedding based methods benefiting the task on FB15k.
+",1,0,0,0,0,0
+17148,Reverse Curriculum Generation for Reinforcement Learning,"  Many relevant tasks require an agent to reach a certain state, or to
+manipulate objects into a desired configuration. For example, we might want a
+robot to align and assemble a gear onto an axle or insert and turn a key in a
+lock. These goal-oriented tasks present a considerable challenge for
+reinforcement learning, since their natural reward function is sparse and
+prohibitive amounts of exploration are required to reach the goal and receive
+some learning signal. Past approaches tackle these problems by exploiting
+expert demonstrations or by manually designing a task-specific reward shaping
+function to guide the learning agent. Instead, we propose a method to learn
+these tasks without requiring any prior knowledge other than obtaining a single
+state in which the task is achieved. The robot is trained in reverse, gradually
+learning to reach the goal from a set of start states increasingly far from the
+goal. Our method automatically generates a curriculum of start states that
+adapts to the agent's performance, leading to efficient training on
+goal-oriented tasks. We demonstrate our approach on difficult simulated
+navigation and fine-grained manipulation problems, not solvable by
+state-of-the-art reinforcement learning methods.
+",1,0,0,0,0,0
+17149,Mutually touching infinite cylinders in the 3D world of lines,"  Recently we gave arguments that only two unique topologically different
+configurations of 7 equal all mutually touching round cylinders (the
+configurations being mirror reflections of each other) are possible in 3D,
+although a whole world of configurations is possible already for round
+cylinders of arbitrary radii. It was found that as many as 9 round cylinders
+(all mutually touching) are possible in 3D while the upper bound for arbitrary
+cylinders was estimated to be not more than 14 under plausible arguments. Now
+by using the chirality and Ring matrices that we introduced earlier for the
+topological classification of line configurations, we have given arguments that
+the maximal number of mutually touching straight infinite cylinders of
+arbitrary cross-section (provided that its boundary is a smooth curve) in 3D
+cannot exceed 10. We generated numerically several configurations of 10
+cylinders, restricting ourselves with elliptic cylinders. Configurations of 8
+and 9 equal elliptic cylinders (all in mutually touching) are generated
+numerically as well. A possibility and restriction of continuous
+transformations from elliptic into round cylinder configurations are discussed.
+Some curious results concerning the properties of the chirality matrix (which
+coincides with Seidel's adjacency matrix important for the Graph theory) are
+presented.
+",0,0,1,0,0,0
+17150,Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields,"  This paper is a tutorial on Formal Concept Analysis (FCA) and its
+applications. FCA is an applied branch of Lattice Theory, a mathematical
+discipline which enables formalisation of concepts as basic units of human
+thinking and analysing data in the object-attribute form. Originated in early
+80s, during the last three decades, it became a popular human-centred tool for
+knowledge representation and data analysis with numerous applications. Since
+the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics
+include Information Retrieval with a focus on visualisation aspects, Machine
+Learning, Data Mining and Knowledge Discovery, Text Mining and several others.
+",1,0,0,1,0,0
+17151,Fully stripped? The dynamics of dark and luminous matter in the massive cluster collision MACSJ0553.4$-$3342,"  We present the results of a multiwavelength investigation of the very X-ray
+luminous galaxy cluster MACSJ0553.4-3342 ($z = 0.4270$; hereafter MACSJ0553).
+Combining high-resolution data obtained with the Hubble Space Telescope and the
+Chandra X-ray Observatory with ground-based galaxy spectroscopy, our analysis
+establishes the system unambiguously as a binary, post-collision merger of
+massive clusters. Key characteristics include perfect alignment of luminous and
+dark matter for one component, a separation of almost 650 kpc (in projection)
+between the dark-matter peak of the other subcluster and the second X-ray peak,
+extremely hot gas (k$T > 15$ keV) at either end of the merger axis, a potential
+cold front in the east, an unusually low gas mass fraction of approximately
+0.075 for the western component, a velocity dispersion of $1490_{-130}^{+104}$
+km s$^{-1}$, and no indication of significant substructure along the line of
+sight. We propose that the MACSJ0553 merger proceeds not in the plane of the
+sky, but at a large inclination angle, is observed very close to turnaround,
+and that the eastern X-ray peak is the cool core of the slightly less massive
+western component that was fully stripped and captured by the eastern
+subcluster during the collision. If correct, this hypothesis would make
+MACSJ0553 a superb target for a competitive study of ram-pressure stripping and
+the collisional behaviour of luminous and dark matter during cluster formation.
+",0,1,0,0,0,0
+17152,When intuition fails in assessing conditional risks: the example of the frog riddle,"  Recently, the educational initiative TED-Ed has published a popular brain
+teaser coined the 'frog riddle', which illustrates non-intuitive implications
+of conditional probabilities. In its intended form, the frog riddle is a
+reformulation of the classic boy-girl paradox. However, the authors alter the
+narrative of the riddle in a form, that subtly changes the way information is
+conveyed. The presented solution, unfortunately, does not take this point into
+full account, and as a consequence, lacks consistency in the sense that
+different parts of the problem are treated on unequal footing. We here review,
+how the mechanism of receiving information matters, and why this is exactly the
+reason that such kind of problems challenge intuitive thinking. Subsequently,
+we present a generalized solution, that accounts for the above difficulties,
+and preserves full logical consistency. Eventually, the relation to the
+boy-girl paradox is discussed.
+",0,1,0,0,0,0
+17153,Tuning parameter selection rules for nuclear norm regularized multivariate linear regression,"  We consider the tuning parameter selection rules for nuclear norm regularized
+multivariate linear regression (NMLR) in high-dimensional setting.
+High-dimensional multivariate linear regression is widely used in statistics
+and machine learning, and regularization technique is commonly applied to deal
+with the special structures in high-dimensional data. As we know, how to select
+the tuning parameter is an essential issue for regularization approach and it
+directly affects the model estimation performance. To the best of our
+knowledge, there are no rules about the tuning parameter selection for NMLR
+from the point of view of optimization. In order to establish such rules, we
+study the duality theory of NMLR. Then, we claim the choice of tuning parameter
+for NMLR is based on the sample data and the solution of NMLR dual problem,
+which is a projection on a nonempty, closed and convex set. Moreover, based on
+the (firm) nonexpansiveness and the idempotence of the projection operator, we
+build four tuning parameter selection rules PSR, PSRi, PSRfn and PSR+.
+Furthermore, we give a sequence of tuning parameters and the corresponding
+intervals for every rule, which states that the rank of the estimation
+coefficient matrix is no more than a fixed number for the tuning parameter in
+the given interval. The relationships between these rules are also discussed
+and PSR+ is the most efficient one to select the tuning parameter. Finally, the
+numerical results are reported on simulation and real data, which show that
+these four tuning parameter selection rules are valuable.
+",0,0,1,1,0,0
+17154,Understanding the evolution of multimedia content in the Internet through BitTorrent glasses,"  Today's Internet traffic is mostly dominated by multimedia content and the
+prediction is that this trend will intensify in the future. Therefore, main
+Internet players, such as ISPs, content delivery platforms (e.g. Youtube,
+Bitorrent, Netflix, etc) or CDN operators, need to understand the evolution of
+multimedia content availability and popularity in order to adapt their
+infrastructures and resources to satisfy clients requirements while they
+minimize their costs. This paper presents a thorough analysis on the evolution
+of multimedia content available in BitTorrent. Specifically, we analyze the
+evolution of four relevant metrics across different content categories: content
+availability, content popularity, content size and user's feedback. To this end
+we leverage a large-scale dataset formed by 4 snapshots collected from the most
+popular BitTorrent portal, namely The Pirate Bay, between Nov. 2009 and Feb.
+2012. Overall our dataset is formed by more than 160k content that attracted
+more than 185M of download sessions.
+",1,0,0,0,0,0
+17155,The weak rate of convergence for the Euler-Maruyama approximation of one-dimensional stochastic differential equations involving the local times of the unknown process,"  In this paper, we consider the weak convergence of the Euler-Maruyama
+approximation for one dimensional stochastic differential equations involving
+the local times of the unknown process. We use a transformation in order to
+remove the local time from the stochastic differential equations and we provide
+the approximation of Euler-maruyama for the stochastic differential equations
+without local time. After that, we conclude the approximation of Euler-maruyama
+for one dimensional stochastic differential equations involving the local times
+of the unknown process , and we provide the rate of weak convergence for any
+function G in a certain class.
+",0,0,1,0,0,0
+17156,Study of deteriorating semiopaque turquoise lead-potassium glass beads at different stages of corrosion using micro-FTIR spectroscopy,"  Nowadays, a problem of historical beadworks conservation in museum
+collections is actual more than ever because of fatal corrosion of the 19th
+century glass beads. Vibrational spectroscopy is a powerful method for
+investigation of glass, namely, of correlation of the structure-chemical
+composition. Therefore, Fourier-transform infrared spectroscopy was used for
+examination of degradation processes in cloudy turquoise glass beads, which in
+contrast to other color ones deteriorate especially strongly. Micro-X-ray
+fluorescence spectrometry of samples has shown that lead-potassium glass
+PbO-K$_2$O-SiO$_2$ with small amount of Cu and Sb was used for manufacture of
+cloudy turquoise beads. Fourier-transform infrared spectroscopy study of the
+beads at different stages of glass corrosion was carried out in the range from
+200 to 4000 cm$^{-1}$ in the attenuated total reflection mode. In all the
+spectra, we have observed shifts of two major absorption bands to low-frequency
+range (~1000 and ~775 cm$^{-1}$) compared to ones typical for amorphous SiO2
+(~1100 and 800 cm$^{-1}$, respectively). Such an effect is connected with
+Pb$^{2+}$ and K$^+$ appending to the glass network. The presence of a weak band
+at ~1630 cm$^{-1}$ in all the spectra is attributed to the adsorption of
+H$_2$O. After annealing of the beads, the band disappeared completely in less
+deteriorated samples and became significantly weaker in more destroyed ones.
+Based on that we conclude that there is adsorbed molecular water on the beads.
+However, products of corrosion (e.g., alkali in the form of white crystals or
+droplets of liquid alkali) were not observed on the glass surface. We have also
+observed glass depolymerisation in the strongly degraded beads, which is
+exhibited in domination of the band peaked at ~1000 cm$^{-1}$.
+",0,1,0,0,0,0
+17157,Stratified surgery and K-theory invariants of the signature operator,"  In work of Higson-Roe the fundamental role of the signature as a homotopy and
+bordism invariant for oriented manifolds is made manifest in how it and related
+secondary invariants define a natural transformation between the
+(Browder-Novikov-Sullivan-Wall) surgery exact sequence and a long exact
+sequence of C*-algebra K-theory groups.
+In recent years the (higher) signature invariants have been extended from
+closed oriented manifolds to a class of stratified spaces known as L-spaces or
+Cheeger spaces. In this paper we show that secondary invariants, such as the
+rho-class, also extend from closed manifolds to Cheeger spaces. We revisit a
+surgery exact sequence for stratified spaces originally introduced by
+Browder-Quinn and obtain a natural transformation analogous to that of
+Higson-Roe. We also discuss geometric applications.
+",0,0,1,0,0,0
+17158,Weighted Tensor Decomposition for Learning Latent Variables with Partial Data,"  Tensor decomposition methods are popular tools for learning latent variables
+given only lower-order moments of the data. However, the standard assumption is
+that we have sufficient data to estimate these moments to high accuracy. In
+this work, we consider the case in which certain dimensions of the data are not
+always observed---common in applied settings, where not all measurements may be
+taken for all observations---resulting in moment estimates of varying quality.
+We derive a weighted tensor decomposition approach that is computationally as
+efficient as the non-weighted approach, and demonstrate that it outperforms
+methods that do not appropriately leverage these less-observed dimensions.
+",0,0,0,1,0,0
+17159,Sparse Bounds for Discrete Quadratic Phase Hilbert Transform,"  Consider the discrete quadratic phase Hilbert Transform acting on $\ell^{2}$
+finitely supported functions $$ H^{\alpha} f(n) : = \sum_{m \neq 0} \frac{e^{2
+\pi i\alpha m^2} f(n - m)}{m}. $$ We prove that, uniformly in $\alpha \in
+\mathbb{T}$, there is a sparse bound for the bilinear form $\langle H^{\alpha}
+f , g \rangle$. The sparse bound implies several mapping properties such as
+weighted inequalities in an intersection of Muckenhoupt and reverse Hölder
+classes.
+",0,0,1,0,0,0
+17160,Fast Distributed Approximation for TAP and 2-Edge-Connectivity,"  The tree augmentation problem (TAP) is a fundamental network design problem,
+in which the input is a graph $G$ and a spanning tree $T$ for it, and the goal
+is to augment $T$ with a minimum set of edges $Aug$ from $G$, such that $T \cup
+Aug$ is 2-edge-connected.
+TAP has been widely studied in the sequential setting. The best known
+approximation ratio of 2 for the weighted case dates back to the work of
+Frederickson and JáJá, SICOMP 1981. Recently, a 3/2-approximation was
+given for the unweighted case by Kortsarz and Nutov, TALG 2016, and recent
+breakthroughs by Adjiashvili, SODA 2017, and by Fiorini et al., 2017, give
+approximations better than 2 for bounded weights.
+In this paper, we provide the first fast distributed approximations for TAP.
+We present a distributed $2$-approximation for weighted TAP which completes in
+$O(h)$ rounds, where $h$ is the height of $T$. When $h$ is large, we show a
+much faster 4-approximation algorithm for the unweighted case, completing in
+$O(D+\sqrt{n}\log^*{n})$ rounds, where $n$ is the number of vertices and $D$ is
+the diameter of $G$.
+Immediate consequences of our results are an $O(D)$-round 2-approximation
+algorithm for the minimum size 2-edge-connected spanning subgraph, which
+significantly improves upon the running time of previous approximation
+algorithms, and an $O(h_{MST}+\sqrt{n}\log^{*}{n})$-round 3-approximation
+algorithm for the weighted case, where $h_{MST}$ is the height of the MST of
+the graph. Additional applications are algorithms for verifying
+2-edge-connectivity and for augmenting the connectivity of any connected
+spanning subgraph to 2.
+Finally, we complement our study with proving lower bounds for distributed
+approximations of TAP.
+",1,0,0,0,0,0
+17161,Generative Adversarial Source Separation,"  Generative source separation methods such as non-negative matrix
+factorization (NMF) or auto-encoders, rely on the assumption of an output
+probability density. Generative Adversarial Networks (GANs) can learn data
+distributions without needing a parametric assumption on the output density. We
+show on a speech source separation experiment that, a multi-layer perceptron
+trained with a Wasserstein-GAN formulation outperforms NMF, auto-encoders
+trained with maximum likelihood, and variational auto-encoders in terms of
+source to distortion ratio.
+",1,0,0,1,0,0
+17162,Bootstrapping Generalization Error Bounds for Time Series,"  We consider the problem of finding confidence intervals for the risk of
+forecasting the future of a stationary, ergodic stochastic process, using a
+model estimated from the past of the process. We show that a bootstrap
+procedure provides valid confidence intervals for the risk, when the data
+source is sufficiently mixing, and the loss function and the estimator are
+suitably smooth. Autoregressive (AR(d)) models estimated by least squares obey
+the necessary regularity conditions, even when mis-specified, and simulations
+show that the finite- sample coverage of our bounds quickly converges to the
+theoretical, asymptotic level. As an intermediate step, we derive sufficient
+conditions for asymptotic independence between empirical distribution functions
+formed by splitting a realization of a stochastic process, of independent
+interest.
+",0,0,1,1,0,0
+17163,Sign reversal of magnetoresistance and p to n transition in Ni doped ZnO thin film,"  We report the magnetoresistance and nonlinear Hall effect studies over a wide
+temperature range in pulsed laser deposited Ni0.07Zn0.93O thin film. Negative
+and positive contributions to magnetoresistance at high and low temperatures
+have been successfully modeled by the localized magnetic moment and two band
+conduction process involving heavy and light hole subbands, respectively.
+Nonlinearity in the Hall resistance also agrees well with the two channel
+conduction model. A negative Hall voltage has been found for T $\gte 50 K$,
+implying a dominant conduction mainly by electrons whereas positive Hall
+voltage for T less than 50 K shows hole dominated conduction in this material.
+Crossover in the sign of magnetoresistance from negative to positive reveals
+the spin polarization of the charge carriers and hence the applicability of Ni
+doped ZnO thin film for spintronic applications.
+",0,1,0,0,0,0
+17164,Proceedings of the Third Workshop on Formal Integrated Development Environment,"  This volume contains the proceedings of F-IDE 2016, the third international
+workshop on Formal Integrated Development Environment, which was held as an FM
+2016 satellite event, on November 8, 2016, in Limassol (Cyprus). High levels of
+safety, security and also privacy standards require the use of formal methods
+to specify and develop compliant software (sub)systems. Any standard comes with
+an assessment process, which requires a complete documentation of the
+application in order to ease the justification of design choices and the review
+of code and proofs. Thus tools are needed for handling specifications, program
+constructs and verification artifacts. The aim of the F-IDE workshop is to
+provide a forum for presenting and discussing research efforts as well as
+experience returns on design, development and usage of formal IDE aiming at
+making formal methods ""easier"" for both specialists and non-specialists.
+",1,0,0,0,0,0
+17165,A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment,"  Animals (especially humans) have an amazing ability to learn new tasks
+quickly, and switch between them flexibly. How brains support this ability is
+largely unknown, both neuroscientifically and algorithmically. One reasonable
+supposition is that modules drawing on an underlying general-purpose sensory
+representation are dynamically allocated on a per-task basis. Recent results
+from neuroscience and artificial intelligence suggest the role of the general
+purpose visual representation may be played by a deep convolutional neural
+network, and give some clues how task modules based on such a representation
+might be discovered and constructed. In this work, we investigate module
+architectures in an embodied two-dimensional touchscreen environment, in which
+an agent's learning must occur via interactions with an environment that emits
+images and rewards, and accepts touches as input. This environment is designed
+to capture the physical structure of the task environments that are commonly
+deployed in visual neuroscience and psychophysics. We show that in this
+context, very simple changes in the nonlinear activations used by such a module
+can significantly influence how fast it is at learning visual tasks and how
+suitable it is for switching to new tasks.
+",1,0,0,1,0,0
+17166,3D Move to See: Multi-perspective visual servoing for improving object views with semantic segmentation,"  In this paper, we present a new approach to visual servoing for robotics,
+referred to as 3D Move to See (3DMTS), based on the principle of finding the
+next best view using a 3D camera array and a robotic manipulator to obtain
+multiple samples of the scene from different perspectives. The method uses
+semantic vision and an objective function applied to each perspective to sample
+a gradient representing the direction of the next best view. The method is
+demonstrated within simulation and on a real robotic platform containing a
+custom 3D camera array for the challenging scenario of robotic harvesting in a
+highly occluded and unstructured environment. It was shown on a real robotic
+platform that by moving the end effector using the gradient of an objective
+function leads to a locally optimal view of the object of interest, even
+amongst occlusions. The overall performance of the 3DMTS method obtained a mean
+increase in target size by 29.3% compared to a baseline method using a single
+RGB-D camera, which obtained 9.17%. The results demonstrate qualitatively and
+quantitatively that the 3DMTS method performed better in most scenarios, and
+yielded three times the target size compared to the baseline method. The
+increased target size in the final view will improve the detection of key
+features of the object of interest for further manipulation, such as grasping
+and harvesting.
+",1,0,0,0,0,0
+17167,Modeling influenza-like illnesses through composite compartmental models,"  Epidemiological models for the spread of pathogens in a population are
+usually only able to describe a single pathogen. This makes their application
+unrealistic in cases where multiple pathogens with similar symptoms are
+spreading concurrently within the same population. Here we describe a method
+which makes possible the application of multiple single-strain models under
+minimal conditions. As such, our method provides a bridge between theoretical
+models of epidemiology and data-driven approaches for modeling of influenza and
+other similar viruses.
+Our model extends the Susceptible-Infected-Recovered model to higher
+dimensions, allowing the modeling of a population infected by multiple viruses.
+We further provide a method, based on an overcomplete dictionary of feasible
+realizations of SIR solutions, to blindly partition the time series
+representing the number of infected people in a population into individual
+components, each representing the effect of a single pathogen.
+We demonstrate the applicability of our proposed method on five years of
+seasonal influenza-like illness (ILI) rates, estimated from Twitter data. We
+demonstrate that our method describes, on average, 44\% of the variance in the
+ILI time series. The individual infectious components derived from our model
+are matched to known viral profiles in the populations, which we demonstrate
+matches that of independently collected epidemiological data. We further show
+that the basic reproductive numbers ($R0$) of the matched components are in
+range known for these pathogens.
+Our results suggest that the proposed method can be applied to other
+pathogens and geographies, providing a simple method for estimating the
+parameters of epidemics in a population.
+",1,1,0,0,0,0
+17168,Notes on the Polish Algorithm,"  We study, with the help of a computer program, the Polish Algorithm for
+finite terms satisfying various algebraic laws, e.g., left distributivity a(bc)
+= (ab)(ac). While the termination of the algorithm for left distributivity
+remains open in general, we can establish some partial results, which might be
+useful towards a positive solution. In contrast, we show the divergence of the
+algorithm for the laws a(bc) = (ab)(cc) and a(bc) = (ab)(a(ac)).
+",0,0,1,0,0,0
+17169,Penalized Maximum Tangent Likelihood Estimation and Robust Variable Selection,"  We introduce a new class of mean regression estimators -- penalized maximum
+tangent likelihood estimation -- for high-dimensional regression estimation and
+variable selection. We first explain the motivations for the key ingredient,
+maximum tangent likelihood estimation (MTE), and establish its asymptotic
+properties. We further propose a penalized MTE for variable selection and show
+that it is $\sqrt{n}$-consistent, enjoys the oracle property. The proposed
+class of estimators consists penalized $\ell_2$ distance, penalized exponential
+squared loss, penalized least trimmed square and penalized least square as
+special cases and can be regarded as a mixture of minimum Kullback-Leibler
+distance estimation and minimum $\ell_2$ distance estimation. Furthermore, we
+consider the proposed class of estimators under the high-dimensional setting
+when the number of variables $d$ can grow exponentially with the sample size
+$n$, and show that the entire class of estimators (including the aforementioned
+special cases) can achieve the optimal rate of convergence in the order of
+$\sqrt{\ln(d)/n}$. Finally, simulation studies and real data analysis
+demonstrate the advantages of the penalized MTE.
+",0,0,0,1,0,0
+17170,A minimally-dissipative low-Mach number solver for complex reacting flows in OpenFOAM,"  Large eddy simulation (LES) has become the de-facto computational tool for
+modeling complex reacting flows, especially in gas turbine applications.
+However, readily usable general-purpose LES codes for complex geometries are
+typically academic or proprietary/commercial in nature. The objective of this
+work is to develop and disseminate an open source LES tool for low-Mach number
+turbulent combustion using the OpenFOAM framework. In particular, a
+collocated-mesh approach suited for unstructured grid formulation is provided.
+Unlike other fluid dynamics models, LES accuracy is intricately linked to
+so-called primary and secondary conservation properties of the numerical
+discretization schemes. This implies that although the solver only evolves
+equations for mass, momentum, and energy, the implied discrete equation for
+kinetic energy (square of velocity) should be minimally-dissipative. Here, a
+specific spatial and temporal discretization is imposed such that this kinetic
+energy dissipation is minimized. The method is demonstrated using manufactured
+solutions approach on regular and skewed meshes, a canonical flow problem, and
+a turbulent sooting flame in a complex domain relevant to gas turbines
+applications.
+",0,1,0,0,0,0
+17171,Surface energy of strained amorphous solids,"  Surface stress and surface energy are fundamental quantities which
+characterize the interface between two materials. Although these quantities are
+identical for interfaces involving only fluids, the Shuttleworth effect
+demonstrates that this is not the case for most interfaces involving solids,
+since their surface energies change with strain. Crystalline materials are
+known to have strain dependent surface energies, but in amorphous materials,
+such as polymeric glasses and elastomers, the strain dependence is debated due
+to a dearth of direct measurements. Here, we utilize contact angle measurements
+on strained glassy and elastomeric solids to address this matter. We show
+conclusively that interfaces involving polymeric glasses exhibit strain
+dependent surface energies, and give strong evidence for the absence of such a
+dependence for incompressible elastomers. The results provide fundamental
+insight into our understanding of the interfaces of amorphous solids and their
+interaction with contacting liquids.
+",0,1,0,0,0,0
+17172,Biaxial magnetic field setup for angular magnetic measurements of thin films and spintronic nanodevices,"  The biaxial magnetic-field setup for angular magnetic measurements of thin
+film and spintronic devices is designed and presented. The setup allows for
+application of the in-plane magnetic field using a quadrupole electromagnet,
+controlled by power supply units and integrated with an electromagnet biaxial
+magnetic field sensor. In addition, the probe station is equipped with a
+microwave circuitry, which enables angle-resolved spin torque oscillation
+measurements. The angular dependencies of magnetoresistance and spin diode
+effect in a giant magnetoresistance strip are shown as an operational
+verification of the experimental setup. We adapted an analytical macrospin
+model to reproduce both the resistance and spin-diode angular dependency
+measurements.
+",0,1,0,0,0,0
+17173,Active matter invasion of a viscous fluid: unstable sheets and a no-flow theorem,"  We investigate the dynamics of a dilute suspension of hydrodynamically
+interacting motile or immotile stress-generating swimmers or particles as they
+invade a surrounding viscous fluid. Colonies of aligned pusher particles are
+shown to elongate in the direction of particle orientation and undergo a
+cascade of transverse concentration instabilities, governed at small times by
+an equation which also describes the Saffman-Taylor instability in a Hele-Shaw
+cell, or Rayleigh-Taylor instability in two-dimensional flow through a porous
+medium. Thin sheets of aligned pusher particles are always unstable, while
+sheets of aligned puller particles can either be stable (immotile particles),
+or unstable (motile particles) with a growth rate which is non-monotonic in the
+force dipole strength. We also prove a surprising ""no-flow theorem"": a
+distribution initially isotropic in orientation loses isotropy immediately but
+in such a way that results in no fluid flow everywhere and for all time.
+",0,0,0,0,1,0
+17174,Interplay of synergy and redundancy in diamond motif,"  The formalism of partial information decomposition provides independent or
+non-overlapping components constituting total information content provided by a
+set of source variables about the target variable. These components are
+recognised as unique information, synergistic information and, redundant
+information. The metric of net synergy, conceived as the difference between
+synergistic and redundant information, is capable of detecting synergy,
+redundancy and, information independence among stochastic variables. And it can
+be quantified, as it is done here, using appropriate combinations of different
+Shannon mutual information terms. Utilisation of such a metric in network
+motifs with the nodes representing different biochemical species, involved in
+information sharing, uncovers rich store for interesting results. In the
+current study, we make use of this formalism to obtain a comprehensive
+understanding of the relative information processing mechanism in a diamond
+motif and two of its sub-motifs namely bifurcation and integration motif
+embedded within the diamond motif. The emerging patterns of synergy and
+redundancy and their effective contribution towards ensuring high fidelity
+information transmission are duly compared in the sub-motifs and independent
+motifs (bifurcation and integration). In this context, the crucial roles played
+by various time scales and activation coefficients in the network topologies
+are especially emphasised. We show that the origin of synergy and redundancy in
+information transmission can be physically justified by decomposing diamond
+motif into bifurcation and integration motif.
+",0,1,0,0,0,0
+17175,Hardy-Sobolev equations with asymptotically vanishing singularity: Blow-up analysis for the minimal energy,"  We study the asymptotic behavior of a sequence of positive solutions
+$(u_{\epsilon})_{\epsilon >0}$ as $\epsilon \to 0$ to the family of equations
+\begin{equation*} \left\{\begin{array}{ll} \Delta
+u_{\epsilon}+a(x)u_{\epsilon}=
+\frac{u_{\epsilon}^{2^*(s_{\epsilon})-1}}{|x|^{s_{\epsilon}}}& \hbox{ in
+}\Omega\\ u_{\epsilon}=0 & \hbox{ on }\partial\Omega. \end{array}\right.
+\end{equation*} where $(s_{\epsilon})_{\epsilon >0}$ is a sequence of positive
+real numbers such that $\lim \limits_{\epsilon \rightarrow 0} s_{\epsilon}=0$,
+$2^{*}(s_{\epsilon}):= \frac{2(n-s_{\epsilon})}{n-2}$ and $\Omega \subset
+\mathbb{R}^{n}$ is a bounded smooth domain such that $0 \in \partial \Omega$.
+When the sequence $(u_{\epsilon})_{\epsilon >0}$ is uniformly bounded in
+$L^{\infty}$, then upto a subsequence it converges strongly to a minimizing
+solution of the stationary Schrödinger equation with critical growth. In
+case the sequence blows up, we obtain strong pointwise control on the blow up
+sequence, and then using the Pohozaev identity localize the point of
+singularity, which in this case can at most be one, and derive precise blow up
+rates. In particular when $n=3$ or $a\equiv 0$ then blow up can occur only at
+an interior point of $\Omega$ or the point $0 \in \partial \Omega$.
+",0,0,1,0,0,0
+17176,"Some results on Ricatti Equations, Floquet Theory and Applications","  In this paper, we present two new results to the classical Floquet theory,
+which provides the Floquet multipliers for two classes of the planar periodic
+system. One these results provides the Floquet multipliers independently of the
+solution of system. To demonstrate the application of these analytical results,
+we consider a cholera epidemic model with phage dynamics and seasonality
+incorporated.
+",0,0,1,0,0,0
+17177,"Size, Shape, and Phase Control in Ultrathin CdSe Nanosheets","  Ultrathin two-dimensional nanosheets raise a rapidly increasing interest due
+to their unique dimensionality-dependent properties. Most of the
+two-dimensional materials are obtained by exfoliation of layered bulk materials
+or are grown on substrates by vapor deposition methods. To produce
+free-standing nanosheets, solution-based colloidal methods are emerging as
+promising routes. In this work, we demonstrate ultrathin CdSe nanosheets with
+controllable size, shape and phase. The key of our approach is the use of
+halogenated alkanes as additives in a hot-injection synthesis. Increasing
+concentrations of bromoalkanes can tune the shape from sexangular to
+quadrangular to triangular and the phase from zinc blende to wurtzite. Geometry
+and crystal structure evolution of the nanosheets take place in the presence of
+halide ions, acting as cadmium complexing agents and as surface X-type ligands,
+according to mass spectrometry and X-ray photoelectron spectroscopies. Our
+experimental findings show that the degree of these changes depends on the
+molecular structure of the halogen alkanes and the type of halogen atom.
+",0,1,0,0,0,0
+17178,Transfer of magnetic order and anisotropy through epitaxial integration of 3$d$ and 4$f$ spin systems,"  Resonant x-ray scattering at the Dy $M_5$ and Ni $L_3$ absorption edges was
+used to probe the temperature and magnetic field dependence of magnetic order
+in epitaxial LaNiO$_3$-DyScO$_3$ superlattices. For superlattices with 2 unit
+cell thick LaNiO$_3$ layers, a commensurate spiral state develops in the Ni
+spin system below 100 K. Upon cooling below $T_{ind} = 18$ K, Dy-Ni exchange
+interactions across the LaNiO$_3$-DyScO$_3$ interfaces induce collinear
+magnetic order of interfacial Dy moments as well as a reorientation of the Ni
+spins to a direction dictated by the strong magneto-crystalline anisotropy of
+Dy. This transition is reversible by an external magnetic field of 3 T.
+Tailored exchange interactions between rare-earth and transition-metal ions
+thus open up new perspectives for the manipulation of spin structures in
+metal-oxide heterostructures and devices.
+",0,1,0,0,0,0
+17179,Exotic limit sets of Teichmüller geodesics in the HHS boundary,"  We answer a question of Durham, Hagen, and Sisto, proving that a
+Teichmüller geodesic ray does not necessarily converge to a unique point in
+the hierarchically hyperbolic space boundary of Teichmüller space. In fact,
+we prove that the limit set can be almost anything allowed by the topology.
+",0,0,1,0,0,0
+17180,"Decoupling ""when to update"" from ""how to update""","  Deep learning requires data. A useful approach to obtain data is to be
+creative and mine data from various sources, that were created for different
+purposes. Unfortunately, this approach often leads to noisy labels. In this
+paper, we propose a meta algorithm for tackling the noisy labels problem. The
+key idea is to decouple ""when to update"" from ""how to update"". We demonstrate
+the effectiveness of our algorithm by mining data for gender classification by
+combining the Labeled Faces in the Wild (LFW) face recognition dataset with a
+textual genderizing service, which leads to a noisy dataset. While our approach
+is very simple to implement, it leads to state-of-the-art results. We analyze
+some convergence properties of the proposed algorithm.
+",1,0,0,0,0,0
+17181,Advection of potential temperature in the atmosphere of irradiated exoplanets: a robust mechanism to explain radius inflation,"  The anomalously large radii of strongly irradiated exoplanets have remained a
+major puzzle in astronomy. Based on a 2D steady state atmospheric circulation
+model, the validity of which is assessed by comparison to 3D calculations, we
+reveal a new mechanism, namely the advection of the potential temperature due
+to mass and longitudinal momentum conservation, a process occuring in the
+Earth's atmosphere or oceans. At depth, the vanishing heating flux forces the
+atmospheric structure to converge to a hotter adiabat than the one obtained
+with 1D calculations, implying a larger radius for the planet. Not only do the
+calculations reproduce the observed radius of HD209458b, but also the observed
+correlation between radius inflation and irradiation for transiting planets.
+Vertical advection of potential temperature induced by non uniform atmospheric
+heating thus provides a robust mechanism explaining the inflated radii of
+irradiated hot Jupiters.
+",0,1,0,0,0,0
+17182,Band depths based on multiple time instances,"  Bands of vector-valued functions $f:T\mapsto\mathbb{R}^d$ are defined by
+considering convex hulls generated by their values concatenated at $m$
+different values of the argument. The obtained $m$-bands are families of
+functions, ranging from the conventional band in case the time points are
+individually considered (for $m=1$) to the convex hull in the functional space
+if the number $m$ of simultaneously considered time points becomes large enough
+to fill the whole time domain. These bands give rise to a depth concept that is
+new both for real-valued and vector-valued functions.
+",0,0,1,1,0,0
+17183,On Biased Correlation Estimation,"  In general, underestimation of risk is something which should be avoided as
+far as possible. Especially in financial asset management, equity risk is
+typically characterized by the measure of portfolio variance, or indirectly by
+quantities which are derived from it. Since there is a linear dependency of the
+variance and the empirical correlation between asset classes, one is compelled
+to control or to avoid the possibility of underestimating correlation
+coefficients. In the present approach, we formalize common practice and
+classify these approaches by computing their probability of underestimation. In
+addition, we introduce a new estimator which is characterized by having the
+advantage of a constant and controllable probability of underestimation. We
+prove that the new estimator is statistically consistent.
+",0,0,1,1,0,0
+17184,Atomic Swaptions: Cryptocurrency Derivatives,"  The atomic swap protocol allows for the exchange of cryptocurrencies on
+different blockchains without the need to trust a third-party. However, market
+participants who desire to hold derivative assets such as options or futures
+would also benefit from trustless exchange. In this paper I propose the atomic
+swaption, which extends the atomic swap to allow for such exchanges. Crucially,
+atomic swaptions do not require the use of oracles. I also introduce the margin
+contract, which provides the ability to create leveraged and short positions.
+Lastly, I discuss how atomic swaptions may be routed on the Lightning Network.
+",0,0,0,0,0,1
+17185,An arbitrary order scheme on generic meshes for miscible displacements in porous media,"  We design, analyse and implement an arbitrary order scheme applicable to
+generic meshes for a coupled elliptic-parabolic PDE system describing miscible
+displacement in porous media. The discretisation is based on several
+adaptations of the Hybrid-High-Order (HHO) method due to Di Pietro et al.
+[Computational Methods in Applied Mathematics, 14(4), (2014)]. The equation
+governing the pressure is discretised using an adaptation of the HHO method for
+variable diffusion, while the discrete concentration equation is based on the
+HHO method for advection-diffusion-reaction problems combined with numerically
+stable flux reconstructions for the advective velocity that we have derived
+using the results of Cockburn et al. [ESAIM: Mathematical Modelling and
+Numerical Analysis, 50(3), (2016)]. We perform some rigorous analysis of the
+method to demonstrate its $L^2$ stability under the irregular data often
+presented by reservoir engineering problems and present several numerical tests
+to demonstrate the quality of the results that are produced by the proposed
+scheme.
+",1,0,0,0,0,0
+17186,"Parametricity, automorphisms of the universe, and excluded middle","  It is known that one can construct non-parametric functions by assuming
+classical axioms. Our work is a converse to that: we prove classical axioms in
+dependent type theory assuming specific instances of non-parametricity. We also
+address the interaction between classical axioms and the existence of
+automorphisms of a type universe. We work over intensional Martin-Löf
+dependent type theory, and in some results assume further principles including
+function extensionality, propositional extensionality, propositional
+truncation, and the univalence axiom.
+",1,0,1,0,0,0
+17187,Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity,"  We extend the classic convergence rate theory for subgradient methods to
+apply to non-Lipschitz functions. For the deterministic projected subgradient
+method, we present a global $O(1/\sqrt{T})$ convergence rate for any convex
+function which is locally Lipschitz around its minimizers. This approach is
+based on Shor's classic subgradient analysis and implies generalizations of the
+standard convergence rates for gradient descent on functions with Lipschitz or
+Hölder continuous gradients. Further, we show a $O(1/\sqrt{T})$ convergence
+rate for the stochastic projected subgradient method on convex functions with
+at most quadratic growth, which improves to $O(1/T)$ under either strong
+convexity or a weaker quadratic lower bound condition.
+",1,0,0,0,0,0
+17188,A quantum dynamics method for excited electrons in molecular aggregate system using a group diabatic Fock matrix,"  We introduce a practical calculation scheme for the description of excited
+electron dynamics in molecular aggregated systems within a locally group
+diabatic Fock representation. This scheme makes it easy to analyze the
+interacting time-dependent excitations of local sites in complex systems. In
+addition, light-electron couplings are considered. The present scheme is
+intended for investigations on the migration dynamics of excited electrons in
+light-energy conversion systems. The scheme was applied to two systems: a
+naphthalene(NPTL)-tetracyanoethylene(TCNE) dimer and a 20-mer circle of
+ethylene molecules. Through local group analyses of the dynamical electrons, we
+obtained an intuitive understanding of the electron transfers between the
+monomers.
+",0,1,0,0,0,0
+17189,Products of topological groups in which all closed subgroups are separable,"  We prove that if $H$ is a topological group such that all closed subgroups of
+$H$ are separable, then the product $G\times H$ has the same property for every
+separable compact group $G$.
+Let $c$ be the cardinality of the continuum. Assuming $2^{\omega_1} = c$, we
+show that there exist:
+(1) pseudocompact topological abelian groups $G$ and $H$ such that all closed
+subgroups of $G$ and $H$ are separable, but the product $G\times H$ contains a
+closed non-separable $\sigma$-compact subgroup;
+(2) pseudocomplete locally convex vector spaces $K$ and $L$ such that all
+closed vector subspaces of $K$ and $L$ are separable, but the product $K\times
+L$ contains a closed non-separable $\sigma$-compact vector subspace.
+",0,0,1,0,0,0
+17190,Low rank solutions to differentiable systems over matrices and applications,"  Differentiable systems in this paper means systems of equations that are
+described by differentiable real functions in real matrix variables. This paper
+proposes algorithms for finding minimal rank solutions to such systems over
+(arbitrary and/or several structured) matrices by using the Levenberg-Marquardt
+method (LM-method) for solving least squares problems. We then apply these
+algorithms to solve several engineering problems such as the low-rank matrix
+completion problem and the low-dimensional Euclidean embedding one. Some
+numerical experiments illustrate the validity of the approach.
+On the other hand, we provide some further properties of low rank solutions
+to systems linear matrix equations. This is useful when the differentiable
+function is linear or quadratic.
+",0,0,1,0,0,0
+17191,A Simple and Realistic Pedestrian Model for Crowd Simulation and Application,"  The simulation of pedestrian crowd that reflects reality is a major challenge
+for researches. Several crowd simulation models have been proposed such as
+cellular automata model, agent-based model, fluid dynamic model, etc. It is
+important to note that agent-based model is able, over others approaches, to
+provide a natural description of the system and then to capture complex human
+behaviors. In this paper, we propose a multi-agent simulation model in which
+pedestrian positions are updated at discrete time intervals. It takes into
+account the major normal conditions of a simple pedestrian situated in a crowd
+such as preferences, realistic perception of environment, etc. Our objective is
+to simulate the pedestrian crowd realistically towards a simulation of
+believable pedestrian behaviors. Typical pedestrian phenomena, including the
+unidirectional and bidirectional movement in a corridor as well as the flow
+through bottleneck, are simulated. The conducted simulations show that our
+model is able to produce realistic pedestrian behaviors. The obtained
+fundamental diagram and flow rate at bottleneck agree very well with classic
+conclusions and empirical study results. It is hoped that the idea of this
+study may be helpful in promoting the modeling and simulation of pedestrian
+crowd in a simple way.
+",1,1,0,0,0,0
+17192,Local connectivity modulates multi-scale relaxation dynamics in a metallic glass-forming system,"  The structural description for the intriguing link between the fast
+vibrational dynamics and slow diffusive dynamics in glass-forming systems is
+one of the most challenging issues in physical science. Here, in a model of
+metallic supercooled liquid, we find that local connectivity as an atomic-level
+structural order parameter tunes the short-time vibrational excitations of the
+icosahedrally coordinated particles and meanwhile modulates their long-time
+relaxation dynamics changing from stretched to compressed exponentials,
+denoting a dynamic transition from subdiffusive to hyperdiffusive motions of
+such particles. Our result indicates that long-time dynamics has an
+atomic-level structural origin which is related to the short-time dynamics,
+thus suggests a structural bridge to link the fast vibrational dynamics and the
+slow structural relaxation in glassy materials.
+",0,1,0,0,0,0
+17193,SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks,"  Purpose: This paper focuses on an automated analysis of surgical motion
+profiles for objective skill assessment and task recognition in robot-assisted
+surgery. Existing techniques heavily rely on conventional statistic measures or
+shallow modelings based on hand-engineered features and gesture segmentation.
+Such developments require significant expert knowledge, are prone to errors,
+and are less efficient in online adaptive training systems. Methods: In this
+work, we present an efficient analytic framework with a parallel deep learning
+architecture, SATR-DL, to assess trainee expertise and recognize surgical
+training activity. Through an end-to-end learning technique, abstract
+information of spatial representations and temporal dynamics is jointly
+obtained directly from raw motion sequences. Results: By leveraging a shared
+high-level representation learning, the resulting model is successful in the
+recognition of trainee skills and surgical tasks, suturing, needle-passing, and
+knot-tying. Meanwhile, we explore the use of ensemble in classification at the
+trial level, where the SATR-DL outperforms state-of-the-art performance by
+achieving accuracies of 0.960 and 1.000 in skill assessment and task
+recognition, respectively. Conclusion: This study highlights the potential of
+SATR-DL to provide improvements for an efficient data-driven assessment in
+intelligent robotic surgery.
+",1,0,0,0,0,0
+17194,Equivalence of weak and strong modes of measures on topological vector spaces,"  A strong mode of a probability measure on a normed space $X$ can be defined
+as a point $u$ such that the mass of the ball centred at $u$ uniformly
+dominates the mass of all other balls in the small-radius limit. Helin and
+Burger weakened this definition by considering only pairwise comparisons with
+balls whose centres differ by vectors in a dense, proper linear subspace $E$ of
+$X$, and posed the question of when these two types of modes coincide. We show
+that, in a more general setting of metrisable vector spaces equipped with
+measures that are finite on bounded sets, the density of $E$ and a uniformity
+condition suffice for the equivalence of these two types of modes. We
+accomplish this by introducing a new, intermediate type of mode. We also show
+that these modes can be inequivalent if the uniformity condition fails. Our
+results shed light on the relationships between among various notions of
+maximum a posteriori estimator in non-parametric Bayesian inference.
+",0,0,1,1,0,0
+17195,Algebras of Quasi-Plücker Coordinates are Koszul,"  Motivated by the theory of quasi-determinants, we study non-commutative
+algebras of quasi-Plücker coordinates. We prove that these algebras provide
+new examples of non-homogeneous quadratic Koszul algebras by showing that their
+quadratic duals have quadratic Gröbner bases.
+",0,0,1,0,0,0
+17196,Doubly-Attentive Decoder for Multi-modal Neural Machine Translation,"  We introduce a Multi-modal Neural Machine Translation model in which a
+doubly-attentive decoder naturally incorporates spatial visual features
+obtained using pre-trained convolutional neural networks, bridging the gap
+between image description and translation. Our decoder learns to attend to
+source-language words and parts of an image independently by means of two
+separate attention mechanisms as it generates words in the target language. We
+find that our model can efficiently exploit not just back-translated in-domain
+multi-modal data but also large general-domain text-only MT corpora. We also
+report state-of-the-art results on the Multi30k data set.
+",1,0,0,0,0,0
+17197,prDeep: Robust Phase Retrieval with a Flexible Deep Network,"  Phase retrieval algorithms have become an important component in many modern
+computational imaging systems. For instance, in the context of ptychography and
+speckle correlation imaging, they enable imaging past the diffraction limit and
+through scattering media, respectively. Unfortunately, traditional phase
+retrieval algorithms struggle in the presence of noise. Progress has been made
+recently on more robust algorithms using signal priors, but at the expense of
+limiting the range of supported measurement models (e.g., to Gaussian or coded
+diffraction patterns). In this work we leverage the regularization-by-denoising
+framework and a convolutional neural network denoiser to create prDeep, a new
+phase retrieval algorithm that is both robust and broadly applicable. We test
+and validate prDeep in simulation to demonstrate that it is robust to noise and
+can handle a variety of system models.
+A MatConvNet implementation of prDeep is available at
+this https URL.
+",0,0,0,1,0,0
+17198,Novel Exotic Magnetic Spin-order in Co5Ge3 Nano-size Materials,"  The Cobalt-germanium (Co-Ge) is a fascinating complex alloy system that has
+unique structure and exhibit range of interesting magnetic properties which
+would change when reduce to nanoscale dimension. At this experimental work, the
+high-aspect-ratio Co5Ge3 nanoparticle with average size of 8nm was synthesized
+by gas aggregation-type cluster-deposition technology. The nanostructure
+morphology of the as-made binary Co5Ge3 nanoparticles demonstrate excellent
+single-crystalline hexagonal structure with mostly preferable growth along
+(110) and (102) directions. In contrast the bulk possess Pauli paramagnetic
+spin-order at all range of temperature, here we discover size-driven new
+magnetic ordering of as-synthesized Co5Ge3 nanoparticles exhibiting
+ferromagnetism at room temperature with saturation magnetization of Ms = 32.2
+emu/cm3. This is first report of observing such new magnetic spin ordering in
+this kind of material at nano-size which the magnetization has lower
+sensitivity to thermal energy fluctuation and exhibit high Curie temperature
+close to 850 K. This ferromagnetic behavior along with higher Curie temperature
+at Co5Ge3 nanoparticles are attributes to low-dimension and quantum-confinement
+effect which imposes strong spin coupling and provides a new set of size-driven
+spin structures in Co5Ge3 nanoparticle which no such magnetic behavior being
+present in the bulk of same material. This fundamental scientific study
+provides important insights into the formation, structural, and the magnetic
+property of sub 10nm Co5Ge3 nanostructure which shall lead to promising
+practical versatile applications for magneto- germanide based nano-devices.
+",0,1,0,0,0,0
+17199,Toward Multimodal Image-to-Image Translation,"  Many image-to-image translation problems are ambiguous, as a single input
+image may correspond to multiple possible outputs. In this work, we aim to
+model a \emph{distribution} of possible outputs in a conditional generative
+modeling setting. The ambiguity of the mapping is distilled in a
+low-dimensional latent vector, which can be randomly sampled at test time. A
+generator learns to map the given input, combined with this latent code, to the
+output. We explicitly encourage the connection between output and the latent
+code to be invertible. This helps prevent a many-to-one mapping from the latent
+code to the output during training, also known as the problem of mode collapse,
+and produces more diverse results. We explore several variants of this approach
+by employing different training objectives, network architectures, and methods
+of injecting the latent code. Our proposed method encourages bijective
+consistency between the latent encoding and output modes. We present a
+systematic comparison of our method and other variants on both perceptual
+realism and diversity.
+",1,0,0,1,0,0
+17200,On the Sample Complexity of the Linear Quadratic Regulator,"  This paper addresses the optimal control problem known as the Linear
+Quadratic Regulator in the case when the dynamics are unknown. We propose a
+multi-stage procedure, called Coarse-ID control, that estimates a model from a
+few experimental trials, estimates the error in that model with respect to the
+truth, and then designs a controller using both the model and uncertainty
+estimate. Our technique uses contemporary tools from random matrix theory to
+bound the error in the estimation procedure. We also employ a recently
+developed approach to control synthesis called System Level Synthesis that
+enables robust control design by solving a convex optimization problem. We
+provide end-to-end bounds on the relative error in control cost that are nearly
+optimal in the number of parameters and that highlight salient properties of
+the system to be controlled such as closed-loop sensitivity and optimal control
+magnitude. We show experimentally that the Coarse-ID approach enables efficient
+computation of a stabilizing controller in regimes where simple control schemes
+that do not take the model uncertainty into account fail to stabilize the true
+system.
+",1,0,0,1,0,0
+17201,Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification,"  Deep neural networks (DNNs) have transformed several artificial intelligence
+research areas including computer vision, speech recognition, and natural
+language processing. However, recent studies demonstrated that DNNs are
+vulnerable to adversarial manipulations at testing time. Specifically, suppose
+we have a testing example, whose label can be correctly predicted by a DNN
+classifier. An attacker can add a small carefully crafted noise to the testing
+example such that the DNN classifier predicts an incorrect label, where the
+crafted testing example is called adversarial example. Such attacks are called
+evasion attacks. Evasion attacks are one of the biggest challenges for
+deploying DNNs in safety and security critical applications such as
+self-driving cars. In this work, we develop new methods to defend against
+evasion attacks. Our key observation is that adversarial examples are close to
+the classification boundary. Therefore, we propose region-based classification
+to be robust to adversarial examples. For a benign/adversarial testing example,
+we ensemble information in a hypercube centered at the example to predict its
+label. In contrast, traditional classifiers are point-based classification,
+i.e., given a testing example, the classifier predicts its label based on the
+testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets
+demonstrate that our region-based classification can significantly mitigate
+evasion attacks without sacrificing classification accuracy on benign examples.
+Specifically, our region-based classification achieves the same classification
+accuracy on testing benign examples as point-based classification, but our
+region-based classification is significantly more robust than point-based
+classification to various evasion attacks.
+",1,0,0,1,0,0
+17202,Partition-free families of sets,"  Let $m(n)$ denote the maximum size of a family of subsets which does not
+contain two disjoint sets along with their union. In 1968 Kleitman proved that
+$m(n) = {n\choose m+1}+\ldots +{n\choose 2m+1}$ if $n=3m+1$. Confirming the
+conjecture of Kleitman, we establish the same equality for the cases $n=3m$ and
+$n=3m+2$, and also determine all extremal families. Unlike the case $n=3m+1$,
+the extremal families are not unique. This is a plausible reason behind the
+relative difficulty of our proofs. We completely settle the case of several
+families as well.
+",1,0,0,0,0,0
+17203,Measuring the Galactic Cosmic Ray Flux with the LISA Pathfinder Radiation Monitor,"  Test mass charging caused by cosmic rays will be a significant source of
+acceleration noise for space-based gravitational wave detectors like LISA.
+Operating between December 2015 and July 2017, the technology demonstration
+mission LISA Pathfinder included a bespoke monitor to help characterise the
+relationship between test mass charging and the local radiation environment.
+The radiation monitor made in situ measurements of the cosmic ray flux while
+also providing information about its energy spectrum. We describe the monitor
+and present measurements which show a gradual 40% increase in count rate
+coinciding with the declining phase of the solar cycle. Modulations of up to
+10% were also observed with periods of 13 and 26 days that are associated with
+co-rotating interaction regions and heliospheric current sheet crossings. These
+variations in the flux above the monitor detection threshold (approximately 70
+MeV) are shown to be coherent with measurements made by the IREM monitor
+on-board the Earth orbiting INTEGRAL spacecraft. Finally we use the measured
+deposited energy spectra, in combination with a GEANT4 model, to estimate the
+galactic cosmic ray differential energy spectrum over the course of the
+mission.
+",0,1,0,0,0,0
+17204,Thread-Modular Static Analysis for Relaxed Memory Models,"  We propose a memory-model-aware static program analysis method for accurately
+analyzing the behavior of concurrent software running on processors with weak
+consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of
+our method is a unified framework for deciding the feasibility of inter-thread
+interferences to avoid propagating spurious data flows during static analysis
+and thus boost the performance of the static analyzer. We formulate the
+checking of interference feasibility as a set of Datalog rules which are both
+efficiently solvable and general enough to capture a range of hardware-level
+memory models. Compared to existing techniques, our method can significantly
+reduce the number of bogus alarms as well as unsound proofs. We implemented the
+method and evaluated it on a large set of multithreaded C programs. Our
+experiments showthe method significantly outperforms state-of-the-art
+techniques in terms of accuracy with only moderate run-time overhead.
+",1,0,0,0,0,0
+17205,Bidirectional Evaluation with Direct Manipulation,"  We present an evaluation update (or simply, update) algorithm for a
+full-featured functional programming language, which synthesizes program
+changes based on output changes. Intuitively, the update algorithm retraces the
+steps of the original evaluation, rewriting the program as needed to reconcile
+differences between the original and updated output values. Our approach,
+furthermore, allows expert users to define custom lenses that augment the
+update algorithm with more advanced or domain-specific program updates.
+To demonstrate the utility of evaluation update, we implement the algorithm
+in Sketch-n-Sketch, a novel direct manipulation programming system for
+generating HTML documents. In Sketch-n-Sketch, the user writes an ML-style
+functional program to generate HTML output. When the user directly manipulates
+the output using a graphical user interface, the update algorithm reconciles
+the changes. We evaluate bidirectional evaluation in Sketch-n-Sketch by
+authoring ten examples comprising approximately 1400 lines of code in total.
+These examples demonstrate how a variety of HTML documents and applications can
+be developed and edited interactively in Sketch-n-Sketch, mitigating the
+tedious edit-run-view cycle in traditional programming environments.
+",1,0,0,0,0,0
+17206,Extreme Event Statistics in a Drifting Markov Chain,"  We analyse extreme event statistics of experimentally realized Markov chains
+with various drifts. Our Markov chains are individual trajectories of a single
+atom diffusing in a one dimensional periodic potential. Based on more than 500
+individual atomic traces we verify the applicability of the Sparre Andersen
+theorem to our system despite the presence of a drift. We present detailed
+analysis of four different rare event statistics for our system: the
+distributions of extreme values, of record values, of extreme value occurrence
+in the chain, and of the number of records in the chain. We observe that for
+our data the shape of the extreme event distributions is dominated by the
+underlying exponential distance distribution extracted from the atomic traces.
+Furthermore, we find that even small drifts influence the statistics of extreme
+events and record values, which is supported by numerical simulations, and we
+identify cases in which the drift can be determined without information about
+the underlying random variable distributions. Our results facilitate the use of
+extreme event statistics as a signal for small drifts in correlated
+trajectories.
+",0,1,0,0,0,0
+17207,On a Possibility of Self Acceleration of Electrons in a Plasma,"  The self-consistent nonlinear interaction of a monoenergetic bunch with cold
+plasma is considered. It is shown that under certain conditions a
+self-acceleration of the bunch tail electrons up to high energies is possible.
+",0,1,0,0,0,0
+17208,An Adaptive Strategy for Active Learning with Smooth Decision Boundary,"  We present the first adaptive strategy for active learning in the setting of
+classification with smooth decision boundary. The problem of adaptivity (to
+unknown distributional parameters) has remained opened since the seminal work
+of Castro and Nowak (2007), which first established (active learning) rates for
+this setting. While some recent advances on this problem establish adaptive
+rates in the case of univariate data, adaptivity in the more practical setting
+of multivariate data has so far remained elusive. Combining insights from
+various recent works, we show that, for the multivariate case, a careful
+reduction to univariate-adaptive strategies yield near-optimal rates without
+prior knowledge of distributional parameters.
+",1,0,0,1,0,0
+17209,Towards Adversarial Retinal Image Synthesis,"  Synthesizing images of the eye fundus is a challenging task that has been
+previously approached by formulating complex models of the anatomy of the eye.
+New images can then be generated by sampling a suitable parameter space. In
+this work, we propose a method that learns to synthesize eye fundus images
+directly from data. For that, we pair true eye fundus images with their
+respective vessel trees, by means of a vessel segmentation technique. These
+pairs are then used to learn a mapping from a binary vessel tree to a new
+retinal image. For this purpose, we use a recent image-to-image translation
+technique, based on the idea of adversarial learning. Experimental results show
+that the original and the generated images are visually different in terms of
+their global appearance, in spite of sharing the same vessel tree.
+Additionally, a quantitative quality analysis of the synthetic retinal images
+confirms that the produced images retain a high proportion of the true image
+set quality.
+",1,0,0,1,0,0
+17210,Tailoring the SiC surface - a morphology study on the epitaxial growth of graphene and its buffer layer,"  We investigate the growth of the graphene buffer layer and the involved step
+bunching behavior of the silicon carbide substrate surface using atomic force
+microscopy. The formation of local buffer layer domains are identified to be
+the origin of undesirably high step edges in excellent agreement with the
+predictions of a general model of step dynamics. The applied polymer-assisted
+sublimation growth method demonstrates that the key principle to suppress this
+behavior is the uniform nucleation of the buffer layer. In this way, the
+silicon carbide surface is stabilized such that ultra-flat surfaces can be
+conserved during graphene growth on a large variety of silicon carbide
+substrate surfaces. The analysis of the experimental results describes
+different growth modes which extend the current understanding of epitaxial
+graphene growth by emphasizing the importance of buffer layer nucleation and
+critical mass transport processes.
+",0,1,0,0,0,0
+17211,Polarization of the Vaccination Debate on Facebook,"  Vaccine hesitancy has been recognized as a major global health threat. Having
+access to any type of information in social media has been suggested as a
+potential powerful influence factor to hesitancy. Recent studies in other
+fields than vaccination show that access to a wide amount of content through
+the Internet without intermediaries resolved into major segregation of the
+users in polarized groups. Users select the information adhering to theirs
+system of beliefs and tend to ignore dissenting information. In this paper we
+assess whether there is polarization in Social Media use in the field of
+vaccination. We perform a thorough quantitative analysis on Facebook analyzing
+2.6M users interacting with 298.018 posts over a time span of seven years and 5
+months. We used community detection algorithms to automatically detect the
+emergent communities from the users activity and to quantify the cohesiveness
+over time of the communities. Our findings show that content consumption about
+vaccines is dominated by the echo-chamber effect and that polarization
+increased over years. Communities emerge from the users consumption habits,
+i.e. the majority of users only consumes information in favor or against
+vaccines, not both. The existence of echo-chambers may explain why social-media
+campaigns providing accurate information may have limited reach, may be
+effective only in sub-groups and might even foment further polarization of
+opinions. The introduction of dissenting information into a sub-group is
+disregarded and can have a backfire effect, further reinforcing the existing
+opinions within the sub-group.
+",1,0,0,0,0,0
+17212,"Predicting Demographics, Moral Foundations, and Human Values from Digital Behaviors","  Personal electronic devices including smartphones give access to behavioural
+signals that can be used to learn about the characteristics and preferences of
+individuals. In this study, we explore the connection between demographic and
+psychological attributes and the digital behavioural records, for a cohort of
+7,633 people, closely representative of the US population with respect to
+gender, age, geographical distribution, education, and income. Along with the
+demographic data, we collected self-reported assessments on validated
+psychometric questionnaires for moral traits and basic human values and
+combined this information with passively collected multi-modal digital data
+from web browsing behaviour and smartphone usage. A machine learning framework
+was then designed to infer both the demographic and psychological attributes
+from the behavioural data. In a cross-validated setting, our models predicted
+demographic attributes with good accuracy as measured by the weighted AUROC
+score (Area Under the Receiver Operating Characteristic), but were less
+performant for the moral traits and human values. These results call for
+further investigation since they are still far from unveiling individuals'
+psychological fabric. This connection, along with the most predictive features
+that we provide for each attribute, might prove useful for designing
+personalised services, communication strategies, and interventions, and can be
+used to sketch a portrait of people with a similar worldview.
+",1,0,0,0,0,0
+17213,Isotonic regression in general dimensions,"  We study the least squares regression function estimator over the class of
+real-valued functions on $[0,1]^d$ that are increasing in each coordinate. For
+uniformly bounded signals and with a fixed, cubic lattice design, we establish
+that the estimator achieves the minimax rate of order
+$n^{-\min\{2/(d+2),1/d\}}$ in the empirical $L_2$ loss, up to poly-logarithmic
+factors. Further, we prove a sharp oracle inequality, which reveals in
+particular that when the true regression function is piecewise constant on $k$
+hyperrectangles, the least squares estimator enjoys a faster, adaptive rate of
+convergence of $(k/n)^{\min(1,2/d)}$, again up to poly-logarithmic factors.
+Previous results are confined to the case $d \leq 2$. Finally, we establish
+corresponding bounds (which are new even in the case $d=2$) in the more
+challenging random design setting. There are two surprising features of these
+results: first, they demonstrate that it is possible for a global empirical
+risk minimisation procedure to be rate optimal up to poly-logarithmic factors
+even when the corresponding entropy integral for the function class diverges
+rapidly; second, they indicate that the adaptation rate for shape-constrained
+estimators can be strictly worse than the parametric rate.
+",0,0,1,1,0,0
+17214,Extraction of Schottky barrier height insensitive to temperature via forward currentvoltage- temperature measurements,"  The thermal stability of most electronic and photo-electronic devices
+strongly depends on the relationship between Schottky Barrier Height (SBH) and
+temperature. In this paper, the possible of thermionic current depicted via
+correct and reliability relationship between forward current and voltage is
+consequently discussed, the intrinsic SBH insensitive to temperature can be
+calculated by modification on Richardson- Dushman`s formula suggested in this
+paper. The results of application on four hetero-junctions prove that the
+method proposed is credible in this paper, this suggests that the I/V/T method
+is a feasible alternative to characterize these heterojunctions.
+",0,1,0,0,0,0
+17215,Emergent Open-Endedness from Contagion of the Fittest,"  In this paper, we study emergent irreducible information in populations of
+randomly generated computable systems that are networked and follow a
+""Susceptible-Infected-Susceptible"" contagion model of imitation of the fittest
+neighbor. We show that there is a lower bound for the stationary prevalence (or
+average density of ""infected"" nodes) that triggers an unlimited increase of the
+expected local emergent algorithmic complexity (or information) of a node as
+the population size grows. We call this phenomenon expected (local) emergent
+open-endedness. In addition, we show that static networks with a power-law
+degree distribution following the Barabási-Albert model satisfy this lower
+bound and, thus, display expected (local) emergent open-endedness.
+",1,0,0,0,0,0
+17216,Incompressible Limit of isentropic Navier-Stokes equations with Navier-slip boundary,"  This paper concerns the low Mach number limit of weak solutions to the
+compressible Navier-Stokes equations for isentropic fluids in a bounded domain
+with a Navier-slip boundary condition. In \cite{DGLM99}, it has been proved
+that if the velocity is imposed the homogeneous Dirichlet boundary condition,
+as the Mach number goes to 0, the velocity of the compressible flow converges
+strongly in $L^2$ under the geometrical assumption (H) on the domain. We
+justify the same strong convergence when the slip length in the Navier
+condition is the reciprocal of the square root of the Mach number.
+",0,0,1,0,0,0
+17217,On Some Exponential Sums Related to the Coulter's Polynomial,"  In this paper, the formulas of some exponential sums over finite field,
+related to the Coulter's polynomial, are settled based on the Coulter's
+theorems on Weil sums, which may have potential application in the construction
+of linear codes with few weights.
+",1,0,0,0,0,0
+17218,Distribution-Preserving k-Anonymity,"  Preserving the privacy of individuals by protecting their sensitive
+attributes is an important consideration during microdata release. However, it
+is equally important to preserve the quality or utility of the data for at
+least some targeted workloads. We propose a novel framework for privacy
+preservation based on the k-anonymity model that is ideally suited for
+workloads that require preserving the probability distribution of the
+quasi-identifier variables in the data. Our framework combines the principles
+of distribution-preserving quantization and k-member clustering, and we
+specialize it to two variants that respectively use intra-cluster and Gaussian
+dithering of cluster centers to achieve distribution preservation. We perform
+theoretical analysis of the proposed schemes in terms of distribution
+preservation, and describe their utility in workloads such as covariate shift
+and transfer learning where such a property is necessary. Using extensive
+experiments on real-world Medical Expenditure Panel Survey data, we demonstrate
+the merits of our algorithms over standard k-anonymization for a hallmark
+health care application where an insurance company wishes to understand the
+risk in entering a new market. Furthermore, by empirically quantifying the
+reidentification risk, we also show that the proposed approaches indeed
+maintain k-anonymity.
+",1,0,0,1,0,0
+17219,Using controlled disorder to probe the interplay between charge order and superconductivity in NbSe2,"  The interplay between superconductivity and charge density waves (CDW) in
+$H$-NbSe2 is not fully understood despite decades of study. Artificially
+introduced disorder can tip the delicate balance between two competing forms of
+long-range order, and reveal the underlying interactions that give rise to
+them. Here we introduce disorders by electron irradiation and measure in-plane
+resistivity, Hall resistivity, X-ray scattering, and London penetration depth.
+With increasing disorder, $T_{\textrm{c}}$ varies nonmonotonically, whereas
+$T_{\textrm{CDW}}$ monotonically decreases and becomes unresolvable above a
+critical irradiation dose where $T_{\textrm{c}}$ drops sharply. Our results
+imply that CDW order initially competes with superconductivity, but eventually
+assists it. We argue that at the transition where the long-range CDW order
+disappears, the cooperation with superconductivity is dramatically suppressed.
+X-ray scattering and Hall resistivity measurements reveal that the short-range
+CDW survives above the transition. Superconductivity persists to much higher
+dose levels, consistent with fully gapped superconductivity and moderate
+interband pairing.
+",0,1,0,0,0,0
+17220,A training process for improving the quality of software projects developed by a practitioner,"  Background: The quality of a software product depends on the quality of the
+software process followed in developing the product. Therefore, many higher
+education institutions (HEI) and software organizations have implemented
+software process improvement (SPI) training courses to improve the software
+quality. Objective: Because the duration of a course is a concern for HEI and
+software organizations, we investigate whether the quality of software projects
+will be improved by reorganizing the activities of the ten assignments of the
+original personal software process (PSP) course into a modified PSP having
+fewer assignments (i.e., seven assignments). Method: The assignments were
+developed by following a modified PSP with fewer assignments but including the
+phases, forms, standards, and logs suggested in the original PSP. The
+measurement of the quality of the software assignments was based on defect
+density. Results: When the activities in the original PSP were reordered into
+fewer assignments, as practitioners progress through the PSP training, the
+defect density improved with statistical significance. Conclusions: Our
+modified PSP could be applied in academy and industrial environments which are
+concerned in the sense of reducing the PSP training time
+",1,0,0,0,0,0
+17221,Gaia Data Release 1. Cross-match with external catalogues - Algorithm and results,"  Although the Gaia catalogue on its own will be a very powerful tool, it is
+the combination of this highly accurate archive with other archives that will
+truly open up amazing possibilities for astronomical research. The advanced
+interoperation of archives is based on cross-matching, leaving the user with
+the feeling of working with one single data archive. The data retrieval should
+work not only across data archives, but also across wavelength domains. The
+first step for seamless data access is the computation of the cross-match
+between Gaia and external surveys. The matching of astronomical catalogues is a
+complex and challenging problem both scientifically and technologically
+(especially when matching large surveys like Gaia). We describe the cross-match
+algorithm used to pre-compute the match of Gaia Data Release 1 (DR1) with a
+selected list of large publicly available optical and IR surveys. The overall
+principles of the adopted cross-match algorithm are outlined. Details are given
+on the developed algorithm, including the methods used to account for position
+errors, proper motions, and environment; to define the neighbours; and to
+define the figure of merit used to select the most probable counterpart.
+Statistics on the results are also given. The results of the cross-match are
+part of the official Gaia DR1 catalogue.
+",0,1,0,0,0,0
+17222,"Masses of Kepler-46b, c from Transit Timing Variations","  We use 16 quarters of the \textit{Kepler} mission data to analyze the transit
+timing variations (TTVs) of the extrasolar planet Kepler-46b (KOI-872). Our
+dynamical fits confirm that the TTVs of this planet (period
+$P=33.648^{+0.004}_{-0.005}$ days) are produced by a non-transiting planet
+Kepler-46c ($P=57.325^{+0.116}_{-0.098}$ days). The Bayesian inference tool
+\texttt{MultiNest} is used to infer the dynamical parameters of Kepler-46b and
+Kepler-46c. We find that the two planets have nearly coplanar and circular
+orbits, with eccentricities $\simeq 0.03$ somewhat higher than previously
+estimated. The masses of the two planets are found to be
+$M_{b}=0.885^{+0.374}_{-0.343}$ and $M_{c}=0.362^{+0.016}_{-0.016}$ Jupiter
+masses, with $M_{b}$ being determined here from TTVs for the first time. Due to
+the precession of its orbital plane, Kepler-46c should start transiting its
+host star in a few decades from now.
+",0,1,0,0,0,0
+17223,Recovering water wave elevation from pressure measurements,"  The reconstruction of water wave elevation from bottom pressure measurements
+is an important issue for coastal applications, but corresponds to a difficult
+mathematical problem. In this paper we present the derivation of a method which
+allows the elevation reconstruction of water waves in intermediate and shallow
+waters. From comparisons with numerical Euler solutions and wave-tank
+experiments we show that our nonlinear method provides much better results of
+the surface elevation reconstruction compared to the linear transfer function
+approach commonly used in coastal applications. More specifically, our
+methodaccurately reproduces the peaked and skewed shape of nonlinear wave
+fields. Therefore, it is particularly relevant for applications on extreme
+waves and wave-induced sediment transport.
+",0,1,0,0,0,0
+17224,Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization,"  The Schatten quasi-norm was introduced to bridge the gap between the trace
+norm and rank function. However, existing algorithms are too slow or even
+impractical for large-scale problems. Motivated by the equivalence relation
+between the trace norm and its bilinear spectral penalty, we define two
+tractable Schatten norms, i.e.\ the bi-trace and tri-trace norms, and prove
+that they are in essence the Schatten-$1/2$ and $1/3$ quasi-norms,
+respectively. By applying the two defined Schatten quasi-norms to various rank
+minimization problems such as MC and RPCA, we only need to solve much smaller
+factor matrices. We design two efficient linearized alternating minimization
+algorithms to solve our problems and establish that each bounded sequence
+generated by our algorithms converges to a critical point. We also provide the
+restricted strong convexity (RSC) based and MC error bounds for our algorithms.
+Our experimental results verified both the efficiency and effectiveness of our
+algorithms compared with the state-of-the-art methods.
+",0,0,0,1,0,0
+17225,Spectral Radii of Truncated Circular Unitary Matrices,"  Consider a truncated circular unitary matrix which is a $p_n$ by $p_n$
+submatrix of an $n$ by $n$ circular unitary matrix by deleting the last $n-p_n$
+columns and rows. Jiang and Qi (2017) proved that the maximum absolute value of
+the eigenvalues (known as spectral radius) of the truncated matrix, after
+properly normalized, converges in distribution to the Gumbel distribution if
+$p_n/n$ is bounded away from $0$ and $1$. In this paper we investigate the
+limiting distribution of the spectral radius under one of the following four
+conditions: (1). $p_n\to\infty$ and $p_n/n\to 0$ as $n\to\infty$; (2).
+$(n-p_n)/n\to 0$ and $(n-p_n)/(\log n)^3\to\infty$ as $n\to\infty$; (3).
+$n-p_n\to\infty$ and $(n-p_n)/\log n\to 0$ as $n\to\infty$ and (4). $n-p_n=k\ge
+1$ is a fixed integer. We prove that the spectral radius converges in
+distribution to the Gumbel distribution under the first three conditions and to
+a reversed Weibull distribution under the fourth condition.
+",0,0,1,1,0,0
+17226,Information Assisted Dictionary Learning for fMRI data analysis,"  In this paper, the task-related fMRI problem is treated in its matrix
+factorization formulation. The focus of the reported work is on the dictionary
+learning (DL) matrix factorization approach. A major novelty of the paper lies
+in the incorporation of well-established assumptions associated with the GLM
+technique, which is currently in use by the neuroscientists. These assumptions
+are embedded as constraints in the DL formulation. In this way, our approach
+provides a framework of combining well-established and understood techniques
+with a more ``modern'' and powerful tool. Furthermore, this paper offers a way
+to relax a major drawback associated with DL techniques; that is, the proper
+tuning of the DL regularization parameter. This parameter plays a critical role
+in DL-based fMRI analysis since it essentially determines the shape and
+structures of the estimated functional brain networks. However, in actual fMRI
+data analysis, the lack of ground truth renders the a priori choice of the
+regularization parameter a truly challenging task. Indeed, the values of the DL
+regularization parameter, associated with the $\ell_1$ sparsity promoting norm,
+do not convey any tangible physical meaning. So it is practically difficult to
+guess its proper value. In this paper, the DL problem is reformulated around a
+sparsity-promoting constraint that can directly be related to the minimum
+amount of voxels that the spatial maps of the functional brain networks occupy.
+Such information is documented and it is readily available to neuroscientists
+and experts in the field.
+The proposed method is tested against a number of other popular techniques
+and the obtained performance gains are reported using a number of synthetic
+fMRI data. Results with real data have also been obtained in the context of a
+number of experiments and will be soon reported in a different publication.
+",0,0,0,1,0,0
+17227,"Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models","  Motivated by the task of clustering either $d$ variables or $d$ points into
+$K$ groups, we investigate efficient algorithms to solve the Peng-Wei (P-W)
+$K$-means semi-definite programming (SDP) relaxation. The P-W SDP has been
+shown in the literature to have good statistical properties in a variety of
+settings, but remains intractable to solve in practice. To this end we propose
+FORCE, a new algorithm to solve this SDP relaxation. Compared to the naive
+interior point method, our method reduces the computational complexity of
+solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to
+$\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an
+$\epsilon$-optimal solution. Our method combines a primal first-order method
+with a dual optimality certificate search, which when successful, allows for
+early termination of the primal method. We show for certain variable clustering
+problems that, with high probability, FORCE is guaranteed to find the optimal
+solution to the SDP relaxation and provide a certificate of exact optimality.
+As verified by our numerical experiments, this allows FORCE to solve the P-W
+SDP with dimensions in the hundreds in only tens of seconds. For a variation of
+the P-W SDP where $K$ is not known a priori a slight modification of FORCE
+reduces the computational complexity of solving this problem as well: from
+$\tilde{O}(d^7\log\epsilon^{-1})$ using a standard SDP solver to
+$\tilde{O}(d^{4}\epsilon^{-1})$.
+",0,0,0,1,0,0
+17228,Two- and three-dimensional wide-field weak lensing mass maps from the Hyper Suprime-Cam Subaru Strategic Program S16A data,"  We present wide-field (167 deg$^2$) weak lensing mass maps from the Hyper
+Supreme-Cam Subaru Strategic Program (HSC-SSP). We compare these weak lensing
+based dark matter maps with maps of the distribution of the stellar mass
+associated with luminous red galaxies. We find a strong correlation between
+these two maps with a correlation coefficient of $\rho=0.54\pm0.03$ (for a
+smoothing size of $8'$). This correlation is detected even with a smaller
+smoothing scale of $2'$ ($\rho=0.34\pm 0.01$). This detection is made uniquely
+possible because of the high source density of the HSC-SSP weak lensing survey
+($\bar{n}\sim 25$ arcmin$^{-2}$). We also present a variety of tests to
+demonstrate that our maps are not significantly affected by systematic effects.
+By using the photometric redshift information associated with source galaxies,
+we reconstruct a three-dimensional mass map. This three-dimensional mass map is
+also found to correlate with the three-dimensional galaxy mass map.
+Cross-correlation tests presented in this paper demonstrate that the HSC-SSP
+weak lensing mass maps are ready for further science analyses.
+",0,1,0,0,0,0
+17229,A Time-spectral Approach to Numerical Weather Prediction,"  Finite difference methods are traditionally used for modelling the time
+domain in numerical weather prediction (NWP). Time-spectral solution is an
+attractive alternative for reasons of accuracy and efficiency and because time
+step limitations associated with causal, CFL-like critera are avoided. In this
+work, the Lorenz 1984 chaotic equations are solved using the time-spectral
+algorithm GWRM. Comparisons of accuracy and efficiency are carried out for both
+explicit and implicit time-stepping algorithms. It is found that the efficiency
+of the GWRM compares well with these methods, in particular at high accuracy.
+For perturbative scenarios, the GWRM was found to be as much as four times
+faster than the finite difference methods. A primary reason is that the GWRM
+time intervals typically are two orders of magnitude larger than those of the
+finite difference methods. The GWRM has the additional advantage to produce
+analytical solutions in the form of Chebyshev series expansions. The results
+are encouraging for pursuing further studies, including spatial dependence, of
+the relevance of time-spectral methods for NWP modelling.
+",0,1,0,0,0,0
+17230,Trivial Constraints on Orbital-free Kinetic Energy Density Functionals,"  Kinetic energy density functionals (KEDFs) are central to orbital-free
+density functional theory. Limitations on the spatial derivative dependencies
+of KEDFs have been claimed from differential virial theorems. We point out a
+central defect in the argument: the relationships are not true for an arbitrary
+density but hold only for the minimizing density and corresponding chemical
+potential. Contrary to the claims therefore, the relationships are not
+constraints and provide no independent information about the spatial derivative
+dependencies of approximate KEDFs. A simple argument also shows that validity
+for arbitrary $v$-representable densities is not restored by appeal to the
+density-potential bijection.
+",0,1,0,0,0,0
+17231,The Multi-layer Information Bottleneck Problem,"  The muti-layer information bottleneck (IB) problem, where information is
+propagated (or successively refined) from layer to layer, is considered. Based
+on information forwarded by the preceding layer, each stage of the network is
+required to preserve a certain level of relevance with regards to a specific
+hidden variable, quantified by the mutual information. The hidden variables and
+the source can be arbitrarily correlated. The optimal trade-off between rates
+of relevance and compression (or complexity) is obtained through a
+single-letter characterization, referred to as the rate-relevance region.
+Conditions of successive refinabilty are given. Binary source with BSC hidden
+variables and binary source with BSC/BEC mixed hidden variables are both proved
+to be successively refinable. We further extend our result to Guassian models.
+A counterexample of successive refinability is also provided.
+",1,0,0,1,0,0
+17232,A geometric approach to non-linear correlations with intrinsic scatter,"  We propose a new mathematical model for $n-k$-dimensional non-linear
+correlations with intrinsic scatter in $n$-dimensional data. The model is based
+on Riemannian geometry, and is naturally symmetric with respect to the measured
+variables and invariant under coordinate transformations. We combine the model
+with a Bayesian approach for estimating the parameters of the correlation
+relation and the intrinsic scatter. A side benefit of the approach is that
+censored and truncated datasets and independent, arbitrary measurement errors
+can be incorporated. We also derive analytic likelihoods for the typical
+astrophysical use case of linear relations in $n$-dimensional Euclidean space.
+We pay particular attention to the case of linear regression in two dimensions,
+and compare our results to existing methods. Finally, we apply our methodology
+to the well-known $M_\text{BH}$-$\sigma$ correlation between the mass of a
+supermassive black hole in the centre of a galactic bulge and the corresponding
+bulge velocity dispersion. The main result of our analysis is that the most
+likely slope of this correlation is $\sim 6$ for the datasets used, rather than
+the values in the range $\sim 4$-$5$ typically quoted in the literature for
+these data.
+",0,1,1,1,0,0
+17233,Computing simplicial representatives of homotopy group elements,"  A central problem of algebraic topology is to understand the homotopy groups
+$\pi_d(X)$ of a topological space $X$. For the computational version of the
+problem, it is well known that there is no algorithm to decide whether the
+fundamental group $\pi_1(X)$ of a given finite simplicial complex $X$ is
+trivial. On the other hand, there are several algorithms that, given a finite
+simplicial complex $X$ that is simply connected (i.e., with $\pi_1(X)$
+trivial), compute the higher homotopy group $\pi_d(X)$ for any given $d\geq 2$.
+%The first such algorithm was given by Brown, and more recently, Čadek et
+al.
+However, these algorithms come with a caveat: They compute the isomorphism
+type of $\pi_d(X)$, $d\geq 2$ as an \emph{abstract} finitely generated abelian
+group given by generators and relations, but they work with very implicit
+representations of the elements of $\pi_d(X)$. Converting elements of this
+abstract group into explicit geometric maps from the $d$-dimensional sphere
+$S^d$ to $X$ has been one of the main unsolved problems in the emerging field
+of computational homotopy theory.
+Here we present an algorithm that, given a~simply connected space $X$,
+computes $\pi_d(X)$ and represents its elements as simplicial maps from a
+suitable triangulation of the $d$-sphere $S^d$ to $X$. For fixed $d$, the
+algorithm runs in time exponential in $size(X)$, the number of simplices of
+$X$. Moreover, we prove that this is optimal: For every fixed $d\geq 2$, we
+construct a family of simply connected spaces $X$ such that for any simplicial
+map representing a generator of $\pi_d(X)$, the size of the triangulation of
+$S^d$ on which the map is defined, is exponential in $size(X)$.
+",1,0,1,0,0,0
+17234,Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling,"  Optimization is becoming a crucial element in industrial applications
+involving sustainable alternative energy systems. During the design of such
+systems, the engineer/decision maker would often encounter noise factors (e.g.
+solar insolation and ambient temperature fluctuations) when their system
+interacts with the environment. In this chapter, the sizing and design
+optimization of the solar powered irrigation system was considered. This
+problem is multivariate, noisy, nonlinear and multiobjective. This design
+problem was tackled by first using the Fuzzy Type II approach to model the
+noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the
+context of a weighted sum framework) was employed to solve this multiobjective
+fuzzy design problem. This method was then used to construct the approximate
+Pareto frontier as well as to identify the best solution option in a fuzzy
+setting. Comprehensive analyses and discussions were performed on the generated
+numerical results with respect to the implemented solution methods.
+",1,0,0,0,0,0
+17235,Convergence Analysis of Gradient EM for Multi-component Gaussian Mixture,"  In this paper, we study convergence properties of the gradient
+Expectation-Maximization algorithm \cite{lange1995gradient} for Gaussian
+Mixture Models for general number of clusters and mixing coefficients. We
+derive the convergence rate depending on the mixing coefficients, minimum and
+maximum pairwise distances between the true centers and dimensionality and
+number of components; and obtain a near-optimal local contraction radius. While
+there have been some recent notable works that derive local convergence rates
+for EM in the two equal mixture symmetric GMM, in the more general case, the
+derivations need structurally different and non-trivial arguments. We use
+recent tools from learning theory and empirical processes to achieve our
+theoretical results.
+",1,0,1,1,0,0
+17236,The Gravitational-Wave Physics,"  The direct detection of gravitational wave by Laser Interferometer
+Gravitational-Wave Observatory indicates the coming of the era of
+gravitational-wave astronomy and gravitational-wave cosmology. It is expected
+that more and more gravitational-wave events will be detected by currently
+existing and planned gravitational-wave detectors. The gravitational waves open
+a new window to explore the Universe and various mysteries will be disclosed
+through the gravitational-wave detection, combined with other cosmological
+probes. The gravitational-wave physics is not only related to gravitation
+theory, but also is closely tied to fundamental physics, cosmology and
+astrophysics. In this review article, three kinds of sources of gravitational
+waves and relevant physics will be discussed, namely gravitational waves
+produced during the inflation and preheating phases of the Universe, the
+gravitational waves produced during the first-order phase transition as the
+Universe cools down and the gravitational waves from the three phases:
+inspiral, merger and ringdown of a compact binary system, respectively. We will
+also discuss the gravitational waves as a standard siren to explore the
+evolution of the Universe.
+",0,1,0,0,0,0
+17237,Multivariant Assertion-based Guidance in Abstract Interpretation,"  Approximations during program analysis are a necessary evil, as they ensure
+essential properties, such as soundness and termination of the analysis, but
+they also imply not always producing useful results. Automatic techniques have
+been studied to prevent precision loss, typically at the expense of larger
+resource consumption. In both cases (i.e., when analysis produces inaccurate
+results and when resource consumption is too high), it is necessary to have
+some means for users to provide information to guide analysis and thus improve
+precision and/or performance. We present techniques for supporting within an
+abstract interpretation framework a rich set of assertions that can deal with
+multivariance/context-sensitivity, and can handle different run-time semantics
+for those assertions that cannot be discharged at compile time. We show how the
+proposed approach can be applied to both improving precision and accelerating
+analysis. We also provide some formal results on the effects of such assertions
+on the analysis results.
+",1,0,0,0,0,0
+17238,An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems,"  Through the development of efficient algorithms, data structures and
+preprocessing techniques, real-world shortest path problems in street networks
+are now very fast to solve. But in reality, the exact travel times along each
+arc in the network may not be known. This lead to the development of robust
+shortest path problems, where all possible arc travel times are contained in a
+so-called uncertainty set of possible outcomes.
+Research in robust shortest path problems typically assumes this set to be
+given, and provides complexity results as well as algorithms depending on its
+shape. However, what can actually be observed in real-world problems are only
+discrete raw data points. The shape of the uncertainty is already a modelling
+assumption. In this paper we test several of the most widely used assumptions
+on the uncertainty set using real-world traffic measurements provided by the
+City of Chicago. We calculate the resulting different robust solutions, and
+evaluate which uncertainty approach is actually reasonable for our data. This
+anchors theoretical research in a real-world application and allows us to point
+out which robust models should be the future focus of algorithmic development.
+",1,0,1,0,0,0
+17239,An experimental comparison of velocities underneath focussed breaking waves,"  Nonlinear wave interactions affect the evolution of steep wave groups, their
+breaking and the associated kinematic field. Laboratory experiments are
+performed to investigate the effect of the underlying focussing mechanism on
+the shape of the breaking wave and its velocity field. In this regard, it is
+found that the shape of the wave spectrum plays a substantial role. Broader
+underlying wave spectra leads to energetic plungers at a relatively low
+amplitude. For narrower spectra waves break at a higher amplitudes but with a
+less energetic spiller. Comparison with standard engineering methods commonly
+used to predict the velocity underneath extreme waves shows that, under certain
+conditions, the measured velocity profile strongly deviates from engineering
+predictions.
+",0,1,0,0,0,0
+17240,Full Momentum and Energy Resolved Spectral Function of a 2D Electronic System,"  The single-particle spectral function measures the density of electronic
+states (DOS) in a material as a function of both momentum and energy, providing
+central insights into phenomena such as superconductivity and Mott insulators.
+While scanning tunneling microscopy (STM) and other tunneling methods have
+provided partial spectral information, until now only angle-resolved
+photoemission spectroscopy (ARPES) has permitted a comprehensive determination
+of the spectral function of materials in both momentum and energy. However,
+ARPES operates only on electronic systems at the material surface and cannot
+work in the presence of applied magnetic fields. Here, we demonstrate a new
+method for determining the full momentum and energy resolved electronic
+spectral function of a two-dimensional (2D) electronic system embedded in a
+semiconductor. In contrast with ARPES, the technique remains operational in the
+presence of large externally applied magnetic fields and functions for
+electronic systems with zero electrical conductivity or with zero electron
+density. It provides a direct high-resolution and high-fidelity probe of the
+dispersion and dynamics of the interacting 2D electron system. By ensuring the
+system of interest remains under equilibrium conditions, we uncover delicate
+signatures of many-body effects involving electron-phonon interactions,
+plasmons, polarons, and a novel phonon analog of the vacuum Rabi splitting in
+atomic systems.
+",0,1,0,0,0,0
+17241,Complete parallel mean curvature surfaces in two-dimensional complex space-forms,"  The purpose of this article is to determine explicitly the complete surfaces
+with parallel mean curvature vector, both in the complex projective plane and
+the complex hyperbolic plane. The main results are as follows: When the
+curvature of the ambient space is positive, there exists a unique such surface
+up to rigid motions of the target space. On the other hand, when the curvature
+of the ambient space is negative, there are `non-trivial' complete parallel
+mean curvature surfaces generated by Jacobi elliptic functions and they exhaust
+such surfaces.
+",0,0,1,0,0,0
+17242,Parallelized Linear Classification with Volumetric Chemical Perceptrons,"  In this work, we introduce a new type of linear classifier that is
+implemented in a chemical form. We propose a novel encoding technique which
+simultaneously represents multiple datasets in an array of microliter-scale
+chemical mixtures. Parallel computations on these datasets are performed as
+robotic liquid handling sequences, whose outputs are analyzed by
+high-performance liquid chromatography. As a proof of concept, we chemically
+encode several MNIST images of handwritten digits and demonstrate successful
+chemical-domain classification of the digits using volumetric perceptrons. We
+additionally quantify the performance of our method with a larger dataset of
+binary vectors and compare the experimental measurements against predicted
+results. Paired with appropriate chemical analysis tools, our approach can work
+on increasingly parallel datasets. We anticipate that related approaches will
+be scalable to multilayer neural networks and other more complex algorithms.
+Much like recent demonstrations of archival data storage in DNA, this work
+blurs the line between chemical and electrical information systems, and offers
+early insight into the computational efficiency and massive parallelism which
+may come with computing in chemical domains.
+",0,0,0,0,1,0
+17243,Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification,"  Multi-label image classification is a fundamental but challenging task in
+computer vision. Great progress has been achieved by exploiting semantic
+relations between labels in recent years. However, conventional approaches are
+unable to model the underlying spatial relations between labels in multi-label
+images, because spatial annotations of the labels are generally not provided.
+In this paper, we propose a unified deep neural network that exploits both
+semantic and spatial relations between labels with only image-level
+supervisions. Given a multi-label image, our proposed Spatial Regularization
+Network (SRN) generates attention maps for all labels and captures the
+underlying relations between them via learnable convolutions. By aggregating
+the regularized classification results with original results by a ResNet-101
+network, the classification performance can be consistently improved. The whole
+deep neural network is trained end-to-end with only image-level annotations,
+thus requires no additional efforts on image annotations. Extensive evaluations
+on 3 public datasets with different types of labels show that our approach
+significantly outperforms state-of-the-arts and has strong generalization
+capability. Analysis of the learned SRN model demonstrates that it can
+effectively capture both semantic and spatial relations of labels for improving
+classification performance.
+",1,0,0,0,0,0
+17244,The critical binary star separation for a planetary system origin of white dwarf pollution,"  The atmospheres of between one quarter and one half of observed single white
+dwarfs in the Milky Way contain heavy element pollution from planetary debris.
+The pollution observed in white dwarfs in binary star systems is, however, less
+clear, because companion star winds can generate a stream of matter which is
+accreted by the white dwarf. Here we (i) discuss the necessity or lack thereof
+of a major planet in order to pollute a white dwarf with orbiting minor planets
+in both single and binary systems, and (ii) determine the critical binary
+separation beyond which the accretion source is from a planetary system. We
+hence obtain user-friendly functions relating this distance to the masses and
+radii of both stars, the companion wind, and the accretion rate onto the white
+dwarf, for a wide variety of published accretion prescriptions. We find that
+for the majority of white dwarfs in known binaries, if pollution is detected,
+then that pollution should originate from planetary material.
+",0,1,0,0,0,0
+17245,A quantum Mirković-Vybornov isomorphism,"  We present a quantization of an isomorphism of Mirković and Vybornov which
+relates the intersection of a Slodowy slice and a nilpotent orbit closure in
+$\mathfrak{gl}_N$ , to a slice between spherical Schubert varieties in the
+affine Grassmannian of $PGL_n$ (with weights encoded by the Jordan types of the
+nilpotent orbits). A quantization of the former variety is provided by a
+parabolic W-algebra and of the latter by a truncated shifted Yangian. Building
+on earlier work of Brundan and Kleshchev, we define an explicit isomorphism
+between these non-commutative algebras, and show that its classical limit is a
+variation of the original isomorphism of Mirković and Vybornov. As a
+corollary, we deduce that the W-algebra is free as a left (or right) module
+over its Gelfand-Tsetlin subalgebra, as conjectured by Futorny, Molev, and
+Ovsienko.
+",0,0,1,0,0,0
+17246,Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach,"  This paper is concerned with a multi-asset mean-variance portfolio selection
+problem under model uncertainty. We develop a continuous time framework for
+taking into account ambiguity aversion about both expected return rates and
+correlation matrix of the assets, and for studying the effects on portfolio
+diversification. We prove a separation principle for the associated robust
+control problem, which allows to reduce the determination of the optimal
+dynamic strategy to the parametric computation of the minimal risk premium
+function. Our results provide a justification for under-diversification, as
+documented in empirical studies. We explicitly quantify the degree of
+under-diversification in terms of correlation and Sharpe ratio ambiguity. In
+particular, we show that an investor with a poor confidence in the expected
+return estimation does not hold any risky asset, and on the other hand, trades
+only one risky asset when the level of ambiguity on correlation matrix is
+large. This extends to the continuous-time setting the results obtained by
+Garlappi, Uppal and Wang [13], and Liu and Zeng [24] in a one-period model. JEL
+Classification: G11, C61 MSC Classification: 91G10, 91G80, 60H30
+",0,0,0,0,0,1
+17247,TensorLayer: A Versatile Library for Efficient Deep Learning Development,"  Deep learning has enabled major advances in the fields of computer vision,
+natural language processing, and multimedia among many others. Developing a
+deep learning system is arduous and complex, as it involves constructing neural
+network architectures, managing training/trained models, tuning optimization
+process, preprocessing and organizing data, etc. TensorLayer is a versatile
+Python library that aims at helping researchers and engineers efficiently
+develop deep learning systems. It offers rich abstractions for neural networks,
+model and data management, and parallel workflow mechanism. While boosting
+efficiency, TensorLayer maintains both performance and scalability. TensorLayer
+was released in September 2016 on GitHub, and has helped people from academia
+and industry develop real-world applications of deep learning.
+",1,0,0,1,0,0
+17248,Effects of excess carriers on native defects in wide bandgap semiconductors: illumination as a method to enhance p-type doping,"  Undesired unintentional doping and doping limits in semiconductors are
+typically caused by compensating defects with low formation energies. Since the
+formation energy of a charged defect depends linearly on the Fermi level,
+doping limits can be especially pronounced in wide bandgap semiconductors where
+the Fermi level can vary substantially. Introduction of non-equilibrium carrier
+concentrations during growth or processing alters the chemical potentials of
+band carriers and thus provides the possibility of modifying populations of
+charged defects in ways impossible at thermal equilibrium. Herein we
+demonstrate that, for an ergodic system with excess carriers, the rates of
+carrier capture and emission involving a defect charge transition level
+rigorously determine the admixture of electron and hole quasi-Fermi levels
+determining the formation energy of non-zero charge states of that defect type.
+To catalog the range of possible responses to excess carriers, we investigate
+the behavior of a single donor-like defect as functions of extrinsic doping and
+energy of the charge transition level. The technologically most important
+finding is that excess carriers will increase the formation energy of
+compensating defects for most values of the charge transition level in the
+bandgap. Thus, it may be possible to overcome limitations on doping imposed by
+native defects. Cases also exist in wide bandgap semiconductors in which the
+concentration of defects with the same charge polarity as the majority dopant
+is either left unchanged or actually increases. The causes of these various
+behaviors are rationalized in terms of the capture and emission rates and
+guidelines for carrying out experimental tests of this model are given.
+",0,1,0,0,0,0
+17249,LATTE: Application Oriented Social Network Embedding,"  In recent years, many research works propose to embed the network structured
+data into a low-dimensional feature space, where each node is represented as a
+feature vector. However, due to the detachment of embedding process with
+external tasks, the learned embedding results by most existing embedding models
+can be ineffective for application tasks with specific objectives, e.g.,
+community detection or information diffusion. In this paper, we propose study
+the application oriented heterogeneous social network embedding problem.
+Significantly different from the existing works, besides the network structure
+preservation, the problem should also incorporate the objectives of external
+applications in the objective function. To resolve the problem, in this paper,
+we propose a novel network embedding framework, namely the ""appLicAtion
+orienTed neTwork Embedding"" (Latte) model. In Latte, the heterogeneous network
+structure can be applied to compute the node ""diffusive proximity"" scores,
+which capture both local and global network structures. Based on these computed
+scores, Latte learns the network representation feature vectors by extending
+the autoencoder model model to the heterogeneous network scenario, which can
+also effectively unite the objectives of network embedding and external
+application tasks. Extensive experiments have been done on real-world
+heterogeneous social network datasets, and the experimental results have
+demonstrated the outstanding performance of Latte in learning the
+representation vectors for specific application tasks.
+",1,0,0,0,0,0
+17250,Giant paramagnetism induced valley polarization of electrons in charge-tunable monolayer MoSe2,"  For applications exploiting the valley pseudospin degree of freedom in
+transition metal dichalcogenide monolayers, efficient preparation of electrons
+or holes in a single valley is essential. Here, we show that a magnetic field
+of 7 Tesla leads to a near-complete valley polarization of electrons in MoSe2
+monolayer with a density 1.6x10^{12} cm^{-2}; in the absence of exchange
+interactions favoring single-valley occupancy, a similar degree of valley
+polarization would have required a pseudospin g-factor exceeding 40. To
+investigate the magnetic response, we use polarization resolved
+photoluminescence as well as resonant reflection measurements. In the latter,
+we observe gate voltage dependent transfer of oscillator strength from the
+exciton to the attractive-Fermi-polaron: stark differences in the spectrum of
+the two light helicities provide a confirmation of valley polarization. Our
+findings suggest an interaction induced giant paramagnetic response of MoSe2,
+which paves the way for valleytronics applications.
+",0,1,0,0,0,0
+17251,Highrisk Prediction from Electronic Medical Records via Deep Attention Networks,"  Predicting highrisk vascular diseases is a significant issue in the medical
+domain. Most predicting methods predict the prognosis of patients from
+pathological and radiological measurements, which are expensive and require
+much time to be analyzed. Here we propose deep attention models that predict
+the onset of the high risky vascular disease from symbolic medical histories
+sequence of hypertension patients such as ICD-10 and pharmacy codes only,
+Medical History-based Prediction using Attention Network (MeHPAN). We
+demonstrate two types of attention models based on 1) bidirectional gated
+recurrent unit (R-MeHPAN) and 2) 1D convolutional multilayer model (C-MeHPAN).
+Two MeHPAN models are evaluated on approximately 50,000 hypertension patients
+with respect to precision, recall, f1-measure and area under the curve (AUC).
+Experimental results show that our MeHPAN methods outperform standard
+classification models. Comparing two MeHPANs, R-MeHPAN provides more better
+discriminative capability with respect to all metrics while C-MeHPAN presents
+much shorter training time with competitive accuracy.
+",1,0,0,1,0,0
+17252,Agent based simulation of the evolution of society as an alternate maximization problem,"  Understanding the evolution of human society, as a complex adaptive system,
+is a task that has been looked upon from various angles. In this paper, we
+simulate an agent-based model with a high enough population tractably. To do
+this, we characterize an entity called \textit{society}, which helps us reduce
+the complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. We
+propose a very realistic setting, where we design a joint alternate
+maximization step algorithm to maximize a certain \textit{fitness} function,
+which we believe simulates the way societies develop. Our key contributions
+include (i) proposing a novel protocol for simulating the evolution of a
+society with cheap, non-optimal joint alternate maximization steps (ii)
+providing a framework for carrying out experiments that adhere to this
+joint-optimization simulation framework (iii) carrying out experiments to show
+that it makes sense empirically (iv) providing an alternate justification for
+the use of \textit{society} in the simulations.
+",1,0,0,1,0,0
+17253,Can a heart rate variability biomarker identify the presence of autism spectrum disorder in eight year old children?,"  Autonomic nervous system (ANS) activity is altered in autism spectrum
+disorder (ASD). Heart rate variability (HRV) derived from electrocardiogram
+(ECG) has been a powerful tool to identify alterations in ANS due to a plethora
+of pathophysiological conditions, including psychological ones such as
+depression. ECG-derived HRV thus carries a yet to be explored potential to be
+used as a diagnostic and follow-up biomarker of ASD. However, few studies have
+explored this potential. In a cohort of boys (ages 8 - 11 years) with (n=18)
+and without ASD (n=18), we tested a set of linear and nonlinear HRV measures,
+including phase rectified signal averaging (PRSA), applied to a segment of ECG
+collected under resting conditions for their predictive properties of ASD. We
+identified HRV measures derived from time, frequency and geometric
+signal-analytical domains which are changed in ASD children relative to peers
+without ASD and correlate to psychometric scores (p<0.05 for each). Receiver
+operating curves area ranged between 0.71 - 0.74 for each HRV measure. Despite
+being a small cohort lacking external validation, these promising preliminary
+results warrant larger prospective validation studies.
+",0,0,0,0,1,0
+17254,Semantic Entity Retrieval Toolkit,"  Unsupervised learning of low-dimensional, semantic representations of words
+and entities has recently gained attention. In this paper we describe the
+Semantic Entity Retrieval Toolkit (SERT) that provides implementations of our
+previously published entity representation models. The toolkit provides a
+unified interface to different representation learning algorithms, fine-grained
+parsing configuration and can be used transparently with GPUs. In addition,
+users can easily modify existing models or implement their own models in the
+framework. After model training, SERT can be used to rank entities according to
+a textual query and extract the learned entity/word representation for use in
+downstream algorithms, such as clustering or recommendation.
+",1,0,0,0,0,0
+17255,Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection,"  In the Best-$k$-Arm problem, we are given $n$ stochastic bandit arms, each
+associated with an unknown reward distribution. We are required to identify the
+$k$ arms with the largest means by taking as few samples as possible. In this
+paper, we make progress towards a complete characterization of the
+instance-wise sample complexity bounds for the Best-$k$-Arm problem. On the
+lower bound side, we obtain a novel complexity term to measure the sample
+complexity that every Best-$k$-Arm instance requires. This is derived by an
+interesting and nontrivial reduction from the Best-$1$-Arm problem. We also
+provide an elimination-based algorithm that matches the instance-wise lower
+bound within doubly-logarithmic factors. The sample complexity of our algorithm
+strictly dominates the state-of-the-art for Best-$k$-Arm (module constant
+factors).
+",1,0,0,1,0,0
+17256,Dimensions of equilibrium measures on a class of planar self-affine sets,"  We study equilibrium measures (Käenmäki measures) supported on
+self-affine sets generated by a finite collection of diagonal and anti-diagonal
+matrices acting on the plane and satisfying the strong separation property. Our
+main result is that such measures are exact dimensional and the dimension
+satisfies the Ledrappier-Young formula, which gives an explicit expression for
+the dimension in terms of the entropy and Lyapunov exponents as well as the
+dimension of the important coordinate projection of the measure. In particular,
+we do this by showing that the Käenmäki measure is equal to the sum of (the
+pushforwards) of two Gibbs measures on an associated subshift of finite type.
+",0,0,1,0,0,0
+17257,Hubble PanCET: An isothermal day-side atmosphere for the bloated gas-giant HAT-P-32Ab,"  We present a thermal emission spectrum of the bloated hot Jupiter HAT-P-32Ab
+from a single eclipse observation made in spatial scan mode with the Wide Field
+Camera 3 (WFC3) aboard the Hubble Space Telescope (HST). The spectrum covers
+the wavelength regime from 1.123 to 1.644 microns which is binned into 14
+eclipse depths measured to an averaged precision of 104 parts-per million. The
+spectrum is unaffected by a dilution from the close M-dwarf companion
+HAT-P-32B, which was fully resolved. We complemented our spectrum with
+literature results and performed a comparative forward and retrieval analysis
+with the 1D radiative-convective ATMO model. Assuming solar abundance of the
+planet atmosphere, we find that the measured spectrum can best be explained by
+the spectrum of a blackbody isothermal atmosphere with Tp = 1995 +/- 17K, but
+can equally-well be described by a spectrum with modest thermal inversion. The
+retrieved spectrum suggests emission from VO at the WFC3 wavelengths and no
+evidence of the 1.4 micron water feature. The emission models with temperature
+profiles decreasing with height are rejected at a high confidence. An
+isothermal or inverted spectrum can imply a clear atmosphere with an absorber,
+a dusty cloud deck or a combination of both. We find that the planet can have
+continuum of values for the albedo and recirculation, ranging from high albedo
+and poor recirculation to low albedo and efficient recirculation. Optical
+spectroscopy of the planet's day-side or thermal emission phase curves can
+potentially resolve the current albedo with recirculation degeneracy.
+",0,1,0,0,0,0
+17258,One Model To Learn Them All,"  Deep learning yields great results across many fields, from speech
+recognition, image classification, to translation. But for each problem,
+getting a deep model to work well involves research into the architecture and a
+long period of tuning. We present a single model that yields good results on a
+number of problems spanning multiple domains. In particular, this single model
+is trained concurrently on ImageNet, multiple translation tasks, image
+captioning (COCO dataset), a speech recognition corpus, and an English parsing
+task. Our model architecture incorporates building blocks from multiple
+domains. It contains convolutional layers, an attention mechanism, and
+sparsely-gated layers. Each of these computational blocks is crucial for a
+subset of the tasks we train on. Interestingly, even if a block is not crucial
+for a task, we observe that adding it never hurts performance and in most cases
+improves it on all tasks. We also show that tasks with less data benefit
+largely from joint training with other tasks, while performance on large tasks
+degrades only slightly if at all.
+",1,0,0,1,0,0
+17259,Porcupine Neural Networks: (Almost) All Local Optima are Global,"  Neural networks have been used prominently in several machine learning and
+statistics applications. In general, the underlying optimization of neural
+networks is non-convex which makes their performance analysis challenging. In
+this paper, we take a novel approach to this problem by asking whether one can
+constrain neural network weights to make its optimization landscape have good
+theoretical properties while at the same time, be a good approximation for the
+unconstrained one. For two-layer neural networks, we provide affirmative
+answers to these questions by introducing Porcupine Neural Networks (PNNs)
+whose weight vectors are constrained to lie over a finite set of lines. We show
+that most local optima of PNN optimizations are global while we have a
+characterization of regions where bad local optimizers may exist. Moreover, our
+theoretical and empirical results suggest that an unconstrained neural network
+can be approximated using a polynomially-large PNN.
+",1,0,0,1,0,0
+17260,Configuration Path Integral Monte Carlo Approach to the Static Density Response of the Warm Dense Electron Gas,"  Precise knowledge of the static density response function (SDRF) of the
+uniform electron gas (UEG) serves as key input for numerous applications, most
+importantly for density functional theory beyond generalized gradient
+approximations. Here we extend the configuration path integral Monte Carlo
+(CPIMC) formalism that was previously applied to the spatially uniform electron
+gas to the case of an inhomogeneous electron gas by adding a spatially periodic
+external potential. This procedure has recently been successfully used in
+permutation blocking path integral Monte Carlo simulations (PB-PIMC) of the
+warm dense electron gas [Dornheim \textit{et al.}, Phys. Rev. E in press,
+arXiv:1706.00315], but this method is restricted to low and moderate densities.
+Implementing this procedure into CPIMC allows us to obtain exact finite
+temperature results for the SDRF of the electron gas at \textit{high to
+moderate densities} closing the gap left open by the PB-PIMC data. In this
+paper we demonstrate how the CPIMC formalism can be efficiently extended to the
+spatially inhomogeneous electron gas and present the first data points.
+Finally, we discuss finite size errors involved in the quantum Monte Carlo
+results for the SDRF in detail and present a solution how to remove them that
+is based on a generalization of ground state techniques.
+",0,1,0,0,0,0
+17261,Superzone gap formation and low lying crystal electric field levels in PrPd$_2$Ge$_2$ single crystal,"  The magnetocrystalline anisotropy exhibited in PrPd$_2$Ge$_2$ single crystal
+has been investigated by measuring the magnetization, magnetic susceptibility,
+electrical resistivity and heat capacity. PrPd$_2$Ge$_2$ crystallizes in the
+well known ThCr$_2$Si$_2$\--type tetragonal structure. The antiferromagnetic
+ordering is confirmed as 5.1~K with the [001]-axis as the easy axis of
+magnetization. A superzone gap formation is observed from the electrical
+resistivity measurement when the current is passed along the [001] direction.
+The crystal electric field (CEF) analysis on the magnetic susceptibility,
+magnetization and the heat capacity measurements confirms a doublet ground
+state with a relatively low over all CEF level splitting. The CEF level
+spacings and the Zeeman splitting at high fields become comparable and lead to
+metamagnetic transition at 34~T due to the CEF level crossing.
+",0,1,0,0,0,0
+17262,Adaptive Real-Time Software Defined MIMO Visible Light Communications using Spatial Multiplexing and Spatial Diversity,"  In this paper, we experimentally demonstrate a real-time software defined
+multiple input multiple output (MIMO) visible light communication (VLC) system
+employing link adaptation of spatial multiplexing and spatial diversity.
+Real-time MIMO signal processing is implemented by using the Field Programmable
+Gate Array (FPGA) based Universal Software Radio Peripheral (USRP) devices.
+Software defined implantation of MIMO VLC can assist in enabling an adaptive
+and reconfigurable communication system without hardware changes. We measured
+the error vector magnitude (EVM), bit error rate (BER) and spectral efficiency
+performance for single carrier M-QAM MIMO VLC using spatial diversity and
+spatial multiplexing. Results show that spatial diversity MIMO VLC improves
+error performance at the cost of spectral efficiency that spatial multiplexing
+should enhance. We propose the adaptive MIMO solution that both modulation
+schema and MIMO schema are dynamically adapted to the changing channel
+conditions for enhancing the error performance and spectral efficiency. The
+average error-free spectral efficiency of adaptive 2x2 MIMO VLC achieved 12
+b/s/Hz over 2 meters indoor dynamic transmission.
+",1,0,1,0,0,0
+17263,Maximum Principle Based Algorithms for Deep Learning,"  The continuous dynamical system approach to deep learning is explored in
+order to devise alternative frameworks for training algorithms. Training is
+recast as a control problem and this allows us to formulate necessary
+optimality conditions in continuous time using the Pontryagin's maximum
+principle (PMP). A modification of the method of successive approximations is
+then used to solve the PMP, giving rise to an alternative training algorithm
+for deep learning. This approach has the advantage that rigorous error
+estimates and convergence results can be established. We also show that it may
+avoid some pitfalls of gradient-based methods, such as slow convergence on flat
+landscapes near saddle points. Furthermore, we demonstrate that it obtains
+favorable initial convergence rate per-iteration, provided Hamiltonian
+maximization can be efficiently carried out - a step which is still in need of
+improvement. Overall, the approach opens up new avenues to attack problems
+associated with deep learning, such as trapping in slow manifolds and
+inapplicability of gradient-based methods for discrete trainable variables.
+",1,0,0,1,0,0
+17264,Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations,"  With the popularity of Linked Open Data (LOD) and the associated rise in
+freely accessible knowledge that can be accessed via LOD, exploiting LOD for
+recommender systems has been widely studied based on various approaches such as
+graph-based or using different machine learning models with LOD-enabled
+features. Many of the previous approaches require construction of an additional
+graph to run graph-based algorithms or to extract path-based features by
+combining user- item interactions (e.g., likes, dislikes) and background
+knowledge from LOD. In this paper, we investigate Factorization Machines (FMs)
+based on particularly lightweight LOD-enabled features which can be directly
+obtained via a public SPARQL Endpoint without any additional effort to
+construct a graph. Firstly, we aim to study whether using FM with these
+lightweight LOD-enabled features can provide competitive performance compared
+to a learning-to-rank approach leveraging LOD as well as other well-established
+approaches such as kNN-item and BPRMF. Secondly, we are interested in finding
+out to what extent each set of LOD-enabled features contributes to the
+recommendation performance. Experimental evaluation on a standard dataset shows
+that our proposed approach using FM with lightweight LOD-enabled features
+provides the best performance compared to other approaches in terms of five
+evaluation metrics. In addition, the study of the recommendation performance
+based on different sets of LOD-enabled features indicate that property-object
+lists and PageRank scores of items are useful for improving the performance,
+and can provide the best performance through using them together for FM. We
+observe that subject-property lists of items does not contribute to the
+recommendation performance but rather decreases the performance.
+",1,0,0,0,0,0
+17265,Wave propagation and homogenization in 2D and 3D lattices: a semi-analytical approach,"  Wave motion in two- and three-dimensional periodic lattices of beam members
+supporting longitudinal and flexural waves is considered. An analytic method
+for solving the Bloch wave spectrum is developed, characterized by a
+generalized eigenvalue equation obtained by enforcing the Floquet condition.
+The dynamic stiffness matrix is shown to be explicitly Hermitian and to admit
+positive eigenvalues. Lattices with hexagonal, rectangular, tetrahedral and
+cubic unit cells are analyzed. The semi-analytical method can be asymptotically
+expanded for low frequency yielding explicit forms for the Christoffel matrix
+describing wave motion in the quasistatic limit.
+",0,1,0,0,0,0
+17266,Waring's problem for unipotent algebraic groups,"  In this paper, we formulate an analogue of Waring's problem for an algebraic
+group $G$. At the field level we consider a morphism of varieties $f\colon
+\mathbb{A}^1\to G$ and ask whether every element of $G(K)$ is the product of a
+bounded number of elements $f(\mathbb{A}^1(K)) = f(K)$. We give an affirmative
+answer when $G$ is unipotent and $K$ is a characteristic zero field which is
+not formally real.
+The idea is the same at the integral level, except one must work with
+schemes, and the question is whether every element in a finite index subgroup
+of $G(\mathcal{O})$ can be written as a product of a bounded number of elements
+of $f(\mathcal{O})$. We prove this is the case when $G$ is unipotent and
+$\mathcal{O}$ is the ring of integers of a totally imaginary number field.
+",0,0,1,0,0,0
+17267,Spreading of localized attacks in spatial multiplex networks,"  Many real-world multilayer systems such as critical infrastructure are
+interdependent and embedded in space with links of a characteristic length.
+They are also vulnerable to localized attacks or failures, such as terrorist
+attacks or natural catastrophes, which affect all nodes within a given radius.
+Here we study the effects of localized attacks on spatial multiplex networks of
+two layers. We find a metastable region where a localized attack larger than a
+critical size induces a nucleation transition as a cascade of failures spreads
+throughout the system, leading to its collapse. We develop a theory to predict
+the critical attack size and find that it exhibits novel scaling behavior. We
+further find that localized attacks in these multiplex systems can induce a
+previously unobserved combination of random and spatial cascades. Our results
+demonstrate important vulnerabilities in real-world interdependent networks and
+show new theoretical features of spatial networks.
+",1,1,0,0,0,0
+17268,Greedy Sparse Signal Reconstruction Using Matching Pursuit Based on Hope-tree,"  The reconstruction of sparse signals requires the solution of an
+$\ell_0$-norm minimization problem in Compressed Sensing. Previous research has
+focused on the investigation of a single candidate to identify the support
+(index of nonzero elements) of a sparse signal. To ensure that the optimal
+candidate can be obtained in each iteration, we propose here an iterative
+greedy reconstruction algorithm (GSRA). First, the intersection of the support
+sets estimated by the Orthogonal Matching Pursuit (OMP) and Subspace Pursuit
+(SP) is set as the initial support set. Then, a hope-tree is built to expand
+the set. Finally, a developed decreasing subspace pursuit method is used to
+rectify the candidate set. Detailed simulation results demonstrate that GSRA is
+more accurate than other typical methods in recovering Gaussian signals, 0--1
+sparse signals, and synthetic signals.
+",1,0,1,0,0,0
+17269,Attack-Aware Multi-Sensor Integration Algorithm for Autonomous Vehicle Navigation Systems,"  In this paper, we propose a fault detection and isolation based attack-aware
+multi-sensor integration algorithm for the detection of cyberattacks in
+autonomous vehicle navigation systems. The proposed algorithm uses an extended
+Kalman filter to construct robust residuals in the presence of noise, and then
+uses a parametric statistical tool to identify cyberattacks. The parametric
+statistical tool is based on the residuals constructed by the measurement
+history rather than one measurement at a time in the properties of
+discrete-time signals and dynamic systems. This approach allows the proposed
+multi-sensor integration algorithm to provide quick detection and low false
+alarm rates for applications in dynamic systems. An example of INS/GNSS
+integration of autonomous navigation systems is presented to validate the
+proposed algorithm by using a software-in-the-loop simulation.
+",1,0,0,0,0,0
+17270,"Turbulence, cascade and singularity in a generalization of the Constantin-Lax-Majda equation","  We study numerically a Constantin-Lax-Majda-De Gregorio model generalized by
+Okamoto, Sakajo and Wunsch, which is a model of fluid turbulence in one
+dimension with an inviscid conservation law. In the presence of the viscosity
+and two types of the large-scale forcings, we show that turbulent cascade of
+the inviscid invariant, which is not limited to quadratic quantity, occurs and
+that properties of this model's turbulent state are related to singularity of
+the inviscid case by adopting standard tools of analyzing fluid turbulence.
+",0,1,0,0,0,0
+17271,Fitting phase--type scale mixtures to heavy--tailed data and distributions,"  We consider the fitting of heavy tailed data and distribution with a special
+attention to distributions with a non--standard shape in the ""body"" of the
+distribution. To this end we consider a dense class of heavy tailed
+distributions introduced recently, employing an EM algorithm for the the
+maximum likelihood estimates of its parameters. We present methods for fitting
+to observed data, histograms, censored data, as well as to theoretical
+distributions. Numerical examples are provided with simulated data and a
+benchmark reinsurance dataset. We empirically demonstrate that our model can
+provide excellent fits to heavy--tailed data/distributions with minimal
+assumptions
+",0,0,1,1,0,0
+17272,Deep Incremental Boosting,"  This paper introduces Deep Incremental Boosting, a new technique derived from
+AdaBoost, specifically adapted to work with Deep Learning methods, that reduces
+the required training time and improves generalisation. We draw inspiration
+from Transfer of Learning approaches to reduce the start-up time to training
+each incremental Ensemble member. We show a set of experiments that outlines
+some preliminary results on some common Deep Learning datasets and discuss the
+potential improvements Deep Incremental Boosting brings to traditional Ensemble
+methods in Deep Learning.
+",1,0,0,1,0,0
+17273,Empirical Likelihood for Linear Structural Equation Models with Dependent Errors,"  We consider linear structural equation models that are associated with mixed
+graphs. The structural equations in these models only involve observed
+variables, but their idiosyncratic error terms are allowed to be correlated and
+non-Gaussian. We propose empirical likelihood (EL) procedures for inference,
+and suggest several modifications, including a profile likelihood, in order to
+improve tractability and performance of the resulting methods. Through
+simulations, we show that when the error distributions are non-Gaussian, the
+use of EL and the proposed modifications may increase statistical efficiency
+and improve assessment of significance.
+",0,0,0,1,0,0
+17274,Grassmannian flows and applications to nonlinear partial differential equations,"  We show how solutions to a large class of partial differential equations with
+nonlocal Riccati-type nonlinearities can be generated from the corresponding
+linearized equations, from arbitrary initial data. It is well known that
+evolutionary matrix Riccati equations can be generated by projecting linear
+evolutionary flows on a Stiefel manifold onto a coordinate chart of the
+underlying Grassmann manifold. Our method relies on extending this idea to the
+infinite dimensional case. The key is an integral equation analogous to the
+Marchenko equation in integrable systems, that represents the coodinate chart
+map. We show explicitly how to generate such solutions to scalar partial
+differential equations of arbitrary order with nonlocal quadratic
+nonlinearities using our approach. We provide numerical simulations that
+demonstrate the generation of solutions to
+Fisher--Kolmogorov--Petrovskii--Piskunov equations with nonlocal
+nonlinearities. We also indicate how the method might extend to more general
+classes of nonlinear partial differential systems.
+",0,1,1,0,0,0
+17275,The Reinhardt Conjecture as an Optimal Control Problem,"  In 1934, Reinhardt conjectured that the shape of the centrally symmetric
+convex body in the plane whose densest lattice packing has the smallest density
+is a smoothed octagon. This conjecture is still open. We formulate the
+Reinhardt Conjecture as a problem in optimal control theory. The smoothed
+octagon is a Pontryagin extremal trajectory with bang-bang control. More
+generally, the smoothed regular $6k+2$-gon is a Pontryagin extremal with
+bang-bang control. The smoothed octagon is a strict (micro) local minimum to
+the optimal control problem. The optimal solution to the Reinhardt problem is a
+trajectory without singular arcs. The extremal trajectories that do not meet
+the singular locus have bang-bang controls with finitely many switching times.
+Finally, we reduce the Reinhardt problem to an optimization problem on a
+five-dimensional manifold. (Each point on the manifold is an initial condition
+for a potential Pontryagin extremal lifted trajectory.) We suggest that the
+Reinhardt conjecture might eventually be fully resolved through optimal control
+theory. Some proofs are computer-assisted using a computer algebra system.
+",0,0,1,0,0,0
+17276,Deep submillimeter and radio observations in the SSA22 field. I. Powering sources and Lyα escape fraction of Lyα blobs,"  We study the heating mechanisms and Ly{\alpha} escape fractions of 35
+Ly{\alpha} blobs (LABs) at z = 3.1 in the SSA22 field. Dust continuum sources
+have been identified in 11 of the 35 LABs, all with star formation rates (SFRs)
+above 100 Msun/yr. Likely radio counterparts are detected in 9 out of 29
+investigated LABs. The detection of submm dust emission is more linked to the
+physical size of the Ly{\alpha} emission than to the Ly{\alpha} luminosities of
+the LABs. A radio excess in the submm/radio detected LABs is common, hinting at
+the presence of active galactic nuclei. Most radio sources without X-ray
+counterparts are located at the centers of the LABs. However, all X-ray
+counterparts avoid the central regions. This may be explained by absorption due
+to exceptionally large column densities along the line-of-sight or by LAB
+morphologies, which are highly orientation dependent. The median Ly{\alpha}
+escape fraction is about 3\% among the submm-detected LABs, which is lower than
+a lower limit of 11\% for the submm-undetected LABs. We suspect that the large
+difference is due to the high dust attenuation supported by the large SFRs, the
+dense large-scale environment as well as large uncertainties in the extinction
+corrections required to apply when interpreting optical data.
+",0,1,0,0,0,0
+17277,Modeling temporal constraints for a system of interactive scores,"  In this chapter we explain briefly the fundamentals of the interactive scores
+formalism. Then we develop a solution for implementing the ECO machine by
+mixing petri nets and constraints propagation. We also present another solution
+for implementing the ECO machine using concurrent constraint programming.
+Finally, we present an extension of interactive score with conditional
+branching.
+",1,0,0,0,0,0
+17278,Electronic structure of ThRu2Si2 studied by angle-resolved photoelectron spectroscopy: Elucidating the contribution of U 5f states in URu2Si2,"  The electronic structure of ThRu2Si2 was studied by angle-resolved
+photoelectron spectroscopy (ARPES) with incident photon energies of hn=655-745
+eV. Detailed band structure and the three-dimensional shapes of Fermi surfaces
+were derived experimentally, and their characteristic features were mostly
+explained by means of band structure calculations based on the density
+functional theory. Comparison of the experimental ARPES spectra of ThRu2Si2
+with those of URu2Si2 shows that they have considerably different spectral
+profiles particularly in the energy range of 1 eV from the Fermi level,
+suggesting that U 5f states are substantially hybridized in these bands. The
+relationship between the ARPES spectra of URu2Si2 and ThRu2Si2 is very
+different from the one between the ARPES spectra of CeRu2Si2 and LaRu2Si2,
+where the intrinsic difference in their spectra is limited only in the very
+vicinity of the Fermi energy. The present result suggests that the U 5f
+electrons in URu2Si2 have strong hybridization with ligand states and have an
+essentially itinerant character.
+",0,1,0,0,0,0
+17279,Non-zero constant curvature factorable surfaces in pseudo-Galilean space,"  Factorable surfaces, i.e. graphs associated with the product of two functions
+of one variable, constitute a wide class of surfaces. Such surfaces in the
+pseudo-Galilean space with zero Gaussian and mean curvature were obtained in
+[1]. In this study, we provide new classification results relating to the
+factorable surfaces with non-zero Gaussian and mean curvature.
+",0,0,1,0,0,0
+17280,Darboux and Binary Darboux Transformations for Discrete Integrable Systems. II. Discrete Potential mKdV Equation,"  The paper presents two results. First it is shown how the discrete potential
+modified KdV equation and its Lax pairs in matrix form arise from the
+Hirota-Miwa equation by a 2-periodic reduction. Then Darboux transformations
+and binary Darboux transformations are derived for the discrete potential
+modified KdV equation and it is shown how these may be used to construct exact
+solutions.
+",0,1,0,0,0,0
+17281,Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates,"  Sequential change-point detection when the distribution parameters are
+unknown is a fundamental problem in statistics and machine learning. When the
+post-change parameters are unknown, we consider a set of detection procedures
+based on sequential likelihood ratios with non-anticipating estimators
+constructed using online convex optimization algorithms such as online mirror
+descent, which provides a more versatile approach to tackle complex situations
+where recursive maximum likelihood estimators cannot be found. When the
+underlying distributions belong to a exponential family and the estimators
+satisfy the logarithm regret property, we show that this approach is nearly
+second-order asymptotically optimal. This means that the upper bound for the
+false alarm rate of the algorithm (measured by the average-run-length) meets
+the lower bound asymptotically up to a log-log factor when the threshold tends
+to infinity. Our proof is achieved by making a connection between sequential
+change-point and online convex optimization and leveraging the logarithmic
+regret bound property of online mirror descent algorithm. Numerical and real
+data examples validate our theory.
+",1,0,1,1,0,0
+17282,Algorithms in the classical Néron Desingularization,"  We give algorithms to construct the Néron Desingularization and the easy
+case from \cite{KK} of the General Néron Desingularization.
+",0,0,1,0,0,0
+17283,Recent Advances in Neural Program Synthesis,"  In recent years, deep learning has made tremendous progress in a number of
+fields that were previously out of reach for artificial intelligence. The
+successes in these problems has led researchers to consider the possibilities
+for intelligent systems to tackle a problem that humans have only recently
+themselves considered: program synthesis. This challenge is unlike others such
+as object recognition and speech translation, since its abstract nature and
+demand for rigor make it difficult even for human minds to attempt. While it is
+still far from being solved or even competitive with most existing methods,
+neural program synthesis is a rapidly growing discipline which holds great
+promise if completely realized. In this paper, we start with exploring the
+problem statement and challenges of program synthesis. Then, we examine the
+fascinating evolution of program induction models, along with how they have
+succeeded, failed and been reimagined since. Finally, we conclude with a
+contrastive look at program synthesis and future research recommendations for
+the field.
+",1,0,0,0,0,0
+17284,Generator Reversal,"  We consider the problem of training generative models with deep neural
+networks as generators, i.e. to map latent codes to data points. Whereas the
+dominant paradigm combines simple priors over codes with complex deterministic
+models, we propose instead to use more flexible code distributions. These
+distributions are estimated non-parametrically by reversing the generator map
+during training. The benefits include: more powerful generative models, better
+modeling of latent structure and explicit control of the degree of
+generalization.
+",1,0,0,1,0,0
+17285,Finite model reasoning over existential rules,"  Ontology-based query answering (OBQA) asks whether a Boolean conjunctive
+query is satisfied by all models of a logical theory consisting of a relational
+database paired with an ontology. The introduction of existential rules (i.e.,
+Datalog rules extended with existential quantifiers in rule-heads) as a means
+to specify the ontology gave birth to Datalog+/-, a framework that has received
+increasing attention in the last decade, with focus also on decidability and
+finite controllability to support effective reasoning. Five basic decidable
+fragments have been singled out: linear, weakly-acyclic, guarded, sticky, and
+shy. Moreover, for all these fragments, except shy, the important property of
+finite controllability has been proved, ensuring that a query is satisfied by
+all models of the theory iff it is satisfied by all its finite models. In this
+paper we complete the picture by demonstrating that finite controllability of
+OBQA holds also for shy ontologies, and it therefore applies to all basic
+decidable Datalog+/- classes. To make the demonstration, we devise a general
+technique to facilitate the process of (dis)proving finite controllability of
+an arbitrary ontological fragment. This paper is under consideration for
+acceptance in TPLP.
+",1,0,0,0,0,0
+17286,On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization,"  Despite their popularity, the practical performance of asynchronous
+stochastic gradient descent methods (ASGD) for solving large scale machine
+learning problems are not as good as theoretical results indicate. We adopt and
+analyze a synchronous K-step averaging stochastic gradient descent algorithm
+which we call K-AVG. We establish the convergence results of K-AVG for
+nonconvex objectives and explain why the K-step delay is necessary and leads to
+better performance than traditional parallel stochastic gradient descent which
+is a special case of K-AVG with $K=1$. We also show that K-AVG scales better
+than ASGD. Another advantage of K-AVG over ASGD is that it allows larger
+stepsizes. On a cluster of $128$ GPUs, K-AVG is faster than ASGD
+implementations and achieves better accuracies and faster convergence for
+\cifar dataset.
+",1,0,0,1,0,0
+17287,Adversarial Neural Machine Translation,"  In this paper, we study a new learning paradigm for Neural Machine
+Translation (NMT). Instead of maximizing the likelihood of the human
+translation as in previous works, we minimize the distinction between human
+translation and the translation given by an NMT model. To achieve this goal,
+inspired by the recent success of generative adversarial networks (GANs), we
+employ an adversarial training architecture and name it as Adversarial-NMT. In
+Adversarial-NMT, the training of the NMT model is assisted by an adversary,
+which is an elaborately designed Convolutional Neural Network (CNN). The goal
+of the adversary is to differentiate the translation result generated by the
+NMT model from that by human. The goal of the NMT model is to produce high
+quality translations so as to cheat the adversary. A policy gradient method is
+leveraged to co-train the NMT model and the adversary. Experimental results on
+English$\rightarrow$French and German$\rightarrow$English translation tasks
+show that Adversarial-NMT can achieve significantly better translation quality
+than several strong baselines.
+",1,0,0,1,0,0
+17288,Surface group amalgams that (don't) act on 3-manifolds,"  We determine which amalgamated products of surface groups identified over
+multiples of simple closed curves are not fundamental groups of 3-manifolds. We
+prove each surface amalgam considered is virtually the fundamental group of a
+3-manifold. We prove that each such surface group amalgam is abstractly
+commensurable to a right-angled Coxeter group from a related family. In an
+appendix, we determine the quasi-isometry classes among these surface amalgams
+and their related right-angled Coxeter groups.
+",0,0,1,0,0,0
+17289,Shading Annotations in the Wild,"  Understanding shading effects in images is critical for a variety of vision
+and graphics problems, including intrinsic image decomposition, shadow removal,
+image relighting, and inverse rendering. As is the case with other vision
+tasks, machine learning is a promising approach to understanding shading - but
+there is little ground truth shading data available for real-world images. We
+introduce Shading Annotations in the Wild (SAW), a new large-scale, public
+dataset of shading annotations in indoor scenes, comprised of multiple forms of
+shading judgments obtained via crowdsourcing, along with shading annotations
+automatically generated from RGB-D imagery. We use this data to train a
+convolutional neural network to predict per-pixel shading information in an
+image. We demonstrate the value of our data and network in an application to
+intrinsic images, where we can reduce decomposition artifacts produced by
+existing algorithms. Our database is available at
+this http URL.
+",1,0,0,0,0,0
+17290,Koszul cycles and Golod rings,"  Let $S$ be the power series ring or the polynomial ring over a field $K$ in
+the variables $x_1,\ldots,x_n$, and let $R=S/I$, where $I$ is proper ideal
+which we assume to be graded if $S$ is the polynomial ring. We give an explicit
+description of the cycles of the Koszul complex whose homology classes generate
+the Koszul homology of $R=S/I$ with respect to $x_1,\ldots,x_n$. The
+description is given in terms of the data of the free $S$-resolution of $R$.
+The result is used to determine classes of Golod ideals, among them proper
+ordinary powers and proper symbolic powers of monomial ideals. Our theory is
+also applied to stretched local rings.
+",0,0,1,0,0,0
+17291,PacGAN: The power of two samples in generative adversarial networks,"  Generative adversarial networks (GANs) are innovative techniques for learning
+generative models of complex data distributions from samples. Despite
+remarkable recent improvements in generating realistic images, one of their
+major shortcomings is the fact that in practice, they tend to produce samples
+with little diversity, even when trained on diverse datasets. This phenomenon,
+known as mode collapse, has been the main focus of several recent advances in
+GANs. Yet there is little understanding of why mode collapse happens and why
+existing approaches are able to mitigate mode collapse. We propose a principled
+approach to handling mode collapse, which we call packing. The main idea is to
+modify the discriminator to make decisions based on multiple samples from the
+same class, either real or artificially generated. We borrow analysis tools
+from binary hypothesis testing---in particular the seminal result of Blackwell
+[Bla53]---to prove a fundamental connection between packing and mode collapse.
+We show that packing naturally penalizes generators with mode collapse, thereby
+favoring generator distributions with less mode collapse during the training
+process. Numerical experiments on benchmark datasets suggests that packing
+provides significant improvements in practice as well.
+",1,0,0,1,0,0
+17292,Stein-like Estimators for Causal Mediation Analysis in Randomized Trials,"  Causal mediation analysis aims to estimate the natural direct and indirect
+effects under clearly specified assumptions. Traditional mediation analysis
+based on Ordinary Least Squares (OLS) relies on the absence of unmeasured
+causes of the putative mediator and outcome. When this assumption cannot be
+justified, Instrumental Variables (IV) estimators can be used in order to
+produce an asymptotically unbiased estimator of the mediator-outcome link.
+However, provided that valid instruments exist, bias removal comes at the cost
+of variance inflation for standard IV procedures such as Two-Stage Least
+Squares (TSLS). A Semi-Parametric Stein-Like (SPSL) estimator has been proposed
+in the literature that strikes a natural trade-off between the unbiasedness of
+the TSLS procedure and the relatively small variance of the OLS estimator.
+Moreover, the SPSL has the advantage that its shrinkage parameter can be
+directly estimated from the data. In this paper, we demonstrate how this
+Stein-like estimator can be implemented in the context of the estimation of
+natural direct and natural indirect effects of treatments in randomized
+controlled trials. The performance of the competing methods is studied in a
+simulation study, in which both the strength of hidden confounding and the
+strength of the instruments are independently varied. These considerations are
+motivated by a trial in mental health evaluating the impact of a primary
+care-based intervention to reduce depression in the elderly.
+",0,0,0,1,0,0
+17293,Structure-Based Subspace Method for Multi-Channel Blind System Identification,"  In this work, a novel subspace-based method for blind identification of
+multichannel finite impulse response (FIR) systems is presented. Here, we
+exploit directly the impeded Toeplitz channel structure in the signal linear
+model to build a quadratic form whose minimization leads to the desired channel
+estimation up to a scalar factor. This method can be extended to estimate any
+predefined linear structure, e.g. Hankel, that is usually encountered in linear
+systems. Simulation findings are provided to highlight the appealing advantages
+of the new structure-based subspace (SSS) method over the standard subspace
+(SS) method in certain adverse identification scenarii.
+",1,0,0,1,0,0
+17294,On Certain Analytical Representations of Cellular Automata,"  We extend a previously introduced semi-analytical representation of a
+decomposition of CA dynamics in arbitrary dimensions and neighborhood schemes
+via the use of certain universal maps in which CA rule vectors are derivable
+from the equivalent of superpotentials. The results justify the search for
+alternative analog models of computation and their possible physical
+connections.
+",0,1,0,0,0,0
+17295,Strong consistency and optimality for generalized estimating equations with stochastic covariates,"  In this article we study the existence and strong consistency of GEE
+estimators, when the generalized estimating functions are martingales with
+random coefficients. Furthermore, we characterize estimating functions which
+are asymptotically optimal.
+",0,0,1,1,0,0
+17296,Synthesis and electronic properties of Ruddlesden-Popper strontium iridate epitaxial thin films stabilized by control of growth kinetics,"  We report on the selective fabrication of high-quality Sr$_2$IrO$_4$ and
+SrIrO$_3$ epitaxial thin films from a single polycrystalline Sr$_2$IrO$_4$
+target by pulsed laser deposition. Using a combination of X-ray diffraction and
+photoemission spectroscopy characterizations, we discover that within a
+relatively narrow range of substrate temperature, the oxygen partial pressure
+plays a critical role in the cation stoichiometric ratio of the films, and
+triggers the stabilization of different Ruddlesden-Popper (RP) phases. Resonant
+X-ray absorption spectroscopy measurements taken at the Ir $L$-edge and the O
+$K$-edge demonstrate the presence of strong spin-orbit coupling, and reveal the
+electronic and orbital structures of both compounds. These results suggest that
+in addition to the conventional thermodynamics consideration, higher members of
+the Sr$_{n+1}$Ir$_n$O$_{3n+1}$ series can possibly be achieved by kinetic
+control away from the thermodynamic limit. These findings offer a new approach
+to the synthesis of ultra-thin films of the RP series of iridates and can be
+extended to other complex oxides with layered structure.
+",0,1,0,0,0,0
+17297,A proof on energy gap for Yang-Mills connection,"  In this note, we prove an ${L^{\frac{n}{2}}}$-energy gap result for
+Yang-Mills connections on a principal $G$-bundle over a compact manifold
+without using Lojasiewicz-Simon gradient inequality (arXiv:1502.00668).
+",0,0,1,0,0,0
+17298,Realisability of Pomsets via Communicating Automata,"  Pomsets are a model of concurrent computations introduced by Pratt. They can
+provide a syntax-oblivious description of semantics of coordination models
+based on asynchronous message-passing, such as Message Sequence Charts (MSCs).
+In this paper, we study conditions that ensure a specification expressed as a
+set of pomsets can be faithfully realised via communicating automata. Our main
+contributions are (i) the definition of a realisability condition accounting
+for termination soundness, (ii) conditions for global specifications with
+""multi-threaded"" participants, and (iii) the definition of realisability
+conditions that can be decided directly over pomsets. A positive by-product of
+our approach is the efficiency gain in the verification of the realisability
+conditions obtained when restricting to specific classes of choreographies
+characterisable in term of behavioural types.
+",1,0,0,0,0,0
+17299,Complex pattern formation driven by the interaction of stable fronts in a competition-diffusion system,"  The ecological invasion problem in which a weaker exotic species invades an
+ecosystem inhabited by two strongly competing native species is modelled by a
+three-species competition-diffusion system. It is known that for a certain
+range of parameter values competitor-mediated coexistence occurs and complex
+spatio-temporal patterns are observed in two spatial dimensions. In this paper
+we uncover the mechanism which generates such patterns. Under some assumptions
+on the parameters the three-species competition-diffusion system admits two
+planarly stable travelling waves. Their interaction in one spatial dimension
+may result in either reflection or merging into a single homoclinic wave,
+depending on the strength of the invading species. This transition can be
+understood by studying the bifurcation structure of the homoclinic wave. In
+particular, a time-periodic homoclinic wave (breathing wave) is born from a
+Hopf bifurcation and its unstable branch acts as a separator between the
+reflection and merging regimes. The same transition occurs in two spatial
+dimensions: the stable regular spiral associated to the homoclinic wave
+destabilizes, giving rise first to an oscillating breathing spiral and then
+breaking up producing a dynamic pattern characterized by many spiral cores. We
+find that these complex patterns are generated by the interaction of two
+planarly stable travelling waves, in contrast with many other well known cases
+of pattern formation where planar instability plays a central role.
+",0,0,0,0,1,0
+17300,Solitons with rings and vortex rings on solitons in nonlocal nonlinear media,"  Nonlocality is a key feature of many physical systems since it prevents a
+catastrophic collapse and a symmetry-breaking azimuthal instability of intense
+wave beams in a bulk self-focusing nonlinear media. This opens up an intriguing
+perspective for stabilization of complex topological structures such as
+higher-order solitons, vortex rings and vortex ring-on-line complexes. Using
+direct numerical simulations, we find a class of cylindrically-symmetric $n$-th
+order spatial solitons having the intensity distribution with a central bright
+spot surrounded by $n$ bright rings of varying size. We investigate dynamical
+properties of these higher-order solitons in a media with thermal nonlocal
+nonlinear response. We show theoretically that a vortex complex of vortex ring
+and vortex line, carrying two independent winding numbers, can be created by
+perturbation of the stable optical vortex soliton in nonlocal nonlinear media.
+",0,1,0,0,0,0
+17301,Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection,"  We introduce Deep-HiTS, a rotation invariant convolutional neural network
+(CNN) model for classifying images of transients candidates into artifacts or
+real sources for the High cadence Transient Survey (HiTS). CNNs have the
+advantage of learning the features automatically from the data while achieving
+high performance. We compare our CNN model against a feature engineering
+approach using random forests (RF). We show that our CNN significantly
+outperforms the RF model reducing the error by almost half. Furthermore, for a
+fixed number of approximately 2,000 allowed false transient candidates per
+night we are able to reduce the miss-classified real transients by
+approximately 1/5. To the best of our knowledge, this is the first time CNNs
+have been used to detect astronomical transient events. Our approach will be
+very useful when processing images from next generation instruments such as the
+Large Synoptic Survey Telescope (LSST). We have made all our code and data
+available to the community for the sake of allowing further developments and
+comparisons at this https URL.
+",1,1,0,0,0,0
+17302,"On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL","  In various approaches to learning, notably in domain adaptation, active
+learning, learning under covariate shift, semi-supervised learning, learning
+with concept drift, and the like, one often wants to compare a baseline
+classifier to one or more advanced (or at least different) strategies. In this
+chapter, we basically argue that if such classifiers, in their respective
+training phases, optimize a so-called surrogate loss that it may also be
+valuable to compare the behavior of this loss on the test set, next to the
+regular classification error rate. It can provide us with an additional view on
+the classifiers' relative performances that error rates cannot capture. As an
+example, limited but convincing empirical results demonstrates that we may be
+able to find semi-supervised learning strategies that can guarantee performance
+improvements with increasing numbers of unlabeled data in terms of
+log-likelihood. In contrast, the latter may be impossible to guarantee for the
+classification error rate.
+",1,0,0,1,0,0
+17303,Do Reichenbachian Common Cause Systems of Arbitrary Finite Size Exist?,"  The principle of common cause asserts that positive correlations between
+causally unrelated events ought to be explained through the action of some
+shared causal factors. Reichenbachian common cause systems are probabilistic
+structures aimed at accounting for cases where correlations of the aforesaid
+sort cannot be explained through the action of a single common cause. The
+existence of Reichenbachian common cause systems of arbitrary finite size for
+each pair of non-causally correlated events was allegedly demonstrated by
+Hofer-Szabó and Rédei in 2006. This paper shows that their proof is
+logically deficient, and we propose an improved proof.
+",1,1,0,1,0,0
+17304,Co-evolution of nodes and links: diversity driven coexistence in cyclic competition of three species,"  When three species compete cyclically in a well-mixed, stochastic system of
+$N$ individuals, extinction is known to typically occur at times scaling as the
+system size $N$. This happens, for example, in rock-paper-scissors games or
+conserved Lotka-Volterra models in which every pair of individuals can interact
+on a complete graph. Here we show that if the competing individuals also have a
+""social temperament"" to be either introverted or extroverted, leading them to
+cut or add links respectively, then long-living state in which all species
+coexist can occur when both introverts and extroverts are present. These states
+are non-equilibrium quasi-steady states, maintained by a subtle balance between
+species competition and network dynamcis. Remarkably, much of the phenomena is
+embodied in a mean-field description. However, an intuitive understanding of
+why diversity stabilizes the co-evolving node and link dynamics remains an open
+issue.
+",0,0,0,0,1,0
+17305,Online Learning with an Almost Perfect Expert,"  We study the multiclass online learning problem where a forecaster makes a
+sequence of predictions using the advice of $n$ experts. Our main contribution
+is to analyze the regime where the best expert makes at most $b$ mistakes and
+to show that when $b = o(\log_4{n})$, the expected number of mistakes made by
+the optimal forecaster is at most $\log_4{n} + o(\log_4{n})$. We also describe
+an adversary strategy showing that this bound is tight and that the worst case
+is attained for binary prediction.
+",0,0,0,1,0,0
+17306,Actively Learning what makes a Discrete Sequence Valid,"  Deep learning techniques have been hugely successful for traditional
+supervised and unsupervised machine learning problems. In large part, these
+techniques solve continuous optimization problems. Recently however, discrete
+generative deep learning models have been successfully used to efficiently
+search high-dimensional discrete spaces. These methods work by representing
+discrete objects as sequences, for which powerful sequence-based deep models
+can be employed. Unfortunately, these techniques are significantly hindered by
+the fact that these generative models often produce invalid sequences. As a
+step towards solving this problem, we propose to learn a deep recurrent
+validator model. Given a partial sequence, our model learns the probability of
+that sequence occurring as the beginning of a full valid sequence. Thus this
+identifies valid versus invalid sequences and crucially it also provides
+insight about how individual sequence elements influence the validity of
+discrete objects. To learn this model we propose an approach inspired by
+seminal work in Bayesian active learning. On a synthetic dataset, we
+demonstrate the ability of our model to distinguish valid and invalid
+sequences. We believe this is a key step toward learning generative models that
+faithfully produce valid discrete objects.
+",1,0,0,1,0,0
+17307,Symmetries and conservation laws of Hamiltonian systems,"  In this paper we study the infinitesimal symmetries, Newtonoid vector fields,
+infinitesimal Noether symmetries and conservation laws of Hamiltonian systems.
+Using the dynamical covariant derivative and Jacobi endomorphism on the
+cotangent bundle we find the invariant equations of infinitesimal symmetries
+and Newtonoid vector fields and prove that the canonical nonlinear connection
+induced by a regular Hamiltonian can be determined by these symmetries.
+Finally, an example from optimal control theory is given.
+",0,0,1,0,0,0
+17308,Fractional differential and fractional integral modified-Bloch equations for PFG anomalous diffusion and their general solutions,"  The studying of anomalous diffusion by pulsed field gradient (PFG) diffusion
+technique still faces challenges. Two different research groups have proposed
+modified Bloch equation for anomalous diffusion. However, these equations have
+different forms and, therefore, yield inconsistent results. The discrepancy in
+these reported modified Bloch equations may arise from different ways of
+combining the fractional diffusion equation with the precession equation where
+the time derivatives have different derivative orders and forms. Moreover, to
+the best of my knowledge, the general PFG signal attenuation expression
+including finite gradient pulse width (FGPW) effect for time-space fractional
+diffusion based on the fractional derivative has yet to be reported by other
+methods. Here, based on different combination strategy, two new modified Bloch
+equations are proposed, which belong to two significantly different types: a
+differential type based on the fractal derivative and an integral type based on
+the fractional derivative. The merit of the integral type modified Bloch
+equation is that the original properties of the contributions from linear or
+nonlinear processes remain unchanged at the instant of the combination. The
+general solutions including the FGPW effect were derived from these two
+equations as well as from two other methods: a method observing the signal
+intensity at the origin and the recently reported effective phase shift
+diffusion equation method. The relaxation effect was also considered. It is
+found that the relaxation behavior influenced by fractional diffusion based on
+the fractional derivative deviates from that of normal diffusion. The general
+solution agrees perfectly with continuous-time random walk (CTRW) simulations
+as well as reported literature results. The new modified Bloch equations is a
+valuable tool to describe PFG anomalous diffusion in NMR and MRI.
+",0,1,0,0,0,0
+17309,Change of the vortex core structure in two-band superconductors at impurity-scattering-driven $s_\pm/s_{++}$ crossover,"  We report a nontrivial transition in the core structure of vortices in
+two-band superconductors as a function of interband impurity scattering. We
+demonstrate that, in addition to singular zeros of the order parameter, the
+vortices there can acquire a circular nodal line around the singular point in
+one of the superconducting components. It results in the formation of the
+peculiar ""moat""-like profile in one of the superconducting gaps. The moat-core
+vortices occur generically in the vicinity of the impurity-induced crossover
+between $s_{\pm}$ and $s_{++}$ states.
+",0,1,0,0,0,0
+17310,Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit,"  Multi-armed bandit (MAB) is a class of online learning problems where a
+learning agent aims to maximize its expected cumulative reward while repeatedly
+selecting to pull arms with unknown reward distributions. We consider a
+scenario where the reward distributions may change in a piecewise-stationary
+fashion at unknown time steps. We show that by incorporating a simple
+change-detection component with classic UCB algorithms to detect and adapt to
+changes, our so-called M-UCB algorithm can achieve nearly optimal regret bound
+on the order of $O(\sqrt{MKT\log T})$, where $T$ is the number of time steps,
+$K$ is the number of arms, and $M$ is the number of stationary segments.
+Comparison with the best available lower bound shows that our M-UCB is nearly
+optimal in $T$ up to a logarithmic factor. We also compare M-UCB with the
+state-of-the-art algorithms in numerical experiments using a public Yahoo!
+dataset to demonstrate its superior performance.
+",0,0,0,1,0,0
+17311,An initial-boundary value problem of the general three-component nonlinear Schrodinger equation with a 4x4 Lax pair on a finite interval,"  We investigate the initial-boundary value problem for the general
+three-component nonlinear Schrodinger (gtc-NLS) equation with a 4x4 Lax pair on
+a finite interval by extending the Fokas unified approach. The solutions of the
+gtc-NLS equation can be expressed in terms of the solutions of a 4x4 matrix
+Riemann-Hilbert (RH) problem formulated in the complex k-plane. Moreover, the
+relevant jump matrices of the RH problem can be explicitly found via the three
+spectral functions arising from the initial data, the Dirichlet-Neumann
+boundary data. The global relation is also established to deduce two distinct
+but equivalent types of representations (i.e., one by using the large k of
+asymptotics of the eigenfunctions and another one in terms of the
+Gelfand-Levitan-Marchenko (GLM) method) for the Dirichlet and Neumann boundary
+value problems. Moreover, the relevant formulae for boundary value problems on
+the finite interval can reduce to ones on the half-line as the length of the
+interval approaches to infinity. Finally, we also give the linearizable
+boundary conditions for the GLM representation.
+",0,1,1,0,0,0
+17312,Deep Learning Microscopy,"  We demonstrate that a deep neural network can significantly improve optical
+microscopy, enhancing its spatial resolution over a large field-of-view and
+depth-of-field. After its training, the only input to this network is an image
+acquired using a regular optical microscope, without any changes to its design.
+We blindly tested this deep learning approach using various tissue samples that
+are imaged with low-resolution and wide-field systems, where the network
+rapidly outputs an image with remarkably better resolution, matching the
+performance of higher numerical aperture lenses, also significantly surpassing
+their limited field-of-view and depth-of-field. These results are
+transformative for various fields that use microscopy tools, including e.g.,
+life sciences, where optical microscopy is considered as one of the most widely
+used and deployed techniques. Beyond such applications, our presented approach
+is broadly applicable to other imaging modalities, also spanning different
+parts of the electromagnetic spectrum, and can be used to design computational
+imagers that get better and better as they continue to image specimen and
+establish new transformations among different modes of imaging.
+",1,1,0,0,0,0
+17313,Effects of pressure impulse and peak pressure of a shock wave on microjet velocity and the onset of cavitation in a microchannel,"  The development of needle-free injection systems utilizing high-speed
+microjets is of great importance to world healthcare. It is thus crucial to
+control the microjets, which are often induced by underwater shock waves. In
+this contribution from fluid-mechanics point of view, we experimentally
+investigate the effect of a shock wave on the velocity of a free surface
+(microjet) and underwater cavitation onset in a microchannel, focusing on the
+pressure impulse and peak pressure of the shock wave. The shock wave used had a
+non-spherically-symmetric peak pressure distribution and a spherically
+symmetric pressure impulse distribution [Tagawa et al., J. Fluid Mech., 2016,
+808, 5-18]. First, we investigate the effect of the shock wave on the jet
+velocity by installing a narrow tube and a hydrophone in different
+configurations in a large water tank, and measuring the shock wave pressure and
+the jet velocity simultaneously. The results suggest that the jet velocity
+depends only on the pressure impulse of the shock wave. We then investigate the
+effect of the shock wave on the cavitation onset by taking measurements in an
+L-shaped microchannel. The results suggest that the probability of cavitation
+onset depends only on the peak pressure of the shock wave. In addition, the jet
+velocity varies according to the presence or absence of cavitation. The above
+findings provide new insights for advancing a control method for high-speed
+microjets.
+",0,1,0,0,0,0
+17314,Clustering with Noisy Queries,"  In this paper, we initiate a rigorous theoretical study of clustering with
+noisy queries (or a faulty oracle). Given a set of $n$ elements, our goal is to
+recover the true clustering by asking minimum number of pairwise queries to an
+oracle. Oracle can answer queries of the form : ""do elements $u$ and $v$ belong
+to the same cluster?"" -- the queries can be asked interactively (adaptive
+queries), or non-adaptively up-front, but its answer can be erroneous with
+probability $p$. In this paper, we provide the first information theoretic
+lower bound on the number of queries for clustering with noisy oracle in both
+situations. We design novel algorithms that closely match this query complexity
+lower bound, even when the number of clusters is unknown. Moreover, we design
+computationally efficient algorithms both for the adaptive and non-adaptive
+settings. The problem captures/generalizes multiple application scenarios. It
+is directly motivated by the growing body of work that use crowdsourcing for
+{\em entity resolution}, a fundamental and challenging data mining task aimed
+to identify all records in a database referring to the same entity. Here crowd
+represents the noisy oracle, and the number of queries directly relates to the
+cost of crowdsourcing. Another application comes from the problem of {\em sign
+edge prediction} in social network, where social interactions can be both
+positive and negative, and one must identify the sign of all pair-wise
+interactions by querying a few pairs. Furthermore, clustering with noisy oracle
+is intimately connected to correlation clustering, leading to improvement
+therein. Finally, it introduces a new direction of study in the popular {\em
+stochastic block model} where one has an incomplete stochastic block model
+matrix to recover the clusters.
+",1,0,0,1,0,0
+17315,Divide-and-Conquer Checkpointing for Arbitrary Programs with No User Annotation,"  Classical reverse-mode automatic differentiation (AD) imposes only a small
+constant-factor overhead in operation count over the original computation, but
+has storage requirements that grow, in the worst case, in proportion to the
+time consumed by the original computation. This storage blowup can be
+ameliorated by checkpointing, a process that reorders application of classical
+reverse-mode AD over an execution interval to tradeoff space \vs\ time.
+Application of checkpointing in a divide-and-conquer fashion to strategically
+chosen nested execution intervals can break classical reverse-mode AD into
+stages which can reduce the worst-case growth in storage from linear to
+sublinear. Doing this has been fully automated only for computations of
+particularly simple form, with checkpoints spanning execution intervals
+resulting from a limited set of program constructs. Here we show how the
+technique can be automated for arbitrary computations. The essential innovation
+is to apply the technique at the level of the language implementation itself,
+thus allowing checkpoints to span any execution interval.
+",1,0,0,0,0,0
+17316,Bow Ties in the Sky II: Searching for Gamma-ray Halos in the Fermi Sky Using Anisotropy,"  Many-degree-scale gamma-ray halos are expected to surround extragalactic
+high-energy gamma ray sources. These arise from the inverse Compton emission of
+an intergalactic population of relativistic electron/positron pairs generated
+by the annihilation of >100 GeV gamma rays on the extragalactic background
+light. These are typically anisotropic due to the jetted structure from which
+they originate or the presence of intergalactic magnetic fields. Here we
+propose a novel method for detecting these inverse-Compton gamma-ray halos
+based upon this anisotropic structure. Specifically, we show that by stacking
+suitably defined angular power spectra instead of images it is possible to
+robustly detect gamma-ray halos with existing Fermi Large Area Telescope (LAT)
+observations for a broad class of intergalactic magnetic fields. Importantly,
+these are largely insensitive to systematic uncertainties within the LAT
+instrumental response or associated with contaminating astronomical sources.
+",0,1,0,0,0,0
+17317,Gain-loss-driven travelling waves in PT-symmetric nonlinear metamaterials,"  In this work we investigate a one-dimensional parity-time (PT)-symmetric
+magnetic metamaterial consisting of split-ring dimers having gain or loss.
+Employing a Melnikov analysis we study the existence of localized travelling
+waves, i.e. homoclinic or heteroclinic solutions. We find conditions under
+which the homoclinic or heteroclinic orbits persist. Our analytical results are
+found to be in good agreement with direct numerical computations. For the
+particular nonlinearity admitting travelling kinks, numerically we observe
+homoclinic snaking in the bifurcation diagram. The Melnikov analysis yields a
+good approximation to one of the boundaries of the snaking profile.
+",0,1,0,0,0,0
+17318,CapsuleGAN: Generative Adversarial Capsule Network,"  We present Generative Adversarial Capsule Network (CapsuleGAN), a framework
+that uses capsule networks (CapsNets) instead of the standard convolutional
+neural networks (CNNs) as discriminators within the generative adversarial
+network (GAN) setting, while modeling image data. We provide guidelines for
+designing CapsNet discriminators and the updated GAN objective function, which
+incorporates the CapsNet margin loss, for training CapsuleGAN models. We show
+that CapsuleGAN outperforms convolutional-GAN at modeling image data
+distribution on MNIST and CIFAR-10 datasets, evaluated on the generative
+adversarial metric and at semi-supervised image classification.
+",0,0,0,1,0,0
+17319,sourceR: Classification and Source Attribution of Infectious Agents among Heterogeneous Populations,"  Zoonotic diseases are a major cause of morbidity, and productivity losses in
+both humans and animal populations. Identifying the source of food-borne
+zoonoses (e.g. an animal reservoir or food product) is crucial for the
+identification and prioritisation of food safety interventions. For many
+zoonotic diseases it is difficult to attribute human cases to sources of
+infection because there is little epidemiological information on the cases.
+However, microbial strain typing allows zoonotic pathogens to be categorised,
+and the relative frequencies of the strain types among the sources and in human
+cases allows inference on the likely source of each infection. We introduce
+sourceR, an R package for quantitative source attribution, aimed at food-borne
+diseases. It implements a fully joint Bayesian model using strain-typed
+surveillance data from both human cases and source samples, capable of
+identifying important sources of infection. The model measures the force of
+infection from each source, allowing for varying survivability, pathogenicity
+and virulence of pathogen strains, and varying abilities of the sources to act
+as vehicles of infection. A Bayesian non-parametric (Dirichlet process)
+approach is used to cluster pathogen strain types by epidemiological behaviour,
+avoiding model overfitting and allowing detection of strain types associated
+with potentially high 'virulence'.
+sourceR is demonstrated using Campylobacter jejuni isolate data collected in
+New Zealand between 2005 and 2008. It enables straightforward attribution of
+cases of zoonotic infection to putative sources of infection by epidemiologists
+and public health decision makers. As sourceR develops, we intend it to become
+an important and flexible resource for food-borne disease attribution studies.
+",0,0,0,1,0,0
+17320,Low resistive edge contacts to CVD-grown graphene using a CMOS compatible metal,"  The exploitation of the excellent intrinsic electronic properties of graphene
+for device applications is hampered by a large contact resistance between the
+metal and graphene. The formation of edge contacts rather than top contacts is
+one of the most promising solutions for realizing low ohmic contacts. In this
+paper the fabrication and characterization of edge contacts to large area
+CVD-grown monolayer graphene by means of optical lithography using CMOS
+compatible metals, i.e. Nickel and Aluminum is reported. Extraction of the
+contact resistance by Transfer Line Method (TLM) as well as the direct
+measurement using Kelvin Probe Force Microscopy demonstrates a very low width
+specific contact resistance.
+",0,1,0,0,0,0
+17321,Uniqueness of planar vortex patch in incompressible steady flow,"  We investigate a steady planar flow of an ideal fluid in a bounded simple
+connected domain and focus on the vortex patch problem with prescribed
+vorticity strength. There are two methods to deal with the existence of
+solutions for this problem: the vorticity method and the stream function
+method. A long standing open problem is whether these two entirely different
+methods result in the same solution. In this paper, we will give a positive
+answer to this problem by studying the local uniqueness of the solutions.
+Another result obtained in this paper is that if the domain is convex, then the
+vortex patch problem has a unique solution.
+",0,0,1,0,0,0
+17322,An Equivalence of Fully Connected Layer and Convolutional Layer,"  This article demonstrates that convolutional operation can be converted to
+matrix multiplication, which has the same calculation way with fully connected
+layer. The article is helpful for the beginners of the neural network to
+understand how fully connected layer and the convolutional layer work in the
+backend. To be concise and to make the article more readable, we only consider
+the linear case. It can be extended to the non-linear case easily through
+plugging in a non-linear encapsulation to the values like this $\sigma(x)$
+denoted as $x^{\prime}$.
+",1,0,0,1,0,0
+17323,Critical Points of Neural Networks: Analytical Forms and Landscape Properties,"  Due to the success of deep learning to solving a variety of challenging
+machine learning tasks, there is a rising interest in understanding loss
+functions for training neural networks from a theoretical aspect. Particularly,
+the properties of critical points and the landscape around them are of
+importance to determine the convergence performance of optimization algorithms.
+In this paper, we provide full (necessary and sufficient) characterization of
+the analytical forms for the critical points (as well as global minimizers) of
+the square loss functions for various neural networks. We show that the
+analytical forms of the critical points characterize the values of the
+corresponding loss functions as well as the necessary and sufficient conditions
+to achieve global minimum. Furthermore, we exploit the analytical forms of the
+critical points to characterize the landscape properties for the loss functions
+of these neural networks. One particular conclusion is that: The loss function
+of linear networks has no spurious local minimum, while the loss function of
+one-hidden-layer nonlinear networks with ReLU activation function does have
+local minimum that is not global minimum.
+",1,0,0,1,0,0
+17324,When the Annihilator Graph of a Commutative Ring Is Planar or Toroidal?,"  Let $R$ be a commutative ring with identity, and let $Z(R)$ be the set of
+zero-divisors of $R$. The annihilator graph of $R$ is defined as the undirected
+graph $AG(R)$ with the vertex set $Z(R)^*=Z(R)\setminus\{0\}$, and two distinct
+vertices $x$ and $y$ are adjacent if and only if $ann_R(xy)\neq ann_R(x)\cup
+ann_R(y)$. In this paper, all rings whose annihilator graphs can be embed on
+the plane or torus are classified.
+",0,0,1,0,0,0
+17325,Econometric modelling and forecasting of intraday electricity prices,"  In the following paper we analyse the ID$_3$-Price on German Intraday
+Continuous Electricity Market using an econometric time series model. A
+multivariate approach is conducted for hourly and quarter-hourly products
+separately. We estimate the model using lasso and elastic net techniques and
+perform an out-of-sample very short-term forecasting study. The model's
+performance is compared with benchmark models and is discussed in detail.
+Forecasting results provide new insights to the German Intraday Continuous
+Electricity Market regarding its efficiency and to the ID$_3$-Price behaviour.
+The supplementary materials are available online.
+",0,0,0,0,0,1
+17326,Matrix-Based Characterization of the Motion and Wrench Uncertainties in Robotic Manipulators,"  Characterization of the uncertainty in robotic manipulators is the focus of
+this paper. Based on the random matrix theory (RMT), we propose uncertainty
+characterization schemes in which the uncertainty is modeled at the macro
+(system) level. This is different from the traditional approaches that model
+the uncertainty in the parametric space of micro (state) level. We show that
+perturbing the system matrices rather than the state of the system provides
+unique advantages especially for robotic manipulators. First, it requires only
+limited statistical information that becomes effective when dealing with
+complex systems where detailed information on their variability is not
+available. Second, the RMT-based models are aware of the system state and
+configuration that are significant factors affecting the level of uncertainty
+in system behavior. In this study, in addition to the motion uncertainty
+analysis that was first proposed in our earlier work, we also develop an
+RMT-based model for the quantification of the static wrench uncertainty in
+multi-agent cooperative systems. This model is aimed to be an alternative to
+the elaborate parametric formulation when only rough bounds are available on
+the system parameters. We discuss that how RMT-based model becomes advantageous
+when the complexity of the system increases. We perform experimental studies on
+a KUKA youBot arm to demonstrate the superiority of the RMT-based motion
+uncertainty models. We show that how these models outperform the traditional
+models built upon Gaussianity assumption in capturing real-system uncertainty
+and providing accurate bounds on the state estimation errors. In addition, to
+experimentally support our wrench uncertainty quantification model, we study
+the behavior of a cooperative system of mobile robots. It is shown that one can
+rely on less demanding RMT-based formulation and yet meets the acceptable
+accuracy.
+",1,0,0,1,0,0
+17327,Good Similar Patches for Image Denoising,"  Patch-based denoising algorithms like BM3D have achieved outstanding
+performance. An important idea for the success of these methods is to exploit
+the recurrence of similar patches in an input image to estimate the underlying
+image structures. However, in these algorithms, the similar patches used for
+denoising are obtained via Nearest Neighbour Search (NNS) and are sometimes not
+optimal. First, due to the existence of noise, NNS can select similar patches
+with similar noise patterns to the reference patch. Second, the unreliable
+noisy pixels in digital images can bring a bias to the patch searching process
+and result in a loss of color fidelity in the final denoising result. We
+observe that given a set of good similar patches, their distribution is not
+necessarily centered at the noisy reference patch and can be approximated by a
+Gaussian component. Based on this observation, we present a patch searching
+method that clusters similar patch candidates into patch groups using Gaussian
+Mixture Model-based clustering, and selects the patch group that contains the
+reference patch as the final patches for denoising. We also use an unreliable
+pixel estimation algorithm to pre-process the input noisy images to further
+improve the patch searching. Our experiments show that our approach can better
+capture the underlying patch structures and can consistently enable the
+state-of-the-art patch-based denoising algorithms, such as BM3D, LPCA and PLOW,
+to better denoise images by providing them with patches found by our approach
+while without modifying these algorithms.
+",1,0,0,0,0,0
+17328,Ginzburg - Landau expansion in strongly disordered attractive Anderson - Hubbard model,"  We have studied disordering effects on the coefficients of Ginzburg - Landau
+expansion in powers of superconducting order - parameter in attractive Anderson
+- Hubbard model within the generalized $DMFT+\Sigma$ approximation. We consider
+the wide region of attractive potentials $U$ from the weak coupling region,
+where superconductivity is described by BCS model, to the strong coupling
+region, where superconducting transition is related with Bose - Einstein
+condensation (BEC) of compact Cooper pairs formed at temperatures essentially
+larger than the temperature of superconducting transition, and the wide range
+of disorder - from weak to strong, where the system is in the vicinity of
+Anderson transition. In case of semi - elliptic bare density of states disorder
+influence upon the coefficients $A$ and $B$ before the square and the fourth
+power of the order - parameter is universal for any value of electron
+correlation and is related only to the general disorder widening of the bare
+band (generalized Anderson theorem). Such universality is absent for the
+gradient term expansion coefficient $C$. In the usual theory of ""dirty""
+superconductors the $C$ coefficient drops with the growth of disorder. In the
+limit of strong disorder in BCS limit the coefficient $C$ is very sensitive to
+the effects of Anderson localization, which lead to its further drop with
+disorder growth up to the region of Anderson insulator. In the region of BCS -
+BEC crossover and in BEC limit the coefficient $C$ and all related physical
+properties are weakly dependent on disorder. In particular, this leads to
+relatively weak disorder dependence of both penetration depth and coherence
+lengths, as well as of related slope of the upper critical magnetic field at
+superconducting transition, in the region of very strong coupling.
+",0,1,0,0,0,0
+17329,Reallocating and Resampling: A Comparison for Inference,"  Simulation-based inference plays a major role in modern statistics, and often
+employs either reallocating (as in a randomization test) or resampling (as in
+bootstrapping). Reallocating mimics random allocation to treatment groups,
+while resampling mimics random sampling from a larger population; does it
+matter whether the simulation method matches the data collection method?
+Moreover, do the results differ for testing versus estimation? Here we answer
+these questions in a simple setting by exploring the distribution of a sample
+difference in means under a basic two group design and four different
+scenarios: true random allocation, true random sampling, reallocating, and
+resampling. For testing a sharp null hypothesis, reallocating is superior in
+small samples, but reallocating and resampling are asymptotically equivalent.
+For estimation, resampling is generally superior, unless the effect is truly
+additive. Moreover, these results hold regardless of whether the data were
+collected by random sampling or random allocation.
+",0,0,1,1,0,0
+17330,An Efficient Algorithm for Bayesian Nearest Neighbours,"  K-Nearest Neighbours (k-NN) is a popular classification and regression
+algorithm, yet one of its main limitations is the difficulty in choosing the
+number of neighbours. We present a Bayesian algorithm to compute the posterior
+probability distribution for k given a target point within a data-set,
+efficiently and without the use of Markov Chain Monte Carlo (MCMC) methods or
+simulation - alongside an exact solution for distributions within the
+exponential family. The central idea is that data points around our target are
+generated by the same probability distribution, extending outwards over the
+appropriate, though unknown, number of neighbours. Once the data is projected
+onto a distance metric of choice, we can transform the choice of k into a
+change-point detection problem, for which there is an efficient solution: we
+recursively compute the probability of the last change-point as we move towards
+our target, and thus de facto compute the posterior probability distribution
+over k. Applying this approach to both a classification and a regression UCI
+data-sets, we compare favourably and, most importantly, by removing the need
+for simulation, we are able to compute the posterior probability of k exactly
+and rapidly. As an example, the computational time for the Ripley data-set is a
+few milliseconds compared to a few hours when using a MCMC approach.
+",1,0,0,1,0,0
+17331,In search of a new economic model determined by logistic growth,"  In this paper we extend the work by Ryuzo Sato devoted to the development of
+economic growth models within the framework of the Lie group theory. We propose
+a new growth model based on the assumption of logistic growth in factors. It is
+employed to derive new production functions and introduce a new notion of wage
+share. In the process it is shown that the new functions compare reasonably
+well against relevant economic data. The corresponding problem of maximization
+of profit under conditions of perfect competition is solved with the aid of one
+of these functions. In addition, it is explained in reasonably rigorous
+mathematical terms why Bowley's law no longer holds true in post-1960 data.
+",0,0,1,0,0,0
+17332,Limits on light WIMPs with a 1 kg-scale germanium detector at 160 eVee physics threshold at the China Jinping Underground Laboratory,"  We report results of a search for light weakly interacting massive particle
+(WIMP) dark matter from the CDEX-1 experiment at the China Jinping Underground
+Laboratory (CJPL). Constraints on WIMP-nucleon spin-independent (SI) and
+spin-dependent (SD) couplings are derived with a physics threshold of 160 eVee,
+from an exposure of 737.1 kg-days. The SI and SD limits extend the lower reach
+of light WIMPs to 2 GeV and improve over our earlier bounds at WIMP mass less
+than 6 GeV.
+",0,1,0,0,0,0
+17333,"A stellar census of the nearby, young 32 Orionis group","  The 32 Orionis group was discovered almost a decade ago and despite the fact
+that it represents the first northern, young (age ~ 25 Myr) stellar aggregate
+within 100 pc of the Sun ($d \simeq 93$ pc), a comprehensive survey for members
+and detailed characterisation of the group has yet to be performed. We present
+the first large-scale spectroscopic survey for new (predominantly M-type)
+members of the group after combining kinematic and photometric data to select
+candidates with Galactic space motion and positions in colour-magnitude space
+consistent with membership. We identify 30 new members, increasing the number
+of known 32 Ori group members by a factor of three and bringing the total
+number of identified members to 46, spanning spectral types B5 to L1. We also
+identify the lithium depletion boundary (LDB) of the group, i.e. the luminosity
+at which lithium remains unburnt in a coeval population. We estimate the age of
+the 32 Ori group independently using both isochronal fitting and LDB analyses
+and find it is essentially coeval with the {\beta} Pictoris moving group, with
+an age of $24\pm4$ Myr. Finally, we have also searched for circumstellar disc
+hosts utilising the AllWISE catalogue. Although we find no evidence for warm,
+dusty discs, we identify several stars with excess emission in the WISE W4-band
+at 22 {\mu}m. Based on the limited number of W4 detections we estimate a debris
+disc fraction of $32^{+12}_{-8}$ per cent for the 32 Ori group.
+",0,1,0,0,0,0
+17334,A High-Level Rule-based Language for Software Defined Network Programming based on OpenFlow,"  This paper proposes XML-Defined Network policies (XDNP), a new high-level
+language based on XML notation, to describe network control rules in Software
+Defined Network environments. We rely on existing OpenFlow controllers
+specifically Floodlight but the novelty of this project is to separate
+complicated language- and framework-specific APIs from policy descriptions.
+This separation makes it possible to extend the current work as a northbound
+higher level abstraction that can support a wide range of controllers who are
+based on different programming languages. By this approach, we believe that
+network administrators can develop and deploy network control policies easier
+and faster.
+",1,0,0,0,0,0
+17335,Domain Objects and Microservices for Systems Development: a roadmap,"  This paper discusses a roadmap to investigate Domain Objects being an
+adequate formalism to capture the peculiarity of microservice architecture, and
+to support Software development since the early stages. It provides a survey of
+both Microservices and Domain Objects, and it discusses plans and reflections
+on how to investigate whether a modeling approach suited to adaptable
+service-based components can also be applied with success to the microservice
+scenario.
+",1,0,0,0,0,0
+17336,Stabilization of prethermal Floquet steady states in a periodically driven dissipative Bose-Hubbard model,"  We discuss the effect of dissipation on heating which occurs in periodically
+driven quantum many body systems. We especially focus on a periodically driven
+Bose-Hubbard model coupled to an energy and particle reservoir. Without
+dissipation, this model is known to undergo parametric instabilities which can
+be considered as an initial stage of heating. By taking the weak on-site
+interaction limit as well as the weak system-reservoir coupling limit, we find
+that parametric instabilities are suppressed if the dissipation is stronger
+than the on-site interaction strength and stable steady states appear. Our
+results demonstrate that periodically-driven systems can emit energy, which is
+absorbed from external drivings, to the reservoir so that they can avoid
+heating.
+",0,1,0,0,0,0
+17337,Compressed Sensing using Generative Models,"  The goal of compressed sensing is to estimate a vector from an
+underdetermined system of noisy linear measurements, by making use of prior
+knowledge on the structure of vectors in the relevant domain. For almost all
+results in this literature, the structure is represented by sparsity in a
+well-chosen basis. We show how to achieve guarantees similar to standard
+compressed sensing but without employing sparsity at all. Instead, we suppose
+that vectors lie near the range of a generative model $G: \mathbb{R}^k \to
+\mathbb{R}^n$. Our main theorem is that, if $G$ is $L$-Lipschitz, then roughly
+$O(k \log L)$ random Gaussian measurements suffice for an $\ell_2/\ell_2$
+recovery guarantee. We demonstrate our results using generative models from
+published variational autoencoder and generative adversarial networks. Our
+method can use $5$-$10$x fewer measurements than Lasso for the same accuracy.
+",1,0,0,1,0,0
+17338,Two-part models with stochastic processes for modelling longitudinal semicontinuous data: computationally efficient inference and modelling the overall marginal mean,"  Several researchers have described two-part models with patient-specific
+stochastic processes for analysing longitudinal semicontinuous data. In theory,
+such models can offer greater flexibility than the standard two-part model with
+patient-specific random effects. However, in practice the high dimensional
+integrations involved in the marginal likelihood (i.e. integrated over the
+stochastic processes) significantly complicates model fitting. Thus
+non-standard computationally intensive procedures based on simulating the
+marginal likelihood have so far only been proposed. In this paper, we describe
+an efficient method of implementation by demonstrating how the high dimensional
+integrations involved in the marginal likelihood can be computed efficiently.
+Specifically, by using a property of the multivariate normal distribution and
+the standard marginal cumulative distribution function identity, we transform
+the marginal likelihood so that the high dimensional integrations are contained
+in the cumulative distribution function of a multivariate normal distribution,
+which can then be efficiently evaluated. Hence maximum likelihood estimation
+can be used to obtain parameter estimates and asymptotic standard errors (from
+the observed information matrix) of model parameters. We describe our proposed
+efficient implementation procedure for the standard two-part model
+parameterisation and when it is of interest to directly model the overall
+marginal mean. The methodology is applied on a psoriatic arthritis data set
+concerning functional disability.
+",0,0,0,1,0,0
+17339,Progressive Image Deraining Networks: A Better and Simpler Baseline,"  Along with the deraining performance improvement of deep networks, their
+structures and learning become more and more complicated and diverse, making it
+difficult to analyze the contribution of various network modules when
+developing new deraining networks. To handle this issue, this paper provides a
+better and simpler baseline deraining network by considering network
+architecture, input and output, and loss functions. Specifically, by repeatedly
+unfolding a shallow ResNet, progressive ResNet (PRN) is proposed to take
+advantage of recursive computation. A recurrent layer is further introduced to
+exploit the dependencies of deep features across stages, forming our
+progressive recurrent network (PReNet). Furthermore, intra-stage recursive
+computation of ResNet can be adopted in PRN and PReNet to notably reduce
+network parameters with graceful degradation in deraining performance. For
+network input and output, we take both stage-wise result and original rainy
+image as input to each ResNet and finally output the prediction of {residual
+image}. As for loss functions, single MSE or negative SSIM losses are
+sufficient to train PRN and PReNet. Experiments show that PRN and PReNet
+perform favorably on both synthetic and real rainy images. Considering its
+simplicity, efficiency and effectiveness, our models are expected to serve as a
+suitable baseline in future deraining research. The source codes are available
+at this https URL.
+",1,0,0,0,0,0
+17340,Optimal Nonparametric Inference under Quantization,"  Statistical inference based on lossy or incomplete samples is of fundamental
+importance in research areas such as signal/image processing, medical image
+storage, remote sensing, signal transmission. In this paper, we propose a
+nonparametric testing procedure based on quantized samples. In contrast to the
+classic nonparametric approach, our method lives on a coarse grid of sample
+information and are simple-to-use. Under mild technical conditions, we
+establish the asymptotic properties of the proposed procedures including
+asymptotic null distribution of the quantization test statistic as well as its
+minimax power optimality. Concrete quantizers are constructed for achieving the
+minimax optimality in practical use. Simulation results and a real data
+analysis are provided to demonstrate the validity and effectiveness of the
+proposed test. Our work bridges the classical nonparametric inference to modern
+lossy data setting.
+",1,0,1,1,0,0
+17341,Nearest neighbor imputation for general parameter estimation in survey sampling,"  Nearest neighbor imputation is popular for handling item nonresponse in
+survey sampling. In this article, we study the asymptotic properties of the
+nearest neighbor imputation estimator for general population parameters,
+including population means, proportions and quantiles. For variance estimation,
+the conventional bootstrap inference for matching estimators with fixed number
+of matches has been shown to be invalid due to the nonsmoothness nature of the
+matching estimator. We propose asymptotically valid replication variance
+estimation. The key strategy is to construct replicates of the estimator
+directly based on linear terms, instead of individual records of variables. A
+simulation study confirms that the new procedure provides valid variance
+estimation.
+",0,0,0,1,0,0
+17342,Time-delay signature suppression in a chaotic semiconductor laser by fiber random grating induced distributed feedback,"  We demonstrate that a semiconductor laser perturbed by the distributed
+feedback from a fiber random grating can emit light chaotically without the
+time delay signature. A theoretical model is developed based on the
+Lang-Kobayashi model in order to numerically explore the chaotic dynamics of
+the laser diode subjected to the random distributed feedback. It is predicted
+that the random distributed feedback is superior to the single reflection
+feedback in suppressing the time-delay signature. In experiments, a massive
+number of feedbacks with randomly varied time delays induced by a fiber random
+grating introduce large numbers of external cavity modes into the semiconductor
+laser, leading to the high dimension of chaotic dynamics and thus the
+concealment of the time delay signature. The obtained time delay signature with
+the maximum suppression is 0.0088, which is the smallest to date.
+",0,1,0,0,0,0
+17343,SAFS: A Deep Feature Selection Approach for Precision Medicine,"  In this paper, we propose a new deep feature selection method based on deep
+architecture. Our method uses stacked auto-encoders for feature representation
+in higher-level abstraction. We developed and applied a novel feature learning
+approach to a specific precision medicine problem, which focuses on assessing
+and prioritizing risk factors for hypertension (HTN) in a vulnerable
+demographic subgroup (African-American). Our approach is to use deep learning
+to identify significant risk factors affecting left ventricular mass indexed to
+body surface area (LVMI) as an indicator of heart damage risk. The results show
+that our feature learning and representation approach leads to better results
+in comparison with others.
+",1,0,0,1,0,0
+17344,Deep Reasoning with Multi-scale Context for Salient Object Detection,"  To detect and segment salient objects accurately, existing methods are
+usually devoted to designing complex network architectures to fuse powerful
+features from the backbone networks. However, they put much less efforts on the
+saliency inference module and only use a few fully convolutional layers to
+perform saliency reasoning from the fused features. However, should feature
+fusion strategies receive much attention but saliency reasoning be ignored a
+lot? In this paper, we find that weakness of the saliency reasoning unit limits
+salient object detection performance, and claim that saliency reasoning after
+multi-scale convolutional features fusion is critical. To verify our findings,
+we first extract multi-scale features with a fully convolutional network, and
+then directly reason from these comprehensive features using a deep yet
+light-weighted network, modified from ShuffleNet, to fast and precisely predict
+salient objects. Such simple design is shown to be capable of reasoning from
+multi-scale saliency features as well as giving superior saliency detection
+performance with less computation cost. Experimental results show that our
+simple framework outperforms the best existing method with 2.3\% and 3.6\%
+promotion for F-measure scores, 2.8\% reduction for MAE score on PASCAL-S,
+DUT-OMRON and SOD datasets respectively.
+",1,0,0,0,0,0
+17345,On Estimation of $L_{r}$-Norms in Gaussian White Noise Models,"  We provide a complete picture of asymptotically minimax estimation of
+$L_r$-norms (for any $r\ge 1$) of the mean in Gaussian white noise model over
+Nikolskii-Besov spaces. In this regard, we complement the work of Lepski,
+Nemirovski and Spokoiny (1999), who considered the cases of $r=1$ (with
+poly-logarithmic gap between upper and lower bounds) and $r$ even (with
+asymptotically sharp upper and lower bounds) over Hölder spaces. We
+additionally consider the case of asymptotically adaptive minimax estimation
+and demonstrate a difference between even and non-even $r$ in terms of an
+investigator's ability to produce asymptotically adaptive minimax estimators
+without paying a penalty.
+",1,0,1,1,0,0
+17346,Secure communications with cooperative jamming: Optimal power allocation and secrecy outage analysis,"  This paper studies the secrecy rate maximization problem of a secure wireless
+communication system, in the presence of multiple eavesdroppers. The security
+of the communication link is enhanced through cooperative jamming, with the
+help of multiple jammers. First, a feasibility condition is derived to achieve
+a positive secrecy rate at the destination. Then, we solve the original secrecy
+rate maximization problem, which is not convex in terms of power allocation at
+the jammers. To circumvent this non-convexity, the achievable secrecy rate is
+approximated for a given power allocation at the jammers and the approximated
+problem is formulated into a geometric programming one. Based on this
+approximation, an iterative algorithm has been developed to obtain the optimal
+power allocation at the jammers. Next, we provide a bisection approach, based
+on one-dimensional search, to validate the optimality of the proposed
+algorithm. In addition, by assuming Rayleigh fading, the secrecy outage
+probability (SOP) of the proposed cooperative jamming scheme is analyzed. More
+specifically, a single-integral form expression for SOP is derived for the most
+general case as well as a closed-form expression for the special case of two
+cooperative jammers and one eavesdropper. Simulation results have been provided
+to validate the convergence and the optimality of the proposed algorithm as
+well as the theoretical derivations of the presented SOP analysis.
+",1,0,1,0,0,0
+17347,Stochastic Calculus with respect to Gaussian Processes: Part I,"  Stochastic integration \textit{wrt} Gaussian processes has raised strong
+interest in recent years, motivated in particular by its applications in
+Internet traffic modeling, biomedicine and finance. The aim of this work is to
+define and develop a White Noise Theory-based anticipative stochastic calculus
+with respect to all Gaussian processes that have an integral representation
+over a real (maybe infinite) interval. Very rich, this class of Gaussian
+processes contains, among many others, Volterra processes (and thus fractional
+Brownian motion) as well as processes the regularity of which varies along the
+time (such as multifractional Brownian motion).A systematic comparison of the
+stochastic calculus (including It{ô} formula) we provide here, to the ones
+given by Malliavin calculus in
+\cite{nualart,MV05,NuTa06,KRT07,KrRu10,LN12,SoVi14,LN12}, and by It{ô}
+stochastic calculus is also made. Not only our stochastic calculus fully
+generalizes and extends the ones originally proposed in \cite{MV05} and in
+\cite{NuTa06} for Gaussian processes, but also the ones proposed in
+\cite{ell,bosw,ben1} for fractional Brownian motion (\textit{resp.} in
+\cite{JLJLV1,JL13,LLVH} for multifractional Brownian motion).
+",0,0,1,0,0,0
+17348,Path-like integrals of lenght on surfaces of constant curvature,"  We naturally associate a measurable space of paths to a couple of orthogonal
+vector fields over a surface and we integrate the length function over it. This
+integral is interpreted as a natural continuous generalization of indirect
+influences on finite graphs and can be thought as a tool to capture geometric
+information of the surface. As a byproduct we calculate volumes in different
+examples of spaces of paths.
+",0,0,1,0,0,0
+17349,Automated Synthesis of Divide and Conquer Parallelism,"  This paper focuses on automated synthesis of divide-and-conquer parallelism,
+which is a common parallel programming skeleton supported by many
+cross-platform multithreaded libraries. The challenges of producing (manually
+or automatically) a correct divide-and-conquer parallel program from a given
+sequential code are two-fold: (1) assuming that individual worker threads
+execute a code identical to the sequential code, the programmer has to provide
+the extra code for dividing the tasks and combining the computation results,
+and (2) sometimes, the sequential code may not be usable as is, and may need to
+be modified by the programmer. We address both challenges in this paper. We
+present an automated synthesis technique for the case where no modifications to
+the sequential code are required, and we propose an algorithm for modifying the
+sequential code to make it suitable for parallelization when some modification
+is necessary. The paper presents theoretical results for when this {\em
+modification} is efficiently possible, and experimental evaluation of the
+technique and the quality of the produced parallel programs.
+",1,0,0,0,0,0
+17350,"Nikol'ski\uı, Jackson and Ul'yanov type inequalities with Muckenhoupt weights","  In the present work we prove a Nikol'ski inequality for trigonometric
+polynomials and Ul'yanov type inequalities for functions in Lebesgue spaces
+with Muckenhoupt weights. Realization result and Jackson inequalities are
+obtained. Simultaneous approximation by polynomials is considered. Some uniform
+norm inequalities are transferred to weighted Lebesgue space.
+",0,0,1,0,0,0
+17351,CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks,"  Inferring model parameters from experimental data is a grand challenge in
+many sciences, including cosmology. This often relies critically on high
+fidelity numerical simulations, which are prohibitively computationally
+expensive. The application of deep learning techniques to generative modeling
+is renewing interest in using high dimensional density estimators as
+computationally inexpensive emulators of fully-fledged simulations. These
+generative models have the potential to make a dramatic shift in the field of
+scientific simulations, but for that shift to happen we need to study the
+performance of such generators in the precision regime needed for science
+applications. To this end, in this work we apply Generative Adversarial
+Networks to the problem of generating weak lensing convergence maps. We show
+that our generator network produces maps that are described by, with high
+statistical confidence, the same summary statistics as the fully simulated
+maps.
+",1,1,0,0,0,0
+17352,Gaussian approximation of maxima of Wiener functionals and its application to high-frequency data,"  This paper establishes an upper bound for the Kolmogorov distance between the
+maximum of a high-dimensional vector of smooth Wiener functionals and the
+maximum of a Gaussian random vector. As a special case, we show that the
+maximum of multiple Wiener-Itô integrals with common orders is
+well-approximated by its Gaussian analog in terms of the Kolmogorov distance if
+their covariance matrices are close to each other and the maximum of the fourth
+cumulants of the multiple Wiener-Itô integrals is close to zero. This may be
+viewed as a new kind of fourth moment phenomenon, which has attracted
+considerable attention in the recent studies of probability. This type of
+Gaussian approximation result has many potential applications to statistics. To
+illustrate this point, we present two statistical applications in
+high-frequency financial econometrics: One is the hypothesis testing problem
+for the absence of lead-lag effects and the other is the construction of
+uniform confidence bands for spot volatility.
+",0,0,1,1,0,0
+17353,A Kronecker-type identity and the representations of a number as a sum of three squares,"  By considering a limiting case of a Kronecker-type identity, we obtain an
+identity found by both Andrews and Crandall. We then use the Andrews-Crandall
+identity to give a new proof of a formula of Gauss for the representations of a
+number as a sum of three squares. From the Kronecker-type identity, we also
+deduce Gauss's theorem that every positive integer is representable as a sum of
+three triangular numbers.
+",0,0,1,0,0,0
+17354,DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction,"  In this paper, we consider the temporal pattern in traffic flow time series,
+and implement a deep learning model for traffic flow prediction. Detrending
+based methods decompose original flow series into trend and residual series, in
+which trend describes the fixed temporal pattern in traffic flow and residual
+series is used for prediction. Inspired by the detrending method, we propose
+DeepTrend, a deep hierarchical neural network used for traffic flow prediction
+which considers and extracts the time-variant trend. DeepTrend has two stacked
+layers: extraction layer and prediction layer. Extraction layer, a fully
+connected layer, is used to extract the time-variant trend in traffic flow by
+feeding the original flow series concatenated with corresponding simple average
+trend series. Prediction layer, an LSTM layer, is used to make flow prediction
+by feeding the obtained trend from the output of extraction layer and
+calculated residual series. To make the model more effective, DeepTrend needs
+first pre-trained layer-by-layer and then fine-tuned in the entire network.
+Experiments show that DeepTrend can noticeably boost the prediction performance
+compared with some traditional prediction models and LSTM with detrending based
+methods.
+",1,0,0,0,0,0
+17355,A new approach to Kaluza-Klein Theory,"  We propose in this paper a new approach to the Kaluza-Klein idea of a five
+dimensional space-time unifying gravitation and electromagnetism, and extension
+to higher-dimensional space-time. By considering a natural geometric definition
+of a matter fluid and abandoning the usual requirement of a Ricci-flat five
+dimensional space-time, we show that a unified geometrical frame can be set for
+gravitation and electromagnetism, giving, by projection on the classical
+4-dimensional space-time, the known Einstein-Maxwell-Lorentz equations for
+charged fluids. Thus, although not introducing new physics, we get a very
+aesthetic presentation of classical physics in the spirit of general
+relativity. The usual physical concepts, such as mass, energy, charge,
+trajectory, Maxwell-Lorentz law, are shown to be only various aspects of the
+geometry, for example curvature, of space-time considered as a Lorentzian
+manifold; that is no physical objects are introduced in space-time, no laws are
+given, everything is only geometry.
+We then extend these ideas to more than 5 dimensions, by considering
+spacetime as a generalization of a $(S^1\times W)$-fiber bundle, that we named
+multi-fibers bundle, where $S^1$ is the circle and $W$ a compact manifold. We
+will use this geometric structure as a possible way to model or encode
+deviations from standard 4-dimensional General Relativity, or ""dark"" effects
+such as dark matter or energy.
+",0,0,1,0,0,0
+17356,Density of orbits of dominant regular self-maps of semiabelian varieties,"  We prove a conjecture of Medvedev and Scanlon in the case of regular
+morphisms of semiabelian varieties. That is, if $G$ is a semiabelian variety
+defined over an algebraically closed field $K$ of characteristic $0$, and
+$\varphi\colon G\to G$ is a dominant regular self-map of $G$ which is not
+necessarily a group homomorphism, we prove that one of the following holds:
+either there exists a non-constant rational fibration preserved by $\varphi$,
+or there exists a point $x\in G(K)$ whose $\varphi$-orbit is Zariski dense in
+$G$.
+",0,0,1,0,0,0
+17357,Asymptotic coverage probabilities of bootstrap percentile confidence intervals for constrained parameters,"  The asymptotic behaviour of the commonly used bootstrap percentile confidence
+interval is investigated when the parameters are subject to linear inequality
+constraints. We concentrate on the important one- and two-sample problems with
+data generated from general parametric distributions in the natural exponential
+family. The focus of this paper is on quantifying the coverage probabilities of
+the parametric bootstrap percentile confidence intervals, in particular their
+limiting behaviour near boundaries. We propose a local asymptotic framework to
+study this subtle coverage behaviour. Under this framework, we discover that
+when the true parameters are on, or close to, the restriction boundary, the
+asymptotic coverage probabilities can always exceed the nominal level in the
+one-sample case; however, they can be, remarkably, both under and over the
+nominal level in the two-sample case. Using illustrative examples, we show that
+the results provide theoretical justification and guidance on applying the
+bootstrap percentile method to constrained inference problems.
+",0,0,1,1,0,0
+17358,Correlations and enlarged superconducting phase of $t$-$J_\perp$ chains of ultracold molecules on optical lattices,"  We compute physical properties across the phase diagram of the $t$-$J_\perp$
+chain with long-range dipolar interactions, which describe ultracold polar
+molecules on optical lattices. Our results obtained by the density-matrix
+renormalization group (DMRG) indicate that superconductivity is enhanced when
+the Ising component $J_z$ of the spin-spin interaction and the charge component
+$V$ are tuned to zero, and even further by the long-range dipolar interactions.
+At low densities, a substantially larger spin gap is obtained. We provide
+evidence that long-range interactions lead to algebraically decaying
+correlation functions despite the presence of a gap. Although this has recently
+been observed in other long-range interacting spin and fermion models, the
+correlations in our case have the peculiar property of having a small and
+continuously varying exponent. We construct simple analytic models and
+arguments to understand the most salient features.
+",0,1,0,0,0,0
+17359,MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks,"  We introduce MinimalRNN, a new recurrent neural network architecture that
+achieves comparable performance as the popular gated RNNs with a simplified
+structure. It employs minimal updates within RNN, which not only leads to
+efficient learning and testing but more importantly better interpretability and
+trainability. We demonstrate that by endorsing the more restrictive update
+rule, MinimalRNN learns disentangled RNN states. We further examine the
+learning dynamics of different RNN structures using input-output Jacobians, and
+show that MinimalRNN is able to capture longer range dependencies than existing
+RNN architectures.
+",1,0,0,1,0,0
+17360,Boolean quadric polytopes are faces of linear ordering polytopes,"  Let $BQP(n)$ be a boolean quadric polytope, $LOP(m)$ be a linear ordering
+polytope. It is shown that $BQP(n)$ is linearly isomorphic to a face of
+$LOP(2n)$.
+",1,0,0,0,0,0
+17361,Sparse Matrix Code Dependence Analysis Simplification at Compile Time,"  Analyzing array-based computations to determine data dependences is useful
+for many applications including automatic parallelization, race detection,
+computation and communication overlap, verification, and shape analysis. For
+sparse matrix codes, array data dependence analysis is made more difficult by
+the use of index arrays that make it possible to store only the nonzero entries
+of the matrix (e.g., in A[B[i]], B is an index array). Here, dependence
+analysis is often stymied by such indirect array accesses due to the values of
+the index array not being available at compile time. Consequently, many
+dependences cannot be proven unsatisfiable or determined until runtime.
+Nonetheless, index arrays in sparse matrix codes often have properties such as
+monotonicity of index array elements that can be exploited to reduce the amount
+of runtime analysis needed. In this paper, we contribute a formulation of array
+data dependence analysis that includes encoding index array properties as
+universally quantified constraints. This makes it possible to leverage existing
+SMT solvers to determine whether such dependences are unsatisfiable and
+significantly reduces the number of dependences that require runtime analysis
+in a set of eight sparse matrix kernels. Another contribution is an algorithm
+for simplifying the remaining satisfiable data dependences by discovering
+equalities and/or subset relationships. These simplifications are essential to
+make a runtime-inspection-based approach feasible.
+",1,0,0,0,0,0
+17362,ICA based on the data asymmetry,"  Independent Component Analysis (ICA) - one of the basic tools in data
+analysis - aims to find a coordinate system in which the components of the data
+are independent. Most of existing methods are based on the minimization of the
+function of fourth-order moment (kurtosis). Skewness (third-order moment) has
+received much less attention.
+In this paper we present a competitive approach to ICA based on the Split
+Gaussian distribution, which is well adapted to asymmetric data. Consequently,
+we obtain a method which works better than the classical approaches, especially
+in the case when the underlying density is not symmetric, which is a typical
+situation in the color distribution in images.
+",0,0,1,1,0,0
+17363,Solid hulls of weighted Banach spaces of analytic functions on the unit disc with exponential weights,"  We study weighted $H^\infty$ spaces of analytic functions on the open unit
+disc in the case of non-doubling weights, which decrease rapidly with respect
+to the boundary distance. We characterize the solid hulls of such spaces and
+give quite explicit representations of them in the case of the most natural
+exponentially decreasing weights.
+",0,0,1,0,0,0
+17364,Line bundles defined by the Schwarz function,"  Cauchy and exponential transforms are characterized, and constructed, as
+canonical holomorphic sections of certain line bundles on the Riemann sphere
+defined in terms of the Schwarz function. A well known natural connection
+between Schwarz reflection and line bundles defined on the Schottky double of a
+planar domain is briefly discussed in the same context.
+",0,0,1,0,0,0
+17365,Collisional excitation of NH3 by atomic and molecular hydrogen,"  We report extensive theoretical calculations on the rotation-inversion
+excitation of interstellar ammonia (NH3) due to collisions with atomic and
+molecular hydrogen (both para- and ortho-H2). Close-coupling calculations are
+performed for total energies in the range 1-2000 cm-1 and rotational cross
+sections are obtained for all transitions among the lowest 17 and 34
+rotation-inversion levels of ortho- and para-NH3, respectively. Rate
+coefficients are deduced for kinetic temperatures up to 200 K. Propensity rules
+for the three colliding partners are discussed and we also compare the new
+results to previous calculations for the spherically symmetrical He and para-H2
+projectiles. Significant differences are found between the different sets of
+calculations. Finally, we test the impact of the new rate coefficients on the
+calibration of the ammonia thermometer. We find that the calibration curve is
+only weakly sensitive to the colliding partner and we confirm that the ammonia
+thermometer is robust.
+",0,1,0,0,0,0
+17366,Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data,"  We consider the multi-view data completion problem, i.e., to complete a
+matrix $\mathbf{U}=[\mathbf{U}_1|\mathbf{U}_2]$ where the ranks of
+$\mathbf{U},\mathbf{U}_1$, and $\mathbf{U}_2$ are given. In particular, we
+investigate the fundamental conditions on the sampling pattern, i.e., locations
+of the sampled entries for finite completability of such a multi-view data
+given the corresponding rank constraints. In contrast with the existing
+analysis on Grassmannian manifold for a single-view matrix, i.e., conventional
+matrix completion, we propose a geometric analysis on the manifold structure
+for multi-view data to incorporate more than one rank constraint. We provide a
+deterministic necessary and sufficient condition on the sampling pattern for
+finite completability. We also give a probabilistic condition in terms of the
+number of samples per column that guarantees finite completability with high
+probability. Finally, using the developed tools, we derive the deterministic
+and probabilistic guarantees for unique completability.
+",1,0,1,0,0,0
+17367,Grid-forming Control for Power Converters based on Matching of Synchronous Machines,"  We consider the problem of grid-forming control of power converters in
+low-inertia power systems. Starting from an average-switch three-phase inverter
+model, we draw parallels to a synchronous machine (SM) model and propose a
+novel grid-forming converter control strategy which dwells upon the main
+characteristic of a SM: the presence of an internal rotating magnetic field. In
+particular, we augment the converter system with a virtual oscillator whose
+frequency is driven by the DC-side voltage measurement and which sets the
+converter pulse-width-modulation signal, thereby achieving exact matching
+between the converter in closed-loop and the SM dynamics. We then provide a
+sufficient condition assuring existence, uniqueness, and global asymptotic
+stability of equilibria in a coordinate frame attached to the virtual
+oscillator angle. By actuating the DC-side input of the converter we are able
+to enforce this sufficient condition. In the same setting, we highlight strict
+incremental passivity, droop, and power-sharing properties of the proposed
+framework, which are compatible with conventional requirements of power system
+operation. We subsequently adopt disturbance decoupling techniques to design
+additional control loops that regulate the DC-side voltage, as well as AC-side
+frequency and amplitude, while in the end validating them with numerical
+experiments.
+",0,0,1,0,0,0
+17368,Characterizing Dust Attenuation in Local Star-Forming Galaxies: Near-Infrared Reddening and Normalization,"  We characterize the near-infrared (NIR) dust attenuation for a sample of
+~5500 local (z<0.1) star-forming galaxies and obtain an estimate of their
+average total-to-selective attenuation $k(\lambda)$. We utilize data from the
+United Kingdom Infrared Telescope (UKIRT) and the Two Micron All-Sky Survey
+(2MASS), which is combined with previously measured UV-optical data for these
+galaxies. The average attenuation curve is slightly lower in the far-UV than
+local starburst galaxies, by roughly 15%, but appears similar at longer
+wavelengths with a total-to-selective normalization at V-band of
+$R_V=3.67\substack{+0.44 \\ -0.35}$. Under the assumption of energy balance,
+the total attenuated energy inferred from this curve is found to be broadly
+consistent with the observed infrared dust emission ($L_{\rm{TIR}}$) in a small
+sample of local galaxies for which far-IR measurements are available. However,
+the significant scatter in this quantity among the sample may reflect large
+variations in the attenuation properties of individual galaxies. We also derive
+the attenuation curve for sub-populations of the main sample, separated
+according to mean stellar population age (via $D_n4000$), specific star
+formation rate, stellar mass, and metallicity, and find that they show only
+tentative trends with low significance, at least over the range which is probed
+by our sample. These results indicate that a single curve is reasonable for
+applications seeking to broadly characterize large samples of galaxies in the
+local Universe, while applications to individual galaxies would yield large
+uncertainties and is not recommended.
+",0,1,0,0,0,0
+17369,Sequential Checking: Reallocation-Free Data-Distribution Algorithm for Scale-out Storage,"  Using tape or optical devices for scale-out storage is one option for storing
+a vast amount of data. However, it is impossible or almost impossible to
+rewrite data with such devices. Thus, scale-out storage using such devices
+cannot use standard data-distribution algorithms because they rewrite data for
+moving between servers constituting the scale-out storage when the server
+configuration is changed. Although using rewritable devices for scale-out
+storage, when server capacity is huge, rewriting data is very hard when server
+constitution is changed. In this paper, a data-distribution algorithm called
+Sequential Checking is proposed, which can be used for scale-out storage
+composed of devices that are hardly able to rewrite data. Sequential Checking
+1) does not need to move data between servers when the server configuration is
+changed, 2) distribute data, the amount of which depends on the server's
+volume, 3) select a unique server when datum is written, and 4) select servers
+when datum is read (there are few such server(s) in most cases) and find out a
+unique server that stores the newest datum from them. These basic
+characteristics were confirmed through proofs and simulations. Data can be read
+by accessing 1.98 servers on average from a storage comprising 256 servers
+under a realistic condition. And it is confirmed by evaluations in real
+environment that access time is acceptable. Sequential Checking makes selecting
+scale-out storage using tape or optical devices or using huge capacity servers
+realistic.
+",1,0,0,0,0,0
+17370,Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels,"  Noisy PN learning is the problem of binary classification when training
+examples may be mislabeled (flipped) uniformly with noise rate rho1 for
+positive examples and rho0 for negative examples. We propose Rank Pruning (RP)
+to solve noisy PN learning and the open problem of estimating the noise rates,
+i.e. the fraction of wrong positive and negative labels. Unlike prior
+solutions, RP is time-efficient and general, requiring O(T) for any
+unrestricted choice of probabilistic classifier with T fitting time. We prove
+RP has consistent noise estimation and equivalent expected risk as learning
+with uncorrupted labels in ideal conditions, and derive closed-form solutions
+when conditions are non-ideal. RP achieves state-of-the-art noise estimation
+and F1, error, and AUC-PR for both MNIST and CIFAR datasets, regardless of the
+amount of noise and performs similarly impressively when a large portion of
+training examples are noise drawn from a third distribution. To highlight, RP
+with a CNN classifier can predict if an MNIST digit is a ""one""or ""not"" with
+only 0.25% error, and 0.46 error across all digits, even when 50% of positive
+examples are mislabeled and 50% of observed positive labels are mislabeled
+negative examples.
+",1,0,0,1,0,0
+17371,code2vec: Learning Distributed Representations of Code,"  We present a neural model for representing snippets of code as continuous
+distributed vectors (""code embeddings""). The main idea is to represent a code
+snippet as a single fixed-length $\textit{code vector}$, which can be used to
+predict semantic properties of the snippet. This is performed by decomposing
+code to a collection of paths in its abstract syntax tree, and learning the
+atomic representation of each path $\textit{simultaneously}$ with learning how
+to aggregate a set of them. We demonstrate the effectiveness of our approach by
+using it to predict a method's name from the vector representation of its body.
+We evaluate our approach by training a model on a dataset of 14M methods. We
+show that code vectors trained on this dataset can predict method names from
+files that were completely unobserved during training. Furthermore, we show
+that our model learns useful method name vectors that capture semantic
+similarities, combinations, and analogies. Comparing previous techniques over
+the same data set, our approach obtains a relative improvement of over 75%,
+being the first to successfully predict method names based on a large,
+cross-project, corpus. Our trained model, visualizations and vector
+similarities are available as an interactive online demo at
+this http URL. The code, data, and trained models are available at
+this https URL.
+",1,0,0,1,0,0
+17372,Learning a Local Feature Descriptor for 3D LiDAR Scans,"  Robust data association is necessary for virtually every SLAM system and
+finding corresponding points is typically a preprocessing step for scan
+alignment algorithms. Traditionally, handcrafted feature descriptors were used
+for these problems but recently learned descriptors have been shown to perform
+more robustly. In this work, we propose a local feature descriptor for 3D LiDAR
+scans. The descriptor is learned using a Convolutional Neural Network (CNN).
+Our proposed architecture consists of a Siamese network for learning a feature
+descriptor and a metric learning network for matching the descriptors. We also
+present a method for estimating local surface patches and obtaining
+ground-truth correspondences. In extensive experiments, we compare our learned
+feature descriptor with existing 3D local descriptors and report highly
+competitive results for multiple experiments in terms of matching accuracy and
+computation time. \end{abstract}
+",1,0,0,0,0,0
+17373,Dynamical tides in exoplanetary systems containing Hot Jupiters: confronting theory and observations,"  We study the effect of dynamical tides associated with the excitation of
+gravity waves in an interior radiative region of the central star on orbital
+evolution in observed systems containing Hot Jupiters. We consider WASP-43,
+Ogle-tr-113, WASP-12, and WASP-18 which contain stars on the main sequence
+(MS). For these systems there are observational estimates regarding the rate of
+change of the orbital period. We also investigate Kepler-91 which contains an
+evolved giant star. We adopt the formalism of Ivanov et al. for calculating the
+orbital evolution.
+For the MS stars we determine expected rates of orbital evolution under
+different assumptions about the amount of dissipation acting on the tides,
+estimate the effect of stellar rotation for the two most rapidly rotating stars
+and compare results with observations. All cases apart from possibly WASP-43
+are consistent with a regime in which gravity waves are damped during their
+propagation over the star. However, at present this is not definitive as
+observational errors are large. We find that although it is expected to apply
+to Kepler-91, linear radiative damping cannot explain this dis- sipation regime
+applying to MS stars. Thus, a nonlinear mechanism may be needed.
+Kepler-91 is found to be such that the time scale for evolution of the star
+is comparable to that for the orbit. This implies that significant orbital
+circularisation may have occurred through tides acting on the star.
+Quasi-static tides, stellar winds, hydrodynamic drag and tides acting on the
+planet have likely played a minor role.
+",0,1,0,0,0,0
+17374,Metastability versus collapse following a quench in attractive Bose-Einstein condensates,"  We consider a Bose-Einstein condensate (BEC) with attractive two-body
+interactions in a cigar-shaped trap, initially prepared in its ground state for
+a given negative scattering length, which is quenched to a larger absolute
+value of the scattering length. Using the mean-field approximation, we compute
+numerically, for an experimentally relevant range of aspect ratios and initial
+strengths of the coupling, two critical values of quench: one corresponds to
+the weakest attraction strength the quench to which causes the system to
+collapse before completing even a single return from the narrow configuration
+(""perihelion"") in its breathing cycle. The other is a similar critical point
+for the occurrence of collapse before completing two returns. In the latter
+case, we also compute the limiting value, as we keep increasing the strength of
+the post-quench attraction towards its critical value, of the time interval
+between the first two perihelia. We also use a Gaussian variational model to
+estimate the critical quenched attraction strength below which the system is
+stable against the collapse for long times. These time intervals and critical
+attraction strengths---apart from being fundamental properties of nonlinear
+dynamics of self-attractive BECs---may provide clues to the design of upcoming
+experiments that are trying to create robust BEC breathers.
+",0,1,0,0,0,0
+17375,A similarity criterion for sequential programs using truth-preserving partial functions,"  The execution of sequential programs allows them to be represented using
+mathematical functions formed by the composition of statements following one
+after the other. Each such statement is in itself a partial function, which
+allows only inputs satisfying a particular Boolean condition to carry forward
+the execution and hence, the composition of such functions (as a result of
+sequential execution of the statements) strengthens the valid set of input
+state variables for the program to complete its execution and halt succesfully.
+With this thought in mind, this paper tries to study a particular class of
+partial functions, which tend to preserve the truth of two given Boolean
+conditions whenever the state variables satisfying one are mapped through such
+functions into a domain of state variables satisfying the other. The existence
+of such maps allows us to study isomorphism between different programs, based
+not only on their structural characteristics (e.g. the kind of programming
+constructs used and the overall input-output transformation), but also the
+nature of computation performed on seemingly different inputs. Consequently, we
+can now relate programs which perform a given type of computation, like a loop
+counting down indefinitely, without caring about the input sets they work on
+individually or the set of statements each program contains.
+",1,0,0,0,0,0
+17376,Subsampling large graphs and invariance in networks,"  Specify a randomized algorithm that, given a very large graph or network,
+extracts a random subgraph. What can we learn about the input graph from a
+single subsample? We derive laws of large numbers for the sampler output, by
+relating randomized subsampling to distributional invariance: Assuming an
+invariance holds is tantamount to assuming the sample has been generated by a
+specific algorithm. That in turn yields a notion of ergodicity. Sampling
+algorithms induce model classes---graphon models, sparse generalizations of
+exchangeable graphs, and random multigraphs with exchangeable edges can all be
+obtained in this manner, and we specialize our results to a number of examples.
+One class of sampling algorithms emerges as special: Roughly speaking, those
+defined as limits of random transformations drawn uniformly from certain
+sequences of groups. Some known pathologies of network models based on graphons
+are explained as a form of selection bias.
+",0,0,1,1,0,0
+17377,Taylor coefficients of non-holomorphic Jacobi forms and applications,"  In this paper, we prove modularity results of Taylor coefficients of certain
+non-holomorphic Jacobi forms. It is well-known that Taylor coefficients of
+holomorphic Jacobi forms are quasimoular forms. However recently there has been
+a wide interest for Taylor coefficients of non-holomorphic Jacobi forms for
+example arising in combinatorics. In this paper, we show that such coefficients
+still inherit modular properties. We then work out the precise spaces in which
+these coefficients lie for two examples.
+",0,0,1,0,0,0
+17378,Beamspace SU-MIMO for Future Millimeter Wave Wireless Communications,"  For future networks (i.e., the fifth generation (5G) wireless networks and
+beyond), millimeter-wave (mmWave) communication with large available unlicensed
+spectrum is a promising technology that enables gigabit multimedia
+applications. Thanks to the short wavelength of mmWave radio, massive antenna
+arrays can be packed into the limited dimensions of mmWave transceivers.
+Therefore, with directional beamforming (BF), both mmWave transmitters (MTXs)
+and mmWave receivers (MRXs) are capable of supporting multiple beams in 5G
+networks. However, for the transmission between an MTX and an MRX, most works
+have only considered a single beam, which means that they do not make full
+potential use of mmWave. Furthermore, the connectivity of single beam
+transmission can easily be blocked. In this context, we propose a single-user
+multi-beam concurrent transmission scheme for future mmWave networks with
+multiple reflected paths. Based on spatial spectrum reuse, the scheme can be
+described as a multiple-input multiple-output (MIMO) technique in beamspace
+(i.e., in the beam-number domain). Moreover, this study investigates the
+challenges and potential solutions for implementing this scheme, including
+multibeam selection, cooperative beam tracking, multi-beam power allocation and
+synchronization. The theoretical and numerical results show that the proposed
+beamspace SU-MIMO can largely improve the achievable rate of the transmission
+between an MTX and an MRX and, meanwhile, can maintain the connectivity.
+",1,0,0,0,0,0
+17379,Learning Robust Visual-Semantic Embeddings,"  Many of the existing methods for learning joint embedding of images and text
+use only supervised information from paired images and its textual attributes.
+Taking advantage of the recent success of unsupervised learning in deep neural
+networks, we propose an end-to-end learning framework that is able to extract
+more robust multi-modal representations across domains. The proposed method
+combines representation learning models (i.e., auto-encoders) together with
+cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn
+joint embeddings for semantic and visual features. A novel technique of
+unsupervised-data adaptation inference is introduced to construct more
+comprehensive embeddings for both labeled and unlabeled data. We evaluate our
+method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with
+a wide range of applications, including zero and few-shot image recognition and
+retrieval, from inductive to transductive settings. Empirically, we show that
+our framework improves over the current state of the art on many of the
+considered tasks.
+",1,0,0,0,0,0
+17380,Quantitative estimates of the surface habitability of Kepler-452b,"  Kepler-452b is currently the best example of an Earth-size planet in the
+habitable zone of a sun-like star, a type of planet whose number of detections
+is expected to increase in the future. Searching for biosignatures in the
+supposedly thin atmospheres of these planets is a challenging goal that
+requires a careful selection of the targets. Under the assumption of a
+rocky-dominated nature for Kepler-452b, we considered it as a test case to
+calculate a temperature-dependent habitability index, $h_{050}$, designed to
+maximize the potential presence of biosignature-producing activity (Silva et
+al.\ 2016). The surface temperature has been computed for a broad range of
+climate factors using a climate model designed for terrestrial-type exoplanets
+(Vladilo et al.\ 2015). After fixing the planetary data according to the
+experimental results (Jenkins et al.\ 2015), we changed the surface gravity,
+CO$_2$ abundance, surface pressure, orbital eccentricity, rotation period, axis
+obliquity and ocean fraction within the range of validity of our model. For
+most choices of parameters we find habitable solutions with $h_{050}>0.2$ only
+for CO$_2$ partial pressure $p_\mathrm{CO_2} \lesssim 0.04$\,bar. At this
+limiting value of CO$_2$ abundance the planet is still habitable if the total
+pressure is $p \lesssim 2$\,bar. In all cases the habitability drops for
+eccentricity $e \gtrsim 0.3$. Changes of rotation period and obliquity affect
+the habitability through their impact on the equator-pole temperature
+difference rather than on the mean global temperature. We calculated the
+variation of $h_{050}$ resulting from the luminosity evolution of the host star
+for a wide range of input parameters. Only a small combination of parameters
+yield habitability-weighted lifetimes $\gtrsim 2$\,Gyr, sufficiently long to
+develop atmospheric biosignatures still detectable at the present time.
+",0,1,0,0,0,0
+17381,Design and implementation of dynamic logic gates and R-S flip-flop using quasiperiodically driven Murali-Lakshmanan-Chua circuit,"  We report the propagation of a square wave signal in a quasi-periodically
+driven Murali-Lakshmanan-Chua (QPDMLC) circuit system. It is observed that
+signal propagation is possible only above a certain threshold strength of the
+square wave or digital signal and all the values above the threshold amplitude
+are termed as 'region of signal propagation'. Then, we extend this region of
+signal propagation to perform various logical operations like AND/NAND/OR/NOR
+and hence it is also designated as the 'region of logical operation'. Based on
+this region, we propose implementing the dynamic logic gates, namely
+AND/NAND/OR/NOR, which can be decided by the asymmetrical input square waves
+without altering the system parameters. Further, we show that a single QPDMLC
+system will produce simultaneously two outputs which are complementary to each
+other. As a result, a single QPDMLC system yields either AND as well as NAND or
+OR as well as NOR gates simultaneously. Then we combine the corresponding two
+QPDMLC systems in a cross-coupled way and report that its dynamics mimics that
+of fundamental R-S flip-flop circuit. All these phenomena have been explained
+with analytical solutions of the circuit equations characterizing the system
+and finally the results are compared with the corresponding numerical and
+experimental analysis.
+",0,1,0,0,0,0
+17382,"Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects","  We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep
+generative model for videos of moving objects. It can reliably discover and
+track objects throughout the sequence of frames, and can also generate future
+frames conditioning on the current frame, thereby simulating expected motion of
+objects. This is achieved by explicitly encoding object presence, locations and
+appearances in the latent variables of the model. SQAIR retains all strengths
+of its predecessor, Attend, Infer, Repeat (AIR, Eslami et. al., 2016),
+including learning in an unsupervised manner, and addresses its shortcomings.
+We use a moving multi-MNIST dataset to show limitations of AIR in detecting
+overlapping or partially occluded objects, and show how SQAIR overcomes them by
+leveraging temporal consistency of objects. Finally, we also apply SQAIR to
+real-world pedestrian CCTV data, where it learns to reliably detect, track and
+generate walking pedestrians with no supervision.
+",0,0,0,1,0,0
+17383,Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks,"  We present a new local descriptor for 3D shapes, directly applicable to a
+wide range of shape analysis problems such as point correspondences, semantic
+segmentation, affordance prediction, and shape-to-scan matching. The descriptor
+is produced by a convolutional network that is trained to embed geometrically
+and semantically similar points close to one another in descriptor space. The
+network processes surface neighborhoods around points on a shape that are
+captured at multiple scales by a succession of progressively zoomed out views,
+taken from carefully selected camera positions. We leverage two extremely large
+sources of data to train our network. First, since our network processes
+rendered views in the form of 2D images, we repurpose architectures pre-trained
+on massive image datasets. Second, we automatically generate a synthetic dense
+point correspondence dataset by non-rigid alignment of corresponding shape
+parts in a large collection of segmented 3D models. As a result of these design
+choices, our network effectively encodes multi-scale local context and
+fine-grained surface detail. Our network can be trained to produce either
+category-specific descriptors or more generic descriptors by learning from
+multiple shape categories. Once trained, at test time, the network extracts
+local descriptors for shapes without requiring any part segmentation as input.
+Our method can produce effective local descriptors even for shapes whose
+category is unknown or different from the ones used while training. We
+demonstrate through several experiments that our learned local descriptors are
+more discriminative compared to state of the art alternatives, and are
+effective in a variety of shape analysis applications.
+",1,0,0,0,0,0
+17384,Alternating minimization for dictionary learning with random initialization,"  We present theoretical guarantees for an alternating minimization algorithm
+for the dictionary learning/sparse coding problem. The dictionary learning
+problem is to factorize vector samples $y^{1},y^{2},\ldots, y^{n}$ into an
+appropriate basis (dictionary) $A^*$ and sparse vectors $x^{1*},\ldots,x^{n*}$.
+Our algorithm is a simple alternating minimization procedure that switches
+between $\ell_1$ minimization and gradient descent in alternate steps.
+Dictionary learning and specifically alternating minimization algorithms for
+dictionary learning are well studied both theoretically and empirically.
+However, in contrast to previous theoretical analyses for this problem, we
+replace the condition on the operator norm (that is, the largest magnitude
+singular value) of the true underlying dictionary $A^*$ with a condition on the
+matrix infinity norm (that is, the largest magnitude term). This not only
+allows us to get convergence rates for the error of the estimated dictionary
+measured in the matrix infinity norm, but also ensures that a random
+initialization will provably converge to the global optimum. Our guarantees are
+under a reasonable generative model that allows for dictionaries with growing
+operator norms, and can handle an arbitrary level of overcompleteness, while
+having sparsity that is information theoretically optimal. We also establish
+upper bounds on the sample complexity of our algorithm.
+",1,0,0,1,0,0
+17385,Optimal Transmission Line Switching under Geomagnetic Disturbances,"  In recent years, there have been increasing concerns about how geomagnetic
+disturbances (GMDs) impact electrical power systems. Geomagnetically-induced
+currents (GICs) can saturate transformers, induce hot spot heating and increase
+reactive power losses. These effects can potentially cause catastrophic damage
+to transformers and severely impact the ability of a power system to deliver
+power. To address this problem, we develop a model of GIC impacts to power
+systems that includes 1) GIC thermal capacity of transformers as a function of
+normal Alternating Current (AC) and 2) reactive power losses as a function of
+GIC. We use this model to derive an optimization problem that protects power
+systems from GIC impacts through line switching, generator redispatch, and load
+shedding. We employ state-of-the-art convex relaxations of AC power flow
+equations to lower bound the objective. We demonstrate the approach on a
+modified RTS96 system and the UIUC 150-bus system and show that line switching
+is an effective means to mitigate GIC impacts. We also provide a sensitivity
+analysis of optimal switching decisions with respect to GMD direction.
+",1,0,0,0,0,0
+17386,Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks,"  In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and
+the segmentation-based multi-scale analysis to locate tampered areas in digital
+images. First, to deal with color input sliding windows of different scales, a
+unified CNN architecture is designed. Then, we elaborately design the training
+procedures of CNNs on sampled training patches. With a set of robust
+multi-scale tampering detectors based on CNNs, complementary tampering
+possibility maps can be generated. Last but not least, a segmentation-based
+method is proposed to fuse the maps and generate the final decision map. By
+exploiting the benefits of both the small-scale and large-scale analyses, the
+segmentation-based multi-scale analysis can lead to a performance leap in
+forgery localization of CNNs. Numerous experiments are conducted to demonstrate
+the effectiveness and efficiency of our method.
+",1,0,0,0,0,0
+17387,The QKP limit of the quantum Euler-Poisson equation,"  In this paper, we consider the derivation of the Kadomtsev-Petviashvili (KP)
+equation for cold ion-acoustic wave in the long wavelength limit of the
+two-dimensional quantum Euler-Poisson system, under different scalings for
+varying directions in the Gardner-Morikawa transform. It is shown that the
+types of the KP equation depend on the scaled quantum parameter $H>0$. The
+QKP-I is derived for $H>2$, QKP-II for $0<H<2$ and the dispersive-less KP (dKP)
+equation for the critical case $H=2$. The rigorous proof for these limits is
+given in the well-prepared initial data case, and the norm that is chosen to
+close the proof is anisotropic in the two directions, in accordance with the
+anisotropic structure of the KP equation as well as the Gardner-Morikawa
+transform. The results can be generalized in several directions.
+",0,0,1,0,0,0
+17388,Variational Implicit Processes,"  This paper introduces the variational implicit processes (VIPs), a Bayesian
+nonparametric method based on a class of highly flexible priors over functions.
+Similar to Gaussian processes (GPs), in implicit processes (IPs), an implicit
+multivariate prior (data simulators, Bayesian neural networks, etc.) is placed
+over any finite collections of random variables. A novel and efficient
+variational inference algorithm for IPs is derived using wake-sleep updates,
+which gives analytic solutions and allows scalable hyper-parameter learning
+with stochastic optimization. Experiments on real-world regression datasets
+demonstrate that VIPs return better uncertainty estimates and superior
+performance over existing inference methods for GPs and Bayesian neural
+networks. With a Bayesian LSTM as the implicit prior, the proposed approach
+achieves state-of-the-art results on predicting power conversion efficiency of
+molecules based on raw chemical formulas.
+",0,0,0,1,0,0
+17389,Silicon Micromachined High-contrast Artificial Dielectrics for Millimeter-wave Transformation Optics Antennas,"  Transformation optics methods and gradient index electromagnetic structures
+rely upon spatially varied arbitrary permittivity. This, along with recent
+interest in millimeter-wave lens-based antennas demands high spatial resolution
+dielectric variation. Perforated media have been used to fabricate gradient
+index structures from microwaves to THz but are often limited in contrast. We
+show that by employing regular polygon unit-cells (hexagon, square, and
+triangle) on matched lattices we can realize very high contrast permittivity
+ranging from 0.1-1.0 of the background permittivity. Silicon micromachining
+(Bosch process) is performed on high resistivity Silicon wafers to achieve a
+minimum permittivity of 1.25 (10% of Silicon) in the WR28 waveguide band,
+specifically targeting the proposed 39 GHz 5G communications band. The method
+is valid into the THz band.
+",0,1,0,0,0,0
+17390,Pseudogap and Fermi surface in the presence of spin-vortex checkerboard for 1/8-doped lanthanum cuprates,"  Lanthanum family of high-temperature cuprate superconductors is known to
+exhibit both spin and charge electronic modulations around doping level 1/8. We
+assume that these modulations have the character of two-dimensional spin-vortex
+checkerboard and investigate whether this assumption is consistent with the
+Fermi surface and the pseudogap measured by angle-resolved photo-emission
+spectroscopy. We also explore the possibility of observing quantum oscillations
+of transport coefficients in such a background. These investigations are based
+on a model of non-interacting spin-1/2 fermions hopping on a square lattice and
+coupled through spins to a magnetic field imitating spin-vortex checkerboard.
+The main results of this article include (i) calculation of Fermi surface
+containing Fermi arcs at the positions in the Brillouin zone largely consistent
+with experiments; (ii) identification of factors complicating the observations
+of quantum oscillations in the presence of spin modulations; and (iii)
+investigation of the symmetries of the resulting electronic energy bands,
+which, in particular, indicates that each band is double-degenerate and, in
+addition, has at least one conical point, where it touches another
+double-degenerate band. We discuss possible implications these cones may have
+for the transport properties and the pseudogap.
+",0,1,0,0,0,0
+17391,Revealing the cluster of slow transients behind a large slow slip event,"  Capable of reaching similar magnitudes to large megathrust earthquakes
+($M_w>7$), slow slip events play a major role in accommodating tectonic motion
+on plate boundaries. These slip transients are the slow release of built-up
+tectonic stress that are geodetically imaged as a predominantly aseismic
+rupture, which is smooth in both time and space. We demonstrate here that large
+slow slip events are in fact a cluster of short-duration slow transients. Using
+a dense catalog of low-frequency earthquakes as a guide, we investigate the
+$M_w7.5$ slow slip event that occurred in 2006 along the subduction interface
+40~km beneath Guerrero, Mexico. We show that while the long-period surface
+displacement as recorded by GPS suggests a six month duration, motion in the
+direction of tectonic release only sporadically occurs over 55 days and its
+surface signature is attenuated by rapid relocking of the plate interface.These
+results demonstrate that our current conceptual model of slow and continuous
+rupture is an artifact of low-resolution geodetic observations of a
+superposition of small, clustered slip events. Our proposed description of slow
+slip as a cluster of slow transients implies that we systematically
+overestimate the duration $T$ and underestimate the moment magnitude $M$ of
+large slow slip events.
+",0,1,0,0,0,0
+17392,General $N$-solitons and their dynamics in several nonlocal nonlinear Schrödinger equations,"  General $N$-solitons in three recently-proposed nonlocal nonlinear
+Schrödinger equations are presented. These nonlocal equations include the
+reverse-space, reverse-time, and reverse-space-time nonlinear Schrödinger
+equations, which are nonlocal reductions of the Ablowitz-Kaup-Newell-Segur
+(AKNS) hierarchy. It is shown that general $N$-solitons in these different
+equations can be derived from the same Riemann-Hilbert solutions of the AKNS
+hierarchy, except that symmetry relations on the scattering data are different
+for these equations. This Riemann-Hilbert framework allows us to identify new
+types of solitons with novel eigenvalue configurations in the spectral plane.
+Dynamics of $N$-solitons in these equations is also explored. In all the three
+nonlocal equations, a generic feature of their solutions is repeated
+collapsing. In addition, multi-solitons can behave very differently from
+fundamental solitons and may not correspond to a nonlinear superposition of
+fundamental solitons.
+",0,1,0,0,0,0
+17393,Revisiting wireless network jamming by SIR-based considerations and Multiband Robust Optimization,"  We revisit the mathematical models for wireless network jamming introduced by
+Commander et al.: we first point out the strong connections with classical
+wireless network design and then we propose a new model based on the explicit
+use of signal-to-interference quantities. Moreover, to address the intrinsic
+uncertain nature of the jamming problem and tackle the peculiar right-hand-side
+(RHS) uncertainty of the problem, we propose an original robust cutting-plane
+algorithm drawing inspiration from Multiband Robust Optimization. Finally, we
+assess the performance of the proposed cutting plane algorithm by experiments
+on realistic network instances.
+",1,0,1,0,0,0
+17394,New models for symbolic data analysis,"  Symbolic data analysis (SDA) is an emerging area of statistics based on
+aggregating individual level data into group-based distributional summaries
+(symbols), and then developing statistical methods to analyse them. It is ideal
+for analysing large and complex datasets, and has immense potential to become a
+standard inferential technique in the near future. However, existing SDA
+techniques are either non-inferential, do not easily permit meaningful
+statistical models, are unable to distinguish between competing models, and are
+based on simplifying assumptions that are known to be false. Further, the
+procedure for constructing symbols from the underlying data is erroneously not
+considered relevant to the resulting statistical analysis. In this paper we
+introduce a new general method for constructing likelihood functions for
+symbolic data based on a desired probability model for the underlying classical
+data, while only observing the distributional summaries. This approach resolves
+many of the conceptual and practical issues with current SDA methods, opens the
+door for new classes of symbol design and construction, in addition to
+developing SDA as a viable tool to enable and improve upon classical data
+analyses, particularly for very large and complex datasets. This work creates a
+new direction for SDA research, which we illustrate through several real and
+simulated data analyses.
+",0,0,0,1,0,0
+17395,Soft Methodology for Cost-and-error Sensitive Classification,"  Many real-world data mining applications need varying cost for different
+types of classification errors and thus call for cost-sensitive classification
+algorithms. Existing algorithms for cost-sensitive classification are
+successful in terms of minimizing the cost, but can result in a high error rate
+as the trade-off. The high error rate holds back the practical use of those
+algorithms. In this paper, we propose a novel cost-sensitive classification
+methodology that takes both the cost and the error rate into account. The
+methodology, called soft cost-sensitive classification, is established from a
+multicriteria optimization problem of the cost and the error rate, and can be
+viewed as regularizing cost-sensitive classification with the error rate. The
+simple methodology allows immediate improvements of existing cost-sensitive
+classification algorithms. Experiments on the benchmark and the real-world data
+sets show that our proposed methodology indeed achieves lower test error rates
+and similar (sometimes lower) test costs than existing cost-sensitive
+classification algorithms. We also demonstrate that the methodology can be
+extended for considering the weighted error rate instead of the original error
+rate. This extension is useful for tackling unbalanced classification problems.
+",1,0,0,0,0,0
+17396,Raman LIDARs and atmospheric calibration for the Cherenkov Telescope Array,"  The Cherenkov Telescope Array (CTA) is the next generation of Imaging
+Atmospheric Cherenkov Telescopes. It will reach a sensitivity and energy
+resolution never obtained until now by any other high energy gamma-ray
+experiment. Understanding the systematic uncertainties in general will be a
+crucial issue for the performance of CTA. It is well known that atmospheric
+conditions contribute particularly in this aspect.Within the CTA consortium
+several groups are currently building Raman LIDARs to be installed on the two
+sites. Raman LIDARs are devices composed of a powerful laser that shoots into
+the atmosphere, a collector that gathers the backscattered light from molecules
+and aerosols, a photo-sensor, an optical module that spectrally selects
+wavelengths of interest, and a read--out system.Unlike currently used elastic
+LIDARs, they can help reduce the systematic uncertainties of the molecular and
+aerosol components of the atmosphere to <5% so that CTA can achieve its energy
+resolution requirements of<10% uncertainty at 1 TeV.All the Raman LIDARs in
+this work have design features that make them different than typical Raman
+LIDARs used in atmospheric science and are characterized by large collecting
+mirrors (2.5m2) and reduced acquisition time.They provide both multiple elastic
+and Raman read-out channels and custom made optics design.In this paper, the
+motivation for Raman LIDARs, the design and the status of advance of these
+technologies are described.
+",0,1,0,0,0,0
+17397,Generalized notions of sparsity and restricted isometry property. Part II: Applications,"  The restricted isometry property (RIP) is a universal tool for data recovery.
+We explore the implication of the RIP in the framework of generalized sparsity
+and group measurements introduced in the Part I paper. It turns out that for a
+given measurement instrument the number of measurements for RIP can be improved
+by optimizing over families of Banach spaces. Second, we investigate the
+preservation of difference of two sparse vectors, which is not trivial in
+generalized models. Third, we extend the RIP of partial Fourier measurements at
+optimal scaling of number of measurements with random sign to far more general
+group structured measurements. Lastly, we also obtain RIP in infinite dimension
+in the context of Fourier measurement concepts with sparsity naturally replaced
+by smoothness assumptions.
+",0,0,0,1,0,0
+17398,Mellin-Meijer-kernel density estimation on $\mathbb{R}^+$,"  Nonparametric kernel density estimation is a very natural procedure which
+simply makes use of the smoothing power of the convolution operation. Yet, it
+performs poorly when the density of a positive variable is to be estimated
+(boundary issues, spurious bumps in the tail). So various extensions of the
+basic kernel estimator allegedly suitable for $\mathbb{R}^+$-supported
+densities, such as those using Gamma or other asymmetric kernels, abound in the
+literature. Those, however, are not based on any valid smoothing operation
+analogous to the convolution, which typically leads to inconsistencies. By
+contrast, in this paper a kernel estimator for $\mathbb{R}^+$-supported
+densities is defined by making use of the Mellin convolution, the natural
+analogue of the usual convolution on $\mathbb{R}^+$. From there, a very
+transparent theory flows and leads to new type of asymmetric kernels strongly
+related to Meijer's $G$-functions. The numerous pleasant properties of this
+`Mellin-Meijer-kernel density estimator' are demonstrated in the paper. Its
+pointwise and $L_2$-consistency (with optimal rate of convergence) is
+established for a large class of densities, including densities unbounded at 0
+and showing power-law decay in their right tail. Its practical behaviour is
+investigated further through simulations and some real data analyses.
+",0,0,1,1,0,0
+17399,Gene Ontology (GO) Prediction using Machine Learning Methods,"  We applied machine learning to predict whether a gene is involved in axon
+regeneration. We extracted 31 features from different databases and trained
+five machine learning models. Our optimal model, a Random Forest Classifier
+with 50 submodels, yielded a test score of 85.71%, which is 4.1% higher than
+the baseline score. We concluded that our models have some predictive
+capability. Similar methodology and features could be applied to predict other
+Gene Ontology (GO) terms.
+",1,0,0,1,0,0
+17400,Dimension Spectra of Lines,"  This paper investigates the algorithmic dimension spectra of lines in the
+Euclidean plane. Given any line L with slope a and vertical intercept b, the
+dimension spectrum sp(L) is the set of all effective Hausdorff dimensions of
+individual points on L. We draw on Kolmogorov complexity and geometrical
+arguments to show that if the effective Hausdorff dimension dim(a, b) is equal
+to the effective packing dimension Dim(a, b), then sp(L) contains a unit
+interval. We also show that, if the dimension dim(a, b) is at least one, then
+sp(L) is infinite. Together with previous work, this implies that the dimension
+spectrum of any line is infinite.
+",1,0,0,0,0,0
+17401,Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search,"  Inference in log-linear models scales linearly in the size of output space in
+the worst-case. This is often a bottleneck in natural language processing and
+computer vision tasks when the output space is feasibly enumerable but very
+large. We propose a method to perform inference in log-linear models with
+sublinear amortized cost. Our idea hinges on using Gumbel random variable
+perturbations and a pre-computed Maximum Inner Product Search data structure to
+access the most-likely elements in sublinear amortized time. Our method yields
+provable runtime and accuracy guarantees. Further, we present empirical
+experiments on ImageNet and Word Embeddings showing significant speedups for
+sampling, inference, and learning in log-linear models.
+",1,0,0,1,0,0
+17402,Temperature Dependence of Magnetic Excitations: Terahertz Magnons above the Curie Temperature,"  When an ordered spin system of a given dimensionality undergoes a second
+order phase transition the dependence of the order parameter i.e. magnetization
+on temperature can be well-described by thermal excitations of elementary
+collective spin excitations (magnons). However, the behavior of magnons
+themselves, as a function of temperature and across the transition temperature
+TC, is an unknown issue. Utilizing spin-polarized high resolution electron
+energy loss spectroscopy we monitor the high-energy (terahertz) magnons,
+excited in an ultrathin ferromagnet, as a function of temperature. We show that
+the magnons' energy and lifetime decrease with temperature. The
+temperature-induced renormalization of the magnons' energy and lifetime depends
+on the wave vector. We provide quantitative results on the temperature-induced
+damping and discuss the possible mechanism e.g., multi-magnon scattering. A
+careful investigation of physical quantities determining the magnons'
+propagation indicates that terahertz magnons sustain their propagating
+character even at temperatures far above TC.
+",0,1,0,0,0,0
+17403,On stable solitons and interactions of the generalized Gross-Pitaevskii equation with PT-and non-PT-symmetric potentials,"  We report the bright solitons of the generalized Gross-Pitaevskii (GP)
+equation with some types of physically relevant parity-time-(PT-) and
+non-PT-symmetric potentials. We find that the constant momentum coefficient can
+modulate the linear stability and complicated transverse power-flows (not
+always from the gain toward loss) of nonlinear modes. However, the varying
+momentum coefficient Gamma(x) can modulate both unbroken linear PT-symmetric
+phases and stability of nonlinear modes. Particularly, the nonlinearity can
+excite the unstable linear mode (i.e., broken linear PT-symmetric phase) to
+stable nonlinear modes. Moreover, we also find stable bright solitons in the
+presence of non-PT-symmetric harmonic-Gaussian potential. The interactions of
+two bright solitons are also illustrated in PT-symmetric potentials. Finally,
+we consider nonlinear modes and transverse power-flows in the three-dimensional
+(3D) GP equation with the generalized PT-symmetric Scarf-II potential
+",0,1,1,0,0,0
+17404,Mechanical properties of borophene films: A reactive molecular dynamics investigation,"  The most recent experimental advances could provide ways for the fabrication
+of several atomic thick and planar forms of boron atoms. For the first time, we
+explore the mechanical properties of five types of boron films with various
+vacancy ratios ranging from 0.1 to 0.15, using molecular dynamics simulations
+with ReaxFF force field. It is found that the Young's modulus and tensile
+strength decrease with increasing the temperature. We found that boron sheets
+exhibit an anisotropic mechanical response due to the different arrangement of
+atoms along the armchair and zigzag directions. At room temperature, 2D Young's
+modulus and fracture stress of these five sheets appear in the range 63 N/m and
+12 N/m, respectively. In addition, the strains at tensile strength are in the
+ranges of 9, 11, and 10 percent at 1, 300, and 600 K, respectively. This
+investigation not only reveals the remarkable stiffness of 2D boron, but
+establishes relations between the mechanical properties of the boron sheets to
+the loading direction, temperature and atomic structures.
+",0,1,0,0,0,0
+17405,Stronger selection can slow down evolution driven by recombination on a smooth fitness landscape,"  Stronger selection implies faster evolution---that is, the greater the force,
+the faster the change. This apparently self-evident proposition, however, is
+derived under the assumption that genetic variation within a population is
+primarily supplied by mutation (i.e.\ mutation-driven evolution). Here, we show
+that this proposition does not actually hold for recombination-driven
+evolution, i.e.\ evolution in which genetic variation is primarily created by
+recombination rather than mutation. By numerically investigating population
+genetics models of recombination, migration and selection, we demonstrate that
+stronger selection can slow down evolution on a perfectly smooth fitness
+landscape. Through simple analytical calculation, this apparently
+counter-intuitive result is shown to stem from two opposing effects of natural
+selection on the rate of evolution. On the one hand, natural selection tends to
+increase the rate of evolution by increasing the fixation probability of fitter
+genotypes. On the other hand, natural selection tends to decrease the rate of
+evolution by decreasing the chance of recombination between immigrants and
+resident individuals. As a consequence of these opposing effects, there is a
+finite selection pressure maximizing the rate of evolution. Hence, stronger
+selection can imply slower evolution if genetic variation is primarily supplied
+by recombination.
+",0,1,0,0,0,0
+17406,Differences Among Noninformative Stopping Rules Are Often Relevant to Bayesian Decisions,"  L.J. Savage once hoped to show that ""the superficially incompatible systems
+of ideas associated on the one hand with [subjective Bayesianism] and on the
+other hand with [classical statistics]...lend each other mutual support and
+clarification."" By 1972, however, he had largely ""lost faith in the devices"" of
+classical statistics. One aspect of those ""devices"" that he found objectionable
+is that differences among the ""stopping rules"" that are used to decide when to
+end an experiment which are ""noninformative"" from a Bayesian perspective can
+affect decisions made using a classical approach. Two experiments that produce
+the same data using different stopping rules seem to differ only in the
+intentions of the experimenters regarding whether or not they would have
+carried on if the data had been different, which seem irrelevant to the
+evidential import of the data and thus to facts about what actions the data
+warrant.
+I argue that classical and Bayesian ideas about stopping rules do in fact
+""lend each other"" the kind of ""mutual support and clarification"" that Savage
+had originally hoped to find. They do so in a kind of case that is common in
+scientific practice, in which those who design an experiment have different
+interests from those who will make decisions in light of its results. I show
+that, in cases of this kind, Bayesian principles provide qualified support for
+the classical statistical practice of ""penalizing"" ""biased"" stopping rules.
+However, they require this practice in a narrower range of circumstances than
+classical principles do, and for different reasons. I argue that classical
+arguments for this practice are compelling in precisely the class of cases in
+which Bayesian principles also require it, and thus that we should regard
+Bayesian principles as clarifying classical statistical ideas about stopping
+rules rather than the reverse.
+",0,0,1,1,0,0
+17407,CNNs are Globally Optimal Given Multi-Layer Support,"  Stochastic Gradient Descent (SGD) is the central workhorse for training
+modern CNNs. Although giving impressive empirical performance it can be slow to
+converge. In this paper we explore a novel strategy for training a CNN using an
+alternation strategy that offers substantial speedups during training. We make
+the following contributions: (i) replace the ReLU non-linearity within a CNN
+with positive hard-thresholding, (ii) reinterpret this non-linearity as a
+binary state vector making the entire CNN linear if the multi-layer support is
+known, and (iii) demonstrate that under certain conditions a global optima to
+the CNN can be found through local descent. We then employ a novel alternation
+strategy (between weights and support) for CNN training that leads to
+substantially faster convergence rates, nice theoretical properties, and
+achieving state of the art results across large scale datasets (e.g. ImageNet)
+as well as other standard benchmarks.
+",1,0,0,0,0,0
+17408,The Kontsevich integral for bottom tangles in handlebodies,"  The Kontsevich integral is a powerful link invariant, taking values in spaces
+of Jacobi diagrams. In this paper, we extend the Kontsevich integral to
+construct a functor on the category of bottom tangles in handlebodies. This
+functor gives a universal finite type invariant of bottom tangles, and refines
+a functorial version of the Le-Murakami-Ohtsuki 3-manifold invariant for
+Lagrangian cobordisms of surfaces.
+",0,0,1,0,0,0
+17409,Commutativity theorems for groups and semigroups,"  In this note we prove a selection of commutativity theorems for various
+classes of semigroups. For instance, if in a separative or completely regular
+semigroup $S$ we have $x^p y^p = y^p x^p$ and $x^q y^q = y^q x^q$ for all
+$x,y\in S$ where $p$ and $q$ are relatively prime, then $S$ is commutative. In
+a separative or inverse semigroup $S$, if there exist three consecutive
+integers $i$ such that $(xy)^i = x^i y^i$ for all $x,y\in S$, then $S$ is
+commutative. Finally, if $S$ is a separative or inverse semigroup satisfying
+$(xy)^3=x^3y^3$ for all $x,y\in S$, and if the cubing map $x\mapsto x^3$ is
+injective, then $S$ is commutative.
+",0,0,1,0,0,0
+17410,Content-based Approach for Vietnamese Spam SMS Filtering,"  Short Message Service (SMS) spam is a serious problem in Vietnam because of
+the availability of very cheap pre-paid SMS packages. There are some systems to
+detect and filter spam messages for English, most of which use machine learning
+techniques to analyze the content of messages and classify them. For
+Vietnamese, there is some research on spam email filtering but none focused on
+SMS. In this work, we propose the first system for filtering Vietnamese spam
+SMS. We first propose an appropriate preprocessing method since existing tools
+for Vietnamese preprocessing cannot give good accuracy on our dataset. We then
+experiment with vector representations and classifiers to find the best model
+for this problem. Our system achieves an accuracy of 94% when labelling spam
+messages while the misclassification rate of legitimate messages is relatively
+small, about only 0.4%. This is an encouraging result compared to that of
+English and can be served as a strong baseline for future development of
+Vietnamese SMS spam prevention systems.
+",1,0,0,0,0,0
+17411,Measuring Software Performance on Linux,"  Measuring and analyzing the performance of software has reached a high
+complexity, caused by more advanced processor designs and the intricate
+interaction between user programs, the operating system, and the processor's
+microarchitecture. In this report, we summarize our experience about how
+performance characteristics of software should be measured when running on a
+Linux operating system and a modern processor. In particular, (1) We provide a
+general overview about hardware and operating system features that may have a
+significant impact on timing and how they interact, (2) we identify sources of
+errors that need to be controlled in order to obtain unbiased measurement
+results, and (3) we propose a measurement setup for Linux to minimize errors.
+Although not the focus of this report, we describe the measurement process
+using hardware performance counters, which can faithfully reflect the real
+bottlenecks on a given processor. Our experiments confirm that our measurement
+setup has a large impact on the results. More surprisingly, however, they also
+suggest that the setup can be negligible for certain analysis methods.
+Furthermore, we found that our setup maintains significantly better performance
+under background load conditions, which means it can be used to improve
+software in high-performance applications.
+",1,0,0,0,0,0
+17412,A general method for calculating lattice Green functions on the branch cut,"  We present a method for calculating the complex Green function $G_{ij}
+(\omega)$ at any real frequency $\omega$ between any two sites $i$ and $j$ on a
+lattice. Starting from numbers of walks on square, cubic, honeycomb,
+triangular, bcc, fcc, and diamond lattices, we derive Chebyshev expansion
+coefficients for $G_{ij} (\omega)$. The convergence of the Chebyshev series can
+be accelerated by constructing functions $f(\omega)$ that mimic the van Hove
+singularities in $G_{ij} (\omega)$ and subtracting their Chebyshev coefficients
+from the original coefficients. We demonstrate this explicitly for the square
+lattice and bcc lattice. Our algorithm achieves typical accuracies of 6--9
+significant figures using 1000 series terms.
+",0,1,0,0,0,0
+17413,"Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems","  Literature on the modeling and simulation of complex adaptive systems (cas)
+has primarily advanced vertically in different scientific domains with
+scientists developing a variety of domain-specific approaches and applications.
+However, while cas researchers are inher-ently interested in an
+interdisciplinary comparison of models, to the best of our knowledge, there is
+currently no single unified framework for facilitating the development,
+comparison, communication and validation of models across different scientific
+domains. In this thesis, we propose first steps towards such a unified
+framework using a combination of agent-based and complex network-based modeling
+approaches and guidelines formulated in the form of a set of four levels of
+usage, which allow multidisciplinary researchers to adopt a suitable framework
+level on the basis of available data types, their research study objectives and
+expected outcomes, thus allowing them to better plan and conduct their
+respective re-search case studies.
+",1,1,0,0,0,0
+17414,Recover Fine-Grained Spatial Data from Coarse Aggregation,"  In this paper, we study a new type of spatial sparse recovery problem, that
+is to infer the fine-grained spatial distribution of certain density data in a
+region only based on the aggregate observations recorded for each of its
+subregions. One typical example of this spatial sparse recovery problem is to
+infer spatial distribution of cellphone activities based on aggregate mobile
+traffic volumes observed at sparsely scattered base stations. We propose a
+novel Constrained Spatial Smoothing (CSS) approach, which exploits the local
+continuity that exists in many types of spatial data to perform sparse recovery
+via finite-element methods, while enforcing the aggregated observation
+constraints through an innovative use of the ADMM algorithm. We also improve
+the approach to further utilize additional geographical attributes. Extensive
+evaluations based on a large dataset of phone call records and a demographical
+dataset from the city of Milan show that our approach significantly outperforms
+various state-of-the-art approaches, including Spatial Spline Regression (SSR).
+",1,0,0,0,0,0
+17415,The Power Allocation Game on Dynamic Networks: Subgame Perfection,"  In the game theory literature, there appears to be little research on
+equilibrium selection for normal-form games with an infinite strategy space and
+discontinuous utility functions. Moreover, many existing selection methods are
+not applicable to games involving both cooperative and noncooperative scenarios
+(e.g., ""games on signed graphs""). With the purpose of equilibrium selection,
+the power allocation game developed in \cite{allocation}, which is a static,
+resource allocation game on signed graphs, will be reformulated into an
+extensive form. Results about the subgame perfect Nash equilibria in the
+extensive-form game will be given. This appears to be the first time that
+subgame perfection based on time-varying graphs is used for equilibrium
+selection in network games. This idea of subgame perfection proposed in the
+paper may be extrapolated to other network games, which will be illustrated
+with a simple example of congestion games.
+",1,0,0,0,0,0
+17416,MIMO Graph Filters for Convolutional Neural Networks,"  Superior performance and ease of implementation have fostered the adoption of
+Convolutional Neural Networks (CNNs) for a wide array of inference and
+reconstruction tasks. CNNs implement three basic blocks: convolution, pooling
+and pointwise nonlinearity. Since the two first operations are well-defined
+only on regular-structured data such as audio or images, application of CNNs to
+contemporary datasets where the information is defined in irregular domains is
+challenging. This paper investigates CNNs architectures to operate on signals
+whose support can be modeled using a graph. Architectures that replace the
+regular convolution with a so-called linear shift-invariant graph filter have
+been recently proposed. This paper goes one step further and, under the
+framework of multiple-input multiple-output (MIMO) graph filters, imposes
+additional structure on the adopted graph filters, to obtain three new (more
+parsimonious) architectures. The proposed architectures result in a lower
+number of model parameters, reducing the computational complexity, facilitating
+the training, and mitigating the risk of overfitting. Simulations show that the
+proposed simpler architectures achieve similar performance as more complex
+models.
+",0,0,0,1,0,0
+17417,An Edge Driven Wavelet Frame Model for Image Restoration,"  Wavelet frame systems are known to be effective in capturing singularities
+from noisy and degraded images. In this paper, we introduce a new edge driven
+wavelet frame model for image restoration by approximating images as piecewise
+smooth functions. With an implicit representation of image singularities sets,
+the proposed model inflicts different strength of regularization on smooth and
+singular image regions and edges. The proposed edge driven model is robust to
+both image approximation and singularity estimation. The implicit formulation
+also enables an asymptotic analysis of the proposed models and a rigorous
+connection between the discrete model and a general continuous variational
+model. Finally, numerical results on image inpainting and deblurring show that
+the proposed model is compared favorably against several popular image
+restoration models.
+",1,0,1,0,0,0
+17418,An exact algorithm exhibiting RS-RSB/easy-hard correspondence for the maximum independent set problem,"  A recently proposed exact algorithm for the maximum independent set problem
+is analyzed. The typical running time is improved exponentially in some
+parameter regions compared to simple binary search. The algorithm also
+overcomes the core transition point, where the conventional leaf removal
+algorithm fails, and works up to the replica symmetry breaking (RSB) transition
+point. This suggests that a leaf removal core itself is not enough for typical
+hardness in the random maximum independent set problem, providing further
+evidence for RSB being the obstacle for algorithms in general.
+",1,1,0,0,0,0
+17419,Flexible Support for Fast Parallel Commutative Updates,"  Privatizing data is a useful strategy for increasing parallelism in a shared
+memory multithreaded program. Independent cores can compute independently on
+duplicates of shared data, combining their results at the end of their
+computations. Conventional approaches to privatization, however, rely on
+explicit static or dynamic memory allocation for duplicated state, increasing
+memory footprint and contention for cache resources, especially in shared
+caches. In this work, we describe CCache, a system for on-demand privatization
+of data manipulated by commutative operations. CCache garners the benefits of
+privatization, without the increase in memory footprint or cache occupancy.
+Each core in CCache dynamically privatizes commutatively manipulated data,
+operating on a copy. Periodically or at the end of its computation, the core
+merges its value with the value resident in memory, and when all cores have
+merged, the in-memory copy contains the up-to-date value. We describe a
+low-complexity architectural implementation of CCache that extends a
+conventional multicore to support on-demand privatization without using
+additional memory for private copies. We evaluate CCache on several high-value
+applications, including random access key-value store, clustering, breadth
+first search and graph ranking, showing speedups upto 3.2X.
+",1,0,0,0,0,0
+17420,Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms,"  Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for
+the 3D visualization of cellular structure and organization at submolecular
+resolution. It enables analyzing the native structures of macromolecular
+complexes and their spatial organization inside single cells. However, due to
+the high degree of structural complexity and practical imaging limitations,
+systematic macromolecular structural recovery inside CECT images remains
+challenging. Particularly, the recovery of a macromolecule is likely to be
+biased by its neighbor structures due to the high molecular crowding. To reduce
+the bias, here we introduce a novel 3D convolutional neural network inspired by
+Fully Convolutional Network and Encoder-Decoder Architecture for the supervised
+segmentation of macromolecules of interest in subtomograms. The tests of our
+models on realistically simulated CECT data demonstrate that our new approach
+has significantly improved segmentation performance compared to our baseline
+approach. Also, we demonstrate that the proposed model has generalization
+ability to segment new structures that do not exist in training data.
+",0,0,0,1,1,0
+17421,CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Processor for Forward and Backward Propagation of Convolutional Neural Networks,"  Large-scale deep convolutional neural networks (CNNs) are widely used in
+machine learning applications. While CNNs involve huge complexity, VLSI (ASIC
+and FPGA) chips that deliver high-density integration of computational
+resources are regarded as a promising platform for CNN's implementation. At
+massive parallelism of computational units, however, the external memory
+bandwidth, which is constrained by the pin count of the VLSI chip, becomes the
+system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of
+the flexibility to be reconfigured for the various parameters of CNNs. This
+paper presents CNN-MERP to address these issues. CNN-MERP incorporates an
+efficient memory hierarchy that significantly reduces the bandwidth
+requirements from multiple optimizations including on/off-chip data allocation,
+data flow optimization and data reuse. The proposed 2-level reconfigurability
+is utilized to enable fast and efficient reconfiguration, which is based on the
+control logic and the multiboot feature of FPGA. As a result, an external
+memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower
+than prior arts. Under limited DRAM bandwidth, a system throughput of
+1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times
+higher than the state-of-the-art FPGA implementations.
+",1,0,0,0,0,0
+17422,Enstrophy Cascade in Decaying Two-Dimensional Quantum Turbulence,"  We report evidence for an enstrophy cascade in large-scale point-vortex
+simulations of decaying two-dimensional quantum turbulence. Devising a method
+to generate quantum vortex configurations with kinetic energy narrowly
+localized near a single length scale, the dynamics are found to be
+well-characterised by a superfluid Reynolds number, $\mathrm{Re_s}$, that
+depends only on the number of vortices and the initial kinetic energy scale.
+Under free evolution the vortices exhibit features of a classical enstrophy
+cascade, including a $k^{-3}$ power-law kinetic energy spectrum, and steady
+enstrophy flux associated with inertial transport to small scales. Clear
+signatures of the cascade emerge for $N\gtrsim 500$ vortices. Simulating up to
+very large Reynolds numbers ($N = 32, 768$ vortices), additional features of
+the classical theory are observed: the Kraichnan-Batchelor constant is found to
+converge to $C' \approx 1.6$, and the width of the $k^{-3}$ range scales as
+$\mathrm{Re_s}^{1/2}$. The results support a universal phenomenology
+underpinning classical and quantum fluid turbulence.
+",0,1,0,0,0,0
+17423,Contextual Multi-armed Bandits under Feature Uncertainty,"  We study contextual multi-armed bandit problems under linear realizability on
+rewards and uncertainty (or noise) on features. For the case of identical noise
+on features across actions, we propose an algorithm, coined {\em NLinRel},
+having $O\left(T^{\frac{7}{8}} \left(\log{(dT)}+K\sqrt{d}\right)\right)$ regret
+bound for $T$ rounds, $K$ actions, and $d$-dimensional feature vectors. Next,
+for the case of non-identical noise, we observe that popular linear hypotheses
+including {\em NLinRel} are impossible to achieve such sub-linear regret.
+Instead, under assumption of Gaussian feature vectors, we prove that a greedy
+algorithm has $O\left(T^{\frac23}\sqrt{\log d}\right)$ regret bound with
+respect to the optimal linear hypothesis. Utilizing our theoretical
+understanding on the Gaussian case, we also design a practical variant of {\em
+NLinRel}, coined {\em Universal-NLinRel}, for arbitrary feature distributions.
+It first runs {\em NLinRel} for finding the `true' coefficient vector using
+feature uncertainties and then adjust it to minimize its regret using the
+statistical feature information. We justify the performance of {\em
+Universal-NLinRel} on both synthetic and real-world datasets.
+",1,0,0,1,0,0
+17424,Asymmetric Variational Autoencoders,"  Variational inference for latent variable models is prevalent in various
+machine learning problems, typically solved by maximizing the Evidence Lower
+Bound (ELBO) of the true data likelihood with respect to a variational
+distribution. However, freely enriching the family of variational distribution
+is challenging since the ELBO requires variational likelihood evaluations of
+the latent variables. In this paper, we propose a novel framework to enrich the
+variational family by incorporating auxiliary variables to the variational
+family. The resulting inference network doesn't require density evaluations for
+the auxiliary variables and thus complex implicit densities over the auxiliary
+variables can be constructed by neural networks. It can be shown that the
+actual variational posterior of the proposed approach is essentially modeling a
+rich probabilistic mixture of simple variational posterior indexed by auxiliary
+variables, thus a flexible inference model can be built. Empirical evaluations
+on several density estimation tasks demonstrates the effectiveness of the
+proposed method.
+",1,0,0,1,0,0
+17425,Dynamics of homogeneous shear turbulence: A key role of the nonlinear transverse cascade in the bypass concept,"  To understand the self-sustenance of subcritical turbulence in spectrally
+stable shear flows, we performed direct numerical simulations of homogeneous
+shear turbulence for different aspect ratios of the flow domain and analyzed
+the dynamical processes in Fourier space. There are no exponentially growing
+modes in such flows and the turbulence is energetically supported only by the
+linear growth of perturbation harmonics due to the shear flow non-normality.
+This non-normality-induced, or nonmodal growth is anisotropic in spectral
+space, which, in turn, leads to anisotropy of nonlinear processes in this
+space. As a result, a transverse (angular) redistribution of harmonics in
+Fourier space appears to be the main nonlinear process in these flows, rather
+than direct or inverse cascades. We refer to this type of nonlinear
+redistribution as the nonlinear transverse cascade. It is demonstrated that the
+turbulence is sustained by a subtle interplay between the linear nonmodal
+growth and the nonlinear transverse cascade that exemplifies a well-known
+bypass scenario of subcritical turbulence. These two basic processes mainly
+operate at large length scales, comparable to the domain size. Therefore, this
+central, small wave number area of Fourier space is crucial in the
+self-sustenance; we defined its size and labeled it as the vital area of
+turbulence. Outside the vital area, the nonmodal growth and the transverse
+cascade are of secondary importance. Although the cascades and the
+self-sustaining process of turbulence are qualitatively the same at different
+aspect ratios, the number of harmonics actively participating in this process
+varies, but always remains quite large. This implies that the self-sustenance
+of subcritical turbulence cannot be described by low-order models.
+",0,1,0,0,0,0
+17426,A taxonomy of learning dynamics in 2 x 2 games,"  Learning would be a convincing method to achieve coordination on an
+equilibrium. But does learning converge, and to what? We answer this question
+in generic 2-player, 2-strategy games, using Experience-Weighted Attraction
+(EWA), which encompasses many extensively studied learning algorithms. We
+exhaustively characterize the parameter space of EWA learning, for any payoff
+matrix, and we understand the generic properties that imply convergent or
+non-convergent behaviour in 2 x 2 games.
+Irrational choice and lack of incentives imply convergence to a mixed
+strategy in the centre of the strategy simplex, possibly far from the Nash
+Equilibrium (NE). In the opposite limit, in which the players quickly modify
+their strategies, the behaviour depends on the payoff matrix: (i) a strong
+discrepancy between the pure strategies describes dominance-solvable games,
+which show convergence to a unique fixed point close to the NE; (ii) a
+preference towards profiles of strategies along the main diagonal describes
+coordination games, with multiple stable fixed points corresponding to the NE;
+(iii) a cycle of best responses defines discoordination games, which commonly
+yield limit cycles or low-dimensional chaos.
+While it is well known that mixed strategy equilibria may be unstable, our
+approach is novel from several perspectives: we fully analyse EWA and provide
+explicit thresholds that define the onset of instability; we find an emerging
+taxonomy of the learning dynamics, without focusing on specific classes of
+games ex-ante; we show that chaos can occur even in the simplest games; we make
+a precise theoretical prediction that can be tested against data on
+experimental learning of discoordination games.
+",0,1,0,0,0,0
+17427,Dynamic attitude planning for trajectory tracking in underactuated VTOL UAVs,"  This paper addresses the trajectory tracking control problem for
+underactuated VTOL UAVs. According to the different actuation mechanisms, the
+most common UAV platforms can achieve only a partial decoupling of attitude and
+position tasks. Since position tracking is of utmost importance for
+applications involving aerial vehicles, we propose a control scheme in which
+position tracking is the primary objective. To this end, this work introduces
+the concept of attitude planner, a dynamical system through which the desired
+attitude reference is processed to guarantee the satisfaction of the primary
+objective: the attitude tracking task is considered as a secondary objective
+which can be realized as long as the desired trajectory satisfies specific
+trackability conditions. Two numerical simulations are performed by applying
+the proposed control law to a hexacopter with and without tilted propellers,
+which accounts for unmodeled dynamics and external disturbances not included in
+the control design model.
+",1,0,0,0,0,0
+17428,The JCMT Transient Survey: Data Reduction and Calibration Methods,"  Though there has been a significant amount of work investigating the early
+stages of low-mass star formation in recent years, the evolution of the mass
+assembly rate onto the central protostar remains largely unconstrained.
+Examining in depth the variation in this rate is critical to understanding the
+physics of star formation. Instabilities in the outer and inner circumstellar
+disk can lead to episodic outbursts. Observing these brightness variations at
+infrared or submillimetre wavelengths sets constraints on the current accretion
+models. The JCMT Transient Survey is a three-year project dedicated to studying
+the continuum variability of deeply embedded protostars in eight nearby
+star-forming regions at a one month cadence. We use the SCUBA-2 instrument to
+simultaneously observe these regions at wavelengths of 450 $\mu$m and 850
+$\mu$m. In this paper, we present the data reduction techniques, image
+alignment procedures, and relative flux calibration methods for 850 $\mu$m
+data. We compare the properties and locations of bright, compact emission
+sources fitted with Gaussians over time. Doing so, we achieve a spatial
+alignment of better than 1"" between the repeated observations and an
+uncertainty of 2-3\% in the relative peak brightness of significant, localised
+emission. This combination of imaging performance is unprecedented in
+ground-based, single dish submillimetre observations. Finally, we identify a
+few sources that show possible and confirmed brightness variations. These
+sources will be closely monitored and presented in further detail in additional
+studies throughout the duration of the survey.
+",0,1,0,0,0,0
+17429,"Synchronization Strings: Explicit Constructions, Local Decoding, and Applications","  This paper gives new results for synchronization strings, a powerful
+combinatorial object that allows to efficiently deal with insertions and
+deletions in various communication settings:
+$\bullet$ We give a deterministic, linear time synchronization string
+construction, improving over an $O(n^5)$ time randomized construction.
+Independently of this work, a deterministic $O(n\log^2\log n)$ time
+construction was just put on arXiv by Cheng, Li, and Wu. We also give a
+deterministic linear time construction of an infinite synchronization string,
+which was not known to be computable before. Both constructions are highly
+explicit, i.e., the $i^{th}$ symbol can be computed in $O(\log i)$ time.
+$\bullet$ This paper also introduces a generalized notion we call
+long-distance synchronization strings that allow for local and very fast
+decoding. In particular, only $O(\log^3 n)$ time and access to logarithmically
+many symbols is required to decode any index.
+We give several applications for these results:
+$\bullet$ For any $\delta<1$ and $\epsilon>0$ we provide an insdel correcting
+code with rate $1-\delta-\epsilon$ which can correct any $O(\delta)$ fraction
+of insdel errors in $O(n\log^3n)$ time. This near linear computational
+efficiency is surprising given that we do not even know how to compute the
+(edit) distance between the decoding input and output in sub-quadratic time. We
+show that such codes can not only efficiently recover from $\delta$ fraction of
+insdel errors but, similar to [Schulman, Zuckerman; TransInf'99], also from any
+$O(\delta/\log n)$ fraction of block transpositions and replications.
+$\bullet$ We show that highly explicitness and local decoding allow for
+infinite channel simulations with exponentially smaller memory and decoding
+time requirements. These simulations can be used to give the first near linear
+time interactive coding scheme for insdel errors.
+",1,0,0,0,0,0
+17430,Independent Component Analysis via Energy-based and Kernel-based Mutual Dependence Measures,"  We apply both distance-based (Jin and Matteson, 2017) and kernel-based
+(Pfister et al., 2016) mutual dependence measures to independent component
+analysis (ICA), and generalize dCovICA (Matteson and Tsay, 2017) to MDMICA,
+minimizing empirical dependence measures as an objective function in both
+deflation and parallel manners. Solving this minimization problem, we introduce
+Latin hypercube sampling (LHS) (McKay et al., 2000), and a global optimization
+method, Bayesian optimization (BO) (Mockus, 1994) to improve the initialization
+of the Newton-type local optimization method. The performance of MDMICA is
+evaluated in various simulation studies and an image data example. When the ICA
+model is correct, MDMICA achieves competitive results compared to existing
+approaches. When the ICA model is misspecified, the estimated independent
+components are less mutually dependent than the observed components using
+MDMICA, while they are prone to be even more mutually dependent than the
+observed components using other approaches.
+",0,0,0,1,0,0
+17431,The local geometry of testing in ellipses: Tight control via localized Kolmogorov widths,"  We study the local geometry of testing a mean vector within a
+high-dimensional ellipse against a compound alternative. Given samples of a
+Gaussian random vector, the goal is to distinguish whether the mean is equal to
+a known vector within an ellipse, or equal to some other unknown vector in the
+ellipse. Such ellipse testing problems lie at the heart of several
+applications, including non-parametric goodness-of-fit testing, signal
+detection in cognitive radio, and regression function testing in reproducing
+kernel Hilbert spaces. While past work on such problems has focused on the
+difficulty in a global sense, we study difficulty in a way that is localized to
+each vector within the ellipse. Our main result is to give sharp upper and
+lower bounds on the localized minimax testing radius in terms of an explicit
+formula involving the Kolmogorov width of the ellipse intersected with a
+Euclidean ball. When applied to particular examples, our general theorems yield
+interesting rates that were not known before: as a particular case, for testing
+in Sobolev ellipses of smoothness $\alpha$, we demonstrate rates that vary from
+$(\sigma^2)^{\frac{4 \alpha}{4 \alpha + 1}}$, corresponding to the classical
+global rate, to the faster rate $(\sigma^2)^{\frac{8
+\alpha}{8 \alpha + 1}}$, achievable for vectors at favorable locations within
+the ellipse. We also show that the optimal test for this problem is achieved by
+a linear projection test that is based on an explicit lower-dimensional
+projection of the observation vector.
+",0,0,1,1,0,0
+17432,A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe,"  Two of the most fundamental prototypes of greedy optimization are the
+matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified
+view on both classes of methods, leading to the first explicit convergence
+rates of matching pursuit methods in an optimization sense, for general sets of
+atoms. We derive sublinear ($1/t$) convergence for both classes on general
+smooth objectives, and linear convergence on strongly convex objectives, as
+well as a clear correspondence of algorithm variants. Our presented algorithms
+and rates are affine invariant, and do not need any incoherence or sparsity
+assumptions.
+",1,0,0,1,0,0
+17433,Subsampled Rényi Differential Privacy and Analytical Moments Accountant,"  We study the problem of subsampling in differential privacy (DP), a question
+that is the centerpiece behind many successful differentially private machine
+learning algorithms. Specifically, we provide a tight upper bound on the
+Rényi Differential Privacy (RDP) (Mironov, 2017) parameters for algorithms
+that: (1) subsample the dataset, and then (2) applies a randomized mechanism M
+to the subsample, in terms of the RDP parameters of M and the subsampling
+probability parameter. Our results generalize the moments accounting technique,
+developed by Abadi et al. (2016) for the Gaussian mechanism, to any subsampled
+RDP mechanism.
+",0,0,0,1,0,0
+17434,"Concurrency and Probability: Removing Confusion, Compositionally","  Assigning a satisfactory truly concurrent semantics to Petri nets with
+confusion and distributed decisions is a long standing problem, especially if
+one wants to fully replace nondeterminism with probability distributions and no
+stochastic structure is desired/allowed. Here we propose a general solution
+based on a recursive, static decomposition of (finite, occurrence) nets in loci
+of decision, called structural branching cells (s-cells). Each s-cell exposes a
+set of alternatives, called transactions, that can be equipped with a general
+probabilistic distribution. The solution is formalised as a transformation from
+a given Petri net to another net whose transitions are the transactions of the
+s-cells and whose places are the places of the original net, with some
+auxiliary structure for bookkeeping. The resulting net is confusion-free,
+namely if a transition is enabled, then all its conflicting alternatives are
+also enabled. Thus sets of conflicting alternatives can be equipped with
+probability distributions, while nonintersecting alternatives are purely
+concurrent and do not introduce any nondeterminism: they are Church-Rosser and
+their probability distributions are independent. The validity of the
+construction is witnessed by a tight correspondence result with the recent
+approach by Abbes and Benveniste (AB) based on recursively stopped
+configurations in event structures. Some advantages of our approach over AB's
+are that: i) s-cells are defined statically and locally in a compositional way,
+whereas AB's branching cells are defined dynamically and globally; ii) their
+recursively stopped configurations correspond to possible executions, but the
+existing concurrency is not made explicit. Instead, our resulting nets are
+equipped with an original concurrency structure exhibiting a so-called complete
+concurrency property.
+",1,0,0,0,0,0
+17435,Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration,"  We propose a technique for multi-task learning from demonstration that trains
+the controller of a low-cost robotic arm to accomplish several complex picking
+and placing tasks, as well as non-prehensile manipulation. The controller is a
+recurrent neural network using raw images as input and generating robot arm
+trajectories, with the parameters shared across the tasks. The controller also
+combines VAE-GAN-based reconstruction with autoregressive multimodal action
+prediction. Our results demonstrate that it is possible to learn complex
+manipulation tasks, such as picking up a towel, wiping an object, and
+depositing the towel to its previous position, entirely from raw images with
+direct behavior cloning. We show that weight sharing and reconstruction-based
+regularization substantially improve generalization and robustness, and
+training on multiple tasks simultaneously increases the success rate on all
+tasks.
+",1,0,0,0,0,0
+17436,Dynamic Word Embeddings,"  We present a probabilistic language model for time-stamped text data which
+tracks the semantic evolution of individual words over time. The model
+represents words and contexts by latent trajectories in an embedding space. At
+each moment in time, the embedding vectors are inferred from a probabilistic
+version of word2vec [Mikolov et al., 2013]. These embedding vectors are
+connected in time through a latent diffusion process. We describe two scalable
+variational inference algorithms--skip-gram smoothing and skip-gram
+filtering--that allow us to train the model jointly over all times; thus
+learning on all data while simultaneously allowing word and context vectors to
+drift. Experimental results on three different corpora demonstrate that our
+dynamic model infers word embedding trajectories that are more interpretable
+and lead to higher predictive likelihoods than competing methods that are based
+on static models trained separately on time slices.
+",0,0,0,1,0,0
+17437,Multiple scattering effect on angular distribution and polarization of radiation by relativistic electrons in a thin crystal,"  The multiple scattering of ultra relativistic electrons in an amorphous
+matter leads to the suppression of the soft part of radiation spectrum (the
+Landau-Pomeranchuk-Migdal effect), and also can change essentially the angular
+distribution of the emitted photons. A similar effect must take place in a
+crystal for the coherent radiation of relativistic electron. The results of the
+theoretical investigation of angular distributions and polarization of
+radiation by a relativistic electron passing through a thin (in comparison with
+a coherence length) crystal at a small angle to the crystal axis are presented.
+The electron trajectories in crystal were simulated using the binary collision
+model which takes into account both coherent and incoherent effects at
+scattering. The angular distribution of radiation and polarization were
+calculated as a sum of radiation from each electron. It is shown that there are
+nontrivial angular distributions of the emitted photons and their polarization
+that are connected to the superposition of the coherent scattering of electrons
+by atomic rows (""doughnut scattering"" effect) and the suppression of radiation
+(similar to the Landau-Pomeranchuk-Migdal effect in an amorphous matter). It is
+also shown that circular polarization of radiation in the considered case is
+identically zero.
+",0,1,0,0,0,0
+17438,Branched coverings of $CP^2$ and other basic 4-manifolds,"  We give necessary and sufficient conditions for a 4-manifold to be a branched
+covering of $CP^2$, $S^2\times S^2$, $S^2 \mathbin{\tilde\times} S^2$ and $S^3
+\times S^1$, which are expressed in terms of the Betti numbers and the
+intersection form of the 4-manifold.
+",0,0,1,0,0,0
+17439,Instantaneous effects of photons on electrons in semiconductors,"  The photoelectric effect established by Einstein is well known, which
+indicates that electrons on lower energy levels can jump up to higher levels by
+absorbing photons, or jump down from higher levels to lower levels and give out
+photons1-3. However, how do photons act on electrons and further on atoms have
+kept unknown up to now. Here we show the results that photons collide on
+electrons with energy-transmission in semiconductors and pass their momenta to
+electrons, which make the electrons jump up from lower energy levels to higher
+levels. We found that (i) photons have rest mass of 7.287exp(-38) kg and
+2.886exp(-35) kg, in vacuum and silicon respectively; (ii) excited by photons
+with energy of 1.12eV, electrons in silicon may jump up from the top of valance
+band to the bottom of conduction band with initial speed of 2.543exp(3) m/s and
+taking time of 4.977exp(-17) s; (iii) acted by photons with energy of 4.6eV,
+the atoms who lose electrons may be catapulted out of the semiconductors by the
+extruded neighbor atoms, and taking time of 2.224exp(-15) s. These results make
+reasonable explanation to rapid thermal annealing, laser ablation and laser
+cutting.
+",0,1,0,0,0,0
+17440,Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-Sequence Model,"  We apply sequence-to-sequence model to mitigate the impact of speech
+recognition errors on open domain end-to-end dialog generation. We cast the
+task as a domain adaptation problem where ASR transcriptions and original text
+are in two different domains. In this paper, our proposed model includes two
+individual encoders for each domain data and make their hidden states similar
+to ensure the decoder predict the same dialog text. The method shows that the
+sequence-to-sequence model can learn the ASR transcriptions and original text
+pair having the same meaning and eliminate the speech recognition errors.
+Experimental results on Cornell movie dialog dataset demonstrate that the
+domain adaption system help the spoken dialog system generate more similar
+responses with the original text answers.
+",1,0,0,0,0,0
+17441,Simultaneous 183 GHz H2O Maser and SiO Observations Towards Evolved Stars Using APEX SEPIA Band 5,"  We investigate the use of 183 GHz H2O masers for characterization of the
+physical conditions and mass loss process in the circumstellar envelopes of
+evolved stars. We used APEX SEPIA Band 5 to observe the 183 GHz H2O line
+towards 2 Red Supergiant and 3 Asymptotic Giant Branch stars. Simultaneously,
+we observed lines in 28SiO v0, 1, 2 and 3, and for 29SiO v0 and 1. We detected
+the 183 GHz H2O line towards all the stars with peak flux densities greater
+than 100 Jy, including a new detection from VY CMa. Towards all 5 targets, the
+water line had indications of being due to maser emission and had higher peak
+flux densities than for the SiO lines. The SiO lines appear to originate from
+both thermal and maser processes. Comparison with simulations and models
+indicate that 183 GHz maser emission is likely to extend to greater radii in
+the circumstellar envelopes than SiO maser emission and to similar or greater
+radii than water masers at 22, 321 and 325 GHz. We speculate that a prominent
+blue-shifted feature in the W Hya 183 GHz spectrum is amplifying the stellar
+continuum, and is located at a similar distance from the star as mainline OH
+maser emission. From a comparison of the individual polarizations, we find that
+the SiO maser linear polarization fraction of several features exceeds the
+maximum fraction allowed under standard maser assumptions and requires strong
+anisotropic pumping of the maser transition and strongly saturated maser
+emission. The low polarization fraction of the H2O maser however, fits with the
+expectation for a non-saturated maser. 183 GHz H2O masers can provide strong
+probes of the mass loss process of evolved stars. Higher angular resolution
+observations of this line using ALMA Band 5 will enable detailed investigation
+of the emission location in circumstellar envelopes and can also provide
+information on magnetic field strength and structure.
+",0,1,0,0,0,0
+17442,What does the free energy principle tell us about the brain?,"  The free energy principle has been proposed as a unifying theory of brain
+function. It is closely related, and in some cases subsumes, earlier unifying
+ideas such as Bayesian inference, predictive coding, and active learning. This
+article clarifies these connections, teasing apart distinctive and shared
+predictions.
+",0,0,0,0,1,0
+17443,Learning with Changing Features,"  In this paper we study the setting where features are added or change
+interpretation over time, which has applications in multiple domains such as
+retail, manufacturing, finance. In particular, we propose an approach to
+provably determine the time instant from which the new/changed features start
+becoming relevant with respect to an output variable in an agnostic
+(supervised) learning setting. We also suggest an efficient version of our
+approach which has the same asymptotic performance. Moreover, our theory also
+applies when we have more than one such change point. Independent post analysis
+of a change point identified by our method for a large retailer revealed that
+it corresponded in time with certain unflattering news stories about a brand
+that resulted in the change in customer behavior. We also applied our method to
+data from an advanced manufacturing plant identifying the time instant from
+which downstream features became relevant. To the best of our knowledge this is
+the first work that formally studies change point detection in a distribution
+independent agnostic setting, where the change point is based on the changing
+relationship between input and output.
+",1,0,0,1,0,0
+17444,Estimation Considerations in Contextual Bandits,"  Contextual bandit algorithms are sensitive to the estimation method of the
+outcome model as well as the exploration method used, particularly in the
+presence of rich heterogeneity or complex outcome models, which can lead to
+difficult estimation problems along the path of learning. We study a
+consideration for the exploration vs. exploitation framework that does not
+arise in multi-armed bandits but is crucial in contextual bandits; the way
+exploration and exploitation is conducted in the present affects the bias and
+variance in the potential outcome model estimation in subsequent stages of
+learning. We develop parametric and non-parametric contextual bandits that
+integrate balancing methods from the causal inference literature in their
+estimation to make it less prone to problems of estimation bias. We provide the
+first regret bound analyses for contextual bandits with balancing in the domain
+of linear contextual bandits that match the state of the art regret bounds. We
+demonstrate the strong practical advantage of balanced contextual bandits on a
+large number of supervised learning datasets and on a synthetic example that
+simulates model mis-specification and prejudice in the initial training data.
+Additionally, we develop contextual bandits with simpler assignment policies by
+leveraging sparse model estimation methods from the econometrics literature and
+demonstrate empirically that in the early stages they can improve the rate of
+learning and decrease regret.
+",1,0,0,1,0,0
+17445,Using solar and load predictions in battery scheduling at the residential level,"  Smart solar inverters can be used to store, monitor and manage a home's solar
+energy. We describe a smart solar inverter system with battery which can either
+operate in an automatic mode or receive commands over a network to charge and
+discharge at a given rate. In order to make battery storage financially viable
+and advantageous to the consumers, effective battery scheduling algorithms can
+be employed. Particularly, when time-of-use tariffs are in effect in the region
+of the inverter, it is possible in some cases to schedule the battery to save
+money for the individual customer, compared to the ""automatic"" mode. Hence,
+this paper presents and evaluates the performance of a novel battery scheduling
+algorithm for residential consumers of solar energy. The proposed battery
+scheduling algorithm optimizes the cost of electricity over next 24 hours for
+residential consumers. The cost minimization is realized by controlling the
+charging/discharging of battery storage system based on the predictions for
+load and solar power generation values. The scheduling problem is formulated as
+a linear programming problem. We performed computer simulations over 83
+inverters using several months of hourly load and PV data. The simulation
+results indicate that key factors affecting the viability of optimization are
+the tariffs and the PV to Load ratio at each inverter. Depending on the tariff,
+savings of between 1% and 10% can be expected over the automatic approach. The
+prediction approach used in this paper is also shown to out-perform basic
+""persistence"" forecasting approaches. We have also examined the approaches for
+improving the prediction accuracy and optimization effectiveness.
+",1,0,0,0,0,0
+17446,Towards Large-Pose Face Frontalization in the Wild,"  Despite recent advances in face recognition using deep learning, severe
+accuracy drops are observed for large pose variations in unconstrained
+environments. Learning pose-invariant features is one solution, but needs
+expensively labeled large-scale data and carefully designed feature learning
+algorithms. In this work, we focus on frontalizing faces in the wild under
+various head poses, including extreme profile views. We propose a novel deep 3D
+Morphable Model (3DMM) conditioned Face Frontalization Generative Adversarial
+Network (GAN), termed as FF-GAN, to generate neutral head pose face images. Our
+framework differs from both traditional GANs and 3DMM based modeling.
+Incorporating 3DMM into the GAN structure provides shape and appearance priors
+for fast convergence with less training data, while also supporting end-to-end
+training. The 3DMM-conditioned GAN employs not only the discriminator and
+generator loss but also a new masked symmetry loss to retain visual quality
+under occlusions, besides an identity loss to recover high frequency
+information. Experiments on face recognition, landmark localization and 3D
+reconstruction consistently show the advantage of our frontalization method on
+faces in the wild datasets.
+",1,0,0,0,0,0
+17447,Managing the Public to Manage Data: Citizen Science and Astronomy,"  Citizen science projects recruit members of the public as volunteers to
+process and produce datasets. These datasets must win the trust of the
+scientific community. The task of securing credibility involves, in part,
+applying standard scientific procedures to clean these datasets. However,
+effective management of volunteer behavior also makes a significant
+contribution to enhancing data quality. Through a case study of Galaxy Zoo, a
+citizen science project set up to generate datasets based on volunteer
+classifications of galaxy morphologies, this paper explores how those involved
+in running the project manage volunteers. The paper focuses on how methods for
+crediting volunteer contributions motivate volunteers to provide higher quality
+contributions and to behave in a way that better corresponds to statistical
+assumptions made when combining volunteer contributions into datasets. These
+methods have made a significant contribution to the success of the project in
+securing trust in these datasets, which have been well used by other
+scientists. Implications for practice are then presented for citizen science
+projects, providing a list of considerations to guide choices regarding how to
+credit volunteer contributions to improve the quality and trustworthiness of
+citizen science-produced datasets.
+",1,1,0,0,0,0
+17448,Modular representations in type A with a two-row nilpotent central character,"  We study the category of representations of $\mathfrak{sl}_{m+2n}$ in
+positive characteristic, whose p-character is a nilpotent whose Jordan type is
+the two-row partition (m+n,n). In a previous paper with Anno, we used
+Bezrukavnikov-Mirkovic-Rumynin's theory of positive characteristic localization
+and exotic t-structures to give a geometric parametrization of the simples
+using annular crossingless matchings. Building on this, here we give
+combinatorial dimension formulae for the simple objects, and compute the
+Jordan-Holder multiplicities of the simples inside the baby Vermas (in special
+case where n=1, i.e. that a subregular nilpotent, these were known from work of
+Jantzen). We use Cautis-Kamnitzer's geometric categorification of the tangle
+calculus to study the images of the simple objects under the [BMR] equivalence.
+The dimension formulae may be viewed as a positive characteristic analogue of
+the combinatorial character formulae for simple objects in parabolic category O
+for $\mathfrak{sl}_{m+2n}$, due to Lascoux and Schutzenberger.
+",0,0,1,0,0,0
+17449,Knowledge Acquisition: A Complex Networks Approach,"  Complex networks have been found to provide a good representation of the
+structure of knowledge, as understood in terms of discoverable concepts and
+their relationships. In this context, the discovery process can be modeled as
+agents walking in a knowledge space. Recent studies proposed more realistic
+dynamics, including the possibility of agents being influenced by others with
+higher visibility or by their own memory. However, rather than dealing with
+these two concepts separately, as previously approached, in this study we
+propose a multi-agent random walk model for knowledge acquisition that
+incorporates both concepts. More specifically, we employed the true self
+avoiding walk alongside a new dynamics based on jumps, in which agents are
+attracted by the influence of others. That was achieved by using a Lévy
+flight influenced by a field of attraction emanating from the agents. In order
+to evaluate our approach, we use a set of network models and two real networks,
+one generated from Wikipedia and another from the Web of Science. The results
+were analyzed globally and by regions. In the global analysis, we found that
+most of the dynamics parameters do not significantly affect the discovery
+dynamics. The local analysis revealed a substantial difference of performance
+depending on the network regions where the dynamics are occurring. In
+particular, the dynamics at the core of networks tend to be more effective. The
+choice of the dynamics parameters also had no significant impact to the
+acquisition performance for the considered knowledge networks, even at the
+local scale.
+",1,1,0,0,0,0
+17450,Frequent flaring in the TRAPPIST-1 system - unsuited for life?,"  We analyze short cadence K2 light curve of the TRAPPIST-1 system. Fourier
+analysis of the data suggests $P_\mathrm{rot}=3.295\pm0.003$ days. The light
+curve shows several flares, of which we analyzed 42 events, these have
+integrated flare energies of $1.26\times10^{30}-1.24\times10^{33}$ ergs.
+Approximately 12% of the flares were complex, multi-peaked eruptions. The
+flaring and the possible rotational modulation shows no obvious correlation.
+The flaring activity of TRAPPIST-1 probably continuously alters the atmospheres
+of the orbiting exoplanets, making these less favorable for hosting life.
+",0,1,0,0,0,0
+17451,Playing Pairs with Pepper,"  As robots become increasingly prevalent in almost all areas of society, the
+factors affecting humans trust in those robots becomes increasingly important.
+This paper is intended to investigate the factor of robot attributes, looking
+specifically at the relationship between anthropomorphism and human development
+of trust. To achieve this, an interaction game, Matching the Pairs, was
+designed and implemented on two robots of varying levels of anthropomorphism,
+Pepper and Husky. Participants completed both pre- and post-test questionnaires
+that were compared and analyzed predominantly with the use of quantitative
+methods, such as paired sample t-tests. Post-test analyses suggested a positive
+relationship between trust and anthropomorphism with $80\%$ of participants
+confirming that the robots' adoption of facial features assisted in
+establishing trust. The results also indicated a positive relationship between
+interaction and trust with $90\%$ of participants confirming this for both
+robots post-test
+",1,0,0,0,0,0
+17452,Wild theories with o-minimal open core,"  Let $T$ be a consistent o-minimal theory extending the theory of densely
+ordered groups and let $T'$ be a consistent theory. Then there is a complete
+theory $T^*$ extending $T$ such that $T$ is an open core of $T^*$, but every
+model of $T^*$ interprets a model of $T'$. If $T'$ is NIP, $T^*$ can be chosen
+to be NIP as well. From this we deduce the existence of an NIP expansion of the
+real field that has no distal expansion.
+",0,0,1,0,0,0
+17453,Objective Bayesian inference with proper scoring rules,"  Standard Bayesian analyses can be difficult to perform when the full
+likelihood, and consequently the full posterior distribution, is too complex
+and difficult to specify or if robustness with respect to data or to model
+misspecifications is required. In these situations, we suggest to resort to a
+posterior distribution for the parameter of interest based on proper scoring
+rules. Scoring rules are loss functions designed to measure the quality of a
+probability distribution for a random variable, given its observed value.
+Important examples are the Tsallis score and the Hyvärinen score, which allow
+us to deal with model misspecifications or with complex models. Also the full
+and the composite likelihoods are both special instances of scoring rules.
+The aim of this paper is twofold. Firstly, we discuss the use of scoring
+rules in the Bayes formula in order to compute a posterior distribution, named
+SR-posterior distribution, and we derive its asymptotic normality. Secondly, we
+propose a procedure for building default priors for the unknown parameter of
+interest that can be used to update the information provided by the scoring
+rule in the SR-posterior distribution. In particular, a reference prior is
+obtained by maximizing the average $\alpha-$divergence from the SR-posterior
+distribution. For $0 \leq |\alpha|<1$, the result is a Jeffreys-type prior that
+is proportional to the square root of the determinant of the Godambe
+information matrix associated to the scoring rule. Some examples are discussed.
+",0,0,1,1,0,0
+17454,Large Area X-ray Proportional Counter (LAXPC) Instrument on AstroSat,"  Large Area X-ray Proportional Counter (LAXPC) is one of the major AstroSat
+payloads. LAXPC instrument will provide high time resolution X-ray observations
+in 3 to 80 keV energy band with moderate energy resolution. A cluster of three
+co-aligned identical LAXPC detectors is used in AstroSat to provide large
+collection area of more than 6000 cm2 . The large detection volume (15 cm
+depth) filled with xenon gas at about 2 atmosphere pressure, results in
+detection efficiency greater than 50%, above 30 keV. With its broad energy
+range and fine time resolution (10 microsecond), LAXPC instrument is well
+suited for timing and spectral studies of a wide variety of known and transient
+X-ray sources in the sky. We have done extensive calibration of all LAXPC
+detectors using radioactive sources as well as GEANT4 simulation of LAXPC
+detectors. We describe in brief some of the results obtained during the payload
+verification phase along with LXAPC capabilities.
+",0,1,0,0,0,0
+17455,"A Counterexample to the Vector Generalization of Costa's EPI, and Partial Resolution","  We give a counterexample to the vector generalization of Costa's entropy
+power inequality (EPI) due to Liu, Liu, Poor and Shamai. In particular, the
+claimed inequality can fail if the matix-valued parameter in the convex
+combination does not commute with the covariance of the additive Gaussian
+noise. Conversely, the inequality holds if these two matrices commute.
+",1,0,0,0,0,0
+17456,Models for Predicting Community-Specific Interest in News Articles,"  In this work, we ask two questions: 1. Can we predict the type of community
+interested in a news article using only features from the article content? and
+2. How well do these models generalize over time? To answer these questions, we
+compute well-studied content-based features on over 60K news articles from 4
+communities on reddit.com. We train and test models over three different time
+periods between 2015 and 2017 to demonstrate which features degrade in
+performance the most due to concept drift. Our models can classify news
+articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0
+ROC AUC. However, while we can predict the community-specific popularity of
+news articles with high accuracy, practitioners should approach these models
+carefully. Predictions are both community-pair dependent and feature group
+dependent. Moreover, these feature groups generalize over time differently,
+with some only degrading slightly over time, but others degrading greatly.
+Therefore, we recommend that community-interest predictions are done in a
+hierarchical structure, where multiple binary classifiers can be used to
+separate community pairs, rather than a traditional multi-class model. Second,
+these models should be retrained over time based on accuracy goals and the
+availability of training data.
+",0,0,0,1,0,0
+17457,On subfiniteness of graded linear series,"  Hilbert's 14th problem studies the finite generation property of the
+intersection of an integral algebra of finite type with a subfield of the field
+of fractions of the algebra. It has a negative answer due to the counterexample
+of Nagata. We show that a subfinite version of Hilbert's 14th problem has a
+confirmative answer. We then establish a graded analogue of this result, which
+permits to show that the subfiniteness of graded linear series does not depend
+on the function field in which we consider it. Finally, we apply the
+subfiniteness result to the study of geometric and arithmetic graded linear
+series.
+",0,0,1,0,0,0
+17458,Natasha 2: Faster Non-Convex Optimization Than SGD,"  We design a stochastic algorithm to train any smooth neural network to
+$\varepsilon$-approximate local minima, using $O(\varepsilon^{-3.25})$
+backpropagations. The best result was essentially $O(\varepsilon^{-4})$ by SGD.
+More broadly, it finds $\varepsilon$-approximate local minima of any smooth
+nonconvex function in rate $O(\varepsilon^{-3.25})$, with only oracle access to
+stochastic gradients.
+",1,0,0,1,0,0
+17459,Evidence for a Dayside Thermal Inversion and High Metallicity for the Hot Jupiter WASP-18b,"  We find evidence for a strong thermal inversion in the dayside atmosphere of
+the highly irradiated hot Jupiter WASP-18b (T$_{eq}=2411K$, $M=10.3M_{J}$)
+based on emission spectroscopy from Hubble Space Telescope secondary eclipse
+observations and Spitzer eclipse photometry. We demonstrate a lack of water
+vapor in either absorption or emission at 1.4$\mu$m. However, we infer emission
+at 4.5$\mu$m and absorption at 1.6$\mu$m that we attribute to CO, as well as a
+non-detection of all other relevant species (e.g., TiO, VO). The most probable
+atmospheric retrieval solution indicates a C/O ratio of 1 and a high
+metallicity (C/H=$283^{+395}_{-138}\times$ solar). The derived composition and
+T/P profile suggest that WASP-18b is the first example of both a planet with a
+non-oxide driven thermal inversion and a planet with an atmospheric metallicity
+inconsistent with that predicted for Jupiter-mass planets at $>2\sigma$. Future
+observations are necessary to confirm the unusual planetary properties implied
+by these results.
+",0,1,0,0,0,0
+17460,$α$-$β$ and $β$-$γ$ phase boundaries of solid oxygen observed by adiabatic magnetocaloric effect,"  The magnetic-field-temperature phase diagram of solid oxygen is investigated
+by the adiabatic magnetocaloric effect (MCE) measurement with pulsed magnetic
+fields. Relatively large temperature decrease with hysteresis is observed at
+just below the $\beta$-$\gamma$ and $\alpha$-$\beta$ phase transition
+temperatures owing to the field-induced transitions. The magnetic field
+dependences of these phase boundaries are obtained as
+$T_\mathrm{\beta\gamma}(H)=43.8-1.55\times10^{-3}H^2$ K and
+$T_\mathrm{\alpha\beta}(H)=23.9-0.73\times10^{-3}H^2$ K. The magnetic
+Clausius-Clapeyron equation quantitatively explains the $H$ dependence of
+$T_\mathrm{\beta\gamma}$, meanwhile, does not $T_\mathrm{\alpha\beta}$. The MCE
+curve at $T_\mathrm{\beta\gamma}$ is of typical first-order, while the curve at
+$T_\mathrm{\alpha\beta}$ seems to have both characteristics of first- and
+second-order transitions. We discuss the order of the $\alpha$-$\beta$ phase
+transition and propose possible reasons for the unusual behavior.
+",0,1,0,0,0,0
+17461,Localization Algorithm with Circular Representation in 2D and its Similarity to Mammalian Brains,"  Extended Kalman filter (EKF) does not guarantee consistent mean and
+covariance under linearization, even though it is the main framework for
+robotic localization. While Lie group improves the modeling of the state space
+in localization, the EKF on Lie group still relies on the arbitrary Gaussian
+assumption in face of nonlinear models. We instead use von Mises filter for
+orientation estimation together with the conventional Kalman filter for
+position estimation, and thus we are able to characterize the first two moments
+of the state estimates. Since the proposed algorithm holds a solid
+probabilistic basis, it is fundamentally relieved from the inconsistency
+problem. Furthermore, we extend the localization algorithm to fully circular
+representation even for position, which is similar to grid patterns found in
+mammalian brains and in recurrent neural networks. The applicability of the
+proposed algorithms is substantiated not only by strong mathematical foundation
+but also by the comparison against other common localization methods.
+",1,0,0,0,1,0
+17462,"Lusin-type approximation of Sobolev by Lipschitz functions, in Gaussian and $RCD(K,\infty)$ spaces","  We establish new approximation results, in the sense of Lusin, of Sobolev
+functions by Lipschitz ones, in some classes of non-doubling metric measure
+structures. Our proof technique relies upon estimates for heat semigroups and
+applies to Gaussian and $RCD(K, \infty)$ spaces. As a consequence, we obtain
+quantitative stability for regular Lagrangian flows in Gaussian settings.
+",0,0,1,0,0,0
+17463,Distributed Coordination for a Class of Nonlinear Multi-agent Systems with Regulation Constraints,"  In this paper, a multi-agent coordination problem with steady-state
+regulation constraints is investigated for a class of nonlinear systems. Unlike
+existing leader-following coordination formulations, the reference signal is
+not given by a dynamic autonomous leader but determined as the optimal solution
+of a distributed optimization problem. Furthermore, we consider a global
+constraint having noisy data observations for the optimization problem, which
+implies that reference signal is not trivially available with existing
+optimization algorithms. To handle those challenges, we present a
+passivity-based analysis and design approach by using only local objective
+function, local data observation and exchanged information from their
+neighbors. The proposed distributed algorithms are shown to achieve the optimal
+steady-state regulation by rejecting the unknown observation disturbances for
+passive nonlinear agents, which are persuasive in various practical problems.
+Applications and simulation examples are then given to verify the effectiveness
+of our design.
+",1,0,1,0,0,0
+17464,Intermodulation distortion of actuated MEMS capacitive switches,"  For the first time, intermodulation distortion of micro-electromechanical
+capacitive switches in the actuated state was analyzed both theoretically and
+experimentally. The distortion, although higher than that of switches in the
+suspended state, was found to decrease with increasing bias voltage but to
+depend weakly on modulation frequencies between 55 kHz and 1.1 MHz. This
+dependence could be explained by the orders-of-magnitude increase of the spring
+constant when the switches were actuated. Additionally, the analysis suggested
+that increasing the spring constant and decreasing the contact roughness could
+improve the linearity of actuated switches. These results are critical to
+micro-electromechanical capacitive switches used in tuners, filters, phase
+shifters, etc. where the linearity of both suspended and actuated states are
+critical.
+",0,1,0,0,0,0
+17465,Parasitic Bipolar Leakage in III-V FETs: Impact of Substrate Architecture,"  InGaAs-based Gate-all-Around (GAA) FETs with moderate to high In content are
+shown experimentally and theoretically to be unsuitable for low-leakage
+advanced CMOS nodes. The primary cause for this is the large leakage penalty
+induced by the Parasitic Bipolar Effect (PBE), which is seen to be particularly
+difficult to remedy in GAA architectures. Experimental evidence of PBE in
+In70Ga30As GAA FETs is demonstrated, along with a simulation-based analysis of
+the PBE behavior. The impact of PBE is investigated by simulation for
+alternative device architectures, such as bulk FinFETs and
+FinFETs-on-insulator. PBE is found to be non-negligible in all standard InGaAs
+FET designs. Practical PBE metrics are introduced and the design of a substrate
+architecture for PBE suppression is elucidated. Finally, it is concluded that
+the GAA architecture is not suitable for low-leakage InGaAs FETs; a bulk FinFET
+is better suited for the role.
+",0,1,0,0,0,0
+17466,Properties of Ultra Gamma Function,"  In this paper we study the integral of type
+\[_{\delta,a}\Gamma_{\rho,b}(x)
+=\Gamma(\delta,a;\rho,b)(x)=\int_{0}^{\infty}t^{x-1}e^{-\frac{t^{\delta}}{a}-\frac{t^{-\rho}}{b}}dt.\]
+Different authors called this integral by different names like ultra gamma
+function, generalized gamma function, Kratzel integral, inverse Gaussian
+integral, reaction-rate probability integral, Bessel integral etc. We prove
+several identities and recurrence relation of above said integral, we called
+this integral as Four Parameter Gamma Function. Also we evaluate relation
+between Four Parameter Gamma Function, p-k Gamma Function and Classical Gamma
+Function. With some conditions we can evaluate Four Parameter Gamma Function in
+term of Hypergeometric function.
+",0,0,1,0,0,0
+17467,Further remarks on liftings of crossed modules,"  In this paper we define the notion of pullback lifting of a lifting crossed
+module over a crossed module morphism and interpret this notion in the category
+of group-groupoid actions as pullback action. Moreover, we give a criterion for
+the lifting of homotopic crossed module morphisms to be homotopic, which will
+be called homotopy lifting property for crossed module morphisms. Finally, we
+investigate some properties of derivations of lifting crossed modules according
+to base crossed module derivations.
+",0,0,1,0,0,0
+17468,Submodular Maximization through the Lens of Linear Programming,"  The simplex algorithm for linear programming is based on the fact that any
+local optimum with respect to the polyhedral neighborhood is also a global
+optimum. We show that a similar result carries over to submodular maximization.
+In particular, every local optimum of a constrained monotone submodular
+maximization problem yields a $1/2$-approximation, and we also present an
+appropriate extension to the non-monotone setting. However, reaching a local
+optimum quickly is a non-trivial task. Moreover, we describe a fast and very
+general local search procedure that applies to a wide range of constraint
+families, and unifies as well as extends previous methods. In our framework, we
+match known approximation guarantees while disentangling and simplifying
+previous approaches. Moreover, despite its generality, we are able to show that
+our local search procedure is slightly faster than previous specialized
+methods. Furthermore, we resolve an open question on the relation between
+linear optimization and submodular maximization; namely, whether a linear
+optimization oracle may be enough to obtain strong approximation algorithms for
+submodular maximization. We show that this is not the case by providing an
+example of a constraint family on a ground set of size $n$ for which, if only
+given a linear optimization oracle, any algorithm for submodular maximization
+with a polynomial number of calls to the linear optimization oracle will have
+an approximation ratio of only $O ( \frac{1}{\sqrt{n}} \cdot \frac{\log
+n}{\log\log n} )$.
+",1,0,0,0,0,0
+17469,Channel Estimation for Diffusive MIMO Molecular Communications,"  In diffusion-based communication, as for molecular systems, the achievable
+data rate is very low due to the slow nature of diffusion and the existence of
+severe inter-symbol interference (ISI). Multiple-input multiple-output (MIMO)
+technique can be used to improve the data rate. Knowledge of channel impulse
+response (CIR) is essential for equalization and detection in MIMO systems.
+This paper presents a training-based CIR estimation for diffusive MIMO (D-MIMO)
+channels. Maximum likelihood and least-squares estimators are derived, and the
+training sequences are designed to minimize the corresponding Cramér-Rao
+bound. Sub-optimal estimators are compared to Cramér-Rao bound to validate
+their performance.
+",1,0,0,1,0,0
+17470,Multi-stage splitting integrators for sampling with modified Hamiltonian Monte Carlo methods,"  Modified Hamiltonian Monte Carlo (MHMC) methods combine the ideas behind two
+popular sampling approaches: Hamiltonian Monte Carlo (HMC) and importance
+sampling. As in the HMC case, the bulk of the computational cost of MHMC
+algorithms lies in the numerical integration of a Hamiltonian system of
+differential equations. We suggest novel integrators designed to enhance
+accuracy and sampling performance of MHMC methods. The novel integrators belong
+to families of splitting algorithms and are therefore easily implemented. We
+identify optimal integrators within the families by minimizing the energy error
+or the average energy error. We derive and discuss in detail the modified
+Hamiltonians of the new integrators, as the evaluation of those Hamiltonians is
+key to the efficiency of the overall algorithms. Numerical experiments show
+that the use of the new integrators may improve very significantly the sampling
+performance of MHMC methods, in both statistical and molecular dynamics
+problems.
+",1,0,0,0,0,0
+17471,Proposal for a High Precision Tensor Processing Unit,"  This whitepaper proposes the design and adoption of a new generation of
+Tensor Processing Unit which has the performance of Google's TPU, yet performs
+operations on wide precision data. The new generation TPU is made possible by
+implementing arithmetic circuits which compute using a new general purpose,
+fractional arithmetic based on the residue number system.
+",1,0,0,0,0,0
+17472,DICOD: Distributed Convolutional Sparse Coding,"  In this paper, we introduce DICOD, a convolutional sparse coding algorithm
+which builds shift invariant representations for long signals. This algorithm
+is designed to run in a distributed setting, with local message passing, making
+it communication efficient. It is based on coordinate descent and uses locally
+greedy updates which accelerate the resolution compared to greedy coordinate
+selection. We prove the convergence of this algorithm and highlight its
+computational speed-up which is super-linear in the number of cores used. We
+also provide empirical evidence for the acceleration properties of our
+algorithm compared to state-of-the-art methods.
+",1,0,0,1,0,0
+17473,On uniqueness results for Dirichlet problems of elliptic systems without DeGiorgi-Nash-Moser regularity,"  We study uniqueness of Dirichlet problems of second order divergence-form
+elliptic systems with transversally independent coefficients on the upper
+half-space in absence of regularity of solutions. To this end, we develop a
+substitute for the fundamental solution used to invert elliptic operators on
+the whole space by means of a representation via abstract single layer
+potentials. We also show that such layer potentials are uniquely determined.
+",0,0,1,0,0,0
+17474,(LaTiO$_3$)$_n$/(LaVO$_3$)$_n$ as a model system for unconventional charge transfer and polar metallicity,"  At interfaces between oxide materials, lattice and electronic reconstructions
+always play important roles in exotic phenomena. In this study, the density
+functional theory and maximally localized Wannier functions are employed to
+investigate the (LaTiO$_3$)$_n$/(LaVO$_3$)$_n$ magnetic superlattices. The
+electron transfer from Ti$^{3+}$ to V$^{3+}$ is predicted, which violates the
+intuitive band alignment based on the electronic structures of LaTiO$_3$ and
+LaVO$_3$. Such unconventional charge transfer quenches the magnetism of
+LaTiO$_3$ layer mostly and leads to metal-insulator transition in the $n=1$
+superlattice when the stacking orientation is altered. In addition, the
+compatibility among the polar structure, ferrimagnetism, and metallicity is
+predicted in the $n=2$ superlattice.
+",0,1,0,0,0,0
+17475,Modeling sorption of emerging contaminants in biofilms,"  A mathematical model for emerging contaminants sorption in multispecies
+biofilms, based on a continuum approach and mass conservation principles is
+presented. Diffusion of contaminants within the biofilm is described using a
+diffusion-reaction equation. Binding sites formation and occupation are modeled
+by two systems of hyperbolic partial differential equations are mutually
+connected through the two growth rate terms. The model is completed with a
+system of hyperbolic equations governing the microbial species growth within
+the biofilm; a system of parabolic equations for substrates diffusion and
+reaction and a nonlinear ordinary differential equation describing the free
+boundary evolution. Two real special cases are modelled. The first one
+describes the dynamics of a free sorbent component diffusing and reacting in a
+multispecies biofilm. In the second illustrative case, the fate of two
+different contaminants has been modelled.
+",0,1,0,0,0,0
+17476,Stabilizing Training of Generative Adversarial Networks through Regularization,"  Deep generative models based on Generative Adversarial Networks (GANs) have
+demonstrated impressive sample quality but in order to work they require a
+careful choice of architecture, parameter initialization, and selection of
+hyper-parameters. This fragility is in part due to a dimensional mismatch or
+non-overlapping support between the model distribution and the data
+distribution, causing their density ratio and the associated f-divergence to be
+undefined. We overcome this fundamental limitation and propose a new
+regularization approach with low computational cost that yields a stable GAN
+training procedure. We demonstrate the effectiveness of this regularizer across
+several architectures trained on common benchmark image generation tasks. Our
+regularization turns GAN models into reliable building blocks for deep
+learning.
+",1,0,0,1,0,0
+17477,Spurious Vanishing Problem in Approximate Vanishing Ideal,"  Approximate vanishing ideal, which is a new concept from computer algebra, is
+a set of polynomials that almost takes a zero value for a set of given data
+points. The introduction of approximation to exact vanishing ideal has played a
+critical role in capturing the nonlinear structures of noisy data by computing
+the approximate vanishing polynomials. However, approximate vanishing has a
+theoretical problem, which is giving rise to the spurious vanishing problem
+that any polynomial turns into an approximate vanishing polynomial by
+coefficient scaling. In the present paper, we propose a general method that
+enables many basis construction methods to overcome this problem. Furthermore,
+a coefficient truncation method is proposed that balances the theoretical
+soundness and computational cost. The experiments show that the proposed method
+overcomes the spurious vanishing problem and significantly increases the
+accuracy of classification.
+",1,0,0,1,0,0
+17478,Opportunities for Two-color Experiments at the SASE3 undulator line of the European XFEL,"  X-ray Free Electron Lasers (XFELs) have been proven to generate short and
+powerful radiation pulses allowing for a wide class of novel experiments. If an
+XFEL facility supports the generation of two X-ray pulses with different
+wavelengths and controllable delay, the range of possible experiments is
+broadened even further to include X-ray-pump/X-ray-probe applications. In this
+work we discuss the possibility of applying a simple and cost-effective method
+for producing two-color pulses at the SASE3 soft X-ray beamline of the European
+XFEL. The technique is based on the installation of a magnetic chicane in the
+baseline undulator and can be accomplished in several steps. We discuss the
+scientific interest of this upgrade for the Small Quantum Systems (SQS)
+instrument, in connection with the high-repetition rate of the European XFEL,
+and we provide start-to-end simulations up to the radiation focus on the
+sample, proving the feasibility of our concept.
+",0,1,0,0,0,0
+17479,Braiding errors in interacting Majorana quantum wires,"  Avenues of Majorana bound states (MBSs) have become one of the primary
+directions towards a possible realization of topological quantum computation.
+For a Y-junction of Kitaev quantum wires, we numerically investigate the
+braiding of MBSs while considering the full quasi-particle background. The two
+central sources of braiding errors are found to be the fidelity loss due to the
+incomplete adiabaticity of the braiding operation as well as the hybridization
+of the MBS. The explicit extraction of the braiding phase in the low-energy
+Majorana sector from the full many-particle Hilbert space allows us to analyze
+the breakdown of the independent-particle picture of Majorana braiding.
+Furthermore, we find nearest-neighbor interactions to significantly affect the
+braiding performance to the better or worse, depending on the sign and
+magnitude of the coupling.
+",0,1,0,0,0,0
+17480,Machine Learning for Structured Clinical Data,"  Research is a tertiary priority in the EHR, where the priorities are patient
+care and billing. Because of this, the data is not standardized or formatted in
+a manner easily adapted to machine learning approaches. Data may be missing for
+a large variety of reasons ranging from individual input styles to differences
+in clinical decision making, for example, which lab tests to issue. Few
+patients are annotated at a research quality, limiting sample size and
+presenting a moving gold standard. Patient progression over time is key to
+understanding many diseases but many machine learning algorithms require a
+snapshot, at a single time point, to create a usable vector form. Furthermore,
+algorithms that produce black box results do not provide the interpretability
+required for clinical adoption. This chapter discusses these challenges and
+others in applying machine learning techniques to the structured EHR (i.e.
+Patient Demographics, Family History, Medication Information, Vital Signs,
+Laboratory Tests, Genetic Testing). It does not cover feature extraction from
+additional sources such as imaging data or free text patient notes but the
+approaches discussed can include features extracted from these sources.
+",1,0,0,0,0,0
+17481,Hidden order and symmetry protected topological states in quantum link ladders,"  We show that whereas spin-1/2 one-dimensional U(1) quantum-link models (QLMs)
+are topologically trivial, when implemented in ladder-like lattices these
+models may present an intriguing ground-state phase diagram, which includes a
+symmetry protected topological (SPT) phase that may be readily revealed by
+analyzing long-range string spin correlations along the ladder legs. We propose
+a simple scheme for the realization of spin-1/2 U(1) QLMs based on
+single-component fermions loaded in an optical lattice with s- and p-bands,
+showing that the SPT phase may be experimentally realized by adiabatic
+preparation.
+",0,1,0,0,0,0
+17482,The effect of prudence on the optimal allocation in possibilistic and mixed models,"  In this paper two portfolio choice models are studied: a purely possibilistic
+model, in which the return of a risky asset is a fuzzy number, and a mixed
+model in which a probabilistic background risk is added. For the two models an
+approximate formula of the optimal allocation is computed, with respect to the
+possibilistic moments associated with fuzzy numbers and the indicators of the
+investor risk preferences (risk aversion, prudence).
+",0,0,0,0,0,1
+17483,On universal operators and universal pairs,"  We study some basic properties of the class of universal operators on Hilbert
+space, and provide new examples of universal operators and universal pairs.
+",0,0,1,0,0,0
+17484,Interleaving Lattice for the APS Linac,"  To realize and test advanced accelerator concepts and hardware, a beamline is
+being reconfigured in the Linac Extension Area (LEA) of APS linac. A
+photo-cathode RF gun installed at the beginning of the APS linac will provide a
+low emittance electron beam into the LEA beamline. The thermionic RF gun beam
+for the APS storage ring, and the photo-cathode RF gun beam for LEA beamline
+will be accelerated through the linac in an interleaved fashion. In this paper,
+the design studies for interleaving lattice realization in APS linac is
+described with initial experiment result
+",0,1,0,0,0,0
+17485,\textit{Ab Initio} Study of the Magnetic Behavior of Metal Hydrides: A Comparison with the Slater-Pauling Curve,"  We investigated the magnetic behavior of metal hydrides FeH$_{x}$, CoH$_{x}$
+and NiH$_{x}$ for several concentrations of hydrogen ($x$) by using Density
+Functional Theory calculations. Several structural phases of the metallic host:
+bcc ($\alpha$), fcc ($\gamma$), hcp ($\varepsilon$), dhcp ($\varepsilon'$),
+tetragonal structure for FeH$_{x}$ and $\varepsilon$-$\gamma$ phases for
+CoH$_{x}$, were studied. We found that for CoH$_{x}$ and NiH$_{x}$ the magnetic
+moment ($m$) decreases regardless the concentration $x$. However, for FeH$_{x}$
+systems, $m$ increases or decreases depending on the variation in $x$. In order
+to find a general trend for these changes of $m$ in magnetic metal hydrides, we
+compare our results with the Slater-Pauling curve for ferromagnetic metallic
+binary alloys. It is found that the $m$ of metal hydrides made of Fe, Co and Ni
+fits the shape of the Slater-Pauling curve as a function of $x$. Our results
+indicate that there are two main effects that determine the $m$ value due to
+hydrogenation: an increase of volume causes $m$ to increase, and the addition
+of an extra electron to the metal always causes it to decrease. We discuss
+these behaviors in detail.
+",0,1,0,0,0,0
+17486,Secret Sharing for Cloud Data Security,"  Cloud computing helps reduce costs, increase business agility and deploy
+solutions with a high return on investment for many types of applications.
+However, data security is of premium importance to many users and often
+restrains their adoption of cloud technologies. Various approaches, i.e., data
+encryption, anonymization, replication and verification, help enforce different
+facets of data security. Secret sharing is a particularly interesting
+cryptographic technique. Its most advanced variants indeed simultaneously
+enforce data privacy, availability and integrity, while allowing computation on
+encrypted data. The aim of this paper is thus to wholly survey secret sharing
+schemes with respect to data security, data access and costs in the
+pay-as-you-go paradigm.
+",1,0,0,0,0,0
+17487,Design and Analysis of a Secure Three Factor User Authentication Scheme Using Biometric and Smart Card,"  Password security can no longer provide enough security in the area of remote
+user authentication. Considering this security drawback, researchers are trying
+to find solution with multifactor remote user authentication system. Recently,
+three factor remote user authentication using biometric and smart card has
+drawn a considerable attention of the researchers. However, most of the current
+proposed schemes have security flaws. They are vulnerable to attacks like user
+impersonation attack, server masquerading attack, password guessing attack,
+insider attack, denial of service attack, forgery attack, etc. Also, most of
+them are unable to provide mutual authentication, session key agreement and
+password, or smart card recovery system. Considering these drawbacks, we
+propose a secure three factor user authentication scheme using biometric and
+smart card. Through security analysis, we show that our proposed scheme can
+overcome drawbacks of existing systems and ensure high security in remote user
+authentication.
+",1,0,0,0,0,0
+17488,Real-World Modeling of a Pathfinding Robot Using Robot Operating System (ROS),"  This paper presents a practical approach towards implementing pathfinding
+algorithms on real-world and low-cost non- commercial hardware platforms. While
+using robotics simulation platforms as a test-bed for our algorithms we easily
+overlook real- world exogenous problems that are developed by external factors.
+Such problems involve robot wheel slips, asynchronous motors, abnormal sensory
+data or unstable power sources. The real-world dynamics tend to be very painful
+even for executing simple algorithms like a Wavefront planner or A-star search.
+This paper addresses designing techniques that tend to be robust as well as
+reusable for any hardware platforms; covering problems like controlling
+asynchronous drives, odometry offset issues and handling abnormal sensory
+feedback. The algorithm implementation medium and hardware design tools have
+been kept general in order to present our work as a serving platform for future
+researchers and robotics enthusiast working in the field of path planning
+robotics.
+",1,0,0,0,0,0
+17489,Understanding Convolution for Semantic Segmentation,"  Recent advances in deep learning, especially deep convolutional neural
+networks (CNNs), have led to significant improvement over previous semantic
+segmentation systems. Here we show how to improve pixel-wise semantic
+segmentation by manipulating convolution-related operations that are of both
+theoretical and practical value. First, we design dense upsampling convolution
+(DUC) to generate pixel-level prediction, which is able to capture and decode
+more detailed information that is generally missing in bilinear upsampling.
+Second, we propose a hybrid dilated convolution (HDC) framework in the encoding
+phase. This framework 1) effectively enlarges the receptive fields (RF) of the
+network to aggregate global information; 2) alleviates what we call the
+""gridding issue"" caused by the standard dilated convolution operation. We
+evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a
+state-of-art result of 80.1% mIOU in the test set at the time of submission. We
+also have achieved state-of-the-art overall on the KITTI road estimation
+benchmark and the PASCAL VOC2012 segmentation task. Our source code can be
+found at this https URL .
+",1,0,0,0,0,0
+17490,Mechanical Failure in Amorphous Solids: Scale Free Spinodal Criticality,"  The mechanical failure of amorphous media is a ubiquitous phenomenon from
+material engineering to geology. It has been noticed for a long time that the
+phenomenon is ""scale-free"", indicating some type of criticality. In spite of
+attempts to invoke ""Self-Organized Criticality"", the physical origin of this
+criticality, and also its universal nature, being quite insensitive to the
+nature of microscopic interactions, remained elusive. Recently we proposed that
+the precise nature of this critical behavior is manifested by a spinodal point
+of a thermodynamic phase transition. Moreover, at the spinodal point there
+exists a divergent correlation length which is associated with the
+system-spanning instabilities (known also as shear bands) which are typical to
+the mechanical yield. Demonstrating this requires the introduction of an ""order
+parameter"" that is suitable for distinguishing between disordered amorphous
+systems, and an associated correlation function, suitable for picking up the
+growing correlation length. The theory, the order parameter, and the
+correlation functions used are universal in nature and can be applied to any
+amorphous solid that undergoes mechanical yield. Critical exponents for the
+correlation length divergence and the system size dependence are estimated. The
+phenomenon is seen at its sharpest in athermal systems, as is explained below;
+in this paper we extend the discussion also to thermal systems, showing that at
+sufficiently high temperatures the spinodal phenomenon is destroyed by thermal
+fluctuations.
+",0,1,0,0,0,0
+17491,Moment conditions in strong laws of large numbers for multiple sums and random measures,"  The validity of the strong law of large numbers for multiple sums $S_n$ of
+independent identically distributed random variables $Z_k$, $k\leq n$, with
+$r$-dimensional indices is equivalent to the integrability of
+$|Z|(\log^+|Z|)^{r-1}$, where $Z$ is the typical summand. We consider the
+strong law of large numbers for more general normalisations, without assuming
+that the summands $Z_k$ are identically distributed, and prove a multiple sum
+generalisation of the Brunk--Prohorov strong law of large numbers. In the case
+of identical finite moments of irder $2q$ with integer $q\geq1$, we show that
+the strong law of large numbers holds with the normalisation $\|n_1\cdots
+n_r\|^{1/2}(\log n_1\cdots\log n_r)^{1/(2q)+\varepsilon}$ for any
+$\varepsilon>0$. The obtained results are also formulated in the setting of
+ergodic theorems for random measures, in particular those generated by marked
+point processes.
+",0,0,1,0,0,0
+17492,Connecting Clump Sizes in Turbulent Disk Galaxies to Instability Theory,"  In this letter we study the mean sizes of Halpha clumps in turbulent disk
+galaxies relative to kinematics, gas fractions, and Toomre Q. We use 100~pc
+resolution HST images, IFU kinematics, and gas fractions of a sample of rare,
+nearby turbulent disks with properties closely matched to z~1.5-2 main-sequence
+galaxies (the DYNAMO sample). We find linear correlations of normalized mean
+clump sizes with both the gas fraction and the velocity dispersion-to-rotation
+velocity ratio of the host galaxy. We show that these correlations are
+consistent with predictions derived from a model of instabilities in a
+self-gravitating disk (the so-called ""violent disk instability model""). We also
+observe, using a two-fluid model for Q, a correlation between the size of
+clumps and self-gravity driven unstable regions. These results are most
+consistent with the hypothesis that massive star forming clumps in turbulent
+disks are the result of instabilities in self-gravitating gas-rich disks, and
+therefore provide a direct connection between resolved clump sizes and this in
+situ mechanism.
+",0,1,0,0,0,0
+17493,Anomalous slowing down of individual human activity due to successive decision-making processes,"  Motivated by a host of empirical evidences revealing the bursty character of
+human dynamics, we develop a model of human activity based on successive
+switching between an hesitation state and a decision-realization state, with
+residency times in the hesitation state distributed according to a heavy-tailed
+Pareto distribution. This model is particularly reminiscent of an individual
+strolling through a randomly distributed human crowd. Using a stochastic model
+based on the concept of anomalous and non-Markovian Lévy walk, we show
+exactly that successive decision-making processes drastically slow down the
+progression of an individual faced with randomly distributed obstacles.
+Specifically, we prove exactly that the average displacement exhibits a
+sublinear scaling with time that finds its origins in: (i) the intrinsically
+non-Markovian character of human activity, and (ii) the power law distribution
+of hesitation times.
+",0,1,0,0,0,0
+17494,"The spectrum, radiation conditions and the Fredholm property for the Dirichlet Laplacian in a perforated plane with semi-infinite inclusions","  We consider the spectral Dirichlet problem for the Laplace operator in the
+plane $\Omega^{\circ}$ with double-periodic perforation but also in the domain
+$\Omega^{\bullet}$ with a semi-infinite foreign inclusion so that the
+Floquet-Bloch technique and the Gelfand transform do not apply directly. We
+describe waves which are localized near the inclusion and propagate along it.
+We give a formulation of the problem with radiation conditions that provides a
+Fredholm operator of index zero. The main conclusion concerns the spectra
+$\sigma^{\circ}$ and $\sigma^{\bullet}$ of the problems in $\Omega^{\circ}$ and
+$\Omega^{\bullet},$ namely we present a concrete geometry which supports the
+relation $\sigma^{\circ}\varsubsetneqq\sigma^{\bullet}$ due to a new non-empty
+spectral band caused by the semi-infinite inclusion called an open waveguide in
+the double-periodic medium.
+",0,0,1,0,0,0
+17495,On the Semantics and Complexity of Probabilistic Logic Programs,"  We examine the meaning and the complexity of probabilistic logic programs
+that consist of a set of rules and a set of independent probabilistic facts
+(that is, programs based on Sato's distribution semantics). We focus on two
+semantics, respectively based on stable and on well-founded models. We show
+that the semantics based on stable models (referred to as the ""credal
+semantics"") produces sets of probability models that dominate infinitely
+monotone Choquet capacities, we describe several useful consequences of this
+result. We then examine the complexity of inference with probabilistic logic
+programs. We distinguish between the complexity of inference when a
+probabilistic program and a query are given (the inferential complexity), and
+the complexity of inference when the probabilistic program is fixed and the
+query is given (the query complexity, akin to data complexity as used in
+database theory). We obtain results on the inferential and query complexity for
+acyclic, stratified, and cyclic propositional and relational programs,
+complexity reaches various levels of the counting hierarchy and even
+exponential levels.
+",1,0,0,0,0,0
+17496,Fitting Probabilistic Index Models on Large Datasets,"  Recently, Thas et al. (2012) introduced a new statistical model for the
+probability index. This index is defined as $P(Y \leq Y^*|X, X^*)$ where Y and
+Y* are independent random response variables associated with covariates X and
+X* [...] Crucially to estimate the parameters of the model, a set of
+pseudo-observations is constructed. For a sample size n, a total of $n(n-1)/2$
+pairwise comparisons between observations is considered. Consequently for large
+sample sizes, it becomes computationally infeasible or even impossible to fit
+the model as the set of pseudo-observations increases nearly quadratically. In
+this dissertation, we provide two solutions to fit a probabilistic index model.
+The first algorithm consists of splitting the entire data set into unique
+partitions. On each of these, we fit the model and then aggregate the
+estimates. A second algorithm is a subsampling scheme in which we select $K <<
+n$ observations without replacement and after B iterations aggregate the
+estimates. In Monte Carlo simulations, we show how the partitioning algorithm
+outperforms the latter [...] We illustrate the partitioning algorithm and the
+interpretation of the probabilistic index model on a real data set (Przybylski
+and Weinstein, 2017) of n = 116,630 where we compare it against the ordinary
+least squares method. By modelling the probabilistic index, we give an
+intuitive and meaningful quantification of the effect of the time adolescents
+spend using digital devices such as smartphones on self-reported mental
+well-being. We show how moderate usage is associated with an increased
+probability of reporting a higher mental well-being compared to random
+adolescents who do not use a smartphone. On the other hand, adolescents who
+excessively use their smartphone are associated with a higher probability of
+reporting a lower mental well-being than randomly chosen peers who do not use a
+smartphone.[...]
+",0,0,0,1,0,0
+17497,BICEP2 / Keck Array IX: New Bounds on Anisotropies of CMB Polarization Rotation and Implications for Axion-Like Particles and Primordial Magnetic Fields,"  We present the strongest constraints to date on anisotropies of CMB
+polarization rotation derived from $150$ GHz data taken by the BICEP2 & Keck
+Array CMB experiments up to and including the 2014 observing season (BK14). The
+definition of polarization angle in BK14 maps has gone through self-calibration
+in which the overall angle is adjusted to minimize the observed $TB$ and $EB$
+power spectra. After this procedure, the $QU$ maps lose sensitivity to a
+uniform polarization rotation but are still sensitive to anisotropies of
+polarization rotation. This analysis places constraints on the anisotropies of
+polarization rotation, which could be generated by CMB photons interacting with
+axion-like pseudoscalar fields or Faraday rotation induced by primordial
+magnetic fields. The sensitivity of BK14 maps ($\sim 3\mu$K-arcmin) makes it
+possible to reconstruct anisotropies of polarization rotation angle and measure
+their angular power spectrum much more precisely than previous attempts. Our
+data are found to be consistent with no polarization rotation anisotropies,
+improving the upper bound on the amplitude of the rotation angle spectrum by
+roughly an order of magnitude compared to the previous best constraints. Our
+results lead to an order of magnitude better constraint on the coupling
+constant of the Chern-Simons electromagnetic term $f_a \geq 1.7\times
+10^2\times (H_I/2\pi)$ ($2\sigma$) than the constraint derived from uniform
+rotation, where $H_I$ is the inflationary Hubble scale. The upper bound on the
+amplitude of the primordial magnetic fields is 30nG ($2\sigma$) from the
+polarization rotation anisotropies.
+",0,1,0,0,0,0
+17498,Unsupervised Object Discovery and Segmentation of RGBD-images,"  In this paper we introduce a system for unsupervised object discovery and
+segmentation of RGBD-images. The system models the sensor noise directly from
+data, allowing accurate segmentation without sensor specific hand tuning of
+measurement noise models making use of the recently introduced Statistical
+Inlier Estimation (SIE) method. Through a fully probabilistic formulation, the
+system is able to apply probabilistic inference, enabling reliable segmentation
+in previously challenging scenarios. In addition, we introduce new methods for
+filtering out false positives, significantly improving the signal to noise
+ratio. We show that the system significantly outperform state-of-the-art in on
+a challenging real-world dataset.
+",1,0,0,0,0,0
+17499,Enabling large-scale viscoelastic calculations via neural network acceleration,"  One of the most significant challenges involved in efforts to understand the
+effects of repeated earthquake cycle activity are the computational costs of
+large-scale viscoelastic earthquake cycle models. Computationally intensive
+viscoelastic codes must be evaluated thousands of times and locations, and as a
+result, studies tend to adopt a few fixed rheological structures and model
+geometries, and examine the predicted time-dependent deformation over short
+(<10 yr) time periods at a given depth after a large earthquake. Training a
+deep neural network to learn a computationally efficient representation of
+viscoelastic solutions, at any time, location, and for a large range of
+rheological structures, allows these calculations to be done quickly and
+reliably, with high spatial and temporal resolution. We demonstrate that this
+machine learning approach accelerates viscoelastic calculations by more than
+50,000%. This magnitude of acceleration will enable the modeling of
+geometrically complex faults over thousands of earthquake cycles across wider
+ranges of model parameters and at larger spatial and temporal scales than have
+been previously possible.
+",0,1,0,0,0,0
+17500,Speaker identification from the sound of the human breath,"  This paper examines the speaker identification potential of breath sounds in
+continuous speech. Speech is largely produced during exhalation. In order to
+replenish air in the lungs, speakers must periodically inhale. When inhalation
+occurs in the midst of continuous speech, it is generally through the mouth.
+Intra-speech breathing behavior has been the subject of much study, including
+the patterns, cadence, and variations in energy levels. However, an often
+ignored characteristic is the {\em sound} produced during the inhalation phase
+of this cycle. Intra-speech inhalation is rapid and energetic, performed with
+open mouth and glottis, effectively exposing the entire vocal tract to enable
+maximum intake of air. This results in vocal tract resonances evoked by
+turbulence that are characteristic of the speaker's speech-producing apparatus.
+Consequently, the sounds of inhalation are expected to carry information about
+the speaker's identity. Moreover, unlike other spoken sounds which are subject
+to active control, inhalation sounds are generally more natural and less
+affected by voluntary influences. The goal of this paper is to demonstrate that
+breath sounds are indeed bio-signatures that can be used to identify speakers.
+We show that these sounds by themselves can yield remarkably accurate speaker
+recognition with appropriate feature representations and classification
+frameworks.
+",1,0,0,1,0,0
+17501,Vector valued maximal Carleson type operators on the weighted Lorentz spaces,"  In this paper, by using the idea of linearizing maximal op-erators originated
+by Charles Fefferman and the TT* method of Stein-Wainger, we establish a
+weighted inequality for vector valued maximal Carleson type operators with
+singular kernels proposed by Andersen and John on the weighted Lorentz spaces
+with vector-valued functions.
+",0,0,1,0,0,0
+17502,Rigidity of square-tiled interval exchange transformations,"  We look at interval exchange transformations defined as first return maps on
+the set of diagonals of a flow of direction $\theta$ on a square-tiled surface:
+using a combinatorial approach, we show that, when the surface has at least one
+true singularity both the flow and the interval exchange are rigid if and only
+if tan $\theta$ has bounded partial quotients. Moreover, if all vertices of the
+squares are singularities of the flat metric, and tan $\theta$ has bounded
+partial quotients, the square-tiled interval exchange transformation T is not
+of rank one. Finally, for another class of surfaces, those defined by the
+unfolding of billiards in Veech triangles, we build an uncountable set of rigid
+directional flows and an uncountable set of rigid interval exchange
+transformations.
+",0,0,1,0,0,0
+17503,Global Marcinkiewicz estimates for nonlinear parabolic equations with nonsmooth coefficients,"  Consider the parabolic equation with measure data \begin{equation*} \left\{
+\begin{aligned} &u_t-{\rm div} \mathbf{a}(D u,x,t)=\mu&\text{in}& \quad
+\Omega_T, &u=0 \quad &\text{on}& \quad \partial_p\Omega_T, \end{aligned}\right.
+\end{equation*} where $\Omega$ is a bounded domain in $\mathbb{R}^n$,
+$\Omega_T=\Omega\times (0,T)$, $\partial_p\Omega_T=(\partial\Omega\times
+(0,T))\cup (\Omega\times\{0\})$, and $\mu$ is a signed Borel measure with
+finite total mass. Assume that the nonlinearity ${\bf a}$ satisfies a small
+BMO-seminorm condition, and $\Omega$ is a Reifenberg flat domain. This paper
+proves a global Marcinkiewicz estimate for the SOLA (Solution Obtained as
+Limits of Approximation) to the parabolic equation.
+",0,0,1,0,0,0
+17504,Can Who-Edits-What Predict Edit Survival?,"  As the number of contributors to online peer-production systems grows, it
+becomes increasingly important to predict whether the edits that users make
+will eventually be beneficial to the project. Existing solutions either rely on
+a user reputation system or consist of a highly specialized predictor that is
+tailored to a specific peer-production system. In this work, we explore a
+different point in the solution space that goes beyond user reputation but does
+not involve any content-based feature of the edits. We view each edit as a game
+between the editor and the component of the project. We posit that the
+probability that an edit is accepted is a function of the editor's skill, of
+the difficulty of editing the component and of a user-component interaction
+term. Our model is broadly applicable, as it only requires observing data about
+who makes an edit, what the edit affects and whether the edit survives or not.
+We apply our model on Wikipedia and the Linux kernel, two examples of
+large-scale peer-production systems, and we seek to understand whether it can
+effectively predict edit survival: in both cases, we provide a positive answer.
+Our approach significantly outperforms those based solely on user reputation
+and bridges the gap with specialized predictors that use content-based
+features. It is simple to implement, computationally inexpensive, and in
+addition it enables us to discover interesting structure in the data.
+",1,0,0,1,0,0
+17505,Introduction to intelligent computing unit 1,"  This brief note highlights some basic concepts required toward understanding
+the evolution of machine learning and deep learning models. The note starts
+with an overview of artificial intelligence and its relationship to biological
+neuron that ultimately led to the evolution of todays intelligent models.
+",1,0,0,1,0,0
+17506,Spatial heterogeneities shape collective behavior of signaling amoeboid cells,"  We present novel experimental results on pattern formation of signaling
+Dictyostelium discoideum amoeba in the presence of a periodic array of
+millimeter-sized pillars. We observe concentric cAMP waves that initiate almost
+synchronously at the pillars and propagate outwards. These waves have higher
+frequency than the other firing centers and dominate the system dynamics. The
+cells respond chemotactically to these circular waves and stream towards the
+pillars, forming periodic Voronoi domains that reflect the periodicity of the
+underlying lattice. We performed comprehensive numerical simulations of a
+reaction-diffusion model to study the characteristics of the boundary
+conditions given by the obstacles. Our simulations show that, the obstacles can
+act as the wave source depending on the imposed boundary condition.
+Interestingly, a critical minimum accumulation of cAMP around the obstacles is
+needed for the pillars to act as the wave source. This critical value is lower
+at smaller production rates of the intracellular cAMP which can be controlled
+in our experiments using caffeine. Experiments and simulations also show that
+in the presence of caffeine the number of firing centers is reduced which is
+crucial in our system for circular waves emitted from the pillars to
+successfully take over the dynamics. These results are crucial to understand
+the signaling mechanism of Dictyostelium cells that experience spatial
+heterogeneities in its natural habitat.
+",0,0,0,0,1,0
+17507,Yamabe Solitons on three-dimensional normal almost paracontact metric manifolds,"  The purpose of the paper is to study Yamabe solitons on three-dimensional
+para-Sasakian, paracosymplectic and para-Kenmotsu manifolds. Mainly, we proved
+that *If the semi-Riemannian metric of a three-dimensional para-Sasakian
+manifold is a Yamabe soliton, then it is of constant scalar curvature, and the
+flow vector field V is Killing. In the next step, we proved that either
+manifold has constant curvature -1 and reduces to an Einstein manifold, or V is
+an infinitesimal automorphism of the paracontact metric structure on the
+manifold. *If the semi-Riemannian metric of a three-dimensional
+paracosymplectic manifold is a Yamabe soliton, then it has constant scalar
+curvature. Furthermore either manifold is $\eta$-Einstein, or Ricci flat. *If
+the semi-Riemannian metric on a three-dimensional para-Kenmotsu manifold is a
+Yamabe soliton, then the manifold is of constant sectional curvature -1,
+reduces to an Einstein manifold. Furthermore, Yamabe soliton is expanding with
+$\lambda$=-6 and the vector field V is Killing. Finally, we construct examples
+to illustrate the results obtained in previous sections.
+",0,0,1,0,0,0
+17508,Clustering and Hitting Times of Threshold Exceedances and Applications,"  We investigate exceedances of the process over a sufficiently high threshold.
+The exceedances determine the risk of hazardous events like climate
+catastrophes, huge insurance claims, the loss and delay in telecommunication
+networks.
+Due to dependence such exceedances tend to occur in clusters. The cluster
+structure of social networks is caused by dependence (social relationships and
+interests) between nodes and possibly heavy-tailed distributions of the node
+degrees. A minimal time to reach a large node determines the first hitting
+time. We derive an asymptotically equivalent distribution and a limit
+expectation of the first hitting time to exceed the threshold $u_n$ as the
+sample size $n$ tends to infinity. The results can be extended to the second
+and, generally, to the $k$th ($k> 2$) hitting times. Applications in
+large-scale networks such as social, telecommunication and recommender systems
+are discussed.
+",0,0,1,1,0,0
+17509,Classification of Local Field Potentials using Gaussian Sequence Model,"  A problem of classification of local field potentials (LFPs), recorded from
+the prefrontal cortex of a macaque monkey, is considered. An adult macaque
+monkey is trained to perform a memory-based saccade. The objective is to decode
+the eye movement goals from the LFP collected during a memory period. The LFP
+classification problem is modeled as that of classification of smooth functions
+embedded in Gaussian noise. It is then argued that using minimax function
+estimators as features would lead to consistent LFP classifiers. The theory of
+Gaussian sequence models allows us to represent minimax estimators as finite
+dimensional objects. The LFP classifier resulting from this mathematical
+endeavor is a spectrum based technique, where Fourier series coefficients of
+the LFP data, followed by appropriate shrinkage and thresholding, are used as
+features in a linear discriminant classifier. The classifier is then applied to
+the LFP data to achieve high decoding accuracy. The function classification
+approach taken in the paper also provides a systematic justification for using
+Fourier series, with shrinkage and thresholding, as features for the problem,
+as opposed to using the power spectrum. It also suggests that phase information
+is crucial to the decision making.
+",0,0,0,1,0,0
+17510,A few explicit examples of complex dynamics of inertia groups on surfaces - a question of Professor Igor Dolgachev,"  We give a few explicit examples which answer an open minded question of
+Professor Igor Dolgachev on complex dynamics of the inertia group of a smooth
+rational curve on a projective K3 surface and its variants for a rational
+surface and a non-projective K3 surface.
+",0,0,1,0,0,0
+17511,Ancestral inference from haplotypes and mutations,"  We consider inference about the history of a sample of DNA sequences,
+conditional upon the haplotype counts and the number of segregating sites
+observed at the present time. After deriving some theoretical results in the
+coalescent setting, we implement rejection sampling and importance sampling
+schemes to perform the inference. The importance sampling scheme addresses an
+extension of the Ewens Sampling Formula for a configuration of haplotypes and
+the number of segregating sites in the sample. The implementations include both
+constant and variable population size models. The methods are illustrated by
+two human Y chromosome data sets.
+",0,0,1,1,0,0
+17512,Ellipsoid Method for Linear Programming made simple,"  In this paper, ellipsoid method for linear programming is derived using only
+minimal knowledge of algebra and matrices. Unfortunately, most authors first
+describe the algorithm, then later prove its correctness, which requires a good
+knowledge of linear algebra.
+",1,0,0,0,0,0
+17513,Collective strong coupling of cold atoms to an all-fiber ring cavity,"  We experimentally demonstrate a ring geometry all-fiber cavity system for
+cavity quantum electrodynamics with an ensemble of cold atoms. The fiber cavity
+contains a nanofiber section which mediates atom-light interactions through an
+evanescent field. We observe well-resolved, vacuum Rabi splitting of the cavity
+transmission spectrum in the weak driving limit due to a collective enhancement
+of the coupling rate by the ensemble of atoms within the evanescent field, and
+we present a simple theoretical model to describe this. In addition, we
+demonstrate a method to control and stabilize the resonant frequency of the
+cavity by utilizing the thermal properties of the nanofiber.
+",0,1,0,0,0,0
+17514,Robust Model-Based Clustering of Voting Records,"  We explore the possibility of discovering extreme voting patterns in the U.S.
+Congressional voting records by drawing ideas from the mixture of contaminated
+normal distributions. A mixture of latent trait models via contaminated normal
+distributions is proposed. We assume that the low dimensional continuous latent
+variable comes from a contaminated normal distribution and, therefore, picks up
+extreme patterns in the observed binary data while clustering. We consider in
+particular such model for the analysis of voting records. The model is applied
+to a U.S. Congressional Voting data set on 16 issues. Note this approach is the
+first instance within the literature of a mixture model handling binary data
+with possible extreme patterns.
+",0,0,0,1,0,0
+17515,The Quest for Solvable Multistate Landau-Zener Models,"  Recently, integrability conditions (ICs) in mutistate Landau-Zener (MLZ)
+theory were proposed [1]. They describe common properties of all known solved
+systems with linearly time-dependent Hamiltonians. Here we show that ICs enable
+efficient computer assisted search for new solvable MLZ models that span
+complexity range from several interacting states to mesoscopic systems with
+many-body dynamics and combinatorially large phase space. This diversity
+suggests that nontrivial solvable MLZ models are numerous. In addition, we
+refine the formulation of ICs and extend the class of solvable systems to
+models with points of multiple diabatic level crossing.
+",0,1,1,0,0,0
+17516,Bayesian Inference of the Multi-Period Optimal Portfolio for an Exponential Utility,"  We consider the estimation of the multi-period optimal portfolio obtained by
+maximizing an exponential utility. Employing Jeffreys' non-informative prior
+and the conjugate informative prior, we derive stochastic representations for
+the optimal portfolio weights at each time point of portfolio reallocation.
+This provides a direct access not only to the posterior distribution of the
+portfolio weights but also to their point estimates together with uncertainties
+and their asymptotic distributions. Furthermore, we present the posterior
+predictive distribution for the investor's wealth at each time point of the
+investment period in terms of a stochastic representation for the future wealth
+realization. This in turn makes it possible to use quantile-based risk measures
+or to calculate the probability of default. We apply the suggested Bayesian
+approach to assess the uncertainty in the multi-period optimal portfolio by
+considering assets from the FTSE 100 in the weeks after the British referendum
+to leave the European Union. The behaviour of the novel portfolio estimation
+method in a precarious market situation is illustrated by calculating the
+predictive wealth, the risk associated with the holding portfolio, and the
+default probability in each period.
+",0,0,1,1,0,0
+17517,Reconstructing the gravitational field of the local universe,"  Tests of gravity at the galaxy scale are in their infancy. As a first step to
+systematically uncovering the gravitational significance of galaxies, we map
+three fundamental gravitational variables -- the Newtonian potential,
+acceleration and curvature -- over the galaxy environments of the local
+universe to a distance of approximately 200 Mpc. Our method combines the
+contributions from galaxies in an all-sky redshift survey, halos from an N-body
+simulation hosting low-luminosity objects, and linear and quasi-linear modes of
+the density field. We use the ranges of these variables to determine the extent
+to which galaxies expand the scope of generic tests of gravity and are capable
+of constraining specific classes of model for which they have special
+significance. Finally, we investigate the improvements afforded by upcoming
+galaxy surveys.
+",0,1,0,0,0,0
+17518,Hierarchical organization of H. Eugene Stanley scientific collaboration community in weighted network representation,"  By mapping the most advanced elements of the contemporary social
+interactions, the world scientific collaboration network develops an extremely
+involved and heterogeneous organization. Selected characteristics of this
+heterogeneity are studied here and identified by focusing on the scientific
+collaboration community of H. Eugene Stanley - one of the most prolific world
+scholars at the present time. Based on the Web of Science records as of March
+28, 2016, several variants of networks are constructed. It is found that the
+Stanley #1 network - this in analogy to the Erdős # - develops a largely
+consistent hierarchical organization and Stanley himself obeys rules of the
+same hierarchy. However, this is seen exclusively in the weighted network
+representation. When such a weighted network is evolving, an existing relevant
+model indicates that the spread of weight gets stimulation to the
+multiplicative bursts over the neighbouring nodes, which leads to a balanced
+growth of interconnections among them. While not exclusive to Stanley, such a
+behaviour is not a rule, however. Networks of other outstanding scholars
+studied here more often develop a star-like form and the central hubs
+constitute the outliers. This study is complemented by a spectral analysis of
+the normalised Laplacian matrices derived from the weighted variants of the
+corresponding networks and, among others, it points to the efficiency of such a
+procedure for identifying the component communities and relations among them in
+the complex weighted networks.
+",1,1,0,0,0,0
+17519,Rational links and DT invariants of quivers,"  We prove that the generating functions for the colored HOMFLY-PT polynomials
+of rational links are specializations of the generating functions of the
+motivic Donaldson-Thomas invariants of appropriate quivers that we naturally
+associate with these links. This shows that the conjectural links-quivers
+correspondence of Kucharski-Reineke-Stošić-Su{\l}kowski as well as the
+LMOV conjecture hold for rational links. Along the way, we extend the
+links-quivers correspondence to tangles and, thus, explore elements of a skein
+theory for motivic Donaldson-Thomas invariants.
+",0,0,1,0,0,0
+17520,G2-structures for N=1 supersymmetric AdS4 solutions of M-theory,"  We study the N=1 supersymmetric solutions of D=11 supergravity obtained as a
+warped product of four-dimensional anti-de-Sitter space with a
+seven-dimensional Riemannian manifold M. Using the octonion bundle structure on
+M we reformulate the Killing spinor equations in terms of sections of the
+octonion bundle on M. The solutions then define a single complexified
+G2-structure on M or equivalently two real G2-structures. We then study the
+torsion of these G2-structures and the relationships between them.
+",0,0,1,0,0,0
+17521,A Neural Stochastic Volatility Model,"  In this paper, we show that the recent integration of statistical models with
+deep recurrent neural networks provides a new way of formulating volatility
+(the degree of variation of time series) models that have been widely used in
+time series analysis and prediction in finance. The model comprises a pair of
+complementary stochastic recurrent neural networks: the generative network
+models the joint distribution of the stochastic volatility process; the
+inference network approximates the conditional distribution of the latent
+variables given the observables. Our focus here is on the formulation of
+temporal dynamics of volatility over time under a stochastic recurrent neural
+network framework. Experiments on real-world stock price datasets demonstrate
+that the proposed model generates a better volatility estimation and prediction
+that outperforms mainstream methods, e.g., deterministic models such as GARCH
+and its variants, and stochastic models namely the MCMC-based model
+\emph{stochvol} as well as the Gaussian process volatility model \emph{GPVol},
+on average negative log-likelihood.
+",1,0,0,1,0,0
+17522,A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds,"  This paper proposes a segmentation-free, automatic and efficient procedure to
+detect general geometric quadric forms in point clouds, where clutter and
+occlusions are inevitable. Our everyday world is dominated by man-made objects
+which are designed using 3D primitives (such as planes, cones, spheres,
+cylinders, etc.). These objects are also omnipresent in industrial
+environments. This gives rise to the possibility of abstracting 3D scenes
+through primitives, thereby positions these geometric forms as an integral part
+of perception and high level 3D scene understanding.
+As opposed to state-of-the-art, where a tailored algorithm treats each
+primitive type separately, we propose to encapsulate all types in a single
+robust detection procedure. At the center of our approach lies a closed form 3D
+quadric fit, operating in both primal & dual spaces and requiring as low as 4
+oriented-points. Around this fit, we design a novel, local null-space voting
+strategy to reduce the 4-point case to 3. Voting is coupled with the famous
+RANSAC and makes our algorithm orders of magnitude faster than its conventional
+counterparts. This is the first method capable of performing a generic
+cross-type multi-object primitive detection in difficult scenes. Results on
+synthetic and real datasets support the validity of our method.
+",1,0,0,0,0,0
+17523,Mass-to-Light versus Color Relations for Dwarf Irregular Galaxies,"  We have determined new relations between $UBV$ colors and mass-to-light
+ratios ($M/L$) for dwarf irregular (dIrr) galaxies, as well as for transformed
+$g^\prime - r^\prime$. These $M/L$ to color relations (MLCRs) are based on
+stellar mass density profiles determined for 34 LITTLE THINGS dwarfs from
+spectral energy distribution fitting to multi-wavelength surface photometry in
+passbands from the FUV to the NIR. These relations can be used to determine
+stellar masses in dIrr galaxies for situations where other determinations of
+stellar mass are not possible. Our MLCRs are shallower than comparable MLCRs in
+the literature determined for spiral galaxies. We divided our dwarf data into
+four metallicity bins and found indications of a steepening of the MLCR with
+increased oxygen abundance, perhaps due to more line blanketing occurring at
+higher metallicity.
+",0,1,0,0,0,0
+17524,Insight into High-order Harmonic Generation from Solids: The Contributions of the Bloch Wave-packets Moving on the Group and Phase Velocities,"  We study numerically the Bloch electron wavepacket dynamics in periodic
+potentials to simulate laser-solid interactions. We introduce a new perspective
+in the coordinate space combined with the motion of the Bloch electron
+wavepackets moving at group and phase velocities under the laser fields. This
+model interprets the origins of the two contributions (intra- and interband
+transitions) of the high-order harmonic generation (HHG) by investigating the
+local and global behavior of the wavepackets. It also elucidates the underlying
+physical picture of the HHG intensity enhancement by means of carrier-envelope
+phase (CEP), chirp and inhomogeneous fields. It provides a deep insight into
+the emission of high-order harmonics from solids. This model is instructive for
+experimental measurements and provides a new avenue to distinguish mechanisms
+of the HHG from solids in diffrent laser fields.
+",0,1,0,0,0,0
+17525,Using deterministic approximations to accelerate SMC for posterior sampling,"  Sequential Monte Carlo has become a standard tool for Bayesian Inference of
+complex models. This approach can be computationally demanding, especially when
+initialized from the prior distribution. On the other hand, deter-ministic
+approximations of the posterior distribution are often available with no
+theoretical guaranties. We propose a bridge sampling scheme starting from such
+a deterministic approximation of the posterior distribution and targeting the
+true one. The resulting Shortened Bridge Sampler (SBS) relies on a sequence of
+distributions that is determined in an adaptive way. We illustrate the
+robustness and the efficiency of the methodology on a large simulation study.
+When applied to network datasets, SBS inference leads to different statistical
+conclusions from the one supplied by the standard variational Bayes
+approximation.
+",0,0,0,1,0,0
+17526,Evaluating (and improving) the correspondence between deep neural networks and human representations,"  Decades of psychological research have been aimed at modeling how people
+learn features and categories. The empirical validation of these theories is
+often based on artificial stimuli with simple representations. Recently, deep
+neural networks have reached or surpassed human accuracy on tasks such as
+identifying objects in natural images. These networks learn representations of
+real-world stimuli that can potentially be leveraged to capture psychological
+representations. We find that state-of-the-art object classification networks
+provide surprisingly accurate predictions of human similarity judgments for
+natural images, but fail to capture some of the structure represented by
+people. We show that a simple transformation that corrects these discrepancies
+can be obtained through convex optimization. We use the resulting
+representations to predict the difficulty of learning novel categories of
+natural images. Our results extend the scope of psychological experiments and
+computational modeling by enabling tractable use of large natural stimulus
+sets.
+",1,0,0,0,0,0
+17527,An approach to nonsolvable base change and descent,"  We present a collection of conjectural trace identities and explain why they
+are equivalent to base change and descent of automorphic representations of
+$\mathrm{GL}_n(\mathbb{A}_F)$ along nonsolvable extensions (under some
+simplifying hypotheses). The case $n=2$ is treated in more detail and
+applications towards the Artin conjecture for icosahedral Galois
+representations are given.
+",0,0,1,0,0,0
+17528,Towards Attack-Tolerant Networks: Concurrent Multipath Routing and the Butterfly Network,"  Targeted attacks against network infrastructure are notoriously difficult to
+guard against. In the case of communication networks, such attacks can leave
+users vulnerable to censorship and surveillance, even when cryptography is
+used. Much of the existing work on network fault-tolerance focuses on random
+faults and does not apply to adversarial faults (attacks). Centralized networks
+have single points of failure by definition, leading to a growing popularity in
+decentralized architectures and protocols for greater fault-tolerance. However,
+centralized network structure can arise even when protocols are decentralized.
+Despite their decentralized protocols, the Internet and World-Wide Web have
+been shown both theoretically and historically to be highly susceptible to
+attack, in part due to emergent structural centralization. When single points
+of failure exist, they are potentially vulnerable to non-technological (i.e.,
+coercive) attacks, suggesting the importance of a structural approach to
+attack-tolerance. We show how the assumption of partial trust transitivity,
+while more realistic than the assumption underlying webs of trust, can be used
+to quantify the effective redundancy of a network as a function of trust
+transitivity. We also prove that the effective redundancy of the wrap-around
+butterfly topology increases exponentially with trust transitivity and describe
+a novel concurrent multipath routing algorithm for constructing paths to
+utilize that redundancy. When portions of network structure can be dictated our
+results can be used to create scalable, attack-tolerant infrastructures. More
+generally, our results provide a theoretical formalism for evaluating the
+effects of network structure on adversarial fault-tolerance.
+",1,0,0,0,0,0
+17529,PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making,"  Reinforcement learning and symbolic planning have both been used to build
+intelligent autonomous agents. Reinforcement learning relies on learning from
+interactions with real world, which often requires an unfeasibly large amount
+of experience. Symbolic planning relies on manually crafted symbolic knowledge,
+which may not be robust to domain uncertainties and changes. In this paper we
+present a unified framework {\em PEORL} that integrates symbolic planning with
+hierarchical reinforcement learning (HRL) to cope with decision-making in a
+dynamic environment with uncertainties.
+Symbolic plans are used to guide the agent's task execution and learning, and
+the learned experience is fed back to symbolic knowledge to improve planning.
+This method leads to rapid policy search and robust symbolic plans in complex
+domains. The framework is tested on benchmark domains of HRL.
+",0,0,0,1,0,0
+17530,Structure of Native Two-dimensional Oxides on III--Nitride Surfaces,"  When pristine material surfaces are exposed to air, highly reactive broken
+bonds can promote the formation of surface oxides with structures and
+properties differing greatly from bulk. Determination of the oxide structure,
+however, is often elusive through the use of indirect diffraction methods or
+techniques that probe only the outer most layer. As a result, surface oxides
+forming on widely used materials, such as group III-nitrides, have not been
+unambiguously resolved, even though critical properties can depend sensitively
+on their presence. In this work, aberration corrected scanning transmission
+electron microscopy reveals directly, and with depth dependence, the structure
+of native two--dimensional oxides that form on AlN and GaN surfaces. Through
+atomic resolution imaging and spectroscopy, we show that the oxide layers are
+comprised of tetrahedra--octahedra cation--oxygen units, similar to bulk
+$\theta$--Al$_2$O$_3$ and $\beta$--Ga$_2$O$_3$. By applying density functional
+theory, we show that the observed structures are more stable than previously
+proposed surface oxide models. We place the impact of these observations in the
+context of key III-nitride growth, device issues, and the recent discovery of
+two-dimnesional nitrides.
+",0,1,0,0,0,0
+17531,Hydrodynamic charge and heat transport on inhomogeneous curved spaces,"  We develop the theory of hydrodynamic charge and heat transport in strongly
+interacting quasi-relativistic systems on manifolds with inhomogeneous spatial
+curvature. In solid-state physics, this is analogous to strain disorder in the
+underlying lattice. In the hydrodynamic limit, we find that the thermal and
+electrical conductivities are dominated by viscous effects, and that the
+thermal conductivity is most sensitive to this disorder. We compare the effects
+of inhomogeneity in the spatial metric to inhomogeneity in the chemical
+potential, and discuss the extent to which our hydrodynamic theory is relevant
+for experimentally realizable condensed matter systems, including suspended
+graphene at the Dirac point.
+",0,1,0,0,0,0
+17532,Simulation assisted machine learning,"  Predicting how a proposed cancer treatment will affect a given tumor can be
+cast as a machine learning problem, but the complexity of biological systems,
+the number of potentially relevant genomic and clinical features, and the lack
+of very large scale patient data repositories make this a unique challenge.
+""Pure data"" approaches to this problem are underpowered to detect
+combinatorially complex interactions and are bound to uncover false
+correlations despite statistical precautions taken (1). To investigate this
+setting, we propose a method to integrate simulations, a strong form of prior
+knowledge, into machine learning, a combination which to date has been largely
+unexplored. The results of multiple simulations (under various uncertainty
+scenarios) are used to compute similarity measures between every pair of
+samples: sample pairs are given a high similarity score if they behave
+similarly under a wide range of simulation parameters. These similarity values,
+rather than the original high dimensional feature data, are used to train
+kernelized machine learning algorithms such as support vector machines, thus
+handling the curse-of-dimensionality that typically affects genomic machine
+learning. Using four synthetic datasets of complex systems--three biological
+models and one network flow optimization model--we demonstrate that when the
+number of training samples is small compared to the number of features, the
+simulation kernel approach dominates over no-prior-knowledge methods. In
+addition to biology and medicine, this approach should be applicable to other
+disciplines, such as weather forecasting, financial markets, and agricultural
+management, where predictive models are sought and informative yet approximate
+simulations are available. The Python SimKern software, the models (in MATLAB,
+Octave, and R), and the datasets are made freely available at
+this https URL .
+",0,0,0,1,1,0
+17533,Rechargeable redox flow batteries: Maximum current density with electrolyte flow reactant penetration in a serpentine flow structure,"  Rechargeable redox flow batteries with serpentine flow field designs have
+been demonstrated to deliver higher current density and power density in medium
+and large-scale stationary energy storage applications. Nevertheless, the
+fundamental mechanisms involved with improved current density in flow batteries
+with flow field designs have not been understood. Here we report a maximum
+current density concept associated with stoichiometric availability of
+electrolyte reactant flow penetration through the porous electrode that can be
+achieved in a flow battery system with a ""zero-gap""serpentine flow field
+architecture. This concept can explain a higher current density achieved within
+allowing reactions of all species soluble in the electrolyte. Further
+validations with experimental data are confirmed by an example of a vanadium
+flow battery with a serpentine flow structure over carbon paper electrode.
+",0,1,0,0,0,0
+17534,Open problems in mathematical physics,"  We present a list of open questions in mathematical physics. After a
+historical introduction, a number of problems in a variety of different fields
+are discussed, with the intention of giving an overall impression of the
+current status of mathematical physics, particularly in the topical fields of
+classical general relativity, cosmology and the quantum realm. This list is
+motivated by the recent article proposing 42 fundamental questions (in physics)
+which must be answered on the road to full enlightenment. But paraphrasing a
+famous quote by the British football manager Bill Shankly, in response to the
+question of whether mathematics can answer the Ultimate Question of Life, the
+Universe, and Everything, mathematics is, of course, much more important than
+that.
+",0,1,1,0,0,0
+17535,Stochastic Bandit Models for Delayed Conversions,"  Online advertising and product recommendation are important domains of
+applications for multi-armed bandit methods. In these fields, the reward that
+is immediately available is most often only a proxy for the actual outcome of
+interest, which we refer to as a conversion. For instance, in web advertising,
+clicks can be observed within a few seconds after an ad display but the
+corresponding sale --if any-- will take hours, if not days to happen. This
+paper proposes and investigates a new stochas-tic multi-armed bandit model in
+the framework proposed by Chapelle (2014) --based on empirical studies in the
+field of web advertising-- in which each action may trigger a future reward
+that will then happen with a stochas-tic delay. We assume that the probability
+of conversion associated with each action is unknown while the distribution of
+the conversion delay is known, distinguishing between the (idealized) case
+where the conversion events may be observed whatever their delay and the more
+realistic setting in which late conversions are censored. We provide
+performance lower bounds as well as two simple but efficient algorithms based
+on the UCB and KLUCB frameworks. The latter algorithm, which is preferable when
+conversion rates are low, is based on a Poissonization argument, of independent
+interest in other settings where aggregation of Bernoulli observations with
+different success probabilities is required.
+",1,0,0,0,0,0
+17536,Fitting Analysis using Differential Evolution Optimization (FADO): Spectral population synthesis through genetic optimization under self-consistency boundary conditions,"  The goal of population spectral synthesis (PSS) is to decipher from the
+spectrum of a galaxy the mass, age and metallicity of its constituent stellar
+populations. This technique has been established as a fundamental tool in
+extragalactic research. It has been extensively applied to large spectroscopic
+data sets, notably the SDSS, leading to important insights into the galaxy
+assembly history. However, despite significant improvements over the past
+decade, all current PSS codes suffer from two major deficiencies that inhibit
+us from gaining sharp insights into the star-formation history (SFH) of
+galaxies and potentially introduce substantial biases in studies of their
+physical properties (e.g., stellar mass, mass-weighted stellar age and specific
+star formation rate). These are i) the neglect of nebular emission in spectral
+fits, consequently, ii) the lack of a mechanism that ensures consistency
+between the best-fitting SFH and the observed nebular emission characteristics
+of a star-forming (SF) galaxy. In this article, we present FADO (Fitting
+Analysis using Differential evolution Optimization): a conceptually novel,
+publicly available PSS tool with the distinctive capability of permitting
+identification of the SFH that reproduces the observed nebular characteristics
+of a SF galaxy. This so-far unique self-consistency concept allows us to
+significantly alleviate degeneracies in current spectral synthesis. The
+innovative character of FADO is further augmented by its mathematical
+foundation: FADO is the first PSS code employing genetic differential evolution
+optimization. This, in conjunction with other unique elements in its
+mathematical concept (e.g., optimization of the spectral library using
+artificial intelligence, convergence test, quasi-parallelization) results in
+key improvements with respect to computational efficiency and uniqueness of the
+best-fitting SFHs.
+",0,1,0,0,0,0
+17537,A Combinatoric Shortcut to Evaluate CHY-forms,"  In \cite{Chen:2016fgi} we proposed a differential operator for the evaluation
+of the multi-dimensional residues on isolated (zero-dimensional) poles.In this
+paper we discuss some new insight on evaluating the (generalized)
+Cachazo-He-Yuan (CHY) forms of the scattering amplitudes using this
+differential operator. We introduce a tableau representation for the
+coefficients appearing in the proposed differential operator. Combining the
+tableaux with the polynomial forms of the scattering equations, the evaluation
+of the generalized CHY form becomes a simple combinatoric problem. It is thus
+possible to obtain the coefficients arising in the differential operator in a
+straightforward way. We present the procedure for a complete solution of the
+$n$-gon amplitudes at one-loop level in a generalized CHY form. We also apply
+our method to fully evaluate the one-loop five-point amplitude in the maximally
+supersymmetric Yang-Mills theory; the final result is identical to the one
+obtained by Q-Cut.
+",0,0,1,0,0,0
+17538,ART: adaptive residual--time restarting for Krylov subspace matrix exponential evaluations,"  In this paper a new restarting method for Krylov subspace matrix exponential
+evaluations is proposed. Since our restarting technique essentially employs the
+residual, some convergence results for the residual are given. We also discuss
+how the restart length can be adjusted after each restart cycle, which leads to
+an adaptive restarting procedure. Numerical tests are presented to compare our
+restarting with three other restarting methods. Some of the algorithms
+described in this paper are a part of the Octave/Matlab package expmARPACK
+available at this http URL.
+",1,0,0,0,0,0
+17539,Nil extensions of simple regular ordered semigroup,"  In this paper, nil extensions of some special type of ordered semigroups,
+such as, simple regular ordered semigroups, left simple and right regular
+ordered semigroup. Moreover, we have characterized complete semilattice
+decomposition of all ordered semigroups which are nil extension of ordered
+semigroup.
+",0,0,1,0,0,0
+17540,The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings,"  We examine a class of embeddings based on structured random matrices with
+orthogonal rows which can be applied in many machine learning applications
+including dimensionality reduction and kernel approximation. For both the
+Johnson-Lindenstrauss transform and the angular kernel, we show that we can
+select matrices yielding guaranteed improved performance in accuracy and/or
+speed compared to earlier methods. We introduce matrices with complex entries
+which give significant further accuracy improvement. We provide geometric and
+Markov chain-based perspectives to help understand the benefits, and empirical
+results which suggest that the approach is helpful in a wider range of
+applications.
+",0,0,0,1,0,0
+17541,The Bayesian optimist's guide to adaptive immune receptor repertoire analysis,"  Probabilistic modeling is fundamental to the statistical analysis of complex
+data. In addition to forming a coherent description of the data-generating
+process, probabilistic models enable parameter inference about given data sets.
+This procedure is well-developed in the Bayesian perspective, in which one
+infers probability distributions describing to what extent various possible
+parameters agree with the data. In this paper we motivate and review
+probabilistic modeling for adaptive immune receptor repertoire data then
+describe progress and prospects for future work, from germline haplotyping to
+adaptive immune system deployment across tissues. The relevant quantities in
+immune sequence analysis include not only continuous parameters such as gene
+use frequency, but also discrete objects such as B cell clusters and lineages.
+Throughout this review, we unravel the many opportunities for probabilistic
+modeling in adaptive immune receptor analysis, including settings for which the
+Bayesian approach holds substantial promise (especially if one is optimistic
+about new computational methods). From our perspective the greatest prospects
+for progress in probabilistic modeling for repertoires concern ancestral
+sequence estimation for B cell receptor lineages, including uncertainty from
+germline genotype, rearrangement, and lineage development.
+",0,0,0,0,1,0
+17542,Predictive Indexing,"  There has been considerable research on automated index tuning in database
+management systems (DBMSs). But the majority of these solutions tune the index
+configuration by retrospectively making computationally expensive physical
+design changes all at once. Such changes degrade the DBMS's performance during
+the process, and have reduced utility during subsequent query processing due to
+the delay between a workload shift and the associated change. A better approach
+is to generate small changes that tune the physical design over time, forecast
+the utility of these changes, and apply them ahead of time to maximize their
+impact.
+This paper presents predictive indexing that continuously improves a
+database's physical design using lightweight physical design changes. It uses a
+machine learning model to forecast the utility of these changes, and
+continuously refines the index configuration of the database to handle evolving
+workloads. We introduce a lightweight hybrid scan operator with which a DBMS
+can make use of partially-built indexes for query processing. Our evaluation
+shows that predictive indexing improves the throughput of a DBMS by 3.5--5.2x
+compared to other state-of-the-art indexing approaches. We demonstrate that
+predictive indexing works seamlessly with other lightweight automated physical
+design tuning methods.
+",1,0,0,0,0,0
+17543,Making Deep Q-learning methods robust to time discretization,"  Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust
+to hyperparameterization, implementation details, or small environment changes
+(Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key
+to making DRL applicable to real world problems. In this paper, we identify
+sensitivity to time discretization in near continuous-time environments as a
+critical factor; this covers, e.g., changing the number of frames per second,
+or the action frequency of the controller. Empirically, we find that
+Q-learning-based approaches such as Deep Q- learning (Mnih et al., 2015) and
+Deep Deterministic Policy Gradient (Lillicrap et al., 2015) collapse with small
+time steps. Formally, we prove that Q-learning does not exist in continuous
+time. We detail a principled way to build an off-policy RL algorithm that
+yields similar performances over a wide range of time discretizations, and
+confirm this robustness empirically.
+",1,0,0,1,0,0
+17544,Anomalous current in diffusive ferromagnetic Josephson junctions,"  We demonstrate that in diffusive superconductor/ferromagnet/superconductor
+(S/F/S) junctions a finite, {\it anomalous}, Josephson current can flow even at
+zero phase difference between the S electrodes. The conditions for the
+observation of this effect are non-coplanar magnetization distribution and a
+broken magnetization inversion symmetry of the superconducting current. The
+latter symmetry is intrinsic for the widely used quasiclassical approximation
+and prevent previous works, based on this approximation, from obtaining the
+Josephson anomalous current. We show that this symmetry can be removed by
+introducing spin-dependent boundary conditions for the quasiclassical equations
+at the superconducting/ferromagnet interfaces in diffusive systems. Using this
+recipe we considered generic multilayer magnetic systems and determine the
+ideal experimental conditions in order to maximize the anomalous current.
+",0,1,0,0,0,0
+17545,Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates,"  Although a majority of the theoretical literature in high-dimensional
+statistics has focused on settings which involve fully-observed data, settings
+with missing values and corruptions are common in practice. We consider the
+problems of estimation and of constructing component-wise confidence intervals
+in a sparse high-dimensional linear regression model when some covariates of
+the design matrix are missing completely at random. We analyze a variant of the
+Dantzig selector [9] for estimating the regression model and we use a
+de-biasing argument to construct component-wise confidence intervals. Our first
+main result is to establish upper bounds on the estimation error as a function
+of the model parameters (the sparsity level s, the expected fraction of
+observed covariates $\rho_*$, and a measure of the signal strength
+$\|\beta^*\|_2$). We find that even in an idealized setting where the
+covariates are assumed to be missing completely at random, somewhat
+surprisingly and in contrast to the fully-observed setting, there is a
+dichotomy in the dependence on model parameters and much faster rates are
+obtained if the covariance matrix of the random design is known. To study this
+issue further, our second main contribution is to provide lower bounds on the
+estimation error showing that this discrepancy in rates is unavoidable in a
+minimax sense. We then consider the problem of high-dimensional inference in
+the presence of missing data. We construct and analyze confidence intervals
+using a de-biased estimator. In the presence of missing data, inference is
+complicated by the fact that the de-biasing matrix is correlated with the pilot
+estimator and this necessitates the design of a new estimator and a novel
+analysis. We also complement our mathematical study with extensive simulations
+on synthetic and semi-synthetic data that show the accuracy of our asymptotic
+predictions for finite sample sizes.
+",0,0,0,1,0,0
+17546,Applications of an algorithm for solving Fredholm equations of the first kind,"  In this paper we use an iterative algorithm for solving Fredholm equations of
+the first kind. The basic algorithm is known and is based on an EM algorithm
+when involved functions are non-negative and integrable. With this algorithm we
+demonstrate two examples involving the estimation of a mixing density and a
+first passage time density function involving Brownian motion. We also develop
+the basic algorithm to include functions which are not necessarily non-negative
+and again present illustrations under this scenario. A self contained proof of
+convergence of all the algorithms employed is presented.
+",0,0,1,1,0,0
+17547,Fully symmetric kernel quadrature,"  Kernel quadratures and other kernel-based approximation methods typically
+suffer from prohibitive cubic time and quadratic space complexity in the number
+of function evaluations. The problem arises because a system of linear
+equations needs to be solved. In this article we show that the weights of a
+kernel quadrature rule can be computed efficiently and exactly for up to tens
+of millions of nodes if the kernel, integration domain, and measure are fully
+symmetric and the node set is a union of fully symmetric sets. This is based on
+the observations that in such a setting there are only as many distinct weights
+as there are fully symmetric sets and that these weights can be solved from a
+linear system of equations constructed out of row sums of certain submatrices
+of the full kernel matrix. We present several numerical examples that show
+feasibility, both for a large number of nodes and in high dimensions, of the
+developed fully symmetric kernel quadrature rules. Most prominent of the fully
+symmetric kernel quadrature rules we propose are those that use sparse grids.
+",1,0,1,1,0,0
+17548,Conditional Accelerated Lazy Stochastic Gradient Descent,"  In this work we introduce a conditional accelerated lazy stochastic gradient
+descent algorithm with optimal number of calls to a stochastic first-order
+oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving
+over the projection-free, Online Frank-Wolfe based stochastic gradient descent
+of Hazan and Kale [2012] with convergence rate
+$O\left(\frac{1}{\varepsilon^4}\right)$.
+",1,0,0,1,0,0
+17549,MMD GAN: Towards Deeper Understanding of Moment Matching Network,"  Generative moment matching network (GMMN) is a deep generative model that
+differs from Generative Adversarial Network (GAN) by replacing the
+discriminator in GAN with a two-sample test based on kernel maximum mean
+discrepancy (MMD). Although some theoretical guarantees of MMD have been
+studied, the empirical performance of GMMN is still not as competitive as that
+of GAN on challenging and large benchmark datasets. The computational
+efficiency of GMMN is also less desirable in comparison with GAN, partially due
+to its requirement for a rather large batch size during the training. In this
+paper, we propose to improve both the model expressiveness of GMMN and its
+computational efficiency by introducing adversarial kernel learning techniques,
+as the replacement of a fixed Gaussian kernel in the original GMMN. The new
+approach combines the key ideas in both GMMN and GAN, hence we name it MMD GAN.
+The new distance measure in MMD GAN is a meaningful loss that enjoys the
+advantage of weak topology and can be optimized via gradient descent with
+relatively small batch sizes. In our evaluation on multiple benchmark datasets,
+including MNIST, CIFAR- 10, CelebA and LSUN, the performance of MMD-GAN
+significantly outperforms GMMN, and is competitive with other representative
+GAN works.
+",1,0,0,1,0,0
+17550,Multipartite entanglement after a quantum quench,"  We study the multipartite entanglement of a quantum many-body system
+undergoing a quantum quench. We quantify multipartite entanglement through the
+quantum Fisher information (QFI) density and we are able to express it after a
+quench in terms of a generalized response function. For pure state initial
+conditions and in the thermodynamic limit, we can express the QFI as the
+fluctuations of an observable computed in the so-called diagonal ensemble. We
+apply the formalism to the dynamics of a quantum Ising chain after a quench in
+the transverse field. In this model the asymptotic state is, in almost all
+cases, more than two-partite entangled. Moreover, starting from the
+ferromagnetic phase, we find a divergence of multipartite entanglement for
+small quenches closely connected to a corresponding divergence of the
+correlation length.
+",0,1,0,0,0,0
+17551,Thermal properties of graphene from path-integral simulations,"  Thermal properties of graphene monolayers are studied by path-integral
+molecular dynamics (PIMD) simulations, which take into account the quantization
+of vibrational modes in the crystalline membrane, and allow one to consider
+anharmonic effects in these properties. This system was studied at temperatures
+in the range from 12 to 2000~K and zero external stress, by describing the
+interatomic interactions through the LCBOPII effective potential. We analyze
+the internal energy and specific heat and compare the results derived from the
+simulations with those yielded by a harmonic approximation for the vibrational
+modes. This approximation turns out to be rather precise up to temperatures of
+about 400~K. At higher temperatures, we observe an influence of the elastic
+energy, due to the thermal expansion of the graphene sheet. Zero-point and
+thermal effects on the in-plane and ""real"" surface of graphene are discussed.
+The thermal expansion coefficient $\alpha$ of the real area is found to be
+positive at all temperatures, in contrast to the expansion coefficient
+$\alpha_p$ of the in-plane area, which is negative at low temperatures, and
+becomes positive for $T \gtrsim$ 1000~K.
+",0,1,0,0,0,0
+17552,Measuring Affectiveness and Effectiveness in Software Systems,"  The summary presented in this paper highlights the results obtained in a
+four-years project aiming at analyzing the development process of software
+artifacts from two points of view: Effectiveness and Affectiveness. The first
+attribute is meant to analyze the productivity of the Open Source Communities
+by measuring the time required to resolve an issue, while the latter provides a
+novel approach for studying the development process by analyzing the
+affectiveness ex-pressed by developers in their comments posted during the
+issue resolution phase. Affectivenes is obtained by measuring Sentiment,
+Politeness and Emotions. All the study presented in this summary are based on
+Jira, one of the most used software repositories.
+",1,0,0,0,0,0
+17553,Intertangled stochastic motifs in networks of excitatory-inhibitory units,"  A stochastic model of excitatory and inhibitory interactions which bears
+universality traits is introduced and studied. The endogenous component of
+noise, stemming from finite size corrections, drives robust inter-nodes
+correlations, that persist at large large distances. Anti-phase synchrony at
+small frequencies is resolved on adjacent nodes and found to promote the
+spontaneous generation of long-ranged stochastic patterns, that invade the
+network as a whole. These patterns are lacking under the idealized
+deterministic scenario, and could provide novel hints on how living systems
+implement and handle a large gallery of delicate computational tasks.
+",0,1,0,0,0,0
+17554,Accurate Computation of the Distribution of Sums of Dependent Log-Normals with Applications to the Black-Scholes Model,"  We present a new Monte Carlo methodology for the accurate estimation of the
+distribution of the sum of dependent log-normal random variables. The
+methodology delivers statistically unbiased estimators for three distributional
+quantities of significant interest in finance and risk management: the left
+tail, or cumulative distribution function, the probability density function,
+and the right tail, or complementary distribution function of the sum of
+dependent log-normal factors. In all of these three cases our methodology
+delivers fast and highly accurate estimators in settings for which existing
+methodology delivers estimators with large variance that tend to underestimate
+the true quantity of interest. We provide insight into the computational
+challenges using theory and numerical experiments, and explain their much wider
+implications for Monte Carlo statistical estimators of rare-event
+probabilities. In particular, we find that theoretically strongly-efficient
+estimators should be used with great caution in practice, because they may
+yield inaccurate results in the pre-limit. Further, this inaccuracy may not be
+detectable from the output of the Monte Carlo simulation, because the
+simulation output may severely underestimate the true variance of the
+estimator.
+",0,0,0,1,0,0
+17555,"The complete unitary dual of non-compact Lie superalgebra su(p,q|m) via the generalised oscillator formalism, and non-compact Young diagrams","  We study the unitary representations of the non-compact real forms of the
+complex Lie superalgebra sl(n|m). Among them, only the real form su(p,q|m)
+(p+q=n) admits nontrivial unitary representations, and all such representations
+are of the highest-weight type (or the lowest-weight type). We extend the
+standard oscillator construction of the unitary representations of non-compact
+Lie superalgebras over standard Fock spaces to generalised Fock spaces which
+allows us to define the action of oscillator determinants raised to non-integer
+powers. We prove that the proposed construction yields all the unitary
+representations including those with continuous labels. The unitary
+representations can be diagrammatically represented by non-compact Young
+diagrams. We apply our general results to the physically important case of
+four-dimensional conformal superalgebra su(2,2|4) and show how it yields
+readily its unitary representations including those corresponding to
+supermultiplets of conformal fields with continuous (anomalous) scaling
+dimensions.
+",0,0,1,0,0,0
+17556,DeepPainter: Painter Classification Using Deep Convolutional Autoencoders,"  In this paper we describe the problem of painter classification, and propose
+a novel approach based on deep convolutional autoencoder neural networks. While
+previous approaches relied on image processing and manual feature extraction
+from paintings, our approach operates on the raw pixel level, without any
+preprocessing or manual feature extraction. We first train a deep convolutional
+autoencoder on a dataset of paintings, and subsequently use it to initialize a
+supervised convolutional neural network for the classification phase.
+The proposed approach substantially outperforms previous methods, improving
+the previous state-of-the-art for the 3-painter classification problem from
+90.44% accuracy (previous state-of-the-art) to 96.52% accuracy, i.e., a 63%
+reduction in error rate.
+",1,0,0,1,0,0
+17557,"Sharp gradient estimate for heat kernels on $RCD^*(K,N)$ metric measure spaces","  In this paper, we will establish an elliptic local Li-Yau gradient estimate
+for weak solutions of the heat equation on metric measure spaces with
+generalized Ricci curvature bounded from below. One of its main applications is
+a sharp gradient estimate for the logarithm of heat kernels. These results seem
+new even for smooth Riemannian manifolds.
+",0,0,1,0,0,0
+17558,Thermodynamic properties of diatomic molecules systems under anharmonic Eckart potential,"  Due to one of the most representative contributions to the energy in diatomic
+molecules being the vibrational, we consider the generalized Morse potential
+(GMP) as one of the typical potential of interaction for one-dimensional
+microscopic systems, which describes local anharmonic effects. From Eckart
+potential (EP) model, it is possible to find a connection with the GMP model,
+as well as obtain the analytical expression for the energy spectrum because it
+is based on $S\,O\left(2,1\right)$ algebras. In this work we find the
+macroscopic properties such as vibrational mean energy $U$, specific heat $C$,
+Helmholtz free energy $F$ and entropy $S$ for a heteronuclear diatomic system,
+along with the exact partition function and its approximation for the high
+temperature region. Finally, we make a comparison between the graphs of some
+thermodynamic functions obtained with the GMP and the Morse potential (MP) for
+$H\,Cl$ molecules.
+",0,1,0,0,0,0
+17559,Effects of ultrasound waves intensity on the removal of Congo red color from the textile industry wastewater by Fe3O4@TiO2 core-shell nanospheres,"  Environmental pollutants, such as colors from the textile industry, affect
+water quality indicators like color, smell, and taste. These substances in the
+water cause the obstruction of filters and membranes and thereby reduce the
+efficiency of advanced water treatment processes. In addition, they are harmful
+to human health because of reaction with disinfectants and production of
+by-products. Iron oxide nanoparticles are considered effective absorbents for
+the removal of pollutants from aqueous environments. In order to increase the
+stability and dispersion, nanospheres with iron oxide core and titanium dioxide
+coating were used in this research and their ability to absorb Congo red color
+was evaluated. Iron oxide-titanium oxide nanospheres were prepared based on the
+coprecipitation method and then their physical properties were determined using
+a tunneling electron microscope (TEM) and an X-ray diffraction device.
+Morphological investigation of the absorbent surface showed that iron
+oxide-titanium oxide nanospheres sized about 5 to 10 nm. X-ray dispersion
+survey also suggested the high purity of the sample. In addition, the
+absorption rate was measured in the presence of ultrasound waves and the
+results indicated that the capacity of the synthesized sample to absorb Congo
+red is greatly dependent on the intensity power of ultrasound waves, as the
+absorption rate reaches 100% at powers above 30 watts.
+",0,1,0,0,0,0
+17560,Enumeration of Tree-like Maps with Arbitrary Number of Vertices,"  This paper provides the generating series for the embedding of tree-like
+graphs of arbitrary number of vertices, accourding to their genus. It applies
+and extends the techniques of Chan, where it was used to give an alternate
+proof of the Goulden and Slofstra formula. Furthermore, this greatly
+generalizes the famous Harer-Zagier formula, which computes the Euler
+characteristic of the moduli space of curves, and is equivalent to the
+computation of one vertex maps.
+",0,0,1,0,0,0
+17561,Depth resolved chemical speciation of a superlattice structure,"  We report results of simultaneous x-ray reflectivity and grazing incidence
+x-ray fluorescence measurements in combination with x-ray standing wave
+assisted depth resolved near edge x-ray absorption measurements to reveal new
+insights on chemical speciation of W in a W-B4C superlattice structure.
+Interestingly, our results show existence of various unusual electronic states
+for the W atoms especially those sitting at the surface and interface boundary
+of a thin film medium as compared to that of the bulk. These observations are
+found to be consistent with the results obtained using first principles
+calculations. Unlike the conventional x-ray absorption measurements the present
+approach has an advantage that it permits the determination of depth resolved
+chemical nature of an element in the thin layered materials at atomic length
+scale resolutions.
+",0,1,0,0,0,0
+17562,Optospintronics in graphene via proximity coupling,"  The observation of micron size spin relaxation makes graphene a promising
+material for applications in spintronics requiring long distance spin
+communication. However, spin dependent scatterings at the contact/graphene
+interfaces affect the spin injection efficiencies and hence prevent the
+material from achieving its full potential. While this major issue could be
+eliminated by nondestructive direct optical spin injection schemes, graphenes
+intrinsically low spin orbit coupling strength and optical absorption place an
+obstacle in their realization. We overcome this challenge by creating sharp
+artificial interfaces between graphene and WSe2 monolayers. Application of a
+circularly polarized light activates the spin polarized charge carriers in the
+WSe2 layer due to its spin coupled valley selective absorption. These carriers
+diffuse into the superjacent graphene layer, transport over a 3.5 um distance,
+and are finally detected electrically using BN/Co contacts in a non local
+geometry. Polarization dependent measurements confirm the spin origin of the
+non local signal.
+",0,1,0,0,0,0
+17563,Control strategy to limit duty cycle impact of earthquakes on the LIGO gravitational-wave detectors,"  Advanced gravitational-wave detectors such as the Laser Interferometer
+Gravitational-Wave Observatories (LIGO) require an unprecedented level of
+isolation from the ground. When in operation, they are expected to observe
+changes in the space-time continuum of less than one thousandth of the diameter
+of a proton. Strong teleseismic events like earthquakes disrupt the proper
+functioning of the detectors, and result in a loss of data until the detectors
+can be returned to their operating states. An earthquake early-warning system,
+as well as a prediction model have been developed to help understand the impact
+of earthquakes on LIGO. This paper describes a control strategy to use this
+early-warning system to reduce the LIGO downtime by 30%. It also presents a
+plan to implement this new earthquake configuration in the LIGO automation
+system.
+",0,1,0,0,0,0
+17564,Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO,"  The design of spacecraft trajectories for missions visiting multiple
+celestial bodies is here framed as a multi-objective bilevel optimization
+problem. A comparative study is performed to assess the performance of
+different Beam Search algorithms at tackling the combinatorial problem of
+finding the ideal sequence of bodies. Special focus is placed on the
+development of a new hybridization between Beam Search and the Population-based
+Ant Colony Optimization algorithm. An experimental evaluation shows all
+algorithms achieving exceptional performance on a hard benchmark problem. It is
+found that a properly tuned deterministic Beam Search always outperforms the
+remaining variants. Beam P-ACO, however, demonstrates lower parameter
+sensitivity, while offering superior worst-case performance. Being an anytime
+algorithm, it is then found to be the preferable choice for certain practical
+applications.
+",1,1,0,0,0,0
+17565,Types and unitary representations of reductive p-adic groups,"  We prove that for every Bushnell-Kutzko type that satisfies a certain
+rigidity assumption, the equivalence of categories between the corresponding
+Bernstein component and the category of modules for the Hecke algebra of the
+type induces a bijection between irreducible unitary representations in the two
+categories. This is a generalization of the unitarity criterion of Barbasch and
+Moy for representations with Iwahori fixed vectors.
+",0,0,1,0,0,0
+17566,Average values of L-functions in even characteristic,"  Let $k = \mathbb{F}_{q}(T)$ be the rational function field over a finite
+field $\mathbb{F}_{q}$, where $q$ is a power of $2$. In this paper we solve the
+problem of averaging the quadratic $L$-functions $L(s, \chi_{u})$ over
+fundamental discriminants. Any separable quadratic extension $K$ of $k$ is of
+the form $K = k(x_{u})$, where $x_{u}$ is a zero of $X^2+X+u=0$ for some $u\in
+k$. We characterize the family $\mathcal I$ (resp. $\mathcal F$, $\mathcal F'$)
+of rational functions $u\in k$ such that any separable quadratic extension $K$
+of $k$ in which the infinite prime $\infty = (1/T)$ of $k$ ramifies (resp.
+splits, is inert) can be written as $K = k(x_{u})$ with a unique $u\in\mathcal
+I$ (resp. $u\in\mathcal F$, $u\in\mathcal F'$). For almost all $s\in\mathbb C$
+with ${\rm Re}(s)\ge \frac{1}2$, we obtain the asymptotic formulas for the
+summation of $L(s,\chi_{u})$ over all $k(x_{u})$ with $u\in \mathcal I$, all
+$k(x_{u})$ with $u\in \mathcal F$ or all $k(x_{u})$ with $u\in \mathcal F'$ of
+given genus. As applications, we obtain the asymptotic mean value formulas of
+$L$-functions at $s=\frac{1}2$ and $s=1$ and the asymptotic mean value formulas
+of the class number $h_{u}$ or the class number times regulator $h_{u} R_{u}$.
+",0,0,1,0,0,0
+17567,"Decoupled molecules with binding polynomials of bidegree (n,2)","  We present a result on the number of decoupled molecules for systems binding
+two different types of ligands. In the case of $n$ and $2$ binding sites
+respectively, we show that, generically, there are $2(n!)^{2}$ decoupled
+molecules with the same binding polynomial. For molecules with more binding
+sites for the second ligand, we provide computational results.
+",1,1,0,0,0,0
+17568,Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization,"  Learning to remember long sequences remains a challenging task for recurrent
+neural networks. Register memory and attention mechanisms were both proposed to
+resolve the issue with either high computational cost to retain memory
+differentiability, or by discounting the RNN representation learning towards
+encoding shorter local contexts than encouraging long sequence encoding.
+Associative memory, which studies the compression of multiple patterns in a
+fixed size memory, were rarely considered in recent years. Although some recent
+work tries to introduce associative memory in RNN and mimic the energy decay
+process in Hopfield nets, it inherits the shortcoming of rule-based memory
+updates, and the memory capacity is limited. This paper proposes a method to
+learn the memory update rule jointly with task objective to improve memory
+capacity for remembering long sequences. Also, we propose an architecture that
+uses multiple such associative memory for more complex input encoding. We
+observed some interesting facts when compared to other RNN architectures on
+some well-studied sequence learning tasks.
+",1,0,0,1,0,0
+17569,McDiarmid Drift Detection Methods for Evolving Data Streams,"  Increasingly, Internet of Things (IoT) domains, such as sensor networks,
+smart cities, and social networks, generate vast amounts of data. Such data are
+not only unbounded and rapidly evolving. Rather, the content thereof
+dynamically evolves over time, often in unforeseen ways. These variations are
+due to so-called concept drifts, caused by changes in the underlying data
+generation mechanisms. In a classification setting, concept drift causes the
+previously learned models to become inaccurate, unsafe and even unusable.
+Accordingly, concept drifts need to be detected, and handled, as soon as
+possible. In medical applications and emergency response settings, for example,
+change in behaviours should be detected in near real-time, to avoid potential
+loss of life. To this end, we introduce the McDiarmid Drift Detection Method
+(MDDM), which utilizes McDiarmid's inequality in order to detect concept drift.
+The MDDM approach proceeds by sliding a window over prediction results, and
+associate window entries with weights. Higher weights are assigned to the most
+recent entries, in order to emphasize their importance. As instances are
+processed, the detection algorithm compares a weighted mean of elements inside
+the sliding window with the maximum weighted mean observed so far. A
+significant difference between the two weighted means, upper-bounded by the
+McDiarmid inequality, implies a concept drift. Our extensive experimentation
+against synthetic and real-world data streams show that our novel method
+outperforms the state-of-the-art. Specifically, MDDM yields shorter detection
+delays as well as lower false negative rates, while maintaining high
+classification accuracies.
+",1,0,0,1,0,0
+17570,Yield in Amorphous Solids: The Ant in the Energy Landscape Labyrinth,"  It has recently been shown that yield in amorphous solids under oscillatory
+shear is a dynamical transition from asymptotically periodic to asymptotically
+chaotic, diffusive dynamics. However, the type and universality class of this
+transition are still undecided. Here we show that the diffusive behavior of the
+vector of coordinates of the particles comprising an amorphous solid when
+subject to oscillatory shear, is analogous to that of a particle diffusing in a
+percolating lattice, the so-called ""ant in the labyrinth"" problem, and that
+yield corresponds to a percolation transition in the lattice. We explain this
+as a transition in the connectivity of the energy landscape, which affects the
+phase-space regions accessible to the coordinate vector for a given maximal
+strain amplitude. This transition provides a natural explanation to the
+observed limit-cycles, periods larger than one and diverging time-scales at
+yield.
+",0,1,0,0,0,0
+17571,Statistical methods in astronomy,"  We present a review of data types and statistical methods often encountered
+in astronomy. The aim is to provide an introduction to statistical applications
+in astronomy for statisticians and computer scientists. We highlight the
+complex, often hierarchical, nature of many astronomy inference problems and
+advocate for cross-disciplinary collaborations to address these challenges.
+",0,1,0,1,0,0
+17572,A note on some algebraic trapdoors for block ciphers,"  We provide sufficient conditions to guarantee that a translation based cipher
+is not vulnerable with respect to the partition-based trapdoor. This trapdoor
+has been introduced, recently, by Bannier et al. (2016) and it generalizes that
+introduced by Paterson in 1999. Moreover, we discuss the fact that studying the
+group generated by the round functions of a block cipher may not be sufficient
+to guarantee security against these trapdoors for the cipher.
+",1,0,1,0,0,0
+17573,Bi-Demographic Changes and Current Account using SVAR Modeling,"  The paper, as a new contribution, aims to explore the impacts of
+bi-demographic structure on the current account and growth. By using a SVAR
+modeling, we track the dynamic impacts between the underlying variables of the
+Saudi economy. New insights have been developed to study the interrelations
+between population growth, current account and economic growth inside the
+neoclassical theory of population. The long-run net impact on economic growth
+of the bi-population growth is negative, due to the typically lower skill sets
+of the immigrant labor population. Besides, the negative long-run contribution
+of immigrant workers to the current account growth largely exceeds that of
+contributions from the native population, because of the increasing levels of
+remittance outflows from the country. We find that a positive shock in
+immigration leads to a negative impact on native active age ratio. Thus, the
+immigrants appear to be more substitutes than complements for native workers.
+",0,0,0,0,0,1
+17574,Supervised Machine Learning for Signals Having RRC Shaped Pulses,"  Classification performances of the supervised machine learning techniques
+such as support vector machines, neural networks and logistic regression are
+compared for modulation recognition purposes. The simple and robust features
+are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals
+having root-raised-cosine shaped pulses are simulated in extreme noisy
+conditions having joint impurities of block fading, lack of symbol and sampling
+synchronization, carrier offset, and additive white Gaussian noise. The
+features are based on sample mean and sample variance of the imaginary part of
+the product of two consecutive complex signal values.
+",1,0,0,0,0,0
+17575,End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks,"  We propose an autoencoding sequence-based transceiver for communication over
+dispersive channels with intensity modulation and direct detection (IM/DD),
+designed as a bidirectional deep recurrent neural network (BRNN). The receiver
+uses a sliding window technique to allow for efficient data stream estimation.
+We find that this sliding window BRNN (SBRNN), based on end-to-end deep
+learning of the communication system, achieves a significant bit-error-rate
+reduction at all examined distances in comparison to previous block-based
+autoencoders implemented as feed-forward neural networks (FFNNs), leading to an
+increase of the transmission distance. We also compare the end-to-end SBRNN
+with a state-of-the-art IM/DD solution based on two level pulse amplitude
+modulation with an FFNN receiver, simultaneously processing multiple received
+symbols and approximating nonlinear Volterra equalization. Our results show
+that the SBRNN outperforms such systems at both 42 and 84\,Gb/s, while training
+fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep
+learning-based systems for communication over nonlinear channels with memory,
+such as the optical IM/DD fiber channel.
+",1,0,0,1,0,0
+17576,Effective difference elimination and Nullstellensatz,"  We prove effective Nullstellensatz and elimination theorems for difference
+equations in sequence rings. More precisely, we compute an explicit function of
+geometric quantities associated to a system of difference equations (and these
+geometric quantities may themselves be bounded by a function of the number of
+variables, the order of the equations, and the degrees of the equations) so
+that for any system of difference equations in variables $\mathbf{x} = (x_1,
+\ldots, x_m)$ and $\mathbf{u} = (u_1, \ldots, u_r)$, if these equations have
+any nontrivial consequences in the $\mathbf{x}$ variables, then such a
+consequence may be seen algebraically considering transforms up to the order of
+our bound. Specializing to the case of $m = 0$, we obtain an effective method
+to test whether a given system of difference equations is consistent.
+",0,0,1,0,0,0
+17577,A note on primitive $1-$normal elements over finite fields,"  Let $q$ be a prime power of a prime $p$, $n$ a positive integer and $\mathbb
+F_{q^n}$ the finite field with $q^n$ elements. The $k-$normal elements over
+finite fields were introduced and characterized by Huczynska et al (2013).
+Under the condition that $n$ is not divisible by $p$, they obtained an
+existence result on primitive $1-$normal elements of $\mathbb F_{q^n}$ over
+$\mathbb F_q$ for $q>2$. In this note, we extend their result to the excluded
+case $q=2$.
+",0,0,1,0,0,0
+17578,Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons,"  Zebrafish pretectal neurons exhibit specificities for large-field optic flow
+patterns associated with rotatory or translatory body motion. We investigate
+the hypothesis that these specificities reflect the input statistics of natural
+optic flow. Realistic motion sequences were generated using computer graphics
+simulating self-motion in an underwater scene. Local retinal motion was
+estimated with a motion detector and encoded in four populations of
+directionally tuned retinal ganglion cells, represented as two signed input
+variables. This activity was then used as input into one of two learning
+networks: a sparse coding network (competitive learning) and backpropagation
+network (supervised learning). Both simulations develop specificities for optic
+flow which are comparable to those found in a neurophysiological study (Kubo et
+al. 2014), and relative frequencies of the various neuronal responses are best
+modeled by the sparse coding approach. We conclude that the optic flow neurons
+in the zebrafish pretectum do reflect the optic flow statistics. The predicted
+vectorial receptive fields show typical optic flow fields but also ""Gabor"" and
+dipole-shaped patterns that likely reflect difference fields needed for
+reconstruction by linear superposition.
+",0,0,0,0,1,0
+17579,Revisiting the problem of audio-based hit song prediction using convolutional neural networks,"  Being able to predict whether a song can be a hit has impor- tant
+applications in the music industry. Although it is true that the popularity of
+a song can be greatly affected by exter- nal factors such as social and
+commercial influences, to which degree audio features computed from musical
+signals (whom we regard as internal factors) can predict song popularity is an
+interesting research question on its own. Motivated by the recent success of
+deep learning techniques, we attempt to ex- tend previous work on hit song
+prediction by jointly learning the audio features and prediction models using
+deep learning. Specifically, we experiment with a convolutional neural net-
+work model that takes the primitive mel-spectrogram as the input for feature
+learning, a more advanced JYnet model that uses an external song dataset for
+supervised pre-training and auto-tagging, and the combination of these two
+models. We also consider the inception model to characterize audio infor-
+mation in different scales. Our experiments suggest that deep structures are
+indeed more accurate than shallow structures in predicting the popularity of
+either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
+by JYnet to gain insights into the result of different models.
+",1,0,0,1,0,0
+17580,Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations,"  We train multi-task autoencoders on linguistic tasks and analyze the learned
+hidden sentence representations. The representations change significantly when
+translation and part-of-speech decoders are added. The more decoders a model
+employs, the better it clusters sentences according to their syntactic
+similarity, as the representation space becomes less entangled. We explore the
+structure of the representation space by interpolating between sentences, which
+yields interesting pseudo-English sentences, many of which have recognizable
+syntactic structure. Lastly, we point out an interesting property of our
+models: The difference-vector between two sentences can be added to change a
+third sentence with similar features in a meaningful way.
+",0,0,0,1,0,0
+17581,Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data,"  Time series forecasting is a crucial component of many important
+applications, ranging from forecasting the stock markets to energy load
+prediction. The high-dimensionality, velocity and variety of the data collected
+in these applications pose significant and unique challenges that must be
+carefully addressed for each of them. In this work, a novel Temporal Logistic
+Neural Bag-of-Features approach, that can be used to tackle these challenges,
+is proposed. The proposed method can be effectively combined with deep neural
+networks, leading to powerful deep learning models for time series analysis.
+However, combining existing BoF formulations with deep feature extractors pose
+significant challenges: the distribution of the input features is not
+stationary, tuning the hyper-parameters of the model can be especially
+difficult and the normalizations involved in the BoF model can cause
+significant instabilities during the training process. The proposed method is
+capable of overcoming these limitations by a employing a novel adaptive scaling
+mechanism and replacing the classical Gaussian-based density estimation
+involved in the regular BoF model with a logistic kernel. The effectiveness of
+the proposed approach is demonstrated using extensive experiments on a
+large-scale financial time series dataset that consists of more than 4 million
+limit orders.
+",1,0,0,1,0,1
+17582,Extended Bose Hubbard model for two leg ladder systems in artificial magnetic fields,"  We investigate the ground state properties of ultracold atoms with long range
+interactions trapped in a two leg ladder configuration in the presence of an
+artificial magnetic field. Using a Gross-Pitaevskii approach and a mean field
+Gutzwiller variational method, we explore both the weakly interacting and
+strongly interacting regime, respectively. We calculate the boundaries between
+the density-wave/supersolid and the Mott-insulator/superfluid phases as a
+function of magnetic flux and uncover regions of supersolidity. The mean-field
+results are confirmed by numerical simulations using a cluster mean field
+approach.
+",0,1,0,0,0,0
+17583,Insensitivity of The Distance Ladder Hubble Constant Determination to Cepheid Calibration Modeling Choices,"  Recent determination of the Hubble constant via Cepheid-calibrated supernovae
+by \citet{riess_2.4_2016} (R16) find $\sim 3\sigma$ tension with inferences
+based on cosmic microwave background temperature and polarization measurements
+from $Planck$. This tension could be an indication of inadequacies in the
+concordance $\Lambda$CDM model. Here we investigate the possibility that the
+discrepancy could instead be due to systematic bias or uncertainty in the
+Cepheid calibration step of the distance ladder measurement by R16. We consider
+variations in total-to-selective extinction of Cepheid flux as a function of
+line-of-sight, hidden structure in the period-luminosity relationship, and
+potentially different intrinsic color distributions of Cepheids as a function
+of host galaxy. Considering all potential sources of error, our final
+determination of $H_0 = 73.3 \pm 1.7~{\rm km/s/Mpc}$ (not including systematic
+errors from the treatment of geometric distances or Type Ia Supernovae) shows
+remarkable robustness and agreement with R16. We conclude systematics from the
+modeling of Cepheid photometry, including Cepheid selection criteria, cannot
+explain the observed tension between Cepheid-variable and CMB-based inferences
+of the Hubble constant. Considering a `model-independent' approach to relating
+Cepheids in galaxies with known distances to Cepheids in galaxies hosting a
+Type Ia supernova and finding agreement with the R16 result, we conclude no
+generalization of the model relating anchor and host Cepheid magnitude
+measurements can introduce significant bias in the $H_0$ inference.
+",0,1,0,0,0,0
+17584,Phrase-based Image Captioning with Hierarchical LSTM Model,"  Automatic generation of caption to describe the content of an image has been
+gaining a lot of research interests recently, where most of the existing works
+treat the image caption as pure sequential data. Natural language, however
+possess a temporal hierarchy structure, with complex dependencies between each
+subsequence. In this paper, we propose a phrase-based hierarchical Long
+Short-Term Memory (phi-LSTM) model to generate image description. In contrast
+to the conventional solutions that generate caption in a pure sequential
+manner, our proposed model decodes image caption from phrase to sentence. It
+consists of a phrase decoder at the bottom hierarchy to decode noun phrases of
+variable length, and an abbreviated sentence decoder at the upper hierarchy to
+decode an abbreviated form of the image description. A complete image caption
+is formed by combining the generated phrases with sentence during the inference
+stage. Empirically, our proposed model shows a better or competitive result on
+the Flickr8k, Flickr30k and MS-COCO datasets in comparison to the state-of-the
+art models. We also show that our proposed model is able to generate more novel
+captions (not seen in the training data) which are richer in word contents in
+all these three datasets.
+",1,0,0,0,0,0
+17585,The three-dimensional structure of swirl-switching in bent pipe flow,"  Swirl-switching is a low-frequency oscillatory phenomenon which affects the
+Dean vortices in bent pipes and may cause fatigue in piping systems. Despite
+thirty years worth of research, the mechanism that causes these oscillations
+and the frequencies that characterise them remain unclear. Here we show that a
+three-dimensional wave-like structure is responsible for the low-frequency
+switching of the dominant Dean vortex. The present study, performed via direct
+numerical simulation, focuses on the turbulent flow through a 90 \degree pipe
+bend preceded and followed by straight pipe segments. A pipe with curvature 0.3
+(defined as ratio between pipe radius and bend radius) is studied for a bulk
+Reynolds number Re = 11 700, corresponding to a friction Reynolds number
+Re_\tau \approx 360. Synthetic turbulence is generated at the inflow section
+and used instead of the classical recycling method in order to avoid the
+interference between recycling and swirl-switching frequencies. The flow field
+is analysed by three-dimensional proper orthogonal decomposition (POD) which
+for the first time allows the identification of the source of swirl-switching:
+a wave-like structure that originates in the pipe bend. Contrary to some
+previous studies, the flow in the upstream pipe does not show any direct
+influence on the swirl-switching modes. Our analysis further shows that a
+three- dimensional characterisation of the modes is crucial to understand the
+mechanism, and that reconstructions based on 2D POD modes are incomplete.
+",0,1,0,0,0,0
+17586,InfoVAE: Information Maximizing Variational Autoencoders,"  A key advance in learning generative models is the use of amortized inference
+distributions that are jointly trained with the models. We find that existing
+training objectives for variational autoencoders can lead to inaccurate
+amortized inference distributions and, in some cases, improving the objective
+provably degrades the inference quality. In addition, it has been observed that
+variational autoencoders tend to ignore the latent variables when combined with
+a decoding distribution that is too flexible. We again identify the cause in
+existing training criteria and propose a new class of objectives (InfoVAE) that
+mitigate these problems. We show that our model can significantly improve the
+quality of the variational posterior and can make effective use of the latent
+features regardless of the flexibility of the decoding distribution. Through
+extensive qualitative and quantitative analyses, we demonstrate that our models
+outperform competing approaches on multiple performance metrics.
+",1,0,0,1,0,0
+17587,Between-class Learning for Image Classification,"  In this paper, we propose a novel learning method for image classification
+called Between-Class learning (BC learning). We generate between-class images
+by mixing two images belonging to different classes with a random ratio. We
+then input the mixed image to the model and train the model to output the
+mixing ratio. BC learning has the ability to impose constraints on the shape of
+the feature distributions, and thus the generalization ability is improved. BC
+learning is originally a method developed for sounds, which can be digitally
+mixed. Mixing two image data does not appear to make sense; however, we argue
+that because convolutional neural networks have an aspect of treating input
+data as waveforms, what works on sounds must also work on images. First, we
+propose a simple mixing method using internal divisions, which surprisingly
+proves to significantly improve performance. Second, we propose a mixing method
+that treats the images as waveforms, which leads to a further improvement in
+performance. As a result, we achieved 19.4% and 2.26% top-1 errors on
+ImageNet-1K and CIFAR-10, respectively.
+",1,0,0,1,0,0
+17588,Girsanov reweighting for path ensembles and Markov state models,"  The sensitivity of molecular dynamics on changes in the potential energy
+function plays an important role in understanding the dynamics and function of
+complex molecules.We present a method to obtain path ensemble averages of a
+perturbed dynamics from a set of paths generated by a reference dynamics. It is
+based on the concept of path probability measure and the Girsanov theorem, a
+result from stochastic analysis to estimate a change of measure of a path
+ensemble. Since Markov state models (MSM) of the molecular dynamics can be
+formulated as a combined phase-space and path ensemble average, the method can
+be extended toreweight MSMs by combining it with a reweighting of the Boltzmann
+distribution. We demonstrate how to efficiently implement the Girsanov
+reweighting in a molecular dynamics simulation program by calculating parts of
+the reweighting factor ""on the fly"" during the simulation, and we benchmark the
+method on test systems ranging from a two-dimensional diffusion process to an
+artificial many-body system and alanine dipeptide and valine dipeptide in
+implicit and explicit water. The method can be used to study the sensitivity of
+molecular dynamics on external perturbations as well as to reweight
+trajectories generated by enhanced sampling schemes to the original dynamics.
+",0,1,0,0,0,0
+17589,Coordination of multi-agent systems via asynchronous cloud communication,"  In this work we study a multi-agent coordination problem in which agents are
+only able to communicate with each other intermittently through a cloud server.
+To reduce the amount of required communication, we develop a self-triggered
+algorithm that allows agents to communicate with the cloud only when necessary
+rather than at some fixed period. Unlike the vast majority of similar works
+that propose distributed event- and/or self-triggered control laws, this work
+doesn't assume agents can be ""listening"" continuously. In other words, when an
+event is triggered by one agent, neighboring agents will not be aware of this
+until the next time they establish communication with the cloud themselves.
+Using a notion of ""promises"" about future control inputs, agents are able to
+keep track of higher quality estimates about their neighbors allowing them to
+stay disconnected from the cloud for longer periods of time while still
+guaranteeing a positive contribution to the global task. We prove that our
+self-triggered coordination algorithm guarantees that the system asymptotically
+reaches the set of desired states. Simulations illustrate our results.
+",1,0,1,0,0,0
+17590,PatternListener: Cracking Android Pattern Lock Using Acoustic Signals,"  Pattern lock has been widely used for authentication to protect user privacy
+on mobile devices (e.g., smartphones and tablets). Given its pervasive usage,
+the compromise of pattern lock could lead to serious consequences. Several
+attacks have been constructed to crack the lock. However, these approaches
+require the attackers to either be physically close to the target device or be
+able to manipulate the network facilities (e.g., WiFi hotspots) used by the
+victims. Therefore, the effectiveness of the attacks is significantly impacted
+by the environment of mobile devices. Also, these attacks are not scalable
+since they cannot easily infer unlock patterns of a large number of devices.
+Motivated by an observation that fingertip motions on the screen of a mobile
+device can be captured by analyzing surrounding acoustic signals on it, we
+propose PatternListener, a novel acoustic attack that cracks pattern lock by
+analyzing imperceptible acoustic signals reflected by the fingertip. It
+leverages speakers and microphones of the victim's device to play imperceptible
+audio and record the acoustic signals reflected by the fingertip. In
+particular, it infers each unlock pattern by analyzing individual lines that
+compose the pattern and are the trajectories of the fingertip. We propose
+several algorithms to construct signal segments according to the captured
+signals for each line and infer possible candidates of each individual line
+according to the signal segments. Finally, we map all line candidates into grid
+patterns and thereby obtain the candidates of the entire unlock pattern. We
+implement a PatternListener prototype by using off-the-shelf smartphones and
+thoroughly evaluate it using 130 unique patterns. The real experimental results
+demonstrate that PatternListener can successfully exploit over 90% patterns
+within five attempts.
+",1,0,0,0,0,0
+17591,Marginal Release Under Local Differential Privacy,"  Many analysis and machine learning tasks require the availability of marginal
+statistics on multidimensional datasets while providing strong privacy
+guarantees for the data subjects. Applications for these statistics range from
+finding correlations in the data to fitting sophisticated prediction models. In
+this paper, we provide a set of algorithms for materializing marginal
+statistics under the strong model of local differential privacy. We prove the
+first tight theoretical bounds on the accuracy of marginals compiled under each
+approach, perform empirical evaluation to confirm these bounds, and evaluate
+them for tasks such as modeling and correlation testing. Our results show that
+releasing information based on (local) Fourier transformations of the input is
+preferable to alternatives based directly on (local) marginals.
+",1,0,0,0,0,0
+17592,A possible flyby anomaly for Juno at Jupiter,"  In the last decades there have been an increasing interest in improving the
+accuracy of spacecraft navigation and trajectory data. In the course of this
+plan some anomalies have been found that cannot, in principle, be explained in
+the context of the most accurate orbital models including all known effects
+from classical dynamics and general relativity. Of particular interest for its
+puzzling nature, and the lack of any accepted explanation for the moment, is
+the flyby anomaly discovered in some spacecraft flybys of the Earth over the
+course of twenty years. This anomaly manifest itself as the impossibility of
+matching the pre and post-encounter Doppler tracking and ranging data within a
+single orbit but, on the contrary, a difference of a few mm$/$s in the
+asymptotic velocities is required to perform the fitting.
+Nevertheless, no dedicated missions have been carried out to elucidate the
+origin of this phenomenon with the objective either of revising our
+understanding of gravity or to improve the accuracy of spacecraft Doppler
+tracking by revealing a conventional origin.
+With the occasion of the Juno mission arrival at Jupiter and the close flybys
+of this planet, that are currently been performed, we have developed an orbital
+model suited to the time window close to the perijove. This model shows that an
+anomalous acceleration of a few mm$/$s$^2$ is also present in this case. The
+chance for overlooked conventional or possible unconventional explanations is
+discussed.
+",0,1,0,0,0,0
+17593,Crossover between various initial conditions in KPZ growth: flat to stationary,"  We conjecture the universal probability distribution at large time for the
+one-point height in the 1D Kardar-Parisi-Zhang (KPZ) stochastic growth
+universality class, with initial conditions interpolating from any one of the
+three main classes (droplet, flat, stationary) on the left, to another on the
+right, allowing for drifts and also for a step near the origin. The result is
+obtained from a replica Bethe ansatz calculation starting from the KPZ
+continuum equation, together with a ""decoupling assumption"" in the large time
+limit. Some cases are checked to be equivalent to previously known results from
+other models in the same class, which provides a test of the method, others
+appear to be new. In particular we obtain the crossover distribution between
+flat and stationary initial conditions (crossover from Airy$_1$ to Airy$_{\rm
+stat}$) in a simple compact form.
+",0,1,1,0,0,0
+17594,Multimodel Response Assessment for Monthly Rainfall Distribution in Some Selected Indian Cities Using Best Fit Probability as a Tool,"  We carry out a study of the statistical distribution of rainfall
+precipitation data for 20 cites in India. We have determined the best-fit
+probability distribution for these cities from the monthly precipitation data
+spanning 100 years of observations from 1901 to 2002. To fit the observed data,
+we considered 10 different distributions. The efficacy of the fits for these
+distributions was evaluated using four empirical non-parametric goodness-of-fit
+tests namely Kolmogorov-Smirnov, Anderson-Darling, Chi-Square, Akaike
+information criterion, and Bayesian Information criterion. Finally, the
+best-fit distribution using each of these tests were reported, by combining the
+results from the model comparison tests. We then find that for most of the
+cities, Generalized Extreme-Value Distribution or Inverse Gaussian Distribution
+most adequately fits the observed data.
+",0,1,0,1,0,0
+17595,Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing,"  Bin Packing problems have been widely studied because of their broad
+applications in different domains. Known as a set of NP-hard problems, they
+have different vari- ations and many heuristics have been proposed for
+obtaining approximate solutions. Specifically, for the 1D variable sized bin
+packing problem, the two key sets of optimization heuristics are the bin
+assignment and the bin allocation. Usually the performance of a single static
+optimization heuristic can not beat that of a dynamic one which is tailored for
+each bin packing instance. Building such an adaptive system requires modeling
+the relationship between bin features and packing perform profiles. The primary
+drawbacks of traditional AI machine learnings for this task are the natural
+limitations of feature engineering, such as the curse of dimensionality and
+feature selection quality. We introduce a deep learning approach to overcome
+the drawbacks by applying a large training data set, auto feature selection and
+fast, accurate labeling. We show in this paper how to build such a system by
+both theoretical formulation and engineering practices. Our prediction system
+achieves up to 89% training accuracy and 72% validation accuracy to select the
+best heuristic that can generate a better quality bin packing solution.
+",1,0,0,1,0,0
+17596,Surges of collective human activity emerge from simple pairwise correlations,"  Human populations exhibit complex behaviors---characterized by long-range
+correlations and surges in activity---across a range of social, political, and
+technological contexts. Yet it remains unclear where these collective behaviors
+come from, or if there even exists a set of unifying principles. Indeed,
+existing explanations typically rely on context-specific mechanisms, such as
+traffic jams driven by work schedules or spikes in online traffic induced by
+significant events. However, analogies with statistical mechanics suggest a
+more general mechanism: that collective patterns can emerge organically from
+fine-scale interactions within a population. Here, across four different modes
+of human activity, we show that the simplest correlations in a
+population---those between pairs of individuals---can yield accurate
+quantitative predictions for the large-scale behavior of the entire population.
+To quantify the minimal consequences of pairwise correlations, we employ the
+principle of maximum entropy, making our description equivalent to an Ising
+model whose interactions and external fields are notably calculated from past
+observations of population activity. In addition to providing accurate
+quantitative predictions, we show that the topology of learned Ising
+interactions resembles the network of inter-human communication within a
+population. Together, these results demonstrate that fine-scale correlations
+can be used to predict large-scale social behaviors, a perspective that has
+critical implications for modeling and resource allocation in human
+populations.
+",1,0,0,0,0,0
+17597,Semantically Enhanced Dynamic Bayesian Network for Detecting Sepsis Mortality Risk in ICU Patients with Infection,"  Although timely sepsis diagnosis and prompt interventions in Intensive Care
+Unit (ICU) patients are associated with reduced mortality, early clinical
+recognition is frequently impeded by non-specific signs of infection and
+failure to detect signs of sepsis-induced organ dysfunction in a constellation
+of dynamically changing physiological data. The goal of this work is to
+identify patient at risk of life-threatening sepsis utilizing a data-centered
+and machine learning-driven approach. We derive a mortality risk predictive
+dynamic Bayesian network (DBN) guided by a customized sepsis knowledgebase and
+compare the predictive accuracy of the derived DBN with the Sepsis-related
+Organ Failure Assessment (SOFA) score, the Quick SOFA (qSOFA) score, the
+Simplified Acute Physiological Score (SAPS-II) and the Modified Early Warning
+Score (MEWS) tools.
+A customized sepsis ontology was used to derive the DBN node structure and
+semantically characterize temporal features derived from both structured
+physiological data and unstructured clinical notes. We assessed the performance
+in predicting mortality risk of the DBN predictive model and compared
+performance to other models using Receiver Operating Characteristic (ROC)
+curves, area under curve (AUROC), calibration curves, and risk distributions.
+The derived dataset consists of 24,506 ICU stays from 19,623 patients with
+evidence of suspected infection, with 2,829 patients deceased at discharge. The
+DBN AUROC was found to be 0.91, which outperformed the SOFA (0.843), qSOFA
+(0.66), MEWS (0.73), and SAPS-II (0.77) scoring tools. Continuous Net
+Reclassification Index and Integrated Discrimination Improvement analysis
+supported the superiority DBN. Compared with conventional rule-based risk
+scoring tools, the sepsis knowledgebase-driven DBN algorithm offers improved
+performance for predicting mortality of infected patients in ICUs.
+",0,0,0,1,0,0
+17598,Hölder regularity of viscosity solutions of some fully nonlinear equations in the Heisenberg group,"  In this paper we prove the Hölder regularity of bounded, uniformly
+continuous, viscosity solutions of some degenerate fully nonlinear equations in
+the Heisenberg group.
+",0,0,1,0,0,0
+17599,Normal form for transverse instability of the line soliton with a nearly critical speed of propagation,"  There exists a critical speed of propagation of the line solitons in the
+Zakharov-Kuznetsov (ZK) equation such that small transversely periodic
+perturbations are unstable for line solitons with larger-than-critical speeds
+and orbitally stable for those with smaller-than-critical speeds. The normal
+form for transverse instability of the line soliton with a nearly critical
+speed of propagation is derived by means of symplectic projections and
+near-identity transformations. Justification of this normal form is provided
+with the energy method. The normal form predicts a transformation of the
+unstable line solitons with larger-than-critical speeds to the orbitally stable
+transversely modulated solitary waves.
+",0,1,1,0,0,0
+17600,The CLaC Discourse Parser at CoNLL-2016,"  This paper describes our submission ""CLaC"" to the CoNLL-2016 shared task on
+shallow discourse parsing. We used two complementary approaches for the task. A
+standard machine learning approach for the parsing of explicit relations, and a
+deep learning approach for non-explicit relations. Overall, our parser achieves
+an F1-score of 0.2106 on the identification of discourse relations (0.3110 for
+explicit relations and 0.1219 for non-explicit relations) on the blind
+CoNLL-2016 test set.
+",1,0,0,0,0,0
+17601,Ordinary differential equations in algebras of generalized functions,"  A local existence and uniqueness theorem for ODEs in the special algebra of
+generalized functions is established, as well as versions including parameters
+and dependence on initial values in the generalized sense. Finally, a Frobenius
+theorem is proved. In all these results, composition of generalized functions
+is based on the notion of c-boundedness.
+",0,0,1,0,0,0
+17602,Interesting Paths in the Mapper,"  The Mapper produces a compact summary of high dimensional data as a
+simplicial complex. We study the problem of quantifying the interestingness of
+subpopulations in a Mapper, which appear as long paths, flares, or loops.
+First, we create a weighted directed graph G using the 1-skeleton of the
+Mapper. We use the average values at the vertices of a target function to
+direct edges (from low to high). The difference between the average values at
+vertices (high-low) is set as the edge's weight. Covariation of the remaining h
+functions (independent variables) is captured by a h-bit binary signature
+assigned to the edge. An interesting path in G is a directed path whose edges
+all have the same signature. We define the interestingness score of such a path
+as a sum of its edge weights multiplied by a nonlinear function of their ranks
+in the path.
+Second, we study three optimization problems on this graph G. In the problem
+Max-IP, we seek an interesting path in G with the maximum interestingness
+score. We show that Max-IP is NP-complete. For the special case when G is a
+directed acyclic graph (DAG), we show that Max-IP can be solved in polynomial
+time - in O(mnd_i) where d_i is the maximum indegree of a vertex in G.
+In the more general problem IP, the goal is to find a collection of
+edge-disjoint interesting paths such that the overall sum of their
+interestingness scores is maximized. We also study a variant of IP termed k-IP,
+where the goal is to identify a collection of edge-disjoint interesting paths
+each with k edges, and their total interestingness score is maximized. While
+k-IP can be solved in polynomial time for k <= 2, we show k-IP is NP-complete
+for k >= 3 even when G is a DAG. We develop polynomial time heuristics for IP
+and k-IP on DAGs.
+",1,0,1,0,0,0
+17603,On a three dimensional vision based collision avoidance model,"  This paper presents a three dimensional collision avoidance approach for
+aerial vehicles inspired by coordinated behaviors in biological groups. The
+proposed strategy aims to enable a group of vehicles to converge to a common
+destination point avoiding collisions with each other and with moving obstacles
+in their environment. The interaction rules lead the agents to adapt their
+velocity vectors through a modification of the relative bearing angle and the
+relative elevation. Moreover the model satisfies the limited field of view
+constraints resulting from individual perception sensitivity. From the proposed
+individual based model, a mean-field kinetic model is derived. Simulations are
+performed to show the effectiveness of the proposed model.
+",0,0,1,0,0,0
+17604,Algorithms for Covering Multiple Barriers,"  In this paper, we consider the problems for covering multiple intervals on a
+line. Given a set $B$ of $m$ line segments (called ""barriers"") on a horizontal
+line $L$ and another set $S$ of $n$ horizontal line segments of the same length
+in the plane, we want to move all segments of $S$ to $L$ so that their union
+covers all barriers and the maximum movement of all segments of $S$ is
+minimized. Previously, an $O(n^3\log n)$-time algorithm was given for the case
+$m=1$. In this paper, we propose an $O(n^2\log n\log \log n+nm\log m)$-time
+algorithm for a more general setting with any $m\geq 1$, which also improves
+the previous work when $m=1$. We then consider a line-constrained version of
+the problem in which the segments of $S$ are all initially on the line $L$.
+Previously, an $O(n\log n)$-time algorithm was known for the case $m=1$. We
+present an algorithm of $O(m\log m+n\log m \log n)$ time for any $m\geq 1$.
+These problems may have applications in mobile sensor barrier coverage in
+wireless sensor networks.
+",1,0,0,0,0,0
+17605,Shattering the glass ceiling? How the institutional context mitigates the gender gap in entrepreneurship,"  We examine how the institutional context affects the relationship between
+gender and opportunity entrepreneurship. To do this, we develop a multi-level
+model that connects feminist theory at the micro-level to institutional theory
+at the macro-level. It is hypothesized that the gender gap in opportunity
+entrepreneurship is more pronounced in low-quality institutional contexts and
+less pronounced in high-quality institutional contexts. Using data from the
+Global Entrepreneurship Monitor (GEM) and regulation data from the economic
+freedom of the world index (EFW), we test our predictions and find evidence in
+support of our model. Our findings suggest that, while there is a gender gap in
+entrepreneurship, these disparities are reduced as the quality of the
+institutional context improves.
+",0,0,0,0,0,1
+17606,An Automated Text Categorization Framework based on Hyperparameter Optimization,"  A great variety of text tasks such as topic or spam identification, user
+profiling, and sentiment analysis can be posed as a supervised learning problem
+and tackle using a text classifier. A text classifier consists of several
+subprocesses, some of them are general enough to be applied to any supervised
+learning problem, whereas others are specifically designed to tackle a
+particular task, using complex and computational expensive processes such as
+lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we
+propose a minimalistic and wide system able to tackle text classification tasks
+independent of domain and language, namely microTC. It is composed by some easy
+to implement text transformations, text representations, and a supervised
+learning algorithm. These pieces produce a competitive classifier even in the
+domain of informally written text. We provide a detailed description of microTC
+along with an extensive experimental comparison with relevant state-of-the-art
+methods. mircoTC was compared on 30 different datasets. Regarding accuracy,
+microTC obtained the best performance in 20 datasets while achieves competitive
+results in the remaining 10. The compared datasets include several problems
+like topic and polarity classification, spam detection, user profiling and
+authorship attribution. Furthermore, it is important to state that our approach
+allows the usage of the technology even without knowledge of machine learning
+and natural language processing.
+",1,0,0,1,0,0
+17607,Abdominal aortic aneurysms and endovascular sealing: deformation and dynamic response,"  Endovascular sealing is a new technique for the repair of abdominal aortic
+aneurysms. Commercially available in Europe since~2013, it takes a
+revolutionary approach to aneurysm repair through minimally invasive
+techniques. Although aneurysm sealing may be thought as more stable than
+conventional endovascular stent graft repairs, post-implantation movement of
+the endoprosthesis has been described, potentially leading to late
+complications. The paper presents for the first time a model, which explains
+the nature of forces, in static and dynamic regimes, acting on sealed abdominal
+aortic aneurysms, with references to real case studies. It is shown that
+elastic deformation of the aorta and of the endoprosthesis induced by static
+forces and vibrations during daily activities can potentially promote undesired
+movements of the endovascular sealing structure.
+",0,1,0,0,0,0
+17608,Affinity Scheduling and the Applications on Data Center Scheduling with Data Locality,"  MapReduce framework is the de facto standard in Hadoop. Considering the data
+locality in data centers, the load balancing problem of map tasks is a special
+case of affinity scheduling problem. There is a huge body of work on affinity
+scheduling, proposing heuristic algorithms which try to increase data locality
+in data centers like Delay Scheduling and Quincy. However, not enough attention
+has been put on theoretical guarantees on throughput and delay optimality of
+such algorithms. In this work, we present and compare different algorithms and
+discuss their shortcoming and strengths. To the best of our knowledge, most
+data centers are using static load balancing algorithms which are not efficient
+in any ways and results in wasting the resources and causing unnecessary delays
+for users.
+",1,0,0,0,0,0
+17609,Multivariate Regression with Gross Errors on Manifold-valued Data,"  We consider the topic of multivariate regression on manifold-valued output,
+that is, for a multivariate observation, its output response lies on a
+manifold. Moreover, we propose a new regression model to deal with the presence
+of grossly corrupted manifold-valued responses, a bottleneck issue commonly
+encountered in practical scenarios. Our model first takes a correction step on
+the grossly corrupted responses via geodesic curves on the manifold, and then
+performs multivariate linear regression on the corrected data. This results in
+a nonconvex and nonsmooth optimization problem on manifolds. To this end, we
+propose a dedicated approach named PALMR, by utilizing and extending the
+proximal alternating linearized minimization techniques. Theoretically, we
+investigate its convergence property, where it is shown to converge to a
+critical point under mild conditions. Empirically, we test our model on both
+synthetic and real diffusion tensor imaging data, and show that our model
+outperforms other multivariate regression models when manifold-valued responses
+contain gross errors, and is effective in identifying gross errors.
+",1,0,1,1,0,0
+17610,Computing an Approximately Optimal Agreeable Set of Items,"  We study the problem of finding a small subset of items that is
+\emph{agreeable} to all agents, meaning that all agents value the subset at
+least as much as its complement. Previous work has shown worst-case bounds,
+over all instances with a given number of agents and items, on the number of
+items that may need to be included in such a subset. Our goal in this paper is
+to efficiently compute an agreeable subset whose size approximates the size of
+the smallest agreeable subset for a given instance. We consider three
+well-known models for representing the preferences of the agents: ordinal
+preferences on single items, the value oracle model, and additive utilities. In
+each of these models, we establish virtually tight bounds on the approximation
+ratio that can be obtained by algorithms running in polynomial time.
+",1,0,0,0,0,0
+17611,3D Sketching using Multi-View Deep Volumetric Prediction,"  Sketch-based modeling strives to bring the ease and immediacy of drawing to
+the 3D world. However, while drawings are easy for humans to create, they are
+very challenging for computers to interpret due to their sparsity and
+ambiguity. We propose a data-driven approach that tackles this challenge by
+learning to reconstruct 3D shapes from one or more drawings. At the core of our
+approach is a deep convolutional neural network (CNN) that predicts occupancy
+of a voxel grid from a line drawing. This CNN provides us with an initial 3D
+reconstruction as soon as the user completes a single drawing of the desired
+shape. We complement this single-view network with an updater CNN that refines
+an existing prediction given a new drawing of the shape created from a novel
+viewpoint. A key advantage of our approach is that we can apply the updater
+iteratively to fuse information from an arbitrary number of viewpoints, without
+requiring explicit stroke correspondences between the drawings. We train both
+CNNs by rendering synthetic contour drawings from hand-modeled shape
+collections as well as from procedurally-generated abstract shapes. Finally, we
+integrate our CNNs in a minimal modeling interface that allows users to
+seamlessly draw an object, rotate it to see its 3D reconstruction, and refine
+it by re-drawing from another vantage point using the 3D reconstruction as
+guidance. The main strengths of our approach are its robustness to freehand
+bitmap drawings, its ability to adapt to different object categories, and the
+continuum it offers between single-view and multi-view sketch-based modeling.
+",1,0,0,0,0,0
+17612,Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier,"  We study the problem of ranking a set of items from nonactively chosen
+pairwise preferences where each item has feature information with it. We
+propose and characterize a very broad class of preference matrices giving rise
+to the Feature Low Rank (FLR) model, which subsumes several models ranging from
+the classic Bradley-Terry-Luce (BTL) (Bradley and Terry 1952) and Thurstone
+(Thurstone 1927) models to the recently proposed blade-chest (Chen and Joachims
+2016) and generic low-rank preference (Rajkumar and Agarwal 2016) models. We
+use the technique of matrix completion in the presence of side information to
+develop the Inductive Pairwise Ranking (IPR) algorithm that provably learns a
+good ranking under the FLR model, in a sample-efficient manner. In practice,
+through systematic synthetic simulations, we confirm our theoretical findings
+regarding improvements in the sample complexity due to the use of feature
+information. Moreover, on popular real-world preference learning datasets, with
+as less as 10% sampling of the pairwise comparisons, our method recovers a good
+ranking.
+",1,0,0,1,0,0
+17613,SAGA and Restricted Strong Convexity,"  SAGA is a fast incremental gradient method on the finite sum problem and its
+effectiveness has been tested on a vast of applications. In this paper, we
+analyze SAGA on a class of non-strongly convex and non-convex statistical
+problem such as Lasso, group Lasso, Logistic regression with $\ell_1$
+regularization, linear regression with SCAD regularization and Correct Lasso.
+We prove that SAGA enjoys the linear convergence rate up to the statistical
+estimation accuracy, under the assumption of restricted strong convexity (RSC).
+It significantly extends the applicability of SAGA in convex and non-convex
+optimization.
+",0,0,0,1,0,0
+17614,Characterization of Traps at Nitrided SiO$_2$/SiC Interfaces near the Conduction Band Edge by using Hall Effect Measurements,"  The effects of nitridation on the density of traps at SiO$_2$/SiC interfaces
+near the conduction band edge were qualitatively examined by a simple, newly
+developed characterization method that utilizes Hall effect measurements and
+split capacitance-voltage measurements. The results showed a significant
+reduction in the density of interface traps near the conduction band edge by
+nitridation, as well as the high density of interface traps that was not
+eliminated by nitridation.
+",0,1,0,0,0,0
+17615,Response theory of the ergodic many-body delocalized phase: Keldysh Finkel'stein sigma models and the 10-fold way,"  We derive the finite temperature Keldysh response theory for interacting
+fermions in the presence of quenched disorder, as applicable to any of the 10
+Altland-Zirnbauer classes in an Anderson delocalized phase with at least a U(1)
+continuous symmetry. In this formulation of the interacting Finkel'stein
+nonlinear sigma model, the statistics of one-body wave functions are encoded by
+the constrained matrix field, while physical correlations follow from the
+hydrodynamic density or spin response field, which decouples the interactions.
+Integrating out the matrix field first, we obtain weak (anti)localization and
+Altshuler-Aronov quantum conductance corrections from the hydrodynamic response
+function. This procedure automatically incorporates the correct infrared
+physics, and in particular gives the Altshuler-Aronov-Khmelnitsky (AAK)
+equations for dephasing of weak (anti)localization due to electron-electron
+collisions. We explicate the method by deriving known quantum corrections in
+two dimensions for the symplectic metal class AII, as well as the spin-SU(2)
+invariant superconductor classes C and CI. We show that conductance corrections
+due to the special modes at zero energy in nonstandard classes are
+automatically cut off by temperature, as previously expected, while the
+Wigner-Dyson class Cooperon modes that persist to all energies are cut by
+dephasing. We also show that for short-ranged interactions, the standard
+self-consistent solution for the dephasing rate is equivalent to a diagrammatic
+summation via the self-consistent Born approximation. This should be compared
+to the AAK solution for long-ranged Coulomb interactions, which exploits the
+Markovian noise correlations induced by thermal fluctuations of the
+electromagnetic field. We discuss prospects for exploring the many-body
+localization transition from the ergodic side as a dephasing catastrophe in
+short-range interacting models.
+",0,1,0,0,0,0
+17616,Classification via Tensor Decompositions of Echo State Networks,"  This work introduces a tensor-based method to perform supervised
+classification on spatiotemporal data processed in an echo state network.
+Typically when performing supervised classification tasks on data processed in
+an echo state network, the entire collection of hidden layer node states from
+the training dataset is shaped into a matrix, allowing one to use standard
+linear algebra techniques to train the output layer. However, the collection of
+hidden layer states is multidimensional in nature, and representing it as a
+matrix may lead to undesirable numerical conditions or loss of spatial and
+temporal correlations in the data.
+This work proposes a tensor-based supervised classification method on echo
+state network data that preserves and exploits the multidimensional nature of
+the hidden layer states. The method, which is based on orthogonal Tucker
+decompositions of tensors, is compared with the standard linear output weight
+approach in several numerical experiments on both synthetic and natural data.
+The results show that the tensor-based approach tends to outperform the
+standard approach in terms of classification accuracy.
+",1,0,0,1,0,0
+17617,Inference on Breakdown Frontiers,"  Given a set of baseline assumptions, a breakdown frontier is the boundary
+between the set of assumptions which lead to a specific conclusion and those
+which do not. In a potential outcomes model with a binary treatment, we
+consider two conclusions: First, that ATE is at least a specific value (e.g.,
+nonnegative) and second that the proportion of units who benefit from treatment
+is at least a specific value (e.g., at least 50\%). For these conclusions, we
+derive the breakdown frontier for two kinds of assumptions: one which indexes
+relaxations of the baseline random assignment of treatment assumption, and one
+which indexes relaxations of the baseline rank invariance assumption. These
+classes of assumptions nest both the point identifying assumptions of random
+assignment and rank invariance and the opposite end of no constraints on
+treatment selection or the dependence structure between potential outcomes.
+This frontier provides a quantitative measure of robustness of conclusions to
+relaxations of the baseline point identifying assumptions. We derive
+$\sqrt{N}$-consistent sample analog estimators for these frontiers. We then
+provide two asymptotically valid bootstrap procedures for constructing lower
+uniform confidence bands for the breakdown frontier. As a measure of
+robustness, estimated breakdown frontiers and their corresponding confidence
+bands can be presented alongside traditional point estimates and confidence
+intervals obtained under point identifying assumptions. We illustrate this
+approach in an empirical application to the effect of child soldiering on
+wages. We find that sufficiently weak conclusions are robust to simultaneous
+failures of rank invariance and random assignment, while some stronger
+conclusions are fairly robust to failures of rank invariance but not
+necessarily to relaxations of random assignment.
+",0,0,0,1,0,0
+17618,Sequential Detection of Three-Dimensional Signals under Dependent Noise,"  We study detection methods for multivariable signals under dependent noise.
+The main focus is on three-dimensional signals, i.e. on signals in the
+space-time domain. Examples for such signals are multifaceted. They include
+geographic and climatic data as well as image data, that are observed over a
+fixed time horizon. We assume that the signal is observed as a finite block of
+noisy samples whereby we are interested in detecting changes from a given
+reference signal. Our detector statistic is based on a sequential partial sum
+process, related to classical signal decomposition and reconstruction
+approaches applied to the sampled signal. We show that this detector process
+converges weakly under the no change null hypothesis that the signal coincides
+with the reference signal, provided that the spatial-temporal partial sum
+process associated to the random field of the noise terms disturbing the
+sampled signal con- verges to a Brownian motion. More generally, we also
+establish the limiting distribution under a wide class of local alternatives
+that allows for smooth as well as discontinuous changes. Our results also cover
+extensions to the case that the reference signal is unknown. We conclude with
+an extensive simulation study of the detection algorithm.
+",0,0,1,1,0,0
+17619,T-Branes at the Limits of Geometry,"  Singular limits of 6D F-theory compactifications are often captured by
+T-branes, namely a non-abelian configuration of intersecting 7-branes with a
+nilpotent matrix of normal deformations. The long distance approximation of
+such 7-branes is a Hitchin-like system in which simple and irregular poles
+emerge at marked points of the geometry. When multiple matter fields localize
+at the same point in the geometry, the associated Higgs field can exhibit
+irregular behavior, namely poles of order greater than one. This provides a
+geometric mechanism to engineer wild Higgs bundles. Physical constraints such
+as anomaly cancellation and consistent coupling to gravity also limit the order
+of such poles. Using this geometric formulation, we unify seemingly different
+wild Hitchin systems in a single framework in which orders of poles become
+adjustable parameters dictated by tuning gauge singlet moduli of the F-theory
+model.
+",0,0,1,0,0,0
+17620,Space-time crystal and space-time group,"  Crystal structures and the Bloch theorem play a fundamental role in condensed
+matter physics. We extend the static crystal to the dynamic ""space-time""
+crystal characterized by the general intertwined space-time periodicities in
+$D+1$ dimensions, which include both the static crystal and the Floquet crystal
+as special cases. A new group structure dubbed ""space-time"" group is
+constructed to describe the discrete symmetries of space-time crystal. Compared
+to space and magnetic groups, space-time group is augmented by ""time-screw""
+rotations and ""time-glide"" reflections involving fractional translations along
+the time direction. A complete classification of the 13 space-time groups in
+1+1D is performed. The Kramers-type degeneracy can arise from the glide
+time-reversal symmetry without the half-integer spinor structure, which
+constrains the winding number patterns of spectral dispersions. In 2+1D,
+non-symmorphic space-time symmetries enforce spectral degeneracies, leading to
+protected Floquet semi-metal states. Our work provides a general framework for
+further studying topological properties of the $D+1$ dimensional space-time
+crystal.
+",0,1,0,0,0,0
+17621,On the Performance of Zero-Forcing Processing in Multi-Way Massive MIMO Relay Networks,"  We consider a multi-way massive multiple-input multiple-output relay network
+with zero-forcing processing at the relay. By taking into account the
+time-division duplex protocol with channel estimation, we derive an analytical
+approximation of the spectral efficiency. This approximation is very tight and
+simple which enables us to analyze the system performance, as well as, to
+compare the spectral efficiency with zero-forcing and maximum-ratio processing.
+Our results show that by using a very large number of relay antennas and with
+the zero-forcing technique, we can simultaneously serve many active users in
+the same time-frequency resource, each with high spectral efficiency.
+",1,0,1,0,0,0
+17622,Motivic rational homotopy type,"  In this paper we introduce and study motives for rational homotopy types.
+",0,0,1,0,0,0
+17623,Preconditioner-free Wiener filtering with a dense noise matrix,"  This work extends the Elsner & Wandelt (2013) iterative method for efficient,
+preconditioner-free Wiener filtering to cases in which the noise covariance
+matrix is dense, but can be decomposed into a sum whose parts are sparse in
+convenient bases. The new method, which uses multiple messenger fields,
+reproduces Wiener filter solutions for test problems, and we apply it to a case
+beyond the reach of the Elsner & Wandelt (2013) method. We compute the Wiener
+filter solution for a simulated Cosmic Microwave Background map that contains
+spatially-varying, uncorrelated noise, isotropic $1/f$ noise, and large-scale
+horizontal stripes (like those caused by the atmospheric noise). We discuss
+simple extensions that can filter contaminated modes or inverse-noise filter
+the data. These techniques help to address complications in the noise
+properties of maps from current and future generations of ground-based
+Microwave Background experiments, like Advanced ACTPol, Simons Observatory, and
+CMB-S4.
+",0,1,0,0,0,0
+17624,"Order-unity argument for structure-generated ""extra"" expansion","  Self-consistent treatment of cosmological structure formation and expansion
+within the context of classical general relativity may lead to ""extra""
+expansion above that expected in a structureless universe. We argue that in
+comparison to an early-epoch, extrapolated Einstein-de Sitter model, about
+10-15% ""extra"" expansion is sufficient at the present to render superfluous the
+""dark energy"" 68% contribution to the energy density budget, and that this is
+observationally realistic.
+",0,1,0,0,0,0
+17625,The least unramified prime which does not split completely,"  Let $K/F$ be a finite extension of number fields of degree $n \geq 2$. We
+establish effective field-uniform unconditional upper bounds for the least norm
+of a prime ideal of $F$ which is degree 1 over $\mathbb{Q}$ and does not ramify
+or split completely in $K$. We improve upon the previous best known general
+estimates due to X. Li when $F = \mathbb{Q}$ and Murty-Patankar when $K/F$ is
+Galois. Our bounds are the first when $K/F$ is not assumed to be Galois and $F
+\neq \mathbb{Q}$.
+",0,0,1,0,0,0
+17626,Crosscorrelation of Rudin-Shapiro-Like Polynomials,"  We consider the class of Rudin-Shapiro-like polynomials, whose $L^4$ norms on
+the complex unit circle were studied by Borwein and Mossinghoff. The polynomial
+$f(z)=f_0+f_1 z + \cdots + f_d z^d$ is identified with the sequence
+$(f_0,f_1,\ldots,f_d)$ of its coefficients. From the $L^4$ norm of a
+polynomial, one can easily calculate the autocorrelation merit factor of its
+associated sequence, and conversely. In this paper, we study the
+crosscorrelation properties of pairs of sequences associated to
+Rudin-Shapiro-like polynomials. We find an explicit formula for the
+crosscorrelation merit factor. A computer search is then used to find pairs of
+Rudin-Shapiro-like polynomials whose autocorrelation and crosscorrelation merit
+factors are simultaneously high. Pursley and Sarwate proved a bound that limits
+how good this combined autocorrelation and crosscorrelation performance can be.
+We find infinite families of polynomials whose performance approaches quite
+close to this fundamental limit.
+",1,0,1,0,0,0
+17627,An Application of Rubi: Series Expansion of the Quark Mass Renormalization Group Equation,"  We highlight how Rule-based Integration (Rubi) is an enhanced method of
+symbolic integration which allows for the integration of many difficult
+integrals not accomplished by other computer algebra systems. Using Rubi, many
+integration techniques become tractable. Integrals are approached using
+step-wise simplification, hence distilling an integral (if the solution is
+unknown) into composite integrals which highlight yet undiscovered integration
+rules. The motivating example we use is the derivation of the updated series
+expansion of the quark mass renormalization group equation (RGE) to five-loop
+order. This series provides the relation between a light quark mass in the
+modified minimal subtraction ($\overline{\text{MS}}$) scheme defined at some
+given scale, e.g. at the tau-lepton mass scale, and another chosen energy
+scale, $s$. This relation explicitly depicts the renormalization scheme
+dependence of the running quark mass on the scale parameter, $s$, and is
+important in accurately determining a light quark mass at a chosen scale. The
+five-loop QCD $\beta(a_s)$ and $\gamma(a_s)$ functions are used in this
+determination.
+",1,0,0,0,0,0
+17628,On the Performance of Wireless Powered Communication With Non-linear Energy Harvesting,"  In this paper, we analyze the performance of a time-slotted multi-antenna
+wireless powered communication (WPC) system, where a wireless device first
+harvests radio frequency (RF) energy from a power station (PS) in the downlink
+to facilitate information transfer to an information receiving station (IRS) in
+the uplink. The main goal of this paper is to provide insights and guidelines
+for the design of practical WPC systems. To this end, we adopt a recently
+proposed parametric non-linear RF energy harvesting (EH) model, which has been
+shown to accurately model the end-to-end non-linearity of practical RF EH
+circuits. In order to enhance the RF power transfer efficiency, maximum ratio
+transmission is adopted at the PS to focus the energy signals on the wireless
+device. Furthermore, at the IRS, maximum ratio combining is used. We analyze
+the outage probability and the average throughput of information transfer,
+assuming Nakagami-$m$ fading uplink and downlink channels. Moreover, we study
+the system performance as a function of the number of PS transmit antennas, the
+number of IRS receive antennas, the transmit power of the PS, the fading
+severity, the transmission rate of the wireless device, and the EH time
+duration. In addition, we obtain a fixed point equation for the optimal
+transmission rate and the optimal EH time duration that maximize the asymptotic
+throughput for high PS transmit powers. All analytical results are corroborated
+by simulations.
+",1,0,0,0,0,0
+17629,Realizing polarization conversion and unidirectional transmission by using a uniaxial crystal plate,"  We show that polarization states of electromagnetic waves can be manipulated
+easily using a single thin uniaxial crystal plate. By performing a rotational
+transformation of the coordinates and controlling the thickness of the plate,
+we can achieve a complete polarization conversion between TE wave and TM wave
+in a spectral band. We show that the off-diagonal element of the permittivity
+is the key for polarization conversion. Our analysis can explain clearly the
+results found in experiments with metamaterials. Finally, we propose a simple
+device to realize unidirectional transmission based on polarization conversion
+and excitation of surface plasmon polaritons.
+",0,1,0,0,0,0
+17630,When Should You Adjust Standard Errors for Clustering?,"  In empirical work in economics it is common to report standard errors that
+account for clustering of units. Typically, the motivation given for the
+clustering adjustments is that unobserved components in outcomes for units
+within clusters are correlated. However, because correlation may occur across
+more than one dimension, this motivation makes it difficult to justify why
+researchers use clustering in some dimensions, such as geographic, but not
+others, such as age cohorts or gender. It also makes it difficult to explain
+why one should not cluster with data from a randomized experiment. In this
+paper, we argue that clustering is in essence a design problem, either a
+sampling design or an experimental design issue. It is a sampling design issue
+if sampling follows a two stage process where in the first stage, a subset of
+clusters were sampled randomly from a population of clusters, while in the
+second stage, units were sampled randomly from the sampled clusters. In this
+case the clustering adjustment is justified by the fact that there are clusters
+in the population that we do not see in the sample. Clustering is an
+experimental design issue if the assignment is correlated within the clusters.
+We take the view that this second perspective best fits the typical setting in
+economics where clustering adjustments are used. This perspective allows us to
+shed new light on three questions: (i) when should one adjust the standard
+errors for clustering, (ii) when is the conventional adjustment for clustering
+appropriate, and (iii) when does the conventional adjustment of the standard
+errors matter.
+",0,0,1,1,0,0
+17631,Approximate homomorphisms on lattices,"  We prove two results concerning an Ulam-type stability problem for
+homomorphisms between lattices. One of them involves estimates by quite general
+error functions; the other deals with approximate (join) homomorphisms in terms
+of certain systems of lattice neighborhoods. As a corollary, we obtain a
+stability result for approximately monotone functions.
+",0,0,1,0,0,0
+17632,Learning Latent Events from Network Message Logs: A Decomposition Based Approach,"  In this communication, we describe a novel technique for event mining using a
+decomposition based approach that combines non-parametric change-point
+detection with LDA. We prove theoretical guarantees about sample-complexity and
+consistency of the approach. In a companion paper, we will perform a thorough
+evaluation of our approach with detailed experiments.
+",0,0,0,1,0,0
+17633,Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,"  Despite significant advances in artificial intelligence (AI) for computer
+vision, its application in medical imaging has been limited by the burden and
+limits of expert-generated labels. We used images from optical coherence
+tomography angiography (OCTA), a relatively new imaging modality that measures
+perfusion of the retinal vasculature, to train an AI algorithm to generate
+vasculature maps from standard structural optical coherence tomography (OCT)
+images of the same retinae, both exceeding the ability and bypassing the need
+for expert labeling. Deep learning was able to infer perfusion of
+microvasculature from structural OCT images with similar fidelity to OCTA and
+significantly better than expert clinicians (P < 0.00001). OCTA suffers from
+need of specialized hardware, laborious acquisition protocols, and motion
+artifacts; whereas our model works directly from standard OCT which are
+ubiquitous and quick to obtain, and allows unlocking of large volumes of
+previously collected standard OCT data both in existing clinical trials and
+clinical practice. This finding demonstrates a novel application of AI to
+medical imaging, whereby subtle regularities between different modalities are
+used to image the same body part and AI is used to generate detailed and
+accurate inferences of tissue function from structure imaging.
+",0,0,0,1,0,0
+17634,Varieties with Ample Tangent Sheaves,"  This paper generalises Mori's famous theorem about ""Projective manifolds with
+ample tangent bundles"" to normal projective varieties in the following way:
+A normal projective variety over $\mathbb{C}$ with ample tangent sheaf is
+isomorphic to the complex projective space.
+",0,0,1,0,0,0
+17635,Water sub-diffusion in membranes for fuel cells,"  We investigate the dynamics of water confined in soft ionic nano-assemblies,
+an issue critical for a general understanding of the multi-scale
+structure-function interplay in advanced materials. We focus in particular on
+hydrated perfluoro-sulfonic acid compounds employed as electrolytes in fuel
+cells. These materials form phase-separated morphologies that show outstanding
+proton-conducting properties, directly related to the state and dynamics of the
+absorbed water. We have quantified water motion and ion transport by combining
+Quasi Elastic Neutron Scattering, Pulsed Field Gradient Nuclear Magnetic
+Resonance, and Molecular Dynamics computer simulation. Effective water and ion
+diffusion coefficients have been determined together with their variation upon
+hydration at the relevant atomic, nanoscopic and macroscopic scales, providing
+a complete picture of transport. We demonstrate that confinement at the
+nanoscale and direct interaction with the charged interfaces produce anomalous
+sub-diffusion, due to a heterogeneous space-dependent dynamics within the ionic
+nanochannels. This is irrespective of the details of the chemistry of the
+hydrophobic confining matrix, confirming the statistical significance of our
+conclusions. Our findings turn out to indicate interesting connections and
+possibilities of cross-fertilization with other domains, including biophysics.
+They also establish fruitful correspondences with advanced topics in
+statistical mechanics, resulting in new possibilities for the analysis of
+Neutron scattering data.
+",0,1,0,0,0,0
+17636,Global Strong Solution of a 2D coupled Parabolic-Hyperbolic Magnetohydrodynamic System,"  The main objective of this paper is to study the global strong solution of
+the parabolic-hyperbolic incompressible magnetohydrodynamic (MHD) model in two
+dimensional space. Based on Agmon, Douglis and Nirenberg's estimates for the
+stationary Stokes equation and the Solonnikov's theorem of
+$L^p$-$L^q$-estimates for the evolution Stokes equation, it is shown that the
+mixed-type MHD equations exist a global strong solution.
+",0,0,1,0,0,0
+17637,Subcritical thermal convection of liquid metals in a rapidly rotating sphere,"  Planetary cores consist of liquid metals (low Prandtl number $Pr$) that
+convect as the core cools. Here we study nonlinear convection in a rotating
+(low Ekman number $Ek$) planetary core using a fully 3D direct numerical
+simulation. Near the critical thermal forcing (Rayleigh number $Ra$),
+convection onsets as thermal Rossby waves, but as the $Ra$ increases, this
+state is superceded by one dominated by advection. At moderate rotation, these
+states (here called the weak branch and strong branch, respectively) are
+smoothly connected. As the planetary core rotates faster, the smooth transition
+is replaced by hysteresis cycles and subcriticality until the weak branch
+disappears entirely and the strong branch onsets in a turbulent state at $Ek <
+10^{-6}$. Here the strong branch persists even as the thermal forcing drops
+well below the linear onset of convection ($Ra=0.7Ra_{crit}$ in this study). We
+highlight the importance of the Reynolds stress, which is required for
+convection to subsist below the linear onset. In addition, the Péclet number
+is consistently above 10 in the strong branch. We further note the presence of
+a strong zonal flow that is nonetheless unimportant to the convective state.
+Our study suggests that, in the asymptotic regime of rapid rotation relevant
+for planetary interiors, thermal convection of liquid metals in a sphere onsets
+through a subcritical bifurcation.
+",0,1,0,0,0,0
+17638,Decision-making processes underlying pedestrian behaviours at signalised crossings: Part 2. Do pedestrians show cultural herding behaviour ?,"  Followership is generally defined as a strategy that evolved to solve social
+coordination problems, and particularly those involved in group movement.
+Followership behaviour is particularly interesting in the context of
+road-crossing behaviour because it involves other principles such as
+risk-taking and evaluating the value of social information. This study sought
+to identify the cognitive mechanisms underlying decision-making by pedestrians
+who follow another person across the road at the green or at the red light in
+two different countries (France and Japan). We used agent-based modelling to
+simulate the road-crossing behaviours of pedestrians. This study showed that
+modelling is a reliable means to test different hypotheses and find the exact
+processes underlying decision-making when crossing the road. We found that two
+processes suffice to simulate pedestrian behaviours. Importantly, the study
+revealed differences between the two nationalities and between sexes in the
+decision to follow and cross at the green and at the red light. Japanese
+pedestrians are particularly attentive to the number of already departed
+pedestrians and the number of waiting pedestrians at the red light, whilst
+their French counterparts only consider the number of pedestrians that have
+already stepped off the kerb, thus showing the strong conformism of Japanese
+people. Finally, the simulations are revealed to be similar to observations,
+not only for the departure latencies but also for the number of crossing
+pedestrians and the rates of illegal crossings. The conclusion suggests new
+solutions for safety in transportation research.
+",0,0,0,0,1,0
+17639,Driven by Excess? Climatic Implications of New Global Mapping of Near-Surface Water-Equivalent Hydrogen on Mars,"  We present improved Mars Odyssey Neutron Spectrometer (MONS) maps of
+near-surface Water-Equivalent Hydrogen (WEH) on Mars that have intriguing
+implications for the global distribution of ""excess"" ice, which occurs when the
+mass fraction of water ice exceeds the threshold amount needed to saturate the
+pore volume in normal soils. We have refined the crossover technique of Feldman
+et al. (2011) by using spatial deconvolution and Gaussian weighting to create
+the first globally self-consistent map of WEH. At low latitudes, our new maps
+indicate that WEH exceeds 15% in several near-equatorial regions, such as
+Arabia Terra, which has important implications for the types of hydrated
+minerals present at low latitudes. At high latitudes, we demonstrate that the
+disparate MONS and Phoenix Robotic Arm (RA) observations of near surface WEH
+can be reconciled by a three-layer model incorporating dry soil over fully
+saturated pore ice over pure excess ice: such a three-layer model can also
+potentially explain the strong anticorrelation of subsurface ice content and
+ice table depth observed at high latitudes. At moderate latitudes, we show that
+the distribution of recently formed impact craters is also consistent with our
+latest MONS results, as both the shallowest ice-exposing crater and deepest
+non-ice-exposing crater at each impact site are in good agreement with our
+predictions of near-surface WEH. Overall, we find that our new mapping is
+consistent with the widespread presence at mid-to-high Martian latitudes of
+recently deposited shallow excess ice reservoirs that are not yet in
+equilibrium with the atmosphere.
+",0,1,0,0,0,0
+17640,On distribution of points with conjugate algebraic integer coordinates close to planar curves,"  Let $\varphi:\mathbb{R}\rightarrow \mathbb{R}$ be a continuously
+differentiable function on an interval $J\subset\mathbb{R}$ and let
+$\boldsymbol{\alpha}=(\alpha_1,\alpha_2)$ be a point with algebraic conjugate
+integer coordinates of degree $\leq n$ and of height $\leq Q$. Denote by
+$\tilde{M}^n_\varphi(Q,\gamma, J)$ the set of points $\boldsymbol{\alpha}$ such
+that $|\varphi(\alpha_1)-\alpha_2|\leq c_1 Q^{-\gamma}$. In this paper we show
+that for a real $0<\gamma<1$ and any sufficiently large $Q$ there exist
+positive values $c_2<c_3$, which are independent of $Q$, such that $c_2\cdot
+Q^{n-\gamma}<# \tilde{M}^n_\varphi(Q,\gamma, J)< c_3\cdot Q^{n-\gamma}$.
+",0,0,1,0,0,0
+17641,A Critical Investigation of Deep Reinforcement Learning for Navigation,"  The navigation problem is classically approached in two steps: an exploration
+step, where map-information about the environment is gathered; and an
+exploitation step, where this information is used to navigate efficiently. Deep
+reinforcement learning (DRL) algorithms, alternatively, approach the problem of
+navigation in an end-to-end fashion. Inspired by the classical approach, we ask
+whether DRL algorithms are able to inherently explore, gather and exploit
+map-information over the course of navigation. We build upon Mirowski et al.
+[2017] work and introduce a systematic suite of experiments that vary three
+parameters: the agent's starting location, the agent's target location, and the
+maze structure. We choose evaluation metrics that explicitly measure the
+algorithm's ability to gather and exploit map-information. Our experiments show
+that when trained and tested on the same maps, the algorithm successfully
+gathers and exploits map-information. However, when trained and tested on
+different sets of maps, the algorithm fails to transfer the ability to gather
+and exploit map-information to unseen maps. Furthermore, we find that when the
+goal location is randomized and the map is kept static, the algorithm is able
+to gather and exploit map-information but the exploitation is far from optimal.
+We open-source our experimental suite in the hopes that it serves as a
+framework for the comparison of future algorithms and leads to the discovery of
+robust alternatives to classical navigation methods.
+",1,0,0,0,0,0
+17642,Performance Evaluation of 3D Correspondence Grouping Algorithms,"  This paper presents a thorough evaluation of several widely-used 3D
+correspondence grouping algorithms, motived by their significance in vision
+tasks relying on correct feature correspondences. A good correspondence
+grouping algorithm is desired to retrieve as many as inliers from initial
+feature matches, giving a rise in both precision and recall. Towards this rule,
+we deploy the experiments on three benchmarks respectively addressing shape
+retrieval, 3D object recognition and point cloud registration scenarios. The
+variety in application context brings a rich category of nuisances including
+noise, varying point densities, clutter, occlusion and partial overlaps. It
+also results to different ratios of inliers and correspondence distributions
+for comprehensive evaluation. Based on the quantitative outcomes, we give a
+summarization of the merits/demerits of the evaluated algorithms from both
+performance and efficiency perspectives.
+",1,0,0,0,0,0
+17643,DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car,"  We present DeepPicar, a low-cost deep neural network based autonomous car
+platform. DeepPicar is a small scale replication of a real self-driving car
+called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN),
+which takes images from a front-facing camera as input and produces car
+steering angles as output. DeepPicar uses the same network architecture---9
+layers, 27 million connections and 250K parameters---and can drive itself in
+real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using
+DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end
+deep learning based real-time control of autonomous vehicles. We also
+systematically compare other contemporary embedded computing platforms using
+the DeepPicar's CNN-based real-time control workload. We find that all tested
+platforms, including the Pi 3, are capable of supporting the CNN-based
+real-time control, from 20 Hz up to 100 Hz, depending on hardware platform.
+However, we find that shared resource contention remains an important issue
+that must be considered in applying CNN models on shared memory based embedded
+computing platforms; we observe up to 11.6X execution time increase in the CNN
+based control loop due to shared resource contention. To protect the CNN
+workload, we also evaluate state-of-the-art cache partitioning and memory
+bandwidth throttling techniques on the Pi 3. We find that cache partitioning is
+ineffective, while memory bandwidth throttling is an effective solution.
+",1,0,0,0,0,0
+17644,Statistics of turbulence in the energy-containing range of Taylor-Couette compared to canonical wall-bounded flows,"  Considering structure functions of the streamwise velocity component in a
+framework akin to the extended self-similarity hypothesis (ESS), de Silva
+\textit{et al.} (\textit{J. Fluid Mech.}, vol. 823,2017, pp. 498-510) observed
+that remarkably the \textit{large-scale} (energy-containing range) statistics
+in canonical wall bounded flows exhibit universal behaviour. In the present
+study, we extend this universality, which was seen to encompass also flows at
+moderate Reynolds number, to Taylor-Couette flow. In doing so, we find that
+also the transversal structure function of the spanwise velocity component
+exhibits the same universal behaviour across all flow types considered. We
+further demonstrate that these observations are consistent with predictions
+developed based on an attached-eddy hypothesis. These considerations also yield
+a possible explanation for the efficacy of the ESS framework by showing that it
+relaxes the self-similarity assumption for the attached eddy contributions. By
+taking the effect of streamwise alignment into account, the attached eddy model
+predicts different behaviour for structure functions in the streamwise and in
+the spanwise directions and that this effect cancels in the ESS-framework ---
+both consistent with the data. Moreover, it is demonstrated here that also the
+additive constants, which were previously believed to be flow dependent, are
+indeed universal at least in turbulent boundary layers and pipe flow where
+high-Reynolds number data are currently available.
+",0,1,0,0,0,0
+17645,Physics-Based Modeling of TID Induced Global Static Leakage in Different CMOS Circuits,"  Compact modeling of inter-device radiation-induced leakage underneath the
+gateless thick STI oxide is presented and validated taking into account CMOS
+technology and hardness parameters, dose-rate and annealing effects, and
+dependence on electric modes under irradiation. It was shown that proposed
+approach can be applied for description of dose dependent static leakage
+currents in complex FPGA circuits.
+",0,1,0,0,0,0
+17646,A multiplier inclusion theorem on product domains,"  In this note it is shown that the class of all multipliers from the
+$d$-parameter Hardy space $H^1_{\mathrm{prod}} (\mathbb{T}^d)$ to $L^2
+(\mathbb{T}^d)$ is properly contained in the class of all multipliers from $L
+\log^{d/2} L (\mathbb{T}^d)$ to $L^2(\mathbb{T}^d)$.
+",0,0,1,0,0,0
+17647,Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains,"  We consider using a battery storage system simultaneously for peak shaving
+and frequency regulation through a joint optimization framework which captures
+battery degradation, operational constraints and uncertainties in customer load
+and regulation signals. Under this framework, using real data we show the
+electricity bill of users can be reduced by up to 15\%. Furthermore, we
+demonstrate that the saving from joint optimization is often larger than the
+sum of the optimal savings when the battery is used for the two individual
+applications. A simple threshold real-time algorithm is proposed and achieves
+this super-linear gain. Compared to prior works that focused on using battery
+storage systems for single applications, our results suggest that batteries can
+achieve much larger economic benefits than previously thought if they jointly
+provide multiple services.
+",1,0,1,0,0,0
+17648,Information Bottleneck in Control Tasks with Recurrent Spiking Neural Networks,"  The nervous system encodes continuous information from the environment in the
+form of discrete spikes, and then decodes these to produce smooth motor
+actions. Understanding how spikes integrate, represent, and process information
+to produce behavior is one of the greatest challenges in neuroscience.
+Information theory has the potential to help us address this challenge.
+Informational analyses of deep and feed-forward artificial neural networks
+solving static input-output tasks, have led to the proposal of the
+\emph{Information Bottleneck} principle, which states that deeper layers encode
+more relevant yet minimal information about the inputs. Such an analyses on
+networks that are recurrent, spiking, and perform control tasks is relatively
+unexplored. Here, we present results from a Mutual Information analysis of a
+recurrent spiking neural network that was evolved to perform the classic
+pole-balancing task. Our results show that these networks deviate from the
+\emph{Information Bottleneck} principle prescribed for feed-forward networks.
+",1,0,0,0,0,0
+17649,Physical Properties of Sub-galactic Clumps at 0.5 $\leq z \leq$ 1.5 in the UVUDF,"  We present an investigation of clumpy galaxies in the Hubble Ultra Deep Field
+at 0.5 $\leq z \leq$ 1.5 in the rest-frame far-ultraviolet (FUV) using HST WFC3
+broadband imaging in F225W, F275W, and F336W. An analysis of 1,404 galaxies
+yields 209 galaxies that host 403 kpc-scale clumps. These host galaxies appear
+to be typical star-forming galaxies, with an average of 2 clumps per galaxy and
+reaching a maximum of 8 clumps. We measure the photometry of the clumps, and
+determine the mass, age, and star formation rates (SFR) utilizing the
+SED-fitting code FAST. We find that clumps make an average contribution of 19%
+to the total rest-frame FUV flux of their host galaxy. Individually, clumps
+contribute a median of 5% to the host galaxy SFR and an average of $\sim$4% to
+the host galaxy mass, with total clump contributions to the host galaxy stellar
+mass ranging widely from less than 1% up to 93%. Clumps in the outskirts of
+galaxies are typically younger, with higher star formation rates, than clumps
+in the inner regions. The results are consistent with clump migration theories
+in which clumps form through violent gravitational instabilities in gas-rich
+turbulent disks, eventually migrate toward the center of the galaxies, and
+coalesce into the bulge.
+",0,1,0,0,0,0
+17650,Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference,"  We propose the use of incomplete dot products (IDP) to dynamically adjust the
+number of input channels used in each layer of a convolutional neural network
+during feedforward inference. IDP adds monotonically non-increasing
+coefficients, referred to as a ""profile"", to the channels during training. The
+profile orders the contribution of each channel in non-increasing order. At
+inference time, the number of channels used can be dynamically adjusted to
+trade off accuracy for lowered power consumption and reduced latency by
+selecting only a beginning subset of channels. This approach allows for a
+single network to dynamically scale over a computation range, as opposed to
+training and deploying multiple networks to support different levels of
+computation scaling. Additionally, we extend the notion to multiple profiles,
+each optimized for some specific range of computation scaling. We present
+experiments on the computation and accuracy trade-offs of IDP for popular image
+classification models and datasets. We demonstrate that, for MNIST and
+CIFAR-10, IDP reduces computation significantly, e.g., by 75%, without
+significantly compromising accuracy. We argue that IDP provides a convenient
+and effective means for devices to lower computation costs dynamically to
+reflect the current computation budget of the system. For example, VGG-16 with
+50% IDP (using only the first 50% of channels) achieves 70% in accuracy on the
+CIFAR-10 dataset compared to the standard network which achieves only 35%
+accuracy when using the reduced channel set.
+",1,0,0,1,0,0
+17651,Graded components of Local cohomology modules II,"  Let $A$ be a commutative Noetherian ring containing a field $K$ of
+characteristic zero and let $R= A[X_1, \ldots, X_m]$. Consider $R$ as standard
+graded with $°A=0$ and $°X_i=1$ for all $i$. We present a few results
+about the behavior of the graded components of local cohomology modules
+$H_I^i(R)$ where $I$ is an arbitrary homogeneous ideal in $R$. We mostly
+restrict our attention to the Vanishing, Tameness and Rigidity problems.
+",0,0,1,0,0,0
+17652,Four-Dimensional Painlevé-Type Equations Associated with Ramified Linear Equations III: Garnier Systems and Fuji-Suzuki Systems,"  This is the last part of a series of three papers entitled ""Four-dimensional
+Painlevé-type equations associated with ramified linear equations"". In this
+series of papers we aim to construct the complete degeneration scheme of
+four-dimensional Painlevé-type equations. In the present paper, we consider
+the degeneration of the Garnier system in two variables and the Fuji-Suzuki
+system.
+",0,0,1,0,0,0
+17653,You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction,"  In the cost per click (CPC) pricing model, an advertiser pays an ad network
+only when a user clicks on an ad; in turn, the ad network gives a share of that
+revenue to the publisher where the ad was impressed. Still, advertisers may be
+unsatisfied with ad networks charging them for ""valueless"" clicks, or so-called
+accidental clicks. [...] Charging advertisers for such clicks is detrimental in
+the long term as the advertiser may decide to run their campaigns on other ad
+networks. In addition, machine-learned click models trained to predict which ad
+will bring the highest revenue may overestimate an ad click-through rate, and
+as a consequence negatively impacting revenue for both the ad network and the
+publisher. In this work, we propose a data-driven method to detect accidental
+clicks from the perspective of the ad network. We collect observations of time
+spent by users on a large set of ad landing pages - i.e., dwell time. We notice
+that the majority of per-ad distributions of dwell time fit to a mixture of
+distributions, where each component may correspond to a particular type of
+clicks, the first one being accidental. We then estimate dwell time thresholds
+of accidental clicks from that component. Using our method to identify
+accidental clicks, we then propose a technique that smoothly discounts the
+advertiser's cost of accidental clicks at billing time. Experiments conducted
+on a large dataset of ads served on Yahoo mobile apps confirm that our
+thresholds are stable over time, and revenue loss in the short term is
+marginal. We also compare the performance of an existing machine-learned click
+model trained on all ad clicks with that of the same model trained only on
+non-accidental clicks. There, we observe an increase in both ad click-through
+rate (+3.9%) and revenue (+0.2%) on ads served by the Yahoo Gemini network when
+using the latter. [...]
+",0,0,0,1,0,0
+17654,Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis,"  An important task for many if not all the scientific domains is efficient
+knowledge integration, testing and codification. It is often solved with model
+construction in a controllable computational environment. In spite of that, the
+throughput of in-silico simulation-based observations become similarly
+intractable for thorough analysis. This is especially the case in molecular
+biology, which served as a subject for this study. In this project, we aimed to
+test some approaches developed to deal with the curse of dimensionality. Among
+these we found dimension reduction techniques especially appealing. They can be
+used to identify irrelevant variability and help to understand critical
+processes underlying high-dimensional datasets. Additionally, we subjected our
+data sets to nonlinear time series analysis, as those are well established
+methods for results comparison. To investigate the usefulness of dimension
+reduction methods, we decided to base our study on a concrete sample set. The
+example was taken from the domain of systems biology concerning dynamic
+evolution of sub-cellular signaling. Particularly, the dataset relates to the
+yeast pheromone pathway and is studied in-silico with a stochastic model. The
+model reconstructs signal propagation stimulated by a mating pheromone. In the
+paper, we elaborate on the reason of multidimensional analysis problem in the
+context of molecular signaling, and next, we introduce the model of choice,
+simulation details and obtained time series dynamics. A description of used
+methods followed by a discussion of results and their biological interpretation
+finalize the paper.
+",0,0,0,1,1,0
+17655,Equilibrium points and basins of convergence in the linear restricted four-body problem with angular velocity,"  The planar linear restricted four-body problem is used in order to determine
+the Newton-Raphson basins of convergence associated with the equilibrium
+points. The parametric variation of the position as well as of the stability of
+the libration points is monitored when the values of the mass parameter $b$ as
+well as of the angular velocity $\omega$ vary in predefined intervals. The
+regions on the configuration $(x,y)$ plane occupied by the basins of attraction
+are revealed using the multivariate version of the Newton-Raphson iterative
+scheme. The correlations between the attracting domains of the equilibrium
+points and the corresponding number of iterations needed for obtaining the
+desired accuracy are also illustrated. We perform a thorough and systematic
+numerical investigation by demonstrating how the parameters $b$ and $\omega$
+influence the shape, the geometry and of course the fractality of the
+converging regions. Our numerical outcomes strongly indicate that these two
+parameters are indeed two of the most influential factors in this dynamical
+system.
+",0,1,0,0,0,0
+17656,Nonmonotonic dependence of polymer glass mechanical response on chain bending stiffness,"  We investigate the mechanical properties of amorphous polymers by means of
+coarse-grained simulations and nonaffine lattice dynamics theory. A small
+increase of polymer chain bending stiffness leads first to softening of the
+material, while hardening happens only upon further strengthening of the
+backbones. This nonmonotonic variation of the storage modulus $G'$ with bending
+stiffness is caused by a competition between additional resistance to
+deformation offered by stiffer backbones and decreased density of the material
+due to a necessary decrease in monomer-monomer coordination. This
+counter-intuitive finding suggests that the strength of polymer glasses may in
+some circumstances be enhanced by softening the bending of constituent chains.
+",0,1,0,0,0,0
+17657,Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation,"  Skin cancer is a major public health problem, as is the most common type of
+cancer and represents more than half of cancer diagnoses worldwide. Early
+detection influences the outcome of the disease and motivates our work. We
+investigate the composition of CNN committees and data augmentation for the the
+ISBI 2017 Melanoma Classification Challenge (named Skin Lesion Analysis towards
+Melanoma Detection) facing the peculiarities of dealing with such a small,
+unbalanced, biological database. For that, we explore committees of
+Convolutional Neural Networks trained over the ISBI challenge training dataset
+artificially augmented by both classical image processing transforms and image
+warping guided by specialist knowledge about the lesion axis and improve the
+final classifier invariance to common melanoma variations.
+",1,0,0,0,0,0
+17658,Arbitrage and Geometry,"  This article introduces the notion of arbitrage for a situation involving a
+collection of investments and a payoff matrix describing the return to an
+investor of each investment under each of a set of possible scenarios. We
+explain the Arbitrage Theorem, discuss its geometric meaning, and show its
+equivalence to Farkas' Lemma. We then ask a seemingly innocent question: given
+a random payoff matrix, what is the probability of an arbitrage opportunity?
+This question leads to some interesting geometry involving hyperplane
+arrangements and related topics.
+",0,0,1,1,0,0
+17659,ADE surfaces and their moduli,"  We define a class of surfaces and surface pairs corresponding to the ADE root
+lattices and construct compactifications of their moduli spaces, generalizing
+Losev-Manin spaces of curves.
+",0,0,1,0,0,0
+17660,An Efficient Descriptor Model for Designing Materials for Solar Cells,"  An efficient descriptor model for fast screening of potential materials for
+solar cell applications is presented. It works for both excitonic and
+non-excitonic solar cells materials, and in addition to the energy gap it
+includes the absorption spectrum ($\alpha(E)$) of the material. The charge
+transport properties of the explored materials are modeled using the
+characteristic diffusion length ($L_{d}$) determined for the respective family
+of compounds. The presented model surpasses the widely used Scharber model
+developed for bulk-heterojunction solar cells [Scharber \textit{et al.,
+Advanced Materials}, 2006, Vol. 18, 789]. Using published experimental data, we
+show that the presented model is more accurate in predicting the achievable
+efficiencies. Although the focus of this work is on organic photovoltaics
+(OPV), for which the original Scharber model was developed, the model presented
+here is applicable also to other solar cell technologies. To model both
+excitonic and non-excitonic systems, two different sets of parameters are used
+to account for the different modes of operation. The analysis of the presented
+descriptor model clearly shows the benefit of including $\alpha(E)$ and $L_{d}$
+in view of improved screening results.
+",0,1,0,0,0,0
+17661,Multiplication of a Schubert polynomial by a Stanley symmetric polynomial,"  We prove, combinatorially, that the product of a Schubert polynomial by a
+Stanley symmetric polynomial is a truncated Schubert polynomial. Using Monk's
+rule, we derive a nonnegative combinatorial formula for the Schubert polynomial
+expansion of a truncated Schubert polynomial. Combining these results, we give
+a nonnegative combinatorial rule for the product of a Schubert and a Schur
+polynomial in the Schubert basis.
+",0,0,1,0,0,0
+17662,Topological nodal line states and a potential catalyst of hydrogen evolution in the TiSi family,"  Topological nodal line (DNL) semimetals, formed by a closed loop of the
+inverted bands in the bulk, result in the nearly flat drumhead-like surface
+states with a high electronic density near the Fermi level. The high catalytic
+active sites associated with the high electronic densities, the good carrier
+mobility, and the proper thermodynamic stabilities with $\Delta
+G_{H^*}$$\approx$0 are currently the prerequisites to seek the alternative
+candidates to precious platinum for catalyzing electrochemical hydrogen (HER)
+production from water. Within this context, it is natural to consider whether
+or not the DNLs are a good candidate for the HER because its non-trivial
+surface states provide a robust platform to activate possibly chemical
+reactions. Here, through first-principles calculations we reported on a new DNL
+TiSi-type family with a closed Dirac nodal line consisting of the linear band
+crossings in the $k_y$ = 0 plane. The hydrogen adsorption on the (010) and
+(110) surfaces yields the $\Delta G_{H^*}$ to be almost zero. The topological
+charge carries have been revealed to participate in this HER. The results are
+highlighting that TiSi not only is a promising catalyst for the HER but also
+paves a new routine to design topological quantum catalyst utilizing the
+topological DNL-induced surface bands as active sites, rather than edge sites-,
+vacancy-, dopant-, strain-, or heterostructure-created active sites.
+",0,1,0,0,0,0
+17663,Boosting Variational Inference: an Optimization Perspective,"  Variational inference is a popular technique to approximate a possibly
+intractable Bayesian posterior with a more tractable one. Recently, boosting
+variational inference has been proposed as a new paradigm to approximate the
+posterior by a mixture of densities by greedily adding components to the
+mixture. However, as is the case with many other variational inference
+algorithms, its theoretical properties have not been studied. In the present
+work, we study the convergence properties of this approach from a modern
+optimization viewpoint by establishing connections to the classic Frank-Wolfe
+algorithm. Our analyses yields novel theoretical insights regarding the
+sufficient conditions for convergence, explicit rates, and algorithmic
+simplifications. Since a lot of focus in previous works for variational
+inference has been on tractability, our work is especially important as a much
+needed attempt to bridge the gap between probabilistic models and their
+corresponding theoretical properties.
+",1,0,0,1,0,0
+17664,Nature of carrier injection in metal/2D semiconductor interface and its implications to the limits of contact resistance,"  Monolayers of transition metal dichalcogenides (TMDCs) exhibit excellent
+electronic and optical properties. However, the performance of these
+two-dimensional (2D) devices are often limited by the large resistance offered
+by the metal contact interface. Till date, the carrier injection mechanism from
+metal to 2D TMDC layers remains unclear, with widely varying reports of
+Schottky barrier height (SBH) and contact resistance (Rc), particularly in the
+monolayer limit. In this work, we use a combination of theory and experiments
+in Au and Ni contacted monolayer MoS2 device to conclude the following points:
+(i) the carriers are injected at the source contact through a cascade of two
+potential barriers - the barrier heights being determined by the degree of
+interaction between the metal and the TMDC layer; (ii) the conventional
+Richardson equation becomes invalid due to the multi-dimensional nature of the
+injection barriers, and using Bardeen-Tersoff theory, we derive the appropriate
+form of the Richardson equation that describes such composite barrier; (iii) we
+propose a novel transfer length method (TLM) based SBH extraction methodology,
+to reliably extract SBH by eliminating any confounding effect of temperature
+dependent channel resistance variation; (iv) we derive the Landauer limit of
+the contact resistance achievable in such devices. A comparison of the limits
+with the experimentally achieved contact resistance reveals plenty of room for
+technological improvements.
+",0,1,0,0,0,0
+17665,Characteristics of stratified flows of Newtonian/non-Newtonian shear-thinning fluids,"  Exact solutions for laminar stratified flows of Newtonian/non-Newtonian
+shear-thinning fluids in horizontal and inclined channels are presented. An
+iterative algorithm is proposed to compute the laminar solution for the general
+case of a Carreau non-Newtonian fluid. The exact solution is used to study the
+effect of the rheology of the shear-thinning liquid on two-phase flow
+characteristics considering both gas/liquid and liquid/liquid systems.
+Concurrent and counter-current inclined systems are investigated, including the
+mapping of multiple solution boundaries. Aspects relevant to practical
+applications are discussed, such as the insitu hold-up, or lubrication effects
+achieved by adding a less viscous phase. A characteristic of this family of
+systems is that, even if the liquid has a complex rheology (Carreau fluid), the
+two-phase stratified flow can behave like the liquid is Newtonian for a wide
+range of operational conditions. The capability of the two-fluid model to yield
+satisfactory predictions in the presence of shear-thinning liquids is tested,
+and an algorithm is proposed to a priori predict if the Newtonian (zero shear
+rate viscosity) behaviour arises for a given operational conditions in order to
+avoid large errors in the predictions of flow characteristics when the
+power-law is considered for modelling the shear-thinning behaviour. Two-fluid
+model closures implied by the exact solution and the effect of a turbulent gas
+layer are also addressed.
+",0,1,0,0,0,0
+17666,Stochastic Chebyshev Gradient Descent for Spectral Optimization,"  A large class of machine learning techniques requires the solution of
+optimization problems involving spectral functions of parametric matrices, e.g.
+log-determinant and nuclear norm. Unfortunately, computing the gradient of a
+spectral function is generally of cubic complexity, as such gradient descent
+methods are rather expensive for optimizing objectives involving the spectral
+function. Thus, one naturally turns to stochastic gradient methods in hope that
+they will provide a way to reduce or altogether avoid the computation of full
+gradients. However, here a new challenge appears: there is no straightforward
+way to compute unbiased stochastic gradients for spectral functions. In this
+paper, we develop unbiased stochastic gradients for spectral-sums, an important
+subclass of spectral functions. Our unbiased stochastic gradients are based on
+combining randomized trace estimators with stochastic truncation of the
+Chebyshev expansions. A careful design of the truncation distribution allows us
+to offer distributions that are variance-optimal, which is crucial for fast and
+stable convergence of stochastic gradient methods. We further leverage our
+proposed stochastic gradients to devise stochastic methods for objective
+functions involving spectral-sums, and rigorously analyze their convergence
+rate. The utility of our methods is demonstrated in numerical experiments.
+",0,0,0,1,0,0
+17667,Poisson Bracket and Symplectic Structure of Covariant Canonical Formalism of Fields,"  The covariant canonical formalism is a covariant extension of the traditional
+canonical formalism of fields. In contrast to the traditional canonical theory,
+it has a remarkable feature that canonical equations of gauge theories or
+gravity are not only manifestly Lorentz covariant but also gauge covariant or
+diffeomorphism covariant. A mathematical peculiarity of the covariant canonical
+formalism is that its canonical coordinates are differential forms on a
+manifold. In the present paper, we find a natural Poisson bracket of this new
+canonical theory, and study symplectic structure behind it. The phase space of
+the theory is identified with a ringed space with the structure sheaf of the
+graded algebra of ""differentiable"" differential forms on the manifold. The
+Poisson and the symplectic structure we found can be even or odd, depending on
+the dimension of the manifold. Our Poisson structure is an example of physical
+application of Poisson structure defined on the graded algebra of differential
+forms.
+",0,0,1,0,0,0
+17668,Cascading Failures in Interdependent Systems: Impact of Degree Variability and Dependence,"  We study cascading failures in a system comprising interdependent
+networks/systems, in which nodes rely on other nodes both in the same system
+and in other systems to perform their function. The (inter-)dependence among
+nodes is modeled using a dependence graph, where the degree vector of a node
+determines the number of other nodes it can potentially cause to fail in each
+system through aforementioned dependency. In particular, we examine the impact
+of the variability and dependence properties of node degrees on the probability
+of cascading failures. We show that larger variability in node degrees hampers
+widespread failures in the system, starting with random failures. Similarly,
+positive correlations in node degrees make it harder to set off an epidemic of
+failures, thereby rendering the system more robust against random failures.
+",1,1,0,1,0,0
+17669,Critical exponent for geodesic currents,"  For any geodesic current we associated a quasi-metric space. For a subclass
+of geodesic currents, called filling, it defines a metric and we study the
+critical exponent associated to this space. We show that is is equal to the
+exponential growth rate of the intersection function for closed curves.
+",0,0,1,0,0,0
+17670,Human and Machine Speaker Recognition Based on Short Trivial Events,"  Trivial events are ubiquitous in human to human conversations, e.g., cough,
+laugh and sniff. Compared to regular speech, these trivial events are usually
+short and unclear, thus generally regarded as not speaker discriminative and so
+are largely ignored by present speaker recognition research. However, these
+trivial events are highly valuable in some particular circumstances such as
+forensic examination, as they are less subjected to intentional change, so can
+be used to discover the genuine speaker from disguised speech. In this paper,
+we collect a trivial event speech database that involves 75 speakers and 6
+types of events, and report preliminary speaker recognition results on this
+database, by both human listeners and machines. Particularly, the deep feature
+learning technique recently proposed by our group is utilized to analyze and
+recognize the trivial events, which leads to acceptable equal error rates
+(EERs) despite the extremely short durations (0.2-0.5 seconds) of these events.
+Comparing different types of events, 'hmm' seems more speaker discriminative.
+",1,0,0,0,0,0
+17671,AdiosStMan: Parallelizing Casacore Table Data System Using Adaptive IO System,"  In this paper, we investigate the Casacore Table Data System (CTDS) used in
+the casacore and CASA libraries, and methods to parallelize it. CTDS provides a
+storage manager plugin mechanism for third-party devel- opers to design and
+implement their own CTDS storage managers. Hav- ing this in mind, we looked
+into various storage backend techniques that can possibly enable parallel I/O
+for CTDS by implementing new storage managers. After carrying on benchmarks
+showing the excellent parallel I/O throughput of the Adaptive IO System
+(ADIOS), we implemented an ADIOS based parallel CTDS storage manager. We then
+applied the CASA MSTransform frequency split task to verify the ADIOS Storage
+Manager. We also ran a series of performance tests to examine the I/O
+throughput in a massively parallel scenario.
+",1,1,0,0,0,0
+17672,Direct Experimental Observation of the Gas Filamentation Effect using a Two-bunch X-ray FEL Beam,"  We report the experimental observation of the filamentation effect in gas
+devices designed for X-ray Free-electron Lasers. The measurements were carried
+out at the Linac Coherent Light Source on the X-ray Correlation Spectroscopy
+(XCS) instrument using a Two-bunch FEL beam at 6.5 keV with 122.5 ns separation
+passing through an Argon gas cell. The relative intensities of the two pulses
+of the Two-bunch beam were measured, after and before the gas cell, from the
+X-ray scattering off thin targets by using fast diodes with sufficient temporal
+resolution. It was found that the after-to-before ratio of the intensities of
+the second pulse was consistently higher than that of the first pulse,
+revealing lower effective attenuation of the gas cell due to the heating and
+subsequent gas density reduction in the beam path by the first pulse. This
+measurement is important in guiding the design and/or mitigating the adverse
+effect in gas devices for high repetition-rate FELs such as the LCLS-II and the
+European XFEL or other future high repetition-rate upgrade to existing FEL
+facilities
+",0,1,0,0,0,0
+17673,Coherence measurements of scattered incoherent light for lensless identification of an object's location and size,"  In absence of a lens to form an image, incoherent or partially coherent light
+scattering off an obstructive or reflective object forms a broad intensity
+distribution in the far field with only feeble spatial features. We show here
+that measuring the complex spatial coherence function can help in the
+identification of the size and location of a one-dimensional object placed in
+the path of a partially coherent light source. The complex coherence function
+is measured in the far field through wavefront sampling, which is performed via
+dynamically reconfigurable slits implemented on a digital micromirror device
+(DMD). The impact of an object -- parameterized by size and location -- that
+either intercepts or reflects incoherent light is studied. The experimental
+results show that measuring the spatial coherence function as a function of the
+separation between two slits located symmetrically around the optical axis can
+identify the object transverse location and angle subtended from the detection
+plane (the ratio of the object width to the axial distance from the detector).
+The measurements are in good agreement with numerical simulations of a forward
+model based on Fresnel propagators. The rapid refresh rate of DMDs may enable
+real-time operation of such a lensless coherency imaging scheme.
+",0,1,0,0,0,0
+17674,MVP2P: Layer-Dependency-Aware Live MVC Video Streaming over Peer-to-Peer Networks,"  Multiview video supports observing a scene from different viewpoints. The
+Joint Video Team (JVT) developed H.264/MVC to enhance the compression
+efficiency for multiview video, however, MVC encoded multiview video (MVC
+video) still requires high bitrates for transmission. This paper investigates
+live MVC video streaming over Peer-to-Peer (P2P) networks. The goal is to
+minimize the server bandwidth costs whist ensuring high streaming quality to
+peers. MVC employs intra-view and inter-view prediction structures, which leads
+to a complicated layer dependency relationship. As the peers' outbound
+bandwidth is shared while supplying all the MVC video layers, the bandwidth
+allocation to one MVC layer affects the available outbound bandwidth of the
+other layers. To optimise the utilisation of the peers' outbound bandwidth for
+providing video layers, a maximum flow based model is proposed which considers
+the MVC video layer dependency and the layer supplying relationship between
+peers. Based on the model, a layer dependency aware live MVC video streaming
+method over a BitTorrent-like P2P network is proposed, named MVP2P. The key
+components of MVP2P include a chunk scheduling strategy and a peer selection
+strategy for receiving peers, and a bandwidth scheduling algorithm for
+supplying peers. To evaluate the efficiency of the proposed solution, MVP2P is
+compared with existing methods considering the constraints of peer bandwidth,
+peer numbers, view switching rates, and peer churns. The test results show that
+MVP2P significantly outperforms the existing methods.
+",1,0,0,0,0,0
+17675,High order finite element simulations for fluid dynamics validated by experimental data from the fda benchmark nozzle model,"  The objective of the present work is to construct a sound mathematical,
+numerical and computational framework relevant to blood flow simulations and to
+assess it through a careful validation against experimental data. We perform
+simulations of a benchmark proposed by the FDA for fluid flow in an idealized
+medical device, under different flow regimes. The results are evaluated using
+metrics proposed in the literature and the findings are in very good agreement
+with the validation experiment.
+",0,1,1,0,0,0
+17676,Twin Networks: Matching the Future for Sequence Generation,"  We propose a simple technique for encouraging generative RNNs to plan ahead.
+We train a ""backward"" recurrent network to generate a given sequence in reverse
+order, and we encourage states of the forward model to predict cotemporal
+states of the backward model. The backward network is used only during
+training, and plays no role during sampling or inference. We hypothesize that
+our approach eases modeling of long-term dependencies by implicitly forcing the
+forward states to hold information about the longer-term future (as contained
+in the backward states). We show empirically that our approach achieves 9%
+relative improvement for a speech recognition task, and achieves significant
+improvement on a COCO caption generation task.
+",1,0,0,1,0,0
+17677,Structure and Randomness of Continuous-Time Discrete-Event Processes,"  Loosely speaking, the Shannon entropy rate is used to gauge a stochastic
+process' intrinsic randomness; the statistical complexity gives the cost of
+predicting the process. We calculate, for the first time, the entropy rate and
+statistical complexity of stochastic processes generated by finite unifilar
+hidden semi-Markov models---memoryful, state-dependent versions of renewal
+processes. Calculating these quantities requires introducing novel mathematical
+objects ({\epsilon}-machines of hidden semi-Markov processes) and new
+information-theoretic methods to stochastic processes.
+",0,1,1,1,0,0
+17678,In situ high resolution real-time quantum efficiency imaging for photocathodes,"  Aspects of the preparation process and performance degradation are two major
+problems of photocathodes. The lack of a means for dynamic quantum efficiency
+measurements results in the inability to observe the inhomogeneity of the
+cathode surface by fine structural analysis and in real time.Here we present a
+simple and scalable technique for in situ real-time quantum efficiency
+diagnosis. An incoherent light source provides uniform illumination on the
+cathode surface, and solenoid magnets are used as lens for focusing and imaging
+the emitted electron beam on a downstream scintillator screen, which converts
+the quantum efficiency information into fluorescence intensity distribution.
+The microscopic discontinuity and the dynamic changes of the quantum efficiency
+of a gallium arsenide photocathode are observed at a resolution of a few
+microns. An unexpected uneven decrease of the quantum efficiency is also
+recorded. The work demonstrates a new observation method for photoemission
+materials research.
+",0,1,0,0,0,0
+17679,Competitive division of a mixed manna,"  A mixed manna contains goods (that everyone likes), bads (that everyone
+dislikes), as well as items that are goods to some agents, but bads or satiated
+to others.
+If all items are goods and utility functions are homothetic, concave (and
+monotone), the Competitive Equilibrium with Equal Incomes maximizes the Nash
+product of utilities: hence it is welfarist (determined utility-wise by the
+feasible set of profiles), single-valued and easy to compute.
+We generalize the Gale-Eisenberg Theorem to a mixed manna. The Competitive
+division is still welfarist and related to the product of utilities or
+disutilities. If the zero utility profile (before any manna) is Pareto
+dominated, the competitive profile is unique and still maximizes the product of
+utilities. If the zero profile is unfeasible, the competitive profiles are the
+critical points of the product of disutilities on the efficiency frontier, and
+multiplicity is pervasive. In particular the task of dividing a mixed manna is
+either good news for everyone, or bad news for everyone.
+We refine our results in the practically important case of linear
+preferences, where the axiomatic comparison between the division of goods and
+that of bads is especially sharp. When we divide goods and the manna improves,
+everyone weakly benefits under the competitive rule; but no reasonable rule to
+divide bads can be similarly Resource Monotonic. Also, the much larger set of
+Non Envious and Efficient divisions of bads can be disconnected so that it will
+admit no continuous selection.
+",1,0,1,0,0,0
+17680,Understanding Black-box Predictions via Influence Functions,"  How can we explain the predictions of a black-box model? In this paper, we
+use influence functions -- a classic technique from robust statistics -- to
+trace a model's prediction through the learning algorithm and back to its
+training data, thereby identifying training points most responsible for a given
+prediction. To scale up influence functions to modern machine learning
+settings, we develop a simple, efficient implementation that requires only
+oracle access to gradients and Hessian-vector products. We show that even on
+non-convex and non-differentiable models where the theory breaks down,
+approximations to influence functions can still provide valuable information.
+On linear models and convolutional neural networks, we demonstrate that
+influence functions are useful for multiple purposes: understanding model
+behavior, debugging models, detecting dataset errors, and even creating
+visually-indistinguishable training-set attacks.
+",1,0,0,1,0,0
+17681,Linear and nonlinear photonic Jackiw-Rebbi states in waveguide arrays,"  We study analytically and numerically the optical analogue of the
+Jackiw-Rebbi states in quantum field theory. These solutions exist at the
+interface of two binary waveguide arrays which are described by two Dirac
+equations with opposite sign masses. We show that these special states are
+topologically robust not only in the linear regime, but also in nonlinear
+regimes (with both focusing and de-focusing nonlinearity). We also reveal that
+one can generate the Jackiw-Rebbi states starting from Dirac solitons.
+",0,1,0,0,0,0
+17682,"A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations","  Progress in science has advanced the development of human society across
+history, with dramatic revolutions shaped by information theory, genetic
+cloning, and artificial intelligence, among the many scientific achievements
+produced in the 20th century. However, the way that science advances itself is
+much less well-understood. In this work, we study the evolution of scientific
+development over the past century by presenting an anatomy of 89 million
+digitalized papers published between 1900 and 2015. We find that science has
+benefited from the shift from individual work to collaborative effort, with
+over 90% of the world-leading innovations generated by collaborations in this
+century, nearly four times higher than they were in the 1900s. We discover that
+rather than the frequent myopic- and self-referencing that was common in the
+early 20th century, modern scientists instead tend to look for literature
+further back and farther around. Finally, we also observe the globalization of
+scientific development from 1900 to 2015, including 25-fold and 7-fold
+increases in international collaborations and citations, respectively, as well
+as a dramatic decline in the dominant accumulation of citations by the US, the
+UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are
+meant to serve as a starter for exploring the visionary ways in which science
+has developed throughout the past century, generating insight into and an
+impact upon the current scientific innovations and funding policies.
+",1,1,0,0,0,0
+17683,Interval-type theorems concerning means,"  Each family $\mathcal{M}$ of means has a natural, partial order (point-wise
+order), that is $M \le N$ iff $M(x) \le N(x)$ for all admissible $x$.
+In this setting we can introduce the notion of interval-type set (a subset
+$\mathcal{I} \subset \mathcal{M}$ such that whenever $M \le P \le N$ for some
+$M,\,N \in \mathcal{I}$ and $P \in \mathcal{M}$ then $P \in \mathcal{I}$). For
+example, in the case of power means there exists a natural isomorphism between
+interval-type sets and intervals contained in real numbers. Nevertheless there
+appear a number of interesting objects for a families which cannot be linearly
+ordered.
+In the present paper we consider this property for Gini means and Hardy
+means. Moreover some results concerning $L^\infty$ metric among (abstract)
+means will be obtained.
+",0,0,1,0,0,0
+17684,Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning,"  We propose a fully distributed actor-critic algorithm approximated by deep
+neural networks, named \textit{Diff-DAC}, with application to single-task and
+to average multitask reinforcement learning (MRL). Each agent has access to
+data from its local task only, but it aims to learn a policy that performs well
+on average for the whole set of tasks. During the learning process, agents
+communicate their value-policy parameters to their neighbors, diffusing the
+information across the network, so that they converge to a common policy, with
+no need for a central node. The method is scalable, since the computational and
+communication costs per agent grow with its number of neighbors. We derive
+Diff-DAC's from duality theory and provide novel insights into the standard
+actor-critic framework, showing that it is actually an instance of the dual
+ascent method that approximates the solution of a linear program. Experiments
+suggest that Diff-DAC can outperform the single previous distributed MRL
+approach (i.e., Dist-MTLPS) and even the centralized architecture.
+",1,0,0,1,0,0
+17685,On locally compact semitopological $0$-bisimple inverse $ω$-semigroups,"  We describe the structure of Hausdorff locally compact semitopological
+$0$-bisimple inverse $\omega$-semigroups with compact maximal subgroups. In
+particular, we show that a Hausdorff locally compact semitopological
+$0$-bisimple inverse $\omega$-semigroup with a compact maximal subgroup is
+either compact or topologically isomorphic to the topological sum of its
+$\mathscr{H}$-classes. We describe the structure of Hausdorff locally compact
+semitopological $0$-bisimple inverse $\omega$-semigroups with a monothetic
+maximal subgroups. In particular we prove the dichotomy for $T_1$ locally
+compact semitopological Reilly semigroup
+$\left(\textbf{B}(\mathbb{Z}_{+},\theta)^0,\tau\right)$ with adjoined zero and
+with a non-annihilating homomorphism $\theta\colon \mathbb{Z}_{+}\to
+\mathbb{Z}_{+}$: $\left(\textbf{B}(\mathbb{Z}_{+},\theta)^0,\tau\right)$ is
+either compact or discrete. At the end we discuss on the remainder under the
+closure of the discrete Reilly semigroup $\textbf{B}(\mathbb{Z}_{+},\theta)^0$
+in a semitopological semigroup.
+",0,0,1,0,0,0
+17686,Theory of Disorder-Induced Half-Integer Thermal Hall Conductance,"  Electrons that are confined to a single Landau level in a two dimensional
+electron gas realize the effects of strong electron-electron repulsion in its
+purest form. The kinetic energy of individual electrons is completely quenched
+and all physical properties are dictated solely by many-body effects. A
+remarkable consequence is the emergence of new quasiparticles with fractional
+charge and exotic quantum statistics of which the most exciting ones are
+non-Abelian quasiparticles. A non-integer quantized thermal Hall conductance
+$\kappa_{xy}$ (in units of temperature times the universal constant $\pi^2
+k_B^2 /3 h$; $h$ is the Planck constant and $k_B$ the Boltzmann constant)
+necessitates the existence of such quasiparticles. It has been predicted, and
+verified numerically, that such states are realized in the clean half-filled
+first Landau level of electrons with Coulomb repulsion, with $\kappa_{xy}$
+being either $3/2$ or $7/2$. Excitingly, a recent experiment has indeed
+observed a half-integer value, which was measured, however, to be
+$\kappa_{xy}=5/2$. We resolve this contradiction within a picture where smooth
+disorder results in the formation of mesoscopic puddles with locally
+$\kappa_{xy}=3/2$ or $7/2$. Interactions between these puddles generate a
+coherent macroscopic state, which is reflected in an extended plateau with
+quantized $\kappa_{xy}=5/2$. The topological properties of quasiparticles at
+large distances are determined by the macroscopic phase, and not by the
+microscopic puddle where they reside. In principle, the same mechanism might
+also allow non-Abelian quasiparticles to emerge from a system comprised of
+microscopic Abelian puddles.
+",0,1,0,0,0,0
+17687,On Security Research Towards Future Mobile Network Generations,"  Over the last decades, numerous security and privacy issues in all three
+active mobile network generations have been revealed that threaten users as
+well as network providers. In view of the newest generation (5G) currently
+under development, we now have the unique opportunity to identify research
+directions for the next generation based on existing security and privacy
+issues as well as already proposed defenses. This paper aims to unify security
+knowledge on mobile phone networks into a comprehensive overview and to derive
+pressing open research questions. To achieve this systematically, we develop a
+methodology that categorizes known attacks by their aim, proposed defenses,
+underlying causes, and root causes. Further, we assess the impact and the
+efficacy of each attack and defense. We then apply this methodology to existing
+literature on attacks and defenses in all three network generations. By doing
+so, we identify ten causes and four root causes of attacks. Mapping the attacks
+to proposed defenses and suggestions for the 5G specification enables us to
+uncover open research questions and challenges for the development of
+next-generation mobile networks. The problems of unsecured pre-authentication
+traffic and jamming attacks exist across all three mobile generations. They
+should be addressed in the future, in particular, to wipe out the class of
+downgrade attacks and, thereby, strengthen the users' privacy. Further advances
+are needed in the areas of inter-operator protocols as well as secure baseband
+implementations. Additionally, mitigations against denial-of-service attacks by
+smart protocol design represent an open research question.
+",1,0,0,0,0,0
+17688,Channel Simulation in Quantum Metrology,"  In this review we discuss how channel simulation can be used to simplify the
+most general protocols of quantum parameter estimation, where unlimited
+entanglement and adaptive joint operations may be employed. Whenever the
+unknown parameter encoded in a quantum channel is completely transferred in an
+environmental program state simulating the channel, the optimal adaptive
+estimation cannot beat the standard quantum limit. In this setting, we
+elucidate the crucial role of quantum teleportation as a primitive operation
+which allows one to completely reduce adaptive protocols over suitable
+teleportation-covariant channels and derive matching upper and lower bounds for
+parameter estimation. For these channels, we may express the quantum Cramér
+Rao bound directly in terms of their Choi matrices. Our review considers both
+discrete- and continuous-variable systems, also presenting some new results for
+bosonic Gaussian channels using an alternative sub-optimal simulation. It is an
+open problem to design simulations for quantum channels that achieve the
+Heisenberg limit.
+",0,1,0,0,0,0
+17689,Ranking Causal Influence of Financial Markets via Directed Information Graphs,"  A non-parametric method for ranking stock indices according to their mutual
+causal influences is presented. Under the assumption that indices reflect the
+underlying economy of a country, such a ranking indicates which countries exert
+the most economic influence in an examined subset of the global economy. The
+proposed method represents the indices as nodes in a directed graph, where the
+edges' weights are estimates of the pair-wise causal influences, quantified
+using the directed information functional. This method facilitates using a
+relatively small number of samples from each index. The indices are then ranked
+according to their net-flow in the estimated graph (sum of the incoming weights
+subtracted from the sum of outgoing weights). Daily and minute-by-minute data
+from nine indices (three from Asia, three from Europe and three from the US)
+were analyzed. The analysis of daily data indicates that the US indices are the
+most influential, which is consistent with intuition that the indices
+representing larger economies usually exert more influence. Yet, it is also
+shown that an index representing a small economy can strongly influence an
+index representing a large economy if the smaller economy is indicative of a
+larger phenomenon. Finally, it is shown that while inter-region interactions
+can be captured using daily data, intra-region interactions require more
+frequent samples.
+",0,0,0,0,0,1
+17690,Shear banding in metallic glasses described by alignments of Eshelby quadrupoles,"  Plastic deformation of metallic glasses performed well below the glass
+transition temperature leads to the formation of shear bands as a result of
+shear localization. It is believed that shear banding originates from
+individual stress concentrators having quadrupolar symmetry. To elucidate the
+underlying mechanisms of shear band formation, microstructural investigations
+were carried out on sheared zones using transmission electron microscopy. Here
+we show evidence of a characteristic signature present in shear bands
+manifested in the form of sinusoidal density variations. We present an
+analytical solution for the observed post-deformation state derived from
+continuum mechanics using an alignment of quadrupolar stress field
+perturbations for the plastic events. Since we observe qualitatively similar
+features for three different types of metallic glasses that span the entire
+range of characteristic properties of metallic glasses, we conclude that the
+reported deformation behavior is generic for all metallic glasses, and thus has
+far-reaching consequences for the deformation behavior of amorphous solids in
+general.
+",0,1,0,0,0,0
+17691,Detection of low dimensionality and data denoising via set estimation techniques,"  This work is closely related to the theories of set estimation and manifold
+estimation.
+Our object of interest is a, possibly lower-dimensional, compact set $S
+\subset {\mathbb R}^d$.
+The general aim is to identify (via stochastic procedures) some qualitative
+or quantitative features of $S$, of geometric or topological character. The
+available information is just a random sample of points drawn on $S$.
+The term ""to identify"" means here to achieve a correct answer almost surely
+(a.s.) when the sample size tends to infinity. More specifically the paper aims
+at giving some partial answers to the following questions: is $S$ full
+dimensional? Is $S$ ""close to a lower dimensional set"" $\mathcal{M}$? If so,
+can we estimate $\mathcal{M}$ or some functionals of $\mathcal{M}$ (in
+particular, the Minkowski content of $\mathcal{M}$)? As an important auxiliary
+tool in the answers of these questions, a denoising procedure is proposed in
+order to partially remove the noise in the original data. The theoretical
+results are complemented with some simulations and graphical illustrations.
+",0,0,1,1,0,0
+17692,Deep Networks with Shape Priors for Nucleus Detection,"  Detection of cell nuclei in microscopic images is a challenging research
+topic, because of limitations in cellular image quality and diversity of
+nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between
+multiple cell nuclei. This has been a topic of enduring interest with promising
+recent success shown by deep learning methods. These methods train for example
+convolutional neural networks (CNNs) with a training set of input images and
+known, labeled nuclei locations. Many of these methods are supplemented by
+spatial or morphological processing. We develop a new approach that we call
+Shape Priors with Convolutional Neural Networks (SP-CNN) to perform
+significantly enhanced nuclei detection. A set of canonical shapes is prepared
+with the help of a domain expert. Subsequently, we present a new network
+structure that can incorporate `expected behavior' of nucleus shapes via two
+components: {\em learnable} layers that perform the nucleus detection and a
+{\em fixed} processing part that guides the learning with prior information.
+Analytically, we formulate a new regularization term that is targeted at
+penalizing false positives while simultaneously encouraging detection inside
+cell nucleus boundary. Experimental results on a challenging dataset reveal
+that SP-CNN is competitive with or outperforms several state-of-the-art
+methods.
+",0,0,0,1,0,0
+17693,"A sheaf-theoretic model for SL(2,C) Floer homology","  Given a Heegaard splitting of a three-manifold Y, we consider the SL(2,C)
+character variety of the Heegaard surface, and two complex Lagrangians
+associated to the handlebodies. We focus on the smooth open subset
+corresponding to irreducible representations. On that subset, the intersection
+of the Lagrangians is an oriented d-critical locus in the sense of Joyce. Bussi
+associates to such an intersection a perverse sheaf of vanishing cycles. We
+prove that in our setting, the perverse sheaf is an invariant of Y, i.e., it is
+independent of the Heegaard splitting. The hypercohomology of this sheaf can be
+viewed as a model for (the dual of) SL(2,C) instanton Floer homology. We also
+present a framed version of this construction, which takes into account
+reducible representations. We give explicit computations for lens spaces and
+Brieskorn spheres, and discuss the connection to the Kapustin-Witten equations
+and Khovanov homology.
+",0,0,1,0,0,0
+17694,Discriminative models for multi-instance problems with tree-structure,"  Modeling network traffic is gaining importance in order to counter modern
+threats of ever increasing sophistication. It is though surprisingly difficult
+and costly to construct reliable classifiers on top of telemetry data due to
+the variety and complexity of signals that no human can manage to interpret in
+full. Obtaining training data with sufficiently large and variable body of
+labels can thus be seen as prohibitive problem. The goal of this work is to
+detect infected computers by observing their HTTP(S) traffic collected from
+network sensors, which are typically proxy servers or network firewalls, while
+relying on only minimal human input in model training phase. We propose a
+discriminative model that makes decisions based on all computer's traffic
+observed during predefined time window (5 minutes in our case). The model is
+trained on collected traffic samples over equally sized time window per large
+number of computers, where the only labels needed are human verdicts about the
+computer as a whole (presumed infected vs. presumed clean). As part of training
+the model itself recognizes discriminative patterns in traffic targeted to
+individual servers and constructs the final high-level classifier on top of
+them. We show the classifier to perform with very high precision, while the
+learned traffic patterns can be interpreted as Indicators of Compromise. In the
+following we implement the discriminative model as a neural network with
+special structure reflecting two stacked multi-instance problems. The main
+advantages of the proposed configuration include not only improved accuracy and
+ability to learn from gross labels, but also automatic learning of server types
+(together with their detectors) which are typically visited by infected
+computers.
+",1,0,0,0,0,0
+17695,Functional advantages offered by many-body coherences in biochemical systems,"  Quantum coherence phenomena driven by electronic-vibrational (vibronic)
+interactions, are being reported in many pulse (e.g. laser) driven chemical and
+biophysical systems. But what systems-level advantage(s) do such many-body
+coherences offer to future technologies? We address this question for pulsed
+systems of general size N, akin to the LHCII aggregates found in green plants.
+We show that external pulses generate vibronic states containing particular
+multipartite entanglements, and that such collective vibronic states increase
+the excitonic transfer efficiency. The strength of these many-body coherences
+and their robustness to decoherence, increase with aggregate size N and do not
+require strong electronic-vibrational coupling. The implications for energy and
+information transport are discussed.
+",0,1,0,0,0,0
+17696,Privacy Mining from IoT-based Smart Homes,"  Recently, a wide range of smart devices are deployed in a variety of
+environments to improve the quality of human life. One of the important
+IoT-based applications is smart homes for healthcare, especially for elders.
+IoT-based smart homes enable elders' health to be properly monitored and taken
+care of. However, elders' privacy might be disclosed from smart homes due to
+non-fully protected network communication or other reasons. To demonstrate how
+serious this issue is, we introduce in this paper a Privacy Mining Approach
+(PMA) to mine privacy from smart homes by conducting a series of deductions and
+analyses on sensor datasets generated by smart homes. The experimental results
+demonstrate that PMA is able to deduce a global sensor topology for a smart
+home and disclose elders' privacy in terms of their house layouts.
+",0,0,0,1,0,0
+17697,Idempotent ordered semigroup,"  An element e of an ordered semigroup $(S,\cdot,\leq)$ is called an ordered
+idempotent if $e\leq e^2$. We call an ordered semigroup $S$ idempotent ordered
+semigroup if every element of $S$ is an ordered idempotent. Every idempotent
+semigroup is a complete semilattice of rectangular idempotent semigroups and in
+this way we arrive to many other important classes of idempotent ordered
+semigroups.
+",0,0,1,0,0,0
+17698,Hybrid bounds for twists of $GL(3)$ $L$-functions,"  Let $\pi$ be a Hecke-Maass cusp form for $SL(3,\mathbb{Z})$ and
+$\chi=\chi_1\chi_2$ a Dirichlet character with $\chi_i$ primitive modulo $M_i$.
+Suppose that $M_1$, $M_2$ are primes such that
+$\max\{(M|t|)^{1/3+2\delta/3},M^{2/5}|t|^{-9/20},
+M^{1/2+2\delta}|t|^{-3/4+2\delta}\}(M|t|)^{\varepsilon}<M_1< \min\{
+(M|t|)^{2/5},(M|t|)^{1/2-8\delta}\}(M|t|)^{-\varepsilon}$ for any
+$\varepsilon>0$, where $M=M_1M_2$, $|t|\geq 1$ and $0<\delta< 1/52$. Then we
+have $$ L\left(\frac{1}{2}+it,\pi\otimes \chi\right)\ll_{\pi,\varepsilon}
+(M|t|)^{3/4-\delta+\varepsilon}. $$
+",0,0,1,0,0,0
+17699,Modeling stochastic skew of FX options using SLV models with stochastic spot/vol correlation and correlated jumps,"  It is known that the implied volatility skew of FX options demonstrates a
+stochastic behavior which is called stochastic skew. In this paper we create
+stochastic skew by assuming the spot/instantaneous variance correlation to be
+stochastic. Accordingly, we consider a class of SLV models with stochastic
+correlation where all drivers - the spot, instantaneous variance and their
+correlation are modeled by Levy processes. We assume all diffusion components
+to be fully correlated as well as all jump components. A new fully implicit
+splitting finite-difference scheme is proposed for solving forward PIDE which
+is used when calibrating the model to market prices of the FX options with
+different strikes and maturities. The scheme is unconditionally stable, of
+second order of approximation in time and space, and achieves a linear
+complexity in each spatial direction. The results of simulation obtained by
+using this model demonstrate capacity of the presented approach in modeling
+stochastic skew.
+",0,0,1,0,0,0
+17700,Computational Study of Amplitude-to-Phase Conversion in a Modified Uni-Traveling Carrier (MUTC) Photodetector,"  We calculate the amplitude-to-phase (AM-to-PM) noise conversion in a modified
+unitraveling carrier (MUTC) photodetector. We obtained two nulls as measured in
+the experiments, and we explain their origin. The nulls appear due to the
+transit time variation when the average photocurrent varies, and the transit
+time variation is due to the change of electron velocity when the average
+photocurrent varies. We also show that the AM-to-PM conversion coefficient
+depends only on the pulse energy and is independent of the pulse duration when
+the duration is less than 500 fs. When the pulse duration is larger than 500
+fs, the nulls of the AM-to-PM conversion coefficient shift to larger average
+photocurrents. This shift occurs because the increase in that pulse duration
+leads to a decrease in the peak photocurrent. The AM-to-PM noise conversion
+coefficient changes as the repetition rate varies. However, the repetition rate
+does not change the AM-to-PM conversion coefficient as a function of input
+optical pulse energy. The repetition rate changes the average photocurrent. We
+propose a design that would in theory improve the performance of the device.
+",0,1,0,0,0,0
+17701,Direct frequency-comb spectroscopy of $6S_{1/2}$-$8S_{1/2}$ transitions of atomic cesium,"  Direct frequency-comb spectroscopy is used to probe the absolute frequencies
+of $6S_{1/2}$-$8S_{1/2}$ two-photon transitions of atomic cesium in hot vapor
+environment. By utilizing the coherent control method of temporally splitting
+the laser spectrum above and below the two-photon resonance frequency,
+Doppler-free absorption is built in two spatially distinct locations and imaged
+for high-precision spectroscopy. Theoretical analysis finds that these
+transition lines are measured with uncertainty below $5\times10^{-10}$, mainly
+contributed from laser-induced AC Stark shift.
+",0,1,0,0,0,0
+17702,Aggregation and Disaggregation of Energetic Flexibility from Distributed Energy Resources,"  A variety of energy resources has been identified as being flexible in their
+electric energy consumption or generation. This energetic flexibility can be
+used for various purposes such as minimizing energy procurement costs or
+providing ancillary services to power grids. To fully leverage the flexibility
+available from distributed small-scale resources, their flexibility must be
+quantified and aggregated.
+This paper introduces a generic and scalable approach for flexible energy
+systems to quantitatively describe and price their flexibility based on
+zonotopic sets. The description proposed allows aggregators to efficiently pool
+the flexibility of large numbers of systems and to make control and market
+decisions on the aggregate level. In addition, an algorithm is presented that
+distributes aggregate-level control decisions among the individual systems of
+the pool in an economically fair and computationally efficient way. Finally, it
+is shown how the zonotopic description of flexibility enables an efficient
+computation of aggregate regulation power bid-curves.
+",1,0,0,0,0,0
+17703,Mapping Walls of Indoor Environment using RGB-D Sensor,"  Inferring walls configuration of indoor environment could help robot
+""understand"" the environment better. This allows the robot to execute a task
+that involves inter-room navigation, such as picking an object in the kitchen.
+In this paper, we present a method to inferring walls configuration from a
+moving RGB-D sensor. Our goal is to combine a simple wall configuration model
+and fast wall detection method in order to get a system that works online, is
+real-time, and does not need a Manhattan World assumption. We tested our
+preliminary work, i.e. wall detection and measurement from moving RGB-D sensor,
+with MIT Stata Center Dataset. The performance of our method is reported in
+terms of accuracy and speed of execution.
+",1,0,0,0,0,0
+17704,The Effects of Memory Replay in Reinforcement Learning,"  Experience replay is a key technique behind many recent advances in deep
+reinforcement learning. Allowing the agent to learn from earlier memories can
+speed up learning and break undesirable temporal correlations. Despite its
+wide-spread application, very little is understood about the properties of
+experience replay. How does the amount of memory kept affect learning dynamics?
+Does it help to prioritize certain experiences? In this paper, we address these
+questions by formulating a dynamical systems ODE model of Q-learning with
+experience replay. We derive analytic solutions of the ODE for a simple
+setting. We show that even in this very simple setting, the amount of memory
+kept can substantially affect the agent's performance. Too much or too little
+memory both slow down learning. Moreover, we characterize regimes where
+prioritized replay harms the agent's learning. We show that our analytic
+solutions have excellent agreement with experiments. Finally, we propose a
+simple algorithm for adaptively changing the memory buffer size which achieves
+consistently good empirical performance.
+",1,0,0,1,0,0
+17705,Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning,"  We investigate the problem of dynamic portfolio optimization in
+continuous-time, finite-horizon setting for a portfolio of two stocks and one
+risk-free asset. The stocks follow the Cointelation model. The proposed
+optimization methods are twofold. In what we call an Stochastic Differential
+Equation approach, we compute the optimal weights using mean-variance criterion
+and power utility maximization. We show that dynamically switching between
+these two optimal strategies by introducing a triggering function can further
+improve the portfolio returns. We contrast this with the machine learning
+clustering methodology inspired by the band-wise Gaussian mixture model. The
+first benefit of the machine learning over the Stochastic Differential Equation
+approach is that we were able to achieve the same results though a simpler
+channel. The second advantage is a flexibility to regime change.
+",0,0,0,0,0,1
+17706,High-mass Starless Clumps in the inner Galactic Plane: the Sample and Dust Properties,"  We report a sample of 463 high-mass starless clump (HMSC) candidates within
+$-60°<l<60°$ and $-1°<b<1°$. This sample has been singled out from
+10861 ATLASGAL clumps. All of these sources are not associated with any known
+star-forming activities collected in SIMBAD and young stellar objects
+identified using color-based criteria. We also make sure that the HMSC
+candidates have neither point sources at 24 and 70 \micron~nor strong extended
+emission at 24 $\mu$m. Most of the identified HMSCs are infrared ($\le24$
+$\mu$m) dark and some are even dark at 70 $\mu$m. Their distribution shows
+crowding in Galactic spiral arms and toward the Galactic center and some
+well-known star-forming complexes. Many HMSCs are associated with large-scale
+filaments. Some basic parameters were attained from column density and dust
+temperature maps constructed via fitting far-infrared and submillimeter
+continuum data to modified blackbodies. The HMSC candidates have sizes, masses,
+and densities similar to clumps associated with Class II methanol masers and
+HII regions, suggesting they will evolve into star-forming clumps. More than
+90% of the HMSC candidates have densities above some proposed thresholds for
+forming high-mass stars. With dust temperatures and luminosity-to-mass ratios
+significantly lower than that for star-forming sources, the HMSC candidates are
+externally heated and genuinely at very early stages of high-mass star
+formation. Twenty sources with equivalent radius $r_\mathrm{eq}<0.15$ pc and
+mass surface density $\Sigma>0.08$ g cm$^{-2}$ could be possible high-mass
+starless cores. Further investigations toward these HMSCs would undoubtedly
+shed light on comprehensively understanding the birth of high-mass stars.
+",0,1,0,0,0,0
+17707,Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics,"  Artificial neural networks that learn to perform Principal Component Analysis
+(PCA) and related tasks using strictly local learning rules have been
+previously derived based on the principle of similarity matching: similar pairs
+of inputs should map to similar pairs of outputs. However, the operation of
+these networks (and of similar networks) requires a fixed-point iteration to
+determine the output corresponding to a given input, which means that dynamics
+must operate on a faster time scale than the variation of the input. Further,
+during these fast dynamics such networks typically ""disable"" learning, updating
+synaptic weights only once the fixed-point iteration has been resolved. Here,
+we derive a network for PCA-based dimensionality reduction that avoids this
+fast fixed-point iteration. The key novelty of our approach is a modification
+of the similarity matching objective to encourage near-diagonality of a
+synaptic weight matrix. We then approximately invert this matrix using a Taylor
+series approximation, replacing the previous fast iterations. In the offline
+setting, our algorithm corresponds to a dynamical system, the stability of
+which we rigorously analyze. In the online setting (i.e., with stochastic
+gradients), we map our algorithm to a familiar neural network architecture and
+give numerical results showing that our method converges at a competitive rate.
+The computational complexity per iteration of our online algorithm is linear in
+the total degrees of freedom, which is in some sense optimal.
+",0,0,0,1,1,0
+17708,Exploring to learn visual saliency: The RL-IAC approach,"  The problem of object localization and recognition on autonomous mobile
+robots is still an active topic. In this context, we tackle the problem of
+learning a model of visual saliency directly on a robot. This model, learned
+and improved on-the-fly during the robot's exploration provides an efficient
+tool for localizing relevant objects within their environment. The proposed
+approach includes two intertwined components. On the one hand, we describe a
+method for learning and incrementally updating a model of visual saliency from
+a depth-based object detector. This model of saliency can also be exploited to
+produce bounding box proposals around objects of interest. On the other hand,
+we investigate an autonomous exploration technique to efficiently learn such a
+saliency model. The proposed exploration, called Reinforcement
+Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's
+exploration so that samples selected by the robot are likely to improve the
+current model of saliency. We then demonstrate that such a saliency model
+learned directly on a robot outperforms several state-of-the-art saliency
+techniques, and that RL-IAC can drastically decrease the required time for
+learning a reliable saliency model.
+",1,0,0,0,0,0
+17709,Fast and Robust Shortest Paths on Manifolds Learned from Data,"  We propose a fast, simple and robust algorithm for computing shortest paths
+and distances on Riemannian manifolds learned from data. This amounts to
+solving a system of ordinary differential equations (ODEs) subject to boundary
+conditions. Here standard solvers perform poorly because they require
+well-behaved Jacobians of the ODE, and usually, manifolds learned from data
+imply unstable and ill-conditioned Jacobians. Instead, we propose a fixed-point
+iteration scheme for solving the ODE that avoids Jacobians. This enhances the
+stability of the solver, while reduces the computational cost. In experiments
+involving both Riemannian metric learning and deep generative models we
+demonstrate significant improvements in speed and stability over both
+general-purpose state-of-the-art solvers as well as over specialized solvers.
+",1,0,0,1,0,0
+17710,Typed Graph Networks,"  Recently, the deep learning community has given growing attention to neural
+architectures engineered to learn problems in relational domains. Convolutional
+Neural Networks employ parameter sharing over the image domain, tying the
+weights of neural connections on a grid topology and thus enforcing the
+learning of a number of convolutional kernels. By instantiating trainable
+neural modules and assembling them in varied configurations (apart from grids),
+one can enforce parameter sharing over graphs, yielding models which can
+effectively be fed with relational data. In this context, vertices in a graph
+can be projected into a hyperdimensional real space and iteratively refined
+over many message-passing iterations in an end-to-end differentiable
+architecture. Architectures of this family have been referred to with several
+definitions in the literature, such as Graph Neural Networks, Message-passing
+Neural Networks, Relational Networks and Graph Networks. In this paper, we
+revisit the original Graph Neural Network model and show that it generalises
+many of the recent models, which in turn benefit from the insight of thinking
+about vertex \textbf{types}. To illustrate the generality of the original
+model, we present a Graph Neural Network formalisation, which partitions the
+vertices of a graph into a number of types. Each type represents an entity in
+the ontology of the problem one wants to learn. This allows - for instance -
+one to assign embeddings to edges, hyperedges, and any number of global
+attributes of the graph. As a companion to this paper we provide a
+Python/Tensorflow library to facilitate the development of such architectures,
+with which we instantiate the formalisation to reproduce a number of models
+proposed in the current literature.
+",1,0,0,1,0,0
+17711,The effect of different in-chain impurities on the magnetic properties of the spin chain compound SrCuO$_2$ probed by NMR,"  The S=1/2 Heisenberg spin chain compound SrCuO2 doped with different amounts
+of nickel (Ni), palladium (Pd), zinc (Zn) and cobalt (Co) has been studied by
+means of Cu nuclear magnetic resonance (NMR). Replacing only a few of the S=1/2
+Cu ions with Ni, Pd, Zn or Co has a major impact on the magnetic properties of
+the spin chain system. In the case of Ni, Pd and Zn an unusual line broadening
+in the low temperature NMR spectra reveals the existence of an impurity-induced
+local alternating magnetization (LAM), while exponentially decaying
+spin-lattice relaxation rates $T_1^{-1}$ towards low temperatures indicate the
+opening of spin gaps. A distribution of gap magnitudes is proven by a stretched
+spin-lattice relaxation and a variation of $T_1^{-1}$ within the broad
+resonance lines. These observations depend strongly on the impurity
+concentration and therefore can be understood using the model of finite
+segments of the spin 1/2 antiferromagnetic Heisenberg chain, i.e. pure chain
+segmentation due to S = 0 impurities. This is surprising for Ni as it was
+previously assumed to be a magnetic impurity with S = 1 which is screened by
+the neighboring copper spins. In order to confirm the S = 0 state of the Ni, we
+performed x-ray absorption spectroscopy (XAS) and compared the measurements to
+simulated XAS spectra based on multiplet ligand-field theory. Furthermore, Zn
+doping leads to much smaller effects on both the NMR spectra and the
+spin-lattice relaxation rates, indicating that Zn avoids occupying Cu sites.
+For magnetic Co impurities, $T_1^{-1}$ does not obey the gap like decrease, and
+the low-temperature spectra get very broad. This could be related to the
+increase of the Neel temperature which was observed by recent muSR and
+susceptibility measurements, and is most likely an effect of the impurity spin
+$S\neq0$.
+",0,1,0,0,0,0
+17712,L-groups and the Langlands program for covering groups: a historical introduction,"  In this joint introduction to an Asterisque volume, we give a short
+discussion of the historical developments in the study of nonlinear covering
+groups, touching on their structure theory, representation theory and the
+theory of automorphic forms. This serves as a historical motivation and sets
+the scene for the papers in the volume. Our discussion is necessarily
+subjective and will undoubtedly leave out the contributions of many authors, to
+whom we apologize in earnest.
+",0,0,1,0,0,0
+17713,Uncertainty Reduction for Stochastic Processes on Complex Networks,"  Many real-world systems are characterized by stochastic dynamical rules where
+a complex network of interactions among individual elements probabilistically
+determines their state. Even with full knowledge of the network structure and
+of the stochastic rules, the ability to predict system configurations is
+generally characterized by a large uncertainty. Selecting a fraction of the
+nodes and observing their state may help to reduce the uncertainty about the
+unobserved nodes. However, choosing these points of observation in an optimal
+way is a highly nontrivial task, depending on the nature of the stochastic
+process and on the structure of the underlying interaction pattern. In this
+paper, we introduce a computationally efficient algorithm to determine
+quasioptimal solutions to the problem. The method leverages network sparsity to
+reduce computational complexity from exponential to almost quadratic, thus
+allowing the straightforward application of the method to mid-to-large-size
+systems. Although the method is exact only for equilibrium stochastic processes
+defined on trees, it turns out to be effective also for out-of-equilibrium
+processes on sparse loopy networks.
+",1,1,0,0,0,0
+17714,Light propagation in Extreme Conditions - The role of optically clear tissues and scattering layers in optical biomedical imaging,"  The field of biomedical imaging has undergone a rapid growth in recent years,
+mostly due to the implementation of ad-hoc designed experimental setups,
+theoretical support methods and numerical reconstructions. Especially for
+biological samples, the high number of scattering events occurring during the
+photon propagation process limit the penetration depth and the possibility to
+outperform direct imaging in thicker and not transparent samples. In this
+thesis, we will examine theoretically and experimentally the scattering process
+from two opposite points of view, focusing also on the continuous stimulus
+offered by the will to tackle some specific challenges in the emerging optical
+imaging science. Firstly, we will discuss the light propagation in diffusive
+biological tissues considering the particular case of the presence of optically
+transparent regions enclosed in a highly scattering environment. The correct
+inclusion of this information, can ultimately lead to higher resolution
+reconstruction, especially in neuroimaging. On the other hand, we will examine
+the extreme case of the three-dimensional imaging of a totally hidden sample,
+in which the phase has been scrambled by a random scattering layer. By making
+use of appropriate numerical methods, we will prove how it is possible to
+outperform such hidden reconstruction in a very efficient way, opening the path
+toward the unexplored field of three-dimensional hidden imaging. Finally, we
+will present how, the properties noticed while addressing these problems,
+leaded us to the development of a novel alignment-free three-dimensional
+tomographic technique that we refer to as Phase-Retrieved Tomography.
+Ultimately, we used this technique for the study of the fluorescence
+distribution in a three-dimensional spherical tumor model, the cancer cell
+spheroid, one of the most important biological model for the study of such
+disease.
+",0,1,0,0,0,0
+17715,Design discussion on the ISDA Common Domain Model,"  A new initiative from the International Swaps and Derivatives Association
+(ISDA) aims to establish a ""Common Domain Model"" (ISDA CDM): a new standard for
+data and process representation across the full range of derivatives
+instruments. Design of the ISDA CDM is at an early stage and the draft
+definition contains considerable complexity. This paper contributes by offering
+insight, analysis and discussion relating to key topics in the design space
+such as data lineage, timestamps, consistency, operations, events, state and
+state transitions.
+",1,0,0,0,0,0
+17716,Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models,"  Hierarchical models are utilized in a wide variety of problems which are
+characterized by task hierarchies, where predictions on smaller subtasks are
+useful for trying to predict a final task. Typically, neural networks are first
+trained for the subtasks, and the predictions of these networks are
+subsequently used as additional features when training a model and doing
+inference for a final task. In this work, we focus on improving learning for
+such hierarchical models and demonstrate our method on the task of speaker
+trait prediction. Speaker trait prediction aims to computationally identify
+which personality traits a speaker might be perceived to have, and has been of
+great interest to both the Artificial Intelligence and Social Science
+communities. Persuasiveness prediction in particular has been of interest, as
+persuasive speakers have a large amount of influence on our thoughts, opinions
+and beliefs. In this work, we examine how leveraging the relationship between
+related speaker traits in a hierarchical structure can help improve our ability
+to predict how persuasive a speaker is. We present a novel algorithm that
+allows us to backpropagate through this hierarchy. This hierarchical model
+achieves a 25% relative error reduction in classification accuracy over current
+state-of-the art methods on the publicly available POM dataset.
+",1,0,0,0,0,0
+17717,Atomistic simulations of dislocation/precipitation interactions in Mg-Al alloys and implications for precipitation hardening,"  Atomistic simulations were carried out to analyze the interaction between $<
+a>$ basal dislocations and precipitates in Mg-Al alloys and the associated
+strengthening mechanisms.
+",0,1,0,0,0,0
+17718,Recovering Dense Tissue Multispectral Signal from in vivo RGB Images,"  Hyperspectral/multispectral imaging (HSI/MSI) contains rich information
+clinical applications, such as 1) narrow band imaging for vascular
+visualisation; 2) oxygen saturation for intraoperative perfusion monitoring and
+clinical decision making [1]; 3) tissue classification and identification of
+pathology [2]. The current systems which provide pixel-level HSI/MSI signal can
+be generally divided into two types: spatial scanning and spectral scanning.
+However, the trade-off between spatial/spectral resolution, the acquisition
+time, and the hardware complexity hampers implementation in real-world
+applications, especially intra-operatively. Acquiring high resolution images in
+real-time is important for HSI/MSI in intra-operative imaging, to alleviate the
+side effect caused by breathing, heartbeat, and other sources of motion.
+Therefore, we developed an algorithm to recover a pixel-level MSI stack using
+only the captured snapshot RGB images from a normal camera. We refer to this
+technique as ""super-spectral-resolution"". The proposed method enables recovery
+of pixel-level-dense MSI signals with 24 spectral bands at ~11 frames per
+second (FPS) on a GPU. Multispectral data captured from porcine bowel and
+sheep/rabbit uteri in vivo has been used for training, and the algorithm has
+been validated using unseen in vivo animal experiments.
+",1,0,0,0,0,0
+17719,Atomically thin gallium layers from solid-melt exfoliation,"  Among the large number of promising two-dimensional (2D) atomic layer
+crystals, true metallic layers are rare. Through combined theoretical and
+experimental approaches, we report on the stability and successful exfoliation
+of atomically thin gallenene sheets, having two distinct atomic arrangements
+along crystallographic twin directions of the parent alpha-gallium. Utilizing
+the weak interface between solid and molten phases of gallium, a solid-melt
+interface exfoliation technique is developed to extract these layers. Phonon
+dispersion calculations show that gallenene can be stabilized with bulk gallium
+lattice parameters. The electronic band structure of gallenene shows a
+combination of partially filled Dirac cone and the non-linear dispersive band
+near Fermi level suggesting that gallenene should behave as a metallic layer.
+Furthermore it is observed that strong interaction of gallenene with other 2D
+semiconductors induces semiconducting to metallic phase transitions in the
+latter paving the way for using gallenene as interesting metallic contacts in
+2D devices.
+",0,1,0,0,0,0
+17720,Novel Universality Classes in Ferroelectric Liquid Crystals,"  Starting from a Langevin formulation of a thermally perturbed nonlinear
+elastic model of the ferroelectric smectic-C$^*$ (SmC${*}$) liquid crystals in
+the presence of an electric field, this article characterizes the hitherto
+unexplored dynamical phase transition from a thermo-electrically forced
+ferroelectric SmC${}^{*}$ phase to a chiral nematic liquid crystalline phase
+and vice versa. The theoretical analysis is based on a combination of dynamic
+renormalization (DRG) and numerical simulation of the emergent model. While the
+DRG architecture predicts a generic transition to the Kardar-Parisi-Zhang (KPZ)
+universality class at dynamic equilibrium, in agreement with recent
+experiments, the numerical simulations of the model show simultaneous existence
+of two phases, one a ""subdiffusive"" (SD) phase characterized by a dynamical
+exponent value of 1, and the other a KPZ phase, characterized by a dynamical
+exponent value of 1.5. The SD phase flows over to the KPZ phase with increased
+external forcing, offering a new universality paradigm, hitherto unexplored in
+the context of ferroelectric liquid crystals.
+",0,1,0,0,0,0
+17721,Feature functional theory - binding predictor (FFT-BP) for the blind prediction of binding free energies,"  We present a feature functional theory - binding predictor (FFT-BP) for the
+protein-ligand binding affinity prediction. The underpinning assumptions of
+FFT-BP are as follows: i) representability: there exists a microscopic feature
+vector that can uniquely characterize and distinguish one protein-ligand
+complex from another; ii) feature-function relationship: the macroscopic
+features, including binding free energy, of a complex is a functional of
+microscopic feature vectors; and iii) similarity: molecules with similar
+microscopic features have similar macroscopic features, such as binding
+affinity. Physical models, such as implicit solvent models and quantum theory,
+are utilized to extract microscopic features, while machine learning algorithms
+are employed to rank the similarity among protein-ligand complexes. A large
+variety of numerical validations and tests confirms the accuracy and robustness
+of the proposed FFT-BP model. The root mean square errors (RMSEs) of FFT-BP
+blind predictions of a benchmark set of 100 complexes, the PDBBind v2007 core
+set of 195 complexes and the PDBBind v2015 core set of 195 complexes are 1.99,
+2.02 and 1.92 kcal/mol, respectively. Their corresponding Pearson correlation
+coefficients are 0.75, 0.80, and 0.78, respectively.
+",1,1,0,0,0,0
+17722,Combining and Steganography of 3D Face Textures,"  One of the serious issues in communication between people is hiding
+information from others, and the best way for this, is deceiving them. Since
+nowadays face images are mostly used in three dimensional format, in this paper
+we are going to steganography 3D face images, detecting which by curious people
+will be impossible. As in detecting face only its texture is important, we
+separate texture from shape matrices, for eliminating half of the extra
+information, steganography is done only for face texture, and for
+reconstructing 3D face, we can use any other shape. Moreover, we will indicate
+that, by using two textures, how two 3D faces can be combined. For a complete
+description of the process, first, 2D faces are used as an input for building
+3D faces, and then 3D textures are hidden within other images.
+",1,0,0,0,0,0
+17723,Degree of sequentiality of weighted automata,"  Weighted automata (WA) are an important formalism to describe quantitative
+properties. Obtaining equivalent deterministic machines is a longstanding
+research problem. In this paper we consider WA with a set semantics, meaning
+that the semantics is given by the set of weights of accepting runs. We focus
+on multi-sequential WA that are defined as finite unions of sequential WA. The
+problem we address is to minimize the size of this union. We call this minimum
+the degree of sequentiality of (the relation realized by) the WA. For a given
+positive integer k, we provide multiple characterizations of relations realized
+by a union of k sequential WA over an infinitary finitely generated group: a
+Lipschitz-like machine independent property, a pattern on the automaton (a new
+twinning property) and a subclass of cost register automata. When possible, we
+effectively translate a WA into an equivalent union of k sequential WA. We also
+provide a decision procedure for our twinning property for commutative
+computable groups thus allowing to compute the degree of sequentiality. Last,
+we show that these results also hold for word transducers and that the
+associated decision problem is Pspace-complete.
+",1,0,0,0,0,0
+17724,On the orders of the non-Frattini elements of a finite group,"  Let $G$ be a finite group and let $p_1,\dots,p_n$ be distinct primes. If $G$
+contains an element of order $p_1\cdots p_n,$ then there is an element in $G$
+which is not contained in the Frattini subgroup of $G$ and whose order is
+divisible by $p_1\cdots p_n.$
+",0,0,1,0,0,0
+17725,Varieties of general type with small volumes,"  Generalize Kobayashi's example for the Noether inequality in dimension three,
+we provide examples of n-folds of general type with small volumes.
+",0,0,1,0,0,0
+17726,Translations in the exponential Orlicz space with Gaussian weight,"  We study the continuity of space translations on non-parametric exponential
+families based on the exponential Orlicz space with Gaussian reference density.
+",0,0,1,1,0,0
+17727,Shrinkage Estimation Strategies in Generalized Ridge Regression Models Under Low/High-Dimension Regime,"  In this study, we propose shrinkage methods based on {\it generalized ridge
+regression} (GRR) estimation which is suitable for both multicollinearity and
+high dimensional problems with small number of samples (large $p$, small $n$).
+Also, it is obtained theoretical properties of the proposed estimators for
+Low/High Dimensional cases. Furthermore, the performance of the listed
+estimators is demonstrated by both simulation studies and real-data analysis,
+and compare its performance with existing penalty methods. We show that the
+proposed methods compare well to competing regularization techniques.
+",0,0,1,1,0,0
+17728,A path integral approach to Bayesian inference in Markov processes,"  We formulate Bayesian updates in Markov processes by means of path integral
+techniques and derive the imaginary-time Schrödinger equation with
+likelihood to direct the inference incorporated as a potential for the
+posterior probability distribution
+",0,0,1,1,0,0
+17729,Strong-coupling charge density wave in a one-dimensional topological metal,"  Scanning tunnelling microscopy and low energy electron diffraction show a
+dimerization-like reconstruction in the one-dimensional atomic chains on
+Bi(114) at low temperatures. While one-dimensional systems are generally
+unstable against such a distortion, its observation is not expected for this
+particular surface, since there are several factors that should prevent it: One
+is the particular spin texture of the Fermi surface, which resembles a
+one-dimensional topological state, and spin protection should hence prevent the
+formation of the reconstruction. The second is the very short nesting vector $2
+k_F$, which is inconsistent with the observed lattice distortion. A
+nesting-driven mechanism of the reconstruction is indeed excluded by the
+absence of any changes in the electronic structure near the Fermi surface, as
+observed by angle-resolved photoemission spectroscopy. However, distinct
+changes in the electronic structure at higher binding energies are found to
+accompany the structural phase transition. This, as well as the observed short
+correlation length of the pairing distortion, suggest that the transition is of
+the strong coupling type and driven by phonon entropy rather than electronic
+entropy.
+",0,1,0,0,0,0
+17730,$\mathfrak A$-principal Hopf hypersurfaces in complex quadrics,"  A real hypersurface in the complex quadric $Q^m=SO_{m+2}/SO_mSO_2$ is said to
+be $\mathfrak A$-principal if its unit normal vector field is singular of type
+$\mathfrak A$-principal everywhere. In this paper, we show that a $\mathfrak
+A$-principal Hopf hypersurface in $Q^m$, $m\geq3$ is an open part of a tube
+around a totally geodesic $Q^{m+1}$ in $Q^m$. We also show that such real
+hypersurfaces are the only contact real hypersurfaces in $Q^m$. %, this answers
+affirmatively a question posted by Berndt (cf. \cite{berndt1})}. The
+classification for pseudo-Einstein real hypersurfaces in $Q^m$, $m\geq3$, is
+also obtained.
+",0,0,1,0,0,0
+17731,On the stochastic phase stability of Ti2AlC-Cr2AlC,"  The quest towards expansion of the MAX design space has been accelerated with
+the recent discovery of several solid solution and ordered phases involving at
+least two MAX end members. Going beyond the nominal MAX compounds enables not
+only fine tuning of existing properties but also entirely new functionality.
+This search, however, has been mostly done through painstaking experiments as
+knowledge of the phase stability of the relevant systems is rather scarce. In
+this work, we report the first attempt to evaluate the finite-temperature
+pseudo-binary phase diagram of the Ti2AlC-Cr2AlC via first-principles-guided
+Bayesian CALPHAD framework that accounts for uncertainties not only in ab
+initio calculations and thermodynamic models but also in synthesis conditions
+in reported experiments. The phase stability analyses are shown to have good
+agreement with previous experiments. The work points towards a promising way of
+investigating phase stability in other MAX Phase systems providing the
+knowledge necessary to elucidate possible synthesis routes for MAX systems with
+unprecedented properties.
+",0,1,0,0,0,0
+17732,Note on Attacking Object Detectors with Adversarial Stickers,"  Deep learning has proven to be a powerful tool for computer vision and has
+seen widespread adoption for numerous tasks. However, deep learning algorithms
+are known to be vulnerable to adversarial examples. These adversarial inputs
+are created such that, when provided to a deep learning algorithm, they are
+very likely to be mislabeled. This can be problematic when deep learning is
+used to assist in safety critical decisions. Recent research has shown that
+classifiers can be attacked by physical adversarial examples under various
+physical conditions. Given the fact that state-of-the-art objection detection
+algorithms are harder to be fooled by the same set of adversarial examples,
+here we show that these detectors can also be attacked by physical adversarial
+examples. In this note, we briefly show both static and dynamic test results.
+We design an algorithm that produces physical adversarial inputs, which can
+fool the YOLO object detector and can also attack Faster-RCNN with relatively
+high success rate based on transferability. Furthermore, our algorithm can
+compress the size of the adversarial inputs to stickers that, when attached to
+the targeted object, result in the detector either mislabeling or not detecting
+the object a high percentage of the time. This note provides a small set of
+results. Our upcoming paper will contain a thorough evaluation on other object
+detectors, and will present the algorithm.
+",1,0,0,0,0,0
+17733,All or Nothing Caching Games with Bounded Queries,"  We determine the value of some search games where our goal is to find all of
+some hidden treasures using queries of bounded size. The answer to a query is
+either empty, in which case we lose, or a location, which contains a treasure.
+We prove that if we need to find $d$ treasures at $n$ possible locations with
+queries of size at most $k$, then our chance of winning is $\frac{k^d}{\binom
+nd}$ if each treasure is at a different location and
+$\frac{k^d}{\binom{n+d-1}d}$ if each location might hide several treasures for
+large enough $n$. Our work builds on some results by Csóka who has studied a
+continuous version of this problem, known as Alpern's Caching Game; we also
+prove that the value of Alpern's Caching Game is $\frac{k^d}{\binom{n+d-1}d}$
+for integer $k$ and large enough $n$.
+",1,0,1,0,0,0
+17734,"HATS-43b, HATS-44b, HATS-45b, and HATS-46b: Four Short Period Transiting Giant Planets in the Neptune-Jupiter Mass Range","  We report the discovery of four short period extrasolar planets transiting
+moderately bright stars from photometric measurements of the HATSouth network
+coupled to additional spectroscopic and photometric follow-up observations.
+While the planet masses range from 0.26 to 0.90 M$_J$, the radii are all
+approximately a Jupiter radii, resulting in a wide range of bulk densities. The
+orbital period of the planets range from 2.7d to 4.7d, with HATS-43b having an
+orbit that appears to be marginally non-circular (e= 0.173$\pm$0.089). HATS-44
+is notable for a high metallicity ([Fe/H]= 0.320$\pm$0.071). The host stars
+spectral types range from late F to early K, and all of them are moderately
+bright (13.3<V<14.4), allowing the execution of future detailed follow-up
+observations. HATS-43b and HATS-46b, with expected transmission signals of 2350
+ppm and 1500 ppm, respectively, are particularly well suited targets for
+atmospheric characterisation via transmission spectroscopy.
+",0,1,0,0,0,0
+17735,Well-balanced mesh-based and meshless schemes for the shallow-water equations,"  We formulate a general criterion for the exact preservation of the ""lake at
+rest"" solution in general mesh-based and meshless numerical schemes for the
+strong form of the shallow-water equations with bottom topography. The main
+idea is a careful mimetic design for the spatial derivative operators in the
+momentum flux equation that is paired with a compatible averaging rule for the
+water column height arising in the bottom topography source term. We prove
+consistency of the mimetic difference operators analytically and demonstrate
+the well-balanced property numerically using finite difference and RBF-FD
+schemes in the one- and two-dimensional cases.
+",0,1,1,0,0,0
+17736,An Enhanced Initial Margin Methodology to Manage Warehoused Credit Risk,"  The use of CVA to cover credit risk is widely spread, but has its
+limitations. Namely, dealers face the problem of the illiquidity of instruments
+used for hedging it, hence forced to warehouse credit risk. As a result,
+dealers tend to offer a limited OTC derivatives market to highly risky
+counterparties. Consequently, those highly risky entities rarely have access to
+hedging services precisely when they need them most. In this paper we propose a
+method to overcome this limitation. We propose to extend the CVA risk-neutral
+framework to compute an initial margin (IM) specific to each counterparty,
+which depends on the credit quality of the entity at stake, transforming the
+effective credit rating of a given netting set to AAA, regardless of the credit
+rating of the counterparty. By transforming CVA requirement into IM ones, as
+proposed in this paper, an institution could rely on the existing mechanisms
+for posting and calling of IM, hence ensuring the operational viability of this
+new form of managing warehoused risk. The main difference with the currently
+standard framework is the creation of a Specific Initial Margin, that depends
+in the credit rating of the counterparty and the characteristics of the netting
+set in question. In this paper we propose a methodology for such transformation
+in a sound manner, and hence this method overcomes some of the limitations of
+the CVA framework.
+",0,0,0,0,0,1
+17737,Ensemble Pruning based on Objection Maximization with a General Distributed Framework,"  Ensemble pruning, selecting a subset of individual learners from an original
+ensemble, alleviates the deficiencies of ensemble learning on the cost of time
+and space. Accuracy and diversity serve as two crucial factors while they
+usually conflict with each other. To balance both of them, we formalize the
+ensemble pruning problem as an objection maximization problem based on
+information entropy. Then we propose an ensemble pruning method including a
+centralized version and a distributed version, in which the latter is to speed
+up the former's execution. At last, we extract a general distributed framework
+for ensemble pruning, which can be widely suitable for most of existing
+ensemble pruning methods and achieve less time consuming without much accuracy
+decline. Experimental results validate the efficiency of our framework and
+methods, particularly with regard to a remarkable improvement of the execution
+speed, accompanied by gratifying accuracy performance.
+",0,0,0,1,0,0
+17738,Psychophysical laws as reflection of mental space properties,"  The paper is devoted to the relationship between psychophysics and physics of
+mind. The basic trends in psychophysics development are briefly discussed with
+special attention focused on Teghtsoonian's hypotheses. These hypotheses pose
+the concept of the universality of inner psychophysics and enable to speak
+about psychological space as an individual object with its own properties.
+Turning to the two-component description of human behavior (I. Lubashevsky,
+Physics of the Human Mind, Springer, 2017) the notion of mental space is
+formulated and human perception of external stimuli is treated as the emergence
+of the corresponding images in the mental space. On one hand, these images are
+caused by external stimuli and their magnitude bears the information about the
+intensity of the corresponding stimuli. On the other hand, the individual
+structure of such images as well as their subsistence after emergence is
+determined only by the properties of mental space on its own. Finally, the
+mental operations of image comparison and their scaling are defined in a way
+allowing for the bounded capacity of human cognition. As demonstrated, the
+developed theory of stimulus perception is able to explain the basic
+regularities of psychophysics, e.g., (i) the regression and range effects
+leading to the overestimation of weak stimuli and the underestimation of strong
+stimuli, (ii) scalar variability (Weber's and Ekman' laws), and (\textit{iii})
+the sequential (memory) effects. As the final result, a solution to the
+Fechner-Stevens dilemma is proposed. This solution posits that Fechner's
+logarithmic law is not a consequences of Weber's law but stems from the
+interplay of uncertainty in evaluating stimulus intensities and the multi-step
+scaling required to overcome the stimulus incommensurability.
+",0,0,0,0,1,0
+17739,Shimura curves in the Prym locus,"  We study Shimura curves of PEL type in $\mathsf{A}_g$ generically contained
+in the Prym locus. We study both the unramified Prym locus, obtained using
+étale double covers, and the ramified Prym locus, corresponding to double
+covers ramified at two points. In both cases we consider the family of all
+double covers compatible with a fixed group action on the base curve. We
+restrict to the case where the family is 1-dimensional and the quotient of the
+base curve by the group is $\mathbb{P}^1$. We give a simple criterion for the
+image of these families under the Prym map to be a Shimura curve. Using
+computer algebra we check all the examples gotten in this way up to genus 28.
+We obtain 43 Shimura curves generically contained in the unramified Prym locus
+and 9 families generically contained in the ramified Prym locus. Most of these
+curves are not generically contained in the Jacobian locus.
+",0,0,1,0,0,0
+17740,Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies,"  Data-driven anomaly detection methods suffer from the drawback of detecting
+all instances that are statistically rare, irrespective of whether the detected
+instances have real-world significance or not. In this paper, we are interested
+in the problem of specifically detecting anomalous instances that are known to
+have high real-world utility, while ignoring the low-utility statistically
+anomalous instances. To this end, we propose a novel method called Latent
+Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution.
+This method uses the EM algorithm to simultaneously incorporate the Geometric
+Entropy Minimization principle for identifying statistical anomalies, and the
+Maximum Entropy Discrimination principle to incorporate utility labels, in
+order to detect high-utility anomalies. We apply our method in both simulated
+and real datasets to demonstrate that it has superior performance over existing
+alternatives that independently pre-process with unsupervised anomaly detection
+algorithms before classifying.
+",1,0,0,1,0,0
+17741,View-Invariant Recognition of Action Style Self-Dissimilarity,"  Self-similarity was recently introduced as a measure of inter-class
+congruence for classification of actions. Herein, we investigate the dual
+problem of intra-class dissimilarity for classification of action styles. We
+introduce self-dissimilarity matrices that discriminate between same actions
+performed by different subjects regardless of viewing direction and camera
+parameters. We investigate two frameworks using these invariant style
+dissimilarity measures based on Principal Component Analysis (PCA) and Fisher
+Discriminant Analysis (FDA). Extensive experiments performed on IXMAS dataset
+indicate remarkably good discriminant characteristics for the proposed
+invariant measures for gender recognition from video data.
+",1,0,0,0,0,0
+17742,Conducting Highly Principled Data Science: A Statistician's Job and Joy,"  Highly Principled Data Science insists on methodologies that are: (1)
+scientifically justified, (2) statistically principled, and (3) computationally
+efficient. An astrostatistics collaboration, together with some reminiscences,
+illustrates the increased roles statisticians can and should play to ensure
+this trio, and to advance the science of data along the way.
+",0,0,0,1,0,0
+17743,Regularizing nonlinear Schroedinger equations through partial off-axis variations,"  We study a class of focusing nonlinear Schroedinger-type equations derived
+recently by Dumas, Lannes and Szeftel within the mathematical description of
+high intensity laser beams [7]. These equations incorporate the possibility of
+a (partial) off-axis variation of the group velocity of such laser beams
+through a second order partial differential operator acting in some, but not
+necessarily all, spatial directions. We study the well-posedness theory for
+such models and obtain a regularizing effect, even in the case of only partial
+off-axis dependence. This provides an answer to an open problem posed in [7].
+",0,0,1,0,0,0
+17744,On h-Lexicalized Restarting Automata,"  Following some previous studies on restarting automata, we introduce a
+refined model - the h-lexicalized restarting automaton (h-RLWW). We argue that
+this model is useful for expressing lexicalized syntax in computational
+linguistics. We compare the input languages, which are the languages
+traditionally considered in automata theory, to the so-called basic and
+h-proper languages, which are (implicitly) used by categorial grammars, the
+original tool for the description of lexicalized syntax. The basic and h-proper
+languages allow us to stress several nice properties of h-lexicalized
+restarting automata, and they are suitable for modeling the analysis by
+reduction and, subsequently, for the development of categories of a lexicalized
+syntax. Based on the fact that a two-way deterministic monotone restarting
+automaton can be transformed into an equivalent deterministic monotone
+RL-automaton in (Marcus) contextual form, we obtain a transformation from
+monotone RLWW-automata that recognize the class CFL of context-free languages
+as their input languages to deterministic monotone h-RLWW-automata that
+recognize CFL through their h-proper languages. Through this transformation we
+obtain automata with the complete correctness preserving property and an
+infinite hierarchy within CFL, based on the size of the read/write window.
+Additionally, we consider h-RLWW-automata that are allowed to perform multiple
+rewrite steps per cycle, and we establish another infinite hierarchy above CFL
+that is based on the number of rewrite steps that may be executed within a
+cycle. The corresponding separation results and their proofs illustrate the
+transparency of h-RLWW-automata that work with the (complete or cyclic)
+correctness preserving property
+",1,0,0,0,0,0
+17745,Bayesian Nonparametric Inference for M/G/1 Queueing Systems,"  In this work, nonparametric statistical inference is provided for the
+continuous-time M/G/1 queueing model from a Bayesian point of view. The
+inference is based on observations of the inter-arrival and service times.
+Beside other characteristics of the system, particular interest is in the
+waiting time distribution which is not accessible in closed form. Thus, we use
+an indirect statistical approach by exploiting the Pollaczek-Khinchine
+transform formula for the Laplace transform of the waiting time distribution.
+Due to this, an estimator is defined and its frequentist validation in terms of
+posterior consistency and posterior normality is studied. It will turn out that
+we can hereby make inference for the observables separately and compose the
+results subsequently by suitable techniques.
+",0,0,1,1,0,0
+17746,Symbol Invariant of Partition and the Construction,"  The symbol is used to describe the Springer correspondence for the classical
+groups. We propose equivalent definitions of symbols for rigid partitions in
+the $B_n$, $C_n$, and $D_n$ theories uniformly. Analysing the new definition of
+symbol in detail, we give rules to construct symbol of a partition, which are
+easy to remember and to operate on. We introduce formal operations of a
+partition, which reduce the difficulties in the proof of the construction
+rules. According these rules, we give a closed formula of symbols for different
+theories uniformly. As applications, previous results can be illustrated more
+clearly by the construction rules of symbol.
+",0,0,1,0,0,0
+17747,Fairness in representation: quantifying stereotyping as a representational harm,"  While harms of allocation have been increasingly studied as part of the
+subfield of algorithmic fairness, harms of representation have received
+considerably less attention. In this paper, we formalize two notions of
+stereotyping and show how they manifest in later allocative harms within the
+machine learning pipeline. We also propose mitigation strategies and
+demonstrate their effectiveness on synthetic datasets.
+",1,0,0,1,0,0
+17748,Probing the possibility of hotspots on the central neutron star in HESS J1731-347,"  The X-ray spectra of the neutron stars located in the centers of supernova
+remnants Cas A and HESS J1731-347 are well fit with carbon atmosphere models.
+These fits yield plausible neutron star sizes for the known or estimated
+distances to these supernova remnants. The evidence in favor of the presence of
+a pure carbon envelope at the neutron star surface is rather indirect and is
+based on the assumption that the emission is generated uniformly by the entire
+stellar surface. Although this assumption is supported by the absence of
+pulsations, the observational upper limit on the pulsed fraction is not very
+stringent. In an attempt to quantify this evidence, we investigate the
+possibility that the observed spectrum of the neutron star in HESS J1731-347 is
+a combination of the spectra produced in a hydrogen atmosphere of the hotspots
+and of the cooler remaining part of the neutron star surface. The lack of
+pulsations in this case has to be explained either by a sufficiently small
+angle between the neutron star spin axis and the line of sight, or by a
+sufficiently small angular distance between the hotspots and the neutron star
+rotation poles. As the observed flux from a non-uniformly emitting neutron star
+depends on the angular distribution of the radiation emerging from the
+atmosphere, we have computed two new grids of pure carbon and pure hydrogen
+atmosphere model spectra accounting for Compton scattering. Using new hydrogen
+models, we have evaluated the probability of a geometry that leads to a pulsed
+fraction below the observed upper limit to be about 8.2 %. Such a geometry thus
+seems to be rather improbable but cannot be excluded at this stage.
+",0,1,0,0,0,0
+17749,Some Elementary Partition Inequalities and Their Implications,"  We prove various inequalities between the number of partitions with the bound
+on the largest part and some restrictions on occurrences of parts. We explore
+many interesting consequences of these partition inequalities. In particular,
+we show that for $L\geq 1$, the number of partitions with $l-s \leq L$ and
+$s=1$ is greater than the number of partitions with $l-s\leq L$ and $s>1$. Here
+$l$ and $s$ are the largest part and the smallest part of the partition,
+respectively.
+",0,0,1,0,0,0
+17750,The terrestrial late veneer from core disruption of a lunar-sized impactor,"  Overabundances in highly siderophile elements (HSEs) of Earth's mantle can be
+explained by conveyance from a singular, immense (3000 km in a diameter) ""Late
+Veneer"" impactor of chondritic composition, subsequent to lunar formation and
+terrestrial core-closure. Such rocky objects of approximately lunar mass (about
+0.01 M_E) ought to be differentiated, such that nearly all of their HSE payload
+is sequestered into iron cores. Here, we analyze the mechanical and chemical
+fate of the core of such a Late Veneer impactor, and trace how its HSEs are
+suspended - and thus pollute - the mantle. For the statistically most-likely
+oblique collision (about 45degree), the impactor's core elongates and
+thereafter disintegrates into a metallic hail of small particles (about 10 m).
+Some strike the orbiting Moon as sesquinary impactors, but most re-accrete to
+Earth as secondaries with further fragmentation. We show that a single oblique
+impactor provides an adequate amount of HSEs to the primordial terrestrial
+silicate reservoirs via oxidation of (<m-sized) metal particles with a hydrous,
+pre-impact, early Hadean Earth.
+",0,1,0,0,0,0
+17751,A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image,"  We consider a Bayesian model for inversion of observed amplitude variation
+with offset (AVO) data into lithology/fluid classes, and study in particular
+how the choice of prior distribution for the lithology/fluid classes influences
+the inversion results. Two distinct prior distributions are considered, a
+simple manually specified Markov random field prior with a first order
+neighborhood and a Markov mesh model with a much larger neighborhood estimated
+from a training image. They are chosen to model both horisontal connectivity
+and vertical thickness distribution of the lithology/fluid classes, and are
+compared on an offshore clastic oil reservoir in the North Sea. We combine both
+priors with the same linearised Gaussian likelihood function based on a
+convolved linearised Zoeppritz relation and estimate properties of the
+resulting two posterior distributions by simulating from these distributions
+with the Metropolis-Hastings algorithm.
+The influence of the prior on the marginal posterior probabilities for the
+lithology/fluid classes is clearly observable, but modest. The importance of
+the prior on the connectivity properties in the posterior realisations,
+however, is much stronger. The larger neighborhood of the Markov mesh prior
+enables it to identify and model connectivity and curvature much better than
+what can be done by the first order neighborhood Markov random field prior. As
+a result, we conclude that the posterior realisations based on the Markov mesh
+prior appear with much higher lateral connectivity, which is geologically
+plausible.
+",0,0,0,1,0,0
+17752,Correct Brillouin zone and electronic structure of BiPd,"  A promising route to the realization of Majorana fermions is in
+non-centrosymmetric superconductors, in which spin-orbit-coupling lifts the
+spin degeneracy of both bulk and surface bands. A detailed assessment of the
+electronic structure is critical to evaluate their suitability for this through
+establishing the topological properties of the electronic structure. This
+requires correct identification of the time-reversal-invariant momenta. One
+such material is BiPd, a recently rediscovered non-centrosymmetric
+superconductor which can be grown in large, high-quality single crystals and
+has been studied by several groups using angular resolved photoemission to
+establish its surface electronic structure. Many of the published electronic
+structure studies on this material are based on a reciprocal unit cell which is
+not the actual Brillouin zone of the material. We show here the consequences of
+this for the electronic structures and show how the inferred topological nature
+of the material is affected.
+",0,1,0,0,0,0
+17753,Even faster sorting of (not only) integers,"  In this paper we introduce RADULS2, the fastest parallel sorter based on
+radix algorithm. It is optimized to process huge amounts of data making use of
+modern multicore CPUs. The main novelties include: extremely optimized
+algorithm for handling tiny arrays (up to about a hundred of records) that
+could appear even billions times as subproblems to handle and improved
+processing of larger subarrays with better use of non-temporal memory stores.
+",1,0,0,0,0,0
+17754,Evidential Deep Learning to Quantify Classification Uncertainty,"  Deterministic neural nets have been shown to learn effective predictors on a
+wide range of machine learning problems. However, as the standard approach is
+to train the network to minimize a prediction loss, the resultant model remains
+ignorant to its prediction confidence. Orthogonally to Bayesian neural nets
+that indirectly infer prediction uncertainty through weight uncertainties, we
+propose explicit modeling of the same using the theory of subjective logic. By
+placing a Dirichlet distribution on the class probabilities, we treat
+predictions of a neural net as subjective opinions and learn the function that
+collects the evidence leading to these opinions by a deterministic neural net
+from data. The resultant predictor for a multi-class classification problem is
+another Dirichlet distribution whose parameters are set by the continuous
+output of a neural net. We provide a preliminary analysis on how the
+peculiarities of our new loss function drive improved uncertainty estimation.
+We observe that our method achieves unprecedented success on detection of
+out-of-distribution queries and endurance against adversarial perturbations.
+",0,0,0,1,0,0
+17755,Neutron interference in the Earth's gravitational field,"  This work relates to the famous experiments, performed in 1975 and 1979 by
+Werner et al., measuring neutron interference and neutron Sagnac effects in the
+earth's gravitational field. Employing the method of Stodolsky in its weak
+field approximation, explicit expressions are derived for the two phase shifts,
+which turn out to be in agreement with the experiments and with the previously
+obtained expressions derived from semi-classical arguments: these expressions
+are simply modified by relativistic correction factors.
+",0,1,0,0,0,0
+17756,Solve For Shortest Paths Problem Within Logarithm Runtime,"  The Shortest Paths Problem (SPP) is no longer unresolved. Just for a large
+scalar of instance on this problem, even we cannot know if an algorithm
+achieves the computing. Those cutting-edge methods are still in the low
+performance. If we go to a strategy the best-first-search to deal with
+computing, it is awkward that the technical barrier from another field: the
+database, which with the capable of Online Oriented. In this paper, we will
+introduce such a synthesis to solve for SPP which comprises various modules
+therein including such database leads to finish the task in a logarithm
+runtime.
+Through experiments taken on three typical instances on mega-scalar data for
+transaction in a common laptop, we show off a totally robust, tractable and
+practical applicability for other projects.
+",1,0,0,0,0,0
+17757,A short proof of the error term in Simpson's rule,"  In this paper we present a short and elementary proof for the error in
+Simpson's rule.
+",0,0,1,0,0,0
+17758,Neutrino mass and dark energy constraints from redshift-space distortions,"  Cosmology in the near future promises a measurement of the sum of neutrino
+masses, a fundamental Standard Model parameter, as well as
+substantially-improved constraints on the dark energy. We use the shape of the
+BOSS redshift-space galaxy power spectrum, in combination with CMB and
+supernova data, to constrain the neutrino masses and the dark energy. Essential
+to this calculation are several recent advances in non-linear cosmological
+perturbation theory, including FFT methods, redshift space distortions, and
+scale-dependent growth. Our 95% confidence upper bound of 200 meV on the sum of
+masses degrades substantially to 770 meV when the dark energy equation of state
+and its first derivative are also allowed to vary, representing a significant
+challenge to current constraints. We also study the impact of additional galaxy
+bias parameters, finding that a velocity bias or a more complicated
+scale-dependent density bias shift the preferred neutrino mass values 20%-30%
+lower while minimally impacting the other cosmological parameters.
+",0,1,0,0,0,0
+17759,Dielectrophoretic assembly of liquid-phase-exfoliated TiS3 nanoribbons for photodetecting applications,"  Liquid-phase-exfoliation is a technique capable of producing large quantities
+of two-dimensional material in suspension. Despite many efforts in the
+optimization of the exfoliation process itself not much has been done towards
+the integration of liquid-phase-exfoliated materials in working solid-state
+devices. In this article, we use dielectrophoresis to direct the assembly of
+liquid-phase-exfoliated TiS3 nanoribbons between two gold electrodes to produce
+photodetectors working in the visible. Through electrical and optical
+measurements we characterize the responsivity of the device and we find values
+as large as 3.8 mA/W, which improve of more than one order of magnitude on the
+state-of-the-art for devices based on liquid-phase-exfoliated two-dimensional
+materials assembled by drop-casting or ink-jet methods.
+",0,1,0,0,0,0
+17760,Motion optimization and parameter identification for a human and lower-back exoskeleton model,"  Designing an exoskeleton to reduce the risk of low-back injury during lifting
+is challenging. Computational models of the human-robot system coupled with
+predictive movement simulations can help to simplify this design process. Here,
+we present a study that models the interaction between a human model actuated
+by muscles and a lower-back exoskeleton. We provide a computational framework
+for identifying the spring parameters of the exoskeleton using an optimal
+control approach and forward-dynamics simulations. This is applied to generate
+dynamically consistent bending and lifting movements in the sagittal plane. Our
+computations are able to predict motions and forces of the human and
+exoskeleton that are within the torque limits of a subject. The identified
+exoskeleton could also yield a considerable reduction of the peak lower-back
+torques as well as the cumulative lower-back load during the movements. This
+work is relevant to the research communities working on human-robot
+interaction, and can be used as a basis for a better human-centered design
+process.
+",1,0,0,0,0,0
+17761,A dataset for Computer-Aided Detection of Pulmonary Embolism in CTA images,"  Todays, researchers in the field of Pulmonary Embolism (PE) analysis need to
+use a publicly available dataset to assess and compare their methods. Different
+systems have been designed for the detection of pulmonary embolism (PE), but
+none of them have used any public datasets. All papers have used their own
+private dataset. In order to fill this gap, we have collected 5160 slices of
+computed tomography angiography (CTA) images acquired from 20 patients, and
+after labeling the image by experts in this field, we provided a reliable
+dataset which is now publicly available. In some situation, PE detection can be
+difficult, for example when it occurs in the peripheral branches or when
+patients have pulmonary diseases (such as parenchymal disease). Therefore, the
+efficiency of CAD systems highly depends on the dataset. In the given dataset,
+66% of PE are located in peripheral branches, and different pulmonary diseases
+are also included.
+",1,0,0,0,0,0
+17762,"Comment on Ben-Amotz and Honig, ""Average entropy dissipation in irreversible mesoscopic processes,"" Phys. Rev. Lett. 96, 020602 (2006)","  We point out that most of the classical thermodynamics results in the paper
+have been known in the literature, see Kestin and Woods, for quite some time
+and are not new, contrary to what the authors imply. As shown by Kestin, these
+results are valid for quasistatic irreversible processes only and not for
+arbitrary irreversible processes as suggested in the paper. Thus, the
+application to the Jarzynski process is limited.
+",0,1,0,0,0,0
+17763,High-transmissivity Silicon Visible-wavelength Metasurface Designs based on Truncated-cone Nanoantennae,"  High-transmissivity all-dielectric metasurfaces have recently attracted
+attention towards the realization of ultra-compact optical devices and systems.
+Silicon based metasurfaces, in particular, are highly promising considering the
+possibility of monolithic integration with VLSI circuits. Realization of
+silicon based metasurfaces operational in the visible wavelengths remains a
+challenge. A numerical study of silicon metasurfaces based on stepped truncated
+cone shaped nanoantenna elements is presented. Metasurfaces based on the
+stepped conical geometry can be designed for operation in the 700nm to 800nm
+wavelength window and achieve full cycle phase response (0 to pi with an
+improved transmittance in comparison with previously reported cylindrical
+geometry [1]. A systematic parameter study of the influence of various
+geometrical parameters on the achievable amplitude and phase coverage is
+reported.
+",0,1,0,0,0,0
+17764,Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data,"  We proposed a probabilistic approach to joint modeling of participants'
+reliability and humans' regularity in crowdsourced affective studies.
+Reliability measures how likely a subject will respond to a question seriously;
+and regularity measures how often a human will agree with other
+seriously-entered responses coming from a targeted population.
+Crowdsourcing-based studies or experiments, which rely on human self-reported
+affect, pose additional challenges as compared with typical crowdsourcing
+studies that attempt to acquire concrete non-affective labels of objects. The
+reliability of participants has been massively pursued for typical
+non-affective crowdsourcing studies, whereas the regularity of humans in an
+affective experiment in its own right has not been thoroughly considered. It
+has been often observed that different individuals exhibit different feelings
+on the same test question, which does not have a sole correct response in the
+first place. High reliability of responses from one individual thus cannot
+conclusively result in high consensus across individuals. Instead, globally
+testing consensus of a population is of interest to investigators. Built upon
+the agreement multigraph among tasks and workers, our probabilistic model
+differentiates subject regularity from population reliability. We demonstrate
+the method's effectiveness for in-depth robust analysis of large-scale
+crowdsourced affective data, including emotion and aesthetic assessments
+collected by presenting visual stimuli to human subjects.
+",1,0,0,1,0,0
+17765,Quantum key distribution protocol with pseudorandom bases,"  Quantum key distribution (QKD) offers a way for establishing
+information-theoretically secure communications. An important part of QKD
+technology is a high-quality random number generator (RNG) for quantum states
+preparation and for post-processing procedures. In the present work, we
+consider a novel class of prepare-and-measure QKD protocols, utilizing
+additional pseudorandomness in the preparation of quantum states. We study one
+of such protocols and analyze its security against the intercept-resend attack.
+We demonstrate that, for single-photon sources, the considered protocol gives
+better secret key rates than the BB84 and the asymmetric BB84 protocol.
+However, the protocol strongly requires single-photon sources.
+",1,0,0,0,0,0
+17766,Deep clustering of longitudinal data,"  Deep neural networks are a family of computational models that have led to a
+dramatical improvement of the state of the art in several domains such as
+image, voice or text analysis. These methods provide a framework to model
+complex, non-linear interactions in large datasets, and are naturally suited to
+the analysis of hierarchical data such as, for instance, longitudinal data with
+the use of recurrent neural networks. In the other hand, cohort studies have
+become a tool of importance in the research field of epidemiology. In such
+studies, variables are measured repeatedly over time, to allow the practitioner
+to study their temporal evolution as trajectories, and, as such, as
+longitudinal data. This paper investigates the application of the advanced
+modelling techniques provided by the deep learning framework in the analysis of
+the longitudinal data provided by cohort studies. Methods: A method for
+visualizing and clustering longitudinal dataset is proposed, and compared to
+other widely used approaches to the problem on both real and simulated
+datasets. Results: The proposed method is shown to be coherent with the
+preexisting procedures on simple tasks, and to outperform them on more complex
+tasks such as the partitioning of longitudinal datasets into non-spherical
+clusters. Conclusion: Deep artificial neural networks can be used to visualize
+longitudinal data in a low dimensional manifold that is much simpler to
+interpret than traditional longitudinal plots are. Consequently, practitioners
+should start considering the use of deep artificial neural networks for the
+analysis of their longitudinal data in studies to come.
+",0,0,0,1,0,0
+17767,Economic Implications of Blockchain Platforms,"  In an economy with asymmetric information, the smart contract in the
+blockchain protocol mitigates uncertainty. Since, as a new trading platform,
+the blockchain triggers segmentation of market and differentiation of agents in
+both the sell and buy sides of the market, it recomposes the asymmetric
+information and generates spreads in asset price and quality between itself and
+a traditional platform. We show that marginal innovation and sophistication of
+the smart contract have non-monotonic effects on the trading value in the
+blockchain platform, its fundamental value, the price of cryptocurrency, and
+consumers' welfare. Moreover, a blockchain manager who controls the level of
+the innovation of the smart contract has an incentive to keep it lower than the
+first best when the underlying information asymmetry is not severe, leading to
+welfare loss for consumers.
+",0,0,0,0,0,1
+17768,Proof Theory and Ordered Groups,"  Ordering theorems, characterizing when partial orders of a group extend to
+total orders, are used to generate hypersequent calculi for varieties of
+lattice-ordered groups (l-groups). These calculi are then used to provide new
+proofs of theorems arising in the theory of ordered groups. More precisely: an
+analytic calculus for abelian l-groups is generated using an ordering theorem
+for abelian groups; a calculus is generated for l-groups and new decidability
+proofs are obtained for the equational theory of this variety and extending
+finite subsets of free groups to right orders; and a calculus for representable
+l-groups is generated and a new proof is obtained that free groups are
+orderable.
+",0,0,1,0,0,0
+17769,DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features,"  As the foundation of driverless vehicle and intelligent robots, Simultaneous
+Localization and Mapping(SLAM) has attracted much attention these days.
+However, non-geometric modules of traditional SLAM algorithms are limited by
+data association tasks and have become a bottleneck preventing the development
+of SLAM. To deal with such problems, many researchers seek to Deep Learning for
+help. But most of these studies are limited to virtual datasets or specific
+environments, and even sacrifice efficiency for accuracy. Thus, they are not
+practical enough.
+We propose DF-SLAM system that uses deep local feature descriptors obtained
+by the neural network as a substitute for traditional hand-made features.
+Experimental results demonstrate its improvements in efficiency and stability.
+DF-SLAM outperforms popular traditional SLAM systems in various scenes,
+including challenging scenes with intense illumination changes. Its versatility
+and mobility fit well into the need for exploring new environments. Since we
+adopt a shallow network to extract local descriptors and remain others the same
+as original SLAM systems, our DF-SLAM can still run in real-time on GPU.
+",1,0,0,0,0,0
+17770,A-NICE-MC: Adversarial Training for MCMC,"  Existing Markov Chain Monte Carlo (MCMC) methods are either based on
+general-purpose and domain-agnostic schemes which can lead to slow convergence,
+or hand-crafting of problem-specific proposals by an expert. We propose
+A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to
+produce samples with desired properties. First, we propose an efficient
+likelihood-free adversarial training method to train a Markov chain and mimic a
+given data distribution. Then, we leverage flexible volume preserving flows to
+obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to
+train efficient Markov chains to sample from a prescribed posterior
+distribution by iteratively improving the quality of both the model and the
+samples. A-NICE-MC provides the first framework to automatically design
+efficient domain-specific MCMC proposals. Empirical results demonstrate that
+A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of
+deep neural networks, and is able to significantly outperform competing methods
+such as Hamiltonian Monte Carlo.
+",1,0,0,1,0,0
+17771,"Starobinsky-like Inflation, Supercosmology and Neutrino Masses in No-Scale Flipped SU(5)","  We embed a flipped ${\rm SU}(5) \times {\rm U}(1)$ GUT model in a no-scale
+supergravity framework, and discuss its predictions for cosmic microwave
+background observables, which are similar to those of the Starobinsky model of
+inflation. Measurements of the tilt in the spectrum of scalar perturbations in
+the cosmic microwave background, $n_s$, constrain significantly the model
+parameters. We also discuss the model's predictions for neutrino masses, and
+pay particular attention to the behaviours of scalar fields during and after
+inflation, reheating and the GUT phase transition. We argue in favor of strong
+reheating in order to avoid excessive entropy production which could dilute the
+generated baryon asymmetry.
+",0,1,0,0,0,0
+17772,"'Viral' Turing Machines, Computation from Noise and Combinatorial Hierarchies","  The interactive computation paradigm is reviewed and a particular example is
+extended to form the stochastic analog of a computational process via a
+transcription of a minimal Turing Machine into an equivalent asynchronous
+Cellular Automaton with an exponential waiting times distribution of effective
+transitions. Furthermore, a special toolbox for analytic derivation of
+recursive relations of important statistical and other quantities is introduced
+in the form of an Inductive Combinatorial Hierarchy.
+",1,1,0,0,0,0
+17773,Estimating model evidence using ensemble-based data assimilation with localization - The model selection problem,"  IIn recent years, there has been a growing interest in applying data
+assimilation (DA) methods, originally designed for state estimation, to the
+model selection problem. In this setting, Carrassi et al. (2017) introduced the
+contextual formulation of model evidence (CME) and showed that CME can be
+efficiently computed using a hierarchy of ensemble-based DA procedures.
+Although Carrassi et al. (2017) analyzed the DA methods most commonly used for
+operational atmospheric and oceanic prediction worldwide, they did not study
+these methods in conjunction with localization to a specific domain. Yet any
+application of ensemble DA methods to realistic geophysical models requires the
+implementation of some form of localization. The present study extends the
+theory for estimating CME to ensemble DA methods with domain localization. The
+domain-localized CME (DL-CME) developed herein is tested for model selection
+with two models: (i) the Lorenz 40-variable mid-latitude atmospheric dynamics
+model (L95); and (ii) the simplified global atmospheric SPEEDY model. The CME
+is compared to the root-mean-square-error (RMSE) as a metric for model
+selection. The experiments show that CME improves systematically over the RMSE,
+and that this skill improvement is further enhanced by applying localization in
+the estimate of the CME, using the DL-CME. The potential use and range of
+applications of the CME and DL-CME as a model selection metric are also
+discussed.
+",0,0,0,1,0,0
+17774,An efficient spectral-Galerkin approximation and error analysis for Maxwell transmission eigenvalue problems in spherical geometries,"  We propose and analyze an efficient spectral-Galerkin approximation for the
+Maxwell transmission eigenvalue problem in spherical geometry. Using a vector
+spherical harmonic expansion, we reduce the problem to a sequence of equivalent
+one-dimensional TE and TM modes that can be solved individually in parallel.
+For the TE mode, we derive associated generalized eigenvalue problems and
+corresponding pole conditions. Then we introduce weighted Sobolev spaces based
+on the pole condition and prove error estimates for the generalized eigenvalue
+problem. The TM mode is a coupled system with four unknown functions, which is
+challenging for numerical calculation. To handle it, we design an effective
+algorithm using Legendre-type vector basis functions. Finally, we provide some
+numerical experiments to validate our theoretical results and demonstrate the
+efficiency of the algorithms.
+",0,0,1,0,0,0
+17775,Mendelian randomization with fine-mapped genetic data: choosing from large numbers of correlated instrumental variables,"  Mendelian randomization uses genetic variants to make causal inferences about
+the effect of a risk factor on an outcome. With fine-mapped genetic data, there
+may be hundreds of genetic variants in a single gene region any of which could
+be used to assess this causal relationship. However, using too many genetic
+variants in the analysis can lead to spurious estimates and inflated Type 1
+error rates. But if only a few genetic variants are used, then the majority of
+the data is ignored and estimates are highly sensitive to the particular choice
+of variants. We propose an approach based on summarized data only (genetic
+association and correlation estimates) that uses principal components analysis
+to form instruments. This approach has desirable theoretical properties: it
+takes the totality of data into account and does not suffer from numerical
+instabilities. It also has good properties in simulation studies: it is not
+particularly sensitive to varying the genetic variants included in the analysis
+or the genetic correlation matrix, and it does not have greatly inflated Type 1
+error rates. Overall, the method gives estimates that are not so precise as
+those from variable selection approaches (such as using a conditional analysis
+or pruning approach to select variants), but are more robust to seemingly
+arbitrary choices in the variable selection step. Methods are illustrated by an
+example using genetic associations with testosterone for 320 genetic variants
+to assess the effect of sex hormone-related pathways on coronary artery disease
+risk, in which variable selection approaches give inconsistent inferences.
+",0,0,0,1,0,0
+17776,Building competitive direct acoustics-to-word models for English conversational speech recognition,"  Direct acoustics-to-word (A2W) models in the end-to-end paradigm have
+received increasing attention compared to conventional sub-word based automatic
+speech recognition models using phones, characters, or context-dependent hidden
+Markov model states. This is because A2W models recognize words from speech
+without any decoder, pronunciation lexicon, or externally-trained language
+model, making training and decoding with such models simple. Prior work has
+shown that A2W models require orders of magnitude more training data in order
+to perform comparably to conventional models. Our work also showed this
+accuracy gap when using the English Switchboard-Fisher data set. This paper
+describes a recipe to train an A2W model that closes this gap and is at-par
+with state-of-the-art sub-word based models. We achieve a word error rate of
+8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder
+or language model. We find that model initialization, training data order, and
+regularization have the most impact on the A2W model performance. Next, we
+present a joint word-character A2W model that learns to first spell the word
+and then recognize it. This model provides a rich output to the user instead of
+simple word hypotheses, making it especially useful in the case of words unseen
+or rarely-seen during training.
+",1,0,0,1,0,0
+17777,Convolutional Dictionary Learning: Acceleration and Convergence,"  Convolutional dictionary learning (CDL or sparsifying CDL) has many
+applications in image processing and computer vision. There has been growing
+interest in developing efficient algorithms for CDL, mostly relying on the
+augmented Lagrangian (AL) method or the variant alternating direction method of
+multipliers (ADMM). When their parameters are properly tuned, AL methods have
+shown fast convergence in CDL. However, the parameter tuning process is not
+trivial due to its data dependence and, in practice, the convergence of AL
+methods depends on the AL parameters for nonconvex CDL problems. To moderate
+these problems, this paper proposes a new practically feasible and convergent
+Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The
+BPG-M-based CDL is investigated with different block updating schemes and
+majorization matrix designs, and further accelerated by incorporating some
+momentum coefficient formulas and restarting techniques. All of the methods
+investigated incorporate a boundary artifacts removal (or, more generally,
+sampling) operator in the learning model. Numerical experiments show that,
+without needing any parameter tuning process, the proposed BPG-M approach
+converges more stably to desirable solutions of lower objective values than the
+existing state-of-the-art ADMM algorithm and its memory-efficient variant do.
+Compared to the ADMM approaches, the BPG-M method using a multi-block updating
+scheme is particularly useful in single-threaded CDL algorithm handling large
+datasets, due to its lower memory requirement and no polynomial computational
+complexity. Image denoising experiments show that, for relatively strong
+additive white Gaussian noise, the filters learned by BPG-M-based CDL
+outperform those trained by the ADMM approach.
+",1,0,1,0,0,0
+17778,The late-time light curve of the type Ia supernova SN 2011fe,"  We present late-time optical $R$-band imaging data from the Palomar Transient
+Factory (PTF) for the nearby type Ia supernova SN 2011fe. The stacked PTF light
+curve provides densely sampled coverage down to $R\simeq22$ mag over 200 to 620
+days past explosion. Combining with literature data, we estimate the
+pseudo-bolometric light curve for this event from 200 to 1600 days after
+explosion, and constrain the likely near-infrared contribution. This light
+curve shows a smooth decline consistent with radioactive decay, except over
+~450 to ~600 days where the light curve appears to decrease faster than
+expected based on the radioactive isotopes presumed to be present, before
+flattening at around 600 days. We model the 200-1600d pseudo-bolometric light
+curve with the luminosity generated by the radioactive decay chains of
+$^{56}$Ni, $^{57}$Ni and $^{55}$Co, and find it is not consistent with models
+that have full positron trapping and no infrared catastrophe (IRC); some
+additional energy escape other than optical/near-IR photons is required.
+However, the light curve is consistent with models that allow for positron
+escape (reaching 75% by day 500) and/or an IRC (with 85% of the flux emerging
+in non-optical wavelengths by day 600). The presence of the $^{57}$Ni decay
+chain is robustly detected, but the $^{55}$Co decay chain is not formally
+required, with an upper mass limit estimated at 0.014 M$_{\odot}$. The
+measurement of the $^{57}$Ni/$^{56}$Ni mass ratio is subject to significant
+systematic uncertainties, but all of our fits require a high ratio >0.031 (>1.3
+in solar abundances).
+",0,1,0,0,0,0
+17779,Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors,"  In this article, we consider Markov chain Monte Carlo(MCMC) algorithms for
+exploring the intractable posterior density associated with Bayesian probit
+linear mixed models under improper priors on the regression coefficients and
+variance components. In particular, we construct the two-block Gibbs sampler
+using the data augmentation (DA) techniques. Furthermore, we prove geometric
+ergodicity of the Gibbs sampler, which is the foundation for building central
+limit theorems for MCMC based estimators and subsequent inferences. The
+conditions for geometric convergence are similar to those guaranteeing
+posterior propriety. We also provide conditions for posterior propriety when
+the design matrices take commonly observed forms. In general, the Haar
+parameter expansion for DA (PX- DA) algorithm is an improvement of the DA
+algorithm and it has been shown that it is theoretically at least as good as
+the DA algorithm. Here we construct a Haar PX-DA algorithm, which has
+essentially the same computational cost as the two-block Gibbs sampler.
+",0,0,1,1,0,0
+17780,The complexity of the Multiple Pattern Matching Problem for random strings,"  We generalise a multiple string pattern matching algorithm, recently proposed
+by Fredriksson and Grabowski [J. Discr. Alg. 7, 2009], to deal with arbitrary
+dictionaries on an alphabet of size $s$. If $r_m$ is the number of words of
+length $m$ in the dictionary, and $\phi(r) = \max_m \ln(s\, m\, r_m)/m$, the
+complexity rate for the string characters to be read by this algorithm is at
+most $\kappa_{{}_\textrm{UB}}\, \phi(r)$ for some constant
+$\kappa_{{}_\textrm{UB}}$. On the other side, we generalise the classical lower
+bound of Yao [SIAM J. Comput. 8, 1979], for the problem with a single pattern,
+to deal with arbitrary dictionaries, and determine it to be at least
+$\kappa_{{}_\textrm{LB}}\, \phi(r)$. This proves the optimality of the
+algorithm, improving and correcting previous claims.
+",1,0,0,0,0,0
+17781,Functoriality and uniformity in Hrushovski's groupoid-cover correspondence,"  The correspondence between definable connected groupoids in a theory $T$ and
+internal generalised imaginary sorts of $T$, established by Hrushovski in
+[""Groupoids, imaginaries and internal covers,"" Turkish Journal of Mathematics,
+2012], is here extended in two ways: First, it is shown that the correspondence
+is in fact an equivalence of categories, with respect to appropriate notions of
+morphism. Secondly, the equivalence of categories is shown to vary uniformly in
+definable families, with respect to an appropriate relativisation of these
+categories. Some elaboration on Hrushovki's original constructions are also
+included.
+",0,0,1,0,0,0
+17782,Simple Conditions for Metastability of Continuous Markov Chains,"  A family $\{Q_{\beta}\}_{\beta \geq 0}$ of Markov chains is said to exhibit
+$\textit{metastable mixing}$ with $\textit{modes}$
+$S_{\beta}^{(1)},\ldots,S_{\beta}^{(k)}$ if its spectral gap (or some other
+mixing property) is very close to the worst conductance
+$\min(\Phi_{\beta}(S_{\beta}^{(1)}), \ldots, \Phi_{\beta}(S_{\beta}^{(k)}))$ of
+its modes. We give simple sufficient conditions for a family of Markov chains
+to exhibit metastability in this sense, and verify that these conditions hold
+for a prototypical Metropolis-Hastings chain targeting a mixture distribution.
+Our work differs from existing work on metastability in that, for the class of
+examples we are interested in, it gives an asymptotically exact formula for the
+spectral gap (rather than a bound that can be very far from sharp) while at the
+same time giving technical conditions that are easier to verify for many
+statistical examples. Our bounds from this paper are used in a companion paper
+to compare the mixing times of the Hamiltonian Monte Carlo algorithm and a
+random walk algorithm for multimodal target distributions.
+",0,0,0,1,0,0
+17783,Deterministic preparation of highly non-classical macroscopic quantum states,"  We present a scheme to deterministically prepare non-classical quantum states
+of a massive mirror including highly non-Gaussian states exhibiting sizeable
+negativity of the Wigner function. This is achieved by exploiting the
+non-linear light-matter interaction in an optomechanical cavity by driving the
+system with optimally designed frequency patterns. Our scheme reveals to be
+resilient against mechanical and optical damping, as well as mechanical thermal
+noise and imperfections in the driving scheme. Our proposal thus opens a
+promising route for table-top experiments to explore and exploit macroscopic
+quantum phenomena.
+",0,1,0,0,0,0
+17784,On Local Optimizers of Acquisition Functions in Bayesian Optimization,"  Bayesian optimization is a sample-efficient method for finding a global
+optimum of an expensive-to-evaluate black-box function. A global solution is
+found by accumulating a pair of query point and corresponding function value,
+repeating these two procedures: (i) learning a surrogate model for the
+objective function using the data observed so far; (ii) the maximization of an
+acquisition function to determine where next to query the objective function.
+Convergence guarantees are only valid when the global optimizer of the
+acquisition function is found and selected as the next query point. In
+practice, however, local optimizers of acquisition functions are also used,
+since searching the exact optimizer of the acquisition function is often a
+non-trivial or time-consuming task. In this paper we present an analysis on the
+behavior of local optimizers of acquisition functions, in terms of
+instantaneous regrets over global optimizers. We also present the performance
+analysis when multi-started local optimizers are used to find the maximum of
+the acquisition function. Numerical experiments confirm the validity of our
+theoretical analysis.
+",1,0,0,1,0,0
+17785,One Password: An Encryption Scheme for Hiding Users' Register Information,"  In recent years, the attack which leverages register information (e.g.
+accounts and passwords) leaked from 3rd party applications to try other
+applications is popular and serious. We call this attack ""database collision"".
+Traditionally, people have to keep dozens of accounts and passwords for
+different applications to prevent this attack. In this paper, we propose a
+novel encryption scheme for hiding users' register information and preventing
+this attack. Specifically, we first hash the register information using
+existing safe hash function. Then the hash string is hidden, instead a
+coefficient vector is stored for verification. Coefficient vectors of the same
+register information are generated randomly for different applications. Hence,
+the original information is hardly cracked by dictionary based attack or
+database collision in practice. Using our encryption scheme, each user only
+needs to keep one password for dozens of applications.
+",1,0,0,0,0,0
+17786,The RoPES project with HARPS and HARPS-N. I. A system of super-Earths orbiting the moderately active K-dwarf HD 176986,"  We report the discovery of a system of two super-Earths orbiting the
+moderately active K-dwarf HD 176986. This work is part of the RoPES RV program
+of G- and K-type stars, which combines radial velocities (RVs) from the HARPS
+and HARPS-N spectrographs to search for short-period terrestrial planets. HD
+176986 b and c are super-Earth planets with masses of 5.74 and 9.18
+M$_{\oplus}$, orbital periods of 6.49 and 16.82 days, and distances of 0.063
+and 0.119 AU in orbits that are consistent with circular. The host star is a
+K2.5 dwarf, and despite its modest level of chromospheric activity (log(R'hk) =
+- 4.90 +- 0.04), it shows a complex activity pattern. Along with the discovery
+of the planets, we study the magnetic cycle and rotation of the star. HD 176986
+proves to be suitable for testing the available RV analysis technique and
+further our understanding of stellar activity.
+",0,1,0,0,0,0
+17787,On the possibility of developing quasi-cw high-power high-pressure laser on 4p-4s transition of ArI with electron beam - optical pumping: quenching of 4s (3P2) lower laser level,"  A new electron beam-optical procedure is proposed for quasi-cw pumping of
+high-pressure large-volume He-Ar laser on 4p[1/2]1 - 4s[3/2]2 argon atom
+transition at the wavelength of 912.5 nm. It consists of creation and
+maintenance of a necessary density of 4s[3/2]2 metastable state in the gain
+medium by a fast electron beam and subsequent optically pumping of the upper
+laser level via the classical three-level scheme using a laser diode.
+Absorption probing is used to study collisional quenching of Ar* metastable in
+electron-beam-excited high-pressure He-Ar mixtures with a low content of argon.
+The rate constants for plasma-chemical reactions Ar*+He+Ar-Ar2*+He (3.6 +-
+0.4)x10-33 cm6/s, Ar+2He-HeAr*+He (4.4 +- 0.9)x10-36 cm6/s and
+Ar*+He-Products+He (2.4 +- 0.3)x10-15 cm3/s are for the first time measured.
+",0,1,0,0,0,0
+17788,An Adaptive Characteristic-wise Reconstruction WENOZ scheme for Gas Dynamic Euler Equations,"  Due to its excellent shock-capturing capability and high resolution, the WENO
+scheme family has been widely used in varieties of compressive flow simulation.
+However, for problems containing strong shocks and contact discontinuities,
+such as the Lax shock tube problem, the WENO scheme still produces numerical
+oscillations. To avoid such numerical oscillations, the characteristic-wise
+construction method should be applied. Compared to component-wise
+reconstruction, characteristic-wise reconstruction leads to much more
+computational cost and thus is not suite for large scale simulation such as
+direct numeric simulation of turbulence. In this paper, an adaptive
+characteristic-wise reconstruction WENO scheme, i.e. the AdaWENO scheme, is
+proposed to improve the computational efficiency of the characteristic-wise
+reconstruction method. The new scheme performs characteristic-wise
+reconstruction near discontinuities while switching to component-wise
+reconstruction for smooth regions. Meanwhile, a new calculation strategy for
+the WENO smoothness indicators is implemented to reduce over-all computational
+cost. Several one dimensional and two dimensional numerical tests are performed
+to validate and evaluate the AdaWENO scheme. Numerical results show that
+AdaWENO maintains essentially non-oscillatory flow field near discontinuities
+as the characteristic-wise reconstruction method. Besieds, compared to the
+component-wise reconstruction, AdaWENO is about 40\% faster which indicates its
+excellent efficiency.
+",0,1,0,0,0,0
+17789,A level set-based structural optimization code using FEniCS,"  This paper presents an educational code written using FEniCS, based on the
+level set method, to perform compliance minimization in structural
+optimization. We use the concept of distributed shape derivative to compute a
+descent direction for the compliance, which is defined as a shape functional.
+The use of the distributed shape derivative is facilitated by FEniCS, which
+allows to handle complicated partial differential equations with a simple
+implementation. The code is written for compliance minimization in the
+framework of linearized elasticity, and can be easily adapted to tackle other
+functionals and partial differential equations. We also provide an extension of
+the code for compliant mechanisms. We start by explaining how to compute shape
+derivatives, and discuss the differences between the distributed and boundary
+expressions of the shape derivative. Then we describe the implementation in
+details, and show the application of this code to some classical benchmarks of
+topology optimization. The code is available at
+this http URL, and the main file is also given in
+the appendix.
+",0,0,1,0,0,0
+17790,Transaction Support over Redis: An Overview,"  This document outlines the approach to supporting cross-node transactions
+over a Redis cluster.
+",1,0,0,0,0,0
+17791,Subgraphs and motifs in a dynamic airline network,"  How does the small-scale topological structure of an airline network behave
+as the network evolves? To address this question, we study the dynamic and
+spatial properties of small undirected subgraphs using 15 years of data on
+Southwest Airlines' domestic route service. We find that this real-world
+network has much in common with random graphs, and describe a possible
+power-law scaling between subgraph counts and the number of edges in the
+network, that appears to be quite robust to changes in network density and
+size. We use analytic formulae to identify statistically over- and
+under-represented subgraphs, known as motifs and anti-motifs, and discover the
+existence of substantial topology transitions. We propose a simple
+subgraph-based node ranking measure, that is not always highly correlated with
+standard node centrality, and can identify important nodes relative to specific
+topologies, and investigate the spatial ""distribution"" of the triangle subgraph
+using graphical tools. Our results have implications for the way in which
+subgraphs can be used to analyze real-world networks.
+",1,0,0,0,0,0
+17792,On the Glitch Phenomenon,"  The Principle of the Glitch states that for any device which makes a discrete
+decision based upon a continuous range of possible inputs, there are inputs for
+which it will take arbitrarily long to reach a decision. The appropriate
+mathematical setting for studying this principle is described. This involves
+defining the concept of continuity for mappings on sets of functions. It can
+then be shown that the glitch principle follows from the continuous behavior of
+the device.
+",1,0,1,0,0,0
+17793,Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel computing methods,"  In this article we perform an asymptotic analysis of Bayesian parallel
+density estimators which are based on logspline density estimation. The
+parallel estimator we introduce is in the spirit of a kernel density estimator
+introduced in recent studies. We provide a numerical procedure that produces
+the density estimator itself in place of the sampling algorithm. We then derive
+an error bound for the mean integrated squared error for the full data
+posterior density estimator. We also investigate the parameters that arise from
+logspline density estimation and the numerical approximation procedure. Our
+investigation identifies specific choices of parameters for logspline density
+estimation that result in the error bound scaling appropriately in relation to
+these choices.
+",0,0,1,1,0,0
+17794,Tunable Weyl and Dirac states in the nonsymmorphic compound $\rm\mathbf{CeSbTe}$,"  Recent interest in topological semimetals has lead to the proposal of many
+new topological phases that can be realized in real materials. Next to Dirac
+and Weyl systems, these include more exotic phases based on manifold band
+degeneracies in the bulk electronic structure. The exotic states in topological
+semimetals are usually protected by some sort of crystal symmetry and the
+introduction of magnetic order can influence these states by breaking time
+reversal symmetry. Here we show that we can realize a rich variety of different
+topological semimetal states in a single material, $\rm CeSbTe$. This compound
+can exhibit different types of magnetic order that can be accessed easily by
+applying a small field. It allows, therefore, for tuning the electronic
+structure and can drive it through a manifold of topologically distinct phases,
+such as the first nonsymmorphic magnetic topological material with an
+eight-fold band crossing at a high symmetry point. Our experimental results are
+backed by a full magnetic group theory analysis and ab initio calculations.
+This discovery introduces a realistic and promising platform for studying the
+interplay of magnetism and topology.
+",0,1,0,0,0,0
+17795,Personalised Query Suggestion for Intranet Search with Temporal User Profiling,"  Recent research has shown the usefulness of using collective user interaction
+data (e.g., query logs) to recommend query modification suggestions for
+Intranet search. However, most of the query suggestion approaches for Intranet
+search follow an ""one size fits all"" strategy, whereby different users who
+submit an identical query would get the same query suggestion list. This is
+problematic, as even with the same query, different users may have different
+topics of interest, which may change over time in response to the user's
+interaction with the system. We address the problem by proposing a personalised
+query suggestion framework for Intranet search. For each search session, we
+construct two temporal user profiles: a click user profile using the user's
+clicked documents and a query user profile using the user's submitted queries.
+We then use the two profiles to re-rank the non-personalised query suggestion
+list returned by a state-of-the-art query suggestion method for Intranet
+search. Experimental results on a large-scale query logs collection show that
+our personalised framework significantly improves the quality of suggested
+queries.
+",1,0,0,0,0,0
+17796,Navigability of Random Geometric Graphs in the Universe and Other Spacetimes,"  Random geometric graphs in hyperbolic spaces explain many common structural
+and dynamical properties of real networks, yet they fail to predict the correct
+values of the exponents of power-law degree distributions observed in real
+networks. In that respect, random geometric graphs in asymptotically de Sitter
+spacetimes, such as the Lorentzian spacetime of our accelerating universe, are
+more attractive as their predictions are more consistent with observations in
+real networks. Yet another important property of hyperbolic graphs is their
+navigability, and it remains unclear if de Sitter graphs are as navigable as
+hyperbolic ones. Here we study the navigability of random geometric graphs in
+three Lorentzian manifolds corresponding to universes filled only with dark
+energy (de Sitter spacetime), only with matter, and with a mixture of dark
+energy and matter as in our universe. We find that these graphs are navigable
+only in the manifolds with dark energy. This result implies that, in terms of
+navigability, random geometric graphs in asymptotically de Sitter spacetimes
+are as good as random hyperbolic graphs. It also establishes a connection
+between the presence of dark energy and navigability of the discretized causal
+structure of spacetime, which provides a basis for a different approach to the
+dark energy problem in cosmology.
+",1,1,0,0,0,0
+17797,Learning Geometric Concepts with Nasty Noise,"  We study the efficient learnability of geometric concept classes -
+specifically, low-degree polynomial threshold functions (PTFs) and
+intersections of halfspaces - when a fraction of the data is adversarially
+corrupted. We give the first polynomial-time PAC learning algorithms for these
+concept classes with dimension-independent error guarantees in the presence of
+nasty noise under the Gaussian distribution. In the nasty noise model, an
+omniscient adversary can arbitrarily corrupt a small fraction of both the
+unlabeled data points and their labels. This model generalizes well-studied
+noise models, including the malicious noise model and the agnostic (adversarial
+label noise) model. Prior to our work, the only concept class for which
+efficient malicious learning algorithms were known was the class of
+origin-centered halfspaces.
+Specifically, our robust learning algorithm for low-degree PTFs succeeds
+under a number of tame distributions -- including the Gaussian distribution
+and, more generally, any log-concave distribution with (approximately) known
+low-degree moments. For LTFs under the Gaussian distribution, we give a
+polynomial-time algorithm that achieves error $O(\epsilon)$, where $\epsilon$
+is the noise rate. At the core of our PAC learning results is an efficient
+algorithm to approximate the low-degree Chow-parameters of any bounded function
+in the presence of nasty noise. To achieve this, we employ an iterative
+spectral method for outlier detection and removal, inspired by recent work in
+robust unsupervised learning. Our aforementioned algorithm succeeds for a range
+of distributions satisfying mild concentration bounds and moment assumptions.
+The correctness of our robust learning algorithm for intersections of
+halfspaces makes essential use of a novel robust inverse independence lemma
+that may be of broader interest.
+",1,0,0,0,0,0
+17798,The relationship between the number of editorial board members and the scientific output of universities in the chemistry field,"  Editorial board members, who are considered the gatekeepers of scientific
+journals, play an important role in academia, and may directly or indirectly
+affect the scientific output of a university. In this article, we used the
+quantile regression method among a sample of 1,387 university in chemistry to
+characterize the correlation between the number of editorial board members and
+the scientific output of their universities. Furthermore, we used time-series
+data and the Granger causality test to explore the causal relationship between
+the number of editorial board members and the number of articles of some top
+universities. Our results suggest that the number of editorial board members is
+positively and significantly related to the scientific output (as measured by
+the number of articles, total number of citations, citations per paper, and h
+index) of their universities. However, the Granger causality test results
+suggest that the causal relationship between the number of editorial board
+members and the number of articles of some top universities is not obvious.
+Combining these findings with the results of qualitative interviews with
+editorial board members, we discuss the causal relationship between the number
+of editorial board members and the scientific output of their universities.
+",1,0,0,1,0,0
+17799,A Non-standard Standard Model,"  This paper examines the Standard Model under the strong-electroweak gauge
+group $SU_S(3)\times U_{EW}(2)$ subject to the condition $u_{EW}(2)\not\cong
+su_I(2)\oplus u_Y(1)$. Physically, the condition ensures that all electroweak
+gauge bosons interact with each other prior to symmetry breaking --- as one
+might expect from $U(2)$ invariance. This represents a crucial shift in the
+notion of physical gauge bosons: Unlike the Standard Model which posits a
+change of Lie algebra basis induced by spontaneous symmetry breaking, here the
+basis is unaltered and $A,\,Z^0,\,W^\pm$ represent (modulo $U_{EW}(2)$ gauge
+transformations) the physical bosons both \emph{before} and after spontaneous
+symmetry breaking.
+Our choice of $u_{EW}(2)$ basis requires some modification of the matter
+field sector of the Standard Model. Careful attention to the product group
+structure calls for strong-electroweak degrees of freedom in the
+$(\mathbf{3},\mathbf{2})$ and the $(\mathbf{3},\overline{\mathbf{2}})$ of
+$SU_S(3)\times U_{EW}(2)$ that possess integer electric charge just like
+leptons. These degrees of freedom play the role of quarks, and they lead to a
+modified Lagrangian that nevertheless reproduces transition rates and cross
+sections equivalent to the Standard Model.
+The close resemblance between quark and lepton electroweak doublets in this
+picture suggests a mechanism for a phase transition between quarks and leptons
+that stems from the product structure of the gauge group. Our hypothesis is
+that the strong and electroweak bosons see each other as a source of
+decoherence. In effect, leptons get identified with the $SU_S(3)$-trace of
+quark representations. This mechanism allows for possible extensions of the
+Standard Model that don't require large inclusive multiplets of matter fields.
+As an example, we propose and investigate a model that turns out to have some
+promising cosmological implications.
+",0,1,0,0,0,0
+17800,Robust functional regression model for marginal mean and subject-specific inferences,"  We introduce flexible robust functional regression models, using various
+heavy-tailed processes, including a Student $t$-process. We propose efficient
+algorithms in estimating parameters for the marginal mean inferences and in
+predicting conditional means as well interpolation and extrapolation for the
+subject-specific inferences. We develop bootstrap prediction intervals for
+conditional mean curves. Numerical studies show that the proposed model
+provides robust analysis against data contamination or distribution
+misspecification, and the proposed prediction intervals maintain the nominal
+confidence levels. A real data application is presented as an illustrative
+example.
+",0,0,0,1,0,0
+17801,Linear theory for single and double flap wavemakers,"  In this paper, we are concerned with deterministic wave generation in a
+hydrodynamic laboratory. A linear wavemaker theory is developed based on the
+fully dispersive water wave equations. The governing field equation is the
+Laplace equation for potential flow with several boundary conditions: the
+dynamic and kinematic boundary condition at the free surface, the lateral
+boundary condition at the wavemaker and the bottom boundary condition. In this
+work, we consider both single-flap and double-flap wavemakers. The velocity
+potential and surface wave elevation are derived, and the relation between the
+propagating wave height and wavemaker stroke is formulated. This formulation is
+then used to find how to operate the wavemaker in an efficient way to generate
+the desired propagating waves with minimal disturbances near the wavemaker.
+",0,1,0,0,0,0
+17802,Image-domain multi-material decomposition for dual-energy CT based on correlation and sparsity of material images,"  Dual energy CT (DECT) enhances tissue characterization because it can produce
+images of basis materials such as soft-tissue and bone. DECT is of great
+interest in applications to medical imaging, security inspection and
+nondestructive testing. Theoretically, two materials with different linear
+attenuation coefficients can be accurately reconstructed using DECT technique.
+However, the ability to reconstruct three or more basis materials is clinically
+and industrially important. Under the assumption that there are at most three
+materials in each pixel, there are a few methods that estimate multiple
+material images from DECT measurements by enforcing sum-to-one and a box
+constraint ([0 1]) derived from both the volume and mass conservation
+assumption. The recently proposed image-domain multi-material decomposition
+(MMD) method introduces edge-preserving regularization for each material image
+which neglects the relations among material images, and enforced the assumption
+that there are at most three materials in each pixel using a time-consuming
+loop over all possible material-triplet in each iteration of optimizing its
+cost function. We propose a new image-domain MMD method for DECT that considers
+the prior information that different material images have common edges and
+encourages sparsity of material composition in each pixel using regularization.
+",0,1,0,0,0,0
+17803,A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model,"  In wind farms, wake interaction leads to losses in power capture and
+accelerated structural degradation when compared to freestanding turbines. One
+method to reduce wake losses is by misaligning the rotor with the incoming flow
+using its yaw actuator, thereby laterally deflecting the wake away from
+downstream turbines. However, this demands an accurate and computationally
+tractable model of the wind farm dynamics. This problem calls for a closed-loop
+solution. This tutorial paper fills the scientific gap by demonstrating the
+full closed-loop controller synthesis cycle using a steady-state surrogate
+model. Furthermore, a novel, computationally efficient and modular
+communication interface is presented that enables researchers to
+straight-forwardly test their control algorithms in large-eddy simulations.
+High-fidelity simulations of a 9-turbine farm show a power production increase
+of up to 11% using the proposed closed-loop controller compared to traditional,
+greedy wind farm operation.
+",1,0,0,0,0,0
+17804,UCB Exploration via Q-Ensembles,"  We show how an ensemble of $Q^*$-functions can be leveraged for more
+effective exploration in deep reinforcement learning. We build on well
+established algorithms from the bandit setting, and adapt them to the
+$Q$-learning setting. We propose an exploration strategy based on
+upper-confidence bounds (UCB). Our experiments show significant gains on the
+Atari benchmark.
+",1,0,0,1,0,0
+17805,Towards a Deep Improviser: a prototype deep learning post-tonal free music generator,"  Two modest-sized symbolic corpora of post-tonal and post-metric keyboard
+music have been constructed, one algorithmic, the other improvised. Deep
+learning models of each have been trained and largely optimised. Our purpose is
+to obtain a model with sufficient generalisation capacity that in response to a
+small quantity of separate fresh input seed material, it can generate outputs
+that are distinctive, rather than recreative of the learned corpora or the seed
+material. This objective has been first assessed statistically, and as judged
+by k-sample Anderson-Darling and Cramer tests, has been achieved. Music has
+been generated using the approach, and informal judgements place it roughly on
+a par with algorithmic and composed music in related forms. Future work will
+aim to enhance the model such that it can be evaluated in relation to
+expression, meaning and utility in real-time performance.
+",1,0,0,0,0,0
+17806,Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data,"  Existing urban boundaries are usually defined by government agencies for
+administrative, economic, and political purposes. Defining urban boundaries
+that consider socio-economic relationships and citizen commute patterns is
+important for many aspects of urban and regional planning. In this paper, we
+describe a method to delineate urban boundaries based upon human interactions
+with physical space inferred from social media. Specifically, we depicted the
+urban boundaries of Great Britain using a mobility network of Twitter user
+spatial interactions, which was inferred from over 69 million geo-located
+tweets. We define the non-administrative anthropographic boundaries in a
+hierarchical fashion based on different physical movement ranges of users
+derived from the collective mobility patterns of Twitter users in Great
+Britain. The results of strongly connected urban regions in the form of
+communities in the network space yield geographically cohesive, non-overlapping
+urban areas, which provide a clear delineation of the non-administrative
+anthropographic urban boundaries of Great Britain. The method was applied to
+both national (Great Britain) and municipal scales (the London metropolis).
+While our results corresponded well with the administrative boundaries, many
+unexpected and interesting boundaries were identified. Importantly, as the
+depicted urban boundaries exhibited a strong instance of spatial proximity, we
+employed a gravity model to understand the distance decay effects in shaping
+the delineated urban boundaries. The model explains how geographical distances
+found in the mobility patterns affect the interaction intensity among different
+non-administrative anthropographic urban areas, which provides new insights
+into human spatial interactions with urban space.
+",1,1,0,0,0,0
+17807,Avoiding a Tragedy of the Commons in the Peer Review Process,"  Peer review is the foundation of scientific publication, and the task of
+reviewing has long been seen as a cornerstone of professional service. However,
+the massive growth in the field of machine learning has put this community
+benefit under stress, threatening both the sustainability of an effective
+review process and the overall progress of the field. In this position paper,
+we argue that a tragedy of the commons outcome may be avoided by emphasizing
+the professional aspects of this service. In particular, we propose a rubric to
+hold reviewers to an objective standard for review quality. In turn, we also
+propose that reviewers be given appropriate incentive. As one possible such
+incentive, we explore the idea of financial compensation on a per-review basis.
+We suggest reasonable funding models and thoughts on long term effects.
+",1,0,0,0,0,0
+17808,Partially Recursive Acceptance Rejection,"  Generating random variates from high-dimensional distributions is often done
+approximately using Markov chain Monte Carlo. In certain cases, perfect
+simulation algorithms exist that allow one to draw exactly from the stationary
+distribution, but most require $O(n \ln(n))$ time, where $n$ measures the size
+of the input. In this work a new protocol for creating perfect simulation
+algorithms that runs in $O(n)$ time for a wider range of parameters on several
+models (such as Strauss, Ising, and random cluster) than was known previously.
+This work represents an extension of the popping algorithms due to Wilson.
+",1,0,1,0,0,0
+17809,Reifenberg Flatness and Oscillation of the Unit Normal Vector,"  We show (under mild topological assumptions) that small oscillation of the
+unit normal vector implies Reifenberg flatness. We then apply this observation
+to the study of chord-arc domains and to a quantitative version of a two-phase
+free boundary problem for harmonic measure previously studied by Kenig-Toro.
+",0,0,1,0,0,0
+17810,Stability and optimality of distributed secondary frequency control schemes in power networks,"  We present a systematic method for designing distributed generation and
+demand control schemes for secondary frequency regulation in power networks
+such that stability and an economically optimal power allocation can be
+guaranteed. A dissipativity condition is imposed on net power supply variables
+to provide stability guarantees. Furthermore, economic optimality is achieved
+by explicit decentralized steady state conditions on the generation and
+controllable demand. We discuss how various classes of dynamics used in recent
+studies fit within our framework and give examples of higher order generation
+and controllable demand dynamics that can be included within our analysis. In
+case of linear dynamics, we discuss how the proposed dissipativity condition
+can be efficiently verified using an appropriate linear matrix inequality.
+Moreover, it is shown how the addition of a suitable observer layer can relax
+the requirement for demand measurements in the employed controller. The
+efficiency and practicality of the proposed results are demonstrated with a
+simulation on the Northeast Power Coordinating Council (NPCC) 140-bus system.
+",1,0,1,0,0,0
+17811,A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks -- Prevention and Prediction for Combating Terrorism,"  Terrorism has become one of the most tedious problems to deal with and a
+prominent threat to mankind. To enhance counter-terrorism, several research
+works are developing efficient and precise systems, data mining is not an
+exception. Immense data is floating in our lives, though the scarce
+availability of authentic terrorist attack data in the public domain makes it
+complicated to fight terrorism. This manuscript focuses on data mining
+classification techniques and discusses the role of United Nations in
+counter-terrorism. It analyzes the performance of classifiers such as Lazy
+Tree, Multilayer Perceptron, Multiclass and Naïve Bayes classifiers for
+observing the trends for terrorist attacks around the world. The database for
+experiment purpose is created from different public and open access sources for
+years 1970-2015 comprising of 156,772 reported attacks causing massive losses
+of lives and property. This work enumerates the losses occurred, trends in
+attack frequency and places more prone to it, by considering the attack
+responsibilities taken as evaluation class.
+",1,0,0,1,0,0
+17812,OVI 6830Å Imaging Polarimetry of Symbiotic Stars,"  I present here the first results from an ongoing pilot project with the 1.6 m
+telescope at the OPD, Brasil, aimed at the detection of the OVI $\lambda$6830
+line via linear polarization in symbiotic stars. The main goal is to
+demonstrate that OVI imaging polarimetry is an efficient technique for
+discovering new symbiotic stars. The OVI $\lambda$6830 line is found in 5 out
+of 9 known symbiotic stars, in which the OVI line has already been
+spectroscopically confirmed, with at least 3-$\sigma$ detection. Three new
+symbiotic star candidates have also been found.
+",0,1,0,0,0,0
+17813,MMGAN: Manifold Matching Generative Adversarial Network,"  It is well-known that GANs are difficult to train, and several different
+techniques have been proposed in order to stabilize their training. In this
+paper, we propose a novel training method called manifold-matching, and a new
+GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds
+representing the vector representations of real and fake images. If these two
+manifolds match, it means that real and fake images are statistically
+identical. To assist the manifold-matching task, we also use i) kernel tricks
+to find better manifold structures, ii) moving-averaged manifolds across
+mini-batches, and iii) a regularizer based on correlation matrix to suppress
+mode collapse.
+We conduct in-depth experiments with three image datasets and compare with
+several state-of-the-art GAN models. 32.4% of images generated by the proposed
+MMGAN are recognized as fake images during our user study (16% enhancement
+compared to other state-of-the-art model). MMGAN achieved an unsupervised
+inception score of 7.8 for CIFAR-10.
+",1,0,0,0,0,0
+17814,Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts,"  Acute respiratory infections have epidemic and pandemic potential and thus
+are being studied worldwide, albeit in many different contexts and study
+formats. Predicting infection from symptom data is critical, though using
+symptom data from varied studies in aggregate is challenging because the data
+is collected in different ways. Accordingly, different symptom profiles could
+be more predictive in certain studies, or even symptoms of the same name could
+have different meanings in different contexts. We assess state-of-the-art
+transfer learning methods for improving prediction of infection from symptom
+data in multiple types of health care data ranging from clinical, to home-visit
+as well as crowdsourced studies. We show interesting characteristics regarding
+six different study types and their feature domains. Further, we demonstrate
+that it is possible to use data collected from one study to predict infection
+in another, at close to or better than using a single dataset for prediction on
+itself. We also investigate in which conditions specific transfer learning and
+domain adaptation methods may perform better on symptom data. This work has the
+potential for broad applicability as we show how it is possible to transfer
+learning from one public health study design to another, and data collected
+from one study may be used for prediction of labels for another, even collected
+through different study designs, populations and contexts.
+",0,0,0,1,1,0
+17815,Constrained empirical Bayes priors on regression coefficients,"  Under model uncertainty, empirical Bayes (EB) procedures can have undesirable
+properties such as extreme estimates of inclusion probabilities (Scott &
+Berger, 2010) or inconsistency under the null model (Liang et al., 2008). To
+avoid these issues, we define empirical Bayes priors with constraints that
+ensure that the estimates of the hyperparameters are at least as ""vague"" as
+those of proper default priors. In our examples, we observe that constrained EB
+procedures are better behaved than their unconstrained counterparts and that
+the Bayesian Information Criterion (BIC) is similar to an intuitively appealing
+constrained EB procedure.
+",0,0,1,1,0,0
+17816,A finite field analogue for Appell series F_3,"  In this paper we introduce a finite field analogue for the Appell series F_3
+and give some reduction formulae and certain generating functions for this
+function over finite fields.
+",0,0,1,0,0,0
+17817,Two-way Two-tape Automata,"  In this article we consider two-way two-tape (alternating) automata accepting
+pairs of words and we study some closure properties of this model. Our main
+result is that such alternating automata are not closed under complementation
+for non-unary alphabets. This improves a similar result of Kari and Moore for
+picture languages. We also show that these deterministic, non-deterministic and
+alternating automata are not closed under composition.
+",1,0,0,0,0,0
+17818,Making 360$^{\circ}$ Video Watchable in 2D: Learning Videography for Click Free Viewing,"  360$^{\circ}$ video requires human viewers to actively control ""where"" to
+look while watching the video. Although it provides a more immersive experience
+of the visual content, it also introduces additional burden for viewers;
+awkward interfaces to navigate the video lead to suboptimal viewing
+experiences. Virtual cinematography is an appealing direction to remedy these
+problems, but conventional methods are limited to virtual environments or rely
+on hand-crafted heuristics. We propose a new algorithm for virtual
+cinematography that automatically controls a virtual camera within a
+360$^{\circ}$ video. Compared to the state of the art, our algorithm allows
+more general camera control, avoids redundant outputs, and extracts its output
+videos substantially more efficiently. Experimental results on over 7 hours of
+real ""in the wild"" video show that our generalized camera control is crucial
+for viewing 360$^{\circ}$ video, while the proposed efficient algorithm is
+essential for making the generalized control computationally tractable.
+",1,0,0,0,0,0
+17819,Zero-Shot Learning by Generating Pseudo Feature Representations,"  Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel
+classes without any training instances. In this paper we present a simple but
+high-performance ZSL approach by generating pseudo feature representations
+(GPFR). Given the dataset of seen classes and side information of unseen
+classes (e.g. attributes), we synthesize feature-level pseudo representations
+for novel concepts, which allows us access to the formulation of unseen class
+predictor. Firstly we design a Joint Attribute Feature Extractor (JAFE) to
+acquire understandings about attributes, then construct a cognitive repository
+of attributes filtered by confidence margins, and finally generate pseudo
+feature representations using a probability based sampling strategy to
+facilitate subsequent training process of class predictor. We demonstrate the
+effectiveness in ZSL settings and the extensibility in supervised recognition
+scenario of our method on a synthetic colored MNIST dataset (C-MNIST). For
+several popular ZSL benchmark datasets, our approach also shows compelling
+results on zero-shot recognition task, especially leading to tremendous
+improvement to state-of-the-art mAP on zero-shot retrieval task.
+",1,0,0,0,0,0
+17820,Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection,"  Speaker change detection (SCD) is an important task in dialog modeling. Our
+paper addresses the problem of text-based SCD, which differs from existing
+audio-based studies and is useful in various scenarios, for example, processing
+dialog transcripts where speaker identities are missing (e.g., OpenSubtitle),
+and enhancing audio SCD with textual information. We formulate text-based SCD
+as a matching problem of utterances before and after a certain decision point;
+we propose a hierarchical recurrent neural network (RNN) with static
+sentence-level attention. Experimental results show that neural networks
+consistently achieve better performance than feature-based approaches, and that
+our attention-based model significantly outperforms non-attention neural
+networks.
+",1,0,0,0,0,0
+17821,Bivariate Discrete Generalized Exponential Distribution,"  In this paper we develop a bivariate discrete generalized exponential
+distribution, whose marginals are discrete generalized exponential distribution
+as proposed by Nekoukhou, Alamatsaz and Bidram (""Discrete generalized
+exponential distribution of a second type"", Statistics, 47, 876 - 887, 2013).
+It is observed that the proposed bivariate distribution is a very flexible
+distribution and the bivariate geometric distribution can be obtained as a
+special case of this distribution. The proposed distribution can be seen as a
+natural discrete analogue of the bivariate generalized exponential distribution
+proposed by Kundu and Gupta (""Bivariate generalized exponential distribution"",
+Journal of Multivariate Analysis, 100, 581 - 593, 2009). We study different
+properties of this distribution and explore its dependence structures. We
+propose a new EM algorithm to compute the maximum likelihood estimators of the
+unknown parameters which can be implemented very efficiently, and discuss some
+inferential issues also. The analysis of one data set has been performed to
+show the effectiveness of the proposed model. Finally we propose some open
+problems and conclude the paper.
+",0,0,0,1,0,0
+17822,"Optimization of Ensemble Supervised Learning Algorithms for Increased Sensitivity, Specificity, and AUC of Population-Based Colorectal Cancer Screenings","  Over 150,000 new people in the United States are diagnosed with colorectal
+cancer each year. Nearly a third die from it (American Cancer Society). The
+only approved noninvasive diagnosis tools currently involve fecal blood count
+tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five
+minutes and are available over the counter for as low as \$15. They are highly
+specific, yet not nearly as sensitive, yielding a high percentage (25%) of
+false negatives (Colon Cancer Alliance). Moreover, FOBT results are far too
+generalized, meaning that a positive result could mean much more than just
+colorectal cancer, and could just as easily mean hemorrhoids, anal fissure,
+proctitis, Crohn's disease, diverticulosis, ulcerative colitis, rectal ulcer,
+rectal prolapse, ischemic colitis, angiodysplasia, rectal trauma, proctitis
+from radiation therapy, and others. Stool DNA tests, the modern benchmark for
+CRC screening, have a much higher sensitivity and specificity, but also cost
+\$600, take two weeks to process, and are not for high-risk individuals or
+people with a history of polyps. To yield a cheap and effective CRC screening
+alternative, a unique ensemble-based classification algorithm is put in place
+that considers the FIT result, BMI, smoking history, and diabetic status of
+patients. This method is tested under ten-fold cross validation to have a .95
+AUC, 92% specificity, 89% sensitivity, .88 F1, and 90% precision. Once
+clinically validated, this test promises to be cheaper, faster, and potentially
+more accurate when compared to a stool DNA test.
+",1,0,0,1,0,0
+17823,More or Less? Predict the Social Influence of Malicious URLs on Social Media,"  Users of Online Social Networks (OSNs) interact with each other more than
+ever. In the context of a public discussion group, people receive, read, and
+write comments in response to articles and postings. In the absence of access
+control mechanisms, OSNs are a great environment for attackers to influence
+others, from spreading phishing URLs, to posting fake news. Moreover, OSN user
+behavior can be predicted by social science concepts which include conformity
+and the bandwagon effect. In this paper, we show how social recommendation
+systems affect the occurrence of malicious URLs on Facebook. We exploit
+temporal features to build a prediction framework, having greater than 75%
+accuracy, to predict whether the following group users' behavior will increase
+or not. Included in this work, we demarcate classes of URLs, including those
+malicious URLs classified as creating critical damage, as well as those of a
+lesser nature which only inflict light damage such as aggressive commercial
+advertisements and spam content. It is our hope that the data and analyses in
+this paper provide a better understanding of OSN user reactions to different
+categories of malicious URLs, thereby providing a way to mitigate the influence
+of these malicious URL attacks.
+",1,0,0,0,0,0
+17824,Path-Following through Control Funnel Functions,"  We present an approach to path following using so-called control funnel
+functions. Synthesizing controllers to ""robustly"" follow a reference trajectory
+is a fundamental problem for autonomous vehicles. Robustness, in this context,
+requires our controllers to handle a specified amount of deviation from the
+desired trajectory. Our approach considers a timing law that describes how fast
+to move along a given reference trajectory and a control feedback law for
+reducing deviations from the reference. We synthesize both feedback laws using
+""control funnel functions"" that jointly encode the control law as well as its
+correctness argument over a mathematical model of the vehicle dynamics. We
+adapt a previously described demonstration-based learning algorithm to
+synthesize a control funnel function as well as the associated feedback law. We
+implement this law on top of a 1/8th scale autonomous vehicle called the
+Parkour car. We compare the performance of our path following approach against
+a trajectory tracking approach by specifying trajectories of varying lengths
+and curvatures. Our experiments demonstrate the improved robustness obtained
+from the use of control funnel functions.
+",1,0,0,0,0,0
+17825,Tunable Anomalous Andreev Reflection and Triplet Pairings in Spin Orbit Coupled Graphene,"  We theoretically study scattering process and superconducting triplet
+correlations in a graphene junction comprised of ferromagnet-RSO-superconductor
+in which RSO stands for a region with Rashba spin orbit interaction. Our
+results reveal spin-polarized subgap transport through the system due to an
+anomalous equal-spin Andreev reflection in addition to conventional back
+scatterings. We calculate equal- and opposite-spin pair correlations near the
+F-RSO interface and demonstrate direct link of the anomalous Andreev reflection
+and equal-spin pairings arised due to the proximity effect in the presence of
+RSO interaction. Moreover, we show that the amplitude of anomalous Andreev
+reflection, and thus the triplet pairings, are experimentally controllable when
+incorporating the influences of both tunable strain and Fermi level in the
+nonsuperconducting region. Our findings can be confirmed by a conductance
+spectroscopy experiment and provide better insights into the proximity-induced
+RSO coupling in graphene layers reported in recent experiments.
+",0,1,0,0,0,0
+17826,Conditional Model Selection in Mixed-Effects Models with cAIC4,"  Model selection in mixed models based on the conditional distribution is
+appropriate for many practical applications and has been a focus of recent
+statistical research. In this paper we introduce the R-package cAIC4 that
+allows for the computation of the conditional Akaike Information Criterion
+(cAIC). Computation of the conditional AIC needs to take into account the
+uncertainty of the random effects variance and is therefore not
+straightforward. We introduce a fast and stable implementation for the
+calculation of the cAIC for linear mixed models estimated with lme4 and
+additive mixed models estimated with gamm4 . Furthermore, cAIC4 offers a
+stepwise function that allows for a fully automated stepwise selection scheme
+for mixed models based on the conditional AIC. Examples of many possible
+applications are presented to illustrate the practical impact and easy handling
+of the package.
+",0,0,0,1,0,0
+17827,Inequalities for the lowest magnetic Neumann eigenvalue,"  We study the ground state energy of the Neumann magnetic Laplacian on planar
+domains. For a constant magnetic field we consider the question whether, under
+an assumption of fixed area, the disc maximizes this eigenvalue. More
+generally, we discuss old and new bounds obtained on this problem.
+",0,0,1,0,0,0
+17828,Automated optimization of large quantum circuits with continuous parameters,"  We develop and implement automated methods for optimizing quantum circuits of
+the size and type expected in quantum computations that outperform classical
+computers. We show how to handle continuous gate parameters and report a
+collection of fast algorithms capable of optimizing large-scale quantum
+circuits. For the suite of benchmarks considered, we obtain substantial
+reductions in gate counts. In particular, we provide better optimization in
+significantly less time than previous approaches, while making minimal
+structural changes so as to preserve the basic layout of the underlying quantum
+algorithms. Our results help bridge the gap between the computations that can
+be run on existing hardware and those that are expected to outperform classical
+computers.
+",1,0,0,0,0,0
+17829,Testing the simplifying assumption in high-dimensional vine copulas,"  Testing the simplifying assumption in high-dimensional vine copulas is a
+difficult task because tests must be based on estimated observations and amount
+to checking constraints on high-dimensional distributions. So far,
+corresponding tests have been limited to single conditional copulas with a
+low-dimensional set of conditioning variables. We propose a novel testing
+procedure that is computationally feasible for high-dimensional data sets and
+that exhibits a power that decreases only slightly with the dimension. By
+discretizing the support of the conditioning variables and incorporating a
+penalty in the test statistic, we mitigate the curse of dimensions by looking
+for the possibly strongest deviation from the simplifying assumption. The use
+of a decision tree renders the test computationally feasible for large
+dimensions. We derive the asymptotic distribution of the test and analyze its
+finite sample performance in an extensive simulation study. The utility of the
+test is demonstrated by its application to 10 data sets with up to 49
+dimensions.
+",0,0,0,1,0,0
+17830,TSP With Locational Uncertainty: The Adversarial Model,"  In this paper we study a natural special case of the Traveling Salesman
+Problem (TSP) with point-locational-uncertainty which we will call the {\em
+adversarial TSP} problem (ATSP). Given a metric space $(X, d)$ and a set of
+subsets $R = \{R_1, R_2, ... , R_n\} : R_i \subseteq X$, the goal is to devise
+an ordering of the regions, $\sigma_R$, that the tour will visit such that when
+a single point is chosen from each region, the induced tour over those points
+in the ordering prescribed by $\sigma_R$ is as short as possible. Unlike the
+classical locational-uncertainty-TSP problem, which focuses on minimizing the
+expected length of such a tour when the point within each region is chosen
+according to some probability distribution, here, we focus on the {\em
+adversarial model} in which once the choice of $\sigma_R$ is announced, an
+adversary selects a point from each region in order to make the resulting tour
+as long as possible. In other words, we consider an offline problem in which
+the goal is to determine an ordering of the regions $R$ that is optimal with
+respect to the ""worst"" point possible within each region being chosen by an
+adversary, who knows the chosen ordering. We give a $3$-approximation when $R$
+is a set of arbitrary regions/sets of points in a metric space. We show how
+geometry leads to improved constant factor approximations when regions are
+parallel line segments of the same lengths, and a polynomial-time approximation
+scheme (PTAS) for the important special case in which $R$ is a set of disjoint
+unit disks in the plane.
+",1,0,0,0,0,0
+17831,Exact solutions to three-dimensional generalized nonlinear Schrodinger equations with varying potential and nonlinearities,"  It is shown that using the similarity transformations, a set of
+three-dimensional p-q nonlinear Schrodinger (NLS) equations with inhomogeneous
+coefficients can be reduced to one-dimensional stationary NLS equation with
+constant or varying coefficients, thus allowing for obtaining exact localized
+and periodic wave solutions. In the suggested reduction the original
+coordinates in the (1+3)-space are mapped into a set of one-parametric
+coordinate surfaces, whose parameter plays the role of the coordinate of the
+one-dimensional equation. We describe the algorithm of finding solutions and
+concentrate on power (linear and nonlinear) potentials presenting a number of
+case examples. Generalizations of the method are also discussed.
+",0,1,1,0,0,0
+17832,A Study of Reinforcement Learning for Neural Machine Translation,"  Recent studies have shown that reinforcement learning (RL) is an effective
+approach for improving the performance of neural machine translation (NMT)
+system. However, due to its instability, successfully RL training is
+challenging, especially in real-world systems where deep models and large
+datasets are leveraged. In this paper, taking several large-scale translation
+tasks as testbeds, we conduct a systematic study on how to train better NMT
+models using reinforcement learning. We provide a comprehensive comparison of
+several important factors (e.g., baseline reward, reward shaping) in RL
+training. Furthermore, to fill in the gap that it remains unclear whether RL is
+still beneficial when monolingual data is used, we propose a new method to
+leverage RL to further boost the performance of NMT systems trained with
+source/target monolingual data. By integrating all our findings, we obtain
+competitive results on WMT14 English- German, WMT17 English-Chinese, and WMT17
+Chinese-English translation tasks, especially setting a state-of-the-art
+performance on WMT17 Chinese-English translation task.
+",0,0,0,1,0,0
+17833,Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification,"  Artificial neural network (ANN) is a very useful tool in solving learning
+problems. Boosting the performances of ANN can be mainly concluded from two
+aspects: optimizing the architecture of ANN and normalizing the raw data for
+ANN. In this paper, a novel method which improves the effects of ANN by
+preprocessing the raw data is proposed. It totally leverages the fact that
+different features should play different roles. The raw data set is firstly
+preprocessed by principle component analysis (PCA), and then its principle
+components are weighted by their corresponding eigenvalues. Several aspects of
+analysis are carried out to analyze its theory and the applicable occasions.
+Three classification problems are launched by an active learning algorithm to
+verify the proposed method. From the empirical results, conclusion comes to the
+fact that the proposed method can significantly improve the performance of ANN.
+",1,0,0,1,0,0
+17834,Maximum likelihood estimation of determinantal point processes,"  Determinantal point processes (DPPs) have wide-ranging applications in
+machine learning, where they are used to enforce the notion of diversity in
+subset selection problems. Many estimators have been proposed, but surprisingly
+the basic properties of the maximum likelihood estimator (MLE) have received
+little attention. The difficulty is that it is a non-concave maximization
+problem, and such functions are notoriously difficult to understand in high
+dimensions, despite their importance in modern machine learning. Here we study
+both the local and global geometry of the expected log-likelihood function. We
+prove several rates of convergence for the MLE and give a complete
+characterization of the case where these are parametric. We also exhibit a
+potential curse of dimensionality where the asymptotic variance of the MLE
+scales exponentially with the dimension of the problem. Moreover, we exhibit an
+exponential number of saddle points, and give evidence that these may be the
+only critical points.
+",0,0,1,1,0,0
+17835,Orthogonal Machine Learning: Power and Limitations,"  Double machine learning provides $\sqrt{n}$-consistent estimates of
+parameters of interest even when high-dimensional or nonparametric nuisance
+parameters are estimated at an $n^{-1/4}$ rate. The key is to employ
+Neyman-orthogonal moment equations which are first-order insensitive to
+perturbations in the nuisance parameters. We show that the $n^{-1/4}$
+requirement can be improved to $n^{-1/(2k+2)}$ by employing a $k$-th order
+notion of orthogonality that grants robustness to more complex or
+higher-dimensional nuisance parameters. In the partially linear regression
+setting popular in causal inference, we show that we can construct second-order
+orthogonal moments if and only if the treatment residual is not normally
+distributed. Our proof relies on Stein's lemma and may be of independent
+interest. We conclude by demonstrating the robustness benefits of an explicit
+doubly-orthogonal estimation procedure for treatment effect.
+",1,0,1,1,0,0
+17836,Numerical investigation of supersonic shock-wave/boundary-layer interaction in transitional and turbulent regime,"  We perform direct numerical simulations of shock-wave/boundary-layer
+interactions (SBLI) at Mach number M = 1.7 to investigate the influence of the
+state of the incoming boundary layer on the interaction properties. We
+reproduce and extend the flow conditions of the experiments performed by
+Giepman et al., in which a spatially evolving laminar boundary layer over a
+flat plate is initially tripped by an array of distributed roughness elements
+and impinged further downstream by an oblique shock wave. Four SBLI cases are
+considered, based on two different shock impingement locations along the
+streamwise direction, corresponding to transitional and turbulent interactions,
+and two different shock strengths, corresponding to flow deflection angles 3
+degreees and 6 degrees. We find that, for all flow cases, shock induced
+separation is not observed, the boundary layer remains attached for the 3
+degrees case and close to incipient separation for the 6 degrees case,
+independent of the state of the incoming boundary layer. The findings of this
+work suggest that a transitional interaction might be the optimal solution for
+practical SBLI applications, as it removes the large separation bubble typical
+of laminar interactions and reduces the extent of the high-friction region
+associated with an incoming turbulent boundary layer.
+",0,1,0,0,0,0
+17837,Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations,"  Latent factor models for recommender systems represent users and items as low
+dimensional vectors. Privacy risks of such systems have previously been studied
+mostly in the context of recovery of personal information in the form of usage
+records from the training data. However, the user representations themselves
+may be used together with external data to recover private user information
+such as gender and age. In this paper we show that user vectors calculated by a
+common recommender system can be exploited in this way. We propose the
+privacy-adversarial framework to eliminate such leakage of private information,
+and study the trade-off between recommender performance and leakage both
+theoretically and empirically using a benchmark dataset. An advantage of the
+proposed method is that it also helps guarantee fairness of results, since all
+implicit knowledge of a set of attributes is scrubbed from the representations
+used by the model, and thus can't enter into the decision making. We discuss
+further applications of this method towards the generation of deeper and more
+insightful recommendations.
+",0,0,0,1,0,0
+17838,Asymptotics for high-dimensional covariance matrices and quadratic forms with applications to the trace functional and shrinkage,"  We establish large sample approximations for an arbitray number of bilinear
+forms of the sample variance-covariance matrix of a high-dimensional vector
+time series using $ \ell_1$-bounded and small $\ell_2$-bounded weighting
+vectors. Estimation of the asymptotic covariance structure is also discussed.
+The results hold true without any constraint on the dimension, the number of
+forms and the sample size or their ratios. Concrete and potential applications
+are widespread and cover high-dimensional data science problems such as tests
+for large numbers of covariances, sparse portfolio optimization and projections
+onto sparse principal components or more general spanning sets as frequently
+considered, e.g. in classification and dictionary learning. As two specific
+applications of our results, we study in greater detail the asymptotics of the
+trace functional and shrinkage estimation of covariance matrices. In shrinkage
+estimation, it turns out that the asymptotics differs for weighting vectors
+bounded away from orthogonaliy and nearly orthogonal ones in the sense that
+their inner product converges to 0.
+",0,0,1,1,0,0
+17839,Bayesian Paragraph Vectors,"  Word2vec (Mikolov et al., 2013) has proven to be successful in natural
+language processing by capturing the semantic relationships between different
+words. Built on top of single-word embeddings, paragraph vectors (Le and
+Mikolov, 2014) find fixed-length representations for pieces of text with
+arbitrary lengths, such as documents, paragraphs, and sentences. In this work,
+we propose a novel interpretation for neural-network-based paragraph vectors by
+developing an unsupervised generative model whose maximum likelihood solution
+corresponds to traditional paragraph vectors. This probabilistic formulation
+allows us to go beyond point estimates of parameters and to perform Bayesian
+posterior inference. We find that the entropy of paragraph vectors decreases
+with the length of documents, and that information about posterior uncertainty
+improves performance in supervised learning tasks such as sentiment analysis
+and paraphrase detection.
+",1,0,0,1,0,0
+17840,Subset Synchronization in Monotonic Automata,"  We study extremal and algorithmic questions of subset and careful
+synchronization in monotonic automata. We show that several synchronization
+problems that are hard in general automata can be solved in polynomial time in
+monotonic automata, even without knowing a linear order of the states preserved
+by the transitions. We provide asymptotically tight bounds on the maximum
+length of a shortest word synchronizing a subset of states in a monotonic
+automaton and a shortest word carefully synchronizing a partial monotonic
+automaton. We provide a complexity framework for dealing with problems for
+monotonic weakly acyclic automata over a three-letter alphabet, and use it to
+prove NP-completeness and inapproximability of problems such as {\sc Finite
+Automata Intersection} and the problem of computing the rank of a subset of
+states in this class. We also show that checking whether a monotonic partial
+automaton over a four-letter alphabet is carefully synchronizing is NP-hard.
+Finally, we give a simple necessary and sufficient condition when a strongly
+connected digraph with a selected subset of vertices can be transformed into a
+deterministic automaton where the corresponding subset of states is
+synchronizing.
+",1,0,0,0,0,0
+17841,Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification,"  The selection of West Java governor is one event that seizes the attention of
+the public is no exception to social media users. Public opinion on a
+prospective regional leader can help predict electability and tendency of
+voters. Data that can be used by the opinion mining process can be obtained
+from Twitter. Because the data is very varied form and very unstructured, it
+must be managed and uninformed using data pre-processing techniques into
+semi-structured data. This semi-structured information is followed by a
+classification stage to categorize the opinion into negative or positive
+opinions. The research methodology uses a literature study where the research
+will examine previous research on a similar topic. The purpose of this study is
+to find the right architecture to develop it into the application of twitter
+opinion mining to know public sentiments toward the election of the governor of
+west java. The result of this research is that Twitter opinion mining is part
+of text mining where opinions in Twitter if they want to be classified, must go
+through the preprocessing text stage first. The preprocessing step required
+from twitter data is cleansing, case folding, POS Tagging and stemming. The
+resulting text mining architecture is an architecture that can be used for text
+mining research with different topics.
+",1,0,0,0,0,0
+17842,Ray: A Distributed Framework for Emerging AI Applications,"  The next generation of AI applications will continuously interact with the
+environment and learn from these interactions. These applications impose new
+and demanding systems requirements, both in terms of performance and
+flexibility. In this paper, we consider these requirements and present Ray---a
+distributed system to address them. Ray implements a unified interface that can
+express both task-parallel and actor-based computations, supported by a single
+dynamic execution engine. To meet the performance requirements, Ray employs a
+distributed scheduler and a distributed and fault-tolerant store to manage the
+system's control state. In our experiments, we demonstrate scaling beyond 1.8
+million tasks per second and better performance than existing specialized
+systems for several challenging reinforcement learning applications.
+",1,0,0,1,0,0
+17843,A blockchain-based Decentralized System for proper handling of temporary Employment contracts,"  Temporary work is an employment situation useful and suitable in all
+occasions in which business needs to adjust more easily and quickly to workload
+fluctuations or maintain staffing flexibility. Temporary workers play therefore
+an important role in many companies, but this kind of activity is subject to a
+special form of legal protections and many aspects and risks must be taken into
+account both employers and employees. In this work we propose a
+blockchain-based system that aims to ensure respect for the rights for all
+actors involved in a temporary employment, in order to provide employees with
+the fair and legal remuneration (including taxes) of work performances and a
+protection in the case employer becomes insolvent. At the same time, our system
+wants to assist the employer in processing contracts with a fully automated and
+fast procedure. To resolve these problems we propose the D-ES (Decentralized
+Employment System). We first model the employment relationship as a state
+system. Then we describe the enabling technology that makes us able to realize
+the D-ES. In facts, we propose the implementation of a DLT (Decentralized
+Ledger Technology) based system, consisting in a blockchain system and of a
+web-based environment. Thanks the decentralized application platforms that
+makes us able to develop smart contracts, we define a discrete event control
+system that works inside the blockchain. In addition, we discuss the temporary
+work in agriculture as a interesting case of study.
+",1,0,0,0,0,0
+17844,Valley polarized relaxation and upconversion luminescence from Tamm-Plasmon Trion-Polaritons with a MoSe2 monolayer,"  Transition metal dichalcogenides represent an ideal testbed to study
+excitonic effects, spin-related phenomena and fundamental light-matter coupling
+in nanoscopic condensed matter systems. In particular, the valley degree of
+freedom, which is unique to such direct band gap monolayers with broken
+inversion symmetry, adds fundamental interest in these materials. Here, we
+implement a Tamm-plasmon structure with an embedded MoSe2 monolayer and study
+the formation of polaritonic quasi-particles. Strong coupling conditions
+between the Tamm-mode and the trion resonance of MoSe2 are established,
+yielding bright luminescence from the polaritonic ground state under
+non-resonant optical excitation. We demonstrate, that tailoring the
+electrodynamic environment of the monolayer results in a significantly
+increased valley polarization. This enhancement can be related to change in
+recombination dynamics shown in time-resolved photoluminescence measurements.
+We furthermore observe strong upconversion luminescence from resonantly excited
+polariton states in the lower polariton branch. This upconverted polariton
+luminescence is shown to preserve the valley polarization of the
+trion-polariton, which paves the way towards combining spin-valley physics and
+exciton scattering experiments.
+",0,1,0,0,0,0
+17845,Machine learning based localization and classification with atomic magnetometers,"  We demonstrate identification of position, material, orientation and shape of
+objects imaged by an $^{85}$Rb atomic magnetometer performing electromagnetic
+induction imaging supported by machine learning. Machine learning maximizes the
+information extracted from the images created by the magnetometer,
+demonstrating the use of hidden data. Localization 2.6 times better than the
+spatial resolution of the imaging system and successful classification up to
+97$\%$ are obtained. This circumvents the need of solving the inverse problem,
+and demonstrates the extension of machine learning to diffusive systems such as
+low-frequency electrodynamics in media. Automated collection of task-relevant
+information from quantum-based electromagnetic imaging will have a relevant
+impact from biomedicine to security.
+",0,1,0,0,0,0
+17846,On the difficulty of finding spines,"  We prove that the set of symplectic lattices in the Siegel space
+$\mathfrak{h}_g$ whose systoles generate a subspace of dimension at least 3 in
+$\mathbb{R}^{2g}$ does not contain any $\mathrm{Sp}(2g,\mathbb{Z})$-equivariant
+deformation retract of $\mathfrak{h}_g$.
+",0,0,1,0,0,0
+17847,Distributed Decoding of Convolutional Network Error Correction Codes,"  A Viterbi-like decoding algorithm is proposed in this paper for generalized
+convolutional network error correction coding. Different from classical Viterbi
+algorithm, our decoding algorithm is based on minimum error weight rather than
+the shortest Hamming distance between received and sent sequences. Network
+errors may disperse or neutralize due to network transmission and convolutional
+network coding. Therefore, classical decoding algorithm cannot be employed any
+more. Source decoding was proposed by multiplying the inverse of network
+transmission matrix, where the inverse is hard to compute. Starting from the
+Maximum A Posteriori (MAP) decoding criterion, we find that it is equivalent to
+the minimum error weight under our model. Inspired by Viterbi algorithm, we
+propose a Viterbi-like decoding algorithm based on minimum error weight of
+combined error vectors, which can be carried out directly at sink nodes and can
+correct any network errors within the capability of convolutional network error
+correction codes (CNECC). Under certain situations, the proposed algorithm can
+realize the distributed decoding of CNECC.
+",1,0,0,0,0,0
+17848,Temperature induced phase transition from cycloidal to collinear antiferromagnetism in multiferroic Bi$_{0.9}$Sm$_{0.1}$FeO$_3$ driven by $f$-$d$ induced magnetic anisotropy,"  In multiferroic BiFeO$_3$ a cycloidal antiferromagnetic structure is coupled
+to a large electric polarization at room temperature, giving rise to
+magnetoelectric functionality that may be exploited in novel multiferroic-based
+devices. In this paper, we demonstrate that by substituting samarium for 10% of
+the bismuth ions the periodicity of the room temperature cycloid is increased,
+and by cooling below $\sim15$ K the magnetic structure tends towards a simple
+G-type antiferromagnet, which is fully established at 1.5 K. We show that this
+transition results from $f-d$ exchange coupling, which induces a local
+anisotropy on the iron magnetic moments that destroys the cycloidal order - a
+result of general significance regarding the stability of non-collinear
+magnetic structures in the presence of multiple magnetic sublattices.
+",0,1,0,0,0,0
+17849,POSEYDON - Converting the DAFNE Collider into a double Positron Facility: a High Duty-Cycle pulse stretcher and a storage ring,"  This project proposes to reuse the DAFNE accelerator complex for producing a
+high intensity (up to 10^10), high-quality beam of high-energy (up to 500 MeV)
+positrons for HEP experiments, mainly - but not only - motivated by light dark
+particles searches. Such a facility would provide a unique source of
+ultra-relativistic, narrow-band and low-emittance positrons, with a high duty
+factor, without employing a cold technology, that would be an ideal facility
+for exploring the existence of light dark matter particles, produced in
+positron-on-target annihilations into a photon+missing mass, and using the
+bump-hunt technique. The PADME experiment, that will use the extracted beam
+from the DAFNE BTF, is indeed limited by the low duty-factor (10^-5=200 ns/20
+ms). The idea is to use a variant of the third of integer resonant extraction,
+with the aim of getting a <10^-6 m rad emittance and, at the same time,
+tailoring the scheme to the peculiar optics of the DAFNE machine. In
+alternative, the possibility of kicking the positrons by means of channelling
+effects in crystals can be evaluated. This would not only increase the
+extraction efficiency but also improve the beam quality, thanks to the high
+collimation of channelled particles.
+",0,1,0,0,0,0
+17850,Phase diagram of hydrogen and a hydrogen-helium mixture at planetary conditions by Quantum Monte Carlo simulations,"  Understanding planetary interiors is directly linked to our ability of
+simulating exotic quantum mechanical systems such as hydrogen (H) and
+hydrogen-helium (H-He) mixtures at high pressures and temperatures. Equations
+of State (EOSs) tables based on Density Functional Theory (DFT), are commonly
+used by planetary scientists, although this method allows only for a
+qualitative description of the phase diagram, due to an incomplete treatment of
+electronic interactions. Here we report Quantum Monte Carlo (QMC) molecular
+dynamics simulations of pure H and H-He mixture. We calculate the first QMC EOS
+at 6000 K for an H-He mixture of a proto-solar composition, and show the
+crucial influence of He on the H metallization pressure. Our results can be
+used to calibrate other EOS calculations and are very timely given the accurate
+determination of Jupiter's gravitational field from the NASA Juno mission and
+the effort to determine its structure.
+",0,1,0,0,0,0
+17851,Identifying exogenous and endogenous activity in social media,"  The occurrence of new events in a system is typically driven by external
+causes and by previous events taking place inside the system. This is a general
+statement, applying to a range of situations including, more recently, to the
+activity of users in Online social networks (OSNs). Here we develop a method
+for extracting from a series of posting times the relative contributions of
+exogenous, e.g. news media, and endogenous, e.g. information cascade. The
+method is based on the fitting of a generalized linear model (GLM) equipped
+with a self-excitation mechanism. We test the method with synthetic data
+generated by a nonlinear Hawkes process, and apply it to a real time series of
+tweets with a given hashtag. In the empirical dataset, the estimated
+contributions of exogenous and endogenous volumes are close to the amounts of
+original tweets and retweets respectively. We conclude by discussing the
+possible applications of the method, for instance in online marketing.
+",1,0,0,0,0,0
+17852,Water flow in Carbon and Silicon Carbide nanotubes,"  In this work the conduction of ion-water solution through two discrete
+bundles of armchair carbon and silicon carbide nanotubes, as useful membranes
+for water desalination, is studied. In order that studies on different types of
+nanotubes be comparable, the chiral vectors of C and Si-C nanotubes are
+selected as (7,7) and (5,5), respectively, so that a similar volume of fluid is
+investigated flowing through two similar dimension membranes. Different
+hydrostatic pressures are applied and the flow rates of water and ions are
+calculated through molecular dynamics simulations. Consequently, according to
+conductance of water per each nanotube, per nanosecond, it is perceived that at
+lower pressures (below 150 MPa) the Si-C nanotubes seem to be more applicable,
+while higher hydrostatic pressures make carbon nanotube membranes more suitable
+for water desalination.
+",0,1,0,0,0,0
+17853,Multidimensional extremal dependence coefficients,"  Extreme values modeling has attracting the attention of researchers in
+diverse areas such as the environment, engineering, or finance. Multivariate
+extreme value distributions are particularly suitable to model the tails of
+multidimensional phenomena. The analysis of the dependence among multivariate
+maxima is useful to evaluate risk. Here we present new multivariate extreme
+value models, as well as, coefficients to assess multivariate extremal
+dependence.
+",0,0,1,1,0,0
+17854,A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas,"  Systems subject to uncertain inputs produce uncertain responses. Uncertainty
+quantification (UQ) deals with the estimation of statistics of the system
+response, given a computational model of the system and a probabilistic model
+of its inputs. In engineering applications it is common to assume that the
+inputs are mutually independent or coupled by a Gaussian or elliptical
+dependence structure (copula). In this paper we overcome such limitations by
+modelling the dependence structure of multivariate inputs as vine copulas. Vine
+copulas are models of multivariate dependence built from simpler pair-copulas.
+The vine representation is flexible enough to capture complex dependencies.
+This paper formalises the framework needed to build vine copula models of
+multivariate inputs and to combine them with virtually any UQ method. The
+framework allows for a fully automated, data-driven inference of the
+probabilistic input model on available input data. The procedure is exemplified
+on two finite element models of truss structures, both subject to inputs with
+non-Gaussian dependence structures. For each case, we analyse the moments of
+the model response (using polynomial chaos expansions), and perform a
+structural reliability analysis to calculate the probability of failure of the
+system (using the first order reliability method and importance sampling).
+Reference solutions are obtained by Monte Carlo simulation. The results show
+that, while the Gaussian assumption yields biased statistics, the vine copula
+representation achieves significantly more precise estimates, even when its
+structure needs to be fully inferred from a limited amount of observations.
+",0,0,0,1,0,0
+17855,Pachinko Prediction: A Bayesian method for event prediction from social media data,"  The combination of large open data sources with machine learning approaches
+presents a potentially powerful way to predict events such as protest or social
+unrest. However, accounting for uncertainty in such models, particularly when
+using diverse, unstructured datasets such as social media, is essential to
+guarantee the appropriate use of such methods. Here we develop a Bayesian
+method for predicting social unrest events in Australia using social media
+data. This method uses machine learning methods to classify individual postings
+to social media as being relevant, and an empirical Bayesian approach to
+calculate posterior event probabilities. We use the method to predict events in
+Australian cities over a period in 2017/18.
+",1,0,0,0,0,0
+17856,A Digital Hardware Fast Algorithm and FPGA-based Prototype for a Novel 16-point Approximate DCT for Image Compression Applications,"  The discrete cosine transform (DCT) is the key step in many image and video
+coding standards. The 8-point DCT is an important special case, possessing
+several low-complexity approximations widely investigated. However, 16-point
+DCT transform has energy compaction advantages. In this sense, this paper
+presents a new 16-point DCT approximation with null multiplicative complexity.
+The proposed transform matrix is orthogonal and contains only zeros and ones.
+The proposed transform outperforms the well-know Walsh-Hadamard transform and
+the current state-of-the-art 16-point approximation. A fast algorithm for the
+proposed transform is also introduced. This fast algorithm is experimentally
+validated using hardware implementations that are physically realized and
+verified on a 40 nm CMOS Xilinx Virtex-6 XC6VLX240T FPGA chip for a maximum
+clock rate of 342 MHz. Rapid prototypes on FPGA for 8-bit input word size shows
+significant improvement in compressed image quality by up to 1-2 dB at the cost
+of only eight adders compared to the state-of-art 16-point DCT approximation
+algorithm in the literature [S. Bouguezel, M. O. Ahmad, and M. N. S. Swamy. A
+novel transform for image compression. In {\em Proceedings of the 53rd IEEE
+International Midwest Symposium on Circuits and Systems (MWSCAS)}, 2010].
+",1,0,0,1,0,0
+17857,Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice,"  It is well known that the initialization of weights in deep neural networks
+can have a dramatic impact on learning speed. For example, ensuring the mean
+squared singular value of a network's input-output Jacobian is $O(1)$ is
+essential for avoiding the exponential vanishing or explosion of gradients. The
+stronger condition that all singular values of the Jacobian concentrate near
+$1$ is a property known as dynamical isometry. For deep linear networks,
+dynamical isometry can be achieved through orthogonal weight initialization and
+has been shown to dramatically speed up learning; however, it has remained
+unclear how to extend these results to the nonlinear setting. We address this
+question by employing powerful tools from free probability theory to compute
+analytically the entire singular value distribution of a deep network's
+input-output Jacobian. We explore the dependence of the singular value
+distribution on the depth of the network, the weight initialization, and the
+choice of nonlinearity. Intriguingly, we find that ReLU networks are incapable
+of dynamical isometry. On the other hand, sigmoidal networks can achieve
+isometry, but only with orthogonal weight initialization. Moreover, we
+demonstrate empirically that deep nonlinear networks achieving dynamical
+isometry learn orders of magnitude faster than networks that do not. Indeed, we
+show that properly-initialized deep sigmoidal networks consistently outperform
+deep ReLU networks. Overall, our analysis reveals that controlling the entire
+distribution of Jacobian singular values is an important design consideration
+in deep learning.
+",1,0,0,1,0,0
+17858,Singular perturbation for abstract elliptic equations and application,"  Boundary value problem for complete second order elliptic equation is
+considered in Banach space. The equation and boundary conditions involve a
+small and spectral parameter. The uniform L_{p}-regularity properties with
+respect to space variable and parameters are established. Here, the explicit
+formula for the solution is given and behavior of solution is derived when the
+small parameter approaches zero. It used to obtain singular perturbation result
+for abstract elliptic equation
+",0,0,1,0,0,0
+17859,Pruning and Nonparametric Multiple Change Point Detection,"  Change point analysis is a statistical tool to identify homogeneity within
+time series data. We propose a pruning approach for approximate nonparametric
+estimation of multiple change points. This general purpose change point
+detection procedure `cp3o' applies a pruning routine within a dynamic program
+to greatly reduce the search space and computational costs. Existing
+goodness-of-fit change point objectives can immediately be utilized within the
+framework. We further propose novel change point algorithms by applying cp3o to
+two popular nonparametric goodness of fit measures: `e-cp3o' uses E-statistics,
+and `ks-cp3o' uses Kolmogorov-Smirnov statistics. Simulation studies highlight
+the performance of these algorithms in comparison with parametric and other
+nonparametric change point methods. Finally, we illustrate these approaches
+with climatological and financial applications.
+",0,0,0,1,0,0
+17860,Context encoding enables machine learning-based quantitative photoacoustics,"  Real-time monitoring of functional tissue parameters, such as local blood
+oxygenation, based on optical imaging could provide groundbreaking advances in
+the diagnosis and interventional therapy of various diseases. While
+photoacoustic (PA) imaging is a novel modality with great potential to measure
+optical absorption deep inside tissue, quantification of the measurements
+remains a major challenge. In this paper, we introduce the first machine
+learning based approach to quantitative PA imaging (qPAI), which relies on
+learning the fluence in a voxel to deduce the corresponding optical absorption.
+The method encodes relevant information of the measured signal and the
+characteristics of the imaging system in voxel-based feature vectors, which
+allow the generation of thousands of training samples from a single simulated
+PA image. Comprehensive in silico experiments suggest that context encoding
+(CE)-qPAI enables highly accurate and robust quantification of the local
+fluence and thereby the optical absorption from PA images.
+",1,1,0,0,0,0
+17861,Simulating the interaction between a falling super-quadric object and a soap film,"  The interaction that occurs between a light solid object and a horizontal
+soap film of a bamboo foam contained in a cylindrical tube is simulated in 3D.
+We vary the shape of the falling object from a sphere to a cube by changing a
+single shape parameter as well as varying the initial orientation and position
+of the object. We investigate in detail how the soap film deforms in all these
+cases, and determine the network and pressure forces that a foam exerts on a
+falling object, due to surface tension and bubble pressure respectively. We
+show that a cubic particle in a particular orientation experiences the largest
+drag force, and that this orientation is also the most likely outcome of
+dropping a cube from an arbitrary orientation through a bamboo foam.
+",0,1,0,0,0,0
+17862,Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach,"  Compared with relational database (RDB), graph database (GDB) is a more
+intuitive expression of the real world. Each node in the GDB is a both storage
+and logic unit. Since it is connected to its neighboring nodes through edges,
+and its neighboring information could be easily obtained in one-step graph
+traversal. It is able to conduct local computation independently and all nodes
+can do their local work in parallel. Then the whole system can be maximally
+analyzed and assessed in parallel to largely improve the computation
+performance without sacrificing the precision of final results. This paper
+firstly introduces graph database, power system graph modeling and potential
+graph computing applications in power systems. Two iterative methods based on
+graph database and PageRank are presented and their convergence are discussed.
+Vertex contraction is proposed to improve the performance by eliminating
+zero-impedance branch. A combination of the two iterative methods is proposed
+to make use of their advantages. Testing results based on a provincial 1425-bus
+system demonstrate that the proposed comprehensive approach is a good candidate
+for power flow analysis.
+",1,0,0,0,0,0
+17863,A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm,"  In this paper, we present an algorithm for the sparse signal recovery problem
+that incorporates damped Gaussian generalized approximate message passing
+(GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning
+(SBL). In particular, GGAMP is used to implement the E-step in SBL in place of
+matrix inversion, leveraging the fact that GGAMP is guaranteed to converge with
+appropriate damping. The resulting GGAMP-SBL algorithm is much more robust to
+arbitrary measurement matrix $\boldsymbol{A}$ than the standard damped GAMP
+algorithm while being much lower complexity than the standard SBL algorithm. We
+then extend the approach from the single measurement vector (SMV) case to the
+temporally correlated multiple measurement vector (MMV) case, leading to the
+GGAMP-TSBL algorithm. We verify the robustness and computational advantages of
+the proposed algorithms through numerical experiments.
+",1,0,0,1,0,0
+17864,Sub-Nanometer Channels Embedded in Two-Dimensional Materials,"  Two-dimensional (2D) materials are among the most promising candidates for
+next-generation electronics due to their atomic thinness, allowing for flexible
+transparent electronics and ultimate length scaling. Thus far, atomically-thin
+p-n junctions, metal-semiconductor contacts, and metal-insulator barriers have
+been demonstrated. While 2D materials achieve the thinnest possible devices,
+precise nanoscale control over the lateral dimensions is also necessary. Here,
+we report the direct synthesis of sub-nanometer-wide 1D MoS2 channels embedded
+within WSe2 monolayers, using a dislocation-catalyzed approach. The 1D channels
+have edges free of misfit dislocations and dangling bonds, forming a coherent
+interface with the embedding 2D matrix. Periodic dislocation arrays produce 2D
+superlattices of coherent MoS2 1D channels in WSe2. Using molecular dynamics
+simulations, we have identified other combinations of 2D materials where 1D
+channels can also be formed. The electronic band structure of these 1D channels
+offer the promise of carrier confinement in a direct-gap material and charge
+separation needed to access the ultimate length scales necessary for future
+electronic applications.
+",0,1,0,0,0,0
+17865,Emotion in Reinforcement Learning Agents and Robots: A Survey,"  This article provides the first survey of computational models of emotion in
+reinforcement learning (RL) agents. The survey focuses on agent/robot emotions,
+and mostly ignores human user emotions. Emotions are recognized as functional
+in decision-making by influencing motivation and action selection. Therefore,
+computational emotion models are usually grounded in the agent's decision
+making architecture, of which RL is an important subclass. Studying emotions in
+RL-based agents is useful for three research fields. For machine learning (ML)
+researchers, emotion models may improve learning efficiency. For the
+interactive ML and human-robot interaction (HRI) community, emotions can
+communicate state and enhance user investment. Lastly, it allows affective
+modelling (AM) researchers to investigate their emotion theories in a
+successful AI agent class. This survey provides background on emotion theory
+and RL. It systematically addresses 1) from what underlying dimensions (e.g.,
+homeostasis, appraisal) emotions can be derived and how these can be modelled
+in RL-agents, 2) what types of emotions have been derived from these
+dimensions, and 3) how these emotions may either influence the learning
+efficiency of the agent or be useful as social signals. We also systematically
+compare evaluation criteria, and draw connections to important RL sub-domains
+like (intrinsic) motivation and model-based RL. In short, this survey provides
+both a practical overview for engineers wanting to implement emotions in their
+RL agents, and identifies challenges and directions for future emotion-RL
+research.
+",1,0,0,1,0,0
+17866,Tensor Networks in a Nutshell,"  Tensor network methods are taking a central role in modern quantum physics
+and beyond. They can provide an efficient approximation to certain classes of
+quantum states, and the associated graphical language makes it easy to describe
+and pictorially reason about quantum circuits, channels, protocols, open
+systems and more. Our goal is to explain tensor networks and some associated
+methods as quickly and as painlessly as possible. Beginning with the key
+definitions, the graphical tensor network language is presented through
+examples. We then provide an introduction to matrix product states. We conclude
+the tutorial with tensor contractions evaluating combinatorial counting
+problems. The first one counts the number of solutions for Boolean formulae,
+whereas the second is Penrose's tensor contraction algorithm, returning the
+number of $3$-edge-colorings of $3$-regular planar graphs.
+",0,1,0,0,0,0
+17867,Cheryl's Birthday,"  We present four logic puzzles and after that their solutions. Joseph Yeo
+designed 'Cheryl's Birthday'. Mike Hartley came up with a novel solution for
+'One Hundred Prisoners and a Light Bulb'. Jonathan Welton designed 'A Blind
+Guess' and 'Abby's Birthday'. Hans van Ditmarsch and Barteld Kooi authored the
+puzzlebook 'One Hundred Prisoners and a Light Bulb' that contains other
+knowledge puzzles, and that can also be found on the webpage
+this http URL dedicated to the book.
+",1,0,0,0,0,0
+17868,Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow,"  Exploration bonus derived from the novelty of the states in an environment
+has become a popular approach to motivate exploration for deep reinforcement
+learning agents in the past few years. Recent methods such as curiosity-driven
+exploration usually estimate the novelty of new observations by the prediction
+errors of their system dynamics models. Due to the capacity limitation of the
+models and difficulty of performing next-frame prediction, however, these
+methods typically fail to balance between exploration and exploitation in
+high-dimensional observation tasks, resulting in the agents forgetting the
+visited paths and exploring those states repeatedly. Such inefficient
+exploration behavior causes significant performance drops, especially in large
+environments with sparse reward signals. In this paper, we propose to introduce
+the concept of optical flow estimation from the field of computer vision to
+deal with the above issue. We propose to employ optical flow estimation errors
+to examine the novelty of new observations, such that agents are able to
+memorize and understand the visited states in a more comprehensive fashion. We
+compare our method against the previous approaches in a number of experimental
+experiments. Our results indicate that the proposed method appears to deliver
+superior and long-lasting performance than the previous methods. We further
+provide a set of comprehensive ablative analysis of the proposed method, and
+investigate the impact of optical flow estimation on the learning curves of the
+DRL agents.
+",1,0,0,0,0,0
+17869,Lenient Multi-Agent Deep Reinforcement Learning,"  Much of the success of single agent deep reinforcement learning (DRL) in
+recent years can be attributed to the use of experience replay memories (ERM),
+which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling
+stored state transitions. However, care is required when using ERMs for
+multi-agent deep reinforcement learning (MA-DRL), as stored transitions can
+become outdated because agents update their policies in parallel [11]. In this
+work we apply leniency [23] to MA-DRL. Lenient agents map state-action pairs to
+decaying temperature values that control the amount of leniency applied towards
+negative policy updates that are sampled from the ERM. This introduces optimism
+in the value-function update, and has been shown to facilitate cooperation in
+tabular fully-cooperative multi-agent reinforcement learning problems. We
+evaluate our Lenient-DQN (LDQN) empirically against the related Hysteretic-DQN
+(HDQN) algorithm [22] as well as a modified version we call scheduled-HDQN,
+that uses average reward learning near terminal states. Evaluations take place
+in extended variations of the Coordinated Multi-Agent Object Transportation
+Problem (CMOTP) [8] which include fully-cooperative sub-tasks and stochastic
+rewards. We find that LDQN agents are more likely to converge to the optimal
+policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN
+agents.
+",1,0,0,0,0,0
+17870,A New Framework for Synthetic Aperture Sonar Micronavigation,"  Synthetic aperture imaging systems achieve constant azimuth resolution by
+coherently summating the observations acquired along the aperture path. At this
+aim, their locations have to be known with subwavelength accuracy. In
+underwater Synthetic Aperture Sonar (SAS), the nature of propagation and
+navigation in water makes the retrieval of this information challenging.
+Inertial sensors have to be employed in combination with signal processing
+techniques, which are usually referred to as micronavigation. In this paper we
+propose a novel micronavigation approach based on the minimization of an error
+function between two contiguous pings having some mutual information. This
+error is obtained by comparing the vector space intersections between the pings
+orthogonal projectors. The effectiveness and generality of the proposed
+approach is demonstrated by means of simulations and by means of an experiment
+performed in a controlled environment.
+",1,0,0,0,0,0
+17871,Emotionalism within People-Oriented Software Design,"  In designing most software applications, much effort is placed upon the
+functional goals, which make a software system useful. However, the failure to
+consider emotional goals, which make a software system pleasurable to use, can
+result in disappointment and system rejection even if utilitarian goals are
+well implemented. Although several studies have emphasized the importance of
+people's emotional goals in developing software, there is little advice on how
+to address these goals in the software system development process. This paper
+proposes a theoretically-sound and practical method by combining the theories
+and techniques of software engineering, requirements engineering, and decision
+making. The outcome of this study is the Emotional Goal Systematic Analysis
+Technique (EG-SAT), which facilitates the process of finding software system
+capabilities to address emotional goals in software design. EG-SAT is easy to
+learn and easy to use technique that helps analysts to gain insights into how
+to address people's emotional goals. To demonstrate the method in use, a
+two-part evaluation is conducted. First, EG-SAT is used to analyze the
+emotional goals of potential users of a mobile learning application that
+provides information about low carbon living for tradespeople and professionals
+in the building industry in Australia. The results of using EG-SAT in this case
+study are compared with a professionally-developed baseline. Second, we ran a
+semi-controlled experiment in which 12 participants were asked to apply EG-SAT
+and another technique on part of our case study. The outcomes show that EG-SAT
+helped participants to both analyse emotional goals and gain valuable insights
+about the functional and non-functional goals for addressing people's emotional
+goals.
+",1,0,0,0,0,0
+17872,Quantifying the Estimation Error of Principal Components,"  Principal component analysis is an important pattern recognition and
+dimensionality reduction tool in many applications. Principal components are
+computed as eigenvectors of a maximum likelihood covariance $\widehat{\Sigma}$
+that approximates a population covariance $\Sigma$, and these eigenvectors are
+often used to extract structural information about the variables (or
+attributes) of the studied population. Since PCA is based on the
+eigendecomposition of the proxy covariance $\widehat{\Sigma}$ rather than the
+ground-truth $\Sigma$, it is important to understand the approximation error in
+each individual eigenvector as a function of the number of available samples.
+The recent results of Kolchinskii and Lounici yield such bounds. In the present
+paper we sharpen these bounds and show that eigenvectors can often be
+reconstructed to a required accuracy from a sample of strictly smaller size
+order.
+",0,0,1,1,0,0
+17873,On Convex Programming Relaxations for the Permanent,"  In recent years, several convex programming relaxations have been proposed to
+estimate the permanent of a non-negative matrix, notably in the works of
+Gurvits and Samorodnitsky. However, the origins of these relaxations and their
+relationships to each other have remained somewhat mysterious. We present a
+conceptual framework, implicit in the belief propagation literature, to
+systematically arrive at these convex programming relaxations for estimating
+the permanent -- as approximations to an exponential-sized max-entropy convex
+program for computing the permanent. Further, using standard convex programming
+techniques such as duality, we establish equivalence of these aforementioned
+relaxations to those based on capacity-like quantities studied by Gurvits and
+Anari et al.
+",1,0,1,0,0,0
+17874,Many cubic surfaces contain rational points,"  Building on recent work of Bhargava--Elkies--Schnidman and Kriz--Li, we
+produce infinitely many smooth cubic surfaces defined over the field of
+rational numbers that contain rational points.
+",0,0,1,0,0,0
+17875,Representation learning of drug and disease terms for drug repositioning,"  Drug repositioning (DR) refers to identification of novel indications for the
+approved drugs. The requirement of huge investment of time as well as money and
+risk of failure in clinical trials have led to surge in interest in drug
+repositioning. DR exploits two major aspects associated with drugs and
+diseases: existence of similarity among drugs and among diseases due to their
+shared involved genes or pathways or common biological effects. Existing
+methods of identifying drug-disease association majorly rely on the information
+available in the structured databases only. On the other hand, abundant
+information available in form of free texts in biomedical research articles are
+not being fully exploited. Word-embedding or obtaining vector representation of
+words from a large corpora of free texts using neural network methods have been
+shown to give significant performance for several natural language processing
+tasks. In this work we propose a novel way of representation learning to obtain
+features of drugs and diseases by combining complementary information available
+in unstructured texts and structured datasets. Next we use matrix completion
+approach on these feature vectors to learn projection matrix between drug and
+disease vector spaces. The proposed method has shown competitive performance
+with state-of-the-art methods. Further, the case studies on Alzheimer's and
+Hypertension diseases have shown that the predicted associations are matching
+with the existing knowledge.
+",1,0,0,0,0,0
+17876,Analysis of a remarkable singularity in a nonlinear DDE,"  In this work we investigate the dynamics of the nonlinear DDE
+(delay-differential equation)
+x''(t)+x(t-T)+x(t)^3=0
+where T is the delay. For T=0 this system is conservative and exhibits no
+limit cycles. For T>0, no matter how small, an infinite number of limit cycles
+exist, their amplitudes going to infinity in the limit as T approaches zero.
+We investigate this situation in three ways: 1) Harmonic Balance, 2)
+Melnikov's integral, and 3) Adding damping to regularize the singularity.
+",0,1,1,0,0,0
+17877,Characterization of Lipschitz functions in terms of variable exponent Lebesgue spaces,"  Our aim is to characterize the Lipschitz functions by variable exponent
+Lebesgue spaces. We give some characterizations of the boundedness of the
+maximal or nonlinear commutators of the Hardy-Littlewood maximal function and
+sharp maximal function in variable exponent Lebesgue spaces when the symbols
+$b$ belong to the Lipschitz spaces, by which some new characterizations of
+Lipschitz spaces and nonnegative Lipschitz functions are obtained. Some
+equivalent relations between the Lipschitz norm and the variable exponent
+Lebesgue norm are also given.
+",0,0,1,0,0,0
+17878,Superconductivity Induced by Interfacial Coupling to Magnons,"  We consider a thin normal metal sandwiched between two ferromagnetic
+insulators. At the interfaces, the exchange coupling causes electrons within
+the metal to interact with magnons in the insulators. This electron-magnon
+interaction induces electron-electron interactions, which, in turn, can result
+in p-wave superconductivity. In the weak-coupling limit, we solve the gap
+equation numerically and estimate the critical temperature. In YIG-Au-YIG
+trilayers, superconductivity sets in at temperatures somewhere in the interval
+between 1 and 10 K. EuO-Au-EuO trilayers require a lower temperature, in the
+range from 0.01 to 1 K.
+",0,1,0,0,0,0
+17879,Privacy Assessment of De-identified Opal Data: A report for Transport for NSW,"  We consider the privacy implications of public release of a de-identified
+dataset of Opal card transactions. The data was recently published at
+this https URL. It
+consists of tap-on and tap-off counts for NSW's four modes of public transport,
+collected over two separate week-long periods. The data has been further
+treated to improve privacy by removing small counts, aggregating some stops and
+routes, and perturbing the counts. This is a summary of our findings.
+",1,0,0,0,0,0
+17880,On the Importance of Correlations in Rational Choice: A Case for Non-Nashian Game Theory,"  The Nash equilibrium paradigm, and Rational Choice Theory in general, rely on
+agents acting independently from each other. This note shows how this
+assumption is crucial in the definition of Rational Choice Theory. It explains
+how a consistent Alternate Rational Choice Theory, as suggested by Jean-Pierre
+Dupuy, can be built on the exact opposite assumption, and how it provides a
+viable account for alternate, actually observed behavior of rational agents
+that is based on correlations between their decisions.
+The end goal of this note is three-fold: (i) to motivate that the Perfect
+Prediction Equilibrium, implementing Dupuy's notion of projected time and
+previously called ""projected equilibrium"", is a reasonable approach in certain
+real situations and a meaningful complement to the Nash paradigm, (ii) to
+summarize common misconceptions about this equilibrium, and (iii) to give a
+concise motivation for future research on non-Nashian game theory.
+",1,0,0,0,0,0
+17881,Reliable estimation of prediction uncertainty for physico-chemical property models,"  The predictions of parameteric property models and their uncertainties are
+sensitive to systematic errors such as inconsistent reference data, parametric
+model assumptions, or inadequate computational methods. Here, we discuss the
+calibration of property models in the light of bootstrapping, a sampling method
+akin to Bayesian inference that can be employed for identifying systematic
+errors and for reliable estimation of the prediction uncertainty. We apply
+bootstrapping to assess a linear property model linking the 57Fe Moessbauer
+isomer shift to the contact electron density at the iron nucleus for a diverse
+set of 44 molecular iron compounds. The contact electron density is calculated
+with twelve density functionals across Jacob's ladder (PWLDA, BP86, BLYP, PW91,
+PBE, M06-L, TPSS, B3LYP, B3PW91, PBE0, M06, TPSSh). We provide systematic-error
+diagnostics and reliable, locally resolved uncertainties for isomer-shift
+predictions. Pure and hybrid density functionals yield average prediction
+uncertainties of 0.06-0.08 mm/s and 0.04-0.05 mm/s, respectively, the latter
+being close to the average experimental uncertainty of 0.02 mm/s. Furthermore,
+we show that both model parameters and prediction uncertainty depend
+significantly on the composition and number of reference data points.
+Accordingly, we suggest that rankings of density functionals based on
+performance measures (e.g., the coefficient of correlation, r2, or the
+root-mean-square error, RMSE) should not be inferred from a single data set.
+This study presents the first statistically rigorous calibration analysis for
+theoretical Moessbauer spectroscopy, which is of general applicability for
+physico-chemical property models and not restricted to isomer-shift
+predictions. We provide the statistically meaningful reference data set MIS39
+and a new calibration of the isomer shift based on the PBE0 functional.
+",0,1,0,0,0,0
+17882,Rethinking Split Manufacturing: An Information-Theoretic Approach with Secure Layout Techniques,"  Split manufacturing is a promising technique to defend against fab-based
+malicious activities such as IP piracy, overbuilding, and insertion of hardware
+Trojans. However, a network flow-based proximity attack, proposed by Wang et
+al. (DAC'16) [1], has demonstrated that most prior art on split manufacturing
+is highly vulnerable. Here in this work, we present two practical layout
+techniques towards secure split manufacturing: (i) gate-level graph coloring
+and (ii) clustering of same-type gates. Our approach shows promising results
+against the advanced proximity attack, lowering its success rate by 5.27x,
+3.19x, and 1.73x on average compared to the unprotected layouts when splitting
+at metal layers M1, M2, and M3, respectively. Also, it largely outperforms
+previous defense efforts; we observe on average 8x higher resilience when
+compared to representative prior art. At the same time, extensive simulations
+on ISCAS'85 and MCNC benchmarks reveal that our techniques incur an acceptable
+layout overhead. Apart from this empirical study, we provide---for the first
+time---a theoretical framework for quantifying the layout-level resilience
+against any proximity-induced information leakage. Towards this end, we
+leverage the notion of mutual information and provide extensive results to
+validate our model.
+",1,0,0,0,0,0
+17883,Unsupervised robotic sorting: Towards autonomous decision making robots,"  Autonomous sorting is a crucial task in industrial robotics which can be very
+challenging depending on the expected amount of automation. Usually, to decide
+where to sort an object, the system needs to solve either an instance retrieval
+(known object) or a supervised classification (predefined set of classes)
+problem. In this paper, we introduce a new decision making module, where the
+robotic system chooses how to sort the objects in an unsupervised way. We call
+this problem Unsupervised Robotic Sorting (URS) and propose an implementation
+on an industrial robotic system, using deep CNN feature extraction and standard
+clustering algorithms. We carry out extensive experiments on various standard
+datasets to demonstrate the efficiency of the proposed image clustering
+pipeline. To evaluate the robustness of our URS implementation, we also
+introduce a complex real world dataset containing images of objects under
+various background and lighting conditions. This dataset is used to fine tune
+the design choices (CNN and clustering algorithm) for URS. Finally, we propose
+a method combining our pipeline with ensemble clustering to use multiple images
+of each object. This redundancy of information about the objects is shown to
+increase the clustering results.
+",1,0,0,0,0,0
+17884,Beyond Whittle: Nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis,"  The Whittle likelihood is widely used for Bayesian nonparametric estimation
+of the spectral density of stationary time series. However, the loss of
+efficiency for non-Gaussian time series can be substantial. On the other hand,
+parametric methods are more powerful if the model is well-specified, but may
+fail entirely otherwise. Therefore, we suggest a nonparametric correction of a
+parametric likelihood taking advantage of the efficiency of parametric models
+while mitigating sensitivities through a nonparametric amendment. Using a
+Bernstein-Dirichlet prior for the nonparametric spectral correction, we show
+posterior consistency and illustrate the performance of our procedure in a
+simulation study and with LIGO gravitational wave data.
+",0,0,0,1,0,0
+17885,A consistent approach to unstructured mesh generation for geophysical models,"  Geophysical model domains typically contain irregular, complex fractal-like
+boundaries and physical processes that act over a wide range of scales.
+Constructing geographically constrained boundary-conforming spatial
+discretizations of these domains with flexible use of anisotropically, fully
+unstructured meshes is a challenge. The problem contains a wide range of scales
+and a relatively large, heterogeneous constraint parameter space. Approaches
+are commonly ad hoc, model or application specific and insufficiently
+described. Development of new spatial domains is frequently time-consuming,
+hard to repeat, error prone and difficult to ensure consistent due to the
+significant human input required. As a consequence, it is difficult to
+reproduce simulations, ensure a provenance in model data handling and
+initialization, and a challenge to conduct model intercomparisons rigorously.
+Moreover, for flexible unstructured meshes, there is additionally a greater
+potential for inconsistencies in model initialization and forcing parameters.
+This paper introduces a consistent approach to unstructured mesh generation for
+geophysical models, that is automated, quick-to-draft and repeat, and provides
+a rigorous and robust approach that is consistent to the source data
+throughout. The approach is enabling further new research in complex
+multi-scale domains, difficult or not possible to achieve with existing
+methods. Examples being actively pursued in a range of geophysical modeling
+efforts are presented alongside the approach, together with the implementation
+library Shingle and a selection of its verification test cases.
+",1,1,0,0,0,0
+17886,Metric Map Merging using RFID Tags & Topological Information,"  A map merging component is crucial for the proper functionality of a
+multi-robot system performing exploration, since it provides the means to
+integrate and distribute the most important information carried by the agents:
+the explored-covered space and its exact (depending on the SLAM accuracy)
+morphology. Map merging is a prerequisite for an intelligent multi-robot team
+aiming to deploy a smart exploration technique. In the current work, a metric
+map merging approach based on environmental information is proposed, in
+conjunction with spatially scattered RFID tags localization. This approach is
+divided into the following parts: the maps approximate rotation calculation via
+the obstacles poses and localized RFID tags, the translation employing the best
+localized common RFID tag and finally the transformation refinement using an
+ICP algorithm.
+",1,0,0,0,0,0
+17887,Learning Local Feature Aggregation Functions with Backpropagation,"  This paper introduces a family of local feature aggregation functions and a
+novel method to estimate their parameters, such that they generate optimal
+representations for classification (or any task that can be expressed as a cost
+function minimization problem). To achieve that, we compose the local feature
+aggregation function with the classifier cost function and we backpropagate the
+gradient of this cost function in order to update the local feature aggregation
+function parameters. Experiments on synthetic datasets indicate that our method
+discovers parameters that model the class-relevant information in addition to
+the local feature space. Further experiments on a variety of motion and visual
+descriptors, both on image and video datasets, show that our method outperforms
+other state-of-the-art local feature aggregation functions, such as Bag of
+Words, Fisher Vectors and VLAD, by a large margin.
+",1,0,0,1,0,0
+17888,Sub-harmonic Injection Locking in Metronomes,"  In this paper, we demonstrate sub-harmonic injection locking (SHIL) in
+mechanical metronomes. To do so, we first formulate metronome's physical
+compact model, focusing on its nonlinear terms for friction and the escapement
+mechanism. Then we analyze metronomes using phase-macromodel-based techniques
+and show that the phase of their oscillation is in fact very immune to periodic
+perturbation at twice its natural frequency, making SHIL difficult. Guided by
+the phase-macromodel-based analysis, we are able to modify the escapement
+mechanism of metronomes such that SHIL can happen more easily. Then we verify
+the occurrence of SHIL in experiments. To our knowledge, this is the first
+demonstration of SHIL in metronomes; As such, it provides many valuable
+insights into the modelling, simulation, analysis and design of nonlinear
+oscillators. The demonstration is also suitable to use for teaching the subject
+of injection locking and SHIL.
+",0,1,0,0,0,0
+17889,Spin Seebeck effect in a polar antiferromagnet $α$-Cu$_{2}$V$_{2}$O$_{7}$,"  We have studied the longitudinal spin Seebeck effect in a polar
+antiferromagnet $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$ in contact with a Pt film.
+Below the antiferromagnetic transition temperature of
+$\alpha$-Cu$_{2}$V$_{2}$O$_{7}$, spin Seebeck voltages whose magnetic field
+dependence is similar to that reported in antiferromagnetic MnF$_{2}$$\mid$Pt
+bilayers are observed. Though a small weak-ferromagnetic moment appears owing
+to the Dzyaloshinskii-Moriya interaction in $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$,
+the magnetic field dependence of spin Seebeck voltages is found to be
+irrelevant to the weak ferromagnetic moments. The dependences of the spin
+Seebeck voltages on magnetic fields and temperature are analyzed by a magnon
+spin current theory. The numerical calculation of spin Seebeck voltages using
+magnetic parameters of $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$ determined by previous
+neutron scattering studies reveals that the magnetic-field and temperature
+dependences of the spin Seebeck voltages for
+$\alpha$-Cu$_{2}$V$_{2}$O$_{7}$$\mid$Pt are governed by the changes in magnon
+lifetimes with magnetic fields and temperature.
+",0,1,0,0,0,0
+17890,Mackey algebras which are Gorenstein,"  We complete the picture available in the literature by showing that the
+integral Mackey algebra is Gorenstein if and only if the group order is
+square-free, in which case it must have Gorenstein dimension one. We illustrate
+this result by looking in details at the examples of the cyclic group of order
+four and the Klein four group.
+",0,0,1,0,0,0
+17891,Efficient acquisition rules for model-based approximate Bayesian computation,"  Approximate Bayesian computation (ABC) is a method for Bayesian inference
+when the likelihood is unavailable but simulating from the model is possible.
+However, many ABC algorithms require a large number of simulations, which can
+be costly. To reduce the computational cost, Bayesian optimisation (BO) and
+surrogate models such as Gaussian processes have been proposed. Bayesian
+optimisation enables one to intelligently decide where to evaluate the model
+next but common BO strategies are not designed for the goal of estimating the
+posterior distribution. Our paper addresses this gap in the literature. We
+propose to compute the uncertainty in the ABC posterior density, which is due
+to a lack of simulations to estimate this quantity accurately, and define a
+loss function that measures this uncertainty. We then propose to select the
+next evaluation location to minimise the expected loss. Experiments show that
+the proposed method often produces the most accurate approximations as compared
+to common BO strategies.
+",0,0,0,1,0,0
+17892,Active Learning for Regression Using Greedy Sampling,"  Regression problems are pervasive in real-world applications. Generally a
+substantial amount of labeled samples are needed to build a regression model
+with good generalization ability. However, many times it is relatively easy to
+collect a large number of unlabeled samples, but time-consuming or expensive to
+label them. Active learning for regression (ALR) is a methodology to reduce the
+number of labeled samples, by selecting the most beneficial ones to label,
+instead of random selection. This paper proposes two new ALR approaches based
+on greedy sampling (GS). The first approach (GSy) selects new samples to
+increase the diversity in the output space, and the second (iGS) selects new
+samples to increase the diversity in both input and output spaces. Extensive
+experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15
+subjects on EEG-based driver drowsiness estimation, verified their
+effectiveness and robustness.
+",0,0,0,1,0,0
+17893,Centroid estimation based on symmetric KL divergence for Multinomial text classification problem,"  We define a new method to estimate centroid for text classification based on
+the symmetric KL-divergence between the distribution of words in training
+documents and their class centroids. Experiments on several standard data sets
+indicate that the new method achieves substantial improvements over the
+traditional classifiers.
+",0,0,0,1,0,0
+17894,Rapid Near-Neighbor Interaction of High-dimensional Data via Hierarchical Clustering,"  Calculation of near-neighbor interactions among high dimensional, irregularly
+distributed data points is a fundamental task to many graph-based or
+kernel-based machine learning algorithms and applications. Such calculations,
+involving large, sparse interaction matrices, expose the limitation of
+conventional data-and-computation reordering techniques for improving space and
+time locality on modern computer memory hierarchies. We introduce a novel
+method for obtaining a matrix permutation that renders a desirable sparsity
+profile. The method is distinguished by the guiding principle to obtain a
+profile that is block-sparse with dense blocks. Our profile model and measure
+capture the essential properties affecting space and time locality, and permit
+variation in sparsity profile without imposing a restriction to a fixed
+pattern. The second distinction lies in an efficient algorithm for obtaining a
+desirable profile, via exploring and exploiting multi-scale cluster structure
+hidden in but intrinsic to the data. The algorithm accomplishes its task with
+key components for lower-dimensional embedding with data-specific principal
+feature axes, hierarchical data clustering, multi-level matrix compression
+storage, and multi-level interaction computations. We provide experimental
+results from case studies with two important data analysis algorithms. The
+resulting performance is remarkably comparable to the BLAS performance for the
+best-case interaction governed by a regularly banded matrix with the same
+sparsity.
+",1,0,0,0,0,0
+17895,Evolution and Recent Developments of the Gaseous Photon Detectors Technologies,"  The evolution and the present status of the gaseous photon detectors
+technologies are reviewed. The most recent developments in several branches of
+the field are described, in particular the installation and commissioning of
+the first large area MPGD-based detectors of single photons on COMPASS RICH-1.
+Investigation of novel detector architectures, different materials and various
+applications are reported, and the quest for visible light gaseous photon
+detectors is discussed. The progress on the use of gaseous photon detector
+related techniques in the field of cryogenic applications and gaseous or liquid
+scintillation imaging are presented.
+",0,1,0,0,0,0
+17896,Superconducting Qubit-Resonator-Atom Hybrid System,"  We propose a hybrid quantum system, where an $LC$ resonator inductively
+interacts with a flux qubit and is capacitively coupled to a Rydberg atom.
+Varying the external magnetic flux bias controls the flux-qubit flipping and
+the flux qubit-resonator interface. The atomic spectrum is tuned via an
+electrostatic field, manipulating the qubit-state transition of atom and the
+atom-resonator coupling. Different types of entanglement of superconducting,
+photonic, and atomic qubits can be prepared via simply tuning the flux bias and
+electrostatic field, leading to the implementation of three-qubit Toffoli logic
+gate.
+",0,1,0,0,0,0
+17897,Heroes and Zeroes: Predicting the Impact of New Video Games on Twitch.tv,"  Video games and the playing thereof have been a fixture of American culture
+since their introduction in the arcades of the 1980s. However, it was not until
+the recent proliferation of broadband connections robust and fast enough to
+handle live video streaming that players of video games have transitioned from
+a content consumer role to a content producer role. Simultaneously, the rise of
+social media has revealed how interpersonal connections drive user engagement
+and interest. In this work, we discuss the recent proliferation of video game
+streaming, particularly on Twitch.tv, analyze trends and patterns in video game
+viewing, and develop predictive models for determining if a new game will have
+substantial impact on the streaming ecosystem.
+",1,0,0,0,0,0
+17898,Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study,"  Learning the latent representation of data in unsupervised fashion is a very
+interesting process that provides relevant features for enhancing the
+performance of a classifier. For speech emotion recognition tasks, generating
+effective features is crucial. Currently, handcrafted features are mostly used
+for speech emotion recognition, however, features learned automatically using
+deep learning have shown strong success in many problems, especially in image
+processing. In particular, deep generative models such as Variational
+Autoencoders (VAEs) have gained enormous success for generating features for
+natural images. Inspired by this, we propose VAEs for deriving the latent
+representation of speech signals and use this representation to classify
+emotions. To the best of our knowledge, we are the first to propose VAEs for
+speech emotion classification. Evaluations on the IEMOCAP dataset demonstrate
+that features learned by VAEs can produce state-of-the-art results for speech
+emotion classification.
+",1,0,0,1,0,0
+17899,Insense: Incoherent Sensor Selection for Sparse Signals,"  Sensor selection refers to the problem of intelligently selecting a small
+subset of a collection of available sensors to reduce the sensing cost while
+preserving signal acquisition performance. The majority of sensor selection
+algorithms find the subset of sensors that best recovers an arbitrary signal
+from a number of linear measurements that is larger than the dimension of the
+signal. In this paper, we develop a new sensor selection algorithm for sparse
+(or near sparse) signals that finds a subset of sensors that best recovers such
+signals from a number of measurements that is much smaller than the dimension
+of the signal. Existing sensor selection algorithms cannot be applied in such
+situations. Our proposed Incoherent Sensor Selection (Insense) algorithm
+minimizes a coherence-based cost function that is adapted from recent results
+in sparse recovery theory. Using six datasets, including two real-world
+datasets on microbial diagnostics and structural health monitoring, we
+demonstrate the superior performance of Insense for sparse-signal sensor
+selection.
+",1,0,0,0,0,0
+17900,Discrete Distribution for a Wiener Process Range and its Properties,"  We introduce the discrete distribution of a Wiener process range. Rather than
+finding some basic distributional properties including hazard rate function,
+moments, Stress-strength parameter and order statistics of this distribution,
+this work studies some basic properties of the truncated version of this
+distribution. The effectiveness of this distribution is established using a
+data set.
+",0,0,1,1,0,0
+17901,Coherent control of flexural vibrations in dual-nanoweb fibers using phase-modulated two-frequency light,"  Coherent control of the resonant response in spatially extended
+optomechanical structures is complicated by the fact that the optical drive is
+affected by the back-action from the generated phonons. Here we report a new
+approach to coherent control based on stimulated Raman-like scattering, in
+which the optical pressure can remain unaffected by the induced vibrations even
+in the regime of strong optomechanical interactions. We demonstrate
+experimentally coherent control of flexural vibrations simultaneously along the
+whole length of a dual-nanoweb fiber, by imprinting steps in the relative phase
+between the components of a two-frequency pump signal,the beat frequency being
+chosen to match a flexural resonance. Furthermore, sequential switching of the
+relative phase at time intervals shorter than the lifetime of the vibrations
+reduces their amplitude to a constant value that is fully adjustable by tuning
+the phase-modulation depth and switching rate. The results may trigger new
+developments in silicon photonics, since such coherent control uniquely
+decouples the amplitude of optomechanical oscillations from power-dependent
+thermal effects and nonlinear optical loss.
+",0,1,0,0,0,0
+17902,Static and Dynamic Magnetic Properties of FeMn/Pt Multilayers,"  Recently we have demonstrated the presence of spin-orbit toque in FeMn/Pt
+multilayers which, in combination with the anisotropy field, is able to rotate
+its magnetization consecutively from 0o to 360o without any external field.
+Here, we report on an investigation of static and dynamic magnetic properties
+of FeMn/Pt multilayers using combined techniques of magnetometry, ferromagnetic
+resonance, inverse spin Hall effect and spin Hall magnetoresistance
+measurements. The FeMn/Pt multilayer was found to exhibit ferromagnetic
+properties, and its temperature dependence of saturation magnetization can be
+fitted well using a phenomenological model by including a finite distribution
+in Curie temperature due to subtle thickness variations across the multilayer
+samples. The non-uniformity in static magnetic properties is also manifested in
+the ferromagnetic resonance spectra, which typically exhibit a broad resonance
+peak. A damping parameter of around 0.106 is derived from the frequency
+dependence of ferromagnetic resonance linewidth, which is comparable to the
+reported values for other types of Pt-based multilayers. Clear inverse spin
+Hall signals and spin Hall magnetoresistance have been observed in all samples
+below the Curie temperature, which corroborate the strong spin-orbit torque
+effect observed previously.
+",0,1,0,0,0,0
+17903,Ultraproducts of crossed product von Neumann algebras,"  We study a relationship between the ultraproduct of a crossed product von
+Neumann algebra and the crossed product of an ultraproduct von Neumann algebra.
+As an application, the continuous core of an ultraproduct von Neumann algebra
+is described.
+",0,0,1,0,0,0
+17904,Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing,"  A fundamental issue for statistical classification models in a streaming
+environment is that the joint distribution between predictor and response
+variables changes over time (a phenomenon also known as concept drifts), such
+that their classification performance deteriorates dramatically. In this paper,
+we first present a hierarchical hypothesis testing (HHT) framework that can
+detect and also adapt to various concept drift types (e.g., recurrent or
+irregular, gradual or abrupt), even in the presence of imbalanced data labels.
+A novel concept drift detector, namely Hierarchical Linear Four Rates (HLFR),
+is implemented under the HHT framework thereafter. By substituting a
+widely-acknowledged retraining scheme with an adaptive training strategy, we
+further demonstrate that the concept drift adaptation capability of HLFR can be
+significantly boosted. The theoretical analysis on the Type-I and Type-II
+errors of HLFR is also performed. Experiments on both simulated and real-world
+datasets illustrate that our methods outperform state-of-the-art methods in
+terms of detection precision, detection delay as well as the adaptability
+across different concept drift types.
+",1,0,0,1,0,0
+17905,Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values,"  The problem of machine learning with missing values is common in many areas.
+A simple approach is to first construct a dataset without missing values simply
+by discarding instances with missing entries or by imputing a fixed value for
+each missing entry, and then train a prediction model with the new dataset. A
+drawback of this naive approach is that the uncertainty in the missing entries
+is not properly incorporated in the prediction. In order to evaluate prediction
+uncertainty, the multiple imputation (MI) approach has been studied, but the
+performance of MI is sensitive to the choice of the probabilistic model of the
+true values in the missing entries, and the computational cost of MI is high
+because multiple models must be trained. In this paper, we propose an
+alternative approach called the Interval-based Prediction Uncertainty Bounding
+(IPUB) method. The IPUB method represents the uncertainties due to missing
+entries as intervals, and efficiently computes the lower and upper bounds of
+the prediction results when all possible training sets constructed by imputing
+arbitrary values in the intervals are considered. The IPUB method can be
+applied to a wide class of convex learning algorithms including penalized
+least-squares regression, support vector machine (SVM), and logistic
+regression. We demonstrate the advantages of the IPUB method by comparing it
+with an existing method in numerical experiment with benchmark datasets.
+",0,0,0,1,0,0
+17906,Methods for Interpreting and Understanding Deep Neural Networks,"  This paper provides an entry point to the problem of interpreting a deep
+neural network model and explaining its predictions. It is based on a tutorial
+given at ICASSP 2017. It introduces some recently proposed techniques of
+interpretation, along with theory, tricks and recommendations, to make most
+efficient use of these techniques on real data. It also discusses a number of
+practical applications.
+",1,0,0,1,0,0
+17907,Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents,"  In this digital era, one thing that still holds the convention is a printed
+archive. Printed documents find their use in many critical domains such as
+contract papers, legal tenders and proof of identity documents. As more
+advanced printing, scanning and image editing techniques are becoming
+available, forgeries on these legal tenders pose a serious threat. Ability to
+easily and reliably identify source printer of a printed document can help a
+lot in reducing this menace. During printing procedure, printer hardware
+introduces certain distortions in printed characters' locations and shapes
+which are invisible to naked eyes. These distortions are referred as geometric
+distortions, their profile (or signature) is generally unique for each printer
+and can be used for printer classification purpose. This paper proposes a set
+of features for characterizing text-line-level geometric distortions, referred
+as geometric distortion signatures and presents a novel system to use them for
+identification of the origin of a printed document. Detailed experiments
+performed on a set of thirteen printers demonstrate that the proposed system
+achieves state of the art performance and gives much higher accuracy under
+small training size constraint. For four training and six test pages of three
+different fonts, the proposed method gives 99\% classification accuracy.
+",1,0,0,0,0,0
+17908,The duration of load effect in lumber as stochastic degradation,"  This paper proposes a gamma process for modelling the damage that accumulates
+over time in the lumber used in structural engineering applications when stress
+is applied. The model separates the stochastic processes representing features
+internal to the piece of lumber on the one hand, from those representing
+external forces due to applied dead and live loads. The model applies those
+external forces through a time-varying population level function designed for
+time-varying loads. The application of this type of model, which is standard in
+reliability analysis, is novel in this context, which has been dominated by
+accumulated damage models (ADMs) over more than half a century. The proposed
+model is compared with one of the traditional ADMs. Our statistical results
+based on a Bayesian analysis of experimental data highlight the limitations of
+using accelerated testing data to assess long-term reliability, as seen in the
+wide posterior intervals. This suggests the need for more comprehensive testing
+in future applications, or to encode appropriate expert knowledge in the priors
+used for Bayesian analysis.
+",0,0,0,1,0,0
+17909,Partial Bridging of Vaccine Efficacy to New Populations,"  Suppose one has data from one or more completed vaccine efficacy trials and
+wishes to estimate the efficacy in a new setting. Often logistical or ethical
+considerations make running another efficacy trial impossible. Fortunately, if
+there is a biomarker that is the primary modifier of efficacy, then the
+biomarker-conditional efficacy may be identical in the completed trials and the
+new setting, or at least informative enough to meaningfully bound this
+quantity. Given a sample of this biomarker from the new population, we might
+hope we can bridge the results of the completed trials to estimate the vaccine
+efficacy in this new population. Unfortunately, even knowing the true
+conditional efficacy in the new population fails to identify the marginal
+efficacy due to the unknown conditional unvaccinated risk. We define a curve
+that partially identifies (lower bounds) the marginal efficacy in the new
+population as a function of the population's marginal unvaccinated risk, under
+the assumption that one can identify bounds on the conditional unvaccinated
+risk in the new population. Interpreting the curve only requires identifying
+plausible regions of the marginal unvaccinated risk in the new population. We
+present a nonparametric estimator of this curve and develop valid lower
+confidence bounds that concentrate at a parametric rate. We use vaccine
+terminology throughout, but the results apply to general binary interventions
+and bounded outcomes.
+",0,0,0,1,0,0
+17910,An induced map between rationalized classifying spaces for fibrations,"  Let $B{ aut}_1X$ be the Dold-Lashof classifying space of orientable
+fibrations with fiber $X$. For a rationally weakly trivial map $f:X\to Y$, our
+strictly induced map $a_f: (Baut_1X)_0\to (Baut_1Y)_0$ induces a natural map
+from a $X_0$-fibration to a $Y_0$-fibration. It is given by a map between the
+differential graded Lie algebras of derivations of Sullivan models. We note
+some conditions that the map $a_f$ admits a section and note some relations
+with the Halperin conjecture. Furthermore we give the obstruction class for a
+lifting of a classifying map $h: B\to (Baut_1Y)_0$ and apply it for liftings of
+$G$-actions on $Y$ for a compact connected Lie group $G$ as the case of $B=BG$
+and evaluating of rational toral ranks as $r_0(Y)\leq r_0(X)$.
+",0,0,1,0,0,0
+17911,The Statistical Recurrent Unit,"  Sophisticated gated recurrent neural network architectures like LSTMs and
+GRUs have been shown to be highly effective in a myriad of applications. We
+develop an un-gated unit, the statistical recurrent unit (SRU), that is able to
+learn long term dependencies in data by only keeping moving averages of
+statistics. The SRU's architecture is simple, un-gated, and contains a
+comparable number of parameters to LSTMs; yet, SRUs perform favorably to more
+sophisticated LSTM and GRU alternatives, often outperforming one or both in
+various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an
+unbiased manner by optimizing respective architectures' hyperparameters in a
+Bayesian optimization scheme for both synthetic and real-world tasks.
+",1,0,0,1,0,0
+17912,Many-body localization in the droplet spectrum of the random XXZ quantum spin chain,"  We study many-body localization properties of the disordered XXZ spin chain
+in the Ising phase. Disorder is introduced via a random magnetic field in the
+$z$-direction. We prove a strong form of dynamical exponential clustering for
+eigenstates in the droplet spectrum: For any pair of local observables
+separated by a distance $\ell$, the sum of the associated correlators over
+these states decays exponentially in $\ell$, in expectation. This exponential
+clustering persists under the time evolution in the droplet spectrum. Our
+result applies to the large disorder regime as well as to the strong Ising
+phase at fixed disorder, with bounds independent of the support of the
+observables.
+",0,0,1,0,0,0
+17913,FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices,"  Deep neural networks show great potential as solutions to many sensing
+application problems, but their excessive resource demand slows down execution
+time, pausing a serious impediment to deployment on low-end devices. To address
+this challenge, recent literature focused on compressing neural network size to
+improve performance. We show that changing neural network size does not
+proportionally affect performance attributes of interest, such as execution
+time. Rather, extreme run-time nonlinearities exist over the network
+configuration space. Hence, we propose a novel framework, called FastDeepIoT,
+that uncovers the non-linear relation between neural network structure and
+execution time, then exploits that understanding to find network configurations
+that significantly improve the trade-off between execution time and accuracy on
+mobile and embedded devices. FastDeepIoT makes two key contributions. First,
+FastDeepIoT automatically learns an accurate and highly interpretable execution
+time model for deep neural networks on the target device. This is done without
+prior knowledge of either the hardware specifications or the detailed
+implementation of the used deep learning library. Second, FastDeepIoT informs a
+compression algorithm how to minimize execution time on the profiled device
+without impacting accuracy. We evaluate FastDeepIoT using three different
+sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
+FastDeepIoT further reduces the neural network execution time by $48\%$ to
+$78\%$ and energy consumption by $37\%$ to $69\%$ compared with the
+state-of-the-art compression algorithms.
+",1,0,0,0,0,0
+17914,Robust MPC for tracking of nonholonomic robots with additive disturbances,"  In this paper, two robust model predictive control (MPC) schemes are proposed
+for tracking control of nonholonomic systems with bounded disturbances:
+tube-MPC and nominal robust MPC (NRMPC). In tube-MPC, the control signal
+consists of a control action and a nonlinear feedback law based on the
+deviation of the actual states from the states of a nominal system. It renders
+the actual trajectory within a tube centered along the optimal trajectory of
+the nominal system. Recursive feasibility and input-to-state stability are
+established and the constraints are ensured by tightening the input domain and
+the terminal region. While in NRMPC, an optimal control sequence is obtained by
+solving an optimization problem based on the current state, and the first
+portion of this sequence is applied to the real system in an open-loop manner
+during each sampling period. The state of nominal system model is updated by
+the actual state at each step, which provides additional a feedback. By
+introducing a robust state constraint and tightening the terminal region,
+recursive feasibility and input-to-state stability are guaranteed. Simulation
+results demonstrate the effectiveness of both strategies proposed.
+",1,0,0,0,0,0
+17915,Investigation on the use of Hidden-Markov Models in automatic transcription of music,"  Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data,
+and have been widely used in two main tasks of Automatic Music Transcription
+(AMT): note segmentation, i.e. identifying the played notes after a multi-pitch
+estimation, and sequential post-processing, i.e. correcting note segmentation
+using training data. In this paper, we employ the multi-pitch estimation method
+called Probabilistic Latent Component Analysis (PLCA), and develop AMT systems
+by integrating different HMM-based modules in this framework. For note
+segmentation, we use two different twostate on/o? HMMs, including a
+higher-order one for duration modeling. For sequential post-processing, we
+focused on a musicological modeling of polyphonic harmonic transitions, using a
+first- and second-order HMMs whose states are defined through candidate note
+mixtures. These different PLCA plus HMM systems have been evaluated
+comparatively on two different instrument repertoires, namely the piano (using
+the MAPS database) and the marovany zither. Our results show that the use of
+HMMs could bring noticeable improvements to transcription results, depending on
+the instrument repertoire.
+",1,0,0,1,0,0
+17916,Network archaeology: phase transition in the recoverability of network history,"  Network growth processes can be understood as generative models of the
+structure and history of complex networks. This point of view naturally leads
+to the problem of network archaeology: Reconstructing all the past states of a
+network from its structure---a difficult permutation inference problem. In this
+paper, we introduce a Bayesian formulation of network archaeology, with a
+generalization of preferential attachment as our generative mechanism. We
+develop a sequential importance sampling algorithm to evaluate the posterior
+averages of this model, as well as an efficient heuristic that uncovers the
+history of a network in linear time. We use these methods to identify and
+characterize a phase transition in the quality of the reconstructed history,
+when they are applied to artificial networks generated by the model itself.
+Despite the existence of a no-recovery phase, we find that non-trivial
+inference is possible in a large portion of the parameter space as well as on
+empirical data.
+",0,0,0,1,0,0
+17917,"Big Data, Data Science, and Civil Rights","  Advances in data analytics bring with them civil rights implications.
+Data-driven and algorithmic decision making increasingly determine how
+businesses target advertisements to consumers, how police departments monitor
+individuals or groups, how banks decide who gets a loan and who does not, how
+employers hire, how colleges and universities make admissions and financial aid
+decisions, and much more. As data-driven decisions increasingly affect every
+corner of our lives, there is an urgent need to ensure they do not become
+instruments of discrimination, barriers to equality, threats to social justice,
+and sources of unfairness. In this paper, we argue for a concrete research
+agenda aimed at addressing these concerns, comprising five areas of emphasis:
+(i) Determining if models and modeling procedures exhibit objectionable bias;
+(ii) Building awareness of fairness into machine learning methods; (iii)
+Improving the transparency and control of data- and model-driven decision
+making; (iv) Looking beyond the algorithm(s) for sources of bias and
+unfairness-in the myriad human decisions made during the problem formulation
+and modeling process; and (v) Supporting the cross-disciplinary scholarship
+necessary to do all of that well.
+",1,0,0,0,0,0
+17918,Global well-posedness for 2-D Boussinesq system with the temperature-dependent viscosity and supercritical dissipation,"  The present paper is dedicated to the global well-posedness issue for the
+Boussinesq system with the temperature-dependent viscosity in $\mathbb{R}^2.$
+We aim at extending the work by Abidi and Zhang ( Adv. Math. 2017 (305)
+1202--1249 ) to a supercritical dissipation for temperature.
+",0,0,1,0,0,0
+17919,"CO~($J = 1-0$) Observations of a Filamentary Molecular Cloud in the Galactic Region Centered at $l = 150\arcdeg, b = 3.5\arcdeg$","  We present large-field (4.25~$\times$~3.75 deg$^2$) mapping observations
+toward the Galactic region centered at $l = 150\arcdeg, b = 3.5\arcdeg$ in the
+$J = 1-0$ emission line of CO isotopologues ($^{12}$CO, $^{13}$CO, and
+C$^{18}$O), using the 13.7 m millimeter-wavelength telescope of the Purple
+Mountain Observatory. Based on the $^{13}$CO observations, we reveal a
+filamentary cloud in the Local Arm at a velocity range of $-$0.5 to
+6.5~km~s$^{-1}$. This molecular cloud contains 1 main filament and 11
+sub-filaments, showing the so-called ""ridge-nest"" structure. The main filament
+and three sub-filaments are also detected in the C$^{18}$O line. The velocity
+structures of most identified filaments display continuous distribution with
+slight velocity gradients. The measured median excitation temperature, line
+width, length, width, and linear mass of the filaments are $\sim$9.28~K,
+0.85~km~s$^{-1}$, 7.30~pc, 0.79~pc, and 17.92~$M_\sun$~pc$^{-1}$, respectively,
+assuming a distance of 400~pc. We find that the four filaments detected in the
+C$^{18}$O line are thermally supercritical, and two of them are in the
+virialized state, and thus tend to be gravitationally bound. We identify in
+total 146 $^{13}$CO clumps in the cloud, about 77$\%$ of the clumps are
+distributed along the filaments. About 56$\%$ of the virialized clumps are
+found to be associated with the supercritical filaments. Three young stellar
+object (YSO) candidates are also identified in the supercritical filaments,
+based on the complementary infrared (IR) data. These results indicate that the
+supercritical filaments, especially the virialized filaments, may contain
+star-forming activities.
+",0,1,0,0,0,0
+17920,Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition,"  Unseen data conditions can inflict serious performance degradation on systems
+relying on supervised machine learning algorithms. Because data can often be
+unseen, and because traditional machine learning algorithms are trained in a
+supervised manner, unsupervised adaptation techniques must be used to adapt the
+model to the unseen data conditions. However, unsupervised adaptation is often
+challenging, as one must generate some hypothesis given a model and then use
+that hypothesis to bootstrap the model to the unseen data conditions.
+Unfortunately, reliability of such hypotheses is often poor, given the mismatch
+between the training and testing datasets. In such cases, a model hypothesis
+confidence measure enables performing data selection for the model adaptation.
+Underlying this approach is the fact that for unseen data conditions, data
+variability is introduced to the model, which the model propagates to its
+output decision, impacting decision reliability. In a fully connected network,
+this data variability is propagated as distortions from one layer to the next.
+This work aims to estimate the propagation of such distortion in the form of
+network activation entropy, which is measured over a short- time running window
+on the activation from each neuron of a given hidden layer, and these
+measurements are then used to compute summary entropy. This work demonstrates
+that such an entropy measure can help to select data for unsupervised model
+adaptation, resulting in performance gains in speech recognition tasks. Results
+from standard benchmark speech recognition tasks show that the proposed
+approach can alleviate the performance degradation experienced under unseen
+data conditions by iteratively adapting the model to the unseen datas acoustic
+condition.
+",1,0,0,1,0,0
+17921,Asymmetric Preheating,"  We study the generation of the matter-antimatter asymmetry during bosonic
+preheating, focusing on the sources of the asymmetry. If the asymmetry appears
+in the multiplication factor of the resonant particle production, the
+matter-antimatter ratio will grow during preheating. On the other hand, if the
+asymmetry does not grow during preheating, one has to find out another reason.
+We consider several scenarios for the asymmetric preheating to distinguish the
+sources of the asymmetry. We also discuss a new baryogenesis scenario, in which
+the asymmetry is generated without introducing neither loop corrections nor
+rotation of a field.
+",0,1,0,0,0,0
+17922,A multi-device dataset for urban acoustic scene classification,"  This paper introduces the acoustic scene classification task of DCASE 2018
+Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task,
+and evaluates the performance of a baseline system in the task. As in previous
+years of the challenge, the task is defined for classification of short audio
+samples into one of predefined acoustic scene classes, using a supervised,
+closed-set classification setup. The newly recorded TUT Urban Acoustic Scenes
+2018 dataset consists of ten different acoustic scenes and was recorded in six
+large European cities, therefore it has a higher acoustic variability than the
+previous datasets used for this task, and in addition to high-quality binaural
+recordings, it also includes data recorded with mobile devices. We also present
+the baseline system consisting of a convolutional neural network and its
+performance in the subtasks using the recommended cross-validation setup.
+",1,0,0,0,0,0
+17923,Asymptotically preserving particle-in-cell methods for inhomogenous strongly magnetized plasmas,"  We propose a class of Particle-In-Cell (PIC) methods for the Vlasov-Poisson
+system with a strong and inhomogeneous external magnetic field with fixed
+direction, where we focus on the motion of particles in the plane orthogonal to
+the magnetic field (so-called poloidal directions). In this regime, the time
+step can be subject to stability constraints related to the smallness of Larmor
+radius and plasma frequency. To avoid this limitation, our approach is based on
+first and higher-order semi-implicit numerical schemes already validated on
+dissipative systems [3] and for homogeneous magnetic fields [10]. Thus, when
+the magnitude of the external magnetic field becomes large, this method
+provides a consistent PIC discretization of the guiding-center system taking
+into account variations of the magnetic field. We carry out some theoretical
+proofs and perform several numerical experiments that establish a solid
+validation of the method and its underlying concepts.
+",0,0,1,0,0,0
+17924,Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates,"  Methodological research rarely generates a broad interest, yet our work on
+the validity of cluster inference methods for functional magnetic resonance
+imaging (fMRI) created intense discussion on both the minutia of our approach
+and its implications for the discipline. In the present work, we take on
+various critiques of our work and further explore the limitations of our
+original work. We address issues about the particular event-related designs we
+used, considering multiple event types and randomisation of events between
+subjects. We consider the lack of validity found with one-sample permutation
+(sign flipping) tests, investigating a number of approaches to improve the
+false positive control of this widely used procedure. We found that the
+combination of a two-sided test and cleaning the data using ICA FIX resulted in
+nominal false positive rates for all datasets, meaning that data cleaning is
+not only important for resting state fMRI, but also for task fMRI. Finally, we
+discuss the implications of our work on the fMRI literature as a whole,
+estimating that at least 10% of the fMRI studies have used the most problematic
+cluster inference method (P = 0.01 cluster defining threshold), and how
+individual studies can be interpreted in light of our findings. These
+additional results underscore our original conclusions, on the importance of
+data sharing and thorough evaluation of statistical methods on realistic null
+data.
+",0,0,0,1,0,0
+17925,An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning,"  Deep learning is an established framework for learning hierarchical data
+representations. While compute power is in abundance, one of the main
+challenges in applying this framework to robotic grasping has been obtaining
+the amount of data needed to learn these representations, and structuring the
+data to the task at hand. Among contemporary approaches in the literature, we
+highlight key properties that have encouraged the use of deep learning
+techniques, and in this paper, detail our experience in developing a simulator
+for collecting cylindrical precision grasps of a multi-fingered dexterous
+robotic hand.
+",1,0,0,1,0,0
+17926,A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry,"  Demographic studies suggest that changes in the retinal vasculature geometry,
+especially in vessel width, are associated with the incidence or progression of
+eye-related or systemic diseases. To date, the main information source for
+width estimation from fundus images has been the intensity profile between
+vessel edges. However, there are many factors affecting the intensity profile:
+pathologies, the central light reflex and local illumination levels, to name a
+few. In this study, we introduce three information sources for width
+estimation. These are the probability profiles of vessel interior, centreline
+and edge locations generated by a deep network. The probability profiles
+provide direct access to vessel geometry and are used in the likelihood
+calculation for a Bayesian method, particle filtering. We also introduce a
+geometric model which can handle non-ideal conditions of the probability
+profiles. Our experiments conducted on the REVIEW dataset yielded consistent
+estimates of vessel width, even in cases when one of the vessel edges is
+difficult to identify. Moreover, our results suggest that the method is better
+than human observers at locating edges of low contrast vessels.
+",1,0,0,0,0,0
+17927,Quantum Harmonic Analysis of the Density Matrix: Basics,"  In this Review we will study rigorously the notion of mixed states and their
+density matrices. We mostly give complete proofs. We will also discuss the
+quantum-mechanical consequences of possible variations of Planck's constant h.
+This Review has been written having in mind two readerships: mathematical
+physicists and quantum physicists. The mathematical rigor is maximal, but the
+language and notation we use throughout should be familiar to physicists.
+",0,0,1,0,0,0
+17928,The bromodomain-containing protein Ibd1 links multiple chromatin related protein complexes to highly expressed genes in Tetrahymena thermophila,"  Background: The chromatin remodelers of the SWI/SNF family are critical
+transcriptional regulators. Recognition of lysine acetylation through a
+bromodomain (BRD) component is key to SWI/SNF function; in most eukaryotes,
+this function is attributed to SNF2/Brg1.
+Results: Using affinity purification coupled to mass spectrometry (AP-MS) we
+identified members of a SWI/SNF complex (SWI/SNFTt) in Tetrahymena thermophila.
+SWI/SNFTt is composed of 11 proteins, Snf5Tt, Swi1Tt, Swi3Tt, Snf12Tt, Brg1Tt,
+two proteins with potential chromatin interacting domains and four proteins
+without orthologs to SWI/SNF proteins in yeast or mammals. SWI/SNFTt subunits
+localize exclusively to the transcriptionally active macronucleus (MAC) during
+growth and development, consistent with a role in transcription. While
+Tetrahymena Brg1 does not contain a BRD, our AP-MS results identified a
+BRD-containing SWI/SNFTt component, Ibd1 that associates with SWI/SNFTt during
+growth but not development. AP-MS analysis of epitope-tagged Ibd1 revealed it
+to be a subunit of several additional protein complexes, including putative
+SWRTt, and SAGATt complexes as well as a putative H3K4-specific histone methyl
+transferase complex. Recombinant Ibd1 recognizes acetyl-lysine marks on
+histones correlated with active transcription. Consistent with our AP-MS and
+histone array data suggesting a role in regulation of gene expression, ChIP-Seq
+analysis of Ibd1 indicated that it primarily binds near promoters and within
+gene bodies of highly expressed genes during growth.
+Conclusions: Our results suggest that through recognizing specific histones
+marks, Ibd1 targets active chromatin regions of highly expressed genes in
+Tetrahymena where it subsequently might coordinate the recruitment of several
+chromatin remodeling complexes to regulate the transcriptional landscape of
+vegetatively growing Tetrahymena cells.
+",0,0,0,0,1,0
+17929,A Bayesian Perspective on Generalization and Stochastic Gradient Descent,"  We consider two questions at the heart of machine learning; how can we
+predict if a minimum will generalize to the test set, and why does stochastic
+gradient descent find minima that generalize well? Our work responds to Zhang
+et al. (2016), who showed deep neural networks can easily memorize randomly
+labeled training data, despite generalizing well on real labels of the same
+inputs. We show that the same phenomenon occurs in small linear models. These
+observations are explained by the Bayesian evidence, which penalizes sharp
+minima but is invariant to model parameterization. We also demonstrate that,
+when one holds the learning rate fixed, there is an optimum batch size which
+maximizes the test set accuracy. We propose that the noise introduced by small
+mini-batches drives the parameters towards minima whose evidence is large.
+Interpreting stochastic gradient descent as a stochastic differential equation,
+we identify the ""noise scale"" $g = \epsilon (\frac{N}{B} - 1) \approx \epsilon
+N/B$, where $\epsilon$ is the learning rate, $N$ the training set size and $B$
+the batch size. Consequently the optimum batch size is proportional to both the
+learning rate and the size of the training set, $B_{opt} \propto \epsilon N$.
+We verify these predictions empirically.
+",1,0,0,1,0,0
+17930,Higher Derivative Field Theories: Degeneracy Conditions and Classes,"  We provide a full analysis of ghost free higher derivative field theories
+with coupled degrees of freedom. Assuming the absence of gauge symmetries, we
+derive the degeneracy conditions in order to evade the Ostrogradsky ghosts, and
+analyze which (non)trivial classes of solutions this allows for. It is shown
+explicitly how Lorentz invariance avoids the propagation of ""half"" degrees of
+freedom. Moreover, for a large class of theories, we construct the field
+redefinitions and/or (extended) contact transformations that put the theory in
+a manifestly first order form. Finally, we identify which class of theories
+cannot be brought to first order form by such transformations.
+",0,1,0,0,0,0
+17931,3D Morphology Prediction of Progressive Spinal Deformities from Probabilistic Modeling of Discriminant Manifolds,"  We introduce a novel approach for predicting the progression of adolescent
+idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray
+images. Recent progress in machine learning have allowed to improve
+classification and prognosis rates, but lack a probabilistic framework to
+measure uncertainty in the data. We propose a discriminative probabilistic
+manifold embedding where locally linear mappings transform data points from
+high-dimensional space to corresponding low-dimensional coordinates. A
+discriminant adjacency matrix is constructed to maximize the separation between
+progressive and non-progressive groups of patients diagnosed with scoliosis,
+while minimizing the distance in latent variables belonging to the same class.
+To predict the evolution of deformation, a baseline reconstruction is projected
+onto the manifold, from which a spatiotemporal regression model is built from
+parallel transport curves inferred from neighboring exemplars. Rate of
+progression is modulated from the spine flexibility and curve magnitude of the
+3D spine deformation. The method was tested on 745 reconstructions from 133
+subjects using longitudinal 3D reconstructions of the spine, with results
+demonstrating the discriminatory framework can identify between progressive and
+non-progressive of scoliotic patients with a classification rate of 81% and
+prediction differences of 2.1$^{o}$ in main curve angulation, outperforming
+other manifold learning methods. Our method achieved a higher prediction
+accuracy and improved the modeling of spatiotemporal morphological changes in
+highly deformed spines compared to other learning methods.
+",1,0,0,1,0,0
+17932,The Galaxy's Veil of Excited Hydrogen,"  Many of the baryons in our Galaxy probably lie outside the well known disk
+and bulge components. Despite a wealth of evidence for the presence of some gas
+in galactic halos, including absorption line systems in the spectra of quasars,
+high velocity neutral hydrogen clouds in our Galaxy halo, line emitting ionised
+hydrogen originating from galactic winds in nearby starburst galaxies, and the
+X-ray coronas surrounding the most massive galaxies, accounting for the gas in
+the halo of any galaxy has been observationally challenging primarily because
+of its low density in the expansive halo. The most sensitive measurements come
+from detecting absorption by the intervening gas in the spectra of distant
+objects such as quasars or distant halo stars, but these have typically been
+limited to a few lines of sight to sufficiently bright objects. Massive
+spectroscopic surveys of millions of objects provide an alternative approach to
+the problem. Here, we present the first evidence for a widely distributed,
+neutral, excited hydrogen component of the Galaxy's halo. It is observed as the
+slight, (0.779 $\pm$ 0.006)\%, absorption of flux near the rest wavelength of
+H$\alpha$ in the combined spectra of hundreds of thousands of galaxy spectra
+and is ubiquitous in high latitude lines of sight. This observation provides an
+avenue to tracing, both spatially and kinematically, the majority of the gas in
+the halo of our Galaxy.
+",0,1,0,0,0,0
+17933,Photonic-chip supercontinuum with tailored spectra for precision frequency metrology,"  Supercontinuum generation using chip-integrated photonic waveguides is a
+powerful approach for spectrally broadening pulsed laser sources with very low
+pulse energies and compact form factors. When pumped with a mode-locked laser
+frequency comb, these waveguides can coherently expand the comb spectrum to
+more than an octave in bandwidth to enable self-referenced stabilization.
+However, for applications in frequency metrology and precision spectroscopy, it
+is desirable to not only support self-referencing, but also to generate
+low-noise combs with customizable broadband spectra. In this work, we
+demonstrate dispersion-engineered waveguides based on silicon nitride that are
+designed to meet these goals and enable precision optical metrology experiments
+across large wavelength spans. We perform a clock comparison measurement and
+report a clock-limited relative frequency instability of $3.8\times10^{-15}$ at
+$\tau = 2$ seconds between a 1550 nm cavity-stabilized reference laser and
+NIST's calcium atomic clock laser at 657 nm using a two-octave
+waveguide-supercontinuum comb.
+",0,1,0,0,0,0
+17934,Endogenizing Epistemic Actions,"  Through a series of examples, we illustrate some important drawbacks that the
+action logic framework suffers from in its ability to represent the dynamics of
+information updates. We argue that these problems stem from the fact that the
+action model, a central construct designed to encode agents' uncertainty about
+actions, is itself effectively common knowledge amongst the agents. In response
+to these difficulties, we motivate and propose an alternative semantics that
+avoids them by (roughly speaking) endogenizing the action model. We discuss the
+relationship to action logic, and provide a sound and complete axiomatization.
+",1,0,0,0,0,0
+17935,The Meaning of Memory Safety,"  We give a rigorous characterization of what it means for a programming
+language to be memory safe, capturing the intuition that memory safety supports
+local reasoning about state. We formalize this principle in two ways. First, we
+show how a small memory-safe language validates a noninterference property: a
+program can neither affect nor be affected by unreachable parts of the state.
+Second, we extend separation logic, a proof system for heap-manipulating
+programs, with a memory-safe variant of its frame rule. The new rule is
+stronger because it applies even when parts of the program are buggy or
+malicious, but also weaker because it demands a stricter form of separation
+between parts of the program state. We also consider a number of pragmatically
+motivated variations on memory safety and the reasoning principles they
+support. As an application of our characterization, we evaluate the security of
+a previously proposed dynamic monitor for memory safety of heap-allocated data.
+",1,0,0,0,0,0
+17936,The Flexible Group Spatial Keyword Query,"  We present a new class of service for location based social networks, called
+the Flexible Group Spatial Keyword Query, which enables a group of users to
+collectively find a point of interest (POI) that optimizes an aggregate cost
+function combining both spatial distances and keyword similarities. In
+addition, our query service allows users to consider the tradeoffs between
+obtaining a sub-optimal solution for the entire group and obtaining an
+optimimized solution but only for a subgroup.
+We propose algorithms to process three variants of the query: (i) the group
+nearest neighbor with keywords query, which finds a POI that optimizes the
+aggregate cost function for the whole group of size n, (ii) the subgroup
+nearest neighbor with keywords query, which finds the optimal subgroup and a
+POI that optimizes the aggregate cost function for a given subgroup size m (m
+<= n), and (iii) the multiple subgroup nearest neighbor with keywords query,
+which finds optimal subgroups and corresponding POIs for each of the subgroup
+sizes in the range [m, n]. We design query processing algorithms based on
+branch-and-bound and best-first paradigms. Finally, we provide theoretical
+bounds and conduct extensive experiments with two real datasets which verify
+the effectiveness and efficiency of the proposed algorithms.
+",1,0,0,0,0,0
+17937,Undersampled windowed exponentials and their applications,"  We characterize the completeness and frame/basis property of a union of
+under-sampled windowed exponentials of the form $$ {\mathcal F}(g): =\{e^{2\pi
+i n x}: n\ge 0\}\cup \{g(x)e^{2\pi i nx}: n<0\} $$ for $L^2[-1/2,1/2]$ by the
+spectra of the Toeplitz operators with symbol $g$. Using this characterization,
+we classify all real-valued functions $g$ such that ${\mathcal F}(g)$ is
+complete or forms a frame/basis. Conversely, we use the classical
+Kadec-1/4-theorem in non-harmonic Fourier series to determine all $\xi$ such
+that the Toeplitz operators with symbol $e^{2\pi i \xi x}$ is injective or
+invertible. These results demonstrate an elegant interaction between frame
+theory of windowed exponentials and Toeplitz operators. Finally, as an
+application, we use our results to answer some open questions in dynamical
+sampling, phase retrieval and derivative samplings on $\ell^2({\mathbb Z})$ and
+Paley-Wiener spaces of bandlimited functions.
+",0,0,1,0,0,0
+17938,Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs,"  Deep learning models are often successfully trained using gradient descent,
+despite the worst case hardness of the underlying non-convex optimization
+problem. The key question is then under what conditions can one prove that
+optimization will succeed. Here we provide a strong result of this kind. We
+consider a neural net with one hidden layer and a convolutional structure with
+no overlap and a ReLU activation function. For this architecture we show that
+learning is NP-complete in the general case, but that when the input
+distribution is Gaussian, gradient descent converges to the global optimum in
+polynomial time. To the best of our knowledge, this is the first global
+optimality guarantee of gradient descent on a convolutional neural network with
+ReLU activations.
+",0,0,1,1,0,0
+17939,Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine,"  Computer based recognition and detection of abnormalities in ECG signals is
+proposed. For this purpose, the Support Vector Machines (SVM) are combined with
+the advantages of Hermite transform representation. SVM represent a special
+type of classification techniques commonly used in medical applications.
+Automatic classification of ECG could make the work of cardiologic departments
+faster and more efficient. It would also reduce the number of false diagnosis
+and, as a result, save lives. The working principle of the SVM is based on
+translating the data into a high dimensional feature space and separating it
+using a linear classificator. In order to provide an optimal representation for
+SVM application, the Hermite transform domain is used. This domain is proved to
+be suitable because of the similarity of the QRS complex with Hermite basis
+functions. The maximal signal information is obtained using a small set of
+features that are used for detection of irregular QRS complexes. The aim of the
+paper is to show that these features can be employed for automatic ECG signal
+analysis.
+",1,0,0,0,0,0
+17940,On the Lipschitz equivalence of self-affine sets,"  Let $A$ be an expanding $d\times d$ matrix with integer entries and
+${\mathcal D}\subset {\mathbb Z}^d$ be a finite digit set. Then the pair $(A,
+{\mathcal D})$ defines a unique integral self-affine set $K=A^{-1}(K+{\mathcal
+D})$. In this paper, by replacing the Euclidean norm with a pseudo-norm $w$ in
+terms of $A$, we construct a hyperbolic graph on $(A, {\mathcal D})$ and show
+that $K$ can be identified with the hyperbolic boundary. Moreover, if $(A,
+{\mathcal D})$ safisfies the open set condition, we also prove that two totally
+disconnected integral self-affine sets are Lipschitz equivalent if an only if
+they have the same $w$-Hausdorff dimension, that is, their digit sets have
+equal cardinality. We extends some well-known results in the self-similar sets
+to the self-affine sets.
+",0,0,1,0,0,0
+17941,The Gross-Pitaevskii equations of a static and spherically symmetric condensate of gravitons,"  In this paper we consider the Dvali and Gómez assumption that the end state
+of a gravitational collapse is a Bose-Einstein condensate of gravitons. We then
+construct the two Gross-Pitaevskii equations for a static and spherically
+symmetric configuration of the condensate. These two equations correspond to
+the constrained minimisation of the gravitational Hamiltonian with respect to
+the redshift and the Newtonian potential, per given number of gravitons. We
+find that the effective geometry of the condensate is the one of a gravastar (a
+DeSitter star) with a sub-Planckian cosmological constant, for masses larger
+than the Planck scale. Thus, a condensate corresponding to a semiclassical
+black hole, is always quantum and weakly coupled. Finally, we obtain that the
+boundary of our gravastar, although it is not the location of a horizon,
+corresponds to the Schwarzschild radius.
+",0,1,0,0,0,0
+17942,Stability and instability in saddle point dynamics - Part I,"  We consider the problem of convergence to a saddle point of a concave-convex
+function via gradient dynamics. Since first introduced by Arrow, Hurwicz and
+Uzawa in [1] such dynamics have been extensively used in diverse areas, there
+are, however, features that render their analysis non trivial. These include
+the lack of convergence guarantees when the function considered is not strictly
+concave-convex and also the non-smoothness of subgradient dynamics. Our aim in
+this two part paper is to provide an explicit characterization to the
+asymptotic behaviour of general gradient and subgradient dynamics applied to a
+general concave-convex function. We show that despite the nonlinearity and
+non-smoothness of these dynamics their $\omega$-limit set is comprised of
+trajectories that solve only explicit linear ODEs that are characterized within
+the paper.
+More precisely, in Part I an exact characterization is provided to the
+asymptotic behaviour of unconstrained gradient dynamics. We also show that when
+convergence to a saddle point is not guaranteed then the system behaviour can
+be problematic, with arbitrarily small noise leading to an unbounded variance.
+In Part II we consider a general class of subgradient dynamics that restrict
+trajectories in an arbitrary convex domain, and show that their limiting
+trajectories are solutions of subgradient dynamics on only affine subspaces.
+The latter is a smooth class of dynamics with an asymptotic behaviour exactly
+characterized in Part I, as solutions to explicit linear ODEs. These results
+are used to formulate corresponding convergence criteria and are demonstrated
+with several examples and applications presented in Part II.
+",1,0,1,0,0,0
+17943,Continual Prediction of Notification Attendance with Classical and Deep Network Approaches,"  We investigate to what extent mobile use patterns can predict -- at the
+moment it is posted -- whether a notification will be clicked within the next
+10 minutes. We use a data set containing the detailed mobile phone usage logs
+of 279 users, who over the course of 5 weeks received 446,268 notifications
+from a variety of apps. Besides using classical gradient-boosted trees, we
+demonstrate how to make continual predictions using a recurrent neural network
+(RNN). The two approaches achieve a similar AUC of ca. 0.7 on unseen users,
+with a possible operation point of 50% sensitivity and 80% specificity
+considering all notification types (an increase of 40% with respect to a
+probabilistic baseline). These results enable automatic, intelligent handling
+of mobile phone notifications without the need for user feedback or
+personalization. Furthermore, they showcase how forego feature-extraction by
+using RNNs for continual predictions directly on mobile usage logs. To the best
+of our knowledge, this is the first work that leverages mobile sensor data for
+continual, context-aware predictions of interruptibility using deep neural
+networks.
+",1,0,0,0,0,0
+17944,"Extensions of Operators, Liftings of Monads and Distributive Laws","  In a previous study, the algebraic formulation of the First Fundamental
+Theorem of Calculus (FFTC) is shown to allow extensions of differential and
+Rota-Baxter operators on the one hand, and to give rise to liftings of monads
+and comonads, and mixed distributive laws on the other. Generalizing the FFTC,
+we consider in this paper a class of constraints between a differential
+operator and a Rota-Baxter operator. For a given constraint, we show that the
+existences of extensions of differential and Rota-Baxter operators, of liftings
+of monads and comonads, and of mixed distributive laws are equivalent. We
+further give a classification of the constraints satisfying these equivalent
+conditions.
+",0,0,1,0,0,0
+17945,Cascaded Incremental Nonlinear Dynamic Inversion Control for MAV Disturbance Rejection,"  Micro Aerial Vehicles (MAVs) are limited in their operation outdoors near
+obstacles by their ability to withstand wind gusts. Currently widespread
+position control methods such as Proportional Integral Derivative control do
+not perform well under the influence of gusts. Incremental Nonlinear Dynamic
+Inversion (INDI) is a sensor-based control technique that can control nonlinear
+systems subject to disturbances. It was developed for the attitude control of
+manned aircraft or MAVs. In this paper we generalize this method to the outer
+loop control of MAVs under severe gust loads. Significant improvements over a
+traditional Proportional Integral Derivative (PID) controller are demonstrated
+in an experiment where the quadrotor flies in and out of a windtunnel exhaust
+at 10 m/s. The control method does not rely on frequent position updates, as is
+demonstrated in an outside experiment using a standard GPS module. Finally, we
+investigate the effect of using a linearization to calculate thrust vector
+increments, compared to a nonlinear calculation. The method requires little
+modeling and is computationally efficient.
+",1,0,0,0,0,0
+17946,"Numerical Evaluation of Elliptic Functions, Elliptic Integrals and Modular Forms","  We describe algorithms to compute elliptic functions and their relatives
+(Jacobi theta functions, modular forms, elliptic integrals, and the
+arithmetic-geometric mean) numerically to arbitrary precision with rigorous
+error bounds for arbitrary complex variables. Implementations in ball
+arithmetic are available in the open source Arb library. We discuss the
+algorithms from a concrete implementation point of view, with focus on
+performance at tens to thousands of digits of precision.
+",1,0,0,0,0,0
+17947,In situ Electric Field Skyrmion Creation in Magnetoelectric Cu$_2$OSeO$_3$,"  Magnetic skyrmions are localized nanometric spin textures with quantized
+winding numbers as the topological invariant. Rapidly increasing attention has
+been paid to the investigations of skyrmions since their experimental discovery
+in 2009, due both to the fundamental properties and the promising potential in
+spintronics based applications. However, controlled creation of skyrmions
+remains a pivotal challenge towards technological applications. Here, we report
+that skyrmions can be created locally by electric field in the magnetoelectric
+helimagnet Cu$\mathsf{_2}$OSeO$\mathsf{_3}$. Using Lorentz transmission
+electron microscopy, we successfully write skyrmions in situ from a helical
+spin background. Our discovery is highly coveted since it implies that
+skyrmionics can be integrated into contemporary field effect transistor based
+electronic technology, where very low energy dissipation can be achieved, and
+hence realizes a large step forward to its practical applications.
+",0,1,0,0,0,0
+17948,An online sequence-to-sequence model for noisy speech recognition,"  Generative models have long been the dominant approach for speech
+recognition. The success of these models however relies on the use of
+sophisticated recipes and complicated machinery that is not easily accessible
+to non-practitioners. Recent innovations in Deep Learning have given rise to an
+alternative - discriminative models called Sequence-to-Sequence models, that
+can almost match the accuracy of state of the art generative models. While
+these models are easy to train as they can be trained end-to-end in a single
+step, they have a practical limitation that they can only be used for offline
+recognition. This is because the models require that the entirety of the input
+sequence be available at the beginning of inference, an assumption that is not
+valid for instantaneous speech recognition. To address this problem, online
+sequence-to-sequence models were recently introduced. These models are able to
+start producing outputs as data arrives, and the model feels confident enough
+to output partial transcripts. These models, like sequence-to-sequence are
+causal - the output produced by the model until any time, $t$, affects the
+features that are computed subsequently. This makes the model inherently more
+powerful than generative models that are unable to change features that are
+computed from the data. This paper highlights two main contributions - an
+improvement to online sequence-to-sequence model training, and its application
+to noisy settings with mixed speech from two speakers.
+",1,0,0,1,0,0
+17949,Existence of infinite Viterbi path for pairwise Markov models,"  For hidden Markov models one of the most popular estimates of the hidden
+chain is the Viterbi path -- the path maximising the posterior probability. We
+consider a more general setting, called the pairwise Markov model, where the
+joint process consisting of finite-state hidden regime and observation process
+is assumed to be a Markov chain. We prove that under some conditions it is
+possible to extend the Viterbi path to infinity for almost every observation
+sequence which in turn enables to define an infinite Viterbi decoding of the
+observation process, called the Viterbi process. This is done by constructing a
+block of observations, called a barrier, which ensures that the Viterbi path
+goes trough a given state whenever this block occurs in the observation
+sequence.
+",0,0,1,1,0,0
+17950,Coding for Segmented Edit Channels,"  This paper considers insertion and deletion channels with the additional
+assumption that the channel input sequence is implicitly divided into segments
+such that at most one edit can occur within a segment. No segment markers are
+available in the received sequence. We propose code constructions for the
+segmented deletion, segmented insertion, and segmented insertion-deletion
+channels based on subsets of Varshamov-Tenengolts codes chosen with
+pre-determined prefixes and/or suffixes. The proposed codes, constructed for
+any finite alphabet, are zero-error and can be decoded segment-by-segment. We
+also derive an upper bound on the rate of any zero-error code for the segmented
+edit channel, in terms of the segment length. This upper bound shows that the
+rate scaling of the proposed codes as the segment length increases is the same
+as that of the maximal code.
+",1,0,0,0,0,0
+17951,Charge compensation at the interface between the polar NaCl(111) surface and a NaCl aqueous solution,"  Periodic supercell models of electric double layers formed at the interface
+between a charged surface and an electrolyte are subject to serious finite size
+errors and require certain adjustments in the treatment of the long-range
+electrostatic interactions. In a previous publication (C. Zhang, M. Sprik,
+Phys. Rev. B 94, 245309 (2016)) we have shown how this can be achieved using
+finite field methods. The test system was the familiar simple point charge
+model of a NaCl aqueous solution confined between two oppositely charged walls.
+Here this method is extended to the interface between the (111) polar surface
+of a NaCl crystal and a high concentration NaCl aqueous solution. The crystal
+is kept completely rigid and the compensating charge screening the polarization
+can only be provided by the electrolyte. We verify that the excess electrolyte
+ionic charge at the interface conforms to the Tasker 1/2 rule for compensating
+charge in the theory of polar rocksalt (111) surfaces. The interface can be
+viewed as an electric double layer with a net charge. We define a generalized
+Helmholtz capacitance $C_\text{H}$ which can be computed by varying the applied
+electric field. We find $C_\text{H} = 8.23 \, \mu \mathrm{Fcm}^{-2}$, which
+should be compared to the $4.23 \, \mu \mathrm{Fcm}^{-2}$ for the (100)
+non-polar surface of the same NaCl crystal. This is rationalized by the
+observation that compensating ions shed their first solvation shell adsorbing
+as contact ions pairs on the polar surface.
+",0,1,0,0,0,0
+17952,CrowdTone: Crowd-powered tone feedback and improvement system for emails,"  In this paper, we present CrowdTone, a system designed to help people set the
+appropriate tone in their email communication. CrowdTone utilizes the context
+and content of an email message to identify and set the appropriate tone
+through a consensus-building process executed by crowd workers. We evaluated
+CrowdTone with 22 participants, who provided a total of 29 emails that they had
+received in the past, and ran them through CrowdTone. Participants and
+professional writers assessed the quality of improvements finding a substantial
+increase in the percentage of emails deemed ""appropriate"" or ""very appropriate""
+- from 25% to more than 90% by recipients, and from 45% to 90% by professional
+writers. Additionally, the recipients' feedback indicated that more than 90% of
+the CrowdTone processed emails showed improvement.
+",1,0,0,0,0,0
+17953,A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines,"  Restricted Boltzmann machines (RBMs) are energy-based neural-networks which
+are commonly used as the building blocks for deep architectures neural
+architectures. In this work, we derive a deterministic framework for the
+training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer
+(TAP) mean-field approximation of widely-connected systems with weak
+interactions coming from spin-glass theory. While the TAP approach has been
+extensively studied for fully-visible binary spin systems, our construction is
+generalized to latent-variable models, as well as to arbitrarily distributed
+real-valued spin systems with bounded support. In our numerical experiments, we
+demonstrate the effective deterministic training of our proposed models and are
+able to show interesting features of unsupervised learning which could not be
+directly observed with sampling. Additionally, we demonstrate how to utilize
+our TAP-based framework for leveraging trained RBMs as joint priors in
+denoising problems.
+",1,1,0,1,0,0
+17954,Integrability conditions for Compound Random Measures,"  Compound random measures (CoRM's) are a flexible and tractable framework for
+vectors of completely random measure. In this paper, we provide conditions to
+guarantee the existence of a CoRM. Furthermore, we prove some interesting
+properties of CoRM's when exponential scores and regularly varying Lévy
+intensities are considered.
+",0,0,1,1,0,0
+17955,Tunable low energy Ps beam for the anti-hydrogen free fall and for testing gravity with a Mach-Zehnder interferometer,"  The test of gravitational force on antimatter in the field of the matter
+gravitational field, produced by earth, can be done by a free fall experiment
+which involves only General Relativity, and with a Mach-Zehnder interferometer
+which involves Quantum Mechanics. This article presents a new method to produce
+a tunable low energy (Ps ) beam suitable for trapping the (Hbar + ) ion in a
+free fall experiment, and suitable for a gravity Mach-Zehnder interferometer
+with (Ps). The low energy (Ps) beam is tunable in the [10 eV, 100 eV] range.
+",0,1,0,0,0,0
+17956,An analysis of incorporating an external language model into a sequence-to-sequence model,"  Attention-based sequence-to-sequence models for automatic speech recognition
+jointly train an acoustic model, language model, and alignment mechanism. Thus,
+the language model component is only trained on transcribed audio-text pairs.
+This leads to the use of shallow fusion with an external language model at
+inference time. Shallow fusion refers to log-linear interpolation with a
+separately trained language model at each step of the beam search. In this
+work, we investigate the behavior of shallow fusion across a range of
+conditions: different types of language models, different decoding units, and
+different tasks. On Google Voice Search, we demonstrate that the use of shallow
+fusion with a neural LM with wordpieces yields a 9.1% relative word error rate
+reduction (WERR) over our competitive attention-based sequence-to-sequence
+model, obviating the need for second-pass rescoring.
+",1,0,0,0,0,0
+17957,Multi-Generator Generative Adversarial Nets,"  We propose a new approach to train the Generative Adversarial Nets (GANs)
+with a mixture of generators to overcome the mode collapsing problem. The main
+intuition is to employ multiple generators, instead of using a single one as in
+the original GAN. The idea is simple, yet proven to be extremely effective at
+covering diverse data modes, easily overcoming the mode collapse and delivering
+state-of-the-art results. A minimax formulation is able to establish among a
+classifier, a discriminator, and a set of generators in a similar spirit with
+GAN. Generators create samples that are intended to come from the same
+distribution as the training data, whilst the discriminator determines whether
+samples are true data or generated by generators, and the classifier specifies
+which generator a sample comes from. The distinguishing feature is that
+internal samples are created from multiple generators, and then one of them
+will be randomly selected as final output similar to the mechanism of a
+probabilistic mixture model. We term our method Mixture GAN (MGAN). We develop
+theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon
+divergence (JSD) between the mixture of generators' distributions and the
+empirical data distribution is minimal, whilst the JSD among generators'
+distributions is maximal, hence effectively avoiding the mode collapse. By
+utilizing parameter sharing, our proposed model adds minimal computational cost
+to the standard GAN, and thus can also efficiently scale to large-scale
+datasets. We conduct extensive experiments on synthetic 2D data and natural
+image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior
+performance of our MGAN in achieving state-of-the-art Inception scores over
+latest baselines, generating diverse and appealing recognizable objects at
+different resolutions, and specializing in capturing different types of objects
+by generators.
+",1,0,0,1,0,0
+17958,Neural Style Transfer: A Review,"  The seminal work of Gatys et al. demonstrated the power of Convolutional
+Neural Networks (CNNs) in creating artistic imagery by separating and
+recombining image content and style. This process of using CNNs to render a
+content image in different styles is referred to as Neural Style Transfer
+(NST). Since then, NST has become a trending topic both in academic literature
+and industrial applications. It is receiving increasing attention and a variety
+of approaches are proposed to either improve or extend the original NST
+algorithm. In this paper, we aim to provide a comprehensive overview of the
+current progress towards NST. We first propose a taxonomy of current algorithms
+in the field of NST. Then, we present several evaluation methods and compare
+different NST algorithms both qualitatively and quantitatively. The review
+concludes with a discussion of various applications of NST and open problems
+for future research. A list of papers discussed in this review, corresponding
+codes, pre-trained models and more comparison results are publicly available at
+this https URL.
+",1,0,0,1,0,0
+17959,Distribution of water in the G327.3-0.6 massive star-forming region,"  We aim at characterizing the large-scale distribution of H2O in G327.3-0.6, a
+massive star-forming region made of individual objects in different
+evolutionary phases. We investigate variations of H2O abundance as function of
+evolution. We present Herschel continuum maps at 89 and 179 $\mu$m of the whole
+region and an APEX map at 350 {\mu}m of the IRDC. New spectral HIFI maps toward
+the IRDC region covering low-energy H2O lines at 987 and 1113 GHz are also
+presented and combined with HIFI pointed observations of the G327 hot core. We
+infer the physical properties of the gas through optical depth analysis and
+radiative transfer modeling. The continuum emission at 89 and 179 {\mu}m
+follows the thermal continuum emission at longer wavelengths, with a peak at
+the position of the hot core, a secondary peak in the Hii region, and an
+arch-like layer of hot gas west of the Hii region. The same morphology is
+observed in the 1113 GHz line, in absorption toward all dust condensations.
+Optical depths of ~80 and 15 are estimated and correspond to column densities
+of 10^15 and 2 10^14 cm-2, for the hot core and IRDC position. These values
+indicate an H2O to H2 ratio of 3 10^-8 toward the hot core; the abundance of
+H2O does not change along the IRDC with values of some 10^-8. Infall (over ~
+20"") is detected toward the hot core position with a rate of 1-1.3 10^-2 M_sun
+/yr, high enough to overcome the radiation pressure due to the stellar
+luminosity. The source structure of the hot core region is complex, with a cold
+outer gas envelope in expansion, situated between the outflow and the observer,
+extending over 0.32 pc. The outflow is seen face-on and centered away from the
+hot core. The distribution of H2O along the IRDC is roughly constant with an
+abundance peak in the more evolved object. These water abundances are in
+agreement with previous studies in other massive objects and chemical models.
+",0,1,0,0,0,0
+17960,Straightening rule for an $m'$-truncated polynomial ring,"  We consider a certain quotient of a polynomial ring categorified by both the
+isomorphic Green rings of the symmetric groups and Schur algebras generated by
+the signed Young permutation modules and mixed powers respectively. They have
+bases parametrised by pairs of partitions whose second partitions are multiples
+of the odd prime $p$ the characteristic of the underlying field. We provide an
+explicit formula rewriting a signed Young permutation module (respectively,
+mixed power) in terms of signed Young permutation modules (respectively, mixed
+powers) labelled by those pairs of partitions. As a result, for each partition
+$\lambda$, we discovered the number of compositions $\delta$ such that $\delta$
+can be rearranged to $\lambda$ and whose partial sums of $\delta$ are not
+divisible by $p$.
+",0,0,1,0,0,0
+17961,Effective Blog Pages Extractor for Better UGC Accessing,"  Blog is becoming an increasingly popular media for information publishing.
+Besides the main content, most of blog pages nowadays also contain noisy
+information such as advertisements etc. Removing these unrelated elements can
+improves user experience, but also can better adapt the content to various
+devices such as mobile phones. Though template-based extractors are highly
+accurate, they may incur expensive cost in that a large number of template need
+to be developed and they will fail once the template is updated. To address
+these issues, we present a novel template-independent content extractor for
+blog pages. First, we convert a blog page into a DOM-Tree, where all elements
+including the title and body blocks in a page correspond to subtrees. Then we
+construct subtree candidate set for the title and the body blocks respectively,
+and extract both spatial and content features for elements contained in the
+subtree. SVM classifiers for the title and the body blocks are trained using
+these features. Finally, the classifiers are used to extract the main content
+from blog pages. We test our extractor on 2,250 blog pages crawled from nine
+blog sites with obviously different styles and templates. Experimental results
+verify the effectiveness of our extractor.
+",1,0,0,0,0,0
+17962,The Principle of Similitude in Biology: From Allometry to the Formulation of Dimensionally Homogenous `Laws',"  Meaningful laws of nature must be independent of the units employed to
+measure the variables. The principle of similitude (Rayleigh 1915) or
+dimensional homogeneity, states that only commensurable quantities (ones having
+the same dimension) may be compared, therefore, meaningful laws of nature must
+be homogeneous equations in their various units of measurement, a result which
+was formalized in the $\rm \Pi$ theorem (Vaschy 1892; Buckingham 1914).
+However, most relations in allometry do not satisfy this basic requirement,
+including the `3/4 Law' (Kleiber 1932) that relates the basal metabolic rate
+and body mass, which it is sometimes claimed to be the most fundamental
+biological rate (Brown et al. 2004) and the closest to a law in life sciences
+(West \& Brown 2004). Using the $\rm \Pi$ theorem, here we show that it is
+possible to construct a unique homogeneous equation for the metabolic rates, in
+agreement with data in the literature. We find that the variations in the
+dependence of the metabolic rates on body mass are secondary, coming from
+variations in the allometric dependence of the heart frequencies. This includes
+not only different classes of animals (mammals, birds, invertebrates) but also
+different exercise conditions (basal and maximal). Our results demonstrate that
+most of the differences found in the allometric exponents (White et al. 2007)
+are due to compare incommensurable quantities and that our dimensionally
+homogenous formula, unify these differences into a single formulation. We
+discuss the ecological implications of this new formulation in the context of
+the Malthusian's, Fenchel's and the total energy consumed in a lifespan
+relations.
+",0,1,0,0,0,0
+17963,New insight into the dynamics of rhodopsin photoisomerization from one-dimensional quantum-classical modeling,"  Characterization of the primary events involved in the $cis-trans$
+photoisomerization of the rhodopsin retinal chromophore was approximated by a
+minimum one-dimensional quantum-classical model. The developed mathematical
+model is identical to that obtained using conventional quantum-classical
+approaches, and multiparametric quantum-chemical or molecular dynamics (MD)
+computations were not required. The quantum subsystem of the model includes
+three electronic states for rhodopsin: (i) the ground state, (ii) the excited
+state, and (iii) the primary photoproduct in the ground state. The resultant
+model is in perfect agreement with experimental data in terms of the quantum
+yield, the time required to reach the conical intersection and to complete the
+quantum evolution, the range of the characteristic low frequencies active
+within the primary events of the $11-cis$ retinal isomerization, and the
+coherent character of the photoreaction. An effective redistribution of excess
+energy between the vibration modes of rhodopsin was revealed by analysis of the
+dissipation process. The results confirm the validity of the minimal model,
+despite its one-dimensional character. The fundamental nature of the
+photoreaction was therefore demonstrated using a minimum mathematical model for
+the first time.
+",0,1,0,0,0,0
+17964,High-order schemes for the Euler equations in cylindrical/spherical coordinates,"  We consider implementations of high-order finite difference Weighted
+Essentially Non-Oscillatory (WENO) schemes for the Euler equations in
+cylindrical and spherical coordinate systems with radial dependence only. The
+main concern of this work lies in ensuring both high-order accuracy and
+conservation. Three different spatial discretizations are assessed: one that is
+shown to be high-order accurate but not conservative, one conservative but not
+high-order accurate, and a new approach that is both high-order accurate and
+conservative. For cylindrical and spherical coordinates, we present convergence
+results for the advection equation and the Euler equations with an acoustics
+problem; we then use the Sod shock tube and the Sedov point-blast problems in
+cylindrical coordinates to verify our analysis and implementations.
+",0,1,1,0,0,0
+17965,Suppressing correlations in massively parallel simulations of lattice models,"  For lattice Monte Carlo simulations parallelization is crucial to make
+studies of large systems and long simulation time feasible, while sequential
+simulations remain the gold-standard for correlation-free dynamics. Here,
+various domain decomposition schemes are compared, concluding with one which
+delivers virtually correlation-free simulations on GPU Extensive simulations of
+the octahedron model for $2+1$ dimensional Karda--Parisi--Zhang surface growth,
+which is very sensitive to correlation in the site-selection dynamics, were
+performed to show self-consistency of the parallel runs and agreement with the
+sequential algorithm. We present a GPU implementation providing a speedup of
+about $30\times$ over a parallel CPU implementation on a single socket and at
+least $180\times$ with respect to the sequential reference.
+",0,1,0,0,0,0
+17966,New bounds on the strength of some restrictions of Hindman's Theorem,"  We prove upper and lower bounds on the effective content and logical strength
+for a variety of natural restrictions of Hindman's Finite Sums Theorem. For
+example, we show that Hindman's Theorem for sums of length at most 2 and 4
+colors implies $\mathsf{ACA}_0$. An emerging {\em leitmotiv} is that the known
+lower bounds for Hindman's Theorem and for its restriction to sums of at most 2
+elements are already valid for a number of restricted versions which have
+simple proofs and better computability- and proof-theoretic upper bounds than
+the known upper bound for the full version of the theorem. We highlight the
+role of a sparsity-like condition on the solution set, which we call apartness.
+",0,0,1,0,0,0
+17967,"3D Face Morphable Models ""In-the-Wild""","  3D Morphable Models (3DMMs) are powerful statistical models of 3D facial
+shape and texture, and among the state-of-the-art methods for reconstructing
+facial shape from single images. With the advent of new 3D sensors, many 3D
+facial datasets have been collected containing both neutral as well as
+expressive faces. However, all datasets are captured under controlled
+conditions. Thus, even though powerful 3D facial shape models can be learnt
+from such data, it is difficult to build statistical texture models that are
+sufficient to reconstruct faces captured in unconstrained conditions
+(""in-the-wild""). In this paper, we propose the first, to the best of our
+knowledge, ""in-the-wild"" 3DMM by combining a powerful statistical model of
+facial shape, which describes both identity and expression, with an
+""in-the-wild"" texture model. We show that the employment of such an
+""in-the-wild"" texture model greatly simplifies the fitting procedure, because
+there is no need to optimize with regards to the illumination parameters.
+Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary
+images. Finally, we have captured the first 3D facial database with relatively
+unconstrained conditions and report quantitative evaluations with
+state-of-the-art performance. Complementary qualitative reconstruction results
+are demonstrated on standard ""in-the-wild"" facial databases. An open source
+implementation of our technique is released as part of the Menpo Project.
+",1,0,0,0,0,0
+17968,Image Segmentation to Distinguish Between Overlapping Human Chromosomes,"  In medicine, visualizing chromosomes is important for medical diagnostics,
+drug development, and biomedical research. Unfortunately, chromosomes often
+overlap and it is necessary to identify and distinguish between the overlapping
+chromosomes. A segmentation solution that is fast and automated will enable
+scaling of cost effective medicine and biomedical research. We apply neural
+network-based image segmentation to the problem of distinguishing between
+partially overlapping DNA chromosomes. A convolutional neural network is
+customized for this problem. The results achieved intersection over union (IOU)
+scores of 94.7% for the overlapping region and 88-94% on the non-overlapping
+chromosome regions.
+",1,0,0,1,0,0
+17969,Implementing Large-Scale Agile Frameworks: Challenges and Recommendations,"  Based on 13 agile transformation cases over 15 years, this article identifies
+nine challenges associated with implementing SAFe, Scrum-at-Scale, Spotify,
+LeSS, Nexus, and other mixed or customised large-scale agile frameworks. These
+challenges should be considered by organizations aspiring to pursue a
+large-scale agile strategy. This article also provides recommendations for
+practitioners and agile researchers.
+",1,0,0,0,0,0
+17970,On The Limiting Distributions of the Total Height On Families of Trees,"  A symbolic-computational algorithm, fully implemented in Maple, is described,
+that computes explicit expressions for generating functions that enable the
+efficient computations of the expectation, variance, and higher moments, of the
+random variable `sum of distances to the root', defined on any given family of
+rooted ordered trees (defined by degree restrictions). Taking limits, we
+confirm, via elementary methods, the fact, due to David Aldous, and expanded by
+Svante Janson and others, that the limiting (scaled) distributions are all the
+same, and coincide with the limiting distribution of the same random variable,
+when it is defined on labeled rooted trees.
+",0,0,1,0,0,0
+17971,HESS J1826$-$130: A Very Hard $γ$-Ray Spectrum Source in the Galactic Plane,"  HESS J1826$-$130 is an unidentified hard spectrum source discovered by
+H.E.S.S. along the Galactic plane, the spectral index being $\Gamma$ = 1.6 with
+an exponential cut-off at about 12 TeV. While the source does not have a clear
+counterpart at longer wavelengths, the very hard spectrum emission at TeV
+energies implies that electrons or protons accelerated up to several hundreds
+of TeV are responsible for the emission. In the hadronic case, the VHE emission
+can be produced by runaway cosmic-rays colliding with the dense molecular
+clouds spatially coincident with the H.E.S.S. source.
+",0,1,0,0,0,0
+17972,Laser opacity in underdense preplasma of solid targets due to quantum electrodynamics effects,"  We investigate how next-generation laser pulses at 10 PW $-$ 200 PW interact
+with a solid target in the presence of a relativistically underdense preplasma
+produced by amplified spontaneous emission (ASE). Laser hole boring and
+relativistic transparency are strongly restrained due to the generation of
+electron-positron pairs and $\gamma$-ray photons via quantum electrodynamics
+(QED) processes. A pair plasma with a density above the initial preplasma
+density is formed, counteracting the electron-free channel produced by the hole
+boring. This pair-dominated plasma can block the laser transport and trigger an
+avalanche-like QED cascade, efficiently transfering the laser energy to
+photons. This renders a 1-$\rm\mu m$-scalelength, underdense preplasma
+completely opaque to laser pulses at this power level. The QED-induced opacity
+therefore sets much higher contrast requirements for such pulse in solid-target
+experiments than expected by classical plasma physics. Our simulations show for
+example, that proton acceleration from the rear of a solid with a preplasma
+would be strongly impaired.
+",0,1,0,0,0,0
+17973,Consequentialist conditional cooperation in social dilemmas with imperfect information,"  Social dilemmas, where mutual cooperation can lead to high payoffs but
+participants face incentives to cheat, are ubiquitous in multi-agent
+interaction. We wish to construct agents that cooperate with pure cooperators,
+avoid exploitation by pure defectors, and incentivize cooperation from the
+rest. However, often the actions taken by a partner are (partially) unobserved
+or the consequences of individual actions are hard to predict. We show that in
+a large class of games good strategies can be constructed by conditioning one's
+behavior solely on outcomes (ie. one's past rewards). We call this
+consequentialist conditional cooperation. We show how to construct such
+strategies using deep reinforcement learning techniques and demonstrate, both
+analytically and experimentally, that they are effective in social dilemmas
+beyond simple matrix games. We also show the limitations of relying purely on
+consequences and discuss the need for understanding both the consequences of
+and the intentions behind an action.
+",1,0,0,0,0,0
+17974,"Casimir free energy of dielectric films: Classical limit, low-temperature behavior and control","  The Casimir free energy of dielectric films, both free-standing in vacuum and
+deposited on metallic or dielectric plates, is investigated. It is shown that
+the values of the free energy depend considerably on whether the calculation
+approach used neglects or takes into account the dc conductivity of film
+material. We demonstrate that there are the material-dependent and universal
+classical limits in the former and latter cases, respectively. The analytic
+behavior of the Casimir free energy and entropy for a free-standing dielectric
+film at low temperature in found. According to our results, the Casimir entropy
+goes to zero when the temperature vanishes if the calculation approach with
+neglected dc conductivity of a film is employed. If the dc conductivity is
+taken into account, the Casimir entropy takes the positive value at zero
+temperature, depending on the parameters of a film, i.e., the Nernst heat
+theorem is violated. By considering the Casimir free energy of silica and
+sapphire films deposited on a Au plate in the framework of two calculation
+approaches, we argue that physically correct values are obtained by
+disregarding the role of dc conductivity. A comparison with the well known
+results for the configuration of two parallel plates is made. Finally, we
+compute the Casimir free energy of silica, sapphire and Ge films deposited on
+high-resistivity Si plates of different thicknesses and demonstrate that it can
+be positive, negative and equal to zero. Possible applications of the obtained
+results to thin films used in microelectronics are discussed.
+",0,1,0,0,0,0
+17975,Discriminant chronicles mining: Application to care pathways analytics,"  Pharmaco-epidemiology (PE) is the study of uses and effects of drugs in well
+defined populations. As medico-administrative databases cover a large part of
+the population, they have become very interesting to carry PE studies. Such
+databases provide longitudinal care pathways in real condition containing
+timestamped care events, especially drug deliveries. Temporal pattern mining
+becomes a strategic choice to gain valuable insights about drug uses. In this
+paper we propose DCM, a new discriminant temporal pattern mining algorithm. It
+extracts chronicle patterns that occur more in a studied population than in a
+control population. We present results on the identification of possible
+associations between hospitalizations for seizure and anti-epileptic drug
+switches in care pathway of epileptic patients.
+",1,0,0,0,0,0
+17976,Adversarial Symmetric Variational Autoencoder,"  A new form of variational autoencoder (VAE) is developed, in which the joint
+distribution of data and codes is considered in two (symmetric) forms: ($i$)
+from observed data fed through the encoder to yield codes, and ($ii$) from
+latent codes drawn from a simple prior and propagated through the decoder to
+manifest data. Lower bounds are learned for marginal log-likelihood fits
+observed data and latent codes. When learning with the variational bound, one
+seeks to minimize the symmetric Kullback-Leibler divergence of joint density
+functions from ($i$) and ($ii$), while simultaneously seeking to maximize the
+two marginal log-likelihoods. To facilitate learning, a new form of adversarial
+training is developed. An extensive set of experiments is performed, in which
+we demonstrate state-of-the-art data reconstruction and generation on several
+image benchmark datasets.
+",1,0,0,0,0,0
+17977,The singular locus of hypersurface sections containing a closed subscheme over finite fields,"  We prove that there exist hypersurfaces that contain a given closed subscheme
+$Z$ of the projective space over a finite field and intersect a given smooth
+scheme $X$ off of $Z$ smoothly, if the intersection $V = Z \cap X$ is smooth.
+Furthermore, we can give a bound on the dimension of the singular locus of the
+hypersurface section and prescribe finitely many local conditions on the
+hypersurface. This is an analogue of a Bertini theorem of Bloch over finite
+fields and is proved using Poonen's closed point sieve. We also show a similar
+theorem for the case where $V$ is not smooth.
+",0,0,1,0,0,0
+17978,A Statistical Perspective on Inverse and Inverse Regression Problems,"  Inverse problems, where in broad sense the task is to learn from the noisy
+response about some unknown function, usually represented as the argument of
+some known functional form, has received wide attention in the general
+scientific disciplines. How- ever, in mainstream statistics such inverse
+problem paradigm does not seem to be as popular. In this article we provide a
+brief overview of such problems from a statistical, particularly Bayesian,
+perspective.
+We also compare and contrast the above class of problems with the perhaps
+more statistically familiar inverse regression problems, arguing that this
+class of problems contains the traditional class of inverse problems. In course
+of our review we point out that the statistical literature is very scarce with
+respect to both the inverse paradigms, and substantial research work is still
+necessary to develop the fields.
+",0,0,1,1,0,0
+17979,Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling,"  This work presents a model reduction approach for problems with coherent
+structures that propagate over time such as convection-dominated flows and
+wave-type phenomena. Traditional model reduction methods have difficulties with
+these transport-dominated problems because propagating coherent structures
+typically introduce high-dimensional features that require high-dimensional
+approximation spaces. The approach proposed in this work exploits the locality
+in space and time of propagating coherent structures to derive efficient
+reduced models. First, full-model solutions are approximated locally in time
+via local reduced spaces that are adapted with basis updates during time
+stepping. The basis updates are derived from querying the full model at a few
+selected spatial coordinates. Second, the locality in space of the coherent
+structures is exploited via an adaptive sampling scheme that selects at which
+components to query the full model for computing the basis updates. Our
+analysis shows that, in probability, the more local the coherent structure is
+in space, the fewer full-model samples are required to adapt the reduced basis
+with the proposed adaptive sampling scheme. Numerical results on benchmark
+examples with interacting wave-type structures and time-varying transport
+speeds and on a model combustor of a single-element rocket engine demonstrate
+the wide applicability of our approach and the significant runtime speedups
+compared to full models and traditional reduced models.
+",1,0,0,0,0,0
+17980,Weighted parallel SGD for distributed unbalanced-workload training system,"  Stochastic gradient descent (SGD) is a popular stochastic optimization method
+in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel
+SGD, often require all nodes to have the same performance or to consume equal
+quantities of data. However, these requirements are difficult to satisfy when
+the parallel SGD algorithms run in a heterogeneous computing environment;
+low-performance nodes will exert a negative influence on the final result. In
+this paper, we propose an algorithm called weighted parallel SGD (WP-SGD).
+WP-SGD combines weighted model parameters from different nodes in the system to
+produce the final output. WP-SGD makes use of the reduction in standard
+deviation to compensate for the loss from the inconsistency in performance of
+nodes in the cluster, which means that WP-SGD does not require that all nodes
+consume equal quantities of data. We also analyze the theoretical feasibility
+of running two other parallel SGD algorithms combined with WP-SGD in a
+heterogeneous environment. The experimental results show that WP-SGD
+significantly outperforms the traditional parallel SGD algorithms on
+distributed training systems with an unbalanced workload.
+",1,0,0,1,0,0
+17981,Neural Lander: Stable Drone Landing Control using Learned Dynamics,"  Precise trajectory control near ground is difficult for multi-rotor drones,
+due to the complex ground effects caused by interactions between multi-rotor
+airflow and the environment. Conventional control methods often fail to
+properly account for these complex effects and fall short in accomplishing
+smooth landing. In this paper, we present a novel deep-learning-based robust
+nonlinear controller (Neural-Lander) that improves control performance of a
+quadrotor during landing. Our approach blends together a nominal dynamics model
+coupled with a Deep Neural Network (DNN) that learns the high-order
+interactions. We employ a novel application of spectral normalization to
+constrain the DNN to have bounded Lipschitz behavior. Leveraging this Lipschitz
+property, we design a nonlinear feedback linearization controller using the
+learned model and prove system stability with disturbance rejection. To the
+best of our knowledge, this is the first DNN-based nonlinear feedback
+controller with stability guarantees that can utilize arbitrarily large neural
+nets. Experimental results demonstrate that the proposed controller
+significantly outperforms a baseline linear proportional-derivative (PD)
+controller in both 1D and 3D landing cases. In particular, we show that
+compared to the PD controller, Neural-Lander can decrease error in z direction
+from 0.13m to zero, and mitigate average x and y drifts by 90% and 34%
+respectively, in 1D landing. Meanwhile, Neural-Lander can decrease z error from
+0.12m to zero, in 3D landing. We also empirically show that the DNN generalizes
+well to new test inputs outside the training domain.
+",1,0,0,0,0,0
+17982,Finite-Time Stabilization of Longitudinal Control for Autonomous Vehicles via a Model-Free Approach,"  This communication presents a longitudinal model-free control approach for
+computing the wheel torque command to be applied on a vehicle. This setting
+enables us to overcome the problem of unknown vehicle parameters for generating
+a suitable control law. An important parameter in this control setting is made
+time-varying for ensuring finite-time stability. Several convincing computer
+simulations are displayed and discussed. Overshoots become therefore smaller.
+The driving comfort is increased and the robustness to time-delays is improved.
+",1,0,1,0,0,0
+17983,"Review of Geraint F. Lewis and Luke A. Barnes, A Fortunate Universe: Life in a Finely Tuned Cosmos","  This new book by cosmologists Geraint F. Lewis and Luke A. Barnes is another
+entry in the long list of cosmology-centered physics books intended for a large
+audience. While many such books aim at advancing a novel scientific theory, A
+Fortunate Universe has no such scientific pretense. Its goals are to assert
+that the universe is fine-tuned for life, to defend that this fact can
+reasonably motivate further scientific inquiry as to why it is so, and to show
+that the multiverse and intelligent design hypotheses are reasonable proposals
+to explain this fine-tuning. This book's potential contribution, therefore,
+lies in how convincingly and efficiently it can make that case.
+",0,1,0,0,0,0
+17984,Introducing SPAIN (SParse Audion INpainter),"  A novel sparsity-based algorithm for audio inpainting is proposed by
+translating the SPADE algorithm by Kitić et. al.---the state-of-the-art for
+audio declipping---into the task of audio inpainting. SPAIN (SParse Audio
+INpainter) comes in synthesis and analysis variants. Experiments show that both
+A-SPAIN and S-SPAIN outperform other sparsity-based inpainting algorithms and
+that A-SPAIN performs on a par with the state-of-the-art method based on linear
+prediction.
+",1,0,0,0,0,0
+17985,Palindromic Decompositions with Gaps and Errors,"  Identifying palindromes in sequences has been an interesting line of research
+in combinatorics on words and also in computational biology, after the
+discovery of the relation of palindromes in the DNA sequence with the HIV
+virus. Efficient algorithms for the factorization of sequences into palindromes
+and maximal palindromes have been devised in recent years. We extend these
+studies by allowing gaps in decompositions and errors in palindromes, and also
+imposing a lower bound to the length of acceptable palindromes.
+We first present an algorithm for obtaining a palindromic decomposition of a
+string of length n with the minimal total gap length in time O(n log n * g) and
+space O(n g), where g is the number of allowed gaps in the decomposition. We
+then consider a decomposition of the string in maximal \delta-palindromes (i.e.
+palindromes with \delta errors under the edit or Hamming distance) and g
+allowed gaps. We present an algorithm to obtain such a decomposition with the
+minimal total gap length in time O(n (g + \delta)) and space O(n g).
+",1,0,0,0,0,0
+17986,End-of-Use Core Triage in Extreme Scenarios Based on a Threshold Approach,"  Remanufacturing is a significant factor in securing sustainability through a
+circular economy. Sorting plays a significant role in remanufacturing
+pre-processing inspections. Its significance can increase when remanufacturing
+facilities encounter extreme situations, such as abnormally huge core arrivals.
+Our main objective in this work is switching from less efficient to a more
+efficient model and to characterize extreme behavior of core arrival in
+remanufacturing and applying the developed model to triage cores. Central
+tendency core flow models are not sufficient to handle extreme situations,
+however, complementary Extreme Value (EV) approaches have shown to improve
+model efficiency. Extreme core flows to remanufacturing facilities are rare but
+still likely and can adversely affect remanufacturing business operations. In
+this investigation, extreme end-of-use core flow is modelled by a threshold
+approach using the Generalized Pareto Distribution (GPD). It is shown that GPD
+has better performance than its maxima-block GEV counterpart from practical and
+data efficiency perspectives. The model is validated by a synthesized big
+dataset, tested by sophisticated statistical Anderson Darling (AD) test, and is
+applied to a case of extreme flow to a valve shop in order to predict
+probability of over-capacity arrivals that is critical in remanufacturing
+business management. Finally, the GPD model combined with triage strategies is
+used to initiate investigations into the efficacy of different triage methods
+in remanufacturing operations.
+",0,0,0,1,0,0
+17987,A temperature-dependent implicit-solvent model of polyethylene glycol in aqueous solution,"  A temperature (T)-dependent coarse-grained (CG) Hamiltonian of polyethylene
+glycol/oxide (PEG/PEO) in aqueous solution is reported to be used in
+implicit-solvent material models in a wide temperature (i.e., solvent quality)
+range. The T-dependent nonbonded CG interactions are derived from a combined
+""bottom-up"" and ""top-down"" approach. The pair potentials calculated from
+atomistic replica-exchange molecular dynamics simulations in combination with
+the iterative Boltzmann inversion are post-refined by benchmarking to
+experimental data of the radius of gyration. For better handling and a fully
+continuous transferability in T-space, the pair potentials are conveniently
+truncated and mapped to an analytic formula with three structural parameters
+expressed as explicit continuous functions of T. It is then demonstrated that
+this model without further adjustments successfully reproduces other
+experimentally known key thermodynamic properties of semi-dilute PEG solutions
+such as the full equation of state (i.e., T-dependent osmotic pressure) for
+various chain lengths as well as their cloud point (or collapse) temperature.
+",0,1,0,0,0,0
+17988,Directional Statistics and Filtering Using libDirectional,"  In this paper, we present libDirectional, a MATLAB library for directional
+statistics and directional estimation. It supports a variety of commonly used
+distributions on the unit circle, such as the von Mises, wrapped normal, and
+wrapped Cauchy distributions. Furthermore, various distributions on
+higher-dimensional manifolds such as the unit hypersphere and the hypertorus
+are available. Based on these distributions, several recursive filtering
+algorithms in libDirectional allow estimation on these manifolds. The
+functionality is implemented in a clear, well-documented, and object-oriented
+structure that is both easy to use and easy to extend.
+",1,0,0,1,0,0
+17989,A solution of the dark energy and its coincidence problem based on local antigravity sources without fine-tuning or new scales,"  A novel idea is proposed for a natural solution of the dark energy and its
+cosmic coincidence problem. The existence of local antigravity sources,
+associated with astrophysical matter configurations distributed throughout the
+universe, can lead to a recent cosmic acceleration effect. Various physical
+theories can be compatible with this idea, but here, in order to test our
+proposal, we focus on quantum originated spherically symmetric metrics matched
+with the cosmological evolution through the simplest Swiss cheese model. In the
+context of asymptotically safe gravity, we have explained the observed amount
+of dark energy using Newton's constant, the galaxy or cluster length scales,
+and dimensionless order one parameters predicted by the theory, without
+fine-tuning or extra unproven energy scales. The interior modified
+Schwarzschild-de Sitter metric allows us to approximately interpret this result
+as that the standard cosmological constant is a composite quantity made of the
+above parameters, instead of a fundamental one.
+",0,1,0,0,0,0
+17990,Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples,"  Self-paced learning and hard example mining re-weight training instances to
+improve learning accuracy. This paper presents two improved alternatives based
+on lightweight estimates of sample uncertainty in stochastic gradient descent
+(SGD): the variance in predicted probability of the correct class across
+iterations of mini-batch SGD, and the proximity of the correct class
+probability to the decision threshold. Extensive experimental results on six
+datasets show that our methods reliably improve accuracy in various network
+architectures, including additional gains on top of other popular training
+techniques, such as residual learning, momentum, ADAM, batch normalization,
+dropout, and distillation.
+",0,0,0,1,0,0
+17991,"Optical signature of Weyl electronic structures in tantalum pnictides Ta$Pn$ ($Pn=$ P, As)","  To investigate the electronic structure of Weyl semimetals Ta$Pn$ ($Pn=$P,
+As), optical conductivity [$\sigma(\omega)$] spectra are measured over a wide
+range of photon energies and temperatures, and these measured values are
+compared with band calculations. Two significant structures can be observed: a
+bending structure at $\hbar\omega\sim$85 meV in TaAs, and peaks at
+$\hbar\omega\sim$ 50 meV (TaP) and $\sim$30 meV (TaAs). The bending structure
+can be explained by the interband transition between saddle points connecting a
+set of $W_2$ Weyl points. The temperature dependence of the peak intensity can
+be fitted by assuming the interband transition between saddle points connecting
+a set of $W_1$ Weyl points. Owing to the different temperature dependence of
+the Drude weight in both materials, it is found that the Weyl points of TaAs
+are located near the Fermi level, whereas those of TaP are further away.
+",0,1,0,0,0,0
+17992,"A study of cyber security in hospitality industry- threats and countermeasures: case study in Reno, Nevada","  The purpose of this study is to analyze cyber security and security practices
+of electronic information and network system, network threats, and techniques
+to prevent the cyber attacks in hotels. Helping the information technology
+directors and chief information officers (CIO) is the aim of this study to
+advance policy for security of electronic information in hotels and suggesting
+some techniques and tools to secure the computer networks. This research is
+completely qualitative while the case study and interviews have done in 5
+random hotels in Reno, Nevada, United States of America. The interview has done
+with 50 hotel guests, 10 front desk employees, 3 IT manager and 2 assistant of
+General manager. The results show that hotels' cyber security is very low and
+hotels are very vulnerable in this regard and at the end, the implications and
+contribution of the study is mentioned.
+",1,0,0,0,0,0
+17993,Weak Fraisse categories,"  We develop the theory of weak Fraisse categories, where the crucial concept
+is the weak amalgamation property, discovered relatively recently in model
+theory. We show that, in a suitable framework, every weak Fraisse category has
+its unique limit, a special object in a bigger category, characterized by
+certain variant of injectivity. This significantly extends the known theory of
+Fraisse limits.
+",0,0,1,0,0,0
+17994,Multiple Stakeholders in Music Recommender Systems,"  Music recommendation services collectively spin billions of songs for
+millions of listeners on a daily basis. Users can typically listen to a variety
+of songs tailored to their personal tastes and preferences. Music is not the
+only type of content encountered in these services, however. Advertisements are
+generally interspersed throughout the music stream to generate revenue for the
+business. Additional content may include artist messaging, ticketing, sports,
+news and weather. In this paper, we discuss issues that arise when multiple
+content providers are stakeholders in the recommendation process. These
+stakeholders each have their own objectives and must work in concert to sustain
+a healthy music recommendation service.
+",1,0,0,0,0,0
+17995,Large time behavior of solution to nonlinear Dirac equation in $1+1$ dimensions,"  This paper studies the large time behavior of solution for a class of
+nonlinear massless Dirac equations in $R^{1+1}$. It is shown that the solution
+will tend to travelling wave solution when time tends to infinity.
+",0,0,1,0,0,0
+17996,Compositions of Functions and Permutations Specified by Minimal Reaction Systems,"  This paper studies mathematical properties of reaction systems that was
+introduced by Enrenfeucht and Rozenberg as computational models inspired by
+biochemical reaction in the living cells. In particular, we continue the study
+on the generative power of functions specified by minimal reaction systems
+under composition initiated by Salomaa. Allowing degenerate reaction systems,
+functions specified by minimal reaction systems over a quarternary alphabet
+that are permutations generate the alternating group on the power set of the
+background set.
+",1,0,0,0,0,0
+17997,The center problem for the Lotka reactions with generalized mass-action kinetics,"  Chemical reaction networks with generalized mass-action kinetics lead to
+power-law dynamical systems. As a simple example, we consider the Lotka
+reactions and the resulting planar ODE. We characterize the parameters
+(positive coefficients and real exponents) for which the unique positive
+equilibrium is a center.
+",0,0,1,0,0,0
+17998,Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure,"  In this paper, we study the missing sample recovery problem using methods
+based on sparse approximation. In this regard, we investigate the algorithms
+used for solving the inverse problem associated with the restoration of missed
+samples of image signal. This problem is also known as inpainting in the
+context of image processing and for this purpose, we suggest an iterative
+sparse recovery algorithm based on constrained $l_1$-norm minimization with a
+new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index,
+is a simplified version of the Structural SIMilarity (SSIM) index, which is
+convex and error-sensitive. The optimization problem incorporating this
+criterion, is then solved via Alternating Direction Method of Multipliers
+(ADMM). Simulation results show the efficiency of the proposed method for
+missing sample recovery of 1D patch vectors and inpainting of 2D image signals.
+",1,0,0,1,0,0
+17999,Skin cancer reorganization and classification with deep neural network,"  As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based
+early detection and recarbonization strategy is critical for melanoma therapy.
+However, well-trained dermatologists dominant the diagnostic accuracy. In order
+to solve this problem, many effort focus on developing automatic image analysis
+systems. Here we report a novel strategy based on deep learning technique, and
+achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1)
+we build a segmentation neural network (skin_segnn), which achieved very high
+lesion boundary detection accuracy; 2) We build another very deep neural
+network based on Google inception v3 network (skin_recnn) and its well-trained
+weight. The novel designed transfer learning based deep neural network
+skin_inceptions_v3_nn helps to achieve a high prediction accuracy.
+",1,0,0,0,0,0
+18000,Rescaled extrapolation for vector-valued functions,"  We extend Rubio de Francia's extrapolation theorem for functions valued in
+UMD Banach function spaces, leading to short proofs of some new and known
+results. In particular we prove Littlewood-Paley-Rubio de Francia-type
+estimates and boundedness of variational Carleson operators for Banach function
+spaces with UMD concavifications.
+",0,0,1,0,0,0
+18001,Separation of the charge density wave and superconducting states by an intermediate semimetal phase in pressurized TaTe2,"  In layered transition metal dichalcogenides (LTMDCs) that display both charge
+density waves (CDWs) and superconductivity, the superconducting state generally
+emerges directly on suppression of the CDW state. Here, however, we report a
+different observation for pressurized TaTe2, a non-superconducting CDW-bearing
+LTMDC at ambient pressure. We find that a superconducting state does not occur
+in TaTe2 after the full suppression of its CDW state, which we observe at about
+3 GPa, but, rather, a non-superconducting semimetal state is observed. At a
+higher pressure, ~21 GPa, where both the semimetal state and the corresponding
+positive magnetoresistance effect are destroyed, superconductivity finally
+emerges and remains present up to ~50 GPa, the high pressure limit of our
+measurements. Our pressure-temperature phase diagram for TaTe2 demonstrates
+that the CDW and the superconducting phases in TaTe2 do not directly transform
+one to the other, but rather are separated by a semimetal state, - the first
+experimental case where the CDW and superconducting states are separated by an
+intermediate phase in LTMDC systems.
+",0,1,0,0,0,0
+18002,Zero distribution for Angelesco Hermite--Padé polynomials,"  We consider the problem of zero distribution of the first kind
+Hermite--Padé polynomials associated with a vector function $\vec f = (f_1,
+\dots, f_s)$ whose components $f_k$ are functions with a finite number of
+branch points in plane. We assume that branch sets of component functions are
+well enough separated (which constitute the Angelesco case). Under this
+condition we prove a theorem on limit zero distribution for such polynomials.
+The limit measures are defined in terms of a known vector equilibrium problem.
+Proof of the theorem is based on the methods developed by H.~Stahl,
+A.~A.~Gonchar and the author. These methods obtained some further
+generalization in the paper in application to systems of polynomials defined by
+systems of complex orthogonality relations.
+Together with the characterization of the limit zero distributions of
+Hermite--Padé polynomials by a vector equilibrium problem we consider an
+alternative characterization using a Riemann surface $\mathcal R(\vec f)$
+associated with $\vec f$. In this terms we present a more general (without
+Angelesco condition) conjecture on the zero distribution of Hermite--Padé
+polynomials.
+Bibliography: 72 items.
+",0,0,1,0,0,0
+18003,Atomic-scale identification of novel planar defect phases in heteroepitaxial YBa$_2$Cu$_3$O$_{7-δ}$ thin films,"  We have discovered two novel types of planar defects that appear in
+heteroepitaxial YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO123) thin films, grown by
+pulsed-laser deposition (PLD) either with or without a
+La$_{2/3}$Ca$_{1/3}$MnO$_3$ (LCMO) overlayer, using the combination of
+high-angle annular dark-field scanning transmission electron microscopy
+(HAADF-STEM) imaging and electron energy loss spectroscopy (EELS) mapping for
+unambiguous identification. These planar lattice defects are based on the
+intergrowth of either a BaO plane between two CuO chains or multiple Y-O layers
+between two CuO$_2$ planes, resulting in non-stoichiometric layer sequences
+that could directly impact the high-$T_c$ superconductivity.
+",0,1,0,0,0,0
+18004,Self-consistent assessment of Englert-Schwinger model on atomic properties,"  Our manuscript investigates a self-consistent solution of the statistical
+atom model proposed by Berthold-Georg Englert and Julian Schwinger (the ES
+model) and benchmarks it against atomic Kohn-Sham and two orbital-free models
+of the Thomas-Fermi-Dirac (TFD)-$\lambda$vW family. Results show that the ES
+model generally offers the same accuracy as the well-known TFD-$\frac{1}{5}$vW
+model; however, the ES model corrects the failure in Pauli potential
+near-nucleus region. We also point to the inability of describing low-$Z$ atoms
+as the foremost concern in improving the present model.
+",0,1,0,0,0,0
+18005,Target volatility option pricing in lognormal fractional SABR model,"  We examine in this article the pricing of target volatility options in the
+lognormal fractional SABR model. A decomposition formula by Ito's calculus
+yields a theoretical replicating strategy for the target volatility option,
+assuming the accessibilities of all variance swaps and swaptions. The same
+formula also suggests an approximation formula for the price of target
+volatility option in small time by the technique of freezing the coefficient.
+Alternatively, we also derive closed formed expressions for a small volatility
+of volatility expansion of the price of target volatility option. Numerical
+experiments show accuracy of the approximations in a reasonably wide range of
+parameters.
+",0,0,0,0,0,1
+18006,Robust Blind Deconvolution via Mirror Descent,"  We revisit the Blind Deconvolution problem with a focus on understanding its
+robustness and convergence properties. Provable robustness to noise and other
+perturbations is receiving recent interest in vision, from obtaining immunity
+to adversarial attacks to assessing and describing failure modes of algorithms
+in mission critical applications. Further, many blind deconvolution methods
+based on deep architectures internally make use of or optimize the basic
+formulation, so a clearer understanding of how this sub-module behaves, when it
+can be solved, and what noise injection it can tolerate is a first order
+requirement. We derive new insights into the theoretical underpinnings of blind
+deconvolution. The algorithm that emerges has nice convergence guarantees and
+is provably robust in a sense we formalize in the paper. Interestingly, these
+technical results play out very well in practice, where on standard datasets
+our algorithm yields results competitive with or superior to the state of the
+art. Keywords: blind deconvolution, robust continuous optimization
+",1,0,0,1,0,0
+18007,One-Dimensional Symmetry Protected Topological Phases and their Transitions,"  We present a unified perspective on symmetry protected topological (SPT)
+phases in one dimension and address the open question of what characterizes
+their phase transitions. In the first part of this work we use symmetry as a
+guide to map various well-known fermionic and spin SPTs to a Kitaev chain with
+coupling of range $\alpha \in \mathbb Z$. This unified picture uncovers new
+properties of old models --such as how the cluster state is the fixed point
+limit of the Affleck-Kennedy-Lieb-Tasaki state in disguise-- and elucidates the
+connection between fermionic and bosonic phases --with the Hubbard chain
+interpolating between four Kitaev chains and a spin chain in the Haldane phase.
+In the second part, we study the topological phase transitions between these
+models in the presence of interactions. This leads us to conjecture that the
+critical point between any SPT with $d$-dimensional edge modes and the trivial
+phase has a central charge $c \geq \log_2 d$. We analytically verify this for
+many known transitions. This agrees with the intuitive notion that the phase
+transition is described by a delocalized edge mode, and that the central charge
+of a conformal field theory is a measure of the gapless degrees of freedom.
+",0,1,0,0,0,0
+18008,"Proceedings Eighth International Symposium on Games, Automata, Logics and Formal Verification","  This volume contains the proceedings of the Eighth International Symposium on
+Games, Automata, Logic and Formal Verification (GandALF 2017). The symposium
+took place in Roma, Italy, from the 20th to the 22nd of September 2017. The
+GandALF symposium was established by a group of Italian computer scientists
+interested in mathematical logic, automata theory, game theory, and their
+applications to the specification, design, and verification of complex systems.
+Its aim is to provide a forum where people from different areas, and possibly
+with different backgrounds, can fruitfully interact. GandALF has a truly
+international spirit, as witnessed by the composition of the program and
+steering committee and by the country distribution of the submitted papers.
+",1,0,0,0,0,0
+18009,Valence Bonds in Random Quantum Magnets: Theory and Application to YbMgGaO4,"  We analyze the effect of quenched disorder on spin-1/2 quantum magnets in
+which magnetic frustration promotes the formation of local singlets. Our
+results include a theory for 2d valence-bond solids subject to weak bond
+randomness, as well as extensions to stronger disorder regimes where we make
+connections with quantum spin liquids. We find, on various lattices, that the
+destruction of a valence-bond solid phase by weak quenched disorder leads
+inevitably to the nucleation of topological defects carrying spin-1/2 moments.
+This renormalizes the lattice into a strongly random spin network with
+interesting low-energy excitations. Similarly when short-ranged valence bonds
+would be pinned by stronger disorder, we find that this putative glass is
+unstable to defects that carry spin-1/2 magnetic moments, and whose residual
+interactions decide the ultimate low energy fate. Motivated by these results we
+conjecture Lieb-Schultz-Mattis-like restrictions on ground states for
+disordered magnets with spin-1/2 per statistical unit cell. These conjectures
+are supported by an argument for 1d spin chains. We apply insights from this
+study to the phenomenology of YbMgGaO$_4$, a recently discovered triangular
+lattice spin-1/2 insulator which was proposed to be a quantum spin liquid. We
+instead explore a description based on the present theory. Experimental
+signatures, including unusual specific heat, thermal conductivity, and
+dynamical structure factor, and their behavior in a magnetic field, are
+predicted from the theory, and compare favorably with existing measurements on
+YbMgGaO$_4$ and related materials.
+",0,1,0,0,0,0
+18010,TuckER: Tensor Factorization for Knowledge Graph Completion,"  Knowledge graphs are structured representations of real world facts. However,
+they typically contain only a small subset of all possible facts. Link
+prediction is a task of inferring missing facts based on existing ones. We
+propose TuckER, a relatively simple but powerful linear model based on Tucker
+decomposition of the binary tensor representation of knowledge graph triples.
+TuckER outperforms all previous state-of-the-art models across standard link
+prediction datasets. We prove that TuckER is a fully expressive model, deriving
+the bound on its entity and relation embedding dimensionality for full
+expressiveness which is several orders of magnitude smaller than the bound of
+previous state-of-the-art models ComplEx and SimplE. We further show that
+several previously introduced linear models can be viewed as special cases of
+TuckER.
+",1,0,0,1,0,0
+18011,Taming Non-stationary Bandits: A Bayesian Approach,"  We consider the multi armed bandit problem in non-stationary environments.
+Based on the Bayesian method, we propose a variant of Thompson Sampling which
+can be used in both rested and restless bandit scenarios. Applying discounting
+to the parameters of prior distribution, we describe a way to systematically
+reduce the effect of past observations. Further, we derive the exact expression
+for the probability of picking sub-optimal arms. By increasing the exploitative
+value of Bayes' samples, we also provide an optimistic version of the
+algorithm. Extensive empirical analysis is conducted under various scenarios to
+validate the utility of proposed algorithms. A comparison study with various
+state-of-the-arm algorithms is also included.
+",1,0,0,1,0,0
+18012,Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization,"  We propose a new randomized coordinate descent method for a convex
+optimization template with broad applications. Our analysis relies on a novel
+combination of four ideas applied to the primal-dual gap function: smoothing,
+acceleration, homotopy, and coordinate descent with non-uniform sampling. As a
+result, our method features the first convergence rate guarantees among the
+coordinate descent methods, that are the best-known under a variety of common
+structure assumptions on the template. We provide numerical evidence to support
+the theoretical results with a comparison to state-of-the-art algorithms.
+",0,0,0,1,0,0
+18013,Guarantees for Greedy Maximization of Non-submodular Functions with Applications,"  We investigate the performance of the standard Greedy algorithm for
+cardinality constrained maximization of non-submodular nondecreasing set
+functions. While there are strong theoretical guarantees on the performance of
+Greedy for maximizing submodular functions, there are few guarantees for
+non-submodular ones. However, Greedy enjoys strong empirical performance for
+many important non-submodular functions, e.g., the Bayesian A-optimality
+objective in experimental design. We prove theoretical guarantees supporting
+the empirical performance. Our guarantees are characterized by a combination of
+the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$. In
+particular, we prove that Greedy enjoys a tight approximation guarantee of
+$\frac{1}{\alpha}(1- e^{-\gamma\alpha})$ for cardinality constrained
+maximization. In addition, we bound the submodularity ratio and curvature for
+several important real-world objectives, including the Bayesian A-optimality
+objective, the determinantal function of a square submatrix and certain linear
+programs with combinatorial constraints. We experimentally validate our
+theoretical findings for both synthetic and real-world applications.
+",1,0,1,0,0,0
+18014,Affine Metrics and Associated Algebroid Structures: Application to General Relativity,"  In this paper, algebroid bundle associated to affine metrics provide an
+structure for unification of gravity and electromagnetism and, geometrization
+of matter.
+",0,0,1,0,0,0
+18015,Controllability of temporal networks: An analysis using higher-order networks,"  The control of complex networks is a significant challenge, especially when
+the network topology of the system to be controlled is dynamic. Addressing this
+challenge, here we introduce a novel approach which allows exploring the
+controllability of temporal networks. Studying six empirical data sets, we
+particularly show that order correlations in the sequence of interactions can
+both increase or decrease the time needed to achieve full controllability.
+Counter-intuitively, we find that this effect can be opposite than the effect
+of order correlations on other dynamical processes. Specifically, we show that
+order correlations that speed up a diffusion process in a given system can slow
+down the control of the same system, and vice-versa. Building on the
+higher-order graphical modeling framework introduced in recent works, we
+further demonstrate that spectral properties of higher-order network topologies
+can be used to analytically explain this phenomenon.
+",1,1,0,0,0,0
+18016,Mapping momentum-dependent electron-phonon coupling and non-equilibrium phonon dynamics with ultrafast electron diffuse scattering,"  Despite their fundamental role in determining material properties, detailed
+momentum-dependent information on the strength of electron-phonon and
+phonon-phonon coupling (EPC and PPC, respectively) across the entire Brillouin
+zone (BZ) has proved difficult to obtain. Here we demonstrate that ultrafast
+electron diffuse scattering (UEDS) directly provides such information. By
+exploiting symmetry-based selection rules and time-resolution, scattering from
+different phonon branches can be distinguished even without energy resolution.
+Using graphite as a model system, we show that UEDS patterns map the relative
+EPC and PPC strength through their profound sensitivity to photoinduced changes
+in phonon populations. We measure strong EPC to the $K$-point transverse
+optical phonon of $A_1'$ symmetry ($K-A_1'$) and along the entire longitudinal
+optical branch between $\Gamma-K$, not only to the $\Gamma-E_{2g}$ phonon as
+previously emphasized. We also determine that the subsequent phonon relaxation
+pathway involves three stages; decay via several identifiable channels to
+transverse acoustic (TA) and longitudinal acoustic (LA) phonons (1-2 ps),
+intraband thermalization of the non-equilibrium TA/LA phonon populations (30-40
+ps) and interband relaxation of the LA/TA modes (115 ps). Combining UEDS with
+ultrafast angle-resolved photoelectron spectroscopy will yield a complete
+picture of the dynamics within and between electron and phonon subsystems,
+helping to unravel complex phases in which the intertwined nature of these
+systems have a strong influence on emergent properties.
+",0,1,0,0,0,0
+18017,Meridional Circulation Dynamics in a Cyclic Convective Dynamo,"  Surface observations indicate that the speed of the solar meridional
+circulation in the photosphere varies in anti-phase with the solar cycle. The
+current explanation for the source of this variation is that inflows into
+active regions alter the global surface pattern of the meridional circulation.
+When these localized inflows are integrated over a full hemisphere, they
+contribute to the slow down of the axisymmetric poleward horizontal component.
+The behavior of this large scale flow deep inside the convection zone remains
+largely unknown. Present helioseismic techniques are not sensitive enough to
+capture the dynamics of this weak large scale flow. Moreover, the large time of
+integration needed to map the meridional circulation inside the convection
+zone, also masks some of the possible dynamics on shorter timescales. In this
+work we examine the dynamics of the meridional circulation that emerges from a
+3D MHD global simulation of the solar convection zone. Our aim is to assess and
+quantify the behavior of meridional circulation deep inside the convection
+zone, where the cyclic large-scale magnetic field can reach considerable
+strength. Our analyses indicate that the meridional circulation morphology and
+amplitude are both highly influenced by the magnetic field, via the impact of
+magnetic torques on the global angular momentum distribution. A dynamic feature
+induced by these magnetic torques is the development of a prominent upward flow
+at mid latitudes in the lower convection zone that occurs near the equatorward
+edge of the toroidal bands and that peaks during cycle maximum. Globally, the
+dynamo-generated large-scale magnetic field drives variations in the meridional
+flow, in stark contrast to the conventional kinematic flux transport view of
+the magnetic field being advected passively by the flow.
+",0,1,0,0,0,0
+18018,Bayesian analysis of 210Pb dating,"  In many studies of environmental change of the past few centuries, 210Pb
+dating is used to obtain chronologies for sedimentary sequences. One of the
+most commonly used approaches to estimate the ages of depths in a sequence is
+to assume a constant rate of supply (CRS) or influx of `unsupported' 210Pb from
+the atmosphere, together with a constant or varying amount of `supported'
+210Pb. Current 210Pb dating models do not use a proper statistical framework
+and thus provide poor estimates of errors. Here we develop a new model for
+210Pb dating, where both ages and values of supported and unsupported 210Pb
+form part of the parameters. We apply our model to a case study from Canada as
+well as to some simulated examples. Our model can extend beyond the current CRS
+approach, deal with asymmetric errors and mix 210Pb with other types of dating,
+thus obtaining more robust, realistic and statistically better defined
+estimates.
+",0,0,0,1,0,0
+18019,Analytic continuation with Padé decomposition,"  The ill-posed analytic continuation problem for Green's functions or
+self-energies can be done using the Padé rational polynomial approximation.
+However, to extract accurate results from this approximation, high precision
+input data of the Matsubara Green's function are needed. The calculation of the
+Matsubara Green's function generally involves a Matsubara frequency summation
+which cannot be evaluated analytically. Numerical summation is requisite but it
+converges slowly with the increase of the Matsubara frequency. Here we show
+that this slow convergence problem can be significantly improved by utilizing
+the Padé decomposition approach to replace the Matsubara frequency summation
+by a Padé frequency summation, and high precision input data can be obtained
+to successfully perform the Padé analytic continuation.
+",0,1,0,0,0,0
+18020,Imperative Functional Programs that Explain their Work,"  Program slicing provides explanations that illustrate how program outputs
+were produced from inputs. We build on an approach introduced in prior work by
+Perera et al., where dynamic slicing was defined for pure higher-order
+functional programs as a Galois connection between lattices of partial inputs
+and partial outputs. We extend this approach to imperative functional programs
+that combine higher-order programming with references and exceptions. We
+present proofs of correctness and optimality of our approach and a
+proof-of-concept implementation and experimental evaluation.
+",1,0,0,0,0,0
+18021,Synthetic dimensions in ultracold molecules: quantum strings and membranes,"  Synthetic dimensions alter one of the most fundamental properties in nature,
+the dimension of space. They allow, for example, a real three-dimensional
+system to act as effectively four-dimensional. Driven by such possibilities,
+synthetic dimensions have been engineered in ongoing experiments with ultracold
+matter. We show that rotational states of ultracold molecules can be used as
+synthetic dimensions extending to many - potentially hundreds of - synthetic
+lattice sites. Microwaves coupling rotational states drive fully controllable
+synthetic inter-site tunnelings, enabling, for example, topological band
+structures. Interactions leads to even richer behavior: when molecules are
+frozen in a real space lattice with uniform synthetic tunnelings, dipole
+interactions cause the molecules to aggregate to a narrow strip in the
+synthetic direction beyond a critical interaction strength, resulting in a
+quantum string or a membrane, with an emergent condensate that lives on this
+string or membrane. All these phases can be detected using measurements of
+rotational state populations.
+",0,1,0,0,0,0
+18022,Self-Organization and The Origins of Life: The Managed-Metabolism Hypothesis,"  The managed-metabolism hypothesis suggests that a cooperation barrier must be
+overcome if self-producing chemical organizations are to transition from
+non-life to life. This barrier prevents un-managed, self-organizing,
+autocatalytic networks of molecular species from individuating into complex,
+cooperative organizations. The barrier arises because molecular species that
+could otherwise make significant cooperative contributions to the success of an
+organization will often not be supported within the organization, and because
+side reactions and other free-riding processes will undermine cooperation. As a
+result, the barrier seriously limits the possibility space that can be explored
+by un-managed organizations, impeding individuation, complex functionality and
+the transition to life. The barrier can be overcome comprehensively by
+appropriate management which implements a system of evolvable constraints. The
+constraints support beneficial co-operators and suppress free riders. In this
+way management can manipulate the chemical processes of an autocatalytic
+organization, producing novel processes that serve the interests of the
+organization as a whole and that could not arise and persist spontaneously in
+an un-managed chemical organization. Management self-organizes because it is
+able to capture some of the benefits that are produced when its management of
+an autocatalytic organization promotes beneficial cooperation. Selection
+therefore favours the emergence of managers that take over and manage chemical
+organizations so as to overcome the cooperation barrier. The managed-metabolism
+hypothesis shows that if management is to overcome the cooperation barrier
+comprehensively, its interventions must be digitally coded. In this way, the
+hypothesis accounts for the two-tiered structure of all living cells in which a
+digitally-coded genetic apparatus manages an analogically-informed metabolism.
+",0,1,0,0,0,0
+18023,"Pile-up Reduction, Bayesian Decomposition and Applications of Silicon Drift Detectors at LCLS","  Silicon drift detectors (SDDs) revolutionized spectroscopy in fields as
+diverse as geology and dentistry. For a subset of experiments at ultra-fast,
+x-ray free-electron lasers (FELs), SDDs can make substantial contributions.
+Often the unknown spectrum is interesting, carrying science data, or the
+background measurement is useful to identify unexpected signals. Many
+measurements involve only several discrete photon energies known a priori. We
+designed a pulse function (a combination of gradual step and exponential decay
+function) and demonstrated that for individual pulses the signal amplitude,
+peaking time, and pulse amplitude are interrelated and the signal amplitude and
+peaking time are obtained for each pulse by fitting. Avoiding pulse shaping
+reduced peaking times to tens of nanoseconds, resulting in reduced pulse
+pile-up and allowing decomposition of remaining pulse pile-up at photon
+separation times down to 100~ns while yielding time-of-arrival information with
+precision of 10~nanoseconds. At pulsed sources or high photon rates, photon
+pile-up still occurs. We showed that the area of one photon peaks is not
+suitable for estimating high photon rates while pile-up spectrum fitting is
+relatively simple and preferable to pile-up spectrum deconvolution. We
+developed a photon pile-up model for constant intensity sources, extended it to
+variable intensity sources (typical for FELs) and used it to fit a complex
+pile-up spectrum, demonstrating its accuracy. Based on the pile-up model, we
+developed a Bayesian pile-up decomposition method that allows decomposing
+pile-up of single events with up to 6 photons from 6 monochromatic lines with
+99% accuracy. The usefulness of SDDs will continue into the x-ray FEL era of
+science. Their successors, the ePixS hybrid pixel detectors, already offer
+hundreds of pixels, each with similar performance to an SDD, in a compact,
+robust and affordable package.
+",0,1,0,0,0,0
+18024,Online Learning to Rank in Stochastic Click Models,"  Online learning to rank is a core problem in information retrieval and
+machine learning. Many provably efficient algorithms have been recently
+proposed for this problem in specific click models. The click model is a model
+of how the user interacts with a list of documents. Though these results are
+significant, their impact on practice is limited, because all proposed
+algorithms are designed for specific click models and lack convergence
+guarantees in other models. In this work, we propose BatchRank, the first
+online learning to rank algorithm for a broad class of click models. The class
+encompasses two most fundamental click models, the cascade and position-based
+models. We derive a gap-dependent upper bound on the $T$-step regret of
+BatchRank and evaluate it on a range of web search queries. We observe that
+BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an
+existing algorithm for the cascade model.
+",1,0,0,1,0,0
+18025,Transforming Coroutining Logic Programs into Equivalent CHR Programs,"  We extend a technique called Compiling Control. The technique transforms
+coroutining logic programs into logic programs that, when executed under the
+standard left-to-right selection rule (and not using any delay features) have
+the same computational behavior as the coroutining program. In recent work, we
+revised Compiling Control and reformulated it as an instance of Abstract
+Conjunctive Partial Deduction. This work was mostly focused on the program
+analysis performed in Compiling Control. In the current paper, we focus on the
+synthesis of the transformed program. Instead of synthesizing a new logic
+program, we synthesize a CHR(Prolog) program which mimics the coroutining
+program. The synthesis to CHR yields programs containing only simplification
+rules, which are particularly amenable to certain static analysis techniques.
+The programs are also more concise and readable and can be ported to CHR
+implementations embedded in other languages than Prolog.
+",1,0,0,0,0,0
+18026,Gap and rings carved by vortices in protoplanetary dust,"  Large-scale vortices in protoplanetary disks are thought to form and survive
+for long periods of time. Hence, they can significantly change the global disk
+evolution and particularly the distribution of the solid particles embedded in
+the gas, possibly explaining asymmetries and dust concentrations recently
+observed at sub-millimeter and millimeter wavelengths. We investigate the
+spatial distribution of dust grains using a simple model of protoplanetary disk
+hosted by a giant gaseous vortex. We explore the dependence of the results on
+grain size and deduce possible consequences and predictions for observations of
+the dust thermal emission at sub-millimeter and millimeter wavelengths. Global
+2D simulations with a bi-fluid code are used to follow the evolution of a
+single population of solid particles aerodynamically coupled to the gas.
+Possible observational signatures of the dust thermal emission are obtained
+using simulators of ALMA and ngVLA observations. We find that a giant vortex
+not only captures dust grains with Stokes number St < 1 but can also affect the
+distribution of larger grains (with St '~' 1) carving a gap associated to a
+ring composed of incompletely trapped particles. The results are presented for
+different particle size and associated to their possible signatures in disk
+observations. Gap clearing in the dust spatial distribution could be due to the
+interaction with a giant gaseous vortex and their associated spiral waves,
+without the gravitational assistance of a planet. Hence, strong dust
+concentrations at short sub-mm wavelengths associated with a gap and an
+irregular ring at longer mm and cm wavelengths could indicate the presence of
+an unseen gaseous vortex.
+",0,1,0,0,0,0
+18027,Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning,"  This paper introduces a new nonlinear dictionary learning method for
+histograms in the probability simplex. The method leverages optimal transport
+theory, in the sense that our aim is to reconstruct histograms using so-called
+displacement interpolations (a.k.a. Wasserstein barycenters) between dictionary
+atoms; such atoms are themselves synthetic histograms in the probability
+simplex. Our method simultaneously estimates such atoms, and, for each
+datapoint, the vector of weights that can optimally reconstruct it as an
+optimal transport barycenter of such atoms. Our method is computationally
+tractable thanks to the addition of an entropic regularization to the usual
+optimal transportation problem, leading to an approximation scheme that is
+efficient, parallel and simple to differentiate. Both atoms and weights are
+learned using a gradient-based descent method. Gradients are obtained by
+automatic differentiation of the generalized Sinkhorn iterations that yield
+barycenters with entropic smoothing. Because of its formulation relying on
+Wasserstein barycenters instead of the usual matrix product between dictionary
+and codes, our method allows for nonlinear relationships between atoms and the
+reconstruction of input data. We illustrate its application in several
+different image processing settings.
+",1,0,0,1,0,0
+18028,A Generalization of Smillie's Theorem on Strongly Cooperative Tridiagonal Systems,"  Smillie (1984) proved an interesting result on the stability of nonlinear,
+time-invariant, strongly cooperative, and tridiagonal dynamical systems. This
+result has found many applications in models from various fields including
+biology, ecology, and chemistry. Smith (1991) has extended Smillie's result and
+proved entrainment in the case where the vector field is time-varying and
+periodic. We use the theory of linear totally nonnegative differential systems
+developed by Schwarz (1970) to give a generalization of these two results. This
+is based on weakening the requirement for strong cooperativity to
+cooperativity, and adding an additional observability-type condition.
+",1,0,0,0,0,0
+18029,Geospatial Semantics,"  Geospatial semantics is a broad field that involves a variety of research
+areas. The term semantics refers to the meaning of things, and is in contrast
+with the term syntactics. Accordingly, studies on geospatial semantics usually
+focus on understanding the meaning of geographic entities as well as their
+counterparts in the cognitive and digital world, such as cognitive geographic
+concepts and digital gazetteers. Geospatial semantics can also facilitate the
+design of geographic information systems (GIS) by enhancing the
+interoperability of distributed systems and developing more intelligent
+interfaces for user interactions. During the past years, a lot of research has
+been conducted, approaching geospatial semantics from different perspectives,
+using a variety of methods, and targeting different problems. Meanwhile, the
+arrival of big geo data, especially the large amount of unstructured text data
+on the Web, and the fast development of natural language processing methods
+enable new research directions in geospatial semantics. This chapter,
+therefore, provides a systematic review on the existing geospatial semantic
+research. Six major research areas are identified and discussed, including
+semantic interoperability, digital gazetteers, geographic information
+retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
+concepts.
+",1,0,0,0,0,0
+18030,Stability of the sum of two solitary waves for (gDNLS) in the energy space,"  In this paper, we continue the study in \cite{MiaoTX:DNLS:Stab}. We use the
+perturbation argument, modulational analysis and the energy argument in
+\cite{MartelMT:Stab:gKdV, MartelMT:Stab:NLS} to show the stability of the sum
+of two solitary waves with weak interactions for the generalized derivative
+Schrödinger equation (gDNLS) in the energy space. Here (gDNLS) hasn't the
+Galilean transformation invariance, the pseudo-conformal invariance and the
+gauge transformation invariance, and the case $\sigma>1$ we considered
+corresponds to the $L^2$-supercritical case.
+",0,0,1,0,0,0
+18031,A BERT Baseline for the Natural Questions,"  This technical note describes a new baseline for the Natural Questions. Our
+model is based on BERT and reduces the gap between the model F1 scores reported
+in the original dataset paper and the human upper bound by 30% and 50% relative
+for the long and short answer tasks respectively. This baseline has been
+submitted to the official NQ leaderboard at
+ai.google.com/research/NaturalQuestions and we plan to opensource the code for
+it in the near future.
+",1,0,0,0,0,0
+18032,Knowledge Evolution in Physics Research: An Analysis of Bibliographic Coupling Networks,"  Even as we advance the frontiers of physics knowledge, our understanding of
+how this knowledge evolves remains at the descriptive levels of Popper and
+Kuhn. Using the APS publications data sets, we ask in this letter how new
+knowledge is built upon old knowledge. We do so by constructing year-to-year
+bibliographic coupling networks, and identify in them validated communities
+that represent different research fields. We then visualize their evolutionary
+relationships in the form of alluvial diagrams, and show how they remain intact
+through APS journal splits. Quantitatively, we see that most fields undergo
+weak Popperian mixing, and it is rare for a field to remain isolated/undergo
+strong mixing. The sizes of fields obey a simple linear growth with
+recombination. We can also reliably predict the merging between two fields, but
+not for the considerably more complex splitting. Finally, we report a case
+study of two fields that underwent repeated merging and splitting around 1995,
+and how these Kuhnian events are correlated with breakthroughs on BEC, quantum
+teleportation, and slow light. This impact showed up quantitatively in the
+citations of the BEC field as a larger proportion of references from during and
+shortly after these events.
+",1,1,0,0,0,0
+18033,On a lower bound for the energy functional on a family of Hamiltonian minimal Lagrangian tori in $\mathbb{C}P^2$,"  We study the energy functional on the set of Lagrangian tori in
+$\mathbb{C}P^2$ . We prove that the value of the energy functional on a certain
+family of Hamiltonian minimal Lagrangian tori in $\mathbb{C}P^2$ is strictly
+larger than energy of the Clifford torus.
+",0,0,1,0,0,0
+18034,Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks,"  Viewing the trajectory of a patient as a dynamical system, a recurrent neural
+network was developed to learn the course of patient encounters in the
+Pediatric Intensive Care Unit (PICU) of a major tertiary care center. Data
+extracted from Electronic Medical Records (EMR) of about 12000 patients who
+were admitted to the PICU over a period of more than 10 years were leveraged.
+The RNN model ingests a sequence of measurements which include physiologic
+observations, laboratory results, administered drugs and interventions, and
+generates temporally dynamic predictions for in-ICU mortality at user-specified
+times. The RNN's ICU mortality predictions offer significant improvements over
+those from two clinically-used scores and static machine learning algorithms.
+",1,0,1,1,0,0
+18035,Global Strichartz estimates for the Schrödinger equation with non zero boundary conditions and applications,"  We consider the Schrödinger equation on a half space in any dimension with
+a class of nonhomogeneous boundary conditions including Dirichlet, Neuman and
+the so-called transparent boundary conditions. Building upon recent local in
+time Strichartz estimates (for Dirichlet boundary conditions), we obtain global
+Strichartz estimates for initial data in $H^s,\ 0\leq s\leq 2$ and boundary
+data in a natural space $\mathcal{H}^s$. For $s\geq 1/2$, the issue of
+compatibility conditions requires a thorough analysis of the $\mathcal{H}^s$
+space. As an application we solve nonlinear Schrödinger equations and
+construct global asymptotically linear solutions for small data. A discussion
+is included on the appropriate notion of scattering in this framework, and the
+optimality of the $\mathcal{H}^s$ space.
+",0,0,1,0,0,0
+18036,Structure of $^{20}$Ne states in the resonance $^{16}$O+$α$ elastic scattering,"  Background
+The nuclear structure of the cluster bands in $^{20}$Ne presents a challenge
+for different theoretical approaches. It is especially difficult to explain the
+broad 0$^+$, 2$^+$ states at 9 MeV excitation energy. Simultaneously, it is
+important to obtain more reliable experimental data for these levels in order
+to quantitatively assess the theoretical framework.
+Purpose
+To obtain new data on $^{20}$Ne $\alpha$ cluster structure. Method Thick
+target inverse kinematics technique was used to study the $^{16}$O+$\alpha$
+resonance elastic scattering and the data were analyzed using an \textit{R}
+matrix approach. The $^{20}$Ne spectrum, the cluster and nucleon spectroscopic
+factors were calculated using cluster-nucleon configuration interaction model
+(CNCIM).
+Results
+We determined the parameters of the broad resonances in
+\textsuperscript{20}Ne: 0$^+$ level at 8.77 $\pm$ 0.150 MeV with a width of 750
+(+500/-220) keV; 2$^+$ level at 8.75 $\pm$ 0.100 MeV with the width of 695
+$\pm$ 120 keV; the width of 9.48 MeV level of 65 $\pm$ 20 keV and showed that
+9.19 MeV, 2$^+$ level (if exists) should have width $\leq$ 10 keV. The detailed
+comparison of the theoretical CNCIM predictions with the experimental data on
+cluster states was made.
+Conclusions
+Our experimental results by the TTIK method generally confirm the adopted
+data on $\alpha$ cluster levels in $^{20}$Ne. The CNCIM gives a good
+description of the $^{20}$Ne positive parity states up to an excitation energy
+of $\sim$ 7 MeV, predicting reasonably well the excitation energy of the states
+and their cluster and single particle properties. At higher excitations, the
+qualitative disagreement with the experimentally observed structure is evident,
+especially for broad resonances.
+",0,1,0,0,0,0
+18037,Terminal-Pairability in Complete Bipartite Graphs,"  We investigate the terminal-pairibility problem in the case when the base
+graph is a complete bipartite graph, and the demand graph is also bipartite
+with the same color classes. We improve the lower bound on maximum value of
+$\Delta(D)$ which still guarantees that the demand graph $D$ is
+terminal-pairable in this setting. We also prove a sharp theorem on the maximum
+number of edges such a demand graph can have.
+",0,0,1,0,0,0
+18038,Rate-optimal Meta Learning of Classification Error,"  Meta learning of optimal classifier error rates allows an experimenter to
+empirically estimate the intrinsic ability of any estimator to discriminate
+between two populations, circumventing the difficult problem of estimating the
+optimal Bayes classifier. To this end we propose a weighted nearest neighbor
+(WNN) graph estimator for a tight bound on the Bayes classification error; the
+Henze-Penrose (HP) divergence. Similar to recently proposed HP estimators
+[berisha2016], the proposed estimator is non-parametric and does not require
+density estimation. However, unlike previous approaches the proposed estimator
+is rate-optimal, i.e., its mean squared estimation error (MSEE) decays to zero
+at the fastest possible rate of $O(1/M+1/N)$ where $M,N$ are the sample sizes
+of the respective populations. We illustrate the proposed WNN meta estimator
+for several simulated and real data sets.
+",0,0,0,1,0,0
+18039,"Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures","  Event detection is a critical feature in data-driven systems as it assists
+with the identification of nominal and anomalous behavior. Event detection is
+increasingly relevant in robotics as robots operate with greater autonomy in
+increasingly unstructured environments. In this work, we present an accurate,
+robust, fast, and versatile measure for skill and anomaly identification. A
+theoretical proof establishes the link between the derivative of the
+log-likelihood of the HMM filtered belief state and the latest emission
+probabilities. The key insight is the inverse relationship in which gradient
+analysis is used for skill and anomaly identification. Our measure showed
+better performance across all metrics than related state-of-the art works. The
+result is broadly applicable to domains that use HMMs for event detection.
+",1,0,0,0,0,0
+18040,Geometric features of Vessiot--Guldberg Lie algebras of conformal and Killing vector fields on $\mathbb{R}^2$,"  This paper locally classifies finite-dimensional Lie algebras of conformal
+and Killing vector fields on $\mathbb{R}^2$ relative to an arbitrary
+pseudo-Riemannian metric. Several results about their geometric properties are
+detailed, e.g. their invariant distributions and induced symplectic structures.
+Findings are illustrated with two examples of physical nature: the
+Milne--Pinney equation and the projective Schrödinger equation on the Riemann
+sphere.
+",0,1,0,0,0,0
+18041,Fermion inter-particle potentials in 5D and a dimensional restriction prescription to 4D,"  This work sets out to compute and discuss effects of spin, velocity and
+dimensionality on inter-particle potentials systematically derived from gauge
+field-theoretic models. We investigate the interaction of fermionic particles
+by the exchange of a vector field in a parity-preserving description in
+five-dimensional $(5D)$ space-time. A particular dimensional reduction
+prescription is adopted $-$ reduction by dimensional restriction $-$ and
+special effects, like a pseudo-spin dependence, show up in four dimensions
+$(4D)$. What we refer to as pseudo-spin shall be duly explained. The main idea
+we try to convey is that the calculation of the potentials in $5D$ and the
+consequent reduction to $4D$ exhibits new effects that are not present if the
+potential is calculated in $4D$ after the action has been reduced.
+",0,1,0,0,0,0
+18042,Characterizations of idempotent discrete uninorms,"  In this paper we provide an axiomatic characterization of the idempotent
+discrete uninorms by means of three conditions only: conservativeness,
+symmetry, and nondecreasing monotonicity. We also provide an alternative
+characterization involving the bisymmetry property. Finally, we provide a
+graphical characterization of these operations in terms of their contour plots,
+and we mention a few open questions for further research.
+",1,0,1,0,0,0
+18043,Constraints on the sum of neutrino masses using cosmological data including the latest extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample,"  We investigate the constraints on the sum of neutrino masses ($\Sigma m_\nu$)
+using the most recent cosmological data, which combines the distance
+measurement from baryonic acoustic oscillation in the extended Baryon
+Oscillation Spectroscopic Survey DR14 quasar sample with the power spectra of
+temperature and polarization anisotropies in the cosmic microwave background
+from the Planck 2015 data release. We also use other low-redshift observations
+including the baryonic acoustic oscillation at relatively low redshifts, the
+supernovae of type Ia and the local measurement of Hubble constant. In the
+standard cosmological constant $\Lambda$ cold dark matter plus massive neutrino
+model, we obtain the $95\%$ \acl{CL} upper limit to be $\Sigma
+m_\nu<0.129~\mathrm{eV}$ for the degenerate mass hierarchy, $\Sigma
+m_{\nu}<0.159~\mathrm{eV}$ for the normal mass hierarchy, and $\Sigma
+m_{\nu}<0.189~\mathrm{eV}$ for the inverted mass hierarchy. Based on Bayesian
+evidence, we find that the degenerate hierarchy is positively supported, and
+the current data combination can not distinguish normal and inverted
+hierarchies. Assuming the degenerate mass hierarchy, we extend our study to
+non-standard cosmological models including the generic dark energy, the spatial
+curvature, and the extra relativistic degrees of freedom, respectively, but
+find these models not favored by the data.
+",0,1,0,0,0,0
+18044,The effect of the choice of neural network depth and breadth on the size of its hypothesis space,"  We show that the number of unique function mappings in a neural network
+hypothesis space is inversely proportional to $\prod_lU_l!$, where $U_{l}$ is
+the number of neurons in the hidden layer $l$.
+",0,0,0,1,0,0
+18045,Fairwashing: the risk of rationalization,"  Black-box explanation is the problem of explaining how a machine learning
+model -- whose internal logic is hidden to the auditor and generally complex --
+produces its outcomes. Current approaches for solving this problem include
+model explanation, outcome explanation as well as model inspection. While these
+techniques can be beneficial by providing interpretability, they can be used in
+a negative manner to perform fairwashing, which we define as promoting the
+perception that a machine learning model respects some ethical values while it
+might not be the case. In particular, we demonstrate that it is possible to
+systematically rationalize decisions taken by an unfair black-box model using
+the model explanation as well as the outcome explanation approaches with a
+given fairness metric. Our solution, LaundryML, is based on a regularized rule
+list enumeration algorithm whose objective is to search for fair rule lists
+approximating an unfair black-box model. We empirically evaluate our
+rationalization technique on black-box models trained on real-world datasets
+and show that one can obtain rule lists with high fidelity to the black-box
+model while being considerably less unfair at the same time.
+",1,0,0,1,0,0
+18046,Volume Dependence of N-Body Bound States,"  We derive the finite-volume correction to the binding energy of an N-particle
+quantum bound state in a cubic periodic volume. Our results are applicable to
+bound states with arbitrary composition and total angular momentum, and in any
+number of spatial dimensions. The only assumptions are that the interactions
+have finite range. The finite-volume correction is a sum of contributions from
+all possible breakup channels. In the case where the separation is into two
+bound clusters, our result gives the leading volume dependence up to
+exponentially small corrections. If the separation is into three or more
+clusters, there is a power-law factor that is beyond the scope of this work,
+however our result again determines the leading exponential dependence. We also
+present two independent methods that use finite-volume data to determine
+asymptotic normalization coefficients. The coefficients are useful to determine
+low-energy capture reactions into weakly bound states relevant for nuclear
+astrophysics. Using the techniques introduced here, one can even extract the
+infinite-volume energy limit using data from a single-volume calculation. The
+derived relations are tested using several exactly solvable systems and
+numerical examples. We anticipate immediate applications to lattice
+calculations of hadronic, nuclear, and cold atomic systems.
+",0,1,1,0,0,0
+18047,Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees,"  Additive models, such as produced by gradient boosting, and full interaction
+models, such as classification and regression trees (CART), are widely used
+algorithms that have been investigated largely in isolation. We show that these
+models exist along a spectrum, revealing never-before-known connections between
+these two approaches. This paper introduces a novel technique called
+tree-structured boosting for creating a single decision tree, and shows that
+this method can produce models equivalent to CART or gradient boosted stumps at
+the extremes by varying a single parameter. Although tree-structured boosting
+is designed primarily to provide both the model interpretability and predictive
+performance needed for high-stake applications like medicine, it also can
+produce decision trees represented by hybrid models between CART and boosted
+stumps that can outperform either of these approaches.
+",1,0,0,1,0,0
+18048,Magnetic field influenced electron-impurity scattering and magnetotransport,"  We formulate a quasiclassical theory ($\omega_c\tau \lesssim 1$ with
+$\omega_c$ as the cyclotron frequency and $\tau$ as the relaxation time) to
+study the influence of magnetic field on electron-impurity scattering process
+in the two-dimensional electron gas. We introduce a general recipe based on an
+abstraction of the detailed impurity scattering process to define the
+scattering parameter such as the incoming and outgoing momentum and coordinate
+jump. In this picture, we can conveniently describe the skew scattering and
+coordinate jump, which will eventually modify the Boltzmann equation. We find
+an anomalous Hall resistivity different from the conventional Boltzmann-Drude
+result and a negative magnetoresistivity parabolic in magnetic field. The
+origin of these results has been analyzed. The relevance between our theory and
+recent simulation and experimental works is also discussed. Our theory
+dominates in dilute impurity system where the correlation effect is negligible.
+",0,1,0,0,0,0
+18049,Evidence for depletion of heavy silicon isotopes at comet 67P/Churyumov-Gerasimenko,"  Context. The Rosetta Orbiter Spectrometer for Ion and Neutral Analysis
+(ROSINA) was designed to measure the composition of the gas in the coma of
+comet 67P/Churyumov-Gerasimenko, the target of the European Space Agency's
+Rosetta mission. In addition to the volatiles, ROSINA measured refractories
+sputtered off the comet by the interaction of solar wind protons with the
+surface of the comet.
+Aims. The origin of different solar system materials is still heavily
+debated. Isotopic ratios can be used to distinguish between different
+reservoirs and investigate processes occurring during the formation of the
+solar system.
+Methods. ROSINA consisted of two mass spectrometers and a pressure sensor. In
+the ROSINA Double Focusing Mass Spectrometer (DFMS), the neutral gas of
+cometary origin was ionized and then deflected in an electric and a magnetic
+field that separated the ions based on their mass-to-charge ratio. The DFMS had
+a high mass resolution, dynamic range, and sensitivity that allowed detection
+of rare species and the known major volatiles.
+Results. We measured the relative abundance of all three stable silicon
+isotopes with the ROSINA instrument on board the Rosetta spacecraft.
+Furthermore, we measured $^{13}$C/$^{12}$C in C$_2$H$_4$, C$_2$H$_5$, and CO.
+The DFMS in situ measurements indicate that the average silicon isotopic
+composition shows depletion in the heavy isotopes $^{29}$Si and $^{30}$Si with
+respect to $^{28}$Si and solar abundances, while $^{13}$C to $^{12}$C is
+analytically indistinguishable from bulk planetary and meteorite compositions.
+Although the origin of the deficiency of the heavy silicon isotopes cannot be
+explained unambiguously, we discuss mechanisms that could have contributed to
+the measured depletion of the isotopes $^{29}$Si and $^{30}$Si.
+",0,1,0,0,0,0
+18050,"Design, Simulation, and Testing of a Flexible Actuated Spine for Quadruped Robots","  Walking quadruped robots face challenges in positioning their feet and
+lifting their legs during gait cycles over uneven terrain. The robot Laika is
+under development as a quadruped with a flexible, actuated spine designed to
+assist with foot movement and balance during these gaits. This paper presents
+the first set of hardware designs for the spine of Laika, a physical prototype
+of those designs, and tests in both hardware and simulations that show the
+prototype's capabilities. Laika's spine is a tensegrity structure, used for its
+advantages with weight and force distribution, and represents the first working
+prototype of a tensegrity spine for a quadruped robot. The spine bends by
+adjusting the lengths of the cables that separate its vertebrae, and twists
+using an actuated rotating vertebra at its center. The current prototype of
+Laika has stiff legs attached to the spine, and is used as a test setup for
+evaluation of the spine itself. This work shows the advantages of Laika's spine
+by demonstrating the spine lifting each of the robot's four feet, both as a
+form of balancing and as a precursor for a walking gait. These foot motions,
+using specific combinations of bending and rotation movements of the spine, are
+measured in both simulation and hardware experiments. Hardware data are used to
+calibrate the simulations, such that the simulations can be used for control of
+balancing or gait cycles in the future. Future work will attach actuated legs
+to Laika's spine, and examine balancing and gait cycles when combined with leg
+movements.
+",1,0,0,0,0,0
+18051,The landscape of NeuroImage-ing research,"  As the field of neuroimaging grows, it can be difficult for scientists within
+the field to gain and maintain a detailed understanding of its ever-changing
+landscape. While collaboration and citation networks highlight important
+contributions within the field, the roles of and relations among specific areas
+of study can remain quite opaque. Here, we apply techniques from network
+science to map the landscape of neuroimaging research documented in the journal
+NeuroImage over the past decade. We create a network in which nodes represent
+research topics, and edges give the degree to which these topics tend to be
+covered in tandem. The network displays small-world architecture, with
+communities characterized by common imaging modalities and medical
+applications, and with bridges that integrate these distinct subfields. Using
+node-level analysis, we quantify the structural roles of individual topics
+within the neuroimaging landscape, and find high levels of clustering within
+the structural MRI subfield as well as increasing participation among topics
+related to psychiatry. The overall prevalence of a topic is unrelated to the
+prevalence of its neighbors, but the degree to which a topic becomes more or
+less popular over time is strongly related to changes in the prevalence of its
+neighbors. Broadly, this work presents a cohesive model for understanding the
+landscape of neuroimaging research across the field, in broad subfields, and
+within specific topic areas.
+",1,0,0,1,0,0
+18052,To tune or not to tune the number of trees in random forest?,"  The number of trees T in the random forest (RF) algorithm for supervised
+learning has to be set by the user. It is controversial whether T should simply
+be set to the largest computationally manageable value or whether a smaller T
+may in some cases be better. While the principle underlying bagging is that
+""more trees are better"", in practice the classification error rate sometimes
+reaches a minimum before increasing again for increasing number of trees. The
+goal of this paper is four-fold: (i) providing theoretical results showing that
+the expected error rate may be a non-monotonous function of the number of trees
+and explaining under which circumstances this happens; (ii) providing
+theoretical results showing that such non-monotonous patterns cannot be
+observed for other performance measures such as the Brier score and the
+logarithmic loss (for classification) and the mean squared error (for
+regression); (iii) illustrating the extent of the problem through an
+application to a large number (n = 306) of datasets from the public database
+OpenML; (iv) finally arguing in favor of setting it to a computationally
+feasible large number, depending on convergence properties of the desired
+performance measure.
+",1,0,0,1,0,0
+18053,SPH Modeling of Short-crested Waves,"  This study investigates short-crested wave breaking over a planar beach by
+using the mesh-free Smoothed Particle Hydrodynamics model, GPUSPH. The
+short-crested waves are created by generating intersecting wave trains in a
+numerical wave basin. We examine the influence of beach slope, incident wave
+height, and incident wave angle on the generated short-crested waves.
+Short-crested wave breaking over a steeper beach generates stronger rip
+currents, and larger circulation cells in front of the beach. Intersecting wave
+trains with a larger incident wave height drive a more complicated
+short-crested wave field including isolated breakers and wave amplitude
+diffraction. Nearshore circulation induced by short-crested wave breaking is
+greatly influenced by the incident wave angle (or the rip current spacing).
+There is no secondary circulation cell between the nodal line and the antinodal
+line if the rip current spacing is narrow. However, there are multiple
+secondary circulation cells observed when the rip current spacing is relatively
+large.
+",0,1,0,0,0,0
+18054,Tunneling Field-Effect Junctions with WS$_2$ barrier,"  Transition metal dichalcogenides (TMDCs), with their two-dimensional
+structures and sizable bandgaps, are good candidates for barrier materials in
+tunneling field-effect transistor (TFET) formed from atomic precision vertical
+stacks of graphene and insulating crystals of a few atomic layers in thickness.
+We report first-principles study of the electronic properties of the
+Graphene/WS$_2$/Graphene sandwich structure revealing strong interface effects
+on dielectric properties and predicting a high ON/OFF ratio with an appropriate
+WS$_2$ thickness and a suitable range of the gate voltage. Both the band
+spin-orbit coupling splitting and the dielectric constant of the WS$_2$ layer
+depend on its thickness when in contact with the graphene electrodes,
+indicating strong influence from graphene across the interfaces. The dielectric
+constant is significantly reduced from the bulk WS$_2$ value. The effective
+barrier height varies with WS$_2$ thickness and can be tuned by a gate voltage.
+These results are critical for future nanoelectronic device designs.
+",0,1,0,0,0,0
+18055,Topological Larkin-Ovchinnikov phase and Majorana zero mode chain in bilayer superconducting topological insulator films,"  We theoretically study bilayer superconducting topological insulator film, in
+which superconductivity exists for both top and bottom surface states. We show
+that an in-plane magnetic field can drive the system into Larkin-Ovchinnikov
+(LO) phase, where electrons are paired with finite momenta. The LO phase is
+topologically non-trivial and characterized by a Z 2 topological invariant,
+leading to a Majorana zero mode chain along the edge perpendicular to in-plane
+magnetic fields.
+",0,1,0,0,0,0
+18056,Optimization and Analysis of Wireless Powered Multi-antenna Cooperative Systems,"  In this paper, we consider a three-node cooperative wireless powered
+communication system consisting of a multi-antenna hybrid access point (H-AP)
+and a single-antenna relay and a single-antenna user. The energy constrained
+relay and user first harvest energy in the downlink and then the relay assists
+the user using the harvested power for information transmission in the uplink.
+The optimal energy beamforming vector and the time split between harvest and
+cooperation are investigated. To reduce the computational complexity,
+suboptimal designs are also studied, where closed-form expressions are derived
+for the energy beamforming vector and the time split. For comparison purposes,
+we also present a detailed performance analysis in terms of the achievable
+outage probability and the average throughput of an intuitive energy
+beamforming scheme, where the H-AP directs all the energy towards the user. The
+findings of the paper suggest that implementing multiple antennas at the H-AP
+can significantly improve the system performance, and the closed-form
+suboptimal energy beamforming vector and time split yields near optimal
+performance. Also, for the intuitive beamforming scheme, a diversity order of
+(N+1)/2 can be achieved, where N is the number of antennas at the H-AP.
+",1,0,0,0,0,0
+18057,Asai cube L-functions and the local Langlands conjecture,"  Let $F$ be a non-archimedean locally compact field. We study a class of
+Langlands-Shahidi pairs $({\bf H},{\bf L})$, consisting of a quasi-split
+connected reductive group $\bf H$ over $F$ and a Levi subgroup $\bf L$ which is
+closely related to a product of restriction of scalars of ${\rm GL}_1$'s or
+${\rm GL}_2$'s. We prove the compatibility of the resulting local factors with
+the Langlands correspondence. In particular, let $E$ be a cubic separable
+extension of $F$. We consider a simply connected quasi-split semisimple group
+$\bf H$ over $F$ of type $D_4$, with triality corresponding to $E$, and let
+$\bf L$ be its Levi subgroup with derived group ${\rm Res}_{E/F} {\rm SL}_2$.
+In this way we obtain Asai cube local factors attached to irreducible smooth
+representations of ${\rm GL}_2(E)$; we prove that they are Weil-Deligne factors
+obtained via the local Langlands correspondence for ${\rm GL}_2(E)$ and tensor
+induction from $E$ to $F$. A consequence is that Asai cube $\gamma$- and
+$\varepsilon$-factors become stable under twists by highly ramified characters.
+",0,0,1,0,0,0
+18058,What's in a game? A theory of game models,"  Game semantics is a rich and successful class of denotational models for
+programming languages. Most game models feature a rather intuitive setup, yet
+surprisingly difficult proofs of such basic results as associativity of
+composition of strategies. We set out to unify these models into a basic
+abstract framework for game semantics, game settings. Our main contribution is
+the generic construction, for any game setting, of a category of games and
+strategies. Furthermore, we extend the framework to deal with innocence, and
+prove that innocent strategies form a subcategory. We finally show that our
+constructions cover many concrete cases, mainly among the early models and the
+very recent sheaf-based ones.
+",1,0,0,0,0,0
+18059,Proposal for the Detection of Magnetic Monopoles in Spin Ice via Nanoscale Magnetometry,"  We present a proposal for applying nanoscale magnetometry to the search for
+magnetic monopoles in the spin ice materials holmium and dysprosium titanate.
+Employing Monte Carlo simulations of the dipolar spin ice model, we find that
+when cooled to below $1.5\,$K these materials exhibit a sufficiently low
+monopole density to enable the direct observation of magnetic fields from
+individual monopoles. At these temperatures we demonstrate that noise
+spectroscopy can capture the intrinsic fluctuations associated with monopole
+dynamics, allowing one to isolate the qualitative effects associated with both
+the Coulomb interaction between monopoles and the topological constraints
+implied by Dirac strings. We describe in detail three different nanoscale
+magnetometry platforms (muon spin rotation, nitrogen vacancy defects, and
+nanoSQUID arrays) that can be used to detect monopoles in these experiments,
+and analyze the advantages of each.
+",0,1,0,0,0,0
+18060,Rule-Based Spanish Morphological Analyzer Built From Spell Checking Lexicon,"  Preprocessing tools for automated text analysis have become more widely
+available in major languages, but non-English tools are often still limited in
+their functionality. When working with Spanish-language text, researchers can
+easily find tools for tokenization and stemming, but may not have the means to
+extract more complex word features like verb tense or mood. Yet Spanish is a
+morphologically rich language in which such features are often identifiable
+from word form. Conjugation rules are consistent, but many special verbs and
+nouns take on different rules. While building a complete dictionary of known
+words and their morphological rules would be labor intensive, resources to do
+so already exist, in spell checkers designed to generate valid forms of known
+words. This paper introduces a set of tools for Spanish-language morphological
+analysis, built using the COES spell checking tools, to label person, mood,
+tense, gender and number, derive a word's root noun or verb infinitive, and
+convert verbs to their nominal form.
+",1,0,0,0,0,0
+18061,Learning Edge Representations via Low-Rank Asymmetric Projections,"  We propose a new method for embedding graphs while preserving directed edge
+information. Learning such continuous-space vector representations (or
+embeddings) of nodes in a graph is an important first step for using network
+information (from social networks, user-item graphs, knowledge bases, etc.) in
+many machine learning tasks.
+Unlike previous work, we (1) explicitly model an edge as a function of node
+embeddings, and we (2) propose a novel objective, the ""graph likelihood"", which
+contrasts information from sampled random walks with non-existent edges.
+Individually, both of these contributions improve the learned representations,
+especially when there are memory constraints on the total size of the
+embeddings. When combined, our contributions enable us to significantly improve
+the state-of-the-art by learning more concise representations that better
+preserve the graph structure.
+We evaluate our method on a variety of link-prediction task including social
+networks, collaboration networks, and protein interactions, showing that our
+proposed method learn representations with error reductions of up to 76% and
+55%, on directed and undirected graphs. In addition, we show that the
+representations learned by our method are quite space efficient, producing
+embeddings which have higher structure-preserving accuracy but are 10 times
+smaller.
+",1,0,0,1,0,0
+18062,"Inference, Prediction, and Control of Networked Epidemics","  We develop a feedback control method for networked epidemic spreading
+processes. In contrast to most prior works which consider mean field, open-loop
+control schemes, the present work develops a novel framework for feedback
+control of epidemic processes which leverages incomplete observations of the
+stochastic epidemic process in order to control the exact dynamics of the
+epidemic outbreak. We develop an observation model for the epidemic process,
+and demonstrate that if the set of observed nodes is sufficiently well
+structured, then the random variables which denote the process' infections are
+conditionally independent given the observations. We then leverage the attained
+conditional independence property to construct tractable mechanisms for the
+inference and prediction of the process state, avoiding the need to use mean
+field approximations or combinatorial representations. We conclude by
+formulating a one-step lookahead controller for the discrete-time
+Susceptible-Infected-Susceptible (SIS) epidemic process which leverages the
+developed Bayesian inference and prediction mechanisms, and causes the epidemic
+to die out at a chosen rate.
+",0,0,1,0,0,0
+18063,The Weisfeiler-Leman algorithm and the diameter of Schreier graphs,"  We prove that the number of iterations taken by the Weisfeiler-Leman
+algorithm for configurations coming from Schreier graphs is closely linked to
+the diameter of the graphs themselves: an upper bound is found for general
+Schreier graphs, and a lower bound holds for particular cases, such as for
+Schreier graphs with $G=\mbox{SL}_{n}({\mathbb F}_{q})$ ($q>2$) acting on
+$k$-tuples of vectors in ${\mathbb F}_{q}^{n}$; moreover, an exact expression
+is found in the case of Cayley graphs.
+",0,0,1,0,0,0
+18064,Correlations between primes in short intervals on curves over finite fields,"  We prove an analogue of the Hardy-Littlewood conjecture on the asymptotic
+distribution of prime constellations in the setting of short intervals in
+function fields of smooth projective curves over finite fields.
+",0,0,1,0,0,0
+18065,Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions,"  Besides enabling an enhanced mobile broadband, next generation of mobile
+networks (5G) are envisioned for the support of massive connectivity of
+heterogeneous Internet of Things (IoT)s. These IoTs are envisioned for a large
+number of use-cases including smart cities, environment monitoring, smart
+vehicles, etc. Unfortunately, most IoTs have very limited computing and storage
+capabilities and need cloud services. Hence, connecting these devices through
+5G systems requires huge spectrum resources in addition to handling the massive
+connectivity and improved security. This article discusses the challenges
+facing the support of IoTs through 5G systems. The focus is devoted to
+discussing physical layer limitations in terms of spectrum resources and radio
+access channel connectivity. We show how sparsity can be exploited for
+addressing these challenges especially in terms of enabling wideband spectrum
+management and handling the connectivity by exploiting device-to-device
+communications and edge-cloud. Moreover, we identify major open problems and
+research directions that need to be explored towards enabling the support of
+massive heterogeneous IoTs through 5G systems.
+",1,0,0,0,0,0
+18066,Existence and convexity of local solutions to degenerate hessian equations,"  In this work, we prove the existence of local convex solution to the
+degenerate Hessian equation
+",0,0,1,0,0,0
+18067,Superexponential estimates and weighted lower bounds for the square function,"  We prove the following superexponential distribution inequality: for any
+integrable $g$ on $[0,1)^{d}$ with zero average, and any $\lambda>0$ \[ |\{ x
+\in [0,1)^{d} \; :\; g \geq\lambda \}| \leq e^{-
+\lambda^{2}/(2^{d}\|S(g)\|_{\infty}^{2})}, \] where $S(g)$ denotes the
+classical dyadic square function in $[0,1)^{d}$. The estimate is sharp when
+dimension $d$ tends to infinity in the sense that the constant $2^{d}$ in the
+denominator cannot be replaced by $C2^{d}$ with $0<C<1$ independent of $d$ when
+$d \to \infty$.
+For $d=1$ this is a classical result of Chang--Wilson--Wolff [4]; however, in
+the case $d>1$ they work with a special square function $S_\infty$, and their
+result does not imply the estimates for the classical square function.
+Using good $\lambda$ inequalities technique we then obtain unweighted and
+weighted $L^p$ lower bounds for $S$; to get the corresponding good $\lambda$
+inequalities we need to modify the classical construction.
+We also show how to obtain our superexponential distribution inequality
+(although with worse constants) from the weighted $L^2$ lower bounds for $S$,
+obtained in [5].
+",0,0,1,0,0,0
+18068,Reframing the S\&P500 Network of Stocks along the \nth{21} Century,"  Since the beginning of the new millennium, stock markets went through every
+state from long-time troughs, trade suspensions to all-time highs. The
+literature on asset pricing hence assumes random processes to be underlying the
+movement of stock returns. Observed procyclicality and time-varying correlation
+of stock returns tried to give the apparently random behavior some sort of
+structure. However, common misperceptions about the co-movement of asset prices
+in the years preceding the \emph{Great Recession} and the \emph{Global
+Commodity Crisis}, is said to have even fueled the crisis' economic impact.
+Here we show how a varying macroeconomic environment influences stocks'
+clustering into communities. From a sample of 296 stocks of the S\&P 500 index,
+distinct periods in between 2004 and 2011 are used to develop networks of
+stocks. The Minimal Spanning Tree analysis of those time-varying networks of
+stocks demonstrates that the crises of 2007-2008 and 2010-2011 drove the market
+to clustered community structures in both periods, helping to restore the stock
+market's ceased order of the pre-crises era. However, a comparison of the
+emergent clusters with the \textit{General Industry Classification Standard}
+conveys the impression that industry sectors do not play a major role in that
+order.
+",0,0,0,0,0,1
+18069,Point-contact spectroscopy of superconducting energy gap in $\rm DyNi_2B_2C$,"  The superconducting energy gap in $\rm DyNi_2B_2C$ has been investigated
+using a point-contact technique based on the Andreev reflection from a normal
+(N)-superconductor (S) boundary, where N is Ag. The observed differential
+resistance $dV/dI$ is well described by the Blonder-Tinkham-Klapwijk (BTK)
+theory based on the BSC density of states with zero broadening parameter.
+Typically, the intensity of the gap structure amounts to several percentage of
+the normal state resistance, which is an order of magnitude less than predicted
+by the theory. For $\rm DyNi_2B_2C$ with $T_c<T_N$ (the Neel temperature), we
+found gap values satisfying the ratio of $2\Delta_0/k_BT_c=3.63\pm 0.05$
+similar to other superconducting nickel-borocarbides, both nonmagnetic and
+magnetic with $T_c\geq T_N$. The superconducting gap nonlinearity is
+superimposed on the antiferromagnetic structure in $dV/dI(V)$ which is
+suppressed at the magnetic field of the order of 3T applied nominally in the
+$ab$-plane and temperature $\geq 11~K$. The observed superconducting properties
+depend on the exact composition and structure at the surface of the crystal.
+",0,1,0,0,0,0
+18070,Parameterized Complexity of Safe Set,"  In this paper we study the problem of finding a small safe set $S$ in a graph
+$G$, i.e. a non-empty set of vertices such that no connected component of
+$G[S]$ is adjacent to a larger component in $G - S$. We enhance our
+understanding of the problem from the viewpoint of parameterized complexity by
+showing that (1) the problem is W[2]-hard when parameterized by the pathwidth
+$pw$ and cannot be solved in time $n^{o(pw)}$ unless the ETH is false, (2) it
+admits no polynomial kernel parameterized by the vertex cover number $vc$
+unless $\mathrm{PH} = \Sigma^{\mathrm{p}}_{3}$, but (3) it is fixed-parameter
+tractable (FPT) when parameterized by the neighborhood diversity $nd$, and (4)
+it can be solved in time $n^{f(cw)}$ for some double exponential function $f$
+where $cw$ is the clique-width. We also present (5) a faster FPT algorithm when
+parameterized by solution size.
+",1,0,0,0,0,0
+18071,A Matrix Variate Skew-t Distribution,"  Although there is ample work in the literature dealing with skewness in the
+multivariate setting, there is a relative paucity of work in the matrix variate
+paradigm. Such work is, for example, useful for modelling three-way data. A
+matrix variate skew-t distribution is derived based on a mean-variance matrix
+normal mixture. An expectation-conditional maximization algorithm is developed
+for parameter estimation. Simulated data are used for illustration.
+",0,0,1,1,0,0
+18072,Status Updates Through Multicast Networks,"  Using age of information as the freshness metric, we examine a multicast
+network in which real-time status updates are generated by the source and sent
+to a group of $n$ interested receivers. We show that in order to keep the
+information freshness at each receiver, the source should terminate the
+transmission of the current update and start sending a new update packet as
+soon as it receives the acknowledgements back from any $k$ out of $n$ nodes. As
+the source stopping threshold $k$ increases, a node is more likely to get the
+latest generated update, but the age of the most recent update is more likely
+to become outdated. We derive the age minimized stopping threshold $k$ that
+balances the likelihood of getting the latest update and the freshness of the
+latest update for shifted exponential link delay. Through numerical evaluations
+for different stopping strategies, we find that waiting for the
+acknowledgements from the earliest $k$ out of $n$ nodes leads to lower average
+age than waiting for a pre-selected group of $k$ nodes. We also observe that a
+properly chosen threshold $k$ can prevent information staleness for increasing
+number of nodes $n$ in the multicast network.
+",1,0,0,0,0,0
+18073,Extension of Convolutional Neural Network with General Image Processing Kernels,"  We applied pre-defined kernels also known as filters or masks developed for
+image processing to convolution neural network. Instead of letting neural
+networks find its own kernels, we used 41 different general-purpose kernels of
+blurring, edge detecting, sharpening, discrete cosine transformation, etc. for
+the first layer of the convolution neural networks. This architecture, thus
+named as general filter convolutional neural network (GFNN), can reduce
+training time by 30% with a better accuracy compared to the regular
+convolutional neural network (CNN). GFNN also can be trained to achieve 90%
+accuracy with only 500 samples. Furthermore, even though these kernels are not
+specialized for the MNIST dataset, we achieved 99.56% accuracy without ensemble
+nor any other special algorithms.
+",1,0,0,1,0,0
+18074,Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network,"  This paper proposes a neural network architecture and training scheme to
+learn the start and end time of sound events (strong labels) in an audio
+recording given just the list of sound events existing in the audio without
+time information (weak labels). We achieve this by using a stacked
+convolutional and recurrent neural network with two prediction layers in
+sequence one for the strong followed by the weak label. The network is trained
+using frame-wise log mel-band energy as the input audio feature, and weak
+labels provided in the dataset as labels for the weak label prediction layer.
+Strong labels are generated by replicating the weak labels as many number of
+times as the frames in the input audio feature, and used for strong label layer
+during training. We propose to control what the network learns from the weak
+and strong labels by different weighting for the loss computed in the two
+prediction layers. The proposed method is evaluated on a publicly available
+dataset of 155 hours with 17 sound event classes. The method achieves the best
+error rate of 0.84 for strong labels and F-score of 43.3% for weak labels on
+the unseen test split.
+",1,0,0,0,0,0
+18075,Categoricity and Universal Classes,"  Let $(\mathcal{K} ,\subseteq )$ be a universal class with
+$LS(\mathcal{K})=\lambda$ categorical in regular $\kappa >\lambda^+$ with
+arbitrarily large models, and let $\mathcal{K}^*$ be the class of all
+$\mathcal{A}\in\mathcal{K}_{>\lambda}$ for which there is $\mathcal{B} \in
+\mathcal{K}_{\ge\kappa}$ such that $\mathcal{A}\subseteq\mathcal{B}$. We prove
+that $\mathcal{K}^*$ is categorical in every $\xi >\lambda^+$,
+$\mathcal{K}_{\ge\beth_{(2^{\lambda^+})^+}} \subseteq \mathcal{K}^{*}$, and the
+models of $\mathcal{K}^*_{>\lambda^+}$ are essentially vector spaces (or
+trivial i.e. disintegrated).
+",0,0,1,0,0,0
+18076,"Water, High-Altitude Condensates, and Possible Methane Depletion in the Atmosphere of the Warm Super-Neptune WASP-107b","  The super-Neptune exoplanet WASP-107b is an exciting target for atmosphere
+characterization. It has an unusually large atmospheric scale height and a
+small, bright host star, raising the possibility of precise constraints on its
+current nature and formation history. We report the first atmospheric study of
+WASP-107b, a Hubble Space Telescope measurement of its near-infrared
+transmission spectrum. We determined the planet's composition with two
+techniques: atmospheric retrieval based on the transmission spectrum and
+interior structure modeling based on the observed mass and radius. The interior
+structure models set a $3\,\sigma$ upper limit on the atmospheric metallicity
+of $30\times$ solar. The transmission spectrum shows strong evidence for water
+absorption ($6.5\,\sigma$ confidence), and the retrieved water abundance is
+consistent with expectations for a solar abundance pattern. The inferred
+carbon-to-oxygen ratio is subsolar at $2.7\,\sigma$ confidence, which we
+attribute to possible methane depletion in the atmosphere. The spectral
+features are smaller than predicted for a cloud-free composition, crossing less
+than one scale height. A thick condensate layer at high altitudes (0.1 - 3
+mbar) is needed to match the observations. We find that physically motivated
+cloud models with moderate sedimentation efficiency ($f_\mathrm{sed} = 0.3$) or
+hazes with a particle size of 0.3 $\mu$m reproduce the observed spectral
+feature amplitude. Taken together, these findings serve as an illustration of
+the diversity and complexity of exoplanet atmospheres. The community can look
+forward to more such results with the high precision and wide spectral coverage
+afforded by future observing facilities.
+",0,1,0,0,0,0
+18077,Partial Order on the set of Boolean Regulatory Functions,"  Logical models have been successfully used to describe regulatory and
+signaling networks without requiring quantitative data. However, existing data
+is insufficient to adequately define a unique model, rendering the
+parametrization of a given model a difficult task.
+Here, we focus on the characterization of the set of Boolean functions
+compatible with a given regulatory structure, i.e. the set of all monotone
+nondegenerate Boolean functions. We then propose an original set of rules to
+locally explore the direct neighboring functions of any function in this set,
+without explicitly generating the whole set. Also, we provide relationships
+between the regulatory functions and their corresponding dynamics.
+Finally, we illustrate the usefulness of this approach by revisiting
+Probabilistic Boolean Networks with the model of T helper cell differentiation
+from Mendoza & Xenarios.
+",1,0,0,0,1,0
+18078,Scalable solvers for complex electromagnetics problems,"  In this work, we present scalable balancing domain decomposition by
+constraints methods for linear systems arising from arbitrary order edge finite
+element discretizations of multi-material and heterogeneous 3D problems. In
+order to enforce the continuity across subdomains of the method, we use a
+partition of the interface objects (edges and faces) into sub-objects
+determined by the variation of the physical coefficients of the problem. For
+multi-material problems, a constant coefficient condition is enough to define
+this sub-partition of the objects. For arbitrarily heterogeneous problems, a
+relaxed version of the method is defined, where we only require that the
+maximal contrast of the physical coefficient in each object is smaller than a
+predefined threshold. Besides, the addition of perturbation terms to the
+preconditioner is empirically shown to be effective in order to deal with the
+case where the two coefficients of the model problem jump simultaneously across
+the interface. The new method, in contrast to existing approaches for problems
+in curl-conforming spaces, preserves the simplicity of the original
+preconditioner, i.e., no spectral information is required, whilst providing
+robustness with regard to coefficient jumps and heterogeneous materials. A
+detailed set of numerical experiments, which includes the application of the
+preconditioner to 3D realistic cases, shows excellent weak scalability
+properties of the implementation of the proposed algorithms.
+",1,0,0,0,0,0
+18079,A flux-splitting method for hyperbolic-equation system of magnetized electron fluids in quasi-neutral plasmas,"  A flux-splitting method is proposed for the hyperbolic-equation system (HES)
+of magnetized electron fluids in quasi-neutral plasmas. The numerical fluxes
+are split into four categories, which are computed by using an upwind method
+which incorporates a flux-vector splitting (FVS) and advection upstream
+splitting method (AUSM). The method is applied to a test calculation condition
+of uniformly distributed and angled magnetic lines of force. All of the
+pseudo-time advancement terms converge monotonically and the conservation laws
+are strictly satisfied in the steady state. The calculation results are
+compared with those computed by using the elliptic-parabolic-equation system
+(EPES) approach using a magnetic-field-aligned mesh (MFAM). Both qualitative
+and quantitative comparisons yield good agreements of results, indicating that
+the HES approach with the flux-splitting method attains a high computational
+accuracy.
+",0,1,0,0,0,0
+18080,TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation,"  We address unsupervised optical flow estimation for ego-centric motion. We
+argue that optical flow can be cast as a geometrical warping between two
+successive video frames and devise a deep architecture to estimate such
+transformation in two stages. First, a dense pixel-level flow is computed with
+a geometric prior imposing strong spatial constraints. Such prior is typical of
+driving scenes, where the point of view is coherent with the vehicle motion. We
+show how such global transformation can be approximated with an homography and
+how spatial transformer layers can be employed to compute the flow field
+implied by such transformation. The second stage then refines the prediction
+feeding a second deeper network. A final reconstruction loss compares the
+warping of frame X(t) with the subsequent frame X(t+1) and guides both
+estimates. The model, which we named TransFlow, performs favorably compared to
+other unsupervised algorithms, and shows better generalization compared to
+supervised methods with a 3x reduction in error on unseen data.
+",1,0,0,0,0,0
+18081,A General Pipeline for 3D Detection of Vehicles,"  Autonomous driving requires 3D perception of vehicles and other objects in
+the in environment. Much of the current methods support 2D vehicle detection.
+This paper proposes a flexible pipeline to adopt any 2D detection network and
+fuse it with a 3D point cloud to generate 3D information with minimum changes
+of the 2D detection networks. To identify the 3D box, an effective model
+fitting algorithm is developed based on generalised car models and score maps.
+A two-stage convolutional neural network (CNN) is proposed to refine the
+detected 3D box. This pipeline is tested on the KITTI dataset using two
+different 2D detection networks. The 3D detection results based on these two
+networks are similar, demonstrating the flexibility of the proposed pipeline.
+The results rank second among the 3D detection algorithms, indicating its
+competencies in 3D detection.
+",0,0,0,1,0,0
+18082,Search for CII Emission on Cosmological Scales at Redshift Z~2.6,"  We present a search for CII emission over cosmological scales at
+high-redshifts. The CII line is a prime candidate to be a tracer of star
+formation over large-scale structure since it is one of the brightest emission
+lines from galaxies. Redshifted CII emission appears in the submillimeter
+regime, meaning it could potentially be present in the higher frequency
+intensity data from the Planck satellite used to measure the cosmic infrared
+background (CIB). We search for CII emission over redshifts z=2-3.2 in the
+Planck 545 GHz intensity map by cross-correlating the 3 highest frequency
+Planck maps with spectroscopic quasars and CMASS galaxies from the Sloan
+Digital Sky Survey III (SDSS-III), which we then use to jointly fit for CII
+intensity, CIB parameters, and thermal Sunyaev-Zeldovich (SZ) emission. We
+report a measurement of an anomalous emission
+$\mathrm{I_\nu}=6.6^{+5.0}_{-4.8}\times10^4$ Jy/sr at 95% confidence, which
+could be explained by CII emission, favoring collisional excitation models of
+CII emission that tend to be more optimistic than models based on CII
+luminosity scaling relations from local measurements; however, a comparison of
+Bayesian information criteria reveal that this model and the CIB & SZ only
+model are equally plausible. Thus, more sensitive measurements will be needed
+to confirm the existence of large-scale CII emission at high redshifts.
+Finally, we forecast that intensity maps from Planck cross-correlated with
+quasars from the Dark Energy Spectroscopic Instrument (DESI) would increase our
+sensitivity to CII emission by a factor of 5, while the proposed Primordial
+Inflation Explorer (PIXIE) could increase the sensitivity further.
+",0,1,0,0,0,0
+18083,Ricci flow and diffeomorphism groups of 3-manifolds,"  We complete the proof of the Generalized Smale Conjecture, apart from the
+case of $RP^3$, and give a new proof of Gabai's theorem for hyperbolic
+3-manifolds. We use an approach based on Ricci flow through singularities,
+which applies uniformly to spherical space forms other than $S^3$ and $RP^3$
+and hyperbolic manifolds, to prove that the moduli space of metrics of constant
+sectional curvature is contractible. As a corollary, for such a 3-manifold $X$,
+the inclusion $\text{Isom} (X,g)\to \text{Diff}(X)$ is a homotopy equivalence
+for any Riemannian metric $g$ of constant sectional curvature.
+",0,0,1,0,0,0
+18084,RLlib: Abstractions for Distributed Reinforcement Learning,"  Reinforcement learning (RL) algorithms involve the deep nesting of highly
+irregular computation patterns, each of which typically exhibits opportunities
+for distributed computation. We argue for distributing RL components in a
+composable way by adapting algorithms for top-down hierarchical control,
+thereby encapsulating parallelism and resource requirements within
+short-running compute tasks. We demonstrate the benefits of this principle
+through RLlib: a library that provides scalable software primitives for RL.
+These primitives enable a broad range of algorithms to be implemented with high
+performance, scalability, and substantial code reuse. RLlib is available at
+this https URL.
+",1,0,0,0,0,0
+18085,A Multiscale-Analysis of Stochastic Bistable Reaction-Diffusion Equations,"  A multiscale analysis of 1D stochastic bistable reaction-diffusion equations
+with additive noise is carried out w.r.t. travelling waves within the
+variational approach to stochastic partial differential equations. It is shown
+with explicit error estimates on appropriate function spaces that up to lower
+order w.r.t. the noise amplitude, the solution can be decomposed into the
+orthogonal sum of a travelling wave moving with random speed and into Gaussian
+fluctuations. A stochastic differential equation describing the speed of the
+travelling wave and a linear stochastic partial differential equation
+describing the fluctuations are derived in terms of the coefficients. Our
+results extend corresponding results obtained for stochastic neural field
+equations to the present class of stochastic dynamics.
+",0,0,1,0,0,0
+18086,MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis,"  Interpretability has emerged as a crucial aspect of machine learning, aimed
+at providing insights into the working of complex neural networks. However,
+existing solutions vary vastly based on the nature of the interpretability
+task, with each use case requiring substantial time and effort. This paper
+introduces MARGIN, a simple yet general approach to address a large set of
+interpretability tasks ranging from identifying prototypes to explaining image
+predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
+influential nodes in a graph, which are defined as those nodes that maximally
+describe a function defined on the graph. By carefully defining task-specific
+graphs and functions, we demonstrate that MARGIN outperforms existing
+approaches in a number of disparate interpretability challenges.
+",1,0,0,1,0,0
+18087,SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability,"  We propose a new technique, Singular Vector Canonical Correlation Analysis
+(SVCCA), a tool for quickly comparing two representations in a way that is both
+invariant to affine transform (allowing comparison between different layers and
+networks) and fast to compute (allowing more comparisons to be calculated than
+with previous methods). We deploy this tool to measure the intrinsic
+dimensionality of layers, showing in some cases needless over-parameterization;
+to probe learning dynamics throughout training, finding that networks converge
+to final representations from the bottom up; to show where class-specific
+information in networks is formed; and to suggest new training regimes that
+simultaneously save computation and overfit less. Code:
+this https URL
+",1,0,0,1,0,0
+18088,Discrete Choice and Rational Inattention: a General Equivalence Result,"  This paper establishes a general equivalence between discrete choice and
+rational inattention models. Matejka and McKay (2015, AER) showed that when
+information costs are modelled using the Shannon entropy function, the
+resulting choice probabilities in the rational inattention model take the
+multinomial logit form. By exploiting convex-analytic properties of the
+discrete choice model, we show that when information costs are modelled using a
+class of generalized entropy functions, the choice probabilities in any
+rational inattention model are observationally equivalent to some additive
+random utility discrete choice model and vice versa. Thus any additive random
+utility model can be given an interpretation in terms of boundedly rational
+behavior. This includes empirically relevant specifications such as the probit
+and nested logit models.
+",0,0,0,1,0,0
+18089,A Hardy inequality for ultraspherical expansions with an application to the sphere,"  We prove a Hardy inequality for ultraspherical expansions by using a proper
+ground state representation. From this result we deduce some uncertainty
+principles for this kind of expansions. Our result also implies a Hardy
+inequality on spheres with a potential having a double singularity.
+",0,0,1,0,0,0
+18090,Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors,"  Bayesian Neural Networks (BNNs) have recently received increasing attention
+for their ability to provide well-calibrated posterior uncertainties. However,
+model selection---even choosing the number of nodes---remains an open question.
+Recent work has proposed the use of a horseshoe prior over node pre-activations
+of a Bayesian neural network, which effectively turns off nodes that do not
+help explain the data. In this work, we propose several modeling and inference
+advances that consistently improve the compactness of the model learned while
+maintaining predictive performance, especially in smaller-sample settings
+including reinforcement learning.
+",0,0,0,1,0,0
+18091,Efficient Reinforcement Learning via Initial Pure Exploration,"  In several realistic situations, an interactive learning agent can practice
+and refine its strategy before going on to be evaluated. For instance, consider
+a student preparing for a series of tests. She would typically take a few
+practice tests to know which areas she needs to improve upon. Based of the
+scores she obtains in these practice tests, she would formulate a strategy for
+maximizing her scores in the actual tests. We treat this scenario in the
+context of an agent exploring a fixed-horizon episodic Markov Decision Process
+(MDP), where the agent can practice on the MDP for some number of episodes (not
+necessarily known in advance) before starting to incur regret for its actions.
+During practice, the agent's goal must be to maximize the probability of
+following an optimal policy. This is akin to the problem of Pure Exploration
+(PE). We extend the PE problem of Multi Armed Bandits (MAB) to MDPs and propose
+a Bayesian algorithm called Posterior Sampling for Pure Exploration (PSPE),
+which is similar to its bandit counterpart. We show that the Bayesian simple
+regret converges at an optimal exponential rate when using PSPE.
+When the agent starts being evaluated, its goal would be to minimize the
+cumulative regret incurred. This is akin to the problem of Reinforcement
+Learning (RL). The agent uses the Posterior Sampling for Reinforcement Learning
+algorithm (PSRL) initialized with the posteriors of the practice phase. We
+hypothesize that this PSPE + PSRL combination is an optimal strategy for
+minimizing regret in RL problems with an initial practice phase. We show
+empirical results which prove that having a lower simple regret at the end of
+the practice phase results in having lower cumulative regret during evaluation.
+",1,0,0,1,0,0
+18092,Stochastic Evolution of Augmented Born--Infeld Equations,"  This paper compares the results of applying a recently developed method of
+stochastic uncertainty quantification designed for fluid dynamics to the
+Born-Infeld model of nonlinear electromagnetism. The similarities in the
+results are striking. Namely, the introduction of Stratonovich cylindrical
+noise into each of their Hamiltonian formulations introduces stochastic Lie
+transport into their dynamics in the same form for both theories. Moreover, the
+resulting stochastic partial differential equations (SPDE) retain their
+unperturbed form, except for an additional term representing induced Lie
+transport by the set of divergence-free vector fields associated with the
+spatial correlations of the cylindrical noise. The explanation for this
+remarkable similarity lies in the method of construction of the Hamiltonian for
+the Stratonovich stochastic contribution to the motion in both cases; which is
+done via pairing spatial correlation eigenvectors for cylindrical noise with
+the momentum map for the deterministic motion. This momentum map is responsible
+for the well-known analogy between hydrodynamics and electromagnetism. The
+momentum map for the Maxwell and Born-Infeld theories of electromagnetism
+treated here is the 1-form density known as the Poynting vector. Two Appendices
+treat the Hamiltonian structures underlying these results.
+",0,1,1,0,0,0
+18093,T-matrix evaluation of acoustic radiation forces on nonspherical objects in Bessel beams,"  Acoustical radiation force (ARF) induced by a single Bessel beam with
+arbitrary order and location on a nonspherical shape is studied with the
+emphasis on the physical mechanism and parameter conditions of negative
+(pulling) forces. Numerical experiments are conducted to verify the T-matrix
+method (TMM) for axial ARFs. This study may guide the experimental set-up to
+find negative axial ARF quickly and effectively based on the predicted
+parameters with TMM, and could be extended for lateral forces. The present work
+could help to design acoustic tweezers numerical toolbox, which provides an
+alternate to the optic tweezers.
+",0,1,0,0,0,0
+18094,On the commutative center of Moufang loops,"  We construct two infinite series of Moufang loops of exponent $3$ whose
+commutative center (i.e. the set of elements that commute with all elements of
+the loop) is not a normal subloop. In particular, we obtain examples of such
+loops of orders $3^8$ and $3^{11}$ one of which can be defined as the Moufang
+triplication of the free Burnside group $B(3,3)$.
+",0,0,1,0,0,0
+18095,The Kontsevich tetrahedral flow in 2D: a toy model,"  In the paper ""Formality conjecture"" (1996) Kontsevich designed a universal
+flow $\dot{\mathcal{P}}=\mathcal{Q}_{a:b}(\mathcal{P})=a\Gamma_{1}+b\Gamma_{2}$
+on the spaces of Poisson structures $\mathcal{P}$ on all affine manifolds of
+dimension $n \geqslant 2$. We prove a claim from $\textit{loc. cit.}$ stating
+that if $n=2$, the flow $\mathcal{Q}_{1:0}=\Gamma_{1}(\mathcal{P})$ is
+Poisson-cohomology trivial: $\Gamma_{1}(\mathcal{P})$ is the Schouten bracket
+of $\mathcal{P}$ with $\mathcal{X}$, for some vector field $\mathcal{X}$; we
+examine the structure of the space of solutions $\mathcal{X}$. Both the
+construction of differential polynomials $\Gamma_{1}(\mathcal{P})$ and
+$\Gamma_{2}(\mathcal{P})$ and the technique to study them remain valid in
+higher dimensions $n \geqslant 3$, but neither the trivializing vector field
+$\mathcal{X}$ nor the setting $b:=0$ survive at $n\geqslant 3$, where the
+balance is $a:b=1:6$.
+",0,0,1,0,0,0
+18096,Twin Learning for Similarity and Clustering: A Unified Kernel Approach,"  Many similarity-based clustering methods work in two separate steps including
+similarity matrix computation and subsequent spectral clustering. However,
+similarity measurement is challenging because it is usually impacted by many
+factors, e.g., the choice of similarity metric, neighborhood size, scale of
+data, noise and outliers. Thus the learned similarity matrix is often not
+suitable, let alone optimal, for the subsequent clustering. In addition,
+nonlinear similarity often exists in many real world data which, however, has
+not been effectively considered by most existing methods. To tackle these two
+challenges, we propose a model to simultaneously learn cluster indicator matrix
+and similarity information in kernel spaces in a principled way. We show
+theoretical relationships to kernel k-means, k-means, and spectral clustering
+methods. Then, to address the practical issue of how to select the most
+suitable kernel for a particular clustering task, we further extend our model
+with a multiple kernel learning ability. With this joint model, we can
+automatically accomplish three subtasks of finding the best cluster indicator
+matrix, the most accurate similarity relations and the optimal combination of
+multiple kernels. By leveraging the interactions between these three subtasks
+in a joint framework, each subtask can be iteratively boosted by using the
+results of the others towards an overall optimal solution. Extensive
+experiments are performed to demonstrate the effectiveness of our method.
+",1,0,0,1,0,0
+18097,Big Data Analysis Using Shrinkage Strategies,"  In this paper, we apply shrinkage strategies to estimate regression
+coefficients efficiently for the high-dimensional multiple regression model,
+where the number of samples is smaller than the number of predictors. We assume
+in the sparse linear model some of the predictors have very weak influence on
+the response of interest. We propose to shrink estimators more than usual.
+Specifically, we use integrated estimation strategies in sub and full models
+and shrink the integrated estimators by incorporating a bounded measurable
+function of some weights. The exhibited double shrunken estimators improve the
+prediction performance of sub models significantly selected from existing
+Lasso-type variable selection methods. Monte Carlo simulation studies as well
+as real examples of eye data and Riboavin data confirm the superior performance
+of the estimators in the high-dimensional regression model.
+",0,0,0,1,0,0
+18098,Interpreter fr topologists,"  Let M be a transitive model of set theory. There is a canonical
+interpretation functor between the category of regular Hausdorff, continuous
+open images of Cech-complete spaces of M and the same category in V, preserving
+many concepts of topology, functional analysis, and dynamics. The functor can
+be further canonically extended to the category of Borel subspaces. This
+greatly simplifies and extends similar results of Fremlin.
+",0,0,1,0,0,0
+18099,Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks,"  We propose a parallel-data-free voice-conversion (VC) method that can learn a
+mapping from source to target speech without relying on parallel data. The
+proposed method is general purpose, high quality, and parallel-data free and
+works without any extra data, modules, or alignment procedure. It also avoids
+over-smoothing, which occurs in many conventional statistical model-based VC
+methods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial
+network (CycleGAN) with gated convolutional neural networks (CNNs) and an
+identity-mapping loss. A CycleGAN learns forward and inverse mappings
+simultaneously using adversarial and cycle-consistency losses. This makes it
+possible to find an optimal pseudo pair from unpaired data. Furthermore, the
+adversarial loss contributes to reducing over-smoothing of the converted
+feature sequence. We configure a CycleGAN with gated CNNs and train it with an
+identity-mapping loss. This allows the mapping function to capture sequential
+and hierarchical structures while preserving linguistic information. We
+evaluated our method on a parallel-data-free VC task. An objective evaluation
+showed that the converted feature sequence was near natural in terms of global
+variance and modulation spectra. A subjective evaluation showed that the
+quality of the converted speech was comparable to that obtained with a Gaussian
+mixture model-based method under advantageous conditions with parallel and
+twice the amount of data.
+",1,0,0,1,0,0
+18100,Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA,"  Least-squares models such as linear regression and Linear Discriminant
+Analysis (LDA) are amongst the most popular statistical learning techniques.
+However, since their computation time increases cubically with the number of
+features, they are inefficient in high-dimensional neuroimaging datasets.
+Fortunately, for k-fold cross-validation, an analytical approach has been
+developed that yields the exact cross-validated predictions in least-squares
+models without explicitly training the model. Its computation time grows with
+the number of test samples. Here, this approach is systematically investigated
+in the context of cross-validation and permutation testing. LDA is used
+exemplarily but results hold for all other least-squares methods. Furthermore,
+a non-trivial extension to multi-class LDA is formally derived. The analytical
+approach is evaluated using complexity calculations, simulations, and
+permutation testing of an EEG/MEG dataset. Depending on the ratio between
+features and samples, the analytical approach is up to 10,000x faster than the
+standard approach (retraining the model on each training set). This allows for
+a fast cross-validation of least-squares models and multi-class LDA in
+high-dimensional data, with obvious applications in multi-dimensional datasets,
+Representational Similarity Analysis, and permutation testing.
+",0,0,0,1,0,0
+18101,"A new, large-scale map of interstellar reddening derived from HI emission","  We present a new map of interstellar reddening, covering the 39\% of the sky
+with low {\rm HI} column densities ($N_{\rm HI} < 4\times10^{20}\,\rm cm^{-2}$
+or $E(B-V)\approx 45\rm\, mmag$) at $16\overset{'}{.}1$ resolution, based on
+all-sky observations of Galactic HI emission by the HI4PI Survey. In this low
+column density regime, we derive a characteristic value of $N_{\rm HI}/E(B-V) =
+8.8\times10^{21}\, \rm\, cm^{2}\, mag^{-1}$ for gas with $|v_{\rm LSR}| <
+90\,\rm km\, s^{-1}$ and find no significant reddening associated with gas at
+higher velocities. We compare our HI-based reddening map with the Schlegel,
+Finkbeiner, and Davis (1998, SFD) reddening map and find them consistent to
+within a scatter of $\simeq 5\,\rm mmag$. Further, the differences between our
+map and the SFD map are in excellent agreement with the low resolution
+($4\overset{\circ}{.}5$) corrections to the SFD map derived by Peek and Graves
+(2010) based on observed reddening toward passive galaxies. We therefore argue
+that our HI-based map provides the most accurate interstellar reddening
+estimates in the low column density regime to date. Our reddening map is made
+publicly available (this http URL).
+",0,1,0,0,0,0
+18102,Seasonal modulation of seismicity: the competing/collaborative effect of the snow and ice load on the lithosphere,"  Seasonal patterns associated with stress modulation, as evidenced by
+earthquake occurrence, have been detected in regions characterized by present
+day mountain building and glacial retreat in the Northern Hemisphere. In the
+Himalaya and the Alps, seismicity is peaking in spring and summer; opposite
+behaviour is observed in the Apennines. This diametrical behaviour, confirmed
+by recent strong earthquakes, well correlates with the dominant tectonic
+regime: peak in spring and summer in shortening areas, peak in fall and winter
+in extensional areas. The analysis of the seasonal effect is extended to
+several shortening (e.g. Zagros and Caucasus) and extensional regions, and
+counter-examples from regions where no seasonal modulation is expected (e.g.
+Tropical Atlantic Ridge). This study generalizes to different seismotectonic
+settings the early observations made about short-term (seasonal) and long-term
+(secular) modulation of seismicity and confirms, with some statistical
+significance, that snow and ice thaw may cause crustal deformations that
+modulate the occurrence of major earthquakes.
+",0,1,0,0,0,0
+18103,Existence and nonexistence of positive solutions to some fully nonlinear equation in one dimension,"  In this paper, we consider the existence (and nonexistence) of solutions to
+\[
+-\mathcal{M}_{\lambda,\Lambda}^\pm (u'') + V(x) u = f(u) \quad {\rm in} \
+\mathbf{R}
+\] where $\mathcal{M}_{\lambda,\Lambda}^+$ and
+$\mathcal{M}_{\lambda,\Lambda}^-$ denote the Pucci operators with $0< \lambda
+\leq \Lambda < \infty$, $V(x)$ is a bounded function, $f(s)$ is a continuous
+function and its typical example is a power-type nonlinearity $f(s)
+=|s|^{p-1}s$ $(p>1)$. In particular, we are interested in positive solutions
+which decay at infinity, and the existence (and nonexistence) of such solutions
+is proved.
+",0,0,1,0,0,0
+18104,Shiba Bound States across the mobility edge in doped InAs nanowires,"  We present a study of Andreev Quantum Dots (QDots) fabricated with
+small-diameter (30 nm) Si-doped InAs nanowires where the Fermi level can be
+tuned across a mobility edge separating localized states from delocalized
+states. The transition to the insulating phase is identified by a drop in the
+amplitude and width of the excited levels and is found to have remarkable
+consequences on the spectrum of superconducting SubGap Resonances (SGRs). While
+at deeply localized levels, only quasiparticles co-tunneling is observed, for
+slightly delocalized levels, Shiba bound states form and a parity changing
+quantum phase transition is identified by a crossing of the bound states at
+zero energy. Finally, in the metallic regime, single Andreev resonances are
+observed.
+",0,1,0,0,0,0
+18105,The Helsinki Neural Machine Translation System,"  We introduce the Helsinki Neural Machine Translation system (HNMT) and how it
+is applied in the news translation task at WMT 2017, where it ranked first in
+both the human and automatic evaluations for English--Finnish. We discuss the
+success of English--Finnish translations and the overall advantage of NMT over
+a strong SMT baseline. We also discuss our submissions for English--Latvian,
+English--Chinese and Chinese--English.
+",1,0,0,0,0,0
+18106,Spatial distribution of nuclei in progressive nucleation: modeling and application,"  Phase transformations ruled by non-simultaneous nucleation and growth do not
+lead to random distribution of nuclei. Since nucleation is only allowed in the
+untransformed portion of space, positions of nuclei are correlated. In this
+article an analytical approach is presented for computing pair-correlation
+function of nuclei in progressive nucleation. This quantity is further employed
+for characterizing the spatial distribution of nuclei through the nearest
+neighbor distribution function. The modeling is developed for nucleation in 2D
+space with power growth law and it is applied to describe electrochemical
+nucleation where correlation effects are significant. Comparison with both
+computer simulations and experimental data lends support to the model which
+gives insights into the transition from Poissonian to correlated nearest
+neighbor probability density.
+",0,1,0,0,0,0
+18107,Finite-time Guarantees for Byzantine-Resilient Distributed State Estimation with Noisy Measurements,"  This work considers resilient, cooperative state estimation in unreliable
+multi-agent networks. A network of agents aims to collaboratively estimate the
+value of an unknown vector parameter, while an {\em unknown} subset of agents
+suffer Byzantine faults. Faulty agents malfunction arbitrarily and may send out
+{\em highly unstructured} messages to other agents in the network. As opposed
+to fault-free networks, reaching agreement in the presence of Byzantine faults
+is far from trivial. In this paper, we propose a computationally-efficient
+algorithm that is provably robust to Byzantine faults. At each iteration of the
+algorithm, a good agent (1) performs a gradient descent update based on noisy
+local measurements, (2) exchanges its update with other agents in its
+neighborhood, and (3) robustly aggregates the received messages using
+coordinate-wise trimmed means. Under mild technical assumptions, we establish
+that good agents learn the true parameter asymptotically in almost sure sense.
+We further complement our analysis by proving (high probability) {\em
+finite-time} convergence rate, encapsulating network characteristics.
+",1,0,0,0,0,0
+18108,Constraining Polarized Foregrounds for EOR Experiments II: Polarization Leakage Simulations in the Avoidance Scheme,"  A critical challenge in the observation of the redshifted 21-cm line is its
+separation from bright Galactic and extragalactic foregrounds. In particular,
+the instrumental leakage of polarized foregrounds, which undergo significant
+Faraday rotation as they propagate through the interstellar medium, may
+harmfully contaminate the 21-cm power spectrum. We develop a formalism to
+describe the leakage due to instrumental widefield effects in visibility-based
+power spectra measured with redundant arrays, extending the delay-spectrum
+approach presented in Parsons et al. (2012). We construct polarized sky models
+and propagate them through the instrument model to simulate realistic full-sky
+observations with the Precision Array to Probe the Epoch of Reionization. We
+find that the leakage due to a population of polarized point sources is
+expected to be higher than diffuse Galactic polarization at any $k$ mode for a
+30~m reference baseline. For the same reference baseline, a foreground-free
+window at $k > 0.3 \, h$~Mpc$^{-1}$ can be defined in terms of leakage from
+diffuse Galactic polarization even under the most pessimistic assumptions. If
+measurements of polarized foreground power spectra or a model of polarized
+foregrounds are given, our method is able to predict the polarization leakage
+in actual 21-cm observations, potentially enabling its statistical subtraction
+from the measured 21-cm power spectrum.
+",0,1,0,0,0,0
+18109,Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts,"  For autonomous robots in dynamic environments mixed with human, it is vital
+to detect impending collision quickly and robustly. The biological visual
+systems evolved over millions of years may provide us efficient solutions for
+collision detection in complex environments. In the cockpit of locusts, two
+Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified
+which respond to looming objects rigorously with high firing rates. Compared to
+LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to
+dark moving objects against bright background in depth while not responding to
+light objects embedded in dark background - a similar situation which ground
+vehicles and robots are facing with. However, little work has been done on
+modeling LGMD2, let alone its potential in robotics and other vision-based
+applications. In this article, we propose a novel way of modeling LGMD2 neuron,
+with biased ON and OFF pathways splitting visual streams into parallel channels
+encoding brightness increments and decrements separately to fulfill its
+selectivity. Moreover, we apply a biophysical mechanism of spike frequency
+adaptation to shape the looming selectivity in such a collision-detecting
+neuron model. The proposed visual neural network has been tested with
+systematic experiments, challenged against synthetic and real physical stimuli,
+as well as image streams from the sensor of a miniature robot. The results
+demonstrated this framework is able to detect looming dark objects embedded in
+bright backgrounds selectively, which make it ideal for ground mobile
+platforms. The robotic experiments also showed its robustness in collision
+detection - it performed well for near range navigation in an arena with many
+obstacles. Its enhanced collision selectivity to dark approaching objects
+versus receding and translating ones has also been verified via systematic
+experiments.
+",1,0,0,0,1,0
+18110,Multifractal invariant measures in expanding piecewise linear coupled maps,"  We analyze invariant measures of two coupled piecewise linear and everywhere
+expanding maps on the synchronization manifold. We observe that though the
+individual maps have simple and smooth functions as their stationary densities,
+they become multifractal as soon as two of them are coupled nonlinearly even
+with a small coupling. For some maps, the multifractal spectrum seems to be
+robust with the coupling or map parameters and for some other maps, there is a
+substantial variation. The origin of the multifractal spectrum here is
+intriguing as it does not seem to conform to the existing theory of
+multifractal functions.
+",0,1,0,0,0,0
+18111,Virtually free finite-normal-subgroup-free groups are strongly verbally closed,"  Any virtually free group $H$ containing no non-trivial finite normal subgroup
+(e.g., the infinite dihedral group) is a retract of any finitely generated
+group containing $H$ as a verbally closed subgroup.
+",0,0,1,0,0,0
+18112,Stochastic Geometry-based Comparison of Secrecy Enhancement Techniques in D2D Networks,"  This letter presents a performance comparison of two popular secrecy
+enhancement techniques in wireless networks: (i) creating guard zones by
+restricting transmissions of legitimate transmitters whenever any eavesdropper
+is detected in their vicinity, and (ii) adding artificial noise to the
+confidential messages to make it difficult for the eavesdroppers to decode
+them. Focusing on a noise-limited regime, we use tools from stochastic geometry
+to derive the secrecy outage probability at the eavesdroppers as well as the
+coverage probability at the legitimate users for both these techniques. Using
+these results, we derive a threshold on the density of the eavesdroppers below
+which no secrecy enhancing technique is required to ensure a target secrecy
+outage probability. For eavesdropper densities above this threshold, we
+concretely characterize the regimes in which each technique outperforms the
+other. Our results demonstrate that guard zone technique is better when the
+distances between the transmitters and their legitimate receivers are higher
+than a certain threshold.
+",1,0,1,0,0,0
+18113,Linear convergence of SDCA in statistical estimation,"  In this paper, we consider stochastic dual coordinate (SDCA) {\em without}
+strongly convex assumption or convex assumption. We show that SDCA converges
+linearly under mild conditions termed restricted strong convexity. This covers
+a wide array of popular statistical models including Lasso, group Lasso, and
+logistic regression with $\ell_1$ regularization, corrected Lasso and linear
+regression with SCAD regularizer. This significantly improves previous
+convergence results on SDCA for problems that are not strongly convex. As a by
+product, we derive a dual free form of SDCA that can handle general
+regularization term, which is of interest by itself.
+",1,0,0,1,0,0
+18114,Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium,"  Regularized inversion methods for image reconstruction are used widely due to
+their tractability and ability to combine complex physical sensor models with
+useful regularity criteria. Such methods motivated the recently developed
+Plug-and-Play prior method, which provides a framework to use advanced
+denoising algorithms as regularizers in inversion. However, the need to
+formulate regularized inversion as the solution to an optimization problem
+limits the possible regularity conditions and physical sensor models.
+In this paper, we introduce Consensus Equilibrium (CE), which generalizes
+regularized inversion to include a much wider variety of both forward
+components and prior components without the need for either to be expressed
+with a cost function. CE is based on the solution of a set of equilibrium
+equations that balance data fit and regularity. In this framework, the problem
+of MAP estimation in regularized inversion is replaced by the problem of
+solving these equilibrium equations, which can be approached in multiple ways.
+The key contribution of CE is to provide a novel framework for fusing
+multiple heterogeneous models of physical sensors or models learned from data.
+We describe the derivation of the CE equations and prove that the solution of
+the CE equations generalizes the standard MAP estimate under appropriate
+circumstances.
+We also discuss algorithms for solving the CE equations, including ADMM with
+a novel form of preconditioning and Newton's method. We give examples to
+illustrate consensus equilibrium and the convergence properties of these
+algorithms and demonstrate this method on some toy problems and on a denoising
+example in which we use an array of convolutional neural network denoisers,
+none of which is tuned to match the noise level in a noisy image but which in
+consensus can achieve a better result than any of them individually.
+",1,0,1,0,0,0
+18115,Using angular pair upweighting to improve 3D clustering measurements,"  Three dimensional galaxy clustering measurements provide a wealth of
+cosmological information. However, obtaining spectra of galaxies is expensive,
+and surveys often only measure redshifts for a subsample of a target galaxy
+population. Provided that the spectroscopic data is representative, we argue
+that angular pair upweighting should be used in these situations to improve the
+3D clustering measurements. We present a toy model showing mathematically how
+such a weighting can improve measurements, and provide a practical example of
+its application using mocks created for the Baryon Oscillation Spectroscopic
+Survey (BOSS). Our analysis of mocks suggests that, if an angular clustering
+measurement is available over twice the area covered spectroscopically,
+weighting gives a $\sim$10-20% reduction of the variance of the monopole
+correlation function on the BAO scale.
+",0,1,0,0,0,0
+18116,Maximal entries of elements in certain matrix monoids,"  Let $L_u=\begin{bmatrix}1 & 0\\u & 1\end{bmatrix}$ and $R_v=\begin{bmatrix}1
+& v\\0 & 1\end{bmatrix}$ be matrices in $SL_2(\mathbb Z)$ with $u, v\geq 1$.
+Since the monoid generated by $L_u$ and $R_v$ is free, we can associate a depth
+to each element based on its product representation. In the cases where $u=v=2$
+and $u=v=3$, Bromberg, Shpilrain, and Vdovina determined the depth $n$ matrices
+containing the maximal entry for each $n\geq 1$. By using ideas from our
+previous work on $(u,v)$-Calkin-Wilf trees, we extend their results for any $u,
+v\geq 1$ and in the process we recover the Fibonacci and some Lucas sequences.
+As a consequence we obtain bounds which guarantee collision resistance on a
+family of hashing functions based on $L_u$ and $R_v$.
+",0,0,1,0,0,0
+18117,Generative Modeling of Multimodal Multi-Human Behavior,"  This work presents a methodology for modeling and predicting human behavior
+in settings with N humans interacting in highly multimodal scenarios (i.e.
+where there are many possible highly-distinct futures). A motivating example
+includes robots interacting with humans in crowded environments, such as
+self-driving cars operating alongside human-driven vehicles or human-robot
+collaborative bin packing in a warehouse. Our approach to model human behavior
+in such uncertain environments is to model humans in the scene as nodes in a
+graphical model, with edges encoding relationships between them. For each
+human, we learn a multimodal probability distribution over future actions from
+a dataset of multi-human interactions. Learning such distributions is made
+possible by recent advances in the theory of conditional variational
+autoencoders and deep learning approximations of probabilistic graphical
+models. Specifically, we learn action distributions conditioned on interaction
+history, neighboring human behavior, and candidate future agent behavior in
+order to take into account response dynamics. We demonstrate the performance of
+such a modeling approach in modeling basketball player trajectories, a highly
+multimodal, multi-human scenario which serves as a proxy for many robotic
+applications.
+",1,0,0,0,0,0
+18118,Deep Sets,"  We study the problem of designing models for machine learning tasks defined
+on \emph{sets}. In contrast to traditional approach of operating on fixed
+dimensional vectors, we consider objective functions defined on sets that are
+invariant to permutations. Such problems are widespread, ranging from
+estimation of population statistics \cite{poczos13aistats}, to anomaly
+detection in piezometer data of embankment dams \cite{Jung15Exploration}, to
+cosmology \cite{Ntampaka16Dynamical,Ravanbakhsh16ICML1}. Our main theorem
+characterizes the permutation invariant functions and provides a family of
+functions to which any permutation invariant objective function must belong.
+This family of functions has a special structure which enables us to design a
+deep network architecture that can operate on sets and which can be deployed on
+a variety of scenarios including both unsupervised and supervised learning
+tasks. We also derive the necessary and sufficient conditions for permutation
+equivariance in deep models. We demonstrate the applicability of our method on
+population statistic estimation, point cloud classification, set expansion, and
+outlier detection.
+",1,0,0,1,0,0
+18119,Assortment Optimization under a Single Transition Model,"  In this paper, we consider a Markov chain choice model with single
+transition. In this model, customers arrive at each product with a certain
+probability. If the arrived product is unavailable, then the seller can
+recommend a subset of available products to the customer and the customer will
+purchase one of the recommended products or choose not to purchase with certain
+transition probabilities. The distinguishing features of the model are that the
+seller can control which products to recommend depending on the arrived product
+and that each customer either purchases a product or leaves the market after
+one transition.
+We study the assortment optimization problem under this model. Particularly,
+we show that this problem is generally NP-Hard even if each product could only
+transit to at most two products. Despite the complexity of the problem, we
+provide polynomial time algorithms for several special cases, such as when the
+transition probabilities are homogeneous with respect to the starting point, or
+when each product can only transit to one other product. We also provide a
+tight performance bound for revenue-ordered assortments. In addition, we
+propose a compact mixed integer program formulation that can solve this problem
+of large size. Through extensive numerical experiments, we show that the
+proposed algorithms can solve the problem efficiently and the obtained
+assortments could significantly improve the revenue of the seller than under
+the Markov chain choice model.
+",1,0,1,0,0,0
+18120,Deconstructing Type III,"  SAS introduced Type III methods to address difficulties in dummy-variable
+models for effects of multiple factors and covariates. Type III methods are
+widely used in practice; they are the default method in many statistical
+computing packages. Type III sums of squares (SSs) are defined by an algorithm,
+and an explicit mathematical formulation does not seem to exist. For that
+reason, their properties have not been rigorously proven. Some that are widely
+believed to be true are not always true. An explicit formulation is derived in
+this paper. It is used as a basis to prove fundamental properties of Type III
+estimable functions and SSs. It is shown that, in any given setting, Type III
+effects include all estimable ANOVA effects, and that if all of an ANOVA effect
+is estimable then the Type III SS tests it exactly. The setting for these
+results is general, comprising linear models for the mean vector of a response
+that include arbitrary sets of effects of factors and covariates.
+",0,0,0,1,0,0
+18121,The Digital Flynn Effect: Complexity of Posts on Social Media Increases over Time,"  Parents and teachers often express concern about the extensive use of social
+media by youngsters. Some of them see emoticons, undecipherable initialisms and
+loose grammar typical for social media as evidence of language degradation. In
+this paper, we use a simple measure of text complexity to investigate how the
+complexity of public posts on a popular social networking site changes over
+time. We analyze a unique dataset that contains texts posted by 942, 336 users
+from a large European city across nine years. We show that the chosen
+complexity measure is correlated with the academic performance of users: users
+from high-performing schools produce more complex texts than users from
+low-performing schools. We also find that complexity of posts increases with
+age. Finally, we demonstrate that overall language complexity of posts on the
+social networking site is constantly increasing. We call this phenomenon the
+digital Flynn effect. Our results may suggest that the worries about language
+degradation are not warranted.
+",1,0,0,0,0,0
+18122,Epistemic Modeling with Justifications,"  Existing logical models do not fairly represent epistemic situations with
+fallible justifications, e.g., Russell's Prime Minister example, though such
+scenarios have long been at the center of epistemic studies. We introduce
+justification epistemic models, JEM, which can handle such scenarios. JEM makes
+justifications prime objects and draws a distinction between accepted and
+knowledge-producing justifications; belief and knowledge become derived
+notions. Furthermore, Kripke models can be viewed as special cases of JEMs with
+additional assumptions of evidence insensitivity and common knowledge of the
+model. We argue that JEM can be applied to a range of epistemic scenarios in
+CS, AI, Game Theory, etc.
+",1,0,0,0,0,0
+18123,Emergent universal critical behavior of the 2D $N$-color Ashkin-Teller model in the presence of correlated disorder,"  We study the critical behavior of the 2D $N$-color Ashkin-Teller model in the
+presence of random bond disorder whose correlations decays with the distance
+$r$ as a power-law $r^{-a}$. We consider the case when the spins of different
+colors sitting at the same site are coupled by the same bond and map this
+problem onto the 2D system of $N/2$ flavors of interacting Dirac fermions in
+the presence of correlated disorder. Using renormalization group we show that
+for $N=2$, a ""weakly universal"" scaling behavior at the continuous transition
+becomes universal with new critical exponents. For $N>2$, the first-order phase
+transition is rounded by the correlated disorder and turns into a continuous
+one.
+",0,1,0,0,0,0
+18124,Transport signatures of topological superconductivity in a proximity-coupled nanowire,"  We study the conductance of a junction between the normal and superconducting
+segments of a nanowire, both of which are subjected to spin-orbit coupling and
+an external magnetic field. We directly compare the transport properties of the
+nanowire assuming two different models for the superconducting segment: one
+where we put superconductivity by hand into the wire, and one where
+superconductivity is induced through a tunneling junction with a bulk s-wave
+superconductor. While these two models are equivalent at low energies and at
+weak coupling between the nanowire and the superconductor, we show that there
+are several interesting qualitative differences away from these two limits. In
+particular, the tunneling model introduces an additional conductance peak at
+the energy corresponding to the bulk gap of the parent superconductor. By
+employing a combination of analytical methods at zero temperature and numerical
+methods at finite temperature, we show that the tunneling model of the
+proximity effect reproduces many more of the qualitative features that are seen
+experimentally in such a nanowire system.
+",0,1,0,0,0,0
+18125,An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation,"  Training of neural machine translation (NMT) models usually uses mini-batches
+for efficiency purposes. During the mini-batched training process, it is
+necessary to pad shorter sentences in a mini-batch to be equal in length to the
+longest sentence therein for efficient computation. Previous work has noted
+that sorting the corpus based on the sentence length before making mini-batches
+reduces the amount of padding and increases the processing speed. However,
+despite the fact that mini-batch creation is an essential step in NMT training,
+widely used NMT toolkits implement disparate strategies for doing so, which
+have not been empirically validated or compared. This work investigates
+mini-batch creation strategies with experiments over two different datasets.
+Our results suggest that the choice of a mini-batch creation strategy has a
+large effect on NMT training and some length-based sorting strategies do not
+always work well compared with simple shuffling.
+",1,0,0,0,0,0
+18126,Network analyses of 4D genome datasets automate detection of community-scale gene structure and plasticity,"  Chromosome conformation capture and Hi-C technologies provide gene-gene
+proximity datasets of stationary cells, revealing chromosome territories,
+topologically associating domains, and chromosome topology. Imaging of tagged
+DNA sequences in live cells through the lac operator reporter system provides
+dynamic datasets of chromosomal loci. Chromosome modeling explores the
+mechanisms underlying 3D genome structure and dynamics. Here, we automate 4D
+genome dataset analysis with network-based tools as an alternative to gene-gene
+proximity statistics and visual structure determination. Temporal network
+models and community detection algorithms are applied to 4D modeling of G1 in
+budding yeast with transient crosslinking of $5 kb$ domains in the nucleolus,
+analyzing datasets from four decades of transient binding timescales. Network
+tools detect and track transient gene communities (clusters) within the
+nucleolus, their size, number, persistence time, and frequency of gene
+exchanges. An optimal, weak binding affinity is revealed that maximizes
+community-scale plasticity whereby large communities persist, frequently
+exchanging genes.
+",0,0,0,0,1,0
+18127,Predicate Pairing for Program Verification,"  It is well-known that the verification of partial correctness properties of
+imperative programs can be reduced to the satisfiability problem for
+constrained Horn clauses (CHCs). However, state-of-the-art solvers for CHCs
+(CHC solvers) based on predicate abstraction are sometimes unable to verify
+satisfiability because they look for models that are definable in a given class
+A of constraints, called A-definable models. We introduce a transformation
+technique, called Predicate Pairing (PP), which is able, in many interesting
+cases, to transform a set of clauses into an equisatisfiable set whose
+satisfiability can be proved by finding an A-definable model, and hence can be
+effectively verified by CHC solvers. We prove that, under very general
+conditions on A, the unfold/fold transformation rules preserve the existence of
+an A-definable model, i.e., if the original clauses have an A-definable model,
+then the transformed clauses have an A-definable model. The converse does not
+hold in general, and we provide suitable conditions under which the transformed
+clauses have an A-definable model iff the original ones have an A-definable
+model. Then, we present the PP strategy which guides the application of the
+transformation rules with the objective of deriving a set of clauses whose
+satisfiability can be proved by looking for A-definable models. PP introduces a
+new predicate defined by the conjunction of two predicates together with some
+constraints. We show through some examples that an A-definable model may exist
+for the new predicate even if it does not exist for its defining atomic
+conjuncts. We also present some case studies showing that PP plays a crucial
+role in the verification of relational properties of programs (e.g., program
+equivalence and non-interference). Finally, we perform an experimental
+evaluation to assess the effectiveness of PP in increasing the power of CHC
+solving.
+",1,0,0,0,0,0
+18128,Entanglement entropy and computational complexity of the Anderson impurity model out of equilibrium I: quench dynamics,"  We study the growth of entanglement entropy in density matrix renormalization
+group calculations of the real-time quench dynamics of the Anderson impurity
+model. We find that with appropriate choice of basis, the entropy growth is
+logarithmic in both the interacting and noninteracting single-impurity models.
+The logarithmic entropy growth is understood from a noninteracting chain model
+as a critical behavior separating regimes of linear growth and saturation of
+entropy, corresponding respectively to an overlapping and gapped energy spectra
+of the set of bath states. We find that with an appropriate choices of basis
+(energy-ordered bath orbitals), logarithmic entropy growth is the generic
+behavior of quenched impurity models. A noninteracting calculation of a
+double-impurity Anderson model supports the conclusion in the multi-impurity
+case. The logarithmic growth of entanglement entropy enables studies of quench
+dynamics to very long times.
+",0,1,0,0,0,0
+18129,Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting,"  In this work, we show that saturating output activation functions, such as
+the softmax, impede learning on a number of standard classification tasks.
+Moreover, we present results showing that the utility of softmax does not stem
+from the normalization, as some have speculated. In fact, the normalization
+makes things worse. Rather, the advantage is in the exponentiation of error
+gradients. This exponential gradient boosting is shown to speed up convergence
+and improve generalization. To this end, we demonstrate faster convergence and
+better performance on diverse classification tasks: image classification using
+CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012. In the
+latter case, using the state-of-the-art neural network architecture, the model
+converged 33% faster with our method (roughly two days of training less) than
+with the standard softmax activation, and with a slightly better performance to
+boot.
+",1,0,0,0,0,0
+18130,A model theoretic Rieffel's theorem of quantum 2-torus,"  We defined a notion of quantum 2-torus $T_\theta$ in ""Masanori Itai and Boris
+Zilber, Notes on a model theory of quantum 2-torus $T_q^2$ for generic $q$,
+arXiv:1503.06045v1 [mathLO]"" and studied its model theoretic property. In this
+note we associate quantum 2-tori $T_\theta$ with the structure over ${\mathbb
+C}_\theta = ({\mathbb C}, +, \cdot, y = x^\theta),$ where $\theta \in {\mathbb
+R} \setminus {\mathbb Q}$, and introduce the notion of geometric isomorphisms
+between such quantum 2-tori.
+We show that this notion is closely connected with the fundamental notion of
+Morita equivalence of non-commutative geometry. Namely, we prove that the
+quantum 2-tori $T_{\theta_1}$ and $T_{\theta_2}$ are Morita equivalent if and
+only if $\theta_2 = {\displaystyle \frac{a \theta_1 + b}{c \theta_1 + d}}$ for
+some $ \left( \begin{array}{cc} a & b \\ c & d \end{array} \right)
+\in {\rm GL}_2({\mathbb Z})$ with $|ad - bc| = 1$. This is our version of
+Rieffel's Theorem in ""M. A. Rieffel and A. Schwarz, Morita equivalence of
+multidimensional noncummutative tori, Internat. J. Math. 10, 2 (1999) 289-299""
+which characterises Morita equivalence of quantum tori in the same terms.
+The result in essence confirms that the representation $T_\theta$ in terms of
+model-theoretic geometry \cite{IZ} is adequate to its original definition in
+terms of non-commutative geometry.
+",0,0,1,0,0,0
+18131,Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets,"  We release two artificial datasets, Simulated Flying Shapes and Simulated
+Planar Manipulator that allow to test the learning ability of video processing
+systems. In particular, the dataset is meant as a tool which allows to easily
+assess the sanity of deep neural network models that aim to encode, reconstruct
+or predict video frame sequences. The datasets each consist of 90000 videos.
+The Simulated Flying Shapes dataset comprises scenes showing two objects of
+equal shape (rectangle, triangle and circle) and size in which one object
+approaches its counterpart. The Simulated Planar Manipulator shows a 3-DOF
+planar manipulator that executes a pick-and-place task in which it has to place
+a size-varying circle on a squared platform. Different from other widely used
+datasets such as moving MNIST [1], [2], the two presented datasets involve
+goal-oriented tasks (e.g. the manipulator grasping an object and placing it on
+a platform), rather than showing random movements. This makes our datasets more
+suitable for testing prediction capabilities and the learning of sophisticated
+motions by a machine learning model. This technical document aims at providing
+an introduction into the usage of both datasets.
+",1,0,0,0,0,0
+18132,Couplings and quantitative contraction rates for Langevin dynamics,"  We introduce a new probabilistic approach to quantify convergence to
+equilibrium for (kinetic) Langevin processes. In contrast to previous analytic
+approaches that focus on the associated kinetic Fokker-Planck equation, our
+approach is based on a specific combination of reflection and synchronous
+coupling of two solutions of the Langevin equation. It yields contractions in a
+particular Wasserstein distance, and it provides rather precise bounds for
+convergence to equilibrium at the borderline between the overdamped and the
+underdamped regime. In particular, we are able to recover kinetic behavior in
+terms of explicit lower bounds for the contraction rate. For example, for a
+rescaled double-well potential with local minima at distance $a$, we obtain a
+lower bound for the contraction rate of order $\Omega (a^{-1})$ provided the
+friction coefficient is of order $\Theta (a^{-1})$.
+",0,0,1,0,0,0
+18133,Stacked transfer learning for tropical cyclone intensity prediction,"  Tropical cyclone wind-intensity prediction is a challenging task considering
+drastic changes climate patterns over the last few decades. In order to develop
+robust prediction models, one needs to consider different characteristics of
+cyclones in terms of spatial and temporal characteristics. Transfer learning
+incorporates knowledge from a related source dataset to compliment a target
+datasets especially in cases where there is lack or data. Stacking is a form of
+ensemble learning focused for improving generalization that has been recently
+used for transfer learning problems which is referred to as transfer stacking.
+In this paper, we employ transfer stacking as a means of studying the effects
+of cyclones whereby we evaluate if cyclones in different geographic locations
+can be helpful for improving generalization performs. Moreover, we use
+conventional neural networks for evaluating the effects of duration on cyclones
+in prediction performance. Therefore, we develop an effective strategy that
+evaluates the relationships between different types of cyclones through
+transfer learning and conventional learning methods via neural networks.
+",1,0,0,1,0,0
+18134,Generalized Gray codes with prescribed ends of small dimensions,"  Given pairwise distinct vertices $\{\alpha_i , \beta_i\}^k_{i=1}$ of the
+$n$-dimensional hypercube $Q_n$ such that the distance of $\alpha_i$ and
+$\beta_i$ is odd, are there paths $P_i$ between $\alpha_i$ and $\beta_i$ such
+that $\{V (P_i)\}^k_{i=1}$ partitions $V(Q_n)$? A positive solution for every
+$n\ge1$ and $k=1$ is known as a Gray code of dimension $n$. In this paper we
+settle this problem for small values of $n$.
+",1,0,0,0,0,0
+18135,Elementary-base cirquent calculus I: Parallel and choice connectives,"  Cirquent calculus is a proof system manipulating circuit-style constructs
+rather than formulas. Using it, this article constructs a sound and complete
+axiomatization CL16 of the propositional fragment of computability logic (the
+game-semantically conceived logic of computational problems - see
+this http URL ) whose logical vocabulary consists
+of negation and parallel and choice connectives, and whose atoms represent
+elementary, i.e. moveless, games.
+",1,0,1,0,0,0
+18136,"Targeted and Imaging-guided In Vivo Photodynamic Therapy of Tumors Using Dual-functional, Aggregation-induced Emission Nanoparticles","  Dual-functional nanoparticles, with the property of aggregation-induced
+emission and the capability of reactive oxygen species, were used to achieve
+passive/active targeting of tumor. Good contrast in in vivo imaging and obvious
+therapeutic efficiency were realized with a low dose of AIE nanoparticles as
+well as a low power density of light, resulting in negligible side effects.
+",0,1,0,0,0,0
+18137,TOSC: an algorithm for the tomography of spotted transit chords,"  Photometric observations of planetary transits may show localized bumps,
+called transit anomalies, due to the possible crossing of photospheric
+starspots. The aim of this work is to analyze the transit anomalies and derive
+the temperature profile inside the transit belt along the transit direction. We
+develop the algorithm TOSC, a tomographic inverse-approach tool which, by means
+of simple algebra, reconstructs the flux distribution along the transit belt.
+We test TOSC against some simulated scenarios. We find that TOSC provides
+robust results for light curves with photometric accuracies better than 1~mmag,
+returning the spot-photosphere temperature contrast with an accuracy better
+than 100~K. TOSC is also robust against the presence of unocculted spots,
+provided that the apparent planetary radius given by the fit of the transit
+light curve is used in place of the true radius. The analysis of real data with
+TOSC returns results consistent with previous studies.
+",0,1,0,0,0,0
+18138,Bayesian uncertainty quantification for epidemic spread on networks,"  While there exist a number of mathematical approaches to modeling the spread
+of disease on a network, analyzing such systems in the presence of uncertainty
+introduces significant complexity. In scenarios where system parameters must be
+inferred from limited observations, general approaches to uncertainty
+quantification can generate approximate distributions of the unknown
+parameters, but these methods often become computationally expensive if the
+underlying disease model is complex. In this paper, we apply the recent
+massively parallelizable Bayesian uncertainty quantification framework $\Pi4U$
+to a model of a disease spreading on a network of communities, showing that the
+method can accurately and tractably recover system parameters and select
+optimal models in this setting.
+",0,0,0,1,0,0
+18139,Switching divergences for spectral learning in blind speech dereverberation,"  When recorded in an enclosed room, a sound signal will most certainly get
+affected by reverberation. This not only undermines audio quality, but also
+poses a problem for many human-machine interaction technologies that use speech
+as their input. In this work, a new blind, two-stage dereverberation approach
+based in a generalized \beta-divergence as a fidelity term over a non-negative
+representation is proposed. The first stage consists of learning the spectral
+structure of the signal solely from the observed spectrogram, while the second
+stage is devoted to model reverberation. Both steps are taken by minimizing a
+cost function in which the aim is put either in constructing a dictionary or a
+good representation by changing the divergence involved. In addition, an
+approach for finding an optimal fidelity parameter for dictionary learning is
+proposed. An algorithm for implementing the proposed method is described and
+tested against state-of-the-art methods. Results show improvements for both
+artificial reverberation and real recordings.
+",1,0,0,0,0,0
+18140,The correlation between the sizes of globular cluster systems and their host dark matter haloes,"  The sizes of entire systems of globular clusters (GCs) depend on the
+formation and destruction histories of the GCs themselves, but also on the
+assembly, merger and accretion history of the dark matter (DM) haloes that they
+inhabit. Recent work has shown a linear relation between total mass of globular
+clusters in the globular cluster system and the mass of its host dark matter
+halo, calibrated from weak lensing. Here we extend this to GC system sizes, by
+studying the radial density profiles of GCs around galaxies in nearby galaxy
+groups. We find that radial density profiles of the GC systems are well fit
+with a de Vaucouleurs profile. Combining our results with those from the
+literature, we find tight relationship ($\sim 0.2$ dex scatter) between the
+effective radius of the GC system and the virial radius (or mass) of its host
+DM halo. The steep non-linear dependence of this relationship ($R_{e, GCS}
+\propto R_{200}^{2.5 - 3}$) is currently not well understood, but is an
+important clue regarding the assembly history of DM haloes and of the GC
+systems that they host.
+",0,1,0,0,0,0
+18141,Variation of field enhancement factor near the emitter tip,"  The field enhancement factor at the emitter tip and its variation in a close
+neighbourhood determines the emitter current in a Fowler-Nordheim like
+formulation. For an axially symmetric emitter with a smooth tip, it is shown
+that the variation can be accounted by a $\cos{\tilde{\theta}}$ factor in
+appropriately defined normalized co-ordinates. This is shown analytically for a
+hemi-ellipsoidal emitter and confirmed numerically for other emitter shapes
+with locally quadratic tips.
+",0,1,0,0,0,0
+18142,Benchmark Environments for Multitask Learning in Continuous Domains,"  As demand drives systems to generalize to various domains and problems, the
+study of multitask, transfer and lifelong learning has become an increasingly
+important pursuit. In discrete domains, performance on the Atari game suite has
+emerged as the de facto benchmark for assessing multitask learning. However, in
+continuous domains there is a lack of agreement on standard multitask
+evaluation environments which makes it difficult to compare different
+approaches fairly. In this work, we describe a benchmark set of tasks that we
+have developed in an extendable framework based on OpenAI Gym. We run a simple
+baseline using Trust Region Policy Optimization and release the framework
+publicly to be expanded and used for the systematic comparison of multitask,
+transfer, and lifelong learning in continuous domains.
+",1,0,0,0,0,0
+18143,A Multiple Linear Regression Approach For Estimating the Market Value of Football Players in Forward Position,"  In this paper, market values of the football players in the forward positions
+are estimated using multiple linear regression by including the physical and
+performance factors in 2017-2018 season. Players from 4 major leagues of Europe
+are examined, and by applying the test for homoscedasticity, a reasonable
+regression model within 0.10 significance level is built, and the most and the
+least affecting factors are explained in detail.
+",0,0,0,1,0,0
+18144,Application of the Huang-Hilbert transform and natural time to the analysis of Seismic Electric Signal activities,"  The Huang-Hilbert transform is applied to Seismic Electric Signal (SES)
+activities in order to decompose them into a number of Intrinsic Mode Functions
+(IMFs) and study which of these functions better represent the SES. The results
+are compared to those obtained from the analysis in a new time domain termed
+natural time after having subtracted the magnetotelluric background from the
+original signal. It is shown that the instantaneous amplitudes of the IMFs can
+be used for the distinction of SES from artificial noises when combined with
+the natural time analysis.
+",0,1,0,0,0,0
+18145,The Hurwitz Subgroups of $E_6(2)$,"  We prove that the exceptional group $E_6(2)$ is not a Hurwitz group. In the
+course of proving this, we complete the classification up to conjugacy of all
+Hurwitz subgroups of $E_6(2)$, in particular, those isomorphic to $L_2(8)$ and
+$L_3(2)$.
+",0,0,1,0,0,0
+18146,Three IQs of AI Systems and their Testing Methods,"  The rapid development of artificial intelligence has brought the artificial
+intelligence threat theory as well as the problem about how to evaluate the
+intelligence level of intelligent products. Both need to find a quantitative
+method to evaluate the intelligence level of intelligence systems, including
+human intelligence. Based on the standard intelligence system and the extended
+Von Neumann architecture, this paper proposes General IQ, Service IQ and Value
+IQ evaluation methods for intelligence systems, depending on different
+evaluation purposes. Among them, the General IQ of intelligence systems is to
+answer the question of whether the artificial intelligence can surpass the
+human intelligence, which is reflected in putting the intelligence systems on
+an equal status and conducting the unified evaluation. The Service IQ and Value
+IQ of intelligence systems are used to answer the question of how the
+intelligent products can better serve the human, reflecting the intelligence
+and required cost of each intelligence system as a product in the process of
+serving human.
+",1,0,0,0,0,0
+18147,Polynomial functors in manifold calculus,"  Let M be a smooth manifold, and let O(M) be the poset of open subsets of M.
+Manifold calculus, due to Goodwillie and Weiss, is a calculus of functors
+suitable for studying contravariant functors (cofunctors) F: O(M)--> Top from
+O(M) to the category of spaces. Weiss showed that polynomial cofunctors of
+degree <= k are determined by their values on O_k(M), where O_k(M) is the full
+subposet of O(M) whose objects are open subsets diffeomorphic to the disjoint
+union of at most k balls. Afterwards Pryor showed that one can replace O_k(M)
+by more general subposets and still recover the same notion of polynomial
+cofunctor. In this paper, we generalize these results to cofunctors from O(M)
+to any simplicial model category C. If conf(k, M) stands for the unordered
+configuration space of k points in M, we also show that the category of
+homogeneous cofunctors O(M) --> C of degree k is weakly equivalent to the
+category of linear cofunctors O(conf(k, M)) --> C provided that C has a zero
+object. Using a completely different approach, we also show that if C is a
+general model category and F: O_k(M) --> C is an isotopy cofunctor, then the
+homotopy right Kan extension of F along the inclusion O_k(M) --> O(M) is also
+an isotopy cofunctor.
+",0,0,1,0,0,0
+18148,Tikhonov Regularization for Long Short-Term Memory Networks,"  It is a well-known fact that adding noise to the input data often improves
+network performance. While the dropout technique may be a cause of memory loss,
+when it is applied to recurrent connections, Tikhonov regularization, which can
+be regarded as the training with additive noise, avoids this issue naturally,
+though it implies regularizer derivation for different architectures. In case
+of feedforward neural networks this is straightforward, while for networks with
+recurrent connections and complicated layers it leads to some difficulties. In
+this paper, a Tikhonov regularizer is derived for Long-Short Term Memory (LSTM)
+networks. Although it is independent of time for simplicity, it considers
+interaction between weights of the LSTM unit, which in theory makes it possible
+to regularize the unit with complicated dependences by using only one parameter
+that measures the input data perturbation. The regularizer that is proposed in
+this paper has three parameters: one to control the regularization process, and
+other two to maintain computation stability while the network is being trained.
+The theory developed in this paper can be applied to get such regularizers for
+different recurrent neural networks with Hadamard products and Lipschitz
+continuous functions.
+",1,0,0,1,0,0
+18149,On the shape operator of relatively parallel hypersurfaces in the $n$-dimensional relative differential geometry,"  We deal with hypersurfaces in the framework of the $n$-dimensional relative
+differential geometry. We consider a hypersurface $\varPhi$ of
+$\mathbb{R}^{n+1}$ with position vector field $\mathbf{x}$, which is relatively
+normalized by a relative normalization $\mathbf{y}$. Then $\mathbf{y}$ is also
+a relative normalization of every member of the one-parameter family
+$\mathcal{F}$ of hypersurfaces $\varPhi_\mu$ with position vector field
+$$\mathbf{x}_\mu = \mathbf{x} + \mu \, \mathbf{y},$$ where $\mu$ is a real
+constant. We call every hypersurface $\varPhi_\mu \in \mathcal{F}$ relatively
+parallel to $\varPhi$ at the ""relative distance"" $\mu$. In this paper we study
+(a) the shape (or Weingarten) operator,
+(b) the relative principal curvatures,
+(c) the relative mean curvature functions and
+(d) the affine normalization
+of a relatively parallel hypersurface $\left( \varPhi_\mu,\mathbf{y}\right)$
+to $\left(\varPhi,\mathbf{y}\right)$.
+",0,0,1,0,0,0
+18150,Soft Pneumatic Gelatin Actuator for Edible Robotics,"  We present a fully edible pneumatic actuator based on gelatin-glycerol
+composite. The actuator is monolithic, fabricated via a molding process, and
+measures 90 mm in length, 20 mm in width, and 17 mm in thickness. Thanks to the
+composite mechanical characteristics similar to those of silicone elastomers,
+the actuator exhibits a bending angle of 170.3 ° and a blocked force of
+0.34 N at the applied pressure of 25 kPa. These values are comparable to
+elastomer based pneumatic actuators. As a validation example, two actuators are
+integrated to form a gripper capable of handling various objects, highlighting
+the high performance and applicability of the edible actuator. These edible
+actuators, combined with other recent edible materials and electronics, could
+lay the foundation for a new type of edible robots.
+",1,0,0,0,0,0
+18151,Deep learning for extracting protein-protein interactions from biomedical literature,"  State-of-the-art methods for protein-protein interaction (PPI) extraction are
+primarily feature-based or kernel-based by leveraging lexical and syntactic
+information. But how to incorporate such knowledge in the recent deep learning
+methods remains an open question. In this paper, we propose a multichannel
+dependency-based convolutional neural network model (McDepCNN). It applies one
+channel to the embedding vector of each word in the sentence, and another
+channel to the embedding vector of the head of the corresponding word.
+Therefore, the model can use richer information obtained from different
+channels. Experiments on two public benchmarking datasets, AIMed and BioInfer,
+demonstrate that McDepCNN compares favorably to the state-of-the-art
+rich-feature and single-kernel based methods. In addition, McDepCNN achieves
+24.4% relative improvement in F1-score over the state-of-the-art methods on
+cross-corpus evaluation and 12% improvement in F1-score over kernel-based
+methods on ""difficult"" instances. These results suggest that McDepCNN
+generalizes more easily over different corpora, and is capable of capturing
+long distance features in the sentences.
+",1,0,0,0,0,0
+18152,Hierarchical VampPrior Variational Fair Auto-Encoder,"  Decision making is a process that is extremely prone to different biases. In
+this paper we consider learning fair representations that aim at removing
+nuisance (sensitive) information from the decision process. For this purpose,
+we propose to use deep generative modeling and adapt a hierarchical Variational
+Auto-Encoder to learn these fair representations. Moreover, we utilize the
+mutual information as a useful regularizer for enforcing fairness of a
+representation. In experiments on two benchmark datasets and two scenarios
+where the sensitive variables are fully and partially observable, we show that
+the proposed approach either outperforms or performs on par with the current
+best model.
+",0,0,0,1,0,0
+18153,MHD Models of Gamma-ray Emission in WR 11,"  Recent reports claiming tentative association of the massive star binary
+system gamma^2 Velorum (WR 11) with a high-energy gamma-ray source observed by
+Fermi-LAT contrast the so-far exclusive role of Eta Carinae as the hitherto
+only detected gamma-ray emitter in the source class of particle-accelerating
+colliding-wind binary systems. We aim to shed light on this claim of
+association by providing dedicated model predictions for the nonthermal photon
+emission spectrum of WR 11. We use three-dimensional magneto-hydrodynamic
+modeling to trace the structure and conditions of the wind-collision region of
+WR 11 throughout its 78.5 day orbit, including the important effect of
+radiative braking in the stellar winds. A transport equation is then solved in
+the wind-collision region to determine the population of relativistic electrons
+and protons which are subsequently used to compute nonthermal photon emission
+components. We find that - if WR 11 be indeed confirmed as the responsible
+object for the observed gamma-ray emission - its radiation will unavoidably be
+of hadronic origin owing to the strong radiation fields in the binary system
+which inhibit the acceleration of electrons to energies suffciently high for
+observable inverse Compton radiation. Different conditions in wind-collision
+region near the apastron and periastron configuration lead to significant
+variability on orbital time scales. The bulk of the hadronic gamma-ray emission
+originates at a 400 solar radii wide region at the apex.
+",0,1,0,0,0,0
+18154,Caulking the Leakage Effect in MEEG Source Connectivity Analysis,"  Simplistic estimation of neural connectivity in MEEG sensor space is
+impossible due to volume conduction. The only viable alternative is to carry
+out connectivity estimation in source space. Among the neuroscience community
+this is claimed to be impossible or misleading due to Leakage: linear mixing of
+the reconstructed sources. To address this problematic we propose a novel
+solution method that caulks the Leakage in MEEG source activity and
+connectivity estimates: BC-VARETA. It is based on a joint estimation of source
+activity and connectivity in the frequency domain representation of MEEG time
+series. To achieve this, we go beyond current methods that assume a fixed
+gaussian graphical model for source connectivity. In contrast we estimate this
+graphical model in a Bayesian framework by placing priors on it, which allows
+for highly optimized computations of the connectivity, via a new procedure
+based on the local quadratic approximation under quite general prior models. A
+further contribution of this paper is the rigorous definition of leakage via
+the Spatial Dispersion Measure and Earth Movers Distance based on the geodesic
+distances over the cortical manifold. Both measures are extended for the first
+time to quantify Connectivity Leakage by defining them on the cartesian product
+of cortical manifolds. Using these measures, we show that BC-VARETA outperforms
+most state of the art inverse solvers by several orders of magnitude.
+",0,0,0,0,1,0
+18155,New ADS Functionality for the Curator,"  In this paper we provide an update concerning the operations of the NASA
+Astrophysics Data System (ADS), its services and user interface, and the
+content currently indexed in its database. As the primary information system
+used by researchers in Astronomy, the ADS aims to provide a comprehensive index
+of all scholarly resources appearing in the literature. With the current effort
+in our community to support data and software citations, we discuss what steps
+the ADS is taking to provide the needed infrastructure in collaboration with
+publishers and data providers. A new API provides access to the ADS search
+interface, metrics, and libraries allowing users to programmatically automate
+discovery and curation tasks. The new ADS interface supports a greater
+integration of content and services with a variety of partners, including ORCID
+claiming, indexing of SIMBAD objects, and article graphics from a variety of
+publishers. Finally, we highlight how librarians can facilitate the ingest of
+gray literature that they curate into our system.
+",1,1,0,0,0,0
+18156,Survey of reasoning using Neural networks,"  Reason and inference require process as well as memory skills by humans.
+Neural networks are able to process tasks like image recognition (better than
+humans) but in memory aspects are still limited (by attention mechanism, size).
+Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve
+small memory contexts, but as context becomes larger than a threshold, it is
+difficult to use them. The Solution is to use large external memory. Still, it
+poses many challenges like, how to train neural networks for discrete memory
+representation, how to describe long term dependencies in sequential data etc.
+Most prominent neural architectures for such tasks are Memory networks:
+inference components combined with long term memory and Neural Turing Machines:
+neural networks using external memory resources. Also, additional techniques
+like attention mechanism, end to end gradient descent on discrete memory
+representation are needed to support these solutions. Preliminary results of
+above neural architectures on simple algorithms (sorting, copying) and Question
+Answering (based on story, dialogs) application are comparable with the state
+of the art. In this paper, I explain these architectures (in general), the
+additional techniques used and the results of their application.
+",1,0,0,0,0,0
+18157,Recurrent Neural Network-based Model Predictive Control for Continuous Pharmaceutical Manufacturing,"  The pharmaceutical industry has witnessed exponential growth in transforming
+operations towards continuous manufacturing to effectively achieve increased
+profitability, reduced waste, and extended product range. Model Predictive
+Control (MPC) can be applied for enabling this vision, in providing superior
+regulation of critical quality attributes. For MPC, obtaining a workable model
+is of fundamental importance, especially in the presence of complex reaction
+kinetics and process dynamics. Whilst physics-based models are desirable, it is
+not always practical to obtain one effective and fit-for-purpose model.
+Instead, within industry, data-driven system-identification approaches have
+been found to be useful and widely deployed in MPC solutions. In this work, we
+demonstrated the applicability of Recurrent Neural Networks (RNNs) for MPC
+applications in continuous pharmaceutical manufacturing. We have shown that
+RNNs are especially well-suited for modeling dynamical systems due to their
+mathematical structure and satisfactory closed-loop control performance can be
+yielded for MPC in continuous pharmaceutical manufacturing.
+",1,0,0,0,0,0
+18158,"A unitary ""quantization commutes with reduction"" map for the adjoint action of a compact Lie group","  Let $K$ be a simply connected compact Lie group and $T^{\ast}(K)$ its
+cotangent bundle. We consider the problem of ""quantization commutes with
+reduction"" for the adjoint action of $K$ on $T^{\ast}(K).$ We quantize both
+$T^{\ast}(K)$ and the reduced phase space using geometric quantization with
+half-forms. We then construct a geometrically natural map from the space of
+invariant elements in the quantization of $T^{\ast}(K)$ to the quantization of
+the reduced phase space. We show that this map is a constant multiple of a
+unitary map.
+",0,0,1,0,0,0
+18159,Electron Paramagnetic Resonance Spectroscopy of Er$^{3+}$:Y$_2$SiO$_5$ Using Josephson Bifurcation Amplifier: Observation of Hyperfine and Quadrupole Structures,"  We performed magnetic field and frequency tunable electron paramagnetic
+resonance spectroscopy of an Er$^{3+}$ doped Y$_2$SiO$_5$ crystal by observing
+the change in flux induced on a direct current-superconducting quantum
+interference device (dc-SQUID) loop of a tunable Josephson bifurcation
+amplifer. The observed spectra show multiple transitions which agree well with
+the simulated energy levels, taking into account the hyperfine and quadrupole
+interactions of $^{167}$Er. The sensing volume is about 0.15 pl, and our
+inferred measurement sensitivity (limited by external flux noise) is
+approximately $1.5\times10^4$ electron spins for a 1 s measurement. The
+sensitivity value is two orders of magnitude better than similar schemes using
+dc-SQUID switching readout.
+",0,1,0,0,0,0
+18160,Step Detection Algorithm For Accurate Distance Estimation Using Dynamic Step Length,"  In this paper, a new Smartphone sensor based algorithm is proposed to detect
+accurate distance estimation. The algorithm consists of two phases, the first
+phase is for detecting the peaks from the Smartphone accelerometer sensor. The
+other one is for detecting the step length which varies from step to step. The
+proposed algorithm is tested and implemented in real environment and it showed
+promising results. Unlike the conventional approaches, the error of the
+proposed algorithm is fixed and is not affected by the long distance.
+Keywords distance estimation, peaks, step length, accelerometer.
+",1,0,0,0,0,0
+18161,Digging Into Self-Supervised Monocular Depth Estimation,"  Depth-sensing is important for both navigation and scene understanding.
+However, procuring RGB images with corresponding depth data for training deep
+models is challenging; large-scale, varied, datasets with ground truth training
+data are scarce. Consequently, several recent methods have proposed treating
+the training of monocular color-to-depth estimation networks as an image
+reconstruction problem, thus forgoing the need for ground truth depth.
+There are multiple concepts and design decisions for these networks that seem
+sensible, but give mixed or surprising results when tested. For example,
+binocular stereo as the source of self-supervision seems cumbersome and hard to
+scale, yet results are less blurry compared to training with monocular videos.
+Such decisions also interplay with questions about architectures, loss
+functions, image scales, and motion handling. In this paper, we propose a
+simple yet effective model, with several general architectural and loss
+innovations, that surpasses all other self-supervised depth estimation
+approaches on KITTI.
+",0,0,0,1,0,0
+18162,The Causality/Repair Connection in Databases: Causality-Programs,"  In this work, answer-set programs that specify repairs of databases are used
+as a basis for solving computational and reasoning problems about causes for
+query answers from databases.
+",1,0,0,0,0,0
+18163,Two-temperature logistic regression based on the Tsallis divergence,"  We develop a variant of multiclass logistic regression that achieves three
+properties: i) We minimize a non-convex surrogate loss which makes the method
+robust to outliers, ii) our method allows transitioning between non-convex and
+convex losses by the choice of the parameters, iii) the surrogate loss is Bayes
+consistent, even in the non-convex case. The algorithm has one weight vector
+per class and the surrogate loss is a function of the linear activations (one
+per class). The surrogate loss of an example with linear activation vector
+$\mathbf{a}$ and class $c$ has the form $-\log_{t_1} \exp_{t_2} (a_c -
+G_{t_2}(\mathbf{a}))$ where the two temperatures $t_1$ and $t_2$ ""temper"" the
+$\log$ and $\exp$, respectively, and $G_{t_2}$ is a generalization of the
+log-partition function. We motivate this loss using the Tsallis divergence. As
+the temperature of the logarithm becomes smaller than the temperature of the
+exponential, the surrogate loss becomes ""more quasi-convex"". Various tunings of
+the temperatures recover previous methods and tuning the degree of
+non-convexity is crucial in the experiments. The choice $t_1<1$ and $t_2>1$
+performs best experimentally. We explain this by showing that $t_1 < 1$ caps
+the surrogate loss and $t_2 >1$ makes the predictive distribution have a heavy
+tail.
+",1,0,0,1,0,0
+18164,Bootstrapped synthetic likelihood,"  Approximate Bayesian computation (ABC) and synthetic likelihood (SL)
+techniques have enabled the use of Bayesian inference for models that may be
+simulated, but for which the likelihood cannot be evaluated pointwise at values
+of an unknown parameter $\theta$. The main idea in ABC and SL is to, for
+different values of $\theta$ (usually chosen using a Monte Carlo algorithm),
+build estimates of the likelihood based on simulations from the model
+conditional on $\theta$. The quality of these estimates determines the
+efficiency of an ABC/SL algorithm. In standard ABC/SL, the only means to
+improve an estimated likelihood at $\theta$ is to simulate more times from the
+model conditional on $\theta$, which is infeasible in cases where the simulator
+is computationally expensive. In this paper we describe how to use
+bootstrapping as a means for improving SL estimates whilst using fewer
+simulations from the model, and also investigate its use in ABC. Further, we
+investigate the use of the bag of little bootstraps as a means for applying
+this approach to large datasets, yielding Monte Carlo algorithms that
+accurately approximate posterior distributions whilst only simulating
+subsamples of the full data. Examples of the approach applied to i.i.d.,
+temporal and spatial data are given.
+",1,0,0,1,0,0
+18165,The Weinstein conjecture for iterated planar contact structures,"  In this paper, we introduce the notions of an iterated planar Lefschetz
+fibration and an iterated planar open book decomposition and prove the
+Weinstein conjecture for contact manifolds supporting an open book that has
+iterated planar pages. For $n\geq 1$, we show that a $(2n+1)$-dimensional
+contact manifold $M$ supporting an iterated planar open book decomposition
+satisfies the Weinstein conjecture.
+",0,0,1,0,0,0
+18166,Privacy-Preserving Deep Learning via Weight Transmission,"  This paper considers the scenario that multiple data owners wish to apply a
+machine learning method over the combined dataset of all owners to obtain the
+best possible learning output but do not want to share the local datasets owing
+to privacy concerns. We design systems for the scenario that the stochastic
+gradient descent (SGD) algorithm is used as the machine learning method because
+SGD (or its variants) is at the heart of recent deep learning techniques over
+neural networks. Our systems differ from existing systems in the following
+features: {\bf (1)} any activation function can be used, meaning that no
+privacy-preserving-friendly approximation is required; {\bf (2)} gradients
+computed by SGD are not shared but the weight parameters are shared instead;
+and {\bf (3)} robustness against colluding parties even in the extreme case
+that only one honest party exists. We prove that our systems, while
+privacy-preserving, achieve the same learning accuracy as SGD and hence retain
+the merit of deep learning with respect to accuracy. Finally, we conduct
+several experiments using benchmark datasets, and show that our systems
+outperform previous system in terms of learning accuracies.
+",0,0,0,1,0,0
+18167,CORRECT: Code Reviewer Recommendation in GitHub Based on Cross-Project and Technology Experience,"  Peer code review locates common coding rule violations and simple logical
+errors in the early phases of software development, and thus reduces overall
+cost. However, in GitHub, identifying an appropriate code reviewer for a pull
+request is a non-trivial task given that reliable information for reviewer
+identification is often not readily available. In this paper, we propose a code
+reviewer recommendation technique that considers not only the relevant
+cross-project work history (e.g., external library experience) but also the
+experience of a developer in certain specialized technologies associated with a
+pull request for determining her expertise as a potential code reviewer. We
+first motivate our technique using an exploratory study with 10 commercial
+projects and 10 associated libraries external to those projects. Experiments
+using 17,115 pull requests from 10 commercial projects and six open source
+projects show that our technique provides 85%--92% recommendation accuracy,
+about 86% precision and 79%--81% recall in code reviewer recommendation, which
+are highly promising. Comparison with the state-of-the-art technique also
+validates the empirical findings and the superiority of our recommendation
+technique.
+",1,0,0,0,0,0
+18168,Charged Perfect Fluid Distribution for Cosmological Universe Interacting With Massive Scalar Field in Brans-Dicke Theory,"  Considering a spherically-symmetric non-static cosmological flat model of
+Robertson-Walker universe we have investigated the problem of perfect fluid
+distribution interacting with the gravitational field in presence of massive
+scalar field and electromagnetic field in B-D theory. Exact solutions have been
+obtained by using a general approach of solving the partial differential
+equations and it has been observed that the electromagnetic field cannot
+survive for the cosmological flat model due to the influence caused by the
+presence of massive scalar field.
+",0,1,0,0,0,0
+18169,Finite homogeneous geometries,"  This paper reproduces the text of a part of the Author's DPhil thesis. It
+gives a proof of the classification of non-trivial, finite homogeneous
+geometries of sufficiently high dimension which does not depend on the
+classification of the finite simple groups.
+",0,0,1,0,0,0
+18170,Weakly-Private Information Retrieval,"  Private information retrieval (PIR) protocols make it possible to retrieve a
+file from a database without disclosing any information about the identity of
+the file being retrieved. These protocols have been rigorously explored from an
+information-theoretic perspective in recent years. While existing protocols
+strictly impose that no information is leaked on the file's identity, this work
+initiates the study of the tradeoffs that can be achieved by relaxing the
+requirement of perfect privacy. In case the user is willing to leak some
+information on the identity of the retrieved file, we study how the PIR rate,
+as well as the upload cost and access complexity, can be improved. For the
+particular case of replicated servers, we propose two weakly-private
+information retrieval schemes based on two recent PIR protocols and a family of
+schemes based on partitioning. Lastly, we compare the performance of the
+proposed schemes.
+",1,0,0,0,0,0
+18171,Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget,"  Iterative algorithms, like gradient descent, are common tools for solving a
+variety of problems, such as model fitting. For this reason, there is interest
+in creating differentially private versions of them. However, their conversion
+to differentially private algorithms is often naive. For instance, a fixed
+number of iterations are chosen, the privacy budget is split evenly among them,
+and at each iteration, parameters are updated with a noisy gradient. In this
+paper, we show that gradient-based algorithms can be improved by a more careful
+allocation of privacy budget per iteration. Intuitively, at the beginning of
+the optimization, gradients are expected to be large, so that they do not need
+to be measured as accurately. However, as the parameters approach their optimal
+values, the gradients decrease and hence need to be measured more accurately.
+We add a basic line-search capability that helps the algorithm decide when more
+accurate gradient measurements are necessary. Our gradient descent algorithm
+works with the recently introduced zCDP version of differential privacy. It
+outperforms prior algorithms for model fitting and is competitive with the
+state-of-the-art for $(\epsilon,\delta)$-differential privacy, a strictly
+weaker definition than zCDP.
+",0,0,0,1,0,0
+18172,"""The universal meaning of the quantum of action"", by Jun Ishiwara","  Commented translation of the paper ""Universelle Bedeutung des
+Wirkungsquantums"", published by Jun Ishiwara in German in the Proceedings of
+Tokyo Mathematico-Physical Society 8 106-116 (1915). In his work, Ishiwara,
+tenured at Sendai University, Japan, proposed - simultaneously with Arnold
+Sommerfeld, William Wilson and Niels Bohr in Europe - the pase-space-integral
+quantization, a rule that would be incorporated into the old-quantum-mechanics
+formalism.
+",0,1,0,0,0,0
+18173,Barrier to recombination of oppositely charged large polarons,"  Electronic charge carriers in ionic materials can self-trap to form large
+polarons. Interference between the ionic displacements associated with
+oppositely charged large polarons increases as they approach one another.
+Initially this interference produces an attractive potential that fosters their
+merger. However, for small enough separations this interference generates a
+repulsive interaction between oppositely charged large polarons. In suitable
+circumstances this repulsion can overwhelm their direct Coulomb attraction.
+Then the resulting net repulsion between oppositely charged large polarons
+constitutes a potential barrier which impedes their recombination.
+",0,1,0,0,0,0
+18174,Metric Reduction and Generalized Holomorphic Structures,"  In this paper, metric reduction in generalized geometry is investigated. We
+show how the Bismut connections on the quotient manifold are obtained from
+those on the original manifold. The result facilitates the analysis of
+generalized K$\ddot{a}$hler reduction, which motivates the concept of metric
+generalized principal bundles and our approach to construct a family of
+generalized holomorphic line bundles over $\mathbb{C}P^2$ equipped with some
+non-trivial generalized K$\ddot{a}$hler structures.
+",0,0,1,0,0,0
+18175,Learning of Optimal Forecast Aggregation in Partial Evidence Environments,"  We consider the forecast aggregation problem in repeated settings, where the
+forecasts are done on a binary event. At each period multiple experts provide
+forecasts about an event. The goal of the aggregator is to aggregate those
+forecasts into a subjective accurate forecast. We assume that experts are
+Bayesian; namely they share a common prior, each expert is exposed to some
+evidence, and each expert applies Bayes rule to deduce his forecast. The
+aggregator is ignorant with respect to the information structure (i.e.,
+distribution over evidence) according to which experts make their prediction.
+The aggregator observes the experts' forecasts only. At the end of each period
+the actual state is realized. We focus on the question whether the aggregator
+can learn to aggregate optimally the forecasts of the experts, where the
+optimal aggregation is the Bayesian aggregation that takes into account all the
+information (evidence) in the system.
+We consider the class of partial evidence information structures, where each
+expert is exposed to a different subset of conditionally independent signals.
+Our main results are positive; We show that optimal aggregation can be learned
+in polynomial time in a quite wide range of instances of the partial evidence
+environments. We provide a tight characterization of the instances where
+learning is possible and impossible.
+",0,0,0,1,0,0
+18176,Distributed Unknown-Input-Observers for Cyber Attack Detection and Isolation in Formation Flying UAVs,"  In this paper, cyber attack detection and isolation is studied on a network
+of UAVs in a formation flying setup. As the UAVs communicate to reach consensus
+on their states while making the formation, the communication network among the
+UAVs makes them vulnerable to a potential attack from malicious adversaries.
+Two types of attacks pertinent to a network of UAVs have been considered: a
+node attack on the UAVs and a deception attack on the communication between the
+UAVs. UAVs formation control presented using a consensus algorithm to reach a
+pre-specified formation. A node and a communication path deception cyber
+attacks on the UAV's network are considered with their respective models in the
+formation setup. For these cyber attacks detection, a bank of Unknown Input
+Observer (UIO) based distributed fault detection scheme proposed to detect and
+identify the compromised UAV in the formation. A rule based on the residuals
+generated using the bank of UIOs are used to detect attacks and identify the
+compromised UAV in the formation. Further, an algorithm developed to remove the
+faulty UAV from the network once an attack detected and the compromised UAV
+isolated while maintaining the formation flight with a missing UAV node.
+",1,0,0,0,0,0
+18177,Exponential Decay of the lengths of Spectral Gaps for Extended Harper's Model with Liouvillean Frequency,"  In this paper, we study the non-self dual extended Harper's model with
+Liouvillean frequency. By establishing quantitative reducibility results
+together with the averaging method, we prove that the lengths of spectral gaps
+decay exponentially.
+",0,0,1,0,0,0
+18178,Topological Landau-Zener Bloch Oscillations in Photonic Floquet Lieb Lattices,"  The Lieb Lattice exhibits intriguing properties that are of general interest
+in both the fundamental physics and practical applications. Here, we
+investigate the topological Landau-Zener Bloch oscillation in a photonic
+Floquet Lieb lattice, where the dimerized helical waveguides is constructed to
+realize the synthetic spin-orbital interaction through the Floquet mechanism,
+rendering us to study the impacts of topological transition from trivial gaps
+to non-trivial ones. The compact localized states of flat bands supported by
+the local symmetry of Lieb lattice will be associated with other bands by
+topological invariants, Chern number, and involved into Landau-Zener transition
+during Bloch oscillation. Importantly, the non-trivial geometrical phases after
+topological transitions will be taken into account for constructive and
+destructive interferences of wave functions. The numerical calculations of
+continuum photonic medium demonstrate reasonable agreements with theoretical
+tight-binding model. Our results provide an ongoing effort to realize designed
+quantum materials with tailored properties.
+",0,1,0,0,0,0
+18179,Assessing the impact of bulk and shear viscosities on large scale structure formation,"  It is analyzed the effects of both bulk and shear viscosities on the
+perturbations, relevant for structure formation in late time cosmology. It is
+shown that shear viscosity can be as effective as the bulk viscosity on
+suppressing the growth of perturbations and delaying the nonlinear regime. A
+statistical analysis of the shear and bulk viscous effects is performed and
+some constraints on these viscous effects are given.
+",0,1,0,0,0,0
+18180,Correlations in eigenfunctions of quantum chaotic systems with sparse Hamiltonian matrices,"  In most realistic models for quantum chaotic systems, the Hamiltonian
+matrices in unperturbed bases have a sparse structure. We study correlations in
+eigenfunctions of such systems and derive explicit expressions for some of the
+correlation functions with respect to energy. The analytical results are tested
+in several models by numerical simulations. An application is given for a
+relation between transition probabilities.
+",0,1,0,0,0,0
+18181,Fast Trajectory Optimization for Legged Robots using Vertex-based ZMP Constraints,"  This paper combines the fast Zero-Moment-Point (ZMP) approaches that work
+well in practice with the broader range of capabilities of a Trajectory
+Optimization formulation, by optimizing over body motion, footholds and Center
+of Pressure simultaneously. We introduce a vertex-based representation of the
+support-area constraint, which can treat arbitrarily oriented point-, line-,
+and area-contacts uniformly. This generalization allows us to create motions
+such quadrupedal walking, trotting, bounding, pacing, combinations and
+transitions between these, limping, bipedal walking and push-recovery all with
+the same approach. This formulation constitutes a minimal representation of the
+physical laws (unilateral contact forces) and kinematic restrictions (range of
+motion) in legged locomotion, which allows us to generate various motion in
+less than a second. We demonstrate the feasibility of the generated motions on
+a real quadruped robot.
+",1,0,1,0,0,0
+18182,Electric Field Properties inside Central Gap of Dipole Micro/Nano Antennas Operating at 30 THz,"  This work investigates the influence of geometric variations in dipole
+micro/nano antennas, regarding their implications on the characteristics of the
+electric field inside the gap space of antenna monopoles. The gap is the
+interface for a metal-Insulator-Metal (MIM) rectifier diode and it needs to be
+carefully optimized, in order to allow better electric current generation by
+tunneling current mechanisms. The arrangement (antenna + diode or rectenna) was
+designed to operate around 30 Terahertz (THz).
+",0,1,0,0,0,0
+18183,Towards Understanding the Invertibility of Convolutional Neural Networks,"  Several recent works have empirically observed that Convolutional Neural Nets
+(CNNs) are (approximately) invertible. To understand this approximate
+invertibility phenomenon and how to leverage it more effectively, we focus on a
+theoretical explanation and develop a mathematical model of sparse signal
+recovery that is consistent with CNNs with random weights. We give an exact
+connection to a particular model of model-based compressive sensing (and its
+recovery algorithms) and random-weight CNNs. We show empirically that several
+learned networks are consistent with our mathematical analysis and then
+demonstrate that with such a simple theoretical framework, we can obtain
+reasonable re- construction results on real images. We also discuss gaps
+between our model assumptions and the CNN trained for classification in
+practical scenarios.
+",1,0,0,1,0,0
+18184,An effective algorithm for hyperparameter optimization of neural networks,"  A major challenge in designing neural network (NN) systems is to determine
+the best structure and parameters for the network given the data for the
+machine learning problem at hand. Examples of parameters are the number of
+layers and nodes, the learning rates, and the dropout rates. Typically, these
+parameters are chosen based on heuristic rules and manually fine-tuned, which
+may be very time-consuming, because evaluating the performance of a single
+parametrization of the NN may require several hours. This paper addresses the
+problem of choosing appropriate parameters for the NN by formulating it as a
+box-constrained mathematical optimization problem, and applying a
+derivative-free optimization tool that automatically and effectively searches
+the parameter space. The optimization tool employs a radial basis function
+model of the objective function (the prediction accuracy of the NN) to
+accelerate the discovery of configurations yielding high accuracy. Candidate
+configurations explored by the algorithm are trained to a small number of
+epochs, and only the most promising candidates receive full training. The
+performance of the proposed methodology is assessed on benchmark sets and in
+the context of predicting drug-drug interactions, showing promising results.
+The optimization tool used in this paper is open-source.
+",1,0,0,0,0,0
+18185,On estimation in varying coefficient models for sparse and irregularly sampled functional data,"  In this paper, we study a smoothness regularization method for a varying
+coefficient model based on sparse and irregularly sampled functional data which
+is contaminated with some measurement errors. We estimate the one-dimensional
+covariance and cross-covariance functions of the underlying stochastic
+processes based on a reproducing kernel Hilbert space approach. We then obtain
+least squares estimates of the coefficient functions. Simulation studies
+demonstrate that the proposed method has good performance. We illustrate our
+method by an analysis of longitudinal primary biliary liver cirrhosis data.
+",0,0,0,1,0,0
+18186,Large-degree asymptotics of rational Painleve-IV functions associated to generalized Hermite polynomials,"  The Painleve-IV equation has three families of rational solutions generated
+by the generalized Hermite polynomials. Each family is indexed by two positive
+integers m and n. These functions have applications to nonlinear wave
+equations, random matrices, fluid dynamics, and quantum mechanics. Numerical
+studies suggest the zeros and poles form a deformed n by m rectangular grid.
+Properly scaled, the zeros and poles appear to densely fill certain curvilinear
+rectangles as m and n tend to infinity with r=m/n fixed. Generalizing a method
+of Bertola and Bothner used to study rational Painleve-II functions, we express
+the generalized Hermite rational Painleve-IV functions in terms of certain
+orthogonal polynomials on the unit circle. Using the Deift-Zhou nonlinear
+steepest-descent method, we asymptotically analyze the associated
+Riemann-Hilbert problem in the limit as n tends to infinity with m=r*n for r
+fixed. We obtain an explicit characterization of the boundary curve and
+determine the leading-order asymptotic expansion of the functions in the
+pole-free region.
+",0,1,1,0,0,0
+18187,Lattice Boltzmann study of chemically-driven self-propelled droplets,"  We numerically study the behavior of self-propelled liquid droplets whose
+motion is triggered by a Marangoni-like flow. This latter is generated by
+variations of surfactant concentration which affect the droplet surface tension
+promoting its motion. In the present paper a model for droplets with a third
+amphiphilic component is adopted. The dynamics is described by Navier-Stokes
+and convection-diffusion equations, solved by lattice Boltzmann method coupled
+with finite-difference schemes. We focus on two cases. First the study of
+self-propulsion of an isolated droplet is carried on and, then, the interaction
+of two self-propelled droplets is investigated. In both cases, when the
+surfactant migrates towards the interface, a quadrupolar vortex of the velocity
+field forms inside the droplet and causes the motion. A weaker dipolar field
+emerges instead when the surfactant is mainly diluted in the bulk. The dynamics
+of two interacting droplets is more complex and strongly depends on their
+reciprocal distance. If, in a head-on collision, droplets are close enough, the
+velocity field initially attracts them until a motionless steady state is
+achieved. If the droplets are vertically shifted, the hydrodynamic field leads
+to an initial reciprocal attraction followed by a scattering along opposite
+directions. This hydrodynamic interaction acts on a separation of some droplet
+radii otherwise it becomes negligible and droplets motion is only driven by
+Marangoni effect. Finally, if one of the droplets is passive, this latter is
+generally advected by the fluid flow generated by the active one.
+",0,1,0,0,0,0
+18188,RDMAvisor: Toward Deploying Scalable and Simple RDMA as a Service in Datacenters,"  RDMA is increasingly adopted by cloud computing platforms to provide low CPU
+overhead, low latency, high throughput network services. On the other hand,
+however, it is still challenging for developers to realize fast deployment of
+RDMA-aware applications in the datacenter, since the performance is highly
+related to many lowlevel details of RDMA operations. To address this problem,
+we present a simple and scalable RDMA as Service (RaaS) to mitigate the impact
+of RDMA operational details. RaaS provides careful message buffer management to
+improve CPU/memory utilization and improve the scalability of RDMA operations.
+These optimized designs lead to simple and flexible programming model for
+common and knowledgeable users. We have implemented a prototype of RaaS, named
+RDMAvisor, and evaluated its performance on a cluster with a large number of
+connections. Our experiment results demonstrate that RDMAvisor achieves high
+throughput for thousand of connections and maintains low CPU and memory
+overhead through adaptive RDMA transport selection.
+",1,0,0,0,0,0
+18189,Towards a better understanding of the matrix product function approximation algorithm in application to quantum physics,"  We recently introduced a method to approximate functions of Hermitian Matrix
+Product Operators or Tensor Trains that are of the form $\mathsf{Tr} f(A)$.
+Functions of this type occur in several applications, most notably in quantum
+physics. In this work we aim at extending the theoretical understanding of our
+method by showing several properties of our algorithm that can be used to
+detect and correct errors in its results. Most importantly, we show that there
+exists a more computationally efficient version of our algorithm for certain
+inputs. To illustrate the usefulness of our finding, we prove that several
+classes of spin Hamiltonians in quantum physics fall into this input category.
+We finally support our findings with numerical results obtained for an example
+from quantum physics.
+",1,1,0,0,0,0
+18190,Explicit polynomial sequences with maximal spaces of partial derivatives and a question of K. Mulmuley,"  We answer a question of K. Mulmuley: In [Efremenko-Landsberg-Schenck-Weyman]
+it was shown that the method of shifted partial derivatives cannot be used to
+separate the padded permanent from the determinant. Mulmuley asked if this
+""no-go"" result could be extended to a model without padding. We prove this is
+indeed the case using the iterated matrix multiplication polynomial. We also
+provide several examples of polynomials with maximal space of partial
+derivatives, including the complete symmetric polynomials. We apply Koszul
+flattenings to these polynomials to have the first explicit sequence of
+polynomials with symmetric border rank lower bounds higher than the bounds
+attainable via partial derivatives.
+",1,0,1,0,0,0
+18191,Affine processes under parameter uncertainty,"  We develop a one-dimensional notion of affine processes under parameter
+uncertainty, which we call non-linear affine processes. This is done as
+follows: given a set of parameters for the process, we construct a
+corresponding non-linear expectation on the path space of continuous processes.
+By a general dynamic programming principle we link this non-linear expectation
+to a variational form of the Kolmogorov equation, where the generator of a
+single affine process is replaced by the supremum over all corresponding
+generators of affine processes with parameters in the parameter set. This
+non-linear affine process yields a tractable model for Knightian uncertainty,
+especially for modelling interest rates under ambiguity.
+We then develop an appropriate Ito-formula, the respective term-structure
+equations and study the non-linear versions of the Vasicek and the
+Cox-Ingersoll-Ross (CIR) model. Thereafter we introduce the non-linear
+Vasicek-CIR model. This model is particularly suitable for modelling interest
+rates when one does not want to restrict the state space a priori and hence the
+approach solves this modelling issue arising with negative interest rates.
+",0,0,0,0,0,1
+18192,Plane graphs without 4- and 5-cycles and without ext-triangular 7-cycles are 3-colorable,"  Listed as No. 53 among the one hundred famous unsolved problems in [J. A.
+Bondy, U. S. R. Murty, Graph Theory, Springer, Berlin, 2008] is Steinberg's
+conjecture, which states that every planar graph without 4- and 5-cycles is
+3-colorable. In this paper, we show that plane graphs without 4- and 5-cycles
+are 3-colorable if they have no ext-triangular 7-cycles. This implies that (1)
+planar graphs without 4-, 5-, 7-cycles are 3-colorable, and (2) planar graphs
+without 4-, 5-, 8-cycles are 3-colorable, which cover a number of known results
+in the literature motivated by Steinberg's conjecture.
+",0,0,1,0,0,0
+18193,Temporal Logic Task Planning and Intermittent Connectivity Control of Mobile Robot Networks,"  In this paper, we develop a distributed intermittent communication and task
+planning framework for mobile robot teams. The goal of the robots is to
+accomplish complex tasks, captured by local Linear Temporal Logic formulas, and
+share the collected information with all other robots and possibly also with a
+user. Specifically, we consider situations where the robot communication
+capabilities are not sufficient to form reliable and connected networks while
+the robots move to accomplish their tasks. In this case, intermittent
+communication protocols are necessary that allow the robots to temporarily
+disconnect from the network in order to accomplish their tasks free of
+communication constraints. We assume that the robots can only communicate with
+each other when they meet at common locations in space. Our distributed control
+framework jointly determines local plans that allow all robots fulfill their
+assigned temporal tasks, sequences of communication events that guarantee
+information exchange infinitely often, and optimal communication locations that
+minimize a desired distance metric. Simulation results verify the efficacy of
+the proposed controllers.
+",1,0,0,0,0,0
+18194,Second-oder analysis in second-oder cone programming,"  The paper conducts a second-order variational analysis for an important class
+of nonpolyhedral conic programs generated by the so-called
+second-order/Lorentz/ice-cream cone $Q$. From one hand, we prove that the
+indicator function of $Q$ is always twice epi-differentiable and apply this
+result to characterizing the uniqueness of Lagrange multipliers at stationary
+points together with an error bound estimate in the general second-order cone
+setting involving ${\cal C}^2$-smooth data. On the other hand, we precisely
+calculate the graphical derivative of the normal cone mapping to $Q$ under the
+weakest metric subregularity constraint qualification and then give an
+application of the latter result to a complete characterization of isolated
+calmness for perturbed variational systems associated with second-order cone
+programs. The obtained results seem to be the first in the literature in these
+directions for nonpolyhedral problems without imposing any nondegeneracy
+assumptions.
+",0,0,1,0,0,0
+18195,Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study,"  Interpreting the performance of deep learning models beyond test set accuracy
+is challenging. Characteristics of individual data points are often not
+considered during evaluation, and each data point is treated equally. We
+examine the impact of a test set question's difficulty to determine if there is
+a relationship between difficulty and performance. We model difficulty using
+well-studied psychometric methods on human response patterns. Experiments on
+Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the
+likelihood of answering a question correctly is impacted by the question's
+difficulty. As DNNs are trained with more data, easy examples are learned more
+quickly than hard examples.
+",1,0,0,0,0,0
+18196,The homotopy theory of coalgebras over simplicial comonads,"  We apply the Acyclicity Theorem of Hess, Kerdziorek, Riehl, and Shipley
+(recently corrected by Garner, Kedziorek, and Riehl) to establishing the
+existence of model category structure on categories of coalgebras over comonads
+arising from simplicial adjunctions, under mild conditions on the adjunction
+and the associated comonad. We study three concrete examples of such
+adjunctions where the left adjoint is comonadic and show that in each case the
+component of the derived counit of the comparison adjunction at any fibrant
+object is an isomorphism, while the component of the derived unit at any
+1-connected object is a weak equivalence. To prove this last result, we explain
+how to construct explicit fibrant replacements for 1-connected coalgebras in
+the image of the canonical comparison functor from the Postnikov decompositions
+of their underlying simplicial sets. We also show in one case that the derived
+unit is precisely the Bousfield-Kan completion map.
+",0,0,1,0,0,0
+18197,Spatio-temporal analysis of regional unemployment rates: A comparison of model based approaches,"  This study aims to analyze the methodologies that can be used to estimate the
+total number of unemployed, as well as the unemployment rates for 28 regions of
+Portugal, designated as NUTS III regions, using model based approaches as
+compared to the direct estimation methods currently employed by INE (National
+Statistical Institute of Portugal). Model based methods, often known as small
+area estimation methods (Rao, 2003), ""borrow strength"" from neighbouring
+regions and in doing so, aim to compensate for the small sample sizes often
+observed in these areas. Consequently, it is generally accepted that model
+based methods tend to produce estimates which have lesser variation. Other
+benefit in employing model based methods is the possibility of including
+auxiliary information in the form of variables of interest and latent random
+structures. This study focuses on the application of Bayesian hierarchical
+models to the Portuguese Labor Force Survey data from the 1st quarter of 2011
+to the 4th quarter of 2013. Three different data modeling strategies are
+considered and compared: Modeling of the total unemployed through Poisson,
+Binomial and Negative Binomial models; modeling of rates using a Beta model;
+and modeling of the three states of the labor market (employed, unemployed and
+inactive) by a Multinomial model. The implementation of these models is based
+on the \textit{Integrated Nested Laplace Approximation} (INLA) approach, except
+for the Multinomial model which is implemented based on the method of Monte
+Carlo Markov Chain (MCMC). Finally, a comparison of the performance of these
+models, as well as the comparison of the results with those obtained by direct
+estimation methods at NUTS III level are given.
+",0,0,0,1,0,0
+18198,Accelerating solutions of one-dimensional unsteady PDEs with GPU-based swept time-space decomposition,"  The expedient design of precision components in aerospace and other high-tech
+industries requires simulations of physical phenomena often described by
+partial differential equations (PDEs) without exact solutions. Modern design
+problems require simulations with a level of resolution difficult to achieve in
+reasonable amounts of time---even in effectively parallelized solvers. Though
+the scale of the problem relative to available computing power is the greatest
+impediment to accelerating these applications, significant performance gains
+can be achieved through careful attention to the details of memory
+communication and access. The swept time-space decomposition rule reduces
+communication between sub-domains by exhausting the domain of influence before
+communicating boundary values. Here we present a GPU implementation of the
+swept rule, which modifies the algorithm for improved performance on this
+processing architecture by prioritizing use of private (shared) memory,
+avoiding interblock communication, and overwriting unnecessary values. It shows
+significant improvement in the execution time of finite-difference solvers for
+one-dimensional unsteady PDEs, producing speedups of 2--9$\times$ for a range
+of problem sizes, respectively, compared with simple GPU versions and
+7--300$\times$ compared with parallel CPU versions. However, for a more
+sophisticated one-dimensional system of equations discretized with a
+second-order finite-volume scheme, the swept rule performs 1.2--1.9$\times$
+worse than a standard implementation for all problem sizes.
+",1,1,0,0,0,0
+18199,Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks,"  This article presents the use of Answer Set Programming (ASP) to mine
+sequential patterns. ASP is a high-level declarative logic programming paradigm
+for high level encoding combinatorial and optimization problem solving as well
+as knowledge representation and reasoning. Thus, ASP is a good candidate for
+implementing pattern mining with background knowledge, which has been a data
+mining issue for a long time. We propose encodings of the classical sequential
+pattern mining tasks within two representations of embeddings (fill-gaps vs
+skip-gaps) and for various kinds of patterns: frequent, constrained and
+condensed. We compare the computational performance of these encodings with
+each other to get a good insight into the efficiency of ASP encodings. The
+results show that the fill-gaps strategy is better on real problems due to
+lower memory consumption. Finally, compared to a constraint programming
+approach (CPSM), another declarative programming paradigm, our proposal showed
+comparable performance.
+",1,0,0,1,0,0
+18200,Fukaya categories in Koszul duality theory,"  In this paper, we define $A_{\infty}$-Koszul duals for directed
+$A_{\infty}$-categories in terms of twists in their $A_{\infty}$-derived
+categories. Then, we compute a concrete formula of $A_{\infty}$-Koszul duals
+for path algebras with directed $A_n$-type Gabriel quivers. To compute an
+$A_\infty$-Koszul dual of such an algebra $A$, we construct a directed
+subcategory of a Fukaya category which are $A_\infty$-derived equivalent to the
+category of $A$-modules and compute Dehn twists as twists. The formula unveils
+all the ext groups of simple modules of the parh algebras and their higher
+composition structures.
+",0,0,1,0,0,0
+18201,BSDEs and SDEs with time-advanced and -delayed coefficients,"  This paper introduces a class of backward stochastic differential equations
+(BSDEs), whose coefficients not only depend on the value of its solutions of
+the present but also the past and the future. For a sufficiently small time
+delay or a sufficiently small Lipschitz constant, the existence and uniqueness
+of such BSDEs is obtained. As an adjoint process, a class of stochastic
+differential equations (SDEs) is introduced, whose coefficients also depend on
+the present, the past and the future of its solutions. The existence and
+uniqueness of such SDEs is proved for a sufficiently small time advance or a
+sufficiently small Lipschitz constant. A duality between such BSDEs and SDEs is
+established.
+",0,0,1,0,0,0
+18202,Characterizing information importance and the effect on the spread in various graph topologies,"  In this paper we present a thorough analysis of the nature of news in
+different mediums across the ages, introducing a unique mathematical model to
+fit the characteristics of information spread. This model enhances the
+information diffusion model to account for conflicting information and the
+topical distribution of news in terms of popularity for a given era. We
+translate this information to a separate graphical node model to determine the
+spread of a news item given a certain category and relevance factor. The two
+models are used as a base for a simulation of information dissemination for
+varying graph topoligies. The simulation is stress-tested and compared against
+real-world data to prove its relevancy. We are then able to use these
+simulations to deduce some conclusive statements about the optimization of
+information spread.
+",1,1,0,0,0,0
+18203,Quantum criticality at the superconductor to insulator transition revealed by specific heat measurements,"  The superconductor-insulator transition (SIT) is considered an excellent
+example of a quantum phase transition which is driven by quantum fluctuations
+at zero temperature. The quantum critical point is characterized by a diverging
+correlation length and a vanishing energy scale. Low energy fluctuations near
+quantum criticality may be experimentally detected by specific heat, $c_{\rm
+p}$, measurements. Here, we use a unique highly sensitive experiment to measure
+$c_{\rm p}$ of two-dimensional granular Pb films through the SIT. The specific
+heat shows the usual jump at the mean field superconducting transition
+temperature $T_{\rm c}^{\rm {mf}}$ marking the onset of Cooper pairs formation.
+As the film thickness is tuned toward the SIT, $T_{\rm c}^{\rm {mf}}$ is
+relatively unchanged, while the magnitude of the jump and low temperature
+specific heat increase significantly. This behaviour is taken as the
+thermodynamic fingerprint of quantum criticality in the vicinity of a quantum
+phase transition.
+",0,1,0,0,0,0
+18204,Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference,"  We introduce a novel formulation of motion planning, for continuous-time
+trajectories, as probabilistic inference. We first show how smooth
+continuous-time trajectories can be represented by a small number of states
+using sparse Gaussian process (GP) models. We next develop an efficient
+gradient-based optimization algorithm that exploits this sparsity and GP
+interpolation. We call this algorithm the Gaussian Process Motion Planner
+(GPMP). We then detail how motion planning problems can be formulated as
+probabilistic inference on a factor graph. This forms the basis for GPMP2, a
+very efficient algorithm that combines GP representations of trajectories with
+fast, structure-exploiting inference via numerical optimization. Finally, we
+extend GPMP2 to an incremental algorithm, iGPMP2, that can efficiently replan
+when conditions change. We benchmark our algorithms against several
+sampling-based and trajectory optimization-based motion planning algorithms on
+planning problems in multiple environments. Our evaluation reveals that GPMP2
+is several times faster than previous algorithms while retaining robustness. We
+also benchmark iGPMP2 on replanning problems, and show that it can find
+successful solutions in a fraction of the time required by GPMP2 to replan from
+scratch.
+",1,0,0,0,0,0
+18205,Polynomially and Infinitesimally Injective Modules,"  The injective polynomial modules for a general linear group $G$ of degree $n$
+are labelled by the partitions with at most $n$ parts. Working over an
+algebraically closed field of characteristic $p$, we consider the question of
+which partitions correspond to polynomially injective modules that are also
+injective as modules for the restricted enveloping algebra of the Lie algebra
+of $G$. The question is related to the ""index of divisibility"" of a polynomial
+module in general, and an explicit answer is given for $n=2$.
+",0,0,1,0,0,0
+18206,Nonstandard Methods in Ramsey Theory and Combinatorial Number Theory,"  The goal of this present manuscript is to introduce the reader to the
+nonstandard method and to provide an overview of its most prominent
+applications in Ramsey theory and combinatorial number theory.
+",0,0,1,0,0,0
+18207,When is Network Lasso Accurate?,"  The ""least absolute shrinkage and selection operator"" (Lasso) method has been
+adapted recently for networkstructured datasets. In particular, this network
+Lasso method allows to learn graph signals from a small number of noisy signal
+samples by using the total variation of a graph signal for regularization.
+While efficient and scalable implementations of the network Lasso are
+available, only little is known about the conditions on the underlying network
+structure which ensure network Lasso to be accurate. By leveraging concepts of
+compressed sensing, we address this gap and derive precise conditions on the
+underlying network topology and sampling set which guarantee the network Lasso
+for a particular loss function to deliver an accurate estimate of the entire
+underlying graph signal. We also quantify the error incurred by network Lasso
+in terms of two constants which reflect the connectivity of the sampled nodes.
+",0,0,0,1,0,0
+18208,T-ROME: A Simple and Energy Efficient Tree Routing Protocol for Low-Power Wake-up Receivers,"  Wireless sensor networks are deployed in many monitoring applications but
+still suffer from short lifetimes originating from limited energy sources and
+storages. Due to their low-power consumption and their on-demand communication
+ability, wake-up receivers represent an energy efficient and simple enhancement
+to wireless sensor nodes and wireless sensor network protocols. In this
+context, wake-up receivers have the ability to increase the network lifetime.
+In this article, we present T-ROME, a simple and energy efficient cross-layer
+routing protocol for wireless sensor nodes containing wake-up receivers. The
+protocol makes use of the different transmission ranges of wake-up and main
+radios in order to save energy by skipping nodes during data transfer. With
+respect to energy consumption and latency, T-ROME outperforms existing
+protocols in many scenarios. Here, we describe and analyze the cross layer
+multi-hop protocol by means of a Markov chain model that we verify using a
+laboratory test setup.
+",1,0,0,0,0,0
+18209,Classifying the Correctness of Generated White-Box Tests: An Exploratory Study,"  White-box test generator tools rely only on the code under test to select
+test inputs, and capture the implementation's output as assertions. If there is
+a fault in the implementation, it could get encoded in the generated tests.
+Tool evaluations usually measure fault-detection capability using the number of
+such fault-encoding tests. However, these faults are only detected, if the
+developer can recognize that the encoded behavior is faulty. We designed an
+exploratory study to investigate how developers perform in classifying
+generated white-box test as faulty or correct. We carried out the study in a
+laboratory setting with 54 graduate students. The tests were generated for two
+open-source projects with the help of the IntelliTest tool. The performance of
+the participants were analyzed using binary classification metrics and by
+coding their observed activities. The results showed that participants
+incorrectly classified a large number of both fault-encoding and correct tests
+(with median misclassification rate 33% and 25% respectively). Thus the real
+fault-detection capability of test generators could be much lower than
+typically reported, and we suggest to take this human factor into account when
+evaluating generated white-box tests.
+",1,0,0,0,0,0
+18210,Numerical Simulations of Saturn's B Ring: Granular Friction as a Mediator between Self-Gravity and Viscous Overstability,"  We study the B ring's complex optical depth structure. The source of this
+structure may be the complex dynamics of the Keplerian shear and the
+self-gravity of the ring particles. The outcome of these dynamic effects
+depends sensitively on the collisional and physical properties of the
+particles. Two mechanisms can emerge that dominate the macroscopic physical
+structure of the ring: self-gravity wakes and viscous overstability. Here we
+study the interplay between these two mechanisms by using our recently
+developed particle collision method that allows us to better model the
+inter-particle contact physics. We find that for a constant ring surface
+density and particle internal density, particles with rough surfaces tend to
+produce axisymmetric ring features associated with the viscous overstability,
+while particles with smoother surfaces produce self-gravity wakes.
+",0,1,0,0,0,0
+18211,Shock-darkening in ordinary chondrites: determination of the pressure-temperature conditions by shock physics mesoscale modeling,"  We determined the shock-darkening pressure range in ordinary chondrites using
+the iSALE shock physics code. We simulated planar shock waves on a mesoscale in
+a sample layer at different nominal pressures. Iron and troilite grains were
+resolved in a porous olivine matrix in the sample layer. We used equations of
+state (Tillotson EoS and ANEOS) and basic strength and thermal properties to
+describe the material phases. We used Lagrangian tracers to record peak shock
+pressures in each material unit. The post-shock temperatures (and the fractions
+of tracers experiencing temperatures above the melting point) for each material
+were estimated after the passage of the shock wave and after reflections of the
+shock at grain boundaries in the heterogeneous materials. The results showed
+that shock-darkening, associated with troilite melt and the onset of olivine
+melt, happened between 40 and 50 GPa - with 52 GPa being the pressure at which
+all tracers in the troilite material reach the melting point. We demonstrate
+the difficulties of shock heating in iron and also the importance of porosity.
+Material impedances, grain shapes and the porosity models available in the
+iSALE code are discussed. We also discussed possible not-shock-related triggers
+for iron melt.
+",0,1,0,0,0,0
+18212,On the Global Fluctuations of Block Gaussian Matrices,"  In this paper we study the global fluctuations of block Gaussian matrices
+within the framework of second-order free probability theory. In order to
+compute the second-order Cauchy transform of these matrices, we introduce a
+matricial second-order conditional expectation and compute the matricial
+second-order Cauchy transform of a certain type of non-commutative random
+variables. As a by-product, using the linearization technique, we obtain the
+second-order Cauchy transform of non-commutative rational functions evaluated
+on selfadjoint Gaussian matrices.
+",0,0,1,0,0,0
+18213,Carnot Efficiency of Publication,"  This paper analyzes publication efficiency in terms of Hirsch-index or
+h-index and total citations, with an analogy to the Carnot efficiency used in
+thermodynamics. Such publication efficiency, with typical value of 30%, can be
+utilized to normalize the research output judgment, favoring quality outputs in
+reduced quantity, which is currently lacking in many discipline.
+",1,0,0,0,0,0
+18214,EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras,"  Event-based cameras have shown great promise in a variety of situations where
+frame based cameras suffer, such as high speed motions and high dynamic range
+scenes. However, developing algorithms for event measurements requires a new
+class of hand crafted algorithms. Deep learning has shown great success in
+providing model free solutions to many problems in the vision community, but
+existing networks have been developed with frame based images in mind, and
+there does not exist the wealth of labeled data for events as there does for
+images for supervised training. To these points, we present EV-FlowNet, a novel
+self-supervised deep learning pipeline for optical flow estimation for event
+based cameras. In particular, we introduce an image based representation of a
+given event stream, which is fed into a self-supervised neural network as the
+sole input. The corresponding grayscale images captured from the same camera at
+the same time as the events are then used as a supervisory signal to provide a
+loss function at training time, given the estimated flow from the network. We
+show that the resulting network is able to accurately predict optical flow from
+events only in a variety of different scenes, with performance competitive to
+image based networks. This method not only allows for accurate estimation of
+dense optical flow, but also provides a framework for the transfer of other
+self-supervised methods to the event-based domain.
+",1,0,0,0,0,0
+18215,Deep Active Learning for Named Entity Recognition,"  Deep learning has yielded state-of-the-art performance on many natural
+language processing tasks including named entity recognition (NER). However,
+this typically requires large amounts of labeled data. In this work, we
+demonstrate that the amount of labeled training data can be drastically reduced
+when deep learning is combined with active learning. While active learning is
+sample-efficient, it can be computationally expensive since it requires
+iterative retraining. To speed this up, we introduce a lightweight architecture
+for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and
+word encoders and a long short term memory (LSTM) tag decoder. The model
+achieves nearly state-of-the-art performance on standard datasets for the task
+while being computationally much more efficient than best performing models. We
+carry out incremental active learning, during the training process, and are
+able to nearly match state-of-the-art performance with just 25\% of the
+original training data.
+",1,0,0,0,0,0
+18216,Bipedal Walking with Corrective Actions in the Tilt Phase Space,"  Many methods exist for a bipedal robot to keep its balance while walking. In
+addition to step size and timing, other strategies are possible that influence
+the stability of the robot without interfering with the target direction and
+speed of locomotion. This paper introduces a multifaceted feedback controller
+that uses numerous different feedback mechanisms, collectively termed
+corrective actions, to stabilise a core keypoint-based gait. The feedback
+controller is experimentally effective, yet free of any physical model of the
+robot, very computationally inexpensive, and requires only a single 6-axis IMU
+sensor. Due to these low requirements, the approach is deemed to be highly
+portable between robots, and was specifically also designed to target lower
+cost robots that have suboptimal sensing, actuation and computational
+resources. The IMU data is used to estimate the yaw-independent tilt
+orientation of the robot, expressed in the so-called tilt phase space, and is
+the source of all feedback provided by the controller. Experimental validation
+is performed in simulation as well as on real robot hardware.
+",1,0,0,0,0,0
+18217,TimeNet: Pre-trained deep recurrent neural network for time series classification,"  Inspired by the tremendous success of deep Convolutional Neural Networks as
+generic feature extractors for images, we propose TimeNet: a deep recurrent
+neural network (RNN) trained on diverse time series in an unsupervised manner
+using sequence to sequence (seq2seq) models to extract features from time
+series. Rather than relying on data from the problem domain, TimeNet attempts
+to generalize time series representation across domains by ingesting time
+series from several domains simultaneously. Once trained, TimeNet can be used
+as a generic off-the-shelf feature extractor for time series. The
+representations or embeddings given by a pre-trained TimeNet are found to be
+useful for time series classification (TSC). For several publicly available
+datasets from UCR TSC Archive and an industrial telematics sensor data from
+vehicles, we observe that a classifier learned over the TimeNet embeddings
+yields significantly better performance compared to (i) a classifier learned
+over the embeddings given by a domain-specific RNN, as well as (ii) a nearest
+neighbor classifier based on Dynamic Time Warping.
+",1,0,0,0,0,0
+18218,Differentially Private Federated Learning: A Client Level Perspective,"  Federated learning is a recent advance in privacy protection. In this
+context, a trusted curator aggregates parameters optimized in decentralized
+fashion by multiple clients. The resulting model is then distributed back to
+all clients, ultimately converging to a joint representative model without
+explicitly having to share the data. However, the protocol is vulnerable to
+differential attacks, which could originate from any party contributing during
+federated optimization. In such an attack, a client's contribution during
+training and information about their data set is revealed through analyzing the
+distributed model. We tackle this problem and propose an algorithm for client
+sided differential privacy preserving federated optimization. The aim is to
+hide clients' contributions during training, balancing the trade-off between
+privacy loss and model performance. Empirical studies suggest that given a
+sufficiently large number of participating clients, our proposed procedure can
+maintain client-level differential privacy at only a minor cost in model
+performance.
+",1,0,0,1,0,0
+18219,"Okapi: Causally Consistent Geo-Replication Made Faster, Cheaper and More Available","  Okapi is a new causally consistent geo-replicated key- value store. Okapi
+leverages two key design choices to achieve high performance. First, it relies
+on hybrid logical/physical clocks to achieve low latency even in the presence
+of clock skew. Second, Okapi achieves higher resource efficiency and better
+availability, at the expense of a slight increase in update visibility latency.
+To this end, Okapi implements a new stabilization protocol that uses a
+combination of vector and scalar clocks and makes a remote update visible when
+its delivery has been acknowledged by every data center. We evaluate Okapi with
+different workloads on Amazon AWS, using three geographically distributed
+regions and 96 nodes. We compare Okapi with two recent approaches to causal
+consistency, Cure and GentleRain. We show that Okapi delivers up to two orders
+of magnitude better performance than GentleRain and that Okapi achieves up to
+3.5x lower latency and a 60% reduction of the meta-data overhead with respect
+to Cure.
+",1,0,0,0,0,0
+18220,Emergence of epithelial cell density waves,"  Epithelial cell monolayers exhibit traveling mechanical waves. We rationalize
+this observation thanks to a hydrodynamic description of the monolayer as a
+compressible, active and polar material. We show that propagating waves of the
+cell density, polarity, velocity and stress fields may be due to a Hopf
+bifurcation occurring above threshold values of active coupling coefficients.
+",0,1,0,0,0,0
+18221,Complex Analysis of Real Functions IV: Non-Integrable Real Functions,"  In the context of the complex-analytic structure within the unit disk
+centered at the origin of the complex plane, that was presented in a previous
+paper, we show that a certain class of non-integrable real functions can be
+represented within that same structure. In previous papers it was shown that
+essentially all integrable real functions, as well as all singular Schwartz
+distributions, can be represented within that same complex-analytic structure.
+The large class of non-integrable real functions which we analyze here can
+therefore be represented side by side with those other real objects, thus
+allowing all these objects to be treated in a unified way.
+",0,0,1,0,0,0
+18222,Convolution Aware Initialization,"  Initialization of parameters in deep neural networks has been shown to have a
+big impact on the performance of the networks (Mishkin & Matas, 2015). The
+initialization scheme devised by He et al, allowed convolution activations to
+carry a constrained mean which allowed deep networks to be trained effectively
+(He et al., 2015a). Orthogonal initializations and more generally orthogonal
+matrices in standard recurrent networks have been proved to eradicate the
+vanishing and exploding gradient problem (Pascanu et al., 2012). Majority of
+current initialization schemes do not take fully into account the intrinsic
+structure of the convolution operator. Using the duality of the Fourier
+transform and the convolution operator, Convolution Aware Initialization builds
+orthogonal filters in the Fourier space, and using the inverse Fourier
+transform represents them in the standard space. With Convolution Aware
+Initialization we noticed not only higher accuracy and lower loss, but faster
+convergence. We achieve new state of the art on the CIFAR10 dataset, and
+achieve close to state of the art on various other tasks.
+",1,0,0,1,0,0
+18223,A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation,"  We consider a multitask estimation problem where nodes in a network are
+divided into several connected clusters, with each cluster performing a
+least-mean-squares estimation of a different random parameter vector. Inspired
+by the adapt-then-combine diffusion strategy, we propose a multitask diffusion
+strategy whose mean stability can be ensured whenever individual nodes are
+stable in the mean, regardless of the inter-cluster cooperation weights. In
+addition, the proposed strategy is able to achieve an asymptotically unbiased
+estimation, when the parameters have same mean. We also develop an
+inter-cluster cooperation weights selection scheme that allows each node in the
+network to locally optimize its inter-cluster cooperation weights. Numerical
+results demonstrate that our approach leads to a lower average steady-state
+network mean-square deviation, compared with using weights selected by various
+other commonly adopted methods in the literature.
+",1,0,0,0,0,0
+18224,High Resolution Observations of the Massive Protostar in IRAS18566+0408,"  We report 3 mm continuum, CH3CN(5-4) and 13CS(2-1) line observations with
+CARMA, in conjunction with 6 and 1.3 cm continuum VLA data, and 12 and 25
+micron broadband data from the Subaru Telescope toward the massive proto-star
+IRAS18566+0408. The VLA data resolve the ionized jet into 4 components aligned
+in the E-W direction. Radio components A, C, and D have flat cm SEDs indicative
+of optically thin emission from ionized gas, and component B has a spectral
+index alpha = 1.0, and a decreasing size with frequency proportional to
+frequency to the -0.5 power. Emission from the CARMA 3 mm continuum, and from
+the 13CS(2-1), and CH3CN(5-4) spectral lines is compact (i.e. < 6700 AU), and
+peaks near the position of VLA cm source, component B. Analysis of these lines
+indicates hot, and dense molecular gas, typical for HMCs. Our Subaru telescope
+observations detect a single compact source, coincident with radio component B,
+demonstrating that most of the energy in IRAS18566+0408 originates from a
+region of size < 2400 AU. We also present UKIRT near-infrared archival data for
+IRAS18566+0408 which show extended K-band emission along the jet direction. We
+detect an E-W velocity shift of about 10 km/sec over the HMC in the CH3CN lines
+possibly tracing the interface of the ionized jet with the surrounding core
+gas. Our data demonstrate the presence of an ionized jet at the base of the
+molecular outflow, and support the hypothesis that massive protostars with
+O-type luminosity form with a mechanism similar to lower mass stars.
+",0,1,0,0,0,0
+18225,MEDL and MEDLA: Methods for Assessment of Scaling by Medians of Log-Squared Nondecimated Wavelet Coefficients,"  High-frequency measurements and images acquired from various sources in the
+real world often possess a degree of self-similarity and inherent regular
+scaling. When data look like a noise, the scaling exponent may be the only
+informative feature that summarizes such data. Methods for the assessment of
+self-similarity by estimating Hurst exponent often involve analysis of rate of
+decay in a spectrum defined in various multiresolution domains. When this
+spectrum is calculated using discrete non-decimated wavelet transforms, due to
+increased autocorrelation in wavelet coefficients, the estimators of $H$ show
+increased bias compared to the estimators that use traditional orthogonal
+transforms. At the same time, non-decimated transforms have a number of
+advantages when employed for calculation of wavelet spectra and estimation of
+Hurst exponents: the variance of the estimator is smaller, input signals and
+images could be of arbitrary size, and due to the shift-invariance, the local
+scaling can be assessed as well. We propose two methods based on robust
+estimation and resampling that alleviate the effect of increased
+autocorrelation while maintaining all advantages of non-decimated wavelet
+transforms. The proposed methods extend the approaches in existing literature
+where the logarithmic transformation and pairing of wavelet coefficients are
+used for lowering the bias. In a simulation study we use fractional Brownian
+motions with a range of theoretical Hurst exponents. For such signals for which
+""true"" $H$ is known, we demonstrate bias reduction and overall reduction of the
+mean-squared error by the two proposed estimators. For fractional Brownian
+motions, both proposed methods yield estimators of $H$ that are asymptotically
+normal and unbiased.
+",0,0,0,1,0,0
+18226,A Two-Level Graph Partitioning Problem Arising in Mobile Wireless Communications,"  In the k-partition problem (k-PP), one is given an edge-weighted undirected
+graph, and one must partition the node set into at most k subsets, in order to
+minimise (or maximise) the total weight of the edges that have their end-nodes
+in the same cluster. Various hierarchical variants of this problem have been
+studied in the context of data mining. We consider a 'two-level' variant that
+arises in mobile wireless communications. We show that an exact algorithm based
+on intelligent preprocessing, cutting planes and symmetry-breaking is capable
+of solving small- and medium-size instances to proven optimality, and providing
+strong lower bounds for larger instances.
+",1,0,1,0,0,0
+18227,Emotion Specification from Musical Stimuli: An EEG Study with AFA and DFA,"  The present study reports interesting findings in regard to emotional arousal
+based activities while listening to two Hindustani classical ragas of contrast
+emotion. EEG data was taken on 5 naive listeners while they listened to two
+ragas Bahar and Mia ki Malhar which are conventionally known to portray
+contrast emotions. The EEG data were analyzed with the help of two robust non
+linear tools viz. Adaptive Fractal Analysis (AFA) and Detrended Fluctuation
+Analysis (DFA). A comparative study of the Hurst Exponents obtained from the
+two methods have been shown which shows that DFA provides more rigorous results
+compared to AFA when it comes to the scaling analysis of biosignal data. The
+results and implications have been discussed in detail.
+",0,1,0,0,0,0
+18228,The light pollution as a surrogate for urban population of the US cities,"  We show that the definition of the city boundaries can have a dramatic
+influence on the scaling behavior of the night-time light (NTL) as a function
+of population (POP) in the US. Precisely, our results show that the arbitrary
+geopolitical definition based on the Metropolitan/Consolidated Metropolitan
+Statistical Areas (MSA/CMSA) leads to a sublinear power-law growth of NTL with
+POP. On the other hand, when cities are defined according to a more natural
+agglomeration criteria, namely, the City Clustering Algorithm (CCA), an
+isometric relation emerges between NTL and population. This discrepancy is
+compatible with results from previous works showing that the scaling behaviors
+of various urban indicators with population can be substantially different for
+distinct definitions of city boundaries. Moreover, considering the CCA
+definition as more adequate than the MSA/CMSA one because the former does not
+violate the expected extensivity between land population and area of their
+generated clusters, we conclude that, without loss of generality, the CCA
+measures of light pollution and population could be interchangeably utilized in
+future studies.
+",0,1,0,0,0,0
+18229,"Analytic calculation of radio emission from parameterized extensive air showers, a tool to extract shower parameters","  The radio intensity and polarization footprint of a cosmic-ray induced
+extensive air shower is determined by the time-dependent structure of the
+current distribution residing in the plasma cloud at the shower front. In turn,
+the time dependence of the integrated charge-current distribution in the plasma
+cloud, the longitudinal shower structure, is determined by interesting physics
+which one would like to extract such as the location and multiplicity of the
+primary cosmic-ray collision or the values of electric fields in the atmosphere
+during thunderstorms. To extract the structure of a shower from its footprint
+requires solving a complicated inverse problem. For this purpose we have
+developed a code that semi-analytically calculates the radio footprint of an
+extensive air shower given an arbitrary longitudinal structure. This code can
+be used in a optimization procedure to extract the optimal longitudinal shower
+structure given a radio footprint. On the basis of air-shower universality we
+propose a simple parametrization of the structure of the plasma cloud. This
+parametrization is based on the results of Monte-Carlo shower simulations.
+Deriving the parametrization also teaches which aspects of the plasma cloud are
+important for understanding the features seen in the radio-emission footprint.
+The calculated radio footprints are compared with microscopic CoREAS
+simulations.
+",0,1,0,0,0,0
+18230,Akhmediev Breathers and Peregrine Solitary Waves in a Quadratic Medium,"  We investigate the formation of optical localized nonlinear structures,
+evolving upon a non-zero background plane wave, in a dispersive quadratic
+medium. We show the existence of quadratic Akhmediev breathers and Peregrine
+solitary waves, in the regime of cascading second-harmonic generation. This
+finding opens a novel path for the excitation of extreme rogue waves and for
+the description of modulation instability in quadratic nonlinear optics.
+",0,1,0,0,0,0
+18231,Novel approaches to spectral properties of correlated electron materials: From generalized Kohn-Sham theory to screened exchange dynamical mean field theory,"  The most intriguing properties of emergent materials are typically
+consequences of highly correlated quantum states of their electronic degrees of
+freedom. Describing those materials from first principles remains a challenge
+for modern condensed matter theory. Here, we review, apply and discuss novel
+approaches to spectral properties of correlated electron materials, assessing
+current day predictive capabilities of electronic structure calculations. In
+particular, we focus on the recent Screened Exchange Dynamical Mean-Field
+Theory scheme and its relation to generalized Kohn-Sham theory. These concepts
+are illustrated on the transition metal pnictide BaCo$_2$As$_2$ and elemental
+zinc and cadmium.
+",0,1,0,0,0,0
+18232,Flux-Rope Twist in Eruptive Flares and CMEs: due to Zipper and Main-Phase Reconnection,"  The nature of three-dimensional reconnection when a twisted flux tube erupts
+during an eruptive flare or coronal mass ejection is considered. The
+reconnection has two phases: first of all, 3D ""zipper reconnection"" propagates
+along the initial coronal arcade, parallel to the polarity inversion line
+(PIL), then subsequent quasi-2D ""main phase reconnection"" in the low corona
+around a flux rope during its eruption produces coronal loops and chromospheric
+ribbons that propagate away from the PIL in a direction normal to it.
+One scenario starts with a sheared arcade: the zipper reconnection creates a
+twisted flux rope of roughly one turn ($2\pi$ radians of twist), and then main
+phase reconnection builds up the bulk of the erupting flux rope with a
+relatively uniform twist of a few turns. A second scenario starts with a
+pre-existing flux rope under the arcade. Here the zipper phase can create a
+core with many turns that depend on the ratio of the magnetic fluxes in the
+newly formed flare ribbons and the new flux rope. Main phase reconnection then
+adds a layer of roughly uniform twist to the twisted central core. Both phases
+and scenarios are modeled in a simple way that assumes the initial magnetic
+flux is fragmented along the PIL. The model uses conservation of magnetic
+helicity and flux, together with equipartition of magnetic helicity, to deduce
+the twist of the erupting flux rope in terms the geometry of the initial
+configuration.
+Interplanetary observations show some flux ropes have a fairly uniform twist,
+which could be produced when the zipper phase and any pre-existing flux rope
+possess small or moderate twist (up to one or two turns). Other interplanetary
+flux ropes have highly twisted cores (up to five turns), which could be
+produced when there is a pre-existing flux rope and an active zipper phase that
+creates substantial extra twist.
+",0,1,0,0,0,0
+18233,On the classification of Kantor pairs and structurable algebras in characteristic 5,"  We observe that any finite-dimensional central simple 5-graded Lie algebra
+over over a field k of characteristic not 2,3 is necessarily classical, i.e. a
+twisted form of a Chevalley Lie algebra. Consequently, the classification of
+central simple structurable algebras and Kantor pairs over fields of
+characteristic 5 derives from the classification of simple algebraic groups.
+Using the classification of nilpotent conjugacy classes, we list all 5-gradings
+on Lie algebras of simple algebraic groups that give rise to simple
+structurable algebras over algebraically closed fields.
+",0,0,1,0,0,0
+18234,Cross-lingual Distillation for Text Classification,"  Cross-lingual text classification(CLTC) is the task of classifying documents
+written in different languages into the same taxonomy of categories. This paper
+presents a novel approach to CLTC that builds on model distillation, which
+adapts and extends a framework originally proposed for model compression. Using
+soft probabilistic predictions for the documents in a label-rich language as
+the (induced) supervisory labels in a parallel corpus of documents, we train
+classifiers successfully for new languages in which labeled training data are
+not available. An adversarial feature adaptation technique is also applied
+during the model training to reduce distribution mismatch. We conducted
+experiments on two benchmark CLTC datasets, treating English as the source
+language and German, French, Japan and Chinese as the unlabeled target
+languages. The proposed approach had the advantageous or comparable performance
+of the other state-of-art methods.
+",1,0,0,0,0,0
+18235,Characterization of Majorana-Ising phase transition in a helical liquid system,"  We map an interacting helical liquid system, coupled to an external magnetic
+field and s-wave superconductor, to an XYZ spin system, and it undergoes
+Majorana-Ising transition by tuning of parameters. In the Majorana state,
+lowest excitation gap decays exponentially with system size, and the system has
+degenerate ground state in the thermodynamic limit. On the contrary, the gap
+opens in the Ising phase even in the thermodynamic limit. We also study other
+criteria to characterize the transition, such as edge spin correlation with its
+neighbor $C(r=1)$, local susceptibility $\chi_i$, superconducting order
+parameter of edge spin $P(r=1)$, and longitudinal structure factor $S(k)$. The
+ground state degeneracy and three other criteria lead to the same critical
+value of parameters for Majorana-Ising phase transition in the thermodynamic
+limit. We study, for the first time, the entanglement spectrum of the reduced
+density matrix of the helical liquid system. The system shows finite Schmidt
+gap and non-degeneracy of the entanglement spectrum in the Ising limit. The
+Schmidt gap closes in the Majorana state, and all the eigenvalues are either
+doubly or multiply degenerate.
+",0,1,0,0,0,0
+18236,Monotonicity and symmetry of nonnegative solutions to $ -Δu=f(u) $ in half-planes and strips,"  We consider nonnegative solutions to $-\Delta u=f(u)$ in half-planes and
+strips, under zero Dirichlet boundary condition. Exploiting a
+rotating$\&$sliding line technique, we prove symmetry and monotonicity
+properties of the solutions, under very general assumptions on the nonlinearity
+$f$. In fact we provide a unified approach that works in all the cases
+$f(0)<0$, $f(0)= 0$ or $f(0)> 0$. Furthermore we make the effort to deal with
+nonlinearities $f$ that may be not locally-Lipschitz continuous. We also
+provide explicite examples showing the sharpness of our assumptions on the
+nonlinear function $f$.
+",0,0,1,0,0,0
+18237,Gini-regularized Optimal Transport with an Application to Spatio-Temporal Forecasting,"  Rapidly growing product lines and services require a finer-granularity
+forecast that considers geographic locales. However the open question remains,
+how to assess the quality of a spatio-temporal forecast? In this manuscript we
+introduce a metric to evaluate spatio-temporal forecasts. This metric is based
+on an Opti- mal Transport (OT) problem. The metric we propose is a constrained
+OT objec- tive function using the Gini impurity function as a regularizer. We
+demonstrate through computer experiments both the qualitative and the
+quantitative charac- teristics of the Gini regularized OT problem. Moreover, we
+show that the Gini regularized OT problem converges to the classical OT
+problem, when the Gini regularized problem is considered as a function of
+{\lambda}, the regularization parame-ter. The convergence to the classical OT
+solution is faster than the state-of-the-art Entropic-regularized OT[Cuturi,
+2013] and results in a numerically more stable algorithm.
+",1,0,0,1,0,0
+18238,Indicators of Conformal Field Theory: entanglement entropy and multiple-point correlators,"  The entanglement entropy (EE) of quantum systems is often used as a test of
+low-energy descriptions by conformal field theory (CFT). Here we point out that
+this is not a reliable indicator, as the EE often shows the same behavior even
+when a CFT description is not correct (as long as the system is asymptotically
+scale-invariant). We use constraints on the scaling dimension given by the CFT
+with SU(2) symmetry to provide alternative tests with two- and four-point
+correlation functions, showing examples for quantum spin models in 1+1
+dimensions. In the case of a critical amplitude-product state expressed in the
+valence-bond basis (where the amplitudes decay as a power law of the bond
+length and the wave function is the product of all bond amplitudes), we show
+that even though the EE exhibits the expected CFT behavior, there is no CFT
+description of this state. We provide numerical tests of the behavior predicted
+by CFT for the correlation functions in the critical transverse-field Ising
+chain and the $J$-$Q$ spin chain, where the conformal structure is well
+understood. That behavior is not reproduced in the amplitude-product state.
+",0,1,0,0,0,0
+18239,Classical Entanglement Structure in the Wavefunction of Inflationary Fluctuations,"  We argue that the preferred classical variables that emerge from a pure
+quantum state are determined by its entanglement structure in the form of
+redundant records: information shared between many subsystems. Focusing on the
+early universe, we ask how classical metric perturbations emerge from vacuum
+fluctuations in an inflationary background. We show that the squeezing of the
+quantum state for super-horizon modes, along with minimal gravitational
+interactions, leads to decoherence and to an exponential number of records of
+metric fluctuations on very large scales, $\lambda/\lambda_{\rm
+Hubble}>\Delta_\zeta^{-2/3}$, where $\Delta_\zeta\lesssim 10^{-5}$ is the
+amplitude of scalar metric fluctuations. This determines a preferred
+decomposition of the inflationary wavefunction into orthogonal ""branches""
+corresponding to classical metric perturbations, which defines an inflationary
+entropy production rate and accounts for the emergence of stochastic,
+inhomogeneous spacetime geometry.
+",0,1,0,0,0,0
+18240,Pulsejet engine dynamics in vertical motion using momentum conservation,"  The momentum conservation law is applied to analyse the dynamics of pulsejet
+engine in vertical motion in a uniform gravitational field in the absence of
+friction. The model predicts existence of a terminal speed given frequency of
+the short pulses. The conditions that the engine does not return to the
+starting position are identified. The number of short periodic pulses after
+which the engine returns to the starting position is found to be independent of
+the exhaust velocity and gravitational field intensity for certain frequency of
+the pulses. The pulsejet engine and turbojet engine aircraft models of dynamics
+are compared. Also the octopus dynamics is modelled. The paper is addressed to
+intermediate undergraduate students of classical mechanics and aerospace
+engineering.
+",0,1,0,0,0,0
+18241,Mitigating Blackout Risk via Maintenance : Inference from Simulation Data,"  Whereas maintenance has been recognized as an important and effective means
+for risk management in power systems, it turns out to be intractable if
+cascading blackout risk is considered due to the extremely high computational
+complexity. In this paper, based on the inference from the blackout simulation
+data, we propose a methodology to efficiently identify the most influential
+component(s) for mitigating cascading blackout risk in a large power system. To
+this end, we first establish an analytic relationship between maintenance
+strategies and blackout risk estimation by inferring from the data of cascading
+outage simulations. Then we formulate the component maintenance decision-making
+problem as a nonlinear 0-1 programming. Afterwards, we quantify the credibility
+of blackout risk estimation, leading to an adaptive method to determine the
+least required number of simulations, which servers as a crucial parameter of
+the optimization model. Finally, we devise two heuristic algorithms to find
+approximate optimal solutions to the model with very high efficiency. Numerical
+experiments well manifest the efficacy and high efficiency of our methodology.
+",0,0,0,1,0,0
+18242,Evolving to Non-round Weingarten Spheres: Integer Linear Hopf Flows,"  In the 1950's Hopf gave examples of non-round convex 2-spheres in Euclidean
+3-space with rotational symmetry that satisfy a linear relationship between
+their principal curvatures. In this paper we investigate conditions under which
+evolving a smooth rotationally symmetric sphere by a linear combination of its
+radii of curvature yields a Hopf sphere. When the coefficients of the flow have
+certain integer values, the fate of an initial sphere is entirely determined by
+the local geometry of its isolated umbilic points. A surprising variety of
+behaviours is uncovered: convergence to round spheres and non-round Hopf
+spheres, as well as divergence to infinity.
+The critical quantity is the rate of vanishing of the astigmatism - the
+difference of the radii of curvature - at the isolated umbilic points. It is
+proven that the size of this quantity versus the coefficient in the flow
+function determines the fate of the evolution.
+The geometric setting for the equation is Radius of Curvature space, viewed
+as a pair of hyperbolic/AdS half-planes joined along their boundary, the
+umbilic horizon. A rotationally symmetric sphere determines a parameterized
+curve in this plane with end-points on the umbilic horizon. The slope of the
+curve at the umbilic horizon is linked by the Codazzi-Mainardi equations to the
+rate of vanishing of astigmatism, and for generic initial conditions can be
+used to determine the outcome of the flow.
+The slope can jump during the flow, and a number of examples are given:
+instant jumps of the initial slope, as well as umbilic circles that contract to
+points in finite time and 'pop' the slope. Finally, we present soliton-like
+solutions: curves that evolve under linear flows by mutual hyperbolic/AdS
+isometries (dilation and translation) of Radius of Curvature space. A
+forthcoming paper will apply these geometric ideas to non-linear curvature
+flows.
+",0,0,1,0,0,0
+18243,On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems,"  We revisit the linear programming approach to deterministic, continuous time,
+infinite horizon discounted optimal control problems. In the first part, we
+relax the original problem to an infinite-dimensional linear program over a
+measure space and prove equivalence of the two formulations under mild
+assumptions, significantly weaker than those found in the literature until now.
+The proof is based on duality theory and mollification techniques for
+constructing approximate smooth subsolutions to the associated
+Hamilton-Jacobi-Bellman equation. In the second part, we assume polynomial data
+and use Lasserre's hierarchy of primal-dual moment-sum-of-squares semidefinite
+relaxations to approximate the value function and design an approximate optimal
+feedback controller. We conclude with an illustrative example.
+",1,0,1,0,0,0
+18244,Granular permittivity representation in extremely near-field light-matter interactions processes,"  Light-matter interaction processes are significantly affected by surrounding
+electromagnetic environment. Dielectric materials are usually introduced into
+an interaction picture via their classical properties, e.g. permittivity,
+appearing in constitutive relations. While this approach was proven to be
+applicable in many occasions, it might face limitations when an emitter is
+situated very close to a material boundary. In this case nonlocal extend of a
+quantum wave function of an emitter becomes comparable with a distance to a
+boundary and a lattice constant of a material. Here a semi-classical model,
+taking into account material's granularity, is developed. In particular,
+spontaneous emission process in the vicinity of flat boundaries is considered.
+The material boundary is divided into a pair areas - far zone is modeled as a
+continuous phase, while the near zone next to a nonlocal emitter is represented
+with a discrete array of polarizable particles. This array resembles optical
+properties of the continuous phase under the standard homogenization procedure.
+Local field effects were shown to lead orders of magnitude corrections to
+spontaneous emission rates in the case of sub-nanometer emitter-surface
+separation distances. The developed mesoscopic model enables addressing few
+aspects of local field corrections in quite complex scenarios, where quantum ab
+initio techniques yet face challenges owing to involved computational
+complexity. The developed method could be utilized for designs of quantum
+sources and networks, enhanced with structured electromagnetic environment.
+",0,1,0,0,0,0
+18245,Application of Superhalogens in the Design of Organic Superconductors,"  Bechgaard salts, (TMTSF)2X (TMTSF = tetramethyl tetraselenafulvalene and X =
+complex anion), form the most popular series of organic superconductors. In
+these salts, TMTSF molecules act as super-electron donor and X as acceptor. We
+computationally examine the electronic structure and properties of X in
+commonly used (TMTSF)2X (X = NO3, BF4, ClO4, PF6) superconductors and notice
+that they belong to the class of superhalogens due to their higher vertical
+detachment energy than halogen anions. This prompted us to choose other
+superhalogens such as X = BO2, BH4, B2F7, AuF6 and study their (TMTSF)2X
+complexes. Our findings suggest that these complexes behave more or less
+similar to those of established (TMTSF)2X superconductors, particularly for X =
+BO2 and B2F7. We, therefore, believe that the concept of superhalogen can be
+successfully applied in the design of novel organic superconductors.
+",0,1,0,0,0,0
+18246,Inhomogeneous hard-core bosonic mixture with checkerboard supersolid phase: Quantum and thermal phase diagram,"  We introduce an inhomogeneous bosonic mixture composed of two kinds of
+hard-core and semi-hard-core bosons with different nilpotency conditions and
+demonstrate that in contrast with the standard hard-core Bose-Hubbard model,
+our bosonic mixture with nearest- and next-nearest-neighbor interactions on a
+square lattice develops the checkerboard supersolid phase characterized by the
+simultaneous superfluid and checkerboard solid orders. Our bosonic mixture is
+created from a two-orbital Bose-Hubbard model including two kinds of bosons: a
+single-orbital boson and a two-orbital boson. By mapping the bosonic mixture to
+an anisotropic inhomogeneous spin model in the presence of a magnetic field, we
+study the ground-state phase diagram of the model by means of cluster mean
+field theory and linear spin-wave theory and show that various phases such as
+solid, superfluid, supersolid, and Mott insulator appear in the phase diagram
+of the mixture. Competition between the interactions and magnetic field causes
+the mixture to undergo different kinds of first- and second-order phase
+transitions. By studying the behavior of the spin-wave excitations, we find the
+reasons of all first- and second-order phase transitions. We also obtain the
+temperature phase diagram of the system using cluster mean field theory. We
+show that the checkerboard supersolid phase persists at finite temperature
+comparable with the interaction energies of bosons.
+",0,1,0,0,0,0
+18247,Width-$k$ Generalizations of Classical Permutation Statistics,"  We introduce new natural generalizations of the classical descent and
+inversion statistics for permutations, called width-$k$ descents and width-$k$
+inversions. These variations induce generalizations of the excedance and major
+statistics, providing a framework in which the most well-known
+equidistributivity results for classical statistics are paralleled. We explore
+additional relationships among the statistics providing specific formulas in
+certain special cases. Moreover, we explore the behavior of these width-$k$
+statistics in the context of pattern avoidance.
+",0,0,1,0,0,0
+18248,Klout Topics for Modeling Interests and Expertise of Users Across Social Networks,"  This paper presents Klout Topics, a lightweight ontology to describe social
+media users' topics of interest and expertise. Klout Topics is designed to: be
+human-readable and consumer-friendly; cover multiple domains of knowledge in
+depth; and promote data extensibility via knowledge base entities. We discuss
+why this ontology is well-suited for text labeling and interest modeling
+applications, and how it compares to available alternatives. We show its
+coverage against common social media interest sets, and examples of how it is
+used to model the interests of over 780M social media users on Klout.com.
+Finally, we open the ontology for external use.
+",1,0,0,0,0,0
+18249,Set-Based Tests for Genetic Association Using the Generalized Berk-Jones Statistic,"  Studying the effects of groups of Single Nucleotide Polymorphisms (SNPs), as
+in a gene, genetic pathway, or network, can provide novel insight into complex
+diseases, above that which can be gleaned from studying SNPs individually.
+Common challenges in set-based genetic association testing include weak effect
+sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with
+single-SNP effects often ranging from extremely sparse to moderately sparse in
+number. Motivated by these challenges, we propose the Generalized Berk-Jones
+(GBJ) test for the association between a SNP-set and outcome. The GBJ extends
+the Berk-Jones (BJ) statistic by accounting for correlation among SNPs, and it
+provides advantages over the Generalized Higher Criticism (GHC) test when
+signals in a SNP-set are moderately sparse. We also provide an analytic p-value
+calculation procedure for SNP-sets of any finite size. Using this p-value
+calculation, we illustrate that the rejection region for GBJ can be described
+as a compromise of those for BJ and GHC. We develop an omnibus statistic as
+well, and we show that this omnibus test is robust to the degree of signal
+sparsity. An additional advantage of our method is the ability to conduct
+inference using individual SNP summary statistics from a genome-wide
+association study. We evaluate the finite sample performance of the GBJ though
+simulation studies and application to gene-level association analysis of breast
+cancer risk.
+",0,0,0,1,0,0
+18250,Comprehensive Modeling of Three-Phase Distribution Systems via the Bus Admittance Matrix,"  The theme of this paper is three-phase distribution system modeling suitable
+for the Z-Bus load-flow. Detailed models of wye and delta constant-power,
+constant-current, and constant-impedance loads are presented. Models of
+transmission lines, voltage regulators, and transformers that build the bus
+admittance matrix (Y-Bus) are laid out. The Z-Bus load-flow is then reviewed
+and the singularity of the Y-Bus in case of certain transformer connections is
+rigorously discussed. Based on realistic assumptions and conventional
+modifications, the invertibility of the Y-Bus is proved. Last but not least,
+the MATLAB scripts that construct the detailed component models for the IEEE
+37-bus, IEEE 123-bus, and 8500-node feeders as well as the European 906-bus
+low-voltage feeder are provided.
+",1,0,1,0,0,0
+18251,$c$-vectors of 2-Calabi--Yau categories and Borel subalgebras of ${\mathfrak{sl}}_{\infty}$,"  We develop a general framework for $c$-vectors of 2-Calabi--Yau categories,
+which deals with cluster tilting subcategories that are not reachable from each
+other and contain infinitely many indecomposable objects. It does not rely on
+iterative sequences of mutations.
+We prove a categorical (infinite-rank) generalization of the
+Nakanishi--Zelevinsky duality for $c$-vectors and establish two formulae for
+the effective computation of $c$-vectors -- one in terms of indices and the
+other in terms of dimension vectors for cluster tilted algebras.
+In this framework, we construct a correspondence between the $c$-vectors of
+the cluster categories ${\mathscr{C}}(A_{\infty})$ of type $A_{\infty}$ due to
+Igusa--Todorov and the roots of the Borel subalgebras of
+${\mathfrak{sl}}_{\infty}$. Contrary to the finite dimensional case, the Borel
+subalgebras of ${\mathfrak{sl}}_{\infty}$ are not conjugate to each other. On
+the categorical side, the cluster tilting subcategories of
+${\mathscr{C}}(A_{\infty})$ exhibit different homological properties. The
+correspondence builds a bridge between the two classes of objects.
+",0,0,1,0,0,0
+18252,Electrostatic interaction between non-identical charged particles at an electrolyte interface,"  In this thesis we study the lateral electrostatic interaction between a pair
+of non-identical, moderately charged colloidal particles trapped at an
+electrolyte interface in the limit of short inter-particle separations. Using a
+simplified model system we solve the problem analytically within the framework
+of linearised Poisson-Boltzmann theory and classical density functional theory.
+In the first step, we calculate the electrostatic potential inside the system
+exactly as well as within the widely used superposition approximation. Then
+these results are used to calculate the surface and line interaction energy
+densities between the particles. Contrary to the case of identical particles,
+depending upon the parameters of the system, we obtain that both the surface
+and the line interaction can vary non-monotonically with varying separation
+between the particles and the superposition approximation fails to predict the
+correct qualitative behaviours in most cases. Additionally, the superposition
+approximation is unable to predict the energy contributions quantitatively even
+at large distances. We also provide expression for the constant (independent of
+the inter-particle separation) interaction parameters, i.e., the surface
+tension, the line tension and the interfacial tension. Our results are expected
+to be of use for modelling particle-interaction at fluid interfaces and, in
+particular, for emulsion stabilization using oppositely charged particles.
+",0,1,0,0,0,0
+18253,The weighted connection and sectional curvature for manifolds with density,"  In this paper we study sectional curvature bounds for Riemannian manifolds
+with density from the perspective of a weighted torsion free connection
+introduced recently by the last two authors. We develop two new tools for
+studying weighted sectional curvature bounds: a new weighted Rauch comparison
+theorem and a modified notion of convexity for distance functions. As
+applications we prove generalizations of theorems of Preissman and Byers for
+negative curvature, the (homeomorphic) quarter-pinched sphere theorem, and
+Cheeger's finiteness theorem. We also improve results of the first two authors
+for spaces of positive weighted sectional curvature and symmetry.
+",0,0,1,0,0,0
+18254,Control and Limit Enforcements for VSC Multi-Terminal HVDC in Newton Power Flow,"  This paper proposes a novel method to automatically enforce controls and
+limits for Voltage Source Converter (VSC) based multi-terminal HVDC in the
+Newton power flow iteration process. A general VSC MT-HVDC model with primary
+PQ or PV control and secondary voltage control is formulated. Both the
+dependent and independent variables are included in the propose formulation so
+that the algebraic variables of the VSC MT-HVDC are adjusted simultaneously.
+The proposed method also maintains the number of equations and the dimension of
+the Jacobian matrix unchanged so that, when a limit is reached and a control is
+released, the Jacobian needs no re-factorization. Simulations on the IEEE
+14-bus and Polish 9241-bus systems are performed to demonstrate the
+effectiveness of the method.
+",1,0,0,0,0,0
+18255,A.Ya. Khintchine's Work in Probability Theory,"  The paper is devoted to the contribution in the Probability Theory of the
+well-known Soviet mathematician Alexander Yakovlevich Khintchine (1894-1959).
+Several of his results are described, in particular those fundamental results
+on the infinitely divisible distributions. Attention is paid also to his
+interaction with Paul Levy. The content of the paper is related to our joint
+book The Legacy of A.Ya. Khintchine's Work in Probability Theory, published in
+2010 by Cambridge Scientific Publishers.
+",0,0,1,0,0,0
+18256,Dynamic Complexity under Definable Changes,"  This paper studies dynamic complexity under definable change operations in
+the DynFO framework by Patnaik and Immerman. It is shown that for changes
+definable by parameter-free first-order formulas, all (uniform) $AC^1$ queries
+can be maintained by first-order dynamic programs. Furthermore, many
+maintenance results for single-tuple changes are extended to more powerful
+change operations: (1) The reachability query for undirected graphs is
+first-order maintainable under single tuple changes and first-order defined
+insertions, likewise the reachability query for directed acyclic graphs under
+quantifier-free insertions. (2) Context-free languages are first-order
+maintainable under $\Sigma_1$-defined changes. These results are complemented
+by several inexpressibility results, for example, that the reachability query
+cannot be maintained by quantifier-free programs under definable,
+quantifier-free deletions.
+",1,0,0,0,0,0
+18257,The Planar Sandwich and Other 1D Planar Heat Flow Test Problems in ExactPack,"  This report documents the implementation of several related 1D heat flow
+problems in the verification package ExactPack. In particular, the planar
+sandwich class defined by Dawes et al., as well as the classes
+PlanarSandwichHot, PlanarSandwichHalf, and other generalizations of the planar
+sandwich problem, are defined and documented here. A rather general treatment
+of 1D heat flow is presented, whose main results have been implemented in the
+class Rod1D. All planar sandwich classes are derived from the parent class
+Rod1D.
+",0,1,0,0,0,0
+18258,Difficulties of Timestamping Archived Web Pages,"  We show that state-of-the-art services for creating trusted timestamps in
+blockchain-based networks do not adequately allow for timestamping of web
+pages. They accept data by value (e.g., images and text), but not by reference
+(e.g., URIs of web pages). Also, we discuss difficulties in repeatedly
+generating the same cryptographic hash value of an archived web page. We then
+introduce several requirements to be fulfilled in order to produce repeatable
+hash values for archived web pages.
+",1,0,0,0,0,0
+18259,Cops and robber on grids and tori,"  This paper is a contribution to the classical cops and robber problem on a
+graph, directed to two-dimensional grids and toroidal grids. These studies are
+generally aimed at determining the minimum number of cops needed to capture the
+robber and proposing algorithms for the capture. We apply some new concepts to
+propose a new solution to the problem on grids that was already solved under a
+different approach, and apply these concepts to give efficient algorithms for
+the capture on toroidal grids. As for grids, we show that two cops suffice even
+in a semi-torus (i.e. a grid with toroidal closure in one dimension) and three
+cops are necessary and sufficient in a torus. Then we treat the problem in
+function of any number k of cops, giving efficient algorithms for grids and
+tori and computing lower and upper bounds on the capture time. Conversely we
+determine the minimum value of k needed for any given capture time and study a
+possible speed-up phenomenon.
+",1,0,0,0,0,0
+18260,Measuring Gender Inequalities of German Professions on Wikipedia,"  Wikipedia is a community-created online encyclopedia; arguably, it is the
+most popular and largest knowledge resource on the Internet. Thus, reliability
+and neutrality are of high importance for Wikipedia. Previous research [3]
+reveals gender bias in Google search results for many professions and
+occupations. Also, Wikipedia was criticized for existing gender bias in
+biographies [4] and gender gap in the editor community [5, 6]. Thus, one could
+expect that gender bias related to professions and occupations may be present
+in Wikipedia. The term gender bias is used here in the sense of conscious or
+unconscious favoritism towards one gender over another [47] with respect to
+professions and occupations. The objective of this work is to identify and
+assess gender bias. To this end, the German Wikipedia articles about
+professions and occupations were analyzed on three dimensions: redirections,
+images, and people mentioned in the articles. This work provides evidence for
+systematic overrepresentation of men in all three dimensions; female bias is
+only present for a few professions.
+",1,0,0,0,0,0
+18261,Contact Adaption during Epidemics: A Multilayer Network Formulation Approach,"  People change their physical contacts as a preventive response to infectious
+disease propagations. Yet, only a few mathematical models consider the coupled
+dynamics of the disease propagation and the contact adaptation process. This
+paper presents a model where each agent has a default contact neighborhood set,
+and switches to a different contact set once she becomes alert about infection
+among her default contacts. Since each agent can adopt either of two possible
+neighborhood sets, the overall contact network switches among 2^N possible
+configurations. Notably, a two-layer network representation can fully model the
+underlying adaptive, state-dependent contact network. Contact adaptation
+influences the size of the disease prevalence and the epidemic threshold---a
+characteristic measure of a contact network robustness against epidemics---in a
+nonlinear fashion. Particularly, the epidemic threshold for the presented
+adaptive contact network belongs to the solution of a nonlinear
+Perron-Frobenius (NPF) problem, which does not depend on the contact adaptation
+rate monotonically. Furthermore, the network adaptation model predicts a
+counter-intuitive scenario where adaptively changing contacts may adversely
+lead to lower network robustness against epidemic spreading if the contact
+adaptation is not fast enough. An original result for a class of NPF problems
+facilitate the analytical developments in this paper.
+",1,0,0,0,0,0
+18262,Estimation in the convolution structure density model. Part I: oracle inequalities,"  We study the problem of nonparametric estimation under $\bL_p$-loss, $p\in
+[1,\infty)$, in the framework of the convolution structure density model on
+$\bR^d$. This observation scheme is a generalization of two classical
+statistical models, namely density estimation under direct and indirect
+observations. In Part I the original pointwise selection rule from a family of
+""kernel-type"" estimators is proposed. For the selected estimator, we prove an
+$\bL_p$-norm oracle inequality and several of its consequences. In Part II the
+problem of adaptive minimax estimation under $\bL_p$--loss over the scale of
+anisotropic Nikol'skii classes is addressed. We fully characterize the behavior
+of the minimax risk for different relationships between regularity parameters
+and norm indexes in the definitions of the functional class and of the risk. We
+prove that the selection rule proposed in Part I leads to the construction of
+an optimally or nearly optimally (up to logarithmic factor) adaptive estimator.
+",0,0,1,1,0,0
+18263,Candidate exoplanet host HD131399A: a nascent Am star,"  Direct imaging suggests that there is a Jovian exoplanet around the primary
+A-star in the triple-star system HD131399. We investigate a high-quality
+spectrum of the primary component HD131399A obtained with FEROS on the ESO/MPG
+2.2m telescope, aiming to characterise the star's atmospheric and fundamental
+parameters, and to determine elemental abundances at high precision and
+accuracy. The aim is to constrain the chemical composition of the birth cloud
+of the system and therefore the bulk composition of the putative planet. A
+hybrid non-local thermal equilibrium (non-LTE) model atmosphere technique is
+adopted for the quantitative spectral analysis. Comparison with the most recent
+stellar evolution models yields the fundamental parameters. The atmospheric and
+fundamental stellar parameters of HD131399A are constrained to Teff=9200+-100
+K, log g=4.37+-0.10, M=1.95+0.08-0.06 Msun, R=1.51+0.13-0.10 Rsun, and log
+L/Lsun=1.17+-0.07, locating the star on the zero-age main sequence. Non-LTE
+effects on the derived metal abundances are often smaller than 0.1dex, but can
+reach up to ~0.8dex for individual lines. The observed lighter elements up to
+calcium are overall consistent with present-day cosmic abundances, with a C/O
+ratio of 0.45$\pm$0.07 by number, while the heavier elements show mild
+overabundances. We conclude that the birth cloud of the system had a standard
+chemical composition, but we witness the onset of the Am phenomenon in the
+slowly rotating star. We furthermore show that non-LTE analyses have the
+potential to solve the remaining discrepancies between observed abundances and
+predictions by diffusion models for Am stars. Moreover, the present case allows
+mass loss, not turbulent mixing, to be identified as the main transport process
+competing with diffusion in very young Am stars.
+",0,1,0,0,0,0
+18264,Toward Quantum Combinatorial Games,"  In this paper, we propose a Quantum variation of combinatorial games,
+generalizing the Quantum Tic-Tac-Toe proposed by Allan Goff. A combinatorial
+game is a two-player game with no chance and no hidden information, such as Go
+or Chess. In this paper, we consider the possibility of playing superpositions
+of moves in such games. We propose different rulesets depending on when
+superposed moves should be played, and prove that all these rulesets may lead
+similar games to different outcomes. We then consider Quantum variations of the
+game of Nim. We conclude with some discussion on the relative interest of the
+different rulesets.
+",1,0,0,0,0,0
+18265,Markov $L_2$ inequality with the Gegenbauer weight,"  For the Gegenbauer weight function $w_{\lambda}(t)=(1-t^2)^{\lambda-1/2}$,
+$\lambda>-1/2$, we denote by $\Vert\cdot\Vert_{w_{\lambda}}$ the associated
+$L_2$-norm, $$ \Vert
+f\Vert_{w_{\lambda}}:=\Big(\int_{-1}^{1}w_{\lambda}(t)f^2(t)\,dt\Big)^{1/2}. $$
+We study the Markov inequality $$ \Vert p^{\prime}\Vert_{w_{\lambda}}\leq
+c_{n}(\lambda)\,\Vert p\Vert_{w_{\lambda}},\qquad p\in \mathcal{P}_n, $$ where
+$\mathcal{P}_n$ is the class of algebraic polynomials of degree not exceeding
+$n$. Upper and lower bounds for the best Markov constant $c_{n}(\lambda)$ are
+obtained, which are valid for all $n\in \mathbb{N}$ and $\lambda>-\frac{1}{2}$.
+",0,0,1,0,0,0
+18266,A Note on Some Approximation Kernels on the Sphere,"  We produce precise estimates for the Kogbetliantz kernel for the
+approximation of functions on the sphere. Furthermore, we propose and study a
+new approximation kernel, which has slightly better properties.
+",0,0,1,0,0,0
+18267,Machine Learning as Statistical Data Assimilation,"  We identify a strong equivalence between neural network based machine
+learning (ML) methods and the formulation of statistical data assimilation
+(DA), known to be a problem in statistical physics. DA, as used widely in
+physical and biological sciences, systematically transfers information in
+observations to a model of the processes producing the observations. The
+correspondence is that layer label in the ML setting is the analog of time in
+the data assimilation setting. Utilizing aspects of this equivalence we discuss
+how to establish the global minimum of the cost functions in the ML context,
+using a variational annealing method from DA. This provides a design method for
+optimal networks for ML applications and may serve as the basis for
+understanding the success of ""deep learning"". Results from an ML example are
+presented.
+When the layer label is taken to be continuous, the Euler-Lagrange equation
+for the ML optimization problem is an ordinary differential equation, and we
+see that the problem being solved is a two point boundary value problem. The
+use of continuous layers is denoted ""deepest learning"". The Hamiltonian version
+provides a direct rationale for back propagation as a solution method for the
+canonical momentum; however, it suggests other solution methods are to be
+preferred.
+",1,0,0,1,0,0
+18268,Nonparametric estimation of the kernel function of symmetric stable moving average random functions,"  We use the empirical normalized (smoothed) periodogram of a $S\alpha S$
+moving average random function to estimate its kernel function from high
+frequency observation data. The weak consistency of the estimator is shown. A
+simulation study of the performance of the estimates rounds up the paper.
+",0,0,1,1,0,0
+18269,Fermions in Two Dimensions: Scattering and Many-Body Properties,"  Ultracold atomic Fermi gases in two-dimensions (2D) are an increasingly
+popular topic of research. The interaction strength between spin-up and
+spin-down particles in two-component Fermi gases can be tuned in experiments,
+allowing for a strongly interacting regime where the gas properties are yet to
+be fully understood. We have probed this regime for 2D Fermi gases by
+performing T=0 ab initio diffusion Monte Carlo calculations. The many-body
+dynamics are largely dependent on the two-body interactions, therefore we start
+with an in-depth look at scattering theory in 2D. We show the partial-wave
+expansion and its relation to the scattering length and effective range. Then
+we discuss our numerical methods for determining these scattering parameters.
+We close out this discussion by illustrating the details of bound states in 2D.
+Transitioning to the many-body system, we use variationally optimized wave
+functions to calculate ground-state properties of the gas over a range of
+interaction strengths. We show results for the energy per particle and
+parametrize an equation of state. We then proceed to determine the chemical
+potential for the strongly interacting gas.
+",0,1,0,0,0,0
+18270,Moving Horizon Estimation for ARMAX process with t-Distribution Noise,"  In this paper, instead of the usual Gaussian noise assumption,
+$t$-distribution noise is assumed. A Maximum Likelihood Estimator using the
+most recent N measurements is proposed for the Auto-Regressive-Moving-Average
+with eXogenous input (ARMAX) process with this assumption. The proposed
+estimator is robust to outliers because the `thick tail' of the t-distribution
+reduces the effect of large errors in the likelihood function. Instead of
+solving the resulting nonlinear estimator numerically, the Influence Function
+is used to formulate a computationally efficient recursive solution, which
+reduces to the traditional Moving Horizon Estimator when the noise is Gaussian.
+The formula for the variance of the estimate is derived. This formula shows
+explicitly how the variance of the estimate is affected by the number of
+measurements and noise variance. The simulation results show that the proposed
+estimator has smaller variance and is more robust to outliers than the Moving
+Window Least-Squares Estimator. For the same accuracy, the proposed estimator
+is an order of magnitude faster than the particle filter.
+",1,0,0,0,0,0
+18271,Chimera states in brain networks: empirical neural vs. modular fractal connectivity,"  Complex spatiotemporal patterns, called chimera states, consist of coexisting
+coherent and incoherent domains and can be observed in networks of coupled
+oscillators. The interplay of synchrony and asynchrony in complex brain
+networks is an important aspect in studies of both brain function and disease.
+We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex
+networks motivated by its potential application to epileptology and epilepsy
+surgery. We compare two topologies: an empirical structural neural connectivity
+derived from diffusion-weighted magnetic resonance imaging and a mathematically
+constructed network with modular fractal connectivity. We analyse the
+properties of chimeras and partially synchronized states, and obtain regions of
+their stability in the parameter planes. Furthermore, we qualitatively simulate
+the dynamics of epileptic seizures and study the influence of the removal of
+nodes on the network synchronizability, which can be useful for applications to
+epileptic surgery.
+",0,1,0,0,0,0
+18272,Bayesian surrogate learning in dynamic simulator-based regression problems,"  The estimation of unknown values of parameters (or hidden variables, control
+variables) that characterise a physical system often relies on the comparison
+of measured data with synthetic data produced by some numerical simulator of
+the system as the parameter values are varied. This process often encounters
+two major difficulties: the generation of synthetic data for each considered
+set of parameter values can be computationally expensive if the system model is
+complicated; and the exploration of the parameter space can be inefficient
+and/or incomplete, a typical example being when the exploration becomes trapped
+in a local optimum of the objection function that characterises the mismatch
+between the measured and synthetic data. A method to address both these issues
+is presented, whereby: a surrogate model (or proxy), which emulates the
+computationally expensive system simulator, is constructed using deep recurrent
+networks (DRN); and a nested sampling (NS) algorithm is employed to perform
+efficient and robust exploration of the parameter space. The analysis is
+performed in a Bayesian context, in which the samples characterise the full
+joint posterior distribution of the parameters, from which parameter estimates
+and uncertainties are easily derived. The proposed approach is compared with
+conventional methods in some numerical examples, for which the results
+demonstrate that one can accelerate the parameter estimation process by at
+least an order of magnitude.
+",1,0,0,1,0,0
+18273,Stationary solutions for the ellipsoidal BGK model in a slab,"  We address the boundary value problem for the ellipsoidal BGK model of the
+Boltzmann equation posed in a bounded interval. The existence of a unique mild
+solution is established under the assumption that the inflow boundary data does
+not concentrate too much around the zero velocity, and the gas is sufficiently
+rarefied.
+",0,0,1,0,0,0
+18274,Raman signatures of monoclinic distortion in (Ba$_{1-x}$Sr$_{x}$)$_{3}$CaNb$_{2}$O$_{9}$ complex perovskites,"  Octahedral tilting is most common distortion process observed in
+centrosymmetric perovskite compounds (ABO$_{3}$). Indeed, crucial physical
+properties of this oxide stem from the tilts of BO$_{6}$ rigid octahedra. In
+microwave ceramics with perovskite-type structure, there is a close relation
+between the temperature coefficient of resonant frequency and tilt system of
+the perovskite structure. However, in many cases, limited access facilities are
+needed to assign correctly the space group, including neutron scattering and
+transmission electron microscopy. Here, we combine the Raman scattering and
+group-theory calculations to probe the structural distortion in the perovskite
+(Ba$_{1-x}$Sr$_{x}$)$_{3}$CaNb$_{2}$O$_{9}$ solid solution, which exhibits a
+structural phase transition at $x$ $\geq$ 0.7, from D$_{3d}^{3}$ trigonal to
+C$_{2h}^{3}$ monoclinic cell. Both phases are related by an octahedral tilting
+distortion ($a^{0}b^{-}b^{-}$ in Glazer notation). Low temperature Raman
+spectra corroborate the group-theoretical predictions for
+Sr$_{3}$CaNb$_{2}$O$_{9}$ compound, since 36 modes detected at 25 K agree well
+with those 42 (25A$_{g}$ $\oplus$ 17B$_{g}$) predicted ones.
+",0,1,0,0,0,0
+18275,On Quadratic Penalties in Elastic Weight Consolidation,"  Elastic weight consolidation (EWC, Kirkpatrick et al, 2017) is a novel
+algorithm designed to safeguard against catastrophic forgetting in neural
+networks. EWC can be seen as an approximation to Laplace propagation (Eskin et
+al, 2004), and this view is consistent with the motivation given by Kirkpatrick
+et al (2017). In this note, I present an extended derivation that covers the
+case when there are more than two tasks. I show that the quadratic penalties in
+EWC are inconsistent with this derivation and might lead to double-counting
+data from earlier tasks.
+",1,0,0,1,0,0
+18276,The design of the ILD forward region,"  Following the decision to reduce the L* from 4.4 m to 4.1 m, the BeamCal had
+to be moved closer to the interaction point. Results of a study of how this
+affects the beamstrahlung and backward scattering backgrounds show that the
+e+e- pair background depositions from beamstrahlung at the BeamCal rises by
+20%. The background from backscattered electrons and positrons in the inner
+pixel layers rises almost by a factor two, so does the number of photons in the
+tracker.
+",0,1,0,0,0,0
+18277,Conduction Channel Formation and Dissolution Due to Oxygen Thermophoresis/Diffusion in Hafnium Oxide Memristors,"  Transition metal oxide memristors, or resistive random-access memory (RRAM)
+switches, are under intense development for storage-class memory because of
+their favorable operating power, endurance, speed, and density. Their
+commercial deployment critically depends on predictive compact models based on
+understanding nanoscale physico-chemical forces, which remains elusive and
+controversial owing to the difficulties in directly observing atomic motions
+during resistive switching, Here, using scanning transmission synchrotron x-ray
+spectromicroscopy to study in-situ switching of hafnium oxide memristors, we
+directly observed the formation of a localized oxygen-deficiency-derived
+conductive channel surrounded by a low-conductivity ring of excess oxygen.
+Subsequent thermal annealing homogenized the segregated oxygen, resetting the
+cells towards their as-grown resistance state. We show that the formation and
+dissolution of the conduction channel are successfully modeled by radial
+thermophoresis and Fick diffusion of oxygen atoms driven by Joule heating. This
+confirmation and quantification of two opposing nanoscale radial forces that
+affect bipolar memristor switching are important components for any future
+physics-based compact model for the electronic switching of these devices.
+",0,1,0,0,0,0
+18278,Improving Massive MIMO Belief Propagation Detector with Deep Neural Network,"  In this paper, deep neural network (DNN) is utilized to improve the belief
+propagation (BP) detection for massive multiple-input multiple-output (MIMO)
+systems. A neural network architecture suitable for detection task is firstly
+introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed
+based on two modified BP detectors, damped BP and max-sum BP. The correction
+factors in these algorithms are optimized through deep learning techniques,
+aiming at improved detection performance. Numerical results are presented to
+demonstrate the performance of the DNN detectors in comparison with various BP
+modifications. The neural network is trained once and can be used for multiple
+online detections. The results show that, compared to other state-of-the-art
+detectors, the DNN detectors can achieve lower bit error rate (BER) with
+improved robustness against various antenna configurations and channel
+conditions at the same level of complexity.
+",0,0,0,1,0,0
+18279,Trend Analysis on the Metadata of Program Comprehension Papers,"  As program comprehension is a vast research area, it is necessary to get an
+overview of its rising and falling trends. We performed an n-gram frequency
+analysis on titles, abstracts and keywords of 1885 articles about program
+comprehension from the years 2000-2014. According to this analysis, the most
+rising trends are feature location and open source systems, the most falling
+ones are program slicing and legacy systems.
+",1,0,0,0,0,0
+18280,Combinatorial views on persistent characters in phylogenetics,"  The so-called binary perfect phylogeny with persistent characters has
+recently been thoroughly studied in computational biology as it is less
+restrictive than the well known binary perfect phylogeny. Here, we focus on the
+notion of (binary) persistent characters, i.e. characters that can be realized
+on a phylogenetic tree by at most one $0 \rightarrow 1$ transition followed by
+at most one $1 \rightarrow 0$ transition in the tree, and analyze these
+characters under different aspects. First, we illustrate the connection between
+persistent characters and Maximum Parsimony, where we characterize persistent
+characters in terms of the first phase of the famous Fitch algorithm.
+Afterwards we focus on the number of persistent characters for a given
+phylogenetic tree. We show that this number solely depends on the balance of
+the tree. To be precise, we develop a formula for counting the number of
+persistent characters for a given phylogenetic tree based on an index of tree
+balance, namely the Sackin index. Lastly, we consider the question of how many
+(carefully chosen) binary characters together with their persistence status are
+needed to uniquely determine a phylogenetic tree and provide an upper bound for
+the number of characters needed.
+",0,0,0,0,1,0
+18281,Distribution of residuals in the nonparametric IV model with application to separability testing,"  We develop a uniform asymptotic expansion for the empirical distribution
+function of residuals in the nonparametric IV regression. Such expansion opens
+a door for construction of a broad range of residual-based specification tests
+in nonparametric IV models. Building on obtained result, we develop a test for
+the separability of unobservables in econometric models with endogeneity. The
+test is based on verifying the independence condition between residuals of the
+NPIV estimator and the instrument and can distinguish between the non-separable
+and the separable specification under endogeneity.
+",0,0,1,1,0,0
+18282,Scene Graph Generation by Iterative Message Passing,"  Understanding a visual scene goes beyond recognizing individual objects in
+isolation. Relationships between objects also constitute rich semantic
+information about the scene. In this work, we explicitly model the objects and
+their relationships using scene graphs, a visually-grounded graphical structure
+of an image. We propose a novel end-to-end model that generates such structured
+scene representation from an input image. The model solves the scene graph
+inference problem using standard RNNs and learns to iteratively improves its
+predictions via message passing. Our joint inference model can take advantage
+of contextual cues to make better predictions on objects and their
+relationships. The experiments show that our model significantly outperforms
+previous methods for generating scene graphs using Visual Genome dataset and
+inferring support relations with NYU Depth v2 dataset.
+",1,0,0,0,0,0
+18283,On the Apparent Power Law in CDM Halo Pseudo Phase Space Density Profiles,"  We investigate the apparent power-law scaling of the pseudo phase space
+density (PPSD) in CDM halos. We study fluid collapse, using the close analogy
+between the gas entropy and the PPSD in the fluid approximation. Our
+hydrodynamic calculations allow for a precise evaluation of logarithmic
+derivatives. For scale-free initial conditions, entropy is a power law in
+Lagrangian (mass) coordinates, but not in Eulerian (radial) coordinates. The
+deviation from a radial power law arises from incomplete hydrostatic
+equilibrium (HSE), linked to bulk inflow and mass accretion, and the
+convergence to the asymptotic central power-law slope is very slow. For more
+realistic collapse, entropy is not a power law with either radius or mass due
+to deviations from HSE and scale-dependent initial conditions. Instead, it is a
+slowly rolling power law that appears approximately linear on a log-log plot.
+Our fluid calculations recover PPSD power-law slopes and residual amplitudes
+similar to N-body simulations, indicating that deviations from a power law are
+not numerical artefacts. In addition, we find that realistic collapse is not
+self-similar: scale lengths such as the shock radius and the turnaround radius
+are not power-law functions of time. We therefore argue that the apparent
+power-law PPSD cannot be used to make detailed dynamical inferences or
+extrapolate halo profiles inward, and that it does not indicate any hidden
+integrals of motion. We also suggest that the apparent agreement between the
+PPSD and the asymptotic Bertschinger slope is purely coincidental.
+",0,1,0,0,0,0
+18284,Heavy tailed approximate identities and $σ$-stable Markov kernels,"  The aim of this paper is to present some results relating the properties of
+stability, concentration and approximation to the identity of convolution
+through not necessarily mollification type families of heavy tailed Markov
+kernels. A particular case is provided by the kernels $K_t$ obtained as the $t$
+mollification of $L^{\sigma(t)}$ selected from the family
+$\mathcal{L}=\{L^{\sigma}:
+\widehat{L^{\sigma}}(\xi)=e^{-|\xi|^\sigma},0<\sigma<2\}$, by a given function
+$\sigma$ with values in the interval $(0,2)$. We show that a basic Harnack type
+inequality, introduced by C.~Calderón in the convolution case, becomes at
+once natural to the setting and useful to connect the concepts of stability,
+concentration and approximation of the identity. Some of the general results
+are extended to spaces of homogeneous type since most of the concepts involved
+in the theory are given in terms of metric and measure.
+",0,0,1,0,0,0
+18285,Spreading of correlations in the Falicov-Kimball model,"  We study dynamical properties of the one- and two-dimensional Falicov-Kimball
+model using lattice Monte Carlo simulations. In particular, we calculate the
+spreading of charge correlations in the equilibrium model and after an
+interaction quench. The results show a reduction of the light-cone velocity
+with interaction strength at low temperature, while the phase velocity
+increases. At higher temperature, the initial spreading is determined by the
+Fermi velocity of the noninteracting system and the maximum range of the
+correlations decreases with increasing interaction strength. Charge order
+correlations in the disorder potential enhance the range of the correlations.
+We also use the numerically exact lattice Monte Carlo results to benchmark the
+accuracy of equilibrium and nonequilibrium dynamical cluster approximation
+calculations. It is shown that the bias introduced by the mapping to a
+periodized cluster is substantial, and that from a numerical point of view, it
+is more efficient to simulate the lattice model directly.
+",0,1,0,0,0,0
+18286,Direct Estimation of Information Divergence Using Nearest Neighbor Ratios,"  We propose a direct estimation method for Rényi and f-divergence measures
+based on a new graph theoretical interpretation. Suppose that we are given two
+sample sets $X$ and $Y$, respectively with $N$ and $M$ samples, where
+$\eta:=M/N$ is a constant value. Considering the $k$-nearest neighbor ($k$-NN)
+graph of $Y$ in the joint data set $(X,Y)$, we show that the average powered
+ratio of the number of $X$ points to the number of $Y$ points among all $k$-NN
+points is proportional to Rényi divergence of $X$ and $Y$ densities. A
+similar method can also be used to estimate f-divergence measures. We derive
+bias and variance rates, and show that for the class of $\gamma$-Hölder
+smooth functions, the estimator achieves the MSE rate of
+$O(N^{-2\gamma/(\gamma+d)})$. Furthermore, by using a weighted ensemble
+estimation technique, for density functions with continuous and bounded
+derivatives of up to the order $d$, and some extra conditions at the support
+set boundary, we derive an ensemble estimator that achieves the parametric MSE
+rate of $O(1/N)$. Our estimators are more computationally tractable than other
+competing estimators, which makes them appealing in many practical
+applications.
+",1,0,0,1,0,0
+18287,Efficiently Learning Mixtures of Mallows Models,"  Mixtures of Mallows models are a popular generative model for ranking data
+coming from a heterogeneous population. They have a variety of applications
+including social choice, recommendation systems and natural language
+processing. Here we give the first polynomial time algorithm for provably
+learning the parameters of a mixture of Mallows models with any constant number
+of components. Prior to our work, only the two component case had been settled.
+Our analysis revolves around a determinantal identity of Zagier which was
+proven in the context of mathematical physics, which we use to show polynomial
+identifiability and ultimately to construct test functions to peel off one
+component at a time.
+To complement our upper bounds, we show information-theoretic lower bounds on
+the sample complexity as well as lower bounds against restricted families of
+algorithms that make only local queries. Together, these results demonstrate
+various impediments to improving the dependence on the number of components.
+They also motivate the study of learning mixtures of Mallows models from the
+perspective of beyond worst-case analysis. In this direction, we show that when
+the scaling parameters of the Mallows models have separation, there are much
+faster learning algorithms.
+",0,0,0,1,0,0
+18288,Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices,"  Optimal scheduling of hydrogen production in dynamic pricing power market can
+maximize the profit of hydrogen producer; however, it highly depends on the
+accurate forecast of hydrogen consumption. In this paper, we propose a deep
+leaning based forecasting approach for predicting hydrogen consumption of fuel
+cell vehicles in future taxi industry. The cost of hydrogen production is
+minimized by utilizing the proposed forecasting tool to reduce the hydrogen
+produced during high cost on-peak hours and guide hydrogen producer to store
+sufficient hydrogen during low cost off-peak hours.
+",0,0,0,1,0,0
+18289,Determination of the thermopower of microscale samples with an AC method,"  A modified AC method based on micro-fabricated heater and resistive
+thermometers has been applied to measure the thermopower of microscale samples.
+A sinusoidal current with frequency {\omega} is passed to the heater to
+generate an oscillatory temperature difference across the sample at a frequency
+2{\omega}, which simultaneously induces an AC thermoelectric voltage, also at
+the frequency 2{\omega}. A key step of the method is to extract amplitude and
+phase of the oscillatory temperature difference by probing the AC temperature
+variation at each individual thermometer. The sign of the thermopower is
+determined by examining the phase difference between the oscillatory
+temperature difference and the AC thermoelectric voltage. The technique has
+been compared with the popular DC method by testing both n-type and p-type thin
+film samples. Both methods yielded consistent results, which verified the
+reliability of the newly proposed AC method.
+",0,1,0,0,0,0
+18290,Food for Thought: Analyzing Public Opinion on the Supplemental Nutrition Assistance Program,"  This project explores public opinion on the Supplemental Nutrition Assistance
+Program (SNAP) in news and social media outlets, and tracks elected
+representatives' voting records on issues relating to SNAP and food insecurity.
+We used machine learning, sentiment analysis, and text mining to analyze
+national and state level coverage of SNAP in order to gauge perceptions of the
+program over time across these outlets. Results indicate that the majority of
+news coverage has negative sentiment, more partisan news outlets have more
+extreme sentiment, and that clustering of negative reporting on SNAP occurs in
+the Midwest. Our final results and tools will be displayed in an on-line
+application that the ACFB Advocacy team can use to inform their communication
+to relevant stakeholders.
+",1,0,0,0,0,0
+18291,"Hasse diagrams of non-isomorphic posets with $n$ elements, $2\leq n \leq 7,$ and the number of posets with $10$ elements, without the aid of any computer program","  Let $P(n)$ be the set of all posets with $n$ elements and $NIP(n)$ the set of
+non-isomorphic posets with $n$ elements. Let $P^{(j)}(n)$, $1\leq j\leq 2^n,$
+be the number of all posets with $n$ elements possessing exactly $j$
+antichains. We have determined the numbers $P^{(j)}(7),$ $1\leq j\leq 128$, and
+using a result of M. Erné \cite{EM4}, we compute $|P(10)|$ without the aid of
+any computer program. We include the Hasse diagrams of all the non-isomorphic
+posets of $P(7)$.
+",0,0,1,0,0,0
+18292,Variable screening with multiple studies,"  Advancement in technology has generated abundant high-dimensional data that
+allows integration of multiple relevant studies. Due to their huge
+computational advantage, variable screening methods based on marginal
+correlation have become promising alternatives to the popular regularization
+methods for variable selection. However, all these screening methods are
+limited to single study so far. In this paper, we consider a general framework
+for variable screening with multiple related studies, and further propose a
+novel two-step screening procedure using a self-normalized estimator for
+high-dimensional regression analysis in this framework. Compared to the
+one-step procedure and rank-based sure independence screening (SIS) procedure,
+our procedure greatly reduces false negative errors while keeping a low false
+positive rate. Theoretically, we show that our procedure possesses the sure
+screening property with weaker assumptions on signal strengths and allows the
+number of features to grow at an exponential rate of the sample size. In
+addition, we relax the commonly used normality assumption and allow
+sub-Gaussian distributions. Simulations and a real transcriptomic application
+illustrate the advantage of our method as compared to the rank-based SIS
+method.
+",0,0,1,1,0,0
+18293,Gaussian-Constrained training for speaker verification,"  Neural models, in particular the d-vector and x-vector architectures, have
+produced state-of-the-art performance on many speaker verification tasks.
+However, two potential problems of these neural models deserve more
+investigation. Firstly, both models suffer from `information leak', which means
+that some parameters participating in model training will be discarded during
+inference, i.e, the layers that are used as the classifier. Secondly, both
+models do not regulate the distribution of the derived speaker vectors. This
+`unconstrained distribution' may degrade the performance of the subsequent
+scoring component, e.g., PLDA. This paper proposes a Gaussian-constrained
+training approach that (1) discards the parametric classifier, and (2) enforces
+the distribution of the derived speaker vectors to be Gaussian. Our experiments
+on the VoxCeleb and SITW databases demonstrated that this new training approach
+produced more representative and regular speaker embeddings, leading to
+consistent performance improvement.
+",1,0,0,0,0,0
+18294,Structural Change in (Economic) Time Series,"  Methods for detecting structural changes, or change points, in time series
+data are widely used in many fields of science and engineering. This chapter
+sketches some basic methods for the analysis of structural changes in time
+series data. The exposition is confined to retrospective methods for univariate
+time series. Several recent methods for dating structural changes are compared
+using a time series of oil prices spanning more than 60 years. The methods
+broadly agree for the first part of the series up to the mid-1980s, for which
+changes are associated with major historical events, but provide somewhat
+different solutions thereafter, reflecting a gradual increase in oil prices
+that is not well described by a step function. As a further illustration, 1990s
+data on the volatility of the Hang Seng stock market index are reanalyzed.
+",0,1,0,1,0,0
+18295,Exploiting Hierarchy in the Abstraction-Based Verification of Statecharts Using SMT Solvers,"  Statecharts are frequently used as a modeling formalism in the design of
+state-based systems. Formal verification techniques are also often applied to
+prove certain properties about the behavior of the system. One of the most
+efficient techniques for formal verification is Counterexample-Guided
+Abstraction Refinement (CEGAR), which reduces the complexity of systems by
+automatically building and refining abstractions. In our paper we present a
+novel adaptation of the CEGAR approach to hierarchical statechart models. First
+we introduce an encoding of the statechart to logical formulas that preserves
+information about the state hierarchy. Based on this encoding we propose
+abstraction and refinement techniques that utilize the hierarchical structure
+of statecharts and also handle variables in the model. The encoding allows us
+to use SMT solvers for the systematic exploration and verification of the
+abstract model, including also bounded model checking. We demonstrate the
+applicability and efficiency of our abstraction techniques with measurements on
+an industry-motivated example.
+",1,0,0,0,0,0
+18296,Modulus consensus in discrete-time signed networks and properties of special recurrent inequalities,"  Recently the dynamics of signed networks, where the ties among the agents can
+be both positive (attractive) or negative (repulsive) have attracted
+substantial attention of the research community. Examples of such networks are
+models of opinion dynamics over signed graphs, recently introduced by Altafini
+(2012,2013) and extended to discrete-time case by Meng et al. (2014). It has
+been shown that under mild connectivity assumptions these protocols provide the
+convergence of opinions in absolute value, whereas their signs may differ. This
+""modulus consensus"" may correspond to the polarization of the opinions (or
+bipartite consensus, including the usual consensus as a special case), or their
+convergence to zero. In this paper, we demonstrate that the phenomenon of
+modulus consensus in the discrete-time Altafini model is a manifestation of a
+more general and profound fact, regarding the solutions of a special recurrent
+inequality. Although such a recurrent inequality does not provide the
+uniqueness of a solution, it can be shown that, under some natural assumptions,
+each of its bounded solutions has a limit and, moreover, converges to
+consensus. A similar property has previously been established for special
+continuous-time differential inequalities (Proskurnikov, Cao, 2016). Besides
+analysis of signed networks, we link the consensus properties of recurrent
+inequalities to the convergence analysis of distributed optimization algorithms
+and the problems of Schur stability of substochastic matrices.
+",1,1,1,0,0,0
+18297,Mobility Transition at Grain Boundaries in Two-Step Sintered 8 mol% Yttria Stabilized Zirconia,"  Stagnation of grain growth is often attributed to impurity segregation.
+Yttria-stabilized cubic zirconia does not evidence any segregation-induced
+slowdown, as its grain growth obeys the parabolic law when the grain size
+increases by more than one order of magnitude. However, lowering the
+temperature below 1300 oC triggers an abrupt slowdown, constraining the average
+grains to grow by less than 0.5 ${\mu}$m in 1000 h despite a relatively large
+driving force imparted in the fine grains of ~0.5 ${\mu}$m. Yet isolated
+pockets of abnormally large grains, along with pockets of abnormally small
+grains, emerge in the same latter sample. Such microstructure bifurcation has
+never been observed before, and can only be explained by an inhomogeneous
+distribution of immobile four-grain junctions. The implications of these
+findings for two-step sintering are discussed.
+",0,1,0,0,0,0
+18298,"Sufficient conditions for the forcing theorem, and turning proper classes into sets","  We present three natural combinatorial properties for class forcing notions,
+which imply the forcing theorem to hold. We then show that all known sufficent
+conditions for the forcing theorem (except for the forcing theorem itself),
+including the three properties presented in this paper, imply yet another
+regularity property for class forcing notions, namely that proper classes of
+the ground model cannot become sets in a generic extension, that is they do not
+have set-sized names in the ground model. We then show that over certain models
+of Gödel-Bernays set theory without the power set axiom, there is a notion of
+class forcing which turns a proper class into a set, however does not satisfy
+the forcing theorem. Moreover, we show that the property of not turning proper
+classes into sets can be used to characterize pretameness over such models of
+Gödel-Bernays set theory.
+",0,0,1,0,0,0
+18299,Cauchy problem for effectively hyperbolic operators with triple characteristics,"  We study the Cauchy problem for effectively hyperbolic operators $P$ with
+principal symbol $p(t, x,\tau,\xi)$ having triple characteristics on $t = 0$.
+Under a condition (E) we show that such operators are strongly hyperbolic, that
+is the Cauchy problem is well posed for $p(t, x,D_t, D_x) + Q(t, x, D_t, D_x)$
+with arbitrary lower order term $Q$. The proof is based on energy estimates
+with weight $t^{-N}$ for a first order pseudo-differential system, where $N$
+depends on lower order terms. For our analysis we construct a non-negative
+definite symmetrizer $S(t)$ and we prove a version of Fefferman-Phong type
+inequality for ${\rm Re}\, (S(t)U, U)_{L^2({\mathbb R}^n)}$ with a lower bound
+$-C t^{-1}\|\langle D \rangle^{-1}U\|_{L^2(\mathbb R^n)}$.
+",0,0,1,0,0,0
+18300,\textit{Ab Initio} results for the Static Structure Factor of the Warm Dense Electron Gas,"  The uniform electron gas at finite temperature is of high current interest
+for warm dense matter research. The complicated interplay of quantum degeneracy
+and Coulomb coupling effects is fully contained in the pair distribution
+function or, equivalently, the static strucutre factor. By combining exact
+quantum Monte Carlo results for large wave vectors with the long-range behavior
+from the Singwi-Tosi-Land-Sjölander approximation, we are able to obtain
+highly accurate data for the static structure factor over the entire $k$-range.
+This allows us to gauge the accuracy of previous approximations and discuss
+their respective shortcomings. Further, our new data will serve as valuable
+input for the computation of other quantities.
+",0,1,0,0,0,0
+18301,Delooping the functor calculus tower,"  We study a connection between mapping spaces of bimodules and of
+infinitesimal bimodules over an operad. As main application and motivation of
+our work, we produce an explicit delooping of the manifold calculus tower
+associated to the space of smooth maps $D^{m}\rightarrow D^{n}$ pf discs,
+$n\geq m$, avoiding any given multisingularity and coinciding with the standard
+inclusion near $\partial D^{m}$. In particular, we give a new proof of the
+delooping of the space of disc embeddings in terms of little discs operads maps
+with the advantage that it can be applied to more general mapping spaces.
+",0,0,1,0,0,0
+18302,Area Law Violations and Quantum Phase Transitions in Modified Motzkin Walk Spin Chains,"  Area law violations for entanglement entropy in the form of a square root has
+recently been studied for one-dimensional frustration-free quantum systems
+based on the Motzkin walks and their variations. Here we consider a Motzkin
+walk with a different Hilbert space on each step of the walk spanned by
+elements of a {\it Symmetric Inverse Semigroup} with the direction of each step
+governed by its algebraic structure. This change alters the number of paths
+allowed in the Motzkin walk and introduces a ground state degeneracy sensitive
+to boundary perturbations. We study the frustration-free spin chains based on
+three symmetric inverse semigroups, $\cS^3_1$, $\cS^3_2$ and $\cS^2_1$. The
+system based on $\cS^3_1$ and $\cS^3_2$ provide examples of quantum phase
+transitions in one dimensions with the former exhibiting a transition between
+the area law and a logarithmic violation of the area law and the latter
+providing an example of transition from logarithmic scaling to a square root
+scaling in the system size, mimicking a colored $\cS^3_1$ system. The system
+with $\cS^2_1$ is much simpler and produces states that continue to obey the
+area law.
+",0,1,0,0,0,0
+18303,Diffusion and confusion of chaotic iteration based hash functions,"  To guarantee the integrity and security of data transmitted through the
+Internet, hash functions are fundamental tools. But recent researches have
+shown that security flaws exist in the most widely used hash functions. So a
+new way to improve their security performance is urgently demanded. In this
+article, we propose new hash functions based on chaotic iterations, which have
+chaotic properties as defined by Devaney. The corresponding diffusion and
+confusion analyzes are provided and a comparative study between the proposed
+hash functions is carried out, to make their use more applicable in any
+security context.
+",1,1,0,0,0,0
+18304,L1188: a promising candidate of cloud-cloud collision triggering the formation of the low- and intermediate-mass stars,"  We present a new large-scale (4 square degrees) simultaneous $^{12}$CO,
+$^{13}$CO, and C$^{18}$O ($J$=1$-$0) mapping of L1188 with the PMO 13.7-m
+telescope. Our observations have revealed that L1188 consists of two nearly
+orthogonal filamentary molecular clouds at two clearly separated velocities.
+Toward the intersection showing large velocity spreads, we find several
+bridging features connecting the two clouds in velocity, and an open arc
+structure which exhibits high excitation temperatures, enhanced $^{12}$CO and
+$^{13}$CO emission, and broad $^{12}$CO line wings. This agrees with the
+scenario that the two clouds are colliding with each other. The distribution of
+young stellar object (YSO) candidates implies an enhancement of star formation
+in the intersection of the two clouds. We suggest that a cloud-cloud collision
+happened in L1188 about 1~Myr ago, possibly triggering the formation of low-
+and intermediate-mass YSOs in the intersection.
+",0,1,0,0,0,0
+18305,Asymmetric Matrix-Valued Covariances for Multivariate Random Fields on Spheres,"  Matrix-valued covariance functions are crucial to geostatistical modeling of
+multivariate spatial data. The classical assumption of symmetry of a
+multivariate covariance function is overlay restrictive and has been considered
+as unrealistic for most of real data applications. Despite of that, the
+literature on asymmetric covariance functions has been very sparse. In
+particular, there is some work related to asymmetric covariances on Euclidean
+spaces, depending on the Euclidean distance. However, for data collected over
+large portions of planet Earth, the most natural spatial domain is a sphere,
+with the corresponding geodesic distance being the natural metric. In this
+work, we propose a strategy based on spatial rotations to generate asymmetric
+covariances for multivariate random fields on the $d$-dimensional unit sphere.
+We illustrate through simulations as well as real data analysis that our
+proposal allows to achieve improvements in the predictive performance in
+comparison to the symmetric counterpart.
+",0,0,1,1,0,0
+18306,A dynamic stochastic blockmodel for interaction lengths,"  We propose a new dynamic stochastic blockmodel that focuses on the analysis
+of interaction lengths in networks. The model does not rely on a discretization
+of the time dimension and may be used to analyze networks that evolve
+continuously over time. The framework relies on a clustering structure on the
+nodes, whereby two nodes belonging to the same latent group tend to create
+interactions and non-interactions of similar lengths. We introduce a fast
+variational expectation-maximization algorithm to perform inference, and adapt
+a widely used clustering criterion to perform model choice. Finally, we test
+our methodology on artificial data, and propose a demonstration on a dataset
+concerning face-to-face interactions between students in a high-school.
+",0,0,0,1,0,0
+18307,Do we agree on user interface aesthetics of Android apps?,"  Context: Visual aesthetics is increasingly seen as an essential factor in
+perceived usability, interaction, and overall appraisal of user interfaces
+especially with respect to mobile applications. Yet, a question that remains is
+how to assess and to which extend users agree on visual aesthetics. Objective:
+This paper analyzes the inter-rater agreement on visual aesthetics of user
+interfaces of Android apps as a basis for guidelines and evaluation models.
+Method: We systematically collected ratings on the visual aesthetics of 100
+user interfaces of Android apps from 10 participants and analyzed the frequency
+distribution, reliability and influencing design aspects. Results: In general,
+user interfaces of Android apps are perceived more ugly than beautiful. Yet,
+raters only moderately agree on the visual aesthetics. Disagreements seem to be
+related to subtle differences with respect to layout, shapes, colors,
+typography, and background images. Conclusion: Visual aesthetics is a key
+factor for the success of apps. However, the considerable disagreement of
+raters on the perceived visual aesthetics indicates the need for a better
+understanding of this software quality with respect to mobile apps.
+",1,0,0,0,0,0
+18308,A Solution for Large-scale Multi-object Tracking,"  A large-scale multi-object tracker based on the generalised labeled
+multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking
+a very large, unknown and time-varying number of objects simultaneously, in the
+presence of a high number of false alarms, as well as misdetections and
+measurement origin uncertainty due to closely spaced objects. The algorithm is
+demonstrated on a simulated large-scale tracking scenario, where the peak
+number objects appearing simultaneously exceeds one million. To evaluate the
+performance of the proposed tracker, we also introduce a new method of applying
+the optimal sub-pattern assignment (OSPA) metric, and an efficient strategy for
+its evaluation in large-scale scenarios.
+",0,0,0,1,0,0
+18309,Empirical Survival Jensen-Shannon Divergence as a Goodness-of-Fit Measure for Maximum Likelihood Estimation and Curve Fitting,"  The coefficient of determination, known as $R^2$, is commonly used as a
+goodness-of-fit criterion for fitting linear models. $R^2$ is somewhat
+controversial when fitting nonlinear models, although it may be generalised on
+a case-by-case basis to deal with specific models such as the logistic model.
+Assume we are fitting a parametric distribution to a data set using, say, the
+maximum likelihood estimation method. A general approach to measure the
+goodness-of-fit of the fitted parameters, which we advocate herein, is to use a
+nonparametric measure for model comparison between the raw data and the fitted
+model. In particular, for this purpose we put forward the {\em Survival
+Jensen-Shannon divergence} ($SJS$) and its empirical counterpart (${\cal
+E}SJS$) as a metric which is bounded, and is a natural generalisation of the
+Jensen-Shannon divergence. We demonstrate, via a straightforward procedure
+making use of the ${\cal E}SJS$, that it can be used as part of maximum
+likelihood estimation or curve fitting as a measure of goodness-of-fit,
+including the construction of a confidence interval for the fitted parametric
+distribution. Furthermore, we show the validity of the proposed method with
+simulated data, and three empirical data sets of interest to researchers in
+sociophysics and econophysics.
+",0,0,0,0,0,1
+18310,Cooperative Localisation of a GPS-Denied UAV using Direction of Arrival Measurements,"  A GPS-denied UAV (Agent B) is localised through INS alignment with the aid of
+a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several
+time instants. Agent B measures the signals' direction of arrival with respect
+to Agent B's inertial navigation frame. Semidefinite programming and the
+Orthogonal Procrustes algorithm are employed, and accuracy is improved through
+maximum likelihood estimation. The method is validated using flight data and
+simulations. A three-agent extension is explored.
+",1,0,0,0,0,0
+18311,Optical Surface Properties and their RF Limitations of European XFEL Cavities,"  The inner surface of superconducting cavities plays a crucial role to achieve
+highest accelerating fields and low losses. The industrial fabrication of
+cavities for the European X-Ray Free Electron Laser (XFEL) and the
+International Linear Collider (ILC) HiGrade Research Project allowed for an
+investigation of this interplay. For the serial inspection of the inner
+surface, the optical inspection robot OBACHT was constructed and to analyze the
+large amount of data, represented in the images of the inner surface, an image
+processing and analysis code was developed and new variables to describe the
+cavity surface were obtained. This quantitative analysis identified vendor
+specific surface properties which allow to perform a quality control and
+assurance during the production. In addition, a strong negative correlation of
+$\rho= -0.93$ with a significance of $6\,\sigma$ of the integrated grain
+boundary area $\sum{\mathrm{A}}$ versus the maximal achievable accelerating
+field $\mathrm{E_{acc,max}}$ has been found.
+",0,1,0,0,0,0
+18312,A comment on `An improved macroscale model for gas slip flow in porous media',"  In a recent paper by Lasseux, Valdés-Parada and Porter (J.~Fluid~Mech.
+\textbf{805} (2016) 118-146), it is found that the apparent gas permeability of
+the porous media is a nonlinear function of the Knudsen number. However, this
+result is highly questionable, because the adopted Navier-Stokes equations and
+the first-order velocity-slip boundary condition are first-order (in terms of
+the Knudsen number) approximations of the Boltzmann equation and the kinetic
+boundary condition for rarefied gas flows. Our numerical simulations based on
+the Bhatnagar-Gross-Krook kinetic equation and regularized 20-moment equations
+prove that the Navier-Stokes equations with the first-order velocity-slip
+boundary condition are only accurate at a very small Knudsen number limit,
+where the apparent gas permeability is a linear function of the Knudsen number.
+",0,1,0,0,0,0
+18313,AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training,"  Highly distributed training of Deep Neural Networks (DNNs) on future compute
+platforms (offering 100 of TeraOps/s of computational capacity) is expected to
+be severely communication constrained. To overcome this limitation, new
+gradient compression techniques are needed that are computationally friendly,
+applicable to a wide variety of layers seen in Deep Neural Networks and
+adaptable to variations in network architectures as well as their
+hyper-parameters. In this paper we introduce a novel technique - the Adaptive
+Residual Gradient Compression (AdaComp) scheme. AdaComp is based on localized
+selection of gradient residues and automatically tunes the compression rate
+depending on local activity. We show excellent results on a wide spectrum of
+state of the art Deep Learning models in multiple domains (vision, speech,
+language), datasets (MNIST, CIFAR10, ImageNet, BN50, Shakespeare), optimizers
+(SGD with momentum, Adam) and network parameters (number of learners,
+minibatch-size etc.). Exploiting both sparsity and quantization, we demonstrate
+end-to-end compression rates of ~200X for fully-connected and recurrent layers,
+and ~40X for convolutional layers, without any noticeable degradation in model
+accuracies.
+",1,0,0,1,0,0
+18314,Halo-independent determination of the unmodulated WIMP signal in DAMA: the isotropic case,"  We present a halo-independent determination of the unmodulated signal
+corresponding to the DAMA modulation if interpreted as due to dark matter
+weakly interacting massive particles (WIMPs). First we show how a modulated
+signal gives information on the WIMP velocity distribution function in the
+Galactic rest frame, from which the unmodulated signal descends. Then we
+perform a mathematically-sound profile likelihood analysis in which we profile
+the likelihood over a continuum of nuisance parameters (namely, the WIMP
+velocity distribution). As a first application of the method, which is very
+general and valid for any class of velocity distributions, we restrict the
+analysis to velocity distributions that are isotropic in the Galactic frame. In
+this way we obtain halo-independent maximum-likelihood estimates and confidence
+intervals for the DAMA unmodulated signal. We find that the estimated
+unmodulated signal is in line with expectations for a WIMP-induced modulation
+and is compatible with the DAMA background+signal rate. Specifically, for the
+isotropic case we find that the modulated amplitude ranges between a few
+percent and about 25% of the unmodulated amplitude, depending on the WIMP mass.
+",0,1,0,0,0,0
+18315,Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?,"  Deep reinforcement learning has achieved many recent successes, but our
+understanding of its strengths and limitations is hampered by the lack of rich
+environments in which we can fully characterize optimal behavior, and
+correspondingly diagnose individual actions against such a characterization.
+Here we consider a family of combinatorial games, arising from work of Erdos,
+Selfridge, and Spencer, and we propose their use as environments for evaluating
+and comparing different approaches to reinforcement learning. These games have
+a number of appealing features: they are challenging for current learning
+approaches, but they form (i) a low-dimensional, simply parametrized
+environment where (ii) there is a linear closed form solution for optimal
+behavior from any state, and (iii) the difficulty of the game can be tuned by
+changing environment parameters in an interpretable way. We use these
+Erdos-Selfridge-Spencer games not only to compare different algorithms, but
+test for generalization, make comparisons to supervised learning, analyse
+multiagent play, and even develop a self play algorithm. Code can be found at:
+this https URL
+",1,0,0,1,0,0
+18316,Memristor equations: incomplete physics and undefined passivity/activity,"  In his seminal paper, Chua presented a fundamental physical claim by
+introducing the memristor, ""The missing circuit element"". The memristor
+equations were originally supposed to represent a passive circuit element
+because, with active circuitry, arbitrary elements can be realized without
+limitations. Therefore, if the memristor equations do not guarantee that the
+circuit element can be realized by a passive system, the fundamental physics
+claim about the memristor as ""missing circuit element"" loses all its weight.
+The question of passivity/activity belongs to physics thus we incorporate
+thermodynamics into the study of this problem. We show that the memristor
+equations are physically incomplete regarding the problem of
+passivity/activity. As a consequence, the claim that the present memristor
+functions describe a passive device lead to unphysical results, such as
+violating the Second Law of thermodynamics, in infinitely large number of
+cases. The seminal memristor equations cannot introduce a new physical circuit
+element without making the model more physical such as providing the
+Fluctuation Dissipation Theory of memristors.
+",1,0,0,0,0,0
+18317,Astrophysical uncertainties on the local dark matter distribution and direct detection experiments,"  The differential event rate in Weakly Interacting Massive Particle (WIMP)
+direct detection experiments depends on the local dark matter density and
+velocity distribution. Accurate modelling of the local dark matter distribution
+is therefore required to obtain reliable constraints on the WIMP particle
+physics properties. Data analyses typically use a simple Standard Halo Model
+which might not be a good approximation to the real Milky Way (MW) halo. We
+review observational determinations of the local dark matter density, circular
+speed and escape speed and also studies of the local dark matter distribution
+in simulated MW-like galaxies. We discuss the effects of the uncertainties in
+these quantities on the energy spectrum and its time and direction dependence.
+Finally we conclude with an overview of various methods for handling these
+astrophysical uncertainties.
+",0,1,0,0,0,0
+18318,On Reliability-Aware Server Consolidation in Cloud Datacenters,"  In the past few years, datacenter (DC) energy consumption has become an
+important issue in technology world. Server consolidation using virtualization
+and virtual machine (VM) live migration allows cloud DCs to improve resource
+utilization and hence energy efficiency. In order to save energy, consolidation
+techniques try to turn off the idle servers, while because of workload
+fluctuations, these offline servers should be turned on to support the
+increased resource demands. These repeated on-off cycles could affect the
+hardware reliability and wear-and-tear of servers and as a result, increase the
+maintenance and replacement costs. In this paper we propose a holistic
+mathematical model for reliability-aware server consolidation with the
+objective of minimizing total DC costs including energy and reliability costs.
+In fact, we try to minimize the number of active PMs and racks, in a
+reliability-aware manner. We formulate the problem as a Mixed Integer Linear
+Programming (MILP) model which is in form of NP-complete. Finally, we evaluate
+the performance of our approach in different scenarios using extensive
+numerical MATLAB simulations.
+",1,0,0,0,0,0
+18319,A novel quantum dynamical approach in electron microscopy combining wave-packet propagation with Bohmian trajectories,"  The numerical analysis of the diffraction features rendered by transmission
+electron microscopy (TEM) typically relies either on classical approximations
+(Monte Carlo simulations) or quantum paraxial tomography (the multislice method
+and any of its variants). Although numerically advan- tageous (relatively
+simple implementations and low computational costs), they involve important
+approximations and thus their range of applicability is limited. To overcome
+such limitations, an alternative, more general approach is proposed, based on
+an optimal combination of wave-packet propagation with the on-the-fly
+computation of associated Bohmian trajectories. For the sake of clarity, but
+without loss of generality, the approach is used to analyze the diffraction of
+an electron beam by a thin aluminum slab as a function of three different
+incidence (work) conditions which are of interest in electron microscopy: the
+probe width, the tilting angle, and the beam energy. Specifically, it is shown
+that, because there is a dependence on particular thresholds of the beam
+energy, this approach provides a clear description of the diffraction process
+at any energy, revealing at the same time any diversion of the beam inside the
+material towards directions that cannot be accounted for by other conventional
+methods, which is of much interest when dealing with relatively low energies
+and/or relatively large tilting angles.
+",0,1,0,0,0,0
+18320,Convex equipartitions of colored point sets,"  We show that any $d$-colored set of points in general position in
+$\mathbb{R}^d$ can be partitioned into $n$ subsets with disjoint convex hulls
+such that the set of points and all color classes are partitioned as evenly as
+possible. This extends results by Holmsen, Kynčl & Valculescu (2017) and
+establishes a special case of their general conjecture. Our proof utilizes a
+result obtained independently by Soberón and by Karasev in 2010, on
+simultaneous equipartitions of $d$ continuous measures in $\mathbb{R}^d$ by $n$
+convex regions. This gives a convex partition of $\mathbb{R}^d$ with the
+desired properties, except that points may lie on the boundaries of the
+regions. In order to resolve the ambiguous assignment of these points, we set
+up a network flow problem. The equipartition of the continuous measures gives a
+fractional flow. The existence of an integer flow then yields the desired
+partition of the point set.
+",0,0,1,0,0,0
+18321,Logic Programming Petri Nets,"  With the purpose of modeling, specifying and reasoning in an integrated
+fashion with procedural and declarative aspects (both commonly present in cases
+or scenarios), the paper introduces Logic Programming Petri Nets (LPPN), an
+extension to the Petri Net notation providing an interface to logic programming
+constructs. Two semantics are presented. First, a hybrid operational semantics
+that separates the process component, treated with Petri nets, from the
+constraint/terminological component, treated with Answer Set Programming (ASP).
+Second, a denotational semantics maps the notation to ASP fully, via Event
+Calculus. These two alternative specifications enable a preliminary evaluation
+in terms of reasoning efficiency.
+",1,0,0,0,0,0
+18322,Unique Continuation through Hyperplane for Higher Order Parabolic and Shrödinger Equations,"  We consider higher order parabolic operator $\partial_t+(-\Delta_x)^m$ and
+higher order Schrödinger operator $i^{-1}\partial_t+(-\Delta_x)^m$ in
+$X=\{(t,x)\in\mathbb{R}^{1+n};~|t|<A,|x_n|<B\}$ where $m$ is any positive
+integer. Under certain lower order and regularity assumptions, we prove that if
+the solution for linear problem vanishes when $x_n>0$, then the solution
+vanishes in $X$. Such results are given globally, and we also prove some
+related local results.
+",0,0,1,0,0,0
+18323,Fashion Conversation Data on Instagram,"  The fashion industry is establishing its presence on a number of
+visual-centric social media like Instagram. This creates an interesting clash
+as fashion brands that have traditionally practiced highly creative and
+editorialized image marketing now have to engage with people on the platform
+that epitomizes impromptu, realtime conversation. What kinds of fashion images
+do brands and individuals share and what are the types of visual features that
+attract likes and comments? In this research, we take both quantitative and
+qualitative approaches to answer these questions. We analyze visual features of
+fashion posts first via manual tagging and then via training on convolutional
+neural networks. The classified images were examined across four types of
+fashion brands: mega couture, small couture, designers, and high street. We
+find that while product-only images make up the majority of fashion
+conversation in terms of volume, body snaps and face images that portray
+fashion items more naturally tend to receive a larger number of likes and
+comments by the audience. Our findings bring insights into building an
+automated tool for classifying or generating influential fashion information.
+We make our novel dataset of {24,752} labeled images on fashion conversations,
+containing visual and textual cues, available for the research community.
+",1,0,0,1,0,0
+18324,Viscous flow in a soft valve,"  Fluid-structure interactions are ubiquitous in nature and technology.
+However, the systems are often so complex that numerical simulations or ad hoc
+assumptions must be used to gain insight into the details of the complex
+interactions between the fluid and solid mechanics. In this paper, we present
+experiments and theory on viscous flow in a simple bioinspired soft valve which
+illustrate essential features of interactions between hydrodynamic and elastic
+forces at low Reynolds numbers. The setup comprises a sphere connected to a
+spring located inside a tapering cylindrical channel. The spring is aligned
+with the central axis of the channel and a pressure drop is applied across the
+sphere, thus forcing the liquid through the narrow gap between the sphere and
+the channel walls. The sphere's equilibrium position is determined by a balance
+between spring and hydrodynamic forces. Since the gap thickness changes with
+the sphere's position, the system has a pressure-dependent hydraulic
+resistance. This leads to a non-linear relation between applied pressure and
+flow rate: flow initially increases with pressure, but decreases when the
+pressure exceeds a certain critical value as the gap closes. To rationalize
+these observations, we propose a mathematical model that reduced the complexity
+of the flow to a two-dimensional lubrication approximation. A closed-form
+expression for the pressure-drop/flow rate is obtained which reveals that the
+flow rate $Q$ depends on the pressure drop $\Delta p$, sphere radius $a$, gap
+thickness $h_0$, and viscosity $\eta$ as $Q\sim \eta^{-1}
+a^{1/2}h_0^{5/2}\left(\Delta p_c-\Delta p\right)^{5/2}\Delta p$, where the
+critical pressure $\Delta p_c$ scales with the spring constant $k$ and sphere
+radius $a$ as $\Delta p_c\sim k a^{-2}$. These predictions compared favorably
+to the results of our experiments with no free parameters.
+",0,1,0,0,0,0
+18325,Superheavy Thermal Dark Matter and Primordial Asymmetries,"  The early universe could feature multiple reheating events, leading to jumps
+in the visible sector entropy density that dilute both particle asymmetries and
+the number density of frozen-out states. In fact, late time entropy jumps are
+usually required in models of Affleck-Dine baryogenesis, which typically
+produces an initial particle-antiparticle asymmetry that is much too large. An
+important consequence of late time dilution, is that a smaller dark matter
+annihilation cross section is needed to obtain the observed dark matter relic
+density. For cosmologies with high scale baryogenesis, followed by
+radiation-dominated dark matter freeze-out, we show that the perturbative
+unitarity mass bound on thermal relic dark matter is relaxed to $10^{10}$ GeV.
+We proceed to study superheavy asymmetric dark matter models, made possible by
+a sizable entropy injection after dark matter freeze-out, and identify how the
+Affleck-Dine mechanism would generate the baryon and dark asymmetries.
+",0,1,0,0,0,0
+18326,Time-dependent population imaging for solid high harmonic generation,"  We propose an intuitive method, called time-dependent population imaging
+(TDPI), to map the dynamical processes of high harmonic generation (HHG) in
+solids by solving the time-dependent Schrödinger equation (TDSE). It is
+shown that the real-time dynamical characteristics of HHG in solids, such as
+the instantaneous photon energies of emitted harmonics, can be read directly
+from the energy-resolved population oscillations of electrons in the TDPIs.
+Meanwhile, the short and long trajectories of solid HHG are illustrated clearly
+from TDPI. By using the TDPI, we also investigate the effects of
+carrier-envelope phase (CEP) in few-cycle pulses and intuitively demonstrate
+the HHG dynamics driven by two-color fields. Our results show that the TDPI
+provides a powerful tool to study the ultrafast dynamics in strong fields for
+various laser-solid configurations and gain an insight into HHG processes in
+solids.
+",0,1,0,0,0,0
+18327,Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics,"  Predicting epidemic dynamics is of great value in understanding and
+controlling diffusion processes, such as infectious disease spread and
+information propagation. This task is intractable, especially when surveillance
+resources are very limited. To address the challenge, we study the problem of
+active surveillance, i.e., how to identify a small portion of system components
+as sentinels to effect monitoring, such that the epidemic dynamics of an entire
+system can be readily predicted from the partial data collected by such
+sentinels. We propose a novel measure, the gamma value, to identify the
+sentinels by modeling a sentinel network with row sparsity structure. We design
+a flexible group sparse Bayesian learning algorithm to mine the sentinel
+network suitable for handling both linear and non-linear dynamical systems by
+using the expectation maximization method and variational approximation. The
+efficacy of the proposed algorithm is theoretically analyzed and empirically
+validated using both synthetic and real-world data.
+",1,0,0,1,0,0
+18328,FNS: an event-driven spiking neural network framework for efficient simulations of large-scale brain models,"  Limitations in processing capabilities and memory of today's computers make
+spiking neuron-based (human) whole-brain simulations inevitably characterized
+by a compromise between bio-plausibility and computational cost. It translates
+into brain models composed of a reduced number of neurons and a simplified
+neuron's mathematical model. Taking advantage of the sparse character of
+brain-like computation, eventdriven technique allows us to carry out efficient
+simulation of large-scale Spiking Neural Networks (SNN). The recent Leaky
+Integrate-and-Fire with Latency (LIFL) spiking neuron model is event-driven
+compatible and exhibits some realistic neuronal features, opening new horizons
+in whole-brain modelling. In this paper we present FNS, a LIFL-based exact
+event-driven spiking neural network framework implemented in Java and oriented
+to wholebrain simulations. FNS combines spiking/synaptic whole-brain modelling
+with the event-driven approach, allowing us to define heterogeneous modules and
+multi-scale connectivity with delayed connections and plastic synapses,
+providing fast simulations at the same time. A novel parallelization strategy
+is also implemented in order to further speed up simulations. This paper
+presents mathematical models, software implementation and simulation routines
+on which FNS is based. Finally, a reduced brain network model (1400 neurons and
+45000 synapses) is synthesized on the basis of real brain structural data, and
+the resulting model activity is compared with associated brain functional
+(source-space MEG) data. The conducted test shows a good matching between the
+activity of model and that of the emulated subject, in outstanding simulation
+times (about 20s for simulating 4s of activity with a normal PC). Dedicated
+sections of stimuli editing and output synthesis allow the neuroscientist to
+introduce and extract brain-like signals, respectively...
+",0,0,0,0,1,0
+18329,"Synchronization, phase slips and coherent structures in area-preserving maps","  The problem of synchronization of coupled Hamiltonian systems exhibits
+interesting features due to the non-uniform or mixed nature (regular and
+chaotic) of the phase space. We study these features by investigating the
+synchronization of unidirectionally coupled area-preserving maps coupled by the
+Pecora-Carroll method. We find that coupled standard maps show complete
+synchronization for values of the nonlinearity parameter at which regular
+structures are still present in phase space. The distribution of
+synchronization times has a power law tail indicating long synchronization
+times for at least some of the synchronizing trajectories. With the
+introduction of coherent structures using parameter perturbation in the system,
+this distribution crosses over to exponential behavior, indicating shorter
+synchronization times, and the number of initial conditions which synchronize
+increases significantly, indicating an enhancement in the basin of
+synchronization. On the other hand, coupled blinking vortex maps display both
+phase synchronization and phase slips, depending on the location of the initial
+conditions. We discuss the implication of our results.
+",0,1,0,0,0,0
+18330,Design of Real-time Semantic Segmentation Decoder for Automated Driving,"  Semantic segmentation remains a computationally intensive algorithm for
+embedded deployment even with the rapid growth of computation power. Thus
+efficient network design is a critical aspect especially for applications like
+automated driving which requires real-time performance. Recently, there has
+been a lot of research on designing efficient encoders that are mostly task
+agnostic. Unlike image classification and bounding box object detection tasks,
+decoders are computationally expensive as well for semantic segmentation task.
+In this work, we focus on efficient design of the segmentation decoder and
+assume that an efficient encoder is already designed to provide shared features
+for a multi-task learning system. We design a novel efficient non-bottleneck
+layer and a family of decoders which fit into a small run-time budget using
+VGG10 as efficient encoder. We demonstrate in our dataset that experimentation
+with various design choices led to an improvement of 10\% from a baseline
+performance.
+",1,0,0,1,0,0
+18331,"Nonparametric covariance estimation for mixed longitudinal studies, with applications in midlife women's health","  Motivated by applications of mixed longitudinal studies, where a group of
+subjects entering the study at different ages (cross-sectional) are followed
+for successive years (longitudinal), we consider nonparametric covariance
+estimation with samples of noisy and partially-observed functional
+trajectories. To ensure model identifiability and estimation consistency, we
+introduce and carefully discuss the reduced rank and neighboring incoherence
+condition. The proposed algorithm is based on a sequential-aggregation scheme,
+which is non-iterative, with only basic matrix operations and closed-form
+solutions in each step. The good performance of the proposed method is
+supported by both theory and numerical experiments. We also apply the proposed
+procedure to a midlife women's working memory study based on the data from the
+Study of Women's Health Across the Nation (SWAN).
+",0,0,1,1,0,0
+18332,Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data,"  The recent Nobel-prize-winning detections of gravitational waves from merging
+black holes and the subsequent detection of the collision of two neutron stars
+in coincidence with electromagnetic observations have inaugurated a new era of
+multimessenger astrophysics. To enhance the scope of this emergent science, we
+proposed the use of deep convolutional neural networks for the detection and
+characterization of gravitational wave signals in real-time. This method, Deep
+Filtering, was initially demonstrated using simulated LIGO noise. In this
+article, we present the extension of Deep Filtering using real data from the
+first observing run of LIGO, for both detection and parameter estimation of
+gravitational waves from binary black hole mergers with continuous data streams
+from multiple LIGO detectors. We show for the first time that machine learning
+can detect and estimate the true parameters of a real GW event observed by
+LIGO. Our comparisons show that Deep Filtering is far more computationally
+efficient than matched-filtering, while retaining similar sensitivity and lower
+errors, allowing real-time processing of weak time-series signals in
+non-stationary non-Gaussian noise, with minimal resources, and also enables the
+detection of new classes of gravitational wave sources that may go unnoticed
+with existing detection algorithms. This approach is uniquely suited to enable
+coincident detection campaigns of gravitational waves and their multimessenger
+counterparts in real-time.
+",1,1,0,0,0,0
+18333,Note on local coisotropic Floer homology and leafwise fixed points,"  I outline a construction of a local Floer homology for a coisotropic
+submanifold of a symplectic manifold and explain how it can be used to show
+that leafwise fixed points of Hamiltonian diffeomorphisms exist.
+",0,0,1,0,0,0
+18334,Geometric Surface-Based Tracking Control of a Quadrotor UAV under Actuator Constraints,"  This paper presents contributions on nonlinear tracking control systems for a
+quadrotor unmanned micro aerial vehicle. New controllers are proposed based on
+nonlinear surfaces composed by tracking errors that evolve directly on the
+nonlinear configuration manifold thus inherently including in the control
+design the nonlinear characteristics of the SE(3) configuration space. In
+particular geometric surface-based controllers are developed, and through
+rigorous stability proofs they are shown to have desirable closed loop
+properties that are almost global. A region of attraction, independent of the
+position error, is produced and its effects are analyzed. A strategy allowing
+the quadrotor to achieve precise attitude tracking while simultaneously
+following a desired position command and complying to actuator constraints in a
+computationally inexpensive manner is derived. This important contribution
+differentiates this work from existing Geometric Nonlinear Control System
+solutions (GNCSs) since the commanded thrusts can be realized by the majority
+of quadrotors produced by the industry. The new features of the proposed GNCSs
+are illustrated by numerical simulations of aggressive maneuvers and a
+comparison with a GNCSs from the bibliography.
+",1,0,0,0,0,0
+18335,Estimating the rate of defects under imperfect sampling inspection - a new approach,"  We consider the problem of estimating the the rate of defects (mean number of
+defects per item), given counts of defects detected by two independent
+imperfect inspectors on a sample of items. In contrast with the well-known
+method of Capture-Recapture, here we {\it{do not}} have information regarding
+the number of defects jointly detected by {\it{both}} inspectors. We solve this
+problem by constructing two types of estimators - a simple moment-type
+estimator, and a more complicated maximum-likelihood estimator. The performance
+of these estimators is studied analytically and by means of simulations. It is
+shown that the maximum-likelihood estimator is superior to the moment-type
+estimator. A systematic comparison with the Capture-Recapture method is also
+made.
+",0,0,0,1,0,0
+18336,Measuring heavy-tailedness of distributions,"  Different questions related with analysis of extreme values and outliers
+arise frequently in practice. To exclude extremal observations and outliers is
+not a good decision because they contain important information about the
+observed distribution. The difficulties with their usage are usually related to
+the estimation of the tail index in case it exists. There are many measures for
+the center of the distribution, e.g. mean, mode, median. There are many
+measures of the variance, asymmetry, and kurtosis, but there is no easy
+characteristic for heavy-tailedness of the observed distribution. Here we
+propose such a measure, give some examples and explore some of its properties.
+This allows us to introduce a classification of the distributions, with respect
+to their heavy-tailedness. The idea is to help and navigate practitioners for
+accurate and easier work in the field of probability distributions.
+Using the properties of the defined characteristics some distribution
+sensitive extremal index estimators are proposed and their properties are
+partially investigated.
+",0,0,0,1,0,0
+18337,Inductive $k$-independent graphs and $c$-colorable subgraphs in scheduling: A review,"  Inductive $k$-independent graphs generalize chordal graphs and have recently
+been advocated in the context of interference-avoiding wireless communication
+scheduling. The NP-hard problem of finding maximum-weight induced $c$-colorable
+subgraphs, which is a generalization of finding maximum independent sets,
+naturally occurs when selecting $c$ sets of pairwise non-conflicting jobs
+(modeled as graph vertices). We investigate the parameterized complexity of
+this problem on inductive $k$-independent graphs. We show that the Independent
+Set problem is W[1]-hard even on 2-simplicial 3-minoes---a subclass of
+inductive 2-independent graphs. In contrast, we prove that the more general
+Maximum $c$-Colorable Subgraph problem is fixed-parameter tractable on
+edge-wise unions of cluster and chordal graphs, which are 2-simplicial. In both
+cases, the parameter is the solution size. Aside from this, we survey other
+graph classes between inductive 1-inductive and inductive 2-inductive graphs
+with applications in scheduling.
+",1,0,0,0,0,0
+18338,The word and order problems for self-similar and automata groups,"  We prove that the word problem is undecidable in functionally recursive
+groups, and that the order problem is undecidable in automata groups, even
+under the assumption that they are contracting.
+",1,0,1,0,0,0
+18339,Machine Teaching: A New Paradigm for Building Machine Learning Systems,"  The current processes for building machine learning systems require
+practitioners with deep knowledge of machine learning. This significantly
+limits the number of machine learning systems that can be created and has led
+to a mismatch between the demand for machine learning systems and the ability
+for organizations to build them. We believe that in order to meet this growing
+demand for machine learning systems we must significantly increase the number
+of individuals that can teach machines. We postulate that we can achieve this
+goal by making the process of teaching machines easy, fast and above all,
+universally accessible.
+While machine learning focuses on creating new algorithms and improving the
+accuracy of ""learners"", the machine teaching discipline focuses on the efficacy
+of the ""teachers"". Machine teaching as a discipline is a paradigm shift that
+follows and extends principles of software engineering and programming
+languages. We put a strong emphasis on the teacher and the teacher's
+interaction with data, as well as crucial components such as techniques and
+design principles of interaction and visualization.
+In this paper, we present our position regarding the discipline of machine
+teaching and articulate fundamental machine teaching principles. We also
+describe how, by decoupling knowledge about machine learning algorithms from
+the process of teaching, we can accelerate innovation and empower millions of
+new uses for machine learning models.
+",1,0,0,1,0,0
+18340,Totally geodesic submanifolds of Teichmuller space,"  We show that any totally geodesic submanifold of Teichmuller space of
+dimension greater than one covers a totally geodesic subvariety, and only
+finitely many totally geodesic subvarieties of dimension greater than one exist
+in each moduli space.
+",0,0,1,0,0,0
+18341,Towards a splitting of the $K(2)$-local string bordism spectrum,"  We show that $K(2)$-locally, the smash product of the string bordism spectrum
+and the spectrum $T_2$ splits into copies of Morava $E$-theories. Here, $T_2$
+is related to the Thom spectrum of the canonical bundle over $\Omega SU(4)$.
+",0,0,1,0,0,0
+18342,Osmotic and diffusio-osmotic flow generation at high solute concentration. I. Mechanical approaches,"  In this paper, we explore various forms of osmotic transport in the regime of
+high solute concentration. We consider both the osmosis across membranes and
+diffusio-osmosis at solid interfaces, driven by solute concentration gradients.
+We follow a mechanical point of view of osmotic transport, which allows us to
+gain much insight into the local mechanical balance underlying osmosis. We
+demonstrate in particular how the general expression of the osmotic pressure
+for mixtures, as obtained classically from the thermodynamic framework, emerges
+from the mechanical balance controlling non-equilibrium transport under solute
+gradients. Expressions for the rejection coefficient of osmosis and the
+diffusio-osmotic mobilities are accordingly obtained. These results generalize
+existing ones in the dilute solute regime to mixtures with arbitrary
+concentrations.
+",0,1,0,0,0,0
+18343,Accurate and Efficient Estimation of Small P-values with the Cross-Entropy Method: Applications in Genomic Data Analysis,"  Small $p$-values are often required to be accurately estimated in large scale
+genomic studies for the adjustment of multiple hypothesis tests and the ranking
+of genomic features based on their statistical significance. For those
+complicated test statistics whose cumulative distribution functions are
+analytically intractable, existing methods usually do not work well with small
+$p$-values due to lack of accuracy or computational restrictions. We propose a
+general approach for accurately and efficiently calculating small $p$-values
+for a broad range of complicated test statistics based on the principle of the
+cross-entropy method and Markov chain Monte Carlo sampling techniques. We
+evaluate the performance of the proposed algorithm through simulations and
+demonstrate its application to three real examples in genomic studies. The
+results show that our approach can accurately evaluate small to extremely small
+$p$-values (e.g. $10^{-6}$ to $10^{-100}$). The proposed algorithm is helpful
+to the improvement of existing test procedures and the development of new test
+procedures in genomic studies.
+",0,0,0,1,0,0
+18344,Does the Testing Level affect the Prevalence of Coincidental Correctness?,"  Researchers have previously shown that Coincidental Correctness (CC) is
+prevalent; however, the benchmarks they used are considered inadequate
+nowadays. They have also recognized the negative impact of CC on the
+effectiveness of fault localization and testing. The aim of this paper is to
+study Coincidental Correctness, using more realistic code, mainly from the
+perspective of unit testing. This stems from the fact that the practice of unit
+testing has grown tremendously in recent years due to the wide adoption of
+software development processes, such as Test-Driven Development. We quantified
+the presence of CC in unit testing using the Defects4J benchmark. This entailed
+manually injecting two code checkers for each of the 395 defects in Defects4J:
+1) a weak checker that detects weak CC tests by monitoring whether the defect
+was reached; and 2) a strong checker that detects strong CC tests by monitoring
+whether the defect was reached and the program has transitioned into an
+infectious state. We also conducted preliminary experiments (using Defects4J,
+NanoXML and JTidy) to assess the pervasiveness of CC at the unit testing level
+in comparison to that at the integration and system levels. Our study showed
+that unit testing is not immune to CC, as it exhibited 7.2x more strong CC
+tests than failing tests and 8.3x more weak CC tests than failing tests.
+However, our preliminary results suggested that it might be less prone to CC
+than integration testing and system testing.
+",1,0,0,0,0,0
+18345,How spread changes affect the order book: Comparing the price responses of order deletions and placements to trades,"  We observe the effects of the three different events that cause spread
+changes in the order book, namely trades, deletions and placement of limit
+orders. By looking at the frequencies of the relative amounts of price changing
+events, we discover that deletions of orders open the bid-ask spread of a stock
+more often than trades do. We see that once the amount of spread changes due to
+deletions exceeds the amount of the ones due to trades, other observables in
+the order book change as well. We then look at how these spread changing events
+affect the prices of stocks, by means of the price response. We not only see
+that the self-response of stocks is positive for both spread changing trades
+and deletions and negative for order placements, but also cross-response to
+other stocks and therefore the market as a whole. In addition, the
+self-response function of spread-changing trades is similar to that of all
+trades. This leads to the conclusion that spread changing deletions and order
+placements have a similar effect on the order book and stock prices over time
+as trades.
+",0,0,0,0,0,1
+18346,High-Dimensional Dependency Structure Learning for Physical Processes,"  In this paper, we consider the use of structure learning methods for
+probabilistic graphical models to identify statistical dependencies in
+high-dimensional physical processes. Such processes are often synthetically
+characterized using PDEs (partial differential equations) and are observed in a
+variety of natural phenomena, including geoscience data capturing atmospheric
+and hydrological phenomena. Classical structure learning approaches such as the
+PC algorithm and variants are challenging to apply due to their high
+computational and sample requirements. Modern approaches, often based on sparse
+regression and variants, do come with finite sample guarantees, but are usually
+highly sensitive to the choice of hyper-parameters, e.g., parameter $\lambda$
+for sparsity inducing constraint or regularization. In this paper, we present
+ACLIME-ADMM, an efficient two-step algorithm for adaptive structure learning,
+which estimates an edge specific parameter $\lambda_{ij}$ in the first step,
+and uses these parameters to learn the structure in the second step. Both steps
+of our algorithm use (inexact) ADMM to solve suitable linear programs, and all
+iterations can be done in closed form in an efficient block parallel manner. We
+compare ACLIME-ADMM with baselines on both synthetic data simulated by partial
+differential equations (PDEs) that model advection-diffusion processes, and
+real data (50 years) of daily global geopotential heights to study information
+flow in the atmosphere. ACLIME-ADMM is shown to be efficient, stable, and
+competitive, usually better than the baselines especially on difficult
+problems. On real data, ACLIME-ADMM recovers the underlying structure of global
+atmospheric circulation, including switches in wind directions at the equator
+and tropics entirely from the data.
+",1,0,0,1,0,0
+18347,Lax orthogonal factorisations in monad-quantale-enriched categories,"  We show that, for a quantale $V$ and a $\mathsf{Set}$-monad $\mathbb{T}$
+laxly extended to $V$-$\mathsf{Rel}$, the presheaf monad on the category of
+$(\mathbb{T},V)$-categories is simple, giving rise to a lax orthogonal
+factorisation system (lofs) whose corresponding weak factorisation system has
+embeddings as left part. In addition, we present presheaf submonads and study
+the LOFSs they define. This provides a method of constructing weak
+factorisation systems on some well-known examples of topological categories
+over $\mathsf{Set}$.
+",0,0,1,0,0,0
+18348,Radiation reaction for spinning bodies in effective field theory I: Spin-orbit effects,"  We compute the leading Post-Newtonian (PN) contributions at linear order in
+the spin to the radiation-reaction acceleration and spin evolution for binary
+systems, which enter at fourth PN order. The calculation is carried out, from
+first principles, using the effective field theory framework for spinning
+compact objects, in both the Newton-Wigner and covariant spin supplementary
+conditions. A non-trivial consistency check is performed on our results by
+showing that the energy loss induced by the resulting radiation-reaction force
+is equivalent to the total emitted power in the far zone, up to so-called
+""Schott terms."" We also find that, at this order, the radiation reaction has no
+net effect on the evolution of the spins. The spin-spin contributions to
+radiation reaction are reported in a companion paper.
+",0,1,0,0,0,0
+18349,On the geometric notion of connection and its expression in tangent categories,"  Tangent categories provide an axiomatic approach to key structural aspects of
+differential geometry that exist not only in the classical category of smooth
+manifolds but also in algebraic geometry, homological algebra, computer
+science, and combinatorics. Generalizing the notion of (linear) connection on a
+smooth vector bundle, Cockett and Cruttwell have defined a notion of connection
+on a differential bundle in an arbitrary tangent category. Herein, we establish
+equivalent formulations of this notion of connection that reduce the amount of
+specified structure required. Further, one of our equivalent formulations
+substantially reduces the number of axioms imposed, and others provide useful
+abstract conceptualizations of connections. In particular, we show that a
+connection on a differential bundle E over M is equivalently given by a single
+morphism K that induces a suitable decomposition of TE as a biproduct. We also
+show that a connection is equivalently given by a vertical connection K for
+which a certain associated diagram is a limit diagram.
+",0,0,1,0,0,0
+18350,Objective priors for the number of degrees of freedom of a multivariate t distribution and the t-copula,"  An objective Bayesian approach to estimate the number of degrees of freedom
+$(\nu)$ for the multivariate $t$ distribution and for the $t$-copula, when the
+parameter is considered discrete, is proposed. Inference on this parameter has
+been problematic for the multivariate $t$ and, for the absence of any method,
+for the $t$-copula. An objective criterion based on loss functions which allows
+to overcome the issue of defining objective probabilities directly is employed.
+The support of the prior for $\nu$ is truncated, which derives from the
+property of both the multivariate $t$ and the $t$-copula of convergence to
+normality for a sufficiently large number of degrees of freedom. The
+performance of the priors is tested on simulated scenarios. The R codes and the
+replication material are available as a supplementary material of the
+electronic version of the paper and on real data: daily logarithmic returns of
+IBM and of the Center for Research in Security Prices Database.
+",0,0,0,1,0,0
+18351,PROBE: Predictive Robust Estimation for Visual-Inertial Navigation,"  Navigation in unknown, chaotic environments continues to present a
+significant challenge for the robotics community. Lighting changes,
+self-similar textures, motion blur, and moving objects are all considerable
+stumbling blocks for state-of-the-art vision-based navigation algorithms. In
+this paper we present a novel technique for improving localization accuracy
+within a visual-inertial navigation system (VINS). We make use of training data
+to learn a model for the quality of visual features with respect to
+localization error in a given environment. This model maps each visual
+observation from a predefined prediction space of visual-inertial predictors
+onto a scalar weight, which is then used to scale the observation covariance
+matrix. In this way, our model can adjust the influence of each observation
+according to its quality. We discuss our choice of predictors and report
+substantial reductions in localization error on 4 km of data from the KITTI
+dataset, as well as on experimental datasets consisting of 700 m of indoor and
+outdoor driving on a small ground rover equipped with a Skybotix VI-Sensor.
+",1,0,0,0,0,0
+18352,Ethical Artificial Intelligence - An Open Question,"  Artificial Intelligence (AI) is an effective science which employs strong
+enough approaches, methods, and techniques to solve unsolvable real world based
+problems. Because of its unstoppable rise towards the future, there are also
+some discussions about its ethics and safety. Shaping an AI friendly
+environment for people and a people friendly environment for AI can be a
+possible answer for finding a shared context of values for both humans and
+robots. In this context, objective of this paper is to address the ethical
+issues of AI and explore the moral dilemmas that arise from ethical algorithms,
+from pre set or acquired values. In addition, the paper will also focus on the
+subject of AI safety. As general, the paper will briefly analyze the concerns
+and potential solutions to solving the ethical issues presented and increase
+readers awareness on AI safety as another related research interest.
+",1,0,0,0,0,0
+18353,Self-Attentive Model for Headline Generation,"  Headline generation is a special type of text summarization task. While the
+amount of available training data for this task is almost unlimited, it still
+remains challenging, as learning to generate headlines for news articles
+implies that the model has strong reasoning about natural language. To overcome
+this issue, we applied recent Universal Transformer architecture paired with
+byte-pair encoding technique and achieved new state-of-the-art results on the
+New York Times Annotated corpus with ROUGE-L F1-score 24.84 and ROUGE-2
+F1-score 13.48. We also present the new RIA corpus and reach ROUGE-L F1-score
+36.81 and ROUGE-2 F1-score 22.15 on it.
+",1,0,0,0,0,0
+18354,Localized Recombining Plasma in G166.0+4.3: A Supernova Remnant with an Unusual Morphology,"  We observed the Galactic mixed-morphology supernova remnant G166.0+4.3 with
+Suzaku. The X-ray spectrum in the western part of the remnant is well
+represented by a one-component ionizing plasma model. The spectrum in the
+northeastern region can be explained by two components. One is the Fe-rich
+component with the electron temperature $kT_e = 0.87_{-0.03}^{+0.02}$ keV. The
+other is the recombining plasma component of lighter elements with $kT_e =
+0.46\pm0.03$ keV, the initial temperature $kT_{init} = 3$ keV (fixed) and the
+ionization parameter $n_et = (6.1_{-0.4}^{+0.5}) \times 10^{11} \rm cm^{-3} s$.
+As the formation process of the recombining plasma, two scenarios, the
+rarefaction and thermal conduction, are considered. The former would not be
+favored since we found the recombining plasma only in the northeastern region
+whereas the latter would explain the origin of the RP. In the latter scenario,
+an RP is anticipated in a part of the remnant where blast waves are in contact
+with cool dense gas. The emission measure suggests higher ambient gas density
+in the northeastern region. The morphology of the radio shell and a GeV
+gamma-ray emission also suggest a molecular cloud in the region.
+",0,1,0,0,0,0
+18355,Visualizing Design Erosion: How Big Balls of Mud are Made,"  Software systems are not static, they have to undergo frequent changes to
+stay fit for purpose, and in the process of doing so, their complexity
+increases. It has been observed that this process often leads to the erosion of
+the systems design and architecture and with it, the decline of many desirable
+quality attributes, such as maintainability. This process can be captured in
+terms of antipatterns-atomic violations of widely accepted design principles.
+We present a visualisation that exposes the design of evolving Java programs,
+highlighting instances of selected antipatterns including their emergence and
+cancerous growth. This visualisation assists software engineers and architects
+in assessing, tracing and therefore combating design erosion. We evaluated the
+effectiveness of the visualisation in four case studies with ten participants.
+",1,0,0,0,0,0
+18356,Hierarchical Representations for Efficient Architecture Search,"  We explore efficient neural architecture search methods and show that a
+simple yet powerful evolutionary algorithm can discover new architectures with
+excellent performance. Our approach combines a novel hierarchical genetic
+representation scheme that imitates the modularized design pattern commonly
+adopted by human experts, and an expressive search space that supports complex
+topologies. Our algorithm efficiently discovers architectures that outperform a
+large number of manually designed models for image classification, obtaining
+top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which
+is competitive with the best existing neural architecture search approaches. We
+also present results using random search, achieving 0.3% less top-1 accuracy on
+CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36
+hours down to 1 hour.
+",1,0,0,1,0,0
+18357,A return to eddy viscosity model for epistemic UQ in RANS closures,"  For the purpose of Uncertainty Quantification (UQ) of Reynolds-Averaged
+Navier-Stokes closures, we introduce a framework in which perturbations in the
+eigenvalues of the anisotropy tensor are made in order to bound a
+Quantity-of-Interest based on limiting states of turbulence. To make the
+perturbations representative of local flow features, we introduce two
+additional transport equations for linear combinations of these aforementioned
+eigenvalues. The location, magnitude and direction of the eigenvalue
+perturbations are now governed by the model transport equations. The general
+behavior of our discrepancy model is determined by two coefficients, resulting
+in a low-dimensional UQ problem. We will furthermore show that the behavior of
+the model is intuitive and rooted in the physical interpretation of
+misalignment between the mean strain and Reynolds stresses.
+",0,1,0,0,0,0
+18358,A Neural Network model with Bidirectional Whitening,"  We present here a new model and algorithm which performs an efficient Natural
+gradient descent for Multilayer Perceptrons. Natural gradient descent was
+originally proposed from a point of view of information geometry, and it
+performs the steepest descent updates on manifolds in a Riemannian space. In
+particular, we extend an approach taken by the ""Whitened neural networks""
+model. We make the whitening process not only in feed-forward direction as in
+the original model, but also in the back-propagation phase. Its efficacy is
+shown by an application of this ""Bidirectional whitened neural networks"" model
+to a handwritten character recognition data (MNIST data).
+",0,0,0,1,0,0
+18359,Regularity gradient estimates for weak solutions of singular quasi-linear parabolic equations,"  This paper studies the Sobolev regularity estimates of weak solutions of a
+class of singular quasi-linear elliptic problems of the form $u_t -
+\mbox{div}[\mathbb{A}(x,t,u,\nabla u)]= \mbox{div}[{\mathbf F}]$ with
+homogeneous Dirichlet boundary conditions over bounded spatial domains. Our
+main focus is on the case that the vector coefficients $\mathbb{A}$ are
+discontinuous and singular in $(x,t)$-variables, and dependent on the solution
+$u$. Global and interior weighted $W^{1,p}(\Omega, \omega)$-regularity
+estimates are established for weak solutions of these equations, where $\omega$
+is a weight function in some Muckenhoupt class of weights. The results obtained
+are even new for linear equations, and for $\omega =1$, because of the
+singularity of the coefficients in $(x,t)$-variables
+",0,0,1,0,0,0
+18360,Learning to Avoid Errors in GANs by Manipulating Input Spaces,"  Despite recent advances, large scale visual artifacts are still a common
+occurrence in images generated by GANs. Previous work has focused on improving
+the generator's capability to accurately imitate the data distribution
+$p_{data}$. In this paper, we instead explore methods that enable GANs to
+actively avoid errors by manipulating the input space. The core idea is to
+apply small changes to each noise vector in order to shift them away from areas
+in the input space that tend to result in errors. We derive three different
+architectures from that idea. The main one of these consists of a simple
+residual module that leads to significantly less visual artifacts, while only
+slightly decreasing diversity. The module is trivial to add to existing GANs
+and costs almost zero computation and memory.
+",1,0,0,1,0,0
+18361,Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search,"  This work proposes a process for efficiently searching over combinations of
+individual object 6D pose hypotheses in cluttered scenes, especially in cases
+involving occlusions and objects resting on each other. The initial set of
+candidate object poses is generated from state-of-the-art object detection and
+global point cloud registration techniques. The best-scored pose per object by
+using these techniques may not be accurate due to overlaps and occlusions.
+Nevertheless, experimental indications provided in this work show that object
+poses with lower ranks may be closer to the real poses than ones with high
+ranks according to registration techniques. This motivates a global
+optimization process for improving these poses by taking into account
+scene-level physical interactions between objects. It also implies that the
+Cartesian product of candidate poses for interacting objects must be searched
+so as to identify the best scene-level hypothesis. To perform the search
+efficiently, the candidate poses for each object are clustered so as to reduce
+their number but still keep a sufficient diversity. Then, searching over the
+combinations of candidate object poses is performed through a Monte Carlo Tree
+Search (MCTS) process that uses the similarity between the observed depth image
+of the scene and a rendering of the scene given the hypothesized pose as a
+score that guides the search procedure. MCTS handles in a principled way the
+tradeoff between fine-tuning the most promising poses and exploring new ones,
+by using the Upper Confidence Bound (UCB) technique. Experimental results
+indicate that this process is able to quickly identify in cluttered scenes
+physically-consistent object poses that are significantly closer to ground
+truth compared to poses found by point cloud registration methods.
+",1,0,0,0,0,0
+18362,On the benefits of output sparsity for multi-label classification,"  The multi-label classification framework, where each observation can be
+associated with a set of labels, has generated a tremendous amount of attention
+over recent years. The modern multi-label problems are typically large-scale in
+terms of number of observations, features and labels, and the amount of labels
+can even be comparable with the amount of observations. In this context,
+different remedies have been proposed to overcome the curse of dimensionality.
+In this work, we aim at exploiting the output sparsity by introducing a new
+loss, called the sparse weighted Hamming loss. This proposed loss can be seen
+as a weighted version of classical ones, where active and inactive labels are
+weighted separately. Leveraging the influence of sparsity in the loss function,
+we provide improved generalization bounds for the empirical risk minimizer, a
+suitable property for large-scale problems. For this new loss, we derive rates
+of convergence linear in the underlying output-sparsity rather than linear in
+the number of labels. In practice, minimizing the associated risk can be
+performed efficiently by using convex surrogates and modern convex optimization
+algorithms. We provide experiments on various real-world datasets demonstrating
+the pertinence of our approach when compared to non-weighted techniques.
+",1,0,1,1,0,0
+18363,"On wrapping number, adequacy and the crossing number of satellite knots","  In this work we establish the tightest lower bound up-to-date for the minimal
+crossing number of a satellite knot based on the minimal crossing number of the
+companion used to build the satellite. If $M$ is the wrapping number of the
+pattern knot, we essentially show that $c(Sat(P,C))>\frac{M^2}{2}c(C)$. The
+existence of this bound will be proven when the companion knot is adequate, and
+it will be further tuned in the case of the companion being alternating.
+",0,0,1,0,0,0
+18364,On the Applicability of Delicious for Temporal Search on Web Archives,"  Web archives are large longitudinal collections that store webpages from the
+past, which might be missing on the current live Web. Consequently, temporal
+search over such collections is essential for finding prominent missing
+webpages and tasks like historical analysis. However, this has been challenging
+due to the lack of popularity information and proper ground truth to evaluate
+temporal retrieval models. In this paper we investigate the applicability of
+external longitudinal resources to identify important and popular websites in
+the past and analyze the social bookmarking service Delicious for this purpose.
+The timestamped bookmarks on Delicious provide explicit cues about popular
+time periods in the past along with relevant descriptors. These are valuable to
+identify important documents in the past for a given temporal query. Focusing
+purely on recall, we analyzed more than 12,000 queries and find that using
+Delicious yields average recall values from 46% up to 100%, when limiting
+ourselves to the best represented queries in the considered dataset. This
+constitutes an attractive and low-overhead approach for quick access into Web
+archives by not dealing with the actual contents.
+",1,0,0,0,0,0
+18365,The Geometry and Topology of Data and Information for Analytics of Processes and Behaviours: Building on Bourdieu and Addressing New Societal Challenges,"  We begin by summarizing the relevance and importance of inductive analytics
+based on the geometry and topology of data and information. Contemporary issues
+are then discussed. These include how sampling data for representativity is
+increasingly to be questioned. While we can always avail of analytics from a
+""bag of tools and techniques"", in the application of machine learning and
+predictive analytics, nonetheless we present the case for Bourdieu and
+Benzécri-based science of data, as follows. This is to construct bridges
+between data sources and position-taking, and decision-making. There is summary
+presentation of a few case studies, illustrating and exemplifying application
+domains.
+",1,0,0,0,0,0
+18366,"Conformal Twists, Yang-Baxter $σ$-models & Holographic Noncommutativity","  Expanding upon earlier results [arXiv:1702.02861], we present a compendium of
+$\sigma$-models associated with integrable deformations of AdS$_5$ generated by
+solutions to homogenous classical Yang-Baxter equation. Each example we study
+from four viewpoints: conformal (Drinfeld) twists, closed string gravity
+backgrounds, open string parameters and proposed dual noncommutative (NC) gauge
+theory. Irrespective of whether the deformed background is a solution to
+supergravity or generalized supergravity, we show that the open string metric
+associated with each gravity background is undeformed AdS$_5$ with constant
+open string coupling and the NC structure $\Theta$ is directly related to the
+conformal twist. One novel feature is that $\Theta$ exhibits ""holographic
+noncommutativity"": while it may exhibit non-trivial dependence on the
+holographic direction, its value everywhere in the bulk is uniquely determined
+by its value at the boundary, thus facilitating introduction of a dual NC gauge
+theory. We show that the divergence of the NC structure $\Theta$ is directly
+related to the unimodularity of the twist. We discuss the implementation of an
+outer automorphism of the conformal algebra as a coordinate transformation in
+the AdS bulk and discuss its implications for Yang-Baxter $\sigma$-models and
+self-T-duality based on fermionic T-duality. Finally, we comment on
+implications of our results for the integrability of associated open strings
+and planar integrability of dual NC gauge theories.
+",0,1,0,0,0,0
+18367,AWEsome: An open-source test platform for airborne wind energy systems,"  In this paper we present AWEsome (Airborne Wind Energy Standardized
+Open-source Model Environment), a test platform for airborne wind energy
+systems that consists of low-cost hardware and is entirely based on open-source
+software. It can hence be used without the need of large financial investments,
+in particular by research groups and startups to acquire first experiences in
+their flight operations, to test novel control strategies or technical designs,
+or for usage in public relations. Our system consists of a modified
+off-the-shelf model aircraft that is controlled by the pixhawk autopilot
+hardware and the ardupilot software for fixed wing aircraft. The aircraft is
+attached to the ground by a tether. We have implemented new flight modes for
+the autonomous tethered flight of the aircraft along periodic patterns. We
+present the principal functionality of our algorithms. We report on first
+successful tests of these modes in real flights.
+",1,0,0,0,0,0
+18368,A one-dimensional mathematical model of collecting lymphatics coupled with an electro-fluid-mechanical contraction model and valve dynamics,"  We propose a one-dimensional model for collecting lymphatics coupled with a
+novel Electro-Fluid-Mechanical Contraction (EFMC) model for dynamical
+contractions, based on a modified FitzHugh-Nagumo model for action potentials.
+The one-dimensional model for a compliant lymphatic vessel is a set of
+hyperbolic Partial Differential Equations (PDEs). The EFMC model combines the
+electrical activity of lymphangions (action potentials) with fluid-mechanical
+feedback (stretch of the lymphatic wall and wall shear stress) and the
+mechanical variation of the lymphatic wall properties (contractions). The EFMC
+model is governed by four Ordinary Differential Equations (ODEs) and
+phenomenologically relies on: (1) environmental calcium influx, (2)
+stretch-activated calcium influx, and (3) contraction inhibitions induced by
+wall shear stresses. We carried out a complete mathematical analysis of the
+stability of the stationary state of the EFMC model. Overall, the EFMC model
+allows imitating the influence of pressure and wall shear stress on the
+frequency of contractions observed experimentally. Lymphatic valves are
+modelled using a well-established lumped-parameter model which allows
+simulating stenotic and regurgitant valves. We analysed several lymphodynamical
+indexes of a single lymphangion for a wide range of upstream and downstream
+pressure combinations. Stenotic and regurgitant valves were modelled, and their
+effects are here quantified. Results for stenotic valves showed in the
+downstream lymphangion that for low frequencies of contractions the Calculated
+Pump Flow (CPF) index remained almost unaltered, while for high frequencies the
+CPF dramatically decreased depending on the severity of the stenosis (up to 93%
+for a severe stenosis). Results for incompetent valves showed that the net flow
+during a lymphatic cycle tends to zero as the degree of incompetence increases.
+",0,1,1,0,0,0
+18369,$A_{n}$-type surface singularity and nondisplaceable Lagrangian tori,"  We prove the existence of a one-parameter family of nondisplaceable
+Lagrangian tori near a linear chain of Lagrangian 2-spheres in a symplectic
+4-manifold. When the symplectic structure is rational we prove that the
+deformed Floer cohomology groups of these tori are nontrivial. The proof uses
+the idea of toric degeneration to analyze the full potential functions with
+bulk deformations of these tori.
+",0,0,1,0,0,0
+18370,Cascade LSTM Based Visual-Inertial Navigation for Magnetic Levitation Haptic Interaction,"  Haptic feedback is essential to acquire immersive experience when interacting
+in virtual or augmented reality. Although the existing promising magnetic
+levitation (maglev) haptic system has advantages of none mechanical friction,
+its performance is limited by its navigation method, which mainly results from
+the challenge that it is difficult to obtain high precision, high frame rate
+and good stability with lightweight design at the same. In this study, we
+propose to perform the visual-inertial fusion navigation based on
+sequence-to-sequence learning for the maglev haptic interaction. Cascade LSTM
+based-increment learning method is first presented to progressively learn the
+increments of the target variables. Then, two cascade LSTM networks are
+separately trained for accomplishing the visual-inertial fusion navigation in a
+loosely-coupled mode. Additionally, we set up a maglev haptic platform as the
+system testbed. Experimental results show that the proposed cascade LSTM
+based-increment learning method can achieve high-precision prediction, and our
+cascade LSTM based visual-inertial fusion navigation method can reach 200Hz
+while maintaining high-precision (the mean absolute error of the position and
+orientation is respectively less than 1mm and 0.02°)navigation for the
+maglev haptic interaction application.
+",1,0,0,0,0,0
+18371,Local Feature Descriptor Learning with Adaptive Siamese Network,"  Although the recent progress in the deep neural network has led to the
+development of learnable local feature descriptors, there is no explicit answer
+for estimation of the necessary size of a neural network. Specifically, the
+local feature is represented in a low dimensional space, so the neural network
+should have more compact structure. The small networks required for local
+feature descriptor learning may be sensitive to initial conditions and learning
+parameters and more likely to become trapped in local minima. In order to
+address the above problem, we introduce an adaptive pruning Siamese
+Architecture based on neuron activation to learn local feature descriptors,
+making the network more computationally efficient with an improved recognition
+rate over more complex networks. Our experiments demonstrate that our learned
+local feature descriptors outperform the state-of-art methods in patch
+matching.
+",1,0,0,1,0,0
+18372,Unbounded cache model for online language modeling with open vocabulary,"  Recently, continuous cache models were proposed as extensions to recurrent
+neural network language models, to adapt their predictions to local changes in
+the data distribution. These models only capture the local context, of up to a
+few thousands tokens. In this paper, we propose an extension of continuous
+cache models, which can scale to larger contexts. In particular, we use a large
+scale non-parametric memory component that stores all the hidden activations
+seen in the past. We leverage recent advances in approximate nearest neighbor
+search and quantization algorithms to store millions of representations while
+searching them efficiently. We conduct extensive experiments showing that our
+approach significantly improves the perplexity of pre-trained language models
+on new distributions, and can scale efficiently to much larger contexts than
+previously proposed local cache models.
+",1,0,0,0,0,0
+18373,Finite-Range Coulomb Gas Models of Banded Random Matrices and Quantum Kicked Rotors,"  Dyson demonstrated an equivalence between infinite-range Coulomb gas models
+and classical random matrix ensembles for study of eigenvalue statistics. We
+introduce finite-range Coulomb gas (FRCG) models via a Brownian matrix process,
+and study them analytically and by Monte-Carlo simulations. These models yield
+new universality classes, and provide a theoretical framework for study of
+banded random matrices (BRM) and quantum kicked rotors (QKR). We demonstrate
+that, for a BRM of bandwidth b and a QKR of chaos parameter {\alpha}, the
+appropriate FRCG model has the effective range d = (b^2)/N = ({\alpha}^2)/N,
+for large N matrix dimensionality. As d increases, there is a transition from
+Poisson to classical random matrix statistics.
+",0,1,0,0,0,0
+18374,Dual Loomis-Whitney inequalities via information theory,"  We establish lower bounds on the volume and the surface area of a geometric
+body using the size of its slices along different directions. In the first part
+of the paper, we derive volume bounds for convex bodies using generalized
+subadditivity properties of entropy combined with entropy bounds for
+log-concave random variables. In the second part, we investigate a new notion
+of Fisher information which we call the $L_1$-Fisher information, and show that
+certain superadditivity properties of the $L_1$-Fisher information lead to
+lower bounds for the surface areas of polyconvex sets in terms of its slices.
+",1,0,0,0,0,0
+18375,Cubic lead perovskite PbMoO3 with anomalous metallic behavior,"  A previously unreported Pb-based perovskite PbMoO$_3$ is obtained by
+high-pressure and high-temperature synthesis. This material crystallizes in the
+$Pm\bar{3}m$ cubic structure at room temperature, making it distinct from
+typical Pb-based perovskite oxides with a structural distortion. PbMoO$_3$
+exhibits a metallic behavior down to 0.1 K with an unusual $T$-sub linear
+dependence of the electrical resistivity. Moreover, a large specific heat is
+observed at low temperatures accompanied by a peak in $C_P/T^3$ around 10 K, in
+marked contrast to the isostructural metallic system SrMoO$_3$. These transport
+and thermal properties for PbMoO$_3$, taking into account anomalously large Pb
+atomic displacements detected through diffraction experiments, are attributed
+to a low-energy vibrational mode, associated with incoherent off-centering of
+lone pair Pb$^{2+}$ cations. We discuss the unusual behavior of the electrical
+resistivity in terms of a polaron-like conduction, mediated by the strong
+coupling between conduction electrons and optical phonons of the local
+low-energy vibrational mode.
+",0,1,0,0,0,0
+18376,On Long Memory Origins and Forecast Horizons,"  Most long memory forecasting studies assume that the memory is generated by
+the fractional difference operator. We argue that the most cited theoretical
+arguments for the presence of long memory do not imply the fractional
+difference operator, and assess the performance of the autoregressive
+fractionally integrated moving average $(ARFIMA)$ model when forecasting series
+with long memory generated by nonfractional processes. We find that high-order
+autoregressive $(AR)$ models produce similar or superior forecast performance
+than $ARFIMA$ models at short horizons. Nonetheless, as the forecast horizon
+increases, the $ARFIMA$ models tend to dominate in forecast performance. Hence,
+$ARFIMA$ models are well suited for forecasts of long memory processes
+regardless of the long memory generating mechanism, particularly for medium and
+long forecast horizons. Additionally, we analyse the forecasting performance of
+the heterogeneous autoregressive ($HAR$) model which imposes restrictions on
+high-order $AR$ models. We find that the structure imposed by the $HAR$ model
+produces better long horizon forecasts than $AR$ models of the same order, at
+the price of inferior short horizon forecasts in some cases. Our results have
+implications for, among others, Climate Econometrics and Financial Econometrics
+models dealing with long memory series at different forecast horizons. We show
+in an example that while a short memory autoregressive moving average $(ARMA)$
+model gives the best performance when forecasting the Realized Variance of the
+S\&P 500 up to a month ahead, the $ARFIMA$ model gives the best performance for
+longer forecast horizons.
+",0,0,1,0,0,0
+18377,Detecting Disguised Plagiarism,"  Source code plagiarism detection is a problem that has been addressed several
+times before; and several tools have been developed for that purpose. In this
+research project we investigated a set of possible disguises that can be
+mechanically applied to plagiarized source code to defeat plagiarism detection
+tools. We propose a preprocessor to be used with existing plagiarism detection
+tools to ""normalize"" source code before checking it, thus making such disguises
+ineffective.
+",1,0,0,0,0,0
+18378,Stellar population synthesis based modelling of the Milky Way using asteroseismology of dwarfs and subgiants from Kepler,"  Early attempts to apply asteroseismology to study the Galaxy have already
+shown unexpected discrepancies for the mass distribution of stars between the
+Galactic models and the data; a result that is still unexplained. Here, we
+revisit the analysis of the asteroseismic sample of dwarf and subgiant stars
+observed by Kepler and investigate in detail the possible causes for the
+reported discrepancy. We investigate two models of the Milky Way based on
+stellar population synthesis, Galaxia and TRILEGAL. In agreement with previous
+results, we find that TRILEGAL predicts more massive stars compared to Galaxia,
+and that TRILEGAL predicts too many blue stars compared to 2MASS observations.
+Both models fail to match the distribution of the stellar sample in $(\log
+g,T_{\rm eff})$ space, pointing to inaccuracies in the models and/or the
+assumed selection function. When corrected for this mismatch in $(\log g,T_{\rm
+eff})$ space, the mass distribution calculated by Galaxia is broader and the
+mean is shifted toward lower masses compared to that of the observed stars.
+This behaviour is similar to what has been reported for the Kepler red giant
+sample. The shift between the mass distributions is equivalent to a change of
+2\% in $\nu_{\rm max}$, which is within the current uncertainty in the
+$\nu_{\rm max}$ scaling relation. Applying corrections to the $\Delta \nu$
+scaling relation predicted by the stellar models makes the observed mass
+distribution significantly narrower, but there is no change to the mean.
+",0,1,0,0,0,0
+18379,EmbNum: Semantic labeling for numerical values with deep metric learning,"  Semantic labeling for numerical values is a task of assigning semantic labels
+to unknown numerical attributes. The semantic labels could be numerical
+properties in ontologies, instances in knowledge bases, or labeled data that
+are manually annotated by domain experts. In this paper, we refer to semantic
+labeling as a retrieval setting where the label of an unknown attribute is
+assigned by the label of the most relevant attribute in labeled data. One of
+the greatest challenges is that an unknown attribute rarely has the same set of
+values with the similar one in the labeled data. To overcome the issue,
+statistical interpretation of value distribution is taken into account.
+However, the existing studies assume a specific form of distribution. It is not
+appropriate in particular to apply open data where there is no knowledge of
+data in advance. To address these problems, we propose a neural numerical
+embedding model (EmbNum) to learn useful representation vectors for numerical
+attributes without prior assumptions on the distribution of data. Then, the
+""semantic similarities"" between the attributes are measured on these
+representation vectors by the Euclidean distance. Our empirical experiments on
+City Data and Open Data show that EmbNum significantly outperforms
+state-of-the-art methods for the task of numerical attribute semantic labeling
+regarding effectiveness and efficiency.
+",0,0,0,1,0,0
+18380,A Network Epidemic Model for Online Community Commissioning Data,"  A statistical model assuming a preferential attachment network, which is
+generated by adding nodes sequentially according to a few simple rules, usually
+describes real-life networks better than a model assuming, for example, a
+Bernoulli random graph, in which any two nodes have the same probability of
+being connected, does. Therefore, to study the propogation of ""infection""
+across a social network, we propose a network epidemic model by combining a
+stochastic epidemic model and a preferential attachment model. A simulation
+study based on the subsequent Markov Chain Monte Carlo algorithm reveals an
+identifiability issue with the model parameters. Finally, the network epidemic
+model is applied to a set of online commissioning data.
+",1,0,0,1,0,0
+18381,Descent and Galois theory for Hopf categories,"  Descent theory for linear categories is developed. Given a linear category as
+an extension of a diagonal category, we introduce descent data, and the
+category of descent data is isomorphic to the category of representations of
+the diagonal category, if some flatness assumptions are satisfied. Then
+Hopf-Galois descent theory for linear Hopf categories, the Hopf algebra version
+of a linear category, is developed. This leads to the notion of Hopf-Galois
+category extension. We have a dual theory, where actions by dual linear Hopf
+categories on linear categories are considered. Hopf-Galois category extensions
+over groupoid algebras correspond to strongly graded linear categories.
+",0,0,1,0,0,0
+18382,On winning strategies for Banach-Mazur games,"  We give topological and game theoretic definitions and theorems nec- essary
+for defining a Banach-Mazur game, and apply these definitions to formalize the
+game. We then state and prove two theorems which give necessary conditions for
+existence of winning strategies for players in a Banach-Mazur game.
+",0,0,1,0,0,0
+18383,Dynamical instability of the electric transport in strongly fluctuating superconductors,"  Theory of the influence of the thermal fluctuations on the electric transport
+beyond linear response in superconductors is developed within the framework of
+the time dependent Ginzburg - Landau approach. The I - V curve is calculated
+using the dynamical self - consistent gaussian approximation. Under certain
+conditions it exhibits a reentrant behaviour acquiring an S - shape form. The
+unstable region below a critical temperature $T^{\ast }$ is determined for
+arbitrary dimensionality ($D=1,2,3$) of the thermal fluctuations. The results
+are applied to analyse the transport data on nanowires and several classes of
+2D superconductors: metallic thin films, layered and atomically thick novel
+materials.
+",0,1,0,0,0,0
+18384,Controlling Multimode Optomechanical Interactions via Interference,"  We demonstrate optomechanical interference in a multimode system, in which an
+optical mode couples to two mechanical modes. A phase-dependent
+excitation-coupling approach is developed, which enables the observation of
+constructive and destructive optomechanical interferences. The destructive
+interference prevents the coupling of the mechanical system to the optical
+mode, suppressing optically-induced mechanical damping. These studies establish
+optomechanical interference as an essential tool for controlling the
+interactions between light and mechanical oscillators.
+",0,1,0,0,0,0
+18385,Online Adaptive Principal Component Analysis and Its extensions,"  We propose algorithms for online principal component analysis (PCA) and
+variance minimization for adaptive settings. Previous literature has focused on
+upper bounding the static adversarial regret, whose comparator is the optimal
+fixed action in hindsight. However, static regret is not an appropriate metric
+when the underlying environment is changing. Instead, we adopt the adaptive
+regret metric from the previous literature and propose online adaptive
+algorithms for PCA and variance minimization, that have sub-linear adaptive
+regret guarantees. We demonstrate both theoretically and experimentally that
+the proposed algorithms can adapt to the changing environments.
+",1,0,0,1,0,0
+18386,Diffusion MRI measurements in challenging head and brain regions via cross-term spatiotemporally encoding,"  Cross-term spatiotemporal encoding (xSPEN) is a recently introduced imaging
+approach delivering single-scan 2D NMR images with unprecedented resilience to
+field inhomogeneities. The method relies on performing a pre-acquisition
+encoding and a subsequent image read out while using the disturbing frequency
+inhomogeneities as part of the image formation processes, rather than as
+artifacts to be overwhelmed by the application of external gradients. This
+study introduces the use of this new single-shot MRI technique as a
+diffusion-monitoring tool, for accessing regions that have hitherto been
+unapproachable by diffusion-weighted imaging (DWI) methods. In order to achieve
+this, xSPEN MRIs intrinsic diffusion weighting effects are formulated using a
+customized, spatially-localized b-matrix analysis; with this, we devise a novel
+diffusion-weighting scheme that both exploits and overcomes xSPENs strong
+intrinsic weighting effects. The ability to provide reliable and robust
+diffusion maps in challenging head and brain regions, including the eyes and
+the optic nerves, is thus demonstrated in humans at 3T; new avenues for imaging
+other body regions are also briefly discussed.
+",0,1,0,0,0,0
+18387,Game-Theoretic Capital Asset Pricing in Continuous Time,"  We derive formulas for the performance of capital assets in continuous time
+from an efficient market hypothesis, with no stochastic assumptions and no
+assumptions about the beliefs or preferences of investors. Our efficient market
+hypothesis says that a speculator with limited means cannot beat a particular
+index by a substantial factor. Our results include a formula that resembles the
+classical CAPM formula for the expected simple return of a security or
+portfolio.
+This version of the article was essentially written in December 2001 but
+remains a working paper.
+",0,0,0,0,0,1
+18388,Full Workspace Generation of Serial-link Manipulators by Deep Learning based Jacobian Estimation,"  Apart from solving complicated problems that require a certain level of
+intelligence, fine-tuned deep neural networks can also create fast algorithms
+for slow, numerical tasks. In this paper, we introduce an improved version of
+[1]'s work, a fast, deep-learning framework capable of generating the full
+workspace of serial-link manipulators. The architecture consists of two neural
+networks: an estimation net that approximates the manipulator Jacobian, and a
+confidence net that measures the confidence of the approximation. We also
+introduce M3 (Manipulability Maps of Manipulators), a MATLAB robotics library
+based on [2](RTB), the datasets generated by which are used by this work.
+Results have shown that not only are the neural networks significantly faster
+than numerical inverse kinematics, it also offers superior accuracy when
+compared to other machine learning alternatives. Implementations of the
+algorithm (based on Keras[3]), including benchmark evaluation script, are
+available at this https URL . The M3
+Library APIs and datasets are also available at
+this https URL .
+",1,0,0,0,0,0
+18389,Linear regression without correspondence,"  This article considers algorithmic and statistical aspects of linear
+regression when the correspondence between the covariates and the responses is
+unknown. First, a fully polynomial-time approximation scheme is given for the
+natural least squares optimization problem in any constant dimension. Next, in
+an average-case and noise-free setting where the responses exactly correspond
+to a linear function of i.i.d. draws from a standard multivariate normal
+distribution, an efficient algorithm based on lattice basis reduction is shown
+to exactly recover the unknown linear function in arbitrary dimension. Finally,
+lower bounds on the signal-to-noise ratio are established for approximate
+recovery of the unknown linear function by any estimator.
+",1,0,1,1,0,0
+18390,Toward sensitive document release with privacy guarantees,"  Privacy has become a serious concern for modern Information Societies. The
+sensitive nature of much of the data that are daily exchanged or released to
+untrusted parties requires that responsible organizations undertake appropriate
+privacy protection measures. Nowadays, much of these data are texts (e.g.,
+emails, messages posted in social media, healthcare outcomes, etc.) that,
+because of their unstructured and semantic nature, constitute a challenge for
+automatic data protection methods. In fact, textual documents are usually
+protected manually, in a process known as document redaction or sanitization.
+To do so, human experts identify sensitive terms (i.e., terms that may reveal
+identities and/or confidential information) and protect them accordingly (e.g.,
+via removal or, preferably, generalization). To relieve experts from this
+burdensome task, in a previous work we introduced the theoretical basis of
+C-sanitization, an inherently semantic privacy model that provides the basis to
+the development of automatic document redaction/sanitization algorithms and
+offers clear and a priori privacy guarantees on data protection; even though
+its potential benefits C-sanitization still presents some limitations when
+applied to practice (mainly regarding flexibility, efficiency and accuracy). In
+this paper, we propose a new more flexible model, named (C, g(C))-sanitization,
+which enables an intuitive configuration of the trade-off between the desired
+level of protection (i.e., controlled information disclosure) and the
+preservation of the utility of the protected data (i.e., amount of semantics to
+be preserved). Moreover, we also present a set of technical solutions and
+algorithms that provide an efficient and scalable implementation of the model
+and improve its practical accuracy, as we also illustrate through empirical
+experiments.
+",1,0,0,0,0,0
+18391,Remarks on the Birch-Swinnerton-Dyer conjecture,"  We give a brief description of the Birch-Swinnerton-Dyer conjecture and
+present related conjectures. We describe the relation between the nilpotent
+orbits of SL(2,R) and CM points.
+",0,0,1,0,0,0
+18392,Trajectory Optimization for Cooperative Dual-band UAV Swarms,"  Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse
+wireless communication fields. They can act as high-altitude flying relays to
+support communications between ground nodes due to their ability to provide
+line-of-sight links. With the flourishing Internet of Things, several types of
+new applications are emerging. In this paper, we focus on bandwidth hungry and
+delay-tolerant applications where multiple pairs of transceivers require the
+support of UAVs to complete their transmissions. To do so, the UAVs have the
+possibility to employ two different bands namely the typical microwave and the
+high-rate millimeter wave bands. In this paper, we develop a generic framework
+to assign UAVs to supported transceivers and optimize their trajectories such
+that a weighted function of the total service time is minimized. Taking into
+account both the communication time needed to relay the message and the flying
+time of the UAVs, a mixed non-linear programming problem aiming at finding the
+stops at which the UAVs hover to forward the data to the receivers is
+formulated. An iterative approach is then developed to solve the problem.
+First, a mixed linear programming problem is optimally solved to determine the
+path of each available UAV. Then, a hierarchical iterative search is executed
+to enhance the UAV stops' locations and reduce the service time. The behavior
+of the UAVs and the benefits of the proposed framework are showcased for
+selected scenarios.
+",1,0,0,0,0,0
+18393,A monad for full ground reference cells,"  We present a denotational account of dynamic allocation of potentially cyclic
+memory cells using a monad on a functor category. We identify the collection of
+heaps as an object in a different functor category equipped with a monad for
+adding hiding/encapsulation capabilities to the heaps. We derive a monad for
+full ground references supporting effect masking by applying a state monad
+transformer to the encapsulation monad. To evaluate the monad, we present a
+denotational semantics for a call-by-value calculus with full ground
+references, and validate associated code transformations.
+",1,0,1,0,0,0
+18394,Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work,"  Deep networks thrive when trained on large scale data collections. This has
+given ImageNet a central role in the development of deep architectures for
+visual object classification. However, ImageNet was created during a specific
+period in time, and as such it is prone to aging, as well as dataset bias
+issues. Moving beyond fixed training datasets will lead to more robust visual
+systems, especially when deployed on robots in new environments which must
+train on the objects they encounter there. To make this possible, it is
+important to break free from the need for manual annotators. Recent work has
+begun to investigate how to use the massive amount of images available on the
+Web in place of manual image annotations. We contribute to this research thread
+with two findings: (1) a study correlating a given level of noisily labels to
+the expected drop in accuracy, for two deep architectures, on two different
+types of noise, that clearly identifies GoogLeNet as a suitable architecture
+for learning from Web data; (2) a recipe for the creation of Web datasets with
+minimal noise and maximum visual variability, based on a visual and natural
+language processing concept expansion strategy. By combining these two results,
+we obtain a method for learning powerful deep object models automatically from
+the Web. We confirm the effectiveness of our approach through object
+categorization experiments using our Web-derived version of ImageNet on a
+popular robot vision benchmark database, and on a lifelong object discovery
+task on a mobile robot.
+",1,0,0,0,0,0
+18395,Existence of either a periodic collisional orbit or infinitely many consecutive collision orbits in the planar circular restricted three-body problem,"  In the restricted three-body problem, consecutive collision orbits are those
+orbits which start and end at collisions with one of the primaries. Interests
+for such orbits arise not only from mathematics but also from various
+engineering problems. In this article, using Floer homology, we show that there
+are either a periodic collisional orbit, or infinitely many consecutive
+collision orbits in the planar circular restricted three-body problem on each
+bounded component of the energy hypersurface for Jacobi energy below the first
+critical value.
+",0,0,1,0,0,0
+18396,Finding AND-OR Hierarchies in Workflow Nets,"  This paper presents the notion of AND-OR reduction, which reduces a WF net to
+a smaller net by iteratively contracting certain well-formed subnets into
+single nodes until no more such contractions are possible. This reduction can
+reveal the hierarchical structure of a WF net, and since it preserves certain
+semantical properties such as soundness, it can help with analysing and
+understanding why a WF net is sound or not. The reduction can also be used to
+verify if a WF net is an AND-OR net. This class of WF nets was introduced in
+earlier work, and arguably describes nets that follow good hierarchical design
+principles. It is shown that the AND-OR reduction is confluent up to
+isomorphism, which means that despite the inherent non-determinism that comes
+from the choice of subnets that are contracted, the final result of the
+reduction is always the same up to the choice of the identity of the nodes.
+Based on this result, a polynomial-time algorithm is presented that computes
+this unique result of the AND-OR reduction. Finally, it is shown how this
+algorithm can be used to verify if a WF net is an AND-OR net.
+",1,0,0,0,0,0
+18397,Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture,"  The present work shows the application of transfer learning for a pre-trained
+deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a
+single workstation with enabled NVIDIA GPU card that takes up to 1 hour to
+complete the training task and archive an overall average accuracy of $94.7\%$.
+The DNN presents a $20\%$ score of misclassification for an external test
+dataset. The accuracy of the proposed methodology is equivalent to ones using
+HSI methodology $(81\%-91\%)$ used for the same task, but with the advantage of
+being independent on special equipment to classify wheat kernel for FHB
+symptoms.
+",0,0,0,1,0,0
+18398,About simple variational splines from the Hamiltonian viewpoint,"  In this paper, we study simple splines on a Riemannian manifold $Q$ from the
+point of view of the Pontryagin maximum principle (PMP) in optimal control
+theory. The control problem consists in finding smooth curves matching two
+given tangent vectors with the control being the curve's acceleration, while
+minimizing a given cost functional. We focus on cubic splines (quadratic cost
+function) and on time-minimal splines (constant cost function) under bounded
+acceleration. We present a general strategy to solve for the optimal
+hamiltonian within the PMP framework based on splitting the variables by means
+of a linear connection. We write down the corresponding hamiltonian equations
+in intrinsic form and study the corresponding hamiltonian dynamics in the case
+$Q$ is the $2$-sphere. We also elaborate on possible applications, including
+landmark cometrics in computational anatomy.
+",0,0,1,0,0,0
+18399,Adaptive Grasp Control through Multi-Modal Interactions for Assistive Prosthetic Devices,"  The hand is one of the most complex and important parts of the human body.
+The dexterity provided by its multiple degrees of freedom enables us to perform
+many of the tasks of daily living which involve grasping and manipulating
+objects of interest. Contemporary prosthetic devices for people with
+transradial amputations or wrist disarticulation vary in complexity, from
+passive prosthetics to complex devices that are body or electrically driven.
+One of the important challenges in developing smart prosthetic hands is to
+create devices which are able to mimic all activities that a person might
+perform and address the needs of a wide variety of users. The approach explored
+here is to develop algorithms that permit a device to adapt its behavior to the
+preferences of the operator through interactions with the wearer. This device
+uses multiple sensing modalities including muscle activity from a myoelectric
+armband, visual information from an on-board camera, tactile input through a
+touchscreen interface, and speech input from an embedded microphone. Presented
+within this paper are the design, software and controls of a platform used to
+evaluate this architecture as well as results from experiments deigned to
+quantify the performance.
+",1,0,0,0,0,0
+18400,Generalization of Effective Conductance Centrality for Egonetworks,"  We study the popular centrality measure known as effective conductance or in
+some circles as information centrality. This is an important notion of
+centrality for undirected networks, with many applications, e.g., for random
+walks, electrical resistor networks, epidemic spreading, etc. In this paper, we
+first reinterpret this measure in terms of modulus (energy) of families of
+walks on the network. This modulus centrality measure coincides with the
+effective conductance measure on simple undirected networks, and extends it to
+much more general situations, e.g., directed networks as well. Secondly, we
+study a variation of this modulus approach in the egocentric network paradigm.
+Egonetworks are networks formed around a focal node (ego) with a specific order
+of neighborhoods. We propose efficient analytical and approximate methods for
+computing these measures on both undirected and directed networks. Finally, we
+describe a simple method inspired by the modulus point-of-view, called shell
+degree, which proved to be a useful tool for network science.
+",1,1,0,0,0,0
+18401,Three-dimensional oscillatory magnetic reconnection,"  Here we detail the dynamic evolution of localised reconnection regions about
+three-dimensional (3D) magnetic null points by using numerical simulation. We
+demonstrate for the first time that reconnection triggered by the localised
+collapse of a 3D null point due to an external MHD wave involves a
+self-generated oscillation, whereby the current sheet and outflow jets undergo
+a reconnection reversal process during which back-pressure formation at the jet
+heads acts to prise open the collapsed field before overshooting the
+equilibrium into an opposite-polarity configuration. The discovery that
+reconnection at fully 3D nulls can proceed naturally in a time-dependent and
+periodic fashion is suggestive that oscillatory reconnection mechanisms may
+play a role in explaining periodicity in astrophysical phenomena associated
+with magnetic reconnection, such as the observed quasi-periodicity of solar and
+stellar flare emission. Furthermore, we find a consequence of oscillatory
+reconnection is the generation of a plethora of freely-propagating MHD waves
+which escape the vicinity of the reconnection region
+",0,1,0,0,0,0
+18402,Limits of Risk Predictability in a Cascading Alternating Renewal Process Model,"  Most risk analysis models systematically underestimate the probability and
+impact of catastrophic events (e.g., economic crises, natural disasters, and
+terrorism) by not taking into account interconnectivity and interdependence of
+risks. To address this weakness, we propose the Cascading Alternating Renewal
+Process (CARP) to forecast interconnected global risks. However, assessments of
+the model's prediction precision are limited by lack of sufficient ground truth
+data. Here, we establish prediction precision as a function of input data size
+by using alternative long ground truth data generated by simulations of the
+CARP model with known parameters. We illustrate the approach on a model of
+fires in artificial cities assembled from basic city blocks with diverse
+housing. The results confirm that parameter recovery variance exhibits power
+law decay as a function of the length of available ground truth data. Using
+CARP, we also demonstrate estimation using a disparate dataset that also has
+dependencies: real-world prediction precision for the global risk model based
+on the World Economic Forum Global Risk Report. We conclude that the CARP model
+is an efficient method for predicting catastrophic cascading events with
+potential applications to emerging local and global interconnected risks.
+",1,1,0,0,0,0
+18403,Self-contracted curves have finite length,"  A curve $\theta$: $I\to E$ in a metric space $E$ equipped with the distance
+$d$, where $I\subset \R$ is a (possibly unbounded) interval, is called
+self-contracted, if for any triple of instances of time $\{t_i\}_{i=1}^3\subset
+I$ with $t_1\leq t_2\leq t_3$ one has $d(\theta(t_3),\theta(t_2))\leq
+d(\theta(t_3),\theta(t_1))$. We prove that if $E$ is a finite-dimensional
+normed space with an arbitrary norm, the trace of $\theta$ is bounded, then
+$\theta$ has finite length, i.e. is rectifiable, thus answering positively the
+question raised in~\cite{Lemenant16sc-rectif}.
+",0,0,1,0,0,0
+18404,Unsupervised robust nonparametric learning of hidden community properties,"  We consider learning of fundamental properties of communities in large noisy
+networks, in the prototypical situation where the nodes or users are split into
+two classes according to a binary property, e.g., according to their opinions
+or preferences on a topic. For learning these properties, we propose a
+nonparametric, unsupervised, and scalable graph scan procedure that is, in
+addition, robust against a class of powerful adversaries. In our setup, one of
+the communities can fall under the influence of a knowledgeable adversarial
+leader, who knows the full network structure, has unlimited computational
+resources and can completely foresee our planned actions on the network. We
+prove strong consistency of our results in this setup with minimal assumptions.
+In particular, the learning procedure estimates the baseline activity of normal
+users asymptotically correctly with probability 1; the only assumption being
+the existence of a single implicit community of asymptotically negligible
+logarithmic size. We provide experiments on real and synthetic data to
+illustrate the performance of our method, including examples with adversaries.
+",1,0,0,1,0,0
+18405,OPEB: Open Physical Environment Benchmark for Artificial Intelligence,"  Artificial Intelligence methods to solve continuous- control tasks have made
+significant progress in recent years. However, these algorithms have important
+limitations and still need significant improvement to be used in industry and
+real- world applications. This means that this area is still in an active
+research phase. To involve a large number of research groups, standard
+benchmarks are needed to evaluate and compare proposed algorithms. In this
+paper, we propose a physical environment benchmark framework to facilitate
+collaborative research in this area by enabling different research groups to
+integrate their designed benchmarks in a unified cloud-based repository and
+also share their actual implemented benchmarks via the cloud. We demonstrate
+the proposed framework using an actual implementation of the classical
+mountain-car example and present the results obtained using a Reinforcement
+Learning algorithm.
+",1,0,0,0,0,0
+18406,The First Measurement of the $2^{3}S_{1} \rightarrow 3^{3}P - 2^{3}P$ Tune-Out Wavelength in He*,"  The workhorse of atomic physics: quantum electrodynamics is one of the best
+tested theories in physics. However recent discrepancies have shed doubt on its
+accuracy for complex atomic systems. To facilitate the development of the
+theory further we aim to measure transition dipole matrix elements of
+metastable helium (He*) (the ideal 3 body test-bed) to the highest accuracy
+thus far. We have undertaken a measurement of the `tune-out wavelength' which
+occurs when the contributions to the dynamic polarizability from all atomic
+transitions sum to zero; thus illuminating an atom with this wavelength of
+light then produces no net energy shift. This provides a strict constraint on
+the transition dipole matrix elements without the complication and inaccuracy
+of other methods.
+Using a novel atom-laser based technique we have made the first measurement
+of the the tune-out wavelength in metastable helium between the
+$3^{3}P_{1,2,3}$ and $2^{3}P_{1,2,3}$ states at 413.07(2)nm which compares well
+with the predicted value\cite{Mitroy2013} of 413.02(9). We have additionally
+developed many of the methods necessary to improve this measurement to the
+100fm level of accuracy where it will form the most accurate determination of
+transition rate information ever made in He* and provide a stringent test for
+atomic QED simulations. We believe this measurement to be one of the most
+sensitive ever made of an optical dipole potential, able to detect changes in
+potentials of $\sim200pK$ and is widely applicable to other species and areas
+of atom optics.
+",0,1,0,0,0,0
+18407,WheelCon: A wheel control-based gaming platform for studying human sensorimotor control,"  Feedback control theory has been extensively implemented to theoretically
+model human sensorimotor control. However, experimental platforms capable of
+manipulating important components of multiple feedback loops lack development.
+This paper describes the WheelCon, which is an open source platform aimed at
+resolving such insufficiencies. WheelCon enables safely simulation of the
+canonical sensorimotor task such as riding a mountain bike down a steep,
+twisting, bumpy trail etc., with provided only a computer, standard display,
+and an inexpensive gaming steering wheel with a force feedback motor. The
+platform provides flexibility, as will be demonstrated in the demos provided,
+so that researchers may manipulate the disturbances, delay, and quantization
+(data rate) in the layered feedback loops, including a high-level advanced plan
+layer and a low-level delayed reflex layer. In this paper, we illustrate
+WheelCon's graphical user interface (GUI), the input and output of existing
+demos, and how to design new games. In addition, we present the basic feedback
+model, and we show the testing results from our demo games which align well
+with prediction from the model. In short, the platform is featured as cheap,
+simple to use, and flexible to program for effective sensorimotor neuroscience
+research and control engineering education.
+",0,0,0,0,1,0
+18408,Depression and Self-Harm Risk Assessment in Online Forums,"  Users suffering from mental health conditions often turn to online resources
+for support, including specialized online support communities or general
+communities such as Twitter and Reddit. In this work, we present a neural
+framework for supporting and studying users in both types of communities. We
+propose methods for identifying posts in support communities that may indicate
+a risk of self-harm, and demonstrate that our approach outperforms strong
+previously proposed methods for identifying such posts. Self-harm is closely
+related to depression, which makes identifying depressed users on general
+forums a crucial related task. We introduce a large-scale general forum dataset
+(""RSDD"") consisting of users with self-reported depression diagnoses matched
+with control users. We show how our method can be applied to effectively
+identify depressed users from their use of language alone. We demonstrate that
+our method outperforms strong baselines on this general forum dataset.
+",1,0,0,0,0,0
+18409,Quantum dilogarithm identities for the square product of A-type Dynkin quivers,"  The famous pentagon identity for quantum dilogarithms has a generalization
+for every Dynkin quiver, due to Reineke. A more advanced generalization is
+associated with a pair of alternating Dynkin quivers, due to Keller. The
+description and proof of Keller's identities involves cluster algebras and
+cluster categories, and the statement of the identity is implicit. In this
+paper we describe Keller's identities explicitly, and prove them by a dimension
+counting argument. Namely, we consider quiver representations
+$\boldsymbol{\mathrm{Rep}}_\gamma$ together with a superpotential function
+$W_\gamma$, and calculate the Betti numbers of the equivariant $W_\gamma$ rapid
+decay cohomology algebra of $\boldsymbol{\mathrm{Rep}}_\gamma$ in two different
+ways corresponding to two natural stratifications of
+$\boldsymbol{\mathrm{Rep}}_\gamma$. This approach is suggested by Kontsevich
+and Soibelman in relation with the Cohomological Hall Algebra of quivers, and
+the associated Donaldson-Thomas invariants.
+",0,0,1,0,0,0
+18410,A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems,"  Traditional Linear Genetic Programming (LGP) algorithms are based only on the
+selection mechanism to guide the search. Genetic operators combine or mutate
+random portions of the individuals, without knowing if the result will lead to
+a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP)
+methods were proposed to overcome this issue through a probability model that
+captures the structure of the fit individuals and use it to sample new
+individuals. This work proposes the use of LGP with a Stochastic Context-Free
+Grammar (SCFG), that has a probability distribution that is updated according
+to selected individuals. We proposed a method for adapting the grammar into the
+linear representation of LGP. Tests performed with the proposed probabilistic
+method, and with two hybrid approaches, on several symbolic regression
+benchmark problems show that the results are statistically better than the
+obtained by the traditional LGP.
+",1,0,0,1,0,0
+18411,"Trace-free ${\rm SL}(2,\mathbb{C})$-representations of arborescent links","  Given a link $L\subset S^3$, a representation $\pi_1(S^3-L)\to{\rm
+SL}(2,\mathbb{C})$ is {\it trace-free} if it sends each meridian to an element
+with trace zero. We present a method for completely determining trace-free
+${\rm SL}(2,\mathbb{C})$-representations for arborescent links. Concrete
+computations are done for a class of 3-bridge arborescent links.
+",0,0,1,0,0,0
+18412,Algorithms for Weighted Sums of Squares Decomposition of Non-negative Univariate Polynomials,"  It is well-known that every non-negative univariate real polynomial can be
+written as the sum of two polynomial squares with real coefficients. When one
+allows a weighted sum of finitely many squares instead of a sum of two squares,
+then one can choose all coefficients in the representation to lie in the field
+generated by the coefficients of the polynomial.
+In this article, we describe, analyze and compare both from the theoretical
+and practical points of view, two algorithms computing such a weighted sums of
+squares decomposition for univariate polynomials with rational coefficients.
+The first algorithm, due to the third author relies on real root isolation,
+quadratic approximations of positive polynomials and square-free decomposition
+but its complexity was not analyzed. We provide bit complexity estimates, both
+on runtime and output size of this algorithm. They are exponential in the
+degree of the input univariate polynomial and linear in the maximum bitsize of
+its complexity. This analysis is obtained using quantifier elimination and root
+isolation bounds.
+The second algorithm, due to Chevillard, Harrison, Joldes and Lauter, relies
+on complex root isolation and square-free decomposition and has been introduced
+for certifying positiveness of polynomials in the context of computer
+arithmetics. Again, its complexity was not analyzed. We provide bit complexity
+estimates, both on runtime and output size of this algorithm, which are
+polynomial in the degree of the input polynomial and linear in the maximum
+bitsize of its complexity. This analysis is obtained using Vieta's formula and
+root isolation bounds.
+Finally, we report on our implementations of both algorithms. While the
+second algorithm is, as expected from the complexity result, more efficient on
+most of examples, we exhibit families of non-negative polynomials for which the
+first algorithm is better.
+",1,0,0,0,0,0
+18413,The Calkin algebra is $\aleph_1$-universal,"  We discuss the existence of (injectively) universal C*-algebras and prove
+that all C*-algebras of density character $\aleph_1$ embed into the Calkin
+algebra, $Q(H)$. Together with other results, this shows that each of the
+following assertions is relatively consistent with ZFC: (i) $Q(H)$ is a
+$2^{\aleph_0}$-universal C*-algebra. (ii) There exists a
+$2^{\aleph_0}$-universal C*-algebra, but $Q(H)$ is not
+$2^{\aleph_0}$-universal. (iii) A $2^{\aleph_0}$-universal C*-algebra does not
+exist. We also prove that it is relatively consistent with ZFC that (iv) there
+is no $\aleph_1$-universal nuclear C*-algebra, and that (v) there is no
+$\aleph_1$-universal simple nuclear C*-algebra.
+",0,0,1,0,0,0
+18414,Learning Structured Semantic Embeddings for Visual Recognition,"  Numerous embedding models have been recently explored to incorporate semantic
+knowledge into visual recognition. Existing methods typically focus on
+minimizing the distance between the corresponding images and texts in the
+embedding space but do not explicitly optimize the underlying structure. Our
+key observation is that modeling the pairwise image-image relationship improves
+the discrimination ability of the embedding model. In this paper, we propose
+the structured discriminative and difference constraints to learn
+visual-semantic embeddings. First, we exploit the discriminative constraints to
+capture the intra- and inter-class relationships of image embeddings. The
+discriminative constraints encourage separability for image instances of
+different classes. Second, we align the difference vector between a pair of
+image embeddings with that of the corresponding word embeddings. The difference
+constraints help regularize image embeddings to preserve the semantic
+relationships among word embeddings. Extensive evaluations demonstrate the
+effectiveness of the proposed structured embeddings for single-label
+classification, multi-label classification, and zero-shot recognition.
+",1,0,0,0,0,0
+18415,Estimators for a Class of Bivariate Measures of Concordance for Copulas,"  In the present paper we propose and study estimators for a wide class of
+bivariate measures of concordance for copulas. These measures of concordance
+are generated by a copula and generalize Spearman's rho and Gini's gamma. In
+the case of Spearman's rho and Gini's gamma the estimators turn out to be the
+usual sample versions of these measures of concordance.
+",0,0,1,1,0,0
+18416,Variational treatment of electron-polyatomic molecule scattering calculations using adaptive overset grids,"  The Complex Kohn variational method for electron-polyatomic molecule
+scattering is formulated using an overset grid representation of the scattering
+wave function. The overset grid consists of a central grid and multiple dense,
+atom-centered subgrids that allow the simultaneous spherical expansions of the
+wave function about multiple centers. Scattering boundary conditions are
+enforced by using a basis formed by the repeated application of the free
+particle Green's function and potential, $\hat{G}^+_0\hat{V}$ on the overset
+grid in a ""Born-Arnoldi"" solution of the working equations. The theory is shown
+to be equivalent to a specific Padé approximant to the $T$-matrix, and has
+rapid convergence properties, both in the number of numerical basis functions
+employed and the number of partial waves employed in the spherical expansions.
+The method is demonstrated in calculations on methane and CF$_4$ in the
+static-exchange approximation, and compared in detail with calculations
+performed with the numerical Schwinger variational approach based on single
+center expansions. An efficient procedure for operating with the free-particle
+Green's function and exchange operators (to which no approximation is made) is
+also described.
+",0,1,0,0,0,0
+18417,Geometry of quantum dynamics in infinite dimension,"  We develop a geometric approach to quantum mechanics based on the concept of
+the Tulczyjew triple. Our approach is genuinely infinite-dimensional and
+including a Lagrangian formalism in which self-adjoint (Schroedinger) operators
+are obtained as Lagrangian submanifolds associated with the Lagrangian. As a
+byproduct we obtain also results concerning coadjoint orbits of the unitary
+group in infinite dimension, embedding of the Hilbert projective space of pure
+states in the unitary group, and an approach to self-adjoint extensions of
+symmetric relations.
+",0,0,1,0,0,0
+18418,Canonical tilting relative generators,"  Given a relatively projective birational morphism $f\colon X\to Y$ of smooth
+algebraic spaces with dimension of fibers bounded by 1, we construct tilting
+relative (over $Y$) generators $T_{X,f}$ and $S_{X,f}$ in $\mathcal{D}^b(X)$.
+We develop a piece of general theory of strict admissible lattice filtrations
+in triangulated categories and show that $\mathcal{D}^b(X)$ has such a
+filtration $\mathcal{L}$ where the lattice is the set of all birational
+decompositions $f \colon X \xrightarrow{g} Z \xrightarrow{h} Y$ with smooth
+$Z$. The $t$-structures related to $T_{X,f}$ and $S_{X,f}$ are proved to be
+glued via filtrations left and right dual to $\mathcal{L}$. We realise all such
+$Z$ as the fine moduli spaces of simple quotients of $\mathcal{O}_X$ in the
+heart of the $t$-structure for which $S_{X,g}$ is a relative projective
+generator over $Y$. This implements the program of interpreting relevant smooth
+contractions of $X$ in terms of a suitable system of $t$-structures on
+$\mathcal{D}^b(X)$.
+",0,0,1,0,0,0
+18419,Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models,"  Current theories hold that brain function is highly related to long-range
+physical connections through axonal bundles, namely extrinsic connectiv-ity.
+However, obtaining a groupwise cortical parcellation based on extrinsic
+connectivity remains challenging. Current parcellation methods are
+compu-tationally expensive; need tuning of several parameters or rely on ad-hoc
+constraints. Furthermore, none of these methods present a model for the
+cortical extrinsic connectivity of the cortex. To tackle these problems, we
+propose a parsimonious model for the extrinsic connectivity and an efficient
+parceling technique based on clustering of tractograms. Our technique allows
+the creation of single subject and groupwise parcellations of the whole cortex.
+The parcellations obtained with our technique are in agreement with structural
+and functional parcellations in the literature. In particular, the motor and
+sensory cortex are subdivided in agreement with the human ho-munculus of
+Penfield. We illustrate this by comparing our resulting parcels with the motor
+strip mapping included in the Human Connectome Project data.
+",0,0,0,1,0,0
+18420,Modeling Biological Problems in Computer Science: A Case Study in Genome Assembly,"  As computer scientists working in bioinformatics/computational biology, we
+often face the challenge of coming up with an algorithm to answer a biological
+question. This occurs in many areas, such as variant calling, alignment, and
+assembly. In this tutorial, we use the example of the genome assembly problem
+to demonstrate how to go from a question in the biological realm to a solution
+in the computer science realm. We show the modeling process step-by-step,
+including all the intermediate failed attempts.
+",1,0,0,0,0,0
+18421,MEC: Memory-efficient Convolution for Deep Neural Network,"  Convolution is a critical component in modern deep neural networks, thus
+several algorithms for convolution have been developed. Direct convolution is
+simple but suffers from poor performance. As an alternative, multiple indirect
+methods have been proposed including im2col-based convolution, FFT-based
+convolution, or Winograd-based algorithm. However, all these indirect methods
+have high memory-overhead, which creates performance degradation and offers a
+poor trade-off between performance and memory consumption. In this work, we
+propose a memory-efficient convolution or MEC with compact lowering, which
+reduces memory-overhead substantially and accelerates convolution process. MEC
+lowers the input matrix in a simple yet efficient/compact way (i.e., much less
+memory-overhead), and then executes multiple small matrix multiplications in
+parallel to get convolution completed. Additionally, the reduced memory
+footprint improves memory sub-system efficiency, improving performance. Our
+experimental results show that MEC reduces memory consumption significantly
+with good speedup on both mobile and server platforms, compared with other
+indirect convolution algorithms.
+",1,0,0,0,0,0
+18422,On age of 6070 Rheinland and 54827 (2001 NQ8) asteroid pair,"  In this paper we present results of our studying of famous very young pair of
+asteroids 6070 Rheinland and 54827 (2001 NQ8). We have done numeric integration
+of orbits of pair with only planet perturbations and include Ceres and Vesta
+effect. We have confirmed results of previous studying, obtained with different
+integrators. And we confirm significant effect of Ceres and Vesta perturbation
+on dynamic of this pair. We find that effect of other massive asteroids is
+insignificant. According our results, more probable age of 6070 Rheinland and
+54827 (2001 NQ8) pair is 16.2 kyrs. Our value of age is very close to most
+recent age determination by Vokrouhlicky et al [12], obtained with different
+method. After the compare our results, we can conclude, that non-gravitational
+forces are small and large number of clones is not necessary in studying of
+this pair. As an additional way of studying of close orbits dynamics, we
+calculate relative velocity in pair during numeric integration. Normal
+component of velocity show a very good convergence at epoch of closest
+encounter in pair.
+",0,1,0,0,0,0
+18423,First-Order Reversal Curves of the Magnetostructural Phase Transition in FeTe,"  We apply the first-order reversal curve (FORC) method, borrowed from studies
+of ferromagnetic materials, to the magneto-structural phase transition of FeTe.
+FORC measurements reveal two features in the hysteretic phase transition, even
+in samples where traditional temperature measurements display only a single
+transition. For Fe1.13Te, the influence of magnetic field suggests that the
+main feature is primarily structural while a smaller, slightly
+higher-temperature transition is magnetic in origin. By contrast Fe1.03Te has a
+single transition which shows a uniform response to magnetic field, indicating
+a stronger coupling of the magnetic and structural phase transitions. We also
+introduce uniaxial stress, which spreads the distribution width without
+changing the underlying energy barrier of the transformation. The work shows
+how FORC can help disentangle the roles of the magnetic and structural phase
+transitions in FeTe.
+",0,1,0,0,0,0
+18424,Data Analytics on Online Labor Markets: Opportunities and Challenges,"  The data-driven economy has led to a significant shortage of data scientists.
+To address this shortage, this study explores the prospects of outsourcing data
+analysis tasks to freelancers available on online labor markets (OLMs) by
+identifying the essential factors for this endeavor. Specifically, we explore
+the skills required from freelancers, collect information about the skills
+present on major OLMs, and identify the main hurdles for out-/crowd-sourcing
+data analysis. Adopting a sequential mixed-method approach, we interviewed 20
+data scientists and subsequently surveyed 80 respondents from OLMs. Besides
+confirming the need for expected skills such as technical/mathematical
+capabilities, it also identifies less known ones such as domain understanding,
+an eye for aesthetic data visualization, good communication skills, and a
+natural understanding of the possibilities/limitations of data analysis in
+general. Finally, it elucidates obstacles for crowdsourcing like the
+communication overhead, knowledge gaps, quality assurance, and data
+confidentiality, which need to be mitigated.
+",1,0,0,0,0,0
+18425,Analyzing the Robustness of Nearest Neighbors to Adversarial Examples,"  Motivated by safety-critical applications, test-time attacks on classifiers
+via adversarial examples has recently received a great deal of attention.
+However, there is a general lack of understanding on why adversarial examples
+arise; whether they originate due to inherent properties of data or due to lack
+of training samples remains ill-understood. In this work, we introduce a
+theoretical framework analogous to bias-variance theory for understanding these
+effects.
+We use our framework to analyze the robustness of a canonical non-parametric
+classifier - the k-nearest neighbors. Our analysis shows that its robustness
+properties depend critically on the value of k - the classifier may be
+inherently non-robust for small k, but its robustness approaches that of the
+Bayes Optimal classifier for fast-growing k. We propose a novel modified
+1-nearest neighbor classifier, and guarantee its robustness in the large sample
+limit. Our experiments suggest that this classifier may have good robustness
+properties even for reasonable data set sizes.
+",1,0,0,1,0,0
+18426,Non Uniform On Chip Power Delivery Network Synthesis Methodology,"  In this paper, we proposed a non-uniform power delivery network (PDN)
+synthesis methodology. It first constructs initial PDN using uniform approach.
+Then preliminary power integrity analysis is performed to derive IR-safe
+candidate window. Congestion map is obtained based global route congestion
+estimation. A self-adaptive non-uniform PDN synthesis is then performed to
+globally and locally optimize PDN over selected regions. The PDN synthesis is
+congestion-driven and IR- guarded. Experimental results show significant timing
+important in trade-off small PDN length reduction with no EM/IR impact. We
+further explored potential power savings using our non-uniform PDN synthesis
+methodology.
+",1,0,0,0,0,0
+18427,Translating ceRNA susceptibilities into correlation functions,"  Competition to bind microRNAs induces an effective positive crosstalk between
+their targets, therefore known as `competing endogenous RNAs' or ceRNAs. While
+such an effect is known to play a significant role in specific conditions,
+estimating its strength from data and, experimentally, in physiological
+conditions appears to be far from simple. Here we show that the susceptibility
+of ceRNAs to different types of perturbations affecting their competitors (and
+hence their tendency to crosstalk) can be encoded in quantities as intuitive
+and as simple to measure as correlation functions. We confirm this scenario by
+extensive numerical simulations and validate it by re-analyzing PTEN's
+crosstalk pattern from TCGA breast cancer dataset. These results clarify the
+links between different quantities used to estimate the intensity of ceRNA
+crosstalk and provide new keys to analyze transcriptional datasets and
+effectively probe ceRNA networks in silico.
+",0,1,0,0,0,0
+18428,Impact of energetic particle orbits on long range frequency chirping of BGK modes,"  Long range frequency chirping of Bernstein-Greene-Kruskal modes, whose
+existence is determined by the fast particles, is investigated in cases where
+these particles do not move freely and their motion is bounded to restricted
+orbits. An equilibrium oscillating potential, which creates different orbit
+topologies of energetic particles, is included into the bump-on-tail
+instability problem of a plasma wave. With respect to fast particles dynamics,
+the extended model captures the range of particles motion (trapped/passing)
+with energy and thus represents a more realistic 1D picture of the long range
+sweeping events observed for weakly damped modes, e.g. global Alfven
+eigenmodes, in tokamaks. The Poisson equation is solved numerically along with
+bounce averaging the Vlasov equation in the adiabatic regime. We demonstrate
+that the shape and the saturation amplitude of the nonlinear mode structure
+depends not only on the amount of deviation from the initial eigenfrequency but
+also on the initial energy of the resonant electrons in the equilibrium
+potential. Similarly, the results reveal that the resonant electrons following
+different equilibrium orbits in the electrostatic potential lead to different
+rates of frequency evolution. As compared to the previous model [Breizman B.N.
+2010 Nucl. Fusion 50 084014], it is shown that the frequency sweeps with lower
+rates. The additional physics included in the model enables a more complete 1D
+description of the range of phenomena observed in experiments.
+",0,1,0,0,0,0
+18429,An Executable Specification of Typing Rules for Extensible Records based on Row Polymorphism,"  Type inference is an application domain that is a natural fit for logic
+programming (LP). LP systems natively support unification, which serves as a
+basic building block of typical type inference algorithms. In particular,
+polymorphic type inference in the Hindley--Milner type system (HM) can be
+succinctly specified and executed in Prolog. In our previous work, we have
+demonstrated that more advanced features of parametric polymorphism beyond HM,
+such as type-constructor polymorphism and kind polymorphism, can be similarly
+specified in Prolog. Here, we demonstrate a specification for records, which is
+one of the most widely supported compound data structures in real-world
+programming languages, and discuss the advantages and limitations of Prolog as
+a specification language for type systems. Record types are specified as
+order-irrelevant collections of named fields mapped to their corresponding
+types. In addition, an open-ended collection is used to support row
+polymorphism for record types to be extensible.
+",1,0,0,0,0,0
+18430,Seasonal Stochastic Volatility and the Samuelson Effect in Agricultural Futures Markets,"  We introduce a multi-factor stochastic volatility model for commodities that
+incorporates seasonality and the Samuelson effect. Conditions on the seasonal
+term under which the corresponding volatility factor is well-defined are given,
+and five different specifications of the seasonality pattern are proposed. We
+calculate the joint characteristic function of two futures prices for different
+maturities in the risk-neutral measure. The model is then presented under the
+physical measure, and its state-space representation is derived, in order to
+estimate the parameters with the Kalman filter for time series of corn, cotton,
+soybean, sugar and wheat futures from 2007 to 2017. The seasonal model
+significantly outperforms the nested non-seasonal model in all five markets,
+and we show which seasonality patterns are particularly well-suited in each
+case. We also confirm the importance of correctly modelling the Samuelson
+effect in order to account for futures with different maturities. Our results
+are clearly confirmed in a robustness check carried out with an alternative
+dataset of constant maturity futures for the same agricultural markets.
+",0,0,0,0,0,1
+18431,A Computational Model of a Single-Photon Avalanche Diode Sensor for Transient Imaging,"  Single-Photon Avalanche Diodes (SPAD) are affordable photodetectors, capable
+to collect extremely fast low-energy events, due to their single-photon
+sensibility. This makes them very suitable for time-of-flight-based range
+imaging systems, allowing to reduce costs and power requirements, without
+sacrifizing much temporal resolution. In this work we describe a computational
+model to simulate the behaviour of SPAD sensors, aiming to provide a realistic
+camera model for time-resolved light transport simulation, with applications on
+prototyping new reconstructions techniques based on SPAD time-of-flight data.
+Our model accounts for the major effects of the sensor on the incoming signal.
+We compare our model against real-world measurements, and apply it to a variety
+of scenarios, including complex multiply-scattered light transport.
+",1,1,0,0,0,0
+18432,On Certain Tilting Modules for SL2,"  We give a complete picture of when the tensor product of an induced module
+and a Weyl module is a tilting module for the algebraic group $SL_2$ over an
+algebraically closed field of characteristic $p$. Whilst the result is
+recursive by nature, we give an explicit statement in terms of the $p$-adic
+expansions of the highest weight of each module.
+",0,0,1,0,0,0
+18433,First-order Methods Almost Always Avoid Saddle Points,"  We establish that first-order methods avoid saddle points for almost all
+initializations. Our results apply to a wide variety of first-order methods,
+including gradient descent, block coordinate descent, mirror descent and
+variants thereof. The connecting thread is that such algorithms can be studied
+from a dynamical systems perspective in which appropriate instantiations of the
+Stable Manifold Theorem allow for a global stability analysis. Thus, neither
+access to second-order derivative information nor randomness beyond
+initialization is necessary to provably avoid saddle points.
+",1,0,0,1,0,0
+18434,Faster Algorithms for Computing Maximal 2-Connected Subgraphs in Sparse Directed Graphs,"  Connectivity related concepts are of fundamental interest in graph theory.
+The area has received extensive attention over four decades, but many problems
+remain unsolved, especially for directed graphs. A directed graph is
+2-edge-connected (resp., 2-vertex-connected) if the removal of any edge (resp.,
+vertex) leaves the graph strongly connected. In this paper we present improved
+algorithms for computing the maximal 2-edge- and 2-vertex-connected subgraphs
+of a given directed graph. These problems were first studied more than 35 years
+ago, with $\widetilde{O}(mn)$ time algorithms for graphs with m edges and n
+vertices being known since the late 1980s. In contrast, the same problems for
+undirected graphs are known to be solvable in linear time. Henzinger et al.
+[ICALP 2015] recently introduced $O(n^2)$ time algorithms for the directed
+case, thus improving the running times for dense graphs. Our new algorithms run
+in time $O(m^{3/2})$, which further improves the running times for sparse
+graphs.
+The notion of 2-connectivity naturally generalizes to k-connectivity for
+$k>2$. For constant values of k, we extend one of our algorithms to compute the
+maximal k-edge-connected in time $O(m^{3/2} \log{n})$, improving again for
+sparse graphs the best known algorithm by Henzinger et al. [ICALP 2015] that
+runs in $O(n^2 \log n)$ time.
+",1,0,0,0,0,0
+18435,Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting,"  Generative adversarial networks (GANs) are an exciting alternative to
+algorithms for solving density estimation problems---using data to assess how
+likely samples are to be drawn from the same distribution. Instead of
+explicitly computing these probabilities, GANs learn a generator that can match
+the given probabilistic source. This paper looks particularly at this matching
+capability in the context of problems with one-dimensional outputs. We identify
+a class of function decompositions with properties that make them well suited
+to the critic role in a leading approach to GANs known as Wasserstein GANs. We
+show that Taylor and Fourier series decompositions belong to our class, provide
+examples of these critics outperforming standard GAN approaches, and suggest
+how they can be scaled to higher dimensional problems in the future.
+",1,0,0,1,0,0
+18436,Fingering instabilities and pattern formation in a two-component dipolar Bose-Einstein condensate,"  We study fingering instabilities and pattern formation at the interface of an
+oppositely polarized two-component Bose-Einstein condensate with strong
+dipole-dipole interactions in three dimensions. It is shown that the rotational
+symmetry is spontaneously broken by fingering instability when the
+dipole-dipole interactions are strengthened. Frog-shaped and mushroom-shaped
+patterns emerge during the dynamics due to the dipolar interactions. We also
+demonstrate the spontaneous density modulation and domain growth of a
+two-component dipolar BEC in the dynamics. Bogoliubov analyses in the
+two-dimensional approximation are performed, and the characteristic lengths of
+the domains are estimated analytically. Patterns resembling those in magnetic
+classical fluids are modulated when the number ratio of atoms, the trap ratio
+of the external potential, or tilted polarization with respect to the z
+direction is varied.
+",0,1,0,0,0,0
+18437,Fast Information-theoretic Bayesian Optimisation,"  Information-theoretic Bayesian optimisation techniques have demonstrated
+state-of-the-art performance in tackling important global optimisation
+problems. However, current information-theoretic approaches require many
+approximations in implementation, introduce often-prohibitive computational
+overhead and limit the choice of kernels available to model the objective. We
+develop a fast information-theoretic Bayesian Optimisation method, FITBO, that
+avoids the need for sampling the global minimiser, thus significantly reducing
+computational overhead. Moreover, in comparison with existing approaches, our
+method faces fewer constraints on kernel choice and enjoys the merits of
+dealing with the output space. We demonstrate empirically that FITBO inherits
+the performance associated with information-theoretic Bayesian optimisation,
+while being even faster than simpler Bayesian optimisation approaches, such as
+Expected Improvement.
+",0,0,0,1,0,0
+18438,ExoMol molecular line lists XX: a comprehensive line list for H$_3^+$,"  H$_3^+$ is a ubiquitous and important astronomical species whose spectrum has
+been observed in the interstellar medium, planets and tentatively in the
+remnants of supernova SN1897a. Its role as a cooler is important for gas giant
+planets and exoplanets, and possibly the early Universe. All this makes the
+spectral properties, cooling function and partition function of H$_3^+$ key
+parameters for astronomical models and analysis. A new high-accuracy, very
+extensive line list for H$_3^+$ called MiZATeP was computed as part of the
+ExoMol project alongside a temperature-dependent cooling function and partition
+function as well as lifetimes for %individual excited states. These data are
+made available in electronic form as supplementary data to this article and at
+this http URL
+",0,1,0,0,0,0
+18439,Robust and Sparse Regression in GLM by Stochastic Optimization,"  The generalized linear model (GLM) plays a key role in regression analyses.
+In high-dimensional data, the sparse GLM has been used but it is not robust
+against outliers. Recently, the robust methods have been proposed for the
+specific example of the sparse GLM. Among them, we focus on the robust and
+sparse linear regression based on the $\gamma$-divergence. The estimator of the
+$\gamma$-divergence has strong robustness under heavy contamination. In this
+paper, we extend the robust and sparse linear regression based on the
+$\gamma$-divergence to the robust and sparse GLM based on the
+$\gamma$-divergence with a stochastic optimization approach in order to obtain
+the estimate. We adopt the randomized stochastic projected gradient descent as
+a stochastic optimization approach and extend the established convergence
+property to the classical first-order necessary condition. By virtue of the
+stochastic optimization approach, we can efficiently estimate parameters for
+very large problems. Particularly, we show the linear regression, logistic
+regression and Poisson regression with $L_1$ regularization in detail as
+specific examples of robust and sparse GLM. In numerical experiments and real
+data analysis, the proposed method outperformed comparative methods.
+",0,0,0,1,0,0
+18440,A Hint-Based Technique for System Level Model-Based Test Case Prioritization,"  Test Case Prioritization (TCP) techniques aim at proposing new test case
+execution orders to favor the achievement of certain testing goal, such as
+fault detection. Current TCP research focus mainly on code-based regression
+testing; however in the Model-Based Testing (MBT) context, we still need more
+investigation. General TCP techniques do not use historical information, since
+this information is often unavailable. Therefore, techniques use different
+sources of information to guide prioritization. We propose a novel technique
+that guides its operation using provided hints, the Hint-Based Adaptive Random
+Prioritization - HARP. Hints are indications or suggestions provided by
+developers about error-prone functionalities. As hints may be hard to collect
+automatically, we also propose an approach of collecting them. To validate our
+findings, we performed an experiment measuring the effect of introducing hints
+to HARP. It shows that hints can improve HARP's performance comparing to its
+baseline. Then, we investigated the ability of developers/managers to provide
+good hints and used them in a case study. This analysis showed that developers
+and managers are able to provide useful hints, which improves HARP's fault
+detection comparing to its baseline. Nonetheless, the provided hints should be
+consensual among the development team members.
+",1,0,0,0,0,0
+18441,Control of birhythmicity: A self-feedback approach,"  Birhythmicity occurs in many natural and artificial systems. In this paper we
+propose a self-feedback scheme to control birhythmicity. To establish the
+efficacy and generality of the proposed control scheme, we apply it on three
+birhythmic oscillators from diverse fields of natural science, namely, an
+energy harvesting system, the p53-Mdm2 network for protein genesis (the OAK
+model) and a glycolysis model (modified Decroly-Goldbeter model). Using the
+harmonic decomposition technique and energy balance method we derive the
+analytical conditions for the control of birhythmicity. A detailed numerical
+bifurcation analysis in the parameter space establishes that the control scheme
+is capable of eliminating birhythmicity and it can also induce transitions
+between different forms of bistability. As the proposed control scheme is quite
+general, it can be applied for control of several real systems, particularly in
+biochemical and engineering systems.
+",0,1,0,0,0,0
+18442,Forecasting the magnitude and onset of El Nino based on climate network,"  El Nino is probably the most influential climate phenomenon on interannual
+time scales. It affects the global climate system and is associated with
+natural disasters and serious consequences in many aspects of human life.
+However, the forecasting of the onset and in particular the magnitude of El
+Nino are still not accurate, at least more than half a year in advance. Here,
+we introduce a new forecasting index based on network links representing the
+similarity of low frequency temporal temperature anomaly variations between
+different sites in the El Nino 3.4 region. We find that significant upward
+trends and peaks in this index forecast with high accuracy both the onset and
+magnitude of El Nino approximately 1 year ahead. The forecasting procedure we
+developed improves in particular the prediction of the magnitude of El Nino and
+is validated based on several, up to more than a century long, datasets.
+",0,1,0,0,0,0
+18443,Weighted $1\times1$ cut-and-project sets in bounded distance to a lattice,"  Recent results of Grepstad and Lev are used to show that weighted
+cut-and-project sets with one-dimensional physical space and one-dimensional
+internal space are bounded distance equivalent to some lattice if the weight
+function $h$ is continuous on the internal space, and if $h$ is either
+piecewise linear, or twice differentiable with bounded curvature.
+",0,0,1,0,0,0
+18444,On Ordinal Invariants in Well Quasi Orders and Finite Antichain Orders,"  We investigate the ordinal invariants height, length, and width of well quasi
+orders (WQO), with particular emphasis on width, an invariant of interest for
+the larger class of orders with finite antichain condition (FAC). We show that
+the width in the class of FAC orders is completely determined by the width in
+the class of WQOs, in the sense that if we know how to calculate the width of
+any WQO then we have a procedure to calculate the width of any given FAC order.
+We show how the width of WQO orders obtained via some classical constructions
+can sometimes be computed in a compositional way. In particular, this allows
+proving that every ordinal can be obtained as the width of some WQO poset. One
+of the difficult questions is to give a complete formula for the width of
+Cartesian products of WQOs. Even the width of the product of two ordinals is
+only known through a complex recursive formula. Although we have not given a
+complete answer to this question we have advanced the state of knowledge by
+considering some more complex special cases and in particular by calculating
+the width of certain products containing three factors. In the course of
+writing the paper we have discovered that some of the relevant literature was
+written on cross-purposes and some of the notions re-discovered several times.
+Therefore we also use the occasion to give a unified presentation of the known
+results.
+",0,0,1,0,0,0
+18445,Indices in XML Databases,"  With XML becoming a standard for business information representation and
+exchange, stor-ing, indexing, and querying XML documents have rapidly become
+major issues in database research. In this context, query processing and
+optimization are primordial, native-XML data-bases not being mature yet. Data
+structures such as indices, which help enhance performances substantially, are
+extensively researched, especially since XML data bear numerous specifici-ties
+with respect to relational data. In this paper, we survey state-of-the-art XML
+indices and discuss the main issues, tradeoffs and future trends in XML
+indexing. We also present an in-dex that we specifically designed for the
+particular architecture of XML data warehouses.
+",1,0,0,0,0,0
+18446,Kernel theorems for modulation spaces,"  We deal with kernel theorems for modulation spaces. We completely
+characterize the continuity of a linear operator on the modulation spaces $M^p$
+for every $1\leq p\leq\infty$, by the membership of its kernel to (mixed)
+modulation spaces. Whereas Feichtinger's kernel theorem (which we recapture as
+a special case) is the modulation space counterpart of Schwartz' kernel theorem
+for temperate distributions, our results do not have a couterpart in
+distribution theory. This reveals the superiority, in some respects, of the
+modulation space formalism upon distribution theory, as already emphasized in
+Feichtinger's manifesto for a post-modern harmonic analysis, tailored to the
+needs of mathematical signal processing. The proof uses in an essential way a
+discretization of the problem by means of Gabor frames. We also show the
+equivalence of the operator norm and the modulation space norm of the
+corresponding kernel. For operators acting on $M^{p,q}$ a similar
+characterization is not expected, but sufficient conditions for boundedness can
+be sated in the same spirit.
+",0,0,1,0,0,0
+18447,On the Performance of Network Parallel Training in Artificial Neural Networks,"  Artificial Neural Networks (ANNs) have received increasing attention in
+recent years with applications that span a wide range of disciplines including
+vital domains such as medicine, network security and autonomous transportation.
+However, neural network architectures are becoming increasingly complex and
+with an increasing need to obtain real-time results from such models, it has
+become pivotal to use parallelization as a mechanism for speeding up network
+training and deployment. In this work we propose an implementation of Network
+Parallel Training through Cannon's Algorithm for matrix multiplication. We show
+that increasing the number of processes speeds up training until the point
+where process communication costs become prohibitive; this point varies by
+network complexity. We also show through empirical efficiency calculations that
+the speedup obtained is superlinear.
+",1,0,0,1,0,0
+18448,Fair Forests: Regularized Tree Induction to Minimize Model Bias,"  The potential lack of fairness in the outputs of machine learning algorithms
+has recently gained attention both within the research community as well as in
+society more broadly. Surprisingly, there is no prior work developing
+tree-induction algorithms for building fair decision trees or fair random
+forests. These methods have widespread popularity as they are one of the few to
+be simultaneously interpretable, non-linear, and easy-to-use. In this paper we
+develop, to our knowledge, the first technique for the induction of fair
+decision trees. We show that our ""Fair Forest"" retains the benefits of the
+tree-based approach, while providing both greater accuracy and fairness than
+other alternatives, for both ""group fairness"" and ""individual fairness.'"" We
+also introduce new measures for fairness which are able to handle multinomial
+and continues attributes as well as regression problems, as opposed to binary
+attributes and labels only. Finally, we demonstrate a new, more robust
+evaluation procedure for algorithms that considers the dataset in its entirety
+rather than only a specific protected attribute.
+",1,0,0,1,0,0
+18449,Discretization of SU(2) and the Orthogonal Group Using Icosahedral Symmetries and the Golden Numbers,"  The vertices of the four dimensional $120$-cell form a non-crystallographic
+root system whose corresponding symmetry group is the Coxeter group $H_{4}$.
+There are two special coordinate representations of this root system in which
+they and their corresponding Coxeter groups involve only rational numbers and
+the golden ratio $\tau$. The two are related by the conjugation $\tau
+\mapsto\tau' = -1/\tau$. This paper investigates what happens when the two root
+systems are combined and the group generated by both versions of $H_{4}$ is
+allowed to operate on them. The result is a new, but infinite, `root system'
+$\Sigma$ which itself turns out to have a natural structure of the unitary
+group $SU(2,\mathcal R)$ over the ring $\mathcal R = \mathbb
+Z[\frac{1}{2},\tau]$ (called here golden numbers). Acting upon it is the
+naturally associated infinite reflection group $H^{\infty}$, which we prove is
+of index $2$ in the orthogonal group $O(4,\mathcal R)$. The paper makes
+extensive use of the quaternions over $\mathcal R$ and leads to highly
+structured discretized filtration of $SU(2)$. We use this to offer a simple and
+effective way to approximate any element of $SU(2)$ to any degree of accuracy
+required using the repeated actions of just five fixed reflections, a process
+that may find application in computational methods in quantum mechanics.
+",0,0,1,0,0,0
+18450,Raman spectra of crystalline secondary amides,"  The study of single-crystal Raman spectra of a series of crystalline
+secondary amides (acetanilide, methacetin, phenacetine, orthorhombic and
+monoclinic polymorphs of paracetamol) as well as simple amides formanilide and
+benzanilide in the temperature range 5-300 K was carried out. The series of
+compounds with the same molecular fragment, i.e. acetamide group, can serve as
+a model system to study the interrelation between the latter and the properties
+of the intermolecular ""peptide-type"" NH...O=C hydrogen bonds. For all the
+""acetamide family"" of compounds, similar changes in the Raman spectra were
+observed on cooling the samples: an appearance of new Amide I(-) and Amide I(+)
+bands that are red and blue shifted respectively from the conventional Amide I
+band by around of 5-10 inverse centimeters. An appropriated changes in the same
+temperature range were observed for the N-H out-of-plane bending (Amide V) and
+N-H stretching vibrations of the N-H...O=C hydrogen bond. All the spectral
+changes on cooling the samples can be supposed to result from delocalization of
+the Amide I and N-H modes and appearance of dynamical splitting at low
+temperature.
+",0,1,0,0,0,0
+18451,Open-World Visual Recognition Using Knowledge Graphs,"  In a real-world setting, visual recognition systems can be brought to make
+predictions for images belonging to previously unknown class labels. In order
+to make semantically meaningful predictions for such inputs, we propose a
+two-step approach that utilizes information from knowledge graphs. First, a
+knowledge-graph representation is learned to embed a large set of entities into
+a semantic space. Second, an image representation is learned to embed images
+into the same space. Under this setup, we are able to predict structured
+properties in the form of relationship triples for any open-world image. This
+is true even when a set of labels has been omitted from the training protocols
+of both the knowledge graph and image embeddings. Furthermore, we append this
+learning framework with appropriate smoothness constraints and show how prior
+knowledge can be incorporated into the model. Both these improvements combined
+increase performance for visual recognition by a factor of six compared to our
+baseline. Finally, we propose a new, extended dataset which we use for
+experiments.
+",1,0,0,1,0,0
+18452,Mass-Imbalanced Ionic Hubbard Chain,"  A repulsive Hubbard model with both spin-asymmetric hopping (${t_\uparrow\neq
+t_\downarrow}$) and a staggered potential (of strength $\Delta$) is studied in
+one dimension. The model is a compound of the mass-imbalanced (${t_\uparrow\neq
+t_\downarrow}$, ${\Delta=0}$) and ionic (${t_\uparrow = t_\downarrow}$,
+${\Delta>0}$) Hubbard models, and may be realized by cold atoms in engineered
+optical lattices. We use mostly mean-field theory to determine the phases and
+phase transitions in the ground state for a half-filled band (one particle per
+site). We find that a period-two modulation of the particle (or charge) density
+and an alternating spin density coexist for arbitrary Hubbard interaction
+strength, ${U\geqslant 0}$. The amplitude of the charge modulation is largest
+at ${U=0}$, decreases with increasing $U$ and tends to zero for
+${U\rightarrow\infty}$. The amplitude for spin alternation increases with $U$
+and tends to saturation for ${U\rightarrow\infty}$. Charge order dominates
+below a critical value $U_c$, whereas magnetic order dominates above. The
+mean-field Hamiltonian has two gap parameters, $\Delta_\uparrow$ and
+$\Delta_\downarrow$, which have to be determined self-consistently. For
+${U<U_c}$ both parameters are positive, for ${U>U_c}$ they have different
+signs, and for ${U=U_c}$ one gap parameter jumps from a positive to a negative
+value. The weakly first-order phase transition at $U_c$ can be interpreted in
+terms of an avoided criticality (or metallicity). The system is reluctant to
+restore a symmetry that has been broken explicitly.
+",0,1,0,0,0,0
+18453,Optical properties of Xe color centers in diamond,"  Optical properties of color centers in diamond have been the subject of
+intense research due to their promising applications in quantum photonics. In
+this work we study the optical properties of Xe related color centers implanted
+into nitrogen rich (type IIA) and an ultrapure, electronic grade diamond. The
+Xe defect has two zero phonon lines at ~ 794 and 811 nm, which can be
+effectively excited using both green and red excitation, however, its emission
+in the nitrogen rich diamond is brighter. Near resonant excitation is performed
+at cryogenic temperatures and luminescence is probed under strong magnetic
+field. Our results are important towards the understanding of the Xe related
+defect and other near infrared color centers in diamond.
+",0,1,0,0,0,0
+18454,"Julian Ernst Besag, 26 March 1945 -- 6 August 2010, a biographical memoir","  Julian Besag was an outstanding statistical scientist, distinguished for his
+pioneering work on the statistical theory and analysis of spatial processes,
+especially conditional lattice systems. His work has been seminal in
+statistical developments over the last several decades ranging from image
+analysis to Markov chain Monte Carlo methods. He clarified the role of
+auto-logistic and auto-normal models as instances of Markov random fields and
+paved the way for their use in diverse applications. Later work included
+investigations into the efficacy of nearest neighbour models to accommodate
+spatial dependence in the analysis of data from agricultural field trials,
+image restoration from noisy data, and texture generation using lattice models.
+",0,0,0,1,0,0
+18455,Wake fields in a rectangular dielectric-lined accelerating structure with transversal isotropic loading,"  Dielectric lined waveguides are under extensive study as accelerating
+structures that can be excited by electron beams. Rectangular dielectric
+structures are used both in proof of principle experiments for new accelerating
+schemes and for studying the electronic properties of the structure loading
+material. Analysis of Cherenkov radiation generated by high current
+relativistic electron bunch passing through a rectangular waveguide with
+transversal isotropic dielectric loading has been carried out. Some of the
+materials used for the waveguide loading of accelerating structures (sapphire,
+ceramic films) possess significant anisotropic properties. In turn, it can
+influence excitation parameters of the wakefields generated by an electron
+beam. General solutions for the fields generated by a relativistic electron
+beam propagating in a rectangular dielectric waveguide have been derived using
+the orthogonal eigenmode decomposition method for the transverse operators of
+the Helmholtz equation. The analytical expression for the combined Cherenkov
+and Coulomb fields in terms of a superposition of LSM and LSE-modes of
+rectangular waveguide with transversal isotropic dielectric loading has been
+obtained. Numerical modelling of the longitudinal and transverse (deflecting)
+wakefields has been carried out as well. It is shown that the dielectric
+anisotropy causes frequency shift in comparison to the dielectric-lined
+waveguide with the isotropic dielectric loading.
+",0,1,0,0,0,0
+18456,Hyperfine Structure of the $B^3Π_1$ State and Predictions of Optical Cycling Behavior in the $X\rightarrow B$ transition of TlF,"  The rotational and hyperfine spectrum of the $X^1\Sigma^+ \rightarrow
+B^3\Pi_1$ transition in TlF molecules was measured using laser-induced
+fluorescence from both a thermal and a cryogenic molecular beam. Rotational and
+hyperfine constants for the $B$ state are obtained. The large magnetic
+hyperfine interaction of the Tl nuclear spin leads to significant mixing of the
+lowest $B$ state rotational levels. Updated, more precise measurements of the
+$B\rightarrow X$ vibrational branching fractions are also presented. The
+combined rovibrational branching fractions allow for the prediction of the
+number of photons that can be scattered in a given TlF optical cycling scheme.
+",0,1,0,0,0,0
+18457,Prevalence of DNSSEC for hospital websites in Illinois,"  The domain name system translates human friendly web addresses to a computer
+readable internet protocol address. This basic infrastructure is insecure and
+can be manipulated. Deployment of technology to secure the DNS system has been
+slow, reaching about 20% of all web sites based in the USA. Little is known
+about the efforts hospitals and health systems make to secure the domain name
+system for their websites. To investigate the prevalence of implementing Domain
+Name System Security Extensions (DNSSEC), we analyzed the websites of the 210
+public hospitals in the state of Illinois, USA. Only one Illinois hospital
+website was found to have implemented DNSSEC by December, 2017.
+",1,0,0,0,0,0
+18458,Mathematical analysis of plasmonic resonance for 2-D photonic crystal,"  In this article, we study the plasmonic resonance of infinite photonic
+crystal mounted by the double negative nanoparticles in two dimensions. The
+corresponding physical model is described by the Helmholz equation with so
+called Bloch wave condition in a periodic domain. By using the quasi-periodic
+layer potential techniques and the spectral theorem of quasi-periodic
+Neumann-Poincar{é} operator, the quasi-static expansion of the near field in
+the presence of nanoparticles is derived. Furthermore, when the magnetic
+permeability of nanoparticles satisfies the Drude model, we give the conditions
+under which the plasmonic resonance occurs, and the rate of blow up of near
+field energy with respect to nanoparticle's bulk electron relaxation rate and
+filling factor are also obtained. It indicates that one can appropriately
+control the bulk electron relaxation rate or filling factor of nanoparticle in
+photonic crystal structure such that the near field energy attains its maximum,
+and enhancing the efficiency of energy utilization.
+",0,0,1,0,0,0
+18459,Thermo-Optical Chaos and Direct Soliton Generation in Microresonators,"  We investigate, numerically and experimentally, the effect of thermo-optical
+(TO) chaos on direct soliton generation (DSG) in microresonators. When the pump
+laser is scanned from blue to red and then stopped at a fixed wavelength, we
+find that the solitons generated sometimes remain (survive) and sometimes
+annihilate subsequent to the end of the scan. We refer to the possibility of
+these different outcomes arising under identical laser scan conditions as
+coexistence of soliton annihilation and survival. Numerical simulations that
+include the thermal dynamics show that the coexistence of soliton annihilation
+and survival is explained by TO chaos accompanying comb generation. The random
+fluctuations of the cavity resonance occurring under TO chaos are also found to
+trigger spontaneous soliton generation after the laser scan ends. The
+coexistence of soliton annihilation and survival as well as spontaneous soliton
+generation are observed experimentally in a silicon-nitride microresonator. The
+elucidation of the role of TO chaos provides important insights into the
+soliton generation dynamics in microresonators, which may eventually facilitate
+straightforward soliton generation in fully-integrated microresonators.
+",0,1,0,0,0,0
+18460,Rigidity of closed metric measure spaces with nonnegative curvature,"  We show that one-dimensional circle is the only case for closed smooth metric
+measure spaces with nonnegative Bakry-Émery Ricci curvature whose spectrum
+of the weighted Laplacian has an optimal positive upper bound. This result
+extends the work of Hang-Wang in the manifold case (Int. Math. Res. Not. 18
+(2007), Art. ID rnm064, 9pp).
+",0,0,1,0,0,0
+18461,Combining Agile with Traditional V Model for Enhancement of Maturity in Software Development,"  In the field of software engineering there are many new archetypes are
+introducing day to day Improve the efficiency and effectiveness of software
+development. Due to dynamic environment organizations are frequently exchanging
+their software constraint to meet their objectives. The propose research is a
+new approach by integrating the traditional V model and agile methodology to
+combining the strength of these models while minimizing their individual
+weakness.The fluctuating requirements of emerging a carried software system and
+accumulative cost of operational software are imposing researchers and experts
+to determine innovative and superior means for emerging software application at
+slight business or at enterprise level are viewing for. Agile methodology has
+its own benefits but there are deficiency several of the features of
+traditional software development methodologies that are essential for success.
+Thats why an embedded approach will be the right answer for software industry
+rather than a pure agile approach. This research shows how agile embedded
+traditional can play a vital role in development of software. A survey
+conducted to find the impact of this approach in industry. Both qualitative and
+quantitative analysis performed.
+",1,0,0,0,0,0
+18462,Histograms of Gaussian normal distribution for feature matching in clutter scenes,"  3D feature descriptor provide information between corresponding models and
+scenes. 3D objection recognition in cluttered scenes, however, remains a
+largely unsolved problem. Practical applications impose several challenges
+which are not fully addressed by existing methods. Especially in cluttered
+scenes there are many feature mismatches between scenes and models. We
+therefore propose Histograms of Gaussian Normal Distribution (HGND) for
+extracting salient features on a local reference frame (LRF) that enables us to
+solve this problem. We propose a LRF on each local surface patches using the
+scatter matrix's eigenvectors. Then the HGND information of each salient point
+is calculated on the LRF, for which we use both the mesh and point data of the
+depth image. Experiments on 45 cluttered scenes of the Bologna Dataset and 50
+cluttered scenes of the UWA Dataset are made to evaluate the robustness and
+descriptiveness of our HGND. Experiments carried out by us demonstrate that
+HGND obtains a more reliable matching rate than state-of-the-art approaches in
+cluttered situations.
+",1,0,0,0,0,0
+18463,The temporalized Massey's method,"  We propose and throughly investigate a temporalized version of the popular
+Massey's technique for rating actors in sport competitions. The method can be
+described as a dynamic temporal process in which team ratings are updated at
+every match according to their performance during the match and the strength of
+the opponent team. Using the Italian soccer dataset, we empirically show that
+the method has a good foresight prediction accuracy.
+",1,1,0,0,0,0
+18464,Introduction to Nonnegative Matrix Factorization,"  In this paper, we introduce and provide a short overview of nonnegative
+matrix factorization (NMF). Several aspects of NMF are discussed, namely, the
+application in hyperspectral imaging, geometry and uniqueness of NMF solutions,
+complexity, algorithms, and its link with extended formulations of polyhedra.
+In order to put NMF into perspective, the more general problem class of
+constrained low-rank matrix approximation problems is first briefly introduced.
+",1,0,1,1,0,0
+18465,Randomized Dynamic Mode Decomposition,"  This paper presents a randomized algorithm for computing the near-optimal
+low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging
+techniques to compute low-rank matrix approximations at a fraction of the cost
+of deterministic algorithms, easing the computational challenges arising in the
+area of `big data'. The idea is to derive a small matrix from the
+high-dimensional data, which is then used to efficiently compute the dynamic
+modes and eigenvalues. The algorithm is presented in a modular probabilistic
+framework, and the approximation quality can be controlled via oversampling and
+power iterations. The effectiveness of the resulting randomized DMD (rDMD)
+algorithm is demonstrated on several benchmark examples of increasing
+complexity, providing an accurate and efficient approach to extract
+spatiotemporal coherent structures from big data in a framework that scales
+with the intrinsic rank of the data, rather than the ambient measurement
+dimension.
+",1,0,1,0,0,0
+18466,On the convergence of a fully discrete scheme of LES type to physically relevant solutions of the incompressible Navier-Stokes,"  Obtaining reliable numerical simulations of turbulent fluids is a challenging
+problem in computational fluid mechanics. The Large Eddy Simulations (LES)
+models are efficient tools to approximate turbulent fluids and an important
+step in the validation of these models is the ability to reproduce relevant
+properties of the flow. In this paper we consider a fully discrete
+approximation of the Navier-Stokes-Voigt model by an implicit Euler algorithm
+(with respect to the time variable) and a Fourier-Galerkin method (in the space
+variables). We prove the convergence to weak solutions of the incompressible
+Navier-Stokes equations satisfying the natural local entropy condition, hence
+selecting the so-called physically relevant solutions
+",0,0,1,0,0,0
+18467,Hardy-Sobolev-Maz'ya inequalities for higher order derivatives on half spaces,"  By using, among other things, the Fourier analysis techniques on hyperbolic
+and symmetric spaces, we establish the Hardy-Sobolev-Maz'ya inequalities for
+higher order derivatives on half spaces. The proof relies on a
+Hardy-Littlewood-Sobolev inequality on hyperbolic spaces which is of its
+independent interest. We also give an alternative proof of Benguria, Frank and
+Loss' work concerning the sharp constant in the Hardy-Sobolev-Maz'ya inequality
+in the three dimensional upper half space. Finally, we show the sharp constant
+in the Hardy-Sobolev-Maz'ya inequality for bi-Laplacian in the upper half space
+of dimension five coincides with the Sobolev constant.
+",0,0,1,0,0,0
+18468,Smooth invariants of focus-focus singularities and obstructions to product decomposition,"  We study focus-focus singularities (also known as nodal singularities, or
+pinched tori) of Lagrangian fibrations on symplectic $4$-manifolds. We show
+that, in contrast to elliptic and hyperbolic singularities, there exist
+homeomorphic focus-focus singularities which are not diffeomorphic.
+Furthermore, we obtain an algebraic description of the moduli space of
+focus-focus singularities up to smooth equivalence, and show that for double
+pinched tori this space is one-dimensional. Finally, we apply our construction
+to disprove Zung's conjecture which says that any non-degenerate singularity
+can be smoothly decomposed into an almost direct product of standard
+singularities.
+",0,1,1,0,0,0
+18469,A note about Euler's inequality and automated reasoning with dynamic geometry,"  Using implicit loci in GeoGebra Euler's $R\geq 2r$ inequality can be
+investigated in a novel way. Some unavoidable side effects of the implicit
+locus computation introduce unexpected algebraic curves. By using a mixture of
+symbolic and numerical methods a possible approach is sketched up to
+investigate the situation.
+",1,0,1,0,0,0
+18470,The MUSE view of He 2-10: no AGN ionization but a sparkling starburst,"  We study the physical and dynamical properties of the ionized gas in the
+prototypical HII galaxy Henize 2-10 using MUSE integral field spectroscopy. The
+large scale dynamics is dominated by extended outflowing bubbles, probably the
+results of massive gas ejection from the central star forming regions. We
+derive a mass outflow rate dMout/dt~0.30 Msun/yr, corresponding to mass loading
+factor eta~0.4, in range with similar measurements in local LIRGs. Such a
+massive outflow has a total kinetic energy that is sustainable by the stellar
+winds and Supernova Remnants expected in the galaxy. We use classical emission
+line diagnostic to study the dust extinction, electron density and ionization
+conditions all across the galaxy, confirming the extreme nature of the highly
+star forming knots in the core of the galaxy, which show high density and high
+ionization parameter. We measure the gas phase metallicity in the galaxy taking
+into account the strong variation of the ionization parameter, finding that the
+external parts of the galaxy have abundances as low as 12 + log(O/H)~8.3, while
+the central star forming knots are highly enriched with super solar
+metallicity. We find no sign of AGN ionization in the galaxy, despite the
+recent claim of the presence of a super massive active Black Hole in the core
+of He~2-10. We therefore reanalyze the X-ray data that were used to propose the
+presence of the AGN, but we conclude that the observed X-ray emission can be
+better explained with sources of a different nature, such as a Supernova
+Remnant.
+",0,1,0,0,0,0
+18471,Modeling Oral Multispecies Biofilm Recovery After Antibacterial Treatment,"  Recovery of multispecies oral biofilms is investigated following treatment by
+chlorhexidine gluconate (CHX), iodine-potassium iodide (IPI) and Sodium
+hypochlorite (NaOCl) both experimentally and theoretically. Experimentally,
+biofilms taken from two donors were exposed to the three antibacterial
+solutions (irrigants) for 10 minutes, respectively. We observe that (a) live
+bacterial cell ratios decline for a week after the exposure and the trend
+reverses beyond a week; after fifteen weeks, live bacterial cell ratios in
+biofilms fully return to their pretreatment levels; (b) NaOCl is shown as the
+strongest antibacterial agent for the oral biofilms; (c) multispecies oral
+biofilms from different donors showed no difference in their susceptibility to
+all the bacterial solutions. Guided by the experiment, a mathematical model for
+biofilm dynamics is developed, accounting for multiple bacterial phenotypes,
+quorum sensing, and growth factor proteins, to describe the nonlinear time
+evolutionary behavior of the biofilms. The model captures time evolutionary
+dynamics of biofilms before and after antibacterial treatment very well. It
+reveals the crucial role played by quorum sensing molecules and growth factors
+in biofilm recovery and verifies that the source of biofilms has a minimal to
+their recovery. The model is also applied to describe the state of biofilms of
+various ages treated by CHX, IPI and NaOCl, taken from different donors. Good
+agreement with experimental data predicted by the model is obtained as well,
+confirming its applicability to modeling biofilm dynamics in general.
+",0,0,0,0,1,0
+18472,On constant multi-commodity flow-cut gaps for directed minor-free graphs,"  The multi-commodity flow-cut gap is a fundamental parameter that affects the
+performance of several divide \& conquer algorithms, and has been extensively
+studied for various classes of undirected graphs. It has been shown by Linial,
+London and Rabinovich \cite{linial1994geometry} and by Aumann and Rabani
+\cite{aumann1998log} that for general $n$-vertex graphs it is bounded by
+$O(\log n)$ and the Gupta-Newman-Rabinovich-Sinclair conjecture
+\cite{gupta2004cuts} asserts that it is $O(1)$ for any family of graphs that
+excludes some fixed minor.
+The flow-cut gap is poorly understood for the case of directed graphs. We
+show that for uniform demands it is $O(1)$ on directed series-parallel graphs,
+and on directed graphs of bounded pathwidth. These are the first constant upper
+bounds of this type for some non-trivial family of directed graphs. We also
+obtain $O(1)$ upper bounds for the general multi-commodity flow-cut gap on
+directed trees and cycles. These bounds are obtained via new embeddings and
+Lipschitz quasipartitions for quasimetric spaces, which generalize analogous
+results form the metric case, and could be of independent interest. Finally, we
+discuss limitations of methods that were developed for undirected graphs, such
+as random partitions, and random embeddings.
+",1,0,0,0,0,0
+18473,Molecular Gas during the Post-Starburst Phase: Low Gas Fractions in Green Valley Seyfert Post-Starburst Galaxies,"  Post-starbursts (PSBs) are candidate for rapidly transitioning from
+star-bursting to quiescent galaxies. We study the molecular gas evolution of
+PSBs at z ~ 0.03 - 0.2. We undertook new CO (2-1) observations of 22 Seyfert
+PSBs candidates using the ARO Submillimeter Telescope. This sample complements
+previous samples of PSBs by including green valley PSBs with Seyfert-like
+emission, allowing us to analyze for the first time the molecular gas
+properties of 116 PSBs with a variety of AGN properties. The distribution of
+molecular gas to stellar mass fractions in PSBs is significantly different than
+normal star-forming galaxies in the COLD GASS survey. The combined samples of
+PSBs with Seyfert-like emission line ratios have a gas fraction distribution
+which is even more significantly different and is broader (~ 0.03-0.3). Most of
+them have lower gas fractions than normal star-forming galaxies. We find a
+highly significant correlation between the WISE 12 micron to 4.6 micron flux
+ratios and molecular gas fractions in both PSBs and normal galaxies. We detect
+molecular gas in 27% of our Seyfert PSBs. Taking into account the upper limits,
+the mean and the dispersion of the distribution of the gas fraction in our
+Seyfert PSB sample are much smaller (mean = 0.025, std dev. = 0.018) than
+previous samples of Seyfert PSBs or PSBs in general (mean ~ 0.1 - 0.2, std dev.
+~ 0.1 - 0.2).
+",0,1,0,0,0,0
+18474,Bit-Reversible Version of Milne's Fourth-Order Time-Reversible Integrator for Molecular Dynamics,"  We point out that two of Milne's fourth-order integrators are well-suited to
+bit-reversible simulations. The fourth-order method improves on the accuracy of
+Levesque and Verlet's algorithm and simplifies the definition of the velocity
+$v$ and energy $e = (q^2 + v^2)/2$ . ( We use this one-dimensional oscillator
+problem as an illustration throughout this paper ). Milne's integrator is
+particularly useful for the analysis of Lyapunov ( exponential ) instability in
+dynamical systems, including manybody molecular dynamics. We include the
+details necessary to the implementation of Milne's Algorithms.
+",0,1,0,0,0,0
+18475,Solvable Hydrodynamics of Quantum Integrable Systems,"  The conventional theory of hydrodynamics describes the evolution in time of
+chaotic many-particle systems from local to global equilibrium. In a quantum
+integrable system, local equilibrium is characterized by a local generalized
+Gibbs ensemble or equivalently a local distribution of pseudo-momenta. We study
+time evolution from local equilibria in such models by solving a certain
+kinetic equation, the ""Bethe-Boltzmann"" equation satisfied by the local
+pseudo-momentum density. Explicit comparison with density matrix
+renormalization group time evolution of a thermal expansion in the XXZ model
+shows that hydrodynamical predictions from smooth initial conditions can be
+remarkably accurate, even for small system sizes. Solutions are also obtained
+in the Lieb-Liniger model for free expansion into vacuum and collisions between
+clouds of particles, which model experiments on ultracold one-dimensional Bose
+gases.
+",0,1,0,0,0,0
+18476,Compressed sensing and optimal denoising of monotone signals,"  We consider the problems of compressed sensing and optimal denoising for
+signals $\mathbf{x_0}\in\mathbb{R}^N$ that are monotone, i.e.,
+$\mathbf{x_0}(i+1) \geq \mathbf{x_0}(i)$, and sparsely varying, i.e.,
+$\mathbf{x_0}(i+1) > \mathbf{x_0}(i)$ only for a small number $k$ of indices
+$i$. We approach the compressed sensing problem by minimizing the total
+variation norm restricted to the class of monotone signals subject to equality
+constraints obtained from a number of measurements $A\mathbf{x_0}$. For random
+Gaussian sensing matrices $A\in\mathbb{R}^{m\times N}$ we derive a closed form
+expression for the number of measurements $m$ required for successful
+reconstruction with high probability. We show that the probability undergoes a
+phase transition as $m$ varies, and depends not only on the number of change
+points, but also on their location. For denoising we regularize with the same
+norm and derive a formula for the optimal regularizer weight that depends only
+mildly on $\mathbf{x_0}$. We obtain our results using the statistical dimension
+tool.
+",1,0,1,1,0,0
+18477,On minimax nonparametric estimation of signal in Gaussian noise,"  For the problem of nonparametric estimation of signal in Gaussian noise we
+point out the strong asymptotically minimax estimators on maxisets for linear
+estimators (see \cite{ker93,rio}). It turns out that the order of rates of
+convergence of Pinsker estimator on this maxisets is worse than the order of
+rates of convergence for the class of linear estimators considered on this
+maxisets. We show that balls in Sobolev spaces are maxisets for Pinsker
+estimators.
+",0,0,1,1,0,0
+18478,Implicit Bias of Gradient Descent on Linear Convolutional Networks,"  We show that gradient descent on full-width linear convolutional networks of
+depth $L$ converges to a linear predictor related to the $\ell_{2/L}$ bridge
+penalty in the frequency domain. This is in contrast to linearly fully
+connected networks, where gradient descent converges to the hard margin linear
+support vector machine solution, regardless of depth.
+",0,0,0,1,0,0
+18479,Oxidation of clofibric acid in aqueous solution using a non-thermal plasma discharge or gamma radiation,"  In this work, we study degradation of clofibric acid (CFA) in aqueous
+solution using either ionizing radiation from a $^{60}$Co source or a
+non-thermal plasma produced by discharges in the air above the solution. The
+results obtained with the two technologies are compared in terms of
+effectiveness of CFA degradation and its by-products. In both cases the CFA
+degradation follows a quasi-exponential decay in time well modelled by a
+kinetic scheme which considers the competition between CFA and all reaction
+intermediates for the reactive species generated in solution as well as the
+amount of the end product formed. A new degradation law is deduced to explain
+the results. Although the end-product CO$_2$ was detected and the CFA
+conversion found to be very high under the studied conditions, HPLC analysis
+reveals several degradation intermediates still bearing the aromatic ring with
+the chlorine substituent. The extent of mineralization is rather limited. The
+energy yield is found to be higher in the gamma radiation experiments.
+",0,1,0,0,0,0
+18480,Quenching current by flux-flow instability in iron-chalcogenides thin films,"  The stability against quench is one of the main issue to be pursued in a
+superconducting material which should be able to perform at very high levels of
+current densities. Here we focus on the connection between the critical current
+$I_c$ and the quenching current $I^*$ associated to the so-called flux-flow
+instability phenomenon, which sets in as an abrupt transition from the flux
+flow state to the normal state. To this purpose, we analyze several
+current-voltage characteristics of three types of iron-based thin films,
+acquired at different temperature and applied magnetic field values. For these
+samples, we discuss the impact of a possible coexistence of intrinsic
+electronic mechanisms and extrinsic thermal effects on the quenching current
+dependence upon the applied magnetic field. The differences between the
+quenching current and the critical current are reported also in the case of
+predominant intrinsic mechanisms. Carrying out a comparison with
+high-temperature cuprate superconductors, we suggest which material can be the
+best trade-off between maximum operating temperature, higher upper critical
+field and stability under high current bias.
+",0,1,0,0,0,0
+18481,New cardinality estimation algorithms for HyperLogLog sketches,"  This paper presents new methods to estimate the cardinalities of data sets
+recorded by HyperLogLog sketches. A theoretically motivated extension to the
+original estimator is presented that eliminates the bias for small and large
+cardinalities. Based on the maximum likelihood principle a second unbiased
+method is derived together with a robust and efficient numerical algorithm to
+calculate the estimate. The maximum likelihood approach can also be applied to
+more than a single HyperLogLog sketch. In particular, it is shown that it gives
+more precise cardinality estimates for union, intersection, or relative
+complements of two sets that are both represented by HyperLogLog sketches
+compared to the conventional technique using the inclusion-exclusion principle.
+All the new methods are demonstrated and verified by extensive simulations.
+",1,0,0,0,0,0
+18482,The EUSO@TurLab Project,"  The TurLab facility is a laboratory, equipped with a 5 m diameter and 1 m
+depth rotating tank, located in the Physics Department of the University of
+Turin. The tank has been built mainly to study problems where system rotation
+plays a key role in the fluid behaviour such as in atmospheric and oceanic
+flows at different scales. The tank can be filled with different fluids of
+variable density, which enables studies in layered conditions such as sea
+waves. The tank can be also used to simulate the terrestrial surface with the
+optical characteristics of different environments such as snow, grass, ocean,
+land with soil, stones etc., fogs and clouds. As it is located in an extremely
+dark place, the light intensity can be controlled artificially. Such
+capabilities of the TurLab facility are applied to perform experiments related
+to the observation of Extreme Energy Cosmic Rays (EECRs) from space using the
+fluorescence technique, as in the case of the JEM-EUSO mission, where the
+diffuse night brightness and artificial light sources can vary significantly in
+time and space inside the Field of View (FoV) of the telescope. Here we will
+report the currently ongoing activity at the TurLab facility in the framework
+of the JEM-EUSO mission (EUSO@TurLab).
+",0,1,0,0,0,0
+18483,A finite element method framework for modeling rotating machines with superconducting windings,"  Electrical machines employing superconductors are attractive solutions in a
+variety of application domains. Numerical models are powerful and necessary
+tools to optimize their design and predict their performance. The
+electromagnetic modeling of superconductors by finite-element method (FEM) is
+usually based on a power-law resistivity for their electrical behavior. The
+implementation of such constitutive law in conventional models of electrical
+machines is quite problematic: the magnetic vector potential directly gives the
+electric field and requires using a power-law depending on it. This power-law
+is a non-bounded function that can generate enormous uneven values in low
+electric field regions that can destroy the reliability of solutions. The
+method proposed here consists in separating the model of an electrical machine
+in two parts, where the magnetic field is calculated with the most appropriate
+formulation: the H-formulation in the part containing the superconductors and
+the A-formulation in the part containing conventional conductors (and possibly
+permanent magnets). The main goal of this work is to determine and to correctly
+apply the continuity conditions on the boundary separating the two regions.
+Depending on the location of such boundary -- in the fixed or rotating part of
+the machine -- the conditions that one needs to apply are different. In
+addition, the application of those conditions requires the use of Lagrange
+multipliers satisfying the field transforms of the electromagnetic quantities
+in the two reference systems, the fixed and the rotating one. In this article,
+several exemplary cases for the possible configurations are presented. In order
+to emphasize and capture the essential point of this modeling strategy, the
+discussed examples are rather simple. Nevertheless, they constitute a solid
+starting point for modeling more complex and realistic devices.
+",0,1,0,0,0,0
+18484,Triangular Decomposition of Matrices in a Domain,"  Deterministic recursive algorithms for the computation of matrix triangular
+decompositions with permutations like LU and Bruhat decomposition are presented
+for the case of commutative domains. This decomposition can be considered as a
+generalization of LU and Bruhat decompositions, because they both may be easily
+obtained from this triangular decomposition. Algorithms have the same
+complexity as the algorithm of matrix multiplication.
+",1,0,0,0,0,0
+18485,Practical Bayesian Optimization for Transportation Simulators,"  We provide a method to solve optimization problem when objective function is
+a complex stochastic simulator of an urban transportation system. To reach this
+goal, a Bayesian optimization framework is introduced. We show how the choice
+of prior and inference algorithm effect the outcome of our optimization
+procedure. We develop dimensionality reduction techniques that allow for our
+optimization techniques to be applicable for real-life problems. We develop a
+distributed, Gaussian Process Bayesian regression and active learning models
+that allow parallel execution of our algorithms and enable usage of high
+performance computing. We present a fully Bayesian approach that is more sample
+efficient and reduces computational budget. Our framework is supported by
+theoretical analysis and an empirical study. We demonstrate our framework on
+the problem of calibrating a multi-modal transportation network of city of
+Bloomington, Illinois. Finally, we discuss directions for further research.
+",0,0,0,1,0,0
+18486,The Ising distribution as a latent variable model,"  It is shown that the Ising distribution can be treated as a latent variable
+model, where a set of N real-valued, correlated random variables are drawn and
+used to generate N binary spins independently. This allows to approximate the
+Ising distribution by a simpler model where the latent variables follow a
+multivariate normal distribution, the so-called Cox distribution. The
+approximation is formally related to an advanced mean field technique known as
+adaptive TAP, and its domain of validity is similar. When valid, it allows a
+principled replacement of the Ising distribution by a distribution much easier
+to sample and manipulate.
+",0,0,0,1,1,0
+18487,Applying DCOP to User Association Problem in Heterogeneous Networks with Markov Chain Based Algorithm,"  Multi-agent systems (MAS) is able to characterize the behavior of individual
+agent and the interaction between agents. Thus, it motivates us to leverage the
+distributed constraint optimization problem (DCOP), a framework of modeling
+MAS, to solve the user association problem in heterogeneous networks (HetNets).
+Two issues we have to consider when we take DCOP into the application of HetNet
+including: (i) How to set up an effective model by DCOP taking account of the
+negtive impact of the increment of users on the modeling process (ii) Which
+kind of algorithms is more suitable to balance the time consumption and the
+quality of soltuion. Aiming to overcome these issues, we firstly come up with
+an ECAV-$\eta$ (Each Connection As Variable) model in which a parameter $\eta$
+with an adequate assignment ($\eta=3$ in this paper) is able to control the
+scale of the model. After that, a Markov chain (MC) based algorithm is proposed
+on the basis of log-sum-exp function. Experimental results show that the
+solution obtained by DCOP framework is better than the one obtained by the
+Max-SINR algorithm. Comparing with the Lagrange dual decomposition based method
+(LDD), the solution performance has been improved since there is no need to
+transform original problem into a satisfied one. In addition, it is also
+apparent that the DCOP based method has better robustness than LDD when the
+number of users increases but the available resource at base stations are
+limited.
+",1,0,0,0,0,0
+18488,Linear growth of streaming instability in pressure bumps,"  Streaming instability is a powerful mechanism which concentrates dust grains
+in pro- toplanetary discs, eventually up to the stage where they collapse
+gravitationally and form planetesimals. Previous studies inferred that it
+should be ineffective in viscous discs, too efficient in inviscid discs, and
+may not operate in local pressure maxima where solids accumulate. From a linear
+analysis of stability, we show that streaming instability behaves differently
+inside local pressure maxima. Under the action of the strong differential
+advection imposed by the bump, a novel unstable mode develops and grows even
+when gas viscosity is large. Hence, pressure bumps are found to be the only
+places where streaming instability occurs in viscous discs. This offers a
+promising way to conciliate models of planet formation with recent observations
+of young discs.
+",0,1,0,0,0,0
+18489,"On the Gevrey regularity for Sums of Squares of vector fields, study of some models","  The micro-local Gevrey regularity of a class of ""sums of squares"" with real
+analytic coefficients is studied in detail. Some partial regularity result is
+also given.
+",0,0,1,0,0,0
+18490,Structure theorems for star-commuting power partial isometries,"  We give a new formulation and proof of a theorem of Halmos and Wallen on the
+structure of power partial isometries on Hilbert space. We then use this
+theorem to give a structure theorem for a finite set of partial isometries
+which star-commute: each operator commutes with the others and with their
+adjoints.
+",0,0,1,0,0,0
+18491,On free Gelfand--Dorfman--Novikov superalgebras and a PBW type theorem,"  We construct a linear basis of a free GDN superalgebra over a field of
+characteristic $\neq 2$.
+As applications, we prove a PBW theorem, that is, any GDN superalgebra can be
+embedded into its universal enveloping commutative associative differential
+superalgebra. An Engel theorem under some assumptions is given.
+",0,0,1,0,0,0
+18492,An inexact iterative Bregman method for optimal control problems,"  In this article we investigate an inexact iterative regularization method
+based on generalized Bregman distances of an optimal control problem with
+control constraints. We show robustness and convergence of the inexact Bregman
+method under a regularity assumption, which is a combination of a source
+condition and a regularity assumption on the active sets. We also take the
+discretization error into account. Numerical results are presented to
+demonstrate the algorithm.
+",0,0,1,0,0,0
+18493,Mixture Models in Astronomy,"  Mixture models combine multiple components into a single probability density
+function. They are a natural statistical model for many situations in
+astronomy, such as surveys containing multiple types of objects, cluster
+analysis in various data spaces, and complicated distribution functions. This
+chapter in the CRC Handbook of Mixture Analysis is concerned with astronomical
+applications of mixture models for cluster analysis, classification, and
+semi-parametric density estimation. We present several classification examples
+from the literature, including identification of a new class, analysis of
+contaminants, and overlapping populations. In most cases, mixtures of normal
+(Gaussian) distributions are used, but it is sometimes necessary to use
+different distribution functions derived from astrophysical experience. We also
+address the use of mixture models for the analysis of spatial distributions of
+objects, like galaxies in redshift surveys or young stars in star-forming
+regions. In the case of galaxy clustering, mixture models may not be the
+optimal choice for understanding the homogeneous and isotropic structure of
+voids and filaments. However, we show that mixture models, using astrophysical
+models for star clusters, may provide a natural solution to the problem of
+subdividing a young stellar population into subclusters. Finally, we explore
+how mixture models can be used for mathematically advanced modeling of data
+with heteroscedastic uncertainties or missing values, providing two example
+algorithms, the measurement error regression model of Kelly (2007) and the
+Extreme Deconvolution model of Bovy et al. (2011). The challenges presented by
+astronomical science, aided by the public availability of catalogs from major
+surveys and missions, are a rich area for collaboration between statisticians
+and astronomers.
+",0,1,0,1,0,0
+18494,Safe Medicine Recommendation via Medical Knowledge Graph Embedding,"  Most of the existing medicine recommendation systems that are mainly based on
+electronic medical records (EMRs) are significantly assisting doctors to make
+better clinical decisions benefiting both patients and caregivers. Even though
+the growth of EMRs is at a lighting fast speed in the era of big data, content
+limitations in EMRs restrain the existed recommendation systems to reflect
+relevant medical facts, such as drug-drug interactions. Many medical knowledge
+graphs that contain drug-related information, such as DrugBank, may give hope
+for the recommendation systems. However, the direct use of these knowledge
+graphs in the systems suffers from robustness caused by the incompleteness of
+the graphs. To address these challenges, we stand on recent advances in graph
+embedding learning techniques and propose a novel framework, called Safe
+Medicine Recommendation (SMR), in this paper. Specifically, SMR first
+constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and
+medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly
+embeds diseases, medicines, patients, and their corresponding relations into a
+shared lower dimensional space. Finally, SMR uses the embeddings to decompose
+the medicine recommendation into a link prediction process while considering
+the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR
+is the first to learn embeddings of a patient-disease-medicine graph for
+medicine recommendation in the world. Extensive experiments on real datasets
+are conducted to evaluate the effectiveness of proposed framework.
+",1,0,0,0,0,0
+18495,Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression,"  Deep neural networks (DNNs) have achieved great success in solving a variety
+of machine learning (ML) problems, especially in the domain of image
+recognition. However, recent research showed that DNNs can be highly vulnerable
+to adversarially generated instances, which look seemingly normal to human
+observers, but completely confuse DNNs. These adversarial samples are crafted
+by adding small perturbations to normal, benign images. Such perturbations,
+while imperceptible to the human eye, are picked up by DNNs and cause them to
+misclassify the manipulated instances with high confidence. In this work, we
+explore and demonstrate how systematic JPEG compression can work as an
+effective pre-processing step in the classification pipeline to counter
+adversarial attacks and dramatically reduce their effects (e.g., Fast Gradient
+Sign Method, DeepFool). An important component of JPEG compression is its
+ability to remove high frequency signal components, inside square blocks of an
+image. Such an operation is equivalent to selective blurring of the image,
+helping remove additive perturbations. Further, we propose an ensemble-based
+technique that can be constructed quickly from a given well-performing DNN, and
+empirically show how such an ensemble that leverages JPEG compression can
+protect a model from multiple types of adversarial attacks, without requiring
+knowledge about the model.
+",1,0,0,0,0,0
+18496,Numerical Simulation of Bloch Equations for Dynamic Magnetic Resonance Imaging,"  Magnetic Resonance Imaging (MRI) is a widely applied non-invasive imaging
+modality based on non-ionizing radiation which gives excellent images and soft
+tissue contrast of living tissues. We consider the modified Bloch problem as a
+model of MRI for flowing spins in an incompressible flow field. After
+establishing the well-posedness of the corresponding evolution problem, we
+analyze its spatial semidiscretization using discontinuous Galerkin methods.
+The high frequency time evolution requires a proper explicit and adaptive
+temporal discretization. The applicability of the approach is shown for basic
+examples.
+",1,0,1,0,0,0
+18497,End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks,"  Speech enhancement model is used to map a noisy speech to a clean speech. In
+the training stage, an objective function is often adopted to optimize the
+model parameters. However, in most studies, there is an inconsistency between
+the model optimization criterion and the evaluation criterion on the enhanced
+speech. For example, in measuring speech intelligibility, most of the
+evaluation metric is based on a short-time objective intelligibility (STOI)
+measure, while the frame based minimum mean square error (MMSE) between
+estimated and clean speech is widely used in optimizing the model. Due to the
+inconsistency, there is no guarantee that the trained model can provide optimal
+performance in applications. In this study, we propose an end-to-end
+utterance-based speech enhancement framework using fully convolutional neural
+networks (FCN) to reduce the gap between the model optimization and evaluation
+criterion. Because of the utterance-based optimization, temporal correlation
+information of long speech segments, or even at the entire utterance level, can
+be considered when perception-based objective functions are used for the direct
+optimization. As an example, we implement the proposed FCN enhancement
+framework to optimize the STOI measure. Experimental results show that the STOI
+of test speech is better than conventional MMSE-optimized speech due to the
+consistency between the training and evaluation target. Moreover, by
+integrating the STOI in model optimization, the intelligibility of human
+subjects and automatic speech recognition (ASR) system on the enhanced speech
+is also substantially improved compared to those generated by the MMSE
+criterion.
+",1,0,0,1,0,0
+18498,The connectivity of graphs of graphs with self-loops and a given degree sequence,"  `Double edge swaps' transform one graph into another while preserving the
+graph's degree sequence, and have thus been used in a number of popular Markov
+chain Monte Carlo (MCMC) sampling techniques. However, while double edge-swaps
+can transform, for any fixed degree sequence, any two graphs inside the classes
+of simple graphs, multigraphs, and pseudographs, this is not true for graphs
+which allow self-loops but not multiedges (loopy graphs). Indeed, we exactly
+characterize the degree sequences where double edge swaps cannot reach every
+valid loopy graph and develop an efficient algorithm to determine such degree
+sequences. The same classification scheme to characterize degree sequences can
+be used to prove that, for all degree sequences, loopy graphs are connected by
+a combination of double and triple edge swaps. Thus, we contribute the first
+MCMC sampler that uniformly samples loopy graphs with any given sequence.
+",1,1,1,0,0,0
+18499,The role of the background in past and future X-ray missions,"  Background has played an important role in X-ray missions, limiting the
+exploitation of science data in several and sometimes unexpected ways. In this
+presentation I review past X-ray missions focusing on some important lessons we
+can learn from them. I then go on discussing prospects for overcoming
+background related limitations in future ones.
+",0,1,0,0,0,0
+18500,Securing Manufacturing Intelligence for the Industrial Internet of Things,"  Widespread interest in the emerging area of predictive analytics is driving
+industries such as manufacturing to explore new approaches to the collection
+and management of data provided from Industrial Internet of Things (IIoT)
+devices. Often, analytics processing for Business Intelligence (BI) is an
+intensive task, and it also presents both an opportunity for competitive
+advantage as well as a security vulnerability in terms of the potential for
+losing Intellectual Property (IP). This article explores two approaches to
+securing BI in the manufacturing domain. Simulation results indicate that a
+Unified Threat Management (UTM) model is simpler to maintain and has less
+potential vulnerabilities than a distributed security model. Conversely, a
+distributed model of security out-performs the UTM model and offers more scope
+for the use of existing hardware resources. In conclusion, a hybrid security
+model is proposed where security controls are segregated into a multi-cloud
+architecture.
+",1,0,0,0,0,0
+18501,Boson-vortex duality in compressible spin-orbit coupled BECs,"  Using a (1+2)-dimensional boson-vortex duality between non-linear
+electrodynamics and a two-component compressible Bose-Einstein condensate (BEC)
+with spin-orbit (SO) coupling, we obtain generalised versions of the
+hydrodynamic continuity and Euler equations where the phase defect and
+non-defect degrees of freedom enter separately. We obtain the generalised
+Magnus force on vortices under SO coupling, and associate the linear
+confinement of vortices due to SO coupling with instanton fluctuations of the
+dual theory.
+",0,1,0,0,0,0
+18502,Communicating Correlated Sources Over an Interference Channel,"  A new coding technique, based on \textit{fixed block-length} codes, is
+proposed for the problem of communicating a pair of correlated sources over a
+$2-$user interference channel. Its performance is analyzed to derive a new set
+of sufficient conditions. The latter is proven to be strictly less binding than
+the current known best, which is due to Liu and Chen [Dec, 2011]. Our findings
+are inspired by Dueck's example [March, 1981].
+",1,0,0,0,0,0
+18503,Simulating polaron biophysics with Rydberg atoms,"  Transport of excitations along proteins can be formulated in a quantum
+physics context, based on the periodicity and vibrational modes of the
+structures. Exact solutions are very challenging to obtain on classical
+computers, however, approximate solutions based on the Davydov ansatz have
+demonstrated the possibility of stabilized solitonic excitations along the
+protein. We propose an alternative study based on a chain of ultracold atoms.
+We investigate the experimental parameters to control such a quantum simulator
+based on dressed Rydberg atoms. We show that there is a feasible range of
+parameters where a quantum simulator can directly mimic the Davydov equations
+and their solutions. Such a quantum simulator opens up new directions for the
+study of transport phenomena in a biophysical context.
+",0,1,0,0,0,0
+18504,"Solubility limit of methyl red and methylene blue in microemulsions and liquid crystals of water, sds and pentanol systems","  Solubility of dyes in amphiphilic association structures of water, SDS and
+penthanol system (i.e. in the phases of microemulsions and liquid crystals) was
+attracted much interest due to its wide industrial and technological
+applications. This research was focused on understanding the solubility
+limitation of methyl red and methylene blue in microemulsion and liquid crystal
+phases. Experimental results showed that the highest solubility of methyl red
+was in LLC, followed by w/o microemulsion and o/w microemulsion, respectively,
+whereas the highest solubility of methylene blue was in w/o microemulsion,
+followed by o/w microemulsion and LLC, respectively. Hence, a chemical dynamics
+strongly played an important role in the solubility limitation of methyl red
+and methylene blue in microemulsions and liquid crystal phases.
+",0,1,0,0,0,0
+18505,Towards Visual Ego-motion Learning in Robots,"  Many model-based Visual Odometry (VO) algorithms have been proposed in the
+past decade, often restricted to the type of camera optics, or the underlying
+motion manifold observed. We envision robots to be able to learn and perform
+these tasks, in a minimally supervised setting, as they gain more experience.
+To this end, we propose a fully trainable solution to visual ego-motion
+estimation for varied camera optics. We propose a visual ego-motion learning
+architecture that maps observed optical flow vectors to an ego-motion density
+estimate via a Mixture Density Network (MDN). By modeling the architecture as a
+Conditional Variational Autoencoder (C-VAE), our model is able to provide
+introspective reasoning and prediction for ego-motion induced scene-flow.
+Additionally, our proposed model is especially amenable to bootstrapped
+ego-motion learning in robots where the supervision in ego-motion estimation
+for a particular camera sensor can be obtained from standard navigation-based
+sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through
+experiments, we show the utility of our proposed approach in enabling the
+concept of self-supervised learning for visual ego-motion estimation in
+autonomous robots.
+",1,0,0,0,0,0
+18506,Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks,"  Prediction of popularity has profound impact for social media, since it
+offers opportunities to reveal individual preference and public attention from
+evolutionary social systems. Previous research, although achieves promising
+results, neglects one distinctive characteristic of social data, i.e.,
+sequentiality. For example, the popularity of online content is generated over
+time with sequential post streams of social media. To investigate the
+sequential prediction of popularity, we propose a novel prediction framework
+called Deep Temporal Context Networks (DTCN) by incorporating both temporal
+context and temporal attention into account. Our DTCN contains three main
+components, from embedding, learning to predicting. With a joint embedding
+network, we obtain a unified deep representation of multi-modal user-post data
+in a common embedding space. Then, based on the embedded data sequence over
+time, temporal context learning attempts to recurrently learn two adaptive
+temporal contexts for sequential popularity. Finally, a novel temporal
+attention is designed to predict new popularity (the popularity of a new
+user-post pair) with temporal coherence across multiple time-scales.
+Experiments on our released image dataset with about 600K Flickr photos
+demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
+with an average of 21.51% relative performance improvement in the popularity
+prediction (Spearman Ranking Correlation).
+",1,0,0,0,0,0
+18507,Key Management and Learning based Two Level Data Security for Metering Infrastructure of Smart Grid,"  In the smart grid, smart meters, and numerous control and monitoring
+applications employ bidirectional wireless communication, where security is a
+critical issue. In key management based encryption method for the smart grid,
+the Trusted Third Party (TTP), and links between the smart meter and the third
+party are assumed to be fully trusted and reliable. However, in wired/wireless
+medium, a man-in-middle may want to interfere, monitor and control the network,
+thus exposing its vulnerability. Acknowledging this, in this paper, we propose
+a novel two level encryption method based on two partially trusted simple
+servers (constitutes the TTP) which implement this method without increasing
+packet overhead. One server is responsible for data encryption between the
+meter and control center/central database, and the other server manages the
+random sequence of data transmission. Numerical calculation shows that the
+number of iterations required to decode a message is large which is quite
+impractical. Furthermore, we introduce One-class support vector machine
+(machine learning) algorithm for node-to-node authentication utilizing the
+location information and the data transmission history (node identity, packet
+size and frequency of transmission). This secures data communication privacy
+without increasing the complexity of the conventional key management scheme.
+",1,0,0,0,0,0
+18508,A numerical study of the homogeneous elliptic equation with fractional order boundary conditions,"  We consider the homogeneous equation ${\mathcal A} u=0$, where ${\mathcal A}$
+is a symmetric and coercive elliptic operator in $H^1(\Omega)$ with $\Omega$
+bounded domain in ${\mathbb R}^d$. The boundary conditions involve fractional
+power $\alpha$, $ 0 < \alpha <1$, of the Steklov spectral operator arising in
+Dirichlet to Neumann map. For such problems we discuss two different numerical
+methods: (1) a computational algorithm based on an approximation of the
+integral representation of the fractional power of the operator and (2)
+numerical technique involving an auxiliary Cauchy problem for an
+ultra-parabolic equation and its subsequent approximation by a time stepping
+technique. For both methods we present numerical experiment for a model
+two-dimensional problem that demonstrate the accuracy, efficiency, and
+stability of the algorithms.
+",0,0,1,0,0,0
+18509,Effects of soft interactions and bound mobility on diffusion in crowded environments: a model of sticky and slippery obstacles,"  Crowded environments modify the diffusion of macromolecules, generally
+slowing their movement and inducing transient anomalous subdiffusion. The
+presence of obstacles also modifies the kinetics and equilibrium behavior of
+tracers. While previous theoretical studies of particle diffusion have
+typically assumed either impenetrable obstacles or binding interactions that
+immobilize the particle, in many cellular contexts bound particles remain
+mobile. Examples include membrane proteins or lipids with some entry and
+diffusion within lipid domains and proteins that can enter into membraneless
+organelles or compartments such as the nucleolus. Using a lattice model, we
+studied the diffusive movement of tracer particles which bind to soft
+obstacles, allowing tracers and obstacles to occupy the same lattice site. For
+sticky obstacles, bound tracer particles are immobile, while for slippery
+obstacles, bound tracers can hop without penalty to adjacent obstacles. In both
+models, binding significantly alters tracer motion. The type and degree of
+motion while bound is a key determinant of the tracer mobility: slippery
+obstacles can allow nearly unhindered diffusion, even at high obstacle filling
+fraction. To mimic compartmentalization in a cell, we examined how obstacle
+size and a range of bound diffusion coefficients affect tracer dynamics. The
+behavior of the model is similar in two and three spatial dimensions. Our work
+has implications for protein movement and interactions within cells.
+",0,1,0,0,0,0
+18510,Thermodynamics of a Quantum Ising system coupled to a spin bath: Zero Temperature Results,"  We study the effect of coupling a spin bath environment to a system which, at
+low energies, can be modeled as a quantum Ising system. A field theoretic
+formalism incorporating both thermal and quantum fluctuations is developed to
+derive results for the thermodynamic properties and response functions, both
+for a toy model and for the $LiHoF_4$ system, in which spin-8 electronic spins
+couple to a spin-$7/2$ nuclear spin bath: the phase transition then occurs in a
+system of electronuclear degrees of freedom, coupled by long-range dipolar
+interactions. The quantum Ising phase transition still exists, and one
+hybridized mode of the Ising and bath spins always goes soft at the transition.
+",0,1,0,0,0,0
+18511,Observation of a Lamb band gap in a polymer waveguide with periodic cross-like cavities,"  The quest for large and low frequency band gaps is one of the principal
+objectives pursued in a number of engineering applications, ranging from noise
+absorption to vibration control, to seismic wave abatement. For this purpose, a
+plethora of complex architectures (including multi-phase materials) and
+multi-physics approaches have been proposed in the past, often involving
+difficulties in their practical realization.
+To address this issue, in this work we propose an easy-to-manufacture design
+able to open large, low frequency complete Lamb band gaps exploiting a suitable
+arrangement of masses and stiffnesses produced by cavities in a monolithic
+material. The performance of the designed structure is evaluated by numerical
+simulations and confirmed by Scanning Laser Doppler Vibrometer (SLDV)
+measurements on an isotropic polyvinyl chloride plate in which a square ring
+region of cross-like cavities is fabricated. The full wave field reconstruction
+clearly confirms the ability of even a limited number of unit cell rows of the
+proposed design to efficiently attenuate Lamb waves. In addition, numerical
+simulations show that the structure allows to shift of the central frequency of
+the BG through geometrical modifications. The design may be of interest for
+applications in which large BGs at low frequencies are required.
+",0,1,0,0,0,0
+18512,Search for nucleon decays with EXO-200,"  A search for instability of nucleons bound in $^{136}$Xe nuclei is reported
+with 223 kg$\cdot$yr exposure of $^{136}$Xe in the EXO-200 experiment. Lifetime
+limits of 3.3$\times 10^{23}$ and 1.9$\times 10^{23}$ yrs are established for
+nucleon decay to $^{133}$Sb and $^{133}$Te, respectively. These are the most
+stringent to date, exceeding the prior decay limits by a factor of 9 and 7,
+respectively.
+",0,1,0,0,0,0
+18513,The multiplicity of massive stars: a 2016 view,"  Massive stars like company. Here, we provide a brief overview of progresses
+made over the last 5 years by a number of medium and large surveys. These
+results provide new insights on the observed and intrinsic multiplicity
+properties of main sequence massive stars and on the initial conditions for
+their future evolution. They also bring new interesting constraints on the
+outcome of the massive star formation process.
+",0,1,0,0,0,0
+18514,Principal Eigenvalue of Mixed Problem for the Fractional Laplacian: Moving the Boundary Conditions,"  We analyze the behavior of the eigenvalues of the following non local mixed
+problem $\left\{ \begin{array}{rcll} (-\Delta)^{s} u &=& \lambda_1(D) \ u
+&\inn\Omega,\\ u&=&0&\inn D,\\ \mathcal{N}_{s}u&=&0&\inn N. \end{array}\right $
+Our goal is to construct different sequences of problems by modifying the
+configuration of the sets $D$ and $N$, and to provide sufficient and necessary
+conditions on the size and the location of these sets in order to obtain
+sequences of eigenvalues that in the limit recover the eigenvalues of the
+Dirichlet or Neumann problem. We will see that the non locality plays a crucial
+role here, since the sets $D$ and $N$ can have infinite measure, a phenomenon
+that does not appear in the local case (see for example \cite{D,D2,CP}).
+",0,0,1,0,0,0
+18515,Two-dimensional Bose and Fermi gases beyond weak coupling,"  Using a formalism based on the two-body S-matrix we study two-dimensional
+Bose and Fermi gases with both attractive and repulsive interactions.
+Approximate analytic expressions, valid at weak coupling and beyond, are
+developed and applied to the Berezinskii-Kosterlitz-Thouless (BKT) transition.
+We successfully recover the correct logarithmic functional form of the critical
+chemical potential and density for the Bose gas. For fermions, the BKT critical
+temperature is calculated in BCS and BEC regimes through consideration of Tan's
+contact.
+",0,1,0,0,0,0
+18516,Anisotropy of magnetic interactions and symmetry of the order parameter in unconventional superconductor Sr$_{2}$RuO$_{4}$,"  Sr$_2$RuO$_4$ is the best candidate for spin-triplet superconductivity, an
+unusual and elusive superconducting state of fundamental importance. In the
+last three decades Sr$_2$RuO$_4$ has been very carefully studied and despite
+its apparent simplicity when compared with strongly correlated high-$T_{c}$
+cuprates, for which the pairing symmetry is understood, there is no scenario
+that can explain all the major experimental observations, a conundrum that has
+generated tremendous interest. Here we present a density-functional based
+analysis of magnetic interactions in Sr$_{2}$RuO$_{4}$ and discuss the role of
+magnetic anisotropy in its unconventional superconductivity. Our goal is
+twofold. First, we access the possibility of the superconducting order
+parameter rotation in an external magnetic field of 200 Oe, and conclude that
+the spin-orbit interaction in this material is several orders of magnitude too
+strong to be consistent with this hypothesis. Thus, the observed invariance of
+the Knight shift across $T_{c}$ has no plausible explanation, and casts doubt
+on using the Knight shift as an ultimate litmus paper for the pairing symmetry.
+Second, we propose a quantitative double-exchange-like model for combining
+itinerant fermions with an anisotropic Heisenberg magnetic Hamiltonian. This
+model is complementary to the Hubbard-model-based calculations published so
+far, and forms an alternative framework for exploring superconducting symmetry
+in Sr$_{2}$RuO$_{4}.$ As an example, we use this model to analyze the
+degeneracy between various $p-$triplet states in the simplest mean-field
+approximation, and show that it splits into a single and two doublets with the
+ground state defined by the competition between the ""Ising"" and ""compass""
+anisotropic terms.
+",0,1,0,0,0,0
+18517,Loss of Regularity of Solutions of the Lighthill Problem for Shock Diffraction for Potential Flow,"  We are concerned with the regularity of solutions of the Lighthill problem
+for shock diffraction by a convex corned wedge, which can be formulated as a
+free boundary problem. In this paper, we prove that there is no regular
+solution that is subsonic up to the wedge corner for potential flow. This
+indicates that, if the solution is subsonic at the wedge corner, at least a
+characteristic discontinuity (vortex sheet or entropy wave) is expected to be
+generated, which is consistent with the experimental and computational results.
+In order to achieve the non-existence result, a weak maximum principle for the
+solution is established, and several other mathematical techniques are
+developed. The methods and techniques developed here are also useful to the
+other problems with similar difficulties.
+",0,0,1,0,0,0
+18518,"Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory","  The European Space Agency (ESA) defines an Earth Observation (EO) Level 2
+product as a multispectral (MS) image corrected for geometric, atmospheric,
+adjacency and topographic effects, stacked with its scene classification map
+(SCM), whose legend includes quality layers such as cloud and cloud-shadow. No
+ESA EO Level 2 product has ever been systematically generated at the ground
+segment. To contribute toward filling an information gap from EO big data to
+the ESA EO Level 2 product, an original Stage 4 validation (Val) of the
+Satellite Image Automatic Mapper (SIAM) lightweight computer program was
+conducted by independent means on an annual Web-Enabled Landsat Data (WELD)
+image composite time-series of the conterminous U.S. The core of SIAM is a one
+pass prior knowledge based decision tree for MS reflectance space
+hyperpolyhedralization into static color names presented in literature in
+recent years. For the sake of readability this paper is split into two. The
+present Part 1 Theory provides the multidisciplinary background of a priori
+color naming in cognitive science, from linguistics to computer vision. To cope
+with dictionaries of MS color names and land cover class names that do not
+coincide and must be harmonized, an original hybrid guideline is proposed to
+identify a categorical variable pair relationship. An original quantitative
+measure of categorical variable pair association is also proposed. The
+subsequent Part 2 Validation discusses Stage 4 Val results collected by an
+original protocol for wall-to-wall thematic map quality assessment without
+sampling where the test and reference map legends can differ. Conclusions are
+that the SIAM-WELD maps instantiate a Level 2 SCM product whose legend is the 4
+class taxonomy of the FAO Land Cover Classification System at the Dichotomous
+Phase Level 1 vegetation/nonvegetation and Level 2 terrestrial/aquatic.
+",1,0,0,0,0,0
+18519,Performance Analysis of MEC Approach for Haplotype Assembly,"  The Minimum Error Correction (MEC) approach is used as a metric for
+reconstruction of haplotypes from NGS reads. In this paper, we show that the
+MEC may encounter with imprecise reconstructed haplotypes for some NGS devices.
+Specifically, using mathematical derivations, we evaluate this approach for the
+SOLiD, Illumina, 454, Ion, Pacific BioSciences, Oxford Nanopore, and 10X
+Genomics devices. Our results reveal that the MEC yields inexact haplotypes for
+the Illumina MiniSeq, 454 GS Junior+, Ion PGM 314, and Oxford Nanopore MK 1
+MinION.
+",0,0,0,0,1,0
+18520,Opinion Recommendation using Neural Memory Model,"  We present opinion recommendation, a novel task of jointly predicting a
+custom review with a rating score that a certain user would give to a certain
+product or service, given existing reviews and rating scores to the product or
+service by other users, and the reviews that the user has given to other
+products and services. A characteristic of opinion recommendation is the
+reliance of multiple data sources for multi-task joint learning, which is the
+strength of neural models. We use a single neural network to model users and
+products, capturing their correlation and generating customised product
+representations using a deep memory network, from which customised ratings and
+reviews are constructed jointly. Results show that our opinion recommendation
+system gives ratings that are closer to real user ratings on Yelp.com data
+compared with Yelp's own ratings, and our methods give better results compared
+to several pipelines baselines using state-of-the-art sentiment rating and
+summarization systems.
+",1,0,0,0,0,0
+18521,Distributed Optimization of Multi-Beam Directional Communication Networks,"  We formulate an optimization problem for maximizing the data rate of a common
+message transmitted from nodes within an airborne network broadcast to a
+central station receiver while maintaining a set of intra-network rate demands.
+Assuming that the network has full-duplex links with multi-beam directional
+capability, we obtain a convex multi-commodity flow problem and use a
+distributed augmented Lagrangian algorithm to solve for the optimal flows
+associated with each beam in the network. For each augmented Lagrangian
+iteration, we propose a scaled gradient projection method to minimize the local
+Lagrangian function that incorporates the local topology of each node in the
+network. Simulation results show fast convergence of the algorithm in
+comparison to simple distributed primal dual methods and highlight performance
+gains over standard minimum distance-based routing.
+",0,0,1,0,0,0
+18522,Mean conservation for density estimation via diffusion using the finite element method,"  We propose boundary conditions for the diffusion equation that maintain the
+initial mean and the total mass of a discrete data sample in the density
+estimation process. A complete study of this framework with numerical
+experiments using the finite element method is presented for the one
+dimensional diffusion equation, some possible applications of this results are
+presented as well. We also comment on a similar methodology for the
+two-dimensional diffusion equation for future applications in two-dimensional
+domains.
+",0,0,1,0,0,0
+18523,Asymptotic properties of maximum likelihood estimator for the growth rate of a stable CIR process based on continuous time observations,"  We consider a stable Cox--Ingersoll--Ross process driven by a standard Wiener
+process and a spectrally positive strictly stable Lévy process, and we study
+asymptotic properties of the maximum likelihood estimator (MLE) for its growth
+rate based on continuous time observations. We distinguish three cases:
+subcritical, critical and supercritical. In all cases we prove strong
+consistency of the MLE in question, in the subcritical case asymptotic
+normality, and in the supercritical case asymptotic mixed normality are shown
+as well. In the critical case the description of the asymptotic behavior of the
+MLE in question remains open.
+",0,0,1,1,0,0
+18524,Indoor Office Wideband Penetration Loss Measurements at 73 GHz,"  This paper presents millimeter wave (mmWave) penetration loss measurements
+and analysis at 73 GHz using a wideband sliding correlator channel sounder in
+an indoor office environment. Penetration loss was measured using a carefully
+controlled measurement setup for many common indoor building materials such as
+glass doors, glass windows, closet doors, steel doors, and whiteboard writing
+walls. Measurements were conducted using narrowbeam transmitter (TX) and
+receiver (RX) horn antennas that were boresight-aligned with a test material
+between the antennas. Overall, 21 different locations were measured for 6
+different materials such that the same type of material was tested in at least
+two locations in order to characterize the effect of penetration loss for
+materials with similar composition. As shown here, attenuation through common
+materials ranged between 0.8 dB/cm and 9.9 dB/cm for co-polarized antennas,
+while cross-polarized antennas exhibited similar attenuation for most
+materials, but up to 23.4 dB/cm of attenuation for others. The penetration loss
+results presented here are useful for site-specific planning tools that will
+model indoor mmWave networks, without the need for expensive measurement
+campaigns.
+",1,0,0,0,0,0
+18525,The SPEDE spectrometer,"  The electron spectrometer, SPEDE, has been developed and will be employed in
+conjunction with the Miniball spectrometer at the HIE-ISOLDE facility, CERN.
+SPEDE allows for direct measurement of internal conversion electrons emitted
+in-flight, without employing magnetic fields to transport or momentum filter
+the electrons. Together with the Miniball spectrometer, it enables simultaneous
+observation of {\gamma} rays and conversion electrons in Coulomb-excitation
+experiments using radioactive ion beams.
+",0,1,0,0,0,0
+18526,Global Stabilization of Triangular Systems with Time-Delayed Dynamic Input Perturbations,"  A control design approach is developed for a general class of uncertain
+strict-feedback-like nonlinear systems with dynamic uncertain input
+nonlinearities with time delays. The system structure considered in this paper
+includes a nominal uncertain strict-feedback-like subsystem, the input signal
+to which is generated by an uncertain nonlinear input unmodeled dynamics that
+is driven by the entire system state (including unmeasured state variables) and
+is also allowed to depend on time delayed versions of the system state variable
+and control input signals. The system also includes additive uncertain
+nonlinear functions, coupled nonlinear appended dynamics, and uncertain dynamic
+input nonlinearities with time-varying uncertain time delays. The proposed
+control design approach provides a globally stabilizing delay-independent
+robust adaptive output-feedback dynamic controller based on a dual dynamic
+high-gain scaling based structure.
+",1,0,1,0,0,0
+18527,Multifractal Analysis of Pulsar Timing Residuals: Assessment of Gravitational Wave Detection,"  We introduce a pipeline including multifractal detrended cross-correlation
+analysis (MF-DXA) modified by either singular value decomposition or the
+adaptive method to examine the statistical properties of the pulsar timing
+residual ($PTR$) induced by a gravitational wave (GW) signal. We propose a new
+algorithm, the so-called irregular-MF-DXA, to deal with irregular data
+sampling. Inspired by the quadrupolar nature of the spatial cross-correlation
+function of a gravitational wave background, a new cross-correlation function,
+$\bar{\sigma}_{\times}$, derived from irregular-MF-DXA will be introduced. We
+show that, this measure reveals the quadrupolar signature in the $PTRs$ induced
+by stochastic GWB. We propose four strategies based on the $y$-intercept of
+fluctuation functions, the generalized Hurst exponent, and the width of the
+singularity spectrum to determine the dimensionless amplitude and power-law
+exponent of the characteristic strain spectrum as
+$\mathcal{H}_c(f)\sim\mathcal{A}_{yr}(f/f_{yr})^{\zeta}$ for stochastic GWB.
+Using the value of Hurst exponent, one can clarify the type of GWs. We apply
+our pipeline to explore 20 millisecond pulsars observed by Parkes Pulsar Timing
+Array. The computed scaling exponents confirm that all data are classified into
+a nonstationary class implying the universality feature. The value of the Hurst
+exponent is in the range $H\in [0.56,0.87]$. The $q$-dependency of the
+generalized Hurst exponent demonstrates that the observed $PTRs$ have
+multifractal behavior, and the source of this multifractality is mainly
+attributed to the correlation of data which is another universality of the
+observed datasets. Multifractal analysis of available $PTRs$ datasets reveals
+an upper bound on the dimensionless amplitude of the GWB, $\mathcal{A}_{yr}<
+2.0\times 10^{-15}$.
+",0,1,0,0,0,0
+18528,Recovery of Bennu's Orientation for the OSIRIS-REx Mission: Implications for the Spin State Accuracy and Geolocation Errors,"  The goal of the OSIRIS-REx mission is to return a sample of asteroid material
+from Near-Earth Asteroid (101955) Bennu. The role of the navigation and flight
+dynamics team is critical for the spacecraft to execute a precisely planned
+sampling maneuver over a specifically-selected landing site. In particular, the
+orientation of Bennu needs to be recovered with good accuracy during orbital
+operations to contribute as small an error as possible to the landing error
+budget. Although Bennu is well characterized from Earth-based radar
+observations, its orientation dynamics are not sufficiently known to exclude
+the presence of a small wobble. To better understand this contingency and
+evaluate how well the orientation can be recovered in the presence of a large
+1$^{\circ}$ wobble, we conduct a comprehensive simulation with the NASA GSFC
+GEODYN orbit determination and geodetic parameter estimation software. We
+describe the dynamic orientation modeling implemented in GEODYN in support of
+OSIRIS-REx operations, and show how both altimetry and imagery data can be used
+as either undifferenced (landmark, direct altimetry) or differenced (image
+crossover, altimetry crossover) measurements. We find that these two different
+types of data contribute differently to the recovery of instrument pointing or
+planetary orientation. When upweighted, the absolute measurements help reduce
+the geolocation errors, despite poorer astrometric (inertial) performance. We
+find that with no wobble present, all the geolocation requirements are met.
+While the presence of a large wobble is detrimental, the recovery is still
+reliable thanks to the combined use of altimetry and imagery data.
+",0,1,0,0,0,0
+18529,Energy Scale of Lorentz Violation in Rainbow Gravity,"  We modify the standard relativistic dispersion relation in a way which breaks
+Lorentz symmetry - the effect is predicted in a high-energy regime of some
+modern theories of quantum gravity. We show that it is possible to realise this
+scenario within the framework of Rainbow Gravity which introduces two new
+energy-dependent functions $f_1(E)$ and $f_2(E)$ into the dispersion relation.
+Additionally, we assume that the gravitational constant $G$ and the
+cosmological constant $\Lambda$ also depend on energy $E$ and introduce the
+scaling function $h(E)$ in order to express this dependence. For cosmological
+applications we specify the functions $f_1$ and $f_2$ in order to fit massless
+particles which allows us to derive modified cosmological equations. Finally,
+by using Hubble+SNIa+BAO(BOSS+Lyman $\alpha$)+CMB data, we constrain the energy
+scale $E_{LV}$ to be at least of the order of $10^{16}$ GeV at $1\sigma$ which
+is the GUT scale or even higher $10^{17}$ GeV at $3\sigma$. Our claim is that
+this energy can be interpreted as the decoupling scale of massless particles
+from spacetime Lorentz violating effects.
+",0,1,0,0,0,0
+18530,How to construct wavelets on local fields of positive characteristic,"  We present an algorithm for construction step wavelets on local fields of
+positive characteristic.
+",0,0,1,0,0,0
+18531,Fates of the dense cores formed by fragmentation of filaments: do they fragment again or not?,"  Fragmentation of filaments into dense cores is thought to be an important
+step in forming stars. The bar-mode instability of spherically collapsing cores
+found in previous linear analysis invokes a possibility of re-fragmentation of
+the cores due to their ellipsoidal (prolate or oblate) deformation. To
+investigate this possibility, here we perform three-dimensional
+self-gravitational hydrodynamics simulations that follow all the way from
+filament fragmentation to subsequent core collapse. We assume the gas is
+polytropic with index \gamma, which determines the stability of the bar-mode.
+For the case that the fragmentation of isolated hydrostatic filaments is
+triggered by the most unstable fragmentation mode, we find the bar mode grows
+as collapse proceeds if \gamma < 1.1, in agreement with the linear analysis.
+However, it takes more than ten orders-of-magnitude increase in the central
+density for the distortion to become non-linear. In addition to this fiducial
+case, we also study non-fiducial ones such as the fragmentation is triggered by
+a fragmentation mode with a longer wavelength and it occurs during radial
+collapse of filaments and find the distortion rapidly grows. In most of
+astrophysical applications, the effective polytropic index of collapsing gas
+exceeds 1.1 before ten orders-of-magnitude increase in the central density.
+Thus, supposing the fiducial case of filament fragmentation, re-fragmentation
+of dense cores would not be likely and their final mass would be determined
+when the filaments fragment.
+",0,1,0,0,0,0
+18532,Inference Related to Common Breaks in a Multivariate System with Joined Segmented Trends with Applications to Global and Hemispheric Temperatures,"  What transpires from recent research is that temperatures and radiative
+forcing seem to be characterized by a linear trend with two changes in the rate
+of growth. The first occurs in the early 60s and indicates a very large
+increase in the rate of growth of both temperature and radiative forcing
+series. This was termed as the ""onset of sustained global warming"". The second
+is related to the more recent so-called hiatus period, which suggests that
+temperatures and total radiative forcing have increased less rapidly since the
+mid-90s compared to the larger rate of increase from 1960 to 1990. There are
+two issues that remain unresolved. The first is whether the breaks in the slope
+of the trend functions of temperatures and radiative forcing are common. This
+is important because common breaks coupled with the basic science of climate
+change would strongly suggest a causal effect from anthropogenic factors to
+temperatures. The second issue relates to establishing formally via a proper
+testing procedure that takes into account the noise in the series, whether
+there was indeed a `hiatus period' for temperatures since the mid 90s. This is
+important because such a test would counter the widely held view that the
+hiatus is the product of natural internal variability. Our paper provides tests
+related to both issues. The results show that the breaks in temperatures and
+radiative forcing are common and that the hiatus is characterized by a
+significant decrease in their rate of growth. The statistical results are of
+independent interest and applicable more generally.
+",0,0,0,1,0,0
+18533,"How to Stop Consensus Algorithms, locally?","  This paper studies problems on locally stopping distributed consensus
+algorithms over networks where each node updates its state by interacting with
+its neighbors and decides by itself whether certain level of agreement has been
+achieved among nodes. Since an individual node is unable to access the states
+of those beyond its neighbors, this problem becomes challenging. In this work,
+we first define the stopping problem for generic distributed algorithms. Then,
+a distributed algorithm is explicitly provided for each node to stop consensus
+updating by exploring the relationship between the so-called local and global
+consensus. Finally, we show both in theory and simulation that its
+effectiveness depends both on the network size and the structure.
+",1,0,0,0,0,0
+18534,Seoul National University Camera II (SNUCAM-II): The New SED Camera for the Lee Sang Gak Telescope (LSGT),"  We present the characteristics and the performance of the new CCD camera
+system, SNUCAM-II (Seoul National University CAMera system II) that was
+installed on the Lee Sang Gak Telescope (LSGT) at the Siding Spring Observatory
+in 2016. SNUCAM-II consists of a deep depletion chip covering a wide wavelength
+from 0.3 {\mu}m to 1.1 {\mu}m with high sensitivity (QE at > 80% over 0.4 to
+0.9 {\mu}m). It is equipped with the SDSS ugriz filters and 13 medium band
+width (50 nm) filters, enabling us to study spectral energy distributions
+(SEDs) of diverse objects from extragalactic sources to solar system objects.
+On LSGT, SNUCAM-II offers 15.7 {\times} 15.7 arcmin field-of-view (FOV) at a
+pixel scale of 0.92 arcsec and a limiting magnitude of g = 19.91 AB mag and
+z=18.20 AB mag at 5{\sigma} with 180 sec exposure time for point source
+detection.
+",0,1,0,0,0,0
+18535,A Bridge Between Hyperparameter Optimization and Larning-to-learn,"  We consider a class of a nested optimization problems involving inner and
+outer objectives. We observe that by taking into explicit account the
+optimization dynamics for the inner objective it is possible to derive a
+general framework that unifies gradient-based hyperparameter optimization and
+meta-learning (or learning-to-learn). Depending on the specific setting, the
+variables of the outer objective take either the meaning of hyperparameters in
+a supervised learning problem or parameters of a meta-learner. We show that
+some recently proposed methods in the latter setting can be instantiated in our
+framework and tackled with the same gradient-based algorithms. Finally, we
+discuss possible design patterns for learning-to-learn and present encouraging
+preliminary experiments for few-shot learning.
+",1,0,0,1,0,0
+18536,Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks,"  Designing a new drug is a lengthy and expensive process. As the space of
+potential molecules is very large (10^23-10^60), a common technique during drug
+discovery is to start from a molecule which already has some of the desired
+properties. An interdisciplinary team of scientists generates hypothesis about
+the required changes to the prototype. In this work, we develop an algorithmic
+unsupervised-approach that automatically generates potential drug molecules
+given a prototype drug. We show that the molecules generated by the system are
+valid molecules and significantly different from the prototype drug. Out of the
+compounds generated by the system, we identified 35 FDA-approved drugs. As an
+example, our system generated Isoniazid - one of the main drugs for
+Tuberculosis. The system is currently being deployed for use in collaboration
+with pharmaceutical companies to further analyze the additional generated
+molecules.
+",0,0,0,1,0,0
+18537,Convex and non-convex regularization methods for spatial point processes intensity estimation,"  This paper deals with feature selection procedures for spatial point
+processes intensity estimation. We consider regularized versions of estimating
+equations based on Campbell theorem derived from two classical functions:
+Poisson likelihood and logistic regression likelihood. We provide general
+conditions on the spatial point processes and on penalty functions which ensure
+consistency, sparsity and asymptotic normality. We discuss the numerical
+implementation and assess finite sample properties in a simulation study.
+Finally, an application to tropical forestry datasets illustrates the use of
+the proposed methods.
+",0,0,1,1,0,0
+18538,Glass Transition in Supercooled Liquids with Medium Range Crystalline Order,"  The origins of rapid dynamical slow down in glass forming liquids in the
+growth of static length scales, possibly associated with identifiable
+structural ordering, is a much debated issue. Growth of medium range
+crystalline order (MRCO) has been observed in various model systems to be
+associated with glassy behaviour. Such observations raise the question about
+the eventual state reached by a glass former, if allowed to relax for
+sufficiently long times. Is a slowly growing crystalline order responsible for
+slow dynamics? Are the molecular mechanisms for glass transition in liquids
+with and without MRCO the same? If yes, glass formers with MRCO provide a
+paradigm for understanding glassy behaviour generically. If not, systems with
+MRCO form a new class of glass forming materials whose molecular mechanism for
+slow dynamics may be easier to understand in terms of growing crystalline
+order, and should be approached in that manner, even while they will not
+provide generic insights. In this study we perform extensive molecular dynamics
+simulations of a number of glass forming liquids in two dimensions and show
+that the static and dynamic properties of glasses with MRCO are different from
+other glass forming liquids with no predominant local order. We also resolve an
+important issue regarding the so-called Point-to-set method for determining
+static length scales, and demonstrate it to be a robust, order agnostic, method
+for determining static correlation lengths in glass formers.
+",0,1,0,0,0,0
+18539,Natural and Artificial Spectral Edges in Exoplanets,"  Technological civilizations may rely upon large-scale photovoltaic arrays to
+harness energy from their host star. Photovoltaic materials, such as silicon,
+possess distinctive spectral features, including an ""artificial edge"" that is
+characteristically shifted in wavelength shortwards of the ""red edge"" of
+vegetation. Future observations of reflected light from exoplanets would be
+able to detect both natural and artificial edges photometrically, if a
+significant fraction of the planet's surface is covered by vegetation or
+photovoltaic arrays respectively. The stellar energy thus tapped can be
+utilized for terraforming activities by transferring heat and light from the
+day side to the night side on tidally locked exoplanets, thereby producing
+detectable artifacts.
+",0,1,0,0,0,0
+18540,The damage inflicted by a computer virus: A new estimation method,"  This paper addressed the issue of estimating the damage caused by a computer
+virus. First, an individual-level delayed SIR model capturing the spreading
+process of a digital virus is derived. Second, the damage inflicted by the
+virus is modeled as the sum of the economic losses and the cost for developing
+the antivirus. Next, the impact of different factors, including the delay and
+the network structure, on the damage is explored by means of computer
+simulations. Thereby some measures of reducing the damage of a virus are
+recommended. To our knowledge, this is the first time the antivirus-developing
+cost is taken into account when estimating the damage of a virus.
+",1,1,0,0,0,0
+18541,Deep Fault Analysis and Subset Selection in Solar Power Grids,"  Non-availability of reliable and sustainable electric power is a major
+problem in the developing world. Renewable energy sources like solar are not
+very lucrative in the current stage due to various uncertainties like weather,
+storage, land use among others. There also exists various other issues like
+mis-commitment of power, absence of intelligent fault analysis, congestion,
+etc. In this paper, we propose a novel deep learning-based system for
+predicting faults and selecting power generators optimally so as to reduce
+costs and ensure higher reliability in solar power systems. The results are
+highly encouraging and they suggest that the approaches proposed in this paper
+have the potential to be applied successfully in the developing world.
+",1,0,0,1,0,0
+18542,Hirzebruch L-polynomials and multiple zeta values,"  We express the coefficients of the Hirzebruch L-polynomials in terms of
+certain alternating multiple zeta values. In particular, we show that every
+monomial in the Pontryagin classes appears with a non-zero coefficient, with
+the expected sign. Similar results hold for the polynomials associated to the
+A-hat genus.
+",0,0,1,0,0,0
+18543,On Nontrivial Zeros of Riemann Zeta Function,"  Let {\Xi} be a function relating to the Riemann zeta function with . In this
+paper, we construct a function containing and {\Xi} , and prove that satisfies
+a nonadjoint boundary value problem to a nonsingular differential equation if
+is any nontrivial zero of {\Xi} . Inspecting properties of and using known
+results of nontrivial zeros of , we derive that nontrivial zeros of all have
+real part equal to , which concludes that Riemann Hypothesis is true.
+",0,0,1,0,0,0
+18544,The reliability of a nutritional meta-analysis study,"  Background: Many researchers have studied the relationship between diet and
+health. There are papers showing an association between the consumption of
+sugar-sweetened beverages and Type 2 diabetes. Many meta-analyses use
+individual studies that do not adjust for multiple testing or multiple modeling
+and thus provide biased estimates of effect. Hence the claims reported in a
+meta-analysis paper may be unreliable if the primary papers do not ensure
+unbiased estimates of effect. Objective: Determine the statistical reliability
+of 10 papers and indirectly the reliability of the meta-analysis study. Method:
+Ten primary papers used in a meta-analysis paper and counted the numbers of
+outcomes, predictors, and covariates. We estimated the size of the potential
+analysis search space available to the authors of these papers; i.e. the number
+of comparisons and models available. Since we noticed that there were
+differences between predictors and covariates cited in the abstract and in the
+text, we applied this formula to information found in the abstracts, Space A,
+as well as the text, Space T, of each primary paper. Results: The median and
+range of the number of comparisons possible across the primary papers are 6.5
+and (2-12,288) for abstracts, and 196,608 and (3,072-117,117,952) the texts.
+Note that the median of 6.5 for Space A is misleading as each primary study has
+60-165 foods not mentioned in the abstract. Conclusion: Given that testing is
+at the 0.05 level and the number of comparisons is very large, nominal
+statistical significance is very weak support for a claim. The claims in these
+papers are not statistically supported and hence are unreliable. Thus, the
+claims of the meta-analysis paper lack evidentiary confirmation.
+",0,0,0,1,0,0
+18545,Composite Fermions on a Torus,"  We achieve an explicit construction of the lowest Landau level (LLL)
+projected wave functions for composite fermions in the periodic (torus)
+geometry. To this end, we first demonstrate how the vortex attachment of the
+composite fermion (CF) theory can be accomplished in the torus geometry to
+produce the ""unprojected"" wave functions satisfying the correct
+(quasi-)periodic boundary conditions. We then consider two methods for
+projecting these wave functions into the LLL. The direct projection produces
+valid wave functions but can be implemented only for very small systems. The
+more powerful and more useful projection method of Jain and Kamilla fails in
+the torus geometry because it does not preserve the periodic boundary
+conditions and thus takes us out of the original Hilbert space. We have
+succeeded in constructing a modified projection method that is consistent with
+both the periodic boundary conditions and the general structure of the CF
+theory. This method is valid for a large class of states of composite fermions,
+called ""proper states,"" which includes the incompressible ground states at
+electron filling factors $\nu=\frac{n}{2pn+ 1}$, their charged and neutral
+excitations, and also the quasidegenerate ground states at arbitrary filling
+factors of the form $\nu=\frac{\nu^*}{2p\nu^*+ 1}$, where $n$ and $p$ are
+integers and $\nu^*$ is the CF filling factor. Comparison with exact results
+known for small systems for the ground and excited states at filling factors
+$\nu=1/3$, 2/5 and 3/7 demonstrates our LLL-projected wave functions to be
+extremely accurate representations of the actual Coulomb eigenstates. Our
+construction enables the study of large systems of composite fermions on the
+torus, thereby opening the possibility of investigating numerous interesting
+questions and phenomena.
+",0,1,0,0,0,0
+18546,ROCKER: A Refinement Operator for Key Discovery,"  The Linked Data principles provide a decentral approach for publishing
+structured data in the RDF format on the Web. In contrast to structured data
+published in relational databases where a key is often provided explicitly,
+finding a set of properties that allows identifying a resource uniquely is a
+non-trivial task. Still, finding keys is of central importance for manifold
+applications such as resource deduplication, link discovery, logical data
+compression and data integration. In this paper, we address this research gap
+by specifying a refinement operator, dubbed ROCKER, which we prove to be
+finite, proper and non-redundant. We combine the theoretical characteristics of
+this operator with two monotonicities of keys to obtain a time-efficient
+approach for detecting keys, i.e., sets of properties that describe resources
+uniquely. We then utilize a hash index to compute the discriminability score
+efficiently. Therewith, we ensure that our approach can scale to very large
+knowledge bases. Results show that ROCKER yields more accurate results, has a
+comparable runtime, and consumes less memory w.r.t. existing state-of-the-art
+techniques.
+",1,0,0,0,0,0
+18547,Double-slit Fraunhofer pattern as the signature of the Josephson effect between Berezinskii superconductors through the ferromagnetic vortex,"  I apply the recently developed formalism of generalized quasiclassical theory
+to show that using hybrid superconducting systems with non-collinear strong
+ferromagnets one can realize the Josephson junction between Berezinskii-type
+superconductors. The reported calculation reproduces main features observed in
+the recent experiment, namely the the slightly asymmetric double-slit
+Fraunhofer interference pattern of the Josephson current through the
+ferromagnetic vortex. The double-slit structure results from the spatially
+inhomogeneous Berezinskii state with the amplitude controlled by the local
+angle between magnetic moments in two ferromagnetic layers. The critical
+current asymmetry by the sign of magnetic field can signal the presence of
+spontaneous supercurrents generated by the non-coplanar magnetic texture near
+the core of the ferromagnetic vortex core. I demonstrate that ferromagnetic
+vortex can induce spontaneous vorticity in the odd-frequency order parameter
+manifesting the possibility of the emergent magnetic field to create
+topological defects.
+",0,1,0,0,0,0
+18548,A class of differential quadratic algebras and their symmetries,"  We study a multi-parametric family of quadratic algebras in four generators,
+which includes coordinate algebras of noncommutative four-planes and, as
+quotient algebras, noncommutative three spheres. Particular subfamilies
+comprise Sklyanin algebras and Connes--Dubois-Violette planes. We determine
+quantum groups of symmetries for the general algebras and construct
+finite-dimensional covariant differential calculi.
+",0,0,1,0,0,0
+18549,Introduction to compact and discrete quantum groups,"  These are notes from introductory lectures at the graduate school
+""Topological Quantum Groups"" in Będlewo (June 28--July 11, 2015). The notes
+present the passage from Hopf algebras to compact quantum groups and sketch the
+notion of discrete quantum groups viewed as duals of compact quantum groups.
+",0,0,1,0,0,0
+18550,An optimization approach to adaptive multi-dimensional capital management,"  Firms should keep capital to offer sufficient protection against the risks
+they are facing. In the insurance context methods have been developed to
+determine the minimum capital level required, but less so in the context of
+firms with multiple business lines including allocation. The individual capital
+reserve of each line can be represented by means of classical models, such as
+the conventional Cramér-Lundberg model, but the challenge lies in soundly
+modelling the correlations between the business lines. We propose a simple yet
+versatile approach that allows for dependence by introducing a common
+environmental factor. We present a novel Bayesian approach to calibrate the
+latent environmental state distribution based on observations concerning the
+claim processes. The calibration approach is adjusted for an environmental
+factor that changes over time. The convergence of the calibration procedure
+towards the true environmental state is deduced. We then point out how to
+determine the optimal initial capital of the different business lines under
+specific constraints on the ruin probability of subsets of business lines. Upon
+combining the above findings, we have developed an easy-to-implement approach
+to capital risk management in a multi-dimensional insurance risk model.
+",0,0,0,0,0,1
+18551,Low Energy Phonons in $Bi_2Sr_2CaCu_2O_{8+δ}$ and their Possible Interaction with Electrons Measured by Inelastic Neutron Scattering,"  Angle-resolved photoemission (ARPES) experiments on copper oxide
+superconductors revealed enigmatic kinks in electronic dispersions near 10 meV
+presumably due to phonons or impuritites. We used inelastic neutron scattering
+to measure phonon branches below 15 meV in a large single crystal sample of
+optimally-doped $Bi_2Sr_2CaCu_2O_{8+\delta}$ (BSCCO). The high quality dataset
+covered several Brilloiun zones with different final energies. In addition to
+acoustic branches, optic branches disperse from 4 meV and 7 meV zone center
+energies. The 4 meV branch interacts with acoustic phonons at small
+wavevectors, which destroys the LA character of the acoustic branch beyond
+~0.15 reciprocal lattice units. We propose a mechanism that explains the low
+energy electronic dispersion features based on this observation.
+",0,1,0,0,0,0
+18552,Spectral properties of complex Airy operator on the semi-axis,"  We prove the theorem on the completeness of the root functions of the
+Schroedinger operator $L=-d^2/dx^2+p(x)$ on the semi-axis $\mathbb R_+$ with a
+complex--valued potential $p(x)$. It is assumed that the potential $p = q \pm
+ir$ is such that the real functions $q$ and $r$ are subject the conditions $$
+q(x) \geqslant c r(x), \quad r(x) \geqslant c_0+ c_1 x^\alpha, \quad \alpha >0,
+$$ where the constants $c, \ c_0\in \mathbb R$, $c_1>0$ and $\arg(\pm i+c) <
+2\alpha\pi/(2+\alpha)$. For the case of the Airy operator $L_c=-d^2/dx^2+cx$,
+$c=const$, this theorem imply the completeness of the system of the
+eigenfunctions of this operator if $|\arg c|<2\pi/3$. Using another technique
+based on the asymptotic behavior of the Airy functions we prove that the
+completeness theorem for the operator $L_c$ remains valid, provided that $|\arg
+c|<5\pi/6$.
+",0,0,1,0,0,0
+18553,Webs and $q$-Howe dualities in types $\mathbf{B}\mathbf{C}\mathbf{D}$,"  We define web categories describing intertwiners for the orthogonal and
+symplectic Lie algebras, and, in the quantized setup, for certain orthogonal
+and symplectic coideal subalgebras. They generalize the Brauer category, and
+allow us to prove quantum versions of some classical type
+$\mathbf{B}\mathbf{C}\mathbf{D}$ Howe dualities.
+",0,0,1,0,0,0
+18554,Structural Controllability of Linear Time-invariant Systems,"  One version of the concept of structural controllability defined for
+single-input systems by Lin and subsequently generalized to multi-input systems
+by others, states that a parameterized matrix pair $(A, B)$ whose nonzero
+entries are distinct parameters, is structurally controllable if values can be
+assigned to the parameters which cause the resulting matrix pair to be
+controllable. In this paper the concept of structural controllability is
+broadened to allow for the possibility that a parameter may appear in more than
+one location in the pair $(A, B)$. Subject to a certain condition on the
+parameterization called the ""binary assumption"", an explicit graph-theoretic
+characterization of such matrix pairs is derived.
+",1,0,0,0,0,0
+18555,Structure of martingale transports in finite dimensions,"  We study the structure of martingale transports in finite dimensions. We
+consider the family $\mathcal{M}(\mu,\nu) $ of martingale measures on
+$\mathbb{R}^N \times \mathbb{R}^N$ with given marginals $\mu,\nu$, and
+construct a family of relatively open convex sets $\{C_x:x\in \mathbb{R}^N \}$,
+which forms a partition of $\mathbb{R}^N$, and such that any martingale
+transport in $\mathcal{M}(\mu,\nu) $ sends mass from $x$ to within
+$\overline{C_x}$, $\mu(dx)$--a.e. Our results extend the analogous
+one-dimensional results of M. Beiglböck and N. Juillet (2016) and M.
+Beiglböck, M. Nutz, and N. Touzi (2015). We conjecture that the decomposition
+is canonical and minimal in the sense that it allows to characterise the
+martingale polar sets, i.e. the sets which have zero mass under all measures in
+$\mathcal{M}(\mu,\nu)$, and offers the martingale analogue of the
+characterisation of transport polar sets proved in M. Beiglböck, M.
+Goldstern, G. Maresch, and W. Schachermayer (2009).
+",0,0,1,0,0,0
+18556,Simplifying branched covering surface-knots by chart moves involving black vertices,"  A branched covering surface-knot is a surface-knot in the form of a branched
+covering over an oriented surface-knot $F$, where we include the case when the
+covering has no branch points. A branched covering surface-knot is presented by
+a graph called a chart on a surface diagram of $F$. We can simplify a branched
+covering surface-knot by an addition of 1-handles with chart loops to a form
+such that its chart is the union of free edges and 1-handles with chart loops.
+We investigate properties of such simplifications for the case when branched
+covering surface-knots have a non-zero number of branch points, using chart
+moves involving black vertices.
+",0,0,1,0,0,0
+18557,$K$-surfaces with free boundaries,"  A well-known question in classical differential geometry and geometric
+analysis asks for a description of possible boundaries of $K$-surfaces, which
+are smooth, compact hypersurfaces in $\mathbb{R}^d$ having constant Gauss
+curvature equal to $K \geq 0$. This question generated a considerable amount of
+remarkable results in the last few decades. Motivated by these developments
+here we study the question of determining a $K$-surface when only part of its
+boundary is fixed, and in addition the surface hits a given manifold at some
+fixed angle. While this general setting is out of reach for us at the present,
+we settle a model case of the problem, which in its analytic formulation
+reduces to a Bernoulli type free boundary problem for the Monge-Ampère
+equation. We study both the cases of 0-curvature and of positive curvature. The
+formulation of the free boundary condition and its regularity are the most
+delicate and challenging questions addressed in this work. In this regard we
+introduce a notion of a Blaschke extension of a solution which might be of
+independent interest.
+The problem we study can also be interpreted as the Alt-Caffarelli problem
+for the Monge-Ampère equation. Moreover, it also relates to the problem of
+isometric embedding of a positive metric on the annulus with partially
+prescribed boundary and optimal transport with free mass.
+",0,0,1,0,0,0
+18558,The graphs of join-semilattices and the shape of congruence lattices of particle lattices,"  We attach to each $\langle 0, \vee \rangle$-semilattice a graph
+$\boldsymbol{G}_{\boldsymbol{S}}$ whose vertices are join-irreducible elements
+of $\boldsymbol{S}$ and whose edges correspond to the reflexive dependency
+relation. We study properties of the graph $\boldsymbol{G}_{\boldsymbol{S}}$
+both when $\boldsymbol{S}$ is a join-semilattice and when it is a lattice. We
+call a $\langle 0, \vee \rangle$-semilattice $\boldsymbol{S}$ particle provided
+that the set of its join-irreducible elements join-generates $\boldsymbol{S}$
+and it satisfies DCC. We prove that the congruence lattice of a particle
+lattice is anti-isomorphic to the lattice of hereditary subsets of the
+corresponding graph that are closed in a certain zero-dimensional topology.
+Thus we extend the result known for principally chain finite lattices.
+",0,0,1,0,0,0
+18559,Optimal Quasi-Gray Codes: The Alphabet Matters,"  A quasi-Gray code of dimension $n$ and length $\ell$ over an alphabet
+$\Sigma$ is a sequence of distinct words $w_1,w_2,\dots,w_\ell$ from $\Sigma^n$
+such that any two consecutive words differ in at most $c$ coordinates, for some
+fixed constant $c>0$. In this paper we are interested in the read and write
+complexity of quasi-Gray codes in the bit-probe model, where we measure the
+number of symbols read and written in order to transform any word $w_i$ into
+its successor $w_{i+1}$.
+We present construction of quasi-Gray codes of dimension $n$ and length $3^n$
+over the ternary alphabet $\{0,1,2\}$ with worst-case read complexity $O(\log
+n)$ and write complexity $2$. This generalizes to arbitrary odd-size alphabets.
+For the binary alphabet, we present quasi-Gray codes of dimension $n$ and
+length at least $2^n - 20n$ with worst-case read complexity $6+\log n$ and
+write complexity $2$. This complements a recent result by Raskin [Raskin '17]
+who shows that any quasi-Gray code over binary alphabet of length $2^n$ has
+read complexity $\Omega(n)$.
+Our results significantly improve on previously known constructions and for
+the odd-size alphabets we break the $\Omega(n)$ worst-case barrier for
+space-optimal (non-redundant) quasi-Gray codes with constant number of writes.
+We obtain our results via a novel application of algebraic tools together with
+the principles of catalytic computation [Buhrman et al. '14, Ben-Or and Cleve
+'92, Barrington '89, Coppersmith and Grossman '75].
+",1,0,0,0,0,0
+18560,Revisiting the pre-main-sequence evolution of stars I. Importance of accretion efficiency and deuterium abundance,"  Recent theoretical work has shown that the pre-main-sequence (PMS) evolution
+of stars is much more complex than previously envisioned. Instead of the
+traditional steady, one-dimensional solution, accretion may be episodic and not
+necessarily symmetrical, thereby affecting the energy deposited inside the star
+and its interior structure. Given this new framework, we want to understand
+what controls the evolution of accreting stars. We use the MESA stellar
+evolution code with various sets of conditions. In particular, we account for
+the (unknown) efficiency of accretion in burying gravitational energy into the
+protostar through a parameter, $\xi$, and we vary the amount of deuterium
+present. We confirm the findings of previous works that the evolution changes
+significantly with the amount of energy that is lost during accretion. We find
+that deuterium burning also regulates the PMS evolution. In the low-entropy
+accretion scenario, the evolutionary tracks in the H-R diagram are
+significantly different from the classical tracks and are sensitive to the
+deuterium content. A comparison of theoretical evolutionary tracks and
+observations allows us to exclude some cold accretion models ($\xi\sim 0$) with
+low deuterium abundances. We confirm that the luminosity spread seen in
+clusters can be explained by models with a somewhat inefficient injection of
+accretion heat. The resulting evolutionary tracks then become sensitive to the
+accretion heat efficiency, initial core entropy, and deuterium content. In this
+context, we predict that clusters with a higher D/H ratio should have less
+scatter in luminosity than clusters with a smaller D/H. Future work on this
+issue should include radiation-hydrodynamic simulations to determine the
+efficiency of accretion heating and further observations to investigate the
+deuterium content in star-forming regions. (abbrev.)
+",0,1,0,0,0,0
+18561,Surface-assisted carrier excitation in plasmonic nanostructure,"  We present a quantum-mechanical model for surface-assisted carrier excitation
+by optical fields in plasmonic nanostructures of arbitrary shape. We derive an
+explicit expression, in terms of local fields inside the metal structure, for
+surface absorbed power and surface scattering rate that determine the
+enhancement of carrier excitation efficiency near the metal-dielectric
+interface. We show that surface scattering is highly sensitive to the local
+field polarization, and can be incorporated into metal dielectric function
+along with phonon and impurity scattering. We also show that the obtained
+surface scattering rate describes surface-assisted plasmon decay (Landau
+damping) in nanostructures larger than the nonlocality scale. Our model can be
+used for calculations of plasmon-assisted hot carrier generation rates in
+photovoltaics and photochemistry applications.
+",0,1,0,0,0,0
+18562,Existence of global weak solutions to the kinetic Hookean dumbbell model for incompressible dilute polymeric fluids,"  We explore the existence of global weak solutions to the Hookean dumbbell
+model, a system of nonlinear partial differential equations that arises from
+the kinetic theory of dilute polymers, involving the unsteady incompressible
+Navier--Stokes equations in a bounded domain in two or three space dimensions,
+coupled to a Fokker--Planck-type parabolic equation. We prove the existence of
+large-data global weak solutions in the case of two space dimensions.
+Indirectly, our proof also rigorously demonstrates that, in two space
+dimensions at least, the Oldroyd-B model is the macroscopic closure of the
+Hookean dumbbell model. In three space dimensions, we prove the existence of
+large-data global weak subsolutions to the model, which are weak solutions with
+a defect measure, where the defect measure appearing in the Navier--Stokes
+momentum equation is the divergence of a symmetric positive semidefinite
+matrix-valued Radon measure.
+",0,0,1,0,0,0
+18563,Tight Bounds for Online Coloring of Basic Graph Classes,"  We resolve a number of long-standing open problems in online graph coloring.
+More specifically, we develop tight lower bounds on the performance of online
+algorithms for fundamental graph classes. An important contribution is that our
+bounds also hold for randomized online algorithms, for which hardly any results
+were known. Technically, we construct lower bounds for chordal graphs. The
+constructions then allow us to derive results on the performance of randomized
+online algorithms for the following further graph classes: trees, planar,
+bipartite, inductive, bounded-treewidth and disk graphs. It shows that the best
+competitive ratio of both deterministic and randomized online algorithms is
+$\Theta(\log n)$, where $n$ is the number of vertices of a graph. Furthermore,
+we prove that this guarantee cannot be improved if an online algorithm has a
+lookahead of size $O(n/\log n)$ or access to a reordering buffer of size
+$n^{1-\epsilon}$, for any $0<\epsilon\leq 1$. A consequence of our results is
+that, for all of the above mentioned graph classes except bipartite graphs, the
+natural $\textit{First Fit}$ coloring algorithm achieves an optimal
+performance, up to constant factors, among deterministic and randomized online
+algorithms.
+",1,0,0,0,0,0
+18564,Comparison of PCA with ICA from data distribution perspective,"  We performed an empirical comparison of ICA and PCA algorithms by applying
+them on two simulated noisy time series with varying distribution parameters
+and level of noise. In general, ICA shows better results than PCA because it
+takes into account higher moments of data distribution. On the other hand, PCA
+remains quite sensitive to the level of correlations among signals.
+",0,0,0,1,0,0
+18565,The Special Polarization Characteristic Features of a Three-Dimensional Terahertz Photonic Crystal with a Silicon Inverse Diamond Structure,"  The band structure of a Si inverse diamond structure whose lattice point
+shape was vacant regular octahedrons was calculated using plane wave expansion
+method and a complete photonic band gap was theoretically confirmed at around
+0.4 THz. It is said that three-dimensional photonic crystals have no
+polarization anisotropy in photonic band gap (stop gap, stop band) of high
+symmetry points in normal incidence. However, it was experimentally confirmed
+that the polarization orientation of a reflected light was different from that
+of a incident light, {I(X,Y)}, where (X,Y) is the coordinate system fixed in
+the photonic crystal. It was studied on a plane (001) at around X point's
+photonic band gap (0.36 - 0.44 THz) for incident light direction [001]
+($\Gamma$-X direction) by rotating a sample in the plane (001), relatively. The
+polarization orientation of the reflected light was parallel to that of the
+incident light for the incident polarization orientation I(1,1), I(1,-1). In
+contrast, the former was perpendicular to the latter for the incident
+polarization orientation I(1,0), I(0,-1) in the vicinity of 0.38 THz. As far as
+the photonic crystal in this work is concerned, method of resolution and
+synthesis of the incident polarization vector isn't apparently able to apply to
+the analysis of experimental results.
+",0,1,0,0,0,0
+18566,Toward Finding Latent Cities with Non-Negative Matrix Factorization,"  In the last decade, digital footprints have been used to cluster population
+activity into functional areas of cities.
+However, a key aspect has been overlooked: we experience our cities not only
+by performing activities at specific destinations, but also by moving from one
+place to another.
+In this paper, we propose to analyze and cluster the city based on how people
+move through it. Particularly, we introduce Mobilicities, automatically
+generated travel patterns inferred from mobile phone network data using NMF, a
+matrix factorization model.
+We evaluate our method in a large city and we find that mobilicities reveal
+latent but at the same time interpretable mobility structures of the city. Our
+results provide evidence on how clustering and visualization of aggregated
+phone logs could be used in planning systems to interactively analyze city
+structure and population activity.
+",1,0,0,0,0,0
+18567,Time-Sensitive Networking for robotics,"  We argue that Time-Sensitive Networking (TSN) will become the de facto
+standard for real-time communications in robotics. We present a review and
+classification of the different communication standards which are relevant for
+the field and introduce the typical problems with traditional switched Ethernet
+networks. We discuss some of the TSN features relevant for deterministic
+communications and evaluate experimentally one of the shaping mechanisms in an
+exemplary robotic scenario. In particular, and based on our results, we claim
+that many of the existing real-time industrial solutions will slowly be
+replaced by TSN. And that this will lead towards a unified landscape of
+physically interoperable robot and robot components.
+",1,0,0,0,0,0
+18568,Active Orthogonal Matching Pursuit for Sparse Subspace Clustering,"  Sparse Subspace Clustering (SSC) is a state-of-the-art method for clustering
+high-dimensional data points lying in a union of low-dimensional subspaces.
+However, while $\ell_1$ optimization-based SSC algorithms suffer from high
+computational complexity, other variants of SSC, such as Orthogonal Matching
+Pursuit-based SSC (OMP-SSC), lose clustering accuracy in pursuit of improving
+time efficiency. In this letter, we propose a novel Active OMP-SSC, which
+improves clustering accuracy of OMP-SSC by adaptively updating data points and
+randomly dropping data points in the OMP process, while still enjoying the low
+computational complexity of greedy pursuit algorithms. We provide heuristic
+analysis of our approach, and explain how these two active steps achieve a
+better tradeoff between connectivity and separation. Numerical results on both
+synthetic data and real-world data validate our analyses and show the
+advantages of the proposed active algorithm.
+",1,0,0,1,0,0
+18569,Experimental and theoretical study of AC losses in variable asymmetrical magnetic environments,"  Measurements of AC losses in a HTS-tape placed in between of two bulk
+magnetic shields of high permeability were performed by applying calorimetric
+techniques for various asymmetrical shielding arrangements. The experiment was
+supported by analytical calculations and finite-element simulations of the
+field and current distributions, based on the Bean model of the critical state.
+The simulated current and field profiles perfectly reproduce the analytic
+solutions known for certain shielding geometries. The evaluation of the
+consequent AC losses exhibits good agreement with measurements for the central
+position of the tape between the magnets but increasing discrepancy when the
+tape is approaching the shields. This can be explained by the increasing
+contribution of the eddy currents and magnetic hysteresis losses in the
+conducting shields.
+",0,1,0,0,0,0
+18570,Robustness of semiparametric efficiency in nearly-true models for two-phase samples,"  We examine the performance of efficient and AIPW estimators under two-phase
+sampling when the complete-data model is nearly correctly specified, in the
+sense that the misspecification is not reliably detectable from the data by any
+possible diagnostic or test. Asymptotic results for these nearly true models
+are obtained by representing them as sequences of misspecified models that are
+mutually contiguous with a correctly specified model. We find that for the
+least-favourable direction of model misspecification the bias in the efficient
+estimator induced can be comparable to the extra variability in the AIPW
+estimator, so that the mean squared error of the efficient estimator is no
+longer lower. This can happen when the most-powerful test for the model
+misspecification still has modest power. We verify that the theoretical results
+agree with simulation in three examples: a simple informative-sampling model
+for a Normal mean, logistic regression in the classical case-control design,
+and linear regression in a two-phase design.
+",0,0,1,1,0,0
+18571,A* CCG Parsing with a Supertag and Dependency Factored Model,"  We propose a new A* CCG parsing model in which the probability of a tree is
+decomposed into factors of CCG categories and its syntactic dependencies both
+defined on bi-directional LSTMs. Our factored model allows the precomputation
+of all probabilities and runs very efficiently, while modeling sentence
+structures explicitly via dependencies. Our model achieves the state-of-the-art
+results on English and Japanese CCG parsing.
+",1,0,0,0,0,0
+18572,Numerical studies of Thompson's group F and related groups,"  We have developed polynomial-time algorithms to generate terms of the
+cogrowth series for groups $\mathbb{Z}\wr \mathbb{Z},$ the lamplighter group,
+$(\mathbb{Z}\wr \mathbb{Z})\wr \mathbb{Z}$ and the Navas-Brin group $B.$ We
+have also given an improved algorithm for the coefficients of Thompson's group
+$F,$ giving 32 terms of the cogrowth series. We develop numerical techniques to
+extract the asymptotics of these various cogrowth series. We present improved
+rigorous lower bounds on the growth-rate of the cogrowth series for Thompson's
+group $F$ using the method from \cite{HHR15} applied to our extended series. We
+also generalise their method by showing that it applies to loops on any locally
+finite graph. Unfortunately, lower bounds less than 16 do not help in
+determining amenability.
+Again for Thompson's group $F$ we prove that, if the group is amenable, there
+cannot be a sub-dominant stretched exponential term in the
+asymptotics\footnote{ }. Yet the numerical data provides compelling evidence
+for the presence of such a term. This observation suggests a potential path to
+a proof of non-amenability: If the universality class of the cogrowth sequence
+can be determined rigorously, it will likely prove non-amenability.
+We estimate the asymptotics of the cogrowth coefficients of $F$ to be $$ c_n
+\sim c \cdot \mu^n \cdot \kappa^{n^\sigma \log^\delta{n}} \cdot n^g,$$ where
+$\mu \approx 15,$ $\kappa \approx 1/e,$ $\sigma \approx 1/2,$ $\delta \approx
+1/2,$ and $g \approx -1.$ The growth constant $\mu$ must be 16 for amenability.
+These two approaches, plus a third based on extrapolating lower bounds, support
+the conjecture \cite{ERvR15, HHR15} that the group is not amenable.
+",0,0,1,0,0,0
+18573,The modularity of action and perception revisited using control theory and active inference,"  The assumption that action and perception can be investigated independently
+is entrenched in theories, models and experimental approaches across the brain
+and mind sciences. In cognitive science, this has been a central point of
+contention between computationalist and 4Es (enactive, embodied, extended and
+embedded) theories of cognition, with the former embracing the ""classical
+sandwich"", modular, architecture of the mind and the latter actively denying
+this separation can be made. In this work we suggest that the modular
+independence of action and perception strongly resonates with the separation
+principle of control theory and furthermore that this principle provides formal
+criteria within which to evaluate the implications of the modularity of action
+and perception. We will also see that real-time feedback with the environment,
+often considered necessary for the definition of 4Es ideas, is not however a
+sufficient condition to avoid the ""classical sandwich"". Finally, we argue that
+an emerging framework in the cognitive and brain sciences, active inference,
+extends ideas derived from control theory to the study of biological systems
+while disposing of the separation principle, describing non-modular models of
+behaviour strongly aligned with 4Es theories of cognition.
+",0,0,0,0,1,0
+18574,Clique-based Method for Social Network Clustering,"  In this article, we develop a clique-based method for social network
+clustering. We introduce a new index to evaluate the quality of clustering
+results, and propose an efficient algorithm based on recursive bipartition to
+maximize an objective function of the proposed index. The optimization problem
+is NP-hard, so we approximate the semi-optimal solution via an implicitly
+restarted Lanczos method. One of the advantages of our algorithm is that the
+proposed index of each community in the clustering result is guaranteed to be
+higher than some predetermined threshold, $p$, which is completely controlled
+by users. We also account for the situation that $p$ is unknown. A statistical
+procedure of controlling both under-clustering and over-clustering errors
+simultaneously is carried out to select localized threshold for each
+subnetwork, such that the community detection accuracy is optimized.
+Accordingly, we propose a localized clustering algorithm based on binary tree
+structure. Finally, we exploit the stochastic blockmodels to conduct simulation
+studies and demonstrate the accuracy and efficiency of our algorithms, both
+numerically and graphically.
+",1,0,0,1,0,0
+18575,Fermionic Matrix Product States and One-Dimensional Short-Range Entangled Phases with Anti-Unitary Symmetries,"  We extend the formalism of Matrix Product States (MPS) to describe
+one-dimensional gapped systems of fermions with both unitary and anti-unitary
+symmetries. Additionally, systems with orientation-reversing spatial symmetries
+are considered. The short-ranged entangled phases of such systems are
+classified by three invariants, which characterize the projective action of the
+symmetry on edge states. We give interpretations of these invariants as
+properties of states on the closed chain. The relationship between fermionic
+MPS systems at an RG fixed point and equivariant algebras is exploited to
+derive a group law for the stacking of fermionic phases. The result generalizes
+known classifications to symmetry groups that are non-trivial extensions of
+fermion parity and time-reversal.
+",0,1,0,0,0,0
+18576,How tracer particles sample the complexity of turbulence,"  On their roller coaster ride through turbulence, tracer particles sample the
+fluctuations of the underlying fields in space and time. Quantitatively
+relating particle and field statistics remains a fundamental challenge in a
+large variety of turbulent flows. We quantify how tracer particles sample
+turbulence by expressing their temporal velocity fluctuations in terms of an
+effective probabilistic sampling of spatial velocity field fluctuations. To
+corroborate our theory, we investigate an extensive suite of direct numerical
+simulations of hydrodynamic turbulence covering a Taylor-scale Reynolds number
+range from 150 to 430. Our approach allows the assessment of particle
+statistics from the knowledge of flow field statistics only, therefore opening
+avenues to a new generation of models for transport in complex flows.
+",0,1,0,0,0,0
+18577,Rare Nash Equilibria and the Price of Anarchy in Large Static Games,"  We study a static game played by a finite number of agents, in which agents
+are assigned independent and identically distributed random types and each
+agent minimizes its objective function by choosing from a set of admissible
+actions that depends on its type. The game is anonymous in the sense that the
+objective function of each agent depends on the actions of other agents only
+through the empirical distribution of their type-action pairs. We study the
+asymptotic behavior of Nash equilibria, as the number of agents tends to
+infinity, first by deriving laws of large numbers characterizes almost sure
+limit points of Nash equilibria in terms of so-called Cournot-Nash equilibria
+of an associated nonatomic game. Our main results are large deviation
+principles that characterize the probability of rare Nash equilibria and
+associated conditional limit theorems describing the behavior of equilibria
+conditioned on a rare event. The results cover situations when neither the
+finite-player game nor the associated nonatomic game has a unique equilibrium.
+In addition, we study the asymptotic behavior of the price of anarchy,
+complementing existing worst-case bounds with new probabilistic bounds in the
+context of congestion games, which are used to model traffic routing in
+networks.
+",1,0,1,0,0,0
+18578,"Componentwise different tail solutions for bivariate stochastic recurrence equations -- with application to GARCH(1,1) processes --","  We study bivariate stochastic recurrence equations (SREs) motivated by
+applications to GARCH(1,1) processes. If coefficient matrices of SREs have
+strictly positive entries, then the Kesten result applies and it gives
+solutions with regularly varying tails. Moreover, the tail indices are the same
+for all coordinates. However, for applications, this framework is too
+restrictive. We study SREs when coefficients are triangular matrices and prove
+that the coordinates of the solution may exhibit regularly varying tails with
+different indices. We also specify each tail index together with its constant.
+The results are used to characterize regular variations of bivariate stationary
+GARCH(1,1) processes.
+",0,0,1,1,0,0
+18579,Partial constraint singularities in elastic rods,"  We present a unified classical treatment of partially constrained elastic
+rods. Partial constraints often entail singularities in both shapes and
+reactions. Our approach encompasses both sleeve and adhesion problems, and
+provides simple and unambiguous derivations of counterintuitive results in the
+literature. Relationships between reaction forces and moments, geometry, and
+adhesion energies follow from the balance of energy during quasistatic motion.
+We also relate our approach to the balance of material momentum and the concept
+of a driving traction. The theory is generalizable and can be applied to a wide
+array of contact, adhesion, gripping, and locomotion problems.
+",0,1,0,0,0,0
+18580,Personalization in Goal-Oriented Dialog,"  The main goal of modeling human conversation is to create agents which can
+interact with people in both open-ended and goal-oriented scenarios. End-to-end
+trained neural dialog systems are an important line of research for such
+generalized dialog models as they do not resort to any situation-specific
+handcrafting of rules. However, incorporating personalization into such systems
+is a largely unexplored topic as there are no existing corpora to facilitate
+such work. In this paper, we present a new dataset of goal-oriented dialogs
+which are influenced by speaker profiles attached to them. We analyze the
+shortcomings of an existing end-to-end dialog system based on Memory Networks
+and propose modifications to the architecture which enable personalization. We
+also investigate personalization in dialog as a multi-task learning problem,
+and show that a single model which shares features among various profiles
+outperforms separate models for each profile.
+",1,0,0,0,0,0
+18581,Maximum Margin Principal Components,"  Principal Component Analysis (PCA) is a very successful dimensionality
+reduction technique, widely used in predictive modeling. A key factor in its
+widespread use in this domain is the fact that the projection of a dataset onto
+its first $K$ principal components minimizes the sum of squared errors between
+the original data and the projected data over all possible rank $K$
+projections. Thus, PCA provides optimal low-rank representations of data for
+least-squares linear regression under standard modeling assumptions. On the
+other hand, when the loss function for a prediction problem is not the
+least-squares error, PCA is typically a heuristic choice of dimensionality
+reduction -- in particular for classification problems under the zero-one loss.
+In this paper we target classification problems by proposing a straightforward
+alternative to PCA that aims to minimize the difference in margin distribution
+between the original and the projected data. Extensive experiments show that
+our simple approach typically outperforms PCA on any particular dataset, in
+terms of classification error, though this difference is not always
+statistically significant, and despite being a filter method is frequently
+competitive with Partial Least Squares (PLS) and Lasso on a wide range of
+datasets.
+",1,0,0,1,0,0
+18582,A New self-propelled magnetic bearing with helical windings,"  In this work a design is proposed for an active, permanent magnet based,
+self-propelled magnetic bearing i.e. levitating motor having the following
+features : (a) simple winding structure, (b) high load supporting capacity, (c)
+no eccentricity sensors, (d) stable confinement in all translational
+dimensions, (e) stable confinement in all rotational dimensions and (f) high
+efficiency. This design uses an architecture consisting of a helically wound
+three-phase stator, and a rotor with the magnets also arranged in a helical
+manner. Active control is used to excite the rotor at a torque angle lying in
+the second quadrant. This torque angle is independent of the rotor's position
+inside the stator cavity hence the control algorithm is similar to that of a
+conventional permanent magnet synchronous motor. It is motivated through a
+physical argument that the bearing rotor develops a lift force proportional to
+the output torque and that it remains stably confined in space. These
+assertions are then proved rigorously through a calculation of the magnetic
+fields, forces and torques. The stiffness matrix of the system is presented and
+a discussion of stable and unstable operating regions is given.
+",0,1,0,0,0,0
+18583,A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection,"  How do we learn an object detector that is invariant to occlusions and
+deformations? Our current solution is to use a data-driven strategy -- collect
+large-scale datasets which have object instances under different conditions.
+The hope is that the final classifier can use these examples to learn
+invariances. But is it really possible to see all the occlusions in a dataset?
+We argue that like categories, occlusions and object deformations also follow a
+long-tail. Some occlusions and deformations are so rare that they hardly
+happen; yet we want to learn a model invariant to such occurrences. In this
+paper, we propose an alternative solution. We propose to learn an adversarial
+network that generates examples with occlusions and deformations. The goal of
+the adversary is to generate examples that are difficult for the object
+detector to classify. In our framework both the original detector and adversary
+are learned in a joint manner. Our experimental results indicate a 2.3% mAP
+boost on VOC07 and a 2.6% mAP boost on VOC2012 object detection challenge
+compared to the Fast-RCNN pipeline. We also release the code for this paper.
+",1,0,0,0,0,0
+18584,Non-thermalization in trapped atomic ion spin chains,"  Linear arrays of trapped and laser cooled atomic ions are a versatile
+platform for studying emergent phenomena in strongly-interacting many-body
+systems. Effective spins are encoded in long-lived electronic levels of each
+ion and made to interact through laser mediated optical dipole forces. The
+advantages of experiments with cold trapped ions, including high spatiotemporal
+resolution, decoupling from the external environment, and control over the
+system Hamiltonian, are used to measure quantum effects not always accessible
+in natural condensed matter samples. In this review we highlight recent work
+using trapped ions to explore a variety of non-ergodic phenomena in long-range
+interacting spin-models which are heralded by memory of out-of-equilibrium
+initial conditions. We observe long-lived memory in static magnetizations for
+quenched many-body localization and prethermalization, while memory is
+preserved in the periodic oscillations of a driven discrete time crystal state.
+",0,1,0,0,0,0
+18585,Generalized Uniformity Testing,"  In this work, we revisit the problem of uniformity testing of discrete
+probability distributions. A fundamental problem in distribution testing,
+testing uniformity over a known domain has been addressed over a significant
+line of works, and is by now fully understood.
+The complexity of deciding whether an unknown distribution is uniform over
+its unknown (and arbitrary) support, however, is much less clear. Yet, this
+task arises as soon as no prior knowledge on the domain is available, or
+whenever the samples originate from an unknown and unstructured universe. In
+this work, we introduce and study this generalized uniformity testing question,
+and establish nearly tight upper and lower bound showing that -- quite
+surprisingly -- its sample complexity significantly differs from the
+known-domain case. Moreover, our algorithm is intrinsically adaptive, in
+contrast to the overwhelming majority of known distribution testing algorithms.
+",1,0,1,1,0,0
+18586,"Refraction in exoplanet atmospheres: Photometric signatures, implications for transmission spectroscopy, and search in Kepler data","  Refraction deflects photons that pass through atmospheres, which affects
+transit light curves. Refraction thus provides an avenue to probe physical
+properties of exoplanet atmospheres and to constrain the presence of clouds and
+hazes. In addition, an effective surface can be imposed by refraction, thereby
+limiting the pressure levels probed by transmission spectroscopy. The main
+objective of the paper is to model the effects of refraction on photometric
+light curves for realistic planets and to explore the dependencies on
+atmospheric physical parameters. We also explore under which circumstances
+transmission spectra are significantly affected by refraction. Finally, we
+search for refraction signatures in photometric residuals in Kepler data. We
+use the model of Hui & Seager (2002) to compute deflection angles and
+refraction transit light curves, allowing us to explore the parameter space of
+atmospheric properties. The observational search is performed by stacking large
+samples of transit light curves from Kepler. We find that out-of-transit
+refraction shoulders are the most easily observable features, which can reach
+peak amplitudes of ~10 parts per million (ppm) for planets around Sun-like
+stars. More typical amplitudes are a few ppm or less for Jovians and at the
+sub-ppm level for super-Earths. Interestingly, the signal-to-noise ratio of any
+refraction residuals for planets orbiting Sun-like hosts are expected to be
+similar for planets orbiting red dwarfs. We also find that the maximum depth
+probed by transmission spectroscopy is not limited by refraction for weakly
+lensing planets, but that the incidence of refraction can vary significantly
+for strongly lensing planets. We find no signs of refraction features in the
+stacked Kepler light curves, which is in agreement with our model predictions.
+",0,1,0,0,0,0
+18587,Balancing Efficiency and Coverage in Human-Robot Dialogue Collection,"  We describe a multi-phased Wizard-of-Oz approach to collecting human-robot
+dialogue in a collaborative search and navigation task. The data is being used
+to train an initial automated robot dialogue system to support collaborative
+exploration tasks. In the first phase, a wizard freely typed robot utterances
+to human participants. For the second phase, this data was used to design a GUI
+that includes buttons for the most common communications, and templates for
+communications with varying parameters. Comparison of the data gathered in
+these phases show that the GUI enabled a faster pace of dialogue while still
+maintaining high coverage of suitable responses, enabling more efficient
+targeted data collection, and improvements in natural language understanding
+using GUI-collected data. As a promising first step towards interactive
+learning, this work shows that our approach enables the collection of useful
+training data for navigation-based HRI tasks.
+",1,0,0,0,0,0
+18588,Reinforcement Learning Based Argument Component Detection,"  Argument component detection (ACD) is an important sub-task in argumentation
+mining. ACD aims at detecting and classifying different argument components in
+natural language texts. Historical annotations (HAs) are important features the
+human annotators consider when they manually perform the ACD task. However, HAs
+are largely ignored by existing automatic ACD techniques. Reinforcement
+learning (RL) has proven to be an effective method for using HAs in some
+natural language processing tasks. In this work, we propose a RL-based ACD
+technique, and evaluate its performance on two well-annotated corpora. Results
+suggest that, in terms of classification accuracy, HAs-augmented RL outperforms
+plain RL by at most 17.85%, and outperforms the state-of-the-art supervised
+learning algorithm by at most 11.94%.
+",1,0,0,0,0,0
+18589,VUNet: Dynamic Scene View Synthesis for Traversability Estimation using an RGB Camera,"  We present VUNet, a novel view(VU) synthesis method for mobile robots in
+dynamic environments, and its application to the estimation of future
+traversability. Our method predicts future images for given virtual robot
+velocity commands using only RGB images at previous and current time steps. The
+future images result from applying two types of image changes to the previous
+and current images: 1) changes caused by different camera pose, and 2) changes
+due to the motion of the dynamic obstacles. We learn to predict these two types
+of changes disjointly using two novel network architectures, SNet and DNet. We
+combine SNet and DNet to synthesize future images that we pass to our
+previously presented method GONet to estimate the traversable areas around the
+robot. Our quantitative and qualitative evaluation indicate that our approach
+for view synthesis predicts accurate future images in both static and dynamic
+environments. We also show that these virtual images can be used to estimate
+future traversability correctly. We apply our view synthesis-based
+traversability estimation method to two applications for assisted
+teleoperation.
+",1,0,0,0,0,0
+18590,Bayesian hypothesis tests with diffuse priors: Can we have our cake and eat it too?,"  We introduce a new class of priors for Bayesian hypothesis testing, which we
+name ""cake priors"". These priors circumvent Bartlett's paradox (also called the
+Jeffreys-Lindley paradox); the problem associated with the use of diffuse
+priors leading to nonsensical statistical inferences. Cake priors allow the use
+of diffuse priors (having one's cake) while achieving theoretically justified
+inferences (eating it too). We demonstrate this methodology for Bayesian
+hypotheses tests for scenarios under which the one and two sample t-tests, and
+linear models are typically derived. The resulting Bayesian test statistic
+takes the form of a penalized likelihood ratio test statistic. By considering
+the sampling distribution under the null and alternative hypotheses we show for
+independent identically distributed regular parametric models that Bayesian
+hypothesis tests using cake priors are Chernoff-consistent, i.e., achieve zero
+type I and II errors asymptotically. Lindley's paradox is also discussed. We
+argue that a true Lindley's paradox will only occur with small probability for
+large sample sizes.
+",0,0,1,1,0,0
+18591,On a Novel Speech Representation Using Multitapered Modified Group Delay Function,"  In this paper, a novel multitaper modified group delay function-based
+representation for speech signals is proposed. With a set of phoneme-based
+experiments, it is shown that the proposed method performs better that an
+existing multitaper magnitude (MT-MAG) estimation technique, in terms of
+variance and MSE, both in spectral- and cepstral-domains. In particular, the
+performance of MT-MOGDF is found to be the best with the Thomson tapers.
+Additionally, the utility of the MT-MOGDF technique is highlighted in a speaker
+recognition experimental setup, where an improvement of around $20\%$ compared
+to the next-best technique is obtained. Moreover, the computational
+requirements of the proposed technique is comparable to that of MT-MAG. The
+proposed feature can be used in for many speech-related applications; in
+particular, it is best suited among those that require information of speaker
+and speech.
+",1,0,0,0,0,0
+18592,Persistent Flows and Non-Reciprocal Interactions in Deterministic Networks,"  This paper studies deterministic consensus networks with discrete-time
+dynamics under persistent flows and non-reciprocal agent interactions. An arc
+describing the interaction strength between two agents is said to be persistent
+if its weight function has an infinite $l_1$ norm. We discuss two balance
+conditions on the interactions between agents which generalize the arc-balance
+and cut-balance conditions in the literature respectively. The proposed
+conditions require that such a balance should be satisfied over each time
+window of a fixed length instead of at each time instant. We prove that in both
+cases global consensus is reached if and only if the persistent graph, which
+consists of all the persistent arcs, contains a directed spanning tree. The
+convergence rates of the system to consensus are also provided in terms of the
+interactions between agents having taken place. The results are obtained under
+a weak condition without assuming the existence of a positive lower bound of
+all the nonzero weights of arcs and are compared with the existing results.
+Illustrative examples are provided to show the critical importance of the
+nontrivial lower boundedness of the self-confidence of the agents.
+",1,0,0,0,0,0
+18593,Linking Fluid and Kinetic Scales in Solar Wind Turbulence,"  We investigate possible links between the large-scale and small-scale
+features of solar wind fluctuations across the frequency break separating fluid
+and kinetic regimes. The aim is to correlate the magnetic field fluctuations
+polarization at dissipative scales with the particular state of turbulence
+within the inertial range of fluctuations. We found clear correlations between
+each type of polarization within the kinetic regime and fluid parameters within
+the inertial range. Moreover, for the first time in literature, we showed that
+left-handed and right-handed polarized fluctuations occupy different areas of
+the plasma instabilities-temperature anisotropy plot, as expected for
+Alfv$\acute{\textrm{e}}$n Ion Cyclotron and Kinetic Alfv$\acute{\textrm{e}}$n
+waves, respectively.
+",0,1,0,0,0,0
+18594,Thresholds For Detecting An Anomalous Path From Noisy Environments,"  We consider the ""searching for a trail in a maze"" composite hypothesis
+testing problem, in which one attempts to detect an anomalous directed path in
+a lattice 2D box of side n based on observations on the nodes of the box. Under
+the signal hypothesis, one observes independent Gaussian variables of unit
+variance at all nodes, with zero, mean off the anomalous path and mean \mu_n on
+it. Under the null hypothesis, one observes i.i.d. standard Gaussians on all
+nodes. Arias-Castro et al. (2008) showed that if the unknown directed path
+under the signal hypothesis has known the initial location, then detection is
+possible (in the minimax sense) if \mu_n >> 1/\sqrt log n, while it is not
+possible if \mu_n << 1/ log n\sqrt log log n. In this paper, we show that this
+result continues to hold even when the initial location of the unknown path is
+not known. As is the case with Arias-Castro et al. (2008), the upper bound here
+also applies when the path is undirected. The improvement is achieved by
+replacing the linear detection statistic used in Arias-Castro et al. (2008)
+with a polynomial statistic, which is obtained by employing a multi-scale
+analysis on a quadratic statistic to bootstrap its performance. Our analysis is
+motivated by ideas developed in the context of the analysis of random polymers
+in Lacoin (2010).
+",0,0,1,1,0,0
+18595,On the topology of real Bott manifolds,"  The main aim of this article is to give a necessary and sufficient condition
+for a real Bott manifold to admit a spin structure and further give a
+combinatorial characterization for the spin structure in terms of the
+associated acyclic digraph.
+",0,0,1,0,0,0
+18596,Relative Error Tensor Low Rank Approximation,"  We consider relative error low rank approximation of $tensors$ with respect
+to the Frobenius norm: given an order-$q$ tensor $A \in
+\mathbb{R}^{\prod_{i=1}^q n_i}$, output a rank-$k$ tensor $B$ for which
+$\|A-B\|_F^2 \leq (1+\epsilon)$OPT, where OPT $= \inf_{\textrm{rank-}k~A'}
+\|A-A'\|_F^2$. Despite the success on obtaining relative error low rank
+approximations for matrices, no such results were known for tensors. One
+structural issue is that there may be no rank-$k$ tensor $A_k$ achieving the
+above infinum. Another, computational issue, is that an efficient relative
+error low rank approximation algorithm for tensors would allow one to compute
+the rank of a tensor, which is NP-hard. We bypass these issues via (1)
+bicriteria and (2) parameterized complexity solutions:
+(1) We give an algorithm which outputs a rank $k' = O((k/\epsilon)^{q-1})$
+tensor $B$ for which $\|A-B\|_F^2 \leq (1+\epsilon)$OPT in $nnz(A) + n \cdot
+\textrm{poly}(k/\epsilon)$ time in the real RAM model. Here $nnz(A)$ is the
+number of non-zero entries in $A$.
+(2) We give an algorithm for any $\delta >0$ which outputs a rank $k$ tensor
+$B$ for which $\|A-B\|_F^2 \leq (1+\epsilon)$OPT and runs in $ ( nnz(A) + n
+\cdot \textrm{poly}(k/\epsilon) + \exp(k^2/\epsilon) ) \cdot n^\delta$ time in
+the unit cost RAM model.
+For outputting a rank-$k$ tensor, or even a bicriteria solution with
+rank-$Ck$ for a certain constant $C > 1$, we show a $2^{\Omega(k^{1-o(1)})}$
+time lower bound under the Exponential Time Hypothesis.
+Our results give the first relative error low rank approximations for tensors
+for a large number of robust error measures for which nothing was known, as
+well as column row and tube subset selection. We also obtain new results for
+matrices, such as $nnz(A)$-time CUR decompositions, improving previous
+$nnz(A)\log n$-time algorithms, which may be of independent interest.
+",1,0,0,0,0,0
+18597,Bayesian Boolean Matrix Factorisation,"  Boolean matrix factorisation aims to decompose a binary data matrix into an
+approximate Boolean product of two low rank, binary matrices: one containing
+meaningful patterns, the other quantifying how the observations can be
+expressed as a combination of these patterns. We introduce the OrMachine, a
+probabilistic generative model for Boolean matrix factorisation and derive a
+Metropolised Gibbs sampler that facilitates efficient parallel posterior
+inference. On real world and simulated data, our method outperforms all
+currently existing approaches for Boolean matrix factorisation and completion.
+This is the first method to provide full posterior inference for Boolean Matrix
+factorisation which is relevant in applications, e.g. for controlling false
+positive rates in collaborative filtering and, crucially, improves the
+interpretability of the inferred patterns. The proposed algorithm scales to
+large datasets as we demonstrate by analysing single cell gene expression data
+in 1.3 million mouse brain cells across 11 thousand genes on commodity
+hardware.
+",1,0,0,1,0,0
+18598,Global existence and convergence of $Q$-curvature flow on manifolds of even dimension,"  Using a negative gradient flow approach, we generalize and unify some
+existence theorems for the problem of prescribing $Q$-curvature first by Baird,
+Fardoun, and Regbaoui (Calc. Var. 27 75-104) for $4$-manifolds with a possible
+sign-changing curvature candidate then by Brendle (Ann. Math. 158 323-343) for
+$n$-manifolds with even $n$ with positive curvature candidate to the case of
+$n$-manifolds of all even dimension with sign-changing curvature candidates.
+Making use of the \L ojasiewicz--Simon inequality, we also address the rate of
+the convergence.
+",0,0,1,0,0,0
+18599,Group Embeddings with Algorithmic Properties,"  We show that every countable group H with solvable word problem (=computable
+group) can be subnormally embedded into a 2-generated group G which also has
+solvable word problem. Moreover, the membership problem for H < G is also
+solvable. We also give estimates of time and space complexity of the word
+problem in G and of the membership problem for H < G.
+",0,0,1,0,0,0
+18600,General purpose graphics-processing-unit implementation of cosmological domain wall network evolution,"  Topological defects unavoidably form at symmetry breaking phase transitions
+in the early Universe. To probe the parameter space of theoretical models and
+set tighter experimental constraints (exploiting the recent advances in
+astrophysical observations), one requires more and more demanding simulations,
+and therefore more hardware resources and computation time. Improving the speed
+and efficiency of existing codes is essential. Here we present a General
+Purpose Graphics Processing Unit implementation of the canonical
+Press-Ryden-Spergel algorithm for the evolution of cosmological domain wall
+networks. This is ported to the Open Computing Language standard, and as a
+consequence significant speed-ups are achieved both in 2D and 3D simulations.
+",0,1,0,0,0,0
+18601,A Consistent Bayesian Formulation for Stochastic Inverse Problems Based on Push-forward Measures,"  We formulate, and present a numerical method for solving, an inverse problem
+for inferring parameters of a deterministic model from stochastic observational
+data (quantities of interest). The solution, given as a probability measure, is
+derived using a Bayesian updating approach for measurable maps that finds a
+posterior probability measure, that when propagated through the deterministic
+model produces a push-forward measure that exactly matches the observed
+probability measure on the data. Our approach for finding such posterior
+measures, which we call consistent Bayesian inference, is simple and only
+requires the computation of the push-forward probability measure induced by the
+combination of a prior probability measure and the deterministic model. We
+establish existence and uniqueness of observation-consistent posteriors and
+present stability and error analysis. We also discuss the relationships between
+consistent Bayesian inference, classical/statistical Bayesian inference, and a
+recently developed measure-theoretic approach for inference. Finally,
+analytical and numerical results are presented to highlight certain properties
+of the consistent Bayesian approach and the differences between this approach
+and the two aforementioned alternatives for inference.
+",0,0,1,1,0,0
+18602,Coherent State Mapping Ring-Polymer Molecular Dynamics for Non-Adiabatic quantum propagations,"  We introduce the coherent state mapping ring-polymer molecular dynamics
+(CS-RPMD), a new method that accurately describes electronic non-adiabatic
+dynamics with explicit nuclear quantization. This new approach is derived by
+using coherent state mapping representation for the electronic degrees of
+freedom (DOF) and the ring-polymer path-integral representation for the nuclear
+DOF. CS-RPMD Hamiltonian does not contain any inter-bead coupling term in the
+state-dependent potential, which is a key feature that ensures correct
+electronic Rabi oscillations. Hamilton's equation of motion is used to sample
+initial configurations and propagate the trajectories, preserving the
+distribution with classical symplectic evolution. In the special one-bead limit
+for mapping variables, CS-RPMD preserves the detailed balance. Numerical tests
+of this method with a two-state model system show a very good agreement with
+exact quantum results over a broad range of electronic couplings.
+",0,1,0,0,0,0
+18603,Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem,"  Stochastic gradient descent in continuous time (SGDCT) provides a
+computationally efficient method for the statistical learning of
+continuous-time models, which are widely used in science, engineering, and
+finance. The SGDCT algorithm follows a (noisy) descent direction along a
+continuous stream of data. The parameter updates occur in continuous time and
+satisfy a stochastic differential equation. This paper analyzes the asymptotic
+convergence rate of the SGDCT algorithm by proving a central limit theorem
+(CLT) for strongly convex objective functions and, under slightly stronger
+conditions, for non-convex objective functions as well. An L$^p$ convergence
+rate is also proven for the algorithm in the strongly convex case. The
+mathematical analysis lies at the intersection of stochastic analysis and
+statistical learning.
+",0,0,1,1,0,0
+18604,KMS states on $C^*$-algebras associated to a family of $*$-commuting local homeomorphisms,"  We consider a family of $*$-commuting local homeomorphisms on a compact
+space, and build a compactly aligned product system of Hilbert bimodules (in
+the sense of Fowler). This product system has a Nica-Toeplitz algebra and a
+Cuntz-Pimsner algebra. Both algebras carry a gauge action of a
+higher-dimensional torus, and there are many possible dynamics obtained by
+composing with different embeddings of the real line in this torus. We study
+the KMS states of these dynamics. For large inverse temperatures, we describe
+the simplex of KMS states on the Nica-Toeplitz algebra. To study KMS states for
+smaller inverse temperature, we consider a preferred dynamics for which there
+is a single critical inverse temperature. We find a KMS state on the
+Nica-Toeplitz algebra at this critical inverse temperature which factors
+through the Cuntz-Pimsner algebra. We then illustrate our results by
+considering backward shifts on the infinite-path spaces of a class of
+$k$-graphs.
+",0,0,1,0,0,0
+18605,Multi-Labelled Value Networks for Computer Go,"  This paper proposes a new approach to a novel value network architecture for
+the game Go, called a multi-labelled (ML) value network. In the ML value
+network, different values (win rates) are trained simultaneously for different
+settings of komi, a compensation given to balance the initiative of playing
+first. The ML value network has three advantages, (a) it outputs values for
+different komi, (b) it supports dynamic komi, and (c) it lowers the mean
+squared error (MSE). This paper also proposes a new dynamic komi method to
+improve game-playing strength. This paper also performs experiments to
+demonstrate the merits of the architecture. First, the MSE of the ML value
+network is generally lower than the value network alone. Second, the program
+based on the ML value network wins by a rate of 67.6% against the program based
+on the value network alone. Third, the program with the proposed dynamic komi
+method significantly improves the playing strength over the baseline that does
+not use dynamic komi, especially for handicap games. To our knowledge, up to
+date, no handicap games have been played openly by programs using value
+networks. This paper provides these programs with a useful approach to playing
+handicap games.
+",1,0,0,0,0,0
+18606,Lifting high-dimensional nonlinear models with Gaussian regressors,"  We study the problem of recovering a structured signal $\mathbf{x}_0$ from
+high-dimensional data $\mathbf{y}_i=f(\mathbf{a}_i^T\mathbf{x}_0)$ for some
+nonlinear (and potentially unknown) link function $f$, when the regressors
+$\mathbf{a}_i$ are iid Gaussian. Brillinger (1982) showed that ordinary
+least-squares estimates $\mathbf{x}_0$ up to a constant of proportionality
+$\mu_\ell$, which depends on $f$. Recently, Plan & Vershynin (2015) extended
+this result to the high-dimensional setting deriving sharp error bounds for the
+generalized Lasso. Unfortunately, both least-squares and the Lasso fail to
+recover $\mathbf{x}_0$ when $\mu_\ell=0$. For example, this includes all even
+link functions. We resolve this issue by proposing and analyzing an alternative
+convex recovery method. In a nutshell, our method treats such link functions as
+if they were linear in a lifted space of higher-dimension. Interestingly, our
+error analysis captures the effect of both the nonlinearity and the problem's
+geometry in a few simple summary parameters.
+",0,0,0,1,0,0
+18607,Functional Conceptual Substratum as a New Cognitive Mechanism for Mathematical Creation,"  We describe a new cognitive ability, i.e., functional conceptual substratum,
+used implicitly in the generation of several mathematical proofs and
+definitions. Furthermore, we present an initial (first-order) formalization of
+this mechanism together with its relation to classic notions like primitive
+positive definability and Diophantiveness. Additionally, we analyze the
+semantic variability of functional conceptual substratum when small syntactic
+modifications are done. Finally, we describe mathematically natural inference
+rules for definitions inspired by functional conceptual substratum and show
+that they are sound and complete w.r.t. standard calculi.
+",0,0,1,0,0,0
+18608,Applying Gromov's Amenable Localization to Geodesic Flows,"  Let $M$ be a compact connected smooth Riemannian $n$-manifold with boundary.
+We combine Gromov's amenable localization technique with the Poincaré
+duality to study the traversally generic geodesic flows on $SM$, the space of
+the spherical tangent bundle. Such flows generate stratifications of $SM$,
+governed by rich universal combinatorics. The stratification reflects the ways
+in which the geodesic flow trajectories interact with the boundary $\d(SM)$.
+Specifically, we get lower estimates of the numbers of connected components of
+these flow-generated strata of any given codimension $k$. These universal
+bounds are expressed in terms of the normed homology $H_k(M; \R)$ and $H_k(DM;
+\R)$, where $DM = M\cup_{\d M} M$ denotes the double of $M$. The norms here are
+the Gromov simplicial semi-norms in homology. The more complex the metric on
+$M$ is, the more numerous the strata of $SM$ and $S(DM)$ are. So one may regard
+our estimates as analogues of the Morse inequalities for the geodesics on
+manifolds with boundary.
+It turns out that some close relatives of the normed homology spaces form
+obstructions to the existence of globally $k$-convex traversally generic
+metrics on $M$.
+",0,0,1,0,0,0
+18609,Modeling the Ellsberg Paradox by Argument Strength,"  We present a formal measure of argument strength, which combines the ideas
+that conclusions of strong arguments are (i) highly probable and (ii) their
+uncertainty is relatively precise. Likewise, arguments are weak when their
+conclusion probability is low or when it is highly imprecise. We show how the
+proposed measure provides a new model of the Ellsberg paradox. Moreover, we
+further substantiate the psychological plausibility of our approach by an
+experiment (N = 60). The data show that the proposed measure predicts human
+inferences in the original Ellsberg task and in corresponding argument strength
+tasks. Finally, we report qualitative data taken from structured interviews on
+folk psychological conceptions on what argument strength means.
+",1,0,1,0,0,0
+18610,"Correction to the paper ""Some remarks on Davie's uniqueness theorem""","  The property 4 in Proposition 2.3 from the paper ""Some remarks on Davie's
+uniqueness theorem"" is replaced with a weaker assertion which is sufficient for
+the proof of the main results. Technical details and improvements are given.
+",0,0,1,0,0,0
+18611,An Estimation and Analysis Framework for the Rasch Model,"  The Rasch model is widely used for item response analysis in applications
+ranging from recommender systems to psychology, education, and finance. While a
+number of estimators have been proposed for the Rasch model over the last
+decades, the available analytical performance guarantees are mostly asymptotic.
+This paper provides a framework that relies on a novel linear minimum
+mean-squared error (L-MMSE) estimator which enables an exact, nonasymptotic,
+and closed-form analysis of the parameter estimation error under the Rasch
+model. The proposed framework provides guidelines on the number of items and
+responses required to attain low estimation errors in tests or surveys. We
+furthermore demonstrate its efficacy on a number of real-world collaborative
+filtering datasets, which reveals that the proposed L-MMSE estimator performs
+on par with state-of-the-art nonlinear estimators in terms of predictive
+performance.
+",0,0,0,1,0,0
+18612,Do planets remember how they formed?,"  One of the most directly observable features of a transiting multi-planet
+system is their size-ordering when ranked in orbital separation. Kepler has
+revealed a rich diversity of outcomes, from perfectly ordered systems, like
+Kepler-80, to ostensibly disordered systems, like Kepler-20. Under the
+hypothesis that systems are born via preferred formation pathways, one might
+reasonably expect non-random size-orderings reflecting these processes.
+However, subsequent dynamical evolution, often chaotic and turbulent in nature,
+may erode this information and so here we ask - do systems remember how they
+formed? To address this, we devise a model to define the entropy of a planetary
+system's size-ordering, by first comparing differences between neighboring
+planets and then extending to accommodate differences across the chain. We
+derive closed-form solutions for many of the micro state occupancies and
+provide public code with look-up tables to compute entropy for up to ten-planet
+systems. All three proposed entropy definitions exhibit the expected property
+that their credible interval increases with respect to a proxy for time. We
+find that the observed Kepler multis display a highly significant deficit in
+entropy compared to a randomly generated population. Incorporating a filter for
+systems deemed likely to be dynamically packed, we show that this result is
+robust against the possibility of missing planets too. Put together, our work
+establishes that Kepler systems do indeed remember something of their younger
+years and highlights the value of information theory for exoplanetary science.
+",0,1,0,0,0,0
+18613,Demarcating circulation regimes of synchronously rotating terrestrial planets within the habitable zone,"  We investigate the atmospheric dynamics of terrestrial planets in synchronous
+rotation within the habitable zone of low-mass stars using the Community
+Atmosphere Model (CAM). The surface temperature contrast between day and night
+hemispheres decreases with an increase in incident stellar flux, which is
+opposite the trend seen on gas giants. We define three dynamical regimes in
+terms of the equatorial Rossby deformation radius and the Rhines length. The
+slow rotation regime has a mean zonal circulation that spans from day to night
+side, with both the Rossby deformation radius and the Rhines length exceeding
+planetary radius, which occurs for planets around stars with effective
+temperatures of 3300 K to 4500 K (rotation period > 20 days). Rapid rotators
+have a mean zonal circulation that partially spans a hemisphere and with banded
+cloud formation beneath the substellar point, with the Rossby deformation
+radius is less than planetary radius, which occurs for planets orbiting stars
+with effective temperatures of less than 3000 K (rotation period < 5 days). In
+between is the Rhines rotation regime, which retains a thermally-direct
+circulation from day to night side but also features midlatitude
+turbulence-driven zonal jets. Rhines rotators occur for planets around stars in
+the range of 3000 K to 3300 K (rotation period ~ 5 to 20 days), where the
+Rhines length is greater than planetary radius but the Rossby deformation
+radius is less than planetary radius. The dynamical state can be
+observationally inferred from comparing the morphology of the thermal emission
+phase curves of synchronously rotating planets.
+",0,1,0,0,0,0
+18614,Versatile Large-Area Custom-Feature van der Waals Epitaxy of Topological Insulators,"  As the focus of applied research in topological insulators (TI) evolves, the
+need to synthesize large-area TI films for practical device applications takes
+center stage. However, constructing scalable and adaptable processes for
+high-quality TI compounds remains a challenge. To this end, a versatile van der
+Waals epitaxy (vdWE) process for custom-feature Bismuth Telluro-Sulfide TI
+growth and fabrication is presented, achieved through selective-area
+fluorination and modification of surface free-energy on mica. The TI features
+grow epitaxially in large single-crystal trigonal domains, exhibiting armchair
+or zigzag crystalline edges highly oriented with the underlying mica lattice
+and only two preferred domain orientations mirrored at $180^\circ$. As-grown
+feature thickness dependence on lateral dimensions and denuded zones at
+boundaries are observed, as explained by a semi-empirical two-species surface
+migration model with robust estimates of growth parameters and elucidating the
+role of selective-area surface modification. Topological surface states
+contribute up to 60% of device conductance at room-temperature, indicating
+excellent electronic quality. High-yield microfabrication and the adaptable
+vdWE growth mechanism with readily alterable precursor and substrate
+combinations, lend the process versatility to realize crystalline TI synthesis
+in arbitrary shapes and arrays suitable for facile integration with processes
+ranging from rapid prototyping to scalable manufacturing.
+",0,1,0,0,0,0
+18615,Sharp off-diagonal weighted norm estimates for the Bergman projection,"  We prove that for $1<p\le q<\infty$, $qp\geq {p'}^2$ or $p'q'\geq q^2$,
+$\frac{1}{p}+\frac{1}{p'}=\frac{1}{q}+\frac{1}{q'}=1$, $$\|\omega
+P_\alpha(f)\|_{L^p(\mathcal{H},y^{\alpha+(2+\alpha)(\frac{q}{p}-1)}dxdy)}\le
+C_{p,q,\alpha}[\omega]_{B_{p,q,\alpha}}^{(\frac{1}{p'}+\frac{1}{q})\max\{1,\frac{p'}{q}\}}\|\omega
+f\|_{L^p(\mathcal{H},y^{\alpha}dxdy)}$$ where $P_\alpha$ is the weighted
+Bergman projection of the upper-half plane $\mathcal{H}$, and
+$$[\omega]_{B_{p,q,\alpha}}:=\sup_{I\subset
+\mathbb{R}}\left(\frac{1}{|I|^{2+\alpha}}\int_{Q_I}\omega^{q}dV_\alpha\right)\left(\frac{1}{|I|^{2+\alpha}}\int_{Q_I}\omega^{-p'}dV_\alpha\right)^{\frac{q}{p'}},$$
+with $Q_I=\{z=x+iy\in \mathbb{C}: x\in I, 0<y<|I|\}$.
+",0,0,1,0,0,0
+18616,An Automated Scalable Framework for Distributing Radio Astronomy Processing Across Clusters and Clouds,"  The Low Frequency Array (LOFAR) radio telescope is an international aperture
+synthesis radio telescope used to study the Universe at low frequencies. One of
+the goals of the LOFAR telescope is to conduct deep wide-field surveys. Here we
+will discuss a framework for the processing of the LOFAR Two Meter Sky Survey
+(LoTSS). This survey will produce close to 50 PB of data within five years.
+These data rates require processing at locations with high-speed access to the
+archived data. To complete the LoTSS project, the processing software needs to
+be made portable and moved to clusters with a high bandwidth connection to the
+data archive. This work presents a framework that makes the LOFAR software
+portable, and is used to scale out LOFAR data reduction. Previous work was
+successful in preprocessing LOFAR data on a cluster of isolated nodes. This
+framework builds upon it and and is currently operational. It is designed to be
+portable, scalable, automated and general. This paper describes its design and
+high level operation and the initial results processing LoTSS data.
+",0,1,0,0,0,0
+18617,Learning multiple visual domains with residual adapters,"  There is a growing interest in learning data representations that work well
+for many different types of problems and data. In this paper, we look in
+particular at the task of learning a single visual representation that can be
+successfully utilized in the analysis of very different types of images, from
+dog breeds to stop signs and digits. Inspired by recent work on learning
+networks that predict the parameters of another, we develop a tunable deep
+network architecture that, by means of adapter residual modules, can be steered
+on the fly to diverse visual domains. Our method achieves a high degree of
+parameter sharing while maintaining or even improving the accuracy of
+domain-specific representations. We also introduce the Visual Decathlon
+Challenge, a benchmark that evaluates the ability of representations to capture
+simultaneously ten very different visual domains and measures their ability to
+recognize well uniformly.
+",1,0,0,1,0,0
+18618,Cosmic quantum optical probing of quantum gravity through a gravitational lensLens,"  We consider the nonunitary quantum dynamics of neutral massless scalar
+particles used to model photons around a massive gravitational lens. The
+gravitational interaction between the lensing mass and asymptotically free
+particles is described by their second-quantized scattering wavefunctions.
+Remarkably, the zero-point spacetime fluctuations can induce significant
+decoherence of the scattered states with spontaneous emission of gravitons,
+thereby reducing the particles' coherence as well as energy. This new effect
+suggests that, when photon polarizations are negligible, such quantum gravity
+phenomena could lead to measurable anomalous redshift of recently studied
+astrophysical lasers through a gravitational lens in the range of black holes
+and galaxy clusters.
+",0,1,0,0,0,0
+18619,Combinatorial Miller-Hagberg Algorithm for Randomization of Dense Networks,"  We propose a slightly revised Miller-Hagberg (MH) algorithm that efficiently
+generates a random network from a given expected degree sequence. The revision
+was to replace the approximated edge probability between a pair of nodes with a
+combinatorically calculated edge probability that better captures the
+likelihood of edge presence especially where edges are dense. The computational
+complexity of this combinatorial MH algorithm is still in the same order as the
+original one. We evaluated the proposed algorithm through several numerical
+experiments. The results demonstrated that the proposed algorithm was
+particularly good at accurately representing high-degree nodes in dense,
+heterogeneous networks. This algorithm may be a useful alternative of other
+more established network randomization methods, given that the data are
+increasingly becoming larger and denser in today's network science research.
+",1,0,0,0,0,0
+18620,Topological Insulators in Random Lattices,"  Our understanding of topological insulators is based on an underlying
+crystalline lattice where the local electronic degrees of freedom at different
+sites hybridize with each other in ways that produce nontrivial band topology,
+and the search for material systems to realize such phases have been strongly
+influenced by this. Here we theoretically demonstrate topological insulators in
+systems with a random distribution of sites in space, i. e., a random lattice.
+This is achieved by constructing hopping models on random lattices whose ground
+states possess nontrivial topological nature (characterized e. g., by Bott
+indices) that manifests as quantized conductances in systems with a boundary.
+By tuning parameters such as the density of sites (for a given range of fermion
+hopping), we can achieve transitions from trivial to topological phases. We
+discuss interesting features of these transitions. In two spatial dimensions,
+we show this for all five symmetry classes (A, AII, D, DIII and C) that are
+known to host nontrivial topology in crystalline systems. We expect similar
+physics to be realizable in any dimension and provide an explicit example of a
+$Z_2$ topological insulator on a random lattice in three spatial dimensions.
+Our study not only provides a deeper understanding of the topological phases of
+non-interacting fermions, but also suggests new directions in the pursuit of
+the laboratory realization of topological quantum matter.
+",0,1,0,0,0,0
+18621,DiGrad: Multi-Task Reinforcement Learning with Shared Actions,"  Most reinforcement learning algorithms are inefficient for learning multiple
+tasks in complex robotic systems, where different tasks share a set of actions.
+In such environments a compound policy may be learnt with shared neural network
+parameters, which performs multiple tasks concurrently. However such compound
+policy may get biased towards a task or the gradients from different tasks
+negate each other, making the learning unstable and sometimes less data
+efficient. In this paper, we propose a new approach for simultaneous training
+of multiple tasks sharing a set of common actions in continuous action spaces,
+which we call as DiGrad (Differential Policy Gradient). The proposed framework
+is based on differential policy gradients and can accommodate multi-task
+learning in a single actor-critic network. We also propose a simple heuristic
+in the differential policy gradient update to further improve the learning. The
+proposed architecture was tested on 8 link planar manipulator and 27 degrees of
+freedom(DoF) Humanoid for learning multi-goal reachability tasks for 3 and 2
+end effectors respectively. We show that our approach supports efficient
+multi-task learning in complex robotic systems, outperforming related methods
+in continuous action spaces.
+",1,0,0,1,0,0
+18622,Narratives of Quantum Theory in the Age of Quantum Technologies,"  Quantum technologies can be presented to the public with or without
+introducing a strange trait of quantum theory responsible for their
+non-classical efficiency. Traditionally the message was centered on the
+superposition principle, while entanglement and properties such as
+contextuality have been gaining ground recently. A less theoretical approach is
+focused on simple protocols that enable technological applications. It results
+in a pragmatic narrative built with the help of the resource paradigm and
+principle-based reconstructions. I discuss the advantages and weaknesses of
+these methods. To illustrate the importance of new metaphors beyond the
+Schrödinger cat, I briefly describe a non-mathematical narrative about
+entanglement that conveys an idea of some of its unusual properties. If quantum
+technologists are to succeed in building trust in their work, they ought to
+provoke an aesthetic perception in the public commensurable with the
+mathematical beauty of quantum theory experienced by the physicist. The power
+of the narrative method lies in its capacity to do so.
+",0,1,0,0,0,0
+18623,Is Information in the Brain Represented in Continuous or Discrete Form?,"  The question of continuous-versus-discrete information representation in the
+brain is a fundamental yet unresolved physiological question. Historically,
+most analyses assume a continuous representation without considering the
+alternative possibility of a discrete representation. Our work explores the
+plausibility of both representations, and answers the question from a
+communications engineering perspective. Drawing on the well-established
+Shannon's communications theory, we posit that information in the brain is
+represented in a discrete form. Using a computer simulation, we show that
+information cannot be communicated reliably between neurons using a continuous
+representation, due to the presence of noise; neural information has to be in a
+discrete form. In addition, we designed 3 (human) behavioral experiments on
+probability estimation and analyzed the data using a novel discrete (quantized)
+model of probability. Under a discrete model of probability, two distinct
+probabilities (say, 0.57 and 0.58) are treated indifferently. We found that
+data from all participants were better fit to discrete models than continuous
+ones. Furthermore, we re-analyzed the data from a published (human) behavioral
+study on intertemporal choice using a novel discrete (quantized) model of
+intertemporal choice. Under such a model, two distinct time delays (say, 16
+days and 17 days) are treated indifferently. We found corroborating results,
+showing that data from all participants were better fit to discrete models than
+continuous ones. In summary, all results reported here support our discrete
+hypothesis of information representation in the brain, which signifies a major
+demarcation from the current understanding of the brain's physiology.
+",0,0,0,0,1,0
+18624,Real-Time Background Subtraction Using Adaptive Sampling and Cascade of Gaussians,"  Background-Foreground classification is a fundamental well-studied problem in
+computer vision. Due to the pixel-wise nature of modeling and processing in the
+algorithm, it is usually difficult to satisfy real-time constraints. There is a
+trade-off between the speed (because of model complexity) and accuracy.
+Inspired by the rejection cascade of Viola-Jones classifier, we decompose the
+Gaussian Mixture Model (GMM) into an adaptive cascade of classifiers. This way
+we achieve a good improvement in speed without compensating for accuracy. In
+the training phase, we learn multiple KDEs for different durations to be used
+as strong prior distribution and detect probable oscillating pixels which
+usually results in misclassifications. We propose a confidence measure for the
+classifier based on temporal consistency and the prior distribution. The
+confidence measure thus derived is used to adapt the learning rate and the
+thresholds of the model, to improve accuracy. The confidence measure is also
+employed to perform temporal and spatial sampling in a principled way. We
+demonstrate a speed-up factor of 5x to 10x and 17 percent average improvement
+in accuracy over several standard videos.
+",1,0,0,1,0,0
+18625,Multilevel nested simulation for efficient risk estimation,"  We investigate the problem of computing a nested expectation of the form
+$\mathbb{P}[\mathbb{E}[X|Y]
+\!\geq\!0]\!=\!\mathbb{E}[\textrm{H}(\mathbb{E}[X|Y])]$ where $\textrm{H}$ is
+the Heaviside function. This nested expectation appears, for example, when
+estimating the probability of a large loss from a financial portfolio. We
+present a method that combines the idea of using Multilevel Monte Carlo (MLMC)
+for nested expectations with the idea of adaptively selecting the number of
+samples in the approximation of the inner expectation, as proposed by (Broadie
+et al., 2011). We propose and analyse an algorithm that adaptively selects the
+number of inner samples on each MLMC level and prove that the resulting MLMC
+method with adaptive sampling has an $\mathcal{O}\left(
+\varepsilon^{-2}|\log\varepsilon|^2 \right)$ complexity to achieve a root
+mean-squared error $\varepsilon$. The theoretical analysis is verified by
+numerical experiments on a simple model problem. We also present a stochastic
+root-finding algorithm that, combined with our adaptive methods, can be used to
+compute other risk measures such as Value-at-Risk (VaR) and Conditional
+Value-at-Risk (CVaR), with the latter being achieved with
+$\mathcal{O}\left(\varepsilon^{-2}\right)$ complexity.
+",0,0,0,0,0,1
+18626,Construction of a relativistic Ornstein-Uhlenbeck process,"  Based on a version of Dudley's Wiener process on the mass shell in the
+momentum Minkowski space of a massive point particle, a model of a relativistic
+Ornstein--Uhlenbeck process is constructed by addition of a specific drift
+term. The invariant distribution of this momentum process as well as other
+associated processes are computed.
+",0,0,1,0,0,0
+18627,Safe Trajectory Synthesis for Autonomous Driving in Unforeseen Environments,"  Path planning for autonomous vehicles in arbitrary environments requires a
+guarantee of safety, but this can be impractical to ensure in real-time when
+the vehicle is described with a high-fidelity model. To address this problem,
+this paper develops a method to perform trajectory design by considering a
+low-fidelity model that accounts for model mismatch. The presented method
+begins by computing a conservative Forward Reachable Set (FRS) of a
+high-fidelity model's trajectories produced when tracking trajectories of a
+low-fidelity model over a finite time horizon. At runtime, the vehicle
+intersects this FRS with obstacles in the environment to eliminate trajectories
+that can lead to a collision, then selects an optimal plan from the remaining
+safe set. By bounding the time for this set intersection and subsequent path
+selection, this paper proves a lower bound for the FRS time horizon and sensing
+horizon to guarantee safety. This method is demonstrated in simulation using a
+kinematic Dubin's car as the low-fidelity model and a dynamic unicycle as the
+high-fidelity model.
+",1,0,0,0,0,0
+18628,"A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark","  Training deep networks is expensive and time-consuming with the training
+period increasing with data size and growth in model parameters. In this paper,
+we provide a framework for distributed training of deep networks over a cluster
+of CPUs in Apache Spark. The framework implements both Data Parallelism and
+Model Parallelism making it suitable to use for deep networks which require
+huge training data and model parameters which are too big to fit into the
+memory of a single machine. It can be scaled easily over a cluster of cheap
+commodity hardware to attain significant speedup and obtain better results
+making it quite economical as compared to farm of GPUs and supercomputers. We
+have proposed a new algorithm for training of deep networks for the case when
+the network is partitioned across the machines (Model Parallelism) along with
+detailed cost analysis and proof of convergence of the same. We have developed
+implementations for Fully-Connected Feedforward Networks, Convolutional Neural
+Networks, Recurrent Neural Networks and Long Short-Term Memory architectures.
+We present the results of extensive simulations demonstrating the speedup and
+accuracy obtained by our framework for different sizes of the data and model
+parameters with variation in the number of worker cores/partitions; thereby
+showing that our proposed framework can achieve significant speedup (upto 11X
+for CNN) and is also quite scalable.
+",1,0,0,1,0,0
+18629,"ICLabel: An automated electroencephalographic independent component classifier, dataset, and website","  The electroencephalogram (EEG) provides a non-invasive, minimally
+restrictive, and relatively low cost measure of mesoscale brain dynamics with
+high temporal resolution. Although signals recorded in parallel by multiple,
+near-adjacent EEG scalp electrode channels are highly-correlated and combine
+signals from many different sources, biological and non-biological, independent
+component analysis (ICA) has been shown to isolate the various source generator
+processes underlying those recordings. Independent components (IC) found by ICA
+decomposition can be manually inspected, selected, and interpreted, but doing
+so requires both time and practice as ICs have no particular order or intrinsic
+interpretations and therefore require further study of their properties.
+Alternatively, sufficiently-accurate automated IC classifiers can be used to
+classify ICs into broad source categories, speeding the analysis of EEG studies
+with many subjects and enabling the use of ICA decomposition in near-real-time
+applications. While many such classifiers have been proposed recently, this
+work presents the ICLabel project comprised of (1) an IC dataset containing
+spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG
+recordings, (2) a website for collecting crowdsourced IC labels and educating
+EEG researchers and practitioners about IC interpretation, and (3) the
+automated ICLabel classifier. The classifier improves upon existing methods in
+two ways: by improving the accuracy of the computed label estimates and by
+enhancing its computational efficiency. The ICLabel classifier outperforms or
+performs comparably to the previous best publicly available method for all
+measured IC categories while computing those labels ten times faster than that
+classifier as shown in a rigorous comparison against all other publicly
+available EEG IC classifiers.
+",1,0,0,1,0,0
+18630,Inference on a New Class of Sample Average Treatment Effects,"  We derive new variance formulas for inference on a general class of estimands
+of causal average treatment effects in a Randomized Control Trial (RCT). We
+generalize Robins (1988) and show that when the estimand of interest is the
+Sample Average Treatment Effect of the Treated (SATT or SATC for controls), a
+consistent variance estimator exists. Although these estimands are equal to the
+Sample Average Treatment Effect (SATE) in expectation, potentially large
+differences in both accuracy and coverage can occur by the change of estimand,
+even asymptotically. Inference on the SATE, even using a conservative
+confidence interval, provides incorrect coverage of the SATT or SATC. We derive
+the variance and limiting distribution of a new and general class of
+estimands---any mixing between SATT and SATC---for which the SATE is a specific
+case. We demonstrate the applicability of the new theoretical results using
+Monte-Carlo simulations and an empirical application with hundreds of online
+experiments with an average sample size of approximately one hundred million
+observations per experiment. An R package, estCI, that implements all the
+proposed estimation procedures is available.
+",0,0,1,1,0,0
+18631,A split step Fourier/discontinuous Galerkin scheme for the Kadomtsev--Petviashvili equation,"  In this paper we propose a method to solve the Kadomtsev--Petviashvili
+equation based on splitting the linear part of the equation from the nonlinear
+part. The linear part is treated using FFTs, while the nonlinear part is
+approximated using a semi-Lagrangian discontinuous Galerkin approach of
+arbitrary order.
+We demonstrate the efficiency and accuracy of the numerical method by
+providing a range of numerical simulations. In particular, we find that our
+approach can outperform the numerical methods considered in the literature by
+up to a factor of five. Although we focus on the Kadomtsev--Petviashvili
+equation in this paper, the proposed numerical scheme can be extended to a
+range of related models as well.
+",0,1,1,0,0,0
+18632,Dense Transformer Networks,"  The key idea of current deep learning methods for dense prediction is to
+apply a model on a regular patch centered on each pixel to make pixel-wise
+predictions. These methods are limited in the sense that the patches are
+determined by network architecture instead of learned from data. In this work,
+we propose the dense transformer networks, which can learn the shapes and sizes
+of patches from data. The dense transformer networks employ an encoder-decoder
+architecture, and a pair of dense transformer modules are inserted into each of
+the encoder and decoder paths. The novelty of this work is that we provide
+technical solutions for learning the shapes and sizes of patches from data and
+efficiently restoring the spatial correspondence required for dense prediction.
+The proposed dense transformer modules are differentiable, thus the entire
+network can be trained. We apply the proposed networks on natural and
+biological image segmentation tasks and show superior performance is achieved
+in comparison to baseline methods.
+",1,0,0,1,0,0
+18633,Tetragonal CH3NH3PbI3 Is Ferroelectric,"  Halide perovskite (HaP) semiconductors are revolutionizing photovoltaic (PV)
+solar energy conversion by showing remarkable performance of solar cells made
+with esp. tetragonal methylammonium lead tri-iodide (MAPbI3). In particular,
+the low voltage loss of these cells implies a remarkably low recombination rate
+of photogenerated carriers. It was suggested that low recombination can be due
+to spatial separation of electrons and holes, a possibility if MAPbI3 is a
+semiconducting ferroelectric, which, however, requires clear experimental
+evidence. As a first step we show that, in operando, MAPbI3 (unlike MAPbBr3) is
+pyroelectric, which implies it can be ferroelectric. The next step, proving it
+is (not) ferroelectric, is challenging, because of the material s relatively
+high electrical conductance (a consequence of an optical band gap suitable for
+PV conversion!) and low stability under high applied bias voltage. This
+excludes normal measurements of a ferroelectric hysteresis loop to prove
+ferroelctricity s hallmark for switchable polarization. By adopting an approach
+suitable for electrically leaky materials as MAPbI3, we show here ferroelectric
+hysteresis from well-characterized single crystals at low temperature (still
+within the tetragonal phase, which is the room temperature stable phase). Using
+chemical etching, we also image polar domains, the structural fingerprint for
+ferroelectricity, periodically stacked along the polar axis of the crystal,
+which, as predicted by theory, scale with the overall crystal size. We also
+succeeded in detecting clear second-harmonic generation, direct evidence for
+the material s non-centrosymmetry. We note that the material s ferroelectric
+nature, can, but not obviously need to be important in a PV cell, operating
+around room temperature.
+",0,1,0,0,0,0
+18634,A Simple and Efficient MapReduce Algorithm for Data Cube Materialization,"  Data cube materialization is a classical database operator introduced in Gray
+et al.~(Data Mining and Knowledge Discovery, Vol.~1), which is critical for
+many analysis tasks. Nandi et al.~(Transactions on Knowledge and Data
+Engineering, Vol.~6) first studied cube materialization for large scale
+datasets using the MapReduce framework, and proposed a sophisticated
+modification of a simple broadcast algorithm to handle a dataset with a 216GB
+cube size within 25 minutes with 2k machines in 2012. We take a different
+approach, and propose a simple MapReduce algorithm which (1) minimizes the
+total number of copy-add operations, (2) leverages locality of computation, and
+(3) balances work evenly across machines. As a result, the algorithm shows
+excellent performance, and materialized a real dataset with a cube size of
+35.0G tuples and 1.75T bytes in 54 minutes, with 0.4k machines in 2014.
+",1,0,0,0,0,0
+18635,"Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations","  The success of deep convolutional architectures is often attributed in part
+to their ability to learn multiscale and invariant representations of natural
+signals. However, a precise study of these properties and how they affect
+learning guarantees is still missing. In this paper, we consider deep
+convolutional representations of signals; we study their invariance to
+translations and to more general groups of transformations, their stability to
+the action of diffeomorphisms, and their ability to preserve signal
+information. This analysis is carried by introducing a multilayer kernel based
+on convolutional kernel networks and by studying the geometry induced by the
+kernel mapping. We then characterize the corresponding reproducing kernel
+Hilbert space (RKHS), showing that it contains a large class of convolutional
+neural networks with homogeneous activation functions. This analysis allows us
+to separate data representation from learning, and to provide a canonical
+measure of model complexity, the RKHS norm, which controls both stability and
+generalization of any learned model. In addition to models in the constructed
+RKHS, our stability analysis also applies to convolutional networks with
+generic activations such as rectified linear units, and we discuss its
+relationship with recent generalization bounds based on spectral norms.
+",1,0,0,1,0,0
+18636,Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition,"  In this paper, we aim at the completion problem of high order tensor data
+with missing entries. The existing tensor factorization and completion methods
+suffer from the curse of dimensionality when the order of tensor N>>3. To
+overcome this problem, we propose an efficient algorithm called TT-WOPT
+(Tensor-train Weighted OPTimization) to find the latent core tensors of tensor
+data and recover the missing entries. Tensor-train decomposition, which has the
+powerful representation ability with linear scalability to tensor order, is
+employed in our algorithm. The experimental results on synthetic data and
+natural image completion demonstrate that our method significantly outperforms
+the other related methods. Especially when the missing rate of data is very
+high, e.g., 85% to 99%, our algorithm can achieve much better performance than
+other state-of-the-art algorithms.
+",1,0,0,0,0,0
+18637,On the inner products of some Deligne--Lusztig type representations,"  In this paper we introduce a family of Deligne--Lusztig type varieties
+attached to connected reductive groups over quotients of discrete valuation
+rings, naturally generalising the higher Deligne--Lusztig varieties and some
+constructions related to the algebraisation problem raised by Lusztig. We
+establish the inner product formula between the representations associated to
+these varieties and the higher Deligne--Lusztig representations.
+",0,0,1,0,0,0
+18638,An invariant for embedded Fano manifolds covered by linear spaces,"  For an embedded Fano manifold $X$, we introduce a new invariant $S_X$ related
+to the dimension of covering linear spaces. The aim of this paper is to
+classify Fano manifolds $X$ which have large $S_X$.
+",0,0,1,0,0,0
+18639,Delivery Latency Trade-Offs of Heterogeneous Contents in Fog Radio Access Networks,"  A Fog Radio Access Network (F-RAN) is a cellular wireless system that enables
+content delivery via the caching of popular content at edge nodes (ENs) and
+cloud processing. The existing information-theoretic analyses of F-RAN systems,
+and special cases thereof, make the assumption that all requests should be
+guaranteed the same delivery latency, which results in identical latency for
+all files in the content library. In practice, however, contents may have
+heterogeneous timeliness requirements depending on the applications that
+operate on them. Given per-EN cache capacity constraint, there exists a
+fundamental trade-off among the delivery latencies of different users'
+requests, since contents that are allocated more cache space generally enjoy
+lower delivery latencies. For the case with two ENs and two users, the optimal
+latency trade-off is characterized in the high-SNR regime in terms of the
+Normalized Delivery Time (NDT) metric. The main results are illustrated by
+numerical examples.
+",1,0,0,0,0,0
+18640,Leaking Uninitialized Secure Enclave Memory via Structure Padding (Extended Abstract),"  Intel software guard extensions (SGX) aims to provide an isolated execution
+environment, known as an enclave, for a user-level process to maximize its
+confidentiality and integrity. In this paper, we study how uninitialized data
+inside a secure enclave can be leaked via structure padding. We found that,
+during ECALL and OCALL, proxy functions that are automatically generated by the
+Intel SGX Software Development Kit (SDK) fully copy structure variables from an
+enclave to the normal memory to return the result of an ECALL function and to
+pass input parameters to an OCALL function. If the structure variables contain
+padding bytes, uninitialized enclave memory, which might contain confidential
+data like a private key, can be copied to the normal memory through the padding
+bytes. We also consider potential countermeasures against these security
+threats.
+",1,0,0,0,0,0
+18641,Asymptotic and numerical analysis of a stochastic PDE model of volume transmission,"  Volume transmission is an important neural communication pathway in which
+neurons in one brain region influence the neurotransmitter concentration in the
+extracellular space of a distant brain region. In this paper, we apply
+asymptotic analysis to a stochastic partial differential equation model of
+volume transmission to calculate the neurotransmitter concentration in the
+extracellular space. Our model involves the diffusion equation in a
+three-dimensional domain with interior holes that randomly switch between being
+either sources or sinks. These holes model nerve varicosities that alternate
+between releasing and absorbing neurotransmitter, according to when they fire
+action potentials. In the case that the holes are small, we compute
+analytically the first two nonzero terms in an asymptotic expansion of the
+average neurotransmitter concentration. The first term shows that the
+concentration is spatially constant to leading order and that this constant is
+independent of many details in the problem. Specifically, this constant first
+term is independent of the number and location of nerve varicosities, neural
+firing correlations, and the size and geometry of the extracellular space. The
+second term shows how these factors affect the concentration at second order.
+Interestingly, the second term is also spatially constant under some mild
+assumptions. We verify our asymptotic results by high-order numerical
+simulation using radial basis function-generated finite differences.
+",0,0,0,0,1,0
+18642,Mixtures of Hidden Truncation Hyperbolic Factor Analyzers,"  The mixture of factor analyzers model was first introduced over 20 years ago
+and, in the meantime, has been extended to several non-Gaussian analogues. In
+general, these analogues account for situations with heavy tailed and/or skewed
+clusters. An approach is introduced that unifies many of these approaches into
+one very general model: the mixture of hidden truncation hyperbolic factor
+analyzers (MHTHFA) model. In the process of doing this, a hidden truncation
+hyperbolic factor analysis model is also introduced. The MHTHFA model is
+illustrated for clustering as well as semi-supervised classification using two
+real datasets.
+",0,0,0,1,0,0
+18643,"Representations on Partially Holomorphic Cohomology Spaces, Revisited","  This is a semi--expository update and rewrite of my 1974 AMS AMS Memoir
+describing Plancherel formulae and partial Dolbeault cohomology realizations
+for standard tempered representations for general real reductive Lie groups.
+Even after so many years, much of that Memoir is up to date, but of course
+there have been a number of refinements, advances and new developments, most of
+which have applied to smaller classes of real reductive Lie groups. Here we
+rewrite that AMS Memoir in in view of these advances and indicate the ties with
+some of the more recent (or at least less classical) approaches to geometric
+realization of unitary representations.
+",0,0,1,0,0,0
+18644,Underground tests of quantum mechanics. Whispers in the cosmic silence?,"  By performing X-rays measurements in the ""cosmic silence"" of the underground
+laboratory of Gran Sasso, LNGS-INFN, we test a basic principle of quantum
+mechanics: the Pauli Exclusion Principle (PEP), for electrons. We present the
+achieved results of the VIP experiment and the ongoing VIP2 measurement aiming
+to gain two orders of magnitude improvement in testing PEP. We also use a
+similar experimental technique to search for radiation (X and gamma) predicted
+by continuous spontaneous localization models, which aim to solve the
+""measurement problem"".
+",0,1,0,0,0,0
+18645,Implications for Post-Processing Nucleosynthesis of Core-Collapse Supernova Models with Lagrangian Particles,"  We investigate core-collapse supernova (CCSN) nucleosynthesis with
+self-consistent, axisymmetric (2D) simulations performed using the
+radiation-hydrodynamics code Chimera. Computational costs have traditionally
+constrained the evolution of the nuclear composition within multidimensional
+CCSN models to, at best, a 14-species $\alpha$-network capable of tracking only
+$(\alpha,\gamma)$ reactions from $^{4}$He to $^{60}$Zn. Such a simplified
+network limits the ability to accurately evolve detailed composition and
+neutronization or calculate the nuclear energy generation rate. Lagrangian
+tracer particles are commonly used to extend the nuclear network evolution by
+incorporating more realistic networks in post-processing nucleosynthesis
+calculations. However, limitations such as poor spatial resolution of the
+tracer particles, inconsistent thermodynamic evolution, including misestimation
+of expansion timescales, and uncertain determination of the multidimensional
+mass-cut at the end of the simulation impose uncertainties inherent to this
+approach. We present a detailed analysis of the impact of such uncertainties
+for four self-consistent axisymmetric CCSN models initiated from stellar
+metallicity, non-rotating progenitors of 12 $M_\odot$, 15 $M_\odot$, 20
+$M_\odot$, and 25 $M_\odot$ and evolved with the smaller $\alpha$-network to
+more than 1 s after the launch of an explosion.
+",0,1,0,0,0,0
+18646,High Performance Parallel Image Reconstruction for New Vacuum Solar Telescope,"  Many technologies have been developed to help improve spatial resolution of
+observational images for ground-based solar telescopes, such as adaptive optics
+(AO) systems and post-processing reconstruction. As any AO system correction is
+only partial, it is indispensable to use post-processing reconstruction
+techniques. In the New Vacuum Solar Telescope (NVST), speckle masking method is
+used to achieve the diffraction limited resolution of the telescope. Although
+the method is very promising, the computation is quite intensive, and the
+amount of data is tremendous, requiring several months to reconstruct
+observational data of one day on a high-end computer. To accelerate image
+reconstruction, we parallelize the program package on a high performance
+cluster. We describe parallel implementation details for several reconstruction
+procedures. The code is written in C language using Message Passing Interface
+(MPI) and optimized for parallel processing in a multi-processor environment.
+We show the excellent performance of parallel implementation, and the whole
+data processing speed is about 71 times faster than before. Finally, we analyze
+the scalability of the code to find possible bottlenecks, and propose several
+ways to further improve the parallel performance. We conclude that the
+presented program is capable of executing in real-time reconstruction
+applications at NVST.
+",0,1,0,0,0,0
+18647,Approximation of Bandwidth for the Interactive Operation in Video on Demand System,"  An interactive session of video-on-demand (VOD) streaming procedure deserves
+smooth data transportation for the viewer, irrespective of their geographic
+location. To access the required video, bandwidth management during the video
+objects transportation at any interactive session is a mandatory prerequisite.
+It has been observed in the domain likes movie on demand, electronic
+encyclopedia, interactive games, and educational resources. The required data
+is imported from the distributed storage servers through the high speed
+backbone network. This paper presents the viewer driven session based
+multi-user model with respect to the overlay mesh network. In virtue of
+reality, the direct implication of this work elaborately shows the required
+bandwidth is a causal part in the video on demand system. The analytic model of
+session based single viewer bandwidth requirement model presents the bandwidth
+requirement for any interactive session like, pause, move slow, rewind, skip
+some number of frames, or move fast with some constant number of frames. This
+work presents the bandwidth requirement model for any interactive session that
+brings the trade-off in data-transportation and storage costs for different
+system resources and also for the various system configurations.
+",1,0,0,0,0,0
+18648,Twisted Recurrence via Polynomial Walks,"  In this paper we show how polynomial walks can be used to establish a twisted
+recurrence for sets of positive density in $\mathbb{Z}^d$. In particular, we
+prove that if $\Gamma \leq \operatorname{GL}_d(\mathbb{Z})$ is finitely
+generated by unipotents and acts irreducibly on $\mathbb{R}^d$, then for any
+set $B \subset \mathbb{Z}^d$ of positive density, there exists $k \geq 1$ such
+that for any $v \in k \mathbb{Z}^d$ one can find $\gamma \in \Gamma$ with
+$\gamma v \in B - B$. Our method does not require the linearity of the action,
+and we prove a twisted recurrence for semigroups of maps from $\mathbb{Z}^d$ to
+$\mathbb{Z}^d$ satisfying some irreducibility and polynomial assumptions. As
+one of the consequences, we prove a non-linear analog of Bogolubov's theorem --
+for any set $B \subset \mathbb{Z}^2$ of positive density, and $p(n) \in
+\mathbb{Z}[n]$, with $p(0) = 0$ and $\operatorname{deg}(p) \geq 2$, there
+exists $k \geq 1$ such that $k \mathbb{Z} \subset \{ x - p(y) \, | \, (x,y) \in
+B-B \}$. Unlike the previous works on twisted recurrence that used recent
+results of Benoist-Quint and Bourgain-Furman-Lindenstrauss-Mozes on
+equidistribution of random walks on automorphism groups of tori, our method
+relies on the classical Weyl equidistribution for polynomial orbits on tori.
+",0,0,1,0,0,0
+18649,Well-posedness and scattering for the Boltzmann equations: Soft potential with cut-off,"  We prove the global existence of the unique mild solution for the Cauchy
+problem of the cut-off Boltzmann equation for soft potential model $\gamma=2-N$
+with initial data small in $L^N_{x,v}$ where $N=2,3$ is the dimension. The
+proof relies on the existing inhomogeneous Strichartz estimates for the kinetic
+equation by Ovcharov and convolution-like estimates for the gain term of the
+Boltzmann collision operator by Alonso, Carneiro and Gamba. The global dynamics
+of the solution is also characterized by showing that the small global solution
+scatters with respect to the kinetic transport operator in $L^N_{x,v}$. Also
+the connection between function spaces and cut-off soft potential model
+$-N<\gamma<2-N$ is characterized in the local well-posedness result for the
+Cauchy problem with large initial data.
+",0,0,1,0,0,0
+18650,Wikipedia in academia as a teaching tool: from averse to proactive faculty profiles,"  This study concerned the active use of Wikipedia as a teaching tool in the
+classroom in higher education, trying to identify different usage profiles and
+their characterization. A questionnaire survey was administrated to all
+full-time and part-time teachers at the Universitat Oberta de Catalunya and the
+Universitat Pompeu Fabra, both in Barcelona, Spain. The questionnaire was
+designed using the Technology Acceptance Model as a reference, including items
+about teachers web 2.0 profile, Wikipedia usage, expertise, perceived
+usefulness, easiness of use, visibility and quality, as well as Wikipedia
+status among colleagues and incentives to use it more actively. Clustering and
+statistical analysis were carried out using the k-medoids algorithm and
+differences between clusters were assessed by means of contingency tables and
+generalized linear models (logit). The respondents were classified in four
+clusters, from less to more likely to adopt and use Wikipedia in the classroom,
+namely averse (25.4%), reluctant (17.9%), open (29.5%) and proactive (27.2%).
+Proactive faculty are mostly men teaching part-time in STEM fields, mainly
+engineering, while averse faculty are mostly women teaching full-time in
+non-STEM fields. Nevertheless, questionnaire items related to visibility,
+quality, image, usefulness and expertise determine the main differences between
+clusters, rather than age, gender or domain. Clusters involving a positive view
+of Wikipedia and at least some frequency of use clearly outnumber those with a
+strictly negative stance. This goes against the common view that faculty
+members are mostly sceptical about Wikipedia. Environmental factors such as
+academic culture and colleagues opinion are more important than faculty
+personal characteristics, especially with respect to what they think about
+Wikipedia quality.
+",1,0,0,0,0,0
+18651,A trans-disciplinary review of deep learning research for water resources scientists,"  Deep learning (DL), a new-generation of artificial neural network research,
+has transformed industries, daily lives and various scientific disciplines in
+recent years. DL represents significant progress in the ability of neural
+networks to automatically engineer problem-relevant features and capture highly
+complex data distributions. I argue that DL can help address several major new
+and old challenges facing research in water sciences such as
+inter-disciplinarity, data discoverability, hydrologic scaling, equifinality,
+and needs for parameter regionalization. This review paper is intended to
+provide water resources scientists and hydrologists in particular with a simple
+technical overview, trans-disciplinary progress update, and a source of
+inspiration about the relevance of DL to water. The review reveals that various
+physical and geoscientific disciplines have utilized DL to address data
+challenges, improve efficiency, and gain scientific insights. DL is especially
+suited for information extraction from image-like data and sequential data.
+Techniques and experiences presented in other disciplines are of high relevance
+to water research. Meanwhile, less noticed is that DL may also serve as a
+scientific exploratory tool. A new area termed 'AI neuroscience,' where
+scientists interpret the decision process of deep networks and derive insights,
+has been born. This budding sub-discipline has demonstrated methods including
+correlation-based analysis, inversion of network-extracted features,
+reduced-order approximations by interpretable models, and attribution of
+network decisions to inputs. Moreover, DL can also use data to condition
+neurons that mimic problem-specific fundamental organizing units, thus
+revealing emergent behaviors of these units. Vast opportunities exist for DL to
+propel advances in water sciences.
+",1,0,0,1,0,0
+18652,A new NS3 Implementation of CCNx 1.0 Protocol,"  The ccns3Sim project is an open source implementation of the CCNx 1.0
+protocols for the NS3 simulator. We describe the implementation and several
+important features including modularity and process delay simulation. The
+ccns3Sim implementation is a fresh NS3-specific implementation. Like NS3
+itself, it uses C++98 standard, NS3 code style, NS3 smart pointers, NS3 xUnit,
+and integrates with the NS3 documentation and manual. A user or developer does
+not need to learn two systems. If one knows NS3, one should be able to get
+started with the CCNx code right away. A developer can easily use their own
+implementation of the layer 3 protocol, layer 4 protocol, forwarder, routing
+protocol, Pending Interest Table (PIT) or Forwarding Information Base (FIB) or
+Content Store (CS). A user may configure or specify a new implementation for
+any of these features at runtime in the simulation script. In this paper, we
+describe the software architecture and give examples of using the simulator. We
+evaluate the implementation with several example experiments on ICN caching.
+",1,0,0,0,0,0
+18653,MultiRefactor: Automated Refactoring To Improve Software Quality,"  In this paper, a new approach is proposed for automated software maintenance.
+The tool is able to perform 26 different refactorings. It also contains a large
+selection of metrics to measure the impact of the refactorings on the software
+and six different search based optimization algorithms to improve the software.
+This tool contains both mono-objective and multi-objective search techniques
+for software improvement and is fully automated. The paper describes the
+various capabilities of the tool, the unique aspects of it, and also presents
+some research results from experimentation. The individual metrics are tested
+across five different codebases to deduce the most effective metrics for
+general quality improvement. It is found that the metrics that relate to more
+specific elements of the code are more useful for driving change in the search.
+The mono-objective genetic algorithm is also tested against the multi-objective
+algorithm to see how comparable the results gained are with three separate
+objectives. When comparing the best solutions of each individual objective the
+multi-objective approach generates suitable improvements in quality in less
+time, allowing for rapid maintenance cycles.
+",1,0,0,0,0,0
+18654,Learning Postural Synergies for Categorical Grasping through Shape Space Registration,"  Every time a person encounters an object with a given degree of familiarity,
+he/she immediately knows how to grasp it. Adaptation of the movement of the
+hand according to the object geometry happens effortlessly because of the
+accumulated knowledge of previous experiences grasping similar objects. In this
+paper, we present a novel method for inferring grasp configurations based on
+the object shape. Grasping knowledge is gathered in a synergy space of the
+robotic hand built by following a human grasping taxonomy. The synergy space is
+constructed through human demonstrations employing a exoskeleton that provides
+force feedback, which provides the advantage of evaluating the quality of the
+grasp. The shape descriptor is obtained by means of a categorical non-rigid
+registration that encodes typical intra-class variations. This approach is
+especially suitable for on-line scenarios where only a portion of the object's
+surface is observable. This method is demonstrated through simulation and real
+robot experiments by grasping objects never seen before by the robot.
+",1,0,0,0,0,0
+18655,Perturbations of self-adjoint operators in semifinite von Neumann algebras: Kato-Rosenblum theorem,"  In the paper, we prove an analogue of the Kato-Rosenblum theorem in a
+semifinite von Neumann algebra. Let $\mathcal{M}$ be a countably decomposable,
+properly infinite, semifinite von Neumann algebra acting on a Hilbert space
+$\mathcal{H}$ and let $\tau$ be a faithful normal semifinite tracial weight of
+$\mathcal M$. Suppose that $H$ and $H_1$ are self-adjoint operators affiliated
+with $\mathcal{M}$. We show that if $H-H_1$ is in $\mathcal{M}\cap
+L^{1}\left(\mathcal{M},\tau\right)$, then the ${norm}$ absolutely continuous
+parts of $H$ and $H_1$ are unitarily equivalent. This implies that the real
+part of a non-normal hyponormal operator in $\mathcal M$ is not a perturbation
+by $\mathcal{M}\cap L^{1}\left(\mathcal{M},\tau\right)$ of a diagonal operator.
+Meanwhile, for $n\ge 2$ and $1\leq p<n$, by modifying Voiculescu's invariant we
+give examples of commuting $n$-tuples of self-adjoint operators in
+$\mathcal{M}$ that are not arbitrarily small perturbations of commuting
+diagonal operators modulo $\mathcal{M}\cap L^{p}\left(\mathcal{M},\tau\right)$.
+",0,0,1,0,0,0
+18656,Dust radiative transfer modelling of the infrared ring around the magnetar SGR 1900$+$14,"  A peculiar infrared ring-like structure was discovered by {\em Spitzer}
+around the strongly magnetised neutron star SGR 1900$+$14. This infrared
+structure was suggested to be due to a dust-free cavity, produced by the SGR
+Giant Flare occurred in 1998, and kept illuminated by surrounding stars. Using
+a 3D dust radiative transfer code, we aimed at reproducing the emission
+morphology and the integrated emission flux of this structure assuming
+different spatial distributions and densities for the dust, and different
+positions for the illuminating stars. We found that a dust-free ellipsoidal
+cavity can reproduce the shape, flux, and spectrum of the ring-like infrared
+emission, provided that the illuminating stars are inside the cavity and that
+the interstellar medium has high gas density ($n_H\sim$1000 cm$^{-3}$). We
+further constrain the emitting region to have a sharp inner boundary and to be
+significantly extended in the radial direction, possibly even just a cavity in
+a smooth molecular cloud. We discuss possible scenarios for the formation of
+the dustless cavity and the particular geometry that allows it to be IR-bright.
+",0,1,0,0,0,0
+18657,Application of transfer matrix and transfer function analysis to grating-type dielectric laser accelerators: ponderomotive focusing of electrons,"  The question of suitability of transfer matrix description of electrons
+traversing grating-type dielectric laser acceleration (DLA) structures is
+addressed. It is shown that although matrix considerations lead to interesting
+insights, the basic transfer properties of DLA cells cannot be described by a
+matrix. A more general notion of a transfer function is shown to be a simple
+and useful tool for formulating problems of particle dynamics in DLA. As an
+example, a focusing structure is proposed which works simultaneously for all
+electron phases.
+",0,1,0,0,0,0
+18658,Discretization-free Knowledge Gradient Methods for Bayesian Optimization,"  This paper studies Bayesian ranking and selection (R&S) problems with
+correlated prior beliefs and continuous domains, i.e. Bayesian optimization
+(BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely
+studied for discrete R&S problems, which sample the one-step Bayes-optimal
+point. When used over continuous domains, previous work on the knowledge
+gradient [Scott et al., 2011, Wu and Frazier, 2016, Wu et al., 2017] often rely
+on a discretized finite approximation. However, the discretization introduces
+error and scales poorly as the dimension of domain grows. In this paper, we
+develop a fast discretization-free knowledge gradient method for Bayesian
+optimization. Our method is not restricted to the fully sequential setting, but
+useful in all settings where knowledge gradient can be used over continuous
+domains. We show how our method can be generalized to handle (i) batch of
+points suggestion (parallel knowledge gradient); (ii) the setting where
+derivative information is available in the optimization process
+(derivative-enabled knowledge gradient). In numerical experiments, we
+demonstrate that the discretization-free knowledge gradient method finds global
+optima significantly faster than previous Bayesian optimization algorithms on
+both synthetic test functions and real-world applications, especially when
+function evaluations are noisy; and derivative-enabled knowledge gradient can
+further improve the performances, even outperforming the gradient-based
+optimizer such as BFGS when derivative information is available.
+",1,0,1,1,0,0
+18659,Measuring the Robustness of Graph Properties,"  In this paper, we propose a perturbation framework to measure the robustness
+of graph properties. Although there are already perturbation methods proposed
+to tackle this problem, they are limited by the fact that the strength of the
+perturbation cannot be well controlled. We firstly provide a perturbation
+framework on graphs by introducing weights on the nodes, of which the magnitude
+of perturbation can be easily controlled through the variance of the weights.
+Meanwhile, the topology of the graphs are also preserved to avoid
+uncontrollable strength in the perturbation. We then extend the measure of
+robustness in the robust statistics literature to the graph properties.
+",1,0,0,1,0,0
+18660,"Better Software Analytics via ""DUO"": Data Mining Algorithms Using/Used-by Optimizers","  This paper claims that a new field of empirical software engineering research
+and practice is emerging: data mining using/used-by optimizers for empirical
+studies, or DUO. For example, data miners can generate the models that are
+explored by optimizers.Also, optimizers can advise how to best adjust the
+control parameters of a data miner. This combined approach acts like an agent
+leaning over the shoulder of an analyst that advises ""ask this question next""
+or ""ignore that problem, it is not relevant to your goals"". Further, those
+agents can help us build ""better"" predictive models, where ""better"" can be
+either greater predictive accuracy, or faster modeling time (which, in turn,
+enables the exploration of a wider range of options). We also caution that the
+era of papers that just use data miners is coming to an end. Results obtained
+from an unoptimized data miner can be quickly refuted, just by applying an
+optimizer to produce a different (and better performing) model. Our conclusion,
+hence, is that for software analytics it is possible, useful and necessary to
+combine data mining and optimization using DUO.
+",1,0,0,0,0,0
+18661,Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks,"  We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
+two-stages deep convolutional neural networks (DCNN). Compared to existing
+DCNN-based DR detection methods, the proposed algorithm have the following
+advantages: (1) Our method can point out the location and type of lesions in
+the fundus images, as well as giving the severity grades of DR. Moreover, since
+retina lesions and DR severity appear with different scales in fundus images,
+the integration of both local and global networks learn more complete and
+specific features for DR analysis. (2) By introducing imbalanced weighting map,
+more attentions will be given to lesion patches for DR grading, which
+significantly improve the performance of the proposed algorithm. In this study,
+we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus
+images from Kaggle competition dataset. Under the guidance of clinical
+ophthalmologists, the experimental results show that our local lesion detection
+net achieve comparable performance with trained human observers, and the
+proposed imbalanced weighted scheme also be proved to significantly improve the
+capability of our DCNN-based DR grading algorithm.
+",1,0,0,0,0,0
+18662,DRYVR:Data-driven verification and compositional reasoning for automotive systems,"  We present the DRYVR framework for verifying hybrid control systems that are
+described by a combination of a black-box simulator for trajectories and a
+white-box transition graph specifying mode switches. The framework includes (a)
+a probabilistic algorithm for learning sensitivity of the continuous
+trajectories from simulation data, (b) a bounded reachability analysis
+algorithm that uses the learned sensitivity, and (c) reasoning techniques based
+on simulation relations and sequential composition, that enable verification of
+complex systems under long switching sequences, from the reachability analysis
+of a simpler system under shorter sequences. We demonstrate the utility of the
+framework by verifying a suite of automotive benchmarks that include powertrain
+control, automatic transmission, and several autonomous and ADAS features like
+automatic emergency braking, lane-merge, and auto-passing controllers.
+",1,0,0,0,0,0
+18663,Deep Reinforcement Learning for Programming Language Correction,"  Novice programmers often struggle with the formal syntax of programming
+languages. To assist them, we design a novel programming language correction
+framework amenable to reinforcement learning. The framework allows an agent to
+mimic human actions for text navigation and editing. We demonstrate that the
+agent can be trained through self-exploration directly from the raw input, that
+is, program text itself, without any knowledge of the formal syntax of the
+programming language. We leverage expert demonstrations for one tenth of the
+training data to accelerate training. The proposed technique is evaluated on
+6975 erroneous C programs with typographic errors, written by students during
+an introductory programming course. Our technique fixes 14% more programs and
+29% more compiler error messages relative to those fixed by a state-of-the-art
+tool, DeepFix, which uses a fully supervised neural machine translation
+approach.
+",1,0,0,0,0,0
+18664,Dispersionless and multicomponent BKP hierarchies with quantum torus symmetries,"  In this article, we will construct the additional perturbative quantum torus
+symmetry of the dispersionless BKP hierarchy basing on the $W_{\infty}$
+infinite dimensional Lie symmetry. These results show that the complete quantum
+torus symmetry is broken from the BKP hierarchy to its dispersionless
+hierarchy. Further a series of additional flows of the multicomponent BKP
+hierarchy will be defined and these flows constitute an $N$-folds direct
+product of the positive half of the quantum torus symmetries.
+",0,1,1,0,0,0
+18665,A Sampling Framework for Solving Physics-driven Inverse Source Problems,"  Partial differential equations are central to describing many physical
+phenomena. In many applications these phenomena are observed through a sensor
+network, with the aim of inferring their underlying properties. Leveraging from
+certain results in sampling and approximation theory, we present a new
+framework for solving a class of inverse source problems for physical fields
+governed by linear partial differential equations. Specifically, we demonstrate
+that the unknown field sources can be recovered from a sequence of, so called,
+generalised measurements by using multidimensional frequency estimation
+techniques. Next we show that---for physics-driven fields---this sequence of
+generalised measurements can be estimated by computing a linear weighted-sum of
+the sensor measurements; whereby the exact weights (of the sums) correspond to
+those that reproduce multidimensional exponentials, when used to linearly
+combine translates of a particular prototype function related to the Green's
+function of our underlying field. Explicit formulae are then derived for the
+sequence of weights, that map sensor samples to the exact sequence of
+generalised measurements when the Green's function satisfies the generalised
+Strang-Fix condition. Otherwise, the same mapping yields a close approximation
+of the generalised measurements. Based on this new framework we develop
+practical, noise robust, sensor network strategies for solving the inverse
+source problem, and then present numerical simulation results to verify their
+performance.
+",1,0,1,0,0,0
+18666,An Application of $h$-principle to Manifold Calculus,"  Manifold calculus is a form of functor calculus that analyzes contravariant
+functors from some categories of manifolds to topological spaces by providing
+analytic approximations to them. In this paper we apply the theory of
+h-principle to construct several examples of analytic functors in this sense.
+We prove that the analytic approximation of the Lagrangian embeddings functor
+$\mathrm{emb}_{\mathrm{Lag}}(-,N)$ is the totally real embeddings functor
+$\mathrm{emb}_{\mathrm{TR}}(-,N)$. Under certain conditions we provide a
+geometric construction for the homotopy fiber of $ \mathrm{emb}(M,N)
+\rightarrow \mathrm{imm}(M,N)$. This construction also provides an example of a
+functor which is itself empty when evaluated on most manifolds but it's
+analytic approximation is almost always non-empty.
+",0,0,1,0,0,0
+18667,Grasping Unknown Objects in Clutter by Superquadric Representation,"  In this paper, a quick and efficient method is presented for grasping unknown
+objects in clutter. The grasping method relies on real-time superquadric (SQ)
+representation of partial view objects and incomplete object modelling, well
+suited for unknown symmetric objects in cluttered scenarios which is followed
+by optimized antipodal grasping. The incomplete object models are processed
+through a mirroring algorithm that assumes symmetry to first create an
+approximate complete model and then fit for SQ representation. The grasping
+algorithm is designed for maximum force balance and stability, taking advantage
+of the quick retrieval of dimension and surface curvature information from the
+SQ parameters. The pose of the SQs with respect to the direction of gravity is
+calculated and used together with the parameters of the SQs and specification
+of the gripper, to select the best direction of approach and contact points.
+The SQ fitting method has been tested on custom datasets containing objects in
+isolation as well as in clutter. The grasping algorithm is evaluated on a PR2
+and real time results are presented. Initial results indicate that though the
+method is based on simplistic shape information, it outperforms other learning
+based grasping algorithms that also work in clutter in terms of time-efficiency
+and accuracy.
+",1,0,0,0,0,0
+18668,Learning to Play with Intrinsically-Motivated Self-Aware Agents,"  Infants are experts at playing, with an amazing ability to generate novel
+structured behaviors in unstructured environments that lack clear extrinsic
+reward signals. We seek to mathematically formalize these abilities using a
+neural network that implements curiosity-driven intrinsic motivation. Using a
+simple but ecologically naturalistic simulated environment in which an agent
+can move and interact with objects it sees, we propose a ""world-model"" network
+that learns to predict the dynamic consequences of the agent's actions.
+Simultaneously, we train a separate explicit ""self-model"" that allows the agent
+to track the error map of its own world-model, and then uses the self-model to
+adversarially challenge the developing world-model. We demonstrate that this
+policy causes the agent to explore novel and informative interactions with its
+environment, leading to the generation of a spectrum of complex behaviors,
+including ego-motion prediction, object attention, and object gathering.
+Moreover, the world-model that the agent learns supports improved performance
+on object dynamics prediction, detection, localization and recognition tasks.
+Taken together, our results are initial steps toward creating flexible
+autonomous agents that self-supervise in complex novel physical environments.
+",0,0,0,1,0,0
+18669,$\ell_1$-minimization method for link flow correction,"  A computational method, based on $\ell_1$-minimization, is proposed for the
+problem of link flow correction, when the available traffic flow data on many
+links in a road network are inconsistent with respect to the flow conservation
+law. Without extra information, the problem is generally ill-posed when a large
+portion of the link sensors are unhealthy. It is possible, however, to correct
+the corrupted link flows \textit{accurately} with the proposed method under a
+recoverability condition if there are only a few bad sensors which are located
+at certain links. We analytically identify the links that are robust to
+miscounts and relate them to the geometric structure of the traffic network by
+introducing the recoverability concept and an algorithm for computing it. The
+recoverability condition for corrupted links is simply the associated
+recoverability being greater than 1. In a more realistic setting, besides the
+unhealthy link sensors, small measurement noises may be present at the other
+sensors. Under the same recoverability condition, our method guarantees to give
+an estimated traffic flow fairly close to the ground-truth data and leads to a
+bound for the correction error. Both synthetic and real-world examples are
+provided to demonstrate the effectiveness of the proposed method.
+",1,1,0,0,0,0
+18670,Current Flow Group Closeness Centrality for Complex Networks,"  Current flow closeness centrality (CFCC) has a better discriminating ability
+than the ordinary closeness centrality based on shortest paths. In this paper,
+we extend this notion to a group of vertices in a weighted graph, and then
+study the problem of finding a subset $S$ of $k$ vertices to maximize its CFCC
+$C(S)$, both theoretically and experimentally. We show that the problem is
+NP-hard, but propose two greedy algorithms for minimizing the reciprocal of
+$C(S)$ with provable guarantees using the monotoncity and supermodularity. The
+first is a deterministic algorithm with an approximation factor
+$(1-\frac{k}{k-1}\cdot\frac{1}{e})$ and cubic running time; while the second is
+a randomized algorithm with a
+$(1-\frac{k}{k-1}\cdot\frac{1}{e}-\epsilon)$-approximation and nearly-linear
+running time for any $\epsilon > 0$. Extensive experiments on model and real
+networks demonstrate that our algorithms are effective and efficient, with the
+second algorithm being scalable to massive networks with more than a million
+vertices.
+",1,0,0,0,0,0
+18671,Visualization of Constraint Handling Rules: Semantics and Applications,"  The work in the paper presents an animation extension ($CHR^{vis}$) to
+Constraint Handling Rules (CHR). Visualizations have always helped programmers
+understand data and debug programs. A picture is worth a thousand words. It can
+help identify where a problem is or show how something works. It can even
+illustrate a relation that was not clear otherwise. $CHR^{vis}$ aims at
+embedding animation and visualization features into CHR programs. It thus
+enables users, while executing programs, to have such executions animated. The
+paper aims at providing the operational semantics for $CHR^{vis}$. The
+correctness of $CHR^{vis}$ programs is also discussed. Some applications of the
+new extension are also introduced.
+",1,0,0,0,0,0
+18672,Simplified Energy Landscape for Modularity Using Total Variation,"  Networks capture pairwise interactions between entities and are frequently
+used in applications such as social networks, food networks, and protein
+interaction networks, to name a few. Communities, cohesive groups of nodes,
+often form in these applications, and identifying them gives insight into the
+overall organization of the network. One common quality function used to
+identify community structure is modularity. In Hu et al. [SIAM J. App. Math.,
+73(6), 2013], it was shown that modularity optimization is equivalent to
+minimizing a particular nonconvex total variation (TV) based functional over a
+discrete domain. They solve this problem, assuming the number of communities is
+known, using a Merriman, Bence, Osher (MBO) scheme.
+We show that modularity optimization is equivalent to minimizing a convex
+TV-based functional over a discrete domain, again, assuming the number of
+communities is known. Furthermore, we show that modularity has no convex
+relaxation satisfying certain natural conditions. We therefore, find a
+manageable non-convex approximation using a Ginzburg Landau functional, which
+provably converges to the correct energy in the limit of a certain parameter.
+We then derive an MBO algorithm with fewer hand-tuned parameters than in Hu et
+al. and which is 7 times faster at solving the associated diffusion equation
+due to the fact that the underlying discretization is unconditionally stable.
+Our numerical tests include a hyperspectral video whose associated graph has
+2.9x10^7 edges, which is roughly 37 times larger than was handled in the paper
+of Hu et al.
+",0,0,1,1,0,0
+18673,"A Hard Look at the Neutron Stars and Accretion Disks in 4U 1636-53, GX 17+2, and 4U 1705-44 with $\emph{NuSTAR}$","  We present $\emph{NuSTAR}$ observations of neutron star (NS) low-mass X-ray
+binaries: 4U 1636-53, GX 17+2, and 4U 1705-44. We observed 4U 1636-53 in the
+hard state, with an Eddington fraction, $F_{\mathrm{Edd}}$, of 0.01; GX 17+2
+and 4U 1705-44 were in the soft state with fractions of 0.57 and 0.10,
+respectively. Each spectrum shows evidence for a relativistically broadened Fe
+K$_{\alpha}$ line. Through accretion disk reflection modeling, we constrain the
+radius of the inner disk in 4U 1636-53 to be $R_{in}=1.03\pm0.03$ ISCO
+(innermost stable circular orbit) assuming a dimensionless spin parameter
+$a_{*}=cJ/GM^{2}=0.0$, and $R_{in}=1.08\pm0.06$ ISCO for $a_{*}=0.3$ (errors
+quoted at 1 $\sigma$). This value proves to be model independent. For
+$a_{*}=0.3$ and $M=1.4\ M_{\odot}$, for example, $1.08\pm0.06$ ISCO translates
+to a physical radius of $R=10.8\pm0.6$ km, and the neutron star would have to
+be smaller than this radius (other outcomes are possible for allowed spin
+parameters and masses). For GX 17+2, $R_{in}=1.00-1.04$ ISCO for $a_{*}=0.0$
+and $R_{in}=1.03-1.30$ ISCO for $a_{*}=0.3$. For $a_{*}=0.3$ and $M=1.4\
+M_{\odot}$, $R_{in}=1.03-1.30$ ISCO translates to $R=10.3-13.0$ km. The inner
+accretion disk in 4U 1705-44 may be truncated just above the stellar surface,
+perhaps by a boundary layer or magnetosphere; reflection models give a radius
+of 1.46-1.64 ISCO for $a_{*}=0.0$ and 1.69-1.93 ISCO for $a_{*}=0.3$. We
+discuss the implications that our results may have on the equation of state of
+ultradense, cold matter and our understanding of the innermost accretion flow
+onto neutron stars with low surface magnetic fields, and systematic errors
+related to the reflection models and spacetime metric around less idealized
+neutron stars.
+",0,1,0,0,0,0
+18674,Approximating Weighted Duo-Preservation in Comparative Genomics,"  Motivated by comparative genomics, Chen et al. [9] introduced the Maximum
+Duo-preservation String Mapping (MDSM) problem in which we are given two
+strings $s_1$ and $s_2$ from the same alphabet and the goal is to find a
+mapping $\pi$ between them so as to maximize the number of duos preserved. A
+duo is any two consecutive characters in a string and it is preserved in the
+mapping if its two consecutive characters in $s_1$ are mapped to same two
+consecutive characters in $s_2$. The MDSM problem is known to be NP-hard and
+there are approximation algorithms for this problem [3, 5, 13], but all of them
+consider only the ""unweighted"" version of the problem in the sense that a duo
+from $s_1$ is preserved by mapping to any same duo in $s_2$ regardless of their
+positions in the respective strings. However, it is well-desired in comparative
+genomics to find mappings that consider preserving duos that are ""closer"" to
+each other under some distance measure [19]. In this paper, we introduce a
+generalized version of the problem, called the Maximum-Weight Duo-preservation
+String Mapping (MWDSM) problem that captures both duos-preservation and
+duos-distance measures in the sense that mapping a duo from $s_1$ to each
+preserved duo in $s_2$ has a weight, indicating the ""closeness"" of the two
+duos. The objective of the MWDSM problem is to find a mapping so as to maximize
+the total weight of preserved duos. In this paper, we give a polynomial-time
+6-approximation algorithm for this problem.
+",1,0,0,0,0,0
+18675,On the virtual singular braid monoid,"  We study the algebraic structures of the virtual singular braid monoid,
+$VSB_n$, and the virtual singular pure braid monoid, $VSP_n$. The monoid
+$VSB_n$ is the splittable extension of $VSP_n$ by the symmetric group $S_n$. We
+also construct a representation of $VSB_n$.
+",0,0,1,0,0,0
+18676,Discrete Games in Endogenous Networks: Equilibria and Policy,"  In games of friendship links and behaviors, I propose $k$-player Nash
+stability---a family of equilibria, indexed by a measure of robustness given by
+the number of permitted link changes, which is (ordinally and cardinally)
+ranked in a probabilistic sense. Application of the proposed framework to
+adolescents' tobacco smoking and friendship decisions suggests that: (a.)
+friendship networks respond to increases of tobacco prices and this response
+amplifies the intended policy effect on smoking, (b.) racially desegregating
+high-schools, via stimulating the social interactions of students with
+different intrinsic propensity to smoke, decreases the overall smoking
+prevalence, (c.) adolescents are averse to sharing friends so that there is a
+rivalry for friendships, (d.) when data on individuals' friendship network is
+not available, the importance of price centered policy tools is underestimated.
+",1,1,0,0,0,0
+18677,Evaluating regulatory reform of network industries: a survey of empirical models based on categorical proxies,"  Proxies for regulatory reforms based on categorical variables are
+increasingly used in empirical evaluation models. We surveyed 63 studies that
+rely on such indices to analyze the effects of entry liberalization,
+privatization, unbundling, and independent regulation of the electricity,
+natural gas, and telecommunications sectors. We highlight methodological issues
+related to the use of these proxies. Next, taking stock of the literature, we
+provide practical advice for the design of the empirical strategy and discuss
+the selection of control and instrumental variables to attenuate endogeneity
+problems undermining identification of the effects of regulatory reforms.
+",0,0,0,0,0,1
+18678,Theory of ground states for classical Heisenberg spin systems II,"  We apply the theory of ground states for classical, finite, Heisenberg spin
+systems previously published to a couple of spin systems that can be considered
+as finite models $K_{12},\,K_{15}$ and $K_{18}$ of the AF Kagome lattice. The
+model $K_{12}$ is isomorphic to the cuboctahedron. In particular, we find
+three-dimensional ground states that cannot be viewed as resulting from the
+well-known independent rotation of subsets of spin vectors. For a couple of
+ground states with translational symmetry we calculate the corresponding wave
+numbers. Finally we study the model $K_{12w}$ without boundary conditions which
+exhibits new phenomena as, e.~g., two-dimensional families of three-dimensional
+ground states.
+",0,1,0,0,0,0
+18679,Relative Property (T) for Nilpotent Subgroups,"  We show that relative Property (T) for the abelianization of a nilpotent
+normal subgroup implies relative Property (T) for the subgroup itself. This and
+other results are a consequence of a theorem of independent interest, which
+states that if $H$ is a closed subgroup of a locally compact group $G$, and $A$
+is a closed subgroup of the center of $H$, such that $A$ is normal in $G$, and
+$(G/A, H/A)$ has relative Property (T), then $(G, H^{(1)})$ has relative
+Property (T), where $H^{(1)}$ is the closure of the commutator subgroup of $H$.
+In fact, the assumption that $A$ is in the center of $H$ can be replaced with
+the weaker assumption that $A$ is abelian and every $H$-invariant finite
+measure on the unitary dual of $A$ is supported on the set of fixed points.
+",0,0,1,0,0,0
+18680,Discriminant analysis in small and large dimensions,"  We study the distributional properties of the linear discriminant function
+under the assumption of normality by comparing two groups with the same
+covariance matrix but different mean vectors. A stochastic representation for
+the discriminant function coefficients is derived which is then used to obtain
+their asymptotic distribution under the high-dimensional asymptotic regime. We
+investigate the performance of the classification analysis based on the
+discriminant function in both small and large dimensions. A stochastic
+representation is established which allows to compute the error rate in an
+efficient way. We further compare the calculated error rate with the optimal
+one obtained under the assumption that the covariance matrix and the two mean
+vectors are known. Finally, we present an analytical expression of the error
+rate calculated in the high-dimensional asymptotic regime. The finite-sample
+properties of the derived theoretical results are assessed via an extensive
+Monte Carlo study.
+",0,0,1,1,0,0
+18681,Sparse Bayesian Inference for Dense Semantic Mapping,"  Despite impressive advances in simultaneous localization and mapping, dense
+robotic mapping remains challenging due to its inherent nature of being a
+high-dimensional inference problem. In this paper, we propose a dense semantic
+robotic mapping technique that exploits sparse Bayesian models, in particular,
+the relevance vector machine, for high-dimensional sequential inference. The
+technique is based on the principle of automatic relevance determination and
+produces sparse models that use a small subset of the original dense training
+set as the dominant basis. The resulting map posterior is continuous, and
+queries can be made efficiently at any resolution. Moreover, the technique has
+probabilistic outputs per semantic class through Bayesian inference. We
+evaluate the proposed relevance vector semantic map using publicly available
+benchmark datasets, NYU Depth V2 and KITTI; and the results show promising
+improvements over the state-of-the-art techniques.
+",1,0,0,0,0,0
+18682,Singularities and Semistable Degenerations for Symplectic Topology,"  We overview our recent work defining and studying normal crossings varieties
+and subvarieties in symplectic topology. This work answers a question of Gromov
+on the feasibility of introducing singular (sub)varieties into symplectic
+topology in the case of normal crossings singularities. It also provides a
+necessary and sufficient condition for smoothing normal crossings symplectic
+varieties. In addition, we explain some connections with other areas of
+mathematics and discuss a few directions for further research.
+",0,0,1,0,0,0
+18683,One-step Local M-estimator for Integrated Jump-Diffusion Models,"  In this paper, robust nonparametric estimators, instead of local linear
+estimators, are adapted for infinitesimal coefficients associated with
+integrated jump-diffusion models to avoid the impact of outliers on accuracy.
+Furthermore, consider the complexity of iteration of the solution for local
+M-estimator, we propose the one-step local M-estimators to release the
+computation burden. Under appropriate regularity conditions, we prove that
+one-step local M-estimators and the fully iterative M-estimators have the same
+performance in consistency and asymptotic normality. Through simulation, our
+method present advantages in bias reduction, robustness and reducing
+computation cost. In addition, the estimators are illustrated empirically
+through stock index under different sampling frequency.
+",0,0,1,1,0,0
+18684,"Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and post-process numerical relativity simulations","  Numerical simulations of Einstein's field equations provide unique insights
+into the physics of compact objects moving at relativistic speeds, and which
+are driven by strong gravitational interactions. Numerical relativity has
+played a key role to firmly establish gravitational wave astrophysics as a new
+field of research, and it is now paving the way to establish whether
+gravitational wave radiation emitted from compact binary mergers is accompanied
+by electromagnetic and astro-particle counterparts. As numerical relativity
+continues to blend in with routine gravitational wave data analyses to validate
+the discovery of gravitational wave events, it is essential to develop open
+source tools to streamline these studies. Motivated by our own experience as
+users and developers of the open source, community software, the Einstein
+Toolkit, we present an open source, Python package that is ideally suited to
+monitor and post-process the data products of numerical relativity simulations,
+and compute the gravitational wave strain at future null infinity in high
+performance environments. We showcase the application of this new package to
+post-process a large numerical relativity catalog and extract higher-order
+waveform modes from numerical relativity simulations of eccentric binary black
+hole mergers and neutron star mergers. This new software fills a critical void
+in the arsenal of tools provided by the Einstein Toolkit Consortium to the
+numerical relativity community.
+",1,1,0,0,0,0
+18685,Uniform confidence bands for nonparametric errors-in-variables regression,"  This paper develops a method to construct uniform confidence bands for a
+nonparametric regression function where a predictor variable is subject to a
+measurement error. We allow for the distribution of the measurement error to be
+unknown, but assume that there is an independent sample from the measurement
+error distribution. The sample from the measurement error distribution need not
+be independent from the sample on response and predictor variables. The
+availability of a sample from the measurement error distribution is satisfied
+if, for example, either 1) validation data or 2) repeated measurements (panel
+data) on the latent predictor variable with measurement errors, one of which is
+symmetrically distributed, are available. The proposed confidence band builds
+on the deconvolution kernel estimation and a novel application of the
+multiplier (or wild) bootstrap method. We establish asymptotic validity of the
+proposed confidence band under ordinary smooth measurement error densities,
+showing that the proposed confidence band contains the true regression function
+with probability approaching the nominal coverage probability. To the best of
+our knowledge, this is the first paper to derive asymptotically valid uniform
+confidence bands for nonparametric errors-in-variables regression. We also
+propose a novel data-driven method to choose a bandwidth, and conduct
+simulation studies to verify the finite sample performance of the proposed
+confidence band. Applying our method to a combination of two empirical data
+sets, we draw confidence bands for nonparametric regressions of medical costs
+on the body mass index (BMI), accounting for measurement errors in BMI.
+Finally, we discuss extensions of our results to specification testing, cases
+with additional error-free regressors, and confidence bands for conditional
+distribution functions.
+",0,0,1,1,0,0
+18686,Unconventional superconductivity in the BiS$_2$-based layered superconductor NdO$_{0.71}$F$_{0.29}$BiS$_2$,"  We investigate the superconducting-gap anisotropy in one of the recently
+discovered BiS$_2$-based superconductors, NdO$_{0.71}$F$_{0.29}$BiS$_2$ ($T_c$
+$\sim$ 5 K), using laser-based angle-resolved photoemission spectroscopy.
+Whereas the previously discovered high-$T_c$ superconductors such as copper
+oxides and iron-based superconductors, which are believed to have
+unconventional superconducting mechanisms, have $3d$ electrons in their
+conduction bands, the conduction band of BiS$_2$-based superconductors mainly
+consists of Bi 6$p$ electrons, and hence the conventional superconducting
+mechanism might be expected. Contrary to this expectation, we observe a
+strongly anisotropic superconducting gap. This result strongly suggests that
+the pairing mechanism for NdO$_{0.71}$F$_{0.29}$BiS$_2$ is unconventional one
+and we attribute the observed anisotropy to competitive or cooperative multiple
+paring interactions.
+",0,1,0,0,0,0
+18687,Low noise sensitivity analysis of Lq-minimization in oversampled systems,"  The class of Lq-regularized least squares (LQLS) are considered for
+estimating a p-dimensional vector \b{eta} from its n noisy linear observations
+y = X\b{eta}+w. The performance of these schemes are studied under the
+high-dimensional asymptotic setting in which p grows linearly with n. In this
+asymptotic setting, phase transition diagrams (PT) are often used for comparing
+the performance of different estimators. Although phase transition analysis is
+shown to provide useful information for compressed sensing, the fact that it
+ignores the measurement noise not only limits its applicability in many
+application areas, but also may lead to misunderstandings. For instance,
+consider a linear regression problem in which n > p and the signal is not
+exactly sparse. If the measurement noise is ignored in such systems,
+regularization techniques, such as LQLS, seem to be irrelevant since even the
+ordinary least squares (OLS) returns the exact solution. However, it is
+well-known that if n is not much larger than p then the regularization
+techniques improve the performance of OLS. In response to this limitation of PT
+analysis, we consider the low-noise sensitivity analysis. We show that this
+analysis framework (i) reveals the advantage of LQLS over OLS, (ii) captures
+the difference between different LQLS estimators even when n > p, and (iii)
+provides a fair comparison among different estimators in high signal-to-noise
+ratios. As an application of this framework, we will show that under mild
+conditions LASSO outperforms other LQLS even when the signal is dense. Finally,
+by a simple transformation we connect our low-noise sensitivity framework to
+the classical asymptotic regime in which n/p goes to infinity and characterize
+how and when regularization techniques offer improvements over ordinary least
+squares, and which regularizer gives the most improvement when the sample size
+is large.
+",0,0,1,1,0,0
+18688,Robust Transceiver Design Based on Interference Alignment for Multi-User Multi-Cell MIMO Networks with Channel Uncertainty,"  In this paper, we firstly exploit the inter-user interference (IUI) and
+inter-cell interference (ICI) as useful references to develop a robust
+transceiver design based on interference alignment for a downlink multi-user
+multi-cell multiple-input multiple-output (MIMO) interference network under
+channel estimation error. At transmitters, we propose a two-tier transmit
+beamforming strategy, we first achieve the inner beamforming direction and
+allocated power by minimizing the interference leakage as well as maximizing
+the system energy efficiency, respectively. Then, for the outer beamformer
+design, we develop an efficient conjugate gradient Grassmann manifold subspace
+tracking algorithm to minimize the distances between the subspace spanned by
+interference and the interference subspace in the time varying channel. At
+receivers, we propose a practical interference alignment based on fast and
+robust fast data projection method (FDPM) subspace tracking algorithm, to
+achieve the receive beamformer under channel uncertainty. Numerical results
+show that our proposed robust transceiver design achieves better performance
+compared with some existing methods in terms of the sum rate and the energy
+efficiency.
+",1,0,0,0,0,0
+18689,Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks,"  Inspired by the recent work of Carleo and Troyer[1], we apply machine
+learning methods to quantum mechanics in this article. The radial basis
+function network in a discrete basis is used as the variational wavefunction
+for the ground state of a quantum system. Variational Monte Carlo(VMC)
+calculations are carried out for some simple Hamiltonians. The results are in
+good agreements with theoretical values. The smallest eigenvalue of a Hermitian
+matrix can also be acquired using VMC calculations. Our results demonstrate
+that machine learning techniques are capable of solving quantum mechanical
+problems.
+",0,1,0,0,0,0
+18690,Scale-free networks are rare,"  A central claim in modern network science is that real-world networks are
+typically ""scale free,"" meaning that the fraction of nodes with degree $k$
+follows a power law, decaying like $k^{-\alpha}$, often with $2 < \alpha < 3$.
+However, empirical evidence for this belief derives from a relatively small
+number of real-world networks. We test the universality of scale-free structure
+by applying state-of-the-art statistical tools to a large corpus of nearly 1000
+network data sets drawn from social, biological, technological, and
+informational sources. We fit the power-law model to each degree distribution,
+test its statistical plausibility, and compare it via a likelihood ratio test
+to alternative, non-scale-free models, e.g., the log-normal. Across domains, we
+find that scale-free networks are rare, with only 4% exhibiting the
+strongest-possible evidence of scale-free structure and 52% exhibiting the
+weakest-possible evidence. Furthermore, evidence of scale-free structure is not
+uniformly distributed across sources: social networks are at best weakly scale
+free, while a handful of technological and biological networks can be called
+strongly scale free. These results undermine the universality of scale-free
+networks and reveal that real-world networks exhibit a rich structural
+diversity that will likely require new ideas and mechanisms to explain.
+",1,0,0,1,1,0
+18691,"Enlargeability, foliations, and positive scalar curvature","  We extend the deep and important results of Lichnerowicz, Connes, and
+Gromov-Lawson which relate geometry and characteristic numbers to the existence
+and non-existence of metrics of positive scalar curvature (PSC). In particular,
+we show: that a spin foliation with Hausdorff homotopy groupoid of an
+enlargeable manifold admits no PSC metric; that any metric of PSC on such a
+foliation is bounded by a multiple of the reciprocal of the foliation K-area of
+the ambient manifold; and that Connes' vanishing theorem for characteristic
+numbers of PSC foliations extends to a vanishing theorem for Haefliger
+cohomology classes.
+",0,0,1,0,0,0
+18692,A simple mathematical model for unemployment: a case study in Portugal with optimal control,"  We propose a simple mathematical model for unemployment. Despite its
+simpleness, we claim that the model is more realistic and useful than recent
+models available in the literature. A case study with real data from Portugal
+supports our claim. An optimal control problem is formulated and solved, which
+provides some non-trivial and interesting conclusions.
+",0,0,0,0,0,1
+18693,Interpreting Deep Neural Networks Through Variable Importance,"  While the success of deep neural networks (DNNs) is well-established across a
+variety of domains, our ability to explain and interpret these methods is
+limited. Unlike previously proposed local methods which try to explain
+particular classification decisions, we focus on global interpretability and
+ask a universally applicable question: given a trained model, which features
+are the most important? In the context of neural networks, a feature is rarely
+important on its own, so our strategy is specifically designed to leverage
+partial covariance structures and incorporate variable dependence into feature
+ranking. Our methodological contributions in this paper are two-fold. First, we
+propose an effect size analogue for DNNs that is appropriate for applications
+with highly collinear predictors (ubiquitous in computer vision). Second, we
+extend the recently proposed ""RelATive cEntrality"" (RATE) measure (Crawford et
+al., 2019) to the Bayesian deep learning setting. RATE applies an information
+theoretic criterion to the posterior distribution of effect sizes to assess
+feature significance. We apply our framework to three broad application areas:
+computer vision, natural language processing, and social science.
+",1,0,0,1,0,0
+18694,Conformality of $1/N$ corrections in SYK-like models,"  The Sachdev-Ye--Kitaev is a quantum mechanical model of $N$ Majorana fermions
+which displays a number of appealing features -- solvability in the strong
+coupling regime, near-conformal invariance and maximal chaos -- which make it a
+suitable model for black holes in the context of the AdS/CFT holography. In
+this paper we show for the colored SYK model and several of its tensor model
+cousins that the next-to-leading order in the $N$ expansion preserves the
+conformal invariance of the $2$-point function in the strong coupling regime,
+up to the contribution of the Goldstone bosons leading to the spontaneous
+breaking of the symmetry and which are already seen in the leading order
+$4$-point function. We also comment on the composite field approach for
+computing correlation functions in colored tensor models.
+",0,1,0,0,0,0
+18695,Segal-type models of higher categories,"  Higher category theory is an exceedingly active area of research, whose rapid
+growth has been driven by its penetration into a diverse range of scientific
+fields. Its influence extends through key mathematical disciplines, notably
+homotopy theory, algebraic geometry and algebra, mathematical physics, to
+encompass important applications in logic, computer science and beyond. Higher
+categories provide a unifying language whose greatest strength lies in its
+ability to bridge between diverse areas and uncover novel applications.
+In this foundational work we introduce a new approach to higher categories.
+It builds upon the theory of iterated internal categories, one of the simplest
+possible higher categorical structures available, by adopting a novel and
+remarkably simple ""weak globularity"" postulate and demonstrating that the
+resulting model provides a fully general theory of weak n-categories. The
+latter are among the most complex of the higher structures, and are crucial for
+applications. We show that this new model of ""weakly globular n-fold
+categories"" is suitably equivalent to the well studied model of weak
+n-categories due to Tamsamani and Simpson.
+",0,0,1,0,0,0
+18696,Low-Mass Dark Matter Search with CDMSlite,"  The SuperCDMS experiment is designed to directly detect weakly interacting
+massive particles (WIMPs) that may constitute the dark matter in our Galaxy.
+During its operation at the Soudan Underground Laboratory, germanium detectors
+were run in the CDMSlite mode to gather data sets with sensitivity specifically
+for WIMPs with masses ${<}$10 GeV/$c^2$. In this mode, a higher detector-bias
+voltage is applied to amplify the phonon signals produced by drifting charges.
+This paper presents studies of the experimental noise and its effect on the
+achievable energy threshold, which is demonstrated to be as low as 56
+eV$_{\text{ee}}$ (electron equivalent energy). The detector-biasing
+configuration is described in detail, with analysis corrections for voltage
+variations to the level of a few percent. Detailed studies of the
+electric-field geometry, and the resulting successful development of a fiducial
+parameter, eliminate poorly measured events, yielding an energy resolution
+ranging from ${\sim}$9 eV$_{\text{ee}}$ at 0 keV to 101 eV$_{\text{ee}}$ at
+${\sim}$10 eV$_{\text{ee}}$. New results are derived for astrophysical
+uncertainties relevant to the WIMP-search limits, specifically examining how
+they are affected by variations in the most probable WIMP velocity and the
+Galactic escape velocity. These variations become more important for WIMP
+masses below 10 GeV/$c^2$. Finally, new limits on spin-dependent low-mass
+WIMP-nucleon interactions are derived, with new parameter space excluded for
+WIMP masses $\lesssim$3 GeV/$c^2$
+",0,1,0,0,0,0
+18697,Gravity with free initial conditions: a solution to the cosmological constant problem testable by CMB B-mode polarization,"  In standard general relativity the universe cannot be started with arbitrary
+initial conditions, because four of the ten components of the Einstein's field
+equations (EFE) are constraints on initial conditions. In the previous work it
+was proposed to extend the gravity theory to allow free initial conditions,
+with a motivation to solve the cosmological constant problem. This was done by
+setting four constraints on metric variations in the action principle, which is
+reasonable because the gravity's physical degrees of freedom are at most six.
+However, there are two problems about this theory; the three constraints in
+addition to the unimodular condition were introduced without clear physical
+meanings, and the flat Minkowski spacetime is unstable against perturbations.
+Here a new set of gravitational field equations is derived by replacing the
+three constraints with new ones requiring that geodesic paths remain geodesic
+against metric variations. The instability problem is then naturally solved.
+Implications for the cosmological constant $\Lambda$ are unchanged; the theory
+converges into EFE with nonzero $\Lambda$ by inflation, but $\Lambda$ varies on
+scales much larger than the present Hubble horizon. Then galaxies are formed
+only in small $\Lambda$ regions, and the cosmological constant problem is
+solved by the anthropic argument. Because of the increased degrees of freedom
+in metric dynamics, the theory predicts new non-oscillatory modes of metric
+anisotropy generated by quantum fluctuation during inflation, and CMB B-mode
+polarization would be observed differently from the standard predictions by
+general relativity.
+",0,1,0,0,0,0
+18698,Gradual Learning of Recurrent Neural Networks,"  Recurrent Neural Networks (RNNs) achieve state-of-the-art results in many
+sequence-to-sequence modeling tasks. However, RNNs are difficult to train and
+tend to suffer from overfitting. Motivated by the Data Processing Inequality
+(DPI), we formulate the multi-layered network as a Markov chain, introducing a
+training method that comprises training the network gradually and using
+layer-wise gradient clipping. We found that applying our methods, combined with
+previously introduced regularization and optimization methods, resulted in
+improvements in state-of-the-art architectures operating in language modeling
+tasks.
+",1,0,0,1,0,0
+18699,Borel subsets of the real line and continuous reducibility,"  We study classes of Borel subsets of the real line $\mathbb{R}$ such as
+levels of the Borel hierarchy and the class of sets that are reducible to the
+set $\mathbb{Q}$ of rationals, endowed with the Wadge quasi-order of
+reducibility with respect to continuous functions on $\mathbb{R}$. Notably, we
+explore several structural properties of Borel subsets of $\mathbb{R}$ that
+diverge from those of Polish spaces with dimension zero. Our first main result
+is on the existence of embeddings of several posets into the restriction of
+this quasi-order to any Borel class that is strictly above the classes of open
+and closed sets, for instance the linear order $\omega_1$, its reverse
+$\omega_1^\star$ and the poset $\mathcal{P}(\omega)/\mathsf{fin}$ of inclusion
+modulo finite error. As a consequence of its proof, it is shown that there are
+no complete sets for these classes. We further extend the previous theorem to
+targets that are reducible to $\mathbb{Q}$. These non-structure results
+motivate the study of further restrictions of the Wadge quasi-order. In our
+second main theorem, we introduce a combinatorial property that is shown to
+characterize those $F_\sigma$ sets that are reducible to $\mathbb{Q}$. This is
+applied to construct a minimal set below $\mathbb{Q}$ and prove its uniqueness
+up to Wadge equivalence. We finally prove several results concerning gaps and
+cardinal characteristics of the Wadge quasi-order and thereby answer questions
+of Brendle and Geschke.
+",0,0,1,0,0,0
+18700,Pairing from dynamically screened Coulomb repulsion in bismuth,"  Recently, Prakash et. al. have discovered bulk superconductivity in single
+crystals of bismuth, which is a semi metal with extremely low carrier density.
+At such low density, we argue that conventional electron-phonon coupling is too
+weak to be responsible for the binding of electrons into Cooper pairs. We study
+a dynamically screened Coulomb interaction with effective attraction generated
+on the scale of the collective plasma modes. We model the electronic states in
+bismuth to include three Dirac pockets with high velocity and one hole pocket
+with a significantly smaller velocity. We find a weak coupling instability,
+which is greatly enhanced by the presence of the hole pocket. Therefore, we
+argue that bismuth is the first material to exhibit superconductivity driven by
+retardation effects of Coulomb repulsion alone. By using realistic parameters
+for bismuth we find that the acoustic plasma mode does not play the central
+role in pairing. We also discuss a matrix element effect, resulting from the
+Dirac nature of the conduction band, which may affect $T_c$ in the $s$-wave
+channel without breaking time-reversal symmetry.
+",0,1,0,0,0,0
+18701,Calabi-Yau metrics on canonical bundles of complex flag manifolds,"  In the present paper we provide a description of complete Calabi-Yau metrics
+on the canonical bundle of generalized complex flag manifolds. By means of Lie
+theory we give an explicit description of complete Ricci-flat Kähler metrics
+obtained through the Calabi ansatz technique. We use this approach to provide
+several explicit examples of noncompact complete Calabi-Yau manifolds, these
+examples include canonical bundles of non-toric flag manifolds (e.g. Grassmann
+manifolds and full flag manifolds).
+",0,0,1,0,0,0
+18702,The ratio of normalizing constants for Bayesian graphical Gaussian model selection,"  Many graphical Gaussian selection methods in a Bayesian framework use the
+G-Wishart as the conjugate prior on the precision matrix. The Bayes factor to
+compare a model governed by a graph G and a model governed by the neighboring
+graph G-e, derived from G by deleting an edge e, is a function of the ratios of
+prior and posterior normalizing constants of the G-Wishart for G and G-e.
+While more recent methods avoid the computation of the posterior ratio,
+computing the ratio of prior normalizing constants, (2) below, has remained a
+computational stumbling block. In this paper, we propose an explicit analytic
+approximation to (2) which is equal to the ratio of two Gamma functions
+evaluated at (delta+d)/2 and (delta+d+1)/2 respectively, where delta is the
+shape parameter of the G-Wishart and d is the number of paths of length two
+between the endpoints of e. This approximation allows us to avoid Monte Carlo
+methods, is computationally inexpensive and is scalable to high-dimensional
+problems. We show that the ratio of the approximation to the true value is
+always between zero and one and so, one cannot incur wild errors.
+In the particular case where the paths between the endpoints of e are
+disjoint, we show that the approximation is very good. When the paths between
+these two endpoints are not disjoint we give a sufficient condition for the
+approximation to be good. Numerical results show that the ratio of the
+approximation to the true value of the prior ratio is always between .55 and 1
+and very often close to 1. We compare the results obtained with a model search
+using our approximation and a search using the double Metropolis-Hastings
+algorithm to compute the prior ratio. The results are extremely close.
+",0,0,1,1,0,0
+18703,Angiogenic Factors produced by Hypoxic Cells are a leading driver of Anastomoses in Sprouting Angiogenesis---a computational study,"  Angiogenesis - the growth of new blood vessels from a pre-existing
+vasculature - is key in both physiological processes and on several
+pathological scenarios such as cancer progression or diabetic retinopathy. For
+the new vascular networks to be functional, it is required that the growing
+sprouts merge either with an existing functional mature vessel or with another
+growing sprout. This process is called anastomosis. We present a systematic 2D
+and 3D computational study of vessel growth in a tissue to address the
+capability of angiogenic factor gradients to drive anastomosis formation. We
+consider that these growth factors are produced only by tissue cells in
+hypoxia, i.e. until nearby vessels merge and become capable of carrying blood
+and irrigating their vicinity. We demonstrate that this increased production of
+angiogenic factors by hypoxic cells is able to promote vessel anastomoses
+events in both 2D and 3D. The simulations also verify that the morphology of
+these networks has an increased resilience toward variations in the endothelial
+cell's proliferation and chemotactic response. The distribution of tissue
+cell`s and the concentration of the growth factors they produce are the major
+factors in determining the final morphology of the network.
+",0,0,0,0,1,0
+18704,Henri Bénard: Thermal convection and vortex shedding,"  We present in this article the work of Henri Bénard (1874-1939), French
+physicist who began the systematic experimental study of two hydrodynamic
+systems: the thermal convection of fluids heated from below (the
+Rayleigh-Bénard convection and the Bénard-Marangoni convection) and the
+periodical vortex shedding behind a bluff body in a flow (the
+Bénard-Kármán vortex street). Across his scientific biography, we review
+the interplay between experiments and theory in these two major subjects of
+fluid mechanics.
+",0,1,0,0,0,0
+18705,Surjective H-Colouring: New Hardness Results,"  A homomorphism from a graph G to a graph H is a vertex mapping f from the
+vertex set of G to the vertex set of H such that there is an edge between
+vertices f(u) and f(v) of H whenever there is an edge between vertices u and v
+of G. The H-Colouring problem is to decide whether or not a graph G allows a
+homomorphism to a fixed graph H. We continue a study on a variant of this
+problem, namely the Surjective H-Colouring problem, which imposes the
+homomorphism to be vertex-surjective. We build upon previous results and show
+that this problem is NP-complete for every connected graph H that has exactly
+two vertices with a self-loop as long as these two vertices are not adjacent.
+As a result, we can classify the computational complexity of Surjective
+H-Colouring for every graph H on at most four vertices.
+",1,0,1,0,0,0
+18706,Subband adaptive filter trained by differential evolution for channel estimation,"  The normalized subband adaptive filter (NSAF) is widely accepted as a
+preeminent adaptive filtering algorithm because of its efficiency under the
+colored excitation. However, the convergence rate of NSAF is slow. To address
+this drawback, in this paper, a variant of the NSAF, called the differential
+evolution (DE)-NSAF (DE-NSAF), is proposed for channel estimation based on DE
+strategy. It is worth noticing that there are several papers concerning
+designing DE strategies for adaptive filter. But their signal models are still
+the single adaptive filter model rather than the fullband adaptive filter model
+considered in this paper. Thus, the problem considered in our work is quite
+different from those. The proposed DE-NSAF algorithm is based on real-valued
+manipulations and has fast convergence rate for searching the global solution
+of optimized weight vector. Moreover, a design step of new algorithm is given
+in detail. Simulation results demonstrate the improved performance of the
+proposed DE-NSAF algorithm in terms of the convergence rate.
+",1,0,0,0,0,0
+18707,Distributionally Robust Games: f-Divergence and Learning,"  In this paper we introduce the novel framework of distributionally robust
+games. These are multi-player games where each player models the state of
+nature using a worst-case distribution, also called adversarial distribution.
+Thus each player's payoff depends on the other players' decisions and on the
+decision of a virtual player (nature) who selects an adversarial distribution
+of scenarios. This paper provides three main contributions. Firstly, the
+distributionally robust game is formulated using the statistical notions of
+$f$-divergence between two distributions, here represented by the adversarial
+distribution, and the exact distribution. Secondly, the complexity of the
+problem is significantly reduced by means of triality theory. Thirdly,
+stochastic Bregman learning algorithms are proposed to speedup the computation
+of robust equilibria. Finally, the theoretical findings are illustrated in a
+convex setting and its limitations are tested with a non-convex non-concave
+function.
+",1,0,1,0,0,0
+18708,Modeling and Reasoning About Wireless Networks: A Graph-based Calculus Approach,"  We propose a graph-based process calculus for modeling and reasoning about
+wireless networks with local broadcasts. Graphs are used at syntactical level
+to describe the topological structures of networks. This calculus is equipped
+with a reduction semantics and a labelled transition semantics. The former is
+used to define weak barbed congruence. The latter is used to define a
+parameterized weak bisimulation emphasizing locations and local broadcasts. We
+prove that weak bisimilarity implies weak barbed congruence. The potential
+applications are illustrated by some examples and two case studies.
+",1,0,0,0,0,0
+18709,"Weyl's law on $RCD^*(K,N)$ metric measure spaces","  In this paper, we will prove the Weyl's law for the asymptotic formula of
+Dirichlet eigenvalues on metric measure spaces with generalized Ricci curvature
+bounded from below.
+",0,0,1,0,0,0
+18710,The area of the Mandelbrot set and Zagier's conjecture,"  We prove Zagier's conjecture regarding the 2-adic valuation of the
+coefficients $\{b_m\}$ that appear in Ewing and Schober's series formula for
+the area of the Mandelbrot set in the case where $m\equiv 2 \mod 4$.
+",0,0,1,0,0,0
+18711,Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization,"  We develop an approximate formula for evaluating a cross-validation estimator
+of predictive likelihood for multinomial logistic regression regularized by an
+$\ell_1$-norm. This allows us to avoid repeated optimizations required for
+literally conducting cross-validation; hence, the computational time can be
+significantly reduced. The formula is derived through a perturbative approach
+employing the largeness of the data size and the model dimensionality. An
+extension to the elastic net regularization is also addressed. The usefulness
+of the approximate formula is demonstrated on simulated data and the ISOLET
+dataset from the UCI machine learning repository.
+",0,1,0,1,0,0
+18712,Realizing an optimization approach inspired from Piagets theory on cognitive development,"  The objective of this paper is to introduce an artificial intelligence based
+optimization approach, which is inspired from Piagets theory on cognitive
+development. The approach has been designed according to essential processes
+that an individual may experience while learning something new or improving his
+/ her knowledge. These processes are associated with the Piagets ideas on an
+individuals cognitive development. The approach expressed in this paper is a
+simple algorithm employing swarm intelligence oriented tasks in order to
+overcome single-objective optimization problems. For evaluating effectiveness
+of this early version of the algorithm, test operations have been done via some
+benchmark functions. The obtained results show that the approach / algorithm
+can be an alternative to the literature in terms of single-objective
+optimization. The authors have suggested the name: Cognitive Development
+Optimization Algorithm (CoDOA) for the related intelligent optimization
+approach.
+",1,0,1,0,0,0
+18713,Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions,"  In conventional chemisorption model, the d-band center theory (augmented
+sometimes with the upper edge of d-band for imporved accuarcy) plays a central
+role in predicting adsorption energies and catalytic activity as a function of
+d-band center of the solid surfaces, but it requires density functional
+calculations that can be quite costly for large scale screening purposes of
+materials. In this work, we propose to use the d-band width of the muffin-tin
+orbital theory (to account for local coordination environment) plus
+electronegativity (to account for adsorbate renormalization) as a simple set of
+alternative descriptors for chemisorption, which do not demand the ab initio
+calculations. This pair of descriptors are then combined with machine learning
+methods, namely, artificial neural network (ANN) and kernel ridge regression
+(KRR), to allow large scale materials screenings. We show, for a toy set of 263
+alloy systems, that the CO adsorption energy can be predicted with a remarkably
+small mean absolute deviation error of 0.05 eV, a significantly improved result
+as compared to 0.13 eV obtained with descriptors including costly d-band center
+calculations in literature. We achieved this high accuracy by utilizing an
+active learning algorithm, without which the accuracy was 0.18 eV otherwise. As
+a practical application of this machine, we identified Cu3Y@Cu as a highly
+active and cost-effective electrochemical CO2 reduction catalyst to produce CO
+with the overpotential 0.37 V lower than Au catalyst.
+",0,1,0,1,0,0
+18714,Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility,"  Extracting significant places or places of interest (POIs) using individuals'
+spatio-temporal data is of fundamental importance for human mobility analysis.
+Classical clustering methods have been used in prior work for detecting POIs,
+but without considering temporal constraints. Usually, the involved parameters
+for clustering are difficult to determine, e.g., the optimal cluster number in
+hierarchical clustering. Currently, researchers either choose heuristic values
+or use spatial distance-based optimization to determine an appropriate
+parameter set. We argue that existing research does not optimally address
+temporal information and thus leaves much room for improvement. Considering
+temporal constraints in human mobility, we introduce an effective clustering
+approach - namely POI clustering with temporal constraints (PC-TC) - to extract
+POIs from spatio-temporal data of human mobility. Following human mobility
+nature in modern society, our approach aims to extract both global POIs (e.g.,
+workplace or university) and local POIs (e.g., library, lab, and canteen).
+Based on two publicly available datasets including 193 individuals, our
+evaluation results show that PC-TC has much potential for next place prediction
+in terms of granularity (i.e., the number of extracted POIs) and
+predictability.
+",0,0,0,1,0,0
+18715,The maximum number of zeros of $r(z) - \overline{z}$ revisited,"  Generalizing several previous results in the literature on rational harmonic
+functions, we derive bounds on the maximum number of zeros of functions $f(z) =
+\frac{p(z)}{q(z)} - \overline{z}$, which depend on both $\mathrm{deg}(p)$ and
+$\mathrm{deg}(q)$. Furthermore, we prove that any function that attains one of
+these upper bounds is regular.
+",0,0,1,0,0,0
+18716,High Luminosity Large Hadron Collider HL-LHC,"  HL-LHC federates the efforts and R&D of a large international community
+towards the ambitious HL- LHC objectives and contributes to establishing the
+European Research Area (ERA) as a focal point of global research cooperation
+and a leader in frontier knowledge and technologies. HL-LHC relies on strong
+participation from various partners, in particular from leading US and Japanese
+laboratories. This participation will be required for the execution of the
+construction phase as a global project. In particular, the US LHC Accelerator
+R&D Program (LARP) has developed some of the key technologies for the HL-LHC,
+such as the large-aperture niobium-tin ($Nb_{3}Sn) quadrupoles and the crab
+cavities. The proposed governance model is tailored accordingly and should pave
+the way for the organization of the construction phase.
+",0,1,0,0,0,0
+18717,Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies,"  Various approaches have been proposed to learn visuo-motor policies for
+real-world robotic applications. One solution is first learning in simulation
+then transferring to the real world. In the transfer, most existing approaches
+need real-world images with labels. However, the labelling process is often
+expensive or even impractical in many robotic applications. In this paper, we
+propose an adversarial discriminative sim-to-real transfer approach to reduce
+the cost of labelling real data. The effectiveness of the approach is
+demonstrated with modular networks in a table-top object reaching task where a
+7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter
+through visual observations. The adversarial transfer approach reduced the
+labelled real data requirement by 50%. Policies can be transferred to real
+environments with only 93 labelled and 186 unlabelled real images. The
+transferred visuo-motor policies are robust to novel (not seen in training)
+objects in clutter and even a moving target, achieving a 97.8% success rate and
+1.8 cm control accuracy.
+",1,0,0,0,0,0
+18718,Algebraic Bethe ansatz for the trigonometric sl(2) Gaudin model with triangular boundary,"  In the derivation of the generating function of the Gaudin Hamiltonians with
+boundary terms, we follow the same approach used previously in the rational
+case, which in turn was based on Sklyanin's method in the periodic case. Our
+derivation is centered on the quasi-classical expansion of the linear
+combination of the transfer matrix of the XXZ Heisenberg spin chain and the
+central element, the so-called Sklyanin determinant. The corresponding Gaudin
+Hamiltonians with boundary terms are obtained as the residues of the generating
+function. By defining the appropriate Bethe vectors which yield strikingly
+simple off-shell action of the generating function, we fully implement the
+algebraic Bethe ansatz, obtaining the spectrum of the generating function and
+the corresponding Bethe equations.
+",0,1,0,0,0,0
+18719,Marked Temporal Dynamics Modeling based on Recurrent Neural Network,"  We are now witnessing the increasing availability of event stream data, i.e.,
+a sequence of events with each event typically being denoted by the time it
+occurs and its mark information (e.g., event type). A fundamental problem is to
+model and predict such kind of marked temporal dynamics, i.e., when the next
+event will take place and what its mark will be. Existing methods either
+predict only the mark or the time of the next event, or predict both of them,
+yet separately. Indeed, in marked temporal dynamics, the time and the mark of
+the next event are highly dependent on each other, requiring a method that
+could simultaneously predict both of them. To tackle this problem, in this
+paper, we propose to model marked temporal dynamics by using a mark-specific
+intensity function to explicitly capture the dependency between the mark and
+the time of the next event. Extensive experiments on two datasets demonstrate
+that the proposed method outperforms state-of-the-art methods at predicting
+marked temporal dynamics.
+",1,0,0,0,0,0
+18720,A Bayesian framework for distributed estimation of arrival rates in asynchronous networks,"  In this paper we consider a network of agents monitoring a spatially
+distributed arrival process. Each node measures the number of arrivals seen at
+its monitoring point in a given time-interval with the objective of estimating
+the unknown local arrival rate. We propose an asynchronous distributed approach
+based on a Bayesian model with unknown hyperparameter, where each node computes
+the minimum mean square error (MMSE) estimator of its local arrival rate in a
+distributed way. As a result, the estimation at each node ""optimally"" fuses the
+information from the whole network through a distributed optimization
+algorithm. Moreover, we propose an ad-hoc distributed estimator, based on a
+consensus algorithm for time-varying and directed graphs, which exhibits
+reduced complexity and exponential convergence. We analyze the performance of
+the proposed distributed estimators, showing that they: (i) are reliable even
+in presence of limited local data, and (ii) improve the estimation accuracy
+compared to the purely decentralized setup. Finally, we provide a statistical
+characterization of the proposed estimators. In particular, for the ad-hoc
+estimator, we show that as the number of nodes goes to infinity its mean square
+error converges to the optimal one. Numerical Monte Carlo simulations confirm
+the theoretical characterization and highlight the appealing performances of
+the estimators.
+",1,0,1,0,0,0
+18721,Rigidity of branching microstructures in shape memory alloys,"  We analyze generic sequences for which the geometrically linear energy
+\[E_\eta(u,\chi):= \eta^{-\frac{2}{3}}\int_{B_{0}(1)} \left| e(u)-
+\sum_{i=1}^3 \chi_ie_i\right|^2 d x+\eta^\frac{1}{3} \sum_{i=1}^3
+|D\chi_i|(B_{0}(1))\] remains bounded in the limit $\eta \to 0$. Here $ e(u)
+:=1/2(Du + Du^T)$ is the (linearized) strain of the displacement $u$, the
+strains $e_i$ correspond to the martensite strains of a shape memory alloy
+undergoing cubic-to-tetragonal transformations and $\chi_i:B_{0}(1) \to
+\{0,1\}$ is the partition into phases. In this regime it is known that in
+addition to simple laminates also branched structures are possible, which if
+austenite was present would enable the alloy to form habit planes.
+In an ansatz-free manner we prove that the alignment of macroscopic
+interfaces between martensite twins is as predicted by well-known rank-one
+conditions. Our proof proceeds via the non-convex, non-discrete-valued
+differential inclusion \[e(u) \in \bigcup_{1\leq i\neq j\leq 3}
+\operatorname{conv} \{e_i,e_j\}\] satisfied by the weak limits of bounded
+energy sequences and of which we classify all solutions. In particular, there
+exist no convex integration solutions of the inclusion with complicated
+geometric structures.
+",0,0,1,0,0,0
+18722,"Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository","  Machine learning qualifies computers to assimilate with data, without being
+solely programmed [1, 2]. Machine learning can be classified as supervised and
+unsupervised learning. In supervised learning, computers learn an objective
+that portrays an input to an output hinged on training input-output pairs [3].
+Most efficient and widely used supervised learning algorithms are K-Nearest
+Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor
+(LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this
+paper is to implement these elegant learning algorithms on eleven different
+datasets from the UCI machine learning repository to observe the variation of
+accuracies for each of the algorithms on all datasets. Analyzing the accuracy
+of the algorithms will give us a brief idea about the relationship of the
+machine learning algorithms and the data dimensionality. All the algorithms are
+developed in Matlab. Upon such accuracy observation, the comparison can be
+built among KNN, SVM, LMNN, and ENN regarding their performances on each
+dataset.
+",0,0,0,1,0,0
+18723,Network Classification in Temporal Networks Using Motifs,"  Network classification has a variety of applications, such as detecting
+communities within networks and finding similarities between those representing
+different aspects of the real world. However, most existing work in this area
+focus on examining static undirected networks without considering directed
+edges or temporality. In this paper, we propose a new methodology that utilizes
+feature representation for network classification based on the temporal motif
+distribution of the network and a null model for comparing against random
+graphs. Experimental results show that our method improves accuracy by up
+$10\%$ compared to the state-of-the-art embedding method in network
+classification, for tasks such as classifying network type, identifying
+communities in email exchange network, and identifying users given their
+app-switching behaviors.
+",1,0,0,0,0,0
+18724,The strength of Ramsey's theorem for pairs and arbitrarily many colors,"  In this paper, we show that $\mathrm{RT}^{2}+\mathsf{WKL}_0$ is a
+$\Pi^{1}_{1}$-conservative extension of $\mathrm{B}\Sigma^0_3$.
+",0,0,1,0,0,0
+18725,Joint Power Allocation and Beamforming for Energy-Efficient Two-Way Multi-Relay Communications,"  This paper considers the joint design of user power allocation and relay
+beamforming in relaying communications, in which multiple pairs of
+single-antenna users exchange information with each other via multiple-antenna
+relays in two time slots. All users transmit their signals to the relays in the
+first time slot while the relays broadcast the beamformed signals to all users
+in the second time slot. The aim is to maximize the system's energy efficiency
+(EE) subject to quality-of-service (QoS) constraints in terms of exchange
+throughput requirements. The QoS constraints are nonconvex with many nonlinear
+cross-terms, so finding a feasible point is already computationally
+challenging. The sum throughput appears in the numerator while the total
+consumption power appears in the denominator of the EE objective function. The
+former is a nonconcave function and the latter is a nonconvex function, making
+fractional programming useless for EE optimization. Nevertheless, efficient
+iterations of low complexity to obtain its optimized solutions are developed.
+The performances of the multiple-user and multiple-relay networks under various
+scenarios are evaluated to show the merit of the paper development.
+",1,0,0,0,0,0
+18726,Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen,"  Armed conflict has led to an unprecedented number of internally displaced
+persons (IDPs) - individuals who are forced out of their homes but remain
+within their country. IDPs often urgently require shelter, food, and
+healthcare, yet prediction of when large fluxes of IDPs will cross into an area
+remains a major challenge for aid delivery organizations. Accurate forecasting
+of IDP migration would empower humanitarian aid groups to more effectively
+allocate resources during conflicts. We show that monthly flow of IDPs from
+province to province in both Syria and Yemen can be accurately forecasted one
+month in advance, using publicly available data. We model monthly IDP flow
+using data on food price, fuel price, wage, geospatial, and news data. We find
+that machine learning approaches can more accurately forecast migration trends
+than baseline persistence models. Our findings thus potentially enable
+proactive aid allocation for IDPs in anticipation of forecasted arrivals.
+",0,0,0,1,0,0
+18727,Entropy? Honest!,"  Here we deconstruct, and then in a reasoned way reconstruct, the concept of
+""entropy of a system,"" paying particular attention to where the randomness may
+be coming from. We start with the core concept of entropy as a COUNT associated
+with a DESCRIPTION; this count (traditionally expressed in logarithmic form for
+a number of good reasons) is in essence the number of possibilities---specific
+instances or ""scenarios,"" that MATCH that description. Very natural (and
+virtually inescapable) generalizations of the idea of description are the
+probability distribution and of its quantum mechanical counterpart, the density
+operator.
+We track the process of dynamically updating entropy as a system evolves.
+Three factors may cause entropy to change: (1) the system's INTERNAL DYNAMICS;
+(2) unsolicited EXTERNAL INFLUENCES on it; and (3) the approximations one has
+to make when one tries to predict the system's future state. The latter task is
+usually hampered by hard-to-quantify aspects of the original description,
+limited data storage and processing resource, and possibly algorithmic
+inadequacy. Factors 2 and 3 introduce randomness into one's predictions and
+accordingly degrade them. When forecasting, as long as the entropy bookkeping
+is conducted in an HONEST fashion, this degradation will ALWAYS lead to an
+entropy increase.
+To clarify the above point we introduce the notion of HONEST ENTROPY, which
+coalesces much of what is of course already done, often tacitly, in responsible
+entropy-bookkeping practice. This notion, we believe, will help to fill an
+expressivity gap in scientific discourse. With its help we shall prove that ANY
+dynamical system---not just our physical universe---strictly obeys Clausius's
+original formulation of the second law of thermodynamics IF AND ONLY IF it is
+invertible. Thus this law is a TAUTOLOGICAL PROPERTY of invertible systems!
+",0,1,0,0,0,0
+18728,Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors,"  We study the problem of generating adversarial examples in a black-box
+setting in which only loss-oracle access to a model is available. We introduce
+a framework that conceptually unifies much of the existing work on black-box
+attacks, and we demonstrate that the current state-of-the-art methods are
+optimal in a natural sense. Despite this optimality, we show how to improve
+black-box attacks by bringing a new element into the problem: gradient priors.
+We give a bandit optimization-based algorithm that allows us to seamlessly
+integrate any such priors, and we explicitly identify and incorporate two
+examples. The resulting methods use two to four times fewer queries and fail
+two to five times less often than the current state-of-the-art.
+",0,0,0,1,0,0
+18729,A Dynamic Model of Central Counterparty Risk,"  We introduce a dynamic model of the default waterfall of derivatives CCPs and
+propose a risk sensitive method for sizing the initial margin (IM), and the
+default fund (DF) and its allocation among clearing members. Using a Markovian
+structure model of joint credit migrations, our evaluation of DF takes into
+account the joint credit quality of clearing members as they evolve over time.
+Another important aspect of the proposed methodology is the use of the time
+consistent dynamic risk measures for computation of IM and DF. We carry out a
+comprehensive numerical study, where, in particular, we analyze the advantages
+of the proposed methodology and its comparison with the currently prevailing
+methods used in industry.
+",0,0,0,0,0,1
+18730,Half-Duplex Base Station with Adaptive Scheduling of the in-Band Uplink-Receptions and Downlink-Transmissions,"  In this paper, we propose a novel reception/transmission scheme for
+half-duplex base stations (BSs). In particular, we propose a half-duplex BS
+that employes in-band uplink-receptions from user 1 and downlink-transmissions
+to user 2, which occur in different time slots. Furthermore, we propose optimal
+adaptive scheduling of the in-band uplink-receptions and downlink-transmissions
+of the BS such that the uplink-downlink rate/throughput region is maximized and
+the outage probabilities of the uplink and downlink channels are minimized.
+Practically, this results in selecting whether in a given time slot the BS
+should receive from user 1 or transmit to user 2 based on the qualities of the
+in-band uplink-reception and downlink-transmission channels. Compared to the
+performance achieved with a conventional full-duplex division (FDD) base
+station, two main gains can be highlighted: 1) Increased uplink-downlink
+rate/throughput region; 2) Doubling of the diversity gain of both the uplink
+and downlink channels.
+",1,0,0,0,0,0
+18731,HAT-P-26b: A Neptune-Mass Exoplanet with a Well Constrained Heavy Element Abundance,"  A correlation between giant-planet mass and atmospheric heavy elemental
+abundance was first noted in the past century from observations of planets in
+our own Solar System, and has served as a cornerstone of planet formation
+theory. Using data from the Hubble and Spitzer Space Telescopes from 0.5 to 5
+microns, we conducted a detailed atmospheric study of the transiting
+Neptune-mass exoplanet HAT-P-26b. We detected prominent H2O absorption bands
+with a maximum base-to-peak amplitude of 525ppm in the transmission spectrum.
+Using the water abundance as a proxy for metallicity, we measured HAT-P-26b's
+atmospheric heavy element content [4.8 (-4.0 +21.5) times solar]. This likely
+indicates that HAT-P-26b's atmosphere is primordial and obtained its gaseous
+envelope late in its disk lifetime, with little contamination from metal-rich
+planetesimals.
+",0,1,0,0,0,0
+18732,Robust Localization Using Range Measurements with Unknown and Bounded Errors,"  Cooperative geolocation has attracted significant research interests in
+recent years. A large number of localization algorithms rely on the
+availability of statistical knowledge of measurement errors, which is often
+difficult to obtain in practice. Compared with the statistical knowledge of
+measurement errors, it can often be easier to obtain the measurement error
+bound. This work investigates a localization problem assuming unknown
+measurement error distribution except for a bound on the error. We first
+formulate this localization problem as an optimization problem to minimize the
+worst-case estimation error, which is shown to be a non-convex optimization
+problem. Then, relaxation is applied to transform it into a convex one.
+Furthermore, we propose a distributed algorithm to solve the problem, which
+will converge in a few iterations. Simulation results show that the proposed
+algorithms are more robust to large measurement errors than existing algorithms
+in the literature. Geometrical analysis providing additional insights is also
+provided.
+",0,0,0,1,0,0
+18733,Intuitionistic Non-Normal Modal Logics: A general framework,"  We define a family of intuitionistic non-normal modal logics; they can bee
+seen as intuitionistic counterparts of classical ones. We first consider
+monomodal logics, which contain only one between Necessity and Possibility. We
+then consider the more important case of bimodal logics, which contain both
+modal operators. In this case we define several interactions between Necessity
+and Possibility of increasing strength, although weaker than duality. For all
+logics we provide both a Hilbert axiomatisation and a cut-free sequent
+calculus, on its basis we also prove their decidability. We then give a
+semantic characterisation of our logics in terms of neighbourhood models. Our
+semantic framework captures modularly not only our systems but also already
+known intuitionistic non-normal modal logics such as Constructive K (CK) and
+the propositional fragment of Wijesekera's Constructive Concurrent Dynamic
+Logic.
+",1,0,0,0,0,0
+18734,Externalities in Socially-Based Resource Sharing Network,"  This paper investigates the impact of link formation between a pair of agents
+on resource availability of other agents in a social cloud network, which is a
+special case of socially-based resource sharing systems. Specifically, we study
+the correlation between externalities, network size, and network density.
+We first conjecture and experimentally support that if an agent experiences
+positive externalities, then its closeness (harmonic centrality measure) should
+increase. Next, we show the following for ring networks: in less populated
+networks no agent experiences positive externalities; in more populated
+networks a set of agents experience positive externalities, and larger the
+distance between agents forming a link, more the number of beneficiaries; and
+the number of beneficiaries is always less than the number of
+non-beneficiaries. Finally, we show that network density is inversely
+proportional to positive externalities, and further, it plays a crucial role in
+determining the kind of externalities.
+",1,0,0,0,0,0
+18735,Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits,"  Consider the problem: given data pair $(\mathbf{x}, \mathbf{y})$ drawn from a
+population with $f_*(x) = \mathbf{E}[\mathbf{y} | \mathbf{x} = x]$, specify a
+neural network and run gradient flow on the weights over time until reaching
+any stationarity. How does $f_t$, the function computed by the neural network
+at time $t$, relate to $f_*$, in terms of approximation and representation?
+What are the provable benefits of the adaptive representation by neural
+networks compared to the pre-specified fixed basis representation in the
+classical nonparametric literature? We answer the above questions via a dynamic
+reproducing kernel Hilbert space (RKHS) approach indexed by the training
+process of neural networks. We show that when reaching any local stationarity,
+gradient flow learns an adaptive RKHS representation, and performs the global
+least squares projection onto the adaptive RKHS, simultaneously. In addition,
+we prove that as the RKHS is data-adaptive and task-specific, the residual for
+$f_*$ lies in a subspace that is smaller than the orthogonal complement of the
+RKHS, formalizing the representation and approximation benefits of neural
+networks.
+",1,0,1,1,0,0
+18736,On utility maximization without passing by the dual problem,"  We treat utility maximization from terminal wealth for an agent with utility
+function $U:\mathbb{R}\to\mathbb{R}$ who dynamically invests in a
+continuous-time financial market and receives a possibly unbounded random
+endowment. We prove the existence of an optimal investment without introducing
+the associated dual problem. We rely on a recent result of Orlicz space theory,
+due to Delbaen and Owari which leads to a simple and transparent proof.
+Our results apply to non-smooth utilities and even strict concavity can be
+relaxed. We can handle certain random endowments with non-hedgeable risks,
+complementing earlier papers. Constraints on the terminal wealth can also be
+incorporated.
+As examples, we treat frictionless markets with finitely many assets and
+large financial markets.
+",0,0,1,0,0,0
+18737,Stochastic Generative Hashing,"  Learning-based binary hashing has become a powerful paradigm for fast search
+and retrieval in massive databases. However, due to the requirement of discrete
+outputs for the hash functions, learning such functions is known to be very
+challenging. In addition, the objective functions adopted by existing hashing
+techniques are mostly chosen heuristically. In this paper, we propose a novel
+generative approach to learn hash functions through Minimum Description Length
+principle such that the learned hash codes maximally compress the dataset and
+can also be used to regenerate the inputs. We also develop an efficient
+learning algorithm based on the stochastic distributional gradient, which
+avoids the notorious difficulty caused by binary output constraints, to jointly
+optimize the parameters of the hash function and the associated generative
+model. Extensive experiments on a variety of large-scale datasets show that the
+proposed method achieves better retrieval results than the existing
+state-of-the-art methods.
+",1,0,0,1,0,0
+18738,Nonlinear learning and learning advantages in evolutionary games,"  The idea of incompetence as a learning or adaptation function was introduced
+in the context of evolutionary games as a fixed parameter. However, live
+organisms usually perform different nonlinear adaptation functions such as a
+power law or exponential fitness growth. Here, we examine how the functional
+form of the learning process may affect the social competition between
+different behavioral types. Further, we extend our results for the evolutionary
+games where fluctuations in the environment affect the behavioral adaptation of
+competing species and demonstrate importance of the starting level of
+incompetence for survival. Hence, we define a new concept of learning
+advantages that becomes crucial when environments are constantly changing and
+requiring rapid adaptation from species. This may lead to the evolutionarily
+weak phase when even evolutionary stable populations become vulnerable to
+invasions.
+",0,0,0,0,1,0
+18739,Information-theoretic Limits for Community Detection in Network Models,"  We analyze the information-theoretic limits for the recovery of node labels
+in several network models. This includes the Stochastic Block Model, the
+Exponential Random Graph Model, the Latent Space Model, the Directed
+Preferential Attachment Model, and the Directed Small-world Model. For the
+Stochastic Block Model, the non-recoverability condition depends on the
+probabilities of having edges inside a community, and between different
+communities. For the Latent Space Model, the non-recoverability condition
+depends on the dimension of the latent space, and how far and spread are the
+communities in the latent space. For the Directed Preferential Attachment Model
+and the Directed Small-world Model, the non-recoverability condition depends on
+the ratio between homophily and neighborhood size. We also consider dynamic
+versions of the Stochastic Block Model and the Latent Space Model.
+",1,0,0,1,0,0
+18740,Proofs of life: molecular-biology reasoning simulates cell behaviors from first principles,"  We axiomatize the molecular-biology reasoning style, verify compliance of the
+standard reference: Ptashne, A Genetic Switch, and present proof-theory-induced
+technologies to predict phenotypes and life cycles from genotypes. The key is
+to note that `reductionist discipline' entails constructive reasoning, i.e.,
+that any argument for a compound property is constructed from more basic
+arguments. Proof theory makes explicit the inner structure of the axiomatized
+reasoning style and allows the permissible dynamics to be presented as a mode
+of computation that can be executed and analyzed. Constructivity and
+executability guarantee simulation when working over domain-specific languages.
+Here, we exhibit phenotype properties for genotype reasons: a molecular-biology
+argument is an open-system concurrent computation that results in compartment
+changes and is performed among processes of physiology change as determined
+from the molecular programming of given DNA. Life cycles are the possible
+sequentializations of the processes. A main implication of our construction is
+that technical correctness provides a complementary perspective on science that
+is as fundamental there as it is for pure mathematics, provided mature
+reductionism exists.
+",0,0,0,0,1,0
+18741,Beyond recursion operators,"  We briefly recall the history of the Nijenhuis torsion of (1,1)-tensors on
+manifolds and of the lesser-known Haantjes torsion. We then show how the
+Haantjes manifolds of Magri and the symplectic-Haantjes structures of Tempesta
+and Tondo generalize the classical approach to integrable systems in the
+bi-hamiltonian and symplectic-Nijenhuis formalisms, the sequence of powers of
+the recursion operator being replaced by a family of commuting Haantjes
+operators.
+",0,0,1,0,0,0
+18742,On Classical Integrability of the Hydrodynamics of Quantum Integrable Systems,"  Recently, a hydrodynamic description of local equilibrium dynamics in quantum
+integrable systems was discovered. In the diffusionless limit, this is
+equivalent to a certain ""Bethe-Boltzmann"" kinetic equation, which has the form
+of an integro-differential conservation law in $(1+1)$D. The purpose of the
+present work is to investigate the sense in which the Bethe-Boltzmann equation
+defines an ""integrable kinetic equation"". To this end, we study a class of $N$
+dimensional systems of evolution equations that arise naturally as
+finite-dimensional approximations to the Bethe-Boltzmann equation. We obtain
+non-local Poisson brackets and Hamiltonian densities for these equations and
+derive an infinite family of first integrals, parameterized by $N$ functional
+degrees of freedom. We find that the conserved charges arising from quantum
+integrability map to Casimir invariants of the hydrodynamic bracket and their
+group velocities map to Hamiltonian flows. Some results from the
+finite-dimensional setting extend to the underlying integro-differential
+equation, providing evidence for its integrability in the hydrodynamic sense.
+",0,1,0,0,0,0
+18743,Transition to turbulence when the Tollmien-Schlichting and bypass routes coexist,"  Plane Poiseuille flow, the pressure driven flow between parallel plates,
+shows a route to turbulence connected with a linear instability to
+Tollmien-Schlichting (TS) waves, and another one, the bypass transition, that
+is triggered with finite amplitude perturbation. We use direct numerical
+simulations to explore the arrangement of the different routes to turbulence
+among the set of initial conditions. For plates that are a distance $2H$ apart
+and in a domain of width $2\pi H$ and length $2\pi H$ the subcritical
+instability to TS waves sets in at $Re_{c}=5815$ that extends down to
+$Re_{TS}\approx4884$. The bypass route becomes available above $Re_E=459$ with
+the appearance of three-dimensional finite-amplitude traveling waves. The
+bypass transition covers a large set of finite amplitude perturbations. Below
+$Re_c$, TS appear for a tiny set of initial conditions that grows with
+increasing Reynolds number. Above $Re_c$ the previously stable region becomes
+unstable via TS waves, but a sharp transition to the bypass route can still be
+identified. Both routes lead to the same turbulent in the final stage of the
+transition, but on different time scales. Similar phenomena can be expected in
+other flows where two or more routes to turbulence compete.
+",0,1,0,0,0,0
+18744,Interactive Exploration and Discovery of Scientific Publications with PubVis,"  With an exponentially growing number of scientific papers published each
+year, advanced tools for exploring and discovering publications of interest are
+becoming indispensable. To empower users beyond a simple keyword search
+provided e.g. by Google Scholar, we present the novel web application PubVis.
+Powered by a variety of machine learning techniques, it combines essential
+features to help researchers find the content most relevant to them. An
+interactive visualization of a large collection of scientific publications
+provides an overview of the field and encourages the user to explore articles
+beyond a narrow research focus. This is augmented by personalized content based
+article recommendations as well as an advanced full text search to discover
+relevant references. The open sourced implementation of the app can be easily
+set up and run locally on a desktop computer to provide access to content
+tailored to the specific needs of individual users. Additionally, a PubVis demo
+with access to a collection of 10,000 papers can be tested online.
+",1,0,0,0,0,0
+18745,Improving phase II oncology trials using best observed RECIST response as an endpoint by modelling continuous tumour measurements,"  In many phase II trials in solid tumours, patients are assessed using
+endpoints based on the Response Evaluation Criteria in Solid Tumours (RECIST)
+scale. Often, analyses are based on the response rate. This is the proportion
+of patients who have an observed tumour shrinkage above a pre-defined level and
+no new tumour lesions. The augmented binary method has been proposed to improve
+the precision of the estimator of the response rate. The method involves
+modelling the tumour shrinkage to avoid dichotomising it. However, in many
+trials the best observed response is used as the primary outcome. In such
+trials, patients are followed until progression, and their best observed RECIST
+outcome is used as the primary endpoint. In this paper, we propose a method
+that extends the augmented binary method so that it can be used when the
+outcome is best observed response. We show through simulated data and data from
+a real phase II cancer trial that this method improves power in both single-arm
+and randomised trials. The average gain in power compared to the traditional
+analysis is equivalent to approximately a 35% increase in sample size. A
+modified version of the method is proposed to reduce the computational effort
+required. We show this modified method maintains much of the efficiency
+advantages.
+",0,0,0,1,0,0
+18746,Evans-Selberg potential on planar domains,"  We provide explicit formulas of Evans kernels, Evans-Selberg potentials and
+fundamental metrics on potential-theoretically parabolic planar domains.
+",0,0,1,0,0,0
+18747,Bridging the Gap Between Computational Photography and Visual Recognition,"  What is the current state-of-the-art for image restoration and enhancement
+applied to degraded images acquired under less than ideal circumstances? Can
+the application of such algorithms as a pre-processing step to improve image
+interpretability for manual analysis or automatic visual recognition to
+classify scene content? While there have been important advances in the area of
+computational photography to restore or enhance the visual quality of an image,
+the capabilities of such techniques have not always translated in a useful way
+to visual recognition tasks. Consequently, there is a pressing need for the
+development of algorithms that are designed for the joint problem of improving
+visual appearance and recognition, which will be an enabling factor for the
+deployment of visual recognition tools in many real-world scenarios. To address
+this, we introduce the UG^2 dataset as a large-scale benchmark composed of
+video imagery captured under challenging conditions, and two enhancement tasks
+designed to test algorithmic impact on visual quality and automatic object
+recognition. Furthermore, we propose a set of metrics to evaluate the joint
+improvement of such tasks as well as individual algorithmic advances, including
+a novel psychophysics-based evaluation regime for human assessment and a
+realistic set of quantitative measures for object recognition performance. We
+introduce six new algorithms for image restoration or enhancement, which were
+created as part of the IARPA sponsored UG^2 Challenge workshop held at CVPR
+2018. Under the proposed evaluation regime, we present an in-depth analysis of
+these algorithms and a host of deep learning-based and classic baseline
+approaches. From the observed results, it is evident that we are in the early
+days of building a bridge between computational photography and visual
+recognition, leaving many opportunities for innovation in this area.
+",1,0,0,0,0,0
+18748,On some conjectures of Samuels and Feige,"  Let $\mu_1 \ge \dotsc \ge \mu_n > 0$ and $\mu_1 + \dotsm + \mu_n = 1$. Let
+$X_1, \dotsc, X_n$ be independent non-negative random variables with $EX_1 =
+\dotsc = EX_n = 1$, and let $Z = \sum_{i=1}^n \mu_i X_i$. Let $M = \max_{1 \le
+i \le n} \mu_i = \mu_1$, and let $\delta > 0$ and $T = 1 + \delta$. Both
+Samuels and Feige formulated conjectures bounding the probability $P(Z < T)$
+from above. We prove that Samuels' conjecture implies a conjecture of Feige.
+",0,0,1,0,0,0
+18749,Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States,"  Making the right decision in traffic is a challenging task that is highly
+dependent on individual preferences as well as the surrounding environment.
+Therefore it is hard to model solely based on expert knowledge. In this work we
+use Deep Reinforcement Learning to learn maneuver decisions based on a compact
+semantic state representation. This ensures a consistent model of the
+environment across scenarios as well as a behavior adaptation function,
+enabling on-line changes of desired behaviors without re-training. The input
+for the neural network is a simulated object list similar to that of Radar or
+Lidar sensors, superimposed by a relational semantic scene description. The
+state as well as the reward are extended by a behavior adaptation function and
+a parameterization respectively. With little expert knowledge and a set of
+mid-level actions, it can be seen that the agent is capable to adhere to
+traffic rules and learns to drive safely in a variety of situations.
+",1,0,0,1,0,0
+18750,Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows,"  We present Sequential Neural Likelihood (SNL), a new method for Bayesian
+inference in simulator models, where the likelihood is intractable but
+simulating data from the model is possible. SNL trains an autoregressive flow
+on simulated data in order to learn a model of the likelihood in the region of
+high posterior density. A sequential training procedure guides simulations and
+reduces simulation cost by orders of magnitude. We show that SNL is more
+robust, more accurate and requires less tuning than related neural-based
+methods, and we discuss diagnostics for assessing calibration, convergence and
+goodness-of-fit.
+",0,0,0,1,0,0
+18751,Creativity: Generating Diverse Questions using Variational Autoencoders,"  Generating diverse questions for given images is an important task for
+computational education, entertainment and AI assistants. Different from many
+conventional prediction techniques is the need for algorithms to generate a
+diverse set of plausible questions, which we refer to as ""creativity"". In this
+paper we propose a creative algorithm for visual question generation which
+combines the advantages of variational autoencoders with long short-term memory
+networks. We demonstrate that our framework is able to generate a large set of
+varying questions given a single input image.
+",1,0,0,0,0,0
+18752,Cooling dynamics of a single trapped ion via elastic collisions with small-mass atoms,"  We demonstrated sympathetic cooling of a single ion in a buffer gas of
+ultracold atoms with small mass. Efficient collisional cooling was realized by
+suppressing collision-induced heating. We attempt to explain the experimental
+results with a simple rate equation model and provide a quantitative discussion
+of the cooling efficiency per collision. The knowledge we obtained in this work
+is an important ingredient for advancing the technique of sympathetic cooling
+of ions with neutral atoms.
+",0,1,0,0,0,0
+18753,Modulational instability in the full-dispersion Camassa-Holm equation,"  We determine the stability and instability of a sufficiently small and
+periodic traveling wave to long wavelength perturbations, for a nonlinear
+dispersive equation which extends a Camassa-Holm equation to include all the
+dispersion of water waves and the Whitham equation to include nonlinearities of
+medium amplitude waves. In the absence of the effects of surface tension, the
+result qualitatively agrees with the Benjamin-Feir instability of a Stokes
+wave. In the presence of the effects of surface tension, it qualitatively
+agrees with those from formal asymptotic expansions of the physical problem and
+it improves upon that for the Whitham equation, correctly predicting the limit
+of strong surface tension. We discuss the modulational stability and
+instability in the Camassa-Holm equation and related models.
+",0,1,1,0,0,0
+18754,Evaluating and Modelling Hanabi-Playing Agents,"  Agent modelling involves considering how other agents will behave, in order
+to influence your own actions. In this paper, we explore the use of agent
+modelling in the hidden-information, collaborative card game Hanabi. We
+implement a number of rule-based agents, both from the literature and of our
+own devising, in addition to an Information Set Monte Carlo Tree Search
+(IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new,
+predictor version that uses a model of the agents with which it is paired. We
+observe a significant improvement in game-playing strength from this agent in
+comparison to IS-MCTS, resulting from its consideration of what the other
+agents in a game would do. In addition, we create a flawed rule-based agent to
+highlight the predictor's capabilities with such an agent.
+",1,0,0,0,0,0
+18755,Rank-related dimension bounds for subspaces of bilinear forms over finite fields,"  Let q be a power of a prime and let V be a vector space of finite dimension n
+over the field of order q. Let Bil(V) denote the set of all bilinear forms
+defined on V x V, let Symm(V) denote the subspace of Bil(V) consisting of
+symmetric bilinear forms, and Alt(V) denote the subspace of alternating
+bilinear forms. Let M denote a subspace of any of the spaces Bil(V), Symm(V),
+or Alt(V). In this paper we investigate hypotheses on the rank of the non-zero
+elements of M which lead to reasonable bounds for dim M. Typically, we look at
+the case where exactly two or three non-zero ranks occur, one of which is
+usually n. In the case that M achieves the maximal dimension predicted by the
+dimension bound, we try to enumerate the number of forms of a given rank in M
+and describe geometric properties of the radicals of the degenerate elements of
+M.
+",0,0,1,0,0,0
+18756,Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks,"  Bayesian Networks have been widely used in the last decades in many fields,
+to describe statistical dependencies among random variables. In general,
+learning the structure of such models is a problem with considerable
+theoretical interest that still poses many challenges. On the one hand, this is
+a well-known NP-complete problem, which is practically hardened by the huge
+search space of possible solutions. On the other hand, the phenomenon of
+I-equivalence, i.e., different graphical structures underpinning the same set
+of statistical dependencies, may lead to multimodal fitness landscapes further
+hindering maximum likelihood approaches to solve the task. Despite all these
+difficulties, greedy search methods based on a likelihood score coupled with a
+regularization term to account for model complexity, have been shown to be
+surprisingly effective in practice. In this paper, we consider the formulation
+of the task of learning the structure of Bayesian Networks as an optimization
+problem based on a likelihood score. Nevertheless, our approach do not adjust
+this score by means of any of the complexity terms proposed in the literature;
+instead, it accounts directly for the complexity of the discovered solutions by
+exploiting a multi-objective optimization procedure. To this extent, we adopt
+NSGA-II and define the first objective function to be the likelihood of a
+solution and the second to be the number of selected arcs. We thoroughly
+analyze the behavior of our method on a wide set of simulated data, and we
+discuss the performance considering the goodness of the inferred solutions both
+in terms of their objective functions and with respect to the retrieved
+structure. Our results show that NSGA-II can converge to solutions
+characterized by better likelihood and less arcs than classic approaches,
+although paradoxically frequently characterized by a lower similarity to the
+target network.
+",0,0,0,1,0,0
+18757,Simultaneous Localization and Layout Model Selection in Manhattan Worlds,"  In this paper, we will demonstrate how Manhattan structure can be exploited
+to transform the Simultaneous Localization and Mapping (SLAM) problem, which is
+typically solved by a nonlinear optimization over feature positions, into a
+model selection problem solved by a convex optimization over higher order
+layout structures, namely walls, floors, and ceilings. Furthermore, we show how
+our novel formulation leads to an optimization procedure that automatically
+performs data association and loop closure and which ultimately produces the
+simplest model of the environment that is consistent with the available
+measurements. We verify our method on real world data sets collected with
+various sensing modalities.
+",1,0,0,0,0,0
+18758,Robust method for finding sparse solutions to linear inverse problems using an L2 regularization,"  We analyzed the performance of a biologically inspired algorithm called the
+Corrected Projections Algorithm (CPA) when a sparseness constraint is required
+to unambiguously reconstruct an observed signal using atoms from an
+overcomplete dictionary. By changing the geometry of the estimation problem,
+CPA gives an analytical expression for a binary variable that indicates the
+presence or absence of a dictionary atom using an L2 regularizer. The
+regularized solution can be implemented using an efficient real-time
+Kalman-filter type of algorithm. The smoother L2 regularization of CPA makes it
+very robust to noise, and CPA outperforms other methods in identifying known
+atoms in the presence of strong novel atoms in the signal.
+",1,0,0,1,0,0
+18759,Humanoid Robot-Application and Influence,"  Application of humanoid robots has been common in the field of healthcare and
+education. It has been recurrently used to improve social behavior and mollify
+distress level among children with autism, cancer and cerebral palsy. This
+article discusses the same from a human factors perspective. It shows how
+people of different age and gender have a different opinion towards the
+application and acceptance of humanoid robots. Additionally, this article
+highlights the influence of cerebral condition and social interaction on a user
+behavior and attitude towards humanoid robots. Our study performed a literature
+review and found that (a) children and elderly individuals prefer humanoid
+robots due to inactive social interaction, (b) The deterministic behavior of
+humanoid robots can be acknowledged to improve social behavior of autistic
+children, (c) Trust on humanoid robots is highly driven by its application and
+a user age, gender, and social life.
+",1,0,0,0,0,0
+18760,Onsager's Conjecture for the Incompressible Euler Equations in Bounded Domains,"  The goal of this note is to show that, also in a bounded domain $\Omega
+\subset \mathbb{R}^n$, with $\partial \Omega\in C^2$, any weak solution,
+$(u(x,t),p(x,t))$, of the Euler equations of ideal incompressible fluid in
+$\Omega\times (0,T) \subset \mathbb{R}^n\times\mathbb{R}_t$, with the
+impermeability boundary condition: $u\cdot \vec n =0$ on
+$\partial\Omega\times(0,T)$, is of constant energy on the interval $(0,T)$
+provided the velocity field $u \in L^3((0,T);
+C^{0,\alpha}(\overline{\Omega}))$, with $\alpha>\frac13\,.$
+",0,1,1,0,0,0
+18761,Fuzzy logic based approaches for gene regulatory network inference,"  The rapid advancement in high-throughput techniques has fueled the generation
+of large volume of biological data rapidly with low cost. Some of these
+techniques are microarray and next generation sequencing which provides genome
+level insight of living cells. As a result, the size of most of the biological
+databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These
+biological data are analyzed using computational techniques for knowledge
+discovery - which is one of the objectives of bioinformatics research. Gene
+regulatory network (GRN) is a gene-gene interaction network which plays pivotal
+role in understanding gene regulation process and disease studies. From the
+last couple of decades, the researchers are interested in developing
+computational algorithms for GRN inference (GRNI) using high-throughput
+experimental data. Several computational approaches have been applied for
+inferring GRN from gene expression data including statistical techniques
+(correlation coefficient), information theory (mutual information), regression
+based approaches, probabilistic approaches (Bayesian networks, naive byes),
+artificial neural networks, and fuzzy logic. The fuzzy logic, along with its
+hybridization with other intelligent approach, is well studied in GRNI due to
+its several advantages. In this paper, we present a consolidated review on
+fuzzy logic and its hybrid approaches for GRNI developed during last two
+decades.
+",0,0,0,0,1,0
+18762,Instrumentation for nuclear magnetic resonance in zero and ultralow magnetic field,"  We review instrumentation for nuclear magnetic resonance (NMR) in zero and
+ultra-low magnetic field (ZULF, below 0.1 $\mu$T) where detection is based on a
+low-cost, non-cryogenic, spin-exchange relaxation free (SERF) $^{87}$Rb atomic
+magnetometer. The typical sensitivity is 20-30 fT/Hz$^{1/2}$ for signal
+frequencies below 1 kHz and NMR linewidths range from Hz all the way down to
+tens of mHz. These features enable precision measurements of chemically
+informative nuclear spin-spin couplings as well as nuclear spin precession in
+ultra-low magnetic fields.
+",0,1,0,0,0,0
+18763,Countable dense homogeneity and the Cantor set,"  It is shown that CH implies the existence of a compact Hausdorff space that
+is countable dense homogeneous, crowded and does not contain topological copies
+of the Cantor set. This contrasts with a previous result by the author which
+says that for any crowded Hausdorff space $X$ of countable $\pi$-weight, if
+${}^\omega{X}$ is countable dense homogeneous, then $X$ must contain a
+topological copy of the Cantor set.
+",0,0,1,0,0,0
+18764,Dimension Estimation Using Random Connection Models,"  Information about intrinsic dimension is crucial to perform dimensionality
+reduction, compress information, design efficient algorithms, and do
+statistical adaptation. In this paper we propose an estimator for the intrinsic
+dimension of a data set. The estimator is based on binary neighbourhood
+information about the observations in the form of two adjacency matrices, and
+does not require any explicit distance information. The underlying graph is
+modelled according to a subset of a specific random connection model, sometimes
+referred to as the Poisson blob model. Computationally the estimator scales
+like n log n, and we specify its asymptotic distribution and rate of
+convergence. A simulation study on both real and simulated data shows that our
+approach compares favourably with some competing methods from the literature,
+including approaches that rely on distance information.
+",0,0,1,1,0,0
+18765,Optimal projection of observations in a Bayesian setting,"  Optimal dimensionality reduction methods are proposed for the Bayesian
+inference of a Gaussian linear model with additive noise in presence of
+overabundant data. Three different optimal projections of the observations are
+proposed based on information theory: the projection that minimizes the
+Kullback-Leibler divergence between the posterior distributions of the original
+and the projected models, the one that minimizes the expected Kullback-Leibler
+divergence between the same distributions, and the one that maximizes the
+mutual information between the parameter of interest and the projected
+observations. The first two optimization problems are formulated as the
+determination of an optimal subspace and therefore the solution is computed
+using Riemannian optimization algorithms on the Grassmann manifold. Regarding
+the maximization of the mutual information, it is shown that there exists an
+optimal subspace that minimizes the entropy of the posterior distribution of
+the reduced model; a basis of the subspace can be computed as the solution to a
+generalized eigenvalue problem; an a priori error estimate on the mutual
+information is available for this particular solution; and that the
+dimensionality of the subspace to exactly conserve the mutual information
+between the input and the output of the models is less than the number of
+parameters to be inferred. Numerical applications to linear and nonlinear
+models are used to assess the efficiency of the proposed approaches, and to
+highlight their advantages compared to standard approaches based on the
+principal component analysis of the observations.
+",0,0,1,1,0,0
+18766,Notes on the Multiplicative Ergodic Theorem,"  The Oseledets Multiplicative Ergodic theorem is a basic result with numerous
+applications throughout dynamical systems. These notes provide an introduction
+to this theorem, as well as subsequent generalizations. They are based on
+lectures at summer schools in Brazil, France, and Russia.
+",0,0,1,0,0,0
+18767,Targeted Learning with Daily EHR Data,"  Electronic health records (EHR) data provide a cost and time-effective
+opportunity to conduct cohort studies of the effects of multiple time-point
+interventions in the diverse patient population found in real-world clinical
+settings. Because the computational cost of analyzing EHR data at daily (or
+more granular) scale can be quite high, a pragmatic approach has been to
+partition the follow-up into coarser intervals of pre-specified length. Current
+guidelines suggest employing a 'small' interval, but the feasibility and
+practical impact of this recommendation has not been evaluated and no formal
+methodology to inform this choice has been developed. We start filling these
+gaps by leveraging large-scale EHR data from a diabetes study to develop and
+illustrate a fast and scalable targeted learning approach that allows to follow
+the current recommendation and study its practical impact on inference. More
+specifically, we map daily EHR data into four analytic datasets using 90, 30,
+15 and 5-day intervals. We apply a semi-parametric and doubly robust estimation
+approach, the longitudinal TMLE, to estimate the causal effects of four dynamic
+treatment rules with each dataset, and compare the resulting inferences. To
+overcome the computational challenges presented by the size of these data, we
+propose a novel TMLE implementation, the 'long-format TMLE', and rely on the
+latest advances in scalable data-adaptive machine-learning software, xgboost
+and h2o, for estimation of the TMLE nuisance parameters.
+",0,0,0,1,0,0
+18768,Stripe-Based Fragility Analysis of Concrete Bridge Classes Using Machine Learning Techniques,"  A framework for the generation of bridge-specific fragility utilizing the
+capabilities of machine learning and stripe-based approach is presented in this
+paper. The proposed methodology using random forests helps to generate or
+update fragility curves for a new set of input parameters with less
+computational effort and expensive re-simulation. The methodology does not
+place any assumptions on the demand model of various components and helps to
+identify the relative importance of each uncertain variable in their seismic
+demand model. The methodology is demonstrated through the case studies of
+multi-span concrete bridges in California. Geometric, material and structural
+uncertainties are accounted for in the generation of bridge models and
+fragility curves. It is also noted that the traditional lognormality assumption
+on the demand model leads to unrealistic fragility estimates. Fragility results
+obtained the proposed methodology curves can be deployed in risk assessment
+platform such as HAZUS for regional loss estimation.
+",0,0,0,1,0,0
+18769,Accountability of AI Under the Law: The Role of Explanation,"  The ubiquity of systems using artificial intelligence or ""AI"" has brought
+increasing attention to how those systems should be regulated. The choice of
+how to regulate AI systems will require care. AI systems have the potential to
+synthesize large amounts of data, allowing for greater levels of
+personalization and precision than ever before---applications range from
+clinical decision support to autonomous driving and predictive policing. That
+said, there exist legitimate concerns about the intentional and unintentional
+negative consequences of AI systems. There are many ways to hold AI systems
+accountable. In this work, we focus on one: explanation. Questions about a
+legal right to explanation from AI systems was recently debated in the EU
+General Data Protection Regulation, and thus thinking carefully about when and
+how explanation from AI systems might improve accountability is timely. In this
+work, we review contexts in which explanation is currently required under the
+law, and then list the technical considerations that must be considered if we
+desired AI systems that could provide kinds of explanations that are currently
+required of humans.
+",1,0,0,1,0,0
+18770,Frequency-oriented sub-sampling by photonic Fourier transform and I/Q demodulation,"  Sub-sampling can acquire directly a passband within a broad radio frequency
+(RF) range, avoiding down-conversion and low-phase-noise tunable local
+oscillation (LO). However, sub-sampling suffers from band folding and
+self-image interference. In this paper we propose a frequency-oriented
+sub-sampling to solve the two problems. With ultrashort optical pulse and a
+pair of chromatic dispersions, the broadband RF signal is firstly short-time
+Fourier-transformed to a spectrum-spread pulse. Then a time slot, corresponding
+to the target spectrum slice, is coherently optical-sampled with
+in-phase/quadrature (I/Q) demodulation. We demonstrate the novel bandpass
+sampling by a numerical example, which shows the desired uneven intensity
+response, i.e. pre-filtering. We show in theory that appropriate time-stretch
+capacity from dispersion can result in pre-filtering bandwidth less than
+sampling rate. Image rejection due to I/Q sampling is also analyzed. A
+proof-of-concept experiment, which is based on a time-lens sampling source and
+chirped fiber Bragg gratings (CFBGs), shows the center-frequency-tunable
+pre-filtered sub-sampling with bandwidth of 6 GHz around, as well as imaging
+rejection larger than 26 dB. Our technique may benefit future broadband RF
+receivers for frequency-agile Radar or channelization.
+",0,1,0,0,0,0
+18771,A moment map picture of relative balanced metrics on extremal Kähler manifolds,"  We give a moment map interpretation of some relatively balanced metrics. As
+an application, we extend a result of S. K. Donaldson on constant scalar
+curvature Kähler metrics to the case of extremal metrics. Namely, we show
+that a given extremal metric is the limit of some specific relatively balanced
+metrics. As a corollary, we recover uniqueness and splitting results for
+extremal metrics in the polarized case.
+",0,0,1,0,0,0
+18772,Traffic models with adversarial vehicle behaviour,"  We examine the impact of adversarial actions on vehicles in traffic. Current
+advances in assisted/autonomous driving technologies are supposed to reduce the
+number of casualties, but this seems to be desired despite the recently proved
+insecurity of in-vehicle communication buses or components. Fortunately to some
+extent, while compromised cars have become a reality, the numerous attacks
+reported so far on in-vehicle electronics are exclusively concerned with
+impairments of a single target. In this work we put adversarial behavior under
+a more complex scenario where driving decisions deluded by corrupted
+electronics can affect more than one vehicle. Particularly, we focus our
+attention on chain collisions involving multiple vehicles that can be amplified
+by simple adversarial interventions, e.g., delaying taillights or falsifying
+speedometer readings. We provide metrics for assessing adversarial impact and
+consider safety margins against adversarial actions. Moreover, we discuss
+intelligent adversarial behaviour by which the creation of rogue platoons is
+possible and speed manipulations become stealthy to human drivers. We emphasize
+that our work does not try to show the mere fact that imprudent speeds and
+headways lead to chain-collisions, but points out that an adversary may favour
+such scenarios (eventually keeping his actions stealthy for human drivers) and
+further asks for quantifying the impact of adversarial activity or whether
+existing traffic regulations are prepared for such situations.
+",1,0,0,0,0,0
+18773,Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments,"  This paper presents two novel control methodologies for the cooperative
+manipulation of an object by N robotic agents. Firstly, we design an adaptive
+control protocol which employs quaternion feedback for the object orientation
+to avoid potential representation singularities. Secondly, we propose a control
+protocol that guarantees predefined transient and steady-state performance for
+the object trajectory. Both methodologies are decentralized, since the agents
+calculate their own signals without communicating with each other, as well as
+robust to external disturbances and model uncertainties. Moreover, we consider
+that the grasping points are rigid, and avoid the need for force/torque
+measurements. Load distribution is also included via a grasp matrix
+pseudo-inverse to account for potential differences in the agents' power
+capabilities. Finally, simulation and experimental results with two robotic
+arms verify the theoretical findings.
+",1,0,0,0,0,0
+18774,Conditionally conjugate mean-field variational Bayes for logistic models,"  Variational Bayes (VB) is a common strategy for approximate Bayesian
+inference, but simple methods are only available for specific classes of models
+including, in particular, representations having conditionally conjugate
+constructions within an exponential family. Models with logit components are an
+apparently notable exception to this class, due to the absence of conjugacy
+between the logistic likelihood and the Gaussian priors for the coefficients in
+the linear predictor. To facilitate approximate inference within this widely
+used class of models, Jaakkola and Jordan (2000) proposed a simple variational
+approach which relies on a family of tangent quadratic lower bounds of logistic
+log-likelihoods, thus restoring conjugacy between these approximate bounds and
+the Gaussian priors. This strategy is still implemented successfully, but less
+attempts have been made to formally understand the reasons underlying its
+excellent performance. To cover this key gap, we provide a formal connection
+between the above bound and a recent Pólya-gamma data augmentation for
+logistic regression. Such a result places the computational methods associated
+with the aforementioned bounds within the framework of variational inference
+for conditionally conjugate exponential family models, thereby allowing recent
+advances for this class to be inherited also by the methods relying on Jaakkola
+and Jordan (2000).
+",0,0,1,1,0,0
+18775,A $\frac{3}{2}$-Approximation Algorithm for Tree Augmentation via Chvátal-Gomory Cuts,"  The weighted tree augmentation problem (WTAP) is a fundamental network design
+problem. We are given an undirected tree $G = (V,E)$, an additional set of
+edges $L$ called links and a cost vector $c \in \mathbb{R}^L_{\geq 1}$. The
+goal is to choose a minimum cost subset $S \subseteq L$ such that $G = (V, E
+\cup S)$ is $2$-edge-connected. In the unweighted case, that is, when we have
+$c_\ell = 1$ for all $\ell \in L$, the problem is called the tree augmentation
+problem (TAP).
+Both problems are known to be APX-hard, and the best known approximation
+factors are $2$ for WTAP by (Frederickson and JáJá, '81) and $\tfrac{3}{2}$
+for TAP due to (Kortsarz and Nutov, TALG '16). In the case where all link costs
+are bounded by a constant $M$, (Adjiashvili, SODA '17) recently gave a $\approx
+1.96418+\varepsilon$-approximation algorithm for WTAP under this assumption.
+This is the first approximation with a better guarantee than $2$ that does not
+require restrictions on the structure of the tree or the links.
+In this paper, we improve Adjiashvili's approximation to a
+$\frac{3}{2}+\varepsilon$-approximation for WTAP under the bounded cost
+assumption. We achieve this by introducing a strong LP that combines
+$\{0,\frac{1}{2}\}$-Chvátal-Gomory cuts for the standard LP for the problem
+with bundle constraints from Adjiashvili. We show that our LP can be solved
+efficiently and that it is exact for some instances that arise at the core of
+Adjiashvili's approach. This results in the improved guarantee of
+$\frac{3}{2}+\varepsilon$. For TAP, this is the best known LP-based result, and
+matches the bound of $\frac{3}{2}+\varepsilon$ achieved by the best SDP-based
+algorithm due to (Cheriyan and Gao, arXiv '15).
+",1,0,0,0,0,0
+18776,On Identifying Disaster-Related Tweets: Matching-based or Learning-based?,"  Social media such as tweets are emerging as platforms contributing to
+situational awareness during disasters. Information shared on Twitter by both
+affected population (e.g., requesting assistance, warning) and those outside
+the impact zone (e.g., providing assistance) would help first responders,
+decision makers, and the public to understand the situation first-hand.
+Effective use of such information requires timely selection and analysis of
+tweets that are relevant to a particular disaster. Even though abundant tweets
+are promising as a data source, it is challenging to automatically identify
+relevant messages since tweet are short and unstructured, resulting to
+unsatisfactory classification performance of conventional learning-based
+approaches. Thus, we propose a simple yet effective algorithm to identify
+relevant messages based on matching keywords and hashtags, and provide a
+comparison between matching-based and learning-based approaches. To evaluate
+the two approaches, we put them into a framework specifically proposed for
+analyzing disaster-related tweets. Analysis results on eleven datasets with
+various disaster types show that our technique provides relevant tweets of
+higher quality and more interpretable results of sentiment analysis tasks when
+compared to learning approach.
+",1,0,0,0,0,0
+18777,Self-Stabilizing Disconnected Components Detection and Rooted Shortest-Path Tree Maintenance in Polynomial Steps,"  We deal with the problem of maintaining a shortest-path tree rooted at some
+process r in a network that may be disconnected after topological changes. The
+goal is then to maintain a shortest-path tree rooted at r in its connected
+component, V\_r, and make all processes of other components detecting that r is
+not part of their connected component. We propose, in the composite atomicity
+model, a silent self-stabilizing algorithm for this problem working in
+semi-anonymous networks, where edges have strictly positive weights. This
+algorithm does not require any a priori knowledge about global parameters of
+the network. We prove its correctness assuming the distributed unfair daemon,
+the most general daemon. Its stabilization time in rounds is at most 3nmax+D,
+where nmax is the maximum number of non-root processes in a connected component
+and D is the hop-diameter of V\_r. Furthermore, if we additionally assume that
+edge weights are positive integers, then it stabilizes in a polynomial number
+of steps: namely, we exhibit a bound in O(maxi nmax^3 n), where maxi is the
+maximum weight of an edge and n is the number of processes.
+",1,0,0,0,0,0
+18778,Optimizing wearable assistive devices with neuromuscular models and optimal control,"  The coupling of human movement dynamics with the function and design of
+wearable assistive devices is vital to better understand the interaction
+between the two. Advanced neuromuscular models and optimal control formulations
+provide the possibility to study and improve this interaction. In addition,
+optimal control can also be used to generate predictive simulations that
+generate novel movements for the human model under varying optimization
+criterion.
+",1,0,0,0,0,0
+18779,Preferential placement for community structure formation,"  Various models have been recently proposed to reflect and predict different
+properties of complex networks. However, the community structure, which is one
+of the most important properties, is not well studied and modeled. In this
+paper, we suggest a principle called ""preferential placement"", which allows to
+model a realistic clustering structure. We provide an extensive empirical
+analysis of the obtained structure as well as some theoretical results.
+",1,1,0,0,0,0
+18780,Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization,"  In this paper, we consider the problem of sequentially optimizing a black-box
+function $f$ based on noisy samples and bandit feedback. We assume that $f$ is
+smooth in the sense of having a bounded norm in some reproducing kernel Hilbert
+space (RKHS), yielding a commonly-considered non-Bayesian form of Gaussian
+process bandit optimization. We provide algorithm-independent lower bounds on
+the simple regret, measuring the suboptimality of a single point reported after
+$T$ rounds, and on the cumulative regret, measuring the sum of regrets over the
+$T$ chosen points. For the isotropic squared-exponential kernel in $d$
+dimensions, we find that an average simple regret of $\epsilon$ requires $T =
+\Omega\big(\frac{1}{\epsilon^2} (\log\frac{1}{\epsilon})^{d/2}\big)$, and the
+average cumulative regret is at least $\Omega\big( \sqrt{T(\log T)^{d/2}}
+\big)$, thus matching existing upper bounds up to the replacement of $d/2$ by
+$2d+O(1)$ in both cases. For the Matérn-$\nu$ kernel, we give analogous
+bounds of the form $\Omega\big( (\frac{1}{\epsilon})^{2+d/\nu}\big)$ and
+$\Omega\big( T^{\frac{\nu + d}{2\nu + d}} \big)$, and discuss the resulting
+gaps to the existing upper bounds.
+",1,0,0,1,0,0
+18781,Hirota bilinear equations for Painlevé transcendents,"  We present some observations on the tau-function for the fourth Painlevé
+equation. By considering a Hirota bilinear equation of order four for this
+tau-function, we describe the general form of the Taylor expansion around an
+arbitrary movable zero. The corresponding Taylor series for the tau-functions
+of the first and second Painlevé equations, as well as that for the
+Weierstrass sigma function, arise naturally as special cases, by setting
+certain parameters to zero.
+",0,1,1,0,0,0
+18782,Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data,"  Representational Similarity Analysis (RSA) aims to explore similarities
+between neural activities of different stimuli. Classical RSA techniques employ
+the inverse of the covariance matrix to explore a linear model between the
+neural activities and task events. However, calculating the inverse of a
+large-scale covariance matrix is time-consuming and can reduce the stability
+and robustness of the final analysis. Notably, it becomes severe when the
+number of samples is too large. For facing this shortcoming, this paper
+proposes a novel RSA method called gradient-based RSA (GRSA). Moreover, the
+proposed method is not restricted to a linear model. In fact, there is a
+growing interest in finding more effective ways of using multi-subject and
+whole-brain fMRI data. Searchlight technique can extend RSA from the localized
+brain regions to the whole-brain regions with smaller memory footprint in each
+process. Based on Searchlight, we propose a new method called Spatiotemporal
+Searchlight GRSA (SSL-GRSA) that generalizes our ROI-based GRSA algorithm to
+the whole-brain data. Further, our approach can handle some computational
+challenges while dealing with large-scale, multi-subject fMRI data.
+Experimental studies on multi-subject datasets confirm that both proposed
+approaches achieve superior performance to other state-of-the-art RSA
+algorithms.
+",0,0,0,1,1,0
+18783,Diffeomorphic random sampling using optimal information transport,"  In this article we explore an algorithm for diffeomorphic random sampling of
+nonuniform probability distributions on Riemannian manifolds. The algorithm is
+based on optimal information transport (OIT)---an analogue of optimal mass
+transport (OMT). Our framework uses the deep geometric connections between the
+Fisher-Rao metric on the space of probability densities and the right-invariant
+information metric on the group of diffeomorphisms. The resulting sampling
+algorithm is a promising alternative to OMT, in particular as our formulation
+is semi-explicit, free of the nonlinear Monge--Ampere equation. Compared to
+Markov Chain Monte Carlo methods, we expect our algorithm to stand up well when
+a large number of samples from a low dimensional nonuniform distribution is
+needed.
+",0,0,1,1,0,0
+18784,On approximations by trigonometric polynomials of classes of functions defined by moduli of smoothness,"  In this paper, we give a characterization of Nikol'ski\u{\i}-Besov type
+classes of functions, given by integral representations of moduli of
+smoothness, in terms of series over the moduli of smoothness. Also, necessary
+and sufficient conditions in terms of monotone or lacunary Fourier coefficients
+for a function to belong to a such a class are given. In order to prove our
+results, we make use of certain recent reverse Copson- and Leindler-type
+inequalities.
+",0,0,1,0,0,0
+18785,Topologically protected Dirac plasmons in graphene,"  Topological optical states exhibit unique immunity to defects and the ability
+to propagate without losses rendering them ideal for photonic applications.A
+powerful class of such states is based on time-reversal symmetry breaking of
+the optical response.However, existing proposals either involve sophisticated
+and bulky structural designs or can only operate in the microwave regime. Here,
+we propose and provide a theoretical proof-of-principle demonstration for
+highly confined topologically protected optical states to be realized at
+infrared frequencies in a simple 2D material structure-a periodically patterned
+graphene monolayer-subject to a magnetic field below 1 tesla. In our graphene
+honeycomb superlattice structures plasmons exhibit substantial nonreciprocal
+behavior at the superlattice junctions, leading to the emergence of
+topologically protected edge states and localized bulk modes enabled by the
+strong magneto-optical response of this material, which leads to
+time-reversal-symmetry breaking already at moderate static magnetic fields. The
+proposed approach is simple and robust for realizing topologically nontrivial
+2D optical states, not only in graphene, but also in other 2D atomic layers,
+and could pave the way for realizing fast, nanoscale, defect-immune devices for
+integrated photonics applications.
+",0,1,0,0,0,0
+18786,The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates,"  In this paper we propose and explore the k-Nearest Neighbour UCB algorithm
+for multi-armed bandits with covariates. We focus on a setting where the
+covariates are supported on a metric space of low intrinsic dimension, such as
+a manifold embedded within a high dimensional ambient feature space. The
+algorithm is conceptually simple and straightforward to implement. The
+k-Nearest Neighbour UCB algorithm does not require prior knowledge of the
+either the intrinsic dimension of the marginal distribution or the time
+horizon. We prove a regret bound for the k-Nearest Neighbour UCB algorithm
+which is minimax optimal up to logarithmic factors. In particular, the
+algorithm automatically takes advantage of both low intrinsic dimensionality of
+the marginal distribution over the covariates and low noise in the data,
+expressed as a margin condition. In addition, focusing on the case of bounded
+rewards, we give corresponding regret bounds for the k-Nearest Neighbour KL-UCB
+algorithm, which is an analogue of the KL-UCB algorithm adapted to the setting
+of multi-armed bandits with covariates. Finally, we present empirical results
+which demonstrate the ability of both the k-Nearest Neighbour UCB and k-Nearest
+Neighbour KL-UCB to take advantage of situations where the data is supported on
+an unknown sub-manifold of a high-dimensional feature space.
+",0,0,0,1,0,0
+18787,Proportional Mean Residual Life Model with Censored Survival Data under Case-cohort Design,"  Proportional mean residual life model is studied for analysing survival data
+from the case-cohort design. To simultaneously estimate the regression
+parameters and the baseline mean residual life function, weighted estimating
+equations based on an inverse selection probability are proposed. The resulting
+regression coefficients estimates are shown to be consistent and asymptotic
+normal with easily estimated variance-covariance. Simulation studies show that
+the proposed estimators perform very well. An application to a real dataset
+from the South Welsh nickel refiners study is also given to illustrate the
+methodology.
+",0,0,1,1,0,0
+18788,Shape differentiation of a steady-state reaction-diffusion problem arising in Chemical Engineering: the case of non-smooth kinetic with dead core,"  In this paper we consider an extension of the results in shape
+differentiation of semilinear equations with smooth nonlinearity presented in
+J.I. Díaz and D. Gómez-Castro: An Application of Shape Differentiation to
+the Effectiveness of a Steady State Reaction-Diffusion Problem Arising in
+Chemical Engineering. Electron. J. Differ. Equations in 2015 to the case in
+which the nonlinearities might be less smooth. Namely we will show that Gateaux
+shape derivatives exists when the nonlinearity is only Lipschitz continuous,
+and we will give a definition of the derivative when the nonlinearity has a
+blow up. In this direction, we will study the case of root-type nonlinearities.
+",0,0,1,0,0,0
+18789,"Analytic evaluation of Coulomb integrals for one, two and three-electron distance operators, $R_{C1}^{-n}R_{D1}^{-m}$, $R_{C1}^{-n}r_{12}^{-m}$ and $r_{12}^{-n}r_{13}^{-m}$ with $n, m=0,1,2$","  The state of the art for integral evaluation is that analytical solutions to
+integrals are far more useful than numerical solutions. We evaluate certain
+integrals analytically that are necessary in some approaches in quantum
+chemistry. In the title, where R stands for nucleus-electron and r for
+electron-electron distances, the $(n,m)=(0,0)$ case is trivial, the
+$(n,m)=(1,0)$ and (0,1) cases are well known, fundamental milestone in
+integration and widely used in computation chemistry, as well as based on
+Laplace transformation with integrand exp(-$a^2t^2$). The rest of the cases are
+new and need the other Laplace transformation with integrand exp(-$a^2t$) also,
+as well as the necessity of a two dimensional version of Boys function comes up
+in case. These analytic expressions (up to Gaussian function integrand) are
+useful for manipulation with higher moments of inter-electronic distances, for
+example in correlation calculations.
+",0,1,0,0,0,0
+18790,Herschel-PACS photometry of faint stars,"  Our aims are to determine flux densities and their photometric accuracy for a
+set of seventeen stars that range in flux from intermediately bright (<2.5 Jy)
+to faint (>5 mJy) in the far-infrared (FIR). We also aim to derive
+signal-to-noise dependence with flux and time, and compare the results with
+predictions from the Herschel exposure-time calculation tool. The PACS faint
+star sample has allowed a comprehensive sensitivity assessment of the PACS
+photometer. Accurate photometry allows us to establish a set of five FIR
+primary standard candidates, namely alpha Ari, epsilon Lep, omega,Cap, HD41047
+and 42Dra, which are 2 -- 20 times fainter than the faintest PACS fiducial
+standard (gamma Dra) with absolute accuracy of <6%. For three of these primary
+standard candidates, essential stellar parameters are known, meaning that a
+dedicated flux model code may be run.
+",0,1,0,0,0,0
+18791,Role of zero synapses in unsupervised feature learning,"  Synapses in real neural circuits can take discrete values, including zero
+(silent or potential) synapses. The computational role of zero synapses in
+unsupervised feature learning of unlabeled noisy data is still unclear, thus it
+is important to understand how the sparseness of synaptic activity is shaped
+during learning and its relationship with receptive field formation. Here, we
+formulate this kind of sparse feature learning by a statistical mechanics
+approach. We find that learning decreases the fraction of zero synapses, and
+when the fraction decreases rapidly around a critical data size, an
+intrinsically structured receptive field starts to develop. Further increasing
+the data size refines the receptive field, while a very small fraction of zero
+synapses remain to act as contour detectors. This phenomenon is discovered not
+only in learning a handwritten digits dataset, but also in learning retinal
+neural activity measured in a natural-movie-stimuli experiment.
+",1,1,0,0,0,0
+18792,On the Pervasiveness of Difference-Convexity in Optimization and Statistics,"  With the increasing interest in applying the methodology of
+difference-of-convex (dc) optimization to diverse problems in engineering and
+statistics, this paper establishes the dc property of many well-known functions
+not previously known to be of this class. Motivated by a quadratic programming
+based recourse function in two-stage stochastic programming, we show that the
+(optimal) value function of a copositive (thus not necessarily convex)
+quadratic program is dc on the domain of finiteness of the program when the
+matrix in the objective function's quadratic term and the constraint matrix are
+fixed. The proof of this result is based on a dc decomposition of a piecewise
+LC1 function (i.e., functions with Lipschitz gradients). Armed with these new
+results and known properties of dc functions existed in the literature, we show
+that many composite statistical functions in risk analysis, including the
+value-at-risk (VaR), conditional value-at-risk (CVaR), expectation-based,
+VaR-based, and CVaR-based random deviation functions are all dc. Adding the
+known class of dc surrogate sparsity functions that are employed as
+approximations of the l_0 function in statistical learning, our work
+significantly expands the family of dc functions and positions them for
+fruitful applications.
+",0,0,1,0,0,0
+18793,Stochastic Dynamic Optimal Power Flow in Distribution Network with Distributed Renewable Energy and Battery Energy Storage,"  The penetration of distributed renewable energy (DRE) greatly raises the risk
+of distribution network operation such as peak shaving and voltage stability.
+Battery energy storage (BES) has been widely accepted as the most potential
+application to cope with the challenge of high penetration of DRE. To cope with
+the uncertainties and variability of DRE, a stochastic day-ahead dynamic
+optimal power flow (DOPF) and its algorithm are proposed. The overall economy
+is achieved by fully considering the DRE, BES, electricity purchasing and
+active power losses. The rainflow algorithm-based cycle counting method of BES
+is incorporated in the DOPF model to capture the cell degradation, greatly
+extending the expected BES lifetime and achieving a better economy. DRE
+scenarios are generated to consider the uncertainties and correlations based on
+the Copula theory. To solve the DOPF model, we propose a Lagrange
+relaxation-based algorithm, which has a significantly reduced complexity with
+respect to the existing techniques. For this reason, the proposed algorithm
+enables much more scenarios incorporated in the DOPF model and better captures
+the DRE uncertainties and correlations. Finally, numerical studies for the
+day-ahead DOPF in the IEEE 123-node test feeder are presented to demonstrate
+the merits of the proposed method. Results show that the actual BES life
+expectancy of the proposed model has increased to 4.89 times compared with the
+traditional ones. The problems caused by DRE are greatly alleviated by fully
+capturing the uncertainties and correlations with the proposed method.
+",0,0,1,0,0,0
+18794,Achievable Rate Region of the Zero-Forcing Precoder in a 2 X 2 MU-MISO Broadcast VLC Channel with Per-LED Peak Power Constraint and Dimming Control,"  In this paper, we consider the 2 X 2 multi-user multiple-input-single-output
+(MU-MISO) broadcast visible light communication (VLC) channel with two light
+emitting diodes (LEDs) at the transmitter and a single photo diode (PD) at each
+of the two users. We propose an achievable rate region of the Zero-Forcing (ZF)
+precoder in this 2 X 2 MU-MISO VLC channel under a per-LED peak and average
+power constraint, where the average optical power emitted from each LED is
+fixed for constant lighting, but is controllable (referred to as dimming
+control in IEEE 802.15.7 standard on VLC). We analytically characterize the
+proposed rate region boundary and show that it is Pareto-optimal. Further
+analysis reveals that the largest rate region is achieved when the fixed
+per-LED average optical power is half of the allowed per-LED peak optical
+power. We also propose a novel transceiver architecture where the channel
+encoder and dimming control are separated which greatly simplifies the
+complexity of the transceiver. A case study of an indoor VLC channel with the
+proposed transceiver reveals that the achievable information rates are
+sensitive to the placement of the LEDs and the PDs. An interesting observation
+is that for a given placement of LEDs in a 5 m X 5 m X 3 m room, even with a
+substantial displacement of the users from their optimum placement, reduction
+in the achievable rates is not significant. This observation could therefore be
+used to define ""coverage zones"" within a room where the reduction in the
+information rates to the two users is within an acceptable tolerance limit.
+",1,0,1,0,0,0
+18795,Autonomous Electric Race Car Design,"  Autonomous driving and electric vehicles are nowadays very active research
+and development areas. In this paper we present the conversion of a standard
+Kyburz eRod into an autonomous vehicle that can be operated in challenging
+environments such as Swiss mountain passes. The overall hardware and software
+architectures are described in detail with a special emphasis on the sensor
+requirements for autonomous vehicles operating in partially structured
+environments. Furthermore, the design process itself and the finalized system
+architecture are presented. The work shows state of the art results in
+localization and controls for self-driving high-performance electric vehicles.
+Test results of the overall system are presented, which show the importance of
+generalizable state estimation algorithms to handle a plethora of conditions.
+",1,0,0,0,0,0
+18796,Temporal Stable Community in Time-Varying Networks,"  Identifying community structure of a complex network provides insight to the
+interdependence between the network topology and emergent collective behaviors
+of networks, while detecting such invariant communities in a time-varying
+network is more challenging. In this paper, we define the temporal stable
+community and newly propose the concept of dynamic modularity to evaluate the
+stable community structures in time-varying networks, which is robust against
+small changes as verified by several empirical time-varying network datasets.
+Besides, using the volatility features of temporal stable communities in
+functional brain networks, we successfully differentiate the ADHD (Attention
+Deficit Hyperactivity Disorder) patients and healthy controls efficiently.
+",0,0,0,0,1,0
+18797,The shape of a rapidly rotating polytrope with index unity,"  We show that the solutions obtained in the paper `An exact solution for
+arbitrarily rotating gaseous polytropes with index unity' by Kong, Zhang, and
+Schubert represent only approximate solutions of the free-boundary
+Euler-Poisson system of equations describing uniformly rotating,
+self-gravitating polytropes with index unity. We discuss the quality of such
+solutions as approximations to the rigidly rotating equilibrium polytropic
+configurations.
+",0,1,0,0,0,0
+18798,Measurement of the Planck constant at the National Institute of Standards and Technology from 2015 to 2017,"  Researchers at the National Institute of Standards and Technology(NIST) have
+measured the value of the Planck constant to be $h =6.626\,069\,934(89)\times
+10^{-34}\,$J$\,$s (relative standard uncertainty $13\times 10^{-9}$). The
+result is based on over 10$\,$000 weighings of masses with nominal values
+ranging from 0.5$\,$kg to 2$\,$kg with the Kibble balance NIST-4. The
+uncertainty has been reduced by more than twofold relative to a previous
+determination because of three factors: (1) a much larger data set than
+previously available, allowing a more realistic, and smaller, Type A
+evaluation; (2) a more comprehensive measurement of the back action of the
+weighing current on the magnet by weighing masses up to 2$\,$kg, decreasing the
+uncertainty associated with magnet non-linearity; (3) a rigorous investigation
+of the dependence of the geometric factor on the coil velocity reducing the
+uncertainty assigned to time-dependent leakage of current in the coil.
+",0,1,0,0,0,0
+18799,Event Stream-Based Process Discovery using Abstract Representations,"  The aim of process discovery, originating from the area of process mining, is
+to discover a process model based on business process execution data. A
+majority of process discovery techniques relies on an event log as an input. An
+event log is a static source of historical data capturing the execution of a
+business process. In this paper we focus on process discovery relying on online
+streams of business process execution events. Learning process models from
+event streams poses both challenges and opportunities, i.e. we need to handle
+unlimited amounts of data using finite memory and, preferably, constant time.
+We propose a generic architecture that allows for adopting several classes of
+existing process discovery techniques in context of event streams. Moreover, we
+provide several instantiations of the architecture, accompanied by
+implementations in the process mining tool-kit ProM (this http URL).
+Using these instantiations, we evaluate several dimensions of stream-based
+process discovery. The evaluation shows that the proposed architecture allows
+us to lift process discovery to the streaming domain.
+",1,0,0,1,0,0
+18800,"On $C$-bases, partition pairs and filtrations for induced or restricted Specht modules","  We obtain alternative explicit Specht filtrations for the induced and the
+restricted Specht modules in the Hecke algebra of the symmetric group (defined
+over the ring $A=\mathbb Z[q^{1/2},q^{-1/2}]$ where $q$ is an indeterminate)
+using $C$-bases for these modules. Moreover, we provide a link between a
+certain $C$-basis for the induced Specht module and the notion of pairs of
+partitions.
+",0,0,1,0,0,0
+18801,Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment,"  Network embeddings, which learn low-dimensional representations for each
+vertex in a large-scale network, have received considerable attention in recent
+years. For a wide range of applications, vertices in a network are typically
+accompanied by rich textual information such as user profiles, paper abstracts,
+etc. We propose to incorporate semantic features into network embeddings by
+matching important words between text sequences for all pairs of vertices. We
+introduce a word-by-word alignment framework that measures the compatibility of
+embeddings between word pairs, and then adaptively accumulates these alignment
+features with a simple yet effective aggregation function. In experiments, we
+evaluate the proposed framework on three real-world benchmarks for downstream
+tasks, including link prediction and multi-label vertex classification. Results
+demonstrate that our model outperforms state-of-the-art network embedding
+methods by a large margin.
+",1,0,0,0,0,0
+18802,Spin Precession Experiments for Light Axionic Dark Matter,"  Axion-like particles are promising candidates to make up the dark matter of
+the universe, but it is challenging to design experiments that can detect them
+over their entire allowed mass range. Dark matter in general, and in particular
+axion-like particles and hidden photons, can be as light as roughly $10^{-22}
+\;\rm{eV}$ ($\sim 10^{-8} \;\rm{Hz}$), with astrophysical anomalies providing
+motivation for the lightest masses (""fuzzy dark matter""). We propose
+experimental techniques for direct detection of axion-like dark matter in the
+mass range from roughly $10^{-13} \;\rm{eV}$ ($\sim 10^2 \;\rm{Hz}$) down to
+the lowest possible masses. In this range, these axion-like particles act as a
+time-oscillating magnetic field coupling only to spin, inducing effects such as
+a time-oscillating torque and periodic variations in the spin-precession
+frequency with the frequency and direction set by fundamental physics. We show
+how these signals can be measured using existing experimental technology,
+including torsion pendulums, atomic magnetometers, and atom interferometry.
+These experiments demonstrate a strong discovery capability, with future
+iterations of these experiments capable of pushing several orders of magnitude
+past current astrophysical bounds.
+",0,1,0,0,0,0
+18803,Persistence barcodes and Laplace eigenfunctions on surfaces,"  We obtain restrictions on the persistence barcodes of Laplace-Beltrami
+eigenfunctions and their linear combinations on compact surfaces with
+Riemannian metrics. Some applications to uniform approximation by linear
+combinations of Laplace eigenfunctions are also discussed.
+",0,0,1,0,0,0
+18804,DFT study of ionic liquids adsorption on circumcoronene shaped graphene,"  Carbon materials have a range of properties such as high electrical
+conductivity, high specific surface area, and mechanical flexibility are
+relevant for electrochemical applications. Carbon materials are utilised in
+energy conversion-and-storage devices along with electrolytes of complementary
+properties. In this work, we study the interaction of highly concentrated
+electrolytes (ionic liquids) at a model carbon surface (circumcoronene) using
+density functional theory methods. Our results indicate the decisive role of
+the dispersion interactions that noticeably strengthen the circumcoronene-ion
+interaction. Also, we focus on the adsorption of halide anions as the
+electrolytes containing these ions are promising for practical use in
+supercapacitors and solar cells.
+",0,1,0,0,0,0
+18805,On the overestimation of the largest eigenvalue of a covariance matrix,"  In this paper, we use a new approach to prove that the largest eigenvalue of
+the sample covariance matrix of a normally distributed vector is bigger than
+the true largest eigenvalue with probability 1 when the dimension is infinite.
+We prove a similar result for the smallest eigenvalue.
+",0,0,1,1,0,0
+18806,Thermodynamic Mechanism of Life and Aging,"  Life is a complex biological phenomenon represented by numerous chemical,
+physical and biological processes performed by a biothermodynamic
+system/cell/organism. Both living organisms and inanimate objects are subject
+to aging, a biological and physicochemical process characterized by changes in
+biological and thermodynamic state. Thus, the same physical laws govern
+processes in both animate and inanimate matter. All life processes lead to
+change of an organism's state. The change of biological and thermodynamic state
+of an organism in time underlies all of three kinds of aging (chronological,
+biological and thermodynamic). Life and aging of an organism both start at the
+moment of fertilization and continue through entire lifespan. Fertilization
+represents formation of a new organism. The new organism represents a new
+thermodynamic system. From the very beginning, it changes its state by changing
+thermodynamic parameters. The change of thermodynamic parameters is observed as
+aging and can be related to change in entropy. Entropy is thus the parameter
+that is related to all others and describes aging in the best manner. In the
+beginning, entropy change appears as a consequence of accumulation of matter
+(growth). Later, decomposition and configurational changes dominate, as a
+consequence of various chemical reactions (free radical, decomposition,
+fragmentation, accumulation of lipofuscin-like substances...).
+",0,0,0,0,1,0
+18807,Flat families of point schemes for connected graded algebras,"  We study truncated point schemes of connected graded algebras as families
+over the parameter space of varying relations for the algebras, proving that
+the families are flat over the open dense locus where the point schemes achieve
+the expected (i.e. minimal) dimension.
+When the truncated point scheme is zero-dimensional we obtain its number of
+points counted with multiplicity via a Chow ring computation. This latter
+application in particular confirms a conjecture of Brazfield to the effect that
+a generic two-generator, two-relator 4-dimensional Artin-Schelter regular
+algebra has seventeen truncated point modules of length six.
+",0,0,1,0,0,0
+18808,Multi-task Learning in the Computerized Diagnosis of Breast Cancer on DCE-MRIs,"  Hand-crafted features extracted from dynamic contrast-enhanced magnetic
+resonance images (DCE-MRIs) have shown strong predictive abilities in
+characterization of breast lesions. However, heterogeneity across medical image
+datasets hinders the generalizability of these features. One of the sources of
+the heterogeneity is the variation of MR scanner magnet strength, which has a
+strong influence on image quality, leading to variations in the extracted image
+features. Thus, statistical decision algorithms need to account for such data
+heterogeneity. Despite the variations, we hypothesize that there exist
+underlying relationships between the features extracted from the datasets
+acquired with different magnet strength MR scanners. We compared the use of a
+multi-task learning (MTL) method that incorporates those relationships during
+the classifier training to support vector machines run on a merged dataset that
+includes cases with various MRI strength images. As a result, higher predictive
+power is achieved with the MTL method.
+",0,1,0,0,0,0
+18809,Real-time Distracted Driver Posture Classification,"  In this paper, we present a new dataset for ""distracted driver"" posture
+estimation. In addition, we propose a novel system that achieves 95.98% driving
+posture estimation classification accuracy. The system consists of a
+genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show
+that a weighted ensemble of classifiers using a genetic algorithm yields in
+better classification confidence. We also study the effect of different visual
+elements (i.e. hands and face) in distraction detection and classification by
+means of face and hand localizations. Finally, we present a thinned version of
+our ensemble that could achieve a 94.29% classification accuracy and operate in
+a realtime environment.
+",1,0,0,0,0,0
+18810,Time Series Cube Data Model,"  The purpose of this document is to create a data model and its serialization
+for expressing generic time series data. Already existing IVOA data models are
+reused as much as possible. The model is also made as generic as possible to be
+open to new extensions but at the same time closed for modifications. This
+enables maintaining interoperability throughout different versions of the data
+model. We define the necessary building blocks for metadata discovery,
+serialization of time series data and understanding it by clients. We present
+several categories of time series science cases with examples of
+implementation. We also take into account the most pressing topics for time
+series providers like tracking original images for every individual point of a
+light curve or time-derived axes like frequency for gravitational wave
+analysis. The main motivation for the creation of a new model is to provide a
+unified time series data publishing standard - not only for light curves but
+also more generic time series data, e.g., radial velocity curves, power
+spectra, hardness ratio, provenance linkage, etc. The flexibility is the most
+crucial part of our model - we are not dependent on any physical domain or
+frame models. While images or spectra are already stable and standardized
+products, the time series related domains are still not completely evolved and
+new ones will likely emerge in near future. That is why we need to keep models
+like Time Series Cube DM independent of any underlying physical models. In our
+opinion, this is the only correct and sustainable way for future development of
+IVOA standards.
+",1,1,0,0,0,0
+18811,Markov modeling of peptide folding in the presence of protein crowders,"  We use Markov state models (MSMs) to analyze the dynamics of a
+$\beta$-hairpin-forming peptide in Monte Carlo (MC) simulations with
+interacting protein crowders, for two different types of crowder proteins
+[bovine pancreatic trypsin inhibitor (BPTI) and GB1]. In these systems, at the
+temperature used, the peptide can be folded or unfolded and bound or unbound to
+crowder molecules. Four or five major free-energy minima can be identified. To
+estimate the dominant MC relaxation times of the peptide, we build MSMs using a
+range of different time resolutions or lag times. We show that stable
+relaxation-time estimates can be obtained from the MSM eigenfunctions through
+fits to autocorrelation data. The eigenfunctions remain sufficiently accurate
+to permit stable relaxation-time estimation down to small lag times, at which
+point simple estimates based on the corresponding eigenvalues have large
+systematic uncertainties. The presence of the crowders have a stabilizing
+effect on the peptide, especially with BPTI crowders, which can be attributed
+to a reduced unfolding rate $k_\text{u}$, while the folding rate $k_\text{f}$
+is left largely unchanged.
+",0,0,0,0,1,0
+18812,Ultra-fast magnetization manipulation using single femtosecond light and hot-electrons pulse,"  Current induced magnetization manipulation is a key issue for spintronic
+application. Therefore, deterministic switching of the magnetization at the
+picoseconds timescale with a single electronic pulse represents a major step
+towards the future developments of ultrafast spintronic. Here, we have studied
+the ultrafast magnetization dynamics in engineered Gdx[FeCo]1-x based structure
+to compare the effect of femtosecond laser and hot-electrons pulses. We
+demonstrate that a single femtosecond hot-electrons pulse allows a
+deterministic magnetization reversal in either Gd-rich and FeCo-rich alloys
+similarly to a femtosecond laser pulse. In addition, we show that the limiting
+factor of such manipulation for perpendicular magnetized films arises from the
+multi-domain formation due to dipolar interaction. By performing time resolved
+measurements under various field, we demonstrate that the same magnetization
+dynamics is observed for both light and hot-electrons excitation and that the
+full magnetization reversal take place within 5 ps. The energy efficiency of
+the ultra-fast current induced magnetization manipulation is optimized thanks
+to the ballistic transport of hot-electrons before reaching the GdFeCo magnetic
+layer.
+",0,1,0,0,0,0
+18813,Grain Boundary Resistance in Copper Interconnects from an Atomistic Model to a Neural Network,"  Orientation effects on the resistivity of copper grain boundaries are studied
+systematically with two different atomistic tight binding methods. A
+methodology is developed to model the resistivity of grain boundaries using the
+Embedded Atom Model, tight binding methods and non-equilibrum Green's functions
+(NEGF). The methodology is validated against first principles calculations for
+small, ultra-thin body grain boundaries (<5nm) with 6.4% deviation in the
+resistivity. A statistical ensemble of 600 large, random structures with grains
+is studied. For structures with three grains, it is found that the distribution
+of resistivities is close to normal. Finally, a compact model for grain
+boundary resistivity is constructed based on a neural network.
+",0,1,0,0,0,0
+18814,Some remarkable infinite product identities involving Fibonacci and Lucas numbers,"  By applying the classic telescoping summation formula and its variants to
+identities involving inverse hyperbolic tangent functions having inverse powers
+of the golden ratio as arguments and employing subtle properties of the
+Fibonacci and Lucas numbers, we derive interesting general infinite product
+identities involving these numbers.
+",0,0,1,0,0,0
+18815,Limit Theorems in Mallows Distance for Processes with Gibssian Dependence,"  In this paper, we explore the connection between convergence in distribution
+and Mallows distance in the context of positively associated random variables.
+Our results extend some known invariance principles for sequences with FKG
+property. Applications for processes with Gibbssian dependence structures are
+included.
+",0,0,1,0,0,0
+18816,Localized-endemic state transition in the susceptible-infected-susceptible model on networks,"  It is a longstanding debate concerning the absence of threshold for the
+susceptible-infected-susceptible spreading model on networks with localized
+state. The key to resolve this controversy is the dynamical interaction
+pattern, which has not been uncovered. Here we show that the interaction
+driving the localized-endemic state transition is not the global interaction
+between a node and all the other nodes on the network, but exists at the level
+of super node composed of highly connected node and its neighbors. The internal
+interactions within a super node induce localized state with limited lifetime,
+while the interactions between neighboring super nodes via a path of two hops
+enable them to avoid trapping in the absorbing state, marking the onset of
+endemic state. The hybrid interactions render highly connected nodes
+exponentially increasing infection density, which truly account for the null
+threshold. These results are crucial for correctly understanding diverse
+recurrent contagion phenomena
+",0,1,0,0,0,0
+18817,Analytic approximation of solutions of parabolic partial differential equations with variable coefficients,"  A complete family of solutions for the one-dimensional reaction-diffusion
+equation \[ u_{xx}(x,t)-q(x)u(x,t) = u_t(x,t) \] with a coefficient $q$
+depending on $x$ is constructed. The solutions represent the images of the heat
+polynomials under the action of a transmutation operator. Their use allows one
+to obtain an explicit solution of the noncharacteristic Cauchy problem for the
+considered equation with sufficiently regular Cauchy data as well as to solve
+numerically initial boundary value problems. In the paper the Dirichlet
+boundary conditions are considered however the proposed method can be easily
+extended onto other standard boundary conditions. The proposed numerical method
+is shown to reveal good accuracy.
+",0,0,1,0,0,0
+18818,Final-State Constrained Optimal Control via a Projection Operator Approach,"  In this paper we develop a numerical method to solve nonlinear optimal
+control problems with final-state constraints. Specifically, we extend the
+PRojection Operator based Netwon's method for Trajectory Optimization (PRONTO),
+which was proposed by Hauser for unconstrained optimal control problems. While
+in the standard method final-state constraints can be only approximately
+handled by means of a terminal penalty, in this work we propose a methodology
+to meet the constraints exactly. Moreover, our method guarantees recursive
+feasibility of the final-state constraint. This is an appealing property
+especially in realtime applications in which one would like to be able to stop
+the computation even if the desired tolerance has not been reached, but still
+satisfy the constraints. Following the same conceptual idea of PRONTO, the
+proposed strategy is based on two main steps which (differently from the
+standard scheme) preserve the feasibility of the final-state constraints: (i)
+solve a quadratic approximation of the nonlinear problem to find a descent
+direction, and (ii) get a (feasible) trajectory by means of a feedback law
+(which turns out to be a nonlinear projection operator). To find the (feasible)
+descent direction we take advantage of final-state constrained Linear Quadratic
+optimal control methods, while the second step is performed by suitably
+designing a constrained version of the trajectory tracking projection operator.
+The effectiveness of the proposed strategy is tested on the optimal state
+transfer of an inverted pendulum.
+",1,0,0,0,0,0
+18819,Robust Tracking with Model Mismatch for Fast and Safe Planning: an SOS Optimization Approach,"  In the pursuit of real-time motion planning, a commonly adopted practice is
+to compute a trajectory by running a planning algorithm on a simplified,
+low-dimensional dynamical model, and then employ a feedback tracking controller
+that tracks such a trajectory by accounting for the full, high-dimensional
+system dynamics. While this strategy of planning with model mismatch generally
+yields fast computation times, there are no guarantees of dynamic feasibility,
+which hampers application to safety-critical systems. Building upon recent work
+that addressed this problem through the lens of Hamilton-Jacobi (HJ)
+reachability, we devise an algorithmic framework whereby one computes, offline,
+for a pair of ""planner"" (i.e., low-dimensional) and ""tracking"" (i.e.,
+high-dimensional) models, a feedback tracking controller and associated
+tracking bound. This bound is then used as a safety margin when generating
+motion plans via the low-dimensional model. Specifically, we harness the
+computational tool of sum-of-squares (SOS) programming to design a bilinear
+optimization algorithm for the computation of the feedback tracking controller
+and associated tracking bound. The algorithm is demonstrated via numerical
+experiments, with an emphasis on investigating the trade-off between the
+increased computational scalability afforded by SOS and its intrinsic
+conservativeness. Collectively, our results enable scaling the appealing
+strategy of planning with model mismatch to systems that are beyond the reach
+of HJ analysis, while maintaining safety guarantees.
+",1,0,0,0,0,0
+18820,Robust Gaussian Stochastic Process Emulation,"  We consider estimation of the parameters of a Gaussian Stochastic Process
+(GaSP), in the context of emulation (approximation) of computer models for
+which the outcomes are real-valued scalars. The main focus is on estimation of
+the GaSP parameters through various generalized maximum likelihood methods,
+mostly involving finding posterior modes; this is because full Bayesian
+analysis in computer model emulation is typically prohibitively expensive. The
+posterior modes that are studied arise from objective priors, such as the
+reference prior. These priors have been studied in the literature for the
+situation of an isotropic covariance function or under the assumption of
+separability in the design of inputs for model runs used in the GaSP
+construction. In this paper, we consider more general designs (e.g., a Latin
+Hypercube Design) with a class of commonly used anisotropic correlation
+functions, which can be written as a product of isotropic correlation
+functions, each having an unknown range parameter and a fixed roughness
+parameter. We discuss properties of the objective priors and marginal
+likelihoods for the parameters of the GaSP and establish the posterior
+propriety of the GaSP parameters, but our main focus is to demonstrate that
+certain parameterizations result in more robust estimation of the GaSP
+parameters than others, and that some parameterizations that are in common use
+should clearly be avoided. These results are applicable to many frequently used
+covariance functions, e.g., power exponential, Mat{é}rn, rational quadratic
+and spherical covariance. We also generalize the results to the GaSP model with
+a nugget parameter. Both theoretical and numerical evidence is presented
+concerning the performance of the studied procedures.
+",0,0,1,1,0,0
+18821,Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST,"  Nine transiting Earth-sized planets have recently been discovered around
+nearby late M dwarfs, including the TRAPPIST-1 planets and two planets
+discovered by the MEarth survey, GJ 1132b and LHS 1140b. These planets are the
+smallest known planets that may have atmospheres amenable to detection with
+JWST. We present model thermal emission and transmission spectra for each
+planet, varying composition and surface pressure of the atmosphere. We base
+elemental compositions on those of Earth, Titan, and Venus and calculate the
+molecular compositions assuming chemical equilibrium, which can strongly depend
+on temperature. Both thermal emission and transmission spectra are sensitive to
+the atmospheric composition; thermal emission spectra are sensitive to surface
+pressure and temperature. We predict the observability of each planet's
+atmosphere with JWST. GJ 1132b and TRAPPIST-1b are excellent targets for
+emission spectroscopy with JWST/MIRI, requiring fewer than 10 eclipse
+observations. Emission photometry for TRAPPIST-1c requires 5-15 eclipses; LHS
+1140b and TRAPPIST-1d, TRAPPIST-1e, and TRAPPIST-1f, which could possibly have
+surface liquid water, may be accessible with photometry. Seven of the nine
+planets are strong candidates for transmission spectroscopy measurements with
+JWST, though the number of transits required depends strongly on the planets'
+actual masses. Using the measured masses, fewer than 20 transits are required
+for a 5 sigma detection of spectral features for GJ 1132b and six of the
+TRAPPIST-1 planets. Dedicated campaigns to measure the atmospheres of these
+nine planets will allow us, for the first time, to probe formation and
+evolution processes of terrestrial planetary atmospheres beyond our solar
+system.
+",0,1,0,0,0,0
+18822,Front Propagation for Nonlocal KPP Reaction-Diffusion Equations in Periodic Media,"  We study front propagation phenomena for a large class of nonlocal KPP-type
+reaction-diffusion equations in oscillatory environments, which model various
+forms of population growth with periodic dependence. The nonlocal diffusion is
+an anisotropic integro-differential operator of order $\alpha \in (0,2)$.
+",0,0,1,0,0,0
+18823,Distributed Functional Observers for LTI Systems,"  We study the problem of designing distributed functional observers for LTI
+systems. Specifically, we consider a setting consisting of a state vector that
+evolves over time according to a dynamical process. A set of nodes distributed
+over a communication network wish to collaboratively estimate certain functions
+of the state. We first show that classical existence conditions for the design
+of centralized functional observers do not directly translate to the
+distributed setting, due to the coupling that exists between the dynamics of
+the functions of interest and the diverse measurements at the various nodes.
+Accordingly, we design transformations that reveal such couplings and identify
+portions of the corresponding dynamics that are locally detectable at each
+sensor node. We provide sufficient conditions on the network, along with state
+estimate update and exchange rules for each node, that guarantee asymptotic
+reconstruction of the functions at each sensor node.
+",0,0,1,0,0,0
+18824,Wavelet eigenvalue regression for $n$-variate operator fractional Brownian motion,"  In this contribution, we extend the methodology proposed in Abry and Didier
+(2017) to obtain the first joint estimator of the real parts of the Hurst
+eigenvalues of $n$-variate OFBM. The procedure consists of a wavelet regression
+on the log-eigenvalues of the sample wavelet spectrum. The estimator is shown
+to be consistent for any time reversible OFBM and, under stronger assumptions,
+also asymptotically normal starting from either continuous or discrete time
+measurements. Simulation studies establish the finite sample effectiveness of
+the methodology and illustrate its benefits compared to univariate-like
+(entrywise) analysis. As an application, we revisit the well-known self-similar
+character of Internet traffic by applying the proposed methodology to 4-variate
+time series of modern, high quality Internet traffic data. The analysis reveals
+the presence of a rich multivariate self-similarity structure.
+",0,0,1,1,0,0
+18825,Deep Neural Network for Analysis of DNA Methylation Data,"  Many researches demonstrated that the DNA methylation, which occurs in the
+context of a CpG, has strong correlation with diseases, including cancer. There
+is a strong interest in analyzing the DNA methylation data to find how to
+distinguish different subtypes of the tumor. However, the conventional
+statistical methods are not suitable for analyzing the highly dimensional DNA
+methylation data with bounded support. In order to explicitly capture the
+properties of the data, we design a deep neural network, which composes of
+several stacked binary restricted Boltzmann machines, to learn the low
+dimensional deep features of the DNA methylation data. Experiments show these
+features perform best in breast cancer DNA methylation data cluster analysis,
+comparing with some state-of-the-art methods.
+",0,0,0,1,1,0
+18826,One look at the rating of scientific publications and corresponding toy-model,"  A toy-model of publications and citations processes is proposed. The model
+shows that the role of randomness in the processes is essential and cannot be
+ignored. Some other aspects of scientific publications rating are discussed.
+",1,0,0,1,0,0
+18827,"Alignment, Orientation, and Coulomb Explosion of Difluoroiodobenzene Studied with the Pixel Imaging Mass Spectrometry (PImMS) Camera","  Laser-induced adiabatic alignment and mixed-field orientation of
+2,6-difluoroiodobenzene (C6H3F2I) molecules are probed by Coulomb explosion
+imaging following either near-infrared strong-field ionization or
+extreme-ultraviolet multi-photon inner-shell ionization using free-electron
+laser pulses. The resulting photoelectrons and fragment ions are captured by a
+double-sided velocity map imaging spectrometer and projected onto two
+position-sensitive detectors. The ion side of the spectrometer is equipped with
+the Pixel Imaging Mass Spectrometry (PImMS) camera, a time-stamping pixelated
+detector that can record the hit positions and arrival times of up to four ions
+per pixel per acquisition cycle. Thus, the time-of-flight trace and ion
+momentum distributions for all fragments can be recorded simultaneously. We
+show that we can obtain a high degree of one- and three-dimensional alignment
+and mixed- field orientation, and compare the Coulomb explosion process induced
+at both wavelengths.
+",0,1,0,0,0,0
+18828,Unsupervised Machine Learning of Open Source Russian Twitter Data Reveals Global Scope and Operational Characteristics,"  We developed and used a collection of statistical methods (unsupervised
+machine learning) to extract relevant information from a Twitter supplied data
+set consisting of alleged Russian trolls who (allegedly) attempted to influence
+the 2016 US Presidential election. These unsupervised statistical methods allow
+fast identification of (i) emergent language communities within the troll
+population, (ii) the transnational scope of the operation and (iii) operational
+characteristics of trolls that can be used for future identification. Using
+natural language processing, manifold learning and Fourier analysis, we
+identify an operation that includes not only the 2016 US election, but also the
+French National and both local and national German elections. We show the
+resulting troll population is composed of users with common, but clearly
+customized, behavioral characteristics.
+",1,0,0,0,0,0
+18829,"Distributed, scalable and gossip-free consensus optimization with application to data analysis","  Distributed algorithms for solving additive or consensus optimization
+problems commonly rely on first-order or proximal splitting methods. These
+algorithms generally come with restrictive assumptions and at best enjoy a
+linear convergence rate. Hence, they can require many iterations or
+communications among agents to converge. In many cases, however, we do not seek
+a highly accurate solution for consensus problems. Based on this we propose a
+controlled relaxation of the coupling in the problem which allows us to compute
+an approximate solution, where the accuracy of the approximation can be
+controlled by the level of relaxation. The relaxed problem can be efficiently
+solved in a distributed way using a combination of primal-dual interior-point
+methods (PDIPMs) and message-passing. This algorithm purely relies on
+second-order methods and thus requires far fewer iterations and communications
+to converge. This is illustrated in numerical experiments, showing its superior
+performance compared to existing methods.
+",0,0,1,0,0,0
+18830,Consistency of Maximum Likelihood for Continuous-Space Network Models,"  Network analysis needs tools to infer distributions over graphs of arbitrary
+size from a single graph. Assuming the distribution is generated by a
+continuous latent space model which obeys certain natural symmetry and
+smoothness properties, we establish three levels of consistency for
+non-parametric maximum likelihood inference as the number of nodes grows: (i)
+the estimated locations of all nodes converge in probability on their true
+locations; (ii) the distribution over locations in the latent space converges
+on the true distribution; and (iii) the distribution over graphs of arbitrary
+size converges.
+",0,0,1,1,0,0
+18831,Consistency Results for Stationary Autoregressive Processes with Constrained Coefficients,"  We consider stationary autoregressive processes with coefficients restricted
+to an ellipsoid, which includes autoregressive processes with absolutely
+summable coefficients. We provide consistency results under different norms for
+the estimation of such processes using constrained and penalized estimators. As
+an application we show some weak form of universal consistency. Simulations
+show that directly including the constraint in the estimation can lead to more
+robust results.
+",0,0,0,1,0,0
+18832,Higher order mobile coverage control with application to localization,"  Most current results on coverage control using mobile sensors require that
+one partitioned cell is associated with precisely one sensor. In this paper, we
+consider a class of coverage control problems involving higher order Voronoi
+partitions, motivated by applications where more than one sensor is required to
+monitor and cover one cell. Such applications are frequent in scenarios
+requiring the sensors to localize targets. We introduce a framework depending
+on a coverage performance function incorporating higher order Voronoi cells and
+then design a gradient-based controller which allows the multi-sensor system to
+achieve a local equilibrium in a distributed manner. The convergence properties
+are studied and related to Lloyd algorithm. We study also the extension to
+coverage of a discrete set of points. In addition, we provide a number of real
+world scenarios where our framework can be applied. Simulation results are also
+provided to show the controller performance.
+",1,0,0,0,0,0
+18833,Borg's Periodicity Theorems for first order self-adjoint systems with complex potentials,"  A self-adjoint first order system with Hermitian $\pi$-periodic potential
+$Q(z)$, integrable on compact sets, is considered. It is shown that all zeros
+of $\Delta + 2e^{-i\int_0^\pi \Im q dt}$ are double zeros if and only if this
+self-adjoint system is unitarily equivalent to one in which $Q(z)$ is
+$\frac{\pi}{2}$-periodic. Furthermore, the zeros of $\Delta - 2e^{-i\int_0^\pi
+\Im q dt}$ are all double zeros if and only if the associated self-adjoint
+system is unitarily equivalent to one in which $Q(z) = \sigma_2 Q(z) \sigma_2$.
+Here $\Delta$ denotes the discriminant of the system and $\sigma_0$, $\sigma_2$
+are Pauli matrices. Finally, it is shown that all instability intervals vanish
+if and only if $Q = r\sigma_0 + q\sigma_2$, for some real valued $\pi$-periodic
+functions $r$ and $q$ integrable on compact sets.
+",0,0,1,0,0,0
+18834,On the Ubiquity of Information Inconsistency for Conjugate Priors,"  Informally, ""Information Inconsistency"" is the property that has been
+observed in many Bayesian hypothesis testing and model selection procedures
+whereby the Bayesian conclusion does not become definitive when the data seems
+to become definitive. An example is that, when performing a t-test using
+standard conjugate priors, the Bayes factor of the alternative hypothesis to
+the null hypothesis remains bounded as the t statistic grows to infinity. This
+paper shows that information inconsistency is ubiquitous in Bayesian hypothesis
+testing under conjugate priors. Yet the title does not fully describe the
+paper, since we also show that theoretically recommended priors, including
+scale mixtures of conjugate priors and adaptive priors, are information
+consistent. Hence the paper is simply a forceful warning that use of conjugate
+priors in testing and model selection is highly problematical, and should be
+replaced by the information consistent alternatives.
+",0,0,1,1,0,0
+18835,Competition evolution of Rayleigh-Taylor bubbles,"  Material mixing induced by a Rayleigh-Taylor instability occurs ubiquitously
+in either nature or engineering when a light fluid pushes against a heavy
+fluid, accompanying with the formation and evolution of chaotic bubbles. Its
+general evolution involves two mechanisms: bubble-merge and bubble-competition.
+The former obeys a universa1 evolution law and has been well-studied, while the
+latter depends on many factors and has not been well-recognized. In this paper,
+we establish a theory for the latter to clarify and quantify the longstanding
+open question: the dependence of bubbles evolution on the dominant factors of
+arbitrary density ratio, broadband initial perturbations and various material
+properties (e.g., viscosity, miscibility, surface tensor). Evolution of the
+most important characteristic quantities, i.e., the diameter of dominant bubble
+$D$ and the height of bubble zone $h$, is derived: (i) the $D$ expands
+self-similarly with steady aspect ratio $\beta \equiv D/h \thickapprox (1{\rm{
++ }}A)/4$, depending only on dimensionless density ratio $A$, and (ii) the $h$
+grows quadratically with constant growth coefficient $\alpha \equiv h/(Ag{t^2})
+\thickapprox [2\phi/{\ln}(2{\eta _{\rm{0}}})]^2$, depending on both
+dimensionless initial perturbation amplitude ${\eta _{\rm{0}}}$ and
+material-property-associated linear growth rate ratio
+$\phi\equiv\Gamma_{actual}/\Gamma_{ideal}\leqslant1$. The theory successfully
+explains the continued puzzle about the widely varying $\alpha\in (0.02,0.12)$
+in experiments and simulations, conducted at all value of $A \in (0,1)$ and
+widely varying value of ${\eta _{\rm{0}}} \in [{10^{ - 7}},{10^{ - 2}}]$ with
+different materials. The good agreement between theory and experiments implies
+that majority of actual mixing depends on initial perturbations and material
+properties, to which more attention should be paid in either natural or
+engineering problems.
+",0,1,0,0,0,0
+18836,Nonlinear oblique projections,"  We construct nonlinear oblique projections along subalgebras of nilpotent Lie
+algebras in terms of the Baker-Campbell-Hausdorff multiplication. We prove that
+these nonlinear projections are real analytic on every Schubert cell of the
+Grassmann manifold whose points are the subalgebras of the nilpotent Lie
+algebra under consideration.
+",0,0,1,0,0,0
+18837,Granger Mediation Analysis of Multiple Time Series with an Application to fMRI,"  It becomes increasingly popular to perform mediation analysis for complex
+data from sophisticated experimental studies. In this paper, we present Granger
+Mediation Analysis (GMA), a new framework for causal mediation analysis of
+multiple time series. This framework is motivated by a functional magnetic
+resonance imaging (fMRI) experiment where we are interested in estimating the
+mediation effects between a randomized stimulus time series and brain activity
+time series from two brain regions. The stable unit treatment assumption for
+causal mediation analysis is thus unrealistic for this type of time series
+data. To address this challenge, our framework integrates two types of models:
+causal mediation analysis across the variables and vector autoregressive models
+across the temporal observations. We further extend this framework to handle
+multilevel data to address individual variability and correlated errors between
+the mediator and the outcome variables. These models not only provide valid
+causal mediation for time series data but also model the causal dynamics across
+time. We show that the modeling parameters in our models are identifiable, and
+we develop computationally efficient methods to maximize the likelihood-based
+optimization criteria. Simulation studies show that our method reduces the
+estimation bias and improve statistical power, compared to existing approaches.
+On a real fMRI data set, our approach not only infers the causal effects of
+brain pathways but accurately captures the feedback effect of the outcome
+region on the mediator region.
+",0,0,0,1,0,0
+18838,Parameterization of Sequence of MFCCs for DNN-based voice disorder detection,"  In this article a DNN-based system for detection of three common voice
+disorders (vocal nodules, polyps and cysts; laryngeal neoplasm; unilateral
+vocal paralysis) is presented. The input to the algorithm is (at least 3-second
+long) audio recording of sustained vowel sound /a:/. The algorithm was
+developed as part of the ""2018 FEMH Voice Data Challenge"" organized by Far
+Eastern Memorial Hospital and obtained score value (defined in the challenge
+specification) of 77.44. This was the second best result before final
+submission. Final challenge results are not yet known during writing of this
+document. The document also reports changes that were made for the final
+submission which improved the score value in cross-validation by 0.6% points.
+",1,0,0,0,0,0
+18839,Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems,"  A key challenge in multi-robot and multi-agent systems is generating
+solutions that are robust to other self-interested or even adversarial parties
+who actively try to prevent the agents from achieving their goals. The
+practicality of existing works addressing this challenge is limited to only
+small-scale synchronous decision-making scenarios or a single agent planning
+its best response against a single adversary with fixed, procedurally
+characterized strategies. In contrast this paper considers a more realistic
+class of problems where a team of asynchronous agents with limited observation
+and communication capabilities need to compete against multiple strategic
+adversaries with changing strategies. This problem necessitates agents that can
+coordinate to detect changes in adversary strategies and plan the best response
+accordingly. Our approach first optimizes a set of stratagems that represent
+these best responses. These optimized stratagems are then integrated into a
+unified policy that can detect and respond when the adversaries change their
+strategies. The near-optimality of the proposed framework is established
+theoretically as well as demonstrated empirically in simulation and hardware.
+",1,0,0,0,0,0
+18840,Structural Connectome Validation Using Pairwise Classification,"  In this work, we study the extent to which structural connectomes and
+topological derivative measures are unique to individual changes within human
+brains. To do so, we classify structural connectome pairs from two large
+longitudinal datasets as either belonging to the same individual or not. Our
+data is comprised of 227 individuals from the Alzheimer's Disease Neuroimaging
+Initiative (ADNI) and 226 from the Parkinson's Progression Markers Initiative
+(PPMI). We achieve 0.99 area under the ROC curve score for features which
+represent either weights or network structure of the connectomes (node degrees,
+PageRank and local efficiency). Our approach may be useful for eliminating
+noisy features as a preprocessing step in brain aging studies and early
+diagnosis classification problems.
+",1,0,0,0,0,0
+18841,Deep Robust Kalman Filter,"  A Robust Markov Decision Process (RMDP) is a sequential decision making model
+that accounts for uncertainty in the parameters of dynamic systems. This
+uncertainty introduces difficulties in learning an optimal policy, especially
+for environments with large state spaces. We propose two algorithms, RTD-DQN
+and Deep-RoK, for solving large-scale RMDPs using nonlinear approximation
+schemes such as deep neural networks. The RTD-DQN algorithm incorporates the
+robust Bellman temporal difference error into a robust loss function, yielding
+robust policies for the agent. The Deep-RoK algorithm is a robust Bayesian
+method, based on the Extended Kalman Filter (EKF), that accounts for both the
+uncertainty in the weights of the approximated value function and the
+uncertainty in the transition probabilities, improving the robustness of the
+agent. We provide theoretical results for our approach and test the proposed
+algorithms on a continuous state domain.
+",1,0,0,1,0,0
+18842,Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach,"  The Fisher information matrix (FIM) is a fundamental quantity to represent
+the characteristics of a stochastic model, including deep neural networks
+(DNNs). The present study reveals novel statistics of FIM that are universal
+among a wide class of DNNs. To this end, we use random weights and large width
+limits, which enables us to utilize mean field theories. We investigate the
+asymptotic statistics of the FIM's eigenvalues and reveal that most of them are
+close to zero while the maximum takes a huge value. This implies that the
+eigenvalue distribution has a long tail. Because the landscape of the parameter
+space is defined by the FIM, it is locally flat in most dimensions, but
+strongly distorted in others. We also demonstrate the potential usage of the
+derived statistics through two exercises. First, small eigenvalues that induce
+flatness can be connected to a norm-based capacity measure of generalization
+ability. Second, the maximum eigenvalue that induces the distortion enables us
+to quantitatively estimate an appropriately sized learning rate for gradient
+methods to converge.
+",0,0,0,1,0,0
+18843,An extension problem and trace Hardy inequality for the sublaplacian on $H$-type groups,"  In this paper we study the extension problem for the sublaplacian on a
+$H$-type group and use the solutions to prove trace Hardy and Hardy
+inequalities for fractional powers of the sublaplacian.
+",0,0,1,0,0,0
+18844,Centered Isotonic Regression: Point and Interval Estimation for Dose-Response Studies,"  Univariate isotonic regression (IR) has been used for nonparametric
+estimation in dose-response and dose-finding studies. One undesirable property
+of IR is the prevalence of piecewise-constant stretches in its estimates,
+whereas the dose-response function is usually assumed to be strictly
+increasing. We propose a simple modification to IR, called centered isotonic
+regression (CIR). CIR's estimates are strictly increasing in the interior of
+the dose range. In the absence of monotonicity violations, CIR and IR both
+return the original observations. Numerical examination indicates that for
+sample sizes typical of dose-response studies and with realistic dose-response
+curves, CIR provides a substantial reduction in estimation error compared with
+IR when monotonicity violations occur. We also develop analytical interval
+estimates for IR and CIR, with good coverage behavior. An R package implements
+these point and interval estimates.
+",0,0,0,1,0,0
+18845,Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection,"  Measuring domain relevance of data and identifying or selecting well-fit
+domain data for machine translation (MT) is a well-studied topic, but denoising
+is not yet. Denoising is concerned with a different type of data quality and
+tries to reduce the negative impact of data noise on MT training, in
+particular, neural MT (NMT) training. This paper generalizes methods for
+measuring and selecting data for domain MT and applies them to denoising NMT
+training. The proposed approach uses trusted data and a denoising curriculum
+realized by online data selection. Intrinsic and extrinsic evaluations of the
+approach show its significant effectiveness for NMT to train on data with
+severe noise.
+",0,0,0,1,0,0
+18846,Discovering Signals from Web Sources to Predict Cyber Attacks,"  Cyber attacks are growing in frequency and severity. Over the past year alone
+we have witnessed massive data breaches that stole personal information of
+millions of people and wide-scale ransomware attacks that paralyzed critical
+infrastructure of several countries. Combating the rising cyber threat calls
+for a multi-pronged strategy, which includes predicting when these attacks will
+occur. The intuition driving our approach is this: during the planning and
+preparation stages, hackers leave digital traces of their activities on both
+the surface web and dark web in the form of discussions on platforms like
+hacker forums, social media, blogs and the like. These data provide predictive
+signals that allow anticipating cyber attacks. In this paper, we describe
+machine learning techniques based on deep neural networks and autoregressive
+time series models that leverage external signals from publicly available Web
+sources to forecast cyber attacks. Performance of our framework across ground
+truth data over real-world forecasting tasks shows that our methods yield a
+significant lift or increase of F1 for the top signals on predicted cyber
+attacks. Our results suggest that, when deployed, our system will be able to
+provide an effective line of defense against various types of targeted cyber
+attacks.
+",1,0,0,1,0,0
+18847,Higher-degree Smoothness of Perturbations I,"  In this paper and its sequels, we give an unified treatment of the
+higher-degree smoothness of admissible perturbations and related results used
+in the global perturbation method for GW and Floer theories.
+",0,0,1,0,0,0
+18848,Space Telescope and Optical Reverberation Mapping Project. V. Optical Spectroscopic Campaign and Emission-Line Analysis for NGC 5548,"  We present the results of an optical spectroscopic monitoring program
+targeting NGC 5548 as part of a larger multi-wavelength reverberation mapping
+campaign. The campaign spanned six months and achieved an almost daily cadence
+with observations from five ground-based telescopes. The H$\beta$ and He II
+$\lambda$4686 broad emission-line light curves lag that of the 5100 $\AA$
+optical continuum by $4.17^{+0.36}_{-0.36}$ days and $0.79^{+0.35}_{-0.34}$
+days, respectively. The H$\beta$ lag relative to the 1158 $\AA$ ultraviolet
+continuum light curve measured by the Hubble Space Telescope is roughly
+$\sim$50% longer than that measured against the optical continuum, and the lag
+difference is consistent with the observed lag between the optical and
+ultraviolet continua. This suggests that the characteristic radius of the
+broad-line region is $\sim$50% larger than the value inferred from optical data
+alone. We also measured velocity-resolved emission-line lags for H$\beta$ and
+found a complex velocity-lag structure with shorter lags in the line wings,
+indicative of a broad-line region dominated by Keplerian motion. The responses
+of both the H$\beta$ and He II $\lambda$4686 emission lines to the driving
+continuum changed significantly halfway through the campaign, a phenomenon also
+observed for C IV, Ly $\alpha$, He II(+O III]), and Si IV(+O IV]) during the
+same monitoring period. Finally, given the optical luminosity of NGC 5548
+during our campaign, the measured H$\beta$ lag is a factor of five shorter than
+the expected value implied by the $R_\mathrm{BLR} - L_\mathrm{AGN}$ relation
+based on the past behavior of NGC 5548.
+",0,1,0,0,0,0
+18849,An Inexact Regularized Newton Framework with a Worst-Case Iteration Complexity of $\mathcal{O}(ε^{-3/2})$ for Nonconvex Optimization,"  An algorithm for solving smooth nonconvex optimization problems is proposed
+that, in the worst-case, takes $\mathcal{O}(\epsilon^{-3/2})$ iterations to
+drive the norm of the gradient of the objective function below a prescribed
+positive real number $\epsilon$ and can take $\mathcal{O}(\epsilon^{-3})$
+iterations to drive the leftmost eigenvalue of the Hessian of the objective
+above $-\epsilon$. The proposed algorithm is a general framework that covers a
+wide range of techniques including quadratically and cubically regularized
+Newton methods, such as the Adaptive Regularisation using Cubics (ARC) method
+and the recently proposed Trust-Region Algorithm with Contractions and
+Expansions (TRACE). The generality of our method is achieved through the
+introduction of generic conditions that each trial step is required to satisfy,
+which in particular allow for inexact regularized Newton steps to be used.
+These conditions center around a new subproblem that can be approximately
+solved to obtain trial steps that satisfy the conditions. A new instance of the
+framework, distinct from ARC and TRACE, is described that may be viewed as a
+hybrid between quadratically and cubically regularized Newton methods.
+Numerical results demonstrate that our hybrid algorithm outperforms a cublicly
+regularized Newton method.
+",0,0,1,0,0,0
+18850,An inverse problem for Maxwell's equations with Lipschitz parameters,"  We consider an inverse boundary value problem for Maxwell's equations, which
+aims to recover the electromagnetic material properties of a body from
+measurements on the boundary. We show that a Lipschitz continuous conductivity,
+electric permittivity, and magnetic permeability are uniquely determined by
+knowledge of all tangential electric and magnetic fields on the boundary of the
+body at a fixed frequency.
+",0,0,1,0,0,0
+18851,Context Aware Robot Navigation using Interactively Built Semantic Maps,"  We discuss the process of building semantic maps, how to interactively label
+entities in them, and how to use them to enable context-aware navigation
+behaviors in human environments. We utilize planar surfaces, such as walls and
+tables, and static objects, such as door signs, as features for our semantic
+mapping approach. Users can interactively annotate these features by having the
+robot follow him/her, entering the label through a mobile app, and performing a
+pointing gesture toward the landmark of interest. Our gesture based approach
+can reliably estimate which object is being pointed at and detect ambiguous
+gestures with probabilistic modeling. Our person following method attempts to
+maximize future utility by a search for future actions assuming constant
+velocity model for the human. We describe a method to extract metric goals from
+a semantic map landmark and to plan a human aware path that takes into account
+the personal spaces of people. Finally, we demonstrate context-awareness for
+person following in two scenarios: interactive labeling and door passing. We
+believe that future navigation approaches and service robotics applications can
+be made more effective by further exploiting the structure of human
+environments.
+",1,0,0,0,0,0
+18852,Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data,"  Multimodal sensory data resembles the form of information perceived by humans
+for learning, and are easy to obtain in large quantities. Compared to unimodal
+data, synchronization of concepts between modalities in such data provides
+supervision for disentangling the underlying explanatory factors of each
+modality. Previous work leveraging multimodal data has mainly focused on
+retaining only the modality-invariant factors while discarding the rest. In
+this paper, we present a partitioned variational autoencoder (PVAE) and several
+training objectives to learn disentangled representations, which encode not
+only the shared factors, but also modality-dependent ones, into separate latent
+variables. Specifically, PVAE integrates a variational inference framework and
+a multimodal generative model that partitions the explanatory factors and
+conditions only on the relevant subset of them for generation. We evaluate our
+model on two parallel speech/image datasets, and demonstrate its ability to
+learn disentangled representations by qualitatively exploring within-modality
+and cross-modality conditional generation with semantics and styles specified
+by examples. For quantitative analysis, we evaluate the classification accuracy
+of automatically discovered semantic units. Our PVAE can achieve over 99%
+accuracy on both modalities.
+",0,0,0,1,0,0
+18853,Blind Gain and Phase Calibration via Sparse Spectral Methods,"  Blind gain and phase calibration (BGPC) is a bilinear inverse problem
+involving the determination of unknown gains and phases of the sensing system,
+and the unknown signal, jointly. BGPC arises in numerous applications, e.g.,
+blind albedo estimation in inverse rendering, synthetic aperture radar
+autofocus, and sensor array auto-calibration. In some cases, sparse structure
+in the unknown signal alleviates the ill-posedness of BGPC. Recently there has
+been renewed interest in solutions to BGPC with careful analysis of error
+bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem,
+and propose to solve it via power iteration, or in the sparsity or joint
+sparsity case, via truncated power iteration. Under certain assumptions, the
+unknown gains, phases, and the unknown signal can be recovered simultaneously.
+Numerical experiments show that power iteration algorithms work not only in the
+regime predicted by our main results, but also in regimes where theoretical
+analysis is limited. We also show that our power iteration algorithms for BGPC
+compare favorably with competing algorithms in adversarial conditions, e.g.,
+with noisy measurement or with a bad initial estimate.
+",1,0,0,0,0,0
+18854,"Reconciling cooperation, biodiversity and stability in complex ecological communities","  Empirical observations show that ecological communities can have a huge
+number of coexisting species, also with few or limited number of resources.
+These ecosystems are characterized by multiple type of interactions, in
+particular displaying cooperative behaviors. However, standard modeling of
+population dynamics based on Lotka-Volterra type of equations predicts that
+ecosystem stability should decrease as the number of species in the community
+increases and that cooperative systems are less stable than communities with
+only competitive and/or exploitative interactions. Here we propose a stochastic
+model of population dynamics, which includes exploitative interactions as well
+as cooperative interactions induced by cross-feeding. The model is exactly
+solved and we obtain results for relevant macro-ecological patterns, such as
+species abundance distributions and correlation functions. In the large system
+size limit, any number of species can coexist for a very general class of
+interaction networks and stability increases as the number of species grows.
+For pure mutualistic/commensalistic interactions we determine the topological
+properties of the network that guarantee species coexistence. We also show that
+the stationary state is globally stable and that inferring species interactions
+through species abundance correlation analysis may be misleading. Our
+theoretical approach thus show that appropriate models of cooperation naturally
+leads to a solution of the long-standing question about complexity-stability
+paradox and on how highly biodiverse communities can coexist.
+",0,0,0,0,1,0
+18855,The Young L Dwarf 2MASS J11193254-1137466 is a Planetary-Mass Binary,"  We have discovered that the extremely red, low-gravity L7 dwarf 2MASS
+J11193254-1137466 is a 0.14"" (3.6 AU) binary using Keck laser guide star
+adaptive optics imaging. 2MASS J11193254-1137466 has previously been identified
+as a likely member of the TW Hydrae Association (TWA). Using our updated
+photometric distance and proper motion, a kinematic analysis based on the
+BANYAN II model gives an 82% probability of TWA membership. At TWA's 10$\pm$3
+Myr age and using hot-start evolutionary models, 2MASS J11193254-1137466AB is a
+pair of $3.7^{+1.2}_{-0.9}$ $M_{\rm Jup}$ brown dwarfs, making it the
+lowest-mass binary discovered to date. We estimate an orbital period of
+$90^{+80}_{-50}$ years. One component is marginally brighter in $K$ band but
+fainter in $J$ band, making this a probable flux-reversal binary, the first
+discovered with such a young age. We also imaged the spectrally similar TWA L7
+dwarf WISEA J114724.10-204021.3 with Keck and found no sign of binarity. Our
+evolutionary model-derived $T_{\rm eff}$ estimate for WISEA J114724.10-204021.3
+is $\approx$230 K higher than for 2MASS J11193254-1137466AB, at odds with their
+spectral similarity. This discrepancy suggests that WISEA J114724.10-204021.3
+may actually be a tight binary with masses and temperatures very similar to
+2MASS J11193254-1137466AB, or further supporting the idea that near-infrared
+spectra of young ultracool dwarfs are shaped by factors other than temperature
+and gravity. 2MASS J11193254-1137466AB will be an essential benchmark for
+testing evolutionary and atmospheric models in the young planetary-mass regime.
+",0,1,0,0,0,0
+18856,Diversity of Abundance Patterns of Light Neutron-capture Elements in Very-metal-poor Stars,"  We determine the abundances of neutron-capture elements from Sr to Eu for
+five very-metal-poor stars (-3<[Fe/H]<-2) in the Milky Way halo to reveal the
+origin of light neutron-capture elements. Previous spectroscopic studies have
+shown evidence of at least two components in the r-process; one referred to as
+the ""main r-process"" and the other as the ""weak r-process,"" which is mainly
+responsible for producing heavy and light neutron-capture elements,
+respectively. Observational studies of metal-poor stars suggest that there is a
+universal pattern in the main r-process, similar to the abundance pattern of
+the r-process component of solar-system material. Still, it is uncertain
+whether the abundance pattern of the weak r-process shows universality or
+diversity, due to the sparseness of measured light neutron-capture elements. We
+have detected the key elements, Mo, Ru, and Pd, in five target stars to give an
+answer to this question. The abundance patterns of light neutron-capture
+elements from Sr to Pd suggest a diversity in the weak r-process. In
+particular, scatter in the abundance ratio between Ru and Pd is significant
+when the abundance patterns are normalized at Zr. Our results are compared with
+the elemental abundances predicted by nucleosynthesis models of supernovae with
+parameters such as electron fraction or proto-neutron-star mass, to investigate
+sources of such diversity in the abundance patterns of light neutron-capture
+elements. This paper presents that the variation in the abundances of observed
+stars can be explained with a small range of parameters, which can serve as
+constraints on future modeling of supernova models.
+",0,1,0,0,0,0
+18857,Hypergames and Cyber-Physical Security for Control Systems,"  The identification of the Stuxnet worm in 2010 provided a highly publicized
+example of a cyber attack used to damage an industrial control system
+physically. This raised public awareness about the possibility of similar
+attacks against other industrial targets -- including critical infrastructure.
+In this paper, we use hypergames to analyze how adversarial perturbations can
+be used to manipulate a system using optimal control. Hypergames form an
+extension of game theory that enables us to model strategic interactions where
+the players may have significantly different perceptions of the game(s) they
+are playing. Past work with hypergames has been limited to relatively simple
+interactions consisting of a small set of discrete choices for each player, but
+here, we apply hypergames to larger systems with continuous variables. We find
+that manipulating constraints can be a more effective attacker strategy than
+directly manipulating objective function parameters. Moreover, the attacker
+need not change the underlying system to carry out a successful attack -- it
+may be sufficient to deceive the defender controlling the system. It is
+possible to scale our approach up to even larger systems, but the ability to do
+so will depend on the characteristics of the system in question, and we
+identify several characteristics that will make those systems amenable to
+hypergame analysis.
+",1,0,0,0,0,0
+18858,Measuring the academic reputation through citation networks via PageRank,"  The objective assessment of the prestige of an academic institution is a
+difficult and hotly debated task. In the last few years, different types of
+University Rankings have been proposed to quantify the excellence of different
+research institutions in the world. Albeit met with criticism in some cases,
+the relevance of university rankings is being increasingly acknowledged:
+indeed, rankings are having a major impact on the design of research policies,
+both at the institutional and governmental level. Yet, the debate on what
+rankings are {\em exactly} measuring is enduring. Here, we address the issue by
+measuring a quantitive and reliable proxy of the academic reputation of a given
+institution and by evaluating its correlation with different university
+rankings. Specifically, we study citation patterns among universities in five
+different Web of Science Subject Categories and use the \pr~algorithm on the
+five resulting citation networks. The rationale behind our work is that
+scientific citations are driven by the reputation of the reference so that the
+PageRank algorithm is expected to yield a rank which reflects the reputation of
+an academic institution in a specific field. Our results allow to quantifying
+the prestige of a set of institutions in a certain research field based only on
+hard bibliometric data. Given the volume of the data analysed, our findings are
+statistically robust and less prone to bias, at odds with ad--hoc surveys often
+employed by ranking bodies in order to attain similar results. Because our
+findings are found to correlate extremely well with the ARWU Subject rankings,
+the approach we propose in our paper may open the door to new, Academic Ranking
+methodologies that go beyond current methods by reconciling the qualitative
+evaluation of Academic Prestige with its quantitative measurements via
+publication impact.
+",1,0,0,0,0,0
+18859,A Model Order Reduction Algorithm for Estimating the Absorption Spectrum,"  The ab initio description of the spectral interior of the absorption spectrum
+poses both a theoretical and computational challenge for modern electronic
+structure theory. Due to the often spectrally dense character of this domain in
+the quantum propagator's eigenspectrum for medium-to-large sized systems,
+traditional approaches based on the partial diagonalization of the propagator
+often encounter oscillatory and stagnating convergence. Electronic structure
+methods which solve the molecular response problem through the solution of
+spectrally shifted linear systems, such as the complex polarization propagator,
+offer an alternative approach which is agnostic to the underlying spectral
+density or domain location. This generality comes at a seemingly high
+computational cost associated with solving a large linear system for each
+spectral shift in some discretization of the spectral domain of interest. We
+present a novel, adaptive solution based on model order reduction techniques
+via interpolation. Model order reduction reduces the computational complexity
+of mathematical models and is ubiquitous in the simulation of dynamical
+systems. The efficiency and effectiveness of the proposed algorithm in the ab
+initio prediction of X-Ray absorption spectra is demonstrated using a test set
+of challenging water clusters which are spectrally dense in the neighborhood of
+the oxygen K-edge. Based on a single, user defined tolerance we automatically
+determine the order of the reduced models and approximate the absorption
+spectrum up to the given tolerance. We also illustrate that the automatically
+determined model order increases logarithmically with the problem dimension,
+compared to a linear increase of the number of eigenvalues within the energy
+window. Furthermore, we observed that the computational cost of the proposed
+algorithm only scales quadratically with respect to the problem dimension.
+",1,1,0,0,0,0
+18860,"Uniform rank gradient, cost and local-global convergence","  We analyze the rank gradient of finitely generated groups with respect to
+sequences of subgroups of finite index that do not necessarily form a chain, by
+connecting it to the cost of p.m.p. actions. We generalize several results that
+were only known for chains before. The connection is made by the notion of
+local-global convergence.
+In particular, we show that for a finitely generated group $\Gamma$ with
+fixed price $c$, every Farber sequence has rank gradient $c-1$. By adapting
+Lackenby's trichotomy theorem to this setting, we also show that in a finitely
+presented amenable group, every sequence of subgroups with index tending to
+infinity has vanishing rank gradient.
+",0,0,1,0,0,0
+18861,Optimal design of a model energy conversion device,"  Fuel cells, batteries, thermochemical and other energy conversion devices
+involve the transport of a number of (electro-)chemical species through
+distinct materials so that they can meet and react at specified multi-material
+interfaces. Therefore, morphology or arrangement of these different materials
+can be critical in the performance of an energy conversion device. In this
+paper, we study a model problem motivated by a solar-driven thermochemical
+conversion device that splits water into hydrogen and oxygen. We formulate the
+problem as a system of coupled multi-material reaction-diffusion equations
+where each species diffuses selectively through a given material and where the
+reaction occurs at multi-material interfaces. We express the problem of optimal
+design of the material arrangement as a saddle point problem and obtain an
+effective functional which shows that regions with very fine phase mixtures of
+the material arise naturally. To explore this further, we introduce a
+phase-field formulation of the optimal design problem, and numerically study
+selected examples.
+",0,1,1,0,0,0
+18862,Mining Communication Data in a Music Community: A Preliminary Analysis,"  Comments play an important role within online creative communities because
+they make it possible to foster the production and improvement of authors'
+artifacts. We investigate how comment-based communication help shape members'
+behavior within online creative communities. In this paper, we report the
+results of a preliminary study aimed at mining the communication network of a
+music community for collaborative songwriting, where users collaborate online
+by first uploading new songs and then by adding new tracks and providing
+feedback in forms of comments.
+",1,0,0,0,0,0
+18863,Multidimensional VlasovPoisson Simulations with High-order Monotonicity- and Positivity-preserving Schemes,"  We develop new numerical schemes for Vlasov--Poisson equations with
+high-order accuracy. Our methods are based on a spatially
+monotonicity-preserving (MP) scheme and are modified suitably so that
+positivity of the distribution function is also preserved. We adopt an
+efficient semi-Lagrangian time integration scheme that is more accurate and
+computationally less expensive than the three-stage TVD Runge-Kutta
+integration. We apply our spatially fifth- and seventh-order schemes to a suite
+of simulations of collisionless self-gravitating systems and electrostatic
+plasma simulations, including linear and nonlinear Landau damping in one
+dimension and Vlasov--Poisson simulations in a six-dimensional phase space. The
+high-order schemes achieve a significantly improved accuracy in comparison with
+the third-order positive-flux-conserved scheme adopted in our previous study.
+With the semi-Lagrangian time integration, the computational cost of our
+high-order schemes does not significantly increase, but remains roughly the
+same as that of the third-order scheme. Vlasov--Poisson simulations on $128^3
+\times 128^3$ mesh grids have been successfully performed on a massively
+parallel computer.
+",0,1,0,0,0,0
+18864,Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy,"  Markov Chain Monte Carlo based Bayesian data analysis has now become the
+method of choice for analyzing and interpreting data in almost all disciplines
+of science. In astronomy, over the last decade, we have also seen a steady
+increase in the number of papers that employ Monte Carlo based Bayesian
+analysis. New, efficient Monte Carlo based methods are continuously being
+developed and explored. In this review, we first explain the basics of Bayesian
+theory and discuss how to set up data analysis problems within this framework.
+Next, we provide an overview of various Monte Carlo based methods for
+performing Bayesian data analysis. Finally, we discuss advanced ideas that
+enable us to tackle complex problems and thus hold great promise for the
+future. We also distribute downloadable computer software (available at
+this https URL ) that implements some of the algorithms and
+examples discussed here.
+",0,1,0,1,0,0
+18865,Stable splitting of mapping spaces via nonabelian Poincaré duality,"  We use nonabelian Poincaré duality to recover the stable splitting of
+compactly supported mapping spaces, $\rm{Map_c}$$(M,\Sigma^nX)$, where $M$ is a
+parallelizable $n$-manifold. Our method for deriving this splitting is new, and
+naturally extends to give a more general stable splitting of the space of
+compactly supported sections of a certain bundle on $M$ with fibers
+$\Sigma^nX$, twisted by the tangent bundle of $M$. This generalization
+incorporates possible $O(n)$-actions on $X$ as well as accommodating
+non-parallelizable manifolds.
+",0,0,1,0,0,0
+18866,Static Gesture Recognition using Leap Motion,"  In this report, an automated bartender system was developed for making orders
+in a bar using hand gestures. The gesture recognition of the system was
+developed using Machine Learning techniques, where the model was trained to
+classify gestures using collected data. The final model used in the system
+reached an average accuracy of 95%. The system raised ethical concerns both in
+terms of user interaction and having such a system in a real world scenario,
+but it could initially work as a complement to a real bartender.
+",1,0,0,1,0,0
+18867,B-spline-like bases for $C^2$ cubics on the Powell-Sabin 12-split,"  For spaces of constant, linear, and quadratic splines of maximal smoothness
+on the Powell-Sabin 12-split of a triangle, the so-called S-bases were recently
+introduced. These are simplex spline bases with B-spline-like properties on the
+12-split of a single triangle, which are tied together across triangles in a
+Bézier-like manner.
+In this paper we give a formal definition of an S-basis in terms of certain
+basic properties. We proceed to investigate the existence of S-bases for the
+aforementioned spaces and additionally the cubic case, resulting in an
+exhaustive list. From their nature as simplex splines, we derive simple
+differentiation and recurrence formulas to other S-bases. We establish a
+Marsden identity that gives rise to various quasi-interpolants and domain
+points forming an intuitive control net, in terms of which conditions for
+$C^0$-, $C^1$-, and $C^2$-smoothness are derived.
+",1,0,0,0,0,0
+18868,Synthesizing Correlations with Computational Likelihood Approach: Vitamin C Data,"  It is known that the primary source of dietary vitamin C is fruit and
+vegetables and the plasma level of vitamin C has been considered a good
+surrogate biomarker of vitamin C intake by fruit and vegetable consumption. To
+combine the information about association between vitamin C intake and the
+plasma level of vitamin C, numerical approximation methods for likelihood
+function of correlation coefficient are studied. The least squares approach is
+used to estimate a log-likelihood function by a function from a space of
+B-splines having desirable mathematical properties. The likelihood interval
+from the Highest Likelihood Regions (HLR) is used for further inference. This
+approach can be easily extended to the realm of meta-analysis involving sample
+correlations from different studies by use of an approximated combined
+likelihood function. The sample correlations between vitamin C intake and serum
+level of vitamin C from many studies are used to illustrate application of this
+approach.
+",0,0,0,1,0,0
+18869,Axion detection via Topological Casimir Effect,"  We propose a new table-top experimental configuration for the direct
+detection of dark matter axions with mass in the $(10^{-6} \rm eV - 10^{-2} \rm
+eV)$ range using non-perturbative effects in a system with non-trivial spatial
+topology. Different from most experimental setups found in literature on direct
+dark matter axion detection, which relies on $\dot{\theta}$ or
+$\vec{\nabla}\theta$, we found that our system is in principle sensitive to a
+static $\theta\geq 10^{-14}$ and can also be used to set limit on the
+fundamental constant $\theta_{\rm QED}$ which becomes the fundamental
+observable parameter of the Maxwell system if some conditions are met.
+Connection with Witten effect when the induced electric charge $e'$ is
+proportional to $\theta$ and the magnetic monopole becomes the dyon with
+non-vanishing $e'=-e \frac{\theta}{2\pi}$ is also discussed.
+",0,1,0,0,0,0
+18870,Novel Feature-Based Clustering of Micro-Panel Data (CluMP),"  Micro-panel data are collected and analysed in many research and industry
+areas. Cluster analysis of micro-panel data is an unsupervised learning
+exploratory method identifying subgroup clusters in a data set which include
+homogeneous objects in terms of the development dynamics of monitored
+variables. The supply of clustering methods tailored to micro-panel data is
+limited. The present paper focuses on a feature-based clustering method,
+introducing a novel two-step characteristic-based approach designed for this
+type of data. The proposed CluMP method aims to identify clusters that are at
+least as internally homogeneous and externally heterogeneous as those obtained
+by alternative methods already implemented in the statistical system R. We
+compare the clustering performance of the devised algorithm with two extant
+methods using simulated micro-panel data sets. Our approach has yielded similar
+or better outcomes than the other methods, the advantage of the proposed
+algorithm being time efficiency which makes it applicable for large data sets.
+",0,0,0,1,0,0
+18871,Ab initio effective Hamiltonians for cuprate superconductors,"  Ab initio low-energy effective Hamiltonians of two typical high-temperature
+copper-oxide superconductors, whose mother compounds are La$_2$CuO$_4$ and
+HgBa$_2$CuO$_4$, are derived by utilizing the multi-scale ab initio scheme for
+correlated electrons (MACE). The effective Hamiltonians obtained in the present
+study serve as platforms of future studies to accurately solve the low-energy
+effective Hamiltonians beyond the density functional theory. It allows further
+study on the superconducting mechanism from the first principles and
+quantitative basis without adjustable parameters not only for the available
+cuprates but also for future design of higher Tc in general. More concretely,
+we derive effective Hamiltonians for three variations, 1)one-band Hamiltonian
+for the antibonding orbital generated from strongly hybridized Cu
+$3d_{x^2-y^2}$ and O $2p_\sigma$ orbitals 2)two-band Hamiltonian constructed
+from the antibonding orbital and Cu $3d_{3z^2-r^2}$ orbital hybridized mainly
+with the apex oxygen $p_z$ orbital 3)three-band Hamiltonian consisting mainly
+of Cu $3d_{x^2-y^2}$ orbitals and two O $2p_\sigma$ orbitals. Differences
+between the Hamiltonians for La$_2$CuO$_4$ and HgBa$_2$CuO$_4$, which have
+relatively low and high critical temperatures, respectively, at optimally doped
+compounds, are elucidated. The main differences are summarized as i) the oxygen
+$2p_\sigma$ orbitals are farther(~3.7eV) below from the Cu $d_{x^2-y^2}$
+orbital for the La compound than the Hg compound(~2.4eV) in the three-band
+Hamiltonian. This causes a substantial difference in the character of the
+$d_{x^2-y^2}-2p_\sigma$ antibonding band at the Fermi level and makes the
+effective onsite Coulomb interaction U larger for the La compound than the Hg
+compound for the two- and one-band Hamiltonians. ii)The ratio of the
+second-neighbor to the nearest transfer t'/t is also substantially different
+(~0.26) for the Hg and ~0.15 for the La compound in the one-band Hamiltonian.
+",0,1,0,0,0,0
+18872,Longitudinal data analysis using matrix completion,"  In clinical practice and biomedical research, measurements are often
+collected sparsely and irregularly in time while the data acquisition is
+expensive and inconvenient. Examples include measurements of spine bone mineral
+density, cancer growth through mammography or biopsy, a progression of defect
+of vision, or assessment of gait in patients with neurological disorders. Since
+the data collection is often costly and inconvenient, estimation of progression
+from sparse observations is of great interest for practitioners.
+From the statistical standpoint, such data is often analyzed in the context
+of a mixed-effect model where time is treated as both random and fixed effect.
+Alternatively, researchers analyze Gaussian processes or functional data where
+observations are assumed to be drawn from a certain distribution of processes.
+These models are flexible but rely on probabilistic assumptions and require
+very careful implementation.
+In this study, we propose an alternative elementary framework for analyzing
+longitudinal data, relying on matrix completion. Our method yields point
+estimates of progression curves by iterative application of the SVD. Our
+framework covers multivariate longitudinal data, regression and can be easily
+extended to other settings.
+We apply our methods to understand trends of progression of motor impairment
+in children with Cerebral Palsy. Our model approximates individual progression
+curves and explains 30% of the variability. Low-rank representation of
+progression trends enables discovering that subtypes of Cerebral Palsy exhibit
+different progression trends.
+",0,0,0,1,0,0
+18873,Emission-line Diagnostics of Nearby HII Regions Including Supernova Hosts,"  We present a new model of the optical nebular emission from HII regions by
+combin- ing the results of the Binary Population and Spectral Synthesis (bpass)
+code with the photoion- ization code cloudy (Ferland et al. 1998). We explore a
+variety of emission-line diagnostics of these star-forming HII regions and
+examine the effects of metallicity and interacting binary evo- lution on the
+nebula emission-line production. We compare the line emission properties of HII
+regions with model stellar populations, and provide new constraints on their
+stellar populations and supernova progenitors. We find that models including
+massive binary stars can successfully match all the observational constraints
+and provide reasonable age and mass estimation of the HII regions and supernova
+progenitors.
+",0,1,0,0,0,0
+18874,On monomial linearisation and supercharacters of pattern subgroups,"  Column closed pattern subgroups $U$ of the finite upper unitriangular groups
+$U_n(q)$ are defined as sets of matrices in $U_n(q)$ having zeros in a
+prescribed set of columns besides the diagonal ones. We explain Jedlitschky's
+construction of monomial linearisation and apply this to $C U$ yielding a
+generalisation of Yan's coadjoint cluster representations. Then we give a
+complete classification of the resulting supercharacters, by describing the
+resulting orbits and determining the Hom-spaces between orbit modules.
+",0,0,1,0,0,0
+18875,The Structural Fate of Individual Multicomponent Metal-Oxide Nanoparticles in Polymer Nanoreactors,"  Multicomponent nanoparticles can be synthesized with either homogeneous or
+phase-segregated architectures depending on the synthesis conditions and
+elements incorporated. To understand the parameters that determine their
+structural fate, multicomponent metal-oxide nanoparticles consisting of
+combinations of Co, Ni, and Cu were synthesized via scanning probe block
+copolymer lithography and characterized using correlated electron microscopy.
+These studies revealed that the miscibility, ratio of the metallic components,
+and the synthesis temperature determine the crystal structure and architecture
+of the nanoparticles. A Co-Ni-O system forms a rock salt structure largely due
+to the miscibility of CoO and NiO, while Cu-Ni-O, which has large miscibility
+gaps, forms either homogeneous oxides, heterojunctions, or alloys depending on
+the annealing temperature and composition. Moreover, a higher ordered
+structure, Co-Ni-Cu-O, was found to follow the behavior of lower ordered
+systems.
+",0,1,0,0,0,0
+18876,On Compression of Unsupervised Neural Nets by Pruning Weak Connections,"  Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep
+Belif Networks(DBNs), are powerful in automatic feature extraction,unsupervised
+weight initialization and density estimation. In this paper,we demonstrate that
+the parameters of these neural nets can be dramatically reduced without
+affecting their performance. We describe a method to reduce the parameters
+required by RBM which is the basic building block for deep architectures.
+Further we propose an unsupervised sparse deep architectures selection
+algorithm to form sparse deep neural networks.Experimental results show that
+there is virtually no loss in either generative or discriminative performance.
+",1,0,0,0,0,0
+18877,A matrix generalization of a theorem of Fine,"  In 1947 Nathan Fine gave a beautiful product for the number of binomial
+coefficients $\binom{n}{m}$, for $m$ in the range $0 \leq m \leq n$, that are
+not divisible by $p$. We give a matrix product that generalizes Fine's formula,
+simultaneously counting binomial coefficients with $p$-adic valuation $\alpha$
+for each $\alpha \geq 0$. For each $n$ this information is naturally encoded in
+a polynomial generating function, and the sequence of these polynomials is
+$p$-regular in the sense of Allouche and Shallit. We also give a further
+generalization to multinomial coefficients.
+",1,0,1,0,0,0
+18878,Training Multi-Task Adversarial Network For Extracting Noise-Robust Speaker Embedding,"  Under noisy environments, to achieve the robust performance of speaker
+recognition is still a challenging task. Motivated by the promising performance
+of multi-task training in a variety of image processing tasks, we explore the
+potential of multi-task adversarial training for learning a noise-robust
+speaker embedding. In this paper we present a novel framework which consists of
+three components: an encoder that extracts noise-robust speaker embedding; a
+classifier that classifies the speakers; a discriminator that discriminates the
+noise type of the speaker embedding. Besides, we propose a training strategy
+using the training accuracy as an indicator to stabilize the multi-class
+adversarial optimization process. We conduct our experiments on the English and
+Mandarin corpus and the experimental results demonstrate that our proposed
+multi-task adversarial training method could greatly outperform the other
+methods without adversarial training in noisy environments. Furthermore,
+experiments indicate that our method is also able to improve the speaker
+verification performance the clean condition.
+",1,0,0,0,0,0
+18879,Playing Games with Bounded Entropy,"  In this paper, we consider zero-sum repeated games in which the maximizer is
+restricted to strategies requiring no more than a limited amount of randomness.
+Particularly, we analyze the maxmin payoff of the maximizer in two models: the
+first model forces the maximizer to randomize her action in each stage just by
+conditioning her decision to outcomes of a given sequence of random source,
+whereas, in the second model, the maximizer is a team of players who are free
+to privately randomize their corresponding actions but do not have access to
+any explicit source of shared randomness needed for cooperation. The works of
+Gossner and Vieille, and Gossner and Tomala adopted the method of types to
+establish their results; however, we utilize the idea of random hashing which
+is the core of randomness extractors in the information theory literature. In
+addition, we adopt the well-studied tool of simulation of a source from another
+source. By utilizing these tools, we are able to simplify the prior results and
+generalize them as well. We characterize the maxmin payoff of the maximizer in
+the repeated games under study. Particularly, the maxmin payoff of the first
+model is fully described by the function $J(h)$ which is the maximum payoff
+that the maximizer can secure in a one-shot game by choosing mixed strategies
+of entropy at most $h$. In the second part of the paper, we study the
+computational aspects of $J(h)$. We offer three explicit lower bounds on the
+entropy-payoff trade-off curve. To do this, we provide and utilize new results
+for the set of distributions that guarantee a certain payoff for Alice. In
+particular, we study how this set of distributions shrinks as we increase the
+security level. While the use of total variation distance is common in game
+theory, our derivation indicates the suitability of utilizing the
+Renyi-divergence of order two.
+",1,0,0,0,0,0
+18880,Exact Hausdorff and packing measures for random self-similar code-trees with necks,"  Random code-trees with necks were introduced recently to generalise the
+notion of $V$-variable and random homogeneous sets. While it is known that the
+Hausdorff and packing dimensions coincide irrespective of overlaps, their exact
+Hausdorff and packing measure has so far been largely ignored. In this article
+we consider the general question of an appropriate gauge function for positive
+and finite Hausdorff and packing measure. We first survey the current state of
+knowledge and establish some bounds on these gauge functions. We then show that
+self-similar code-trees do not admit a gauge functions that simultaneously give
+positive and finite Hausdorff measure almost surely. This surprising result is
+in stark contrast to the random recursive model and sheds some light on the
+question of whether $V$-variable sets interpolate between random homogeneous
+and random recursive sets. We conclude by discussing implications of our
+results.
+",0,0,1,0,0,0
+18881,On Modules over a G-set,"  Let R be a commutative ring with unity, M a module over R and let S be a
+G-set for a finite group G. We define a set MS to be the set of elements
+expressed as the formal finite sum of the form similar to the elements of group
+ring RG. The set MS is a module over the group ring RG under the addition and
+the scalar multiplication similar to the RG-module MG. With this notion, we not
+only generalize but also unify the theories of both of the group algebra and
+the group module, and we also establish some significant properties of MS. In
+particular, we describe a method for decomposing a given RG-module MS as a
+direct sum of RG-submodules. Furthermore, we prove the semisimplicity problem
+of MS with regard to the properties of M, S and G.
+",0,0,1,0,0,0
+18882,Polynomial-time algorithms for the Longest Induced Path and Induced Disjoint Paths problems on graphs of bounded mim-width,"  We give the first polynomial-time algorithms on graphs of bounded maximum
+induced matching width (mim-width) for problems that are not locally checkable.
+In particular, we give $n^{\mathcal{O}(w)}$-time algorithms on graphs of
+mim-width at most $w$, when given a decomposition, for the following problems:
+Longest Induced Path, Induced Disjoint Paths and $H$-Induced Topological Minor
+for fixed $H$. Our results imply that the following graph classes have
+polynomial-time algorithms for these three problems: Interval and Bi-Interval
+graphs, Circular Arc, Permutation and Circular Permutation graphs, Convex
+graphs, $k$-Trapezoid, Circular $k$-Trapezoid, $k$-Polygon, Dilworth-$k$ and
+Co-$k$-Degenerate graphs for fixed $k$.
+",1,0,0,0,0,0
+18883,Bug or Not? Bug Report Classification Using N-Gram IDF,"  Previous studies have found that a significant number of bug reports are
+misclassified between bugs and non-bugs, and that manually classifying bug
+reports is a time-consuming task. To address this problem, we propose a bug
+reports classification model with N-gram IDF, a theoretical extension of
+Inverse Document Frequency (IDF) for handling words and phrases of any length.
+N-gram IDF enables us to extract key terms of any length from texts, these key
+terms can be used as the features to classify bug reports. We build
+classification models with logistic regression and random forest using features
+from N-gram IDF and topic modeling, which is widely used in various software
+engineering tasks. With a publicly available dataset, our results show that our
+N-gram IDF-based models have a superior performance than the topic-based models
+on all of the evaluated cases. Our models show promising results and have a
+potential to be extended to other software engineering tasks.
+",1,0,0,0,0,0
+18884,Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection,"  Cell nuclei detection is a challenging research topic because of limitations
+in cellular image quality and diversity of nuclear morphology, i.e. varying
+nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been
+a topic of enduring interest with promising recent success shown by deep
+learning methods. These methods train Convolutional Neural Networks (CNNs) with
+a training set of input images and known, labeled nuclei locations. Many such
+methods are supplemented by spatial or morphological processing. Using a set of
+canonical cell nuclei shapes, prepared with the help of a domain expert, we
+develop a new approach that we call Shape Priors with Convolutional Neural
+Networks (SP-CNN). We further extend the network to introduce a shape prior
+(SP) layer and then allowing it to become trainable (i.e. optimizable). We call
+this network tunable SP-CNN (TSP-CNN). In summary, we present new network
+structures that can incorporate 'expected behavior' of nucleus shapes via two
+components: learnable layers that perform the nucleus detection and a fixed
+processing part that guides the learning with prior information. Analytically,
+we formulate two new regularization terms that are targeted at: 1) learning the
+shapes, 2) reducing false positives while simultaneously encouraging detection
+inside the cell nucleus boundary. Experimental results on two challenging
+datasets reveal that the proposed SP-CNN and TSP-CNN can outperform
+state-of-the-art alternatives.
+",1,0,0,1,0,0
+18885,Anisotropic hydrodynamic turbulence in accretion disks,"  Recently, the vertical shear instability (VSI) has become an attractive
+purely hydrodynamic candidate for the anomalous angular momentum transport
+required for weakly ionized accretion disks. In direct three-dimensional
+numerical simulations of VSI turbulence in disks, a meridional circulation
+pattern was observed that is opposite to the usual viscous flow behavior. Here,
+we investigate whether this feature can possibly be explained by an anisotropy
+of the VSI turbulence. Using three-dimensional hydrodynamical simulations, we
+calculate the turbulent Reynolds stresses relevant for angular momentum
+transport for a representative section of a disk.
+We find that the vertical stress is significantly stronger than the radial
+stress. Using our results in viscous disk simulations with different viscosity
+coefficients for the radial and vertical direction, we find good agreement with
+the VSI turbulence for the stresses and meridional flow; this provides
+additional evidence for the anisotropy. The results are important with respect
+to the transport of small embedded particles in disks.
+",0,1,0,0,0,0
+18886,Optimal Algorithms for Distributed Optimization,"  In this paper, we study the optimal convergence rate for distributed convex
+optimization problems in networks. We model the communication restrictions
+imposed by the network as a set of affine constraints and provide optimal
+complexity bounds for four different setups, namely: the function $F(\xb)
+\triangleq \sum_{i=1}^{m}f_i(\xb)$ is strongly convex and smooth, either
+strongly convex or smooth or just convex. Our results show that Nesterov's
+accelerated gradient descent on the dual problem can be executed in a
+distributed manner and obtains the same optimal rates as in the centralized
+version of the problem (up to constant or logarithmic factors) with an
+additional cost related to the spectral gap of the interaction matrix. Finally,
+we discuss some extensions to the proposed setup such as proximal friendly
+functions, time-varying graphs, improvement of the condition numbers.
+",1,0,0,1,0,0
+18887,Evaluation of Classical Features and Classifiers in Brain-Computer Interface Tasks,"  Brain-Computer Interface (BCI) uses brain signals in order to provide a new
+method for communication between human and outside world. Feature extraction,
+selection and classification are among the main matters of concerns in signal
+processing stage of BCI. In this article, we present our findings about the
+most effective features and classifiers in some brain tasks. Six different
+groups of classical features and twelve classifiers have been examined in nine
+datasets of brain signal. The results indicate that energy of brain signals in
+{\alpha} and \b{eta} frequency bands, together with some statistical parameters
+are more effective, comparing to the other types of extracted features. In
+addition, Bayesian classifier with Gaussian distribution assumption and also
+Support Vector Machine (SVM) show to classify different BCI datasets more
+accurately than the other classifiers. We believe that the results can give an
+insight about a strategy for blind classification of brain signals in
+brain-computer interface.
+",1,0,0,1,0,0
+18888,On exceptional compact homogeneous geometries of type C3,"  We provide a uniform framework to study the exceptional homogeneous compact
+geometries of type C3. This framework is then used to show that these are
+simply connected, answering a question by Kramer and Lytchak, and to calculate
+the full automorphism groups.
+",0,0,1,0,0,0
+18889,Intensity estimation of transaction arrivals on the intraday electricity market,"  In the following paper we present a simple intensity estimation method of
+transaction arrivals on the intraday electricity market. Assuming the
+interarrival times distribution, we utilize a maximum likelihood estimation.
+The method's performance is briefly tested using German Intraday Continuous
+data. Despite the simplicity of the method, the results are encouraging. The
+supplementary materials containing the R-codes and the data are attached to
+this paper.
+",0,0,0,1,0,1
+18890,Capacitive Mechanism of Oxygen Functional Groups on Carbon Surface in Supercapacitors,"  Oxygen functional groups are one of the most important subjects in the study
+of electrochemical properties of carbon materials which can change the
+wettability, conductivity and pore size distributions of carbon materials, and
+can occur redox reactions. In the electrode materials of carbon-based
+supercapacitors, the oxygen functional groups have widely been used to improve
+the capacitive performance. In this paper, we not only analyzed the reasons for
+the increase of the capacity that promoted by oxygen functional groups in the
+charge-discharge cycling tests, but also analyzed the mechanism how the
+pseudocapacitance was provided by the oxygen functional groups in the
+acid/alkaline aqueous electrolyte. Moreover, we also discussed the effect of
+the oxygen functional groups in electrochemical impedance spectroscopy.
+",0,1,0,0,0,0
+18891,On conditional least squares estimation for affine diffusions based on continuous time observations,"  We study asymptotic properties of conditional least squares estimators for
+the drift parameters of two-factor affine diffusions based on continuous time
+observations. We distinguish three cases: subcritical, critical and
+supercritical. For all the drift parameters, in the subcritical and
+supercritical cases, asymptotic normality and asymptotic mixed normality is
+proved, while in the critical case, non-standard asymptotic behavior is
+described.
+",0,0,1,1,0,0
+18892,Discretization error estimates for penalty formulations of a linearized Canham-Helfrich type energy,"  This paper is concerned with minimization of a fourth-order linearized
+Canham-Helfrich energy subject to Dirichlet boundary conditions on curves
+inside the domain. Such problems arise in the modeling of the mechanical
+interaction of biomembranes with embedded particles. There, the curve
+conditions result from the imposed particle--membrane coupling. We prove
+almost-$H^{\frac{5}{2}}$ regularity of the solution and then consider two
+possible penalty formulations. For the combination of these penalty
+formulations with a Bogner-Fox-Schmit finite element discretization we prove
+discretization error estimates which are optimal in view of the solution's
+reduced regularity. The error estimates are based on a general estimate for
+linear penalty problems in Hilbert spaces. Finally, we illustrate the
+theoretical results by numerical computations. An important feature of the
+presented discretization is that it does not require to resolve the particle
+boundary. This is crucial in order to avoid re-meshing if the presented problem
+arises as subproblem in a model where particles are allowed to move or rotate.
+",0,0,1,0,0,0
+18893,On the Humphreys conjecture on support varieties of tilting modules,"  Let $G$ be a simply-connected semisimple algebraic group over an
+algebraically closed field of characteristic $p$, assumed to be larger than the
+Coxeter number. The ""support variety"" of a $G$-module $M$ is a certain closed
+subvariety of the nilpotent cone of $G$, defined in terms of cohomology for the
+first Frobenius kernel $G_1$. In the 1990s, Humphreys proposed a conjectural
+description of the support varieties of tilting modules; this conjecture has
+been proved for $G = \mathrm{SL}_n$ in earlier work of the second author.
+In this paper, we show that for any $G$, the support variety of a tilting
+module always contains the variety predicted by Humphreys, and that they
+coincide (i.e., the Humphreys conjecture is true) when $p$ is sufficiently
+large. We also prove variants of these statements involving ""relative support
+varieties.""
+",0,0,1,0,0,0
+18894,Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks,"  Machine learning models have been widely used in security applications such
+as intrusion detection, spam filtering, and virus or malware detection.
+However, it is well-known that adversaries are always trying to adapt their
+attacks to evade detection. For example, an email spammer may guess what
+features spam detection models use and modify or remove those features to avoid
+detection. There has been some work on making machine learning models more
+robust to such attacks. However, one simple but promising approach called {\em
+randomization} is underexplored. This paper proposes a novel
+randomization-based approach to improve robustness of machine learning models
+against evasion attacks. The proposed approach incorporates randomization into
+both model training time and model application time (meaning when the model is
+used to detect attacks). We also apply this approach to random forest, an
+existing ML method which already has some degree of randomness. Experiments on
+intrusion detection and spam filtering data show that our approach further
+improves robustness of random-forest method. We also discuss how this approach
+can be applied to other ML models.
+",0,0,0,1,0,0
+18895,Further extension of the generalized Hurwitz-Lerch Zeta function of two variables,"  The main aim of this paper is to give a new generalization of Hurwitz-Lerch
+Zeta function of two variables.Also, we investigate several interesting
+properties such as integral representations, summation formula and a connection
+with generalized hypergeometric function. To strengthen the main results we
+also consider many important special cases.
+",0,0,1,0,0,0
+18896,A Physarum-inspired model for the probit-based stochastic user equilibrium problem,"  Stochastic user equilibrium is an important issue in the traffic assignment
+problems, tradition models for the stochastic user equilibrium problem are
+designed as mathematical programming problems. In this article, a
+Physarum-inspired model for the probit-based stochastic user equilibrium
+problem is proposed. There are two main contributions of our work. On the one
+hand, the origin Physarum model is modified to find the shortest path in
+traffic direction networks with the properties of two-way traffic
+characteristic. On the other hand, the modified Physarum-inspired model could
+get the equilibrium flows when traveller's perceived transportation cost
+complies with normal distribution. The proposed method is constituted with a
+two-step procedure. First, the modified Physarum model is applied to get the
+auxiliary flows. Second, the auxiliary flows are averaged to obtain the
+equilibrium flows. Numerical examples are conducted to illustrate the
+performance of the proposed method, which is compared with the Method of
+Successive Average method.
+",1,0,0,0,0,0
+18897,Going Higher in First-Order Quantifier Alternation Hierarchies on Words,"  We investigate quantifier alternation hierarchies in first-order logic on
+finite words. Levels in these hierarchies are defined by counting the number of
+quantifier alternations in formulas. We prove that one can decide membership of
+a regular language in the levels $\mathcal{B}{\Sigma}_2$ (finite boolean
+combinations of formulas having only one alternation) and ${\Sigma}_3$
+(formulas having only two alternations and beginning with an existential
+block). Our proofs work by considering a deeper problem, called separation,
+which, once solved for lower levels, allows us to solve membership for higher
+levels.
+",1,0,0,0,0,0
+18898,Invariance of Ideal Limit Points,"  Let $\mathcal{I}$ be an analytic P-ideal [respectively, a summable ideal] on
+the positive integers and let $(x_n)$ be a sequence taking values in a metric
+space $X$. First, it is shown that the set of ideal limit points of $(x_n)$ is
+an $F_\sigma$-set [resp., a closet set]. Let us assume that $X$ is also
+separable and the ideal $\mathcal{I}$ satisfies certain additional assumptions,
+which however includes several well-known examples, e.g., the collection of
+sets with zero asymptotic density, sets with zero logarithmic density, and some
+summable ideals. Then, it is shown that the set of ideal limit points of
+$(x_n)$ is equal to the set of ideal limit points of almost all its
+subsequences.
+",0,0,1,0,0,0
+18899,"Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching","  The unprecedented demand for large amount of data has catalyzed the trend of
+combining human insights with machine learning techniques, which facilitate the
+use of crowdsourcing to enlist label information both effectively and
+efficiently. The classic work on crowdsourcing mainly focuses on the label
+inference problem under the categorization setting. However, inferring the true
+label requires sophisticated aggregation models that usually can only perform
+well under certain assumptions. Meanwhile, no matter how complicated the
+aggregation model is, the true model that generated the crowd labels remains
+unknown. Therefore, the label inference problem can never infer the ground
+truth perfectly. Based on the fact that the crowdsourcing labels are abundant
+and utilizing aggregation will lose such kind of rich annotation information
+(e.g., which worker provided which labels), we believe that it is critical to
+take the diverse labeling abilities of the crowdsourcing workers as well as
+their correlations into consideration. To address the above challenge, we
+propose to tackle three research problems, namely inference, learning, and
+teaching.
+",0,0,0,1,0,0
+18900,Learning With Errors and Extrapolated Dihedral Cosets,"  The hardness of the learning with errors (LWE) problem is one of the most
+fruitful resources of modern cryptography. In particular, it is one of the most
+prominent candidates for secure post-quantum cryptography. Understanding its
+quantum complexity is therefore an important goal. We show that under quantum
+polynomial time reductions, LWE is equivalent to a relaxed version of the
+dihedral coset problem (DCP), which we call extrapolated DCP (eDCP). The extent
+of extrapolation varies with the LWE noise rate. By considering different
+extents of extrapolation, our result generalizes Regev's famous proof that if
+DCP is in BQP (quantum poly-time) then so is LWE (FOCS'02). We also discuss a
+connection between eDCP and Childs and Van Dam's algorithm for generalized
+hidden shift problems (SODA'07). Our result implies that a BQP solution for LWE
+might not require the full power of solving DCP, but rather only a solution for
+its relaxed version, eDCP, which could be easier.
+",1,0,0,0,0,0
+18901,Fermi bubbles: high latitude X-ray supersonic shell,"  The nature of the bipolar, $\gamma$-ray Fermi bubbles (FB) is still unclear,
+in part because their faint, high-latitude X-ray counterpart has until now
+eluded a clear detection. We stack ROSAT data at varying distances from the FB
+edges, thus boosting the signal and identifying an expanding shell behind the
+southwest, southeast, and northwest edges, albeit not in the dusty northeast
+sector near Loop I. A Primakoff-like model for the underlying flow is invoked
+to show that the signals are consistent with halo gas heated by a strong,
+forward shock to $\sim$keV temperatures. Assuming ion--electron thermal
+equilibrium then implies a $\sim10^{56}$ erg event near the Galactic centre
+$\sim7$ Myr ago. However, the reported high absorption-line velocities suggest
+a preferential shock-heating of ions, and thus more energetic ($\sim 10^{57}$
+erg), younger ($\lesssim 3$ Myr) FBs.
+",0,1,0,0,0,0
+18902,Passivity-Based Generalization of Primal-Dual Dynamics for Non-Strictly Convex Cost Functions,"  In this paper, we revisit primal-dual dynamics for convex optimization and
+present a generalization of the dynamics based on the concept of passivity. It
+is then proved that supplying a stable zero to one of the integrators in the
+dynamics allows one to eliminate the assumption of strict convexity on the cost
+function based on the passivity paradigm together with the invariance principle
+for Caratheodory systems. We then show that the present algorithm is also a
+generalization of existing augmented Lagrangian-based primal-dual dynamics, and
+discuss the benefit of the present generalization in terms of noise reduction
+and convergence speed.
+",1,0,0,0,0,0
+18903,"An integral formula for the powered sum of the independent, identically and normally distributed random variables","  The distribution of the sum of r-th power of standard normal random variables
+is a generalization of the chi-squared distribution. In this paper, we
+represent the probability density function of the random variable by an
+one-dimensional absolutely convergent integral with the characteristic
+function. Our integral formula is expected to be applied for evaluation of the
+density function. Our integral formula is based on the inversion formula, and
+we utilize a summation method. We also discuss on our formula in the view point
+of hyperfunctions.
+",0,0,1,0,0,0
+18904,A Dynamic Boosted Ensemble Learning Method Based on Random Forest,"  We propose a dynamic boosted ensemble learning method based on random forest
+(DBRF), a novel ensemble algorithm that incorporates the notion of hard example
+mining into Random Forest (RF) and thus combines the high accuracy of Boosting
+algorithm with the strong generalization of Bagging algorithm. Specifically, we
+propose to measure the quality of each leaf node of every decision tree in the
+random forest to determine hard examples. By iteratively training and then
+removing easy examples from training data, we evolve the random forest to focus
+on hard examples dynamically so as to learn decision boundaries better. Data
+can be cascaded through these random forests learned in each iteration in
+sequence to generate predictions, thus making RF deep. We also propose to use
+evolution mechanism and smart iteration mechanism to improve the performance of
+the model. DBRF outperforms RF on three UCI datasets and achieved
+state-of-the-art results compared to other deep models. Moreover, we show that
+DBRF is also a new way of sampling and can be very useful when learning from
+imbalanced data.
+",0,0,0,1,0,0
+18905,"Inter-Operator Resource Management for Millimeter Wave, Multi-Hop Backhaul Networks","  In this paper, a novel framework is proposed for optimizing the operation and
+performance of a large-scale, multi-hop millimeter wave (mmW) backhaul within a
+wireless small cell network (SCN) that encompasses multiple mobile network
+operators (MNOs). The proposed framework enables the small base stations (SBSs)
+to jointly decide on forming the multi-hop, mmW links over backhaul
+infrastructure that belongs to multiple, independent MNOs, while properly
+allocating resources across those links. In this regard, the problem is
+addressed using a novel framework based on matching theory that is composed to
+two, highly inter-related stages: a multi-hop network formation stage and a
+resource management stage. One unique feature of this framework is that it
+jointly accounts for both wireless channel characteristics and economic factors
+during both network formation and resource management. The multi-hop network
+formation stage is formulated as a one-to-many matching game which is solved
+using a novel algorithm, that builds on the so-called deferred acceptance
+algorithm and is shown to yield a stable and Pareto optimal multi-hop mmW
+backhaul network. Then, a one-to-many matching game is formulated to enable
+proper resource allocation across the formed multi-hop network. This game is
+then shown to exhibit peer effects and, as such, a novel algorithm is developed
+to find a stable and optimal resource management solution that can properly
+cope with these peer effects. Simulation results show that the proposed
+framework yields substantial gains, in terms of the average sum rate, reaching
+up to 27% and 54%, respectively, compared to a non-cooperative scheme in which
+inter-operator sharing is not allowed and a random allocation approach. The
+results also show that our framework provides insights on how to manage pricing
+and the cost of the cooperative mmW backhaul network for the MNOs.
+",1,0,0,0,0,0
+18906,An Online Learning Approach to Generative Adversarial Networks,"  We consider the problem of training generative models with a Generative
+Adversarial Network (GAN). Although GANs can accurately model complex
+distributions, they are known to be difficult to train due to instabilities
+caused by a difficult minimax optimization problem. In this paper, we view the
+problem of training GANs as finding a mixed strategy in a zero-sum game.
+Building on ideas from online learning we propose a novel training method named
+Chekhov GAN 1 . On the theory side, we show that our method provably converges
+to an equilibrium for semi-shallow GAN architectures, i.e. architectures where
+the discriminator is a one layer network and the generator is arbitrary. On the
+practical side, we develop an efficient heuristic guided by our theoretical
+results, which we apply to commonly used deep GAN architectures. On several
+real world tasks our approach exhibits improved stability and performance
+compared to standard GAN training.
+",1,0,0,1,0,0
+18907,Weakly supervised CRNN system for sound event detection with large-scale unlabeled in-domain data,"  Sound event detection (SED) is typically posed as a supervised learning
+problem requiring training data with strong temporal labels of sound events.
+However, the production of datasets with strong labels normally requires
+unaffordable labor cost. It limits the practical application of supervised SED
+methods. The recent advances in SED approaches focuses on detecting sound
+events by taking advantages of weakly labeled or unlabeled training data. In
+this paper, we propose a joint framework to solve the SED task using
+large-scale unlabeled in-domain data. In particular, a state-of-the-art general
+audio tagging model is first employed to predict weak labels for unlabeled
+data. On the other hand, a weakly supervised architecture based on the
+convolutional recurrent neural network (CRNN) is developed to solve the strong
+annotations of sound events with the aid of the unlabeled data with predicted
+labels. It is found that the SED performance generally increases as more
+unlabeled data is added into the training. To address the noisy label problem
+of unlabeled data, an ensemble strategy is applied to increase the system
+robustness. The proposed system is evaluated on the SED dataset of DCASE 2018
+challenge. It reaches a F1-score of 21.0%, resulting in an improvement of 10%
+over the baseline system.
+",1,0,0,0,0,0
+18908,Revealing the Coulomb interaction strength in a cuprate superconductor,"  We study optimally doped
+Bi$_{2}$Sr$_{2}$Ca$_{0.92}$Y$_{0.08}$Cu$_{2}$O$_{8+\delta}$ (Bi2212) using
+angle-resolved two-photon photoemission spectroscopy. Three spectral features
+are resolved near 1.5, 2.7, and 3.6 eV above the Fermi level. By tuning the
+photon energy, we determine that the 2.7-eV feature arises predominantly from
+unoccupied states. The 1.5- and 3.6-eV features reflect unoccupied states whose
+spectral intensities are strongly modulated by the corresponding occupied
+states. These unoccupied states are consistent with the prediction from a
+cluster perturbation theory based on the single-band Hubbard model. Through
+this comparison, a Coulomb interaction strength U of 2.7 eV is extracted. Our
+study complements equilibrium photoemission spectroscopy and provides a direct
+spectroscopic measurement of the unoccupied states in cuprates. The determined
+Coulomb U indicates that the charge-transfer gap of optimally doped Bi2212 is
+1.1 eV.
+",0,1,0,0,0,0
+18909,Ordering dynamics of self-propelled particles in an inhomogeneous medium,"  Ordering dynamics of self-propelled particles in an inhomogeneous medium in
+two-dimensions is studied. We write coarse-grained hydrodynamic equations of
+motion for coarse-grained density and velocity fields in the presence of an
+external random disorder field, which is quenched in time. The strength of
+inhomogeneity is tuned from zero disorder (clean system) to large disorder. In
+the clean system, the velocity field grows algebraically as $L_{\rm V} \sim
+t^{0.5}$. The density field does not show clean power-law growth; however, it
+follows $L_{\rm \rho} \sim t^{0.8}$ approximately. In the inhomogeneous system,
+we find a disorder dependent growth. For both the density and the velocity,
+growth slow down with increasing strength of disorder. The velocity shows a
+disorder dependent power-law growth $L_{\rm V}(t,\Delta) \sim t^{1/\bar z_{\rm
+V}(\Delta)}$ for intermediate times. At late times, there is a crossover to
+logarithmic growth $L_{\rm V}(t,\Delta) \sim (\ln t)^{1/\varphi}$, where
+$\varphi$ is a disorder independent exponent. Two-point correlation functions
+for the velocity shows dynamical scaling, but the density does not.
+",0,1,0,0,0,0
+18910,Inflationary preheating dynamics with ultracold atoms,"  We discuss the amplification of loop corrections in quantum many-body systems
+through dynamical instabilities. As an example, we investigate both
+analytically and numerically a two-component ultracold atom system in one
+spatial dimension. The model features a tachyonic instability, which
+incorporates characteristic aspects of the mechanisms for particle production
+in early-universe inflaton models. We establish a direct correspondence between
+measureable macroscopic growth rates for occupation numbers of the ultracold
+Bose gas and the underlying microscopic processes in terms of Feynman loop
+diagrams. We analyze several existing ultracold atom setups featuring dynamical
+instabilities and propose optimized protocols for their experimental
+realization. We demonstrate that relevant dynamical processes can be enhanced
+using a seeding procedure for unstable modes and clarify the role of initial
+quantum fluctuations and the generation of a non-linear secondary stage for the
+amplification of modes.
+",0,1,0,0,0,0
+18911,Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning,"  Reinforcement learning (RL), while often powerful, can suffer from slow
+learning speeds, particularly in high dimensional spaces. The autonomous
+decomposition of tasks and use of hierarchical methods hold the potential to
+significantly speed up learning in such domains. This paper proposes a novel
+practical method that can autonomously decompose tasks, by leveraging
+association rule mining, which discovers hidden relationship among entities in
+data mining. We introduce a novel method called ARM-HSTRL (Association Rule
+Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning).
+It extracts temporal and structural relationships of sub-goals in RL, and
+multi-task RL. In particular,it finds sub-goals and relationship among them. It
+is shown the significant efficiency and performance of the proposed method in
+two main topics of RL.
+",1,0,0,0,0,0
+18912,Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection,"  Motivation: Epigenetic heterogeneity within a tumour can play an important
+role in tumour evolution and the emergence of resistance to treatment. It is
+increasingly recognised that the study of DNA methylation (DNAm) patterns along
+the genome -- so-called `epialleles' -- offers greater insight into epigenetic
+dynamics than conventional analyses which examine DNAm marks individually.
+Results: We have developed a Bayesian model to infer which epialleles are
+present in multiple regions of the same tumour. We apply our method to reduced
+representation bisulfite sequencing (RRBS) data from multiple regions of one
+lung cancer tumour and a matched normal sample. The model borrows information
+from all tumour regions to leverage greater statistical power. The total number
+of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all
+automatically inferred from the data. Uncertainty as to which epiallele an
+observed sequencing read originated from is explicitly incorporated by
+marginalising over the appropriate posterior densities. The degree to which
+tumour samples are contaminated with normal tissue can be estimated and
+corrected for. By tracing the distribution of epialleles throughout the tumour
+we can infer the phylogenetic history of the tumour, identify epialleles that
+differ between normal and cancer tissue, and define a measure of global
+epigenetic disorder.
+",0,0,1,1,0,0
+18913,A bootstrap test to detect prominent Granger-causalities across frequencies,"  Granger-causality in the frequency domain is an emerging tool to analyze the
+causal relationship between two time series. We propose a bootstrap test on
+unconditional and conditional Granger-causality spectra, as well as on their
+difference, to catch particularly prominent causality cycles in relative terms.
+In particular, we consider a stochastic process derived applying independently
+the stationary bootstrap to the original series. Our null hypothesis is that
+each causality or causality difference is equal to the median across
+frequencies computed on that process. In this way, we are able to disambiguate
+causalities which depart significantly from the median one obtained ignoring
+the causality structure. Our test shows power one as the process tends to
+non-stationarity, thus being more conservative than parametric alternatives. As
+an example, we infer about the relationship between money stock and GDP in the
+Euro Area via our approach, considering inflation, unemployment and interest
+rates as conditioning variables. We point out that during the period 1999-2017
+the money stock aggregate M1 had a significant impact on economic output at all
+frequencies, while the opposite relationship is significant only at high
+frequencies.
+",0,0,0,1,0,1
+18914,Optimal segregation of proteins: phase transitions and symmetry breaking,"  Asymmetric segregation of key proteins at cell division -- be it a beneficial
+or deleterious protein -- is ubiquitous in unicellular organisms and often
+considered as an evolved trait to increase fitness in a stressed environment.
+Here, we provide a general framework to describe the evolutionary origin of
+this asymmetric segregation. We compute the population fitness as a function of
+the protein segregation asymmetry $a$, and show that the value of $a$ which
+optimizes the population growth manifests a phase transition between symmetric
+and asymmetric partitioning phases. Surprisingly, the nature of phase
+transition is different for the case of beneficial proteins as opposed to
+proteins which decrease the single-cell growth rate. Our study elucidates the
+optimization problem faced by evolution in the context of protein segregation,
+and motivates further investigation of asymmetric protein segregation in
+biological systems.
+",0,0,0,0,1,0
+18915,Optimal make-take fees for market making regulation,"  We consider an exchange who wishes to set suitable make-take fees to attract
+liquidity on its platform. Using a principal-agent approach, we are able to
+describe in quasi-explicit form the optimal contract to propose to a market
+maker. This contract depends essentially on the market maker inventory
+trajectory and on the volatility of the asset. We also provide the optimal
+quotes that should be displayed by the market maker. The simplicity of our
+formulas allows us to analyze in details the effects of optimal contracting
+with an exchange, compared to a situation without contract. We show in
+particular that it leads to higher quality liquidity and lower trading costs
+for investors.
+",0,0,0,0,0,1
+18916,Spontaneous symmetry breaking due to the trade-off between attractive and repulsive couplings,"  Spontaneous symmetry breaking (SSB) is an important phenomenon observed in
+various fields including physics and biology. In this connection, we here show
+that the trade-off between attractive and repulsive couplings can induce
+spontaneous symmetry breaking in a homogeneous system of coupled oscillators.
+With a simple model of a system of two coupled Stuart-Landau oscillators, we
+demonstrate how the tendency of attractive coupling in inducing in-phase
+synchronized (IPS) oscillations and the tendency of repulsive coupling in
+inducing out-of-phase synchronized (OPS) oscillations compete with each other
+and give rise to symmetry breaking oscillatory (SBO) states and interesting
+multistabilities. Further, we provide explicit expressions for synchronized and
+anti-synchronized oscillatory states as well as the so called oscillation death
+(OD) state and study their stability. If the Hopf bifurcation parameter
+(${\lambda}$) is greater than the natural frequency ($\omega$) of the system,
+the attractive coupling favours the emergence of an anti-symmetric OD state via
+a Hopf bifurcation whereas the repulsive coupling favours the emergence of a
+similar state through a saddle-node bifurcation. We show that an increase in
+the repulsive coupling not only destabilizes the IPS state but also facilitates
+the re-entrance of the IPS state.
+",0,1,0,0,0,0
+18917,Evolutionary Image Composition Using Feature Covariance Matrices,"  Evolutionary algorithms have recently been used to create a wide range of
+artistic work. In this paper, we propose a new approach for the composition of
+new images from existing ones, that retain some salient features of the
+original images. We introduce evolutionary algorithms that create new images
+based on a fitness function that incorporates feature covariance matrices
+associated with different parts of the images. This approach is very flexible
+in that it can work with a wide range of features and enables targeting
+specific regions in the images. For the creation of the new images, we propose
+a population-based evolutionary algorithm with mutation and crossover operators
+based on random walks. Our experimental results reveal a spectrum of
+aesthetically pleasing images that can be obtained with the aid of our
+evolutionary process.
+",1,0,0,0,0,0
+18918,Community Detection with Colored Edges,"  In this paper, we prove a sharp limit on the community detection problem with
+colored edges. We assume two equal-sized communities and there are $m$
+different types of edges. If two vertices are in the same community, the
+distribution of edges follows $p_i=\alpha_i\log{n}/n$ for $1\leq i \leq m$,
+otherwise the distribution of edges is $q_i=\beta_i\log{n}/n$ for $1\leq i \leq
+m$, where $\alpha_i$ and $\beta_i$ are positive constants and $n$ is the total
+number of vertices. Under these assumptions, a fundamental limit on community
+detection is characterized using the Hellinger distance between the two
+distributions. If $\sum_{i=1}^{m} {(\sqrt{\alpha_i} - \sqrt{\beta_i})}^2 >2$,
+then the community detection via maximum likelihood (ML) estimator is possible
+with high probability. If $\sum_{i=1}^m {(\sqrt{\alpha_i} - \sqrt{\beta_i})}^2
+< 2$, the probability that the ML estimator fails to detect the communities
+does not go to zero.
+",1,1,0,0,0,0
+18919,"Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling","  Nonlocal neural networks have been proposed and shown to be effective in
+several computer vision tasks, where the nonlocal operations can directly
+capture long-range dependencies in the feature space. In this paper, we study
+the nature of diffusion and damping effect of nonlocal networks by doing
+spectrum analysis on the weight matrices of the well-trained networks, and then
+propose a new formulation of the nonlocal block. The new block not only learns
+the nonlocal interactions but also has stable dynamics, thus allowing deeper
+nonlocal structures. Moreover, we interpret our formulation from the general
+nonlocal modeling perspective, where we make connections between the proposed
+nonlocal network and other nonlocal models, such as nonlocal diffusion process
+and Markov jump process.
+",0,0,0,1,0,0
+18920,Proof of FLT by Algebra Identities and Linear Algebra,"  The main aim of the present paper is to represent an exact and simple proof
+for FLT by using properties of the algebra identities and linear algebra.
+",0,0,1,0,0,0
+18921,An Observational Diagnostic for Distinguishing Between Clouds and Haze in Hot Exoplanet Atmospheres,"  The nature of aerosols in hot exoplanet atmospheres is one of the primary
+vexing questions facing the exoplanet field. The complex chemistry, multiple
+formation pathways, and lack of easily identifiable spectral features
+associated with aerosols make it especially challenging to constrain their key
+properties. We propose a transmission spectroscopy technique to identify the
+primary aerosol formation mechanism for the most highly irradiated hot Jupiters
+(HIHJs). The technique is based on the expectation that the two key types of
+aerosols -- photochemically generated hazes and equilibrium condensate clouds
+-- are expected to form and persist in different regions of a highly irradiated
+planet's atmosphere. Haze can only be produced on the permanent daysides of
+tidally-locked hot Jupiters, and will be carried downwind by atmospheric
+dynamics to the evening terminator (seen as the trailing limb during transit).
+Clouds can only form in cooler regions on the night side and morning terminator
+of HIHJs (seen as the leading limb during transit). Because opposite limbs are
+expected to be impacted by different types of aerosols, ingress and egress
+spectra, which primarily probe opposing sides of the planet, will reveal the
+dominant aerosol formation mechanism. We show that the benchmark HIHJ,
+WASP-121b, has a transmission spectrum consistent with partial aerosol coverage
+and that ingress-egress spectroscopy would constrain the location and formation
+mechanism of those aerosols. In general, using this diagnostic we find that
+observations with JWST and potentially with HST should be able to distinguish
+between clouds and haze for currently known HIHJs.
+",0,1,0,0,0,0
+18922,$\mathcal{P}$-schemes and Deterministic Polynomial Factoring over Finite Fields,"  We introduce a family of mathematical objects called $\mathcal{P}$-schemes,
+where $\mathcal{P}$ is a poset of subgroups of a finite group $G$. A
+$\mathcal{P}$-scheme is a collection of partitions of the right coset spaces
+$H\backslash G$, indexed by $H\in\mathcal{P}$, that satisfies a list of axioms.
+These objects generalize the classical notion of association schemes as well as
+the notion of $m$-schemes (Ivanyos et al. 2009).
+Based on $\mathcal{P}$-schemes, we develop a unifying framework for the
+problem of deterministic factoring of univariate polynomials over finite fields
+under the generalized Riemann hypothesis (GRH).
+",1,0,1,0,0,0
+18923,Similarity Preserving Representation Learning for Time Series Analysis,"  A considerable amount of machine learning algorithms take instance-feature
+matrices as their inputs. As such, they cannot directly analyze time series
+data due to its temporal nature, usually unequal lengths, and complex
+properties. This is a great pity since many of these algorithms are effective,
+robust, efficient, and easy to use. In this paper, we bridge this gap by
+proposing an efficient representation learning framework that is able to
+convert a set of time series with equal or unequal lengths to a matrix format.
+In particular, we guarantee that the pairwise similarities between time series
+are well preserved after the transformation. The learned feature representation
+is particularly suitable to the class of learning problems that are sensitive
+to data similarities. Given a set of $n$ time series, we first construct an
+$n\times n$ partially observed similarity matrix by randomly sampling $O(n \log
+n)$ pairs of time series and computing their pairwise similarities. We then
+propose an extremely efficient algorithm that solves a highly non-convex and
+NP-hard problem to learn new features based on the partially observed
+similarity matrix. We use the learned features to conduct experiments on both
+data classification and clustering tasks. Our extensive experimental results
+demonstrate that the proposed framework is both effective and efficient.
+",1,0,0,0,0,0
+18924,Modeling Social Organizations as Communication Networks,"  We identify the ""organization"" of a human social group as the communication
+network(s) within that group. We then introduce three theoretical approaches to
+analyzing what determines the structures of human organizations. All three
+approaches adopt a group-selection perspective, so that the group's network
+structure is (approximately) optimal, given the information-processing
+limitations of agents within the social group, and the exogenous welfare
+function of the overall group. In the first approach we use a new sub-field of
+telecommunications theory called network coding, and focus on a welfare
+function that involves the ability of the organization to convey information
+among the agents. In the second approach we focus on a scenario where agents
+within the organization must allocate their future communication resources when
+the state of the future environment is uncertain. We show how this formulation
+can be solved with a linear program. In the third approach, we introduce an
+information synthesis problem in which agents within an organization receive
+information from various sources and must decide how to transform such
+information and transmit the results to other agents in the organization. We
+propose leveraging the computational power of neural networks to solve such
+problems. These three approaches formalize and synthesize work in fields
+including anthropology, archeology, economics and psychology that deal with
+organization structure, theory of the firm, span of control and cognitive
+limits on communication.
+",1,1,0,0,0,0
+18925,The Ricci flow on solvmanifolds of real type,"  We show that for any solvable Lie group of real type, any homogeneous Ricci
+flow solution converges in Cheeger-Gromov topology to a unique non-flat
+solvsoliton, which is independent of the initial left-invariant metric. As an
+application, we obtain results on the isometry groups of non-flat solvsoliton
+metrics and Einstein solvmanifolds.
+",0,0,1,0,0,0
+18926,Model-based clustering of multi-tissue gene expression data,"  Recently, it has become feasible to generate large-scale, multi-tissue gene
+expression data, where expression profiles are obtained from multiple tissues
+or organs sampled from dozens to hundreds of individuals. When traditional
+clustering methods are applied to this type of data, important information is
+lost, because they either require all tissues to be analyzed independently,
+ignoring dependencies and similarities between tissues, or to merge tissues in
+a single, monolithic dataset, ignoring individual characteristics of tissues.
+We developed a Bayesian model-based multi-tissue clustering algorithm, revamp,
+which can incorporate prior information on physiological tissue similarity, and
+which results in a set of clusters, each consisting of a core set of genes
+conserved across tissues as well as differential sets of genes specific to one
+or more subsets of tissues. Using data from seven vascular and metabolic
+tissues from over 100 individuals in the STockholm Atherosclerosis Gene
+Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by
+revamp are more enriched for tissue-dependent protein-protein interactions
+compared to alternative approaches. We further demonstrate that revamp results
+in easily interpretable multi-tissue gene expression associations to key
+coronary artery disease processes and clinical phenotypes in the STAGE
+individuals. Revamp is implemented in the Lemon-Tree software, available at
+this https URL
+",0,0,0,0,1,0
+18927,Fastest Convergence for Q-learning,"  The Zap Q-learning algorithm introduced in this paper is an improvement of
+Watkins' original algorithm and recent competitors in several respects. It is a
+matrix-gain algorithm designed so that its asymptotic variance is optimal.
+Moreover, an ODE analysis suggests that the transient behavior is a close match
+to a deterministic Newton-Raphson implementation. This is made possible by a
+two time-scale update equation for the matrix gain sequence.
+The analysis suggests that the approach will lead to stable and efficient
+computation even for non-ideal parameterized settings. Numerical experiments
+confirm the quick convergence, even in such non-ideal cases.
+A secondary goal of this paper is tutorial. The first half of the paper
+contains a survey on reinforcement learning algorithms, with a focus on minimum
+variance algorithms.
+",1,0,1,0,0,0
+18928,New Bounds on the Field Size for Maximally Recoverable Codes Instantiating Grid-like Topologies,"  In recent years, the rapidly increasing amounts of data created and processed
+through the internet resulted in distributed storage systems employing erasure
+coding based schemes. Aiming to balance the tradeoff between data recovery for
+correlated failures and efficient encoding and decoding, distributed storage
+systems employing maximally recoverable codes came up. Unifying a number of
+topologies considered both in theory and practice, Gopalan \cite{Gopalan2017}
+initiated the study of maximally recoverable codes for grid-like topologies.
+In this paper, we focus on the maximally recoverable codes that instantiate
+grid-like topologies $T_{m\times n}(1,b,0)$. To characterize the property of
+codes for these topologies, we introduce the notion of \emph{pseudo-parity
+check matrix}. Then, using the hypergraph independent set approach, we
+establish the first polynomial upper bound on the field size needed for
+achieving the maximal recoverability in topologies $T_{m\times n}(1,b,0)$, when
+$n$ is large enough. And we further improve this general upper bound for
+topologies $T_{4\times n}(1,2,0)$ and $T_{3\times n}(1,3,0)$. By relating the
+problem to generalized \emph{Sidon sets} in $\mathbb{F}_q$, we also obtain
+non-trivial lower bounds on the field size for maximally recoverable codes that
+instantiate topologies $T_{4\times n}(1,2,0)$ and $T_{3\times n}(1,3,0)$.
+",1,0,0,0,0,0
+18929,Two-Step Disentanglement for Financial Data,"  In this work, we address the problem of disentanglement of factors that
+generate a given data into those that are correlated with the labeling and
+those that are not. Our solution is simpler than previous solutions and employs
+adversarial training in a straightforward manner. We demonstrate the new method
+on visual datasets as well as on financial data. In order to evaluate the
+latter, we developed a hypothetical trading strategy whose performance is
+affected by the performance of the disentanglement, namely, it trades better
+when the factors are better separated.
+",1,0,0,1,0,0
+18930,Optimal control of a Vlasov-Poisson plasma by an external magnetic field - Analysis of a tracking type optimal control problem,"  In the paper ""Optimal control of a Vlasov-Poisson plasma by an external
+magnetic field - The basics for variational calculus"" [arXiv:1708.02464] we
+have already introduced a set of admissible magnetic fields and we have proved
+that each of those fields induces a unique strong solution of the
+Vlasov-Poisson system. We have also established that the field-state operator
+that maps any admissible field onto its corresponding solution is continuous
+and weakly compact. In this paper we will show that this operator is also
+Fréchet differentiable and we will continue to analyze the optimal control
+problem that was introduced in [arXiv:1708.02464]. More precisely, we will
+establish necessary and sufficient conditions for local optimality and we will
+show that an optimal solution is unique under certain conditions.
+",0,0,1,0,0,0
+18931,Towards an algebraic natural proofs barrier via polynomial identity testing,"  We observe that a certain kind of algebraic proof - which covers essentially
+all known algebraic circuit lower bounds to date - cannot be used to prove
+lower bounds against VP if and only if what we call succinct hitting sets exist
+for VP. This is analogous to the Razborov-Rudich natural proofs barrier in
+Boolean circuit complexity, in that we rule out a large class of lower bound
+techniques under a derandomization assumption. We also discuss connections
+between this algebraic natural proofs barrier, geometric complexity theory, and
+(algebraic) proof complexity.
+",1,0,1,0,0,0
+18932,In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference,"  In recent years, a great deal of interest has focused on conducting inference
+on the parameters in a linear model in the high-dimensional setting. In this
+paper, we consider a simple and very naïve two-step procedure for this
+task, in which we (i) fit a lasso model in order to obtain a subset of the
+variables; and (ii) fit a least squares model on the lasso-selected set.
+Conventional statistical wisdom tells us that we cannot make use of the
+standard statistical inference tools for the resulting least squares model
+(such as confidence intervals and $p$-values), since we peeked at the data
+twice: once in running the lasso, and again in fitting the least squares model.
+However, in this paper, we show that under a certain set of assumptions, with
+high probability, the set of variables selected by the lasso is deterministic.
+Consequently, the naïve two-step approach can yield confidence intervals
+that have asymptotically correct coverage, as well as p-values with proper
+Type-I error control. Furthermore, this two-step approach unifies two existing
+camps of work on high-dimensional inference: one camp has focused on inference
+based on a sub-model selected by the lasso, and the other has focused on
+inference using a debiased version of the lasso estimator.
+",0,0,1,1,0,0
+18933,Asymptotic Analysis of Plausible Tree Hash Modes for SHA-3,"  Discussions about the choice of a tree hash mode of operation for a
+standardization have recently been undertaken. It appears that a single tree
+mode cannot address adequately all possible uses and specifications of a
+system. In this paper, we review the tree modes which have been proposed, we
+discuss their problems and propose remedies. We make the reasonable assumption
+that communicating systems have different specifications and that software
+applications are of different types (securing stored content or live-streamed
+content). Finally, we propose new modes of operation that address the resource
+usage problem for the three most representative categories of devices and we
+analyse their asymptotic behavior.
+",1,0,0,0,0,0
+18934,Neurally Plausible Model of Robot Reaching Inspired by Infant Motor Babbling,"  In this paper we present a neurally plausible model of robot reaching
+inspired by human infant reaching that is based on embodied artificial
+intelligence, which emphasizes the importance of the sensory-motor interaction
+of an agent and the world. This model encompasses both learning sensory-motor
+correlations through motor babbling and also arm motion planning using
+spreading activation. This model is organized in three layers of neural maps
+with parallel structures representing the same sensory-motor space. The motor
+babbling period shapes the structure of the three neural maps as well as the
+connections within and between them. We describe an implementation of this
+model and an investigation of this implementation using a simple reaching task
+on a humanoid robot. The robot has learned successfully to plan reaching
+motions from a test set with high accuracy and smoothness.
+",1,0,0,0,0,0
+18935,When Will AI Exceed Human Performance? Evidence from AI Experts,"  Advances in artificial intelligence (AI) will transform modern life by
+reshaping transportation, health, science, finance, and the military. To adapt
+public policy, we need to better anticipate these advances. Here we report the
+results from a large survey of machine learning researchers on their beliefs
+about progress in AI. Researchers predict AI will outperform humans in many
+activities in the next ten years, such as translating languages (by 2024),
+writing high-school essays (by 2026), driving a truck (by 2027), working in
+retail (by 2031), writing a bestselling book (by 2049), and working as a
+surgeon (by 2053). Researchers believe there is a 50% chance of AI
+outperforming humans in all tasks in 45 years and of automating all human jobs
+in 120 years, with Asian respondents expecting these dates much sooner than
+North Americans. These results will inform discussion amongst researchers and
+policymakers about anticipating and managing trends in AI.
+",1,0,0,0,0,0
+18936,E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks,"  Recurrent Neural Networks (RNNs) are a key technology for emerging
+applications such as automatic speech recognition, machine translation or image
+description. Long Short Term Memory (LSTM) networks are the most successful RNN
+implementation, as they can learn long term dependencies to achieve high
+accuracy. Unfortunately, the recurrent nature of LSTM networks significantly
+constrains the amount of parallelism and, hence, multicore CPUs and many-core
+GPUs exhibit poor efficiency for RNN inference. In this paper, we present
+E-PUR, an energy-efficient processing unit tailored to the requirements of LSTM
+computation. The main goal of E-PUR is to support large recurrent neural
+networks for low-power mobile devices. E-PUR provides an efficient hardware
+implementation of LSTM networks that is flexible to support diverse
+applications. One of its main novelties is a technique that we call Maximizing
+Weight Locality (MWL), which improves the temporal locality of the memory
+accesses for fetching the synaptic weights, reducing the memory requirements by
+a large extent. Our experimental results show that E-PUR achieves real-time
+performance for different LSTM networks, while reducing energy consumption by
+orders of magnitude with respect to general-purpose processors and GPUs, and it
+requires a very small chip area. Compared to a modern mobile SoC, an NVIDIA
+Tegra X1, E-PUR provides an average energy reduction of 92x.
+",1,0,0,0,0,0
+18937,Increasing Geminid meteor shower activity,"  Mathematical modelling has shown that activity of the Geminid meteor shower
+should rise with time, and that was confirmed by analysis of visual
+observations 1985--2016. We do not expect any outburst activity of the Geminid
+shower in 2017, even though the asteroid (3200) Phaethon has close approach to
+Earth in December of 2017. A small probability to observe dust ejected at
+perihelia 2009--2016 still exists.
+",0,1,0,0,0,0
+18938,The Bennett-Orlicz norm,"  Lederer and van de Geer (2013) introduced a new Orlicz norm, the
+Bernstein-Orlicz norm, which is connected to Bernstein type inequalities. Here
+we introduce another Orlicz norm, the Bennett-Orlicz norm, which is connected
+to Bennett type inequalities. The new Bennett-Orlicz norm yields inequalities
+for expectations of maxima which are potentially somewhat tighter than those
+resulting from the Bernstein-Orlicz norm when they are both applicable. We
+discuss cross connections between these norms, exponential inequalities of the
+Bernstein, Bennett, and Prokhorov types, and make comparisons with results of
+Talagrand (1989, 1994), and Boucheron, Lugosi, and Massart (2013).
+",0,0,1,1,0,0
+18939,Temporal Justification Logic,"  Justification logics are modal-like logics with the additional capability of
+recording the reason, or justification, for modalities in syntactic structures,
+called justification terms. Justification logics can be seen as explicit
+counterparts to modal logics. The behavior and interaction of agents in
+distributed system is often modeled using logics of knowledge and time. In this
+paper, we sketch some preliminary ideas on how the modal knowledge part of such
+logics of knowledge and time could be replaced with an appropriate
+justification logic.
+",1,0,0,0,0,0
+18940,Simultaneous Multiparty Communication Complexity of Composed Functions,"  In the Number On the Forehead (NOF) multiparty communication model, $k$
+players want to evaluate a function $F : X_1 \times\cdots\times X_k\rightarrow
+Y$ on some input $(x_1,\dots,x_k)$ by broadcasting bits according to a
+predetermined protocol. The input is distributed in such a way that each player
+$i$ sees all of it except $x_i$. In the simultaneous setting, the players
+cannot speak to each other but instead send information to a referee. The
+referee does not know the players' input, and cannot give any information back.
+At the end, the referee must be able to recover $F(x_1,\dots,x_k)$ from what
+she obtained.
+A central open question, called the $\log n$ barrier, is to find a function
+which is hard to compute for $polylog(n)$ or more players (where the $x_i$'s
+have size $poly(n)$) in the simultaneous NOF model. This has important
+applications in circuit complexity, as it could help to separate $ACC^0$ from
+other complexity classes. One of the candidates belongs to the family of
+composed functions. The input to these functions is represented by a $k\times
+(t\cdot n)$ boolean matrix $M$, whose row $i$ is the input $x_i$ and $t$ is a
+block-width parameter. A symmetric composed function acting on $M$ is specified
+by two symmetric $n$- and $kt$-variate functions $f$ and $g$, that output
+$f\circ g(M)=f(g(B_1),\dots,g(B_n))$ where $B_j$ is the $j$-th block of width
+$t$ of $M$. As the majority function $MAJ$ is conjectured to be outside of
+$ACC^0$, Babai et. al. suggested to study $MAJ\circ MAJ_t$, with $t$ large
+enough.
+So far, it was only known that $t=1$ is not enough for $MAJ\circ MAJ_t$ to
+break the $\log n$ barrier in the simultaneous deterministic NOF model. In this
+paper, we extend this result to any constant block-width $t>1$, by giving a
+protocol of cost $2^{O(2^t)}\log^{2^{t+1}}(n)$ for any symmetric composed
+function when there are $2^{\Omega(2^t)}\log n$ players.
+",1,0,0,0,0,0
+18941,Online Learning for Changing Environments using Coin Betting,"  A key challenge in online learning is that classical algorithms can be slow
+to adapt to changing environments. Recent studies have proposed ""meta""
+algorithms that convert any online learning algorithm to one that is adaptive
+to changing environments, where the adaptivity is analyzed in a quantity called
+the strongly-adaptive regret. This paper describes a new meta algorithm that
+has a strongly-adaptive regret bound that is a factor of $\sqrt{\log(T)}$
+better than other algorithms with the same time complexity, where $T$ is the
+time horizon. We also extend our algorithm to achieve a first-order (i.e.,
+dependent on the observed losses) strongly-adaptive regret bound for the first
+time, to our knowledge. At its heart is a new parameter-free algorithm for the
+learning with expert advice (LEA) problem in which experts sometimes do not
+output advice for consecutive time steps (i.e., \emph{sleeping} experts). This
+algorithm is derived by a reduction from optimal algorithms for the so-called
+coin betting problem. Empirical results show that our algorithm outperforms
+state-of-the-art methods in both learning with expert advice and metric
+learning scenarios.
+",1,0,0,1,0,0
+18942,On the global sup-norm of GL(3) cusp forms,"  Let $\phi$ be a spherical Hecke-Maass cusp form on the non-compact space
+$\mathrm{PGL}_3(\mathbb{Z})\backslash\mathrm{PGL}_3(\mathbb{R})$. We establish
+various pointwise upper bounds for $\phi$ in terms of its Laplace eigenvalue
+$\lambda_\phi$. These imply, for $\phi$ arithmetically normalized and tempered
+at the archimedean place, the bound $\|\phi\|_\infty\ll_\epsilon
+\lambda_{\phi}^{39/40+\epsilon}$ for the global sup-norm (without restriction
+to a compact subset). On the way, we derive a new uniform upper bound for the
+$\mathrm{GL}_3$ Jacquet-Whittaker function.
+",0,0,1,0,0,0
+18943,Four revolutions in physics and the second quantum revolution -- a unification of force and matter by quantum information,"  Newton's mechanical revolution unifies the motion of planets in the sky and
+falling of apple on earth. Maxwell's electromagnetic revolution unifies
+electricity, magnetism, and light. Einstein's relativity revolution unifies
+space with time, and gravity with space-time distortion. The quantum revolution
+unifies particle with waves, and energy with frequency. Each of those
+revolution changes our world view. In this article, we will describe a
+revolution that is happening now: the second quantum revolution which unifies
+matter/space with information. In other words, the new world view suggests that
+elementary particles (the bosonic force particles and fermionic matter
+particles) all originated from quantum information (qubits): they are
+collective excitations of an entangled qubit ocean that corresponds to our
+space. The beautiful geometric Yang-Mills gauge theory and the strange Fermi
+statistics of matter particles now have a common algebraic quantum
+informational origin.
+",0,1,0,0,0,0
+18944,Concept Drift Learning with Alternating Learners,"  Data-driven predictive analytics are in use today across a number of
+industrial applications, but further integration is hindered by the requirement
+of similarity among model training and test data distributions. This paper
+addresses the need of learning from possibly nonstationary data streams, or
+under concept drift, a commonly seen phenomenon in practical applications. A
+simple dual-learner ensemble strategy, alternating learners framework, is
+proposed. A long-memory model learns stable concepts from a long relevant time
+window, while a short-memory model learns transient concepts from a small
+recent window. The difference in prediction performance of these two models is
+monitored and induces an alternating policy to select, update and reset the two
+models. The method features an online updating mechanism to maintain the
+ensemble accuracy, and a concept-dependent trigger to focus on relevant data.
+Through empirical studies the method demonstrates effective tracking and
+prediction when the steaming data carry abrupt and/or gradual changes.
+",1,0,0,1,0,0
+18945,A new proof of the competitive exclusion principle in the chemostat,"  We give an new proof of the well-known competitive exclusion principle in the
+chemostat model with $n$ species competing for a single resource, for any set
+of increasing growth functions. The proof is constructed by induction on the
+number of the species, after being ordered. It uses elementary analysis and
+comparisons of solutions of ordinary differential equations.
+",0,0,1,0,0,0
+18946,From Random Differential Equations to Structural Causal Models: the stochastic case,"  Random Differential Equations provide a natural extension of Ordinary
+Differential Equations to the stochastic setting. We show how, and under which
+conditions, every equilibrium state of a Random Differential Equation (RDE) can
+be described by a Structural Causal Model (SCM), while pertaining the causal
+semantics. This provides an SCM that captures the stochastic and causal
+behavior of the RDE, which can model both cycles and confounders. This enables
+the study of the equilibrium states of the RDE by applying the theory and
+statistical tools available for SCMs, for example, marginalizations and Markov
+properties, as we illustrate by means of an example. Our work thus provides a
+direct connection between two fields that so far have been developing in
+isolation.
+",0,0,0,1,0,0
+18947,Ties That Bind - Characterizing Classes by Attributes and Social Ties,"  Given a set of attributed subgraphs known to be from different classes, how
+can we discover their differences? There are many cases where collections of
+subgraphs may be contrasted against each other. For example, they may be
+assigned ground truth labels (spam/not-spam), or it may be desired to directly
+compare the biological networks of different species or compound networks of
+different chemicals.
+In this work we introduce the problem of characterizing the differences
+between attributed subgraphs that belong to different classes. We define this
+characterization problem as one of partitioning the attributes into as many
+groups as the number of classes, while maximizing the total attributed quality
+score of all the given subgraphs.
+We show that our attribute-to-class assignment problem is NP-hard and an
+optimal $(1 - 1/e)$-approximation algorithm exists. We also propose two
+different faster heuristics that are linear-time in the number of attributes
+and subgraphs. Unlike previous work where only attributes were taken into
+account for characterization, here we exploit both attributes and social ties
+(i.e. graph structure).
+Through extensive experiments, we compare our proposed algorithms, show
+findings that agree with human intuition on datasets from Amazon co-purchases,
+Congressional bill sponsorships, and DBLP co-authorships. We also show that our
+approach of characterizing subgraphs is better suited for sense-making than
+discriminating classification approaches.
+",1,1,0,0,0,0
+18948,Probing dark matter with star clusters: a dark matter core in the ultra-faint dwarf Eridanus II,"  We present a new technique to probe the central dark matter (DM) density
+profile of galaxies that harnesses both the survival and observed properties of
+star clusters. As a first application, we apply our method to the `ultra-faint'
+dwarf Eridanus II (Eri II) that has a lone star cluster ~45 pc from its centre.
+Using a grid of collisional $N$-body simulations, incorporating the effects of
+stellar evolution, external tides and dynamical friction, we show that a DM
+core for Eri II naturally reproduces the size and the projected position of its
+star cluster. By contrast, a dense cusped galaxy requires the cluster to lie
+implausibly far from the centre of Eri II (>1 kpc), with a high inclination
+orbit that must be observed at a particular orbital phase. Our results,
+therefore, favour a dark matter core. This implies that either a cold DM cusp
+was `heated up' at the centre of Eri II by bursty star formation, or we are
+seeing an evidence for physics beyond cold DM.
+",0,1,0,0,0,0
+18949,Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s),"  We propose the ambiguity problem for the foreground object segmentation task
+and motivate the importance of estimating and accounting for this ambiguity
+when designing vision systems. Specifically, we distinguish between images
+which lead multiple annotators to segment different foreground objects
+(ambiguous) versus minor inter-annotator differences of the same object. Taking
+images from eight widely used datasets, we crowdsource labeling the images as
+""ambiguous"" or ""not ambiguous"" to segment in order to construct a new dataset
+we call STATIC. Using STATIC, we develop a system that automatically predicts
+which images are ambiguous. Experiments demonstrate the advantage of our
+prediction system over existing saliency-based methods on images from vision
+benchmarks and images taken by blind people who are trying to recognize objects
+in their environment. Finally, we introduce a crowdsourcing system to achieve
+cost savings for collecting the diversity of all valid ""ground truth""
+foreground object segmentations by collecting extra segmentations only when
+ambiguity is expected. Experiments show our system eliminates up to 47% of
+human effort compared to existing crowdsourcing methods with no loss in
+capturing the diversity of ground truths.
+",1,0,0,0,0,0
+18950,On Some Generalized Polyhedral Convex Constructions,"  Generalized polyhedral convex sets, generalized polyhedral convex functions
+on locally convex Hausdorff topological vector spaces, and the related
+constructions such as sum of sets, sum of functions, directional derivative,
+infimal convolution, normal cone, conjugate function, subdifferential, are
+studied thoroughly in this paper. Among other things, we show how a generalized
+polyhedral convex set can be characterized via the finiteness of the number of
+its faces. In addition, it is proved that the infimal convolution of a
+generalized polyhedral convex function and a polyhedral convex function is a
+polyhedral convex function. The obtained results can be applied to scalar
+optimization problems described by generalized polyhedral convex sets and
+generalized polyhedral convex functions.
+",0,0,1,0,0,0
+18951,mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion,"  Proteins are commonly used by biochemical industry for numerous processes.
+Refining these proteins' properties via mutations causes stability effects as
+well. Accurate computational method to predict how mutations affect protein
+stability are necessary to facilitate efficient protein design. However,
+accuracy of predictive models is ultimately constrained by the limited
+availability of experimental data. We have developed mGPfusion, a novel
+Gaussian process (GP) method for predicting protein's stability changes upon
+single and multiple mutations. This method complements the limited experimental
+data with large amounts of molecular simulation data. We introduce a Bayesian
+data fusion model that re-calibrates the experimental and in silico data
+sources and then learns a predictive GP model from the combined data. Our
+protein-specific model requires experimental data only regarding the protein of
+interest and performs well even with few experimental measurements. The
+mGPfusion models proteins by contact maps and infers the stability effects
+caused by mutations with a mixture of graph kernels. Our results show that
+mGPfusion outperforms state-of-the-art methods in predicting protein stability
+on a dataset of 15 different proteins and that incorporating molecular
+simulation data improves the model learning and prediction accuracy.
+",0,0,0,1,1,0
+18952,How Do Software Startups Pivot? Empirical Results from a Multiple Case Study,"  In order to handle intense time pressure and survive in dynamic market,
+software startups have to make crucial decisions constantly on whether to
+change directions or stay on chosen courses, or in the terms of Lean Startup,
+to pivot or to persevere. The existing research and knowledge on software
+startup pivots are very limited. In this study, we focused on understanding the
+pivoting processes of software startups, and identified the triggering factors
+and pivot types. To achieve this, we employed a multiple case study approach,
+and analyzed the data obtained from four software startups. The initial
+findings show that different software startups make different types of pivots
+related to business and technology during their product development life cycle.
+The pivots are triggered by various factors including negative customer
+feedback.
+",1,0,0,0,0,0
+18953,Some criteria for Wind Riemannian completeness and existence of Cauchy hypersurfaces,"  Recently, a link between Lorentzian and Finslerian Geometries has been
+carried out, leading to the notion of wind Riemannian structure (WRS), a
+generalization of Finslerian Randers metrics. Here, we further develop this
+notion and its applications to spacetimes, by introducing some
+characterizations and criteria for the completeness of WRS's.
+As an application, we consider a general class of spacetimes admitting a time
+function $t$ generated by the flow of a complete Killing vector field
+(generalized standard stationary spacetimes or, more precisely, SSTK ones) and
+derive simple criteria ensuring that its slices $t=$ constant are Cauchy.
+Moreover, a brief summary on the Finsler/Lorentz link for readers with some
+acquaintance in Lorentzian Geometry, plus some simple examples in Mathematical
+Relativity, are provided.
+",0,0,1,0,0,0
+18954,A generalization of Schönemann's theorem via a graph theoretic method,"  Recently, Grynkiewicz et al. [{\it Israel J. Math.} {\bf 193} (2013),
+359--398], using tools from additive combinatorics and group theory, proved
+necessary and sufficient conditions under which the linear congruence
+$a_1x_1+\cdots +a_kx_k\equiv b \pmod{n}$, where $a_1,\ldots,a_k,b,n$ ($n\geq
+1$) are arbitrary integers, has a solution $\langle x_1,\ldots,x_k \rangle \in
+\Z_{n}^k$ with all $x_i$ distinct modulo $n$. So, it would be an interesting
+problem to give an explicit formula for the number of such solutions. Quite
+surprisingly, this problem was first considered, in a special case, by
+Schönemann almost two centuries ago(!) but his result seems to have been
+forgotten. Schönemann [{\it J. Reine Angew. Math.} {\bf 1839} (1839),
+231--243] proved an explicit formula for the number of such solutions when
+$b=0$, $n=p$ a prime, and $\sum_{i=1}^k a_i \equiv 0 \pmod{p}$ but $\sum_{i \in
+I} a_i \not\equiv 0 \pmod{p}$ for all $I\varsubsetneq \lbrace 1, \ldots,
+k\rbrace$. In this paper, we generalize Schönemann's theorem using a result
+on the number of solutions of linear congruences due to D. N. Lehmer and also a
+result on graph enumeration recently obtained by Ardila et al. [{\it Int. Math.
+Res. Not.} {\bf 2015} (2015), 3830--3877]. This seems to be a rather uncommon
+method in the area; besides, our proof technique or its modifications may be
+useful for dealing with other cases of this problem (or even the general case)
+or other relevant problems.
+",1,0,0,0,0,0
+18955,Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning,"  In order for robots to perform mission-critical tasks, it is essential that
+they are able to quickly adapt to changes in their environment as well as to
+injuries and or other bodily changes. Deep reinforcement learning has been
+shown to be successful in training robot control policies for operation in
+complex environments. However, existing methods typically employ only a single
+policy. This can limit the adaptability since a large environmental
+modification might require a completely different behavior compared to the
+learning environment. To solve this problem, we propose Map-based Multi-Policy
+Reinforcement Learning (MMPRL), which aims to search and store multiple
+policies that encode different behavioral features while maximizing the
+expected reward in advance of the environment change. Thanks to these policies,
+which are stored into a multi-dimensional discrete map according to its
+behavioral feature, adaptation can be performed within reasonable time without
+retraining the robot. An appropriate pre-trained policy from the map can be
+recalled using Bayesian optimization. Our experiments show that MMPRL enables
+robots to quickly adapt to large changes without requiring any prior knowledge
+on the type of injuries that could occur. A highlight of the learned behaviors
+can be found here: this https URL .
+",1,0,0,0,0,0
+18956,Testing convexity of a discrete distribution,"  Based on the convex least-squares estimator, we propose two different
+procedures for testing convexity of a probability mass function supported on N
+with an unknown finite support. The procedures are shown to be asymptotically
+calibrated.
+",0,0,1,1,0,0
+18957,Small and Strong Formulations for Unions of Convex Sets from the Cayley Embedding,"  There is often a significant trade-off between formulation strength and size
+in mixed integer programming (MIP). When modeling convex disjunctive
+constraints (e.g. unions of convex sets), adding auxiliary continuous variables
+can sometimes help resolve this trade-off. However, standard formulations that
+use such auxiliary continuous variables can have a worse-than-expected
+computational effectiveness, which is often attributed precisely to these
+auxiliary continuous variables. For this reason, there has been considerable
+interest in constructing strong formulations that do not use continuous
+auxiliary variables. We introduce a technique to construct formulations without
+these detrimental continuous auxiliary variables. To develop this technique we
+introduce a natural non-polyhedral generalization of the Cayley embedding of a
+family of polytopes and show it inherits many geometric properties of the
+original embedding. We then show how the associated formulation technique can
+be used to construct small and strong formulation for a wide range of
+disjunctive constraints. In particular, we show it can recover and generalize
+all known strong formulations without continuous auxiliary variables.
+",0,0,1,0,0,0
+18958,Safe Robotic Grasping: Minimum Impact-Force Grasp Selection,"  This paper addresses the problem of selecting from a choice of possible
+grasps, so that impact forces will be minimised if a collision occurs while the
+robot is moving the grasped object along a post-grasp trajectory. Such
+considerations are important for safety in human-robot interaction, where even
+a certified ""human-safe"" (e.g. compliant) arm may become hazardous once it
+grasps and begins moving an object, which may have significant mass, sharp
+edges or other dangers. Additionally, minimising collision forces is critical
+to preserving the longevity of robots which operate in uncertain and hazardous
+environments, e.g. robots deployed for nuclear decommissioning, where removing
+a damaged robot from a contaminated zone for repairs may be extremely difficult
+and costly. Also, unwanted collisions between a robot and critical
+infrastructure (e.g. pipework) in such high-consequence environments can be
+disastrous. In this paper, we investigate how the safety of the post-grasp
+motion can be considered during the pre-grasp approach phase, so that the
+selected grasp is optimal in terms applying minimum impact forces if a
+collision occurs during a desired post-grasp manipulation. We build on the
+methods of augmented robot-object dynamics models and ""effective mass"" and
+propose a method for combining these concepts with modern grasp and trajectory
+planners, to enable the robot to achieve a grasp which maximises the safety of
+the post-grasp trajectory, by minimising potential collision forces. We
+demonstrate the effectiveness of our approach through several experiments with
+both simulated and real robots.
+",1,0,0,0,0,0
+18959,Nonparametric Kernel Density Estimation for Univariate Curent Status Data,"  We derive estimators of the density of the event times of current status
+data. The estimators are derived for the situations where the distribution of
+the observation times is known and where this distribution is unknown. The
+density estimators are constructed from kernel estimators of the density of
+transformed current status data, which have a distribution similar to uniform
+deconvolution data. Expansions of the expectation and variance as well as
+asymptotic normality are derived. A reference density based bandwidth selection
+method is proposed. A simulated example is presented.
+",0,0,1,1,0,0
+18960,Non-Kähler Mirror Symmetry of the Iwasawa Manifold,"  We propose a new approach to the Mirror Symmetry Conjecture in a form
+suitable to possibly non-Kähler compact complex manifolds whose canonical
+bundle is trivial. We apply our methods by proving that the Iwasawa manifold
+$X$, a well-known non-Kähler compact complex manifold of dimension $3$, is
+its own mirror dual to the extent that its Gauduchon cone, replacing the
+classical Kähler cone that is empty in this case, corresponds to what we call
+the local universal family of essential deformations of $X$. These are obtained
+by removing from the Kuranishi family the two ""superfluous"" dimensions of
+complex parallelisable deformations that have a similar geometry to that of the
+Iwasawa manifold. The remaining four dimensions are shown to have a clear
+geometric meaning including in terms of the degeneration at $E_2$ of the
+Frölicher spectral sequence. On the local moduli space of ""essential"" complex
+structures, we obtain a canonical Hodge decomposition of weight $3$ and a
+variation of Hodge structures, construct coordinates and Yukawa couplings while
+implicitly proving a local Torelli theorem. On the metric side of the mirror,
+we construct a variation of Hodge structures parametrised by a subset of the
+complexified Gauduchon cone of the Iwasawa manifold using the sGG property of
+all the small deformations of this manifold proved in earlier joint work of the
+author with L. Ugarte. Finally, we define a mirror map linking the two
+variations of Hodge structures and we highlight its properties.
+",0,0,1,0,0,0
+18961,Estimation and Inference for Moments of Ratios with Robustness against Large Trimming Bias,"  Empirical researchers often trim observations with small denominator A when
+they estimate moments of the form E[B/A]. Large trimming is a common practice
+to mitigate variance, but it incurs large trimming bias. This paper provides a
+novel method of correcting large trimming bias. If a researcher is willing to
+assume that the joint distribution between A and B is smooth, then a large
+trimming bias may be estimated well. With the bias correction, we also develop
+a valid and robust inference result for E[B/A].
+",0,0,0,1,0,0
+18962,FPGA-based real-time 105-channel data acquisition platform for imaging system,"  In this paper, a real-time 105-channel data acquisition platform based on
+FPGA for imaging will be implemented for mm-wave imaging systems. PC platform
+is also realized for imaging results monitoring purpose. Mm-wave imaging
+expands our vision by letting us see things under poor visibility conditions.
+With this extended vision ability, a wide range of military imaging missions
+would benefit, such as surveillance, precision targeting, navigation, and
+rescue. Based on the previously designed imager modules, this project would go
+on finishing the PCB design (both schematic and layout) of the following signal
+processing systems consisting of Programmable Gain Amplifier(PGA) (4 PGA for
+each ADC) and 16-channel Analog to Digital Converter (ADC) (7 ADC in total).
+Then the system verification would be performed on the Artix-7 35T Arty FPGA
+with the developing of proper controlling code to configure the ADC and realize
+the communication between the FPGA and the PC (through both UART and Ethernet).
+For the verification part, a simple test on a breadboard with a simple analog
+input (generated from a resistor divider) would first be performed. After the
+PCB design is finished, the whole system would be tested again with a precise
+reference and analog input.
+",1,0,0,0,0,0
+18963,Zn-induced in-gap electronic states in La214 probed by uniform magnetic susceptibility: relevance to the suppression of superconducting Tc,"  Substitution of isovalent non-magnetic defects, such as Zn, in CuO2 plane
+strongly modifies the magnetic properties of strongly electron correlated hole
+doped cuprate superconductors. The reason for enhanced uniform magnetic
+susceptibility, \c{hi}, in Zn substituted cuprates is debatable. So far, the
+observed magnetic behavior has been analyzed mainly in terms of two somewhat
+contrasting scenarios, (a) that due to independent localized moments appearing
+in the vicinity of Zn arising because of the strong electronic/magnetic
+correlations present in the host compound and (b) that due to transfer of
+quasiparticle spectral weight and creation of weakly localized low energy
+electronic states associated with each Zn atom in place of an in-plane Cu. If
+the second scenario is correct, one should expect a direct correspondence
+between Zn induced suppression of superconducting transition temperature, Tc,
+and the extent of the enhanced magnetic susceptibility at low temperature. In
+this case, the low-T enhancement of \c{hi} would be due to weakly localized
+quasiparticle states at low energy and these electronic states will be
+precluded from taking part in Cooper pairing. We explore this second
+possibility by analyzing the \c{hi}(T) data for La2-xSrxCu1-yZnyO4 with
+different hole contents, p (= x), and Zn concentrations (y) in this paper.
+Results of our analysis support this scenario.
+",0,1,0,0,0,0
+18964,Predicate Specialization for Definitional Higher-order Logic Programs,"  Higher-order logic programming is an interesting extension of traditional
+logic programming that allows predicates to appear as arguments and variables
+to be used where predicates typically occur. Higher-order characteristics are
+indeed desirable but on the other hand they are also usually more expensive to
+support. In this paper we propose a program specialization technique based on
+partial evaluation that can be applied to a modest but useful class of
+higher-order logic programs and can transform them into first-order programs
+without introducing additional data structures. The resulting first-order
+programs can be executed by conventional logic programming interpreters and
+benefit from other optimizations that might be available. We provide an
+implementation and experimental results that suggest the efficiency of the
+transformation.
+",1,0,0,0,0,0
+18965,Metastability and bifurcation in superconducting nanorings,"  We describe an approach, based on direct numerical solution of the Usadel
+equation, to finding stationary points of the free energy of superconducting
+nanorings. We consider both uniform (equilibrium) solutions and the critical
+droplets that mediate activated transitions between them. For the uniform
+solutions, we compute the critical current as a function of the temperature,
+thus obtaining a correction factor to Bardeen's 1962 interpolation formula. For
+the droplets, we present a metastability chart that shows the activation energy
+as a function of the temperature and current. A comparison of the activation
+energy for a ring to experimental results for a wire connected to
+superconducting leads reveals a discrepancy at large currents. We discuss
+possible reasons for it. We also discuss the nature of the bifurcation point at
+which the droplet merges with the uniform solution.
+",0,1,0,0,0,0
+18966,Origin of life in a digital microcosm,"  While all organisms on Earth descend from a common ancestor, there is no
+consensus on whether the origin of this ancestral self-replicator was a one-off
+event or whether it was only the final survivor of multiple origins. Here we
+use the digital evolution system Avida to study the origin of self-replicating
+computer programs. By using a computational system, we avoid many of the
+uncertainties inherent in any biochemical system of self-replicators (while
+running the risk of ignoring a fundamental aspect of biochemistry). We
+generated the exhaustive set of minimal-genome self-replicators and analyzed
+the network structure of this fitness landscape. We further examined the
+evolvability of these self-replicators and found that the evolvability of a
+self-replicator is dependent on its genomic architecture. We studied the
+differential ability of replicators to take over the population when competed
+against each other (akin to a primordial-soup model of biogenesis) and found
+that the probability of a self-replicator out-competing the others is not
+uniform. Instead, progenitor (most-recent common ancestor) genotypes are
+clustered in a small region of the replicator space. Our results demonstrate
+how computational systems can be used as test systems for hypotheses concerning
+the origin of life.
+",1,1,0,0,0,0
+18967,The equivalence of two tax processes,"  We introduce two models of taxation, the latent and natural tax processes,
+which have both been used to represent loss-carry-forward taxation on the
+capital of an insurance company. In the natural tax process, the tax rate is a
+function of the current level of capital, whereas in the latent tax process,
+the tax rate is a function of the capital that would have resulted if no tax
+had been paid. Whereas up to now these two types of tax processes have been
+treated separately, we show that, in fact, they are essentially equivalent.
+This allows a unified treatment, translating results from one model to the
+other. Significantly, we solve the question of existence and uniqueness for the
+natural tax process, which is defined via an integral equation. Our results
+clarify the existing literature on processes with tax.
+",0,0,0,0,0,1
+18968,Space-Time Geostatistical Models with both Linear and Seasonal Structures in the Temporal Components,"  We provide a novel approach to model space-time random fields where the
+temporal argument is decomposed into two parts. The former captures the linear
+argument, which is related, for instance, to the annual evolution of the field.
+The latter is instead a circular variable describing, for instance, monthly
+observations. The basic intuition behind this construction is to consider a
+random field defined over space (a compact set of the $d$-dimensional Euclidean
+space) across time, which is considered as the product space $\mathbb{R} \times
+\mathbb{S}^1$, with $\mathbb{S}^1$ being the unit circle. Under such framework,
+we derive new parametric families of covariance functions. In particular, we
+focus on two classes of parametric families. The former being parenthetical to
+the Gneiting class of covariance functions. The latter is instead obtained by
+proposing a new Lagrangian framework for the space-time domain considered in
+the manuscript. Our findings are illustrated through a real dataset of surface
+air temperatures. We show that the incorporation of both temporal variables can
+produce significant improvements in the predictive performances of the model.
+We also discuss the extension of this approach for fields defined spatially on
+a sphere, which allows to model space-time phenomena over large portions of
+planet Earth.
+",0,0,1,1,0,0
+18969,Stability and Robust Regulation of Passive Linear Systems,"  We study the stability of coupled impedance passive regular linear systems
+under power-preserving interconnections. We present new conditions for strong,
+exponential, and non-uniform stability of the closed-loop system. We apply the
+stability results to the construction of passive error feedback controllers for
+robust output tracking and disturbance rejection for strongly stabilizable
+passive systems. In the case of nonsmooth reference and disturbance signals we
+present conditions for non-uniform rational and logarithmic rates of
+convergence of the output. The results are illustrated with examples on
+designing controllers for linear wave and heat equations, and on studying the
+stability of a system of coupled partial differential equations.
+",0,0,1,0,0,0
+18970,Bayes Minimax Competitors of Preliminary Test Estimators in k Sample Problems,"  In this paper, we consider the estimation of a mean vector of a multivariate
+normal population where the mean vector is suspected to be nearly equal to mean
+vectors of $k-1$ other populations. As an alternative to the preliminary test
+estimator based on the test statistic for testing hypothesis of equal means, we
+derive empirical and hierarchical Bayes estimators which shrink the sample mean
+vector toward a pooled mean estimator given under the hypothesis. The
+minimaxity of those Bayesian estimators are shown, and their performances are
+investigated by simulation.
+",0,0,1,1,0,0
+18971,Non-orthogonal Multiple Access for High-reliable and Low-latency V2X Communications,"  In this paper, we consider a dense vehicular communication network where each
+vehicle broadcasts its safety information to its neighborhood in each
+transmission period. Such applications require low latency and high
+reliability, and thus, we propose a non-orthogonal multiple access scheme to
+reduce the latency and to improve the packet reception probability. In the
+proposed scheme, the BS performs the semi-persistent scheduling to optimize the
+time scheduling and allocate frequency resources in a non-orthogonal manner
+while the vehicles autonomously perform distributed power control. We formulate
+the centralized scheduling and resource allocation problem as equivalent to a
+multi-dimensional stable roommate matching problem, in which the users and
+time/frequency resources are considered as disjoint sets of players to be
+matched with each other. We then develop a novel rotation matching algorithm,
+which converges to a q-exchange stable matching after a limited number of
+iterations. Simulation results show that the proposed scheme outperforms the
+traditional orthogonal multiple access scheme in terms of the latency and
+reliability.
+",1,0,0,0,0,0
+18972,Cohomology monoids of monoids with coefficients in semimodules II,"  We relate the old and new cohomology monoids of an arbitrary monoid $M$ with
+coefficients in semimodules over $M$, introduced in the author's previous
+papers, to monoid and group extensions. More precisely, the old and new second
+cohomology monoids describe Schreier extensions of semimodules by monoids, and
+the new third cohomology monoid is related to a certain group extension
+problem.
+",0,0,1,0,0,0
+18973,Lower Bounds for Higher-Order Convex Optimization,"  State-of-the-art methods in convex and non-convex optimization employ
+higher-order derivative information, either implicitly or explicitly. We
+explore the limitations of higher-order optimization and prove that even for
+convex optimization, a polynomial dependence on the approximation guarantee and
+higher-order smoothness parameters is necessary. As a special case, we show
+Nesterov's accelerated cubic regularization method to be nearly tight.
+",1,0,0,1,0,0
+18974,Superconductivity in La1-xCexOBiSSe: carrier doping by mixed valence of Ce ions,"  We report the effects of Ce substitution on structural, electronic, and
+magnetic properties of layered bismuth-chalcogenide La1-xCexOBiSSe (x = 0-0.9),
+which are newly obtained in this study. Metallic conductivity was observed for
+x > 0.1 because of electron carriers induced by mixed valence of Ce ions, as
+revealed by bond valence sum calculation and magnetization measurements. Zero
+resistivity and clear diamagnetic susceptibility were obtained for x = 0.2-0.6,
+indicating the emergence of bulk superconductivity in these compounds.
+Dome-shaped superconductivity phase diagram with the highest transition
+temperature (Tc) of 3.1 K, which is slightly lower than that of F-doped
+LaOBiSSe (Tc = 3.7 K), was established. The present study clearly shows that
+the mixed valence of Ce ions can be utilized as an alternative approach for
+electron-doping in layered bismuth-chalcogenides to induce superconductivity.
+",0,1,0,0,0,0
+18975,Gaussian Processes Over Graphs,"  We propose Gaussian processes for signals over graphs (GPG) using the apriori
+knowledge that the target vectors lie over a graph. We incorporate this
+information using a graph- Laplacian based regularization which enforces the
+target vectors to have a specific profile in terms of graph Fourier transform
+coeffcients, for example lowpass or bandpass graph signals. We discuss how the
+regularization affects the mean and the variance in the prediction output. In
+particular, we prove that the predictive variance of the GPG is strictly
+smaller than the conventional Gaussian process (GP) for any non-trivial graph.
+We validate our concepts by application to various real-world graph signals.
+Our experiments show that the performance of the GPG is superior to GP for
+small training data sizes and under noisy training.
+",0,0,0,1,0,0
+18976,Differences of Type I error rates for ANOVA and Multilevel-Linear-Models using SAS and SPSS for repeated measures designs,"  To derive recommendations on how to analyze longitudinal data, we examined
+Type I error rates of Multilevel Linear Models (MLM) and repeated measures
+Analysis of Variance (rANOVA) using SAS and SPSS.We performed a simulation with
+the following specifications: To explore the effects of high numbers of
+measurement occasions and small sample sizes on Type I error, measurement
+occasions of m = 9 and 12 were investigated as well as sample sizes of n = 15,
+20, 25 and 30. Effects of non-sphericity in the population on Type I error were
+also inspected: 5,000 random samples were drawn from two populations containing
+neither a within-subject nor a between-group effect. They were analyzed
+including the most common options to correct rANOVA and MLM-results: The
+Huynh-Feldt-correction for rANOVA (rANOVA-HF) and the Kenward-Roger-correction
+for MLM (MLM-KR), which could help to correct progressive bias of MLM with an
+unstructured covariance matrix (MLM-UN). Moreover, uncorrected rANOVA and MLM
+assuming a compound symmetry covariance structure (MLM-CS) were also taken into
+account. The results showed a progressive bias for MLM-UN for small samples
+which was stronger in SPSS than in SAS. Moreover, an appropriate bias
+correction for Type I error via rANOVA-HF and an insufficient correction by
+MLM-UN-KR for n < 30 were found. These findings suggest MLM-CS or rANOVA if
+sphericity holds and a correction of a violation via rANOVA-HF. If an analysis
+requires MLM, SPSS yields more accurate Type I error rates for MLM-CS and SAS
+yields more accurate Type I error rates for MLM-UN.
+",0,0,0,1,0,0
+18977,Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes,"  We consider alternate formulations of recently proposed hierarchical Nearest
+Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) for improved
+convergence, faster computing time, and more robust and reproducible Bayesian
+inference. Algorithms are defined that improve CPU memory management and
+exploit existing high-performance numerical linear algebra libraries.
+Computational and inferential benefits are assessed for alternate NNGP
+specifications using simulated datasets and remotely sensed light detection and
+ranging (LiDAR) data collected over the US Forest Service Tanana Inventory Unit
+(TIU) in a remote portion of Interior Alaska. The resulting data product is the
+first statistically robust map of forest canopy for the TIU.
+",0,0,0,1,0,0
+18978,Variegation and space weathering on asteroid 21 Lutetia,"  During the flyby in 2010, the OSIRIS camera on-board Rosetta acquired
+hundreds of high-resolution images of asteroid Lutetia's surface through a
+range of narrow-band filters. While Lutetia appears very bland in the visible
+wavelength range, Magrin et al. (2012) tentatively identified UV color
+variations in the Baetica cluster, a group of relatively young craters close to
+the north pole. As Lutetia remains a poorly understood asteroid, such color
+variations may provide clues to the nature of its surface. We take the color
+analysis one step further. First we orthorectify the images using a shape model
+and improved camera pointing, then apply a variety of techniques (photometric
+correction, principal component analysis) to the resulting color cubes. We
+characterize variegation in the Baetica crater cluster at high spatial
+resolution, identifying crater rays and small, fresh impact craters. We argue
+that at least some of the color variation is due to space weathering, which
+makes Lutetia's regolith redder and brighter.
+",0,1,0,0,0,0
+18979,Rokhlin dimension for compact quantum group actions,"  We show that, for a given compact or discrete quantum group $G$, the class of
+actions of $G$ on C*-algebras is first-order axiomatizable in the logic for
+metric structures. As an application, we extend the notion of Rokhlin property
+for $G$-C*-algebra, introduced by Barlak, Szabó, and Voigt in the case when
+$G$ is second countable and coexact, to an arbitrary compact quantum group $G$.
+All the the preservations and rigidity results for Rokhlin actions of second
+countable coexact compact quantum groups obtained by Barlak, Szabó, and
+Voigt are shown to hold in this general context. As a further application, we
+extend the notion of equivariant order zero dimension for equivariant
+*-homomorphisms, introduced in the classical setting by the first and third
+authors, to actions of compact quantum groups. This allows us to define the
+Rokhlin dimension of an action of a compact quantum group on a C*-algebra,
+recovering the Rokhlin property as Rokhlin dimension zero. We conclude by
+establishing a preservation result for finite nuclear dimension and finite
+decomposition rank when passing to fixed point algebras and crossed products by
+compact quantum group actions with finite Rokhlin dimension.
+",0,0,1,0,0,0
+18980,Parametric Identification Using Weighted Null-Space Fitting,"  In identification of dynamical systems, the prediction error method using a
+quadratic cost function provides asymptotically efficient estimates under
+Gaussian noise and additional mild assumptions, but in general it requires
+solving a non-convex optimization problem. An alternative class of methods uses
+a non-parametric model as intermediate step to obtain the model of interest.
+Weighted null-space fitting (WNSF) belongs to this class. It is a weighted
+least-squares method consisting of three steps. In the first step, a high-order
+ARX model is estimated. In a second least-squares step, this high-order
+estimate is reduced to a parametric estimate. In the third step, weighted least
+squares is used to reduce the variance of the estimates. The method is flexible
+in parametrization and suitable for both open- and closed-loop data. In this
+paper, we show that WNSF provides estimates with the same asymptotic properties
+as PEM with a quadratic cost function when the model orders are chosen
+according to the true system. Also, simulation studies indicate that WNSF may
+be competitive with state-of-the-art methods.
+",1,0,0,0,0,0
+18981,Learning Large-Scale Bayesian Networks with the sparsebn Package,"  Learning graphical models from data is an important problem with wide
+applications, ranging from genomics to the social sciences. Nowadays datasets
+often have upwards of thousands---sometimes tens or hundreds of thousands---of
+variables and far fewer samples. To meet this challenge, we have developed a
+new R package called sparsebn for learning the structure of large, sparse
+graphical models with a focus on Bayesian networks. While there are many
+existing software packages for this task, this package focuses on the unique
+setting of learning large networks from high-dimensional data, possibly with
+interventions. As such, the methods provided place a premium on scalability and
+consistency in a high-dimensional setting. Furthermore, in the presence of
+interventions, the methods implemented here achieve the goal of learning a
+causal network from data. Additionally, the sparsebn package is fully
+compatible with existing software packages for network analysis.
+",1,0,0,1,0,0
+18982,An Asymptotically Optimal Index Policy for Finite-Horizon Restless Bandits,"  We consider restless multi-armed bandit (RMAB) with a finite horizon and
+multiple pulls per period. Leveraging the Lagrangian relaxation, we approximate
+the problem with a collection of single arm problems. We then propose an
+index-based policy that uses optimal solutions of the single arm problems to
+index individual arms, and offer a proof that it is asymptotically optimal as
+the number of arms tends to infinity. We also use simulation to show that this
+index-based policy performs better than the state-of-art heuristics in various
+problem settings.
+",0,0,1,0,0,0
+18983,Effective holographic theory of charge density waves,"  We use Gauge/Gravity duality to write down an effective low energy
+holographic theory of charge density waves. We consider a simple gravity model
+which breaks translations spontaneously in the dual field theory in a
+homogeneous manner, capturing the low energy dynamics of phonons coupled to
+conserved currents. We first focus on the leading two-derivative action, which
+leads to excited states with non-zero strain. We show that including subleading
+quartic derivative terms leads to dynamical instabilities of AdS$_2$
+translation invariant states and to stable phases breaking translations
+spontaneously. We compute analytically the real part of the electric
+conductivity. The model allows to construct Lifshitz-like hyperscaling
+violating quantum critical ground states breaking translations spontaneously.
+At these critical points, the real part of the dc conductivity can be metallic
+or insulating.
+",0,1,0,0,0,0
+18984,Interactive Certificates for Polynomial Matrices with Sub-Linear Communication,"  We develop and analyze new protocols to verify the correctness of various
+computations on matrices over F[x], where F is a field. The properties we
+verify concern an F[x]-module and therefore cannot simply rely on
+previously-developed linear algebra certificates which work only for vector
+spaces. Our protocols are interactive certificates, often randomized, and
+featuring a constant number of rounds of communication between the prover and
+verifier. We seek to minimize the communication cost so that the amount of data
+sent during the protocol is significantly smaller than the size of the result
+being verified, which can be useful when combining protocols or in some
+multi-party settings. The main tools we use are reductions to existing linear
+algebra certificates and a new protocol to verify that a given vector is in the
+F[x]-linear span of a given matrix.
+",1,0,0,0,0,0
+18985,Cost Functions for Robot Motion Style,"  We focus on autonomously generating robot motion for day to day physical
+tasks that is expressive of a certain style or emotion. Because we seek
+generalization across task instances and task types, we propose to capture
+style via cost functions that the robot can use to augment its nominal task
+cost and task constraints in a trajectory optimization process. We compare two
+approaches to representing such cost functions: a weighted linear combination
+of hand-designed features, and a neural network parameterization operating on
+raw trajectory input. For each cost type, we learn weights for each style from
+user feedback. We contrast these approaches to a nominal motion across
+different tasks and for different styles in a user study, and find that they
+both perform on par with each other, and significantly outperform the baseline.
+Each approach has its advantages: featurized costs require learning fewer
+parameters and can perform better on some styles, but neural network
+representations do not require expert knowledge to design features and could
+even learn more complex, nuanced costs than an expert can easily design.
+",1,0,0,0,0,0
+18986,Revealing the basins of convergence in the planar equilateral restricted four-body problem,"  The planar equilateral restricted four-body problem where two of the
+primaries have equal masses is used in order to determine the Newton-Raphson
+basins of convergence associated with the equilibrium points. The parametric
+variation of the position of the libration points is monitored when the value
+of the mass parameter $m_3$ varies in predefined intervals. The regions on the
+configuration $(x,y)$ plane occupied by the basins of attraction are revealed
+using the multivariate version of the Newton-Raphson iterative scheme. The
+correlations between the attracting domains of the equilibrium points and the
+corresponding number of iterations needed for obtaining the desired accuracy
+are also illustrated. We perform a thorough and systematic numerical
+investigation by demonstrating how the dynamical parameter $m_3$ influences the
+shape, the geometry and the degree of fractality of the converging regions. Our
+numerical outcomes strongly indicate that the mass parameter is indeed one of
+the most influential factors in this dynamical system.
+",0,1,0,0,0,0
+18987,The Eccentric Kozai-Lidov mechanism for Outer Test Particle,"  The secular approximation of the hierarchical three body systems has been
+proven to be very useful in addressing many astrophysical systems, from
+planets, stars to black holes. In such a system two objects are on a tight
+orbit, and the tertiary is on a much wider orbit. Here we study the dynamics of
+a system by taking the tertiary mass to zero and solve the hierarchical three
+body system up to the octupole level of approximation. We find a rich dynamics
+that the outer orbit undergoes due to gravitational perturbations from the
+inner binary. The nominal result of the precession of the nodes is mostly
+limited for the lowest order of approximation, however, when the octupole-level
+of approximation is introduced the system becomes chaotic, as expected, and the
+tertiary oscillates below and above 90deg, similarly to the non-test particle
+flip behavior (e.g., Naoz 2016). We provide the Hamiltonian of the system and
+investigate the dynamics of the system from the quadrupole to the octupole
+level of approximations. We also analyze the chaotic and quasi-periodic orbital
+evolution by studying the surfaces of sections. Furthermore, including general
+relativity, we show case the long term evolution of individual debris disk
+particles under the influence of a far away interior eccentric planet. We show
+that this dynamics can naturally result in retrograde objects and a puffy disk
+after a long timescale evolution (few Gyr) for initially aligned configuration.
+",0,1,0,0,0,0
+18988,A more symmetric picture for Kasparov's KK-bifunctor,"  For C*-algebras $A$ and $B$, we generalize the notion of a quasihomomorphism
+from $A$ to $B$, due to Cuntz, by considering quasihomomorphisms from some
+C*-algebra $C$ to $B$ such that $C$ surjects onto $A$, and the two maps forming
+a quasihomomorphism agree on the kernel of this surjection. Under an additional
+assumption, the group of homotopy classes of such generalized
+quasihomomorphisms coincides with $KK(A,B)$. This makes the definition of
+Kasparov's bifunctor slightly more symmetric and gives more flexibility for
+constructing elements of $KK$-groups. These generalized quasihomomorphisms can
+be viewed as pairs of maps directly from $A$ (instead of various $C$'s), but
+these maps need not be $*$-homomorphisms.
+",0,0,1,0,0,0
+18989,Spot dynamics in a reaction-diffusion model of plant root hair initiation,"  We study pattern formation in a 2-D reaction-diffusion (RD) sub-cellular
+model characterizing the effect of a spatial gradient of a plant hormone
+distribution on a family of G-proteins associated with root-hair (RH)
+initiation in the plant cell Arabidopsis thaliana. The activation of these
+G-proteins, known as the Rho of Plants (ROPs), by the plant hormone auxin, is
+known to promote certain protuberances on root hair cells, which are crucial
+for both anchorage and the uptake of nutrients from the soil. Our mathematical
+model for the activation of ROPs by the auxin gradient is an extension of the
+model of Payne and Grierson [PLoS ONE, 12(4), (2009)], and consists of a
+two-component Schnakenberg-type RD system with spatially heterogeneous
+coefficients on a 2-D domain. The nonlinear kinetics in this RD system model
+the nonlinear interactions between the active and inactive forms of ROPs. By
+using a singular perturbation analysis to study 2-D localized spatial patterns
+of active ROPs, it is shown that the spatial variations in the nonlinear
+reaction kinetics, due to the auxin gradient, lead to a slow spatial alignment
+of the localized regions of active ROPs along the longitudinal midline of the
+plant cell. Numerical bifurcation analysis, together with time-dependent
+numerical simulations of the RD system are used to illustrate both 2-D
+localized patterns in the model, and the spatial alignment of localized
+structures.
+",0,1,0,0,0,0
+18990,Incorporating Covariates into Integrated Factor Analysis of Multi-View Data,"  In modern biomedical research, it is ubiquitous to have multiple data sets
+measured on the same set of samples from different views (i.e., multi-view
+data). For example, in genetic studies, multiple genomic data sets at different
+molecular levels or from different cell types are measured for a common set of
+individuals to investigate genetic regulation. Integration and reduction of
+multi-view data have the potential to leverage information in different data
+sets, and to reduce the magnitude and complexity of data for further
+statistical analysis and interpretation. In this paper, we develop a novel
+statistical model, called supervised integrated factor analysis (SIFA), for
+integrative dimension reduction of multi-view data while incorporating
+auxiliary covariates. The model decomposes data into joint and individual
+factors, capturing the joint variation across multiple data sets and the
+individual variation specific to each set respectively. Moreover, both joint
+and individual factors are partially informed by auxiliary covariates via
+nonparametric models. We devise a computationally efficient
+Expectation-Maximization (EM) algorithm to fit the model under some
+identifiability conditions. We apply the method to the Genotype-Tissue
+Expression (GTEx) data, and provide new insights into the variation
+decomposition of gene expression in multiple tissues. Extensive simulation
+studies and an additional application to a pediatric growth study demonstrate
+the advantage of the proposed method over competing methods.
+",0,0,0,1,0,0
+18991,Batch-normalized joint training for DNN-based distant speech recognition,"  Improving distant speech recognition is a crucial step towards flexible
+human-machine interfaces. Current technology, however, still exhibits a lack of
+robustness, especially when adverse acoustic conditions are met. Despite the
+significant progress made in the last years on both speech enhancement and
+speech recognition, one potential limitation of state-of-the-art technology
+lies in composing modules that are not well matched because they are not
+trained jointly. To address this concern, a promising approach consists in
+concatenating a speech enhancement and a speech recognition deep neural network
+and to jointly update their parameters as if they were within a single bigger
+network. Unfortunately, joint training can be difficult because the output
+distribution of the speech enhancement system may change substantially during
+the optimization procedure. The speech recognition module would have to deal
+with an input distribution that is non-stationary and unnormalized. To mitigate
+this issue, we propose a joint training approach based on a fully
+batch-normalized architecture. Experiments, conducted using different datasets,
+tasks and acoustic conditions, revealed that the proposed framework
+significantly overtakes other competitive solutions, especially in challenging
+environments.
+",1,0,0,0,0,0
+18992,Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects,"  This paper describes a preliminary study for producing and distributing a
+large-scale database of embeddings from the Portuguese Twitter stream. We start
+by experimenting with a relatively small sample and focusing on three
+challenges: volume of training data, vocabulary size and intrinsic evaluation
+metrics. Using a single GPU, we were able to scale up vocabulary size from 2048
+words embedded and 500K training examples to 32768 words over 10M training
+examples while keeping a stable validation loss and approximately linear trend
+on training time per epoch. We also observed that using less than 50\% of the
+available training examples for each vocabulary size might result in
+overfitting. Results on intrinsic evaluation show promising performance for a
+vocabulary size of 32768 words. Nevertheless, intrinsic evaluation metrics
+suffer from over-sensitivity to their corresponding cosine similarity
+thresholds, indicating that a wider range of metrics need to be developed to
+track progress.
+",1,0,0,0,0,0
+18993,A temperate exo-Earth around a quiet M dwarf at 3.4 parsecs,"  The combination of high-contrast imaging and high-dispersion spectroscopy,
+which has successfully been used to detect the atmosphere of a giant planet, is
+one of the most promising potential probes of the atmosphere of Earth-size
+worlds. The forthcoming generation of extremely large telescopes (ELTs) may
+obtain sufficient contrast with this technique to detect O$_2$ in the
+atmosphere of those worlds that orbit low-mass M dwarfs. This is strong
+motivation to carry out a census of planets around cool stars for which
+habitable zones can be resolved by ELTs, i.e. for M dwarfs within $\sim$5
+parsecs. Our HARPS survey has been a major contributor to that sample of nearby
+planets. Here we report on our radial velocity observations of Ross 128
+(Proxima Virginis, GJ447, HIP 57548), an M4 dwarf just 3.4 parsec away from our
+Sun. This source hosts an exo-Earth with a projected mass $m \sin i = 1.35
+M_\oplus$ and an orbital period of 9.9 days. Ross 128 b receives $\sim$1.38
+times as much flux as Earth from the Sun and its equilibrium ranges in
+temperature between 269 K for an Earth-like albedo and 213 K for a Venus-like
+albedo. Recent studies place it close to the inner edge of the conventional
+habitable zone. An 80-day long light curve from K2 campaign C01 demonstrates
+that Ross~128~b does not transit. Together with the All Sky Automated Survey
+(ASAS) photometry and spectroscopic activity indices, the K2 photometry shows
+that Ross 128 rotates slowly and has weak magnetic activity. In a habitability
+context, this makes survival of its atmosphere against erosion more likely.
+Ross 128 b is the second closest known exo-Earth, after Proxima Centauri b (1.3
+parsec), and the closest temperate planet known around a quiet star. The 15 mas
+planet-star angular separation at maximum elongation will be resolved by ELTs
+($>$ 3$\lambda/D$) in the optical bands of O$_2$.
+",0,1,0,0,0,0
+18994,Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning,"  Low-rank structures play important role in recent advances of many problems
+in image science and data science. As a natural extension of low-rank
+structures for data with nonlinear structures, the concept of the
+low-dimensional manifold structure has been considered in many data processing
+problems. Inspired by this concept, we consider a manifold based low-rank
+regularization as a linear approximation of manifold dimension. This
+regularization is less restricted than the global low-rank regularization, and
+thus enjoy more flexibility to handle data with nonlinear structures. As
+applications, we demonstrate the proposed regularization to classical inverse
+problems in image sciences and data sciences including image inpainting, image
+super-resolution, X-ray computer tomography (CT) image reconstruction and
+semi-supervised learning. We conduct intensive numerical experiments in several
+image restoration problems and a semi-supervised learning problem of
+classifying handwritten digits using the MINST data. Our numerical tests
+demonstrate the effectiveness of the proposed methods and illustrate that the
+new regularization methods produce outstanding results by comparing with many
+existing methods.
+",1,0,1,0,0,0
+18995,History-aware Autonomous Exploration in Confined Environments using MAVs,"  Many scenarios require a robot to be able to explore its 3D environment
+online without human supervision. This is especially relevant for inspection
+tasks and search and rescue missions. To solve this high-dimensional path
+planning problem, sampling-based exploration algorithms have proven successful.
+However, these do not necessarily scale well to larger environments or spaces
+with narrow openings. This paper presents a 3D exploration planner based on the
+principles of Next-Best Views (NBVs). In this approach, a Micro-Aerial Vehicle
+(MAV) equipped with a limited field-of-view depth sensor randomly samples its
+configuration space to find promising future viewpoints. In order to obtain
+high sampling efficiency, our planner maintains and uses a history of visited
+places, and locally optimizes the robot's orientation with respect to
+unobserved space. We evaluate our method in several simulated scenarios, and
+compare it against a state-of-the-art exploration algorithm. The experiments
+show substantial improvements in exploration time ($2\times$ faster),
+computation time, and path length, and advantages in handling difficult
+situations such as escaping dead-ends (up to $20\times$ faster). Finally, we
+validate the on-line capability of our algorithm on a computational constrained
+real world MAV.
+",1,0,0,0,0,0
+18996,A factor-model approach for correlation scenarios and correlation stress-testing,"  In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives
+portfolio, the so-called `London Whale', partly as a consequence of
+de-correlations of non-perfectly correlated positions that were supposed to
+hedge each other. Motivated by this case, we devise a factor model for
+correlations that allows for scenario-based stress testing of correlations. We
+derive a number of analytical results related to a portfolio of homogeneous
+assets. Using the concept of Mahalanobis distance, we show how to identify
+adverse scenarios of correlation risk. In addition, we demonstrate how
+correlation and volatility stress tests can be combined. As an example, we
+apply the factor-model approach to the ""London Whale"" portfolio and determine
+the value-at-risk impact from correlation changes. Since our findings are
+particularly relevant for large portfolios, where even small correlation
+changes can have a large impact, a further application would be to stress test
+portfolios of central counterparties, which are of systemically relevant size.
+",0,0,0,0,0,1
+18997,Topology data analysis of critical transitions in financial networks,"  We develop a topology data analysis-based method to detect early signs for
+critical transitions in financial data. From the time-series of multiple stock
+prices, we build time-dependent correlation networks, which exhibit topological
+structures. We compute the persistent homology associated to these structures
+in order to track the changes in topology when approaching a critical
+transition. As a case study, we investigate a portfolio of stocks during a
+period prior to the US financial crisis of 2007-2008, and show the presence of
+early signs of the critical transition.
+",0,1,1,0,0,0
+18998,Construction of exact constants of motion and effective models for many-body localized systems,"  One of the defining features of many-body localization is the presence of
+extensively many quasi-local conserved quantities. These constants of motion
+constitute a corner-stone to an intuitive understanding of much of the
+phenomenology of many-body localized systems arising from effective
+Hamiltonians. They may be seen as local magnetization operators smeared out by
+a quasi-local unitary. However, accurately identifying such constants of motion
+remains a challenging problem. Current numerical constructions often capture
+the conserved operators only approximately restricting a conclusive
+understanding of many-body localization. In this work, we use methods from the
+theory of quantum many-body systems out of equilibrium to establish a new
+approach for finding a complete set of exact constants of motion which are in
+addition guaranteed to represent Pauli-$z$ operators. By this we are able to
+construct and investigate the proposed effective Hamiltonian using exact
+diagonalization. Hence, our work provides an important tool expected to further
+boost inquiries into the breakdown of transport due to quenched disorder.
+",0,1,0,0,0,0
+18999,Toric manifolds over cyclohedra,"  We study the action of the dihedral group on the (equivariant) cohomology of
+the toric manifolds associated with cycle graphs.
+",0,0,1,0,0,0
+19000,Use of Genome Information-Based Potentials to Characterize Human Adaptation,"  As a living information and communications system, the genome encodes
+patterns in single nucleotide polymorphisms (SNPs) reflecting human adaption
+that optimizes population survival in differing environments. This paper
+mathematically models environmentally induced adaptive forces that quantify
+changes in the distribution of SNP frequencies between populations. We make
+direct connections between biophysical methods (e.g. minimizing genomic free
+energy) and concepts in population genetics. Our unbiased computer program
+scanned a large set of SNPs in the major histocompatibility complex region, and
+flagged an altitude dependency on a SNP associated with response to oxygen
+deprivation. The statistical power of our double-blind approach is demonstrated
+in the flagging of mathematical functional correlations of SNP
+information-based potentials in multiple populations with specific
+environmental parameters. Furthermore, our approach provides insights for new
+discoveries on the biology of common variants. This paper demonstrates the
+power of biophysical modeling of population diversity for better understanding
+genome-environment interactions in biological phenomenon.
+",0,0,0,0,1,0
+19001,Communication-Efficient Algorithms for Decentralized and Stochastic Optimization,"  We present a new class of decentralized first-order methods for nonsmooth and
+stochastic optimization problems defined over multiagent networks. Considering
+that communication is a major bottleneck in decentralized optimization, our
+main goal in this paper is to develop algorithmic frameworks which can
+significantly reduce the number of inter-node communications. We first propose
+a decentralized primal-dual method which can find an $\epsilon$-solution both
+in terms of functional optimality gap and feasibility residual in
+$O(1/\epsilon)$ inter-node communication rounds when the objective functions
+are convex and the local primal subproblems are solved exactly. Our major
+contribution is to present a new class of decentralized primal-dual type
+algorithms, namely the decentralized communication sliding (DCS) methods, which
+can skip the inter-node communications while agents solve the primal
+subproblems iteratively through linearizations of their local objective
+functions. By employing DCS, agents can still find an $\epsilon$-solution in
+$O(1/\epsilon)$ (resp., $O(1/\sqrt{\epsilon})$) communication rounds for
+general convex functions (resp., strongly convex functions), while maintaining
+the $O(1/\epsilon^2)$ (resp., $O(1/\epsilon)$) bound on the total number of
+intra-node subgradient evaluations. We also present a stochastic counterpart
+for these algorithms, denoted by SDCS, for solving stochastic optimization
+problems whose objective function cannot be evaluated exactly. In comparison
+with existing results for decentralized nonsmooth and stochastic optimization,
+we can reduce the total number of inter-node communication rounds by orders of
+magnitude while still maintaining the optimal complexity bounds on intra-node
+stochastic subgradient evaluations. The bounds on the subgradient evaluations
+are actually comparable to those required for centralized nonsmooth and
+stochastic optimization.
+",1,0,1,0,0,0
+19002,A Machine Learning Approach to Shipping Box Design,"  Having the right assortment of shipping boxes in the fulfillment warehouse to
+pack and ship customer's online orders is an indispensable and integral part of
+nowadays eCommerce business, as it will not only help maintain a profitable
+business but also create great experiences for customers. However, it is an
+extremely challenging operations task to strategically select the best
+combination of tens of box sizes from thousands of feasible ones to be
+responsible for hundreds of thousands of orders daily placed on millions of
+inventory products. In this paper, we present a machine learning approach to
+tackle the task by formulating the box design problem prescriptively as a
+generalized version of weighted $k$-medoids clustering problem, where the
+parameters are estimated through a variety of descriptive analytics. We test
+this machine learning approach on fulfillment data collected from Walmart U.S.
+eCommerce, and our approach is shown to be capable of improving the box
+utilization rate by more than $10\%$.
+",0,0,0,1,0,0
+19003,Precise but Natural Specification for Robot Tasks,"  We present Flipper, a natural language interface for describing high-level
+task specifications for robots that are compiled into robot actions. Flipper
+starts with a formal core language for task planning that allows expressing
+rich temporal specifications and uses a semantic parser to provide a natural
+language interface. Flipper provides immediate visual feedback by executing an
+automatically constructed plan of the task in a graphical user interface. This
+allows the user to resolve potentially ambiguous interpretations. Flipper
+extends itself via naturalization: its users can add definitions for
+utterances, from which Flipper induces new rules and adds them to the core
+language, gradually growing a more and more natural task specification
+language. Flipper improves the naturalization by generalizing the definition
+provided by users. Unlike other task-specification systems, Flipper enables
+natural language interactions while maintaining the expressive power and formal
+precision of a programming language. We show through an initial user study that
+natural language interactions and generalization can considerably ease the
+description of tasks. Moreover, over time, users employ more and more concepts
+outside of the initial core language. Such extensions are available to the
+Flipper community, and users can use concepts that others have defined.
+",1,0,0,0,0,0
+19004,Rigorous Analysis for Efficient Statistically Accurate Algorithms for Solving Fokker-Planck Equations in Large Dimensions,"  This article presents a rigorous analysis for efficient statistically
+accurate algorithms for solving the Fokker-Planck equations associated with
+high-dimensional nonlinear turbulent dynamical systems with conditional
+Gaussian structures. Despite the conditional Gaussianity, these nonlinear
+systems contain many strong non-Gaussian features such as intermittency and
+fat-tailed probability density functions (PDFs). The algorithms involve a
+hybrid strategy that requires only a small number of samples $L$ to capture
+both the transient and the equilibrium non-Gaussian PDFs with high accuracy.
+Here, a conditional Gaussian mixture in a high-dimensional subspace via an
+extremely efficient parametric method is combined with a judicious Gaussian
+kernel density estimation in the remaining low-dimensional subspace. Rigorous
+analysis shows that the mean integrated squared error in the recovered PDFs in
+the high-dimensional subspace is bounded by the inverse square root of the
+determinant of the conditional covariance, where the conditional covariance is
+completely determined by the underlying dynamics and is independent of $L$.
+This is fundamentally different from a direct application of kernel methods to
+solve the full PDF, where $L$ needs to increase exponentially with the
+dimension of the system and the bandwidth shrinks. A detailed comparison
+between different methods justifies that the efficient statistically accurate
+algorithms are able to overcome the curse of dimensionality. It is also shown
+with mathematical rigour that these algorithms are robust in long time provided
+that the system is controllable and stochastically stable. Particularly,
+dynamical systems with energy-conserving quadratic nonlinearity as in many
+geophysical and engineering turbulence are proved to have these properties.
+",0,0,1,1,0,0
+19005,Rapid processing of 85Kr/Kr ratios using Atom Trap Trace Analysis,"  We report a methodology for measuring 85Kr/Kr isotopic abundances using Atom
+Trap Trace Analysis (ATTA) that increases sample measurement throughput by over
+an order of magnitude to 6 samples per 24 hours. The noble gas isotope 85Kr
+(half-life = 10.7 yr) is a useful tracer for young groundwater in the age range
+of 5-50 years. ATTA, an efficient and selective laser-based atom counting
+method, has recently been applied to 85Kr/Kr isotopic abundance measurements,
+requiring 5-10 microliters of krypton gas at STP extracted from 50-100 L of
+water. Previously a single such measurement required 48 hours. Our new method
+demonstrates that we can measure 85Kr/Kr ratios with 3-5% relative uncertainty
+every 4 hours, on average, with the same sample requirements.
+",0,1,0,0,0,0
+19006,Adapting Everyday Manipulation Skills to Varied Scenarios,"  We address the problem of executing tool-using manipulation skills in
+scenarios where the objects to be used may vary. We assume that point clouds of
+the tool and target object can be obtained, but no interpretation or further
+knowledge about these objects is provided. The system must interpret the point
+clouds and decide how to use the tool to complete a manipulation task with a
+target object; this means it must adjust motion trajectories appropriately to
+complete the task. We tackle three everyday manipulations: scraping material
+from a tool into a container, cutting, and scooping from a container. Our
+solution encodes these manipulation skills in a generic way, with parameters
+that can be filled in at run-time via queries to a robot perception module; the
+perception module abstracts the functional parts for the tool and extracts key
+parameters that are needed for the task. The approach is evaluated in
+simulation and with selected examples on a PR2 robot.
+",1,0,0,0,0,0
+19007,Effect of Particle Number Conservation on the Berry Phase Resulting from Transport of a Bound Quasiparticle around a Superfluid Vortex,"  Motivated by understanding Majorana zero modes in topological superfluids in
+particle-number conserving framework beyond the present framework, we study the
+effect of particle number conservation on the Berry phase resulting from
+transport of a bound quasiparticle around a superfluid vortex. We find that
+particle-number non-conserving calculations based on Bogoliubov-de Gennes (BdG)
+equations are unable to capture the correct physics when the quasiparticle is
+within the penetration depth of the vortex core where the superfluid velocity
+is non-zero. Particle number conservation is crucial for deriving the correct
+Berry phase in this context, and the Berry phase takes non-universal values
+depending on the system parameters and the external trap imposed to bind the
+quasiparticle. Of particular relevance to Majorana physics are the findings
+that superfluid condensate affects the part of the Berry phase not accounted
+for in the standard BdG framework, and that the superfluid many-body ground
+state of odd number of fermions involves superfluid condensate deformation due
+to the presence of the bound quasiparticle - an effect which is beyond the
+description of the BdG equations.
+",0,1,0,0,0,0
+19008,A new topological insulator - β-InTe strained in the layer plane,"  We have investigated the band structure of the bulk crystal and the (001)
+surface of the \beta-InTe layered crystal subjected to biaxial stretching in
+the layer plane. The calculation has been carried out using the full-potential
+linearized augmented plane wave method (FP LAPW) implemented in WIEN2k. It has
+been shown that at the strain \Deltaa/a=0.06, where a is the lattice parameter
+in the layer plane, the band gap in the electronic spectrum collapses. With
+further strain increase a band inversion occurs. The inclusion of the
+spin-orbit interaction reopens the gap in the electronic spectrum of a bulk
+crystal, and our calculations show that the spectrum of the surface states has
+the form of a Dirac cone, typical for topological insulators.
+",0,1,0,0,0,0
+19009,Stock management (Gestão de estoques),"  There is a great need to stock materials for production, but storing
+materials comes at a cost. Lack of organization in the inventory can result in
+a very high cost for the final product, in addition to generating other
+problems in the production chain. In this work we present mathematical and
+statistical methods applicable to stock management. The stock analysis using
+ABC curves serves to identify which are the priority items, the most expensive
+and with the highest turnover (demand), and thus determine, through stock
+control models, the purchase lot size and the periodicity that minimize the
+total costs of storing these materials. Using the Economic Order Quantity (EOQ)
+model and the (Q,R) model, the inventory costs of a company were minimized. The
+comparison of the results provided by the models was performed.
+",0,0,0,1,0,0
+19010,From Principal Subspaces to Principal Components with Linear Autoencoders,"  The autoencoder is an effective unsupervised learning model which is widely
+used in deep learning. It is well known that an autoencoder with a single
+fully-connected hidden layer, a linear activation function and a squared error
+cost function trains weights that span the same subspace as the one spanned by
+the principal component loading vectors, but that they are not identical to the
+loading vectors. In this paper, we show how to recover the loading vectors from
+the autoencoder weights.
+",0,0,0,1,0,0
+19011,A critical nonlinear elliptic equation with non local regional diffusion,"  In this article we are interested in the nonlocal regional Schrödinger
+equation with critical exponent \begin{eqnarray*} &\epsilon^{2\alpha}
+(-\Delta)_{\rho}^{\alpha}u + u = \lambda u^q + u^{2_{\alpha}^{*}-1} \mbox{ in }
+\mathbb{R}^{N}, \\ & u \in H^{\alpha}(\mathbb{R}^{N}), \end{eqnarray*} where
+$\epsilon$ is a small positive parameter, $\alpha \in (0,1)$, $q\in
+(1,2_{\alpha}^{*}-1)$, $2_{\alpha}^{*} = \frac{2N}{N-2\alpha}$ is the critical
+Sobolev exponent, $\lambda >0$ is a parameter and $(-\Delta)_{\rho}^{\alpha}$
+is a variational version of the regional laplacian, whose range of scope is a
+ball with radius $\rho(x)>0$. We study the existence of a ground state and we
+analyze the behavior of semi-classical solutions as $\varepsilon\to 0$.
+",0,0,1,0,0,0
+19012,"Don't Decay the Learning Rate, Increase the Batch Size","  It is common practice to decay the learning rate. Here we show one can
+usually obtain the same learning curve on both training and test sets by
+instead increasing the batch size during training. This procedure is successful
+for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum,
+and Adam. It reaches equivalent test accuracies after the same number of
+training epochs, but with fewer parameter updates, leading to greater
+parallelism and shorter training times. We can further reduce the number of
+parameter updates by increasing the learning rate $\epsilon$ and scaling the
+batch size $B \propto \epsilon$. Finally, one can increase the momentum
+coefficient $m$ and scale $B \propto 1/(1-m)$, although this tends to slightly
+reduce the test accuracy. Crucially, our techniques allow us to repurpose
+existing training schedules for large batch training with no hyper-parameter
+tuning. We train ResNet-50 on ImageNet to $76.1\%$ validation accuracy in under
+30 minutes.
+",1,0,0,1,0,0
+19013,Collisions of Dark Matter Axion Stars with Astrophysical Sources,"  If QCD axions form a large fraction of the total mass of dark matter, then
+axion stars could be very abundant in galaxies. As a result, collisions with
+each other, and with other astrophysical bodies, can occur. We calculate the
+rate and analyze the consequences of three classes of collisions, those
+occurring between a dilute axion star and: another dilute axion star, an
+ordinary star, or a neutron star. In all cases we attempt to quantify the most
+important astrophysical uncertainties; we also pay particular attention to
+scenarios in which collisions lead to collapse of otherwise stable axion stars,
+and possible subsequent decay through number changing interactions. Collisions
+between two axion stars can occur with a high total rate, but the low relative
+velocity required for collapse to occur leads to a very low total rate of
+collapses. On the other hand, collisions between an axion star and an ordinary
+star have a large rate, $\Gamma_\odot \sim 3000$ collisions/year/galaxy, and
+for sufficiently heavy axion stars, it is plausible that most or all such
+collisions lead to collapse. We identify in this case a parameter space which
+has a stable region and a region in which collision triggers collapse, which
+depend on the axion number ($N$) in the axion star, and a ratio of mass to
+radius cubed characterizing the ordinary star ($M_s/R_s^3$). Finally, we
+revisit the calculation of collision rates between axion stars and neutron
+stars, improving on previous estimates by taking cylindrical symmetry of the
+neutron star distribution into account. Collapse and subsequent decay through
+collision processes, if occurring with a significant rate, can affect dark
+matter phenomenology and the axion star mass distribution.
+",0,1,0,0,0,0
+19014,Practical Algorithms for Best-K Identification in Multi-Armed Bandits,"  In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$
+stochastic bandit arms with unknown reward distributions. Our goal is to
+identify the $K$ arms with the largest means with high confidence, by drawing
+samples from the arms adaptively. This problem is motivated by various
+practical applications and has attracted considerable attention in the past
+decade. In this paper, we propose new practical algorithms for the Best-$K$-Arm
+problem, which have nearly optimal sample complexity bounds (matching the lower
+bound up to logarithmic factors) and outperform the state-of-the-art algorithms
+for the Best-$K$-Arm problem (even for $K=1$) in practice.
+",1,0,0,1,0,0
+19015,Hyperfield Grassmannians,"  In a recent paper Baker and Bowler introduced matroids over hyperfields,
+offering a common generalization of matroids, oriented matroids, and linear
+subspaces of based vector spaces. This paper introduces the notion of a
+topological hyperfield and explores the generalization of Grassmannians and
+realization spaces to this context, particularly in relating the (hyper)fields
+R and C to hyperfields arising in matroid theory and in tropical geometry.
+",0,0,1,0,0,0
+19016,Statistical estimation in a randomly structured branching population,"  We consider a binary branching process structured by a stochastic trait that
+evolves according to a diffusion process that triggers the branching events, in
+the spirit of Kimmel's model of cell division with parasite infection. Based on
+the observation of the trait at birth of the first n generations of the
+process, we construct nonparametric estimator of the transition of the
+associated bifurcating chain and study the parametric estimation of the
+branching rate. In the limit, as n tends to infinity, we obtain asymptotic
+efficiency in the parametric case and minimax optimality in the nonparametric
+case.
+",0,0,1,0,0,0
+19017,Discovering Eastern European PCs by hacking them. Today,"  Computer science would not be the same without personal computers. In the
+West the so called PC revolution started in the late '70s and has its roots in
+hobbyists and do-it-yourself clubs. In the following years the diffusion of
+home and personal computers has made the discipline closer to many people. A
+bit later, to a lesser extent, yet in a similar way, the revolution took place
+also in East European countries. Today, the scenario of personal computing has
+completely changed, however the computers of the '80s are still objects of
+fascination for a number of retrocomputing fans who enjoy using, programming
+and hacking the old 8-bits. The paper highlights the continuity between
+yesterday's hobbyists and today's retrocomputing enthusiasts, particularly
+focusing on East European PCs. Besides the preservation of old hardware and
+software, the community is engaged in the development of emulators and cross
+compilers. Such tools can be used for historical investigation, for example to
+trace the origins of the BASIC interpreters loaded in the ROMs of East European
+PCs.
+",1,0,0,0,0,0
+19018,Neural Rating Regression with Abstractive Tips Generation for Recommendation,"  Recently, some E-commerce sites launch a new interaction box called Tips on
+their mobile apps. Users can express their experience and feelings or provide
+suggestions using short texts typically several words or one sentence. In
+essence, writing some tips and giving a numerical rating are two facets of a
+user's product assessment action, expressing the user experience and feelings.
+Jointly modeling these two facets is helpful for designing a better
+recommendation system. While some existing models integrate text information
+such as item specifications or user reviews into user and item latent factors
+for improving the rating prediction, no existing works consider tips for
+improving recommendation quality. We propose a deep learning based framework
+named NRT which can simultaneously predict precise ratings and generate
+abstractive tips with good linguistic quality simulating user experience and
+feelings. For abstractive tips generation, gated recurrent neural networks are
+employed to ""translate"" user and item latent representations into a concise
+sentence. Extensive experiments on benchmark datasets from different domains
+show that NRT achieves significant improvements over the state-of-the-art
+methods. Moreover, the generated tips can vividly predict the user experience
+and feelings.
+",1,0,0,0,0,0
+19019,Calibration Uncertainty for Advanced LIGO's First and Second Observing Runs,"  Calibration of the Advanced LIGO detectors is the quantification of the
+detectors' response to gravitational waves. Gravitational waves incident on the
+detectors cause phase shifts in the interferometer laser light which are read
+out as intensity fluctuations at the detector output. Understanding this
+detector response to gravitational waves is crucial to producing accurate and
+precise gravitational wave strain data. Estimates of binary black hole and
+neutron star parameters and tests of general relativity require well-calibrated
+data, as miscalibrations will lead to biased results. We describe the method of
+producing calibration uncertainty estimates for both LIGO detectors in the
+first and second observing runs.
+",0,1,0,0,0,0
+19020,Entanglement transitions induced by large deviations,"  The probability of large deviations of the smallest Schmidt eigenvalue for
+random pure states of bipartite systems, denoted as $A$ and $B$, is computed
+analytically using a Coulomb gas method. It is shown that this probability, for
+large $N$, goes as $\exp[-\beta N^2\Phi(\zeta)]$, where the parameter $\beta$
+is the Dyson index of the ensemble, $\zeta$ is the large deviation parameter
+while the rate function $\Phi(\zeta)$ is calculated exactly. Corresponding
+equilibrium Coulomb charge density is derived for its large deviations. Effects
+of the large deviations of the extreme (largest and smallest) Schmidt
+eigenvalues on the bipartite entanglement are studied using the von Neumann
+entropy. Effect of these deviations is also studied on the entanglement between
+subsystems $1$ and $2$, obtained by further partitioning the subsystem $A$,
+using the properties of the density matrix's partial transpose
+$\rho_{12}^\Gamma$. The density of states of $\rho_{12}^\Gamma$ is found to be
+close to the Wigner's semicircle law with these large deviations. The
+entanglement properties are captured very well by a simple random matrix model
+for the partial transpose. The model predicts the entanglement transition
+across a critical large deviation parameter $\zeta$. Log negativity is used to
+quantify the entanglement between subsystems $1$ and $2$. Analytical formulas
+for it are derived using the simple model. Numerical simulations are in
+excellent agreement with the analytical results.
+",0,0,1,1,0,0
+19021,"Understanding System Characteristics of Online Erasure Coding on Scalable, Distributed and Large-Scale SSD Array Systems","  Large-scale systems with arrays of solid state disks (SSDs) have become
+increasingly common in many computing segments. To make such systems resilient,
+we can adopt erasure coding such as Reed-Solomon (RS) code as an alternative to
+replication because erasure coding can offer a significantly lower storage cost
+than replication. To understand the impact of using erasure coding on system
+performance and other system aspects such as CPU utilization and network
+traffic, we build a storage cluster consisting of approximately one hundred
+processor cores with more than fifty high-performance SSDs, and evaluate the
+cluster with a popular open-source distributed parallel file system, Ceph. Then
+we analyze behaviors of systems adopting erasure coding from the following five
+viewpoints, compared with those of systems using replication: (1) storage
+system I/O performance; (2) computing and software overheads; (3) I/O
+amplification; (4) network traffic among storage nodes; (5) the impact of
+physical data layout on performance of RS-coded SSD arrays. For all these
+analyses, we examine two representative RS configurations, which are used by
+Google and Facebook file systems, and compare them with triple replication that
+a typical parallel file system employs as a default fault tolerance mechanism.
+Lastly, we collect 54 block-level traces from the cluster and make them
+available for other researchers.
+",1,0,0,0,0,0
+19022,Three Questions on Special Homeomorphisms on Subgroups of $R$ and $R^\infty$,"  We provide justifications for two questions on special maps on subgroups of
+the reals. We will show that the questions can be treated from different points
+of view. We also discuss two versions of Anderson's Involution Conjecture.
+",0,0,1,0,0,0
+19023,Nearly Semiparametric Efficient Estimation of Quantile Regression,"  As a competitive alternative to least squares regression, quantile regression
+is popular in analyzing heterogenous data. For quantile regression model
+specified for one single quantile level $\tau$, major difficulties of
+semiparametric efficient estimation are the unavailability of a parametric
+efficient score and the conditional density estimation. In this paper, with the
+help of the least favorable submodel technique, we first derive the
+semiparametric efficient scores for linear quantile regression models that are
+assumed for a single quantile level, multiple quantile levels and all the
+quantile levels in $(0,1)$ respectively. Our main discovery is a one-step
+(nearly) semiparametric efficient estimation for the regression coefficients of
+the quantile regression models assumed for multiple quantile levels, which has
+several advantages: it could be regarded as an optimal way to pool information
+across multiple/other quantiles for efficiency gain; it is computationally
+feasible and easy to implement, as the initial estimator is easily available;
+due to the nature of quantile regression models under investigation, the
+conditional density estimation is straightforward by plugging in an initial
+estimator. The resulting estimator is proved to achieve the corresponding
+semiparametric efficiency lower bound under regularity conditions. Numerical
+studies including simulations and an example of birth weight of children
+confirms that the proposed estimator leads to higher efficiency compared with
+the Koenker-Bassett quantile regression estimator for all quantiles of
+interest.
+",0,0,0,1,0,0
+19024,Relevance of backtracking paths in epidemic spreading on networks,"  The understanding of epidemics on networks has greatly benefited from the
+recent application of message-passing approaches, which allow to derive exact
+results for irreversible spreading (i.e. diseases with permanent acquired
+immunity) in locally-tree like topologies. This success has suggested the
+application of the same approach to reversible epidemics, for which an
+individual can contract the epidemic and recover repeatedly. The underlying
+assumption is that backtracking paths (i.e. an individual is reinfected by a
+neighbor he/she previously infected) do not play a relevant role. In this paper
+we show that this is not the case for reversible epidemics, since the neglect
+of backtracking paths leads to a formula for the epidemic threshold that is
+qualitatively incorrect in the large size limit. Moreover we define a modified
+reversible dynamics which explicitly forbids direct backtracking events and
+show that this modification completely upsets the phenomenology.
+",1,0,0,0,0,0
+19025,Hybrid graphene tunneling photoconductor with interface engineering towards fast photoresponse and high responsivity,"  Hybrid graphene photoconductor/phototransistor has achieved giant
+photoresponsivity, but its response speed dramatically degrades as the expense
+due to the long lifetime of trapped interfacial carriers. In this work, by
+intercalating a large-area atomically thin MoS2 film into a hybrid graphene
+photoconductor, we have developed a prototype tunneling photoconductor, which
+exhibits a record-fast response (rising time ~17 ns) and a high responsivity
+(~$3\times10^4$ A/W at 635 nm and 16.8 nW illumination) across the broad
+spectral range. We demonstrate that the photo-excited carriers generated in
+silicon are transferred into graphene through a tunneling process rather than
+carrier drift. The atomically thin MoS2 film not only serves as tunneling layer
+but also passivates surface states, which in combination delivers a superior
+response speed (~3 order of magnitude improved than a device without MoS2
+layer), while the responsivity remains high. This intriguing tunneling
+photoconductor integrates both fast response and high responsivity and thus has
+significant potential in practical applications of optoelectronic devices.
+",0,1,0,0,0,0
+19026,Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care,"  This paper proposes a real-time embedded fall detection system using a
+DVS(Dynamic Vision Sensor) that has never been used for traditional fall
+detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal
+Network). The first contribution is building a DVS Falls Dataset, which made
+our network to recognize a much greater variety of falls than the existing
+datasets that existed before and solved privacy issues using the DVS. Secondly,
+we introduce the DVS-TN : optimized deep learning network to detect falls using
+DVS. Finally, we implemented a fall detection system which can run on
+low-computing H/W with real-time, and tested on DVS Falls Dataset that takes
+into account various falls situations. Our approach achieved 95.5% on the
+F1-score and operates at 31.25 FPS on NVIDIA Jetson TX1 board.
+",1,0,0,1,0,0
+19027,A Distributed Algorithm for Solving Linear Algebraic Equations Over Random Networks,"  In this paper, we consider the problem of solving linear algebraic equations
+of the form $Ax=b$ among multi agents which seek a solution by using local
+information in presence of random communication topologies. The equation is
+solved by $m$ agents where each agent only knows a subset of rows of the
+partitioned matrix $[A,b]$. We formulate the problem such that this formulation
+does not need the distribution of random interconnection graphs. Therefore,
+this framework includes asynchronous updates or unreliable communication
+protocols without B-connectivity assumption. We apply the random
+Krasnoselskii-Mann iterative algorithm which converges almost surely and in
+mean square to a solution of the problem for any matrices $A$ and $b$ and any
+initial conditions of agents' states. We demonestrate that the limit point to
+which the agents' states converge is determined by the unique solution of a
+convex optimization problem regardless of the distribution of random
+communication graphs. Eventually, we show by two numerical examples that the
+rate of convergence of the algorithm cannot be guaranteed.
+",1,0,0,0,0,0
+19028,Doping-induced quantum cross-over in Er$_2$Ti$_{2-x}$Sn$_x$O$_7$,"  We present the results of the investigation of magnetic properties of the
+Er$_2$Ti$_{2-x}$Sn$_x$O$_7$ series. For small doping values the ordering
+temperature decreases linearly with $x$ while the moment configuration remains
+the same as in the $x = 0$ parent compound. Around $x = 1.7$ doping level we
+observe a change in the behavior, where the ordering temperature starts to
+increase and new magnetic Bragg peaks appear. For the first time we present
+evidence of a long-range order (LRO) in Er$_2$Sn$_2$O$_7$ ($x = 2.0$) below
+$T_N = 130$ mK. It is revealed that the moment configuration corresponds to a
+Palmer-Chalker type with a value of the magnetic moment significantly
+renormalized compared to $x = 0$. We discuss our results in the framework of a
+possible quantum phase transition occurring close to $x = 1.7$.
+",0,1,0,0,0,0
+19029,Stochastic Block Models with Multiple Continuous Attributes,"  The stochastic block model (SBM) is a probabilistic model for community
+structure in networks. Typically, only the adjacency matrix is used to perform
+SBM parameter inference. In this paper, we consider circumstances in which
+nodes have an associated vector of continuous attributes that are also used to
+learn the node-to-community assignments and corresponding SBM parameters. While
+this assumption is not realistic for every application, our model assumes that
+the attributes associated with the nodes in a network's community can be
+described by a common multivariate Gaussian model. In this augmented,
+attributed SBM, the objective is to simultaneously learn the SBM connectivity
+probabilities with the multivariate Gaussian parameters describing each
+community. While there are recent examples in the literature that combine
+connectivity and attribute information to inform community detection, our model
+is the first augmented stochastic block model to handle multiple continuous
+attributes. This provides the flexibility in biological data to, for example,
+augment connectivity information with continuous measurements from multiple
+experimental modalities. Because the lack of labeled network data often makes
+community detection results difficult to validate, we highlight the usefulness
+of our model for two network prediction tasks: link prediction and
+collaborative filtering. As a result of fitting this attributed stochastic
+block model, one can predict the attribute vector or connectivity patterns for
+a new node in the event of the complementary source of information
+(connectivity or attributes, respectively). We also highlight two biological
+examples where the attributed stochastic block model provides satisfactory
+performance in the link prediction and collaborative filtering tasks.
+",1,0,0,1,0,0
+19030,Asymptotic Independence of Bivariate Order Statistics,"  It is well known that an extreme order statistic and a central order
+statistic (os) as well as an intermediate os and a central os from a sample of
+iid univariate random variables get asymptotically independent as the sample
+size increases. We extend this result to bivariate random variables, where the
+os are taken componentwise. An explicit representation of the conditional
+distribution of bivariate os turns out to be a powerful tool.
+",0,0,1,1,0,0
+19031,Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks,"  Material recognition enables robots to incorporate knowledge of material
+properties into their interactions with everyday objects. For example, material
+recognition opens up opportunities for clearer communication with a robot, such
+as ""bring me the metal coffee mug"", and recognizing plastic versus metal is
+crucial when using a microwave or oven. However, collecting labeled training
+data with a robot is often more difficult than unlabeled data. We present a
+semi-supervised learning approach for material recognition that uses generative
+adversarial networks (GANs) with haptic features such as force, temperature,
+and vibration. Our approach achieves state-of-the-art results and enables a
+robot to estimate the material class of household objects with ~90% accuracy
+when 92% of the training data are unlabeled. We explore how well this approach
+can recognize the material of new objects and we discuss challenges facing
+generalization. To motivate learning from unlabeled training data, we also
+compare results against several common supervised learning classifiers. In
+addition, we have released the dataset used for this work which consists of
+time-series haptic measurements from a robot that conducted thousands of
+interactions with 72 household objects.
+",1,0,0,1,0,0
+19032,Level set shape and topology optimization of finite strain bilateral contact problems,"  This paper presents a method for the optimization of multi-component
+structures comprised of two and three materials considering large motion
+sliding contact and separation along interfaces. The structural geometry is
+defined by an explicit level set method, which allows for both shape and
+topology changes. The mechanical model assumes finite strains, a nonlinear
+elastic material behavior, and a quasi-static response. Identification of
+overlapping surface position is handled by a coupled parametric representation
+of contact surfaces. A stabilized Lagrange method and an active set strategy
+are used to model frictionless contact and separation. The mechanical model is
+discretized by the extended finite element method which maintains a clear
+definition of geometry. Face-oriented ghost penalization and dynamic relaxation
+are implemented to improve the stability of the physical response prediction. A
+nonlinear programming scheme is used to solve the optimization problem, which
+is regularized by introducing a perimeter penalty into the objective function.
+Sensitivities are determined by the adjoint method. The main characteristics of
+the proposed method are studied by numerical examples in two dimensions. The
+numerical results demonstrate improved design performance when compared to
+models optimized with a small strain assumption. Additionally, examples with
+load path dependent objectives display non-intuitive designs.
+",0,0,1,0,0,0
+19033,Thermal distortions of non-Gaussian beams in Fabry-Perot cavities,"  Thermal effects are already important in currently operating interferometric
+gravitational wave detectors. Planned upgrades of these detectors involve
+increasing optical power to combat quantum shot noise. We consider the
+ramifications of this increased power for one particular class of laser
+beams--wide, flat-topped, mesa beams. In particular we model a single mesa beam
+Fabry-Perot cavity having thermoelastically deformed mirrors. We calculate the
+intensity profile of the fundamental cavity eigenmode in the presence of
+thermal perturbations, and the associated changes in thermal noise. We also
+outline an idealized method of correcting for such effects. At each stage we
+contrast our results with those of a comparable Gaussian beam cavity. Although
+we focus on mesa beams the techniques described are applicable to any
+azimuthally symmetric system.
+",0,1,0,0,0,0
+19034,Rigidity and trace properties of divergence-measure vector fields,"  We show some rigidity properties of divergence-free vector fields defined on
+half-spaces. As an application, we prove the existence of the classical trace
+for a bounded, divergence-measure vector field $\xi$ defined on the Euclidean
+plane, at almost every point of a locally oriented rectifiable set $S$, under
+the assumption that its weak normal trace $[\xi\cdot \nu_S]$ attains a local
+maximum for the norm of $\xi$ at the point.
+",0,0,1,0,0,0
+19035,A note on the Almansi property,"  The first goal of this note is to study the Almansi property on an
+m-dimensional model in the sense of Greene and Wu and, more generally, in a
+Riemannian geometric setting. In particular, we shall prove that the only model
+on which the Almansi property is verified is the Euclidean space R^m. In the
+second part of the paper we shall study Almansi's property and biharmonicity
+for functions which depend on the distance from a given submanifold. Finally,
+in the last section we provide an extension to the semi-Euclidean case R^{p,q}
+which includes the proof of the classical Almansi property in R^m as a special
+instance.
+",0,0,1,0,0,0
+19036,The Quantum Complexity of Computing Schatten $p$-norms,"  We consider the quantum complexity of computing Schatten $p$-norms and
+related quantities, and find that the problem of estimating these quantities is
+closely related to the one clean qubit model of computation. We show that the
+problem of approximating $\text{Tr}\, (|A|^p)$ for a log-local $n$-qubit
+Hamiltonian $A$ and $p=\text{poly}(n)$, up to a suitable level of accuracy, is
+contained in DQC1; and that approximating this quantity up to a somewhat higher
+level of accuracy is DQC1-hard. In some cases the level of accuracy achieved by
+the quantum algorithm is substantially better than a natural classical
+algorithm for the problem. The same problem can be solved for arbitrary sparse
+matrices in BQP. One application of the algorithm is the approximate
+computation of the energy of a graph.
+",1,0,0,0,0,0
+19037,Bounds for fidelity of semiclassical Lagrangian states in K{ä}hler quantization,"  We define mixed states associated with submanifolds with probability
+densities in quantizable closed K{ä}hler manifolds. Then, we address the
+problem of comparing two such states via their fidelity. Firstly, we estimate
+the sub-fidelity and super-fidelity of two such states, giving lower and upper
+bounds for their fidelity, when the underlying submanifolds are two Lagrangian
+submanifolds intersecting transversally at a finite number of points, in the
+semiclassical limit. Secondly, we investigate a family of examples on the
+sphere, for which we manage to obtain a better upper bound for the fidelity. We
+conclude by stating a conjecture regarding the fidelity in the general case.
+",0,0,1,0,0,0
+19038,Precision measurement of antiproton to proton ratio with the Alpha Magnetic Spectrometer on the International Space Station,"  A precision measurement by AMS of the antiproton-to-proton flux ratio in
+primary cosmic rays in the absolute rigidity range from 1 to 450 GV is
+presented based on $3.49\times10^5$ antiproton events and $2.42\times10^9$
+proton events. Above $\sim60$ GV the antiproton to proton flux ratio is
+consistent with being rigidity independent. A decreasing behaviour is expected
+for this ratio considering the traditional models for the secondary antiproton
+flux.
+",0,1,0,0,0,0
+19039,Stability criteria for the 2D $α$-Euler equations,"  We derive analogues of the classical Rayleigh, Fjortoft and Arnold stability
+and instability theorems in the context of the 2D $\alpha$-Euler equations.
+",0,1,0,0,0,0
+19040,Superconducting spin valves controlled by spiral re-orientation in B20-family magnets,"  We propose a superconducting spin-triplet valve, which consists of a
+superconductor and an itinerant magnetic material, with the magnet showing an
+intrinsic non-collinear order characterized by a wave vector that may be
+aligned in a few equivalent preferred directions under control of a weak
+external magnetic field. Re-orienting the spiral direction allows one to
+controllably modify long-range spin-triplet superconducting correlations,
+leading to spin-valve switching behavior. Our results indicate that the
+spin-valve effect may be noticeable. This bilayer may be used as a magnetic
+memory element for cryogenic nanoelectronics. It has the following advantages
+in comparison to superconducting spin valves proposed previously: (i) it
+contains only one magnetic layer, which may be more easily fabricated and
+controlled, (ii) its ground states are separated by a potential barrier, which
+solves the ""half-select"" problem of the addressed switch of memory elements.
+",0,1,0,0,0,0
+19041,Experience Recommendation for Long Term Safe Learning-based Model Predictive Control in Changing Operating Conditions,"  Learning has propelled the cutting edge of performance in robotic control to
+new heights, allowing robots to operate with high performance in conditions
+that were previously unimaginable. The majority of the work, however, assumes
+that the unknown parts are static or slowly changing. This limits them to
+static or slowly changing environments. However, in the real world, a robot may
+experience various unknown conditions. This paper presents a method to extend
+an existing single mode GP-based safe learning controller to learn an
+increasing number of non-linear models for the robot dynamics. We show that
+this approach enables a robot to re-use past experience from a large number of
+previously visited operating conditions, and to safely adapt when a new and
+distinct operating condition is encountered. This allows the robot to achieve
+safety and high performance in an large number of operating conditions that do
+not have to be specified ahead of time. Our approach runs independently from
+the controller, imposing no additional computation time on the control loop
+regardless of the number of previous operating conditions considered. We
+demonstrate the effectiveness of our approach in experiment on a 900\,kg ground
+robot with both physical and artificial changes to its dynamics. All of our
+experiments are conducted using vision for localization.
+",1,0,0,0,0,0
+19042,Calderón-type inequalities for affine frames,"  We prove sharp upper and lower bounds for generalized Calderón's sums
+associated to frames on LCA groups generated by affine actions of cocompact
+subgroup translations and general measurable families of automorphisms. The
+proof makes use of techniques of analysis on metric spaces, and relies on a
+counting estimate of lattice points inside metric balls. We will deduce as
+special cases Calderón-type inequalities for families of expanding
+automorphisms as well as for LCA-Gabor systems.
+",0,0,1,0,0,0
+19043,Magnetism in Semiconducting Molybdenum Dichalcogenides,"  Transition metal dichalcogenides (TMDs) are interesting for understanding
+fundamental physics of two-dimensional materials (2D) as well as for many
+emerging technologies, including spin electronics. Here, we report the
+discovery of long-range magnetic order below TM = 40 K and 100 K in bulk
+semiconducting TMDs 2H-MoTe2 and 2H-MoSe2, respectively, by means of muon
+spin-rotation (muSR), scanning tunneling microscopy (STM), as well as density
+functional theory (DFT) calculations. The muon spin rotation measurements show
+the presence of a large and homogeneous internal magnetic fields at low
+temperatures in both compounds indicative of long-range magnetic order. DFT
+calculations show that this magnetism is promoted by the presence of defects in
+the crystal. The STM measurements show that the vast majority of defects in
+these materials are metal vacancies and chalcogen-metal antisites which are
+randomly distributed in the lattice at the sub-percent level. DFT indicates
+that the antisite defects are magnetic with a magnetic moment in the range of
+0.9-2.8 mu_B. Further, we find that the magnetic order stabilized in 2H-MoTe2
+and 2H-MoSe2 is highly sensitive to hydrostatic pressure. These observations
+establish 2H-MoTe2 and 2H-MoSe2 as a new class of magnetic semiconductors and
+opens a path to studying the interplay of 2D physics and magnetism in these
+interesting semiconductors.
+",0,1,0,0,0,0
+19044,Characteristic Polynomial of Certain Hyperplane Arrangements through Graph Theory,"  We give a formula for computing the characteristic polynomial for certain
+hyperplane arrangements in terms of the number of bipartite graphs of given
+rank and cardinality.
+",0,0,1,0,0,0
+19045,Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models,"  The process to certify highly Automated Vehicles has not yet been defined by
+any country in the world. Currently, companies test Automated Vehicles on
+public roads, which is time-consuming and inefficient. We proposed the
+Accelerated Evaluation concept, which uses a modified statistics of the
+surrounding vehicles and the Importance Sampling theory to reduce the
+evaluation time by several orders of magnitude, while ensuring the evaluation
+results are statistically accurate. In this paper, we further improve the
+accelerated evaluation concept by using Piecewise Mixture Distribution models,
+instead of Single Parametric Distribution models. We developed and applied this
+idea to forward collision control system reacting to vehicles making cut-in
+lane changes. The behavior of the cut-in vehicles was modeled based on more
+than 403,581 lane changes collected by the University of Michigan Safety Pilot
+Model Deployment Program. Simulation results confirm that the accuracy and
+efficiency of the Piecewise Mixture Distribution method outperformed single
+parametric distribution methods in accuracy and efficiency, and accelerated the
+evaluation process by almost four orders of magnitude.
+",1,0,0,0,0,0
+19046,Phase shift's influence of two strong pulsed laser waves on effective interaction of electrons,"  The phase shift's influence of two strong pulsed laser waves on effective
+interaction of electrons was studied. Considerable amplification of electrons
+repulsion in the certain range of phase shifts and waves intensities is shown.
+That leads to electrons scatter on greater distances than without an external
+field. The value of the distance can be greater on 2-3 order of magnitude. Also
+considerable influence of the phase shift of pulses of waves on the possibility
+of effective attraction of electrons is shown.
+",0,1,0,0,0,0
+19047,Topological Sieving of Rings According to their Rigidity,"  We present a novel mechanism for resolving the mechanical rigidity of
+nanoscopic circular polymers that flow in a complex environment. The emergence
+of a regime of negative differential mobility induced by topological
+interactions between the rings and the substrate is the key mechanism for
+selective sieving of circular polymers with distinct flexibilities. A simple
+model accurately describes the sieving process observed in molecular dynamics
+simulations and yields experimentally verifiable analytical predictions, which
+can be used as a reference guide for improving filtration procedures of
+circular filaments. The topological sieving mechanism we propose ought to be
+relevant also in probing the microscopic details of complex substrates.
+",0,0,0,0,1,0
+19048,A structural Markov property for decomposable graph laws that allows control of clique intersections,"  We present a new kind of structural Markov property for probabilistic laws on
+decomposable graphs, which allows the explicit control of interactions between
+cliques, so is capable of encoding some interesting structure. We prove the
+equivalence of this property to an exponential family assumption, and discuss
+identifiability, modelling, inferential and computational implications.
+",0,0,0,1,0,0
+19049,Fisher consistency for prior probability shift,"  We introduce Fisher consistency in the sense of unbiasedness as a desirable
+property for estimators of class prior probabilities. Lack of Fisher
+consistency could be used as a criterion to dismiss estimators that are
+unlikely to deliver precise estimates in test datasets under prior probability
+and more general dataset shift. The usefulness of this unbiasedness concept is
+demonstrated with three examples of classifiers used for quantification:
+Adjusted Classify & Count, EM-algorithm and CDE-Iterate. We find that Adjusted
+Classify & Count and EM-algorithm are Fisher consistent. A counter-example
+shows that CDE-Iterate is not Fisher consistent and, therefore, cannot be
+trusted to deliver reliable estimates of class probabilities.
+",1,0,0,1,0,0
+19050,Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders,"  The notion of disentangled autoencoders was proposed as an extension to the
+variational autoencoder by introducing a disentanglement parameter $\beta$,
+controlling the learning pressure put on the possible underlying latent
+representations. For certain values of $\beta$ this kind of autoencoders is
+capable of encoding independent input generative factors in separate elements
+of the code, leading to a more interpretable and predictable model behaviour.
+In this paper we quantify the effects of the parameter $\beta$ on the model
+performance and disentanglement. After training multiple models with the same
+value of $\beta$, we establish the existence of consistent variance in one of
+the disentanglement measures, proposed in literature. The negative consequences
+of the disentanglement to the autoencoder's discriminative ability are also
+asserted while varying the amount of examples available during training.
+",1,0,0,1,0,0
+19051,An omnibus test for the global null hypothesis,"  Global hypothesis tests are a useful tool in the context of, e.g, clinical
+trials, genetic studies or meta analyses, when researchers are not interested
+in testing individual hypotheses, but in testing whether none of the hypotheses
+is false. There are several possibilities how to test the global null
+hypothesis when the individual null hypotheses are independent. If it is
+assumed that many of the individual null hypotheses are false, combinations
+tests have been recommended to maximise power. If, however, it is assumed that
+only one or a few null hypotheses are false, global tests based on individual
+test statistics are more powerful (e.g., Bonferroni or Simes test). However,
+usually there is no a-priori knowledge on the number of false individual null
+hypotheses. We therefore propose an omnibus test based on the combination of
+p-values. We show that this test yields an impressive overall performance. The
+proposed method is implemented in the R-package omnibus.
+",0,0,0,1,0,0
+19052,Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification,"  We prove that the ordinary least-squares (OLS) estimator attains nearly
+minimax optimal performance for the identification of linear dynamical systems
+from a single observed trajectory. Our upper bound relies on a generalization
+of Mendelson's small-ball method to dependent data, eschewing the use of
+standard mixing-time arguments. Our lower bounds reveal that these upper bounds
+match up to logarithmic factors. In particular, we capture the correct
+signal-to-noise behavior of the problem, showing that more unstable linear
+systems are easier to estimate. This behavior is qualitatively different from
+arguments which rely on mixing-time calculations that suggest that unstable
+systems are more difficult to estimate. We generalize our technique to provide
+bounds for a more general class of linear response time-series.
+",0,0,0,1,0,0
+19053,Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification,"  Motivated by applications in protein function prediction, we consider a
+challenging supervised classification setting in which positive labels are
+scarce and there are no explicit negative labels. The learning algorithm must
+thus select which unlabeled examples to use as negative training points,
+possibly ending up with an unbalanced learning problem. We address these issues
+by proposing an algorithm that combines active learning (for selecting negative
+examples) with imbalance-aware learning (for mitigating the label imbalance).
+In our experiments we observe that these two techniques operate
+synergistically, outperforming state-of-the-art methods on standard protein
+function prediction benchmarks.
+",0,0,0,0,1,0
+19054,Superconductivity in quantum wires: A symmetry analysis,"  We study properties of quantim wires with spin-orbit coupling and time
+reversal symmetry breaking, in normal and superconducting states. Electronic
+band structures are classified according to quasi-one-dimensional magnetic
+point groups, or magnetic classes. The latter belong to one of three distinct
+types, depending on the way the time reversal operation appears in the group
+elements. The superconducting gap functions are constructed using antiunitary
+operations and have different symmetry properties depending on the type of the
+magnetic point group. We obtain the spectrum of the Andreev boundary modes near
+the end of the wire in a model-independent way, using the semiclassical
+approach with the boundary conditions described by a phenomenological
+scattering matrix. Explicit expressions for the bulk topological invariants
+controlling the number of the boundary zero modes are presented in the general
+multiband case for two types of the magnetic point groups, corresponding to
+DIII and BDI symmetry classes.
+",0,1,0,0,0,0
+19055,Exponentially convergent data assimilation algorithm for Navier-Stokes equations,"  The paper presents a new state estimation algorithm for a bilinear equation
+representing the Fourier- Galerkin (FG) approximation of the Navier-Stokes (NS)
+equations on a torus in R2. This state equation is subject to uncertain but
+bounded noise in the input (Kolmogorov forcing) and initial conditions, and its
+output is incomplete and contains bounded noise. The algorithm designs a
+time-dependent gain such that the estimation error converges to zero
+exponentially. The sufficient condition for the existence of the gain are
+formulated in the form of algebraic Riccati equations. To demonstrate the
+results we apply the proposed algorithm to the reconstruction a chaotic fluid
+flow from incomplete and noisy data.
+",0,1,0,0,0,0
+19056,A Multi-Modal Approach to Infer Image Affect,"  The group affect or emotion in an image of people can be inferred by
+extracting features about both the people in the picture and the overall makeup
+of the scene. The state-of-the-art on this problem investigates a combination
+of facial features, scene extraction and even audio tonality. This paper
+combines three additional modalities, namely, human pose, text-based tagging
+and CNN extracted features / predictions. To the best of our knowledge, this is
+the first time all of the modalities were extracted using deep neural networks.
+We evaluate the performance of our approach against baselines and identify
+insights throughout this paper.
+",0,0,0,1,0,0
+19057,Joint Tilt Angle Adaptation and Beamforming in Multicell Multiuser Cellular Networks,"  3D beamforming is a promising approach for interference coordination in
+cellular networks which brings significant improvements in comparison with
+conventional 2D beamforming techniques. This paper investigates the problem of
+joint beamforming design and tilt angle adaptation of the BS antenna array for
+maximizing energy efficiency (EE) in downlink of multi-cell multi-user
+coordinated cellular networks. An iterative algorithm based on fractional
+programming approach is introduced to solve the resulting non-convex
+optimization problem. In each iteration, users are clustered based on their
+elevation angle. Then, optimization of the tilt angle is carried out through a
+reduced complexity greedy search to find the best tilt angle for a given
+placement of the users. Numerical results show that the proposed algorithm
+achieves higher EE compared to the 2D beamforming techniques.
+",1,0,1,0,0,0
+19058,Testing Degree Corrections in Stochastic Block Models,"  We study sharp detection thresholds for degree corrections in Stochastic
+Block Models in the context of a goodness of fit problem. When degree
+corrections are relatively dense, a simple test based on the total number of
+edges is asymptotically optimal. For sparse degree corrections in non-dense
+graphs, simple degree based Higher Criticism Test (Mukherjee, Mukherjee, Sen
+2016) is optimal with sharp constants. In contrast, for dense graphs, the
+optimal procedure runs in two stages. It involves running a suitable community
+recovery algorithm in stage 1, followed by a Higher Criticism Test based on a
+linear combination of within and across (estimated) community degrees in stage
+2. The necessity of the two step procedure is demonstrated by the failure of
+the ordinary Maximum Degree Test in achieving sharp constants. As necessary
+tools we also derive asymptotic distribution of the Maximum Degree in
+Stochastic Block Models along with moderate deviation and local central limit
+type asymptotics of positive linear combinations of independent Binomial random
+variables.
+",0,0,1,1,0,0
+19059,A Survey on Mobile Edge Computing: The Communication Perspective,"  Driven by the visions of Internet of Things and 5G communications, recent
+years have seen a paradigm shift in mobile computing, from the centralized
+Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of
+MEC is to push mobile computing, network control and storage to the network
+edges (e.g., base stations and access points) so as to enable
+computation-intensive and latency-critical applications at the resource-limited
+mobile devices. MEC promises dramatic reduction in latency and mobile energy
+consumption, tackling the key challenges for materializing 5G vision. The
+promised gains of MEC have motivated extensive efforts in both academia and
+industry on developing the technology. A main thrust of MEC research is to
+seamlessly merge the two disciplines of wireless communications and mobile
+computing, resulting in a wide-range of new designs ranging from techniques for
+computation offloading to network architectures. This paper provides a
+comprehensive survey of the state-of-the-art MEC research with a focus on joint
+radio-and-computational resource management. We also present a research outlook
+consisting of a set of promising directions for MEC research, including MEC
+system deployment, cache-enabled MEC, mobility management for MEC, green MEC,
+as well as privacy-aware MEC. Advancements in these directions will facilitate
+the transformation of MEC from theory to practice. Finally, we introduce recent
+standardization efforts on MEC as well as some typical MEC application
+scenarios.
+",1,0,1,0,0,0
+19060,Deep Learning Approximation: Zero-Shot Neural Network Speedup,"  Neural networks offer high-accuracy solutions to a range of problems, but are
+costly to run in production systems because of computational and memory
+requirements during a forward pass. Given a trained network, we propose a
+techique called Deep Learning Approximation to build a faster network in a tiny
+fraction of the time required for training by only manipulating the network
+structure and coefficients without requiring re-training or access to the
+training data. Speedup is achieved by by applying a sequential series of
+independent optimizations that reduce the floating-point operations (FLOPs)
+required to perform a forward pass. First, lossless optimizations are applied,
+followed by lossy approximations using singular value decomposition (SVD) and
+low-rank matrix decomposition. The optimal approximation is chosen by weighing
+the relative accuracy loss and FLOP reduction according to a single parameter
+specified by the user. On PASCAL VOC 2007 with the YOLO network, we show an
+end-to-end 2x speedup in a network forward pass with a 5% drop in mAP that can
+be re-gained by finetuning.
+",0,0,0,1,0,0
+19061,Frequency truncated discrete-time system norm,"  Multirate digital signal processing and model reduction applications require
+computation of the frequency truncated norm of a discrete-time system. This
+paper explains how to compute the frequency truncated norm of a discrete-time
+system. To this end, a much-generalized problem of integrating a transfer
+function of a discrete-time system given in the descriptor form over an
+interval of limited frequencies is also discussed along with its computation.
+",1,0,0,0,0,0
+19062,Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation,"  Recent work has shown that state-of-the-art classifiers are quite brittle, in
+the sense that a small adversarial change of an originally with high confidence
+correctly classified input leads to a wrong classification again with high
+confidence. This raises concerns that such classifiers are vulnerable to
+attacks and calls into question their usage in safety-critical systems. We show
+in this paper for the first time formal guarantees on the robustness of a
+classifier by giving instance-specific lower bounds on the norm of the input
+manipulation required to change the classifier decision. Based on this analysis
+we propose the Cross-Lipschitz regularization functional. We show that using
+this form of regularization in kernel methods resp. neural networks improves
+the robustness of the classifier without any loss in prediction performance.
+",1,0,0,1,0,0
+19063,Complete DFM Model for High-Performance Computing SoCs with Guard Ring and Dummy Fill Effect,"  For nanotechnology, the semiconductor device is scaled down dramatically with
+additional strain engineering for device enhancement, the overall device
+characteristic is no longer dominated by the device size but also circuit
+layout. The higher order layout effects, such as well proximity effect (WPE),
+oxide spacing effect (OSE) and poly spacing effect (PSE), play an important
+role for the device performance, it is critical to understand Design for
+Manufacturability (DFM) impacts with various layout topology toward the overall
+circuit performance. Currently, the layout effects (WPE, OSE and PSE) are
+validated through digital standard cell and analog differential pair test
+structure. However, two analog layout structures: the guard ring and dummy fill
+impact are not well studied yet, then, this paper describes the current mirror
+test circuit to examine the guard ring and dummy fills DFM impacts using TSMC
+28nm HPM process.
+",1,0,0,0,0,0
+19064,Investigating the potential of social network data for transport demand models,"  Location-based social network data offers the promise of collecting the data
+from a large base of users over a longer span of time at negligible cost. While
+several studies have applied social network data to activity and mobility
+analysis, a comparison with travel diaries and general statistics has been
+lacking. In this paper, we analysed geo-referenced Twitter activities from a
+large number of users in Singapore and neighbouring countries. By combining
+this data, population statistics and travel diaries and applying clustering
+techniques, we addressed detection of activity locations, as well as spatial
+separation and transitions between these locations. Kernel density estimation
+performs best to detect activity locations due to the scattered nature of the
+twitter data; more activity locations are detected per user than reported in
+the travel survey. The descriptive analysis shows that determining home
+locations is more difficult than detecting work locations for most planning
+zones. Spatial separations between detected activity locations from Twitter
+data - as reported in a travel survey and captured by public transport smart
+card data - are mostly similarly distributed, but also show relevant
+differences for very short and very long distances. This also holds for the
+transitions between zones. Whether the differences between Twitter data and
+other data sources stem from differences in the population sub-sample,
+clustering methodology, or whether social networks are being used significantly
+more at specific locations must be determined by further research. Despite
+these shortcomings, location-based social network data offers a promising data
+source for insights into activity locations and mobility patterns, especially
+for regions where travel survey data is not readily available.
+",1,1,0,0,0,0
+19065,Generalized Internal Boundaries (GIB),"  Representing large-scale motions and topological changes in the finite volume
+(FV) framework, while at the same time preserving the accuracy of the numerical
+solution, is difficult. In this paper, we present a robust, highly efficient
+method designed to achieve this capability. The proposed approach conceptually
+shares many of the characteristics of the cut-cell interface tracking method,
+but without the need for complex cell splitting/merging operations. The heart
+of the new technique is to align existing mesh facets with the geometry to be
+represented. We then modify the matrix contributions from these facets such
+that they are represented in an identical fashion to traditional boundary
+conditions. The collection of such faces is named a Generalised Internal
+Boundary (GIB). In order to introduce motion into the system, we rely on the
+classical ALE (Arbitrary Lagrangian-Eulerian) approach, but with the caveat
+that the non-time-dependent motion of elements instantaneously crossing the
+interface is handled separately from the time dependent component. The new
+methodology is validated through comparison with: a) a body-fitted grid
+simulation of an oscillating two dimensional cylinder and b) experimental
+results of a butterfly valve.
+",1,1,0,0,0,0
+19066,Reducing Certification Granularity to Increase Adaptability of Avionics Software,"  A strong certification process is required to insure the safety of airplanes,
+and more specifically the robustness of avionics applications. To implement
+this process, the development of avionics software must follow long and costly
+procedures. Most of these procedures have to be reexecuted each time the
+software is modified. In this paper, we propose a framework to reduce the cost
+and time impact of a software modification. With this new approach, the piece
+of software likely to change is isolated from the rest of the application, so
+it can be certified independently. This helps the system integrator to adapt an
+avionics application to the specificities of the target airplane, without the
+need for a new certification of the application.
+",1,0,0,0,0,0
+19067,Evolutionary multiplayer games on graphs with edge diversity,"  Evolutionary game dynamics in structured populations has been extensively
+explored in past decades. However, most previous studies assume that payoffs of
+individuals are fully determined by the strategic behaviors of interacting
+parties and social ties between them only serve as the indicator of the
+existence of interactions. This assumption neglects important information
+carried by inter-personal social ties such as genetic similarity, geographic
+proximity, and social closeness, which may crucially affect the outcome of
+interactions. To model these situations, we present a framework of evolutionary
+multiplayer games on graphs with edge diversity, where different types of edges
+describe diverse social ties. Strategic behaviors together with social ties
+determine the resulting payoffs of interactants. Under weak selection, we
+provide a general formula to predict the success of one behavior over the
+other. We apply this formula to various examples which cannot be dealt with
+using previous models, including the division of labor and relationship- or
+edge-dependent games. We find that labor division facilitates collective
+cooperation by decomposing a many-player game into several games of smaller
+sizes. The evolutionary process based on relationship-dependent games can be
+approximated by interactions under a transformed and unified game. Our work
+stresses the importance of social ties and provides effective methods to reduce
+the calculating complexity in analyzing the evolution of realistic systems.
+",0,0,0,0,1,0
+19068,Robust Consensus for Multi-Agent Systems Communicating over Stochastic Uncertain Networks,"  In this paper, we study the robust consensus problem for a set of
+discrete-time linear agents to coordinate over an uncertain communication
+network, which is to achieve consensus against the transmission errors and
+noises resulted from the information exchange between the agents. We model the
+network by means of communication links subject to multiplicative stochastic
+uncertainties, which are susceptible to describing packet dropout, random
+delay, and fading phenomena. Different communication topologies, such as
+undirected graphs and leader-follower graphs, are considered. We derive
+sufficient conditions for robust consensus in the mean square sense. This
+results unveil intrinsic constraints on consensus attainment imposed by the
+network synchronizability, the unstable agent dynamics, and the channel
+uncertainty variances. Consensus protocols are designed based on the state
+information transmitted over the uncertain channels, by solving a modified
+algebraic Riccati equation.
+",1,0,0,0,0,0
+19069,Fractional Topological Elasticity and Fracton Order,"  We analyze the ""higher rank"" gauge theories, that capture some of the
+phenomenology of the Fracton order. It is shown that these theories loose gauge
+invariance when arbitrarily weak and smooth curvature is introduced. We propose
+a resolution to this problem by introducing a theory invariant under
+area-preserving diffeomorphisms, which reduce to the ""higher rank"" gauge
+transformations upon linearization around a flat background. The proposed
+theory is \emph{geometric} in nature and is interpreted as a theory of
+\emph{fractional topological elasticity}. This theory exhibits the Fracton
+phenomenology. We explore the conservation laws, topological excitations,
+linear response, various kinematical constraints, and canonical structure of
+the theory. Finally, we emphasize that the very structure of Riemann-Cartan
+geometry, which we use to formulate the theory, encodes the Fracton
+phenomenology, suggesting that the Fracton order itself is \emph{geometric} in
+nature.
+",0,1,0,0,0,0
+19070,Perturbing Eisenstein polynomials over local fields,"  Let $K$ be a local field whose residue field has characteristic $p$ and let
+$L/K$ be a finite separable totally ramified extension. Let $\pi_L$ be a
+uniformizer for $L$ and let $f(X)$ be the minimum polynomial for $\pi_L$ over
+$K$. Suppose $\tilde{\pi}_L$ is another uniformizer for $L$ such that
+$\tilde{\pi}_L\equiv\pi_L+r\pi_L^{\ell+1} \pmod{\pi_L^{\ell+2}}$ for some
+$\ell\ge1$ and $r\in O_K$. Let $\tilde{f}(X)$ be the minimum polynomial for
+$\tilde{\pi}_L$ over $K$. In this paper we give congruences for the
+coefficients of $\tilde{f}(X)$ in terms of $r$ and the coefficients of $f(X)$.
+These congruences improve and extend work of Krasner.
+",0,0,1,0,0,0
+19071,Galactic Orbits of Globular Clusters in the Region of the Galactic Bulge,"  Galactic orbits have been constructed over long time intervals for ten
+globular clusters located near the Galactic center. A model with an axially
+symmetric gravitational potential for the Galaxy was initially applied, after
+which a non-axially symmetric potential corresponding to the central bar was
+added. Variations in the trajectories of all these globular clusters in the XY
+plane due to the influence of the bar were detected. These were greatest for
+the cluster Terzan 4 in the meridional (RZ) plane. The globular clusters Terzan
+1, Terzan 2, Terzan 4, Terzan 9, NGC 6522, and NGC 6558 always remained within
+the Galactic bulge, no farther than 4 kpc from the Galactic center.
+",0,1,0,0,0,0
+19072,Streamlines for Motion Planning in Underwater Currents,"  Motion planning for underwater vehicles must consider the effect of ocean
+currents. We present an efficient method to compute reachability and cost
+between sample points in sampling-based motion planning that supports
+long-range planning over hundreds of kilometres in complicated flows. The idea
+is to search a reduced space of control inputs that consists of stream
+functions whose level sets, or streamlines, optimally connect two given points.
+Such stream functions are generated by superimposing a control input onto the
+underlying current flow. A streamline represents the resulting path that a
+vehicle would follow as it is carried along by the current given that control
+input. We provide rigorous analysis that shows how our method avoids exhaustive
+search of the control space, and demonstrate simulated examples in complicated
+flows including a traversal along the east coast of Australia, using actual
+current predictions, between Sydney and Brisbane.
+",1,0,0,0,0,0
+19073,"Eddington-Limited Accretion in z~2 WISE-selected Hot, Dust-Obscured Galaxies","  Hot, Dust-Obscured Galaxies, or ""Hot DOGs"", are a rare, dusty, hyperluminous
+galaxy population discovered by the WISE mission. Predominantly at redshifts
+2-3, they include the most luminous known galaxies in the universe. Their high
+luminosities likely come from accretion onto highly obscured super massive
+black holes (SMBHs). We have conducted a pilot survey to measure the SMBH
+masses of five z~2 Hot DOGs via broad H_alpha emission lines, using
+Keck/MOSFIRE and Gemini/FLAMINGOS-2. We detect broad H_alpha emission in all
+five Hot DOGs. We find substantial corresponding SMBH masses for these Hot DOGs
+(~ 10^{9} M_sun), and their derived Eddington ratios are close to unity. These
+z~2 Hot DOGs are the most luminous AGNs at given BH masses, suggesting they are
+accreting at the maximum rates for their BHs. A similar property is found for
+known z~6 quasars. Our results are consistent with scenarios in which Hot DOGs
+represent a transitional, high-accretion phase between obscured and unobscured
+quasars. Hot DOGs may mark a special evolutionary stage before the red quasar
+and optical quasar phases, and they may be present at other cosmic epochs.
+",0,1,0,0,0,0
+19074,Boltzmann Transport in Nanostructures as a Friction Effect,"  Surface scattering is the key limiting factor to thermal transport in
+dielectric crystals as the length scales are reduced or when temperature is
+lowered. To explain this phenomenon, it is commonly assumed that the mean free
+paths of heat carriers are bound by the crystal size and that thermal
+conductivity is reduced in a manner proportional to such mean free paths. We
+show here that these conclusions rely on simplifying assumptions and
+approximated transport models. Instead, starting from the linearized Boltzmann
+transport equation in the relaxon basis, we show how the problem can be reduced
+to a set of decoupled linear differential equations. Then, the heat flow can be
+interpreted as a hydrodynamic phenomenon, with the relaxon gas being slowed
+down in proximity of a surface by friction effects, similar to the flux of a
+viscous fluid in a pipe. As an example, we study a ribbon and a trench of
+monolayer molybdenum disulphide, describing the procedure to reconstruct the
+temperature and thermal conductivity profile in the sample interior and showing
+how to estimate the effect of nanostructuring. The approach is general and
+could be extended to other transport carriers, such as electrons, or extended
+to materials of higher dimensionality and to different geometries, such as thin
+films.
+",0,1,0,0,0,0
+19075,Agile Software Engineering and Systems Engineering at SKA Scale,"  Systems Engineering (SE) is the set of processes and documentation required
+for successfully realising large-scale engineering projects, but the classical
+approach is not a good fit for software-intensive projects, especially when the
+needs of the different stakeholders are not fully known from the beginning, and
+requirement priorities might change. The SKA is the ultimate software-enabled
+telescope, with enormous amounts of computing hardware and software required to
+perform its data reduction. We give an overview of the system and software
+engineering processes in the SKA1 development, and the tension between
+classical and agile SE.
+",1,1,0,0,0,0
+19076,Prime geodesic theorem of Gallagher type,"  We reduce the exponent in the error term of the prime geodesic theorem for
+compact Riemann surfaces from $\frac{3}{4}$ to $\frac{7}{10}$ outside a set of
+finite logarithmic measure.
+",0,0,1,0,0,0
+19077,PriMaL: A Privacy-Preserving Machine Learning Method for Event Detection in Distributed Sensor Networks,"  This paper introduces PriMaL, a general PRIvacy-preserving MAchine-Learning
+method for reducing the privacy cost of information transmitted through a
+network. Distributed sensor networks are often used for automated
+classification and detection of abnormal events in high-stakes situations, e.g.
+fire in buildings, earthquakes, or crowd disasters. Such networks might
+transmit privacy-sensitive information, e.g. GPS location of smartphones, which
+might be disclosed if the network is compromised. Privacy concerns might slow
+down the adoption of the technology, in particular in the scenario of social
+sensing where participation is voluntary, thus solutions are needed which
+improve privacy without compromising on the event detection accuracy. PriMaL is
+implemented as a machine-learning layer that works on top of an existing event
+detection algorithm. Experiments are run in a general simulation framework, for
+several network topologies and parameter values. The privacy footprint of
+state-of-the-art event detection algorithms is compared within the proposed
+framework. Results show that PriMaL is able to reduce the privacy cost of a
+distributed event detection algorithm below that of the corresponding
+centralized algorithm, within the bounds of some assumptions about the
+protocol. Moreover the performance of the distributed algorithm is not
+statistically worse than that of the centralized algorithm.
+",1,0,0,0,0,0
+19078,Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU,"  The online problem of computing the top eigenvector is fundamental to machine
+learning. In both adversarial and stochastic settings, previous results (such
+as matrix multiplicative weight update, follow the regularized leader, follow
+the compressed leader, block power method) either achieve optimal regret but
+run slow, or run fast at the expense of loosing a $\sqrt{d}$ factor in total
+regret where $d$ is the matrix dimension.
+We propose a $\textit{follow-the-compressed-leader (FTCL)}$ framework which
+achieves optimal regret without sacrificing the running time. Our idea is to
+""compress"" the matrix strategy to dimension 3 in the adversarial setting, or
+dimension 1 in the stochastic setting. These respectively resolve two open
+questions regarding the design of optimal and efficient algorithms for the
+online eigenvector problem.
+",1,0,1,1,0,0
+19079,Solving satisfiability using inclusion-exclusion,"  Using Maple, we implement a SAT solver based on the principle of
+inclusion-exclusion and the Bonferroni inequalities. Using randomly generated
+input, we investigate the performance of our solver as a function of the number
+of variables and number of clauses. We also test it against Maple's built-in
+tautology procedure. Finally, we implement the Lovász local lemma with Maple
+and discuss its applicability to SAT.
+",1,0,0,0,0,0
+19080,Isometric immersions into manifolds with metallic structures,"  We consider submanifolds into Riemannian manifold with metallic structures.
+We obtain some new results for hypersurfaces in these spaces and we express the
+fundamental theorem of submanifolds into products spaces in terms of metallic
+structures. Moreover, we define new structures called complex metallic
+structures. We show that these structures are linked with complex structures.
+Then, we consider submanifolds into Riemannian manifold with such structures
+with a focus on invariant submanifolds and hypersurfaces. We also express in
+particular the fundamental theorem of submanifolds of complex space form in
+terms of complex metallic structures.
+",0,0,1,0,0,0
+19081,Demonstration of an ac Josephson junction laser,"  Superconducting electronic devices have re-emerged as contenders for both
+classical and quantum computing due to their fast operation speeds, low
+dissipation and long coherence times. An ultimate demonstration of coherence is
+lasing. We use one of the fundamental aspects of superconductivity, the ac
+Josephson effect, to demonstrate a laser made from a Josephson junction
+strongly coupled to a multi-mode superconducting cavity. A dc voltage bias to
+the junction provides a source of microwave photons, while the circuit's
+nonlinearity allows for efficient down-conversion of higher order Josephson
+frequencies down to the cavity's fundamental mode. The simple fabrication and
+operation allows for easy integration with a range of quantum devices, allowing
+for efficient on-chip generation of coherent microwave photons at low
+temperatures.
+",0,1,0,0,0,0
+19082,"Integrable systems, symmetries and quantization","  These notes correspond to a mini-course given at the Poisson 2016 conference
+in Geneva. Starting from classical integrable systems in the sense of
+Liouville, we explore the notion of non-degenerate singularity and expose
+recent research in connection with semi-toric systems. The quantum and
+semiclassical counterpart will also be presented, in the viewpoint of the
+inverse question: from the quantum mechanical spectrum, can you recover the
+classical system?
+",0,1,1,0,0,0
+19083,Detecting Friedel oscillations in ultracold Fermi gases,"  Investigating Friedel oscillations in ultracold gases would complement the
+studies performed on solid state samples with scanning-tunneling microscopes.
+In atomic quantum gases interactions and external potentials can be tuned
+freely and the inherently slower dynamics allow to access non-equilibrium
+dynamics following a potential or interaction quench. Here, we examine how
+Friedel oscillations can be observed in current ultracold gas experiments under
+realistic conditions. To this aim we numerically calculate the amplitude of the
+Friedel oscillations which a potential barrier provokes in a 1D Fermi gas and
+compare it to the expected atomic and photonic shot noise in a density
+measurement. We find that to detect Friedel oscillations the signal from
+several thousand one-dimensional systems has to be averaged. However, as up to
+100 parallel one-dimensional systems can be prepared in a single run with
+present experiments, averaging over about 100 images is sufficient.
+",0,1,0,0,0,0
+19084,Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems,"  While variational methods have been among the most powerful tools for solving
+linear inverse problems in imaging, deep (convolutional) neural networks have
+recently taken the lead in many challenging benchmarks. A remaining drawback of
+deep learning approaches is their requirement for an expensive retraining
+whenever the specific problem, the noise level, noise type, or desired measure
+of fidelity changes. On the contrary, variational methods have a plug-and-play
+nature as they usually consist of separate data fidelity and regularization
+terms.
+In this paper we study the possibility of replacing the proximal operator of
+the regularization used in many convex energy minimization algorithms by a
+denoising neural network. The latter therefore serves as an implicit natural
+image prior, while the data term can still be chosen independently. Using a
+fixed denoising neural network in exemplary problems of image deconvolution
+with different blur kernels and image demosaicking, we obtain state-of-the-art
+reconstruction results. These indicate the high generalizability of our
+approach and a reduction of the need for problem-specific training.
+Additionally, we discuss novel results on the analysis of possible optimization
+algorithms to incorporate the network into, as well as the choices of algorithm
+parameters and their relation to the noise level the neural network is trained
+on.
+",1,0,0,0,0,0
+19085,Heated-Up Softmax Embedding,"  Metric learning aims at learning a distance which is consistent with the
+semantic meaning of the samples. The problem is generally solved by learning an
+embedding for each sample such that the embeddings of samples of the same
+category are compact while the embeddings of samples of different categories
+are spread-out in the feature space. We study the features extracted from the
+second last layer of a deep neural network based classifier trained with the
+cross entropy loss on top of the softmax layer. We show that training
+classifiers with different temperature values of softmax function leads to
+features with different levels of compactness. Leveraging these insights, we
+propose a ""heating-up"" strategy to train a classifier with increasing
+temperatures, leading the corresponding embeddings to achieve state-of-the-art
+performance on a variety of metric learning benchmarks.
+",0,0,0,1,0,0
+19086,Fast Matrix Inversion and Determinant Computation for Polarimetric Synthetic Aperture Radar,"  This paper introduces a fast algorithm for simultaneous inversion and
+determinant computation of small sized matrices in the context of fully
+Polarimetric Synthetic Aperture Radar (PolSAR) image processing and analysis.
+The proposed fast algorithm is based on the computation of the adjoint matrix
+and the symmetry of the input matrix. The algorithm is implemented in a general
+purpose graphical processing unit (GPGPU) and compared to the usual approach
+based on Cholesky factorization. The assessment with simulated observations and
+data from an actual PolSAR sensor show a speedup factor of about two when
+compared to the usual Cholesky factorization. Moreover, the expressions
+provided here can be implemented in any platform.
+",1,0,0,1,0,0
+19087,The imprint of neutrinos on clustering in redshift-space,"  (abridged) We investigate the signatures left by the cosmic neutrino
+background on the clustering of matter, CDM+baryons and halos in redshift-space
+using a set of more than 1000 N-body and hydrodynamical simulations with
+massless and massive neutrinos. We find that the effect neutrinos induce on the
+clustering of CDM+baryons in redshift-space on small scales is almost entirely
+due to the change in $\sigma_8$. Neutrinos imprint a characteristic signature
+in the quadrupole of the matter (CDM+baryons+neutrinos) field on small scales,
+that can be used to disentangle the effect of $\sigma_8$ and $M_\nu$. We show
+that the effect of neutrinos on the clustering of halos is very different, on
+all scales, to the one induced by $\sigma_8$. We find that the effects of
+neutrinos of the growth rate of CDM+baryons ranges from $\sim0.3\%$ to $2\%$ on
+scales $k\in[0.01, 0.5]~h{\rm Mpc}^{-1}$ for neutrinos with masses $M_\nu
+\leqslant 0.15$ eV. We compute the bias between the momentum of halos and the
+momentum of CDM+baryon and find it to be 1 on large scales for all models with
+massless and massive neutrinos considered. This point towards a velocity bias
+between halos and total matter on large scales that it is important to account
+for in order to extract unbiased neutrino information from velocity/momentum
+surveys such as kSZ observations. We show that baryonic effects can affect the
+clustering of matter and CDM+baryons in redshift-space by up to a few percent
+down to $k=0.5~h{\rm Mpc}^{-1}$. We find that hydrodynamics and astrophysical
+processes, as implemented in our simulations, only distort the relative effect
+that neutrinos induce on the anisotropic clustering of matter, CDM+baryons and
+halos in redshift-space by less than $1\%$. Thus, the effect of neutrinos in
+the fully non-linear regime can be written as a transfer function with very
+weak dependence on astrophysics.
+",0,1,0,0,0,0
+19088,Quantum Privacy-Preserving Data Analytics,"  Data analytics (such as association rule mining and decision tree mining) can
+discover useful statistical knowledge from a big data set. But protecting the
+privacy of the data provider and the data user in the process of analytics is a
+serious issue. Usually, the privacy of both parties cannot be fully protected
+simultaneously by a classical algorithm. In this paper, we present a quantum
+protocol for data mining that can much better protect privacy than the known
+classical algorithms: (1) if both the data provider and the data user are
+honest, the data user can know nothing about the database except the
+statistical results, and the data provider can get nearly no information about
+the results mined by the data user; (2) if the data user is dishonest and tries
+to disclose private information of the other, she/he will be detected with a
+high probability; (3) if the data provider tries to disclose the privacy of the
+data user, she/he cannot get any useful information since the data user hides
+his privacy among noises.
+",1,0,0,0,0,0
+19089,Magnetic Properties of Transition-Metal Adsorbed ot-Phosphorus Monolayer: A First-principles and Monte Carlo Study,"  Using the first-principles and Monte Carlo methods, here we systematically
+study magnetic properties of monolayer octagonal-tetragonal phosphorus with 3d
+transition-metal (TM) adatoms. Different from the puckered hexagonal black
+phosphorus monolayer (phosphorene or $\alpha$-P), the octagonal-tetragonal
+phase of 2D phosphorus (named as ot-P or $\epsilon$-P in this article) is
+buckled with octagon-tetragon structure. Our calculations show that all TMs,
+except the closed-shell Zn atom, are able to strongly bind onto monolayer
+$ot$-P with significant binding energies. Local magnetic moments (up to 6
+$\mu$B) on adatoms of Sc, Ti, V, Cr, Mn, Fe and Co originate from the exchange
+and crystal-field splitting of TM 3d orbitals. The magnetic coupling between
+localized magnetic states of adatoms is dependent on adatomic distances and
+directions. Lastly, the uniformly magnetic order is investigated to screening
+two-dimensional dilute ferromagnets with high Curie temperature for
+applications of spintronics. It is found that ot-P with V atoms homogeneously
+adsorbed at the centre of octagons with a concentration of 5% has the most
+stable ferromagnetic ground state. Its Curie temperature is estimated to be 173
+K using the Monte Carlo method.
+",0,1,0,0,0,0
+19090,A bootstrap for the number of $\mathbb{F}_{q^r}$-rational points on a curve over $\mathbb{F}_q$,"  In this note we present a fast algorithm that finds for any $r$ the number
+$N_r$ of $\mathbb{F}_{q^r}$ rational points on a smooth absolutely irreducible
+curve $C$ defined over $\mathbb{F}_{q}$ assuming that we know $N_1,\cdots,N_g$,
+where $g$ is the genus of $C$. The proof of its validity is given in detail and
+its working are illustrated with several examples. In an Appendix we list the
+Python function in which we have implemented the algorithm together with other
+routines used in the examples.
+",0,0,1,0,0,0
+19091,Doping anatase TiO2 with group V-b and VI-b transition metal atoms: a hybrid functional first-principles study,"  We investigate the role of transition metal atoms of group V-b (V, Nb, and
+Ta) and VI-b (Cr, Mo, and W) as n- or p-type dopants in anatase TiO2 using
+thermodynamic principles and density functional theory with the
+Heyd-Scuseria-Ernzerhof HSE06 hybrid functional. The HSE06 functional provides
+a realistic value for the band gap, which ensures a correct classification of
+dopants as shallow or deep donors or acceptors. Defect formation energies and
+thermodynamic transition levels are calculated taking into account the
+constraints imposed by the stability of TiO2 and the solubility limit of the
+impurities. Nb, Ta, W and Mo are identified as shallow donors. Although W
+provides two electrons, Nb and Ta show a considerably lower formation energy,
+in particular under O-poor conditions. Mo donates in principle one electron,
+but under specific conditions can turn into a double donor. V impurities are
+deep donors and Cr shows up as an amphoteric defect, thereby acting as an
+electron trapping center in n-type TiO2 especially under O-rich conditions. A
+comparison with the available experimental data yields excellent agreement.
+",0,1,0,0,0,0
+19092,Patch-planting spin-glass solution for benchmarking,"  We introduce an algorithm to generate (not solve) spin-glass instances with
+planted solutions of arbitrary size and structure. First, a set of small
+problem patches with open boundaries is solved either exactly or with a
+heuristic, and then the individual patches are stitched together to create a
+large problem with a known planted solution. Because in these problems
+frustration is typically smaller than in random problems, we first assess the
+typical computational complexity of the individual patches using population
+annealing Monte Carlo, and introduce an approach that allows one to fine-tune
+the typical computational complexity of the patch-planted system. The scaling
+of the typical computational complexity of these planted instances with various
+numbers of patches and patch sizes is investigated and compared to random
+instances.
+",0,1,0,0,0,0
+19093,Evidence for the formation of comet 67P/Churyumov-Gerasimenko through gravitational collapse of a bound clump of pebbles,"  The processes that led to the formation of the planetary bodies in the Solar
+System are still not fully understood. Using the results obtained with the
+comprehensive suite of instruments on-board ESA's Rosetta mission, we present
+evidence that comet 67P/Churyumov-Gerasimenko likely formed through the gentle
+gravitational collapse of a bound clump of mm-sized dust aggregates
+(""pebbles""), intermixed with microscopic ice particles. This formation scenario
+leads to a cometary make-up that is simultaneously compatible with the global
+porosity, homogeneity, tensile strength, thermal inertia, vertical temperature
+profiles, sizes and porosities of emitted dust, and the steep increase in
+water-vapour production rate with decreasing heliocentric distance, measured by
+the instruments on-board the Rosetta spacecraft and the Philae lander. Our
+findings suggest that the pebbles observed to be abundant in protoplanetary
+discs around young stars provide the building material for comets and other
+minor bodies.
+",0,1,0,0,0,0
+19094,Detection of virial shocks in stacked Fermi-LAT clusters,"  Galaxy clusters are thought to grow by accreting mass through large-scale,
+strong, yet elusive, virial shocks. Such a shock is expected to accelerate
+relativistic electrons, thus generating a spectrally-flat leptonic virial-ring.
+However, until now, only the nearby Coma cluster has shown evidence for a
+$\gamma$-ray virial ring. We stack Fermi-LAT data for the 112 most massive,
+high latitude, extended clusters, enhancing the ring sensitivity by rescaling
+clusters to their virial radii and utilizing the expected flat energy spectrum.
+In addition to a central unresolved, hard signal (detected at the $\sim
+5.8\sigma$ confidence level), probably dominated by AGN, we identify (at the
+$5.8\sigma$ confidence level) a bright, spectrally-flat $\gamma$-ray ring at
+the expected virial shock position. The ring signal implies that the shock
+deposits $\sim 0.6\%$ (with an interpretation uncertainty factor $\sim2$) of
+the thermal energy in relativistic electrons over a Hubble time. This result,
+consistent with the Coma signal, validates and calibrates the virial shock
+model, and indicates that the cumulative emission from such shocks
+significantly contributes to the diffuse extragalactic $\gamma$-ray and
+low-frequency radio backgrounds.
+",0,1,0,0,0,0
+19095,Closed-form formulae of hyperbolic metamaterial made by stacked hole-array layers working at terahertz or microwave radiation,"  A metamaterial made by stacked hole-array layers known as a fishnet
+metamaterial behaves as a hyperbolic metamaterial at wavelength much longer
+than hole-array period. However, the analytical formulae of effective
+parameters of a fishnet metamaterial have not been reported hindering the
+design of deep-subwavelength imaging devices using this structure. We report
+the new closed-form formulae of effective parameters comprising anisotropic
+dispersion relation of a fishnet metamaterial working at terahertz or microwave
+frequency. These effective parameters of a fishnet metamaterial are consistent
+with those obtained by quasi-full solutions using known effective parameters of
+a hole-array layer working at frequency below its spoof plasma frequency with
+the superlattice period much smaller than the hole-array period. We also
+theoretically demonstrate the deep-subwavelength focusing at {\lambda}/83 using
+the composite structure of a slit-array layer and a fishnet metamaterial. It is
+found that the focused intensity inside a fishnet metamaterial is several times
+larger than that without the fishnet metamaterial, but the transmitted
+intensity is still restricted by large-wavevector difference in air and a
+fishnet metamaterial. Our effective parameters may aid the next-generation
+deep-subwavelength imaging devices working at terahertz or microwave radiation.
+",0,1,0,0,0,0
+19096,RMPflow: A Computational Graph for Automatic Motion Policy Generation,"  We develop a novel policy synthesis algorithm, RMPflow, based on
+geometrically consistent transformations of Riemannian Motion Policies (RMPs).
+RMPs are a class of reactive motion policies designed to parameterize
+non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task
+spaces. Given a set of RMPs designed for individual tasks, RMPflow can
+consistently combine these local policies to generate an expressive global
+policy, while simultaneously exploiting sparse structure for computational
+efficiency. We study the geometric properties of RMPflow and provide sufficient
+conditions for stability. Finally, we experimentally demonstrate that
+accounting for the geometry of task policies can simplify classically difficult
+problems, such as planning through clutter on high-DOF manipulation systems.
+",1,0,0,0,0,0
+19097,Long-range correlations and fractal dynamics in C. elegans: changes with aging and stress,"  Reduced motor control is one of the most frequent features associated with
+aging and disease. Nonlinear and fractal analyses have proved to be useful in
+investigating human physiological alterations with age and disease. Similar
+findings have not been established for any of the model organisms typically
+studied by biologists, though. If the physiology of a simpler model organism
+displays the same characteristics, this fact would open a new research window
+on the control mechanisms that organisms use to regulate physiological
+processes during aging and stress. Here, we use a recently introduced animal
+tracking technology to simultaneously follow tens of Caenorhabdits elegans for
+several hours and use tools from fractal physiology to quantitatively evaluate
+the effects of aging and temperature stress on nematode motility. Similarly to
+human physiological signals, scaling analysis reveals long-range correlations
+in numerous motility variables, fractal properties in behavioral shifts, and
+fluctuation dynamics over a wide range of timescales. These properties change
+as a result of a superposition of age and stress-related adaptive mechanisms
+that regulate motility.
+",0,1,0,1,0,0
+19098,A Computational Approach to Extinction Events in Chemical Reaction Networks with Discrete State Spaces,"  Recent work of M.D. Johnston et al. has produced sufficient conditions on the
+structure of a chemical reaction network which guarantee that the corresponding
+discrete state space system exhibits an extinction event. The conditions
+consist of a series of systems of equalities and inequalities on the edges of a
+modified reaction network called a domination-expanded reaction network. In
+this paper, we present a computational implementation of these conditions
+written in Python and apply the program on examples drawn from the biochemical
+literature, including a model of polyamine metabolism in mammals and a model of
+the pentose phosphate pathway in Trypanosoma brucei. We also run the program on
+458 models from the European Bioinformatics Institute's BioModels Database and
+report our results.
+",0,0,1,0,0,0
+19099,Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media,"  A person's weight status can have profound implications on their life,
+ranging from mental health, to longevity, to financial income. At the societal
+level, ""fat shaming"" and other forms of ""sizeism"" are a growing concern, while
+increasing obesity rates are linked to ever raising healthcare costs. For these
+reasons, researchers from a variety of backgrounds are interested in studying
+obesity from all angles. To obtain data, traditionally, a person would have to
+accurately self-report their body-mass index (BMI) or would have to see a
+doctor to have it measured. In this paper, we show how computer vision can be
+used to infer a person's BMI from social media images. We hope that our tool,
+which we release, helps to advance the study of social aspects related to body
+weight.
+",1,0,0,0,0,0
+19100,Recursive simplex stars,"  This paper proposes a new method which builds a simplex based approximation
+of a $d-1$-dimensional manifold $M$ separating a $d$-dimensional compact set
+into two parts, and an efficient algorithm classifying points according to this
+approximation. In a first variant, the approximation is made of simplices that
+are defined in the cubes of a regular grid covering the compact set, from
+boundary points that approximate the intersection between $M$ and the edges of
+the cubes. All the simplices defined in a cube share the barycentre of the
+boundary points located in the cube and include simplices similarly defined in
+cube facets, and so on recursively. In a second variant, the Kuhn triangulation
+is used to break the cubes into simplices and the approximation is defined in
+these simplices from the boundary points computed on their edges, with the same
+principle. Both the approximation in cubes and in simplices define a separating
+surface on the whole grid and classifying a point on one side or the other of
+this surface requires only a small number (at most $d$) of simple tests. Under
+some conditions on the definition of the boundary points and on the reach of
+$M$, for both variants the Hausdorff distance between $M$ and its approximation
+decreases like $\mathcal{O}(d n_G^{-2})$, where $n_G$ is the number of points
+on each axis of the grid. The approximation in cubes requires computing less
+boundary points than the approximation in simplices but the latter is always a
+manifold and is more accurate for a given value of $n_G$. The paper reports
+tests of the method when varying $n_G$ and the dimensionality of the space (up
+to 9).
+",1,0,0,0,0,0
+19101,An Analytic Formula for Numbers of Restricted Partitions from Conformal Field Theory,"  We study the correlators of irregular vertex operators in two-dimensional
+conformal field theory (CFT) in order to propose an exact analytic formula for
+calculating numbers of partitions, that is:
+1) for given $N,k$, finding the total number $\lambda(N|k)$ of length $k$
+partitions of $N$: $N=n_1+...+n_k;0<n_1\leq{n_2}...\leq{n_k}$.
+2) finding the total number $\lambda(N)=\sum_{k=1}^N\lambda(N|k)$ of
+partitions of a natural number $N$
+We propose an exact analytic expression for $\lambda(N|k)$ by relating
+two-point short-distance correlation functions of irregular vertex operators in
+$c=1$ conformal field theory ( the form of the operators is established in this
+paper): with the first correlator counting the partitions in the upper
+half-plane and the second one obtained from the first correlator by conformal
+transformations of the form $f(z)=h(z)e^{-{i\over{z}}}$ where $h(z)$ is regular
+and non-vanishing at $z=0$. The final formula for $\lambda(N|k)$ is given in
+terms of regularized ($\epsilon$-ordered) finite series in the generalized
+higher-derivative Schwarzians and incomplete Bell polynomials of the above
+conformal transformation at $z=i\epsilon$ ($\epsilon\rightarrow{0}$)
+",0,0,1,0,0,0
+19102,Hyperopic Cops and Robbers,"  We introduce a new variant of the game of Cops and Robbers played on graphs,
+where the robber is invisible unless outside the neighbor set of a cop. The
+hyperopic cop number is the corresponding analogue of the cop number, and we
+investigate bounds and other properties of this parameter. We characterize the
+cop-win graphs for this variant, along with graphs with the largest possible
+hyperopic cop number. We analyze the cases of graphs with diameter 2 or at
+least 3, focusing on when the hyperopic cop number is at most one greater than
+the cop number. We show that for planar graphs, as with the usual cop number,
+the hyperopic cop number is at most 3. The hyperopic cop number is considered
+for countable graphs, and it is shown that for connected chains of graphs, the
+hyperopic cop density can be any real number in $[0,1/2].$
+",1,0,0,0,0,0
+19103,New pinching estimates for Inverse curvature flows in space forms,"  We prove new pinching estimate for the inverse curvature flow of strictly
+convex hypersurfaces in the space form $N$ of constant sectional curvature
+$K_N$ with speed given by $F^{-\alpha}$, where $\alpha\in (0,1]$ for $K_N=0,-1$
+and $\alpha=1$ for $K_N=1$, $F$ is a smooth, symmetric homogeneous of degree
+one function which is inverse concave and has dual $F_*$ approaching zero on
+the boundary of the positive cone $\Gamma_+$. We show that the ratio of the
+largest principal curvature to the smallest principal curvature of the flow
+hypersurface is controlled by its initial value. This can be used to prove the
+smooth convergence of the flow.
+",0,0,1,0,0,0
+19104,"automan: a simple, Python-based, automation framework for numerical computing","  We present an easy-to-use, Python-based framework that allows a researcher to
+automate their computational simulations. In particular the framework
+facilitates assembling several long-running computations and producing various
+plots from the data produced by these computations. The framework makes it
+possible to reproduce every figure made for a publication with a single
+command. It also allows one to distribute the computations across a network of
+computers. The framework has been used to write research papers in numerical
+computing. This paper discusses the design of the framework, and the benefits
+of using it. The ideas presented are general and should help researchers
+organize their computations for better reproducibility.
+",1,0,0,0,0,0
+19105,Extensive characterization of a high Reynolds number decelerating boundary layer using advanced optical metrology,"  An experiment conducted in the framework of the EUHIT project and designed to
+characterize large scale structures in an adverse pressure gradient boundary
+layer flow is presented. Up to 16 sCMOS cameras were used in order to perform
+large scale turbulent boundary layer PIV measurements with a large field of
+view and appropriate spatial resolution. To access the span-wise / wall-normal
+signature of the structures as well, stereoscopic PIV measurements in
+span-wise/wall-normal planes were performed at specific stream-wise locations.
+To complement these large field of view measurements, long-range micro-PIV,
+time resolved near wall velocity profiles and film-based measurements were
+performed in order to determine the wall-shear stress and its fluctuations at
+some specific locations along the model.
+",0,1,0,0,0,0
+19106,Q-analogues of the Fibo-Stirling numbers,"  Let $F_n$ denote the $n^{th}$ Fibonacci number relative to the initial
+conditions $F_0=0$ and $F_1=1$. Bach, Paudyal, and Remmel introduced Fibonacci
+analogues of the Stirling numbers called Fibo-Stirling numbers of the first and
+second kind. These numbers serve as the connection coefficients between the
+Fibo-falling factorial basis $\{(x)_{\downarrow_{F,n}}:n \geq 0\}$ and the
+Fibo-rising factorial basis $\{(x)_{\uparrow_{F,n}}:n \geq 0\}$ which are
+defined by $(x)_{\downarrow_{F,0}} = (x)_{\uparrow_{F,0}} = 1$ and for $k \geq
+1$, $(x)_{\downarrow_{F,k}} = x(x-F_1) \cdots (x-F_{k-1})$ and
+$(x)_{\uparrow_{F,k}} = x(x+F_1) \cdots (x+F_{k-1})$. We gave a general rook
+theory model which allowed us to give combinatorial interpretations of the
+Fibo-Stirling numbers of the first and second kind.
+There are two natural $q$-analogues of the falling and rising Fibo-factorial
+basis. That is, let $[x]_q = \frac{q^x-1}{q-1}$. Then we let
+$[x]_{\downarrow_{q,F,0}} = \overline{[x]}_{\downarrow_{q,F,0}} =
+[x]_{\uparrow_{q,F,0}} = \overline{[x]}_{\uparrow_{q,F,0}}=1$ and, for $k > 0$,
+we let $[x]_{\downarrow_{q,F,k}} = [x]_q [x-F_1]_q \cdots [x-F_{k-1}]_q$,
+$\overline{[x]}_{\downarrow_{q,F,k}}= [x]_q ([x]_q-[F_1]_q) \cdots
+([x]_q-[F_{k-1}]_q)$, $[x]_{\uparrow_{q,F,k}}= [x]_q [x+F_1]_q \cdots
+[x+F_{k-1}]_q$, and $\overline{[x]}_{\uparrow_{q,F,k}}= [x]_q ([x]_q+[F_1]_q)
+\cdots ([x]_q+[F_{k-1}]_q)$.
+In this paper, we show we can modify the rook theory model of Bach, Paudyal,
+and Remmel to give combinatorial interpretations for the two different types
+$q$-analogues of the Fibo-Stirling numbers which arise as the connection
+coefficients between the two different $q$-analogues of the Fibonacci falling
+and rising factorial bases. \end{abstract}
+",0,0,1,0,0,0
+19107,Hybrid Dirac Semimetal in CaAgBi Materials Family,"  Based on their formation mechanisms, Dirac points in three-dimensional
+systems can be classified as accidental or essential. The former can be further
+distinguished into type-I and type-II, depending on whether the Dirac cone
+spectrum is completely tipped over along certain direction. Here, we predict
+the coexistence of all three kinds of Dirac points in the low-energy band
+structure of CaAgBi-family materials with a stuffed Wurtzite structure. Two
+pairs of accidental Dirac points reside on the rotational axis, with one pair
+being type-I and the other pair type-II; while another essential Dirac point is
+pinned at the high symmetry point on the Brillouin zone boundary. Due to broken
+inversion symmetry, the band degeneracy around accidental Dirac points is
+completely lifted except along the rotational axis, which may enable the
+splitting of chiral carriers at a ballistic p-n junction with a double negative
+refraction effect. We clarify their symmetry protections, and find both the
+Dirac-cone and Fermi arc topological surface states.
+",0,1,0,0,0,0
+19108,On the martingale property in the rough Bergomi model,"  We consider a class of fractional stochastic volatility models (including the
+so-called rough Bergomi model), where the volatility is a superlinear function
+of a fractional Gaussian process. We show that the stock price is a true
+martingale if and only if the correlation $\rho$ between the driving Brownian
+motions of the stock and the volatility is nonpositive. We also show that for
+each $\rho<0$ and $m> \frac{1}{1-\rho^2}$, the $m$-th moment of the stock
+price is infinite at each positive time.
+",0,0,0,0,0,1
+19109,Weighted Surface Algebras,"  A finite-dimensional algebra $A$ over an algebraically closed field $K$ is
+called periodic if it is periodic under the action of the syzygy operator in
+the category of $A-A-$ bimodules. The periodic algebras are self-injective and
+occur naturally in the study of tame blocks of group algebras, actions of
+finite groups on spheres, hypersurface singularities of finite Cohen-Macaulay
+type, and Jacobian algebras of quivers with potentials. Recently, the tame
+periodic algebras of polynomial growth have been classified and it is natural
+to attempt to classify all tame periodic algebras. We introduce the weighted
+surface algebras of triangulated surfaces with arbitrarily oriented triangles
+and describe their basic properties. In particular, we prove that all these
+algebras, except the singular tetrahedral algebras, are symmetric tame periodic
+algebras of period $4$. Moreover, we describe the socle deformations of the
+weighted surface algebras and prove that all these algebras are symmetric tame
+periodic algebras of period $4$. The main results of the paper form an
+important step towards a classification of all periodic symmetric tame algebras
+of non-polynomial growth, and lead to a complete description of all algebras of
+generalized quaternion type. Further, the orbit closures of the weighted
+surface algebras (and their socle deformations) in the affine varieties of
+associative $K$-algebra structures contain wide classes of tame symmetric
+algebras related to algebras of dihedral and semidihedral types, which occur in
+the study of blocks of group algebras with dihedral and semidihedral defect
+groups.
+",0,0,1,0,0,0
+19110,A Statistical Learning Approach to Modal Regression,"  This paper studies the nonparametric modal regression problem systematically
+from a statistical learning view. Originally motivated by pursuing a
+theoretical understanding of the maximum correntropy criterion based regression
+(MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is
+essentially modal regression. We show that nonparametric modal regression
+problem can be approached via the classical empirical risk minimization. Some
+efforts are then made to develop a framework for analyzing and implementing
+modal regression. For instance, the modal regression function is described, the
+modal regression risk is defined explicitly and its \textit{Bayes} rule is
+characterized; for the sake of computational tractability, the surrogate modal
+regression risk, which is termed as the generalization risk in our study, is
+introduced. On the theoretical side, the excess modal regression risk, the
+excess generalization risk, the function estimation error, and the relations
+among the above three quantities are studied rigorously. It turns out that
+under mild conditions, function estimation consistency and convergence may be
+pursued in modal regression as in vanilla regression protocols, such as mean
+regression, median regression, and quantile regression. However, it outperforms
+these regression models in terms of robustness as shown in our study from a
+re-descending M-estimation view. This coincides with and in return explains the
+merits of MCCR on robustness. On the practical side, the implementation issues
+of modal regression including the computational algorithm and the tuning
+parameters selection are discussed. Numerical assessments on modal regression
+are also conducted to verify our findings empirically.
+",0,0,1,1,0,0
+19111,Teaching a Machine to Read Maps with Deep Reinforcement Learning,"  The ability to use a 2D map to navigate a complex 3D environment is quite
+remarkable, and even difficult for many humans. Localization and navigation is
+also an important problem in domains such as robotics, and has recently become
+a focus of the deep reinforcement learning community. In this paper we teach a
+reinforcement learning agent to read a map in order to find the shortest way
+out of a random maze it has never seen before. Our system combines several
+state-of-the-art methods such as A3C and incorporates novel elements such as a
+recurrent localization cell. Our agent learns to localize itself based on 3D
+first person images and an approximate orientation angle. The agent generalizes
+well to bigger mazes, showing that it learned useful localization and
+navigation capabilities.
+",1,0,0,1,0,0
+19112,The fan beam model for the pulse evolution of PSR J0737-3039B,"  Average radio pulse profile of a pulsar B in a double pulsar system PSR
+J0737-3039A/B exhibits an interesting behaviour. During the observation period
+between 2003 and 2009, the profile evolves from a single-peaked to a
+double-peaked form, following disappearance in 2008 indicating that the
+geodetic precession of the pulsar is a possible origin of such behaviour. The
+known pulsar beam models can be used to determine the geometry of PSR
+J0737-3039B in the context of the precession. We study how the fan-beam
+geometry performs in explaining the observed variations of the radio profile
+morphology. It is shown that the fan beam can successfully reproduce the
+observed evolution of the pulse width, and should be considered as a serious
+alternative for the conal-like models.
+",0,1,0,0,0,0
+19113,Resilience of Core-Periphery Networks in the Case of Rich-Club,"  Core-periphery networks are structures that present a set of central and
+densely connected nodes, namely the core, and a set of non-central and sparsely
+connected nodes, namely the periphery. The rich-club refers to a set in which
+the highest degree nodes show a high density of connections. Thus, a network
+that displays a rich-club can be interpreted as a core-periphery network in
+which the core is made up by a number of hubs. In this paper, we test the
+resilience of networks showing a progressively denser rich-club and we observe
+how this structure is able to affect the network measures in terms of both
+cohesion and efficiency in information flow. Additionally, we consider the case
+in which, instead of making the core denser, we add links to the periphery.
+These two procedures of core and periphery thickening delineate a decision
+process in the placement of new links and allow us to conduct a scenario
+analysis that can be helpful in the comprehension and supervision of complex
+networks under the resilience perspective. The advantages of the two
+procedures, as well as their implications, are discussed in relation to both
+network effciency and node heterogeneity.
+",1,0,0,0,0,0
+19114,Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams,"  The last decade has seen a surge of interest in adaptive learning algorithms
+for data stream classification, with applications ranging from predicting ozone
+level peaks, learning stock market indicators, to detecting computer security
+violations. In addition, a number of methods have been developed to detect
+concept drifts in these streams. Consider a scenario where we have a number of
+classifiers with diverse learning styles and different drift detectors.
+Intuitively, the current 'best' (classifier, detector) pair is application
+dependent and may change as a result of the stream evolution. Our research
+builds on this observation. We introduce the $\mbox{Tornado}$ framework that
+implements a reservoir of diverse classifiers, together with a variety of drift
+detection algorithms. In our framework, all (classifier, detector) pairs
+proceed, in parallel, to construct models against the evolving data streams. At
+any point in time, we select the pair which currently yields the best
+performance. We further incorporate two novel stacking-based drift detection
+methods, namely the $\mbox{FHDDMS}$ and $\mbox{FHDDMS}_{add}$ approaches. The
+experimental evaluation confirms that the current 'best' (classifier, detector)
+pair is not only heavily dependent on the characteristics of the stream, but
+also that this selection evolves as the stream flows. Further, our
+$\mbox{FHDDMS}$ variants detect concept drifts accurately in a timely fashion
+while outperforming the state-of-the-art.
+",1,0,0,1,0,0
+19115,Extremes in Random Graphs Models of Complex Networks,"  Regarding the analysis of Web communication, social and complex networks the
+fast finding of most influential nodes in a network graph constitutes an
+important research problem. We use two indices of the influence of those nodes,
+namely, PageRank and a Max-linear model. We consider the PageRank %both as
+%Galton-Watson branching process and as an autoregressive process with a random
+number of random coefficients that depend on ranks of incoming nodes and their
+out-degrees and assume that the coefficients are independent and distributed
+with regularly varying tail and with the same tail index. Then it is proved
+that the tail index and the extremal index are the same for both PageRank and
+the Max-linear model and the values of these indices are found. The
+achievements are based on the study of random sequences of a random length and
+the comparison of the distribution of their maxima and linear combinations.
+",0,0,1,1,0,0
+19116,Estimates of the Reconstruction Error in Partially Redressed Warped Frames Expansions,"  In recent work, redressed warped frames have been introduced for the analysis
+and synthesis of audio signals with non-uniform frequency and time resolutions.
+In these frames, the allocation of frequency bands or time intervals of the
+elements of the representation can be uniquely described by means of a warping
+map. Inverse warping applied after time-frequency sampling provides the key to
+reduce or eliminate dispersion of the warped frame elements in the conjugate
+variable, making it possible, e.g., to construct frequency warped frames with
+synchronous time alignment through frequency. The redressing procedure is
+however exact only when the analysis and synthesis windows have compact support
+in the domain where warping is applied. This implies that frequency warped
+frames cannot have compact support in the time domain. This property is
+undesirable when online computation is required. Approximations in which the
+time support is finite are however possible, which lead to small reconstruction
+errors. In this paper we study the approximation error for compactly supported
+frequency warped analysis-synthesis elements, providing a few examples and case
+studies.
+",1,0,0,0,0,0
+19117,On the Optimality of Secret Key Agreement via Omniscience,"  For the multiterminal secret key agreement problem under a private source
+model, it is known that the maximum key rate, i.e., the secrecy capacity, can
+be achieved through communication for omniscience, but the omniscience strategy
+can be strictly suboptimal in terms of minimizing the public discussion rate.
+While a single-letter characterization is not known for the minimum discussion
+rate needed for achieving the secrecy capacity, we derive single-letter lower
+and upper bounds that yield some simple conditions for omniscience to be
+discussion-rate optimal. These conditions turn out to be enough to deduce the
+optimality of omniscience for a large class of sources including the
+hypergraphical sources. Through conjectures and examples, we explore other
+source models to which our methods do not easily extend.
+",1,0,0,0,0,0
+19118,Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process,"  Constructing a smart wheelchair on a commercially available powered
+wheelchair (PWC) platform avoids a host of seating, mechanical design and
+reliability issues but requires methods of predicting and controlling the
+motion of a device never intended for robotics. Analog joystick inputs are
+subject to black-box transformations which may produce intuitive and adaptable
+motion control for human operators, but complicate robotic control approaches;
+furthermore, installation of standard axle mounted odometers on a commercial
+PWC is difficult. In this work, we present an integrated hardware and software
+system for predicting the motion of a commercial PWC platform that does not
+require any physical or electronic modification of the chair beyond plugging
+into an industry standard auxiliary input port. This system uses an RGB-D
+camera and an Arduino interface board to capture motion data, including visual
+odometry and joystick signals, via ROS communication. Future motion is
+predicted using an autoregressive sparse Gaussian process model. We evaluate
+the proposed system on real-world short-term path prediction experiments.
+Experimental results demonstrate the system's efficacy when compared to a
+baseline neural network model.
+",1,0,0,0,0,0
+19119,Kernelized Hashcode Representations for Relation Extraction,"  Kernel methods have produced state-of-the-art results for a number of NLP
+tasks such as relation extraction, but suffer from poor scalability due to the
+high cost of computing kernel similarities between natural language structures.
+A recently proposed technique, kernelized locality-sensitive hashing (KLSH),
+can significantly reduce the computational cost, but is only applicable to
+classifiers operating on kNN graphs. Here we propose to use random subspaces of
+KLSH codes for efficiently constructing an explicit representation of NLP
+structures suitable for general classification methods. Further, we propose an
+approach for optimizing the KLSH model for classification problems by
+maximizing an approximation of mutual information between the KLSH codes
+(feature vectors) and the class labels. We evaluate the proposed approach on
+biomedical relation extraction datasets, and observe significant and robust
+improvements in accuracy w.r.t. state-of-the-art classifiers, along with
+drastic (orders-of-magnitude) speedup compared to conventional kernel methods.
+",1,0,0,1,0,0
+19120,Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?,"  When you see a person in a crowd, occluded by other persons, you miss visual
+information that can be used to recognize, re-identify or simply classify him
+or her. You can imagine its appearance given your experience, nothing more.
+Similarly, AI solutions can try to hallucinate missing information with
+specific deep learning architectures, suitably trained with people with and
+without occlusions. The goal of this work is to generate a complete image of a
+person, given an occluded version in input, that should be a) without occlusion
+b) similar at pixel level to a completely visible people shape c) capable to
+conserve similar visual attributes (e.g. male/female) of the original one. For
+the purpose, we propose a new approach by integrating the state-of-the-art of
+neural network architectures, namely U-nets and GANs, as well as discriminative
+attribute classification nets, with an architecture specifically designed to
+de-occlude people shapes. The network is trained to optimize a Loss function
+which could take into account the aforementioned objectives. As well we propose
+two datasets for testing our solution: the first one, occluded RAP, created
+automatically by occluding real shapes of the RAP dataset (which collects also
+attributes of the people aspect); the second is a large synthetic dataset, AiC,
+generated in computer graphics with data extracted from the GTA video game,
+that contains 3D data of occluded objects by construction. Results are
+impressive and outperform any other previous proposal. This result could be an
+initial step to many further researches to recognize people and their behavior
+in an open crowded world.
+",1,0,0,0,0,0
+19121,"John, the semi-conductor : a tool for comprovisation","  This article presents ""John"", an open-source software designed to help
+collective free improvisation. It provides generated screen-scores running on
+distributed, reactive web-browsers. The musicians can then concurrently edit
+the scores in their own browser. John is used by ONE, a septet playing
+improvised electro-acoustic music with digital musical instruments (DMI). One
+of the original features of John is that its design takes care of leaving the
+musician's attention as free as possible. Firstly, a quick review of the
+context of screen-based scores will help situate this research in the history
+of contemporary music notation. Then I will trace back how improvisation
+sessions led to John's particular ""notational perspective"". A brief description
+of the software will precede a discussion about the various aspects guiding its
+design.
+",1,0,0,0,0,0
+19122,Eigenvalue and Eigenfunction for the $PT$-symmetric Potential $V = - (ix)^N$,"  The real energy spectrum from the $PT$-symmetric Hamiltonian $H = p^2 -
+(ix)^N$ with $x\in\mathbb{C}$ was examined within one pair of Stokes wedges in
+1998 by Bender and Boettcher. For this Hamiltonian we discuss the following
+three questions. First, since their paper used a Runge-Kutta method to
+integrate along a path at the center of the Stokes wedges to calculate
+eigenvalues $E$ with high accuracy, we wonder if the same eigenvalues can be
+obtained if integrate along some other paths in different shapes. Second, what
+the corresponding eigenfunctions look like? Should the eigenfunctions be
+independent from the shapes of path or not? Third, since for large $N$ the
+Hamiltonian contains many pairs of Stokes wedges symmetric with respect to the
+imaginary axis of $x$, thus multiple families of real energy spectrum can be
+obtained. What do they look like? Any relation among them?
+",0,0,1,0,0,0
+19123,Random walks on activity-driven networks with attractiveness,"  Virtually all real-world networks are dynamical entities. In social networks,
+the propensity of nodes to engage in social interactions (activity) and their
+chances to be selected by active nodes (attractiveness) are heterogeneously
+distributed. Here, we present a time-varying network model where each node and
+the dynamical formation of ties are characterised by these two features. We
+study how these properties affect random walk processes unfolding on the
+network when the time scales describing the process and the network evolution
+are comparable. We derive analytical solutions for the stationary state and the
+mean first passage time of the process and we study cases informed by empirical
+observations of social networks. Our work shows that previously disregarded
+properties of real social systems such heterogeneous distributions of activity
+and attractiveness as well as the correlations between them, substantially
+affect the dynamical process unfolding on the network.
+",1,1,0,0,0,0
+19124,Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data,"  B cells develop high affinity receptors during the course of affinity
+maturation, a cyclic process of mutation and selection. At the end of affinity
+maturation, a number of cells sharing the same ancestor (i.e. in the same
+""clonal family"") are released from the germinal center, their amino acid
+frequency profile reflects the allowed and disallowed substitutions at each
+position. These clonal-family-specific frequency profiles, called ""substitution
+profiles"", are useful for studying the course of affinity maturation as well as
+for antibody engineering purposes. However, most often only a single sequence
+is recovered from each clonal family in a sequencing experiment, making it
+impossible to construct a clonal-family-specific substitution profile. Given
+the public release of many high-quality large B cell receptor datasets, one may
+ask whether it is possible to use such data in a prediction model for
+clonal-family-specific substitution profiles. In this paper, we present the
+method ""Substitution Profiles Using Related Families"" (SPURF), a penalized
+tensor regression framework that integrates information from a rich assemblage
+of datasets to predict the clonal-family-specific substitution profile for any
+single input sequence. Using this framework, we show that substitution profiles
+from similar clonal families can be leveraged together with simulated
+substitution profiles and germline gene sequence information to improve
+prediction. We fit this model on a large public dataset and validate the
+robustness of our approach on an external dataset. Furthermore, we provide a
+command-line tool in an open-source software package
+(this https URL) implementing these ideas and providing easy
+prediction using our pre-fit models.
+",0,0,0,0,1,0
+19125,Energy Efficient Mobile Edge Computing in Dense Cellular Networks,"  Merging Mobile Edge Computing (MEC), which is an emerging paradigm to meet
+the increasing computation demands from mobile devices, with the dense
+deployment of Base Stations (BSs), is foreseen as a key step towards the next
+generation mobile networks. However, new challenges arise for designing energy
+efficient networks since radio access resources and computing resources of BSs
+have to be jointly managed, and yet they are complexly coupled with traffic in
+both spatial and temporal domains. In this paper, we address the challenge of
+incorporating MEC into dense cellular networks, and propose an efficient online
+algorithm, called ENGINE (ENErgy constrained offloadINg and slEeping) which
+makes joint computation offloading and BS sleeping decisions in order to
+maximize the quality of service while keeping the energy consumption low. Our
+algorithm leverages Lyapunov optimization technique, works online and achieves
+a close-to-optimal performance without using future information. Our simulation
+results show that our algorithm can effectively reduce energy consumption
+without sacrificing the user quality of service.
+",1,0,0,0,0,0
+19126,Delone dynamical systems and spectral convergence,"  In the realm of Delone sets in locally compact, second countable, Hausdorff
+groups, we develop a dynamical systems approach in order to study the
+continuity behavior of measured quantities arising from point sets. A special
+focus is both on the autocorrelation, as well as on the density of states for
+random bounded operators. It is shown that for uniquely ergodic limit systems,
+the latter measures behave continuously with respect to the Chabauty-Fell
+convergence of hulls. In the special situation of Euclidean spaces, our results
+complement recent developments in describing spectra as topological limits: we
+show that the measured quantities under consideration can be approximated via
+periodic analogs.
+",0,0,1,0,0,0
+19127,On biconservative surfaces in Euclidean spaces,"  In this paper, we study biconservative surfaces with parallel normalized mean
+curvature vector in $\mathbb{E}^4$. We obtain complete local classification in
+$\mathbb{E}^4$ for a biconservative PNMCV surface. We also give an example to
+show the existence of PNMCV biconservative surfaces in $\mathbb{E}^4$.
+",0,0,1,0,0,0
+19128,Automatic implementation of material laws: Jacobian calculation in a finite element code with TAPENADE,"  In an effort to increase the versatility of finite element codes, we explore
+the possibility of automatically creating the Jacobian matrix necessary for the
+gradient-based solution of nonlinear systems of equations. Particularly, we aim
+to assess the feasibility of employing the automatic differentiation tool
+TAPENADE for this purpose on a large Fortran codebase that is the result of
+many years of continuous development. As a starting point we will describe the
+special structure of finite element codes and the implications that this code
+design carries for an efficient calculation of the Jacobian matrix. We will
+also propose a first approach towards improving the efficiency of such a
+method. Finally, we will present a functioning method for the automatic
+implementation of the Jacobian calculation in a finite element software, but
+will also point out important shortcomings that will have to be addressed in
+the future.
+",1,1,0,0,0,0
+19129,Asymptotic analysis for Hamilton-Jacobi equations with large drift term,"  We investigate the asymptotic behavior of solutions of Hamilton-Jacobi
+equations with large drift term in an open subset of two-dimensional Euclidean
+space. When the drift is given by $\varepsilon^{-1} (H_{x_2}, -H_{x_1})$ of a
+Hamiltonian $H$, with $\varepsilon > 0$, we establish the convergence, as
+$\varepsilon \to 0+$, of solutions of the Hamilton-Jacobi equations and
+identify the limit of the solutions as the solution of systems of ordinary
+differential equations on a graph. This result generalizes the previous one
+obtained by the author to the case where the Hamiltonian $H$ admits a
+degenerate critical point and, as a consequence, the graph may have segments
+more than four at a node.
+",0,0,1,0,0,0
+19130,An Upper Bound of the Minimal Dispersion via Delta Covers,"  For a point set of $n$ elements in the $d$-dimensional unit cube and a class
+of test sets we are interested in the largest volume of a test set which does
+not contain any point. For all natural numbers $n$, $d$ and under the
+assumption of a $delta$-cover with cardinality $\vert \Gamma_\delta \vert$ we
+prove that there is a point set, such that the largest volume of such a test
+set without any point is bounded by $\frac{\log \vert \Gamma_\delta \vert}{n} +
+\delta$. For axis-parallel boxes on the unit cube this leads to a volume of at
+most $\frac{4d}{n}\log(\frac{9n}{d})$ and on the torus to $\frac{4d}{n}\log
+(2n)$.
+",1,0,1,0,0,0
+19131,Three-dimensional band structure of LaSb and CeSb:Absence of band inversion,"  We have performed angle-resolved photoemission spectroscopy (ARPES) of LaSb
+and CeSb, a candidate of topological insulator. Using soft-x-ray photons, we
+have accurately determined the three-dimensional bulk band structure and
+revealed that the band inversion at the Brillouin-zone corner - a prerequisite
+for realizing topological-insulator phase - is absent in both LaSb and CeSb.
+Moreover, unlike the ARPES data obtained with soft-x-ray photons, those with
+vacuum ultraviolet (VUV) photons were found to suffer significant $k_z$
+broadening. These results suggest that LaSb and CeSb are topologically trivial
+semimetals, and unusual Dirac-cone-like states observed with VUV photons are
+not of the topological origin.
+",0,1,0,0,0,0
+19132,Stellar-to-halo mass relation of cluster galaxies,"  In the hierarchical formation model, galaxy clusters grow by accretion of
+smaller groups or isolated galaxies. During the infall into the centre of a
+cluster, the properties of accreted galaxies change. In particular, both
+observations and numerical simulations suggest that its dark matter halo is
+stripped by the tidal forces of the host.
+We use galaxy-galaxy weak lensing to measure the average mass of dark matter
+haloes of satellite galaxies as a function of projected distance to the centre
+of the host, for different stellar mass bins. Assuming that the stellar
+component of the galaxy is less disrupted by tidal stripping, stellar mass can
+be used as a proxy of the infall mass. We study the stellar to halo mass
+relation of satellites as a function of the cluster-centric distance to measure
+tidal stripping.
+We use the shear catalogues of the DES science verification archive, the
+CFHTLenS and the CFHT Stripe 82 (CS82) surveys, and we select satellites from
+the redMaPPer catalogue of clusters. For galaxies located in the outskirts of
+clusters, we find a stellar to halo mass relation in good agreement with the
+theoretical expectations from \citet{moster2013} for central galaxies. In the
+centre of the cluster, we find that this relation is shifted to smaller halo
+mass for a given stellar mass. We interpret this finding as further evidence
+for tidal stripping of dark matter haloes in high density environments.
+",0,1,0,0,0,0
+19133,Undecidability and Finite Automata,"  Using a novel rewriting problem, we show that several natural decision
+problems about finite automata are undecidable (i.e., recursively unsolvable).
+In contrast, we also prove three related problems are decidable. We apply one
+result to prove the undecidability of a related problem about k-automatic sets
+of rational numbers.
+",1,0,0,0,0,0
+19134,A globally stable attractor that is locally unstable everywhere,"  We construct two examples of invariant manifolds that despite being locally
+unstable at every point in the transverse direction are globally stable. Using
+numerical simulations we show that these invariant manifolds temporarily repel
+nearby trajectories but act as global attractors. We formulate an explanation
+for such global stability in terms of the `rate of rotation' of the stable and
+unstable eigenvectors spanning the normal subspace associated with each point
+of the invariant manifold. We discuss the role of this rate of rotation on the
+transitions between the stable and unstable regimes.
+",0,1,0,0,0,0
+19135,On Centers and Central Lines of Triangles in the Elliptic Plane,"  We determine barycentric coordinates of triangle centers in the elliptic
+plane. The main focus is put on centers that lie on lines whose euclidean limit
+(triangle excess --> 0) is the Euler line or the Brocard line. We also
+investigate curves which can serve in elliptic geometry as substitutes for the
+euclidean nine-point-circle, the first Lemoine circle or the apollonian
+circles.
+",0,0,1,0,0,0
+19136,Stopping Active Learning based on Predicted Change of F Measure for Text Classification,"  During active learning, an effective stopping method allows users to limit
+the number of annotations, which is cost effective. In this paper, a new
+stopping method called Predicted Change of F Measure will be introduced that
+attempts to provide the users an estimate of how much performance of the model
+is changing at each iteration. This stopping method can be applied with any
+base learner. This method is useful for reducing the data annotation bottleneck
+encountered when building text classification systems.
+",1,0,0,1,0,0
+19137,Statistical properties of random clique networks,"  In this paper, a random clique network model to mimic the large clustering
+coefficient and the modular structure that exist in many real complex networks,
+such as social networks, artificial networks, and protein interaction networks,
+is introduced by combining the random selection rule of the Erdös and Rényi
+(ER) model and the concept of cliques. We find that random clique networks
+having a small average degree differ from the ER network in that they have a
+large clustering coefficient and a power law clustering spectrum, while
+networks having a high average degree have similar properties as the ER model.
+In addition, we find that the relation between the clustering coefficient and
+the average degree shows a non-monotonic behavior and that the degree
+distributions can be fit by multiple Poisson curves; we explain the origin of
+such novel behaviors and degree distributions.
+",1,1,0,0,0,0
+19138,"Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset","  The paucity of videos in current action classification datasets (UCF-101 and
+HMDB-51) has made it difficult to identify good video architectures, as most
+methods obtain similar performance on existing small-scale benchmarks. This
+paper re-evaluates state-of-the-art architectures in light of the new Kinetics
+Human Action Video dataset. Kinetics has two orders of magnitude more data,
+with 400 human action classes and over 400 clips per class, and is collected
+from realistic, challenging YouTube videos. We provide an analysis on how
+current architectures fare on the task of action classification on this dataset
+and how much performance improves on the smaller benchmark datasets after
+pre-training on Kinetics.
+We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on
+2D ConvNet inflation: filters and pooling kernels of very deep image
+classification ConvNets are expanded into 3D, making it possible to learn
+seamless spatio-temporal feature extractors from video while leveraging
+successful ImageNet architecture designs and even their parameters. We show
+that, after pre-training on Kinetics, I3D models considerably improve upon the
+state-of-the-art in action classification, reaching 80.9% on HMDB-51 and 98.0%
+on UCF-101.
+",1,0,0,0,0,0
+19139,Selfish Cops and Active Robber: Multi-Player Pursuit Evasion on Graphs,"  We introduce and study the game of ""Selfish Cops and Active Robber"" (SCAR)
+which can be seen as an multiplayer variant of the ""classic"" two-player Cops
+and Robbers (CR) game. In classic CR all cops are controlled by a single
+player, who has no preference over which cop captures the robber. In SCAR, on
+the other hand, each of N-1 cops is controlled by a separate player, and a
+single robber is controlled by the N-th player; and the capturing cop player
+receives a higher reward than the non-capturing ones. Consequently, SCAR is an
+N-player pursuit game on graphs, in which each cop player has an increased
+motive to be the one who captures the robber. The focus of our study is the
+existence and properties of SCAR Nash Equilibria (NE). In particular, we prove
+that SCAR always has one NE in deterministic positional strategies and (for N
+greater than two) another in deterministic nonpositional strategies.
+Furthermore, we study conditions which, at equilibrium, guarantee either
+capture or escape of the robber and show that (because of the antagonism
+between the ""selfish"" cop players) the robber may, in certain SCAR
+configurations, be captured later than he would be in classic CR, or even not
+captured at all. Finally we define the selfish cop number of a graph and study
+its connection to the classic cop number.
+",1,0,0,0,0,0
+19140,Singly-Thermostated Ergodicity in Gibbs' Canonical Ensemble and the 2016 Ian Snook Prize Award,"  The 2016 Snook Prize has been awarded to Diego Tapias, Alessandro Bravetti,
+and David Sanders for their paper -- Ergodicity of One-Dimensional Systems
+Coupled to the Logistic Thermostat. They introduced a relatively stiff
+hyperbolic tangent thermostat force and successfully tested its ability to
+reproduce Gibbs' canonical distribution for the harmonic oscillator, the
+quartic oscillator, and the Mexican Hat potentials. Their work constitutes an
+effective response to the 2016 Ian Snook Prize Award goal -- Finding ergodic
+algorithms for Gibbs' canonical ensemble using a single thermostat variable. We
+confirm their work here and highlight an interesting feature of the Mexican Hat
+problem when it is solved with an adaptive integrator.
+",0,1,0,0,0,0
+19141,Robust Estimation via Robust Gradient Estimation,"  We provide a new computationally-efficient class of estimators for risk
+minimization. We show that these estimators are robust for general statistical
+models: in the classical Huber epsilon-contamination model and in heavy-tailed
+settings. Our workhorse is a novel robust variant of gradient descent, and we
+provide conditions under which our gradient descent variant provides accurate
+estimators in a general convex risk minimization problem. We provide specific
+consequences of our theory for linear regression, logistic regression and for
+estimation of the canonical parameters in an exponential family. These results
+provide some of the first computationally tractable and provably robust
+estimators for these canonical statistical models. Finally, we study the
+empirical performance of our proposed methods on synthetic and real datasets,
+and find that our methods convincingly outperform a variety of baselines.
+",0,0,0,1,0,0
+19142,SMT Solving for Vesicle Traffic Systems in Cells,"  In biology, there are several questions that translate to combinatorial
+search. For example, vesicle traffic systems that move cargo within eukaryotic
+cells have been proposed to exhibit several graph properties such as three
+connectivity. These properties are consequences of underlying biophysical
+constraints. A natural question for biologists is: what are the possible
+networks for various combinations of those properties? In this paper, we
+present novel SMT based encodings of the properties over vesicle traffic
+systems and a tool that searches for the networks that satisfies the properties
+using SMT solvers. In our experiments, we show that our tool can search for
+networks of sizes that are considered to be relevant by biologists.
+",1,0,0,0,0,0
+19143,Endemicity and prevalence of multipartite viruses under heterogeneous between-host transmission,"  Multipartite viruses replicate through a puzzling evolutionary strategy.
+Their genome is segmented into two or more parts, and encapsidated in separate
+particles that appear to propagate independently. Completing the replication
+cycle, however, requires the full genome, so that a persistent infection of a
+host requires the concurrent presence of several particles. This represents an
+apparent evolutionary drawback of multipartitism, while its advantages remain
+unclear. A transition from monopartite to multipartite viral forms has been
+described in vitro under conditions of high multiplicity of infection,
+suggesting that cooperation between defective mutants is a plausible
+evolutionary pathway towards multipartitism. However, it is unknown how the
+putative advantages that multipartitism might enjoy affect its epidemiology, or
+if an explicit advantage is needed to explain its ecological persistence. To
+disentangle which mechanisms might contribute to the rise and fixation of
+multipartitism, we here investigate the interaction between viral spreading
+dynamics and host population structure. We set up a compartmental model of the
+spread of a virus in its different forms and explore its epidemiology using
+both analytical and numerical techniques. We uncover that the impact of host
+contact structure on spreading dynamics entails a rich phenomenology of
+ecological relationships that includes cooperation, competition, and
+commensality. Furthermore, we find out that multipartitism might rise to
+fixation even in the absence of explicit microscopic advantages. Multipartitism
+allows the virus to colonize environments that could not be invaded by the
+monopartite form, while homogeneous contacts between hosts facilitate its
+spread. We conjecture that there might have been an increase in the diversity
+and prevalence of multipartite viral forms concomitantly with the expansion of
+agricultural practices.
+",0,0,0,0,1,0
+19144,Correlating Cell Shape and Cellular Stress in Motile Confluent Tissues,"  Collective cell migration is a highly regulated process involved in wound
+healing, cancer metastasis and morphogenesis. Mechanical interactions among
+cells provide an important regulatory mechanism to coordinate such collective
+motion. Using a Self-Propelled Voronoi (SPV) model that links cell mechanics to
+cell shape and cell motility, we formulate a generalized mechanical inference
+method to obtain the spatio-temporal distribution of cellular stresses from
+measured traction forces in motile tissues and show that such traction-based
+stresses match those calculated from instantaneous cell shapes. We additionally
+use stress information to characterize the rheological properties of the
+tissue. We identify a motility-induced swim stress that adds to the interaction
+stress to determine the global contractility or extensibility of epithelia. We
+further show that the temporal correlation of the interaction shear stress
+determines an effective viscosity of the tissue that diverges at the
+liquid-solid transition, suggesting the possibility of extracting rheological
+information directly from traction data.
+",0,1,0,0,0,0
+19145,Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks,"  We consider the problem of learning function classes computed by neural
+networks with various activations (e.g. ReLU or Sigmoid), a task believed to be
+computationally intractable in the worst-case. A major open problem is to
+understand the minimal assumptions under which these classes admit provably
+efficient algorithms. In this work we show that a natural distributional
+assumption corresponding to {\em eigenvalue decay} of the Gram matrix yields
+polynomial-time algorithms in the non-realizable setting for expressive classes
+of networks (e.g. feed-forward networks of ReLUs). We make no assumptions on
+the structure of the network or the labels. Given sufficiently-strong
+polynomial eigenvalue decay, we obtain {\em fully}-polynomial time algorithms
+in {\em all} the relevant parameters with respect to square-loss. Milder decay
+assumptions also lead to improved algorithms. This is the first purely
+distributional assumption that leads to polynomial-time algorithms for networks
+of ReLUs, even with one hidden layer. Further, unlike prior distributional
+assumptions (e.g., the marginal distribution is Gaussian), eigenvalue decay has
+been observed in practice on common data sets.
+",1,0,0,0,0,0
+19146,A New Steganographic Technique Matching the Secret Message and Cover image Binary Value,"  Steganography involves hiding a secret message or image inside another cover
+image. Changes are made in the cover image without affecting visual quality of
+the image. In contrast to cryptography, Steganography provides complete secrecy
+of the communication. Security of very sensitive data can be enhanced by
+combining cryptography and steganography. A new technique that uses the concept
+of Steganography to obtain the position values from an image is suggested. This
+paper proposes a new method where no change is made to the cover image, only
+the pixel position LSB (Least Significant Bit) values that match with the
+secret message bit values are noted in a separate position file. At the sending
+end the position file along with the cover image is sent. At the receiving end
+the position file is opened only with a secret key. The bit positions are taken
+from the position file and the LSB values from the positions are combined to
+get ASCII values and then form characters of the secret message
+",1,0,0,0,0,0
+19147,On topological fluid mechanics of non-ideal systems and virtual frozen-in dynamics,"  Euler and Navier-Stokes have variant systems with dynamical invariance of
+helicity and thus (weak) topological equivalence, allowing a strong `frozen-in'
+(to, or, dually, `Lie-carried' by the \textit{virtual} velocity $V$)
+formulation of the vorticity with a flavor of `inverse Helmholtz theorem'. We
+remark on the non-ideal (statistical) topological fluid mechanics (TFM) for (1)
+the Constantin-Iyer formulation of Navier-Stokes, (2) our own extension of the
+Gallavotti-Cohen type dynamical ensembles of modified Navier-Stokes with
+energy-helicity constraints and (3) the Galerkin truncated Euler, as the
+typical case variants with dynamical time reversibility and helicity
+invariance. Ideal TFM is thus bridged with non-ideal flows. An example virtual
+(Lie-)carrier of the vorticity in a Galerkin-truncated Euler system is
+calculated to demonstrate the issue of determining $V$.
+",0,1,0,0,0,0
+19148,Topological AdS/CFT,"  We define a holographic dual to the Donaldson-Witten topological twist of
+$\mathcal{N}=2$ gauge theories on a Riemannian four-manifold. This is described
+by a class of asymptotically locally hyperbolic solutions to $\mathcal{N}=4$
+gauged supergravity in five dimensions, with the four-manifold as conformal
+boundary. Under AdS/CFT, minus the logarithm of the partition function of the
+gauge theory is identified with the holographically renormalized supergravity
+action. We show that the latter is independent of the metric on the boundary
+four-manifold, as required for a topological theory. Supersymmetric solutions
+in the bulk satisfy first order differential equations for a twisted $Sp(1)$
+structure, which extends the quaternionic Kahler structure that exists on any
+Riemannian four-manifold boundary. We comment on applications and extensions,
+including generalizations to other topological twists.
+",0,0,1,0,0,0
+19149,Using highly uniform and smooth Selenium colloids as low-loss magnetodielectric building blocks of optical metafluids,"  We systematically analyzed magnetodielectric resonances of Se colloids for
+the first time to exploit the possibility for use as building blocks of
+all-dielectric optical metafluids. By taking synergistic advantages of Se
+colloids, including (i) high-refractive-index at optical frequencies, (ii)
+unprecedented structural uniformity, and (iii) versatile access to copious
+quantities, the Kerker-type directional light scattering resulting from
+efficient coupling between strong electric and magnetic resonances were
+observed directly from Se colloidal suspension. Thus, the use of Se colloid as
+a generic magnetodielectric building block highlights an opportunity for the
+fluidic low-loss optical antenna, which can be processed via spin-coating and
+painting.
+",0,1,0,0,0,0
+19150,A Vector Field Method for Radiating Black Hole Spacetimes,"  We develop a commuting vector field method for a general class of radiating
+spacetimes. The metrics considered are certain long range perturbations of
+Minkowski space including those constructed from global stability problems in
+general relativity. Our method provides sharp peeling estimates for solutions
+to both linear and nonlinear (null form) scalar fields.
+",0,0,1,0,0,0
+19151,Experimental Constraint on an Exotic Spin- and Velocity-Dependent Interaction in the Sub-meV Range of Axion Mass with a Spin-Exchange Relaxation-Free Magnetometer,"  We conducted a search for an exotic spin- and velocity-dependent interaction
+for polarized electrons with an experimental approach based on a
+high-sensitivity spin-exchange relaxation-free (SERF) magnetometer, which
+serves as both a source of polarized electrons and a magnetic-field sensor. The
+experiment aims to sensitively detect magnetic-fieldlike effects from the
+exotic interaction between the polarized electrons in a SERF vapor cell and
+unpolarized nucleons of a closely located solid-state mass. We report
+experimental results on the interaction with 82 h of data averaging, which sets
+an experimental limit on the coupling strength around $10^{-19}$ for the axion
+mass $m_a \lesssim 10^{-3}$ eV, within the important axion window.
+",0,1,0,0,0,0
+19152,Generalization Error in Deep Learning,"  Deep learning models have lately shown great performance in various fields
+such as computer vision, speech recognition, speech translation, and natural
+language processing. However, alongside their state-of-the-art performance, it
+is still generally unclear what is the source of their generalization ability.
+Thus, an important question is what makes deep neural networks able to
+generalize well from the training set to new data. In this article, we provide
+an overview of the existing theory and bounds for the characterization of the
+generalization error of deep neural networks, combining both classical and more
+recent theoretical and empirical results.
+",0,0,0,1,0,0
+19153,Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation,"  Although block compressive sensing (BCS) makes it tractable to sense
+large-sized images and video, its recovery performance has yet to be
+significantly improved because its recovered images or video usually suffer
+from blurred edges, loss of details, and high-frequency oscillatory artifacts,
+especially at a low subrate. This paper addresses these problems by designing a
+modified total variation technique that employs multi-block gradient
+processing, a denoised Lagrangian multiplier, and patch-based sparse
+representation. In the case of video, the proposed recovery method is able to
+exploit both spatial and temporal similarities. Simulation results confirm the
+improved performance of the proposed method for compressive sensing of images
+and video in terms of both objective and subjective qualities.
+",1,0,0,0,0,0
+19154,A multi-phase-field method for surface tension-induced elasticity,"  A consistent treatment of the coupling of surface energy and elasticity
+within the multi-phase- field framework is presented. The model accurately
+reproduces stress distribution in a number of analytically tractable, yet
+non-trivial, cases including different types of spherical heterogeneities and a
+thin plate suspending in a gas environment. It is then used to study the stress
+distribution inside elastic bodies with non-spherical geometries, such as a
+solid ellipsoid and a sintered structure. In these latter cases, it is shown
+that the interplay between deformation and spatially variable surface curvature
+leads to heterogeneous stress distribution across the specimen.
+",0,1,0,0,0,0
+19155,On the restriction theorem for paraboloid in $\mathbb R^4$,"  We prove that recent breaking by Zahl of the $\frac32$ barrier in Wolff's
+estimate on the Kakeya maximal operator in $\mathbb R^4$ leads to improving the
+$\frac{14}{5}$ threshold for the restriction problem for the paraboloid in
+$\mathbb R^4$. One of the ingredients is a new trilinear estimate. The proofs
+are deliberately presented in a nontechnical and concise format, so as to make
+the arguments more readable and focus attention on the key tools.
+",0,0,1,0,0,0
+19156,"Existence of Stein Kernels under a Spectral Gap, and Discrepancy Bound","  We establish existence of Stein kernels for probability measures on
+$\mathbb{R}^d$ satisfying a Poincaré inequality, and obtain bounds on the
+Stein discrepancy of such measures. Applications to quantitative central limit
+theorems are discussed, including a new CLT in Wasserstein distance $W_2$ with
+optimal rate and dependence on the dimension. As a byproduct, we obtain a
+stability version of an estimate of the Poincaré constant of probability
+measures under a second moment constraint. The results extend more generally to
+the setting of converse weighted Poincaré inequalities. The proof is based on
+simple arguments of calculus of variations.
+Further, we establish two general properties enjoyed by the Stein
+discrepancy, holding whenever a Stein kernel exists: Stein discrepancy is
+strictly decreasing along the CLT, and it controls the skewness of a random
+vector.
+",1,0,1,0,0,0
+19157,High-order asynchrony-tolerant finite difference schemes for partial differential equations,"  Synchronizations of processing elements (PEs) in massively parallel
+simulations, which arise due to communication or load imbalances between PEs,
+significantly affect the scalability of scientific applications. We have
+recently proposed a method based on finite-difference schemes to solve partial
+differential equations in an asynchronous fashion -- synchronization between
+PEs is relaxed at a mathematical level. While standard schemes can maintain
+their stability in the presence of asynchrony, their accuracy is drastically
+affected. In this work, we present a general methodology to derive
+asynchrony-tolerant (AT) finite difference schemes of arbitrary order of
+accuracy, which can maintain their accuracy when synchronizations are relaxed.
+We show that there are several choices available in selecting a stencil to
+derive these schemes and discuss their effect on numerical and computational
+performance. We provide a simple classification of schemes based on the stencil
+and derive schemes that are representative of different classes. Their
+numerical error is rigorously analyzed within a statistical framework to obtain
+the overall accuracy of the solution. Results from numerical experiments are
+used to validate the performance of the schemes.
+",0,1,0,0,0,0
+19158,Hazard Analysis and Risk Assessment for an Automated Unmanned Protective Vehicle,"  For future application of automated vehicles in public traffic, ensuring
+functional safety is essential. In this context, a hazard analysis and risk
+assessment is an important input for designing functionally vehicle automation
+systems. In this contribution, we present a detailed hazard analysis and risk
+assessment (HARA) according to the ISO 26262 standard for a specific Level 4
+application, namely an unmanned protective vehicle operated without human
+supervision for motorway hard shoulder roadworks.
+",1,0,0,0,0,0
+19159,Detailed experimental and numerical analysis of a cylindrical cup deep drawing: pros and cons of using solid-shell elements,"  The Swift test was originally proposed as a formability test to reproduce the
+conditions observed in deep drawing operations. This test consists on forming a
+cylindrical cup from a circular blank, using a flat bottom cylindrical punch
+and has been extensively studied using both analytical and numerical methods.
+This test can also be combined with the Demeri test, which consists in cutting
+a ring from the wall of a cylindrical cup, in order to open it afterwards to
+measure the springback. This combination allows their use as benchmark test, in
+order to improve the knowledge concerning the numerical simulation models,
+through the comparison between experimental and numerical results. The focus of
+this study is the experimental and numerical analyses of the Swift cup test,
+followed by the Demeri test, performed with an AA5754-O alloy at room
+temperature. In this context, a detailed analysis of the punch force evolution,
+the thickness evolution along the cup wall, the earing profile, the strain
+paths and their evolution and the ring opening is performed. The numerical
+simulation is performed using the finite element code ABAQUS, with solid and
+solid-shell elements, in order to compare the computational efficiency of these
+type of elements. The results show that the solid-shell element is more
+cost-effective than the solid, presenting global accurate predictions, excepted
+for the thinning zones. Both the von Mises and the Hill48 yield criteria
+predict the strain distributions in the final cup quite accurately. However,
+improved knowledge concerning the stress states is still required, because the
+Hill48 criterion showed difficulties in the correct prediction of the
+springback, whatever the type of finite element adopted.
+",0,1,0,0,0,0
+19160,Hubble Frontier Fields: systematic errors in strong lensing models of galaxy clusters - Implications for cosmography,"  Strong gravitational lensing by galaxy clusters is a fundamental tool to
+study dark matter and constrain the geometry of the Universe. Recently, the
+Hubble Space Telescope Frontier Fields programme has allowed a significant
+improvement of mass and magnification measurements but lensing models still
+have a residual root mean square between 0.2 arcsec and few arcsec- onds, not
+yet completely understood. Systematic errors have to be better understood and
+treated in order to use strong lensing clusters as reliable cosmological
+probes. We have analysed two simulated Hubble-Frontier-Fields-like clusters
+from the Hubble Frontier Fields Comparison Challenge, Ares and Hera. We use
+several estimators (relative bias on magnification, den- sity profiles,
+ellipticity and orientation) to quantify the goodness of our reconstructions by
+comparing our multiple models, optimized with the parametric software LENSTOOL
+, with the input models. We have quantified the impact of systematic errors
+arising, first, from the choice of different density profiles and
+configurations and, secondly, from the availability of con- straints
+(spectroscopic or photometric redshifts, redshift ranges of the background
+sources) in the parametric modelling of strong lensing galaxy clusters and
+therefore on the retrieval of cosmological parameters. We find that
+substructures in the outskirts have a significant im- pact on the position of
+the multiple images, yielding tighter cosmological contours. The need for
+wide-field imaging around massive clusters is thus reinforced. We show that
+competitive cosmological constraints can be obtained also with complex
+multimodal clusters and that photometric redshifts improve the constraints on
+cosmological parameters when considering a narrow range of (spectroscopic)
+redshifts for the sources.
+",0,1,0,0,0,0
+19161,A Note on the Spectral Transfer Morphisms for Affine Hecke Algebras,"  E. Opdam introduced the tool of spectral transfer morphism (STM) of affine
+Hecke algebras to study the formal degrees of unipotent discrete series
+representations. He established a uniqueness property of STM for the affine
+Hecke algebras associated of unipotent discrete series representations. Based
+on this result, Opdam gave an explanation for Lusztig's arithmetic/geometric
+correspondence (in Lusztig's classification of unipotent representations of
+$p$-adic adjoint simple groups) in terms of harmonic analysis, and partitioned
+the unipotent discrete series representations into $L$-packets based on the
+Lusztig-Langlands parameters. The present paper provides some omitted details
+for the argument of the uniqueness property of STM. In the last section, we
+prove that three finite morphisms of algebraic tori are spectral transfer
+morphisms, and hence complete the proof of the uniqueness property.
+",0,0,1,0,0,0
+19162,Batch-Expansion Training: An Efficient Optimization Framework,"  We propose Batch-Expansion Training (BET), a framework for running a batch
+optimizer on a gradually expanding dataset. As opposed to stochastic
+approaches, batches do not need to be resampled i.i.d. at every iteration, thus
+making BET more resource efficient in a distributed setting, and when
+disk-access is constrained. Moreover, BET can be easily paired with most batch
+optimizers, does not require any parameter-tuning, and compares favorably to
+existing stochastic and batch methods. We show that when the batch size grows
+exponentially with the number of outer iterations, BET achieves optimal
+$O(1/\epsilon)$ data-access convergence rate for strongly convex objectives.
+Experiments in parallel and distributed settings show that BET performs better
+than standard batch and stochastic approaches.
+",1,0,0,0,0,0
+19163,Coordinate Descent with Bandit Sampling,"  Coordinate descent methods usually minimize a cost function by updating a
+random decision variable (corresponding to one coordinate) at a time. Ideally,
+we would update the decision variable that yields the largest decrease in the
+cost function. However, finding this coordinate would require checking all of
+them, which would effectively negate the improvement in computational
+tractability that coordinate descent is intended to afford. To address this, we
+propose a new adaptive method for selecting a coordinate. First, we find a
+lower bound on the amount the cost function decreases when a coordinate is
+updated. We then use a multi-armed bandit algorithm to learn which coordinates
+result in the largest lower bound by interleaving this learning with
+conventional coordinate descent updates except that the coordinate is selected
+proportionately to the expected decrease. We show that our approach improves
+the convergence of coordinate descent methods both theoretically and
+experimentally.
+",1,0,0,1,0,0
+19164,Closing the gap for pseudo-polynomial strip packing,"  The set of 2-dimensional packing problems builds an important class of
+optimization problems and Strip Packing together with 2-dimensional Bin Packing
+and 2-dimensional Knapsack is one of the most famous of these problems. Given a
+set of rectangular axis parallel items and a strip with bounded width and
+infinite height the objective is to find a packing of the items into the strip
+which minimizes the packing height. We speak of pseudo-polynomial Strip Packing
+if we consider algorithms with pseudo-polynomial running time with respect to
+the width of the strip.
+It is known that there is no pseudo-polynomial algorithm for Strip Packing
+with a ratio better than $5/4$ unless $\mathrm{P} = \mathrm{NP}$. The best
+algorithm so far has a ratio of $(4/3 + \varepsilon)$. In this paper, we close
+this gap between inapproximability result and best known algorithm by
+presenting an algorithm with approximation ratio $(5/4 + \varepsilon)$ and thus
+categorize the problem accurately. The algorithm uses a structural result which
+states that each optimal solution can be transformed such that it has one of a
+polynomial number of different forms. The strength of this structural result is
+that it applies to other problem settings as well for example to Strip Packing
+with rotations (90 degrees) and Contiguous Moldable Task Scheduling. This fact
+enabled us to present algorithms with approximation ratio $(5/4 + \varepsilon)$
+for these problems as well.
+",1,0,0,0,0,0
+19165,"Notes on the replica symmetric solution of the classical and quantum SK model, including the matrix of second derivatives and the spin glass susceptibility","  A review of the replica symmetric solution of the classical and quantum,
+infinite-range, Sherrington-Kirkpatrick spin glass is presented.
+",0,1,0,0,0,0
+19166,Self-supervised Knowledge Distillation Using Singular Value Decomposition,"  To solve deep neural network (DNN)'s huge training dataset and its high
+computation issue, so-called teacher-student (T-S) DNN which transfers the
+knowledge of T-DNN to S-DNN has been proposed. However, the existing T-S-DNN
+has limited range of use, and the knowledge of T-DNN is insufficiently
+transferred to S-DNN. To improve the quality of the transferred knowledge from
+T-DNN, we propose a new knowledge distillation using singular value
+decomposition (SVD). In addition, we define a knowledge transfer as a
+self-supervised task and suggest a way to continuously receive information from
+T-DNN. Simulation results show that a S-DNN with a computational cost of 1/5 of
+the T-DNN can be up to 1.1\% better than the T-DNN in terms of classification
+accuracy. Also assuming the same computational cost, our S-DNN outperforms the
+S-DNN driven by the state-of-the-art distillation with a performance advantage
+of 1.79\%. code is available on this https URL\_SVD.
+",0,0,0,1,0,0
+19167,Can justice be fair when it is blind? How social network structures can promote or prevent the evolution of despotism,"  Hierarchy is an efficient way for a group to organize, but often goes along
+with inequality that benefits leaders. To control despotic behaviour, followers
+can assess leaders decisions by aggregating their own and their neighbours
+experience, and in response challenge despotic leaders. But in hierarchical
+social networks, this interactional justice can be limited by (i) the high
+influence of a small clique who are treated better, and (ii) the low
+connectedness of followers. Here we study how the connectedness of a social
+network affects the co-evolution of despotism in leaders and tolerance to
+despotism in followers. We simulate the evolution of a population of agents,
+where the influence of an agent is its number of social links. Whether a leader
+remains in power is controlled by the overall satisfaction of group members, as
+determined by their joint assessment of the leaders behaviour. We demonstrate
+that centralization of a social network around a highly influential clique
+greatly increases the level of despotism. This is because the clique is more
+satisfied, and their higher influence spreads their positive opinion of the
+leader throughout the network. Finally, our results suggest that increasing the
+connectedness of followers limits despotism while maintaining hierarchy.
+",0,0,0,0,1,0
+19168,GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization,"  Bioinformatics tools have been developed to interpret gene expression data at
+the gene set level, and these gene set based analyses improve the biologists'
+capability to discover functional relevance of their experiment design. While
+elucidating gene set individually, inter gene sets association is rarely taken
+into consideration. Deep learning, an emerging machine learning technique in
+computational biology, can be used to generate an unbiased combination of gene
+set, and to determine the biological relevance and analysis consistency of
+these combining gene sets by leveraging large genomic data sets. In this study,
+we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model
+with the incorporation of a priori defined gene sets that retain the crucial
+biological features in the latent layer. We introduced the concept of the gene
+superset, an unbiased combination of gene sets with weights trained by the
+autoencoder, where each node in the latent layer is a superset. Trained with
+genomic data from TCGA and evaluated with their accompanying clinical
+parameters, we showed gene supersets' ability of discriminating tumor subtypes
+and their prognostic capability. We further demonstrated the biological
+relevance of the top component gene sets in the significant supersets. Using
+autoencoder model and gene superset at its latent layer, we demonstrated that
+gene supersets retain sufficient biological information with respect to tumor
+subtypes and clinical prognostic significance. Superset also provides high
+reproducibility on survival analysis and accurate prediction for cancer
+subtypes.
+",0,0,0,1,1,0
+19169,Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter,"  Given a nonconvex function that is an average of $n$ smooth functions, we
+design stochastic first-order methods to find its approximate stationary
+points. The convergence of our new methods depends on the smallest (negative)
+eigenvalue $-\sigma$ of the Hessian, a parameter that describes how nonconvex
+the function is.
+Our methods outperform known results for a range of parameter $\sigma$, and
+can be used to find approximate local minima. Our result implies an interesting
+dichotomy: there exists a threshold $\sigma_0$ so that the currently fastest
+methods for $\sigma>\sigma_0$ and for $\sigma<\sigma_0$ have different
+behaviors: the former scales with $n^{2/3}$ and the latter scales with
+$n^{3/4}$.
+",1,0,1,1,0,0
+19170,Higher-Order Bounded Model Checking,"  We present a Bounded Model Checking technique for higher-order programs. The
+vehicle of our study is a higher-order calculus with general references. Our
+technique is a symbolic state syntactical translation based on SMT solvers,
+adapted to a setting where the values passed and stored during computation can
+be functions of arbitrary order. We prove that our algorithm is sound, and
+devise an optimisation based on points-to analysis to improve scalability. We
+moreover provide a prototype implementation of the algorithm with experimental
+results showcasing its performance.
+",1,0,0,0,0,0
+19171,Smoothing for the fractional Schrodinger equation on the torus and the real line,"  In this paper we study the cubic fractional nonlinear Schrodinger equation
+(NLS) on the torus and on the real line. Combining the normal form and the
+restricted norm methods we prove that the nonlinear part of the solution is
+smoother than the initial data. Our method applies to both focusing and
+defocusing nonlinearities. In the case of full dispersion (NLS) and on the
+torus, the gain is a full derivative, while on the real line we get a
+derivative smoothing with an $\epsilon$ loss. Our result lowers the regularity
+requirement of a recent theorem of Kappeler et al. on the periodic defocusing
+cubic NLS, and extends it to the focusing case and to the real line. We also
+obtain estimates on the higher order Sobolev norms of the global smooth
+solutions in the defocusing case.
+",0,0,1,0,0,0
+19172,Deep Learning Based Cryptographic Primitive Classification,"  Cryptovirological augmentations present an immediate, incomparable threat.
+Over the last decade, the substantial proliferation of crypto-ransomware has
+had widespread consequences for consumers and organisations alike. Established
+preventive measures perform well, however, the problem has not ceased. Reverse
+engineering potentially malicious software is a cumbersome task due to platform
+eccentricities and obfuscated transmutation mechanisms, hence requiring
+smarter, more efficient detection strategies. The following manuscript presents
+a novel approach for the classification of cryptographic primitives in compiled
+binary executables using deep learning. The model blueprint, a DCNN, is
+fittingly configured to learn from variable-length control flow diagnostics
+output from a dynamic trace. To rival the size and variability of contemporary
+data compendiums, hence feeding the model cognition, a methodology for the
+procedural generation of synthetic cryptographic binaries is defined, utilising
+core primitives from OpenSSL with multivariate obfuscation, to draw a vastly
+scalable distribution. The library, CryptoKnight, rendered an algorithmic pool
+of AES, RC4, Blowfish, MD5 and RSA to synthesis combinable variants which are
+automatically fed in its core model. Converging at 91% accuracy, CryptoKnight
+is successfully able to classify the sample algorithms with minimal loss.
+",1,0,0,0,0,0
+19173,"The Alexandrov-Fenchel type inequalities, revisited","  Various Alexandrov-Fenchel type inequalities have appeared and played
+important roles in convex geometry, matrix theory and complex algebraic
+geometry. It has been noticed for some time that they share some striking
+analogies and have intimate relationships. The purpose of this article is to
+shed new light on this by comparatively investigating them in several aspects.
+\emph{The principal result} in this article is a complete solution to the
+equality characterization problem of various Alexandrov-Fenchel type
+inequalities for intersection numbers of nef and big classes on compact
+Kähler manifolds, extending earlier results of Boucksom-Favre-Jonsson,
+Fu-Xiao and Xiao-Lehmann. Our proof combines a result of Dinh-Nguyên on
+Kähler geometry and an idea in convex geometry tracing back to Shephard. In
+addition to this central result, we also give a geometric proof of the complex
+version of the Alexandrov-Fenchel type inequality for mixed discriminants and a
+determinantal type generalization of various Alexandrov-Fenchel type
+inequalities.
+",0,0,1,0,0,0
+19174,Development of non-modal shear induced instabilities in atmospheric tornadoes,"  In this paper we consider the role of nonmodal instabilities in the dynamics
+of atmospheric tornadoes. For this purpose we consider the Euler equation,
+continuity equation and the equation of state and linearise them. As an example
+we study several different velocity profiles: the so-called Rankine vortex
+model; the Burgers-Rott vortex model; Sullivan and modified Sullivan vortex
+models. It has been shown that in the two dimensional Rankine vortex model no
+instability appears in the inner region of a tornado. On the contrary, outside
+this area the physical system undergoes strong exponential instability. We have
+found that initially perturbed velocity components lead to amplified sound wave
+excitations. The similar results have been shown in Burgers-Rott vortex model
+as well. As it was numerically estimated, in this case, the unstable wave
+increases its energy by a factor of $400$ only in $\sim 0.5$min. According to
+the numerical study, in Sullivan and modified Sullivan models, the instability
+does not differ much by the growth. Despite the fact that in the inner area the
+exponential instability does not appear in a purely two dimensional case, we
+have found that in the modified Sullivan vortex even a small contribution from
+vertical velocities can drive unstable nonmodal waves.
+",0,1,0,0,0,0
+19175,DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling,"  Face modeling has been paid much attention in the field of visual computing.
+There exist many scenarios, including cartoon characters, avatars for social
+media, 3D face caricatures as well as face-related art and design, where
+low-cost interactive face modeling is a popular approach especially among
+amateur users. In this paper, we propose a deep learning based sketching system
+for 3D face and caricature modeling. This system has a labor-efficient
+sketching interface, that allows the user to draw freehand imprecise yet
+expressive 2D lines representing the contours of facial features. A novel CNN
+based deep regression network is designed for inferring 3D face models from 2D
+sketches. Our network fuses both CNN and shape based features of the input
+sketch, and has two independent branches of fully connected layers generating
+independent subsets of coefficients for a bilinear face representation. Our
+system also supports gesture based interactions for users to further manipulate
+initial face models. Both user studies and numerical results indicate that our
+sketching system can help users create face models quickly and effectively. A
+significantly expanded face database with diverse identities, expressions and
+levels of exaggeration is constructed to promote further research and
+evaluation of face modeling techniques.
+",1,0,0,0,0,0
+19176,Time-Inhomogeneous Branching Processes Conditioned on Non-Extinction,"  In this paper, we consider time-inhomogeneous branching processes and
+time-inhomogeneous birth-and-death processes, in which the offspring
+distribution and birth and death rates (respectively) vary in time. A classical
+result of branching processes states that in the critical regime, a process
+conditioned on non-extinction and normalized will converge in distribution to a
+standard exponential. In a paper of Jagers, time-inhomogeneous branching
+processes are shown to exhibit this convergence as well. In this paper, the
+hypotheses of Jagers' result are relaxed, further hypotheses are presented for
+convergence in moments, and the result is extended to the continuous-time
+analogue of time-inhomogeneous birth-and-death processes. In particular, the
+new hypotheses suggest a simple characterization of the critical regime.
+",0,0,1,0,0,0
+19177,The Dirichlet-to-Neumann operator for quantum graphs,"  For a compact, connected metric graphs with a boundary that consists of $k$
+vertices, we prove that an arbitrary symmetric $k\times k$ matrix with real
+entries can be realized as the Dirichlet-to-Neumann operator for the Laplacian
+plus a constant.
+",0,0,1,0,0,0
+19178,A study of the dual problem of the one-dimensional L-infinity optimal transport problem with applications,"  The Monge-Kantorovich problem for the infinite Wasserstein distance presents
+several peculiarities. Among them the lack of convexity and then of a direct
+duality. We study in dimension 1 the dual problem introduced by Barron, Bocea
+and Jensen. We construct a couple of Kantorovich potentials which is ""as less
+trivial as possible"". More precisely, we build a potential which is non
+constant around any point that the plan which is locally optimal moves at
+maximal distance. As an application, we show that the set of points which are
+displaced to maximal distance by a locally optimal transport plan is minimal.
+",0,0,1,0,0,0
+19179,Strong coupling Bose polarons in a BEC,"  We use a non-perturbative renormalization group approach to develop a unified
+picture of the Bose polaron problem, where a mobile impurity is strongly
+interacting with a surrounding Bose-Einstein condensate (BEC). A detailed
+theoretical analysis of the phase diagram is presented and the
+polaron-to-molecule transition is discussed. For attractive polarons we argue
+that a description in terms of an effective Fröhlich Hamiltonian with
+renormalized parameters is possible. Its strong coupling regime is realized
+close to a Feshbach resonance, where we predict a sharp increase of the
+effective mass. Already for weaker interactions, before the polaron mass
+diverges, we predict a transition to a regime where states exist below the
+polaron energy and the attractive polaron is no longer the ground state. On the
+repulsive side of the Feshbach resonance we recover the repulsive polaron,
+which has a finite lifetime because it can decay into low-lying molecular
+states. We show for the entire range of couplings that the polaron energy has
+logarithmic corrections in comparison with predictions by the mean-field
+approach. We demonstrate that they are a consequence of the polaronic mass
+renormalization which is due to quantum fluctuations of correlated phonons in
+the polaron cloud.
+",0,1,0,0,0,0
+19180,Triangle packing in (sparse) tournaments: approximation and kernelization,"  Given a tournament T and a positive integer k, the C_3-Pakcing-T problem asks
+if there exists a least k (vertex-)disjoint directed 3-cycles in T. This is the
+dual problem in tournaments of the classical minimal feedback vertex set
+problem. Surprisingly C_3-Pakcing-T did not receive a lot of attention in the
+literature. We show that it does not admit a PTAS unless P=NP, even if we
+restrict the considered instances to sparse tournaments, that is tournaments
+with a feedback arc set (FAS) being a matching. Focusing on sparse tournaments
+we provide a (1+6/(c-1)) approximation algorithm for sparse tournaments having
+a linear representation where all the backward arcs have ""length"" at least c.
+Concerning kernelization, we show that C_3-Pakcing-T admits a kernel with O(m)
+vertices, where m is the size of a given feedback arc set. In particular, we
+derive a O(k) vertices kernel for C_3-Pakcing-T when restricted to sparse
+instances. On the negative size, we show that C_3-Pakcing-T does not admit a
+kernel of (total bit) size O(k^{2-\epsilon}) unless NP is a subset of coNP /
+Poly. The existence of a kernel in O(k) vertices for C_3-Pakcing-T remains an
+open question.
+",1,0,0,0,0,0
+19181,"Asymptotics of Chebyshev Polynomials, II. DCT Subsets of $\mathbb{R}$","  We prove Szegő-Widom asymptotics for the Chebyshev polynomials of a
+compact subset of $\mathbb{R}$ which is regular for potential theory and obeys
+the Parreau-Widom and DCT conditions.
+",0,0,1,0,0,0
+19182,Tracking Urban Human Activity from Mobile Phone Calling Patterns,"  Timings of human activities are marked by circadian clocks which in turn are
+entrained to different environmental signals. In an urban environment the
+presence of artificial lighting and various social cues tend to disrupt the
+natural entrainment with the sunlight. However, it is not completely understood
+to what extent this is the case. Here we exploit the large-scale data analysis
+techniques to study the mobile phone calling activity of people in large cities
+to infer the dynamics of urban daily rhythms. From the calling patterns of
+about 1,000,000 users spread over different cities but lying inside the same
+time-zone, we show that the onset and termination of the calling activity
+synchronizes with the east-west progression of the sun. We also find that the
+onset and termination of the calling activity of users follows a yearly
+dynamics, varying across seasons, and that its timings are entrained to solar
+midnight. Furthermore, we show that the average mid-sleep time of people living
+in urban areas depends on the age and gender of each cohort as a result of
+biological and social factors.
+",0,1,0,0,0,0
+19183,Efficient conversion from rotating matrix to rotation axis and angle by extending Rodrigues' formula,"  In computational 3D geometric problems involving rotations, it is often that
+people have to convert back and forth between a rotational matrix and a
+rotation described by an axis and a corresponding angle. For this purpose,
+Rodrigues' rotation formula is a very popular expression to use because of its
+simplicity and efficiency. Nevertheless, while converting a rotation matrix to
+an axis of rotation and the rotation angle, there exists ambiguity. Further
+judgement or even manual interference may be necessary in some situations. An
+extension of the Rodrigues' formula helps to find the sine and cosine values of
+the rotation angle with respect to a given rotation axis is found and this
+simple extension may help to accelerate many applications.
+",1,0,0,0,0,0
+19184,Statistical methods for characterizing transfusion-related changes in regional oxygenation using Near-infrared spectroscopy (NIRS) in preterm infants,"  Near infrared spectroscopy (NIRS) is an imaging-based diagnostic tool that
+provides non-invasive and continuous evaluation of regional tissue oxygenation
+in real-time. In recent years, NIRS has show promise as a useful monitoring
+technology to help detect relative tissue ischemia that could lead to
+significant morbidity and mortality in preterm infants. However, some issues
+inherent in NIRS technology use on neonates, such as wide fluctuation in
+signals, signal dropout and low limit of detection of the device, pose
+challenges that may obscure reliable interpretation of the NIRS measurements
+using current methods of analysis. In this paper, we propose new statistical
+methods to analyse mesenteric rSO2 (regional oxygenation) produced by NIRS to
+evaluate oxygenation in intestinal tissues and investigate oxygenation response
+to red blood cell transfusion (RBC) in preterm infants. We present a mean area
+under the curve (MAUC) measure and a slope measure to capture the mean rSO2
+level and temporal trajectory of rSO2, respectively. Estimation methods are
+developed for these measures and nonparametric testing procedures are proposed
+to detect RBC-related changes in mesenteric oxygenation in preterm infants.
+Through simulation studies, we show that the proposed methods demonstrate
+improved accuracy in characterizing the mean level and changing pattern of
+mesenteric rSO2 and also increased statistical power in detecting RBC-related
+changes, as compared with standard approaches. We apply our methods to a NIRS
+study in preterm infants receiving RBC transfusion from Emory Univerity to
+evaluate the pre- and post-transfusion mesenteric oxygenation in preterm
+infants.
+",0,0,0,1,0,0
+19185,To understand deep learning we need to understand kernel learning,"  Generalization performance of classifiers in deep learning has recently
+become a subject of intense study. Deep models, typically over-parametrized,
+tend to fit the training data exactly. Despite this ""overfitting"", they perform
+well on test data, a phenomenon not yet fully understood.
+The first point of our paper is that strong performance of overfitted
+classifiers is not a unique feature of deep learning. Using six real-world and
+two synthetic datasets, we establish experimentally that kernel machines
+trained to have zero classification or near zero regression error perform very
+well on test data, even when the labels are corrupted with a high level of
+noise. We proceed to give a lower bound on the norm of zero loss solutions for
+smooth kernels, showing that they increase nearly exponentially with data size.
+We point out that this is difficult to reconcile with the existing
+generalization bounds. Moreover, none of the bounds produce non-trivial results
+for interpolating solutions.
+Second, we show experimentally that (non-smooth) Laplacian kernels easily fit
+random labels, a finding that parallels results for ReLU neural networks. In
+contrast, fitting noisy data requires many more epochs for smooth Gaussian
+kernels. Similar performance of overfitted Laplacian and Gaussian classifiers
+on test, suggests that generalization is tied to the properties of the kernel
+function rather than the optimization process.
+Certain key phenomena of deep learning are manifested similarly in kernel
+methods in the modern ""overfitted"" regime. The combination of the experimental
+and theoretical results presented in this paper indicates a need for new
+theoretical ideas for understanding properties of classical kernel methods. We
+argue that progress on understanding deep learning will be difficult until more
+tractable ""shallow"" kernel methods are better understood.
+",0,0,0,1,0,0
+19186,"Atomic Data Revisions for Transitions Relevant to Observations of Interstellar, Circumgalactic, and Intergalactic Matter","  Measurements of element abundances in galaxies from astrophysical
+spectroscopy depend sensitively on the atomic data used. With the goal of
+making the latest atomic data accessible to the community, we present a
+compilation of selected atomic data for resonant absorption lines at
+wavelengths longward of 911.753 {\AA} (the \ion{H}{1} Lyman limit), for key
+heavy elements (heavier than atomic number 5) of astrophysical interest. In
+particular, we focus on the transitions of those ions that have been observed
+in the Milky Way interstellar medium (ISM), the circumgalactic medium (CGM) of
+the Milky Way and/or other galaxies, and the intergalactic medium (IGM).
+We provide wavelengths, oscillator strengths, associated accuracy grades, and
+references to the oscillator strength determinations. We also attempt to
+compare and assess the recent oscillator strength determinations. For about
+22\% of the lines that have updated oscillator strength values, the differences
+between the former values and the updated ones are $\gtrsim$~0.1 dex.
+Our compilation will be a useful resource for absorption line studies of the
+ISM, as well as studies of the CGM and IGM traced by sight lines to quasars and
+gamma-ray bursts. Studies (including those enabled by future generations of
+extremely large telescopes) of absorption by galaxies against the light of
+background galaxies will also benefit from our compilation.
+",0,1,0,0,0,0
+19187,Using Nonlinear Normal Modes for Execution of Efficient Cyclic Motions in Soft Robots,"  With the aim of getting closer to the performance of the animal
+muscleskeletal system, elastic elements are purposefully introduced in the
+mechanical structure of soft robots. Indeed, previous works have extensively
+shown that elasticity can endow robots with the ability of performing tasks
+with increased efficiency, peak performances, and mechanical robustness.
+However, despite the many achievements, a general theory of efficient motions
+in soft robots is still lacking. Most of the literature focuses on specific
+examples, or imposes a prescribed behavior through dynamic cancellations, thus
+defeating the purpose of introducing elasticity in the first place. This paper
+aims at making a step towards establishing such a general framework. To this
+end, we leverage on the theory of oscillations in nonlinear dynamical systems,
+and we take inspiration from state of the art theories about how the human
+central nervous system manages the muscleskeletal system. We propose to
+generate regular and efficient motions in soft robots by stabilizing
+sub-manifolds of the state space on which the system would naturally evolve. We
+select these sub-manifolds as the nonlinear continuation of linear eigenspaces,
+called nonlinear normal modes. In such a way, efficient oscillatory behaviors
+can be excited. We show the effectiveness of the methods in simulations on an
+elastic inverted pendulum, and experimentally on a segmented elastic leg.
+",1,0,0,0,0,0
+19188,Efficient Deep Learning on Multi-Source Private Data,"  Machine learning models benefit from large and diverse datasets. Using such
+datasets, however, often requires trusting a centralized data aggregator. For
+sensitive applications like healthcare and finance this is undesirable as it
+could compromise patient privacy or divulge trade secrets. Recent advances in
+secure and privacy-preserving computation, including trusted hardware enclaves
+and differential privacy, offer a way for mutually distrusting parties to
+efficiently train a machine learning model without revealing the training data.
+In this work, we introduce Myelin, a deep learning framework which combines
+these privacy-preservation primitives, and use it to establish a baseline level
+of performance for fully private machine learning.
+",0,0,0,1,0,0
+19189,Analytical solution of the integral equation for partial wave Coulomb t-matrices at excited-state energy,"  Starting from the integral representation of the three-dimensional Coulomb
+transition matrix elaborated by us formerly with the use of specific symmetry
+of the interaction in a four-dimensional Euclidean space introduced by Fock,
+the possibility of the analytical solving of the integral equation for the
+partial wave transition matrices at the excited bound state energy has been
+studied. New analytical expressions for the partial s-, p- and d-wave Coulomb
+t-matrices for like-charged particles and the expression for the partial d-wave
+t-matrix for unlike-charged particles at the energy of the first excited bound
+state have been derived.
+",0,1,0,0,0,0
+19190,Equilateral $p$-gons in $\mathbb R^d$ and deformed spheres and mod $p$ Fadell-Husseini index,"  We introduce the concept of $r$-equilateral $m$-gons. We prove the existence
+of $r$-equilateral $p$-gons in $\mathbb R^d$ if $r<d$ and the existence of
+equilateral $p$-gons in the image of continuous injective maps $f:S^d\to
+\mathbb R^{d+1}$. Our ideas are based mainly in the paper of Y. Soibelman
+\cite{soibelman}, in which the topological Borsuk number of $\mathbb{R}^2$ is
+calculated by means of topological methods and the paper of P. Blagojević and
+G. Ziegler \cite{blagojevictetrahedra} where Fadell-Husseini index is used for
+solving a problem related to the topological Borsuk problem for $\mathbb{R}^3$.
+",0,0,1,0,0,0
+19191,Jónsson posets,"  According to Kearnes and Oman (2013), an ordered set $P$ is \emph{Jónsson}
+if it is infinite and the cardinality of every proper initial segment of $P$ is
+strictly less than the cardinaliy of $P$. We examine the structure of Jónsson
+posets.
+",0,0,1,0,0,0
+19192,Hypotheses testing on infinite random graphs,"  Drawing on some recent results that provide the formalism necessary to
+definite stationarity for infinite random graphs, this paper initiates the
+study of statistical and learning questions pertaining to these objects.
+Specifically, a criterion for the existence of a consistent test for complex
+hypotheses is presented, generalizing the corresponding results on time series.
+As an application, it is shown how one can test that a tree has the Markov
+property, or, more generally, to estimate its memory.
+",1,0,1,1,0,0
+19193,On compact splitting complex submanifolds of quotients of bounded symmetric domains,"  In the current article our primary objects of study are compact complex
+submanifolds of quotient manifolds of irreducible bounded symmetric domains by
+torsion free discrete lattices of automorphisms. We are interested in the
+characterization of the totally geodesic submanifolds among compact splitting
+complex submanifolds, i.e. under the assumption that the tangent sequence
+splits holomorphically over the submanifold.
+",0,0,1,0,0,0
+19194,Octupolar Tensors for Liquid Crystals,"  A third-order three-dimensional symmetric traceless tensor, called the
+\emph{octupolar} tensor, has been introduced to study tetrahedratic nematic
+phases in liquid crystals. The octupolar \emph{potential}, a scalar-valued
+function generated on the unit sphere by that tensor, should ideally have four
+maxima capturing the most probable molecular orientations (on the vertices of a
+tetrahedron), but it was recently found to possess an equally generic variant
+with \emph{three} maxima instead of four. It was also shown that the
+irreducible admissible region for the octupolar tensor in a three-dimensional
+parameter space is bounded by a dome-shaped surface, beneath which is a
+\emph{separatrix} surface connecting the two generic octupolar states. The
+latter surface, which was obtained through numerical continuation, may be
+physically interpreted as marking a possible \emph{intra-octupolar} transition.
+In this paper, by using the resultant theory of algebraic geometry and the
+E-characteristic polynomial of spectral theory of tensors, we give a
+closed-form, algebraic expression for both the dome-shaped surface and the
+separatrix surface. This turns the envisaged intra-octupolar transition into a
+quantitative, possibly observable prediction. Some other properties of
+octupolar tensors are also studied.
+",0,0,1,0,0,0
+19195,Efficient boundary corrected Strang splitting,"  Strang splitting is a well established tool for the numerical integration of
+evolution equations. It allows the application of tailored integrators for
+different parts of the vector field. However, it is also prone to order
+reduction in the case of non-trivial boundary conditions. This order reduction
+can be remedied by correcting the boundary values of the intermediate splitting
+step. In this paper, three different approaches for constructing such a
+correction in the case of inhomogeneous Dirichlet, Neumann, and mixed boundary
+conditions are presented. Numerical examples that illustrate the effectivity
+and benefits of these corrections are included.
+",0,1,0,0,0,0
+19196,SOS-convex Semi-algebraic Programs and its Applications to Robust Optimization: A Tractable Class of Nonsmooth Convex Optimization,"  In this paper, we introduce a new class of nonsmooth convex functions called
+SOS-convex semialgebraic functions extending the recently proposed notion of
+SOS-convex polynomials. This class of nonsmooth convex functions covers many
+common nonsmooth functions arising in the applications such as the Euclidean
+norm, the maximum eigenvalue function and the least squares functions with
+$\ell_1$-regularization or elastic net regularization used in statistics and
+compressed sensing. We show that, under commonly used strict feasibility
+conditions, the optimal value and an optimal solution of SOS-convex
+semi-algebraic programs can be found by solving a single semi-definite
+programming problem (SDP). We achieve the results by using tools from
+semi-algebraic geometry, convex-concave minimax theorem and a recently
+established Jensen inequality type result for SOS-convex polynomials. As an
+application, we outline how the derived results can be applied to show that
+robust SOS-convex optimization problems under restricted spectrahedron data
+uncertainty enjoy exact SDP relaxations. This extends the existing exact SDP
+relaxation result for restricted ellipsoidal data uncertainty and answers the
+open questions left in [Optimization Letters 9, 1-18(2015)] on how to recover a
+robust solution from the semi-definite programming relaxation in this broader
+setting.
+",0,0,1,0,0,0
+19197,An Uncertainty Principle for Estimates of Floquet Multipliers,"  We derive a Cramér-Rao lower bound for the variance of Floquet multiplier
+estimates that have been constructed from stable limit cycles perturbed by
+noise. To do so, we consider perturbed periodic orbits in the plane. We use a
+periodic autoregressive process to model the intersections of these orbits with
+cross sections, then passing to the limit of a continuum of sections to obtain
+a bound that depends on the continuous flow restricted to the (nontrivial)
+Floquet mode. We compare our bound against the empirical variance of estimates
+constructed using several cross sections. The section-based estimates are close
+to being optimal. We posit that the utility of our bound persists in higher
+dimensions when computed along Floquet modes for real and distinct multipliers.
+Our bound elucidates some of the empirical observations noted in the
+literature; e.g., (a) it is the number of cycles (as opposed to the frequency
+of observations) that drives the variance of estimates to zero, and (b) the
+estimator variance has a positive lower bound as the noise amplitude tends to
+zero.
+",0,0,0,1,0,0
+19198,Shielding Google's language toxicity model against adversarial attacks,"  Lack of moderation in online communities enables participants to incur in
+personal aggression, harassment or cyberbullying, issues that have been
+accentuated by extremist radicalisation in the contemporary post-truth politics
+scenario. This kind of hostility is usually expressed by means of toxic
+language, profanity or abusive statements. Recently Google has developed a
+machine-learning-based toxicity model in an attempt to assess the hostility of
+a comment; unfortunately, it has been suggested that said model can be deceived
+by adversarial attacks that manipulate the text sequence of the comment. In
+this paper we firstly characterise such adversarial attacks as using
+obfuscation and polarity transformations. The former deceives by corrupting
+toxic trigger content with typographic edits, whereas the latter deceives by
+grammatical negation of the toxic content. Then, we propose a two--stage
+approach to counter--attack these anomalies, bulding upon a recently proposed
+text deobfuscation method and the toxicity scoring model. Lastly, we conducted
+an experiment with approximately 24000 distorted comments, showing how in this
+way it is feasible to restore toxicity of the adversarial variants, while
+incurring roughly on a twofold increase in processing time. Even though novel
+adversary challenges would keep coming up derived from the versatile nature of
+written language, we anticipate that techniques combining machine learning and
+text pattern recognition methods, each one targeting different layers of
+linguistic features, would be needed to achieve robust detection of toxic
+language, thus fostering aggression--free digital interaction.
+",1,0,0,0,0,0
+19199,Diagrammatic Monte-Carlo for weak-coupling expansion of non-Abelian lattice field theories: large-N U(N)xU(N) principal chiral model,"  We develop numerical tools for Diagrammatic Monte-Carlo simulations of
+non-Abelian lattice field theories in the t'Hooft large-N limit based on the
+weak-coupling expansion. First we note that the path integral measure of such
+theories contributes a bare mass term in the effective action which is
+proportional to the bare coupling constant. This mass term renders the
+perturbative expansion infrared-finite and allows to study it directly in the
+large-N and infinite-volume limits using the Diagrammatic Monte-Carlo approach.
+On the exactly solvable example of a large-N O(N) sigma model in D=2 dimensions
+we show that this infrared-finite weak-coupling expansion contains, in addition
+to powers of bare coupling, also powers of its logarithm, reminiscent of
+re-summed perturbation theory in thermal field theory and resurgent
+trans-series without exponential terms. We numerically demonstrate the
+convergence of these double series to the manifestly non-perturbative dynamical
+mass gap. We then develop a Diagrammatic Monte-Carlo algorithm for sampling
+planar diagrams in the large-N matrix field theory, and apply it to study this
+infrared-finite weak-coupling expansion for large-N U(N)xU(N) nonlinear sigma
+model (principal chiral model) in D=2. We sample up to 12 leading orders of the
+weak-coupling expansion, which is the practical limit set by the increasingly
+strong sign problem at high orders. Comparing Diagrammatic Monte-Carlo with
+conventional Monte-Carlo simulations extrapolated to infinite N, we find a good
+agreement for the energy density as well as for the critical temperature of the
+""deconfinement"" transition. Finally, we comment on the applicability of our
+approach to planar QCD at zero and finite density.
+",0,1,0,0,0,0
+19200,Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks,"  There is an increasing interest on accelerating neural networks for real-time
+applications. We study the student-teacher strategy, in which a small and fast
+student network is trained with the auxiliary information learned from a large
+and accurate teacher network. We propose to use conditional adversarial
+networks to learn the loss function to transfer knowledge from teacher to
+student. The proposed method is particularly effective for relatively small
+student networks. Moreover, experimental results show the effect of network
+size when the modern networks are used as student. We empirically study the
+trade-off between inference time and classification accuracy, and provide
+suggestions on choosing a proper student network.
+",1,0,0,0,0,0
+19201,Transductive Zero-Shot Learning with Adaptive Structural Embedding,"  Zero-shot learning (ZSL) endows the computer vision system with the
+inferential capability to recognize instances of a new category that has never
+seen before. Two fundamental challenges in it are visual-semantic embedding and
+domain adaptation in cross-modality learning and unseen class prediction steps,
+respectively. To address both challenges, this paper presents two corresponding
+methods named Adaptive STructural Embedding (ASTE) and Self-PAsed Selective
+Strategy (SPASS), respectively. Specifically, ASTE formulates the
+visualsemantic interactions in a latent structural SVM framework to adaptively
+adjust the slack variables to embody the different reliableness among training
+instances. In this way, the reliable instances are imposed with small
+punishments, wheras the less reliable instances are imposed with more severe
+punishments. Thus, it ensures a more discriminative embedding. On the other
+hand, SPASS offers a framework to alleviate the domain shift problem in ZSL,
+which exploits the unseen data in an easy to hard fashion. Particularly, SPASS
+borrows the idea from selfpaced learning by iteratively selecting the unseen
+instances from reliable to less reliable to gradually adapt the knowledge from
+the seen domain to the unseen domain. Subsequently, by combining SPASS and
+ASTE, we present a self-paced Transductive ASTE (TASTE) method to progressively
+reinforce the classification capacity. Extensive experiments on three benchmark
+datasets (i.e., AwA, CUB, and aPY) demonstrate the superiorities of ASTE and
+TASTE. Furthermore, we also propose a fast training (FT) strategy to improve
+the efficiency of most of existing ZSL methods. The FT strategy is surprisingly
+simple and general enough, which can speed up the training time of most
+existing methods by 4~300 times while holding the previous performance.
+",1,0,0,0,0,0
+19202,A geometric attractor mechanism for self-organization of entorhinal grid modules,"  Grid cells in the medial entorhinal cortex (mEC) respond when an animal
+occupies a periodic lattice of ""grid fields"" in the environment. The grids are
+organized in modules with spatial periods clustered around discrete values
+separated by constant ratios reported in the range 1.3-1.8. We propose a
+mechanism for dynamical self-organization in the mEC that can produce this
+modular structure. In attractor network models of grid formation, the period of
+a single module is set by the length scale of recurrent inhibition between
+neurons. We show that grid cells will instead form a hierarchy of discrete
+modules if a continuous increase in inhibition distance along the dorso-ventral
+axis of the mEC is accompanied by excitatory interactions along this axis.
+Moreover, constant scale ratios between successive modules arise through
+geometric relationships between triangular grids, whose lattice constants are
+separated by $\sqrt{3} \approx 1.7$, $\sqrt{7}/2 \approx 1.3$, or other ratios.
+We discuss how the interactions required by our model might be tested
+experimentally and realized by circuits in the mEC.
+",0,0,0,0,1,0
+19203,Attacking Strategies and Temporal Analysis Involving Facebook Discussion Groups,"  Online social network (OSN) discussion groups are exerting significant
+effects on political dialogue. In the absence of access control mechanisms, any
+user can contribute to any OSN thread. Individuals can exploit this
+characteristic to execute targeted attacks, which increases the potential for
+subsequent malicious behaviors such as phishing and malware distribution. These
+kinds of actions will also disrupt bridges among the media, politicians, and
+their constituencies.
+For the concern of Security Management, blending malicious cyberattacks with
+online social interactions has introduced a brand new challenge. In this paper
+we describe our proposal for a novel approach to studying and understanding the
+strategies that attackers use to spread malicious URLs across Facebook
+discussion groups. We define and analyze problems tied to predicting the
+potential for attacks focused on threads created by news media organizations.
+We use a mix of macro static features and the micro dynamic evolution of posts
+and threads to identify likely targets with greater than 90% accuracy. One of
+our secondary goals is to make such predictions within a short (10 minute) time
+frame. It is our hope that the data and analyses presented in this paper will
+support a better understanding of attacker strategies and footprints, thereby
+developing new system management methodologies in handing cyber attacks on
+social networks.
+",1,0,0,0,0,0
+19204,Perception-based energy functions in seam-cutting,"  Image stitching is challenging in consumer-level photography, due to
+alignment difficulties in unconstrained shooting environment. Recent studies
+show that seam-cutting approaches can effectively relieve artifacts generated
+by local misalignment. Normally, seam-cutting is described in terms of energy
+minimization, however, few of existing methods consider human perception in
+their energy functions, which sometimes causes that a seam with minimum energy
+is not most invisible in the overlapping region. In this paper, we propose a
+novel perception-based energy function in the seam-cutting framework, which
+considers the nonlinearity and the nonuniformity of human perception in energy
+minimization. Our perception-based approach adopts a sigmoid metric to
+characterize the perception of color discrimination, and a saliency weight to
+simulate that human eyes incline to pay more attention to salient objects. In
+addition, our seam-cutting composition can be easily implemented into other
+stitching pipelines. Experiments show that our method outperforms the
+seam-cutting method of the normal energy function, and a user study
+demonstrates that our composed results are more consistent with human
+perception.
+",1,0,0,0,0,0
+19205,Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo,"  Probabilistic modeling provides the capability to represent and manipulate
+uncertainty in data, models, predictions and decisions. We are concerned with
+the problem of learning probabilistic models of dynamical systems from measured
+data. Specifically, we consider learning of probabilistic nonlinear state-space
+models. There is no closed-form solution available for this problem, implying
+that we are forced to use approximations. In this tutorial we will provide a
+self-contained introduction to one of the state-of-the-art methods---the
+particle Metropolis--Hastings algorithm---which has proven to offer a practical
+approximation. This is a Monte Carlo based method, where the particle filter is
+used to guide a Markov chain Monte Carlo method through the parameter space.
+One of the key merits of the particle Metropolis--Hastings algorithm is that it
+is guaranteed to converge to the ""true solution"" under mild assumptions,
+despite being based on a particle filter with only a finite number of
+particles. We will also provide a motivating numerical example illustrating the
+method using a modeling language tailored for sequential Monte Carlo methods.
+The intention of modeling languages of this kind is to open up the power of
+sophisticated Monte Carlo methods---including particle
+Metropolis--Hastings---to a large group of users without requiring them to know
+all the underlying mathematical details.
+",1,0,0,1,0,0
+19206,Deep Projective 3D Semantic Segmentation,"  Semantic segmentation of 3D point clouds is a challenging problem with
+numerous real-world applications. While deep learning has revolutionized the
+field of image semantic segmentation, its impact on point cloud data has been
+limited so far. Recent attempts, based on 3D deep learning approaches
+(3D-CNNs), have achieved below-expected results. Such methods require
+voxelizations of the underlying point cloud data, leading to decreased spatial
+resolution and increased memory consumption. Additionally, 3D-CNNs greatly
+suffer from the limited availability of annotated datasets.
+In this paper, we propose an alternative framework that avoids the
+limitations of 3D-CNNs. Instead of directly solving the problem in 3D, we first
+project the point cloud onto a set of synthetic 2D-images. These images are
+then used as input to a 2D-CNN, designed for semantic segmentation. Finally,
+the obtained prediction scores are re-projected to the point cloud to obtain
+the segmentation results. We further investigate the impact of multiple
+modalities, such as color, depth and surface normals, in a multi-stream network
+architecture. Experiments are performed on the recent Semantic3D dataset. Our
+approach sets a new state-of-the-art by achieving a relative gain of 7.9 %,
+compared to the previous best approach.
+",1,0,0,0,0,0
+19207,Dynamic Pricing with Finitely Many Unknown Valuations,"  Motivated by posted price auctions where buyers are grouped in an unknown
+number of latent types characterized by their private values for the good on
+sale, we investigate revenue maximization in stochastic dynamic pricing when
+the distribution of buyers' private values is supported on an unknown set of
+points in [0,1] of unknown cardinality K. This setting can be viewed as an
+instance of a stochastic K-armed bandit problem where the location of the arms
+(the K unknown valuations) must be learned as well. In the distribution-free
+case, we show that our setting is just as hard as K-armed stochastic bandits:
+we prove that no algorithm can achieve a regret significantly better than
+$\sqrt{KT}$, (where T is the time horizon) and present an efficient algorithm
+matching this lower bound up to logarithmic factors. In the
+distribution-dependent case, we show that for all K>2 our setting is strictly
+harder than K-armed stochastic bandits by proving that it is impossible to
+obtain regret bounds that grow logarithmically in time or slower. On the other
+hand, when a lower bound $\gamma>0$ on the smallest drop in the demand curve is
+known, we prove an upper bound on the regret of order $(1/\Delta+(\log \log
+T)/\gamma^2)(K\log T)$. This is a significant improvement on previously known
+regret bounds for discontinuous demand curves, that are at best of order
+$(K^{12}/\gamma^8)\sqrt{T}$. When K=2 in the distribution-dependent case, the
+hardness of our setting reduces to that of a stochastic 2-armed bandit: we
+prove that an upper bound of order $(\log T)/\Delta$ (up to $\log\log$ factors)
+on the regret can be achieved with no information on the demand curve. Finally,
+we show a $O(\sqrt{T})$ upper bound on the regret for the setting in which the
+buyers' decisions are nonstochastic, and the regret is measured with respect to
+the best between two fixed valuations one of which is known to the seller.
+",0,0,0,1,0,0
+19208,"Haar systems, KMS states on von Neumann algebras and $C^*$-algebras on dynamically defined groupoids and Noncommutative Integration","  We analyse certain Haar systems associated to groupoids obtained by certain
+natural equivalence relations of dynamical nature on sets like
+$\{1,2,...,d\}^\mathbb{Z}$, $\{1,2,...,d\}^\mathbb{N}$, $S^1\times S^1$, or
+$(S^1)^\mathbb{N}$, where $S^1$ is the unitary circle. We also describe
+properties of transverse functions, quasi-invariant probabilities and KMS
+states for some examples of von Neumann algebras (and also $C^*$-Algebras)
+associated to these groupoids. We relate some of these KMS states with Gibbs
+states of Thermodynamic Formalism. While presenting new results, we will also
+describe in detail several examples and basic results on the above topics. In
+other words it is also a survey paper. Some known results on non-commutative
+integration are presented, more precisely, the relation of transverse measures,
+cocycles and quasi-invariant probabilities.
+We describe the results in a language which is more familiar to people in
+Dynamical Systems. Our intention is to study Haar systems, quasi-invariant
+probabilities and von Neumann algebras as a topic on measure theory
+(intersected with ergodic theory) avoiding questions of algebraic nature
+(which, of course, are also extremely important).
+",0,0,1,0,0,0
+19209,Restoring a smooth function from its noisy integrals,"  Numerical (and experimental) data analysis often requires the restoration of
+a smooth function from a set of sampled integrals over finite bins. We present
+the bin hierarchy method that efficiently computes the maximally smooth
+function from the sampled integrals using essentially all the information
+contained in the data. We perform extensive tests with different classes of
+functions and levels of data quality, including Monte Carlo data suffering from
+a severe sign problem and physical data for the Green's function of the
+Fröhlich polaron.
+",0,1,0,1,0,0
+19210,Adversarial Active Learning for Deep Networks: a Margin Based Approach,"  We propose a new active learning strategy designed for deep neural networks.
+The goal is to minimize the number of data annotation queried from an oracle
+during training. Previous active learning strategies scalable for deep networks
+were mostly based on uncertain sample selection. In this work, we focus on
+examples lying close to the decision boundary. Based on theoretical works on
+margin theory for active learning, we know that such examples may help to
+considerably decrease the number of annotations. While measuring the exact
+distance to the decision boundaries is intractable, we propose to rely on
+adversarial examples. We do not consider anymore them as a threat instead we
+exploit the information they provide on the distribution of the input space in
+order to approximate the distance to decision boundaries. We demonstrate
+empirically that adversarial active queries yield faster convergence of CNNs
+trained on MNIST, the Shoe-Bag and the Quick-Draw datasets.
+",0,0,0,1,0,0
+19211,Improving SIEM capabilities through an enhanced probe for encrypted Skype traffic detection,"  Nowadays, the Security Information and Event Management (SIEM) systems take
+on great relevance in handling security issues for critical infrastructures as
+Internet Service Providers. Basically, a SIEM has two main functions: i) the
+collection and the aggregation of log data and security information from
+disparate network devices (routers, firewalls, intrusion detection systems, ad
+hoc probes and others) and ii) the analysis of the gathered data by
+implementing a set of correlation rules aimed at detecting potential suspicious
+events as the presence of encrypted real-time traffic. In the present work, the
+authors propose an enhanced implementation of a SIEM where a particular focus
+is given to the detection of encrypted Skype traffic by using an ad-hoc
+developed enhanced probe (ESkyPRO) conveniently governed by the SIEM itself.
+Such enhanced probe, able to interact with an agent counterpart deployed into
+the SIEM platform, is designed by exploiting some machine learning concepts.
+The main purpose of the proposed ad-hoc SIEM is to correlate the information
+received by ESkyPRO and other types of data obtained by an Intrusion Detection
+System (IDS) probe in order to make the encrypted Skype traffic detection as
+accurate as possible.
+",1,0,0,0,0,0
+19212,Escaping Saddle Points with Adaptive Gradient Methods,"  Adaptive methods such as Adam and RMSProp are widely used in deep learning
+but are not well understood. In this paper, we seek a crisp, clean and precise
+characterization of their behavior in nonconvex settings. To this end, we first
+provide a novel view of adaptive methods as preconditioned SGD, where the
+preconditioner is estimated in an online manner. By studying the preconditioner
+on its own, we elucidate its purpose: it rescales the stochastic gradient noise
+to be isotropic near stationary points, which helps escape saddle points.
+Furthermore, we show that adaptive methods can efficiently estimate the
+aforementioned preconditioner. By gluing together these two components, we
+provide the first (to our knowledge) second-order convergence result for any
+adaptive method. The key insight from our analysis is that, compared to SGD,
+adaptive methods escape saddle points faster, and can converge faster overall
+to second-order stationary points.
+",1,0,0,1,0,0
+19213,The Effect of Temperature on Cu-K-In-Se Thin Films,"  Films of Cu-K-In-Se were co-evaporated at varied K/(K+Cu) compositions and
+substrate temperatures (with constant (K+Cu)/In ~ 0.85). Increased Na
+composition on the substrate's surface and decreased growth temperature were
+both found to favor Cu1-xKxInSe2 (CKIS) alloy formation, relative to
+mixed-phase CuInSe2 + KInSe2 formation. Structures from X-ray diffraction
+(XRD), band gaps, resistivities, minority carrier lifetimes and carrier
+concentrations from time-resolved photoluminescence were in agreement with
+previous reports, where low K/(K+Cu) composition films exhibited properties
+promising for photovoltaic (PV) absorbers. Films grown at 400-500 C were then
+annealed to 600 C under Se, which caused K loss by evaporation in proportion to
+initial K/(K+Cu) composition. Similar to growth temperature, annealing drove
+CKIS alloy consumption and CuInSe2 + KInSe2 production, as evidenced by high
+temperature XRD. Annealing also decomposed KInSe2 and formed K2In12Se19. At
+high temperature the KInSe2 crystal lattice gradually contracted as temperature
+and time increased, as well as just time. Evaporative loss of K during
+annealing could accompany the generation of vacancies on K lattice sites, and
+may explain the KInSe2 lattice contraction. This knowledge of Cu-K-In-Se
+material chemistry may be used to predict and control minor phase impurities in
+Cu(In,Ga)(Se,S)2 PV absorbers-where impurities below typical detection limits
+may have played a role in recent world record PV efficiencies that utilized KF
+post-deposition treatments.
+",0,1,0,0,0,0
+19214,Simulations of the Solar System's Early Dynamical Evolution with a Self-Gravitating Planetesimal Disk,"  Over the course of last decade, the Nice model has dramatically changed our
+view of the solar system's formation and early evolution. Within the context of
+this model, a transient period of planet-planet scattering is triggered by
+gravitational interactions between the giant planets and a massive primordial
+planetesimal disk, leading to a successful reproduction of the solar system's
+present-day architecture. In typical realizations of the Nice model,
+self-gravity of the planetesimal disk is routinely neglected, as it poses a
+computational bottleneck to the calculations. Recent analyses have shown,
+however, that a self-gravitating disk can exhibit behavior that is dynamically
+distinct, and this disparity may have significant implications for the solar
+system's evolutionary path. In this work, we explore this discrepancy utilizing
+a large suite of Nice odel simulations with and without a self-gravitating
+planetesimal disk, taking advantage of the inherently parallel nature of
+graphic processing units. Our simulations demonstrate that self-consistent
+modeling of particle interactions does not lead to significantly different
+final planetary orbits from those obtained within conventional simulations.
+Moreover, self-gravitating calculations show similar planetesimal evolution to
+non-self-gravitating numerical experiments after dynamical instability is
+triggered, suggesting that the orbital clustering observed in the distant
+Kuiper belt is unlikely to have a self-gravitational origin.
+",0,1,0,0,0,0
+19215,On weak Fraisse limits,"  Using the natural action of $S_\infty$ we show that a countable hereditary
+class $\cC$ of finitely generated structures has the joint embedding property
+(JEP) and the weak amalgamation property (WAP) if and only if there is a
+structure $M$ whose isomorphism type is comeager in the space of all countable,
+infinitely generated structures with age in $\cC$. In this case, $M$ is the
+weak Fraïssé limit of $\cC$.
+This applies in particular to countable structures with generic automorphisms
+and recovers a result by Kechris and Rosendal [\textit{Proc.\ Lond.\ Math.\
+Soc.,\ 2007}].
+",0,0,1,0,0,0
+19216,Promoting Saving for College Through Data Science,"  The cost of attending college has been steadily rising and in 10 years is
+estimated to reach $140,000 for a 4-year public university. Recent surveys
+estimate just over half of US families are saving for college. State-operated
+529 college savings plans are an effective way for families to plan and save
+for future college costs, but only 3% of families currently use them. The
+Office of the Illinois State Treasurer (Treasurer) administers two 529 plans to
+help its residents save for college. In order to increase the number of
+families saving for college, the Treasurer and Civis Analytics used data
+science techniques to identify the people most likely to sign up for a college
+savings plan. In this paper, we will discuss the use of person matching to join
+accountholder data from the Treasurer to the Civis National File, as well as
+the use of lookalike modeling to identify new potential signups. In order to
+avoid reinforcing existing demographic imbalances in who saves for college, the
+lookalike models used were ensured to be racially and economically balanced. We
+will also discuss how these new signup targets were then individually served
+digital ads to encourage opening college savings accounts.
+",1,0,0,0,0,0
+19217,Rotation Blurring: Use of Artificial Blurring to Reduce Cybersickness in Virtual Reality First Person Shooters,"  Users of Virtual Reality (VR) systems often experience vection, the
+perception of self-motion in the absence of any physical movement. While
+vection helps to improve presence in VR, it often leads to a form of motion
+sickness called cybersickness. Cybersickness is a major deterrent to large
+scale adoption of VR.
+Prior work has discovered that changing vection (changing the perceived speed
+or moving direction) causes more severe cybersickness than steady vection
+(walking at a constant speed or in a constant direction). Based on this idea,
+we try to reduce the cybersickness caused by character movements in a First
+Person Shooter (FPS) game in VR. We propose Rotation Blurring (RB), uniformly
+blurring the screen during rotational movements to reduce cybersickness. We
+performed a user study to evaluate the impact of RB in reducing cybersickness.
+We found that the blurring technique led to an overall reduction in sickness
+levels of the participants and delayed its onset. Participants who experienced
+acute levels of cybersickness benefited significantly from this technique.
+",1,0,0,0,0,0
+19218,Comparative Benchmarking of Causal Discovery Techniques,"  In this paper we present a comprehensive view of prominent causal discovery
+algorithms, categorized into two main categories (1) assuming acyclic and no
+latent variables, and (2) allowing both cycles and latent variables, along with
+experimental results comparing them from three perspectives: (a) structural
+accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual
+inference. For (b) and (c) we train causal Bayesian networks with structures as
+predicted by each causal discovery technique to carry out counterfactual or
+standard predictive inference. We compare causal algorithms on two pub- licly
+available and one simulated datasets having different sample sizes: small,
+medium and large. Experiments show that structural accuracy of a technique does
+not necessarily correlate with higher accuracy of inferencing tasks. Fur- ther,
+surveyed structure learning algorithms do not perform well in terms of
+structural accuracy in case of datasets having large number of variables.
+",1,0,0,1,0,0
+19219,Sensitivity of Love and quasi-Rayleigh waves to model parameters,"  We examine the sensitivity of the Love and the quasi-Rayleigh waves to model
+parameters. Both waves are guided waves that propagate in the same model of an
+elastic layer above an elastic halfspace. We study their dispersion curves
+without any simplifying assumptions, beyond the standard approach of elasticity
+theory in isotropic media. We examine the sensitivity of both waves to
+elasticity parameters, frequency and layer thickness, for varying frequency and
+different modes. In the case of Love waves, we derive and plot the absolute
+value of a dimensionless sensitivity coefficient in terms of partial
+derivatives, and perform an analysis to find the optimum frequency for
+determining the layer thickness. For a coherency of the background information,
+we briefly review the Love-wave dispersion relation and provide details of the
+less common derivation of the quasi-Rayleigh relation in an appendix. We
+compare that derivation to past results in the literature, finding certain
+discrepancies among them.
+",0,1,0,0,0,0
+19220,The image size of iterated rational maps over finite fields,"  Let $\varphi:\mathbb{F}_q\to\mathbb{F}_q$ be a rational map on a fixed finite
+field. We give explicit asymptotic formulas for the size of image sets
+$\varphi^n(\mathbb{F}_q)$ as a function of $n$. This is done by using
+properties of the Galois groups of iterated maps, whose connection to the
+question of the size of image sets is established via Chebotarev's Density
+Theorem. We then apply these results to provide explicit bounds on the
+proportion of periodic points in $\mathbb{F}_q$ in terms of $q$ for certain
+rational maps.
+",0,0,1,0,0,0
+19221,Noncommutative modular symbols and Eisenstein series,"  We form real-analytic Eisenstein series twisted by Manin's noncommutative
+modular symbols. After developing their basic properties, these series are
+shown to have meromorphic continuations to the entire complex plane and satisfy
+functional equations in some cases. This theory neatly contains and generalizes
+earlier work in the literature on the properties of Eisenstein series twisted
+by classical modular symbols.
+",0,0,1,0,0,0
+19222,Some rigidity characterizations on critical metrics for quadratic curvature functionals,"  We study closed $n$-dimensional manifolds of which the metrics are critical
+for quadratic curvature functionals involving the Ricci curvature, the scalar
+curvature and the Riemannian curvature tensor on the space of Riemannian
+metrics with unit volume. Under some additional integral conditions, we
+classify such manifolds. Moreover, under some curvature conditions, the result
+that a critical metric must be Einstein is proved.
+",0,0,1,0,0,0
+19223,Profinite completions of Burnside-type quotients of surface groups,"  Using quantum representations of mapping class groups we prove that profinite
+completions of Burnside-type surface group quotients are not virtually
+prosolvable, in general. Further, we construct infinitely many finite simple
+characteristic quotients of surface groups.
+",0,0,1,0,0,0
+19224,Learning Infinite RBMs with Frank-Wolfe,"  In this work, we propose an infinite restricted Boltzmann machine~(RBM),
+whose maximum likelihood estimation~(MLE) corresponds to a constrained convex
+optimization. We consider the Frank-Wolfe algorithm to solve the program, which
+provides a sparse solution that can be interpreted as inserting a hidden unit
+at each iteration, so that the optimization process takes the form of a
+sequence of finite models of increasing complexity. As a side benefit, this can
+be used to easily and efficiently identify an appropriate number of hidden
+units during the optimization. The resulting model can also be used as an
+initialization for typical state-of-the-art RBM training algorithms such as
+contrastive divergence, leading to models with consistently higher test
+likelihood than random initialization.
+",1,0,0,1,0,0
+19225,New low-mass eclipsing binary systems in Praesepe discovered by K2,"  We present the discovery of four low-mass ($M<0.6$ $M_\odot$) eclipsing
+binary (EB) systems in the sub-Gyr old Praesepe open cluster using Kepler/K2
+time-series photometry and Keck/HIRES spectroscopy. We present a new Gaussian
+process eclipsing binary model, GP-EBOP, as well as a method of simultaneously
+determining effective temperatures and distances for EBs. Three of the reported
+systems (AD 3814, AD 2615 and AD 1508) are detached and double-lined, and
+precise solutions are presented for the first two. We determine masses and
+radii to 1-3% precision for AD 3814 and to 5-6% for AD 2615. Together with
+effective temperatures determined to $\sim$50 K precision, we test the PARSEC
+v1.2 and BHAC15 stellar evolution models. Our EB parameters are more consistent
+with the PARSEC models, primarily because the BHAC15 temperature scale is
+hotter than our data over the mid M-dwarf mass range probed. Both ADs 3814 and
+2615, which have orbital periods of 6.0 and 11.6 days, are circularized but not
+synchronized. This suggests that either synchronization proceeds more slowly in
+fully convective stars than the theory of equilibrium tides predicts or
+magnetic braking is currently playing a more important role than tidal forces
+in the spin evolution of these binaries. The fourth system (AD 3116) comprises
+a brown dwarf transiting a mid M-dwarf, which is the first such system
+discovered in a sub-Gyr open cluster. Finally, these new discoveries increase
+the number of characterized EBs in sub-Gyr open clusters by 20% (40%) below
+$M<1.5$ $M_{\odot}$ ($M<0.6$ $M_{\odot}$).
+",0,1,0,0,0,0
+19226,The impact of the halide cage on the electronic properties of fully inorganic caesium lead halide perovskites,"  Perovskite solar cells with record power conversion efficiency are fabricated
+by alloying both hybrid and fully inorganic compounds. While the basic
+electronic properties of the hybrid perovskites are now well understood, key
+electronic parameters for solar cell performance, such as the exciton binding
+energy of fully inorganic perovskites, are still unknown. By performing magneto
+transmission measurements, we determine with high accuracy the exciton binding
+energy and reduced mass of fully inorganic CsPbX$_3$ perovskites (X=I, Br, and
+an alloy of these). The well behaved (continuous) evolution of the band gap
+with temperature in the range $4-270$\,K suggests that fully inorganic
+perovskites do not undergo structural phase transitions like their hybrid
+counterparts. The experimentally determined dielectric constants indicate that
+at low temperature, when the motion of the organic cation is frozen, the
+dielectric screening mechanism is essentially the same both for hybrid and
+inorganic perovskites, and is dominated by the relative motion of atoms within
+the lead-halide cage.
+",0,1,0,0,0,0
+19227,Discovery of Giant Radio Galaxies from NVSS: Radio & Infrared Properties,"  Giant radio galaxies (GRGs) are one of the largest astrophysical sources in
+the Universe with an overall projected linear size of ~0.7 Mpc or more. Last
+six decades of radio astronomy research has led to the detection of thousands
+of radio galaxies. But only ~ 300 of them can be classified as GRGs. The
+reasons behind their large size and rarity are unknown. We carried out a
+systematic search for these radio giants and found a large sample of GRGs. In
+this paper, we report the discovery of 25 GRGs from NVSS, in the redshift range
+(z) ~ 0.07 to 0.67. Their physical sizes range from ~0.8 Mpc to ~4 Mpc. Eight
+of these GRGs have sizes greater than 2Mpc which is a rarity. In this paper,
+for the first time, we investigate the mid-IR properties of the optical hosts
+of the GRGs and classify them securely into various AGN types using the WISE
+mid-IR colours. Using radio and IR data, four of the hosts of GRGs were
+observed to be radio loud quasars that extend up to 2 Mpc in radio size. These
+GRGs missed detection in earlier searches possibly because of their highly
+diffuse nature, low surface brightness and lack of optical data. The new GRGs
+are a significant addition to the existing sample that will contribute to
+better understanding of the physical properties of radio giants.
+",0,1,0,0,0,0
+19228,SGD: General Analysis and Improved Rates,"  We propose a general yet simple theorem describing the convergence of SGD
+under the arbitrary sampling paradigm. Our theorem describes the convergence of
+an infinite array of variants of SGD, each of which is associated with a
+specific probability law governing the data selection rule used to form
+mini-batches. This is the first time such an analysis is performed, and most of
+our variants of SGD were never explicitly considered in the literature before.
+Our analysis relies on the recently introduced notion of expected smoothness
+and does not rely on a uniform bound on the variance of the stochastic
+gradients. By specializing our theorem to different mini-batching strategies,
+such as sampling with replacement and independent sampling, we derive exact
+expressions for the stepsize as a function of the mini-batch size. With this we
+can also determine the mini-batch size that optimizes the total complexity, and
+show explicitly that as the variance of the stochastic gradient evaluated at
+the minimum grows, so does the optimal mini-batch size. For zero variance, the
+optimal mini-batch size is one. Moreover, we prove insightful
+stepsize-switching rules which describe when one should switch from a constant
+to a decreasing stepsize regime.
+",1,0,0,1,0,0
+19229,Nonconvex One-bit Single-label Multi-label Learning,"  We study an extreme scenario in multi-label learning where each training
+instance is endowed with a single one-bit label out of multiple labels. We
+formulate this problem as a non-trivial special case of one-bit rank-one matrix
+sensing and develop an efficient non-convex algorithm based on alternating
+power iteration. The proposed algorithm is able to recover the underlying
+low-rank matrix model with linear convergence. For a rank-$k$ model with $d_1$
+features and $d_2$ classes, the proposed algorithm achieves $O(\epsilon)$
+recovery error after retrieving $O(k^{1.5}d_1 d_2/\epsilon)$ one-bit labels
+within $O(kd)$ memory. Our bound is nearly optimal in the order of
+$O(1/\epsilon)$. This significantly improves the state-of-the-art sampling
+complexity of one-bit multi-label learning. We perform experiments to verify
+our theory and evaluate the performance of the proposed algorithm.
+",1,0,0,1,0,0
+19230,Two Categories of Indoor Interactive Dynamics of a Large-scale Human Population in a WiFi covered university campus,"  To explore large-scale population indoor interactions, we analyze 18,715
+users' WiFi access logs recorded in a Chinese university campus during 3
+months, and define two categories of human interactions, the event interaction
+(EI) and the temporal interaction (TI). The EI helps construct a transmission
+graph, and the TI helps build an interval graph. The dynamics of EIs show that
+their active durations are truncated power-law distributed, which is
+independent on the number of involved individuals. The transmission duration
+presents a truncated power-law behavior at the daily timescale with weekly
+periodicity. Besides, those `leaf' individuals in the aggregated contact
+network may participate in the `super-connecting cliques' in the aggregated
+transmission graph. Analyzing the dynamics of the interval graph, we find that
+the probability distribution of TIs' inter-event duration also displays a
+truncated power-law pattern at the daily timescale with weekly periodicity,
+while the pairwise individuals with burst interactions are prone to randomly
+select their interactive locations, and those individuals with periodic
+interactions have preferred interactive locations.
+",1,1,0,0,0,0
+19231,Robust estimators for generalized linear models with a dispersion parameter,"  Highly robust and efficient estimators for the generalized linear model with
+a dispersion parameter are proposed. The estimators are based on three steps.
+In the first step the maximum rank correlation estimator is used to
+consistently estimate the slopes up to a scale factor. In the second step, the
+scale factor, the intercept, and the dispersion parameter are consistently
+estimated using a MT-estimator of a simple regression model. The combined
+estimator is highly robust but inefficient. Then, randomized quantile residuals
+based on the initial estimators are used to detect outliers to be rejected and
+to define a set S of observations to be retained. Finally, a conditional
+maximum likelihood (CML) estimator given the observations in S is computed. We
+show that, under the model, S tends to the complete sample for increasing
+sample size. Therefore, the CML tends to the unconditional maximum likelihood
+estimator. It is therefore highly efficient, while maintaining the high degree
+of robustness of the initial estimator. The case of the negative binomial
+regression model is studied in detail.
+",0,0,0,1,0,0
+19232,Abstracting Event-Driven Systems with Lifestate Rules,"  We present lifestate rules--an approach for abstracting event-driven object
+protocols. Developing applications against event-driven software frameworks is
+notoriously difficult. One reason why is that to create functioning
+applications, developers must know about and understand the complex protocols
+that abstract the internal behavior of the framework. Such protocols intertwine
+the proper registering of callbacks to receive control from the framework with
+appropriate application programming interface (API) calls to delegate back to
+it. Lifestate rules unify lifecycle and typestate constraints in one common
+specification language. Our primary contribution is a model of event-driven
+systems from which lifestate rules can be derived. We then apply specification
+mining techniques to learn lifestate specifications for Android framework
+types. In the end, our implementation is able to find several rules that
+characterize actual behavior of the Android framework.
+",1,0,0,0,0,0
+19233,Boltzmann Encoded Adversarial Machines,"  Restricted Boltzmann Machines (RBMs) are a class of generative neural network
+that are typically trained to maximize a log-likelihood objective function. We
+argue that likelihood-based training strategies may fail because the objective
+does not sufficiently penalize models that place a high probability in regions
+where the training data distribution has low probability. To overcome this
+problem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is
+an RBM trained against an adversary that uses the hidden layer activations of
+the RBM to discriminate between the training data and the probability
+distribution generated by the model. We present experiments demonstrating that
+BEAMs outperform RBMs and GANs on multiple benchmarks.
+",0,0,0,1,0,0
+19234,"A Non-Gaussian, Nonparametric Structure for Gene-Gene and Gene-Environment Interactions in Case-Control Studies Based on Hierarchies of Dirichlet Processes","  It is becoming increasingly clear that complex interactions among genes and
+environmental factors play crucial roles in triggering complex diseases. Thus,
+understanding such interactions is vital, which is possible only through
+statistical models that adequately account for such intricate, albeit unknown,
+dependence structures. Bhattacharya & Bhattacharya (2016b) attempt such
+modeling, relating finite mixtures composed of Dirichlet processes that
+represent unknown number of genetic sub-populations through a hierarchical
+matrix-normal structure that incorporates gene-gene interactions, and possible
+mutations, induced by environmental variables. However, the product dependence
+structure implied by their matrix-normal model seems to be too simple to be
+appropriate for general complex, realistic situations. In this article, we
+propose and develop a novel nonparametric Bayesian model for case-control
+genotype data using hierarchies of Dirichlet processes that offers a more
+realistic and nonparametric dependence structure between the genes, induced by
+the environmental variables. In this regard, we propose a novel and highly
+parallelisable MCMC algorithm that is rendered quite efficient by the
+combination of modern parallel computing technology, effective Gibbs sampling
+steps, retrospective sampling and Transformation based Markov Chain Monte Carlo
+(TMCMC). We use appropriate Bayesian hypothesis testing procedures to detect
+the roles of genes and environment in case-control studies. We apply our ideas
+to 5 biologically realistic case-control genotype datasets simulated under
+distinct set-ups, and obtain encouraging results in each case. We finally apply
+our ideas to a real, myocardial infarction dataset, and obtain interesting
+results on gene-gene and gene-environment interaction, while broadly agreeing
+with the results reported in the literature.
+",0,0,0,1,0,0
+19235,An Efficient Bayesian Robust Principal Component Regression,"  Principal component regression is a linear regression model with principal
+components as regressors. This type of modelling is particularly useful for
+prediction in settings with high-dimensional covariates. Surprisingly, the
+existing literature treating of Bayesian approaches is relatively sparse. In
+this paper, we aim at filling some gaps through the following practical
+contribution: we introduce a Bayesian approach with detailed guidelines for a
+straightforward implementation. The approach features two characteristics that
+we believe are important. First, it effectively involves the relevant principal
+components in the prediction process. This is achieved in two steps. The first
+one is model selection; the second one is to average out the predictions
+obtained from the selected models according to model averaging mechanisms,
+allowing to account for model uncertainty. The model posterior probabilities
+are required for model selection and model averaging. For this purpose, we
+include a procedure leading to an efficient reversible jump algorithm. The
+second characteristic of our approach is whole robustness, meaning that the
+impact of outliers on inference gradually vanishes as they approach plus or
+minus infinity. The conclusions obtained are consequently consistent with the
+majority of observations (the bulk of the data).
+",0,0,0,1,0,0
+19236,Bayesian estimation from few samples: community detection and related problems,"  We propose an efficient meta-algorithm for Bayesian estimation problems that
+is based on low-degree polynomials, semidefinite programming, and tensor
+decomposition. The algorithm is inspired by recent lower bound constructions
+for sum-of-squares and related to the method of moments. Our focus is on sample
+complexity bounds that are as tight as possible (up to additive lower-order
+terms) and often achieve statistical thresholds or conjectured computational
+thresholds.
+Our algorithm recovers the best known bounds for community detection in the
+sparse stochastic block model, a widely-studied class of estimation problems
+for community detection in graphs. We obtain the first recovery guarantees for
+the mixed-membership stochastic block model (Airoldi et el.) in constant
+average degree graphs---up to what we conjecture to be the computational
+threshold for this model. We show that our algorithm exhibits a sharp
+computational threshold for the stochastic block model with multiple
+communities beyond the Kesten--Stigum bound---giving evidence that this task
+may require exponential time.
+The basic strategy of our algorithm is strikingly simple: we compute the
+best-possible low-degree approximation for the moments of the posterior
+distribution of the parameters and use a robust tensor decomposition algorithm
+to recover the parameters from these approximate posterior moments.
+",1,0,0,1,0,0
+19237,X-ray Transform and Boundary Rigidity for Asymptotically Hyperbolic Manifolds,"  We consider the boundary rigidity problem for asymptotically hyperbolic
+manifolds. We show injectivity of the X-ray transform in several cases and
+consider the non-linear inverse problem which consists of recovering a metric
+from boundary measurements for the geodesic flow.
+",0,0,1,0,0,0
+19238,Pinning of longitudinal phonons in holographic spontaneous helices,"  We consider the spontaneous breaking of translational symmetry and identify
+the associated Goldstone mode -- a longitudinal phonon -- in a holographic
+model with Bianchi VII helical symmetry. For the first time in holography, we
+observe the pinning of this mode after introducing a source for explicit
+breaking compatible with the helical symmetry of our setup. We study the
+dispersion relation of the resulting pseudo-Goldstone mode, uncovering how its
+speed and mass gap depend on the amplitude of the source and temperature. In
+addition, we extract the optical conductivity as a function of frequency, which
+reveals a metal-insulator transition as a consequence of the pinning.
+",0,1,0,0,0,0
+19239,An Extension of the Method of Brackets. Part 1,"  The method of brackets is an efficient method for the evaluation of a large
+class of definite integrals on the half-line. It is based on a small collection
+of rules, some of which are heuristic. The extension discussed here is based on
+the concepts of null and divergent series. These are formal representations of
+functions, whose coefficients $a_{n}$ have meromorphic representations for $n
+\in \mathbb{C}$, but might vanish or blow up when $n \in \mathbb{N}$. These
+ideas are illustrated with the evaluation of a variety of entries from the
+classical table of integrals by Gradshteyn and Ryzhik.
+",0,0,1,0,0,0
+19240,optimParallel: an R Package Providing Parallel Versions of the Gradient-Based Optimization Methods of optim(),"  The R package optimParallel provides a parallel version of the gradient-based
+optimization methods of optim(). The main function of the package is
+optimParallel(), which has the same usage and output as optim(). Using
+optimParallel() can significantly reduce optimization times. We introduce the R
+package and illustrate its implementation, which takes advantage of the lexical
+scoping mechanism of R.
+",0,0,0,1,0,0
+19241,The barocaloric effect: A Spin-off of the Discovery of High-Temperature Superconductivity,"  Some key results obtained in joint research projects with Alex Müller are
+summarized, concentrating on the invention of the barocaloric effect and its
+application for cooling as well as on important findings in the field of
+high-temperature superconductivity resulting from neutron scattering
+experiments.
+",0,1,0,0,0,0
+19242,Spontaneous currents in superconducting systems with strong spin-orbit coupling,"  We show that Rashba spin-orbit coupling at the interface between a
+superconductor and a ferromagnet should produce a spontaneous current in the
+atomic thickness region near the interface. This current is counter-balanced by
+the superconducting screening current flowing in the region of the width of the
+London penetration depth near the interface. Such current carrying state
+creates a magnetic field near the superconductor surface, generates a stray
+magnetic field outside the sample edges, changes the slope of the temperature
+dependence of the critical field $H_{c3}$ and may generate the spontaneous
+Abrikosov vortices near the interface.
+",0,1,0,0,0,0
+19243,Catching Loosely Synchronized Behavior in Face of Camouflage,"  Fraud has severely detrimental impacts on the business of social networks and
+other online applications. A user can become a fake celebrity by purchasing
+""zombie followers"" on Twitter. A merchant can boost his reputation through fake
+reviews on Amazon. This phenomenon also conspicuously exists on Facebook, Yelp
+and TripAdvisor, etc. In all the cases, fraudsters try to manipulate the
+platform's ranking mechanism by faking interactions between the fake accounts
+they control and the target customers.
+",1,0,0,0,0,0
+19244,Three-dimensional localized-delocalized Anderson transition in the time domain,"  Systems which can spontaneously reveal periodic evolution are dubbed time
+crystals. This is in analogy with space crystals that display periodic behavior
+in configuration space. While space crystals are modelled with the help of
+space periodic potentials, crystalline phenomena in time can be modelled by
+periodically driven systems. Disorder in the periodic driving can lead to
+Anderson localization in time: the probability for detecting a system at a
+fixed point of configuration space becomes exponentially localized around a
+certain moment in time. We here show that a three-dimensional system exposed to
+a properly disordered pseudo-periodic driving may display a
+localized-delocalized Anderson transition in the time domain, in strong analogy
+with the usual three-dimensional Anderson transition in disordered systems.
+Such a transition could be experimentally observed with ultra-cold atomic
+gases.
+",0,1,0,0,0,0
+19245,Socio-economic constraints to maximum human lifespan,"  The analysis of the demographic transition of the past century and a half,
+using both empirical data and mathematical models, has rendered a wealth of
+well-established facts, including the dramatic increases in life expectancy.
+Despite these insights, such analyses have also occasionally triggered debates
+which spill over many disciplines, from genetics, to biology, or demography.
+Perhaps the hottest discussion is happening around the question of maximum
+human lifespan, which --besides its fascinating historical and philosophical
+interest-- poses urgent pragmatic warnings on a number of issues in public and
+private decision-making. In this paper, we add to the controversy some results
+which, based on purely statistical grounds, suggest that the maximum human
+lifespan is not fixed, or has not reached yet a plateau. Quite the contrary,
+analysis on reliable data for over 150 years in more than 20 industrialized
+countries point at a sustained increase in the maximum age at death.
+Furthermore, were this trend to continue, a limitless lifespan could be
+achieved by 2102. Finally, we quantify the dependence of increases in the
+maximum lifespan on socio-economic factors. Our analysis indicates that in some
+countries the observed rising patterns can only be sustained by progressively
+larger increases in GDP, setting the problem of longevity in a context of
+diminishing returns.
+",0,0,0,1,1,0
+19246,SD-CPS: Taming the Challenges of Cyber-Physical Systems with a Software-Defined Approach,"  Cyber-Physical Systems (CPS) revolutionize various application domains with
+integration and interoperability of networking, computing systems, and
+mechanical devices. Due to its scale and variety, CPS faces a number of
+challenges and opens up a few research questions in terms of management,
+fault-tolerance, and scalability. We propose a software-defined approach
+inspired by Software-Defined Networking (SDN), to address the challenges for a
+wider CPS adoption. We thus design a middleware architecture for the correct
+and resilient operation of CPS, to manage and coordinate the interacting
+devices centrally in the cyberspace whilst not sacrificing the functionality
+and performance benefits inherent to a distributed execution.
+",1,0,0,0,0,0
+19247,Dual Ore's theorem on distributive intervals of finite groups,"  This paper gives a self-contained group-theoretic proof of a dual version of
+a theorem of Ore on distributive intervals of finite groups. We deduce a bridge
+between combinatorics and representations in finite group theory.
+",0,0,1,0,0,0
+19248,A construction of trivial Beltrami coefficients,"  A measurable function $\mu$ on the unit disk $\mathbb{D}$ of the complex
+plane with $\|\mu\|_\infty<1$ is sometimes called a Beltrami coefficient. We
+say that $\mu$ is trivial if it is the complex dilatation $f_{\bar z}/f_z$ of a
+quasiconformal automorphism $f$ of $\mathbb{D}$ satisfying the trivial boundary
+condition $f(z)=z,~|z|=1.$ Since it is not easy to solve the Beltrami equation
+explicitly, to detect triviality of a given Beltrami coefficient is a hard
+problem, in general. In the present article, we offer a sufficient condition
+for a Beltrami coefficient to be trivial. Our proof is based on Betker's
+theorem on Löwner chains.
+",0,0,1,0,0,0
+19249,A GNS construction of three-dimensional abelian Dijkgraaf-Witten theories,"  We give a detailed account of the so-called ""universal construction"" that
+aims to extend invariants of closed manifolds, possibly with additional
+structure, to topological field theories and show that it amounts to a
+generalization of the GNS construction. We apply this construction to an
+invariant defined in terms of the groupoid cardinality of groupoids of bundles
+to recover Dijkgraaf-Witten theories, including the vector spaces obtained as a
+linearization of spaces of principal bundles.
+",0,0,1,0,0,0
+19250,Long-path formation in a deformed microdisk laser,"  An asymmetric resonant cavity can be used to form a path that is much longer
+than the cavity size. We demonstrate this capability for a deformed microdisk
+equipped with two linear waveguides, by constructing a multiply reflected
+periodic orbit that is confined by total internal reflection within the
+deformed microdisk and outcoupled by the two linear waveguides. Resonant mode
+analysis reveals that the modes corresponding to the periodic orbit are
+characterized by high quality factors. From measured spectral and far-field
+data, we confirm that the fabricated devices can form a path about 9.3 times
+longer than the average diameter of the deformed microdisk.
+",0,1,0,0,0,0
+19251,Spitzer Observations of Large Amplitude Variables in the LMC and IC 1613,"  The 3.6 and 4.5 micron characteristics of AGB variables in the LMC and IC1613
+are discussed. For C-rich Mira variables there is a very clear
+period-luminosity-colour relation, where the [3.6]-[4.5] colour is associated
+with the amount of circumstellar material and correlated with the pulsation
+amplitude. The [4.5] period-luminosity relation for dusty stars is
+approximately one mag brighter than for their naked counterparts with
+comparable periods.
+",0,1,0,0,0,0
+19252,Synergistic Team Composition,"  Effective teams are crucial for organisations, especially in environments
+that require teams to be constantly created and dismantled, such as software
+development, scientific experiments, crowd-sourcing, or the classroom. Key
+factors influencing team performance are competences and personality of team
+members. Hence, we present a computational model to compose proficient and
+congenial teams based on individuals' personalities and their competences to
+perform tasks of different nature. With this purpose, we extend Wilde's
+post-Jungian method for team composition, which solely employs individuals'
+personalities. The aim of this study is to create a model to partition agents
+into teams that are balanced in competences, personality and gender. Finally,
+we present some preliminary empirical results that we obtained when analysing
+student performance. Results show the benefits of a more informed team
+composition that exploits individuals' competences besides information about
+their personalities.
+",1,0,0,0,0,0
+19253,The inverse hull of 0-left cancellative semigroups,"  Given a semigroup S with zero, which is left-cancellative in the sense that
+st=sr \neq 0 implies that t=r, we construct an inverse semigroup called the
+inverse hull of S, denoted H(S). When S admits least common multiples, in a
+precise sense defined below, we study the idempotent semilattice of H(S), with
+a focus on its spectrum. When S arises as the language semigroup for a subsift
+X on a finite alphabet, we discuss the relationship between H(S) and several
+C*-algebras associated to X appearing in the literature.
+",0,0,1,0,0,0
+19254,The Hadamard Determinant Inequality - Extensions to Operators on a Hilbert Space,"  A generalization of classical determinant inequalities like Hadamard's
+inequality and Fischer's inequality is studied. For a version of the
+inequalities originally proved by Arveson for positive operators in von Neumann
+algebras with a tracial state, we give a different proof. We also improve and
+generalize to the setting of finite von Neumann algebras, some `Fischer-type'
+inequalities by Matic for determinants of perturbed positive-definite matrices.
+In the process, a conceptual framework is established for viewing these
+inequalities as manifestations of Jensen's inequality in conjunction with the
+theory of operator monotone and operator convex functions on $[0,\infty)$. We
+place emphasis on documenting necessary and sufficient conditions for equality
+to hold.
+",0,0,1,0,0,0
+19255,Experimental results : Reinforcement Learning of POMDPs using Spectral Methods,"  We propose a new reinforcement learning algorithm for partially observable
+Markov decision processes (POMDP) based on spectral decomposition methods.
+While spectral methods have been previously employed for consistent learning of
+(passive) latent variable models such as hidden Markov models, POMDPs are more
+challenging since the learner interacts with the environment and possibly
+changes the future observations in the process. We devise a learning algorithm
+running through epochs, in each epoch we employ spectral techniques to learn
+the POMDP parameters from a trajectory generated by a fixed policy. At the end
+of the epoch, an optimization oracle returns the optimal memoryless planning
+policy which maximizes the expected reward based on the estimated POMDP model.
+We prove an order-optimal regret bound with respect to the optimal memoryless
+policy and efficient scaling with respect to the dimensionality of observation
+and action spaces.
+",1,0,0,1,0,0
+19256,Discreteness of silting objects and t-structures in triangulated categories,"  We introduce the notion of ST-pairs of triangulated subcategories, a
+prototypical example of which is the pair of the bound homotopy category and
+the bound derived category of a finite-dimensional algebra. For an ST-pair
+$(\C,\D)$, we construct an order-preserving map from silting objects in $\C$ to
+bounded $t$-structures on $\D$ and show that the map is bijective if and only
+if $\C$ is silting-discrete if and only if $\D$ is $t$-discrete. Based on a
+work of Qiu and Woolf, the above result is applied to show that if $\C$ is
+silting-discrete then the stability space of $\D$ is contractible. This is used
+to obtain the contractibility of the stability spaces of some Calabi--Yau
+triangulated categories associated to Dynkin quivers.
+",0,0,1,0,0,0
+19257,Sketching the order of events,"  We introduce features for massive data streams. These stream features can be
+thought of as ""ordered moments"" and generalize stream sketches from ""moments of
+order one"" to ""ordered moments of arbitrary order"". In analogy to classic
+moments, they have theoretical guarantees such as universality that are
+important for learning algorithms.
+",1,0,1,1,0,0
+19258,Deep Multi-view Learning to Rank,"  We study the problem of learning to rank from multiple sources. Though
+multi-view learning and learning to rank have been studied extensively leading
+to a wide range of applications, multi-view learning to rank as a synergy of
+both topics has received little attention. The aim of the paper is to propose a
+composite ranking method while keeping a close correlation with the individual
+rankings simultaneously. We propose a multi-objective solution to ranking by
+capturing the information of the feature mapping from both within each view as
+well as across views using autoencoder-like networks. Moreover, a novel
+end-to-end solution is introduced to enhance the joint ranking with minimum
+view-specific ranking loss, so that we can achieve the maximum global view
+agreements within a single optimization process. The proposed method is
+validated on a wide variety of ranking problems, including university ranking,
+multi-view lingual text ranking and image data ranking, providing superior
+results.
+",0,0,0,1,0,0
+19259,One-Shot Visual Imitation Learning via Meta-Learning,"  In order for a robot to be a generalist that can perform a wide range of
+jobs, it must be able to acquire a wide variety of skills quickly and
+efficiently in complex unstructured environments. High-capacity models such as
+deep neural networks can enable a robot to represent complex skills, but
+learning each skill from scratch then becomes infeasible. In this work, we
+present a meta-imitation learning method that enables a robot to learn how to
+learn more efficiently, allowing it to acquire new skills from just a single
+demonstration. Unlike prior methods for one-shot imitation, our method can
+scale to raw pixel inputs and requires data from significantly fewer prior
+tasks for effective learning of new skills. Our experiments on both simulated
+and real robot platforms demonstrate the ability to learn new tasks,
+end-to-end, from a single visual demonstration.
+",1,0,0,0,0,0
+19260,Second variation of Selberg zeta functions and curvature asymptotics,"  We give an explicit formula for the second variation of the logarithm of the
+Selberg zeta function, $Z(s)$, on Teichmüller space. We then use this formula
+to determine the asymptotic behavior as $s \to \infty$ of the second variation.
+As a consequence, we determine the signature of the Hessian of $\log Z(s)$ for
+sufficiently large $s$. As a further consequence, the asymptotic behavior of
+the second variation of $\log Z(s)$ shows that the Ricci curvature of the Hodge
+bundle $H^0(\mathcal K^m_t)\mapsto t$ over Teichmüller space agrees with the
+Quillen curvature up to a term of exponential decay, $O(s^2 e^{-l_0 s}),$ where
+$l_0$ is the length of the shortest closed hyperbolic geodesic.
+",0,0,1,0,0,0
+19261,Magnetic Fields Threading Black Holes: restrictions from general relativity and implications for astrophysical black holes,"  The idea that black hole spin is instrumental in the generation of powerful
+jets in active galactic nuclei and X-ray binaries is arguably the most
+contentious claim in black hole astrophysics. Because jets are thought to
+originate in the context of electromagnetism, and the modeling of Maxwell
+fields in curved spacetime around black holes is challenging, various
+approximations are made in numerical simulations that fall under the guise of
+'ideal magnetohydrodynamics'. But the simplifications of this framework may
+struggle to capture relevant details of real astrophysical environments near
+black holes. In this work, we highlight tension between analytic and numerical
+results, specifically between the analytically derived conserved Noether
+currents for rotating black hole spacetimes and the results of general
+relativistic numerical simulations (GRMHD). While we cannot definitively
+attribute the issue to any specific approximation used in the numerical
+schemes, there seem to be natural candidates, which we explore. GRMHD
+notwithstanding, if electromagnetic fields around rotating black holes are
+brought to the hole by accretion, we show from first principles that prograde
+accreting disks likely experience weaker large-scale black hole-threading
+fields, implying weaker jets than in retrograde configurations.
+",0,1,0,0,0,0
+19262,Distance covariance for stochastic processes,"  The distance covariance of two random vectors is a measure of their
+dependence. The empirical distance covariance and correlation can be used as
+statistical tools for testing whether two random vectors are independent. We
+propose an analogs of the distance covariance for two stochastic processes
+defined on some interval. Their empirical analogs can be used to test the
+independence of two processes.
+",0,0,1,1,0,0
+19263,On Nonparametric Regression using Data Depth,"  We investigate nonparametric regression methods based on statistical depth
+functions. These nonparametric regression procedures can be used in situations,
+where the response is multivariate and the covariate is a random element in a
+metric space. This includes regression with functional covariate as a special
+case. Our objective is to study different features of the conditional
+distribution of the response given the covariate. We construct measures of the
+center and the spread of the conditional distribution using depth based
+nonparametric regression procedures. We establish the asymptotic consistency of
+those measures and develop a test for heteroscedasticity based on the measure
+of conditional spread. The usefulness of the methodology is demonstrated in
+some real datasets. In one dataset consisting of Italian household expenditure
+data for the period 1973 to 1992, we regress the expenditure for different
+items on their prices. In another dataset, our responses are the nutritional
+contents of different meat samples measured by their protein, fat and moisture
+contents, and the functional covariate is the absorbance spectra of the meat
+samples.
+",0,0,0,1,0,0
+19264,A Backward Simulation Method for Stochastic Optimal Control Problems,"  A number of optimal decision problems with uncertainty can be formulated into
+a stochastic optimal control framework. The Least-Squares Monte Carlo (LSMC)
+algorithm is a popular numerical method to approach solutions of such
+stochastic control problems as analytical solutions are not tractable in
+general. This paper generalizes the LSMC algorithm proposed in Shen and Weng
+(2017) to solve a wide class of stochastic optimal control models. Our
+algorithm has three pillars: a construction of auxiliary stochastic control
+model, an artificial simulation of the post-action value of state process, and
+a shape-preserving sieve estimation method which equip the algorithm with a
+number of merits including bypassing forward simulation and control
+randomization, evading extrapolating the value function, and alleviating
+computational burden of the tuning parameter selection. The efficacy of the
+algorithm is corroborated by an application to pricing equity-linked insurance
+products.
+",0,0,0,0,0,1
+19265,Damped Posterior Linearization Filter,"  The iterated posterior linearization filter (IPLF) is an algorithm for
+Bayesian state estimation that performs the measurement update using iterative
+statistical regression. The main result behind IPLF is that the posterior
+approximation is more accurate when the statistical regression of measurement
+function is done in the posterior instead of the prior as is done in
+non-iterative Kalman filter extensions. In IPLF, each iteration in principle
+gives a better posterior estimate to obtain a better statistical regression and
+more accurate posterior estimate in the next iteration. However, IPLF may
+diverge. IPLF's fixed- points are not described as solutions to an optimization
+problem, which makes it challenging to improve its convergence properties. In
+this letter, we introduce a double-loop version of IPLF, where the inner loop
+computes the posterior mean using an optimization algorithm. Simulation results
+are presented to show that the proposed algorithm has better convergence than
+IPLF and its accuracy is similar to or better than other state-of-the-art
+algorithms.
+",0,0,1,1,0,0
+19266,Status updates through M/G/1/1 queues with HARQ,"  We consider a system where randomly generated updates are to be transmitted
+to a monitor, but only a single update can be in the transmission service at a
+time. Therefore, the source has to prioritize between the two possible
+transmission policies: preempting the current update or discarding the new one.
+We consider Poisson arrivals and general service time, and refer to this system
+as the M/G/1/1 queue. We start by studying the average status update age and
+the optimal update arrival rate for these two schemes under general service
+time distribution. We then apply these results on two practical scenarios in
+which updates are sent through an erasure channel using (a) an infinite
+incremental redundancy (IIR) HARQ system and (b) a fixed redundancy (FR) HARQ
+system. We show that in both schemes the best strategy would be not to preempt.
+Moreover, we also prove that, from an age point of view, IIR is better than FR.
+",1,0,0,0,0,0
+19267,A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials,"  Most analyses of randomised trials with incomplete outcomes make untestable
+assumptions and should therefore be subjected to sensitivity analyses. However,
+methods for sensitivity analyses are not widely used. We propose a mean score
+approach for exploring global sensitivity to departures from missing at random
+or other assumptions about incomplete outcome data in a randomised trial. We
+assume a single outcome analysed under a generalised linear model. One or more
+sensitivity parameters, specified by the user, measure the degree of departure
+from missing at random in a pattern mixture model. Advantages of our method are
+that its sensitivity parameters are relatively easy to interpret and so can be
+elicited from subject matter experts; it is fast and non-stochastic; and its
+point estimate, standard error and confidence interval agree perfectly with
+standard methods when particular values of the sensitivity parameters make
+those standard methods appropriate. We illustrate the method using data from a
+mental health trial.
+",0,0,1,1,0,0
+19268,On the union complexity of families of axis-parallel rectangles with a low packing number,"  Let R be a family of n axis-parallel rectangles with packing number p-1,
+meaning that among any p of the rectangles, there are two with a non-empty
+intersection. We show that the union complexity of R is at most O(n+p^2), and
+that the (<=k)-level complexity of R is at most O(kn+k^2p^2). Both upper bounds
+are tight.
+",1,0,1,0,0,0
+19269,Using photo-ionisation models to derive carbon and oxygen gas-phase abundances in the rest UV,"  We present a new method to derive oxygen and carbon abundances using the
+ultraviolet (UV) lines emitted by the gas-phase ionised by massive stars. The
+method is based on the comparison of the nebular emission-line ratios with
+those predicted by a large grid of photo-ionisation models. Given the large
+dispersion in the O/H - C/O plane, our method firstly fixes C/O using ratios of
+appropriate emission lines and, in a second step, calculates O/H and the
+ionisation parameter from carbon lines in the UV. We find abundances totally
+consistent with those provided by the direct method when we apply this method
+to a sample of objects with an empirical determination of the electron
+temperature using optical emission lines. The proposed methodology appears as a
+powerful tool for systematic studies of nebular abundances in star-forming
+galaxies at high redshift.
+",0,1,0,0,0,0
+19270,A Probabilistic Framework for Location Inference from Social Media,"  We study the extent to which we can infer users' geographical locations from
+social media. Location inference from social media can benefit many
+applications, such as disaster management, targeted advertising, and news
+content tailoring. In recent years, a number of algorithms have been proposed
+for identifying user locations on social media platforms such as Twitter and
+Facebook from message contents, friend networks, and interactions between
+users. In this paper, we propose a novel probabilistic model based on factor
+graphs for location inference that offers several unique advantages for this
+task. First, the model generalizes previous methods by incorporating content,
+network, and deep features learned from social context. The model is also
+flexible enough to support both supervised learning and semi-supervised
+learning. Second, we explore several learning algorithms for the proposed
+model, and present a Two-chain Metropolis-Hastings (MH+) algorithm, which
+improves the inference accuracy. Third, we validate the proposed model on three
+different genres of data - Twitter, Weibo, and Facebook - and demonstrate that
+the proposed model can substantially improve the inference accuracy (+3.3-18.5%
+by F1-score) over that of several state-of-the-art methods.
+",1,0,0,0,0,0
+19271,"The Novel ABALONE Photosensor Technology: 4-Year Long Tests of Vacuum Integrity, Internal Pumping and Afterpulsing","  The ABALONE Photosensor Technology (U.S. Patent 9064678 2015) has the
+capability of supplanting the expensive 80 year old Photomultiplier Tube (PMT)
+manufacture by providing a modern and cost effective alternative product. An
+ABALONE Photosensor comprises only three monolithic glass components, sealed
+together by our new thin film adhesive. In 2013, we left one of the early
+ABALONE Photosensor prototypes intact for continuous stress testing, and here
+we report its long term vacuum integrity. The exceptionally low ion
+afterpulsing rate (approximately two orders of magnitude lower than in PMTs)
+has been constantly improving. We explain the physical and technological
+reasons for this achievement. Due to the cost-effectiveness and the specific
+combination of features, including low level of radioactivity, integration into
+large-area panels, and robustness, this technology can open new horizons in the
+fields of fundamental physics, functional medical imaging, and nuclear
+security.
+",0,1,0,0,0,0
+19272,Multi-robot Dubins Coverage with Autonomous Surface Vehicles,"  In large scale coverage operations, such as marine exploration or aerial
+monitoring, single robot approaches are not ideal, as they may take too long to
+cover a large area. In such scenarios, multi-robot approaches are preferable.
+Furthermore, several real world vehicles are non-holonomic, but can be modeled
+using Dubins vehicle kinematics. This paper focuses on environmental monitoring
+of aquatic environments using Autonomous Surface Vehicles (ASVs). In
+particular, we propose a novel approach for solving the problem of complete
+coverage of a known environment by a multi-robot team consisting of Dubins
+vehicles. It is worth noting that both multi-robot coverage and Dubins vehicle
+coverage are NP-complete problems. As such, we present two heuristics methods
+based on a variant of the traveling salesman problem -- k-TSP -- formulation
+and clustering algorithms that efficiently solve the problem. The proposed
+methods are tested both in simulations to assess their scalability and with a
+team of ASVs operating on a lake to ensure their applicability in real world.
+",1,0,0,0,0,0
+19273,New Fairness Metrics for Recommendation that Embrace Differences,"  We study fairness in collaborative-filtering recommender systems, which are
+sensitive to discrimination that exists in historical data. Biased data can
+lead collaborative filtering methods to make unfair predictions against
+minority groups of users. We identify the insufficiency of existing fairness
+metrics and propose four new metrics that address different forms of
+unfairness. These fairness metrics can be optimized by adding fairness terms to
+the learning objective. Experiments on synthetic and real data show that our
+new metrics can better measure fairness than the baseline, and that the
+fairness objectives effectively help reduce unfairness.
+",1,0,0,0,0,0
+19274,Graph heat mixture model learning,"  Graph inference methods have recently attracted a great interest from the
+scientific community, due to the large value they bring in data interpretation
+and analysis. However, most of the available state-of-the-art methods focus on
+scenarios where all available data can be explained through the same graph, or
+groups corresponding to each graph are known a priori. In this paper, we argue
+that this is not always realistic and we introduce a generative model for mixed
+signals following a heat diffusion process on multiple graphs. We propose an
+expectation-maximisation algorithm that can successfully separate signals into
+corresponding groups, and infer multiple graphs that govern their behaviour. We
+demonstrate the benefits of our method on both synthetic and real data.
+",1,0,0,1,0,0
+19275,Technical Report: Reactive Navigation in Partially Known Non-Convex Environments,"  This paper presents a provably correct method for robot navigation in 2D
+environments cluttered with familiar but unexpected non-convex, star-shaped
+obstacles as well as completely unknown, convex obstacles. We presuppose a
+limited range onboard sensor, capable of recognizing, localizing and
+(leveraging ideas from constructive solid geometry) generating online from its
+catalogue of the familiar, non-convex shapes an implicit representation of each
+one. These representations underlie an online change of coordinates to a
+completely convex model planning space wherein a previously developed online
+construction yields a provably correct reactive controller that is pulled back
+to the physically sensed representation to generate the actual robot commands.
+We extend the construction to differential drive robots, and suggest the
+empirical utility of the proposed control architecture using both formal proofs
+and numerical simulations.
+",1,0,0,0,0,0
+19276,Automatic smoothness detection of the resolvent Krylov subspace method for the approximation of $C_0$-semigroups,"  The resolvent Krylov subspace method builds approximations to operator
+functions $f(A)$ times a vector $v$. For the semigroup and related operator
+functions, this method is proved to possess the favorable property that the
+convergence is automatically faster when the vector $v$ is smoother. The user
+of the method does not need to know the presented theory and alterations of the
+method are not necessary in order to adapt to the (possibly unknown) smoothness
+of $v$. The findings are illustrated by numerical experiments.
+",0,0,1,0,0,0
+19277,On the observability of Pauli crystals,"  The best known manifestation of the Fermi-Dirac statistics is the Pauli
+exclusion principle: no two identical fermions can occupy the same one-particle
+state. This principle enforces high order correlations in systems of many
+identical fermions and is responsible for a particular geometric arrangement of
+trapped particles even when all mutual interactions are absent [1]. These
+geometric structures, called Pauli crystals, are predicted for a system of $N$
+identical atoms trapped in a harmonic potential. They emerge as the most
+frequent configurations in a collection of single-shot pictures of the system.
+Here we study how fragile Pauli crystals are when realistic experimental
+limitations are taken into account. The influence of the number of single-shots
+pictures available to analysis, thermal fluctuations and finite efficiency of
+detection are considered. The role of these sources of noise on the possibility
+of experimental observation of Pauli crystals is shown and conditions necessary
+for the detection of the geometrical arrangements of particles are identified.
+",0,1,0,0,0,0
+19278,Interpolating Between Choices for the Approximate Intermediate Value Theorem,"  This paper proves the approximate intermediate value theorem, constructively
+and from notably weak hypotheses: from pointwise rather than uniform
+continuity, without assuming that reals are presented with rational
+approximants, and without using countable choice. The theorem is that if a
+pointwise continuous function has both a negative and a positive value, then it
+has values arbitrarily close to 0. The proof builds on the usual classical
+proof by bisection, which repeatedly selects the left or right half of an
+interval; the algorithm here selects an interval of half the size in a
+continuous way, interpolating between those two possibilities.
+",1,0,1,0,0,0
+19279,HAZMAT II: Ultraviolet Variability of Low-Mass Stars in the GALEX Archive,"  The ultraviolet (UV) light from a host star influences a planet's atmospheric
+photochemistry and will affect interpretations of exoplanetary spectra from
+future missions like the James Webb Space Telescope. These effects will be
+particularly critical in the study of planetary atmospheres around M dwarfs,
+including Earth-sized planets in the habitable zone. Given the higher activity
+levels of M dwarfs compared to Sun-like stars, time resolved UV data are needed
+for more accurate input conditions for exoplanet atmospheric modeling. The
+Galaxy Evolution Explorer (\emph{GALEX}) provides multi-epoch photometric
+observations in two UV bands: near-ultraviolet (NUV; 1771 -- 2831 \AA) and
+far-ultraviolet (FUV; 1344 -- 1786 \AA). Within 30 pc of Earth, there are 357
+and 303 M dwarfs in the NUV and FUV bands, respectively, with multiple\GALEX
+observations. Simultaneous NUV and FUV detections exist for 145 stars in
+both\GALEX bands. Our analyses of these data show that low-mass stars are
+typically more variable in the FUV than the NUV. Median variability increases
+with later spectral types in the NUV with no clear trend in the FUV. We find
+evidence that flares increase the FUV flux density far more than the NUV flux
+density, leading to variable FUV to NUV flux density ratios in the \GALEX\
+bandpasses.The ratio of FUV to NUV flux is important for interpreting the
+presence of atmospheric molecules in planetary atmospheres such as oxygen and
+methane as a high FUV to NUV ratio may cause false-positive biosignature
+detections. This ratio of flux density in the\GALEX\ bands spans three orders
+of magnitude in our sample, from 0.008 to 4.6, and is 1 to 2 orders of
+magnitude higher than for G dwarfs like the Sun. These results characterize the
+UV behavior for the largest set of low-mass stars to date.
+",0,1,0,0,0,0
+19280,Exactly solvable Schrödinger equation with double-well potential for hydrogen bond,"  We construct a double-well potential for which the Schrödinger equation can
+be exactly solved via reducing to the confluent Heun's one. Thus the wave
+function is expressed via the confluent Heun's function. The latter is
+tabulated in {\sl {Maple}} so that the obtained solution is easily treated. The
+potential is infinite at the boundaries of the final interval that makes it to
+be highly suitable for modeling hydrogen bonds (both ordinary and low-barrier
+ones). We exemplify theoretical results by detailed treating the hydrogen bond
+in $KHCO_3$ and show their good agreement with literature experimental data.
+",0,1,0,0,0,0
+19281,The co-evolution of emotional well-being with weak and strong friendship ties,"  Social ties are strongly related to well-being. But what characterizes this
+relationship? This study investigates social mechanisms explaining how social
+ties affect well-being through social integration and social influence, and how
+well-being affects social ties through social selection. We hypothesize that
+highly integrated individuals - those with more extensive and dense friendship
+networks - report higher emotional well-being than others. Moreover, emotional
+well-being should be influenced by the well-being of close friends. Finally,
+well-being should affect friendship selection when individuals prefer others
+with higher levels of well-being, and others whose well-being is similar to
+theirs. We test our hypotheses using longitudinal social network and well-being
+data of 117 individuals living in a graduate housing community. The application
+of a novel extension of Stochastic Actor-Oriented Models for ordered networks
+(ordered SAOMs) allows us to detail and test our hypotheses for weak- and
+strong-tied friendship networks simultaneously. Results do not support our
+social integration and social influence hypotheses but provide evidence for
+selection: individuals with higher emotional well-being tend to have more
+strong-tied friends, and there are homophily processes regarding emotional
+well-being in strong-tied networks. Our study highlights the two-directional
+relationship between social ties and well-being, and demonstrates the
+importance of considering different tie strengths for various social processes.
+",1,0,0,1,0,0
+19282,A magnetic version of the Smilansky-Solomyak model,"  We analyze spectral properties of two mutually related families of magnetic
+Schrödinger operators, $H_{\mathrm{Sm}}(A)=(i \nabla +A)^2+\omega^2
+y^2+\lambda y \delta(x)$ and $H(A)=(i \nabla +A)^2+\omega^2 y^2+ \lambda y^2
+V(x y)$ in $L^2(R^2)$, with the parameters $\omega>0$ and $\lambda<0$, where
+$A$ is a vector potential corresponding to a homogeneous magnetic field
+perpendicular to the plane and $V$ is a regular nonnegative and compactly
+supported potential. We show that the spectral properties of the operators
+depend crucially on the one-dimensional Schrödinger operators $L=
+-\frac{\mathrm{d}^2}{\mathrm{d}x^2} +\omega^2 +\lambda \delta (x)$ and $L (V)=
+- \frac{\mathrm{d}^2}{\mathrm{d}x^2} +\omega^2 +\lambda V(x)$, respectively.
+Depending on whether the operators $L$ and $L(V)$ are positive or not, the
+spectrum of $H_{\mathrm{Sm}}(A)$ and $H(V)$ exhibits a sharp transition.
+",0,0,1,0,0,0
+19283,John-Nirenberg Radius and Collapse in Conformal Geometry,"  Given a positive function $u\in W^{1,n}$, we define its John-Nirenberg radius
+at point $x$ to be the supreme of the radius such that $\int_{B_t}|\nabla\log
+u|^n<\epsilon_0^n$ when $n>2$, and $\int_{B_t}|\nabla u|^2<\epsilon_0^2$ when
+$n=2$. We will show that for a collapsing sequence in a fixed conformal class
+under some curvature conditions, the radius is bounded below by a positive
+constant. As applications, we will study the convergence of a conformal metric
+sequence on a $4$-manifold with bounded $\|K\|_{W^{1,2}}$, and prove a
+generalized Hélein's Convergence Theorem.
+",0,0,1,0,0,0
+19284,Deep Prior,"  The recent literature on deep learning offers new tools to learn a rich
+probability distribution over high dimensional data such as images or sounds.
+In this work we investigate the possibility of learning the prior distribution
+over neural network parameters using such tools. Our resulting variational
+Bayes algorithm generalizes well to new tasks, even when very few training
+examples are provided. Furthermore, this learned prior allows the model to
+extrapolate correctly far from a given task's training data on a meta-dataset
+of periodic signals.
+",1,0,0,1,0,0
+19285,Generalized two-dimensional linear discriminant analysis with regularization,"  Recent advances show that two-dimensional linear discriminant analysis
+(2DLDA) is a successful matrix based dimensionality reduction method. However,
+2DLDA may encounter the singularity issue theoretically and the sensitivity to
+outliers. In this paper, a generalized Lp-norm 2DLDA framework with
+regularization for an arbitrary $p>0$ is proposed, named G2DLDA. There are
+mainly two contributions of G2DLDA: one is G2DLDA model uses an arbitrary
+Lp-norm to measure the between-class and within-class scatter, and hence a
+proper $p$ can be selected to achieve the robustness. The other one is that by
+introducing an extra regularization term, G2DLDA achieves better generalization
+performance, and solves the singularity problem. In addition, G2DLDA can be
+solved through a series of convex problems with equality constraint, and it has
+closed solution for each single problem. Its convergence can be guaranteed
+theoretically when $1\leq p\leq2$. Preliminary experimental results on three
+contaminated human face databases show the effectiveness of the proposed
+G2DLDA.
+",0,0,0,1,0,0
+19286,Time-Frequency Audio Features for Speech-Music Classification,"  Distinct striation patterns are observed in the spectrograms of speech and
+music. This motivated us to propose three novel time-frequency features for
+speech-music classification. These features are extracted in two stages. First,
+a preset number of prominent spectral peak locations are identified from the
+spectra of each frame. These important peak locations obtained from each frame
+are used to form Spectral peak sequences (SPS) for an audio interval. In second
+stage, these SPS are treated as time series data of frequency locations. The
+proposed features are extracted as periodicity, average frequency and
+statistical attributes of these spectral peak sequences. Speech-music
+categorization is performed by learning binary classifiers on these features.
+We have experimented with Gaussian mixture models, support vector machine and
+random forest classifiers. Our proposal is validated on four datasets and
+benchmarked against three baseline approaches. Experimental results establish
+the validity of our proposal.
+",1,0,0,0,0,0
+19287,Kinodynamic Planning on Constraint Manifolds,"  This paper presents a motion planner for systems subject to kinematic and
+dynamic constraints. The former appear when kinematic loops are present in the
+system, such as in parallel manipulators, in robots that cooperate to achieve a
+given task, or in situations involving contacts with the environment. The
+latter are necessary to obtain realistic trajectories, taking into account the
+forces acting on the system. The kinematic constraints make the state space
+become an implicitly-defined manifold, which complicates the application of
+common motion planning techniques. To address this issue, the planner
+constructs an atlas of the state space manifold incrementally, and uses this
+atlas both to generate random states and to dynamically simulate the steering
+of the system towards such states. The resulting tools are then exploited to
+construct a rapidly-exploring random tree (RRT) over the state space. To the
+best of our knowledge, this is the first randomized kinodynamic planner for
+implicitly-defined state spaces. The test cases presented in this paper
+validate the approach in significantly-complex systems.
+",1,0,0,0,0,0
+19288,Binary Classification from Positive-Confidence Data,"  Can we learn a binary classifier from only positive data, without any
+negative data or unlabeled data? We show that if one can equip positive data
+with confidence (positive-confidence), one can successfully learn a binary
+classifier, which we name positive-confidence (Pconf) classification. Our work
+is related to one-class classification which is aimed at ""describing"" the
+positive class by clustering-related methods, but one-class classification does
+not have the ability to tune hyper-parameters and their aim is not on
+""discriminating"" positive and negative classes. For the Pconf classification
+problem, we provide a simple empirical risk minimization framework that is
+model-independent and optimization-independent. We theoretically establish the
+consistency and an estimation error bound, and demonstrate the usefulness of
+the proposed method for training deep neural networks through experiments.
+",1,0,0,1,0,0
+19289,Skewing Methods for Variance-Stabilizing Local Linear Regression Estimation,"  It is well-known that kernel regression estimators do not produce a constant
+estimator variance over a domain. To correct this problem, Nishida and Kanazawa
+(2015) proposed a variance-stabilizing (VS) local variable bandwidth for Local
+Linear (LL) regression estimator. In contrast, Choi and Hall (1998) proposed
+the skewing (SK) methods for a univariate LL estimator and constructed a convex
+combination of one LL estimator and two SK estimators that are symmetrically
+placed on both sides of the LL estimator (the convex combination (CC)
+estimator) to eliminate higher-order terms in its asymptotic bias. To obtain a
+CC estimator with a constant estimator variance without employing the VS local
+variable bandwidth, the weight in the convex combination must be determined
+locally to produce a constant estimator variance. In this study, we compare the
+performances of two VS methods for a CC estimator and find cases in which the
+weighting method can superior to the VS bandwidth method in terms of the degree
+of variance stabilization.
+",0,0,0,1,0,0
+19290,Proximodistal Exploration in Motor Learning as an Emergent Property of Optimization,"  To harness the complexity of their high-dimensional bodies during
+sensorimotor development, infants are guided by patterns of freezing and
+freeing of degrees of freedom. For instance, when learning to reach, infants
+free the degrees of freedom in their arm proximodistally, i.e. from joints that
+are closer to the body to those that are more distant. Here, we formulate and
+study computationally the hypothesis that such patterns can emerge
+spontaneously as the result of a family of stochastic optimization processes
+(evolution strategies with covariance-matrix adaptation), without an innate
+encoding of a maturational schedule. In particular, we present simulated
+experiments with an arm where a computational learner progressively acquires
+reaching skills through adaptive exploration, and we show that a proximodistal
+organization appears spontaneously, which we denote PDFF (ProximoDistal
+Freezing and Freeing of degrees of freedom). We also compare this emergent
+organization between different arm morphologies -- from human-like to quite
+unnatural ones -- to study the effect of different kinematic structures on the
+emergence of PDFF. Keywords: human motor learning; proximo-distal exploration;
+stochastic optimization; modelling; evolution strategies; cross-entropy
+methods; policy search; morphology.}
+",1,0,0,0,0,0
+19291,Near-optimal Sample Complexity Bounds for Robust Learning of Gaussians Mixtures via Compression Schemes,"  We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary
+and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to
+error $\varepsilon$ in total variation distance. This improves both the known
+upper bounds and lower bounds for this problem. For mixtures of axis-aligned
+Gaussians, we show that $\tilde{O}(k d / \varepsilon^2)$ samples suffice,
+matching a known lower bound. Moreover, these results hold in the
+agnostic-learning/robust-estimation setting as well, where the target
+distribution is only approximately a mixture of Gaussians.
+The upper bound is shown using a novel technique for distribution learning
+based on a notion of `compression.' Any class of distributions that allows such
+a compression scheme can also be learned with few samples. Moreover, if a class
+of distributions has such a compression scheme, then so do the classes of
+products and mixtures of those distributions. The core of our main result is
+showing that the class of Gaussians in $\mathbb{R}^d$ admits a small-sized
+compression scheme.
+",1,0,1,0,0,0
+19292,Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME,"  We present a heuristic based algorithm to induce \textit{nonmonotonic} logic
+programs that will explain the behavior of XGBoost trained classifiers. We use
+the technique based on the LIME approach to locally select the most important
+features contributing to the classification decision. Then, in order to explain
+the model's global behavior, we propose the LIME-FOLD algorithm ---a
+heuristic-based inductive logic programming (ILP) algorithm capable of learning
+non-monotonic logic programs---that we apply to a transformed dataset produced
+by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm.
+Our experiments with UCI standard benchmarks suggest a significant improvement
+in terms of classification evaluation metrics. Meanwhile, the number of induced
+rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system.
+",0,0,0,1,0,0
+19293,Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation,"  Manipulation of deformable objects, such as ropes and cloth, is an important
+but challenging problem in robotics. We present a learning-based system where a
+robot takes as input a sequence of images of a human manipulating a rope from
+an initial to goal configuration, and outputs a sequence of actions that can
+reproduce the human demonstration, using only monocular images as input. To
+perform this task, the robot learns a pixel-level inverse dynamics model of
+rope manipulation directly from images in a self-supervised manner, using about
+60K interactions with the rope collected autonomously by the robot. The human
+demonstration provides a high-level plan of what to do and the low-level
+inverse model is used to execute the plan. We show that by combining the high
+and low-level plans, the robot can successfully manipulate a rope into a
+variety of target shapes using only a sequence of human-provided images for
+direction.
+",1,0,0,0,0,0
+19294,Online learning with graph-structured feedback against adaptive adversaries,"  We derive upper and lower bounds for the policy regret of $T$-round online
+learning problems with graph-structured feedback, where the adversary is
+nonoblivious but assumed to have a bounded memory. We obtain upper bounds of
+$\widetilde O(T^{2/3})$ and $\widetilde O(T^{3/4})$ for strongly-observable and
+weakly-observable graphs, respectively, based on analyzing a variant of the
+Exp3 algorithm. When the adversary is allowed a bounded memory of size 1, we
+show that a matching lower bound of $\widetilde\Omega(T^{2/3})$ is achieved in
+the case of full-information feedback. We also study the particular loss
+structure of an oblivious adversary with switching costs, and show that in such
+a setting, non-revealing strongly-observable feedback graphs achieve a lower
+bound of $\widetilde\Omega(T^{2/3})$, as well.
+",0,0,0,1,0,0
+19295,Dynamical characteristics of electromagnetic field under conditions of total reflection,"  The dynamical characteristics of electromagnetic fields include energy,
+momentum, angular momentum (spin) and helicity. We analyze their spatial
+distributions near the planar interface between two transparent and
+non-dispersive media, when the incident monochromatic plane wave with arbitrary
+polarization is totally reflected, and an evanescent wave is formed in the
+medium with lower optical density. Based on the recent arguments in favor of
+the Minkowski definition of the electromagnetic momentum in a material medium
+[Phys. Rev. A 83, 013823 (2011); 86, 055802 (2012); Phys. Rev. Lett. 119,
+073901 (2017)], we derive the explicit expressions for the dynamical
+characteristics in both media, with special attention to their behavior at the
+interface. Especially, the ""extraordinary"" spin and momentum components
+orthogonal to the plane of incidence are described, and the canonical (spin -
+orbital) momentum decomposition is performed that contains no singular terms.
+The field energy, helicity, the spin momentum and orbital momentum components
+are everywhere regular but experience discontinuities at the interface; the
+spin components parallel to the interface appear to be continuous, which
+testifies for the consistency of the adopted Minkowski picture. The results
+supply a meaningful example of the electromagnetic momentum decomposition, with
+separation of spatial and polarization degrees of freedom, in inhomogeneous
+media, and can be used in engineering the structured fields designed for
+optical sorting, dispatching and micromanipulation.
+",0,1,0,0,0,0
+19296,Functors induced by Cauchy extension of C*-algebras,"  In this paper we give three functors $\mathfrak{P}$, $[\cdot]_K$ and
+$\mathfrak{F}$ on the category of C$^\ast$-algebras. The functor $\mathfrak{P}$
+assigns to each C$^\ast$-algebra $\mathcal{A}$ a pre-C$^\ast$-algebra
+$\mathfrak{P}(\mathcal{A})$ with completion $[\mathcal{A}]_K$. The functor
+$[\cdot]_K$ assigns to each C$^\ast$-algebra $\mathcal{A}$ the Cauchy extension
+$[\mathcal{A}]_K$ of $\mathcal{A}$ by a non-unital C$^\ast$-algebra
+$\mathfrak{F}(\mathcal{A})$. Some properties of these functors are also given.
+In particular, we show that the functors $[\cdot]_K$ and $\mathfrak{F}$ are
+exact and the functor $\mathfrak{P}$ is normal exact.
+",0,0,1,0,0,0
+19297,Hierarchically cocompact classifying spaces for mapping class groups of surfaces,"  We define the notion of a hierarchically cocompact classifying space for a
+family of subgroups of a group. Our main application is to show that the
+mapping class group $\mbox{Mod}(S)$ of any connected oriented compact surface
+$S$, possibly with punctures and boundary components and with negative Euler
+characteristic has a hierarchically cocompact model for the family of virtually
+cyclic subgroups of dimension at most $\mbox{vcd} \mbox{Mod}(S)+1$. When the
+surface is closed, we prove that this bound is optimal. In particular, this
+answers a question of Lück for mapping class groups of surfaces.
+",0,0,1,0,0,0
+19298,A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks,"  An explosion of high-throughput DNA sequencing in the past decade has led to
+a surge of interest in population-scale inference with whole-genome data.
+Recent work in population genetics has centered on designing inference methods
+for relatively simple model classes, and few scalable general-purpose inference
+techniques exist for more realistic, complex models. To achieve this, two
+inferential challenges need to be addressed: (1) population data are
+exchangeable, calling for methods that efficiently exploit the symmetries of
+the data, and (2) computing likelihoods is intractable as it requires
+integrating over a set of correlated, extremely high-dimensional latent
+variables. These challenges are traditionally tackled by likelihood-free
+methods that use scientific simulators to generate datasets and reduce them to
+hand-designed, permutation-invariant summary statistics, often leading to
+inaccurate inference. In this work, we develop an exchangeable neural network
+that performs summary statistic-free, likelihood-free inference. Our framework
+can be applied in a black-box fashion across a variety of simulation-based
+tasks, both within and outside biology. We demonstrate the power of our
+approach on the recombination hotspot testing problem, outperforming the
+state-of-the-art.
+",0,0,0,1,1,0
+19299,Coherent single-atom superradiance,"  Quantum effects, prevalent in the microscopic scale, generally elusive in
+macroscopic systems due to dissipation and decoherence. Quantum phenomena in
+large systems emerge only when particles are strongly correlated as in
+superconductors and superfluids. Cooperative interaction of correlated atoms
+with electromagnetic fields leads to superradiance, the enhanced quantum
+radiation phenomenon, exhibiting novel physics such as quantum Dicke phase and
+ultranarrow linewidth for optical clocks. Recent researches to imprint atomic
+correlation directly demonstrated controllable collective atom-field
+interactions. Here, we report cavity-mediated coherent single-atom
+superradiance. Single atoms with predefined correlation traverse a high-Q
+cavity one by one, emitting photons cooperatively with the atoms already gone
+through the cavity. Such collective behavior of time-separated atoms is
+mediated by the long-lived cavity field. As a result, a coherent field is
+generated in the steady state, whose intensity varies as the square of the
+number of traversing atoms during the cavity decay time, exhibiting more than
+ten-fold enhancement from noncollective cases. The correlation among single
+atoms is prepared with the aligned atomic phase achieved by nanometer-precision
+position control of atoms with a nanohole-array aperture. The present work
+deepens our understanding of the collective matter-light interaction and
+provides an advanced platform for phase-controlled atom-field interactions.
+",0,1,0,0,0,0
+19300,Recurrent Deterministic Policy Gradient Method for Bipedal Locomotion on Rough Terrain Challenge,"  This paper presents a deep learning framework that is capable of solving
+partially observable locomotion tasks based on our novel interpretation of
+Recurrent Deterministic Policy Gradient (RDPG). We study on bias of sampled
+error measure and its variance induced by the partial observability of
+environment and subtrajectory sampling, respectively. Three major improvements
+are introduced in our RDPG based learning framework: tail-step bootstrap of
+interpolated temporal difference, initialisation of hidden state using past
+trajectory scanning, and injection of external experiences learned by other
+agents. The proposed learning framework was implemented to solve the
+Bipedal-Walker challenge in OpenAI's gym simulation environment where only
+partial state information is available. Our simulation study shows that the
+autonomous behaviors generated by the RDPG agent are highly adaptive to a
+variety of obstacles and enables the agent to effectively traverse rugged
+terrains for long distance with higher success rate than leading contenders.
+",1,0,0,0,0,0
+19301,Diversified essential properties in halogenated graphenes,"  The significant halogenation effects on the essential properties of graphene
+are investigated by the first-principles method. The geometric structures,
+electronic properties, and magnetic configurations are greatly diversified
+under the various halogen adsorptions. Fluorination, with the strong
+multi-orbital chemical bondings, can create the buckled graphene structure,
+while the other halogenations do not change the planar {\sigma} bonding in the
+presence of single-orbital hybridization. Electronic structures consist of the
+carbon-, adatom- and (carbon, adatom)-dominated energy bands. All halogenated
+graphenes belong to hole-doped metals except that fluorinated systems are
+middle-gap semiconductors at sufficiently high concentration. Moreover, the
+metallic ferromagnetism is revealed in certain adatom distributions. The
+unusual hybridization-induced features are clearly evidenced in many van Hove
+singularities of the density of states. The structure- and adatom-enriched
+essential properties are compared with the measured results, and potential
+applications are also discussed.
+",0,1,0,0,0,0
+19302,Quantifying genuine multipartite correlations and their pattern complexity,"  We propose an information-theoretic framework to quantify multipartite
+correlations in classical and quantum systems, answering questions such as:
+what is the amount of seven-partite correlations in a given state of ten
+particles? We identify measures of genuine multipartite correlations, i.e.
+statistical dependencies which cannot be ascribed to bipartite correlations,
+satisfying a set of desirable properties. Inspired by ideas developed in
+complexity science, we then introduce the concept of weaving to classify states
+which display different correlation patterns, but cannot be distinguished by
+correlation measures. The weaving of a state is defined as the weighted sum of
+correlations of every order. Weaving measures are good descriptors of the
+complexity of correlation structures in multipartite systems.
+",0,1,0,0,0,0
+19303,Pulsating low-mass white dwarfs in the frame of new evolutionary sequences: IV. The secular rate of period change,"  We present a theoretical assessment of the expected temporal rates of change
+of periods ($\dot{\Pi}$) for low-mass ($M_{\star}/M_{\sun} \lesssim 0.45$) and
+extremely low-mass (ELM, $M_{\star}/M_{\sun} \lesssim 0.18-0.20$) white-dwarf
+stars, based on fully evolutionary low-mass He-core white dwarf and pre-white
+dwarf models. Our analysis is based on a large set of adiabatic periods of
+radial and nonradial pulsation modes computed on a suite of low-mass He-core
+white dwarf and pre-white dwarf models with masses ranging from $0.1554$ to
+$0.4352 M_{\sun}$. We compute the secular rates of period change of radial
+($\ell= 0$) and nonradial ($\ell= 1, 2$) $g$ and $p$ modes for stellar models
+representative of ELMV and pre-ELMV stars, as well as for stellar objects that
+are evolving just before the occurrence of CNO flashes at the early cooling
+branches. We found that the theoretically expected magnitude of $\dot{\Pi}$ of
+$g$ modes for pre-ELMVs are by far larger than for ELMVs. In turn, $\dot{\Pi}$
+of $g$ modes for models evolving before the occurrence of CNO flashes are
+larger than the maximum values of the rates of period change predicted for
+pre-ELMV stars. Regarding $p$ and radial modes, we found that the larger
+absolute values of $\dot{\Pi}$ correspond to pre-ELMV models. We conclude that
+any eventual measurement of a rate of period change for a given pulsating
+low-mass pre-white dwarf or white dwarf star could shed light about its
+evolutionary status. Also, in view of the systematic difficulties in the
+spectroscopic classification of stars of the ELM Survey, an eventual
+measurement of $\dot{\Pi}$ could help to confirm that a given pulsating star is
+an authentic low-mass white dwarf and not a star from another stellar
+population.
+",0,1,0,0,0,0
+19304,Normalized Maximum Likelihood with Luckiness for Multivariate Normal Distributions,"  The normalized maximum likelihood (NML) is one of the most important
+distribution in coding theory and statistics. NML is the unique solution (if
+exists) to the pointwise minimax regret problem. However, NML is not defined
+even for simple family of distributions such as the normal distributions. Since
+there does not exist any meaningful minimax-regret distribution for such case,
+it is pointed out that NML with luckiness (LNML) can be employed as an
+alternative to NML. In this paper, we develop the closed form of LNMLs for
+multivariate normal distributions.
+",0,0,1,1,0,0
+19305,Tackling Diversity and Heterogeneity by Vertical Memory Management,"  Existing memory management mechanisms used in commodity computing machines
+typically adopt hardware based address interleaving and OS directed random
+memory allocation to service generic application requests. These conventional
+memory management mechanisms are challenged by contention at multiple memory
+levels, a daunting variety of workload behaviors, and an increasingly
+complicated memory hierarchy. Our ISCA-41 paper proposes vertical partitioning
+to eliminate shared resource contention at multiple levels in the memory
+hierarchy. Combined with horizontal memory management policies, our framework
+supports a flexible policy space for tackling diverse application needs in
+production environment and is suitable for future heterogeneous memory systems.
+",1,0,0,0,0,0
+19306,From the Icosahedron to E8,"  The regular icosahedron is connected to many exceptional objects in
+mathematics. Here we describe two constructions of the $\mathrm{E}_8$ lattice
+from the icosahedron. One uses a subring of the quaternions called the
+""icosians"", while the other uses du Val's work on the resolution of Kleinian
+singularities. Together they link the golden ratio, the quaternions, the
+quintic equation, the 600-cell, and the Poincare homology 3-sphere. We leave it
+as a challenge to the reader to find the connection between these two
+constructions.
+",0,0,1,0,0,0
+19307,Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation,"  This paper demonstrates the feasibility of implementing Real-Time State
+Estimators (RTSEs) for Active Distribution Networks (ADNs) in
+Field-Programmable Gate Arrays (FPGAs) by presenting an operational prototype.
+The prototype is based on a Linear State Estimator (LSE) that uses
+synchrophasor measurements from Phasor Measurement Units (PMUs). The underlying
+algorithm is the Sequential Discrete Kalman Filter (SDKF), an equivalent
+formulation of the Discrete Kalman Filter (DKF) for the case of uncorrelated
+measurement noise. In this regard, this work formally proves the equivalence
+the SDKF and the DKF, and highlights the suitability of the SDKF for an FPGA
+implementation by means of a computational complexity analysis. The developed
+prototype is validated using a case study adapted from the IEEE 34-node
+distribution test feeder.
+",0,0,0,1,0,0
+19308,Game-Theoretic Semantics for ATL+ with Applications to Model Checking,"  We develop game-theoretic semantics (GTS) for the fragment ATL+ of the full
+Alternating-time Temporal Logic ATL*, essentially extending a recently
+introduced GTS for ATL. We first show that the new game-theoretic semantics is
+equivalent to the standard semantics of ATL+ (based on perfect recall
+strategies). We then provide an analysis, based on the new semantics, of the
+memory and time resources needed for model checking ATL+. Based on that, we
+establish that strategies that use only a very limited amount of memory suffice
+for ATL+. Furthermore, using the GTS we provide a new algorithm for model
+checking of ATL+ and identify a natural hierarchy of tractable fragments of
+ATL+ that extend ATL.
+",1,0,1,0,0,0
+19309,Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification,"  The wide usage of Machine Learning (ML) has lead to research on the attack
+vectors and vulnerability of these systems. The defenses in this area are
+however still an open problem, and often lead to an arms race. We define a
+naive, secure classifier at test time and show that a Gaussian Process (GP) is
+an instance of this classifier given two assumptions: one concerns the
+distances in the training data, the other rejection at test time. Using these
+assumptions, we are able to show that a classifier is either secure, or
+generalizes and thus learns. Our analysis also points towards another factor
+influencing robustness, the curvature of the classifier. This connection is not
+unknown for linear models, but GP offer an ideal framework to study this
+relationship for nonlinear classifiers. We evaluate on five security and two
+computer vision datasets applying test and training time attacks and membership
+inference. We show that we only change which attacks are needed to succeed,
+instead of alleviating the threat. Only for membership inference, there is a
+setting in which attacks are unsuccessful (<10% increase in accuracy over
+random guess). Given these results, we define a classification scheme based on
+voting, ParGP. This allows us to decide how many points vote and how large the
+agreement on a class has to be. This ensures a classification output only in
+cases when there is evidence for a decision, where evidence is parametrized. We
+evaluate this scheme and obtain promising results.
+",0,0,0,1,0,0
+19310,Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis,"  We analyse a linear regression problem with nonconvex regularization called
+smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis
+for Gaussian random data. We propose an approximate message passing (AMP)
+algorithm considering nonconvex regularization, namely SCAD-AMP, and
+analytically show that the stability condition corresponds to the de
+Almeida--Thouless condition in spin glass literature. Through asymptotic
+analysis, we show the correspondence between the density evolution of SCAD-AMP
+and the replica symmetric solution. Numerical experiments confirm that for a
+sufficiently large system size, SCAD-AMP achieves the optimal performance
+predicted by the replica method. Through replica analysis, a phase transition
+between replica symmetric (RS) and replica symmetry breaking (RSB) region is
+found in the parameter space of SCAD. The appearance of the RS region for a
+nonconvex penalty is a significant advantage that indicates the region of
+smooth landscape of the optimization problem. Furthermore, we analytically show
+that the statistical representation performance of the SCAD penalty is better
+than that of L1-based methods, and the minimum representation error under RS
+assumption is obtained at the edge of the RS/RSB phase. The correspondence
+between the convergence of the existing coordinate descent algorithm and RS/RSB
+transition is also indicated.
+",1,0,0,1,0,0
+19311,Concurrence Topology of Some Cancer Genomics Data,"  The topological data analysis method ""concurrence topology"" is applied to
+mutation frequencies in 69 genes in glioblastoma data. In dimension 1 some
+apparent ""mutual exclusivity"" is found. By simulation of data having
+approximately the same second order dependence structure as that found in the
+data, it appears that one triple of mutations, PTEN, RB1, TP53, exhibits mutual
+exclusivity that depends on special features of the third order dependence and
+may reflect global dependence among a larger group of genes. A bootstrap
+analysis suggests that this form of mutual exclusivity is not uncommon in the
+population from which the data were drawn.
+",0,0,0,1,0,0
+19312,Quasimomentum of an elementary excitation for a system of point bosons with zero boundary conditions,"  As is known, an elementary excitation of a many-particle system with
+boundaries is not characterized by a definite momentum. Here, we obtain the
+formula for the quasimomentum of an elementary excitation for a one-dimensional
+system of $N$ spinless point bosons with zero boundary conditions (BCs). We
+also find that the dispersion law $E(p)$ of the system with zero BCs coincides
+with that of a system with periodic BCs. The elementary excitations are defined
+within a new approach proposed earlier by the author. This approach is
+mathematically equivalent to the traditional approach by Lieb, but differs from
+it by a simpler way of enumeration of excited states and leads to a single
+dispersion law (instead of two ones in the Lieb's approach).
+",0,1,0,0,0,0
+19313,Moving to VideoKifu: the last steps toward a fully automatic record-keeping of a Go game,"  In a previous paper [ arXiv:1508.03269 ] we described the techniques we
+successfully employed for automatically reconstructing the whole move sequence
+of a Go game by means of a set of pictures. Now we describe how it is possible
+to reconstruct the move sequence by means of a video stream (which may be
+provided by an unattended webcam), possibly in real-time. Although the basic
+algorithms remain the same, we will discuss the new problems that arise when
+dealing with videos, with special care for the ones that could block a
+real-time analysis and require an improvement of our previous techniques or
+even a completely brand new approach. Eventually we present a number of
+preliminary but positive experimental results supporting the effectiveness of
+the software we are developing, built on the ideas here outlined.
+",1,0,0,0,0,0
+19314,Malware Detection by Eating a Whole EXE,"  In this work we introduce malware detection from raw byte sequences as a
+fruitful research area to the larger machine learning community. Building a
+neural network for such a problem presents a number of interesting challenges
+that have not occurred in tasks such as image processing or NLP. In particular,
+we note that detection from raw bytes presents a sequence problem with over two
+million time steps and a problem where batch normalization appear to hinder the
+learning process. We present our initial work in building a solution to tackle
+this problem, which has linear complexity dependence on the sequence length,
+and allows for interpretable sub-regions of the binary to be identified. In
+doing so we will discuss the many challenges in building a neural network to
+process data at this scale, and the methods we used to work around them.
+",1,0,0,1,0,0
+19315,On the Complexity of Model Checking for Syntactically Maximal Fragments of the Interval Temporal Logic HS with Regular Expressions,"  In this paper, we investigate the model checking (MC) problem for Halpern and
+Shoham's interval temporal logic HS. In the last years, interval temporal logic
+MC has received an increasing attention as a viable alternative to the
+traditional (point-based) temporal logic MC, which can be recovered as a
+special case. Most results have been obtained under the homogeneity assumption,
+that constrains a proposition letter to hold over an interval if and only if it
+holds over each component state. Recently, Lomuscio and Michaliszyn proposed a
+way to relax such an assumption by exploiting regular expressions to define the
+behaviour of proposition letters over intervals in terms of their component
+states. When homogeneity is assumed, the exact complexity of MC is a difficult
+open question for full HS and for its two syntactically maximal fragments
+AA'BB'E' and AA'EB'E'. In this paper, we provide an asymptotically optimal
+bound to the complexity of these two fragments under the more expressive
+semantic variant based on regular expressions by showing that their MC problem
+is AEXP_pol-complete, where AEXP_pol denotes the complexity class of problems
+decided by exponential-time bounded alternating Turing Machines making a
+polynomially bounded number of alternations.
+",1,0,0,0,0,0
+19316,Deforming 3-manifolds of bounded geometry and uniformly positive scalar curvature,"  We prove that the moduli space of complete Riemannian metrics of bounded
+geometry and uniformly positive scalar curvature on an orientable 3-manifold is
+path-connected. This generalizes the main result of the fourth author [Mar12]
+in the compact case. The proof uses Ricci flow with surgery as well as
+arguments involving performing infinite connected sums with control on the
+geometry.
+",0,0,1,0,0,0
+19317,Optimal and Myopic Information Acquisition,"  We consider the problem of optimal dynamic information acquisition from many
+correlated information sources. Each period, the decision-maker jointly takes
+an action and allocates a fixed number of observations across the available
+sources. His payoff depends on the actions taken and on an unknown state. In
+the canonical setting of jointly normal information sources, we show that the
+optimal dynamic information acquisition rule proceeds myopically after finitely
+many periods. If signals are acquired in large blocks each period, then the
+optimal rule turns out to be myopic from period 1. These results demonstrate
+the possibility of robust and ""simple"" optimal information acquisition, and
+simplify the analysis of dynamic information acquisition in a widely used
+informational environment.
+",1,0,1,1,0,0
+19318,Infinitely many minimal classes of graphs of unbounded clique-width,"  The celebrated theorem of Robertson and Seymour states that in the family of
+minor-closed graph classes, there is a unique minimal class of graphs of
+unbounded tree-width, namely, the class of planar graphs. In the case of
+tree-width, the restriction to minor-closed classes is justified by the fact
+that the tree-width of a graph is never smaller than the tree-width of any of
+its minors. This, however, is not the case with respect to clique-width, as the
+clique-width of a graph can be (much) smaller than the clique-width of its
+minor. On the other hand, the clique-width of a graph is never smaller than the
+clique-width of any of its induced subgraphs, which allows us to be restricted
+to hereditary classes (that is, classes closed under taking induced subgraphs),
+when we study clique-width. Up to date, only finitely many minimal hereditary
+classes of graphs of unbounded clique-width have been discovered in the
+literature. In the present paper, we prove that the family of such classes is
+infinite. Moreover, we show that the same is true with respect to linear
+clique-width.
+",1,0,1,0,0,0
+19319,A Passivity-Based Approach to Nash Equilibrium Seeking over Networks,"  In this paper we consider the problem of distributed Nash equilibrium (NE)
+seeking over networks, a setting in which players have limited local
+information. We start from a continuous-time gradient-play dynamics that
+converges to an NE under strict monotonicity of the pseudo-gradient and assumes
+perfect information, i.e., instantaneous all-to-all player communication. We
+consider how to modify this gradient-play dynamics in the case of partial, or
+networked information between players. We propose an augmented gradient-play
+dynamics with correction in which players communicate locally only with their
+neighbours to compute an estimate of the other players' actions. We derive the
+new dynamics based on the reformulation as a multi-agent coordination problem
+over an undirected graph. We exploit incremental passivity properties and show
+that a synchronizing, distributed Laplacian feedback can be designed using
+relative estimates of the neighbours. Under a strict monotonicity property of
+the pseudo-gradient, we show that the augmented gradient-play dynamics
+converges to consensus on the NE of the game. We further discuss two cases that
+highlight the tradeoff between properties of the game and the communication
+graph.
+",0,0,1,0,0,0
+19320,Magnetic and dielectric investigations of $γ$ - Fe${_2}$WO${_6}$,"  The magnetic, thermodynamic and dielectric properties of the $\gamma$ -
+Fe${_2}$WO${_6}$ system is reported. Crystallizing in the centrosymmetric
+$Pbcn$ space group, this particular polymorph exhibits a number of different
+magnetic transitions, all of which are seen to exhibit a finite
+magneto-dielectric coupling. At the lowest measured temperatures, the magnetic
+ground state appears to be glass-like, as evidenced by the waiting time
+dependence of the magnetic relaxation. Also reflected in the frequency
+dependent dielectric measurements, these signatures possibly arise as a
+consequence of the oxygen non-stoichiometry, which promotes an inhomogeneous
+magnetic and electronic ground state.
+",0,1,0,0,0,0
+19321,Linear centralization classifier,"  A classification algorithm, called the Linear Centralization Classifier
+(LCC), is introduced. The algorithm seeks to find a transformation that best
+maps instances from the feature space to a space where they concentrate towards
+the center of their own classes, while maximimizing the distance between class
+centers. We formulate the classifier as a quadratic program with quadratic
+constraints. We then simplify this formulation to a linear program that can be
+solved effectively using a linear programming solver (e.g., simplex-dual). We
+extend the formulation for LCC to enable the use of kernel functions for
+non-linear classification applications. We compare our method with two standard
+classification methods (support vector machine and linear discriminant
+analysis) and four state-of-the-art classification methods when they are
+applied to eight standard classification datasets. Our experimental results
+show that LCC is able to classify instances more accurately (based on the area
+under the receiver operating characteristic) in comparison to other tested
+methods on the chosen datasets. We also report the results for LCC with a
+particular kernel to solve for synthetic non-linear classification problems.
+",1,0,0,1,0,0
+19322,Spatio-temporal Person Retrieval via Natural Language Queries,"  In this paper, we address the problem of spatio-temporal person retrieval
+from multiple videos using a natural language query, in which we output a tube
+(i.e., a sequence of bounding boxes) which encloses the person described by the
+query. For this problem, we introduce a novel dataset consisting of videos
+containing people annotated with bounding boxes for each second and with five
+natural language descriptions. To retrieve the tube of the person described by
+a given natural language query, we design a model that combines methods for
+spatio-temporal human detection and multimodal retrieval. We conduct
+comprehensive experiments to compare a variety of tube and text representations
+and multimodal retrieval methods, and present a strong baseline in this task as
+well as demonstrate the efficacy of our tube representation and multimodal
+feature embedding technique. Finally, we demonstrate the versatility of our
+model by applying it to two other important tasks.
+",1,0,0,0,0,0
+19323,Thermo-elasto-plastic simulations of femtosecond laser-induced multiple-cavity in fused silica,"  The formation and the interaction of multiple cavities, induced by tightly
+focused femtosecond laser pulses, are studied by using a developed numerical
+tool, including the thermo-elasto-plastic material response. Simulations are
+performed in fused silica in cases of one, two, and four spots of laser energy
+deposition. The relaxation of the heated matter, launching shock waves in the
+surrounding cold material, leads to cavity formation and emergence of areas
+where cracks may be induced. Results show that the laser-induced structure
+shape depends on the energy deposition configuration and demonstrate the
+potential of the used numerical tool to obtain the desired designed structure
+or technological process.
+",0,1,0,0,0,0
+19324,Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species,"  Machine-learning potentials (MLPs) for atomistic simulations are a promising
+alternative to conventional classical potentials. Current approaches rely on
+descriptors of the local atomic environment with dimensions that increase
+quadratically with the number of chemical species. In this article, we
+demonstrate that such a scaling can be avoided in practice. We show that a
+mathematically simple and computationally efficient descriptor with constant
+complexity is sufficient to represent transition-metal oxide compositions and
+biomolecules containing 11 chemical species with a precision of around 3
+meV/atom. This insight removes a perceived bound on the utility of MLPs and
+paves the way to investigate the physics of previously inaccessible materials
+with more than ten chemical species.
+",0,1,0,0,0,0
+19325,Randomized Constraints Consensus for Distributed Robust Linear Programming,"  In this paper we consider a network of processors aiming at cooperatively
+solving linear programming problems subject to uncertainty. Each node only
+knows a common cost function and its local uncertain constraint set. We propose
+a randomized, distributed algorithm working under time-varying, asynchronous
+and directed communication topology. The algorithm is based on a local
+computation and communication paradigm. At each communication round, nodes
+perform two updates: (i) a verification in which they check-in a randomized
+setup-the robust feasibility (and hence optimality) of the candidate optimal
+point, and (ii) an optimization step in which they exchange their candidate
+bases (minimal sets of active constraints) with neighbors and locally solve an
+optimization problem whose constraint set includes: a sampled constraint
+violating the candidate optimal point (if it exists), agent's current basis and
+the collection of neighbor's basis. As main result, we show that if a processor
+successfully performs the verification step for a sufficient number of
+communication rounds, it can stop the algorithm since a consensus has been
+reached. The common solution is-with high confidence-feasible (and hence
+optimal) for the entire set of uncertainty except a subset having arbitrary
+small probability measure. We show the effectiveness of the proposed
+distributed algorithm on a multi-core platform in which the nodes communicate
+asynchronously.
+",0,0,1,0,0,0
+19326,Continuum limit of the vibrational properties of amorphous solids,"  The low-frequency vibrational and low-temperature thermal properties of
+amorphous solids are markedly different from those of crystalline solids. This
+situation is counter-intuitive because any solid material is expected to behave
+as a homogeneous elastic body in the continuum limit, in which vibrational
+modes are phonons following the Debye law. A number of phenomenological
+explanations have been proposed, which assume elastic heterogeneities, soft
+localized vibrations, and so on. Recently, the microscopic mean-field theories
+have been developed to predict the universal non-Debye scaling law. Considering
+these theoretical arguments, it is absolutely necessary to directly observe the
+nature of the low-frequency vibrations of amorphous solids and determine the
+laws that such vibrations obey. Here, we perform an extremely large-scale
+vibrational mode analysis of a model amorphous solid. We find that the scaling
+law predicted by the mean-field theory is violated at low frequency, and in the
+continuum limit, the vibrational modes converge to a mixture of phonon modes
+following the Debye law and soft localized modes following another universal
+non-Debye scaling law.
+",0,1,0,0,0,0
+19327,Existence of smooth solutions of multi-term Caputo-type fractional differential equations,"  This paper deals with the initial value problem for the multi-term fractional
+differential equation. The fractional derivative is defined in the Caputo
+sense. Firstly the initial value problem is transformed into a equivalent
+Volterra-type integral equation under appropriate assumptions. Then new
+existence results for smooth solutions are established by using the Schauder
+fixed point theorem.
+",0,0,1,0,0,0
+19328,Classes of elementary function solutions to the CEV model. I,"  The CEV model subsumes some of the previous option pricing models. An
+important parameter in the model is the parameter b, the elasticity of
+volatility. For b=0, b=-1/2, and b=-1 the CEV model reduces respectively to the
+BSM model, the square-root model of Cox and Ross, and the Bachelier model. Both
+in the case of the BSM model and in the case of the CEV model it has become
+traditional to begin a discussion of option pricing by starting with the
+vanilla European calls and puts. In the case of BSM model simpler solutions are
+the log and power solutions. These contracts, despite the simplicity of their
+mathematical description, are attracting increasing attention as a trading
+instrument. Similar simple solutions have not been studied so far in a
+systematic fashion for the CEV model. We use Kovacic's algorithm to derive, for
+all half-integer values of b, all solutions ""in quadratures"" of the CEV
+ordinary differential equation. These solutions give rise, by separation of
+variables, to simple solutions to the CEV partial differential equation. In
+particular, when b=...,-5/2,-2,-3/2,-1, 1, 3/2, 2, 5/2,..., we obtain four
+classes of denumerably infinite elementary function solutions, when b=-1/2 and
+b=1/2 we obtain two classes of denumerably infinite elementary function
+solutions, whereas, when b=0 we find two elementary function solutions. In the
+derived solutions we have also dispensed with the unnecessary assumption made
+in the the BSM model asserting that the underlying asset pays no dividends
+during the life of the option.
+",0,0,0,0,0,1
+19329,Congruent families and invariant tensors,"  Classical results of Chentsov and Campbell state that -- up to constant
+multiples -- the only $2$-tensor field of a statistical model which is
+invariant under congruent Markov morphisms is the Fisher metric and the only
+invariant $3$-tensor field is the Amari-Chentsov tensor. We generalize this
+result for arbitrary degree $n$, showing that any family of $n$-tensors which
+is invariant under congruent Markov morphisms is algebraically generated by the
+canonical tensor fields defined in an earlier paper.
+",0,0,1,1,0,0
+19330,Quality of Information in Mobile Crowdsensing: Survey and Research Challenges,"  Smartphones have become the most pervasive devices in people's lives, and are
+clearly transforming the way we live and perceive technology. Today's
+smartphones benefit from almost ubiquitous Internet connectivity and come
+equipped with a plethora of inexpensive yet powerful embedded sensors, such as
+accelerometer, gyroscope, microphone, and camera. This unique combination has
+enabled revolutionary applications based on the mobile crowdsensing paradigm,
+such as real-time road traffic monitoring, air and noise pollution, crime
+control, and wildlife monitoring, just to name a few. Differently from prior
+sensing paradigms, humans are now the primary actors of the sensing process,
+since they become fundamental in retrieving reliable and up-to-date information
+about the event being monitored. As humans may behave unreliably or
+maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
+more important than ever. In this paper, we provide a new framework for
+defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
+current state-of-the-art on the topic. We also outline novel research
+challenges, along with possible directions of future work.
+",1,0,0,0,0,0
+19331,SimBlock: A Blockchain Network Simulator,"  Blockchain, which is a technology for distributedly managing ledger
+information over multiple nodes without a centralized system, has elicited
+increasing attention. Performing experiments on actual blockchains are
+difficult because a large number of nodes in wide areas are necessary. In this
+study, we developed a blockchain network simulator SimBlock for such
+experiments. Unlike the existing simulators, SimBlock can easily change
+behavior of node, so that it enables to investigate the influence of nodes'
+behavior on blockchains. We compared some simulation results with the measured
+values in actual blockchains to demonstrate the validity of this simulator.
+Furthermore, to show practical usage, we conducted two experiments which
+clarify the influence of neighbor node selection algorithms and relay networks
+on the block propagation time. The simulator could depict the effects of the
+two techniques on block propagation time. The simulator will be publicly
+available in a few months.
+",1,0,0,0,0,0
+19332,Bayesian Fused Lasso regression for dynamic binary networks,"  We propose a multinomial logistic regression model for link prediction in a
+time series of directed binary networks. To account for the dynamic nature of
+the data we employ a dynamic model for the model parameters that is strongly
+connected with the fused lasso penalty. In addition to promoting sparseness,
+this prior allows us to explore the presence of change points in the structure
+of the network. We introduce fast computational algorithms for estimation and
+prediction using both optimization and Bayesian approaches. The performance of
+the model is illustrated using simulated data and data from a financial trading
+network in the NYMEX natural gas futures market. Supplementary material
+containing the trading network data set and code to implement the algorithms is
+available online.
+",0,0,0,1,0,0
+19333,Indefinite Kernel Logistic Regression,"  Traditionally, kernel learning methods requires positive definitiveness on
+the kernel, which is too strict and excludes many sophisticated similarities,
+that are indefinite, in multimedia area. To utilize those indefinite kernels,
+indefinite learning methods are of great interests. This paper aims at the
+extension of the logistic regression from positive semi-definite kernels to
+indefinite kernels. The model, called indefinite kernel logistic regression
+(IKLR), keeps consistency to the regular KLR in formulation but it essentially
+becomes non-convex. Thanks to the positive decomposition of an indefinite
+matrix, IKLR can be transformed into a difference of two convex models, which
+follows the use of concave-convex procedure. Moreover, we employ an inexact
+solving scheme to speed up the sub-problem and develop a concave-inexact-convex
+procedure (CCICP) algorithm with theoretical convergence analysis. Systematical
+experiments on multi-modal datasets demonstrate the superiority of the proposed
+IKLR method over kernel logistic regression with positive definite kernels and
+other state-of-the-art indefinite learning based algorithms.
+",1,0,0,1,0,0
+19334,Microstructure and thickening of dense suspensions under extensional and shear flows,"  Dense suspensions are non-Newtonian fluids which exhibit strong shear
+thickening and normal stress differences. Using numerical simulation of
+extensional and shear flows, we investigate how rheological properties are
+determined by the microstructure which is built under flows and by the
+interactions between particles. By imposing extensional and shear flows, we can
+assess the degree of flow-type dependence in regimes below and above
+thickening. Even when the flow-type dependence is hindered, nondissipative
+responses, such as normal stress differences, are present and characterise the
+non-Newtonian behaviour of dense suspensions.
+",0,1,0,0,0,0
+19335,Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots,"  One of the open challenges in designing robots that operate successfully in
+the unpredictable human environment is how to make them able to predict what
+actions they can perform on objects, and what their effects will be, i.e., the
+ability to perceive object affordances. Since modeling all the possible world
+interactions is unfeasible, learning from experience is required, posing the
+challenge of collecting a large amount of experiences (i.e., training data).
+Typically, a manipulative robot operates on external objects by using its own
+hands (or similar end-effectors), but in some cases the use of tools may be
+desirable, nevertheless, it is reasonable to assume that while a robot can
+collect many sensorimotor experiences using its own hands, this cannot happen
+for all possible human-made tools.
+Therefore, in this paper we investigate the developmental transition from
+hand to tool affordances: what sensorimotor skills that a robot has acquired
+with its bare hands can be employed for tool use? By employing a visual and
+motor imagination mechanism to represent different hand postures compactly, we
+propose a probabilistic model to learn hand affordances, and we show how this
+model can generalize to estimate the affordances of previously unseen tools,
+ultimately supporting planning, decision-making and tool selection tasks in
+humanoid robots. We present experimental results with the iCub humanoid robot,
+and we publicly release the collected sensorimotor data in the form of a hand
+posture affordances dataset.
+",1,0,0,1,0,0
+19336,Rewriting in Free Hypergraph Categories,"  We study rewriting for equational theories in the context of symmetric
+monoidal categories where there is a separable Frobenius monoid on each object.
+These categories, also called hypergraph categories, are increasingly relevant:
+Frobenius structures recently appeared in cross-disciplinary applications,
+including the study of quantum processes, dynamical systems and natural
+language processing. In this work we give a combinatorial characterisation of
+arrows of a free hypergraph category as cospans of labelled hypergraphs and
+establish a precise correspondence between rewriting modulo Frobenius structure
+on the one hand and double-pushout rewriting of hypergraphs on the other. This
+interpretation allows to use results on hypergraphs to ensure decidability of
+confluence for rewriting in a free hypergraph category. Our results generalise
+previous approaches where only categories generated by a single object (props)
+were considered.
+",1,0,0,0,0,0
+19337,Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix,"  In this work we addressed the issue of applying a stochastic classifier and a
+local, fuzzy confusion matrix under the framework of multi-label
+classification. We proposed a novel solution to the problem of correcting label
+pairwise ensembles. The main step of the correction procedure is to compute
+classifier-specific competence and cross-competence measures, which estimates
+error pattern of the underlying classifier. At the fusion phase we employed two
+weighting approaches based on information theory. The classifier weights
+promote base classifiers which are the most susceptible to the correction based
+on the fuzzy confusion matrix. During the experimental study, the proposed
+approach was compared against two reference methods. The comparison was made in
+terms of six different quality criteria. The conducted experiments reveals that
+the proposed approach eliminates one of main drawbacks of the original
+FCM-based approach i.e. the original approach is vulnerable to the imbalanced
+class/label distribution. What is more, the obtained results shows that the
+introduced method achieves satisfying classification quality under all
+considered quality criteria. Additionally, the impact of fluctuations of data
+set characteristics is reduced.
+",1,0,0,1,0,0
+19338,Wilcoxon Rank-Based Tests for Clustered Data with R Package clusrank,"  Wilcoxon Rank-based tests are distribution-free alternatives to the popular
+two-sample and paired t-tests. For independent data, they are available in
+several R packages such as stats and coin. For clustered data, in spite of the
+recent methodological developments, there did not exist an R package that makes
+them available at one place. We present a package clusrank where the latest
+developments are implemented and wrapped under a unified user-friendly
+interface. With different methods dispatched based on the inputs, this package
+offers great flexibility in rank-based tests for various clustered data. Exact
+tests based on permutations are also provided for some methods. Details of the
+major schools of different methods are briefly reviewed. Usages of the package
+clusrank are illustrated with simulated data as well as a real dataset from an
+ophthalmological study. The package also enables convenient comparison between
+selected methods under settings that have not been studied before and the
+results are discussed.
+",0,0,0,1,0,0
+19339,Latent heterogeneous multilayer community detection,"  We propose a method for simultaneously detecting shared and unshared
+communities in heterogeneous multilayer weighted and undirected networks. The
+multilayer network is assumed to follow a generative probabilistic model that
+takes into account the similarities and dissimilarities between the
+communities. We make use of a variational Bayes approach for jointly inferring
+the shared and unshared hidden communities from multilayer network
+observations. We show the robustness of our approach compared to state-of-the
+art algorithms in detecting disparate (shared and private) communities on
+synthetic data as well as on real genome-wide fibroblast proliferation dataset.
+",1,0,0,1,0,0
+19340,Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection,"  In this paper, we analyze the behavior of the multivariate symmetric
+uncertainty (MSU) measure through the use of statistical simulation techniques
+under various mixes of informative and non-informative randomly generated
+features. Experiments show how the number of attributes, their cardinalities,
+and the sample size affect the MSU. We discovered a condition that preserves
+good quality in the MSU under different combinations of these three factors,
+providing a new useful criterion to help drive the process of dimension
+reduction.
+",1,0,0,1,0,0
+19341,Approximation Dynamics,"  We describe the approximation of a continuous dynamical system on a p. l.
+manifold or Cantor set by a tractable system. A system is tractable when it has
+a finite number of chain components and, with respect to a given full
+background measure, almost every point is generic for one of a finite number of
+ergodic invariant measures with non-overlapping supports. The approximations
+use non-degenerate simplicial dynamical systems for p. l. manifolds and
+shift-like dynamical systems for Cantor Sets.
+",0,0,1,0,0,0
+19342,Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance,"  The Wasserstein distance between two probability measures on a metric space
+is a measure of closeness with applications in statistics, probability, and
+machine learning. In this work, we consider the fundamental question of how
+quickly the empirical measure obtained from $n$ independent samples from $\mu$
+approaches $\mu$ in the Wasserstein distance of any order. We prove sharp
+asymptotic and finite-sample results for this rate of convergence for general
+measures on general compact metric spaces. Our finite-sample results show the
+existence of multi-scale behavior, where measures can exhibit radically
+different rates of convergence as $n$ grows.
+",0,0,1,1,0,0
+19343,Ensuring patients privacy in a cryptographic-based-electronic health records using bio-cryptography,"  Several recent works have proposed and implemented cryptography as a means to
+preserve privacy and security of patients health data. Nevertheless, the
+weakest point of electronic health record (EHR) systems that relied on these
+cryptographic schemes is key management. Thus, this paper presents the
+development of privacy and security system for cryptography-based-EHR by taking
+advantage of the uniqueness of fingerprint and iris characteristic features to
+secure cryptographic keys in a bio-cryptography framework. The results of the
+system evaluation showed significant improvements in terms of time efficiency
+of this approach to cryptographic-based-EHR. Both the fuzzy vault and fuzzy
+commitment demonstrated false acceptance rate (FAR) of 0%, which reduces the
+likelihood of imposters gaining successful access to the keys protecting
+patients protected health information. This result also justifies the
+feasibility of implementing fuzzy key binding scheme in real applications,
+especially fuzzy vault which demonstrated a better performance during key
+reconstruction.
+",1,0,0,0,0,0
+19344,A Dynamically Reconfigurable Terahertz Array Antenna for Near-field Imaging Applications,"  A proof of concept for high speed near-field imaging with sub-wavelength
+resolution using SLM is presented. An 8 channel THz detector array antenna with
+an electrode gap of 100 um and length of 5 mm is fabricated using the
+commercially available GaAs semiconductor substrate. Each array antenna can be
+excited simultaneously by spatially reconfiguring the optical probe beam and
+the THz electric field can be recorded using 8 channel lock-in amplifiers. By
+scanning the probe beam along the length of the array antenna, a 2D image can
+be obtained with amplitude, phase and frequency information.
+",0,1,0,0,0,0
+19345,Stationary C*-dynamical systems,"  We introduce the notion of stationary actions in the context of C*-algebras.
+We develop the basics of the theory, and provide applications to several
+ergodic theoretical and operator algebraic rigidity problems.
+",0,0,1,0,0,0
+19346,The impact of imbalanced training data on machine learning for author name disambiguation,"  In supervised machine learning for author name disambiguation, negative
+training data are often dominantly larger than positive training data. This
+paper examines how the ratios of negative to positive training data can affect
+the performance of machine learning algorithms to disambiguate author names in
+bibliographic records. On multiple labeled datasets, three classifiers -
+Logistic Regression, Naïve Bayes, and Random Forest - are trained through
+representative features such as coauthor names, and title words extracted from
+the same training data but with various positive-negative training data ratios.
+Results show that increasing negative training data can improve disambiguation
+performance but with a few percent of performance gains and sometimes degrade
+it. Logistic Regression and Naïve Bayes learn optimal disambiguation models
+even with a base ratio (1:1) of positive and negative training data. Also, the
+performance improvement by Random Forest tends to quickly saturate roughly
+after 1:10 ~ 1:15. These findings imply that contrary to the common practice
+using all training data, name disambiguation algorithms can be trained using
+part of negative training data without degrading much disambiguation
+performance while increasing computational efficiency. This study calls for
+more attention from author name disambiguation scholars to methods for machine
+learning from imbalanced data.
+",0,0,0,1,0,0
+19347,SIGNet: Scalable Embeddings for Signed Networks,"  Recent successes in word embedding and document embedding have motivated
+researchers to explore similar representations for networks and to use such
+representations for tasks such as edge prediction, node label prediction, and
+community detection. Such network embedding methods are largely focused on
+finding distributed representations for unsigned networks and are unable to
+discover embeddings that respect polarities inherent in edges. We propose
+SIGNet, a fast scalable embedding method suitable for signed networks. Our
+proposed objective function aims to carefully model the social structure
+implicit in signed networks by reinforcing the principles of social balance
+theory. Our method builds upon the traditional word2vec family of embedding
+approaches and adds a new targeted node sampling strategy to maintain
+structural balance in higher-order neighborhoods. We demonstrate the
+superiority of SIGNet over state-of-the-art methods proposed for both signed
+and unsigned networks on several real world datasets from different domains. In
+particular, SIGNet offers an approach to generate a richer vocabulary of
+features of signed networks to support representation and reasoning.
+",1,0,0,1,0,0
+19348,Spread of hate speech in online social media,"  The present online social media platform is afflicted with several issues,
+with hate speech being on the predominant forefront. The prevalence of online
+hate speech has fueled horrific real-world hate-crime such as the mass-genocide
+of Rohingya Muslims, communal violence in Colombo and the recent massacre in
+the Pittsburgh synagogue. Consequently, It is imperative to understand the
+diffusion of such hateful content in an online setting. We conduct the first
+study that analyses the flow and dynamics of posts generated by hateful and
+non-hateful users on Gab (gab.com) over a massive dataset of 341K users and 21M
+posts. Our observations confirms that hateful content diffuse farther, wider
+and faster and have a greater outreach than those of non-hateful users. A
+deeper inspection into the profiles and network of hateful and non-hateful
+users reveals that the former are more influential, popular and cohesive. Thus,
+our research explores the interesting facets of diffusion dynamics of hateful
+users and broadens our understanding of hate speech in the online world.
+",1,0,0,0,0,0
+19349,GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams,"  Designing adaptive classifiers for an evolving data stream is a challenging
+task due to the data size and its dynamically changing nature. Combining
+individual classifiers in an online setting, the ensemble approach, is a
+well-known solution. It is possible that a subset of classifiers in the
+ensemble outperforms others in a time-varying fashion. However, optimum weight
+assignment for component classifiers is a problem which is not yet fully
+addressed in online evolving environments. We propose a novel data stream
+ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble
+(GOOWE), which assigns optimum weights to the component classifiers using a
+sliding window containing the most recent data instances. We map vote scores of
+individual classifiers and true class labels into a spatial environment. Based
+on the Euclidean distance between vote scores and ideal-points, and using the
+linear least squares (LSQ) solution, we present a novel, dynamic, and online
+weighting approach. While LSQ is used for batch mode ensemble classifiers, it
+is the first time that we adapt and use it for online environments by providing
+a spatial modeling of online ensembles. In order to show the robustness of the
+proposed algorithm, we use real-world datasets and synthetic data generators
+using the MOA libraries. First, we analyze the impact of our weighting system
+on prediction accuracy through two scenarios. Second, we compare GOOWE with 8
+state-of-the-art ensemble classifiers in a comprehensive experimental
+environment. Our experiments show that GOOWE provides improved reactions to
+different types of concept drift compared to our baselines. The statistical
+tests indicate a significant improvement in accuracy, with conservative time
+and memory requirements.
+",1,0,0,0,0,0
+19350,Graph Product Multilayer Networks: Spectral Properties and Applications,"  This paper aims to establish theoretical foundations of graph product
+multilayer networks (GPMNs), a family of multilayer networks that can be
+obtained as a graph product of two or more factor networks. Cartesian, direct
+(tensor), and strong product operators are considered, and then generalized. We
+first describe mathematical relationships between GPMNs and their factor
+networks regarding their degree/strength, adjacency, and Laplacian spectra, and
+then show that those relationships can still hold for nonsimple and generalized
+GPMNs. Applications of GPMNs are discussed in three areas: predicting epidemic
+thresholds, modeling propagation in nontrivial space and time, and analyzing
+higher-order properties of self-similar networks. Directions of future research
+are also discussed.
+",1,1,0,0,0,0
+19351,Multilayer flows in molecular networks identify biological modules in the human proteome,"  A variety of complex systems exhibit different types of relationships
+simultaneously that can be modeled by multiplex networks. A typical problem is
+to determine the community structure of such systems that, in general, depend
+on one or more parameters to be tuned. In this study we propose one measure,
+grounded on information theory, to find the optimal value of the relax rate
+characterizing Multiplex Infomap, the generalization of the Infomap algorithm
+to the realm of multilayer networks. We evaluate our methodology on synthetic
+networks, to show that the most representative community structure can be
+reliably identified when the most appropriate relax rate is used. Capitalizing
+on these results, we use this measure to identify the most reliable meso-scale
+functional organization in the human protein-protein interaction multiplex
+network and compare the observed clusters against a collection of independently
+annotated gene sets from the Molecular Signatures Database (MSigDB). Our
+analysis reveals that modules obtained with the optimal value of the relax rate
+are biologically significant and, remarkably, with higher functional content
+than the ones obtained from the aggregate representation of the human proteome.
+Our framework allows us to characterize the meso-scale structure of those
+multilayer systems whose layers are not explicitly interconnected each other --
+as in the case of edge-colored models -- the ones describing most biological
+networks, from proteomes to connectomes.
+",0,0,0,0,1,0
+19352,Using Artificial Neural Networks (ANN) to Control Chaos,"  Controlling Chaos could be a big factor in getting great stable amounts of
+energy out of small amounts of not necessarily stable resources. By definition,
+Chaos is getting huge changes in the system's output due to unpredictable small
+changes in initial conditions, and that means we could take advantage of this
+fact and select the proper control system to manipulate system's initial
+conditions and inputs in general and get a desirable output out of otherwise a
+Chaotic system. That was accomplished by first building some known chaotic
+circuit (Chua circuit) and the NI's MultiSim was used to simulate the ANN
+control system. It was shown that this technique can also be used to stabilize
+some hard to stabilize electronic systems.
+",1,1,0,0,0,0
+19353,Glassy quantum dynamics in translation invariant fracton models,"  We investigate relaxation in the recently discovered ""fracton"" models and
+discover that these models naturally host glassy quantum dynamics in the
+absence of quenched disorder. We begin with a discussion of ""type I"" fracton
+models, in the taxonomy of Vijay, Haah, and Fu. We demonstrate that in these
+systems, the mobility of charges is suppressed exponentially in the inverse
+temperature. We further demonstrate that when a zero temperature type I fracton
+model is placed in contact with a finite temperature heat bath, the approach to
+equilibrium is a logarithmic function of time over an exponentially wide window
+of time scales. Generalizing to the more complex ""type II"" fracton models, we
+find that the charges exhibit subdiffusion upto a relaxation time that diverges
+at low temperatures as a super-exponential function of inverse temperature.
+This behaviour is reminiscent of ""nearly localized"" disordered systems, but
+occurs with a translation invariant three-dimensional Hamiltonian. We also
+conjecture that fracton models with conserved charge may support a phase which
+is a thermal metal but a charge insulator.
+",0,1,0,0,0,0
+19354,Saliency Guided Hierarchical Robust Visual Tracking,"  A saliency guided hierarchical visual tracking (SHT) algorithm containing
+global and local search phases is proposed in this paper. In global search, a
+top-down saliency model is novelly developed to handle abrupt motion and
+appearance variation problems. Nineteen feature maps are extracted first and
+combined with online learnt weights to produce the final saliency map and
+estimated target locations. After the evaluation of integration mechanism, the
+optimum candidate patch is passed to the local search. In local search, a
+superpixel based HSV histogram matching is performed jointly with an L2-RLS
+tracker to take both color distribution and holistic appearance feature of the
+object into consideration. Furthermore, a linear refinement search process with
+fast iterative solver is implemented to attenuate the possible negative
+influence of dominant particles. Both qualitative and quantitative experiments
+are conducted on a series of challenging image sequences. The superior
+performance of the proposed method over other state-of-the-art algorithms is
+demonstrated by comparative study.
+",1,0,0,0,0,0
+19355,Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images,"  The present study shows that the performance of CNN is not significantly
+different from the best classical methods and human doctors for classifying
+mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does
+not need tumor segmentation or feature calculation, it is more convenient and
+more objective than the classical methods. However, CNN does not make use of
+the import diagnostic features, which have been proved more discriminative than
+the texture features for classifying small-sized lymph nodes. Therefore,
+incorporating the diagnostic features into CNN is a promising direction for
+future research.
+",1,1,0,0,0,0
+19356,A Semantics for Probabilistic Control-Flow Graphs,"  This article develops a novel operational semantics for probabilistic
+control-flow graphs (pCFGs) of probabilistic imperative programs with random
+assignment and ""observe"" (or conditioning) statements. The semantics transforms
+probability distributions (on stores) as control moves from one node to another
+in pCFGs. We relate this semantics to a standard, expectation-transforming,
+denotational semantics of structured probabilistic imperative programs, by
+translating structured programs into (unstructured) pCFGs, and proving adequacy
+of the translation. This shows that the operational semantics can be used
+without loss of information, and is faithful to the ""intended"" semantics and
+hence can be used to reason about, for example, the correctness of
+transformations (as we do in a companion article).
+",1,0,0,0,0,0
+19357,Generalized Slow Roll in the Unified Effective Field Theory of Inflation,"  We provide a compact and unified treatment of power spectrum observables for
+the effective field theory (EFT) of inflation with the complete set of
+operators that lead to second-order equations of motion in metric perturbations
+in both space and time derivatives, including Horndeski and GLPV theories. We
+relate the EFT operators in ADM form to the four additional free functions of
+time in the scalar and tensor equations. Using the generalized slow roll
+formalism, we show that each power spectrum can be described by an integral
+over a single source that is a function of its respective sound horizon. With
+this correspondence, existing model independent constraints on the source
+function can be simply reinterpreted in the more general inflationary context.
+By expanding these sources around an optimized freeze-out epoch, we also
+provide characterizations of these spectra in terms of five slow-roll
+hierarchies whose leading order forms are compact and accurate as long as EFT
+coefficients vary only on timescales greater than an efold. We also clarify the
+relationship between the unitary gauge observables employed in the EFT and the
+comoving gauge observables of the post-inflationary universe.
+",0,1,0,0,0,0
+19358,MgO thickness-induced spin reorientation transition in Co0.9Fe0.1/MgO/Co0.9Fe0.1 structure,"  The magnetic anisotropy (MA) of Mo/Au/Co0.9Fe0.1/Au/MgO(0.7 - 3
+nm)/Au/Co0.9Fe0.1/Au heterostructure has been investigated at room temperature
+as a function of MgO layer thickness (tMgO). Our studies show that while the MA
+of the top layer does not change its character upon variation of tMgO, the
+uniaxial out-of-plane MA of the bottom one undergoes a spin reorientation
+transition at tMgO of about 0.8 nm, switching to the regime where the
+coexistence of in- and out-of-plane magnetization alignments is observed. The
+magnitudes of the magnetic anisotropy constants have been determined from
+ferromagnetic resonance and dc-magnetometry measurements. The origin of MA
+evolution has been attributed to a presence of an interlayer exchange coupling
+(IEC) between Co0.9Fe0.1 layers through the thin MgO film.
+",0,1,0,0,0,0
+19359,Bjerrum Pairs in Ionic Solutions: a Poisson-Boltzmann Approach,"  Ionic solutions are often regarded as fully dissociated ions dispersed in a
+polar solvent. While this picture holds for dilute solutions, at higher ionic
+concentrations, oppositely charged ions can associate into dimers, referred to
+as Bjerrum pairs. We consider the formation of such pairs within the nonlinear
+Poisson-Boltzmann framework, and investigate their effects on bulk and
+interfacial properties of electrolytes. Our findings show that pairs can reduce
+the magnitude of the dielectric decrement of ionic solutions as the ionic
+concentration increases. We describe the effect of pairs on the Debye screening
+length, and relate our results to recent surface-force experiments.
+Furthermore, we show that Bjerrum pairs reduce the ionic concentration in bulk
+electrolyte and at the proximity of charged surfaces, while they enhance the
+attraction between oppositely charged surfaces.
+",0,1,0,0,0,0
+19360,Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding,"  Network embedding methodologies, which learn a distributed vector
+representation for each vertex in a network, have attracted considerable
+interest in recent years. Existing works have demonstrated that vertex
+representation learned through an embedding method provides superior
+performance in many real-world applications, such as node classification, link
+prediction, and community detection. However, most of the existing methods for
+network embedding only utilize topological information of a vertex, ignoring a
+rich set of nodal attributes (such as, user profiles of an online social
+network, or textual contents of a citation network), which is abundant in all
+real-life networks. A joint network embedding that takes into account both
+attributional and relational information entails a complete network information
+and could further enrich the learned vector representations. In this work, we
+present Neural-Brane, a novel Neural Bayesian Personalized Ranking based
+Attributed Network Embedding. For a given network, Neural-Brane extracts latent
+feature representation of its vertices using a designed neural network model
+that unifies network topological information and nodal attributes; Besides, it
+utilizes Bayesian personalized ranking objective, which exploits the proximity
+ordering between a similar node-pair and a dissimilar node-pair. We evaluate
+the quality of vertex embedding produced by Neural-Brane by solving the node
+classification and clustering tasks on four real-world datasets. Experimental
+results demonstrate the superiority of our proposed method over the
+state-of-the-art existing methods.
+",0,0,0,1,0,0
+19361,Pairwise $k$-Semi-Stratifiable Bispaces and Topological Ordered Spaces,"  In this paper, we continue to study pairwise ($k$-semi-)stratifiable
+bitopological spaces. Some new characterizations of pairwise
+$k$-semi-stratifiable bitopological spaces are provided. Relationships between
+pairwise stratifiable and pairwise $k$-semi-stratifiable bitopological spaces
+are further investigated, and an open question recently posed by Li and Lin in
+\cite{LL} is completely solved. We also study the quasi-pseudo-metrizability of
+a topological ordered space $(X, \tau, \preccurlyeq)$. It is shown that if $(X,
+\tau, \preccurlyeq)$ is a ball transitive topological ordered $C$- and
+$I$-space such that $\tau$ is metrizable, then its associated bitopological
+space $(X,\tau^{\flat},\tau^{\natural})$ is quasi-pseudo-metrizable. This
+result provides a partial affirmative answer to a problem in \cite{KM}.
+",0,0,1,0,0,0
+19362,Ontology based system to guide internship assignment process,"  Internship assignment is a complicated process for universities since it is
+necessary to take into account a multiplicity of variables to establish a
+compromise between companies' requirements and student competencies acquired
+during the university training. These variables build up a complex relations
+map that requires the formulation of an exhaustive and rigorous conceptual
+scheme. In this research a domain ontological model is presented as support to
+the student's decision making for opportunities of University studies level of
+the University Lumiere Lyon 2 (ULL) education system. The ontology is designed
+and created using methodological approach offering the possibility of improving
+the progressive creation, capture and knowledge articulation. In this paper, we
+draw a balance taking the demands of the companies across the capabilities of
+the students. This will be done through the establishment of an ontological
+model of an educational learners' profile and the internship postings which are
+written in a free text and using uncontrolled vocabulary. Furthermore, we
+outline the process of semantic matching which improves the quality of query
+results.
+",1,0,0,0,0,0
+19363,Vacancy-driven extended stability of cubic metastable Ta-Al-N and Nb-Al-N phases,"  Quantum mechanical calculations had been previously applied to predict phase
+stability in many ternary and multinary nitride systems. While the predictions
+were very accurate for the Ti-Al-N system, some discrepancies between theory
+and experiment were obtained in the case of other systems. Namely, in the case
+of Ta-Al-N, the calculations tend to overestimate the minimum Al content
+necessary to obtain a metastable solid solution with a cubic structure. In this
+work, we present a comprehensive study of the impact of vacancies on the phase
+fields in quasi-binary TaN-AlN and NbN-AlN systems. Our calculations clearly
+show that presence of point defects strongly enlarges the cubic phase field in
+the TaN-AlN system, while the effect is less pronounced in the NbN-AlN case.
+The present phase stability predictions agree better with experimental
+observations of physical vapour deposited thin films reported in the literature
+than that based on perfect, non-defected structures. This study shows that a
+representative structural model is crucial for a meaningful comparison with
+experimental data.
+",0,1,0,0,0,0
+19364,Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation,"  This paper addresses the question: Why do neural dialog systems generate
+short and meaningless replies? We conjecture that, in a dialog system, an
+utterance may have multiple equally plausible replies, causing the deficiency
+of neural networks in the dialog application. We propose a systematic way to
+mimic the dialog scenario in a machine translation system, and manage to
+reproduce the phenomenon of generating short and less meaningful sentences in
+the translation setting, showing evidence of our conjecture.
+",1,0,0,0,0,0
+19365,Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain,"  In this paper we tackle the problem of visually predicting surface friction
+for environments with diverse surfaces, and integrating this knowledge into
+biped robot locomotion planning. The problem is essential for autonomous robot
+locomotion since diverse surfaces with varying friction abound in the real
+world, from wood to ceramic tiles, grass or ice, which may cause difficulties
+or huge energy costs for robot locomotion if not considered. We propose to
+estimate friction and its uncertainty from visual estimation of material
+classes using convolutional neural networks, together with probability
+distribution functions of friction associated with each material. We then
+robustly integrate the friction predictions into a hierarchical (footstep and
+full-body) planning method using chance constraints, and optimize the same
+trajectory costs at both levels of the planning method for consistency. Our
+solution achieves fully autonomous perception and locomotion on slippery
+terrain, which considers not only friction and its uncertainty, but also
+collision, stability and trajectory cost. We show promising friction prediction
+results in real pictures of outdoor scenarios, and planning experiments on a
+real robot facing surfaces with different friction.
+",1,0,0,0,0,0
+19366,Non-Archimedean Replicator Dynamics and Eigen's Paradox,"  We present a new non-Archimedean model of evolutionary dynamics, in which the
+genomes are represented by p-adic numbers. In this model the genomes have a
+variable length, not necessarily bounded, in contrast with the classical models
+where the length is fixed. The time evolution of the concentration of a given
+genome is controlled by a p-adic evolution equation. This equation depends on a
+fitness function f and on mutation measure Q. By choosing a mutation measure of
+Gibbs type, and by using a p-adic version of the Maynard Smith Ansatz, we show
+the existence of threshold function M_{c}(f,Q), such that the long term
+survival of a genome requires that its length grows faster than M_{c}(f,Q).
+This implies that Eigen's paradox does not occur if the complexity of genomes
+grows at the right pace. About twenty years ago, Scheuring and Poole, Jeffares,
+Penny proposed a hypothesis to explain Eigen's paradox. Our mathematical model
+shows that this biological hypothesis is feasible, but it requires p-adic
+analysis instead of real analysis. More exactly, the Darwin-Eigen cycle
+proposed by Poole et al. takes place if the length of the genomes exceeds
+M_{c}(f,Q).
+",0,0,0,0,1,0
+19367,D-optimal design for multivariate polynomial regression via the Christoffel function and semidefinite relaxations,"  We present a new approach to the design of D-optimal experiments with
+multivariate polynomial regressions on compact semi-algebraic design spaces. We
+apply the moment-sum-of-squares hierarchy of semidefinite programming problems
+to solve numerically and approximately the optimal design problem. The geometry
+of the design is recovered with semidefinite programming duality theory and the
+Christoffel polynomial.
+",0,0,1,1,0,0
+19368,A dynamic network model to measure exposure diversification in the Austrian interbank market,"  We propose a statistical model for weighted temporal networks capable of
+measuring the level of heterogeneity in a financial system. Our model focuses
+on the level of diversification of financial institutions; that is, whether
+they are more inclined to distribute their assets equally among partners, or if
+they rather concentrate their commitment towards a limited number of
+institutions. Crucially, a Markov property is introduced to capture time
+dependencies and to make our measures comparable across time. We apply the
+model on an original dataset of Austrian interbank exposures. The temporal span
+encompasses the onset and development of the financial crisis in 2008 as well
+as the beginnings of European sovereign debt crisis in 2011. Our analysis
+highlights an overall increasing trend for network homogeneity, whereby core
+banks have a tendency to distribute their market exposures more equally across
+their partners.
+",0,0,0,1,0,1
+19369,Universal scaling in the Knight shift anomaly of doped periodic Anderson model,"  We report a Dynamical Cluster Approximation (DCA) investigation of the doped
+periodic Anderson model (PAM) to explain the universal scaling in the Knight
+shift anomaly predicted by the phenomenological two-fluid model and confirmed
+in many heavy-fermion compounds. We calculate the quantitative evolution of the
+orbital-dependent magnetic susceptibility and reproduce correctly the two-fluid
+prediction in a large range of doping and hybridization. Our results confirm
+the presence of a temperature/energy scale $T^{\ast}$ for the universal scaling
+and show distinctive behavors of the Knight shift anomaly in response to other
+""orders"" at low temperatures. However, comparison with the temperature
+evolution of the calculated resistivity and quasiparticle spectral peak
+indicates a different characteristic temperature from $T^*$, in contradiction
+with the experimental observation in CeCoIn$_5$ and other compounds. This
+reveals a missing piece in the current model calculations in explaining the
+two-fluid phenomenology.
+",0,1,0,0,0,0
+19370,Reinforcement Learning Algorithm Selection,"  This paper formalises the problem of online algorithm selection in the
+context of Reinforcement Learning. The setup is as follows: given an episodic
+task and a finite number of off-policy RL algorithms, a meta-algorithm has to
+decide which RL algorithm is in control during the next episode so as to
+maximize the expected return. The article presents a novel meta-algorithm,
+called Epochal Stochastic Bandit Algorithm Selection (ESBAS). Its principle is
+to freeze the policy updates at each epoch, and to leave a rebooted stochastic
+bandit in charge of the algorithm selection. Under some assumptions, a thorough
+theoretical analysis demonstrates its near-optimality considering the
+structural sampling budget limitations. ESBAS is first empirically evaluated on
+a dialogue task where it is shown to outperform each individual algorithm in
+most configurations. ESBAS is then adapted to a true online setting where
+algorithms update their policies after each transition, which we call SSBAS.
+SSBAS is evaluated on a fruit collection task where it is shown to adapt the
+stepsize parameter more efficiently than the classical hyperbolic decay, and on
+an Atari game, where it improves the performance by a wide margin.
+",1,0,1,1,0,0
+19371,"On periodic solutions of nonlinear wave equations, including Einstein equations with a negative cosmological constant","  We construct periodic solutions of nonlinear wave equations using analytic
+continuation. The construction applies in particular to Einstein equations,
+leading to infinite-dimensional families of time-periodic solutions of the
+vacuum, or of the Einstein-Maxwell-dilaton-scalar
+fields-Yang-Mills-Higgs-Chern-Simons-$f(R)$ equations, with a negative
+cosmological constant.
+",0,0,1,0,0,0
+19372,"Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories","  Attribute-based recognition models, due to their impressive performance and
+their ability to generalize well on novel categories, have been widely adopted
+for many computer vision applications. However, usually both the attribute
+vocabulary and the class-attribute associations have to be provided manually by
+domain experts or large number of annotators. This is very costly and not
+necessarily optimal regarding recognition performance, and most importantly, it
+limits the applicability of attribute-based models to large scale data sets. To
+tackle this problem, we propose an end-to-end unsupervised attribute learning
+approach. We utilize online text corpora to automatically discover a salient
+and discriminative vocabulary that correlates well with the human concept of
+semantic attributes. Moreover, we propose a deep convolutional model to
+optimize class-attribute associations with a linguistic prior that accounts for
+noise and missing data in text. In a thorough evaluation on ImageNet, we
+demonstrate that our model is able to efficiently discover and learn semantic
+attributes at a large scale. Furthermore, we demonstrate that our model
+outperforms the state-of-the-art in zero-shot learning on three data sets:
+ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable
+attribute-based learning on ImageNet and will share the attributes and
+associations for future research.
+",1,0,0,0,0,0
+19373,Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data,"  In this paper, a scale mixture of Normal distributions model is developed for
+classification and clustering of data having outliers and missing values. The
+classification method, based on a mixture model, focuses on the introduction of
+latent variables that gives us the possibility to handle sensitivity of model
+to outliers and to allow a less restrictive modelling of missing data.
+Inference is processed through a Variational Bayesian Approximation and a
+Bayesian treatment is adopted for model learning, supervised classification and
+clustering.
+",0,0,0,1,0,0
+19374,Energy stable discretization of Allen-Cahn type problems modeling the motion of phase boundaries,"  We study the systematic numerical approximation of a class of Allen-Cahn type
+problems modeling the motion of phase interfaces. The common feature of these
+models is an underlying gradient flow structure which gives rise to a decay of
+an associated energy functional along solution trajectories. We first study the
+discretization in space by a conforming Galerkin approximation of a variational
+principle which characterizes smooth solutions of the problem. Well-posedness
+of the resulting semi-discretization is established and the energy decay along
+discrete solution trajectories is proven. A problem adapted implicit
+time-stepping scheme is then proposed and we establish its well-posed and decay
+of the free energy for the fully discrete scheme. Some details about the
+numerical realization by finite elements are discussed, in particular the
+iterative solution of the nonlinear problems arising in every time-step. The
+theoretical results are illustrated by numerical tests which also provide
+further evidence for asymptotic expansions of the interface velocities derived
+by Alber et al.
+",0,0,1,0,0,0
+19375,Hidden space reconstruction inspires link prediction in complex networks,"  As a fundamental challenge in vast disciplines, link prediction aims to
+identify potential links in a network based on the incomplete observed
+information, which has broad applications ranging from uncovering missing
+protein-protein interaction to predicting the evolution of networks. One of the
+most influential methods rely on similarity indices characterized by the common
+neighbors or its variations. We construct a hidden space mapping a network into
+Euclidean space based solely on the connection structures of a network.
+Compared with real geographical locations of nodes, our reconstructed locations
+are in conformity with those real ones. The distances between nodes in our
+hidden space could serve as a novel similarity metric in link prediction. In
+addition, we hybrid our hidden space method with other state-of-the-art
+similarity methods which substantially outperforms the existing methods on the
+prediction accuracy. Hence, our hidden space reconstruction model provides a
+fresh perspective to understand the network structure, which in particular
+casts a new light on link prediction.
+",1,1,0,0,0,0
+19376,Maximum principles for the fractional p-Laplacian and symmetry of solutions,"  In this paper, we consider nonlinear equations involving the fractional
+p-Laplacian $$ (-\lap)_p^s u(x)) \equiv C_{n,s,p} PV \int_{\mathbb{R}^n}
+\frac{|u(x)-u(y)|^{p-2}[u(x)-u(y)]}{|x-z|^{n+ps}} dz= f(x,u).$$
+We prove a {\em maximum principle for anti-symmetric functions} and obtain
+other key ingredients for carrying on the method of moving planes, such as {\em
+a key boundary estimate lemma}. Then we establish radial symmetry and
+monotonicity for positive solutions to semilinear equations involving the
+fractional p-Laplacian in a unit ball and in the whole space. We believe that
+the methods developed here can be applied to a variety of problems involving
+nonlinear nonlocal operators.
+",0,0,1,0,0,0
+19377,On slowly rotating axisymmetric solutions of the Einstein-Euler equations,"  In recent works we have constructed axisymmetric solutions to the
+Euler-Poisson equations which give mathematical models of slowly uniformly
+rotating gaseous stars. We try to extend this result to the study of solutions
+of the Einstein-Euler equations in the framework of the general theory of
+relativity. Although many interesting studies have been done about axisymmetric
+metric in the general theory of relativity, they are restricted to the region
+of the vacuum. Mathematically rigorous existence theorem of the axisymmetric
+interior solutions of the stationary metric corresponding to the
+energy-momentum tensor of the perfect fluid with non-zero pressure may be not
+yet established until now except only one found in the pioneering work by U.
+Heilig done in 1993. In this article, along a different approach to that of
+Heilig's work, axisymmetric stationary solutions of the Einstein-Euler
+equations are constructed near those of the Euler-Poisson equations when the
+speed of light is sufficiently large in the considered system of units, or,
+when the gravitational field is sufficiently weak.
+",0,0,1,0,0,0
+19378,Gapped paramagnetic state in a frustrated spin-$\frac{1}{2}$ Heisenberg antiferromagnet on the cross-striped square lattice,"  We implement the coupled cluster method to very high orders of approximation
+to study the spin-$\frac{1}{2}$ $J_{1}$--$J_{2}$ Heisenberg model on a
+cross-striped square lattice. Every nearest-neighbour pair of sites on the
+square lattice has an isotropic antiferromagnetic exchange bond of strength
+$J_{1}>0$, while the basic square plaquettes in alternate columns have either
+both or neither next-nearest-neighbour (diagonal) pairs of sites connected by
+an equivalent frustrating bond of strength $J_{2} \equiv \alpha J_{1} > 0$. By
+studying the magnetic order parameter (i.e., the average local on-site
+magnetization) in the range $0 \leq \alpha \leq 1$ of the frustration parameter
+we find that the quasiclassical antiferromagnetic Néel and (so-called)
+double Néel states form the stable ground-state phases in the respective
+regions $\alpha < \alpha_{1a}^{c} = 0.46(1)$ and $\alpha > \alpha_{1b}^{c} =
+0.615(5)$. The double Néel state has Néel
+($\cdots\uparrow\downarrow\uparrow\downarrow\cdots$) ordering along the
+(column) direction parallel to the stripes of squares with both or no $J_{2}$
+bonds, and spins alternating in a pairwise
+($\cdots\uparrow\uparrow\downarrow\downarrow\uparrow\uparrow\downarrow\downarrow\cdots$)
+fashion along the perpendicular (row) direction, so that the parallel pairs
+occur on squares with both $J_{2}$ bonds present. Further explicit calculations
+of both the triplet spin gap and the zero-field uniform transverse magnetic
+susceptibility provide compelling evidence that the ground-state phase over all
+or most of the intermediate regime $\alpha_{1a}^{c} < \alpha < \alpha_{1b}^{c}$
+is a gapped state with no discernible long-range magnetic order.
+",0,1,0,0,0,0
+19379,Clouds in the atmospheres of extrasolar planets. V. The impact of CO2 ice clouds on the outer boundary of the habitable zone,"  Clouds have a strong impact on the climate of planetary atmospheres. The
+potential scattering greenhouse effect of CO2 ice clouds in the atmospheres of
+terrestrial extrasolar planets is of particular interest because it might
+influence the position and thus the extension of the outer boundary of the
+classic habitable zone around main sequence stars. Here, the impact of CO2 ice
+clouds on the surface temperatures of terrestrial planets with CO2 dominated
+atmospheres, orbiting different types of stars is studied. Additionally, their
+corresponding effect on the position of the outer habitable zone boundary is
+evaluated. For this study, a radiative-convective atmospheric model is used the
+calculate the surface temperatures influenced by CO2 ice particles. The clouds
+are included using a parametrised cloud model. The atmospheric model includes a
+general discrete ordinate radiative transfer that can describe the anisotropic
+scattering by the cloud particles accurately. A net scattering greenhouse
+effect caused by CO2 clouds is only obtained in a rather limited parameter
+range which also strongly depends on the stellar effective temperature. For
+cool M-stars, CO2 clouds only provide about 6 K of additional greenhouse
+heating in the best case scenario. On the other hand, the surface temperature
+for a planet around an F-type star can be increased by 30 K if carbon dioxide
+clouds are present. Accordingly, the extension of the habitable zone due to
+clouds is quite small for late-type stars. Higher stellar effective
+temperatures, on the other hand, can lead to outer HZ boundaries about 0.5 au
+farther out than the corresponding clear-sky values.
+",0,1,0,0,0,0
+19380,Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks,"  Magnetic resonance imaging (MRI) has been proposed as a complimentary method
+to measure bone quality and assess fracture risk. However, manual segmentation
+of MR images of bone is time-consuming, limiting the use of MRI measurements in
+the clinical practice. The purpose of this paper is to present an automatic
+proximal femur segmentation method that is based on deep convolutional neural
+networks (CNNs). This study had institutional review board approval and written
+informed consent was obtained from all subjects. A dataset of volumetric
+structural MR images of the proximal femur from 86 subject were
+manually-segmented by an expert. We performed experiments by training two
+different CNN architectures with multiple number of initial feature maps and
+layers, and tested their segmentation performance against the gold standard of
+manual segmentations using four-fold cross-validation. Automatic segmentation
+of the proximal femur achieved a high dice similarity score of 0.94$\pm$0.05
+with precision = 0.95$\pm$0.02, and recall = 0.94$\pm$0.08 using a CNN
+architecture based on 3D convolution exceeding the performance of 2D CNNs. The
+high segmentation accuracy provided by CNNs has the potential to help bring the
+use of structural MRI measurements of bone quality into clinical practice for
+management of osteoporosis.
+",1,0,0,1,0,0
+19381,Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter,"  In Crowdfunding platforms, people turn their prototype ideas into real
+products by raising money from the crowd, or invest in someone else's projects.
+In reward-based crowdfunding platforms such as Kickstarter and Indiegogo,
+selecting accurate reward delivery duration becomes crucial for creators,
+backers, and platform providers to keep the trust between the creators and the
+backers, and the trust between the platform providers and users. According to
+Kickstarter, 35% backers did not receive rewards on time. Unfortunately, little
+is known about on-time and late reward delivery projects, and there is no prior
+work to estimate reward delivery duration. To fill the gap, in this paper, we
+(i) extract novel features that reveal latent difficulty levels of project
+rewards; (ii) build predictive models to identify whether a creator will
+deliver all rewards in a project on time or not; and (iii) build a regression
+model to estimate accurate reward delivery duration (i.e., how long it will
+take to produce and deliver all the rewards). Experimental results show that
+our models achieve good performance -- 82.5% accuracy, 78.1 RMSE, and 0.108
+NRMSE at the first 5% of the longest reward delivery duration.
+",1,0,0,0,0,0
+19382,Bayesian Model-Agnostic Meta-Learning,"  Learning to infer Bayesian posterior from a few-shot dataset is an important
+step towards robust meta-learning due to the model uncertainty inherent in the
+problem. In this paper, we propose a novel Bayesian model-agnostic
+meta-learning method. The proposed method combines scalable gradient-based
+meta-learning with nonparametric variational inference in a principled
+probabilistic framework. During fast adaptation, the method is capable of
+learning complex uncertainty structure beyond a point estimate or a simple
+Gaussian approximation. In addition, a robust Bayesian meta-update mechanism
+with a new meta-loss prevents overfitting during meta-update. Remaining an
+efficient gradient-based meta-learner, the method is also model-agnostic and
+simple to implement. Experiment results show the accuracy and robustness of the
+proposed method in various tasks: sinusoidal regression, image classification,
+active learning, and reinforcement learning.
+",0,0,0,1,0,0
+19383,Distributed matching scheme and a flexible deterministic matching algorithm for arbitrary systems,"  We discuss the distributed matching scheme in accelerators where control of
+transverse beam phase space, oscillation, and transport is accomplished by
+flexible distribution of focusing elements beyond dedicated matching sections.
+Besides freeing accelerator design from fixed matching sections, such a scheme
+has many operational advantages, and enables fluid optics manipulation not
+possible in conventional schemes. Combined with an interpolation scheme this
+can bring about a new paradigm for efficient, flexible, and robust optics
+control. A rigorous and deterministic algorithm is developed for its
+realization. The algorithm is a matching tool in its own right with unique
+characteristics in robustness and determinism. The beam phase space dynamics is
+naturally integrated into the algorithm, instead of being treated as generic
+numerical parameters as in traditional schemes. It is applicable to a wider
+range of problems, such as trading-off between competing options for desired
+machine states.
+",0,1,0,0,0,0
+19384,Group Metrics for Graph Products of Cyclic Groups,"  We complement the characterization of the graph products of cyclic groups
+$G(\Gamma, \mathfrak{p})$ admitting a Polish group topology of [9] with the
+following result. Let $G = G(\Gamma, \mathfrak{p})$, then the following are
+equivalent: (i) there is a metric on $\Gamma$ which induces a separable
+topology in which $E_{\Gamma}$ is closed; (ii) $G(\Gamma, \mathfrak{p})$ is
+embeddable into a Polish group; (iii) $G(\Gamma, \mathfrak{p})$ is embeddable
+into a non-Archimedean Polish group. We also construct left-invariant separable
+group ultrametrics for $G = G(\Gamma, \mathfrak{p})$ and $\Gamma$ a closed
+graph on the Baire space, which is of independent interest.
+",0,0,1,0,0,0
+19385,Robust Online Multi-Task Learning with Correlative and Personalized Structures,"  Multi-Task Learning (MTL) can enhance a classifier's generalization
+performance by learning multiple related tasks simultaneously. Conventional MTL
+works under the offline or batch setting, and suffers from expensive training
+cost and poor scalability. To address such inefficiency issues, online learning
+techniques have been applied to solve MTL problems. However, most existing
+algorithms of online MTL constrain task relatedness into a presumed structure
+via a single weight matrix, which is a strict restriction that does not always
+hold in practice. In this paper, we propose a robust online MTL framework that
+overcomes this restriction by decomposing the weight matrix into two
+components: the first one captures the low-rank common structure among tasks
+via a nuclear norm and the second one identifies the personalized patterns of
+outlier tasks via a group lasso. Theoretical analysis shows the proposed
+algorithm can achieve a sub-linear regret with respect to the best linear model
+in hindsight. Even though the above framework achieves good performance, the
+nuclear norm that simply adds all nonzero singular values together may not be a
+good low-rank approximation. To improve the results, we use a log-determinant
+function as a non-convex rank approximation. The gradient scheme is applied to
+optimize log-determinant function and can obtain a closed-form solution for
+this refined problem. Experimental results on a number of real-world
+applications verify the efficacy of our method.
+",1,0,0,1,0,0
+19386,Cobordism maps on PFH induced by Lefschetz fibration over higher genus base,"  In this note, we discuss the cobordism maps on periodic Floer homology(PFH)
+induced by Lefschetz fibration. In the first part of the note, we define the
+cobordism maps on PFH induced by Lefschetz fibration via Seiberg Witten theory
+and the isomorphism between PFH and Seiberg Witten cohomology. The second part
+is to define the cobordism maps induced by Lefschetz fibration provided that
+the cobordism satisfies certain conditions. Under certain monotone assumptions,
+we show that these two definitions in fact are equivalent.
+",0,0,1,0,0,0
+19387,A short note on the order of the Zhang-Liu matrices over arbitrary fields,"  We give necessary and sufficient conditions for the Zhang-Liu matrices to be
+diagonalizable over arbitrary fields and provide the eigen-decomposition when
+it is possible. We use this result to calculate the order of these matrices
+over any arbitrary field. This generalizes a result of the second author.
+",0,0,1,0,0,0
+19388,Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017),"  This is the Proceedings of the 2017 ICML Workshop on Human Interpretability
+in Machine Learning (WHI 2017), which was held in Sydney, Australia, August 10,
+2017. Invited speakers were Tony Jebara, Pang Wei Koh, and David Sontag.
+",1,0,0,1,0,0
+19389,Ferromagnetic transition in a one-dimensional spin-orbit-coupled metal and its mapping to a critical point in smectic liquid crystals,"  We study the quantum phase transition between a paramagnetic and
+ferromagnetic metal in the presence of Rashba spin-orbit coupling in one
+dimension. Using bosonization, we analyze the transition by means of
+renormalization group, controlled by an $\varepsilon$-expansion around the
+upper critical dimension of two. We show that the presence of Rashba spin-orbit
+coupling allows for a new nonlinear term in the bosonized action, which
+generically leads to a fluctuation driven first-order transition. We further
+demonstrate that the Euclidean action of this system maps onto a classical
+smectic-A -- C phase transition in a magnetic field in two dimensions. We show
+that the smectic transition is second-order and is controlled by a new critical
+point.
+",0,1,0,0,0,0
+19390,Riemannian almost product manifolds generated by a circulant structure,"  A 4-dimensional Riemannian manifold equipped with a circulant structure,
+which is an isometry with respect to the metric and its fourth power is the
+identity, is considered. The almost product manifold associated with the
+considered manifold is studied. The relation between the covariant derivatives
+of the almost product structure and the circulant structure is obtained. The
+conditions for the covariant derivative of the circulant structure, which imply
+that an almost product manifold belongs to each of the basic classes of the
+Staikova-Gribachev classification, are given.
+",0,0,1,0,0,0
+19391,Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis,"  Modern software systems provide many configuration options which
+significantly influence their non-functional properties. To understand and
+predict the effect of configuration options, several sampling and learning
+strategies have been proposed, albeit often with significant cost to cover the
+highly dimensional configuration space. Recently, transfer learning has been
+applied to reduce the effort of constructing performance models by transferring
+knowledge about performance behavior across environments. While this line of
+research is promising to learn more accurate models at a lower cost, it is
+unclear why and when transfer learning works for performance modeling. To shed
+light on when it is beneficial to apply transfer learning, we conducted an
+empirical study on four popular software systems, varying software
+configurations and environmental conditions, such as hardware, workload, and
+software versions, to identify the key knowledge pieces that can be exploited
+for transfer learning. Our results show that in small environmental changes
+(e.g., homogeneous workload change), by applying a linear transformation to the
+performance model, we can understand the performance behavior of the target
+environment, while for severe environmental changes (e.g., drastic workload
+change) we can transfer only knowledge that makes sampling more efficient,
+e.g., by reducing the dimensionality of the configuration space.
+",1,0,0,1,0,0
+19392,"Critique of Barbosa's ""P != NP Proof""","  We review André Luiz Barbosa's paper ""P != NP Proof,"" in which the classes
+P and NP are generalized and claimed to be proven separate. We highlight
+inherent ambiguities in Barbosa's definitions, and show that attempts to
+resolve this ambiguity lead to flaws in the proof of his main result.
+",1,0,0,0,0,0
+19393,"Two-Armed Bandit Problem, Data Processing, and Parallel Version of the Mirror Descent Algorithm","  We consider the minimax setup for the two-armed bandit problem as applied to
+data processing if there are two alternative processing methods available with
+different a priori unknown efficiencies. One should determine the most
+effective method and provide its predominant application. To this end we use
+the mirror descent algorithm (MDA). It is well-known that corresponding minimax
+risk has the order $N^{1/2}$ with $N$ being the number of processed data. We
+improve significantly the theoretical estimate of the factor using Monte-Carlo
+simulations. Then we propose a parallel version of the MDA which allows
+processing of data by packets in a number of stages. The usage of parallel
+version of the MDA ensures that total time of data processing depends mostly on
+the number of packets but not on the total number of data. It is quite
+unexpectedly that the parallel version behaves unlike the ordinary one even if
+the number of packets is large. Moreover, the parallel version considerably
+improves control performance because it provides significantly smaller value of
+the minimax risk. We explain this result by considering another parallel
+modification of the MDA which behavior is close to behavior of the ordinary
+version. Our estimates are based on invariant descriptions of the algorithms.
+All estimates are obtained by Monte-Carlo simulations. It's worth noting that
+parallel version performs well only for methods with close efficiencies. If
+efficiencies differ significantly then one should use the combined algorithm
+which at initial sufficiently short control horizon uses ordinary version and
+then switches to the parallel version of the MDA.
+",0,0,1,1,0,0
+19394,Bearing fault diagnosis under varying working condition based on domain adaptation,"  Traditional intelligent fault diagnosis of rolling bearings work well only
+under a common assumption that the labeled training data (source domain) and
+unlabeled testing data (target domain) are drawn from the same distribution.
+When the distribution changes, most fault diagnosis models need to be rebuilt
+from scratch using newly recollected labeled training data. However, it is
+expensive or impossible to annotate huge amount of training data to rebuild
+such new model. Meanwhile, large amounts of labeled training data have not been
+fully utilized yet, which is apparently a waste of resources. As one of the
+important research directions of transfer learning, domain adaptation (DA)
+typically aims at minimizing the differences between distributions of different
+domains in order to minimize the cross-domain prediction error by taking full
+advantage of information coming from both source and target domains. In this
+paper, we present one of the first studies on unsupervised DA in the field of
+fault diagnosis of rolling bearings under varying working conditions and a
+novel diagnosis strategy based on unsupervised DA using subspace alignment (SA)
+is proposed. After processed by unsupervised DA with SA, the distributions of
+training data and testing data become close and the classifier trained on
+training data can be used to classify the testing data. Experimental results on
+the 60 domain adaptation diagnosis problems under varying working condition in
+Case Western Reserve benchmark data and 12 domain adaptation diagnosis problems
+under varying working conditions in our new data are given to demonstrate the
+effectiveness of the proposed method. The proposed methods can effectively
+distinguish not only bearing faults categories but also fault severities.
+",1,0,0,0,0,0
+19395,"Dialectical Rough Sets, Parthood and Figures of Opposition-1","  In one perspective, the main theme of this research revolves around the
+inverse problem in the context of general rough sets that concerns the
+existence of rough basis for given approximations in a context. Granular
+operator spaces and variants were recently introduced by the present author as
+an optimal framework for anti-chain based algebraic semantics of general rough
+sets and the inverse problem. In the framework, various sub-types of crisp and
+non-crisp objects are identifiable that may be missed in more restrictive
+formalism. This is also because in the latter cases concepts of complementation
+and negation are taken for granted - while in reality they have a complicated
+dialectical basis. This motivates a general approach to dialectical rough sets
+building on previous work of the present author and figures of opposition. In
+this paper dialectical rough logics are invented from a semantic perspective, a
+concept of dialectical predicates is formalised, connection with dialetheias
+and glutty negation are established, parthood analyzed and studied from the
+viewpoint of classical and dialectical figures of opposition by the present
+author. Her methods become more geometrical and encompass parthood as a primary
+relation (as opposed to roughly equivalent objects) for algebraic semantics.
+",1,0,1,0,0,0
+19396,On quasi-hereditary algebras,"  In this paper we introduce an easily verifiable sufficient condition to
+determine whether an algebra is quasi-hereditary. In the case of monomial
+algebras, we give conditions that are both necessary and sufficient to show
+whether an algebra is quasi-hereditary.
+",0,0,1,0,0,0
+19397,Clustering Analysis on Locally Asymptotically Self-similar Processes with Known Number of Clusters,"  We study the problems of clustering locally asymptotically self-similar
+stochastic processes, when the true number of clusters is priorly known. A new
+covariance-based dissimilarity measure is introduced, from which the so-called
+approximately asymptotically consistent clustering algorithms are obtained. In
+a simulation study, clustering data sampled from multifractional Brownian
+motions is performed to illustrate the approximated asymptotic consistency of
+the proposed algorithms.
+",0,0,0,1,0,0
+19398,The OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS): Spectral Maps of the Asteroid Bennu,"  The OSIRIS-REx Visible and Infrared Spectrometer (OVIRS) is a point
+spectrometer covering the spectral range of 0.4 to 4.3 microns (25,000-2300
+cm-1). Its primary purpose is to map the surface composition of the asteroid
+Bennu, the target asteroid of the OSIRIS-REx asteroid sample return mission.
+The information it returns will help guide the selection of the sample site. It
+will also provide global context for the sample and high spatial resolution
+spectra that can be related to spatially unresolved terrestrial observations of
+asteroids. It is a compact, low-mass (17.8 kg), power efficient (8.8 W
+average), and robust instrument with the sensitivity needed to detect a 5%
+spectral absorption feature on a very dark surface (3% reflectance) in the
+inner solar system (0.89-1.35 AU). It, in combination with the other
+instruments on the OSIRIS-REx Mission, will provide an unprecedented view of an
+asteroid's surface.
+",0,1,0,0,0,0
+19399,The effect of the smoothness of fractional type operators over their commutators with Lipschitz symbols on weighted spaces,"  We prove boundedness results for integral operators of fractional type and
+their higher order commutators between weighted spaces, including $L^p$-$L^q$,
+$L^p$-$BMO$ and $L^p$-Lipschitz estimates. The kernels of such operators
+satisfy certain size condition and a Lipschitz type regularity, and the symbol
+of the commutator belongs to a Lipschitz class. We also deal with commutators
+of fractional type operators with less regular kernels satisfying a
+Hörmander's type inequality. As far as we know, these last results are new
+even in the unweighted case. Moreover, we give a characterization result
+involving symbols of the commutators and continuity results for extreme values
+of $p$.
+",0,0,1,0,0,0
+19400,Fast mean-reversion asymptotics for large portfolios of stochastic volatility models,"  We consider a large portfolio limit where the asset prices evolve according
+certain stochastic volatility models with default upon hitting a lower barrier.
+When the asset prices and the volatilities are correlated via systemic Brownian
+Motions, that limit exist and it is described by a SPDE on the positive
+half-space with Dirichlet boundary conditions which has been studied in
+\cite{HK17}. We study the convergence of the total mass of a solution to this
+stochastic initial-boundary value problem when the mean-reversion coefficients
+of the volatilities are multiples of a parameter that tends to infinity. When
+the volatilities of the volatilities are multiples of the square root of the
+same parameter, the convergence is extremely weak. On the other hand, when the
+volatilities of the volatilities are independent of this exploding parameter,
+the volatilities converge to their means and we can have much better
+approximations. Our aim is to use such approximations to improve the accuracy
+of certain risk-management methods in markets where fast volatility
+mean-reversion is observed.
+",0,0,0,0,0,1
+19401,Poincaré profiles of groups and spaces,"  We introduce a spectrum of monotone coarse invariants for metric measure
+spaces called Poincaré profiles. The two extremes of this spectrum
+determine the growth of the space, and the separation profile as defined by
+Benjamini--Schramm--Timár. In this paper we focus on properties of the
+Poincaré profiles of groups with polynomial growth, and of hyperbolic
+spaces, where we deduce a striking connection between these profiles and
+conformal dimension. One application of our results is that there is a
+collection of hyperbolic Coxeter groups, indexed by a countable dense subset of
+$(1,\infty)$, such that $G_s$ does not coarsely embed into $G_t$ whenever
+$s<t$.
+",0,0,1,0,0,0
+19402,NMR Study of the New Magnetic Superconductor CaK(Fe$0.951Ni0.049)4As4: Microscopic Coexistence of Hedgehog Spin-vortex Crystal and Superconductivity,"  Coexistence of a new-type antiferromagnetic (AFM) state, the so-called
+hedgehog spin-vortex crystal (SVC), and superconductivity (SC) is evidenced by
+$^{75}$As nuclear magnetic resonance study on single-crystalline
+CaK(Fe$_{0.951}$Ni$_{0.049}$)$_4$As$_4$. The hedgehog SVC order is clearly
+demonstrated by the direct observation of the internal magnetic induction along
+the $c$ axis at the As1 site (close to K) and a zero net internal magnetic
+induction at the As2 site (close to Ca) below an AFM ordering temperature
+$T_{\rm N}$ $\sim$ 52 K. The nuclear spin-lattice relaxation rate 1/$T_1$ shows
+a distinct decrease below $T_{\rm c}$ $\sim$ 10 K, providing also unambiguous
+evidence for the microscopic coexistence. Furthermore, based on the analysis of
+the 1/$T_1$ data, the hedgehog SVC-type spin correlations are found to be
+enhanced below $T$ $\sim$ 150 K in the paramagnetic state. These results
+indicate the hedgehog SVC-type spin correlations play an important role for the
+appearance of SC in the new magnetic superconductor.
+",0,1,0,0,0,0
+19403,Light spanners for bounded treewidth graphs imply light spanners for $H$-minor-free graphs,"  Grigni and Hung~\cite{GH12} conjectured that H-minor-free graphs have
+$(1+\epsilon)$-spanners that are light, that is, of weight $g(|H|,\epsilon)$
+times the weight of the minimum spanning tree for some function $g$. This
+conjecture implies the {\em efficient} polynomial-time approximation scheme
+(PTAS) of the traveling salesperson problem in $H$-minor free graphs; that is,
+a PTAS whose running time is of the form $2^{f(\epsilon)}n^{O(1)}$ for some
+function $f$. The state of the art PTAS for TSP in H-minor-free-graphs has
+running time $n^{1/\epsilon^c}$. We take a further step toward proving this
+conjecture by showing that if the bounded treewidth graphs have light greedy
+spanners, then the conjecture is true. We also prove that the greedy spanner of
+a bounded pathwidth graph is light and discuss the possibility of extending our
+proof to bounded treewidth graphs.
+",1,0,0,0,0,0
+19404,Modeling Daily Seasonality of Mexico City Ozone using Nonseparable Covariance Models on Circles Cross Time,"  Mexico City tracks ground-level ozone levels to assess compliance with
+national ambient air quality standards and to prevent environmental health
+emergencies. Ozone levels show distinct daily patterns, within the city, and
+over the course of the year. To model these data, we use covariance models over
+space, circular time, and linear time. We review existing models and develop
+new classes of nonseparable covariance models of this type, models appropriate
+for quasi-periodic data collected at many locations. With these covariance
+models, we use nearest-neighbor Gaussian processes to predict hourly ozone
+levels at unobserved locations in April and May, the peak ozone season, to
+infer compliance to Mexican air quality standards and to estimate respiratory
+health risk associated with ozone. Predicted compliance with air quality
+standards and estimated respiratory health risk vary greatly over space and
+time. In some regions, we predict exceedance of national standards for more
+than a third of the hours in April and May. On many days, we predict that
+nearly all of Mexico City exceeds nationally legislated ozone thresholds at
+least once. In peak regions, we estimate respiratory risk for ozone to be 55%
+higher on average than the annual average risk and as much at 170% higher on
+some days.
+",0,0,0,1,0,0
+19405,Huygens-Fresnel Picture for Electron-Molecule Elastic Scattering,"  The elastic scattering cross sections for a slow electron by C2 and H2
+molecules have been calculated within the framework of the non-overlapping
+atomic potential model. For the amplitudes of the multiple electron scattering
+by a target the wave function of the molecular continuum is represented as a
+combination of a plane wave and two spherical waves generated by the centers of
+atomic spheres. This wave function obeys the Huygens-Fresnel principle
+according to which the electron wave scattering by a system of two centers is
+accompanied by generation of two spherical waves; their interaction creates a
+diffraction pattern far from the target. Each of the Huygens waves, in turn, is
+a superposition of the partial spherical waves with different orbital angular
+momenta l and their projections m. The amplitudes of these partial waves are
+defined by the corresponding phases of electron elastic scattering by an
+isolated atomic potential. In numerical calculations the s- and p-phase shifts
+are taken into account. So the number of interfering electron waves is equal to
+eight: two of which are the s-type waves and the remaining six waves are of the
+p-type with different m values. The calculation of the scattering amplitudes in
+closed form (rather than in the form of S-matrix expansion) is reduced to
+solving a system of eight inhomogeneous algebraic equations. The differential
+and total cross sections of electron scattering by fixed-in-space molecules and
+randomly oriented ones have been calculated as well. We conclude by discussing
+the special features of the S-matrix method for the case of arbitrary
+non-spherical potentials.
+",0,1,0,0,0,0
+19406,When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity,"  Predicting fine-grained interests of users with temporal behavior is
+important to personalization and information filtering applications. However,
+existing interest prediction methods are incapable of capturing the subtle
+degreed user interests towards particular items, and the internal time-varying
+drifting attention of individuals is not studied yet. Moreover, the prediction
+process can also be affected by inter-personal influence, known as behavioral
+mutual infectivity. Inspired by point process in modeling temporal point
+process, in this paper we present a deep prediction method based on two
+recurrent neural networks (RNNs) to jointly model each user's continuous
+browsing history and asynchronous event sequences in the context of inter-user
+behavioral mutual infectivity. Our model is able to predict the fine-grained
+interest from a user regarding a particular item and corresponding timestamps
+when an occurrence of event takes place. The proposed approach is more flexible
+to capture the dynamic characteristic of event sequences by using the temporal
+point process to model event data and timely update its intensity function by
+RNNs. Furthermore, to improve the interpretability of the model, the attention
+mechanism is introduced to emphasize both intra-personal and inter-personal
+behavior influence over time. Experiments on real datasets demonstrate that our
+model outperforms the state-of-the-art methods in fine-grained user interest
+prediction.
+",1,0,0,1,0,0
+19407,Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer,"  Quantitative extraction of high-dimensional mineable data from medical images
+is a process known as radiomics. Radiomics is foreseen as an essential
+prognostic tool for cancer risk assessment and the quantification of
+intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying
+tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET
+and CT images of 300 patients from four different cohorts were analyzed for the
+risk assessment of locoregional recurrences (LR) and distant metastases (DM) in
+head-and-neck cancer. Prediction models combining radiomic and clinical
+variables were constructed via random forests and imbalance-adjustment
+strategies using two of the four cohorts. Independent validation of the
+prediction and prognostic performance of the models was carried out on the
+other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).
+Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the
+potential of radiomics for assessing the risk of specific tumour outcomes using
+multiple stratification groups. This could have important clinical impact,
+notably by allowing for a better personalization of chemo-radiation treatments
+for head-and-neck cancer patients from different risk groups.
+",1,0,0,0,0,0
+19408,Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data,"  Recently, two new indicators (Equalized Mean-based Normalized Proportion
+Cited, EMNPC; Mean-based Normalized Proportion Cited, MNPC) were proposed which
+are intended for sparse scientometrics data. The indicators compare the
+proportion of mentioned papers (e.g. on Facebook) of a unit (e.g., a researcher
+or institution) with the proportion of mentioned papers in the corresponding
+fields and publication years (the expected values). In this study, we propose a
+third indicator (Mantel-Haenszel quotient, MHq) belonging to the same indicator
+family. The MHq is based on the MH analysis - an established method in
+statistics for the comparison of proportions. We test (using citations and
+assessments by peers, i.e. F1000Prime recommendations) if the three indicators
+can distinguish between different quality levels as defined on the basis of the
+assessments by peers. Thus, we test their convergent validity. We find that the
+indicator MHq is able to distinguish between the quality levels in most cases
+while MNPC and EMNPC are not. Since the MHq is shown in this study to be a
+valid indicator, we apply it to six types of zero-inflated altmetrics data and
+test whether different altmetrics sources are related to quality. The results
+for the various altmetrics demonstrate that the relationship between altmetrics
+(Wikipedia, Facebook, blogs, and news data) and assessments by peers is not as
+strong as the relationship between citations and assessments by peers.
+Actually, the relationship between citations and peer assessments is about two
+to three times stronger than the association between altmetrics and assessments
+by peers.
+",1,0,0,0,0,0
+19409,Gravity Formality,"  We show that Willwacher's cyclic formality theorem can be extended to
+preserve natural Gravity operations on cyclic multivector fields and cyclic
+multidifferential operators. We express this in terms of a homotopy Gravity
+quasi-isomorphism with explicit local formulas. For this, we develop operadic
+tools related to mixed complexes and cyclic homology and prove that the operad
+$\mathsf M_\circlearrowleft$ of natural operations on cyclic operators is
+formal and hence quasi-isomorphic to the Gravity operad.
+",0,0,1,0,0,0
+19410,On the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems Using Multivariate Statistical Process Control,"  Process Control Systems (PCSs) are the operating core of Critical
+Infrastructures (CIs). As such, anomaly detection has been an active research
+field to ensure CI normal operation. Previous approaches have leveraged network
+level data for anomaly detection, or have disregarded the existence of process
+disturbances, thus opening the possibility of mislabelling disturbances as
+attacks and vice versa. In this paper we present an anomaly detection and
+diagnostic system based on Multivariate Statistical Process Control (MSPC),
+that aims to distinguish between attacks and disturbances. For this end, we
+expand traditional MSPC to monitor process level and controller level data. We
+evaluate our approach using the Tennessee-Eastman process. Results show that
+our approach can be used to distinguish disturbances from intrusions to a
+certain extent and we conclude that the proposed approach can be extended with
+other sources of data for improving results.
+",1,0,0,0,0,0
+19411,Quantum hydrodynamic approximations to the finite temperature trapped Bose gases,"  For the quantum kinetic system modelling the Bose-Einstein Condensate that
+accounts for interactions between condensate and excited atoms, we use the
+Chapman-Enskog expansion to derive its hydrodynamic approximations, include
+both Euler and Navier-Stokes approximations. The hydrodynamic approximations
+describe not only the macroscopic behavior of the BEC but also its coupling
+with the non-condensates, which agrees with Landau's two fluid theory.
+",0,1,0,0,0,0
+19412,Game-theoretic dynamic investment model with incomplete information: futures contracts,"  Over the past few years, the futures market has been successfully developing
+in the North-West region. Futures markets are one of the most effective and
+liquid-visible trading mechanisms. A large number of buyers are forced to
+compete with each other and raise their prices. A large number of sellers make
+them reduce prices. Thus, the gap between the prices of offers of buyers and
+sellers is reduced due to high competition, and this is a good criterion for
+the liquidity of the market. This high degree of liquidity contributed to the
+fact that futures trading took such an important role in commerce and finance.
+A multi-step, non-cooperative n persons game is formalized and studied
+",0,0,0,0,0,1
+19413,Deep Collaborative Learning for Visual Recognition,"  Deep neural networks are playing an important role in state-of-the-art visual
+recognition. To represent high-level visual concepts, modern networks are
+equipped with large convolutional layers, which use a large number of filters
+and contribute significantly to model complexity. For example, more than half
+of the weights of AlexNet are stored in the first fully-connected layer (4,096
+filters).
+We formulate the function of a convolutional layer as learning a large visual
+vocabulary, and propose an alternative way, namely Deep Collaborative Learning
+(DCL), to reduce the computational complexity. We replace a convolutional layer
+with a two-stage DCL module, in which we first construct a couple of smaller
+convolutional layers individually, and then fuse them at each spatial position
+to consider feature co-occurrence. In mathematics, DCL can be explained as an
+efficient way of learning compositional visual concepts, in which the
+vocabulary size increases exponentially while the model complexity only
+increases linearly. We evaluate DCL on a wide range of visual recognition
+tasks, including a series of multi-digit number classification datasets, and
+some generic image classification datasets such as SVHN, CIFAR and ILSVRC2012.
+We apply DCL to several state-of-the-art network structures, improving the
+recognition accuracy meanwhile reducing the number of parameters (16.82% fewer
+in AlexNet).
+",1,0,0,0,0,0
+19414,Co-location Epidemic Tracking on London Public Transports Using Low Power Mobile Magnetometer,"  The public transports provide an ideal means to enable contagious diseases
+transmission. This paper introduces a novel idea to detect co-location of
+people in such environment using just the ubiquitous geomagnetic field sensor
+on the smart phone. Essentially, given that all passengers must share the same
+journey between at least two consecutive stations, we have a long window to
+match the user trajectory. Our idea was assessed over a painstakingly survey of
+over 150 kilometres of travelling distance, covering different parts of London,
+using the overground trains, the underground tubes and the buses.
+",1,0,0,0,0,0
+19415,"Waring-Goldbach Problem: One Square, Four Cubes and Higher Powers","  Let $\mathcal{P}_r$ denote an almost-prime with at most $r$ prime factors,
+counted according to multiplicity. In this paper, it is proved that, for
+$12\leqslant b\leqslant 35$ and for every sufficiently large odd integer $N$,
+the equation \begin{equation*}
+N=x^2+p_1^3+p_2^3+p_3^3+p_4^3+p_5^4+p_6^b \end{equation*} is solvable with
+$x$ being an almost-prime $\mathcal{P}_{r(b)}$ and the other variables primes,
+where $r(b)$ is defined in the Theorem. This result constitutes an improvement
+upon that of Lü and Mu.
+",0,0,1,0,0,0
+19416,Realizability of tropical canonical divisors,"  We use recent results by Bainbridge-Chen-Gendron-Grushevsky-Moeller on
+compactifications of strata of abelian differentials to give a comprehensive
+solution to the realizability problem for effective tropical canonical divisors
+in equicharacteristic zero. Given a pair $(\Gamma, D)$ consisting of a stable
+tropical curve $\Gamma$ and a divisor $D$ in the canonical linear system on
+$\Gamma$, we give a purely combinatorial condition to decide whether there is a
+smooth curve $X$ over a non-Archimedean field whose stable reduction has
+$\Gamma$ as its dual tropical curve together with a effective canonical divisor
+$K_X$ that specializes to $D$. Along the way, we develop a moduli-theoretic
+framework to understand Baker's specialization of divisors from algebraic to
+tropical curves as a natural toroidal tropicalization map in the sense of
+Abramovich-Caporaso-Payne.
+",0,0,1,0,0,0
+19417,Causal inference for interfering units with cluster and population level treatment allocation programs,"  Interference arises when an individual's potential outcome depends on the
+individual treatment level, but also on the treatment level of others. A common
+assumption in the causal inference literature in the presence of interference
+is partial interference, implying that the population can be partitioned in
+clusters of individuals whose potential outcomes only depend on the treatment
+of units within the same cluster. Previous literature has defined average
+potential outcomes under counterfactual scenarios where treatments are randomly
+allocated to units within a cluster. However, within clusters there may be
+units that are more or less likely to receive treatment based on covariates or
+neighbors' treatment. We define new estimands that describe average potential
+outcomes for realistic counterfactual treatment allocation programs, extending
+existing estimands to take into consideration the units' covariates and
+dependence between units' treatment assignment. We further propose entirely new
+estimands for population-level interventions over the collection of clusters,
+which correspond in the motivating setting to regulations at the federal (vs.
+cluster or regional) level. We discuss these estimands, propose unbiased
+estimators and derive asymptotic results as the number of clusters grows.
+Finally, we estimate effects in a comparative effectiveness study of power
+plant emission reduction technologies on ambient ozone pollution.
+",0,0,1,1,0,0
+19418,"A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation","  We present a unifying framework to solve several computer vision problems
+with event cameras: motion, depth and optical flow estimation. The main idea of
+our framework is to find the point trajectories on the image plane that are
+best aligned with the event data by maximizing an objective function: the
+contrast of an image of warped events. Our method implicitly handles data
+association between the events, and therefore, does not rely on additional
+appearance information about the scene. In addition to accurately recovering
+the motion parameters of the problem, our framework produces motion-corrected
+edge-like images with high dynamic range that can be used for further scene
+analysis. The proposed method is not only simple, but more importantly, it is,
+to the best of our knowledge, the first method that can be successfully applied
+to such a diverse set of important vision tasks with event cameras.
+",1,0,0,0,0,0
+19419,TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing,"  Machine learning models are notoriously difficult to interpret and debug.
+This is particularly true of neural networks. In this work, we introduce
+automated software testing techniques for neural networks that are well-suited
+to discovering errors which occur only for rare inputs. Specifically, we
+develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF,
+random mutations of inputs to a neural network are guided by a coverage metric
+toward the goal of satisfying user-specified constraints. We describe how fast
+approximate nearest neighbor algorithms can provide this coverage metric. We
+then discuss the application of CGF to the following goals: finding numerical
+errors in trained neural networks, generating disagreements between neural
+networks and quantized versions of those networks, and surfacing undesirable
+behavior in character level language models. Finally, we release an open source
+library called TensorFuzz that implements the described techniques.
+",0,0,0,1,0,0
+19420,Inferring directed climatic interactions with renormalized partial directed coherence and directed partial correlation,"  Inferring interactions between processes promises deeper insight into
+mechanisms underlying network phenomena. Renormalised partial directed
+coherence (rPDC) is a frequency-domain representation of the concept of Granger
+causality while directed partial correlation (DPC) is an alternative approach
+for quantifying Granger causality in the time domain. Both methodologies have
+been successfully applied to neurophysiological signals for detecting directed
+relationships. This paper introduces their application to climatological time
+series. We first discuss the application to ENSO -- Monsoon interaction, and
+then apply the methodologies to the more challenging air-sea interaction in the
+South Atlantic Convergence Zone (SACZ). While in the first case the results
+obtained are fully consistent with present knowledge in climate modeling, in
+the second case the results are, as expected, less clear, and to fully
+elucidate the SACZ air-sea interaction, further investigations on the
+specificity and sensitivity of these methodologies are needed.
+",0,1,0,0,0,0
+19421,Conditions for the invertibility of dual energy data,"  The Alvarez-Macovski method [Alvarez, R. E and Macovski, A.,
+""Energy-selective reconstructions in X-ray computerized tomography"", Phys. Med.
+Biol. (1976), 733--44] requires the inversion of the transformation from the
+line integrals of the basis set coefficients to measurements with multiple
+x-ray spectra. Analytical formulas for invertibility of the transformation from
+two measurements to two line integrals are derived. It is found that
+non-invertible systems have near zero Jacobian determinants on a nearly
+straight line in the line integrals plane. Formulas are derived for the points
+where the line crosses the axes, thus determining the line. Additional formulas
+are derived for the values of the terms of the Jacobian determinant at the
+endpoints of the line of non-invertibility. The formulas are applied to a set
+of spectra including one suggested by Levine that is not invertible as well as
+similar spectra that are invertible and voltage switched x-ray tube spectra
+that are also invertible. An iterative inverse transformation algorithm
+exhibits large errors with non-invertible spectra.
+",0,1,0,0,0,0
+19422,"Examples of lattice-polarized K3 surfaces with automorphic discriminant, and Lorentzian Kac--Moody algebras","  Using our results about Lorentzian Kac--Moody algebras and arithmetic mirror
+symmetry, we give six series of examples of lattice-polarized K3 surfaces with
+automorphic discriminant.
+",0,0,1,0,0,0
+19423,Quantum ferrofluid turbulence,"  We study the elementary characteristics of turbulence in a quantum ferrofluid
+through the context of a dipolar Bose gas condensing from a highly
+non-equilibrium thermal state. Our simulations reveal that the dipolar
+interactions drive the emergence of polarized turbulence and density
+corrugations. The superfluid vortex lines and density fluctuations adopt a
+columnar or stratified configuration, depending on the sign of the dipolar
+interactions, with the vortices tending to form in the low density regions to
+minimize kinetic energy. When the interactions are dominantly dipolar, the
+decay of vortex line length is enhanced, closely following a $t^{-3/2}$
+behaviour. This system poses exciting prospects for realizing stratified
+quantum turbulence and new levels of generating and controlling turbulence
+using magnetic fields.
+",0,1,0,0,0,0
+19424,Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book,"  An extensive empirical literature documents a generally negative correlation,
+named the ""leverage effect,"" between asset returns and changes of volatility.
+It is more challenging to establish such a return-volatility relationship for
+jumps in high-frequency data. We propose new nonparametric methods to assess
+and test for a discontinuous leverage effect --- i.e. a relation between
+contemporaneous jumps in prices and volatility. The methods are robust to
+market microstructure noise and build on a newly developed price-jump
+localization and estimation procedure. Our empirical investigation of six years
+of transaction data from 320 NASDAQ firms displays no unconditional negative
+correlation between price and volatility cojumps. We show, however, that there
+is a strong relation between price-volatility cojumps if one conditions on the
+sign of price jumps and whether the price jumps are market-wide or
+idiosyncratic. Firms' volatility levels strongly explain the cross-section of
+discontinuous leverage while debt-to-equity ratios have no significant
+explanatory power.
+",0,0,1,1,0,0
+19425,Group representations that resist worst-case sampling,"  Motivated by expansion in Cayley graphs, we show that there exist infinitely
+many groups $G$ with a nontrivial irreducible unitary representation whose
+average over every set of $o(\log\log|G|)$ elements of $G$ has operator norm $1
+- o(1)$. This answers a question of Lovett, Moore, and Russell, and strengthens
+their negative answer to a question of Wigderson.
+The construction is the affine group of $\mathbb{F}_p$ and uses the fact that
+for every $A \subset \mathbb{F}_p\setminus\{0\}$, there is a set of size
+$\exp(\exp(O(|A|)))$ that is almost invariant under both additive and
+multiplicatpive translations by elements of $A$.
+",0,0,1,0,0,0
+19426,On the nearly smooth complex spaces,"  We introduce a class of normal complex spaces having only mild sin-gularities
+(close to quotient singularities) for which we generalize the notion of a
+(analytic) fundamental class for an analytic cycle and also the notion of a
+relative fundamental class for an analytic family of cycles. We also generalize
+to these spaces the geometric intersection theory for analytic cycles with
+rational positive coefficients and show that it behaves well with respect to
+analytic families of cycles. We prove that this intersection theory has most of
+the usual properties of the standard geometric intersection theory on complex
+manifolds, but with the exception that the intersection cycle of two cycles
+with positive integral coefficients that intersect properly may have rational
+coefficients. AMS classification. 32 C 20-32 C 25-32 C 36.
+",0,0,1,0,0,0
+19427,Semi-supervised learning of hierarchical representations of molecules using neural message passing,"  With the rapid increase of compound databases available in medicinal and
+material science, there is a growing need for learning representations of
+molecules in a semi-supervised manner. In this paper, we propose an
+unsupervised hierarchical feature extraction algorithm for molecules (or more
+generally, graph-structured objects with fixed number of types of nodes and
+edges), which is applicable to both unsupervised and semi-supervised tasks. Our
+method extends recently proposed Paragraph Vector algorithm and incorporates
+neural message passing to obtain hierarchical representations of subgraphs. We
+applied our method to an unsupervised task and demonstrated that it outperforms
+existing proposed methods in several benchmark datasets. We also experimentally
+showed that semi-supervised tasks enhanced predictive performance compared with
+supervised ones with labeled molecules only.
+",1,0,0,1,0,0
+19428,Tracy-Widom at each edge of real covariance and MANOVA estimators,"  We study the sample covariance matrix for real-valued data with general
+population covariance, as well as MANOVA-type covariance estimators in variance
+components models under null hypotheses of global sphericity. In the limit as
+matrix dimensions increase proportionally, the asymptotic spectra of such
+estimators may have multiple disjoint intervals of support, possibly
+intersecting the negative half line. We show that the distribution of the
+extremal eigenvalue at each regular edge of the support has a GOE Tracy-Widom
+limit. Our proof extends a comparison argument of Ji Oon Lee and Kevin
+Schnelli, replacing a continuous Green function flow by a discrete Lindeberg
+swapping scheme.
+",0,0,1,1,0,0
+19429,Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images,"  Lung nodule classification is a class imbalanced problem because nodules are
+found with much lower frequency than non-nodules. In the class imbalanced
+problem, conventional classifiers tend to be overwhelmed by the majority class
+and ignore the minority class. We therefore propose cascaded convolutional
+neural networks to cope with the class imbalanced problem. In the proposed
+approach, multi-stage convolutional neural networks that perform as
+single-sided classifiers filter out obvious non-nodules. Successively, a
+convolutional neural network trained with a balanced data set calculates nodule
+probabilities. The proposed method achieved the sensitivity of 92.4\% and 94.5%
+at 4 and 8 false positives per scan in Free Receiver Operating Characteristics
+(FROC) curve analysis, respectively.
+",1,0,0,0,0,0
+19430,Monochromatic knots and other unusual electromagnetic disturbances: light localised in 3D,"  We introduce and examine a collection of unusual electromagnetic
+disturbances. Each of these is an exact, monochromatic solution of Maxwell's
+equations in free space with looped electric and magnetic field lines of finite
+extent and a localised appearance in all three spatial dimensions. Included are
+the first explicit examples of monochromatic electromagnetic knots. We also
+consider the generation of our unusual electromagnetic disturbances in the
+laboratory, at both low and high frequencies, and highlight possible directions
+for future research, including the use of unusual electromagnetic disturbances
+as the basis of a new form of three-dimensional display.
+",0,1,0,0,0,0
+19431,Zero-cycles of degree one on Skorobogatov's bielliptic surface,"  Skorobogatov constructed a bielliptic surface which is a counterexample to
+the Hasse principle not explained by the Brauer-Manin obstruction. We show that
+this surface has a $0$-cycle of degree 1, as predicted by a conjecture of
+Colliot-Thélène.
+",0,0,1,0,0,0
+19432,Locally Nameless Permutation Types,"  We define ""Locally Nameless Permutation Types"", which fuse permutation types
+as used in Nominal Isabelle with the locally nameless representation. We show
+that this combination is particularly useful when formalizing programming
+languages where bound names may become free during execution (""extrusion""),
+common in process calculi. It inherits the generic definition of permutations
+and support, and associated lemmas, from the Nominal approach, and the ability
+to stay close to pencil-and-paper proofs from the locally nameless approach. We
+explain how to use cofinite quantification in this setting, show why reasoning
+about renaming is more important here than in languages without extrusion, and
+provide results about infinite support, necessary when reasoning about
+countable choice.
+",1,0,0,0,0,0
+19433,On dimension-free variational inequalities for averaging operators in $\mathbb R^d$,"  We study dimension-free $L^p$ inequalities for $r$-variations of the
+Hardy--Littlewood averaging operators defined over symmetric convex bodies in
+$\mathbb R^d$.
+",0,0,1,0,0,0
+19434,One-particle density matrix of trapped one-dimensional impenetrable bosons from conformal invariance,"  The one-particle density matrix of the one-dimensional Tonks-Girardeau gas
+with inhomogeneous density profile is calculated, thanks to a recent
+observation that relates this system to a two-dimensional conformal field
+theory in curved space. The result is asymptotically exact in the limit of
+large particle density and small density variation, and holds for arbitrary
+trapping potentials. In the particular case of a harmonic trap, we recover a
+formula obtained by Forrester et al. [Phys. Rev. A 67, 043607 (2003)] from a
+different method.
+",0,1,1,0,0,0
+19435,Image Analysis Using a Dual-Tree $M$-Band Wavelet Transform,"  We propose a 2D generalization to the $M$-band case of the dual-tree
+decomposition structure (initially proposed by N. Kingsbury and further
+investigated by I. Selesnick) based on a Hilbert pair of wavelets. We
+particularly address (\textit{i}) the construction of the dual basis and
+(\textit{ii}) the resulting directional analysis. We also revisit the necessary
+pre-processing stage in the $M$-band case. While several reconstructions are
+possible because of the redundancy of the representation, we propose a new
+optimal signal reconstruction technique, which minimizes potential estimation
+errors. The effectiveness of the proposed $M$-band decomposition is
+demonstrated via denoising comparisons on several image types (natural,
+texture, seismics), with various $M$-band wavelets and thresholding strategies.
+Significant improvements in terms of both overall noise reduction and direction
+preservation are observed.
+",1,1,1,0,0,0
+19436,Nonlinear Cauchy-Riemann Equations and Liouville Equation For Conformal Metrics,"  We introduce the Nonlinear Cauchy-Riemann equations as Bäcklund
+transformations for several nonlinear and linear partial differential
+equations. From these equations we treat in details the Laplace and the
+Liouville equations by deriving general solution for the nonlinear Liouville
+equation. By Möbius transformation we relate solutions for the Poincare
+model of hyperbolic geometry, the Klein model in half-plane and the
+pseudo-sphere. Conformal form of the constant curvature metrics in these
+geometries, stereographic projections and special solutions are discussed. Then
+we introduce the hyperbolic analog of the Riemann sphere, which we call the
+Riemann pseudosphere. We identify point at infinity on this pseudosphere and
+show that it can be used in complex analysis as an alternative to usual Riemann
+sphere to extend the complex plane. Interpretation of symmetric and antipodal
+points on both, the Riemann sphere and the Riemann pseudo-sphere, are given. By
+Möbius transformation and homogenous coordinates, the most general solution
+of Liouville equation as discussed by Crowdy is derived.
+",0,1,1,0,0,0
+19437,SmartPaste: Learning to Adapt Source Code,"  Deep Neural Networks have been shown to succeed at a range of natural
+language tasks such as machine translation and text summarization. While tasks
+on source code (ie, formal languages) have been considered recently, most work
+in this area does not attempt to capitalize on the unique opportunities offered
+by its known syntax and structure. In this work, we introduce SmartPaste, a
+first task that requires to use such information. The task is a variant of the
+program repair problem that requires to adapt a given (pasted) snippet of code
+to surrounding, existing source code. As first solutions, we design a set of
+deep neural models that learn to represent the context of each variable
+location and variable usage in a data flow-sensitive way. Our evaluation
+suggests that our models can learn to solve the SmartPaste task in many cases,
+achieving 58.6% accuracy, while learning meaningful representation of variable
+usages.
+",1,0,0,0,0,0
+19438,An Outcome Model Approach to Translating a Randomized Controlled Trial Results to a Target Population,"  Participants enrolled into randomized controlled trials (RCTs) often do not
+reflect real-world populations. Previous research in how best to translate RCT
+results to target populations has focused on weighting RCT data to look like
+the target data. Simulation work, however, has suggested that an outcome model
+approach may be preferable. Here we describe such an approach using source data
+from the 2x2 factorial NAVIGATOR trial which evaluated the impact of valsartan
+and nateglinide on cardiovascular outcomes and new-onset diabetes in a
+pre-diabetic population. Our target data consisted of people with pre-diabetes
+serviced at our institution. We used Random Survival Forests to develop
+separate outcome models for each of the 4 treatments, estimating the 5-year
+risk difference for progression to diabetes and estimated the treatment effect
+in our local patient populations, as well as sub-populations, and the results
+compared to the traditional weighting approach. Our models suggested that the
+treatment effect for valsartan in our patient population was the same as in the
+trial, whereas for nateglinide treatment effect was stronger than observed in
+the original trial. Our effect estimates were more efficient than the weighting
+approach.
+",0,0,0,1,0,0
+19439,Influence of parameterized small-scale gravity waves on the migrating diurnal tide in Earth's thermosphere,"  Effects of subgrid-scale gravity waves (GWs) on the diurnal migrating tides
+are investigated from the mesosphere to the upper thermosphere for September
+equinox conditions, using a general circulation model coupled with the extended
+spectral nonlinear GW parameterization of Yiğit et al (2008). Simulations
+with GW effects cut-off above the turbopause and included in the entire
+thermosphere have been conducted. GWs appreciably impact the mean circulation
+and cool the thermosphere down by up to 12-18%. GWs significantly affect the
+winds modulated by the diurnal migrating tide, in particular in the
+low-latitude mesosphere and lower thermosphere and in the high-latitude
+thermosphere. These effects depend on the mutual correlation of the diurnal
+phases of the GW forcing and tides: GWs can either enhance or reduce the tidal
+amplitude. In the low-latitude MLT, the correlation between the direction of
+the deposited GW momentum and the tidal phase is positive due to propagation of
+a broad spectrum of GW harmonics through the alternating winds. In the Northern
+Hemisphere high-latitude thermosphere, GWs act against the tide due to an
+anti-correlation of tidal wind and GW momentum, while in the Southern
+high-latitudes they weakly enhance the tidal amplitude via a combination of a
+partial correlation of phases and GW-induced changes of the circulation. The
+variable nature of GW effects on the thermal tide can be captured in GCMs
+provided that a GW parameterization (1) considers a broad spectrum of
+harmonics, (2) properly describes their propagation, and (3) correctly accounts
+for the physics of wave breaking/saturation.
+",0,1,0,0,0,0
+19440,A Statistical Model for Ideal Team Selection for A National Cricket Squad,"  Cricket is a game played between two teams which consists of eleven players
+each. Nowadays cricket game is becoming more and more popular in Bangladesh and
+other South Asian Countries. Before a match people are very enthusiastic about
+team squads and ""Which players are playing today?"", ""How well will MR. X
+perform today?"" are the million dollar questions before a big match. This
+article will propose a method using statistical data analysis for recommending
+a national team squad. Recent match scorecards for domestic and international
+matches played by a specific team in recent years are used to recommend the
+ideal squad. Impact point or rating points of all players in different
+conditions are calculated and the best ones from different categories are
+chosen to form optimal line-ups. To evaluate the efficiency of impact point
+system, it will be tested with real time match data to see how much accuracy it
+gives.
+",0,0,0,1,0,0
+19441,The geometry of some generalized affine Springer fibers,"  We study basic geometric properties of some group analogue of affine Springer
+fibers and compare with the classical Lie algebra affine Springer fibers. The
+main purpose is to formulate a conjecture that relates the number of
+irreducible components of such varieties for a reductive group $G$ to certain
+weight multiplicities defined by the Langlands dual group $\hat{G}$. We prove
+our conjecture in the case of unramified conjugacy class.
+",0,0,1,0,0,0
+19442,Modelling of a Permanent Magnet Synchronous Machine Using Isogeometric Analysis,"  Isogeometric analysis (IGA) is used to simulate a permanent magnet
+synchronous machine. IGA uses non-uniform rational B-splines to parametrise the
+domain and to approximate the solution space, thus allowing for the exact
+description of the geometries even on the coarsest level of mesh refinement.
+Given the properties of the isogeometric basis functions, this choice
+guarantees a higher accuracy than the classical finite element method.
+For dealing with the different stator and rotor topologies, the domain is
+split into two non-overlapping parts on which Maxwell's equations are solved
+independently in the context of a classical Dirichlet-to-Neumann domain
+decomposition scheme. The results show good agreement with the ones obtained by
+the classical finite element approach.
+",1,0,0,0,0,0
+19443,Learning and Trust in Auction Markets,"  In this paper, we study behavior of bidders in an experimental launch of a
+new advertising auction platform by Zillow, as Zillow switched from negotiated
+contracts to using auctions in several geographically isolated markets. A
+unique feature of this experiment is that the bidders in this market are real
+estate agents that bid on their own behalf, not using third-party
+intermediaries. To help bidders, Zillow also provided a recommendation tool
+that suggested a bid for each bidder.
+Our main focus in this paper is on the decisions of bidders whether or not to
+adopt the platform-provided bid recommendation. We observe that a significant
+proportion of bidders do not use the recommended bid. Using the bid history of
+the agents we infer their value, and compare the agents' regret with their
+actual bidding history with results they would have obtained following the
+recommendation. We find that for half of the agents not following the
+recommendation, the increased effort of experimenting with alternate bids
+results in increased regret, i.e., they get decreased net value out of the
+system. The proportion of agents not following the recommendation slowly
+declines as markets mature, but it remains large in most markets that we
+observe. We argue that the main reason for this phenomenon is the lack of trust
+in the platform-provided tool.
+Our work provides an empirical insight into possible design choices for
+auction-based online advertising platforms. While search advertising platforms
+(such as Google or Bing) allow bidders to submit bids on their own, many
+display advertising platforms (such as Facebook) optimize bids on bidders'
+behalf and eliminate the need for bids. Our empirical analysis shows that the
+latter approach is preferred for markets where bidders are individuals, who
+don't have access to third party tools, and who may question the fairness of
+platform-provided suggestions.
+",1,0,0,0,0,0
+19444,"Like trainer, like bot? Inheritance of bias in algorithmic content moderation","  The internet has become a central medium through which `networked publics'
+express their opinions and engage in debate. Offensive comments and personal
+attacks can inhibit participation in these spaces. Automated content moderation
+aims to overcome this problem using machine learning classifiers trained on
+large corpora of texts manually annotated for offence. While such systems could
+help encourage more civil debate, they must navigate inherently normatively
+contestable boundaries, and are subject to the idiosyncratic norms of the human
+raters who provide the training data. An important objective for platforms
+implementing such measures might be to ensure that they are not unduly biased
+towards or against particular norms of offence. This paper provides some
+exploratory methods by which the normative biases of algorithmic content
+moderation systems can be measured, by way of a case study using an existing
+dataset of comments labelled for offence. We train classifiers on comments
+labelled by different demographic subsets (men and women) to understand how
+differences in conceptions of offence between these groups might affect the
+performance of the resulting models on various test sets. We conclude by
+discussing some of the ethical choices facing the implementers of algorithmic
+moderation systems, given various desired levels of diversity of viewpoints
+amongst discussion participants.
+",1,0,0,0,0,0
+19445,Holographic Neural Architectures,"  Representation learning is at the heart of what makes deep learning
+effective. In this work, we introduce a new framework for representation
+learning that we call ""Holographic Neural Architectures"" (HNAs). In the same
+way that an observer can experience the 3D structure of a holographed object by
+looking at its hologram from several angles, HNAs derive Holographic
+Representations from the training set. These representations can then be
+explored by moving along a continuous bounded single dimension. We show that
+HNAs can be used to make generative networks, state-of-the-art regression
+models and that they are inherently highly resistant to noise. Finally, we
+argue that because of their denoising abilities and their capacity to
+generalize well from very few examples, models based upon HNAs are particularly
+well suited for biological applications where training examples are rare or
+noisy.
+",0,0,0,1,1,0
+19446,An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists,"  Topological Data Analysis (tda) is a recent and fast growing eld providing a
+set of new topological and geometric tools to infer relevant features for
+possibly complex data. This paper is a brief introduction, through a few
+selected topics, to basic fundamental and practical aspects of tda for non
+experts. 1 Introduction and motivation Topological Data Analysis (tda) is a
+recent eld that emerged from various works in applied (algebraic) topology and
+computational geometry during the rst decade of the century. Although one can
+trace back geometric approaches for data analysis quite far in the past, tda
+really started as a eld with the pioneering works of Edelsbrunner et al. (2002)
+and Zomorodian and Carlsson (2005) in persistent homology and was popularized
+in a landmark paper in 2009 Carlsson (2009). tda is mainly motivated by the
+idea that topology and geometry provide a powerful approach to infer robust
+qualitative, and sometimes quantitative, information about the structure of
+data-see, e.g. Chazal (2017). tda aims at providing well-founded mathematical,
+statistical and algorithmic methods to infer, analyze and exploit the complex
+topological and geometric structures underlying data that are often represented
+as point clouds in Euclidean or more general metric spaces. During the last few
+years, a considerable eort has been made to provide robust and ecient data
+structures and algorithms for tda that are now implemented and available and
+easy to use through standard libraries such as the Gudhi library (C++ and
+Python) Maria et al. (2014) and its R software interface Fasy et al. (2014a).
+Although it is still rapidly evolving, tda now provides a set of mature and
+ecient tools that can be used in combination or complementary to other data
+sciences tools. The tdapipeline. tda has recently known developments in various
+directions and application elds. There now exist a large variety of methods
+inspired by topological and geometric approaches. Providing a complete overview
+of all these existing approaches is beyond the scope of this introductory
+survey. However, most of them rely on the following basic and standard pipeline
+that will serve as the backbone of this paper: 1. The input is assumed to be a
+nite set of points coming with a notion of distance-or similarity between them.
+This distance can be induced by the metric in the ambient space (e.g. the
+Euclidean metric when the data are embedded in R d) or come as an intrinsic
+metric dened by a pairwise distance matrix. The denition of the metric on the
+data is usually given as an input or guided by the application. It is however
+important to notice that the choice of the metric may be critical to reveal
+interesting topological and geometric features of the data.
+",1,0,1,1,0,0
+19447,Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures,"  The resemblance between the methods used in quantum-many body physics and in
+machine learning has drawn considerable attention. In particular, tensor
+networks (TNs) and deep learning architectures bear striking similarities to
+the extent that TNs can be used for machine learning. Previous results used
+one-dimensional TNs in image recognition, showing limited scalability and
+flexibilities. In this work, we train two-dimensional hierarchical TNs to solve
+image recognition problems, using a training algorithm derived from the
+multipartite entanglement renormalization ansatz. This approach introduces
+novel mathematical connections among quantum many-body physics, quantum
+information theory, and machine learning. While keeping the TN unitary in the
+training phase, TN states are defined, which optimally encode classes of images
+into quantum many-body states. We study the quantum features of the TN states,
+including quantum entanglement and fidelity. We find these quantities could be
+novel properties that characterize the image classes, as well as the machine
+learning tasks. Our work could contribute to the research on
+identifying/modeling quantum artificial intelligences.
+",0,1,0,1,0,0
+19448,Hierarchical Imitation and Reinforcement Learning,"  We study how to effectively leverage expert feedback to learn sequential
+decision-making policies. We focus on problems with sparse rewards and long
+time horizons, which typically pose significant challenges in reinforcement
+learning. We propose an algorithmic framework, called hierarchical guidance,
+that leverages the hierarchical structure of the underlying problem to
+integrate different modes of expert interaction. Our framework can incorporate
+different combinations of imitation learning (IL) and reinforcement learning
+(RL) at different levels, leading to dramatic reductions in both expert effort
+and cost of exploration. Using long-horizon benchmarks, including Montezuma's
+Revenge, we demonstrate that our approach can learn significantly faster than
+hierarchical RL, and be significantly more label-efficient than standard IL. We
+also theoretically analyze labeling cost for certain instantiations of our
+framework.
+",0,0,0,1,0,0
+19449,Social learning in a simple task allocation game,"  We investigate the effects of social interactions in task al- location using
+Evolutionary Game Theory (EGT). We propose a simple task-allocation game and
+study how different learning mechanisms can give rise to specialised and non-
+specialised colonies under different ecological conditions. By combining
+agent-based simulations and adaptive dynamics we show that social learning can
+result in colonies of generalists or specialists, depending on ecological
+parameters. Agent-based simulations further show that learning dynamics play a
+crucial role in task allocation. In particular, introspective individual
+learning readily favours the emergence of specialists, while a process
+resembling task recruitment favours the emergence of generalists.
+",1,0,0,0,0,0
+19450,Guided projections for analysing the structure of high-dimensional data,"  A powerful data transformation method named guided projections is proposed
+creating new possibilities to reveal the group structure of high-dimensional
+data in the presence of noise variables. Utilising projections onto a space
+spanned by a selection of a small number of observations allows measuring the
+similarity of other observations to the selection based on orthogonal and score
+distances. Observations are iteratively exchanged from the selection creating a
+non-random sequence of projections which we call guided projections. In
+contrast to conventional projection pursuit methods, which typically identify a
+low-dimensional projection revealing some interesting features contained in the
+data, guided projections generate a series of projections that serve as a basis
+not just for diagnostic plots but to directly investigate the group structure
+in data. Based on simulated data we identify the strengths and limitations of
+guided projections in comparison to commonly employed data transformation
+methods. We further show the relevance of the transformation by applying it to
+real-world data sets.
+",0,0,0,1,0,0
+19451,Limits of Predictability of Cascading Overload Failures in Spatially-Embedded Networks with Distributed Flows,"  Cascading failures are a critical vulnerability of complex information or
+infrastructure networks. Here we investigate the properties of load-based
+cascading failures in real and synthetic spatially-embedded network structures,
+and propose mitigation strategies to reduce the severity of damages caused by
+such failures. We introduce a stochastic method for optimal heterogeneous
+distribution of resources (node capacities) subject to a fixed total cost.
+Additionally, we design and compare the performance of networks with N-stable
+and (N-1)-stable network-capacity allocations by triggering cascades using
+various real-world node-attack and node-failure scenarios. We show that failure
+mitigation through increased node protection can be effectively achieved
+against single node failures. However, mitigating against multiple node
+failures is much more difficult due to the combinatorial increase in possible
+failures. We analyze the robustness of the system with increasing protection,
+and find that a critical tolerance exists at which the system undergoes a phase
+transition, and above which the network almost completely survives an attack.
+Moreover, we show that cascade-size distributions measured in this region
+exhibit a power-law decay. Finally, we find a strong correlation between
+cascade sizes induced by individual nodes and sets of nodes. We also show that
+network topology alone is a weak factor in determining the progression of
+cascading failures.
+",1,1,0,0,0,0
+19452,Strong electron-hole symmetric Rashba spin-orbit coupling in graphene/monolayer transition metal dichalcogenide heterostructures,"  Despite its extremely weak intrinsic spin-orbit coupling (SOC), graphene has
+been shown to acquire considerable SOC by proximity coupling with exfoliated
+transition metal dichalcogenides (TMDs). Here we demonstrate strong induced
+Rashba SOC in graphene that is proximity coupled to a monolayer TMD film, MoS2
+or WSe2, grown by chemical vapor deposition with drastically different Fermi
+level positions. Graphene/TMD heterostructures are fabricated with a
+pickup-transfer technique utilizing hexagonal boron nitride, which serves as a
+flat template to promote intimate contact and therefore a strong interfacial
+interaction between TMD and graphene as evidenced by quenching of the TMD
+photoluminescence. We observe strong induced graphene SOC that manifests itself
+in a pronounced weak anti-localization (WAL) effect in the graphene
+magnetoconductance. The spin relaxation rate extracted from the WAL analysis
+varies linearly with the momentum scattering time and is independent of the
+carrier type. This indicates a dominantly Dyakonov-Perel spin relaxation
+mechanism caused by the induced Rashba SOC. Our analysis yields a Rashba SOC
+energy of ~1.5 meV in graphene/WSe2 and ~0.9 meV in graphene/MoS2,
+respectively. The nearly electron-hole symmetric nature of the induced Rashba
+SOC provides a clue to possible underlying SOC mechanisms.
+",0,1,0,0,0,0
+19453,Robust Statistics for Image Deconvolution,"  We present a blind multiframe image-deconvolution method based on robust
+statistics. The usual shortcomings of iterative optimization of the likelihood
+function are alleviated by minimizing the M-scale of the residuals, which
+achieves more uniform convergence across the image. We focus on the
+deconvolution of astronomical images, which are among the most challenging due
+to their huge dynamic ranges and the frequent presence of large noise-dominated
+regions in the images. We show that high-quality image reconstruction is
+possible even in super-resolution and without the use of traditional
+regularization terms. Using a robust \r{ho}-function is straightforward to
+implement in a streaming setting and, hence our method is applicable to the
+large volumes of astronomy images. The power of our method is demonstrated on
+observations from the Sloan Digital Sky Survey (Stripe 82) and we briefly
+discuss the feasibility of a pipeline based on Graphical Processing Units for
+the next generation of telescope surveys.
+",1,1,0,0,0,0
+19454,Nash and Wardrop equilibria in aggregative games with coupling constraints,"  We consider the framework of aggregative games, in which the cost function of
+each agent depends on his own strategy and on the average population strategy.
+As first contribution, we investigate the relations between the concepts of
+Nash and Wardrop equilibrium. By exploiting a characterization of the two
+equilibria as solutions of variational inequalities, we bound their distance
+with a decreasing function of the population size. As second contribution, we
+propose two decentralized algorithms that converge to such equilibria and are
+capable of coping with constraints coupling the strategies of different agents.
+Finally, we study the applications of charging of electric vehicles and of
+route choice on a road network.
+",1,0,1,0,0,0
+19455,Magnetoresistance in the superconducting state at the (111) LaAlO$_3$/SrTiO$_3$ interface,"  Condensed matter systems that simultaneously exhibit superconductivity and
+ferromagnetism are rare due the antagonistic relationship between conventional
+spin-singlet superconductivity and ferromagnetic order. In materials in which
+superconductivity and magnetic order is known to coexist (such as some
+heavy-fermion materials), the superconductivity is thought to be of an
+unconventional nature. Recently, the conducting gas that lives at the interface
+between the perovskite band insulators LaAlO$_3$ (LAO) and SrTiO$_3$ (STO) has
+also been shown to host both superconductivity and magnetism. Most previous
+research has focused on LAO/STO samples in which the interface is in the (001)
+crystal plane. Relatively little work has focused on the (111) crystal
+orientation, which has hexagonal symmetry at the interface, and has been
+predicted to have potentially interesting topological properties, including
+unconventional superconducting pairing states. Here we report measurements of
+the magnetoresistance of (111) LAO/STO heterostructures at temperatures at
+which they are also superconducting. As with the (001) structures, the
+magnetoresistance is hysteretic, indicating the coexistence of magnetism and
+superconductivity, but in addition, we find that this magnetoresistance is
+anisotropic. Such an anisotropic response is completely unexpected in the
+superconducting state, and suggests that (111) LAO/STO heterostructures may
+support unconventional superconductivity.
+",0,1,0,0,0,0
+19456,Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data,"  The paper presents the application of Variational Autoencoders (VAE) for data
+dimensionality reduction and explorative analysis of mass spectrometry imaging
+data (MSI). The results confirm that VAEs are capable of detecting the patterns
+associated with the different tissue sub-types with performance than standard
+approaches.
+",1,0,0,1,0,0
+19457,Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings,"  In this paper, we propose a new algorithm for learning general
+latent-variable probabilistic graphical models using the techniques of
+predictive state representation, instrumental variable regression, and
+reproducing-kernel Hilbert space embeddings of distributions. Under this new
+learning framework, we first convert latent-variable graphical models into
+corresponding latent-variable junction trees, and then reduce the hard
+parameter learning problem into a pipeline of supervised learning problems,
+whose results will then be used to perform predictive belief propagation over
+the latent junction tree during the actual inference procedure. We then give
+proofs of our algorithm's correctness, and demonstrate its good performance in
+experiments on one synthetic dataset and two real-world tasks from
+computational biology and computer vision - classifying DNA splice junctions
+and recognizing human actions in videos.
+",1,0,0,1,0,0
+19458,Can interacting dark energy solve the $H_0$ tension?,"  The answer is Yes! We indeed find that interacting dark energy can alleviate
+the current tension on the value of the Hubble constant $H_0$ between the
+Cosmic Microwave Background anisotropies constraints obtained from the Planck
+satellite and the recent direct measurements reported by Riess et al. 2016. The
+combination of these two datasets points towards an evidence for a non-zero
+dark matter-dark energy coupling $\xi$ at more than two standard deviations,
+with $\xi=-0.26_{-0.12}^{+0.16}$ at $95\%$ CL. However the $H_0$ tension is
+better solved when the equation of state of the interacting dark energy
+component is allowed to freely vary, with a phantom-like equation of state
+$w=-1.184\pm0.064$ (at $68 \%$ CL), ruling out the pure cosmological constant
+case, $w=-1$, again at more than two standard deviations. When Planck data are
+combined with external datasets, as BAO, JLA Supernovae Ia luminosity
+distances, cosmic shear or lensing data, we find good consistency with the
+cosmological constant scenario and no compelling evidence for a dark
+matter-dark energy coupling.
+",0,1,0,0,0,0
+19459,Fast Switching Dual Fabry-Perot Cavity Optical Refractometry - Methodologies for Accurate Assessment of Gas Density,"  Dual Fabry-Perot cavity based optical refractometry (DFPC-OR) has a high
+potential for assessments of gas density. However, drifts of the FP cavity
+often limit its performance. We show that by the use of two narrow-linewidth
+fiber lasers locked to two high finesse cavities and Allan-Werle plots that
+drift-free DFPC-OR can be obtained for short measurement times (for which the
+drifts of the cavity can be disregarded). Based on this, a novel strategy,
+termed fast switching DFPC-OR (FS-DFPC-OR), is presented. A set of novel
+methodologies for assessment of both gas density and flow rates (in particular
+from small leaks) that are not restricted by the conventional limitations
+imposed by the drifts of the cavity are presented. The methodologies deal with
+assessments in both open and closed (finite-sized) compartments. They
+circumvent the problem with volumetric expansion, i.e. that the gas density in
+a measurement cavity is not the same as that in the closed external compartment
+that should be assessed, by performing a pair of measurements in rapid
+succession; the first one serves the purpose of assessing the density of the
+gas that has been transferred into the measurement cavity by the gas
+equilibration process, while the 2nd is used to automatically calibrate the
+system with respect to the relative volumes of the measurement cavity and the
+external compartment. The methodologies for assessments of leak rates comprise
+triple cavity evacuation assessments, comprising two measurements performed in
+rapid succession, supplemented by a 3rd measurement a certain time thereafter.
+A clear explanation of why the technique has such a small temperature
+dependence is given. It is concluded that FS-DFPC-OR constitutes a novel
+strategy that can be used for precise and accurate assessment of gas number
+density and gas flows under a variety of conditions, in particular
+non-temperature stabilized ones.
+",0,1,0,0,0,0
+19460,A Large Self-Annotated Corpus for Sarcasm,"  We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for
+sarcasm research and for training and evaluating systems for sarcasm detection.
+The corpus has 1.3 million sarcastic statements -- 10 times more than any
+previous dataset -- and many times more instances of non-sarcastic statements,
+allowing for learning in both balanced and unbalanced label regimes. Each
+statement is furthermore self-annotated -- sarcasm is labeled by the author,
+not an independent annotator -- and provided with user, topic, and conversation
+context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm
+detection, and evaluate baseline methods.
+",1,0,0,0,0,0
+19461,Aerosol properties in the atmospheres of extrasolar giant planets,"  We use a model of aerosol microphysics to investigate the impact of
+high-altitude photochemical aerosols on the transmission spectra and
+atmospheric properties of close-in exoplanets, such as HD209458b and HD189733b.
+The results depend strongly on the temperature profiles in the middle and upper
+atmosphere that are poorly understood. Nevertheless, our model of HD189733b,
+based on the most recently inferred temperature profiles, produces an aerosol
+distribution that matches the observed transmission spectrum. We argue that the
+hotter temperature of HD209458b inhibits the production of high-altitude
+aerosols and leads to the appearance of a more clear atmosphere than on
+HD189733b. The aerosol distribution also depends on the particle composition,
+the photochemical production, and the atmospheric mixing. Due to degeneracies
+among these inputs, current data cannot constrain the aerosol properties in
+detail. Instead, our work highlights the role of different factors in
+controlling the aerosol distribution that will prove useful in understanding
+different observations, including those from future missions. For the
+atmospheric mixing efficiency suggested by general circulation models (GCMs) we
+find that aerosol particles are small ($\sim$nm) and probably spherical. We
+further conclude that composition based on complex hydrocarbons (soots) is the
+most likely candidate to survive the high temperatures in hot Jupiter
+atmospheres. Such particles would have a significant impact on the energy
+balance of HD189733b's atmosphere and should be incorporated in future studies
+of atmospheric structure. We also evaluate the contribution of external sources
+in the photochemical aerosol formation and find that their spectral signature
+is not consistent with observations.
+",0,1,0,0,0,0
+19462,Influence of random opinion change in complex networks,"  Opinion formation in the population has attracted extensive research
+interest. Various models have been introduced and studied, including the ones
+with individuals' free will allowing them to change their opinions. Such
+models, however, have not taken into account the fact that individuals with
+different opinions may have different levels of loyalty, and consequently,
+different probabilities of changing their opinions. In this work, we study on
+how the non-uniform distribution of the opinion changing probability may affect
+the final state of opinion distribution. By simulating a few different cases
+with different symmetric and asymmetric non-uniform patterns of opinion
+changing probabilities, we demonstrate the significant effects that the
+different loyalty levels of different opinions have on the final state of the
+opinion distribution.
+",1,1,0,0,0,0
+19463,We Are Not Your Real Parents: Telling Causal from Confounded using MDL,"  Given data over variables $(X_1,...,X_m, Y)$ we consider the problem of
+finding out whether $X$ jointly causes $Y$ or whether they are all confounded
+by an unobserved latent variable $Z$. To do so, we take an
+information-theoretic approach based on Kolmogorov complexity. In a nutshell,
+we follow the postulate that first encoding the true cause, and then the
+effects given that cause, results in a shorter description than any other
+encoding of the observed variables.
+The ideal score is not computable, and hence we have to approximate it. We
+propose to do so using the Minimum Description Length (MDL) principle. We
+compare the MDL scores under the models where $X$ causes $Y$ and where there
+exists a latent variables $Z$ confounding both $X$ and $Y$ and show our scores
+are consistent. To find potential confounders we propose using latent factor
+modeling, in particular, probabilistic PCA (PPCA).
+Empirical evaluation on both synthetic and real-world data shows that our
+method, CoCa, performs very well -- even when the true generating process of
+the data is far from the assumptions made by the models we use. Moreover, it is
+robust as its accuracy goes hand in hand with its confidence.
+",1,0,0,1,0,0
+19464,Evolution of eccentricity and inclination of hot protoplanets embedded in radiative discs,"  We study the evolution of the eccentricity and inclination of protoplanetary
+embryos and low-mass protoplanets (from a fraction of an Earth mass to a few
+Earth masses) embedded in a protoplanetary disc, by means of three dimensional
+hydrodynamics calculations with radiative transfer in the diffusion limit. When
+the protoplanets radiate in the surrounding disc the energy released by the
+accretion of solids, their eccentricity and inclination experience a growth
+toward values which depend on the luminosity to mass ratio of the planet, which
+are comparable to the disc's aspect ratio and which are reached over timescales
+of a few thousand years. This growth is triggered by the appearance of a hot,
+under-dense region in the vicinity of the planet. The growth rate of the
+eccentricity is typically three times larger than that of the inclination. In
+long term calculations, we find that the excitation of eccentricity and the
+excitation of inclination are not independent. In the particular case in which
+a planet has initially a very small eccentricity and inclination, the
+eccentricity largely overruns the inclination. When the eccentricity reaches
+its asymptotic value, the growth of inclination is quenched, yielding an
+eccentric orbit with a very low inclination. As a side result, we find that the
+eccentricity and inclination of non-luminous planets are damped more vigorously
+in radiative discs than in isothermal discs.
+",0,1,0,0,0,0
+19465,Definitions in mathematics,"  We discuss various forms of definitions in mathematics and describe rules
+governing them.
+",0,0,1,0,0,0
+19466,Excellence in prime characteristic,"  Fix any field $K$ of characteristic $p$ such that $[K:K^p]$ is finite. We
+discuss excellence for Noetherian domains whose fraction field is $K$, showing
+for example, that $R$ is excellent if and only if the Frobenius map is finite
+on $R$. Furthermore, we show $R$ is excellent if and only if it admits some
+non-zero $p^{-e}$-linear map for $R$ or equivalently, that $R$ is a solid
+$R$-algebra under Frobenius. In particular, this means that Frobenius split
+Noetherian domains that are generically $F$-finite are always excellent. We
+also show that non-excellent rings are abundant and easy to construct in prime
+characteristic, even within the world of regular local rings of dimension one
+in function fields. This paper is mostly expository in nature.
+",0,0,1,0,0,0
+19467,Asymptotically optimal private estimation under mean square loss,"  We consider the minimax estimation problem of a discrete distribution with
+support size $k$ under locally differential privacy constraints. A
+privatization scheme is applied to each raw sample independently, and we need
+to estimate the distribution of the raw samples from the privatized samples. A
+positive number $\epsilon$ measures the privacy level of a privatization
+scheme.
+In our previous work (arXiv:1702.00610), we proposed a family of new
+privatization schemes and the corresponding estimator. We also proved that our
+scheme and estimator are order optimal in the regime $e^{\epsilon} \ll k$ under
+both $\ell_2^2$ and $\ell_1$ loss. In other words, for a large number of
+samples the worst-case estimation loss of our scheme was shown to differ from
+the optimal value by at most a constant factor. In this paper, we eliminate
+this gap by showing asymptotic optimality of the proposed scheme and estimator
+under the $\ell_2^2$ (mean square) loss. More precisely, we show that for any
+$k$ and $\epsilon,$ the ratio between the worst-case estimation loss of our
+scheme and the optimal value approaches $1$ as the number of samples tends to
+infinity.
+",1,0,1,1,0,0
+19468,Coulomb repulsion of holes and competition between d_{x^2-y^2}-wave and s-wave parings in cuprate superconductors,"  The effect of the Coulomb repulsion of holes on the Cooper instability in an
+ensemble of spin-polaron quasiparticles has been analyzed, taking into account
+the peculiarities of the crystallographic structure of the CuO$_2$ plane, which
+are associated with the presence of two oxygen ions and one copper ion in the
+unit cell, as well as the strong spin-fermion coupling. The investigation of
+the possibility of implementation superconducting phases with d-wave and s-wave
+pairing of the order parameter symmetry has shown that in the entire doping
+region only the d-wave pairing satisfies the self-consistency equations, while
+there is no solution for the s-wave pairing. This result completely corresponds
+to the experimental data on cuprate HTSC. It has been demonstrated analytically
+that the intersite Coulomb interaction does not affect the superconducting
+d-wave pairing, because its Fourier transform $V_q$ does not appear in the
+kernel of the corresponding integral equation.
+",0,1,0,0,0,0
+19469,Modeling Game Avatar Synergy and Opposition through Embedding in Multiplayer Online Battle Arena Games,"  Multiplayer Online Battle Arena (MOBA) games have received increasing
+worldwide popularity recently. In such games, players compete in teams against
+each other by controlling selected game avatars, each of which is designed with
+different strengths and weaknesses. Intuitively, putting together game avatars
+that complement each other (synergy) and suppress those of opponents
+(opposition) would result in a stronger team. In-depth understanding of synergy
+and opposition relationships among game avatars benefits player in making
+decisions in game avatar drafting and gaining better prediction of match
+events. However, due to intricate design and complex interactions between game
+avatars, thorough understanding of their relationships is not a trivial task.
+In this paper, we propose a latent variable model, namely Game Avatar
+Embedding (GAE), to learn avatars' numerical representations which encode
+synergy and opposition relationships between pairs of avatars. The merits of
+our model are twofold: (1) the captured synergy and opposition relationships
+are sensible to experienced human players' perception; (2) the learned
+numerical representations of game avatars allow many important downstream
+tasks, such as similar avatar search, match outcome prediction, and avatar pick
+recommender. To our best knowledge, no previous model is able to simultaneously
+support both features. Our quantitative and qualitative evaluations on real
+match data from three commercial MOBA games illustrate the benefits of our
+model.
+",1,0,0,0,0,0
+19470,Design and Development of Effective Transmission Mechanisms on a Tendon Driven Hand Orthosis for Stroke Patients,"  Tendon-driven hand orthoses have advantages over exoskeletons with respect to
+wearability and safety because of their low-profile design and ability to fit a
+range of patients without requiring custom joint alignment. However, no
+existing study on a wearable tendon-driven hand orthosis for stroke patients
+presents evidence that such devices can overcome spasticity given repeated use
+and fatigue, or discusses transmission efficiency. In this study, we propose
+two designs that provide effective force transmission by increasing moment arms
+around finger joints. We evaluate the designs with geometric models and
+experiment using a 3D-printed artificial finger to find force and joint angle
+characteristics of the suggested structures. We also perform clinical tests
+with stroke patients to demonstrate the feasibility of the designs. The testing
+supports the hypothesis that the proposed designs efficiently elicit extension
+of the digits in patients with spasticity as compared to existing baselines.
+",1,0,0,0,0,0
+19471,"eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys","  For years security machine learning research has promised to obviate the need
+for signature based detection by automatically learning to detect indicators of
+attack. Unfortunately, this vision hasn't come to fruition: in fact, developing
+and maintaining today's security machine learning systems can require
+engineering resources that are comparable to that of signature-based detection
+systems, due in part to the need to develop and continuously tune the
+""features"" these machine learning systems look at as attacks evolve. Deep
+learning, a subfield of machine learning, promises to change this by operating
+on raw input signals and automating the process of feature design and
+extraction. In this paper we propose the eXpose neural network, which uses a
+deep learning approach we have developed to take generic, raw short character
+strings as input (a common case for security inputs, which include artifacts
+like potentially malicious URLs, file paths, named pipes, named mutexes, and
+registry keys), and learns to simultaneously extract features and classify
+using character-level embeddings and convolutional neural network. In addition
+to completely automating the feature design and extraction process, eXpose
+outperforms manual feature extraction based baselines on all of the intrusion
+detection problems we tested it on, yielding a 5%-10% detection rate gain at
+0.1% false positive rate compared to these baselines.
+",1,0,0,0,0,0
+19472,"A Software Reuse Approach and Its Effect On Software Quality, An Empirical Study for The Software Industry","  Software reusability has become much interesting because of increased quality
+and reduce cost. A good process of software reuse leads to enhance the
+reliability, productivity, quality and the reduction of time and cost. Current
+reuse techniques focuses on the reuse of software artifact which grounded on
+anticipated functionality whereas, the non-functional (quality) aspect are also
+important. So, Software reusability used here to expand quality and
+productivity of software. It improves overall quality of software in minimum
+energy and time. Main objective of this study was to present a reuse approach
+that discovered that how software reuse improves the quality in Software
+Industry. The V&V technique used for this purpose which is part of software
+quality management process, it checks the quality and correctness during the
+software life cycle. A survey study conducted as QUESTIONAIR to find the impact
+of reuse approach on quality attributes which are requirement specification and
+design specification. Other quality enhancement techniques like ad hoc, CBSE,
+MBSE, Product line, COTS reuse checked on existing software industry. Results
+analyzed with the help of MATLAB tool as it provides effective data management,
+wide range of options, better output organization, to check weather quality
+enhancement technique is affected due to reusability and how quality will
+improve.
+",1,0,0,0,0,0
+19473,Portfolio Optimization with Nondominated Priors and Unbounded Parameters,"  We consider classical Merton problem of terminal wealth maximization in
+finite horizon. We assume that the drift of the stock is following
+Ornstein-Uhlenbeck process and the volatility of it is following GARCH(1)
+process. In particular, both mean and volatility are unbounded. We assume that
+there is Knightian uncertainty on the parameters of both mean and volatility.
+We take that the investor has logarithmic utility function, and solve the
+corresponding utility maximization problem explicitly. To the best of our
+knowledge, this is the first work on utility maximization with unbounded mean
+and volatility in Knightian uncertainty under nondominated priors.
+",0,0,0,0,0,1
+19474,Measuring heterogeneity in urban expansion via spatial entropy,"  The lack of efficiency in urban diffusion is a debated issue, important for
+biologists, urban specialists, planners and statisticians, both in developed
+and new developing countries. Many approaches have been considered to measure
+urban sprawl, i.e. chaotic urban expansion; such idea of chaos is here linked
+to the concept of entropy. Entropy, firstly introduced in information theory,
+rapidly became a standard tool in ecology, biology and geography to measure the
+degree of heterogeneity among observations; in these contexts, entropy measures
+should include spatial information. The aim of this paper is to employ a
+rigorous spatial entropy based approach to measure urban sprawl associated to
+the diffusion of metropolitan cities. In order to assess the performance of the
+considered measures, a comparative study is run over alternative urban
+scenarios; afterwards, measures are used to quantify the degree of disorder in
+the urban expansion of three cities in Europe. Results are easily interpretable
+and can be used both as an absolute measure of urban sprawl and for comparison
+over space and time.
+",0,0,0,1,0,0
+19475,Bayesian Cluster Enumeration Criterion for Unsupervised Learning,"  We derive a new Bayesian Information Criterion (BIC) by formulating the
+problem of estimating the number of clusters in an observed data set as
+maximization of the posterior probability of the candidate models. Given that
+some mild assumptions are satisfied, we provide a general BIC expression for a
+broad class of data distributions. This serves as a starting point when
+deriving the BIC for specific distributions. Along this line, we provide a
+closed-form BIC expression for multivariate Gaussian distributed variables. We
+show that incorporating the data structure of the clustering problem into the
+derivation of the BIC results in an expression whose penalty term is different
+from that of the original BIC. We propose a two-step cluster enumeration
+algorithm. First, a model-based unsupervised learning algorithm partitions the
+data according to a given set of candidate models. Subsequently, the number of
+clusters is determined as the one associated with the model for which the
+proposed BIC is maximal. The performance of the proposed two-step algorithm is
+tested using synthetic and real data sets.
+",1,0,1,1,0,0
+19476,Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation,"  Combinatorial interaction testing is an important software testing technique
+that has seen lots of recent interest. It can reduce the number of test cases
+needed by considering interactions between combinations of input parameters.
+Empirical evidence shows that it effectively detects faults, in particular, for
+highly configurable software systems. In real-world software testing, the input
+variables may vary in how strongly they interact, variable strength
+combinatorial interaction testing (VS-CIT) can exploit this for higher
+effectiveness. The generation of variable strength test suites is a
+non-deterministic polynomial-time (NP) hard computational problem
+\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
+population-based algorithms such as particle swarm optimization (PSO) can be
+efficient compared to alternatives for VS-CIT problems. Nevertheless, they
+require detailed control for the exploitation and exploration trade-off to
+avoid premature convergence (i.e. being trapped in local optima) as well as to
+enhance the solution diversity. Here, we present a new variant of PSO based on
+Mamdani fuzzy inference system
+\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
+selection of its global and local search operations. We detail the design of
+this combined algorithm and evaluate it through experiments on multiple
+synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
+global and local search operations is, at least, feasible as it performs only
+second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
+best mean test suite size, the fuzzy adaptation even outperforms DPSO
+occasionally. We discuss the reasons behind this performance and outline
+relevant areas of future work.
+",1,0,0,0,0,0
+19477,Is Life Most Likely Around Sun-like Stars?,"  We consider the habitability of Earth-analogs around stars of different
+masses, which is regulated by the stellar lifetime, stellar wind-induced
+atmospheric erosion, and biologically active ultraviolet (UV) irradiance. By
+estimating the timescales associated with each of these processes, we show that
+they collectively impose limits on the habitability of Earth-analogs. We
+conclude that planets orbiting most M-dwarfs are not likely to host life, and
+that the highest probability of complex biospheres is for planets around K- and
+G-type stars. Our analysis suggests that the current existence of life near the
+Sun is slightly unusual, but not significantly anomalous.
+",0,1,0,0,0,0
+19478,Maximum Entropy Generators for Energy-Based Models,"  Unsupervised learning is about capturing dependencies between variables and
+is driven by the contrast between the probable vs. improbable configurations of
+these variables, often either via a generative model that only samples probable
+ones or with an energy function (unnormalized log-density) that is low for
+probable ones and high for improbable ones. Here, we consider learning both an
+energy function and an efficient approximate sampling mechanism. Whereas the
+discriminator in generative adversarial networks (GANs) learns to separate data
+and generator samples, introducing an entropy maximization regularizer on the
+generator can turn the interpretation of the critic into an energy function,
+which separates the training distribution from everything else, and thus can be
+used for tasks like anomaly or novelty detection. Then, we show how Markov
+Chain Monte Carlo can be done in the generator latent space whose samples can
+be mapped to data space, producing better samples. These samples are used for
+the negative phase gradient required to estimate the log-likelihood gradient of
+the data space energy function. To maximize entropy at the output of the
+generator, we take advantage of recently introduced neural estimators of mutual
+information. We find that in addition to producing a useful scoring function
+for anomaly detection, the resulting approach produces sharp samples while
+covering the modes well, leading to high Inception and Frechet scores.
+",1,0,0,1,0,0
+19479,A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols,"  Convolutional neural networks (CNNs) have shown promising results on several
+segmentation tasks in magnetic resonance (MR) images. However, the accuracy of
+CNNs may degrade severely when segmenting images acquired with different
+scanners and/or protocols as compared to the training data, thus limiting their
+practical utility. We address this shortcoming in a lifelong multi-domain
+learning setting by treating images acquired with different scanners or
+protocols as samples from different, but related domains. Our solution is a
+single CNN with shared convolutional filters and domain-specific batch
+normalization layers, which can be tuned to new domains with only a few
+($\approx$ 4) labelled images. Importantly, this is achieved while retaining
+performance on the older domains whose training data may no longer be
+available. We evaluate the method for brain structure segmentation in MR
+images. Results demonstrate that the proposed method largely closes the gap to
+the benchmark, which is training a dedicated CNN for each scanner.
+",0,0,0,1,0,0
+19480,Hybrid Kinematic Control for Rigid Body Pose Stabilization using Dual Quaternions,"  In this paper, we address the rigid body pose stabilization problem using
+dual quaternion formalism. We propose a hybrid control strategy to design a
+switching control law with hysteresis in such a way that the global asymptotic
+stability of the closed-loop system is guaranteed and such that the global
+attractivity of the stabilization pose does not exhibit chattering, a problem
+that is present in all discontinuous-based feedback controllers. Using
+numerical simulations, we illustrate the problems that arise from existing
+results in the literature -- as unwinding and chattering -- and verify the
+effectiveness of the proposed controller to solve the robust global pose
+stability problem.
+",1,0,1,0,0,0
+19481,On 2d-4d motivic wall-crossing formulas,"  In this paper we propose definitions and examples of categorical enhancements
+of the data involved in the $2d$-$4d$ wall-crossing formulas which generalize
+both Cecotti-Vafa and Kontsevich-Soibelman motivic wall-crossing formulas.
+",0,0,1,0,0,0
+19482,On Quitting: Performance and Practice in Online Game Play,"  We study the relationship between performance and practice by analyzing the
+activity of many players of a casual online game. We find significant
+heterogeneity in the improvement of player performance, given by score, and
+address this by dividing players into similar skill levels and segmenting each
+player's activity into sessions, i.e., sequence of game rounds without an
+extended break. After disaggregating data, we find that performance improves
+with practice across all skill levels. More interestingly, players are more
+likely to end their session after an especially large improvement, leading to a
+peak score in their very last game of a session. In addition, success is
+strongly correlated with a lower quitting rate when the score drops, and only
+weakly correlated with skill, in line with psychological findings about the
+value of persistence and ""grit"": successful players are those who persist in
+their practice despite lower scores. Finally, we train an epsilon-machine, a
+type of hidden Markov model, and find a plausible mechanism of game play that
+can predict player performance and quitting the game. Our work raises the
+possibility of real-time assessment and behavior prediction that can be used to
+optimize human performance.
+",1,0,0,0,0,0
+19483,Video Labeling for Automatic Video Surveillance in Security Domains,"  Beyond traditional security methods, unmanned aerial vehicles (UAVs) have
+become an important surveillance tool used in security domains to collect the
+required annotated data. However, collecting annotated data from videos taken
+by UAVs efficiently, and using these data to build datasets that can be used
+for learning payoffs or adversary behaviors in game-theoretic approaches and
+security applications, is an under-explored research question. This paper
+presents VIOLA, a novel labeling application that includes (i) a workload
+distribution framework to efficiently gather human labels from videos in a
+secured manner; (ii) a software interface with features designed for labeling
+videos taken by UAVs in the domain of wildlife security. We also present the
+evolution of VIOLA and analyze how the changes made in the development process
+relate to the efficiency of labeling, including when seemingly obvious
+improvements did not lead to increased efficiency. VIOLA enables collecting
+massive amounts of data with detailed information from challenging security
+videos such as those collected aboard UAVs for wildlife security. VIOLA will
+lead to the development of new approaches that integrate deep learning for
+real-time detection and response.
+",1,0,0,0,0,0
+19484,On density of subgraphs of Cartesian products,"  In this paper, we extend two classical results about the density of subgraphs
+of hypercubes to subgraphs $G$ of Cartesian products $G_1\times\cdots\times
+G_m$ of arbitrary connected graphs. Namely, we show that
+$\frac{|E(G)|}{|V(G)|}\le \lceil 2\max\{
+\text{dens}(G_1),\ldots,\text{dens}(G_m)\} \rceil\log|V(G)|$, where
+$\text{dens}(H)$ is the maximum ratio $\frac{|E(H')|}{|V(H')|}$ over all
+subgraphs $H'$ of $H$. We introduce the notions of VC-dimension
+$\text{VC-dim}(G)$ and VC-density $\text{VC-dens}(G)$ of a subgraph $G$ of a
+Cartesian product $G_1\times\cdots\times G_m$, generalizing the classical
+Vapnik-Chervonenkis dimension of set-families (viewed as subgraphs of
+hypercubes). We prove that if $G_1,\ldots,G_m$ belong to the class ${\mathcal
+G}(H)$ of all finite connected graphs not containing a given graph $H$ as a
+minor, then for any subgraph $G$ of $G_1\times\cdots\times G_m$ a sharper
+inequality $\frac{|E(G)|}{|V(G)|}\le \text{VC-dim}(G)\alpha(H)$ holds, where
+$\alpha(H)$ is the density of the graphs from ${\mathcal G}(H)$. We refine and
+sharpen those two results to several specific graph classes. We also derive
+upper bounds (some of them polylogarithmic) for the size of adjacency labeling
+schemes of subgraphs of Cartesian products.
+",1,0,0,0,0,0
+19485,Replica Analysis for Maximization of Net Present Value,"  In this paper, we use replica analysis to determine the investment strategy
+that can maximize the net present value for portfolios containing multiple
+development projects. Replica analysis was developed in statistical mechanical
+informatics and econophysics to evaluate disordered systems, and here we use it
+to formulate the maximization of the net present value as an optimization
+problem under budget and investment concentration constraints. Furthermore, we
+confirm that a common approach from operations research underestimates the true
+maximal net present value as the maximal expected net present value by
+comparing our results with the maximal expected net present value as derived in
+operations research. Moreover, it is shown that the conventional method for
+estimating the net present value does not consider variance in the cash flow.
+",0,0,0,0,0,1
+19486,On some properties of weak solutions to elliptic equations with divergence-free drifts,"  We discuss the local properties of weak solutions to the equation $-\Delta u
++ b\cdot\nabla u=0$. The corresponding theory is well-known in the case $b\in
+L_n$, where $n$ is the dimension of the space. Our main interest is focused on
+the case $b\in L_2$. In this case the structure assumption $\operatorname{div}
+b=0$ turns out to be crucial.
+",0,0,1,0,0,0
+19487,The heptagon-wheel cocycle in the Kontsevich graph complex,"  The real vector space of non-oriented graphs is known to carry a differential
+graded Lie algebra structure. Cocycles in the Kontsevich graph complex,
+expressed using formal sums of graphs on $n$ vertices and $2n-2$ edges, induce
+-- under the orientation mapping -- infinitesimal symmetries of classical
+Poisson structures on arbitrary finite-dimensional affine real manifolds.
+Willwacher has stated the existence of a nontrivial cocycle that contains the
+$(2\ell+1)$-wheel graph with a nonzero coefficient at every
+$\ell\in\mathbb{N}$. We present detailed calculations of the differential of
+graphs; for the tetrahedron and pentagon-wheel cocycles, consisting at $\ell =
+1$ and $\ell = 2$ of one and two graphs respectively, the cocycle condition
+$d(\gamma) = 0$ is verified by hand. For the next, heptagon-wheel cocycle
+(known to exist at $\ell = 3$), we provide an explicit representative: it
+consists of 46 graphs on 8 vertices and 14 edges.
+",0,0,1,0,0,0
+19488,Maneuver Regulation for Accelerating Bodies in Atmospheric Environments,"  In order to address the need for an affordable reduced gravity test platform,
+this work focuses on the analysis and implementation of atmospheric
+acceleration tracking with an autonomous aerial vehicle. As proof of concept,
+the vehicle is designed with the objective of flying accurate reduced-gravity
+parabolas. Suggestions from both academia and industry were taken into account,
+as well as requirements imposed by a regulatory agency. The novelty of this
+work is the Proportional Integral Ramp Quadratic PIRQ controller, which is
+employed to counteract the aerodynamic forces impeding the vehicles constant
+acceleration during the maneuver. The stability of the free-fall maneuver under
+this controller is studied in detail via the formation of the transverse
+dynamics and the application of the circle criterion. The implementation of
+such a controller is then outlined, and the PIRQ controller is validated
+through a flight test, where the vehicle successfully tracks Martian gravity
+0.378 G's with a standard deviation of 0.0426.
+",1,0,0,0,0,0
+19489,A Hybrid Deep Learning Approach for Texture Analysis,"  Texture classification is a problem that has various applications such as
+remote sensing and forest species recognition. Solutions tend to be custom fit
+to the dataset used but fails to generalize. The Convolutional Neural Network
+(CNN) in combination with Support Vector Machine (SVM) form a robust selection
+between powerful invariant feature extractor and accurate classifier. The
+fusion of experts provides stability in classification rates among different
+datasets.
+",1,0,0,0,0,0
+19490,Accounting Noise and the Pricing of CoCos,"  Contingent Convertible bonds (CoCos) are debt instruments that convert into
+equity or are written down in times of distress. Existing pricing models assume
+conversion triggers based on market prices and on the assumption that markets
+can always observe all relevant firm information. But all Cocos issued so far
+have triggers based on accounting ratios and/or regulatory intervention. We
+incorporate that markets receive information through noisy accounting reports
+issued at discrete time instants, which allows us to distinguish between market
+and accounting values, and between automatic triggers and regulator-mandated
+conversions. Our second contribution is to incorporate that coupon payments are
+contingent too: their payment is conditional on the Maximum Distributable
+Amount not being exceeded. We examine the impact of CoCo design parameters,
+asset volatility and accounting noise on the price of a CoCo; and investigate
+the interaction between CoCo design features, the capital structure of the
+issuing bank and their implications for risk taking and investment incentives.
+Finally, we use our model to explain the crash in CoCo prices after Deutsche
+Bank's profit warning in February 2016.
+",0,0,0,0,0,1
+19491,An Improved Algorithm for E-Generalization,"  E-generalization computes common generalizations of given ground terms w.r.t.
+a given equational background theory E. In 2005 [arXiv:1403.8118], we had
+presented a computation approach based on standard regular tree grammar
+algorithms, and a Prolog prototype implementation. In this report, we present
+algorithmic improvements, prove them correct and complete, and give some
+details of an efficiency-oriented implementation in C that allows us to handle
+problems larger by several orders of magnitude.
+",1,0,0,0,0,0
+19492,On quartic double fivefolds and the matrix factorizations of exceptional quaternionic representations,"  We study quartic double fivefolds from the perspective of Fano manifolds of
+Calabi-Yau type and that of exceptional quaternionic representations. We first
+prove that the generic quartic double fivefold can be represented, in a finite
+number of ways, as a double cover of P^5 ramified along a linear section of the
+Sp 12-invariant quartic in P^31. Then, using the geometry of the Vinberg's type
+II decomposition of some exceptional quaternionic representations, and backed
+by some cohomological computations performed by Macaulay2, we prove the
+existence of a spherical rank 6 vector bundle on such a generic quartic double
+fivefold. We finally use the existence this vector bundle to prove that the
+homological unit of the CY-3 category associated by Kuznetsov to the derived
+category of a generic quartic double fivefold is C $\oplus$ C[3].
+",0,0,1,0,0,0
+19493,Atmospheric thermal tides and planetary spin I. The complex interplay between stratification and rotation,"  Thermal atmospheric tides can torque telluric planets away from spin-orbit
+synchronous rotation, as observed in the case of Venus. They thus participate
+to determine the possible climates and general circulations of the atmospheres
+of these planets. In this work, we write the equations governing the dynamics
+of thermal tides in a local vertically-stratified section of a rotating
+planetary atmosphere by taking into account the effects of the complete
+Coriolis acceleration on tidal waves. This allows us to derive analytically the
+tidal torque and the tidally dissipated energy, which we use to discuss the
+possible regimes of tidal dissipation and examine the key role played by
+stratification.
+In agreement with early studies, we find that the frequency dependence of the
+thermal atmospheric tidal torque in the vicinity of synchronization can be
+approximated by a Maxwell model. This behaviour corresponds to weakly stably
+stratified or convective fluid layers, as observed in ADLM2016a. A strong
+stable stratification allows gravity waves to propagate, which makes the tidal
+torque become negligible. The transition is continuous between these two
+regimes. The traditional approximation appears to be valid in thin atmospheres
+and in regimes where the rotation frequency is dominated by the forcing or the
+buoyancy frequencies.
+Depending on the stability of their atmospheres with respect to convection,
+observed exoplanets can be tidally driven toward synchronous or asynchronous
+final rotation rates. The domain of applicability of the traditional
+approximation is rigorously constrained by calculations.
+",0,1,0,0,0,0
+19494,Inferring Generative Model Structure with Static Analysis,"  Obtaining enough labeled data to robustly train complex discriminative models
+is a major bottleneck in the machine learning pipeline. A popular solution is
+combining multiple sources of weak supervision using generative models. The
+structure of these models affects training label quality, but is difficult to
+learn without any ground truth labels. We instead rely on these weak
+supervision sources having some structure by virtue of being encoded
+programmatically. We present Coral, a paradigm that infers generative model
+structure by statically analyzing the code for these heuristics, thus reducing
+the data required to learn structure significantly. We prove that Coral's
+sample complexity scales quasilinearly with the number of heuristics and number
+of relations found, improving over the standard sample complexity, which is
+exponential in $n$ for identifying $n^{\textrm{th}}$ degree relations.
+Experimentally, Coral matches or outperforms traditional structure learning
+approaches by up to 3.81 F1 points. Using Coral to model dependencies instead
+of assuming independence results in better performance than a fully supervised
+model by 3.07 accuracy points when heuristics are used to label radiology data
+without ground truth labels.
+",1,0,0,1,0,0
+19495,"Maslov, Chern-Weil and Mean Curvature","  We provide an integral formula for the Maslov index of a pair $(E,F)$ over a
+surface $\Sigma$, where $E\rightarrow\Sigma$ is a complex vector bundle and
+$F\subset E_{|\partial\Sigma}$ is a totally real subbundle. As in Chern-Weil
+theory, this formula is written in terms of the curvature of $E$ plus a
+boundary contribution.
+When $(E,F)$ is obtained via an immersion of $(\Sigma,\partial\Sigma)$ into a
+pair $(M,L)$ where $M$ is Kähler and $L$ is totally real, the formula allows
+us to control the Maslov index in terms of the geometry of $(M,L)$. We exhibit
+natural conditions on $(M,L)$ which lead to bounds and monotonicity results.
+",0,0,1,0,0,0
+19496,Tuning Goodness-of-Fit Tests,"  As modern precision cosmological measurements continue to show agreement with
+the broad features of the standard $\Lambda$-Cold Dark Matter ($\Lambda$CDM)
+cosmological model, we are increasingly motivated to look for small departures
+from the standard model's predictions which might not be detected with standard
+approaches. While searches for extensions and modifications of $\Lambda$CDM
+have to date turned up no convincing evidence of beyond-the-standard-model
+cosmology, the list of models compared against $\Lambda$CDM is by no means
+complete and is often governed by readily-coded modifications to standard
+Boltzmann codes. Also, standard goodness-of-fit methods such as a naive
+$\chi^2$ test fail to put strong pressure on the null $\Lambda$CDM hypothesis,
+since modern datasets have orders of magnitudes more degrees of freedom than
+$\Lambda$CDM. Here we present a method of tuning goodness-of-fit tests to
+detect potential sub-dominant extra-$\Lambda$CDM signals present in the data
+through compressing observations in a way that maximizes extra-$\Lambda$CDM
+signal variation over noise and $\Lambda$CDM variation. This method, based on a
+Karhunen-Loève transformation of the data, is tuned to be maximally
+sensitive to particular types of variations characteristic of the tuning model;
+but, unlike direct model comparison, the test is also sensitive to features
+that only partially mimic the tuning model. As an example of its use, we apply
+this method in the context of a nonstandard primordial power spectrum compared
+against the $2015$ $Planck$ CMB temperature and polarization power spectrum. We
+find weak evidence of extra-$\Lambda$CDM physics, conceivably due to known
+systematics in the 2015 Planck polarization release.
+",0,1,0,0,0,0
+19497,The Lubin-Tate stack and Gross-Hopkins duality,"  Morava $E$-theory $E$ is an $E_\infty$-ring with an action of the Morava
+stabilizer group $\Gamma$. We study the derived stack $\operatorname{Spf}
+E/\Gamma$. Descent-theoretic techniques allow us to deduce a theorem of
+Hopkins-Mahowald-Sadofsky on the $K(n)$-local Picard group, as well as a recent
+result of Barthel-Beaudry-Stojanoska on the Anderson duals of higher real
+$K$-theories.
+",0,0,1,0,0,0
+19498,Generalized Probabilistic Bisection for Stochastic Root-Finding,"  We consider numerical schemes for root finding of noisy responses through
+generalizing the Probabilistic Bisection Algorithm (PBA) to the more practical
+context where the sampling distribution is unknown and location-dependent. As
+in standard PBA, we rely on a knowledge state for the approximate posterior of
+the root location. To implement the corresponding Bayesian updating, we also
+carry out inference of oracle accuracy, namely learning the probability of
+correct response. To this end we utilize batched querying in combination with a
+variety of frequentist and Bayesian estimators based on majority vote, as well
+as the underlying functional responses, if available. For guiding sampling
+selection we investigate both Information Directed sampling, as well as
+Quantile sampling. Our numerical experiments show that these strategies perform
+quite differently; in particular we demonstrate the efficiency of randomized
+quantile sampling which is reminiscent of Thompson sampling. Our work is
+motivated by the root-finding sub-routine in pricing of Bermudan financial
+derivatives, illustrated in the last section of the paper.
+",1,0,0,1,0,0
+19499,Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic,"  Inspired by the recent evolution of deep neural networks (DNNs) in machine
+learning, we explore their application to PL-related topics. This paper is the
+first step towards this goal; we propose a proof-synthesis method for the
+negation-free propositional logic in which we use a DNN to obtain a guide of
+proof search. The idea is to view the proof-synthesis problem as a translation
+from a proposition to its proof. We train seq2seq, which is a popular network
+in neural machine translation, so that it generates a proof encoded as a
+$\lambda$-term of a given proposition. We implement the whole framework and
+empirically observe that a generated proof term is close to a correct proof in
+terms of the tree edit distance of AST. This observation justifies using the
+output from a trained seq2seq model as a guide for proof search.
+",1,0,0,0,0,0
+19500,Fibonacci words in hyperbolic Pascal triangles,"  The hyperbolic Pascal triangle ${\cal HPT}_{4,q}$ $(q\ge5)$ is a new
+mathematical construction, which is a geometrical generalization of Pascal's
+arithmetical triangle. In the present study we show that a natural pattern of
+rows of ${\cal HPT}_{4,5}$ is almost the same as the sequence consisting of
+every second term of the well-known Fibonacci words. Further, we give a
+generalization of the Fibonacci words using the hyperbolic Pascal triangles.
+The geometrical properties of a ${\cal HPT}_{4,q}$ imply a graph structure
+between the finite Fibonacci words.
+",1,0,0,0,0,0
+19501,A Review on Quantile Regression for Stochastic Computer Experiments,"  We report on an empirical study of the main strategies for conditional
+quantile estimation in the context of stochastic computer experiments. To
+ensure adequate diversity, six metamodels are presented, divided into three
+categories based on order statistics, functional approaches, and those of
+Bayesian inspiration. The metamodels are tested on several problems
+characterized by the size of the training set, the input dimension, the
+quantile order and the value of the probability density function in the
+neighborhood of the quantile. The metamodels studied reveal good contrasts in
+our set of 480 experiments, enabling several patterns to be extracted. Based on
+our results, guidelines are proposed to allow users to select the best method
+for a given problem.
+",1,0,0,1,0,0
+19502,Scalability of Voltage-Controlled Filamentary and Nanometallic Resistance Memories,"  Much effort has been devoted to device and materials engineering to realize
+nanoscale resistance random access memory (RRAM) for practical applications,
+but there still lacks a rational physical basis to be relied on to design
+scalable devices spanning many length scales. In particular, the critical
+switching criterion is not clear for RRAM devices in which resistance changes
+are limited to localized nanoscale filaments that experience concentrated heat,
+electric current and field. Here, we demonstrate voltage-controlled resistance
+switching for macro and nano devices in both filamentary RRAM and nanometallic
+RRAM, the latter switches uniformly and does not require forming. As a result,
+using a constant current density as the compliance, we have achieved
+area-scalability for the low resistance state of the filamentary RRAM, and for
+both the low and high resistance states of the nanometallic RRAM. This finding
+will help design area-scalable RRAM at the nanoscale.
+",0,1,0,0,0,0
+19503,Functional geometry of protein-protein interaction networks,"  Motivation: Protein-protein interactions (PPIs) are usually modelled as
+networks. These networks have extensively been studied using graphlets, small
+induced subgraphs capturing the local wiring patterns around nodes in networks.
+They revealed that proteins involved in similar functions tend to be similarly
+wired. However, such simple models can only represent pairwise relationships
+and cannot fully capture the higher-order organization of protein interactions,
+including protein complexes. Results: To model the multi-sale organization of
+these complex biological systems, we utilize simplicial complexes from
+computational geometry. The question is how to mine these new representations
+of PPI networks to reveal additional biological information. To address this,
+we define simplets, a generalization of graphlets to simplicial complexes. By
+using simplets, we define a sensitive measure of similarity between simplicial
+complex network representations that allows for clustering them according to
+their data types better than clustering them by using other state-of-the-art
+measures, e.g., spectral distance, or facet distribution distance. We model
+human and baker's yeast PPI networks as simplicial complexes that capture PPIs
+and protein complexes as simplices. On these models, we show that our newly
+introduced simplet-based methods cluster proteins by function better than the
+clustering methods that use the standard PPI networks, uncovering the new
+underlying functional organization of the cell. We demonstrate the existence of
+the functional geometry in the PPI data and the superiority of our
+simplet-based methods to effectively mine for new biological information hidden
+in the complexity of the higher order organization of PPI networks.
+",0,0,0,0,1,0
+19504,A Two-Layer Component-Based Allocation for Embedded Systems with GPUs,"  Component-based development is a software engineering paradigm that can
+facilitate the construction of embedded systems and tackle its complexities.
+The modern embedded systems have more and more demanding requirements. One way
+to cope with such versatile and growing set of requirements is to employ
+heterogeneous processing power, i.e., CPU-GPU architectures. The new CPU-GPU
+embedded boards deliver an increased performance but also introduce additional
+complexity and challenges. In this work, we address the component-to-hardware
+allocation for CPU-GPU embedded systems. The allocation for such systems is
+much complex due to the increased amount of GPU-related information. For
+example, while in traditional embedded systems the allocation mechanism may
+consider only the CPU memory usage of components to find an appropriate
+allocation scheme, in heterogeneous systems, the GPU memory usage needs also to
+be taken into account in the allocation process. This paper aims at decreasing
+the component-to-hardware allocation complexity by introducing a 2-layer
+component-based architecture for heterogeneous embedded systems. The detailed
+CPU-GPU information of the system is abstracted at a high-layer by compacting
+connected components into single units that behave as regular components. The
+allocator, based on the compacted information received from the high-level
+layer, computes, with a decreased complexity, feasible allocation schemes. In
+the last part of the paper, the 2-layer allocation method is evaluated using an
+existing embedded system demonstrator; namely, an underwater robot.
+",1,0,0,0,0,0
+19505,Sales Forecast in E-commerce using Convolutional Neural Network,"  Sales forecast is an essential task in E-commerce and has a crucial impact on
+making informed business decisions. It can help us to manage the workforce,
+cash flow and resources such as optimizing the supply chain of manufacturers
+etc. Sales forecast is a challenging problem in that sales is affected by many
+factors including promotion activities, price changes, and user preferences
+etc. Traditional sales forecast techniques mainly rely on historical sales data
+to predict future sales and their accuracies are limited. Some more recent
+learning-based methods capture more information in the model to improve the
+forecast accuracy. However, these methods require case-by-case manual feature
+engineering for specific commercial scenarios, which is usually a difficult,
+time-consuming task and requires expert knowledge. To overcome the limitations
+of existing methods, we propose a novel approach in this paper to learn
+effective features automatically from the structured data using the
+Convolutional Neural Network (CNN). When fed with raw log data, our approach
+can automatically extract effective features from that and then forecast sales
+using those extracted features. We test our method on a large real-world
+dataset from CaiNiao.com and the experimental results validate the
+effectiveness of our method.
+",1,0,0,0,0,0
+19506,On normalization of inconsistency indicators in pairwise comparisons,"  In this study, we provide mathematical and practice-driven justification for
+using $[0,1]$ normalization of inconsistency indicators in pairwise
+comparisons. The need for normalization, as well as problems with the lack of
+normalization, are presented. A new type of paradox of infinity is described.
+",1,0,0,0,0,0
+19507,Gamma factors of intertwining periods and distinction for inner forms of $\GL(n)$,"  Let $F$ be a $p$-adic fied, $E$ be a quadratic extension of $F$, and $D$ be
+an $F$-division algebra of odd index. Set $H=\mathrm{GL}m,D)$ and
+$G=\mathrm{GL}(m,D\otimes_F E)$, we carry out a fine study of local
+intertwining open periods attached to $H$-distinguished induced representations
+of inner forms of $G$. These objects have been studied globally in \cite{JLR}
+and \cite{LR}, and locally in \cite{BD08}. Here we give sufficient conditions
+for the local intertwining periods to have singularities. By a local/global
+method, we also compute in terms of Asai gamma factors the proportionality
+constants involved in their functional equations with respect to certain
+intertwining operators. As a consequence, we classify distinguished unitary and
+ladder representations of $G$, extending respectively the results of \cite{M14}
+and \cite{G15} for $D=F$, which both relied at some crucial step on the theory
+of Bernstein-Zelevinsky derivatives. We make use of one of the main results of
+\cite{BP17} in our setting, which in the case of the group $G$, asserts that
+the Jacquet-Langlands correspondence preserves distinction. Such a result is
+for discrete series representations, but our method in fact allows us to use it
+only for cuspidal representations of $G$.
+",0,0,1,0,0,0
+19508,Spin-flip scattering selection in a controlled molecular junction,"  A simple double-decker molecule with magnetic anisotropy, nickelocene, is
+attached to the metallic tip of a low-temperature scanning tunneling
+microscope. In the presence of a Cu(100) surface, the conductance around the
+Fermi energy is governed by spin-flip scattering, the nature of which is
+determined by the tunneling barrier thickness. The molecular tip exhibits
+inelastic spin-flip scattering in the tunneling regime, while in the contact
+regime a Kondo ground state is stabilized causing an order of magnitude change
+in the zero-bias conductance. First principle calculations show that
+nickelocene reversibly switches from a spin 1 to 1/2 between the two transport
+regimes.
+",0,1,0,0,0,0
+19509,Mean-Field Sparse Jurdjevic--Quinn Control,"  We consider nonlinear transport equations with non-local velocity, describing
+the time-evolution of a measure, which in practice may represent the density of
+a crowd. Such equations often appear by taking the mean-field limit of
+finite-dimensional systems modelling collective dynamics. We first give a sense
+to dissipativity of these mean-field equations in terms of Lie derivatives of a
+Lyapunov function depending on the measure. Then, we address the problem of
+controlling such equations by means of a time-varying bounded control action
+localized on a time-varying control subset with bounded Lebesgue measure
+(sparsity space constraint). Finite-dimensional versions are given by
+control-affine systems, which can be stabilized by the well known
+Jurdjevic--Quinn procedure. In this paper, assuming that the uncontrolled
+dynamics are dissipative, we develop an approach in the spirit of the classical
+Jurdjevic--Quinn theorem, showing how to steer the system to an invariant
+sublevel of the Lyapunov function. The control function and the control domain
+are designed in terms of the Lie derivatives of the Lyapunov function, and
+enjoy sparsity properties in the sense that the control support is small.
+Finally, we show that our result applies to a large class of kinetic equations
+modelling multi-agent dynamics.
+",0,0,1,0,0,0
+19510,Sampling from Social Networks with Attributes,"  Sampling from large networks represents a fundamental challenge for social
+network research. In this paper, we explore the sensitivity of different
+sampling techniques (node sampling, edge sampling, random walk sampling, and
+snowball sampling) on social networks with attributes. We consider the special
+case of networks (i) where we have one attribute with two values (e.g., male
+and female in the case of gender), (ii) where the size of the two groups is
+unequal (e.g., a male majority and a female minority), and (iii) where nodes
+with the same or different attribute value attract or repel each other (i.e.,
+homophilic or heterophilic behavior). We evaluate the different sampling
+techniques with respect to conserving the position of nodes and the visibility
+of groups in such networks. Experiments are conducted both on synthetic and
+empirical social networks. Our results provide evidence that different network
+sampling techniques are highly sensitive with regard to capturing the expected
+centrality of nodes, and that their accuracy depends on relative group size
+differences and on the level of homophily that can be observed in the network.
+We conclude that uninformed sampling from social networks with attributes thus
+can significantly impair the ability of researchers to draw valid conclusions
+about the centrality of nodes and the visibility or invisibility of groups in
+social networks.
+",1,1,0,0,0,0
+19511,Exploiting Investors Social Network for Stock Prediction in China's Market,"  Recent works have shown that social media platforms are able to influence the
+trends of stock price movements. However, existing works have majorly focused
+on the U.S. stock market and lacked attention to certain emerging countries
+such as China, where retail investors dominate the market. In this regard, as
+retail investors are prone to be influenced by news or other social media,
+psychological and behavioral features extracted from social media platforms are
+thought to well predict stock price movements in the China's market. Recent
+advances in the investor social network in China enables the extraction of such
+features from web-scale data. In this paper, on the basis of tweets from
+Xueqiu, a popular Chinese Twitter-like social platform specialized for
+investors, we analyze features with regard to collective sentiment and
+perception on stock relatedness and predict stock price movements by employing
+nonlinear models. The features of interest prove to be effective in our
+experiments.
+",0,0,0,0,0,1
+19512,Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks,"  Can we make a famous rap singer like Eminem sing whatever our favorite song?
+Singing style transfer attempts to make this possible, by replacing the vocal
+of a song from the source singer to the target singer. This paper presents a
+method that learns from unpaired data for singing style transfer using
+generative adversarial networks.
+",1,0,0,0,0,0
+19513,A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots,"  We present a robust deep learning based 6 degrees-of-freedom (DoF)
+localization system for endoscopic capsule robots. Our system mainly focuses on
+localization of endoscopic capsule robots inside the GI tract using only visual
+information captured by a mono camera integrated to the robot. The proposed
+system is a 23-layer deep convolutional neural network (CNN) that is capable to
+estimate the pose of the robot in real time using a standard CPU. The dataset
+for the evaluation of the system was recorded inside a surgical human stomach
+model with realistic surface texture, softness, and surface liquid properties
+so that the pre-trained CNN architecture can be transferred confidently into a
+real endoscopic scenario. An average error of 7:1% and 3:4% for translation and
+rotation has been obtained, respectively. The results accomplished from the
+experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images
+performs accurately inside the GI tract and is robust to various challenges
+posed by reflection distortions, lens imperfections, vignetting, noise, motion
+blur, low resolution, and lack of unique landmarks to track.
+",1,0,0,0,0,0
+19514,On a cross-diffusion system arising in image denosing,"  We study a generalization of a cross-diffusion problem deduced from a
+nonlinear complex-variable diffusion model for signal and image denoising.
+We prove the existence of weak solutions of the time-independent problem with
+fidelity terms under mild conditions on the data problem. Then, we show that
+this translates on the well-posedness of a quasi-steady state approximation of
+the evolution problem, and also prove the existence of weak solutions of the
+latter under more restrictive hypothesis.
+We finally perform some numerical simulations for image denoising, comparing
+the performance of the cross-diffusion model and its corresponding scalar
+Perona-Malik equation.
+",0,0,1,0,0,0
+19515,"Locally Repairable Codes with Multiple $(r_{i}, δ_{i})$-Localities","  In distributed storage systems, locally repairable codes (LRCs) are
+introduced to realize low disk I/O and repair cost. In order to tolerate
+multiple node failures, the LRCs with \emph{$(r, \delta)$-locality} are further
+proposed. Since hot data is not uncommon in a distributed storage system, both
+Zeh \emph{et al.} and Kadhe \emph{et al.} focus on the LRCs with \emph{multiple
+localities or unequal localities} (ML-LRCs) recently, which said that the
+localities among the code symbols can be different. ML-LRCs are attractive and
+useful in reducing repair cost for hot data. In this paper, we generalize the
+ML-LRCs to the $(r,\delta)$-locality case of multiple node failures, and define
+an LRC with multiple $(r_{i}, \delta_{i})_{i\in [s]}$ localities ($s\ge 2$),
+where $r_{1}\leq r_{2}\leq\dots\leq r_{s}$ and
+$\delta_{1}\geq\delta_{2}\geq\dots\geq\delta_{s}\geq2$. Such codes ensure that
+some hot data could be repaired more quickly and have better failure-tolerance
+in certain cases because of relatively smaller $r_{i}$ and larger $\delta_{i}$.
+Then, we derive a Singleton-like upper bound on the minimum distance for the
+proposed LRCs by employing the regenerating-set technique. Finally, we obtain a
+class of explicit and structured constructions of optimal ML-LRCs, and further
+extend them to the cases of multiple $(r_{i}, \delta)_{i\in [s]}$ localities.
+",1,0,0,0,0,0
+19516,Reconciling Enumerative and Symbolic Search in Syntax-Guided Synthesis,"  Syntax-guided synthesis aims to find a program satisfying semantic
+specification as well as user-provided structural hypothesis. For syntax-guided
+synthesis there are two main search strategies: concrete search, which
+systematically or stochastically enumerates all possible solutions, and
+symbolic search, which interacts with a constraint solver to solve the
+synthesis problem. In this paper, we propose a concolic synthesis framework
+which combines the best of the two worlds. Based on a decision tree
+representation, our framework works by enumerating tree heights from the
+smallest possible one to larger ones. For each fixed height, the framework
+symbolically searches a solution through the counterexample-guided inductive
+synthesis approach. To compensate the exponential blow-up problem with the
+concolic synthesis framework, we identify two fragments of synthesis problems
+and develop purely symbolic and more efficient procedures. The two fragments
+are decidable as these procedures are terminating and complete. We implemented
+our synthesis procedures and compared with state-of-the-art synthesizers on a
+range of benchmarks. Experiments show that our algorithms are promising.
+",1,0,0,0,0,0
+19517,Periodic solutions of semilinear Duffing equations with impulsive effects,"  In this paper we are concerned with the existence of periodic solutions for
+semilinear Duffing equations with impulsive effects. Firstly for the autonomous
+one, basing on Poincaré-Birkhoff twist theorem, we prove the existence of
+infinitely many periodic solutions. Secondly, as for the nonautonomous case,
+the impulse brings us great challenges for the study, and there are only
+finitely many periodic solutions, which is quite different from the
+corresponding equation without impulses. Here, taking the autonomous one as an
+auxiliary equation, we find the relation between these two equations and then
+obtain the result also by Poincaré-Birkhoff twist theorem.
+",0,1,1,0,0,0
+19518,Spectral and scattering theory for perturbed block Toeplitz operators,"  We analyse spectral properties of a class of compact perturbations of block
+Toeplitz operators associated with analytic symbols. In particular, a limiting
+absorption principle and the absence of singular continuous spectrum are shown.
+The existence and the completeness of wave operators are also obtained. Our
+study is based on the construction of a conjugate operator in Mourre sense for
+the corresponding Laurent operators.
+",0,0,1,0,0,0
+19519,Robust adaptive droop control for DC microgrids,"  There are tradeoffs between current sharing among distributed resources and
+DC bus voltage stability when conventional droop control is used in DC
+microgrids. As current sharing approaches the setpoint, bus voltage deviation
+increases. Previous studies have suggested using secondary control utilizing
+linear controllers to overcome drawbacks of droop control. However, linear
+control design depends on an accurate model of the system. The derivation of
+such a model is challenging because the noise and disturbances caused by the
+coupling between sources, loads, and switches in microgrids are
+under-represented. This under-representation makes linear modeling and control
+insufficient. Hence, in this paper, we propose a robust adaptive control to
+adjust droop characteristics to satisfy both current sharing and bus voltage
+stability. First, the time-varying models of DC microgrids are derived. Second,
+the improvements for the adaptive control method are presented. Third, the
+application of the enhanced adaptive method to DC microgrids is presented to
+satisfy the system objective. Fourth, simulation and experimental results on a
+microgrid show that the adaptive method precisely shares current between two
+distributed resources and maintains the nominal bus voltage. Last, the
+comparative study validates the effectiveness of the proposed method over the
+conventional method.
+",0,0,1,0,0,0
+19520,Off-diagonal asymptotic properties of Bergman kernels associated to analytic Kähler potentials,"  We prove a new off-diagonal asymptotic of the Bergman kernels associated to
+tensor powers of a positive line bundle on a compact Kähler manifold. We show
+that if the Kähler potential is real analytic, then the Bergman kernel
+accepts a complete asymptotic expansion in a neighborhood of the diagonal of
+shrinking size $k^{-\frac14}$. These improve the earlier results in the subject
+for smooth potentials, where an expansion exists in a $k^{-\frac12}$
+neighborhood of the diagonal. We obtain our results by finding upper bounds of
+the form $C^m m!^{2}$ for the Bergman coefficients $b_m(x, \bar y)$, which is
+an interesting problem on its own. We find such upper bounds using the method
+of Berman-Berndtsson-Sjöstrand. We also show that sharpening these upper
+bounds would improve the rate of shrinking neighborhoods of the diagonal $x=y$
+in our results. In the special case of metrics with local constant holomorphic
+sectional curvatures, we obtain off-diagonal asymptotic in a fixed (as $k \to
+\infty$) neighborhood of the diagonal, which recovers a result of Berman [Ber]
+(see Remark 3.5 of [Ber] for higher dimensions). In this case, we also find an
+explicit formula for the Bergman kernel mod $O(e^{-k \delta} )$.
+",0,0,1,0,0,0
+19521,Network Backboning with Noisy Data,"  Networks are powerful instruments to study complex phenomena, but they become
+hard to analyze in data that contain noise. Network backbones provide a tool to
+extract the latent structure from noisy networks by pruning non-salient edges.
+We describe a new approach to extract such backbones. We assume that edge
+weights are drawn from a binomial distribution, and estimate the error-variance
+in edge weights using a Bayesian framework. Our approach uses a more realistic
+null model for the edge weight creation process than prior work. In particular,
+it simultaneously considers the propensity of nodes to send and receive
+connections, whereas previous approaches only considered nodes as emitters of
+edges. We test our model with real world networks of different types (flows,
+stocks, co-occurrences, directed, undirected) and show that our Noise-Corrected
+approach returns backbones that outperform other approaches on a number of
+criteria. Our approach is scalable, able to deal with networks with millions of
+edges.
+",1,1,0,0,0,0
+19522,The 2017 DAVIS Challenge on Video Object Segmentation,"  We present the 2017 DAVIS Challenge on Video Object Segmentation, a public
+dataset, benchmark, and competition specifically designed for the task of video
+object segmentation. Following the footsteps of other successful initiatives,
+such as ILSVRC and PASCAL VOC, which established the avenue of research in the
+fields of scene classification and semantic segmentation, the DAVIS Challenge
+comprises a dataset, an evaluation methodology, and a public competition with a
+dedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on
+the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which
+has fostered the development of several novel state-of-the-art video object
+segmentation techniques. In this paper we describe the scope of the benchmark,
+highlight the main characteristics of the dataset, define the evaluation
+metrics of the competition, and present a detailed analysis of the results of
+the participants to the challenge.
+",1,0,0,0,0,0
+19523,How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow,"  Context: The success of Stack Overflow and other community-based
+question-and-answer (Q&A) sites depends mainly on the will of their members to
+answer others' questions. In fact, when formulating requests on Q&A sites, we
+are not simply seeking for information. Instead, we are also asking for other
+people's help and feedback. Understanding the dynamics of the participation in
+Q&A communities is essential to improve the value of crowdsourced knowledge.
+Objective: In this paper, we investigate how information seekers can increase
+the chance of eliciting a successful answer to their questions on Stack
+Overflow by focusing on the following actionable factors: affect, presentation
+quality, and time.
+Method: We develop a conceptual framework of factors potentially influencing
+the success of questions in Stack Overflow. We quantitatively analyze a set of
+over 87K questions from the official Stack Overflow dump to assess the impact
+of actionable factors on the success of technical requests. The information
+seeker reputation is included as a control factor. Furthermore, to understand
+the role played by affective states in the success of questions, we
+qualitatively analyze questions containing positive and negative emotions.
+Finally, a survey is conducted to understand how Stack Overflow users perceive
+the guideline suggestions for writing questions.
+Results: We found that regardless of user reputation, successful questions
+are short, contain code snippets, and do not abuse with uppercase characters.
+As regards affect, successful questions adopt a neutral emotional style.
+Conclusion: We provide evidence-based guidelines for writing effective
+questions on Stack Overflow that software engineers can follow to increase the
+chance of getting technical help. As for the role of affect, we empirically
+confirmed community guidelines that suggest avoiding rudeness in question
+writing.
+",1,0,0,0,0,0
+19524,Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays,"  Massive multiple-input multiple-output (MIMO) systems, which utilize a large
+number of antennas at the base station, are expected to enhance network
+throughput by enabling improved multiuser MIMO techniques. To deploy many
+antennas in reasonable form factors, base stations are expected to employ
+antenna arrays in both horizontal and vertical dimensions, which is known as
+full-dimension (FD) MIMO. The most popular two-dimensional array is the uniform
+planar array (UPA), where antennas are placed in a grid pattern. To exploit the
+full benefit of massive MIMO in frequency division duplexing (FDD), the
+downlink channel state information (CSI) should be estimated, quantized, and
+fed back from the receiver to the transmitter. However, it is difficult to
+accurately quantize the channel in a computationally efficient manner due to
+the high dimensionality of the massive MIMO channel. In this paper, we develop
+both narrowband and wideband CSI quantizers for FD-MIMO taking the properties
+of realistic channels and the UPA into consideration. To improve quantization
+quality, we focus on not only quantizing dominant radio paths in the channel,
+but also combining the quantized beams. We also develop a hierarchical beam
+search approach, which scans both vertical and horizontal domains jointly with
+moderate computational complexity. Numerical simulations verify that the
+performance of the proposed quantizers is better than that of previous CSI
+quantization techniques.
+",1,0,0,0,0,0
+19525,A Novel Stretch Energy Minimization Algorithm for Equiareal Parameterizations,"  Surface parameterizations have been widely applied to computer graphics and
+digital geometry processing. In this paper, we propose a novel stretch energy
+minimization (SEM) algorithm for the computation of equiareal parameterizations
+of simply connected open surfaces with a very small area distortion and a
+highly improved computational efficiency. In addition, the existence of
+nontrivial limit points of the SEM algorithm is guaranteed under some mild
+assumptions of the mesh quality. Numerical experiments indicate that the
+efficiency, accuracy, and robustness of the proposed SEM algorithm outperform
+other state-of-the-art algorithms. Applications of the SEM on surface remeshing
+and surface registration for simply connected open surfaces are demonstrated
+thereafter. Thanks to the SEM algorithm, the computations for these
+applications can be carried out efficiently and robustly.
+",1,0,0,0,0,0
+19526,Time Complexity Analysis of a Distributed Stochastic Optimization in a Non-Stationary Environment,"  In this paper, we consider a distributed stochastic optimization problem
+where the goal is to minimize the time average of a cost function subject to a
+set of constraints on the time averages of related stochastic processes called
+penalties. We assume that the state of the system is evolving in an independent
+and non-stationary fashion and the ""common information"" available at each node
+is distributed and delayed. Such stochastic optimization is an integral part of
+many important problems in wireless networks such as scheduling, routing,
+resource allocation and crowd sensing. We propose an approximate distributed
+Drift- Plus-Penalty (DPP) algorithm, and show that it achieves a time average
+cost (and penalties) that is within epsilon > 0 of the optimal cost (and
+constraints) with high probability. Also, we provide a condition on the
+convergence time t for this result to hold. In particular, for any delay D >= 0
+in the common information, we use a coupling argument to prove that the
+proposed algorithm converges almost surely to the optimal solution. We use an
+application from wireless sensor network to corroborate our theoretical
+findings through simulation results.
+",1,0,1,0,0,0
+19527,Robust Counterfactual Inferences using Feature Learning and their Applications,"  In a wide variety of applications, including personalization, we want to
+measure the difference in outcome due to an intervention and thus have to deal
+with counterfactual inference. The feedback from a customer in any of these
+situations is only 'bandit feedback' - that is, a partial feedback based on
+whether we chose to intervene or not. Typically randomized experiments are
+carried out to understand whether an intervention is overall better than no
+intervention. Here we present a feature learning algorithm to learn from a
+randomized experiment where the intervention in consideration is most effective
+and where it is least effective rather than only focusing on the overall
+impact, thus adding a context to our learning mechanism and extract more
+information. From the randomized experiment, we learn the feature
+representations which divide the population into subpopulations where we
+observe statistically significant difference in average customer feedback
+between those who were subjected to the intervention and those who were not,
+with a level of significance l, where l is a configurable parameter in our
+model. We use this information to derive the value of the intervention in
+consideration for each instance in the population. With experiments, we show
+that using this additional learning, in future interventions, the context for
+each instance could be leveraged to decide whether to intervene or not.
+",0,0,0,1,0,0
+19528,Regularity of symbolic powers and Arboricity of matroids,"  Let $\Delta$ be a simplicial complex of a matroid $M$. In this paper, we
+explicitly compute the regularity of all the symbolic powers of a
+Stanley-Reisner ideal $I_\Delta$ in terms of combinatorial data of the matroid
+$M$. In order to do that, we provide a sharp bound between the arboricity of
+$M$ and the circumference of its dual $M^*$.
+",0,0,1,0,0,0
+19529,Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting,"  We propose a max-pooling based loss function for training Long Short-Term
+Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low
+CPU, memory, and latency requirements. The max-pooling loss training can be
+further guided by initializing with a cross-entropy loss trained network. A
+posterior smoothing based evaluation approach is employed to measure keyword
+spotting performance. Our experimental results show that LSTM models trained
+using cross-entropy loss or max-pooling loss outperform a cross-entropy loss
+trained baseline feed-forward Deep Neural Network (DNN). In addition,
+max-pooling loss trained LSTM with randomly initialized network performs better
+compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss
+trained LSTM initialized with a cross-entropy pre-trained network shows the
+best performance, which yields $67.6\%$ relative reduction compared to baseline
+feed-forward DNN in Area Under the Curve (AUC) measure.
+",1,0,0,1,0,0
+19530,On Packet Scheduling with Adversarial Jamming and Speedup,"  In Packet Scheduling with Adversarial Jamming packets of arbitrary sizes
+arrive over time to be transmitted over a channel in which instantaneous
+jamming errors occur at times chosen by the adversary and not known to the
+algorithm. The transmission taking place at the time of jamming is corrupt, and
+the algorithm learns this fact immediately. An online algorithm maximizes the
+total size of packets it successfully transmits and the goal is to develop an
+algorithm with the lowest possible asymptotic competitive ratio, where the
+additive constant may depend on packet sizes.
+Our main contribution is a universal algorithm that works for any speedup and
+packet sizes and, unlike previous algorithms for the problem, it does not need
+to know these properties in advance. We show that this algorithm guarantees
+1-competitiveness with speedup 4, making it the first known algorithm to
+maintain 1-competitiveness with a moderate speedup in the general setting of
+arbitrary packet sizes. We also prove a lower bound of $\phi+1\approx 2.618$ on
+the speedup of any 1-competitive deterministic algorithm, showing that our
+algorithm is close to the optimum.
+Additionally, we formulate a general framework for analyzing our algorithm
+locally and use it to show upper bounds on its competitive ratio for speedups
+in $[1,4)$ and for several special cases, recovering some previously known
+results, each of which had a dedicated proof. In particular, our algorithm is
+3-competitive without speedup, matching both the (worst-case) performance of
+the algorithm by Jurdzinski et al. and the lower bound by Anta et al.
+",1,0,0,0,0,0
+19531,Coarse-Grid Computational Fluid Dynamic (CG-CFD) Error Prediction using Machine Learning,"  Despite the progress in high performance computing, Computational Fluid
+Dynamics (CFD) simulations are still computationally expensive for many
+practical engineering applications such as simulating large computational
+domains and highly turbulent flows. One of the major reasons of the high
+expense of CFD is the need for a fine grid to resolve phenomena at the relevant
+scale, and obtain a grid-independent solution. The fine grid requirements often
+drive the computational time step size down, which makes long transient
+problems prohibitively expensive. In the research presented, the feasibility of
+a Coarse Grid CFD (CG-CFD) approach is investigated by utilizing Machine
+Learning (ML) algorithms. Relying on coarse grids increases the discretization
+error. Hence, a method is suggested to produce a surrogate model that predicts
+the CG-CFD local errors to correct the variables of interest. Given
+high-fidelity data, a surrogate model is trained to predict the CG-CFD local
+errors as a function of the coarse grid local features. ML regression
+algorithms are utilized to construct a surrogate model that relates the local
+error and the coarse grid features. This method is applied to a
+three-dimensional flow in a lid driven cubic cavity domain. The performance of
+the method was assessed by training the surrogate model on the flow full field
+spatial data and tested on new data (from flows of different Reynolds number
+and/or computed by different grid sizes). The proposed method maximizes the
+benefit of the available data and shows potential for a good predictive
+capability.
+",0,1,0,0,0,0
+19532,EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection,"  In this paper, we propose a deep learning based approach for facial action
+unit detection by enhancing and cropping the regions of interest. The approach
+is implemented by adding two novel nets (layers): the enhancing layers and the
+cropping layers, to a pretrained CNN model. For the enhancing layers, we
+designed an attention map based on facial landmark features and applied it to a
+pretrained neural network to conduct enhanced learning (The E-Net). For the
+cropping layers, we crop facial regions around the detected landmarks and
+design convolutional layers to learn deeper features for each facial region
+(C-Net). We then fuse the E-Net and the C-Net to obtain our Enhancing and
+Cropping (EAC) Net, which can learn both feature enhancing and region cropping
+functions. Our approach shows significant improvement in performance compared
+to the state-of-the-art methods applied to BP4D and DISFA AU datasets.
+",1,0,0,0,0,0
+19533,Influence of surface and bulk water ice on the reactivity of a water-forming reaction,"  On the surface of icy dust grains in the dense regions of the interstellar
+medium a rich chemistry can take place. Due to the low temperature, reactions
+that proceed via a barrier can only take place through tunneling. The reaction
+H + H$_2$O$_2$ $\rightarrow$ H$_2$O + OH is such a case with a gas-phase
+barrier of $\sim$26.5 kJ/mol. Still the reaction is known to be involved in
+water formation on interstellar grains. Here, we investigate the influence of a
+water ice surface and of bulk ice on the reaction rate constant. Rate constants
+are calculated using instanton theory down to 74 K. The ice is taken into
+account via multiscale modeling, describing the reactants and the direct
+surrounding at the quantum mechanical level with density functional theory
+(DFT), while the rest of the ice is modeled on the molecular mechanical level
+with a force field. We find that H$_2$O$_2$ binding energies cannot be captured
+by a single value, but rather depend on the number of hydrogen bonds with
+surface molecules. In highly amorphous surroundings the binding site can block
+the routes of attack and impede the reaction. Furthermore, the activation
+energies do not correlate with the binding energies of the same sites. The
+unimolecular rate constants related to the Langmuir-Hinshelwood mechanism
+increase as the activation energy decreases. Thus, we provide a lower limit for
+the rate constant and argue that rate constants can have values up to two order
+of magnitude larger than this limit.
+",0,1,0,0,0,0
+19534,Impact of energy dissipation on interface shapes and on rates for dewetting from liquid substrates,"  We revisit the fundamental problem of liquid-liquid dewetting and perform a
+detailed comparison of theoretical predictions based on thin-film models with
+experimental measurements obtained by atomic force microscopy (AFM).
+Specifically, we consider the dewetting of a liquid polystyrene (PS) layer from
+a liquid polymethyl methacrylate (PMMA) layer, where the thicknesses and the
+viscosities of PS and PMMA layers are similar. The excellent agreement of
+experiment and theory reveals that dewetting rates for such systems follow no
+universal power law, in contrast to dewetting scenarios on solid substrates.
+Our new energetic approach allows to assess the physical importance of
+different contributions to the energy-dissipation mechanism, for which we
+analyze the local flow fields and the local dissipation rates.
+",0,1,0,0,0,0
+19535,Resonance control of graphene drum resonator in nonlinear regime by standing wave of light,"  We demonstrate the control of resonance characteristics of a drum type
+graphene mechanical resonator in nonlinear oscillation regime by the
+photothermal effect, which is induced by a standing wave of light between a
+graphene and a substrate. Unlike the conventional Duffing type nonlinearity,
+the resonance characteristics in nonlinear oscillation regime is modulated by
+the standing wave of light despite a small variation amplitude. From numerical
+calculations with a combination of equations of heat and motion with Duffing
+type nonlinearity, this can be explained that the photothermal effect causes
+delayed modulation of stress or tension of the graphene.
+",0,1,0,0,0,0
+19536,Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos,"  A surrogate model approximates a computationally expensive solver. Polynomial
+Chaos is a method to construct surrogate models by summing combinations of
+carefully chosen polynomials. The polynomials are chosen to respect the
+probability distributions of the uncertain input variables (parameters); this
+allows for both uncertainty quantification and global sensitivity analysis.
+In this paper we apply these techniques to a commercial solver for the
+estimation of peak gas rate and cumulative gas extraction from a coal seam gas
+well. The polynomial expansion is shown to honour the underlying geophysics
+with low error when compared to a much more complex and computationally slower
+commercial solver. We make use of advanced numerical integration techniques to
+achieve this accuracy using relatively small amounts of training data.
+",0,0,1,0,0,0
+19537,Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards,"  The most data-efficient algorithms for reinforcement learning in robotics are
+model-based policy search algorithms, which alternate between learning a
+dynamical model of the robot and optimizing a policy to maximize the expected
+return given the model and its uncertainties. However, the current algorithms
+lack an effective exploration strategy to deal with sparse or misleading reward
+scenarios: if they do not experience any state with a positive reward during
+the initial random exploration, it is very unlikely to solve the problem. Here,
+we propose a novel model-based policy search algorithm, Multi-DEX, that
+leverages a learned dynamical model to efficiently explore the task space and
+solve tasks with sparse rewards in a few episodes. To achieve this, we frame
+the policy search problem as a multi-objective, model-based policy optimization
+problem with three objectives: (1) generate maximally novel state trajectories,
+(2) maximize the expected return and (3) keep the system in state-space regions
+for which the model is as accurate as possible. We then optimize these
+objectives using a Pareto-based multi-objective optimization algorithm. The
+experiments show that Multi-DEX is able to solve sparse reward scenarios (with
+a simulated robotic arm) in much lower interaction time than VIME, TRPO,
+GEP-PG, CMA-ES and Black-DROPS.
+",1,0,0,1,0,0
+19538,Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification,"  This work explores the trade-off between the number of samples required to
+accurately build models of dynamical systems and the degradation of performance
+in various control objectives due to a coarse approximation. In particular, we
+show that simple models can be easily fit from input/output data and are
+sufficient for achieving various control objectives. We derive bounds on the
+number of noisy input/output samples from a stable linear time-invariant system
+that are sufficient to guarantee that the corresponding finite impulse response
+approximation is close to the true system in the $\mathcal{H}_\infty$-norm. We
+demonstrate that these demands are lower than those derived in prior art which
+aimed to accurately identify dynamical models. We also explore how different
+physical input constraints, such as power constraints, affect the sample
+complexity. Finally, we show how our analysis fits within the established
+framework of robust control, by demonstrating how a controller designed for an
+approximate system provably meets performance objectives on the true system.
+",1,0,1,0,0,0
+19539,"Construction,sensitivity index, and synchronization speed of optimal networks","  The stability (or instability) of synchronization is important in a number of
+real world systems, including the power grid, the human brain and biological
+cells. For identical synchronization, the synchronizability of a network, which
+can be measured by the range of coupling strength that admits stable
+synchronization, can be optimized for a given number of nodes and links.
+Depending on the geometric degeneracy of the Laplacian eigenvectors, optimal
+networks can be classified into different sensitivity levels, which we define
+as a network's sensitivity index. We introduce an efficient and explicit way to
+construct optimal networks of arbitrary size over a wide range of sensitivity
+and link densities. Using coupled chaotic oscillators, we study synchronization
+dynamics on optimal networks, showing that cospectral optimal networks can have
+drastically different speed of synchronization. Such difference in dynamical
+stability is found to be closely related to the different structural
+sensitivity of these networks: generally, networks with high sensitivity index
+are slower to synchronize, and, surprisingly, may not synchronize at all,
+despite being theoretically stable under linear stability analysis.
+",0,1,1,0,0,0
+19540,A Vorticity-Preserving Hydrodynamical Scheme for Modeling Accretion Disk Flows,"  Vortices, turbulence, and unsteady non-laminar flows are likely both
+prominent and dynamically important features of astrophysical disks. Such
+strongly nonlinear phenomena are often difficult, however, to simulate
+accurately, and are generally amenable to analytic treatment only in idealized
+form. In this paper, we explore the evolution of compressible two-dimensional
+flows using an implicit dual-time hydrodynamical scheme that strictly conserves
+vorticity (if applied to simulate inviscid flows for which Kelvin's Circulation
+Theorem is applicable). The algorithm is based on the work of Lerat, Falissard
+& Side (2007), who proposed it in the context of terrestrial applications such
+as the blade-vortex interactions generated by helicopter rotors. We present
+several tests of Lerat et al.'s vorticity-preserving approach, which we have
+implemented to second-order accuracy, providing side-by-side comparisons with
+other algorithms that are frequently used in protostellar disk simulations. The
+comparison codes include one based on explicit, second-order van-Leer
+advection, one based on spectral methods, and another that implements a
+higher-order Godunov solver. Our results suggest that Lerat et al's algorithm
+will be useful for simulations of astrophysical environments in which vortices
+play a dynamical role, and where strong shocks are not expected.
+",0,1,0,0,0,0
+19541,Direct simulation of liquid-gas-solid flow with a free surface lattice Boltzmann method,"  Direct numerical simulation of liquid-gas-solid flows is uncommon due to the
+considerable computational cost. As the grid spacing is determined by the
+smallest involved length scale, large grid sizes become necessary -- in
+particular if the bubble-particle aspect ratio is on the order of 10 or larger.
+Hence, it arises the question of both feasibility and reasonability. In this
+paper, we present a fully parallel, scalable method for direct numerical
+simulation of bubble-particle interaction at a size ratio of 1-2 orders of
+magnitude that makes simulations feasible on currently available
+super-computing resources. With the presented approach, simulations of bubbles
+in suspension columns consisting of more than $100\,000$ fully resolved
+particles become possible. Furthermore, we demonstrate the significance of
+particle-resolved simulations by comparison to previous unresolved solutions.
+The results indicate that fully-resolved direct numerical simulation is indeed
+necessary to predict the flow structure of bubble-particle interaction problems
+correctly.
+",0,1,0,0,0,0
+19542,TALL: Temporal Activity Localization via Language Query,"  This paper focuses on temporal localization of actions in untrimmed videos.
+Existing methods typically train classifiers for a pre-defined list of actions
+and apply them in a sliding window fashion. However, activities in the wild
+consist of a wide combination of actors, actions and objects; it is difficult
+to design a proper activity list that meets users' needs. We propose to
+localize activities by natural language queries. Temporal Activity Localization
+via Language (TALL) is challenging as it requires: (1) suitable design of text
+and video representations to allow cross-modal matching of actions and language
+queries; (2) ability to locate actions accurately given features from sliding
+windows of limited granularity. We propose a novel Cross-modal Temporal
+Regression Localizer (CTRL) to jointly model text query and video clips, output
+alignment scores and action boundary regression results for candidate clips.
+For evaluation, we adopt TaCoS dataset, and build a new dataset for this task
+on top of Charades by adding sentence temporal annotations, called
+Charades-STA. We also build complex sentence queries in Charades-STA for test.
+Experimental results show that CTRL outperforms previous methods significantly
+on both datasets.
+",1,0,0,0,0,0
+19543,2D granular flows with the $μ(I)$ rheology and side walls friction: a well balanced multilayer discretization,"  We present here numerical modelling of granular flows with the $\mu(I)$
+rheology in confined channels. The contribution is twofold: (i) a model to
+approximate the Navier-Stokes equations with the $\mu(I)$ rheology through an
+asymptotic analysis. Under the hypothesis of a one-dimensional flow, this model
+takes into account side walls friction; (ii) a multilayer discretization
+following Fernández-Nieto et al. (J. Fluid Mech., vol. 798, 2016, pp.
+643-681). In this new numerical scheme, we propose an appropriate treatment of
+the rheological terms through a hydrostatic reconstruction which allows this
+scheme to be well-balanced and therefore to deal with dry areas. Based on
+academic tests, we first evaluate the influence of the width of the channel on
+the normal profiles of the downslope velocity thanks to the multilayer approach
+that is intrinsically able to describe changes from Bagnold to S-shaped (and
+vice versa) velocity profiles. We also check the well balance property of the
+proposed numerical scheme. We show that approximating side walls friction using
+single-layer models may lead to strong errors. Secondly, we compare the
+numerical results with experimental data on granular collapses. We show that
+the proposed scheme allows us to qualitatively reproduce the deposit in the
+case of a rigid bed (i. e. dry area) and that the error made by replacing the
+dry area by a small layer of material may be large if this layer is not thin
+enough. The proposed model is also able to reproduce the time evolution of the
+free surface and of the flow/no-flow interface. In addition, it reproduces the
+effect of erosion for granular flows over initially static material lying on
+the bed. This is possible when using a variable friction coefficient $\mu(I)$
+but not with a constant friction coefficient.
+",0,1,0,0,0,0
+19544,A scientists' view of scientometrics: Not everything that counts can be counted,"  Like it or not, attempts to evaluate and monitor the quality of academic
+research have become increasingly prevalent worldwide. Performance reviews
+range from at the level of individuals, through research groups and
+departments, to entire universities. Many of these are informed by, or
+functions of, simple scientometric indicators and the results of such exercises
+impact onto careers, funding and prestige. However, there is sometimes a
+failure to appreciate that scientometrics are, at best, very blunt instruments
+and their incorrect usage can be misleading. Rather than accepting the rise and
+fall of individuals and institutions on the basis of such imprecise measures,
+calls have been made for indicators be regularly scrutinised and for
+improvements to the evidence base in this area. It is thus incumbent upon the
+scientific community, especially the physics, complexity-science and
+scientometrics communities, to scrutinise metric indicators. Here, we review
+recent attempts to do this and show that some metrics in widespread use cannot
+be used as reliable indicators research quality.
+",1,1,0,0,0,0
+19545,On analyzing and evaluating privacy measures for social networks under active attack,"  Widespread usage of complex interconnected social networks such as Facebook,
+Twitter and LinkedIn in modern internet era has also unfortunately opened the
+door for privacy violation of users of such networks by malicious entities. In
+this article we investigate, both theoretically and empirically, privacy
+violation measures of large networks under active attacks that was recently
+introduced in (Information Sciences, 328, 403-417, 2016). Our theoretical
+result indicates that the network manager responsible for prevention of privacy
+violation must be very careful in designing the network if its topology does
+not contain a cycle. Our empirical results shed light on privacy violation
+properties of eight real social networks as well as a large number of synthetic
+networks generated by both the classical Erdos-Renyi model and the scale-free
+random networks generated by the Barabasi-Albert preferential-attachment model.
+",1,0,0,0,0,0
+19546,Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise,"  Recent developments have established the vulnerability of deep reinforcement
+learning to policy manipulation attacks via intentionally perturbed inputs,
+known as adversarial examples. In this work, we propose a technique for
+mitigation of such attacks based on addition of noise to the parameter space of
+deep reinforcement learners during training. We experimentally verify the
+effect of parameter-space noise in reducing the transferability of adversarial
+examples, and demonstrate the promising performance of this technique in
+mitigating the impact of whitebox and blackbox attacks at both test and
+training times.
+",0,0,0,1,0,0
+19547,Multi-Observation Elicitation,"  We study loss functions that measure the accuracy of a prediction based on
+multiple data points simultaneously. To our knowledge, such loss functions have
+not been studied before in the area of property elicitation or in machine
+learning more broadly. As compared to traditional loss functions that take only
+a single data point, these multi-observation loss functions can in some cases
+drastically reduce the dimensionality of the hypothesis required. In
+elicitation, this corresponds to requiring many fewer reports; in empirical
+risk minimization, it corresponds to algorithms on a hypothesis space of much
+smaller dimension. We explore some examples of the tradeoff between
+dimensionality and number of observations, give some geometric
+characterizations and intuition for relating loss functions and the properties
+that they elicit, and discuss some implications for both elicitation and
+machine-learning contexts.
+",1,0,0,0,0,0
+19548,"Next Stop ""NoOps"": Enabling Cross-System Diagnostics Through Graph-based Composition of Logs and Metrics","  Performing diagnostics in IT systems is an increasingly complicated task, and
+it is not doable in satisfactory time by even the most skillful operators.
+Systems and their architecture change very rapidly in response to business and
+user demand. Many organizations see value in the maintenance and management
+model of NoOps that stands for No Operations. One of the implementations of
+this model is a system that is maintained automatically without any human
+intervention. The path to NoOps involves not only precise and fast diagnostics
+but also reusing as much knowledge as possible after the system is reconfigured
+or changed. The biggest challenge is to leverage knowledge on one IT system and
+reuse this knowledge for diagnostics of another, different system. We propose a
+framework of weighted graphs which can transfer knowledge, and perform
+high-quality diagnostics of IT systems. We encode all possible data in a graph
+representation of a system state and automatically calculate weights of these
+graphs. Then, thanks to the evaluation of similarity between graphs, we
+transfer knowledge about failures from one system to another and use it for
+diagnostics. We successfully evaluate the proposed approach on Spark, Hadoop,
+Kafka and Cassandra systems.
+",1,0,0,0,0,0
+19549,Davenport-Heilbronn Theorems for Quotients of Class Groups,"  We prove a generalization of the Davenport-Heilbronn theorem to quotients of
+ideal class groups of quadratic fields by the primes lying above a fixed set of
+rational primes $S$. Additionally, we obtain average sizes for the relaxed
+Selmer group $\mathrm{Sel}_3^S(K)$ and for
+$\mathcal{O}_{K,S}^\times/(\mathcal{O}_{K,S}^\times)^3$ as $K$ varies among
+quadratic fields with a fixed signature ordered by discriminant.
+",0,0,1,0,0,0
+19550,Learning Universal Adversarial Perturbations with Generative Models,"  Neural networks are known to be vulnerable to adversarial examples, inputs
+that have been intentionally perturbed to remain visually similar to the source
+input, but cause a misclassification. It was recently shown that given a
+dataset and classifier, there exists so called universal adversarial
+perturbations, a single perturbation that causes a misclassification when
+applied to any input. In this work, we introduce universal adversarial
+networks, a generative network that is capable of fooling a target classifier
+when it's generated output is added to a clean sample from a dataset. We show
+that this technique improves on known universal adversarial attacks.
+",1,0,0,1,0,0
+19551,Large-Scale Classification using Multinomial Regression and ADMM,"  We present a novel method for learning the weights in multinomial logistic
+regression based on the alternating direction method of multipliers (ADMM). In
+each iteration, our algorithm decomposes the training into three steps; a
+linear least-squares problem for the weights, a global variable update
+involving a separable cross-entropy loss function, and a trivial dual variable
+update The least-squares problem can be factorized in the off-line phase, and
+the separability in the global variable update allows for efficient
+parallelization, leading to faster convergence. We compare our method with
+stochastic gradient descent for linear classification as well as for transfer
+learning and show that the proposed ADMM-Softmax leads to improved
+generalization and convergence.
+",1,0,0,1,0,0
+19552,First order dipolar phase transition in the Dicke model with infinitely coordinated frustrating interaction,"  We found analytically a first order quantum phase transition in the Cooper
+pair box array of $N$ low-capacitance Josephson junctions capacitively coupled
+to a resonant photon in a microwave cavity. The Hamiltonian of the system maps
+on the extended Dicke Hamiltonian of $N$ spins one-half with infinitely
+coordinated antiferromagnetic (frustrating) interaction. This interaction
+arises from the gauge-invariant coupling of the Josephson junctions phases to
+the vector potential of the resonant photon field. In $N \gg 1$ semiclassical
+limit, we found a critical coupling at which ground state of the system
+switches to the one with a net collective electric dipole moment of the Cooper
+pair boxes coupled to superradiant equilibrium photonic condensate. This phase
+transition changes from the first to second order if the frustrating
+interaction is switched off. A self-consistently `rotating' Holstein-Primakoff
+representation for the Cartesian components of the total superspin is proposed,
+that enables to trace both the first and the second order quantum phase
+transitions in the extended and standard Dicke models respectively.
+",0,1,0,0,0,0
+19553,Parsimonious Bayesian deep networks,"  Combining Bayesian nonparametrics and a forward model selection strategy, we
+construct parsimonious Bayesian deep networks (PBDNs) that infer
+capacity-regularized network architectures from the data and require neither
+cross-validation nor fine-tuning when training the model. One of the two
+essential components of a PBDN is the development of a special infinite-wide
+single-hidden-layer neural network, whose number of active hidden units can be
+inferred from the data. The other one is the construction of a greedy
+layer-wise learning algorithm that uses a forward model selection criterion to
+determine when to stop adding another hidden layer. We develop both Gibbs
+sampling and stochastic gradient descent based maximum a posteriori inference
+for PBDNs, providing state-of-the-art classification accuracy and interpretable
+data subtypes near the decision boundaries, while maintaining low computational
+complexity for out-of-sample prediction.
+",0,0,0,1,0,0
+19554,Learning to Play Othello with Deep Neural Networks,"  Achieving superhuman playing level by AlphaGo corroborated the capabilities
+of convolutional neural architectures (CNNs) for capturing complex spatial
+patterns. This result was to a great extent due to several analogies between Go
+board states and 2D images CNNs have been designed for, in particular
+translational invariance and a relatively large board. In this paper, we verify
+whether CNN-based move predictors prove effective for Othello, a game with
+significantly different characteristics, including a much smaller board size
+and complete lack of translational invariance. We compare several CNN
+architectures and board encodings, augment them with state-of-the-art
+extensions, train on an extensive database of experts' moves, and examine them
+with respect to move prediction accuracy and playing strength. The empirical
+evaluation confirms high capabilities of neural move predictors and suggests a
+strong correlation between prediction accuracy and playing strength. The best
+CNNs not only surpass all other 1-ply Othello players proposed to date but
+defeat (2-ply) Edax, the best open-source Othello player.
+",1,0,0,1,0,0
+19555,A causal modelling framework for reference-based imputation and tipping point analysis,"  We consider estimating the ""de facto"" or effectiveness estimand in a
+randomised placebo-controlled or standard-of-care-controlled drug trial with
+quantitative outcome, where participants who discontinue an investigational
+treatment are not followed up thereafter. Carpenter et al (2013) proposed
+reference-based imputation methods which use a reference arm to inform the
+distribution of post-discontinuation outcomes and hence to inform an imputation
+model. However, the reference-based imputation methods were not formally
+justified. We present a causal model which makes an explicit assumption in a
+potential outcomes framework about the maintained causal effect of treatment
+after discontinuation. We show that the ""jump to reference"", ""copy reference""
+and ""copy increments in reference"" reference-based imputation methods, with the
+control arm as the reference arm, are special cases of the causal model with
+specific assumptions about the causal treatment effect. Results from simulation
+studies are presented. We also show that the causal model provides a flexible
+and transparent framework for a tipping point sensitivity analysis in which we
+vary the assumptions made about the causal effect of discontinued treatment. We
+illustrate the approach with data from two longitudinal clinical trials.
+",0,0,0,1,0,0
+19556,"The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race","  Recent studies in social media spam and automation provide anecdotal
+argumentation of the rise of a new generation of spambots, so-called social
+spambots. Here, for the first time, we extensively study this novel phenomenon
+on Twitter and we provide quantitative evidence that a paradigm-shift exists in
+spambot design. First, we measure current Twitter's capabilities of detecting
+the new social spambots. Later, we assess the human performance in
+discriminating between genuine accounts, social spambots, and traditional
+spambots. Then, we benchmark several state-of-the-art techniques proposed by
+the academic literature. Results show that neither Twitter, nor humans, nor
+cutting-edge applications are currently capable of accurately detecting the new
+social spambots. Our results call for new approaches capable of turning the
+tide in the fight against this raising phenomenon. We conclude by reviewing the
+latest literature on spambots detection and we highlight an emerging common
+research trend based on the analysis of collective behaviors. Insights derived
+from both our extensive experimental campaign and survey shed light on the most
+promising directions of research and lay the foundations for the arms race
+against the novel social spambots. Finally, to foster research on this novel
+phenomenon, we make publicly available to the scientific community all the
+datasets used in this study.
+",1,0,0,0,0,0
+19557,Weakly-Supervised Spatial Context Networks,"  We explore the power of spatial context as a self-supervisory signal for
+learning visual representations. In particular, we propose spatial context
+networks that learn to predict a representation of one image patch from another
+image patch, within the same image, conditioned on their real-valued relative
+spatial offset. Unlike auto-encoders, that aim to encode and reconstruct
+original image patches, our network aims to encode and reconstruct intermediate
+representations of the spatially offset patches. As such, the network learns a
+spatially conditioned contextual representation. By testing performance with
+various patch selection mechanisms we show that focusing on object-centric
+patches is important, and that using object proposal as a patch selection
+mechanism leads to the highest improvement in performance. Further, unlike
+auto-encoders, context encoders [21], or other forms of unsupervised feature
+learning, we illustrate that contextual supervision (with pre-trained model
+initialization) can improve on existing pre-trained model performance. We build
+our spatial context networks on top of standard VGG_19 and CNN_M architectures
+and, among other things, show that we can achieve improvements (with no
+additional explicit supervision) over the original ImageNet pre-trained VGG_19
+and CNN_M models in object categorization and detection on VOC2007.
+",1,0,0,0,0,0
+19558,Variation of ionizing continuum: the main driver of Broad Absorption Line Variability,"  We present a statistical analysis of the variability of broad absorption
+lines (BALs) in quasars using the large multi-epoch spectroscopic dataset of
+the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We divide the sample
+into two groups according to the pattern of the variation of C iv BAL with
+respect to that of continuum: the equivalent widths (EW) of the BAL decreases
+(increases) when the continuum brightens (dims) as group T1; and the variation
+of EW and continuum in the opposite relation as group T2. We find that T2 has
+significantly (P_T<10-6 , Students T Test) higher EW ratios (R) of Si iv to C
+iv BAL than T1. Our result agrees with the prediction of photoionization models
+that C +3 column density increases (decreases) if there is a (or no) C +3
+ionization front while R decreases with the incident continuum. We show that
+BAL variabilities in at least 80% quasars are driven by the variation of
+ionizing continuum while other models that predict uncorrelated BAL and
+continuum variability contribute less than 20%. Considering large uncertainty
+in the continuum flux calibration, the latter fraction may be much smaller.
+When the sample is binned into different time interval between the two
+observations, we find significant difference in the distribution of R between
+T1 and T2 in all time-bins down to a deltaT < 6 days, suggesting that BAL
+outflow in a fraction of quasars has a recombination time scale of only a few
+days.
+",0,1,0,0,0,0
+19559,Low Dimensional Atomic Norm Representations in Line Spectral Estimation,"  The line spectral estimation problem consists in recovering the frequencies
+of a complex valued time signal that is assumed to be sparse in the spectral
+domain from its discrete observations. Unlike the gridding required by the
+classical compressed sensing framework, line spectral estimation reconstructs
+signals whose spectral supports lie continuously in the Fourier domain. If
+recent advances have shown that atomic norm relaxation produces highly robust
+estimates in this context, the computational cost of this approach remains,
+however, the major flaw for its application to practical systems.
+In this work, we aim to bridge the complexity issue by studying the atomic
+norm minimization problem from low dimensional projection of the signal
+samples. We derive conditions on the sub-sampling matrix under which the
+partial atomic norm can be expressed by a low-dimensional semidefinite program.
+Moreover, we illustrate the tightness of this relaxation by showing that it is
+possible to recover the original signal in poly-logarithmic time for two
+specific sub-sampling patterns.
+",1,0,0,0,0,0
+19560,The Trio Identity for Quasi-Monte Carlo Error,"  Monte Carlo methods approximate integrals by sample averages of integrand
+values. The error of Monte Carlo methods may be expressed as a trio identity:
+the product of the variation of the integrand, the discrepancy of the sampling
+measure, and the confounding. The trio identity has different versions,
+depending on whether the integrand is deterministic or Bayesian and whether the
+sampling measure is deterministic or random. Although the variation and the
+discrepancy are common in the literature, the confounding is relatively unknown
+and under-appreciated. Theory and examples are used to show how the cubature
+error may be reduced by employing the low discrepancy sampling that defines
+quasi-Monte Carlo methods. The error may also be reduced by rewriting the
+integral in terms of a different integrand. Finally, the confounding explains
+why the cubature error might decay at a rate different from that of the
+discrepancy.
+",0,0,1,0,0,0
+19561,Weak type operator Lipschitz and commutator estimates for commuting tuples,"  Let $f: \mathbb{R}^d \to\mathbb{R}$ be a Lipschitz function. If $B$ is a
+bounded self-adjoint operator and if $\{A_k\}_{k=1}^d$ are commuting bounded
+self-adjoint operators such that $[A_k,B]\in L_1(H),$ then
+$$\|[f(A_1,\cdots,A_d),B]\|_{1,\infty}\leq
+c(d)\|\nabla(f)\|_{\infty}\max_{1\leq k\leq d}\|[A_k,B]\|_1,$$ where $c(d)$ is
+a constant independent of $f$, $\mathcal{M}$ and $A,B$ and
+$\|\cdot\|_{1,\infty}$ denotes the weak $L_1$-norm. If $\{X_k\}_{k=1}^d$
+(respectively, $\{Y_k\}_{k=1}^d$) are commuting bounded self-adjoint operators
+such that $X_k-Y_k\in L_1(H),$ then
+$$\|f(X_1,\cdots,X_d)-f(Y_1,\cdots,Y_d)\|_{1,\infty}\leq
+c(d)\|\nabla(f)\|_{\infty}\max_{1\leq k\leq d}\|X_k-Y_k\|_1.$$
+",0,0,1,0,0,0
+19562,Fast Spectral Ranking for Similarity Search,"  Despite the success of deep learning on representing images for particular
+object retrieval, recent studies show that the learned representations still
+lie on manifolds in a high dimensional space. This makes the Euclidean nearest
+neighbor search biased for this task. Exploring the manifolds online remains
+expensive even if a nearest neighbor graph has been computed offline. This work
+introduces an explicit embedding reducing manifold search to Euclidean search
+followed by dot product similarity search. This is equivalent to linear graph
+filtering of a sparse signal in the frequency domain. To speed up online
+search, we compute an approximate Fourier basis of the graph offline. We
+improve the state of art on particular object retrieval datasets including the
+challenging Instre dataset containing small objects. At a scale of 10^5 images,
+the offline cost is only a few hours, while query time is comparable to
+standard similarity search.
+",1,0,0,0,0,0
+19563,Transition of multi-diffusive states in a biased periodic potential,"  We study a frequency-dependent damping model of hyper-diffusion within the
+generalized Langevin equation. The model allows for the colored noise defined
+by its spectral density, assumed to be proportional to $\omega^{\delta-1}$ at
+low frequencies with $0<\delta<1$ (sub-Ohmic damping) or $1<\delta<2$
+(super-Ohmic damping), where the frequency-dependent damping is deduced from
+the noise by means of the fluctuation-dissipation theorem. It is shown that for
+super-Ohmic damping and certain parameters, the diffusive process of the
+particle in a titled periodic potential undergos sequentially four
+time-regimes: thermalization, hyper-diffusion, collapse and asymptotical
+restoration. For analysing transition phenomenon of multi-diffusive states, we
+demonstrate that the first exist time of the particle escaping from the locked
+state into the running state abides by an exponential distribution. The concept
+of equivalent velocity trap is introduced in the present model, moreover,
+reformation of ballistic diffusive system is also considered as a marginal
+situation, however there does not exhibit the collapsed state of diffusion.
+",0,1,0,0,0,0
+19564,Deformable Classifiers,"  Geometric variations of objects, which do not modify the object class, pose a
+major challenge for object recognition. These variations could be rigid as well
+as non-rigid transformations. In this paper, we design a framework for training
+deformable classifiers, where latent transformation variables are introduced,
+and a transformation of the object image to a reference instantiation is
+computed in terms of the classifier output, separately for each class. The
+classifier outputs for each class, after transformation, are compared to yield
+the final decision. As a by-product of the classification this yields a
+transformation of the input object to a reference pose, which can be used for
+downstream tasks such as the computation of object support. We apply a two-step
+training mechanism for our framework, which alternates between optimizing over
+the latent transformation variables and the classifier parameters to minimize
+the loss function. We show that multilayer perceptrons, also known as deep
+networks, are well suited for this approach and achieve state of the art
+results on the rotated MNIST and the Google Earth dataset, and produce
+competitive results on MNIST and CIFAR-10 when training on smaller subsets of
+training data.
+",0,0,0,1,0,0
+19565,Energy Dissipation in Hamiltonian Chains of Rotators,"  We discuss, in the context of energy flow in high-dimensional systems and
+Kolmogorov-Arnol'd-Moser (KAM) theory, the behavior of a chain of rotators
+(rotors) which is purely Hamiltonian, apart from dissipation at just one end.
+We derive bounds on the dissipation rate which become arbitrarily small in
+certain physical regimes, and we present numerical evidence that these bounds
+are sharp. We relate this to the decoupling of non-resonant terms as is known
+in KAM problems.
+",0,1,1,0,0,0
+19566,"Computational complexity, torsion-freeness of homoclinic Floer homology, and homoclinic Morse inequalities","  Floer theory was originally devised to estimate the number of 1-periodic
+orbits of Hamiltonian systems. In earlier works, we constructed Floer homology
+for homoclinic orbits on two dimensional manifolds using combinatorial
+techniques. In the present paper, we study theoretic aspects of computational
+complexity of homoclinic Floer homology. More precisely, for finding the
+homoclinic points and immersions that generate the homology and its boundary
+operator, we establish sharp upper bounds in terms of iterations of the
+underlying symplectomorphism. This prepares the ground for future numerical
+works.
+Although originally aimed at numerics, the above bounds provide also purely
+algebraic applications, namely
+1) Torsion-freeness of primary homoclinic Floer homology.
+2) Morse type inequalities for primary homoclinic orbits.
+",0,0,1,0,0,0
+19567,Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models,"  Bayesian inference for stochastic volatility models using MCMC methods highly
+depends on actual parameter values in terms of sampling efficiency. While draws
+from the posterior utilizing the standard centered parameterization break down
+when the volatility of volatility parameter in the latent state equation is
+small, non-centered versions of the model show deficiencies for highly
+persistent latent variable series. The novel approach of
+ancillarity-sufficiency interweaving has recently been shown to aid in
+overcoming these issues for a broad class of multilevel models. In this paper,
+we demonstrate how such an interweaving strategy can be applied to stochastic
+volatility models in order to greatly improve sampling efficiency for all
+parameters and throughout the entire parameter range. Moreover, this method of
+""combining best of different worlds"" allows for inference for parameter
+constellations that have previously been infeasible to estimate without the
+need to select a particular parameterization beforehand.
+",0,0,0,1,0,0
+19568,Simple root flows for Hitchin representations,"  We study simple root flows and Liouville currents for Hitchin
+representations. We show that the Liouville current is associated to the
+measure of maximal entropy for a simple root flow, derive a Liouville volume
+rigidity result, and construct a Liouville pressure metric on the Hitchin
+component.
+",0,0,1,0,0,0
+19569,On the Origin of Deep Learning,"  This paper is a review of the evolutionary history of deep learning models.
+It covers from the genesis of neural networks when associationism modeling of
+the brain is studied, to the models that dominate the last decade of research
+in deep learning like convolutional neural networks, deep belief networks, and
+recurrent neural networks. In addition to a review of these models, this paper
+primarily focuses on the precedents of the models above, examining how the
+initial ideas are assembled to construct the early models and how these
+preliminary models are developed into their current forms. Many of these
+evolutionary paths last more than half a century and have a diversity of
+directions. For example, CNN is built on prior knowledge of biological vision
+system; DBN is evolved from a trade-off of modeling power and computation
+complexity of graphical models and many nowadays models are neural counterparts
+of ancient linear models. This paper reviews these evolutionary paths and
+offers a concise thought flow of how these models are developed, and aims to
+provide a thorough background for deep learning. More importantly, along with
+the path, this paper summarizes the gist behind these milestones and proposes
+many directions to guide the future research of deep learning.
+",1,0,0,1,0,0
+19570,Control of Asynchronous Imitation Dynamics on Networks,"  Imitation is widely observed in populations of decision-making agents. Using
+our recent convergence results for asynchronous imitation dynamics on networks,
+we consider how such networks can be efficiently driven to a desired
+equilibrium state by offering payoff incentives for using a certain strategy,
+either uniformly or targeted to individuals. In particular, if for each
+available strategy, agents playing that strategy receive maximum payoff when
+their neighbors play that same strategy, we show that providing incentives to
+agents in a network that is at equilibrium will result in convergence to a
+unique new equilibrium. For the case when a uniform incentive can be offered to
+all agents, this result allows the computation of the optimal incentive using a
+binary search algorithm. When incentives can be targeted to individual agents,
+we propose an algorithm to select which agents should be chosen based on
+iteratively maximizing a ratio of the number of agents who adopt the desired
+strategy to the payoff incentive required to get those agents to do so.
+Simulations demonstrate that the proposed algorithm computes near-optimal
+targeted payoff incentives for a range of networks and payoff distributions in
+coordination games.
+",1,1,0,0,0,0
+19571,Finsler structures on holomorphic Lie algebroids,"  Complex Finsler vector bundles have been studied mainly by T. Aikou, who
+defined complex Finsler structures on holomorphic vector bundles. In this
+paper, we consider the more general case of a holomorphic Lie algebroid E and
+we introduce Finsler structures, partial and Chern-Finsler connections on it.
+First, we recall some basic notions on holomorphic Lie algebroids. Then, using
+an idea from E. Martinez, we introduce the concept of complexified prolongation
+of such an algebroid. Also, we study nonlinear and linear connections on the
+tangent bundle of E and on the prolongation of E and we investigate the
+relation between their coefficients. The analogue of the classical
+Chern-Finsler connection is defined and studied in the paper for the case of
+the holomorphic Lie algebroid.
+",0,0,1,0,0,0
+19572,The role of complex analysis in modeling economic growth,"  Development and growth are complex and tumultuous processes. Modern economic
+growth theories identify some key determinants of economic growth. However, the
+relative importance of the determinants remains unknown, and additional
+variables may help clarify the directions and dimensions of the interactions.
+The novel stream of literature on economic complexity goes beyond aggregate
+measures of productive inputs, and considers instead a more granular and
+structural view of the productive possibilities of countries, i.e. their
+capabilities. Different endowments of capabilities are crucial ingredients in
+explaining differences in economic performances. In this paper we employ
+economic fitness, a measure of productive capabilities obtained through complex
+network techniques. Focusing on the combined roles of fitness and some more
+traditional drivers of growth, we build a bridge between economic growth
+theories and the economic complexity literature. Our findings, in agreement
+with other recent empirical studies, show that fitness plays a crucial role in
+fostering economic growth and, when it is included in the analysis, can be
+either complementary to traditional drivers of growth or can completely
+overshadow them.
+",0,0,0,0,0,1
+19573,Inputs from Hell: Generating Uncommon Inputs from Common Samples,"  Generating structured input files to test programs can be performed by
+techniques that produce them from a grammar that serves as the specification
+for syntactically correct input files. Two interesting scenarios then arise for
+effective testing. In the first scenario, software engineers would like to
+generate inputs that are as similar as possible to the inputs in common usage
+of the program, to test the reliability of the program. More interesting is the
+second scenario where inputs should be as dissimilar as possible from normal
+usage. This is useful for robustness testing and exploring yet uncovered
+behavior. To provide test cases for both scenarios, we leverage a context-free
+grammar to parse a set of sample input files that represent the program's
+common usage, and determine probabilities for individual grammar production as
+they occur during parsing the inputs. Replicating these probabilities during
+grammar-based test input generation, we obtain inputs that are close to the
+samples. Inverting these probabilities yields inputs that are strongly
+dissimilar to common inputs, yet still valid with respect to the grammar. Our
+evaluation on three common input formats (JSON, JavaScript, CSS) shows the
+effectiveness of these approaches in obtaining instances from both sets of
+inputs.
+",1,0,0,0,0,0
+19574,On equivariant formal deformation theory,"  Using the set-up of deformation categories of Talpo and Vistoli, we
+re-interpret and generalize, in the context of cartesian morphisms in abstract
+categories, some results of Rim concerning obstructions against extensions of
+group actions in infinitesimal deformations. Furthermore, we observe that
+finite étale coverings can be infinitesimally extended and the resulting
+formal scheme is algebraizable. Finally, we show that pre-Tango structures
+survive under pullbacks with respect to finite, generically étale surjections
+$\pi:X\rightarrow Y$, and record some consequences regarding Kodaira vanishing
+in degree one.
+",0,0,1,0,0,0
+19575,A self-consistent cloud model for brown dwarfs and young giant exoplanets: comparison with photometric and spectroscopic observations,"  We developed a simple, physical and self-consistent cloud model for brown
+dwarfs and young giant exoplanets. We compared different parametrisations for
+the cloud particle size, by either fixing particle radii, or fixing the mixing
+efficiency (parameter fsed) or estimating particle radii from simple
+microphysics. The cloud scheme with simple microphysics appears as the best
+parametrisation by successfully reproducing the observed photometry and spectra
+of brown dwarfs and young giant exoplanets. In particular, it reproduces the
+L-T transition, due to the condensation of silicate and iron clouds below the
+visible/near-IR photosphere. It also reproduces the reddening observed for
+low-gravity objects, due to an increase of cloud optical depth for low gravity.
+In addition, we found that the cloud greenhouse effect shifts chemical
+equilibriums, increasing the abundances of species stable at high temperature.
+This effect should significantly contribute to the strong variation of methane
+abundance at the L-T transition and to the methane depletion observed on young
+exoplanets. Finally, we predict the existence of a continuum of brown dwarfs
+and exoplanets for absolute J magnitude=15-18 and J-K color=0-3, due to the
+evolution of the L-T transition with gravity. This self-consistent model
+therefore provides a general framework to understand the effects of clouds and
+appears well-suited for atmospheric retrievals.
+",0,1,0,0,0,0
+19576,Consistency Between the Luminosity Function of Resolved Millisecond Pulsars and the Galactic Center Excess,"  Fermi Large Area Telescope data reveal an excess of GeV gamma rays from the
+direction of the Galactic Center and bulge. Several explanations have been
+proposed for this excess including an unresolved population of millisecond
+pulsars (MSPs) and self-annihilating dark matter. It has been claimed that a
+key discriminant for or against the MSP explanation can be extracted from the
+properties of the luminosity function describing this source population.
+Specifically, is the luminosity function of the putative MSPs in the Galactic
+Center consistent with that characterizing the resolved MSPs in the Galactic
+disk? To investigate this we have used a Bayesian Markov Chain Monte Carlo to
+evaluate the posterior distribution of the parameters of the MSP luminosity
+function describing both resolved MSPs and the Galactic Center excess. At
+variance with some other claims, our analysis reveals that, within current
+uncertainties, both data sets can be well fit with the same luminosity
+function.
+",0,1,0,0,0,0
+19577,First functionality tests of a 64 x 64 pixel DSSC sensor module connected to the complete ladder readout,"  The European X-ray Free Electron Laser (XFEL.EU) will provide every 0.1 s a
+train of 2700 spatially coherent ultrashort X-ray pulses at 4.5 MHz repetition
+rate. The Small Quantum Systems (SQS) instrument and the Spectroscopy and
+Coherent Scattering instrument (SCS) operate with soft X-rays between 0.5 keV -
+6keV. The DEPFET Sensor with Signal Compression (DSSC) detector is being
+developed to meet the requirements set by these two XFEL.EU instruments. The
+DSSC imager is a 1 mega-pixel camera able to store up to 800 single-pulse
+images per train. The so-called ladder is the basic unit of the DSSC detector.
+It is the single unit out of sixteen identical-units composing the
+DSSC-megapixel camera, containing all representative electronic components of
+the full-size system and allows testing the full electronic chain. Each DSSC
+ladder has a focal plane sensor with 128 x 512 pixels. The read-out ASIC
+provides full-parallel readout of the sensor pixels. Every read-out channel
+contains an amplifier and an analog filter, an up-to 9 bit ADC and the digital
+memory. The ASIC amplifier have a double front-end to allow one to use either
+DEPFET sensors or Mini-SDD sensors. In the first case, the signal compression
+is a characteristic intrinsic of the sensor; in the second case, the
+compression is implemented at the first amplification stage. The goal of signal
+compression is to meet the requirement of single-photon detection capability
+and wide dynamic range. We present the first results of measurements obtained
+using a 64 x 64 pixel DEPFET sensor attached to the full final electronic and
+data-acquisition chain.
+",0,1,0,0,0,0
+19578,Label Propagation on K-partite Graphs with Heterophily,"  In this paper, for the first time, we study label propagation in
+heterogeneous graphs under heterophily assumption. Homophily label propagation
+(i.e., two connected nodes share similar labels) in homogeneous graph (with
+same types of vertices and relations) has been extensively studied before.
+Unfortunately, real-life networks are heterogeneous, they contain different
+types of vertices (e.g., users, images, texts) and relations (e.g.,
+friendships, co-tagging) and allow for each node to propagate both the same and
+opposite copy of labels to its neighbors. We propose a $\mathcal{K}$-partite
+label propagation model to handle the mystifying combination of heterogeneous
+nodes/relations and heterophily propagation. With this model, we develop a
+novel label inference algorithm framework with update rules in near-linear time
+complexity. Since real networks change over time, we devise an incremental
+approach, which supports fast updates for both new data and evidence (e.g.,
+ground truth labels) with guaranteed efficiency. We further provide a utility
+function to automatically determine whether an incremental or a re-modeling
+approach is favored. Extensive experiments on real datasets have verified the
+effectiveness and efficiency of our approach, and its superiority over the
+state-of-the-art label propagation methods.
+",1,0,0,0,0,0
+19579,On LoRaWAN Scalability: Empirical Evaluation of Susceptibility to Inter-Network Interference,"  Appearing on the stage quite recently, the Low Power Wide Area Networks
+(LPWANs) are currently getting much of attention. In the current paper we study
+the susceptibility of one LPWAN technology, namely LoRaWAN, to the
+inter-network interferences. By means of excessive empirical measurements
+employing the certified commercial transceivers, we characterize the effect of
+modulation coding schemes (known for LoRaWAN as data rates (DRs)) of a
+transmitter and an interferer on probability of successful packet delivery
+while operating in EU 868 MHz band. We show that in reality the transmissions
+with different DRs in the same frequency channel can negatively affect each
+other and that the high DRs are influenced by interferences more severely than
+the low ones. Also, we show that the LoRa-modulated DRs are affected by the
+interferences much less than the FSK-modulated one. Importantly, the presented
+results provide insight into the network-level operation of the LoRa LPWAN
+technology in general, and its scalability potential in particular. The results
+can also be used as a reference for simulations and analyses or for defining
+the communication parameters for real-life applications.
+",1,0,0,0,0,0
+19580,DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution,"  The impacts of climate change are felt by most critical systems, such as
+infrastructure, ecological systems, and power-plants. However, contemporary
+Earth System Models (ESM) are run at spatial resolutions too coarse for
+assessing effects this localized. Local scale projections can be obtained using
+statistical downscaling, a technique which uses historical climate observations
+to learn a low-resolution to high-resolution mapping. Depending on statistical
+modeling choices, downscaled projections have been shown to vary significantly
+terms of accuracy and reliability. The spatio-temporal nature of the climate
+system motivates the adaptation of super-resolution image processing techniques
+to statistical downscaling. In our work, we present DeepSD, a generalized
+stacked super resolution convolutional neural network (SRCNN) framework for
+statistical downscaling of climate variables. DeepSD augments SRCNN with
+multi-scale input channels to maximize predictability in statistical
+downscaling. We provide a comparison with Bias Correction Spatial
+Disaggregation as well as three Automated-Statistical Downscaling approaches in
+downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km)
+over the Continental United States. Furthermore, a framework using the NASA
+Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM
+models with multiple emission scenarios.
+",1,0,0,0,0,0
+19581,Statistical analysis of the ambiguities in the asteroid period determinations,"  Among asteroids there exist ambiguities in their rotation period
+determinations. They are due to incomplete coverage of the rotation, noise
+and/or aliases resulting from gaps between separate lightcurves. To help to
+remove such uncertainties, basic characteristic of the lightcurves resulting
+from constraints imposed by the asteroid shapes and geometries of observations
+should be identified. We simulated light variations of asteroids which shapes
+were modelled as Gaussian random spheres, with random orientations of spin
+vectors and phase angles changed every $5^\circ$ from $0^\circ$ to $65^\circ$.
+This produced 1.4 mln lightcurves. For each simulated lightcurve Fourier
+analysis has been made and the harmonic of the highest amplitude was recorded.
+From the statistical point of view, all lightcurves observed at phase angles
+$\alpha < 30^\circ$, with peak-to-peak amplitudes $A>0.2$ mag are bimodal.
+Second most frequently dominating harmonic is the first one, with the 3rd
+harmonic following right after. For 1% of lightcurves with amplitudes $A < 0.1$
+mag and phase angles $\alpha < 40^\circ$ 4th harmonic dominates.
+",0,1,0,0,0,0
+19582,The Assistive Multi-Armed Bandit,"  Learning preferences implicit in the choices humans make is a well studied
+problem in both economics and computer science. However, most work makes the
+assumption that humans are acting (noisily) optimally with respect to their
+preferences. Such approaches can fail when people are themselves learning about
+what they want. In this work, we introduce the assistive multi-armed bandit,
+where a robot assists a human playing a bandit task to maximize cumulative
+reward. In this problem, the human does not know the reward function but can
+learn it through the rewards received from arm pulls; the robot only observes
+which arms the human pulls but not the reward associated with each pull. We
+offer sufficient and necessary conditions for successfully assisting the human
+in this framework. Surprisingly, better human performance in isolation does not
+necessarily lead to better performance when assisted by the robot: a human
+policy can do better by effectively communicating its observed rewards to the
+robot. We conduct proof-of-concept experiments that support these results. We
+see this work as contributing towards a theory behind algorithms for
+human-robot interaction.
+",1,0,0,1,0,0
+19583,Localized Manifold Harmonics for Spectral Shape Analysis,"  The use of Laplacian eigenfunctions is ubiquitous in a wide range of computer
+graphics and geometry processing applications. In particular, Laplacian
+eigenbases allow generalizing the classical Fourier analysis to manifolds. A
+key drawback of such bases is their inherently global nature, as the Laplacian
+eigenfunctions carry geometric and topological structure of the entire
+manifold. In this paper, we introduce a new framework for local spectral shape
+analysis. We show how to efficiently construct localized orthogonal bases by
+solving an optimization problem that in turn can be posed as the
+eigendecomposition of a new operator obtained by a modification of the standard
+Laplacian. We study the theoretical and computational aspects of the proposed
+framework and showcase our new construction on the classical problems of shape
+approximation and correspondence. We obtain significant improvement compared to
+classical Laplacian eigenbases as well as other alternatives for constructing
+localized bases.
+",1,0,0,0,0,0
+19584,How to Search the Internet Archive Without Indexing It,"  Significant parts of cultural heritage are produced on the web during the
+last decades. While easy accessibility to the current web is a good baseline,
+optimal access to the past web faces several challenges. This includes dealing
+with large-scale web archive collections and lacking of usage logs that contain
+implicit human feedback most relevant for today's web search. In this paper, we
+propose an entity-oriented search system to support retrieval and analytics on
+the Internet Archive. We use Bing to retrieve a ranked list of results from the
+current web. In addition, we link retrieved results to the WayBack Machine;
+thus allowing keyword search on the Internet Archive without processing and
+indexing its raw archived content. Our search system complements existing web
+archive search tools through a user-friendly interface, which comes close to
+the functionalities of modern web search engines (e.g., keyword search, query
+auto-completion and related query suggestion), and provides a great benefit of
+taking user feedback on the current web into account also for web archive
+search. Through extensive experiments, we conduct quantitative and qualitative
+analyses in order to provide insights that enable further research on and
+practical applications of web archives.
+",1,0,0,0,0,0
+19585,Quantum Dot at a Luttinger liquid edge - Exact solution via Bethe Ansatz,"  We study a system consisting of a Luttinger liquid coupled to a quantum dot
+on the boundary. The Luttinger liquid is expressed in terms of fermions
+interacting via density-density coupling and the dot is modeled as an
+interacting resonant level on to which the bulk fermions can tunnel. We solve
+the Hamiltonian exactly and construct all eigenstates. We study both the zero
+and finite temperature properties of the system, in particular we compute the
+exact dot occupation as a function of the dot energy in all parameter regimes.
+The system is seen to flow from weak to to strong coupling for all values of
+the bulk interaction, with the flow characterized by a non-perturbative Kondo
+scale. We identify the critical exponents at the weak and strong coupling
+regimes.
+",0,1,0,0,0,0
+19586,Optimal Jittered Sampling for two Points in the Unit Square,"  Jittered Sampling is a refinement of the classical Monte Carlo sampling
+method. Instead of picking $n$ points randomly from $[0,1]^2$, one partitions
+the unit square into $n$ regions of equal measure and then chooses a point
+randomly from each partition. Currently, no good rules for how to partition the
+space are available. In this paper, we present a solution for the special case
+of subdividing the unit square by a decreasing function into two regions so as
+to minimize the expected squared $\mathcal{L}_2-$discrepancy. The optimal
+partitions are given by a \textit{highly} nonlinear integral equation for which
+we determine an approximate solution. In particular, there is a break of
+symmetry and the optimal partition is not into two sets of equal measure. We
+hope this stimulates further interest in the construction of good partitions.
+",0,0,1,0,0,0
+19587,Brain structural connectivity atrophy in Alzheimer's disease,"  Analysis and quantification of brain structural changes, using Magnetic
+resonance imaging (MRI), are increasingly used to define novel biomarkers of
+brain pathologies, such as Alzheimer's disease (AD). Network-based models of
+the brain have shown that both local and global topological properties can
+reveal patterns of disease propagation. On the other hand, intra-subject
+descriptions cannot exploit the whole information context, accessible through
+inter-subject comparisons. To address this, we developed a novel approach,
+which models brain structural connectivity atrophy with a multiplex network and
+summarizes it within a classification score. On an independent dataset
+multiplex networks were able to correctly segregate, from normal controls (NC),
+AD patients and subjects with mild cognitive impairment that will convert to AD
+(cMCI) with an accuracy of, respectively, $0.86 \pm 0.01$ and $0.84 \pm 0.01$.
+The model also shows that illness effects are maximally detected by parceling
+the brain in equal volumes of $3000$ $mm^3$ (""patches""), without any $a$
+$priori$ segmentation based on anatomical features. A direct comparison to
+standard voxel-based morphometry on the same dataset showed that the multiplex
+network approach had higher sensitivity. This method is general and can have
+twofold potential applications: providing a reliable tool for clinical trials
+and a disease signature of neurodegenerative pathologies.
+",0,1,0,0,0,0
+19588,Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry,"  This work proposes a visual odometry method that combines points and plane
+primitives, extracted from a noisy depth camera. Depth measurement uncertainty
+is modelled and propagated through the extraction of geometric primitives to
+the frame-to-frame motion estimation, where pose is optimized by weighting the
+residuals of 3D point and planes matches, according to their uncertainties.
+Results on an RGB-D dataset show that the combination of points and planes,
+through the proposed method, is able to perform well in poorly textured
+environments, where point-based odometry is bound to fail.
+",1,0,0,0,0,0
+19589,Autoignition of Butanol Isomers at Low to Intermediate Temperature and Elevated Pressure,"  Autoignition delay experiments for the isomers of butanol, including n-,
+sec-, tert-, and iso-butanol, have been performed using a heated rapid
+compression machine. For a compressed pressure of 15 bar, the compressed
+temperatures have been varied in the range of 725-855 K for all the
+stoichiometric fuel/oxidizer mixtures. Over the conditions investigated in this
+study, the ignition delay decreases monotonically as temperature increases and
+exhibits single-stage characteristics. Experimental ignition delays are also
+compared to simulations computed using three kinetic mechanisms available in
+the literature. Reasonable agreement is found for three isomers (tert-, iso-,
+and n-butanol).
+",0,1,0,0,0,0
+19590,On the $k$-abelian complexity of the Cantor sequence,"  In this paper, we prove that for every integer $k \geq 1$, the $k$-abelian
+complexity function of the Cantor sequence $\mathbf{c} = 101000101\cdots$ is a
+$3$-regular sequence.
+",1,0,1,0,0,0
+19591,Sharp rates of convergence for accumulated spectrograms,"  We investigate an inverse problem in time-frequency localization: the
+approximation of the symbol of a time-frequency localization operator from
+partial spectral information by the method of accumulated spectrograms (the sum
+of the spectrograms corresponding to large eigenvalues). We derive a sharp
+bound for the rate of convergence of the accumulated spectrogram, improving on
+recent results.
+",0,0,1,0,0,0
+19592,Putting a Face to the Voice: Fusing Audio and Visual Signals Across a Video to Determine Speakers,"  In this paper, we present a system that associates faces with voices in a
+video by fusing information from the audio and visual signals. The thesis
+underlying our work is that an extremely simple approach to generating (weak)
+speech clusters can be combined with visual signals to effectively associate
+faces and voices by aggregating statistics across a video. This approach does
+not need any training data specific to this task and leverages the natural
+coherence of information in the audio and visual streams. It is particularly
+applicable to tracking speakers in videos on the web where a priori information
+about the environment (e.g., number of speakers, spatial signals for
+beamforming) is not available. We performed experiments on a real-world dataset
+using this analysis framework to determine the speaker in a video. Given a
+ground truth labeling determined by human rater consensus, our approach had
+~71% accuracy.
+",1,0,0,0,0,0
+19593,Topological Interplay between Knots and Entangled Vortex-Membranes,"  In this paper, the Kelvin wave and knot dynamics are studied on three
+dimensional smoothly deformed entangled vortex-membranes in five dimensional
+space. Owing to the existence of local Lorentz invariance and diffeomorphism
+invariance, in continuum limit gravity becomes an emergent phenomenon on 3+1
+dimensional zero-lattice (a lattice of projected zeroes): On the one hand, the
+deformed zero-lattice can be denoted by curved space-time for knots; on the
+other hand, the knots as topological defect of 3+1 dimensional zero-lattice
+indicates matter may curve space-time. This work would help researchers to
+understand the mystery in gravity.
+",0,1,0,0,0,0
+19594,The first and second fundamental theorems of invariant theory for the quantum general linear supergroup,"  We develop the non-commutative polynomial version of the invariant theory for
+the quantum general linear supergroup ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$. A
+non-commutative ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-module superalgebra
+$\mathcal{P}^{k|l}_{\,r|s}$ is constructed, which is the quantum analogue of
+the supersymmetric algebra over $\mathbb{C}^{k|l}\otimes \mathbb{C}^{m|n}\oplus
+\mathbb{C}^{r|s}\otimes (\mathbb{C}^{m|n})^{\ast}$. We analyse the structure of
+the subalgebra of ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-invariants in
+$\mathcal{P}^{k|l}_{\,r|s}$ by using the quantum super analogue of Howe
+duality.
+The subalgebra of ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-invariants in
+$\mathcal{P}^{k|l}_{\,r|s}$ is shown to be finitely generated. We determine its
+generators and establish a surjective superalgebra homomorphism from a braided
+supersymmetric algebra onto it. This establishes the first fundamental theorem
+of invariant theory for ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$.
+We show that the above mentioned superalgebra homomorphism is an isomorphism
+if and only if $m\geq \min\{k,r\}$ and $n\geq \min\{l,s\}$, and obtain a
+monomial basis for the subalgebra of invariants in this case. When the
+homomorphism is not injective, we give a representation theoretical description
+of the generating elements of the kernel associated to the partition
+$((m+1)^{n+1})$, producing the second fundamental theorem of invariant theory
+for ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$.
+We consider two applications of our results. A complete treatment of the
+non-commutative polynomial version of invariant theory for ${\rm{
+U}}_q(\mathfrak{gl}_{m})$ is obtained as the special case with $n=0$, where an
+explicit SFT is proved, which we believe to be new. The FFT and SFT of the
+invariant theory for the general linear superalgebra are recovered from the
+classical (i.e., $q\to 1$) limit of our results.
+",0,0,1,0,0,0
+19595,Poly-Spline Finite Element Method,"  We introduce an integrated meshing and finite element method pipeline
+enabling black-box solution of partial differential equations in the volume
+enclosed by a boundary representation. We construct a hybrid
+hexahedral-dominant mesh, which contains a small number of star-shaped
+polyhedra, and build a set of high-order basis on its elements, combining
+triquadratic B-splines, triquadratic hexahedra (27 degrees of freedom), and
+harmonic elements. We demonstrate that our approach converges cubically under
+refinement, while requiring around 50% of the degrees of freedom than a
+similarly dense hexahedral mesh composed of triquadratic hexahedra. We validate
+our approach solving Poisson's equation on a large collection of models, which
+are automatically processed by our algorithm, only requiring the user to
+provide boundary conditions on their surface.
+",1,0,0,0,0,0
+19596,"Turing Completeness of Finite, Epistemic Programs","  In this note, we show the class of finite, epistemic programs to be Turing
+complete. Epistemic programs is a widely used update mechanism used in
+epistemic logic, where it such are a special type of action models: One which
+does not contain postconditions.
+",1,0,0,0,0,0
+19597,Spontaneous domain formation in disordered copolymers as a mechanism for chromosome structuring,"  Motivated by the problem of domain formation in chromosomes, we studied a
+co--polymer model where only a subset of the monomers feel attractive
+interactions. These monomers are displaced randomly from a regularly-spaced
+pattern, thus introducing some quenched disorder in the system. Previous work
+has shown that in the case of regularly-spaced interacting monomers this chain
+can fold into structures characterized by multiple distinct domains of
+consecutive segments. In each domain, attractive interactions are balanced by
+the entropy cost of forming loops. We show by advanced replica-exchange
+simulations that adding disorder in the position of the interacting monomers
+further stabilizes these domains. The model suggests that the partitioning of
+the chain into well-defined domains of consecutive monomers is a spontaneous
+property of heteropolymers. In the case of chromosomes, evolution could have
+acted on the spacing of interacting monomers to modulate in a simple way the
+underlying domains for functional reasons.
+",0,0,0,0,1,0
+19598,Reconstruction of Correlated Sources with Energy Harvesting Constraints in Delay-constrained and Delay-tolerant Communication Scenarios,"  In this paper, we investigate the reconstruction of time-correlated sources
+in a point-to-point communications scenario comprising an energy-harvesting
+sensor and a Fusion Center (FC). Our goal is to minimize the average distortion
+in the reconstructed observations by using data from previously encoded sources
+as side information. First, we analyze a delay-constrained scenario, where the
+sources must be reconstructed before the next time slot. We formulate the
+problem in a convex optimization framework and derive the optimal transmission
+(i.e., power and rate allocation) policy. To solve this problem, we propose an
+iterative algorithm based on the subgradient method. Interestingly, the
+solution to the problem consists of a coupling between a two-dimensional
+directional water-filling algorithm (for power allocation) and a reverse
+water-filling algorithm (for rate allocation). Then we find a more general
+solution to this problem in a delay-tolerant scenario where the time horizon
+for source reconstruction is extended to multiple time slots. Finally, we
+provide some numerical results that illustrate the impact of delay and
+correlation in the power and rate allocation policies, and in the resulting
+reconstruction distortion. We also discuss the performance gap exhibited by a
+heuristic online policy derived from the optimal (offline) one.
+",1,0,0,0,0,0
+19599,Future of Flexible Robotic Endoscopy Systems,"  Robotics enables a variety of unconventional actuation strategies to be used
+for endoscopes, resulting in reduced trauma to the GI tract. For transmission
+of force to distally mounted endoscopic instruments, robotically actuated
+tendon sheath mechanisms are the current state of the art. Robotics in surgical
+endoscopy enables an ergonomic mapping of the surgeon movements to remotely
+controlled slave arms, facilitating tissue manipulation. The learning curve for
+difficult procedures such as endoscopic submucosal dissection and
+full-thickness resection can be significantly reduced. Improved surgical
+outcomes are also observed from clinical and pre-clinical trials. The
+technology behind master-slave surgical robotics will continue to mature, with
+the addition of position and force sensors enabling better control and tactile
+feedback. More robotic assisted GI luminal and NOTES surgeries are expected to
+be conducted in future, and gastroenterologists will have a key collaborative
+role to play.
+",1,1,0,0,0,0
+19600,Shear-driven parametric instability in a precessing sphere,"  The present numerical study aims at shedding light on the mechanism
+underlying the precessional instability in a sphere. Precessional instabilities
+in the form of parametric resonance due to topographic coupling have been
+reported in a spheroidal geometry both analytically and numerically. We show
+that such parametric resonances can also develop in spherical geometry due to
+the conical shear layers driven by the Ekman pumping singularities at the
+critical latitudes. Scaling considerations lead to a stability criterion of the
+form, $|P_o|>O(E^{4/5})$, where $P_o$ represents the Poincaré number and $E$
+the Ekman number. The predicted threshold is consistent with our numerical
+simulations as well as previous experimental results. When the precessional
+forcing is supercriticial, our simulations show evidence of an inverse cascade,
+i.e. small scale flows merging into large scale cyclones with a retrograde
+drift. Finally, it is shown that this instability mechanism may be relevant to
+precessing celestial bodies such as the Earth and Earth's moon.
+",0,1,0,0,0,0
+19601,"Linear-Size Hopsets with Small Hopbound, and Distributed Routing with Low Memory","  For a positive parameter $\beta$, the $\beta$-bounded distance between a pair
+of vertices $u,v$ in a weighted undirected graph $G = (V,E,\omega)$ is the
+length of the shortest $u-v$ path in $G$ with at most $\beta$ edges, aka {\em
+hops}. For $\beta$ as above and $\epsilon>0$, a {\em $(\beta,\epsilon)$-hopset}
+of $G = (V,E,\omega)$ is a graph $G' =(V,H,\omega_H)$ on the same vertex set,
+such that all distances in $G$ are $(1+\epsilon)$-approximated by
+$\beta$-bounded distances in $G\cup G'$.
+Hopsets are a fundamental graph-theoretic and graph-algorithmic construct,
+and they are widely used for distance-related problems in a variety of
+computational settings. Currently existing constructions of hopsets produce
+hopsets either with $\Omega(n \log n)$ edges, or with a hopbound
+$n^{\Omega(1)}$. In this paper we devise a construction of {\em linear-size}
+hopsets with hopbound $(\log n)^{\log^{(3)}n+O(1)}$. This improves the previous
+bound almost exponentially.
+We also devise efficient implementations of our construction in PRAM and
+distributed settings. The only existing PRAM algorithm \cite{EN16} for
+computing hopsets with a constant (i.e., independent of $n$) hopbound requires
+$n^{\Omega(1)}$ time. We devise a PRAM algorithm with polylogarithmic running
+time for computing hopsets with a constant hopbound, i.e., our running time is
+exponentially better than the previous one. Moreover, these hopsets are also
+significantly sparser than their counterparts from \cite{EN16}.
+We use our hopsets to devise a distributed routing scheme that exhibits
+near-optimal tradeoff between individual memory requirement
+$\tilde{O}(n^{1/k})$ of vertices throughout preprocessing and routing phases of
+the algorithm, and stretch $O(k)$, along with a near-optimal construction time
+$\approx D + n^{1/2 + 1/k}$, where $D$ is the hop-diameter of the input graph.
+",1,0,0,0,0,0
+19602,The Openpipeflow Navier--Stokes Solver,"  Pipelines are used in a huge range of industrial processes involving fluids,
+and the ability to accurately predict properties of the flow through a pipe is
+of fundamental engineering importance. Armed with parallel MPI, Arnoldi and
+Newton--Krylov solvers, the Openpipeflow code can be used in a range of
+settings, from large-scale simulation of highly turbulent flow, to the detailed
+analysis of nonlinear invariant solutions (equilibria and periodic orbits) and
+their influence on the dynamics of the flow.
+",0,1,0,0,0,0
+19603,Logical and Inequality Implications for Reducing the Size and Complexity of Quadratic Unconstrained Binary Optimization Problems,"  The quadratic unconstrained binary optimization (QUBO) problem arises in
+diverse optimization applications ranging from Ising spin problems to classical
+problems in graph theory and binary discrete optimization. The use of
+preprocessing to transform the graph representing the QUBO problem into a
+smaller equivalent graph is important for improving solution quality and time
+for both exact and metaheuristic algorithms and is a step towards mapping large
+scale QUBO to hardware graphs used in quantum annealing computers. In an
+earlier paper (Lewis and Glover, 2016) a set of rules was introduced that
+achieved significant QUBO reductions as verified through computational testing.
+Here this work is extended with additional rules that provide further
+reductions that succeed in exactly solving 10% of the benchmark QUBO problems.
+An algorithm and associated data structures to efficiently implement the entire
+set of rules is detailed and computational experiments are reported that
+demonstrate their efficacy.
+",1,0,0,0,0,0
+19604,Optimal Decentralized Economical-sharing Criterion and Scheme for Microgrid,"  In order to address the economical dispatch problem in islanded microgrid,
+this letter proposes an optimal criterion and two decentralized
+economical-sharing schemes. The criterion is to judge whether global optimal
+economical-sharing can be realized via a decentralized manner. On the one hand,
+if the system cost functions meet this criterion, the corresponding
+decentralized droop method is proposed to achieve the global optimal dispatch.
+Otherwise, if the system does not meet this criterion, a modified method to
+achieve suboptimal dispatch is presented. The advantages of these methods are
+convenient,effective and communication-less.
+",1,0,0,0,0,0
+19605,Unbiased inference for discretely observed hidden Markov model diffusions,"  We develop an importance sampling (IS) type estimator for Bayesian joint
+inference on the model parameters and latent states of a class of hidden Markov
+models. The hidden state dynamics is a diffusion process and noisy observations
+are obtained at discrete points in time. We suppose that the diffusion dynamics
+can not be simulated exactly and hence one must time-discretise the diffusion.
+Our approach is based on particle marginal Metropolis--Hastings, particle
+filters, and multilevel Monte Carlo. The resulting IS type estimator leads to
+inference without a bias from the time-discretisation. We give convergence
+results and recommend allocations for algorithm inputs. In contrast to existing
+unbiased methods requiring strong conditions on the diffusion and tailored
+solutions, our method relies on standard Euler approximations of the diffusion.
+Our method is parallelisable, and can be computationally efficient. The
+user-friendly approach is illustrated with two examples.
+",0,0,0,1,0,0
+19606,Electric propulsion reliability: statistical analysis of on-orbit anomalies and comparative analysis of electric versus chemical propulsion failure rates,"  With a few hundred spacecraft launched to date with electric propulsion (EP),
+it is possible to conduct an epidemiological study of EP on orbit reliability.
+The first objective of the present work was to undertake such a study and
+analyze EP track record of on orbit anomalies and failures by different
+covariates. The second objective was to provide a comparative analysis of EP
+failure rates with those of chemical propulsion. After a thorough data
+collection, 162 EP-equipped satellites launched between January 1997 and
+December 2015 were included in our dataset for analysis. Several statistical
+analyses were conducted, at the aggregate level and then with the data
+stratified by severity of the anomaly, by orbit type, and by EP technology.
+Mean Time To Anomaly (MTTA) and the distribution of the time to anomaly were
+investigated, as well as anomaly rates. The important findings in this work
+include the following: (1) Post-2005, EP reliability has outperformed that of
+chemical propulsion; (2) Hall thrusters have robustly outperformed chemical
+propulsion, and they maintain a small but shrinking reliability advantage over
+gridded ion engines. Other results were also provided, for example the
+differentials in MTTA of minor and major anomalies for gridded ion engines and
+Hall thrusters. It was shown that: (3) Hall thrusters exhibit minor anomalies
+very early on orbit, which might be indicative of infant anomalies, and thus
+would benefit from better ground testing and acceptance procedures; (4) Strong
+evidence exists that EP anomalies (onset and likelihood) and orbit type are
+dependent, a dependence likely mediated by either the space environment or
+differences in thrusters duty cycles; (5) Gridded ion thrusters exhibit both
+infant and wear-out failures, and thus would benefit from a reliability growth
+program that addresses both these types of problems.
+",0,1,0,1,0,0
+19607,A Bayesian Framework for Cosmic String Searches in CMB Maps,"  There exists various proposals to detect cosmic strings from Cosmic Microwave
+Background (CMB) or 21 cm temperature maps. Current proposals do not aim to
+find the location of strings on sky maps, all of these approaches can be
+thought of as a statistic on a sky map. We propose a Bayesian interpretation of
+cosmic string detection and within that framework, we derive a connection
+between estimates of cosmic string locations and cosmic string tension $G\mu$.
+We use this Bayesian framework to develop a machine learning framework for
+detecting strings from sky maps and outline how to implement this framework
+with neural networks. The neural network we trained was able to detect and
+locate cosmic strings on noiseless CMB temperature map down to a string tension
+of $G\mu=5 \times10^{-9}$ and when analyzing a CMB temperature map that does
+not contain strings, the neural network gives a 0.95 probability that
+$G\mu\leq2.3\times10^{-9}$.
+",0,1,0,0,0,0
+19608,Constraining the giant planets' initial configuration from their evolution: implications for the timing of the planetary instability,"  Recent works on planetary migration show that the orbital structure of the
+Kuiper belt can be very well reproduced if before the onset of the planetary
+instability Neptune underwent a long-range planetesimal-driven migration up to
+$\sim$28 au. However, considering that all giant planets should have been
+captured in mean motion resonances among themselves during the gas-disk phase,
+it is not clear whether such a very specific evolution for Neptune is possible,
+nor whether the instability could have happened at late times. Here, we first
+investigate which initial resonant configuration of the giant planets can be
+compatible with Neptune being extracted from the resonant chain and migrating
+to $\sim$28 au before that the planetary instability happened. We address the
+late instability issue by investigating the conditions where the planets can
+stay in resonance for about 400 My. Our results indicate that this can happen
+only in the case where the planetesimal disk is beyond a specific minimum
+distance $\delta_{stab}$ from Neptune. Then, if there is a sufficient amount of
+dust produced in the planetesimal disk, that drifts inwards, Neptune can enter
+in a slow dust-driven migration phase for hundreds of Mys until it reaches a
+critical distance $\delta_{mig}$ from the disk. From that point, faster
+planetesimal-driven migration takes over and Neptune continues migrating
+outward until the instability happens. We conclude that, although an early
+instability reproduces more easily the evolution of Neptune required to explain
+the structure of the Kuiper belt, such evolution is also compatible with a late
+instability.
+",0,1,0,0,0,0
+19609,General AI Challenge - Round One: Gradual Learning,"  The General AI Challenge is an initiative to encourage the wider artificial
+intelligence community to focus on important problems in building intelligent
+machines with more general scope than is currently possible. The challenge
+comprises of multiple rounds, with the first round focusing on gradual
+learning, i.e. the ability to re-use already learned knowledge for efficiently
+learning to solve subsequent problems. In this article, we will present details
+of the first round of the challenge, its inspiration and aims. We also outline
+a more formal description of the challenge and present a preliminary analysis
+of its curriculum, based on ideas from computational mechanics. We believe,
+that such formalism will allow for a more principled approach towards
+investigating tasks in the challenge, building new curricula and for
+potentially improving consequent challenge rounds.
+",1,0,0,0,0,0
+19610,DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks,"  A key enabler for optimizing business processes is accurately estimating the
+probability distribution of a time series future given its past. Such
+probabilistic forecasts are crucial for example for reducing excess inventory
+in supply chains. In this paper we propose DeepAR, a novel methodology for
+producing accurate probabilistic forecasts, based on training an
+auto-regressive recurrent network model on a large number of related time
+series. We show through extensive empirical evaluation on several real-world
+forecasting data sets that our methodology is more accurate than
+state-of-the-art models, while requiring minimal feature engineering.
+",1,0,0,1,0,0
+19611,Algebraic and logistic investigations on free lattices,"  Lorenzen's ""Algebraische und logistische Untersuchungen über freie
+Verbände"" appeared in 1951 in The journal of symbolic logic. These
+""Investigations"" have immediately been recognised as a landmark in the history
+of infinitary proof theory, but their approach and method of proof have not
+been incorporated into the corpus of proof theory. More precisely, Lorenzen
+proves the admissibility of cut by double induction, on the cut formula and on
+the complexity of the derivations, without using any ordinal assignment,
+contrary to the presentation of cut elimination in most standard texts on proof
+theory. This translation has the intent of giving a new impetus to their
+reception.
+The ""Investigations"" are best known for providing a constructive proof of
+consistency for ramified type theory without axiom of reducibility. They do so
+by showing that it is a part of a trivially consistent ""inductive calculus""
+that describes our knowledge of arithmetic without detour. The proof resorts
+only to the inductive definition of formulas and theorems.
+They propose furthermore a definition of a semilattice, of a distributive
+lattice, of a pseudocomplemented semilattice, and of a countably complete
+boolean lattice as deductive calculuses, and show how to present them for
+constructing the respective free object over a given preordered set.
+This translation is published with the kind permission of Lorenzen's
+daughter, Jutta Reinhardt.
+",0,0,1,0,0,0
+19612,Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms,"  Online interactive recommender systems strive to promptly suggest to
+consumers appropriate items (e.g., movies, news articles) according to the
+current context including both the consumer and item content information.
+However, such context information is often unavailable in practice for the
+recommendation, where only the users' interaction data on items can be
+utilized. Moreover, the lack of interaction records, especially for new users
+and items, worsens the performance of recommendation further. To address these
+issues, collaborative filtering (CF), one of the recommendation techniques
+relying on the interaction data only, as well as the online multi-armed bandit
+mechanisms, capable of achieving the balance between exploitation and
+exploration, are adopted in the online interactive recommendation settings, by
+assuming independent items (i.e., arms). Nonetheless, the assumption rarely
+holds in reality, since the real-world items tend to be correlated with each
+other (e.g., two articles with similar topics). In this paper, we study online
+interactive collaborative filtering problems by considering the dependencies
+among items. We explicitly formulate the item dependencies as the clusters on
+arms, where the arms within a single cluster share the similar latent topics.
+In light of the topic modeling techniques, we come up with a generative model
+to generate the items from their underlying topics. Furthermore, an efficient
+online algorithm based on particle learning is developed for inferring both
+latent parameters and states of our model. Additionally, our inferred model can
+be naturally integrated with existing multi-armed selection strategies in the
+online interactive collaborating setting. Empirical studies on two real-world
+applications, online recommendations of movies and news, demonstrate both the
+effectiveness and efficiency of the proposed approach.
+",1,0,0,0,0,0
+19613,Learning Latent Features with Pairwise Penalties in Matrix Completion,"  Low-rank matrix completion (MC) has achieved great success in many real-world
+data applications. A latent feature model formulation is usually employed and,
+to improve prediction performance, the similarities between latent variables
+can be exploited by pairwise learning, e.g., the graph regularized matrix
+factorization (GRMF) method. However, existing GRMF approaches often use a
+squared L2 norm to measure the pairwise difference, which may be overly
+influenced by dissimilar pairs and lead to inferior prediction. To fully
+empower pairwise learning for matrix completion, we propose a general
+optimization framework that allows a rich class of (non-)convex pairwise
+penalty functions. A new and efficient algorithm is further developed to
+uniformly solve the optimization problem, with a theoretical convergence
+guarantee. In an important situation where the latent variables form a small
+number of subgroups, its statistical guarantee is also fully characterized. In
+particular, we theoretically characterize the complexity-regularized maximum
+likelihood estimator, as a special case of our framework. It has a better error
+bound when compared to the standard trace-norm regularized matrix completion.
+We conduct extensive experiments on both synthetic and real datasets to
+demonstrate the superior performance of this general framework.
+",0,0,0,1,0,0
+19614,Renewal theorems and mixing for non Markov flows with infinite measure,"  We obtain results on mixing for a large class of (not necessarily Markov)
+infinite measure semiflows and flows. Erickson proved, amongst other things, a
+strong renewal theorem in the corresponding i.i.d. setting. Using operator
+renewal theory, we extend Erickson's methods to the deterministic (i.e.
+non-i.i.d.) continuous time setting and obtain results on mixing as a
+consequence.
+Our results apply to intermittent semiflows and flows of Pomeau-Manneville
+type (both Markov and nonMarkov), and to semiflows and flows over
+Collet-Eckmann maps with nonintegrable roof function.
+",0,0,1,0,0,0
+19615,Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods,"  Many cognitive, sensory and motor processes have correlates in oscillatory
+neural sources, which are embedded as a subspace into the recorded brain
+signals. Decoding such processes from noisy
+magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires the
+use of data-driven analysis methods. The objective evaluation of such decoding
+algorithms on experimental raw signals, however, is a challenge: the amount of
+available M/EEG data typically is limited, labels can be unreliable, and raw
+signals often are contaminated with artifacts. The latter is specifically
+problematic, if the artifacts stem from behavioral confounds of the oscillatory
+neural processes of interest.
+To overcome some of these problems, simulation frameworks have been
+introduced for benchmarking decoding methods. Generating artificial brain
+signals, however, most simulation frameworks make strong and partially
+unrealistic assumptions about brain activity, which limits the generalization
+of obtained results to real-world conditions.
+In the present contribution, we thrive to remove many shortcomings of current
+simulation frameworks and propose a versatile alternative, that allows for
+objective evaluation and benchmarking of novel data-driven decoding methods for
+neural signals. Its central idea is to utilize post-hoc labelings of arbitrary
+M/EEG recordings. This strategy makes it paradigm-agnostic and allows to
+generate comparatively large datasets with noiseless labels. Source code and
+data of the novel simulation approach are made available for facilitating its
+adoption.
+",1,0,0,1,0,0
+19616,"Sums of two cubes as twisted perfect powers, revisited","  In this paper, we sharpen earlier work of the first author, Luca and
+Mulholland, showing that the Diophantine equation $$ A^3+B^3 = q^\alpha C^p, \,
+\, ABC \neq 0, \, \, \gcd (A,B) =1, $$ has, for ""most"" primes $q$ and suitably
+large prime exponents $p$, no solutions. We handle a number of (presumably
+infinite) families where no such conclusion was hitherto known. Through further
+application of certain {\it symplectic criteria}, we are able to make some
+conditional statements about still more values of $q$, a sample such result is
+that, for all but $O(\sqrt{x}/\log x)$ primes $q$ up to $x$, the equation $$
+A^3 + B^3 = q C^p. $$ has no solutions in coprime, nonzero integers $A, B$
+and $C$, for a positive proportion of prime exponents $p$.
+",0,0,1,0,0,0
+19617,A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications,"  Bilevel optimization is defined as a mathematical program, where an
+optimization problem contains another optimization problem as a constraint.
+These problems have received significant attention from the mathematical
+programming community. Only limited work exists on bilevel problems using
+evolutionary computation techniques; however, recently there has been an
+increasing interest due to the proliferation of practical applications and the
+potential of evolutionary algorithms in tackling these problems. This paper
+provides a comprehensive review on bilevel optimization from the basic
+principles to solution strategies; both classical and evolutionary. A number of
+potential application problems are also discussed. To offer the readers
+insights on the prominent developments in the field of bilevel optimization, we
+have performed an automated text-analysis of an extended list of papers
+published on bilevel optimization to date. This paper should motivate
+evolutionary computation researchers to pay more attention to this practical
+yet challenging area.
+",1,0,1,0,0,0
+19618,"Speaker Recognition with Cough, Laugh and ""Wei""","  This paper proposes a speaker recognition (SRE) task with trivial speech
+events, such as cough and laugh. These trivial events are ubiquitous in
+conversations and less subjected to intentional change, therefore offering
+valuable particularities to discover the genuine speaker from disguised speech.
+However, trivial events are often short and idiocratic in spectral patterns,
+making SRE extremely difficult. Fortunately, we found a very powerful deep
+feature learning structure that can extract highly speaker-sensitive features.
+By employing this tool, we studied the SRE performance on three types of
+trivial events: cough, laugh and ""Wei"" (a short Chinese ""Hello""). The results
+show that there is rich speaker information within these trivial events, even
+for cough that is intuitively less speaker distinguishable. With the deep
+feature approach, the EER can reach 10%-14% with the three trivial events,
+despite their extremely short durations (0.2-1.0 seconds).
+",1,0,0,0,0,0
+19619,Explicit equations for two-dimensional water waves with constant vorticity,"  Governing equations for two-dimensional inviscid free-surface flows with
+constant vorticity over arbitrary non-uniform bottom profile are presented in
+exact and compact form using conformal variables. An efficient and very
+accurate numerical method for this problem is developed.
+",0,1,0,0,0,0
+19620,On the Global Limiting Absorption Principle for Massless Dirac Operators,"  We prove a global limiting absorption principle on the entire real line for
+free, massless Dirac operators $H_0 = \alpha \cdot (-i \nabla)$ for all space
+dimensions $n \in \mathbb{N}$, $n \geq 2$. This is a new result for all
+dimensions other than three, in particular, it applies to the two-dimensional
+case which is known to be of some relevance in applications to graphene.
+We also prove an essential self-adjointness result for first-order
+matrix-valued differential operators with Lipschitz coefficients.
+",0,0,1,0,0,0
+19621,Rotation Averaging and Strong Duality,"  In this paper we explore the role of duality principles within the problem of
+rotation averaging, a fundamental task in a wide range of computer vision
+applications. In its conventional form, rotation averaging is stated as a
+minimization over multiple rotation constraints. As these constraints are
+non-convex, this problem is generally considered challenging to solve globally.
+We show how to circumvent this difficulty through the use of Lagrangian
+duality. While such an approach is well-known it is normally not guaranteed to
+provide a tight relaxation. Based on spectral graph theory, we analytically
+prove that in many cases there is no duality gap unless the noise levels are
+severe. This allows us to obtain certifiably global solutions to a class of
+important non-convex problems in polynomial time.
+We also propose an efficient, scalable algorithm that out-performs general
+purpose numerical solvers and is able to handle the large problem instances
+commonly occurring in structure from motion settings. The potential of this
+proposed method is demonstrated on a number of different problems, consisting
+of both synthetic and real-world data.
+",1,0,0,0,0,0
+19622,Active Learning with Gaussian Processes for High Throughput Phenotyping,"  A looming question that must be solved before robotic plant phenotyping
+capabilities can have significant impact to crop improvement programs is
+scalability. High Throughput Phenotyping (HTP) uses robotic technologies to
+analyze crops in order to determine species with favorable traits, however, the
+current practices rely on exhaustive coverage and data collection from the
+entire crop field being monitored under the breeding experiment. This works
+well in relatively small agricultural fields but can not be scaled to the
+larger ones, thus limiting the progress of genetics research. In this work, we
+propose an active learning algorithm to enable an autonomous system to collect
+the most informative samples in order to accurately learn the distribution of
+phenotypes in the field with the help of a Gaussian Process model. We
+demonstrate the superior performance of our proposed algorithm compared to the
+current practices on sorghum phenotype data collection.
+",1,0,0,1,0,0
+19623,Comparative Autoignition Trends in the Butanol Isomers at Elevated Pressure,"  Autoignition experiments of stoichiometric mixtures of s-, t-, and i-butanol
+in air have been performed using a heated rapid compression machine (RCM). At
+compressed pressures of 15 and 30 bar and for compressed temperatures in the
+range of 715-910 K, no evidence of a negative temperature coefficient region in
+terms of ignition delay response is found. The present experimental results are
+also compared with previously reported RCM data of n-butanol in air. The order
+of reactivity of the butanols is
+n-butanol>s-butanol$\approx$i-butanol>t-butanol at the lower pressure, but
+changes to n-butanol>t-butanol>s-butanol>i-butanol at higher pressure. In
+addition, t-butanol shows pre-ignition heat release behavior, which is
+especially evident at higher pressures. To help identify the controlling
+chemistry leading to this pre-ignition heat release, off-stoichiometric
+experiments are further performed at 30 bar compressed pressure, for t-butanol
+at $\phi$ = 0.5 and $\phi$ = 2.0 in air. For these experiments, higher fuel
+loading (i.e. $\phi$ = 2.0) causes greater pre-ignition heat release (as
+indicated by greater pressure rise) than the stoichiometric or $\phi$ = 0.5
+cases. Comparison of the experimental ignition delays with the simulated
+results using two literature kinetic mechanisms shows generally good agreement,
+and one mechanism is further used to explore and compare the fuel decomposition
+pathways of the butanol isomers. Using this mechanism, the importance of peroxy
+chemistry in the autoignition of the butanol isomers is highlighted and
+discussed.
+",0,1,0,0,0,0
+19624,A cross-vendor and cross-state analysis of the GPS-probe data latency,"  Crowdsourced GPS probe data has become a major source of real-time traffic
+information applications. In addition to traditional traveler advisory systems
+such as dynamic message signs (DMS) and 511 systems, probe data is being used
+for automatic incident detection, Integrated Corridor Management (ICM), end of
+queue warning systems, and mobility-related smartphone applications. Several
+private sector vendors offer minute by minute network-wide travel time and
+speed probe data. The quality of such data in terms of deviation of the
+reported travel time and speeds from ground-truth has been extensively studied
+in recent years, and as a result concerns over the accuracy of probe data has
+mostly faded away. However, the latency of probe data, defined as the lag
+between the time that disturbance in traffic speed is reported in the
+outsourced data feed, and the time that the traffic is perturbed, has become a
+subject of interest. The extent of latency of probe data for real-time
+applications is critical, so it is important to have a good understanding of
+the amount of latency and its influencing factors. This paper uses high-quality
+independent Bluetooth/Wi-Fi re-identification data collected on multiple
+freeway segments in three different states, to measure the latency of the
+vehicle probe data provided by three major vendors. The statistical
+distribution of the latency and its sensitivity to speed slowdown and recovery
+periods are discussed.
+",0,0,0,1,0,0
+19625,Determining Phonon Coherence Using Photon Sideband Detection,"  Generating and detection coherent high-frequency heat-carrying phonons has
+been a great topic of interest in recent years. While there have been
+successful attempts in generating and observing coherent phonons, rigorous
+techniques to characterize and detect these phonon coherence in a crystalline
+material have been lagging compared to what has been achieved for photons. One
+main challenge is a lack of detailed understanding of how detection signals for
+phonons can be related to coherence. The quantum theory of photoelectric
+detection has greatly advanced the ability to characterize photon coherence in
+the last century and a similar theory for phonon detection is necessary. Here,
+we re-examine the optical sideband fluorescence technique that has been used
+detect high frequency phonons in materials with optically active defects. We
+apply the quantum theory of photodetection to the sideband technique and
+propose signatures in sideband photon-counting statistics and second-order
+correlation measurement of sideband signals that indicates the degree of phonon
+coherence. Our theory can be implemented in recently performed experiments to
+bridge the gap of determining phonon coherence to be on par with that of
+photons.
+",0,1,0,0,0,0
+19626,Landscape of Configurational Density of States for Discrete Large Systems,"  For classical many-body systems, our recent study reveals that expectation
+value of internal energy, structure, and free energy can be well characterized
+by a single specially-selected microscopic structure. This finding relies on
+the fact that configurational density of states (CDOS) for typical classical
+system before applying interatomic interaction can be well characterized by
+multidimensional gaussian distribution. Although gaussian distribution is an
+well-known and widely-used function in diverse fields, it is quantitatively
+unclear why the CDOS takes gaussian when system size gets large, even for
+projected CDOS onto a single chosen coordination. Here we demonstrate that for
+equiatomic binary system, one-dimensional CDOS along coordination of pair
+correlation can be reasonably described by gaussian distribution under an
+appropriate condition, whose deviation from real CDOS mainly reflects the
+existence of triplet closed link consisting of the pair figure considered. The
+present result thus significantly makes advance in analytic determination of
+the special microscopic states to characterized macroscopic physical property
+in equilibrium state.
+",0,1,0,0,0,0
+19627,On the domain of Dirac and Laplace type operators on stratified spaces,"  We consider a generalized Dirac operator on a compact stratified space with
+an iterated cone-edge metric. Assuming a spectral Witt condition, we prove its
+essential self-adjointness and identify its domain and the domain of its square
+with weighted edge Sobolev spaces. This sharpens previous results where the
+minimal domain is shown only to be a subset of an intersection of weighted edge
+Sobolev spaces. Our argument does not rely on microlocal techniques and is very
+explicit. The novelty of our approach is the use of an abstract functional
+analytic notion of interpolation scales. Our results hold for the Gauss-Bonnet
+and spin Dirac operators satisfying a spectral Witt condition.
+",0,0,1,0,0,0
+19628,Modeling Influence with Semantics in Social Networks: a Survey,"  The discovery of influential entities in all kinds of networks (e.g. social,
+digital, or computer) has always been an important field of study. In recent
+years, Online Social Networks (OSNs) have been established as a basic means of
+communication and often influencers and opinion makers promote politics,
+events, brands or products through viral content. In this work, we present a
+systematic review across i) online social influence metrics, properties, and
+applications and ii) the role of semantic in modeling OSNs information. We end
+up with the conclusion that both areas can jointly provide useful insights
+towards the qualitative assessment of viral user-generated content, as well as
+for modeling the dynamic properties of influential content and its flow
+dynamics.
+",1,0,0,0,0,0
+19629,Differentiable Submodular Maximization,"  We consider learning of submodular functions from data. These functions are
+important in machine learning and have a wide range of applications, e.g. data
+summarization, feature selection and active learning. Despite their
+combinatorial nature, submodular functions can be maximized approximately with
+strong theoretical guarantees in polynomial time. Typically, learning the
+submodular function and optimization of that function are treated separately,
+i.e. the function is first learned using a proxy objective and subsequently
+maximized. In contrast, we show how to perform learning and optimization
+jointly. By interpreting the output of greedy maximization algorithms as
+distributions over sequences of items and smoothening these distributions, we
+obtain a differentiable objective. In this way, we can differentiate through
+the maximization algorithms and optimize the model to work well with the
+optimization algorithm. We theoretically characterize the error made by our
+approach, yielding insights into the tradeoff of smoothness and accuracy. We
+demonstrate the effectiveness of our approach for jointly learning and
+optimizing on synthetic maximum cut data, and on real world applications such
+as product recommendation and image collection summarization.
+",0,0,0,1,0,0
+19630,On the Support of Weight Modules for Affine Kac-Moody-Algebras,"  An irreducible weight module of an affine Kac-Moody algebra $\mathfrak{g}$ is
+called dense if its support is equal to a coset in $\mathfrak{h}^{*}/Q$.
+Following a conjecture of V. Futorny about affine Kac-Moody algebras
+$\mathfrak{g}$, an irreducible weight $\mathfrak{g}$-module is dense if and
+only if it is cuspidal (i.e. not a quotient of an induced module). The
+conjecture is confirmed for $\mathfrak{g}=A_{2}^{\left(1\right)}$,
+$A_{3}^{\left(1\right)}$ and$A_{4}^{\left(1\right)}$ and a classification of
+the supports of the irreducible weight $\mathfrak{g}$-modules obtained. For all
+$A_{n}^{\left(1\right)}$ the problem is reduced to finding primitive elements
+for only finitely many cases, all lying below a certain bound. For the
+left-over finitely many cases an algorithm is proposed, which leads to the
+solution of Futorny's conjecture for the cases $A_{2}^{\left(1\right)}$ and
+$A_{3}^{\left(1\right)}$. Yet, the solution of the case
+$A_{4}^{\left(1\right)}$ required additional combinatorics.
+For the proofs, a new category of hypoabelian Lie subalgebras,
+pre-prosolvable subalgebras, and a subclass thereof, quasicone subalgebras, is
+introduced and its tropical matrix algebra structure outlined.
+",0,0,1,0,0,0
+19631,The middle-scale asymptotics of Wishart matrices,"  We study the behavior of a real $p$-dimensional Wishart random matrix with
+$n$ degrees of freedom when $n,p\rightarrow\infty$ but $p/n\rightarrow 0$. We
+establish the existence of phase transitions when $p$ grows at the order
+$n^{(K+1)/(K+3)}$ for every $k\in\mathbb{N}$, and derive expressions for
+approximating densities between every two phase transitions. To do this, we
+make use of a novel tool we call the G-transform of a distribution, which is
+closely related to the characteristic function. We also derive an extension of
+the $t$-distribution to the real symmetric matrices, which naturally appears as
+the conjugate distribution to the Wishart under a G-transformation, and show
+its empirical spectral distribution obeys a semicircle law when $p/n\rightarrow
+0$. Finally, we discuss how the phase transitions of the Wishart distribution
+might originate from changes in rates of convergence of symmetric $t$
+statistics.
+",0,0,1,1,0,0
+19632,Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping,"  In this paper, we propose an online learning algorithm based on a
+Rao-Blackwellized particle filter for spatial concept acquisition and mapping.
+We have proposed a nonparametric Bayesian spatial concept acquisition model
+(SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in
+the theoretical framework of the Bayesian generative model. The proposed method
+can simultaneously learn place categories and lexicons while incrementally
+generating an environmental map. Furthermore, the proposed method has scene
+image features and a language model added to SpCoA. In the experiments, we
+tested online learning of spatial concepts and environmental maps in a novel
+environment of which the robot did not have a map. Then, we evaluated the
+results of online learning of spatial concepts and lexical acquisition. The
+experimental results demonstrated that the robot was able to more accurately
+learn the relationships between words and the place in the environmental map
+incrementally by using the proposed method.
+",1,0,0,0,0,0
+19633,The Method of Arbitrarily Large Moments to Calculate Single Scale Processes in Quantum Field Theory,"  We device a new method to calculate a large number of Mellin moments of
+single scale quantities using the systems of differential and/or difference
+equations obtained by integration-by-parts identities between the corresponding
+Feynman integrals of loop corrections to physical quantities. These scalar
+quantities have a much simpler mathematical structure than the complete
+quantity. A sufficiently large set of moments may even allow the analytic
+reconstruction of the whole quantity considered, holding in case of first order
+factorizing systems. In any case, one may derive highly precise numerical
+representations in general using this method, which is otherwise completely
+analytic.
+",1,0,1,0,0,0
+19634,Unraveling the escape dynamics and the nature of the normally hyperbolic invariant manifolds in tidally limited star clusters,"  The escape mechanism of orbits in a star cluster rotating around its parent
+galaxy in a circular orbit is investigated. A three degrees of freedom model is
+used for describing the dynamical properties of the Hamiltonian system. The
+gravitational field of the star cluster is represented by a smooth and
+spherically symmetric Plummer potential. We distinguish between ordered and
+chaotic orbits as well as between trapped and escaping orbits, considering only
+unbounded motion for several energy levels. The Smaller Alignment Index (SALI)
+method is used for determining the regular or chaotic nature of the orbits. The
+basins of escape are located and they are also correlated with the
+corresponding escape time of the orbits. Areas of bounded regular or chaotic
+motion and basins of escape were found to coexist in the $(x,z)$ plane. The
+properties of the normally hyperbolic invariant manifolds (NHIMs), located in
+the vicinity of the index-1 Lagrange points $L_1$ and $L_2$, are also explored.
+These manifolds are of paramount importance as they control the flow of stars
+over the saddle points, while they also trigger the formation of tidal tails
+observed in star clusters. Bifurcation diagrams of the Lyapunov periodic orbits
+as well as restrictions of the Poincaré map to the NHIMs are deployed for
+elucidating the dynamics in the neighbourhood of the saddle points. The
+extended tidal tails, or tidal arms, formed by stars with low velocity which
+escape through the Lagrange points are monitored. The numerical results of this
+work are also compared with previous related work.
+",0,1,0,0,0,0
+19635,The ALMA Early Science View of FUor/EXor objects. III. The Slow and Wide Outflow of V883 Ori,"  We present Atacama Large Millimeter/ sub-millimeter Array (ALMA) observations
+of V883 Ori, an FU Ori object. We describe the molecular outflow and envelope
+of the system based on the $^{12}$CO and $^{13}$CO emissions, which together
+trace a bipolar molecular outflow. The C$^{18}$O emission traces the rotational
+motion of the circumstellar disk. From the $^{12}$CO blue-shifted emission, we
+estimate a wide opening angle of $\sim$ 150$^{^{\circ}}$ for the outflow
+cavities. Also, we find that the outflow is very slow (characteristic velocity
+of only 0.65 km~s$^{-1}$), which is unique for an FU Ori object. We calculate
+the kinematic properties of the outflow in the standard manner using the
+$^{12}$CO and $^{13}$CO emissions. In addition, we present a P Cygni profile
+observed in the high-resolution optical spectrum, evidence of a wind driven by
+the accretion and being the cause for the particular morphology of the
+outflows. We discuss the implications of our findings and the rise of these
+slow outflows during and/or after the formation of a rotationally supported
+disk.
+",0,1,0,0,0,0
+19636,Hydrodynamical models of cometary HII regions,"  We have modelled the evolution of cometary HII regions produced by zero-age
+main-sequence stars of O and B spectral types, which are driving strong winds
+and are born off-centre from spherically symmetric cores with power-law
+($\alpha = 2$) density slopes. A model parameter grid was produced that spans
+stellar mass, age and core density. Exploring this parameter space we
+investigated limb-brightening, a feature commonly seen in cometary HII regions.
+We found that stars with mass $M_\star \geq 12\, \mathrm{M}_\odot$ produce this
+feature. Our models have a cavity bounded by a contact discontinuity separating
+hot shocked wind and ionised ambient gas that is similar in size to the
+surrounding HII region. Due to early pressure confinement we did not see shocks
+outside of the contact discontinuity for stars with $M_\star \leq 40\,
+\mathrm{M}_\odot$, but the cavities were found to continue to grow. The cavity
+size in each model plateaus as the HII region stagnates. The spectral energy
+distributions of our models are similar to those from identical stars evolving
+in uniform density fields. The turn-over frequency is slightly lower in our
+power-law models due to a higher proportion of low density gas covered by the
+HII regions.
+",0,1,0,0,0,0
+19637,Feature Selection based on the Local Lift Dependence Scale,"  This paper uses a classical approach to feature selection: minimization of a
+cost function applied on estimated joint distributions. However, the search
+space in which such minimization is performed is extended. In the original
+formulation, the search space is the Boolean lattice of features sets (BLFS),
+while, in the present formulation, it is a collection of Boolean lattices of
+ordered pairs (features, associated value) (CBLOP), indexed by the elements of
+the BLFS. In this approach, we may not only select the features that are most
+related to a variable Y, but also select the values of the features that most
+influence the variable or that are most prone to have a specific value of Y. A
+local formulation of Shanon's mutual information is applied on a CBLOP to
+select features, namely, the Local Lift Dependence Scale, an scale for
+measuring variable dependence in multiple resolutions. The main contribution of
+this paper is to define and apply this local measure, which permits to analyse
+local properties of joint distributions that are neglected by the classical
+Shanon's global measure. The proposed approach is applied to a dataset
+consisting of student performances on a university entrance exam, as well as on
+undergraduate courses. The approach is also applied to two datasets of the UCI
+Machine Learning Repository.
+",1,0,0,1,0,0
+19638,Weyl Rings and enhanced susceptibilities in Pyrochlore Iridates: $k\cdot p$ Analysis of Cluster Dynamical Mean-Field Theory Results,"  We match analytic results to numerical calculations to provide a detailed
+picture of the metal-insulator and topological transitions found in density
+functional plus cluster dynamical mean-field calculations of pyrochlore
+iridates. We discuss the transition from Weyl metal to Weyl semimetal regimes,
+and then analyse in detail the properties of the Weyl semimetal phase and its
+evolution into the topologically trivial insulator. The energy scales in the
+Weyl semimetal phase are found to be very small, as are the anisotropy
+parameters. The electronic structure can to a good approximation be described
+as `Weyl rings' and one of the two branches that contributes to the Weyl bands
+is essentially flat, leading to enhanced susceptibilities. The optical
+longitudinal and Hall conductivities are determined; the frequency dependence
+includes pronounced features that reveal the basic energy scales of the Weyl
+semimetal phase.
+",0,1,0,0,0,0
+19639,Inconsistency of Template Estimation by Minimizing of the Variance/Pre-Variance in the Quotient Space,"  We tackle the problem of template estimation when data have been randomly
+deformed under a group action in the presence of noise. In order to estimate
+the template, one often minimizes the variance when the influence of the
+transformations have been removed (computation of the Fr{é}chet mean in the
+quotient space). The consistency bias is defined as the distance (possibly
+zero) between the orbit of the template and the orbit of one element which
+minimizes the variance. In the first part, we restrict ourselves to isometric
+group action, in this case the Hilbertian distance is invariant under the group
+action. We establish an asymptotic behavior of the consistency bias which is
+linear with respect to the noise level. As a result the inconsistency is
+unavoidable as soon as the noise is enough. In practice, template estimation
+with a finite sample is often done with an algorithm called ""max-max"". In the
+second part, also in the case of isometric group finite, we show the
+convergence of this algorithm to an empirical Karcher mean. Our numerical
+experiments show that the bias observed in practice can not be attributed to
+the small sample size or to a convergence problem but is indeed due to the
+previously studied inconsistency. In a third part, we also present some
+insights of the case of a non invariant distance with respect to the group
+action. We will see that the inconsistency still holds as soon as the noise
+level is large enough. Moreover we prove the inconsistency even when a
+regularization term is added.
+",0,0,1,1,0,0
+19640,Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Erro,"  Bayesian inverse modeling is important for a better understanding of
+hydrological processes. However, this approach can be computationally demanding
+as it usually requires a large number of model evaluations. To address this
+issue, one can take advantage of surrogate modeling techniques. Nevertheless,
+when approximation error of the surrogate model is neglected in inverse
+modeling, the inversion result will be biased. In this paper, we develop a
+surrogate-based Bayesian inversion framework that explicitly quantifies and
+gradually reduces the approximation error of the surrogate. Specifically, two
+strategies are proposed and compared. The first strategy works by obtaining an
+ensemble of sparse polynomial chaos expansion (PCE) surrogates with Markov
+chain Monte Carlo sampling, while the second one uses Gaussian process (GP) to
+simulate the approximation error of a single sparse PCE surrogate. The two
+strategies can also be applied with other surrogates, thus they have general
+applicability. By adaptively refining the surrogate over the posterior
+distribution, we can gradually reduce the surrogate approximation error to a
+small level. Demonstrated with three case studies involving
+high-dimensionality, multi-modality and a real-world application, respectively,
+it is found that both strategies can reduce the bias introduced by surrogate
+modeling, while the second strategy has a better performance as it integrates
+two methods (i.e., sparse PCE and GP) that complement each other.
+",0,0,0,1,0,0
+19641,On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox,"  One-Class Classification (OCC) has been prime concern for researchers and
+effectively employed in various disciplines. But, traditional methods based
+one-class classifiers are very time consuming due to its iterative process and
+various parameters tuning. In this paper, we present six OCC methods based on
+extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed
+classifiers mainly lie in two categories: reconstruction based and boundary
+based, which supports both types of learning viz., online and offline learning.
+Out of various proposed methods, four are offline and remaining two are online
+methods. Out of four offline methods, two methods perform random feature
+mapping and two methods perform kernel feature mapping. Kernel feature mapping
+based approaches have been tested with RBF kernel and online version of
+one-class classifiers are tested with both types of nodes viz., additive and
+RBF. It is well known fact that threshold decision is a crucial factor in case
+of OCC, so, three different threshold deciding criteria have been employed so
+far and analyses the effectiveness of one threshold deciding criteria over
+another. Further, these methods are tested on two artificial datasets to check
+there boundary construction capability and on eight benchmark datasets from
+different discipline to evaluate the performance of the classifiers. Our
+proposed classifiers exhibit better performance compared to ten traditional
+one-class classifiers and ELM based two one-class classifiers. Through proposed
+one-class classifiers, we intend to expand the functionality of the most used
+toolbox for OCC i.e. DD toolbox. All of our methods are totally compatible with
+all the present features of the toolbox.
+",1,0,0,1,0,0
+19642,2nd order PDEs: geometric and functional considerations,"  INTRODUCTION
+This papers deals with partial differential equations of second order,
+linear, with constant and not constant coefficients, in two variables, which
+admit real characteristics. I face the study of PDEs with the mentality of the
+applied physicist, but with a weakness for formalization: look inside the black
+box of the formulas, try to compact them (for example, proceeding from an
+inverse transformation of coordinates) and make them smart (in the context,
+reformulating the theory by means of differential operators and related
+invariants), applying them with awareness and then connecting them to geometry
+or to spatial categories, which are in mathematics what is closest to the
+sensible reality. Finally, proposing examples that are exercise and
+corroborating for theory.
+TOPICS
+The geometric meaning of invariant to a differential operator. Operator
+Principal Part and its factorization: commutativity and product with and
+without residues(first order terms). Related conditions by operators and
+invariants derivatives. Coordinate transformation by invariants and expression
+of the hyperbolic and parabolic operators in the new coordinates. Properties of
+the Jacobian Matrix and relations between invariants derivatives and inverse
+coordinates transformation or the initial variables derivatives. Commutativity
+conditions and product without residues in terms of inverse coordinate
+transformations that allow to build commutative differential operators or whose
+product is without residues (or both). Diffeomorphisms and plane
+transformations: new operators and invariants in the new coordinate space which
+lead to the chain rule in compact form. Conclusive considerations and examples
+who compares different methods of solution.
+",0,0,1,0,0,0
+19643,Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression,"  Machine Learning models have been shown to be vulnerable to adversarial
+examples, ie. the manipulation of data by a attacker to defeat a defender's
+classifier at test time. We present a novel probabilistic definition of
+adversarial examples in perfect or limited knowledge setting using prior
+probability distributions on the defender's classifier. Using the asymptotic
+properties of the logistic regression, we derive a closed-form expression of
+the intensity of any adversarial perturbation, in order to achieve a given
+expected misclassification rate. This technique is relevant in a threat model
+of known model specifications and unknown training data. To our knowledge, this
+is the first method that allows an attacker to directly choose the probability
+of attack success. We evaluate our approach on two real-world datasets.
+",0,0,0,1,0,0
+19644,Data Aggregation and Packet Bundling of Uplink Small Packets for Monitoring Applications in LTE,"  In cellular massive Machine-Type Communications (MTC), a device can transmit
+directly to the base station (BS) or through an aggregator (intermediate node).
+While direct device-BS communication has recently been in the focus of 5G/3GPP
+research and standardization efforts, the use of aggregators remains a less
+explored topic. In this paper we analyze the deployment scenarios in which
+aggregators can perform cellular access on behalf of multiple MTC devices. We
+study the effect of packet bundling at the aggregator, which alleviates
+overhead and resource waste when sending small packets. The aggregators give
+rise to a tradeoff between access congestion and resource starvation and we
+show that packet bundling can minimize resource starvation, especially for
+smaller numbers of aggregators. Under the limitations of the considered model,
+we investigate the optimal settings of the network parameters, in terms of
+number of aggregators and packet-bundle size. Our results show that, in
+general, data aggregation can benefit the uplink massive MTC in LTE, by
+reducing the signalling overhead.
+",1,0,0,0,0,0
+19645,The GTC exoplanet transit spectroscopy survey. VII. Detection of sodium in WASP-52b's cloudy atmosphere,"  We report the first detection of sodium absorption in the atmosphere of the
+hot Jupiter WASP-52b. We observed one transit of WASP-52b with the
+low-resolution Optical System for Imaging and low-Intermediate-Resolution
+Integrated Spectroscopy (OSIRIS) at the 10.4 m Gran Telescopio Canarias (GTC).
+The resulting transmission spectrum, covering the wavelength range from 522 nm
+to 903 nm, is flat and featureless, except for the significant narrow
+absorption signature at the sodium doublet, which can be explained by an
+atmosphere in solar composition with clouds at 1 mbar. A cloud-free atmosphere
+is stringently ruled out. By assessing the absorption depths of sodium in
+various bin widths, we find that temperature increases towards lower
+atmospheric pressure levels, with a positive temperature gradient of 0.88 +/-
+0.65 K/km, possibly indicative of upper atmospheric heating and a temperature
+inversion.
+",0,1,0,0,0,0
+19646,That's Enough: Asynchrony with Standard Choreography Primitives,"  Choreographies are widely used for the specification of concurrent and
+distributed software architectures. Since asynchronous communications are
+ubiquitous in real-world systems, previous works have proposed different
+approaches for the formal modelling of asynchrony in choreographies. Such
+approaches typically rely on ad-hoc syntactic terms or semantics for capturing
+the concept of messages in transit, yielding different formalisms that have to
+be studied separately.
+In this work, we take a different approach, and show that such extensions are
+not needed to reason about asynchronous communications in choreographies.
+Rather, we demonstrate how a standard choreography calculus already has all the
+needed expressive power to encode messages in transit (and thus asynchronous
+communications) through the primitives of process spawning and name mobility.
+The practical consequence of our results is that we can reason about real-world
+systems within a choreography formalism that is simpler than those hitherto
+proposed.
+",1,0,0,0,0,0
+19647,Doing Things Twice (Or Differently): Strategies to Identify Studies for Targeted Validation,"  The ""reproducibility crisis"" has been a highly visible source of scientific
+controversy and dispute. Here, I propose and review several avenues for
+identifying and prioritizing research studies for the purpose of targeted
+validation. Of the various proposals discussed, I identify scientific data
+science as being a strategy that merits greater attention among those
+interested in reproducibility. I argue that the tremendous potential of
+scientific data science for uncovering high-value research studies is a
+significant and rarely discussed benefit of the transition to a fully
+open-access publishing model.
+",1,1,0,0,0,0
+19648,SciSports: Learning football kinematics through two-dimensional tracking data,"  SciSports is a Dutch startup company specializing in football analytics. This
+paper describes a joint research effort with SciSports, during the Study Group
+Mathematics with Industry 2018 at Eindhoven, the Netherlands. The main
+challenge that we addressed was to automatically process empirical football
+players' trajectories, in order to extract useful information from them. The
+data provided to us was two-dimensional positional data during entire matches.
+We developed methods based on Newtonian mechanics and the Kalman filter,
+Generative Adversarial Nets and Variational Autoencoders. In addition, we
+trained a discriminator network to recognize and discern different movement
+patterns of players. The Kalman-filter approach yields an interpretable model,
+in which a small number of player-dependent parameters can be fit; in theory
+this could be used to distinguish among players. The
+Generative-Adversarial-Nets approach appears promising in theory, and some
+initial tests showed an improvement with respect to the baseline, but the
+limits in time and computational power meant that we could not fully explore
+it. We also trained a Discriminator network to distinguish between two players
+based on their trajectories; after training, the network managed to distinguish
+between some pairs of players, but not between others. After training, the
+Variational Autoencoders generated trajectories that are difficult to
+distinguish, visually, from the data. These experiments provide an indication
+that deep generative models can learn the underlying structure and statistics
+of football players' trajectories. This can serve as a starting point for
+determining player qualities based on such trajectory data.
+",0,0,0,1,0,0
+19649,A Model-based Projection Technique for Segmenting Customers,"  We consider the problem of segmenting a large population of customers into
+non-overlapping groups with similar preferences, using diverse preference
+observations such as purchases, ratings, clicks, etc. over subsets of items. We
+focus on the setting where the universe of items is large (ranging from
+thousands to millions) and unstructured (lacking well-defined attributes) and
+each customer provides observations for only a few items. These data
+characteristics limit the applicability of existing techniques in marketing and
+machine learning. To overcome these limitations, we propose a model-based
+projection technique, which transforms the diverse set of observations into a
+more comparable scale and deals with missing data by projecting the transformed
+data onto a low-dimensional space. We then cluster the projected data to obtain
+the customer segments. Theoretically, we derive precise necessary and
+sufficient conditions that guarantee asymptotic recovery of the true customer
+segments. Empirically, we demonstrate the speed and performance of our method
+in two real-world case studies: (a) 84% improvement in the accuracy of new
+movie recommendations on the MovieLens data set and (b) 6% improvement in the
+performance of similar item recommendations algorithm on an offline dataset at
+eBay. We show that our method outperforms standard latent-class and
+demographic-based techniques.
+",1,0,0,1,0,0
+19650,Distinct Effects of Cr Bulk Doping and Surface Deposition on the Chemical Environment and Electronic Structure of the Topological Insulator Bi2Se3,"  In this report, it is shown that Cr doped into the bulk and Cr deposited on
+the surface of Bi2Se3 films produced by molecular beam epitaxy (MBE) have
+strikingly different effects on both the electronic structure and chemical
+environment.
+",0,1,0,0,0,0
+19651,High-Performance Code Generation though Fusion and Vectorization,"  We present a technique for automatically transforming kernel-based
+computations in disparate, nested loops into a fused, vectorized form that can
+reduce intermediate storage needs and lead to improved performance on
+contemporary hardware.
+We introduce representations for the abstract relationships and data
+dependencies of kernels in loop nests and algorithms for manipulating them into
+more efficient form; we similarly introduce techniques for determining data
+access patterns for stencil-like array accesses and show how this can be used
+to elide storage and improve vectorization.
+We discuss our prototype implementation of these ideas---named HFAV---and its
+use of a declarative, inference-based front-end to drive transformations, and
+we present results for some prominent codes in HPC.
+",1,0,0,0,0,0
+19652,Language Model Pre-training for Hierarchical Document Representations,"  Hierarchical neural architectures are often used to capture long-distance
+dependencies and have been applied to many document-level tasks such as
+summarization, document segmentation, and sentiment analysis. However,
+effective usage of such a large context can be difficult to learn, especially
+in the case where there is limited labeled data available. Building on the
+recent success of language model pretraining methods for learning flat
+representations of text, we propose algorithms for pre-training hierarchical
+document representations from unlabeled data. Unlike prior work, which has
+focused on pre-training contextual token representations or context-independent
+{sentence/paragraph} representations, our hierarchical document representations
+include fixed-length sentence/paragraph representations which integrate
+contextual information from the entire documents. Experiments on document
+segmentation, document-level question answering, and extractive document
+summarization demonstrate the effectiveness of the proposed pre-training
+algorithms.
+",1,0,0,0,0,0
+19653,Interpretation of Semantic Tweet Representations,"  Research in analysis of microblogging platforms is experiencing a renewed
+surge with a large number of works applying representation learning models for
+applications like sentiment analysis, semantic textual similarity computation,
+hashtag prediction, etc. Although the performance of the representation
+learning models has been better than the traditional baselines for such tasks,
+little is known about the elementary properties of a tweet encoded within these
+representations, or why particular representations work better for certain
+tasks. Our work presented here constitutes the first step in opening the
+black-box of vector embeddings for tweets. Traditional feature engineering
+methods for high-level applications have exploited various elementary
+properties of tweets. We believe that a tweet representation is effective for
+an application because it meticulously encodes the application-specific
+elementary properties of tweets. To understand the elementary properties
+encoded in a tweet representation, we evaluate the representations on the
+accuracy to which they can model each of those properties such as tweet length,
+presence of particular words, hashtags, mentions, capitalization, etc. Our
+systematic extensive study of nine supervised and four unsupervised tweet
+representations against most popular eight textual and five social elementary
+properties reveal that Bi-directional LSTMs (BLSTMs) and Skip-Thought Vectors
+(STV) best encode the textual and social properties of tweets respectively.
+FastText is the best model for low resource settings, providing very little
+degradation with reduction in embedding size. Finally, we draw interesting
+insights by correlating the model performance obtained for elementary property
+prediction tasks with the highlevel downstream applications.
+",1,0,0,0,0,0
+19654,LoIDE: a web-based IDE for Logic Programming - Preliminary Technical Report,"  Logic-based paradigms are nowadays widely used in many different fields, also
+thank to the availability of robust tools and systems that allow the
+development of real-world and industrial applications.
+In this work we present LoIDE, an advanced and modular web-editor for
+logic-based languages that also integrates with state-of-the-art solvers.
+",1,0,0,0,0,0
+19655,Introduction to finite mixtures,"  Mixture models have been around for over 150 years, as an intuitively simple
+and practical tool for enriching the collection of probability distributions
+available for modelling data. In this chapter we describe the basic ideas of
+the subject, present several alternative representations and perspectives on
+these models, and discuss some of the elements of inference about the unknowns
+in the models. Our focus is on the simplest set-up, of finite mixture models,
+but we discuss also how various simplifying assumptions can be relaxed to
+generate the rich landscape of modelling and inference ideas traversed in the
+rest of this book.
+",0,0,0,1,0,0
+19656,On the complexity of the projective splitting and Spingarn's methods for the sum of two maximal monotone operators,"  In this work we study the pointwise and ergodic iteration-complexity of a
+family of projective splitting methods proposed by Eckstein and Svaiter, for
+finding a zero of the sum of two maximal monotone operators. As a consequence
+of the complexity analysis of the projective splitting methods, we obtain
+complexity bounds for the two-operator case of Spingarn's partial inverse
+method. We also present inexact variants of two specific instances of this
+family of algorithms, and derive corresponding convergence rate results.
+",0,0,1,0,0,0
+19657,Demystifying Relational Latent Representations,"  Latent features learned by deep learning approaches have proven to be a
+powerful tool for machine learning. They serve as a data abstraction that makes
+learning easier by capturing regularities in data explicitly. Their benefits
+motivated their adaptation to relational learning context. In our previous
+work, we introduce an approach that learns relational latent features by means
+of clustering instances and their relations. The major drawback of latent
+representations is that they are often black-box and difficult to interpret.
+This work addresses these issues and shows that (1) latent features created by
+clustering are interpretable and capture interesting properties of data; (2)
+they identify local regions of instances that match well with the label, which
+partially explains their benefit; and (3) although the number of latent
+features generated by this approach is large, often many of them are highly
+redundant and can be removed without hurting performance much.
+",1,0,0,1,0,0
+19658,A direct method to compute the galaxy count angular correlation function including redshift-space distortions,"  In the near future, cosmology will enter the wide and deep galaxy survey area
+allowing high-precision studies of the large scale structure of the universe in
+three dimensions. To test cosmological models and determine their parameters
+accurately, it is natural to confront data with exact theoretical expectations
+expressed in the observational parameter space (angles and redshift). The
+data-driven galaxy number count fluctuations on redshift shells, can be used to
+build correlation functions $C(\theta; z_1, z_2)$ on and between shells which
+can probe the baryonic acoustic oscillations, the distance-redshift distortions
+as well as gravitational lensing and other relativistic effects. Transforming
+the model to the data space usually requires the computation of the angular
+power spectrum $C_\ell(z_1, z_2)$ but this appears as an artificial and
+inefficient step plagued by apodization issues. In this article we show that it
+is not necessary and present a compact expression for $C(\theta; z_1, z_2)$
+that includes directly the leading density and redshift space distortions terms
+from the full linear theory. It can be evaluated using a fast integration
+method based on Clenshaw-Curtis quadrature and Chebyshev polynomial series.
+This new method to compute the correlation functions without any Limber
+approximation, allows us to produce and discuss maps of the correlation
+function directly in the observable space and is a significant step towards
+disentangling the data from the tested models.
+",0,1,0,0,0,0
+19659,Contributors profile modelization in crowdsourcing platforms,"  The crowdsourcing consists in the externalisation of tasks to a crowd of
+people remunerated to execute this ones. The crowd, usually diversified, can
+include users without qualification and/or motivation for the tasks. In this
+paper we will introduce a new method of user expertise modelization in the
+crowdsourcing platforms based on the theory of belief functions in order to
+identify serious and qualificated users.
+",1,0,0,0,0,0
+19660,Discrete versions of the Li-Yau gradient estimate,"  We study positive solutions to the heat equation on graphs. We prove variants
+of the Li-Yau gradient estimate and the differential Harnack inequality. For
+some graphs, we can show the estimates to be sharp. We establish new
+computation rules for differential operators on discrete spaces and introduce a
+relaxation function that governs the time dependency in the differential
+Harnack estimate.
+",0,0,1,0,0,0
+19661,Efficient Modelling & Forecasting with range based volatility models and application,"  This paper considers an alternative method for fitting CARR models using
+combined estimating functions (CEF) by showing its usefulness in applications
+in economics and quantitative finance. The associated information matrix for
+corresponding new estimates is derived to calculate the standard errors. A
+simulation study is carried out to demonstrate its superiority relative to
+other two competitors: linear estimating functions (LEF) and the maximum
+likelihood (ML). Results show that CEF estimates are more efficient than LEF
+and ML estimates when the error distribution is mis-specified. Taking a real
+data set from financial economics, we illustrate the usefulness and
+applicability of the CEF method in practice and report reliable forecast values
+to minimize the risk in the decision making process.
+",0,0,1,1,0,0
+19662,A Constrained Shortest Path Scheme for Virtual Network Service Management,"  Virtual network services that span multiple data centers are important to
+support emerging data-intensive applications in fields such as bioinformatics
+and retail analytics. Successful virtual network service composition and
+maintenance requires flexible and scalable 'constrained shortest path
+management' both in the management plane for virtual network embedding (VNE) or
+network function virtualization service chaining (NFV-SC), as well as in the
+data plane for traffic engineering (TE). In this paper, we show analytically
+and empirically that leveraging constrained shortest paths within recent VNE,
+NFV-SC and TE algorithms can lead to network utilization gains (of up to 50%)
+and higher energy efficiency. The management of complex VNE, NFV-SC and TE
+algorithms can be, however, intractable for large scale substrate networks due
+to the NP-hardness of the constrained shortest path problem. To address such
+scalability challenges, we propose a novel, exact constrained shortest path
+algorithm viz., 'Neighborhoods Method' (NM). Our NM uses novel search space
+reduction techniques and has a theoretical quadratic speed-up making it
+practically faster (by an order of magnitude) than recent branch-and-bound
+exhaustive search solutions. Finally, we detail our NM-based SDN controller
+implementation in a real-world testbed to further validate practical NM
+benefits for virtual network services.
+",1,0,0,0,0,0
+19663,Tunneling of Glashow-Weinberg-Salam model particles from Black Hole Solutions in Rastall Theory,"  Using the semiclassical WKB approximation and Hamilton-Jacobi method, we
+solve an equation of motion for the Glashow-Weinberg-Salam model, which is
+important for understanding the unified gauge-theory of weak and
+electromagnetic interactions. We calculate the tunneling rate of the massive
+charged W-bosons in a background of electromagnetic field to investigate the
+Hawking temperature of black holes surrounded by perfect fluid in Rastall
+theory. Then, we study the quantum gravity effects on the generalized Proca
+equation with generalized uncertainty principle (GUP) on this background. We
+show that quantum gravity effects leave the remnants on the Hawking temperature
+and the Hawking radiation becomes nonthermal.
+",0,1,0,0,0,0
+19664,"Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey","  Natural language and symbols are intimately correlated. Recent advances in
+machine learning (ML) and in natural language processing (NLP) seem to
+contradict the above intuition: symbols are fading away, erased by vectors or
+tensors called distributed and distributional representations. However, there
+is a strict link between distributed/distributional representations and
+symbols, being the first an approximation of the second. A clearer
+understanding of the strict link between distributed/distributional
+representations and symbols will certainly lead to radically new deep learning
+networks. In this paper we make a survey that aims to draw the link between
+symbolic representations and distributed/distributional representations. This
+is the right time to revitalize the area of interpreting how symbols are
+represented inside neural networks.
+",1,0,0,0,0,0
+19665,Joint Embedding of Graphs,"  Feature extraction and dimension reduction for networks is critical in a wide
+variety of domains. Efficiently and accurately learning features for multiple
+graphs has important applications in statistical inference on graphs. We
+propose a method to jointly embed multiple undirected graphs. Given a set of
+graphs, the joint embedding method identifies a linear subspace spanned by rank
+one symmetric matrices and projects adjacency matrices of graphs into this
+subspace. The projection coefficients can be treated as features of the graphs.
+We also propose a random graph model which generalizes classical random graph
+model and can be used to model multiple graphs. We show through theory and
+numerical experiments that under the model, the joint embedding method produces
+estimates of parameters with small errors. Via simulation experiments, we
+demonstrate that the joint embedding method produces features which lead to
+state of the art performance in classifying graphs. Applying the joint
+embedding method to human brain graphs, we find it extract interpretable
+features that can be used to predict individual composite creativity index.
+",1,0,0,1,0,0
+19666,Tuples of polynomials over finite fields with pairwise coprimality conditions,"  Let $q$ be a prime power. We estimate the number of tuples of degree bounded
+monic polynomials $(Q_1,\ldots,Q_v) \in (\mathbb{F}_q[z])^v$ that satisfy given
+pairwise coprimality conditions. We show how this generalises from monic
+polynomials in finite fields to Dedekind domains with finite norms.
+",0,0,1,0,0,0
+19667,Pythagorean theorem of Sharpe ratio,"  In the present paper, using a replica analysis, we examine the portfolio
+optimization problem handled in previous work and discuss the minimization of
+investment risk under constraints of budget and expected return for the case
+that the distribution of the hyperparameters of the mean and variance of the
+return rate of each asset are not limited to a specific probability family.
+Findings derived using our proposed method are compared with those in previous
+work to verify the effectiveness of our proposed method. Further, we derive a
+Pythagorean theorem of the Sharpe ratio and macroscopic relations of
+opportunity loss. Using numerical experiments, the effectiveness of our
+proposed method is demonstrated for a specific situation.
+",0,1,1,0,0,0
+19668,Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models,"  Researchers often summarize their work in the form of scientific posters.
+Posters provide a coherent and efficient way to convey core ideas expressed in
+scientific papers. Generating a good scientific poster, however, is a complex
+and time consuming cognitive task, since such posters need to be readable,
+informative, and visually aesthetic. In this paper, for the first time, we
+study the challenging problem of learning to generate posters from scientific
+papers. To this end, a data-driven framework, that utilizes graphical models,
+is proposed. Specifically, given content to display, the key elements of a good
+poster, including attributes of each panel and arrangements of graphical
+elements are learned and inferred from data. During the inference stage, an MAP
+inference framework is employed to incorporate some design principles. In order
+to bridge the gap between panel attributes and the composition within each
+panel, we also propose a recursive page splitting algorithm to generate the
+panel layout for a poster. To learn and validate our model, we collect and
+release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which
+consists of scientific papers and corresponding posters with exhaustively
+labelled panels and attributes. Qualitative and quantitative results indicate
+the effectiveness of our approach.
+",1,0,0,0,0,0
+19669,Transformable Biomimetic Liquid Metal Chameleon,"  Liquid metal (LM) is of current core interest for a wide variety of newly
+emerging areas. However, the functional materials thus made so far by LM only
+could display a single silver-white appearance. Here in this study, the new
+conceptual colorful LM marbles working like transformable biomimetic chameleons
+were proposed and fabricated from LM droplets through encasing them with
+fluorescent nano-particles. We demonstrated that this unique LM marble can be
+manipulated into various stable magnificent appearances as one desires. And it
+can also splitt and merge among different colors. Such multifunctional LM
+chameleon is capable of responding to the outside electric-stimulus and
+realizing shape transformation and discoloration behaviors as well. Further
+more, the electric-stimuli has been disclosed to be an easy going way to
+trigger the release of nano/micro-particles from the LM. The present
+fluorescent biomimetic liquid metal chameleon is expected to offer important
+opportunities for diverse unconventional applications, especially in a wide
+variety of functional smart material and color changeable soft robot areas.
+",0,1,0,0,0,0
+19670,Local Private Hypothesis Testing: Chi-Square Tests,"  The local model for differential privacy is emerging as the reference model
+for practical applications collecting and sharing sensitive information while
+satisfying strong privacy guarantees. In the local model, there is no trusted
+entity which is allowed to have each individual's raw data as is assumed in the
+traditional curator model for differential privacy. So, individuals' data are
+usually perturbed before sharing them.
+We explore the design of private hypothesis tests in the local model, where
+each data entry is perturbed to ensure the privacy of each participant.
+Specifically, we analyze locally private chi-square tests for goodness of fit
+and independence testing, which have been studied in the traditional, curator
+model for differential privacy.
+",1,0,1,1,0,0
+19671,Rapid point-of-care Hemoglobin measurement through low-cost optics and Convolutional Neural Network based validation,"  A low-cost, robust, and simple mechanism to measure hemoglobin would play a
+critical role in the modern health infrastructure. Consistent sample
+acquisition has been a long-standing technical hurdle for photometer-based
+portable hemoglobin detectors which rely on micro cuvettes and dry chemistry.
+Any particulates (e.g. intact red blood cells (RBCs), microbubbles, etc.) in a
+cuvette's sensing area drastically impact optical absorption profile, and
+commercial hemoglobinometers lack the ability to automatically detect faulty
+samples. We present the ground-up development of a portable, low-cost and open
+platform with equivalent accuracy to medical-grade devices, with the addition
+of CNN-based image processing for rapid sample viability prechecks. The
+developed platform has demonstrated precision to the nearest $0.18[g/dL]$ of
+hemoglobin, an R^2 = 0.945 correlation to hemoglobin absorption curves reported
+in literature, and a 97% detection accuracy of poorly-prepared samples. We see
+the developed hemoglobin device/ML platform having massive implications in
+rural medicine, and consider it an excellent springboard for robust deep
+learning optical spectroscopy: a currently untapped source of data for
+detection of countless analytes.
+",0,1,0,1,0,0
+19672,Families of sets with no matchings of sizes 3 and 4,"  In this paper, we study the following classical question of extremal set
+theory: what is the maximum size of a family of subsets of $[n]$ such that no
+$s$ sets from the family are pairwise disjoint? This problem was first posed by
+Erd\H os and resolved for $n\equiv 0, -1\ (\mathrm{mod }\ s)$ by Kleitman in
+the 60s. Very little progress was made on the problem until recently. The only
+result was a very lengthy resolution of the case $s=3,\ n\equiv 1\ (\mathrm{mod
+}\ 3)$ by Quinn, which was written in his PhD thesis and never published in a
+refereed journal. In this paper, we give another, much shorter proof of Quinn's
+result, as well as resolve the case $s=4,\ n\equiv 2\ (\mathrm{mod }\ 4)$. This
+complements the results in our recent paper, where, in particular, we answered
+the question in the case $n\equiv -2\ (\mathrm{mod }\ s)$ for $s\ge 5$.
+",1,0,1,0,0,0
+19673,Light fields in complex media: mesoscopic scattering meets wave control,"  The newly emerging field of wave front shaping in complex media has recently
+seen enormous progress. The driving force behind these advances has been the
+experimental accessibility of the information stored in the scattering matrix
+of a disordered medium, which can nowadays routinely be exploited to focus
+light as well as to image or to transmit information even across highly turbid
+scattering samples. We will provide an overview of these new techniques, of
+their experimental implementations as well as of the underlying theoretical
+concepts following from mesoscopic scattering theory. In particular, we will
+highlight the intimate connections between quantum transport phenomena and the
+scattering of light fields in disordered media, which can both be described by
+the same theoretical concepts. We also put particular emphasis on how the above
+topics relate to application-oriented research fields such as optical imaging,
+sensing and communication.
+",0,1,0,0,0,0
+19674,Tailoring Product Ownership in Large-Scale Agile,"  In large-scale agile projects, product owners undertake a range of
+challenging and varied activities beyond those conventionally associated with
+that role. Using in-depth research interviews from 93 practitioners working in
+cross-border teams, from 21 organisations, our rich empirical data offers a
+unique international perspective into product owner activities. We found that
+the leaders of large-scale agile projects create product owner teams. Product
+owner team members undertake sponsor, intermediary and release plan master
+activities to manage scale. They undertake communicator and traveller
+activities to manage distance and technical architect, governor and risk
+assessor activities to manage governance. Based on our findings, we describe
+product owner behaviors that are valued by experienced product owners and their
+line managers.
+",1,0,0,0,0,0
+19675,"Gravitational wave, collider and dark matter signals from a scalar singlet electroweak baryogenesis","  We analyse a simple extension of the SM with just an additional scalar
+singlet coupled to the Higgs boson. We discuss the possible probes for
+electroweak baryogenesis in this model including collider searches,
+gravitational wave and direct dark matter detection signals. We show that a
+large portion of the model parameter space exists where the observation of
+gravitational waves would allow detection while the indirect collider searches
+would not.
+",0,1,0,0,0,0
+19676,Canonical correlation coefficients of high-dimensional Gaussian vectors: finite rank case,"  Consider a Gaussian vector $\mathbf{z}=(\mathbf{x}',\mathbf{y}')'$,
+consisting of two sub-vectors $\mathbf{x}$ and $\mathbf{y}$ with dimensions $p$
+and $q$ respectively, where both $p$ and $q$ are proportional to the sample
+size $n$. Denote by $\Sigma_{\mathbf{u}\mathbf{v}}$ the population
+cross-covariance matrix of random vectors $\mathbf{u}$ and $\mathbf{v}$, and
+denote by $S_{\mathbf{u}\mathbf{v}}$ the sample counterpart. The canonical
+correlation coefficients between $\mathbf{x}$ and $\mathbf{y}$ are known as the
+square roots of the nonzero eigenvalues of the canonical correlation matrix
+$\Sigma_{\mathbf{x}\mathbf{x}}^{-1}\Sigma_{\mathbf{x}\mathbf{y}}\Sigma_{\mathbf{y}\mathbf{y}}^{-1}\Sigma_{\mathbf{y}\mathbf{x}}$.
+In this paper, we focus on the case that $\Sigma_{\mathbf{x}\mathbf{y}}$ is of
+finite rank $k$, i.e. there are $k$ nonzero canonical correlation coefficients,
+whose squares are denoted by $r_1\geq\cdots\geq r_k>0$. We study the sample
+counterparts of $r_i,i=1,\ldots,k$, i.e. the largest $k$ eigenvalues of the
+sample canonical correlation matrix
+$§_{\mathbf{x}\mathbf{x}}^{-1}§_{\mathbf{x}\mathbf{y}}§_{\mathbf{y}\mathbf{y}}^{-1}§_{\mathbf{y}\mathbf{x}}$,
+denoted by $\lambda_1\geq\cdots\geq \lambda_k$. We show that there exists a
+threshold $r_c\in(0,1)$, such that for each $i\in\{1,\ldots,k\}$, when $r_i\leq
+r_c$, $\lambda_i$ converges almost surely to the right edge of the limiting
+spectral distribution of the sample canonical correlation matrix, denoted by
+$d_{+}$. When $r_i>r_c$, $\lambda_i$ possesses an almost sure limit in
+$(d_{+},1]$. We also obtain the limiting distribution of $\lambda_i$'s under
+appropriate normalization. Specifically, $\lambda_i$ possesses Gaussian type
+fluctuation if $r_i>r_c$, and follows Tracy-Widom distribution if $r_i<r_c$.
+Some applications of our results are also discussed.
+",0,0,1,1,0,0
+19677,Synthesizing Normalized Faces from Facial Identity Features,"  We present a method for synthesizing a frontal, neutral-expression image of a
+person's face given an input face photograph. This is achieved by learning to
+generate facial landmarks and textures from features extracted from a
+facial-recognition network. Unlike previous approaches, our encoding feature
+vector is largely invariant to lighting, pose, and facial expression.
+Exploiting this invariance, we train our decoder network using only frontal,
+neutral-expression photographs. Since these photographs are well aligned, we
+can decompose them into a sparse set of landmark points and aligned texture
+maps. The decoder then predicts landmarks and textures independently and
+combines them using a differentiable image warping operation. The resulting
+images can be used for a number of applications, such as analyzing facial
+attributes, exposure and white balance adjustment, or creating a 3-D avatar.
+",1,0,0,1,0,0
+19678,One-Shot Coresets: The Case of k-Clustering,"  Scaling clustering algorithms to massive data sets is a challenging task.
+Recently, several successful approaches based on data summarization methods,
+such as coresets and sketches, were proposed. While these techniques provide
+provably good and small summaries, they are inherently problem dependent - the
+practitioner has to commit to a fixed clustering objective before even
+exploring the data. However, can one construct small data summaries for a wide
+range of clustering problems simultaneously? In this work, we affirmatively
+answer this question by proposing an efficient algorithm that constructs such
+one-shot summaries for k-clustering problems while retaining strong theoretical
+guarantees.
+",1,0,0,1,0,0
+19679,Multiple nodal solutions of nonlinear Choquard equations,"  In this paper, we consider the existence of multiple nodal solutions of the
+nonlinear Choquard equation \begin{equation*} \ \ \ \ (P)\ \ \ \ \begin{cases}
+-\Delta u+u=(|x|^{-1}\ast|u|^p)|u|^{p-2}u \ \ \ \text{in}\ \mathbb{R}^3, \ \ \
+\ \\ u\in H^1(\mathbb{R}^3),\\ \end{cases} \end{equation*} where $p\in
+(\frac{5}{2},5)$. We show that for any positive integer $k$, problem $(P)$ has
+at least a radially symmetrical solution changing sign exactly $k$-times.
+",0,0,1,0,0,0
+19680,"Computational Sufficiency, Reflection Groups, and Generalized Lasso Penalties","  We study estimators with generalized lasso penalties within the computational
+sufficiency framework introduced by Vu (2018, arXiv:1807.05985). By
+representing these penalties as support functions of zonotopes and more
+generally Minkowski sums of line segments and rays, we show that there is a
+natural reflection group associated with the underlying optimization problem. A
+consequence of this point of view is that for large classes of estimators
+sharing the same penalty, the penalized least squares estimator is
+computationally minimal sufficient. This means that all such estimators can be
+computed by refining the output of any algorithm for the least squares case. An
+interesting technical component is our analysis of coordinate descent on the
+dual problem. A key insight is that the iterates are obtained by reflecting and
+averaging, so they converge to an element of the dual feasible set that is
+minimal with respect to a ordering induced by the group associated with the
+penalty. Our main application is fused lasso/total variation denoising and
+isotonic regression on arbitrary graphs. In those cases the associated group is
+a permutation group.
+",0,0,0,1,0,0
+19681,Driving Simulator Platform for Development and Evaluation of Safety and Emergency Systems,"  According to data from the United Nations, more than 3000 people have died
+each day in the world due to road traffic collision. Considering recent
+researches, the human error may be considered as the main responsible for these
+fatalities. Because of this, researchers seek alternatives to transfer the
+vehicle control from people to autonomous systems. However, providing this
+technological innovation for the people may demand complex challenges in the
+legal, economic and technological areas. Consequently, carmakers and
+researchers have divided the driving automation in safety and emergency systems
+that improve the driver perception on the road. This may reduce the human
+error. Therefore, the main contribution of this study is to propose a driving
+simulator platform to develop and evaluate safety and emergency systems, in the
+first design stage. This driving simulator platform has an advantage: a
+flexible software structure.This allows in the simulation one adaptation for
+development or evaluation of a system. The proposed driving simulator platform
+was tested in two applications: cooperative vehicle system development and the
+influence evaluation of a Driving Assistance System (\textit{DAS}) on a driver.
+In the cooperative vehicle system development, the results obtained show that
+the increment of the time delay in the communication among vehicles ($V2V$) is
+determinant for the system performance. On the other hand, in the influence
+evaluation of a \textit{DAS} in a driver, it was possible to conclude that the
+\textit{DAS'} model does not have the level of influence necessary in a driver
+to avoid an accident.
+",1,0,0,0,0,0
+19682,Electrical transient laws in neuronal microdomains based on electro-diffusion,"  The current-voltage (I-V) conversion characterizes the physiology of cellular
+microdomains and reflects cellular communication, excitability, and electrical
+transduction. Yet deriving such I-V laws remains a major challenge in most
+cellular microdomains due to their small sizes and the difficulty of accessing
+voltage with a high nanometer precision. We present here novel analytical
+relations derived for different numbers of ionic species inside a neuronal
+micro/nano-domains, such as dendritic spines. When a steady-state current is
+injected, we find a large deviation from the classical Ohm's law, showing that
+the spine neck resistance is insuficent to characterize electrical properties.
+For a constricted spine neck, modeled by a hyperboloid, we obtain a new I-V law
+that illustrates the consequences of narrow passages on electrical conduction.
+Finally, during a fast current transient, the local voltage is modulated by the
+distance between activated voltage-gated channels. To conclude,
+electro-diffusion laws can now be used to interpret voltage distribution in
+neuronal microdomains.
+",0,0,0,0,1,0
+19683,Characterization of a Deuterium-Deuterium Plasma Fusion Neutron Generator,"  We characterize the neutron output of a deuterium-deuterium plasma fusion
+neutron generator, model 35-DD-W-S, manufactured by NSD/Gradel-Fusion. The
+measured energy spectrum is found to be dominated by neutron peaks at 2.2 MeV
+and 2.7 MeV. A detailed GEANT4 simulation accurately reproduces the measured
+energy spectrum and confirms our understanding of the fusion process in this
+generator. Additionally, a contribution of 14.1 MeV neutrons from
+deuterium-tritium fusion is found at a level of~$3.5\%$, from tritium produced
+in previous deuterium-deuterium reactions. We have measured both the absolute
+neutron flux as well as its relative variation on the operational parameters of
+the generator. We find the flux to be proportional to voltage $V^{3.32 \pm
+0.14}$ and current $I^{0.97 \pm 0.01}$. Further, we have measured the angular
+dependence of the neutron emission with respect to the polar angle. We conclude
+that it is well described by isotropic production of neutrons within the
+cathode field cage.
+",0,1,0,0,0,0
+19684,Turbulent Mass Inhomogeneities induced by a point-source,"  We describe how turbulence distributes tracers away from a localized source
+of injection, and analyse how the spatial inhomogeneities of the concentration
+field depend on the amount of randomness in the injection mechanism. For that
+purpose, we contrast the mass correlations induced by purely random injections
+with those induced by continuous injections in the environment. Using the
+Kraichnan model of turbulent advection, whereby the underlying velocity field
+is assumed to be shortly correlated in time, we explicitly identify scaling
+regions for the statistics of the mass contained within a shell of radius $r$
+and located at a distance $\rho$ away from the source. The two key parameters
+are found to be (i) the ratio $s^2$ between the absolute and the relative
+timescales of dispersion and (ii) the ratio $\Lambda$ between the size of the
+cloud and its distance away from the source. When the injection is random, only
+the former is relevant, as previously shown by Celani, Martins-Afonso $\&$
+Mazzino, $J. Fluid. Mech$, 2007 in the case of an incompressible fluid. It is
+argued that the space partition in terms of $s^2$ and $\Lambda$ is a robust
+feature of the injection mechanism itself, which should remain relevant beyond
+the Kraichnan model. This is for instance the case in a generalised version of
+the model, where the absolute dispersion is prescribed to be ballistic rather
+than diffusive.
+",0,1,0,0,0,0
+19685,Berezin-toeplitz quantization and complex weyl quantization of the torus t${}^2$,"  In this paper, we give a correspondence between the Berezin-Toeplitz and the
+complex Weyl quantizations of the torus $ \mathbb{T}^2$. To achieve this, we
+use the correspondence between the Berezin-Toeplitz and the complex Weyl
+quantizations of the complex plane and a relation between the Berezin-Toeplitz
+quantization of a periodic symbol on the real phase space $\mathbb{R}^2$ and
+the Berezin-Toeplitz quantization of a symbol on the torus $ \mathbb{T}^2 $.
+",0,0,1,0,0,0
+19686,Conformal predictive distributions with kernels,"  This paper reviews the checkered history of predictive distributions in
+statistics and discusses two developments, one from recent literature and the
+other new. The first development is bringing predictive distributions into
+machine learning, whose early development was so deeply influenced by two
+remarkable groups at the Institute of Automation and Remote Control. The second
+development is combining predictive distributions with kernel methods, which
+were originated by one of those groups, including Emmanuel Braverman.
+",1,0,0,1,0,0
+19687,Degree bound for toric envelope of a linear algebraic group,"  Algorithms working with linear algebraic groups often represent them via
+defining polynomial equations. One can always choose defining equations for an
+algebraic group to be of the degree at most the degree of the group as an
+algebraic variety. However, the degree of a linear algebraic group $G \subset
+\mathrm{GL}_n(C)$ can be arbitrarily large even for $n = 1$. One of the key
+ingredients of Hrushovski's algorithm for computing the Galois group of a
+linear differential equation was an idea to `approximate' every algebraic
+subgroup of $\mathrm{GL}_n(C)$ by a `similar' group so that the degree of the
+latter is bounded uniformly in $n$. Making this uniform bound computationally
+feasible is crucial for making the algorithm practical.
+In this paper, we derive a single-exponential degree bound for such an
+approximation (we call it toric envelope), which is qualitatively optimal. As
+an application, we improve the quintuply exponential bound for the first step
+of the Hrushovski's algorithm due to Feng to a single-exponential bound. For
+the cases $n = 2, 3$ often arising in practice, we further refine our general
+bound.
+",1,0,0,0,0,0
+19688,The Modified Lommel functions: monotonic pattern and inequalities,"  This article studies the monotonicity, log-convexity of the modified Lommel
+functions by using its power series and infinite product representation. Same
+properties for the ratio of the modified Lommel functions with the Lommel
+function, $\sinh$ and $\cosh$ are also discussed. As a consequence, some
+Turán type and reverse Turán type inequalities are given. A Rayleigh type
+function for the Lommel functions are derived and as an application, we obtain
+the Redheffer-type inequality.
+",0,0,1,0,0,0
+19689,Anticipating epileptic seizures through the analysis of EEG synchronization as a data classification problem,"  Epilepsy is a neurological disorder arising from anomalies of the electrical
+activity in the brain, affecting about 0.5--0.8\% of the world population.
+Several studies investigated the relationship between seizures and brainwave
+synchronization patterns, pursuing the possibility of identifying interictal,
+preictal, ictal and postictal states. In this work, we introduce a graph-based
+model of the brain interactions developed to study synchronization patterns in
+the electroencephalogram (EEG) signals. The aim is to develop a
+patient-specific approach, also for a real-time use, for the prediction of
+epileptic seizures' occurrences. Different synchronization measures of the EEG
+signals and easily computable functions able to capture in real-time the
+variations of EEG synchronization have been considered. Both standard and
+ad-hoc classification algorithms have been developed and used. Results on scalp
+EEG signals show that this simple and computationally viable processing is able
+to highlight the changes in the synchronization corresponding to the preictal
+state.
+",0,0,0,0,1,0
+19690,On distances in lattices from algebraic number fields,"  In this paper, we study a classical construction of lattices from number
+fields and obtain a series of new results about their minimum distance and
+other characteristics by introducing a new measure of algebraic numbers. In
+particular, we show that when the number fields have few complex embeddings,
+the minimum distances of these lattices can be computed exactly.
+",0,0,1,0,0,0
+19691,"Significance of distinct electron correlation effects in determining the P,T-odd electric dipole moment of $^{171}$Yb","  Parity and time-reversal violating electric dipole moment (EDM) of $^{171}$Yb
+is calculated accounting for the electron correlation effects over the
+Dirac-Hartree-Fock (DHF) method in the relativistic Rayleigh-Schrödinger
+many-body perturbation theory, with the second (MBPT(2) method) and third order
+(MBPT(3) method) approximations, and two variants of all-order relativistic
+many-body approaches, in the random phase approximation (RPA) and
+coupled-cluster (CC) method with singles and doubles (CCSD method) framework.
+We consider electron-nucleus tensor-pseudotensor (T-PT) and nuclear Schiff
+moment (NSM) interactions as the predominant sources that induce EDM in a
+diamagnetic atomic system. Our results from the CCSD method to EDM ($d_a$) of
+$^{171}$Yb due to the T-PT and NSM interactions are found to be $d_a = 4.85(6)
+\times 10^{-20} \langle \sigma \rangle C_T \ |e| \ cm$ and $d_a=2.89(4) \times
+10^{-17} {S/(|e|\ fm^3)}$, respectively, where $C_T$ is the T-PT coupling
+constant and $S$ is the NSM. These values differ significantly from the earlier
+calculations. The reason for the same has been attributed to large correlation
+effects arising through non-RPA type of interactions among the electrons in
+this atom that are observed by analyzing the differences in the RPA and CCSD
+results. This has been further scrutinized from the MBPT(2) and MBPT(3) results
+and their roles have been demonstrated explicitly.
+",0,1,0,0,0,0
+19692,Facilitating information system development with Panoramic view on data,"  The increasing amount of information and the absence of an effective tool for
+assisting users with minimal technical knowledge lead us to use associative
+thinking paradigm for implementation of a software solution - Panorama. In this
+study, we present object recognition process, based on context + focus
+information visualization techniques, as a foundation for realization of
+Panorama. We show that user can easily define data vocabulary of selected
+domain that is furthermore used as the application framework. The purpose of
+Panorama approach is to facilitate software development of certain problem
+domains by shortening the Software Development Life Cycle with minimizing the
+impact of implementation, review and maintenance phase. Our approach is focused
+on using and updating data vocabulary by users without extensive programming
+skills. Panorama therefore facilitates traversing through data by following
+associations where user does not need to be familiar with the query language,
+the data structure and does not need to know the problem domain fully. Our
+approach has been verified by detailed comparison to existing approaches and in
+an experiment by implementing selected use cases. The results confirmed that
+Panorama fits problem domains with emphasis on data oriented rather than ones
+with process oriented aspects. In such cases the development of selected
+problem domains is shortened up to 25%, where emphasis is mainly on analysis,
+logical design and testing, while omitting physical design and programming,
+which is performed automatically by Panorama tool.
+",1,0,0,0,0,0
+19693,Neutron Diffraction and $μ$SR Studies of Two Polymorphs of Nickel Niobate (NiNb$_2$O$_6$),"  Neutron diffraction and muon spin relaxation ($\mu$SR) studies are presented
+for the newly characterized polymorph of NiNb$_2$O$_6$ ($\beta$-NiNb$_2$O$_6$)
+with space group P4$_2$/n and $\mu$SR data only for the previously known
+columbite structure polymorph with space group Pbcn. The magnetic structure of
+the P4$_2$/n form was determined from neutron diffraction using both powder and
+single crystal data. Powder neutron diffraction determined an ordering wave
+vector $\vec{k}$ = ($\frac{1}{2},\frac{1}{2},\frac{1}{2}$). Single crystal data
+confirmed the same $\vec{k}$-vector and showed that the correct magnetic
+structure consists of antiferromagnetically-coupled chains running along the a
+or b-axes in adjacent Ni$^{2+}$ layers perpendicular to the c-axis, which is
+consistent with the expected exchange interaction hierarchy in this system. The
+refined magnetic structure is compared with the known magnetic structures of
+the closely related tri-rutile phases, NiSb$_2$O$_6$ and NiTa$_2$O$_6$. $\mu$SR
+data finds a transition temperature of $T_N \sim$ 15 K for this system, while
+the columbite polymorph exhibits a lower $T_N =$ 5.7(3) K. Our $\mu$SR
+measurements also allowed us to estimate the critical exponent of the order
+parameter $\beta$ for each polymorph. We found $\beta =$ 0.25(3) and 0.16(2)
+for the $\beta$ and columbite polymorphs respectively. The single crystal
+neutron scattering data gives a value for the critical exponent $\beta
+=$~0.28(3) for $\beta$-NiNb$_2$O$_6$, in agreement with the $\mu$SR value.
+While both systems have $\beta$ values less than 0.3, which is indicative of
+reduced dimensionality, this effect appears to be much stronger for the
+columbite system. In other words, although both systems appear to
+well-described by $S = 1$ spin chains, the interchain interactions in the
+$\beta$-polymorph are likely much larger.
+",0,1,0,0,0,0
+19694,Semiclassical Propagation: Hilbert Space vs. Wigner Representation,"  A unified viewpoint on the van Vleck and Herman-Kluk propagators in Hilbert
+space and their recently developed counterparts in Wigner representation is
+presented. It is shown that the numerical protocol for the Herman-Kluk
+propagator, which contains the van Vleck one as a particular case, coincides in
+both representations. The flexibility of the Wigner version in choosing the
+Gaussians' width for the underlying coherent states, being not bound to minimal
+uncertainty, is investigated numerically on prototypical potentials. Exploiting
+this flexibility provides neither qualitative nor quantitative improvements.
+Thus, the well-established Herman-Kluk propagator in Hilbert space remains the
+best choice to date given the large number of semiclassical developments and
+applications based on it.
+",0,1,0,0,0,0
+19695,Dynamic analysis and control PID path of a model type gantry crane,"  This paper presents an alternate form for the dynamic modelling of a
+mechanical system that simulates in real life a gantry crane type, using
+Euler's classical mechanics and Lagrange formalism, which allows find the
+equations of motion that our model describe. Moreover, it has a basic model
+design system using the SolidWorks software, based on the material and
+dimensions of the model provides some physical variables necessary for
+modelling. In order to verify the theoretical results obtained, a contrast was
+made between solutions obtained by simulation in SimMechanics-Matlab and
+Euler-Lagrange equations system, has been solved through Matlab libraries for
+solving equation's systems of the type and order obtained. The force is
+determined, but not as exerted by the spring, as this will be the control
+variable. The objective to bring the mass of the pendulum from one point to
+another with a specified distance without the oscillation from it, so that, the
+answer is overdamped. This article includes an analysis of PID control in which
+the equations of motion of Euler-Lagrange are rewritten in the state space,
+once there, they were implemented in Simulink to get the natural response of
+the system to a step input in F and then draw the desired trajectories.
+",1,1,0,0,0,0
+19696,Dynamic Minimum Spanning Forest with Subpolynomial Worst-case Update Time,"  We present a Las Vegas algorithm for dynamically maintaining a minimum
+spanning forest of an $n$-node graph undergoing edge insertions and deletions.
+Our algorithm guarantees an $O(n^{o(1)})$ worst-case update time with high
+probability. This significantly improves the two recent Las Vegas algorithms by
+Wulff-Nilsen [STOC'17] with update time $O(n^{0.5-\epsilon})$ for some constant
+$\epsilon>0$ and, independently, by Nanongkai and Saranurak [STOC'17] with
+update time $O(n^{0.494})$ (the latter works only for maintaining a spanning
+forest).
+Our result is obtained by identifying the common framework that both two
+previous algorithms rely on, and then improve and combine the ideas from both
+works. There are two main algorithmic components of the framework that are
+newly improved and critical for obtaining our result. First, we improve the
+update time from $O(n^{0.5-\epsilon})$ in Wulff-Nilsen [STOC'17] to
+$O(n^{o(1)})$ for decrementally removing all low-conductance cuts in an
+expander undergoing edge deletions. Second, by revisiting the ""contraction
+technique"" by Henzinger and King [1997] and Holm et al. [STOC'98], we show a
+new approach for maintaining a minimum spanning forest in connected graphs with
+very few (at most $(1+o(1))n$) edges. This significantly improves the previous
+approach in [Wulff-Nilsen STOC'17] and [Nanongkai and Saranurak STOC'17] which
+is based on Frederickson's 2-dimensional topology tree and illustrates a new
+application to this old technique.
+",1,0,0,0,0,0
+19697,Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference,"  The rising popularity of intelligent mobile devices and the daunting
+computational cost of deep learning-based models call for efficient and
+accurate on-device inference schemes. We propose a quantization scheme that
+allows inference to be carried out using integer-only arithmetic, which can be
+implemented more efficiently than floating point inference on commonly
+available integer-only hardware. We also co-design a training procedure to
+preserve end-to-end model accuracy post quantization. As a result, the proposed
+quantization scheme improves the tradeoff between accuracy and on-device
+latency. The improvements are significant even on MobileNets, a model family
+known for run-time efficiency, and are demonstrated in ImageNet classification
+and COCO detection on popular CPUs.
+",1,0,0,1,0,0
+19698,airpred: A Flexible R Package Implementing Methods for Predicting Air Pollution,"  Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants
+regulated by the Environmental Protection Agency in the United States. There is
+strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of
+mortality and hospitalization. Large scale epidemiological studies on the
+health effects of PM$_{2.5}$ provide the necessary evidence base for lowering
+the safety standards and inform regulatory policy. However, ambient monitors of
+PM$_{2.5}$ (as well as monitors for other pollutants) are sparsely located
+across the U.S., and therefore studies based only on the levels of PM$_{2.5}$
+measured from the monitors would inevitably exclude large amounts of the
+population. One approach to resolving this issue has been developing models to
+predict local PM$_{2.5}$, NO$_2$, and ozone based on satellite, meteorological,
+and land use data. This process typically relies developing a prediction model
+that relies on large amounts of input data and is highly computationally
+intensive to predict levels of air pollution in unmonitored areas. We have
+developed a flexible R package that allows for environmental health researchers
+to design and train spatio-temporal models capable of predicting multiple
+pollutants, including PM$_{2.5}$. We utilize H2O, an open source big data
+platform, to achieve both performance and scalability when used in conjunction
+with cloud or cluster computing systems.
+",0,0,0,1,0,0
+19699,Accurate and Efficient Profile Matching in Knowledge Bases,"  A profile describes a set of properties, e.g. a set of skills a person may
+have, a set of skills required for a particular job, or a set of abilities a
+football player may have with respect to a particular team strategy. Profile
+matching aims to determine how well a given profile fits to a requested
+profile. The approach taken in this article is grounded in a matching theory
+that uses filters in lattices to represent profiles, and matching values in the
+interval [0,1]: the higher the matching value the better is the fit. Such
+lattices can be derived from knowledge bases exploiting description logics to
+represent the knowledge about profiles. An interesting first question is, how
+human expertise concerning the matching can be exploited to obtain most
+accurate matchings. It will be shown that if a set of filters together with
+matching values by some human expert is given, then under some mild
+plausibility assumptions a matching measure can be determined such that the
+computed matching values preserve the rankings given by the expert. A second
+question concerns the efficient querying of databases of profile instances. For
+matching queries that result in a ranked list of profile instances matching a
+given one it will be shown how corresponding top-k queries can be evaluated on
+grounds of pre-computed matching values, which in turn allows the maintenance
+of the knowledge base to be decoupled from the maintenance of profile
+instances. In addition, it will be shown how the matching queries can be
+exploited for gap queries that determine how profile instances need to be
+extended in order to improve in the rankings. Finally, the theory of matching
+will be extended beyond the filters, which lead to a matching theory that
+exploits fuzzy sets or probabilistic logic with maximum entropy semantics. It
+will be shown that added fuzzy links can be captured by extending the
+underlying lattice.
+",1,0,0,0,0,0
+19700,Boötes-HiZELS: an optical to near-infrared survey of emission-line galaxies at $\bf z=0.4-4.7$,"  We present a sample of $\sim 1000$ emission line galaxies at $z=0.4-4.7$ from
+the $\sim0.7$deg$^2$ High-$z$ Emission Line Survey (HiZELS) in the Boötes
+field identified with a suite of six narrow-band filters at $\approx 0.4-2.1$
+$\mu$m. These galaxies have been selected on their Ly$\alpha$ (73), {\sc [Oii]}
+(285), H$\beta$/{\sc [Oiii]} (387) or H$\alpha$ (362) emission-line, and have
+been classified with optical to near-infrared colours. A subsample of 98
+sources have reliable redshifts from multiple narrow-band (e.g. [O{\sc
+ii}]-H$\alpha$) detections and/or spectroscopy. In this survey paper, we
+present the observations, selection and catalogs of emitters. We measure number
+densities of Ly$\alpha$, [O{\sc ii}], H$\beta$/{\sc [Oiii]} and H$\alpha$ and
+confirm strong luminosity evolution in star-forming galaxies from $z\sim0.4$ to
+$\sim 5$, in agreement with previous results. To demonstrate the usefulness of
+dual-line emitters, we use the sample of dual [O{\sc ii}]-H$\alpha$ emitters to
+measure the observed [O{\sc ii}]/H$\alpha$ ratio at $z=1.47$. The observed
+[O{\sc ii}]/H$\alpha$ ratio increases significantly from 0.40$\pm0.01$ at
+$z=0.1$ to 0.52$\pm0.05$ at $z=1.47$, which we attribute to either decreasing
+dust attenuation with redshift, or due to a bias in the (typically)
+fiber-measurements in the local Universe which only measure the central kpc
+regions. At the bright end, we find that both the H$\alpha$ and Ly$\alpha$
+number densities at $z\approx2.2$ deviate significantly from a Schechter form,
+following a power-law. We show that this is driven entirely by an increasing
+X-ray/AGN fraction with line-luminosity, which reaches $\approx 100$ \% at
+line-luminosities $L\gtrsim3\times10^{44}$ erg s$^{-1}$.
+",0,1,0,0,0,0
+19701,Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting,"  Tensor factorization with hard and/or soft constraints has played an
+important role in signal processing and data analysis. However, existing
+algorithms for constrained tensor factorization have two drawbacks: (i) they
+require matrix-inversion; and (ii) they cannot (or at least is very difficult
+to) handle structured regularizations. We propose a new tensor factorization
+algorithm that circumvents these drawbacks. The proposed method is built upon
+alternating optimization, and each subproblem is solved by a primal-dual
+splitting algorithm, yielding an efficient and flexible algorithmic framework
+to constrained tensor factorization. The advantages of the proposed method over
+a state-of-the-art constrained tensor factorization algorithm, called AO-ADMM,
+are demonstrated on regularized nonnegative tensor factorization.
+",1,0,0,0,0,0
+19702,Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes,"  In this paper, we propose a replay attack spoofing detection system for
+automatic speaker verification using multitask learning of noise classes. We
+define the noise that is caused by the replay attack as replay noise. We
+explore the effectiveness of training a deep neural network simultaneously for
+replay attack spoofing detection and replay noise classification. The
+multi-task learning includes classifying the noise of playback devices,
+recording environments, and recording devices as well as the spoofing
+detection. Each of the three types of the noise classes also includes a genuine
+class. The experiment results on the ASVspoof2017 datasets demonstrate that the
+performance of our proposed system is improved by 30% relatively on the
+evaluation set.
+",1,0,0,1,0,0
+19703,On One Property of Tikhonov Regularization Algorithm,"  For linear inverse problem with Gaussian random noise we show that Tikhonov
+regularization algorithm is minimax in the class of linear estimators and is
+asymptotically minimax in the sense of sharp asymptotic in the class of all
+estimators. The results are valid if some a priori information on a Fourier
+coefficients of solution is provided. For trigonometric basis this a priori
+information implies that the solution belongs to a ball in Besov space
+$B^r_{2\infty}$.
+",0,0,1,1,0,0
+19704,Observation of Extra Photon Recoil in a Distorted Optical Field,"  Light carries momentum which induces on atoms a recoil for each photon
+absorbed. In vacuum, for a monochromatic beam of frequency $\nu$, the global
+momentum per photon is bounded by general principles and is smaller than $h
+\nu/c$ leading to a limit on the recoil. However, locally this limit can be
+broken. In this paper, we give a general formula to calculate the recoil in
+vacuum. We show that in a laser beam with a distorted optical field, there are
+regions where the recoil can be higher than this limit. Using atoms placed in
+those regions we are able to measure directly the extra recoil.
+",0,1,0,0,0,0
+19705,Weak Poincaré Inequalities for Convergence Rate of Degenerate Diffusion Processes,"  For a contraction $C_0$-semigroup on a separable Hilbert space, the decay
+rate is estimated by using the weak Poincaré inequalities for the symmetric
+and anti-symmetric part of the generator. As applications, non-exponential
+convergence rate is characterized for a class of degenerate diffusion
+processes, so that the study of hypocoercivity is extended. Concrete examples
+are presented.
+",0,0,1,0,0,0
+19706,rTraceroute: Réunion Traceroute Visualisation,"  Traceroute is the main tools to explore Internet path. It provides limited
+information about each node along the path. However, Traceroute cannot go
+further in statistics analysis, or \emph{Man-Machine Interface (MMI)}.
+Indeed, there are no graphical tool that is able to draw all paths used by IP
+routes. We present a new tool that can handle more than 1,000 Traceroute
+results, map them, identify graphically MPLS links, get information of usage of
+all routes (in percent) to improve the knowledge between countries' links.
+rTraceroute want to go deeper in usage of atomic traces. In this paper, we will
+discuss the concept of rTraceroute and present some example of usage.
+",1,0,0,0,0,0
+19707,Effects of heterogeneity in site-site couplings for tight-binding models on scale-invariant structures,"  We studied the thermodynamic behaviors of non-interacting bosons and fermions
+trapped by a scale-invariant branching structure of adjustable degree of
+heterogeneity. The full energy spectrum in tight-binding approximation was
+analytically solved . We found that the log-periodic oscillation of the
+specific heat for Fermi gas depended on the heterogeneity of hopping. Also, low
+dimensional Bose-Einstein condensation occurred only for non-homogeneous setup.
+",0,1,0,0,0,0
+19708,Are generative deep models for novelty detection truly better?,"  Many deep models have been recently proposed for anomaly detection. This
+paper presents comparison of selected generative deep models and classical
+anomaly detection methods on an extensive number of non--image benchmark
+datasets. We provide statistical comparison of the selected models, in many
+configurations, architectures and hyperparamaters. We arrive to conclusion that
+performance of the generative models is determined by the process of selection
+of their hyperparameters. Specifically, performance of the deep generative
+models deteriorates with decreasing amount of anomalous samples used in
+hyperparameter selection. In practical scenarios of anomaly detection, none of
+the deep generative models systematically outperforms the kNN.
+",0,0,0,1,0,0
+19709,Minimizing the waiting time for a one-way shuttle service,"  Consider a terminal in which users arrive continuously over a finite period
+of time at a variable rate known in advance. A fleet of shuttles has to carry
+the users over a fixed trip. What is the shuttle schedule that minimizes their
+waiting time? This is the question addressed in the present paper. We propose
+efficient algorithms for several variations of this question with proven
+performance guarantees. The techniques used are of various types (convex
+optimization, shortest paths,...). The paper ends with numerical experiments
+showing that most of our algorithms behave also well in practice.
+",0,0,1,0,0,0
+19710,Two weight bump conditions for matrix weights,"  In this paper we extend the theory of two weight, $A_p$ bump conditions to
+the setting of matrix weights. We prove two matrix weight inequalities for
+fractional maximal operators, fractional and singular integrals, sparse
+operators and averaging operators. As applications we prove quantitative, one
+weight estimates, in terms of the matrix $A_p$ constant, for singular
+integrals, and prove a Poincaré inequality related to those that appear in
+the study of degenerate elliptic PDEs.
+",0,0,1,0,0,0
+19711,Legendrian ribbons and strongly quasipositive links in an open book,"  We show that a link in an open book can be realized as a strongly
+quasipositive braid if and only if it bounds a Legendrian ribbon with respect
+to the associated contact structure. This generalizes a result due to Baader
+and Ishikawa for links in the three-sphere. We highlight some related
+techniques for determining whether or not a link is strongly quasipositive,
+emphasizing applications to fibered links and satellites.
+",0,0,1,0,0,0
+19712,Δ-cumulants in terms of moments,"  The \Delta-convolution of real probability measures, introduced by Bożejko,
+generalizes both free and boolean convolutions. It is linearized by the
+\Delta-cumulants, and Yoshida gave a combinatorial formula for moments in terms
+of \Delta-cumulants, that implicitly defines the latter. It relies on the
+definition of an appropriate weight on noncrossing partitions. We give here two
+different expressions for the \Delta-cumulants: the first one is a simple
+variant of Lagrange inversion formula, and the second one is a combinatorial
+inversion of Yoshida's formula involving Schröder trees.
+",0,0,1,0,0,0
+19713,A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds,"  When classifying point clouds, a large amount of time is devoted to the
+process of engineering a reliable set of features which are then passed to a
+classifier of choice. Generally, such features - usually derived from the
+3D-covariance matrix - are computed using the surrounding neighborhood of
+points. While these features capture local information, the process is usually
+time-consuming, and requires the application at multiple scales combined with
+contextual methods in order to adequately describe the diversity of objects
+within a scene. In this paper we present a 1D-fully convolutional network that
+consumes terrain-normalized points directly with the corresponding spectral
+data,if available, to generate point-wise labeling while implicitly learning
+contextual features in an end-to-end fashion. Our method uses only the
+3D-coordinates and three corresponding spectral features for each point.
+Spectral features may either be extracted from 2D-georeferenced images, as
+shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted
+directly for passive-derived point clouds,i.e. from muliple-view imagery. We
+train our network by splitting the data into square regions, and use a pooling
+layer that respects the permutation-invariance of the input points. Evaluated
+using the ISPRS 3D Semantic Labeling Contest, our method scored second place
+with an overall accuracy of 81.6%. We ranked third place with a mean F1-score
+of 63.32%, surpassing the F1-score of the method with highest accuracy by
+1.69%. In addition to labeling 3D-point clouds, we also show that our method
+can be easily extended to 2D-semantic segmentation tasks, with promising
+initial results.
+",1,0,0,1,0,0
+19714,Boundedness properties in function spaces,"  Some boundedness properties of function spaces (considered as topological
+groups) are studied.
+",0,0,1,0,0,0
+19715,Linear-size CDAWG: new repetition-aware indexing and grammar compression,"  In this paper, we propose a novel approach to combine \emph{compact directed
+acyclic word graphs} (CDAWGs) and grammar-based compression. This leads us to
+an efficient self-index, called Linear-size CDAWGs (L-CDAWGs), which can be
+represented with $O(\tilde e_T \log n)$ bits of space allowing for $O(\log
+n)$-time random and $O(1)$-time sequential accesses to edge labels, and $O(m
+\log \sigma + occ)$-time pattern matching. Here, $\tilde e_T$ is the number of
+all extensions of maximal repeats in $T$, $n$ and $m$ are respectively the
+lengths of the text $T$ and a given pattern, $\sigma$ is the alphabet size, and
+$occ$ is the number of occurrences of the pattern in $T$. The repetitiveness
+measure $\tilde e_T$ is known to be much smaller than the text length $n$ for
+highly repetitive text. For constant alphabets, our L-CDAWGs achieve $O(m +
+occ)$ pattern matching time with $O(e_T^r \log n)$ bits of space, which
+improves the pattern matching time of Belazzougui et al.'s run-length
+BWT-CDAWGs by a factor of $\log \log n$, with the same space complexity. Here,
+$e_T^r$ is the number of right extensions of maximal repeats in $T$. As a
+byproduct, our result gives a way of constructing an SLP of size $O(\tilde
+e_T)$ for a given text $T$ in $O(n + \tilde e_T \log \sigma)$ time.
+",1,0,0,0,0,0
+19716,Topic Lifecycle on Social Networks: Analyzing the Effects of Semantic Continuity and Social Communities,"  Topic lifecycle analysis on Twitter, a branch of study that investigates
+Twitter topics from their birth through lifecycle to death, has gained immense
+mainstream research popularity. In the literature, topics are often treated as
+one of (a) hashtags (independent from other hashtags), (b) a burst of keywords
+in a short time span or (c) a latent concept space captured by advanced text
+analysis methodologies, such as Latent Dirichlet Allocation (LDA). The first
+two approaches are not capable of recognizing topics where different users use
+different hashtags to express the same concept (semantically related), while
+the third approach misses out the user's explicit intent expressed via
+hashtags. In our work, we use a word embedding based approach to cluster
+different hashtags together, and the temporal concurrency of the hashtag
+usages, thus forming topics (a semantically and temporally related group of
+hashtags).We present a novel analysis of topic lifecycles with respect to
+communities. We characterize the participation of social communities in the
+topic clusters, and analyze the lifecycle of topic clusters with respect to
+such participation. We derive first-of-its-kind novel insights with respect to
+the complex evolution of topics over communities and time: temporal morphing of
+topics over hashtags within communities, how the hashtags die in some
+communities but morph into some other hashtags in some other communities (that,
+it is a community-level phenomenon), and how specific communities adopt to
+specific hashtags. Our work is fundamental in the space of topic lifecycle
+modeling and understanding in communities: it redefines our understanding of
+topic lifecycles and shows that the social boundaries of topic lifecycles are
+deeply ingrained with community behavior.
+",1,0,0,0,0,0
+19717,Unstable Footwear as a Speed-Dependent Noise-Based Training Gear to Exercise Inverted Pendulum Motion During Walking,"  Previous research on unstable footwear has suggested that it may induce
+plantar mechanical noise during walking. The purpose of this study was to
+explore whether unstable footwear could be considered as a noise-based training
+gear to exercise body center of mass (CoM) motion during walking or not. Ground
+reaction forces were collected among 24 healthy young women walking at speeds
+between 3 and 6 km h-1 with control running shoes and unstable rocker-bottom
+shoes. The external mechanical work, the recovery of mechanical energy of the
+CoM during and within the step cycles, and the phase shift between potential
+and kinetic energy curves of the CoM were computed. Our findings support the
+idea that unstable rocker-bottom footwear could serve as a speed-dependent
+noise- based training gear to exercise CoM motion during walking. At slow
+speed, it acts as a stochastic resonance or facilitator, whereas at brisk speed
+it acts as a constraint.
+",0,1,0,0,0,0
+19718,Gaussian autoregressive process with dependent innovations. Some asymptotic results,"  In this paper we introduce a modified version of a gaussian standard
+first-order autoregressive process where we allow for a dependence structure
+between the state variable $Y_{t-1}$ and the next innovation $\xi_t$. We call
+this model dependent innovations gaussian AR(1) process (DIG-AR(1)). We analyze
+the moment and temporal dependence properties of the new model. After proving
+that the OLS estimator does not consistently estimate the autoregressive
+parameter, we introduce an infeasible estimator and we provide its
+$\sqrt{T}$-asymptotic normality.
+",0,0,1,1,0,0
+19719,Structured Peer Learning Program - An Innovative Approach to Computer Science Education,"  Structured Peer Learning (SPL) is a form of peer-based supplemental
+instruction that focuses on mentoring, guidance, and development of technical,
+communication, and social skills in both the students receiving assistance and
+the students in teaching roles. This paper explores the methodology, efficacy,
+and reasoning behind the practical realization of a SPL program designed to
+increase student knowledge and success in undergraduate Computer Science
+courses. Students expressed an increased level of comfort when asking for help
+from student teachers versus traditional educational resources, historically
+showed an increased average grade in lower-level courses, and felt that the
+program positively impacted their desire to continue in or switch to a Computer
+major. Additionally, results indicated that advances in programming, analytical
+thinking, and abstract analysis skills were evident in not only the students
+but also the student teachers, suggesting a strong bidirectional flow of
+knowledge.
+",1,0,0,0,0,0
+19720,Analysis of the possibility for time-optimal control of the scanning system of the GREEN-WAKE's project lidar,"  The monograph represents analysis of the possibility for time-optimal control
+of a prototype of an aircraft-mounted scanning-imaging system of a Light
+Detection and Ranging based wake vortex detection system. The study is a part
+of the research project ""Demonstration of Light Detection and Ranging based
+wake vortex detection system incorporating an atmospheric hazard map"" or
+GREEN-WAKE (Project ID 213254) of the European Union Seventh Framework Program
+for Research and Technological Development. The scanning system comprises two
+light mirror actuators. The study is decomposed into several group of problems.
+The first and second groups consider the mathematical models of the scanning
+system and the mirror actuators. The third group of problems deals with the
+design of closed loop tracking control systems of both the large and small
+mirror actuators. The control of each one system is synthesized as a near
+time-optimal control of the precise linear model of the mirror actuator. The
+control algorithms realize the state of the art method for synthesis of
+time-optimal control of any order for a class of linear time-optimal control
+problems developed in the author's dissertation. The last discovers and
+theoretically proves new properties of a class of linear time-optimal control
+problems. From the point of view of the control synthesis algorithms the main
+advantage is that the time-optimal control is produced by a multistage
+procedure within the class of problems but without the need of describing the
+hyper-surfaces of switching over. The fourth group of problems considers
+modeling the real scan picture but with inclusion also of the Coulomb's
+friction model. The fifth group of problems investigates the ways of
+improvement of the real scan picture. As a result an excellent repetition and
+clearness of the scanning alongside with symmetry and matching the scan pattern
+are seen.
+",1,0,0,0,0,0
+19721,Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples,"  Recently, researchers have discovered that the state-of-the-art object
+classifiers can be fooled easily by small perturbations in the input
+unnoticeable to human eyes. It is also known that an attacker can generate
+strong adversarial examples if she knows the classifier parameters. Conversely,
+a defender can robustify the classifier by retraining if she has access to the
+adversarial examples. We explain and formulate this adversarial example problem
+as a two-player continuous zero-sum game, and demonstrate the fallacy of
+evaluating a defense or an attack as a static problem. To find the best
+worst-case defense against whitebox attacks, we propose a continuous minimax
+optimization algorithm. We demonstrate the minimax defense with two types of
+attack classes -- gradient-based and neural network-based attacks. Experiments
+with the MNIST and the CIFAR-10 datasets demonstrate that the defense found by
+numerical minimax optimization is indeed more robust than non-minimax defenses.
+We discuss directions for improving the result toward achieving robustness
+against multiple types of attack classes.
+",1,0,0,1,0,0
+19722,From Attention to Participation: Reviewing and Modelling Engagement with Computers,"  Over the last decades, the Internet and mobile technology have consolidated
+the digital as a public sphere of life. Designers are asked to create engaging
+digital experiences. However, in some cases engagement is seen as a
+psychological state, while in others it emphasizes a participative vein. In
+this paper, I review and discuss both and propose a new definition to clarify
+the concept engagement with computers. Thus, engagement is a quality of an
+active connection between a user and a computing product, either a website or a
+mobile phone app. Studying it requires understanding a set of aspects like the
+user's affect, motivation and attention, as well as the product's design,
+content and composition. Finally, I propose explaining these concepts aligned
+with engagement and integrate them into a preliminary model to measure the
+manifestations.
+",1,0,0,0,0,0
+19723,"Circularizing Planet Nine through dynamical friction with an extended, cold planetesimal belt","  Unexpected clustering in the orbital elements of minor bodies beyond the
+Kuiper belt has led to speculations that our solar system actually hosts nine
+planets, the eight established plus a hypothetical ""Planet Nine"". Several
+recent studies have shown that a planet with a mass of about 10 Earth masses on
+a distant eccentric orbit with perihelion far beyond the Kuiper belt could
+create and maintain this clustering. The evolutionary path resulting in an
+orbit such as the one suggested for Planet Nine is nevertheless not easily
+explained. Here we investigate whether a planet scattered away from the
+giant-planet region could be lifted to an orbit similar to the one suggested
+for Planet Nine through dynamical friction with a cold, distant planetesimal
+belt. Recent simulations of planetesimal formation via the streaming
+instability suggest that planetesimals can readily form beyond 100au. We
+explore this circularisation by dynamical friction with a set of numerical
+simulations. We find that a planet that is scattered from the region close to
+Neptune onto an eccentric orbit has a 20-30% chance of obtaining an orbit
+similar to that of Planet Nine after 4.6Gyr. Our simulations also result in
+strong or partial clustering of the planetesimals; however, whether or not this
+clustering is observable depends on the location of the inner edge of the
+planetesimal belt. If the inner edge is located at 200au the degree of
+clustering amongst observable objects is significant.
+",0,1,0,0,0,0
+19724,Tactics of Adversarial Attack on Deep Reinforcement Learning Agents,"  We introduce two tactics to attack agents trained by deep reinforcement
+learning algorithms using adversarial examples, namely the strategically-timed
+attack and the enchanting attack. In the strategically-timed attack, the
+adversary aims at minimizing the agent's reward by only attacking the agent at
+a small subset of time steps in an episode. Limiting the attack activity to
+this subset helps prevent detection of the attack by the agent. We propose a
+novel method to determine when an adversarial example should be crafted and
+applied. In the enchanting attack, the adversary aims at luring the agent to a
+designated target state. This is achieved by combining a generative model and a
+planning algorithm: while the generative model predicts the future states, the
+planning algorithm generates a preferred sequence of actions for luring the
+agent. A sequence of adversarial examples is then crafted to lure the agent to
+take the preferred sequence of actions. We apply the two tactics to the agents
+trained by the state-of-the-art deep reinforcement learning algorithm including
+DQN and A3C. In 5 Atari games, our strategically timed attack reduces as much
+reward as the uniform attack (i.e., attacking at every time step) does by
+attacking the agent 4 times less often. Our enchanting attack lures the agent
+toward designated target states with a more than 70% success rate. Videos are
+available at this http URL
+",1,0,0,1,0,0
+19725,Differentially Private Distributed Learning for Language Modeling Tasks,"  One of the big challenges in machine learning applications is that training
+data can be different from the real-world data faced by the algorithm. In
+language modeling, users' language (e.g. in private messaging) could change in
+a year and be completely different from what we observe in publicly available
+data. At the same time, public data can be used for obtaining general knowledge
+(i.e. general model of English). We study approaches to distributed fine-tuning
+of a general model on user private data with the additional requirements of
+maintaining the quality on the general data and minimization of communication
+costs. We propose a novel technique that significantly improves prediction
+quality on users' language compared to a general model and outperforms gradient
+compression methods in terms of communication efficiency. The proposed
+procedure is fast and leads to an almost 70% perplexity reduction and 8.7
+percentage point improvement in keystroke saving rate on informal English
+texts. We also show that the range of tasks our approach is applicable to is
+not limited by language modeling only. Finally, we propose an experimental
+framework for evaluating differential privacy of distributed training of
+language models and show that our approach has good privacy guarantees.
+",1,0,0,0,0,0
+19726,Tensor Train Neighborhood Preserving Embedding,"  In this paper, we propose a Tensor Train Neighborhood Preserving Embedding
+(TTNPE) to embed multi-dimensional tensor data into low dimensional tensor
+subspace. Novel approaches to solve the optimization problem in TTNPE are
+proposed. For this embedding, we evaluate novel trade-off gain among
+classification, computation, and dimensionality reduction (storage) for
+supervised learning. It is shown that compared to the state-of-the-arts tensor
+embedding methods, TTNPE achieves superior trade-off in classification,
+computation, and dimensionality reduction in MNIST handwritten digits and
+Weizmann face datasets.
+",1,0,0,1,0,0
+19727,Explicit three dimensional topology optimization via Moving Morphable Void (MMV) approach,"  Three dimensional (3D) topology optimization problems always involve huge
+numbers of Degrees of Freedom (DOFs) in finite element analysis (FEA) and
+design variables in numerical optimization, respectively. This will inevitably
+lead to large computational efforts in the solution process. In the present
+paper, an efficient and explicit topology optimization approach which can
+reduce not only the number of design variables but also the number of degrees
+of freedom in FEA is proposed based on the Moving Morphable Voids (MMVs)
+solution framework. This is achieved by introducing a set of geometry
+parameters (e.g., control points of B-spline surfaces) to describe the boundary
+of a structure explicitly and removing the unnecessary DOFs from the FE model
+at every step of numerical optimization. Numerical examples demonstrate that
+the proposed approach does can overcome the bottleneck problems associated with
+a 3D topology optimization problem in a straightforward way and enhance the
+solution efficiency significantly.
+",0,1,0,0,0,0
+19728,The index of compact simple Lie groups,"  Let M be an irreducible Riemannian symmetric space. The index i(M) of M is
+the minimal codimension of a (non-trivial) totally geodesic submanifold of M.
+The purpose of this note is to determine the index i(M) for all irreducible
+Riemannian symmetric spaces M of type (II) and (IV).
+",0,0,1,0,0,0
+19729,Cell Tracking via Proposal Generation and Selection,"  Microscopy imaging plays a vital role in understanding many biological
+processes in development and disease. The recent advances in automation of
+microscopes and development of methods and markers for live cell imaging has
+led to rapid growth in the amount of image data being captured. To efficiently
+and reliably extract useful insights from these captured sequences, automated
+cell tracking is essential. This is a challenging problem due to large
+variation in the appearance and shapes of cells depending on many factors
+including imaging methodology, biological characteristics of cells, cell matrix
+composition, labeling methodology, etc. Often cell tracking methods require a
+sequence-specific segmentation method and manual tuning of many tracking
+parameters, which limits their applicability to sequences other than those they
+are designed for. In this paper, we propose 1) a deep learning based cell
+proposal method, which proposes candidates for cells along with their scores,
+and 2) a cell tracking method, which links proposals in adjacent frames in a
+graphical model using edges representing different cellular events and poses
+joint cell detection and tracking as the selection of a subset of cell and edge
+proposals. Our method is completely automated and given enough training data
+can be applied to a wide variety of microscopy sequences. We evaluate our
+method on multiple fluorescence and phase contrast microscopy sequences
+containing cells of various shapes and appearances from ISBI cell tracking
+challenge, and show that our method outperforms existing cell tracking methods.
+Code is available at: this https URL
+",1,0,0,0,0,0
+19730,Geometric Properties of Isostables and Basins of Attraction of Monotone Systems,"  In this paper, we study geometric properties of basins of attraction of
+monotone systems. Our results are based on a combination of monotone systems
+theory and spectral operator theory. We exploit the framework of the Koopman
+operator, which provides a linear infinite-dimensional description of nonlinear
+dynamical systems and spectral operator-theoretic notions such as eigenvalues
+and eigenfunctions. The sublevel sets of the dominant eigenfunction form a
+family of nested forward-invariant sets and the basin of attraction is the
+largest of these sets. The boundaries of these sets, called isostables, allow
+studying temporal properties of the system. Our first observation is that the
+dominant eigenfunction is increasing in every variable in the case of monotone
+systems. This is a strong geometric property which simplifies the computation
+of isostables. We also show how variations in basins of attraction can be
+bounded under parametric uncertainty in the vector field of monotone systems.
+Finally, we study the properties of the parameter set for which a monotone
+system is multistable. Our results are illustrated on several systems of two to
+four dimensions.
+",1,0,1,0,0,0
+19731,Elongation and shape changes in organisms with cell walls: a dialogue between experiments and models,"  The generation of anisotropic shapes occurs during morphogenesis of almost
+all organisms. With the recent renewal of the interest in mechanical aspects of
+morphogenesis, it has become clear that mechanics contributes to anisotropic
+forms in a subtle interaction with various molecular actors. Here, we consider
+plants, fungi, oomycetes, and bacteria, and we review the mechanisms by which
+elongated shapes are generated and maintained. We focus on theoretical models
+of the interplay between growth and mechanics, in relation with experimental
+data, and discuss how models may help us improve our understanding of the
+underlying biological mechanisms.
+",0,0,0,0,1,0
+19732,Localic completion of uniform spaces,"  We extend the notion of localic completion of generalised metric spaces by
+Steven Vickers to the setting of generalised uniform spaces. A generalised
+uniform space (gus) is a set X equipped with a family of generalised metrics on
+X, where a generalised metric on X is a map from the product of X to the upper
+reals satisfying zero self-distance law and triangle inequality.
+For a symmetric generalised uniform space, the localic completion lifts its
+generalised uniform structure to a point-free generalised uniform structure.
+This point-free structure induces a complete generalised uniform structure on
+the set of formal points of the localic completion that gives the standard
+completion of the original gus with Cauchy filters.
+We extend the localic completion to a full and faithful functor from the
+category of locally compact uniform spaces into that of overt locally compact
+completely regular formal topologies. Moreover, we give an elementary
+characterisation of the cover of the localic completion of a locally compact
+uniform space that simplifies the existing characterisation for metric spaces.
+These results generalise the corresponding results for metric spaces by Erik
+Palmgren.
+Furthermore, we show that the localic completion of a symmetric gus is
+equivalent to the point-free completion of the uniform formal topology
+associated with the gus.
+We work in Aczel's constructive set theory CZF with the Regular Extension
+Axiom. Some of our results also require Countable Choice.
+",1,0,1,0,0,0
+19733,Kinetic inhibition of MHD-shocks in the vicinity of a parallel magnetic field,"  According to magnetohydrodynamics (MHD), the encounter of two collisional
+magnetized plasmas at high velocity gives rise to shock waves. Investigations
+conducted so far have found that the same conclusion still holds in the case of
+collisionless plasmas. For the case of a flow-aligned field, MHD stipulates
+that the field and the fluid are disconnected, so that the shock produced is
+independent of the field. We present a violation of this MHD prediction when
+considering the encounter of two cold pair plasmas along a flow-aligned
+magnetic field. As the guiding magnetic field grows, isotropization is
+progressively suppressed, resulting in a strong influence of the field on the
+resulting structure. A micro-physics analysis allows to understand the
+mechanisms at work. Particle-in-cell simulations also support our conclusions
+and show that the results are not restricted to a strictly parallel field.
+",0,1,0,0,0,0
+19734,Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things,"  In this paper, we propose Squeezed Convolutional Variational AutoEncoder
+(SCVAE) for anomaly detection in time series data for Edge Computing in
+Industrial Internet of Things (IIoT). The proposed model is applied to labeled
+time series data from UCI datasets for exact performance evaluation, and
+applied to real world data for indirect model performance comparison. In
+addition, by comparing the models before and after applying Fire Modules from
+SqueezeNet, we show that model size and inference times are reduced while
+similar levels of performance is maintained.
+",1,0,0,0,0,0
+19735,Global Weisfeiler-Lehman Graph Kernels,"  Most state-of-the-art graph kernels only take local graph properties into
+account, i.e., the kernel is computed with regard to properties of the
+neighborhood of vertices or other small substructures. On the other hand,
+kernels that do take global graph propertiesinto account may not scale well to
+large graph databases. Here we propose to start exploring the space between
+local and global graph kernels, striking the balance between both worlds.
+Specifically, we introduce a novel graph kernel based on the $k$-dimensional
+Weisfeiler-Lehman algorithm. Unfortunately, the $k$-dimensional
+Weisfeiler-Lehman algorithm scales exponentially in $k$. Consequently, we
+devise a stochastic version of the kernel with provable approximation
+guarantees using conditional Rademacher averages. On bounded-degree graphs, it
+can even be computed in constant time. We support our theoretical results with
+experiments on several graph classification benchmarks, showing that our
+kernels often outperform the state-of-the-art in terms of classification
+accuracies.
+",1,0,0,1,0,0
+19736,An analytical Model which Determines the Apparent T1 for Modified Look-Locker Inversion Recovery (MOLLI) -- Analysis of the Longitudinal Relaxation under the Influence of Discontinuous Balanced and Spoiled Gradient Echo Readouts,"  Quantitative nuclear magnetic resonance imaging (MRI) shifts more and more
+into the focus of clinical research. Especially determination of relaxation
+times without/and with contrast agents becomes the foundation of tissue
+characterization, e.g. in cardiac MRI for myocardial fibrosis. Techniques which
+assess longitudinal relaxation times rely on repetitive application of readout
+modules, which are interrupted by free relaxation periods, e.g. the Modified
+Look-Locker Inversion Recovery = MOLLI sequence. These discontinuous sequences
+reveal an apparent relaxation time, and, by techniques extrapolated from
+continuous readout sequences, the real T1 is determined. What is missing is a
+rigorous analysis of the dependence of the apparent relaxation time on its real
+partner, readout sequence parameters and biological parameters as heart rate.
+This is provided in this paper for the discontinuous balanced steady state free
+precession (bSSFP) and spoiled gradient echo readouts. It turns out that the
+apparente longitudinal relaxation rate is the time average of the relaxation
+rates during the readout module, and free relaxation period. Knowing the heart
+rate our results vice versa allow to determine the real T1 from its measured
+apparent partner.
+",0,1,0,0,0,0
+19737,Updated Constraints on the Dark Matter Interpretation of CDMS-II-Si Data,"  We present an updated halo-dependent and halo-independent analysis of viable
+light WIMP dark matter candidates which could account for the excess observed
+in CDMS-II-Si. We include recent constraints from LUX, PandaX-II, and PICO-60,
+as well as projected sensitivities for XENON1T, SuperCDMS SNOLAB, LZ, DARWIN,
+DarkSide-20k, and PICO-250, on candidates with spin-independent isospin
+conserving and isospin-violating interactions, and either elastic or exothermic
+scattering. We show that there exist dark matter candidates which can explain
+the CDMS-II-Si data and remain very marginally consistent with the null results
+of all current experiments, however such models are highly tuned, making a dark
+matter interpretation of CDMS-II-Si very unlikely. We find that these models
+can only be ruled out in the future by an experiment comparable to LZ or
+PICO-250.
+",0,1,0,0,0,0
+19738,"Emulating Simulations of Cosmic Dawn for 21cm Power Spectrum Constraints on Cosmology, Reionization, and X-ray Heating","  Current and upcoming radio interferometric experiments are aiming to make a
+statistical characterization of the high-redshift 21cm fluctuation signal
+spanning the hydrogen reionization and X-ray heating epochs of the universe.
+However, connecting 21cm statistics to underlying physical parameters is
+complicated by the theoretical challenge of modeling the relevant physics at
+computational speeds quick enough to enable exploration of the high dimensional
+and weakly constrained parameter space. In this work, we use machine learning
+algorithms to build a fast emulator that mimics expensive simulations of the
+21cm signal across a wide parameter space to high precision. We embed our
+emulator within a Markov-Chain Monte Carlo framework, enabling it to explore
+the posterior distribution over a large number of model parameters, including
+those that govern the Epoch of Reionization, the Epoch of X-ray Heating, and
+cosmology. As a worked example, we use our emulator to present an updated
+parameter constraint forecast for the Hydrogen Epoch of Reionization Array
+experiment, showing that its characterization of a fiducial 21cm power spectrum
+will considerably narrow the allowed parameter space of reionization and
+heating parameters, and could help strengthen Planck's constraints on
+$\sigma_8$. We provide both our generalized emulator code and its
+implementation specifically for 21cm parameter constraints as publicly
+available software.
+",0,1,0,0,0,0
+19739,Fluctuations and Noise Signatures of Driven Magnetic Skyrmions,"  Magnetic skyrmions are particle-like objects with topologically-protected
+stability which can be set into motion with an applied current. Using a
+particle-based model we simulate current-driven magnetic skyrmions interacting
+with random quenched disorder and examine the skyrmion velocity fluctuations
+parallel and perpendicular to the direction of motion as a function of
+increasing drive. We show that the Magnus force contribution to skyrmion
+dynamics combined with the random pinning produces an isotropic effective
+shaking temperature. As a result, the skyrmions form a moving crystal at large
+drives instead of the moving smectic state observed in systems with a
+negligible Magnus force where the effective shaking temperature is anisotropic.
+We demonstrate that spectral analysis of the velocity noise fluctuations can be
+used to identify dynamical phase transitions and to extract information about
+the different dynamic phases, and show how the velocity noise fluctuations are
+correlated with changes in the skyrmion Hall angle, transport features, and
+skyrmion lattice structure.
+",0,1,0,0,0,0
+19740,Metallic maps between metallic Riemannian manifolds and constancy of certain maps,"  In this paper, we introduce metallic maps between metallic Riemannian
+manifolds, provide an example and obtain certain conditions for such maps to be
+totally geodesic. We also give a sufficient condition for a map between
+metallic Riemannian manifolds to be harmonic map. Then we investigate the
+constancy of certain maps between metallic Riemannian manifolds and various
+manifolds by imposing the holomorphic-like condition. Moreover, we check the
+reverse case and show that some such maps are constant if there is a condition
+for this.
+",0,0,1,0,0,0
+19741,Hashing as Tie-Aware Learning to Rank,"  Hashing, or learning binary embeddings of data, is frequently used in nearest
+neighbor retrieval. In this paper, we develop learning to rank formulations for
+hashing, aimed at directly optimizing ranking-based evaluation metrics such as
+Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We
+first observe that the integer-valued Hamming distance often leads to tied
+rankings, and propose to use tie-aware versions of AP and NDCG to evaluate
+hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive
+their continuous relaxations, and perform gradient-based optimization with deep
+neural networks. Our results establish the new state-of-the-art for image
+retrieval by Hamming ranking in common benchmarks.
+",1,0,0,1,0,0
+19742,Models of compact stars on paraboloidal spacetime satisfying Karmarkar condition,"  A new exact solution of Einstein's field equations on the background of
+paraboloidal spacetime using Karmarkar condition is reported. The physical
+acceptability conditions of the model are investigated and found that the model
+is compatible with a number of compact star candidates like Her X-1, LMC X-4,
+EXO 1785-248, PSR J1903+327, Vela X-1 and PSR J1614-2230. A noteworthy feature
+of the model is that it is geometrically significant and simple in form.
+",0,1,0,0,0,0
+19743,Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification,"  Implicit discourse relation classification is of great challenge due to the
+lack of connectives as strong linguistic cues, which motivates the use of
+annotated implicit connectives to improve the recognition. We propose a feature
+imitation framework in which an implicit relation network is driven to learn
+from another neural network with access to connectives, and thus encouraged to
+extract similarly salient features for accurate classification. We develop an
+adversarial model to enable an adaptive imitation scheme through competition
+between the implicit network and a rival feature discriminator. Our method
+effectively transfers discriminability of connectives to the implicit features,
+and achieves state-of-the-art performance on the PDTB benchmark.
+",1,0,0,1,0,0
+19744,An Input Reconstruction Approach for Command Following in Linear MIMO Systems,"  The idea of posing a command following or tracking control problem as an
+input reconstruction problem is explored in the paper. For a class of square
+MIMO systems with known dynamics, by pretending that reference commands are
+actual outputs of the system, input reconstruction methods can be used to
+determine control action that will result in a system following desired
+reference commands. A feedback controller which is a combination of an unbiased
+state estimator and an input reconstructor that ensures unbiased tracking of
+reference commands is proposed. Simulations and real-time implementation are
+presented to demonstrate utility of the proposed idea. Conditions under which
+proposed controller may be used for non-square systems are also discussed.
+",1,0,0,0,0,0
+19745,Simulation Framework for Cooperative Adaptive Cruise Control with Empirical DSRC Module,"  Wireless communication plays a vital role in the promising performance of
+connected and automated vehicle (CAV) technology. This paper proposes a
+Vissim-based microscopic traffic simulation framework with an analytical
+dedicated short-range communication (DSRC) module for packet reception. Being
+derived from ns-2, a packet-level network simulator, the DSRC probability
+module takes into account the imperfect wireless communication that occurs in
+real-world deployment. Four managed lane deployment strategies are evaluated
+using the proposed framework. While the average packet reception rate is above
+93\% among all tested scenarios, the results reveal that the reliability of the
+vehicle-to-vehicle (V2V) communication can be influenced by the deployment
+strategies. Additionally, the proposed framework exhibits desirable scalability
+for traffic simulation and it is able to evaluate transportation-network-level
+deployment strategies in the near future for CAV technologies.
+",1,0,0,0,0,0
+19746,Synchronization of Kuramoto oscillators in a bidirectional frequency-dependent tree network,"  This paper studies the synchronization of a finite number of Kuramoto
+oscillators in a frequency-dependent bidirectional tree network. We assume that
+the coupling strength of each link in each direction is equal to the product of
+a common coefficient and the exogenous frequency of its corresponding head
+oscillator. We derive a sufficient condition for the common coupling strength
+in order to guarantee frequency synchronization in tree networks. Moreover, we
+discuss the dependency of the obtained bound on both the graph structure and
+the way that exogenous frequencies are distributed. Further, we present an
+application of the obtained result by means of an event-triggered algorithm for
+achieving frequency synchronization in a star network assuming that the common
+coupling coefficient is given.
+",1,0,0,0,0,0
+19747,On Exchangeability in Network Models,"  We derive representation theorems for exchangeable distributions on finite
+and infinite graphs using elementary arguments based on geometric and
+graph-theoretic concepts. Our results elucidate some of the key differences,
+and their implications, between statistical network models that are finitely
+exchangeable and models that define a consistent sequence of probability
+distributions on graphs of increasing size.
+",0,0,1,1,0,0
+19748,Replicator equation on networks with degree regular communities,"  The replicator equation is one of the fundamental tools to study evolutionary
+dynamics in well-mixed populations. This paper contributes to the literature on
+evolutionary graph theory, providing a version of the replicator equation for a
+family of connected networks with communities, where nodes in the same
+community have the same degree. This replicator equation is applied to the
+study of different classes of games, exploring the impact of the graph
+structure on the equilibria of the evolutionary dynamics.
+",0,0,0,0,1,0
+19749,Go game formal revealing by Ising model,"  Go gaming is a struggle for territory control between rival, black and white,
+stones on a board. We model the Go dynamics in a game by means of the Ising
+model whose interaction coefficients reflect essential rules and tactics
+employed in Go to build long-term strategies. At any step of the game, the
+energy functional of the model provides the control degree (strength) of a
+player over the board. A close fit between predictions of the model with actual
+games is obtained.
+",1,0,0,0,0,0
+19750,High resolution structural characterisation of laser-induced defect clusters inside diamond,"  Laser writing with ultrashort pulses provides a potential route for the
+manufacture of three-dimensional wires, waveguides and defects within diamond.
+We present a transmission electron microscopy (TEM) study of the intrinsic
+structure of the laser modifications and reveal a complex distribution of
+defects. Electron energy loss spectroscopy (EELS) indicates that the majority
+of the irradiated region remains as $sp^3$ bonded diamond.
+Electrically-conductive paths are attributed to the formation of multiple
+nano-scale, $sp^2$-bonded graphitic wires and a network of strain-relieving
+micro-cracks.
+",0,1,0,0,0,0
+19751,Neural networks for post-processing ensemble weather forecasts,"  Ensemble weather predictions require statistical post-processing of
+systematic errors to obtain reliable and accurate probabilistic forecasts.
+Traditionally, this is accomplished with distributional regression models in
+which the parameters of a predictive distribution are estimated from a training
+period. We propose a flexible alternative based on neural networks that can
+incorporate nonlinear relationships between arbitrary predictor variables and
+forecast distribution parameters that are automatically learned in a
+data-driven way rather than requiring pre-specified link functions. In a case
+study of 2-meter temperature forecasts at surface stations in Germany, the
+neural network approach significantly outperforms benchmark post-processing
+methods while being computationally more affordable. Key components to this
+improvement are the use of auxiliary predictor variables and station-specific
+information with the help of embeddings. Furthermore, the trained neural
+network can be used to gain insight into the importance of meteorological
+variables thereby challenging the notion of neural networks as uninterpretable
+black boxes. Our approach can easily be extended to other statistical
+post-processing and forecasting problems. We anticipate that recent advances in
+deep learning combined with the ever-increasing amounts of model and
+observation data will transform the post-processing of numerical weather
+forecasts in the coming decade.
+",0,0,0,1,0,0
+19752,Power maps in finite groups,"  In recent work, Pomerance and Shparlinski have obtained results on the number
+of cycles in the functional graph of the map $x \mapsto x^a$ in
+$\mathbb{F}_p^*$. We prove similar results for other families of finite groups.
+In particular, we obtain estimates for the number of cycles for cyclic groups,
+symmetric groups, dihedral groups and $SL_2(\mathbb{F}_q)$. We also show that
+the cyclic group of order $n$ minimizes the number of cycles among all
+nilpotent groups of order $n$ for a fixed exponent. Finally, we pose several
+problems.
+",0,0,1,0,0,0
+19753,"Permutation polynomials, fractional polynomials, and algebraic curves","  In this note we prove a conjecture by Li, Qu, Li, and Fu on permutation
+trinomials over $\mathbb{F}_3^{2k}$. In addition, new examples and
+generalizations of some families of permutation polynomials of
+$\mathbb{F}_{3^k}$ and $\mathbb{F}_{5^k}$ are given. We also study permutation
+quadrinomials of type $Ax^{q(q-1)+1} + Bx^{2(q-1)+1} + Cx^{q} + x$. Our method
+is based on the investigation of an algebraic curve associated with a
+{fractional polynomial} over a finite field.
+",0,0,1,0,0,0
+19754,Recursive Whitening Transformation for Speaker Recognition on Language Mismatched Condition,"  Recently in speaker recognition, performance degradation due to the channel
+domain mismatched condition has been actively addressed. However, the
+mismatches arising from language is yet to be sufficiently addressed. This
+paper proposes an approach which employs recursive whitening transformation to
+mitigate the language mismatched condition. The proposed method is based on the
+multiple whitening transformation, which is intended to remove un-whitened
+residual components in the dataset associated with i-vector length
+normalization. The experiments were conducted on the Speaker Recognition
+Evaluation 2016 trials of which the task is non-English speaker recognition
+using development dataset consist of both a large scale out-of-domain (English)
+dataset and an extremely low-quantity in-domain (non-English) dataset. For
+performance comparison, we develop a state-of- the-art system using deep neural
+network and bottleneck feature, which is based on a phonetically aware model.
+From the experimental results, along with other prior studies, effectiveness of
+the proposed method on language mismatched condition is validated.
+",1,0,0,0,0,0
+19755,Learning Representations for Soft Skill Matching,"  Employers actively look for talents having not only specific hard skills but
+also various soft skills. To analyze the soft skill demands on the job market,
+it is important to be able to detect soft skill phrases from job advertisements
+automatically. However, a naive matching of soft skill phrases can lead to
+false positive matches when a soft skill phrase, such as friendly, is used to
+describe a company, a team, or another entity, rather than a desired candidate.
+In this paper, we propose a phrase-matching-based approach which
+differentiates between soft skill phrases referring to a candidate vs.
+something else. The disambiguation is formulated as a binary text
+classification problem where the prediction is made for the potential soft
+skill based on the context where it occurs. To inform the model about the soft
+skill for which the prediction is made, we develop several approaches,
+including soft skill masking and soft skill tagging.
+We compare several neural network based approaches, including CNN, LSTM and
+Hierarchical Attention Model. The proposed tagging-based input representation
+using LSTM achieved the highest recall of 83.92% on the job dataset when fixing
+a precision to 95%.
+",0,0,0,1,0,0
+19756,Time-reversal and spatial reflection symmetry localization anomalies in (2+1)D topological phases of matter,"  We study a class of anomalies associated with time-reversal and spatial
+reflection symmetry in (2+1)D topological phases of matter. In these systems,
+the topological quantum numbers of the quasiparticles, such as the fusion rules
+and braiding statistics, possess a $\mathbb{Z}_2$ symmetry which can be
+associated with either time-reversal (denoted $\mathbb{Z}_2^{\bf T})$ or
+spatial reflections. Under this symmetry, correlation functions of all Wilson
+loop operators in the low energy topological quantum field theory (TQFT) are
+invariant. However, the theories that we study possess a severe anomaly
+associated with the failure to consistently localize the symmetry action to the
+quasiparticles, precluding even defining a notion of symmetry
+fractionalization. We present simple sufficient conditions which determine when
+$\mathbb{Z}_2^{\bf T}$ symmetry localization anomalies exist. We present an
+infinite series of TQFTs with such anomalies, some examples of which include
+USp$(4)_2$ and SO$(4)_4$ Chern-Simons (CS) theory. The theories that we find
+with these $\mathbb{Z}_2^{\bf T}$ anomalies can be obtained by gauging the
+unitary $\mathbb{Z}_2$ subgroup of a different TQFT with a $\mathbb{Z}_4^{\bf
+T}$ symmetry. We show that the anomaly can be resolved in several ways: (1) the
+true symmetry of the theory is $\mathbb{Z}_4^{\bf T}$, or (2) the theory can be
+considered to be a theory of fermions, with ${\bf T}^2 = (-1)^{N_f}$
+corresponding to fermion parity. Finally, we demonstrate that theories with the
+$\mathbb{Z}_2^{\bf T}$ localization anomaly can be compatible with
+$\mathbb{Z}_2^{\bf T}$ if they are ""pseudo-realized"" at the surface of a (3+1)D
+symmetry-enriched topological phase. The ""pseudo-realization"" refers to the
+fact that the bulk (3+1)D system is described by a dynamical $\mathbb{Z}_2$
+gauge theory and thus only a subset of the quasiparticles are confined to the
+surface.
+",0,1,0,0,0,0
+19757,Extending the modeling of the anisotropic galaxy power spectrum to $k = 0.4 \ h\mathrm{Mpc}^{-1}$,"  We present a new model for the redshift-space power spectrum of galaxies and
+demonstrate its accuracy in modeling the monopole, quadrupole, and hexadecapole
+of the galaxy density field down to scales of $k = 0.4 \ h\mathrm{Mpc}^{-1}$.
+The model describes the clustering of galaxies in the context of a halo model
+and the clustering of the underlying halos in redshift space using a
+combination of Eulerian perturbation theory and $N$-body simulations. The
+modeling of redshift-space distortions is done using the so-called distribution
+function approach. The final model has 13 free parameters, and each parameter
+is physically motivated rather than a nuisance parameter, which allows the use
+of well-motivated priors. We account for the Finger-of-God effect from centrals
+and both isolated and non-isolated satellites rather than using a single
+velocity dispersion to describe the combined effect. We test and validate the
+accuracy of the model on several sets of high-fidelity $N$-body simulations, as
+well as realistic mock catalogs designed to simulate the BOSS DR12 CMASS data
+set. The suite of simulations covers a range of cosmologies and galaxy bias
+models, providing a rigorous test of the level of theoretical systematics
+present in the model. The level of bias in the recovered values of $f \sigma_8$
+is found to be small. When including scales to $k = 0.4 \ h\mathrm{Mpc}^{-1}$,
+we find 15-30\% gains in the statistical precision of $f \sigma_8$ relative to
+$k = 0.2 \ h\mathrm{Mpc}^{-1}$ and a roughly 10-15\% improvement for the
+perpendicular Alcock-Paczynski parameter $\alpha_\perp$. Using the BOSS DR12
+CMASS mocks as a benchmark for comparison, we estimate an uncertainty on $f
+\sigma_8$ that is $\sim$10-20\% larger than other similar Fourier-space RSD
+models in the literature that use $k \leq 0.2 \ h\mathrm{Mpc}^{-1}$, suggesting
+that these models likely have a too-limited parametrization.
+",0,1,0,0,0,0
+19758,Finite rank and pseudofinite groups,"  It is proven that an infinite finitely generated group cannot be elementarily
+equivalent to an ultraproduct of finite groups of a given Prüfer rank.
+Furthermore, it is shown that an infinite finitely generated group of finite
+Prüfer rank is not pseudofinite.
+",0,0,1,0,0,0
+19759,Thermographic measurements of the spin Peltier effect in metal/yttrium-iron-garnet junction systems,"  The spin Peltier effect (SPE), heat-current generation due to spin-current
+injection, in various metal (Pt, W, and Au single layers and Pt/Cu
+bilayer)/ferrimagnetic insulator (yttrium iron garnet: YIG) junction systems
+has been investigated by means of a lock-in thermography (LIT) method. The SPE
+is excited by a spin current across the metal/YIG interface, which is generated
+by applying a charge current to the metallic layer via the spin Hall effect.
+The LIT method enables the thermal imaging of the SPE free from the
+Joule-heating contribution. Importantly, we observed spin-current-induced
+temperature modulation not only in the Pt/YIG and W/YIG systems but also in the
+Au/YIG and Pt/Cu/YIG systems, excluding the possible contamination by anomalous
+Ettingshausen effects due to proximity-induced ferromagnetism near the
+metal/YIG interface. As demonstrated in our previous study, the SPE signals are
+confined only in the vicinity of the metal/YIG interface; we buttress this
+conclusion by reducing a spatial blur due to thermal diffusion in an infrared
+emission layer on the sample surface used for the LIT measurements. We also
+found that the YIG-thickness dependence of the SPE is similar to that of the
+spin Seebeck effect measured in the same Pt/YIG sample, implying the reciprocal
+relation between them.
+",0,1,0,0,0,0
+19760,"Number-theoretic aspects of 1D localization: ""popcorn function"" with Lifshitz tails and its continuous approximation by the Dedekind $η$","  We discuss the number-theoretic properties of distributions appearing in
+physical systems when an observable is a quotient of two independent
+exponentially weighted integers. The spectral density of ensemble of linear
+polymer chains distributed with the law $\sim f^L$ ($0<f<1$), where $L$ is the
+chain length, serves as a particular example. At $f\to 1$, the spectral density
+can be expressed through the discontinuous at all rational points, Thomae
+(""popcorn"") function. We suggest a continuous approximation of the popcorn
+function, based on the Dedekind $\eta$-function near the real axis. Moreover,
+we provide simple arguments, based on the ""Euclid orchard"" construction, that
+demonstrate the presence of Lifshitz tails, typical for the 1D Anderson
+localization, at the spectral edges. We emphasize that the ultrametric
+structure of the spectral density is ultimately connected with number-theoretic
+relations on asymptotic modular functions. We also pay attention to connection
+of the Dedekind $\eta$-function near the real axis to invariant measures of
+some continued fractions studied by Borwein and Borwein in 1993.
+",0,1,1,0,0,0
+19761,Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates,"  In this paper, we describe a phenomenon, which we named ""super-convergence"",
+where neural networks can be trained an order of magnitude faster than with
+standard training methods. The existence of super-convergence is relevant to
+understanding why deep networks generalize well. One of the key elements of
+super-convergence is training with one learning rate cycle and a large maximum
+learning rate. A primary insight that allows super-convergence training is that
+large learning rates regularize the training, hence requiring a reduction of
+all other forms of regularization in order to preserve an optimal
+regularization balance. We also derive a simplification of the Hessian Free
+optimization method to compute an estimate of the optimal learning rate.
+Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet
+datasets, and resnet, wide-resnet, densenet, and inception architectures. In
+addition, we show that super-convergence provides a greater boost in
+performance relative to standard training when the amount of labeled training
+data is limited. The architectures and code to replicate the figures in this
+paper are available at github.com/lnsmith54/super-convergence. See
+this http URL for an application of
+super-convergence to win the DAWNBench challenge (see
+this https URL).
+",1,0,0,1,0,0
+19762,Space of initial conditions for a cubic Hamiltonian system,"  In this paper we perform the analysis that leads to the space of initial
+conditions for the Hamiltonian system $q' = p^2 + zq + \alpha$, $p' = -q^2 - zp
+- \beta$, studied by the author in an earlier article. By compactifying the
+phase space of the system from $\mathbb{C}^2$ to $\mathbb{CP}^2$ three base
+points arise in the standard coordinate charts covering the complex projective
+space. Each of these is removed by a sequence of three blow-ups, a construction
+to regularise the system at these points. The resulting space, where the
+exceptional curves introduced after the first and second blow-up are removed,
+is the so-called Okamoto's space of initial conditions for this system which,
+at every point, defines a regular initial value problem in some coordinate
+chart of the space.
+",0,1,1,0,0,0
+19763,Electronic properties of WS$_2$ on epitaxial graphene on SiC(0001),"  This work reports an electronic and micro-structural study of an appealing
+system for optoelectronics: tungsten disulphide WS$_2$ on epitaxial graphene
+(EG) on SiC(0001). The WS$_2$ is grown via chemical vapor deposition (CVD) onto
+the EG. Low-energy electron diffraction (LEED) measurements assign the
+zero-degree orientation as the preferential azimuthal alignment for WS$_2$/EG.
+The valence-band (VB) structure emerging from this alignment is investigated by
+means of photoelectron spectroscopy measurements, with both high space and
+energy resolution. We find that the spin-orbit splitting of monolayer WS$_2$ on
+graphene is of 462 meV, larger than what is reported to date for other
+substrates. We determine the value of the work function for the WS$_2$/EG to be
+4.5$\pm$0.1 eV. A large shift of the WS$_2$ VB maximum is observed as well ,
+due to the lowering of the WS$_2$ work function caused by the donor-like
+interfacial states of EG. Density functional theory (DFT) calculations carried
+out on a coincidence supercell confirm the experimental band structure to an
+excellent degree. X-ray photoemission electron microscopy (XPEEM) measurements
+performed on single WS$_2$ crystals confirm the van der Waals nature of the
+interface coupling between the two layers. In virtue of its band alignment and
+large spin-orbit splitting, this system gains strong appeal for optical
+spin-injection experiments and opto-spintronic applications in general.
+",0,1,0,0,0,0
+19764,Electrical transport and optical band gap of NiFe$_\textrm{2}$O$_\textrm{x}$ thin films,"  We fabricated NiFe$_\textrm{2}$O$_\textrm{x}$ thin films on
+MgAl$_2$O$_4$(001) substrates by reactive dc magnetron co-sputtering varying
+the oxygen partial pressure during deposition. The fabrication of a variable
+material with oxygen deficiency leads to controllable electrical and optical
+properties which would be beneficial for the investigations of the transport
+phenomena and would, therefore, promote the use of such materials in spintronic
+and spin caloritronic applications. We used several characterization techniques
+in order to investigate the film properties, focusing on their structural,
+magnetic, electrical, and optical properties. From the electrical resistivity
+measurements we obtained the conduction mechanisms that govern the systems in
+high and low temperature regimes, extracting low thermal activation energies
+which unveil extrinsic transport mechanisms. The thermal activation energy
+decreases in the less oxidized samples revealing the pronounced contribution of
+a large amount of electronic states localized in the band gap to the electrical
+conductivity. Hall effect measurements showed the mixed-type semiconducting
+character of our films. The optical band gaps were determined via
+ultraviolet-visible spectroscopy. They follow a similar trend as the thermal
+activation energy, with lower band gap values in the less oxidized samples.
+",0,1,0,0,0,0
+19765,Towards parallelizable sampling-based Nonlinear Model Predictive Control,"  This paper proposes a new sampling-based nonlinear model predictive control
+(MPC) algorithm, with a bound on complexity quadratic in the prediction horizon
+N and linear in the number of samples. The idea of the proposed algorithm is to
+use the sequence of predicted inputs from the previous time step as a warm
+start, and to iteratively update this sequence by changing its elements one by
+one, starting from the last predicted input and ending with the first predicted
+input. This strategy, which resembles the dynamic programming principle, allows
+for parallelization up to a certain level and yields a suboptimal nonlinear MPC
+algorithm with guaranteed recursive feasibility, stability and improved cost
+function at every iteration, which is suitable for real-time implementation.
+The complexity of the algorithm per each time step in the prediction horizon
+depends only on the horizon, the number of samples and parallel threads, and it
+is independent of the measured system state. Comparisons with the fmincon
+nonlinear optimization solver on benchmark examples indicate that as the
+simulation time progresses, the proposed algorithm converges rapidly to the
+""optimal"" solution, even when using a small number of samples.
+",1,0,0,0,0,0
+19766,Quantum decoherence from entanglement during inflation,"  We study the primary entanglement effect on the decoherence of fields reduced
+density matrix which are in interaction with another fields or independent mode
+functions. We show that the primary entanglement has a significant role in
+decoherence of the system quantum state. We find that the existence of
+entanglement could couple dynamical equations coming from Schrödinger
+equation. We show if one wants to see no effect of the entanglement parameter
+in decoherence then interaction terms in Hamiltonian can not be independent
+from each other. Generally, including the primary entanglement destroys the
+independence of the interaction terms. Our results could be generalized to
+every scalar quantum field theory with a well defined quantization in a given
+curved space time.
+",0,1,0,0,0,0
+19767,Breast density classification with deep convolutional neural networks,"  Breast density classification is an essential part of breast cancer
+screening. Although a lot of prior work considered this problem as a task for
+learning algorithms, to our knowledge, all of them used small and not
+clinically realistic data both for training and evaluation of their models. In
+this work, we explore the limits of this task with a data set coming from over
+200,000 breast cancer screening exams. We use this data to train and evaluate a
+strong convolutional neural network classifier. In a reader study, we find that
+our model can perform this task comparably to a human expert.
+",1,0,0,1,0,0
+19768,Spatial Integration by a Dielectric Slab Waveguide and its Planar Graphene-based Counterpart,"  Motivated by the recent progress in analog computing [Science 343, 160
+(2014)], a new approach to perform spatial integration is presented using a
+dielectric slab waveguide. Our approach is indeed based on the fact that the
+transmission coefficient of a simple dielectric slab waveguide at its mode
+excitation angle matches the Green's function of first order integration.
+Inspired by the mentioned dielectric-based integrator, we further demonstrate
+its graphene-based counterpart. The latter is not only reconfigurable but also
+highly miniaturized in contrast to the previously reported designs [Opt.
+Commun. 338, 457 (2015)]. Such integrators have the potential to be used in
+ultrafast analog computation and signal processing.
+",0,1,0,0,0,0
+19769,Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders,"  A crucial challenge in image-based modeling of biomedical data is to identify
+trends and features that separate normality and pathology. In many cases, the
+morphology of the imaged object exhibits continuous change as it deviates from
+normality, and thus a generative model can be trained to model this
+morphological continuum. Moreover, given side information that correlates to
+certain trend in morphological change, a latent variable model can be
+regularized such that its latent representation reflects this side information.
+In this work, we use the Wasserstein Auto-encoder to model this pathology
+continuum, and apply the Hilbert-Schmitt Independence Criterion (HSIC) to
+enforce dependency between certain latent features and the provided side
+information. We experimentally show that the model can provide disentangled and
+interpretable latent representations and also generate a continuum of
+morphological changes that corresponds to change in the side information.
+",1,0,0,1,0,0
+19770,Control Problems with Vanishing Lie Bracket Arising from Complete Odd Circulant Evolutionary Games,"  We study an optimal control problem arising from a generalization of
+rock-paper-scissors in which the number of strategies may be selected from any
+positive odd number greater than 1 and in which the payoff to the winner is
+controlled by a control variable $\gamma$. Using the replicator dynamics as the
+equations of motion, we show that a quasi-linearization of the problem admits a
+special optimal control form in which explicit dynamics for the controller can
+be identified. We show that all optimal controls must satisfy a specific second
+order differential equation parameterized by the number of strategies in the
+game. We show that as the number of strategies increases, a limiting case
+admits a closed form for the open-loop optimal control. In performing our
+analysis we show necessary conditions on an optimal control problem that allow
+this analytic approach to function.
+",1,0,0,0,0,0
+19771,Teaching DevOps in Corporate Environments: An experience report,"  This paper describes our experience of training a team of developers of an
+East-European phone service provider. The training experience was structured in
+two sessions of two days each conducted in different weeks with a gap of about
+fifteen days. The first session was dedicated to the Continuous Integration
+Delivery Pipeline, and the second on Agile methods. We summarize the activity,
+its preparation and delivery and draw some conclusions out of it on our
+mistakes and how future session should be addressed.
+",1,0,0,0,0,0
+19772,WKB solutions of difference equations and reconstruction by the topological recursion,"  The purpose of this article is to analyze the connection between
+Eynard-Orantin topological recursion and formal WKB solutions of a
+$\hbar$-difference equation: $\Psi(x+\hbar)=\left(e^{\hbar\frac{d}{dx}}\right)
+\Psi(x)=L(x;\hbar)\Psi(x)$ with $L(x;\hbar)\in GL_2( (\mathbb{C}(x))[\hbar])$.
+In particular, we extend the notion of determinantal formulas and topological
+type property proposed for formal WKB solutions of $\hbar$-differential systems
+to this setting. We apply our results to a specific $\hbar$-difference system
+associated to the quantum curve of the Gromov-Witten invariants of
+$\mathbb{P}^1$ for which we are able to prove that the correlation functions
+are reconstructed from the Eynard-Orantin differentials computed from the
+topological recursion applied to the spectral curve $y=\cosh^{-1}\frac{x}{2}$.
+Finally, identifying the large $x$ expansion of the correlation functions,
+proves a recent conjecture made by B. Dubrovin and D. Yang regarding a new
+generating series for Gromov-Witten invariants of $\mathbb{P}^1$.
+",0,1,1,0,0,0
+19773,Diagrammatic Exciton Basis Theory of the Photophysics of Pentacene Dimers,"  Covalently linked acene dimers are of interest as candidates for
+intramolecular singlet fission. We report many-electron calculations of the
+energies and wavefunctions of the optical singlets, the lowest triplet exciton
+and the triplet-triplet biexciton, as well as the final states of excited state
+absorptions from these states in a family of phenyl-linked pentacene dimers.
+While it is difficult to distinguish between the triplet and the
+triplet-triplet from their transient absorptions in the 500-600 nm region, by
+comparing theoretical transient absorption spectra against published and
+unpublished experimental transient absorptions in the near and mid infrared we
+conclude that the end product of photoexcitation in these particular
+bipentacenes is the bound triplet-triplet and not free triplets. We predict
+additional transient absorptions at even longer wavelengths, beyond 1500 nm, to
+the equivalent of the classic 2$^1$A$_g^-$ in linear polyenes.
+",0,1,0,0,0,0
+19774,Developing and evaluating an interactive tutorial on degenerate perturbation theory,"  We discuss an investigation of student difficulties with degenerate
+perturbation theory (DPT) carried out in advanced quantum mechanics courses by
+administering free-response and multiple-choice questions and conducting
+individual interviews with students. We find that students share many common
+difficulties related to this topic. We used the difficulties found via research
+as resources to develop and evaluate a Quantum Interactive Learning Tutorial
+(QuILT) which strives to help students develop a functional understanding of
+DPT. We discuss the development of the DPT QuILT and its preliminary evaluation
+in the undergraduate and graduate courses.
+",0,1,0,0,0,0
+19775,Time optimal sampled-data controls for heat equations,"  In this paper, we first design a time optimal control problem for the heat
+equation with sampled-data controls, and then use it to approximate a time
+optimal control problem for the heat equation with distributed controls. Our
+design is reasonable from perspective of sampled-data controls. And it might
+provide a right way for the numerical approach of a time optimal distributed
+control problem, via the corresponding semi-discretized (in time variable) time
+optimal control problem.
+The study of such a time optimal sampled-data control problem is not easy,
+because it may have infinitely many optimal controls. We find connections among
+this problem, a minimal norm sampled-data control problem and a minimization
+problem. And obtain some properties on these problems. Based on these, we not
+only build up error estimates for optimal time and optimal controls between the
+time optimal sampled-data control problem and the time optimal distributed
+control problem, in terms of the sampling period, but also prove that such
+estimates are optimal in some sense.
+",0,0,1,0,0,0
+19776,Malware distributions and graph structure of the Web,"  Knowledge about the graph structure of the Web is important for understanding
+this complex socio-technical system and for devising proper policies supporting
+its future development. Knowledge about the differences between clean and
+malicious parts of the Web is important for understanding potential treats to
+its users and for devising protection mechanisms. In this study, we conduct
+data science methods on a large crawl of surface and deep Web pages with the
+aim to increase such knowledge. To accomplish this, we answer the following
+questions. Which theoretical distributions explain important local
+characteristics and network properties of websites? How are these
+characteristics and properties different between clean and malicious
+(malware-affected) websites? What is the prediction power of local
+characteristics and network properties to classify malware websites? To the
+best of our knowledge, this is the first large-scale study describing the
+differences in global properties between malicious and clean parts of the Web.
+In other words, our work is building on and bridging the gap between
+\textit{Web science} that tackles large-scale graph representations and
+\textit{Web cyber security} that is concerned with malicious activities on the
+Web. The results presented herein can also help antivirus vendors in devising
+approaches to improve their detection algorithms.
+",1,0,0,0,0,0
+19777,Depth creates no more spurious local minima,"  We show that for any convex differentiable loss function, a deep linear
+network has no spurious local minima as long as it is true for the two layer
+case. When applied to the quadratic loss, our result immediately implies the
+powerful result in [Kawaguchi 2016] that there is no spurious local minima in
+deep linear networks. Further, with the recent work [Zhou and Liang 2018], we
+can remove all the assumptions in [Kawaguchi 2016]. Our proof is short and
+elementary. It builds on the recent work of [Laurent and von Brecht 2018] and
+uses a new rank one perturbation argument.
+",1,0,0,1,0,0
+19778,On the Application of ISO 26262 in Control Design for Automated Vehicles,"  Research on automated vehicles has experienced an explosive growth over the
+past decade. A main obstacle to their practical realization, however, is a
+convincing safety concept. This question becomes ever more important as more
+sophisticated algorithms are used and the vehicle automation level increases.
+The field of functional safety offers a systematic approach to identify
+possible sources of risk and to improve the safety of a vehicle. It is based on
+practical experience across the aerospace, process and other industries over
+multiple decades. This experience is compiled in the functional safety standard
+for the automotive domain, ISO 26262, which is widely adopted throughout the
+automotive industry. However, its applicability and relevance for highly
+automated vehicles is subject to a controversial debate. This paper takes a
+critical look at the discussion and summarizes the main steps of ISO 26262 for
+a safe control design for automated vehicles.
+",1,0,0,0,0,0
+19779,Semi-wavefront solutions in models of collective movements with density-dependent diffusivity,"  This paper deals with a nonhomogeneous scalar parabolic equation with
+possibly degenerate diffusion term; the process has only one stationary state.
+The equation can be interpreted as modeling collective movements (crowd
+dynamics, for instance). We first prove the existence of semi-wavefront
+solutions for every wave speed; their properties are investigated. Then, a
+family of travelling wave solutions is constructed by a suitable combination of
+the previous semi-wavefront solutions. Proofs exploit comparison-type
+techniques and are carried out in the case of one spatial variable; the
+extension to the general case is straightforward.
+",0,0,1,0,0,0
+19780,Self-Regulated Transport in Photonic Crystals with Phase-Changing Defects,"  Phase changing materials (PCM) are widely used for optical data recording,
+sensing, all-optical switching, and optical limiting. Our focus here is on the
+case when the change in the transmission characteristics of the optical
+material is caused by the input light itself. Specifically, the light-induced
+heating triggers the phase transition in the PCM. In this paper, using a
+numerical example, we demonstrate that incorporating the PCM in a photonic
+structure can lead to a dramatic modification of the effects of light-induced
+phase transition, as compared to a stand-alone sample of the same PCM. Our
+focus is on short pulses. We discuss some possible applications of such
+phase-changing photonic structures for optical sensing and limiting.
+",0,1,0,0,0,0
+19781,Assessing randomness in case assignment: the case study of the Brazilian Supreme Court,"  Sortition, i.e., random appointment for public duty, has been employed by
+societies throughout the years, especially for duties related to the judicial
+system, as a firewall designated to prevent illegitimate interference between
+parties in a legal case and agents of the legal system. In judicial systems of
+modern western countries, random procedures are mainly employed to select the
+jury, the court and/or the judge in charge of judging a legal case, so that
+they have a significant role in the course of a case. Therefore, these random
+procedures must comply with some principles, as statistical soundness; complete
+auditability; open-source programming; and procedural, cryptographical and
+computational security. Nevertheless, some of these principles are neglected by
+some random procedures in judicial systems, that are, in some cases, performed
+in secrecy and are not auditable by the involved parts. The assignment of cases
+in the Brazilian Supreme Court (Supremo Tribunal Federal) is an example of such
+procedures, for it is performed by a closed-source algorithm, unknown to the
+public and to the parts involved in the judicial cases, that allegedly assign
+the cases randomly to the justice chairs based on their caseload.
+In this context, this article presents a review of how sortition has been
+employed historically by societies, and discusses how Mathematical Statistics
+may be applied to random procedures of the judicial system, as it has been
+applied for almost a century on clinical trials, for example. Based on this
+discussion, a statistical model for assessing randomness in case assignment is
+proposed and applied to the Brazilian Supreme Court in order to shed light on
+how this assignment process is performed by the closed-source algorithm.
+Guidelines for random procedures are outlined and topics for further researches
+presented.
+",0,0,0,1,0,0
+19782,Particle picture representation of the non-symmetric Rosenblatt process and Hermite processes of any order,"  We provide a particle picture representation for the non-symmetric Rosenblatt
+process and for Hermite processes of any order, extending the result of
+Bojdecki, Gorostiza and Talarczyk in~\cite{FILT}. We show that these processes
+can be obtained as limits in the sense of finite-dimensional distributions of
+certain functionals of a system of particles evolving according to symmetric
+stable Lévy motions. In the case of $k$-Hermite processes the corresponding
+functional involves $k$-intersection local time of symmetric stable Lévy
+processes
+",0,0,1,0,0,0
+19783,Houdini: Fooling Deep Structured Prediction Models,"  Generating adversarial examples is a critical step for evaluating and
+improving the robustness of learning machines. So far, most existing methods
+only work for classification and are not designed to alter the true performance
+measure of the problem at hand. We introduce a novel flexible approach named
+Houdini for generating adversarial examples specifically tailored for the final
+performance measure of the task considered, be it combinatorial and
+non-decomposable. We successfully apply Houdini to a range of applications such
+as speech recognition, pose estimation and semantic segmentation. In all cases,
+the attacks based on Houdini achieve higher success rate than those based on
+the traditional surrogates used to train the models while using a less
+perceptible adversarial perturbation.
+",1,0,0,1,0,0
+19784,Don't relax: early stopping for convex regularization,"  We consider the problem of designing efficient regularization algorithms when
+regularization is encoded by a (strongly) convex functional. Unlike classical
+penalization methods based on a relaxation approach, we propose an iterative
+method where regularization is achieved via early stopping. Our results show
+that the proposed procedure achieves the same recovery accuracy as penalization
+methods, while naturally integrating computational considerations. An empirical
+analysis on a number of problems provides promising results with respect to the
+state of the art.
+",1,0,1,0,0,0
+19785,Interplay between social influence and competitive strategical games in multiplex networks,"  We present a model that takes into account the coupling between evolutionary
+game dynamics and social influence. Importantly, social influence and game
+dynamics take place in different domains, which we model as different layers of
+a multiplex network. We show that the coupling between these dynamical
+processes can lead to cooperation in scenarios where the pure game dynamics
+predicts defection. In addition, we show that the structure of the network
+layers and the relation between them can further increase cooperation.
+Remarkably, if the layers are related in a certain way, the system can reach a
+polarized metastable state.These findings could explain the prevalence of
+polarization observed in many social dilemmas.
+",1,1,0,0,0,0
+19786,Data Augmentation for Low-Resource Neural Machine Translation,"  The quality of a Neural Machine Translation system depends substantially on
+the availability of sizable parallel corpora. For low-resource language pairs
+this is not the case, resulting in poor translation quality. Inspired by work
+in computer vision, we propose a novel data augmentation approach that targets
+low-frequency words by generating new sentence pairs containing rare words in
+new, synthetically created contexts. Experimental results on simulated
+low-resource settings show that our method improves translation quality by up
+to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.
+",1,0,0,0,0,0
+19787,A Lagrangian approach to modeling heat flux driven close-contact melting,"  Close-contact melting refers to the process of a heat source melting its way
+into a phase-change material. Of special interest is the close-contact melting
+velocity, or more specifically the relative velocity between the heat source
+and the phase-change material. In this work, we present a novel numerical
+approach to simulate quasi-steady, heat flux driven close-contact melting. It
+extends existing approaches found in the literature, and, for the first time,
+allows to study the impact of a spatially varying heat flux distribution. We
+will start by deriving the governing equations in a Lagrangian reference frame
+fixed to the heat source. Exploiting the narrowness of the melt film enables us
+to reduce the momentum balance to the Reynolds equation, which is coupled to
+the energy balance via the velocity field. We particularize our derivation for
+two simple, yet technically relevant geometries, namely a 3d circular disc and
+a 2d planar heat source. An iterative solution procedure for the coupled system
+is described in detail and discussed on the basis of a convergence study.
+Furthermore, we present an extension to allow for rotational melting modes.
+Various test cases demonstrate the proficiency of our method. In particular, we
+will utilize the method to assess the efficiency of the close-contact melting
+process and to quantify the model error introduced if convective losses are
+neglected. Finally, we will draw conclusions and present an outlook to future
+work.
+",0,1,0,0,0,0
+19788,Spectral Graph Analysis: A Unified Explanation and Modern Perspectives,"  Complex networks or graphs are ubiquitous in sciences and engineering:
+biological networks, brain networks, transportation networks, social networks,
+and the World Wide Web, to name a few. Spectral graph theory provides a set of
+useful techniques and models for understanding `patterns of interconnectedness'
+in a graph. Our prime focus in this paper is on the following question: Is
+there a unified explanation and description of the fundamental spectral graph
+methods? There are at least two reasons to be interested in this question.
+Firstly, to gain a much deeper and refined understanding of the basic
+foundational principles, and secondly, to derive rich consequences with
+practical significance for algorithm design. However, despite half a century of
+research, this question remains one of the most formidable open issues, if not
+the core problem in modern network science. The achievement of this paper is to
+take a step towards answering this question by discovering a simple, yet
+universal statistical logic of spectral graph analysis. The prescribed
+viewpoint appears to be good enough to accommodate almost all existing spectral
+graph techniques as a consequence of just one single formalism and algorithm.
+",0,0,1,1,0,0
+19789,ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting,"  Accurate demand forecasts can help on-line retail organizations better plan
+their supply-chain processes. The challenge, however, is the large number of
+associative factors that result in large, non-stationary shifts in demand,
+which traditional time series and regression approaches fail to model. In this
+paper, we propose a Neural Network architecture called AR-MDN, that
+simultaneously models associative factors, time-series trends and the variance
+in the demand. We first identify several causal features and use a combination
+of feature embeddings, MLP and LSTM to represent them. We then model the output
+density as a learned mixture of Gaussian distributions. The AR-MDN can be
+trained end-to-end without the need for additional supervision. We experiment
+on a dataset of an year's worth of data over tens-of-thousands of products from
+Flipkart. The proposed architecture yields a significant improvement in
+forecasting accuracy when compared with existing alternatives.
+",0,0,0,1,0,0
+19790,Extreme Dimension Reduction for Handling Covariate Shift,"  In the covariate shift learning scenario, the training and test covariate
+distributions differ, so that a predictor's average loss over the training and
+test distributions also differ. In this work, we explore the potential of
+extreme dimension reduction, i.e. to very low dimensions, in improving the
+performance of importance weighting methods for handling covariate shift, which
+fail in high dimensions due to potentially high train/test covariate divergence
+and the inability to accurately estimate the requisite density ratios. We first
+formulate and solve a problem optimizing over linear subspaces a combination of
+their predictive utility and train/test divergence within. Applying it to
+simulated and real data, we show extreme dimension reduction helps sometimes
+but not always, due to a bias introduced by dimension reduction.
+",1,0,0,1,0,0
+19791,Landau-Zener transitions for Majorana fermions,"  One-dimensional systems obtained as low-energy limits of hybrid
+superconductor-topological insulator devices provide means of production,
+transport, and destruction of Majorana bound states (MBSs) by variations of the
+magnetic flux. When two or more pairs of MBSs are present in the intermediate
+state, there is a possibility of a Landau-Zener transition, wherein even a slow
+variation of the flux leads to production of a quasiparticle pair. We study
+numerically a version of this process, with four MBSs produced and subsequently
+destroyed, and find that, quite universally, the probability of quasiparticle
+production in it is 50%. This implies that the effect may be a limiting factor
+in applications requiring a high degree of quantum coherence.
+",0,1,0,0,0,0
+19792,Deep Hyperspherical Learning,"  Convolution as inner product has been the founding basis of convolutional
+neural networks (CNNs) and the key to end-to-end visual representation
+learning. Benefiting from deeper architectures, recent CNNs have demonstrated
+increasingly strong representation abilities. Despite such improvement, the
+increased depth and larger parameter space have also led to challenges in
+properly training a network. In light of such challenges, we propose
+hyperspherical convolution (SphereConv), a novel learning framework that gives
+angular representations on hyperspheres. We introduce SphereNet, deep
+hyperspherical convolution networks that are distinct from conventional inner
+product based convolutional networks. In particular, SphereNet adopts
+SphereConv as its basic convolution operator and is supervised by generalized
+angular softmax loss - a natural loss formulation under SphereConv. We show
+that SphereNet can effectively encode discriminative representation and
+alleviate training difficulty, leading to easier optimization, faster
+convergence and comparable (even better) classification accuracy over
+convolutional counterparts. We also provide some theoretical insights for the
+advantages of learning on hyperspheres. In addition, we introduce the learnable
+SphereConv, i.e., a natural improvement over prefixed SphereConv, and
+SphereNorm, i.e., hyperspherical learning as a normalization method.
+Experiments have verified our conclusions.
+",1,0,0,1,0,0
+19793,Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching,"  This paper presents a robotic pick-and-place system that is capable of
+grasping and recognizing both known and novel objects in cluttered
+environments. The key new feature of the system is that it handles a wide range
+of object categories without needing any task-specific training data for novel
+objects. To achieve this, it first uses a category-agnostic affordance
+prediction algorithm to select and execute among four different grasping
+primitive behaviors. It then recognizes picked objects with a cross-domain
+image classification framework that matches observed images to product images.
+Since product images are readily available for a wide range of objects (e.g.,
+from the web), the system works out-of-the-box for novel objects without
+requiring any additional training data. Exhaustive experimental results
+demonstrate that our multi-affordance grasping achieves high success rates for
+a wide variety of objects in clutter, and our recognition algorithm achieves
+high accuracy for both known and novel grasped objects. The approach was part
+of the MIT-Princeton Team system that took 1st place in the stowing task at the
+2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are
+available online at this http URL
+",1,0,0,0,0,0
+19794,Toward an enumerative geometry with quadratic forms,"  We develop various aspects of classical enumerative geometry, including Euler
+characteristics and formulas for counting degenerate fibres in a pencil, with
+the classical numerical formulas being replaced by identitites in the
+Grothendieck-Witt group of quadratic forms with coefficients in the base-field.
+",0,0,1,0,0,0
+19795,Robust reputation-based ranking on multipartite rating networks,"  The spread of online reviews, ratings and opinions and its growing influence
+on people's behavior and decisions boosted the interest to extract meaningful
+information from this data deluge. Hence, crowdsourced ratings of products and
+services gained a critical role in business, governments, and others. We
+propose a new reputation-based ranking system utilizing multipartite rating
+subnetworks, that clusters users by their similarities, using Kolmogorov
+complexity. Our system is novel in that it reflects a diversity of
+opinions/preferences by assigning possibly distinct rankings, for the same
+item, for different groups of users. We prove the convergence and efficiency of
+the system and show that it copes better with spamming/spurious users, and it
+is more robust to attacks than state-of-the-art approaches.
+",1,0,0,0,0,0
+19796,Duality and upper bounds in optimal stochastic control governed by partial differential equations,"  A dual control problem is presented for the optimal stochastic control of a
+system governed by partial differential equations. Relationships between the
+optimal values of the original and the dual problems are investigated and two
+duality theorems are proved. The dual problem serves to provide upper bounds
+for the optimal and maximum value of the original one or even to give the
+optimal value.
+",0,0,1,0,0,0
+19797,On Sampling from Massive Graph Streams,"  We propose Graph Priority Sampling (GPS), a new paradigm for order-based
+reservoir sampling from massive streams of graph edges. GPS provides a general
+way to weight edge sampling according to auxiliary and/or size variables so as
+to accomplish various estimation goals of graph properties. In the context of
+subgraph counting, we show how edge sampling weights can be chosen so as to
+minimize the estimation variance of counts of specified sets of subgraphs. In
+distinction with many prior graph sampling schemes, GPS separates the functions
+of edge sampling and subgraph estimation. We propose two estimation frameworks:
+(1) Post-Stream estimation, to allow GPS to construct a reference sample of
+edges to support retrospective graph queries, and (2) In-Stream estimation, to
+allow GPS to obtain lower variance estimates by incrementally updating the
+subgraph count estimates during stream processing. Unbiasedness of subgraph
+estimators is established through a new Martingale formulation of graph stream
+order sampling, which shows that subgraph estimators, written as a product of
+constituent edge estimators are unbiased, even when computed at different
+points in the stream. The separation of estimation and sampling enables
+significant resource savings relative to previous work. We illustrate our
+framework with applications to triangle and wedge counting. We perform a
+large-scale experimental study on real-world graphs from various domains and
+types. GPS achieves high accuracy with less than 1% error for triangle and
+wedge counting, while storing a small fraction of the graph with average update
+times of a few microseconds per edge. Notably, for a large Twitter graph with
+more than 260M edges, GPS accurately estimates triangle counts with less than
+1% error, while storing only 40K edges.
+",1,0,1,1,0,0
+19798,"Optimal Investment, Demand and Arbitrage under Price Impact","  This paper studies the optimal investment problem with random endowment in an
+inventory-based price impact model with competitive market makers. Our goal is
+to analyze how price impact affects optimal policies, as well as both pricing
+rules and demand schedules for contingent claims. For exponential market makers
+preferences, we establish two effects due to price impact: constrained trading,
+and non-linear hedging costs. To the former, wealth processes in the impact
+model are identified with those in a model without impact, but with constrained
+trading, where the (random) constraint set is generically neither closed nor
+convex. Regarding hedging, non-linear hedging costs motivate the study of
+arbitrage free prices for the claim. We provide three such notions, which
+coincide in the frictionless case, but which dramatically differ in the
+presence of price impact. Additionally, we show arbitrage opportunities, should
+they arise from claim prices, can be exploited only for limited position sizes,
+and may be ignored if outweighed by hedging considerations. We also show that
+arbitrage inducing prices may arise endogenously in equilibrium, and that
+equilibrium positions are inversely proportional to the market makers'
+representative risk aversion. Therefore, large positions endogenously arise in
+the limit of either market maker risk neutrality, or a large number of market
+makers.
+",0,0,0,0,0,1
+19799,Amplitude Mediated Chimera States with Active and Inactive Oscillators,"  The emergence and nature of amplitude mediated chimera states,
+spatio-temporal patterns of co-existing coherent and incoherent regions, are
+investigated for a globally coupled system of active and inactive
+Ginzburg-Landau oscillators. The existence domain of such states is found to
+shrink and shift in parametric space as the fraction of inactive oscillators is
+increased. The role of inactive oscillators is found to be two fold - they get
+activated to form a separate region of coherent oscillations and in addition
+decrease the common collective frequency of the coherent regions by their
+presence. The dynamical origin of these effects is delineated through a
+detailed bifurcation analysis of a reduced model equation that is based on a
+mean field approximation. Our results may have practical implications for the
+robustness of such states in biological or physical systems where age related
+deterioration in the functionality of components can occur.
+",0,1,0,0,0,0
+19800,Astronomical random numbers for quantum foundations experiments,"  Photons from distant astronomical sources can be used as a classical source
+of randomness to improve fundamental tests of quantum nonlocality,
+wave-particle duality, and local realism through Bell's inequality and
+delayed-choice quantum eraser tests inspired by Wheeler's cosmic-scale
+Mach-Zehnder interferometer gedankenexperiment. Such sources of random numbers
+may also be useful for information-theoretic applications such as key
+distribution for quantum cryptography. Building on the design of an
+""astronomical random-number generator"" developed for the recent ""cosmic Bell""
+experiment [Handsteiner et al., Phys. Rev. Lett. 118, 060401 (2017)], in this
+paper we report on the design and characterization of a device that, with
+20-nanosecond latency, outputs a bit based on whether the wavelength of an
+incoming photon is greater than or less than 700 nm. Using the one-meter
+telescope at the Jet Propulsion Laboratory (JPL) Table Mountain Observatory, we
+generated random bits from astronomical photons in both color channels from 50
+stars of varying color and magnitude, and from 12 quasars with redshifts up to
+$z = 3.9$. With stars, we achieved bit rates of $\sim 1 \times 10^6$ Hz /
+m$^2$, limited by saturation for our single-photon detectors, and with quasars
+of magnitudes between 12.9 and 16, we achieved rates between $\sim 10^2$ and $2
+\times 10^3$ Hz /m$^2$. For bright quasars, the resulting bitstreams exhibit
+sufficiently low amounts of statistical predictability as quantified by the
+mutual information. In addition, a sufficiently high fraction of bits generated
+are of true astronomical origin in order to address both the locality and
+freedom-of-choice loopholes when used to set the measurement settings in a test
+of the Bell-CHSH inequality.
+",0,1,0,0,0,0
+19801,Application of generative autoencoder in de novo molecular design,"  A major challenge in computational chemistry is the generation of novel
+molecular structures with desirable pharmacological and physiochemical
+properties. In this work, we investigate the potential use of autoencoder, a
+deep learning methodology, for de novo molecular design. Various generative
+autoencoders were used to map molecule structures into a continuous latent
+space and vice versa and their performance as structure generator was assessed.
+Our results show that the latent space preserves chemical similarity principle
+and thus can be used for the generation of analogue structures. Furthermore,
+the latent space created by autoencoders were searched systematically to
+generate novel compounds with predicted activity against dopamine receptor type
+2 and compounds similar to known active compounds not included in the training
+set were identified.
+",1,0,0,1,0,0
+19802,Prospects for Measuring Cosmic Microwave Background Spectral Distortions in the Presence of Foregrounds,"  Measurements of cosmic microwave background spectral distortions have
+profound implications for our understanding of physical processes taking place
+over a vast window in cosmological history. Foreground contamination is
+unavoidable in such measurements and detailed signal-foreground separation will
+be necessary to extract cosmological science. We present MCMC-based spectral
+distortion detection forecasts in the presence of Galactic and extragalactic
+foregrounds for a range of possible experimental configurations, focusing on
+the Primordial Inflation Explorer (PIXIE) as a fiducial concept. We consider
+modifications to the baseline PIXIE mission (operating 12 months in distortion
+mode), searching for optimal configurations using a Fisher approach. Using only
+spectral information, we forecast an extended PIXIE mission to detect the
+expected average non-relativistic and relativistic thermal Sunyaev-Zeldovich
+distortions at high significance (194$\sigma$ and 11$\sigma$, respectively),
+even in the presence of foregrounds. The $\Lambda$CDM Silk damping $\mu$-type
+distortion is not detected without additional modifications of the instrument
+or external data. Galactic synchrotron radiation is the most problematic source
+of contamination in this respect, an issue that could be mitigated by combining
+PIXIE data with future ground-based observations at low frequencies ($\nu <
+15-30$GHz). Assuming moderate external information on the synchrotron spectrum,
+we project an upper limit of $|\mu| < 3.6\times 10^{-7}$ (95\% c.l.), slightly
+more than one order of magnitude above the fiducial $\Lambda$CDM signal from
+the damping of small-scale primordial fluctuations, but a factor of $\simeq
+250$ improvement over the current upper limit from COBE/FIRAS. This limit could
+be further reduced to $|\mu| < 9.4\times 10^{-8}$ (95\% c.l.) with more
+optimistic assumptions about low-frequency information. (Abridged)
+",0,1,0,0,0,0
+19803,The Fog of War: A Machine Learning Approach to Forecasting Weather on Mars,"  For over a decade, scientists at NASA's Jet Propulsion Laboratory (JPL) have
+been recording measurements from the Martian surface as a part of the Mars
+Exploration Rovers mission. One quantity of interest has been the opacity of
+Mars's atmosphere for its importance in day-to-day estimations of the amount of
+power available to the rover from its solar arrays. This paper proposes the use
+of neural networks as a method for forecasting Martian atmospheric opacity that
+is more effective than the current empirical model. The more accurate
+prediction provided by these networks would allow operators at JPL to make more
+accurate predictions of the amount of energy available to the rover when they
+plan activities for coming sols.
+",1,1,0,0,0,0
+19804,Substrate inhibition imposes fitness penalty at high protein stability,"  Proteins are only moderately stable. It has long been debated whether this
+narrow range of stabilities is solely a result of neutral drift towards lower
+stability or purifying selection against excess stability is also at work - for
+which no experimental evidence was found so far. Here we show that mutations
+outside the active site in the essential E. coli enzyme adenylate kinase result
+in stability-dependent increase in substrate inhibition by AMP, thereby
+impairing overall enzyme activity at high stability. Such inhibition caused
+substantial fitness defects not only in the presence of excess substrate but
+also under physiological conditions. In the latter case, substrate inhibition
+caused differential accumulation of AMP in the stationary phase for the
+inhibition prone mutants. Further, we show that changes in flux through Adk
+could accurately describe the variation in fitness effects. Taken together,
+these data suggest that selection against substrate inhibition and hence excess
+stability may have resulted in a narrow range of optimal stability observed for
+modern proteins.
+",0,0,0,0,1,0
+19805,Exact relativistic Toda chain eigenfunctions from Separation of Variables and gauge theory,"  We provide a proposal, motivated by Separation of Variables and gauge theory
+arguments, for constructing exact solutions to the quantum Baxter equation
+associated to the $N$-particle relativistic Toda chain and test our proposal
+against numerical results. Quantum Mechanical non-perturbative corrections,
+essential in order to obtain a sensible solution, are taken into account in our
+gauge theory approach by considering codimension two defects on curved
+backgrounds (squashed $S^5$ and degenerate limits) rather than flat space; this
+setting also naturally incorporates exact quantization conditions and energy
+spectrum of the relativistic Toda chain as well as its modular dual structure.
+",0,1,0,0,0,0
+19806,Simple Compact Monotone Tree Drawings,"  A monotone drawing of a graph G is a straight-line drawing of G such that
+every pair of vertices is connected by a path that is monotone with respect to
+some direction.
+Trees, as a special class of graphs, have been the focus of several papers
+and, recently, He and He~\cite{mt:4} showed how to produce a monotone drawing
+of an arbitrary $n$-vertex tree that is contained in a $12n \times 12n$ grid.
+All monotone tree drawing algorithms that have appeared in the literature
+consider rooted ordered trees and they draw them so that (i) the root of the
+tree is drawn at the origin of the drawing, (ii) the drawing is confined in the
+first quadrant, and (iii) the ordering/embedding of the tree is respected. In
+this paper, we provide a simple algorithm that has the exact same
+characteristics and, given an $n$-vertex rooted tree $T$, it outputs a monotone
+drawing of $T$ that fits on a $n \times n$ grid.
+For unrooted ordered trees, we present an algorithms that produces monotone
+drawings that respect the ordering and fit in an $(n+1) \times (\frac{n}{2}
++1)$ grid, while, for unrooted non-ordered trees we produce monotone drawings
+of good aspect ratio which fit on a grid of size at most $\left\lfloor
+\frac{3}{4} \left(n+2\right)\right\rfloor \times \left\lfloor \frac{3}{4}
+\left(n+2\right)\right\rfloor$.
+",1,0,0,0,0,0
+19807,Context-Free Path Querying by Matrix Multiplication,"  Graph data models are widely used in many areas, for example, bioinformatics,
+graph databases. In these areas, it is often required to process queries for
+large graphs. Some of the most common graph queries are navigational queries.
+The result of query evaluation is a set of implicit relations between nodes of
+the graph, i.e. paths in the graph. A natural way to specify these relations is
+by specifying paths using formal grammars over the alphabet of edge labels. An
+answer to a context-free path query in this approach is usually a set of
+triples (A, m, n) such that there is a path from the node m to the node n,
+whose labeling is derived from a non-terminal A of the given context-free
+grammar. This type of queries is evaluated using the relational query
+semantics. Another example of path query semantics is the single-path query
+semantics which requires presenting a single path from the node m to the node
+n, whose labeling is derived from a non-terminal A for all triples (A, m, n)
+evaluated using the relational query semantics. There is a number of algorithms
+for query evaluation which use these semantics but all of them perform poorly
+on large graphs. One of the most common technique for efficient big data
+processing is the use of a graphics processing unit (GPU) to perform
+computations, but these algorithms do not allow to use this technique
+efficiently. In this paper, we show how the context-free path query evaluation
+using these query semantics can be reduced to the calculation of the matrix
+transitive closure. Also, we propose an algorithm for context-free path query
+evaluation which uses relational query semantics and is based on matrix
+operations that make it possible to speed up computations by using a GPU.
+",1,0,0,0,0,0
+19808,Equivalence of sparse and Carleson coefficients for general sets,"  We remark that sparse and Carleson coefficients are equivalent for every
+countable collection of Borel sets and hence, in particular, for dyadic
+rectangles, the case relevant to the theory of bi-parameter singular integrals.
+The key observation is that a dual refomulation by I. E. Verbitsky for
+Carleson coefficients over dyadic cubes holds also for Carleson coefficients
+over general sets. We give a simple proof for this reformulation.
+",0,0,1,0,0,0
+19809,Discovering Business Rules from Business Process Models,"  Discovering business rules from business process models are of advantage to
+ensure the compliance of business processes with business rules. Furthermore it
+provides the agility of business processes in case of business rules evolution.
+Current approaches are limited on types of rules that can be discovered. This
+paper analyses the expression power of some popular business process modelling
+languages in embedding business rules in its presentation and provides
+indicators to extract various types of business rules from business process
+models.
+",1,0,0,0,0,0
+19810,Fast Learning and Prediction for Object Detection using Whitened CNN Features,"  We combine features extracted from pre-trained convolutional neural networks
+(CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of
+both: the high detection performance of CNNs, automatic feature engineering,
+fast model learning from few training samples and efficient sliding-window
+detection. The Adaptive Real-Time Object Detection System (ARTOS) has been
+refactored broadly to be used in combination with Caffe for the experimental
+studies reported in this work.
+",1,0,0,0,0,0
+19811,N-body simulations of planet formation via pebble accretion I: First Results,"  Context. Planet formation with pebbles has been proposed to solve a couple of
+long-standing issues in the classical formation model. Some sophisticated
+simulations have been done to confirm the efficiency of pebble accretion.
+However, there has not been any global N-body simulations that compare the
+outcomes of planet formation via pebble accretion with observed extrasolar
+planetary systems. Aims. In this paper, we study the effects of a range of
+initial parameters of planet formation via pebble accretion, and present the
+first results of our simulations. Methods. We incorporate the pebble accretion
+model by Ida et al. (2016) in the N-body code SyMBA (Duncan et al. 1998), along
+with the effects of gas accretion, eccentricity and inclination damping and
+planet migration in the disc. Results. We confirm that pebble accretion leads
+to a variety of planetary systems, but have difficulty in reproducing observed
+properties of exoplanetary systems, such as planetary mass, semimajor axis, and
+eccentricity distributions. The main reason behind this is a too-efficient type
+I migration, which sensitively depends on the disc model. However, our
+simulations also lead to a few interesting predictions. First, we find that
+formation efficiencies of planets depend on the stellar metallicities, not only
+for giant planets, but also for Earths (Es) and Super-Earths (SEs). The
+dependency for Es/SEs is subtle. Although higher metallicity environments lead
+to faster formation of a larger number of Es/SEs, they also tend to be lost
+later via dynamical instability. Second, our results indicate that a wide range
+of bulk densities observed for Es and SEs is a natural consequence of dynamical
+evolution of planetary systems. Third, the ejection trend of our simulations
+suggest that one free-floating E/SE may be expected for two smaller-mass
+planets.
+",0,1,0,0,0,0
+19812,Local character of Kim-independence,"  We show that NSOP$_{1}$ theories are exactly the theories in which
+Kim-independence satisfies a form of local character. In particular, we show
+that if $T$ is NSOP$_{1}$, $M\models T$, and $p$ is a type over $M$, then the
+collection of elementary substructures of size $\left|T\right|$ over which $p$
+does not Kim-fork is a club of $\left[M\right]^{\left|T\right|}$ and that this
+characterizes NSOP$_{1}$.
+We also present a new phenomenon we call dual local-character for
+Kim-independence in NSOP$_{1}$-theories.
+",0,0,1,0,0,0
+19813,A Further Analysis of The Role of Heterogeneity in Coevolutionary Spatial Games,"  Heterogeneity has been studied as one of the most common explanations of the
+puzzle of cooperation in social dilemmas. A large number of papers have been
+published discussing the effects of increasing heterogeneity in structured
+populations of agents, where it has been established that heterogeneity may
+favour cooperative behaviour if it supports agents to locally coordinate their
+strategies. In this paper, assuming an existing model of a heterogeneous
+weighted network, we aim to further this analysis by exploring the relationship
+(if any) between heterogeneity and cooperation. We adopt a weighted network
+which is fully populated by agents playing both the Prisoner's Dilemma or the
+Optional Prisoner's Dilemma games with coevolutionary rules, i.e., not only the
+strategies but also the link weights evolve over time. Surprisingly, results
+show that the heterogeneity of link weights (states) on their own does not
+always promote cooperation; rather cooperation is actually favoured by the
+increase in the number of overlapping states and not by the heterogeneity
+itself. We believe that these results can guide further research towards a more
+accurate analysis of the role of heterogeneity in social dilemmas.
+",1,0,0,0,0,0
+19814,Transport properties of the Azimuthal Magnetorotational Instability,"  The magnetorotational instability (MRI) is thought to be a powerful source of
+turbulence in Keplerian accretion disks. Motivated by recent laboratory
+experiments, we study the MRI driven by an azimuthal magnetic field in an
+electrically conducting fluid sheared between two concentric rotating
+cylinders. By adjusting the rotation rates of the cylinders, we approximate
+angular velocity profiles $\omega \propto r^{q}$. We perform direct numerical
+simulations of a steep profile close to the Rayleigh line $q \gtrsim -2 $ and a
+quasi-Keplerian profile $q \approx -3/2$ and cover wide ranges of Reynolds
+($Re\le 4\cdot10^4$) and magnetic Prandtl numbers ($0\le Pm \le 1$). In the
+quasi-Keplerian case, the onset of instability depends on the magnetic Reynolds
+number, with $Rm_c \approx 50$, and angular momentum transport scales as
+$\sqrt{Pm} Re^2$ in the turbulent regime. The ratio of Maxwell to Reynolds
+stresses is set by $Rm$. At the onset of instability both stresses have similar
+magnitude, whereas the Reynolds stress vanishes or becomes even negative as
+$Rm$ increases. For the profile close to the Rayleigh line, the instability
+shares these properties as long as $Pm\gtrsim0.1$, but exhibits a markedly
+different character if $Pm\rightarrow 0$, where the onset of instability is
+governed by the Reynolds number, with $Re_c \approx 1250$, transport is via
+Reynolds stresses and scales as $Re^2$. At intermediate $Pm=0.01$ we observe a
+continuous transition from one regime to the other, with a crossover at
+$Rm=\mathcal{O}(100)$. Our results give a comprehensive picture of angular
+momentum transport of the MRI with an imposed azimuthal field.
+",0,1,0,0,0,0
+19815,New bounds for the Probability of Causation in Mediation Analysis,"  An individual has been subjected to some exposure and has developed some
+outcome. Using data on similar individuals, we wish to evaluate, for this case,
+the probability that the outcome was in fact caused by the exposure. Even with
+the best possible experimental data on exposure and outcome, we typically can
+not identify this ""probability of causation"" exactly, but we can provide
+information in the form of bounds for it. Under appropriate assumptions, these
+bounds can be tightened if we can make other observations (e.g., on
+non-experimental cases), measure additional variables (e.g., covariates) or
+measure complete mediators. In this work we propose new bounds for the case
+that a third variable mediates partially the effect of the exposure on the
+outcome.
+",0,0,1,1,0,0
+19816,Searching for a cosmological preferred direction with 147 rotationally supported galaxies,"  It is well known that the Milgrom's MOND (modified Newtonian dynamics)
+explains well the mass discrepancy problem in galaxy rotation curves. The MOND
+predicts a universal acceleration scale below which the Newtonian dynamics is
+invalid yet. The universal acceleration scale we got from the SPARC dataset is
+$g_†=1.02\times10^{-10} \rm m~s^{-2}$. Milgrom suggested that the
+acceleration scale may be a fingerprint of cosmology on local dynamics and
+related with the Hubble constant $g_†\sim cH_0$. In this paper, we use the
+hemisphere comparison method with the SPARC dataset to investigate the spatial
+anisotropy on the acceleration scale. We find that the hemisphere of the
+maximum acceleration scale is in the direction $(l,b) =
+({175.5^\circ}^{+6^\circ}_{-10^\circ}, {-6.5^\circ}^{+8^\circ}_{-3^\circ})$
+with $g_{†,max}=1.10\times10^{-10} \rm m~s^{-2}$, while the hemisphere of
+the minimum acceleration scale is in the opposite direction $(l,b) =
+({355.5^\circ}^{+6^\circ}_{-10^\circ}, {6.5^\circ}^{+3^\circ}_{-8^\circ})$ with
+$g_{†,min}=0.76\times10^{-10} \rm m~s^{-2}$. The maximum anisotropy level
+reaches up to $0.37\pm0.04$. Robust tests present that such a level of
+anisotropy can't be reproduced by a statistically isotropic data. In addition,
+we show that the spatial anisotropy on the acceleration scale has little
+correlation with the non-uniform distribution of the SPARC data points in sky.
+We also find that the maximum anisotropy direction is close with other
+cosmological preferred directions, especially the direction of the ""Australia
+dipole"" for the fine structure constant.
+",0,1,0,0,0,0
+19817,MultiFIT: A Multivariate Multiscale Framework for Independence Tests,"  We present a framework for testing independence between two random vectors
+that is scalable to massive data. We break down the multivariate test into
+univariate tests of independence on a collection of $2\times 2$ contingency
+tables, constructed by sequentially discretizing the sample space. This
+transforms a complex problem that traditionally requires quadratic
+computational complexity with respect to the sample size into one that scales
+almost linearly with the sample size. We further consider the scenario when the
+dimensionality of the random vectors grows large, in which case the curse of
+dimensionality arises in the proposed framework through an explosion in the
+number of univariate tests to be completed. To overcome this difficulty we
+propose a data-adaptive version of our method that completes a fraction of the
+univariate tests judged to be more likely to contain evidence for dependency.
+We demonstrate the tremendous computational advantage of the algorithm in
+comparison to existing approaches while achieving desirable statistical power
+through an extensive simulation study. In addition, we illustrate how our
+method can be used for learning the nature of the underlying dependency. We
+demonstrate the use of our method through analyzing a data set from flow
+cytometry.
+",0,0,0,1,0,0
+19818,Stability of algebraic varieties and Kahler geometry,"  This is a survey article, based on the author's lectures in the 2015 AMS
+Summer Research Institute in Algebraic Geometry, and to appear in the
+Proceedings.
+",0,0,1,0,0,0
+19819,Scratch iridescence: Wave-optical rendering of diffractive surface structure,"  The surface of metal, glass and plastic objects is often characterized by
+microscopic scratches caused by manufacturing and/or wear. A closer look onto
+such scratches reveals iridescent colors with a complex dependency on viewing
+and lighting conditions. The physics behind this phenomenon is well understood;
+it is caused by diffraction of the incident light by surface features on the
+order of the optical wavelength. Existing analytic models are able to reproduce
+spatially unresolved microstructure such as the iridescent appearance of
+compact disks and similar materials. Spatially resolved scratches, on the other
+hand, have proven elusive due to the highly complex wave-optical light
+transport simulations needed to account for their appearance. In this paper, we
+propose a wave-optical shading model based on non-paraxial scalar diffraction
+theory to render this class of effects. Our model expresses surface roughness
+as a collection of line segments. To shade a point on the surface, the
+individual diffraction patterns for contributing scratch segments are computed
+analytically and superimposed coherently. This provides natural transitions
+from localized glint-like iridescence to smooth BRDFs representing the
+superposition of many reflections at large viewing distances. We demonstrate
+that our model is capable of recreating the overall appearance as well as
+characteristic detail effects observed on real-world examples.
+",1,0,0,0,0,0
+19820,An oscillation criterion for delay differential equations with several non-monotone arguments,"  The oscillatory behavior of the solutions to a differential equation with
+several non-monotone delay arguments and non-negative coefficients is studied.
+A new sufficient oscillation condition, involving lim sup, is obtained. An
+example illustrating the significance of the result is also given.
+",0,0,1,0,0,0
+19821,Item Recommendation with Variational Autoencoders and Heterogenous Priors,"  In recent years, Variational Autoencoders (VAEs) have been shown to be highly
+effective in both standard collaborative filtering applications and extensions
+such as incorporation of implicit feedback. We extend VAEs to collaborative
+filtering with side information, for instance when ratings are combined with
+explicit text feedback from the user. Instead of using a user-agnostic standard
+Gaussian prior, we incorporate user-dependent priors in the latent VAE space to
+encode users' preferences as functions of the review text. Taking into account
+both the rating and the text information to represent users in this multimodal
+latent space is promising to improve recommendation quality. Our proposed model
+is shown to outperform the existing VAE models for collaborative filtering (up
+to 29.41% relative improvement in ranking metric) along with other baselines
+that incorporate both user ratings and text for item recommendation.
+",0,0,0,1,0,0
+19822,Boltzmann Exploration Done Right,"  Boltzmann exploration is a classic strategy for sequential decision-making
+under uncertainty, and is one of the most standard tools in Reinforcement
+Learning (RL). Despite its widespread use, there is virtually no theoretical
+understanding about the limitations or the actual benefits of this exploration
+scheme. Does it drive exploration in a meaningful way? Is it prone to
+misidentifying the optimal actions or spending too much time exploring the
+suboptimal ones? What is the right tuning for the learning rate? In this paper,
+we address several of these questions in the classic setup of stochastic
+multi-armed bandits. One of our main results is showing that the Boltzmann
+exploration strategy with any monotone learning-rate sequence will induce
+suboptimal behavior. As a remedy, we offer a simple non-monotone schedule that
+guarantees near-optimal performance, albeit only when given prior access to key
+problem parameters that are typically not available in practical situations
+(like the time horizon $T$ and the suboptimality gap $\Delta$). More
+importantly, we propose a novel variant that uses different learning rates for
+different arms, and achieves a distribution-dependent regret bound of order
+$\frac{K\log^2 T}{\Delta}$ and a distribution-independent bound of order
+$\sqrt{KT}\log K$ without requiring such prior knowledge. To demonstrate the
+flexibility of our technique, we also propose a variant that guarantees the
+same performance bounds even if the rewards are heavy-tailed.
+",1,0,0,1,0,0
+19823,Deep reinforcement learning from human preferences,"  For sophisticated reinforcement learning (RL) systems to interact usefully
+with real-world environments, we need to communicate complex goals to these
+systems. In this work, we explore goals defined in terms of (non-expert) human
+preferences between pairs of trajectory segments. We show that this approach
+can effectively solve complex RL tasks without access to the reward function,
+including Atari games and simulated robot locomotion, while providing feedback
+on less than one percent of our agent's interactions with the environment. This
+reduces the cost of human oversight far enough that it can be practically
+applied to state-of-the-art RL systems. To demonstrate the flexibility of our
+approach, we show that we can successfully train complex novel behaviors with
+about an hour of human time. These behaviors and environments are considerably
+more complex than any that have been previously learned from human feedback.
+",1,0,0,1,0,0
+19824,Entanglement spectroscopy on a quantum computer,"  We present a quantum algorithm to compute the entanglement spectrum of
+arbitrary quantum states. The interesting universal part of the entanglement
+spectrum is typically contained in the largest eigenvalues of the density
+matrix which can be obtained from the lower Renyi entropies through the
+Newton-Girard method. Obtaining the $p$ largest eigenvalues
+($\lambda_1>\lambda_2\ldots>\lambda_p$) requires a parallel circuit depth of
+$\mathcal{O}(p(\lambda_1/\lambda_p)^p)$ and $\mathcal{O}(p\log(N))$ qubits
+where up to $p$ copies of the quantum state defined on a Hilbert space of size
+$N$ are needed as the input. We validate this procedure for the entanglement
+spectrum of the topologically-ordered Laughlin wave function corresponding to
+the quantum Hall state at filling factor $\nu=1/3$. Our scaling analysis
+exposes the tradeoffs between time and number of qubits for obtaining the
+entanglement spectrum in the thermodynamic limit using finite-size digital
+quantum computers. We also illustrate the utility of the second Renyi entropy
+in predicting a topological phase transition and in extracting the localization
+length in a many-body localized system.
+",0,1,0,0,0,0
+19825,Light Attenuation Length of High Quality Linear Alkyl Benzene as Liquid Scintillator Solvent for the JUNO Experiment,"  The Jiangmen Underground Neutrino Observatory (JUNO) is a multipurpose
+neutrino experiment with a 20 kt liquid scintillator detector designed to
+determine the neutrino mass hierarchy, and measure the neutrino oscillation
+parameters. Linear alkyl benzene (LAB) will be used as the solvent for the
+liquid scintillation system in the central detector of JUNO. For this purpose,
+we have prepared LAB samples, and have measured their light attenuation
+lengths, with one achieving a length of 25.8 m, comparable to the diameter of
+the JUNO detector.
+",0,1,0,0,0,0
+19826,OIL: Observational Imitation Learning,"  Recent work has explored the problem of autonomous navigation by imitating a
+teacher and learning an end-to-end policy, which directly predicts controls
+from raw images. However, these approaches tend to be sensitive to mistakes by
+the teacher and do not scale well to other environments or vehicles. To this
+end, we propose Observational Imitation Learning (OIL), a novel imitation
+learning variant that supports online training and automatic selection of
+optimal behavior by observing multiple imperfect teachers. We apply our
+proposed methodology to the challenging problems of autonomous driving and UAV
+racing. For both tasks, we utilize the Sim4CV simulator that enables the
+generation of large amounts of synthetic training data and also allows for
+online learning and evaluation. We train a perception network to predict
+waypoints from raw image data and use OIL to train another network to predict
+controls from these waypoints. Extensive experiments demonstrate that our
+trained network outperforms its teachers, conventional imitation learning (IL)
+and reinforcement learning (RL) baselines and even humans in simulation. The
+project website is available at this https URL
+",1,0,0,0,0,0
+19827,A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation,"  State space models in which the system state is a finite set--called the
+multi-object state--have generated considerable interest in recent years.
+Smoothing for state space models provides better estimation performance than
+filtering by using the full posterior rather than the filtering density. In
+multi-object state estimation, the Bayes multi-object filtering recursion
+admits an analytic solution known as the Generalized Labeled Multi-Bernoulli
+(GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate
+the multi-object posterior. We also propose an implementation of this so-called
+multi-scan GLMB posterior recursion using a similar approach to the GLMB filter
+implementation.
+",0,0,0,1,0,0
+19828,On the Complexity of Sampling Nodes Uniformly from a Graph,"  We study a number of graph exploration problems in the following natural
+scenario: an algorithm starts exploring an undirected graph from some seed
+node; the algorithm, for an arbitrary node $v$ that it is aware of, can ask an
+oracle to return the set of the neighbors of $v$. (In social network analysis,
+a call to this oracle corresponds to downloading the profile page of user $v$
+in a social network.) The goal of the algorithm is to either learn something
+(e.g., average degree) about the graph, or to return some random function of
+the graph (e.g., a uniform-at-random node), while accessing/downloading as few
+nodes of the graph as possible. Motivated by practical applications, we study
+the complexities of a variety of problems in terms of the graph's mixing time
+and average degree -- two measures that are believed to be quite small in
+real-world social networks, and that have often been used in the applied
+literature to bound the performance of online exploration algorithms. Our main
+result is that the algorithm has to access $\Omega\left(t_{\rm mix} d_{\rm avg}
+\epsilon^{-2} \ln \delta^{-1}\right)$ nodes to obtain, with probability at
+least $1-\delta$, an $\epsilon$-additive approximation of the average of a
+bounded function on the nodes of a graph -- this lower bound matches the
+performance of an algorithm that was proposed in the literature. We also give
+tight bounds for the problem of returning a close-to-uniform-at-random node
+from the graph. Finally, we give lower bounds for the problems of estimating
+the average degree of the graph, and the number of nodes of the graph.
+",1,0,0,0,0,0
+19829,A structure theorem for almost low-degree functions on the slice,"  The Fourier-Walsh expansion of a Boolean function $f \colon \{0,1\}^n
+\rightarrow \{0,1\}$ is its unique representation as a multilinear polynomial.
+The Kindler-Safra theorem (2002) asserts that if in the expansion of $f$, the
+total weight on coefficients beyond degree $k$ is very small, then $f$ can be
+approximated by a Boolean-valued function depending on at most $O(2^k)$
+variables.
+In this paper we prove a similar theorem for Boolean functions whose domain
+is the `slice' ${{[n]}\choose{pn}} = \{x \in \{0,1\}^n\colon \sum_i x_i =
+pn\}$, where $0 \ll p \ll 1$, with respect to their unique representation as
+harmonic multilinear polynomials. We show that if in the representation of
+$f\colon {{[n]}\choose{pn}} \rightarrow \{0,1\}$, the total weight beyond
+degree $k$ is at most $\epsilon$, where $\epsilon = \min(p, 1-p)^{O(k)}$, then
+$f$ can be $O(\epsilon)$-approximated by a degree-$k$ Boolean function on the
+slice, which in turn depends on $O(2^{k})$ coordinates. This proves a
+conjecture of Filmus, Kindler, Mossel, and Wimmer (2015). Our proof relies on
+hypercontractivity, along with a novel kind of a shifting procedure.
+In addition, we show that the approximation rate in the Kindler-Safra theorem
+can be improved from $\epsilon + \exp(O(k)) \epsilon^{1/4}$ to
+$\epsilon+\epsilon^2 (2\ln(1/\epsilon))^k/k!$, which is tight in terms of the
+dependence on $\epsilon$ and misses at most a factor of $2^{O(k)}$ in the
+lower-order term.
+",1,0,0,0,0,0
+19830,Oxygen-vacancy driven electron localization and itinerancy in rutile-based TiO$_2$,"  Oxygen-deficient TiO$_2$ in the rutile structure as well as the Ti$_3$O$_5$
+Magn{é}li phase is investigated within the charge self-consistent combination
+of density functional theory (DFT) with dynamical mean-field theory (DMFT). It
+is shown that an isolated oxygen vacancy (V$_{\rm O}$) in titanium dioxide is
+not sufficient to metallize the system at low temperatures. In a semiconducting
+phase, an in-gap state is identified at $\varepsilon_{\rm IG}^{\hfill}\sim
+-0.75\,$eV\, in excellent agreement with experimental data. Band-like impurity
+levels, resulting from a threefold V$_{\rm O}$-Ti coordination as well as
+entangled $(t_{2g},e_g)$ states, become localized due to site-dependent
+electronic correlations. Charge localization and strong orbital polarization
+occur in the V$_{\rm O}$-near Ti ions, which details can be modified by a
+variation of the correlated subspace. At higher oxygen vacancy concentration, a
+correlated metal is stabilized in the Magn{é}li phase. A V$_{\rm O}$-defect
+rutile structure of identical stoichiometry shows key differences in the
+orbital-resolved character and the spectral properties. Charge
+disproportionation is vital in the oxygen-deficient compounds, but obvious
+metal-insulator transitions driven or sustained by charge order are not
+identified.
+",0,1,0,0,0,0
+19831,3D printable multimaterial cellular auxetics with tunable stiffness,"  Auxetic materials are a novel class of mechanical metamaterials which exhibit
+an interesting property of negative Poisson ratio by virtue of their
+architecture rather than composition. It has been well established that a wide
+range of negative Poisson ratio can be obtained by varying the geometry and
+architecture of the cellular materials. However, the limited range of stiffness
+values obtained from a given geometry restricts their applications. Research
+trials have revealed that multi-material cellular designs have the capability
+to generate range of stiffness values as per the requirement of application.
+With the advancements in 3D printing, multi-material cellular designs can be
+realized in practice. In this work, multi-material cellular designs are
+investigated using finite element method. It was observed that introduction of
+material gradient/distribution in the cell provides a means to tune cellular
+stiffness as per the specific requirement. These results will aid in the design
+of wearable auxetic impact protection devices which rely on stiffness gradients
+and variable auxeticity.
+",0,1,0,0,0,0
+19832,MACS J0416.1-2403: Impact of line-of-sight structures on strong gravitational lensing modelling of galaxy clusters,"  Exploiting the powerful tool of strong gravitational lensing by galaxy
+clusters to study the highest-redshift Universe and cluster mass distributions
+relies on precise lens mass modelling. In this work, we present the first
+attempt at modelling line-of-sight mass distribution in addition to that of the
+cluster, extending previous modelling techniques that assume mass distributions
+to be on a single lens plane. We focus on the Hubble Frontier Field cluster
+MACS J0416.1-2403, and our multi-plane model reproduces the observed image
+positions with a rms offset of ~0.53"". Starting from this best-fitting model,
+we simulate a mock cluster that resembles MACS J0416.1-2403 in order to explore
+the effects of line-of-sight structures on cluster mass modelling. By
+systematically analysing the mock cluster under different model assumptions, we
+find that neglecting the lensing environment has a significant impact on the
+reconstruction of image positions (rms ~0.3""); accounting for line-of-sight
+galaxies as if they were at the cluster redshift can partially reduce this
+offset. Moreover, foreground galaxies are more important to include into the
+model than the background ones. While the magnification factors of the lensed
+multiple images are recovered within ~10% for ~95% of them, those ~5% that lie
+near critical curves can be significantly affected by the exclusion of the
+lensing environment in the models (up to a factor of ~200). In addition,
+line-of-sight galaxies cannot explain the apparent discrepancy in the
+properties of massive subhalos between MACS J0416.1-2403 and N-body simulated
+clusters. Since our model of MACS J0416.1-2403 with line-of-sight galaxies only
+reduced modestly the rms offset in the image positions, we conclude that
+additional complexities, such as more flexible halo shapes, would be needed in
+future models of MACS J0416.1-2403.
+",0,1,0,0,0,0
+19833,What can you do with a rock? Affordance extraction via word embeddings,"  Autonomous agents must often detect affordances: the set of behaviors enabled
+by a situation. Affordance detection is particularly helpful in domains with
+large action spaces, allowing the agent to prune its search space by avoiding
+futile behaviors. This paper presents a method for affordance extraction via
+word embeddings trained on a Wikipedia corpus. The resulting word vectors are
+treated as a common knowledge database which can be queried using linear
+algebra. We apply this method to a reinforcement learning agent in a text-only
+environment and show that affordance-based action selection improves
+performance most of the time. Our method increases the computational complexity
+of each learning step but significantly reduces the total number of steps
+needed. In addition, the agent's action selections begin to resemble those a
+human would choose.
+",1,0,0,0,0,0
+19834,On Generalized Gibbs Ensembles with an infinite set of conserved charges,"  We revisit the question of whether and how the steady states arising after
+non-equilibrium time evolution in integrable models (and in particular in the
+XXZ spin chain) can be described by the so-called Generalized Gibbs Ensemble
+(GGE). It is known that the micro-canonical ensemble built on a complete set of
+charges correctly describes the long-time limit of local observables, and
+recently a canonical ensemble was built by Ilievski et. al. using particle
+occupation number operators. Here we provide an alternative construction by
+considering truncated GGE's (tGGE's) that only include a finite number of well
+localized conserved operators. It is shown that the tGGE's can approximate the
+steady states with arbitrary precision, i.e. all physical observables are
+exactly reproduced in the infinite truncation limit. In addition, we show that
+a complete canonical ensemble can in fact be built in terms of a new (discrete)
+set of charges built as linear combinations of the standard ones.
+Our general arguments are applied to concrete quench situations in the XXZ
+chain, where the initial states are simple two-site or four-site product
+states. Depending on the quench we find that numerical results for the local
+correlators can be obtained with remarkable precision using truncated GGE's
+with only 10-100 charges.
+",0,1,0,0,0,0
+19835,High resolution ion trap time-of-flight mass spectrometer for cold trapped ion experiments,"  Trapping molecular ions that have been sympathetically cooled with
+laser-cooled atomic ions is a useful platform for exploring cold ion chemistry.
+We designed and characterized a new experimental apparatus for probing chemical
+reaction dynamics between molecular cations and neutral radicals at
+temperatures below 1 K. The ions are trapped in a linear quadrupole
+radio-frequency trap and sympathetically cooled by co-trapped, laser-cooled,
+atomic ions. The ion trap is coupled to a time-of-flight mass spectrometer to
+readily identify product ion species, as well as to accurately determine
+trapped ion numbers. We discuss, and present in detail, the design of this ion
+trap time-of-flight mass spectrometer, as well as the electronics required for
+driving the trap and mass spectrometer. Furthermore, we measure the performance
+of this system, which yields mass resolutions of $m/\Delta{}m \geq 1100$ over a
+wide mass range, and discuss its relevance for future measurements in chemical
+reaction kinetics and dynamics.
+",0,1,0,0,0,0
+19836,The role of local-geometrical-orders on the growth of dynamic-length-scales in glass-forming liquids,"  The precise nature of complex structural relaxation as well as an explanation
+for the precipitous growth of relaxation time in cooling glass-forming liquids
+are essential to the understanding of vitrification of liquids. The dramatic
+increase of relaxation time is believed to be caused by the growth of one or
+more correlation lengths, which has received much attention recently. Here, we
+report a direct link between the growth of a specific local-geometrical-order
+and an increase of dynamic-length-scale as the atomic dynamics in metallic
+glass-forming liquids slow down. Although several types of local
+geometrical-orders are present in these metallic liquids, the growth of
+icosahedral ordering is found to be directly related to the increase of the
+dynamic-length-scale. This finding suggests an intriguing scenario that the
+transient icosahedral ordering could be the origin of the dynamic-length-scale
+in metallic glass-forming liquids.
+",0,1,0,0,0,0
+19837,Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs,"  A regularized optimization problem over a large unstructured graph is
+studied, where the regularization term is tied to the graph geometry. Typical
+regularization examples include the total variation and the Laplacian
+regularizations over the graph. When applying the proximal gradient algorithm
+to solve this problem, there exist quite affordable methods to implement the
+proximity operator (backward step) in the special case where the graph is a
+simple path without loops. In this paper, an algorithm, referred to as ""Snake"",
+is proposed to solve such regularized problems over general graphs, by taking
+benefit of these fast methods. The algorithm consists in properly selecting
+random simple paths in the graph and performing the proximal gradient algorithm
+over these simple paths. This algorithm is an instance of a new general
+stochastic proximal gradient algorithm, whose convergence is proven.
+Applications to trend filtering and graph inpainting are provided among others.
+Numerical experiments are conducted over large graphs.
+",1,0,0,1,0,0
+19838,Stability of the coexistent superconducting-nematic phase under the presence of intersite interactions,"  We analyze the effect of intersite-interaction terms on the stability of the
+coexisting superconucting-nematic phase (SC+N) within the extended Hubbard and
+$t$-$J$-$U$ models on the square lattice. In order to take into account the
+correlation effects with a proper precision, we use the approach based on the
+\textit{diagrammatic expansion of the Gutzwiller wave function} (DE-GWF), which
+goes beyond the renormalized mean field theory (RMFT) in a systematic manner.
+As a starting point of our analysis we discuss the stability region of the SC+N
+phase on the intrasite Coulomb repulsion-hole doping plane for the case of the
+Hubbard model. Next, we show that the exchange interaction term enhances
+superconductivity while suppresses the nematicity, whereas the intersite
+Coulomb repulsion term acts in the opposite manner. The competing character of
+the SC and N phases interplay is clearly visible throughout the analysis. A
+universal conclusion is that the nematic phase does not survive within the
+$t$-$J$-$U$ model with the value of $J$ integral typical for the high-T$_C$
+cuprates ($J\approx 0.1$eV). For the sake of completeness, the effect of the
+correlated hopping term is also analyzed. Thus the present discussion contains
+all relevant two-site interaction terms which appear in the parametrized
+one-band model within the second quantization scheme. At the end, the influence
+of the higher-order terms of the diagrammatic expansion on the rotational
+symmetry breaking is also shown by comparing the DE-GWF results with those
+corresponding to the RMFT.
+",0,1,0,0,0,0
+19839,Multi-Advisor Reinforcement Learning,"  We consider tackling a single-agent RL problem by distributing it to $n$
+learners. These learners, called advisors, endeavour to solve the problem from
+a different focus. Their advice, taking the form of action values, is then
+communicated to an aggregator, which is in control of the system. We show that
+the local planning method for the advisors is critical and that none of the
+ones found in the literature is flawless: the egocentric planning overestimates
+values of states where the other advisors disagree, and the agnostic planning
+is inefficient around danger zones. We introduce a novel approach called
+empathic and discuss its theoretical aspects. We empirically examine and
+validate our theoretical findings on a fruit collection task.
+",1,0,0,1,0,0
+19840,Using CMB spectral distortions to distinguish between dark matter solutions to the small-scale crisis,"  The dissipation of small-scale perturbations in the early universe produces a
+distortion in the blackbody spectrum of cosmic microwave background photons. In
+this work, we propose to use these distortions as a probe of the microphysics
+of dark matter on scales $1\,\textrm{Mpc}^{-1}\lesssim k \lesssim
+10^{4}\,\textrm{Mpc}^{-1}$. We consider in particular models in which the dark
+matter is kinetically coupled to either neutrinos or photons until shortly
+before recombination, and compute the photon heating rate and the resultant
+$\mu$-distortion in both cases. We show that the $\mu$-parameter is generally
+enhanced relative to $\Lambda$CDM for interactions with neutrinos, and may be
+either enhanced or suppressed in the case of interactions with photons. The
+deviations from the $\Lambda$CDM signal are potentially within the sensitivity
+reach of a PRISM-like experiment if $\sigma_{\textrm{DM}-\gamma} \gtrsim
+1.1\times10^{-30} \left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$
+and $\sigma_{\textrm{DM}-\nu} \gtrsim 4.8\times 10^{-32}
+\left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$ for time-independent
+cross sections, and $\sigma^{0}_{\textrm{DM}-\gamma} \gtrsim 1.8 \times
+10^{-40} \left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$ and
+$\sigma^{0}_{\textrm{DM}-\nu} \gtrsim 2.5 \times 10^{-47}
+\left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$ for cross sections
+scaling as temperature squared, coinciding with the parameter regions in which
+late kinetic decoupling may serve as a solution to the small-scale crisis.
+Furthermore, these $\mu$-distortion signals differ from those of warm dark
+matter (no deviation from $\Lambda$CDM) and a suppressed primordial power
+spectrum (strongly suppressed or a negative $\mu$-parameter), demonstrating
+that CMB spectral distortion can potentially be used to distinguish between
+solutions to the small-scale crisis.
+",0,1,0,0,0,0
+19841,Gradient Hyperalignment for multi-subject fMRI data alignment,"  Multi-subject fMRI data analysis is an interesting and challenging problem in
+human brain decoding studies. The inherent anatomical and functional
+variability across subjects make it necessary to do both anatomical and
+functional alignment before classification analysis. Besides, when it comes to
+big data, time complexity becomes a problem that cannot be ignored. This paper
+proposes Gradient Hyperalignment (Gradient-HA) as a gradient-based functional
+alignment method that is suitable for multi-subject fMRI datasets with large
+amounts of samples and voxels. The advantage of Gradient-HA is that it can
+solve independence and high dimension problems by using Independent Component
+Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using
+multi-classification tasks on big data demonstrates that Gradient-HA method has
+less time complexity and better or comparable performance compared with other
+state-of-the-art functional alignment methods.
+",0,0,0,1,1,0
+19842,Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer,"  Knowledge transfer between tasks can improve the performance of learned
+models, but requires an accurate estimate of the inter-task relationships to
+identify the relevant knowledge to transfer. These inter-task relationships are
+typically estimated based on training data for each task, which is inefficient
+in lifelong learning settings where the goal is to learn each consecutive task
+rapidly from as little data as possible. To reduce this burden, we develop a
+lifelong learning method based on coupled dictionary learning that utilizes
+high-level task descriptions to model the inter-task relationships. We show
+that using task descriptors improves the performance of the learned task
+policies, providing both theoretical justification for the benefit and
+empirical demonstration of the improvement across a variety of learning
+problems. Given only the descriptor for a new task, the lifelong learner is
+also able to accurately predict a model for the new task through zero-shot
+learning using the coupled dictionary, eliminating the need to gather training
+data before addressing the task.
+",1,0,0,1,0,0
+19843,Giant Unification Theory of the Grand Unification and Gravitation Theories,"  Because the grand unification theory of gauge theories of strong, weak and
+electromagnetic interactions is based on principal bundle theory, and
+gravitational theory is based on the tangent vector bundle theory, so people
+cannot unify the these four basic interactions in principal bundle theory. This
+Letter discovers and gives giant unification theory of the grand unification
+theory and gravitation theory, i.e., the giant unification theory of strong,
+weak, electromagnetic and gravitational interactions according to the general
+fiber bundle theory, symmetry, quantitative causal principle (QCP) and so on.
+Consequently, the research of this Letter is based on the rigorous scientific
+bases of mathematics and physics. The Lagrangians of the well-known fundamental
+physics interactions are unifiedly deduced from QCP and satisfy the gauge
+invariant principle of general gauge fields interacting with Fermion and/or
+boson fields. The geometry and physics meanings of gauge invariant property of
+different physical systems are revealed, and it is discovered that all the
+Lagrangians of the well-known fundamental physics interactions are composed of
+the invariant quantities in corresponding spacetime structures. The
+difficulties that fundamental physics interactions and Noether theorem are not
+able to be unifiedly given and investigated are overcome, the unified
+description and origin of the fundamental physics interactions and Noether
+theorem are shown by QCP, their two-order general Euler-Lagrange Equations and
+corresponding Noether conservation currents are derived in general curved
+spacetime. Therefore, using the new unification theory, a lot of research works
+about different branches of physics etc can be renewedly done and expressed
+simpler with clear quantitative causal physical meanings.
+",0,1,0,0,0,0
+19844,Generalized Rich-Club Ordering in Networks,"  Rich-club ordering refers to the tendency of nodes with a high degree to be
+more interconnected than expected. In this paper we consider the concept of
+rich-club ordering when generalized to structural measures that differ from the
+node degree and to non-structural measures (i.e. to node metadata). The
+differences in considering rich-club ordering (RCO) with respect to both
+structural and non-structural measures is then discussed in terms of employed
+coefficients and of appropriate null models (link rewiring vs metadata
+reshuffling). Once a framework for the evaluation of generalized rich-club
+ordering (GRCO) is defined, we investigate such a phenomenon in real networks
+provided with node metadata. By considering different notions of node richness,
+we compare structural and non-structural rich-club ordering, observing how
+external information about the network nodes is able to validate the presence
+of rich-clubs in networked systems.
+",1,0,0,0,0,0
+19845,Integrating Proactive Mode Changes in Mixed Criticality Systems,"  In this work, we propose to integrate prediction algorithms to the scheduling
+of mode changes under the Earliest-Deadline-First and Fixed-priority scheduling
+in mixed-criticality real-time systems. The method proactively schedules a mode
+change in the system based on state variables such as laxity, to the percentage
+difference in the temporal distance between the completion time of the instance
+of a task and its respective deadline, by the deadline (D) stipulated for the
+task, in order to minimize deadline misses. The simulation model was validated
+against an analytical model prior to the logical integration of the
+Kalman-based prediction algorithm. Two study cases were presented, one covering
+earliest-deadline first and the other the fixed-priority scheduling approach.
+The results showed the gains in the adoption of the prediction approach for
+both scheduling paradigms by presenting a significant reduction of the number
+of missed deadlines for low-criticality tasks.
+",1,0,0,0,0,0
+19846,Leveraging Continuous Material Averaging for Inverse Electromagnetic Design,"  Inverse electromagnetic design has emerged as a way of efficiently designing
+active and passive electromagnetic devices. This maturing strategy involves
+optimizing the shape or topology of a device in order to improve a figure of
+merit--a process which is typically performed using some form of steepest
+descent algorithm. Naturally, this requires that we compute the gradient of a
+figure of merit which describes device performance, potentially with respect to
+many design variables. In this paper, we introduce a new strategy based on
+smoothing abrupt material interfaces which enables us to efficiently compute
+these gradients with high accuracy irrespective of the resolution of the
+underlying simulation. This has advantages over previous approaches to shape
+and topology optimization in nanophotonics which are either prone to gradient
+errors or place important constraints on the shape of the device. As a
+demonstration of this new strategy, we optimize a non-adiabatic waveguide taper
+between a narrow and wide waveguide. This optimization leads to a non-intuitive
+design with a very low insertion loss of only 0.041 dB at 1550 nm.
+",0,1,0,0,0,0
+19847,Robust Speech Recognition Using Generative Adversarial Networks,"  This paper describes a general, scalable, end-to-end framework that uses the
+generative adversarial network (GAN) objective to enable robust speech
+recognition. Encoders trained with the proposed approach enjoy improved
+invariance by learning to map noisy audio to the same embedding space as that
+of clean audio. Unlike previous methods, the new framework does not rely on
+domain expertise or simplifying assumptions as are often needed in signal
+processing, and directly encourages robustness in a data-driven way. We show
+the new approach improves simulated far-field speech recognition of vanilla
+sequence-to-sequence models without specialized front-ends or preprocessing.
+",1,0,0,0,0,0
+19848,Minimal inequalities for an infinite relaxation of integer programs,"  We show that maximal $S$-free convex sets are polyhedra when $S$ is the set
+of integral points in some rational polyhedron of $\mathbb{R}^n$. This result
+extends a theorem of Lovász characterizing maximal lattice-free convex sets.
+Our theorem has implications in integer programming. In particular, we show
+that maximal $S$-free convex sets are in one-to-one correspondence with minimal
+inequalities.
+",0,0,1,0,0,0
+19849,The population of SNe/SNRs in the starburst galaxy Arp 220. A self-consistent analysis of 20 years of VLBI monitoring,"  The nearby ultra-luminous infrared galaxy (ULIRG) Arp 220 is an excellent
+laboratory for studies of extreme astrophysical environments. For 20 years,
+Very Long Baseline Interferometry (VLBI) has been used to monitor a population
+of compact sources thought to be supernovae (SNe), supernova remnants (SNRs)
+and possibly active galactic nuclei (AGNs). Using new and archival VLBI data
+spanning 20 years, we obtain 23 high-resolution radio images of Arp 220 at
+wavelengths from 18 cm to 2 cm. From model-fitting to the images we obtain
+estimates of flux densities and sizes of all detected sources. We detect radio
+continuum emission from 97 compact sources and present flux densities and sizes
+for all analysed observation epochs. We find evidence for a LD-relation within
+Arp 220, with larger sources being less luminous. We find a compact source LF
+$n(L)\propto L^\beta$ with $\beta=-2.02\pm0.11$, similar to SNRs in normal
+galaxies. Based on simulations we argue that there are many relatively large
+and weak sources below our detection threshold. The rapidly declining object
+0.2227+0.482 is proposed as a possible AGN candidate. The observations can be
+explained by a mixed population of SNe and SNRs, where the former expand in a
+dense circumstellar medium (CSM) and the latter interact with the surrounding
+interstellar medium (ISM). Several sources (9 of the 94 with fitted sizes) are
+likely luminous, type IIn SNe. This number of luminous SNe correspond to few
+percent of the total number of SNe in Arp 220 which is consistent with a total
+SN-rate of 4 yr$^{-1}$ as inferred from the total radio emission given a normal
+stellar initial mass function (IMF). Based on the fitted luminosity function,
+we argue that emission from all compact sources, also below our detection
+threshold, make up at most 20\% of the total radio emission at GHz frequencies.
+",0,1,0,0,0,0
+19850,Dirac and Chiral Quantum Spin Liquids on the Honeycomb Lattice in a Magnetic Field,"  Motivated by recent experimental observations in $\alpha$-RuCl$_3$, we study
+the $K$-$\Gamma$ model on the honeycomb lattice in an external magnetic field.
+By a slave-particle representation and Variational Monte Carlo calculations, we
+reproduce the phase transition from zigzag magnetic order to a field-induced
+disordered phase. The nature of this state depends crucially on the field
+orientation. For particular field directions in the honeycomb plane, we find a
+gapless Dirac spin liquid, in agreement with recent experiments on
+$\alpha$-RuCl$_3$. For a range of out-of-plane fields, we predict the existence
+of a Kalmeyer-Laughlin-type chiral spin liquid, which would show an
+integer-quantized thermal Hall effect.
+",0,1,0,0,0,0
+19851,Secular Dynamics of an Exterior Test Particle: The Inverse Kozai and Other Eccentricity-Inclination Resonances,"  The behavior of an interior test particle in the secular 3-body problem has
+been studied extensively. A well-known feature is the Lidov-Kozai resonance in
+which the test particle's argument of periapse librates about $\pm 90^\circ$
+and large oscillations in eccentricity and inclination are possible. Less
+explored is the inverse problem: the dynamics of an exterior test particle and
+an interior perturber. We survey numerically the inverse secular problem,
+expanding the potential to hexadecapolar order and correcting an error in the
+published expansion. Four secular resonances are uncovered that persist in full
+$N$-body treatments (in what follows, $\varpi$ and $\Omega$ are the longitudes
+of periapse and of ascending node, $\omega$ is the argument of periapse, and
+subscripts 1 and 2 refer to the inner perturber and outer test particle): (i)
+an orbit-flipping quadrupole resonance requiring a non-zero perturber
+eccentricity $e_1$, in which $\Omega_2-\varpi_1$ librates about $\pm 90^\circ$;
+(ii) a hexadecapolar resonance (the ""inverse Kozai"" resonance) for perturbers
+that are circular or nearly so and inclined by $I \simeq 63^\circ/117^\circ$,
+in which $\omega_2$ librates about $\pm 90^\circ$ and which can vary the
+particle eccentricity by $\Delta e_2 \simeq 0.2$ and lead to orbit crossing;
+(iii) an octopole ""apse-aligned"" resonance at $I \simeq 46^\circ/107^\circ$
+wherein $\varpi_2 - \varpi_1$ librates about $0^\circ$ and $\Delta e_2$ grows
+with $e_1$; and (iv) an octopole resonance at $I \simeq 73^\circ/134^\circ$
+wherein $\varpi_2 + \varpi_1 - 2 \Omega_2$ librates about $0^\circ$ and $\Delta
+e_2$ can be as large as 0.3 for small $e_1 \neq 0$. The more eccentric the
+perturber, the more the particle's eccentricity and inclination vary; also,
+more polar orbits are more chaotic. Our inverse solutions may be applied to the
+Kuiper belt and debris disks, circumbinary planets, and stellar systems.
+",0,1,0,0,0,0
+19852,"A pulsed, mono-energetic and angular-selective UV photo-electron source for the commissioning of the KATRIN experiment","  The KATRIN experiment aims to determine the neutrino mass scale with a
+sensitivity of 200 meV/c^2 (90% C.L.) by a precision measurement of the shape
+of the tritium $\beta$-spectrum in the endpoint region. The energy analysis of
+the decay electrons is achieved by a MAC-E filter spectrometer. To determine
+the transmission properties of the KATRIN main spectrometer, a mono-energetic
+and angular-selective electron source has been developed. In preparation for
+the second commissioning phase of the main spectrometer, a measurement phase
+was carried out at the KATRIN monitor spectrometer where the device was
+operated in a MAC-E filter setup for testing. The results of these measurements
+are compared with simulations using the particle-tracking software
+""Kassiopeia"", which was developed in the KATRIN collaboration over recent
+years.
+",0,1,0,0,0,0
+19853,Carrier frequency modulation of an acousto-optic modulator for laser stabilization,"  The stabilization of lasers to absolute frequency references is a fundamental
+requirement in several areas of atomic, molecular and optical physics. A range
+of techniques are available to produce a suitable reference onto which one can
+'lock' the laser, many of which depend on the specific internal structure of
+the reference or are sensitive to laser intensity noise. We present a novel
+method using the frequency modulation of an acousto-optic modulator's carrier
+(drive) signal to generate two spatially separated beams, with a frequency
+difference of only a few MHz. These beams are used to probe a narrow absorption
+feature and the difference in their detected signals leads to a dispersion-like
+feature suitable for wavelength stabilization of a diode laser. This simple and
+versatile method only requires a narrow absorption line and is therefore
+suitable for both atomic and cavity based stabilization schemes. To demonstrate
+the suitability of this method we lock an external cavity diode laser near the
+$^{85}\mathrm{Rb}\,5S_{1/2}\rightarrow5P_{3/2}, F=3\rightarrow F^{\prime}=4$
+using sub-Doppler pump probe spectroscopy and also demonstrate excellent
+agreement between the measured signal and a theoretical model.
+",0,1,0,0,0,0
+19854,Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method,"  In this paper, a new type of 3D bin packing problem (BPP) is proposed, in
+which a number of cuboid-shaped items must be put into a bin one by one
+orthogonally. The objective is to find a way to place these items that can
+minimize the surface area of the bin. This problem is based on the fact that
+there is no fixed-sized bin in many real business scenarios and the cost of a
+bin is proportional to its surface area. Our research shows that this problem
+is NP-hard. Based on previous research on 3D BPP, the surface area is
+determined by the sequence, spatial locations and orientations of items. Among
+these factors, the sequence of items plays a key role in minimizing the surface
+area. Inspired by recent achievements of deep reinforcement learning (DRL)
+techniques, especially Pointer Network, on combinatorial optimization problems
+such as TSP, a DRL-based method is applied to optimize the sequence of items to
+be packed into the bin. Numerical results show that the method proposed in this
+paper achieve about 5% improvement than heuristic method.
+",1,0,0,0,0,0
+19855,Entendendo o Pensamento Computacional,"  The goal of this article is to clarify the meaning of Computational Thinking.
+We differentiate logical from computational reasoning and discuss the
+importance of Computational Thinking in solving problems. The three pillars of
+Computational Thinking - Abstraction, Automation and Analysis - are outlined,
+highlighting the role of each one in developing the skills needed for the
+problem-solving process.
+-----
+O objetivo deste artigo é esclarecer o significado de Pensamento
+Computacional. Diferencia-se o raciocínio lógico do computacional e
+discute-se a importância do Pensamento Computacional na resolução de
+problemas. Os três pilares do Pensamento Computacional - Abstração,
+Automação e Análise - são delineados, destacando-se o papel de cada
+um deles no desenvolvimento das habilidades necessárias para o processo de
+solução de problemas.
+",1,0,0,0,0,0
+19856,Estimation of samples relevance by their histograms,"  The problem of the estimation of relevance to a set of histograms generated
+by samples of a discrete time process is discussed on the base of the
+variational principles proposed in the previous paper [1]. Some conditions for
+dimension reduction of corresponding linear programming problems are presented
+also.
+",0,0,1,0,0,0
+19857,On the synthesis of acoustic sources with controllable near fields,"  In this paper we present a strategy for the the synthesis of acoustic sources
+with controllable near fields in free space and finite depth homogeneous ocean
+environments. We first present the theoretical results at the basis of our
+discussion and then, to illustrate our findings we focus on the following three
+particular examples:
+1. acoustic source approximating a prescribed field pattern in a given
+bounded sub- region of its near field. 2. acoustic source approximating
+different prescribed field patterns in given disjoint bounded near field
+sub-regions. 3. acoustic source approximating a prescribed back-propagating
+field in a given bounded near field sub-region while maintaining a very low far
+field signature.
+For each of these three examples, we discuss the optimization scheme used to
+approx- imate their solutions and support our claims through relevant numerical
+simulations.
+",0,0,1,0,0,0
+19858,Inverse Protein Folding Problem via Quadratic Programming,"  This paper presents a method of reconstruction a primary structure of a
+protein that folds into a given geometrical shape. This method predicts the
+primary structure of a protein and restores its linear sequence of amino acids
+in the polypeptide chain using the tertiary structure of a molecule. Unknown
+amino acids are determined according to the principle of energy minimization.
+This study represents inverse folding problem as a quadratic optimization
+problem and uses different relaxation techniques to reduce it to the problem of
+convex optimizations. Computational experiment compares the quality of these
+approaches on real protein structures.
+",0,0,1,0,0,0
+19859,Spoken Language Understanding on the Edge,"  We consider the problem of performing Spoken Language Understanding (SLU) on
+small devices typical of IoT applications. Our contributions are twofold.
+First, we outline the design of an embedded, private-by-design SLU system and
+show that it has performance on par with cloud-based commercial solutions.
+Second, we release the datasets used in our experiments in the interest of
+reproducibility and in the hope that they can prove useful to the SLU
+community.
+",1,0,0,0,0,0
+19860,On the link between column density distribution and density scaling relation in star formation regions,"  We present a method to derive the density scaling relation $\langle n\rangle
+\propto L^{-\alpha}$ in regions of star formation or in their turbulent
+vicinities from straightforward binning of the column-density distribution
+($N$-pdf). The outcome of the method is studied for three types of $N$-pdf:
+power law ($7/5\le\alpha\le5/3$), lognormal ($0.7\lesssim\alpha\lesssim1.4$)
+and combination of lognormals. In the last case, the method of Stanchev et al.
+(2015) was also applied for comparison and a very weak (or close to zero)
+correlation was found. We conclude that the considered `binning approach'
+reflects rather the local morphology of the $N$-pdf with no reference to the
+physical conditions in a considered region. The rough consistency of the
+derived slopes with the widely adopted Larson's (1981) value $\alpha\sim1.1$ is
+suggested to support claims that the density-size relation in molecular clouds
+is indeed an artifact of the observed $N$-pdf.
+",0,1,0,0,0,0
+19861,Measuring Effectiveness of Video Advertisements,"  Advertisements are unavoidable in modern society. Times Square is notorious
+for its incessant display of advertisements. Its popularity is worldwide and
+smaller cities possess miniature versions of the display, such as Pittsburgh
+and its digital works in Oakland on Forbes Avenue. Tokyo's Ginza district
+recently rose to popularity due to its upscale shops and constant onslaught of
+advertisements to pedestrians. Advertisements arise in other mediums as well.
+For example, they help popular streaming services, such as Spotify, Hulu, and
+Youtube TV gather significant streams of revenue to reduce the cost of monthly
+subscriptions for consumers. Ads provide an additional source of money for
+companies and entire industries to allocate resources toward alternative
+business motives. They are attractive to companies and nearly unavoidable for
+consumers. One challenge for advertisers is examining a advertisement's
+effectiveness or usefulness in conveying a message to their targeted
+demographics. Rather than constructing a single, static image of content, a
+video advertisement possesses hundreds of frames of data with varying scenes,
+actors, objects, and complexity. Therefore, measuring effectiveness of video
+advertisements is important to impacting a billion-dollar industry. This paper
+explores the combination of human-annotated features and common video
+processing techniques to predict effectiveness ratings of advertisements
+collected from Youtube. This task is seen as a binary (effective vs.
+non-effective), four-way, and five-way machine learning classification task.
+The first findings in terms of accuracy and inference on this dataset, as well
+as some of the first ad research, on a small dataset are presented. Accuracies
+of 84\%, 65\%, and 55\% are reached on the binary, four-way, and five-way tasks
+respectively.
+",1,0,0,0,0,0
+19862,Multilinear compressive sensing and an application to convolutional linear networks,"  We study a deep linear network endowed with a structure. It takes the form of
+a matrix $X$ obtained by multiplying $K$ matrices (called factors and
+corresponding to the action of the layers). The action of each layer (i.e. a
+factor) is obtained by applying a fixed linear operator to a vector of
+parameters satisfying a constraint. The number of layers is not limited.
+Assuming that $X$ is given and factors have been estimated, the error between
+the product of the estimated factors and $X$ (i.e. the reconstruction error) is
+either the statistical or the empirical risk. In this paper, we provide
+necessary and sufficient conditions on the network topology under which a
+stability property holds. The stability property requires that the error on the
+parameters defining the factors (i.e. the stability of the recovered
+parameters) scales linearly with the reconstruction error (i.e. the risk).
+Therefore, under these conditions on the network topology, any successful
+learning task leads to stably defined features and therefore interpretable
+layers/network.In order to do so, we first evaluate how the Segre embedding and
+its inverse distort distances. Then, we show that any deep structured linear
+network can be cast as a generic multilinear problem (that uses the Segre
+embedding). This is the {\em tensorial lifting}. Using the tensorial lifting,
+we provide necessary and sufficient conditions for the identifiability of the
+factors (up to a scale rearrangement). We finally provide the necessary and
+sufficient condition called \NSPlong~(because of the analogy with the usual
+Null Space Property in the compressed sensing framework) which guarantees that
+the stability property holds. We illustrate the theory with a practical example
+where the deep structured linear network is a convolutional linear network. As
+expected, the conditions are rather strong but not empty. A simple test on the
+network topology can be implemented to test if the condition holds.
+",0,0,1,1,0,0
+19863,Normalization of closed Ekedahl-Oort strata,"  We apply our theory of partial flag spaces developed in previous articles to
+study a group-theoretical generalization of the canonical filtration of a
+truncated Barsotti-Tate group of level 1. As an application, we determine
+explicitly the normalization of the Zariski closures of Ekedahl-Oort strata of
+Shimura varieties of Hodge-type as certain closed coarse strata in the
+associated partial flag spaces.
+",0,0,1,0,0,0
+19864,Robustness of functional networks at criticality against structural defects,"  The robustness of dynamical properties of neuronal networks against
+structural damages is a central problem in computational and experimental
+neuroscience. Research has shown that the cortical network of a healthy brain
+works near a critical state, and moreover, that functional neuronal networks
+often have scale-free and small-world properties. In this work, we study how
+the robustness of simple functional networks at criticality is affected by
+structural defects. In particular, we consider a 2D Ising model at the critical
+temperature and investigate how its functional network changes with the
+increasing degree of structural defects. We show that the scale-free and
+small-world properties of the functional network at criticality are robust
+against large degrees of structural lesions while the system remains below the
+percolation limit. Although the Ising model is only a conceptual description of
+a two-state neuron, our research reveals fundamental robustness properties of
+functional networks derived from classical statistical mechanics models.
+",0,0,0,0,1,0
+19865,Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning,"  Maximizing the area under the receiver operating characteristic curve (AUC)
+is a standard approach to imbalanced classification. So far, various supervised
+AUC optimization methods have been developed and they are also extended to
+semi-supervised scenarios to cope with small sample problems. However, existing
+semi-supervised AUC optimization methods rely on strong distributional
+assumptions, which are rarely satisfied in real-world problems. In this paper,
+we propose a novel semi-supervised AUC optimization method that does not
+require such restrictive assumptions. We first develop an AUC optimization
+method based only on positive and unlabeled data (PU-AUC) and then extend it to
+semi-supervised learning by combining it with a supervised AUC optimization
+method. We theoretically prove that, without the restrictive distributional
+assumptions, unlabeled data contribute to improving the generalization
+performance in PU and semi-supervised AUC optimization methods. Finally, we
+demonstrate the practical usefulness of the proposed methods through
+experiments.
+",1,0,0,1,0,0
+19866,Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering,"  Motivated by problems in data clustering, we establish general conditions
+under which families of nonparametric mixture models are identifiable, by
+introducing a novel framework involving clustering overfitted \emph{parametric}
+(i.e. misspecified) mixture models. These identifiability conditions generalize
+existing conditions in the literature, and are flexible enough to include for
+example mixtures of Gaussian mixtures. In contrast to the recent literature on
+estimating nonparametric mixtures, we allow for general nonparametric mixture
+components, and instead impose regularity assumptions on the underlying mixing
+measure. As our primary application, we apply these results to partition-based
+clustering, generalizing the notion of a Bayes optimal partition from classical
+parametric model-based clustering to nonparametric settings. Furthermore, this
+framework is constructive so that it yields a practical algorithm for learning
+identified mixtures, which is illustrated through several examples on real
+data. The key conceptual device in the analysis is the convex, metric geometry
+of probability measures on metric spaces and its connection to the Wasserstein
+convergence of mixing measures. The result is a flexible framework for
+nonparametric clustering with formal consistency guarantees.
+",0,0,0,1,0,0
+19867,The geometric classification of Leibniz algebras,"  We describe all rigid algebras and all irreducible components in the variety
+of four dimensional Leibniz algebras $\mathfrak{Leib}_4$ over $\mathbb{C}.$ In
+particular, we prove that the Grunewald--O'Halloran conjecture is not valid and
+the Vergne conjecture is valid for $\mathfrak{Leib}_4.$
+",0,0,1,0,0,0
+19868,Discrete fundamental groups of Warped Cones and expanders,"  In this paper we compute the discrete fundamental groups of warped cones. As
+an immediate consequence, this allows us to show that there exist coarsely
+simply-connected expanders and superexpanders. This also provides a strong
+coarse invariant of warped cones and implies that many warped cones cannot be
+coarsely equivalent to any box space.
+",0,0,1,0,0,0
+19869,Simulated Tempering Method in the Infinite Switch Limit with Adaptive Weight Learning,"  We investigate the theoretical foundations of the simulated tempering method
+and use our findings to design efficient algorithms. Employing a large
+deviation argument first used for replica exchange molecular dynamics [Plattner
+et al., J. Chem. Phys. 135:134111 (2011)], we demonstrate that the most
+efficient approach to simulated tempering is to vary the temperature infinitely
+rapidly. In this limit, we can replace the equations of motion for the
+temperature and physical variables by averaged equations for the latter alone,
+with the forces rescaled according to a position-dependent function defined in
+terms of temperature weights. The averaged equations are similar to those used
+in Gao's integrated-over-temperature method, except that we show that it is
+better to use a continuous rather than a discrete set of temperatures. We give
+a theoretical argument for the choice of the temperature weights as the
+reciprocal partition function, thereby relating simulated tempering to
+Wang-Landau sampling. Finally, we describe a self-consistent algorithm for
+simultaneously sampling the canonical ensemble and learning the weights during
+simulation. This algorithm is tested on a system of harmonic oscillators as
+well as a continuous variant of the Curie-Weiss model, where it is shown to
+perform well and to accurately capture the second-order phase transition
+observed in this model.
+",0,0,0,1,0,0
+19870,Analysis and Modeling of 3D Indoor Scenes,"  We live in a 3D world, performing activities and interacting with objects in
+the indoor environments everyday. Indoor scenes are the most familiar and
+essential environments in everyone's life. In the virtual world, 3D indoor
+scenes are also ubiquitous in 3D games and interior design. With the fast
+development of VR/AR devices and the emerging applications, the demand of
+realistic 3D indoor scenes keeps growing rapidly. Currently, designing detailed
+3D indoor scenes requires proficient 3D designing and modeling skills and is
+often time-consuming. For novice users, creating realistic and complex 3D
+indoor scenes is even more difficult and challenging.
+Many efforts have been made in different research communities, e.g. computer
+graphics, vision and robotics, to capture, analyze and generate the 3D indoor
+data. This report mainly focuses on the recent research progress in graphics on
+geometry, structure and semantic analysis of 3D indoor data and different
+modeling techniques for creating plausible and realistic indoor scenes. We
+first review works on understanding and semantic modeling of scenes from
+captured 3D data of the real world. Then, we focus on the virtual scenes
+composed of 3D CAD models and study methods for 3D scene analysis and
+processing. After that, we survey various modeling paradigms for creating 3D
+indoor scenes and investigate human-centric scene analysis and modeling, which
+bridge indoor scene studies of graphics, vision and robotics. At last, we
+discuss open problems in indoor scene processing that might bring interests to
+graphics and all related communities.
+",1,0,0,0,0,0
+19871,Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy,"  Advances in astronomy are intimately linked to advances in digital signal
+processing (DSP). This special issue is focused upon advances in DSP within
+radio astronomy. The trend within that community is to use off-the-shelf
+digital hardware where possible and leverage advances in high performance
+computing. In particular, graphics processing units (GPUs) and field
+programmable gate arrays (FPGAs) are being used in place of
+application-specific circuits (ASICs); high-speed Ethernet and Infiniband are
+being used for interconnect in place of custom backplanes. Further, to lower
+hurdles in digital engineering, communities have designed and released
+general-purpose FPGA-based DSP systems, such as the CASPER ROACH board, ASTRON
+Uniboard and CSIRO Redback board. In this introductory article, we give a brief
+historical overview, a summary of recent trends, and provide an outlook on
+future directions.
+",0,1,0,0,0,0
+19872,HPDedup: A Hybrid Prioritized Data Deduplication Mechanism for Primary Storage in the Cloud,"  Eliminating duplicate data in primary storage of clouds increases the
+cost-efficiency of cloud service providers as well as reduces the cost of users
+for using cloud services. Existing primary deduplication techniques either use
+inline caching to exploit locality in primary workloads or use post-processing
+deduplication running in system idle time to avoid the negative impact on I/O
+performance. However, neither of them works well in the cloud servers running
+multiple services or applications for the following two reasons: Firstly, the
+temporal locality of duplicate data writes may not exist in some primary
+storage workloads thus inline caching often fails to achieve good deduplication
+ratio. Secondly, the post-processing deduplication allows duplicate data to be
+written into disks, therefore does not provide the benefit of I/O deduplication
+and requires high peak storage capacity. This paper presents HPDedup, a Hybrid
+Prioritized data Deduplication mechanism to deal with the storage system shared
+by applications running in co-located virtual machines or containers by fusing
+an inline and a post-processing process for exact deduplication. In the inline
+deduplication phase, HPDedup gives a fingerprint caching mechanism that
+estimates the temporal locality of duplicates in data streams from different
+VMs or applications and prioritizes the cache allocation for these streams
+based on the estimation. HPDedup also allows different deduplication threshold
+for streams based on their spatial locality to reduce the disk fragmentation.
+The post-processing phase removes duplicates whose fingerprints are not able to
+be cached due to the weak temporal locality from disks. Our experimental
+results show that HPDedup clearly outperforms the state-of-the-art primary
+storage deduplication techniques in terms of inline cache efficiency and
+primary deduplication efficiency.
+",1,0,0,0,0,0
+19873,Two-domain and three-domain limit cycles in a typical aeroelastic system with freeplay in pitch,"  Freeplay is a significant source of nonlinearity in aeroelastic systems and
+is strictly regulated by airworthiness authorities. It splits the phase plane
+of such systems into three piecewise linear subdomains. Depending on the
+location of the freeplay, limit cycle oscillations can result that span either
+two or three of these subdomains. The purpose of this work is to demonstrate
+the existence of two-domain cycles both theoretically and experimentally. A
+simple aeroelastic system with pitch, plunge and control deflection degrees of
+freedom is investigated in the presence of freeplay in pitch. It is shown that
+two-domain and three-domain cycles can result from a grazing bifurcation and
+propagate in the decreasing airspeed direction. Close to the bifurcation, the
+two limit cycle branches interact with each other and aperiodic oscillations
+ensue. Equivalent linearization is used to derive the conditions of existence
+of each type of limit cycle and to predict their amplitudes and frequencies.
+Comparisons with measurements from wind tunnel experiments demonstrate that the
+theory describes these phenomena with accuracy.
+",0,1,0,0,0,0
+19874,Preference Modeling by Exploiting Latent Components of Ratings,"  Understanding user preference is essential to the optimization of recommender
+systems. As a feedback of user's taste, rating scores can directly reflect the
+preference of a given user to a given product. Uncovering the latent components
+of user ratings is thus of significant importance for learning user interests.
+In this paper, a new recommendation approach, called LCR, was proposed by
+investigating the latent components of user ratings. The basic idea is to
+decompose an existing rating into several components via a cost-sensitive
+learning strategy. Specifically, each rating is assigned to several latent
+factor models and each model is updated according to its predictive errors.
+Afterwards, these accumulated predictive errors of models are utilized to
+decompose a rating into several components, each of which is treated as an
+independent part to retrain the latent factor models. Finally, all latent
+factor models are combined linearly to estimate predictive ratings for users.
+In contrast to existing methods, LCR provides an intuitive preference modeling
+strategy via multiple component analysis at an individual perspective.
+Meanwhile, it is verified by the experimental results on several benchmark
+datasets that the proposed method is superior to the state-of-art methods in
+terms of recommendation accuracy.
+",1,0,0,0,0,0
+19875,Inductive Representation Learning on Large Graphs,"  Low-dimensional embeddings of nodes in large graphs have proved extremely
+useful in a variety of prediction tasks, from content recommendation to
+identifying protein functions. However, most existing approaches require that
+all nodes in the graph are present during training of the embeddings; these
+previous approaches are inherently transductive and do not naturally generalize
+to unseen nodes. Here we present GraphSAGE, a general, inductive framework that
+leverages node feature information (e.g., text attributes) to efficiently
+generate node embeddings for previously unseen data. Instead of training
+individual embeddings for each node, we learn a function that generates
+embeddings by sampling and aggregating features from a node's local
+neighborhood. Our algorithm outperforms strong baselines on three inductive
+node-classification benchmarks: we classify the category of unseen nodes in
+evolving information graphs based on citation and Reddit post data, and we show
+that our algorithm generalizes to completely unseen graphs using a multi-graph
+dataset of protein-protein interactions.
+",1,0,0,1,0,0
+19876,Milnor and Tjurina numbers for a hypersurface germ with isolated singularity,"  Assume that $f:(\mathbb{C}^n,0) \to (\mathbb{C},0)$ is an analytic function
+germ at the origin with only isolated singularity. Let $\mu$ and $\tau$ be the
+corresponding Milnor and Tjurina numbers. We show that $\dfrac{\mu}{\tau} \leq
+n$. As an application, we give a lower bound for the Tjurina number in terms of
+$n$ and the multiplicity of $f$ at the origin.
+",0,0,1,0,0,0
+19877,Analysis of the Gibbs Sampler for Gaussian hierarchical models via multigrid decomposition,"  We study the convergence properties of the Gibbs Sampler in the context of
+posterior distributions arising from Bayesian analysis of Gaussian hierarchical
+models. We consider centred and non-centred parameterizations as well as their
+hybrids including the full family of partially non-centred parameterizations.
+We develop a novel methodology based on multi-grid decompositions to derive
+analytic expressions for the convergence rates of the algorithm for an
+arbitrary number of layers in the hierarchy, while previous work was typically
+limited to the two-level case. Our work gives a complete understanding for the
+three-level symmetric case and this gives rise to approximations for the
+non-symmetric case. We also give analogous, if less explicit, results for
+models of arbitrary level. This theory gives rise to simple and
+easy-to-implement guidelines for the practical implementation of Gibbs samplers
+on conditionally Gaussian hierarchical models.
+",0,0,0,1,0,0
+19878,A Cosmic Selection Rule for Glueball Dark Matter Relic Density,"  We point out a unique mechanism to produce the relic abundance for glueball
+dark matter from a gauged $SU(N)_d$ hidden sector which is bridged to the
+standard model sector through heavy vectorlike quarks colored under gauge
+interactions from both sides. A necessary ingredient of our assumption is that
+the vectorlike quarks, produced either thermally or non-thermally, are abundant
+enough to dominate the universe for some time in the early universe. They later
+undergo dark color confinement and form unstable vectorlike-quarkonium states
+which annihilate decay and reheat the visible and dark sectors. The ratio of
+entropy dumped into two sectors and the final energy budget in the dark
+glueballs is only determined by low energy parameters, including the intrinsic
+scale of the dark $SU(N)_d$, $\Lambda_d$, and number of dark colors, $N_d$, but
+depend weakly on parameters in the ultraviolet such as the vectorlike quark
+mass or the initial condition. We call this a cosmic selection rule for the
+glueball dark matter relic density.
+",0,1,0,0,0,0
+19879,Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network,"  In this paper, we propose a method using a three dimensional convolutional
+neural network (3-D-CNN) to fuse together multispectral (MS) and hyperspectral
+(HS) images to obtain a high resolution hyperspectral image. Dimensionality
+reduction of the hyperspectral image is performed prior to fusion in order to
+significantly reduce the computational time and make the method more robust to
+noise. Experiments are performed on a data set simulated using a real
+hyperspectral image. The results obtained show that the proposed approach is
+very promising when compared to conventional methods. This is especially true
+when the hyperspectral image is corrupted by additive noise.
+",1,0,0,1,0,0
+19880,The fundamental Lepage form in variational theory for submanifolds,"  A setting for global variational geometry on Grassmann fibrations is
+presented. The integral variational functionals for finite dimensional immersed
+submanifolds are studied by means of the fundamental Lepage equivalent of a
+homogeneous Lagrangian, which can be regarded as a generalization of the
+well-known Hilbert form in the classical mechanics. Prolongations of
+immersions, diffeomorphisms and vector fields to the Grassmann fibrations are
+introduced as geometric tools for the variations of immersions. The first
+infinitesimal variation formula together with its consequences, the
+Euler-Lagrange equations for extremal submanifolds and the Noether theorem for
+invariant variational functionals are proved. The theory is illustrated on the
+variational functional for minimal submanifolds.
+",0,0,1,0,0,0
+19881,Adding educational funcionalities to classic board games,"  In this paper we revisit some classic board games like Pachisi or the Game of
+Gosse. The main contribution of the paper is to design and add some
+functionalities to the games in order to transform them in serious games, that
+is, in games with learning and educational purposes. To do that, at the
+beginning of the game, players choose one or several topics and during the
+game, players have to anwers questions on these topics in order to move their
+markers. We choose classic board games because a lot of people are familiar
+with them so it is very easy to start to play without wasting time learning
+game rules and, we think that this is an important element to make the game
+more attractive to people. To enlarge the number of potential users we have
+implement the games just using html and javascript and the games can be used in
+any web browser, in any computer (including tablets) , in any computer
+arquitecture (Windows, Mac, Linux) and no internet/server conexion is required.
+Associated software is distributed under Creative Commons
+Attribution-NonCommercial-ShareAlike 3.0 licence and can be obtained at
+this http URL
+",1,0,0,0,0,0
+19882,Conceptual Frameworks for Building Online Citizen Science Projects,"  In recent years, citizen science has grown in popularity due to a number of
+reasons, including the emphasis on informal learning and creativity potential
+associated with these initiatives. Citizen science projects address research
+questions from various domains, ranging from Ecology to Astronomy. Due to the
+advancement of communication technologies, which makes outreach and engagement
+of wider communities easier, scientists are keen to turn their own research
+into citizen science projects. However, the development, deployment and
+management of these projects remains challenging. One of the most important
+challenges is building the project itself. There is no single tool or
+framework, which guides the step-by-step development of the project, since
+every project has specific characteristics, such as geographical constraints or
+volunteers' mode of participation. Therefore, in this article, we present a
+series of conceptual frameworks for categorisation, decision and deployment,
+which guide a citizen science project creator in every step of creating a new
+project starting from the research question to project deployment. The
+frameworks are designed with consideration to the properties of already
+existing citizen science projects and could be easily extended to include other
+dimensions, which are not currently perceived.
+",1,0,0,0,0,0
+19883,SDN Architecture and Southbound APIs for IPv6 Segment Routing Enabled Wide Area Networks,"  The SRv6 architecture (Segment Routing based on IPv6 data plane) is a
+promising solution to support services like Traffic Engineering, Service
+Function Chaining and Virtual Private Networks in IPv6 backbones and
+datacenters. The SRv6 architecture has interesting scalability properties as it
+reduces the amount of state information that needs to be configured in the
+nodes to support the network services. In this paper, we describe the
+advantages of complementing the SRv6 technology with an SDN based approach in
+backbone networks. We discuss the architecture of a SRv6 enabled network based
+on Linux nodes. In addition, we present the design and implementation of the
+Southbound API between the SDN controller and the SRv6 device. We have defined
+a data-model and four different implementations of the API, respectively based
+on gRPC, REST, NETCONF and remote Command Line Interface (CLI). Since it is
+important to support both the development and testing aspects we have realized
+an Intent based emulation system to build realistic and reproducible
+experiments. This collection of tools automate most of the configuration
+aspects relieving the experimenter from a significant effort. Finally, we have
+realized an evaluation of some performance aspects of our architecture and of
+the different variants of the Southbound APIs and we have analyzed the effects
+of the configuration updates in the SRv6 enabled nodes.
+",1,0,0,0,0,0
+19884,Topology-optimized Dual-Polarization Dirac Cones,"  We apply a large-scale computational technique, known as topology
+optimization, to the inverse design of photonic Dirac cones. In particular, we
+report on a variety of photonic crystal geometries, realizable in simple
+isotropic dielectric materials, which exhibit dual-polarization and
+dual-wavelength Dirac cones. We demonstrate the flexibility of this technique
+by designing photonic crystals of different symmetry types, such as ones with
+four-fold and six-fold rotational symmetry, which possess Dirac cones at
+different points within the Brillouin zone. The demonstrated and related
+optimization techniques could open new avenues to band-structure engineering
+and manipulating the propagation of light in periodic media, with possible
+applications in exotic optical phenomena such as effective zero-index media and
+topological photonics.
+",0,1,0,0,0,0
+19885,Reconstruction of Word Embeddings from Sub-Word Parameters,"  Pre-trained word embeddings improve the performance of a neural model at the
+cost of increasing the model size. We propose to benefit from this resource
+without paying the cost by operating strictly at the sub-lexical level. Our
+approach is quite simple: before task-specific training, we first optimize
+sub-word parameters to reconstruct pre-trained word embeddings using various
+distance measures. We report interesting results on a variety of tasks: word
+similarity, word analogy, and part-of-speech tagging.
+",1,0,0,0,0,0
+19886,Deep Submodular Functions,"  We start with an overview of a class of submodular functions called SCMMs
+(sums of concave composed with non-negative modular functions plus a final
+arbitrary modular). We then define a new class of submodular functions we call
+{\em deep submodular functions} or DSFs. We show that DSFs are a flexible
+parametric family of submodular functions that share many of the properties and
+advantages of deep neural networks (DNNs). DSFs can be motivated by considering
+a hierarchy of descriptive concepts over ground elements and where one wishes
+to allow submodular interaction throughout this hierarchy. Results in this
+paper show that DSFs constitute a strictly larger class of submodular functions
+than SCMMs. We show that, for any integer $k>0$, there are $k$-layer DSFs that
+cannot be represented by a $k'$-layer DSF for any $k'<k$. This implies that,
+like DNNs, there is a utility to depth, but unlike DNNs, the family of DSFs
+strictly increase with depth. Despite this, we show (using a ""backpropagation""
+like method) that DSFs, even with arbitrarily large $k$, do not comprise all
+submodular functions. In offering the above results, we also define the notion
+of an antitone superdifferential of a concave function and show how this
+relates to submodular functions (in general), DSFs (in particular), negative
+second-order partial derivatives, continuous submodularity, and concave
+extensions. To further motivate our analysis, we provide various special case
+results from matroid theory, comparing DSFs with forms of matroid rank, in
+particular the laminar matroid. Lastly, we discuss strategies to learn DSFs,
+and define the classes of deep supermodular functions, deep difference of
+submodular functions, and deep multivariate submodular functions, and discuss
+where these can be useful in applications.
+",1,0,0,0,0,0
+19887,Inverse problems for the wave equation with under-determined data,"  We consider the inverse problems of determining the potential or the damping
+coefficient appearing in the wave equation. We will prove the unique
+determination of these coefficients from the one point measurement. Since our
+problem is under-determined, so some extra assumption on the coefficients is
+required to prove the uniqueness.
+",0,0,1,0,0,0
+19888,Spatial interactions and oscillatory tragedies of the commons,"  A tragedy of the commons (TOC) occurs when individuals acting in their own
+self-interest deplete commonly-held resources, leading to a worse outcome than
+had they cooperated. Over time, the depletion of resources can change
+incentives for subsequent actions. Here, we investigate long-term feedback
+between game and environment across a continuum of incentives in an
+individual-based framework. We identify payoff-dependent transition rules that
+lead to oscillatory TOC-s in stochastic simulations and the mean field limit.
+Further extending the stochastic model, we find that spatially explicit
+interactions can lead to emergent, localized dynamics, including the
+propagation of cooperative wave fronts and cluster formation of both social
+context and resources. These dynamics suggest new mechanisms underlying how
+TOCs arise and how they might be averted.
+",0,0,0,0,1,0
+19889,An upper bound on transport,"  The linear growth of operators in local quantum systems leads to an effective
+lightcone even if the system is non-relativistic. We show that consistency of
+diffusive transport with this lightcone places an upper bound on the
+diffusivity: $D \lesssim v^2 \tau_\text{eq}$. The operator growth velocity $v$
+defines the lightcone and $\tau_\text{eq}$ is the local equilibration
+timescale, beyond which the dynamics of conserved densities is diffusive. We
+verify that the bound is obeyed in various weakly and strongly interacting
+theories. In holographic models this bound establishes a relation between the
+hydrodynamic and leading non-hydrodynamic quasinormal modes of planar black
+holes. Our bound relates transport data --- including the electrical
+resistivity and the shear viscosity --- to the local equilibration time, even
+in the absence of a quasiparticle description. In this way, the bound sheds
+light on the observed $T$-linear resistivity of many unconventional metals, the
+shear viscosity of the quark-gluon plasma and the spin transport of unitary
+fermions.
+",0,1,0,0,0,0
+19890,Taylor expansion in linear logic is invertible,"  Each Multiplicative Exponential Linear Logic (MELL) proof-net can be expanded
+into a differential net, which is its Taylor expansion. We prove that two
+different MELL proof-nets have two different Taylor expansions. As a corollary,
+we prove a completeness result for MELL: We show that the relational model is
+injective for MELL proof-nets, i.e. the equality between MELL proof-nets in the
+relational model is exactly axiomatized by cut-elimination.
+",1,0,0,0,0,0
+19891,Collision-Free Multi Robot Trajectory Optimization in Unknown Environments using Decentralized Trajectory Planning,"  Multi robot systems have the potential to be utilized in a variety of
+applications. In most of the previous works, the trajectory generation for
+multi robot systems is implemented in known environments. To overcome that we
+present an online trajectory optimization algorithm that utilizes communication
+of robots' current states to account to the other robots while using local
+object based maps for identifying obstacles. Based upon this data, we predict
+the trajectory expected to be traversed by the robots and utilize that to avoid
+collisions by formulating regions of free space that the robot can be without
+colliding with other robots and obstacles. A trajectory is optimized
+constraining the robot to remain within this region.The proposed method is
+tested in simulations on Gazebo using ROS.
+",1,0,0,0,0,0
+19892,On the bottom of spectra under coverings,"  For a Riemannian covering $M_1\to M_0$ of complete Riemannian manifolds with
+boundary (possibly empty) and respective fundamental groups
+$\Gamma_1\subseteq\Gamma_0$, we show that the bottoms of the spectra of $M_0$
+and $M_1$ coincide if the right action of $\Gamma_0$ on
+$\Gamma_1\backslash\Gamma_0$ is amenable.
+",0,0,1,0,0,0
+19893,Theory of ground states for classical Heisenberg spin systems IV,"  We extend the theory of ground states of classical Heisenberg spin systems
+previously published to the case where the interaction with an external
+magnetic field is described by a Zeeman term. The ground state problem for the
+Heisenberg-Zeeman Hamiltonian can be reduced first to the relative ground state
+problem, and, in a second step, to the absolute ground state problem for pure
+Heisenberg Hamiltonians depending on an additional Lagrange parameter. We
+distinguish between continuous and discontinuous reduction. Moreover, there are
+various general statements about Heisenberg-Zeeman systems that will be proven
+under most general assumptions. One topic is the connection between the minimal
+energy functions $E_{min}$ for the Heisenberg energy and $H_{min}$ for the
+Heisenberg-Zeeman energy which turn out to be essentially mutual
+Legendre-Fenchel transforms. This generalization of the traditional Legendre
+transform is especially suited to cope with situations where the function
+$E_{min}$ is not convex and consequently there is a magnetization jump at a
+critical field. Another topic is magnetization and the occurrence of threshold
+fields $B_{thr}$ and saturation fields $B_{sat}$, where we provide a general
+formula for the latter. We suggest a distinction between ferromagnetic and
+anti-ferromagnetic systems based on the vanishing of $B_{sat}$ for the former
+ones. Parabolic systems are defined in such a way that $E_{min}$ and $H_{min}$
+have a particularly simple form and studied in detail. For a large class of
+parabolic systems the relative ground states can be constructed from the
+absolute ground state by means of a so-called umbrella family. Finally we
+provide a counter-example of a parabolic system where this construction is not
+possible.
+",0,1,0,0,0,0
+19894,Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model,"  We present a framework for vision-based model predictive control (MPC) for
+the task of aggressive, high-speed autonomous driving. Our approach uses deep
+convolutional neural networks to predict cost functions from input video which
+are directly suitable for online trajectory optimization with MPC. We
+demonstrate the method in a high speed autonomous driving scenario, where we
+use a single monocular camera and a deep convolutional neural network to
+predict a cost map of the track in front of the vehicle. Results are
+demonstrated on a 1:5 scale autonomous vehicle given the task of high speed,
+aggressive driving.
+",1,0,0,0,0,0
+19895,K-theory of line bundles and smooth varieties,"  We give a $K$-theoretic criterion for a quasi-projective variety to be
+smooth. If $\mathbb{L}$ is a line bundle corresponding to an ample invertible
+sheaf on $X$, it suffices that $K_q(X) = K_q(\mathbb{L})$ for all
+$q\le\dim(X)+1$.
+",0,0,1,0,0,0
+19896,Local Two-Sample Testing: A New Tool for Analysing High-Dimensional Astronomical Data,"  Modern surveys have provided the astronomical community with a flood of
+high-dimensional data, but analyses of these data often occur after their
+projection to lower-dimensional spaces. In this work, we introduce a local
+two-sample hypothesis test framework that an analyst may directly apply to data
+in their native space. In this framework, the analyst defines two classes based
+on a response variable of interest (e.g. higher-mass galaxies versus lower-mass
+galaxies) and determines at arbitrary points in predictor space whether the
+local proportions of objects that belong to the two classes significantly
+differs from the global proportion.
+Our framework has a potential myriad of uses throughout astronomy; here, we
+demonstrate its efficacy by applying it to a sample of 2487 i-band-selected
+galaxies observed by the HST ACS in four of the CANDELS program fields. For
+each galaxy, we have seven morphological summary statistics along with an
+estimated stellar mass and star-formation rate. We perform two studies: one in
+which we determine regions of the seven-dimensional space of morphological
+statistics where high-mass galaxies are significantly more numerous than
+low-mass galaxies, and vice-versa, and another study where we use SFR in place
+of mass. We find that we are able to identify such regions, and show how
+high-mass/low-SFR regions are associated with concentrated and undisturbed
+galaxies while galaxies in low-mass/high-SFR regions appear more extended
+and/or disturbed than their high-mass/low-SFR counterparts.
+",0,1,0,0,0,0
+19897,Are You Tampering With My Data?,"  We propose a novel approach towards adversarial attacks on neural networks
+(NN), focusing on tampering the data used for training instead of generating
+attacks on trained models. Our network-agnostic method creates a backdoor
+during training which can be exploited at test time to force a neural network
+to exhibit abnormal behaviour. We demonstrate on two widely used datasets
+(CIFAR-10 and SVHN) that a universal modification of just one pixel per image
+for all the images of a class in the training set is enough to corrupt the
+training procedure of several state-of-the-art deep neural networks causing the
+networks to misclassify any images to which the modification is applied. Our
+aim is to bring to the attention of the machine learning community, the
+possibility that even learning-based methods that are personally trained on
+public datasets can be subject to attacks by a skillful adversary.
+",0,0,0,1,0,0
+19898,Answering Spatial Multiple-Set Intersection Queries Using 2-3 Cuckoo Hash-Filters,"  We show how to answer spatial multiple-set intersection queries in O(n(log
+w)/w + kt) expected time, where n is the total size of the t sets involved in
+the query, w is the number of bits in a memory word, k is the output size, and
+c is any fixed constant. This improves the asymptotic performance over previous
+solutions and is based on an interesting data structure, known as 2-3 cuckoo
+hash-filters. Our results apply in the word-RAM model (or practical RAM model),
+which allows for constant-time bit-parallel operations, such as bitwise AND,
+OR, NOT, and MSB (most-significant 1-bit), as exist in modern CPUs and GPUs.
+Our solutions apply to any multiple-set intersection queries in spatial data
+sets that can be reduced to one-dimensional range queries, such as spatial join
+queries for one-dimensional points or sets of points stored along space-filling
+curves, which are used in GIS applications.
+",1,0,0,0,0,0
+19899,A framework for cascade size calculations on random networks,"  We present a framework to calculate the cascade size evolution for a large
+class of cascade models on random network ensembles in the limit of infinite
+network size. Our method is exact and applies to network ensembles with almost
+arbitrary degree distribution, degree-degree correlations and, in case of
+threshold models, with arbitrary threshold distribution. With our approach, we
+shift the perspective from the known branching process approximations to the
+iterative update of suitable probability distributions. Such distributions are
+key to capture cascade dynamics that involve possibly continuous quantities and
+that depend on the cascade history, e.g. if load is accumulated over time.
+These distributions respect the Markovian nature of the studied random
+processes. Random variables capture the impact of nodes that have failed at any
+point in the past on their neighborhood. As a proof of concept, we provide two
+examples: (a) Constant load models that cover many of the analytically
+tractable cascade models, and, as a highlight, (b) a fiber bundle model that
+was not tractable by branching process approximations before. Our derivations
+cover the whole cascade dynamics, not only their steady state. This allows to
+include interventions in time or further model complexity in the analysis.
+",1,1,0,0,0,0
+19900,A Gaussian Process Regression Model for Distribution Inputs,"  Monge-Kantorovich distances, otherwise known as Wasserstein distances, have
+received a growing attention in statistics and machine learning as a powerful
+discrepancy measure for probability distributions. In this paper, we focus on
+forecasting a Gaussian process indexed by probability distributions. For this,
+we provide a family of positive definite kernels built using transportation
+based distances. We provide a probabilistic understanding of these kernels and
+characterize the corresponding stochastic processes. We prove that the Gaussian
+processes indexed by distributions corresponding to these kernels can be
+efficiently forecast, opening new perspectives in Gaussian process modeling.
+",0,0,1,1,0,0
+19901,The intrinsic stable normal cone,"  We construct an analog of the intrinsic normal cone of Behrend-Fantechi in
+the equivariant motivic stable homotopy category over a base-scheme B and
+construct a fundament class in E-cohomology for any cohomology theory E in
+SH(B). For affine B, a perfect obstruction theory gives rise to a virtual
+fundamental class in a twisted Borel-Moore E-homology for arbitrary E. This
+includes motivic cohomology (homotopy invariant) K-theory algebraic cobordism
+and the oriented Chow groups of Barge-Morel and Fasel. In the case of motivic
+cohomology, we recover the constructions of Behrend-Fantechi, with values in
+the Chow group.
+",0,0,1,0,0,0
+19902,Globular cluster formation with multiple stellar populations from hierarchical star cluster complexes,"  Most old globular clusters (GCs) in the Galaxy are observed to have internal
+chemical abundance spreads in light elements. We discuss a new GC formation
+scenario based on hierarchical star formation within fractal molecular clouds.
+In the new scenario, a cluster of bound and unbound star clusters (`star
+cluster complex', SCC) that have a power-law cluster mass function with a slope
+(beta) of 2 is first formed from a massive gas clump developed in a dwarf
+galaxy. Such cluster complexes and beta=2 are observed and expected from
+hierarchical star formation. The most massive star cluster (`main cluster'),
+which is the progenitor of a GC, can accrete gas ejected from asymptotic giant
+branch (AGB) stars initially in the cluster and other low-mass clusters before
+the clusters are tidally stripped or destroyed to become field stars in the
+dwarf. The SCC is initially embedded in a giant gas hole created by numerous
+supernovae of the SCC so that cold gas outside the hole can be accreted onto
+the main cluster later. New stars formed from the accreted gas have chemical
+abundances that are different from those of the original SCC. Using
+hydrodynamical simulations of GC formation based on this scenario, we show that
+the main cluster with the initial mass as large as [2-5]x10^5 Msun can accrete
+more than 10^5 Msun gas from AGB stars of the SCC. We suggest that merging of
+hierarchical star cluster complexes can play key roles in stellar halo
+formation around GCs and self-enrichment processes of GCs.
+",0,1,0,0,0,0
+19903,Multiple Source Domain Adaptation with Adversarial Training of Neural Networks,"  While domain adaptation has been actively researched in recent years, most
+theoretical results and algorithms focus on the single-source-single-target
+adaptation setting. Naive application of such algorithms on multiple source
+domain adaptation problem may lead to suboptimal solutions. As a step toward
+bridging the gap, we propose a new generalization bound for domain adaptation
+when there are multiple source domains with labeled instances and one target
+domain with unlabeled instances. Compared with existing bounds, the new bound
+does not require expert knowledge about the target distribution, nor the
+optimal combination rule for multisource domains. Interestingly, our theory
+also leads to an efficient learning strategy using adversarial neural networks:
+we show how to interpret it as learning feature representations that are
+invariant to the multiple domain shifts while still being discriminative for
+the learning task. To this end, we propose two models, both of which we call
+multisource domain adversarial networks (MDANs): the first model optimizes
+directly our bound, while the second model is a smoothed approximation of the
+first one, leading to a more data-efficient and task-adaptive model. The
+optimization tasks of both models are minimax saddle point problems that can be
+optimized by adversarial training. To demonstrate the effectiveness of MDANs,
+we conduct extensive experiments showing superior adaptation performance on
+three real-world datasets: sentiment analysis, digit classification, and
+vehicle counting.
+",1,0,0,1,0,0
+19904,OSSOS: V. Diffusion in the orbit of a high-perihelion distant Solar System object,"  We report the discovery of the minor planet 2013 SY$_{99}$, on an
+exceptionally distant, highly eccentric orbit. With a perihelion of 50.0 au,
+2013 SY$_{99}$'s orbit has a semi-major axis of $730 \pm 40$ au, the largest
+known for a high-perihelion trans-Neptunian object (TNO), well beyond those of
+(90377) Sedna and 2012 VP$_{113}$. Yet, with an aphelion of $1420 \pm 90$ au,
+2013 SY$_{99}$'s orbit is interior to the region influenced by Galactic tides.
+Such TNOs are not thought to be produced in the current known planetary
+architecture of the Solar System, and they have informed the recent debate on
+the existence of a distant giant planet. Photometry from the
+Canada-France-Hawaii Telescope, Gemini North and Subaru indicate 2013 SY$_{99}$
+is $\sim 250$ km in diameter and moderately red in colour, similar to other
+dynamically excited TNOs. Our dynamical simulations show that Neptune's weak
+influence during 2013 SY$_{99}$'s perihelia encounters drives diffusion in its
+semi-major axis of hundreds of astronomical units over 4 Gyr. The overall
+symmetry of random walks in semi-major axis allow diffusion to populate 2013
+SY$_{99}$'s orbital parameter space from the 1000-2000 au inner fringe of the
+Oort cloud. Diffusion affects other known TNOs on orbits with perihelia of 45
+to 49 au and semi-major axes beyond 250 au, providing a formation mechanism
+that implies an extended population, gently cycling into and returning from the
+inner fringe of the Oort cloud.
+",0,1,0,0,0,0
+19905,Solution of the Lindblad equation for spin helix states,"  Using Lindblad dynamics we study quantum spin systems with dissipative
+boundary dynamics that generate a stationary nonequilibrium state with a
+non-vanishing spin current that is locally conserved except at the boundaries.
+We demonstrate that with suitably chosen boundary target states one can solve
+the many-body Lindblad equation exactly in any dimension. As solution we obtain
+pure states at any finite value of the dissipation strength and any system
+size. They are characterized by a helical stationary magnetization profile and
+a superdiffusive ballistic current of order one, independent of system size
+even when the quantum spin system is not integrable. These results are derived
+in explicit form for the one-dimensional spin-1/2 Heisenberg chain and its
+higher-spin generalizations (which include for spin-1 the integrable
+Zamolodchikov-Fateev model and the bi-quadratic Heisenberg chain). The
+extension of the results to higher dimensions is straightforward.
+",0,1,0,0,0,0
+19906,Global bifurcation map of the homogeneus states in the Gray-Scott model,"  We study the spatially homogeneous time dependent solutions and their
+bifurcations of the Gray-Scott model. We find the global map of bifurcations by
+a combination of rigorous verification of the existence of Takens Bogdanov and
+a Bautin bifurcations, in the space of two parameters k and F. With the aid of
+numerical continuation of local bifurcation curves we give a global description
+of all the possible bifurcations
+",0,0,1,0,0,0
+19907,Stochastic Composite Least-Squares Regression with convergence rate O(1/n),"  We consider the minimization of composite objective functions composed of the
+expectation of quadratic functions and an arbitrary convex function. We study
+the stochastic dual averaging algorithm with a constant step-size, showing that
+it leads to a convergence rate of O(1/n) without strong convexity assumptions.
+This thus extends earlier results on least-squares regression with the
+Euclidean geometry to (a) all convex regularizers and constraints, and (b) all
+geome-tries represented by a Bregman divergence. This is achieved by a new
+proof technique that relates stochastic and deterministic recursions.
+",0,0,1,1,0,0
+19908,Fisher GAN,"  Generative Adversarial Networks (GANs) are powerful models for learning
+complex distributions. Stable training of GANs has been addressed in many
+recent works which explore different metrics between distributions. In this
+paper we introduce Fisher GAN which fits within the Integral Probability
+Metrics (IPM) framework for training GANs. Fisher GAN defines a critic with a
+data dependent constraint on its second order moments. We show in this paper
+that Fisher GAN allows for stable and time efficient training that does not
+compromise the capacity of the critic, and does not need data independent
+constraints such as weight clipping. We analyze our Fisher IPM theoretically
+and provide an algorithm based on Augmented Lagrangian for Fisher GAN. We
+validate our claims on both image sample generation and semi-supervised
+classification using Fisher GAN.
+",1,0,0,1,0,0
+19909,Language as a matrix product state,"  We propose a statistical model for natural language that begins by
+considering language as a monoid, then representing it in complex matrices with
+a compatible translation invariant probability measure. We interpret the
+probability measure as arising via the Born rule from a translation invariant
+matrix product state.
+",1,1,0,1,0,0
+19910,On the application of Mattis-Bardeen theory in strongly disordered superconductors,"  The low energy optical conductivity of conventional superconductors is
+usually well described by Mattis-Bardeen (MB) theory which predicts the onset
+of absorption above an energy corresponding to twice the superconducing (SC)
+gap parameter Delta. Recent experiments on strongly disordered superconductors
+have challenged the application of the MB formulas due to the occurrence of
+additional spectral weight at low energies below 2Delta. Here we identify three
+crucial items which have to be included in the analysis of optical-conductivity
+data for these systems: (a) the correct identification of the optical threshold
+in the Mattis-Bardeen theory, and its relation with the gap value extracted
+from the measured density of states, (b) the gauge-invariant evaluation of the
+current-current response function, needed to account for the optical absorption
+by SC collective modes, and (c) the inclusion into the MB formula of the energy
+dependence of the density of states present already above Tc. By computing the
+optical conductvity in the disordered attractive Hubbard model we analyze the
+relevance of all these items, and we provide a compelling scheme for the
+analysis and interpretation of the optical data in real materials.
+",0,1,0,0,0,0
+19911,Nanoscale Magnetic Imaging using Circularly Polarized High-Harmonic Radiation,"  This work demonstrates nanoscale magnetic imaging using bright circularly
+polarized high-harmonic radiation. We utilize the magneto-optical contrast of
+worm-like magnetic domains in a Co/Pd multilayer structure, obtaining
+quantitative amplitude and phase maps by lensless imaging. A
+diffraction-limited spatial resolution of 49 nm is achieved with iterative
+phase reconstruction enhanced by a holographic mask. Harnessing the unique
+coherence of high harmonics, this approach will facilitate quantitative,
+element-specific and spatially-resolved studies of ultrafast magnetization
+dynamics, advancing both fundamental and applied aspects of nanoscale
+magnetism.
+",0,1,0,0,0,0
+19912,Towards integrated superconducting detectors on lithium niobate waveguides,"  Superconducting detectors are now well-established tools for low-light
+optics, and in particular quantum optics, boasting high-efficiency, fast
+response and low noise. Similarly, lithium niobate is an important platform for
+integrated optics given its high second-order nonlinearity, used for high-speed
+electro-optic modulation and polarization conversion, as well as frequency
+conversion and sources of quantum light. Combining these technologies addresses
+the requirements for a single platform capable of generating, manipulating and
+measuring quantum light in many degrees of freedom, in a compact and
+potentially scalable manner. We will report on progress integrating tungsten
+transition-edge sensors (TESs) and amorphous tungsten silicide superconducting
+nanowire single-photon detectors (SNSPDs) on titanium in-diffused lithium
+niobate waveguides. The travelling-wave design couples the evanescent field
+from the waveguides into the superconducting absorber. We will report on
+simulations and measurements of the absorption, which we can characterize at
+room temperature prior to cooling down the devices. Independently, we show how
+the detectors respond to flood illumination, normally incident on the devices,
+demonstrating their functionality.
+",0,1,0,0,0,0
+19913,The Hilbert scheme of 11 points in A^3 is irreducible,"  We prove that the Hilbert scheme of 11 points on a smooth threefold is
+irreducible. In the course of the proof, we present several known and new
+techniques for producing curves on the Hilbert scheme.
+",0,0,1,0,0,0
+19914,Dynamically reconfigurable metal-semiconductor Yagi-Uda nanoantenna,"  We propose a novel type of tunable Yagi-Uda nanoantenna composed of
+metal-dielectric (Ag-Ge) core-shell nanoparticles. We show that, due to the
+combination of two types of resonances in each nanoparticle, such hybrid
+Yagi-Uda nanoantenna can operate in two different regimes. Besides the
+conventional nonresonant operation regime at low frequencies, characterized by
+highly directive emission in the forward direction, there is another one at
+higher frequencies caused by hybrid magneto-electric response of the core-shell
+nanoparticles. This regime is based on the excitation of the van Hove
+singularity, and emission in this regime is accompanied by high values of
+directivity and Purcell factor within the same narrow frequency range. Our
+analysis reveals the possibility of flexible dynamical tuning of the hybrid
+nanoantenna emission pattern via electron-hole plasma excitation by 100
+femtosecond pump pulse with relatively low peak intensities $\sim$200
+MW/cm$^2$.
+",0,1,0,0,0,0
+19915,Synthesis and In Situ Modification of Hierarchical SAPO-34 by PEG with Different Molecular Weights; Application in MTO Process,"  Modified structures of SAPO-34 were prepared using polyethylene glycol as the
+mesopores generating agent. The synthesized catalysts were applied in
+methanol-to-olefins (MTO) process. All modified synthesized catalysts were
+characterized via XRD, XRF, FESEM, FTIR, N2 adsorption-desorption techniques,
+and temperature-programmed NH3 desorption and they were compared with
+conventional microporous SAPO-34. Introduction of non-ionic PEG capping agent
+affected the degree of homogeneity and integrity of the synthesis media and
+thus reduced the number of nuclei and order of coordination structures
+resulting in larger and less crystalline particles compared with the
+conventional sample. During the calcination process, decomposition of absorbed
+PEG moieties among the piled up SAPO patches formed a great portion of tuned
+mesopores into the microporous matrix. These tailored mesopores were served as
+auxiliary diffusion pathways in MTO reaction. The effects of molecular weight
+of PEG and PEG/Al molar ratio on the properties of the synthesized materials
+were investigated in order to optimize their MTO reaction performance. It was
+revealed that both of these two parameters can significantly change the
+structural composition and physicochemical properties of resultant products.
+Using PEG with MW of 6000 has led to the formation of RHO and CHA structural
+frameworks i.e. DNL-6 and SAPO-34, simultaneously, while addition of PEG with
+MW of 4000 resulted the formation of pure SAPO-34 phase. Altering the PEG/Al
+molar ratio in the precursor significantly influenced the porosity and acidity
+of the synthesized silicoaluminophosphate products. SAPO-34 impregnated with
+PEG molecular weight of 4000 and PEG/Al molar ratio of 0.0125 showed superior
+catalytic stability in MTO reaction because of the tuned bi-modal porosity and
+tailored acidity pattern.
+",0,1,0,0,0,0
+19916,Support Feature Machines,"  Support Vector Machines (SVMs) with various kernels have played dominant role
+in machine learning for many years, finding numerous applications. Although
+they have many attractive features interpretation of their solutions is quite
+difficult, the use of a single kernel type may not be appropriate in all areas
+of the input space, convergence problems for some kernels are not uncommon, the
+standard quadratic programming solution has $O(m^3)$ time and $O(m^2)$ space
+complexity for $m$ training patterns. Kernel methods work because they
+implicitly provide new, useful features. Such features, derived from various
+kernels and other vector transformations, may be used directly in any machine
+learning algorithm, facilitating multiresolution, heterogeneous models of data.
+Therefore Support Feature Machines (SFM) based on linear models in the extended
+feature spaces, enabling control over selection of support features, give at
+least as good results as any kernel-based SVMs, removing all problems related
+to interpretation, scaling and convergence. This is demonstrated for a number
+of benchmark datasets analyzed with linear discrimination, SVM, decision trees
+and nearest neighbor methods.
+",1,0,0,1,0,0
+19917,Voltage Control Using Eigen Value Decomposition of Fast Decoupled Load Flow Jacobian,"  Voltage deviations occur frequently in power systems. If the violation at
+some buses falls outside the prescribed range, it will be necessary to correct
+the problem by controlling reactive power resources. In this paper, an optimal
+algorithm is proposed to solve this problem by identifying the voltage buses,
+that will have a maximum effect on the affected buses, and setting their new
+set-points. This algorithm is based on the Eigen-Value Decomposition of the
+fast decoupled load flow Jacobian matrix. Different Case studies including IEEE
+9, 14, 30 and 57 bus systems have been used to verify the method.
+",1,0,0,0,0,0
+19918,Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer,"  Convolutional Neural Networks (CNNs) are a popular deep learning architecture
+widely applied in different domains, in particular in classifying over images,
+for which the concept of convolution with a filter comes naturally.
+Unfortunately, the requirement of a distance (or, at least, of a neighbourhood
+function) in the input feature space has so far prevented its direct use on
+data types such as omics data. However, a number of omics data are metrizable,
+i.e., they can be endowed with a metric structure, enabling to adopt a
+convolutional based deep learning framework, e.g., for prediction. We propose a
+generalized solution for CNNs on omics data, implemented through a dedicated
+Keras layer. In particular, for metagenomics data, a metric can be derived from
+the patristic distance on the phylogenetic tree. For transcriptomics data, we
+combine Gene Ontology semantic similarity and gene co-expression to define a
+distance; the function is defined through a multilayer network where 3 layers
+are defined by the GO mutual semantic similarity while the fourth one by gene
+co-expression. As a general tool, feature distance on omics data is enabled by
+OmicsConv, a novel Keras layer, obtaining OmicsCNN, a dedicated deep learning
+framework. Here we demonstrate OmicsCNN on gut microbiota sequencing data, for
+Inflammatory Bowel Disease (IBD) 16S data, first on synthetic data and then a
+metagenomics collection of gut microbiota of 222 IBD patients.
+",0,0,0,1,0,0
+19919,Neural Network Multitask Learning for Traffic Flow Forecasting,"  Traditional neural network approaches for traffic flow forecasting are
+usually single task learning (STL) models, which do not take advantage of the
+information provided by related tasks. In contrast to STL, multitask learning
+(MTL) has the potential to improve generalization by transferring information
+in training signals of extra tasks. In this paper, MTL based neural networks
+are used for traffic flow forecasting. For neural network MTL, a
+backpropagation (BP) network is constructed by incorporating traffic flows at
+several contiguous time instants into an output layer. Nodes in the output
+layer can be seen as outputs of different but closely related STL tasks.
+Comprehensive experiments on urban vehicular traffic flow data and comparisons
+with STL show that MTL in BP neural networks is a promising and effective
+approach for traffic flow forecasting.
+",1,0,0,0,0,0
+19920,A note on a new paradox in superluminal signalling,"  The Tolman paradox is well known as a base for demonstrating the causality
+violation by faster-than-light signals within special relativity. It is
+constructed using a two-way exchange of faster-than-light signals between two
+inertial observers who are in a relative motion receding one from another.
+Recently a one-way superluminal signalling arrangement was suggested as a
+possible construction of a causal paradox. In this note we show that this
+suggestion is not correct, and no causality principle violation can occur in
+any one-way signalling by the use of faster-than-light particles and signals.
+",0,1,0,0,0,0
+19921,The Compressed Overlap Index,"  For analysing text algorithms, for computing superstrings, or for testing
+random number generators, one needs to compute all overlaps between any pairs
+of words in a given set. The positions of overlaps of a word onto itself, or of
+two words, are needed to compute the absence probability of a word in a random
+text, or the numbers of common words shared by two random texts. In all these
+contexts, one needs to compute or to query overlaps between pairs of words in a
+given set. For this sake, we designed COvI, a compressed overlap index that
+supports multiple queries on overlaps: like computing the correlation of two
+words, or listing pairs of words whose longest overlap is maximal among all
+possible pairs. COvI stores overlaps in a hierarchical and non-redundant
+manner. We propose an implementation that can handle datasets of millions of
+words and still answer queries efficiently. Comparison with a baseline solution
+- called FullAC - relying on the Aho-Corasick automaton shows that COvI
+provides significant advantages. For similar construction times, COvI requires
+half the memory FullAC, and still solves complex queries much faster.
+",1,0,0,0,0,0
+19922,An intuitive approach to the unified theory of spin-relaxation,"  Spin-relaxation is conventionally discussed using two different approaches
+for materials with and without inversion symmetry. The former is known as the
+Elliott-Yafet (EY) theory and for the latter the D'yakonov-Perel' (DP) theory
+applies, respectively. We discuss herein a simple and intuitive approach to
+demonstrate that the two seemingly disparate mechanisms are closely related. A
+compelling analogy between the respective Hamiltonian is presented and that the
+usual derivation of spin-relaxation times, in the respective frameworks of the
+two theories, can be performed. The result also allows to obtain the less
+canonical spin-relaxation regimes; the generalization of the EY when the
+material has a large quasiparticle broadening and the DP mechanism in ultrapure
+semiconductors. The method also allows a practical and intuitive numerical
+implementation of the spin-relaxation calculation, which is demonstrated for
+MgB$_2$ that has anomalous spin-relaxation properties.
+",0,1,0,0,0,0
+19923,Weighted Random Walk Sampling for Multi-Relational Recommendation,"  In the information overloaded web, personalized recommender systems are
+essential tools to help users find most relevant information. The most
+heavily-used recommendation frameworks assume user interactions that are
+characterized by a single relation. However, for many tasks, such as
+recommendation in social networks, user-item interactions must be modeled as a
+complex network of multiple relations, not only a single relation. Recently
+research on multi-relational factorization and hybrid recommender models has
+shown that using extended meta-paths to capture additional information about
+both users and items in the network can enhance the accuracy of recommendations
+in such networks. Most of this work is focused on unweighted heterogeneous
+networks, and to apply these techniques, weighted relations must be simplified
+into binary ones. However, information associated with weighted edges, such as
+user ratings, which may be crucial for recommendation, are lost in such
+binarization. In this paper, we explore a random walk sampling method in which
+the frequency of edge sampling is a function of edge weight, and apply this
+generate extended meta-paths in weighted heterogeneous networks. With this
+sampling technique, we demonstrate improved performance on multiple data sets
+both in terms of recommendation accuracy and model generation efficiency.
+",1,0,0,0,0,0
+19924,Understanding the Twitter Usage of Humanities and Social Sciences Academic Journals,"  Scholarly communication has the scope to transcend the limitations of the
+physical world through social media extended coverage and shortened information
+paths. Accordingly, publishers have created profiles for their journals in
+Twitter to promote their publications and to initiate discussions with public.
+This paper investigates the Twitter presence of humanities and social sciences
+(HSS) journal titles obtained from mainstream citation indices, by analysing
+the interaction and communication patterns. This study utilizes webometric data
+collection, descriptive analysis, and social network analysis. Findings
+indicate that the presence of HSS journals in Twitter across disciplines is not
+yet substantial. Sharing of general websites appears to be the key activity
+performed by HSS journals in Twitter. Among them, web content from news portals
+and magazines are highly disseminated. Sharing of research articles and
+retweeting was not majorly observed. Inter-journal communication is apparent
+within the same citation index, but it is very minimal with journals from the
+other index. However, there seems to be an effort to broaden communication
+beyond the research community, reaching out to connect with the public.
+",1,0,0,0,0,0
+19925,Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting,"  Machine learning algorithms, when applied to sensitive data, pose a distinct
+threat to privacy. A growing body of prior work demonstrates that models
+produced by these algorithms may leak specific private information in the
+training data to an attacker, either through the models' structure or their
+observable behavior. However, the underlying cause of this privacy risk is not
+well understood beyond a handful of anecdotal accounts that suggest overfitting
+and influence might play a role.
+This paper examines the effect that overfitting and influence have on the
+ability of an attacker to learn information about the training data from
+machine learning models, either through training set membership inference or
+attribute inference attacks. Using both formal and empirical analyses, we
+illustrate a clear relationship between these factors and the privacy risk that
+arises in several popular machine learning algorithms. We find that overfitting
+is sufficient to allow an attacker to perform membership inference and, when
+the target attribute meets certain conditions about its influence, attribute
+inference attacks. Interestingly, our formal analysis also shows that
+overfitting is not necessary for these attacks and begins to shed light on what
+other factors may be in play. Finally, we explore the connection between
+membership inference and attribute inference, showing that there are deep
+connections between the two that lead to effective new attacks.
+",1,0,0,1,0,0
+19926,Nanoplatelets as material system between strong confinement and weak confinement,"  Recently, the fabrication of CdSe nanoplatelets became an important research
+topic. Nanoplatelets are often described as having a similar electronic
+structure as 2D dimensional quantum wells and are promoted as colloidal quantum
+wells with monolayer precision width. In this paper, we show, that
+nanoplatelets are not ideal quantum wells, but cover depending on the size: the
+strong confinement regime, an intermediate regime and a Coulomb dominated
+regime. Thus, nanoplatelets are an ideal platform to study the physics in these
+regimes. Therefore, the exciton states of the nanoplatelets are numerically
+calculated by solving the full four dimensional Schrödinger equation. We
+compare the results with approximate solutions from semiconductor quantum well
+and quantum dot theory. The paper can also act as review of these concepts for
+the colloidal nanoparticle community.
+",0,1,0,0,0,0
+19927,Combined Thermal Control and GNC: An Enabling Technology for CubeSat Surface Probes and Small Robots,"  Advances in GNC, particularly from miniaturized control electronics,
+reaction-wheels and attitude determination sensors make it possible to design
+surface probes and small robots to perform surface exploration and science on
+low-gravity environments. These robots would use their reaction wheels to roll,
+hop and tumble over rugged surfaces. These robots could provide 'Google
+Streetview' quality images of off-world surfaces and perform some unique
+science using penetrometers. These systems can be powered by high-efficiency
+fuel cells that operate at 60-65 % and utilize hydrogen and oxygen electrolyzed
+from water. However, one of the major challenges that prevent these probes and
+robots from performing long duration surface exploration and science is thermal
+design and control. In the inner solar system, during the day time, there is
+often enough solar-insolation to keep these robots warm and power these
+devices, but during eclipse the temperatures falls well below storage
+temperature. We have developed a thermal control system that utilizes chemicals
+to store and dispense heat when needed. The system takes waste products, such
+as water from these robots and transfers them to a thermochemical storage
+system. These thermochemical storage systems when mixed with water (a waste
+product from a PEM fuel cell) releases heat. Under eclipse, the heat from the
+thermochemical storage system is released to keep the probe warm enough to
+survive. In sunlight, solar photovoltaics are used to electrolyze the water and
+reheat the thermochemical storage system to release the water. Our research has
+showed thermochemical storage systems are a feasible solution for use on
+surface probes and robots for applications on the Moon, Mars and asteroids.
+",1,1,0,0,0,0
+19928,Denoising Adversarial Autoencoders,"  Unsupervised learning is of growing interest because it unlocks the potential
+held in vast amounts of unlabelled data to learn useful representations for
+inference. Autoencoders, a form of generative model, may be trained by learning
+to reconstruct unlabelled input data from a latent representation space. More
+robust representations may be produced by an autoencoder if it learns to
+recover clean input samples from corrupted ones. Representations may be further
+improved by introducing regularisation during training to shape the
+distribution of the encoded data in latent space. We suggest denoising
+adversarial autoencoders, which combine denoising and regularisation, shaping
+the distribution of latent space using adversarial training. We introduce a
+novel analysis that shows how denoising may be incorporated into the training
+and sampling of adversarial autoencoders. Experiments are performed to assess
+the contributions that denoising makes to the learning of representations for
+classification and sample synthesis. Our results suggest that autoencoders
+trained using a denoising criterion achieve higher classification performance,
+and can synthesise samples that are more consistent with the input data than
+those trained without a corruption process.
+",1,0,0,1,0,0
+19929,Monodromy map for tropical Dolbeault cohomology,"  We define monodromy maps for tropical Dolbeault cohomology of algebraic
+varieties over non-Archimedean fields. We propose a conjecture of Hodge
+isomorphisms via monodromy maps, and provide some evidence.
+",0,0,1,0,0,0
+19930,Randomness Evaluation with the Discrete Fourier Transform Test Based on Exact Analysis of the Reference Distribution,"  In this paper, we study the problems in the discrete Fourier transform (DFT)
+test included in NIST SP 800-22 released by the National Institute of Standards
+and Technology (NIST), which is a collection of tests for evaluating both
+physical and pseudo-random number generators for cryptographic applications.
+The most crucial problem in the DFT test is that its reference distribution of
+the test statistic is not derived mathematically but rather numerically
+estimated, the DFT test for randomness is based on a pseudo-random number
+generator (PRNG). Therefore, the present DFT test should not be used unless the
+reference distribution is mathematically derived. Here, we prove that a power
+spectrum, which is a component of the test statistic, follows a chi-squared
+distribution with 2 degrees of freedom. Based on this fact, we propose a test
+whose reference distribution of the test statistic is mathematically derived.
+Furthermore, the results of testing non-random sequences and several PRNGs
+showed that the proposed test is more reliable and definitely more sensitive
+than the present DFT test.
+",1,0,0,1,0,0
+19931,Analogies Explained: Towards Understanding Word Embeddings,"  Word embeddings generated by neural network methods such as word2vec (W2V)
+are well known to exhibit seemingly linear behaviour, e.g. the embeddings of
+analogy ""woman is to queen as man is to king"" approximately describe a
+parallelogram. This property is particularly intriguing since the embeddings
+are not trained to achieve it. Several explanations have been proposed, but
+each introduces assumptions that do not hold in practice. We derive a
+probabilistically grounded definition of paraphrasing and show it can be
+re-interpreted as word transformation, a mathematical description of ""$w_x$ is
+to $w_y$"". From these concepts we prove existence of the linear relationship
+between W2V-type embeddings that underlies the analogical phenomenon, and
+identify explicit error terms in the relationship.
+",1,0,0,1,0,0
+19932,Zero sum partition into sets of the same order and its applications,"  We will say that an Abelian group $\Gamma$ of order $n$ has the
+$m$-\emph{zero-sum-partition property} ($m$-\textit{ZSP-property}) if $m$
+divides $n$, $m\geq 2$ and there is a partition of $\Gamma$ into pairwise
+disjoint subsets $A_1, A_2,\ldots , A_t$, such that $|A_i| = m$ and $\sum_{a\in
+A_i}a = g_0$ for $1 \leq i \leq t$, where $g_0$ is the identity element of
+$\Gamma$.
+In this paper we study the $m$-ZSP property of $\Gamma$. We show that
+$\Gamma$ has $m$-ZSP if and only if $|\Gamma|$ is odd or $m\geq 3$ and $\Gamma$
+has more than one involution. We will apply the results to the study of group
+distance magic graphs as well as to generalized Kotzig arrays.
+",0,0,1,0,0,0
+19933,Strongly Coupled Dark Energy with Warm dark matter vs. LCDM,"  Cosmologies including strongly Coupled (SC) Dark Energy (DE) and Warm dark
+matter (SCDEW) are based on a conformally invariant (CI) attractor solution
+modifying the early radiative expansion. Then, aside of radiation, a kinetic
+field $\Phi$ and a DM component account for a stationary fraction, $\sim 1\,
+\%$, of the total energy. Most SCDEW predictions are hardly distinguishable
+from LCDM, while SCDEW alleviates quite a few LCDM conceptual problems, as well
+as its difficulties to meet data below the average galaxy scale. The CI
+expansion begins at the inflation end, when $\Phi$ (future DE) possibly plays a
+role in reheating, and ends at the Higgs' scale. Afterwards, a number of viable
+options is open, allowing for the transition from the CI expansion to the
+present Universe. In this paper: (i) We show how the attractor is recovered
+when the spin degrees of freedom decreases. (ii) We perform a detailed
+comparison of CMB anisotropy and polarization spectra for SCDEW and LCDM,
+including tensor components, finding negligible discrepancies. (iii) Linear
+spectra exhibit a greater parameter dependence at large $k$'s, but are still
+consistent with data for suitable parameter choices. (iv) We also compare
+previous simulation results with fresh data on galaxy concentration. Finally,
+(v) we outline numerical difficulties at high $k$. This motivates a second
+related paper, where such problems are treated in a quantitative way.
+",0,1,0,0,0,0
+19934,Clausal Analysis of First-order Proof Schemata,"  Proof schemata are a variant of LK-proofs able to simulate various induction
+schemes in first-order logic by adding so called proof links to the standard
+first-order LK-calculus. Proof links allow proofs to reference proofs thus
+giving proof schemata a recursive structure. Unfortunately, applying reductive
+cut- elimination is non-trivial in the presence of proof links. Borrowing the
+concept of lazy instantiation from functional programming, we evaluate proof
+links locally allowing reductive cut-elimination to proceed past them. Though,
+this method cannot be used to obtain cut-free proof schemata, we nonetheless
+obtain important results concerning the schematic CERES method, that is a
+method of cut-elimination for proof schemata based on resolution. In ""Towards a
+clausal analysis of cut-elimination"", it was shown that reductive
+cut-elimination transforms a given LK-proof in such a way that a subsumption
+relation holds between the pre- and post-transformation characteristic clause
+sets, i.e. the clause set representing the cut-structure of an LK-proof. Let
+CL(A') be the characteristic clause set of a normal form A' of an LK-proof A
+that is reached by performing reductive cut-elimination on A without atomic cut
+elimination. Then CL(A') is subsumed by all characteristic clause sets
+extractable from any application of reductive cut-elimination to A. Such a
+normal form is referred to as an ACNF top and plays an essential role in
+methods of cut-elimination by resolution. These results can be extended to
+proof schemata through our ""lazy instantiation"" of proof links, and provides an
+essential step toward a complete cut-elimination method for proof schemata.
+",1,0,1,0,0,0
+19935,Emotion Intensities in Tweets,"  This paper examines the task of detecting intensity of emotion from text. We
+create the first datasets of tweets annotated for anger, fear, joy, and sadness
+intensities. We use a technique called best--worst scaling (BWS) that improves
+annotation consistency and obtains reliable fine-grained scores. We show that
+emotion-word hashtags often impact emotion intensity, usually conveying a more
+intense emotion. Finally, we create a benchmark regression system and conduct
+experiments to determine: which features are useful for detecting emotion
+intensity, and, the extent to which two emotions are similar in terms of how
+they manifest in language.
+",1,0,0,0,0,0
+19936,Modelling the descent of nitric oxide during the elevated stratopause event of January 2013,"  Using simulations with a whole-atmosphere chemistry-climate model nudged by
+meteorological analyses, global satellite observations of nitrogen oxide (NO)
+and water vapour by the Sub-Millimetre Radiometer instrument (SMR), of
+temperature by the Microwave Limb Sounder (MLS), as well as local radar
+observations, this study examines the recent major stratospheric sudden warming
+accompanied by an elevated stratopause event (ESE) that occurred in January
+2013. We examine dynamical processes during the ESE, including the role of
+planetary wave, gravity wave and tidal forcing on the initiation of the descent
+in the mesosphere-lower thermosphere (MLT) and its continuation throughout the
+mesosphere and stratosphere, as well as the impact of model eddy diffusion. We
+analyse the transport of NO and find the model underestimates the large descent
+of NO compared to SMR observations. We demonstrate that the discrepancy arises
+abruptly in the MLT region at a time when the resolved wave forcing and the
+planetary wave activity increase, just before the elevated stratopause reforms.
+The discrepancy persists despite doubling the model eddy diffusion. While the
+simulations reproduce an enhancement of the semi-diurnal tide following the
+onset of the 2013 SSW, corroborating new meteor radar observations at high
+northern latitudes over Trondheim (63.4$^{\circ}$N), the modelled tidal
+contribution to the forcing of the mean meridional circulation and to the
+descent is a small portion of the resolved wave forcing, and lags it by about
+ten days.
+",0,1,0,0,0,0
+19937,Optimal Evidence Accumulation on Social Networks,"  A fundamental question in biology is how organisms integrate sensory and
+social evidence to make decisions. However, few models describe how both these
+streams of information can be combined to optimize choices. Here we develop a
+normative model for collective decision making in a network of agents
+performing a two-alternative forced choice task. We assume that rational
+(Bayesian) agents in this network make private measurements, and observe the
+decisions of their neighbors until they accumulate sufficient evidence to make
+an irreversible choice. As each agent communicates its decision to those
+observing it, the flow of social information is described by a directed graph.
+The decision-making process in this setting is intuitive, but can be complex.
+We describe when and how the absence of a decision of a neighboring agent
+communicates social information, and how an agent must marginalize over all
+unobserved decisions. We also show how decision thresholds and network
+connectivity affect group evidence accumulation, and describe the dynamics of
+decision making in social cliques. Our model provides a bridge between the
+abstractions used in the economics literature and the evidence accumulator
+models used widely in neuroscience and psychology.
+",1,0,0,0,1,0
+19938,Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm,"  Conventional seismic techniques for detecting the subsurface geologic
+features are challenged by limited data coverage, computational inefficiency,
+and subjective human factors. We developed a novel data-driven geological
+feature detection approach based on pre-stack seismic measurements. Our
+detection method employs an efficient and accurate machine-learning detection
+approach to extract useful subsurface geologic features automatically.
+Specifically, our method is based on kernel ridge regression model. The
+conventional kernel ridge regression can be computationally prohibited because
+of the large volume of seismic measurements. We employ a data reduction
+technique in combination with the conventional kernel ridge regression method
+to improve the computational efficiency and reduce memory usage. In particular,
+we utilize a randomized numerical linear algebra technique, named Nyström
+method, to effectively reduce the dimensionality of the feature space without
+compromising the information content required for accurate detection. We
+provide thorough computational cost analysis to show efficiency of our new
+geological feature detection methods. We further validate the performance of
+our new subsurface geologic feature detection method using synthetic surface
+seismic data for 2D acoustic and elastic velocity models. Our numerical
+examples demonstrate that our new detection method significantly improves the
+computational efficiency while maintaining comparable accuracy. Interestingly,
+we show that our method yields a speed-up ratio on the order of $\sim10^2$ to
+$\sim 10^3$ in a multi-core computational environment.
+",1,0,0,1,0,0
+19939,Metastable Modular Metastructures for On-Demand Reconfiguration of Band Structures and Non-Reciprocal Wave Propagation,"  We present a novel approach to achieve adaptable band structures and
+non-reciprocal wave propagation by exploring and exploiting the concept of
+metastable modular metastructures. Through studying the dynamics of wave
+propagation in a chain composed of finite metastable modules, we provide
+experimental and analysis results on non-reciprocal wave propagation and unveil
+the underlying mechanisms in accomplishing such unidirectional energy
+transmission. Utilizing the property adaptation feature afforded via
+transitioning amongst metastable states, we uncovered an unprecedented bandgap
+reconfiguration characteristic, which enables the adaptivity of wave
+propagation within the metastructure. Overall, this investigation elucidates
+the rich dynamics attainable by periodicity, nonlinearity, asymmetry, and
+metastability, and creates a new class of adaptive structural and material
+systems capable of realizing tunable bandgaps and non-reciprocal wave
+transmissions.
+",0,1,0,0,0,0
+19940,Learning to Plan Chemical Syntheses,"  From medicines to materials, small organic molecules are indispensable for
+human well-being. To plan their syntheses, chemists employ a problem solving
+technique called retrosynthesis. In retrosynthesis, target molecules are
+recursively transformed into increasingly simpler precursor compounds until a
+set of readily available starting materials is obtained. Computer-aided
+retrosynthesis would be a highly valuable tool, however, past approaches were
+slow and provided results of unsatisfactory quality. Here, we employ Monte
+Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS
+was combined with an expansion policy network that guides the search, and an
+""in-scope"" filter network to pre-select the most promising retrosynthetic
+steps. These deep neural networks were trained on 12 million reactions, which
+represents essentially all reactions ever published in organic chemistry. Our
+system solves almost twice as many molecules and is 30 times faster in
+comparison to the traditional search method based on extracted rules and
+hand-coded heuristics. Finally after a 60 year history of computer-aided
+synthesis planning, chemists can no longer distinguish between routes generated
+by a computer system and real routes taken from the scientific literature. We
+anticipate that our method will accelerate drug and materials discovery by
+assisting chemists to plan better syntheses faster, and by enabling fully
+automated robot synthesis.
+",1,1,0,0,0,0
+19941,Projective embedding of pairs and logarithmic K-stability,"  Let $\hat{L}$ be the projective completion of an ample line bundle $L$ over
+$D$, a smooth projective manifold. Hwang-Singer \cite{HwangS} have constructed
+complete CSCK metric on $\hat{L}\backslash D$. When the corresponding \kahler
+form is in the cohomology class of a rational divisor $A$ and when $L$ has
+negative CSCK metric on $D$, we show that the Kodaira embedding induced by
+orthonormal basis of the Bergman space of $kA$ is almost balanced. As a
+corollary, $(\hat{L},D,cA,0)$ is K-semistable.
+",0,0,1,0,0,0
+19942,Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network,"  To ensure that a robot is able to accomplish an extensive range of tasks, it
+is necessary to achieve a flexible combination of multiple behaviors. This is
+because the design of task motions suited to each situation would become
+increasingly difficult as the number of situations and the types of tasks
+performed by them increase. To handle the switching and combination of multiple
+behaviors, we propose a method to design dynamical systems based on point
+attractors that accept (i) ""instruction signals"" for instruction-driven
+switching. We incorporate the (ii) ""instruction phase"" to form a point
+attractor and divide the target task into multiple subtasks. By forming an
+instruction phase that consists of point attractors, the model embeds a subtask
+in the form of trajectory dynamics that can be manipulated using sensory and
+instruction signals. Our model comprises two deep neural networks: a
+convolutional autoencoder and a multiple time-scale recurrent neural network.
+In this study, we apply the proposed method to manipulate soft materials. To
+evaluate our model, we design a cloth-folding task that consists of four
+subtasks and three patterns of instruction signals, which indicate the
+direction of motion. The results depict that the robot can perform the required
+task by combining subtasks based on sensory and instruction signals. And, our
+model determined the relations among these signals using its internal dynamics.
+",1,0,0,0,0,0
+19943,The evolution of magnetic fields in hot stars,"  Over the last decade, tremendous strides have been achieved in our
+understanding of magnetism in main sequence hot stars. In particular, the
+statistical occurrence of their surface magnetism has been established (~10%)
+and the field origin is now understood to be fossil. However, fundamental
+questions remain: how do these fossil fields evolve during the post-main
+sequence phases, and how do they influence the evolution of hot stars from the
+main sequence to their ultimate demise? Filling the void of known magnetic
+evolved hot (OBA) stars, studying the evolution of their fossil magnetic fields
+along stellar evolution, and understanding the impact of these fields on the
+angular momentum, rotation, mass loss, and evolution of the star itself, is
+crucial to answering these questions, with far reaching consequences, in
+particular for the properties of the precursors of supernovae explosions and
+stellar remnants. In the framework of the BRITE spectropolarimetric survey and
+LIFE project, we have discovered the first few magnetic hot supergiants. Their
+longitudinal surface magnetic field is very weak but their configuration
+resembles those of main sequence hot stars. We present these first
+observational results and propose to interpret them at first order in the
+context of magnetic flux conservation as the radius of the star expands with
+evolution. We then also consider the possible impact of stellar structure
+changes along evolution.
+",0,1,0,0,0,0
+19944,Polynomial-Time Methods to Solve Unimodular Quadratic Programs With Performance Guarantees,"  We develop polynomial-time heuristic methods to solve unimodular quadratic
+programs (UQPs) approximately, which are known to be NP-hard. In the UQP
+framework, we maximize a quadratic function of a vector of complex variables
+with unit modulus. Several problems in active sensing and wireless
+communication applications boil down to UQP. With this motivation, we present
+three new heuristic methods with polynomial-time complexity to solve the UQP
+approximately. The first method is called dominant-eigenvector-matching; here
+the solution is picked that matches the complex arguments of the dominant
+eigenvector of the Hermitian matrix in the UQP formulation. We also provide a
+performance guarantee for this method. The second method, a greedy strategy, is
+shown to provide a performance guarantee of (1-1/e) with respect to the optimal
+objective value given that the objective function possesses a property called
+string submodularity. The third heuristic method is called row-swap greedy
+strategy, which is an extension to the greedy strategy and utilizes certain
+properties of the UQP to provide a better performance than the greedy strategy
+at the expense of an increase in computational complexity. We present numerical
+results to demonstrate the performance of these heuristic methods, and also
+compare the performance of these methods against a standard heuristic method
+called semidefinite relaxation.
+",1,0,1,0,0,0
+19945,Can the Wild Bootstrap be Tamed into a General Analysis of Covariance Model?,"  It is well known that the F test is severly affected by heteroskedasticity in
+unbalanced analysis of covariance (ANCOVA) models. Currently available remedies
+for such a scenario are either based on heteroskedasticity-consistent
+covariance matrix estimation (HCCME) or bootstrap techniques. However, the
+HCCME approach tends to be liberal in small samples. Therefore, we propose a
+combination of HCCME and a wild bootstrap technique. We prove the theoretical
+validity of our approach and investigate its performance in an extensive
+simulation study in comparison to existing procedures. The results indicate
+that our proposed test remedies all problems of the ANCOVA F test and its
+heteroskedasticityconsistent alternatives. Our test only requires very general
+conditions, thus being applicable in a broad range of real-life settings.
+",0,0,0,1,0,0
+19946,Efficient K-Shot Learning with Regularized Deep Networks,"  Feature representations from pre-trained deep neural networks have been known
+to exhibit excellent generalization and utility across a variety of related
+tasks. Fine-tuning is by far the simplest and most widely used approach that
+seeks to exploit and adapt these feature representations to novel tasks with
+limited data. Despite the effectiveness of fine-tuning, itis often sub-optimal
+and requires very careful optimization to prevent severe over-fitting to small
+datasets. The problem of sub-optimality and over-fitting, is due in part to the
+large number of parameters used in a typical deep convolutional neural network.
+To address these problems, we propose a simple yet effective regularization
+method for fine-tuning pre-trained deep networks for the task of k-shot
+learning. To prevent overfitting, our key strategy is to cluster the model
+parameters while ensuring intra-cluster similarity and inter-cluster diversity
+of the parameters, effectively regularizing the dimensionality of the parameter
+search space. In particular, we identify groups of neurons within each layer of
+a deep network that shares similar activation patterns. When the network is to
+be fine-tuned for a classification task using only k examples, we propagate a
+single gradient to all of the neuron parameters that belong to the same group.
+The grouping of neurons is non-trivial as neuron activations depend on the
+distribution of the input data. To efficiently search for optimal groupings
+conditioned on the input data, we propose a reinforcement learning search
+strategy using recurrent networks to learn the optimal group assignments for
+each network layer. Experimental results show that our method can be easily
+applied to several popular convolutional neural networks and improve upon other
+state-of-the-art fine-tuning based k-shot learning strategies by more than10%
+",1,0,0,1,0,0
+19947,The cohomology ring of some Hopf algebras,"  Let p be a prime, and k be a field of characteristic p. We investigate the
+algebra structure and the structure of the cohomology ring for the connected
+Hopf algebras of dimension p^3, which appear in the classification obtained in
+[V.C. Nguyen, L.-H. Wang and X.-T. Wang, Classification of connected Hopf
+algebras of dimension p^3, J. Algebra 424 (2015), 473-505]. The list consists
+of 23 algebras together with two infinite families. We identify the Morita type
+of the algebra, and in almost all cases this is sufficient to clarify the
+structure of the cohomology ring.
+",0,0,1,0,0,0
+19948,Eckart ro-vibrational Hamiltonians via the gateway Hamilton operator: theory and practice,"  Recently, a general expression for Eckart-frame Hamilton operators has been
+obtained by the gateway Hamiltonian method ({\it J. Chem. Phys.} {\bf 142},
+174107 (2015); {\it ibid.} {\bf 143}, 064104 (2015)). The kinetic energy
+operator in this general Hamiltonian is nearly identical with that of the
+Eckart-Watson operator even when curvilinear vibrational coordinates are
+employed. Its different realizations correspond to different methods of
+calculating Eckart displacements. There are at least two different methods for
+calculating such displacements: rotation and projection. In this communication
+the application of Eckart Hamiltonian operators constructed by rotation and
+projection, respectively, is numerically demonstrated in calculating
+vibrational energy levels. The numerical examples confirm that there is no need
+for rotation to construct an Eckart ro-vibrational Hamiltonian. The application
+of the gateway method is advantageous even when rotation is used, since it
+obviates the need for differentiation of the matrix rotating into the Eckart
+frame. Simple geometrical arguments explain that there are infinitely many
+different methods for calculating Eckart displacements. The geometrical picture
+also suggests that a unique Eckart displacement vector may be defined as the
+shortest (mass-weighted) Eckart displacement vector among Eckart displacement
+vectors corresponding to configurations related by rotation. Its length, as
+shown analytically and demonstrated by way of numerical examples, is equal to
+or less than that of the Eckart displacement vector one can obtain by rotation
+to the Eckart frame.
+",0,1,0,0,0,0
+19949,Optimal Timing to Trade Along a Randomized Brownian Bridge,"  This paper studies an optimal trading problem that incorporates the trader's
+market view on the terminal asset price distribution and uninformative noise
+embedded in the asset price dynamics. We model the underlying asset price
+evolution by an exponential randomized Brownian bridge (rBb) and consider
+various prior distributions for the random endpoint. We solve for the optimal
+strategies to sell a stock, call, or put, and analyze the associated delayed
+liquidation premia. We solve for the optimal trading strategies numerically and
+compare them across different prior beliefs. Among our results, we find that
+disconnected continuation/exercise regions arise when the trader prescribe a
+two-point discrete distribution and double exponential distribution.
+",0,0,0,0,0,1
+19950,Inflationary Features and Shifts in Cosmological Parameters from Planck 2015 Data,"  We explore the relationship between features in the Planck 2015 temperature
+and polarization data, shifts in the cosmological parameters, and features from
+inflation. Residuals in the temperature data at low multipole $\ell$, which are
+responsible for the high $H_0\approx 70$ km s$^{-1}$Mpc$^{-1}$ and low
+$\sigma_8\Omega_m^{1/2}$ values from $\ell<1000$ in power-law $\Lambda$CDM
+models, are better fit to inflationary features with a $1.9\sigma$ preference
+for running of the running of the tilt or a stronger $99\%$ CL local
+significance preference for a sharp drop in power around $k=0.004$ Mpc$^{-1}$
+in generalized slow roll and a lower $H_0\approx 67$ km s$^{-1}$Mpc$^{-1}$. The
+same in-phase acoustic residuals at $\ell>1000$ that drive the global $H_0$
+constraints and appear as a lensing anomaly also favor running parameters which
+allow even lower $H_0$, but not once lensing reconstruction is considered.
+Polarization spectra are intrinsically highly sensitive to these parameter
+shifts, and even more so in the Planck 2015 TE data due to an outlier at $\ell
+\approx 165$, which disfavors the best fit $H_0$ $\Lambda$CDM solution by more
+than $2\sigma$, and high $H_0$ value at almost $3\sigma$. Current polarization
+data also slightly enhance the significance of a sharp suppression of
+large-scale power but leave room for large improvements in the future with
+cosmic variance limited $E$-mode measurements.
+",0,1,0,0,0,0
+19951,Tree based weighted learning for estimating individualized treatment rules with censored data,"  Estimating individualized treatment rules is a central task for personalized
+medicine. [zhao2012estimating] and [zhang2012robust] proposed outcome weighted
+learning to estimate individualized treatment rules directly through maximizing
+the expected outcome without modeling the response directly. In this paper, we
+extend the outcome weighted learning to right censored survival data without
+requiring either an inverse probability of censoring weighting or a
+semiparametric modeling of the censoring and failure times as done in
+[zhao2015doubly]. To accomplish this, we take advantage of the tree based
+approach proposed in [zhu2012recursively] to nonparametrically impute the
+survival time in two different ways. The first approach replaces the reward of
+each individual by the expected survival time, while in the second approach
+only the censored observations are imputed by their conditional expected
+failure times. We establish consistency and convergence rates for both
+estimators. In simulation studies, our estimators demonstrate improved
+performance compared to existing methods. We also illustrate the proposed
+method on a phase III clinical trial of non-small cell lung cancer.
+",0,0,1,1,0,0
+19952,Fine-Tuning in the Context of Bayesian Theory Testing,"  Fine-tuning in physics and cosmology is often used as evidence that a theory
+is incomplete. For example, the parameters of the standard model of particle
+physics are ""unnaturally"" small (in various technical senses), which has driven
+much of the search for physics beyond the standard model. Of particular
+interest is the fine-tuning of the universe for life, which suggests that our
+universe's ability to create physical life forms is improbable and in need of
+explanation, perhaps by a multiverse. This claim has been challenged on the
+grounds that the relevant probability measure cannot be justified because it
+cannot be normalized, and so small probabilities cannot be inferred. We show
+how fine-tuning can be formulated within the context of Bayesian theory testing
+(or \emph{model selection}) in the physical sciences. The normalizability
+problem is seen to be a general problem for testing any theory with free
+parameters, and not a unique problem for fine-tuning. Physical theories in fact
+avoid such problems in one of two ways. Dimensional parameters are bounded by
+the Planck scale, avoiding troublesome infinities, and we are not compelled to
+assume that dimensionless parameters are distributed uniformly, which avoids
+non-normalizability.
+",0,1,0,0,0,0
+19953,Nuclear physics insights for new-physics searches using nuclei: Neutrinoless $ββ$ decay and dark matter direct detection,"  Experiments using nuclei to probe new physics beyond the Standard Model, such
+as neutrinoless $\beta\beta$ decay searches testing whether neutrinos are their
+own antiparticle, and direct detection experiments aiming to identify the
+nature of dark matter, require accurate nuclear physics input for optimizing
+their discovery potential and for a correct interpretation of their results.
+This demands a detailed knowledge of the nuclear structure relevant for these
+processes. For instance, neutrinoless $\beta\beta$ decay nuclear matrix
+elements are very sensitive to the nuclear correlations in the initial and
+final nuclei, and the spin-dependent nuclear structure factors of dark matter
+scattering depend on the subtle distribution of the nuclear spin among all
+nucleons. In addition, nucleons are composite and strongly interacting, which
+implies that many-nucleon processes are necessary for a correct description of
+nuclei and their interactions. It is thus crucial that theoretical studies and
+experimental analyses consider $\beta$ decays and dark matter interactions with
+a coupling to two nucleons, called two-nucleon currents.
+",0,1,0,0,0,0
+19954,On the (Statistical) Detection of Adversarial Examples,"  Machine Learning (ML) models are applied in a variety of tasks such as
+network intrusion detection or Malware classification. Yet, these models are
+vulnerable to a class of malicious inputs known as adversarial examples. These
+are slightly perturbed inputs that are classified incorrectly by the ML model.
+The mitigation of these adversarial inputs remains an open problem. As a step
+towards understanding adversarial examples, we show that they are not drawn
+from the same distribution than the original data, and can thus be detected
+using statistical tests. Using thus knowledge, we introduce a complimentary
+approach to identify specific inputs that are adversarial. Specifically, we
+augment our ML model with an additional output, in which the model is trained
+to classify all adversarial inputs. We evaluate our approach on multiple
+adversarial example crafting methods (including the fast gradient sign and
+saliency map methods) with several datasets. The statistical test flags sample
+sets containing adversarial inputs confidently at sample sizes between 10 and
+100 data points. Furthermore, our augmented model either detects adversarial
+examples as outliers with high accuracy (> 80%) or increases the adversary's
+cost - the perturbation added - by more than 150%. In this way, we show that
+statistical properties of adversarial examples are essential to their
+detection.
+",1,0,0,1,0,0
+19955,Search Rank Fraud De-Anonymization in Online Systems,"  We introduce the fraud de-anonymization problem, that goes beyond fraud
+detection, to unmask the human masterminds responsible for posting search rank
+fraud in online systems. We collect and study search rank fraud data from
+Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters
+recruited from 6 crowdsourcing sites. We propose Dolos, a fraud
+de-anonymization system that leverages traits and behaviors extracted from
+these studies, to attribute detected fraud to crowdsourcing site fraudsters,
+thus to real identities and bank accounts. We introduce MCDense, a min-cut
+dense component detection algorithm to uncover groups of user accounts
+controlled by different fraudsters, and leverage stylometry and deep learning
+to attribute them to crowdsourcing site profiles. Dolos correctly identified
+the owners of 95% of fraudster-controlled communities, and uncovered fraudsters
+who promoted as many as 97.5% of fraud apps we collected from Google Play. When
+evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6
+months, Dolos identified 1,056 apps with suspicious reviewer groups. We report
+orthogonal evidence of their fraud, including fraud duplicates and fraud
+re-posts.
+",1,0,0,0,0,0
+19956,Instabilities in Interacting Binary Stars,"  The types of instability in the interacting binary stars are reviewed. The
+project ""Inter-Longitude Astronomy"" is a series of smaller projects on concrete
+stars or groups of stars. It has no special funds, and is supported from
+resources and grants of participating organizations, when informal working
+groups are created. Totally we studied 1900+ variable stars of different types.
+The characteristic timescale is from seconds to decades and (extrapolating)
+even more. The monitoring of the first star of our sample AM Her was initiated
+by Prof. V.P. Tsesevich (1907-1983). Since more than 358 ADS papers were
+published. Some highlights of our photometric and photo-polarimetric monitoring
+and mathematical modelling of interacting binary stars of different types are
+presented: classical, asynchronous, intermediate polars and magnetic dwarf
+novae (DO Dra) with 25 timescales corresponding to different physical
+mechanisms and their combinations (part ""Polar""); negative and positive
+superhumpers in nova-like and many dwarf novae stars (""Superhumper""); eclipsing
+""non-magnetic"" cataclysmic variables; symbiotic systems (""Symbiosis"");
+super-soft sources (SSS, QR And); spotted (and not spotted) eclipsing variables
+with (and without) evidence for a current mass transfer (""Eclipser"") with a
+special emphasis on systems with a direct impact of the stream into the gainer
+star's atmosphere, or V361 Lyr-type stars. Other parts of the ILA project are
+""Stellar Bell"" (interesting pulsating variables of different types and periods
+- M, SR, RV Tau, RR Lyr, Delta Sct) and ""Novice""(=""New Variable"") discoveries
+and classification with a subsequent monitoring for searching and studying
+possible multiple components of variability. Special mathematical methods have
+been developed to create a set of complementary software for statistically
+optimal modelling of variable stars of different types.
+",0,1,0,0,0,0
+19957,Interaction blockade for bosons in an asymmetric double well,"  The interaction blockade phenomenon isolates the motion of a single quantum
+particle within a multi-particle system, in particular for coherent
+oscillations in and out of a region affected by the blockade mechanism. For
+identical quantum particles with Bose statistics, the presence of the other
+particles is still felt by a bosonic stimulation factor $\sqrt{N}$ that speeds
+up the coherent oscillations, where $N$ is the number of bosons. Here we
+propose an experiment to observe this enhancement factor with a small number of
+bosonic atoms. The proposed protocol realises an asymmetric double well
+potential with multiple optical tweezer laser beams. The ability to adjust bias
+independently of the coherent coupling between the wells allows the potential
+to be loaded with different particle numbers while maintaining the resonance
+condition needed for coherent oscillations. Numerical simulations with up to
+three bosons in a realistic potential generated by three optical tweezers
+predict that the relevant avoided level crossing can be probed and the expected
+bosonic enhancement factor observed.
+",0,1,0,0,0,0
+19958,SPIDER: CMB polarimetry from the edge of space,"  SPIDER is a balloon-borne instrument designed to map the polarization of the
+millimeter-wave sky at large angular scales. SPIDER targets the B-mode
+signature of primordial gravitational waves in the cosmic microwave background
+(CMB), with a focus on mapping a large sky area with high fidelity at multiple
+frequencies. SPIDER's first longduration balloon (LDB) flight in January 2015
+deployed a total of 2400 antenna-coupled Transition Edge Sensors (TESs) at 90
+GHz and 150 GHz. In this work we review the design and in-flight performance of
+the SPIDER instrument, with a particular focus on the measured performance of
+the detectors and instrument in a space-like loading and radiation environment.
+SPIDER's second flight in December 2018 will incorporate payload upgrades and
+new receivers to map the sky at 285 GHz, providing valuable information for
+cleaning polarized dust emission from CMB maps.
+",0,1,0,0,0,0
+19959,Wind Shear and Turbulence on Titan : Huygens Analysis,"  Wind shear measured by Doppler tracking of the Huygens probe is evaluated,
+and found to be within the range anticipated by pre-flight assessments (namely
+less than two times the Brunt-Vaisala frequency). The strongest large-scale
+shear encountered was ~5 m/s/km, a level associated with 'Light' turbulence in
+terrestrial aviation. Near-surface winds (below 4km) have small-scale
+fluctuations of ~0.2 m/s , indicated both by probe tilt and Doppler tracking,
+and the characteristics of the fluctuation, of interest for future missions to
+Titan, can be reproduced with a simple autoregressive (AR(1)) model. The
+turbulent dissipation rate at an altitude of ~500m is found to be 16 cm2/sec3,
+which may be a useful benchmark for atmospheric circulation models.
+",0,1,0,0,0,0
+19960,Composable security in relativistic quantum cryptography,"  Relativistic protocols have been proposed to overcome some impossibility
+results in classical and quantum cryptography. In such a setting, one takes the
+location of honest players into account, and uses the fact that information
+cannot travel faster than the speed of light to limit the abilities of
+dishonest agents. For example, various relativistic bit commitment protocols
+have been proposed. Although it has been shown that bit commitment is
+sufficient to construct oblivious transfer and thus multiparty computation,
+composing specific relativistic protocols in this way is known to be insecure.
+A composable framework is required to perform such a modular security analysis
+of construction schemes, but no known frameworks can handle models of
+computation in Minkowski space.
+By instantiating the systems model from the Abstract Cryptography framework
+with Causal Boxes, we obtain such a composable framework, in which messages are
+assigned a location in Minkowski space (or superpositions thereof). This allows
+us to analyse relativistic protocols and to derive novel possibility and
+impossibility results. We show that (1) coin flipping can be constructed from
+the primitive channel with delay, (2) biased coin flipping, bit commitment and
+channel with delay are all impossible without further assumptions, and (3) it
+is impossible to improve a channel with delay. Note that the impossibility
+results also hold in the computational and bounded storage settings. This
+implies in particular non-composability of all proposed relativistic bit
+commitment protocols, of bit commitment in the bounded storage model, and of
+biased coin flipping.
+",1,0,0,0,0,0
+19961,Tensor networks demonstrate the robustness of localization and symmetry protected topological phases,"  We prove that all eigenstates of many-body localized symmetry protected
+topological systems with time reversal symmetry have four-fold degenerate
+entanglement spectra in the thermodynamic limit. To that end, we employ unitary
+quantum circuits where the number of sites the gates act on grows linearly with
+the system size. We find that the corresponding matrix product operator
+representation has similar local symmetries as matrix product ground states of
+symmetry protected topological phases. Those local symmetries give rise to a
+$\mathbb{Z}_2$ topological index, which is robust against arbitrary
+perturbations so long as they do not break time reversal symmetry or drive the
+system out of the fully many-body localized phase.
+",0,1,0,0,0,0
+19962,Formation of Intermediate-Mass Black Holes through Runaway Collisions in the First Star Clusters,"  We study the formation of massive black holes in the first star clusters. We
+first locate star-forming gas clouds in proto-galactic haloes of $\gtrsim
+\!10^7\,{\rm M}_{\odot}$ in cosmological hydrodynamics simulations and use them
+to generate the initial conditions for star clusters with masses of $\sim
+\!10^5\,{\rm M}_{\odot}$. We then perform a series of direct-tree hybrid
+$N$-body simulations to follow runaway stellar collisions in the dense star
+clusters. In all the cluster models except one, runaway collisions occur within
+a few million years, and the mass of the central, most massive star reaches
+$\sim \!400-1900\,{\rm M}_{\odot}$. Such very massive stars collapse to leave
+intermediate-mass black holes (IMBHs). The diversity of the final masses may be
+attributed to the differences in a few basic properties of the host haloes such
+as mass, central gas velocity dispersion, and mean gas density of the central
+core. Finally, we derive the IMBH mass to cluster mass ratios, and compare them
+with the observed black hole to bulge mass ratios in the present-day Universe.
+",0,1,0,0,0,0
+19963,Mean Reverting Portfolios via Penalized OU-Likelihood Estimation,"  We study an optimization-based approach to con- struct a mean-reverting
+portfolio of assets. Our objectives are threefold: (1) design a portfolio that
+is well-represented by an Ornstein-Uhlenbeck process with parameters estimated
+by maximum likelihood, (2) select portfolios with desirable characteristics of
+high mean reversion and low variance, and (3) select a parsimonious portfolio,
+i.e. find a small subset of a larger universe of assets that can be used for
+long and short positions. We present the full problem formulation, a
+specialized algorithm that exploits partial minimization, and numerical
+examples using both simulated and empirical price data.
+",0,0,0,1,0,1
+19964,When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies,"  We show how third-party web trackers can deanonymize users of
+cryptocurrencies. We present two distinct but complementary attacks. On most
+shopping websites, third party trackers receive information about user
+purchases for purposes of advertising and analytics. We show that, if the user
+pays using a cryptocurrency, trackers typically possess enough information
+about the purchase to uniquely identify the transaction on the blockchain, link
+it to the user's cookie, and further to the user's real identity. Our second
+attack shows that if the tracker is able to link two purchases of the same user
+to the blockchain in this manner, it can identify the user's entire cluster of
+addresses and transactions on the blockchain, even if the user employs
+blockchain anonymity techniques such as CoinJoin. The attacks are passive and
+hence can be retroactively applied to past purchases. We discuss several
+mitigations, but none are perfect.
+",1,0,0,0,0,0
+19965,Performance Evaluation of Container-based Virtualization for High Performance Computing Environments,"  Virtualization technologies have evolved along with the development of
+computational environments since virtualization offered needed features at that
+time such as isolation, accountability, resource allocation, resource fair
+sharing and so on. Novel processor technologies bring to commodity computers
+the possibility to emulate diverse environments where a wide range of
+computational scenarios can be run. Along with processors evolution, system
+developers have created different virtualization mechanisms where each new
+development enhanced the performance of previous virtualized environments.
+Recently, operating system-based virtualization technologies captured the
+attention of communities abroad (from industry to academy and research) because
+their important improvements on performance area.
+In this paper, the features of three container-based operating systems
+virtualization tools (LXC, Docker and Singularity) are presented. LXC, Docker,
+Singularity and bare metal are put under test through a customized single node
+HPL-Benchmark and a MPI-based application for the multi node testbed. Also the
+disk I/O performance, Memory (RAM) performance, Network bandwidth and GPU
+performance are tested for the COS technologies vs bare metal. Preliminary
+results and conclusions around them are presented and discussed.
+",1,0,0,0,0,0
+19966,An integral formula for the $Q$-prime curvature in 3-dimensional CR geometry,"  We give an integral formula for the total $Q^\prime$-curvature of a
+three-dimensional CR manifold with positive CR Yamabe constant and nonnegative
+Paneitz operator. Our derivation includes a relationship between the Green's
+functions of the CR Laplacian and the $P^\prime$-operator.
+",0,0,1,0,0,0
+19967,Intelligent Device Discovery in the Internet of Things - Enabling the Robot Society,"  The Internet of Things (IoT) is continuously growing to connect billions of
+smart devices anywhere and anytime in an Internet-like structure, which enables
+a variety of applications, services and interactions between human and objects.
+In the future, the smart devices are supposed to be able to autonomously
+discover a target device with desired features and generate a set of entirely
+new services and applications that are not supervised or even imagined by human
+beings. The pervasiveness of smart devices, as well as the heterogeneity of
+their design and functionalities, raise a major concern: How can a smart device
+efficiently discover a desired target device? In this paper, we propose a
+Social-Aware and Distributed (SAND) scheme that achieves a fast, scalable and
+efficient device discovery in the IoT. The proposed SAND scheme adopts a novel
+device ranking criteria that measures the device's degree, social relationship
+diversity, clustering coefficient and betweenness. Based on the device ranking
+criteria, the discovery request can be guided to travel through critical
+devices that stand at the major intersections of the network, and thus quickly
+reach the desired target device by contacting only a limited number of
+intermediate devices. With the help of such an intelligent device discovery as
+SAND, the IoT devices, as well as other computing facilities, software and data
+on the Internet, can autonomously establish new social connections with each
+other as human being do. They can formulate self-organized computing groups to
+perform required computing tasks, facilitate a fusion of a variety of computing
+service, network service and data to generate novel applications and services,
+evolve from the individual aritificial intelligence to the collaborative
+intelligence, and eventually enable the birth of a robot society.
+",1,0,0,0,0,0
+19968,Super-blockers and the effect of network structure on information cascades,"  Modelling information cascades over online social networks is important in
+fields from marketing to civil unrest prediction, however the underlying
+network structure strongly affects the probability and nature of such cascades.
+Even with simple cascade dynamics the probability of large cascades are almost
+entirely dictated by network properties, with well-known networks such as
+Erdos-Renyi and Barabasi-Albert producing wildly different cascades from the
+same model. Indeed, the notion of 'superspreaders' has arisen to describe
+highly influential nodes promoting global cascades in a social network. Here we
+use a simple model of global cascades to show that the presence of locality in
+the network increases the probability of a global cascade due to the increased
+vulnerability of connecting nodes. Rather than 'super-spreaders', we find that
+the presence of these highly connected 'super-blockers' in heavy-tailed
+networks in fact reduces the probability of global cascades, while promoting
+information spread when targeted as the initial spreader.
+",1,0,0,0,0,0
+19969,"Super-Gaussian, super-diffusive transport of multi-mode active matter","  Living cells exhibit multi-mode transport that switches between an active,
+self-propelled motion and a seemingly passive, random motion. Cellular
+decision-making over transport mode switching is a stochastic process that
+depends on the dynamics of the intracellular chemical network regulating the
+cell migration process. Here, we propose a theory and an exactly solvable model
+of multi-mode active matter. Our exact model study shows that the reversible
+transition between a passive mode and an active mode is the origin of the
+anomalous, super-Gaussian transport dynamics, which has been observed in
+various experiments for multi-mode active matter. We also present the
+generalization of our model to encompass complex multi-mode matter with
+arbitrary internal state chemical dynamics and internal state dependent
+transport dynamics.
+",0,1,0,0,0,0
+19970,Height functions for motives,"  We define various height functions for motives over number fields. We compare
+these height functions with classical height functions on algebraic varieties,
+and also with analogous height functions for variations of Hodge structures on
+curves over C. These comparisons provide new questions on motives over number
+fields.
+",0,0,1,0,0,0
+19971,On the dimension effect of regularized linear discriminant analysis,"  This paper studies the dimension effect of the linear discriminant analysis
+(LDA) and the regularized linear discriminant analysis (RLDA) classifiers for
+large dimensional data where the observation dimension $p$ is of the same order
+as the sample size $n$. More specifically, built on properties of the Wishart
+distribution and recent results in random matrix theory, we derive explicit
+expressions for the asymptotic misclassification errors of LDA and RLDA
+respectively, from which we gain insights of how dimension affects the
+performance of classification and in what sense. Motivated by these results, we
+propose adjusted classifiers by correcting the bias brought by the unequal
+sample sizes. The bias-corrected LDA and RLDA classifiers are shown to have
+smaller misclassification rates than LDA and RLDA respectively. Several
+interesting examples are discussed in detail and the theoretical results on
+dimension effect are illustrated via extensive simulation studies.
+",0,0,1,1,0,0
+19972,Plasma Wake Accelerators: Introduction and Historical Overview,"  Fundamental questions on the nature of matter and energy have found answers
+thanks to the use of particle accelerators. Societal applications, such as
+cancer treatment or cancer imaging, illustrate the impact of accelerators in
+our current life. Today, accelerators use metallic cavities that sustain
+electricfields with values limited to about 100 MV/m. Because of their ability
+to support extreme accelerating gradients, the plasma medium has recently been
+proposed for future cavity-like accelerating structures. This contribution
+highlights the tremendous evolution of plasma accelerators driven by either
+laser or particle beams that allow the production of high quality particle
+beams with a degree of tunability and a set of parameters that make them very
+pertinent for many applications.
+",0,1,0,0,0,0
+19973,$R$-triviality of some exceptional groups,"  The main aim of this paper is to prove $R$-triviality for simple, simply
+connected algebraic groups with Tits index $E_{8,2}^{78}$ or $E_{7,1}^{78}$,
+defined over a field $k$ of arbitrary characteristic. Let $G$ be such a group.
+We prove that there exists a quadratic extension $K$ of $k$ such that $G$ is
+$R$-trivial over $K$, i.e., for any extension $F$ of $K$, $G(F)/R=\{1\}$, where
+$G(F)/R$ denotes the group of $R$-equivalence classes in $G(F)$, in the sense
+of Manin (see \cite{M}). As a consequence, it follows that the variety $G$ is
+retract $K$-rational and that the Kneser-Tits conjecture holds for these groups
+over $K$. Moreover, $G(L)$ is projectively simple as an abstract group for any
+field extension $L$ of $K$. In their monograph (\cite{TW}) J. Tits and Richard
+Weiss conjectured that for an Albert division algebra $A$ over a field $k$, its
+structure group $Str(A)$ is generated by scalar homotheties and its
+$U$-operators. This is known to be equivalent to the Kneser-Tits conjecture for
+groups with Tits index $E_{8,2}^{78}$. We settle this conjecture for Albert
+division algebras which are first constructions, in affirmative. These results
+are obtained as corollaries to the main result, which shows that if $A$ is an
+Albert division algebra which is a first construction and $\Gamma$ its
+structure group, i.e., the algebraic group of the norm similarities of $A$,
+then $\Gamma(F)/R=\{1\}$ for any field extension $F$ of $k$, i.e., $\Gamma$ is
+$R$-trivial.
+",0,0,1,0,0,0
+19974,Aperture synthesis imaging of the carbon AGB star R Sculptoris: Detection of a complex structure and a dominating spot on the stellar disk,"  We present near-infrared interferometry of the carbon-rich asymptotic giant
+branch (AGB) star R Sculptoris.
+The visibility data indicate a broadly circular resolved stellar disk with a
+complex substructure. The observed AMBER squared visibility values show drops
+at the positions of CO and CN bands, indicating that these lines form in
+extended layers above the photosphere. The AMBER visibility values are best fit
+by a model without a wind. The PIONIER data are consistent with the same model.
+We obtain a Rosseland angular diameter of 8.9+-0.3 mas, corresponding to a
+Rosseland radius of 355+-55 Rsun, an effective temperature of 2640+-80 K, and a
+luminosity of log L/Lsun=3.74+-0.18. These parameters match evolutionary tracks
+of initial mass 1.5+-0.5 Msun and current mass 1.3+-0.7 Msun. The reconstructed
+PIONIER images exhibit a complex structure within the stellar disk including a
+dominant bright spot located at the western part of the stellar disk. The spot
+has an H-band peak intensity of 40% to 60% above the average intensity of the
+limb-darkening-corrected stellar disk. The contrast between the minimum and
+maximum intensity on the stellar disk is about 1:2.5.
+Our observations are broadly consistent with predictions by dynamic
+atmosphere and wind models, although models with wind appear to have a
+circumstellar envelope that is too extended compared to our observations. The
+detected complex structure within the stellar disk is most likely caused by
+giant convection cells, resulting in large-scale shock fronts, and their
+effects on clumpy molecule and dust formation seen against the photosphere at
+distances of 2-3 stellar radii.
+",0,1,0,0,0,0
+19975,Intrinsically Sparse Long Short-Term Memory Networks,"  Long Short-Term Memory (LSTM) has achieved state-of-the-art performances on a
+wide range of tasks. Its outstanding performance is guaranteed by the long-term
+memory ability which matches the sequential data perfectly and the gating
+structure controlling the information flow. However, LSTMs are prone to be
+memory-bandwidth limited in realistic applications and need an unbearable
+period of training and inference time as the model size is ever-increasing. To
+tackle this problem, various efficient model compression methods have been
+proposed. Most of them need a big and expensive pre-trained model which is a
+nightmare for resource-limited devices where the memory budget is strictly
+limited. To remedy this situation, in this paper, we incorporate the Sparse
+Evolutionary Training (SET) procedure into LSTM, proposing a novel model dubbed
+SET-LSTM. Rather than starting with a fully-connected architecture, SET-LSTM
+has a sparse topology and dramatically fewer parameters in both phases,
+training and inference. Considering the specific architecture of LSTMs, we
+replace the LSTM cells and embedding layers with sparse structures and further
+on, use an evolutionary strategy to adapt the sparse connectivity to the data.
+Additionally, we find that SET-LSTM can provide many different good
+combinations of sparse connectivity to substitute the overparameterized
+optimization problem of dense neural networks. Evaluated on four sentiment
+analysis classification datasets, the results demonstrate that our proposed
+model is able to achieve usually better performance than its fully connected
+counterpart while having less than 4\% of its parameters.
+",1,0,0,0,0,0
+19976,Inferencing into the void: problems with implicit populations Comments on `Empirical software engineering experts on the use of students and professionals in experiments',"  I welcome the contribution from Falessi et al. [1] hereafter referred to as
+F++ , and the ensuing debate. Experimentation is an important tool within
+empirical software engineering, so how we select participants is clearly a
+relevant question. Moreover as F++ point out, the question is considerably more
+nuanced than the simple dichotomy it might appear to be at first sight.
+This commentary is structured as follows. In Section 2 I briefly summarise
+the arguments of F++ and comment on their approach. Next, in Section 3, I take
+a step back to consider the nature of representativeness in inferential
+arguments and the need for careful definition. Then I give three examples of
+using different types of participant to consider impact. I conclude by arguing,
+largely in agreement with F++, that the question of whether student
+participants are representative or not depends on the target population.
+However, we need to give careful consideration to defining that population and,
+in particular, not to overlook the representativeness of tasks and environment.
+This is facilitated by explicit description of the target populations.
+",1,0,0,0,0,0
+19977,NoScope: Optimizing Neural Network Queries over Video at Scale,"  Recent advances in computer vision-in the form of deep neural networks-have
+made it possible to query increasing volumes of video data with high accuracy.
+However, neural network inference is computationally expensive at scale:
+applying a state-of-the-art object detector in real time (i.e., 30+ frames per
+second) to a single video requires a $4000 GPU. In response, we present
+NoScope, a system for querying videos that can reduce the cost of neural
+network video analysis by up to three orders of magnitude via
+inference-optimized model search. Given a target video, object to detect, and
+reference neural network, NoScope automatically searches for and trains a
+sequence, or cascade, of models that preserves the accuracy of the reference
+network but is specialized to the target video and are therefore far less
+computationally expensive. NoScope cascades two types of models: specialized
+models that forego the full generality of the reference model but faithfully
+mimic its behavior for the target video and object; and difference detectors
+that highlight temporal differences across frames. We show that the optimal
+cascade architecture differs across videos and objects, so NoScope uses an
+efficient cost-based optimizer to search across models and cascades. With this
+approach, NoScope achieves two to three order of magnitude speed-ups
+(265-15,500x real-time) on binary classification tasks over fixed-angle webcam
+and surveillance video while maintaining accuracy within 1-5% of
+state-of-the-art neural networks.
+",1,0,0,0,0,0
+19978,TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization,"  Recent research implies that training and inference of deep neural networks
+(DNN) can be computed with low precision numerical representations of the
+training/test data, weights and gradients without a general loss in accuracy.
+The benefit of such compact representations is twofold: they allow a
+significant reduction of the communication bottleneck in distributed DNN
+training and faster neural network implementations on hardware accelerators
+like FPGAs. Several quantization methods have been proposed to map the original
+32-bit floating point problem to low-bit representations. While most related
+publications validate the proposed approach on a single DNN topology, it
+appears to be evident, that the optimal choice of the quantization method and
+number of coding bits is topology dependent. To this end, there is no general
+theory available, which would allow users to derive the optimal quantization
+during the design of a DNN topology. In this paper, we present a quantization
+tool box for the TensorFlow framework. TensorQuant allows a transparent
+quantization simulation of existing DNN topologies during training and
+inference. TensorQuant supports generic quantization methods and allows
+experimental evaluation of the impact of the quantization on single layers as
+well as on the full topology. In a first series of experiments with
+TensorQuant, we show an analysis of fix-point quantizations of popular CNN
+topologies.
+",1,0,0,1,0,0
+19979,Information-geometrical characterization of statistical models which are statistically equivalent to probability simplexes,"  The probability simplex is the set of all probability distributions on a
+finite set and is the most fundamental object in the finite probability theory.
+In this paper we give a characterization of statistical models on finite sets
+which are statistically equivalent to probability simplexes in terms of
+$\alpha$-families including exponential families and mixture families. The
+subject has a close relation to some fundamental aspects of information
+geometry such as $\alpha$-connections and autoparallelity.
+",1,0,1,1,0,0
+19980,The Network Nullspace Property for Compressed Sensing of Big Data over Networks,"  We present a novel condition, which we term the net- work nullspace property,
+which ensures accurate recovery of graph signals representing massive
+network-structured datasets from few signal values. The network nullspace
+property couples the cluster structure of the underlying network-structure with
+the geometry of the sampling set. Our results can be used to design efficient
+sampling strategies based on the network topology.
+",1,0,0,1,0,0
+19981,On physically redundant and irrelevant features when applying Lie-group symmetry analysis to hydrodynamic stability analysis,"  Every linear system of partial differential equations (PDEs) admits a scaling
+symmetry in its dependent variables. In conjunction with other admitted
+symmetries of linear type, the associated invariant solution condition poses a
+linear eigenvalue problem. If this problem is structured such that the spectral
+theorem applies, then the general solution of the considered linear PDE system
+is obtained by summing or integrating the invariant eigenfunctions (modes) over
+all eigenvalues, depending on whether the spectrum of the operator is discrete
+or continuous. By first studying the 1-D diffusion equation as a demonstrating
+example, this method is then applied to a relevant 2-D problem from
+hydrodynamic stability analysis. The aim of this study is to draw attention to
+the following two independent facts that need to be addressed in future studies
+when constructing solutions for linear PDEs with the method of Lie-symmetries:
+(i) Although each new symmetry leads to a mathematically different spectral
+decomposition, they may all be physically redundant to standard ones and do not
+reveal a new physical mechanism behind the overall considered dynamical
+process, as incorrectly asserted, for example, in the recent studies by the
+group of Oberlack et al. Hence, with regard to linear stability analysis, no
+physically ""new"" or more ""general"" modes are generated by this method than the
+ones already established. (ii) Next to the eigenvalue parameters, each single
+mode can also acquire non-system parameters, depending on the choice of its
+underlying symmetry. These symmetry-induced parameters, however, are all
+physically irrelevant, since their effect on a single mode will cancel when
+considering all modes collectively. In particular, the collective action of all
+single modes is identical for all symmetry-based decompositions and thus
+indistinguishable when considering the full physical fields.
+",0,1,0,0,0,0
+19982,Transforming Musical Signals through a Genre Classifying Convolutional Neural Network,"  Convolutional neural networks (CNNs) have been successfully applied on both
+discriminative and generative modeling for music-related tasks. For a
+particular task, the trained CNN contains information representing the decision
+making or the abstracting process. One can hope to manipulate existing music
+based on this 'informed' network and create music with new features
+corresponding to the knowledge obtained by the network. In this paper, we
+propose a method to utilize the stored information from a CNN trained on
+musical genre classification task. The network was composed of three
+convolutional layers, and was trained to classify five-second song clips into
+five different genres. After training, randomly selected clips were modified by
+maximizing the sum of outputs from the network layers. In addition to the
+potential of such CNNs to produce interesting audio transformation, more
+information about the network and the original music could be obtained from the
+analysis of the generated features since these features indicate how the
+network 'understands' the music.
+",1,0,0,0,0,0
+19983,AMTnet: Action-Micro-Tube Regression by End-to-end Trainable Deep Architecture,"  Dominant approaches to action detection can only provide sub-optimal
+solutions to the problem, as they rely on seeking frame-level detections, to
+later compose them into ""action tubes"" in a post-processing step. With this
+paper we radically depart from current practice, and take a first step towards
+the design and implementation of a deep network architecture able to classify
+and regress whole video subsets, so providing a truly optimal solution of the
+action detection problem. In this work, in particular, we propose a novel deep
+net framework able to regress and classify 3D region proposals spanning two
+successive video frames, whose core is an evolution of classical region
+proposal networks (RPNs). As such, our 3D-RPN net is able to effectively encode
+the temporal aspect of actions by purely exploiting appearance, as opposed to
+methods which heavily rely on expensive flow maps. The proposed model is
+end-to-end trainable and can be jointly optimised for action localisation and
+classification in a single step. At test time the network predicts
+""micro-tubes"" encompassing two successive frames, which are linked up into
+complete action tubes via a new algorithm which exploits the temporal encoding
+learned by the network and cuts computation time by 50%. Promising results on
+the J-HMDB-21 and UCF-101 action detection datasets show that our model does
+outperform the state-of-the-art when relying purely on appearance.
+",1,0,0,0,0,0
+19984,Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering,"  This work introduces a novel estimation method, called LOVE, of the entries
+and structure of a loading matrix A in a sparse latent factor model X = AZ + E,
+for an observable random vector X in Rp, with correlated unobservable factors Z
+\in RK, with K unknown, and independent noise E. Each row of A is scaled and
+sparse. In order to identify the loading matrix A, we require the existence of
+pure variables, which are components of X that are associated, via A, with one
+and only one latent factor. Despite the fact that the number of factors K, the
+number of the pure variables, and their location are all unknown, we only
+require a mild condition on the covariance matrix of Z, and a minimum of only
+two pure variables per latent factor to show that A is uniquely defined, up to
+signed permutations. Our proofs for model identifiability are constructive, and
+lead to our novel estimation method of the number of factors and of the set of
+pure variables, from a sample of size n of observations on X. This is the first
+step of our LOVE algorithm, which is optimization-free, and has low
+computational complexity of order p2. The second step of LOVE is an easily
+implementable linear program that estimates A. We prove that the resulting
+estimator is minimax rate optimal up to logarithmic factors in p. The model
+structure is motivated by the problem of overlapping variable clustering,
+ubiquitous in data science. We define the population level clusters as groups
+of those components of X that are associated, via the sparse matrix A, with the
+same unobservable latent factor, and multi-factor association is allowed.
+Clusters are respectively anchored by the pure variables, and form overlapping
+sub-groups of the p-dimensional random vector X. The Latent model approach to
+OVErlapping clustering is reflected in the name of our algorithm, LOVE.
+",0,0,1,1,0,0
+19985,Asymptotic profile of solutions for some wave equations with very strong structural damping,"  We consider the Cauchy problem in R^n for some types of damped wave
+equations. We derive asymptotic profiles of solutions with weighted
+L^{1,1}(R^n) initial data by employing a simple method introduced by the first
+author. The obtained results will include regularity loss type estimates, which
+are essentially new in this kind of equations.
+",0,0,1,0,0,0
+19986,Spontaneous generation of fractional vortex-antivortex pairs at single edges of high-Tc superconductors,"  Unconventional d-wave superconductors with pair-breaking edges are predicted
+to have ground states with spontaneously broken time-reversal and translational
+symmetries. We use the quasiclassical theory of superconductivity to
+demonstrate that such phases can exist at any single pair-breaking facet. This
+implies that a greater variety of systems, not necessarily mesoscopic in size,
+should be unstable to such symmetry breaking. The density of states averaged
+over the facet displays a broad peak centered at zero energy, which is
+consistent with experimental findings of a broad zero-bias conductance peak
+with a temperature-independent width at low temperatures.
+",0,1,0,0,0,0
+19987,A Survey of Neuromorphic Computing and Neural Networks in Hardware,"  Neuromorphic computing has come to refer to a variety of brain-inspired
+computers, devices, and models that contrast the pervasive von Neumann computer
+architecture. This biologically inspired approach has created highly connected
+synthetic neurons and synapses that can be used to model neuroscience theories
+as well as solve challenging machine learning problems. The promise of the
+technology is to create a brain-like ability to learn and adapt, but the
+technical challenges are significant, starting with an accurate neuroscience
+model of how the brain works, to finding materials and engineering
+breakthroughs to build devices to support these models, to creating a
+programming framework so the systems can learn, to creating applications with
+brain-like capabilities. In this work, we provide a comprehensive survey of the
+research and motivations for neuromorphic computing over its history. We begin
+with a 35-year review of the motivations and drivers of neuromorphic computing,
+then look at the major research areas of the field, which we define as
+neuro-inspired models, algorithms and learning approaches, hardware and
+devices, supporting systems, and finally applications. We conclude with a broad
+discussion on the major research topics that need to be addressed in the coming
+years to see the promise of neuromorphic computing fulfilled. The goals of this
+work are to provide an exhaustive review of the research conducted in
+neuromorphic computing since the inception of the term, and to motivate further
+work by illuminating gaps in the field where new research is needed.
+",1,0,0,0,0,0
+19988,Junctions of refined Wilson lines and one-parameter deformation of quantum groups,"  We study junctions of Wilson lines in refined SU(N) Chern-Simons theory and
+their local relations. We focus on junctions of Wilson lines in antisymmetric
+and symmetric powers of the fundamental representation and propose a set of
+local relations which realize one-parameter deformations of quantum groups
+$\dot{U}_{q}(\mathfrak{sl}_{m})$ and $\dot{U}_{q}(\mathfrak{sl}_{n|m})$.
+",0,0,1,0,0,0
+19989,Online estimation of the asymptotic variance for averaged stochastic gradient algorithms,"  Stochastic gradient algorithms are more and more studied since they can deal
+efficiently and online with large samples in high dimensional spaces. In this
+paper, we first establish a Central Limit Theorem for these estimates as well
+as for their averaged version in general Hilbert spaces. Moreover, since having
+the asymptotic normality of estimates is often unusable without an estimation
+of the asymptotic variance, we introduce a new recursive algorithm for
+estimating this last one, and we establish its almost sure rate of convergence
+as well as its rate of convergence in quadratic mean. Finally, two examples
+consisting in estimating the parameters of the logistic regression and
+estimating geometric quantiles are given.
+",0,0,1,1,0,0
+19990,The response of the terrestrial bow shock and magnetopause of the long term decline in solar polar fields,"  The location of the terrestrial magnetopause (MP) and it's subsolar stand-off
+distance depends not only on the solar wind dynamic pressure and the
+interplanetary magnetic field (IMF), both of which play a crucial role in
+determining it's shape, but also on the nature of the processes involved in the
+interaction between the solar wind and the magnetosphere. The stand-off
+distance of the earth's MP and bow shock (BS) also define the extent of
+terrestrial magnetic fields into near-earth space on the sunward side and have
+important consequences for space weather. However, asymmetries due to the
+direction of the IMF are hard to account for, making it nearly impossible to
+favour any specific model over the other in estimating the extent of the MP or
+BS. Thus, both numerical and empirical models have been used and compared to
+estimate the BS and MP stand-off distances as well as the MP shape, in the
+period Jan. 1975-Dec. 2016, covering solar cycles 21-24. The computed MP and BS
+stand-off distances have been found to be increasing steadily over the past two
+decades, since ~1995, spanning solar cycles 23 and 24. The increasing trend is
+consistent with earlier reported studies of a long term and steady decline in
+solar polar magnetic fields and solar wind micro-turbulence levels. The present
+study, thus, highlights the response of the terrestrial magnetosphere to the
+long term global changes in both solar and solar wind activity, through a
+detailed study of the extent and shape of the terrestrial MP and BS over the
+past four solar cycles, a period spanning the last four decades.
+",0,1,0,0,0,0
+19991,Superconductivity of barium-VI synthesized via compression at low temperatures,"  Using a membrane-driven diamond anvil cell and both ac magnetic
+susceptibility and electrical resistivity measurements, we have characterized
+the superconducting phase diagram of elemental barium to pressures as high as
+65 GPa. We have determined the superconducting properties of the recently
+discovered Ba-VI crystal structure, which can only be accessed via the
+application of pressure at low temperature. We find that Ba-VI exhibits a
+maximum Tc near 8 K, which is substantially higher than the maximum Tc found
+when pressure is applied at room temperature.
+",0,1,0,0,0,0
+19992,Self-Supervised Damage-Avoiding Manipulation Strategy Optimization via Mental Simulation,"  Everyday robotics are challenged to deal with autonomous product handling in
+applications like logistics or retail, possibly causing damage on the items
+during manipulation. Traditionally, most approaches try to minimize physical
+interaction with goods. However, we propose to take into account any unintended
+motion of objects in the scene and to learn manipulation strategies in a
+self-supervised way which minimize the potential damage. The presented approach
+consists of a planning method that determines the optimal sequence to
+manipulate a number of objects in a scene with respect to possible damage by
+simulating interaction and hence anticipating scene dynamics. The planned
+manipulation sequences are taken as input to a machine learning process which
+generalizes to new, unseen scenes in the same application scenario. This
+learned manipulation strategy is continuously refined in a self-supervised
+optimization cycle dur- ing load-free times of the system. Such a
+simulation-in-the-loop setup is commonly known as mental simulation and allows
+for efficient, fully automatic generation of training data as opposed to
+classical supervised learning approaches. In parallel, the generated
+manipulation strategies can be deployed in near-real time in an anytime
+fashion. We evaluate our approach on one industrial scenario (autonomous
+container unloading) and one retail scenario (autonomous shelf replenishment).
+",1,0,0,0,0,0
+19993,Adversarial Deep Learning for Robust Detection of Binary Encoded Malware,"  Malware is constantly adapting in order to avoid detection. Model based
+malware detectors, such as SVM and neural networks, are vulnerable to so-called
+adversarial examples which are modest changes to detectable malware that allows
+the resulting malware to evade detection. Continuous-valued methods that are
+robust to adversarial examples of images have been developed using saddle-point
+optimization formulations. We are inspired by them to develop similar methods
+for the discrete, e.g. binary, domain which characterizes the features of
+malware. A specific extra challenge of malware is that the adversarial examples
+must be generated in a way that preserves their malicious functionality. We
+introduce methods capable of generating functionally preserved adversarial
+malware examples in the binary domain. Using the saddle-point formulation, we
+incorporate the adversarial examples into the training of models that are
+robust to them. We evaluate the effectiveness of the methods and others in the
+literature on a set of Portable Execution~(PE) files. Comparison prompts our
+introduction of an online measure computed during training to assess general
+expectation of robustness.
+",0,0,0,1,0,0
+19994,"Simple closed curves, finite covers of surfaces, and power subgroups of Out(F_n)","  We construct examples of finite covers of punctured surfaces where the first
+rational homology is not spanned by lifts of simple closed curves. More
+generally, for any set $\mathcal{O} \subset F_n$ which is contained in the
+union of finitely many $Aut(F_n)$-orbits, we construct finite-index normal
+subgroups of $F_n$ whose first rational homology is not spanned by powers of
+elements of $\mathcal{O}$. These examples answer questions of Farb-Hensel,
+Looijenga, and Marche. We also show that the quotient of $Out(F_n)$ by the
+subgroup generated by kth powers of transvections often contains infinite order
+elements, strengthening a result of Bridson-Vogtmann saying that it is often
+infinite. Finally, for any set $\mathcal{O} \subset F_n$ which is contained in
+the union of finitely many $Aut(F_n)$-orbits, we construct integral linear
+representations of free groups that have infinite image and map all elements of
+$\mathcal{O}$ to torsion elements.
+",0,0,1,0,0,0
+19995,Hard Mixtures of Experts for Large Scale Weakly Supervised Vision,"  Training convolutional networks (CNN's) that fit on a single GPU with
+minibatch stochastic gradient descent has become effective in practice.
+However, there is still no effective method for training large CNN's that do
+not fit in the memory of a few GPU cards, or for parallelizing CNN training. In
+this work we show that a simple hard mixture of experts model can be
+efficiently trained to good effect on large scale hashtag (multilabel)
+prediction tasks. Mixture of experts models are not new (Jacobs et. al. 1991,
+Collobert et. al. 2003), but in the past, researchers have had to devise
+sophisticated methods to deal with data fragmentation. We show empirically that
+modern weakly supervised data sets are large enough to support naive
+partitioning schemes where each data point is assigned to a single expert.
+Because the experts are independent, training them in parallel is easy, and
+evaluation is cheap for the size of the model. Furthermore, we show that we can
+use a single decoding layer for all the experts, allowing a unified feature
+embedding space. We demonstrate that it is feasible (and in fact relatively
+painless) to train far larger models than could be practically trained with
+standard CNN architectures, and that the extra capacity can be well used on
+current datasets.
+",1,0,0,1,0,0
+19996,Single-hole GPR reflection imaging of solute transport in a granitic aquifer,"  Identifying transport pathways in fractured rock is extremely challenging as
+flow is often organized in a few fractures that occupy a very small portion of
+the rock volume. We demonstrate that saline tracer experiments combined with
+single-hole ground penetrating radar (GPR) reflection imaging can be used to
+monitor saline tracer movement within mm-aperture fractures. A dipole tracer
+test was performed in a granitic aquifer by injecting a saline solution in a
+known fracture, while repeatedly acquiring single-hole GPR sections in the
+pumping borehole located 6 m away. The final depth-migrated difference sections
+make it possible to identify consistent temporal changes over a 30 m depth
+interval at locations corresponding to fractures previously imaged in GPR
+sections acquired under natural flow and tracer-free conditions. The experiment
+allows determining the dominant flow paths of the injected tracer and the
+velocity (0.4-0.7 m/min) of the tracer front.
+",0,1,0,0,0,0
+19997,Episodic Torque-Luminosity Correlations and Anticorrelations of GX 1+4,"  We analyse archival CGRO-BATSE X-ray flux and spin frequency measurements of
+GX 1+4 over a time span of 3000 days. We systematically search for time
+dependent variations of torque luminosity correlation. Our preliminary results
+indicate that the correlation shifts from being positive to negative on time
+scales of few 100 days.
+",0,1,0,0,0,0
+19998,Applications of Fractional Calculus to Newtonian Mechanics,"  We investigate some basic applications of Fractional Calculus (FC) to
+Newtonian mechanics. After a brief review of FC, we consider a possible
+generalization of Newton's second law of motion and apply it to the case of a
+body subject to a constant force. In our second application of FC to Newtonian
+gravity, we consider a generalized fractional gravitational potential and
+derive the related circular orbital velocities. This analysis might be used as
+a tool to model galactic rotation curves, in view of the dark matter problem.
+Both applications have a pedagogical value in connecting fractional calculus to
+standard mechanics and can be used as a starting point for a more advanced
+treatment of fractional mechanics.
+",0,1,0,0,0,0
+19999,Radial Surface Density Profiles of Gas and Dust in the Debris Disk around 49 Ceti,"  We present ~0.4 resolution images of CO(3-2) and associated continuum
+emission from the gas-bearing debris disk around the nearby A star 49 Ceti,
+observed with the Atacama Large Millimeter/Submillimeter Array (ALMA). We
+analyze the ALMA visibilities in tandem with the broad-band spectral energy
+distribution to measure the radial surface density profiles of dust and gas
+emission from the system. The dust surface density decreases with radius
+between ~100 and 310 au, with a marginally significant enhancement of surface
+density at a radius of ~110 au. The SED requires an inner disk of small grains
+in addition to the outer disk of larger grains resolved by ALMA. The gas disk
+exhibits a surface density profile that increases with radius, contrary to most
+previous spatially resolved observations of circumstellar gas disks. While ~80%
+of the CO flux is well described by an axisymmetric power-law disk in Keplerian
+rotation about the central star, residuals at ~20% of the peak flux exhibit a
+departure from axisymmetry suggestive of spiral arms or a warp in the gas disk.
+The radial extent of the gas disk (~220 au) is smaller than that of the dust
+disk (~300 au), consistent with recent observations of other gas-bearing debris
+disks. While there are so far only three broad debris disks with well
+characterized radial dust profiles at millimeter wavelengths, 49 Ceti's disk
+shows a markedly different structure from two radially resolved gas-poor debris
+disks, implying that the physical processes generating and sculpting the gas
+and dust are fundamentally different.
+",0,1,0,0,0,0
+20000,Spontaneously broken translational symmetry at edges of high-temperature superconductors: thermodynamics in magnetic field,"  We investigate equilibrium properties, including structure of the order
+parameter, superflow patterns, and thermodynamics of low-temperature surface
+phases of layered d_{x^2-y^2}-wave superconductors in magnetic field. At zero
+external magnetic field, time-reversal symmetry and continuous translational
+symmetry along the edge are broken spontaneously in a second order phase
+transition at a temperature $T^*\approx 0.18 T_c$, where $T_c$ is the
+superconducting transition temperature. At the phase transition there is a jump
+in the specific heat that scales with the ratio between the edge length $D$ and
+layer area ${\cal A}$ as $(D\xi_0/{\cal A})\Delta C_d$, where $\Delta C_d$ is
+the jump in the specific heat at the d-wave superconducting transition and
+$\xi_0$ is the superconducting coherence length. The phase with broken symmetry
+is characterized by a gauge invariant superfluid momentum ${\bf p}_s$ that
+forms a non-trivial planar vector field with a chain of sources and sinks along
+the edges with a period of approximately $12\xi_0$, and saddle point
+disclinations in the interior. To find out the relative importance of
+time-reversal and translational symmetry breaking we apply an external field
+that breaks time-reversal symmetry explicitly. We find that the phase
+transition into the state with the non-trivial ${\bf p}_s$ vector field keeps
+its main signatures, and is still of second order. In the external field, the
+saddle point disclinations are pushed towards the edges, and thereby a chain of
+edge motifs are formed, where each motif contains a source, a sink, and a
+saddle point. Due to a competing paramagnetic response at the edges, the phase
+transition temperature $T^*$ is slowly suppressed with increasing magnetic
+field strength, but the phase with broken symmetry survives into the mixed
+state.
+",0,1,0,0,0,0
+20001,Asymmetric Spin-wave Dispersion on Fe(110): Direct Evidence of Dzyaloshinskii--Moriya Interaction,"  The influence of the Dzyaloshinskii-Moriya interaction on the spin-wave
+dispersion in an Fe double layer grown on W(110) is measured for the first
+time. It is demonstrated that the Dzyaloshinskii-Moriya interaction breaks the
+degeneracy of spin waves and leads to an asymmetric spin-wave dispersion
+relation. An extended Heisenberg spin Hamiltonian is employed to obtain the
+longitudinal component of the Dzyaloshinskii-Moriya vectors from the
+experimentally measured energy asymmetry.
+",0,1,0,0,0,0
+20002,Consumption smoothing in the working-class households of interwar Japan,"  I analyze Osaka factory worker households in the early 1920s, whether
+idiosyncratic income shocks were shared efficiently, and which consumption
+categories were robust to shocks. While the null hypothesis of full
+risk-sharing of total expenditures was rejected, factory workers maintained
+their households, in that they paid for essential expenditures (rent,
+utilities, and commutation) during economic hardship. Additionally, children's
+education expenditures were possibly robust to idiosyncratic income shocks. The
+results suggest that temporary income is statistically significantly increased
+if disposable income drops due to idiosyncratic shocks. Historical documents
+suggest microfinancial lending and saving institutions helped mitigate
+risk-based vulnerabilities.
+",0,0,0,0,0,1
+20003,Observations and Modelling of the Pre-Flare Period of the 29 March 2014 X1 Flare,"  On the 29 March 2014 NOAA active region (AR) 12017 produced an X1 flare which
+was simultaneously observed by an unprecedented number of observatories. We
+have investigated the pre-flare period of this flare from 14:00 UT until 19:00
+UT using joint observations made by the Interface Region Imaging Spectrometer
+(IRIS) and the Hinode Extreme Ultraviolet Imaging Spectrometer (EIS). Spectral
+lines providing coverage of the solar atmosphere from chromosphere to the
+corona were analysed to investigate pre-flare activity within the AR. The
+results of the investigation have revealed evidence of strongly blue-shifted
+plasma flows, with velocities up to 200 km/s, being observed 40 minutes prior
+to flaring. These flows are located along the filament present in the active
+region and are both spatially discrete and transient. In order to constrain the
+possible explanations for this activity, we undertake non-potential magnetic
+field modelling of the active region. This modelling indicates the existence of
+a weakly twisted flux rope along the polarity inversion line in the region
+where a filament and the strong pre-flare flows are observed. We then discuss
+how these observations relate to the current models of flare triggering. We
+conclude that the most likely drivers of the observed activity are internal
+reconnection in the flux rope, early onset of the flare reconnection, or tether
+cutting reconnection along the filament.
+",0,1,0,0,0,0
+20004,"Wild ramification and K(pi, 1) spaces","  We prove that every connected affine scheme of positive characteristic is a
+K(pi, 1) space for the etale topology. The main ingredient is the special case
+of the affine space over a field k. This is dealt with by induction on n, using
+a key ""Bertini-type""' statement regarding the wild ramification of l-adic local
+systems on affine spaces, which might be of independent interest. Its proof
+uses in an essential way recent advances in higher ramification theory due to
+T. Saito. We also give rigid analytic and mixed characteristic versions of the
+main result.
+",0,0,1,0,0,0
+20005,A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading,"  Deep neural network models have been proven to be very successful in image
+classification tasks, also for medical diagnosis, but their main concern is its
+lack of interpretability. They use to work as intuition machines with high
+statistical confidence but unable to give interpretable explanations about the
+reported results. The vast amount of parameters of these models make difficult
+to infer a rationale interpretation from them. In this paper we present a
+diabetic retinopathy interpretable classifier able to classify retine images
+into the different levels of disease severity and of explaining its results by
+assigning a score for every point in the hidden and input space, evaluating its
+contribution to the final classification in a linear way. The generated visual
+maps can be interpreted by an expert in order to compare its own knowledge with
+the interpretation given by the model.
+",1,0,0,1,0,0
+20006,Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM,"  Arabic word segmentation is essential for a variety of NLP applications such
+as machine translation and information retrieval. Segmentation entails breaking
+words into their constituent stems, affixes and clitics. In this paper, we
+compare two approaches for segmenting four major Arabic dialects using only
+several thousand training examples for each dialect. The two approaches involve
+posing the problem as a ranking problem, where an SVM ranker picks the best
+segmentation, and as a sequence labeling problem, where a bi-LSTM RNN coupled
+with CRF determines where best to segment words. We are able to achieve solid
+segmentation results for all dialects using rather limited training data. We
+also show that employing Modern Standard Arabic data for domain adaptation and
+assuming context independence improve overall results.
+",1,0,0,0,0,0
+20007,Iterative bidding in electricity markets: rationality and robustness,"  This paper studies an electricity market consisting of an independent system
+operator (ISO) and a group of generators. The goal is to solve the DC optimal
+power flow (DC-OPF) problem: have the generators collectively meet the power
+demand while minimizing the aggregate generation cost and respecting line flow
+limits in the network. The ISO by itself cannot solve the DC-OPF problem as
+generators are strategic and do not share their cost functions. Instead, each
+generator submits to the ISO a bid, consisting of the price per unit of
+electricity at which it is willing to provide power. Based on the bids, the ISO
+decides how much production to allocate to each generator to minimize the total
+payment while meeting the load and satisfying the line limits. We provide a
+provably correct, decentralized iterative scheme, termed BID ADJUSTMENT
+ALGORITHM, for the resulting Bertrand competition game. Regarding convergence,
+we show that the algorithm takes the generators' bids to any desired
+neighborhood of the efficient Nash equilibrium at a linear convergence rate. As
+a consequence, the optimal production of the generators converges to the
+optimizer of the DC-OPF problem. Regarding robustness, we show that the
+algorithm is robust to affine perturbations in the bid adjustment scheme and
+that there is no incentive for any individual generator to deviate from the
+algorithm by using an alternative bid update scheme. We also establish the
+algorithm robustness to collusion, i.e., we show that, as long as each bus with
+generation has a generator following the strategy, there is no incentive for
+any group of generators to share information with the intent of tricking the
+system to obtain a higher payoff. Simulations illustrate our results.
+",1,0,1,0,0,0
+20008,Short-baseline electron antineutrino disappearance study by using neutrino sources from $^{13}$C + $^{9}$Be reaction,"  To investigate the existence of sterile neutrino, we propose a new neutrino
+production method using $^{13}$C beams and a $^{9}$Be target for short-baseline
+electron antineutrino (${\bar{\nu}}_{e}$) disappearance study. The production
+of secondary unstable isotopes which can emit neutrinos from the $^{13}$C +
+$^{9}$Be reaction is calculated with three different nucleus-nucleus (AA)
+reaction models. Different isotope yields are obtained using these models, but
+the results of the neutrino flux are found to have unanimous similarities. This
+feature gives an opportunity to study neutrino oscillation through shape
+analysis. In this work, expected neutrino flux and event rates are discussed in
+detail through intensive simulation of the light ion collision reaction and the
+neutrino flux from the beta decay of unstable isotopes followed by this
+collision. Together with the reactor and accelerator anomalies, the present
+proposed ${\bar{\nu}}_{e}$ source is shown to be a practically alternative test
+of the existence of the $\Delta m^{2}$ $\sim$ 1 eV$^{2}$ scale sterile
+neutrino.
+",0,1,0,0,0,0
+20009,Temperature-dependent non-covalent protein-protein interactions explain normal and inverted solubility in a mutant of human gamma D-crystallin,"  Protein crystal production is a major bottleneck for the structural
+characterisation of proteins. To advance beyond large-scale screening, rational
+strategies for protein crystallization are crucial. Understanding how chemical
+anisotropy (or patchiness) of the protein surface due to the variety of amino
+acid side chains in contact with solvent, contributes to protein protein
+contact formation in the crystal lattice is a major obstacle to predicting and
+optimising crystallization. The relative scarcity of sophisticated theoretical
+models that include sufficient detail to link collective behaviour, captured in
+protein phase diagrams, and molecular level details, determined from
+high-resolution structural information is a further barrier. Here we present
+two crystals structures for the P23TR36S mutant of gamma D-crystallin, each
+with opposite solubility behaviour, one melts when heated, the other when
+cooled. When combined with the protein phase diagram and a tailored patchy
+particle model we show that a single temperature dependent interaction is
+sufficient to stabilise the inverted solubility crystal. This contact, at the
+P23T substitution site, relates to a genetic cataract and reveals at a
+molecular level, the origin of the lowered and retrograde solubility of the
+protein. Our results show that the approach employed here may present an
+alternative strategy for the rationalization of protein crystallization.
+",0,0,0,0,1,0
+20010,Planetary Candidates Observed by Kepler. VIII. A Fully Automated Catalog With Measured Completeness and Reliability Based on Data Release 25,"  We present the Kepler Object of Interest (KOI) catalog of transiting
+exoplanets based on searching four years of Kepler time series photometry (Data
+Release 25, Q1-Q17). The catalog contains 8054 KOIs of which 4034 are planet
+candidates with periods between 0.25 and 632 days. Of these candidates, 219 are
+new and include two in multi-planet systems (KOI-82.06 and KOI-2926.05), and
+ten high-reliability, terrestrial-size, habitable zone candidates. This catalog
+was created using a tool called the Robovetter which automatically vets the
+DR25 Threshold Crossing Events (TCEs, Twicken et al. 2016). The Robovetter also
+vetted simulated data sets and measured how well it was able to separate TCEs
+caused by noise from those caused by low signal-to-noise transits. We discusses
+the Robovetter and the metrics it uses to sort TCEs. For orbital periods less
+than 100 days the Robovetter completeness (the fraction of simulated transits
+that are determined to be planet candidates) across all observed stars is
+greater than 85%. For the same period range, the catalog reliability (the
+fraction of candidates that are not due to instrumental or stellar noise) is
+greater than 98%. However, for low signal-to-noise candidates between 200 and
+500 days around FGK dwarf stars, the Robovetter is 76.7% complete and the
+catalog is 50.5% reliable. The KOI catalog, the transit fits and all of the
+simulated data used to characterize this catalog are available at the NASA
+Exoplanet Archive.
+",0,1,0,0,0,0
+20011,Dark Photons from Captured Inelastic Dark Matter Annihilation: Charged Particle Signatures,"  The dark sector may contain a dark photon that kinetically mixes with the
+Standard Model photon, allowing dark matter to interact weakly with normal
+matter. In previous work we analyzed the implications of this scenario for dark
+matter capture by the Sun. Dark matter will gather in the core of the Sun and
+annihilate to dark photons. These dark photons travel outwards from the center
+of the Sun and may decay to produce positrons that can be detected by the Alpha
+Magnetic Spectrometer (AMS-02) on the ISS. We found that the dark photon
+parameter space accessible to this analysis is largely constrained by strong
+limits on the spin-independent WIMP-nucleon cross section from direct detection
+experiments. In this paper we build upon previous work by considering the case
+where the dark sector contains two species of Dirac fermion that are nearly
+degenerate in mass and couple inelastically to the dark photon. We find that
+for small values of the mass splitting $\Delta \sim 100 ~\text{keV}$, the
+predicted positron signal at AMS-02 remains largely unchanged from the
+previously considered elastic case while constraints from direct detection are
+relaxed, leaving a region of parameter space with dark matter mass $100
+~\text{GeV} \lesssim m_X \lesssim 10 ~\text{TeV}$, dark photon mass $1
+~\text{MeV} \lesssim m_{A'} \lesssim 100 ~\text{MeV}$, and kinetic mixing
+parameter $10^{-9} \lesssim \varepsilon \lesssim 10^{-8}$ that is untouched by
+supernova observations and fixed target experiments but where an inelastic dark
+sector may still be discovered using existing AMS-02 data.
+",0,1,0,0,0,0
+20012,A Polya Contagion Model for Networks,"  A network epidemics model based on the classical Polya urn scheme is
+investigated. Temporal contagion processes are generated on the network nodes
+using a modified Polya sampling scheme that accounts for spatial infection
+among neighbouring nodes. The stochastic properties and the asymptotic
+behaviour of the resulting network contagion process are analyzed. Unlike the
+classical Polya process, the network process is noted to be non-stationary in
+general, although it is shown to be time-invariant in its first and some of its
+second-order statistics and to satisfy martingale convergence properties under
+certain conditions. Three classical Polya processes, one computational and two
+analytical, are proposed to statistically approximate the contagion process of
+each node, showing a good fit for a range of system parameters. Finally,
+empirical results compare and contrast our model with the well-known discrete
+time SIS model.
+",1,1,1,0,0,0
+20013,Compressive Statistical Learning with Random Feature Moments,"  We describe a general framework --compressive statistical learning-- for
+resource-efficient large-scale learning: the training collection is compressed
+in one pass into a low-dimensional sketch (a vector of random empirical
+generalized moments) that captures the information relevant to the considered
+learning task. A near-minimizer of the risk is computed from the sketch through
+the solution of a nonlinear least squares problem. We investigate sufficient
+sketch sizes to control the generalization error of this procedure. The
+framework is illustrated on compressive clustering, compressive Gaussian
+mixture Modeling with fixed known variance, and compressive PCA.
+",1,0,1,1,0,0
+20014,Bandit Structured Prediction for Neural Sequence-to-Sequence Learning,"  Bandit structured prediction describes a stochastic optimization framework
+where learning is performed from partial feedback. This feedback is received in
+the form of a task loss evaluation to a predicted output structure, without
+having access to gold standard structures. We advance this framework by lifting
+linear bandit learning to neural sequence-to-sequence learning problems using
+attention-based recurrent neural networks. Furthermore, we show how to
+incorporate control variates into our learning algorithms for variance
+reduction and improved generalization. We present an evaluation on a neural
+machine translation task that shows improvements of up to 5.89 BLEU points for
+domain adaptation from simulated bandit feedback.
+",1,0,0,1,0,0
+20015,Dynamic Input Structure and Network Assembly for Few-Shot Learning,"  The ability to learn from a small number of examples has been a difficult
+problem in machine learning since its inception. While methods have succeeded
+with large amounts of training data, research has been underway in how to
+accomplish similar performance with fewer examples, known as one-shot or more
+generally few-shot learning. This technique has been shown to have promising
+performance, but in practice requires fixed-size inputs making it impractical
+for production systems where class sizes can vary. This impedes training and
+the final utility of few-shot learning systems. This paper describes an
+approach to constructing and training a network that can handle arbitrary
+example sizes dynamically as the system is used.
+",1,0,0,1,0,0
+20016,Face R-CNN,"  Faster R-CNN is one of the most representative and successful methods for
+object detection, and has been becoming increasingly popular in various
+objection detection applications. In this report, we propose a robust deep face
+detection approach based on Faster R-CNN. In our approach, we exploit several
+new techniques including new multi-task loss function design, online hard
+example mining, and multi-scale training strategy to improve Faster R-CNN in
+multiple aspects. The proposed approach is well suited for face detection, so
+we call it Face R-CNN. Extensive experiments are conducted on two most popular
+and challenging face detection benchmarks, FDDB and WIDER FACE, to demonstrate
+the superiority of the proposed approach over state-of-the-arts.
+",1,0,0,0,0,0
+20017,Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach,"  Data-target pairing is an important step towards multi-target localization
+for the intelligent operation of unmanned systems. Target localization plays a
+crucial role in numerous applications, such as search, and rescue missions,
+traffic management and surveillance. The objective of this paper is to present
+an innovative target location learning approach, where numerous machine
+learning approaches, including K-means clustering and supported vector machines
+(SVM), are used to learn the data pattern across a list of spatially
+distributed sensors. To enable the accurate data association from different
+sensors for accurate target localization, appropriate data pre-processing is
+essential, which is then followed by the application of different machine
+learning algorithms to appropriately group data from different sensors for the
+accurate localization of multiple targets. Through simulation examples, the
+performance of these machine learning algorithms is quantified and compared.
+",1,0,0,1,0,0
+20018,Calipso: Physics-based Image and Video Editing through CAD Model Proxies,"  We present Calipso, an interactive method for editing images and videos in a
+physically-coherent manner. Our main idea is to realize physics-based
+manipulations by running a full physics simulation on proxy geometries given by
+non-rigidly aligned CAD models. Running these simulations allows us to apply
+new, unseen forces to move or deform selected objects, change physical
+parameters such as mass or elasticity, or even add entire new objects that
+interact with the rest of the underlying scene. In Calipso, the user makes
+edits directly in 3D; these edits are processed by the simulation and then
+transfered to the target 2D content using shape-to-image correspondences in a
+photo-realistic rendering process. To align the CAD models, we introduce an
+efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
+non-rigid alignment while preserving the high-level structure of the input
+shape. Moreover, the user can choose to exploit image flow to estimate scene
+motion, producing coherent physical behavior with ambient dynamics. We
+demonstrate Calipso's physics-based editing on a wide range of examples
+producing myriad physical behavior while preserving geometric and visual
+consistency.
+",1,0,0,0,0,0
+20019,"Maximal (120,8)-arcs in projective planes of order 16 and related designs","  The resolutions and maximal sets of compatible resolutions of all 2-(120,8,1)
+designs arising frommaximal (120,8)-arcs in the known projective planes of
+order 16 are computed. It is shown that each of these designs is embeddable in
+a unique way in a projective plane of order 16.
+",0,0,1,0,0,0
+20020,First order sentences about random graphs: small number of alternations,"  Spectrum of a first order sentence is the set of all $\alpha$ such that $G(n,
+n^{-\alpha})$ does not obey zero-one law w.r.t. this sentence. We have proved
+that the minimal number of quantifier alternations of a first order sentence
+with an infinite spectrum equals 3. We have also proved that the spectrum of a
+first order sentence with a quantifier depth 4 has no limit points except
+possibly the points 1/2 and 3/5.
+",0,0,1,0,0,0
+20021,New Methods for Metadata Extraction from Scientific Literature,"  Within the past few decades we have witnessed digital revolution, which moved
+scholarly communication to electronic media and also resulted in a substantial
+increase in its volume. Nowadays keeping track with the latest scientific
+achievements poses a major challenge for the researchers. Scientific
+information overload is a severe problem that slows down scholarly
+communication and knowledge propagation across the academia. Modern research
+infrastructures facilitate studying scientific literature by providing
+intelligent search tools, proposing similar and related documents, visualizing
+citation and author networks, assessing the quality and impact of the articles,
+and so on. In order to provide such high quality services the system requires
+the access not only to the text content of stored documents, but also to their
+machine-readable metadata. Since in practice good quality metadata is not
+always available, there is a strong demand for a reliable automatic method of
+extracting machine-readable metadata directly from source documents. This
+research addresses these problems by proposing an automatic, accurate and
+flexible algorithm for extracting wide range of metadata directly from
+scientific articles in born-digital form. Extracted information includes basic
+document metadata, structured full text and bibliography section. Designed as a
+universal solution, proposed algorithm is able to handle a vast variety of
+publication layouts with high precision and thus is well-suited for analyzing
+heterogeneous document collections. This was achieved by employing supervised
+and unsupervised machine-learning algorithms trained on large, diverse
+datasets. The evaluation we conducted showed good performance of proposed
+metadata extraction algorithm. The comparison with other similar solutions also
+proved our algorithm performs better than competition for most metadata types.
+",1,0,0,0,0,0
+20022,Unexpected biases in prime factorizations and Liouville functions for arithmetic progressions,"  We introduce a refinement of the classical Liouville function to primes in
+arithmetic progressions. Using this, we discover new biases in the appearances
+of primes in a given arithmetic progression in the prime factorizations of
+integers. For example, we observe that the primes of the form $4k+1$ tend to
+appear an even number of times in the prime factorization of a given integer,
+more so than for primes of the form $4k+3$. We are led to consider variants of
+Pólya's conjecture, supported by extensive numerical evidence, and its
+relation to other conjectures.
+",0,0,1,0,0,0
+20023,Nonconvection and uniqueness in Navier-Stokes equation,"  In the presence of a certain class of functions we show that there exists a
+smooth solution to Navier-Stokes equation. This solution entertains the
+property of being nonconvective. We introduce a definition for any possible
+solution to the problem with minimum assumptions on the existence and the
+regularity of such solution. Then we prove that the proposed class of functions
+represents the unique solution to the problem and consequently we conclude that
+there exists no convective solutions to the problem in the sense of the given
+definition.
+",0,0,1,0,0,0
+20024,An alternative approach for compatibility of two discrete conditional distributions,"  Conditional specification of distributions is a developing area with
+increasing applications. In the finite discrete case, a variety of compatible
+conditions can be derived. In this paper, we propose an alternative approach to
+study the compatibility of two conditional probability distributions under the
+finite discrete setup. A technique based on rank-based criterion is shown to be
+particularly convenient for identifying compatible distributions corresponding
+to complete conditional specification including the case with zeros.The
+proposed methods are illustrated with several examples.
+",0,0,1,1,0,0
+20025,System of unbiased representatives for a collection of bicolorings,"  Let $\mathcal{B}$ denote a set of bicolorings of $[n]$, where each bicoloring
+is a mapping of the points in $[n]$ to $\{-1,+1\}$.
+For each $B \in \mathcal{B}$, let $Y_B=(B(1),\ldots,B(n))$.
+For each $A \subseteq [n]$, let $X_A \in \{0,1\}^n$ denote the incidence
+vector of $A$.
+A non-empty set $A$ is said to be an `unbiased representative' for a
+bicoloring $B \in \mathcal{B}$ if $\left\langle X_A,Y_B\right\rangle =0$.
+Given a set $\mathcal{B}$ of bicolorings, we study the minimum cardinality of
+a family $\mathcal{A}$ consisting of subsets of $[n]$ such that every
+bicoloring in $\mathcal{B}$ has an unbiased representative in $\mathcal{A}$.
+",1,0,0,0,0,0
+20026,Stability and instability in saddle point dynamics Part II: The subgradient method,"  In part I we considered the problem of convergence to a saddle point of a
+concave-convex function via gradient dynamics and an exact characterization was
+given to their asymptotic behaviour. In part II we consider a general class of
+subgradient dynamics that provide a restriction in an arbitrary convex domain.
+We show that despite the nonlinear and non-smooth character of these dynamics
+their $\omega$-limit set is comprised of solutions to only linear ODEs. In
+particular, we show that the latter are solutions to subgradient dynamics on
+affine subspaces which is a smooth class of dynamics the asymptotic properties
+of which have been exactly characterized in part I. Various convergence
+criteria are formulated using these results and several examples and
+applications are also discussed throughout the manuscript.
+",1,0,1,0,0,0
+20027,"Refactoring Legacy JavaScript Code to Use Classes: The Good, The Bad and The Ugly","  JavaScript systems are becoming increasingly complex and large. To tackle the
+challenges involved in implementing these systems, the language is evolving to
+include several constructions for programming- in-the-large. For example,
+although the language is prototype-based, the latest JavaScript standard, named
+ECMAScript 6 (ES6), provides native support for implementing classes. Even
+though most modern web browsers support ES6, only a very few applications use
+the class syntax. In this paper, we analyze the process of migrating structures
+that emulate classes in legacy JavaScript code to adopt the new syntax for
+classes introduced by ES6. We apply a set of migration rules on eight legacy
+JavaScript systems. In our study, we document: (a) cases that are
+straightforward to migrate (the good parts); (b) cases that require manual and
+ad-hoc migration (the bad parts); and (c) cases that cannot be migrated due to
+limitations and restrictions of ES6 (the ugly parts). Six out of eight systems
+(75%) contain instances of bad and/or ugly cases. We also collect the
+perceptions of JavaScript developers about migrating their code to use the new
+syntax for classes.
+",1,0,0,0,0,0
+20028,Parylene-C microfibrous thin films as phononic crystals,"  Phononic bandgaps of Parylene-C microfibrous thin films (muFTFs) were
+computationally determined by treating them as phononic crystals comprising
+identical microfibers arranged either on a square or a hexagonal lattice. The
+microfibers could be columnar,chevronic, or helical in shape, and the host
+medium could be either water or air. All bandgaps were observed to lie in the
+0.01-to-162.9-MHz regime, for microfibers of realistically chosen dimensions.
+The upper limit of the frequency of bandgaps was the highest for the columnar
+muFTF and the lowest for the chiral muFTF. More bandgaps exist when the host
+medium is water than air. Complete bandgaps were observed for the columnar
+muFTF with microfibers arranged on a hexagonal lattice in air, the chevronic
+muFTF with microfibers arranged on a square lattice in water, and the chiral
+muFTF with microfibers arranged on a hexagonal lattice in either air or water.
+The softness of the Parylene-C muFTFs makes them mechanically tunable, and
+their bandgaps can be exploited in multiband ultrasonic filters.
+",0,1,0,0,0,0
+20029,A General Theory for Training Learning Machine,"  Though the deep learning is pushing the machine learning to a new stage,
+basic theories of machine learning are still limited. The principle of
+learning, the role of the a prior knowledge, the role of neuron bias, and the
+basis for choosing neural transfer function and cost function, etc., are still
+far from clear. In this paper, we present a general theoretical framework for
+machine learning. We classify the prior knowledge into common and
+problem-dependent parts, and consider that the aim of learning is to maximally
+incorporate them. The principle we suggested for maximizing the former is the
+design risk minimization principle, while the neural transfer function, the
+cost function, as well as pretreatment of samples, are endowed with the role
+for maximizing the latter. The role of the neuron bias is explained from a
+different angle. We develop a Monte Carlo algorithm to establish the
+input-output responses, and we control the input-output sensitivity of a
+learning machine by controlling that of individual neurons. Applications of
+function approaching and smoothing, pattern recognition and classification, are
+provided to illustrate how to train general learning machines based on our
+theory and algorithm. Our method may in addition induce new applications, such
+as the transductive inference.
+",1,0,0,1,0,0
+20030,Semiparametric spectral modeling of the Drosophila connectome,"  We present semiparametric spectral modeling of the complete larval Drosophila
+mushroom body connectome. Motivated by a thorough exploratory data analysis of
+the network via Gaussian mixture modeling (GMM) in the adjacency spectral
+embedding (ASE) representation space, we introduce the latent structure model
+(LSM) for network modeling and inference. LSM is a generalization of the
+stochastic block model (SBM) and a special case of the random dot product graph
+(RDPG) latent position model, and is amenable to semiparametric GMM in the ASE
+representation space. The resulting connectome code derived via semiparametric
+GMM composed with ASE captures latent connectome structure and elucidates
+biologically relevant neuronal properties.
+",0,0,0,1,0,0
+20031,A Large Term Rewrite System Modelling a Pioneering Cryptographic Algorithm,"  We present a term rewrite system that formally models the Message
+Authenticator Algorithm (MAA), which was one of the first cryptographic
+functions for computing a Message Authentication Code and was adopted, between
+1987 and 2001, in international standards (ISO 8730 and ISO 8731-2) to ensure
+the authenticity and integrity of banking transactions. Our term rewrite system
+is large (13 sorts, 18 constructors, 644 non-constructors, and 684 rewrite
+rules), confluent, and terminating. Implementations in thirteen different
+languages have been automatically derived from this model and used to validate
+200 official test vectors for the MAA.
+",1,0,0,0,0,0
+20032,Malleability of complex networks,"  Most complex networks are not static, but evolve along time. Given a specific
+configuration of one such changing network, it becomes a particularly
+interesting issue to quantify the diversity of possible unfoldings of its
+topology. In this work, we suggest the concept of malleability of a network,
+which is defined as the exponential of the entropy of the probabilities of each
+possible unfolding with respect to a given configuration. We calculate the
+malleability with respect to specific measurements of the involved topologies.
+More specifically, we identify the possible topologies derivable from a given
+configuration and calculate some topological measurement of them (e.g.
+clustering coefficient, shortest path length, assortativity, etc.), leading to
+respective probabilities being associated to each possible measurement value.
+Though this approach implies some level of degeneracy in the mapping from
+topology to measurement space, it still paves the way to inferring the
+malleability of specific network types with respect to given topological
+measurements. We report that the malleability, in general, depends on each
+specific measurement, with the average shortest path length and degree
+assortativity typically leading to large malleability values. The maximum
+malleability was observed for the Wikipedia network and the minimum for the
+Watts-Strogatz model.
+",1,0,0,0,0,0
+20033,Identifying Critical Risks of Cascading Failures in Power Systems,"  Potential critical risks of cascading failures in power systems can be
+identified by exposing those critical electrical elements on which certain
+initial disturbances may cause maximum disruption to power transmission
+networks. In this work, we investigate cascading failures in power systems
+described by the direct current (DC) power flow equations, while initial
+disturbances take the form of altering admittance of elements. The disruption
+is quantified with the remaining transmission power at the end of cascading
+process. In particular, identifying the critical elements and the corresponding
+initial disturbances causing the worst-case cascading blackout is formulated as
+a dynamic optimization problem (DOP) in the framework of optimal control
+theory, where the entire propagation process of cascading failures is put under
+consideration. An Identifying Critical Risk Algorithm (ICRA) based on the
+maximum principle is proposed to solve the DOP. Simulation results on the IEEE
+9-Bus and the IEEE 14-Bus test systems are presented to demonstrate the
+effectiveness of the algorithm.
+",1,0,0,0,0,0
+20034,Radiation Hardness Test of Eljen EJ-500 Optical Cement,"  We present a comprehensive account of the proton radiation hardness of Eljen
+Technology's EJ-500 optical cement used in the construction of experiment
+detectors. The cement was embedded into five plastic scintillator tiles which
+were each exposed to one of five different levels of radiation by a 50 MeV
+proton beam produced at the 88-Inch Cyclotron at Lawrence Berkeley National
+Laboratory. A cosmic ray telescope setup was used to measure signal amplitudes
+before and after irradiation. Another post-radiation measurement was taken four
+months after the experiment to investigate whether the radiation damage to the
+cement recovers after a short amount of time. We verified that the radiation
+damage to the tiles increased with increasing dose but showed significant
+improvement after the four months time interval.
+",0,1,0,0,0,0
+20035,Triviality of the ground-state metastate in long-range Ising spin glasses in one dimension,"  We consider the one-dimensional model of a spin glass with independent
+Gaussian-distributed random interactions, that have mean zero and variance
+$1/|i-j|^{2\sigma}$, between the spins at sites $i$ and $j$ for all $i\neq j$.
+It is known that, for $\sigma>1$, there is no phase transition at any non-zero
+temperature in this model. We prove rigorously that, for $\sigma>3/2$, any
+Newman-Stein metastate for the ground states (i.e.\ the frequencies with which
+distinct ground states are observed in finite size samples in the limit of
+infinite size, for given disorder) is trivial and unique. In other words, for
+given disorder and asymptotically at large sizes, the same ground state, or its
+global spin flip, is obtained (almost) always. The proof consists of two parts:
+one is a theorem (based on one by Newman and Stein for short-range
+two-dimensional models), valid for all $\sigma>1$, that establishes triviality
+under a convergence hypothesis on something similar to the energies of domain
+walls, and the other (based on older results for the one-dimensional model)
+establishes that the hypothesis is true for $\sigma>3/2$. In addition, we
+derive heuristic scaling arguments and rigorous exponent inequalities which
+tend to support the validity of the hypothesis under broader conditions. The
+constructions of various metastates are extended to all values $\sigma>1/2$.
+Triviality of the metastate in bond-diluted power-law models for $\sigma>1$ is
+proved directly.
+",0,1,0,0,0,0
+20036,Thermodynamics of BTZ Black Holes in Gravity's Rainbow,"  In this paper, we deform the thermodynamics of a BTZ black hole from rainbow
+functions in gravity's rainbow. The rainbow functions will be motivated from
+results in loop quantum gravity and Noncommutative geometry. It will be
+observed that the thermodynamics gets deformed due to these rainbow functions,
+indicating the existence of a remnant. However, the Gibbs free energy does not
+get deformed due to these rainbow functions, and so the critical behaviour from
+Gibbs does not change by this deformation.This is because the deformation in
+the entropy cancel's out the temperature deformation.
+",0,1,0,0,0,0
+20037,Nondegeneracy and the Jacobi fields of rotationally symmetric solutions to the Cahn-Hillard equation,"  In this paper we study rotationally symmetric solutions of the Cahn-Hilliard
+equation in $\mathbb R^3$ constructed by the authors. These solutions form a
+one parameter family analog to the family of Delaunay surfaces and in fact the
+zero level sets of their blowdowns approach these surfaces. Presently we go a
+step further and show that their stability properties are inherited from the
+stability properties of the Delaunay surfaces. Our main result states that the
+rotationally symmetric solutions are non degenerate and that they have exactly
+$6$ Jacobi fields of temperate growth coming from the natural invariances of
+the problem (3 translations and 2 rotations) and the variation of the Delaunay
+parameter.
+",0,0,1,0,0,0
+20038,Quasiflats in hierarchically hyperbolic spaces,"  The rank of a hierarchically hyperbolic space is the maximal number of
+unbounded factors of standard product regions; this coincides with the maximal
+dimension of a quasiflat for hierarchically hyperbolic groups. Noteworthy
+examples where the rank coincides with familiar quantities include: the
+dimension of maximal Dehn twist flats for mapping class groups, the maximal
+rank of a free abelian subgroup for right-angled Coxeter groups and
+right-angled Artin groups (in the latter this coincides with the clique number
+of the defining graph), and, for the Weil-Petersson metric the rank is half the
+complex dimension of Teichmuller space.
+We prove that in a HHS, any quasiflat of dimension equal to the rank lies
+within finite distance of a union of standard orthants (under a very mild
+condition satisfied by all natural examples). This resolves outstanding
+conjectures when applied to a number of different groups and spaces. The
+mapping class group case resolves a conjecture of Farb, in Teichmuller space
+this resolves a question of Brock, and in the context of CAT(0) cubical groups
+it strengthens previous results (so as to handle, for example, the right-angled
+Coxeter case).
+An important ingredient, is our proof that the hull of any finite set in an
+HHS is quasi-isometric to a cube complex of dimension equal to the rank.
+We deduce a number of applications; for instance we show that any
+quasi-isometry between HHS induces a quasi-isometry between certain simpler
+HHS. This allows one, for example, to distinguish quasi-isometry classes of
+right-angled Artin/Coxeter groups.
+Another application is that our tools, in many cases, allow one to reduce the
+problem of quasi-isometric rigidity for a given HHG to a combinatorial problem.
+As a template, we give a new proof of quasi-isometric rigidity of mapping class
+groups, using simpler combinatorial arguments than in previous proofs.
+",0,0,1,0,0,0
+20039,Learning to Skim Text,"  Recurrent Neural Networks are showing much promise in many sub-areas of
+natural language processing, ranging from document classification to machine
+translation to automatic question answering. Despite their promise, many
+recurrent models have to read the whole text word by word, making it slow to
+handle long documents. For example, it is difficult to use a recurrent network
+to read a book and answer questions about it. In this paper, we present an
+approach of reading text while skipping irrelevant information if needed. The
+underlying model is a recurrent network that learns how far to jump after
+reading a few words of the input text. We employ a standard policy gradient
+method to train the model to make discrete jumping decisions. In our benchmarks
+on four different tasks, including number prediction, sentiment analysis, news
+article classification and automatic Q\&A, our proposed model, a modified LSTM
+with jumping, is up to 6 times faster than the standard sequential LSTM, while
+maintaining the same or even better accuracy.
+",1,0,0,0,0,0
+20040,Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications,"  Statistical analysis (SA) is a complex process to deduce population
+properties from analysis of data. It usually takes a well-trained analyst to
+successfully perform SA, and it becomes extremely challenging to apply SA to
+big data applications. We propose to use deep neural networks to automate the
+SA process. In particular, we propose to construct convolutional neural
+networks (CNNs) to perform automatic model selection and parameter estimation,
+two most important SA tasks. We refer to the resulting CNNs as the neural model
+selector and the neural model estimator, respectively, which can be properly
+trained using labeled data systematically generated from candidate models.
+Simulation study shows that both the selector and estimator demonstrate
+excellent performances. The idea and proposed framework can be further extended
+to automate the entire SA process and have the potential to revolutionize how
+SA is performed in big data analytics.
+",1,0,0,1,0,0
+20041,Duty to Delete on Non-Volatile Memory,"  We firstly suggest new cache policy applying the duty to delete invalid cache
+data on Non-volatile Memory (NVM). This cache policy includes generating random
+data and overwriting the random data into invalid cache data. Proposed cache
+policy is more economical and effective regarding perfect deletion of data. It
+is ensure that the invalid cache data in NVM is secure against malicious
+hackers.
+",1,0,0,0,0,0
+20042,Geometry of Log-Concave Density Estimation,"  Shape-constrained density estimation is an important topic in mathematical
+statistics. We focus on densities on $\mathbb{R}^d$ that are log-concave, and
+we study geometric properties of the maximum likelihood estimator (MLE) for
+weighted samples. Cule, Samworth, and Stewart showed that the logarithm of the
+optimal log-concave density is piecewise linear and supported on a regular
+subdivision of the samples. This defines a map from the space of weights to the
+set of regular subdivisions of the samples, i.e. the face poset of their
+secondary polytope. We prove that this map is surjective. In fact, every
+regular subdivision arises in the MLE for some set of weights with positive
+probability, but coarser subdivisions appear to be more likely to arise than
+finer ones. To quantify these results, we introduce a continuous version of the
+secondary polytope, whose dual we name the Samworth body. This article
+establishes a new link between geometric combinatorics and nonparametric
+statistics, and it suggests numerous open problems.
+",0,0,0,1,0,0
+20043,Antenna Arrays for Line-of-Sight Massive MIMO: Half Wavelength is not Enough,"  The aim of this paper is to analyze the array synthesis for 5 G massive MIMO
+systems in the line-of-sight working condition. The main result of the
+numerical investigation performed is that non-uniform arrays are the natural
+choice in this kind of application. In particular, by using non-equispaced
+arrays, we show that it is possible to achieve a better average condition
+number of the channel matrix and a significantly higher spectral efficiency.
+Furthermore, we verify that increasing the array size is beneficial also for
+circular arrays, and we provide some useful rules-of-thumb for antenna array
+design for massive MIMO applications. These results are in contrast to the
+widely-accepted idea in the 5 G massive MIMO literature, in which the
+half-wavelength linear uniform array is universally adopted.
+",1,0,0,0,0,0
+20044,Gravitational octree code performance evaluation on Volta GPU,"  In this study, the gravitational octree code originally optimized for the
+Fermi, Kepler, and Maxwell GPU architectures is adapted to the Volta
+architecture. The Volta architecture introduces independent thread scheduling
+requiring either the insertion of the explicit synchronizations at appropriate
+locations or the enforcement of the same implicit synchronizations as do the
+Pascal or earlier architectures by specifying \texttt{-gencode
+arch=compute\_60,code=sm\_70}. The performance measurements on Tesla V100, the
+current flagship GPU by NVIDIA, revealed that the $N$-body simulations of the
+Andromeda galaxy model with $2^{23} = 8388608$ particles took $3.8 \times
+10^{-2}$~s or $3.3 \times 10^{-2}$~s per step for each case. Tesla V100
+achieves a 1.4 to 2.2-fold acceleration in comparison with Tesla P100, the
+flagship GPU in the previous generation. The observed speed-up of 2.2 is
+greater than 1.5, which is the ratio of the theoretical peak performance of the
+two GPUs. The independence of the units for integer operations from those for
+floating-point number operations enables the overlapped execution of integer
+and floating-point number operations. It hides the execution time of the
+integer operations leading to the speed-up rate above the theoretical peak
+performance ratio. Tesla V100 can execute $N$-body simulation with up to $25
+\times 2^{20} = 26214400$ particles, and it took $2.0 \times 10^{-1}$~s per
+step. It corresponds to $3.5$~TFlop/s, which is 22\% of the single-precision
+theoretical peak performance.
+",1,0,0,0,0,0
+20045,"Instanton bundles on the flag variety F(0,1,2)","  Instanton bundles on $\mathbb{P}^3$ have been at the core of the research in
+Algebraic Geometry during the last thirty years. Motivated by the recent
+extension of their definition to other Fano threefolds of Picard number one, we
+develop the theory of instanton bundles on the complete flag variety
+$F:=F(0,1,2)$ of point-lines on $\mathbb{P}^2$. After giving for them two
+different monadic presentations, we use it to show that the moduli space
+$MI_F(k)$ of instanton bundles of charge $k$ is a geometric GIT quotient with a
+generically smooth component of dim $8k-3$. Finally we study their locus of
+jumping conics.
+",0,0,1,0,0,0
+20046,Non-linear motor control by local learning in spiking neural networks,"  Learning weights in a spiking neural network with hidden neurons, using
+local, stable and online rules, to control non-linear body dynamics is an open
+problem. Here, we employ a supervised scheme, Feedback-based Online Local
+Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking
+neurons with hidden layers, to control a two-link arm so as to reproduce a
+desired state trajectory. The network first learns an inverse model of the
+non-linear dynamics, i.e. from state trajectory as input to the network, it
+learns to infer the continuous-time command that produced the trajectory.
+Connection weights are adjusted via a local plasticity rule that involves
+pre-synaptic firing and post-synaptic feedback of the error in the inferred
+command. We choose a network architecture, termed differential feedforward,
+that gives the lowest test error from different feedforward and recurrent
+architectures. The learned inverse model is then used to generate a
+continuous-time motor command to control the arm, given a desired trajectory.
+",1,0,0,1,0,0
+20047,Training Deep Neural Networks via Optimization Over Graphs,"  In this work, we propose to train a deep neural network by distributed
+optimization over a graph. Two nonlinear functions are considered: the
+rectified linear unit (ReLU) and a linear unit with both lower and upper
+cutoffs (DCutLU). The problem reformulation over a graph is realized by
+explicitly representing ReLU or DCutLU using a set of slack variables. We then
+apply the alternating direction method of multipliers (ADMM) to update the
+weights of the network layerwise by solving subproblems of the reformulated
+problem. Empirical results suggest that the ADMM-based method is less sensitive
+to overfitting than the stochastic gradient descent (SGD) and Adam methods.
+",1,0,0,0,0,0
+20048,Calculation of Effective Interaction Potential During Positron Channeling in Ionic Crystals,"  An analytical expression is received for the effective interaction potential
+of a fast charged particle with the ionic crystal CsCl near the direction of
+<100> axis as a function of the temperature of the medium. By numerical
+analysis it is shown that the effective potential of axial channeling of
+positrons along the axis <100> of negatively charged ions practically does not
+depend on temperature of the media
+",0,1,0,0,0,0
+20049,The Follower Count Fallacy: Detecting Twitter Users with Manipulated Follower Count,"  Online Social Networks (OSN) are increasingly being used as platform for an
+effective communication, to engage with other users, and to create a social
+worth via number of likes, followers and shares. Such metrics and crowd-sourced
+ratings give the OSN user a sense of social reputation which she tries to
+maintain and boost to be more influential. Users artificially bolster their
+social reputation via black-market web services. In this work, we identify
+users which manipulate their projected follower count using an unsupervised
+local neighborhood detection method. We identify a neighborhood of the user
+based on a robust set of features which reflect user similarity in terms of the
+expected follower count. We show that follower count estimation using our
+method has 84.2% accuracy with a low error rate. In addition, we estimate the
+follower count of the user under suspicion by finding its neighborhood drawn
+from a large random sample of Twitter. We show that our method is highly
+tolerant to synthetic manipulation of followers. Using the deviation of
+predicted follower count from the displayed count, we are also able to detect
+customers with a high precision of 98.62%
+",1,0,0,0,0,0
+20050,Bergman kernel estimates and Toeplitz operators on holomorphic line bundles,"  We characterize operator-theoretic properties (boundedness, compactness, and
+Schatten class membership) of Toeplitz operators with positive measure symbols
+on Bergman spaces of holomorphic hermitian line bundles over Kähler
+Cartan-Hadamard manifolds in terms of geometric or operator-theoretic
+properties of measures.
+",0,0,1,0,0,0
+20051,HST PanCET program: A Cloudy Atmosphere for the promising JWST target WASP-101b,"  We present results from the first observations of the Hubble Space Telescope
+(HST) Panchromatic Comparative Exoplanet Treasury (PanCET) program for
+WASP-101b, a highly inflated hot Jupiter and one of the community targets
+proposed for the James Webb Space Telescope (JWST) Early Release Science (ERS)
+program. From a single HST Wide Field Camera 3 (WFC3) observation, we find that
+the near-infrared transmission spectrum of WASP-101b contains no significant
+H$_2$O absorption features and we rule out a clear atmosphere at 13{\sigma}.
+Therefore, WASP-101b is not an optimum target for a JWST ERS program aimed at
+observing strong molecular transmission features. We compare WASP-101b to the
+well studied and nearly identical hot Jupiter WASP-31b. These twin planets show
+similar temperature-pressure profiles and atmospheric features in the
+near-infrared. We suggest exoplanets in the same parameter space as WASP-101b
+and WASP-31b will also exhibit cloudy transmission spectral features. For
+future HST exoplanet studies, our analysis also suggests that a lower count
+limit needs to be exceeded per pixel on the detector in order to avoid unwanted
+instrumental systematics.
+",0,1,0,0,0,0
+20052,Quantum gauge symmetry of reducible gauge theory,"  We derive the gaugeon formalism of the Kalb-Ramond field theory, a reducible
+gauge theory, which discusses the quantum gauge freedom. In gaugeon formalism,
+theory admits quantum gauge symmetry which leaves the action form-invariant.
+The BRST symmetric gaugeon formalism is also studied which introduces the
+gaugeon ghost fields and gaugeon ghosts of ghosts fields. To replace the
+Yokoyama subsidiary conditions by a single Kugo-Ojima type condition the virtue
+of BRST symmetry is utilized. Under generalized BRST transformations, we show
+that the gaugeon fields appear naturally in the reducible gauge theory.
+",0,1,0,0,0,0
+20053,The Tian Pseudo-Atom Method,"  In this work, the authors give a new method for phase determination, the Tian
+pseudo atom method (TPAM) or pseudo atom method (PAM) for short. In this new
+method, the figure of merit function, Rtian, replaces Rcf in the charge
+flipping algorithm. The key difference between Rcf and Rtian is the oberved
+structure factor was replaced by the pseudo structure factor. The test results
+show that Rtian is more powerful and robust than Rcf to estimate the correct
+structure especially with low resolution data. Therefore, the pseudo atom
+method could overcome the charge flipping method's defeat to some extent. In
+theory, the pseudo atom method could deal with quite low resolution data but it
+needs a further test.
+",0,1,0,0,0,0
+20054,"The Phenotypes of Fluctuating Flow: Development of Distribution Networks in Biology and the Trade-off between Efficiency, Cost, and Resilience","  Complex distribution networks are pervasive in biology. Examples include
+nutrient transport in the slime mold $Physarum$ $polycephalum$ as well as
+mammalian and plant venation. Adaptive rules are believed to guide development
+of these networks and lead to a reticulate, hierarchically nested topology that
+is both efficient and resilient against perturbations. However, as of yet no
+mechanism is known that can generate such networks on all scales. We show how
+hierarchically organized reticulation can be generated and maintained through
+spatially collective load fluctuations on a particular length scale. We
+demonstrate that the resulting network topologies represent a trade-off between
+optimizing power dissipation, construction cost, and damage robustness and
+identify the Pareto-efficient front that evolution is expected to favor and
+select for. We show that the typical fluctuation length scale controls the
+position of the networks on the Pareto front and thus on the spectrum of
+venation phenotypes. We compare the Pareto archetypes predicted by our model
+with examples of real leaf networks.
+",0,1,0,0,0,0
+20055,Recent Trends in Deep Learning Based Natural Language Processing,"  Deep learning methods employ multiple processing layers to learn hierarchical
+representations of data and have produced state-of-the-art results in many
+domains. Recently, a variety of model designs and methods have blossomed in the
+context of natural language processing (NLP). In this paper, we review
+significant deep learning related models and methods that have been employed
+for numerous NLP tasks and provide a walk-through of their evolution. We also
+summarize, compare and contrast the various models and put forward a detailed
+understanding of the past, present and future of deep learning in NLP.
+",1,0,0,0,0,0
+20056,Volatile memory forensics for the Robot Operating System,"  The increasing impact of robotics on industry and on society will unavoidably
+lead to the involvement of robots in incidents and mishaps. In such cases,
+forensic analyses are key techniques to provide useful evidence on what
+happened, and try to prevent future incidents. This article discusses volatile
+memory forensics for the Robot Operating System (ROS). The authors start by
+providing a general overview of forensic techniques in robotics and then
+present a robotics-specific Volatility plugin named linux_rosnode, packaged
+within the ros_volatility project and aimed to extract evidence from robot's
+volatile memory. They demonstrate how this plugin can be used to detect a
+specific attack pattern on ROS, where a publisher node is unregistered
+externally, leading to denial of service and disruption of robotic behaviors.
+Step-by-step, common practices are introduced for performing forensic analysis
+and several techniques to capture memory are described. The authors finalize by
+introducing some future remarks while providing references to reproduce their
+work.
+",1,0,0,0,0,0
+20057,HYDRA: HYbrid Design for Remote Attestation (Using a Formally Verified Microkernel),"  Remote Attestation (RA) allows a trusted entity (verifier) to securely
+measure internal state of a remote untrusted hardware platform (prover). RA can
+be used to establish a static or dynamic root of trust in embedded and
+cyber-physical systems. It can also be used as a building block for other
+security services and primitives, such as software updates and patches,
+verifiable deletion and memory resetting. There are three major classes of RA
+designs: hardware-based, software-based, and hybrid, each with its own set of
+benefits and drawbacks. This paper presents the first hybrid RA design, called
+HYDRA, that builds upon formally verified software components that ensure
+memory isolation and protection, as well as enforce access control to memory
+and other resources. HYDRA obtains these properties by using the formally
+verified seL4 microkernel. (Until now, this was only attainable with purely
+hardware-based designs.) Using seL4 requires fewer hardware modifications to
+the underlying microprocessor. Building upon a formally verified software
+component increases confidence in security of the overall design of HYDRA and
+its implementation. We instantiate HYDRA on two commodity hardware platforms
+and assess the performance and overhead of performing RA on such platforms via
+experimentation; we show that HYDRA can attest 10MB of memory in less than
+500msec when using a Speck-based message authentication code (MAC) to compute a
+cryptographic checksum over the memory to be attested.
+",1,0,0,0,0,0
+20058,Social Robots for People with Developmental Disabilities: A User Study on Design Features of a Graphical User Interface,"  Social robots, also known as service or assistant robots, have been developed
+to improve the quality of human life in recent years. The design of socially
+capable and intelligent robots can vary, depending on the target user groups.
+In this work, we assess the effect of social robots' roles, functions, and
+communication approaches in the context of a social agent providing service or
+entertainment to users with developmental disabilities. In this paper, we
+describe an exploratory study of interface design for a social robot that
+assists people suffering from developmental disabilities. We developed series
+of prototypes and tested one in a user study that included three residents with
+various function levels. This entire study had been recorded for the following
+qualitative data analysis. Results show that each design factor played a
+different role in delivering information and in increasing engagement. We also
+note that some of the fundamental design principles that would work for
+ordinary users did not apply to our target user group. We conclude that social
+robots could benefit our target users, and acknowledge that these robots were
+not suitable for certain scenarios based on the feedback from our users.
+",1,0,0,0,0,0
+20059,Inverse problem for multi-species mean field models in the low temperature phase,"  In this paper we solve the inverse problem for a class of mean field models
+(Curie-Weiss model and its multi-species version) when multiple thermodynamic
+states are present, as in the low temperature phase where the phase space is
+clustered. The inverse problem consists in reconstructing the model parameters
+starting from configuration data generated according to the distribution of the
+model. We show that the application of the inversion procedure without taking
+into account the presence of many states produces very poor inference results.
+This problem is overcomed using the clustering algorithm. When the system has
+two symmetric states of positive and negative magnetization, the parameter
+reconstruction can be also obtained with smaller computational effort simply by
+flipping the sign of the magnetizations from positive to negative (or
+viceversa). The parameter reconstruction fails when the system is critical: in
+this case we give the correct inversion formulas for the Curie-Weiss model and
+we show that they can be used to measuring how much the system is close to
+criticality.
+",0,1,0,0,0,0
+20060,Inertial Odometry on Handheld Smartphones,"  Building a complete inertial navigation system using the limited quality data
+provided by current smartphones has been regarded challenging, if not
+impossible. This paper shows that by careful crafting and accounting for the
+weak information in the sensor samples, smartphones are capable of pure
+inertial navigation. We present a probabilistic approach for orientation and
+use-case free inertial odometry, which is based on double-integrating rotated
+accelerations. The strength of the model is in learning additive and
+multiplicative IMU biases online. We are able to track the phone position,
+velocity, and pose in real-time and in a computationally lightweight fashion by
+solving the inference with an extended Kalman filter. The information fusion is
+completed with zero-velocity updates (if the phone remains stationary),
+altitude correction from barometric pressure readings (if available), and
+pseudo-updates constraining the momentary speed. We demonstrate our approach
+using an iPad and iPhone in several indoor dead-reckoning applications and in a
+measurement tool setup.
+",1,0,0,1,0,0
+20061,Asymptotic network models of subwavelength metamaterials formed by closely packed photonic and phononic crystals,"  We demonstrate that photonic and phononic crystals consisting of closely
+spaced inclusions constitute a versatile class of subwavelength metamaterials.
+Intuitively, the voids and narrow gaps that characterise the crystal form an
+interconnected network of Helmholtz-like resonators. We use this intuition to
+argue that these continuous photonic (phononic) crystals are in fact
+asymptotically equivalent, at low frequencies, to discrete capacitor-inductor
+(mass-spring) networks whose lumped parameters we derive explicitly. The
+crystals are tantamount to metamaterials as their entire acoustic branch, or
+branches when the discrete analogue is polyatomic, is squeezed into a
+subwavelength regime where the ratio of wavelength to period scales like the
+ratio of period to gap width raised to the power 1/4; at yet larger wavelengths
+we accordingly find a comparably large effective refractive index. The fully
+analytical dispersion relations predicted by the discrete models yield
+dispersion curves that agree with those from finite-element simulations of the
+continuous crystals. The insight gained from the network approach is used to
+show that, surprisingly, the continuum created by a closely packed hexagonal
+lattice of cylinders is represented by a discrete honeycomb lattice. The
+analogy is utilised to show that the hexagonal continuum lattice has a
+Dirac-point degeneracy that is lifted in a controlled manner by specifying the
+area of a symmetry-breaking defect.
+",0,1,0,0,0,0
+20062,The Hydrogen Epoch of Reionization Array Dish III: Measuring Chromaticity of Prototype Element with Reflectometry,"  The experimental efforts to detect the redshifted 21 cm signal from the Epoch
+of Reionization (EoR) are limited predominantly by the chromatic instrumental
+systematic effect. The delay spectrum methodology for 21 cm power spectrum
+measurements brought new attention to the critical impact of an antenna's
+chromaticity on the viability of making this measurement. This methodology
+established a straightforward relationship between time-domain response of an
+instrument and the power spectrum modes accessible to a 21 cm EoR experiment.
+We examine the performance of a prototype of the Hydrogen Epoch of Reionization
+Array (HERA) array element that is currently observing in Karoo desert, South
+Africa. We present a mathematical framework to derive the beam integrated
+frequency response of a HERA prototype element in reception from the return
+loss measurements between 100-200 MHz and determined the extent of additional
+foreground contamination in the delay space. The measurement reveals excess
+spectral structures in comparison to the simulation studies of the HERA
+element. Combined with the HERA data analysis pipeline that incorporates
+inverse covariance weighting in optimal quadratic estimation of power spectrum,
+we find that in spite of its departure from the simulated response, HERA
+prototype element satisfies the necessary criteria posed by the foreground
+attenuation limits and potentially can measure the power spectrum at spatial
+modes as low as $k_{\parallel} > 0.1h$~Mpc$^{-1}$. The work highlights a
+straightforward method for directly measuring an instrument response and
+assessing its impact on 21 cm EoR power spectrum measurements for future
+experiments that will use reflector-type antenna.
+",0,1,0,0,0,0
+20063,Unifying the micro and macro properties of AGN feeding and feedback,"  We unify the feeding and feedback of supermassive black holes with the global
+properties of galaxies, groups, and clusters, by linking for the first time the
+physical mechanical efficiency at the horizon and Mpc scale. The macro hot halo
+is tightly constrained by the absence of overheating and overcooling as probed
+by X-ray data and hydrodynamic simulations ($\varepsilon_{\rm BH} \simeq$
+10$^{-3}\, T_{\rm x,7.4}$). The micro flow is shaped by general relativistic
+effects tracked by state-of-the-art GR-RMHD simulations ($\varepsilon_\bullet
+\simeq$ 0.03). The SMBH properties are tied to the X-ray halo temperature
+$T_{\rm x}$, or related cosmic scaling relation (as $L_{\rm x}$). The model is
+minimally based on first principles, as conservation of energy and mass
+recycling. The inflow occurs via chaotic cold accretion (CCA), the rain of cold
+clouds condensing out of the quenched cooling flow and recurrently funneled via
+inelastic collisions. Within 100s gravitational radii, the accretion energy is
+transformed into ultrafast 10$^4$ km s$^{-1}$ outflows (UFOs) ejecting most of
+the inflowing mass. At larger radii the energy-driven outflow entrains
+progressively more mass: at roughly kpc scale, the velocities of the
+hot/warm/cold outflows are a few 10$^3$, 1000, 500 km s$^{-1}$, with median
+mass rates ~10, 100, several 100 M$_\odot$ yr$^{-1}$, respectively. The unified
+CCA model is consistent with the observations of nuclear UFOs, and ionized,
+neutral, and molecular macro outflows. We provide step-by-step implementation
+for subgrid simulations, (semi)analytic works, or observational interpretations
+which require self-regulated AGN feedback at coarse scales, avoiding the
+a-posteriori fine-tuning of efficiencies.
+",0,1,0,0,0,0
+20064,Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning,"  The inherent noise in the observed (e.g., scanned) binary document image
+degrades the image quality and harms the compression ratio through breaking the
+pattern repentance and adding entropy to the document images. In this paper, we
+design a cost function in Bayesian framework with dictionary learning.
+Minimizing our cost function produces a restored image which has better quality
+than that of the observed noisy image, and a dictionary for representing and
+encoding the image. After the restoration, we use this dictionary (from the
+same cost function) to encode the restored image following the
+symbol-dictionary framework by JBIG2 standard with the lossless mode.
+Experimental results with a variety of document images demonstrate that our
+method improves the image quality compared with the observed image, and
+simultaneously improves the compression ratio. For the test images with
+synthetic noise, our method reduces the number of flipped pixels by 48.2% and
+improves the compression ratio by 36.36% as compared with the best encoding
+methods. For the test images with real noise, our method visually improves the
+image quality, and outperforms the cutting-edge method by 28.27% in terms of
+the compression ratio.
+",1,0,0,0,0,0
+20065,A First Look at Ad Blocking Apps on Google Play,"  Online advertisers and analytics services (or trackers), are constantly
+tracking users activities as they access web services either through browsers
+or a mobile apps. Numerous tools such as browser plugins and specialized mobile
+apps have been proposed to limit intrusive advertisements and prevent tracking
+on desktop computing and mobile phones. For desktop computing, browser plugins
+are heavily studied for their usability and efficiency issues, however, tools
+that block ads and prevent trackers in mobile platforms, have received the
+least or no attention.
+In this paper, we present a first look at 97 Android adblocking apps (or
+adblockers), extracted from more than 1.5 million apps from Google Play, that
+promise to block advertisements and analytics services. With our data
+collection and analysis pipeline of the Android adblockers, we reveal the
+presences of third-party tracking libraries and sensitive permissions for
+critical resources on user mobile devices as well as have malware in the source
+codes. We analyze users' reviews for the in-effectiveness of adblockers in
+terms of not blocking ads and trackers. We found that a significant fraction of
+adblockers are not fulfilling their advertised functionality.
+",1,0,0,0,0,0
+20066,Nondegeneracy of the traveling lump solution to the $2+1$ Toda lattice,"  We consider the $2+1$ Toda system \[ \frac{1}{4}\Delta
+q_{n}=e^{q_{n-1}-q_{n}}-e^{q_{n}-q_{n+1}}\text{ in }\mathbb{R}^{2},\
+n\in\mathbb{Z}. \] It has a traveling wave type solution $\left\{ Q_{n}\right\}
+$ satisfying $Q_{n+1}(x,y)=Q_{n}(x+\frac{1}{2\sqrt{2}},y)$, and is explicitly
+given by \[ Q_{n}\left( x,y\right) =\ln\frac{\frac{1}{4}+\left(
+n-1+2\sqrt{2}x\right) ^{2}+4y^{2}}{\frac{1}{4}+\left( n+2\sqrt{2}x\right)
+^{2}+4y^{2}}. \] In this paper we prove that \{$Q_{n}$\} is nondegenerate.
+",0,0,1,0,0,0
+20067,One side continuity of meromorphic mappings between real analytic hypersurfaces,"  We prove that a meromorphic mapping, which sends a peace of a real analytic
+strictly pseudoconvex hypersurface in $\cc^2$ to a compact subset of $\cc^N$
+which doesn't contain germs of non-constant complex curves is continuous from
+the concave side of the hypersurface. This implies the analytic continuability
+along CR-paths of germs of holomorphic mappings from real analytic
+hypersurfaces with non-vanishing Levi form to the locally spherical ones in all
+dimensions.
+",0,0,1,0,0,0
+20068,Generic Camera Attribute Control using Bayesian Optimization,"  Cameras are the most widely exploited sensor in both robotics and computer
+vision communities. Despite their popularity, two dominant attributes (i.e.,
+gain and exposure time) have been determined empirically and images are
+captured in very passive manner. In this paper, we present an active and
+generic camera attribute control scheme using Bayesian optimization. We extend
+from our previous work [1] in two aspects. First, we propose a method that
+jointly controls camera gain and exposure time. Secondly, to speed up the
+Bayesian optimization process, we introduce image synthesis using the camera
+response function (CRF). These synthesized images allowed us to diminish the
+image acquisition time during the Bayesian optimization phase, substantially
+improving overall control performance. The proposed method is validated both in
+an indoor and an outdoor environment where light condition rapidly changes.
+Supplementary material is available at this https URL .
+",1,0,0,0,0,0
+20069,Far-from-equilibrium energy flow and entanglement entropy,"  The time evolution of the energy transport triggered in a strongly coupled
+system by a temperature gradient is holographically related to the evolution of
+an asymptotically AdS black brane. We study the far-from-equilibrium properties
+of such a system by using the AdS/CFT correspondence. In particular, we
+describe the appearance of a steady state, and study the information flow by
+computing the time evolution of the holographic entanglement entropy. Some
+universal properties of the quenching process are presented.
+",0,1,0,0,0,0
+20070,Algebraic Aspects of Conditional Independence and Graphical Models,"  This chapter of the forthcoming Handbook of Graphical Models contains an
+overview of basic theorems and techniques from algebraic geometry and how they
+can be applied to the study of conditional independence and graphical models.
+It also introduces binomial ideals and some ideas from real algebraic geometry.
+When random variables are discrete or Gaussian, tools from computational
+algebraic geometry can be used to understand implications between conditional
+independence statements. This is accomplished by computing primary
+decompositions of conditional independence ideals. As examples the chapter
+presents in detail the graphical model of a four cycle and the intersection
+axiom, a certain implication of conditional independence statements. Another
+important problem in the area is to determine all constraints on a graphical
+model, for example, equations determined by trek separation. The full set of
+equality constraints can be determined by computing the model's vanishing
+ideal. The chapter illustrates these techniques and ideas with examples from
+the literature and provides references for further reading.
+",0,0,1,1,0,0
+20071,A Survey of Riccati Equation Results in Negative Imaginary Systems Theory and Quantum Control Theory,"  This paper presents a survey of some new applications of algebraic Riccati
+equations. In particular, the paper surveys some recent results on the use of
+algebraic Riccati equations in testing whether a system is negative imaginary
+and in synthesizing state feedback controllers which make the closed loop
+system negative imaginary. The paper also surveys the use of Riccati equation
+methods in the control of quantum linear systems including coherent $H^\infty$
+control.
+",1,0,1,0,0,0
+20072,"The circumstellar disk HD$\,$169142: gas, dust and planets acting in concert?","  HD$\,$169142 is an excellent target to investigate signs of planet-disk
+interaction due to the previous evidence of gap structures. We performed J-band
+(~1.2{\mu}m) polarized intensity imaging of HD169142 with VLT/SPHERE. We
+observe polarized scattered light down to 0.16"" (~19 au) and find an inner gap
+with a significantly reduced scattered light flux. We confirm the previously
+detected double ring structure peaking at 0.18"" (~21 au) and 0.56"" (~66 au),
+and marginally detect a faint third gap at 0.70""-0.73"" (~82-85 au). We explore
+dust evolution models in a disk perturbed by two giant planets, as well as
+models with a parameterized dust size distribution. The dust evolution model is
+able to reproduce the ring locations and gap widths in polarized intensity, but
+fails to reproduce their depths. It, however, gives a good match with the ALMA
+dust continuum image at 1.3 mm. Models with a parameterized dust size
+distribution better reproduce the gap depth in scattered light, suggesting that
+dust filtration at the outer edges of the gaps is less effective. The pile-up
+of millimeter grains in a dust trap and the continuous distribution of small
+grains throughout the gap likely require a more efficient dust fragmentation
+and dust diffusion in the dust trap. Alternatively, turbulence or charging
+effects might lead to a reservoir of small grains at the surface layer that is
+not affected by the dust growth and fragmentation cycle dominating the dense
+disk midplane. The exploration of models shows that extracting planet
+properties such as mass from observed gap profiles is highly degenerate.
+",0,1,0,0,0,0
+20073,Quantitative analysis of the influence of keV He ion bombardment on exchange bias layer systems,"  The mechanism of ion bombardment induced magnetic patterning of exchange bias
+layer systems for creating engineered magnetic stray field landscapes is still
+unclear. We compare results from vectorial magneto-optic Kerr effect
+measurements to a recently proposed model with time dependent rotatable
+magnetic anisotropy. Results show massive reduction of rotational magnetic
+anisotropy compared to all other magnetic anisotropies. We disprove the
+assumption of comparable weakening of all magnetic anisotropies and show that
+ion bombardment mainly influences smaller grains in the antiferromagnet.
+",0,1,0,0,0,0
+20074,Automatic classification of automorphisms of lower-dimensional Lie algebras,"  We implement two algorithms in MATHEMATICA for classifying automorphisms of
+lower-dimensional non-commutative Lie algebras. The first algorithm is a
+brute-force approach whereas the second is an evolutionary strategy. These
+algorithms are delivered as the MATHEMATICA package cwsAutoClass. In order to
+facilitate the application of this package to symmetry Lie algebras of
+differential equations, we also provide a package, cwsLieSymTools, for
+manipulating finite-dimensional Lie algebras of vector fields. In particular,
+this package allows the computations of Lie brackets, structure constants, and
+the visualization of commutator tables. Several examples are provided to
+illustrate the pertinence of our approach.
+",0,0,1,0,0,0
+20075,Information-Theoretic Understanding of Population Risk Improvement with Model Compression,"  We show that model compression can improve the population risk of a
+pre-trained model, by studying the tradeoff between the decrease in the
+generalization error and the increase in the empirical risk with model
+compression. We first prove that model compression reduces an
+information-theoretic bound on the generalization error; this allows for an
+interpretation of model compression as a regularization technique to avoid
+overfitting. We then characterize the increase in empirical risk with model
+compression using rate distortion theory. These results imply that the
+population risk could be improved by model compression if the decrease in
+generalization error exceeds the increase in empirical risk. We show through a
+linear regression example that such a decrease in population risk due to model
+compression is indeed possible. Our theoretical results further suggest that
+the Hessian-weighted $K$-means clustering compression approach can be improved
+by regularizing the distance between the clustering centers. We provide
+experiments with neural networks to support our theoretical assertions.
+",1,0,0,1,0,0
+20076,Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries,"  This paper presents a procedure to retrieve subsets of relevant documents
+from large text collections for Content Analysis, e.g. in social sciences.
+Document retrieval for this purpose needs to take account of the fact that
+analysts often cannot describe their research objective with a small set of key
+terms, especially when dealing with theoretical or rather abstract research
+interests. Instead, it is much easier to define a set of paradigmatic documents
+which reflect topics of interest as well as targeted manner of speech. Thus, in
+contrast to classic information retrieval tasks we employ manually compiled
+collections of reference documents to compose large queries of several hundred
+key terms, called dictionaries. We extract dictionaries via Topic Models and
+also use co-occurrence data from reference collections. Evaluations show that
+the procedure improves retrieval results for this purpose compared to
+alternative methods of key term extraction as well as neglecting co-occurrence
+data.
+",1,0,0,0,0,0
+20077,Cepheids with the eyes of photometric space telescopes,"  Space photometric missions have been steadily accumulating observations of
+Cepheids in recent years, leading to a flow of new discoveries. In this short
+review we summarize the findings provided by the early missions such as WIRE,
+MOST, and CoRoT, and the recent results of the Kepler and K2 missions. The
+surprising and fascinating results from the high-precision, quasi-continuous
+data include the detection of the amplitude increase of Polaris, and exquisite
+details about V1154 Cyg within the original Kepler field of view. We also
+briefly discuss the current opportunities with the K2 mission, and the
+prospects of the TESS space telescope regarding Cepheids.
+",0,1,0,0,0,0
+20078,Explicitly correlated formalism for second-order single-particle Green's function,"  We present an explicitly correlated formalism for the second-order
+single-particle Green's function method (GF2-F12) that does not assume the
+popular diagonal approximation, and describes the energy dependence of the
+explicitly correlated terms. For small and medium organic molecules the basis
+set errors of ionization potentials of GF2-F12 are radically improved relative
+to GF2: the performance of GF2-F12/aug- cc-pVDZ is better than that of
+GF2/aug-cc-pVQZ, at a significantly lower cost.
+",0,1,0,0,0,0
+20079,Relative stability associated to quantised extremal Kähler metrics,"  We study algebro-geometric consequences of the quantised extremal Kähler
+metrics, introduced in the previous work of the author. We prove that the
+existence of quantised extremal metrics implies weak relative Chow
+polystability. As a consequence, we obtain asymptotic weak relative Chow
+polystability and $K$-semistability of extremal manifolds by using quantised
+extremal metrics; this gives an alternative proof of the results of Mabuchi and
+Stoppa--Székelyhidi. In proving them, we further provide an explicit local
+density formula for the equivariant Riemann--Roch theorem.
+",0,0,1,0,0,0
+20080,Beyond the EULA: Improving consent for data mining,"  Companies and academic researchers may collect, process, and distribute large
+quantities of personal data without the explicit knowledge or consent of the
+individuals to whom the data pertains. Existing forms of consent often fail to
+be appropriately readable and ethical oversight of data mining may not be
+sufficient. This raises the question of whether existing consent instruments
+are sufficient, logistically feasible, or even necessary, for data mining. In
+this chapter, we review the data collection and mining landscape, including
+commercial and academic activities, and the relevant data protection concerns,
+to determine the types of consent instruments used. Using three case studies,
+we use the new paradigm of human-data interaction to examine whether these
+existing approaches are appropriate. We then introduce an approach to consent
+that has been empirically demonstrated to improve on the state of the art and
+deliver meaningful consent. Finally, we propose some best practices for data
+collectors to ensure their data mining activities do not violate the
+expectations of the people to whom the data relate.
+",1,0,0,0,0,0
+20081,Estimating a Separably-Markov Random Field (SMuRF) from Binary Observations,"  A fundamental problem in neuroscience is to characterize the dynamics of
+spiking from the neurons in a circuit that is involved in learning about a
+stimulus or a contingency. A key limitation of current methods to analyze
+neural spiking data is the need to collapse neural activity over time or
+trials, which may cause the loss of information pertinent to understanding the
+function of a neuron or circuit. We introduce a new method that can determine
+not only the trial-to-trial dynamics that accompany the learning of a
+contingency by a neuron, but also the latency of this learning with respect to
+the onset of a conditioned stimulus. The backbone of the method is a separable
+two-dimensional (2D) random field (RF) model of neural spike rasters, in which
+the joint conditional intensity function of a neuron over time and trials
+depends on two latent Markovian state sequences that evolve separately but in
+parallel. Classical tools to estimate state-space models cannot be applied
+readily to our 2D separable RF model. We develop efficient statistical and
+computational tools to estimate the parameters of the separable 2D RF model. We
+apply these to data collected from neurons in the pre-frontal cortex (PFC) in
+an experiment designed to characterize the neural underpinnings of the
+associative learning of fear in mice. Overall, the separable 2D RF model
+provides a detailed, interpretable, characterization of the dynamics of neural
+spiking that accompany the learning of a contingency.
+",1,0,0,1,0,0
+20082,Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms,"  Deep reinforcement learning enables algorithms to learn complex behavior,
+deal with continuous action spaces and find good strategies in environments
+with high dimensional state spaces. With deep reinforcement learning being an
+active area of research and many concurrent inventions, we decided to focus on
+a relatively simple robotic task to evaluate a set of ideas that might help to
+solve recent reinforcement learning problems. We test a newly created
+combination of two commonly used reinforcement learning methods, whether it is
+able to learn more effectively than a baseline. We also compare different ideas
+to preprocess information before it is fed to the reinforcement learning
+algorithm. The goal of this strategy is to reduce training time and eventually
+help the algorithm to converge. The concluding evaluation proves the general
+applicability of the described concepts by testing them using a simulated
+environment. These concepts might be reused for future experiments.
+",1,0,0,0,0,0
+20083,The First Detection of Gravitational Waves,"  This article deals with the first detection of gravitational waves by the
+advanced Laser Interferometer Gravitational Wave Observatory (LIGO) detectors
+on 14 September 2015, where the signal was generated by two stellar mass black
+holes with masses 36 $ M_{\odot}$ and 29 $ M_{\odot}$ that merged to form a 62
+$ M_{\odot}$ black hole, releasing 3 $M_{\odot}$ energy in gravitational waves,
+almost 1.3 billion years ago. We begin by providing a brief overview of
+gravitational waves, their sources and the gravitational wave detectors. We
+then describe in detail the first detection of gravitational waves from a
+binary black hole merger. We then comment on the electromagnetic follow up of
+the detection event with various telescopes. Finally, we conclude with the
+discussion on the tests of gravity and fundamental physics with the first
+gravitational wave detection event.
+",0,1,0,0,0,0
+20084,Remarkably strong chemisorption of nitric oxide on insulating oxide films promoted by hybrid structure,"  The remarkably strong chemical adsorption behaviors of nitric oxide on
+magnesia (001) film deposited on metal substrate have been investigated by
+employing periodic density functional calculations with Van der Waals
+corrections. The molybdenum supported magnesia (001) show significantly
+enhanced adsorption properties and the nitric oxide is chemisorbed strongly and
+preferably trapped in flat adsorption configuration on metal supported oxide
+film, due to the substantially large adsorption energies and transformation
+barriers. The analysis of Bader charges, projected density of states,
+differential charge densities, electron localization function, highest occupied
+orbital and particular orbital with largest Mg-NO-Mg bonding coefficients, are
+applied to reveal the electronic adsorption properties and characteristics of
+bonding between nitric oxide and surface as well as the bonding within the
+hybrid structure. The strong chemical binding of nitric oxide on magnesia
+deposited on molybdenum slab offers new opportunities for toxic gas detection
+and treatment. We anticipate that hybrid structure promoted remarkable chemical
+adsorption of nitric oxide on magnesia in this study will provide versatile
+strategy for enhancing chemical reactivity and properties of insulating oxide.
+",0,1,0,0,0,0
+20085,Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes,"  In this work we propose approaches to effectively transfer knowledge from
+weakly labeled web audio data. We first describe a convolutional neural network
+(CNN) based framework for sound event detection and classification using weakly
+labeled audio data. Our model trains efficiently from audios of variable
+lengths; hence, it is well suited for transfer learning. We then propose
+methods to learn representations using this model which can be effectively used
+for solving the target task. We study both transductive and inductive transfer
+learning tasks, showing the effectiveness of our methods for both domain and
+task adaptation. We show that the learned representations using the proposed
+CNN model generalizes well enough to reach human level accuracy on ESC-50 sound
+events dataset and set state of art results on this dataset. We further use
+them for acoustic scene classification task and once again show that our
+proposed approaches suit well for this task as well. We also show that our
+methods are helpful in capturing semantic meanings and relations as well.
+Moreover, in this process we also set state-of-art results on Audioset dataset,
+relying on balanced training set.
+",1,0,0,0,0,0
+20086,A priori estimates for the free-boundary Euler equations with surface tension in three dimensions,"  We derive a priori estimates for the incompressible free-boundary Euler
+equations with surface tension in three spatial dimensions. Working in
+Lagrangian coordinates, we provide a priori estimates for the local existence
+when the initial velocity, which is rotational, belongs to $H^3$ and the trace
+of initial velocity on the free boundary to $H^{3.5}$, thus lowering the
+requirement on the regularity of initial data in the Lagrangian setting. Our
+methods are direct and involve three key elements: estimates for the pressure,
+the boundary regularity provided by the mean curvature, and the Cauchy
+invariance.
+",0,0,1,0,0,0
+20087,Koszul duality via suspending Lefschetz fibrations,"  Let $M$ be a Liouville 6-manifold which is the smooth fiber of a Lefschetz
+fibration on $\mathbb{C}^4$ constructed by suspending a Lefschetz fibration on
+$\mathbb{C}^3$. We prove that for many examples including stabilizations of
+Milnor fibers of hypersurface cusp singularities, the compact Fukaya category
+$\mathcal{F}(M)$ and the wrapped Fukaya category $\mathcal{W}(M)$ are related
+through $A_\infty$-Koszul duality, by identifying them with cyclic and
+Calabi-Yau completions of the same quiver algebra. This implies the
+split-generation of the compact Fukaya category $\mathcal{F}(M)$ by vanishing
+cycles. Moreover, new examples of Liouville manifolds which admit
+quasi-dilations in the sense of Seidel-Solomon are obtained.
+",0,0,1,0,0,0
+20088,Attractor of Cantor Type with Positive Measure,"  We construct an iterated function system consisting of strictly increasing
+contractions $f,g\colon [0,1]\to [0,1]$ with $f([0,1])\cap g([0,1])=\emptyset$
+and such that its attractor has positive Lebesgue measure.
+",0,0,1,0,0,0
+20089,Generating online social networks based on socio-demographic attributes,"  Recent years have seen tremendous growth of many online social networks such
+as Facebook, LinkedIn and MySpace. People connect to each other through these
+networks forming large social communities providing researchers rich datasets
+to understand, model and predict social interactions and behaviors. New
+contacts in these networks can be formed due to an individual's demographic
+attributes such as age group, gender, geographic location, or due to a
+network's structural dynamics such as triadic closure and preferential
+attachment, or a combination of both demographic and structural
+characteristics.
+A number of network generation models have been proposed in the last decade
+to explain the structure, evolution and processes taking place in different
+types of networks, and notably social networks. Network generation models
+studied in the literature primarily consider structural properties, and in some
+cases an individual's demographic profile in the formation of new social
+contacts. These models do not present a mechanism to combine both structural
+and demographic characteristics for the formation of new links. In this paper,
+we propose a new network generation algorithm which incorporates both these
+characteristics to model network formation. We use different publicly available
+Facebook datasets as benchmarks to demonstrate the correctness of the proposed
+network generation model. The proposed model is flexible and thus can generate
+networks with varying demographic and structural properties.
+",1,1,0,0,0,0
+20090,Optimal control of diffuser shapes for confined turbulent shear flows,"  A model for the development of turbulent shear flows, created by non-uniform
+parallel flows in a confining channel, is used to identify the diffuser shape
+that maximises pressure recovery when the inflow is non-uniform. Wide diffuser
+angles tend to accentuate the non- uniform flow, causing poor pressure
+recovery. On the other hand, shallow diffuser angles create longer regions with
+large wall drag, which is also detrimental to pressure recovery. Thus, optimal
+diffuser shapes strike a balance between the two effects. We use a simple model
+which describes the evolution of an approximate flow profile and pressure in
+the diffuser. The model equations form the dynamics of an optimal control
+problem where the control is the diffuser channel shape. A numerical
+optimisation approach is used to solve the optimal control problem and we use
+analytical results to interpret the numerics in some limiting cases. The
+results of the optimisation are compared to calculations from computational
+fluid dynamics.
+",0,1,0,0,0,0
+20091,Criteria for Solar Car Optimized Route Estimation,"  This paper gives a thorough overview of Solar Car Optimized Route Estimation
+(SCORE), novel route optimization scheme for solar vehicles based on solar
+irradiance and target distance. In order to conduct the optimization, both data
+collection and the optimization algorithm itself have to be performed using
+appropriate hardware. Here we give an insight to both stages, hardware and
+software used and present some results of the SCORE system together with
+certain improvements of its fusion and optimization criteria. Results and the
+limited applicability of SCORE are discussed together with an overview of
+future research plans and comparison with state-of-the-art solar vehicle
+optimization solutions.
+",1,0,1,0,0,0
+20092,Dykes for filtering ocean waves using c-shaped vertical cylinders,"  The present study investigates a way to design dykes which can filter the
+wavelengths of ocean surface waves. This offers the possibility to achieve a
+structure that can attenuate waves associated with storm swell, without
+affecting coastline in other conditions. Our approach is based on low frequency
+resonances in metamaterials combined with Bragg frequencies for which waves
+cannot propagate in periodic lattices.
+",0,1,0,0,0,0
+20093,Origin of Non-axisymmetric Features of Virgo Cluster Early-type Dwarf Galaxies. I. Bar Formation and Recurrent Buckling,"  A fraction of early-type dwarf galaxies in the Virgo cluster have a disk
+component and even possess disk features such as bar, lens, and spiral arms. In
+this study, we construct 15 galaxy models that resemble VCC856, considered to
+be an infalling progenitor of disk dwarf galaxies, within observational error
+ranges, and use $N$-body simulations to study their long-term dynamical
+evolution in isolation as well as the formation of bar in them. We find that
+dwarf disk galaxies readily form bars unless they have an excessively
+concentrated halo or a hot disk. This suggests that infalling dwarf disk
+galaxies are intrinsically unstable to bar formation, even without any external
+perturbation, accounting for a population of barred dwarf galaxies in the
+outskirts of the Virgo cluster. The bars form earlier and stronger in galaxies
+with a lower fraction of counter-streaming motions, lower halo concentration,
+lower velocity anisotropy, and thinner disk. Similarly to normal disk galaxies,
+dwarf disk galaxies also undergo recurrent buckling instabilities. The first
+buckling instability tends to shorten the bar and to thicken the disk, and
+drives a dynamical transition in the bar pattern speed as well as mass inflow
+rate. In nine models, the bars regrow after the mild first buckling instability
+due to the efficient transfer of disk angular momentum to the halo, and are
+subject to recurrent buckling instabilities to turn into X-shaped bulges.
+",0,1,0,0,0,0
+20094,First results from the DEAP-3600 dark matter search with argon at SNOLAB,"  This paper reports the first results of a direct dark matter search with the
+DEAP-3600 single-phase liquid argon (LAr) detector. The experiment was
+performed 2 km underground at SNOLAB (Sudbury, Canada) utilizing a large target
+mass, with the LAr target contained in a spherical acrylic vessel of 3600 kg
+capacity. The LAr is viewed by an array of PMTs, which would register
+scintillation light produced by rare nuclear recoil signals induced by dark
+matter particle scattering. An analysis of 4.44 live days (fiducial exposure of
+9.87 tonne-days) of data taken with the nearly full detector during the initial
+filling phase demonstrates the detector performance and the best electronic
+recoil rejection using pulse-shape discrimination in argon, with leakage
+$<1.2\times 10^{-7}$ (90% C.L.) between 16 and 33 keV$_{ee}$. No candidate
+signal events are observed, which results in the leading limit on WIMP-nucleon
+spin-independent cross section on argon, $<1.2\times 10^{-44}$ cm$^2$ for a 100
+GeV/c$^2$ WIMP mass (90% C.L.).
+",0,1,0,0,0,0
+20095,Camera Calibration by Global Constraints on the Motion of Silhouettes,"  We address the problem of epipolar geometry using the motion of silhouettes.
+Such methods match epipolar lines or frontier points across views, which are
+then used as the set of putative correspondences. We introduce an approach that
+improves by two orders of magnitude the performance over state-of-the-art
+methods, by significantly reducing the number of outliers in the putative
+matching. We model the frontier points' correspondence problem as constrained
+flow optimization, requiring small differences between their coordinates over
+consecutive frames. Our approach is formulated as a Linear Integer Program and
+we show that due to the nature of our problem, it can be solved efficiently in
+an iterative manner. Our method was validated on four standard datasets
+providing accurate calibrations across very different viewpoints.
+",1,0,0,0,0,0
+20096,Ann: A domain-specific language for the effective design and validation of Java annotations,"  This paper describes a new modelling language for the effective design and
+validation of Java annotations. Since their inclusion in the 5th edition of
+Java, annotations have grown from a useful tool for the addition of meta-data
+to play a central role in many popular software projects. Usually they are not
+conceived in isolation, but in groups, with dependency and integrity
+constraints between them. However, the native support provided by Java for
+expressing this design is very limited.
+To overcome its deficiencies and make explicit the rich conceptual model
+which lies behind a set of annotations, we propose a domain-specific modelling
+language. The proposal has been implemented as an Eclipse plug-in, including an
+editor and an integrated code generator that synthesises annotation processors.
+The environment also integrates a model finder, able to detect unsatisfiable
+constraints between different annotations, and to provide examples of correct
+annotation usages for validation. The language has been tested using a real set
+of annotations from the Java Persistence API (JPA). Within this subset we have
+found enough rich semantics expressible with Ann and omitted nowadays by the
+Java language, which shows the benefits of Ann in a relevant field of
+application.
+",1,0,0,0,0,0
+20097,Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery,"  The new wave of successful generative models in machine learning has
+increased the interest in deep learning driven de novo drug design. However,
+assessing the performance of such generative models is notoriously difficult.
+Metrics that are typically used to assess the performance of such generative
+models are the percentage of chemically valid molecules or the similarity to
+real molecules in terms of particular descriptors, such as the partition
+coefficient (logP) or druglikeness. However, method comparison is difficult
+because of the inconsistent use of evaluation metrics, the necessity for
+multiple metrics, and the fact that some of these measures can easily be
+tricked by simple rule-based systems. We propose a novel distance measure
+between two sets of molecules, called Fréchet ChemNet distance (FCD), that
+can be used as an evaluation metric for generative models. The FCD is similar
+to a recently established performance metric for comparing image generation
+methods, the Fréchet Inception Distance (FID). Whereas the FID uses one of
+the hidden layers of InceptionNet, the FCD utilizes the penultimate layer of a
+deep neural network called ChemNet, which was trained to predict drug
+activities. Thus, the FCD metric takes into account chemically and biologically
+relevant information about molecules, and also measures the diversity of the
+set via the distribution of generated molecules. The FCD's advantage over
+previous metrics is that it can detect if generated molecules are a) diverse
+and have similar b) chemical and c) biological properties as real molecules. We
+further provide an easy-to-use implementation that only requires the SMILES
+representation of the generated molecules as input to calculate the FCD.
+Implementations are available at: this https URL
+",0,0,0,1,1,0
+20098,RANSAC Algorithms for Subspace Recovery and Subspace Clustering,"  We consider the RANSAC algorithm in the context of subspace recovery and
+subspace clustering. We derive some theory and perform some numerical
+experiments. We also draw some correspondences with the methods of Hardt and
+Moitra (2013) and Chen and Lerman (2009b).
+",0,0,1,1,0,0
+20099,Convex Formulations for Fair Principal Component Analysis,"  Though there is a growing body of literature on fairness for supervised
+learning, the problem of incorporating fairness into unsupervised learning has
+been less well-studied. This paper studies fairness in the context of principal
+component analysis (PCA). We first present a definition of fairness for
+dimensionality reduction, and our definition can be interpreted as saying that
+a reduction is fair if information about a protected class (e.g., race or
+gender) cannot be inferred from the dimensionality-reduced data points. Next,
+we develop convex optimization formulations that can improve the fairness (with
+respect to our definition) of PCA and kernel PCA. These formulations are
+semidefinite programs (SDP's), and we demonstrate the effectiveness of our
+formulations using several datasets. We conclude by showing how our approach
+can be used to perform a fair (with respect to age) clustering of health data
+that may be used to set health insurance rates.
+",0,0,0,1,0,0
+20100,Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture,"  In the research of the impact of gestures using by a lecturer, one
+challenging task is to infer the attention of a group of audiences. Two
+important measurements that can help infer the level of attention are eye
+movement data and Electroencephalography (EEG) data. Under the fundamental
+assumption that a group of people would look at the same place if they all pay
+attention at the same time, we apply a method, ""Time Warp Edit Distance"", to
+calculate the similarity of their eye movement trajectories. Moreover, we also
+cluster eye movement pattern of audiences based on these pair-wised similarity
+metrics. Besides, since we don't have a direct metric for the ""attention""
+ground truth, a visual assessment would be beneficial to evaluate the
+gesture-attention relationship. Thus we also implement a visualization tool.
+",1,0,0,0,0,0
+20101,Infinite symmetric ergodic index and related examples in infinite measure,"  We define an infinite measure-preserving transformation to have infinite
+symmetric ergodic index if all finite Cartesian products of the transformation
+and its inverse are ergodic, and show that infinite symmetric ergodic index
+does not imply that all products of powers are conservative, so does not imply
+power weak mixing. We provide a sufficient condition for $k$-fold and infinite
+symmetric ergodic index and use it to answer a question on the relationship
+between product conservativity and product ergodicity. We also show that a
+class of rank-one transformations that have infinite symmetric ergodic index
+are not power weakly mixing, and precisely characterize a class of power weak
+transformations that generalizes existing examples.
+",0,0,1,0,0,0
+20102,Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature,"  An exciting branch of machine learning research focuses on methods for
+learning, optimizing, and integrating unknown functions that are difficult or
+costly to evaluate. A popular Bayesian approach to this problem uses a Gaussian
+process (GP) to construct a posterior distribution over the function of
+interest given a set of observed measurements, and selects new points to
+evaluate using the statistics of this posterior. Here we extend these methods
+to exploit derivative information from the unknown function. We describe
+methods for Bayesian optimization (BO) and Bayesian quadrature (BQ) in settings
+where first and second derivatives may be evaluated along with the function
+itself. We perform sampling-based inference in order to incorporate uncertainty
+over hyperparameters, and show that both hyperparameter and function
+uncertainty decrease much more rapidly when using derivative information.
+Moreover, we introduce techniques for overcoming ill-conditioning issues that
+have plagued earlier methods for gradient-enhanced Gaussian processes and
+kriging. We illustrate the efficacy of these methods using applications to real
+and simulated Bayesian optimization and quadrature problems, and show that
+exploting derivatives can provide substantial gains over standard methods.
+",0,0,0,1,0,0
+20103,Permanency of the age-structured population model on several temporally variable patches,"  We consider a system of nonlinear partial differential equations that
+describes an age-structured population inhabiting several temporally varying
+patches. We prove existence and uniqueness of solution and analyze its
+large-time behavior in cases when the environment is constant and when it
+changes periodically. A pivotal assumption is that individuals can disperse and
+that each patch can be reached from every other patch, directly or through
+several intermediary patches. We introduce the net reproductive operator and
+characteristic equations for time-independent and periodical models and prove
+that permanency is defined by the net reproductive rate for the whole system.
+If the net reproductive rate is less or equal to one, extinction on all patches
+is imminent. Otherwise, permanency on all patches is guaranteed. The proof is
+based on a new approach to analysis of large-time stability.
+",0,1,1,0,0,0
+20104,Rapid laser-induced photochemical conversion of sol-gel precursors to In2O3 layers and their application in thin-film transistors,"  We report the development of indium oxide (In2O3) transistors via a single
+step laser-induced photochemical conversion process of a sol-gel metal oxide
+precursor. Through careful optimization of the laser annealing conditions we
+demonstrated successful conversion of the precursor to In2O3 and its subsequent
+implementation in n-channel transistors with electron mobility up to 13 cm2/Vs.
+Importantly, the process does not require thermal annealing making it
+compatible with temperature sensitive materials such as plastic. On the other
+hand, the spatial conversion/densification of the sol-gel layer eliminates
+additional process steps associated with semiconductor patterning and hence
+significantly reduces fabrication complexity and cost. Our work demonstrates
+unambiguously that laser-induced photochemical conversion of sol-gel metal
+oxide precursors can be rapid and compatible with large-area electronics
+manufacturing.
+",0,1,0,0,0,0
+20105,Testing for Change in Stochastic Volatility with Long Range Dependence,"  In this paper, change-point problems for long memory stochastic volatility
+models are considered. A general testing problem which includes various
+alternative hypotheses is discussed. Under the hypothesis of stationarity the
+limiting behavior of CUSUM- and Wilcoxon-type test statistics is derived. In
+this context, a limit theorem for the two-parameter empirical process of long
+memory stochastic volatility time series is proved. In particular, it is shown
+that the asymptotic distribution of CUSUM test statistics may not be affected
+by long memory, unlike Wilcoxon test statistics which are typically influenced
+by long range dependence. To avoid the estimation of nuisance parameters in
+applications, the usage of self-normalized test statistics is proposed. The
+theoretical results are accompanied by simulation studies which characterize
+the finite sample behavior of the considered testing procedures when testing
+for changes in mean, in variance, and in the tail index.
+",0,0,1,1,0,0
+20106,Kinematics effects of atmospheric friction in spacecraft flybys,"  Gravity assist manoeuvres are one of the most succesful techniques in
+astrodynamics. In these trajectories the spacecraft comes very close to the
+surface of the Earth, or other Solar system planets or moons, and, as a
+consequence, it experiences the effect of atmospheric friction by the outer
+layers of the Earth's atmosphere or ionosphere.
+In this paper we analyze a standard atmospheric model to estimate the density
+profile during the two Galileo flybys, the NEAR and the Juno flyby. We show
+that, even allowing for a margin of uncertainty in the spacecraft cross-section
+and the drag coefficient, the observed -8 mm/sec anomalous velocity decrease
+during the second Galileo flyby of December, 8th, 1992 cannot be attributed
+only to atmospheric friction. On the other hand, for perigees on the border
+between the termosphere and the exosphere the friction only accounts for a
+fraction of a millimeter per second in the final asymptotic velocity.
+",0,1,0,0,0,0
+20107,Interval vs. Point Temporal Logic Model Checking: an Expressiveness Comparison,"  In the last years, model checking with interval temporal logics is emerging
+as a viable alternative to model checking with standard point-based temporal
+logics, such as LTL, CTL, CTL*, and the like. The behavior of the system is
+modeled by means of (finite) Kripke structures, as usual. However, while
+temporal logics which are interpreted ""point-wise"" describe how the system
+evolves state-by-state, and predicate properties of system states, those which
+are interpreted ""interval-wise"" express properties of computation stretches,
+spanning a sequence of states. A proposition letter is assumed to hold over a
+computation stretch (interval) if and only if it holds over each component
+state (homogeneity assumption). A natural question arises: is there any
+advantage in replacing points by intervals as the primary temporal entities, or
+is it just a matter of taste?
+In this paper, we study the expressiveness of Halpern and Shoham's interval
+temporal logic (HS) in model checking, in comparison with those of LTL, CTL,
+and CTL*. To this end, we consider three semantic variants of HS: the
+state-based one, introduced by Montanari et al., that allows time to branch
+both in the past and in the future, the computation-tree-based one, that allows
+time to branch in the future only, and the trace-based variant, that disallows
+time to branch. These variants are compared among themselves and to the
+aforementioned standard logics, getting a complete picture. In particular, we
+show that HS with trace-based semantics is equivalent to LTL (but at least
+exponentially more succinct), HS with computation-tree-based semantics is
+equivalent to finitary CTL*, and HS with state-based semantics is incomparable
+with all of them (LTL, CTL, and CTL*).
+",1,0,0,0,0,0
+20108,Calculations for electron-impact ionization of magnesium and calcium atoms in the method of interacting configurations in the complex number representation,"  Next investigations in our program of transition from the He atom to the
+complex atoms description have been presented. The method of interacting
+configurations in the complex number representation is under consideration. The
+spectroscopic characteristics of the Mg and Ca atoms in the problem of the
+electron-impact ionization of these atoms are investigated. The energies and
+the widths of the lowest autoionizing states of Mg and Ca atoms are calculated.
+Few results in the photoionization problem on the autoionizing states above the
+n=2 threshold of helium-like Be ion are presented.
+",0,1,0,0,0,0
+20109,An Information Theoretic Approach to Sample Acquisition and Perception in Planetary Robotics,"  An important and emerging component of planetary exploration is sample
+retrieval and return to Earth. Obtaining and analyzing rock samples can provide
+unprecedented insight into the geology, geo-history and prospects for finding
+past life and water. Current methods of exploration rely on mission scientists
+to identify objects of interests and this presents major operational
+challenges. Finding objects of interests will require systematic and efficient
+methods to quickly and correctly evaluate the importance of hundreds if not
+thousands of samples so that the most interesting are saved for further
+analysis by the mission scientists. In this paper, we propose an automated
+information theoretic approach to identify shapes of interests using a library
+of predefined interesting shapes. These predefined shapes maybe human input or
+samples that are then extrapolated by the shape matching system using the
+Superformula to judge the importance of newly obtained objects. Shape samples
+are matched to a library of shapes using the eigenfaces approach enabling
+categorization and prioritization of the sample. The approach shows robustness
+to simulated sensor noise of up to 20%. The effect of shape parameters and
+rotational angle on shape matching accuracy has been analyzed. The approach
+shows significant promise and efforts are underway in testing the algorithm
+with real rock samples.
+",1,1,0,0,0,0
+20110,Global solvability of the Navier-Stokes equations with a free surface in the maximal $L_p\text{-}L_q$ regularity class,"  We consider the motion of incompressible viscous fluids bounded above by a
+free surface and below by a solid surface in the $N$-dimensional Euclidean
+space for $N\geq 2$ when the gravity is not taken into account. The aim of this
+paper is to show the global solvability of the Naiver-Stokes equations with a
+free surface, describing the above-mentioned motion, in the maximal
+$L_p\text{-}L_q$ regularity class. Our approach is based on the maximal
+$L_p\text{-}L_q$ regularity with exponential stability for the linearized
+equations, and solutions to the original nonlinear problem are also
+exponentially stable.
+",0,0,1,0,0,0
+20111,The fundamental group of the complement of the singular locus of Lauricella's $F_C$,"  We study the fundamental group of the complement of the singular locus of
+Lauricella's hypergeometric function $F_C$ of $n$ variables. The singular locus
+consists of $n$ hyperplanes and a hypersurface of degree $2^{n-1}$ in the
+complex $n$-space. We derive some relations that holds for general $n\geq 3$.
+We give an explicit presentation of the fundamental groupin the
+three-dimensional case. We also consider a presentation of the fundamental
+group of $2^3$-covering of this space.
+In the version 2, we omit some of the calculations. For all the calculations,
+refer to the version 1 (arXiv:1710.09594v1) of this article.
+",0,0,1,0,0,0
+20112,Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis,"  Spectral Clustering (SC) is a widely used data clustering method which first
+learns a low-dimensional embedding $U$ of data by computing the eigenvectors of
+the normalized Laplacian matrix, and then performs k-means on $U^\top$ to get
+the final clustering result. The Sparse Spectral Clustering (SSC) method
+extends SC with a sparse regularization on $UU^\top$ by using the block
+diagonal structure prior of $UU^\top$ in the ideal case. However, encouraging
+$UU^\top$ to be sparse leads to a heavily nonconvex problem which is
+challenging to solve and the work (Lu, Yan, and Lin 2016) proposes a convex
+relaxation in the pursuit of this aim indirectly. However, the convex
+relaxation generally leads to a loose approximation and the quality of the
+solution is not clear. This work instead considers to solve the nonconvex
+formulation of SSC which directly encourages $UU^\top$ to be sparse. We propose
+an efficient Alternating Direction Method of Multipliers (ADMM) to solve the
+nonconvex SSC and provide the convergence guarantee. In particular, we prove
+that the sequences generated by ADMM always exist a limit point and any limit
+point is a stationary point. Our analysis does not impose any assumptions on
+the iterates and thus is practical. Our proposed ADMM for nonconvex problems
+allows the stepsize to be increasing but upper bounded, and this makes it very
+efficient in practice. Experimental analysis on several real data sets verifies
+the effectiveness of our method.
+",1,0,0,0,0,0
+20113,Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings,"  A hyperbolic space has been shown to be more capable of modeling complex
+networks than a Euclidean space. This paper proposes an explicit update rule
+along geodesics in a hyperbolic space. The convergence of our algorithm is
+theoretically guaranteed, and the convergence rate is better than the
+conventional Euclidean gradient descent algorithm. Moreover, our algorithm
+avoids the ""bias"" problem of existing methods using the Riemannian gradient.
+Experimental results demonstrate the good performance of our algorithm in the
+\Poincare embeddings of knowledge base data.
+",0,0,0,1,0,0
+20114,A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches,"  We present a parameterized approach to produce personalized variable length
+summaries of soccer matches. Our approach is based on temporally segmenting the
+soccer video into 'plays', associating a user-specifiable 'utility' for each
+type of play and using 'bin-packing' to select a subset of the plays that add
+up to the desired length while maximizing the overall utility (volume in
+bin-packing terms). Our approach systematically allows a user to override the
+default weights assigned to each type of play with individual preferences and
+thus see a highly personalized variable length summarization of soccer matches.
+We demonstrate our approach based on the output of an end-to-end pipeline that
+we are building to produce such summaries. Though aspects of the overall
+end-to-end pipeline are human assisted at present, the results clearly show
+that the proposed approach is capable of producing semantically meaningful and
+compelling summaries. Besides the obvious use of producing summaries of
+superior league matches for news broadcasts, we anticipate our work to promote
+greater awareness of the local matches and junior leagues by producing
+consumable summaries of them.
+",1,0,0,0,0,0
+20115,Towards Robust Neural Networks via Random Self-ensemble,"  Recent studies have revealed the vulnerability of deep neural networks: A
+small adversarial perturbation that is imperceptible to human can easily make a
+well-trained deep neural network misclassify. This makes it unsafe to apply
+neural networks in security-critical applications. In this paper, we propose a
+new defense algorithm called Random Self-Ensemble (RSE) by combining two
+important concepts: {\bf randomness} and {\bf ensemble}. To protect a targeted
+model, RSE adds random noise layers to the neural network to prevent the strong
+gradient-based attacks, and ensembles the prediction over random noises to
+stabilize the performance. We show that our algorithm is equivalent to ensemble
+an infinite number of noisy models $f_\epsilon$ without any additional memory
+overhead, and the proposed training procedure based on noisy stochastic
+gradient descent can ensure the ensemble model has a good predictive
+capability. Our algorithm significantly outperforms previous defense techniques
+on real data sets. For instance, on CIFAR-10 with VGG network (which has 92\%
+accuracy without any attack), under the strong C\&W attack within a certain
+distortion tolerance, the accuracy of unprotected model drops to less than
+10\%, the best previous defense technique has $48\%$ accuracy, while our method
+still has $86\%$ prediction accuracy under the same level of attack. Finally,
+our method is simple and easy to integrate into any neural network.
+",1,0,0,1,0,0
+20116,Prospects for detection of intermediate-mass black holes in globular clusters using integrated-light spectroscopy,"  The detection of intermediate mass black holes (IMBHs) in Galactic globular
+clusters (GCs) has so far been controversial. In order to characterize the
+effectiveness of integrated-light spectroscopy through integral field units, we
+analyze realistic mock data generated from state-of-the-art Monte Carlo
+simulations of GCs with a central IMBH, considering different setups and
+conditions varying IMBH mass, cluster distance, and accuracy in determination
+of the center. The mock observations are modeled with isotropic Jeans models to
+assess the success rate in identifying the IMBH presence, which we find to be
+primarily dependent on IMBH mass. However, even for a IMBH of considerable mass
+(3% of the total GC mass), the analysis does not yield conclusive results in 1
+out of 5 cases, because of shot noise due to bright stars close to the IMBH
+line-of-sight. This stochastic variability in the modeling outcome grows with
+decreasing BH mass, with approximately 3 failures out of 4 for IMBHs with 0.1%
+of total GC mass. Finally, we find that our analysis is generally unable to
+exclude at 68% confidence an IMBH with mass of $10^3~M_\odot$ in snapshots
+without a central BH. Interestingly, our results are not sensitive to GC
+distance within 5-20 kpc, nor to mis-identification of the GC center by less
+than 2'' (<20% of the core radius). These findings highlight the value of
+ground-based integral field spectroscopy for large GC surveys, where systematic
+failures can be accounted for, but stress the importance of discrete kinematic
+measurements that are less affected by stochasticity induced by bright stars.
+",0,1,0,0,0,0
+20117,A Multi-frequency analysis of possible Dark Matter Contributions to M31 Gamma-Ray Emissions,"  We examine the possibility of a dark matter (DM) contribution to the recently
+observed gamma-ray spectrum seen in the M31 galaxy. In particular, we apply
+limits on Weakly Interacting Massive Particle DM annihilation cross-sections
+derived from the Coma galaxy cluster and the Reticulum II dwarf galaxy to
+determine the maximal flux contribution by DM annihilation to both the M31
+gamma-ray spectrum and that of the Milky-Way galactic centre. We limit the
+energy range between 1 and 12 GeV in M31 and galactic centre spectra due to the
+limited range of former's data, as well as to encompass the high-energy
+gamma-ray excess observed in the latter target. In so doing, we will make use
+of Fermi-LAT data for all mentioned targets, as well as diffuse radio data for
+the Coma cluster. The multi-target strategy using both Coma and Reticulum II to
+derive cross-section limits, as well as multi-frequency data, ensures that our
+results are robust against the various uncertainties inherent in modelling of
+indirect DM emissions.
+Our results indicate that, when a Navarro-Frenk-White (or shallower) radial
+density profile is assumed, severe constraints can be imposed upon the fraction
+of the M31 and galactic centre spectra that can be accounted for by DM, with
+the best limits arising from cross-section constraints from Coma radio data and
+Reticulum II gamma-ray limits. These particular limits force all the studied
+annihilation channels to contribute 1% or less to the total integrated
+gamma-ray flux within both M31 and galactic centre targets. In contrast,
+considerably more, 10-100%, of the flux can be attributed to DM when a
+contracted Navarro-Frenk-White profile is assumed. This demonstrates how
+sensitive DM contributions to gamma-ray emissions are to the possibility of
+cored profiles in galaxies.
+",0,1,0,0,0,0
+20118,Tensor Decompositions for Modeling Inverse Dynamics,"  Modeling inverse dynamics is crucial for accurate feedforward robot control.
+The model computes the necessary joint torques, to perform a desired movement.
+The highly non-linear inverse function of the dynamical system can be
+approximated using regression techniques. We propose as regression method a
+tensor decomposition model that exploits the inherent three-way interaction of
+positions x velocities x accelerations. Most work in tensor factorization has
+addressed the decomposition of dense tensors. In this paper, we build upon the
+decomposition of sparse tensors, with only small amounts of nonzero entries.
+The decomposition of sparse tensors has successfully been used in relational
+learning, e.g., the modeling of large knowledge graphs. Recently, the approach
+has been extended to multi-class classification with discrete input variables.
+Representing the data in high dimensional sparse tensors enables the
+approximation of complex highly non-linear functions. In this paper we show how
+the decomposition of sparse tensors can be applied to regression problems.
+Furthermore, we extend the method to continuous inputs, by learning a mapping
+from the continuous inputs to the latent representations of the tensor
+decomposition, using basis functions. We evaluate our proposed model on a
+dataset with trajectories from a seven degrees of freedom SARCOS robot arm. Our
+experimental results show superior performance of the proposed functional
+tensor model, compared to challenging state-of-the art methods.
+",1,0,0,1,0,0
+20119,Synthesis and Hydrogen Sorption Characteristics of Mechanically Alloyed Mg(NixMn1-x)2 Intermetallics,"  New ternary Mg-Ni-Mn intermetallics have been successfully synthesized by
+High Energy Ball Milling (HEBM) and have been studied as possible materials for
+efficient hydrogen storage applications. The microstructures of the as-cast and
+milled alloys were characterized by means of X-ray Powder Diffraction (XRD) and
+Scanning Electron Microscopy (SEM) both prior and after the hydrogenation
+process, while the hydrogen storage characteristics (P-c-T) and the kinetics
+were measured by using a commercial and automatically controlled Sievert-type
+apparatus. The hydrogenation and dehydrogenation measurements were performed at
+four different temperatures 150-200-250-300oC and the results showed that the
+kinetics for both the hydrogenation and dehydrogenation process are very fast
+for operation temperatures 250 and 300oC, but for temperatures below 200oC the
+hydrogenation process becomes very slow and the dehydrogenation process cannot
+be achieved.
+",0,1,0,0,0,0
+20120,Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective,"  Recent advances in the field of network embedding have shown the
+low-dimensional network representation is playing a critical role in network
+analysis. However, most of the existing principles of network embedding do not
+incorporate auxiliary information such as content and labels of nodes flexibly.
+In this paper, we take a matrix factorization perspective of network embedding,
+and incorporate structure, content and label information of the network
+simultaneously. For structure, we validate that the matrix we construct
+preserves high-order proximities of the network. Label information can be
+further integrated into the matrix via the process of random walk sampling to
+enhance the quality of embedding in an unsupervised manner, i.e., without
+leveraging downstream classifiers. In addition, we generalize the Skip-Gram
+Negative Sampling model to integrate the content of the network in a matrix
+factorization framework. As a consequence, network embedding can be learned in
+a unified framework integrating network structure and node content as well as
+label information simultaneously. We demonstrate the efficacy of the proposed
+model with the tasks of semi-supervised node classification and link prediction
+on a variety of real-world benchmark network datasets.
+",1,0,0,1,0,0
+20121,Incremental Principal Component Analysis Exact implementation and continuity corrections,"  This paper describes some applications of an incremental implementation of
+the principal component analysis (PCA). The algorithm updates the
+transformation coefficients matrix on-line for each new sample, without the
+need to keep all the samples in memory. The algorithm is formally equivalent to
+the usual batch version, in the sense that given a sample set the
+transformation coefficients at the end of the process are the same. The
+implications of applying the PCA in real time are discussed with the help of
+data analysis examples. In particular we focus on the problem of the continuity
+of the PCs during an on-line analysis.
+",1,0,0,1,0,0
+20122,More investment in Research and Development for better Education in the future?,"  The question in this paper is whether R&D efforts affect education
+performance in small classes. Merging two datasets collected from the PISA
+studies and the World Development Indicators and using Learning Bayesian
+Networks, we prove the existence of a statistical causal relationship between
+investment in R&D of a country and its education performance (PISA scores). We
+also prove that the effect of R\&D on Education is long term as a country has
+to invest at least 10 years before beginning to improve the level of young
+pupils.
+",0,0,0,1,0,0
+20123,Discriminative Modeling of Social Influence for Prediction and Explanation in Event Cascades,"  The global dynamics of event cascades are often governed by the local
+dynamics of peer influence. However, detecting social influence from
+observational data is challenging, due to confounds like homophily and
+practical issues like missing data. In this work, we propose a novel
+discriminative method to detect influence from observational data. The core of
+the approach is to train a ranking algorithm to predict the source of the next
+event in a cascade, and compare its out-of-sample accuracy against a
+competitive baseline which lacks access to features corresponding to social
+influence. Using synthetically generated data, we provide empirical evidence
+that this method correctly identifies influence in the presence of confounds,
+and is robust to both missing data and misspecification --- unlike popular
+alternatives. We also apply the method to two real-world datasets: (1) cascades
+of co-sponsorship of legislation in the U.S. House of Representatives, on a
+social network of shared campaign donors; (2) rumors about the Higgs boson
+discovery, on a follower network of $10^5$ Twitter accounts. Our model
+identifies the role of peer influence in these scenarios, and uses it to make
+more accurate predictions about the future trajectory of cascades.
+",1,0,0,0,0,0
+20124,Online codes for analog signals,"  We revisit a classical scenario in communication theory: a source is
+generating a waveform which we sample at regular intervals; we wish to
+transform the signal in such a way as to minimize distortion in its
+reconstruction, despite noise. The transformation must be online (also called
+causal), in order to enable real-time signaling. The noise model we consider is
+adversarial $\ell_1$-bounded; this is the ""atomic norm"" convex relaxation of
+the standard adversary model in discrete-alphabet communications, namely
+sparsity (low Hamming weight). We require that our encoding not increase the
+power of the original signal.
+In the ""block coding"" setting such encoding is possible due to the existence
+of large almost-Euclidean sections in $\ell_1$ spaces (established in the work
+of Dvoretzky, Milman, Kašin, and Figiel, Lindenstrauss and Milman).
+Our main result is that an analogous result is achievable even online.
+Equivalently, we show a ""lower triangular"" version of $\ell_1$ Dvoretzky
+theorems. In terms of communication, the result has the following form: If the
+signal is a stream of reals $x_1,\ldots$, one per unit time, which we encode
+causally into $\rho$ (a constant) reals per unit time (forming altogether an
+output stream $\mathcal{E}(x)$), and if the adversarial noise added to this
+encoded stream up to time $s$ is a vector $\vec{y}$, then at time $s$ the
+decoder's reconstruction of the input prefix $x_{[s]}$ is accurate in a
+time-weighted $\ell_2$ norm, to within $s^{-1/2+\delta}$ (any $\delta>0$) times
+the adversary's noise as measured in a time-weighted $\ell_1$ norm. The
+time-weighted decoding norm forces increasingly accurate reconstruction of the
+distant past, while the time-weighted noise norm permits only vanishing effect
+from noise in the distant past.
+Encoding is linear, and decoding is performed by an LP analogous to those
+used in compressed sensing.
+",1,0,0,0,0,0
+20125,Statistical test for fractional Brownian motion based on detrending moving average algorithm,"  Motivated by contemporary and rich applications of anomalous diffusion
+processes we propose a new statistical test for fractional Brownian motion,
+which is one of the most popular models for anomalous diffusion systems. The
+test is based on detrending moving average statistic and its probability
+distribution. Using the theory of Gaussian quadratic forms we determined it as
+a generalized chi-squared distribution. The proposed test could be generalized
+for statistical testing of any centered non-degenerate Gaussian process.
+Finally, we examine the test via Monte Carlo simulations for two exemplary
+scenarios of subdiffusive and superdiffusive dynamics.
+",0,0,0,1,0,0
+20126,Fast Radio Map Construction and Position Estimation via Direct Mapping for WLAN Indoor Localization System,"  The main limitation that constrains the fast and comprehensive application of
+Wireless Local Area Network (WLAN) based indoor localization systems with
+Received Signal Strength (RSS) positioning algorithms is the building of the
+fingerprinting radio map, which is time-consuming especially when the indoor
+environment is large and/or with high frequent changes. Different approaches
+have been proposed to reduce workload, including fingerprinting deployment and
+update efforts, but the performance degrades greatly when the workload is
+reduced below a certain level. In this paper, we propose an indoor localization
+scenario that applies metric learning and manifold alignment to realize direct
+mapping localization (DML) using a low resolution radio map with single sample
+of RSS that reduces the fingerprinting workload by up to 87\%. Compared to
+previous work. The proposed two localization approaches, DML and $k$ nearest
+neighbors based on reconstructed radio map (reKNN), were shown to achieve less
+than 4.3\ m and 3.7\ m mean localization error respectively in a typical office
+environment with an area of approximately 170\ m$^2$, while the unsupervised
+localization with perturbation algorithm was shown to achieve 4.7\ m mean
+localization error with 8 times more workload than the proposed methods. As for
+the room level localization application, both DML and reKNN can meet the
+requirement with at most 9\ m of localization error which is enough to tell
+apart different rooms with over 99\% accuracy.
+",1,0,0,1,0,0
+20127,Lagrangian solutions to the Vlasov-Poisson system with a point charge,"  We consider the Cauchy problem for the repulsive Vlasov-Poisson system in the
+three dimensional space, where the initial datum is the sum of a diffuse
+density, assumed to be bounded and integrable, and a point charge. Under some
+decay assumptions for the diffuse density close to the point charge, under
+bounds on the total energy, and assuming that the initial total diffuse charge
+is strictly less than one, we prove existence of global Lagrangian solutions.
+Our result extends the Eulerian theory of [16], proving that solutions are
+transported by the flow trajectories. The proof is based on the ODE theory
+developed in [8] in the setting of vector fields with anisotropic regularity,
+where some components of the gradient of the vector field is a singular
+integral of a measure.
+",0,0,1,0,0,0
+20128,MITHRIL: Mining Sporadic Associations for Cache Prefetching,"  The growing pressure on cloud application scalability has accentuated storage
+performance as a critical bottle- neck. Although cache replacement algorithms
+have been extensively studied, cache prefetching - reducing latency by
+retrieving items before they are actually requested remains an underexplored
+area. Existing approaches to history-based prefetching, in particular, provide
+too few benefits for real systems for the resources they cost. We propose
+MITHRIL, a prefetching layer that efficiently exploits historical patterns in
+cache request associations. MITHRIL is inspired by sporadic association rule
+mining and only relies on the timestamps of requests. Through evaluation of 135
+block-storage traces, we show that MITHRIL is effective, giving an average of a
+55% hit ratio increase over LRU and PROBABILITY GRAPH, a 36% hit ratio gain
+over AMP at reasonable cost. We further show that MITHRIL can supplement any
+cache replacement algorithm and be readily integrated into existing systems.
+Furthermore, we demonstrate the improvement comes from MITHRIL being able to
+capture mid-frequency blocks.
+",1,0,0,0,0,0
+20129,On 2-level polytopes arising in combinatorial settings,"  2-level polytopes naturally appear in several areas of pure and applied
+mathematics, including combinatorial optimization, polyhedral combinatorics,
+communication complexity, and statistics. In this paper, we present a study of
+some 2-level polytopes arising in combinatorial settings. Our first
+contribution is proving that v(P)*f(P) is upper bounded by d*2^(d+1), for a
+large collection of families of such polytopes P. Here v(P) (resp. f(P)) is the
+number of vertices (resp. facets) of P, and d is its dimension. Whether this
+holds for all 2-level polytopes was asked in [Bohn et al., ESA 2015], and
+experimental results from [Fiorini et al., ISCO 2016] showed it true up to
+dimension 7. The key to most of our proofs is a deeper understanding of the
+relations among those polytopes and their underlying combinatorial structures.
+This leads to a number of results that we believe to be of independent
+interest: a trade-off formula for the number of cliques and stable sets in a
+graph; a description of stable matching polytopes as affine projections of
+certain order polytopes; and a linear-size description of the base polytope of
+matroids that are 2-level in terms of cuts of an associated tree.
+",1,0,1,0,0,0
+20130,Learned Optimizers that Scale and Generalize,"  Learning to learn has emerged as an important direction for achieving
+artificial intelligence. Two of the primary barriers to its adoption are an
+inability to scale to larger problems and a limited ability to generalize to
+new tasks. We introduce a learned gradient descent optimizer that generalizes
+well to new tasks, and which has significantly reduced memory and computation
+overhead. We achieve this by introducing a novel hierarchical RNN architecture,
+with minimal per-parameter overhead, augmented with additional architectural
+features that mirror the known structure of optimization tasks. We also develop
+a meta-training ensemble of small, diverse optimization tasks capturing common
+properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM
+on problems in this corpus. More importantly, it performs comparably or better
+when applied to small convolutional neural networks, despite seeing no neural
+networks in its meta-training set. Finally, it generalizes to train Inception
+V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps,
+optimization problems that are of a vastly different scale than those it was
+trained on. We release an open source implementation of the meta-training
+algorithm.
+",1,0,0,1,0,0
+20131,Toward Optimal Run Racing: Application to Deep Learning Calibration,"  This paper aims at one-shot learning of deep neural nets, where a highly
+parallel setting is considered to address the algorithm calibration problem -
+selecting the best neural architecture and learning hyper-parameter values
+depending on the dataset at hand. The notoriously expensive calibration problem
+is optimally reduced by detecting and early stopping non-optimal runs. The
+theoretical contribution regards the optimality guarantees within the multiple
+hypothesis testing framework. Experimentations on the Cifar10, PTB and Wiki
+benchmarks demonstrate the relevance of the approach with a principled and
+consistent improvement on the state of the art with no extra hyper-parameter.
+",1,0,0,0,0,0
+20132,Bernstein - von Mises theorems for statistical inverse problems I: Schrödinger equation,"  The inverse problem of determining the unknown potential $f>0$ in the partial
+differential equation $$\frac{\Delta}{2} u - fu =0 \text{ on } \mathcal O
+~~\text{s.t. } u = g \text { on } \partial \mathcal O,$$ where $\mathcal O$ is
+a bounded $C^\infty$-domain in $\mathbb R^d$ and $g>0$ is a given function
+prescribing boundary values, is considered. The data consist of the solution
+$u$ corrupted by additive Gaussian noise. A nonparametric Bayesian prior for
+the function $f$ is devised and a Bernstein - von Mises theorem is proved which
+entails that the posterior distribution given the observations is approximated
+in a suitable function space by an infinite-dimensional Gaussian measure that
+has a `minimal' covariance structure in an information-theoretic sense. As a
+consequence the posterior distribution performs valid and optimal frequentist
+statistical inference on $f$ in the small noise limit.
+",0,0,1,1,0,0
+20133,Tailoring Architecture Centric Design Method with Rapid Prototyping,"  Many engineering processes exist in the industry, text books and
+international standards. However, in practice rarely any of the processes are
+followed consistently and literally. It is observed across industries the
+processes are altered based on the requirements of the projects. Two features
+commonly lacking from many engineering processes are, 1) the formal capacity to
+rapidly develop prototypes in the rudimentary stage of the project, 2)
+transitioning of requirements into architectural designs, when and how to
+evaluate designs and how to use the throw away prototypes throughout the system
+lifecycle. Prototypes are useful for eliciting requirements, generating
+customer feedback and identifying, examining or mitigating risks in a project
+where the product concept is at a cutting edge or not fully perceived. Apart
+from the work that the product is intended to do, systemic properties like
+availability, performance and modifiability matter as much as functionality.
+Architects must even these concerns with the method they select to promote
+these systemic properties and at the same time equip the stakeholders with the
+desired functionality. Architectural design and prototyping is one of the key
+ways to build the right product embedded with the desired systemic properties.
+Once the product is built it can be almost impossible to retrofit the system
+with the desired attributes. This paper customizes the architecture centric
+development method with rapid prototyping to achieve the above-mentioned goals
+and reducing the number of iterations across the stages of ACDM.
+",1,0,0,0,0,0
+20134,Easy High-Dimensional Likelihood-Free Inference,"  We introduce a framework using Generative Adversarial Networks (GANs) for
+likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC)
+where we replace the black-box simulator model with an approximator network and
+generate a rich set of summary features in a data driven fashion. On benchmark
+data sets, our approach improves on others with respect to scalability, ability
+to handle high dimensional data and complex probability distributions.
+",1,0,0,1,0,0
+20135,Narrow-line Laser Cooling by Adiabatic Transfer,"  We propose and demonstrate a novel laser cooling mechanism applicable to
+particles with narrow-linewidth optical transitions. By sweeping the frequency
+of counter-propagating laser beams in a sawtooth manner, we cause adiabatic
+transfer back and forth between the ground state and a long-lived optically
+excited state. The time-ordering of these adiabatic transfers is determined by
+Doppler shifts, which ensures that the associated photon recoils are in the
+opposite direction to the particle's motion. This ultimately leads to a robust
+cooling mechanism capable of exerting large forces via a weak transition and
+with reduced reliance on spontaneous emission. We present a simple intuitive
+model for the resulting frictional force, and directly demonstrate its efficacy
+for increasing the total phase-space density of an atomic ensemble. We rely on
+both simulation and experimental studies using the 7.5~kHz linewidth $^1$S$_0$
+to $^3$P$_1$ transition in $^{88}$Sr. The reduced reliance on spontaneous
+emission may allow this adiabatic sweep method to be a useful tool for cooling
+particles that lack closed cycling transitions, such as molecules.
+",0,1,0,0,0,0
+20136,Assessing the Effect of Stellar Companions from High-Resolution Imaging of Kepler Objects of Interest,"  We report on 176 close (<2"") stellar companions detected with high-resolution
+imaging near 170 hosts of Kepler Objects of Interest. These Kepler targets were
+prioritized for imaging follow-up based on the presence of small planets, so
+most of the KOIs in these systems (176 out of 204) have nominal radii <6 R_E .
+Each KOI in our sample was observed in at least 2 filters with adaptive optics,
+speckle imaging, lucky imaging, or HST. Multi-filter photometry provides color
+information on the companions, allowing us to constrain their stellar
+properties and assess the probability that the companions are physically bound.
+We find that 60 -- 80% of companions within 1"" are bound, and the bound
+fraction is >90% for companions within 0.5""; the bound fraction decreases with
+increasing angular separation. This picture is consistent with simulations of
+the binary and background stellar populations in the Kepler field. We also
+reassess the planet radii in these systems, converting the observed
+differential magnitudes to a contamination in the Kepler bandpass and
+calculating the planet radius correction factor, $X_R = R_p (true) / R_p
+(single)$. Under the assumption that planets in bound binaries are equally
+likely to orbit the primary or secondary, we find a mean radius correction
+factor for planets in stellar multiples of $X_R = 1.65$. If stellar
+multiplicity in the Kepler field is similar to the solar neighborhood, then
+nearly half of all Kepler planets may have radii underestimated by an average
+of 65%, unless vetted using high resolution imaging or spectroscopy.
+",0,1,0,0,0,0
+20137,Semi-Supervised Learning via New Deep Network Inversion,"  We exploit a recently derived inversion scheme for arbitrary deep neural
+networks to develop a new semi-supervised learning framework that applies to a
+wide range of systems and problems. The approach outperforms current
+state-of-the-art methods on MNIST reaching $99.14\%$ of test set accuracy while
+using $5$ labeled examples per class. Experiments with one-dimensional signals
+highlight the generality of the method. Importantly, our approach is simple,
+efficient, and requires no change in the deep network architecture.
+",1,0,0,1,0,0
+20138,Using English as Pivot to Extract Persian-Italian Parallel Sentences from Non-Parallel Corpora,"  The effectiveness of a statistical machine translation system (SMT) is very
+dependent upon the amount of parallel corpus used in the training phase. For
+low-resource language pairs there are not enough parallel corpora to build an
+accurate SMT. In this paper, a novel approach is presented to extract bilingual
+Persian-Italian parallel sentences from a non-parallel (comparable) corpus. In
+this study, English is used as the pivot language to compute the matching
+scores between source and target sentences and candidate selection phase.
+Additionally, a new monolingual sentence similarity metric, Normalized Google
+Distance (NGD) is proposed to improve the matching process. Moreover, some
+extensions of the baseline system are applied to improve the quality of
+extracted sentences measured with BLEU. Experimental results show that using
+the new pivot based extraction can increase the quality of bilingual corpus
+significantly and consequently improves the performance of the Persian-Italian
+SMT system.
+",1,0,0,0,0,0
+20139,Intrinsic geometry and analysis of Finsler structures,"  In this short note, we prove that if $F$ is a weak upper semicontinuous
+admissible Finsler structure on a domain in $\mathbb{R}^n$, $n\geq 2$, then the
+intrinsic distance and differential structures coincide.
+",0,0,1,0,0,0
+20140,Combining Prediction of Human Decisions with ISMCTS in Imperfect Information Games,"  Monte Carlo Tree Search (MCTS) has been extended to many imperfect
+information games. However, due to the added complexity that uncertainty
+introduces, these adaptations have not reached the same level of practical
+success as their perfect information counterparts. In this paper we consider
+the development of agents that perform well against humans in imperfect
+information games with partially observable actions. We introduce the
+Semi-Determinized-MCTS (SDMCTS), a variant of the Information Set MCTS
+algorithm (ISMCTS). More specifically, SDMCTS generates a predictive model of
+the unobservable portion of the opponent's actions from historical behavioral
+data. Next, SDMCTS performs simulations on an instance of the game where the
+unobservable portion of the opponent's actions are determined. Thereby, it
+facilitates the use of the predictive model in order to decrease uncertainty.
+We present an implementation of the SDMCTS applied to the Cheat Game, a
+well-known card game, with partially observable (and often deceptive) actions.
+Results from experiments with 120 subjects playing a head-to-head Cheat Game
+against our SDMCTS agents suggest that SDMCTS performs well against humans, and
+its performance improves as the predictive model's accuracy increases.
+",1,0,0,0,0,0
+20141,The KLASH Proposal,"  We propose a search of galactic axions with mass about 0.2 microeV using a
+large volume resonant cavity, about 50 m^3, cooled down to 4 K and immersed in
+a moderate axial magnetic field of about 0.6 T generated inside the
+superconducting magnet of the KLOE experiment located at the National
+Laboratory of Frascati of INFN. This experiment, called KLASH (KLoe magnet for
+Axion SearcH) in the following, has a potential sensitivity on the
+axion-to-photon coupling, g_agg, of about 6x10^-17 GeV-1, reaching the region
+predicted by KSVZ and DFSZ models of QCD axions.
+",0,1,0,0,0,0
+20142,Dimensionality-Driven Learning with Noisy Labels,"  Datasets with significant proportions of noisy (incorrect) class labels
+present challenges for training accurate Deep Neural Networks (DNNs). We
+propose a new perspective for understanding DNN generalization for such
+datasets, by investigating the dimensionality of the deep representation
+subspace of training samples. We show that from a dimensionality perspective,
+DNNs exhibit quite distinctive learning styles when trained with clean labels
+versus when trained with a proportion of noisy labels. Based on this finding,
+we develop a new dimensionality-driven learning strategy, which monitors the
+dimensionality of subspaces during training and adapts the loss function
+accordingly. We empirically demonstrate that our approach is highly tolerant to
+significant proportions of noisy labels, and can effectively learn
+low-dimensional local subspaces that capture the data distribution.
+",0,0,0,1,0,0
+20143,Delving into adversarial attacks on deep policies,"  Adversarial examples have been shown to exist for a variety of deep learning
+architectures. Deep reinforcement learning has shown promising results on
+training agent policies directly on raw inputs such as image pixels. In this
+paper we present a novel study into adversarial attacks on deep reinforcement
+learning polices. We compare the effectiveness of the attacks using adversarial
+examples vs. random noise. We present a novel method for reducing the number of
+times adversarial examples need to be injected for a successful attack, based
+on the value function. We further explore how re-training on random noise and
+FGSM perturbations affects the resilience against adversarial examples.
+",1,0,0,1,0,0
+20144,On two functions arising in the study of the Euler and Carmichael quotients,"  We investigate two arithmetic functions naturally occurring in the study of
+the Euler and Carmichael quotients. The functions are related to the frequency
+of vanishing of the Euler and Carmichael quotients. We obtain several results
+concerning the relations between these functions as well as their typical and
+extreme values.
+",0,0,1,0,0,0
+20145,Lurking Variable Detection via Dimensional Analysis,"  Lurking variables represent hidden information, and preclude a full
+understanding of phenomena of interest. Detection is usually based on
+serendipity -- visual detection of unexplained, systematic variation. However,
+these approaches are doomed to fail if the lurking variables do not vary. In
+this article, we address these challenges by introducing formal hypothesis
+tests for the presence of lurking variables, based on Dimensional Analysis.
+These procedures utilize a modified form of the Buckingham Pi theorem to
+provide structure for a suitable null hypothesis. We present analytic tools for
+reasoning about lurking variables in physical phenomena, construct procedures
+to handle cases of increasing complexity, and present examples of their
+application to engineering problems. The results of this work enable
+algorithm-driven lurking variable detection, complementing a traditionally
+inspection-based approach.
+",0,0,0,1,0,0
+20146,System calibration method for Fourier ptychographic microscopy,"  Fourier ptychographic microscopy (FPM) is a recently proposed quantitative
+phase imaging technique with high resolution and wide field-of-view (FOV). In
+current FPM imaging platforms, systematic error sources come from the
+aberrations, LED intensity fluctuation, parameter imperfections and noise,
+which will severely corrupt the reconstruction results with artifacts. Although
+these problems have been researched and some special methods have been proposed
+respectively, there is no method to solve all of them. However, the systematic
+error is a mixture of various sources in the real situation. It is difficult to
+distinguish a kind of error source from another due to the similar artifacts.
+To this end, we report a system calibration procedure, termed SC-FPM, based on
+the simulated annealing (SA) algorithm, LED intensity correction and adaptive
+step-size strategy, which involves the evaluation of an error matric at each
+iteration step, followed by the re-estimation of accurate parameters. The great
+performance has been achieved both in simulation and experiments. The reported
+system calibration scheme improves the robustness of FPM and relaxes the
+experiment conditions, which makes the FPM more pragmatic.
+",0,1,0,0,0,0
+20147,Categories for Dynamic Epistemic Logic,"  The primary goal of this paper is to recast the semantics of modal logic, and
+dynamic epistemic logic (DEL) in particular, in category-theoretic terms. We
+first review the category of relations and categories of Kripke frames, with
+particular emphasis on the duality between relations and adjoint homomorphisms.
+Using these categories, we then reformulate the semantics of DEL in a more
+categorical and algebraic form. Several virtues of the new formulation will be
+demonstrated: The DEL idea of updating a model into another is captured
+naturally by the categorical perspective -- which emphasizes a family of
+objects and structural relationships among them, as opposed to a single object
+and structure on it. Also, the categorical semantics of DEL can be merged
+straightforwardly with a standard categorical semantics for first-order logic,
+providing a semantics for first-order DEL.
+",1,0,1,0,0,0
+20148,Accurate Pouring with an Autonomous Robot Using an RGB-D Camera,"  Robotic assistants in a home environment are expected to perform various
+complex tasks for their users. One particularly challenging task is pouring
+drinks into cups, which for successful completion, requires the detection and
+tracking of the liquid level during a pour to determine when to stop. In this
+paper, we present a novel approach to autonomous pouring that tracks the liquid
+level using an RGB-D camera and adapts the rate of pouring based on the liquid
+level feedback. We thoroughly evaluate our system on various types of liquids
+and under different conditions, conducting over 250 pours with a PR2 robot. The
+results demonstrate that our approach is able to pour liquids to a target
+height with an accuracy of a few millimeters.
+",1,0,0,0,0,0
+20149,Restriction of Odd Degree Characters of $\mathfrak{S}_n$,"  Let $n$ and $k$ be natural numbers such that $2^k < n$. We study the
+restriction to $\mathfrak{S}_{n-2^k}$ of odd-degree irreducible characters of
+the symmetric group $\mathfrak{S}_n$. This analysis completes the study begun
+in [Ayyer A., Prasad A., Spallone S., Sem. Lothar. Combin. 75 (2015), Art.
+B75g, 13 pages] and recently developed in [Isaacs I.M., Navarro G., Olsson
+J.B., Tiep P.H., J. Algebra 478 (2017), 271-282].
+",0,0,1,0,0,0
+20150,CSGNet: Neural Shape Parser for Constructive Solid Geometry,"  We present a neural architecture that takes as input a 2D or 3D shape and
+outputs a program that generates the shape. The instructions in our program are
+based on constructive solid geometry principles, i.e., a set of boolean
+operations on shape primitives defined recursively. Bottom-up techniques for
+this shape parsing task rely on primitive detection and are inherently slow
+since the search space over possible primitive combinations is large. In
+contrast, our model uses a recurrent neural network that parses the input shape
+in a top-down manner, which is significantly faster and yields a compact and
+easy-to-interpret sequence of modeling instructions. Our model is also more
+effective as a shape detector compared to existing state-of-the-art detection
+techniques. We finally demonstrate that our network can be trained on novel
+datasets without ground-truth program annotations through policy gradient
+techniques.
+",1,0,0,0,0,0
+20151,Composite Weyl nodes stabilized by screw symmetry with and without time reversal,"  We classify the band degeneracies in 3D crystals with screw symmetry $n_m$
+and broken $\mathcal P*\mathcal T$ symmetry, where $\mathcal P$ stands for
+spatial inversion and $\mathcal T$ for time reversal. The generic degeneracies
+along symmetry lines are Weyl nodes: Chiral contact points between pairs of
+bands. They can be single nodes with a chiral charge of magnitude $|\chi|=1$ or
+composite nodes with $|\chi|=2$ or $3$, and the possible $\chi$ values only
+depend on the order $n$ of the axis, not on the pitch $m/n$ of the screw.
+Double Weyl nodes require $n=4$ or 6, and triple nodes require $n=6$. In all
+cases the bands split linearly along the axis, and for composite nodes the
+splitting is quadratic on the orthogonal plane. This is true for triple as well
+as double nodes, due to the presence in the effective two-band Hamiltonian of a
+nonchiral quadratic term that masks the chiral cubic dispersion. If $\mathcal
+T$ symmetry is present and $\mathcal P$ is broken there may exist on some
+symmetry lines Weyl nodes pinned to $\mathcal T$-invariant momenta, which in
+some cases are unavoidable. In the absence of other symmetries their
+classification depends on $n$, $m$, and the type of $\mathcal T$ symmetry. With
+spinless $\mathcal T$ such $\mathcal T$-invariant Weyl nodes are always double
+nodes, while with spinful $\mathcal T$ they can be single or triple nodes.
+$\mathcal T$-invariant triples nodes can occur not only on 6-fold axes but also
+on 3-fold ones, and their in-plane band splitting is cubic, not quadratic as in
+the case of generic triple nodes. These rules are illustrated by means of
+first-principles calculations for hcp cobalt, a $\mathcal T$-broken, $\mathcal
+P$-invariant crystal with $6_3$ symmetry, and for trigonal tellurium and
+hexagonal NbSi$_2$, which are $\mathcal T$-invariant, $\mathcal P$-broken
+crystals with 3-fold and 6-fold screw symmetry respectively.
+",0,1,0,0,0,0
+20152,Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study,"  Deep Neural Networks (DNNs) are very popular these days, and are the subject
+of a very intense investigation. A DNN is made by layers of internal units (or
+neurons), each of which computes an affine combination of the output of the
+units in the previous layer, applies a nonlinear operator, and outputs the
+corresponding value (also known as activation). A commonly-used nonlinear
+operator is the so-called rectified linear unit (ReLU), whose output is just
+the maximum between its input value and zero. In this (and other similar cases
+like max pooling, where the max operation involves more than one input value),
+one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where
+the continuous variables correspond to the output values of each unit, and a
+binary variable is associated with each ReLU to model its yes/no nature. In
+this paper we discuss the peculiarity of this kind of 0-1 MILP models, and
+describe an effective bound-tightening technique intended to ease its solution.
+We also present possible applications of the 0-1 MILP model arising in feature
+visualization and in the construction of adversarial examples. Preliminary
+computational results are reported, aimed at investigating (on small DNNs) the
+computational performance of a state-of-the-art MILP solver when applied to a
+known test case, namely, hand-written digit recognition.
+",1,0,0,0,0,0
+20153,Directionality Fields generated by a Local Hilbert Transform,"  We propose a new approach based on a local Hilbert transform to design
+non-Hermitian potentials generating arbitrary vector fields of directionality,
+p(r), with desired shapes and topologies. We derive a local Hilbert transform
+to systematically build such potentials, by modifying background potentials
+(being either regular or random, extended or localized). In particular, we
+explore particular directionality fields, for instance in the form of a focus
+to create sinks for probe fields (which could help to increase absorption at
+the sink), or to generate vortices in the probe fields. Physically, the
+proposed directionality fields provide a flexible new mechanism for dynamically
+shaping and precise control over probe fields leading to novel effects in wave
+dynamics.
+",0,1,0,0,0,0
+20154,Bulk Eigenvalue Correlation Statistics of Random Biregular Bipartite Graphs,"  This paper is the second chapter of three of the author's undergraduate
+thesis. In this paper, we consider the random matrix ensemble given by $(d_b,
+d_w)$-regular graphs on $M$ black vertices and $N$ white vertices, where $d_b
+\in [N^{\gamma}, N^{2/3 - \gamma}]$ for any $\gamma > 0$. We simultaneously
+prove that the bulk eigenvalue correlation statistics for both normalized
+adjacency matrices and their corresponding covariance matrices are stable for
+short times. Combined with an ergodicity analysis of the Dyson Brownian motion
+in another paper, this proves universality of bulk eigenvalue correlation
+statistics, matching normalized adjacency matrices with the GOE and the
+corresponding covariance matrices with the Gaussian Wishart Ensemble.
+",0,0,1,1,0,0
+20155,A local weighted Axler-Zheng theorem in $\mathbb{C}^n$,"  The well-known Axler-Zheng theorem characterizes compactness of finite sums
+of finite products of Toeplitz operators on the unit disk in terms of the
+Berezin transform of these operators. Subsequently this theorem was generalized
+to other domains and appeared in different forms, including domains in
+$\mathbb{C}^n$ on which the $\overline{\partial}$-Neumann operator $N$ is
+compact. In this work we remove the assumption on $N$, and we study weighted
+Bergman spaces on smooth bounded pseudoconvex domains. We prove a local version
+of the Axler-Zheng theorem characterizing compactness of Toeplitz operators in
+the algebra generated by symbols continuous up to the boundary in terms of the
+behavior of the Berezin transform at strongly pseudoconvex points. We employ a
+Forelli-Rudin type inflation method to handle the weights.
+",0,0,1,0,0,0
+20156,Spatial modulation of Joule losses to increase the normal zone propagation velocity in (RE)BaCuO tapes,"  This paper presents a simple approach to increase the normal zone propagation
+velocity in (RE)BaCuO thin films grown on a flexible metallic substrate, also
+called superconducting tapes. The key idea behind this approach is to use a
+specific geometry of the silver thermal stabilizer that surrounds the
+superconducting tape. More specifically, a very thin layer of silver stabilizer
+is deposited on top of the superconductor layer, typically less than 100 nm,
+while the remaining stabilizer (still silver) is deposited on the substrate
+side. Normal zone propagation velocities up to 170 cm/s have been measured
+experimentally, corresponding to a stabilizer thickness of 20 nm on top of the
+superconductor layer. This is one order of magnitude faster than the speed
+measured on actual commercial tapes. Our results clearly demonstrate that a
+very thin stabilizer on top of the superconductor layer leads to high normal
+zone propagation velocities. The experimental values are in good agreement with
+predictions realized by finite element simulations. Furthermore, the
+propagation of the normal zone during the quench was recorded in situ and in
+real time using a high-speed camera. Due to high Joule losses generated on both
+edges of the tape sample, a ""U-shaped"" profile could be observed at the
+boundaries between the superconducting and the normal zones, which matches very
+closely the profile predicted by the simulations.
+",0,1,0,0,0,0
+20157,Vortex Thermometry for Turbulent Two-Dimensional Fluids,"  We introduce a new method of statistical analysis to characterise the
+dynamics of turbulent fluids in two dimensions. We establish that, in
+equilibrium, the vortex distributions can be uniquely connected to the
+temperature of the vortex gas, and apply this vortex thermometry to
+characterise simulations of decaying superfluid turbulence. We confirm the
+hypothesis of vortex evaporative heating leading to Onsager vortices proposed
+in Phys. Rev. Lett. 113, 165302 (2014), and find previously unidentified vortex
+power-law distributions that emerge from the dynamics.
+",0,1,0,0,0,0
+20158,The Hurwitz-type theorem for the regular Coulomb wave function via Hankel determinants,"  We derive a closed formula for the determinant of the Hankel matrix whose
+entries are given by sums of negative powers of the zeros of the regular
+Coulomb wave function. This new identity applied together with results of
+Grommer and Chebotarev allows us to prove a Hurwitz-type theorem about the
+zeros of the regular Coulomb wave function. As a particular case, we obtain a
+new proof of the classical Hurwitz's theorem from the theory of Bessel
+functions that is based on algebraic arguments. In addition, several Hankel
+determinants with entries given by the Rayleigh function and Bernoulli numbers
+are also evaluated.
+",0,0,1,0,0,0
+20159,An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients,"  In this technical report, we consider an approach that combines the PPO
+objective and K-FAC natural gradient optimization, for which we call PPOKFAC.
+We perform a range of empirical analysis on various aspects of the algorithm,
+such as sample complexity, training speed, and sensitivity to batch size and
+training epochs. We observe that PPOKFAC is able to outperform PPO in terms of
+sample complexity and speed in a range of MuJoCo environments, while being
+scalable in terms of batch size. In spite of this, it seems that adding more
+epochs is not necessarily helpful for sample efficiency, and PPOKFAC seems to
+be worse than its A2C counterpart, ACKTR.
+",0,0,0,1,0,0
+20160,On the density of sets avoiding parallelohedron distance 1,"  The maximal density of a measurable subset of R^n avoiding Euclidean
+distance1 is unknown except in the trivial case of dimension 1. In this paper,
+we consider thecase of a distance associated to a polytope that tiles space,
+where it is likely that the setsavoiding distance 1 are of maximal density
+2^-n, as conjectured by Bachoc and Robins. We prove that this is true for n =
+2, and for the Voronoï regions of the lattices An, n >= 2.
+",0,0,1,0,0,0
+20161,Adversarial Deep Structured Nets for Mass Segmentation from Mammograms,"  Mass segmentation provides effective morphological features which are
+important for mass diagnosis. In this work, we propose a novel end-to-end
+network for mammographic mass segmentation which employs a fully convolutional
+network (FCN) to model a potential function, followed by a CRF to perform
+structured learning. Because the mass distribution varies greatly with pixel
+position, the FCN is combined with a position priori. Further, we employ
+adversarial training to eliminate over-fitting due to the small sizes of
+mammogram datasets. Multi-scale FCN is employed to improve the segmentation
+performance. Experimental results on two public datasets, INbreast and
+DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
+than state-of-the-art approaches.
+\footnote{this https URL}
+",1,0,0,0,0,0
+20162,The downward directed grounds hypothesis and very large cardinals,"  A transitive model $M$ of ZFC is called a ground if the universe $V$ is a set
+forcing extension of $M$. We show that the grounds of $V$ are downward
+set-directed. Consequently, we establish some fundamental theorems on the
+forcing method and the set-theoretic geology. For instance, (1) the mantle, the
+intersection of all grounds, must be a model of ZFC. (2) $V$ has only set many
+grounds if and only if the mantle is a ground. We also show that if the
+universe has some very large cardinal, then the mantle must be a ground.
+",0,0,1,0,0,0
+20163,A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking,"  Wild-land fire fighting is a hazardous job. A key task for firefighters is to
+observe the ""fire front"" to chart the progress of the fire and areas that will
+likely spread next. Lack of information of the fire front causes many
+accidents. Using Unmanned Aerial Vehicles (UAVs) to cover wildfire is promising
+because it can replace humans in hazardous fire tracking and significantly
+reduce operation costs. In this paper we propose a distributed control
+framework designed for a team of UAVs that can closely monitor a wildfire in
+open space, and precisely track its development. The UAV team, designed for
+flexible deployment, can effectively avoid in-flight collisions and cooperate
+well with neighbors. They can maintain a certain height level to the ground for
+safe flight above fire. Experimental results are conducted to demonstrate the
+capabilities of the UAV team in covering a spreading wildfire.
+",1,0,0,0,0,0
+20164,Rayleigh-Brillouin light scattering spectroscopy of nitrous oxide (N$_2$O),"  High signal-to-noise and high-resolution light scattering spectra are
+measured for nitrous oxide (N$_2$O) gas at an incident wavelength of 403.00 nm,
+at 90$^\circ$ scattering, at room temperature and at gas pressures in the range
+$0.5-4$ bar. The resulting Rayleigh-Brillouin light scattering spectra are
+compared to a number of models describing in an approximate manner the
+collisional dynamics and energy transfer in this gaseous medium of this
+polyatomic molecular species. The Tenti-S6 model, based on macroscopic gas
+transport coefficients, reproduces the scattering profiles in the entire
+pressure range at less than 2\% deviation at a similar level as does the
+alternative kinetic Grad's 6-moment model, which is based on the internal
+collisional relaxation as a decisive parameter. A hydrodynamic model fails to
+reproduce experimental spectra for the low pressures of 0.5-1 bar, but yields
+very good agreement ($< 1$\%) in the pressure range $2-4$ bar. While these
+three models have a different physical basis the internal molecular relaxation
+derived can for all three be described in terms of a bulk viscosity of $\eta_b
+\sim (6 \pm 2) \times 10^{-5}$ Pa$\cdot$s. A 'rough-sphere' model, previously
+shown to be effective to describe light scattering in SF$_6$ gas, is not found
+to be suitable, likely in view of the non-sphericity and asymmetry of the N-N-O
+structured linear polyatomic molecule.
+",0,1,0,0,0,0
+20165,Competition between Chaotic and Non-Chaotic Phases in a Quadratically Coupled Sachdev-Ye-Kitaev Model,"  The Sachdev-Ye-Kitaev (SYK) model is a concrete solvable model to study
+non-Fermi liquid properties, holographic duality and maximally chaotic
+behavior. In this work, we consider a generalization of the SYK model that
+contains two SYK models with different number of Majorana modes coupled by
+quadratic terms. This model is also solvable, and the solution shows a
+zero-temperature quantum phase transition between two non-Fermi liquid chaotic
+phases. This phase transition is driven by tuning the ratio of two mode
+numbers, and a Fermi liquid non-chaotic phase sits at the critical point with
+equal mode number. At finite temperature, the Fermi liquid phase expands to a
+finite regime. More intriguingly, a different non-Fermi liquid phase emerges at
+finite temperature. We characterize the phase diagram in term of the spectral
+function, the Lyapunov exponent and the entropy. Our results illustrate a
+concrete example of quantum phase transition and critical regime between two
+non-Fermi liquid phases.
+",0,1,0,0,0,0
+20166,Symmetry Realization via a Dynamical Inverse Higgs Mechanism,"  The Ward identities associated with spontaneously broken symmetries can be
+saturated by Goldstone bosons. However, when space-time symmetries are broken,
+the number of Goldstone bosons necessary to non-linearly realize the symmetry
+can be less than the number of broken generators. The loss of Goldstones may be
+due to a redundancy or the generation of a gap. This phenomena is called an
+Inverse Higgs Mechanism (IHM). However, there are cases when a Goldstone boson
+associated with a broken generator does not appear in the low energy theory
+despite the lack of the existence of an associated IHM. In this paper we will
+show that in such cases the relevant broken symmetry can be realized, without
+the aid of an associated Goldstone, if there exists a proper set of operator
+constraints, which we call a Dynamical Inverse Higgs Mechanism (DIHM). We
+consider the spontaneous breaking of boosts, rotations and conformal
+transformations in the context of Fermi liquids, finding three possible paths
+to symmetry realization: pure Goldstones, no Goldstones and DIHM, or some
+mixture thereof. We show that in the two dimensional degenerate electron system
+the DIHM route is the only consistent way to realize spontaneously broken
+boosts and dilatations, while in three dimensions these symmetries could just
+as well be realized via the inclusion of non-derivatively coupled Goldstone
+bosons. We have present the action, including the leading order
+non-linearities, for the rotational Goldstone (angulon), and discuss the
+constraint associated with the possible DIHM that would need to be imposed to
+remove it from the spectrum. Finally we discuss the conditions under which
+Goldstone bosons are non-derivatively coupled, a necessary condition for the
+existence of a Dynamical Inverse Higgs Constraint (DIHC), generalizaing the
+results for Vishwanath and Wantanabe.
+",0,1,0,0,0,0
+20167,Growth of strontium ruthenate films by hybrid molecular beam epitaxy,"  We report on the growth of epitaxial Sr2RuO4 films using a hybrid molecular
+beam epitaxy approach in which a volatile precursor containing RuO4 is used to
+supply ruthenium and oxygen. The use of the precursor overcomes a number of
+issues encountered in traditional MBE that uses elemental metal sources.
+Phase-pure, epitaxial thin films of Sr2RuO4 are obtained. At high substrate
+temperatures, growth proceeds in a layer-by-layer mode with intensity
+oscillations observed in reflection high-energy electron diffraction. Films are
+of high structural quality, as documented by x-ray diffraction, atomic force
+microscopy, and transmission electron microscopy. The method should be suitable
+for the growth of other complex oxides containing ruthenium, opening up
+opportunities to investigate thin films that host rich exotic ground states.
+",0,1,0,0,0,0
+20168,Possible Evidence for the Stochastic Acceleration of Secondary Antiprotons by Supernova Remnants,"  The antiproton-to-proton ratio in the cosmic-ray spectrum is a sensitive
+probe of new physics. Using recent measurements of the cosmic-ray antiproton
+and proton fluxes in the energy range of 1-1000 GeV, we study the contribution
+to the $\bar{p}/p$ ratio from secondary antiprotons that are produced and
+subsequently accelerated within individual supernova remnants. We consider
+several well-motivated models for cosmic-ray propagation in the interstellar
+medium and marginalize our results over the uncertainties related to the
+antiproton production cross section and the time-, charge-, and
+energy-dependent effects of solar modulation. We find that the increase in the
+$\bar{p}/p$ ratio observed at rigidities above $\sim$ 100 GV cannot be
+accounted for within the context of conventional cosmic-ray propagation models,
+but is consistent with scenarios in which cosmic-ray antiprotons are produced
+and subsequently accelerated by shocks within a given supernova remnant. In
+light of this, the acceleration of secondary cosmic rays in supernova remnants
+is predicted to substantially contribute to the cosmic-ray positron spectrum,
+accounting for a significant fraction of the observed positron excess.
+",0,1,0,0,0,0
+20169,The Host Galaxy and Redshift of the Repeating Fast Radio Burst FRB 121102,"  The precise localization of the repeating fast radio burst (FRB 121102) has
+provided the first unambiguous association (chance coincidence probability
+$p\lesssim3\times10^{-4}$) of an FRB with an optical and persistent radio
+counterpart. We report on optical imaging and spectroscopy of the counterpart
+and find that it is an extended ($0.6^{\prime\prime}-0.8^{\prime\prime}$)
+object displaying prominent Balmer and [OIII] emission lines. Based on the
+spectrum and emission line ratios, we classify the counterpart as a
+low-metallicity, star-forming, $m_{r^\prime} = 25.1$ AB mag dwarf galaxy at a
+redshift of $z=0.19273(8)$, corresponding to a luminosity distance of 972 Mpc.
+From the angular size, the redshift, and luminosity, we estimate the host
+galaxy to have a diameter $\lesssim4$ kpc and a stellar mass of
+$M_*\sim4-7\times 10^{7}\,M_\odot$, assuming a mass-to-light ratio between 2 to
+3$\,M_\odot\,L_\odot^{-1}$. Based on the H$\alpha$ flux, we estimate the star
+formation rate of the host to be $0.4\,M_\odot\,\mathrm{yr^{-1}}$ and a
+substantial host dispersion measure depth $\lesssim 324\,\mathrm{pc\,cm^{-3}}$.
+The net dispersion measure contribution of the host galaxy to FRB 121102 is
+likely to be lower than this value depending on geometrical factors. We show
+that the persistent radio source at FRB 121102's location reported by Marcote
+et al (2017) is offset from the galaxy's center of light by $\sim$200 mas and
+the host galaxy does not show optical signatures for AGN activity. If FRB
+121102 is typical of the wider FRB population and if future interferometric
+localizations preferentially find them in dwarf galaxies with low metallicities
+and prominent emission lines, they would share such a preference with long
+gamma ray bursts and superluminous supernovae.
+",0,1,0,0,0,0
+20170,Thicket Density,"  Thicket density is a new measure of the complexity of a set system, having
+the same relationship to stable formulas that VC density has to NIP formulas.
+It satisfies a Sauer-Shelah type dichotomy that has applications in both model
+theory and the theory of algorithms
+",0,0,1,0,0,0
+20171,"Uniformizations of stable $(γ,n)$-gonal Riemann surfaces","  A $(\gamma,n)$-gonal pair is a pair $(S,f)$, where $S$ is a closed Riemann
+surface and $f:S \to R$ is a degree $n$ holomorphic map onto a closed Riemann
+surface $R$ of genus $\gamma$. If the signature of $(S,f)$ is of hyperbolic
+type, then there is pair $(\Gamma,G)$, called an uniformization of $(S,f)$,
+where $G$ is a Fuchsian group acting on the unit disc ${\mathbb D}$ containing
+$\Gamma$ as an index $n$ subgroup, so that $f$ is induced by the inclusion of
+$\Gamma <G$. The uniformization is uniquely determined by $(S,f)$, up to
+conjugation by holomorphic automorphisms of ${\mathbb D}$, and it permits to
+provide natural complex orbifold structures on the Hurwitz spaces parametrizing
+(twisted) isomorphic classes of pairs topologically equivalent to $(S,f)$. In
+order to produce certain compactifications of these Hurwitz spaces, one needs
+to consider the so called stable $(\gamma,n)$-gonal pairs, which are natural
+geometrical deformations of $(\gamma,n)$-gonal pairs. Due to the above, it
+seems interesting to search for uniformizations of stable $(\gamma,n)$-gonal
+pairs, in terms of certain class of Kleinian groups. In this paper we review
+such uniformizations by using noded Fuchsian groups, which are (geometric)
+limits of quasiconformal deformations of Fuchsian groups, and which provide
+uniformizations of stable Riemann orbifolds. These uniformizations permit to
+obtain a compactification of the Hurwitz spaces with a complex orbifold
+structure, these being quotients of the augmented Teichmüller space of $G$ by
+a suitable finite index subgroup of its modular group.
+",0,0,1,0,0,0
+20172,"If you are not paying for it, you are the product: How much do advertisers pay to reach you?","  Online advertising is progressively moving towards a programmatic model in
+which ads are matched to actual interests of individuals collected as they
+browse the web. Letting the huge debate around privacy aside, a very important
+question in this area, for which little is known, is: How much do advertisers
+pay to reach an individual? In this study, we develop a first of its kind
+methodology for computing exactly that -- the price paid for a web user by the
+ad ecosystem -- and we do that in real time. Our approach is based on tapping
+on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted
+prices for winning bids paid by advertisers in order to place targeted ads. Our
+main technical contribution is a method for tallying winning bids even when
+they are encrypted. We achieve this by training a model using as ground truth
+prices obtained by running our own ""probe"" ad-campaigns. We design our
+methodology through a browser extension and a back-end server that provides it
+with fresh models for encrypted bids. We validate our methodology using a one
+year long trace of 1600 mobile users and demonstrate that it can estimate a
+user's advertising worth with more than 82% accuracy.
+",1,0,0,0,0,0
+20173,Circularly polarized vacuum field in three-dimensional chiral photonic crystals probed by quantum dot emission,"  The quantum nature of light-matter interactions in a circularly polarized
+vacuum field was probed by spontaneous emission from quantum dots in
+three-dimensional chiral photonic crystals. Due to the circularly polarized
+eigenmodes along the helical axis in the GaAs-based mirror-asymmetric
+structures we studied, we observed highly circularly polarized emission from
+the quantum dots. Both spectroscopic and time-resolved measurements confirmed
+that the obtained circularly polarized light was influenced by a large
+difference in the photonic density of states between the orthogonal components
+of the circular polarization in the vacuum field.
+",0,1,0,0,0,0
+20174,Critical behavior of quasi-two-dimensional semiconducting ferromagnet CrGeTe$_3$,"  The critical properties of the single-crystalline semiconducting ferromagnet
+CrGeTe$_3$ were investigated by bulk dc magnetization around the paramagnetic
+to ferromagnetic phase transition. Critical exponents $\beta = 0.200\pm0.003$
+with critical temperature $T_c = 62.65\pm0.07$ K and $\gamma = 1.28\pm0.03$
+with $T_c = 62.75\pm0.06$ K are obtained by the Kouvel-Fisher method whereas
+$\delta = 7.96\pm0.01$ is obtained by the critical isotherm analysis at $T_c =
+62.7$ K. These critical exponents obey the Widom scaling relation $\delta =
+1+\gamma/\beta$, indicating self-consistency of the obtained values. With these
+critical exponents the isotherm $M(H)$ curves below and above the critical
+temperatures collapse into two independent universal branches, obeying the
+single scaling equation $m = f_\pm(h)$, where $m$ and $h$ are renormalized
+magnetization and field, respectively. The determined exponents match well with
+those calculated from the results of renormalization group approach for a
+two-dimensional Ising system coupled with long-range interaction between spins
+decaying as $J(r)\approx r^{-(d+\sigma)}$ with $\sigma=1.52$.
+",0,1,0,0,0,0
+20175,Scale-free Monte Carlo method for calculating the critical exponent $γ$ of self-avoiding walks,"  We implement a scale-free version of the pivot algorithm and use it to sample
+pairs of three-dimensional self-avoiding walks, for the purpose of efficiently
+calculating an observable that corresponds to the probability that pairs of
+self-avoiding walks remain self-avoiding when they are concatenated. We study
+the properties of this Markov chain, and then use it to find the critical
+exponent $\gamma$ for self-avoiding walks to unprecedented accuracy. Our final
+estimate for $\gamma$ is $1.15695300(95)$.
+",0,1,1,0,0,0
+20176,The quadratic M-convexity testing problem,"  M-convex functions, which are a generalization of valuated matroids, play a
+central role in discrete convex analysis. Quadratic M-convex functions
+constitute a basic and important subclass of M-convex functions, which has a
+close relationship with phylogenetics as well as valued constraint satisfaction
+problems. In this paper, we consider the quadratic M-convexity testing problem
+(QMCTP), which is the problem of deciding whether a given quadratic function on
+$\{0,1\}^n$ is M-convex. We show that QMCTP is co-NP-complete in general, but
+is polynomial-time solvable under a natural assumption. Furthermore, we propose
+an $O(n^2)$-time algorithm for solving QMCTP in the polynomial-time solvable
+case.
+",0,0,1,0,0,0
+20177,Distributed Bayesian Matrix Factorization with Limited Communication,"  Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank
+representations of matrices and for predicting missing values and providing
+confidence intervals. Scaling up the posterior inference for massive-scale
+matrices is challenging and requires distributing both data and computation
+over many workers, making communication the main computational bottleneck.
+Embarrassingly parallel inference would remove the communication needed, by
+using completely independent computations on different data subsets, but it
+suffers from the inherent unidentifiability of BMF solutions. We introduce a
+hierarchical decomposition of the joint posterior distribution, which couples
+the subset inferences, allowing for embarrassingly parallel computations in a
+sequence of at most three stages. Using an efficient approximate
+implementation, we show improvements empirically on both real and simulated
+data. Our distributed approach is able to achieve a speed-up of almost an order
+of magnitude over the full posterior, with a negligible effect on predictive
+accuracy. Our method outperforms state-of-the-art embarrassingly parallel MCMC
+methods in accuracy, and achieves results competitive to other available
+distributed and parallel implementations of BMF.
+",1,0,0,1,0,0
+20178,Optimized Certificate Revocation List Distribution for Secure V2X Communications,"  The successful deployment of safe and trustworthy Connected and Autonomous
+Vehicles (CAVs) will highly depend on the ability to devise robust and
+effective security solutions to resist sophisticated cyber attacks and patch up
+critical vulnerabilities. Pseudonym Public Key Infrastructure (PPKI) is a
+promising approach to secure vehicular networks as well as ensure data and
+location privacy, concealing the vehicles' real identities. Nevertheless,
+pseudonym distribution and management affect PPKI scalability due to the
+significant number of digital certificates required by a single vehicle. In
+this paper, we focus on the certificate revocation process and propose a
+versatile and low-complexity framework to facilitate the distribution of the
+Certificate Revocation Lists (CRL) issued by the Certification Authority (CA).
+CRL compression is achieved through optimized Bloom filters, which guarantee a
+considerable overhead reduction with a configurable rate of false positives.
+Our results show that the distribution of compressed CRLs can significantly
+enhance the system scalability without increasing the complexity of the
+revocation process.
+",1,0,0,0,0,0
+20179,Optical response of highly reflective film used in the water Cherenkov muon veto of the XENON1T dark matter experiment,"  The XENON1T experiment is the most recent stage of the XENON Dark Matter
+Search, aiming for the direct detection of Weakly Interacting Massive Particles
+(WIMPs). To reach its projected sensitivity, the background has to be reduced
+by two orders of magnitude compared to its predecessor XENON100. This requires
+a water Cherenkov muon veto surrounding the XENON1T TPC, both to shield
+external backgrounds and to tag muon-induced energetic neutrons through
+detection of a passing muon or the secondary shower induced by a muon
+interacting in the surrounding rock. The muon veto is instrumented with $84$
+$8""$ PMTs with high quantum efficiency (QE) in the Cherenkov regime and the
+walls of the watertank are clad with the highly reflective DF2000MA foil by 3M.
+Here, we present a study of the reflective properties of this foil, as well as
+the measurement of its wavelength shifting (WLS) properties. Further, we
+present the impact of reflectance and WLS on the detection efficiency of the
+muon veto, using a Monte Carlo simulation carried out with Geant4. The
+measurements yield a specular reflectance of $\approx100\%$ for wavelengths
+larger than $400\,$nm, while $\approx90\%$ of the incoming light below
+$370\,$nm is absorbed by the foil. Approximately $3-7.5\%$ of the light hitting
+the foil within the wavelength range $250\,$nm $\leq \lambda \leq 390\,$nm is
+used for the WLS process. The intensity of the emission spectrum of the WLS
+light is slightly dependent on the absorbed wavelength and shows the shape of a
+rotational-vibrational fluorescence spectrum, peaking at around $\lambda
+\approx 420\,$nm. Adjusting the reflectance values to the measured ones in the
+Monte Carlo simulation originally used for the muon veto design, the veto
+detection efficiency remains unchanged. Including the wavelength shifting in
+the Monte Carlo simulation leads to an increase of the efficiency of
+approximately $0.5\%$.
+",0,1,0,0,0,0
+20180,On spectral partitioning of signed graphs,"  We argue that the standard graph Laplacian is preferable for spectral
+partitioning of signed graphs compared to the signed Laplacian. Simple examples
+demonstrate that partitioning based on signs of components of the leading
+eigenvectors of the signed Laplacian may be meaningless, in contrast to
+partitioning based on the Fiedler vector of the standard graph Laplacian for
+signed graphs. We observe that negative eigenvalues are beneficial for spectral
+partitioning of signed graphs, making the Fiedler vector easier to compute.
+",1,0,1,1,0,0
+20181,Sensor Fusion for Public Space Utilization Monitoring in a Smart City,"  Public space utilization is crucial for urban developers to understand how
+efficient a place is being occupied in order to improve existing or future
+infrastructures. In a smart cities approach, implementing public space
+monitoring with Internet-of-Things (IoT) sensors appear to be a viable
+solution. However, choice of sensors often is a challenging problem and often
+linked with scalability, coverage, energy consumption, accuracy, and privacy.
+To get the most from low cost sensor with aforementioned design in mind, we
+proposed data processing modules for capturing public space utilization with
+Renewable Wireless Sensor Network (RWSN) platform using pyroelectric infrared
+(PIR) and analog sound sensor. We first proposed a calibration process to
+remove false alarm of PIR sensor due to the impact of weather and environment.
+We then demonstrate how the sounds sensor can be processed to provide various
+insight of a public space. Lastly, we fused both sensors and study a particular
+public space utilization based on one month data to unveil its usage.
+",1,0,0,1,0,0
+20182,Symplectic integrators for second-order linear non-autonomous equations,"  Two families of symplectic methods specially designed for second-order
+time-dependent linear systems are presented. Both are obtained from the Magnus
+expansion of the corresponding first-order equation, but otherwise they differ
+in significant aspects. The first family is addressed to problems with low to
+moderate dimension, whereas the second is more appropriate when the dimension
+is large, in particular when the system corresponds to a linear wave equation
+previously discretised in space. Several numerical experiments illustrate the
+main features of the new schemes.
+",0,0,1,0,0,0
+20183,"Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations","  The recent breakthroughs of deep reinforcement learning (DRL) technique in
+Alpha Go and playing Atari have set a good example in handling large state and
+actions spaces of complicated control problems. The DRL technique is comprised
+of (i) an offline deep neural network (DNN) construction phase, which derives
+the correlation between each state-action pair of the system and its value
+function, and (ii) an online deep Q-learning phase, which adaptively derives
+the optimal action and updates value estimates. In this paper, we first present
+the general DRL framework, which can be widely utilized in many applications
+with different optimization objectives. This is followed by the introduction of
+three specific applications: the cloud computing resource allocation problem,
+the residential smart grid task scheduling problem, and building HVAC system
+optimal control problem. The effectiveness of the DRL technique in these three
+cyber-physical applications have been validated. Finally, this paper
+investigates the stochastic computing-based hardware implementations of the DRL
+framework, which consumes a significant improvement in area efficiency and
+power consumption compared with binary-based implementation counterparts.
+",1,0,0,0,0,0
+20184,Experimental observation of node-line-like surface states in LaBi,"  In a Dirac nodal line semimetal, the bulk conduction and valence bands touch
+at extended lines in the Brillouin zone. To date, most of the theoretically
+predicted and experimentally discovered nodal lines derive from the bulk bands
+of two- and three-dimensional materials. Here, based on combined angle-resolved
+photoemission spectroscopy measurements and first-principles calculations, we
+report the discovery of node-line-like surface states on the (001) surface of
+LaBi. These bands derive from the topological surface states of LaBi and bridge
+the band gap opened by spin-orbit coupling and band inversion. Our
+first-principles calculations reveal that these ""nodal lines"" have a tiny gap,
+which is beyond typical experimental resolution. These results may provide
+important information to understand the extraordinary physical properties of
+LaBi, such as the extremely large magnetoresistance and resistivity plateau.
+",0,1,0,0,0,0
+20185,Manin's conjecture for a class of singular cubic hypersurfaces,"  Let $n$ be a positive multiple of $4$. We establish an asymptotic formula for
+the number of rational points of bounded height on singular cubic hypersurfaces
+$S_n$ defined by $$ x^3=(y_1^2 + \cdots + y_n^2)z . $$ This result is new in
+two aspects: first, it can be viewed as a modest start on the study of density
+of rational points on those singular cubic hypersurfaces which are not covered
+by the classical theorems of Davenport or Heath-Brown; second, it proves
+Manin's conjecture for singular cubic hypersurfaces $S_n$ defined above.
+",0,0,1,0,0,0
+20186,A Flexible Approach to Automated RNN Architecture Generation,"  The process of designing neural architectures requires expert knowledge and
+extensive trial and error. While automated architecture search may simplify
+these requirements, the recurrent neural network (RNN) architectures generated
+by existing methods are limited in both flexibility and components. We propose
+a domain-specific language (DSL) for use in automated architecture search which
+can produce novel RNNs of arbitrary depth and width. The DSL is flexible enough
+to define standard architectures such as the Gated Recurrent Unit and Long
+Short Term Memory and allows the introduction of non-standard RNN components
+such as trigonometric curves and layer normalization. Using two different
+candidate generation techniques, random search with a ranking function and
+reinforcement learning, we explore the novel architectures produced by the RNN
+DSL for language modeling and machine translation domains. The resulting
+architectures do not follow human intuition yet perform well on their targeted
+tasks, suggesting the space of usable RNN architectures is far larger than
+previously assumed.
+",1,0,0,1,0,0
+20187,Convexification of Neural Graph,"  Traditionally, most complex intelligence architectures are extremely
+non-convex, which could not be well performed by convex optimization. However,
+this paper decomposes complex structures into three types of nodes: operators,
+algorithms and functions. Iteratively, propagating from node to node along
+edge, we prove that ""regarding the tree-structured neural graph, it is nearly
+convex in each variable, when the other variables are fixed."" In fact, the
+non-convex properties stem from circles and functions, which could be
+transformed to be convex with our proposed \textit{\textbf{scale mechanism}}.
+Experimentally, we justify our theoretical analysis by two practical
+applications.
+",0,0,0,1,0,0
+20188,Pseudogaps in strongly interacting Fermi gases,"  A central challenge in modern condensed matter physics is developing the
+tools for understanding nontrivial yet unordered states of matter. One
+important idea to emerge in this context is that of a ""pseudogap"": the fact
+that under appropriate circumstances the normal state displays a suppression of
+the single particle spectral density near the Fermi level, reminiscent of the
+gaps seen in ordered states of matter. While these concepts arose in a solid
+state context, it is now being explored in cold gases. This article reviews the
+current experimental and theoretical understanding of the normal state of
+strongly interacting Fermi gases, with particular focus on the phenomonology
+which is traditionally associated with the pseudogap.
+",0,1,0,0,0,0
+20189,Resolution-Exact Planner for Thick Non-Crossing 2-Link Robots,"  We consider the path planning problem for a 2-link robot amidst polygonal
+obstacles. Our robot is parametrizable by the lengths $\ell_1, \ell_2>0$ of its
+two links, the thickness $\tau \ge 0$ of the links, and an angle $\kappa$ that
+constrains the angle between the 2 links to be strictly greater than $\kappa$.
+The case $\tau>0$ and $\kappa \ge 0$ corresponds to ""thick non-crossing""
+robots. This results in a novel 4DOF configuration space ${\mathbb R}^2\times
+({\mathbb T}\setminus\Delta(\kappa))$ where ${\mathbb T}$ is the torus and
+$\Delta(\kappa)$ the diagonal band of width $\kappa$. We design a
+resolution-exact planner for this robot using the framework of Soft Subdivision
+Search (SSS). First, we provide an analysis of the space of forbidden angles,
+leading to a soft predicate for classifying configuration boxes. We further
+exploit the T/R splitting technique which was previously introduced for
+self-crossing thin 2-link robots. Our open-source implementation in Core
+Library achieves real-time performance for a suite of combinatorially
+non-trivial obstacle sets. Experimentally, our algorithm is significantly
+better than any of the state-of-art sampling algorithms we looked at, in timing
+and in success rate.
+",1,0,0,0,0,0
+20190,Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks,"  Heterogeneous information networks (HINs) are ubiquitous in real-world
+applications. In the meantime, network embedding has emerged as a convenient
+tool to mine and learn from networked data. As a result, it is of interest to
+develop HIN embedding methods. However, the heterogeneity in HINs introduces
+not only rich information but also potentially incompatible semantics, which
+poses special challenges to embedding learning in HINs. With the intention to
+preserve the rich yet potentially incompatible information in HIN embedding, we
+propose to study the problem of comprehensive transcription of heterogeneous
+information networks. The comprehensive transcription of HINs also provides an
+easy-to-use approach to unleash the power of HINs, since it requires no
+additional supervision, expertise, or feature engineering. To cope with the
+challenges in the comprehensive transcription of HINs, we propose the HEER
+algorithm, which embeds HINs via edge representations that are further coupled
+with properly-learned heterogeneous metrics. To corroborate the efficacy of
+HEER, we conducted experiments on two large-scale real-words datasets with an
+edge reconstruction task and multiple case studies. Experiment results
+demonstrate the effectiveness of the proposed HEER model and the utility of
+edge representations and heterogeneous metrics. The code and data are available
+at this https URL.
+",1,0,0,0,0,0
+20191,Generic Dynamical Phase Transition in One-Dimensional Bulk-Driven Lattice Gases with Exclusion,"  Dynamical phase transitions are crucial features of the fluctuations of
+statistical systems, corresponding to boundaries between qualitatively
+different mechanisms of maintaining unlikely values of dynamical observables
+over long periods of time. They manifest themselves in the form of
+non-analyticities in the large deviation function of those observables. In this
+paper, we look at bulk-driven exclusion processes with open boundaries. It is
+known that the standard asymmetric simple exclusion process exhibits a
+dynamical phase transition in the large deviations of the current of particles
+flowing through it. That phase transition has been described thanks to specific
+calculation methods relying on the model being exactly solvable, but more
+general methods have also been used to describe the extreme large deviations of
+that current, far from the phase transition. We extend those methods to a large
+class of models based on the ASEP, where we add arbitrary spatial
+inhomogeneities in the rates and short-range potentials between the particles.
+We show that, as for the regular ASEP, the large deviation function of the
+current scales differently with the size of the system if one considers very
+high or very low currents, pointing to the existence of a dynamical phase
+transition between those two regimes: high current large deviations are
+extensive in the system size, and the typical states associated to them are
+Coulomb gases, which are correlated ; low current large deviations do not
+depend on the system size, and the typical states associated to them are
+anti-shocks, consistently with a hydrodynamic behaviour. Finally, we illustrate
+our results numerically on a simple example, and we interpret the transition in
+terms of the current pushing beyond its maximal hydrodynamic value, as well as
+relate it to the appearance of Tracy-Widom distributions in the relaxation
+statistics of such models.
+",0,1,0,0,0,0
+20192,Semianalytical calculation of the zonal-flow oscillation frequency in stellarators,"  Due to their capability to reduce turbulent transport in magnetized plasmas,
+understanding the dynamics of zonal flows is an important problem in the fusion
+programme. Since the pioneering work by Rosenbluth and Hinton in axisymmetric
+tokamaks, it is known that studying the linear and collisionless relaxation of
+zonal flow perturbations gives valuable information and physical insight.
+Recently, the problem has been investigated in stellarators and it has been
+found that in these devices the relaxation process exhibits a characteristic
+feature: a damped oscillation. The frequency of this oscillation might be a
+relevant parameter in the regulation of turbulent transport, and therefore its
+efficient and accurate calculation is important. Although an analytical
+expression can be derived for the frequency, its numerical evaluation is not
+simple and has not been exploited systematically so far. Here, a numerical
+method for its evaluation is considered, and the results are compared with
+those obtained by calculating the frequency from gyrokinetic simulations. This
+""semianalytical"" approach for the determination of the zonal-flow frequency
+reveals accurate and faster than the one based on gyrokinetic simulations.
+",0,1,0,0,0,0
+20193,Geometric phase of a moving dipole under a magnetic field at a distance,"  We predict a geometric quantum phase shift of a moving electric dipole in the
+presence of an external magnetic field at a distance. On the basis of the
+Lorentz-covariant field interaction approach, we show that a geometric phase
+appears under the condition that the dipole is moving in the field-free region,
+which is distinct from the topological He-McKellar-Wilkens phase generated by a
+direct overlap of the dipole and the field. We discuss the experimental
+feasibility of detecting this phase with atomic interferometry and argue that
+detection of this phase would result in a deeper understanding of the locality
+in quantum electromagnetic interaction.
+",0,1,0,0,0,0
+20194,A refined count of Coxeter element factorizations,"  For well-generated complex reflection groups, Chapuy and Stump gave a simple
+product for a generating function counting reflection factorizations of a
+Coxeter element by their length. This is refined here to record the number of
+reflections used from each orbit of hyperplanes. The proof is case-by-case via
+the classification of well-generated groups. It implies a new expression for
+the Coxeter number, expressed via data coming from a hyperplane orbit; a
+case-free proof of this due to J. Michel is included.
+",0,0,1,0,0,0
+20195,Tuning the magnetism of the top-layer FeAs on BaFe$_{2}$As$_{2}$(001): First-principles study,"  The magnetic properties of BaFe$_{2}$As$_{2}$(001) surface have been studied
+by using first-principles electronic structure calculations. We find that for
+As-terminated surface the magnetic ground state of the top-layer FeAs is in the
+staggered dimer antiferromagnetic (AFM) order, while for Ba-terminated surface
+the collinear (single stripe) AFM order is the most stable. When a certain
+coverage of Ba or K atoms are deposited onto the As-terminated surface, the
+calculated energy differences among different AFM orders for the top-layer FeAs
+on BaFe$_{2}$As$_{2}$(001) can be much reduced, indicating enhanced spin
+fluctuations. To identify the novel staggered dimer AFM order for the As
+termination, we have simulated the scanning tunneling microscopy (STM) image
+for this state, which shows a different $\sqrt{2}\times\sqrt{2}$ pattern from
+the case of half Ba coverage. Our results suggest: i) the magnetic properties
+of the top-layer FeAs on BaFe$_{2}$As$_{2}$(001) can be tuned effectively by
+surface doping; ii) both the surface termination and the AFM order in the
+top-layer FeAs can affect the STM image of BaFe$_{2}$As$_{2}$(001).
+",0,1,0,0,0,0
+20196,Cubature methods to solve BSDEs: Error expansion and complexity control,"  We obtain an explicit error expansion for the solution of Backward Stochastic
+Differential Equations (BSDEs) using the cubature on Wiener spaces method. The
+result is proved under a mild strengthening of the assumptions needed for the
+application of the cubature method. The explicit expansion can then be used to
+construct implementable higher order approximations via Richardson-Romberg
+extrapolation. To allow for an effective efficiency improvement of the
+interpolated algorithm, we introduce an additional projection on sparse grids,
+and study the resulting complexity reduction. Numerical examples are provided
+to illustrate our results.
+",0,0,1,0,0,0
+20197,An Earth-mass Planet in a 1-AU Orbit around an Ultracool Dwarf,"  We combine $Spitzer$ and ground-based KMTNet microlensing observations to
+identify and precisely measure an Earth-mass ($1.43^{+0.45}_{-0.32} M_\oplus$)
+planet OGLE-2016-BLG-1195Lb at $1.16^{+0.16}_{-0.13}$ AU orbiting a
+$0.078^{+0.016}_{-0.012} M_\odot$ ultracool dwarf. This is the lowest-mass
+microlensing planet to date. At $3.91^{+0.42}_{-0.46}$ kpc, it is the third
+consecutive case among the $Spitzer$ ""Galactic distribution"" planets toward the
+Galactic bulge that lies in the Galactic disk as opposed to the bulge itself,
+hinting at a skewed distribution of planets. Together with previous
+microlensing discoveries, the seven Earth-size planets orbiting the ultracool
+dwarf TRAPPIST-1, and the detection of disks around young brown dwarfs,
+OGLE-2016-BLG-1195Lb suggests that such planets might be common around
+ultracool dwarfs. It therefore sheds light on the formation of both ultracool
+dwarfs and planetary systems at the limit of low-mass protoplanetary disks.
+",0,1,0,0,0,0
+20198,Distributed Statistical Estimation and Rates of Convergence in Normal Approximation,"  This paper presents a class of new algorithms for distributed statistical
+estimation that exploit divide-and-conquer approach. We show that one of the
+key benefits of the divide-and-conquer strategy is robustness, an important
+characteristic for large distributed systems. We establish connections between
+performance of these distributed algorithms and the rates of convergence in
+normal approximation, and prove non-asymptotic deviations guarantees, as well
+as limit theorems, for the resulting estimators. Our techniques are illustrated
+through several examples: in particular, we obtain new results for the
+median-of-means estimator, as well as provide performance guarantees for
+distributed maximum likelihood estimation.
+",0,0,1,1,0,0
+20199,Connecting HL Tau to the Observed Exoplanet Sample,"  The Atacama Large Millimeter/submilimeter Array (ALMA) recently revealed a
+set of nearly concentric gaps in the protoplanetary disk surrounding the young
+star HL Tau. If these are carved by forming gas giants, this provides the first
+set of orbital initial conditions for planets as they emerge from their birth
+disks. Using N-body integrations, we have followed the evolution of the system
+for 5 Gyr to explore the possible outcomes. We find that HL Tau initial
+conditions scaled down to the size of typically observed exoplanet orbits
+naturally produce several populations in the observed exoplanet sample. First,
+for a plausible range of planetary masses, we can match the observed
+eccentricity distribution of dynamically excited radial velocity giant planets
+with eccentricities $>$ 0.2. Second, we roughly obtain the observed rate of hot
+Jupiters around FGK stars. Finally, we obtain a large efficiency of planetary
+ejections of $\approx 2$ per HL Tau-like system, but the small fraction of
+stars observed to host giant planets makes it hard to match the rate of
+free-floating planets inferred from microlensing observations. In view of
+upcoming GAIA results, we also provide predictions for the expected mutual
+inclination distribution, which is significantly broader than the absolute
+inclination distributions typically considered by previous studies.
+",0,1,0,0,0,0
+20200,How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics,"  Endowing robots with the capability of assessing risk and making risk-aware
+decisions is widely considered a key step toward ensuring safety for robots
+operating under uncertainty. But, how should a robot quantify risk? A natural
+and common approach is to consider the framework whereby costs are assigned to
+stochastic outcomes - an assignment captured by a cost random variable.
+Quantifying risk then corresponds to evaluating a risk metric, i.e., a mapping
+from the cost random variable to a real number. Yet, the question of what
+constitutes a ""good"" risk metric has received little attention within the
+robotics community. The goal of this paper is to explore and partially address
+this question by advocating axioms that risk metrics in robotics applications
+should satisfy in order to be employed as rational assessments of risk. We
+discuss general representation theorems that precisely characterize the class
+of metrics that satisfy these axioms (referred to as distortion risk metrics),
+and provide instantiations that can be used in applications. We further discuss
+pitfalls of commonly used risk metrics in robotics, and discuss additional
+properties that one must consider in sequential decision making tasks. Our hope
+is that the ideas presented here will lead to a foundational framework for
+quantifying risk (and hence safety) in robotics applications.
+",1,0,0,0,0,0
+20201,Pitfalls and Best Practices in Algorithm Configuration,"  Good parameter settings are crucial to achieve high performance in many areas
+of artificial intelligence (AI), such as propositional satisfiability solving,
+AI planning, scheduling, and machine learning (in particular deep learning).
+Automated algorithm configuration methods have recently received much attention
+in the AI community since they replace tedious, irreproducible and error-prone
+manual parameter tuning and can lead to new state-of-the-art performance.
+However, practical applications of algorithm configuration are prone to several
+(often subtle) pitfalls in the experimental design that can render the
+procedure ineffective. We identify several common issues and propose best
+practices for avoiding them. As one possibility for automatically handling as
+many of these as possible, we also propose a tool called GenericWrapper4AC.
+",1,0,0,0,0,0
+20202,Critical behaviors in contagion dynamics,"  We study the critical behavior of a general contagion model where nodes are
+either active (e.g. with opinion A, or functioning) or inactive (e.g. with
+opinion B, or damaged). The transitions between these two states are determined
+by (i) spontaneous transitions independent of the neighborhood, (ii)
+transitions induced by neighboring nodes and (iii) spontaneous reverse
+transitions. The resulting dynamics is extremely rich including limit cycles
+and random phase switching. We derive a unifying mean-field theory.
+Specifically, we analytically show that the critical behavior of systems whose
+dynamics is governed by processes (i-iii) can only exhibit three distinct
+regimes: (a) uncorrelated spontaneous transition dynamics (b) contact process
+dynamics and (c) cusp catastrophes. This ends a long-standing debate on the
+universality classes of complex contagion dynamics in mean-field and
+substantially deepens its mathematical understanding.
+",0,1,1,0,0,0
+20203,Machine Learning in Appearance-based Robot Self-localization,"  An appearance-based robot self-localization problem is considered in the
+machine learning framework. The appearance space is composed of all possible
+images, which can be captured by a robot's visual system under all robot
+localizations. Using recent manifold learning and deep learning techniques, we
+propose a new geometrically motivated solution based on training data
+consisting of a finite set of images captured in known locations of the robot.
+The solution includes estimation of the robot localization mapping from the
+appearance space to the robot localization space, as well as estimation of the
+inverse mapping for modeling visual image features. The latter allows solving
+the robot localization problem as the Kalman filtering problem.
+",1,0,0,1,0,0
+20204,Towards Communication-Aware Robust Topologies,"  We currently witness the emergence of interesting new network topologies
+optimized towards the traffic matrices they serve, such as demand-aware
+datacenter interconnects (e.g., ProjecToR) and demand-aware overlay networks
+(e.g., SplayNets). This paper introduces a formal framework and approach to
+reason about and design such topologies. We leverage a connection between the
+communication frequency of two nodes and the path length between them in the
+network, which depends on the entropy of the communication matrix. Our main
+contribution is a novel robust, yet sparse, family of network topologies which
+guarantee an expected path length that is proportional to the entropy of the
+communication patterns.
+",1,0,0,0,0,0
+20205,Zero-shot Domain Adaptation without Domain Semantic Descriptors,"  We propose a method to infer domain-specific models such as classifiers for
+unseen domains, from which no data are given in the training phase, without
+domain semantic descriptors. When training and test distributions are
+different, standard supervised learning methods perform poorly. Zero-shot
+domain adaptation attempts to alleviate this problem by inferring models that
+generalize well to unseen domains by using training data in multiple source
+domains. Existing methods use observed semantic descriptors characterizing
+domains such as time information to infer the domain-specific models for the
+unseen domains. However, it cannot always be assumed that such metadata can be
+used in real-world applications. The proposed method can infer appropriate
+domain-specific models without any semantic descriptors by introducing the
+concept of latent domain vectors, which are latent representations for the
+domains and are used for inferring the models. The latent domain vector for the
+unseen domain is inferred from the set of the feature vectors in the
+corresponding domain, which is given in the testing phase. The domain-specific
+models consist of two components: the first is for extracting a representation
+of a feature vector to be predicted, and the second is for inferring model
+parameters given the latent domain vector. The posterior distributions of the
+latent domain vectors and the domain-specific models are parametrized by neural
+networks, and are optimized by maximizing the variational lower bound using
+stochastic gradient descent. The effectiveness of the proposed method was
+demonstrated through experiments using one regression and two classification
+tasks.
+",0,0,0,1,0,0
+20206,Network modelling of topological domains using Hi-C data,"  Genome-wide chromosome conformation capture techniques such as Hi-C enable
+the generation of 3D genome contact maps and offer new pathways toward
+understanding the spatial organization of genome. One specific feature of the
+3D organization is known as topologically associating domains (TADs), which are
+densely interacting, contiguous chromatin regions playing important roles in
+regulating gene expression. A few algorithms have been proposed to detect TADs.
+In particular, the structure of Hi-C data naturally inspires application of
+community detection methods. However, one of the drawbacks of community
+detection is that most methods take exchangeability of the nodes in the network
+for granted; whereas the nodes in this case, i.e. the positions on the
+chromosomes, are not exchangeable. We propose a network model for detecting
+TADs using Hi-C data that takes into account this non-exchangeability. In
+addition, our model explicitly makes use of cell-type specific CTCF binding
+sites as biological covariates and can be used to identify conserved TADs
+across multiple cell types. The model leads to a likelihood objective that can
+be efficiently optimized via relaxation. We also prove that when suitably
+initialized, this model finds the underlying TAD structure with high
+probability. Using simulated data, we show the advantages of our method and the
+caveats of popular community detection methods, such as spectral clustering, in
+this application. Applying our method to real Hi-C data, we demonstrate the
+domains identified have desirable epigenetic features and compare them across
+different cell types. The code is available upon request.
+",0,0,0,1,0,0
+20207,On a Possible Giant Impact Origin for the Colorado Plateau,"  It is proposed and substantiated that an extraterrestrial object of the
+approximate size and mass of Planet Mars, impacting the Earth in an oblique
+angle along an approximately NE-SW route (with respect to the current
+orientation of the North America continent) around 750 million years ago (750
+Ma), is likely to be the direct cause of a chain of events which led to the
+rifting of the Rodinia supercontinent and the severing of the foundation of the
+Colorado Plateau from its surrounding craton.
+It is further argued that the impactor most likely originated as a rouge
+exoplanet produced during one of the past crossings of our Solar System through
+the Galactic spiral arms in its orbital motion around the center of the Milky
+Way Galaxy. Recent work has shown that the sites of galactic spiral arms are
+locations of density-wave collisionless shocks. The perturbations from such
+shock are known lead to the formation of massive stars, which evolve quickly
+and die as supernovae. The blastwaves from supernova explosions, in addition to
+the collisionless shocks at the spiral arms, can perturb the orbits of the
+streaming disk matter, occasionally producing rogue exoplanets that can reach
+the inner confines of our Solar System. The similarity between the period of
+spiral-arm crossings of our Solar System to the period of major extinction
+events in the Phanerozoic Eon of the Earth's history, as well as to the period
+of the supercontinent cycle (the so-called Wilson Cycle), indicates that the
+global environment of the Milky Way Galaxy may have played a major role in
+initiating Earth's past tectonic activities.
+",0,1,0,0,0,0
+20208,Reversing Parallel Programs with Blocks and Procedures,"  We show how to reverse a while language extended with blocks, local
+variables, procedures and the interleaving parallel composition. Annotation is
+defined along with a set of operational semantics capable of storing necessary
+reversal information, and identifiers are introduced to capture the
+interleaving order of an execution. Inversion is defined with a set of
+operational semantics that use saved information to undo an execution. We prove
+that annotation does not alter the behaviour of the original program, and that
+inversion correctly restores the initial program state.
+",1,0,0,0,0,0
+20209,Detection of the Stellar Intracluster Medium in Perseus (Abell 426),"  Hubble Space Telescope photometry from the ACS/WFC and WFPC2 cameras is used
+to detect and measure globular clusters (GCs) in the central region of the rich
+Perseus cluster of galaxies. A detectable population of Intragalactic GCs is
+found extending out to at least 500 kpc from the cluster center. These objects
+display luminosity and color (metallicity) distributions that are entirely
+normal for GC populations. Extrapolating from the limited spatial coverage of
+the HST fields, we estimate very roughly that the entire Perseus cluster should
+contain ~50000 or more IGCs, but a targetted wide-field survey will be needed
+for a more definitive answer. Separate brief results are presented for the rich
+GC systems in NGC 1272 and NGC 1275, the two largest Perseus ellipticals. For
+NGC 1272 we find a specific frequency S_N = 8, while for the central giant NGC
+1275, S_N ~ 12. In both these giant galaxies, the GC colors are well matched by
+bimodal distributions, with the majority in the blue (metal-poor) component.
+This preliminary study suggests that Perseus is a prime target for a more
+comprehensive deep imaging survey of Intragalactic GCs.
+",0,1,0,0,0,0
+20210,Binets: fundamental building blocks for phylogenetic networks,"  Phylogenetic networks are a generalization of evolutionary trees that are
+used by biologists to represent the evolution of organisms which have undergone
+reticulate evolution. Essentially, a phylogenetic network is a directed acyclic
+graph having a unique root in which the leaves are labelled by a given set of
+species. Recently, some approaches have been developed to construct
+phylogenetic networks from collections of networks on 2- and 3-leaved networks,
+which are known as binets and trinets, respectively. Here we study in more
+depth properties of collections of binets, one of the simplest possible types
+of networks into which a phylogenetic network can be decomposed. More
+specifically, we show that if a collection of level-1 binets is compatible with
+some binary network, then it is also compatible with a binary level-1 network.
+Our proofs are based on useful structural results concerning lowest stable
+ancestors in networks. In addition, we show that, although the binets do not
+determine the topology of the network, they do determine the number of
+reticulations in the network, which is one of its most important parameters. We
+also consider algorithmic questions concerning binets. We show that deciding
+whether an arbitrary set of binets is compatible with some network is at least
+as hard as the well-known Graph Isomorphism problem. However, if we restrict to
+level-1 binets, it is possible to decide in polynomial time whether there
+exists a binary network that displays all the binets. We also show that to find
+a network that displays a maximum number of the binets is NP-hard, but that
+there exists a simple polynomial-time 1/3-approximation algorithm for this
+problem. It is hoped that these results will eventually assist in the
+development of new methods for constructing phylogenetic networks from
+collections of smaller networks.
+",1,0,1,0,0,0
+20211,G-Deformations of maps into projective space,"  $G$-deformability of maps into projective space is characterised by the
+existence of certain Lie algebra valued 1-forms. This characterisation gives a
+unified way to obtain well known results regarding deformability in different
+geometries.
+",0,0,1,0,0,0
+20212,Cloudless atmospheres for young low-gravity substellar objects,"  Atmospheric modeling of low-gravity (VL-G) young brown dwarfs remains a
+challenge. The presence of very thick clouds has been suggested because of
+their extremely red near-infrared (NIR) spectra, but no cloud models provide a
+good fit to the data with a radius compatible with evolutionary models for
+these objects. We show that cloudless atmospheres assuming a temperature
+gradient reduction caused by fingering convection provides a very good model to
+match the observed VL-G NIR spectra. The sequence of extremely red colors in
+the NIR for atmospheres with effective temperature from ~2000 K down to ~1200 K
+is very well reproduced with predicted radii typical of young low-gravity
+objects. Future observations with NIRSPEC and MIRI on the James Webb Space
+Telescope (JWST) will provide more constrains in the mid-infrared, helping to
+confirm/refute whether or not the NIR reddening is caused by fingering
+convection. We suggest that the presence/absence of clouds will be directly
+determined by the silicate absorption features that can be observed with MIRI.
+JWST will therefore be able to better characterize the atmosphere of these hot
+young brown dwarfs and their low-gravity exoplanet analogues.
+",0,1,0,0,0,0
+20213,On the State of the Art of Evaluation in Neural Language Models,"  Ongoing innovations in recurrent neural network architectures have provided a
+steady influx of apparently state-of-the-art results on language modelling
+benchmarks. However, these have been evaluated using differing code bases and
+limited computational resources, which represent uncontrolled sources of
+experimental variation. We reevaluate several popular architectures and
+regularisation methods with large-scale automatic black-box hyperparameter
+tuning and arrive at the somewhat surprising conclusion that standard LSTM
+architectures, when properly regularised, outperform more recent models. We
+establish a new state of the art on the Penn Treebank and Wikitext-2 corpora,
+as well as strong baselines on the Hutter Prize dataset.
+",1,0,0,0,0,0
+20214,Weakly Supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning,"  Separating audio mixtures into individual instrument tracks has been a long
+standing challenging task. We introduce a novel weakly supervised audio source
+separation approach based on deep adversarial learning. Specifically, our loss
+function adopts the Wasserstein distance which directly measures the
+distribution distance between the separated sources and the real sources for
+each individual source. Moreover, a global regularization term is added to
+fulfill the spectrum energy preservation property regardless separation. Unlike
+state-of-the-art weakly supervised models which often involve deliberately
+devised constraints or careful model selection, our approach need little prior
+model specification on the data, and can be straightforwardly learned in an
+end-to-end fashion. We show that the proposed method performs competitively on
+public benchmark against state-of-the-art weakly supervised methods.
+",1,0,0,0,0,0
+20215,Proof of a conjecture of Kløve on permutation codes under the Chebychev distance,"  Let $d$ be a positive integer and $x$ a real number. Let $A_{d, x}$ be a
+$d\times 2d$ matrix with its entries $$ a_{i,j}=\left\{ \begin{array}{ll} x\ \
+& \mbox{for} \ 1\leqslant j\leqslant d+1-i, 1\ \ & \mbox{for} \ d+2-i\leqslant
+j\leqslant d+i, 0\ \ & \mbox{for} \ d+1+i\leqslant j\leqslant 2d. \end{array}
+\right. $$ Further, let $R_d$ be a set of sequences of integers as follows:
+$$R_d=\{(\rho_1, \rho_2,\ldots, \rho_d)|1\leqslant \rho_i\leqslant d+i,
+1\leqslant i \leqslant d,\ \mbox{and}\ \rho_r\neq \rho_s\ \mbox{for}\ r\neq
+s\}.$$ and define $$\Omega_d(x)=\sum_{\rho\in R_d}a_{1,\rho_1}a_{2,
+\rho_2}\ldots a_{d,\rho_d}.$$ In order to give a better bound on the size of
+spheres of permutation codes under the Chebychev distance, Kl{\o}ve introduced
+the above function and conjectured that $$\Omega_d(x)=\sum_{m=0}^d{d\choose
+m}(m+1)^d(x-1)^{d-m}.$$ In this paper, we settle down this conjecture
+positively.
+",1,0,0,0,0,0
+20216,Herschel observations of the Galactic HII region RCW 79,"  Triggered star formation around HII regions could be an important process.
+The Galactic HII region RCW 79 is a prototypical object for triggered high-mass
+star formation. We take advantage of Herschel data from the surveys HOBYS,
+""Evolution of Interstellar Dust"", and Hi-Gal to extract compact sources in this
+region, complemented with archival 2MASS, Spitzer, and WISE data to determine
+the physical parameters of the sources (e.g., envelope mass, dust temperature,
+and luminosity) by fitting the spectral energy distribution. We obtained a
+sample of 50 compact sources, 96% of which are situated in the
+ionization-compressed layer of cold and dense gas that is characterized by the
+column density PDF with a double-peaked lognormal distribution. The 50 sources
+have sizes of 0.1-0.4 pc with a typical value of 0.2 pc, temperatures of 11-26
+K, envelope masses of 6-760 $M_\odot$, densities of 0.1-44 $\times$ $10^5$
+cm$^{-3}$, and luminosities of 19-12712 $L_\odot$. The sources are classified
+into 16 class 0, 19 intermediate, and 15 class I objects. Their distribution
+follows the evolutionary tracks in the diagram of bolometric luminosity versus
+envelope mass (Lbol-Menv) well. A mass threshold of 140 $M_\odot$, determined
+from the Lbol-Menv diagram, yields 12 candidate massive dense cores that may
+form high-mass stars. The core formation efficiency (CFE) for the 8 massive
+condensations shows an increasing trend of the CFE with density. This suggests
+that the denser the condensation, the higher the fraction of its mass
+transformation into dense cores, as previously observed in other high-mass
+star-forming regions.
+",0,1,0,0,0,0
+20217,Software-Defined Robotics -- Idea & Approach,"  The methodology of Software-Defined Robotics hierarchical-based and
+stand-alone framework can be designed and implemented to program and control
+different sets of robots, regardless of their manufacturers' parameters and
+specifications, with unified commands and communications. This framework
+approach will increase the capability of (re)programming a specific group of
+robots during the runtime without affecting the others as desired in the
+critical missions and industrial operations, expand the shared bandwidth,
+enhance the reusability of code, leverage the computational processing power,
+decrease the unnecessary analyses of vast supplemental electrical components
+for each robot, as well as get advantages of the most state-of-the-art
+industrial trends in the cloud-based computing, Virtual Machines (VM), and
+Robot-as-a-Service (RaaS) technologies.
+",1,0,0,0,0,0
+20218,Stability interchanges in a curved Sitnikov problem,"  We consider a curved Sitnikov problem, in which an infinitesimal particle
+moves on a circle under the gravitational influence of two equal masses in
+Keplerian motion within a plane perpendicular to that circle. There are two
+equilibrium points, whose stability we are studying. We show that one of the
+equilibrium points undergoes stability interchanges as the semi-major axis of
+the Keplerian ellipses approaches the diameter of that circle. To derive this
+result, we first formulate and prove a general theorem on stability
+interchanges, and then we apply it to our model. The motivation for our model
+resides with the $n$-body problem in spaces of constant curvature.
+",0,1,1,0,0,0
+20219,Hardy Spaces over Half-strip Domains,"  We define Hardy spaces $H^p(\Omega_\pm)$ on half-strip domain~$\Omega_+$ and
+$\Omega_-= \mathbb{C}\setminus\overline{\Omega_+}$, where $0<p<\infty$, and
+prove that functions in $H^p(\Omega_\pm)$ has non-tangential boundary limit
+a.e. on $\Gamma$, the common boundary of $\Omega_\pm$. We then prove that
+Cauchy integral of functions in $L^p(\Gamma)$ are in $H^p(\Omega_\pm)$, where
+$1<p<\infty$, that is, Cauchy transform is bounded. Besides, if $1\leqslant
+p<\infty$, then $H^p(\Omega_\pm)$ functions are the Cauchy integral of their
+non-tangential boundary limits. We also establish an isomorphism between
+$H^p(\Omega_\pm)$ and $H^p(\mathbb{C}_\pm)$, the classical Hardy spaces over
+upper and lower half complex planes.
+",0,0,1,0,0,0
+20220,An Optimization Framework with Flexible Inexact Inner Iterations for Nonconvex and Nonsmooth Programming,"  In recent years, numerous vision and learning tasks have been (re)formulated
+as nonconvex and nonsmooth programmings(NNPs). Although some algorithms have
+been proposed for particular problems, designing fast and flexible optimization
+schemes with theoretical guarantee is a challenging task for general NNPs. It
+has been investigated that performing inexact inner iterations often benefit to
+special applications case by case, but their convergence behaviors are still
+unclear. Motivated by these practical experiences, this paper designs a novel
+algorithmic framework, named inexact proximal alternating direction method
+(IPAD) for solving general NNPs. We demonstrate that any numerical algorithms
+can be incorporated into IPAD for solving subproblems and the convergence of
+the resulting hybrid schemes can be consistently guaranteed by a series of
+simple error conditions. Beyond the guarantee in theory, numerical experiments
+on both synthesized and real-world data further demonstrate the superiority and
+flexibility of our IPAD framework for practical use.
+",1,0,1,0,0,0
+20221,Attitude and angular velocity tracking for a rigid body using geometric methods on the two-sphere,"  The control task of tracking a reference pointing direction (the attitude
+about the pointing direction is irrelevant) while obtaining a desired angular
+velocity (PDAV) around the pointing direction using geometric techniques is
+addressed here. Existing geometric controllers developed on the two-sphere only
+address the tracking of a reference pointing direction while driving the
+angular velocity about the pointing direction to zero. In this paper a tracking
+controller on the two-sphere, able to address the PDAV control task, is
+developed globally in a geometric frame work, to avoid problems related to
+other attitude representations such as unwinding (quaternions) or singularities
+(Euler angles). An attitude error function is constructed resulting in a
+control system with desired tracking performance for rotational maneuvers with
+large initial attitude/angular velocity errors and the ability to negotiate
+bounded modeling inaccuracies. The tracking ability of the developed control
+system is evaluated by comparing its performance with an existing geometric
+controller on the two-sphere and by numerical simulations, showing improved
+performance for large initial attitude errors, smooth transitions between
+desired angular velocities and the ability to negotiate bounded modeling
+inaccuracies.
+",1,0,1,0,0,0
+20222,Miscomputation in software: Learning to live with errors,"  Computer programs do not always work as expected. In fact, ominous warnings
+about the desperate state of the software industry continue to be released with
+almost ritualistic regularity. In this paper, we look at the 60 years history
+of programming and at the different practical methods that software community
+developed to live with programming errors. We do so by observing a class of
+students discussing different approaches to programming errors. While learning
+about the different methods for dealing with errors, we uncover basic
+assumptions that proponents of different paradigms follow. We learn about the
+mathematical attempt to eliminate errors through formal methods, scientific
+method based on testing, a way of building reliable systems through engineering
+methods, as well as an artistic approach to live coding that accepts errors as
+a creative inspiration. This way, we can explore the differences and
+similarities among the different paradigms. By inviting proponents of different
+methods into a single discussion, we hope to open potential for new thinking
+about errors. When should we use which of the approaches? And what can software
+development learn from mathematics, science, engineering and art? When
+programming or studying programming, we are often enclosed in small communities
+and we take our basic assumptions for granted. Through the discussion in this
+paper, we attempt to map the large and rich space of programming ideas and
+provide reference points for exploring, perhaps foreign, ideas that can
+challenge some of our assumptions.
+",1,0,0,0,0,0
+20223,The Combinatorics of Weighted Vector Compositions,"  A vector composition of a vector $\mathbf{\ell}$ is a matrix $\mathbf{A}$
+whose rows sum to $\mathbf{\ell}$. We define a weighted vector composition as a
+vector composition in which the column values of $\mathbf{A}$ may appear in
+different colors. We study vector compositions from different viewpoints: (1)
+We show how they are related to sums of random vectors and (2) how they allow
+to derive formulas for partial derivatives of composite functions. (3) We study
+congruence properties of the number of weighted vector compositions, for fixed
+and arbitrary number of parts, many of which are analogous to those of ordinary
+binomial coefficients and related quantities. Via the Central Limit Theorem and
+their multivariate generating functions, (4) we also investigate the asymptotic
+behavior of several special cases of numbers of weighted vector compositions.
+Finally, (5) we conjecture an extension of a primality criterion due to Mann
+and Shanks in the context of weighted vector compositions.
+",1,0,1,0,0,0
+20224,Fast Linear Model for Knowledge Graph Embeddings,"  This paper shows that a simple baseline based on a Bag-of-Words (BoW)
+representation learns surprisingly good knowledge graph embeddings. By casting
+knowledge base completion and question answering as supervised classification
+problems, we observe that modeling co-occurences of entities and relations
+leads to state-of-the-art performance with a training time of a few minutes
+using the open sourced library fastText.
+",1,0,0,1,0,0
+20225,Deep learning for plasma tomography using the bolometer system at JET,"  Deep learning is having a profound impact in many fields, especially those
+that involve some form of image processing. Deep neural networks excel in
+turning an input image into a set of high-level features. On the other hand,
+tomography deals with the inverse problem of recreating an image from a number
+of projections. In plasma diagnostics, tomography aims at reconstructing the
+cross-section of the plasma from radiation measurements. This reconstruction
+can be computed with neural networks. However, previous attempts have focused
+on learning a parametric model of the plasma profile. In this work, we use a
+deep neural network to produce a full, pixel-by-pixel reconstruction of the
+plasma profile. For this purpose, we use the overview bolometer system at JET,
+and we introduce an up-convolutional network that has been trained and tested
+on a large set of sample tomograms. We show that this network is able to
+reproduce existing reconstructions with a high level of accuracy, as measured
+by several metrics.
+",0,1,0,1,0,0
+20226,Assembly Bias and Splashback in Galaxy Clusters,"  We use publicly available data for the Millennium Simulation to explore the
+implications of the recent detection of assembly bias and splashback signatures
+in a large sample of galaxy clusters. These were identified in the SDSS/DR8
+photometric data by the redMaPPer algorithm and split into high- and
+low-concentration subsamples based on the projected positions of cluster
+members. We use simplified versions of these procedures to build cluster
+samples of similar size from the simulation data. These match the observed
+samples quite well and show similar assembly bias and splashback signals.
+Previous theoretical work has found the logarithmic slope of halo density
+profiles to have a well-defined minimum whose depth decreases and whose radius
+increases with halo concentration. Projected profiles for the observed and
+simulated cluster samples show trends with concentration which are opposite to
+these predictions. In addition, for high-concentration clusters the minimum
+slope occurs at significantly smaller radius than predicted. We show that these
+discrepancies all reflect confusion between splashback features and features
+imposed on the profiles by the cluster identification and concentration
+estimation procedures. The strong apparent assembly bias is not reflected in
+the three-dimensional distribution of matter around clusters. Rather it is a
+consequence of the preferential contamination of low-concentration clusters by
+foreground or background groups.
+",0,1,0,0,0,0
+20227,Accelerating Kernel Classifiers Through Borders Mapping,"  Support vector machines (SVM) and other kernel techniques represent a family
+of powerful statistical classification methods with high accuracy and broad
+applicability. Because they use all or a significant portion of the training
+data, however, they can be slow, especially for large problems. Piecewise
+linear classifiers are similarly versatile, yet have the additional advantages
+of simplicity, ease of interpretation and, if the number of component linear
+classifiers is not too large, speed. Here we show how a simple, piecewise
+linear classifier can be trained from a kernel-based classifier in order to
+improve the classification speed. The method works by finding the root of the
+difference in conditional probabilities between pairs of opposite classes to
+build up a representation of the decision boundary. When tested on 17 different
+datasets, it succeeded in improving the classification speed of a SVM for 9 of
+them by factors as high as 88 times or more. The method is best suited to
+problems with continuum features data and smooth probability functions. Because
+the component linear classifiers are built up individually from an existing
+classifier, rather than through a simultaneous optimization procedure, the
+classifier is also fast to train.
+",1,0,0,1,0,0
+20228,Information Criterion for Minimum Cross-Entropy Model Selection,"  This paper considers the problem of approximating a density when it can be
+evaluated up to a normalizing constant at a finite number of points. This
+density approximation problem is ubiquitous in machine learning, such as
+approximating a posterior density for Bayesian inference and estimating an
+optimal density for importance sampling. Approximating the density with a
+parametric model can be cast as a model selection problem. This problem cannot
+be addressed with traditional approaches that maximize the (marginal)
+likelihood of a model, for example, using the Akaike information criterion
+(AIC) or Bayesian information criterion (BIC). We instead aim to minimize the
+cross-entropy that gauges the deviation of a parametric model from the target
+density. We propose a novel information criterion called the cross-entropy
+information criterion (CIC) and prove that the CIC is an asymptotically
+unbiased estimator of the cross-entropy (up to a multiplicative constant) under
+some regularity conditions. We propose an iterative method to approximate the
+target density by minimizing the CIC. We demonstrate that the proposed method
+selects a parametric model that well approximates the target density.
+",0,0,0,1,0,0
+20229,Scaling relations in large-Prandtl-number natural thermal convection,"  In this study we follow Grossmann and Lohse, Phys. Rev. Lett. 86 (2001), who
+derived various scalings regimes for the dependence of the Nusselt number $Nu$
+and the Reynolds number $Re$ on the Rayleigh number $Ra$ and the Prandtl number
+$Pr$. We focus on theoretical arguments as well as on numerical simulations for
+the case of large-$Pr$ natural thermal convection. Based on an analysis of
+self-similarity of the boundary layer equations, we derive that in this case
+the limiting large-$Pr$ boundary-layer dominated regime is I$_\infty^<$,
+introduced and defined in [1], with the scaling relations $Nu\sim
+Pr^0\,Ra^{1/3}$ and $Re\sim Pr^{-1}\,Ra^{2/3}$. Our direct numerical
+simulations for $Ra$ from $10^4$ to $10^9$ and $Pr$ from 0.1 to 200 show that
+the regime I$_\infty^<$ is almost indistinguishable from the regime
+III$_\infty$, where the kinetic dissipation is bulk-dominated. With increasing
+$Ra$, the scaling relations undergo a transition to those in IV$_u$ of
+reference [1], where the thermal dissipation is determined by its bulk
+contribution.
+",0,1,0,0,0,0
+20230,"The renormalization method from continuous to discrete dynamical systems: asymptotic solutions, reductions and invariant manifolds","  The renormalization method based on the Taylor expansion for asymptotic
+analysis of differential equations is generalized to difference equations. The
+proposed renormalization method is based on the Newton-Maclaurin expansion.
+Several basic theorems on the renormalization method are proven. Some
+interesting applications are given, including asymptotic solutions of quantum
+anharmonic oscillator and discrete boundary layer, the reductions and invariant
+manifolds of some discrete dynamics systems. Furthermore, the homotopy
+renormalization method based on the Newton-Maclaurin expansion is proposed and
+applied to those difference equations including no a small parameter.
+",0,0,1,0,0,0
+20231,A Stress/Displacement Virtual Element Method for Plane Elasticity Problems,"  The numerical approximation of 2D elasticity problems is considered, in the
+framework of the small strain theory and in connection with the mixed
+Hellinger-Reissner variational formulation. A low-order Virtual Element Method
+(VEM) with a-priori symmetric stresses is proposed. Several numerical tests are
+provided, along with a rigorous stability and convergence analysis.
+",0,0,1,0,0,0
+20232,Estimation of the lead-lag parameter between two stochastic processes driven by fractional Brownian motions,"  In this paper, we consider the problem of estimating the lead-lag parameter
+between two stochastic processes driven by fractional Brownian motions (fBMs)
+of the Hurst parameter greater than 1/2. First we propose a lead-lag model
+between two stochastic processes involving fBMs, and then construct a
+consistent estimator of the lead-lag parameter with possible convergence rate.
+Our estimator has the following two features. Firstly, we can construct the
+lead-lag estimator without using the Hurst parameters of the underlying fBMs.
+Secondly, our estimator can deal with some non-synchronous and irregular
+observations. We explicitly calculate possible convergence rate when the
+observation times are (1) synchronous and equidistant, and (2) given by the
+Poisson sampling scheme. We also present numerical simulations of our results
+using the R package YUIMA.
+",0,0,1,1,0,0
+20233,Multi-resolution polymer Brownian dynamics with hydrodynamic interactions,"  A polymer model given in terms of beads, interacting through Hookean springs
+and hydrodynamic forces, is studied. Brownian dynamics description of this
+bead-spring polymer model is extended to multiple resolutions. Using this
+multiscale approach, a modeller can efficiently look at different regions of
+the polymer in different spatial and temporal resolutions with scalings given
+for the number of beads, statistical segment length and bead radius in order to
+maintain macro-scale properties of the polymer filament. The Boltzmann
+distribution of a Gaussian chain for differing statistical segment lengths
+gives a Langevin equation for the multi-resolution model with a mobility tensor
+for different bead sizes. Using the pre-averaging approximation, the
+translational diffusion coefficient is obtained as a function of the inverse of
+a matrix and then in closed form in the long-chain limit. This is then
+confirmed with numerical experiments.
+",0,1,0,0,0,0
+20234,Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network,"  Distributed learning is an effective way to analyze big data. In distributed
+regression, a typical approach is to divide the big data into multiple blocks,
+apply a base regression algorithm on each of them, and then simply average the
+output functions learnt from these blocks. Since the average process will
+decrease the variance, not the bias, bias correction is expected to improve the
+learning performance if the base regression algorithm is a biased one.
+Regularization kernel network is an effective and widely used method for
+nonlinear regression analysis. In this paper we will investigate a bias
+corrected version of regularization kernel network. We derive the error bounds
+when it is applied to a single data set and when it is applied as a base
+algorithm in distributed regression. We show that, under certain appropriate
+conditions, the optimal learning rates can be reached in both situations.
+",1,0,0,1,0,0
+20235,Cluster-based Kriging Approximation Algorithms for Complexity Reduction,"  Kriging or Gaussian Process Regression is applied in many fields as a
+non-linear regression model as well as a surrogate model in the field of
+evolutionary computation. However, the computational and space complexity of
+Kriging, that is cubic and quadratic in the number of data points respectively,
+becomes a major bottleneck with more and more data available nowadays. In this
+paper, we propose a general methodology for the complexity reduction, called
+cluster Kriging, where the whole data set is partitioned into smaller clusters
+and multiple Kriging models are built on top of them. In addition, four Kriging
+approximation algorithms are proposed as candidate algorithms within the new
+framework. Each of these algorithms can be applied to much larger data sets
+while maintaining the advantages and power of Kriging. The proposed algorithms
+are explained in detail and compared empirically against a broad set of
+existing state-of-the-art Kriging approximation methods on a well-defined
+testing framework. According to the empirical study, the proposed algorithms
+consistently outperform the existing algorithms. Moreover, some practical
+suggestions are provided for using the proposed algorithms.
+",1,0,0,1,0,0
+20236,How to model fake news,"  Over the past three years it has become evident that fake news is a danger to
+democracy. However, until now there has been no clear understanding of how to
+define fake news, much less how to model it. This paper addresses both these
+issues. A definition of fake news is given, and two approaches for the
+modelling of fake news and its impact in elections and referendums are
+introduced. The first approach, based on the idea of a representative voter, is
+shown to be suitable to obtain a qualitative understanding of phenomena
+associated with fake news at a macroscopic level. The second approach, based on
+the idea of an election microstructure, describes the collective behaviour of
+the electorate by modelling the preferences of individual voters. It is shown
+through a simulation study that the mere knowledge that pieces of fake news may
+be in circulation goes a long way towards mitigating the impact of fake news.
+",1,0,0,0,0,1
+20237,Analysis of Dropout in Online Learning,"  Deep learning is the state-of-the-art in fields such as visual object
+recognition and speech recognition. This learning uses a large number of layers
+and a huge number of units and connections. Therefore, overfitting is a serious
+problem with it, and the dropout which is a kind of regularization tool is
+used. However, in online learning, the effect of dropout is not well known.
+This paper presents our investigation on the effect of dropout in online
+learning. We analyzed the effect of dropout on convergence speed near the
+singular point. Our results indicated that dropout is effective in online
+learning. Dropout tends to avoid the singular point for convergence speed near
+that point.
+",1,0,0,1,0,0
+20238,TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease,"  The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE)
+Challenge compares the performance of algorithms at predicting future evolution
+of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants
+train their models and algorithms on historical data from the Alzheimer's
+Disease Neuroimaging Initiative (ADNI) study or any other datasets to which
+they have access. Participants are then required to make monthly forecasts over
+a period of 5 years from January 2018, of three key outcomes for ADNI-3
+rollover participants: clinical diagnosis, Alzheimer's Disease Assessment Scale
+Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. These
+individual forecasts are later compared with the corresponding future
+measurements in ADNI-3 (obtained after the TADPOLE submission deadline). The
+first submission phase of TADPOLE was open for prize-eligible submissions
+between 15 June and 15 November 2017. The submission system remains open via
+the website: this https URL, although since 15 November
+2017 submissions are not eligible for the first round of prizes. This paper
+describes the design of the TADPOLE Challenge.
+",0,0,0,1,1,0
+20239,A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction,"  The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
+Inspired by recent advances in deep learning, we propose a framework for
+reconstructing MR images from undersampled data using a deep cascade of
+convolutional neural networks to accelerate the data acquisition process. We
+show that for Cartesian undersampling of 2D cardiac MR images, the proposed
+method outperforms the state-of-the-art compressed sensing approaches, such as
+dictionary learning-based MRI (DLMRI) reconstruction, in terms of
+reconstruction error, perceptual quality and reconstruction speed for both
+3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the
+method proposed is approximately twice as small, allowing to preserve
+anatomical structures more faithfully. Using our method, each image can be
+reconstructed in 23 ms, which is fast enough to enable real-time applications.
+",1,0,0,0,0,0
+20240,Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM,"  We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR)
+model. We learn to listen and write characters with a joint Connectionist
+Temporal Classification (CTC) and attention-based encoder-decoder network. The
+encoder is a deep Convolutional Neural Network (CNN) based on the VGG network.
+The CTC network sits on top of the encoder and is jointly trained with the
+attention-based decoder. During the beam search process, we combine the CTC
+predictions, the attention-based decoder predictions and a separately trained
+LSTM language model. We achieve a 5-10\% error reduction compared to prior
+systems on spontaneous Japanese and Chinese speech, and our end-to-end model
+beats out traditional hybrid ASR systems.
+",1,0,0,0,0,0
+20241,The Social and Work Structure of an Afterschool Math Club,"  This study focuses on the social structure and interpersonal dynamics of an
+afterschool math club for middle schoolers. Using social network analysis, two
+networks were formed and analyzed: The network of friendship relationships and
+the network of working relationships. The interconnections and correlations
+between friendship relationships, working relationships, and student opinion
+surveys are studied. A core working group of talented students emerged from the
+network of working relations. This group acted a central go-between for other
+members in the club. This core working group expanded into largest friendship
+group in the friendship network. Although there were working isolates, they
+were not found to be socially isolated. Students who were less popular tended
+to report a greater favorable impact from club participation. Implications for
+the study of the social structure of afterschool STEM clubs and classrooms are
+discussed.
+",0,0,1,0,0,0
+20242,Fourier Transform of Schwartz Algebras on Groups in the Harish-Chandra class,"  It is well-known that the Harish-Chandra transform, $f\mapsto\mathcal{H}f,$
+is a topological isomorphism of the spherical (Schwartz) convolution algebra
+$\mathcal{C}^{p}(G//K)$ (where $K$ is a maximal compact subgroup of any
+arbitrarily chosen group $G$ in the Harish-Chandra class and $0<p\leq2$) onto
+the (Schwartz) multiplication algebra
+$\bar{\mathcal{Z}}({\mathfrak{F}}^{\epsilon})$ (of $\mathfrak{w}-$invariant
+members of $\mathcal{Z}({\mathfrak{F}}^{\epsilon}),$ with $\epsilon=(2/p)-1$).
+The same cannot however be said of the full Schwartz convolution algebra
+$\mathcal{C}^{p}(G),$ except for few specific examples of groups (notably
+$G=SL(2,\mathbb{R})$) and for some notable values of $p$ (with restrictions on
+$G$ and/or on $\mathcal{C}^{p}(G)$). Nevertheless the full Harish-Chandra
+Plancherel formula on $G$ is known for all of
+$\mathcal{C}^{2}(G)=:\mathcal{C}(G).$ In order to then understand the structure
+of Harish-Chandra transform more clearly and to compute the image of
+$\mathcal{C}^{p}(G)$ under it (without any restriction) we derive an absolutely
+convergent series expansion (in terms of known functions) for the
+Harish-Chandra transform by an application of the full Plancherel formula on
+$G.$ This leads to a computation of the image of $\mathcal{C}(G)$ under the
+Harish-Chandra transform which may be seen as a concrete realization of
+Arthur's result and be easily extended to all of $\mathcal{C}^{p}(G)$ in much
+the same way as it is known in the work of Trombi and Varadarajan.
+",0,0,1,0,0,0
+20243,Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation,"  We consider the estimation of a signal from the knowledge of its noisy linear
+random Gaussian projections. A few examples where this problem is relevant are
+compressed sensing, sparse superposition codes, and code division multiple
+access. There has been a number of works considering the mutual information for
+this problem using the replica method from statistical physics. Here we put
+these considerations on a firm rigorous basis. First, we show, using a
+Guerra-Toninelli type interpolation, that the replica formula yields an upper
+bound to the exact mutual information. Secondly, for many relevant practical
+cases, we present a converse lower bound via a method that uses spatial
+coupling, state evolution analysis and the I-MMSE theorem. This yields a single
+letter formula for the mutual information and the minimal-mean-square error for
+random Gaussian linear estimation of all discrete bounded signals. In addition,
+we prove that the low complexity approximate message-passing algorithm is
+optimal outside of the so-called hard phase, in the sense that it
+asymptotically reaches the minimal-mean-square error. In this work spatial
+coupling is used primarily as a proof technique. However our results also prove
+two important features of spatially coupled noisy linear random Gaussian
+estimation. First there is no algorithmically hard phase. This means that for
+such systems approximate message-passing always reaches the minimal-mean-square
+error. Secondly, in a proper limit the mutual information associated to such
+systems is the same as the one of uncoupled linear random Gaussian estimation.
+",1,1,1,0,0,0
+20244,A Memristor-Based Optimization Framework for AI Applications,"  Memristors have recently received significant attention as ubiquitous
+device-level components for building a novel generation of computing systems.
+These devices have many promising features, such as non-volatility, low power
+consumption, high density, and excellent scalability. The ability to control
+and modify biasing voltages at the two terminals of memristors make them
+promising candidates to perform matrix-vector multiplications and solve systems
+of linear equations. In this article, we discuss how networks of memristors
+arranged in crossbar arrays can be used for efficiently solving optimization
+and machine learning problems. We introduce a new memristor-based optimization
+framework that combines the computational merit of memristor crossbars with the
+advantages of an operator splitting method, alternating direction method of
+multipliers (ADMM). Here, ADMM helps in splitting a complex optimization
+problem into subproblems that involve the solution of systems of linear
+equations. The capability of this framework is shown by applying it to linear
+programming, quadratic programming, and sparse optimization. In addition to
+ADMM, implementation of a customized power iteration (PI) method for
+eigenvalue/eigenvector computation using memristor crossbars is discussed. The
+memristor-based PI method can further be applied to principal component
+analysis (PCA). The use of memristor crossbars yields a significant speed-up in
+computation, and thus, we believe, has the potential to advance optimization
+and machine learning research in artificial intelligence (AI).
+",1,0,0,1,0,0
+20245,Estimating parameters of a directed weighted graph model with beta-distributed edge-weights,"  We introduce a directed, weighted random graph model, where the edge-weights
+are independent and beta-distributed with parameters depending on their
+endpoints. We will show that the row- and column-sums of the transformed
+edge-weight matrix are sufficient statistics for the parameters, and use the
+theory of exponential families to prove that the ML estimate of the parameters
+exists and is unique. Then an algorithm to find this estimate is introduced
+together with convergence proof that uses properties of the digamma function.
+Simulation results and applications are also presented.
+",0,0,1,1,0,0
+20246,On the Performance of Reduced-Complexity Transmit/Receive Diversity Systems over MIMO-V2V Channel Model,"  In this letter, we investigate the performance of multiple-input
+multiple-output techniques in a vehicle-to-vehicle communication system. We
+consider both transmit antenna selection with maximal-ratio combining and
+transmit antenna selection with selection combining. The channel propagation
+model between two vehicles is represented as n*Rayleigh distribution, which has
+been shown to be a realistic model for vehicle-to-vehicle communication
+scenarios. We derive tight analytical expressions for the outage probability
+and amount of fading of the post-processing signal-to-noise ratio.
+",1,0,0,0,0,0
+20247,Internal migration and education: A cross-national comparison,"  Migration the main process shaping patterns of human settlement within and
+between countries. It is widely acknowledged to be integral to the process of
+human development as it plays a significant role in enhancing educational
+outcomes. At regional and national levels, internal migration underpins the
+efficient functioning of the economy by bringing knowledge and skills to the
+locations where they are needed. It is the multi-dimensional nature of
+migration that underlines its significance in the process of human development.
+Human mobility extends in the spatial domain from local travel to international
+migration, and in the temporal dimension from short-term stays to permanent
+relocations. Classification and measurement of such phenomena is inevitably
+complex, which has severely hindered progress in comparative research, with
+very few large-scale cross-national comparisons of migration. The linkages
+between migration and education have been explored in a separate line of
+inquiry that has predominantly focused on country-specific analyses as to the
+ways in which migration affects educational outcomes and how educational
+attainment affects migration behaviour. A recurrent theme has been the
+educational selectivity of migrants, which in turn leads to an increase of
+human capital in some regions, primarily cities, at the expense of others.
+Questions have long been raised as to the links between education and migration
+in response to educational expansion, but have not yet been fully answered
+because of the absence, until recently, of adequate data for comparative
+analysis of migration. In this paper, we bring these two separate strands of
+research together to systematically explore links between internal migration
+and education across a global sample of 57 countries at various stages of
+development, using data drawn from the IPUMS database.
+",0,0,0,0,0,1
+20248,Free differential Lie Rota-Baxter algebras and Gröbner-Shirshov bases,"  We establish the Gröbner-Shirshov bases theory for differential Lie
+$\Omega$-algebras. As an application, we give a linear basis of a free
+differential Lie Rota-Baxter algebra on a set.
+",0,0,1,0,0,0
+20249,Conversion Rate Optimization through Evolutionary Computation,"  Conversion optimization means designing a web interface so that as many users
+as possible take a desired action on it, such as register or purchase. Such
+design is usually done by hand, testing one change at a time through A/B
+testing, or a limited number of combinations through multivariate testing,
+making it possible to evaluate only a small fraction of designs in a vast
+design space. This paper describes Sentient Ascend, an automatic conversion
+optimization system that uses evolutionary optimization to create effective web
+interface designs. Ascend makes it possible to discover and utilize
+interactions between the design elements that are difficult to identify
+otherwise. Moreover, evaluation of design candidates is done in parallel
+online, i.e. with a large number of real users interacting with the system. A
+case study on an existing media site shows that significant improvements (i.e.
+over 43%) are possible beyond human design. Ascend can therefore be seen as an
+approach to massively multivariate conversion optimization, based on a
+massively parallel interactive evolution.
+",1,0,0,0,0,0
+20250,Emergence of spatial curvature,"  This paper investigates the phenomenon of emergence of spatial curvature.
+This phenomenon is absent in the Standard Cosmological Model, which has a flat
+and fixed spatial curvature (small perturbations are considered in the Standard
+Cosmological Model but their global average vanishes, leading to spatial
+flatness at all times). This paper shows that with the nonlinear growth of
+cosmic structures the global average deviates from zero. The analysis is based
+on the {\em silent universes} (a wide class of inhomogeneous cosmological
+solutions of the Einstein equations). The initial conditions are set in the
+early universe as perturbations around the $\Lambda$CDM model with $\Omega_m =
+0.31$, $\Omega_\Lambda = 0.69$, and $H_0 = 67.8$ km s$^{-1}$ Mpc$^{-1}$. As the
+growth of structures becomes nonlinear, the model deviates from the
+$\Lambda$CDM model, and at the present instant if averaged over a domain ${\cal
+D}$ with volume $V = (2150\,{\rm Mpc})^3$ (at these scales the cosmic variance
+is negligibly small) gives: $\Omega_m^{\cal D} = 0.22$, $\Omega_\Lambda^{\cal
+D} = 0.61$, $\Omega_{\cal R}^{\cal D} = 0.15$ (in the FLRW limit $\Omega_{\cal
+R}^{\cal D} \to \Omega_k$), and $\langle H \rangle_{\cal D} = 72.2$ km s$^{-1}$
+Mpc$^{-1}$. Given the fact that low-redshift observations favor higher values
+of the Hubble constant and lower values of matter density, compared to the CMB
+constraints, the emergence of the spatial curvature in the low-redshift
+universe could be a possible solution to these discrepancies.
+",0,1,0,0,0,0
+20251,Asymptotic generalized bivariate extreme with random index,"  In many biological, agricultural, military activity problems and in some
+quality control problems, it is almost impossible to have a fixed sample size,
+because some observations are always lost for various reasons. Therefore, the
+sample size itself is considered frequently to be a random variable (rv). The
+class of limit distribution functions (df's) of the random bivariate extreme
+generalized order statistics (GOS) from independent and identically distributed
+RV's are fully characterized. When the random sample size is assumed to be
+independent of the basic variables and its df is assumed to converge weakly to
+a non-degenerate limit, the necessary and sufficient conditions for the weak
+convergence of the random bivariate extreme GOS are obtained. Furthermore, when
+the interrelation of the random size and the basic rv's is not restricted,
+sufficient conditions for the convergence and the forms of the limit df's are
+deduced. Illustrative examples are given which lend further support to our
+theoretical results.
+",0,0,1,1,0,0
+20252,On problems in the calculus of variations in increasingly elongated domains,"  We consider minimization problems in the calculus of variations set in a
+sequence of domains the size of which tends to infinity in certain directions
+and such that the data only depend on the coordinates in the directions that
+remain constant. We study the asymptotic behavior of minimizers in various
+situations and show that they converge in an appropriate sense toward
+minimizers of a related energy functional in the constant directions.
+",0,0,1,0,0,0
+20253,Inequalities related to Symmetrized Harmonic Convex Functions,"  In this paper, we extend the Hermite-Hadamard type $\dot{I}$scan inequality
+to the class of symmetrized harmonic convex functions. The corresponding
+version for harmonic h-convex functions is also investigated. Furthermore, we
+establish Hermite-Hadamard type inequalites for the product of a harmonic
+convex function with a symmetrized harmonic convex function.
+",0,0,1,0,0,0
+20254,Parameter estimation for fractional Ornstein-Uhlenbeck processes of general Hurst parameter,"  This paper provides several statistical estimators for the drift and
+volatility parameters of an Ornstein-Uhlenbeck process driven by fractional
+Brownian motion, whose observations can be made either continuously or at
+discrete time instants. First and higher order power variations are used to
+estimate the volatility parameter. The almost sure convergence of the
+estimators and the corresponding central limit theorems are obtained for all
+the Hurst parameter range $H\in (0, 1)$. The least squares estimator is used
+for the drift parameter. A central limit theorem is proved when the Hurst
+parameter $H \in (0, 1/2)$ and a noncentral limit theorem is proved for
+$H\in[3/4, 1)$. Thus, the open problem left in the paper by Hu and Nualart
+(2010) is completely solved, where a central limit theorem for least squares
+estimator is proved for $H\in [1/2, 3/4)$.
+",0,0,1,1,0,0
+20255,"E-polynomials of $PGL(2,\mathbb{C})$-character varieties of surface groups","  In this paper, we compute the E-polynomials of the
+$PGL(2,\mathbb{C})$-character varieties associated to surfaces of genus $g$
+with one puncture, for any holonomy around it, and compare it with its
+Langlands dual case, $SL(2,\mathbb{C})$. The study is based on the
+stratification of the space of representations and on the analysis of the
+behaviour of the E-polynomial under fibrations.
+",0,0,1,0,0,0
+20256,Security Analysis of Cache Replacement Policies,"  Modern computer architectures share physical resources between different
+programs in order to increase area-, energy-, and cost-efficiency.
+Unfortunately, sharing often gives rise to side channels that can be exploited
+for extracting or transmitting sensitive information. We currently lack
+techniques for systematic reasoning about this interplay between security and
+efficiency. In particular, there is no established way for quantifying security
+properties of shared caches.
+In this paper, we propose a novel model that enables us to characterize
+important security properties of caches. Our model encompasses two aspects: (1)
+The amount of information that can be absorbed by a cache, and (2) the amount
+of information that can effectively be extracted from the cache by an
+adversary. We use our model to compute both quantities for common cache
+replacement policies (FIFO, LRU, and PLRU) and to compare their isolation
+properties. We further show how our model for information extraction leads to
+an algorithm that can be used to improve the bounds delivered by the CacheAudit
+static analyzer.
+",1,0,0,0,0,0
+20257,Supercongruences related to ${}_3F_2(1)$ involving harmonic numbers,"  We show various supercongruences for truncated series which involve central
+binomial coefficients and harmonic numbers. The corresponding infinite series
+are also evaluated.
+",0,0,1,0,0,0
+20258,Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition,"  Layer normalization is a recently introduced technique for normalizing the
+activities of neurons in deep neural networks to improve the training speed and
+stability. In this paper, we introduce a new layer normalization technique
+called Dynamic Layer Normalization (DLN) for adaptive neural acoustic modeling
+in speech recognition. By dynamically generating the scaling and shifting
+parameters in layer normalization, DLN adapts neural acoustic models to the
+acoustic variability arising from various factors such as speakers, channel
+noises, and environments. Unlike other adaptive acoustic models, our proposed
+approach does not require additional adaptation data or speaker information
+such as i-vectors. Moreover, the model size is fixed as it dynamically
+generates adaptation parameters. We apply our proposed DLN to deep
+bidirectional LSTM acoustic models and evaluate them on two benchmark datasets
+for large vocabulary ASR experiments: WSJ and TED-LIUM release 2. The
+experimental results show that our DLN improves neural acoustic models in terms
+of transcription accuracy by dynamically adapting to various speakers and
+environments.
+",1,0,0,0,0,0
+20259,First-Order vs. Second-Order Encodings for LTLf-to-Automata Translation,"  Translating formulas of Linear Temporal Logic (LTL) over finite traces, or
+LTLf, to symbolic Deterministic Finite Automata (DFA) plays an important role
+not only in LTLf synthesis, but also in synthesis for Safety LTL formulas. The
+translation is enabled by using MONA, a powerful tool for symbolic, BDD-based,
+DFA construction from logic specifications. Recent works used a first-order
+encoding of LTLf formulas to translate LTLf to First Order Logic (FOL), which
+is then fed to MONA to get the symbolic DFA. This encoding was shown to perform
+well, but other encodings have not been studied. Specifically, the natural
+question of whether second-order encoding, which has significantly simpler
+quantificational structure, can outperform first-order encoding remained open.
+In this paper we address this challenge and study second-order encodings for
+LTLf formulas. We first introduce a specific MSO encoding that captures the
+semantics of LTLf in a natural way and prove its correctness. We then explore
+is a Compact MSO encoding, which benefits from automata-theoretic minimization,
+thus suggesting a possible practical advantage. To that end, we propose a
+formalization of symbolic DFA in second-order logic, thus developing a novel
+connection between BDDs and MSO. We then show by empirical evaluations that the
+first-order encoding does perform better than both second-order encodings. The
+conclusion is that first-order encoding is a better choice than second-order
+encoding in LTLf-to-Automata translation.
+",1,0,0,0,0,0
+20260,Regularity results and parametrices of semi-linear boundary problems of product type,"  This short note describes the benefit one obtains from a specific
+construction of a family of parametrices for a class of elliptic boundary value
+problems perturbed by non-linear terms of product type. The construction is
+based on the Boutet de Monvel calculus of pseudo-differential boundary
+operators for the linear elliptic parts, and on paradifferential operators for
+the product terms.
+",0,0,1,0,0,0
+20261,"$\left( β, \varpi \right)$-stability for cross-validation and the choice of the number of folds","  In this paper, we introduce a new concept of stability for cross-validation,
+called the $\left( \beta, \varpi \right)$-stability, and use it as a new
+perspective to build the general theory for cross-validation. The $\left(
+\beta, \varpi \right)$-stability mathematically connects the generalization
+ability and the stability of the cross-validated model via the Rademacher
+complexity. Our result reveals mathematically the effect of cross-validation
+from two sides: on one hand, cross-validation picks the model with the best
+empirical generalization ability by validating all the alternatives on test
+sets; on the other hand, cross-validation may compromise the stability of the
+model selection by causing subsampling error. Moreover, the difference between
+training and test errors in q\textsuperscript{th} round, sometimes referred to
+as the generalization error, might be autocorrelated on q. Guided by the ideas
+above, the $\left( \beta, \varpi \right)$-stability help us derivd a new class
+of Rademacher bounds, referred to as the one-round/convoluted Rademacher
+bounds, for the stability of cross-validation in both the i.i.d.\ and
+non-i.i.d.\ cases. For both light-tail and heavy-tail losses, the new bounds
+quantify the stability of the one-round/average test error of the
+cross-validated model in terms of its one-round/average training error, the
+sample sizes $n$, number of folds $K$, the tail property of the loss (encoded
+as Orlicz-$\Psi_\nu$ norms) and the Rademacher complexity of the model class
+$\Lambda$. The new class of bounds not only quantitatively reveals the
+stability of the generalization ability of the cross-validated model, it also
+shows empirically the optimal choice for number of folds $K$, at which the
+upper bound of the one-round/average test error is lowest, or, to put it in
+another way, where the test error is most stable.
+",1,0,1,1,0,0
+20262,Directional convexity of harmonic mappings,"  The convolution properties are discussed for the complex-valued harmonic
+functions in the unit disk $\mathbb{D}$ constructed from the harmonic shearing
+of the analytic function $\phi(z):=\int_0^z
+(1/(1-2\xi\textit{e}^{\textit{i}\mu}\cos\nu+\xi^2\textit{e}^{2\textit{i}\mu}))\textit{d}\xi$,
+where $\mu$ and $\nu$ are real numbers. For any real number $\alpha$ and
+harmonic function $f=h+\overline{g}$, define an analytic function
+$f_{\alpha}:=h+\textit{e}^{-2\textit{i}\alpha}g$. Let $\mu_1$ and $\mu_2$
+$(\mu_1+\mu_2=\mu)$ be real numbers, and $f=h+\overline{g}$ and
+$F=H+\overline{G}$ be locally-univalent and sense-preserving harmonic functions
+such that $f_{\mu_1}*F_{\mu_2}=\phi$. It is shown that the convolution $f*F$ is
+univalent and convex in the direction of $-\mu$, provided it is locally
+univalent and sense-preserving. Also, local-univalence of the above convolution
+$f*F$ is shown for some specific analytic dilatations of $f$ and $F$.
+Furthermore, if $g\equiv0$ and both the analytic functions $f_{\mu_1}$ and
+$F_{\mu_2}$ are convex, then the convolution $f*F$ is shown to be convex. These
+results extends the work done by Dorff \textit{et al.} to a larger class of
+functions.
+",0,0,1,0,0,0
+20263,Optimal Non-uniform Deployments in Ultra-Dense Finite-Area Cellular Networks,"  Network densification and heterogenisation through the deployment of small
+cellular access points (picocells and femtocells) are seen as key mechanisms in
+handling the exponential increase in cellular data traffic. Modelling such
+networks by leveraging tools from Stochastic Geometry has proven particularly
+useful in understanding the fundamental limits imposed on network coverage and
+capacity by co-channel interference. Most of these works however assume
+infinite sized and uniformly distributed networks on the Euclidean plane. In
+contrast, we study finite sized non-uniformly distributed networks, and find
+the optimal non-uniform distribution of access points which maximises network
+coverage for a given non-uniform distribution of mobile users, and vice versa.
+",1,0,0,0,0,0
+20264,"Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder","  We investigate the integration of a planning mechanism into an
+encoder-decoder architecture with an explicit alignment for character-level
+machine translation. We develop a model that plans ahead when it computes
+alignments between the source and target sequences, constructing a matrix of
+proposed future alignments and a commitment vector that governs whether to
+follow or recompute the plan. This mechanism is inspired by the strategic
+attentive reader and writer (STRAW) model. Our proposed model is end-to-end
+trainable with fully differentiable operations. We show that it outperforms a
+strong baseline on three character-level decoder neural machine translation on
+WMT'15 corpus. Our analysis demonstrates that our model can compute
+qualitatively intuitive alignments and achieves superior performance with fewer
+parameters.
+",1,0,0,0,0,0
+20265,Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding,"  In this paper, we propose a novel recurrent neural network architecture for
+speech separation. This architecture is constructed by unfolding the iterations
+of a sequential iterative soft-thresholding algorithm (ISTA) that solves the
+optimization problem for sparse nonnegative matrix factorization (NMF) of
+spectrograms. We name this network architecture deep recurrent NMF (DR-NMF).
+The proposed DR-NMF network has three distinct advantages. First, DR-NMF
+provides better interpretability than other deep architectures, since the
+weights correspond to NMF model parameters, even after training. This
+interpretability also provides principled initializations that enable faster
+training and convergence to better solutions compared to conventional random
+initialization. Second, like many deep networks, DR-NMF is an order of
+magnitude faster at test time than NMF, since computation of the network output
+only requires evaluating a few layers at each time step. Third, when a limited
+amount of training data is available, DR-NMF exhibits stronger generalization
+and separation performance compared to sparse NMF and state-of-the-art
+long-short term memory (LSTM) networks. When a large amount of training data is
+available, DR-NMF achieves lower yet competitive separation performance
+compared to LSTM networks.
+",1,0,0,1,0,0
+20266,Birth of isolated nested cylinders and limit cycles in 3D piecewise smooth vector fields with symmetry,"  Our start point is a 3D piecewise smooth vector field defined in two zones
+and presenting a shared fold curve for the two smooth vector fields considered.
+Moreover, these smooth vector fields are symmetric relative to the fold curve,
+giving raise to a continuum of nested topological cylinders such that each
+orthogonal section of these cylinders is filled by centers. First we prove that
+the normal form considered represents a whole class of piecewise smooth vector
+fields. After we perturb the initial model in order to obtain exactly
+$\mathcal{L}$ invariant planes containing centers. A second perturbation of the
+initial model also is considered in order to obtain exactly $k$ isolated
+cylinders filled by periodic orbits. Finally, joining the two previous
+bifurcations we are able to exhibit a model, preserving the symmetry relative
+to the fold curve, and having exactly $k.\mathcal{L}$ limit cycles.
+",0,0,1,0,0,0
+20267,The difficulty of folding self-folding origami,"  Why is it difficult to refold a previously folded sheet of paper? We show
+that even crease patterns with only one designed folding motion inevitably
+contain an exponential number of `distractor' folding branches accessible from
+a bifurcation at the flat state. Consequently, refolding a sheet requires
+finding the ground state in a glassy energy landscape with an exponential
+number of other attractors of higher energy, much like in models of protein
+folding (Levinthal's paradox) and other NP-hard satisfiability (SAT) problems.
+As in these problems, we find that refolding a sheet requires actuation at
+multiple carefully chosen creases. We show that seeding successful folding in
+this way can be understood in terms of sub-patterns that fold when cut out
+(`folding islands'). Besides providing guidelines for the placement of active
+hinges in origami applications, our results point to fundamental limits on the
+programmability of energy landscapes in sheets.
+",0,1,0,0,0,0
+20268,Integrable Trotterization: Local Conservation Laws and Boundary Driving,"  We discuss a general procedure to construct an integrable real-time
+trotterization of interacting lattice models. As an illustrative example we
+consider a spin-$1/2$ chain, with continuous time dynamics described by the
+isotropic ($XXX$) Heisenberg Hamiltonian. For periodic boundary conditions
+local conservation laws are derived from an inhomogeneous transfer matrix and a
+boost operator is constructed. In the continuous time limit these local charges
+reduce to the known integrals of motion of the Heisenberg chain. In a simple
+Kraus representation we also examine the nonequilibrium setting, where our
+integrable cellular automaton is driven by stochastic processes at the
+boundaries. We show explicitly, how an exact nonequilibrium steady state
+density matrix can be written in terms of a staggered matrix product ansatz.
+This simple trotterization scheme, in particular in the open system framework,
+could prove to be a useful tool for experimental simulations of the lattice
+models in terms of trapped ion and atom optics setups.
+",0,1,0,0,0,0
+20269,The Multivariate Hawkes Process in High Dimensions: Beyond Mutual Excitation,"  The Hawkes process is a class of point processes whose future depends on its
+own history. Previous theoretical work on the Hawkes process is limited to the
+case of a mutually-exciting process, in which a past event can only increase
+the occurrence of future events. However, in neuronal networks and other
+real-world applications, inhibitory relationships may be present. In this
+paper, we develop a new approach for establishing the properties of the Hawkes
+process without the restriction to mutual excitation. To this end, we employ a
+thinning process representation and a coupling construction to bound the
+dependence coefficient of the Hawkes process. Using recent developments on
+weakly dependent sequences, we establish a concentration inequality for
+second-order statistics of the Hawkes process. We apply this concentration
+inequality in order to establish theoretical results for penalized regression
+and clustering analysis in the high-dimensional regime. Our theoretical results
+are corroborated by simulation studies and an application to a neuronal spike
+train data set.
+",0,0,0,1,0,0
+20270,Aerial-Ground collaborative sensing: Third-Person view for teleoperation,"  Rapid deployment and operation are key requirements in time critical
+application, such as Search and Rescue (SaR). Efficiently teleoperated ground
+robots can support first-responders in such situations. However, first-person
+view teleoperation is sub-optimal in difficult terrains, while a third-person
+perspective can drastically increase teleoperation performance. Here, we
+propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide
+third-person perspective to ground robots. While our approach is based on local
+visual servoing, it further leverages the global localization of several ground
+robots to seamlessly transfer between these ground robots in GPS-denied
+environments. Therewith one MAV can support multiple ground robots on a demand
+basis. Furthermore, our system enables different visual detection regimes, and
+enhanced operability, and return-home functionality. We evaluate our system in
+real-world SaR scenarios.
+",1,0,0,0,0,0
+20271,A universal thin film model for Ginzburg-Landau energy with dipolar interaction,"  We present an analytical treatment of a three-dimensional variational model
+of a system that exhibits a second-order phase transition in the presence of
+dipolar interactions. Within the framework of Ginzburg-Landau theory, we
+concentrate on the case in which the domain occupied by the sample has the
+shape of a flat thin film and obtain a reduced two-dimensional, non-local
+variational model that describes the energetics of the system in terms of the
+order parameter averages across the film thickness. Namely, we show that the
+reduced two-dimensional model is in a certain sense asymptotically equivalent
+to the original three-dimensional model for small film thicknesses. Using this
+asymptotic equivalence, we analyze two different thin film limits for the full
+three-dimensional model via the methods of $\Gamma$-convergence applied to the
+reduced two-dimensional model. In the first regime, in which the film thickness
+vanishes while all other parameters remain fixed, we recover the local
+two-dimensional Ginzburg-Landau model. On the other hand, when the film
+thickness vanishes while the sample's lateral dimensions diverge at the right
+rate, we show that the system exhibits a transition from homogeneous to
+spatially modulated global energy minimizers. We identify a sharp threshold for
+this transition.
+",0,1,1,0,0,0
+20272,LOCATA challenge: speaker localization with a planar array,"  This document describes our submission to the 2018 LOCalization And TrAcking
+(LOCATA) challenge (Tasks 1, 3, 5). We estimate the 3D position of a speaker
+using the Global Coherence Field (GCF) computed from multiple microphone pairs
+of a DICIT planar array. One of the main challenges when using such an array
+with omnidirectional microphones is the front-back ambiguity, which is
+particularly evident in Task 5. We address this challenge by post-processing
+the peaks of the GCF and exploiting the attenuation introduced by the frame of
+the array. Moreover, the intermittent nature of speech and the changing
+orientation of the speaker make localization difficult. For Tasks 3 and 5, we
+also employ a Particle Filter (PF) that favors the spatio-temporal continuity
+of the localization results.
+",1,0,0,0,0,0
+20273,Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers,"  The number of component classifiers chosen for an ensemble greatly impacts
+the prediction ability. In this paper, we use a geometric framework for a
+priori determining the ensemble size, which is applicable to most of existing
+batch and online ensemble classifiers. There are only a limited number of
+studies on the ensemble size examining Majority Voting (MV) and Weighted
+Majority Voting (WMV). Almost all of them are designed for batch-mode, hardly
+addressing online environments. Big data dimensions and resource limitations,
+in terms of time and memory, make determination of ensemble size crucial,
+especially for online environments. For the MV aggregation rule, our framework
+proves that the more strong components we add to the ensemble, the more
+accurate predictions we can achieve. For the WMV aggregation rule, our
+framework proves the existence of an ideal number of components, which is equal
+to the number of class labels, with the premise that components are completely
+independent of each other and strong enough. While giving the exact definition
+for a strong and independent classifier in the context of an ensemble is a
+challenging task, our proposed geometric framework provides a theoretical
+explanation of diversity and its impact on the accuracy of predictions. We
+conduct a series of experimental evaluations to show the practical value of our
+theorems and existing challenges.
+",1,0,0,1,0,0
+20274,Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers,"  Low-rank modeling has many important applications in computer vision and
+machine learning. While the matrix rank is often approximated by the convex
+nuclear norm, the use of nonconvex low-rank regularizers has demonstrated
+better empirical performance. However, the resulting optimization problem is
+much more challenging. Recent state-of-the-art requires an expensive full SVD
+in each iteration. In this paper, we show that for many commonly-used nonconvex
+low-rank regularizers, a cutoff can be derived to automatically threshold the
+singular values obtained from the proximal operator. This allows such operator
+being efficiently approximated by power method. Based on it, we develop a
+proximal gradient algorithm (and its accelerated variant) with inexact proximal
+splitting and prove that a convergence rate of O(1/T) where T is the number of
+iterations is guaranteed. Furthermore, we show the proposed algorithm can be
+well parallelized, which achieves nearly linear speedup w.r.t the number of
+threads. Extensive experiments are performed on matrix completion and robust
+principal component analysis, which shows a significant speedup over the
+state-of-the-art. Moreover, the matrix solution obtained is more accurate and
+has a lower rank than that of the nuclear norm regularizer.
+",1,0,0,1,0,0
+20275,Computational Eco-Systems for Handwritten Digits Recognition,"  Inspired by the importance of diversity in biological system, we built an
+heterogeneous system that could achieve this goal. Our architecture could be
+summarized in two basic steps. First, we generate a diverse set of
+classification hypothesis using both Convolutional Neural Networks, currently
+the state-of-the-art technique for this task, among with other traditional and
+innovative machine learning techniques. Then, we optimally combine them through
+Meta-Nets, a family of recently developed and performing ensemble methods.
+",0,0,0,1,0,0
+20276,Compressing networks with super nodes,"  Community detection is a commonly used technique for identifying groups in a
+network based on similarities in connectivity patterns. To facilitate community
+detection in large networks, we recast the network to be partitioned into a
+smaller network of 'super nodes', each super node comprising one or more nodes
+in the original network. To define the seeds of our super nodes, we apply the
+'CoreHD' ranking from dismantling and decycling. We test our approach through
+the analysis of two common methods for community detection: modularity
+maximization with the Louvain algorithm and maximum likelihood optimization for
+fitting a stochastic block model. Our results highlight that applying community
+detection to the compressed network of super nodes is significantly faster
+while successfully producing partitions that are more aligned with the local
+network connectivity, more stable across multiple (stochastic) runs within and
+between community detection algorithms, and overlap well with the results
+obtained using the full network.
+",1,1,0,0,0,0
+20277,Mode specific electronic friction in dissociative chemisorption on metal surfaces: H$_2$ on Ag(111),"  Electronic friction and the ensuing nonadiabatic energy loss play an
+important role in chemical reaction dynamics at metal surfaces. Using molecular
+dynamics with electronic friction evaluated on-the-fly from Density Functional
+Theory, we find strong mode dependence and a dominance of nonadiabatic energy
+loss along the bond stretch coordinate for scattering and dissociative
+chemisorption of H$_2$ on the Ag(111) surface. Exemplary trajectories with
+varying initial conditions indicate that this mode-specificity translates into
+modulated energy loss during a dissociative chemisorption event. Despite minor
+nonadiabatic energy loss of about 5\%, the directionality of friction forces
+induces dynamical steering that affects individual reaction outcomes,
+specifically for low-incidence energies and vibrationally excited molecules.
+Mode-specific friction induces enhanced loss of rovibrational rather than
+translational energy and will be most visible in its effect on final energy
+distributions in molecular scattering experiments.
+",0,1,0,0,0,0
+20278,The quantum auxiliary linear problem & quantum Darboux-Backlund transformations,"  We explore the notion of the quantum auxiliary linear problem and the
+associated problem of quantum Backlund transformations (BT). In this context we
+systematically construct the analogue of the classical formula that provides
+the whole hierarchy of the time components of Lax pairs at the quantum level
+for both closed and open integrable lattice models. The generic time evolution
+operator formula is particularly interesting and novel at the quantum level
+when dealing with systems with open boundary conditions. In the same frame we
+show that the reflection K-matrix can also be viewed as a particular type of
+BT, fixed at the boundaries of the system. The q-oscillator (q-boson) model, a
+variant of the Ablowitz-Ladik model, is then employed as a paradigm to
+illustrate the method. Particular emphasis is given to the time part of the
+quantum BT as possible connections and applications to the problem of quantum
+quenches as well as the time evolution of local quantum impurities are evident.
+A discussion on the use of Bethe states as well as coherent states for the
+study of the time evolution is also presented.
+",0,1,0,0,0,0
+20279,Simplified Minimal Gated Unit Variations for Recurrent Neural Networks,"  Recurrent neural networks with various types of hidden units have been used
+to solve a diverse range of problems involving sequence data. Two of the most
+recent proposals, gated recurrent units (GRU) and minimal gated units (MGU),
+have shown comparable promising results on example public datasets. In this
+paper, we introduce three model variants of the minimal gated unit (MGU) which
+further simplify that design by reducing the number of parameters in the
+forget-gate dynamic equation. These three model variants, referred to simply as
+MGU1, MGU2, and MGU3, were tested on sequences generated from the MNIST dataset
+and from the Reuters Newswire Topics (RNT) dataset. The new models have shown
+similar accuracy to the MGU model while using fewer parameters and thus
+lowering training expense. One model variant, namely MGU2, performed better
+than MGU on the datasets considered, and thus may be used as an alternate to
+MGU or GRU in recurrent neural networks.
+",1,0,0,1,0,0
+20280,Parallel G-duplex and C-duplex DNA with Uninterrupted Spines of AgI-Mediated Base Pairs,"  Hydrogen bonding between nucleobases produces diverse DNA structural motifs,
+including canonical duplexes, guanine (G) quadruplexes and cytosine (C)
+i-motifs. Incorporating metal-mediated base pairs into nucleic acid structures
+can introduce new functionalities and enhanced stabilities. Here we
+demonstrate, using mass spectrometry (MS), ion mobility spectrometry (IMS) and
+fluorescence resonance energy transfer (FRET), that parallel-stranded
+structures consisting of up to 20 G-Ag(I)-G contiguous base pairs are formed
+when natural DNA sequences are mixed with silver cations in aqueous solution.
+FRET indicates that duplexes formed by poly(cytosine) strands with 20
+contiguous C-Ag(I)-C base pairs are also parallel. Silver-mediated G-duplexes
+form preferentially over G-quadruplexes, and the ability of Ag+ to convert
+G-quadruplexes into silver-paired duplexes may provide a new route to
+manipulating these biologically relevant structures. IMS indicates that
+G-duplexes are linear and more rigid than B-DNA. DFT calculations were used to
+propose structures compatible with the IMS experiments. Such inexpensive,
+defect-free and soluble DNA-based nanowires open new directions in the design
+of novel metal-mediated DNA nanotechnology.
+",0,0,0,0,1,0
+20281,Constraints on Vacuum Energy from Structure Formation and Nucleosynthesis,"  This paper derives an upper limit on the density $\rho_{\scriptstyle\Lambda}$
+of dark energy based on the requirement that cosmological structure forms
+before being frozen out by the eventual acceleration of the universe. By
+allowing for variations in both the cosmological parameters and the strength of
+gravity, the resulting constraint is a generalization of previous limits. The
+specific parameters under consideration include the amplitude $Q$ of the
+primordial density fluctuations, the Planck mass $M_{\rm pl}$, the
+baryon-to-photon ratio $\eta$, and the density ratio $\Omega_M/\Omega_b$. In
+addition to structure formation, we use considerations from stellar structure
+and Big Bang Nucleosynthesis (BBN) to constrain these quantities. The resulting
+upper limit on the dimensionless density of dark energy becomes
+$\rho_{\scriptstyle\Lambda}/M_{\rm pl}^4<10^{-90}$, which is $\sim30$ orders of
+magnitude larger than the value in our universe
+$\rho_{\scriptstyle\Lambda}/M_{\rm pl}^4\sim10^{-120}$. This new limit is much
+less restrictive than previous constraints because additional parameters are
+allowed to vary. With these generalizations, a much wider range of universes
+can develop cosmic structure and support observers. To constrain the
+constituent parameters, new BBN calculations are carried out in the regime
+where $\eta$ and $G=M_{\rm pl}^{-2}$ are much larger than in our universe. If
+the BBN epoch were to process all of the protons into heavier elements, no
+hydrogen would be left behind to make water, and the universe would not be
+viable. However, our results show that some hydrogen is always left over, even
+under conditions of extremely large $\eta$ and $G$, so that a wide range of
+alternate universes are potentially habitable.
+",0,1,0,0,0,0
+20282,Dynamic Mobile Edge Caching with Location Differentiation,"  Mobile edge caching enables content delivery directly within the radio access
+network, which effectively alleviates the backhaul burden and reduces
+round-trip latency. To fully exploit the edge resources, the most popular
+contents should be identified and cached. Observing that content popularity
+varies greatly at different locations, to maximize local hit rate, this paper
+proposes an online learning algorithm that dynamically predicts content hit
+rate, and makes location-differentiated caching decisions. Specifically, a
+linear model is used to estimate the future hit rate. Considering the
+variations in user demand, a perturbation is added to the estimation to account
+for uncertainty. The proposed learning algorithm requires no training phase,
+and hence is adaptive to the time-varying content popularity profile.
+Theoretical analysis indicates that the proposed algorithm asymptotically
+approaches the optimal policy in the long term. Extensive simulations based on
+real world traces show that, the proposed algorithm achieves higher hit rate
+and better adaptiveness to content popularity fluctuation, compared with other
+schemes.
+",1,0,0,0,0,0
+20283,Online Learning for Distribution-Free Prediction,"  We develop an online learning method for prediction, which is important in
+problems with large and/or streaming data sets. We formulate the learning
+approach using a covariance-fitting methodology, and show that the resulting
+predictor has desirable computational and distribution-free properties: It is
+implemented online with a runtime that scales linearly in the number of
+samples; has a constant memory requirement; avoids local minima problems; and
+prunes away redundant feature dimensions without relying on restrictive
+assumptions on the data distribution. In conjunction with the split conformal
+approach, it also produces distribution-free prediction confidence intervals in
+a computationally efficient manner. The method is demonstrated on both real and
+synthetic datasets.
+",1,0,0,1,0,0
+20284,"Thermal transitions, pseudogap behavior and BCS-BEC crossover in Fermi-Fermi mixtures","  We study the mass imbalanced Fermi-Fermi mixture within the framework of a
+two-dimensional lattice fermion model. Based on the thermodynamic and species
+dependent quasiparticle behavior we map out the finite temperature phase
+diagram of this system and show that unlike the balanced Fermi superfluid there
+are now two different pseudogap regimes as PG-I and PG-II. While within the
+PG-I regime both the fermionic species are pseudogapped, PG-II corresponds to
+the regime where pseudogap feature survives only in the light species. We
+believe that the single particle spectral features that we discuss in this
+paper are observable through the species resolved radio frequency spectroscopy
+and momentum resolved photo emission spectroscopy measurements on systems such
+as, 6$_{Li}$-40$_{K}$ mixture. We further investigate the interplay between the
+population and mass imbalances and report that at a fixed population imbalance
+the BCS-BEC crossover in a Fermi-Fermi mixture would require a critical
+interaction (U$_{c}$), for the realization of the uniform superfluid state. The
+effect of imbalance in mass on the exotic Fulde-Ferrell-Larkin-Ovchinnikov
+(FFLO) superfluid phase has been probed in detail in terms of the thermodynamic
+and quasiparticle behavior of this phase. It has been observed that in spite of
+the s-wave symmetry of the pairing field a nodal superfluid gap is realized in
+the LO regime. Our results on the various thermal scales and regimes are
+expected to serve as benchmarks for the experimental observations on
+6$_{Li}$-40$_{K}$ mixture.
+",0,1,0,0,0,0
+20285,Spectral Approximation for Ergodic CMV Operators with an Application to Quantum Walks,"  We establish concrete criteria for fully supported absolutely continuous
+spectrum for ergodic CMV matrices and purely absolutely continuous spectrum for
+limit-periodic CMV matrices. We proceed by proving several variational
+estimates on the measure of the spectrum and the vanishing set of the Lyapunov
+exponent for CMV matrices, which represent CMV analogues of results obtained
+for Schrödinger operators due to Y.\ Last in the early 1990s. Having done so,
+we combine those estimates with results from inverse spectral theory to obtain
+purely absolutely continuous spectrum.
+",0,0,1,0,0,0
+20286,Quantitative Photoacoustic Imaging in the Acoustic Regime using SPIM,"  While in standard photoacoustic imaging the propagation of sound waves is
+modeled by the standard wave equation, our approach is based on a generalized
+wave equation with variable sound speed and material density, respectively. In
+this paper we present an approach for photoacoustic imaging, which in addition
+to recovering of the absorption density parameter, the imaging parameter of
+standard photoacoustics, also allows to reconstruct the spatially varying sound
+speed and density, respectively, of the medium. We provide analytical
+reconstruction formulas for all three parameters based in a linearized model
+based on single plane illumination microscopy (SPIM) techniques.
+",0,0,1,0,0,0
+20287,Integrated Deep and Shallow Networks for Salient Object Detection,"  Deep convolutional neural network (CNN) based salient object detection
+methods have achieved state-of-the-art performance and outperform those
+unsupervised methods with a wide margin. In this paper, we propose to integrate
+deep and unsupervised saliency for salient object detection under a unified
+framework. Specifically, our method takes results of unsupervised saliency
+(Robust Background Detection, RBD) and normalized color images as inputs, and
+directly learns an end-to-end mapping between inputs and the corresponding
+saliency maps. The color images are fed into a Fully Convolutional Neural
+Networks (FCNN) adapted from semantic segmentation to exploit high-level
+semantic cues for salient object detection. Then the results from deep FCNN and
+RBD are concatenated to feed into a shallow network to map the concatenated
+feature maps to saliency maps. Finally, to obtain a spatially consistent
+saliency map with sharp object boundaries, we fuse superpixel level saliency
+map at multi-scale. Extensive experimental results on 8 benchmark datasets
+demonstrate that the proposed method outperforms the state-of-the-art
+approaches with a margin.
+",1,0,0,0,0,0
+20288,Feedback Capacity over Networks,"  In this paper, we investigate the fundamental limitations of feedback
+mechanism in dealing with uncertainties for network systems. The study of
+maximum capability of feedback control was pioneered in Xie and Guo (2000) for
+scalar systems with nonparametric nonlinear uncertainty. In a network setting,
+nodes with unknown and nonlinear dynamics are interconnected through a directed
+interaction graph. Nodes can design feedback controls based on all available
+information, where the objective is to stabilize the network state. Using
+information structure and decision pattern as criteria, we specify three
+categories of network feedback laws, namely the
+global-knowledge/global-decision, network-flow/local-decision, and
+local-flow/local-decision feedback. We establish a series of network capacity
+characterizations for these three fundamental types of network control laws.
+First of all, we prove that for global-knowledge/global-decision and
+network-flow/local-decision control where nodes know the information flow
+across the entire network, there exists a critical number
+$\big(3/2+\sqrt{2}\big)/\|A_{\mathrm{G}}\|_\infty$, where $3/2+\sqrt{2}$ is as
+known as the Xie-Guo constant and $A_{\mathrm{G}}$ is the network adjacency
+matrix, defining exactly how much uncertainty in the node dynamics can be
+overcome by feedback. Interestingly enough, the same feedback capacity can be
+achieved under max-consensus enhanced local flows where nodes only observe
+information flows from neighbors as well as extreme (max and min) states in the
+network. Next, for local-flow/local-decision control, we prove that there
+exists a structure-determined value being a lower bound of the network feedback
+capacity. These results reveal the important connection between network
+structure and fundamental capabilities of in-network feedback control.
+",1,0,0,0,0,0
+20289,Flexural phonons in supported graphene: from pinning to localization,"  We identify graphene layer on a disordered substrate as a possible system
+where Anderson localization of phonons can be observed. Generally, observation
+of localization for scattering waves is not simple, because the Rayleigh
+scattering is inversely proportional to a high power of wavelength. The
+situation is radically different for the out of plane vibrations, so-called
+flexural phonons, scattered by pinning centers induced by a substrate. In this
+case, the scattering time for vanishing wave vector tends to a finite limit.
+One may, therefore, expect that physics of the flexural phonons exhibits
+features characteristic for electron localization in two dimensions, albeit
+without complications caused by the electron-electron interactions. We confirm
+this idea by calculating statistical properties of the Anderson localization of
+flexural phonons for a model of elastic sheet in the presence of the pinning
+centers. Finally, we discuss possible manifestations of the flexural phonons,
+including the localized ones, in the electronic thermal conductance.
+",0,1,0,0,0,0
+20290,"Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack","  Many approaches for testing configurable software systems start from the same
+assumption: it is impossible to test all configurations. This motivated the
+definition of variability-aware abstractions and sampling techniques to cope
+with large configuration spaces. Yet, there is no theoretical barrier that
+prevents the exhaustive testing of all configurations by simply enumerating
+them, if the effort required to do so remains acceptable. Not only this: we
+believe there is lots to be learned by systematically and exhaustively testing
+a configurable system. In this case study, we report on the first ever
+endeavour to test all possible configurations of an industry-strength, open
+source configurable software system, JHipster, a popular code generator for web
+applications. We built a testing scaffold for the 26,000+ configurations of
+JHipster using a cluster of 80 machines during 4 nights for a total of 4,376
+hours (182 days) CPU time. We find that 35.70% configurations fail and we
+identify the feature interactions that cause the errors. We show that sampling
+strategies (like dissimilarity and 2-wise): (1) are more effective to find
+faults than the 12 default configurations used in the JHipster continuous
+integration; (2) can be too costly and exceed the available testing budget. We
+cross this quantitative analysis with the qualitative assessment of JHipster's
+lead developers.
+",1,0,0,0,0,0
+20291,Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells,"  We propose a reinforcement learning (RL) based closed loop power control
+algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an
+indoor environment served by small cells. The main contributions of our paper
+are to 1) use RL to solve performance tuning problems in an indoor cellular
+network for voice bearers and 2) show that our derived lower bound loss in
+effective signal to interference plus noise ratio due to neighboring cell
+failure is sufficient for VoLTE power control purposes in practical cellular
+networks. In our simulation, the proposed RL-based power control algorithm
+significantly improves both voice retainability and mean opinion score compared
+to current industry standards. The improvement is due to maintaining an
+effective downlink signal to interference plus noise ratio against adverse
+network operational issues and faults.
+",1,0,0,1,0,0
+20292,Ancient shrinking spherical interfaces in the Allen-Cahn flow,"  We consider the parabolic Allen-Cahn equation in $\mathbb{R}^n$, $n\ge 2$,
+$$u_t= \Delta u + (1-u^2)u \quad \hbox{ in } \mathbb{R}^n \times (-\infty,
+0].$$ We construct an ancient radially symmetric solution $u(x,t)$ with any
+given number $k$ of transition layers between $-1$ and $+1$. At main order they
+consist of $k$ time-traveling copies of $w$ with spherical interfaces distant
+$O(\log |t| )$ one to each other as $t\to -\infty$. These interfaces are
+resemble at main order copies of the {\em shrinking sphere} ancient solution to
+mean the flow by mean curvature of surfaces: $|x| = \sqrt{- 2(n-1)t}$. More
+precisely, if $w(s)$ denotes the heteroclinic 1-dimensional solution of $w'' +
+(1-w^2)w=0$ $w(\pm \infty)= \pm 1$ given by $w(s) = \tanh \left(\frac
+s{\sqrt{2}} \right) $ we have $$ u(x,t) \approx \sum_{j=1}^k
+(-1)^{j-1}w(|x|-\rho_j(t)) - \frac 12 (1+ (-1)^{k}) \quad \hbox{ as } t\to
+-\infty $$ where
+$$\rho_j(t)=\sqrt{-2(n-1)t}+\frac{1}{\sqrt{2}}\left(j-\frac{k+1}{2}\right)\log\left(\frac
+{|t|}{\log |t| }\right)+ O(1),\quad j=1,\ldots ,k.$$
+",0,0,1,0,0,0
+20293,Measuring the Eccentricity of Items,"  The long-tail phenomenon tells us that there are many items in the tail.
+However, not all tail items are the same. Each item acquires different kinds of
+users. Some items are loved by the general public, while some items are
+consumed by eccentric fans. In this paper, we propose a novel metric, item
+eccentricity, to incorporate this difference between consumers of the items.
+Eccentric items are defined as items that are consumed by eccentric users. We
+used this metric to analyze two real-world datasets of music and movies and
+observed the characteristics of items in terms of eccentricity. The results
+showed that our defined eccentricity of an item does not change much over time,
+and classified eccentric and noneccentric items present significantly distinct
+characteristics. The proposed metric effectively separates the eccentric and
+noneccentric items mixed in the tail, which could not be done with the previous
+measures, which only consider the popularity of items.
+",1,0,0,0,0,0
+20294,Dataset: Rare Event Classification in Multivariate Time Series,"  A real-world dataset is provided from a pulp-and-paper manufacturing
+industry. The dataset comes from a multivariate time series process. The data
+contains a rare event of paper break that commonly occurs in the industry. The
+data contains sensor readings at regular time-intervals (x's) and the event
+label (y). The primary purpose of the data is thought to be building a
+classification model for early prediction of the rare event. However, it can
+also be used for multivariate time series data exploration and building other
+supervised and unsupervised models.
+",0,0,0,1,0,0
+20295,Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues,"  An experimental setup for consecutive measurement of ion and x-ray absorption
+in tissue or other materials is introduced. With this setup using a 3D-printed
+sample container, the reference stopping-power ratio (SPR) of materials can be
+measured with an uncertainty of below 0.1%. A total of 65 porcine and bovine
+tissue samples were prepared for measurement, comprising five samples each of
+13 tissue types representing about 80% of the total body mass (three different
+muscle and fatty tissues, liver, kidney, brain, heart, blood, lung and bone).
+Using a standard stoichiometric calibration for single-energy CT (SECT) as well
+as a state-of-the-art dual-energy CT (DECT) approach, SPR was predicted for all
+tissues and then compared to the measured reference. With the SECT approach,
+the SPRs of all tissues were predicted with a mean error of (-0.84 $\pm$ 0.12)%
+and a mean absolute error of (1.27 $\pm$ 0.12)%. In contrast, the DECT-based
+SPR predictions were overall consistent with the measured reference with a mean
+error of (-0.02 $\pm$ 0.15)% and a mean absolute error of (0.10 $\pm$ 0.15)%.
+Thus, in this study, the potential of DECT to decrease range uncertainty could
+be confirmed in biological tissue.
+",0,1,0,0,0,0
+20296,How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks,"  In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi
+Oreskes and Erik Conway describe the ""tobacco strategy"", which was used by the
+tobacco industry to influence policy makers regarding the health risks of
+tobacco products. The strategy involved two parts, consisting of (1) promoting
+and sharing independent research supporting the industry's preferred position
+and (2) funding additional research, but selectively publishing the results. We
+introduce a model of the Tobacco Strategy, and use it to argue that both prongs
+of the strategy can be extremely effective--even when policy makers rationally
+update on all evidence available to them. As we elaborate, this model helps
+illustrate the conditions under which the Tobacco Strategy is particularly
+successful. In addition, we show how journalists engaged in ""fair"" reporting
+can inadvertently mimic the effects of industry on public belief.
+",1,0,0,0,0,0
+20297,A complete and partial integrability technique of the Lorenz system,"  In this paper we deal with the well-known nonlinear Lorenz system that
+describes the deterministic chaos phenomenon. We consider an interesting
+problem with time-varying phenomena in quantum optics. Then we establish from
+the motion equations the passage to the Lorenz system. Furthermore, we show
+that the reduction to the third order non linear equation can be performed.
+Therefore, the obtained differential equation can be analytically solved in
+some special cases and transformed to Abel, Dufing, Painlevé and
+generalized Emden-Fowler equations. So, a motivating technique that permitted a
+complete and partial integrability of the Lorenz system is presented.
+",0,1,0,0,0,0
+20298,Comparision of the definitions of generalized solution of the Cauchy problem for quasi-linear equation,"  In preprint we consider and compare different definitions of generalized
+solution of the Cauchy problem for 1d-scalar quasilinear equation (conservation
+law). We start from the classical approaches goes back to I.M. Gelfand, O.A.
+Oleinik, S.N. Kruzhkov and move to the modern finite-difference approximations
+approaches belongs to A.A. Shananin and G.M. Henkin. We discuss the conditions
+that provide definitions to be equivalent.
+",0,0,1,0,0,0
+20299,On The Complexity of Sparse Label Propagation,"  This paper investigates the computational complexity of sparse label
+propagation which has been proposed recently for processing network structured
+data. Sparse label propagation amounts to a convex optimization problem and
+might be considered as an extension of basis pursuit from sparse vectors to
+network structured datasets. Using a standard first-order oracle model, we
+characterize the number of iterations for sparse label propagation to achieve a
+prescribed accuracy. In particular, we derive an upper bound on the number of
+iterations required to achieve a certain accuracy and show that this upper
+bound is sharp for datasets having a chain structure (e.g., time series).
+",0,0,0,1,0,0
+20300,Detecting Qualia in Natural and Artificial Agents,"  The Hard Problem of consciousness has been dismissed as an illusion. By
+showing that computers are capable of experiencing, we show that they are at
+least rudimentarily conscious with potential to eventually reach
+superconsciousness. The main contribution of the paper is a test for confirming
+certain subjective experiences in a tested agent. We follow with analysis of
+benefits and problems with conscious machines and implications of such
+capability on future of computing, machine rights and artificial intelligence
+safety.
+",1,0,0,0,0,0
+20301,The interplay between Steinberg algebras and partial skew rings,"  We study the interplay between Steinberg algebras and partial skew rings: For
+a partial action of a group in a Hausdorff, locally compact, totally
+disconnected topological space, we realize the associated partial skew group
+ring as a Steinberg algebra (over the transformation groupoid attached to the
+partial action). We then apply this realization to characterize diagonal
+preserving isomorphisms of partial skew group rings, over commutative algebras,
+in terms of continuous orbit equivalence of the associated partial actions.
+Finally, we show that any Steinberg algebra, associated to a Hausdorff ample
+groupoid, can be seen as a partial skew inverse semigroup ring.
+",0,0,1,0,0,0
+20302,Type-II Dirac Photons,"  The Dirac equation for relativistic electron waves is the parent model for
+Weyl and Majorana fermions as well as topological insulators. Simulation of
+Dirac physics in three-dimensional photonic crystals, though fundamentally
+important for topological phenomena at optical frequencies, encounters the
+challenge of synthesis of both Kramers double degeneracy and parity inversion.
+Here we show how type-II Dirac points---exotic Dirac relativistic waves yet to
+be discovered---are robustly realized through the nonsymmorphic screw symmetry.
+The emergent type-II Dirac points carry nontrivial topology and are the mother
+states of type-II Weyl points. The proposed all-dielectric architecture enables
+robust cavity states at photonic-crystal---air interfaces and anomalous
+refraction, with very low energy dissipation.
+",0,1,0,0,0,0
+20303,Improving Network Robustness against Adversarial Attacks with Compact Convolution,"  Though Convolutional Neural Networks (CNNs) have surpassed human-level
+performance on tasks such as object classification and face verification, they
+can easily be fooled by adversarial attacks. These attacks add a small
+perturbation to the input image that causes the network to misclassify the
+sample. In this paper, we focus on neutralizing adversarial attacks by compact
+feature learning. In particular, we show that learning features in a closed and
+bounded space improves the robustness of the network. We explore the effect of
+L2-Softmax Loss, that enforces compactness in the learned features, thus
+resulting in enhanced robustness to adversarial perturbations. Additionally, we
+propose compact convolution, a novel method of convolution that when
+incorporated in conventional CNNs improves their robustness. Compact
+convolution ensures feature compactness at every layer such that they are
+bounded and close to each other. Extensive experiments show that Compact
+Convolutional Networks (CCNs) neutralize multiple types of attacks, and perform
+better than existing methods in defending adversarial attacks, without
+incurring any additional training overhead compared to CNNs.
+",1,0,0,1,0,0
+20304,From sudden quench to adiabatic dynamics in the attractive Hubbard model,"  We study the crossover between the sudden quench limit and the adiabatic
+dynamics of superconducting states in the attractive Hubbard model. We focus on
+the dynamics induced by the change of the attractive interaction during a
+finite ramp time which is varied in order to track the evolution of the
+dynamical phase diagram from the sudden quench to the equilibrium limit. Two
+different dynamical regimes are realized for quenches towards weak and strong
+coupling interactions. At weak coupling the dynamics depends only on the energy
+injected into the system, whereas a dynamics retaining memory of the initial
+state takes place at strong coupling. We show that this is related to a sharp
+transition between a weak and a strong coupling quench dynamical regime, which
+defines the boundaries beyond which a dynamics independent from the initial
+state is recovered. Comparing the dynamics in the superconducting and
+non-superconducting phases we argue that this is due to the lack of an
+adiabatic connection to the equilibrium ground state for non-equilibrium
+superconducting states in the strong coupling quench regime.
+",0,1,0,0,0,0
+20305,Strongly exchange-coupled and surface-state-modulated magnetization dynamics in Bi2Se3/YIG heterostructures,"  We report strong interfacial exchange coupling in Bi2Se3/yttrium iron garnet
+(YIG) bilayers manifested as large in-plane interfacial magnetic anisotropy
+(IMA) and enhancement of damping probed by ferromagnetic resonance (FMR). The
+IMA and spin mixing conductance reached a maximum when Bi2Se3 was around 6
+quintuple-layer (QL) thick. The unconventional Bi2Se3 thickness dependence of
+the IMA and spin mixing conductance are correlated with the evolution of
+surface band structure of Bi2Se3, indicating that topological surface states
+play an important role in the magnetization dynamics of YIG.
+Temperature-dependent FMR of Bi2Se3/YIG revealed signatures of magnetic
+proximity effect of $T_c$ as high as 180 K, and an effective field parallel to
+the YIG magnetization direction at low temperature. Our study sheds light on
+the effects of topological insulators on magnetization dynamics, essential for
+development of TI-based spintronic devices.
+",0,1,0,0,0,0
+20306,Survivable Probability of SDN-enabled Cloud Networking with Random Physical Link Failure,"  Software-driven cloud networking is a new paradigm in orchestrating physical
+resources (CPU, network bandwidth, energy, storage) allocated to network
+functions, services, and applications, which is commonly modeled as a
+cross-layer network. This model carries a physical network representing the
+physical infrastructure, a logical network showing demands, and
+logical-to-physical node/link mappings. In such networks, a single failure in
+the physical network may trigger cascading failures in the logical network and
+disable network services and connectivity. In this paper, we propose an
+evaluation metric, survivable probability, to evaluate the reliability of such
+networks under random physical link failure(s). We propose the concept of base
+protecting spanning tree and prove the necessary and sufficient conditions for
+its existence and relation to survivability. We then develop mathematical
+programming formulations for reliable cross-layer network routing design with
+the maximal reliable probability. Computation results demonstrate the viability
+of our approach.
+",1,0,0,0,0,0
+20307,Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers,"  PID control architectures are widely used in industrial applications. Despite
+their low number of open parameters, tuning multiple, coupled PID controllers
+can become tedious in practice. In this paper, we extend PILCO, a model-based
+policy search framework, to automatically tune multivariate PID controllers
+purely based on data observed on an otherwise unknown system. The system's
+state is extended appropriately to frame the PID policy as a static state
+feedback policy. This renders PID tuning possible as the solution of a finite
+horizon optimal control problem without further a priori knowledge. The
+framework is applied to the task of balancing an inverted pendulum on a seven
+degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast
+and data-efficient policy learning, even on complex real world problems.
+",1,0,0,1,0,0
+20308,Engineering a Simplified 0-Bit Consistent Weighted Sampling,"  The Min-Hashing approach to sketching has become an important tool in data
+analysis, information retrial, and classification. To apply it to real-valued
+datasets, the ICWS algorithm has become a seminal approach that is widely used,
+and provides state-of-the-art performance for this problem space. However, ICWS
+suffers a computational burden as the sketch size K increases. We develop a new
+Simplified approach to the ICWS algorithm, that enables us to obtain over 20x
+speedups compared to the standard algorithm. The veracity of our approach is
+demonstrated empirically on multiple datasets and scenarios, showing that our
+new Simplified CWS obtains the same quality of results while being an order of
+magnitude faster.
+",0,0,0,1,0,0
+20309,Quantifying macroeconomic expectations in stock markets using Google Trends,"  Among other macroeconomic indicators, the monthly release of U.S.
+unemployment rate figures in the Employment Situation report by the U.S. Bureau
+of Labour Statistics gets a lot of media attention and strongly affects the
+stock markets. I investigate whether a profitable investment strategy can be
+constructed by predicting the likely changes in U.S. unemployment before the
+official news release using Google query volumes for related search terms. I
+find that massive new data sources of human interaction with the Internet not
+only improves U.S. unemployment rate predictability, but can also enhance
+market timing of trading strategies when considered jointly with macroeconomic
+data. My results illustrate the potential of combining extensive behavioural
+data sets with economic data to anticipate investor expectations and stock
+market moves.
+",0,0,0,0,0,1
+20310,Effects of Incomplete Ionization on Beta - Ga2O3 Power Devices: Unintentional Donor with Energy 110 meV,"  Understanding the origin of unintentional doping in Ga2O3 is key to
+increasing breakdown voltages of Ga2O3 based power devices. Therefore,
+transport and capacitance spectroscopy studies have been performed to better
+understand the origin of unintentional doping in Ga2O3. Previously unobserved
+unintentional donors in commercially available (-201) Ga2O3 substrates have
+been electrically characterized via temperature dependent Hall effect
+measurements up to 1000 K and found to have a donor energy of 110 meV. The
+existence of the unintentional donor is confirmed by temperature dependent
+admittance spectroscopy, with an activation energy of 131 meV determined via
+that technique, in agreement with Hall effect measurements. With the
+concentration of this donor determined to be in the mid to high 10^16 cm^-3
+range, elimination of this donor from the drift layer of Ga2O3 power
+electronics devices will be key to pushing the limits of device performance.
+Indeed, analytical assessment of the specific on-resistance (Ronsp) and
+breakdown voltage of Schottky diodes containing the 110 meV donor indicates
+that incomplete ionization increases Ronsp and decreases breakdown voltage as
+compared to Ga2O3 Schottky diodes containing only the shallow donor. The
+reduced performance due to incomplete ionization occurs in addition to the
+usual tradeoff between Ronsp and breakdown voltage. To achieve 10 kV operation
+in Ga2O3 Schottky diode devices, analysis indicates that the concentration of
+110 meV donors must be reduced below 5x10^14 cm^-3 to limit the increase in
+Ronsp to one percent.
+",0,1,0,0,0,0
+20311,A single coordinate framework for optic flow and binocular disparity,"  Optic flow is two dimensional, but no special qualities are attached to one
+or other of these dimensions. For binocular disparity, on the other hand, the
+terms 'horizontal' and 'vertical' disparities are commonly used. This is odd,
+since binocular disparity and optic flow describe essentially the same thing.
+The difference is that, generally, people tend to fixate relatively close to
+the direction of heading as they move, meaning that fixation is close to the
+optic flow epipole, whereas, for binocular vision, fixation is close to the
+head-centric midline, i.e. approximately 90 degrees from the binocular epipole.
+For fixating animals, some separations of flow may lead to simple algorithms
+for the judgement of surface structure and the control of action. We consider
+the following canonical flow patterns that sum to produce overall flow: (i)
+'towards' flow, the component of translational flow produced by approaching (or
+retreating from) the fixated object, which produces pure radial flow on the
+retina; (ii) 'sideways' flow, the remaining component of translational flow,
+which is produced by translation of the optic centre orthogonal to the
+cyclopean line of sight and (iii) 'vergence' flow, rotational flow produced by
+a counter-rotation of the eye in order to maintain fixation. A general flow
+pattern could also include (iv) 'cyclovergence' flow, produced by rotation of
+one eye relative to the other about the line of sight. We consider some
+practical advantages of dividing up flow in this way when an observer fixates
+as they move. As in some previous treatments, we suggest that there are certain
+tasks for which it is sensible to consider 'towards' flow as one component and
+'sideways' + 'vergence' flow as another.
+",0,0,0,0,1,0
+20312,Gain control with A-type potassium current: IA as a switch between divisive and subtractive inhibition,"  Neurons process information by transforming barrages of synaptic inputs into
+spiking activity. Synaptic inhibition suppresses the output firing activity of
+a neuron, and is commonly classified as having a subtractive or divisive effect
+on a neuron's output firing activity. Subtractive inhibition can narrow the
+range of inputs that evoke spiking activity by eliminating responses to
+non-preferred inputs. Divisive inhibition is a form of gain control: it
+modifies firing rates while preserving the range of inputs that evoke firing
+activity. Since these two ""modes"" of inhibition have distinct impacts on neural
+coding, it is important to understand the biophysical mechanisms that
+distinguish these response profiles.
+We use simulations and mathematical analysis of a neuron model to find the
+specific conditions for which inhibitory inputs have subtractive or divisive
+effects. We identify a novel role for the A-type Potassium current (IA). In our
+model, this fast-activating, slowly- inactivating outward current acts as a
+switch between subtractive and divisive inhibition. If IA is strong (large
+maximal conductance) and fast (activates on a time-scale similar to spike
+initiation), then inhibition has a subtractive effect on neural firing. In
+contrast, if IA is weak or insufficiently fast-activating, then inhibition has
+a divisive effect on neural firing. We explain these findings using dynamical
+systems methods to define how a spike threshold condition depends on synaptic
+inputs and IA.
+Our findings suggest that neurons can ""self-regulate"" the gain control
+effects of inhibition via combinations of synaptic plasticity and/or modulation
+of the conductance and kinetics of A-type Potassium channels. This novel role
+for IA would add flexibility to neurons and networks, and may relate to recent
+observations of divisive inhibitory effects on neurons in the nucleus of the
+solitary tract.
+",0,0,0,0,1,0
+20313,Cyclic Datatypes modulo Bisimulation based on Second-Order Algebraic Theories,"  Cyclic data structures, such as cyclic lists, in functional programming are
+tricky to handle because of their cyclicity. This paper presents an
+investigation of categorical, algebraic, and computational foundations of
+cyclic datatypes. Our framework of cyclic datatypes is based on second-order
+algebraic theories of Fiore et al., which give a uniform setting for syntax,
+types, and computation rules for describing and reasoning about cyclic
+datatypes. We extract the ""fold"" computation rules from the categorical
+semantics based on iteration categories of Bloom and Esik. Thereby, the rules
+are correct by construction. We prove strong normalisation using the General
+Schema criterion for second-order computation rules. Rather than the fixed
+point law, we particularly choose Bekic law for computation, which is a key to
+obtaining strong normalisation. We also prove the property of ""Church-Rosser
+modulo bisimulation"" for the computation rules. Combining these results, we
+have a remarkable decidability result of the equational theory of cyclic data
+and fold.
+",1,0,0,0,0,0
+20314,High-order harmonic generation from highly-excited states in acetylene,"  High-order harmonic generation (HHG) from aligned acetylene molecules
+interacting with mid infra-red (IR), linearly polarized laser pulses is studied
+theoretically using a mixed quantum-classical approach in which the electrons
+are described using time-dependent density functional theory while the ions are
+treated classically. We find that for molecules aligned perpendicular to the
+laser polarization axis, HHG arises from the highest-occupied molecular orbital
+(HOMO) while for molecules aligned along the laser polarization axis, HHG is
+dominated by the HOMO-1. In the parallel orientation we observe a double
+plateau with an inner plateau that is produced by ionization from and
+recombination back to an autoionizing state. Two pieces of evidence support
+this idea. Firstly, by choosing a suitably tuned vacuum ultraviolet pump pulse
+that directly excites the autoionizing state we observe a dramatic enhancement
+of all harmonics in the inner plateau. Secondly, in certain circumstances, the
+position of the inner plateau cut-off does not agree with the classical
+three-step model. We show that this discrepancy can be understood in terms of a
+minimum in the dipole recombination matrix element from the continuum to the
+autoionizing state. As far as we are aware, this represents the first
+observation of harmonic enhancement over a wide range of frequencies arising
+from autoionizing states in molecules.
+",0,1,0,0,0,0
+20315,Cellulyzer - Automated analysis and interactive visualization/simulation of select cellular processes,"  Here we report on a set of programs developed at the ZMBH Bio-Imaging
+Facility for tracking real-life images of cellular processes. These programs
+perform 1) automated tracking; 2) quantitative and comparative track analyses
+of different images in different groups; 3) different interactive visualization
+schemes; and 4) interactive realistic simulation of different cellular
+processes for validation and optimal problem-specific adjustment of image
+acquisition parameters (tradeoff between speed, resolution, and quality with
+feedback from the very final results). The collection of programs is primarily
+developed for the common bio-image analysis software ImageJ (as a single Java
+Plugin). Some programs are also available in other languages (C++ and
+Javascript) and may be run simply with a web-browser; even on a low-end Tablet
+or Smartphone. The programs are available at
+this https URL
+",1,1,0,0,0,0
+20316,Vprop: Variational Inference using RMSprop,"  Many computationally-efficient methods for Bayesian deep learning rely on
+continuous optimization algorithms, but the implementation of these methods
+requires significant changes to existing code-bases. In this paper, we propose
+Vprop, a method for Gaussian variational inference that can be implemented with
+two minor changes to the off-the-shelf RMSprop optimizer. Vprop also reduces
+the memory requirements of Black-Box Variational Inference by half. We derive
+Vprop using the conjugate-computation variational inference method, and
+establish its connections to Newton's method, natural-gradient methods, and
+extended Kalman filters. Overall, this paper presents Vprop as a principled,
+computationally-efficient, and easy-to-implement method for Bayesian deep
+learning.
+",1,0,0,1,0,0
+20317,Hidden symmetries in $N$-layer dielectric stacks,"  The optical properties of a multilayer system of dielectric media with
+arbitrary $N$ layers is investigated. Each layer is one of two dielectric
+media, with thickness one-quarter the wavelength of light in that medium,
+corresponding to a central frequency. Using the transfer matrix method, the
+transmittance $T$ is calculated for all possible $2^N$ sequences for small $N$.
+Unexpectedly, it is found that instead of $2^N$ different values of $T$ at the
+central frequency ($T_0$), there are either $(N/2+1)$ or $(N+1)$ discrete
+values of $T_0$ for even or odd $N$, respectively. We explain the high
+degeneracy in the $T_0$ values by defining new symmetry operations that do not
+change $T_0$. Analytical formulae were derived for the $T_0$ values and their
+degeneracy as functions of $N$ and an integer parameter for each sequence we
+call ""charge"". Additionally, the bandwidth of the transmission spectra at $f_0$
+is investigated, revealing some asymptotic behavior at large $N$.
+",0,1,0,0,0,0
+20318,Solvability regions of affinely parameterized quadratic equations,"  Quadratic systems of equations appear in several applications. The results in
+this paper are motivated by quadratic systems of equations that describe
+equilibrium behavior of physical infrastructure networks like the power and gas
+grids. The quadratic systems in infrastructure networks are parameterized- the
+parameters can represent uncertainty (estimation error in resistance/inductance
+of a power transmission line, for example)or controllable decision variables
+(power outputs of generators,for example). It is then of interest to understand
+conditions on the parameters under which the quadratic system is guaranteed to
+have a solution within a specified set (for example, bounds on voltages and
+flows in a power grid). Given nominal values of the parameters at which the
+quadratic system has a solution and the Jacobian of the quadratic system at the
+solution i snon-singular, we develop a general framework to construct convex
+regions around the nominal value such that the system is guaranteed to have a
+solution within a given distance of the nominal solution. We show that several
+results from recen tliterature can be recovered as special cases of our
+framework,and demonstrate our approach on several benchmark power systems.
+",1,0,1,0,0,0
+20319,Mid-price estimation for European corporate bonds: a particle filtering approach,"  In most illiquid markets, there is no obvious proxy for the market price of
+an asset. The European corporate bond market is an archetypal example of such
+an illiquid market where mid-prices can only be estimated with a statistical
+model. In this OTC market, dealers / market makers only have access, indeed, to
+partial information about the market. In real-time, they know the price
+associated with their trades on the dealer-to-dealer (D2D) and dealer-to-client
+(D2C) markets, they know the result of the requests for quotes (RFQ) they
+answered, and they have access to composite prices (e.g., Bloomberg CBBT). This
+paper presents a Bayesian method for estimating the mid-price of corporate
+bonds by using the real-time information available to a dealer. This method
+relies on recent ideas coming from the particle filtering (PF) / sequential
+Monte-Carlo (SMC) literature.
+",0,0,0,0,0,1
+20320,Matching RGB Images to CAD Models for Object Pose Estimation,"  We propose a novel method for 3D object pose estimation in RGB images, which
+does not require pose annotations of objects in images in the training stage.
+We tackle the pose estimation problem by learning how to establish
+correspondences between RGB images and rendered depth images of CAD models.
+During training, our approach only requires textureless CAD models and aligned
+RGB-D frames of a subset of object instances, without explicitly requiring pose
+annotations for the RGB images. We employ a deep quadruplet convolutional
+neural network for joint learning of suitable keypoints and their associated
+descriptors in pairs of rendered depth images which can be matched across
+modalities with aligned RGB-D views. During testing, keypoints are extracted
+from a query RGB image and matched to keypoints extracted from rendered depth
+images, followed by establishing 2D-3D correspondences. The object's pose is
+then estimated using the RANSAC and PnP algorithms. We conduct experiments on
+the recently introduced Pix3D dataset and demonstrate the efficacy of our
+proposed approach in object pose estimation as well as generalization to object
+instances not seen during training.
+",1,0,0,0,0,0
+20321,A Model for Paired-Multinomial Data and Its Application to Analysis of Data on a Taxonomic Tree,"  In human microbiome studies, sequencing reads data are often summarized as
+counts of bacterial taxa at various taxonomic levels specified by a taxonomic
+tree. This paper considers the problem of analyzing two repeated measurements
+of microbiome data from the same subjects. Such data are often collected to
+assess the change of microbial composition after certain treatment, or the
+difference in microbial compositions across body sites. Existing models for
+such count data are limited in modeling the covariance structure of the counts
+and in handling paired multinomial count data. A new probability distribution
+is proposed for paired-multinomial count data, which allows flexible covariance
+structure and can be used to model repeatedly measured multivariate count data.
+Based on this distribution, a test statistic is developed for testing the
+difference in compositions based on paired multinomial count data. The proposed
+test can be applied to the count data observed on a taxonomic tree in order to
+test difference in microbiome compositions and to identify the subtrees with
+different subcompositions. Simulation results indicate that proposed test has
+correct type 1 errors and increased power compared to some commonly used
+methods. An analysis of an upper respiratory tract microbiome data set is used
+to illustrate the proposed methods.
+",0,0,0,1,0,0
+20322,Adhesion-induced Discontinuous Transitions and Classifying Social Networks,"  Transition points mark qualitative changes in the macroscopic properties of
+large complex systems. Explosive transitions, exhibiting properties of both
+continuous and discontinuous phase transitions, have recently been uncovered in
+network growth processes. Real networks not only grow but often also
+restructure, yet common network restructuring processes, such as small world
+rewiring, do not exhibit phase transitions. Here, we uncover a class of
+intrinsically discontinuous transitions emerging in network restructuring
+processes controlled by \emph{adhesion} -- the preference of a chosen link to
+remain connected to its end node. Deriving a master equation for the temporal
+network evolution and working out an analytic solution, we identify genuinely
+discontinuous transitions in non-growing networks, separating qualitatively
+distinct phases with monotonic and with peaked degree distributions.
+Intriguingly, our analysis of heuristic data indicates a separation between the
+same two forms of degree distributions distinguishing abstract from
+face-to-face social networks.
+",1,0,0,0,0,0
+20323,Numerical simulation of polynomial-speed convergence phenomenon,"  We provide a hybrid method that captures the polynomial speed of convergence
+and polynomial speed of mixing for Markov processes. The hybrid method that we
+introduce is based on the coupling technique and renewal theory. We propose to
+replace some estimates in classical results about the ergodicity of Markov
+processes by numerical simulations when the corresponding analytical proof is
+difficult. After that, all remaining conclusions can be derived from rigorous
+analysis. Then we apply our results to two 1D microscopic heat conduction
+models. The mixing rate of these two models are expected to be polynomial but
+very difficult to prove. In both examples, our numerical results match the
+expected polynomial mixing rate well.
+",0,0,1,0,0,0
+20324,Efficient determination of optimised multi-arm multi-stage experimental designs with control of generalised error-rates,"  Primarily motivated by the drug development process, several publications
+have now presented methodology for the design of multi-arm multi-stage
+experiments with normally distributed outcome variables of known variance.
+Here, we extend these past considerations to allow the design of what we refer
+to as an abcd multi-arm multi-stage experiment. We provide a proof of how
+strong control of the a-generalised type-I familywise error-rate can be
+ensured. We then describe how to attain the power to reject at least b out of c
+false hypotheses, which is related to controlling the b-generalised type-II
+familywise error-rate. Following this, we detail how a design can be optimised
+for a scenario in which rejection of any d null hypotheses brings about
+termination of the experiment. We achieve this by proposing a highly
+computationally efficient approach for evaluating the performance of a
+candidate design. Finally, using a real clinical trial as a motivating example,
+we explore the effect of the design's control parameters on the statistical
+operating characteristics.
+",0,0,0,1,0,0
+20325,Application of the Waveform Relaxation Technique to the Co-Simulation of Power Converter Controller and Electrical Circuit Models,"  In this paper we present the co-simulation of a PID class power converter
+controller and an electrical circuit by means of the waveform relaxation
+technique. The simulation of the controller model is characterized by a
+fixed-time stepping scheme reflecting its digital implementation, whereas a
+circuit simulation usually employs an adaptive time stepping scheme in order to
+account for a wide range of time constants within the circuit model. In order
+to maintain the characteristic of both models as well as to facilitate model
+replacement, we treat them separately by means of input/output relations and
+propose an application of a waveform relaxation algorithm. Furthermore, the
+maximum and minimum number of iterations of the proposed algorithm are
+mathematically analyzed. The concept of controller/circuit coupling is
+illustrated by an example of the co-simulation of a PI power converter
+controller and a model of the main dipole circuit of the Large Hadron Collider.
+",1,1,0,0,0,0
+20326,Some Investigations about the Properties of Maximum Likelihood Estimations Based on Lower Record Values for a Sub-Family of the Exponential Family,"  Here, in this paper it has been considered a sub family of exponential
+family. Maximum likelihood estimations (MLE) for the parameter of this family,
+probability density function, and cumulative density function based on a sample
+and based on lower record values have been obtained. It has been considered
+Mean Square Error (MSE) as a criterion for determining which is better in
+different situations. Additionally, it has been proved some theories about the
+relations between MLE based on lower record values and based on a random
+sample. Also, some interesting asymptotically properties for these estimations
+have been shown during some theories.
+",0,0,1,1,0,0
+20327,Some Insights on Synthesizing Optimal Linear Quadratic Controller Using Krotov's Sufficiency Conditions,"  This paper revisits the problem of optimal control law design for linear
+systems using the global optimal control framework introduced by Vadim Krotov.
+Krotov's approach is based on the idea of total decomposition of the original
+optimal control problem (OCP) with respect to time, by an $ad$ $hoc$ choice of
+the so-called Krotov's function or solving function, thereby providing
+sufficient conditions for the existence of global solution based on another
+optimization problem, which is completely equivalent to the original OCP. It is
+well known that the solution of this equivalent optimization problem is
+obtained using an iterative method. In this paper, we propose suitable Krotov's
+functions for linear quadratic OCP and subsequently, show that by imposing
+convexity condition on this equivalent optimization problem, there is no need
+to compute an iterative solution. We also give some key insights into the
+solution procedure of the linear quadratic OCP using the proposed methodology
+in contrast to the celebrated Calculus of Variations (CoV) and
+Hamilton-Jacobi-Bellman (HJB) equation based approach.
+",1,0,0,0,0,0
+20328,Schumann resonance transients and the search for gravitational waves,"  Schumann resonance transients which propagate around the globe can
+potentially generate a correlated background in widely separated gravitational
+wave detectors. We show that due to the distribution of lightning hotspots
+around the globe these transients have characteristic time lags, and this
+feature can be useful to further suppress such a background, especially in
+searches of the stochastic gravitational-wave background. A brief review of the
+corresponding literature on Schumann resonances and lightnings is also given.
+",0,1,0,0,0,0
+20329,Learning to Fly by Crashing,"  How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
+obstacles? One approach is to use a small dataset collected by human experts:
+however, high capacity learning algorithms tend to overfit when trained with
+little data. An alternative is to use simulation. But the gap between
+simulation and real world remains large especially for perception problems. The
+reason most research avoids using large-scale real data is the fear of crashes!
+In this paper, we propose to bite the bullet and collect a dataset of crashes
+itself! We build a drone whose sole purpose is to crash into objects: it
+samples naive trajectories and crashes into random objects. We crash our drone
+11,500 times to create one of the biggest UAV crash dataset. This dataset
+captures the different ways in which a UAV can crash. We use all this negative
+flying data in conjunction with positive data sampled from the same
+trajectories to learn a simple yet powerful policy for UAV navigation. We show
+that this simple self-supervised model is quite effective in navigating the UAV
+even in extremely cluttered environments with dynamic obstacles including
+humans. For supplementary video see: this https URL
+",1,0,0,0,0,0
+20330,Estimating Graphlet Statistics via Lifting,"  Exploratory analysis over network data is often limited by our ability to
+efficiently calculate graph statistics, which can provide a model-free
+understanding of macroscopic properties of a network. This work introduces a
+framework for estimating the graphlet count - the number of occurrences of a
+small subgraph motif (e.g. a wedge or a triangle) in the network. For massive
+graphs, where accessing the whole graph is not possible, the only viable
+algorithms are those which act locally by making a limited number of vertex
+neighborhood queries.
+We introduce a Monte Carlo sampling technique for graphlet counts, called
+lifting, which can simultaneously sample all graphlets of size up to $k$
+vertices. We outline three variants of lifted graphlet counts: the ordered,
+unordered, and shotgun estimators. We prove that our graphlet count updates are
+unbiased for the true graphlet count, have low correlation between samples, and
+have a controlled variance. We compare the experimental performance of lifted
+graphlet counts to the state-of-the art graphlet sampling procedures: Waddling
+and the pairwise subgraph random walk.
+",0,0,0,1,0,0
+20331,Calibration for the (Computationally-Identifiable) Masses,"  As algorithms increasingly inform and influence decisions made about
+individuals, it becomes increasingly important to address concerns that these
+algorithms might be discriminatory. The output of an algorithm can be
+discriminatory for many reasons, most notably: (1) the data used to train the
+algorithm might be biased (in various ways) to favor certain populations over
+others; (2) the analysis of this training data might inadvertently or
+maliciously introduce biases that are not borne out in the data. This work
+focuses on the latter concern.
+We develop and study multicalbration -- a new measure of algorithmic fairness
+that aims to mitigate concerns about discrimination that is introduced in the
+process of learning a predictor from data. Multicalibration guarantees accurate
+(calibrated) predictions for every subpopulation that can be identified within
+a specified class of computations. We think of the class as being quite rich;
+in particular, it can contain many overlapping subgroups of a protected group.
+We show that in many settings this strong notion of protection from
+discrimination is both attainable and aligned with the goal of obtaining
+accurate predictions. Along the way, we present new algorithms for learning a
+multicalibrated predictor, study the computational complexity of this task, and
+draw new connections to computational learning models such as agnostic
+learning.
+",1,0,0,1,0,0
+20332,The distribution of old stars around the Milky Way's central black hole I: Star counts,"  (abridged) In this paper we revisit the problem of inferring the innermost
+structure of the Milky Way's nuclear star cluster via star counts, to clarify
+whether it displays a core or a cusp around the central black hole. Through
+image stacking and improved PSF fitting we push the completeness limit about
+one magnitude deeper than in previous, comparable work. Contrary to previous
+work, we analyse the stellar density in well-defined magnitude ranges in order
+to be able to constrain stellar masses and ages. The RC and brighter giant
+stars display a core-like surface density profile within a projected radius
+R<0.3 pc of the central black hole, in agreement with previous studies, but
+show a cusp-like surface density distribution at larger R. The surface density
+of the fainter stars can be described well by a single power-law at R<2 pc. The
+cusp-like profile of the faint stars persists even if we take into account the
+possible contamination of stars in this brightness range by young pre-main
+sequence stars. The data are inconsistent with a core-profile for the faint
+stars.Finally, we show that a 3D Nuker law provides a very good description of
+the cluster structure. We conclude that the observed stellar density at the
+Galactic Centre, as it can be inferred with current instruments, is consistent
+with the existence of a stellar cusp around the Milky Way's central black hole,
+Sgr A*. This cusp is well developed inside the influence radius of about 3 pc
+of Sgr A* and can be described by a single three-dimensional power-law with an
+exponent gamma=1.23+-0.05. The apparent lack of RC stars and brighter giants at
+projected distances of R < 0.3 pc (R<8"") of the massive black hole may indicate
+that some mechanism has altered their distribution or intrinsic luminosity.
+",0,1,0,0,0,0
+20333,On infinite order differential operators in fractional viscoelasticity,"  In this paper we discuss some general properties of viscoelastic models
+defined in terms of constitutive equations involving infinitely many
+derivatives (of integer and fractional order). In particular, we consider as a
+working example the recently developed Bessel models of linear viscoelasticiy
+that, for short times, behave like fractional Maxwell bodies of order $1/2$.
+",0,1,1,0,0,0
+20334,Gap structure of FeSe determined by field-angle-resolved specific heat measurements,"  Quasiparticle excitations in FeSe were studied by means of specific heat
+($C$) measurements on a high-quality single crystal under rotating magnetic
+fields. The field dependence of $C$ shows three-stage behavior with different
+slopes, indicating the existence of three gaps ($\Delta_1$, $\Delta_2$, and
+$\Delta_3$). In the low-temperature and low-field region, the azimuthal-angle
+($\phi$) dependence of $C$ shows a four-fold symmetric oscillation with sign
+change. On the other hand, the polar-angle ($\theta$) dependence manifests as
+an anisotropy-inverted two-fold symmetry with unusual shoulder behavior.
+Combining the angle-resolved results and the theoretical calculation, the
+smaller gap $\Delta_1$ is proved to have two vertical-line nodes or gap minima
+along the $k_z$ direction, and is determined to reside on the electron-type
+$\varepsilon$ band. $\Delta_2$ is found to be related to the electron-type
+$\delta$ band, and is isotropic in the $ab$-plane but largely anisotropic out
+of the plane. $\Delta_3$ residing on the hole-type $\alpha$ band shows a small
+out-of-plane anisotropy with a strong Pauli-paramagnetic effect.
+",0,1,0,0,0,0
+20335,Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets,"  This paper presents a model based on Deep Learning algorithms of LSTM and GRU
+for facilitating an anomaly detection in Large Hadron Collider superconducting
+magnets. We used high resolution data available in Post Mortem database to
+train a set of models and chose the best possible set of their
+hyper-parameters. Using Deep Learning approach allowed to examine a vast body
+of data and extract the fragments which require further experts examination and
+are regarded as anomalies. The presented method does not require tedious manual
+threshold setting and operator attention at the stage of the system setup.
+Instead, the automatic approach is proposed, which achieves according to our
+experiments accuracy of 99%. This is reached for the largest dataset of 302 MB
+and the following architecture of the network: single layer LSTM, 128 cells, 20
+epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam.
+All the experiments were run on GPU Nvidia Tesla K80
+",0,1,0,0,0,0
+20336,Electro-Oxidation of Ni42 Steel: A highly Active Bifunctional Electrocatalyst,"  Janus type Water-Splitting Catalysts have attracted highest attention as a
+tool of choice for solar to fuel conversion. AISI Ni 42 steel was upon harsh
+anodization converted in a bifunctional electrocatalyst. Oxygen evolution
+reaction- (OER) and hydrogen evolution reaction (HER) are highly efficiently
+and steadfast catalyzed at pH 7, 13, 14, 14.6 (OER) respectively at pH 0, 1,
+13, 14, 14.6 (HER). The current density taken from long-term OER measurements
+in pH 7 buffer solution upon the electro activated steel at 491 mV
+overpotential was around 4 times higher (4 mA/cm2) in comparison with recently
+developed OER electrocatalysts. The very strong voltage-current behavior of the
+catalyst shown in OER polarization experiments at both pH 7 and at pH 13 were
+even superior to those known for IrO2-RuO2. No degradation of the catalyst was
+detected even when conditions close to standard industrial operations were
+applied to the catalyst. A stable Ni-, Fe- oxide based passivating layer
+sufficiently protected the bare metal for further oxidation. Quantitative
+charge to oxygen- (OER) and charge to hydrogen (HER) conversion was confirmed.
+High resolution XPS spectra showed that most likely gamma-NiO(OH) and FeO(OH)
+are the catalytic active OER and NiO is the catalytic active HER species.
+",0,1,0,0,0,0
+20337,Shape-constrained partial identification of a population mean under unknown probabilities of sample selection,"  A prevailing challenge in the biomedical and social sciences is to estimate a
+population mean from a sample obtained with unknown selection probabilities.
+Using a well-known ratio estimator, Aronow and Lee (2013) proposed a method for
+partial identification of the mean by allowing the unknown selection
+probabilities to vary arbitrarily between two fixed extreme values. In this
+paper, we show how to leverage auxiliary shape constraints on the population
+outcome distribution, such as symmetry or log-concavity, to obtain tighter
+bounds on the population mean. We use this method to estimate the performance
+of Aymara students---an ethnic minority in the north of Chile---in a national
+educational standardized test. We implement this method in the new statistical
+software package scbounds for R.
+",0,0,1,1,0,0
+20338,Warped metrics for location-scale models,"  This paper argues that a class of Riemannian metrics, called warped metrics,
+plays a fundamental role in statistical problems involving location-scale
+models. The paper reports three new results : i) the Rao-Fisher metric of any
+location-scale model is a warped metric, provided that this model satisfies a
+natural invariance condition, ii) the analytic expression of the sectional
+curvature of this metric, iii) the exact analytic solution of the geodesic
+equation of this metric. The paper applies these new results to several
+examples of interest, where it shows that warped metrics turn location-scale
+models into complete Riemannian manifolds of negative sectional curvature. This
+is a very suitable situation for developing algorithms which solve problems of
+classification and on-line estimation. Thus, by revealing the connection
+between warped metrics and location-scale models, the present paper paves the
+way to the introduction of new efficient statistical algorithms.
+",0,0,1,1,0,0
+20339,Detecting Changes in Time Series Data using Volatility Filters,"  This work develops techniques for the sequential detection and location
+estimation of transient changes in the volatility (standard deviation) of time
+series data. In particular, we introduce a class of change detection algorithms
+based on the windowed volatility filter. The first method detects changes by
+employing a convex combination of two such filters with differing window sizes,
+such that the adaptively updated convex weight parameter is then used as an
+indicator for the detection of instantaneous power changes. Moreover, the
+proposed adaptive filtering based method is readily extended to the
+multivariate case by using recent advances in distributed adaptive filters,
+thereby using cooperation between the data channels for more effective
+detection of change points. Furthermore, this work also develops a novel change
+point location estimator based on the differenced output of the volatility
+filter. Finally, the performance of the proposed methods were evaluated on both
+synthetic and real world data.
+",1,0,0,0,0,0
+20340,A minimax and asymptotically optimal algorithm for stochastic bandits,"  We propose the kl-UCB ++ algorithm for regret minimization in stochastic
+bandit models with exponential families of distributions. We prove that it is
+simultaneously asymptotically optimal (in the sense of Lai and Robbins' lower
+bound) and minimax optimal. This is the first algorithm proved to enjoy these
+two properties at the same time. This work thus merges two different lines of
+research with simple and clear proofs.
+",0,0,1,1,0,0
+20341,Eigenfunctions of Periodic Differential Operators Analytic in a Strip,"  Ordinary differential operators with periodic coefficients analytic in a
+strip act on a Hardy-Hilbert space of analytic functions with inner product
+defined by integration over a period on the boundary of the strip. Simple
+examples show that eigenfunctions may form a complete set for a narrow strip,
+but completeness may be lost for a wide strip. Completeness of the
+eigenfunctions in the Hardy-Hilbert space is established for regular second
+order operators with matrix-valued coefficients when the leading coefficient
+satisfies a positive real part condition throughout the strip.
+",0,0,1,0,0,0
+20342,Infinite ergodic index of the ehrenfest wind-tree model,"  The set of all possible configurations of the Ehrenfest wind-tree model
+endowed with the Hausdorff topology is a compact metric space. For a typical
+configuration we show that the wind-tree dynamics has infinite ergodic index in
+almost every direction. In particular some ergodic theorems can be applied to
+show that if we start with a large number of initially parallel particles their
+directions decorrelate as the dynamics evolve answering the question posed by
+the Ehrenfests.
+",0,0,1,0,0,0
+20343,Arrays of strongly-coupled atoms in a one-dimensional waveguide,"  We study the cooperative optical coupling between regularly spaced atoms in a
+one-dimensional waveguide using decompositions to subradiant and superradiant
+collective excitation eigenmodes, direct numerical solutions, and analytical
+transfer-matrix methods. We illustrate how the spectrum of transmitted light
+through the waveguide including the emergence of narrow Fano resonances can be
+understood by the resonance features of the eigenmodes. We describe a method
+based on superradiant and subradiant modes to engineer the optical response of
+the waveguide and to store light. The stopping of light is obtained by
+transferring an atomic excitation to a subradiant collective mode with the zero
+radiative resonance linewidth by controlling the level shift of an atom in the
+waveguide. Moreover, we obtain an exact analytic solution for the transmitted
+light through the waveguide for the case of a regular lattice of atoms and
+provide a simple description how the light transmission may present large
+resonance shifts when the lattice spacing is close, but not exactly equal, to
+half of the wavelength of the light. Experimental imperfections such as
+fluctuations of the positions of the atoms and loss of light from the waveguide
+are easily quantified in the numerical simulations, which produce the natural
+result that the optical response of the atomic array tends toward the response
+of a gas with random atomic positions.
+",0,1,0,0,0,0
+20344,"MEG-Derived Functional Tractography, Results for Normal and Concussed Cohorts","  Measures of neuroelectric activity from each of 18 automatically identified
+white matter tracts were extracted from resting MEG recordings from a
+normative, n=588, and a chronic TBI, traumatic brain injury, n=63, cohort, 60
+of whose TBIs were mild. Activity in the TBI cohort was significantly reduced
+compared with the norms for ten of the tracts, p < 10-6 for each. Significantly
+reduced activity (p < 10-3) was seen in more than one tract in seven mTBI
+individuals and one member of the normative cohort.
+",0,0,0,0,1,0
+20345,Coincidence of magnetic and valence quantum critical points in CeRhIn5 under pressure,"  We present accurate electrical resistivity measurements along the two
+principle crystallographic axes of the pressure-induced heavy-fermion
+superconductor CeRhIn5 up to 5.63 GPa. For both directions, a valence crossover
+line is identified in the p-T plane and the extrapolation of this line to zero
+temperature coincides with the collapse of the magnetic ordering temperature.
+Furthermore, it is found that the p-T phase diagram of CeRhIn5 in the valence
+crossover region is very similar to that of CeCu2Si2. These results point to
+the essential role of Ce-4f electron delocalization in both destroying magnetic
+order and realizing superconductivity in CeRhIn5 under pressure.
+",0,1,0,0,0,0
+20346,Sequential Inverse Approximation of a Regularized Sample Covariance Matrix,"  One of the goals in scaling sequential machine learning methods pertains to
+dealing with high-dimensional data spaces. A key related challenge is that many
+methods heavily depend on obtaining the inverse covariance matrix of the data.
+It is well known that covariance matrix estimation is problematic when the
+number of observations is relatively small compared to the number of variables.
+A common way to tackle this problem is through the use of a shrinkage estimator
+that offers a compromise between the sample covariance matrix and a
+well-conditioned matrix, with the aim of minimizing the mean-squared error. We
+derived sequential update rules to approximate the inverse shrinkage estimator
+of the covariance matrix. The approach paves the way for improved large-scale
+machine learning methods that involve sequential updates.
+",0,0,0,1,0,0
+20347,"Sesqui-arrays, a generalisation of triple arrays","  A triple array is a rectangular array containing letters, each letter
+occurring equally often with no repeats in rows or columns, such that the
+number of letters common to two rows, two columns, or a row and a column are
+(possibly different) non-zero constants. Deleting the condition on the letters
+common to a row and a column gives a double array. We propose the term
+\emph{sesqui-array} for such an array when only the condition on pairs of
+columns is deleted. Thus all triple arrays are sesqui-arrays.
+In this paper we give three constructions for sesqui-arrays. The first gives
+$(n+1)\times n^2$ arrays on $n(n+1)$ letters for $n\geq 2$. (Such an array for
+$n=2$ was found by Bagchi.) This construction uses Latin squares. The second
+uses the \emph{Sylvester graph}, a subgraph of the Hoffman--Singleton graph, to
+build a good block design for $36$ treatments in $42$ blocks of size~$6$, and
+then uses this in a $7\times 36$ sesqui-array for $42$ letters.
+We also give a construction for $K\times(K-1)(K-2)/2$ sesqui-arrays on
+$K(K-1)/2$ letters. This construction uses biplanes. It starts with a block of
+a biplane and produces an array which satisfies the requirements for a
+sesqui-array except possibly that of having no repeated letters in a row or
+column. We show that this condition holds if and only if the \emph{Hussain
+chains} for the selected block contain no $4$-cycles. A sufficient condition
+for the construction to give a triple array is that each Hussain chain is a
+union of $3$-cycles; but this condition is not necessary, and we give a few
+further examples.
+We also discuss the question of which of these arrays provide good designs
+for experiments.
+",0,0,1,1,0,0
+20348,Investigating how well contextual features are captured by bi-directional recurrent neural network models,"  Learning algorithms for natural language processing (NLP) tasks traditionally
+rely on manually defined relevant contextual features. On the other hand,
+neural network models using an only distributional representation of words have
+been successfully applied for several NLP tasks. Such models learn features
+automatically and avoid explicit feature engineering. Across several domains,
+neural models become a natural choice specifically when limited characteristics
+of data are known. However, this flexibility comes at the cost of
+interpretability. In this paper, we define three different methods to
+investigate ability of bi-directional recurrent neural networks (RNNs) in
+capturing contextual features. In particular, we analyze RNNs for sequence
+tagging tasks. We perform a comprehensive analysis on general as well as
+biomedical domain datasets. Our experiments focus on important contextual words
+as features, which can easily be extended to analyze various other feature
+types. We also investigate positional effects of context words and show how the
+developed methods can be used for error analysis.
+",1,0,0,0,0,0
+20349,A Hybridizable Discontinuous Galerkin solver for the Grad-Shafranov equation,"  In axisymmetric fusion reactors, the equilibrium magnetic configuration can
+be expressed in terms of the solution to a semi-linear elliptic equation known
+as the Grad-Shafranov equation, the solution of which determines the poloidal
+component of the magnetic field. When the geometry of the confinement region is
+known, the problem becomes an interior Dirichlet boundary value problem. We
+propose a high order solver based on the Hybridizable Discontinuous Galerkin
+method. The resulting algorithm (1) provides high order of convergence for the
+flux function and its gradient, (2) incorporates a novel method for handling
+piecewise smooth geometries by extension from polygonal meshes, (3) can handle
+geometries with non-smooth boundaries and x-points, and (4) deals with the
+semi-linearity through an accelerated two-grid fixed-point iteration. The
+effectiveness of the algorithm is verified with computations for cases where
+analytic solutions are known on configurations similar to those of actual
+devices (ITER with single null and double null divertor, NSTX, ASDEX upgrade,
+and Field Reversed Configurations).
+",0,1,0,0,0,0
+20350,Sketching Linear Classifiers over Data Streams,"  We introduce a new sub-linear space sketch---the Weight-Median Sketch---for
+learning compressed linear classifiers over data streams while supporting the
+efficient recovery of large-magnitude weights in the model. This enables
+memory-limited execution of several statistical analyses over streams,
+including online feature selection, streaming data explanation, relative
+deltoid detection, and streaming estimation of pointwise mutual information.
+Unlike related sketches that capture the most frequently-occurring features (or
+items) in a data stream, the Weight-Median Sketch captures the features that
+are most discriminative of one stream (or class) compared to another. The
+Weight-Median Sketch adopts the core data structure used in the Count-Sketch,
+but, instead of sketching counts, it captures sketched gradient updates to the
+model parameters. We provide a theoretical analysis that establishes recovery
+guarantees for batch and online learning, and demonstrate empirical
+improvements in memory-accuracy trade-offs over alternative memory-budgeted
+methods, including count-based sketches and feature hashing.
+",1,0,0,1,0,0
+20351,Tamed to compatible when b^(2+) = 1 and b^1 = 2,"  Weiyi Zhang noticed recently a gap in the proof of the main theorem of the
+authors article ""Tamed to compatible: Symplectic forms via moduli space
+integration"" [T] for the case when the symplectic 4-manifold in question has
+first Betti number 2 (and necessarily self-dual second Betti number 1). This
+note explains how to fill this gap.
+",0,0,1,0,0,0
+20352,The Mass Transference Principle: Ten Years On,"  In this article we discuss the Mass Transference Principle due to Beresnevich
+and Velani and survey several generalisations and variants, both deterministic
+and random. Using a Hausdorff measure analogue of the inhomogeneous
+Khintchine-Groshev Theorem, proved recently via an extension of the Mass
+Transference Principle to systems of linear forms, we give an alternative proof
+of a general inhomogeneous Jarn\'{\i}k-Besicovitch Theorem which was originally
+proved by Levesley. We additionally show that without monotonicity Levesley's
+theorem no longer holds in general. Thereafter, we discuss recent advances by
+Wang, Wu and Xu towards mass transference principles where one transitions from
+$\limsup$ sets defined by balls to $\limsup$ sets defined by rectangles (rather
+than from ""balls to balls"" as is the case in the original Mass Transference
+Principle). Furthermore, we consider mass transference principles for
+transitioning from rectangles to rectangles and extend known results using a
+slicing technique. We end this article with a brief survey of random analogues
+of the Mass Transference Principle.
+",0,0,1,0,0,0
+20353,Ground-state properties of Ca$_2$ from narrow line two-color photoassociation,"  By two-color photoassociation of $^{40}$Ca four weakly bound vibrational
+levels in the Ca$_2$ \Xpot ground state potential were measured, using highly
+spin-forbidden transitions to intermediate states of the coupled system
+$^3\Pi_{u}$ and $^3\Sigma^+ _{u}$ near the ${^3P_1}$+${^1S_0}$ asymptote. From
+the observed binding energies, including the least bound state, the long range
+dispersion coefficients $\mathrm{C}_6, \mathrm{C}_8,\mathrm{C}_{10}$ and a
+precise value for the s-wave scattering length of 308.5(50)~$a_0$ were derived.
+From mass scaling we also calculated the corresponding scattering length for
+other natural isotopes. From the Autler-Townes splitting of the spectra, the
+molecular Rabi frequency has been determined as function of the laser intensity
+for one bound-bound transition. The observed value for the Rabi-frequency is in
+good agreement with calculated transition moments based on the derived
+potentials, assuming a dipole moment being independent of internuclear
+separation for the atomic pair model.
+",0,1,0,0,0,0
+20354,On the semisimplicity of the cyclotomic quiver Hecke algebra of type C,"  We provide criteria for the cyclotomic quiver Hecke algebras of type C to be
+semisimple. In the semisimple case, we construct the irreducible modules.
+",0,0,1,0,0,0
+20355,"On the origin of the shallow and ""replica"" bands in FeSe monolayer superconductors","  We compare electronic structures of single FeSe layer films on SrTiO$_3$
+substrate (FeSe/STO) and K$_x$Fe$_{2-y}$Se$_{2}$ superconductors obtained from
+extensive LDA and LDA+DMFT calculations with the results of ARPES experiments.
+It is demonstrated that correlation effects on Fe-3d states are sufficient in
+principle to explain the formation of the shallow electron -- like bands at the
+M(X)-point. However, in FeSe/STO these effects alone are apparently
+insufficient for the simultaneous elimination of the hole -- like Fermi surface
+around the $\Gamma$-point which is not observed in ARPES experiments. Detailed
+comparison of ARPES detected and calculated quasiparticle bands shows
+reasonable agreement between theory and experiment. Analysis of the bands with
+respect to their origin and orbital composition shows, that for FeSe/STO system
+the experimentally observed ""replica"" quasiparticle band at the M-point
+(usually attributed to forward scattering interactions with optical phonons in
+SrTiO$_3$ substrate) can be reasonably understood just as the LDA calculated
+Fe-3d$_{xy}$ band, renormalized by electronic correlations. The only
+manifestation of the substrate reduces to lifting the degeneracy between
+Fe-3d$_{xz}$ and Fe-3d$_{yz}$ bands in the vicinity of M-point. For the case of
+K$_x$Fe$_{2-y}$Se$_{2}$ most bands observed in ARPES can also be understood as
+correlation renormalized Fe-3d LDA calculated bands, with overall semi --
+quantitative agreement with LDA+DMFT calculations.
+",0,1,0,0,0,0
+20356,A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers,"  Regularization techniques are widely employed in optimization-based
+approaches for solving ill-posed inverse problems in data analysis and
+scientific computing. These methods are based on augmenting the objective with
+a penalty function, which is specified based on prior domain-specific expertise
+to induce a desired structure in the solution. We consider the problem of
+learning suitable regularization functions from data in settings in which
+precise domain knowledge is not directly available. Previous work under the
+title of `dictionary learning' or `sparse coding' may be viewed as learning a
+regularization function that can be computed via linear programming. We
+describe generalizations of these methods to learn regularizers that can be
+computed and optimized via semidefinite programming. Our framework for learning
+such semidefinite regularizers is based on obtaining structured factorizations
+of data matrices, and our algorithmic approach for computing these
+factorizations combines recent techniques for rank minimization problems along
+with an operator analog of Sinkhorn scaling. Under suitable conditions on the
+input data, our algorithm provides a locally linearly convergent method for
+identifying the correct regularizer that promotes the type of structure
+contained in the data. Our analysis is based on the stability properties of
+Operator Sinkhorn scaling and their relation to geometric aspects of
+determinantal varieties (in particular tangent spaces with respect to these
+varieties). The regularizers obtained using our framework can be employed
+effectively in semidefinite programming relaxations for solving inverse
+problems.
+",1,0,1,1,0,0
+20357,A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network,"  Network models are applied in numerous domains where data can be represented
+as a system of interactions among pairs of actors. While both statistical and
+mechanistic network models are increasingly capable of capturing various
+dependencies amongst these actors, these dependencies imply the lack of
+independence. This poses statistical challenges for analyzing such data,
+especially when there is only a single observed network, and often leads to
+intractable likelihoods regardless of the modeling paradigm, which limit the
+application of existing statistical methods for networks. We explore a
+subsampling bootstrap procedure to serve as the basis for goodness of fit and
+model selection with a single observed network that circumvents the
+intractability of such likelihoods. Our approach is based on flexible
+resampling distributions formed from the single observed network, allowing for
+finer and higher dimensional comparisons than simply point estimates of
+quantities of interest. We include worked examples for model selection, with
+simulation, and assessment of goodness of fit, with duplication-divergence
+model fits for yeast (S.cerevisiae) protein-protein interaction data from the
+literature. The proposed procedure produces a flexible resampling distribution
+that can be based on any statistics of one's choosing and can be employed
+regardless of choice of model.
+",0,0,0,1,0,0
+20358,Nonlinear Acceleration of Stochastic Algorithms,"  Extrapolation methods use the last few iterates of an optimization algorithm
+to produce a better estimate of the optimum. They were shown to achieve optimal
+convergence rates in a deterministic setting using simple gradient iterates.
+Here, we study extrapolation methods in a stochastic setting, where the
+iterates are produced by either a simple or an accelerated stochastic gradient
+algorithm. We first derive convergence bounds for arbitrary, potentially biased
+perturbations, then produce asymptotic bounds using the ratio between the
+variance of the noise and the accuracy of the current point. Finally, we apply
+this acceleration technique to stochastic algorithms such as SGD, SAGA, SVRG
+and Katyusha in different settings, and show significant performance gains.
+",0,0,1,0,0,0
+20359,Motion of Massive Particles in Rindler Space and the Problem of Fall at the Centre,"  The motion of a massive particle in Rindler space has been studied and
+obtained the geodesics of motion. The orbits in Rindler space are found to be
+quite different from that of Schwarzschild case. The paths are not like the
+Perihelion Precession type. Further we have set up the non-relativistic
+Schrodinger equation for the particle in the quantum mechanical scenario in
+presence of background constant gravitational field and investigated the
+problem of fall of the particle at the center. This problem is also treated
+classically. Unlike the conventional scenario, here the fall occurs at the
+surface of a sphere of unit radius.
+",0,1,0,0,0,0
+20360,Measuring Sample Quality with Kernels,"  Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid
+sampling at the cost of more biased inference. Since standard MCMC diagnostics
+fail to detect these biases, researchers have developed computable Stein
+discrepancy measures that provably determine the convergence of a sample to its
+target distribution. This approach was recently combined with the theory of
+reproducing kernels to define a closed-form kernel Stein discrepancy (KSD)
+computable by summing kernel evaluations across pairs of sample points. We
+develop a theory of weak convergence for KSDs based on Stein's method,
+demonstrate that commonly used KSDs fail to detect non-convergence even for
+Gaussian targets, and show that kernels with slowly decaying tails provably
+determine convergence for a large class of target distributions. The resulting
+convergence-determining KSDs are suitable for comparing biased, exact, and
+deterministic sample sequences and simpler to compute and parallelize than
+alternative Stein discrepancies. We use our tools to compare biased samplers,
+select sampler hyperparameters, and improve upon existing KSD approaches to
+one-sample hypothesis testing and sample quality improvement.
+",1,0,0,1,0,0
+20361,Diversity of preferences can increase collective welfare in sequential exploration problems,"  In search engines, online marketplaces and other human-computer interfaces
+large collectives of individuals sequentially interact with numerous
+alternatives of varying quality. In these contexts, trial and error
+(exploration) is crucial for uncovering novel high-quality items or solutions,
+but entails a high cost for individual users. Self-interested decision makers,
+are often better off imitating the choices of individuals who have already
+incurred the costs of exploration. Although imitation makes sense at the
+individual level, it deprives the group of additional information that could
+have been gleaned by individual explorers. In this paper we show that in such
+problems, preference diversity can function as a welfare enhancing mechanism.
+It leads to a consistent increase in the quality of the consumed alternatives
+that outweighs the increased cost of search for the users.
+",1,0,0,0,0,0
+20362,Epidemic spread in interconnected directed networks,"  In the real world, many complex systems interact with other systems. In
+addition, the intra- or inter-systems for the spread of information about
+infectious diseases and the transmission of infectious diseases are often not
+random, but with direction. Hence, in this paper, we build epidemic model based
+on an interconnected directed network, which can be considered as the
+generalization of undirected networks and bipartite networks. By using the
+mean-field approach, we establish the Susceptible-Infectious-Susceptible model
+on this network. We theoretically analyze the model, and obtain the basic
+reproduction number, which is also the generalization of the critical number
+corresponding to undirected or bipartite networks. And we prove the global
+stability of disease-free and endemic equilibria via the basic reproduction
+number as a forward bifurcation parameter. We also give a condition for
+epidemic prevalence only on a single subnetwork. Furthermore, we carry out
+numerical simulations, and find that the independence between each node's in-
+and out-degrees greatly reduce the impact of the network's topological
+structure on disease spread.
+",0,1,0,0,0,0
+20363,Controllability and maximum matchings of complex networks,"  Previously, the controllability problem of a linear time-invariant dynamical
+system was mapped to the maximum matching (MM) problem on the bipartite
+representation of the underlying directed graph, and the sizes of MMs on random
+bipartite graphs were calculated analytically with the cavity method at zero
+temperature limit. Here we present an alternative theory to estimate MM sizes
+based on the core percolation theory and the perfect matching of cores. Our
+theory is much more simplified and easily interpreted, and can estimate MM
+sizes on random graphs with or without symmetry between out- and in-degree
+distributions. Our result helps to illuminate the fundamental connection
+between the controllability problem and the underlying structure of complex
+systems.
+",1,0,0,0,0,0
+20364,"Dimers, crystals and quantum Kostka numbers","  We relate the counting of honeycomb dimer configurations on the cylinder to
+the counting of certain vertices in Kirillov-Reshetikhin crystal graphs. We
+show that these dimer configurations yield the quantum Kostka numbers of the
+small quantum cohomology ring of the Grassmannian, i.e. the expansion
+coefficients when multiplying a Schubert class repeatedly with different Chern
+classes. This allows one to derive sum rules for Gromov-Witten invariants.
+",0,0,1,0,0,0
+20365,Generalized Task-Parameterized Skill Learning,"  Programming by demonstration has recently gained much attention due to its
+user-friendly and natural way to transfer human skills to robots. In order to
+facilitate the learning of multiple demonstrations and meanwhile generalize to
+new situations, a task-parameterized Gaussian mixture model (TP-GMM) has been
+recently developed. This model has achieved reliable performance in areas such
+as human-robot collaboration and dual-arm manipulation. However, the crucial
+task frames and associated parameters in this learning framework are often set
+by the human teacher, which renders three problems that have not been addressed
+yet: (i) task frames are treated equally, without considering their individual
+importance, (ii) task parameters are defined without taking into account
+additional task constraints, such as robot joint limits and motion smoothness,
+and (iii) a fixed number of task frames are pre-defined regardless of whether
+some of them may be redundant or even irrelevant for the task at hand. In this
+paper, we generalize the task-parameterized learning by addressing the
+aforementioned problems. Moreover, we provide a novel learning perspective
+which allows the robot to refine and adapt previously learned skills in a low
+dimensional space. Several examples are studied in both simulated and real
+robotic systems, showing the applicability of our approach.
+",1,0,0,0,0,0
+20366,Nonparametric Bayesian volatility learning under microstructure noise,"  Aiming at financial applications, we study the problem of learning the
+volatility under market microstructure noise. Specifically, we consider noisy
+discrete time observations from a stochastic differential equation and develop
+a novel computational method to learn the diffusion coefficient of the
+equation. We take a nonparametric Bayesian approach, where we model the
+volatility function a priori as piecewise constant. Its prior is specified via
+the inverse Gamma Markov chain. Sampling from the posterior is accomplished by
+incorporating the Forward Filtering Backward Simulation algorithm in the Gibbs
+sampler. Good performance of the method is demonstrated on two representative
+synthetic data examples. Finally, we apply the method on the EUR/USD exchange
+rate dataset.
+",0,0,0,0,0,1
+20367,Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals,"  Supervised learning based methods for source localization, being data driven,
+can be adapted to different acoustic conditions via training and have been
+shown to be robust to adverse acoustic environments. In this paper, a
+convolutional neural network (CNN) based supervised learning method for
+estimating the direction-of-arrival (DOA) of multiple speakers is proposed.
+Multi-speaker DOA estimation is formulated as a multi-class multi-label
+classification problem, where the assignment of each DOA label to the input
+feature is treated as a separate binary classification problem. The phase
+component of the short-time Fourier transform (STFT) coefficients of the
+received microphone signals are directly fed into the CNN, and the features for
+DOA estimation are learnt during training. Utilizing the assumption of disjoint
+speaker activity in the STFT domain, a novel method is proposed to train the
+CNN with synthesized noise signals. Through experimental evaluation with both
+simulated and measured acoustic impulse responses, the ability of the proposed
+DOA estimation approach to adapt to unseen acoustic conditions and its
+robustness to unseen noise type is demonstrated. Through additional empirical
+investigation, it is also shown that with an array of M microphones our
+proposed framework yields the best localization performance with M-1
+convolution layers. The ability of the proposed method to accurately localize
+speakers in a dynamic acoustic scenario with varying number of sources is also
+shown.
+",1,0,0,0,0,0
+20368,Follow-up of eROSITA and Euclid Galaxy Clusters with XMM-Newton,"  A revolution in galaxy cluster science is only a few years away. The survey
+machines eROSITA and Euclid will provide cluster samples of never-before-seen
+statistical quality. XMM-Newton will be the key instrument to exploit these
+rich datasets in terms of detailed follow-up of the cluster hot gas content,
+systematically characterizing sub-samples as well as exotic new objects.
+",0,1,0,0,0,0
+20369,Effect of viscosity ratio on the self-sustained instabilities in planar immiscible jets,"  Previous studies have shown that intermediate surface tension has a
+counterintuitive destabilizing effect on 2-phase planar jets. Here, the
+transition process in confined 2D jets of two fluids with varying viscosity
+ratio is investigated using DNS. Neutral curves for persistent oscillations are
+found by recording the norm of the velocity residuals in DNS for over 1000
+nondimensional time units, or until the signal has reached a constant level in
+a logarithmic scale - either a converged steady state, or a ""statistically
+steady"" oscillatory state. Oscillatory final states are found for all viscosity
+ratios (0.1-10). For uniform viscosity (m=1), the first bifurcation is through
+a surface tension-driven global instability. For low viscosity of the outer
+fluid, there is a mode competition between a steady asymmetric Coanda-type
+attachment mode and the surface tension-induced mode. At moderate surface
+tension, the Coanda-type attachment dominates and eventually triggers
+time-dependent convective bursts. At high surface tension, the surface
+tension-dominated mode dominates. For high viscosity of the outer fluid,
+persistent oscillations appear due to a strong convective instability. Finally,
+the m=1 jet remains unstable far from the inlet when the shear profile is
+nearly constant. Comparing this to a parallel Couette flow (without inflection
+points), we show that in both flows, a hidden interfacial mode brought out by
+surface tension becomes temporally and absolutely unstable in an intermediate
+Weber and Reynolds regime. An energy analysis of the Couette setup shows that
+surface tension, although dissipative, induces a velocity field near the
+interface which extracts energy from the flow through a viscous mechanism. This
+study highlights the rich dynamics of immiscible planar uniform-density jets,
+where several self-sustained and convective mechanisms compete depending on the
+exact parameters.
+",0,1,0,0,0,0
+20370,On the Effects of Batch and Weight Normalization in Generative Adversarial Networks,"  Generative adversarial networks (GANs) are highly effective unsupervised
+learning frameworks that can generate very sharp data, even for data such as
+images with complex, highly multimodal distributions. However GANs are known to
+be very hard to train, suffering from problems such as mode collapse and
+disturbing visual artifacts. Batch normalization (BN) techniques have been
+introduced to address the training. Though BN accelerates the training in the
+beginning, our experiments show that the use of BN can be unstable and
+negatively impact the quality of the trained model. The evaluation of BN and
+numerous other recent schemes for improving GAN training is hindered by the
+lack of an effective objective quality measure for GAN models. To address these
+issues, we first introduce a weight normalization (WN) approach for GAN
+training that significantly improves the stability, efficiency and the quality
+of the generated samples. To allow a methodical evaluation, we introduce
+squared Euclidean reconstruction error on a test set as a new objective
+measure, to assess training performance in terms of speed, stability, and
+quality of generated samples. Our experiments with a standard DCGAN
+architecture on commonly used datasets (CelebA, LSUN bedroom, and CIFAR-10)
+indicate that training using WN is generally superior to BN for GANs, achieving
+10% lower mean squared loss for reconstruction and significantly better
+qualitative results than BN. We further demonstrate the stability of WN on a
+21-layer ResNet trained with the CelebA data set. The code for this paper is
+available at this https URL
+",1,0,0,1,0,0
+20371,A noise-immune cavity-assisted non-destructive detection for an optical lattice clock in the quantum regime,"  We present and implement a non-destructive detection scheme for the
+transition probability readout of an optical lattice clock. The scheme relies
+on a differential heterodyne measurement of the dispersive properties of
+lattice-trapped atoms enhanced by a high finesse cavity. By design, this scheme
+offers a 1st order rejection of the technical noise sources, an enhanced
+signal-to-noise ratio, and an homogeneous atom-cavity coupling. We
+theoretically show that this scheme is optimal with respect to the photon shot
+noise limit. We experimentally realize this detection scheme in an operational
+strontium optical lattice clock. The resolution is on the order of a few atoms
+with a photon scattering rate low enough to keep the atoms trapped after
+detection. This scheme opens the door to various different interrogations
+protocols, which reduce the frequency instability, including atom recycling,
+zero-dead time clocks with a fast repetition rate, and sub quantum projection
+noise frequency stability.
+",0,1,0,0,0,0
+20372,Remarks on planar edge-chromatic critical graphs,"  The only open case of Vizing's conjecture that every planar graph with
+$\Delta\geq 6$ is a class 1 graph is $\Delta = 6$. We give a short proof of the
+following statement: there is no 6-critical plane graph $G$, such that every
+vertex of $G$ is incident to at most three 3-faces. A stronger statement
+without restriction to critical graphs is stated in \cite{Wang_Xu_2013}.
+However, the proof given there works only for critical graphs. Furthermore, we
+show that every 5-critical plane graph has a 3-face which is adjacent to a
+$k$-face $(k\in \{3,4\})$.
+For $\Delta = 5$ our result gives insights into the structure of planar
+$5$-critical graphs, and the result for $\Delta=6$ gives support for the truth
+of Vizing's planar graph conjecture.
+",0,0,1,0,0,0
+20373,The LOFAR window on star-forming galaxies and AGN - curved radio SEDs and IR-radio correlation at $0 < z < 2.5$,"  We present a study of the low-frequency radio properties of star forming (SF)
+galaxies and active galactic nuclei (AGN) up to redshift $z=2.5$. The new
+spectral window probed by the Low Frequency Array (LOFAR) allows us to
+reconstruct the radio continuum emission from 150 MHz to 1.4 GHz to an
+unprecedented depth for a radio-selected sample of $1542$ galaxies in $\sim 7~
+\rm{deg}^2$ of the LOFAR Boötes field. Using the extensive multi-wavelength
+dataset available in Boötes and detailed modelling of the FIR to UV spectral
+energy distribution (SED), we are able to separate the star-formation (N=758)
+and the AGN (N=784) dominated populations. We study the shape of the radio SEDs
+and their evolution across cosmic time and find significant differences in the
+spectral curvature between the SF galaxy and AGN populations. While the radio
+spectra of SF galaxies exhibit a weak but statistically significant flattening,
+AGN SEDs show a clear trend to become steeper towards lower frequencies. No
+evolution of the spectral curvature as a function of redshift is found for SF
+galaxies or AGN. We investigate the redshift evolution of the infrared-radio
+correlation (IRC) for SF galaxies and find that the ratio of total infrared to
+1.4 GHz radio luminosities decreases with increasing redshift: $ q_{\rm 1.4GHz}
+= (2.45 \pm 0.04) \times (1+z)^{-0.15 \pm 0.03} $. Similarly, $q_{\rm 150MHz}$
+shows a redshift evolution following $ q_{\rm 150GHz} = (1.72 \pm 0.04) \times
+(1+z)^{-0.22 \pm 0.05}$. Calibration of the 150 MHz radio luminosity as a star
+formation rate tracer suggests that a single power-law extrapolation from
+$q_{\rm 1.4GHz}$ is not an accurate approximation at all redshifts.
+",0,1,0,0,0,0
+20374,Interplay between the Inverse Scattering Method and Fokas's Unified Transform with an Application,"  It is known that the initial-boundary value problem for certain integrable
+partial differential equations (PDEs) on the half-line with integrable boundary
+conditions can be mapped to a special case of the Inverse Scattering Method
+(ISM) on the full-line. This can also be established within the so-called
+Unified Transform (UT) for initial-boundary value problems with linearizable
+boundary conditions. In this paper, we show a converse to this statement within
+the Ablowitz-Kaup-Newell-Segur (AKNS) scheme: the ISM on the full-line can be
+mapped to an initial-boundary value problem with linearizable boundary
+conditions. To achieve this, we need a matrix version of the UT that was
+introduced by the author to study integrable PDEs on star-graphs. As an
+application of the result, we show that the new, nonlocal reduction of the AKNS
+scheme introduced by Ablowitz and Musslimani to obtain the nonlocal Nonlinear
+Schrödinger (NLS) equation can be recast as an old, local reduction, thus
+putting the nonlocal NLS and the NLS equations on equal footing from the point
+of view of the reduction group theory of Mikhailov.
+",0,1,1,0,0,0
+20375,GNC of the SphereX Robot for Extreme Environment Exploration on Mars,"  Wheeled ground robots are limited from exploring extreme environments such as
+caves, lava tubes and skylights. Small robots that can utilize unconventional
+mobility through hopping, flying or rolling can overcome these limitations.
+Mul-tiple robots operating as a team offer significant benefits over a single
+large ro-bot, as they are not prone to single-point failure, enable distributed
+command and control and enable execution of tasks in parallel. These robots can
+complement large rovers and landers, helping to explore inaccessible sites,
+obtaining samples and for planning future exploration missions. Our robots, the
+SphereX, are 3-kg in mass, spherical and contain computers equivalent to
+current smartphones. They contain an array of guidance, navigation and control
+sensors and electronics. SphereX contains room for a 1-kg science payload,
+including for sample return. Our work in this field has recognized the need for
+miniaturized chemical mobility systems that provide power and propulsion. Our
+research explored the use of miniature rockets, including solid rockets,
+bi-propellants including RP1/hydrogen-peroxide and
+polyurethane/ammonium-perchlorate. These propulsion options provide maximum
+flight times of 10 minutes on Mars. Flying, especially hovering consumes
+significant fuel; hence, we have been developing our robots to perform
+ballistic hops that enable the robots to travel efficiently over long
+distances. Techniques are being developed to enable mid-course correction
+during a ballistic hop. Using multiple cameras, it is possible to reconstitute
+an image scene from motion blur. Hence our approach is to enable photo mapping
+as the robots travel on a ballistic hop. The same images would also be used for
+navigation and path planning. Using our proposed design approach, we are
+developing low-cost methods for surface exploration of planetary bodies using a
+network of small robots.
+",1,1,0,0,0,0
+20376,Stable Signatures for Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence,"  When studying flocking/swarming behaviors in animals one is interested in
+quantifying and comparing the dynamics of the clustering induced by the
+coalescence and disbanding of animals in different groups. In a similar vein,
+studying the dynamics of social networks leads to the problem of characterizing
+groups/communities as they form and disperse throughout time.
+Motivated by this, we study the problem of obtaining persistent homology
+based summaries of time-dependent data. Given a finite dynamic graph (DG), we
+first construct a zigzag persistence module arising from linearizing the
+dynamic transitive graph naturally induced from the input DG. Based on standard
+results, we then obtain a persistence diagram or barcode from this zigzag
+persistence module. We prove that these barcodes are stable under perturbations
+in the input DG under a suitable distance between DGs that we identify.
+More precisely, our stability theorem can be interpreted as providing a lower
+bound for the distance between DGs. Since it relies on barcodes, and their
+bottleneck distance, this lower bound can be computed in polynomial time from
+the DG inputs.
+Since DGs can be given rise by applying the Rips functor (with a fixed
+threshold) to dynamic metric spaces, we are also able to derive related stable
+invariants for these richer class of dynamic objects.
+Along the way, we propose a summarization of dynamic graphs that captures
+their time-dependent clustering features which we call formigrams. These
+set-valued functions generalize the notion of dendrogram, a prevalent tool for
+hierarchical clustering. In order to elucidate the relationship between our
+distance between two DGs and the bottleneck distance between their associated
+barcodes, we exploit recent advances in the stability of zigzag persistence due
+to Botnan and Lesnick, and to Bjerkevik.
+",0,0,1,0,0,0
+20377,A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space,"  We are developing a system for human-robot communication that enables people
+to communicate with robots in a natural way and is focused on solving problems
+in a shared space. Our strategy for developing this system is fundamentally
+data-driven: we use data from multiple input sources and train key components
+with various machine learning techniques. We developed a web application that
+is collecting data on how two humans communicate to accomplish a task, as well
+as a mobile laboratory that is instrumented to collect data on how two humans
+communicate to accomplish a task in a physically shared space. The data from
+these systems will be used to train and fine-tune the second stage of our
+system, in which the robot will be simulated through software. A physical robot
+will be used in the final stage of our project. We describe these instruments,
+a test-suite and performance metrics designed to evaluate and automate the data
+gathering process as well as evaluate an initial data set.
+",1,0,0,0,0,0
+20378,Discursive Landscapes and Unsupervised Topic Modeling in IR: A Validation of Text-As-Data Approaches through a New Corpus of UN Security Council Speeches on Afghanistan,"  The recent turn towards quantitative text-as-data approaches in IR brought
+new ways to study the discursive landscape of world politics. Here seen as
+complementary to qualitative approaches, quantitative assessments have the
+advantage of being able to order and make comprehensible vast amounts of text.
+However, the validity of unsupervised methods applied to the types of text
+available in large quantities needs to be established before they can speak to
+other studies relying on text and discourse as data. In this paper, we
+introduce a new text corpus of United Nations Security Council (UNSC) speeches
+on Afghanistan between 2001 and 2017; we study this corpus through unsupervised
+topic modeling (LDA) with the central aim to validate the topic categories that
+the LDA identifies; and we discuss the added value, and complementarity, of
+quantitative text-as-data approaches. We set-up two tests using mixed- method
+approaches. Firstly, we evaluate the identified topics by assessing whether
+they conform with previous qualitative work on the development of the situation
+in Afghanistan. Secondly, we use network analysis to study the underlying
+social structures of what we will call 'speaker-topic relations' to see whether
+they correspondent to know divisions and coalitions in the UNSC. In both cases
+we find that the unsupervised LDA indeed provides valid and valuable outputs.
+In addition, the mixed-method approaches themselves reveal interesting patterns
+deserving future qualitative research. Amongst these are the coalition and
+dynamics around the 'women and human rights' topic as part of the UNSC debates
+on Afghanistan.
+",1,0,0,0,0,0
+20379,Lattice Gas with Molecular Dynamics Collision Operator,"  We introduce a lattice gas implementation that is based on coarse-graining a
+Molecular Dynamics (MD) simulation. Such a lattice gas is similar to standard
+lattice gases, but its collision operator is informed by an underlying MD
+simulation. This can be considered an optimal lattice gas implementation
+because it allows for the representation of any system that can be simulated
+with MD. We show here that equilibrium behavior of the popular lattice
+Boltzmann algorithm is consistent with this optimal lattice gas. This
+comparison allows us to make a more accurate identification of the expressions
+for temperature and pressure in lattice Boltzmann simulations which turn out to
+be related not only to the physical temperature and pressure but also to the
+lattice discretization. We show that for any spatial discretization we need to
+choose a particular temporal discretization to recover the lattice Boltzmann
+equilibrium.
+",0,1,0,0,0,0
+20380,From dynamical systems with time-varying delay to circle maps and Koopmanism,"  In the present paper we investigate the influence of the retarded access by a
+time-varying delay on the dynamics of delay systems. We show that there are two
+universality classes of delays, which lead to fundamental differences in
+dynamical quantities such as the Lyapunov spectrum. Therefore we introduce an
+operator theoretic framework, where the solution operator of the delay system
+is decomposed into the Koopman operator describing the delay access and an
+operator similar to the solution operator known from systems with constant
+delay. The Koopman operator corresponds to an iterated map, called access map,
+which is defined by the iteration of the delayed argument of the delay
+equation. The dynamics of this one-dimensional iterated map determines the
+universality classes of the infinite-dimensional state dynamics governed by the
+delay differential equation. In this way, we connect the theory of time-delay
+systems with the theory of circle maps and the framework of the Koopman
+operator. In the present paper we extend our previous work [Otto, Müller, and
+Radons, Phys. Rev. Lett. 118, 044104 (2017)], by elaborating the mathematical
+details and presenting further results also on the Lyapunov vectors.
+",0,1,1,0,0,0
+20381,Engineering Frequency-dependent Superfluidity in Bose-Fermi Mixtures,"  Unconventional superconductivity or superfluidity are among the most exciting
+and fascinating quantum states in condensed matter physics. Usually these
+states are characterized by non-trivial spatial symmetry of the pairing order
+parameter, such as in $^{3}He$ and high-$T_{c}$ cuprates. Besides spatial
+dependence the order parameter could have unconventional frequency dependence,
+which is also allowed by Fermi-Dirac statistics. For instance, odd-frequency
+pairing is an exciting paradigm when discussing exotic superfluidity or
+superconductivity and is yet to be realized in the experiments. In this paper
+we propose a symmetry-based method of controlling frequency dependence of the
+pairing order parameter via manipulating the inversion symmetry of the system.
+First, a toy model is introduced to illustrate that frequency dependence of the
+order parameter can be adjusted by controlling the inversion symmetry of the
+system. Second, taking advantage of the recent rapid developments of shaken
+optical lattices in ultracold gases, we propose a Bose-Fermi mixture to realize
+such frequency dependent superfluids. The key idea is introducing the
+frequency-dependent attraction between Fermions mediated by Bogoliubov phonons
+with asymmetric dispersion. Our proposal should pave an alternative way for
+exploring frequency-dependent superconductors or superfluids with cold atoms.
+",0,1,0,0,0,0
+20382,In-Silico Proportional-Integral Moment Control of Stochastic Reaction Networks with Applications to Gene Expression (with Dimerization),"  The problem of controlling the mean and the variance of a species of interest
+in a simple gene expression is addressed. It is shown that the protein mean
+level can be globally and robustly tracked to any desired value using a simple
+PI controller that satisfies certain sufficient conditions. Controlling both
+the mean and variance however requires an additional control input, e.g. the
+mRNA degradation rate, and local robust tracking of mean and variance is proved
+to be achievable using multivariable PI control, provided that the reference
+point satisfies necessary conditions imposed by the system. Even more
+importantly, it is shown that there exist PI controllers that locally, robustly
+and simultaneously stabilize all the equilibrium points inside the admissible
+region. The results are then extended to the mean control of a gene expression
+with protein dimerization. It is shown that the moment closure problem can be
+circumvented without invoking any moment closure technique. Local stabilization
+and convergence of the average dimer population to any desired reference value
+is ensured using a pure integral control law. Explicit bounds on the controller
+gain are provided and shown to be valid for any reference value. As a
+byproduct, an explicit upper-bound of the variance of the monomer species,
+acting on the system as unknown input due to the moment openness, is obtained.
+The results are illustrated by simulation.
+",1,0,0,0,1,0
+20383,Big enterprise registration data imputation: Supporting spatiotemporal analysis of industries in China,"  Big, fine-grained enterprise registration data that includes time and
+location information enables us to quantitatively analyze, visualize, and
+understand the patterns of industries at multiple scales across time and space.
+However, data quality issues like incompleteness and ambiguity, hinder such
+analysis and application. These issues become more challenging when the volume
+of data is immense and constantly growing. High Performance Computing (HPC)
+frameworks can tackle big data computational issues, but few studies have
+systematically investigated imputation methods for enterprise registration data
+in this type of computing environment. In this paper, we propose a big data
+imputation workflow based on Apache Spark as well as a bare-metal computing
+cluster, to impute enterprise registration data. We integrated external data
+sources, employed Natural Language Processing (NLP), and compared several
+machine-learning methods to address incompleteness and ambiguity problems found
+in enterprise registration data. Experimental results illustrate the
+feasibility, efficiency, and scalability of the proposed HPC-based imputation
+framework, which also provides a reference for other big georeferenced text
+data processing. Using these imputation results, we visualize and briefly
+discuss the spatiotemporal distribution of industries in China, demonstrating
+the potential applications of such data when quality issues are resolved.
+",1,0,0,0,0,0
+20384,Fast Simulation of Vehicles with Non-deformable Tracks,"  This paper presents a novel technique that allows for both computationally
+fast and sufficiently plausible simulation of vehicles with non-deformable
+tracks. The method is based on an effect we have called Contact Surface Motion.
+A comparison with several other methods for simulation of tracked vehicle
+dynamics is presented with the aim to evaluate methods that are available
+off-the-shelf or with minimum effort in general-purpose robotics simulators.
+The proposed method is implemented as a plugin for the open-source
+physics-based simulator Gazebo using the Open Dynamics Engine.
+",1,0,0,0,0,0
+20385,Persistent Hidden States and Nonlinear Transformation for Long Short-Term Memory,"  Recurrent neural networks (RNNs) have been drawing much attention with great
+success in many applications like speech recognition and neural machine
+translation. Long short-term memory (LSTM) is one of the most popular RNN units
+in deep learning applications. LSTM transforms the input and the previous
+hidden states to the next states with the affine transformation, multiplication
+operations and a nonlinear activation function, which makes a good data
+representation for a given task. The affine transformation includes rotation
+and reflection, which change the semantic or syntactic information of
+dimensions in the hidden states. However, considering that a model interprets
+the output sequence of LSTM over the whole input sequence, the dimensions of
+the states need to keep the same type of semantic or syntactic information
+regardless of the location in the sequence. In this paper, we propose a simple
+variant of the LSTM unit, persistent recurrent unit (PRU), where each dimension
+of hidden states keeps persistent information across time, so that the space
+keeps the same meaning over the whole sequence. In addition, to improve the
+nonlinear transformation power, we add a feedforward layer in the PRU
+structure. In the experiment, we evaluate our proposed methods with three
+different tasks, and the results confirm that our methods have better
+performance than the conventional LSTM.
+",0,0,0,1,0,0
+20386,Satellite Image-based Localization via Learned Embeddings,"  We propose a vision-based method that localizes a ground vehicle using
+publicly available satellite imagery as the only prior knowledge of the
+environment. Our approach takes as input a sequence of ground-level images
+acquired by the vehicle as it navigates, and outputs an estimate of the
+vehicle's pose relative to a georeferenced satellite image. We overcome the
+significant viewpoint and appearance variations between the images through a
+neural multi-view model that learns location-discriminative embeddings in which
+ground-level images are matched with their corresponding satellite view of the
+scene. We use this learned function as an observation model in a filtering
+framework to maintain a distribution over the vehicle's pose. We evaluate our
+method on different benchmark datasets and demonstrate its ability localize
+ground-level images in environments novel relative to training, despite the
+challenges of significant viewpoint and appearance variations.
+",1,0,0,0,0,0
+20387,Optimality of codes with respect to error probability in Gaussian noise,"  We consider geometrical optimization problems related to optimizing the error
+probability in the presence of a Gaussian noise. One famous questions in the
+field is the ""weak simplex conjecture"". We discuss possible approaches to it,
+and state related conjectures about the Gaussian measure, in particular, the
+conjecture about minimizing of the Gaussian measure of a simplex. We also
+consider antipodal codes, apply the Šidák inequality and establish some
+theoretical and some numerical results about their optimality.
+",1,0,1,0,0,0
+20388,Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs,"  Time delay neural networks (TDNNs) are an effective acoustic model for large
+vocabulary speech recognition. The strength of the model can be attributed to
+its ability to effectively model long temporal contexts. However, current TDNN
+models are relatively shallow, which limits the modelling capability. This
+paper proposes a method of increasing the network depth by deepening the kernel
+used in the TDNN temporal convolutions. The best performing kernel consists of
+three fully connected layers with a residual (ResNet) connection from the
+output of the first to the output of the third. The addition of
+spectro-temporal processing as the input to the TDNN in the form of a
+convolutional neural network (CNN) and a newly designed Grid-RNN was
+investigated. The Grid-RNN strongly outperforms a CNN if different sets of
+parameters for different frequency bands are used and can be further enhanced
+by using a bi-directional Grid-RNN. Experiments using the multi-genre broadcast
+(MGB3) English data (275h) show that deep kernel TDNNs reduces the word error
+rate (WER) by 6% relative and when combined with the frequency dependent
+Grid-RNN gives a relative WER reduction of 9%.
+",0,0,0,1,0,0
+20389,A Structured Self-attentive Sentence Embedding,"  This paper proposes a new model for extracting an interpretable sentence
+embedding by introducing self-attention. Instead of using a vector, we use a
+2-D matrix to represent the embedding, with each row of the matrix attending on
+a different part of the sentence. We also propose a self-attention mechanism
+and a special regularization term for the model. As a side effect, the
+embedding comes with an easy way of visualizing what specific parts of the
+sentence are encoded into the embedding. We evaluate our model on 3 different
+tasks: author profiling, sentiment classification, and textual entailment.
+Results show that our model yields a significant performance gain compared to
+other sentence embedding methods in all of the 3 tasks.
+",1,0,0,0,0,0
+20390,When Neurons Fail,"  We view a neural network as a distributed system of which neurons can fail
+independently, and we evaluate its robustness in the absence of any (recovery)
+learning phase. We give tight bounds on the number of neurons that can fail
+without harming the result of a computation. To determine our bounds, we
+leverage the fact that neural activation functions are Lipschitz-continuous.
+Our bound is on a quantity, we call the \textit{Forward Error Propagation},
+capturing how much error is propagated by a neural network when a given number
+of components is failing, computing this quantity only requires looking at the
+topology of the network, while experimentally assessing the robustness of a
+network requires the costly experiment of looking at all the possible inputs
+and testing all the possible configurations of the network corresponding to
+different failure situations, facing a discouraging combinatorial explosion.
+We distinguish the case of neurons that can fail and stop their activity
+(crashed neurons) from the case of neurons that can fail by transmitting
+arbitrary values (Byzantine neurons). Interestingly, as we show in the paper,
+our bound can easily be extended to the case where synapses can fail.
+We show how our bound can be leveraged to quantify the effect of memory cost
+reduction on the accuracy of a neural network, to estimate the amount of
+information any neuron needs from its preceding layer, enabling thereby a
+boosting scheme that prevents neurons from waiting for unnecessary signals. We
+finally discuss the trade-off between neural networks robustness and learning
+cost.
+",0,0,0,1,0,0
+20391,Heavy Traffic Limit for a Tandem Queue with Identical Service Times,"  We consider a two-node tandem queueing network in which the upstream queue is
+M/G/1 and each job reuses its upstream service requirement when moving to the
+downstream queue. Both servers employ the first-in-first-out policy. We
+investigate the amount of work in the second queue at certain embedded arrival
+time points, namely when the upstream queue has just emptied. We focus on the
+case of infinite-variance service times and obtain a heavy traffic process
+limit for the embedded Markov chain.
+",0,0,1,0,0,0
+20392,Autonomous Sweet Pepper Harvesting for Protected Cropping Systems,"  In this letter, we present a new robotic harvester (Harvey) that can
+autonomously harvest sweet pepper in protected cropping environments. Our
+approach combines effective vision algorithms with a novel end-effector design
+to enable successful harvesting of sweet peppers. Initial field trials in
+protected cropping environments, with two cultivar, demonstrate the efficacy of
+this approach achieving a 46% success rate for unmodified crop, and 58% for
+modified crop. Furthermore, for the more favourable cultivar we were also able
+to detach 90% of sweet peppers, indicating that improvements in the grasping
+success rate would result in greatly improved harvesting performance.
+",1,0,0,0,0,0
+20393,AMI SZ observation of galaxy-cluster merger CIZA J2242+5301: perpendicular flows of gas and dark matter,"  AMI observations towards CIZA J2242+5301, in comparison with observations of
+weak gravitational lensing and X-ray emission from the literature, are used to
+investigate the behaviour of non-baryonic dark matter (NBDM) and gas during the
+merger. Analysis of the Sunyaev-Zel'dovich (SZ) signal indicates the presence
+of high pressure gas elongated perpendicularly to the X-ray and weak-lensing
+morphologies which, given the merger-axis constraints in the literature,
+implies that high pressure gas is pushed out into a linear structure during
+core passing. Simulations in the literature closely matching the inferred
+merger scenario show the formation of gas density and temperature structures
+perpendicular to the merger axis. These SZ observations are challenging for
+modified gravity theories in which NBDM is not the dominant contributor to
+galaxy-cluster gravity.
+",0,1,0,0,0,0
+20394,Development of a computer-aided design software for dental splint in orthognathic surgery,"  In the orthognathic surgery, dental splints are important and necessary to
+help the surgeon reposition the maxilla or mandible. However, the traditional
+methods of manual design of dental splints are difficult and time-consuming.
+The research on computer-aided design software for dental splints is rarely
+reported. Our purpose is to develop a novel special software named EasySplint
+to design the dental splints conveniently and efficiently. The design can be
+divided into two steps, which are the generation of initial splint base and the
+Boolean operation between it and the maxilla-mandibular model. The initial
+splint base is formed by ruled surfaces reconstructed using the manually picked
+points. Then, a method to accomplish Boolean operation based on the distance
+filed of two meshes is proposed. The interference elimination can be conducted
+on the basis of marching cubes algorithm and Boolean operation. The accuracy of
+the dental splint can be guaranteed since the original mesh is utilized to form
+the result surface. Using EasySplint, the dental splints can be designed in
+about 10 minutes and saved as a stereo lithography (STL) file for 3D printing
+in clinical applications. Three phantom experiments were conducted and the
+efficiency of our method was demonstrated.
+",1,1,0,0,0,0
+20395,"Energy-transport systems for optical lattices: derivation, analysis, simulation","  Energy-transport equations for the transport of fermions in optical lattices
+are formally derived from a Boltzmann transport equation with a periodic
+lattice potential in the diffusive limit. The limit model possesses a formal
+gradient-flow structure like in the case of the energy-transport equations for
+semiconductors. At the zeroth-order high temperature limit, the
+energy-transport equations reduce to the whole-space logarithmic diffusion
+equation which has some unphysical properties. Therefore, the first-order
+expansion is derived and analyzed. The existence of weak solutions to the
+time-discretized system for the particle and energy densities with periodic
+boundary conditions is proved. The difficulties are the nonstandard degeneracy
+and the quadratic gradient term. The main tool of the proof is a result on the
+strong convergence of the gradients of the approximate solutions. Numerical
+simulations in one space dimension show that the particle density converges to
+a constant steady state if the initial energy density is sufficiently large,
+otherwise the particle density converges to a nonconstant steady state.
+",0,0,1,0,0,0
+20396,"First principles study of structural, magnetic and electronic properties of CrAs","  We report ab initio density functional calculations of the structural and
+magnetic properties, and the electronic structure of CrAs. To simulate the
+observed pressure-driven experimental results, we perform our analysis for
+different volumes of the unit cell, showing that the structural, magnetic and
+electronic properties strongly depend on the size of the cell. We find that the
+calculated quantities are in good agreement with the experimental data, and we
+review our results in terms of the observed superconductivity.
+",0,1,0,0,0,0
+20397,Subspace Robust Wasserstein distances,"  Making sense of Wasserstein distances between discrete measures in
+high-dimensional settings remains a challenge. Recent work has advocated a
+two-step approach to improve robustness and facilitate the computation of
+optimal transport, using for instance projections on random real lines, or a
+preliminary quantization to reduce the number of points. We propose in this
+work a new robust variant of the Wasserstein distance. This quantity captures
+the maximal possible distance that can be realized between these two measures,
+after they have been projected orthogonally on a lower k dimensional subspace.
+We show that this distance inherits several favorably properties of OT, and
+that computing it can be cast as a convex problem involving the top k
+eigenvalues of the second order moment matrix of the displacements induced by a
+transport plan. We provide algorithms to approximate the computation of this
+saddle point using entropic regularization, and illustrate the interest of this
+approach empirically.
+",1,0,0,1,0,0
+20398,Wind accretion onto compact objects,"  X-ray emission associated to accretion onto compact objects displays
+important levels of photometric and spectroscopic time-variability. When the
+accretor orbits a Supergiant star, it captures a fraction of the supersonic
+radiatively-driven wind which forms shocks in its vicinity. The amplitude and
+stability of this gravitational beaming of the flow conditions the mass
+accretion rate responsible, in fine, for the X-ray luminosity of those
+Supergiant X-ray Binaries. The capacity of this low angular momentum inflow to
+form a disc-like structure susceptible to be the stage of well-known
+instabilities remains at stake. Using state-of-the-art numerical setups, we
+characterized the structure of a Bondi-Hoyle-Lyttleton flow onto a compact
+object, from the shock down to the vicinity of the accretor, typically five
+orders of magnitude smaller. The evolution of the mass accretion rate and of
+the bow shock which forms around the accretor (transverse structure, opening
+angle, stability, temperature profile) with the Mach number of the incoming
+flow is described in detail. The robustness of those simulations based on the
+High Performance Computing MPI-AMRVAC code is supported by the topology of the
+inner sonic surface, in agreement with theoretical expectations. We developed a
+synthetic model of mass transfer in Supergiant X-ray Binaries which couples the
+launching of the wind accordingly to the stellar parameters, the orbital
+evolution of the streamlines in a modified Roche potential and the accretion
+process. We show that the shape of the permanent flow is entirely determined by
+the mass ratio, the filling factor, the Eddington factor and the alpha-force
+multiplier. Provided scales such as the orbital period are known, we can trace
+back the observables to evaluate the mass accretion rates, the accretion
+mechanism (stream or wind-dominated) and the shearing of the inflow.
+",0,1,0,0,0,0
+20399,Riemannian Stein Variational Gradient Descent for Bayesian Inference,"  We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian
+inference method that generalizes Stein Variational Gradient Descent (SVGD) to
+Riemann manifold. The benefits are two-folds: (i) for inference tasks in
+Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information
+geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the
+unique advantages of SVGD to the Riemannian world. To appropriately transfer to
+Riemann manifolds, we conceive novel and non-trivial techniques for RSVGD,
+which are required by the intrinsically different characteristics of general
+Riemann manifolds from Euclidean spaces. We also discover Riemannian Stein's
+Identity and Riemannian Kernelized Stein Discrepancy. Experimental results show
+the advantages over SVGD of exploring distribution geometry and the advantages
+of particle-efficiency, iteration-effectiveness and approximation flexibility
+over other inference methods on Riemann manifolds.
+",0,0,0,1,0,0
+20400,Suspension-thermal noise in spring-antispring systems for future gravitational-wave detectors,"  Spring-antispring systems have been investigated as possible low-frequency
+seismic isolation in high-precision optical experiments. These systems provide
+the possibility to tune the fundamental resonance frequency to, in principle,
+arbitrarily low values, and at the same time maintain a compact design of the
+isolation system. It was argued though that thermal noise in spring-antispring
+systems would not be as small as one may naively expect from lowering the
+fundamental resonance frequency. In this paper, we present a detailed
+calculation of the suspension thermal noise for a specific spring-antispring
+system, namely the Roberts linkage. We find a concise expression of the
+suspension thermal noise spectrum, which assumes a form very similar to the
+well-known expression for a simple pendulum. It is found that while the Roberts
+linkage can provide strong seismic isolation due to a very low fundamental
+resonance frequency, its thermal noise is rather determined by the dimension of
+the system. We argue that this is true for all horizontal mechanical isolation
+systems with spring-antispring dynamics. This imposes strict requirements on
+mechanical spring-antispring systems for the seismic isolation in potential
+future low-frequency gravitational-wave detectors as we discuss for the four
+main concepts: atom-interferometric, superconducting, torsion-bars, and
+conventional laser interferometer.
+",0,1,0,0,0,0
+20401,Fooling Sets and the Spanning Tree Polytope,"  In the study of extensions of polytopes of combinatorial optimization
+problems, a notorious open question is that for the size of the smallest
+extended formulation of the Minimum Spanning Tree problem on a complete graph
+with $n$ nodes. The best known lower bound is $\Omega(n^2)$, the best known
+upper bound is $O(n^3)$.
+In this note we show that the venerable fooling set method cannot be used to
+improve the lower bound: every fooling set for the Spanning Tree polytope has
+size $O(n^2)$.
+",1,0,1,0,0,0
+20402,From Azéma supermartingales of finite honest times to optional semimartingales of class-($Σ$),"  Given a finite honest time, we derive representations for the additive and
+multiplicative decomposition of it's Azéma supermartingale in terms of
+optional supermartingales and its running supremum. We then extend the notion
+of semimartingales of class-$(\Sigma)$ to optional semimartingales with jumps
+in its finite variation part, allowing one to establish formulas similar to the
+Madan-Roynette-Yor option pricing formulas for larger class of processes.
+Finally, we introduce the optional multiplicative systems associated with
+positive submartingales and apply them to construct random times with given
+Azéma supermartingale.
+",0,0,0,0,0,1
+20403,Single versus Double Blind Reviewing at WSDM 2017,"  In this paper we study the implications for conference program committees of
+using single-blind reviewing, in which committee members are aware of the names
+and affiliations of paper authors, versus double-blind reviewing, in which this
+information is not visible to committee members. WSDM 2017, the 10th ACM
+International ACM Conference on Web Search and Data Mining, performed a
+controlled experiment in which each paper was reviewed by four committee
+members. Two of these four reviewers were chosen from a pool of committee
+members who had access to author information; the other two were chosen from a
+disjoint pool who did not have access to this information. This information
+asymmetry persisted through the process of bidding for papers, reviewing
+papers, and entering scores. Reviewers in the single-blind condition typically
+bid for 22% fewer papers, and preferentially bid for papers from top
+institutions. Once papers were allocated to reviewers, single-blind reviewers
+were significantly more likely than their double-blind counterparts to
+recommend for acceptance papers from famous authors and top institutions. The
+estimated odds multipliers are 1.63 for famous authors and 1.58 and 2.10 for
+top universities and companies respectively, so the result is tangible. For
+female authors, the associated odds multiplier of 0.78 is not statistically
+significant in our study. However, a meta-analysis places this value in line
+with that of other experiments, and in the context of this larger aggregate the
+gender effect is also statistically significant.
+",1,0,0,0,0,0
+20404,Scale-variant Topological Information for Characterizing Complex Networks,"  Real-world networks are difficult to characterize because of the variation of
+topological scales, the non-dyadic complex interactions, and the fluctuations.
+Here, we propose a general framework to address these problems via a
+methodology grounded on topology data analysis. By observing the diffusion
+process in a network at a single specified timescale, we can map the network
+nodes to a point cloud, which contains the topological information of the
+network at a single scale. We then calculate the point clouds constructed over
+variable timescales, which provide scale-variant topological information and
+enable a deep understanding of the network structure and functionality.
+Experiments on synthetic and real-world data demonstrate the effectiveness of
+our framework in identifying network models, classifying real-world networks
+and detecting transition points in time-evolving networks. Our work presents a
+unified analysis that is potentially applicable to more complicated network
+structures such as multilayer and multiplex networks.
+",1,0,0,0,0,0
+20405,Topological orbital superfluid with chiral d-wave order in a rotating optical lattice,"  Topological superfluid is an exotic state of quantum matter that possesses a
+nodeless superfluid gap in the bulk and Andreev edge modes at the boundary of a
+finite system. Here, we study a multi-orbital superfluid driven by attractive
+s-wave interaction in a rotating optical lattice. Interestingly, we find that
+the rotation induces the inter- orbital hybridization and drives the system
+into topological orbital superfluid in accordance with intrinsically chiral
+d-wave pairing characteristics. Thanks to the conservation of spin, the
+topological orbital superfluid supports four rather than two chiral Andreev
+edge modes at the boundary of the lattice. Moreover, we find that the intrinsic
+harmonic confining potential forms a circular spatial barrier which accumulates
+atoms and supports a mass current under injection of small angular momentum as
+external driving force. This feature provides an experimentally detectable
+phenomenon to verify the topological orbital superfluid with chiral d-wave
+order in a rotating optical lattice.
+",0,1,0,0,0,0
+20406,Optimal client recommendation for market makers in illiquid financial products,"  The process of liquidity provision in financial markets can result in
+prolonged exposure to illiquid instruments for market makers. In this case,
+where a proprietary position is not desired, pro-actively targeting the right
+client who is likely to be interested can be an effective means to offset this
+position, rather than relying on commensurate interest arising through natural
+demand. In this paper, we consider the inference of a client profile for the
+purpose of corporate bond recommendation, based on typical recorded information
+available to the market maker. Given a historical record of corporate bond
+transactions and bond meta-data, we use a topic-modelling analogy to develop a
+probabilistic technique for compiling a curated list of client recommendations
+for a particular bond that needs to be traded, ranked by probability of
+interest. We show that a model based on Latent Dirichlet Allocation offers
+promising performance to deliver relevant recommendations for sales traders.
+",0,0,0,1,0,0
+20407,Design of an Audio Interface for Patmos,"  This paper describes the design and implementation of an audio interface for
+the Patmos processor, which runs on an Altera DE2-115 FPGA board. This board
+has an audio codec included, the WM8731. The interface described in this work
+allows to receive and send audio from and to the WM8731, and to synthesize,
+store or manipulate audio signals writing C programs for Patmos. The audio
+interface described in this paper is intended to be used with the Patmos
+processor. Patmos is an open source RISC ISAs with a load-store architecture,
+that is optimized for Real-Time Systems. Patmos is part of a project founded by
+the European Union called T-CREST (Time-predictable Multi-Core Architecture for
+Embedded Systems).[5] The structure of this project is integrated with the
+Patmos project: new hardware modules have been added as IOs, which allow the
+communication between the processor and the audio codec. These modules include
+a clock generator for the audio chip, ADC and DAC modules for the audio
+conversion from analog to digital and vice versa, and an I2C module which
+allows setting configuration parameters on the audio codec. Moreover, a top
+module has been created, which connects all the modules previously mentioned
+between them, to Patmos and to the WM8731, using the external pins of the FPGA.
+",1,0,0,0,0,0
+20408,A Classifying Variational Autoencoder with Application to Polyphonic Music Generation,"  The variational autoencoder (VAE) is a popular probabilistic generative
+model. However, one shortcoming of VAEs is that the latent variables cannot be
+discrete, which makes it difficult to generate data from different modes of a
+distribution. Here, we propose an extension of the VAE framework that
+incorporates a classifier to infer the discrete class of the modeled data. To
+model sequential data, we can combine our Classifying VAE with a recurrent
+neural network such as an LSTM. We apply this model to algorithmic music
+generation, where our model learns to generate musical sequences in different
+keys. Most previous work in this area avoids modeling key by transposing data
+into only one or two keys, as opposed to the 10+ different keys in the original
+music. We show that our Classifying VAE and Classifying VAE+LSTM models
+outperform the corresponding non-classifying models in generating musical
+samples that stay in key. This benefit is especially apparent when trained on
+untransposed music data in the original keys.
+",1,0,0,1,0,0
+20409,On Estimation of Isotonic Piecewise Constant Signals,"  Consider a sequence of real data points $X_1,\ldots, X_n$ with underlying
+means $\theta^*_1,\dots,\theta^*_n$. This paper starts from studying the
+setting that $\theta^*_i$ is both piecewise constant and monotone as a function
+of the index $i$. For this, we establish the exact minimax rate of estimating
+such monotone functions, and thus give a non-trivial answer to an open problem
+in the shape-constrained analysis literature. The minimax rate involves an
+interesting iterated logarithmic dependence on the dimension, a phenomenon that
+is revealed through characterizing the interplay between the isotonic shape
+constraint and model selection complexity. We then develop a penalized
+least-squares procedure for estimating the vector
+$\theta^*=(\theta^*_1,\dots,\theta^*_n)^T$. This estimator is shown to achieve
+the derived minimax rate adaptively. For the proposed estimator, we further
+allow the model to be misspecified and derive oracle inequalities with the
+optimal rates, and show there exists a computationally efficient algorithm to
+compute the exact solution.
+",0,0,1,1,0,0
+20410,Deep Stacked Stochastic Configuration Networks for Non-Stationary Data Streams,"  The concept of stochastic configuration networks (SCNs) others a solid
+framework for fast implementation of feedforward neural networks through
+randomized learning. Unlike conventional randomized approaches, SCNs provide an
+avenue to select appropriate scope of random parameters to ensure the universal
+approximation property. In this paper, a deep version of stochastic
+configuration networks, namely deep stacked stochastic configuration network
+(DSSCN), is proposed for modeling non-stationary data streams. As an extension
+of evolving stochastic connfiguration networks (eSCNs), this work contributes a
+way to grow and shrink the structure of deep stochastic configuration networks
+autonomously from data streams. The performance of DSSCN is evaluated by six
+benchmark datasets. Simulation results, compared with prominent data stream
+algorithms, show that the proposed method is capable of achieving comparable
+accuracy and evolving compact and parsimonious deep stacked network
+architecture.
+",0,0,0,1,0,0
+20411,Extracting Syntactic Patterns from Databases,"  Many database columns contain string or numerical data that conforms to a
+pattern, such as phone numbers, dates, addresses, product identifiers, and
+employee ids. These patterns are useful in a number of data processing
+applications, including understanding what a specific field represents when
+field names are ambiguous, identifying outlier values, and finding similar
+fields across data sets. One way to express such patterns would be to learn
+regular expressions for each field in the database. Unfortunately, exist- ing
+techniques on regular expression learning are slow, taking hundreds of seconds
+for columns of just a few thousand values. In contrast, we develop XSystem, an
+efficient method to learn patterns over database columns in significantly less
+time. We show that these patterns can not only be built quickly, but are
+expressive enough to capture a number of key applications, including detecting
+outliers, measuring column similarity, and assigning semantic labels to columns
+(based on a library of regular expressions). We evaluate these applications
+with datasets that range from chemical databases (based on a collaboration with
+a pharmaceutical company), our university data warehouse, and open data from
+MassData.gov.
+",1,0,0,0,0,0
+20412,Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification,"  We address the problem of multi-class classification in the case where the
+number of classes is very large. We propose a double sampling strategy on top
+of a multi-class to binary reduction strategy, which transforms the original
+multi-class problem into a binary classification problem over pairs of
+examples. The aim of the sampling strategy is to overcome the curse of
+long-tailed class distributions exhibited in majority of large-scale
+multi-class classification problems and to reduce the number of pairs of
+examples in the expanded data. We show that this strategy does not alter the
+consistency of the empirical risk minimization principle defined over the
+double sample reduction. Experiments are carried out on DMOZ and Wikipedia
+collections with 10,000 to 100,000 classes where we show the efficiency of the
+proposed approach in terms of training and prediction time, memory consumption,
+and predictive performance with respect to state-of-the-art approaches.
+",1,0,0,1,0,0
+20413,"Understanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents","  Travel time on a route varies substantially by time of day and from day to
+day. It is critical to understand to what extent this variation is correlated
+with various factors, such as weather, incidents, events or travel demand level
+in the context of dynamic networks. This helps a better decision making for
+infrastructure planning and real-time traffic operation. We propose a
+data-driven approach to understand and predict highway travel time using
+spatio-temporal features of those factors, all of which are acquired from
+multiple data sources. The prediction model holistically selects the most
+related features from a high-dimensional feature space by correlation analysis,
+principle component analysis and LASSO. We test and compare the performance of
+several regression models in predicting travel time 30 min in advance via two
+case studies: (1) a 6-mile highway corridor of I-270N in D.C. region, and (2) a
+2.3-mile corridor of I-376E in Pittsburgh region. We found that some
+bottlenecks scattered in the network can imply congestion on those corridors at
+least 30 minutes in advance, including those on the alternative route to the
+corridors of study. In addition, real-time travel time is statistically related
+to incidents on some specific locations, morning/afternoon travel demand,
+visibility, precipitation, wind speed/gust and the weather type. All those
+spatio-temporal information together help improve prediction accuracy,
+comparing to using only speed data. In both case studies, random forest shows
+the most promise, reaching a root-mean-squared error of 16.6\% and 17.0\%
+respectively in afternoon peak hours for the entire year of 2014.
+",0,0,0,1,0,0
+20414,The power of sum-of-squares for detecting hidden structures,"  We study planted problems---finding hidden structures in random noisy
+inputs---through the lens of the sum-of-squares semidefinite programming
+hierarchy (SoS). This family of powerful semidefinite programs has recently
+yielded many new algorithms for planted problems, often achieving the best
+known polynomial-time guarantees in terms of accuracy of recovered solutions
+and robustness to noise. One theme in recent work is the design of spectral
+algorithms which match the guarantees of SoS algorithms for planted problems.
+Classical spectral algorithms are often unable to accomplish this: the twist in
+these new spectral algorithms is the use of spectral structure of matrices
+whose entries are low-degree polynomials of the input variables. We prove that
+for a wide class of planted problems, including refuting random constraint
+satisfaction problems, tensor and sparse PCA, densest-k-subgraph, community
+detection in stochastic block models, planted clique, and others, eigenvalues
+of degree-d matrix polynomials are as powerful as SoS semidefinite programs of
+roughly degree d. For such problems it is therefore always possible to match
+the guarantees of SoS without solving a large semidefinite program. Using
+related ideas on SoS algorithms and low-degree matrix polynomials (and inspired
+by recent work on SoS and the planted clique problem by Barak et al.), we prove
+new nearly-tight SoS lower bounds for the tensor and sparse principal component
+analysis problems. Our lower bounds for sparse principal component analysis are
+the first to suggest that going beyond existing algorithms for this problem may
+require sub-exponential time.
+",1,0,0,0,0,0
+20415,An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis,"  Polynomial chaos expansions (PCE) have seen widespread use in the context of
+uncertainty quantification. However, their application to structural
+reliability problems has been hindered by the limited performance of PCE in the
+tails of the model response and due to the lack of local metamodel error
+estimates. We propose a new method to provide local metamodel error estimates
+based on bootstrap resampling and sparse PCE. An initial experimental design is
+iteratively updated based on the current estimation of the limit-state surface
+in an active learning algorithm. The greedy algorithm uses the bootstrap-based
+local error estimates for the polynomial chaos predictor to identify the best
+candidate set of points to enrich the experimental design. We demonstrate the
+effectiveness of this approach on a well-known analytical benchmark
+representing a series system, on a truss structure and on a complex realistic
+frame structure problem.
+",0,0,0,1,0,0
+20416,Experimental Tests of Spirituality,"  We currently harness technologies that could shed new light on old
+philosophical questions, such as whether our mind entails anything beyond our
+body or whether our moral values reflect universal truth.
+",0,0,0,0,1,0
+20417,Chern classes and Gromov--Witten theory of projective bundles,"  We prove that the Gromov--Witten theory (GWT) of a projective bundle can be
+determined by the Chern classes and the GWT of the base. It completely answers
+a question raised in a previous paper (arXiv:1607.00740). Its consequences
+include that the GWT of the blow-up of X at a smooth subvariety Z is uniquely
+determined by GWT of X, Z plus some topological data.
+",0,0,1,0,0,0
+20418,Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations,"  We introduce the concept of numerical Gaussian processes, which we define as
+Gaussian processes with covariance functions resulting from temporal
+discretization of time-dependent partial differential equations. Numerical
+Gaussian processes, by construction, are designed to deal with cases where: (1)
+all we observe are noisy data on black-box initial conditions, and (2) we are
+interested in quantifying the uncertainty associated with such noisy data in
+our solutions to time-dependent partial differential equations. Our method
+circumvents the need for spatial discretization of the differential operators
+by proper placement of Gaussian process priors. This is an attempt to construct
+structured and data-efficient learning machines, which are explicitly informed
+by the underlying physics that possibly generated the observed data. The
+effectiveness of the proposed approach is demonstrated through several
+benchmark problems involving linear and nonlinear time-dependent operators. In
+all examples, we are able to recover accurate approximations of the latent
+solutions, and consistently propagate uncertainty, even in cases involving very
+long time integration.
+",1,0,1,1,0,0
+20419,Learning from Experience: A Dynamic Closed-Loop QoE Optimization for Video Adaptation and Delivery,"  The quality of experience (QoE) is known to be subjective and
+context-dependent. Identifying and calculating the factors that affect QoE is
+indeed a difficult task. Recently, a lot of effort has been devoted to estimate
+the users QoE in order to improve video delivery. In the literature, most of
+the QoE-driven optimization schemes that realize trade-offs among different
+quality metrics have been addressed under the assumption of homogenous
+populations. Nevertheless, people perceptions on a given video quality may not
+be the same, which makes the QoE optimization harder. This paper aims at taking
+a step further in order to address this limitation and meet users profiles. To
+do so, we propose a closed-loop control framework based on the
+users(subjective) feedbacks to learn the QoE function and optimize it at the
+same time. Our simulation results show that our system converges to a steady
+state, where the resulting QoE function noticeably improves the users
+feedbacks.
+",1,0,0,0,0,0
+20420,Deep CNN based feature extractor for text-prompted speaker recognition,"  Deep learning is still not a very common tool in speaker verification field.
+We study deep convolutional neural network performance in the text-prompted
+speaker verification task. The prompted passphrase is segmented into word
+states - i.e. digits -to test each digit utterance separately. We train a
+single high-level feature extractor for all states and use cosine similarity
+metric for scoring. The key feature of our network is the Max-Feature-Map
+activation function, which acts as an embedded feature selector. By using
+multitask learning scheme to train the high-level feature extractor we were
+able to surpass the classic baseline systems in terms of quality and achieved
+impressive results for such a novice approach, getting 2.85% EER on the RSR2015
+evaluation set. Fusion of the proposed and the baseline systems improves this
+result.
+",0,0,0,1,0,0
+20421,Data-Injection Attacks in Stochastic Control Systems: Detectability and Performance Tradeoffs,"  Consider a stochastic process being controlled across a communication
+channel. The control signal that is transmitted across the control channel can
+be replaced by a malicious attacker. The controller is allowed to implement any
+arbitrary detection algorithm to detect if an attacker is present. This work
+characterizes some fundamental limitations of when such an attack can be
+detected, and quantifies the performance degradation that an attacker that
+seeks to be undetected or stealthy can introduce.
+",1,0,1,0,0,0
+20422,Efficient Version-Space Reduction for Visual Tracking,"  Discrminative trackers, employ a classification approach to separate the
+target from its background. To cope with variations of the target shape and
+appearance, the classifier is updated online with different samples of the
+target and the background. Sample selection, labeling and updating the
+classifier is prone to various sources of errors that drift the tracker. We
+introduce the use of an efficient version space shrinking strategy to reduce
+the labeling errors and enhance its sampling strategy by measuring the
+uncertainty of the tracker about the samples. The proposed tracker, utilize an
+ensemble of classifiers that represents different hypotheses about the target,
+diversify them using boosting to provide a larger and more consistent coverage
+of the version-space and tune the classifiers' weights in voting. The proposed
+system adjusts the model update rate by promoting the co-training of the
+short-memory ensemble with a long-memory oracle. The proposed tracker
+outperformed state-of-the-art trackers on different sequences bearing various
+tracking challenges.
+",1,0,0,0,0,0
+20423,Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts,"  Most of the JavaScript code deployed in the wild has been minified, a process
+in which identifier names are replaced with short, arbitrary and meaningless
+names. Minified code occupies less space, but also makes the code extremely
+difficult to manually inspect and understand. This paper presents Context2Name,
+a deep learningbased technique that partially reverses the effect of
+minification by predicting natural identifier names for minified names. The
+core idea is to predict from the usage context of a variable a name that
+captures the meaning of the variable. The approach combines a lightweight,
+token-based static analysis with an auto-encoder neural network that summarizes
+usage contexts and a recurrent neural network that predict natural names for a
+given usage context. We evaluate Context2Name with a large corpus of real-world
+JavaScript code and show that it successfully predicts 47.5% of all minified
+identifiers while taking only 2.9 milliseconds on average to predict a name. A
+comparison with the state-of-the-art tools JSNice and JSNaughty shows that our
+approach performs comparably in terms of accuracy while improving in terms of
+efficiency. Moreover, Context2Name complements the state-of-the-art by
+predicting 5.3% additional identifiers that are missed by both existing tools.
+",1,0,0,1,0,0
+20424,Reconstructing fluid dynamics with micro-finite element,"  In the theory of the Navier-Stokes equations, the viscous fluid in
+incompressible flow is modelled as a homogeneous and dense assemblage of
+constituent ""fluid particles"" with viscous stress proportional to rate of
+strain. The crucial concept of fluid flow is the velocity of the particle that
+is accelerated by the pressure and viscous interaction around it. In this
+paper, by virtue of the alternative constituent ""micro-finite element"", we
+introduce a set of new intrinsic quantities, called the vortex fields, to
+characterise the relative orientation between elements and the feature of
+micro-eddies in the element, while the description of viscous interaction in
+fluid returns to the initial intuition that the interlayer friction is
+proportional to the slip strength. Such a framework enables us to reconstruct
+the dynamics theory of viscous fluid, in which the flowing fluid can be
+modelled as a finite covering of elements and consequently indicated by a
+space-time differential manifold that admits complex topological evolution.
+",0,1,0,0,0,0
+20425,Towards Black-box Iterative Machine Teaching,"  In this paper, we make an important step towards the black-box machine
+teaching by considering the cross-space machine teaching, where the teacher and
+the learner use different feature representations and the teacher can not fully
+observe the learner's model. In such scenario, we study how the teacher is
+still able to teach the learner to achieve faster convergence rate than the
+traditional passive learning. We propose an active teacher model that can
+actively query the learner (i.e., make the learner take exams) for estimating
+the learner's status and provably guide the learner to achieve faster
+convergence. The sample complexities for both teaching and query are provided.
+In the experiments, we compare the proposed active teacher with the omniscient
+teacher and verify the effectiveness of the active teacher model.
+",1,0,0,1,0,0
+20426,On Generalization and Regularization in Deep Learning,"  Why do large neural network generalize so well on complex tasks such as image
+classification or speech recognition? What exactly is the role regularization
+for them? These are arguably among the most important open questions in machine
+learning today. In a recent and thought provoking paper [C. Zhang et al.]
+several authors performed a number of numerical experiments that hint at the
+need for novel theoretical concepts to account for this phenomenon. The paper
+stirred quit a lot of excitement among the machine learning community but at
+the same time it created some confusion as discussions on OpenReview.net
+testifies. The aim of this pedagogical paper is to make this debate accessible
+to a wider audience of data scientists without advanced theoretical knowledge
+in statistical learning. The focus here is on explicit mathematical definitions
+and on a discussion of relevant concepts, not on proofs for which we provide
+references.
+",0,0,1,1,0,0
+20427,Nonparametric Inference for Auto-Encoding Variational Bayes,"  We would like to learn latent representations that are low-dimensional and
+highly interpretable. A model that has these characteristics is the Gaussian
+Process Latent Variable Model. The benefits and negative of the GP-LVM are
+complementary to the Variational Autoencoder, the former provides interpretable
+low-dimensional latent representations while the latter is able to handle large
+amounts of data and can use non-Gaussian likelihoods. Our inspiration for this
+paper is to marry these two approaches and reap the benefits of both. In order
+to do so we will introduce a novel approximate inference scheme inspired by the
+GP-LVM and the VAE. We show experimentally that the approximation allows the
+capacity of the generative bottle-neck (Z) of the VAE to be arbitrarily large
+without losing a highly interpretable representation, allowing reconstruction
+quality to be unlimited by Z at the same time as a low-dimensional space can be
+used to perform ancestral sampling from as well as a means to reason about the
+embedded data.
+",1,0,0,1,0,0
+20428,Imbalanced Malware Images Classification: a CNN based Approach,"  Deep convolutional neural networks (CNNs) can be applied to malware binary
+detection through images classification. The performance, however, is degraded
+due to the imbalance of malware families (classes). To mitigate this issue, we
+propose a simple yet effective weighted softmax loss which can be employed as
+the final layer of deep CNNs. The original softmax loss is weighted, and the
+weight value can be determined according to class size. A scaling parameter is
+also included in computing the weight. Proper selection of this parameter has
+been studied and an empirical option is given. The weighted loss aims at
+alleviating the impact of data imbalance in an end-to-end learning fashion. To
+validate the efficacy, we deploy the proposed weighted loss in a pre-trained
+deep CNN model and fine-tune it to achieve promising results on malware images
+classification. Extensive experiments also indicate that the new loss function
+can fit other typical CNNs with an improved classification performance.
+",1,0,0,1,0,0
+20429,Integrated optical force sensors using focusing photonic crystal arrays,"  Mechanical oscillators are at the heart of many sensor applications. Recently
+several groups have developed oscillators that are probed optically, fabricated
+from high-stress silicon nitride films. They exhibit outstanding force
+sensitivities of a few aN/Hz$^{1/2}$ and can also be made highly reflective,
+for efficient detection. The optical read-out usually requires complex
+experimental setups, including positioning stages and bulky cavities, making
+them impractical for real applications. In this paper we propose a novel way of
+building fully integrated all-optical force sensors based on low-loss silicon
+nitride mechanical resonators with a photonic crystal reflector. We can
+circumvent previous limitations in stability and complexity by simulating a
+suspended focusing photonic crystal, purely made of silicon nitride. Our design
+allows for an all integrated sensor, built out of a single block that
+integrates a full Fabry-Pérot cavity, without the need for assembly or
+alignment. The presented simulations will allow for a radical simplification of
+sensors based on high-Q silicon nitride membranes. Our results comprise, to the
+best of our knowledge, the first simulations of a focusing mirror made from a
+mechanically suspended flat membrane with subwavelength thickness. Cavity
+lengths between a few hundred $\mu$m and mm should be directly realizable.
+",0,1,0,0,0,0
+20430,Comparison of Signaling and Media Approaches to Detect VoIP SPIT Attack,"  IP networks became the most dominant type of information networks nowadays.
+It provides a number of services and makes it easy for users to be connected.
+IP networks provide an efficient way with a large number of services compared
+to other ways of voice communication. This leads to the migration to make voice
+calls via IP networks. Despite the wide range of IP networks services,
+availability, and its capabilities, there still a large number of security
+threats that affect IP networks and for sure affecting other services based on
+it and voice is one of them. This paper discusses reasons of migration from
+making voice calls via IP networks and leaving legacy networks, requirements to
+be available in IP networks to support voice transport, and concentrating on
+SPIT attack and its detection methods. Experiments took place to compare the
+different approaches used to detect spam over VoIP networks.
+",1,0,0,0,0,0
+20431,Coupling functions: Universal insights into dynamical interaction mechanisms,"  The dynamical systems found in Nature are rarely isolated. Instead they
+interact and influence each other. The coupling functions that connect them
+contain detailed information about the functional mechanisms underlying the
+interactions and prescribe the physical rule specifying how an interaction
+occurs. Here, we aim to present a coherent and comprehensive review
+encompassing the rapid progress made recently in the analysis, understanding
+and applications of coupling functions. The basic concepts and characteristics
+of coupling functions are presented through demonstrative examples of different
+domains, revealing the mechanisms and emphasizing their multivariate nature.
+The theory of coupling functions is discussed through gradually increasing
+complexity from strong and weak interactions to globally-coupled systems and
+networks. A variety of methods that have been developed for the detection and
+reconstruction of coupling functions from measured data is described. These
+methods are based on different statistical techniques for dynamical inference.
+Stemming from physics, such methods are being applied in diverse areas of
+science and technology, including chemistry, biology, physiology, neuroscience,
+social sciences, mechanics and secure communications. This breadth of
+application illustrates the universality of coupling functions for studying the
+interaction mechanisms of coupled dynamical systems.
+",0,1,0,0,0,0
+20432,Uncertainty quantification for radio interferometric imaging: II. MAP estimation,"  Uncertainty quantification is a critical missing component in radio
+interferometric imaging that will only become increasingly important as the
+big-data era of radio interferometry emerges. Statistical sampling approaches
+to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC) sampling,
+can in principle recover the full posterior distribution of the image, from
+which uncertainties can then be quantified. However, for massive data sizes,
+like those anticipated from the Square Kilometre Array (SKA), it will be
+difficult if not impossible to apply any MCMC technique due to its inherent
+computational cost. We formulate Bayesian inference problems with
+sparsity-promoting priors (motivated by compressive sensing), for which we
+recover maximum a posteriori (MAP) point estimators of radio interferometric
+images by convex optimisation. Exploiting recent developments in the theory of
+probability concentration, we quantify uncertainties by post-processing the
+recovered MAP estimate. Three strategies to quantify uncertainties are
+developed: (i) highest posterior density credible regions; (ii) local credible
+intervals (cf. error bars) for individual pixels and superpixels; and (iii)
+hypothesis testing of image structure. These forms of uncertainty
+quantification provide rich information for analysing radio interferometric
+observations in a statistically robust manner. Our MAP-based methods are
+approximately $10^5$ times faster computationally than state-of-the-art MCMC
+methods and, in addition, support highly distributed and parallelised
+algorithmic structures. For the first time, our MAP-based techniques provide a
+means of quantifying uncertainties for radio interferometric imaging for
+realistic data volumes and practical use, and scale to the emerging big-data
+era of radio astronomy.
+",0,1,0,1,0,0
+20433,Semialgebraic Invariant Synthesis for the Kannan-Lipton Orbit Problem,"  The \emph{Orbit Problem} consists of determining, given a linear
+transformation $A$ on $\mathbb{Q}^d$, together with vectors $x$ and $y$,
+whether the orbit of $x$ under repeated applications of $A$ can ever reach $y$.
+This problem was famously shown to be decidable by Kannan and Lipton in the
+1980s.
+In this paper, we are concerned with the problem of synthesising suitable
+\emph{invariants} $\mathcal{P} \subseteq \mathbb{R}^d$, \emph{i.e.}, sets that
+are stable under $A$ and contain $x$ and not $y$, thereby providing compact and
+versatile certificates of non-reachability. We show that whether a given
+instance of the Orbit Problem admits a semialgebraic invariant is decidable,
+and moreover in positive instances we provide an algorithm to synthesise
+suitable invariants of polynomial size.
+It is worth noting that the existence of \emph{semilinear} invariants, on the
+other hand, is (to the best of our knowledge) not known to be decidable.
+",1,0,1,0,0,0
+20434,Design and characterization of the Large-Aperture Experiment to Detect the Dark Age (LEDA) radiometer systems,"  The Large-Aperture Experiment to Detect the Dark Age (LEDA) was designed to
+detect the predicted O(100)mK sky-averaged absorption of the Cosmic Microwave
+Background by Hydrogen in the neutral pre- and intergalactic medium just after
+the cosmological Dark Age. The spectral signature would be associated with
+emergence of a diffuse Ly$\alpha$ background from starlight during 'Cosmic
+Dawn'. Recently, Bowman et al. (2018) have reported detection of this predicted
+absorption feature, with an unexpectedly large amplitude of 530 mK, centered at
+78 MHz. Verification of this result by an independent experiment, such as LEDA,
+is pressing. In this paper, we detail design and characterization of the LEDA
+radiometer systems, and a first-generation pipeline that instantiates a signal
+path model. Sited at the Owens Valley Radio Observatory Long Wavelength Array,
+LEDA systems include the station correlator, five well-separated redundant dual
+polarization radiometers and backend electronics. The radiometers deliver a
+30-85MHz band (16<z<34) and operate as part of the larger interferometric
+array, for purposes ultimately of in situ calibration. Here, we report on the
+LEDA system design, calibration approach, and progress in characterization as
+of January 2016. The LEDA systems are currently being modified to improve
+performance near 78 MHz in order to verify the purported absorption feature.
+",0,1,0,0,0,0
+20435,Automatic Bayesian Density Analysis,"  Making sense of a dataset in an automatic and unsupervised fashion is a
+challenging problem in statistics and AI. Classical approaches for density
+estimation are usually not flexible enough to deal with the uncertainty
+inherent to real-world data: they are often restricted to fixed latent
+interaction models and homogeneous likelihoods; they are sensitive to missing,
+corrupt and anomalous data; moreover, their expressiveness generally comes at
+the price of intractable inference. As a result, supervision from statisticians
+is usually needed to find the right model for the data. However, as domain
+experts do not necessarily have to be experts in statistics, we propose
+Automatic Bayesian Density Analysis (ABDA) to make density estimation
+accessible at large. ABDA automates the selection of adequate likelihood models
+from arbitrarily rich dictionaries while modeling their interactions via a deep
+latent structure adaptively learned from data as a sum-product network. ABDA
+casts uncertainty estimation at these local and global levels into a joint
+Bayesian inference problem, providing robust and yet tractable inference.
+Extensive empirical evidence shows that ABDA is a suitable tool for automatic
+exploratory analysis of heterogeneous tabular data, allowing for missing value
+estimation, statistical data type and likelihood discovery, anomaly detection
+and dependency structure mining, on top of providing accurate density
+estimation.
+",0,0,0,1,0,0
+20436,MLCapsule: Guarded Offline Deployment of Machine Learning as a Service,"  With the widespread use of machine learning (ML) techniques, ML as a service
+has become increasingly popular. In this setting, an ML model resides on a
+server and users can query the model with their data via an API. However, if
+the user's input is sensitive, sending it to the server is not an option.
+Equally, the service provider does not want to share the model by sending it to
+the client for protecting its intellectual property and pay-per-query business
+model. In this paper, we propose MLCapsule, a guarded offline deployment of
+machine learning as a service. MLCapsule executes the machine learning model
+locally on the user's client and therefore the data never leaves the client.
+Meanwhile, MLCapsule offers the service provider the same level of control and
+security of its model as the commonly used server-side execution. In addition,
+MLCapsule is applicable to offline applications that require local execution.
+Beyond protecting against direct model access, we demonstrate that MLCapsule
+allows for implementing defenses against advanced attacks on machine learning
+models such as model stealing/reverse engineering and membership inference.
+",0,0,0,1,0,0
+20437,Brownian forgery of statistical dependences,"  The balance held by Brownian motion between temporal regularity and
+randomness is embodied in a remarkable way by Levy's forgery of continuous
+functions. Here we describe how this property can be extended to forge
+arbitrary dependences between two statistical systems, and then establish a new
+Brownian independence test based on fluctuating random paths. We also argue
+that this result allows revisiting the theory of Brownian covariance from a
+physical perspective and opens the possibility of engineering nonlinear
+correlation measures from more general functional integrals.
+",0,0,1,1,0,0
+20438,Internal sizes in $μ$-abstract elementary classes,"  Working in the context of $\mu$-abstract elementary classes ($\mu$-AECs) -
+or, equivalently, accessible categories with all morphisms monomorphisms - we
+examine the two natural notions of size that occur, namely cardinality of
+underlying sets and internal size. The latter, purely category-theoretic,
+notion generalizes e.g. density character in complete metric spaces and
+cardinality of orthogonal bases in Hilbert spaces. We consider the relationship
+between these notions under mild set-theoretic hypotheses, including weakenings
+of the singular cardinal hypothesis. We also establish preliminary results on
+the existence and categoricity spectra of $\mu$-AECs, including specific
+examples showing dramatic failures of the eventual categoricity conjecture
+(with categoricity defined using cardinality) in $\mu$-AECs.
+",0,0,1,0,0,0
+20439,Seasonal forecasts of the summer 2016 Yangtze River basin rainfall,"  The Yangtze River has been subject to heavy flooding throughout history, and
+in recent times severe floods such as those in 1998 have resulted in heavy loss
+of life and livelihoods. Dams along the river help to manage flood waters, and
+are important sources of electricity for the region. Being able to forecast
+high-impact events at long lead times therefore has enormous potential benefit.
+Recent improvements in seasonal forecasting mean that dynamical climate models
+can start to be used directly for operational services. The teleconnection from
+El Niño to Yangtze River basin rainfall meant that the strong El Niño in
+winter 2015/2016 provided a valuable opportunity to test the application of a
+dynamical forecast system.
+This paper therefore presents a case study of a real time seasonal forecast
+for the Yangtze River basin, building on previous work demonstrating the
+retrospective skill of such a forecast. A simple forecasting methodology is
+presented, in which the forecast probabilities are derived from the historical
+relationship between hindcast and observations. Its performance for 2016 is
+discussed. The heavy rainfall in the May-June-July period was correctly
+forecast well in advance. August saw anomalously low rainfall, and the
+forecasts for the June-July-August period correctly showed closer to average
+levels. The forecasts contributed to the confidence of decision-makers across
+the Yangtze River basin. Trials of climate services such as this help to
+promote appropriate use of seasonal forecasts, and highlight areas for future
+improvements.
+",0,1,0,0,0,0
+20440,Modeling Interference Via Symmetric Treatment Decomposition,"  Classical causal inference assumes a treatment meant for a given unit does
+not have an effect on other units. When this ""no interference"" assumption is
+violated, new types of spillover causal effects arise, and causal inference
+becomes much more difficult. In addition, interference introduces a unique
+complication where outcomes may transmit treatment influences to each other,
+which is a relationship that has some features of a causal one, but is
+symmetric. In settings where detailed temporal information on outcomes is not
+available, addressing this complication using statistical inference methods
+based on Directed Acyclic Graphs (DAGs) (Ogburn & VanderWeele, 2014) leads to
+conceptual difficulties.
+In this paper, we develop a new approach to decomposing the spillover effect
+into direct (also known as the contagion effect) and indirect (also known as
+the infectiousness effect) components that extends the DAG based treatment
+decomposition approach to mediation found in (Robins & Richardson, 2010) to
+causal chain graph models (Lauritzen & Richardson, 2002). We show that when
+these components of the spillover effect are identified in these models, they
+have an identifying functional, which we call the symmetric mediation formula,
+that generalizes the mediation formula in DAGs (Pearl, 2011). We further show
+that, unlike assumptions in classical mediation analysis, an assumption
+permitting identification in our setting leads to restrictions on the observed
+data law, making the assumption empirically falsifiable. Finally, we discuss
+statistical inference for the components of the spillover effect in the special
+case of two interacting outcomes, and discuss a maximum likelihood estimator,
+and a doubly robust estimator.
+",0,0,0,1,0,0
+20441,A Bayesian algorithm for distributed network localization using distance and direction data,"  A reliable, accurate, and affordable positioning service is highly required
+in wireless networks. In this paper, the novel Message Passing Hybrid
+Localization (MPHL) algorithm is proposed to solve the problem of cooperative
+distributed localization using distance and direction estimates. This hybrid
+approach combines two sensing modalities to reduce the uncertainty in
+localizing the network nodes. A statistical model is formulated for the
+problem, and approximate minimum mean square error (MMSE) estimates of the node
+locations are computed. The proposed MPHL is a distributed algorithm based on
+belief propagation (BP) and Markov chain Monte Carlo (MCMC) sampling. It
+improves the identifiability of the localization problem and reduces its
+sensitivity to the anchor node geometry, compared to distance-only or
+direction-only localization techniques. For example, the unknown location of a
+node can be found if it has only a single neighbor; and a whole network can be
+localized using only a single anchor node. Numerical results are presented
+showing that the average localization error is significantly reduced in almost
+every simulation scenario, about 50% in most cases, compared to the competing
+algorithms.
+",1,0,0,1,0,0
+20442,Bounds for multivariate residues and for the polynomials in the elimination theorem,"  We present several upper bounds for the height of global residues of rational
+forms on an affine variety. As a consequence, we deduce upper bounds for the
+height of the coefficients in the Bergman-Weil trace formula.
+We also present upper bounds for the degree and the height of the polynomials
+in the elimination theorem on an affine variety. This is an arithmetic analogue
+of Jelonek's effective elimination theorem, that plays a crucial role in the
+proof of our bounds for the height of global residues.
+",0,0,1,0,0,0
+20443,An adsorbed gas estimation model for shale gas reservoirs via statistical learning,"  Shale gas plays an important role in reducing pollution and adjusting the
+structure of world energy. Gas content estimation is particularly significant
+in shale gas resource evaluation. There exist various estimation methods, such
+as first principle methods and empirical models. However, resource evaluation
+presents many challenges, especially the insufficient accuracy of existing
+models and the high cost resulting from time-consuming adsorption experiments.
+In this research, a low-cost and high-accuracy model based on geological
+parameters is constructed through statistical learning methods to estimate
+adsorbed shale gas content
+",0,1,0,1,0,0
+20444,A Semantic Loss Function for Deep Learning with Symbolic Knowledge,"  This paper develops a novel methodology for using symbolic knowledge in deep
+learning. From first principles, we derive a semantic loss function that
+bridges between neural output vectors and logical constraints. This loss
+function captures how close the neural network is to satisfying the constraints
+on its output. An experimental evaluation shows that it effectively guides the
+learner to achieve (near-)state-of-the-art results on semi-supervised
+multi-class classification. Moreover, it significantly increases the ability of
+the neural network to predict structured objects, such as rankings and paths.
+These discrete concepts are tremendously difficult to learn, and benefit from a
+tight integration of deep learning and symbolic reasoning methods.
+",1,0,0,1,0,0
+20445,Chaotic properties of a turbulent isotropic fluid,"  By tracking the divergence of two initially close trajectories in phase space
+in an Eulerian approach to forced turbulence, the relation between the maximal
+Lyapunov exponent $\lambda$, and the Reynolds number $Re$ is measured using
+direct numerical simulations, performed on up to $2048^3$ collocation points.
+The Lyapunov exponent is found to solely depend on the Reynolds number with
+$\lambda \propto Re^{0.53}$ and that after a transient period the divergence of
+trajectories grows at the same rate at all scales. Finally a linear divergence
+is seen that is dependent on the energy forcing rate. Links are made with other
+chaotic systems.
+",0,1,0,0,0,0
+20446,White light emission from silicon nanoparticles,"  As one of the most important semiconductors, silicon (Si) has been used to
+fabricate electronic devices, waveguides, detectors, and solar cells etc.
+However, its indirect bandgap hinders the use of Si for making good emitters1.
+For integrated photonic circuits, Si-based emitters with sizes in the range of
+100-300 nm are highly desirable. Here, we show that efficient white light
+emission can be realized in spherical and cylindrical Si nanoparticles with
+feature sizes of ~200 nm. The up-converted luminescence appears at the magnetic
+and electric multipole resonances when the nanoparticles are resonantly excited
+at their magnetic and electric dipole resonances by using femtosecond (fs)
+laser pulses with ultralow low energy of ~40 pJ. The lifetime of the white
+light is as short as ~52 ps, almost three orders of magnitude smaller than the
+state-of-the-art results reported so far for Si (~10 ns). Our finding paves the
+way for realizing efficient Si-based emitters compatible with current
+semiconductor fabrication technology, which can be integrated to photonic
+circuits.
+",0,1,0,0,0,0
+20447,The collapse of ecosystem engineer populations,"  Humans are the ultimate ecosystem engineers who have profoundly transformed
+the world's landscapes in order to enhance their survival. Somewhat
+paradoxically, however, sometimes the unforeseen effect of this ecosystem
+engineering is the very collapse of the population it intended to protect. Here
+we use a spatial version of a standard population dynamics model of ecosystem
+engineers to study the colonization of unexplored virgin territories by a small
+settlement of engineers. We find that during the expansion phase the population
+density reaches values much higher than those the environment can support in
+the equilibrium situation. When the colonization front reaches the boundary of
+the available space, the population density plunges sharply and attains its
+equilibrium value. The collapse takes place without warning and happens just
+after the population reaches its peak number. We conclude that overpopulation
+and the consequent collapse of an expanding population of ecosystem engineers
+is a natural consequence of the nonlinear feedback between the population and
+environment variables.
+",0,1,0,0,0,0
+20448,Highly Viscous Microjet Generator,"  This paper describes a simple yet novel system for generating a highly
+viscous microjet. The jet is produced inside a wettable thin tube partially
+submerged in a liquid. The gas-liquid interface inside the tube, which is
+initially concave, is kept much deeper than that outside the tube. An impulsive
+force applied at the bottom of a liquid container leads to significant
+acceleration of the liquid inside the tube followed by flow-focusing due to the
+concave interface. The jet generation process can be divided into two parts
+that occur in different time scales, i.e. the Impact time (impact duration $\le
+O(10^{-4})$ s) and Focusing time (focusing duration $\gg O(10^{-4})$ s). In
+Impact time, the liquid accelerates suddenly due to the impact. In Focusing
+time, the microjet emerges due to flow-focusing. In order to explain the sudden
+acceleration inside the tube in Impact time, we develop a physical model based
+on a pressure impulse approach. Numerical simulations confirm the proposed
+model, indicating that the basic mechanism of the acceleration of the liquid
+due to the impulsive force is elucidated. Remarkably, the viscous effect is
+negligible in Impact time. In contrast, in Focusing time, the viscosity plays
+an important role in the microjet generation. We experimentally and numerically
+investigate the velocity of microjets with various viscosities. We find that
+higher viscosities lead to reduction of the jet velocity, which can be
+described by using Reynolds number (the ratio between the inertia force and the
+viscous force). This novel device may be a starting point for next-generation
+technologies, such as high-viscosity inkjet printers including bioprinters and
+needle-free injection devices for minimally invasive medical treatments.
+",0,1,0,0,0,0
+20449,Section problems for configuration spaces of surfaces,"  In this paper we give a close-to-sharp answer to the basic questions: When is
+there a continuous way to add a point to a configuration of $n$ ordered points
+on a surface $S$ of finite type so that all the points are still distinct? When
+this is possible, what are all the ways to do it? More precisely, let
+PConf$_n(S)$ be the space of ordered $n$-tuples of distinct points in $S$. Let
+$f_n(S): \text{PConf}_{n+1}(S) \to \text{PConf}_n(S)$ be the map given by
+$f_n(x_0,x_1,\ldots,x_n):=(x_1,\ldots,x_n)$. We will classify all continuous
+sections of $f_n$ by proving:
+1. If $S=\mathbb{R}^2$ and $n>3$, any section of $f_{n}(S)$ is either ""adding
+a point at infinity"" or ""adding a point near $x_k$"". (We define these two terms
+in Section 2.1, whether we can define ""adding a point near $x_k$"" or ""adding a
+point at infinity"" depends in a delicate way on properties of $S$.)
+2. If $S=S^2$ a $2$-sphere and $n>4$, any section of $f_{n}(S)$ is ""adding a
+point near $x_k$"", if $S=S^2$ and $n=2$, the bundle $f_n(S)$ does not have a
+section. (We define this term in Section 3.2)
+3. If $S=S_g$ a surface of genus $g>1$ and for $n>1$, the bundle $f_{n}(S)$
+does not have a section.
+",0,0,1,0,0,0
+20450,Odd-triplet superconductivity in single-level quantum dots,"  We study the interplay of spin and charge coherence in a single-level quantum
+dot. A tunnel coupling to a superconducting lead induces superconducting
+correlations in the dot. With full spin symmetry retained, only even-singlet
+superconducting correlations are generated. An applied magnetic field or
+attached ferromagnetic leads partially or fully reduce the spin symmetry, and
+odd-triplet superconducting correlations are generated as well. For
+single-level quantum dots, no other superconducting correlations are possible.
+We analyze, with the help of a diagrammatic real-time technique, the interplay
+of spin symmetry and superconductivity and its signatures in electronic
+transport, in particular current and shot noise.
+",0,1,0,0,0,0
+20451,From Neuronal Models to Neuronal Dynamics and Image Processing,"  This paper is an introduction to the membrane potential equation for neurons.
+Its properties are described, as well as sample applications. Networks of these
+equations can be used for modeling neuronal systems, which also process images
+and video sequences, respectively. Specifically, (i) a dynamic retina is
+proposed (based on a reaction-diffusion system), which predicts afterimages and
+simple visual illusions, (ii) a system for texture segregation (texture
+elements are understood as even-symmetric contrast features), and (iii) a
+network for detecting object approaches (inspired by the locust visual system).
+",0,0,0,0,1,0
+20452,A compilation of LEGO Technic parts to support learning experiments on linkages,"  We present a compilation of LEGO Technic parts to provide easy-to-build
+constructions of basic planar linkages. Some technical issues and their
+possible solutions are discussed. To solve questions on fine details---like
+deciding whether the motion is an exactly straight line or not---we refer to
+the dynamic mathematics software tool GeoGebra.
+",0,0,1,0,0,0
+20453,On the Casas-Alvero conjecture,"  The conjecture is formulated in an affine structure and linked with
+dimension=1 of the defined CA sets. Then some known results are proved in this
+context. The short intended proof relies on a direct yet unclear statement
+about homogeneous dependence of algebraic equations. This might not be a
+complete proof or even one on the right track, but it may provoke more thoughts
+in this respect as expected.
+",0,0,1,0,0,0
+20454,Effect of compressibility and aspect ratio on performance of long elastic seals,"  Recent experiments show no statistical impact of seal length on the
+performance of long elastomeric seals in relatively smooth test fixtures.
+Motivated by these results, we analytically and computationally investigate the
+combined effects of seal length and compressibility on the maximum differential
+pressure a seal can support. We present a Saint-Venant type analytic shear lag
+solution for slightly compressible seals with large aspect ratios, which
+compares well with nonlinear finite element simulations in regions far from the
+ends of the seal. However, at the high- and low-pressure ends, where fracture
+is observed experimentally, the analytic solution is in poor agreement with
+detailed finite element calculations. Nevertheless, we show that the analytic
+solution provides far-field stress measures that correlate, over a range of
+aspect ratios and bulk moduli, the calculated energy release rates for the
+growth of small cracks at the two ends of the seal. Thus a single finite
+element simulation coupled with the analytic solution can be used to determine
+tendencies for fracture at the two ends of the seal over a wide range of
+geometry and compressibility. Finally, using a hypothetical critical energy
+release rate, predictions for whether a crack on the high-pressure end will
+begin to grow before or after a crack on the low-pressure end begins to grow
+are made using the analytic solution and compared with finite element
+simulations for finite deformation, hyperelastic seals.
+",0,1,0,0,0,0
+20455,Integral models of reductive groups and integral Mumford-Tate groups,"  Let $G$ be a reductive algebraic group over a $p$-adic field or number field
+$K$, and let $V$ be a $K$-linear faithful representation of $G$. A lattice
+$\Lambda$ in the vector space $V$ defines a model $\hat{G}_{\Lambda}$ of $G$
+over $\mathscr{O}_K$. One may wonder to what extent $\Lambda$ is determined by
+the group scheme $\hat{G}_{\Lambda}$. In this paper we prove that up to a
+natural equivalence relation on the set of lattices there are only finitely
+many $\Lambda$ corresponding to one model $\hat{G}_{\Lambda}$. Furthermore, we
+relate this fact to moduli spaces of abelian varieties as follows: let
+$\mathscr{A}_{g,n}$ be the moduli space of principally polarised abelian
+varieties of dimension $g$ with level $n$ structure. We prove that there are at
+most finitely many special subvarieties of $\mathscr{A}_{g,n}$ with a given
+integral generic Mumford-Tate group.
+",0,0,1,0,0,0
+20456,A proof of Boca's Theorem,"  We give a general method of extending unital completely positive maps to
+amalgamated free products of C*-algebras. As an application we give a dilation
+theoretic proof of Boca's Theorem.
+",0,0,1,0,0,0
+20457,Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations,"  Convolution with Green's function of a differential operator appears in a lot
+of applications e.g. Lippmann-Schwinger integral equation. Algorithms for
+computing such are usually non-trivial and require non-uniform mesh. However,
+recently Vico, Greengard and Ferrando developed method for computing
+convolution with smooth functions with compact support with spectral accuracy,
+requiring nothing more than Fast Fourier Transform (FFT). Their approach is
+very suitable for the low-rank tensor implementation which we develop using
+Quantized Tensor Train (QTT) decomposition.
+",1,0,0,0,0,0
+20458,"Mott insulators of hardcore bosons in 1D: many-body orders, entanglement, edge modes","  Many-body phenomena were always an integral part of physics comprising of
+collective behaviors through self-organization, in systems consisting of many
+components and degrees of freedom. We investigate the collective behaviors of
+strongly interacting particles confined in one dimension. We show that
+many-body orders with topological characteristics can be found at the Mott
+insulator limit for hardcore bosons, at different fillings, without considering
+the spin degree of freedom or long-range microscopic interactions. These orders
+have unique properties like weak or strong quantum correlations (entanglement),
+quantified by the entanglement entropy, edge excitations/modes and gapped
+energy spectrum with highly degenerate ground state, bearing resemblance to
+topologically ordered phases of matter.
+",0,1,0,0,0,0
+20459,Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects,"  When performing Bayesian data analysis using a general linear mixed model,
+the resulting posterior density is almost always analytically intractable.
+However, if proper conditionally conjugate priors are used, there is a simple
+two-block Gibbs sampler that is geometrically ergodic in nearly all practical
+settings, including situations where $p > n$ (Abrahamsen and Hobert, 2017).
+Unfortunately, the (conditionally conjugate) multivariate normal prior on
+$\beta$ does not perform well in the high-dimensional setting where $p \gg n$.
+In this paper, we consider an alternative model in which the multivariate
+normal prior is replaced by the normal-gamma shrinkage prior developed by
+Griffin and Brown (2010). This change leads to a much more complex posterior
+density, and we develop a simple MCMC algorithm for exploring it. This
+algorithm, which has both deterministic and random scan components, is easier
+to analyze than the more obvious three-step Gibbs sampler. Indeed, we prove
+that the new algorithm is geometrically ergodic in most practical settings.
+",0,0,1,1,0,0
+20460,Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction,"  With the increasing of electric vehicle (EV) adoption in recent years, the
+impact of EV charging activities to the power grid becomes more and more
+significant. In this article, an optimal scheduling algorithm which combines
+smart EV charging and V2G gird service is developed to integrate EVs into power
+grid as distributed energy resources, with improved system cost performance.
+Specifically, an optimization problem is formulated and solved at each EV
+charging station according to control signal from aggregated control center and
+user charging behavior prediction by mean estimation and linear regression. The
+control center collects distributed optimization results and updates the
+control signal, periodically. The iteration continues until it converges to
+optimal scheduling. Experimental result shows this algorithm helps fill the
+valley and shave the peak in electric load profiles within a microgrid, while
+the energy demand of individual driver can be satisfied.
+",1,0,1,0,0,0
+20461,Horcrux: A Password Manager for Paranoids,"  Vulnerabilities in password managers are unremitting because current designs
+provide large attack surfaces, both at the client and server. We describe and
+evaluate Horcrux, a password manager that is designed holistically to minimize
+and decentralize trust, while retaining the usability of a traditional password
+manager. The prototype Horcrux client, implemented as a Firefox add-on, is
+split into two components, with code that has access to the user's master's
+password and any key material isolated into a small auditable component,
+separate from the complexity of managing the user interface. Instead of
+exposing actual credentials to the DOM, a dummy username and password are
+autofilled by the untrusted component. The trusted component intercepts and
+modifies POST requests before they are encrypted and sent over the network. To
+avoid trusting a centralized store, stored credentials are secret-shared over
+multiple servers. To provide domain and username privacy, while maintaining
+resilience to off-line attacks on a compromised password store, we incorporate
+cuckoo hashing in a way that ensures an attacker cannot determine if a guessed
+master password is correct. Our approach only works for websites that do not
+manipulate entered credentials in the browser client, so we conducted a
+large-scale experiment that found the technique appears to be compatible with
+over 98% of tested login forms.
+",1,0,0,0,0,0
+20462,Cross-validation,"  This text is a survey on cross-validation. We define all classical
+cross-validation procedures, and we study their properties for two different
+goals: estimating the risk of a given estimator, and selecting the best
+estimator among a given family. For the risk estimation problem, we compute the
+bias (which can also be corrected) and the variance of cross-validation
+methods. For estimator selection, we first provide a first-order analysis
+(based on expectations). Then, we explain how to take into account second-order
+terms (from variance computations, and by taking into account the usefulness of
+overpenalization). This allows, in the end, to provide some guidelines for
+choosing the best cross-validation method for a given learning problem.
+",0,0,1,1,0,0
+20463,Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guarantees,"  Objective: Using traditional approaches, a Brain-Computer Interface (BCI)
+requires the collection of calibration data for new subjects prior to online
+use. Calibration time can be reduced or eliminated e.g.~by transfer of a
+pre-trained classifier or unsupervised adaptive classification methods which
+learn from scratch and adapt over time. While such heuristics work well in
+practice, none of them can provide theoretical guarantees. Our objective is to
+modify an event-related potential (ERP) paradigm to work in unison with the
+machine learning decoder to achieve a reliable calibration-less decoding with a
+guarantee to recover the true class means.
+Method: We introduce learning from label proportions (LLP) to the BCI
+community as a new unsupervised, and easy-to-implement classification approach
+for ERP-based BCIs. The LLP estimates the mean target and non-target responses
+based on known proportions of these two classes in different groups of the
+data. We modified a visual ERP speller to meet the requirements of the LLP. For
+evaluation, we ran simulations on artificially created data sets and conducted
+an online BCI study with N=13 subjects performing a copy-spelling task.
+Results: Theoretical considerations show that LLP is guaranteed to minimize
+the loss function similarly to a corresponding supervised classifier. It
+performed well in simulations and in the online application, where 84.5% of
+characters were spelled correctly on average without prior calibration.
+Significance: The continuously adapting LLP classifier is the first
+unsupervised decoder for ERP BCIs guaranteed to find the true class means. This
+makes it an ideal solution to avoid a tedious calibration and to tackle
+non-stationarities in the data. Additionally, LLP works on complementary
+principles compared to existing unsupervised methods, allowing for their
+further enhancement when combined with LLP.
+",1,0,0,1,0,0
+20464,Generalized Index Coding Problem and Discrete Polymatroids,"  The index coding problem has been generalized recently to accommodate
+receivers which demand functions of messages and which possess functions of
+messages. The connections between index coding and matroid theory have been
+well studied in the recent past. Index coding solutions were first connected to
+multi linear representation of matroids. For vector linear index codes discrete
+polymatroids which can be viewed as a generalization of the matroids was used.
+It was shown that a vector linear solution to an index coding problem exists if
+and only if there exists a representable discrete polymatroid satisfying
+certain conditions. In this work we explore the connections between generalized
+index coding and discrete polymatroids. The conditions that needs to be
+satisfied by a representable discrete polymatroid for a generalized index
+coding problem to have a vector linear solution is established. From a discrete
+polymatroid we construct an index coding problem with coded side information
+and shows that if the index coding problem has a certain optimal length
+solution then the discrete polymatroid satisfies certain properties. From a
+matroid we construct a similar generalized index coding problem and shows that
+the index coding problem has a binary scalar linear solution of optimal length
+if and only if the matroid is binary representable.
+",1,0,1,0,0,0
+20465,Reconstruction via the intrinsic geometric structures of interior transmission eigenfunctions,"  We are concerned with the inverse scattering problem of extracting the
+geometric structures of an unknown/inaccessible inhomogeneous medium by using
+the corresponding acoustic far-field measurement. Using the intrinsic geometric
+properties of the so-called interior transmission eigenfunctions, we develop a
+novel inverse scattering scheme. The proposed method can efficiently capture
+the cusp singularities of the support of the inhomogeneous medium. If further a
+priori information is available on the support of the medium, say, it is a
+convex polyhedron, then one can actually recover its shape. Both theoretical
+analysis and numerical experiments are provided. Our reconstruction method is
+new to the literature and opens up a new direction in the study of inverse
+scattering problems.
+",0,0,1,0,0,0
+20466,Thermoelectric phase diagram of the SrTiO3-SrNbO3 solid solution system,"  Thermoelectric energy conversion - the exploitation of the Seebeck effect to
+convert waste heat into electricity - has attracted an increasing amount of
+research attention for energy harvesting technology. Niobium-doped strontium
+titanate (SrTi1-xNbxO3) is one of the most promising thermoelectric material
+candidates, particularly as it poses a much lesser environmental risk in
+comparison to materials based on heavy metal elements. Two-dimensional electron
+confinement, e.g. through the formation of superlattices or two-dimensional
+electron gases, is recognized as an effective strategy to improve the
+thermoelectric performance of SrTi1-xNbxO3. Although electron confinement is
+closely related to the electronic structure, the fundamental electronic phase
+behavior of the SrTi1-xNbxO3 solid solution system has yet to be
+comprehensively investigated. Here, we present a thermoelectric phase diagram
+for the SrTi1-xNbxO3 (0.05 =< x =< 1) solid solution system, which we derived
+from the characterization of epitaxial films. We observed two thermoelectric
+phase boundaries in the system, which originate from the step-like decrease in
+carrier effective mass at x ~ 0.3, and from a local minimum in carrier
+relaxation time at x ~ 0.5. The origins of these phase boundaries are
+considered to be related to isovalent/heterovalent B-site substitution:
+parabolic Ti 3d orbitals dominate electron conduction for compositions with x <
+0.3, whereas the Nb 4d orbital dominates when x > 0.3. At x ~ 0.5, a tetragonal
+distortion of the lattice, in which the B-site is composed of Ti4+ and Nb4+
+ions, leads to the formation of tail-like impurity bands, which maximizes the
+electron scattering. These results provide a foundation for further research
+into improving the thermoelectric performance of SrTi1-xNbxO3.
+",0,1,0,0,0,0
+20467,"AMPA, NMDA and GABAA receptor mediated network burst dynamics in cortical cultures in vitro","  In this work we study the excitatory AMPA, and NMDA, and inhibitory GABAA
+receptor mediated dynamical changes in neuronal networks of neonatal rat cortex
+in vitro. Extracellular network-wide activity was recorded with 59 planar
+electrodes simultaneously under different pharmacological conditions. We
+analyzed the changes of overall network activity and network-wide burst
+frequency between baseline and AMPA receptor (AMPA-R) or NMDA receptor (NMDA-R)
+driven activity, as well as between the latter states and disinhibited
+activity. Additionally, spatiotemporal structures of pharmacologically modified
+bursts and recruitment of electrodes during the network bursts were studied.
+Our results show that AMPA-R and NMDA-R receptors have clearly distinct roles
+in network dynamics. AMPA-Rs are in greater charge to initiate network wide
+bursts. Therefore NMDA-Rs maintain the already initiated activity. GABAA
+receptors (GABAA-Rs) inhibit AMPA-R driven network activity more strongly than
+NMDA-R driven activity during the bursts.
+",0,0,0,0,1,0
+20468,Coarse fundamental groups and box spaces,"  We use a coarse version of the fundamental group first introduced by Barcelo,
+Kramer, Laubenbacher and Weaver to show that box spaces of finitely presented
+groups detect the normal subgroups used to construct the box space, up to
+isomorphism. As a consequence we have that two finitely presented groups admit
+coarsely equivalent box spaces if and only if they are commensurable via normal
+subgroups. We also provide an example of two filtrations $(N_i)$ and $(M_i)$ of
+a free group $F$ such that $M_i>N_i$ for all $i$ with $[M_i:N_i]$ uniformly
+bounded, but with $\Box_{(N_i)}F$ not coarsely equivalent to $\Box_{(M_i)}F$.
+Finally, we give some applications of the main theorem for rank gradient and
+the first $\ell^2$ Betti number, and show that the main theorem can be used to
+construct infinitely many coarse equivalence classes of box spaces with various
+properties.
+",0,0,1,0,0,0
+20469,Algebraic entropy of (integrable) lattice equations and their reductions,"  We study the growth of degrees in many autonomous and non-autonomous lattice
+equations defined by quad rules with corner boundary values, some of which are
+known to be integrable by other characterisations. Subject to an enabling
+conjecture, we prove polynomial growth for a large class of equations which
+includes the Adler-Bobenko-Suris equations and Viallet's $Q_V$ and its
+non-autonomous generalization. Our technique is to determine the ambient degree
+growth of the projective version of the lattice equations and to conjecture the
+growth of their common factors at each lattice vertex, allowing the true degree
+growth to be found. The resulting degrees satisfy a linear partial difference
+equation which is universal, i.e. the same for all the integrable lattice
+equations considered. When we take periodic reductions of these equations,
+which includes staircase initial conditions, we obtain from this linear partial
+difference equation an ordinary difference equation for degrees that implies
+quadratic or linear degree growth. We also study growth of degree of several
+non-integrable lattice equations. Exponential growth of degrees of these
+equations, and their mapping reductions, is also proved subject to a
+conjecture.
+",0,1,0,0,0,0
+20470,Measurement of mirror birefringence with laser heterodyne polarimetry,"  A laser heterodyne polarimeter (LHP) designed for the measurement of the
+birefringence of dielectric super-mirrors is described and initial results are
+reported. The LHP does not require an optical resonator and so promises
+unprecedented accuracy in the measurement of the birefringence of individual
+mirrors. The working principle of the LHP can be applied to the measurement of
+vacuum birefringence and potentially ALPS (Any Light Particle Search).
+",0,1,0,0,0,0
+20471,Individual position diversity in dependence socioeconomic networks increases economic output,"  The availability of big data recorded from massively multiplayer online
+role-playing games (MMORPGs) allows us to gain a deeper understanding of the
+potential connection between individuals' network positions and their economic
+outputs. We use a statistical filtering method to construct dependence networks
+from weighted friendship networks of individuals. We investigate the 30
+distinct motif positions in the 13 directed triadic motifs which represent
+microscopic dependences among individuals. Based on the structural similarity
+of motif positions, we further classify individuals into different groups. The
+node position diversity of individuals is found to be positively correlated
+with their economic outputs. We also find that the economic outputs of leaf
+nodes are significantly lower than that of the other nodes in the same motif.
+Our findings shed light on understanding the influence of network structure on
+economic activities and outputs in socioeconomic system.
+",1,1,0,0,0,0
+20472,A formula for the nonsymmetric Opdam's hypergeometric function of type $A_2$,"  The aim of this paper is to give an explicit formula for the nonsymmetric
+Heckman-Opdam's hypergeometric function of type $A_2$. This is obtained by
+differentiating the corresponding symmetric hypergeometric function.
+",0,0,1,0,0,0
+20473,A new algorithm for irreducible decomposition of representations of finite groups,"  An algorithm for irreducible decomposition of representations of finite
+groups over fields of characteristic zero is described. The algorithm uses the
+fact that the decomposition induces a partition of the invariant inner product
+into a complete set of mutually orthogonal projectors. By expressing the
+projectors through the basis elements of the centralizer ring of the
+representation, the problem is reduced to solving systems of quadratic
+equations. The current implementation of the algorithm is able to split
+representations of dimensions up to hundreds of thousands. Examples of
+calculations are given.
+",1,0,0,0,0,0
+20474,Stability and Grothendieck,"  This note is a commentary on the model-theoretic interpretation of
+Grothendieck's double limit characterization of weak relative compactness.
+",0,0,1,0,0,0
+20475,Visual Analogies between Atari Games for Studying Transfer Learning in RL,"  In this work, we ask the following question: Can visual analogies, learned in
+an unsupervised way, be used in order to transfer knowledge between pairs of
+games and even play one game using an agent trained for another game? We
+attempt to answer this research question by creating visual analogies between a
+pair of games: a source game and a target game. For example, given a video
+frame in the target game, we map it to an analogous state in the source game
+and then attempt to play using a trained policy learned for the source game. We
+demonstrate convincing visual mapping between four pairs of games (eight
+mappings), which are used to evaluate three transfer learning approaches.
+",0,0,0,1,0,0
+20476,Learning in the Repeated Secretary Problem,"  In the classical secretary problem, one attempts to find the maximum of an
+unknown and unlearnable distribution through sequential search. In many
+real-world searches, however, distributions are not entirely unknown and can be
+learned through experience. To investigate learning in such a repeated
+secretary problem we conduct a large-scale behavioral experiment in which
+people search repeatedly from fixed distributions. In contrast to prior
+investigations that find no evidence for learning in the classical scenario, in
+the repeated setting we observe substantial learning resulting in near-optimal
+stopping behavior. We conduct a Bayesian comparison of multiple behavioral
+models which shows that participants' behavior is best described by a class of
+threshold-based models that contains the theoretically optimal strategy.
+Fitting such a threshold-based model to data reveals players' estimated
+thresholds to be surprisingly close to the optimal thresholds after only a
+small number of games.
+",1,0,0,0,0,0
+20477,Flexible Mixture Modeling on Constrained Spaces,"  This paper addresses challenges in flexibly modeling multimodal data that lie
+on constrained spaces. Applications include climate or crime measurements in a
+geographical area, or flow-cytometry experiments, where unsuitable recordings
+are discarded. A simple approach to modeling such data is through the use of
+mixture models, with each component following an appropriate truncated
+distribution. Problems arise when the truncation involves complicated
+constraints, leading to difficulties in specifying the component distributions,
+and in evaluating their normalization constants. Bayesian inference over the
+parameters of these models results in posterior distributions that are
+doubly-intractable. We address this problem via an algorithm based on rejection
+sampling and data augmentation. We view samples from a truncated distribution
+as outcomes of a rejection sampling scheme, where proposals are made from a
+simple mixture model, and are rejected if they violate the constraints. Our
+scheme proceeds by imputing the rejected samples given mixture parameters, and
+then resampling parameters given all samples. We study two modeling approaches:
+mixtures of truncated components and truncated mixtures of components. In both
+situations, we describe exact Markov chain Monte Carlo sampling algorithms, as
+well as approximations that bound the number of rejected samples, achieving
+computational efficiency and lower variance at the cost of asymptotic bias.
+Overall, our methodology only requires practitioners to provide an indicator
+function for the set of interest. We present results on simulated data and
+apply our algorithm to two problems, one involving flow-cytometry data, and the
+other, crime recorded in the city of Chicago.
+",0,0,0,1,0,0
+20478,Universal equilibrium scaling functions at short times after a quench,"  By analyzing spin-spin correlation functions at relatively short distances,
+we show that equilibrium near-critical properties can be extracted at short
+times after quenches into the vicinity of a quantum critical point. The time
+scales after which equilibrium properties can be extracted are sufficiently
+short so that the proposed scheme should be viable for quantum simulators of
+spin models based on ultracold atoms or trapped ions. Our results, analytic as
+well as numeric, are for one-dimensional spin models, either integrable or
+nonintegrable, but we expect our conclusions to be valid in higher dimensions
+as well.
+",0,1,0,0,0,0
+20479,First Results from CUORE: A Search for Lepton Number Violation via $0νββ$ Decay of $^{130}$Te,"  The CUORE experiment, a ton-scale cryogenic bolometer array, recently began
+operation at the Laboratori Nazionali del Gran Sasso in Italy. The array
+represents a significant advancement in this technology, and in this work we
+apply it for the first time to a high-sensitivity search for a
+lepton-number--violating process: $^{130}$Te neutrinoless double-beta decay.
+Examining a total TeO$_2$ exposure of 86.3 kg$\cdot$yr, characterized by an
+effective energy resolution of (7.7 $\pm$ 0.5) keV FWHM and a background in the
+region of interest of (0.014 $\pm$ 0.002) counts/(keV$\cdot$kg$\cdot$yr), we
+find no evidence for neutrinoless double-beta decay. The median statistical
+sensitivity of this search is $7.0\times10^{24}$ yr. Including systematic
+uncertainties, we place a lower limit on the decay half-life of
+$T^{0\nu}_{1/2}$($^{130}$Te) > $1.3\times 10^{25}$ yr (90% C.L.). Combining
+this result with those of two earlier experiments, Cuoricino and CUORE-0, we
+find $T^{0\nu}_{1/2}$($^{130}$Te) > $1.5\times 10^{25}$ yr (90% C.L.), which is
+the most stringent limit to date on this decay. Interpreting this result as a
+limit on the effective Majorana neutrino mass, we find $m_{\beta\beta}<(110 -
+520)$ meV, where the range reflects the nuclear matrix element estimates
+employed.
+",0,1,0,0,0,0
+20480,Bernoulli-Carlitz and Cauchy-Carlitz numbers with Stirling-Carlitz numbers,"  Recently, the Cauchy-Carlitz number was defined as the counterpart of the
+Bernoulli-Carlitz number. Both numbers can be expressed explicitly in terms of
+so-called Stirling-Carlitz numbers. In this paper, we study the second analogue
+of Stirling-Carlitz numbers and give some general formulae, including Bernoulli
+and Cauchy numbers in formal power series with complex coefficients, and
+Bernoulli-Carlitz and Cauchy-Carlitz numbers in function fields. We also give
+some applications of Hasse-Teichmüller derivative to hypergeometric Bernoulli
+and Cauchy numbers in terms of associated Stirling numbers.
+",0,0,1,0,0,0
+20481,Irreducible network backbones: unbiased graph filtering via maximum entropy,"  Networks provide an informative, yet non-redundant description of complex
+systems only if links represent truly dyadic relationships that cannot be
+directly traced back to node-specific properties such as size, importance, or
+coordinates in some embedding space. In any real-world network, some links may
+be reducible, and others irreducible, to such local properties. This dichotomy
+persists despite the steady increase in data availability and resolution, which
+actually determines an even stronger need for filtering techniques aimed at
+discerning essential links from non-essential ones. Here we introduce a
+rigorous method that, for any desired level of statistical significance,
+outputs the network backbone that is irreducible to the local properties of
+nodes, i.e. their degrees and strengths. Unlike previous approaches, our method
+employs an exact maximum-entropy formulation guaranteeing that the filtered
+network encodes only the links that cannot be inferred from local information.
+Extensive empirical analysis confirms that this approach uncovers essential
+backbones that are otherwise hidden amidst many redundant relationships and
+inaccessible to other methods. For instance, we retrieve the hub-and-spoke
+skeleton of the US airport network and many specialised patterns of
+international trade. Being irreducible to local transportation and economic
+constraints of supply and demand, these backbones single out genuinely
+higher-order wiring principles.
+",1,1,0,0,0,0
+20482,Synkhronos: a Multi-GPU Theano Extension for Data Parallelism,"  We present Synkhronos, an extension to Theano for multi-GPU computations
+leveraging data parallelism. Our framework provides automated execution and
+synchronization across devices, allowing users to continue to write serial
+programs without risk of race conditions. The NVIDIA Collective Communication
+Library is used for high-bandwidth inter-GPU communication. Further
+enhancements to the Theano function interface include input slicing (with
+aggregation) and input indexing, which perform common data-parallel computation
+patterns efficiently. One example use case is synchronous SGD, which has
+recently been shown to scale well for a growing set of deep learning problems.
+When training ResNet-50, we achieve a near-linear speedup of 7.5x on an NVIDIA
+DGX-1 using 8 GPUs, relative to Theano-only code running a single GPU in
+isolation. Yet Synkhronos remains general to any data-parallel computation
+programmable in Theano. By implementing parallelism at the level of individual
+Theano functions, our framework uniquely addresses a niche between manual
+multi-device programming and prescribed multi-GPU training routines.
+",1,0,0,0,0,0
+20483,The PomXYZ Proteins Self-Organize on the Bacterial Nucleoid to Stimulate Cell Division,"  Cell division site positioning is precisely regulated to generate correctly
+sized and shaped daughters. We uncover a novel strategy to position the FtsZ
+cytokinetic ring at midcell in the social bacterium Myxococcus xanthus. PomX,
+PomY and the nucleoid-binding ParA/MinD ATPase PomZ self-assemble forming a
+large nucleoid-associated complex that localizes at the division site before
+FtsZ to directly guide and stimulate division. PomXYZ localization is generated
+through self-organized biased random motion on the nucleoid towards midcell and
+constrained motion at midcell. Experiments and theory show that PomXYZ motion
+is produced by diffusive PomZ fluxes on the nucleoid into the complex. Flux
+differences scale with the intracellular asymmetry of the complex and are
+converted into a local PomZ concentration gradient across the complex with
+translocation towards the higher PomZ concentration. At midcell, fluxes
+equalize resulting in constrained motion. Flux-based mechanisms may represent a
+general paradigm for positioning of macromolecular structures in bacteria.
+",0,0,0,0,1,0
+20484,Quantitative Results on Diophantine Equations in Many Variables,"  We consider a system of polynomials $f_1,\ldots, f_R\in
+\mathbb{Z}[x_1,\ldots, x_n]$ of the same degree with non-singular local zeros
+and in many variables. Generalising the work of Birch (1962) we find
+quantitative asymptotics (in terms of the maximum of the absolute value of the
+coefficients of these polynomials) for the number of integer zeros of this
+system within a growing box. Using a quantitative version of the
+Nullstellensatz, we obtain a quantitative strong approximation result, i.e. an
+upper bound on the smallest integer zero provided the system of polynomials is
+non-singular.
+",0,0,1,0,0,0
+20485,Generalized Springer correspondence for symmetric spaces associated to orthogonal groups,"  Let $G = GL_N$ over an algebraically closed field of odd characteristic, and
+$\theta$ an involutive automorphism on $G$ such that $H = (G^{\theta})^0$ is
+isomorphic to $SO_N$. Then $G^{\iota\theta} = \{ g \in G \mid \theta(g) =
+g^{-1} \}$ is regarded as a symmetric space $G/G^{\theta}$. Let
+$G^{\iota\theta}_{uni}$ be the set of unipotent elements in $G^{\iota\theta}$.
+$H$ acts on $G^{\iota\theta}_{uni}$ by the conjugation. As an analogue of the
+generalized Springer correspondence in the case of reductive groups, we
+establish in this paper the generalized Springer correspondence between
+$H$-orbits in $G^{\iota\theta}_{uni}$ and irreducible representations of
+various symmetric groups.
+",0,0,1,0,0,0
+20486,An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit,"  Our work focuses on the problem of predicting the transfer of pediatric
+patients from the general ward of a hospital to the pediatric intensive care
+unit. Using data collected over 5.5 years from the electronic health records of
+two medical facilities, we develop classifiers based on adaptive boosting and
+gradient tree boosting. We further combine these learned classifiers into an
+ensemble model and compare its performance to a modified pediatric early
+warning score (PEWS) baseline that relies on expert defined guidelines. To
+gauge model generalizability, we perform an inter-facility evaluation where we
+train our algorithm on data from one facility and perform evaluation on a
+hidden test dataset from a separate facility. We show that improvements are
+witnessed over the PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80
+vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73).
+",1,0,0,1,0,0
+20487,Effects of Network Structure on the Performance of a Modeled Traffic Network under Drivers' Bounded Rationality,"  We propose a minority route choice game to investigate the effect of the
+network structure on traffic network performance under the assumption of
+drivers' bounded rationality. We investigate ring-and-hub topologies to capture
+the nature of traffic networks in cities, and employ a minority game-based
+inductive learning process to model the characteristic behavior under the route
+choice scenario. Through numerical experiments, we find that topological
+changes in traffic networks induce a phase transition from an uncongested phase
+to a congested phase. Understanding this phase transition is helpful in
+planning new traffic networks.
+",1,1,0,0,0,0
+20488,Sneak into Devil's Colony- A study of Fake Profiles in Online Social Networks and the Cyber Law,"  Massive content about user's social, personal and professional life stored on
+Online Social Networks (OSNs) has attracted not only the attention of
+researchers and social analysts but also the cyber criminals. These cyber
+criminals penetrate illegally into an OSN by establishing fake profiles or by
+designing bots and exploit the vulnerabilities of an OSN to carry out illegal
+activities. With the growth of technology cyber crimes have been increasing
+manifold. Daily reports of the security and privacy threats in the OSNs demand
+not only the intelligent automated detection systems that can identify and
+alleviate fake profiles in real time but also the reinforcement of the security
+and privacy laws to curtail the cyber crime. In this paper, we have studied
+various categories of fake profiles like compromised profiles, cloned profiles
+and online bots (spam-bots, social-bots, like-bots and influential-bots) on
+different OSN sites along with existing cyber laws to mitigate their threats.
+In order to design fake profile detection systems, we have highlighted
+different category of fake profile features which are capable to distinguish
+different kinds of fake entities from real ones. Another major challenges faced
+by researchers while building the fake profile detection systems is the
+unavailability of data specific to fake users. The paper addresses this
+challenge by providing extremely obliging data collection techniques along with
+some existing data sources. Furthermore, an attempt is made to present several
+machine learning techniques employed to design different fake profile detection
+systems.
+",1,0,0,0,0,0
+20489,Preserving Data-Privacy with Added Noises: Optimal Estimation and Privacy Analysis,"  Networked system often relies on distributed algorithms to achieve a global
+computation goal with iterative local information exchanges between neighbor
+nodes. To preserve data privacy, a node may add a random noise to its original
+data for information exchange at each iteration. Nevertheless, a neighbor node
+can estimate other's original data based on the information it received. The
+estimation accuracy and data privacy can be measured in terms of $(\epsilon,
+\delta)$-data-privacy, defined as the probability of $\epsilon$-accurate
+estimation (the difference of an estimation and the original data is within
+$\epsilon$) is no larger than $\delta$ (the disclosure probability). How to
+optimize the estimation and analyze data privacy is a critical and open issue.
+In this paper, a theoretical framework is developed to investigate how to
+optimize the estimation of neighbor's original data using the local information
+received, named optimal distributed estimation. Then, we study the disclosure
+probability under the optimal estimation for data privacy analysis. We further
+apply the developed framework to analyze the data privacy of the
+privacy-preserving average consensus algorithm and identify the optimal noises
+for the algorithm.
+",1,0,0,0,0,0
+20490,Geometric tuning of self-propulsion for Janus catalytic particles,"  Catalytic swimmers have attracted much attention as alternatives to
+biological systems for examining collective microscopic dynamics and the
+response to physico-chemical signals. Yet, understanding and predicting even
+the most fundamental characteristics of their individual propulsion still
+raises important challenges. While chemical asymmetry is widely recognized as
+the cornerstone of catalytic propulsion, different experimental studies have
+reported that particles with identical chemical properties may propel in
+opposite directions. Here, we show that, beyond its chemical properties, the
+detailed shape of a catalytic swimmer plays an essential role in determining
+its direction of motion, demonstrating the compatibility of the classical
+theoretical framework with experimental observations.
+",0,1,0,0,0,0
+20491,On the Bogolubov-de Gennes Equations,"  We consider the Bogolubov-de Gennes equations giving an equivalent
+formulation of the BCS theory of superconductivity. We are interested in the
+case when the magnetic field is present. We (a) discuss their general features,
+(b) isolate key physical classes of solutions (normal, vortex and vortex
+lattice states) and (c) prove existence of the normal, vortex and vortex
+lattice states and stability/instability of the normal states for large/small
+temperature or/and magnetic fields.
+",0,0,1,0,0,0
+20492,"High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking","  Penalized likelihood methods are widely used for high-dimensional regression.
+Although many methods have been proposed and the associated theory is now
+well-developed, the relative efficacy of different methods in finite-sample
+settings, as encountered in practice, remains incompletely understood. There is
+therefore a need for empirical investigations in this area that can offer
+practical insight and guidance to users of these methods. In this paper we
+present a large-scale comparison of penalized regression methods. We
+distinguish between three related goals: prediction, variable selection and
+variable ranking. Our results span more than 1,800 data-generating scenarios,
+allowing us to systematically consider the influence of various factors (sample
+size, dimensionality, sparsity, signal strength and multicollinearity). We
+consider several widely-used methods (Lasso, Elastic Net, Ridge Regression,
+SCAD, the Dantzig Selector as well as Stability Selection). We find
+considerable variation in performance between methods, with results dependent
+on details of the data-generating scenario and the specific goal. Our results
+support a `no panacea' view, with no unambiguous winner across all scenarios,
+even in this restricted setting where all data align well with the assumptions
+underlying the methods. Lasso is well-behaved, performing competitively in many
+scenarios, while SCAD is highly variable. Substantial benefits from a
+Ridge-penalty are only seen in the most challenging scenarios with strong
+multi-collinearity. The results are supported by semi-synthetic analyzes using
+gene expression data from cancer samples. Our empirical results complement
+existing theory and provide a resource to compare methods across a range of
+scenarios and metrics.
+",0,0,0,1,0,0
+20493,Using Human Brain Activity to Guide Machine Learning,"  Machine learning is a field of computer science that builds algorithms that
+learn. In many cases, machine learning algorithms are used to recreate a human
+ability like adding a caption to a photo, driving a car, or playing a game.
+While the human brain has long served as a source of inspiration for machine
+learning, little effort has been made to directly use data collected from
+working brains as a guide for machine learning algorithms. Here we demonstrate
+a new paradigm of ""neurally-weighted"" machine learning, which takes fMRI
+measurements of human brain activity from subjects viewing images, and infuses
+these data into the training process of an object recognition learning
+algorithm to make it more consistent with the human brain. After training,
+these neurally-weighted classifiers are able to classify images without
+requiring any additional neural data. We show that our neural-weighting
+approach can lead to large performance gains when used with traditional machine
+vision features, as well as to significant improvements with already
+high-performing convolutional neural network features. The effectiveness of
+this approach points to a path forward for a new class of hybrid machine
+learning algorithms which take both inspiration and direct constraints from
+neuronal data.
+",1,0,0,0,0,0
+20494,Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning,"  We use a deep learning model trained only on a patient's blood oxygenation
+data (measurable with an inexpensive fingertip sensor) to predict impending
+hypoxemia (low blood oxygen) more accurately than trained anesthesiologists
+with access to all the data recorded in a modern operating room. We also
+provide a simple way to visualize the reason why a patient's risk is low or
+high by assigning weight to the patient's past blood oxygen values. This work
+has the potential to provide cutting-edge clinical decision support in
+low-resource settings, where rates of surgical complication and death are
+substantially greater than in high-resource areas.
+",1,0,0,1,0,0
+20495,Merging fragments of classical logic,"  We investigate the possibility of extending the non-functionally complete
+logic of a collection of Boolean connectives by the addition of further Boolean
+connectives that make the resulting set of connectives functionally complete.
+More precisely, we will be interested in checking whether an axiomatization for
+Classical Propositional Logic may be produced by merging Hilbert-style calculi
+for two disjoint incomplete fragments of it. We will prove that the answer to
+that problem is a negative one, unless one of the components includes only
+top-like connectives.
+",1,0,1,0,0,0
+20496,Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series,"  We propose a new variational Bayes estimator for high-dimensional copulas
+with discrete, or a combination of discrete and continuous, margins. The method
+is based on a variational approximation to a tractable augmented posterior, and
+is faster than previous likelihood-based approaches. We use it to estimate
+drawable vine copulas for univariate and multivariate Markov ordinal and mixed
+time series. These have dimension $rT$, where $T$ is the number of observations
+and $r$ is the number of series, and are difficult to estimate using previous
+methods. The vine pair-copulas are carefully selected to allow for
+heteroskedasticity, which is a feature of most ordinal time series data. When
+combined with flexible margins, the resulting time series models also allow for
+other common features of ordinal data, such as zero inflation, multiple modes
+and under- or over-dispersion. Using six example series, we illustrate both the
+flexibility of the time series copula models, and the efficacy of the
+variational Bayes estimator for copulas of up to 792 dimensions and 60
+parameters. This far exceeds the size and complexity of copula models for
+discrete data that can be estimated using previous methods.
+",0,0,0,1,0,0
+20497,COSMO: Contextualized Scene Modeling with Boltzmann Machines,"  Scene modeling is very crucial for robots that need to perceive, reason about
+and manipulate the objects in their environments. In this paper, we adapt and
+extend Boltzmann Machines (BMs) for contextualized scene modeling. Although
+there are many models on the subject, ours is the first to bring together
+objects, relations, and affordances in a highly-capable generative model. For
+this end, we introduce a hybrid version of BMs where relations and affordances
+are introduced with shared, tri-way connections into the model. Moreover, we
+contribute a dataset for relation estimation and modeling studies. We evaluate
+our method in comparison with several baselines on object estimation,
+out-of-context object detection, relation estimation, and affordance estimation
+tasks. Moreover, to illustrate the generative capability of the model, we show
+several example scenes that the model is able to generate.
+",1,0,0,0,0,0
+20498,Learning Large-Scale Topological Maps Using Sum-Product Networks,"  In order to perform complex actions in human environments, an autonomous
+robot needs the ability to understand the environment, that is, to gather and
+maintain spatial knowledge. Topological map is commonly used for representing
+large scale, global maps such as floor plans. Although much work has been done
+in topological map extraction, we have found little previous work on the
+problem of learning the topological map using a probabilistic model. Learning a
+topological map means learning the structure of the large-scale space and
+dependency between places, for example, how the evidence of a group of places
+influence the attributes of other places. This is an important step towards
+planning complex actions in the environment. In this thesis, we consider the
+problem of using probabilistic deep learning model to learn the topological
+map, which is essentially a sparse undirected graph where nodes represent
+places annotated with their semantic attributes (e.g. place category). We
+propose to use a novel probabilistic deep model, Sum-Product Networks (SPNs),
+due to their unique properties. We present two methods for learning topological
+maps using SPNs: the place grid method and the template-based method. We
+contribute an algorithm that builds SPNs for graphs using template models. Our
+experiments evaluate the ability of our models to enable robots to infer
+semantic attributes and detect maps with novel semantic attribute arrangements.
+Our results demonstrate their understanding of the topological map structure
+and spatial relations between places.
+",1,0,0,0,0,0
+20499,Entanglement scaling and spatial correlations of the transverse field Ising model with perturbations,"  We study numerically the entanglement entropy and spatial correlations of the
+one dimensional transverse field Ising model with three different
+perturbations. First, we focus on the out of equilibrium, steady state with an
+energy current passing through the system. By employing a variety of
+matrix-product state based methods, we confirm the phase diagram and compute
+the entanglement entropy. Second, we consider a small perturbation that takes
+the system away from integrability and calculate the correlations, the central
+charge and the entanglement entropy. Third, we consider periodically weakened
+bonds, exploring the phase diagram and entanglement properties first in the
+situation when the weak and strong bonds alternate (period two-bonds) and then
+the general situation of a period of n bonds. In the latter case we find a
+critical weak bond that scales with the transverse field as $J'_c/J$ =
+$(h/J)^n$, where $J$ is the strength of the strong bond, $J'$ of the weak bond
+and $h$ the transverse field. We explicitly show that the energy current is not
+a conserved quantity in this case.
+",0,1,0,0,0,0
+20500,Anyonic excitations of hardcore anyons,"  Strongly interacting many-body systems consisting of fermions or bosons can
+host exotic quasiparticles with anyonic statistics. Here, we demonstrate that
+many-body systems of anyons can also form anyonic quasi-particles. The charge
+and statistics of the emergent anyons can be different from those of the
+original anyons.
+",0,1,0,0,0,0
+20501,Chaotic behavior in Casimir oscillators: A case study for phase change materials,"  Casimir forces between material surfaces at close proximity of less than 200
+nm can lead to increased chaotic behavior of actuating devices depending on the
+strength of the Casimir interaction. We investigate these phenomena for phase
+change materials in torsional oscillators, where the amorphous to crystalline
+phase transitions lead to transitions between high and low Casimir force and
+torque states respectively, without material compositions. For a conservative
+system bifurcation curve and Poincare maps analysis show the absence of chaotic
+behavior but with the crystalline phase (high force/torque state) favoring more
+unstable behavior and stiction. However, for a non-conservative system chaotic
+behavior can occur introducing significant risk for stiction, which is again
+more pronounced for the crystalline phase. The latter illustrates the more
+general scenario that stronger Casimir forces and torques increase the
+possibility for chaotic behavior. The latter is making impossible to predict
+whether stiction or stable actuation will occur on a long term basis, and it is
+setting limitations in the design of micro/nano devices operating at short
+range nanoscale separations.
+",0,1,0,0,0,0
+20502,Confluence in Probabilistic Rewriting,"  Driven by the interest of reasoning about probabilistic programming
+languages, we set out to study a notion of unicity of normal forms for them. To
+provide a tractable proof method for it, we define a property of distribution
+confluence which is shown to imply the desired uniqueness (even for infinite
+sequences of reduction) and further properties. We then carry over several
+criteria from the classical case, such as Newman's lemma, to simplify proving
+confluence in concrete languages. Using these criteria, we obtain simple proofs
+of confluence for $\lambda_1$, an affine probabilistic $\lambda$-calculus, and
+for Q$^*$, a quantum programming language for which a related property has
+already been proven in the literature.
+",1,0,0,0,0,0
+20503,Structure preserving schemes for mean-field equations of collective behavior,"  In this paper we consider the development of numerical schemes for mean-field
+equations describing the collective behavior of a large group of interacting
+agents. The schemes are based on a generalization of the classical Chang-Cooper
+approach and are capable to preserve the main structural properties of the
+systems, namely nonnegativity of the solution, physical conservation laws,
+entropy dissipation and stationary solutions. In particular, the methods here
+derived are second order accurate in transient regimes whereas they can reach
+arbitrary accuracy asymptotically for large times. Several examples are
+reported to show the generality of the approach.
+",0,1,0,0,0,0
+20504,Localization optoacoustic tomography,"  Localization-based imaging has revolutionized fluorescence optical microscopy
+and has also enabled unprecedented ultrasound images of microvascular
+structures in deep tissues. Herein, we introduce a new concept of localization
+optoacoustic tomography (LOAT) that employs rapid sequential acquisition of
+three-dimensional optoacoustic images from flowing absorbing particles. We show
+that the new method enables breaking through the spatial resolution barrier of
+acoustic diffraction while further enhancing the visibility of structures under
+limited-view tomographic conditions. Given the intrinsic sensitivity of
+optoacoustics to multiple hemodynamic and oxygenation parameters, LOAT may
+enable new level of performance in studying functional and anatomical
+alterations of microcirculation.
+",0,1,0,0,0,0
+20505,Reduced-basis approach to many-body localization,"  Within the standard model of many-body localization, i.e., the disordered
+chain of spinless fermions, we investigate how the interaction affects the
+many-body states in the basis of noninteracting localized Anderson states. From
+this starting point we follow the approach that uses a reduced basis of
+many-body states. Together with an extrapolation to the full basis, it proves
+to be efficient for the evaluation of the stiffnesses of local observables,
+which remain finite within the non-ergodic regime and represent the hallmark of
+the many-body localization (MBL). The method enables a larger span of system
+sizes and, within the MBL regime, allows for a more careful analysis of the
+size scaling of calculated quantities. On the other hand, the survival
+stiffness as the representative of non--local quantities, reveals limitations
+of the reduced-basis approach.
+",0,1,0,0,0,0
+20506,Electron-correlation study of Y III-Tc VII ions using a relativistic coupled-cluster theory,"  Spectroscopic properties, useful for plasma diagnostics and astrophysics, of
+a few rubidium-like ions are studied here. We choose one of the simplest, but
+correlationally challenging series where $d-$ and $f-$ orbitals are present in
+the core and/or valence shells with $4d$ $^2D_{3/2}$ as the ground state. We
+study different correlation characteristics of this series and make precise
+calculations of electronic structure and rates of electromagnetic transitions.
+Our calculated lifetimes and transition rates are compared with other available
+experimental and theoretical values. Radiative rates of vacuum ultra-violet
+electromagnetic transitions of the long lived Tc$^{6+}$ ion, useful in several
+areas of physics and chemistry, are estimated. To the best of our knowledge,
+there is no literature for most of these transitions.
+",0,1,0,0,0,0
+20507,Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection,"  This paper presents the submissions by the University of Zurich to the
+SIGMORPHON 2017 shared task on morphological reinflection. The task is to
+predict the inflected form given a lemma and a set of morpho-syntactic
+features. We focus on neural network approaches that can tackle the task in a
+limited-resource setting. As the transduction of the lemma into the inflected
+form is dominated by copying over lemma characters, we propose two recurrent
+neural network architectures with hard monotonic attention that are strong at
+copying and, yet, substantially different in how they achieve this. The first
+approach is an encoder-decoder model with a copy mechanism. The second approach
+is a neural state-transition system over a set of explicit edit actions,
+including a designated COPY action. We experiment with character alignment and
+find that naive, greedy alignment consistently produces strong results for some
+languages. Our best system combination is the overall winner of the SIGMORPHON
+2017 Shared Task 1 without external resources. At a setting with 100 training
+samples, both our approaches, as ensembles of models, outperform the next best
+competitor.
+",1,0,0,0,0,0
+20508,Krylov methods for low-rank commuting generalized Sylvester equations,"  We consider generalizations of the Sylvester matrix equation, consisting of
+the sum of a Sylvester operator and a linear operator $\Pi$ with a particular
+structure. More precisely, the commutator of the matrix coefficients of the
+operator $\Pi$ and the Sylvester operator coefficients are assumed to be
+matrices with low rank. We show (under certain additional conditions) low-rank
+approximability of this problem, i.e., the solution to this matrix equation can
+be approximated with a low-rank matrix. Projection methods have successfully
+been used to solve other matrix equations with low-rank approximability. We
+propose a new projection method for this class of matrix equations. The choice
+of subspace is a crucial ingredient for any projection method for matrix
+equations. Our method is based on an adaption and extension of the extended
+Krylov subspace method for Sylvester equations. A constructive choice of the
+starting vector/block is derived from the low-rank commutators. We illustrate
+the effectiveness of our method by solving large-scale matrix equations arising
+from applications in control theory and the discretization of PDEs. The
+advantages of our approach in comparison to other methods are also illustrated.
+",0,0,1,0,0,0
+20509,Atomic-scale origin of dynamic viscoelastic response and creep in disordered solids,"  Viscoelasticity has been described since the time of Maxwell as an
+interpolation of purely viscous and purely elastic response, but its
+microscopic atomic-level mechanism in solids has remained elusive. We studied
+three model disordered solids: a random lattice, the bond-depleted fcc lattice,
+and the fcc lattice with vacancies. Within the harmonic approximation for
+central-force lattices, we applied sum-rules for viscoelastic response derived
+on the basis of non-affine atomic motions. The latter motions are a direct
+result of local structural disorder, and in particular, of the lack of
+inversion-symmetry in disordered lattices. By defining a suitable quantitative
+and general atomic-level measure of nonaffinity and inversion-symmetry, we show
+that the viscoelastic responses of all three systems collapse onto a master
+curve upon normalizing by the overall strength of inversion-symmetry breaking
+in each system. Close to the isostatic point for central-force lattices,
+power-law creep $G(t)\sim t^{-1/2}$ emerges as a consequence of the interplay
+between soft vibrational modes and non-affine dynamics, and various analytical
+scalings, supported by numerical calculations, are predicted by the theory.
+",0,1,0,0,0,0
+20510,On-sky closed loop correction of atmospheric dispersion for high-contrast coronagraphy and astrometry,"  Adaptive optic (AO) systems delivering high levels of wavefront correction
+are now common at observatories. One of the main limitations to image quality
+after wavefront correction comes from atmospheric refraction. An Atmospheric
+dispersion compensator (ADC) is employed to correct for atmospheric refraction.
+The correction is applied based on a look-up table consisting of dispersion
+values as a function of telescope elevation angle. The look-up table based
+correction of atmospheric dispersion results in imperfect compensation leading
+to the presence of residual dispersion in the point-spread function (PSF) and
+is insufficient when sub-milliarcsecond precision is required. The presence of
+residual dispersion can limit the achievable contrast while employing
+high-performance coronagraphs or can compromise high-precision astrometric
+measurements. In this paper, we present the first on-sky closed-loop correction
+of atmospheric dispersion by directly using science path images. The concept
+behind the measurement of dispersion utilizes the chromatic scaling of focal
+plane speckles. An adaptive speckle grid generated with a deformable mirror
+(DM) that has a sufficiently large number of actuators is used to accurately
+measure the residual dispersion and subsequently correct it by driving the ADC.
+We have demonstrated with the Subaru Coronagraphic Extreme AO (SCExAO) system
+on-sky closed-loop correction of residual dispersion to < 1 mas across H-band.
+This work will aid in the direct detection of habitable exoplanets with
+upcoming extremely large telescopes (ELTs) and also provide a diagnostic tool
+to test the performance of instruments which require sub-milliarcsecond
+correction.
+",0,1,0,0,0,0
+20511,Experimental observations and modelling of intrinsic rotation reversals in tokamaks,"  The progress made in understanding spontaneous toroidal rotation reversals in
+tokamaks is reviewed and current ideas to solve this ten-year-old puzzle are
+explored. The paper includes a summarial synthesis of the experimental
+observations in AUG, C-Mod, KSTAR, MAST and TCV tokamaks, reasons why turbulent
+momentum transport is thought to be responsible for the reversals, a review of
+the theory of turbulent momentum transport and suggestions for future
+investigations.
+",0,1,0,0,0,0
+20512,"Where, When, and How mmWave is Used in 5G and Beyon","  Wireless engineers and business planners commonly raise the question on
+where, when, and how millimeter-wave (mmWave) will be used in 5G and beyond.
+Since the next generation network is not just a new radio access standard, but
+instead an integration of networks for vertical markets with diverse
+applications, answers to the question depend on scenarios and use cases to be
+deployed. This paper gives four 5G mmWave deployment examples and describes in
+chronological order the scenarios and use cases of their probable deployment,
+including expected system architectures and hardware prototypes. The paper
+starts with 28 GHz outdoor backhauling for fixed wireless access and moving
+hotspots, which will be demonstrated at the PyeongChang winter Olympic games in
+2018. The second deployment example is a 60 GHz unlicensed indoor access system
+at the Tokyo-Narita airport, which is combined with Mobile Edge Computing (MEC)
+to enable ultra-high speed content download with low latency. The third example
+is mmWave mesh network to be used as a micro Radio Access Network ({\mu}-RAN),
+for cost-effective backhauling of small-cell Base Stations (BSs) in dense urban
+scenarios. The last example is mmWave based Vehicular-to-Vehicular (V2V) and
+Vehicular-to-Everything (V2X) communications system, which enables automated
+driving by exchanging High Definition (HD) dynamic map information between cars
+and Roadside Units (RSUs). For 5G and beyond, mmWave and MEC will play
+important roles for a diverse set of applications that require both ultra-high
+data rate and low latency communications.
+",1,0,0,0,0,0
+20513,"A simple script language for choreography of multiple, synchronizing non-anthropomorphic robots","  The scripting language described in this document is (in the first place)
+intended to be used on robots developed by Anja M{\o}lle Lindelof and Henning
+Christiansen as part of a research project about robots performing on stage.
+The target robots are expected to appear as familiar domestic objects that
+take their own life, so to speak, and perhaps perform together with human
+players, creating at illusion of a communication between them. In the current
+version, these robots' common behaviour is determined uniquely by a script
+written in the language described here -- the only possible autonomy for the
+robots is action to correct dynamically for inaccuracies that arise during a
+performance.
+The present work is preliminary and has not been compared to properly to
+other research work in this area, and the testing is still limited. A first
+implementation on small Lego Mindstorms based robots is under development by
+Mads Saustrup Fox as part of his master thesis work.
+",1,0,0,0,0,0
+20514,Stability results for abstract evolution equations with intermittent time-delay feedback,"  We consider abstract evolution equations with on-off time delay feedback.
+Without the time delay term, the model is described by an exponentially stable
+semigroup. We show that, under appropriate conditions involving the delay term,
+the system remains asymptotically stable. Under additional assumptions
+exponential stability results are also obtained. Concrete examples illustrating
+the abstract results are finally given.
+",0,0,1,0,0,0
+20515,Solar wind turbulent cascade from MHD to sub-ion scales: large-size 3D hybrid particle-in-cell simulations,"  Spectral properties of the turbulent cascade from fluid to kinetic scales in
+collisionless plasmas are investigated by means of large-size three-dimensional
+(3D) hybrid (fluid electrons, kinetic protons) particle-in-cell simulations.
+Initially isotropic Alfvènic fluctuations rapidly develop a strongly
+anisotropic turbulent cascade, mainly in the direction perpendicular to the
+ambient magnetic field. The omnidirectional magnetic field spectrum shows a
+double power-law behavior over almost two decades in wavenumber, with a
+Kolmogorov-like index at large scales, a spectral break around ion scales, and
+a steepening at sub-ion scales. Power laws are also observed in the spectra of
+the ion bulk velocity, density, and electric field, both at magnetohydrodynamic
+(MHD) and at kinetic scales. Despite the complex structure, the omnidirectional
+spectra of all fields at ion and sub-ion scales are in remarkable quantitative
+agreement with those of a two-dimensional (2D) simulation with similar physical
+parameters. This provides a partial, a-posteriori validation of the 2D
+approximation at kinetic scales. Conversely, at MHD scales, the spectra of the
+density and of the velocity (and, consequently, of the electric field) exhibit
+differences between the 2D and 3D cases. Although they can be partly ascribed
+to the lower spatial resolution, the main reason is likely the larger
+importance of compressible effects in a full geometry. Our findings are also in
+remarkable quantitative agreement with solar wind observations.
+",0,1,0,0,0,0
+20516,Blind source separation of tensor-valued time series,"  The blind source separation model for multivariate time series generally
+assumes that the observed series is a linear transformation of an unobserved
+series with temporally uncorrelated or independent components. Given the
+observations, the objective is to find a linear transformation that recovers
+the latent series. Several methods for accomplishing this exist and three
+particular ones are the classic SOBI and the recently proposed generalized FOBI
+(gFOBI) and generalized JADE (gJADE), each based on the use of joint lagged
+moments. In this paper we generalize the methodologies behind these algorithms
+for tensor-valued time series. We assume that our data consists of a tensor
+observed at each time point and that the observations are linear
+transformations of latent tensors we wish to estimate. The tensorial
+generalizations are shown to have particularly elegant forms and we show that
+each of them is Fisher consistent and orthogonal equivariant. Comparing the new
+methods with the original ones in various settings shows that the tensorial
+extensions are superior to both their vector-valued counterparts and to two
+existing tensorial dimension reduction methods for i.i.d. data. Finally,
+applications to fMRI-data and video processing show that the methods are
+capable of extracting relevant information from noisy high-dimensional data.
+",0,0,1,1,0,0
+20517,Mixed Cages,"  We introduce the notion of a $[z, r; g]$-mixed cage. A $[z, r; g]$-mixed cage
+is a mixed graph $G$, $z$-regular by arcs, $r$-regular by edges, with girth $g$
+and minimum order. In this paper we prove the existence of $[z, r ;g]$-mixed
+cages and exhibit families of mixed cages for some specific values. We also
+give lower and upper bounds for some choices of $z, r$ and $g$. In particular
+we present the first results on $[z,r;g]$- mixed cages for $z=1$ and any $r\geq
+1$ and $g\geq 3$, and for any $z\geq 1$, $r=1$ and $g=4$.
+",0,0,1,0,0,0
+20518,Single-beam dielectric-microsphere trapping with optical heterodyne detection,"  A technique to levitate and measure the three-dimensional position of
+micrometer-sized dielectric spheres with heterodyne detection is presented. The
+two radial degrees of freedom are measured by interfering light transmitted
+through the microsphere with a reference wavefront, while the axial degree of
+freedom is measured from the phase of the light reflected from the surface of
+the microsphere. This method pairs the simplicity and accessibility of single
+beam optical traps to a measurement of displacement that is intrinsically
+calibrated by the wavelength of the trapping light and has exceptional immunity
+to stray light. A theoretical shot noise limit of
+$1.3\times10^{-13}\,\text{m}/\sqrt{\text{Hz}}$ for the radial degrees of
+freedom, and $3.0\times10^{-15} \, \text{m}/\sqrt{\text{Hz}}$ for the axial
+degree of freedom can be obtained in the system described. The measured
+acceleration noise in the radial direction is $7.5\times10^{-5} \,
+(\text{m/s}^2)/\sqrt{\text{Hz}}$.
+",0,1,0,0,0,0
+20519,An Empirical Analysis of Traceability in the Monero Blockchain,"  Monero is a privacy-centric cryptocurrency that allows users to obscure their
+transactions by including chaff coins, called ""mixins,"" along with the actual
+coins they spend. In this paper, we empirically evaluate two weaknesses in
+Monero's mixin sampling strategy. First, about 62% of transaction inputs with
+one or more mixins are vulnerable to ""chain-reaction"" analysis -- that is, the
+real input can be deduced by elimination. Second, Monero mixins are sampled in
+such a way that they can be easily distinguished from the real coins by their
+age distribution; in short, the real input is usually the ""newest"" input. We
+estimate that this heuristic can be used to guess the real input with 80%
+accuracy over all transactions with 1 or more mixins. Next, we turn to the
+Monero ecosystem and study the importance of mining pools and the former
+anonymous marketplace AlphaBay on the transaction volume. We find that after
+removing mining pool activity, there remains a large amount of potentially
+privacy-sensitive transactions that are affected by these weaknesses. We
+propose and evaluate two countermeasures that can improve the privacy of future
+transactions.
+",1,0,0,0,0,0
+20520,Automatic Face Image Quality Prediction,"  Face image quality can be defined as a measure of the utility of a face image
+to automatic face recognition. In this work, we propose (and compare) two
+methods for automatic face image quality based on target face quality values
+from (i) human assessments of face image quality (matcher-independent), and
+(ii) quality values computed from similarity scores (matcher-dependent). A
+support vector regression model trained on face features extracted using a deep
+convolutional neural network (ConvNet) is used to predict the quality of a face
+image. The proposed methods are evaluated on two unconstrained face image
+databases, LFW and IJB-A, which both contain facial variations with multiple
+quality factors. Evaluation of the proposed automatic face image quality
+measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two
+face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face
+quality to select subsets of face images and video frames for matching
+templates (i.e., multiple faces per subject) in the IJB-A protocol. To our
+knowledge, this is the first work to utilize human assessments of face image
+quality in designing a predictor of unconstrained face quality that is shown to
+be effective in cross-database evaluation.
+",1,0,0,0,0,0
+20521,Stochastic Block Models are a Discrete Surface Tension,"  Networks, which represent agents and interactions between them, arise in
+myriad applications throughout the sciences, engineering, and even the
+humanities. To understand large-scale structure in a network, a common task is
+to cluster a network's nodes into sets called ""communities"" such that there are
+dense connections within communities but sparse connections between them. A
+popular and statistically principled method to perform such clustering is to
+use a family of generative models known as stochastic block models (SBMs). In
+this paper, we show that maximum likelihood estimation in an SBM is a network
+analog of a well-known continuum surface-tension problem that arises from an
+application in metallurgy. To illustrate the utility of this bridge, we
+implement network analogs of three surface-tension algorithms, with which we
+successfully recover planted community structure in synthetic networks and
+which yield fascinating insights on empirical networks from the field of
+hyperspectral video segmentation.
+",1,0,0,1,0,0
+20522,Robust d-wave pairing symmetry in multi-orbital cobalt high temperature superconductors,"  The pairing symmetry of the newly proposed cobalt high temperature
+(high-$T_c$) superconductors formed by vertex shared cation-anion tetrahedral
+complexes is studied by the methods of mean field, random phase approximation
+(RPA) and functional renormalization group (FRG) analysis. The results of all
+these methods show that the $d_{x^2-y^2}$ pairing symmetry is robustly favored
+near half filling. The RPA and FRG methods, which are valid in weak interaction
+regions, predict that the superconducting state is also strongly orbital
+selective, namely the $d_{x^2-y^2}$ orbital that has the largest density near
+half filling among the three $t_{2g}$ orbitals dominates superconducting
+pairing. These results suggest that the new materials, if synthesized, can
+provide indisputable test to high-$T_c$ pairing mechanism and the validity of
+different theoretical methods.
+",0,1,0,0,0,0
+20523,Thermodynamic properties of Ba$_2$CoSi$_2$O$_6$Cl$_2$ in strong magnetic field: Realization of flat-band physics in a highly frustrated quantum magnet,"  The search for flat-band solid-state realizations is a crucial issue to
+verify or to challenge theoretical predictions for quantum many-body flat-band
+systems. For frustrated quantum magnets flat bands lead to various
+unconventional properties related to the existence of localized many-magnon
+states. The recently synthesized magnetic compound Ba$_2$CoSi$_2$O$_6$Cl$_2$
+seems to be an almost perfect candidate to observe these features in
+experiments. We develop a theory for Ba$_2$CoSi$_2$O$_6$Cl$_2$ by adapting the
+localized-magnon concept to this compound. We first show that our theory
+describes the known experimental facts and then we propose new experimental
+studies to detect a field-driven phase transition related to a
+Wigner-crystal-like ordering of localized magnons at low temperatures.
+",0,1,0,0,0,0
+20524,Fisher Information and Natural Gradient Learning of Random Deep Networks,"  A deep neural network is a hierarchical nonlinear model transforming input
+signals to output signals. Its input-output relation is considered to be
+stochastic, being described for a given input by a parameterized conditional
+probability distribution of outputs. The space of parameters consisting of
+weights and biases is a Riemannian manifold, where the metric is defined by the
+Fisher information matrix. The natural gradient method uses the steepest
+descent direction in a Riemannian manifold, so it is effective in learning,
+avoiding plateaus. It requires inversion of the Fisher information matrix,
+however, which is practically impossible when the matrix has a huge number of
+dimensions. Many methods for approximating the natural gradient have therefore
+been introduced. The present paper uses statistical neurodynamical method to
+reveal the properties of the Fisher information matrix in a net of random
+connections under the mean field approximation. We prove that the Fisher
+information matrix is unit-wise block diagonal supplemented by small order
+terms of off-block-diagonal elements, which provides a justification for the
+quasi-diagonal natural gradient method by Y. Ollivier. A unitwise
+block-diagonal Fisher metrix reduces to the tensor product of the Fisher
+information matrices of single units. We further prove that the Fisher
+information matrix of a single unit has a simple reduced form, a sum of a
+diagonal matrix and a rank 2 matrix of weight-bias correlations. We obtain the
+inverse of Fisher information explicitly. We then have an explicit form of the
+natural gradient, without relying on the numerical matrix inversion, which
+drastically speeds up stochastic gradient learning.
+",0,0,0,1,0,0
+20525,Dynamic of plumes and scaling during the melting of a Phase Change Material heated from below,"  We identify and describe the main dynamic regimes occurring during the
+melting of the PCM n-octadecane in horizontal layers of several sizes heated
+from below. This configuration allows to cover a wide range of effective
+Rayleigh numbers on the liquid PCM phase, up to $\sim 10^9$, without changing
+any external parameter control. We identify four different regimes as time
+evolves: (i) the conductive regime, (ii) linear regime, (iii) coarsening regime
+and (iv) turbulent regime. The first two regimes appear at all domain sizes.
+However the third and fourth regime require a minimum advance of the
+solid/liquid interface to develop, and we observe them only for large enough
+domains.
+The transition to turbulence takes places after a secondary instability that
+forces the coarsening of the thermal plumes. Each one of the melting regimes
+creates a distinct solid/liquid front that characterizes the internal state of
+the melting process. We observe that most of the magnitudes of the melting
+process are ruled by power laws, although not all of them. Thus the number of
+plumes, some regimes of the Rayleigh number as a function of time, the number
+of plumes after the primary and secondary instability, the thermal and kinetic
+boundary layers follow simple power laws. In particular, we find that the
+Nusselt number scales with the Rayleigh number as $Nu \sim Ra^{0.29}$ in the
+turbulent regime, consistent with theories and experiments on Rayleigh-Bénard
+convection that show an exponent $2/7$.
+",0,1,0,0,0,0
+20526,Parallel mean curvature surfaces in four-dimensional homogeneous spaces,"  We survey different classification results for surfaces with parallel mean
+curvature immersed into some Riemannian homogeneous four-manifolds, including
+real and complex space forms, and product spaces. We provide a common framework
+for this problem, with special attention to the existence of holomorphic
+quadratic differentials on such surfaces. The case of spheres with parallel
+mean curvature is also explained in detail, as well as the state-of-the-art
+advances in the general problem.
+",0,0,1,0,0,0
+20527,"Recent implementations, applications, and extensions of the Locally Optimal Block Preconditioned Conjugate Gradient method (LOBPCG)","  Since introduction [A. Knyazev, Toward the optimal preconditioned
+eigensolver: Locally optimal block preconditioned conjugate gradient method,
+SISC (2001) DOI:10.1137/S1064827500366124] and efficient parallel
+implementation [A. Knyazev et al., Block locally optimal preconditioned
+eigenvalue xolvers (BLOPEX) in HYPRE and PETSc, SISC (2007)
+DOI:10.1137/060661624], LOBPCG has been used is a wide range of applications in
+mechanics, material sciences, and data sciences. We review its recent
+implementations and applications, as well as extensions of the local optimality
+idea beyond standard eigenvalue problems.
+",1,0,0,1,0,0
+20528,An evaluation of cosmological models from expansion and growth of structure measurements,"  We compare a large suite of theoretical cosmological models to observational
+data from the cosmic microwave background, baryon acoustic oscillation
+measurements of expansion, Type Ia SNe measurements of expansion, redshift
+space distortion measurements of the growth of structure, and the local Hubble
+constant. Our theoretical models include parametrizations of dark energy as
+well as physical models of dark energy and modified gravity. We determine the
+constraints on the model parameters, incorporating the redshift space
+distortion data directly in the analysis. To determine whether models can be
+ruled out, we evaluate the $p$ value (the probability under the model of
+obtaining data as bad or worse than the observed data). In our comparison, we
+find the well known tension of H$_0$ with the other data; no model resolves
+this tension successfully. Among the models we consider, the large scale growth
+of structure data does not affect the modified gravity models as a category
+particularly differently than dark energy models; it matters for some modified
+gravity models but not others, and the same is true for dark energy models. We
+compute predicted observables for each model under current observational
+constraints, and identify models for which future observational constraints
+will be particularly informative.
+",0,1,0,0,0,0
+20529,The variety of $2$-dimensional algebras over an algebraically closed field,"  The work is devoted to the variety of $2$-dimensional algebras over an
+algebraically closed field. Firstly, we classify such algebras modulo
+isomorphism. Then we describe the degenerations and the closures of principal
+algebra series in the variety under consideration. Finally, we apply our
+results to obtain analogous descriptions for the subvarieties of flexible, and
+bicommutative algebras. In particular, we describe rigid algebras and
+irreducible components for these subvarieties.
+",0,0,1,0,0,0
+20530,A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM,"  We present a Bayesian object observation model for complete probabilistic
+semantic SLAM. Recent studies on object detection and feature extraction have
+become important for scene understanding and 3D mapping. However, 3D shape of
+the object is too complex to formulate the probabilistic observation model;
+therefore, performing the Bayesian inference of the object-oriented features as
+well as their pose is less considered. Besides, when the robot equipped with an
+RGB mono camera only observes the projected single view of an object, a
+significant amount of the 3D shape information is abandoned. Due to these
+limitations, semantic SLAM and viewpoint-independent loop closure using
+volumetric 3D object shape is challenging. In order to enable the complete
+formulation of probabilistic semantic SLAM, we approximate the observation
+model of a 3D object with a tractable distribution. We also estimate the
+variational likelihood from the 2D image of the object to exploit its observed
+single view. In order to evaluate the proposed method, we perform pose and
+feature estimation, and demonstrate that the automatic loop closure works
+seamlessly without additional loop detector in various environments.
+",1,0,0,0,0,0
+20531,Generation of optical frequency combs via four-wave mixing processes for low- and medium-resolution astronomy,"  We investigate the generation of optical frequency combs through a cascade of
+four-wave mixing processes in nonlinear fibres with optimised parameters. The
+initial optical field consists of two continuous-wave lasers with frequency
+separation larger than 40 GHz (312.7 pm at 1531 nm). It propagates through
+three nonlinear fibres. The first fibre serves to pulse shape the initial
+sinusoidal-square pulse, while a strong pulse compression down to sub-100 fs
+takes place in the second fibre which is an amplifying erbium-doped fibre. The
+last stage is a low-dispersion highly nonlinear fibre where the frequency comb
+bandwidth is increased and the line intensity is equalised. We model this
+system using the generalised nonlinear Schrödinger equation and investigate
+it in terms of fibre lengths, fibre dispersion, laser frequency separation and
+input powers with the aim to minimise the frequency comb noise. With the
+support of the numerical results, a frequency comb is experimentally generated,
+first in the near infra-red and then it is frequency-doubled into the visible
+spectral range. Using a MUSE-type spectrograph, we evaluate the comb
+performance for astronomical wavelength calibration in terms of equidistancy of
+the comb lines and their stability.
+",0,1,0,0,0,0
+20532,Cross-referencing Social Media and Public Surveillance Camera Data for Disaster Response,"  Physical media (like surveillance cameras) and social media (like Instagram
+and Twitter) may both be useful in attaining on-the-ground information during
+an emergency or disaster situation. However, the intersection and reliability
+of both surveillance cameras and social media during a natural disaster are not
+fully understood. To address this gap, we tested whether social media is of
+utility when physical surveillance cameras went off-line during Hurricane Irma
+in 2017. Specifically, we collected and compared geo-tagged Instagram and
+Twitter posts in the state of Florida during times and in areas where public
+surveillance cameras went off-line. We report social media content and
+frequency and content to determine the utility for emergency managers or first
+responders during a natural disaster.
+",1,0,0,0,0,0
+20533,Nonlinear Dynamics of a Viscous Bubbly Fluid,"  A physical model of a three-dimensional flow of a viscous bubbly fluid in an
+intermediate regime between bubble formation and breakage is presented. The
+model is based on mechanics and thermodynamics of a single bubble coupled to
+the dynamics of a viscous fluid as a whole, and takes into account multiple
+physical effects, including gravity, viscosity, and surface tension.
+Dimensionless versions of the resulting nonlinear model are obtained, and
+values of dimensionless parameters are estimated for typical magma flows in
+horizontal subaerial lava fields and vertical volcanic conduits.
+Exact solutions of the resulting system of nonlinear equations corresponding
+to equilibrium flows and traveling waves are analyzed in the one-dimensional
+setting. Generalized Su-Gardner-type perturbation analysis is employed to study
+approximate solutions of the model in the long-wave ansatz. Simplified
+nonlinear partial differential equations (PDE) satisfied by the leading terms
+of the perturbation solutions are systematically derived. It is shown that for
+specific classes of perturbations, approximate solutions of the bubbly fluid
+model arise from solutions of the classical diffusion, Burgers,
+variable-coefficient Burgers, and Korteweg-de Vries equations.
+",0,1,0,0,0,0
+20534,ConvNet-Based Localization of Anatomical Structures in 3D Medical Images,"  Localization of anatomical structures is a prerequisite for many tasks in
+medical image analysis. We propose a method for automatic localization of one
+or more anatomical structures in 3D medical images through detection of their
+presence in 2D image slices using a convolutional neural network (ConvNet).
+A single ConvNet is trained to detect presence of the anatomical structure of
+interest in axial, coronal, and sagittal slices extracted from a 3D image. To
+allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling
+is applied. After detection, 3D bounding boxes are created by combining the
+output of the ConvNet in all slices.
+In the experiments 200 chest CT, 100 cardiac CT angiography (CTA), and 100
+abdomen CT scans were used. The heart, ascending aorta, aortic arch, and
+descending aorta were localized in chest CT scans, the left cardiac ventricle
+in cardiac CTA scans, and the liver in abdomen CT scans. Localization was
+evaluated using the distances between automatically and manually defined
+reference bounding box centroids and walls.
+The best results were achieved in localization of structures with clearly
+defined boundaries (e.g. aortic arch) and the worst when the structure boundary
+was not clearly visible (e.g. liver). The method was more robust and accurate
+in localization multiple structures.
+",1,0,0,0,0,0
+20535,Gradient estimates for heat kernels and harmonic functions,"  Let $(X,d,\mu)$ be a doubling metric measure space endowed with a Dirichlet
+form $\E$ deriving from a ""carré du champ"". Assume that $(X,d,\mu,\E)$
+supports a scale-invariant $L^2$-Poincaré inequality. In this article, we
+study the following properties of harmonic functions, heat kernels and Riesz
+transforms for $p\in (2,\infty]$:
+(i) $(G_p)$: $L^p$-estimate for the gradient of the associated heat
+semigroup;
+(ii) $(RH_p)$: $L^p$-reverse Hölder inequality for the gradients of
+harmonic functions;
+(iii) $(R_p)$: $L^p$-boundedness of the Riesz transform ($p<\infty$);
+(iv) $(GBE)$: a generalised Bakry-Émery condition.
+We show that, for $p\in (2,\infty)$, (i), (ii) (iii) are equivalent, while
+for $p=\infty$, (i), (ii), (iv) are equivalent.
+Moreover, some of these equivalences still hold under weaker conditions than
+the $L^2$-Poincaré inequality.
+Our result gives a characterisation of Li-Yau's gradient estimate of heat
+kernels for $p=\infty$, while for $p\in (2,\infty)$ it is a substantial
+improvement as well as a generalisation of earlier results by
+Auscher-Coulhon-Duong-Hofmann [7] and Auscher-Coulhon [6]. Applications to
+isoperimetric inequalities and Sobolev inequalities are given. Our results
+apply to Riemannian and sub-Riemannian manifolds as well as to non-smooth
+spaces, and to degenerate elliptic/parabolic equations in these settings.
+",0,0,1,0,0,0
+20536,The Deep Underground Neutrino Experiment -- DUNE: the precision era of neutrino physics,"  The last decade was remarkable for neutrino physics. In particular, the
+phenomenon of neutrino flavor oscillations has been firmly established by a
+series of independent measurements. All parameters of the neutrino mixing are
+now known and we have elements to plan a judicious exploration of new scenarios
+that are opened by these recent advances. With precise measurements, we can
+test the 3-neutrino paradigm, neutrino mass hierarchy and CP asymmetry in the
+lepton sector. The future long-baseline experiments are considered to be a
+fundamental tool to deepen our knowledge of electroweak interactions. The Deep
+Underground Neutrino Experiment -- DUNE will detect a broad-band neutrino beam
+from Fermilab in an underground massive Liquid Argon Time-Projection Chamber at
+an L/E of about $10^3$ km / GeV to reach good sensitivity for CP-phase
+measurements and the determination of the mass hierarchy. The dimensions and
+the depth of the Far Detector also create an excellent opportunity to look for
+rare signals like proton decay to study violation of baryonic number, as well
+as supernova neutrino bursts, broadening the scope of the experiment to
+astrophysics and associated impacts in cosmology. In this presentation, we will
+discuss the physics motivations and the main experimental features of the DUNE
+project required to reach its scientific goals.
+",0,1,0,0,0,0
+20537,Variations of $q$-Garnier system,"  We study several variants of q-Garnier system corresponding to various
+directions of discrete time evolutions. We also investigate a relation between
+the $q$-Garnier system and Suzuki's higher order $q$-Painlev/'e system by using
+a duality of the $q$-KP system.
+",0,1,1,0,0,0
+20538,Quantum Quench dynamics in Non-local Luttinger Model: Rigorous Results,"  We investigate, in the Luttinger model with fixed box potential, the time
+evolution of an inhomogeneous state prepared as a localized fermion added to
+the noninteracting ground state. We proved that, if the state is evolved with
+the interacting Hamiltonian, the averaged density has two peaks moving in
+opposite directions, with a constant but renormalized velocity. We also proved
+that a dynamical `Landau quasi-particle weight' appears in the oscillating part
+of the averaged density, asymptotically vanishing with large time. The results
+are proved with the Mattis-Lieb diagonalization method. A simpler proof with
+the exact Bosonization formulas is also provided.
+",0,0,1,0,0,0
+20539,Dynamics of Relaxed Inflation,"  The cosmological relaxation of the electroweak scale has been proposed as a
+mechanism to address the hierarchy problem of the Standard Model. A field, the
+relaxion, rolls down its potential and, in doing so, scans the squared mass
+parameter of the Higgs, relaxing it to a parametrically small value. In this
+work, we promote the relaxion to an inflaton. We couple it to Abelian gauge
+bosons, thereby introducing the necessary dissipation mechanism which slows
+down the field in the last stages. We describe a novel reheating mechanism,
+which relies on the gauge-boson production leading to strong electromagnetic
+fields, and proceeds via the vacuum production of electron-positron pairs
+through the Schwinger effect. We refer to this mechanism as Schwinger
+reheating. We discuss the cosmological dynamics of the model and the
+phenomenological constraints from CMB and other experiments. We find that a
+cutoff close to the Planck scale may be achieved. In its minimal form, the
+model does not generate sufficient curvature perturbations and additional
+ingredients, such as a curvaton field, are needed.
+",0,1,0,0,0,0
+20540,Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling,"  In solving hard computational problems, semidefinite program (SDP)
+relaxations often play an important role because they come with a guarantee of
+optimality. Here, we focus on a popular semidefinite relaxation of K-means
+clustering which yields the same solution as the non-convex original
+formulation for well segregated datasets. We report an unexpected finding: when
+data contains (greater than zero-dimensional) manifolds, the SDP solution
+captures such geometrical structures. Unlike traditional manifold embedding
+techniques, our approach does not rely on manually defining a kernel but rather
+enforces locality via a nonnegativity constraint. We thus call our approach
+NOnnegative MAnifold Disentangling, or NOMAD. To build an intuitive
+understanding of its manifold learning capabilities, we develop a theoretical
+analysis of NOMAD on idealized datasets. While NOMAD is convex and the globally
+optimal solution can be found by generic SDP solvers with polynomial time
+complexity, they are too slow for modern datasets. To address this problem, we
+analyze a non-convex heuristic and present a new, convex and yet efficient,
+algorithm, based on the conditional gradient method. Our results render NOMAD a
+versatile, understandable, and powerful tool for manifold learning.
+",1,0,0,0,0,0
+20541,Dispersive optical detection of magnetic Feshbach resonances in ultracold gases,"  Magnetically tunable Feshbach resonances in ultracold atomic systems are
+chiefly identified and characterized through time consuming atom loss
+spectroscopy. We describe an off-resonant dispersive optical probing technique
+to rapidly locate Feshbach resonances and demonstrate the method by locating
+four resonances of $^{87}$Rb, between the $|\rm{F} = 1, \rm{m_F}=1 \rangle$ and
+$|\rm{F} = 2, \rm{m_F}=0 \rangle$ states. Despite the loss features being
+$\lesssim0.1$ G wide, we require only 21 experimental runs to explore a
+magnetic field range >18 G, where $1~\rm{G}=10^{-4}$ T. The resonances consist
+of two known s-wave features in the vicinity of 9 G and 18 G and two previously
+unobserved p-wave features near 5 G and 10 G. We further utilize the dispersive
+approach to directly characterize the two-body loss dynamics for each Feshbach
+resonance.
+",0,1,0,0,0,0
+20542,Unveiling Bias Compensation in Turbo-Based Algorithms for (Discrete) Compressed Sensing,"  In Compressed Sensing, a real-valued sparse vector has to be recovered from
+an underdetermined system of linear equations. In many applications, however,
+the elements of the sparse vector are drawn from a finite set. Adapted
+algorithms incorporating this additional knowledge are required for the
+discrete-valued setup. In this paper, turbo-based algorithms for both cases are
+elucidated and analyzed from a communications engineering perspective, leading
+to a deeper understanding of the algorithm. In particular, we gain the
+intriguing insight that the calculation of extrinsic values is equal to the
+unbiasing of a biased estimate and present an improved algorithm.
+",1,0,0,0,0,0
+20543,Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances,"  Momentum methods such as Polyak's heavy ball (HB) method, Nesterov's
+accelerated gradient (AG) as well as accelerated projected gradient (APG)
+method have been commonly used in machine learning practice, but their
+performance is quite sensitive to noise in the gradients. We study these
+methods under a first-order stochastic oracle model where noisy estimates of
+the gradients are available. For strongly convex problems, we show that the
+distribution of the iterates of AG converges with the accelerated
+$O(\sqrt{\kappa}\log(1/\varepsilon))$ linear rate to a ball of radius
+$\varepsilon$ centered at a unique invariant distribution in the 1-Wasserstein
+metric where $\kappa$ is the condition number as long as the noise variance is
+smaller than an explicit upper bound we can provide. Our analysis also
+certifies linear convergence rates as a function of the stepsize, momentum
+parameter and the noise variance; recovering the accelerated rates in the
+noiseless case and quantifying the level of noise that can be tolerated to
+achieve a given performance. In the special case of strongly convex quadratic
+objectives, we can show accelerated linear rates in the $p$-Wasserstein metric
+for any $p\geq 1$ with improved sensitivity to noise for both AG and HB through
+a non-asymptotic analysis under some additional assumptions on the noise
+structure. Our analysis for HB and AG also leads to improved non-asymptotic
+convergence bounds in suboptimality for both deterministic and stochastic
+settings which is of independent interest. To the best of our knowledge, these
+are the first linear convergence results for stochastic momentum methods under
+the stochastic oracle model. We also extend our results to the APG method and
+weakly convex functions showing accelerated rates when the noise magnitude is
+sufficiently small.
+",1,0,0,1,0,0
+20544,Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations,"  The recovery of approximately sparse or compressible coefficients in a
+Polynomial Chaos Expansion is a common goal in modern parametric uncertainty
+quantification (UQ). However, relatively little effort in UQ has been directed
+toward theoretical and computational strategies for addressing the sparse
+corruptions problem, where a small number of measurements are highly corrupted.
+Such a situation has become pertinent today since modern computational
+frameworks are sufficiently complex with many interdependent components that
+may introduce hardware and software failures, some of which can be difficult to
+detect and result in a highly polluted simulation result.
+In this paper we present a novel compressive sampling-based theoretical
+analysis for a regularized $\ell^1$ minimization algorithm that aims to recover
+sparse expansion coefficients in the presence of measurement corruptions. Our
+recovery results are uniform, and prescribe algorithmic regularization
+parameters in terms of a user-defined a priori estimate on the ratio of
+measurements that are believed to be corrupted. We also propose an iteratively
+reweighted optimization algorithm that automatically refines the value of the
+regularization parameter, and empirically produces superior results. Our
+numerical results test our framework on several medium-to-high dimensional
+examples of solutions to parameterized differential equations, and demonstrate
+the effectiveness of our approach.
+",0,0,1,0,0,0
+20545,Learning to Remember Rare Events,"  Despite recent advances, memory-augmented deep neural networks are still
+limited when it comes to life-long and one-shot learning, especially in
+remembering rare events. We present a large-scale life-long memory module for
+use in deep learning. The module exploits fast nearest-neighbor algorithms for
+efficiency and thus scales to large memory sizes. Except for the
+nearest-neighbor query, the module is fully differentiable and trained
+end-to-end with no extra supervision. It operates in a life-long manner, i.e.,
+without the need to reset it during training.
+Our memory module can be easily added to any part of a supervised neural
+network. To show its versatility we add it to a number of networks, from simple
+convolutional ones tested on image classification to deep sequence-to-sequence
+and recurrent-convolutional models. In all cases, the enhanced network gains
+the ability to remember and do life-long one-shot learning. Our module
+remembers training examples shown many thousands of steps in the past and it
+can successfully generalize from them. We set new state-of-the-art for one-shot
+learning on the Omniglot dataset and demonstrate, for the first time, life-long
+one-shot learning in recurrent neural networks on a large-scale machine
+translation task.
+",1,0,0,0,0,0
+20546,Stacking-dependent electronic structure of trilayer graphene resolved by nanospot angle-resolved photoemission spectroscopy,"  The crystallographic stacking order in multilayer graphene plays an important
+role in determining its electronic structure. In trilayer graphene,
+rhombohedral stacking (ABC) is particularly intriguing, exhibiting a flat band
+with an electric-field tunable band gap. Such electronic structure is distinct
+from simple hexagonal stacking (AAA) or typical Bernal stacking (ABA), and is
+promising for nanoscale electronics, optoelectronics applications. So far clean
+experimental electronic spectra on the first two stackings are missing because
+the samples are usually too small in size (um or nm scale) to be resolved by
+conventional angle-resolved photoemission spectroscopy (ARPES). Here by using
+ARPES with nanospot beam size (NanoARPES), we provide direct experimental
+evidence for the coexistence of three different stackings of trilayer graphene
+and reveal their distinctive electronic structures directly. By fitting the
+experimental data, we provide important experimental band parameters for
+describing the electronic structure of trilayer graphene with different
+stackings.
+",0,1,0,0,0,0
+20547,Interaction energy between vortices of vector fields on Riemannian surfaces,"  We study a variational Ginzburg-Landau type model depending on a small
+parameter $\epsilon>0$ for (tangent) vector fields on a $2$-dimensional
+Riemannian surface. As $\epsilon\to 0$, the vector fields tend to be of unit
+length and will have singular points of a (non-zero) index, called vortices.
+Our main result determines the interaction energy between these vortices as a
+$\Gamma$-limit (at the second order) as $\epsilon\to 0$.
+",0,0,1,0,0,0
+20548,Multimodal Nonlinear Microscope based on a Compact Fiber-format Laser Source,"  We present a multimodal non-linear optical (NLO) laser-scanning microscope,
+based on a compact fiber-format excitation laser and integrating coherent
+anti-Stokes Raman scattering (CARS), stimulated Raman scattering (SRS) and
+two-photon-excitation fluorescence (TPEF) on a single platform. We demonstrate
+its capabilities in simultaneously acquiring CARS and SRS images of a blend of
+6-{\mu}m poly(methyl methacrylate) beads and 3-{\mu}m polystyrene beads. We
+then apply it to visualize cell walls and chloroplast of an unprocessed fresh
+leaf of Elodea aquatic plant via SRS and TPEF modalities, respectively. The
+presented NLO microscope, developed in house using off-the-shelf components,
+offers full accessibility to the optical path and ensures its easy
+re-configurability and flexibility.
+",0,1,0,0,0,0
+20549,The Diffuse Light of the Universe - On the microwave background before and after its discovery: open questions,"  In 1965, the discovery of a new type of uniform radiation, located between
+radiowaves and infrared light, was accidental. Known today as Cosmic Microwave
+background (CMB), this diffuse radiation is commonly interpreted as a fossil
+light released in an early hot and dense universe and constitutes today the
+main 'pilar' of the big bang cosmology. Considerable efforts have been devoted
+to derive fundamental cosmological parameters from the characteristics of this
+radiation that led to a surprising universe that is shaped by at least three
+major unknown components: inflation, dark matter and dark energy. This is an
+important weakness of the present consensus cosmological model that justifies
+raising several questions on the CMB interpretation. Can we consider its
+cosmological nature as undisputable? Do other possible interpretations exist in
+the context of other cosmological theories or simply as a result of other
+physical mechanisms that could account for it? In an effort to questioning the
+validity of scientific hypotheses and the under-determination of theories
+compared to observations, we examine here the difficulties that still exist on
+the interpretation of this diffuse radiation and explore other proposed tracks
+to explain its origin. We discuss previous historical concepts of diffuse
+radiation before and after the CMB discovery and underline the limit of our
+present understanding.
+",0,1,0,0,0,0
+20550,Classification of $5$-Dimensional Complex Nilpotent Leibniz Algebras,"  Leibniz algebras are certain generalization of Lie algebras. In this paper we
+give the classification of $5-$dimensional complex non-Lie nilpotent Leibniz
+algebras. We use the canonical forms for the congruence classes of matrices of
+bilinear forms to classify the case $\dim(A^2)=3$ and $\dim(Leib(A))=1$ which
+can be applied to higher dimensions. The remaining cases are classified via
+direct method.
+",0,0,1,0,0,0
+20551,Haptics of Screwing and Unscrewing for its Application in Smart Factories for Disassembly,"  Reconstruction of skilled humans sensation and control system often leads to
+a development of robust control for the robots. We are developing an unscrewing
+robot for the automated disassembly which requires a comprehensive control
+system, but unscrewing experiments with robots are often limited to several
+conditions. On the contrary, humans typically have a broad range of screwing
+experiences and sensations throughout their lives, and we conducted an
+experiment to find these haptic patterns. Results show that people apply axial
+force to the screws to avoid screwdriver slippage (cam-outs), which is one of
+the key problems during screwing and unscrewing, and this axial force is
+proportional to the torque which is required for screwing. We have found that
+type of the screw head influences the amount of axial force applied. Using this
+knowledge an unscrewing robot for the smart disassembly factory RecyBot is
+developed, and experiments confirm the optimality of the strategy, used by
+humans. Finally, a methodology for robust unscrewing algorithm design is
+presented as a generalization of the findings. It can seriously speed up the
+development of the screwing and unscrewing robots and tools.
+",1,0,0,0,0,0
+20552,$G$-invariant Szegö kernel asymptotics and CR reduction,"  Let $(X, T^{1,0}X)$ be a compact connected orientable CR manifold of
+dimension $2n+1$ with non-degenerate Levi curvature. Assume that $X$ admits a
+connected compact Lie group action $G$. Under certain natural assumptions about
+the group action $G$, we show that the $G$-invariant Szegö kernel for $(0,q)$
+forms is a complex Fourier integral operator, smoothing away $\mu^{-1}(0)$ and
+there is a precise description of the singularity near $\mu^{-1}(0)$, where
+$\mu$ denotes the CR moment map. We apply our result to the case when $X$
+admits a transversal CR $S^1$ action and deduce an asymptotic expansion for the
+$m$-th Fourier component of the $G$-invariant Szegö kernel for $(0,q)$ forms
+as $m \to+\infty$. As an application, we show that if $m$ large enough,
+quantization commutes with reduction.
+",0,0,1,0,0,0
+20553,Self-similar resistive circuits as fractal-like structures,"  In the present work we explore resistive circuits where the individual
+resistors are arranged in fractal-like patterns. These circuits have some of
+the characteristics typically found in geometric fractals, namely
+self-similarity and scale invariance. Considering resistive circuits as graphs,
+we propose a definition of self-similar circuits which mimics a self-similar
+fractal. General properties of the resistive circuits generated by this
+approach are investigated, and interesting examples are commented in detail.
+Specifically, we consider self-similar resistive series, tree-like resistive
+networks and Sierpinski's configurations with resistors.
+",0,1,0,0,0,0
+20554,DynaPhoPy: A code for extracting phonon quasiparticles from molecular dynamics simulations,"  We have developed a computational code, DynaPhoPy, that allow us to extract
+the microscopic anharmonic phonon properties from molecular dynamics (MD)
+simulations using the normal-mode-decomposition technique as presented by Sun
+et al. [T. Sun, D. Zhang, R. Wentzcovitch, 2014]. Using this code we calculated
+the quasiparticle phonon frequencies and linewidths of crystalline silicon at
+different temperatures using both of first-principles and the Tersoff empirical
+potential approaches. In this work we show the dependence of these properties
+on the temperature using both approaches and compare them with reported
+experimental data obtained by Raman spectroscopy [M. Balkanski, R. Wallis, E.
+Haro, 1983 and R. Tsu, J. G. Hernandez, 1982].
+",0,1,0,0,0,0
+20555,Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models,"  The change detection problem is to determine if the Markov network structures
+of two Markov random fields differ from one another given two sets of samples
+drawn from the respective underlying distributions. We study the trade-off
+between the sample sizes and the reliability of change detection, measured as a
+minimax risk, for the important cases of the Ising models and the Gaussian
+Markov random fields restricted to the models which have network structures
+with $p$ nodes and degree at most $d$, and obtain information-theoretic lower
+bounds for reliable change detection over these models. We show that for the
+Ising model, $\Omega\left(\frac{d^2}{(\log d)^2}\log p\right)$ samples are
+required from each dataset to detect even the sparsest possible changes, and
+that for the Gaussian, $\Omega\left( \gamma^{-2} \log(p)\right)$ samples are
+required from each dataset to detect change, where $\gamma$ is the smallest
+ratio of off-diagonal to diagonal terms in the precision matrices of the
+distributions. These bounds are compared to the corresponding results in
+structure learning, and closely match them under mild conditions on the model
+parameters. Thus, our change detection bounds inherit partial tightness from
+the structure learning schemes in previous literature, demonstrating that in
+certain parameter regimes, the naive structure learning based approach to
+change detection is minimax optimal up to constant factors.
+",1,0,1,0,0,0
+20556,How Usable are Rust Cryptography APIs?,"  Context: Poor usability of cryptographic APIs is a severe source of
+vulnerabilities. Aim: We wanted to find out what kind of cryptographic
+libraries are present in Rust and how usable they are. Method: We explored
+Rust's cryptographic libraries through a systematic search, conducted an
+exploratory study on the major libraries and a controlled experiment on two of
+these libraries with 28 student participants. Results: Only half of the major
+libraries explicitly focus on usability and misuse resistance, which is
+reflected in their current APIs. We found that participants were more
+successful using rust-crypto which we considered less usable than ring before
+the experiment. Conclusion: We discuss API design insights and make
+recommendations for the design of crypto libraries in Rust regarding the detail
+and structure of the documentation, higher-level APIs as wrappers for the
+existing low-level libraries, and selected, good-quality example code to
+improve the emerging cryptographic libraries of Rust.
+",1,0,0,0,0,0
+20557,Augmenting End-to-End Dialog Systems with Commonsense Knowledge,"  Building dialog agents that can converse naturally with humans is a
+challenging yet intriguing problem of artificial intelligence. In open-domain
+human-computer conversation, where the conversational agent is expected to
+respond to human responses in an interesting and engaging way, commonsense
+knowledge has to be integrated into the model effectively. In this paper, we
+investigate the impact of providing commonsense knowledge about the concepts
+covered in the dialog. Our model represents the first attempt to integrating a
+large commonsense knowledge base into end-to-end conversational models. In the
+retrieval-based scenario, we propose the Tri-LSTM model to jointly take into
+account message and commonsense for selecting an appropriate response. Our
+experiments suggest that the knowledge-augmented models are superior to their
+knowledge-free counterparts in automatic evaluation.
+",1,0,0,0,0,0
+20558,A framework for Multi-A(rmed)/B(andit) testing with online FDR control,"  We propose an alternative framework to existing setups for controlling false
+alarms when multiple A/B tests are run over time. This setup arises in many
+practical applications, e.g. when pharmaceutical companies test new treatment
+options against control pills for different diseases, or when internet
+companies test their default webpages versus various alternatives over time.
+Our framework proposes to replace a sequence of A/B tests by a sequence of
+best-arm MAB instances, which can be continuously monitored by the data
+scientist. When interleaving the MAB tests with an an online false discovery
+rate (FDR) algorithm, we can obtain the best of both worlds: low sample
+complexity and any time online FDR control. Our main contributions are: (i) to
+propose reasonable definitions of a null hypothesis for MAB instances; (ii) to
+demonstrate how one can derive an always-valid sequential p-value that allows
+continuous monitoring of each MAB test; and (iii) to show that using rejection
+thresholds of online-FDR algorithms as the confidence levels for the MAB
+algorithms results in both sample-optimality, high power and low FDR at any
+point in time. We run extensive simulations to verify our claims, and also
+report results on real data collected from the New Yorker Cartoon Caption
+contest.
+",1,0,0,1,0,0
+20559,Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization,"  This paper presents a statistical method of single-channel speech enhancement
+that uses a variational autoencoder (VAE) as a prior distribution on clean
+speech. A standard approach to speech enhancement is to train a deep neural
+network (DNN) to take noisy speech as input and output clean speech. Although
+this supervised approach requires a very large amount of pair data for
+training, it is not robust against unknown environments. Another approach is to
+use non-negative matrix factorization (NMF) based on basis spectra trained on
+clean speech in advance and those adapted to noise on the fly. This
+semi-supervised approach, however, causes considerable signal distortion in
+enhanced speech due to the unrealistic assumption that speech spectrograms are
+linear combinations of the basis spectra. Replacing the poor linear generative
+model of clean speech in NMF with a VAE---a powerful nonlinear deep generative
+model---trained on clean speech, we formulate a unified probabilistic
+generative model of noisy speech. Given noisy speech as observed data, we can
+sample clean speech from its posterior distribution. The proposed method
+outperformed the conventional DNN-based method in unseen noisy environments.
+",1,0,0,1,0,0
+20560,BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning,"  Understanding the global optimality in deep learning (DL) has been attracting
+more and more attention recently. Conventional DL solvers, however, have not
+been developed intentionally to seek for such global optimality. In this paper
+we propose a novel approximation algorithm, BPGrad, towards optimizing deep
+models globally via branch and pruning. Our BPGrad algorithm is based on the
+assumption of Lipschitz continuity in DL, and as a result it can adaptively
+determine the step size for current gradient given the history of previous
+updates, wherein theoretically no smaller steps can achieve the global
+optimality. We prove that, by repeating such branch-and-pruning procedure, we
+can locate the global optimality within finite iterations. Empirically an
+efficient solver based on BPGrad for DL is proposed as well, and it outperforms
+conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the
+tasks of object recognition, detection, and segmentation.
+",1,0,0,1,0,0
+20561,Lagrangian Flow Network approach to an open flow model,"  Concepts and tools from network theory, the so-called Lagrangian Flow Network
+framework, have been successfully used to obtain a coarse-grained description
+of transport by closed fluid flows. Here we explore the application of this
+methodology to open chaotic flows, and check it with numerical results for a
+model open flow, namely a jet with a localized wave perturbation. We find that
+network nodes with high values of out-degree and of finite-time entropy in the
+forward-in-time direction identify the location of the chaotic saddle and its
+stable manifold, whereas nodes with high in-degree and backwards finite-time
+entropy highlight the location of the saddle and its unstable manifold. The
+cyclic clustering coefficient, associated to the presence of periodic orbits,
+takes non-vanishing values at the location of the saddle itself.
+",0,1,0,0,0,0
+20562,Some new gradient estimates for two nonlinear parabolic equations under Ricci flow,"  In this paper, by maximum principle and cutoff function, we investigate
+gradient estimates for positive solutions to two nonlinear parabolic equations
+under Ricci flow. The related Harnack inequalities are deduced. An result about
+positive solutions on closed manifolds under Ricci flow is abtained. As
+applications, gradient estimates and Harnack inequalities for positive
+solutions to the heat equation under Ricci flow are derived. These results in
+the paper can be regard as generalizing the gradient estimates of Li-Yau, J. Y.
+Li, Hamilton and Li-Xu to the Ricci flow. Our results also improve the
+estimates of S. P. Liu and J. Sun to the nonlinear parabolic equation under
+Ricci flow.
+",0,0,1,0,0,0
+20563,On the pointwise iteration-complexity of a dynamic regularized ADMM with over-relaxation stepsize,"  In this paper, we extend the improved pointwise iteration-complexity result
+of a dynamic regularized alternating direction method of multipliers (ADMM) for
+a new stepsize domain. In this complexity analysis, the stepsize parameter can
+even be chosen in the interval $(0,2)$ instead of interval
+$(0,(1+\sqrt{5})/2)$. As usual, our analysis is established by interpreting
+this ADMM variant as an instance of a hybrid proximal extragradient framework
+applied to a specific monotone inclusion problem.
+",0,0,1,0,0,0
+20564,DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders,"  This paper presents a novel deep learning-based method for learning a
+functional representation of mammalian neural images. The method uses a deep
+convolutional denoising autoencoder (CDAE) for generating an invariant, compact
+representation of in situ hybridization (ISH) images. While most existing
+methods for bio-imaging analysis were not developed to handle images with
+highly complex anatomical structures, the results presented in this paper show
+that functional representation extracted by CDAE can help learn features of
+functional gene ontology categories for their classification in a highly
+accurate manner. Using this CDAE representation, our method outperforms the
+previous state-of-the-art classification rate, by improving the average AUC
+from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates
+on input images that were downsampled significantly with respect to the
+original ones to make it computationally feasible.
+",1,0,0,1,0,0
+20565,Nonlocal heat equations in the Heisenberg group,"  We study the following nonlocal diffusion equation in the Heisenberg group
+$\mathbb{H}_n$, \[ u_t(z,s,t)=J\ast u(z,s,t)-u(z,s,t), \] where $\ast$ denote
+convolution product and $J$ satisfies appropriated hypothesis. For the Cauchy
+problem we obtain that the asymptotic behavior of the solutions is the same
+form that the one for the heat equation in the Heisenberg group. To obtain this
+result we use the spherical transform related to the pair
+$(U(n),\mathbb{H}_n)$. Finally we prove that solutions of properly rescaled
+nonlocal Dirichlet problem converge uniformly to the solution of the
+corresponding Dirichlet problem for the classical heat equation in the
+Heisenberg group.
+",0,0,1,0,0,0
+20566,Motion Planning Networks,"  Fast and efficient motion planning algorithms are crucial for many
+state-of-the-art robotics applications such as self-driving cars. Existing
+motion planning methods such as RRT*, A*, and D*, become ineffective as their
+computational complexity increases exponentially with the dimensionality of the
+motion planning problem. To address this issue, we present a neural
+network-based novel planning algorithm which generates end-to-end
+collision-free paths irrespective of the obstacles' geometry. The proposed
+method, called MPNet (Motion Planning Network), comprises of a Contractive
+Autoencoder which encodes the given workspaces directly from a point cloud
+measurement, and a deep feedforward neural network which takes the workspace
+encoding, start and goal configuration, and generates end-to-end feasible
+motion trajectories for the robot to follow. We evaluate MPNet on multiple
+planning problems such as planning of a point-mass robot, rigid-body, and 7 DOF
+Baxter robot manipulators in various 2D and 3D environments. The results show
+that MPNet is not only consistently computationally efficient in all 2D and 3D
+environments but also show remarkable generalization to completely unseen
+environments. The results also show that computation time of MPNet consistently
+remains less than 1 second which is significantly lower than existing
+state-of-the-art motion planning algorithms. Furthermore, through transfer
+learning, the MPNet trained in one scenario (e.g., indoor living places) can
+also quickly adapt to new scenarios (e.g., factory floors) with a little amount
+of data.
+",1,0,0,1,0,0
+20567,Query Expansion Techniques for Information Retrieval: a Survey,"  With the ever increasing size of web, relevant information extraction on the
+Internet with a query formed by a few keywords has become a big challenge. To
+overcome this, query expansion (QE) plays a crucial role in improving the
+Internet searches, where the user's initial query is reformulated to a new
+query by adding new meaningful terms with similar significance. QE -- as part
+of information retrieval (IR) -- has long attracted researchers' attention. It
+has also become very influential in the field of personalized social document,
+Question Answering over Linked Data (QALD), and, Text Retrieval Conference
+(TREC) and REAL sets. This paper surveys QE techniques in IR from 1960 to 2017
+with respect to core techniques, data sources used, weighting and ranking
+methodologies, user participation and applications (of QE techniques) --
+bringing out similarities and differences.
+",1,0,0,0,0,0
+20568,Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis,"  This paper fills a gap in aspect-based sentiment analysis and aims to present
+a new method for preparing and analysing texts concerning opinion and
+generating user-friendly descriptive reports in natural language. We present a
+comprehensive set of techniques derived from Rhetorical Structure Theory and
+sentiment analysis to extract aspects from textual opinions and then build an
+abstractive summary of a set of opinions. Moreover, we propose aspect-aspect
+graphs to evaluate the importance of aspects and to filter out unimportant ones
+from the summary. Additionally, the paper presents a prototype solution of data
+flow with interesting and valuable results. The proposed method's results
+proved the high accuracy of aspect detection when applied to the gold standard
+dataset.
+",1,0,0,0,0,0
+20569,Commutativity of integral quasi-arithmetic means on measure spaces,"  Let $(X, \mathscr{L}, \lambda)$ and $(Y, \mathscr{M}, \mu)$ be finite measure
+spaces for which there exist $A \in \mathscr{L}$ and $B \in \mathscr{M}$ with
+$0 < \lambda(A) < \lambda(X)$ and $0 < \mu(B) < \mu(Y)$, and let $I\subseteq
+\mathbf{R}$ be a non-empty interval. We prove that, if $f$ and $g$ are
+continuous bijections $I \to \mathbf{R}^+$, then the equation $$
+f^{-1}\!\left(\int_X f\!\left(g^{-1}\!\left(\int_Y g \circ
+h\;d\mu\right)\right)d \lambda\right)\! = g^{-1}\!\left(\int_Y
+g\!\left(f^{-1}\!\left(\int_X f \circ h\;d\lambda\right)\right)d \mu\right)$$
+is satisfied by every $\mathscr{L} \otimes \mathscr{M}$-measurable simple
+function $h: X \times Y \to I$ if and only if $f=c g$ for some $c \in
+\mathbf{R}^+$ (it is easy to see that the equation is well posed). An
+analogous, but essentially different, result, with $f$ and $g$ replaced by
+continuous injections $I \to \mathbf R$ and $\lambda(X)=\mu(Y)=1$, was recently
+obtained in [Indag. Math. 27 (2016), 945-953].
+",0,0,1,0,0,0
+20570,Moderate Deviation for Random Elliptic PDEs with Small Noise,"  Partial differential equations with random inputs have become popular models
+to characterize physical systems with uncertainty coming from, e.g., imprecise
+measurement and intrinsic randomness. In this paper, we perform asymptotic rare
+event analysis for such elliptic PDEs with random inputs. In particular, we
+consider the asymptotic regime that the noise level converges to zero
+suggesting that the system uncertainty is low, but does exists. We develop
+sharp approximations of the probability of a large class of rare events.
+",0,0,1,0,0,0
+20571,Music Transformer,"  Music relies heavily on repetition to build structure and meaning.
+Self-reference occurs on multiple timescales, from motifs to phrases to reusing
+of entire sections of music, such as in pieces with ABA structure. The
+Transformer (Vaswani et al., 2017), a sequence model based on self-attention,
+has achieved compelling results in many generation tasks that require
+maintaining long-range coherence. This suggests that self-attention might also
+be well-suited to modeling music. In musical composition and performance,
+however, relative timing is critically important. Existing approaches for
+representing relative positional information in the Transformer modulate
+attention based on pairwise distance (Shaw et al., 2018). This is impractical
+for long sequences such as musical compositions since their memory complexity
+for intermediate relative information is quadratic in the sequence length. We
+propose an algorithm that reduces their intermediate memory requirement to
+linear in the sequence length. This enables us to demonstrate that a
+Transformer with our modified relative attention mechanism can generate
+minute-long compositions (thousands of steps, four times the length modeled in
+Oore et al., 2018) with compelling structure, generate continuations that
+coherently elaborate on a given motif, and in a seq2seq setup generate
+accompaniments conditioned on melodies. We evaluate the Transformer with our
+relative attention mechanism on two datasets, JSB Chorales and
+Piano-e-Competition, and obtain state-of-the-art results on the latter.
+",1,0,0,1,0,0
+20572,Residual-Based Detections and Unified Architecture for Massive MIMO Uplink,"  Massive multiple-input multiple-output (M-MIMO) technique brings better
+energy efficiency and coverage but higher computational complexity than
+small-scale MIMO. For linear detections such as minimum mean square error
+(MMSE), prohibitive complexity lies in solving large-scale linear equations.
+For a better trade-off between bit-error-rate (BER) performance and
+computational complexity, iterative linear algorithms like conjugate gradient
+(CG) have been applied and have shown their feasibility in recent years. In
+this paper, residual-based detection (RBD) algorithms are proposed for M-MIMO
+detection, including minimal residual (MINRES) algorithm, generalized minimal
+residual (GMRES) algorithm, and conjugate residual (CR) algorithm. RBD
+algorithms focus on the minimization of residual norm per iteration, whereas
+most existing algorithms focus on the approximation of exact signal. Numerical
+results have shown that, for $64$-QAM $128\times 8$ MIMO, RBD algorithms are
+only $0.13$ dB away from the exact matrix inversion method when BER$=10^{-4}$.
+Stability of RBD algorithms has also been verified in various correlation
+conditions. Complexity comparison has shown that, CR algorithm require $87\%$
+less complexity than the traditional method for $128\times 60$ MIMO. The
+unified hardware architecture is proposed with flexibility, which guarantees a
+low-complexity implementation for a family of RBD M-MIMO detectors.
+",1,0,0,0,0,0
+20573,Sparsity constrained split feasibility for dose-volume constraints in inverse planning of intensity-modulated photon or proton therapy,"  A split feasibility formulation for the inverse problem of
+intensity-modulated radiation therapy (IMRT) treatment planning with
+dose-volume constraints (DVCs) included in the planning algorithm is presented.
+It involves a new type of sparsity constraint that enables the inclusion of a
+percentage-violation constraint in the model problem and its handling by
+continuous (as opposed to integer) methods. We propose an iterative algorithmic
+framework for solving such a problem by applying the feasibility-seeking
+CQ-algorithm of Byrne combined with the automatic relaxation method (ARM) that
+uses cyclic projections. Detailed implementation instructions are furnished.
+Functionality of the algorithm was demonstrated through the creation of an
+intensity-modulated proton therapy plan for a simple 2D C-shaped geometry and
+also for a realistic base-of-skull chordoma treatment site. Monte Carlo
+simulations of proton pencil beams of varying energy were conducted to obtain
+dose distributions for the 2D test case. A research release of the Pinnacle3
+proton treatment planning system was used to extract pencil beam doses for a
+clinical base-of-skull chordoma case. In both cases the beamlet doses were
+calculated to satisfy dose-volume constraints according to our new algorithm.
+Examination of the dose-volume histograms following inverse planning with our
+algorithm demonstrated that it performed as intended. The application of our
+proposed algorithm to dose-volume constraint inverse planning was successfully
+demonstrated. Comparison with optimized dose distributions from the research
+release of the Pinnacle3 treatment planning system showed the algorithm could
+achieve equivalent or superior results.
+",0,1,1,0,0,0
+20574,On 2-Verma modules for quantum $\mathfrak{sl}_2$,"  In this paper we study the superalgebra $A_n$, introduced by the authors in
+previous work on categorification of Verma modules for quantum
+$\mathfrak{sl}_2$. The superalgebra $A_n$ is akin to the nilHecke algebra, and
+shares similar properties. In particular, we prove a uniqueness result about
+2-Verma modules on $\Bbbk$-linear 2-categories.
+",0,0,1,0,0,0
+20575,On the Number of Single-Peaked Narcissistic or Single-Crossing Narcissistic Preference Profiles,"  We investigate preference profiles for a set $\mathcal{V}$ of voters, where
+each voter $i$ has a preference order $\succ_i$ on a finite set $A$ of
+alternatives (that is, a linear order on $A$) such that for each two
+alternatives $a,b\in A$, voter $i$ prefers $a$ to $b$ if $a\succ_i b$. Such a
+profile is narcissistic if each alternative $a$ is preferred the most by at
+least one voter. It is single-peaked if there is a linear order
+$\triangleright^{\text{sp}}$ on the alternatives such that each voter's
+preferences on the alternatives along the order $\triangleright^{\text{sp}}$
+are either strictly increasing, or strictly decreasing, or first strictly
+increasing and then strictly decreasing. It is single-crossing if there is a
+linear order $\triangleright^{\text{sc}}$ on the voters such that each pair of
+alternatives divides the order $\triangleright^{\text{sc}}$ into at most two
+suborders, where in each suborder, all voters have the same linear order on
+this pair.
+We show that for $n$ voters and $n$ alternatives,the number of single-peaked
+narcissistic profiles is $\prod_{i=2}^{n-1} \binom{n-1}{i-1}$ while the number
+of single-crossing narcissistic profiles is $2^{\binom{n-1}{2}}$.
+",0,0,1,0,0,0
+20576,Planet formation and disk-planet interactions,"  This review is based on lectures given at the 45th Saas-Fee Advanced Course
+'From Protoplanetary Disks to Planet Formation' held in March 2015 in Les
+Diablerets, Switzerland. Starting with an overview of the main characterictics
+of the Solar System and extrasolar planets, we describe the planet formation
+process in terms of the sequential accretion scenario. First the growth
+processes of dust particles to planetesimals and subsequently to terrestrial
+planets or planetary cores are presented. This is followed by the formation
+process of the giant planets either by core accretion or gravitational
+instability. Finally, the dynamical evolution of the orbital elements as driven
+by disk-planet interaction and the overall evolution of multi-object systems is
+presented.
+",0,1,0,0,0,0
+20577,Cherednik algebras and Calogero-Moser cells,"  Using the representation theory of Cherednik algebras at $t=0$ and a Galois
+covering of the Calogero-Moser space, we define the notions of left, right and
+two-sided Calogero-Moser cells for any finite complex reflection group. To each
+Caloger-Moser two-sided cell is associated a Calogero-Moser family, while to
+each Calogero-Moser left cell is associated a Calogero-Moser cellular
+representation. We study properties of these objects and we conjecture that,
+whenever the reflection group is real (i.e. is a Coxeter group), these notions
+coincide with the one of Kazhdan-Lusztig left, right and two-sided cells,
+Kazhdan-Lusztig families and Kazhdan-Lusztig cellular representations.
+",0,0,1,0,0,0
+20578,Khovanov complexes of rational tangles,"  We show that the Khovanov complex of a rational tangle has a very simple
+representative whose backbone of non-zero morphisms forms a zig-zag.
+Furthermore, this minimal complex can be computed quickly by an inductive
+algorithm. (For example, we calculate $Kh(8_2)$ by hand.) We find that the
+bigradings of the subobjects in these minimal complexes can be described by
+matrix actions, which after a change of basis is the reduced Burau
+representation of $B_3$.
+",0,0,1,0,0,0
+20579,Asymmetric Connectedness of Fears in the U.S. Financial Sector,"  We study how shocks to the forward-looking expectations of investors buying
+call and put options transmit across the financial system. We introduce a new
+contagion measure, called asymmetric fear connectedness (AFC), which captures
+the information related to ""fear"" on the two sides of the options market and
+can be used as a forward-looking systemic risk monitoring tool. The decomposed
+connectedness measures provide timely predictive information for near-future
+macroeconomic conditions and uncertainty indicators, and they contain
+additional valuable information that is not included in the aggregate
+connectedness measure. The role of a positive/negative ""fear""
+transmitter/receiver emerges clearly when we focus more closely on
+idiosyncratic events for financial institutions. We identify banks that are
+predominantly positive/negative receivers of ""fear"", as well as banks that
+positively/negatively transmit ""fear"" in the financial system.
+",0,0,0,0,0,1
+20580,Neural Network Based Speaker Classification and Verification Systems with Enhanced Features,"  This work presents a novel framework based on feed-forward neural network for
+text-independent speaker classification and verification, two related systems
+of speaker recognition. With optimized features and model training, it achieves
+100% classification rate in classification and less than 6% Equal Error Rate
+(ERR), using merely about 1 second and 5 seconds of data respectively. Features
+with stricter Voice Active Detection (VAD) than the regular one for speech
+recognition ensure extracting stronger voiced portion for speaker recognition,
+speaker-level mean and variance normalization helps to eliminate the
+discrepancy between samples from the same speaker. Both are proven to improve
+the system performance. In building the neural network speaker classifier, the
+network structure parameters are optimized with grid search and dynamically
+reduced regularization parameters are used to avoid training terminated in
+local minimum. It enables the training goes further with lower cost. In speaker
+verification, performance is improved with prediction score normalization,
+which rewards the speaker identity indices with distinct peaks and penalizes
+the weak ones with high scores but more competitors, and speaker-specific
+thresholding, which significantly reduces ERR in the ROC curve. TIMIT corpus
+with 8K sampling rate is used here. First 200 male speakers are used to train
+and test the classification performance. The testing files of them are used as
+in-domain registered speakers, while data from the remaining 126 male speakers
+are used as out-of-domain speakers, i.e. imposters in speaker verification.
+",1,0,0,0,0,0
+20581,Slimness of graphs,"  Slimness of a graph measures the local deviation of its metric from a tree
+metric. In a graph $G=(V,E)$, a geodesic triangle $\bigtriangleup(x,y,z)$ with
+$x, y, z\in V$ is the union $P(x,y) \cup P(x,z) \cup P(y,z)$ of three shortest
+paths connecting these vertices. A geodesic triangle $\bigtriangleup(x,y,z)$ is
+called $\delta$-slim if for any vertex $u\in V$ on any side $P(x,y)$ the
+distance from $u$ to $P(x,z) \cup P(y,z)$ is at most $\delta$, i.e. each path
+is contained in the union of the $\delta$-neighborhoods of two others. A graph
+$G$ is called $\delta$-slim, if all geodesic triangles in $G$ are
+$\delta$-slim. The smallest value $\delta$ for which $G$ is $\delta$-slim is
+called the slimness of $G$. In this paper, using the layering partition
+technique, we obtain sharp bounds on slimness of such families of graphs as (1)
+graphs with cluster-diameter $\Delta(G)$ of a layering partition of $G$, (2)
+graphs with tree-length $\lambda$, (3) graphs with tree-breadth $\rho$, (4)
+$k$-chordal graphs, AT-free graphs and HHD-free graphs. Additionally, we show
+that the slimness of every 4-chordal graph is at most 2 and characterize those
+4-chordal graphs for which the slimness of every of its induced subgraph is at
+most 1.
+",1,0,0,0,0,0
+20582,SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems,"  Despite their immense popularity, deep learning-based acoustic systems are
+inherently vulnerable to adversarial attacks, wherein maliciously crafted
+audios trigger target systems to misbehave. In this paper, we present
+SirenAttack, a new class of attacks to generate adversarial audios. Compared
+with existing attacks, SirenAttack highlights with a set of significant
+features: (i) versatile -- it is able to deceive a range of end-to-end acoustic
+systems under both white-box and black-box settings; (ii) effective -- it is
+able to generate adversarial audios that can be recognized as specific phrases
+by target acoustic systems; and (iii) stealthy -- it is able to generate
+adversarial audios indistinguishable from their benign counterparts to human
+perception. We empirically evaluate SirenAttack on a set of state-of-the-art
+deep learning-based acoustic systems (including speech command recognition,
+speaker recognition and sound event classification), with results showing the
+versatility, effectiveness, and stealthiness of SirenAttack. For instance, it
+achieves 99.45% attack success rate on the IEMOCAP dataset against the ResNet18
+model, while the generated adversarial audios are also misinterpreted by
+multiple popular ASR platforms, including Google Cloud Speech, Microsoft Bing
+Voice, and IBM Speech-to-Text. We further evaluate three potential defense
+methods to mitigate such attacks, including adversarial training, audio
+downsampling, and moving average filtering, which leads to promising directions
+for further research.
+",1,0,0,0,0,0
+20583,k-server via multiscale entropic regularization,"  We present an $O((\log k)^2)$-competitive randomized algorithm for the
+$k$-server problem on hierarchically separated trees (HSTs). This is the first
+$o(k)$-competitive randomized algorithm for which the competitive ratio is
+independent of the size of the underlying HST. Our algorithm is designed in the
+framework of online mirror descent where the mirror map is a multiscale
+entropy. When combined with Bartal's static HST embedding reduction, this leads
+to an $O((\log k)^2 \log n)$-competitive algorithm on any $n$-point metric
+space. We give a new dynamic HST embedding that yields an $O((\log k)^3 \log
+\Delta)$-competitive algorithm on any metric space where the ratio of the
+largest to smallest non-zero distance is at most $\Delta$.
+",1,0,1,0,0,0
+20584,Some Large Sample Results for the Method of Regularized Estimators,"  We present a general framework for studying regularized estimators; i.e.,
+estimation problems wherein ""plug-in"" type estimators are either ill-defined or
+ill-behaved. We derive primitive conditions that imply consistency and
+asymptotic linear representation for regularized estimators, allowing for
+slower than $\sqrt{n}$ estimators as well as infinite dimensional parameters.
+We also provide data-driven methods for choosing tuning parameters that, under
+some conditions, achieve the aforementioned results. We illustrate the scope of
+our approach by studying a wide range of applications, revisiting known results
+and deriving new ones.
+",0,0,1,0,0,0
+20585,Coregionalised Locomotion Envelopes - A Qualitative Approach,"  'Sharing of statistical strength' is a phrase often employed in machine
+learning and signal processing. In sensor networks, for example, missing
+signals from certain sensors may be predicted by exploiting their correlation
+with observed signals acquired from other sensors. For humans, our hands move
+synchronously with our legs, and we can exploit these implicit correlations for
+predicting new poses and for generating new natural-looking walking sequences.
+We can also go much further and exploit this form of transfer learning, to
+develop new control schemas for robust control of rehabilitation robots. In
+this short paper we introduce coregionalised locomotion envelopes - a method
+for multi-dimensional manifold regression, on human locomotion variates. Herein
+we render a qualitative description of this method.
+",1,0,0,1,0,0
+20586,Optimal Invariant Tests in an Instrumental Variables Regression With Heteroskedastic and Autocorrelated Errors,"  This paper uses model symmetries in the instrumental variable (IV) regression
+to derive an invariant test for the causal structural parameter. Contrary to
+popular belief, we show there exist model symmetries when equation errors are
+heteroskedastic and autocorrelated (HAC). Our theory is consistent with
+existing results for the homoskedastic model (Andrews, Moreira and Stock(2006}
+and Chamberlain (2007}), but in general uses information on the structural
+parameter beyond the Anderson-Rubin, score, and rank statistics. This suggests
+that tests based only the Anderson-Rubin and score statistics discard
+information on the causal parameter of interest. We apply our theory to
+construct designs in which these tests indeed have power arbitrarily close to
+size. Other tests, including other adaptations to the CLR test, do not suffer
+the same deficiencies. Finally, we use the model symmetries to propose novel
+weighted-average power tests for the HAC-IV model.
+",0,0,1,1,0,0
+20587,Longitudinal electric field: from Maxwell equation to non-locality in time and space,"  In this paper we use the classical electrodynamics to show that the Lorenz
+gauge can be incompatible with some particular solutions of the d Alembert
+equations for electromagnetic potentials. In its turn, the d Alembert equations
+for the elec- tromagnetic potentials is the result of application of the Lorenz
+gauge to general equations for the potentials. The last ones is the
+straightforward consequence of Maxwell equations. Since the d Alembert
+equations and the electromagnetic poten- tials are necessary for quantum
+electrodynamics formulation, one should oblige to satisfy these equations also
+in classical case. The solution of d Alembert equations, which modifies
+longitudinal electric field is found. The requirement of this modifi- cation
+follows from the necessity to satisfy the physical condition of impossibility
+of instantaneous transferring of interaction in space.
+",0,1,0,0,0,0
+20588,Estimating linear functionals of a sparse family of Poisson means,"  Assume that we observe a sample of size n composed of p-dimensional signals,
+each signal having independent entries drawn from a scaled Poisson distribution
+with an unknown intensity. We are interested in estimating the sum of the n
+unknown intensity vectors, under the assumption that most of them coincide with
+a given 'background' signal. The number s of p-dimensional signals different
+from the background signal plays the role of sparsity and the goal is to
+leverage this sparsity assumption in order to improve the quality of estimation
+as compared to the naive estimator that computes the sum of the observed
+signals. We first introduce the group hard thresholding estimator and analyze
+its mean squared error measured by the squared Euclidean norm. We establish a
+nonasymptotic upper bound showing that the risk is at most of the order of
+{\sigma}^2(sp + s^2sqrt(p)) log^3/2(np). We then establish lower bounds on the
+minimax risk over a properly defined class of collections of s-sparse signals.
+These lower bounds match with the upper bound, up to logarithmic terms, when
+the dimension p is fixed or of larger order than s^2. In the case where the
+dimension p increases but remains of smaller order than s^2, our results show a
+gap between the lower and the upper bounds, which can be up to order sqrt(p).
+",0,0,1,1,0,0
+20589,Fabrication of porous microrings via laser printing and ion-beam post-etching,"  Pulsed-laser dry printing of noble-metal microrings with a tunable internal
+porous structure, which can be revealed via an ion-beam etching post-procedure,
+was demonstrated. Abundance and average size of the pores inside the microrings
+were shown to be tuned in a wide range by varying incident pulse energy and a
+nitrogen doping level controlled in the process of magnetron deposition of the
+gold film in the appropriate gaseous environment. The fabricated porous
+microrings were shown to provide many-fold near-field enhancement of incident
+electromagnetic fields, which was confirmed by mapping of the characteristic
+Raman band of a nanometer-thick covering layer of Rhodamine 6G dye molecules
+and supporting finite-difference time-domain calculations. The proposed laser
+printing/ion-beam etching approach is demonstrated to be a unique tool aimed at
+designing and fabricating multifunctional plasmonic structures and metasurfaces
+for spectroscopic bioidentification based on surface-enhanced infrared
+absorption, Raman scattering and photoluminescence detection schemes.
+",0,1,0,0,0,0
+20590,Performance Measurements of Supercomputing and Cloud Storage Solutions,"  Increasing amounts of data from varied sources, particularly in the fields of
+machine learning and graph analytics, are causing storage requirements to grow
+rapidly. A variety of technologies exist for storing and sharing these data,
+ranging from parallel file systems used by supercomputers to distributed block
+storage systems found in clouds. Relatively few comparative measurements exist
+to inform decisions about which storage systems are best suited for particular
+tasks. This work provides these measurements for two of the most popular
+storage technologies: Lustre and Amazon S3. Lustre is an open-source, high
+performance, parallel file system used by many of the largest supercomputers in
+the world. Amazon's Simple Storage Service, or S3, is part of the Amazon Web
+Services offering, and offers a scalable, distributed option to store and
+retrieve data from anywhere on the Internet. Parallel processing is essential
+for achieving high performance on modern storage systems. The performance tests
+used span the gamut of parallel I/O scenarios, ranging from single-client,
+single-node Amazon S3 and Lustre performance to a large-scale, multi-client
+test designed to demonstrate the capabilities of a modern storage appliance
+under heavy load. These results show that, when parallel I/O is used correctly
+(i.e., many simultaneous read or write processes), full network bandwidth
+performance is achievable and ranged from 10 gigabits/s over a 10 GigE S3
+connection to 0.35 terabits/s using Lustre on a 1200 port 10 GigE switch. These
+results demonstrate that S3 is well-suited to sharing vast quantities of data
+over the Internet, while Lustre is well-suited to processing large quantities
+of data locally.
+",1,1,0,0,0,0
+20591,Multi-Objective Event-triggered Consensus of Linear Multi-agent Systems,"  This paper proposes a distributed consensus algorithm for linear event-based
+heterogeneous multi-agent systems (MAS). The proposed scheme is event-triggered
+in the sense that an agent selectively transmits its information within its
+local neighbourhood based on a directed network topology under the fulfillment
+of certain conditions. Using the Lyapunov stability theorem, the system
+constraints and event-triggering condition are expressed in terms of several
+linear matrix inequalities (LMIs) to derive the consensus parameters. The
+objective is to design the transmission threshold and minimum-norm
+heterogeneous control gains which collectively ensure an exponential consensus
+convergence rate for the closed-loop systems. The LMI computed control gains
+are robust to uncertainty with some deviation from their nominal values
+allowed. The practicability of the proposed event-based framework is further
+studied by proving the Zeno behaviour exclusion. Numerical simulations quantify
+the advantages of our event-triggered consensus approach in second-order,
+linear and heterogeneous multi-agent systems.
+",1,0,0,0,0,0
+20592,Private and Secure Coordination of Match-Making for Heavy-Duty Vehicle Platooning,"  A secure and private framework for inter-agent communication and coordination
+is developed. This allows an agent, in our case a fleet owner, to ask questions
+or submit queries in an encrypted fashion using semi-homomorphic encryption.
+The submitted query can be about the interest of the other fleet owners for
+using a road at a specific time of the day, for instance, for the purpose of
+collaborative vehicle platooning. The other agents can then provide appropriate
+responses without knowing the content of the questions or the queries. Strong
+privacy and security guarantees are provided for the agent who is submitting
+the queries. It is also shown that the amount of the information that this
+agent can extract from the other agent is bounded. In fact, with submitting one
+query, a sophisticated agent can at most extract the answer to two queries.
+This secure communication platform is used subsequently to develop a
+distributed coordination mechanisms among fleet owners.
+",1,0,1,0,0,0
+20593,Kernel Robust Bias-Aware Prediction under Covariate Shift,"  Under covariate shift, training (source) data and testing (target) data
+differ in input space distribution, but share the same conditional label
+distribution. This poses a challenging machine learning task. Robust Bias-Aware
+(RBA) prediction provides the conditional label distribution that is robust to
+the worstcase logarithmic loss for the target distribution while matching
+feature expectation constraints from the source distribution. However,
+employing RBA with insufficient feature constraints may result in high
+certainty predictions for much of the source data, while leaving too much
+uncertainty for target data predictions. To overcome this issue, we extend the
+representer theorem to the RBA setting, enabling minimization of regularized
+expected target risk by a reweighted kernel expectation under the source
+distribution. By applying kernel methods, we establish consistency guarantees
+and demonstrate better performance of the RBA classifier than competing methods
+on synthetically biased UCI datasets as well as datasets that have natural
+covariate shift.
+",1,0,0,1,0,0
+20594,Degeneration in VAE: in the Light of Fisher Information Loss,"  While enormous progress has been made to Variational Autoencoder (VAE) in
+recent years, similar to other deep networks, VAE with deep networks suffers
+from the problem of degeneration, which seriously weakens the correlation
+between the input and the corresponding latent codes, deviating from the goal
+of the representation learning. To investigate how degeneration affects VAE
+from a theoretical perspective, we illustrate the information transmission in
+VAE and analyze the intermediate layers of the encoders/decoders. Specifically,
+we propose a Fisher Information measure for the layer-wise analysis. With such
+measure, we demonstrate that information loss is ineluctable in feed-forward
+networks and causes the degeneration in VAE. We show that skip connections in
+VAE enable the preservation of information without changing the model
+architecture. We call this class of VAE equipped with skip connections as SCVAE
+and perform a range of experiments to show its advantages in information
+preservation and degeneration mitigation.
+",0,0,0,1,0,0
+20595,Room-temperature spin transport in n-Ge probed by four-terminal nonlocal measurements,"  We demonsrtate electrical spin injection and detection in $n$-type Ge
+($n$-Ge) at room temperature using four-terminal nonlocal spin-valve and
+Hanle-effect measurements in lateral spin-valve (LSV) devices with
+Heusler-alloy Schottky tunnel contacts. The spin diffusion length
+($\lambda$$_{\rm Ge}$) of the Ge layer used ($n \sim$ 1 $\times$ 10$^{19}$
+cm$^{-3}$) at 296 K is estimated to be $\sim$ 0.44 $\pm$ 0.02 $\mu$m.
+Room-temperature spin signals can be observed reproducibly at the low bias
+voltage range ($\le$ 0.7 V) for LSVs with relatively low resistance-area
+product ($RA$) values ($\le$ 1 k$\Omega$$\mu$m$^{2}$). This means that the
+Schottky tunnel contacts used here are more suitable than ferromagnet/MgO
+tunnel contacts ($RA \ge$ 100 k$\Omega$$\mu$m$^{2}$) for developing Ge
+spintronic applications.
+",0,1,0,0,0,0
+20596,Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning,"  We present an algorithm for rapidly learning controllers for robotics
+systems. The algorithm follows the model-based reinforcement learning paradigm,
+and improves upon existing algorithms; namely Probabilistic learning in Control
+(PILCO) and a sample-based version of PILCO with neural network dynamics
+(Deep-PILCO). We propose training a neural network dynamics model using
+variational dropout with truncated Log-Normal noise. This allows us to obtain a
+dynamics model with calibrated uncertainty, which can be used to simulate
+controller executions via rollouts. We also describe set of techniques,
+inspired by viewing PILCO as a recurrent neural network model, that are crucial
+to improve the convergence of the method. We test our method on a variety of
+benchmark tasks, demonstrating data-efficiency that is competitive with PILCO,
+while being able to optimize complex neural network controllers. Finally, we
+assess the performance of the algorithm for learning motor controllers for a
+six legged autonomous underwater vehicle. This demonstrates the potential of
+the algorithm for scaling up the dimensionality and dataset sizes, in more
+complex control tasks.
+",1,0,0,0,0,0
+20597,Stealthy Deception Attacks Against SCADA Systems,"  SCADA protocols for Industrial Control Systems (ICS) are vulnerable to
+network attacks such as session hijacking. Hence, research focuses on network
+anomaly detection based on meta--data (message sizes, timing, command
+sequence), or on the state values of the physical process. In this work we
+present a class of semantic network-based attacks against SCADA systems that
+are undetectable by the above mentioned anomaly detection. After hijacking the
+communication channels between the Human Machine Interface (HMI) and
+Programmable Logic Controllers (PLCs), our attacks cause the HMI to present a
+fake view of the industrial process, deceiving the human operator into taking
+manual actions. Our most advanced attack also manipulates the messages
+generated by the operator's actions, reversing their semantic meaning while
+causing the HMI to present a view that is consistent with the attempted human
+actions. The attacks are totaly stealthy because the message sizes and timing,
+the command sequences, and the data values of the ICS's state all remain
+legitimate.
+We implemented and tested several attack scenarios in the test lab of our
+local electric company, against a real HMI and real PLCs, separated by a
+commercial-grade firewall. We developed a real-time security assessment tool,
+that can simultaneously manipulate the communication to multiple PLCs and cause
+the HMI to display a coherent system--wide fake view. Our tool is configured
+with message-manipulating rules written in an ICS Attack Markup Language (IAML)
+we designed, which may be of independent interest. Our semantic attacks all
+successfully fooled the operator and brought the system to states of blackout
+and possible equipment damage.
+",1,0,0,0,0,0
+20598,Beta-rhythm oscillations and synchronization transition in network models of Izhikevich neurons: effect of topology and synaptic type,"  Despite their significant functional roles, beta-band oscillations are least
+understood. Synchronization in neuronal networks have attracted much attention
+in recent years with the main focus on transition type. Whether one obtains
+explosive transition or a continuous transition is an important feature of the
+neuronal network which can depend on network structure as well as synaptic
+types. In this study we consider the effect of synaptic interaction (electrical
+and chemical) as well as structural connectivity on synchronization transition
+in network models of Izhikevich neurons which spike regularly with beta
+rhythms. We find a wide range of behavior including continuous transition,
+explosive transition, as well as lack of global order. The stronger electrical
+synapses are more conducive to synchronization and can even lead to explosive
+synchronization. The key network element which determines the order of
+transition is found to be the clustering coefficient and not the small world
+effect, or the existence of hubs in a network. These results are in contrast to
+previous results which use phase oscillator models such as the Kuramoto model.
+Furthermore, we show that the patterns of synchronization changes when one goes
+to the gamma band. We attribute such a change to the change in the refractory
+period of Izhikevich neurons which changes significantly with frequency.
+",0,0,0,0,1,0
+20599,Renyi Differential Privacy,"  We propose a natural relaxation of differential privacy based on the Renyi
+divergence. Closely related notions have appeared in several recent papers that
+analyzed composition of differentially private mechanisms. We argue that the
+useful analytical tool can be used as a privacy definition, compactly and
+accurately representing guarantees on the tails of the privacy loss.
+We demonstrate that the new definition shares many important properties with
+the standard definition of differential privacy, while additionally allowing
+tighter analysis of composite heterogeneous mechanisms.
+",1,0,0,0,0,0
+20600,Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure,"  In this paper, we propose a quality enhancement network for Versatile Video
+Coding (VVC) compressed videos by jointly exploiting spatial details and
+temporal structure (SDTS). The network consists of a temporal structure
+prediction subnet and a spatial detail enhancement subnet. The former subnet is
+used to estimate and compensate the temporal motion across frames, and the
+spatial detail subnet is used to reduce the compression artifacts and enhance
+the reconstruction quality of the VVC compressed video. Experimental results
+demonstrate the effectiveness of our SDTS-based approach. It offers over
+7.82$\%$ BD-rate saving on the common test video sequences and achieves the
+state-of-the-art performance.
+",1,0,0,0,0,0
+20601,Discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. A discrete Lawson correspondence,"  The main result of this paper is a discrete Lawson correspondence between
+discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. This is a
+correspondence between two discrete isothermic surfaces. We show that this
+correspondence is an isometry in the following sense: it preserves the metric
+coefficients introduced previously by Bobenko and Suris for isothermic nets.
+Exactly as in the smooth case, this is a correspondence between nets with the
+same Lax matrices, and the immersion formulas also coincide with the smooth
+case.
+",0,1,1,0,0,0
+20602,Network Model Selection Using Task-Focused Minimum Description Length,"  Networks are fundamental models for data used in practically every
+application domain. In most instances, several implicit or explicit choices
+about the network definition impact the translation of underlying data to a
+network representation, and the subsequent question(s) about the underlying
+system being represented. Users of downstream network data may not even be
+aware of these choices or their impacts. We propose a task-focused network
+model selection methodology which addresses several key challenges. Our
+approach constructs network models from underlying data and uses minimum
+description length (MDL) criteria for selection. Our methodology measures
+efficiency, a general and comparable measure of the network's performance of a
+local (i.e. node-level) predictive task of interest. Selection on efficiency
+favors parsimonious (e.g. sparse) models to avoid overfitting and can be
+applied across arbitrary tasks and representations. We show stability,
+sensitivity, and significance testing in our methodology.
+",1,0,0,0,0,0
+20603,Concentration of curvature and Lipschitz invariants of holomorphic functions of two variables,"  By combining analytic and geometric viewpoints on the concentration of the
+curvature of the Milnor fibre, we prove that Lipschitz homeomorphisms preserve
+the zones of multi-scale curvature concentration as well as the gradient canyon
+structure of holomorphic functions of two variables. This yields the first new
+Lipschitz invariants after those discovered by Henry and Parusinski in 2003.
+",0,0,1,0,0,0
+20604,Beam tuning and bunch length measurement in the bunch compression operation at the cERL,"  Realization of a short bunch beam by manipulating the longitudinal phase
+space distribution with a finite longitudinal dispersion following an off-crest
+accelera- tion is a widely used technique. The technique was applied in a
+compact test accelerator of an energy-recovery linac scheme for compressing the
+bunch length at the return loop. A diagnostic system utilizing coherent
+transition radiation was developed for the beam tuning and for estimating the
+bunch length. By scanning the beam parameters, we experimentally found the best
+condition for the bunch compression. The RMS bunch length of 250+-50 fs was
+obtained at a bunch charge of 2 pC. This result confirmed the design and the
+tuning pro- cedure of the bunch compression operation for the future
+energy-recovery linac (ERL).
+",0,1,0,0,0,0
+20605,Long-Lived Ultracold Molecules with Electric and Magnetic Dipole Moments,"  We create fermionic dipolar $^{23}$Na$^6$Li molecules in their triplet ground
+state from an ultracold mixture of $^{23}$Na and $^6$Li. Using
+magneto-association across a narrow Feshbach resonance followed by a two-photon
+STIRAP transfer to the triplet ground state, we produce $3\,{\times}\,10^4$
+ground state molecules in a spin-polarized state. We observe a lifetime of
+$4.6\,\text{s}$ in an isolated molecular sample, approaching the $p$-wave
+universal rate limit. Electron spin resonance spectroscopy of the triplet state
+was used to determine the hyperfine structure of this previously unobserved
+molecular state.
+",0,1,0,0,0,0
+20606,Bilinear generalized Radon transforms in the plane,"  Let $\sigma$ be arc-length measure on $S^1\subset \mathbb R^2$ and $\Theta$
+denote rotation by an angle $\theta \in (0, \pi]$. Define a model bilinear
+generalized Radon transform, $$B_{\theta}(f,g)(x)=\int_{S^1} f(x-y)g(x-\Theta
+y)\, d\sigma(y),$$ an analogue of the linear generalized Radon transforms of
+Guillemin and Sternberg \cite{GS} and Phong and Stein (e.g.,
+\cite{PhSt91,St93}). Operators such as $B_\theta$ are motivated by problems in
+geometric measure theory and combinatorics. For $\theta<\pi$, we show that
+$B_{\theta}: L^p({\Bbb R}^2) \times L^q({\Bbb R}^2) \to L^r({\Bbb R}^2)$ if
+$\left(\frac{1}{p},\frac{1}{q},\frac{1}{r}\right)\in Q$, the polyhedron with
+the vertices $(0,0,0)$, $(\frac{2}{3}, \frac{2}{3}, 1)$, $(0, \frac{2}{3},
+\frac{1}{3})$, $(\frac{2}{3},0,\frac{1}{3})$, $(1,0,1)$, $(0,1,1)$ and
+$(\frac{1}{2},\frac{1}{2},\frac{1}{2})$, except for $\left(
+\frac{1}{2},\frac{1}{2},\frac{1}{2} \right)$, where we obtain a restricted
+strong type estimate. For the degenerate case $\theta=\pi$, a more restrictive
+set of exponents holds. In the scale of normed spaces, $p,q,r \ge 1$, the type
+set $Q$ is sharp. Estimates for the same exponents are also proved for a class
+of bilinear generalized Radon transforms in $\mathbb R^2$ of the form $$
+B(f,g)(x)=\int \int \delta(\phi_1(x,y)-t_1)\delta(\phi_2(x,z)-t_2)
+\delta(\phi_3(y,z)-t_3) f(y)g(z) \psi(y,z) \, dy\, dz, $$ where $\delta$
+denotes the Dirac distribution, $t_1,t_2,t_3\in\mathbb R$, $\psi$ is a smooth
+cut-off and the defining functions $\phi_j$ satisfy some natural geometric
+assumptions.
+",0,0,1,0,0,0
+20607,Diffusion of new products with recovering consumers,"  We consider the diffusion of new products in the discrete Bass-SIR model, in
+which consumers who adopt the product can later ""recover"" and stop influencing
+their peers to adopt the product. To gain insight into the effect of the social
+network structure on the diffusion, we focus on two extreme cases. In the
+""most-connected"" configuration where all consumers are inter-connected
+(complete network), averaging over all consumers leads to an aggregate model,
+which combines the Bass model for diffusion of new products with the SIR model
+for epidemics. In the ""least-connected"" configuration where consumers are
+arranged on a circle and each consumer can only be influenced by his left
+neighbor (one-sided 1D network), averaging over all consumers leads to a
+different aggregate model which is linear, and can be solved explicitly. We
+conjecture that for any other network, the diffusion is bounded from below and
+from above by that on a one-sided 1D network and on a complete network,
+respectively. When consumers are arranged on a circle and each consumer can be
+influenced by his left and right neighbors (two-sided 1D network), the
+diffusion is strictly faster than on a one-sided 1D network. This is different
+from the case of non-recovering adopters, where the diffusion on one-sided and
+on two-sided 1D networks is identical. We also propose a nonlinear model for
+recoveries, and show that consumers' heterogeneity has a negligible effect on
+the aggregate diffusion.
+",1,1,0,0,0,0
+20608,Answering Complex Questions Using Open Information Extraction,"  While there has been substantial progress in factoid question-answering (QA),
+answering complex questions remains challenging, typically requiring both a
+large body of knowledge and inference techniques. Open Information Extraction
+(Open IE) provides a way to generate semi-structured knowledge for QA, but to
+date such knowledge has only been used to answer simple questions with
+retrieval-based methods. We overcome this limitation by presenting a method for
+reasoning with Open IE knowledge, allowing more complex questions to be
+handled. Using a recently proposed support graph optimization framework for QA,
+we develop a new inference model for Open IE, in particular one that can work
+effectively with multiple short facts, noise, and the relational structure of
+tuples. Our model significantly outperforms a state-of-the-art structured
+solver on complex questions of varying difficulty, while also removing the
+reliance on manually curated knowledge.
+",1,0,0,0,0,0
+20609,On Testing Quantum Programs,"  A quantum computer (QC) can solve many computational problems more
+efficiently than a classic one. The field of QCs is growing: companies (such as
+DWave, IBM, Google, and Microsoft) are building QC offerings. We position that
+software engineers should look into defining a set of software engineering
+practices that apply to QC's software. To start this process, we give examples
+of challenges associated with testing such software and sketch potential
+solutions to some of these challenges.
+",1,0,0,0,0,0
+20610,Degree weighted recurrence networks for the analysis of time series data,"  Recurrence networks are powerful tools used effectively in the nonlinear
+analysis of time series data. The analysis in this context is done mostly with
+unweighted and undirected complex networks constructed with specific criteria
+from the time series. In this work, we propose a novel method to construct
+""weighted recurrence network""(WRN) from a time series and show how it can
+reveal useful information regarding the structure of a chaotic attractor, which
+the usual unweighted recurrence network cannot provide. Especially, we find the
+node strength distribution of the WRN, from every chaotic attractor follows a
+power law (with exponential tail) with the index characteristic to the fractal
+structure of the attractor. This leads to a new class among complex networks,
+to which networks from all standard chaotic attractors are found to belong. In
+addition, we present generalized definitions for clustering coefficient and
+characteristic path length and show that these measures can effectively
+discriminate chaotic dynamics from white noise and $1/f$ colored noise. Our
+results indicate that the WRN and the associated measures can become
+potentially important tools for the analysis of short and noisy time series
+from the real world systems as they are clearly demarked from that of noisy or
+stochastic systems.
+",0,1,0,0,0,0
+20611,Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age,"  As Ocean General Circulation Models (OGCMs) move into the petascale age,
+where the output from global high-resolution model runs can be of the order of
+hundreds of terabytes in size, tools to analyse the output of these models will
+need to scale up too. Lagrangian Ocean Analysis, where virtual particles are
+tracked through hydrodynamic fields, is an increasingly popular way to analyse
+OGCM output, by mapping pathways and connectivity of biotic and abiotic
+particulates. However, the current software stack of Lagrangian Ocean Analysis
+codes is not dynamic enough to cope with the increasing complexity, scale and
+need for customisation of use-cases. Furthermore, most community codes are
+developed for stand-alone use, making it a nontrivial task to integrate virtual
+particles at runtime of the OGCM. Here, we introduce the new Parcels code,
+which was designed from the ground up to be sufficiently scalable to cope with
+petascale computing. We highlight its API design that combines flexibility and
+customisation with the ability to optimise for HPC workflows, following the
+paradigm of domain-specific languages. Parcels is primarily written in Python,
+utilising the wide range of tools available in the scientific Python ecosystem,
+while generating low-level C-code and using Just-In-Time compilation for
+performance-critical computation. We show a worked-out example of its API, and
+validate the accuracy of the code against seven idealised test cases. This
+version~0.9 of Parcels is focussed on laying out the API, with future work
+concentrating on optimisation, efficiency and at-runtime coupling with OGCMs.
+",1,1,0,0,0,0
+20612,TFLMS: Large Model Support in TensorFlow by Graph Rewriting,"  While accelerators such as GPUs have limited memory, deep neural networks are
+becoming larger and will not fit with the memory limitation of accelerators for
+training. We propose an approach to tackle this problem by rewriting the
+computational graph of a neural network, in which swap-out and swap-in
+operations are inserted to temporarily store intermediate results on CPU
+memory. In particular, we first revise the concept of a computational graph by
+defining a concrete semantics for variables in a graph. We then formally show
+how to derive swap-out and swap-in operations from an existing graph and
+present rules to optimize the graph. To realize our approach, we developed a
+module in TensorFlow, named TFLMS. TFLMS is published as a pull request in the
+TensorFlow repository for contributing to the TensorFlow community. With TFLMS,
+we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size,
+respectively. In particular, we were able to train 3DUNet using images of size
+of $192^3$ for image segmentation, which, without TFLMS, had been done only by
+dividing the images to smaller images, which affects the accuracy.
+",0,0,0,1,0,0
+20613,Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties,"  Understanding the relationship between the structure of light-harvesting
+systems and their excitation energy transfer properties is of fundamental
+importance in many applications including the development of next generation
+photovoltaics. Natural light harvesting in photosynthesis shows remarkable
+excitation energy transfer properties, which suggests that pigment-protein
+complexes could serve as blueprints for the design of nature inspired devices.
+Mechanistic insights into energy transport dynamics can be gained by leveraging
+numerically involved propagation schemes such as the hierarchical equations of
+motion (HEOM). Solving these equations, however, is computationally costly due
+to the adverse scaling with the number of pigments. Therefore virtual
+high-throughput screening, which has become a powerful tool in material
+discovery, is less readily applicable for the search of novel excitonic
+devices. We propose the use of artificial neural networks to bypass the
+computational limitations of established techniques for exploring the
+structure-dynamics relation in excitonic systems. Once trained, our neural
+networks reduce computational costs by several orders of magnitudes. Our
+predicted transfer times and transfer efficiencies exhibit similar or even
+higher accuracies than frequently used approximate methods such as secular
+Redfield theory
+",0,1,0,1,0,0
+20614,Multi-Player Bandits: A Trekking Approach,"  We study stochastic multi-armed bandits with many players. The players do not
+know the number of players, cannot communicate with each other and if multiple
+players select a common arm they collide and none of them receive any reward.
+We consider the static scenario, where the number of players remains fixed, and
+the dynamic scenario, where the players enter and leave at any time. We provide
+algorithms based on a novel `trekking approach' that guarantees constant regret
+for the static case and sub-linear regret for the dynamic case with high
+probability. The trekking approach eliminates the need to estimate the number
+of players resulting in fewer collisions and improved regret performance
+compared to the state-of-the-art algorithms. We also develop an epoch-less
+algorithm that eliminates any requirement of time synchronization across the
+players provided each player can detect the presence of other players on an
+arm. We validate our theoretical guarantees using simulation based and real
+test-bed based experiments.
+",0,0,0,1,0,0
+20615,High-resolution investigation of spinal cord and spine,"  High-resolution non-invasive 3D study of intact spine and spinal cord
+morphology on the level of complex vascular and neuronal organization is a
+crucial issue for the development of treatments for the injuries and
+pathologies of central nervous system (CNS). X-ray phase contrast tomography
+enables high quality 3D visualization in ex-vivo mouse model of both vascular
+and neuronal network of the soft spinal cord tissue at the scale from
+millimeters to hundreds of nanometers without any contrast agents and
+sectioning. Until now, 3D high resolution visualization of spinal cord mostly
+has been limited by imaging of organ extracted from vertebral column because
+high absorbing boney tissue drastically reduces the morphological details of
+soft tissue in image. However, the extremely destructive procedure of bones
+removal leads to sample deterioration and, therefore, to the lack of
+considerable part of information about the object. In this work we present the
+data analysis procedure to get high resolution and high contrast 3D images of
+intact mice spinal cord surrounded by vertebras, preserving all richness of
+micro-details of the spinal cord inhabiting inside. Our results are the first
+step forward to the difficult way toward the high- resolution investigation of
+in-vivo model central nervous system.
+",0,1,0,0,0,0
+20616,Bohr--Rogosinski radius for analytic functions,"  There are a number of articles which deal with Bohr's phenomenon whereas only
+a few papers appeared in the literature on Rogosinski's radii for analytic
+functions defined on the unit disk $|z|<1$. In this article, we introduce and
+investigate Bohr-Rogosinski's radii for analytic functions defined for $|z|<1$.
+Also, we prove several different improved versions of the classical Bohr's
+inequality. Finally, we also discuss the Bohr-Rogosinski's radius for a class
+of subordinations. All the results are proved to be sharp.
+",0,0,1,0,0,0
+20617,Methods to locate Saddle Points in Complex Landscapes,"  We present a class of simple algorithms that allows to find the reaction path
+in systems with a complex potential energy landscape. The approach does not
+need any knowledge on the product state and does not require the calculation of
+any second derivatives. The underlying idea is to use two nearby points in
+configuration space to locate the path of slowest ascent. By introducing a weak
+noise term, the algorithm is able to find even low-lying saddle points that are
+not reachable by means of a slowest ascent path. Since the algorithm makes only
+use of the value of the potential and its gradient, the computational effort to
+find saddles is linear in the number of degrees of freedom, if the potential is
+short-ranged. We test the performance of the algorithm for two potential energy
+landscapes. For the Müller-Brown surface we find that the algorithm always
+finds the correct saddle point. For the modified Müller-Brown surface, which
+has a saddle point that is not reachable by means of a slowest ascent path, the
+algorithm is still able to find this saddle point with high probability.
+",0,1,0,0,0,0
+20618,On the Complexity of Opinions and Online Discussions,"  In an increasingly polarized world, demagogues who reduce complexity down to
+simple arguments based on emotion are gaining in popularity. Are opinions and
+online discussions falling into demagoguery? In this work, we aim to provide
+computational tools to investigate this question and, by doing so, explore the
+nature and complexity of online discussions and their space of opinions,
+uncovering where each participant lies.
+More specifically, we present a modeling framework to construct latent
+representations of opinions in online discussions which are consistent with
+human judgements, as measured by online voting. If two opinions are close in
+the resulting latent space of opinions, it is because humans think they are
+similar. Our modeling framework is theoretically grounded and establishes a
+surprising connection between opinions and voting models and the sign-rank of a
+matrix. Moreover, it also provides a set of practical algorithms to both
+estimate the dimension of the latent space of opinions and infer where opinions
+expressed by the participants of an online discussion lie in this space.
+Experiments on a large dataset from Yahoo! News, Yahoo! Finance, Yahoo! Sports,
+and the Newsroom app suggest that unidimensional opinion models may often be
+unable to accurately represent online discussions, provide insights into human
+judgements and opinions, and show that our framework is able to circumvent
+language nuances such as sarcasm or humor by relying on human judgements
+instead of textual analysis.
+",1,0,0,1,0,0
+20619,Free energy distribution of the stationary O'Connell-Yor directed random polymer model,"  We study the semi-discrete directed polymer model introduced by O'Connell-Yor
+in its stationary regime, based on our previous work on the stationary
+$q$-totally asymmetric simple exclusion process ($q$-TASEP) using a two-sided
+$q$-Whittaker process. We give a formula for the free energy distribution of
+the polymer model in terms of Fredholm determinant and show that the universal
+KPZ stationary distribution appears in the long time limit. We also consider
+the limit to the stationary KPZ equation and discuss the connections with
+previously found formulas.
+",0,1,1,0,0,0
+20620,Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13),"  In this paper, we introduce the use of a personalized Gaussian Process model
+(pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor
+of Alzheimer's Disease (AD) in the cognitive domain -- using data from each
+patient's previous visits, and testing on future (held-out) data. We start by
+learning a population-level model using multi-modal data from previously seen
+patients using a base Gaussian Process (GP) regression. The personalized GP
+(pGP) is formed by adapting the base GP sequentially over time to a new
+(target) patient using domain adaptive GPs. We extend this personalized
+approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24
+months. We compare this approach to a GP model trained only on past data of the
+target patients (tGP), as well as to a new approach that combines pGP with tGP.
+We find that the new approach, combining pGP with tGP, leads to large
+improvements in accurately forecasting future ADAS-Cog13 scores.
+",0,0,0,1,0,0
+20621,Analysis and Control of a Non-Standard Hyperbolic PDE Traffic Flow Model,"  The paper provides results for a non-standard, hyperbolic, 1-D, nonlinear
+traffic flow model on a bounded domain. The model consists of two first-order
+PDEs with a dynamic boundary condition that involves the time derivative of the
+velocity. The proposed model has features that are important from a
+traffic-theoretic point of view: is completely anisotropic and information
+travels forward exactly at the same speed as traffic. It is shown that, for all
+physically meaningful initial conditions, the model admits a globally defined,
+unique, classical solution that remains positive and bounded for all times.
+Moreover, it is shown that global stabilization can be achieved for arbitrary
+equilibria by means of an explicit boundary feedback law. The stabilizing
+feedback law depends only on the inlet velocity and consequently, the
+measurement requirements for the implementation of the proposed boundary
+feedback law are minimal. The efficiency of the proposed boundary feedback law
+is demonstrated by means of a numerical example.
+",1,0,1,0,0,0
+20622,Learning Robust Representations for Computer Vision,"  Unsupervised learning techniques in computer vision often require learning
+latent representations, such as low-dimensional linear and non-linear
+subspaces. Noise and outliers in the data can frustrate these approaches by
+obscuring the latent spaces.
+Our main goal is deeper understanding and new development of robust
+approaches for representation learning. We provide a new interpretation for
+existing robust approaches and present two specific contributions: a new robust
+PCA approach, which can separate foreground features from dynamic background,
+and a novel robust spectral clustering method, that can cluster facial images
+with high accuracy. Both contributions show superior performance to standard
+methods on real-world test sets.
+",1,0,0,1,0,0
+20623,Detection Estimation and Grid matching of Multiple Targets with Single Snapshot Measurements,"  In this work, we explore the problems of detecting the number of narrow-band,
+far-field targets and estimating their corresponding directions from single
+snapshot measurements. The principles of sparse signal recovery (SSR) are used
+for the single snapshot detection and estimation of multiple targets. In the
+SSR framework, the DoA estimation problem is grid based and can be posed as the
+lasso optimization problem. However, the SSR framework for DoA estimation gives
+rise to the grid mismatch problem, when the unknown targets (sources) are not
+matched with the estimation grid chosen for the construction of the array
+steering matrix at the receiver. The block sparse recovery framework is known
+to mitigate the grid mismatch problem by jointly estimating the targets and
+their corresponding offsets from the estimation grid using the group lasso
+estimator. The corresponding detection problem reduces to estimating the
+optimal regularization parameter ($\tau$) of the lasso (in case of perfect
+grid-matching) or group-lasso estimation problem for achieving the required
+probability of correct detection ($P_c$). We propose asymptotic and finite
+sample test statistics for detecting the number of sources with the required
+$P_c$ at moderate to high signal to noise ratios. Once the number of sources
+are detected, or equivalently the optimal $\hat{\tau}$ is estimated, the
+corresponding estimation and grid matching of the DoAs can be performed by
+solving the lasso or group-lasso problem at $\hat{\tau}$
+",0,0,0,1,0,0
+20624,Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels,"  The latent feature relational model (LFRM) is a generative model for
+graph-structured data to learn a binary vector representation for each node in
+the graph. The binary vector denotes the node's membership in one or more
+communities. At its core, the LFRM miller2009nonparametric is an overlapping
+stochastic blockmodel, which defines the link probability between any pair of
+nodes as a bilinear function of their community membership vectors. Moreover,
+using a nonparametric Bayesian prior (Indian Buffet Process) enables learning
+the number of communities automatically from the data. However, despite its
+appealing properties, inference in LFRM remains a challenge and is typically
+done via MCMC methods. This can be slow and may take a long time to converge.
+In this work, we develop a small-variance asymptotics based framework for the
+non-parametric Bayesian LFRM. This leads to an objective function that retains
+the nonparametric Bayesian flavor of LFRM, while enabling us to design
+deterministic inference algorithms for this model, that are easy to implement
+(using generic or specialized optimization routines) and are fast in practice.
+Our results on several benchmark datasets demonstrate that our algorithm is
+competitive to methods such as MCMC, while being much faster.
+",0,0,0,1,0,0
+20625,Towards a Science of Mind,"  The ancient mind/body problem continues to be one of deepest mysteries of
+science and of the human spirit. Despite major advances in many fields, there
+is still no plausible link between subjective experience (qualia) and its
+realization in the body. This paper outlines some of the elements of a rigorous
+science of mind (SoM) - key ideas include scientific realism of mind, agnostic
+mysterianism, careful attention to language, and a focus on concrete
+(touchstone) questions and results.
+",1,0,0,0,1,0
+20626,Resistance distance criterion for optimal slack bus selection,"  We investigate the dependence of transmission losses on the choice of a slack
+bus in high voltage AC transmission networks. We formulate a transmission loss
+minimization problem in terms of slack variables representing the additional
+power injection that each generator provides to compensate the transmission
+losses. We show analytically that for transmission lines having small,
+homogeneous resistance over reactance ratios ${r/x\ll1}$, transmission losses
+are generically minimal in the case of a unique \textit{slack bus} instead of a
+distributed slack bus. For the unique slack bus scenario, to lowest order in
+${r/x}$, transmission losses depend linearly on a resistance distance based
+indicator measuring the separation of the slack bus candidate from the rest of
+the network. We confirm these results numerically for several IEEE and Pegase
+testcases, and show that our predictions qualitatively hold also in the case of
+lines having inhomogeneous ${r/x}$ ratios, with optimal slack bus choices
+reducing transmission losses by ${10}\%$ typically.
+",1,1,0,0,0,0
+20627,Interpolation in the Presence of Domain Inhomogeneity,"  Standard interpolation techniques are implicitly based on the assumption that
+the signal lies on a homogeneous domain. In this letter, the proposed
+interpolation method instead exploits prior information about domain
+inhomogeneity, characterized by different, potentially overlapping, subdomains.
+By introducing a domain-similarity metric for each sample, the interpolation
+process is then based on a domain-informed consistency principle. We illustrate
+and demonstrate the feasibility of domain-informed linear interpolation in 1D,
+and also, on a real fMRI image in 2D. The results show the benefit of
+incorporating domain knowledge so that, for example, sharp domain boundaries
+can be recovered by the interpolation, if such information is available.
+",0,0,1,0,0,0
+20628,Lectures on the mean values of functionals -- An elementary introduction to infinite-dimensional probability,"  This is an elementary introduction to infinite-dimensional probability. In
+the lectures, we compute the exact mean values of some functionals on C[0,1]
+and L[0,1] by considering these functionals as infinite-dimensional random
+variables. The results show that there exist the complete concentration of
+measure phenomenon for these mean values since the variances are all zeroes.
+",0,1,1,1,0,0
+20629,Synthesis of Optimal Resilient Control Strategies,"  Repair mechanisms are important within resilient systems to maintain the
+system in an operational state after an error occurred. Usually, constraints on
+the repair mechanisms are imposed, e.g., concerning the time or resources
+required (such as energy consumption or other kinds of costs). For systems
+modeled by Markov decision processes (MDPs), we introduce the concept of
+resilient schedulers, which represent control strategies guaranteeing that
+these constraints are always met within some given probability. Assigning
+rewards to the operational states of the system, we then aim towards resilient
+schedulers which maximize the long-run average reward, i.e., the expected mean
+payoff. We present a pseudo-polynomial algorithm that decides whether a
+resilient scheduler exists and if so, yields an optimal resilient scheduler. We
+show also that already the decision problem asking whether there exists a
+resilient scheduler is PSPACE-hard.
+",1,0,0,0,0,0
+20630,Limits of the Kucera-Gacs coding method,"  Every real is computable from a Martin-Loef random real. This well known
+result in algorithmic randomness was proved by Kucera and Gacs. In this survey
+article we discuss various approaches to the problem of coding an arbitrary
+real into a Martin-Loef random real,and also describe new results concerning
+optimal methods of coding. We start with a simple presentation of the original
+methods of Kucera and Gacs and then rigorously demonstrate their limitations in
+terms of the size of the redundancy in the codes that they produce. Armed with
+a deeper understanding of these methods, we then proceed to motivate and
+illustrate aspects of the new coding method that was recently introduced by
+Barmpalias and Lewis-Pye and which achieves optimal logarithmic redundancy, an
+exponential improvement over the original redundancy bounds.
+",0,0,1,0,0,0
+20631,Subspace Tracking Algorithms for Millimeter Wave MIMO Channel Estimation with Hybrid Beamforming,"  This paper proposes the use of subspace tracking algorithms for performing
+MIMO channel estimation at millimeter wave (mmWave) frequencies. Using a
+subspace approach, we develop a protocol enabling the estimation of the right
+(resp. left) singular vectors at the transmitter (resp. receiver) side; then,
+we adapt the projection approximation subspace tracking with deflation (PASTd)
+and the orthogonal Oja (OOJA) algorithms to our framework and obtain two
+channel estimation algorithms. The hybrid analog/digital nature of the
+beamformer is also explicitly taken into account at the algorithm design stage.
+Numerical results show that the proposed estimation algorithms are effective,
+and that they perform better than two relevant competing alternatives available
+in the open literature.
+",1,0,0,0,0,0
+20632,Thomas Precession for Dressed Particles,"  We consider a particle dressed with boundary gravitons in three-dimensional
+Minkowski space. The existence of BMS transformations implies that the
+particle's wavefunction picks up a Berry phase when subjected to changes of
+reference frames that trace a closed path in the asymptotic symmetry group. We
+evaluate this phase and show that, for BMS superrotations, it provides a
+gravitational generalization of Thomas precession. In principle, such phases
+are observable signatures of asymptotic symmetries.
+",0,1,1,0,0,0
+20633,Exponential random graphs behave like mixtures of stochastic block models,"  We study the behavior of exponential random graphs in both the sparse and the
+dense regime. We show that exponential random graphs are approximate mixtures
+of graphs with independent edges whose probability matrices are critical points
+of an associated functional, thereby satisfying a certain matrix equation. In
+the dense regime, every solution to this equation is close to a block matrix,
+concluding that the exponential random graph behaves roughly like a mixture of
+stochastic block models. We also show existence and uniqueness of solutions to
+this equation for several families of exponential random graphs, including the
+case where the subgraphs are counted with positive weights and the case where
+all weights are small in absolute value. In particular, this generalizes some
+of the results in a paper by Chatterjee and Diaconis from the dense regime to
+the sparse regime and strengthens their bounds from the cut-metric to the
+one-metric.
+",1,0,1,1,0,0
+20634,Certificate Enhanced Data-Flow Analysis,"  Proof-carrying-code was proposed as a solution to ensure a trust relationship
+between two parties: a (heavyweight) analyzer and a (lightweight) checker. The
+analyzer verifies the conformance of a given application to a specified
+property and generates a certificate attesting the validity of the analysis
+result. It suffices then for the checker just to test the consistency of the
+proof instead of constructing it. We set out to study the applicability of this
+technique in the context of data- flow analysis. In particular, we want to know
+if there is a significant performance difference between the analyzer and the
+checker. Therefore, we developed a tool, called DCert, implementing an
+inter-procedural context and flow-sensitive data-flow analyzer and checker for
+Android. Applying our tool to real-world large applications, we found out that
+checking can be up to 8 times faster than verification. This important gain in
+time suggests a potential for equipping applications on app stores with
+certificates that can be checked on mobile devices which are limited in
+computation and storage resources. We describe our implementation and report on
+experimental results.
+",1,0,0,0,0,0
+20635,Small cells in a Poisson hyperplane tessellation,"  Until now, little was known about properties of small cells in a Poisson
+hyperplane tessellation. The few existing results were either heuristic or
+applying only to the two dimensional case and for very specific size
+functionals and directional distributions. This paper fills this gap by
+providing a systematic study of small cells in a Poisson hyperplane
+tessellation of arbitrary dimension, arbitrary directional distribution
+$\varphi$ and with respect to an arbitrary size functional $\Sigma$. More
+precisely, we investigate the distribution of the typical cell $Z$, conditioned
+on the event $\{\Sigma(Z)<a\}$, where $a\to0$ and $\Sigma$ is a size
+functional, i.e. a functional on the set of convex bodies which is continuous,
+not identically zero, homogeneous of degree $k>0$, and increasing with respect
+to set inclusion. We focus on the number of facets and the shape of such small
+cells. We show in various general settings that small cells tend to minimize
+the number of facets and that they have a non degenerated limit shape
+distribution which depends on the size $\Sigma$ and the directional
+distribution. We also exhibit a class of directional distribution for which
+cells with small inradius do not tend to minimize the number of facets.
+",0,0,1,0,0,0
+20636,Privacy-Aware Guessing Efficiency,"  We investigate the problem of guessing a discrete random variable $Y$ under a
+privacy constraint dictated by another correlated discrete random variable $X$,
+where both guessing efficiency and privacy are assessed in terms of the
+probability of correct guessing. We define $h(P_{XY}, \epsilon)$ as the maximum
+probability of correctly guessing $Y$ given an auxiliary random variable $Z$,
+where the maximization is taken over all $P_{Z|Y}$ ensuring that the
+probability of correctly guessing $X$ given $Z$ does not exceed $\epsilon$. We
+show that the map $\epsilon\mapsto h(P_{XY}, \epsilon)$ is strictly increasing,
+concave, and piecewise linear, which allows us to derive a closed form
+expression for $h(P_{XY}, \epsilon)$ when $X$ and $Y$ are connected via a
+binary-input binary-output channel. For $(X^n, Y^n)$ being pairs of independent
+and identically distributed binary random vectors, we similarly define
+$\underline{h}_n(P_{X^nY^n}, \epsilon)$ under the assumption that $Z^n$ is also
+a binary vector. Then we obtain a closed form expression for
+$\underline{h}_n(P_{X^nY^n}, \epsilon)$ for sufficiently large, but nontrivial
+values of $\epsilon$.
+",1,0,1,1,0,0
+20637,Positioning services of a travel agency in social networks,"  In this paper the methods of forming a travel company customer base by means
+of social networks are observed. These methods are made to involve web-users of
+the social networks (VK.com and Facebook) for positioning of the service of the
+travel agency ""New Europe"" on the Internet. The methods of applying the
+maintenance activities and interests of web-users are also used. So, the main
+method of information exchanging in modern network society is on-line social
+networks. The rapid development and improvement of such information and
+communication technologies is a key factor in the positioning of the travel
+agency brand in the global information space. The absence of time and space
+restrictions and the speed of spreading of the information among an aim
+audience of social networks create all the conditions for effective
+popularization of the travel agency ""New Europe"" and its service in the
+Internet.
+",1,0,0,0,0,0
+20638,Inverse Mapping for Rainfall-Runoff Models using History Matching Approach,"  In this paper, we consider two rainfall-runoff computer models. The first
+model is Matlab-Simulink model which simulates runoff from windrow compost pad
+(located at the Bioconversion Center in Athens, GA) over a period of time based
+on rainfall events. The second model is Soil Water Assessment Tool (SWAT) which
+estimates surface runoff in the Middle Oconee River in Athens, GA. The input
+parameter spaces of both models are sensitive and high dimensional, the model
+output for every input combination is a time-series of runoff, and these two
+computer models generate a wide spectrum of outputs including some that are far
+from reality. In order to improve the prediction accuracy, in this paper we
+propose to apply a history matching approach for calibrating these hydrological
+models, which also gives better insights for improved management of these
+systems.
+",0,0,0,1,0,0
+20639,Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE,"  Classification of imbalanced datasets is a challenging task for standard
+algorithms. Although many methods exist to address this problem in different
+ways, generating artificial data for the minority class is a more general
+approach compared to algorithmic modifications. SMOTE algorithm and its
+variations generate synthetic samples along a line segment that joins minority
+class instances. In this paper we propose Geometric SMOTE (G-SMOTE) as a
+generalization of the SMOTE data generation mechanism. G-SMOTE generates
+synthetic samples in a geometric region of the input space, around each
+selected minority instance. While in the basic configuration this region is a
+hyper-sphere, G-SMOTE allows its deformation to a hyper-spheroid and finally to
+a line segment, emulating, in the last case, the SMOTE mechanism. The
+performance of G-SMOTE is compared against multiple standard oversampling
+algorithms. We present empirical results that show a significant improvement in
+the quality of the generated data when G-SMOTE is used as an oversampling
+algorithm.
+",1,0,0,0,0,0
+20640,Liveness Verification and Synthesis: New Algorithms for Recursive Programs,"  We consider the problems of liveness verification and liveness synthesis for
+recursive programs. The liveness verification problem (LVP) is to decide
+whether a given omega-context-free language is contained in a given
+omega-regular language. The liveness synthesis problem (LSP) is to compute a
+strategy so that a given omega-context-free game, when played along the
+strategy, is guaranteed to derive a word in a given omega-regular language. The
+problems are known to be EXPTIME-complete and EXPTIME-complete, respectively.
+Our contributions are new algorithms with optimal time complexity. For LVP, we
+generalize recent lasso-finding algorithms (also known as Ramsey-based
+algorithms) from finite to recursive programs. For LSP, we generalize a recent
+summary-based algorithm from finite to infinite words. Lasso finding and
+summaries have proven to be efficient in a number of implementations for the
+finite state and finite word setting.
+",1,0,0,0,0,0
+20641,A semiparametric approach for bivariate extreme exceedances,"  Inference over tails is performed by applying only the results of extreme
+value theory. Whilst such theory is well defined and flexible enough in the
+univariate case, multivariate inferential methods often require the imposition
+of arbitrary constraints not fully justifed by the underlying theory. In
+contrast, our approach uses only the constraints imposed by theory. We build on
+previous, theoretically justified work for marginal exceedances over a high,
+unknown threshold, by combining it with flexible, semiparametric copulae
+specifications to investigate extreme dependence. Whilst giving probabilistic
+judgements about the extreme regime of all marginal variables, our approach
+formally uses the full dataset and allows for a variety of patterns of
+dependence, be them extremal or not. A new probabilistic criterion quantifying
+the possibility that the data exhibits asymptotic independence is introduced
+and its robustness empirically studied. Estimation of functions of interest in
+extreme value analyses is performed via MCMC algorithms. Attention is also
+devoted to the prediction of new extreme observations. Our approach is
+evaluated through a series of simulations, applied to real data sets and
+assessed against competing approaches. Evidence demonstrates that the bulk of
+the data does not bias and improves the inferential process for the extremal
+dependence.
+",0,0,0,1,0,0
+20642,Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network,"  The Soil Moisture Active Passive (SMAP) mission has delivered valuable
+sensing of surface soil moisture since 2015. However, it has a short time span
+and irregular revisit schedule. Utilizing a state-of-the-art time-series deep
+learning neural network, Long Short-Term Memory (LSTM), we created a system
+that predicts SMAP level-3 soil moisture data with atmospheric forcing,
+model-simulated moisture, and static physiographic attributes as inputs. The
+system removes most of the bias with model simulations and improves predicted
+moisture climatology, achieving small test root-mean-squared error (<0.035) and
+high correlation coefficient >0.87 for over 75\% of Continental United States,
+including the forested Southeast. As the first application of LSTM in
+hydrology, we show the proposed network avoids overfitting and is robust for
+both temporal and spatial extrapolation tests. LSTM generalizes well across
+regions with distinct climates and physiography. With high fidelity to SMAP,
+LSTM shows great potential for hindcasting, data assimilation, and weather
+forecasting.
+",0,0,0,1,0,0
+20643,Note on Green Function Formalism and Topological Invariants,"  It has been discovered previously that the topological order parameter could
+be identified from the topological data of the Green function, namely the
+(generalized) TKNN invariant in general dimensions, for both non-interacting
+and interacting systems. In this note, we show that this phenomena has a clear
+geometric derivation. This proposal could be regarded as an alternative proof
+for the identification of the corresponding topological invariant and
+topological order parameter.
+",0,0,1,0,0,0
+20644,Unsupervised Document Embedding With CNNs,"  We propose a new model for unsupervised document embedding. Leading existing
+approaches either require complex inference or use recurrent neural networks
+(RNN) that are difficult to parallelize. We take a different route and develop
+a convolutional neural network (CNN) embedding model. Our CNN architecture is
+fully parallelizable resulting in over 10x speedup in inference time over RNN
+models. Parallelizable architecture enables to train deeper models where each
+successive layer has increasingly larger receptive field and models longer
+range semantic structure within the document. We additionally propose a fully
+unsupervised learning algorithm to train this model based on stochastic forward
+prediction. Empirical results on two public benchmarks show that our approach
+produces comparable to state-of-the-art accuracy at a fraction of computational
+cost.
+",1,0,0,1,0,0
+20645,A metric model for the functional architecture of the visual cortex,"  The purpose of this work is to construct a model for the functional
+architecture of the primary visual cortex (V1), based on a structure of metric
+measure space induced by the underlying organization of receptive profiles
+(RPs) of visual cells. In order to account for the horizontal connectivity of
+V1 in such a context, a diffusion process compatible with the geometry of the
+space is defined following the classical approach of K.-T. Sturm. The
+construction of our distance function does neither require any group
+parameterization of the family of RPs, nor involve any differential structure.
+As such, it adapts to non-parameterized sets of RPs, possibly obtained through
+numerical procedures; it also allows to model the lateral connectivity arising
+from non-differential metrics such as the one induced on a pinwheel surface by
+a family of filters of vanishing scale. On the other hand, when applied to the
+classical framework of Gabor filters, this construction yields a distance
+approximating the sub-Riemannian structure proposed as a model for V1 by G.
+Citti and A. Sarti [J Math Imaging Vis 24: 307 (2006)], thus showing itself to
+be consistent with existing cortex models.
+",0,0,0,0,1,0
+20646,Bayesian parameter identification in Cahn-Hilliard models for biological growth,"  We consider the inverse problem of parameter estimation in a diffuse
+interface model for tumour growth. The model consists of a fourth-order
+Cahn--Hilliard system and contains three phenomenological parameters: the
+tumour proliferation rate, the nutrient consumption rate, and the chemotactic
+sensitivity. We study the inverse problem within the Bayesian framework and
+construct the likelihood and noise for two typical observation settings. One
+setting involves an infinite-dimensional data space where we observe the full
+tumour. In the second setting we observe only the tumour volume, hence the data
+space is finite-dimensional. We show the well-posedness of the posterior
+measure for both settings, building upon and improving the analytical results
+in [C. Kahle and K.F. Lam, Appl. Math. Optim. (2018)]. A numerical example
+involving synthetic data is presented in which the posterior measure is
+numerically approximated by the Sequential Monte Carlo approach with tempering.
+",0,0,0,1,0,0
+20647,Transport Phase Diagram and Anderson Localization in Hyperuniform Disordered Photonic Materials,"  Hyperuniform disordered photonic materials (HDPM) are spatially correlated
+dielectric structures with unconventional optical properties. They can be
+transparent to long-wavelength radiation while at the same time have isotropic
+band gaps in another frequency range. This phenomenon raises fundamental
+questions concerning photon transport through disordered media. While optical
+transparency is robust against recurrent multiple scattering, little is known
+about other transport regimes like diffusive multiple scattering or Anderson
+localization. Here we investigate band gaps, and we report Anderson
+localization in two-dimensional stealthy HDPM using numerical simulations of
+the density of states and optical transport statistics. To establish a unified
+view, we propose a transport phase diagram. Our results show that, depending
+only on the degree of correlation, a dielectric material can transition from
+localization behavior to a bandgap crossing an intermediate regime dominated by
+tunneling between weakly coupled states.
+",0,1,0,0,0,0
+20648,Nearly Maximally Predictive Features and Their Dimensions,"  Scientific explanation often requires inferring maximally predictive features
+from a given data set. Unfortunately, the collection of minimal maximally
+predictive features for most stochastic processes is uncountably infinite. In
+such cases, one compromises and instead seeks nearly maximally predictive
+features. Here, we derive upper-bounds on the rates at which the number and the
+coding cost of nearly maximally predictive features scales with desired
+predictive power. The rates are determined by the fractal dimensions of a
+process' mixed-state distribution. These results, in turn, show how widely-used
+finite-order Markov models can fail as predictors and that mixed-state
+predictive features offer a substantial improvement.
+",1,1,0,1,0,0
+20649,A Survey Of Cross-lingual Word Embedding Models,"  Cross-lingual representations of words enable us to reason about word meaning
+in multilingual contexts and are a key facilitator of cross-lingual transfer
+when developing natural language processing models for low-resource languages.
+In this survey, we provide a comprehensive typology of cross-lingual word
+embedding models. We compare their data requirements and objective functions.
+The recurring theme of the survey is that many of the models presented in the
+literature optimize for the same objectives, and that seemingly different
+models are often equivalent modulo optimization strategies, hyper-parameters,
+and such. We also discuss the different ways cross-lingual word embeddings are
+evaluated, as well as future challenges and research horizons.
+",1,0,0,0,0,0
+20650,Space-efficient classical and quantum algorithms for the shortest vector problem,"  A lattice is the integer span of some linearly independent vectors. Lattice
+problems have many significant applications in coding theory and cryptographic
+systems for their conjectured hardness. The Shortest Vector Problem (SVP),
+which is to find the shortest non-zero vector in a lattice, is one of the
+well-known problems that are believed to be hard to solve, even with a quantum
+computer. In this paper we propose space-efficient classical and quantum
+algorithms for solving SVP. Currently the best time-efficient algorithm for
+solving SVP takes $2^{n+o(n)}$ time and $2^{n+o(n)}$ space. Our classical
+algorithm takes $2^{2.05n+o(n)}$ time to solve SVP with only $2^{0.5n+o(n)}$
+space. We then modify our classical algorithm to a quantum version, which can
+solve SVP in time $2^{1.2553n+o(n)}$ with $2^{0.5n+o(n)}$ classical space and
+only poly(n) qubits.
+",1,0,0,0,0,0
+20651,Low Power SI Class E Power Amplifier and RF Switch For Health Care,"  This research was to design a 2.4 GHz class E Power Amplifier (PA) for health
+care, with 0.18um Semiconductor Manufacturing International Corporation CMOS
+technology by using Cadence software. And also RF switch was designed at
+cadence software with power Jazz 180nm SOI process. The ultimate goal for such
+application is to reach high performance and low cost, and between high
+performance and low power consumption design. This paper introduces the design
+of a 2.4GHz class E power amplifier and RF switch design. PA consists of
+cascade stage with negative capacitance. This power amplifier can transmit
+16dBm output power to a 50{\Omega} load. The performance of the power amplifier
+and switch meet the specification requirements of the desired.
+",1,0,0,0,0,0
+20652,Improved approximation algorithm for the Dense-3-Subhypergraph Problem,"  The study of Dense-$3$-Subhypergraph problem was initiated in Chlamt{á}c
+et al. [Approx'16]. The input is a universe $U$ and collection ${\cal S}$ of
+subsets of $U$, each of size $3$, and a number $k$. The goal is to choose a set
+$W$ of $k$ elements from the universe, and maximize the number of sets, $S\in
+{\cal S}$ so that $S\subseteq W$. The members in $U$ are called {\em vertices}
+and the sets of ${\cal S}$ are called the {\em hyperedges}. This is the
+simplest extension into hyperedges of the case of sets of size $2$ which is the
+well known Dense $k$-subgraph problem.
+The best known ratio for the Dense-$3$-Subhypergraph is $O(n^{0.69783..})$ by
+Chlamt{á}c et al. We improve this ratio to $n^{0.61802..}$. More
+importantly, we give a new algorithm that approximates Dense-$3$-Subhypergraph
+within a ratio of $\tilde O(n/k)$, which improves the ratio of $O(n^2/k^2)$ of
+Chlamt{á}c et al.
+We prove that under the {\em log density conjecture} (see Bhaskara et al.
+[STOC'10]) the ratio cannot be better than $\Omega(\sqrt{n})$ and demonstrate
+some cases in which this optimum can be attained.
+",1,0,0,0,0,0
+20653,A Survey of Security Assessment Ontologies,"  A literature survey on ontologies concerning the Security Assessment domain
+has been carried out to uncover initiatives that aim at formalizing concepts
+from the Security Assessment field of research. A preliminary analysis and a
+discussion on the selected works are presented. Our main contribution is an
+updated literature review, describing key characteristics, results, research
+issues, and application domains of the papers. We have also detected gaps in
+the Security Assessment literature that could be the subject of further studies
+in the field. This work is meant to be useful for security researchers who wish
+to adopt a formal approach in their methods.
+",1,0,0,0,0,0
+20654,Distributed Online Learning of Event Definitions,"  Logic-based event recognition systems infer occurrences of events in time
+using a set of event definitions in the form of first-order rules. The Event
+Calculus is a temporal logic that has been used as a basis in event recognition
+applications, providing among others, direct connections to machine learning,
+via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system
+that learns event definitions in the form of Event Calculus theories, in a
+single pass over a data stream. In this work we present a version of OLED that
+allows for distributed, online learning. We evaluate our approach on a
+benchmark activity recognition dataset and show that we can significantly
+reduce training times, exchanging minimal information between processing nodes.
+",1,0,0,0,0,0
+20655,Solving Graph Isomorphism Problem for a Special case,"  Graph isomorphism is an important computer science problem. The problem for
+the general case is unknown to be in polynomial time. The base algorithm for
+the general case works in quasi-polynomial time. The solutions in polynomial
+time for some special type of classes are known. In this work, we have worked
+with a special type of graphs. We have proposed a method to represent these
+graphs and finding isomorphism between these graphs. The method uses a modified
+version of the degree list of a graph and neighbourhood degree list. These
+special type of graphs have a property that neighbourhood degree list of any
+two immediate neighbours is different for every vertex.The representation
+becomes invariant to the order in which the node was selected for giving the
+representation making the isomorphism problem trivial for this case. The
+algorithm works in $O(n^4)$ time, where n is the number of vertices present in
+the graph. The proposed algorithm runs faster than quasi-polynomial time for
+the graphs used in the study.
+",1,0,0,0,0,0
+20656,Selective inference for effect modification via the lasso,"  Effect modification occurs when the effect of the treatment on an outcome
+varies according to the level of other covariates and often has important
+implications in decision making. When there are tens or hundreds of covariates,
+it becomes necessary to use the observed data to select a simpler model for
+effect modification and then make valid statistical inference. We propose a two
+stage procedure to solve this problem. First, we use Robinson's transformation
+to decouple the nuisance parameters from the treatment effect of interest and
+use machine learning algorithms to estimate the nuisance parameters. Next,
+after plugging in the estimates of the nuisance parameters, we use the Lasso to
+choose a low-complexity model for effect modification. Compared to a full model
+consisting of all the covariates, the selected model is much more
+interpretable. Compared to the univariate subgroup analyses, the selected model
+greatly reduces the number of false discoveries. We show that the conditional
+selective inference for the selected model is asymptotically valid given the
+rate assumptions in classical semiparametric regression. Extensive simulation
+studies are conducted to verify the asymptotic results and an epidemiological
+application is used to demonstrate the method.
+",0,0,1,1,0,0
+20657,Is Climate Change Controversial? Modeling Controversy as Contention Within Populations,"  A growing body of research focuses on computationally detecting controversial
+topics and understanding the stances people hold on them. Yet gaps remain in
+our theoretical and practical understanding of how to define controversy, how
+it manifests, and how to measure it. In this paper, we introduce a novel
+measure we call ""contention"", defined with respect to a topic and a population.
+We model contention from a mathematical standpoint. We validate our model by
+examining a diverse set of sources: real-world polling data sets, actual voter
+data, and Twitter coverage on several topics. In our publicly-released Twitter
+data set of nearly 100M tweets, we examine several topics such as Brexit, the
+2016 U.S. Elections, and ""The Dress"", and cross-reference them with other
+sources. We demonstrate that the contention measure holds explanatory power for
+a wide variety of observed phenomena, such as controversies over climate change
+and other topics that are well within scientific consensus. Finally, we
+re-examine the notion of controversy, and present a theoretical framework that
+defines it in terms of population. We present preliminary evidence suggesting
+that contention is one dimension of controversy, along with others, such as
+""importance"". Our new contention measure, along with the hypothesized model of
+controversy, suggest several avenues for future work in this emerging
+interdisciplinary research area.
+",1,1,0,0,0,0
+20658,Defending Against Adversarial Attacks by Leveraging an Entire GAN,"  Recent work has shown that state-of-the-art models are highly vulnerable to
+adversarial perturbations of the input. We propose cowboy, an approach to
+detecting and defending against adversarial attacks by using both the
+discriminator and generator of a GAN trained on the same dataset. We show that
+the discriminator consistently scores the adversarial samples lower than the
+real samples across multiple attacks and datasets. We provide empirical
+evidence that adversarial samples lie outside of the data manifold learned by
+the GAN. Based on this, we propose a cleaning method which uses both the
+discriminator and generator of the GAN to project the samples back onto the
+data manifold. This cleaning procedure is independent of the classifier and
+type of attack and thus can be deployed in existing systems.
+",0,0,0,1,0,0
+20659,Spectral Graph Convolutions for Population-based Disease Prediction,"  Exploiting the wealth of imaging and non-imaging information for disease
+prediction tasks requires models capable of representing, at the same time,
+individual features as well as data associations between subjects from
+potentially large populations. Graphs provide a natural framework for such
+tasks, yet previous graph-based approaches focus on pairwise similarities
+without modelling the subjects' individual characteristics and features. On the
+other hand, relying solely on subject-specific imaging feature vectors fails to
+model the interaction and similarity between subjects, which can reduce
+performance. In this paper, we introduce the novel concept of Graph
+Convolutional Networks (GCN) for brain analysis in populations, combining
+imaging and non-imaging data. We represent populations as a sparse graph where
+its vertices are associated with image-based feature vectors and the edges
+encode phenotypic information. This structure was used to train a GCN model on
+partially labelled graphs, aiming to infer the classes of unlabelled nodes from
+the node features and pairwise associations between subjects. We demonstrate
+the potential of the method on the challenging ADNI and ABIDE databases, as a
+proof of concept of the benefit from integrating contextual information in
+classification tasks. This has a clear impact on the quality of the
+predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
+state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
+significantly outperforming standard linear classifiers where only individual
+features are considered.
+",1,0,0,1,0,0
+20660,An independence system as knot invariant,"  An independence system (with respect to the unknotting number) is defined for
+a classical knot diagram. It is proved that the independence system is a knot
+invariant for alternating knots. The exchange property for minimal unknotting
+sets are also discussed. It is shown that there exists an infinite family of
+knot diagrams whose corresponding independence systems are matroids. In
+contrast, infinite families of knot diagrams exist whose independence systems
+are not matroids.
+",0,0,1,0,0,0
+20661,A cavity-induced artificial gauge field in a Bose-Hubbard ladder,"  We consider theoretically ultracold interacting bosonic atoms confined to
+quasi-one-dimensional ladder structures formed by optical lattices and coupled
+to the field of an optical cavity. The atoms can collect a spatial phase
+imprint during a cavity-assisted tunneling along a rung via Raman transitions
+employing a cavity mode and a transverse running wave pump beam. By adiabatic
+elimination of the cavity field we obtain an effective Hamiltonian for the
+bosonic atoms, with a self-consistency condition. Using the numerical density
+matrix renormalization group method, we obtain a rich steady state diagram of
+self-organized steady states. Transitions between superfluid to Mott-insulating
+states occur, on top of which we can have Meissner, vortex liquid, and vortex
+lattice phases. Also a state that explicitly breaks the symmetry between the
+two legs of the ladder, namely the biased-ladder phase is dynamically
+stabilized.
+",0,1,0,0,0,0
+20662,A Review of Laser-Plasma Ion Acceleration,"  An overview of research on laser-plasma based acceleration of ions is given.
+The experimental state of the art is summarized and recent progress is
+discussed. The basic acceleration processes are briefly reviewed with an
+outlook on hybrid mechanisms and novel concepts. Finally, we put focus on the
+development of engineered targets for enhanced acceleration and of all-optical
+methods for beam post-acceleration and control.
+",0,1,0,0,0,0
+20663,Minimax Optimal Estimators for Additive Scalar Functionals of Discrete Distributions,"  In this paper, we consider estimators for an additive functional of $\phi$,
+which is defined as $\theta(P;\phi)=\sum_{i=1}^k\phi(p_i)$, from $n$ i.i.d.
+random samples drawn from a discrete distribution $P=(p_1,...,p_k)$ with
+alphabet size $k$. We propose a minimax optimal estimator for the estimation
+problem of the additive functional. We reveal that the minimax optimal rate is
+characterized by the divergence speed of the fourth derivative of $\phi$ if the
+divergence speed is high. As a result, we show there is no consistent estimator
+if the divergence speed of the fourth derivative of $\phi$ is larger than
+$p^{-4}$. Furthermore, if the divergence speed of the fourth derivative of
+$\phi$ is $p^{4-\alpha}$ for $\alpha \in (0,1)$, the minimax optimal rate is
+obtained within a universal multiplicative constant as $\frac{k^2}{(n\ln
+n)^{2\alpha}} + \frac{k^{2-2\alpha}}{n}$.
+",1,0,1,1,0,0
+20664,Van der Waals Heterostructures Based on Allotropes of Phosphorene and MoSe2,"  The van der Waals heterostructures of allotropes of phosphorene (${\alpha}$-
+and $\beta-P$) with MoSe2 (H-, T-, ZT- and SO-MoSe2) are investigated in the
+framework of state-of-the-art density functional theory. The semiconducting
+heterostructures, $\beta$-P /H-MoSe2 and ${\alpha}$-P / H-MoSe2, forms
+anti-type structures with type I and type II band alignments, respectively,
+whose bands are tunable with external electric field. ${\alpha}$-P / ZT-MoSe2
+and ${\alpha}$-P / SO-MoSe2 form ohmic semiconductor-metal contacts while
+Schottky barrier in $\beta$-P / T-MoSe2 can be reduced to zero by external
+electric field to form ohmic contact which is useful to realize
+high-performance devices. Simulated STM images of given heterostructures reveal
+that ${\alpha}$-P can be used as a capping layer to differentiate between
+various allotropes of underlying MoSe2. The dielectric response of considered
+heterostructures is highly anisotropic in terms of lateral and vertical
+polarization. The tunable electronic and dielectric response of van der Waals
+phosphorene/MoSe2 heterostructure may find potentials applications in the
+fabrication of optoelectronic devices.
+",0,1,0,0,0,0
+20665,On separated solutions of logistic population equation with harvesting,"  We provide a surprising answer to a question raised in S. Ahmad and A.C.
+Lazer [2], and extend the results of that paper.
+",0,0,1,0,0,0
+20666,Nonreciprocal Electromagnetic Scattering from a Periodically Space-Time Modulated Slab and Application to a Quasisonic Isolator,"  Scattering of obliquely incident electromagnetic waves from periodically
+space-time modulated slabs is investigated. It is shown that such structures
+operate as nonreciprocal harmonic generators and spatial-frequency filters. For
+oblique incidences, low-frequency harmonics are filtered out in the form of
+surface waves, while high-frequency harmonics are transmitted as space waves.
+In the quasisonic regime, where the velocity of the space-time modulation is
+close to the velocity of the electromagnetic waves in the background medium,
+the incident wave is strongly coupled to space-time harmonics in the forward
+direction, while in the backward direction it exhibits low coupling to other
+harmonics. This nonreciprocity is leveraged for the realization of an
+electromagnetic isolator in the quasisonic regime and is experimentally
+demonstrated at microwave frequencies.
+",0,1,0,0,0,0
+20667,Story Cloze Ending Selection Baselines and Data Examination,"  This paper describes two supervised baseline systems for the Story Cloze Test
+Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using
+features based on word embeddings and semantic similarity computation. We
+further implement a neural LSTM system with different encoding strategies that
+try to model the relation between the story and the provided endings. Our
+experiments show that a model using representation features based on average
+word embedding vectors over the given story words and the candidate ending
+sentences words, joint with similarity features between the story and candidate
+ending representations performed better than the neural models. Our best model
+achieves an accuracy of 72.42, ranking 3rd in the official evaluation.
+",1,0,0,0,0,0
+20668,Linear Quadratic Optimal Control Problems with Fixed Terminal States and Integral Quadratic Constraints,"  This paper is concerned with a linear quadratic (LQ, for short) optimal
+control problem with fixed terminal states and integral quadratic constraints.
+A Riccati equation with infinite terminal value is introduced, which is
+uniquely solvable and whose solution can be approximated by the solution for a
+suitable unconstrained LQ problem with penalized terminal state. Using results
+from duality theory, the optimal control is explicitly derived by solving the
+Riccati equation together with an optimal parameter selection problem. It turns
+out that the optimal control is not only a feedback of the current state, but
+also a feedback of the target (terminal state). Some examples are presented to
+illustrate the theory developed.
+",0,0,1,0,0,0
+20669,A new sampling density condition for shift-invariant spaces,"  Let $X=\{x_i:i\in\mathbb{Z}\}$, $\dots<x_{i-1}<x_i<x_{i+1}<\dots$, be a
+sampling set which is separated by a constant $\gamma>0$. Under certain
+conditions on $\phi$, it is proved that if there exists a positive integer
+$\nu$ such that
+$$\delta_\nu:=\sup\limits_{i\in\mathbb{Z}}(x_{i+\nu}-x_i)<\dfrac{\nu}{2\pi}\left(\dfrac{c_{k}^2}{M_{2k}}\right)^{\frac{1}{4k}},$$
+then every function belonging to a shift-invariant space $V(\phi)$ can be
+reconstructed stably from its nonuniform sample values
+$\{f^{(j)}(x_i):j=0,1,\dots, k-1, i\in\mathbb{Z}\}$, where $c_k$ is a
+Wirtinger-Sobolev constant and $M_{2k}$ is a constant in Bernstein-type
+inequality of $V(\phi)$. Further, when $k=1$, the maximum gap $\delta_\nu<\nu$
+is sharp for certain shift-invariant spaces.
+",0,0,1,0,0,0
+20670,Experimental Design via Generalized Mean Objective Cost of Uncertainty,"  The mean objective cost of uncertainty (MOCU) quantifies the performance cost
+of using an operator that is optimal across an uncertainty class of systems as
+opposed to using an operator that is optimal for a particular system.
+MOCU-based experimental design selects an experiment to maximally reduce MOCU,
+thereby gaining the greatest reduction of uncertainty impacting the operational
+objective. The original formulation applied to finding optimal system
+operators, where optimality is with respect to a cost function, such as
+mean-square error; and the prior distribution governing the uncertainty class
+relates directly to the underlying physical system. Here we provide a
+generalized MOCU and the corresponding experimental design. We then demonstrate
+how this new formulation includes as special cases MOCU-based experimental
+design methods developed for materials science and genomic networks when there
+is experimental error. Most importantly, we show that the classical Knowledge
+Gradient and Efficient Global Optimization experimental design procedures are
+actually implementations of MOCU-based experimental design under their modeling
+assumptions.
+",0,0,0,1,0,0
+20671,Parallelizing Over Artificial Neural Network Training Runs with Multigrid,"  Artificial neural networks are a popular and effective machine learning
+technique. Great progress has been made parallelizing the expensive training
+phase of an individual network, leading to highly specialized pieces of
+hardware, many based on GPU-type architectures, and more concurrent algorithms
+such as synthetic gradients. However, the training phase continues to be a
+bottleneck, where the training data must be processed serially over thousands
+of individual training runs. This work considers a multigrid reduction in time
+(MGRIT) algorithm that is able to parallelize over the thousands of training
+runs and converge to the exact same solution as traditional training would
+provide. MGRIT was originally developed to provide parallelism for time
+evolution problems that serially step through a finite number of time-steps.
+This work recasts the training of a neural network similarly, treating neural
+network training as an evolution equation that evolves the network weights from
+one step to the next. Thus, this work concerns distributed computing approaches
+for neural networks, but is distinct from other approaches which seek to
+parallelize only over individual training runs. The work concludes with
+supporting numerical results for two model problems.
+",1,0,0,0,0,0
+20672,Learning the Structure of Generative Models without Labeled Data,"  Curating labeled training data has become the primary bottleneck in machine
+learning. Recent frameworks address this bottleneck with generative models to
+synthesize labels at scale from weak supervision sources. The generative
+model's dependency structure directly affects the quality of the estimated
+labels, but selecting a structure automatically without any labeled data is a
+distinct challenge. We propose a structure estimation method that maximizes the
+$\ell_1$-regularized marginal pseudolikelihood of the observed data. Our
+analysis shows that the amount of unlabeled data required to identify the true
+structure scales sublinearly in the number of possible dependencies for a broad
+class of models. Simulations show that our method is 100$\times$ faster than a
+maximum likelihood approach and selects $1/4$ as many extraneous dependencies.
+We also show that our method provides an average of 1.5 F1 points of
+improvement over existing, user-developed information extraction applications
+on real-world data such as PubMed journal abstracts.
+",1,0,0,1,0,0
+20673,Robust Loss Functions under Label Noise for Deep Neural Networks,"  In many applications of classifier learning, training data suffers from label
+noise. Deep networks are learned using huge training data where the problem of
+noisy labels is particularly relevant. The current techniques proposed for
+learning deep networks under label noise focus on modifying the network
+architecture and on algorithms for estimating true labels from noisy labels. An
+alternate approach would be to look for loss functions that are inherently
+noise-tolerant. For binary classification there exist theoretical results on
+loss functions that are robust to label noise. In this paper, we provide some
+sufficient conditions on a loss function so that risk minimization under that
+loss function would be inherently tolerant to label noise for multiclass
+classification problems. These results generalize the existing results on
+noise-tolerant loss functions for binary classification. We study some of the
+widely used loss functions in deep networks and show that the loss function
+based on mean absolute value of error is inherently robust to label noise. Thus
+standard back propagation is enough to learn the true classifier even under
+label noise. Through experiments, we illustrate the robustness of risk
+minimization with such loss functions for learning neural networks.
+",1,0,0,1,0,0
+20674,A quality model for evaluating and choosing a stream processing framework architecture,"  Today, we have to deal with many data (Big data) and we need to make
+decisions by choosing an architectural framework to analyze these data coming
+from different area. Due to this, it become problematic when we want to process
+these data, and even more, when it is continuous data. When you want to process
+some data, you have to first receive it, store it, and then query it. This is
+what we call Batch Processing. It works well when you process big amount of
+data, but it finds its limits when you want to get fast (or real-time)
+processing results, such as financial trades, sensors, user session activity,
+etc. The solution to this problem is stream processing. Stream processing
+approach consists of data arriving record by record and rather than storing it,
+the processing should be done directly. Therefore, direct results are needed
+with a latency that may vary in real-time.
+In this paper, we propose an assessment quality model to evaluate and choose
+stream processing frameworks. We describe briefly different architectural
+frameworks such as Kafka, Spark Streaming and Flink that address the stream
+processing. Using our quality model, we present a decision tree to support
+engineers to choose a framework following the quality aspects. Finally, we
+evaluate our model doing a case study to Twitter and Netflix streaming.
+",1,0,0,0,0,0
+20675,On the Hardness of Inventory Management with Censored Demand Data,"  We consider a repeated newsvendor problem where the inventory manager has no
+prior information about the demand, and can access only censored/sales data. In
+analogy to multi-armed bandit problems, the manager needs to simultaneously
+""explore"" and ""exploit"" with her inventory decisions, in order to minimize the
+cumulative cost. We make no probabilistic assumptions---importantly,
+independence or time stationarity---regarding the mechanism that creates the
+demand sequence. Our goal is to shed light on the hardness of the problem, and
+to develop policies that perform well with respect to the regret criterion,
+that is, the difference between the cumulative cost of a policy and that of the
+best fixed action/static inventory decision in hindsight, uniformly over all
+feasible demand sequences. We show that a simple randomized policy, termed the
+Exponentially Weighted Forecaster, combined with a carefully designed cost
+estimator, achieves optimal scaling of the expected regret (up to logarithmic
+factors) with respect to all three key primitives: the number of time periods,
+the number of inventory decisions available, and the demand support. Through
+this result, we derive an important insight: the benefit from ""information
+stalking"" as well as the cost of censoring are both negligible in this dynamic
+learning problem, at least with respect to the regret criterion. Furthermore,
+we modify the proposed policy in order to perform well in terms of the tracking
+regret, that is, using as benchmark the best sequence of inventory decisions
+that switches a limited number of times. Numerical experiments suggest that the
+proposed approach outperforms existing ones (that are tailored to, or
+facilitated by, time stationarity) on nonstationary demand models. Finally, we
+extend the proposed approach and its analysis to a ""combinatorial"" version of
+the repeated newsvendor problem.
+",1,0,0,1,0,0
+20676,Social Media Analysis For Organizations: Us Northeastern Public And State Libraries Case Study,"  Social networking sites such as Twitter have provided a great opportunity for
+organizations such as public libraries to disseminate information for public
+relations purposes. However, there is a need to analyze vast amounts of social
+media data. This study presents a computational approach to explore the content
+of tweets posted by nine public libraries in the northeastern United States of
+America. In December 2017, this study extracted more than 19,000 tweets from
+the Twitter accounts of seven state libraries and two urban public libraries.
+Computational methods were applied to collect the tweets and discover
+meaningful themes. This paper shows how the libraries have used Twitter to
+represent their services and provides a starting point for different
+organizations to evaluate the themes of their public tweets.
+",1,0,0,1,0,0
+20677,Weakly tripotent rings,"  We study the class of rings $R$ with the property that for $x\in R$ at least
+one of the elements $x$ and $1+x$ are tripotent.
+",0,0,1,0,0,0
+20678,ML for Flood Forecasting at Scale,"  Effective riverine flood forecasting at scale is hindered by a multitude of
+factors, most notably the need to rely on human calibration in current
+methodology, the limited amount of data for a specific location, and the
+computational difficulty of building continent/global level models that are
+sufficiently accurate. Machine learning (ML) is primed to be useful in this
+scenario: learned models often surpass human experts in complex
+high-dimensional scenarios, and the framework of transfer or multitask learning
+is an appealing solution for leveraging local signals to achieve improved
+global performance. We propose to build on these strengths and develop ML
+systems for timely and accurate riverine flood prediction.
+",1,0,0,1,0,0
+20679,Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis,"  We consider the frequency domain form of proper orthogonal decomposition
+(POD) called spectral proper orthogonal decomposition (SPOD). Spectral POD is
+derived from a space-time POD problem for statistically stationary flows and
+leads to modes that each oscillate at a single frequency. This form of POD goes
+back to the original work of Lumley (Stochastic tools in turbulence, Academic
+Press, 1970), but has been overshadowed by a space-only form of POD since the
+1990s. We clarify the relationship between these two forms of POD and show that
+SPOD modes represent structures that evolve coherently in space and time while
+space-only POD modes in general do not. We also establish a relationship
+between SPOD and dynamic mode decomposition (DMD); we show that SPOD modes are
+in fact optimally averaged DMD modes obtained from an ensemble DMD problem for
+stationary flows. Accordingly, SPOD modes represent structures that are dynamic
+in the same sense as DMD modes but also optimally account for the statistical
+variability of turbulent flows. Finally, we establish a connection between SPOD
+and resolvent analysis. The key observation is that the resolvent-mode
+expansion coefficients must be regarded as statistical quantities to ensure
+convergent approximations of the flow statistics. When the expansion
+coefficients are uncorrelated, we show that SPOD and resolvent modes are
+identical. Our theoretical results and the overall utility of SPOD are
+demonstrated using two example problems: the complex Ginzburg-Landau equation
+and a turbulent jet.
+",0,1,0,0,0,0
+20680,An optimal transportation approach for assessing almost stochastic order,"  When stochastic dominance $F\leq_{st}G$ does not hold, we can improve
+agreement to stochastic order by suitably trimming both distributions. In this
+work we consider the $L_2-$Wasserstein distance, $\mathcal W_2$, to stochastic
+order of these trimmed versions. Our characterization for that distance
+naturally leads to consider a $\mathcal W_2$-based index of disagreement with
+stochastic order, $\varepsilon_{\mathcal W_2}(F,G)$. We provide asymptotic
+results allowing to test $H_0: \varepsilon_{\mathcal W_2}(F,G)\geq
+\varepsilon_0$ vs $H_a: \varepsilon_{\mathcal W_2}(F,G)<\varepsilon_0$, that,
+under rejection, would give statistical guarantee of almost stochastic
+dominance. We include a simulation study showing a good performance of the
+index under the normal model.
+",0,0,0,1,0,0
+20681,The Effect of Electron Lens as Landau Damping Device on Single Particle Dynamics in HL-LHC,"  An electron lens can serve as an effective mechanism for suppressing coherent
+instabilities in high intensity storage rings through nonlinear amplitude
+dependent betatron tune shift. However, the addition of a strong localized
+nonlinear focusing element to the accelerator lattice may lead to undesired
+effects in particle dynamics. We evaluate the effect of a Gaussian electron
+lens on single particle motion in HL-LHC using numerical tracking simulations,
+and compare the results to the case when an equal tune spread is generated by
+conventional octupole magnets.
+",0,1,0,0,0,0
+20682,Search for sterile neutrinos in holographic dark energy cosmology: Reconciling Planck observation with the local measurement of the Hubble constant,"  We search for sterile neutrinos in the holographic dark energy cosmology by
+using the latest observational data. To perform the analysis, we employ the
+current cosmological observations, including the cosmic microwave background
+temperature power spectrum data from the Planck mission, the baryon acoustic
+oscillation measurements, the type Ia supernova data, the redshift space
+distortion measurements, the shear data of weak lensing observation, the Planck
+lensing measurement, and the latest direct measurement of $H_0$ as well. We
+show that, compared to the $\Lambda$CDM cosmology, the holographic dark energy
+cosmology with sterile neutrinos can relieve the tension between the Planck
+observation and the direct measurement of $H_0$ much better. Once we include
+the $H_0$ measurement in the global fit, we find that the hint of the existence
+of sterile neutrinos in the holographic dark energy cosmology can be given.
+Under the constraint of the all-data combination, we obtain $N_{\rm eff}=
+3.76\pm0.26$ and $m_{\nu,\rm sterile}^{\rm eff}< 0.215\,\rm eV$, indicating
+that the detection of $\Delta N_{\rm eff}>0$ in the holographic dark energy
+cosmology is at the $2.75\sigma$ level and the massless or very light sterile
+neutrino is favored by the current observations.
+",0,1,0,0,0,0
+20683,Simulation of high temperature superconductors and experimental validation,"  In this work, we present a parallel, fully-distributed finite element
+numerical framework to simulate the low-frequency electromagnetic response of
+superconducting devices, which allows to efficiently exploit HPC platforms. We
+select the so-called H-formulation, which uses the magnetic field as a state
+variable. Nédélec elements (of arbitrary order) are required for an
+accurate approximation of the H-formulation for modelling electromagnetic
+fields along interfaces between regions with high contrast medium properties.
+An h-adaptive mesh refinement technique customized for Nédélec elements
+leads to a structured fine mesh in areas of interest whereas a smart coarsening
+is obtained in other regions. The composition of a tailored, robust, parallel
+nonlinear solver completes the exposition of the developed tools to tackle the
+problem. First, a comparison against experimental data is performed to show the
+availability of the finite element approximation to model the physical
+phenomena. Then, a selected state-of-the-art 3D benchmark is reproduced,
+focusing on the parallel performance of the algorithms.
+",1,1,0,0,0,0
+20684,Hardware Translation Coherence for Virtualized Systems,"  To improve system performance, modern operating systems (OSes) often
+undertake activities that require modification of virtual-to-physical page
+translation mappings. For example, the OS may migrate data between physical
+frames to defragment memory and enable superpages. The OS may migrate pages of
+data between heterogeneous memory devices. We refer to all such activities as
+page remappings. Unfortunately, page remappings are expensive. We show that
+translation coherence is a major culprit and that systems employing
+virtualization are especially badly affected by their overheads. In response,
+we propose hardware translation invalidation and coherence or HATRIC, a readily
+implementable hardware mechanism to piggyback translation coherence atop
+existing cache coherence protocols. We perform detailed studies using KVM-based
+virtualization, showing that HATRIC achieves up to 30% performance and 10%
+energy benefits, for per-CPU area overheads of 2%. We also quantify HATRIC's
+benefits on systems running Xen and find up to 33% performance improvements.
+",1,0,0,0,0,0
+20685,MotifMark: Finding Regulatory Motifs in DNA Sequences,"  The interaction between proteins and DNA is a key driving force in a
+significant number of biological processes such as transcriptional regulation,
+repair, recombination, splicing, and DNA modification. The identification of
+DNA-binding sites and the specificity of target proteins in binding to these
+regions are two important steps in understanding the mechanisms of these
+biological activities. A number of high-throughput technologies have recently
+emerged that try to quantify the affinity between proteins and DNA motifs.
+Despite their success, these technologies have their own limitations and fall
+short in precise characterization of motifs, and as a result, require further
+downstream analysis to extract useful and interpretable information from a
+haystack of noisy and inaccurate data. Here we propose MotifMark, a new
+algorithm based on graph theory and machine learning, that can find binding
+sites on candidate probes and rank their specificity in regard to the
+underlying transcription factor. We developed a pipeline to analyze
+experimental data derived from compact universal protein binding microarrays
+and benchmarked it against two of the most accurate motif search methods. Our
+results indicate that MotifMark can be a viable alternative technique for
+prediction of motif from protein binding microarrays and possibly other related
+high-throughput techniques.
+",1,0,0,0,0,0
+20686,Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning,"  This paper presents KeypointNet, an end-to-end geometric reasoning framework
+to learn an optimal set of category-specific 3D keypoints, along with their
+detectors. Given a single image, KeypointNet extracts 3D keypoints that are
+optimized for a downstream task. We demonstrate this framework on 3D pose
+estimation by proposing a differentiable objective that seeks the optimal set
+of keypoints for recovering the relative pose between two views of an object.
+Our model discovers geometrically and semantically consistent keypoints across
+viewing angles and instances of an object category. Importantly, we find that
+our end-to-end framework using no ground-truth keypoint annotations outperforms
+a fully supervised baseline using the same neural network architecture on the
+task of pose estimation. The discovered 3D keypoints on the car, chair, and
+plane categories of ShapeNet are visualized at this http URL.
+",0,0,0,1,0,0
+20687,Multi-State Trajectory Approach to Non-Adiabatic Dynamics: General Formalism and the Active State Trajectory Approximation,"  A general theoretical framework is derived for the recently developed
+multi-state trajectory (MST) approach from the time dependent Schrödinger
+equation, resulting in equations of motion for coupled nuclear-electronic
+dynamics equivalent to Hamilton dynamics or Heisenberg equation based on a new
+multistate Meyer-Miller (MM) model. The derived MST formalism incorporates both
+diabatic and adiabatic representations as limiting cases, and reduces to
+Ehrenfest or Born-Oppenheimer dynamics in the mean field or the single state
+limits, respectively. By quantizing nuclear dynamics to a particular active
+state, the MST algorithm does not suffer from the instability caused by the
+negative instant electronic population variables unlike the standard MM
+dynamics. Furthermore the multistate representation for electron coupled
+nuclear dynamics with each state associated with one individual trajectory
+presumably captures single state dynamics better than the mean field
+description. The coupled electronic-nuclear coherence is incorporated
+consistently in the MST framework with no ad-hoc state switch and the
+associated momentum adjustment or parameters for artificial decoherence, unlike
+the original or modified surface hopping treatments. The implementation of the
+MST approach to benchmark problems shows reasonably good agreement with exact
+quantum calculations, and the results in both representations are similar in
+accuracy. The active state trajectory (AST) approximation of the MST approach
+provides a consistent interpretation to trajectory surface hopping, which
+predicts the transition probabilities reasonably well for multiple nonadiabatic
+transitions and conical intersection problems.
+",0,1,0,0,0,0
+20688,Combining Homotopy Methods and Numerical Optimal Control to Solve Motion Planning Problems,"  This paper presents a systematic approach for computing local solutions to
+motion planning problems in non-convex environments using numerical optimal
+control techniques. It extends the range of use of state-of-the-art numerical
+optimal control tools to problem classes where these tools have previously not
+been applicable. Today these problems are typically solved using motion
+planners based on randomized or graph search. The general principle is to
+define a homotopy that perturbs, or preferably relaxes, the original problem to
+an easily solved problem. By combining a Sequential Quadratic Programming (SQP)
+method with a homotopy approach that gradually transforms the problem from a
+relaxed one to the original one, practically relevant locally optimal solutions
+to the motion planning problem can be computed. The approach is demonstrated in
+motion planning problems in challenging 2D and 3D environments, where the
+presented method significantly outperforms a state-of-the-art open-source
+optimizing sampled-based planner commonly used as benchmark.
+",0,0,1,0,0,0
+20689,High quality atomically thin PtSe2 films grown by molecular beam epitaxy,"  Atomically thin PtSe2 films have attracted extensive research interests for
+potential applications in high-speed electronics, spintronics and
+photodetectors. Obtaining high quality, single crystalline thin films with
+large size is critical. Here we report the first successful layer-by-layer
+growth of high quality PtSe2 films by molecular beam epitaxy. Atomically thin
+films from 1 ML to 22 ML have been grown and characterized by low-energy
+electron diffraction, Raman spectroscopy and X-ray photoemission spectroscopy.
+Moreover, a systematic thickness dependent study of the electronic structure is
+revealed by angle-resolved photoemission spectroscopy (ARPES), and helical spin
+texture is revealed by spin-ARPES. Our work provides new opportunities for
+growing large size single crystalline films for investigating the physical
+properties and potential applications of PtSe2.
+",0,1,0,0,0,0
+20690,NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images,"  We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in
+an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable
+reward function is designed keeping in mind the cost and weight constraints for
+micro drone with minimum number of sensing modalities. Collection of small
+number of expert data and knowledge based data aggregation is integrated into
+the RL process to aid convergence. Experimentation is carried out on a Parrot
+AR drone in different indoor arenas and the results are compared with other
+baseline technologies. We demonstrate how the drone successfully avoids
+obstacles and navigates across different arenas.
+",1,0,0,1,0,0
+20691,"Fast, Accurate and Fully Parallelizable Digital Image Correlation","  Digital image correlation (DIC) is a widely used optical metrology for
+surface deformation measurements. DIC relies on nonlinear optimization method.
+Thus an initial guess is quite important due to its influence on the converge
+characteristics of the algorithm. In order to obtain a reliable, accurate
+initial guess, a reliability-guided digital image correlation (RG-DIC) method,
+which is able to intelligently obtain a reliable initial guess without using
+time-consuming integer-pixel registration, was proposed. However, the RG-DIC
+and its improved methods are path-dependent and cannot be fully parallelized.
+Besides, it is highly possible that RG-DIC fails in the full-field analysis of
+deformation without manual intervention if the deformation fields contain large
+areas of discontinuous deformation. Feature-based initial guess is highly
+robust while it is relatively time-consuming. Recently, path-independent
+algorithm, fast Fourier transform-based cross correlation (FFT-CC) algorithm,
+was proposed to estimate the initial guess. Complete parallelizability is the
+major advantage of the FFT-CC algorithm, while it is sensitive to small
+deformation. Wu et al proposed an efficient integer-pixel search scheme, but
+the parameters of this algorithm are set by the users empirically. In this
+technical note, a fully parallelizable DIC method is proposed. Different from
+RG-DIC method, the proposed method divides DIC algorithm into two parts:
+full-field initial guess estimation and sub-pixel registration. The proposed
+method has the following benefits: 1) providing a pre-knowledge of deformation
+fields; 2) saving computational time; 3) reducing error propagation; 4)
+integratability with well-established DIC algorithms; 5) fully
+parallelizability.
+",0,1,0,0,0,0
+20692,Discovering the effect of nonlocal payoff calculation on the stabilty of ESS: Spatial patterns of Hawk-Dove game in metapopulations,"  The classical idea of evolutionarily stable strategy (ESS) modeling animal
+behavior does not involve any spatial dependence. We considered a spatial
+Hawk-Dove game played by animals in a patchy environment with wrap around
+boundaries. We posit that each site contains the same number of individuals. An
+evolution equation for analyzing the stability of the ESS is found as the mean
+dynamics of the classical frequency dependent Moran process coupled via
+migration and nonlocal payoff calculation in 1D and 2D habitats. The linear
+stability analysis of the model is performed and conditions to observe spatial
+patterns are investigated. For the nearest neighbor interactions (including von
+Neumann and Moore neighborhoods in 2D) we concluded that it is possible to
+destabilize the ESS of the game and observe pattern formation when the
+dispersal rate is small enough. We numerically investigate the spatial patterns
+arising from the replicator equations coupled via nearest neighbor payoff
+calculation and dispersal.
+",0,0,0,0,1,0
+20693,Analysis of the measurements of anisotropic a.c. vortex resistivity in tilted magnetic fields,"  Measurements of the high-frequency complex resistivity in superconductors are
+a tool often used to obtain the vortex parameters, such as the vortex
+viscosity, the pinning constant and the depinning frequency. In anisotropic
+superconductors, the extraction of these quantities from the measurements faces
+new difficulties due to the tensor nature of the electromagnetic problem. The
+problem is specifically intricate when the magnetic field is tilted with
+respect to the crystallographic axes. Partial solutions exist in the
+free-flux-flow (no pinning) and Campbell (pinning dominated) regimes. In this
+paper we develop a full tensor model for the vortex motion complex resistivity,
+including flux-flow, pinning, and creep. We give explicit expressions for the
+tensors involved. We obtain that, despite the complexity of the physics, some
+parameters remain scalar in nature. We show that under specific circumstances
+the directly measured quantities do not reflect the true vortex parameters, and
+we give procedures to derive the true vortex parameters from measurements taken
+with arbitrary field orientations. Finally, we discuss the applicability of the
+angular scaling properties to the measured and transformed vortex parameters
+and we exploit these properties as a tool to unveil the existence of
+directional pinning.
+",0,1,0,0,0,0
+20694,Deep Convolutional Neural Network to Detect J-UNIWARD,"  This paper presents an empirical study on applying convolutional neural
+networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG
+steganographic method. Experiments guiding the architectural design of the CNNs
+have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of
+size 512x512. Results have verified that both the pooling method and the depth
+of the CNNs are critical for performance. Results have also proved that a
+20-layer CNN, in general, outperforms the most sophisticated feature-based
+methods, but its advantage gradually diminishes on hard-to-detect cases. To
+show that the performance generalizes to large-scale databases and to different
+cover sizes, one experiment has been conducted on the CLS-LOC dataset of
+ImageNet containing more than one million covers cropped to unified size of
+256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently
+proposed for large-scale JPEG steganalysis by 35%. Source code is available via
+GitHub: this https URL
+",1,0,0,0,0,0
+20695,Observing Power-Law Dynamics of Position-Velocity Correlation in Anomalous Diffusion,"  In this letter we present a measurement of the phase-space density
+distribution (PSDD) of ultra-cold \Rb atoms performing 1D anomalous diffusion.
+The PSDD is imaged using a direct tomographic method based on Raman velocity
+selection. It reveals that the position-velocity correlation function
+$C_{xv}(t)$ builds up on a timescale related to the initial conditions of the
+ensemble and then decays asymptotically as a power-law. We show that the decay
+follows a simple scaling theory involving the power-law asymptotic dynamics of
+position and velocity. The generality of this scaling theory is confirmed using
+Monte-Carlo simulations of two distinct models of anomalous diffusion.
+",0,1,0,0,0,0
+20696,"Modular curves, invariant theory and $E_8$","  The $E_8$ root lattice can be constructed from the modular curve $X(13)$ by
+the invariant theory for the simple group $\text{PSL}(2, 13)$. This gives a
+different construction of the $E_8$ root lattice. It also gives an explicit
+construction of the modular curve $X(13)$.
+",0,0,1,0,0,0
+20697,Analysis of Approximate Stochastic Gradient Using Quadratic Constraints and Sequential Semidefinite Programs,"  We present convergence rate analysis for the approximate stochastic gradient
+method, where individual gradient updates are corrupted by computation errors.
+We develop stochastic quadratic constraints to formulate a small linear matrix
+inequality (LMI) whose feasible set characterizes convergence properties of the
+approximate stochastic gradient. Based on this LMI condition, we develop a
+sequential minimization approach to analyze the intricate trade-offs that
+couple stepsize selection, convergence rate, optimization accuracy, and
+robustness to gradient inaccuracy. We also analytically solve this LMI
+condition and obtain theoretical formulas that quantify the convergence
+properties of the approximate stochastic gradient under various assumptions on
+the loss functions.
+",0,0,0,1,0,0
+20698,Invariant holomorphic discs in some non-convex domains,"  We give a description of complex geodesics and we study the structure of
+stationary discs in some non-convex domains for which complex geodesics are not
+unique.
+",0,0,1,0,0,0
+20699,MIMIX: a Bayesian Mixed-Effects Model for Microbiome Data from Designed Experiments,"  Recent advances in bioinformatics have made high-throughput microbiome data
+widely available, and new statistical tools are required to maximize the
+information gained from these data. For example, analysis of high-dimensional
+microbiome data from designed experiments remains an open area in microbiome
+research. Contemporary analyses work on metrics that summarize collective
+properties of the microbiome, but such reductions preclude inference on the
+fine-scale effects of environmental stimuli on individual microbial taxa. Other
+approaches model the proportions or counts of individual taxa as response
+variables in mixed models, but these methods fail to account for complex
+correlation patterns among microbial communities. In this paper, we propose a
+novel Bayesian mixed-effects model that exploits cross-taxa correlations within
+the microbiome, a model we call MIMIX (MIcrobiome MIXed model). MIMIX offers
+global tests for treatment effects, local tests and estimation of treatment
+effects on individual taxa, quantification of the relative contribution from
+heterogeneous sources to microbiome variability, and identification of latent
+ecological subcommunities in the microbiome. MIMIX is tailored to large
+microbiome experiments using a combination of Bayesian factor analysis to
+efficiently represent dependence between taxa and Bayesian variable selection
+methods to achieve sparsity. We demonstrate the model using a simulation
+experiment and on a 2x2 factorial experiment of the effects of nutrient
+supplement and herbivore exclusion on the foliar fungal microbiome of
+$\textit{Andropogon gerardii}$, a perennial bunchgrass, as part of the global
+Nutrient Network research initiative.
+",0,0,0,1,0,0
+20700,SpatEntropy: Spatial Entropy Measures in R,"  This article illustrates how to measure the heterogeneity of spatial data
+presenting a finite number of categories via computation of spatial entropy.
+The R package SpatEntropy contains functions for the computation of entropy and
+spatial entropy measures. The extension to spatial entropy measures is a unique
+feature of SpatEntropy. In addition to the traditional version of Shannon's
+entropy, the package includes Batty's spatial entropy, O'Neill's entropy, Li
+and Reynolds' contagion index, Karlstrom and Ceccato's entropy, Leibovici's
+entropy, Parresol and Edwards' entropy and Altieri's entropy. The package is
+able to work with both areal and point data. This paper is a general
+description of SpatEntropy, as well as its necessary theoretical background,
+and an introduction for new users.
+",0,0,0,1,0,0
+20701,Evidence for universality in the initial planetesimal mass function,"  Planetesimals may form from the gravitational collapse of dense particle
+clumps initiated by the streaming instability. We use simulations of
+aerodynamically coupled gas-particle mixtures to investigate whether the
+properties of planetesimals formed in this way depend upon the sizes of the
+particles that participate in the instability. Based on three high resolution
+simulations that span a range of dimensionless stopping time $6 \times 10^{-3}
+\leq \tau \leq 2$ no statistically significant differences in the initial
+planetesimal mass function are found. The mass functions are fit by a
+power-law, ${\rm d}N / {\rm d}M_p \propto M_p^{-p}$, with $p=1.5-1.7$ and
+errors of $\Delta p \approx 0.1$. Comparing the particle density fields prior
+to collapse, we find that the high wavenumber power spectra are similarly
+indistinguishable, though the large-scale geometry of structures induced via
+the streaming instability is significantly different between all three cases.
+We interpret the results as evidence for a near-universal slope to the mass
+function, arising from the small-scale structure of streaming-induced
+turbulence.
+",0,1,0,0,0,0
+20702,On covering systems of integers,"  A covering system of the integers is a finite collection of modular residue
+classes $\{a_m \bmod{m}\}_{m \in S}$ whose union is all integers. Given a
+finite set $S$ of moduli, it is often difficult to tell whether there is a
+choice of residues modulo elements of $S$ covering the integers. Hough has
+shown that if the smallest modulus in $S$ is at least $10^{16}$, then there is
+none. However, the question of whether there is a covering of the integers with
+all odd moduli remains open. We consider multiplicative restrictions on the set
+of moduli to generalize Hough's negative solution to the minimum modulus
+problem. In particular, we find that every covering system of the integers has
+a modulus divisible by a prime number less than or equal to $19$. Hough and
+Nielsen have shown that every covering system has a modulus divisible by either
+$2$ or $3$.
+",0,0,1,0,0,0
+20703,Asymptotically safe cosmology - a status report,"  Asymptotic Safety, based on a non-Gaussian fixed point of the gravitational
+renormalization group flow, provides an elegant mechanism for completing the
+gravitational force at sub-Planckian scales. At high energies the fixed point
+controls the scaling of couplings such that unphysical divergences are absent
+while the emergence of classical low-energy physics is linked to a crossover
+between two renormalization group fixed points. These features make Asymptotic
+Safety an attractive framework for cosmological model building. The resulting
+scenarios may naturally give rise to a quantum gravity driven inflationary
+phase in the very early universe and an almost scale-free fluctuation spectrum.
+Moreover, effective descriptions arising from an renormalization group
+improvement permit a direct comparison to cosmological observations as, e.g.
+Planck data.
+",0,1,0,0,0,0
+20704,Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction,"  Future predictions on sequence data (e.g., videos or audios) require the
+algorithms to capture non-Markovian and compositional properties of high-level
+semantics. Context-free grammars are natural choices to capture such
+properties, but traditional grammar parsers (e.g., Earley parser) only take
+symbolic sentences as inputs. In this paper, we generalize the Earley parser to
+parse sequence data which is neither segmented nor labeled. This generalized
+Earley parser integrates a grammar parser with a classifier to find the optimal
+segmentation and labels, and makes top-down future predictions. Experiments
+show that our method significantly outperforms other approaches for future
+human activity prediction.
+",0,0,0,1,0,0
+20705,COCrIP: Compliant OmniCrawler In-pipeline Robot,"  This paper presents a modular in-pipeline climbing robot with a novel
+compliant foldable OmniCrawler mechanism. The circular cross-section of the
+OmniCrawler module enables a holonomic motion to facilitate the alignment of
+the robot in the direction of bends. Additionally, the crawler mechanism
+provides a fair amount of traction, even on slippery surfaces. These advantages
+of crawler modules have been further supplemented by incorporating active
+compliance in the module itself which helps to negotiate sharp bends in small
+diameter pipes. The robot has a series of 3 such compliant foldable modules
+interconnected by the links via passive joints. For the desirable pipe diameter
+and curvature of the bends, the spring stiffness value for each passive joint
+is determined by formulating a constrained optimization problem using the
+quasi-static model of the robot. Moreover, a minimum friction coefficient value
+between the module-pipe surface which can be vertically climbed by the robot
+without slipping is estimated. The numerical simulation results have further
+been validated by experiments on real robot prototype.
+",1,0,0,0,0,0
+20706,An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics,"  This paper presents a learning-based approach for impromptu trajectory
+tracking for non-minimum phase systems, i.e., systems with unstable inverse
+dynamics. Inversion-based feedforward approaches are commonly used for
+improving tracking performance; however, these approaches are not directly
+applicable to non-minimum phase systems due to their inherent instability. In
+order to resolve the instability issue, existing methods have assumed that the
+system model is known and used pre-actuation or inverse approximation
+techniques. In this work, we propose an approach for learning a stable,
+approximate inverse of a non-minimum phase baseline system directly from its
+input-output data. Through theoretical discussions, simulations, and
+experiments on two different platforms, we show the stability of our proposed
+approach and its effectiveness for high-accuracy, impromptu tracking. Our
+approach also shows that including more information in the training, as is
+commonly assumed to be useful, does not lead to better performance but may
+trigger instability and impact the effectiveness of the overall approach.
+",1,0,0,0,0,0
+20707,Decay Estimates for 1-D Parabolic PDEs with Boundary Disturbances,"  In this work decay estimates are derived for the solutions of 1-D linear
+parabolic PDEs with disturbances at both boundaries and distributed
+disturbances. The decay estimates are given in the L2 and H1 norms of the
+solution and discontinuous disturbances are allowed. Although an eigenfunction
+expansion for the solution is exploited for the proof of the decay estimates,
+the estimates do not require knowledge of the eigenvalues and the
+eigenfunctions of the corresponding Sturm-Liouville operator. Examples show
+that the obtained results can be applied for the stability analysis of
+parabolic PDEs with nonlocal terms.
+",1,0,1,0,0,0
+20708,GEANT4 Simulation of Nuclear Interaction Induced Soft Errors in Digital Nanoscale Electronics: Interrelation Between Proton and Heavy Ion Impacts,"  A simple and self-consistent approach has been proposed for simulation of the
+proton-induced soft error rate based on the heavy ion induced single event
+upset cross-section data and vice versa. The approach relies on the GEANT4
+assisted Monte Carlo simulation of the secondary particle LET spectra produced
+by nuclear interactions. The method has been validated with the relevant
+in-flight soft error rate data for space protons and heavy ions. An approximate
+analytical relation is proposed and validated for a fast recalculation between
+the two types of experimental data.
+",0,1,0,0,0,0
+20709,Analysis and Measurement of the Transfer Matrix of a 9-cell 1.3-GHz Superconducting Cavity,"  Superconducting linacs are capable of producing intense, stable, high-quality
+electron beams that have found widespread applications in science and industry.
+The 9-cell 1.3-GHz superconducting standing-wave accelerating RF cavity
+originally developed for $e^+/e^-$ linear-collider applications [B. Aunes, {\em
+et al.} Phys. Rev. ST Accel. Beams {\bf 3}, 092001 (2000)] has been broadly
+employed in various superconducting-linac designs. In this paper we discuss the
+transfer matrix of such a cavity and present its measurement performed at the
+Fermilab Accelerator Science and Technology (FAST) facility. The experimental
+results are found to be in agreement with analytical calculations and numerical
+simulations.
+",0,1,0,0,0,0
+20710,Size-aware Sharding For Improving Tail Latencies in In-memory Key-value Stores,"  This paper introduces the concept of size-aware sharding to improve tail
+latencies for in-memory key-value stores, and describes its implementation in
+the Minos key-value store. Tail latencies are crucial in distributed
+applications with high fan-out ratios, because overall response time is
+determined by the slowest response. Size-aware sharding distributes requests
+for keys to cores according to the size of the item associated with the key. In
+particular, requests for small and large items are sent to disjoint subsets of
+cores. Size-aware sharding improves tail latencies by avoiding head-of-line
+blocking, in which a request for a small item gets queued behind a request for
+a large item. Alternative size-unaware approaches to sharding, such as
+keyhash-based sharding, request dispatching and stealing do not avoid
+head-of-line blocking, and therefore exhibit worse tail latencies. The
+challenge in implementing size-aware sharding is to maintain high throughput by
+avoiding the cost of software dispatching and by achieving load balancing
+between different cores. Minos uses hardware dispatch for all requests for
+small items, which form the very large majority of all requests. It achieves
+load balancing by adapting the number of cores handling requests for small and
+large items to their relative presence in the workload. We compare Minos to
+three state-of-the-art designs of in-memory KV stores. Compared to its closest
+competitor, Minos achieves a 99th percentile latency that is up to two orders
+of magnitude lower. Put differently, for a given value for the 99th percentile
+latency equal to 10 times the mean service time, Minos achieves a throughput
+that is up to 7.4 times higher.
+",1,0,0,0,0,0
+20711,Abell 2744 may be a supercluster aligned along the sightline,"  To explain the unusual richness and compactness of the Abell 2744, we propose
+a hypothesis that it may be a rich supercluster aligned along the sightline,
+and present a supporting evidence obtained numerically from the MultiDark
+Planck 2 simulations with a linear box size of $1\,h^{-1}$Gpc. Applying the
+friends-of-friends (FoF) algorithm with a linkage length of $0.33$ to a sample
+of the cluster-size halos from the simulations, we identify the superclusters
+and investigate how many superclusters have filamentary branches that would
+appear to be similar to the Abell 2744 if the filamentary axis is aligned with
+the sightline. Generating randomly a unit vector as a sightline at the position
+of the core member of each supercluster and projecting the positions of the
+members onto the plane perpendicular to the direction of the sightline, we
+measure two dimensional distances ($R_{2d}$) of the member halos from the core
+for each supercluster. Defining a Abell 2744-like spuercluster as the one
+having a filamentary branch composed of eight or more members with $R_{2d}\le
+1\,$Mpc and masses comparable to those of the observed Abell 2744
+substructures, we find one Abell 2744-like supercluster at $z=0.3$ and two at
+$z=0$. Repeating the same analysis but with the data from the Big MultiDark
+Planck simulations performed on a larger box of linear size of
+$2.5\,h^{-1}$Mpc, we find that the number of the Abell 2744-like superclusters
+at $z=0$ increases up to eighteen, among which three are found more massive
+than $5\times 10^{15}\,M_{\odot}$.
+",0,1,0,0,0,0
+20712,Classification of Pressure Gradient of Human Common Carotid Artery and Ascending Aorta on the Basis of Age and Gender,"  The current work is done to see which artery has more chance of having
+cardiovascular diseases by measuring value of pressure gradient in the common
+carotid artery (CCA) and ascending aorta according to age and gender. Pressure
+gradient is determined in the CCA and ascending aorta of presumed healthy
+volunteers, having age between 10 and 60 years. A real 2D model of both aorta
+and common carotid artery is constructed for different age groups using
+computational fluid dynamics (CFD). Pressure gradient of both the arteries are
+calculated and compared for different age groups and gender. It is found that
+with increase in diameter of common carotid artery and ascending aorta with
+advancing age pressure gradient decreases. The value of pressure gradient of
+aorta is found less than common carotid artery in both cases of age and gender.
+",0,1,0,0,0,0
+20713,Second-order and local characteristics of network intensity functions,"  The last decade has witnessed an increase of interest in the spatial analysis
+of structured point patterns over networks whose analysis is challenging
+because of geometrical complexities and unique methodological problems. In this
+context, it is essential to incorporate the network specificity into the
+analysis as the locations of events are restricted to areas covered by line
+segments. Relying on concepts originating from graph theory, we extend the
+notions of first-order network intensity functions to second-order and local
+network intensity functions. We consider two types of local indicators of
+network association functions which can be understood as adaptations of the
+primary ideas of local analysis on the plane. We develop the node-wise and
+cross-hierarchical type of local functions. A real dataset on urban
+disturbances is also presented.
+",0,0,0,1,0,0
+20714,Modeling of hysteresis loop and its applications in ferroelectric materials,"  In order to understand the physical hysteresis loops clearly, we constructed
+a novel model, which is combined with the electric field, the temperature, and
+the stress as one synthetically parameter. This model revealed the shape of
+hysteresis loop was determined by few variables in ferroelectric materials: the
+saturation of polarization, the coercive field, the electric susceptibility and
+the equivalent field. Comparison with experimental results revealed the model
+can retrace polarization versus electric field and temperature. As a
+applications of this model, the calculate formula of energy storage efficiency,
+the electrocaloric effect, and the P(E,T) function have also been included in
+this article.
+",0,1,0,0,0,0
+20715,Online Factorization and Partition of Complex Networks From Random Walks,"  Finding the reduced-dimensional structure is critical to understanding
+complex networks. Existing approaches such as spectral clustering are
+applicable only when the full network is explicitly observed. In this paper, we
+focus on the online factorization and partition of implicit large-scale
+networks based on observations from an associated random walk. We formulate
+this into a nonconvex stochastic factorization problem and propose an efficient
+and scalable stochastic generalized Hebbian algorithm. The algorithm is able to
+process dependent state-transition data dynamically generated by the underlying
+network and learn a low-dimensional representation for each vertex. By applying
+a diffusion approximation analysis, we show that the continuous-time limiting
+process of the stochastic algorithm converges globally to the ""principal
+components"" of the Markov chain and achieves a nearly optimal sample
+complexity. Once given the learned low-dimensional representations, we further
+apply clustering techniques to recover the network partition. We show that when
+the associated Markov process is lumpable, one can recover the partition
+exactly with high probability. We apply the proposed approach to model the
+traffic flow of Manhattan as city-wide random walks. By using our algorithm to
+analyze the taxi trip data, we discover a latent partition of the Manhattan
+city that closely matches the traffic dynamics.
+",1,0,1,1,0,0
+20716,Powerful genome-wide design and robust statistical inference in two-sample summary-data Mendelian randomization,"  Two-sample summary-data Mendelian randomization (MR) has become a popular
+research design to estimate the causal effect of risk exposures. With the
+sample size of GWAS continuing to increase, it is now possible to utilize
+genetic instruments that are only weakly associated with the exposure. To
+maximize the statistical power of MR, we propose a genome-wide design where
+more than a thousand genetic instruments are used. For the statistical
+analysis, we use an empirical partially Bayes approach where instruments are
+weighted according to their strength, thus weak instruments bring less
+variation to the estimator. The estimator is highly efficient with many weak
+genetic instruments and is robust to balanced and/or sparse pleiotropy. We
+apply our method to estimate the causal effect of body mass index (BMI) and
+major blood lipids on cardiovascular disease outcomes and obtain substantially
+shorter confidence intervals. Some new and statistically significant findings
+are: the estimated causal odds ratio of BMI on ischemic stroke is 1.19 (95% CI:
+1.07--1.32, p-value < 0.001); the estimated causal odds ratio of high-density
+lipoprotein cholesterol (HDL-C) on coronary artery disease (CAD) is 0.78 (95%
+CI 0.73--0.84, p-value < 0.001). However, the estimated effect of HDL-C becomes
+substantially smaller and statistically non-significant when we only use the
+strong instruments. By employing a genome-wide design and robust statistical
+methods, the statistical power of MR studies can be greatly improved. Our
+empirical results suggest that, even though the relationship between HDL-C and
+CAD appears to be highly heterogeneous, it may be too soon to completely
+dismiss the HDL hypothesis.
+",0,0,0,1,0,0
+20717,A Benchmark on Reliability of Complex Discrete Systems: Emergency Power Supply of a Nuclear Power Plant,"  This paper contains two parts: the description of a real electrical system,
+with many redundancies, reconfigurations and repairs, then the description of a
+reliability model of this system, based on the BDMP (Boolean logic Driven
+Markov Processes) formalism and partial results of a reliability and
+availability calculation made from this model.
+",1,0,0,0,0,0
+20718,Riemannian Gaussian distributions on the space of positive-definite quaternion matrices,"  Recently, Riemannian Gaussian distributions were defined on spaces of
+positive-definite real and complex matrices. The present paper extends this
+definition to the space of positive-definite quaternion matrices. In order to
+do so, it develops the Riemannian geometry of the space of positive-definite
+quaternion matrices, which is shown to be a Riemannian symmetric space of
+non-positive curvature. The paper gives original formulae for the Riemannian
+metric of this space, its geodesics, and distance function. Then, it develops
+the theory of Riemannian Gaussian distributions, including the exact expression
+of their probability density, their sampling algorithm and statistical
+inference.
+",0,0,1,1,0,0
+20719,Analisis of the power flow in Low Voltage DC grids,"  Power flow in a low voltage direct current grid (LVDC) is a non-linear
+problem just as its counterpart ac. This paper demonstrates that, unlike in ac
+grids, convergence and uniqueness of the solution can be guaranteed in this
+type of grids. The result is not a linearization nor an approximation, but an
+analysis of the set of non-linear algebraic equations, which is valid for any
+LVDC grid regardless its size, topology or load condition. Computer simulation
+corroborate the theoretical analysis.
+",0,0,1,0,0,0
+20720,Multi-scale bilinear restriction estimates for general phases,"  We prove (adjoint) bilinear restriction estimates for general phases at
+different scales in the full non-endpoint mixed norm range, and give bounds
+with a sharp and explicit dependence on the phases. These estimates have
+applications to high-low frequency interactions for solutions to partial
+differential equations, as well as to the linear restriction problem for
+surfaces with degenerate curvature. As a consequence, we obtain new bilinear
+restriction estimates for elliptic phases and wave/Klein-Gordon interactions in
+the full bilinear range, and give a refined Strichartz inequality for the
+Klein-Gordon equation. In addition, we extend these bilinear estimates to hold
+in adapted function spaces by using a transference type principle which holds
+for vector valued waves.
+",0,0,1,0,0,0
+20721,Program algebra for Turing-machine programs,"  This note presents an algebraic theory of instruction sequences with
+instructions for Turing tapes as basic instructions, the behaviours produced by
+the instruction sequences concerned under execution, and the interaction
+between such behaviours and the Turing tapes provided by an execution
+environment. This theory provides a setting for investigating issues relating
+to computability and computational complexity that is more general than the
+closely related Turing-machine models of computation. The theory is essentially
+an instantiation of a parameterized algebraic theory which is the basis of a
+line of research in which issues relating to a wide variety of subjects from
+computer science have been rigorously investigated thinking in terms of
+instruction sequences.
+",1,0,0,0,0,0
+20722,"CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions","  How can we find patterns and anomalies in a tensor, or multi-dimensional
+array, in an efficient and directly interpretable way? How can we do this in an
+online environment, where a new tensor arrives each time step? Finding patterns
+and anomalies in a tensor is a crucial problem with many applications,
+including building safety monitoring, patient health monitoring, cyber
+security, terrorist detection, and fake user detection in social networks.
+Standard PARAFAC and Tucker decomposition results are not directly
+interpretable. Although a few sampling-based methods have previously been
+proposed towards better interpretability, they need to be made faster, more
+memory efficient, and more accurate.
+In this paper, we propose CTD, a fast, accurate, and directly interpretable
+tensor decomposition method based on sampling. CTD-S, the static version of
+CTD, provably guarantees a high accuracy that is 17 ~ 83x more accurate than
+that of the state-of-the-art method. Also, CTD-S is made 5 ~ 86x faster, and 7
+~ 12x more memory-efficient than the state-of-the-art method by removing
+redundancy. CTD-D, the dynamic version of CTD, is the first interpretable
+dynamic tensor decomposition method ever proposed. Also, it is made 2 ~ 3x
+faster than already fast CTD-S by exploiting factors at previous time step and
+by reordering operations. With CTD, we demonstrate how the results can be
+effectively interpreted in the online distributed denial of service (DDoS)
+attack detection.
+",1,0,0,1,0,0
+20723,Memory Augmented Control Networks,"  Planning problems in partially observable environments cannot be solved
+directly with convolutional networks and require some form of memory. But, even
+memory networks with sophisticated addressing schemes are unable to learn
+intelligent reasoning satisfactorily due to the complexity of simultaneously
+learning to access memory and plan. To mitigate these challenges we introduce
+the Memory Augmented Control Network (MACN). The proposed network architecture
+consists of three main parts. The first part uses convolutions to extract
+features and the second part uses a neural network-based planning module to
+pre-plan in the environment. The third part uses a network controller that
+learns to store those specific instances of past information that are necessary
+for planning. The performance of the network is evaluated in discrete grid
+world environments for path planning in the presence of simple and complex
+obstacles. We show that our network learns to plan and can generalize to new
+environments.
+",1,0,0,0,0,0
+20724,Community Recovery in a Preferential Attachment Graph,"  A message passing algorithm is derived for recovering communities within a
+graph generated by a variation of the Barabási-Albert preferential
+attachment model. The estimator is assumed to know the arrival times, or order
+of attachment, of the vertices. The derivation of the algorithm is based on
+belief propagation under an independence assumption. Two precursors to the
+message passing algorithm are analyzed: the first is a degree thresholding (DT)
+algorithm and the second is an algorithm based on the arrival times of the
+children (C) of a given vertex, where the children of a given vertex are the
+vertices that attached to it. Comparison of the performance of the algorithms
+shows it is beneficial to know the arrival times, not just the number, of the
+children. The probability of correct classification of a vertex is
+asymptotically determined by the fraction of vertices arriving before it. Two
+extensions of Algorithm C are given: the first is based on joint likelihood of
+the children of a fixed set of vertices; it can sometimes be used to seed the
+message passing algorithm. The second is the message passing algorithm.
+Simulation results are given.
+",1,0,0,1,0,0
+20725,The descriptive look at the size of subsets of groups,"  We explore the Borel complexity of some basic families of subsets of a
+countable group (large, small, thin, sparse and other) defined by the size of
+their elements. Applying the obtained results to the Stone-Čech
+compactification $\beta G$ of $G$, we prove, in particular, that the closure of
+the minimal ideal of $\beta G$ is of type $F_{\sigma\delta}$.
+",0,0,1,0,0,0
+20726,The Impact of Antenna Height Difference on the Performance of Downlink Cellular Networks,"  Capable of significantly reducing cell size and enhancing spatial reuse,
+network densification is shown to be one of the most dominant approaches to
+expand network capacity. Due to the scarcity of available spectrum resources,
+nevertheless, the over-deployment of network infrastructures, e.g., cellular
+base stations (BSs), would strengthen the inter-cell interference as well, thus
+in turn deteriorating the system performance. On this account, we investigate
+the performance of downlink cellular networks in terms of user coverage
+probability (CP) and network spatial throughput (ST), aiming to shed light on
+the limitation of network densification. Notably, it is shown that both CP and
+ST would be degraded and even diminish to be zero when BS density is
+sufficiently large, provided that practical antenna height difference (AHD)
+between BSs and users is involved to characterize pathloss. Moreover, the
+results also reveal that the increase of network ST is at the expense of the
+degradation of CP. Therefore, to balance the tradeoff between user and network
+performance, we further study the critical density, under which ST could be
+maximized under the CP constraint. Through a special case study, it follows
+that the critical density is inversely proportional to the square of AHD. The
+results in this work could provide helpful guideline towards the application of
+network densification in the next-generation wireless networks.
+",1,0,0,0,0,0
+20727,Spin-wave propagation in cubic anisotropic materials,"  The information carrier of modern technologies is the electron charge whose
+transport inevitably generates Joule heating. Spin-waves, the collective
+precessional motion of electron spins, do not involve moving charges and thus
+avoid Joule heating. In this respect, magnonic devices in which the information
+is carried by spin-waves attract interest for low-power computing. However
+implementation of magnonic devices for practical use suffers from low spin-wave
+signal and on/off ratio. Here we demonstrate that cubic anisotropic materials
+can enhance spin-wave signals by improving spin-wave amplitude as well as group
+velocity and attenuation length. Furthermore, cubic anisotropic material shows
+an enhanced on/off ratio through a laterally localized edge mode, which closely
+mimics the gate-controlled conducting channel in traditional field-effect
+transistors. These attractive features of cubic anisotropic materials will
+invigorate magnonics research towards wave-based functional devices.
+",0,1,0,0,0,0
+20728,Relaxing Exclusive Control in Boolean Games,"  In the typical framework for boolean games (BG) each player can change the
+truth value of some propositional atoms, while attempting to make her goal
+true. In standard BG goals are propositional formulas, whereas in iterated BG
+goals are formulas of Linear Temporal Logic. Both notions of BG are
+characterised by the fact that agents have exclusive control over their set of
+atoms, meaning that no two agents can control the same atom. In the present
+contribution we drop the exclusivity assumption and explore structures where an
+atom can be controlled by multiple agents. We introduce Concurrent Game
+Structures with Shared Propositional Control (CGS-SPC) and show that they ac-
+count for several classes of repeated games, including iterated boolean games,
+influence games, and aggregation games. Our main result shows that, as far as
+verification is concerned, CGS-SPC can be reduced to concurrent game structures
+with exclusive control. This result provides a polynomial reduction for the
+model checking problem of specifications in Alternating-time Temporal Logic on
+CGS-SPC.
+",1,0,0,0,0,0
+20729,Representing de Rham cohomology classes on an open Riemann surface by holomorphic forms,"  Let $X$ be a connected open Riemann surface. Let $Y$ be an Oka domain in the
+smooth locus of an analytic subvariety of $\mathbb C^n$, $n\geq 1$, such that
+the convex hull of $Y$ is all of $\mathbb C^n$. Let $\mathscr O_*(X, Y)$ be the
+space of nondegenerate holomorphic maps $X\to Y$. Take a holomorphic $1$-form
+$\theta$ on $X$, not identically zero, and let $\pi:\mathscr O_*(X,Y) \to
+H^1(X,\mathbb C^n)$ send a map $g$ to the cohomology class of $g\theta$. Our
+main theorem states that $\pi$ is a Serre fibration. This result subsumes the
+1971 theorem of Kusunoki and Sainouchi that both the periods and the divisor of
+a holomorphic form on $X$ can be prescribed arbitrarily. It also subsumes two
+parametric h-principles in minimal surface theory proved by Forstneric and
+Larusson in 2016.
+",0,0,1,0,0,0
+20730,Unidirectional control of optically induced spin waves,"  Unidirectional control of optically induced spin waves in a rare-earth iron
+garnet crystal is demonstrated. We observed the interference of two spin-wave
+packets with different initial phases generated by circularly polarized light
+pulses. This interference results in unidirectional propagation if the
+spin-wave sources are spaced apart at 1/4 of the wavelength of the spin waves
+and the initial phase difference is set to pi/2. The propagating direction of
+the spin wave is switched by the polarization helicity of the light pulses.
+Moreover, in a numerical simulation, applying more than two spin-wave sources
+with a suitable polarization and spot shape, arbitrary manipulation of the spin
+wave by the phased array method was replicated.
+",0,1,0,0,0,0
+20731,Value Prediction Network,"  This paper proposes a novel deep reinforcement learning (RL) architecture,
+called Value Prediction Network (VPN), which integrates model-free and
+model-based RL methods into a single neural network. In contrast to typical
+model-based RL methods, VPN learns a dynamics model whose abstract states are
+trained to make option-conditional predictions of future values (discounted sum
+of rewards) rather than of future observations. Our experimental results show
+that VPN has several advantages over both model-free and model-based baselines
+in a stochastic environment where careful planning is required but building an
+accurate observation-prediction model is difficult. Furthermore, VPN
+outperforms Deep Q-Network (DQN) on several Atari games even with
+short-lookahead planning, demonstrating its potential as a new way of learning
+a good state representation.
+",1,0,0,0,0,0
+20732,Scalable Generalized Linear Bandits: Online Computation and Hashing,"  Generalized Linear Bandits (GLBs), a natural extension of the stochastic
+linear bandits, has been popular and successful in recent years. However,
+existing GLBs scale poorly with the number of rounds and the number of arms,
+limiting their utility in practice. This paper proposes new, scalable solutions
+to the GLB problem in two respects. First, unlike existing GLBs, whose
+per-time-step space and time complexity grow at least linearly with time $t$,
+we propose a new algorithm that performs online computations to enjoy a
+constant space and time complexity. At its heart is a novel Generalized Linear
+extension of the Online-to-confidence-set Conversion (GLOC method) that takes
+\emph{any} online learning algorithm and turns it into a GLB algorithm. As a
+special case, we apply GLOC to the online Newton step algorithm, which results
+in a low-regret GLB algorithm with much lower time and memory complexity than
+prior work. Second, for the case where the number $N$ of arms is very large, we
+propose new algorithms in which each next arm is selected via an inner product
+search. Such methods can be implemented via hashing algorithms (i.e.,
+""hash-amenable"") and result in a time complexity sublinear in $N$. While a
+Thompson sampling extension of GLOC is hash-amenable, its regret bound for
+$d$-dimensional arm sets scales with $d^{3/2}$, whereas GLOC's regret bound
+scales with $d$. Towards closing this gap, we propose a new hash-amenable
+algorithm whose regret bound scales with $d^{5/4}$. Finally, we propose a fast
+approximate hash-key computation (inner product) with a better accuracy than
+the state-of-the-art, which can be of independent interest. We conclude the
+paper with preliminary experimental results confirming the merits of our
+methods.
+",1,0,0,1,0,0
+20733,Dome of magnetic order inside the nematic phase of sulfur-substituted FeSe under pressure,"  The pressure dependence of the structural, magnetic and superconducting
+transitions and of the superconducting upper critical field were studied in
+sulfur-substituted Fe(Se$_{1-x}$S$_{x}$). Resistance measurements were
+performed on single crystals with three substitution levels ($x$=0.043, 0.096,
+0.12) under hydrostatic pressures up to 1.8 GPa and in magnetic fields up to 9
+T, and compared to data on pure FeSe. Our results illustrate the effects of
+chemical and physical pressure on Fe(Se$_{1-x}$S$_{x}$). On increasing sulfur
+content, magnetic order in the low-pressure range is strongly suppressed to a
+small dome-like region in the phase diagrams. However, $T_s$ is much less
+suppressed by sulfur substitution and $T_c$ of Fe(Se$_{1-x}$S$_{x}$) exhibits
+similar non-monotonic pressure dependence with a local maximum and a local
+minimum present in the low pressure range for all $x$. The local maximum in
+$T_c$ coincides with the emergence of the magnetic order above $T_c$. At this
+pressure the slope of the upper critical field decreases abruptly. The minimum
+of $T_c$ correlates with a broad maximum of the upper critical field slope
+normalized by $T_c$.
+",0,1,0,0,0,0
+20734,ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge,"  This paper describes Luminoso's participation in SemEval 2017 Task 2,
+""Multilingual and Cross-lingual Semantic Word Similarity"", with a system based
+on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses
+on general knowledge that relates the meanings of words and phrases. Our
+submission to SemEval was an update of previous work that builds high-quality,
+multilingual word embeddings from a combination of ConceptNet and
+distributional semantics. Our system took first place in both subtasks. It
+ranked first in 4 out of 5 of the separate languages, and also ranked first in
+all 10 of the cross-lingual language pairs.
+",1,0,0,0,0,0
+20735,The symmetrized topological complexity of the circle,"  We determine the symmetrized topological complexity of the circle, using
+primarily just general topology.
+",0,0,1,0,0,0
+20736,Intertwining operators among twisted modules associated to not-necessarily-commuting automorphisms,"  We introduce intertwining operators among twisted modules or twisted
+intertwining operators associated to not-necessarily-commuting automorphisms of
+a vertex operator algebra. Let $V$ be a vertex operator algebra and let
+$g_{1}$, $g_{2}$ and $g_{3}$ be automorphisms of $V$. We prove that for
+$g_{1}$-, $g_{2}$- and $g_{3}$-twisted $V$-modules $W_{1}$, $W_{2}$ and
+$W_{3}$, respectively, such that the vertex operator map for $W_{3}$ is
+injective, if there exists a twisted intertwining operator of type
+${W_{3}\choose W_{1}W_{2}}$ such that the images of its component operators
+span $W_{3}$, then $g_{3}=g_{1}g_{2}$. We also construct what we call the
+skew-symmetry and contragredient isomorphisms between spaces of twisted
+intertwining operators among twisted modules of suitable types. The proofs of
+these results involve careful analysis of the analytic extensions corresponding
+to the actions of the not-necessarily-commuting automorphisms of the vertex
+operator algebra.
+",0,0,1,0,0,0
+20737,Enabling Visual Design Verification Analytics - From Prototype Visualizations to an Analytics Tool using the Unity Game Engine,"  The ever-increasing architectural complexity in contemporary ASIC projects
+turns Design Verification (DV) into a highly advanced endeavor. Pressing needs
+for short time-to-market has made automation a key solution in DV. However,
+recurring execution of large regression suites inevitably leads to challenging
+amounts of test results. Following the design science paradigm, we present an
+action research study to introduce visual analytics in a commercial ASIC
+project. We develop a cityscape visualization tool using the game engine Unity.
+Initial evaluations are promising, suggesting that the tool offers a novel
+approach to identify error-prone parts of the design, as well as coverage
+holes.
+",1,0,0,0,0,0
+20738,Self-regulation promotes cooperation in social networks,"  Cooperative behavior in real social dilemmas is often perceived as a
+phenomenon emerging from norms and punishment. To overcome this paradigm, we
+highlight the interplay between the influence of social networks on
+individuals, and the activation of spontaneous self-regulating mechanisms,
+which may lead them to behave cooperatively, while interacting with others and
+taking conflicting decisions over time. By extending Evolutionary game theory
+over networks, we prove that cooperation partially or fully emerges whether
+self-regulating mechanisms are sufficiently stronger than social pressure.
+Interestingly, even few cooperative individuals act as catalyzing agents for
+the cooperation of others, thus activating a recruiting mechanism, eventually
+driving the whole population to cooperate.
+",1,0,0,0,0,0
+20739,Magnetic properties of nanoparticles compacts with controlled broadening of the particle size distribution,"  Binary random compacts with different proportions of small (volume V) and
+large (volume 2V) bare maghemite nanoparticles (NPs) are used to investigate
+the effect of controllably broadening the particle size distribution on the
+magnetic properties of magnetic NP assemblies with strong dipolar interaction.
+A series of eight random mixtures of highly uniform 9.0 and 11.5 nm diameter
+maghemite particles prepared by thermal decomposition are studied. In spite of
+severely broadened size distributions in the mixed samples, well defined
+superspin glass transition temperatures are observed across the series, their
+values increasing linearly with the weight fraction of large particles.
+",0,1,0,0,0,0
+20740,A Generative Model for Exploring Structure Regularities in Attributed Networks,"  Many real-world networks known as attributed networks contain two types of
+information: topology information and node attributes. It is a challenging task
+on how to use these two types of information to explore structural
+regularities. In this paper, by characterizing potential relationship between
+link communities and node attributes, a principled statistical model named
+PSB_PG that generates link topology and node attributes is proposed. This model
+for generating links is based on the stochastic blockmodels following a Poisson
+distribution. Therefore, it is capable of detecting a wide range of network
+structures including community structures, bipartite structures and other
+mixture structures. The model for generating node attributes assumes that node
+attributes are high dimensional and sparse and also follow a Poisson
+distribution. This makes the model be uniform and the model parameters can be
+directly estimated by expectation-maximization (EM) algorithm. Experimental
+results on artificial networks and real networks containing various structures
+have shown that the proposed model PSB_PG is not only competitive with the
+state-of-the-art models, but also provides good semantic interpretation for
+each community via the learned relationship between the community and its
+related attributes.
+",1,0,0,0,0,0
+20741,On the maximal halfspace depth of permutation-invariant distributions on the simplex,"  We compute the maximal halfspace depth for a class of permutation-invariant
+distributions on the probability simplex. The derivations are based on
+stochastic ordering results that so far were only showed to be relevant for the
+Behrens-Fisher problem.
+",0,0,1,1,0,0
+20742,Theoretical Evaluation of Li et al.'s Approach for Improving a Binary Watermark-Based Scheme in Remote Sensing Data Communications,"  This letter is about a principal weakness of the published article by Li et
+al. in 2014. It seems that the mentioned work has a terrible conceptual mistake
+while presenting its theoretical approach. In fact, the work has tried to
+design a new attack and its effective solution for a basic watermarking
+algorithm by Zhu et al. published in 2013, however in practice, we show the Li
+et al.'s approach is not correct to obtain the aim. For disproof of the
+incorrect approach, we only apply a numerical example as the counterexample of
+the Li et al.'s approach.
+",1,0,0,0,0,0
+20743,Bioinformatics and Medicine in the Era of Deep Learning,"  Many of the current scientific advances in the life sciences have their
+origin in the intensive use of data for knowledge discovery. In no area this is
+so clear as in bioinformatics, led by technological breakthroughs in data
+acquisition technologies. It has been argued that bioinformatics could quickly
+become the field of research generating the largest data repositories, beating
+other data-intensive areas such as high-energy physics or astroinformatics.
+Over the last decade, deep learning has become a disruptive advance in machine
+learning, giving new live to the long-standing connectionist paradigm in
+artificial intelligence. Deep learning methods are ideally suited to
+large-scale data and, therefore, they should be ideally suited to knowledge
+discovery in bioinformatics and biomedicine at large. In this brief paper, we
+review key aspects of the application of deep learning in bioinformatics and
+medicine, drawing from the themes covered by the contributions to an ESANN 2018
+special session devoted to this topic.
+",0,0,0,1,1,0
+20744,A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously,"  Capabilities of detecting temporal relations between two events can benefit
+many applications. Most of existing temporal relation classifiers were trained
+in a supervised manner. Instead, we explore the observation that regular event
+pairs show a consistent temporal relation despite of their various contexts,
+and these rich contexts can be used to train a contextual temporal relation
+classifier, which can further recognize new temporal relation contexts and
+identify new regular event pairs. We focus on detecting after and before
+temporal relations and design a weakly supervised learning approach that
+extracts thousands of regular event pairs and learns a contextual temporal
+relation classifier simultaneously. Evaluation shows that the acquired regular
+event pairs are of high quality and contain rich commonsense knowledge and
+domain specific knowledge. In addition, the weakly supervised trained temporal
+relation classifier achieves comparable performance with the state-of-the-art
+supervised systems.
+",1,0,0,0,0,0
+20745,Privacy with Estimation Guarantees,"  We study the central problem in data privacy: how to share data with an
+analyst while providing both privacy and utility guarantees to the user that
+owns the data. In this setting, we present an estimation-theoretic analysis of
+the privacy-utility trade-off (PUT). Here, an analyst is allowed to reconstruct
+(in a mean-squared error sense) certain functions of the data (utility), while
+other private functions should not be reconstructed with distortion below a
+certain threshold (privacy). We demonstrate how $\chi^2$-information captures
+the fundamental PUT in this case and provide bounds for the best PUT. We
+propose a convex program to compute privacy-assuring mappings when the
+functions to be disclosed and hidden are known a priori and the data
+distribution is known. We derive lower bounds on the minimum mean-squared error
+of estimating a target function from the disclosed data and evaluate the
+robustness of our approach when an empirical distribution is used to compute
+the privacy-assuring mappings instead of the true data distribution. We
+illustrate the proposed approach through two numerical experiments.
+",1,0,0,0,0,0
+20746,A line of CFTs: from generalized free fields to SYK,"  We point out that there is a simple variant of the SYK model, which we call
+cSYK, that is $SL(2,R)$ invariant for all values of the coupling. The
+modification consists of replacing the UV part of the SYK action with a
+quadratic bilocal term. The corresponding bulk dual is a non-gravitational
+theory in a rigid AdS$_2$ background. At weak coupling cSYK is a generalized
+free field theory; at strong coupling, it approaches the infrared of SYK. The
+existence of this line of fixed points explains the previously found connection
+between the three-point function of bilinears in these two theories at large
+$q$.
+",0,1,0,0,0,0
+20747,Miura transformations for discrete Painlevé equations coming from the affine E$_8$ Weyl group,"  We derive integrable equations starting from autonomous mappings with a
+general form inspired by the additive systems associated to the affine Weyl
+group E$_8^{(1)}$. By deautonomisation we obtain two hitherto unknown systems,
+one of which turns out to be a linearisable one, and we show that both these
+systems arise from the deautonomisation of a non-QRT mapping. In order to
+unambiguously prove the integrability of these nonautonomous systems, we
+introduce a series of Miura transformations which allows us to prove that one
+of these systems is indeed a discrete Painlevé equation, related to the
+affine Weyl group E$_7^{(1)}$, and to cast it in canonical form. A similar
+sequence of Miura transformations allows us to effectively linearise the second
+system we obtain. An interesting off-shoot of our calculations is that the
+series of Miura transformations, when applied at the autonomous limit, allows
+one to transform a non-QRT invariant into a QRT one.
+",0,1,1,0,0,0
+20748,Virtual Molecular Dynamics,"  Molecular dynamics is based on solving Newton's equations for many-particle
+systems that evolve along complex, highly fluctuating trajectories. The orbital
+instability and short-time complexity of Newtonian orbits is in sharp contrast
+to the more coherent behavior of collective modes such as density profiles. The
+notion of virtual molecular dynamics is introduced here based on temporal
+coarse-graining via Pade approximants and the Ito formula for stochastic
+processes. It is demonstrated that this framework leads to significant
+efficiency over traditional molecular dynamics and avoids the need to introduce
+coarse-grained variables and phenomenological equations for their evolution. In
+this framework, an all-atom trajectory is represented by a Markov chain of
+virtual atomic states at a discrete sequence of timesteps, transitions between
+which are determined by an integration of conventional molecular dynamics with
+Pade approximants and a microstate energy annealing methodology. The latter is
+achieved by a conventional and an MD NVE energy minimization schemes. This
+multiscale framework is demonstrated for a pertussis toxin subunit undergoing a
+structural transition, a T=1 capsid-like structure of HPV16 L1 protein, and two
+coalescing argon droplets.
+",0,1,0,0,0,0
+20749,Regression with genuinely functional errors-in-covariates,"  Contamination of covariates by measurement error is a classical problem in
+multivariate regression, where it is well known that failing to account for
+this contamination can result in substantial bias in the parameter estimators.
+The nature and degree of this effect on statistical inference is also
+understood to crucially depend on the specific distributional properties of the
+measurement error in question. When dealing with functional covariates,
+measurement error has thus far been modelled as additive white noise over the
+observation grid. Such a setting implicitly assumes that the error arises
+purely at the discrete sampling stage, otherwise the model can only be viewed
+in a weak (stochastic differential equation) sense, white noise not being a
+second-order process. Departing from this simple distributional setting can
+have serious consequences for inference, similar to the multivariate case, and
+current methodology will break down. In this paper, we consider the case when
+the additive measurement error is allowed to be a valid stochastic process. We
+propose a novel estimator of the slope parameter in a functional linear model,
+for scalar as well as functional responses, in the presence of this general
+measurement error specification. The proposed estimator is inspired by the
+multivariate regression calibration approach, but hinges on recent advances on
+matrix completion methods for functional data in order to handle the nontrivial
+(and unknown) error covariance structure. The asymptotic properties of the
+proposed estimators are derived. We probe the performance of the proposed
+estimator of slope using simulations and observe that it substantially improves
+upon the spectral truncation estimator based on the erroneous observations,
+i.e., ignoring measurement error. We also investigate the behaviour of the
+estimators on a real dataset on hip and knee angle curves during a gait cycle.
+",0,0,0,1,0,0
+20750,The distance between a naive cumulative estimator and its least concave majorant,"  We consider the process $\widehat\Lambda_n-\Lambda_n$, where $\Lambda_n$ is a
+cadlag step estimator for the primitive $\Lambda$ of a nonincreasing function
+$\lambda$ on $[0,1]$, and $\widehat\Lambda_n$ is the least concave majorant of
+$\Lambda_n$. We extend the results in Kulikov and Lopuhaä (2006, 2008) to the
+general setting considered in Durot (2007). Under this setting we prove that a
+suitably scaled version of $\widehat\Lambda_n-\Lambda_n$ converges in
+distribution to the corresponding process for two-sided Brownian motion with
+parabolic drift and we establish a central limit theorem for the $L_p$-distance
+between $\widehat\Lambda_n$ and $\Lambda_n$.
+",0,0,1,1,0,0
+20751,Controllability of impulse controlled systems of heat equations coupled by constant matrices,"  This paper studies the approximate and null controllability for impulse
+controlled systems of heat equations coupled by a pair (A,B) of constant
+matrices. We present a necessary and sufficient condition for the approximate
+controllability, which is exactly Kalman's controllability rank condition of
+(A,B). We prove that when such a system is approximately controllable, the
+approximate controllability over an interval [0,T] can be realized by adding
+controls at arbitrary n different control instants
+0<\tau_1<\tau_2<\cdots<\tau_n<T, provided that \tau_n-\tau_1<d_A, where
+d_A=\min\{\pi/|Im \lambda| : \lambda\in \sigma(A)\}. We also show that in
+general, such systems are not null controllable.
+",0,0,1,0,0,0
+20752,Nearly Optimal Constructions of PIR and Batch Codes,"  In this work we study two families of codes with availability, namely private
+information retrieval (PIR) codes and batch codes. While the former requires
+that every information symbol has $k$ mutually disjoint recovering sets, the
+latter asks this property for every multiset request of $k$ information
+symbols. The main problem under this paradigm is to minimize the number of
+redundancy symbols. We denote this value by $r_P(n,k), r_B(n,k)$, for PIR,
+batch codes, respectively, where $n$ is the number of information symbols.
+Previous results showed that for any constant $k$, $r_P(n,k) =
+\Theta(\sqrt{n})$ and $r_B(n,k)=O(\sqrt{n}\log(n)$. In this work we study the
+asymptotic behavior of these codes for non-constant $k$ and specifically for
+$k=\Theta(n^\epsilon)$. We also study the largest value of $k$ such that the
+rate of the codes approaches 1, and show that for all $\epsilon<1$,
+$r_P(n,n^\epsilon) = o(n)$, while for batch codes, this property holds for all
+$\epsilon< 0.5$.
+",1,0,0,0,0,0
+20753,Submillimeter Array CO(2-1) Imaging of the NGC 6946 Giant Molecular Clouds,"  We present a CO(2-1) mosaic map of the spiral galaxy NGC 6946 by combining
+data from the Submillimeter Array and the IRAM 30 m telescope. We identify 390
+giant molecular clouds (GMCs) from the nucleus to 4.5 kpc in the disk. GMCs in
+the inner 1 kpc are generally more luminous and turbulent, some of which have
+luminosities >10^6 K km/s pc^2 and velocity dispersions >10 km/s. Large-scale
+bar-driven dynamics likely regulate GMC properties in the nuclear region.
+Similar to the Milky Way and other disk galaxies, GMC mass function of NGC 6946
+has a shallower slope (index>-2) in the inner region, and a steeper slope
+(index<-2) in the outer region. This difference in mass spectra may be
+indicative of different cloud formation pathways: gravitational instabilities
+might play a major role in the nuclear region, while cloud coalescence might be
+dominant in the outer disk. Finally, the NGC 6946 clouds are similar to those
+in M33 in terms of statistical properties, but they are generally less luminous
+and turbulent than the M51 clouds.
+",0,1,0,0,0,0
+20754,Optical and Near-Infrared Spectra of sigma Orionis Isolated Planetary-mass Objects,"  We have obtained low-resolution optical (0.7-0.98 micron) and near-infrared
+(1.11-1.34 micron and 0.8-2.5 micron) spectra of twelve isolated planetary-mass
+candidates (J = 18.2-19.9 mag) of the 3-Myr sigma Orionis star cluster with a
+view to determining the spectroscopic properties of very young, substellar
+dwarfs and assembling a complete cluster mass function. We have classified our
+targets by visual comparison with high- and low-gravity standards and by
+measuring newly defined spectroscopic indices. We derived L0-L4.5 and M9-L2.5
+using high- and low-gravity standards, respectively. Our targets reveal clear
+signposts of youth, thus corroborating their cluster membership and planetary
+masses (6-13 Mjup). These observations complete the sigma Orionis mass function
+by spectroscopically confirming the planetary-mass domain to a confidence level
+of $\sim$75 percent. The comparison of our spectra with BT-Settl solar
+metallicity model atmospheres yields a temperature scale of 2350-1800 K and a
+low surface gravity of log g ~ 4.0 [cm/s2], as would be expected for young
+planetary-mass objects. We discuss the properties of the cluster least-massive
+population as a function of spectral type. We have also obtained the first
+optical spectrum of S Ori 70, a T dwarf in the direction of sigma Orionis. Our
+data provide reference optical and near-infrared spectra of very young L dwarfs
+and a mass function that may be used as templates for future studies of
+low-mass substellar objects and exoplanets. The extrapolation of the sigma
+Orionis mass function to the solar neighborhood may indicate that isolated
+planetary-mass objects with temperatures of 200-300 K and masses in the
+interval 6-13-Mjup may be as numerous as very low-mass stars.
+",0,1,0,0,0,0
+20755,The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight,"  The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale,
+aggressive indoor flight dataset collected using a custom-built quadrotor
+platform for use in evaluation of agile perception.Inspired by the potential of
+future high-speed fully-autonomous drone racing, the Blackbird dataset contains
+over 10 hours of flight data from 168 flights over 17 flight trajectories and 5
+environments at velocities up to $7.0ms^-1$. Each flight includes sensor data
+from 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz
+IMU, $\sim190Hz$ motor speed sensors, and 360Hz millimeter-accurate motion
+capture ground truth. Camera images for each flight were photorealistically
+rendered using FlightGoggles across a variety of environments to facilitate
+easy experimentation of high performance perception algorithms. The dataset is
+available for download at this http URL
+",1,0,0,0,0,0
+20756,An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification,"  Motivation: Cellular Electron CryoTomography (CECT) is an emerging 3D imaging
+technique that visualizes subcellular organization of single cells at
+submolecular resolution and in near-native state. CECT captures large numbers
+of macromolecular complexes of highly diverse structures and abundances.
+However, the structural complexity and imaging limits complicate the systematic
+de novo structural recovery and recognition of these macromolecular complexes.
+Efficient and accurate reference-free subtomogram averaging and classification
+represent the most critical tasks for such analysis. Existing subtomogram
+alignment based methods are prone to the missing wedge effects and low
+signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based
+methods rely on integration operations, which are in principle computationally
+infeasible for accurate calculation.
+Results: Built on existing works, we propose an integrated method, Fast
+Alignment Maximum Likelihood method (FAML), which uses fast subtomogram
+alignment to sample sub-optimal rigid transformations. The transformations are
+then used to approximate integrals for maximum-likelihood update of subtomogram
+averages through expectation-maximization algorithm. Our tests on simulated and
+experimental subtomograms showed that, compared to our previously developed
+fast alignment method (FA), FAML is significantly more robust to noise and
+missing wedge effects with moderate increases of computation cost.Besides, FAML
+performs well with significantly fewer input subtomograms when the FA method
+fails. Therefore, FAML can serve as a key component for improved construction
+of initial structural models from macromolecules captured by CECT.
+",0,0,0,1,1,0
+20757,Local and global existence of solutions to a strongly damped wave equation of the $p$-Laplacian type,"  This article focuses on a quasilinear wave equation of $p$-Laplacian type: $$
+u_{tt} - \Delta_p u - \Delta u_t=0$$ in a bounded domain
+$\Omega\subset\mathbb{R}^3$ with a sufficiently smooth boundary
+$\Gamma=\partial\Omega$ subject to a generalized Robin boundary condition
+featuring boundary damping and a nonlinear source term. The operator
+$\Delta_p$, $2 < p < 3$, denotes the classical $p$-Laplacian. The nonlinear
+boundary term $f (u)$ is a source feedback that is allowed to have a
+supercritical exponent, in the sense that the associated Nemytskii operator is
+not locally Lipschitz from $W^{1,p}(\Omega)$ into $L^2(\Gamma)$. Under suitable
+assumptions on the parameters we provide a rigorous proof of existence of a
+local weak solution which can be extended globally in time provided the source
+term satisfies an appropriate growth condition.
+",0,0,1,0,0,0
+20758,Quasi-flat representations of uniform groups and quantum groups,"  Given a discrete group $\Gamma=<g_1,\ldots,g_M>$ and a number $K\in\mathbb
+N$, a unitary representation $\rho:\Gamma\to U_K$ is called quasi-flat when the
+eigenvalues of each $\rho(g_i)\in U_K$ are uniformly distributed among the
+$K$-th roots of unity. The quasi-flat representations of $\Gamma$ form
+altogether a parametric matrix model $\pi:\Gamma\to C(X,U_K)$.
+We compute here the universal model space $X$ for various classes of discrete
+groups, notably with results in the case where $\Gamma$ is metabelian. We are
+particularly interested in the case where $X$ is a union of compact homogeneous
+spaces, and where the induced representation $\tilde{\pi}:C^*(\Gamma)\to
+C(X,U_K)$ is stationary in the sense that it commutes with the Haar
+functionals. We present several positive and negative results on this subject.
+We also discuss similar questions for the discrete quantum groups, proving a
+stationarity result for the discrete dual of the twisted orthogonal group
+$O_2^{-1}$.
+",0,0,1,0,0,0
+20759,Nonparametric Preference Completion,"  We consider the task of collaborative preference completion: given a pool of
+items, a pool of users and a partially observed item-user rating matrix, the
+goal is to recover the \emph{personalized ranking} of each user over all of the
+items. Our approach is nonparametric: we assume that each item $i$ and each
+user $u$ have unobserved features $x_i$ and $y_u$, and that the associated
+rating is given by $g_u(f(x_i,y_u))$ where $f$ is Lipschitz and $g_u$ is a
+monotonic transformation that depends on the user. We propose a $k$-nearest
+neighbors-like algorithm and prove that it is consistent. To the best of our
+knowledge, this is the first consistency result for the collaborative
+preference completion problem in a nonparametric setting. Finally, we
+demonstrate the performance of our algorithm with experiments on the Netflix
+and Movielens datasets.
+",1,0,0,1,0,0
+20760,Real-Time Reconstruction of Counting Process through Queues,"  For the emerging Internet of Things (IoT), one of the most critical problems
+is the real-time reconstruction of signals from a set of aged measurements.
+During the reconstruction, distortion occurs between the observed signal and
+the reconstructed signal due to sampling and transmission. In this paper, we
+focus on minimizing the average distortion defined as the 1-norm of the
+difference of the two signals under the scenario that a Poisson counting
+process is reconstructed in real-time on a remote monitor. Especially, we
+consider the reconstruction under uniform sampling policy and two non-uniform
+sampling policies, i.e., the threshold-based policy and the zero-wait policy.
+For each of the policy, we derive the closed-form expression of the average
+distortion by dividing the overall distortion area into polygons and analyzing
+their structures. It turns out that the polygons are built up by sub-polygons
+that account for distortions caused by sampling and transmission. The
+closed-form expressions of the average distortion help us find the optimal
+sampling parameters that achieve the minimum distortion. Simulation results are
+provided to validate our conclusion.
+",1,0,0,0,0,0
+20761,Discrepancy-Based Algorithms for Non-Stationary Rested Bandits,"  We study the multi-armed bandit problem where the rewards are realizations of
+general non-stationary stochastic processes, a setting that generalizes many
+existing lines of work and analyses. In particular, we present a theoretical
+analysis and derive regret guarantees for rested bandits in which the reward
+distribution of each arm changes only when we pull that arm. Remarkably, our
+regret bounds are logarithmic in the number of rounds under several natural
+conditions. We introduce a new algorithm based on classical UCB ideas combined
+with the notion of weighted discrepancy, a useful tool for measuring the
+non-stationarity of a stochastic process. We show that the notion of
+discrepancy can be used to design very general algorithms and a unified
+framework for the analysis of multi-armed rested bandit problems with
+non-stationary rewards. In particular, we show that we can recover the regret
+guarantees of many specific instances of bandit problems with non-stationary
+rewards that have been studied in the literature. We also provide experiments
+demonstrating that our algorithms can enjoy a significant improvement in
+practice compared to standard benchmarks.
+",1,0,0,0,0,0
+20762,Kondo lattice heavy fermion behavior in CeRh2Ga2,"  The physical properties of an intermetallic compound CeRh2Ga2 have been
+investigated by magnetic susceptibility \chi(T), isothermal magnetization M(H),
+heat capacity C_p(T), electrical resistivity \rho(T), thermal conductivity
+\kappa(T) and thermopower S(T) measurements. CeRh2Ga2 is found to crystallize
+with CaBe2Ge2-type primitive tetragonal structure (space group P4/nmm). No
+evidence of long range magnetic order is seen down to 1.8 K. The \chi(T) data
+show paramagnetic behavior with an effective moment \mu_eff ~ 2.5 \mu_B/Ce
+indicating Ce^3+ valence state of Ce ions. The \rho(T) data exhibit Kondo
+lattice behavior with a metallic ground state. The low-T C_p(T) data yield an
+enhanced Sommerfeld coefficient \gamma = 130(2) mJ/mol K^2 characterizing
+CeRh2Ga2 as a moderate heavy fermion system. The high-T C_p(T) and \rho(T) show
+an anomaly near 255 K, reflecting a phase transition. The \kappa(T) suggests
+phonon dominated thermal transport with considerably higher values of Lorenz
+number L(T) compared to the theoretical Sommerfeld value L_0.
+",0,1,0,0,0,0
+20763,Khovanov homology and periodic links,"  Based on the results of the second author, we define an equivariant version
+of Lee and Bar-Natan homology for periodic links and show that there exists an
+equivariant spectral sequence from the equivariant Khovanov homology to
+equivariant Lee homology. As a result we obtain new obstructions for a link to
+be periodic. These obstructions generalize previous results of Przytycki and of
+the second author.
+",0,0,1,0,0,0
+20764,A Note on Spectral Clustering and SVD of Graph Data,"  Spectral clustering and Singular Value Decomposition (SVD) are both widely
+used technique for analyzing graph data. In this note, I will present their
+connections using simple linear algebra, aiming to provide some in-depth
+understanding for future research.
+",1,0,0,0,0,0
+20765,An explicit analysis of the entropic penalty in linear programming,"  Solving linear programs by using entropic penalization has recently attracted
+new interest in the optimization community, since this strategy forms the basis
+for the fastest-known algorithms for the optimal transport problem, with many
+applications in modern large-scale machine learning. Crucial to these
+applications has been an analysis of how quickly solutions to the penalized
+program approach true optima to the original linear program. More than 20 years
+ago, Cominetti and San Martín showed that this convergence is exponentially
+fast; however, their proof is asymptotic and does not give any indication of
+how accurately the entropic program approximates the original program for any
+particular choice of the penalization parameter. We close this long-standing
+gap in the literature regarding entropic penalization by giving a new proof of
+the exponential convergence, valid for any linear program. Our proof is
+non-asymptotic, yields explicit constants, and has the virtue of being
+extremely simple. We provide matching lower bounds and show that the entropic
+approach does not lead to a near-linear time approximation scheme for the
+linear assignment problem.
+",0,0,0,1,0,0
+20766,Deconstructing the Tail at Scale Effect Across Network Protocols,"  Network latencies have become increasingly important for the performance of
+web servers and cloud computing platforms. Identifying network-related tail
+latencies and reasoning about their potential causes is especially important to
+gauge application run-time in online data-intensive applications, where the
+99th percentile latency of individual operations can significantly affect the
+the overall latency of requests.
+This paper deconstructs the ""tail at scale"" effect across TCP-IP, UDP-IP, and
+RDMA network protocols. Prior scholarly works have analyzed tail latencies
+caused by extrinsic network parameters like network congestion and flow
+fairness. Contrary to existing literature, we identify surprising rare tails in
+TCP-IP round-trip measurements that are as enormous as 110x higher than the
+median latency. Our experimental design eliminates network congestion as a
+tail-inducing factor. Moreover, we observe similar extreme tails in UDP-IP
+packet exchanges, ruling out additional TCP-IP protocol operations as the root
+cause of tail latency. However, we are unable to reproduce similar tail
+latencies in RDMA packet exchanges, which leads us to conclude that the TCP/UDP
+protocol stack within the operating system kernel is likely the primary source
+of extreme latency tails.
+",1,0,0,0,0,0
+20767,Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis,"  Accurate diagnosis of psychiatric disorders plays a critical role in
+improving quality of life for patients and potentially supports the development
+of new treatments. Many studies have been conducted on machine learning
+techniques that seek brain imaging data for specific biomarkers of disorders.
+These studies have encountered the following dilemma: An end-to-end
+classification overfits to a small number of high-dimensional samples but
+unsupervised feature-extraction has the risk of extracting a signal of no
+interest. In addition, such studies often provided only diagnoses for patients
+without presenting the reasons for these diagnoses. This study proposed a deep
+neural generative model of resting-state functional magnetic resonance imaging
+(fMRI) data. The proposed model is conditioned by the assumption of the
+subject's state and estimates the posterior probability of the subject's state
+given the imaging data, using Bayes' rule. This study applied the proposed
+model to diagnose schizophrenia and bipolar disorders. Diagnosis accuracy was
+improved by a large margin over competitive approaches, namely a support vector
+machine, logistic regression, and multilayer perceptron with or without
+unsupervised feature-extractors in addition to a Gaussian mixture model. The
+proposed model visualizes brain regions largely related to the disorders, thus
+motivating further biological investigation.
+",1,0,0,1,0,0
+20768,Deep Learning for Patient-Specific Kidney Graft Survival Analysis,"  An accurate model of patient-specific kidney graft survival distributions can
+help to improve shared-decision making in the treatment and care of patients.
+In this paper, we propose a deep learning method that directly models the
+survival function instead of estimating the hazard function to predict survival
+times for graft patients based on the principle of multi-task learning. By
+learning to jointly predict the time of the event, and its rank in the cox
+partial log likelihood framework, our deep learning approach outperforms, in
+terms of survival time prediction quality and concordance index, other common
+methods for survival analysis, including the Cox Proportional Hazards model and
+a network trained on the cox partial log-likelihood.
+",1,0,0,1,0,0
+20769,When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks,"  We introduce a framework for the modeling of sequential data capturing
+pathways of varying lengths observed in a network. Such data are important,
+e.g., when studying click streams in information networks, travel patterns in
+transportation systems, information cascades in social networks, biological
+pathways or time-stamped social interactions. While it is common to apply graph
+analytics and network analysis to such data, recent works have shown that
+temporal correlations can invalidate the results of such methods. This raises a
+fundamental question: when is a network abstraction of sequential data
+justified? Addressing this open question, we propose a framework which combines
+Markov chains of multiple, higher orders into a multi-layer graphical model
+that captures temporal correlations in pathways at multiple length scales
+simultaneously. We develop a model selection technique to infer the optimal
+number of layers of such a model and show that it outperforms previously used
+Markov order detection techniques. An application to eight real-world data sets
+on pathways and temporal networks shows that it allows to infer graphical
+models which capture both topological and temporal characteristics of such
+data. Our work highlights fallacies of network abstractions and provides a
+principled answer to the open question when they are justified. Generalizing
+network representations to multi-order graphical models, it opens perspectives
+for new data mining and knowledge discovery algorithms.
+",1,1,0,0,0,0
+20770,Outlier-robust moment-estimation via sum-of-squares,"  We develop efficient algorithms for estimating low-degree moments of unknown
+distributions in the presence of adversarial outliers. The guarantees of our
+algorithms improve in many cases significantly over the best previous ones,
+obtained in recent works of Diakonikolas et al, Lai et al, and Charikar et al.
+We also show that the guarantees of our algorithms match information-theoretic
+lower-bounds for the class of distributions we consider. These improved
+guarantees allow us to give improved algorithms for independent component
+analysis and learning mixtures of Gaussians in the presence of outliers.
+Our algorithms are based on a standard sum-of-squares relaxation of the
+following conceptually-simple optimization problem: Among all distributions
+whose moments are bounded in the same way as for the unknown distribution, find
+the one that is closest in statistical distance to the empirical distribution
+of the adversarially-corrupted sample.
+",1,0,0,1,0,0
+20771,Clustering High Dimensional Dynamic Data Streams,"  We present data streaming algorithms for the $k$-median problem in
+high-dimensional dynamic geometric data streams, i.e. streams allowing both
+insertions and deletions of points from a discrete Euclidean space $\{1, 2,
+\ldots \Delta\}^d$. Our algorithms use $k \epsilon^{-2} poly(d \log \Delta)$
+space/time and maintain with high probability a small weighted set of points (a
+coreset) such that for every set of $k$ centers the cost of the coreset
+$(1+\epsilon)$-approximates the cost of the streamed point set. We also provide
+algorithms that guarantee only positive weights in the coreset with additional
+logarithmic factors in the space and time complexities. We can use this
+positively-weighted coreset to compute a $(1+\epsilon)$-approximation for the
+$k$-median problem by any efficient offline $k$-median algorithm. All previous
+algorithms for computing a $(1+\epsilon)$-approximation for the $k$-median
+problem over dynamic data streams required space and time exponential in $d$.
+Our algorithms can be generalized to metric spaces of bounded doubling
+dimension.
+",1,0,0,0,0,0
+20772,Edge contact angle and modified Kelvin equation for condensation in open pores,"  We consider capillary condensation transitions occurring in open slits of
+width $L$ and finite height $H$ immersed in a reservoir of vapour. In this case
+the pressure at which condensation occurs is closer to saturation compared to
+that occurring in an infinite slit ($H=\infty$) due to the presence of two
+menisci which are pinned near the open ends. Using macroscopic arguments we
+derive a modified Kelvin equation for the pressure, $p_{cc}(L;H)$, at which
+condensation occurs and show that the two menisci are characterised by an edge
+contact angle $\theta_e$ which is always larger than the equilibrium contact
+angle $\theta$, only equal to it in the limit of macroscopic $H$. For walls
+which are completely wet ($\theta=0$) the edge contact angle depends only on
+the aspect ratio of the capillary and is well described by $\theta_e\approx
+\sqrt{\pi L/2H}$ for large $H$. Similar results apply for condensation in
+cylindrical pores of finite length. We have tested these predictions against
+numerical results obtained using a microscopic density functional model where
+the presence of an edge contact angle characterising the shape of the menisci
+is clearly visible from the density profiles. Below the wetting temperature
+$T_w$ we find very good agreement for slit pores of widths of just a few tens
+of molecular diameters while above $T_w$ the modified Kelvin equation only
+becomes accurate for much larger systems.
+",0,1,0,0,0,0
+20773,Thresholds for hanger slackening and cable shortening in the Melan equation for suspension bridges,"  The Melan equation for suspension bridges is derived by assuming small
+displacements of the deck and inextensible hangers. We determine the thresholds
+for the validity of the Melan equation when the hangers slacken, thereby
+violating the inextensibility assumption. To this end, we preliminarily study
+the possible shortening of the cables: it turns out that there is a striking
+difference between even and odd vibrating modes since the former never shorten
+the cables. These problems are studied both on beams and plates.
+",0,1,1,0,0,0
+20774,Bendable Cuboid Robot Path Planning with Collision Avoidance using Generalized $L_p$ Norms,"  Optimal path planning problems for rigid and deformable (bendable) cuboid
+robots are considered by providing an analytic safety constraint using
+generalized $L_p$ norms. For regular cuboid robots, level sets of weighted
+$L_p$ norms generate implicit approximations of their surfaces. For bendable
+cuboid robots a weighted $L_p$ norm in polar coordinates implicitly
+approximates the surface boundary through a specified level set. Obstacle
+volumes, in the environment to navigate within, are presumed to be
+approximately described as sub-level sets of weighted $L_p$ norms. Using these
+approximate surface models, the optimal safe path planning problem is
+reformulated as a two stage optimization problem, where the safety constraint
+depends on a point on the robot which is closest to the obstacle in the
+obstacle's distance metric. A set of equality and inequality constraints are
+derived to replace the closest point problem, which is then defines additional
+analytic constraints on the original path planning problem. Combining all the
+analytic constraints with logical AND operations leads to a general optimal
+safe path planning problem. Numerically solving the problem involve conversion
+to a nonlinear programing problem. Simulations for rigid and bendable cuboid
+robot verify the proposed method.
+",1,0,0,0,0,0
+20775,"Quotients of triangulated categories and Equivalences of Buchweitz, Orlov and Amiot--Guo--Keller","  We give a sufficient condition for a Verdier quotient $\ct/\cs$ of a
+triangulated category $\ct$ by a thick subcategory $\cs$ to be realized inside
+of $\ct$ as an ideal quotient. As applications, we deduce three significant
+results by Buchweitz, Orlov and Amiot--Guo--Keller.
+",0,0,1,0,0,0
+20776,Sorting sums of binary decision summands,"  A sum where each of the $N$ summands can be independently chosen from two
+choices yields $2^N$ possible summation outcomes. There is an
+$\mathcal{O}(K^2)$-algorithm that finds the $K$ smallest/largest of these sums
+by evading the enumeration of all sums.
+",1,0,0,0,0,0
+20777,A Pursuit of Temporal Accuracy in General Activity Detection,"  Detecting activities in untrimmed videos is an important but challenging
+task. The performance of existing methods remains unsatisfactory, e.g., they
+often meet difficulties in locating the beginning and end of a long complex
+action. In this paper, we propose a generic framework that can accurately
+detect a wide variety of activities from untrimmed videos. Our first
+contribution is a novel proposal scheme that can efficiently generate
+candidates with accurate temporal boundaries. The other contribution is a
+cascaded classification pipeline that explicitly distinguishes between
+relevance and completeness of a candidate instance. On two challenging temporal
+activity detection datasets, THUMOS14 and ActivityNet, the proposed framework
+significantly outperforms the existing state-of-the-art methods, demonstrating
+superior accuracy and strong adaptivity in handling activities with various
+temporal structures.
+",1,0,0,0,0,0
+20778,Adjusting systematic bias in high dimensional principal component scores,"  Principal component analysis continues to be a powerful tool in dimension
+reduction of high dimensional data. We assume a variance-diverging model and
+use the high-dimension, low-sample-size asymptotics to show that even though
+the principal component directions are not consistent, the sample and
+prediction principal component scores can be useful in revealing the population
+structure. We further show that these scores are biased, and the bias is
+asymptotically decomposed into rotation and scaling parts. We propose methods
+of bias-adjustment that are shown to be consistent and work well in the finite
+but high dimensional situations with small sample sizes. The potential
+advantage of bias-adjustment is demonstrated in a classification setting.
+",0,0,1,1,0,0
+20779,Dynamical Exploration of Amplitude Bistability in Engineered Quantum Systems,"  Nonlinear systems, whose outputs are not directly proportional to their
+inputs, are well known to exhibit many interesting and important phenomena
+which have profoundly changed our technological landscape over the last 50
+years. Recently the ability to engineer quantum metamaterials through
+hybridisation has allowed to explore these nonlinear effects in systems with no
+natural analogue. Here we investigate amplitude bistability, which is one of
+the most fundamental nonlinear phenomena, in a hybrid system composed of a
+superconducting resonator inductively coupled to an ensemble of
+nitrogen-vacancy centres. One of the exciting properties of this spin system is
+its extremely long spin life-time, more than ten orders of magnitude longer
+than other relevant timescales of the hybrid system. This allows us to
+dynamically explore this nonlinear regime of cavity quantum electrodynamics
+(cQED) and demonstrate a critical slowing down of the cavity population on the
+order of several tens of thousands of seconds - a timescale much longer than
+observed so far for this effect. Our results provide the foundation for future
+quantum technologies based on nonlinear phenomena.
+",0,1,0,0,0,0
+20780,Map Memorization and Forgetting in the IARA Autonomous Car,"  In this work, we present a novel strategy for correcting imperfections in
+occupancy grid maps called map decay. The objective of map decay is to correct
+invalid occupancy probabilities of map cells that are unobservable by sensors.
+The strategy was inspired by an analogy between the memory architecture
+believed to exist in the human brain and the maps maintained by an autonomous
+vehicle. It consists in merging sensory information obtained during runtime
+(online) with a priori data from a high-precision map constructed offline. In
+map decay, cells observed by sensors are updated using traditional occupancy
+grid mapping techniques and unobserved cells are adjusted so that their
+occupancy probabilities tend to the values found in the offline map. This
+strategy is grounded in the idea that the most precise information available
+about an unobservable cell is the value found in the high-precision offline
+map. Map decay was successfully tested and is still in use in the IARA
+autonomous vehicle from Universidade Federal do Espírito Santo.
+",1,0,0,0,0,0
+20781,Effective Subgroup Separability of Finitely Generated Nilpotent Groups,"  This paper studies effective separability for subgroups of finitely generated
+nilpotent groups and more broadly effective subgroup separability of finitely
+generated nilpotent groups. We provide upper and lower bounds that are
+polynomial with respect to the logarithm of the word length for infinite index
+subgroups of nilpotent groups. In the case of normal subgroups, we provide an
+exact computation generalizing work of the second author. We introduce a
+function that quantifies subgroup separability, and we provide polynomial upper
+and lower bounds. We finish by demonstrating that our results extend to
+virtually nilpotent groups.
+",0,0,1,0,0,0
+20782,External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising,"  Most of existing image denoising methods learn image priors from either
+external data or the noisy image itself to remove noise. However, priors
+learned from external data may not be adaptive to the image to be denoised,
+while priors learned from the given noisy image may not be accurate due to the
+interference of corrupted noise. Meanwhile, the noise in real-world noisy
+images is very complex, which is hard to be described by simple distributions
+such as Gaussian distribution, making real-world noisy image denoising a very
+challenging problem. We propose to exploit the information in both external
+data and the given noisy image, and develop an external prior guided internal
+prior learning method for real-world noisy image denoising. We first learn
+external priors from an independent set of clean natural images. With the aid
+of learned external priors, we then learn internal priors from the given noisy
+image to refine the prior model. The external and internal priors are
+formulated as a set of orthogonal dictionaries to efficiently reconstruct the
+desired image. Extensive experiments are performed on several real-world noisy
+image datasets. The proposed method demonstrates highly competitive denoising
+performance, outperforming state-of-the-art denoising methods including those
+designed for real-world noisy images.
+",1,0,0,0,0,0
+20783,A Study of MAC Address Randomization in Mobile Devices and When it Fails,"  MAC address randomization is a privacy technique whereby mobile devices
+rotate through random hardware addresses in order to prevent observers from
+singling out their traffic or physical location from other nearby devices.
+Adoption of this technology, however, has been sporadic and varied across
+device manufacturers. In this paper, we present the first wide-scale study of
+MAC address randomization in the wild, including a detailed breakdown of
+different randomization techniques by operating system, manufacturer, and model
+of device.
+We then identify multiple flaws in these implementations which can be
+exploited to defeat randomization as performed by existing devices. First, we
+show that devices commonly make improper use of randomization by sending
+wireless frames with the true, global address when they should be using a
+randomized address. We move on to extend the passive identification techniques
+of Vanhoef et al. to effectively defeat randomization in ~96% of Android
+phones. Finally, we show a method that can be used to track 100% of devices
+using randomization, regardless of manufacturer, by exploiting a previously
+unknown flaw in the way existing wireless chipsets handle low-level control
+frames.
+",1,0,0,0,0,0
+20784,Completion of the integrable coupling systems,"  In this paper, we proposed an procedure to construct the completion of the
+integrable system by adding a perturbation to the generalized matrix problem,
+which can be used to continuous integrable couplings, discrete integrable
+couplings and super integrable couplings. As example, we construct the
+completion of the Kaup-Newell (KN) integrable coupling, the
+Wadati-Konno-Ichikawa (WKI) integrable couplingsis, vector
+Ablowitz-Kaup-Newell-Segur (vAKNS) integrable couplings, the Volterra
+integrable couplings, Dirac type integrable couplings and NLS-mKdV type
+integrable couplings.
+",0,1,0,0,0,0
+20785,On the wildness of cambrian lattices,"  In this note, we investigate the representation type of the cambrian lattices
+and some other related lattices. The result is expressed as a very simple
+trichotomy. When the rank of the underlined Coxeter group is at most 2, the
+lattices are of finite representation type. When the Coxeter group is a
+reducible group of type A 3 1 , the lattices are of tame representation type.
+In all the other cases they are of wild representation type.
+",0,0,1,0,0,0
+20786,Random Projections For Large-Scale Regression,"  Fitting linear regression models can be computationally very expensive in
+large-scale data analysis tasks if the sample size and the number of variables
+are very large. Random projections are extensively used as a dimension
+reduction tool in machine learning and statistics. We discuss the applications
+of random projections in linear regression problems, developed to decrease
+computational costs, and give an overview of the theoretical guarantees of the
+generalization error. It can be shown that the combination of random
+projections with least squares regression leads to similar recovery as ridge
+regression and principal component regression. We also discuss possible
+improvements when averaging over multiple random projections, an approach that
+lends itself easily to parallel implementation.
+",0,0,1,1,0,0
+20787,Properties of linear groups with restricted unipotent elements,"  We consider linear groups which do not contain unipotent elements of infinite
+order, which includes all linear groups in positive characteristic, and show
+that this class of groups has good properties which resemble those held by
+groups of non positive curvature and which do not hold for arbitrary
+characteristic zero linear groups. In particular if such a linear group is
+finitely generated then centralisers virtually split and all finitely generated
+abelian subgroups are undistorted. If further the group is virtually torsion
+free (which always holds in characteristic zero) then we have a strong property
+on small subgroups: any subgroup either contains a non abelian free group or is
+finitely generated and virtually abelian, hence also undistorted. We present
+applications, including that the mapping class group of a surface having genus
+at least 3 has no faithful linear representation which is complex unitary or
+over any field of positive characteristic.
+",0,0,1,0,0,0
+20788,Sparse Approximation of 3D Meshes using the Spectral Geometry of the Hamiltonian Operator,"  The discrete Laplace operator is ubiquitous in spectral shape analysis, since
+its eigenfunctions are provably optimal in representing smooth functions
+defined on the surface of the shape. Indeed, subspaces defined by its
+eigenfunctions have been utilized for shape compression, treating the
+coordinates as smooth functions defined on the given surface. However, surfaces
+of shapes in nature often contain geometric structures for which the general
+smoothness assumption may fail to hold. At the other end, some explicit mesh
+compression algorithms utilize the order by which vertices that represent the
+surface are traversed, a property which has been ignored in spectral
+approaches. Here, we incorporate the order of vertices into an operator that
+defines a novel spectral domain. We propose a method for representing 3D meshes
+using the spectral geometry of the Hamiltonian operator, integrated within a
+sparse approximation framework. We adapt the concept of a potential function
+from quantum physics and incorporate vertex ordering information into the
+potential, yielding a novel data-dependent operator. The potential function
+modifies the spectral geometry of the Laplacian to focus on regions with finer
+details of the given surface. By sparsely encoding the geometry of the shape
+using the proposed data-dependent basis, we improve compression performance
+compared to previous results that use the standard Laplacian basis and spectral
+graph wavelets.
+",1,0,0,0,0,0
+20789,Improved Convergence Rates for Distributed Resource Allocation,"  In this paper, we develop a class of decentralized algorithms for solving a
+convex resource allocation problem in a network of $n$ agents, where the agent
+objectives are decoupled while the resource constraints are coupled. The agents
+communicate over a connected undirected graph, and they want to collaboratively
+determine a solution to the overall network problem, while each agent only
+communicates with its neighbors. We first study the connection between the
+decentralized resource allocation problem and the decentralized consensus
+optimization problem. Then, using a class of algorithms for solving consensus
+optimization problems, we propose a novel class of decentralized schemes for
+solving resource allocation problems in a distributed manner. Specifically, we
+first propose an algorithm for solving the resource allocation problem with an
+$o(1/k)$ convergence rate guarantee when the agents' objective functions are
+generally convex (could be nondifferentiable) and per agent local convex
+constraints are allowed; We then propose a gradient-based algorithm for solving
+the resource allocation problem when per agent local constraints are absent and
+show that such scheme can achieve geometric rate when the objective functions
+are strongly convex and have Lipschitz continuous gradients. We have also
+provided scalability/network dependency analysis. Based on these two
+algorithms, we have further proposed a gradient projection-based algorithm
+which can handle smooth objective and simple constraints more efficiently.
+Numerical experiments demonstrates the viability and performance of all the
+proposed algorithms.
+",1,0,1,0,0,0
+20790,Sensory Metrics of Neuromechanical Trust,"  Today digital sources supply an unprecedented component of human sensorimotor
+data, the consumption of which is correlated with poorly understood maladies
+such as Internet Addiction Disorder and Internet Gaming Disorder. This paper
+offers a mathematical understanding of human sensorimotor processing as
+multiscale, continuous-time vibratory interaction. We quantify human
+informational needs using the signal processing metrics of entropy, noise,
+dimensionality, continuity, latency, and bandwidth. Using these metrics, we
+define the trust humans experience as a primitive statistical algorithm
+processing finely grained sensorimotor data from neuromechanical interaction.
+This definition of neuromechanical trust implies that artificial sensorimotor
+inputs and interactions that attract low-level attention through frequent
+discontinuities and enhanced coherence will decalibrate a brain's
+representation of its world over the long term by violating the implicit
+statistical contract for which self-calibration evolved. This approach allows
+us to model addiction in general as the result of homeostatic regulation gone
+awry in novel environments and digital dependency as a sub-case in which the
+decalibration caused by digital sensorimotor data spurs yet more consumption of
+them. We predict that institutions can use these sensorimotor metrics to
+quantify media richness to improve employee well-being; that dyads and
+family-size groups will bond and heal best through low-latency, high-resolution
+multisensory interaction such as shared meals and reciprocated touch; and that
+individuals can improve sensory and sociosensory resolution through deliberate
+sensory reintegration practices. We conclude that we humans are the victims of
+our own success, our hands so skilled they fill the world with captivating
+things, our eyes so innocent they follow eagerly.
+",0,1,0,0,0,0
+20791,Quantum algorithms for training Gaussian Processes,"  Gaussian processes (GPs) are important models in supervised machine learning.
+Training in Gaussian processes refers to selecting the covariance functions and
+the associated parameters in order to improve the outcome of predictions, the
+core of which amounts to evaluating the logarithm of the marginal likelihood
+(LML) of a given model. LML gives a concrete measure of the quality of
+prediction that a GP model is expected to achieve. The classical computation of
+LML typically carries a polynomial time overhead with respect to the input
+size. We propose a quantum algorithm that computes the logarithm of the
+determinant of a Hermitian matrix, which runs in logarithmic time for sparse
+matrices. This is applied in conjunction with a variant of the quantum linear
+system algorithm that allows for logarithmic time computation of the form
+$\mathbf{y}^TA^{-1}\mathbf{y}$, where $\mathbf{y}$ is a dense vector and $A$ is
+the covariance matrix. We hence show that quantum computing can be used to
+estimate the LML of a GP with exponentially improved efficiency under certain
+conditions.
+",0,0,0,1,0,0
+20792,Detecting Oriented Text in Natural Images by Linking Segments,"  Most state-of-the-art text detection methods are specific to horizontal Latin
+text and are not fast enough for real-time applications. We introduce Segment
+Linking (SegLink), an oriented text detection method. The main idea is to
+decompose text into two locally detectable elements, namely segments and links.
+A segment is an oriented box covering a part of a word or text line; A link
+connects two adjacent segments, indicating that they belong to the same word or
+text line. Both elements are detected densely at multiple scales by an
+end-to-end trained, fully-convolutional neural network. Final detections are
+produced by combining segments connected by links. Compared with previous
+methods, SegLink improves along the dimensions of accuracy, speed, and ease of
+training. It achieves an f-measure of 75.0% on the standard ICDAR 2015
+Incidental (Challenge 4) benchmark, outperforming the previous best by a large
+margin. It runs at over 20 FPS on 512x512 images. Moreover, without
+modification, SegLink is able to detect long lines of non-Latin text, such as
+Chinese.
+",1,0,0,0,0,0
+20793,Factorizable Module Algebras,"  The aim of this paper is to introduce and study a large class of
+$\mathfrak{g}$-module algebras which we call factorizable by generalizing the
+Gauss factorization of (square or rectangular) matrices. This class includes
+coordinate algebras of corresponding reductive groups $G$, their parabolic
+subgroups, basic affine spaces and many others. It turns out that tensor
+products of factorizable algebras are also factorizable and it is easy to
+create a factorizable algebra out of virtually any $\mathfrak{g}$-module
+algebra. We also have quantum versions of all these constructions in the
+category of $U_q(\mathfrak{g})$-module algebras. Quite surprisingly, our
+quantum factorizable algebras are naturally acted on by the quantized
+enveloping algebra $U_q(\mathfrak{g}^*)$ of the dual Lie bialgebra
+$\mathfrak{g}^*$ of $\mathfrak{g}$.
+",0,0,1,0,0,0
+20794,Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors,"  Robot awareness of human actions is an essential research problem in robotics
+with many important real-world applications, including human-robot
+collaboration and teaming. Over the past few years, depth sensors have become a
+standard device widely used by intelligent robots for 3D perception, which can
+also offer human skeletal data in 3D space. Several methods based on skeletal
+data were designed to enable robot awareness of human actions with satisfactory
+accuracy. However, previous methods treated all body parts and features equally
+important, without the capability to identify discriminative body parts and
+features. In this paper, we propose a novel simultaneous Feature And Body-part
+Learning (FABL) approach that simultaneously identifies discriminative body
+parts and features, and efficiently integrates all available information
+together to enable real-time robot awareness of human behaviors. We formulate
+FABL as a regression-like optimization problem with structured
+sparsity-inducing norms to model interrelationships of body parts and features.
+We also develop an optimization algorithm to solve the formulated problem,
+which possesses a theoretical guarantee to find the optimal solution. To
+evaluate FABL, three experiments were performed using public benchmark
+datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter
+robot in practical assistive living applications. Experimental results show
+that our FABL approach obtains a high recognition accuracy with a processing
+speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method
+to enable real-time robot awareness of human behaviors in practical robotics
+applications.
+",1,0,0,0,0,0
+20795,Off-diagonal estimates of some Bergman-type operators on tube domains over symmetric cones,"  We obtain some necessary and sufficient conditions for the boundedness of a
+family of positive operators defined on symmetric cones, we then deduce
+off-diagonal boundedness of associated Bergman-type operators in tube domains
+over symmetric cones.
+",0,0,1,0,0,0
+20796,Provenance and Pseudo-Provenance for Seeded Learning-Based Automated Test Generation,"  Many methods for automated software test generation, including some that
+explicitly use machine learning (and some that use ML more broadly conceived)
+derive new tests from existing tests (often referred to as seeds). Often, the
+seed tests from which new tests are derived are manually constructed, or at
+least simpler than the tests that are produced as the final outputs of such
+test generators. We propose annotation of generated tests with a provenance
+(trail) showing how individual generated tests of interest (especially failing
+tests) derive from seed tests, and how the population of generated tests
+relates to the original seed tests. In some cases, post-processing of generated
+tests can invalidate provenance information, in which case we also propose a
+method for attempting to construct ""pseudo-provenance"" describing how the tests
+could have been (partly) generated from seeds.
+",1,0,0,1,0,0
+20797,Learning Solving Procedure for Artificial Neural Network,"  It is expected that progress toward true artificial intelligence will be
+achieved through the emergence of a system that integrates representation
+learning and complex reasoning (LeCun et al. 2015). In response to this
+prediction, research has been conducted on implementing the symbolic reasoning
+of a von Neumann computer in an artificial neural network (Graves et al. 2016;
+Graves et al. 2014; Reed et al. 2015). However, these studies have many
+limitations in realizing neural-symbolic integration (Jaeger. 2016). Here, we
+present a new learning paradigm: a learning solving procedure (LSP) that learns
+the procedure for solving complex problems. This is not accomplished merely by
+learning input-output data, but by learning algorithms through a solving
+procedure that obtains the output as a sequence of tasks for a given input
+problem. The LSP neural network system not only learns simple problems of
+addition and multiplication, but also the algorithms of complicated problems,
+such as complex arithmetic expression, sorting, and Hanoi Tower. To realize
+this, the LSP neural network structure consists of a deep neural network and
+long short-term memory, which are recursively combined. Through
+experimentation, we demonstrate the efficiency and scalability of LSP and its
+validity as a mechanism of complex reasoning.
+",1,0,0,0,0,0
+20798,On primordial black holes from an inflection point,"  Recently, it has been claimed that inflationary models with an inflection
+point in the scalar potential can produce a large resonance in the power
+spectrum of curvature perturbation. In this paper however we show that the
+previous analyses are incorrect. The reason is twofold: firstly, the inflaton
+is over-shot from a stage of standard inflation and so deviates from the
+slow-roll attractor before reaching the inflection. Secondly, on the (or close
+to) the inflection point, the ultra-slow-roll trajectory supersede the
+slow-roll one and thus, the slow-roll approximations used in the literature
+cannot be used. We then reconsider the model and provide a recipe for how to
+produce nevertheless a large peak in the matter power spectrum via fine-tuning
+of parameters.
+",0,1,0,0,0,0
+20799,Dropout as a Low-Rank Regularizer for Matrix Factorization,"  Regularization for matrix factorization (MF) and approximation problems has
+been carried out in many different ways. Due to its popularity in deep
+learning, dropout has been applied also for this class of problems. Despite its
+solid empirical performance, the theoretical properties of dropout as a
+regularizer remain quite elusive for this class of problems. In this paper, we
+present a theoretical analysis of dropout for MF, where Bernoulli random
+variables are used to drop columns of the factors. We demonstrate the
+equivalence between dropout and a fully deterministic model for MF in which the
+factors are regularized by the sum of the product of squared Euclidean norms of
+the columns. Additionally, we inspect the case of a variable sized
+factorization and we prove that dropout achieves the global minimum of a convex
+approximation problem with (squared) nuclear norm regularization. As a result,
+we conclude that dropout can be used as a low-rank regularizer with data
+dependent singular-value thresholding.
+",1,0,0,1,0,0
+20800,Introduction to a Temporal Graph Benchmark,"  A temporal graph is a data structure, consisting of nodes and edges in which
+the edges are associated with time labels. To analyze the temporal graph, the
+first step is to find a proper graph dataset/benchmark. While many temporal
+graph datasets exist online, none could be found that used the interval labels
+in which each edge is associated with a starting and ending time. Therefore we
+create a temporal graph data based on Wikipedia reference graph for temporal
+analysis. This report aims to provide more details of this graph benchmark to
+those who are interested in using it.
+",1,1,0,0,0,0
+20801,Orbifold equivalence: structure and new examples,"  Orbifold equivalence is a notion of symmetry that does not rely on group
+actions. Among other applications, it leads to surprising connections between
+hitherto unrelated singularities. While the concept can be defined in a very
+general category-theoretic language, we focus on the most explicit setting in
+terms of matrix factorisations, where orbifold equivalences arise from defects
+with special properties. Examples are relatively difficult to construct, but we
+uncover some structural features that distinguish orbifold equivalences -- most
+notably a finite perturbation expansion. We use those properties to devise a
+search algorithm, then present some new examples including Arnold
+singularities.
+",0,0,1,0,0,0
+20802,Efficient Attention using a Fixed-Size Memory Representation,"  The standard content-based attention mechanism typically used in
+sequence-to-sequence models is computationally expensive as it requires the
+comparison of large encoder and decoder states at each time step. In this work,
+we propose an alternative attention mechanism based on a fixed size memory
+representation that is more efficient. Our technique predicts a compact set of
+K attention contexts during encoding and lets the decoder compute an efficient
+lookup that does not need to consult the memory. We show that our approach
+performs on-par with the standard attention mechanism while yielding inference
+speedups of 20% for real-world translation tasks and more for tasks with longer
+sequences. By visualizing attention scores we demonstrate that our models learn
+distinct, meaningful alignments.
+",1,0,0,0,0,0
+20803,Multiplicities of bifurcation sets of Pham singularities,"  The local multiplicities of the Maxwell sets in the spaces of versal
+deformations of Pham holomorphic function singularities are calculated. A
+similar calculation for some other bifurcation sets (generalized Stokes' sets)
+defined by more complicated relations between the critical values is given.
+Aplications to the complexity of algorithms enumerating topologically distinct
+morsifications of complicated real function singularities are discussed.
+",0,0,1,0,0,0
+20804,Fast and scalable Gaussian process modeling with applications to astronomical time series,"  The growing field of large-scale time domain astronomy requires methods for
+probabilistic data analysis that are computationally tractable, even with large
+datasets. Gaussian Processes are a popular class of models used for this
+purpose but, since the computational cost scales, in general, as the cube of
+the number of data points, their application has been limited to small
+datasets. In this paper, we present a novel method for Gaussian Process
+modeling in one-dimension where the computational requirements scale linearly
+with the size of the dataset. We demonstrate the method by applying it to
+simulated and real astronomical time series datasets. These demonstrations are
+examples of probabilistic inference of stellar rotation periods, asteroseismic
+oscillation spectra, and transiting planet parameters. The method exploits
+structure in the problem when the covariance function is expressed as a mixture
+of complex exponentials, without requiring evenly spaced observations or
+uniform noise. This form of covariance arises naturally when the process is a
+mixture of stochastically-driven damped harmonic oscillators -- providing a
+physical motivation for and interpretation of this choice -- but we also
+demonstrate that it can be a useful effective model in some other cases. We
+present a mathematical description of the method and compare it to existing
+scalable Gaussian Process methods. The method is fast and interpretable, with a
+range of potential applications within astronomical data analysis and beyond.
+We provide well-tested and documented open-source implementations of this
+method in C++, Python, and Julia.
+",0,1,0,1,0,0
+20805,Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs,"  Motivations Short-read accuracy is important for downstream analyses such as
+genome assembly and hybrid long-read correction. Despite much work on
+short-read correction, present-day correctors either do not scale well on large
+data sets or consider reads as mere suites of k-mers, without taking into
+account their full-length read information. Results We propose a new method to
+correct short reads using de Bruijn graphs, and implement it as a tool called
+Bcool. As a first st ep, Bcool constructs a compacted de Bruijn graph from the
+reads. This graph is filtered on the basis of k-mer abundance then of unitig
+abundance, thereby removing from most sequencing errors. The cleaned graph is
+then used as a reference on which the reads are mapped to correct them. We show
+that this approach yields more accurate reads than k-mer-spectrum correctors
+while being scalable to human-size genomic datasets and beyond. Availability
+and Implementation The implementation is open source and available at http:
+//github.com/Malfoy/BCOOL under the Affero GPL license. Contact Antoine
+Limasset antoine.limasset@gmail.com & Jean-François Flot jflot@ulb.ac.be &
+Pierre Peterlongo pierre.peterlongo@inria.fr
+",1,0,0,0,0,0
+20806,Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning,"  Patient pain can be detected highly reliably from facial expressions using a
+set of facial muscle-based action units (AUs) defined by the Facial Action
+Coding System (FACS). A key characteristic of facial expression of pain is the
+simultaneous occurrence of pain-related AU combinations, whose automated
+detection would be highly beneficial for efficient and practical pain
+monitoring. Existing general Automated Facial Expression Recognition (AFER)
+systems prove inadequate when applied specifically for detecting pain as they
+either focus on detecting individual pain-related AUs but not on combinations
+or they seek to bypass AU detection by training a binary pain classifier
+directly on pain intensity data but are limited by lack of enough labeled data
+for satisfactory training. In this paper, we propose a new approach that mimics
+the strategy of human coders of decoupling pain detection into two consecutive
+tasks: one performed at the individual video-frame level and the other at
+video-sequence level. Using state-of-the-art AFER tools to detect single AUs at
+the frame level, we propose two novel data structures to encode AU combinations
+from single AU scores. Two weakly supervised learning frameworks namely
+multiple instance learning (MIL) and multiple clustered instance learning
+(MCIL) are employed corresponding to each data structure to learn pain from
+video sequences. Experimental results show an 87% pain recognition accuracy
+with 0.94 AUC (Area Under Curve) on the UNBC-McMaster Shoulder Pain Expression
+dataset. Tests on long videos in a lung cancer patient video dataset
+demonstrates the potential value of the proposed system for pain monitoring in
+clinical settings.
+",1,0,0,1,0,0
+20807,A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects,"  Tracking humans that are interacting with the other subjects or environment
+remains unsolved in visual tracking, because the visibility of the human of
+interests in videos is unknown and might vary over time. In particular, it is
+still difficult for state-of-the-art human trackers to recover complete human
+trajectories in crowded scenes with frequent human interactions. In this work,
+we consider the visibility status of a subject as a fluent variable, whose
+change is mostly attributed to the subject's interaction with the surrounding,
+e.g., crossing behind another object, entering a building, or getting into a
+vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the
+causal-effect relations between an object's visibility fluent and its
+activities, and develop a probabilistic graph model to jointly reason the
+visibility fluent change (e.g., from visible to invisible) and track humans in
+videos. We formulate this joint task as an iterative search of a feasible
+causal graph structure that enables fast search algorithm, e.g., dynamic
+programming method. We apply the proposed method on challenging video sequences
+to evaluate its capabilities of estimating visibility fluent changes of
+subjects and tracking subjects of interests over time. Results with comparisons
+demonstrate that our method outperforms the alternative trackers and can
+recover complete trajectories of humans in complicated scenarios with frequent
+human interactions.
+",1,0,0,0,0,0
+20808,Exact Affine Counter Automata,"  We introduce an affine generalization of counter automata, and analyze their
+ability as well as affine finite automata. Our contributions are as follows. We
+show that there is a language that can be recognized by exact realtime affine
+counter automata but by neither 1-way deterministic pushdown automata nor
+realtime deterministic k-counter automata. We also show that a certain promise
+problem, which is conjectured not to be solved by two-way quantum finite
+automata in polynomial time, can be solved by Las Vegas affine finite automata.
+Lastly, we show that how a counter helps for affine finite automata by showing
+that the language MANYTWINS, which is conjectured not to be recognized by
+affine, quantum or classical finite state models in polynomial time, can be
+recognized by affine counter automata with one-sided bounded-error in realtime.
+",1,0,0,0,0,0
+20809,Preorder characterizations of lower separation axioms and their applications to foliations and flows,"  In this paper, we characterize several lower separation axioms $C_0, C_D$,
+$C_R$, $C_N$, $\lambda$-space, nested, $S_{YS}$, $S_{YY}$, $S_{YS}$, and
+$S_{\delta}$ using pre-order. To analyze topological properties of (resp.
+dynamical systems) foliations, we introduce notions of topology (resp.
+dynamical systems) for foliations. Then proper (resp. compact, minimal,
+recurrent) foliations are characterized by separation axioms. Conversely, lower
+separation axioms are interpreted into the condition for foliations and several
+relations of them are described. Moreover, we introduce some notions for
+topologies from dynamical systems and foliation theory.
+",0,0,1,0,0,0
+20810,Convergence of row sequences of simultaneous Padé-Faber approximants,"  We consider row sequences of vector valued Padé-Faber approximants
+(simultaneous Padé-Faber approximants) and prove a Montessus de Ballore
+type theorem.
+",0,0,1,0,0,0
+20811,A Deep Convolutional Neural Network for Background Subtraction,"  In this work, we present a novel background subtraction system that uses a
+deep Convolutional Neural Network (CNN) to perform the segmentation. With this
+approach, feature engineering and parameter tuning become unnecessary since the
+network parameters can be learned from data by training a single CNN that can
+handle various video scenes. Additionally, we propose a new approach to
+estimate background model from video. For the training of the CNN, we employed
+randomly 5 percent video frames and their ground truth segmentations taken from
+the Change Detection challenge 2014(CDnet 2014). We also utilized
+spatial-median filtering as the post-processing of the network outputs. Our
+method is evaluated with different data-sets, and the network outperforms the
+existing algorithms with respect to the average ranking over different
+evaluation metrics. Furthermore, due to the network architecture, our CNN is
+capable of real time processing.
+",1,0,0,0,0,0
+20812,A second main theorem for holomorphic curve intersecting hypersurfaces,"  In this paper, we establish a second main theorem for holomorphic curve
+intersecting hypersurfaces in general position in projective space with level
+of truncation. As an application, we reduce the number hypersurfaces in
+uniqueness problem for holomorphic curve of authors before.
+",0,0,1,0,0,0
+20813,Partially hyperbolic diffeomorphisms with one-dimensional neutral center on 3-manifolds,"  We prove that for any partially hyperbolic diffeomorphism with one
+dimensional neutral center on a 3-manifold, the center stable and center
+unstable foliations are complete; moreover, each leaf of center stable and
+center unstable foliations is a cylinder, a M$\ddot{o}$bius band or a plane.
+Further properties of the Bonatti-Parwani-Potrie type of partially hyperbolic
+diffeomorphisms are studied. Such examples are obtained by composing the time
+$m$-map (for $m>0$ large) of a non-transitive Anosov flow $\phi_t$ on an
+orientable 3-manifold with Dehn twists along some transverse tori, and the
+examples are partially hyperbolic with one-dimensional neutral center. We prove
+that the center foliation gives a topologically Anosov flow which is
+topologically equivalent to $\phi_t$. We also prove that for the precise
+example constructed by Bonatti-Parwani-Potrie, the center stable and center
+unstable foliations are robustly complete.
+",0,0,1,0,0,0
+20814,Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network,"  Speech enhancement (SE) aims to reduce noise in speech signals. Most SE
+techniques focus on addressing audio information only. In this work, inspired
+by multimodal learning, which utilizes data from different modalities, and the
+recent success of convolutional neural networks (CNNs) in SE, we propose an
+audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual
+streams into a unified network model. In the proposed AVDCNN SE model, audio
+and visual data are first processed using individual CNNs, and then, fused into
+a joint network to generate enhanced speech at the output layer. The AVDCNN
+model is trained in an end-to-end manner, and parameters are jointly learned
+through back-propagation. We evaluate enhanced speech using five objective
+criteria. Results show that the AVDCNN yields notably better performance,
+compared with an audio-only CNN-based SE model and two conventional SE
+approaches, confirming the effectiveness of integrating visual information into
+the SE process.
+",1,0,0,1,0,0
+20815,"Finding structure in the dark: coupled dark energy, weak lensing, and the mildly nonlinear regime","  We reexamine interactions between the dark sectors of cosmology, with a focus
+on robust constraints that can be obtained using only mildly nonlinear scales.
+While it is well known that couplings between dark matter and dark energy can
+be constrained to the percent level when including the full range of scales
+probed by future optical surveys, calibrating matter power spectrum emulators
+to all possible choices of potentials and couplings requires many
+computationally expensive n-body simulations. Here we show that lensing and
+clustering of galaxies in combination with the Cosmic Microwave Background
+(CMB) is capable of probing the dark sector coupling to the few percent level
+for a given class of models, using only linear and quasi-linear Fourier modes.
+These scales can, in principle, be described by semi-analytical techniques such
+as the effective field theory of large-scale structure.
+",0,1,0,0,0,0
+20816,Semantic Autoencoder for Zero-Shot Learning,"  Existing zero-shot learning (ZSL) models typically learn a projection
+function from a feature space to a semantic embedding space (e.g.~attribute
+space). However, such a projection function is only concerned with predicting
+the training seen class semantic representation (e.g.~attribute prediction) or
+classification. When applied to test data, which in the context of ZSL contains
+different (unseen) classes without training data, a ZSL model typically suffers
+from the project domain shift problem. In this work, we present a novel
+solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the
+encoder-decoder paradigm, an encoder aims to project a visual feature vector
+into the semantic space as in the existing ZSL models. However, the decoder
+exerts an additional constraint, that is, the projection/code must be able to
+reconstruct the original visual feature. We show that with this additional
+reconstruction constraint, the learned projection function from the seen
+classes is able to generalise better to the new unseen classes. Importantly,
+the encoder and decoder are linear and symmetric which enable us to develop an
+extremely efficient learning algorithm. Extensive experiments on six benchmark
+datasets demonstrate that the proposed SAE outperforms significantly the
+existing ZSL models with the additional benefit of lower computational cost.
+Furthermore, when the SAE is applied to supervised clustering problem, it also
+beats the state-of-the-art.
+",1,0,0,0,0,0
+20817,An Information-Theoretic Optimality Principle for Deep Reinforcement Learning,"  We methodologically address the problem of Q-value overestimation in deep
+reinforcement learning to handle high-dimensional state spaces efficiently. By
+adapting concepts from information theory, we introduce an intrinsic penalty
+signal encouraging reduced Q-value estimates. The resultant algorithm
+encompasses a wide range of learning outcomes containing deep Q-networks as a
+special case. Different learning outcomes can be demonstrated by tuning a
+Lagrange multiplier accordingly. We furthermore propose a novel scheduling
+scheme for this Lagrange multiplier to ensure efficient and robust learning. In
+experiments on Atari, our algorithm outperforms other algorithms (e.g. deep and
+double deep Q-networks) in terms of both game-play performance and sample
+complexity. These results remain valid under the recently proposed dueling
+architecture.
+",1,0,0,1,0,0
+20818,Noise-synchronizability of opinion dynamics,"  With the analysis of noise-induced synchronization of opinion dynamics with
+bounded confidence (BC), a natural and fundamental question is what opinion
+structures can be synchronized by noise. In the traditional Hegselmann-Krause
+(HK) model, each agent examines the opinion values of all the other ones and
+then choose neighbors to update its own opinion according to the BC scheme. In
+reality, people are more likely to interchange opinions with only some
+individuals, resulting in a predetermined local discourse relationship as
+introduced by the DeGroot model. In this paper, we consider an opinion dynamics
+that combines the schemes of BC and local discourse topology and investigate
+its synchronization induced by noise. The new model endows the heterogeneous HK
+model with a time-varying discourse topology. With the proposed definition of
+noise-synchronizability, it is shown that the compound noisy model is almost
+surely noise-synchronizable if and only if the time-varying discourse graph is
+uniformly jointly connected, taking the noise-induced synchronization of the
+classical heterogeneous HK model as a special case. As a natural implication,
+the result for the first time builds the equivalence between the connectivity
+of discourse graph and the beneficial effect of noise for opinion consensus.
+",1,0,0,0,0,0
+20819,Conformer-selection by matter-wave interference,"  We establish that matter-wave interference at near-resonant ultraviolet
+optical gratings can be used to spatially separate individual conformers of
+complex molecules. Our calculations show that the conformational purity of the
+prepared beam can be close to 100% and that all molecules remain in their
+electronic ground state. The proposed technique is independent of the dipole
+moment and the spin of the molecule and thus paves the way for
+structure-sensitive experiments with hydrocarbons and biomolecules, such as
+neurotransmitters and hormones, which evaded conformer-pure isolation so far
+",0,1,0,0,0,0
+20820,Building a Neural Machine Translation System Using Only Synthetic Parallel Data,"  Recent works have shown that synthetic parallel data automatically generated
+by translation models can be effective for various neural machine translation
+(NMT) issues. In this study, we build NMT systems using only synthetic parallel
+data. As an efficient alternative to real parallel data, we also present a new
+type of synthetic parallel corpus. The proposed pseudo parallel data are
+distinct from previous works in that ground truth and synthetic examples are
+mixed on both sides of sentence pairs. Experiments on Czech-German and
+French-German translations demonstrate the efficacy of the proposed pseudo
+parallel corpus, which shows not only enhanced results for bidirectional
+translation tasks but also substantial improvement with the aid of a ground
+truth real parallel corpus.
+",1,0,0,0,0,0
+20821,Learning to Predict Indoor Illumination from a Single Image,"  We propose an automatic method to infer high dynamic range illumination from
+a single, limited field-of-view, low dynamic range photograph of an indoor
+scene. In contrast to previous work that relies on specialized image capture,
+user input, and/or simple scene models, we train an end-to-end deep neural
+network that directly regresses a limited field-of-view photo to HDR
+illumination, without strong assumptions on scene geometry, material
+properties, or lighting. We show that this can be accomplished in a three step
+process: 1) we train a robust lighting classifier to automatically annotate the
+location of light sources in a large dataset of LDR environment maps, 2) we use
+these annotations to train a deep neural network that predicts the location of
+lights in a scene from a single limited field-of-view photo, and 3) we
+fine-tune this network using a small dataset of HDR environment maps to predict
+light intensities. This allows us to automatically recover high-quality HDR
+illumination estimates that significantly outperform previous state-of-the-art
+methods. Consequently, using our illumination estimates for applications like
+3D object insertion, we can achieve results that are photo-realistic, which is
+validated via a perceptual user study.
+",1,0,0,1,0,0
+20822,$H^\infty$-calculus for semigroup generators on BMO,"  We prove that the negative infinitesimal generator $L$ of a semigroup of
+positive contractions on $L^\infty$ has a bounded $H^\infty(S_\eta^0)$-calculus
+on BMO$(\sqrt L)$ for any angle $\eta>\pi/2$, provided the semigroup satisfies
+Bakry-Emry's $\Gamma_2 $ criterion. Our arguments only rely on the properties
+of the underlying semigroup and works well in the noncommutative setting. A key
+ingredient of our argument is a quasi monotone property for the subordinated
+semigroup $T_{t,\alpha}=e^{-tL^\alpha},0<\alpha<1$, that is proved in the first
+half of the article.
+",0,0,1,0,0,0
+20823,A Bayesian Game without epsilon equilibria,"  We present a three player Bayesian game for which there is no epsilon
+equilibria in Borel measurable strategies for small enough epsilon, however
+there are non-measurable equilibria.
+",1,0,1,0,0,0
+20824,Towards Robust Interpretability with Self-Explaining Neural Networks,"  Most recent work on interpretability of complex machine learning models has
+focused on estimating $\textit{a posteriori}$ explanations for previously
+trained models around specific predictions. $\textit{Self-explaining}$ models
+where interpretability plays a key role already during learning have received
+much less attention. We propose three desiderata for explanations in general --
+explicitness, faithfulness, and stability -- and show that existing methods do
+not satisfy them. In response, we design self-explaining models in stages,
+progressively generalizing linear classifiers to complex yet architecturally
+explicit models. Faithfulness and stability are enforced via regularization
+specifically tailored to such models. Experimental results across various
+benchmark datasets show that our framework offers a promising direction for
+reconciling model complexity and interpretability.
+",0,0,0,1,0,0
+20825,"Deep Learning Scaling is Predictable, Empirically","  Deep learning (DL) creates impactful advances following a virtuous recipe:
+model architecture search, creating large training data sets, and scaling
+computation. It is widely believed that growing training sets and models should
+improve accuracy and result in better products. As DL application domains grow,
+we would like a deeper understanding of the relationships between training set
+size, computational scale, and model accuracy improvements to advance the
+state-of-the-art.
+This paper presents a large scale empirical characterization of
+generalization error and model size growth as training sets grow. We introduce
+a methodology for this measurement and test four machine learning domains:
+machine translation, language modeling, image processing, and speech
+recognition. Our empirical results show power-law generalization error scaling
+across a breadth of factors, resulting in power-law exponents---the ""steepness""
+of the learning curve---yet to be explained by theoretical work. Further, model
+improvements only shift the error but do not appear to affect the power-law
+exponent. We also show that model size scales sublinearly with data size. These
+scaling relationships have significant implications on deep learning research,
+practice, and systems. They can assist model debugging, setting accuracy
+targets, and decisions about data set growth. They can also guide computing
+system design and underscore the importance of continued computational scaling.
+",1,0,0,1,0,0
+20826,Coupled Self-Organized Hydrodynamics and Stokes models for suspensions of active particles,"  We derive macroscopic dynamics for self-propelled particles in a fluid. The
+starting point is a coupled Vicsek-Stokes system. The Vicsek model describes
+self-propelled agents interacting through alignment. It provides a
+phenomenological description of hydrodynamic interactions between agents at
+high density. Stokes equations describe a low Reynolds number fluid. These two
+dynamics are coupled by the interaction between the agents and the fluid. The
+fluid contributes to rotating the particles through Jeffery's equation.
+Particle self-propulsion induces a force dipole on the fluid. After
+coarse-graining we obtain a coupled Self-Organised Hydrodynamics (SOH)-Stokes
+system. We perform a linear stability analysis for this system which shows that
+both pullers and pushers have unstable modes. We conclude by providing
+extensions of the Vicsek-Stokes model including short-distance repulsion,
+finite particle inertia and finite Reynolds number fluid regime.
+",0,1,1,0,0,0
+20827,Tunable Spin-Orbit Torques in Cu-Ta Binary Alloy Heterostructures,"  The spin Hall effect (SHE) is found to be strong in heavy transition metals
+(HM), such as Ta and W, in their amorphous and/or high resistivity form. In
+this work, we show that by employing a Cu-Ta binary alloy as buffer layer in an
+amorphous Cu$_{100-x}$Ta$_{x}$-based magnetic heterostructure with
+perpendicular magnetic anisotropy (PMA), the SHE-induced damping-like
+spin-orbit torque (DL-SOT) efficiency $|\xi_{DL}|$ can be linearly tuned by
+adjusting the buffer layer resistivity. Current-induced SOT switching can also
+be achieved in these Cu$_{100-x}$Ta$_{x}$-based magnetic heterostructures, and
+we find the switching behavior better explained by a SOT-assisted domain wall
+propagation picture. Through systematic studies on Cu$_{100-x}$Ta$_{x}$-based
+samples with various compositions, we determine the lower bound of spin Hall
+conductivity
+$|\sigma_{SH}|\approx2.02\times10^{4}[\hbar/2e]\Omega^{-1}\cdot\operatorname{m}^{-1}$
+in the Ta-rich regime. Based on the idea of resistivity tuning, we further
+demonstrate that $|\xi_{DL}|$ can be enhanced from 0.087 for pure Ta to 0.152
+by employing a resistive TaN buffer layer.
+",0,1,0,0,0,0
+20828,On Properties of Nests: Some Answers and Questions,"  By considering nests on a given space, we explore order-theoretical and
+topological properties that are closely related to the structure of a nest. In
+particular, we see how subbases given by two dual nests can be an indicator of
+how close or far are the properties of the space from the structure of a
+linearly ordered space. Having in mind that the term interlocking nest is a key
+tool to a general solution of the orderability problem, we give a
+characterization of interlocking nest via closed sets in the Alexandroff
+topology and via lower sets, respectively. We also characterize bounded subsets
+of a given set in terms of nests and, finally, we explore the possibility of
+characterizing topological groups via properties of nests. All sections are
+followed by a number of open questions, which may give new directions to the
+orderability problem.
+",0,0,1,0,0,0
+20829,Differential Characters of Drinfeld Modules and de Rham Cohomology,"  We introduce differential characters of Drinfeld modules. These are
+function-field analogues of Buium's p-adic differential characters of elliptic
+curves and of Manin's differential characters of elliptic curves in
+differential algebra, both of which have had notable Diophantine applications.
+We determine the structure of the group of differential characters. This shows
+the existence of a family of interesting differential modular functions on the
+moduli of Drinfeld modules. It also leads to a canonical $F$-crystal equipped
+with a map to the de Rham cohomology of the Drinfeld module. This $F$-crystal
+is of a differential-algebraic nature, and the relation to the classical
+cohomological realizations is presently not clear.
+",0,0,1,0,0,0
+20830,Strong submeasures and several applications,"  A strong submeasure on a compact metric space X is a sub-linear and bounded
+operator on the space of continuous functions on X. A strong submeasure is
+positive if it is non-decreasing. By Hahn-Banach theorem, a positive strong
+submeasure is the supremum of a non-empty collection of measures whose masses
+are uniformly bounded from above.
+We give several applications of strong submeasures in various diverse topics,
+thus illustrate the usefulness of this classical but largely overlooked notion.
+The applications include:
+- Pullback and pushforward of all measures by meromorphic selfmaps of compact
+complex varieties.
+- The existence of invariant positive strong submeasures for meromorphic maps
+between compact complex varieties, a notion of entropy for such submeasures
+(which coincide with the classical ones in good cases) and a version of the
+Variation Principle.
+- Intersection of every positive closed (1,1) currents on compact Kähler
+manifolds. Explicit calculations are given for self-intersection of the current
+of integration of some curves $C$ in a compact Kähler surface where the
+self-intersection in cohomology is negative.
+All of these points are new and have not been previously given in work by
+other authors. In addition, we will apply the same ideas to entropy of
+transcendental maps of $\mathbb{C}$ and $\mathbb{C}^2$.
+",0,0,1,0,0,0
+20831,Understanding Career Progression in Baseball Through Machine Learning,"  Professional baseball players are increasingly guaranteed expensive long-term
+contracts, with over 70 deals signed in excess of \$90 million, mostly in the
+last decade. These are substantial sums compared to a typical franchise
+valuation of \$1-2 billion. Hence, the players to whom a team chooses to give
+such a contract can have an enormous impact on both competitiveness and profit.
+Despite this, most published approaches examining career progression in
+baseball are fairly simplistic. We applied four machine learning algorithms to
+the problem and soundly improved upon existing approaches, particularly for
+batting data.
+",1,0,0,1,0,0
+20832,Testing the validity of the local and global GKLS master equations on an exactly solvable model,"  When deriving a master equation for a multipartite weakly-interacting open
+quantum systems, dissipation is often addressed \textit{locally} on each
+component, i.e. ignoring the coherent couplings, which are later added `by
+hand'. Although simple, the resulting local master equation (LME) is known to
+be thermodynamically inconsistent. Otherwise, one may always obtain a
+consistent \textit{global} master equation (GME) by working on the energy basis
+of the full interacting Hamiltonian. Here, we consider a two-node `quantum
+wire' connected to two heat baths. The stationary solution of the LME and GME
+are obtained and benchmarked against the exact result. Importantly, in our
+model, the validity of the GME is constrained by the underlying secular
+approximation. Whenever this breaks down (for resonant weakly-coupled nodes),
+we observe that the LME, in spite of being thermodynamically flawed: (a)
+predicts the correct steady state, (b) yields the exact asymptotic heat
+currents, and (c) reliably reflects the correlations between the nodes. In
+contrast, the GME fails at all three tasks. Nonetheless, as the inter-node
+coupling grows, the LME breaks down whilst the GME becomes correct. Hence, the
+global and local approach may be viewed as \textit{complementary} tools, best
+suited to different parameter regimes.
+",0,1,0,0,0,0
+20833,Unsupervised Contact Learning for Humanoid Estimation and Control,"  This work presents a method for contact state estimation using fuzzy
+clustering to learn contact probability for full, six-dimensional humanoid
+contacts. The data required for training is solely from proprioceptive sensors
+- endeffector contact wrench sensors and inertial measurement units (IMUs) -
+and the method is completely unsupervised. The resulting cluster means are used
+to efficiently compute the probability of contact in each of the six
+endeffector degrees of freedom (DoFs) independently. This clustering-based
+contact probability estimator is validated in a kinematics-based base state
+estimator in a simulation environment with realistic added sensor noise for
+locomotion over rough, low-friction terrain on which the robot is subject to
+foot slip and rotation. The proposed base state estimator which utilizes these
+six DoF contact probability estimates is shown to perform considerably better
+than that which determines kinematic contact constraints purely based on
+measured normal force.
+",1,0,0,0,0,0
+20834,Learning to Sequence Robot Behaviors for Visual Navigation,"  Recent literature in the robotics community has focused on learning robot
+behaviors that abstract out lower-level details of robot control. To fully
+leverage the efficacy of such behaviors, it is necessary to select and sequence
+them to achieve a given task. In this paper, we present an approach to both
+learn and sequence robot behaviors, applied to the problem of visual navigation
+of mobile robots. We construct a layered representation of control policies
+composed of low- level behaviors and a meta-level policy. The low-level
+behaviors enable the robot to locomote in a particular environment while
+avoiding obstacles, and the meta-level policy actively selects the low-level
+behavior most appropriate for the current situation based purely on visual
+feedback. We demonstrate the effectiveness of our method on three simulated
+robot navigation tasks: a legged hexapod robot which must successfully traverse
+varying terrain, a wheeled robot which must navigate a maze-like course while
+avoiding obstacles, and finally a wheeled robot navigating in the presence of
+dynamic obstacles. We show that by learning control policies in a layered
+manner, we gain the ability to successfully traverse new compound environments
+composed of distinct sub-environments, and outperform both the low-level
+behaviors in their respective sub-environments, as well as a hand-crafted
+selection of low-level policies on these compound environments.
+",1,0,0,0,0,0
+20835,"Evaluation complexity bounds for smooth constrained nonlinear optimisation using scaled KKT conditions, high-order models and the criticality measure $χ$","  Evaluation complexity for convexly constrained optimization is considered and
+it is shown first that the complexity bound of $O(\epsilon^{-3/2})$ proved by
+Cartis, Gould and Toint (IMAJNA 32(4) 2012, pp.1662-1695) for computing an
+$\epsilon$-approximate first-order critical point can be obtained under
+significantly weaker assumptions. Moreover, the result is generalized to the
+case where high-order derivatives are used, resulting in a bound of
+$O(\epsilon^{-(p+1)/p})$ evaluations whenever derivatives of order $p$ are
+available. It is also shown that the bound of
+$O(\epsilon_P^{-1/2}\epsilon_D^{-3/2})$ evaluations ($\epsilon_P$ and
+$\epsilon_D$ being primal and dual accuracy thresholds) suggested by Cartis,
+Gould and Toint (SINUM, 2015) for the general nonconvex case involving both
+equality and inequality constraints can be generalized to a bound of
+$O(\epsilon_P^{-1/p}\epsilon_D^{-(p+1)/p})$ evaluations under similarly
+weakened assumptions. This paper is variant of a companion report (NTR-11-2015,
+University of Namur, Belgium) which uses a different first-order criticality
+measure to obtain the same complexity bounds.
+",1,0,1,0,0,0
+20836,One level density of low-lying zeros of quadratic and quartic Hecke $L$-functions,"  In this paper, we prove some one level density results for the low-lying
+zeros of famliies of quadratic and quartic Hecke $L$-functions of the Gaussian
+field. As corollaries, we deduce that, respectively, at least $94.27 \%$ and
+$5\%$ of the members of the quadratic family and the quartic family do not
+vanish at the central point.
+",0,0,1,0,0,0
+20837,An Online Convex Optimization Approach to Dynamic Network Resource Allocation,"  Existing approaches to online convex optimization (OCO) make sequential
+one-slot-ahead decisions, which lead to (possibly adversarial) losses that
+drive subsequent decision iterates. Their performance is evaluated by the
+so-called regret that measures the difference of losses between the online
+solution and the best yet fixed overall solution in hindsight. The present
+paper deals with online convex optimization involving adversarial loss
+functions and adversarial constraints, where the constraints are revealed after
+making decisions, and can be tolerable to instantaneous violations but must be
+satisfied in the long term. Performance of an online algorithm in this setting
+is assessed by: i) the difference of its losses relative to the best dynamic
+solution with one-slot-ahead information of the loss function and the
+constraint (that is here termed dynamic regret); and, ii) the accumulated
+amount of constraint violations (that is here termed dynamic fit). In this
+context, a modified online saddle-point (MOSP) scheme is developed, and proved
+to simultaneously yield sub-linear dynamic regret and fit, provided that the
+accumulated variations of per-slot minimizers and constraints are sub-linearly
+growing with time. MOSP is also applied to the dynamic network resource
+allocation task, and it is compared with the well-known stochastic dual
+gradient method. Under various scenarios, numerical experiments demonstrate the
+performance gain of MOSP relative to the state-of-the-art.
+",1,0,1,1,0,0
+20838,Learning causal Bayes networks using interventional path queries in polynomial time and sample complexity,"  Causal discovery from empirical data is a fundamental problem in many
+scientific domains. Observational data allows for identifiability only up to
+Markov equivalence class. In this paper we first propose a polynomial time
+algorithm for learning the exact correctly-oriented structure of the transitive
+reduction of any causal Bayesian networks with high probability, by using
+interventional path queries. Each path query takes as input an origin node and
+a target node, and answers whether there is a directed path from the origin to
+the target. This is done by intervening the origin node and observing samples
+from the target node. We theoretically show the logarithmic sample complexity
+for the size of interventional data per path query, for continuous and discrete
+networks. We further extend our work to learn the transitive edges using
+logarithmic sample complexity (albeit in time exponential in the maximum number
+of parents for discrete networks). This allows us to learn the full network. We
+also provide an analysis of imperfect interventions.
+",1,0,0,1,0,0
+20839,The Fredholm alternative for the $p$-Laplacian in exterior domains,"  We investigate the Fredholm alternative for the $p$-Laplacian in an exterior
+domain which is the complement of the closed unit ball in $\mathbb{R}^N$
+($N\geq 2$). By employing techniques of Calculus of Variations we obtain the
+multiplicity of solutions. The striking difference between our case and the
+entire space case is also discussed.
+",0,0,1,0,0,0
+20840,Thermodynamic Stabilization of Precipitates through Interface Segregation: Chemical Effects,"  Precipitation hardening, which relies on a high density of intermetallic
+precipitates, is a commonly utilized technique for strengthening structural
+alloys. Structural alloys are commonly strengthened through a high density of
+small size intermetallic precipitates. At high temperatures, however, the
+precipitates coarsen to reduce the excess energy of the interface, resulting in
+a significant reduction in the strengthening provided by the precipitates. In
+certain ternary alloys, the secondary solute segregates to the interface and
+results in the formation of a high density of nanosize precipitates that
+provide enhanced strength and are resistant to coarsening. To understand the
+chemical effects involved, and to identify such systems, we develop a
+thermodynamic model using the framework of the regular nanocrystalline solution
+model. For various global compositions, temperatures and thermodynamic
+parameters, equilibrium configuration of Mg-Sn-Zn alloy is evaluated by
+minimizing the Gibbs free energy function with respect to the region-specific
+(bulk solid-solution, interface and precipitate) concentrations and sizes. The
+results show that Mg$_2$Sn precipitates can be stabilized to nanoscale sizes
+through Zn segregation to Mg/Mg$_2$Sn interface, and the precipitates can be
+stabilized against coarsening at high-temperatures by providing a larger Zn
+concentration in the system. Together with the inclusion of elastic strain
+energy effects and the input of computationally informed interface
+thermodynamic parameters in the future, the model is expected to provide a more
+realistic prediction of segregation and precipitate stabilization in ternary
+alloys of structural importance.
+",0,1,0,0,0,0
+20841,Wavelength Does Not Equal Pressure: Vertical Contribution Functions and their Implications for Mapping Hot Jupiters,"  Multi-band phase variations in principle allow us to infer the longitudinal
+temperature distributions of planets as a function of height in their
+atmospheres. For example, 3.6 micron emission originates from deeper layers of
+the atmosphere than 4.5 micron due to greater water vapor absorption at the
+longer wavelength. Since heat transport efficiency increases with pressure, we
+expect thermal phase curves at 3.6 micron to exhibit smaller amplitudes and
+greater phase offsets than at 4.5 micron; this trend is not observed. Of the
+seven hot Jupiters with full-orbit phase curves at 3.6 and 4.5 micron, all have
+greater phase amplitude at 3.6 micron than at 4.5 micron, while four of seven
+exhibit a greater phase offset at 3.6 micron. We use a 3D
+radiative-hydrodynamic model to calculate theoretical phase curves of HD
+189733b, assuming thermo-chemical equilibrium. The model exhibits temperature,
+pressure, and wavelength dependent opacity, primarily driven by carbon
+chemistry: CO is energetically favored on the dayside, while CH4 is favored on
+the cooler nightside. Infrared opacity therefore changes by orders of magnitude
+between day and night, producing dramatic vertical shifts in the
+wavelength-specific photospheres, which would complicate eclipse or phase
+mapping with spectral data. The model predicts greater relative phase amplitude
+and greater phase offset at 3.6 micron than at 4.5 micron, in agreement with
+the data. Our model qualitatively explains the observed phase curves, but is in
+tension with current thermo-chemical kinetics models that predict zonally
+uniform atmospheric composition due to transport of CO from the hot regions of
+the atmosphere.
+",0,1,0,0,0,0
+20842,Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction,"  We introduce a new algorithm, called CDER, for supervised machine learning
+that merges the multi-scale geometric properties of Cover Trees with the
+information-theoretic properties of entropy. CDER applies to a training set of
+labeled pointclouds embedded in a common Euclidean space. If typical
+pointclouds corresponding to distinct labels tend to differ at any scale in any
+sub-region, CDER can identify these differences in (typically) linear time,
+creating a set of distributional coordinates which act as a feature extraction
+mechanism for supervised learning. We describe theoretical properties and
+implementation details of CDER, and illustrate its benefits on several
+synthetic examples.
+",1,0,0,1,0,0
+20843,Design Considerations for Proposed Fermilab Integrable RCS,"  Integrable optics is an innovation in particle accelerator design that
+provides strong nonlinear focusing while avoiding parametric resonances. One
+promising application of integrable optics is to overcome the traditional
+limits on accelerator intensity imposed by betatron tune-spread and collective
+instabilities. The efficacy of high-intensity integrable accelerators will be
+undergo comprehensive testing over the next several years at the Fermilab
+Integrable Optics Test Accelerator (IOTA) and the University of Maryland
+Electron Ring (UMER). We propose an integrable Rapid-Cycling Synchrotron (iRCS)
+as a replacement for the Fermilab Booster to achieve multi-MW beam power for
+the Fermilab high-energy neutrino program. We provide a overview of the machine
+parameters and discuss an approach to lattice optimization. Integrable optics
+requires arcs with integer-pi phase advance followed by drifts with matched
+beta functions. We provide an example integrable lattice with features of a
+modern RCS - long dispersion-free drifts, low momentum compaction,
+superperiodicity, chromaticity correction, separate-function magnets, and
+bounded beta functions.
+",0,1,0,0,0,0
+20844,Consistent Estimation in General Sublinear Preferential Attachment Trees,"  We propose an empirical estimator of the preferential attachment function $f$
+in the setting of general preferential attachment trees. Using a supercritical
+continuous-time branching process framework, we prove the almost sure
+consistency of the proposed estimator. We perform simulations to study the
+empirical properties of our estimators.
+",0,0,1,1,0,0
+20845,Learned Watershed: End-to-End Learning of Seeded Segmentation,"  Learned boundary maps are known to outperform hand- crafted ones as a basis
+for the watershed algorithm. We show, for the first time, how to train
+watershed computation jointly with boundary map prediction. The estimator for
+the merging priorities is cast as a neural network that is con- volutional
+(over space) and recurrent (over iterations). The latter allows learning of
+complex shape priors. The method gives the best known seeded segmentation
+results on the CREMI segmentation challenge.
+",1,0,0,0,0,0
+20846,MAGIC Contributions to the 35th International Cosmic Ray Conference (ICRC2017),"  MAGIC (Major Atmospheric Gamma Imaging Cherenkov) is a system of two 17 m
+diameter, F/1.03 Imaging Atmospheric Cherenkov Telescopes (IACT). They are
+dedicated to the observation of gamma rays from galactic and extragalactic
+sources in the very high energy range (VHE, 30 GeV to 100 TeV). This submission
+contains links to the proceedings for the 35th International Cosmic Ray
+Conference (ICRC2017), held in Bexco, Busan, Korea from the 12th to the 17th of
+July, 2017.
+",0,1,0,0,0,0
+20847,Efficient Hidden Vector Encryptions and Its Applications,"  Predicate encryption is a new paradigm of public key encryption that enables
+searches on encrypted data. Using the predicate encryption, we can search
+keywords or attributes on encrypted data without decrypting the ciphertexts. In
+predicate encryption, a ciphertext is associated with attributes and a token
+corresponds to a predicate. The token that corresponds to a predicate $f$ can
+decrypt the ciphertext associated with attributes $x$ if and only if $f(x)=1$.
+Hidden vector encryption (HVE) is a special kind of predicate encryption. In
+this thesis, we consider the efficiency, the generality, and the security of
+HVE schemes. The results of this thesis are described as follows.
+The first results of this thesis are efficient HVE schemes where the token
+consists of just four group elements and the decryption only requires four
+bilinear map computations, independent of the number of attributes in the
+ciphertext. The construction uses composite order bilinear groups and is
+selectively secure under the well-known assumptions. The second results are
+efficient HVE schemes that are secure under any kind of pairing types. To
+achieve our goals, we proposed a general framework that converts HVE schemes
+from composite order bilinear groups to prime order bilinear groups. Using the
+framework, we convert the previous HVE schemes from composite order bilinear
+groups to prime order bilinear groups. The third results are fully secure HVE
+schemes with short tokens. Previous HVE schemes were proven to be secure only
+in the selective security model where the capabilities of the adversaries are
+severely restricted. Using the dual system encryption techniques, we construct
+fully secure HVE schemes with match revealing property in composite order
+groups.
+",1,0,0,0,0,0
+20848,The Galaxy Clustering Crisis in Abundance Matching,"  Galaxy clustering on small scales is significantly under-predicted by
+sub-halo abundance matching (SHAM) models that populate (sub-)haloes with
+galaxies based on peak halo mass, $M_{\rm peak}$. SHAM models based on the peak
+maximum circular velocity, $V_{\rm peak}$, have had much better success. The
+primary reason $M_{\rm peak}$ based models fail is the relatively low abundance
+of satellite galaxies produced in these models compared to those based on
+$V_{\rm peak}$. Despite success in predicting clustering, a simple $V_{\rm
+peak}$ based SHAM model results in predictions for galaxy growth that are at
+odds with observations. We evaluate three possible remedies that could ""save""
+mass-based SHAM: (1) SHAM models require a significant population of ""orphan""
+galaxies as a result of artificial disruption/merging of sub-haloes in modern
+high resolution dark matter simulations; (2) satellites must grow significantly
+after their accretion; and (3) stellar mass is significantly affected by halo
+assembly history. No solution is entirely satisfactory. However, regardless of
+the particulars, we show that popular SHAM models based on $M_{\rm peak}$
+cannot be complete physical models as presented. Either $V_{\rm peak}$ truly is
+a better predictor of stellar mass at $z\sim 0$ and it remains to be seen how
+the correlation between stellar mass and $V_{\rm peak}$ comes about, or SHAM
+models are missing vital component(s) that significantly affect galaxy
+clustering.
+",0,1,0,0,0,0
+20849,Supplying Dark Energy from Scalar Field Dark Matter,"  We consider the hypothesis that dark matter and dark energy consists of
+ultra-light self-interacting scalar particles. It is found that the
+Klein-Gordon equation with only two free parameters (mass and self-coupling) on
+a Schwarzschild background, at the galactic length-scales has the solution
+which corresponds to Bose-Einstein condensate, behaving as dark matter, while
+the constant solution at supra-galactic scales can explain dark energy.
+",0,1,0,0,0,0
+20850,GHz-Band Integrated Magnetic Inductors,"  The demand on mobile electronics to continue to shrink in size while increase
+in efficiency drives the demand on the internal passive components to do the
+same. Power amplifiers require inductors with small form factors, high quality
+factors, and high operating frequency in the single-digit GHz range. This work
+explores the use of magnetic materials to satisfy the needs of power amplifier
+inductor applications. This paper discusses the optimization choices regarding
+material selection, device design, and fabrication methodology. The inductors
+achieved here present the best performance to date for an integrated magnetic
+core inductor at high frequencies with a 1 nH inductance and peak quality
+factor of 4 at ~3 GHz. Such compact inductors show potential for efficiently
+meeting the need of mobile electronics in the future.
+",0,1,0,0,0,0
+20851,Acylindrical actions on projection complexes,"  We simplify the construction of projection complexes due to
+Bestvina-Bromberg-Fujiwara. To do so, we introduce a sharper version of the
+Behrstock inequality, and show that it can always be enforced. Furthermore, we
+use the new setup to prove acylindricity results for the action on the
+projection complexes. We also treat quasi-trees of metric spaces associated to
+projection complexes, and prove an acylindricity criterion in that context as
+well.
+",0,0,1,0,0,0
+20852,Introducing Geometric Algebra to Geometric Computing Software Developers: A Computational Thinking Approach,"  Designing software systems for Geometric Computing applications can be a
+challenging task. Software engineers typically use software abstractions to
+hide and manage the high complexity of such systems. Without the presence of a
+unifying algebraic system to describe geometric models, the use of software
+abstractions alone can result in many design and maintenance problems.
+Geometric Algebra (GA) can be a universal abstract algebraic language for
+software engineering geometric computing applications. Few sources, however,
+provide enough information about GA-based software implementations targeting
+the software engineering community. In particular, successfully introducing GA
+to software engineers requires quite different approaches from introducing GA
+to mathematicians or physicists. This article provides a high-level
+introduction to the abstract concepts and algebraic representations behind the
+elegant GA mathematical structure. The article focuses on the conceptual and
+representational abstraction levels behind GA mathematics with sufficient
+references for more details. In addition, the article strongly recommends
+applying the methods of Computational Thinking in both introducing GA to
+software engineers, and in using GA as a mathematical language for developing
+Geometric Computing software systems.
+",1,0,0,0,0,0
+20853,Recovering Nonuniform Planted Partitions via Iterated Projection,"  In the planted partition problem, the $n$ vertices of a random graph are
+partitioned into $k$ ""clusters,"" and edges between vertices in the same cluster
+and different clusters are included with constant probability $p$ and $q$,
+respectively (where $0 \le q < p \le 1$). We give an efficient spectral
+algorithm that recovers the clusters with high probability, provided that the
+sizes of any two clusters are either very close or separated by $\geq
+\Omega(\sqrt n)$. We also discuss a generalization of planted partition in
+which the algorithm's input is not a random graph, but a random real symmetric
+matrix with independent above-diagonal entries.
+Our algorithm is an adaptation of a previous algorithm for the uniform case,
+i.e., when all clusters are size $n / k \geq \Omega(\sqrt n)$. The original
+algorithm recovers the clusters one by one via iterated projection: it
+constructs the orthogonal projection operator onto the dominant $k$-dimensional
+eigenspace of the random graph's adjacency matrix, uses it to recover one of
+the clusters, then deletes it and recurses on the remaining vertices. We show
+herein that a similar algorithm works in the nonuniform case.
+",1,0,0,0,0,0
+20854,Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory,"  Business Architecture (BA) plays a significant role in helping organizations
+understand enterprise structures and processes, and align them with strategic
+objectives. However, traditional BAs are represented in fixed structure with
+static model elements and fail to dynamically capture business insights based
+on internal and external data. To solve this problem, this paper introduces the
+graph theory into BAs with aim of building extensible data-driven analytics and
+automatically generating business insights. We use IBM's Component Business
+Model (CBM) as an example to illustrate various ways in which graph theory can
+be leveraged for data-driven analytics, including what and how business
+insights can be obtained. Future directions for applying graph theory to
+business architecture analytics are discussed.
+",0,0,0,1,0,0
+20855,Beyond Sparsity: Tree Regularization of Deep Models for Interpretability,"  The lack of interpretability remains a key barrier to the adoption of deep
+models in many applications. In this work, we explicitly regularize deep models
+so human users might step through the process behind their predictions in
+little time. Specifically, we train deep time-series models so their
+class-probability predictions have high accuracy while being closely modeled by
+decision trees with few nodes. Using intuitive toy examples as well as medical
+tasks for treating sepsis and HIV, we demonstrate that this new tree
+regularization yields models that are easier for humans to simulate than
+simpler L1 or L2 penalties without sacrificing predictive power.
+",1,0,0,1,0,0
+20856,Phase-type distributions in population genetics,"  Probability modelling for DNA sequence evolution is well established and
+provides a rich framework for understanding genetic variation between samples
+of individuals from one or more populations. We show that both classical and
+more recent models for coalescence (with or without recombination) can be
+described in terms of the so-called phase-type theory, where complicated and
+tedious calculations are circumvented by the use of matrices. The application
+of phase-type theory consists of describing the stochastic model as a Markov
+model by appropriately setting up a state space and calculating the
+corresponding intensity and reward matrices. Formulae of interest are then
+expressed in terms of these aforementioned matrices. We illustrate this by a
+few examples calculating the mean, variance and even higher order moments of
+the site frequency spectrum in the multiple merger coalescent models, and by
+analysing the mean and variance for the number of segregating sites for
+multiple samples in the two-locus ancestral recombination graph. We believe
+that phase-type theory has great potential as a tool for analysing probability
+models in population genetics. The compact matrix notation is useful for
+clarification of current models, in particular their formal manipulation
+(calculation), but also for further development or extensions.
+",0,0,0,1,1,0
+20857,Robust Wald-type test in GLM with random design based on minimum density power divergence estimators,"  We consider the problem of robust inference under the important generalized
+linear model (GLM) with stochastic covariates. We derive the properties of the
+minimum density power divergence estimator of the parameters in GLM with random
+design and used this estimator to propose a robust Wald-type test for testing
+any general composite null hypothesis about the GLM. The asymptotic and
+robustness properties of the proposed test are also examined for the GLM with
+random design. Application of the proposed robust inference procedures to the
+popular Poisson regression model for analyzing count data is discussed in
+detail both theoretically and numerically with some interesting real data
+examples.
+",0,0,0,1,0,0
+20858,Locally Smoothed Neural Networks,"  Convolutional Neural Networks (CNN) and the locally connected layer are
+limited in capturing the importance and relations of different local receptive
+fields, which are often crucial for tasks such as face verification, visual
+question answering, and word sequence prediction. To tackle the issue, we
+propose a novel locally smoothed neural network (LSNN) in this paper. The main
+idea is to represent the weight matrix of the locally connected layer as the
+product of the kernel and the smoother, where the kernel is shared over
+different local receptive fields, and the smoother is for determining the
+importance and relations of different local receptive fields. Specifically, a
+multi-variate Gaussian function is utilized to generate the smoother, for
+modeling the location relations among different local receptive fields.
+Furthermore, the content information can also be leveraged by setting the mean
+and precision of the Gaussian function according to the content. Experiments on
+some variant of MNIST clearly show our advantages over CNN and locally
+connected layer.
+",1,0,0,1,0,0
+20859,Asymptotic properties of a componentwise ARH(1) plug-in predictor,"  This paper presents new results on prediction of linear processes in function
+spaces. The autoregressive Hilbertian process framework of order one (ARH(1)
+process framework) is adopted. A componentwise estimator of the autocorrelation
+operator is formulated, from the moment-based estimation of its diagonal
+coefficients, with respect to the orthogonal eigenvectors of the
+auto-covariance operator, which are assumed to be known. Mean-square
+convergence to the theoretical autocorrelation operator, in the space of
+Hilbert-Schmidt operators, is proved. Consistency then follows in that space.
+For the associated ARH(1) plug-in predictor, mean absolute convergence to the
+corresponding conditional expectation, in the considered Hilbert space, is
+obtained. Hence, consistency in that space also holds. A simulation study is
+undertaken to illustrate the finite-large sample behavior of the formulated
+componentwise estimator and predictor. The performance of the presented
+approach is compared with alternative approaches in the previous and current
+ARH(1) framework literature, including the case of unknown eigenvectors.
+",0,0,1,1,0,0
+20860,The Effect of Population Control Policies on Societal Fragmentation,"  Population control policies are proposed and in some places employed as a
+means towards curbing population growth. This paper is concerned with a
+disturbing side-effect of such policies, namely, the potential risk of societal
+fragmentation due to changes in the distribution of family sizes. This effect
+is illustrated in some simple settings and demonstrated by simulation. In
+addition, the dependence of societal fragmentation on family size distribution
+is analyzed. In particular, it is shown that under the studied model, any
+population control policy that disallows families of 3 or more children incurs
+the possible risk of societal fragmentation.
+",1,1,0,0,0,0
+20861,Optimizing Beam Transport in Rapidly Compressing Beams on the Neutralized Drift Compression Experiment - II,"  The Neutralized Drift Compression Experiment-II (NDCX-II) is an induction
+linac that generates intense pulses of 1.2 MeV helium ions for heating matter
+to extreme conditions. Here, we present recent results on optimizing beam
+transport. The NDCX-II beamline includes a 1-meter-long drift section
+downstream of the last transport solenoid, which is filled with
+charge-neutralizing plasma that enables rapid longitudinal compression of an
+intense ion beam against space-charge forces. The transport section on NDCX-II
+consists of 28 solenoids. Finding optimal field settings for a group of
+solenoids requires knowledge of the envelope parameters of the beam. Imaging
+the beam on scintillator gives the radius of the beam, but the envelope angle
+dr/dz is not measured directly. We demonstrate how the parameters of the beam
+envelope (r, dr/dz, and emittance) can be reconstructed from a series of images
+taken at varying B-field strengths of a solenoid upstream of the scintillator.
+We use this technique to evaluate emittance at several points in the NDCX-II
+beamline and for optimizing the trajectory of the beam at the entry of the
+plasma-filled drift section.
+",0,1,0,0,0,0
+20862,Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations,"  Many structured prediction problems (particularly in vision and language
+domains) are ambiguous, with multiple outputs being correct for an input - e.g.
+there are many ways of describing an image, multiple ways of translating a
+sentence; however, exhaustively annotating the applicability of all possible
+outputs is intractable due to exponentially large output spaces (e.g. all
+English sentences). In practice, these problems are cast as multi-class
+prediction, with the likelihood of only a sparse set of annotations being
+maximized - unfortunately penalizing for placing beliefs on plausible but
+unannotated outputs. We make and test the following hypothesis - for a given
+input, the annotations of its neighbors may serve as an additional supervisory
+signal. Specifically, we propose an objective that transfers supervision from
+neighboring examples. We first study the properties of our developed method in
+a controlled toy setup before reporting results on multi-label classification
+and two image-grounded sequence modeling tasks - captioning and question
+generation. We evaluate using standard task-specific metrics and measures of
+output diversity, finding consistent improvements over standard maximum
+likelihood training and other baselines.
+",0,0,0,1,0,0
+20863,Direct and indirect seismic inversion: interpretation of certain mathematical theorems,"  Quantitative methods are more familiar to most geophysicists with direct
+inversion or indirect inversion. We will discuss seismic inversion in a high
+level sense without getting into the actual algorithms. We will stay with
+meta-equations and argue pros and cons based on certain mathematical theorems.
+",0,1,0,0,0,0
+20864,Kan's combinatorial spectra and their sheaves revisited,"  We define a right Cartan-Eilenberg structure on the category of Kan's
+combinatorial spectra, and the category of sheaves of such spectra, assuming
+some conditions. In both structures, we use the geometric concept of homotopy
+equivalence as the strong equivalence. In the case of sheaves, we use local
+equivalence as the weak equivalence. This paper is the first step in a
+larger-scale program of investigating sheaves of spectra from a geometric
+viewpoint.
+",0,0,1,0,0,0
+20865,Adiabatic Quantum Computing for Binary Clustering,"  Quantum computing for machine learning attracts increasing attention and
+recent technological developments suggest that especially adiabatic quantum
+computing may soon be of practical interest. In this paper, we therefore
+consider this paradigm and discuss how to adopt it to the problem of binary
+clustering. Numerical simulations demonstrate the feasibility of our approach
+and illustrate how systems of qubits adiabatically evolve towards a solution.
+",0,0,0,1,0,0
+20866,Similarity Search Over Graphs Using Localized Spectral Analysis,"  This paper provides a new similarity detection algorithm. Given an input set
+of multi-dimensional data points, where each data point is assumed to be
+multi-dimensional, and an additional reference data point for similarity
+finding, the algorithm uses kernel method that embeds the data points into a
+low dimensional manifold. Unlike other kernel methods, which consider the
+entire data for the embedding, our method selects a specific set of kernel
+eigenvectors. The eigenvectors are chosen to separate between the data points
+and the reference data point so that similar data points can be easily
+identified as being distinct from most of the members in the dataset.
+",1,0,0,0,0,0
+20867,"Reminiscences of Julian Schwinger: Late Harvard, Early UCLA Years (1968-1981)","  These are reminiscences of my interactions with Julian Schwinger from 1968
+through 1981 and beyond.
+",0,1,0,0,0,0
+20868,Site-resolved imaging of a bosonic Mott insulator using ytterbium atoms,"  We demonstrate site-resolved imaging of a strongly correlated quantum system
+without relying on laser-cooling techniques during fluorescence imaging. We
+observed the formation of Mott shells in the insulating regime and realized
+thermometry on the atomic cloud. This work proves the feasibility of the
+noncooled approach and opens the door to extending the detection technology to
+new atomic species.
+",0,1,0,0,0,0
+20869,"A unified continuum and variational multiscale formulation for fluids, solids, and fluid-structure interaction","  We develop a unified continuum modeling framework for viscous fluids and
+hyperelastic solids using the Gibbs free energy as the thermodynamic potential.
+This framework naturally leads to a pressure primitive variable formulation for
+the continuum body, which is well-behaved in both compressible and
+incompressible regimes. Our derivation also provides a rational justification
+of the isochoric-volumetric additive split of free energies in nonlinear
+continuum mechanics. The variational multiscale analysis is performed for the
+continuum model to construct a foundation for numerical discretization. We
+first consider the continuum body instantiated as a hyperelastic material and
+develop a variational multiscale formulation for the hyper-elastodynamic
+problem. The generalized-alpha method is applied for temporal discretization. A
+segregated algorithm for the nonlinear solver is designed and carefully
+analyzed. Second, we apply the new formulation to construct a novel unified
+formulation for fluid-solid coupled problems. The variational multiscale
+formulation is utilized for spatial discretization in both fluid and solid
+subdomains. The generalized-alpha method is applied for the whole continuum
+body, and optimal high-frequency dissipation is achieved in both fluid and
+solid subproblems. A new predictor multi-corrector algorithm is developed based
+on the segregated algorithm to attain a good balance between robustness and
+efficiency. The efficacy of the new formulations is examined in several
+benchmark problems. The results indicate that the proposed modeling and
+numerical methodologies constitute a promising technology for biomedical and
+engineering applications, particularly those necessitating incompressible
+models.
+",0,1,0,0,0,0
+20870,Time Assignment System and Its Performance aboard the Hitomi Satellite,"  Fast timing capability in X-ray observation of astrophysical objects is one
+of the key properties for the ASTRO-H (Hitomi) mission. Absolute timing
+accuracies of 350 micro second or 35 micro second are required to achieve
+nominal scientific goals or to study fast variabilities of specific sources.
+The satellite carries a GPS receiver to obtain accurate time information, which
+is distributed from the central onboard computer through the large and complex
+SpaceWire network. The details on the time system on the hardware and software
+design are described. In the distribution of the time information, the
+propagation delays and jitters affect the timing accuracy. Six other items
+identified within the timing system will also contribute to absolute time
+error. These error items have been measured and checked on ground to ensure the
+time error budgets meet the mission requirements. The overall timing
+performance in combination with hardware performance, software algorithm, and
+the orbital determination accuracies, etc, under nominal conditions satisfies
+the mission requirements of 35 micro second. This work demonstrates key points
+for space-use instruments in hardware and software designs and calibration
+measurements for fine timing accuracy on the order of microseconds for
+mid-sized satellites using the SpaceWire (IEEE1355) network.
+",0,1,0,0,0,0
+20871,Common fixed points via $λ$-sequences in $G$-metric spaces,"  In this article, we use $\lambda$-sequences to derive common fixed points for
+a family of self-mappings defined on a complete $G$-metric space. We imitate
+some existing techniques in our proofs and show that the tools emlyed can be
+used at a larger scale. These results generalise well known results in the
+literature.
+",0,0,1,0,0,0
+20872,Bidirectional Nested Weighted Automata,"  Nested weighted automata (NWA) present a robust and convenient
+automata-theoretic formalism for quantitative specifications. Previous works
+have considered NWA that processed input words only in the forward direction.
+It is natural to allow the automata to process input words backwards as well,
+for example, to measure the maximal or average time between a response and the
+preceding request. We therefore introduce and study bidirectional NWA that can
+process input words in both directions. First, we show that bidirectional NWA
+can express interesting quantitative properties that are not expressible by
+forward-only NWA. Second, for the fundamental decision problems of emptiness
+and universality, we establish decidability and complexity results for the new
+framework which match the best-known results for the special case of
+forward-only NWA. Thus, for NWA, the increased expressiveness of
+bidirectionality is achieved at no additional computational complexity. This is
+in stark contrast to the unweighted case, where bidirectional finite automata
+are no more expressive but exponentially more succinct than their forward-only
+counterparts.
+",1,0,0,0,0,0
+20873,Uniform Shapiro-Lopatinski conditions and boundary value problems on manifolds with bounded geometry,"  We study the regularity of the solutions of second order boundary value
+problems on manifolds with boundary and bounded geometry. We first show that
+the regularity property of a given boundary value problem $(P, C)$ is
+equivalent to the uniform regularity of the natural family $(P_x, C_x)$ of
+associated boundary value problems in local coordinates. We verify that this
+property is satisfied for the Dirichlet boundary conditions and strongly
+elliptic operators via a compactness argument. We then introduce a uniform
+Shapiro-Lopatinski regularity condition, which is a modification of the
+classical one, and we prove that it characterizes the boundary value problems
+that satisfy the usual regularity property. We also show that the natural Robin
+boundary conditions always satisfy the uniform Shapiro-Lopatinski regularity
+condition, provided that our operator satisfies the strong Legendre condition.
+This is achieved by proving that ""well-posedness implies regularity"" via a
+modification of the classical ""Nirenberg trick"". When combining our regularity
+results with the Poincaré inequality of (Ammann-Grosse-Nistor, preprint
+2015), one obtains the usual well-posedness results for the classical boundary
+value problems in the usual scale of Sobolev spaces, thus extending these
+important, well-known theorems from smooth, bounded domains, to manifolds with
+boundary and bounded geometry. As we show in several examples, these results do
+not hold true anymore if one drops the bounded geometry assumption. We also
+introduce a uniform Agmon condition and show that it is equivalent to the
+coerciveness. Consequently, we prove a well-posedness result for parabolic
+equations whose elliptic generator satisfies the uniform Agmon condition.
+",0,0,1,0,0,0
+20874,A Fourier-Chebyshev Spectral Method for Cavitation Computation in Nonlinear Elasticity,"  A Fourier-Chebyshev spectral method is proposed in this paper for solving the
+cavitation problem in nonlinear elasticity. The interpolation error for the
+cavitation solution is analyzed, the elastic energy error estimate for the
+discrete cavitation solution is obtained, and the convergence of the method is
+proved. An algorithm combined a gradient type method with a damped quasi-Newton
+method is applied to solve the discretized nonlinear equilibrium equations.
+Numerical experiments show that the Fourier-Chebyshev spectral method is
+efficient and capable of producing accurate numerical cavitation solutions.
+",0,0,1,0,0,0
+20875,Learning Criticality in an Embodied Boltzmann Machine,"  Many biological and cognitive systems do not operate deep into one or other
+regime of activity. Instead, they exploit critical surfaces poised at
+transitions in their parameter space. The pervasiveness of criticality in
+natural systems suggests that there may be general principles inducing this
+behaviour. However, there is a lack of conceptual models explaining how
+embodied agents propel themselves towards these critical points. In this paper,
+we present a learning model driving an embodied Boltzmann Machine towards
+critical behaviour by maximizing the heat capacity of the network. We test and
+corroborate the model implementing an embodied agent in the mountain car
+benchmark, controlled by a Boltzmann Machine that adjust its weights according
+to the model. We find that the neural controller reaches a point of
+criticality, which coincides with a transition point of the behaviour of the
+agent between two regimes of behaviour, maximizing the synergistic information
+between its sensors and the hidden and motor neurons. Finally, we discuss the
+potential of our learning model to study the contribution of criticality to the
+behaviour of embodied living systems in scenarios not necessarily constrained
+by biological restrictions of the examples of criticality we find in nature.
+",1,1,0,0,0,0
+20876,A contemporary look at Hermann Hankel's 1861 pioneering work on Lagrangian fluid dynamics,"  The present paper is a companion to the paper by Villone and Rampf (2017),
+titled ""Hermann Hankel's On the general theory of motion of fluids, an essay
+including an English translation of the complete Preisschrift from 1861""
+together with connected documents. Here we give a critical assessment of
+Hankel's work, which covers many important aspects of fluid dynamics considered
+from a Lagrangian-coordinates point of view: variational formulation in the
+spirit of Hamilton for elastic (barotropic) fluids, transport (we would now say
+Lie transport) of vorticity, the Lagrangian significance of Clebsch variables,
+etc. Hankel's work is also put in the perspective of previous and future work.
+Hence, the action spans about two centuries: from Lagrange's 1760-1761 Turin
+paper on variational approaches to mechanics and fluid mechanics problems to
+Arnold's 1966 founding paper on the geometrical/variational formulation of
+incompressible flow. The 22-year old Hankel - who was to die 12 years later -
+emerges as a highly innovative master of mathematical fluid dynamics, fully
+deserving Riemann's assessment that his Preisschrift contains ""all manner of
+good things.""
+",0,1,1,0,0,0
+20877,A Dynamic Edge Exchangeable Model for Sparse Temporal Networks,"  We propose a dynamic edge exchangeable network model that can capture sparse
+connections observed in real temporal networks, in contrast to existing models
+which are dense. The model achieved superior link prediction accuracy on
+multiple data sets when compared to a dynamic variant of the blockmodel, and is
+able to extract interpretable time-varying community structures from the data.
+In addition to sparsity, the model accounts for the effect of social influence
+on vertices' future behaviours. Compared to the dynamic blockmodels, our model
+has a smaller latent space. The compact latent space requires a smaller number
+of parameters to be estimated in variational inference and results in a
+computationally friendly inference algorithm.
+",0,0,0,1,0,0
+20878,Current-mode Memristor Crossbars for Neuromemristive Systems,"  Motivated by advantages of current-mode design, this brief contribution
+explores the implementation of weight matrices in neuromemristive systems via
+current-mode memristor crossbar circuits. After deriving theoretical results
+for the range and distribution of weights in the current-mode design, it is
+shown that any weight matrix based on voltage-mode crossbars can be mapped to a
+current-mode crossbar if the voltage-mode weights are carefully bounded. Then,
+a modified gradient descent rule is derived for the current-mode design that
+can be used to perform backpropagation training. Behavioral simulations on the
+MNIST dataset indicate that both voltage and current-mode designs are able to
+achieve similar accuracy and have similar defect tolerance. However, analysis
+of trained weight distributions reveals that current-mode and voltage-mode
+designs may use different feature representations.
+",1,0,0,1,0,0
+20879,Incomplete Gauss sums modulo primes,"  We obtain a new bound for incomplete Gauss sums modulo primes. Our argument
+falls under the framework of Vinogradov's method which we use to reduce the
+problem under consideration to bounding the number of solutions to two distinct
+systems of congruences. The first is related to Vinogradov's mean value
+theorem, although the second does not appear to have been considered before.
+Our bound improves on current results in the range $N\ge
+q^{2k^{-1/2}+O(k^{-3/2})}$.
+",0,0,1,0,0,0
+20880,FAST Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI,"  Functional Magnetic Resonance Imaging is a noninvasive tool used to study
+brain function. Detecting activation is challenged by many factors, and even
+more so in low-signal scenarios that arise in the performance of high-level
+cognitive tasks. We provide a fully automated and fast adaptive smoothing and
+thresholding (FAST) algorithm that uses smoothing and extreme value theory on
+correlated statistical parametric maps for thresholding. Performance on
+experiments spanning a range of low-signal settings is very encouraging. The
+methodology also performs well in a study to identify the cerebral regions that
+perceive only-auditory-reliable and only-visual-reliable speech stimuli as well
+as those that perceive one but not the other.
+",0,0,1,1,0,0
+20881,Improper multiferroicity and colossal dielectric constants in Bi$_{2}$CuO$_{4}$,"  The layered cuprate Bi$_{2}$CuO$_{4}$ is investigated using magnetic,
+dielectric and pyroelectric measurements. This system is observed to be an
+improper multiferroic, with a robust ferroelectric state being established near
+the magnetic transition. Magnetic and dielectric measurements indicate the
+presence of a region above the antiferromagnetic Neel temperature with
+concomitant polar and magnetic short range order. Bi$_{2}$CuO$_{4}$ is also
+seen to exhibit colossal dielectric constants at higher temperatures with
+clearly distinguishable grain and grain boundary contributions, both of which
+exhibit non-Debye relaxation.
+",0,1,0,0,0,0
+20882,Suppression of plasma echoes and Landau damping in Sobolev spaces by weak collisions in a Vlasov-Fokker-Planck equation,"  In this paper, we study Landau damping in the weakly collisional limit of a
+Vlasov-Fokker-Planck equation with nonlinear collisions in the phase-space
+$(x,v) \in \mathbb T_x^n \times \mathbb R^n_v$. The goal is four-fold: (A) to
+understand how collisions suppress plasma echoes and enable Landau damping in
+agreement with linearized theory in Sobolev spaces, (B) to understand how phase
+mixing accelerates collisional relaxation, (C) to understand better how the
+plasma returns to global equilibrium during Landau damping, and (D) to rule out
+that collision-driven nonlinear instabilities dominate. We give an estimate for
+the scaling law between Knudsen number and the maximal size of the perturbation
+necessary for linear theory to be accurate in Sobolev regularity. We conjecture
+this scaling to be sharp (up to logarithmic corrections) due to potential
+nonlinear echoes in the collisionless model.
+",0,0,1,0,0,0
+20883,Deep Learning for Semantic Segmentation on Minimal Hardware,"  Deep learning has revolutionised many fields, but it is still challenging to
+transfer its success to small mobile robots with minimal hardware.
+Specifically, some work has been done to this effect in the RoboCup humanoid
+football domain, but results that are performant and efficient and still
+generally applicable outside of this domain are lacking. We propose an approach
+conceptually different from those taken previously. It is based on semantic
+segmentation and does achieve these desired properties. In detail, it is being
+able to process full VGA images in real-time on a low-power mobile processor.
+It can further handle multiple image dimensions without retraining, it does not
+require specific domain knowledge for achieving a high frame rate and it is
+applicable on a minimal mobile hardware.
+",1,0,0,1,0,0
+20884,"Overpartition $M2$-rank differences, class number relations, and vector-valued mock Eisenstein series","  We prove that the generating function of overpartition $M2$-rank differences
+is, up to coefficient signs, a component of the vector-valued mock Eisenstein
+series attached to a certain quadratic form. We use this to compute analogs of
+the class number relations for $M2$-rank differences. As applications we split
+the Kronecker-Hurwitz relation into its ""even"" and ""odd"" parts and calculate
+sums over Hurwitz class numbers of the form $\sum_{r \in \mathbb{Z}} H(n -
+2r^2)$.
+",0,0,1,0,0,0
+20885,Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph,"  We study the decentralized machine learning scenario where many users
+collaborate to learn personalized models based on (i) their local datasets and
+(ii) a similarity graph over the users' learning tasks. Our approach trains
+nonlinear classifiers in a multi-task boosting manner without exchanging
+personal data and with low communication costs. When background knowledge about
+task similarities is not available, we propose to jointly learn the
+personalized models and a sparse collaboration graph through an alternating
+optimization procedure. We analyze the convergence rate, memory consumption and
+communication complexity of our decentralized algorithms, and demonstrate the
+benefits of our approach compared to competing techniques on synthetic and real
+datasets.
+",1,0,0,1,0,0
+20886,On the well-posedness of SPDEs with singular drift in divergence form,"  We prove existence and uniqueness of strong solutions for a class of
+second-order stochastic PDEs with multiplicative Wiener noise and drift of the
+form $\operatorname{div} \gamma(\nabla \cdot)$, where $\gamma$ is a maximal
+monotone graph in $\mathbb{R}^n \times \mathbb{R}^n$ obtained as the
+subdifferential of a convex function satisfying very mild assumptions on its
+behavior at infinity. The well-posedness result complements the corresponding
+one in our recent work arXiv:1612.08260 where, under the additional assumption
+that $\gamma$ is single-valued, a solution with better integrability and
+regularity properties is constructed. The proof given here, however, is
+self-contained.
+",0,0,1,0,0,0
+20887,Trace norm regularization and faster inference for embedded speech recognition RNNs,"  We propose and evaluate new techniques for compressing and speeding up dense
+matrix multiplications as found in the fully connected and recurrent layers of
+neural networks for embedded large vocabulary continuous speech recognition
+(LVCSR). For compression, we introduce and study a trace norm regularization
+technique for training low rank factored versions of matrix multiplications.
+Compared to standard low rank training, we show that our method leads to good
+accuracy versus number of parameter trade-offs and can be used to speed up
+training of large models. For speedup, we enable faster inference on ARM
+processors through new open sourced kernels optimized for small batch sizes,
+resulting in 3x to 7x speed ups over the widely used gemmlowp library. Beyond
+LVCSR, we expect our techniques and kernels to be more generally applicable to
+embedded neural networks with large fully connected or recurrent layers.
+",1,0,0,1,0,0
+20888,Scalable and Robust Sparse Subspace Clustering Using Randomized Clustering and Multilayer Graphs,"  Sparse subspace clustering (SSC) is one of the current state-of-the-art
+methods for partitioning data points into the union of subspaces, with strong
+theoretical guarantees. However, it is not practical for large data sets as it
+requires solving a LASSO problem for each data point, where the number of
+variables in each LASSO problem is the number of data points. To improve the
+scalability of SSC, we propose to select a few sets of anchor points using a
+randomized hierarchical clustering method, and, for each set of anchor points,
+solve the LASSO problems for each data point allowing only anchor points to
+have a non-zero weight (this reduces drastically the number of variables). This
+generates a multilayer graph where each layer corresponds to a different set of
+anchor points. Using the Grassmann manifold of orthogonal matrices, the shared
+connectivity among the layers is summarized within a single subspace. Finally,
+we use $k$-means clustering within that subspace to cluster the data points,
+similarly as done by spectral clustering in SSC. We show on both synthetic and
+real-world data sets that the proposed method not only allows SSC to scale to
+large-scale data sets, but that it is also much more robust as it performs
+significantly better on noisy data and on data with close susbspaces and
+outliers, while it is not prone to oversegmentation.
+",0,0,0,1,0,0
+20889,Deep Health Care Text Classification,"  Health related social media mining is a valuable apparatus for the early
+recognition of the diverse antagonistic medicinal conditions. Mostly, the
+existing methods are based on machine learning with knowledge-based learning.
+This working note presents the Recurrent neural network (RNN) and Long
+short-term memory (LSTM) based embedding for automatic health text
+classification in the social media mining. For each task, two systems are built
+and that classify the tweet at the tweet level. RNN and LSTM are used for
+extracting features and non-linear activation function at the last layer
+facilitates to distinguish the tweets of different categories. The experiments
+are conducted on 2nd Social Media Mining for Health Applications Shared Task at
+AMIA 2017. The experiment results are considerable; however the proposed method
+is appropriate for the health text classification. This is primarily due to the
+reason that, it doesn't rely on any feature engineering mechanisms.
+",1,0,0,0,0,0
+20890,"Study of cost functionals for ptychographic phase retrieval to improve the robustness against noise, and a proposal for another noise-robust ptychographic phase retrieval scheme","  Recently, efforts have been made to improve ptychography phase retrieval
+algorithms so that they are more robust against noise. Often the algorithm is
+adapted by changing the cost functional that needs to be minimized. In
+particular, it has been suggested that the cost functional should be obtained
+using a maximum-likelihood approach that takes the noise statistics into
+account. Here, we consider the different choices of cost functional, and to how
+they affect the reconstruction results. We find that seemingly the only
+consistently reliable way to improve reconstruction results in the presence of
+noise is to reduce the step size of the update function. In addition, a
+noise-robust ptychographic reconstruction method has been proposed that relies
+on adapting the intensity constraints
+",1,1,0,0,0,0
+20891,Predicting Native Language from Gaze,"  A fundamental question in language learning concerns the role of a speaker's
+first language in second language acquisition. We present a novel methodology
+for studying this question: analysis of eye-movement patterns in second
+language reading of free-form text. Using this methodology, we demonstrate for
+the first time that the native language of English learners can be predicted
+from their gaze fixations when reading English. We provide analysis of
+classifier uncertainty and learned features, which indicates that differences
+in English reading are likely to be rooted in linguistic divergences across
+native languages. The presented framework complements production studies and
+offers new ground for advancing research on multilingualism.
+",1,0,0,0,0,0
+20892,A form of Schwarz's lemma and a bound for the Kobayashi metric on convex domains,"  We present a form of Schwarz's lemma for holomorphic maps between convex
+domains $D_1$ and $D_2$. This result provides a lower bound on the distance
+between the images of relatively compact subsets of $D_1$ and the boundary of
+$D_2$. This is a natural improvement of an old estimate by Bernal-González
+that takes into account the geometry of $\partial{D_1}$. We also provide a new
+estimate for the Kobayashi metric on bounded convex domains.
+",0,0,1,0,0,0
+20893,The COM-negative binomial distribution: modeling overdispersion and ultrahigh zero-inflated count data,"  In this paper, we focus on the COM-type negative binomial distribution with
+three parameters, which belongs to COM-type $(a,b,0)$ class distributions and
+family of equilibrium distributions of arbitrary birth-death process. Besides,
+we show abundant distributional properties such as overdispersion and
+underdispersion, log-concavity, log-convexity (infinite divisibility), pseudo
+compound Poisson, stochastic ordering and asymptotic approximation. Some
+characterizations including sum of equicorrelated geometrically distributed
+random variables, conditional distribution, limit distribution of COM-negative
+hypergeometric distribution, and Stein's identity are given for theoretical
+properties. COM-negative binomial distribution was applied to overdispersion
+and ultrahigh zero-inflated data sets. With the aid of ratio regression, we
+employ maximum likelihood method to estimate the parameters and the
+goodness-of-fit are evaluated by the discrete Kolmogorov-Smirnov test.
+",0,0,1,1,0,0
+20894,Efficient Contextual Bandits in Non-stationary Worlds,"  Most contextual bandit algorithms minimize regret against the best fixed
+policy, a questionable benchmark for non-stationary environments that are
+ubiquitous in applications. In this work, we develop several efficient
+contextual bandit algorithms for non-stationary environments by equipping
+existing methods for i.i.d. problems with sophisticated statistical tests so as
+to dynamically adapt to a change in distribution.
+We analyze various standard notions of regret suited to non-stationary
+environments for these algorithms, including interval regret, switching regret,
+and dynamic regret. When competing with the best policy at each time, one of
+our algorithms achieves regret $\mathcal{O}(\sqrt{ST})$ if there are $T$ rounds
+with $S$ stationary periods, or more generally
+$\mathcal{O}(\Delta^{1/3}T^{2/3})$ where $\Delta$ is some non-stationarity
+measure. These results almost match the optimal guarantees achieved by an
+inefficient baseline that is a variant of the classic Exp4 algorithm. The
+dynamic regret result is also the first one for efficient and fully adversarial
+contextual bandit.
+Furthermore, while the results above require tuning a parameter based on the
+unknown quantity $S$ or $\Delta$, we also develop a parameter free algorithm
+achieving regret $\min\{S^{1/4}T^{3/4}, \Delta^{1/5}T^{4/5}\}$. This improves
+and generalizes the best existing result $\Delta^{0.18}T^{0.82}$ by Karnin and
+Anava (2016) which only holds for the two-armed bandit problem.
+",1,0,0,1,0,0
+20895,Search Engine Drives the Evolution of Social Networks,"  The search engine is tightly coupled with social networks and is primarily
+designed for users to acquire interested information. Specifically, the search
+engine assists the information dissemination for social networks, i.e.,
+enabling users to access interested contents with keywords-searching and
+promoting the process of contents-transferring from the source users directly
+to potential interested users. Accompanying such processes, the social network
+evolves as new links emerge between users with common interests. However, there
+is no clear understanding of such a ""chicken-and-egg"" problem, namely, new
+links encourage more social interactions, and vice versa. In this paper, we aim
+to quantitatively characterize the social network evolution phenomenon driven
+by a search engine. First, we propose a search network model for social network
+evolution. Second, we adopt two performance metrics, namely, degree
+distribution and network diameter. Theoretically, we prove that the degree
+distribution follows an intensified power-law, and the network diameter
+shrinks. Third, we quantitatively show that the search engine accelerates the
+rumor propagation in social networks. Finally, based on four real-world data
+sets (i.e., CDBLP, Facebook, Weibo Tweets, P2P), we verify our theoretical
+findings. Furthermore, we find that the search engine dramatically increases
+the speed of rumor propagation.
+",1,1,0,0,0,0
+20896,End-to-End Learning of Semantic Grasping,"  We consider the task of semantic robotic grasping, in which a robot picks up
+an object of a user-specified class using only monocular images. Inspired by
+the two-stream hypothesis of visual reasoning, we present a semantic grasping
+framework that learns object detection, classification, and grasp planning in
+an end-to-end fashion. A ""ventral stream"" recognizes object class while a
+""dorsal stream"" simultaneously interprets the geometric relationships necessary
+to execute successful grasps. We leverage the autonomous data collection
+capabilities of robots to obtain a large self-supervised dataset for training
+the dorsal stream, and use semi-supervised label propagation to train the
+ventral stream with only a modest amount of human supervision. We
+experimentally show that our approach improves upon grasping systems whose
+components are not learned end-to-end, including a baseline method that uses
+bounding box detection. Furthermore, we show that jointly training our model
+with auxiliary data consisting of non-semantic grasping data, as well as
+semantically labeled images without grasp actions, has the potential to
+substantially improve semantic grasping performance.
+",1,0,0,1,0,0
+20897,Von Neumann Regular Cellular Automata,"  For any group $G$ and any set $A$, a cellular automaton (CA) is a
+transformation of the configuration space $A^G$ defined via a finite memory set
+and a local function. Let $\text{CA}(G;A)$ be the monoid of all CA over $A^G$.
+In this paper, we investigate a generalisation of the inverse of a CA from the
+semigroup-theoretic perspective. An element $\tau \in \text{CA}(G;A)$ is von
+Neumann regular (or simply regular) if there exists $\sigma \in \text{CA}(G;A)$
+such that $\tau \circ \sigma \circ \tau = \tau$ and $\sigma \circ \tau \circ
+\sigma = \sigma$, where $\circ$ is the composition of functions. Such an
+element $\sigma$ is called a generalised inverse of $\tau$. The monoid
+$\text{CA}(G;A)$ itself is regular if all its elements are regular. We
+establish that $\text{CA}(G;A)$ is regular if and only if $\vert G \vert = 1$
+or $\vert A \vert = 1$, and we characterise all regular elements in
+$\text{CA}(G;A)$ when $G$ and $A$ are both finite. Furthermore, we study
+regular linear CA when $A= V$ is a vector space over a field $\mathbb{F}$; in
+particular, we show that every regular linear CA is invertible when $G$ is
+torsion-free elementary amenable (e.g. when $G=\mathbb{Z}^d, \ d \in
+\mathbb{N}$) and $V=\mathbb{F}$, and that every linear CA is regular when $V$
+is finite-dimensional and $G$ is locally finite with $\text{Char}(\mathbb{F})
+\nmid o(g)$ for all $g \in G$.
+",1,0,1,0,0,0
+20898,A Distance Between Filtered Spaces Via Tripods,"  We present a simplified treatment of stability of filtrations on finite
+spaces. Interestingly, we can lift the stability result for combinatorial
+filtrations from [CSEM06] to the case when two filtrations live on different
+spaces without directly invoking the concept of interleaving. We then prove
+that this distance is intrinsic by constructing explicit geodesics between any
+pair of filtered spaces. Finally we use this construction to obtain a
+strengthening of the stability result.
+",1,0,1,0,0,0
+20899,Robust Shape Estimation for 3D Deformable Object Manipulation,"  Existing shape estimation methods for deformable object manipulation suffer
+from the drawbacks of being off-line, model dependent, noise-sensitive or
+occlusion-sensitive, and thus are not appropriate for manipulation tasks
+requiring high precision. In this paper, we present a real-time shape
+estimation approach for autonomous robotic manipulation of 3D deformable
+objects. Our method fulfills all the requirements necessary for the
+high-quality deformable object manipulation in terms of being real-time,
+model-free and robust to noise and occlusion. These advantages are accomplished
+using a joint tracking and reconstruction framework, in which we track the
+object deformation by aligning a reference shape model with the stream input
+from the RGB-D camera, and simultaneously upgrade the reference shape model
+according to the newly captured RGB-D data. We have evaluated the quality and
+robustness of our real-time shape estimation pipeline on a set of deformable
+manipulation tasks implemented on physical robots. Videos are available at
+this https URL
+",1,0,0,0,0,0
+20900,Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning,"  In recent years, reinforcement learning (RL) methods have been applied to
+model gameplay with great success, achieving super-human performance in various
+environments, such as Atari, Go, and Poker. However, those studies mostly focus
+on winning the game and have largely ignored the rich and complex human
+motivations, which are essential for understanding different players' diverse
+behaviors. In this paper, we present a novel method called Multi-Motivation
+Behavior Modeling (MMBM) that takes the multifaceted human motivations into
+consideration and models the underlying value structure of the players using
+inverse RL. Our approach does not require the access to the dynamic of the
+system, making it feasible to model complex interactive environments such as
+massively multiplayer online games. MMBM is tested on the World of Warcraft
+Avatar History dataset, which recorded over 70,000 users' gameplay spanning
+three years period. Our model reveals the significant difference of value
+structures among different player groups. Using the results of motivation
+modeling, we also predict and explain their diverse gameplay behaviors and
+provide a quantitative assessment of how the redesign of the game environment
+impacts players' behaviors.
+",0,0,0,1,0,0
+20901,Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization,"  In this paper we demonstrate how genetic algorithms can be used to reverse
+engineer an evaluation function's parameters for computer chess. Our results
+show that using an appropriate mentor, we can evolve a program that is on par
+with top tournament-playing chess programs, outperforming a two-time World
+Computer Chess Champion. This performance gain is achieved by evolving a
+program with a smaller number of parameters in its evaluation function to mimic
+the behavior of a superior mentor which uses a more extensive evaluation
+function. In principle, our mentor-assisted approach could be used in a wide
+range of problems for which appropriate mentors are available.
+",1,0,0,1,0,0
+20902,Universal Constraints on the Location of Extrema of Eigenfunctions of Non-Local Schrödinger Operators,"  We derive a lower bound on the location of global extrema of eigenfunctions
+for a large class of non-local Schrödinger operators in convex domains under
+Dirichlet exterior conditions, featuring the symbol of the kinetic term, the
+strength of the potential, and the corresponding eigenvalue, and involving a
+new universal constant. We show a number of probabilistic and spectral
+geometric implications, and derive a Faber-Krahn type inequality for non-local
+operators. Our study also extends to potentials with compact support, and we
+establish bounds on the location of extrema relative to the boundary edge of
+the support or level sets around minima of the potential.
+",0,0,1,0,0,0
+20903,InScript: Narrative texts annotated with script information,"  This paper presents the InScript corpus (Narrative Texts Instantiating Script
+structure). InScript is a corpus of 1,000 stories centered around 10 different
+scenarios. Verbs and noun phrases are annotated with event and participant
+types, respectively. Additionally, the text is annotated with coreference
+information. The corpus shows rich lexical variation and will serve as a unique
+resource for the study of the role of script knowledge in natural language
+processing.
+",1,0,0,0,0,0
+20904,Semimetallic and charge-ordered $α$-(BEDT-TTF)$_2$I$_3$: on the role of disorder in dc transport and dielectric properties,"  $\alpha$-(BEDT-TTF)$_2$I$_3$ is a prominent example of charge ordering among
+organic conductors. In this work we explore the details of transport within the
+charge-ordered as well as semimetallic phase at ambient pressure. In the
+high-temperature semimetallic phase, the mobilities and concentrations of both
+electrons and holes conspire in such a way to create an almost
+temperature-independent conductivity as well as a low Hall effect. We explain
+these phenomena as a consequence of a predominantly inter-pocket scattering
+which equalizes mobilities of the two types of charge carriers. At low
+temperatures, within the insulating charge-ordered phase two channels of
+conduction can be discerned: a temperature-dependent activation which follows
+the mean-field behavior, and a nearest-neighbor hopping contribution. Together
+with negative magnetoresistance, the latter relies on the presence of disorder.
+The charge-ordered phase also features a prominent dielectric peak which bears
+a similarity to relaxor ferroelectrics. Its dispersion is determined by
+free-electron screening and pushed by disorder well below the transition
+temperature. The source of this disorder can be found in the anion layers which
+randomly perturb BEDT-TTF molecules through hydrogen bonds.
+",0,1,0,0,0,0
+20905,Robust Photometric Stereo Using Learned Image and Gradient Dictionaries,"  Photometric stereo is a method for estimating the normal vectors of an object
+from images of the object under varying lighting conditions. Motivated by
+several recent works that extend photometric stereo to more general objects and
+lighting conditions, we study a new robust approach to photometric stereo that
+utilizes dictionary learning. Specifically, we propose and analyze two
+approaches to adaptive dictionary regularization for the photometric stereo
+problem. First, we propose an image preprocessing step that utilizes an
+adaptive dictionary learning model to remove noise and other non-idealities
+from the image dataset before estimating the normal vectors. We also propose an
+alternative model where we directly apply the adaptive dictionary
+regularization to the normal vectors themselves during estimation. We study the
+practical performance of both methods through extensive simulations, which
+demonstrate the state-of-the-art performance of both methods in the presence of
+noise.
+",0,0,0,1,0,0
+20906,Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness,"  We present a micro aerial vehicle (MAV) system, built with inexpensive
+off-the-shelf hardware, for autonomously following trails in unstructured,
+outdoor environments such as forests. The system introduces a deep neural
+network (DNN) called TrailNet for estimating the view orientation and lateral
+offset of the MAV with respect to the trail center. The DNN-based controller
+achieves stable flight without oscillations by avoiding overconfident behavior
+through a loss function that includes both label smoothing and entropy reward.
+In addition to the TrailNet DNN, the system also utilizes vision modules for
+environmental awareness, including another DNN for object detection and a
+visual odometry component for estimating depth for the purpose of low-level
+obstacle detection. All vision systems run in real time on board the MAV via a
+Jetson TX1. We provide details on the hardware and software used, as well as
+implementation details. We present experiments showing the ability of our
+system to navigate forest trails more robustly than previous techniques,
+including autonomous flights of 1 km.
+",1,0,0,0,0,0
+20907,Origin of Charge Separation at Organic Photovoltaic Heterojunctions: A Mesoscale Quantum Mechanical View,"  The high efficiency of charge generation within organic photovoltaic blends
+apparently contrasts with the strong ""classical"" attraction between newly
+formed electron-hole pairs. Several factors have been identified as possible
+facilitators of charge dissociation, such as quantum mechanical coherence and
+delocalization, structural and energetic disorder, built-in electric fields,
+nanoscale intermixing of the donor and acceptor components of the blends. Our
+mesoscale quantum-chemical model allows an unbiased assessment of their
+relative importance, through excited-state calculations on systems containing
+thousands of donor and acceptor sites. The results on several model
+heterojunctions confirm that the classical model severely overestimates the
+binding energy of the electron-hole pairs, produced by vertical excitation from
+the electronic ground state. Using physically sensible parameters for the
+individual materials, we find that the quantum mechanical energy difference
+between the lowest interfacial charge transfer states and the fully separated
+electron and hole is of the order of the thermal energy.
+",0,1,0,0,0,0
+20908,Spectral Decimation for Families of Self-Similar Symmetric Laplacians on the Sierpinski Gasket,"  We construct a one-parameter family of Laplacians on the Sierpinski Gasket
+that are symmetric and self-similar for the 9-map iterated function system
+obtained by iterating the standard 3-map iterated function system. Our main
+result is the fact that all these Laplacians satisfy a version of spectral
+decimation that builds a precise catalog of eigenvalues and eigenfunctions for
+any choice of the parameter. We give a number of applications of this spectral
+decimation. We also prove analogous results for fractal Laplacians on the unit
+Interval, and this yields an analogue of the classical Sturm-Liouville theory
+for the eigenfunctions of these one-dimensional Laplacians.
+",0,0,1,0,0,0
+20909,Identification of multi-object dynamical systems: consistency and Fisher information,"  Learning the model parameters of a multi-object dynamical system from partial
+and perturbed observations is a challenging task. Despite recent numerical
+advancements in learning these parameters, theoretical guarantees are extremely
+scarce. In this article, we study the identifiability of these parameters and
+the consistency of the corresponding maximum likelihood estimate (MLE) under
+assumptions on the different components of the underlying multi-object system.
+In order to understand the impact of the various sources of observation noise
+on the ability to learn the model parameters, we study the asymptotic variance
+of the MLE through the associated Fisher information matrix. For example, we
+show that specific aspects of the multi-target tracking (MTT) problem such as
+detection failures and unknown data association lead to a loss of information
+which is quantified in special cases of interest.
+",0,0,1,1,0,0
+20910,Elliptic curves maximal over extensions of finite base fields,"  Given an elliptic curve $E$ over a finite field $\mathbb{F}_q$ we study the
+finite extensions $\mathbb{F}_{q^n}$ of $\mathbb{F}_q$ such that the number of
+$\mathbb{F}_{q^n}$-rational points on $E$ attains the Hasse upper bound. We
+obtain an upper bound on the degree $n$ for $E$ ordinary using an estimate for
+linear forms in logarithms, which allows us to compute the pairs of isogeny
+classes of such curves and degree $n$ for small $q$. Using a consequence of
+Schmidt's Subspace Theorem, we improve the upper bound to $n\leq 11$ for
+sufficiently large $q$. We also show that there are infinitely many isogeny
+classes of ordinary elliptic curves with $n=3$.
+",0,0,1,0,0,0
+20911,CURE: Curvature Regularization For Missing Data Recovery,"  Missing data recovery is an important and yet challenging problem in imaging
+and data science. Successful models often adopt certain carefully chosen
+regularization. Recently, the low dimension manifold model (LDMM) was
+introduced by S.Osher et al. and shown effective in image inpainting. They
+observed that enforcing low dimensionality on image patch manifold serves as a
+good image regularizer. In this paper, we observe that having only the low
+dimension manifold regularization is not enough sometimes, and we need
+smoothness as well. For that, we introduce a new regularization by combining
+the low dimension manifold regularization with a higher order Curvature
+Regularization, and we call this new regularization CURE for short. The key
+step of solving CURE is to solve a biharmonic equation on a manifold. We
+further introduce a weighted version of CURE, called WeCURE, in a similar
+manner as the weighted nonlocal Laplacian (WNLL) method. Numerical experiments
+for image inpainting and semi-supervised learning show that the proposed CURE
+and WeCURE significantly outperform LDMM and WNLL respectively.
+",1,0,0,0,0,0
+20912,Nonlinear parametric excitation effect induces stability transitions in swimming direction of flexible superparamagnetic microswimmers,"  Microscopic artificial swimmers have recently become highly attractive due to
+their promising potential for biomedical applications. The pioneering work of
+Dreyfus et al (2005) has demonstrated the motion of a microswimmer with an
+undulating chain of superparamagnetic beads, which is actuated by an
+oscillating external magnetic field. Interestingly, it has also been
+theoretically predicted that the swimming direction of this swimmer will
+undergo a $90^\circ$-transition when the magnetic field's oscillations
+amplitude is increased above a critical value of $\sqrt{2}$. In this work, we
+further investigate this transition both theoretically and experimentally by
+using numerical simulations and presenting a novel flexible microswimmer with a
+superparamagnetic head. We realize the $90^\circ$-transition in swimming
+direction, prove that this effect depends on both frequency and amplitude of
+the oscillating magnetic field, and demonstrate the existence of an optimal
+amplitude, under which, maximal swimming speed can be achieved. By
+asymptotically analyzing the dynamic motion of microswimmer with a minimal
+two-link model, we reveal that the stability transitions representing the
+changes in the swimming direction are induced by the effect of nonlinear
+parametric excitation.
+",0,1,0,0,0,0
+20913,$L^p$ Mapping Properties for the Cauchy-Riemann Equations on Lipschitz Domains Admitting Subelliptic Estimates,"  We show that on bounded Lipschitz pseudoconvex domains that admit good weight
+functions the $\overline{\partial}$-Neumann operators $N_q,
+\overline{\partial}^* N_{q}$, and $\overline{\partial} N_{q}$ are bounded on
+$L^p$ spaces for some values of $p$ greater than 2.
+",0,0,1,0,0,0
+20914,Fast dose optimization for rotating shield brachytherapy,"  Purpose: To provide a fast computational method, based on the proximal graph
+solver (POGS) - a convex optimization solver using the alternating direction
+method of multipliers (ADMM), for calculating an optimal treatment plan in
+rotating shield brachytherapy (RSBT). RSBT treatment planning has more degrees
+of freedom than conventional high-dose-rate brachytherapy (HDR-BT) due to the
+addition of emission direction, and this necessitates a fast optimization
+technique to enable clinical usage. // Methods: The multi-helix RSBT (H-RSBT)
+delivery technique was considered with five representative cervical cancer
+patients. Treatment plans were generated for all patients using the POGS method
+and the previously considered commercial solver IBM CPLEX. The rectum, bladder,
+sigmoid, high-risk clinical target volume (HR-CTV), and HR-CTV boundary were
+the structures considered in our optimization problem, called the asymmetric
+dose-volume optimization with smoothness control. Dose calculation resolution
+was 1x1x3 mm^3 for all cases. The H-RSBT applicator has 6 helices, with 33.3 mm
+of translation along the applicator per helical rotation and 1.7 mm spacing
+between dwell positions, yielding 17.5 degree emission angle spacing per 5 mm
+along the applicator.// Results: For each patient, HR-CTV D90, HR-CTV D100,
+rectum D2cc, sigmoid D2cc, and bladder D2cc matched within 1% for CPLEX and
+POGS. Also, we obtained similar EQD2 figures between CPLEX and POGS. POGS was
+around 18 times faster than CPLEX. Over all patients, total optimization times
+were 32.1-65.4 seconds for CPLEX and 2.1-3.9 seconds for POGS. // Conclusions:
+POGS substantially reduced treatment plan optimization time around 18 times for
+RSBT with similar HR-CTV D90, OAR D2cc values, and EQD2 figure relative to
+CPLEX, which is significant progress toward clinical translation of RSBT. POGS
+is also applicable to conventional HDR-BT.
+",0,1,1,0,0,0
+20915,3D Modeling of Electric Fields in the LUX Detector,"  This work details the development of a three-dimensional (3D) electric field
+model for the LUX detector. The detector took data during two periods of
+searching for weakly interacting massive particle (WIMP) searches. After the
+first period completed, a time-varying non-uniform negative charge developed in
+the polytetrafluoroethylene (PTFE) panels that define the radial boundary of
+the detector's active volume. This caused electric field variations in the
+detector in time, depth and azimuth, generating an electrostatic
+radially-inward force on electrons on their way upward to the liquid surface.
+To map this behavior, 3D electric field maps of the detector's active volume
+were built on a monthly basis. This was done by fitting a model built in COMSOL
+Multiphysics to the uniformly distributed calibration data that were collected
+on a regular basis. The modeled average PTFE charge density increased over the
+course of the exposure from -3.6 to $-5.5~\mu$C/m$^2$. From our studies, we
+deduce that the electric field magnitude varied while the mean value of the
+field of $\sim200$~V/cm remained constant throughout the exposure. As a result
+of this work the varying electric fields and their impact on event
+reconstruction and discrimination were successfully modeled.
+",0,1,0,0,0,0
+20916,Optimal Communication Strategies in Networked Cyber-Physical Systems with Adversarial Elements,"  This paper studies optimal communication and coordination strategies in
+cyber-physical systems for both defender and attacker within a game-theoretic
+framework. We model the communication network of a cyber-physical system as a
+sensor network which involves one single Gaussian source observed by many
+sensors, subject to additive independent Gaussian observation noises. The
+sensors communicate with the estimator over a coherent Gaussian multiple access
+channel. The aim of the receiver is to reconstruct the underlying source with
+minimum mean squared error. The scenario of interest here is one where some of
+the sensors are captured by the attacker and they act as the adversary
+(jammer): they strive to maximize distortion. The receiver (estimator) knows
+the captured sensors but still cannot simply ignore them due to the multiple
+access channel, i.e., the outputs of all sensors are summed to generate the
+estimator input. We show that the ability of transmitter sensors to secretly
+agree on a random event, that is ""coordination"", plays a key role in the
+analysis...
+",1,0,0,0,0,0
+20917,First- and Second-Order Models of Recursive Arithmetics,"  We study a quadruple of interrelated subexponential subsystems of arithmetic
+WKL$_0^-$, RCA$^-_0$, I$\Delta_0$, and $\Delta$RA$_1$, which complement the
+similarly related quadruple WKL$_0$, RCA$_0$, I$\Sigma_1$, and PRA studied by
+Simpson, and the quadruple WKL$_0^\ast$, RCA$_0^\ast$, I$\Delta_0$(exp), and
+EFA studied by Simpson and Smith. We then explore the space of subexponential
+arithmetic theories between I$\Delta_0$ and I$\Delta_0$(exp). We introduce and
+study first- and second-order theories of recursive arithmetic $A$RA$_1$ and
+$A$RA$_2$ capable of characterizing various computational complexity classes
+and based on function algebras $A$, studied by Clote and others.
+",1,0,0,0,0,0
+20918,Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks,"  Nowadays, modern earth observation programs produce huge volumes of satellite
+images time series (SITS) that can be useful to monitor geographical areas
+through time. How to efficiently analyze such kind of information is still an
+open question in the remote sensing field. Recently, deep learning methods
+proved suitable to deal with remote sensing data mainly for scene
+classification (i.e. Convolutional Neural Networks - CNNs - on single images)
+while only very few studies exist involving temporal deep learning approaches
+(i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series.
+In this letter we evaluate the ability of Recurrent Neural Networks, in
+particular the Long-Short Term Memory (LSTM) model, to perform land cover
+classification considering multi-temporal spatial data derived from a time
+series of satellite images. We carried out experiments on two different
+datasets considering both pixel-based and object-based classification. The
+obtained results show that Recurrent Neural Networks are competitive compared
+to state-of-the-art classifiers, and may outperform classical approaches in
+presence of low represented and/or highly mixed classes. We also show that
+using the alternative feature representation generated by LSTM can improve the
+performances of standard classifiers.
+",1,0,0,0,0,0
+20919,Disentangling by Factorising,"  We define and address the problem of unsupervised learning of disentangled
+representations on data generated from independent factors of variation. We
+propose FactorVAE, a method that disentangles by encouraging the distribution
+of representations to be factorial and hence independent across the dimensions.
+We show that it improves upon $\beta$-VAE by providing a better trade-off
+between disentanglement and reconstruction quality. Moreover, we highlight the
+problems of a commonly used disentanglement metric and introduce a new metric
+that does not suffer from them.
+",0,0,0,1,0,0
+20920,Symmetries and synchronization in multilayer random networks,"  In the light of the recently proposed scenario of asymmetry-induced
+synchronization (AISync), in which dynamical uniformity and consensus in a
+distributed system would demand certain asymmetries in the underlying network,
+we investigate here the influence of some regularities in the interlayer
+connection patterns on the synchronization properties of multilayer random
+networks. More specifically, by considering a Stuart-Landau model of complex
+oscillators with random frequencies, we report for multilayer networks a
+dynamical behavior that could be also classified as a manifestation of AISync.
+We show, namely, that the presence of certain symmetries in the interlayer
+connection pattern tends to diminish the synchronization capability of the
+whole network or, in other words, asymmetries in the interlayer connections
+would enhance synchronization in such structured networks. Our results might
+help the understanding not only of the AISync mechanism itself, but also its
+possible role in the determination of the interlayer connection pattern of
+multilayer and other structured networks with optimal synchronization
+properties.
+",0,1,0,0,0,0
+20921,Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation,"  As part of autonomous car driving systems, semantic segmentation is an
+essential component to obtain a full understanding of the car's environment.
+One difficulty, that occurs while training neural networks for this purpose, is
+class imbalance of training data. Consequently, a neural network trained on
+unbalanced data in combination with maximum a-posteriori classification may
+easily ignore classes that are rare in terms of their frequency in the dataset.
+However, these classes are often of highest interest. We approach such
+potential misclassifications by weighting the posterior class probabilities
+with the prior class probabilities which in our case are the inverse
+frequencies of the corresponding classes in the training dataset. More
+precisely, we adopt a localized method by computing the priors pixel-wise such
+that the impact can be analyzed at pixel level as well. In our experiments, we
+train one network from scratch using a proprietary dataset containing 20,000
+annotated frames of video sequences recorded from street scenes. The evaluation
+on our test set shows an increase of average recall with regard to instances of
+pedestrians and info signs by $25\%$ and $23.4\%$, respectively. In addition,
+we significantly reduce the non-detection rate for instances of the same
+classes by $61\%$ and $38\%$.
+",1,0,0,1,0,0
+20922,Muchnik degrees and cardinal characteristics,"  We provide a pair of dual results, each stating the coincidence of highness
+properties from computability theory. We provide an analogous pair of dual
+results on the coincidence of cardinal characteristics within ZFC.
+A mass problem is a set of functions on $\omega$. For mass problems $\mathcal
+C, \mathcal D$, one says that $\mathcal C$ is Muchnik reducible to $\mathcal D$
+if each function in $\mathcal D$ computes a function in $\mathcal C$. In this
+paper we view highness properties as mass problems, and compare them with
+respect to Muchnik reducibility and its uniform strengthening, Medvedev
+reducibility.
+Let $\mathcal D(p)$ be the mass problem of infinite bit sequences $y$ (i.e.,
+0,1 valued functions) such that for each computable bit sequence $x$, the
+asymptotic lower density $\underline \rho$ of the agreement bit sequence $x
+\leftrightarrow y$ is at most $p$ (this sequence takes the value 1 at a bit
+position iff $x$ and $y$ agree).
+We show that all members of this family of mass problems parameterized by a
+real $p$ with $0 < p<1/2 $ have the same complexity in the sense of Muchnik
+reducibility. This also yields a new version of Monin's affirmative answer to
+the ""Gamma question"", whether $\Gamma(A)< 1/2$ implies $\Gamma(A)=0$ for each
+Turing oracle $A$.
+We also show, together with Joseph Miller, that for any order function~$g$
+there exists a faster growing order function $h $ such that $\mathrm{IOE}(g) $
+is strictly Muchnik below $\mathrm{IOE}(h)$.
+We study cardinal characteristics analogous to the highness properties above.
+For instance, $\mathfrak d (p)$ is the least size of a set $G$ of bit sequences
+so that for each bit sequence $x$ there is a bit sequence $y$ in $G$ so that
+$\underline \rho (x \leftrightarrow y) >p$. We prove within ZFC all the
+coincidences of cardinal characteristics that are the analogs of the results
+above.
+",0,0,1,0,0,0
+20923,Existence of solutions for a semirelativistic Hartree equation with unbounded potentials,"  We prove the existence of a solution to the semirelativistic Hartree equation
+$$\sqrt{-\Delta+m^2}u+ V(x) u = A(x)\left( W * |u|^p \right) |u|^{p-2}u $$
+under suitable growth assumption on the potential functions $V$ and $A$. In
+particular, both can be unbounded from above.
+",0,0,1,0,0,0
+20924,Lattice Operations on Terms over Similar Signatures,"  Unification and generalization are operations on two terms computing
+respectively their greatest lower bound and least upper bound when the terms
+are quasi-ordered by subsumption up to variable renaming (i.e., $t_1\preceq
+t_2$ iff $t_1 = t_2\sigma$ for some variable substitution $\sigma$). When term
+signatures are such that distinct functor symbols may be related with a fuzzy
+equivalence (called a similarity), these operations can be formally extended to
+tolerate mismatches on functor names and/or arity or argument order. We
+reformulate and extend previous work with a declarative approach defining
+unification and generalization as sets of axioms and rules forming a complete
+constraint-normalization proof system. These include the Reynolds-Plotkin
+term-generalization procedures, Maria Sessa's ""weak"" unification with partially
+fuzzy signatures and its corresponding generalization, as well as novel
+extensions of such operations to fully fuzzy signatures (i.e., similar functors
+with possibly different arities). One advantage of this approach is that it
+requires no modification of the conventional data structures for terms and
+substitutions. This and the fact that these declarative specifications are
+efficiently executable conditional Horn-clauses offers great practical
+potential for fuzzy information-handling applications.
+",1,0,0,0,0,0
+20925,Towards Physically Safe Reinforcement Learning under Supervision,"  This paper addresses the question of how a previously available control
+policy $\pi_s$ can be used as a supervisor to more quickly and safely train a
+new learned control policy $\pi_L$ for a robot. A weighted average of the
+supervisor and learned policies is used during trials, with a heavier weight
+initially on the supervisor, in order to allow safe and useful physical trials
+while the learned policy is still ineffective. During the process, the weight
+is adjusted to favor the learned policy. As weights are adjusted, the learned
+network must compensate so as to give safe and reasonable outputs under the
+different weights. A pioneer network is introduced that pre-learns a policy
+that performs similarly to the current learned policy under the planned next
+step for new weights; this pioneer network then replaces the currently learned
+network in the next set of trials. Experiments in OpenAI Gym demonstrate the
+effectiveness of the proposed method.
+",1,0,0,1,0,0
+20926,Complex Valued Risk Diversification,"  Risk diversification is one of the dominant concerns for portfolio managers.
+Various portfolio constructions have been proposed to minimize the risk of the
+portfolio under some constrains including expected returns. We propose a
+portfolio construction method that incorporates the complex valued principal
+component analysis into the risk diversification portfolio construction. The
+proposed method is verified to outperform the conventional risk parity and risk
+diversification portfolio constructions.
+",0,0,0,0,0,1
+20927,An Overview on Application of Machine Learning Techniques in Optical Networks,"  Today's telecommunication networks have become sources of enormous amounts of
+widely heterogeneous data. This information can be retrieved from network
+traffic traces, network alarms, signal quality indicators, users' behavioral
+data, etc. Advanced mathematical tools are required to extract meaningful
+information from these data and take decisions pertaining to the proper
+functioning of the networks from the network-generated data. Among these
+mathematical tools, Machine Learning (ML) is regarded as one of the most
+promising methodological approaches to perform network-data analysis and enable
+automated network self-configuration and fault management. The adoption of ML
+techniques in the field of optical communication networks is motivated by the
+unprecedented growth of network complexity faced by optical networks in the
+last few years. Such complexity increase is due to the introduction of a huge
+number of adjustable and interdependent system parameters (e.g., routing
+configurations, modulation format, symbol rate, coding schemes, etc.) that are
+enabled by the usage of coherent transmission/reception technologies, advanced
+digital signal processing and compensation of nonlinear effects in optical
+fiber propagation. In this paper we provide an overview of the application of
+ML to optical communications and networking. We classify and survey relevant
+literature dealing with the topic, and we also provide an introductory tutorial
+on ML for researchers and practitioners interested in this field. Although a
+good number of research papers have recently appeared, the application of ML to
+optical networks is still in its infancy: to stimulate further work in this
+area, we conclude the paper proposing new possible research directions.
+",0,0,0,1,0,0
+20928,Study of Minor Actinides Transmutation in PWR MOX fuel,"  The management of long-lived radionuclides in spent fuel is a key issue to
+achieve the closed nuclear fuel cycle and the sustainable development of
+nuclear energy. Partitioning-Transmutation is supposed to be an efficient
+method to treat the long-lived radionuclides in spent fuel. Some Minor
+Actinides (MAs) have very long half-lives among the radionuclides in the spent
+fuel. Accordingly, the study of MAs transmutation is a significant work for the
+post-processing of spent fuel.
+In the present work, the transmutations in Pressurized Water Reactor (PWR)
+mixed oxide (MOX) fuel are investigated through the Monte Carlo based code RMC.
+Two kinds of MAs, $^{237}$Np and five MAs ($^{237}$Np, $^{241}$Am, $^{243}$Am,
+$^{244}$Cm and $^{245}$Cm) are incorporated homogeneously into the MOX fuel
+assembly. The transmutation of MAs is simulated with different initial MOX
+concentrations.
+The results indicate an overall nice efficiency of transmutation in both
+initial MOX concentrations, especially for the two kinds of MAs primarily
+generated in the UOX fuel, $^{237}$Np and $^{241}$Am. In addition, the
+inclusion of $^{237}$Np in MOX has no large influence for other MAs, while the
+transmutation efficiency of $^{237}$Np is excellent. The transmutation of MAs
+in MOX fuel depletion is expected to be a new, efficient nuclear spent fuel
+management method for the future nuclear power generation.
+",0,1,0,0,0,0
+20929,Undesired parking spaces and contractible pieces of the noncrossing partition link,"  There are two natural simplicial complexes associated to the noncrossing
+partition lattice: the order complex of the full lattice and the order complex
+of the lattice with its bounding elements removed. The latter is a complex that
+we call the noncrossing partition link because it is the link of an edge in the
+former. The first author and his coauthors conjectured that various collections
+of simplices of the noncrossing partition link (determined by the undesired
+parking spaces in the corresponding parking functions) form contractible
+subcomplexes. In this article we prove their conjecture by combining the fact
+that the star of a simplex in a flag complex is contractible with the second
+author's theory of noncrossing hypertrees.
+",0,0,1,0,0,0
+20930,Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization,"  Due to their simplicity and excellent performance, parallel asynchronous
+variants of stochastic gradient descent have become popular methods to solve a
+wide range of large-scale optimization problems on multi-core architectures.
+Yet, despite their practical success, support for nonsmooth objectives is still
+lacking, making them unsuitable for many problems of interest in machine
+learning, such as the Lasso, group Lasso or empirical risk minimization with
+convex constraints.
+In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse
+method inspired by SAGA, a variance reduced incremental gradient algorithm. The
+proposed method is easy to implement and significantly outperforms the state of
+the art on several nonsmooth, large-scale problems. We prove that our method
+achieves a theoretical linear speedup with respect to the sequential version
+under assumptions on the sparsity of gradients and block-separability of the
+proximal term. Empirical benchmarks on a multi-core architecture illustrate
+practical speedups of up to 12x on a 20-core machine.
+",1,0,1,1,0,0
+20931,Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems,"  We consider the inverse problem of recovering an unknown functional parameter
+$u$ in a separable Banach space, from a noisy observation $y$ of its image
+through a known possibly non-linear ill-posed map ${\mathcal G}$. The data $y$
+is finite-dimensional and the noise is Gaussian. We adopt a Bayesian approach
+to the problem and consider Besov space priors (see Lassas et al. 2009), which
+are well-known for their edge-preserving and sparsity-promoting properties and
+have recently attracted wide attention especially in the medical imaging
+community.
+Our key result is to show that in this non-parametric setup the maximum a
+posteriori (MAP) estimates are characterized by the minimizers of a generalized
+Onsager--Machlup functional of the posterior. This is done independently for
+the so-called weak and strong MAP estimates, which as we show coincide in our
+context. In addition, we prove a form of weak consistency for the MAP
+estimators in the infinitely informative data limit. Our results are remarkable
+for two reasons: first, the prior distribution is non-Gaussian and does not
+meet the smoothness conditions required in previous research on non-parametric
+MAP estimates. Second, the result analytically justifies existing uses of the
+MAP estimate in finite but high dimensional discretizations of Bayesian inverse
+problems with the considered Besov priors.
+",0,0,1,1,0,0
+20932,Non-LTE line formation of Fe in late-type stars IV: Modelling of the solar centre-to-limb variation in 3D,"  Our ability to model the shapes and strengths of iron lines in the solar
+spectrum is a critical test of the accuracy of the solar iron abundance, which
+sets the absolute zero-point of all stellar metallicities. We use an extensive
+463-level Fe atom with new photoionisation cross-sections for FeI as well as
+quantum mechanical calculations of collisional excitation and charge transfer
+with neutral hydrogen; the latter effectively remove a free parameter that has
+hampered all previous line formation studies of Fe in non-local thermodynamic
+equilibrium (NLTE). For the first time, we use realistic 3D NLTE calculations
+of Fe for a quantitative comparison to solar observations. We confront our
+theoretical line profiles with observations taken at different viewing angles
+across the solar disk with the Swedish 1-m Solar Telescope. We find that 3D
+modelling well reproduces the observed centre-to-limb behaviour of spectral
+lines overall, but highlight aspects that may require further work, especially
+cross-sections for inelastic collisions with electrons. Our inferred solar iron
+abundance is log(eps(Fe))=7.48+-0.04.
+",0,1,0,0,0,0
+20933,"Transmission clusters in the HIV-1 epidemic among men who have sex with men in Montreal, Quebec, Canada","  Background. Several studies have used phylogenetics to investigate Human
+Immunodeficiency Virus (HIV) transmission among Men who have Sex with Men
+(MSMs) in Montreal, Quebec, Canada, revealing many transmission clusters. The
+Quebec HIV genotyping program sequence database now includes viral sequences
+from close to 4,000 HIV-positive individuals classified as MSMs. In this paper,
+we investigate clustering in those data by comparing results from several
+methods: the conventional Bayesian and maximum likelihood-bootstrap methods,
+and two more recent algorithms, DM-PhyClus, a Bayesian algorithm that produces
+a measure of uncertainty for proposed partitions, and the Gap Procedure, a fast
+distance-based approach. We estimate cluster growth by focusing on recent cases
+in the Primary HIV Infection (PHI) stage. Results. The analyses reveal
+considerable overlap between cluster estimates obtained from conventional
+methods. The Gap Procedure and DM-PhyClus rely on different cluster definitions
+and as a result, suggest moderately different partitions. All estimates lead to
+similar conclusions about cluster expansion: several large clusters have
+experienced sizeable growth, and a few new transmission clusters are likely
+emerging. Conclusions. The lack of a gold standard measure for clustering
+quality makes picking a best estimate among those proposed difficult. Work
+aiming to refine clustering criteria would be required to improve estimates.
+Nevertheless, the results unanimously stress the role that clusters play in
+promoting HIV incidence among MSMs.
+",0,0,0,1,0,0
+20934,Conformal blocks attached to twisted groups,"  The aim of this paper is to generalize the notion of conformal blocks to the
+situation in which the Lie algebra they are attached to is not defined over a
+field, but depends on covering data of curves. The result will be a sheaf of
+conformal blocks on the Hurwitz stack parametrizing Galois coverings of curves.
+Many features of the classical sheaves of conformal blocks are proved to hold
+in this more general setting, in particular the fusion rules, the propagation
+of vacua and the WZW connection.
+",0,0,1,0,0,0
+20935,Statistical study on propagation characteristics of Omega signals (VLF) in magnetosphere detected by the Akebono satellite,"  This paper shows a statistical analysis of 10.2 kHz Omega broadcasts of an
+artificial signal broadcast from ground stations, propagated in the
+plasmasphere, and detected using an automatic detection method we developed. We
+study the propagation patterns of the Omega signals to understand the
+propagation characteristics that are strongly affected by plasmaspheric
+electron density and the ambient magnetic field. We show the unique propagation
+patterns of the Omega 10.2 kHz signal when it was broadcast from two
+high-middle-latitude stations. We use about eight years of data captured by the
+Poynting flux analyzer subsystem on board the Akebono satellite from October
+1989 to September 1997. We demonstrate that the signals broadcast from almost
+the same latitude (in geomagnetic coordinates) propagated differently depending
+on the geographic latitude. We also study propagation characteristics as a
+function of local time, season, and solar activity. The Omega signal tended to
+propagate farther on the nightside than on the dayside and was more widely
+distributed during winter than during summer. When solar activity was at
+maximum, the Omega signal propagated at a lower intensity level. In contrast,
+when solar activity was at minimum, the Omega signal propagated at a higher
+intensity and farther from the transmitter station.
+",0,1,0,0,0,0
+20936,"Thermally induced stresses in boulders on airless body surfaces, and implications for rock breakdown","  This work investigates the macroscopic thermomechanical behavior of lunar
+boulders by modeling their response to diurnal thermal forcing. Our results
+reveal a bimodal, spatiotemporally-complex stress response. During sunrise,
+stresses occur in the boulders' interiors that are associated with large-scale
+temperature gradients developed due to overnight cooling. During sunset,
+stresses occur at the boulders' exteriors due to the cooling and contraction of
+the surface. Both kinds of stresses are on the order of 10 MPa in 1 m boulders
+and decrease for smaller diameters, suggesting that larger boulders break down
+more quickly. Boulders <30 cm exhibit a weak response to thermal forcing,
+suggesting a threshold below which crack propagation may not occur. Boulders of
+any size buried by regolith are shielded from thermal breakdown. As boulders
+increase in size (>1 m), stresses increase to several 10s of MPa as the
+behavior of their surfaces approaches that of an infinite halfspace. As the
+thermal wave loses contact with the boulder interior, stresses become limited
+to the near-surface. This suggests that the survival time of a boulder is not
+only controlled by the amplitude of induced stress, but also by its diameter as
+compared to the diurnal skin depth. While stresses on the order of 10 MPa are
+enough to drive crack propagation in terrestrial environments, crack
+propagation rates in vacuum are not well constrained. We explore the
+relationship between boulder size, stress, and the direction of crack
+propagation, and discuss the implications for the relative breakdown rates and
+estimated lifetimes of boulders on airless body surfaces.
+",0,1,0,0,0,0
+20937,Some observations about generalized quantifiers in logics of imperfect information,"  We analyze the definitions of generalized quantifiers of imperfect
+information that have been proposed by F.Engström. We argue that these
+definitions are just embeddings of the first-order generalized quantifiers into
+team semantics, and fail to capture an adequate notion of team-theoretical
+generalized quantifier, save for the special cases in which the quantifiers are
+applied to flat formulas. We also criticize the meaningfulness of the
+monotone/nonmonotone distinction in this context. We make some proposals for a
+more adequate definition of generalized quantifiers of imperfect information.
+",0,0,1,0,0,0
+20938,Predicting Demographics of High-Resolution Geographies with Geotagged Tweets,"  In this paper, we consider the problem of predicting demographics of
+geographic units given geotagged Tweets that are composed within these units.
+Traditional survey methods that offer demographics estimates are usually
+limited in terms of geographic resolution, geographic boundaries, and time
+intervals. Thus, it would be highly useful to develop computational methods
+that can complement traditional survey methods by offering demographics
+estimates at finer geographic resolutions, with flexible geographic boundaries
+(i.e. not confined to administrative boundaries), and at different time
+intervals. While prior work has focused on predicting demographics and health
+statistics at relatively coarse geographic resolutions such as the county-level
+or state-level, we introduce an approach to predict demographics at finer
+geographic resolutions such as the blockgroup-level. For the task of predicting
+gender and race/ethnicity counts at the blockgroup-level, an approach adapted
+from prior work to our problem achieves an average correlation of 0.389
+(gender) and 0.569 (race) on a held-out test dataset. Our approach outperforms
+this prior approach with an average correlation of 0.671 (gender) and 0.692
+(race).
+",1,0,0,1,0,0
+20939,Text Compression for Sentiment Analysis via Evolutionary Algorithms,"  Can textual data be compressed intelligently without losing accuracy in
+evaluating sentiment? In this study, we propose a novel evolutionary
+compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression),
+which makes use of Parts-of-Speech tags to compress text in a way that
+sacrifices minimal classification accuracy when used in conjunction with
+sentiment analysis algorithms. An analysis of PARSEC with eight commercial and
+non-commercial sentiment analysis algorithms on twelve English sentiment data
+sets reveals that accurate compression is possible with (0%, 1.3%, 3.3%) loss
+in sentiment classification accuracy for (20%, 50%, 75%) data compression with
+PARSEC using LingPipe, the most accurate of the sentiment algorithms. Other
+sentiment analysis algorithms are more severely affected by compression. We
+conclude that significant compression of text data is possible for sentiment
+analysis depending on the accuracy demands of the specific application and the
+specific sentiment analysis algorithm used.
+",1,0,0,1,0,0
+20940,Training large margin host-pathogen protein-protein interaction predictors,"  Detection of protein-protein interactions (PPIs) plays a vital role in
+molecular biology. Particularly, infections are caused by the interactions of
+host and pathogen proteins. It is important to identify host-pathogen
+interactions (HPIs) to discover new drugs to counter infectious diseases.
+Conventional wet lab PPI prediction techniques have limitations in terms of
+large scale application and budget. Hence, computational approaches are
+developed to predict PPIs. This study aims to develop large margin machine
+learning models to predict interspecies PPIs with a special interest in
+host-pathogen protein interactions (HPIs). Especially, we focus on seeking
+answers to three queries that arise while developing an HPI predictor. 1) How
+should we select negative samples? 2) What should be the size of negative
+samples as compared to the positive samples? 3) What type of margin violation
+penalty should be used to train the predictor? We compare two available methods
+for negative sampling. Moreover, we propose a new method of assigning weights
+to each training example in weighted SVM depending on the distance of the
+negative examples from the positive examples. We have also developed a web
+server for our HPI predictor called HoPItor (Host Pathogen Interaction
+predicTOR) that can predict interactions between human and viral proteins. This
+webserver can be accessed at the URL:
+this http URL.
+",1,0,0,1,0,0
+20941,A Useful Solution of the Coupon Collector's Problem,"  The Coupon Collector's Problem is one of the few mathematical problems that
+make news headlines regularly. The reasons for this are on one hand the immense
+popularity of soccer albums (called Paninimania) and on the other hand that no
+solution is known that is able to take into account all effects such as
+replacement (limited purchasing of missing stickers) or swapping. In previous
+papers we have proven that the classical assumptions are not fulfilled in
+practice. Therefore we define new assumptions that match reality. Based on
+these assumptions we are able to derive formulae for the mean number of
+stickers needed (and the associated standard deviation) that are able to take
+into account all effects that occur in practical collecting. Thus collectors
+can estimate the average cost of completion of an album and its standard
+deviation just based on elementary calculations. From a practical point of view
+we consider the Coupon Collector's problem as solved.
+-----
+Das Sammelbilderproblem ist eines der wenigen mathematischen Probleme, die
+regelmä{\ss}ig in den Schlagzeilen der Nachrichten vorkommen. Dies liegt
+einerseits an der gro{\ss}en Popularität von Fu{\ss}ball-Sammelbildern
+(Paninimania genannt) und andererseits daran, dass es bisher keine Lösung
+gibt, die alle relevanten Effekte wie Nachkaufen oder Tauschen
+berücksichtigt. Wir haben bereits nachgewiesen, dass die klassischen Annahmen
+nicht der Realität entsprechen. Deshalb stellen wir neue Annahmen auf, die
+die Praxis besser abbilden. Darauf aufbauend können wir Formeln für die
+mittlere Anzahl benötigter Bilder (sowie deren Standardabweichung) ableiten,
+die alle in der Praxis relevanten Effekte berücksichtigen. Damit können
+Sammler die mittleren Kosten eines Albums sowie deren Standardabweichung nur
+mit Hilfe von elementaren Rechnungen bestimmen. Für praktische Zwecke ist das
+Sammelbilderproblem damit gelöst.
+",0,0,1,0,0,0
+20942,Accretion driven turbulence in filaments I: Non-gravitational accretion,"  We study accretion driven turbulence for different inflow velocities in star
+forming filaments using the code ramses. Filaments are rarely isolated objects
+and their gravitational potential will lead to radially dominated accretion. In
+the non-gravitational case, accretion by itself can already provoke
+non-isotropic, radially dominated turbulent motions responsible for the complex
+structure and non-thermal line widths observed in filaments. We find that there
+is a direct linear relation between the absolute value of the total density
+weighted velocity dispersion and the infall velocity. The turbulent velocity
+dispersion in the filaments is independent of sound speed or any net flow along
+the filament. We show that the density weighted velocity dispersion acts as an
+additional pressure term supporting the filament in hydrostatic equilibrium.
+Comparing to observations, we find that the projected non-thermal line width
+variation is generally subsonic independent of inflow velocity.
+",0,1,0,0,0,0
+20943,Contextual Outlier Interpretation,"  Outlier detection plays an essential role in many data-driven applications to
+identify isolated instances that are different from the majority. While many
+statistical learning and data mining techniques have been used for developing
+more effective outlier detection algorithms, the interpretation of detected
+outliers does not receive much attention. Interpretation is becoming
+increasingly important to help people trust and evaluate the developed models
+through providing intrinsic reasons why the certain outliers are chosen. It is
+difficult, if not impossible, to simply apply feature selection for explaining
+outliers due to the distinct characteristics of various detection models,
+complicated structures of data in certain applications, and imbalanced
+distribution of outliers and normal instances. In addition, the role of
+contrastive contexts where outliers locate, as well as the relation between
+outliers and contexts, are usually overlooked in interpretation. To tackle the
+issues above, in this paper, we propose a novel Contextual Outlier
+INterpretation (COIN) method to explain the abnormality of existing outliers
+spotted by detectors. The interpretability for an outlier is achieved from
+three aspects: outlierness score, attributes that contribute to the
+abnormality, and contextual description of its neighborhoods. Experimental
+results on various types of datasets demonstrate the flexibility and
+effectiveness of the proposed framework compared with existing interpretation
+approaches.
+",1,0,0,1,0,0
+20944,Game Theory for Secure Critical Interdependent Gas-Power-Water Infrastructure,"  A city's critical infrastructure such as gas, water, and power systems, are
+largely interdependent since they share energy, computing, and communication
+resources. This, in turn, makes it challenging to endow them with fool-proof
+security solutions. In this paper, a unified model for interdependent
+gas-power-water infrastructure is presented and the security of this model is
+studied using a novel game-theoretic framework. In particular, a zero-sum
+noncooperative game is formulated between a malicious attacker who seeks to
+simultaneously alter the states of the gas-power-water critical infrastructure
+to increase the power generation cost and a defender who allocates
+communication resources over its attack detection filters in local areas to
+monitor the infrastructure. At the mixed strategy Nash equilibrium of this
+game, numerical results show that the expected power generation cost deviation
+is 35\% lower than the one resulting from an equal allocation of resources over
+the local filters. The results also show that, at equilibrium, the
+interdependence of the power system on the natural gas and water systems can
+motivate the attacker to target the states of the water and natural gas systems
+to change the operational states of the power grid. Conversely, the defender
+allocates a portion of its resources to the water and natural gas states of the
+interdependent system to protect the grid from state deviations.
+",1,0,0,0,0,0
+20945,Implicit Entity Linking in Tweets,"  Over the years, Twitter has become one of the largest communication platforms
+providing key data to various applications such as brand monitoring, trend
+detection, among others. Entity linking is one of the major tasks in natural
+language understanding from tweets and it associates entity mentions in text to
+corresponding entries in knowledge bases in order to provide unambiguous
+interpretation and additional con- text. State-of-the-art techniques have
+focused on linking explicitly mentioned entities in tweets with reasonable
+success. However, we argue that in addition to explicit mentions i.e. The movie
+Gravity was more ex- pensive than the mars orbiter mission entities (movie
+Gravity) can also be mentioned implicitly i.e. This new space movie is crazy.
+you must watch it!. This paper introduces the problem of implicit entity
+linking in tweets. We propose an approach that models the entities by
+exploiting their factual and contextual knowledge. We demonstrate how to use
+these models to perform implicit entity linking on a ground truth dataset with
+397 tweets from two domains, namely, Movie and Book. Specifically, we show: 1)
+the importance of linking implicit entities and its value addition to the
+standard entity linking task, and 2) the importance of exploiting contextual
+knowledge associated with an entity for linking their implicit mentions. We
+also make the ground truth dataset publicly available to foster the research in
+this new research area.
+",1,0,0,0,0,0
+20946,Non-cocompact Group Actions and $π_1$-Semistability at Infinity,"  A finitely presented 1-ended group $G$ has {\it semistable fundamental group
+at infinity} if $G$ acts geometrically on a simply connected and locally
+compact ANR $Y$ having the property that any two proper rays in $Y$ are
+properly homotopic. This property of $Y$ captures a notion of connectivity at
+infinity stronger than ""1-ended"", and is in fact a feature of $G$, being
+independent of choices. It is a fundamental property in the homotopical study
+of finitely presented groups. While many important classes of groups have been
+shown to have semistable fundamental group at infinity, the question of whether
+every $G$ has this property has been a recognized open question for nearly
+forty years. In this paper we attack the problem by considering a proper {\it
+but non-cocompact} action of a group $J$ on such an $Y$. This $J$ would
+typically be a subgroup of infinite index in the geometrically acting
+over-group $G$; for example $J$ might be infinite cyclic or some other subgroup
+whose semistability properties are known. We divide the semistability property
+of $G$ into a $J$-part and a ""perpendicular to $J$"" part, and we analyze how
+these two parts fit together. Among other things, this analysis leads to a
+proof (in a companion paper) that a class of groups previously considered to be
+likely counter examples do in fact have the semistability property.
+",0,0,1,0,0,0
+20947,Robust Distributed Control of DC Microgrids with Time-Varying Power Sharing,"  This paper addresses the problem of output voltage regulation for multiple
+DC/DC converters connected to a microgrid, and prescribes a scheme for sharing
+power among different sources. This architecture is structured in such a way
+that it admits quantifiable analysis of the closed-loop performance of the
+network of converters; the analysis simplifies to studying closed-loop
+performance of an equivalent {\em single-converter} system. The proposed
+architecture allows for the proportion in which the sources provide power to
+vary with time; thus overcoming limitations of our previous designs.
+Additionally, the proposed control framework is suitable to both centralized
+and decentralized implementations, i.e., the same control architecture can be
+employed for voltage regulation irrespective of the availability of common
+load-current (or power) measurement, without the need to modify controller
+parameters. The performance becomes quantifiably better with better
+communication of the demanded load to all the controllers at all the converters
+(in the centralized case); however guarantees viability when such communication
+is absent. Case studies comprising of battery, PV and generic sources are
+presented and demonstrate the enhanced performance of prescribed optimal
+controllers for voltage regulation and power sharing.
+",1,0,1,0,0,0
+20948,Quantum Lower Bounds for Tripartite Versions of the Hidden Shift and the Set Equality Problems,"  In this paper, we study quantum query complexity of the following rather
+natural tripartite generalisations (in the spirit of the 3-sum problem) of the
+hidden shift and the set equality problems, which we call the 3-shift-sum and
+the 3-matching-sum problems.
+The 3-shift-sum problem is as follows: given a table of $3\times n$ elements,
+is it possible to circularly shift its rows so that the sum of the elements in
+each column becomes zero? It is promised that, if this is not the case, then no
+3 elements in the table sum up to zero. The 3-matching-sum problem is defined
+similarly, but it is allowed to arbitrarily permute elements within each row.
+For these problems, we prove lower bounds of $\Omega(n^{1/3})$ and
+$\Omega(\sqrt n)$, respectively. The second lower bound is tight.
+The lower bounds are proven by a novel application of the dual learning graph
+framework and by using representation-theoretic tools.
+",1,0,0,0,0,0
+20949,A Parallel Direct Cut Algorithm for High-Order Overset Methods with Application to a Spinning Golf Ball,"  Overset methods are commonly employed to enable the effective simulation of
+problems involving complex geometries and moving objects such as rotorcraft.
+This paper presents a novel overset domain connectivity algorithm based upon
+the direct cut approach suitable for use with GPU-accelerated solvers on
+high-order curved grids. In contrast to previous methods it is capable of
+exploiting the highly data-parallel nature of modern accelerators. Further, the
+approach is also substantially more efficient at handling the curved grids
+which arise within the context of high-order methods. An implementation of this
+new algorithm is presented and combined with a high-order fluid dynamics code.
+The algorithm is validated against several benchmark problems, including flow
+over a spinning golf ball at a Reynolds number of 150,000.
+",0,1,0,0,0,0
+20950,A data assimilation algorithm: the paradigm of the 3D Leray-alpha model of turbulence,"  In this paper we survey the various implementations of a new data
+assimilation (downscaling) algorithm based on spatial coarse mesh measurements.
+As a paradigm, we demonstrate the application of this algorithm to the 3D
+Leray-$\alpha$ subgrid scale turbulence model. Most importantly, we use this
+paradigm to show that it is not always necessary that one has to collect coarse
+mesh measurements of all the state variables, that are involved in the
+underlying evolutionary system, in order to recover the corresponding exact
+reference solution. Specifically, we show that in the case of the 3D
+Leray$-\alpha$ model of turbulence the solutions of the algorithm, constructed
+using only coarse mesh observations of any two components of the
+three-dimensional velocity field, and without any information of the third
+component, converge, at an exponential rate in time, to the corresponding exact
+reference solution of the 3D Leray$-\alpha$ model. This study serves as an
+addendum to our recent work on abridged continuous data assimilation for the 2D
+Navier-Stokes equations. Notably, similar results have also been recently
+established for the 3D viscous Planetary Geostrophic circulation model in which
+we show that coarse mesh measurements of the temperature alone are sufficient
+for recovering, through our data assimilation algorithm, the full solution;
+viz. the three components of velocity vector field and the temperature.
+Consequently, this proves the Charney conjecture for the 3D Planetary
+Geostrophic model; namely, that the history of the large spatial scales of
+temperature is sufficient for determining all the other quantities (state
+variables) of the model.
+",0,1,1,0,0,0
+20951,Playing a true Parrondo's game with a three state coin on a quantum walk,"  Playing a Parrondo's game with a qutrit is the subject of this paper. We show
+that a true quantum Parrondo's game can be played with a 3 state coin(qutrit)
+in a 1D quantum walk in contrast to the fact that playing a true Parrondo's
+game with a 2 state coin(qubit) in 1D quantum walk fails in the asymptotic
+limits.
+",1,1,0,0,0,0
+20952,Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data,"  We analyse multimodal time-series data corresponding to weight, sleep and
+steps measurements. We focus on predicting whether a user will successfully
+achieve his/her weight objective. For this, we design several deep long
+short-term memory (LSTM) architectures, including a novel cross-modal LSTM
+(X-LSTM), and demonstrate their superiority over baseline approaches. The
+X-LSTM improves parameter efficiency by processing each modality separately and
+allowing for information flow between them by way of recurrent
+cross-connections. We present a general hyperparameter optimisation technique
+for X-LSTMs, which allows us to significantly improve on the LSTM and a prior
+state-of-the-art cross-modal approach, using a comparable number of parameters.
+Finally, we visualise the model's predictions, revealing implications about
+latent variables in this task.
+",1,0,0,1,0,0
+20953,Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions,"  The key idea of variational auto-encoders (VAEs) resembles that of
+traditional auto-encoder models in which spatial information is supposed to be
+explicitly encoded in the latent space. However, the latent variables in VAEs
+are vectors, which can be interpreted as multiple feature maps of size 1x1.
+Such representations can only convey spatial information implicitly when
+coupled with powerful decoders. In this work, we propose spatial VAEs that use
+feature maps of larger size as latent variables to explicitly capture spatial
+information. This is achieved by allowing the latent variables to be sampled
+from matrix-variate normal (MVN) distributions whose parameters are computed
+from the encoder network. To increase dependencies among locations on latent
+feature maps and reduce the number of parameters, we further propose spatial
+VAEs via low-rank MVN distributions. Experimental results show that the
+proposed spatial VAEs outperform original VAEs in capturing rich structural and
+spatial information.
+",1,0,0,1,0,0
+20954,Optimal Ramp Schemes and Related Combinatorial Objects,"  In 1996, Jackson and Martin proved that a strong ideal ramp scheme is
+equivalent to an orthogonal array. However, there was no good characterization
+of ideal ramp schemes that are not strong. Here we show the equivalence of
+ideal ramp schemes to a new variant of orthogonal arrays that we term augmented
+orthogonal arrays. We give some constructions for these new kinds of arrays,
+and, as a consequence, we also provide parameter situations where ideal ramp
+schemes exist but strong ideal ramp schemes do not exist.
+",1,0,0,0,0,0
+20955,Do Neural Nets Learn Statistical Laws behind Natural Language?,"  The performance of deep learning in natural language processing has been
+spectacular, but the reasons for this success remain unclear because of the
+inherent complexity of deep learning. This paper provides empirical evidence of
+its effectiveness and of a limitation of neural networks for language
+engineering. Precisely, we demonstrate that a neural language model based on
+long short-term memory (LSTM) effectively reproduces Zipf's law and Heaps' law,
+two representative statistical properties underlying natural language. We
+discuss the quality of reproducibility and the emergence of Zipf's law and
+Heaps' law as training progresses. We also point out that the neural language
+model has a limitation in reproducing long-range correlation, another
+statistical property of natural language. This understanding could provide a
+direction for improving the architectures of neural networks.
+",1,0,0,0,0,0
+20956,Super-speeds with Zero-RAM: Next Generation Large-Scale Optimization in Your Laptop!,"  This article presents the novel breakthrough general purpose algorithm for
+large scale optimization problems. The novel algorithm is capable of achieving
+breakthrough speeds for very large-scale optimization on general purpose
+laptops and embedded systems. Application of the algorithm to the Griewank
+function was possible in up to 1 billion decision variables in double precision
+took only 64485 seconds (~18 hours) to solve, while consuming 7,630 MB (7.6 GB)
+or RAM on a single threaded laptop CPU. It shows that the algorithm is
+computationally and memory (space) linearly efficient, and can find the optimal
+or near-optimal solution in a fraction of the time and memory that many
+conventional algorithms require. It is envisaged that this will open up new
+possibilities of real-time large-scale problems on personal laptops and
+embedded systems.
+",1,0,0,0,0,0
+20957,Recoverable Energy of Dissipative Electromagnetic Systems,"  Ambiguities in the definition of stored energy within distributed or
+radiating electromagnetic systems motivate the discussion of the well-defined
+concept of recoverable energy. This concept is commonly overlooked by the
+community and the purpose of this communication is to recall its existence and
+to discuss its relationship to fractional bandwidth. Using a rational function
+approximation of a system's input impedance, the recoverable energy of lumped
+and radiating systems is calculated in closed form and is related to stored
+energy and fractional bandwidth. Lumped circuits are also used to demonstrate
+the relationship between recoverable energy and the energy stored within
+equivalent circuits produced by the minimum phase-shift Darlington's synthesis
+procedure.
+",0,1,0,0,0,0
+20958,Elliptic Hall algebra on $\mathbb{F}_1$,"  We construct Hall algebra of elliptic curve over $\mathbb{F}_1$ using the
+theory of monoidal scheme due to Deitmar and the theory of Hall algebra for
+monoidal representations due to Szczesny. The resulting algebra is shown to be
+a specialization of elliptic Hall algebra studied by Burban and Schiffmann.
+Thus our algebra is isomorphic to the skein algebra for torus by the recent
+work of Morton and Samuelson.
+",0,0,1,0,0,0
+20959,Approximate Bayesian inference with queueing networks and coupled jump processes,"  Queueing networks are systems of theoretical interest that give rise to
+complex families of stochastic processes, and find widespread use in the
+performance evaluation of interconnected resources. Yet, despite their
+importance within applications, and in comparison to their counterpart
+stochastic models in genetics or mathematical biology, there exist few relevant
+approaches for transient inference and uncertainty quantification tasks in
+these systems. This is a consequence of strong computational impediments and
+distinctive properties of the Markov jump processes induced by queueing
+networks. In this paper, we offer a comprehensive overview of the inferential
+challenge and its comparison to analogue tasks within related mathematical
+domains. We then discuss a model augmentation over an approximating network
+system, and present a flexible and scalable variational Bayesian framework,
+which is targeted at general-form open and closed queueing systems, with varied
+service disciplines and priorities. The inferential procedure is finally
+validated in a couple of uncertainty quantification tasks for network service
+rates.
+",1,0,0,1,0,0
+20960,Universal features of price formation in financial markets: perspectives from Deep Learning,"  Using a large-scale Deep Learning approach applied to a high-frequency
+database containing billions of electronic market quotes and transactions for
+US equities, we uncover nonparametric evidence for the existence of a universal
+and stationary price formation mechanism relating the dynamics of supply and
+demand for a stock, as revealed through the order book, to subsequent
+variations in its market price. We assess the model by testing its
+out-of-sample predictions for the direction of price moves given the history of
+price and order flow, across a wide range of stocks and time periods. The
+universal price formation model is shown to exhibit a remarkably stable
+out-of-sample prediction accuracy across time, for a wide range of stocks from
+different sectors. Interestingly, these results also hold for stocks which are
+not part of the training sample, showing that the relations captured by the
+model are universal and not asset-specific.
+The universal model --- trained on data from all stocks --- outperforms, in
+terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear
+models trained on time series of any given stock, showing that the universal
+nature of price formation weighs in favour of pooling together financial data
+from various stocks, rather than designing asset- or sector-specific models as
+commonly done. Standard data normalizations based on volatility, price level or
+average spread, or partitioning the training data into sectors or categories
+such as large/small tick stocks, do not improve training results. On the other
+hand, inclusion of price and order flow history over many past observations is
+shown to improve forecasting performance, showing evidence of path-dependence
+in price dynamics.
+",0,0,0,1,0,1
+20961,A New Tracking Algorithm for Multiple Colloidal Particles Close to Contact,"  In this paper, we propose a new algorithm based on radial symmetry center
+method to track colloidal particles close to contact, where the optical images
+of the particles start to overlap in digital video microscopy. This overlapping
+effect is important to observe the pair interaction potential in colloidal
+studies and it appears as additional interaction in the measurement of the
+interaction with conventional tracking analysis. The proposed algorithm in this
+work is simple, fast and applicable for not only two particles but also three
+and more particles without any modification. The algorithm uses gradient
+vectors of the particle intensity distribution, which allows us to use a part
+of the symmetric intensity distribution in the calculation of the actual
+particle position. In this study, simulations are performed to see the
+performance of the proposed algorithm for two and three particles, where the
+simulation images are generated by using fitted curve to experimental particle
+image for different sized particles. As a result, the algorithm yields the
+maximum error smaller than 2 nm for 5.53 {\mu}m silica particles in contact
+condition.
+",0,1,0,0,0,0
+20962,Critical Percolation Without Fine Tuning on the Surface of a Topological Superconductor,"  We present numerical evidence that most two-dimensional surface states of a
+bulk topological superconductor (TSC) sit at an integer quantum Hall plateau
+transition. We study TSC surface states in class CI with quenched disorder.
+Low-energy (finite-energy) surface states were expected to be critically
+delocalized (Anderson localized). We confirm the low-energy picture, but find
+instead that finite-energy states are also delocalized, with universal
+statistics that are independent of the TSC winding number, and consistent with
+the spin quantum Hall plateau transition (percolation).
+",0,1,0,0,0,0
+20963,Low-luminosity stellar wind accretion onto neutron stars in HMXBs,"  Features and applications of quasi-spherical settling accretion onto rotating
+magnetized neutron stars in high-mass X-ray binaries are discussed. The
+settling accretion occurs in wind-fed HMXBs when the plasma cooling time is
+longer than the free-fall time from the gravitational capture radius, which can
+take place in low-luminosity HMXBs with $L_x\lesssim 4\times 10^{36}$ erg/s. We
+briefly review the implications of the settling accretion, focusing on the SFXT
+phenomenon, which can be related to instability of the quasi-spherical
+convective shell above the neutron star magnetosphere due to magnetic
+reconnection from fast temporarily magnetized winds from OB-supergiant. If a
+young neutron star in a wind-fed HMXB is rapidly rotating, the propeller regime
+in a quasi-spherical hot shell occurs. We show that X-ray spectral and temporal
+properties of enigmatic $\gamma$ Cas Be-stars are consistent with failed
+settling accretion regime onto a propelling neutron star. The subsequent
+evolutionary stage of $\gamma$ Cas and its analogs should be the X Per-type
+binaries comprising low-luminosity slowly rotating X-ray pulsars.
+",0,1,0,0,0,0
+20964,Faithful Inversion of Generative Models for Effective Amortized Inference,"  Inference amortization methods share information across multiple
+posterior-inference problems, allowing each to be carried out more efficiently.
+Generally, they require the inversion of the dependency structure in the
+generative model, as the modeller must learn a mapping from observations to
+distributions approximating the posterior. Previous approaches have involved
+inverting the dependency structure in a heuristic way that fails to capture
+these dependencies correctly, thereby limiting the achievable accuracy of the
+resulting approximations. We introduce an algorithm for faithfully, and
+minimally, inverting the graphical model structure of any generative model.
+Such inverses have two crucial properties: (a) they do not encode any
+independence assertions that are absent from the model and; (b) they are local
+maxima for the number of true independencies encoded. We prove the correctness
+of our approach and empirically show that the resulting minimally faithful
+inverses lead to better inference amortization than existing heuristic
+approaches.
+",1,0,0,1,0,0
+20965,A social Network Analysis of the Operations Research/Industrial Engineering Faculty Hiring Network,"  We study the U.S. Operations Research/Industrial-Systems Engineering (ORIE)
+faculty hiring network, consisting of 1,179 faculty origin and destination data
+together with attribute data from 83 ORIE departments. A social network
+analysis of faculty hires can reveal important patterns in an academic field,
+such as the existence of a hierarchy or sociological aspects such as the
+presence of communities of departments. We first statistically test for the
+existence of a linear hierarchy in the network and for its steepness. We find a
+near linear hierarchical order of the departments, proposing a new index for
+hiring networks, which we contrast with other indicators of hierarchy,
+including published rankings. A single index is not capable to capture the full
+structure of a complex network, however, so we next fit a latent exponential
+random graph model (ERGM) to the network, which is able to reproduce its main
+observed characteristics: high incidence of self-hiring, skewed out-degree
+distribution, low density and clustering. Finally, we use the latent variables
+in the ERGM to simplify the network to one where faculty hires take place among
+three groups of departments. We contrast our findings with those reported for
+other related disciplines, Computer Science and Business.
+",1,0,0,1,0,0
+20966,One-sample aggregate data meta-analysis of medians,"  An aggregate data meta-analysis is a statistical method that pools the
+summary statistics of several selected studies to estimate the outcome of
+interest. When considering a continuous outcome, typically each study must
+report the same measure of the outcome variable and its spread (e.g., the
+sample mean and its standard error). However, some studies may instead report
+the median along with various measures of spread. Recently, the task of
+incorporating medians in meta-analysis has been achieved by estimating the
+sample mean and its standard error from each study that reports a median in
+order to meta-analyze the means. In this paper, we propose two alternative
+approaches to meta-analyze data that instead rely on medians. We systematically
+compare these approaches via simulation study to each other and to methods that
+transform the study-specific medians and spread into sample means and their
+standard errors. We demonstrate that the proposed median-based approaches
+perform better than the transformation-based approaches, especially when
+applied to skewed data and data with high inter-study variance. In addition,
+when meta-analyzing data that consists of medians, we show that the
+median-based approaches perform considerably better than or comparably to the
+best-case scenario for a transformation approach: conducting a meta-analysis
+using the actual sample mean and standard error of the mean of each study.
+Finally, we illustrate these approaches in a meta-analysis of patient delay in
+tuberculosis diagnosis.
+",0,0,0,1,0,0
+20967,QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts,"  Large inter-datacenter transfers are crucial for cloud service efficiency and
+are increasingly used by organizations that have dedicated wide area networks
+between datacenters. A recent work uses multicast forwarding trees to reduce
+the bandwidth needs and improve completion times of point-to-multipoint
+transfers. Using a single forwarding tree per transfer, however, leads to poor
+performance because the slowest receiver dictates the completion time for all
+receivers. Using multiple forwarding trees per transfer alleviates this
+concern--the average receiver could finish early; however, if done naively,
+bandwidth usage would also increase and it is apriori unclear how best to
+partition receivers, how to construct the multiple trees and how to determine
+the rate and schedule of flows on these trees. This paper presents QuickCast, a
+first solution to these problems. Using simulations on real-world network
+topologies, we see that QuickCast can speed up the average receiver's
+completion time by as much as $10\times$ while only using $1.04\times$ more
+bandwidth; further, the completion time for all receivers also improves by as
+much as $1.6\times$ faster at high loads.
+",1,0,0,0,0,0
+20968,Contemporary machine learning: a guide for practitioners in the physical sciences,"  Machine learning is finding increasingly broad application in the physical
+sciences. This most often involves building a model relationship between a
+dependent, measurable output and an associated set of controllable, but
+complicated, independent inputs. We present a tutorial on current techniques in
+machine learning -- a jumping-off point for interested researchers to advance
+their work. We focus on deep neural networks with an emphasis on demystifying
+deep learning. We begin with background ideas in machine learning and some
+example applications from current research in plasma physics. We discuss
+supervised learning techniques for modeling complicated functions, beginning
+with familiar regression schemes, then advancing to more sophisticated deep
+learning methods. We also address unsupervised learning and techniques for
+reducing the dimensionality of input spaces. Along the way, we describe methods
+for practitioners to help ensure that their models generalize from their
+training data to as-yet-unseen test data. We describe classes of tasks --
+predicting scalars, handling images, fitting time-series -- and prepare the
+reader to choose an appropriate technique. We finally point out some
+limitations to modern machine learning and speculate on some ways that
+practitioners from the physical sciences may be particularly suited to help.
+",1,1,0,0,0,0
+20969,Uniform diamond coatings on WC-Co hard alloy cutting inserts deposited by a microwave plasma CVD,"  Polycrystalline diamond coatings have been grown on cemented carbide
+substrates with different aspect ratios by a microwave plasma CVD in
+methane-hydrogen gas mixtures. To protect the edges of the substrates from
+non-uniform heating due to the plasma edge effect, a special plateholder with
+pockets for group growth has been used. The difference in heights of the
+substrates and plateholder, and its influence on the diamond film mean grain
+size, growth rate, phase composition and stress was investigated. The substrate
+temperature range, within which uniform diamond films are produced with good
+adhesion, is determined. The diamond-coated cutting inserts produced at
+optimized process exhibited a reduction of cutting force and wear resistance by
+a factor of two, and cutting efficiency increase by 4.3 times upon turning A390
+Al-Si alloy as compared to performance of uncoated tools.
+",0,1,0,0,0,0
+20970,Analysing Soccer Games with Clustering and Conceptors,"  We present a new approach for identifying situations and behaviours, which we
+call ""moves"", from soccer games in the 2D simulation league. Being able to
+identify key situations and behaviours are useful capabilities for analysing
+soccer matches, anticipating opponent behaviours to aid selection of
+appropriate tactics, and also as a prerequisite for automatic learning of
+behaviours and policies. To support a wide set of strategies, our goal is to
+identify situations from data, in an unsupervised way without making use of
+pre-defined soccer specific concepts such as ""pass"" or ""dribble"". The recurrent
+neural networks we use in our approach act as a high-dimensional projection of
+the recent history of a situation on the field. Similar situations, i.e., with
+similar histories, are found by clustering of network states. The same networks
+are also used to learn so-called conceptors, that are lower-dimensional
+manifolds that describe trajectories through a high-dimensional state space
+that enable situation-specific predictions from the same neural network. With
+the proposed approach, we can segment games into sequences of situations that
+are learnt in an unsupervised way, and learn conceptors that are useful for the
+prediction of the near future of the respective situation.
+",1,0,0,0,0,0
+20971,On the Efficient Simulation of the Left-Tail of the Sum of Correlated Log-normal Variates,"  The sum of Log-normal variates is encountered in many challenging
+applications such as in performance analysis of wireless communication systems
+and in financial engineering. Several approximation methods have been developed
+in the literature, the accuracy of which is not ensured in the tail regions.
+These regions are of primordial interest wherein small probability values have
+to be evaluated with high precision. Variance reduction techniques are known to
+yield accurate, yet efficient, estimates of small probability values. Most of
+the existing approaches, however, have considered the problem of estimating the
+right-tail of the sum of Log-normal random variables (RVS). In the present
+work, we consider instead the estimation of the left-tail of the sum of
+correlated Log-normal variates with Gaussian copula under a mild assumption on
+the covariance matrix. We propose an estimator combining an existing
+mean-shifting importance sampling approach with a control variate technique.
+The main result is that the proposed estimator has an asymptotically vanishing
+relative error which represents a major finding in the context of the left-tail
+simulation of the sum of Log-normal RVs. Finally, we assess by various
+simulation results the performances of the proposed estimator compared to
+existing estimators.
+",0,0,1,1,0,0
+20972,Why optional stopping is a problem for Bayesians,"  Recently, optional stopping has been a subject of debate in the Bayesian
+psychology community. Rouder (2014) argues that optional stopping is no problem
+for Bayesians, and even recommends the use of optional stopping in practice, as
+do Wagenmakers et al. (2012). This article addresses the question whether
+optional stopping is problematic for Bayesian methods, and specifies under
+which circumstances and in which sense it is and is not. By slightly varying
+and extending Rouder's (2014) experiment, we illustrate that, as soon as the
+parameters of interest are equipped with default or pragmatic priors - which
+means, in most practical applications of Bayes Factor hypothesis testing -
+resilience to optional stopping can break down. We distinguish between four
+types of default priors, each having their own specific issues with optional
+stopping, ranging from no-problem-at-all (Type 0 priors) to quite severe (Type
+II and III priors).
+",0,0,1,1,0,0

From 933fa2ed64d0812029ec85b4e2d1cb359d99f836 Mon Sep 17 00:00:00 2001
From: Simran Shaikh <141727239+SimranShaikh20@users.noreply.github.com>
Date: Sun, 10 Nov 2024 18:47:53 +0530
Subject: [PATCH 2/7] Add files via upload

---
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 create mode 100644 Research Topic Prediction using Deep Learning/Dataset/test.csv

diff --git a/Research Topic Prediction using Deep Learning/Dataset/test.csv b/Research Topic Prediction using Deep Learning/Dataset/test.csv
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--- /dev/null
+++ b/Research Topic Prediction using Deep Learning/Dataset/test.csv	
@@ -0,0 +1,132507 @@
+ID,TITLE,ABSTRACT
+20973,Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization,"  We present novel understandings of the Gamma-Poisson (GaP) model, a
+probabilistic matrix factorization model for count data. We show that GaP can
+be rewritten free of the score/activation matrix. This gives us new insights
+about the estimation of the topic/dictionary matrix by maximum marginal
+likelihood estimation. In particular, this explains the robustness of this
+estimator to over-specified values of the factorization rank, especially its
+ability to automatically prune irrelevant dictionary columns, as empirically
+observed in previous work. The marginalization of the activation matrix leads
+in turn to a new Monte Carlo Expectation-Maximization algorithm with favorable
+properties.
+"
+20974,Laboratory mid-IR spectra of equilibrated and igneous meteorites. Searching for observables of planetesimal debris,"  Meteorites contain minerals from Solar System asteroids with different
+properties (like size, presence of water, core formation). We provide new
+mid-IR transmission spectra of powdered meteorites to obtain templates of how
+mid-IR spectra of asteroidal debris would look like. This is essential for
+interpreting mid-IR spectra of past and future space observatories, like the
+James Webb Space Telescope. We show that the transmission spectra of wet and
+dry chondrites, carbonaceous and ordinary chondrites and achondrite and
+chondrite meteorites are distinctly different in a way one can distinguish in
+astronomical mid-IR spectra. The two observables that spectroscopically
+separate the different meteorites groups (and thus the different types of
+parent bodies) are the pyroxene-olivine feature strength ratio and the peak
+shift of the olivine spectral features due to an increase in the iron
+concentration of the olivine.
+"
+20975,Case For Static AMSDU Aggregation in WLANs,"  Frame aggregation is a mechanism by which multiple frames are combined into a
+single transmission unit over the air. Frames aggregated at the AMSDU level use
+a common CRC check to enforce integrity. For longer aggregated AMSDU frames,
+the packet error rate increases significantly for the same bit error rate.
+Hence, multiple studies have proposed doing AMSDU aggregation adaptively based
+on the error rate. This study evaluates if there is a \emph{practical}
+advantage in doing adaptive AMSDU aggregation based on the link bit error rate.
+Evaluations on a model show that instead of implementing a complex adaptive
+AMSDU frame aggregation mechanism which impact queuing and other implementation
+aspects, it is easier to influence packet error rate with traditional
+mechanisms while keeping the AMSDU aggregation logic simple.
+"
+20976,The $Gaia$-ESO Survey: the inner disk intermediate-age open cluster NGC 6802,"  Milky Way open clusters are very diverse in terms of age, chemical
+composition, and kinematic properties. Intermediate-age and old open clusters
+are less common, and it is even harder to find them inside the solar
+Galactocentric radius, due to the high mortality rate and strong extinction
+inside this region. NGC 6802 is one of the inner disk open clusters (IOCs)
+observed by the $Gaia$-ESO survey (GES). This cluster is an important target
+for calibrating the abundances derived in the survey due to the kinematic and
+chemical homogeneity of the members in open clusters. Using the measurements
+from $Gaia$-ESO internal data release 4 (iDR4), we identify 95 main-sequence
+dwarfs as cluster members from the GIRAFFE target list, and eight giants as
+cluster members from the UVES target list. The dwarf cluster members have a
+median radial velocity of $13.6\pm1.9$ km s$^{-1}$, while the giant cluster
+members have a median radial velocity of $12.0\pm0.9$ km s$^{-1}$ and a median
+[Fe/H] of $0.10\pm0.02$ dex. The color-magnitude diagram of these cluster
+members suggests an age of $0.9\pm0.1$ Gyr, with $(m-M)_0=11.4$ and
+$E(B-V)=0.86$. We perform the first detailed chemical abundance analysis of NGC
+6802, including 27 elemental species. To gain a more general picture about
+IOCs, the measurements of NGC 6802 are compared with those of other IOCs
+previously studied by GES, that is, NGC 4815, Trumpler 20, NGC 6705, and
+Berkeley 81. NGC 6802 shows similar C, N, Na, and Al abundances as other IOCs.
+These elements are compared with nucleosynthetic models as a function of
+cluster turn-off mass. The $\alpha$, iron-peak, and neutron-capture elements
+are also explored in a self-consistent way.
+"
+20977,Witness-Functions versus Interpretation-Functions for Secrecy in Cryptographic Protocols: What to Choose?,"  Proving that a cryptographic protocol is correct for secrecy is a hard task.
+One of the strongest strategies to reach this goal is to show that it is
+increasing, which means that the security level of every single atomic message
+exchanged in the protocol, safely evaluated, never deceases. Recently, two
+families of functions have been proposed to measure the security level of
+atomic messages. The first one is the family of interpretation-functions. The
+second is the family of witness-functions. In this paper, we show that the
+witness-functions are more efficient than interpretation-functions. We give a
+detailed analysis of an ad-hoc protocol on which the witness-functions succeed
+in proving its correctness for secrecy while the interpretation-functions fail
+to do so.
+"
+20978,Pairwise Difference Estimation of High Dimensional Partially Linear Model,"  This paper proposes a regularized pairwise difference approach for estimating
+the linear component coefficient in a partially linear model, with consistency
+and exact rates of convergence obtained in high dimensions under mild scaling
+requirements. Our analysis reveals interesting features such as (i) the
+bandwidth parameter automatically adapts to the model and is actually
+tuning-insensitive; and (ii) the procedure could even maintain fast rate of
+convergence for $\alpha$-Hölder class of $\alpha\leq1/2$. Simulation studies
+show the advantage of the proposed method, and application of our approach to a
+brain imaging data reveals some biological patterns which fail to be recovered
+using competing methods.
+"
+20979,Dissecting the multivariate extremal index and tail dependence,"  A central issue in the theory of extreme values focuses on suitable
+conditions such that the well-known results for the limiting distributions of
+the maximum of i.i.d. sequences can be applied to stationary ones. In this
+context, the extremal index appears as a key parameter to capture the effect of
+temporal dependence on the limiting distribution of the maxima. The
+multivariate extremal index corresponds to a generalization of this concept to
+a multivariate context and affects the tail dependence structure within the
+marginal sequences and between them. As it is a function, the inference becomes
+more difficult, and it is therefore important to obtain characterizations,
+namely bounds based on the marginal dependence that are easier to estimate. In
+this work we present two decompositions that emphasize different types of
+information contained in the multivariate extremal index, an upper limit better
+than those found in the literature and we analyze its role in dependence on the
+limiting model of the componentwise maxima of a stationary sequence. We will
+illustrate the results with examples of recognized interest in applications.
+"
+20980,"Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy","  Astrophysics and cosmology are rich with data. The advent of wide-area
+digital cameras on large aperture telescopes has led to ever more ambitious
+surveys of the sky. Data volumes of entire surveys a decade ago can now be
+acquired in a single night and real-time analysis is often desired. Thus,
+modern astronomy requires big data know-how, in particular it demands highly
+efficient machine learning and image analysis algorithms. But scalability is
+not the only challenge: Astronomy applications touch several current machine
+learning research questions, such as learning from biased data and dealing with
+label and measurement noise. We argue that this makes astronomy a great domain
+for computer science research, as it pushes the boundaries of data analysis. In
+the following, we will present this exciting application area for data
+scientists. We will focus on exemplary results, discuss main challenges, and
+highlight some recent methodological advancements in machine learning and image
+analysis triggered by astronomical applications.
+"
+20981,Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog,"  A number of recent works have proposed techniques for end-to-end learning of
+communication protocols among cooperative multi-agent populations, and have
+simultaneously found the emergence of grounded human-interpretable language in
+the protocols developed by the agents, all learned without any human
+supervision!
+In this paper, using a Task and Tell reference game between two agents as a
+testbed, we present a sequence of 'negative' results culminating in a
+'positive' one -- showing that while most agent-invented languages are
+effective (i.e. achieve near-perfect task rewards), they are decidedly not
+interpretable or compositional.
+In essence, we find that natural language does not emerge 'naturally',
+despite the semblance of ease of natural-language-emergence that one may gather
+from recent literature. We discuss how it is possible to coax the invented
+languages to become more and more human-like and compositional by increasing
+restrictions on how two agents may communicate.
+"
+20982,Properties and Origin of Galaxy Velocity Bias in the Illustris Simulation,"  We use the hydrodynamical galaxy formation simulations from the Illustris
+suite to study the origin and properties of galaxy velocity bias, i.e., the
+difference between the velocity distributions of galaxies and dark matter
+inside halos. We find that galaxy velocity bias is a decreasing function of the
+ratio of galaxy stellar mass to host halo mass. In general, central galaxies
+are not at rest with respect to dark matter halos or the core of halos, with a
+velocity dispersion above 0.04 times that of the dark matter. The central
+galaxy velocity bias is found to be mostly caused by the close interactions
+between the central and satellite galaxies. For satellite galaxies, the
+velocity bias is related to their dynamical and tidal evolution history after
+being accreted onto the host halos. It depends on the time after the accretion
+and their distances from the halo centers, with massive satellites generally
+moving more slowly than the dark matter. The results are in broad agreements
+with those inferred from modeling small-scale redshift-space galaxy clustering
+data, and the study can help improve models of redshift-space galaxy
+clustering.
+"
+20983,Computer Modeling of Halogen Bonds and Other $σ$-Hole Interactions,"  In the field of noncovalent interactions a new paradigm has recently become
+popular. It stems from the analysis of molecular electrostatic potentials and
+introduces a label, which has recently attracted enormous attention. The label
+is {\sigma}-hole, and it was first used in connection with halogens. It
+initiated a renaissance of interest in halogenated compounds, and later on,
+when found also on other groups of atoms (chalcogens, pnicogens, tetrels and
+aerogens), it resulted in a new direction of research of intermolecular
+interactions. In this review, we summarize advances from about the last 10
+years in understanding those interactions related to {\sigma}-hole. We pay
+particular attention to theoretical and computational techniques, which play a
+crucial role in the field.
+"
+20984,Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets,"  We propose an end-to-end affect recognition approach using a Convolutional
+Neural Network (CNN) that handles multiple languages, with applications to
+emotion and personality recognition from speech. We lay the foundation of a
+universal model that is trained on multiple languages at once. As affect is
+shared across all languages, we are able to leverage shared information between
+languages and improve the overall performance for each one. We obtained an
+average improvement of 12.8% on emotion and 10.1% on personality when compared
+with the same model trained on each language only. It is end-to-end because we
+directly take narrow-band raw waveforms as input. This allows us to accept as
+input audio recorded from any source and to avoid the overhead and information
+loss of feature extraction. It outperforms a similar CNN using spectrograms as
+input by 12.8% for emotion and 6.3% for personality, based on F-scores.
+Analysis of the network parameters and layers activation shows that the network
+learns and extracts significant features in the first layer, in particular
+pitch, energy and contour variations. Subsequent convolutional layers instead
+capture language-specific representations through the analysis of
+supra-segmental features. Our model represents an important step for the
+development of a fully universal affect recognizer, able to recognize
+additional descriptors, such as stress, and for the future implementation into
+affective interactive systems.
+"
+20985,A Variational Characterization of Fluid Sloshing with Surface Tension,"  We consider the sloshing problem for an incompressible, inviscid,
+irrotational fluid in an open container, including effects due to surface
+tension on the free surface. We restrict ourselves to a constant contact angle
+and seek time-harmonic solutions of the linearized problem, which describes the
+time-evolution of the fluid due to a small initial disturbance of the surface
+at rest. As opposed to the zero surface tension case, where the problem reduces
+to a partial differential equation for the velocity potential, we obtain a
+coupled system for the velocity potential and the free surface displacement. We
+derive a new variational formulation of the coupled problem and establish the
+existence of solutions using the direct method from the calculus of variations.
+We prove a domain monotonicity result for the fundamental sloshing eigenvalue.
+In the limit of zero surface tension, we recover the variational formulation of
+the mixed Steklov-Neumann eigenvalue problem and give the first-order
+perturbation formula for a simple eigenvalue.
+"
+20986,diagnoseIT: Expertengestützte automatische Diagnose von Performance-Probleme in Enterprise-Anwendungen (Abschlussbericht),"  This is the final report of the collaborative research project diagnoseIT on
+expert-guided automatic diagnosis of performance problems in enterprise
+applications.
+"
+20987,Statistical Inference in Political Networks Research,"  Researchers interested in statistically modeling network data have a
+well-established and quickly growing set of approaches from which to choose.
+Several of these methods have been regularly applied in research on political
+networks, while others have yet to permeate the field. Here, we review the most
+prominent methods of inferential network analysis---both for cross-sectionally
+and longitudinally observed networks including (temporal) exponential random
+graph models, latent space models, the quadratic assignment procedure, and
+stochastic actor oriented models. For each method, we summarize its analytic
+form, identify prominent published applications in political science and
+discuss computational considerations. We conclude with a set of guidelines for
+selecting a method for a given application.
+"
+20988,Controller-jammer game models of Denial of Service in control systems operating over packet-dropping links,"  The paper introduces a class of zero-sum games between the adversary and
+controller as a scenario for a `denial of service' in a networked control
+system. The communication link is modeled as a set of transmission regimes
+controlled by a strategic jammer whose intention is to wage an attack on the
+plant by choosing a most damaging regime-switching strategy. We demonstrate
+that even in the one-step case, the introduced games admit a saddle-point
+equilibrium, at which the jammer's optimal policy is to randomize in a region
+of the plant's state space, thus requiring the controller to undertake a
+nontrivial response which is different from what one would expect in a standard
+stochastic control problem over a packet dropping link. The paper derives
+conditions for the introduced games to have such a saddle-point equilibrium.
+Furthermore, we show that in more general multi-stage games, these conditions
+provide `greedy' jamming strategies for the adversary.
+"
+20989,Conservation of spin supercurrents in superconductors,"  We demonstrate that spin supercurrents are conserved upon transmission
+through a conventional superconductor, even in the presence of spin-dependent
+scattering by impurities with magnetic moments or spin-orbit coupling. This is
+fundamentally different from conventional spin currents, which decay in the
+presence of such scattering, and this has important implications for the usage
+of superconducting materials in spintronic hybrid structures.
+"
+20990,Driver Identification Using Automobile Sensor Data from a Single Turn,"  As automotive electronics continue to advance, cars are becoming more and
+more reliant on sensors to perform everyday driving operations. These sensors
+are omnipresent and help the car navigate, reduce accidents, and provide
+comfortable rides. However, they can also be used to learn about the drivers
+themselves. In this paper, we propose a method to predict, from sensor data
+collected at a single turn, the identity of a driver out of a given set of
+individuals. We cast the problem in terms of time series classification, where
+our dataset contains sensor readings at one turn, repeated several times by
+multiple drivers. We build a classifier to find unique patterns in each
+individual's driving style, which are visible in the data even on such a short
+road segment. To test our approach, we analyze a new dataset collected by AUDI
+AG and Audi Electronics Venture, where a fleet of test vehicles was equipped
+with automotive data loggers storing all sensor readings on real roads. We show
+that turns are particularly well-suited for detecting variations across
+drivers, especially when compared to straightaways. We then focus on the 12
+most frequently made turns in the dataset, which include rural, urban, highway
+on-ramps, and more, obtaining accurate identification results and learning
+useful insights about driver behavior in a variety of settings.
+"
+20991,Same-different problems strain convolutional neural networks,"  The robust and efficient recognition of visual relations in images is a
+hallmark of biological vision. We argue that, despite recent progress in visual
+recognition, modern machine vision algorithms are severely limited in their
+ability to learn visual relations. Through controlled experiments, we
+demonstrate that visual-relation problems strain convolutional neural networks
+(CNNs). The networks eventually break altogether when rote memorization becomes
+impossible, as when intra-class variability exceeds network capacity. Motivated
+by the comparable success of biological vision, we argue that feedback
+mechanisms including attention and perceptual grouping may be the key
+computational components underlying abstract visual reasoning.\
+"
+20992,Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end,"  In this work we propose a system for visual question answering. Our
+architecture is composed of two parts, the first part creates the logical
+knowledge base given the image. The second part evaluates questions against the
+knowledge base. Differently from previous work, the knowledge base is
+represented using hyper-dimensional computing. This choice has the advantage
+that all the operations in the system, namely creating the knowledge base and
+evaluating the questions against it, are differentiable, thereby making the
+system easily trainable in an end-to-end fashion.
+"
+20993,The Inner Structure of Time-Dependent Signals,"  This paper shows how a time series of measurements of an evolving system can
+be processed to create an inner time series that is unaffected by any
+instantaneous invertible, possibly nonlinear transformation of the
+measurements. An inner time series contains information that does not depend on
+the nature of the sensors, which the observer chose to monitor the system.
+Instead, it encodes information that is intrinsic to the evolution of the
+observed system. Because of its sensor-independence, an inner time series may
+produce fewer false negatives when it is used to detect events in the presence
+of sensor drift. Furthermore, if the observed physical system is comprised of
+non-interacting subsystems, its inner time series is separable; i.e., it
+consists of a collection of time series, each one being the inner time series
+of an isolated subsystem. Because of this property, an inner time series can be
+used to detect a specific behavior of one of the independent subsystems without
+using blind source separation to disentangle that subsystem from the others.
+The method is illustrated by applying it to: 1) an analytic example; 2) the
+audio waveform of one speaker; 3) video images from a moving camera; 4)
+mixtures of audio waveforms of two speakers.
+"
+20994,Classical properties of the leading eigenstates of quantum dissipative systems,"  By analyzing a paradigmatic example of the theory of dissipative systems --
+the classical and quantum dissipative standard map -- we are able to explain
+the main features of the decay to the quantum equilibrium state. The classical
+isoperiodic stable structures typically present in the parameter space of these
+kind of systems play a fundamental role. In fact, we have found that the period
+of stable structures that are near in this space determines the phase of the
+leading eigenstates of the corresponding quantum superoperator. Moreover, the
+eigenvectors show a strong localization on the corresponding periodic orbits
+(limit cycles). We show that this sort of scarring phenomenon (an established
+property of Hamiltonian and projectively open systems) is present in the
+dissipative case and it is of extreme simplicity.
+"
+20995,Towards Binary-Valued Gates for Robust LSTM Training,"  Long Short-Term Memory (LSTM) is one of the most widely used recurrent
+structures in sequence modeling. It aims to use gates to control information
+flow (e.g., whether to skip some information or not) in the recurrent
+computations, although its practical implementation based on soft gates only
+partially achieves this goal. In this paper, we propose a new way for LSTM
+training, which pushes the output values of the gates towards 0 or 1. By doing
+so, we can better control the information flow: the gates are mostly open or
+closed, instead of in a middle state, which makes the results more
+interpretable. Empirical studies show that (1) Although it seems that we
+restrict the model capacity, there is no performance drop: we achieve better or
+comparable performances due to its better generalization ability; (2) The
+outputs of gates are not sensitive to their inputs: we can easily compress the
+LSTM unit in multiple ways, e.g., low-rank approximation and low-precision
+approximation. The compressed models are even better than the baseline models
+without compression.
+"
+20996,GUB Covers and Power-Indexed formulations for Wireless Network Design,"  We propose a pure 0-1 formulation for the wireless network design problem,
+i.e. the problem of configuring a set of transmitters to provide service
+coverage to a set of receivers. In contrast with classical mixed integer
+formulations, where power emissions are represented by continuous variables, we
+consider only a finite set of powers values. This has two major advantages: it
+better fits the usual practice and eliminates the sources of numerical problems
+which heavily affect continuous models. A crucial ingredient of our approach is
+an effective basic formulation for the single knapsack problem representing the
+coverage condition of a receiver. This formulation is based on the GUB cover
+inequalities introduced by Wolsey (1990) and its core is an extension of the
+exact formulation of the GUB knapsack polytope with two GUB constraints. This
+special case corresponds to the very common practical situation where only one
+major interferer is present. We assess the effectiveness of our formulation by
+comprehensive computational results over realistic instances of two typical
+technologies, namely WiMAX and DVB-T.
+"
+20997,Orbital contributions to the electron g-factor in semiconductor nanowires,"  Recent experiments on Majorana fermions in semiconductor nanowires [Albrecht
+et al., Nat. 531, 206 (2016)] revealed a surprisingly large electronic Landé
+g-factor, several times larger than the bulk value - contrary to the
+expectation that confinement reduces the g-factor. Here we assess the role of
+orbital contributions to the electron g-factor in nanowires and quantum dots.
+We show that an LS coupling in higher subbands leads to an enhancement of the
+g-factor of an order of magnitude or more for small effective mass
+semiconductors. We validate our theoretical finding with simulations of InAs
+and InSb, showing that the effect persists even if cylindrical symmetry is
+broken. A huge anisotropy of the enhanced g-factors under magnetic field
+rotation allows for a straightforward experimental test of this theory.
+"
+20998,Comprehensive routing strategy on multilayer networks,"  Designing an efficient routing strategy is of great importance to alleviate
+traffic congestion in multilayer networks. In this work, we design an effective
+routing strategy for multilayer networks by comprehensively considering the
+roles of nodes' local structures in micro-level, as well as the macro-level
+differences in transmission speeds between different layers. Both numerical and
+analytical results indicate that our proposed routing strategy can reasonably
+redistribute the traffic load of the low speed layer to the high speed layer,
+and thus the traffic capacity of multilayer networks are significantly enhanced
+compared with the monolayer low speed networks. There is an optimal combination
+of macro- and micro-level control parameters at which can remarkably alleviate
+the congestion and thus maximize the traffic capacity for a given multilayer
+network. Moreover, we find that increasing the size and the average degree of
+the high speed layer can enhance the traffic capacity of multilayer networks
+more effectively. We finally verify that real-world network topology does not
+invalidate the results. The theoretical predictions agree well with the
+numerical simulations.
+"
+20999,Long short-term memory and learning-to-learn in networks of spiking neurons,"  Recurrent networks of spiking neurons (RSNNs) underlie the astounding
+computing and learning capabilities of the brain. But computing and learning
+capabilities of RSNN models have remained poor, at least in comparison with
+artificial neural networks (ANNs). We address two possible reasons for that.
+One is that RSNNs in the brain are not randomly connected or designed according
+to simple rules, and they do not start learning as a tabula rasa network.
+Rather, RSNNs in the brain were optimized for their tasks through evolution,
+development, and prior experience. Details of these optimization processes are
+largely unknown. But their functional contribution can be approximated through
+powerful optimization methods, such as backpropagation through time (BPTT).
+A second major mismatch between RSNNs in the brain and models is that the
+latter only show a small fraction of the dynamics of neurons and synapses in
+the brain. We include neurons in our RSNN model that reproduce one prominent
+dynamical process of biological neurons that takes place at the behaviourally
+relevant time scale of seconds: neuronal adaptation. We denote these networks
+as LSNNs because of their Long short-term memory. The inclusion of adapting
+neurons drastically increases the computing and learning capability of RSNNs if
+they are trained and configured by deep learning (BPTT combined with a rewiring
+algorithm that optimizes the network architecture). In fact, the computational
+performance of these RSNNs approaches for the first time that of LSTM networks.
+In addition RSNNs with adapting neurons can acquire abstract knowledge from
+prior learning in a Learning-to-Learn (L2L) scheme, and transfer that knowledge
+in order to learn new but related tasks from very few examples. We demonstrate
+this for supervised learning and reinforcement learning.
+"
+21000,Detecting Molecular Rotational Dynamics Complementing the Low-Frequency Terahertz Vibrations in a Zirconium-Based Metal-Organic Framework,"  We show clear experimental evidence of co-operative terahertz (THz) dynamics
+observed below 3 THz (~100 cm-1), for a low-symmetry Zr-based metal-organic
+framework (MOF) structure, termed MIL-140A [ZrO(O2C-C6H4-CO2)]. Utilizing a
+combination of high-resolution inelastic neutron scattering and synchrotron
+radiation far-infrared spectroscopy, we measured low-energy vibrations
+originating from the hindered rotations of organic linkers, whose energy
+barriers and detailed dynamics have been elucidated via ab initio density
+functional theory (DFT) calculations. For completeness, we obtained Raman
+spectra and characterized the alterations to the complex pore architecture
+caused by the THz rotations. We discovered an array of soft modes with
+trampoline-like motions, which could potentially be the source of anomalous
+mechanical phenomena, such as negative linear compressibility and negative
+thermal expansion. Our results also demonstrate coordinated shear dynamics
+(~2.5 THz), a mechanism which we have shown to destabilize MOF crystals, in the
+exact crystallographic direction of the minimum shear modulus (Gmin).
+"
+21001,A southern-sky total intensity source catalogue at 2.3 GHz from S-band Polarisation All-Sky Survey data,"  The S-band Polarisation All-Sky Survey (S-PASS) has observed the entire
+southern sky using the 64-metre Parkes radio telescope at 2.3GHz with an
+effective bandwidth of 184MHz. The surveyed sky area covers all declinations
+$\delta\leq 0^\circ$. To analyse compact sources the survey data have been
+re-processed to produce a set of 107 Stokes $I$ maps with 10.75arcmin
+resolution and the large scale emission contribution filtered out. In this
+paper we use these Stokes $I$ images to create a total intensity southern-sky
+extragalactic source catalogue at 2.3GHz. The source catalogue contains 23,389
+sources and covers a sky area of 16,600deg$^2$, excluding the Galactic plane
+for latitudes $|b|<10^\circ$. Approximately 8% of catalogued sources are
+resolved. S-PASS source positions are typically accurate to within 35arcsec. At
+a flux density of 225mJy the S-PASS source catalogue is more than 95% complete,
+and $\sim$94% of S-PASS sources brighter than 500mJy beam$^{-1}$ have a
+counterpart at lower frequencies.
+"
+21002,Mechanomyography based closed-loop Functional Electrical Stimulation cycling system,"  Functional Electrical Stimulation (FES) systems are successful in restoring
+motor function and supporting paralyzed users. Commercially available FES
+products are open loop, meaning that the system is unable to adapt to changing
+conditions with the user and their muscles which results in muscle fatigue and
+poor stimulation protocols. This is because it is difficult to close the loop
+between stimulation and monitoring of muscle contraction using adaptive
+stimulation. FES causes electrical artefacts which make it challenging to
+monitor muscle contractions with traditional methods such as electromyography
+(EMG). We look to overcome this limitation by combining FES with novel
+mechanomyographic (MMG) sensors to be able to monitor muscle activity during
+stimulation in real time. To provide a meaningful task we built an FES cycling
+rig with a software interface that enabled us to perform adaptive recording and
+stimulation, and then combine this with sensors to record forces applied to the
+pedals using force sensitive resistors (FSRs), crank angle position using a
+magnetic incremental encoder and inputs from the user using switches and a
+potentiometer. We illustrated this with a closed-loop stimulation algorithm
+that used the inputs from the sensors to control the output of a programmable
+RehaStim 1 FES stimulator (Hasomed) in real-time. This recumbent bicycle rig
+was used as a testing platform for FES cycling. The algorithm was designed to
+respond to a change in requested speed (RPM) from the user and change the
+stimulation power (% of maximum current mA) until this speed was achieved and
+then maintain it.
+"
+21003,QT2S: A System for Monitoring Road Traffic via Fine Grounding of Tweets,"  Social media platforms provide continuous access to user generated content
+that enables real-time monitoring of user behavior and of events. The
+geographical dimension of such user behavior and events has recently caught a
+lot of attention in several domains: mobility, humanitarian, or
+infrastructural. While resolving the location of a user can be straightforward,
+depending on the affordances of their device and/or of the application they are
+using, in most cases, locating a user demands a larger effort, such as
+exploiting textual features. On Twitter for instance, only 2% of all tweets are
+geo-referenced. In this paper, we present a system for zoomed-in grounding
+(below city level) for short messages (e.g., tweets). The system combines
+different natural language processing and machine learning techniques to
+increase the number of geo-grounded tweets, which is essential to many
+applications such as disaster response and real-time traffic monitoring.
+"
+21004,A Benchmark for Dose Finding Studies with Continuous Outcomes,"  An important tool to evaluate the performance of any design is an optimal
+benchmark proposed by O'Quigley and others (2002, Biostatistics 3(1), 51-56)
+that provides an upper bound on the performance of a design under a given
+scenario. The original benchmark can be applied to dose finding studies with a
+binary endpoint only. However, there is a growing interest in dose finding
+studies involving continuous outcomes, but no benchmark for such studies has
+been developed. We show that the original benchmark and its extension by Cheung
+(2014, Biometrics 70(2), 389-397), when looked at from a different perspective,
+can be generalised to various settings with several discrete and continuous
+outcomes. We illustrate and compare the benchmark performance in the setting of
+a Phase I clinical trial with continuous toxicity endpoint and in the setting
+of a Phase I/II clinical trial with continuous efficacy outcome. We show that
+the proposed benchmark provides an accurate upper bound for model-based dose
+finding methods and serves as a powerful tool for evaluating designs.
+"
+21005,Wavefront retrieval through random pupil plane phase probes: Gerchberg-Saxton approach,"  A pupil plane wavefront reconstruction procedure is proposed based on
+analysis of a sequence of focal plane images corresponding to a sequence of
+random pupil plane phase probes. The developed method provides the unique
+nontrivial solution of wavefront retrieval problem and shows global convergence
+to this solution demonstrated using a Gerchberg-Saxton implementation. The
+method is general and can be used in any optical system that includes
+deformable mirrors for active/adaptive wavefront correction. The presented
+numerical simulation and lab experimental results show low noise sensitivity,
+high reliability and robustness of the proposed approach for high quality
+optical wavefront restoration. Laboratory experiments have shown $\lambda$/14
+rms accuracy in retrieval of a poked DM actuator fiducial pattern with spatial
+resolution of 20-30$~\mu$m that is comparable with accuracy of direct
+high-resolution interferometric measurements.
+"
+21006,Uplink Non-Orthogonal Multiple Access for 5G Wireless Networks,"  Orthogonal Frequency Division Multiple Access (OFDMA) as well as other
+orthogonal multiple access techniques fail to achieve the system capacity limit
+in the uplink due to the exclusivity in resource allocation. This issue is more
+prominent when fairness among the users is considered in the system. Current
+Non-Orthogonal Multiple Access (NOMA) techniques introduce redundancy by
+coding/spreading to facilitate the users' signals separation at the receiver,
+which degrade the system spectral efficiency. Hence, in order to achieve higher
+capacity, more efficient NOMA schemes need to be developed. In this paper, we
+propose a NOMA scheme for uplink that removes the resource allocation
+exclusivity and allows more than one user to share the same subcarrier without
+any coding/spreading redundancy. Joint processing is implemented at the
+receiver to detect the users' signals. However, to control the receiver
+complexity, an upper limit on the number of users per subcarrier needs to be
+imposed. In addition, a novel subcarrier and power allocation algorithm is
+proposed for the new NOMA scheme that maximizes the users' sum-rate. The
+link-level performance evaluation has shown that the proposed scheme achieves
+bit error rate close to the single-user case. Numerical results show that the
+proposed NOMA scheme can significantly improve the system performance in terms
+of spectral efficiency and fairness comparing to OFDMA.
+"
+21007,Epsilon-approximations and epsilon-nets,"  The use of random samples to approximate properties of geometric
+configurations has been an influential idea for both combinatorial and
+algorithmic purposes. This chapter considers two related
+notions---$\epsilon$-approximations and $\epsilon$-nets---that capture the most
+important quantitative properties that one would expect from a random sample
+with respect to an underlying geometric configuration.
+"
+21008,A continuous framework for fairness,"  Increasingly, discrimination by algorithms is perceived as a societal and
+legal problem. As a response, a number of criteria for implementing algorithmic
+fairness in machine learning have been developed in the literature. This paper
+proposes the Continuous Fairness Algorithm (CFA$\theta$) which enables a
+continuous interpolation between different fairness definitions. More
+specifically, we make three main contributions to the existing literature.
+First, our approach allows the decision maker to continuously vary between
+concepts of individual and group fairness. As a consequence, the algorithm
+enables the decision maker to adopt intermediate ""worldviews"" on the degree of
+discrimination encoded in algorithmic processes, adding nuance to the extreme
+cases of ""we're all equal"" (WAE) and ""what you see is what you get"" (WYSIWYG)
+proposed so far in the literature. Second, we use optimal transport theory, and
+specifically the concept of the barycenter, to maximize decision maker utility
+under the chosen fairness constraints. Third, the algorithm is able to handle
+cases of intersectionality, i.e., of multi-dimensional discrimination of
+certain groups on grounds of several criteria. We discuss three main examples
+(college admissions; credit application; insurance contracts) and map out the
+policy implications of our approach. The explicit formalization of the
+trade-off between individual and group fairness allows this post-processing
+approach to be tailored to different situational contexts in which one or the
+other fairness criterion may take precedence.
+"
+21009,Counting Subwords Occurrences in Base-b Expansions,"  We count the number of distinct (scattered) subwords occurring in the base-b
+expansion of the non-negative integers. More precisely, we consider the
+sequence $(S_b(n))_{n\ge 0}$ counting the number of positive entries on each
+row of a generalization of the Pascal triangle to binomial coefficients of
+base-$b$ expansions. By using a convenient tree structure, we provide
+recurrence relations for $(S_b(n))_{n\ge 0}$ leading to the $b$-regularity of
+the latter sequence. Then we deduce the asymptotics of the summatory function
+of the sequence $(S_b(n))_{n\ge 0}$.
+"
+21010,Tensor Renormalization Group with Randomized Singular Value Decomposition,"  An algorithm of the tensor renormalization group is proposed based on a
+randomized algorithm for singular value decomposition. Our algorithm is
+applicable to a broad range of two-dimensional classical models. In the case of
+a square lattice, its computational complexity and memory usage are
+proportional to the fifth and the third power of the bond dimension,
+respectively, whereas those of the conventional implementation are of the sixth
+and the fourth power. The oversampling parameter larger than the bond dimension
+is sufficient to reproduce the same result as full singular value decomposition
+even at the critical point of the two-dimensional Ising model.
+"
+21011,Cohomology of symplectic groups and Meyer's signature theorem,"  Meyer showed that the signature of a closed oriented surface bundle over a
+surface is a multiple of $4$, and can be computed using an element of
+$H^2(\mathsf{Sp}(2g, \mathbb{Z}),\mathbb{Z})$. Denoting by $1 \to \mathbb{Z}
+\to \widetilde{\mathsf{Sp}(2g,\mathbb{Z})} \to \mathsf{Sp}(2g,\mathbb{Z}) \to
+1$ the pullback of the universal cover of $\mathsf{ Sp}(2g,\mathbb{R})$,
+Deligne proved that every finite index subgroup of $\widetilde{\mathsf {Sp}(2g,
+\mathbb{Z})}$ contains $2\mathbb{Z}$. As a consequence, a class in the second
+cohomology of any finite quotient of $\mathsf{Sp}(2g, \mathbb{Z})$ can at most
+enable us to compute the signature of a surface bundle modulo $8$. We show that
+this is in fact possible and investigate the smallest quotient of
+$\mathsf{Sp}(2g, \mathbb{Z})$ that contains this information. This quotient
+$\mathfrak{H}$ is a non-split extension of $\mathsf {Sp}(2g,2)$ by an
+elementary abelian group of order $2^{2g+1}$. There is a central extension
+$1\to \mathbb{Z}/2\to\tilde{\mathfrak{H}}\to\mathfrak{H}\to 1$, and
+$\tilde{\mathfrak{H}}$ appears as a quotient of the metaplectic double cover
+$\mathsf{Mp}(2g,\mathbb{Z})=\widetilde{\mathsf{Sp}(2g,\mathbb{Z})}/2\mathbb{Z}$.
+It is an extension of $\mathsf{Sp}(2g,2)$ by an almost extraspecial group of
+order $2^{2g+2}$, and has a faithful irreducible complex representation of
+dimension $2^g$. Provided $g\ge 4$, $\widetilde{\mathfrak{H}}$ is the universal
+central extension of $\mathfrak{H}$. Putting all this together, we provide a
+recipe for computing the signature modulo $8$, and indicate some consequences.
+"
+21012,The Newman--Shapiro problem,"  We give a negative answer to the Newman--Shapiro problem on weighted
+approximation for entire functions formulated in 1966 and motivated by the
+theory of operators on the Fock space. There exists a function in the Fock
+space such that its exponential multiples do not approximate some entire
+multiples in the space. Furthermore, we establish several positive results
+under different restrictions on the function in question.
+"
+21013,randUTV: A blocked randomized algorithm for computing a rank-revealing UTV factorization,"  This manuscript describes the randomized algorithm randUTV for computing a so
+called UTV factorization efficiently. Given a matrix $A$, the algorithm
+computes a factorization $A = UTV^{*}$, where $U$ and $V$ have orthonormal
+columns, and $T$ is triangular (either upper or lower, whichever is preferred).
+The algorithm randUTV is developed primarily to be a fast and easily
+parallelized alternative to algorithms for computing the Singular Value
+Decomposition (SVD). randUTV provides accuracy very close to that of the SVD
+for problems such as low-rank approximation, solving ill-conditioned linear
+systems, determining bases for various subspaces associated with the matrix,
+etc. Moreover, randUTV produces highly accurate approximations to the singular
+values of $A$. Unlike the SVD, the randomized algorithm proposed builds a UTV
+factorization in an incremental, single-stage, and non-iterative way, making it
+possible to halt the factorization process once a specified tolerance has been
+met. Numerical experiments comparing the accuracy and speed of randUTV to the
+SVD are presented. These experiments demonstrate that in comparison to column
+pivoted QR, which is another factorization that is often used as a relatively
+economic alternative to the SVD, randUTV compares favorably in terms of speed
+while providing far higher accuracy.
+"
+21014,"Continuity of nonlinear eigenvalues in $CD(K,\infty)$ spaces with respect to measured Gromov-Hausdorff convergence","  In this note we prove in the nonlinear setting of $CD(K,\infty)$ spaces the
+stability of the Krasnoselskii spectrum of the Laplace operator $-\Delta$ under
+measured Gromov-Hausdorff convergence, under an additional compactness
+assumption satisfied, for instance, by sequences of $CD^*(K,N)$ metric measure
+spaces with uniformly bounded diameter. Additionally, we show that every
+element $\lambda$ in the Krasnoselskii spectrum is indeed an eigenvalue, namely
+there exists a nontrivial $u$ satisfying the eigenvalue equation $- \Delta u =
+\lambda u$.
+"
+21015,Modeling WiFi Traffic for White Space Prediction in Wireless Sensor Networks,"  Cross Technology Interference (CTI) is a prevalent phenomenon in the 2.4 GHz
+unlicensed spectrum causing packet losses and increased channel contention. In
+particular, WiFi interference is a severe problem for low-power wireless
+networks as its presence causes a significant degradation of the overall
+performance. In this paper, we propose a proactive approach based on WiFi
+interference modeling for accurately predicting transmission opportunities for
+low-power wireless networks. We leverage statistical analysis of real-world
+WiFi traces to learn aggregated traffic characteristics in terms of
+Inter-Arrival Time (IAT) that, once captured into a specific 2nd order Markov
+Modulated Poisson Process (MMPP(2)) model, enable accurate estimation of
+interference. We further use a hidden Markov model (HMM) for channel occupancy
+prediction. We evaluated the performance of i) the MMPP(2) traffic model w.r.t.
+real-world traces and an existing Pareto model for accurately characterizing
+the WiFi traffic and, ii) compared the HMM based white space prediction to
+random channel access. We report encouraging results for using interference
+modeling for white space prediction.
+"
+21016,Dyson models under renormalization and in weak fields,"  We consider one-dimensional long-range spin models (usually called Dyson
+models), consisting of Ising ferromagnets with slowly decaying long-range pair
+potentials of the form $\frac{1}{|i-j|^{\alpha}}$ mainly focusing on the range
+of slow decays $1 < \alpha \leq 2$. We describe two recent results, one about
+renormalization and one about the effect of external fields at low temperature.
+The first result states that a decimated long-range Gibbs measure in one
+dimension becomes non-Gibbsian, in the same vein as comparable results in
+higher dimensions for short-range models. The second result addresses the
+behaviour of such models under inhomogeneous fields, in particular external
+fields which decay to zero polynomially as $(|i|+1)^{- \gamma}$. We study how
+the critical decay power of the field, $\gamma$, for which the phase transition
+persists and the decay power $\alpha$ of the Dyson model compare, extending
+recent results for short-range models on lattices and on trees. We also briefly
+point out some analogies between these results.
+"
+21017,Coupling geometry on binary bipartite networks: hypotheses testing on pattern geometry and nestedness,"  Upon a matrix representation of a binary bipartite network, via the
+permutation invariance, a coupling geometry is computed to approximate the
+minimum energy macrostate of a network's system. Such a macrostate is supposed
+to constitute the intrinsic structures of the system, so that the coupling
+geometry should be taken as information contents, or even the nonparametric
+minimum sufficient statistics of the network data. Then pertinent null and
+alternative hypotheses, such as nestedness, are to be formulated according to
+the macrostate. That is, any efficient testing statistic needs to be a function
+of this coupling geometry. These conceptual architectures and mechanisms are by
+and large still missing in community ecology literature, and rendered
+misconceptions prevalent in this research area. Here the algorithmically
+computed coupling geometry is shown consisting of deterministic multiscale
+block patterns, which are framed by two marginal ultrametric trees on row and
+column axes, and stochastic uniform randomness within each block found on the
+finest scale. Functionally a series of increasingly larger ensembles of matrix
+mimicries is derived by conforming to the multiscale block configurations. Here
+matrix mimicking is meant to be subject to constraints of row and column sums
+sequences. Based on such a series of ensembles, a profile of distributions
+becomes a natural device for checking the validity of testing statistics or
+structural indexes. An energy based index is used for testing whether network
+data indeed contains structural geometry. A new version block-based nestedness
+index is also proposed. Its validity is checked and compared with the existing
+ones. A computing paradigm, called Data Mechanics, and its application on one
+real data network are illustrated throughout the developments and discussions
+in this paper.
+"
+21018,"MUSE-inspired view of the quasar Q2059-360, its Lyman alpha blob, and its neighborhood","  The radio-quiet quasar Q2059-360 at redshift $z=3.08$ is known to be close to
+a small Lyman $\alpha$ blob (LAB) and to be absorbed by a proximate damped
+Ly$\alpha$ (PDLA) system.
+Here, we present the Multi Unit Spectroscopic Explorer (MUSE) integral field
+spectroscopy follow-up of this quasi-stellar object (QSO). Our primary goal is
+to characterize this LAB in detail by mapping it both spatially and spectrally
+using the Ly$\alpha$ line, and by looking for high-ionization lines to
+constrain the emission mechanism.
+Combining the high sensitivity of the MUSE integral field spectrograph
+mounted on the Yepun telescope at ESO-VLT with the natural coronagraph provided
+by the PDLA, we map the LAB down to the QSO position, after robust subtraction
+of QSO light in the spectral domain.
+In addition to confirming earlier results for the small bright component of
+the LAB, we unveil a faint filamentary emission protruding to the south over
+about 80 pkpc (physical kpc); this results in a total size of about 120 pkpc.
+We derive the velocity field of the LAB (assuming no transfer effects) and map
+the Ly$\alpha$ line width. Upper limits are set to the flux of the N V $\lambda
+1238-1242$, C IV $\lambda 1548-1551$, He II $\lambda 1640$, and C III] $\lambda
+1548-1551$ lines. We have discovered two probable Ly$\alpha$ emitters at the
+same redshift as the LAB and at projected distances of 265 kpc and 207 kpc from
+the QSO; their Ly$\alpha$ luminosities might well be enhanced by the QSO
+radiation. We also find an emission line galaxy at $z=0.33$ near the line of
+sight to the QSO.
+This LAB shares the same general characteristics as the 17 others surrounding
+radio-quiet QSOs presented previously. However, there are indications that it
+may be centered on the PDLA galaxy rather than on the QSO.
+"
+21019,Community Detection on Euclidean Random Graphs,"  We study the problem of community detection (CD) on Euclidean random
+geometric graphs where each vertex has two latent variables: a binary community
+label and a $\mathbb{R}^d$ valued location label which forms the support of a
+Poisson point process of intensity $\lambda$. A random graph is then drawn with
+edge probabilities dependent on both the community and location labels. In
+contrast to the stochastic block model (SBM) that has no location labels, the
+resulting random graph contains many more short loops due to the geometric
+embedding. We consider the recovery of the community labels, partial and exact,
+using the random graph and the location labels. We establish phase transitions
+for both sparse and logarithmic degree regimes, and provide bounds on the
+location of the thresholds, conjectured to be tight in the case of exact
+recovery. We also show that the threshold of the distinguishability problem,
+i.e., the testing between our model and the null model without community labels
+exhibits no phase-transition and in particular, does not match the weak
+recovery threshold (in contrast to the SBM).
+"
+21020,Load balancing with heterogeneous schedulers,"  Load balancing is a common approach in web server farms or inventory routing
+problems. An important issue in such systems is to determine the server to
+which an incoming request should be routed to optimize a given performance
+criteria. In this paper, we assume the server's scheduling disciplines to be
+heterogeneous. More precisely, a server implements a scheduling discipline
+which belongs to the class of limited processor sharing (LPS-$d$) scheduling
+disciplines. Under LPS-$d$, up to $d$ jobs can be served simultaneously, and
+hence, includes as special cases First Come First Served ($d=1$) and Processor
+Sharing ($d=\infty$).
+In order to obtain efficient heuristics, we model the above load-balancing
+framework as a multi-armed restless bandit problem. Using the relaxation
+technique, as first developed in the seminal work of Whittle, we derive
+Whittle's index policy for general cost functions and obtain a closed-form
+expression for Whittle's index in terms of the steady-state distribution.
+Through numerical computations, we investigate the performance of Whittle's
+index with two different performance criteria: linear cost criterion and a cost
+criterion that depends on the first and second moment of the throughput. Our
+results show that \emph{(i)} the structure of Whittle's index policy can
+strongly depend on the scheduling discipline implemented in the server, i.e.,
+on $d$, and that \emph{(ii)} Whittle's index policy significantly outperforms
+standard dispatching rules such as Join the Shortest Queue (JSQ), Join the
+Shortest Expected Workload (JSEW), and Random Server allocation (RSA).
+"
+21021,An Information Theoretic Framework for Active De-anonymization in Social Networks Based on Group Memberships,"  In this paper, a new mathematical formulation for the problem of
+de-anonymizing social network users by actively querying their membership in
+social network groups is introduced. In this formulation, the attacker has
+access to a noisy observation of the group membership of each user in the
+social network. When an unidentified victim visits a malicious website, the
+attacker uses browser history sniffing to make queries regarding the victim's
+social media activity. Particularly, it can make polar queries regarding the
+victim's group memberships and the victim's identity. The attacker receives
+noisy responses to her queries. The goal is to de-anonymize the victim with the
+minimum number of queries. Starting with a rigorous mathematical model for this
+active de-anonymization problem, an upper bound on the attacker's expected
+query cost is derived, and new attack algorithms are proposed which achieve
+this bound. These algorithms vary in computational cost and performance. The
+results suggest that prior heuristic approaches to this problem provide
+sub-optimal solutions.
+"
+21022,Geobiodynamics and Roegenian Economic Systems,"  This mathematical essay brings together ideas from Economics, Geobiodynamics
+and Thermodynamics. Its purpose is to obtain real models of complex
+evolutionary systems. More specifically, the essay defines Roegenian Economy
+and links Geobiodynamics and Roegenian Economy. In this context, we discuss the
+isomorphism between the concepts and techniques of Thermodynamics and
+Economics. Then we describe a Roegenian economic system like a Carnot group.
+After we analyse the phase equilibrium for two heterogeneous economic systems.
+The European Union Economics appears like Cartesian product of Roegenian
+economic systems and its Balance is analysed in details. A Section at the end
+describes the ""economic black holes"" as small parts of a a global economic
+system in which national income is so great that it causes others poor
+enrichment. These ideas can be used to improve our knowledge and understanding
+of the nature of development and evolution of thermodynamic-economic systems.
+"
+21023,The effects of the overshooting of the convective core on main-sequence turnoffs of young- and intermediate-age star clusters,"  Recent investigations have shown that the extended main-sequence turnoffs
+(eMSTOs) are a common feature of intermediate-age star clusters in the
+Magellanic Clouds. The eMSTOs are also found in the color-magnitude diagram
+(CMD) of young-age star clusters. The origin of the eMSTOs is still an open
+question. Moreover, asteroseismology shows that the value of the overshooting
+parameter $\delta_{\rm ov}$ of the convective core is not fixed for the stars
+with an approximatelly equal mass. Thus the MSTO of star clusters may be
+affected by the overshooting of the convective core (OVCC). We calculated the
+effects of the OVCC with different $\delta_{\rm ov}$ on the MSTO of young- and
+intermediate-age star clusters. \textbf{If $\delta_{\rm ov}$ varies between
+stars in a cluster,} the observed eMSTOs of young- and intermediate-age star
+clusters can be explained well by the effects. The equivalent age spreads of
+MSTO caused by the OVCC are related to the age of star clusters and are in good
+agreement with observed results of many clusters. Moreover, the observed eMSTOs
+of NGC 1856 are reproduced by the coeval populations with different
+$\delta_{\rm ov}$. The eMSTOs of star clusters may be relevant to the effects
+of the OVCC. The effects of the OVCC \textbf{are similar to that of rotation in
+some respects. But the effects cannot result in a significant split of main
+sequence of young star clusters at $m_{U}\lesssim 21$.} The presence of a rapid
+rotation can make the split of main sequence of young star clusters more
+significant.
+"
+21024,First eigenvalue estimates of Dirichlet-to-Neumann operators on graphs,"  Following Escobar [Esc97] and Jammes [Jam15], we introduce two types of
+isoperimetric constants and give lower bound estimates for the first nontrivial
+eigenvalues of Dirichlet-to-Neumann operators on finite graphs with boundary
+respectively.
+"
+21025,Air-burst Generated Tsunamis,"  This paper examines the questions of whether smaller asteroids that burst in
+the air over water can generate tsunamis that could pose a threat to distant
+locations. Such air burst-generated tsunamis are qualitatively different than
+the more frequently studied earthquake-generated tsunamis, and differ as well
+from impact asteroids. Numerical simulations are presented using the shallow
+water equations in several settings, demonstrating very little tsunami threat
+from this scenario. A model problem with an explicit solution that demonstrates
+and explains the same phenomena found in the computations is analyzed. We
+discuss the question of whether compressibility and dispersion are important
+effects that should be included, and show results from a more sophisticated
+model problem using the linearized Euler equations that begins to addresses
+this.
+"
+21026,A new design principle of robust onion-like networks self-organized in growth,"  Today's economy, production activity, and our life are sustained by social
+and technological network infrastructures, while new threats of network attacks
+by destructing loops have been found recently in network science. We inversely
+take into account the weakness, and propose a new design principle for
+incrementally growing robust networks. The networks are self-organized by
+enhancing interwoven long loops. In particular, we consider the range-limited
+approximation of linking by intermediations in a few hops, and show the strong
+robustness in the growth without degrading efficiency of paths. Moreover, we
+demonstrate that the tolerance of connectivity is reformable even from
+extremely vulnerable real networks according to our proposed growing process
+with some investment. These results may indicate a prospective direction to the
+future growth of our network infrastructures.
+"
+21027,Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization,"  Stochastic Gradient Descent (SGD) has played a central role in machine
+learning. However, it requires a carefully hand-picked stepsize for fast
+convergence, which is notoriously tedious and time-consuming to tune. Over the
+last several years, a plethora of adaptive gradient-based algorithms have
+emerged to ameliorate this problem. They have proved efficient in reducing the
+labor of tuning in practice, but many of them lack theoretic guarantees even in
+the convex setting. In this paper, we propose new surrogate losses to cast the
+problem of learning the optimal stepsizes for the stochastic optimization of a
+non-convex smooth objective function onto an online convex optimization
+problem. This allows the use of no-regret online algorithms to compute optimal
+stepsizes on the fly. In turn, this results in a SGD algorithm with self-tuned
+stepsizes that guarantees convergence rates that are automatically adaptive to
+the level of noise.
+"
+21028,On Categorical Time Series Models With Covariates,"  We study the problem of stationarity and ergodicity for autoregressive
+multinomial logistic time series models which possibly include a latent process
+and are defined by a GARCH-type recursive equation. We improve considerably
+upon the existing results related to stationarity and ergodicity conditions of
+such models. Proofs are based on theory developed for chains with complete
+connections. This approach is based on a useful coupling technique which is
+utilized for studying ergodicity of more general finite-state stochastic
+processes. Such processes generalize finite-state Markov chains by assuming
+infinite order models of past values. For finite order Markov chains, we also
+discuss ergodicity properties when some strongly exogenous covariates are
+considered in the dynamics of the process.
+"
+21029,Layered Coding for Energy Harvesting Communication Without CSIT,"  Due to stringent constraints on resources, it may be infeasible to acquire
+the current channel state information at the transmitter in energy harvesting
+communication systems. In this paper, we optimize an energy harvesting
+transmitter, communicating over a slow fading channel, using layered coding.
+The transmitter has access to the channel statistics, but does not know the
+exact channel state. In layered coding, the codewords are first designed for
+each of the channel states at different rates, and then the codewords are
+either time-multiplexed or superimposed before the transmission, leading to two
+transmission strategies. The receiver then decodes the information adaptively
+based on the realized channel state. The transmitter is equipped with a
+finite-capacity battery having non-zero internal resistance. In each of the
+transmission strategies, we first formulate and study an average rate
+maximization problem with non-causal knowledge of the harvested power
+variations. Further, assuming statistical knowledge and causal information of
+the harvested power variations, we propose a sub-optimal algorithm, and compare
+with the stochastic dynamic programming based solution and a greedy policy.
+"
+21030,The automorphisms of Petit's algebras,"  Let $\sigma$ be an automorphism of a field $K$ with fixed field $F$. We study
+the automorphisms of nonassociative unital algebras which are canonical
+generalizations of the associative quotient algebras $K[t;\sigma]/fK[t;\sigma]$
+obtained when the twisted polynomial $f\in K[t;\sigma]$ is invariant, and were
+first defined by Petit. We compute all their automorphisms if $\sigma$ commutes
+with all automorphisms in ${\rm Aut}_F(K)$ and $n\geq m-1$, where $n$ is the
+order of $\sigma$ and $m$ the degree of $f$,and obtain partial results for
+$n<m-1$. In the case where $K/F$ is a finite Galois field extension, we obtain
+more detailed information on the structure of the automorphism groups of these
+nonassociative unital algebras over $F$. We also briefly investigate when two
+such algebras are isomorphic.
+"
+21031,A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation,"  Cellular electron cryo-tomography enables the 3D visualization of cellular
+organization in the near-native state and at submolecular resolution. However,
+the contents of cellular tomograms are often complex, making it difficult to
+automatically isolate different in situ cellular components. In this paper, we
+propose a convolutional autoencoder-based unsupervised approach to provide a
+coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
+that the autoencoder can be used for efficient and coarse characterization of
+features of macromolecular complexes and surfaces, such as membranes. In
+addition, the autoencoder can be used to detect non-cellular features related
+to sample preparation and data collection, such as carbon edges from the grid
+and tomogram boundaries. The autoencoder is also able to detect patterns that
+may indicate spatial interactions between cellular components. Furthermore, we
+demonstrate that our autoencoder can be used for weakly supervised semantic
+segmentation of cellular components, requiring a very small amount of manual
+annotation.
+"
+21032,Probabilistic Rule Realization and Selection,"  Abstraction and realization are bilateral processes that are key in deriving
+intelligence and creativity. In many domains, the two processes are approached
+through rules: high-level principles that reveal invariances within similar yet
+diverse examples. Under a probabilistic setting for discrete input spaces, we
+focus on the rule realization problem which generates input sample
+distributions that follow the given rules. More ambitiously, we go beyond a
+mechanical realization that takes whatever is given, but instead ask for
+proactively selecting reasonable rules to realize. This goal is demanding in
+practice, since the initial rule set may not always be consistent and thus
+intelligent compromises are needed. We formulate both rule realization and
+selection as two strongly connected components within a single and symmetric
+bi-convex problem, and derive an efficient algorithm that works at large scale.
+Taking music compositional rules as the main example throughout the paper, we
+demonstrate our model's efficiency in not only music realization (composition)
+but also music interpretation and understanding (analysis).
+"
+21033,Asymptotic and numerical analysis of a porous medium model for transpiration-driven sap flow in trees,"  We develop a 3D porous medium model for sap flow within a tree stem, which
+consists of a nonlinear parabolic partial differential equation with a suitable
+transpiration source term. Using an asymptotic analysis, we derive approximate
+series solutions for the liquid saturation and sap velocity for a general class
+of coefficient functions. Several important non-dimensional parameters are
+identified that can be used to characterize various flow regimes. We
+investigate the relative importance of stem aspect ratio versus anisotropy in
+the sapwood hydraulic conductivity, and how these two effects impact the radial
+and vertical components of sap velocity. The analytical results are validated
+by means of a second-order finite volume discretization of the governing
+equations, and comparisons are drawn to experimental results on Norway spruce
+trees.
+"
+21034,Risk Estimators for Choosing Regularization Parameters in Ill-Posed Problems - Properties and Limitations,"  This paper discusses the properties of certain risk estimators recently
+proposed to choose regularization parameters in ill-posed problems. A simple
+approach is Stein's unbiased risk estimator (SURE), which estimates the risk in
+the data space, while a recent modification (GSURE) estimates the risk in the
+space of the unknown variable. It seems intuitive that the latter is more
+appropriate for ill-posed problems, since the properties in the data space do
+not tell much about the quality of the reconstruction. We provide theoretical
+studies of both estimators for linear Tikhonov regularization in a finite
+dimensional setting and estimate the quality of the risk estimators, which also
+leads to asymptotic convergence results as the dimension of the problem tends
+to infinity. Unlike previous papers, who studied image processing problems with
+a very low degree of ill-posedness, we are interested in the behavior of the
+risk estimators for increasing ill-posedness. Interestingly, our theoretical
+results indicate that the quality of the GSURE risk can deteriorate
+asymptotically for ill-posed problems, which is confirmed by a detailed
+numerical study. The latter shows that in many cases the GSURE estimator leads
+to extremely small regularization parameters, which obviously cannot stabilize
+the reconstruction. Similar but less severe issues with respect to robustness
+also appear for the SURE estimator, which in comparison to the rather
+conservative discrepancy principle leads to the conclusion that regularization
+parameter choice based on unbiased risk estimation is not a reliable procedure
+for ill-posed problems. A similar numerical study for sparsity regularization
+demonstrates that the same issue appears in nonlinear variational
+regularization approaches.
+"
+21035,Analyzing Effects of Seasonal Variations in Wind Generation and Load on Voltage Profiles,"  This paper presents a methodology for building daily profiles of wind
+generation and load for different seasons to assess their impacts on voltage
+violations. The measurement-based wind models showed very high accuracy when
+validated against several years of actual wind power data. System load modeling
+was carried out by analyzing the seasonal trends that occur in residential,
+commercial, and industrial loads. When the proposed approach was implemented on
+the IEEE 118-bus system, it could identify violations in bus voltage profiles
+that the season-independent model could not capture. The results of the
+proposed approach are expected to provide better visualization of the problems
+that seasonal variations in wind power and load might cause to the electric
+power grid.
+"
+21036,Imitation Learning from Imperfect Demonstration,"  Imitation learning (IL) aims to learn an optimal policy from demonstrations.
+However, such demonstrations are often imperfect since collecting optimal ones
+is costly. To effectively learn from imperfect demonstrations, we propose a
+novel approach that utilizes confidence scores, which describe the quality of
+demonstrations. More specifically, we propose two confidence-based IL methods,
+namely two-step importance weighting IL (2IWIL) and generative adversarial IL
+with imperfect demonstration and confidence (IC-GAIL). We show that confidence
+scores given only to a small portion of sub-optimal demonstrations
+significantly improve the performance of IL both theoretically and empirically.
+"
+21037,Some Bounds on Binary LCD Codes,"  A linear code with a complementary dual (or LCD code) is defined to be a
+linear code $C$ whose dual code $C^{\perp}$ satisfies $C \cap C^{\perp}$=
+$\left\{ \mathbf{0}\right\} $. Let $LCD{[}n,k{]}$ denote the maximum of
+possible values of $d$ among $[n,k,d]$ binary LCD codes. We give exact values
+of $LCD{[}n,k{]}$ for $1 \le k \le n \le 12$.
+We also show that $LCD[n,n-i]=2$ for any $i\geq2$ and $n\geq2^{i}$.
+Furthermore, we show that $LCD[n,k]\leq LCD[n,k-1]$ for $k$ odd and
+$LCD[n,k]\leq LCD[n,k-2]$ for $k$ even.
+"
+21038,Control-Oriented Learning on the Fly,"  This paper focuses on developing a strategy for control of systems whose
+dynamics are almost entirely unknown. This situation arises naturally in a
+scenario where a system undergoes a critical failure. In that case, it is
+imperative to retain the ability to satisfy basic control objectives in order
+to avert an imminent catastrophe. A prime example of such an objective is the
+reach-avoid problem, where a system needs to move to a certain state in a
+constrained state space. To deal with limitations on our knowledge of system
+dynamics, we develop a theory of myopic control. The primary goal of myopic
+control is to, at any given time, optimize the current direction of the system
+trajectory, given solely the information obtained about the system until that
+time. We propose an algorithm that uses small perturbations in the control
+effort to learn local dynamics while simultaneously ensuring that the system
+moves in a direction that appears to be nearly optimal, and provide hard bounds
+for its suboptimality. We additionally verify the usefulness of the algorithm
+on a simulation of a damaged aircraft seeking to avoid a crash, as well as on
+an example of a Van der Pol oscillator.
+"
+21039,Multi Agent Driven Data Mining For Knowledge Discovery in Cloud Computing,"  Today, huge amount of data is available on the web. Now there is a need to
+convert that data in knowledge which can be useful for different purposes. This
+paper depicts the use of data mining process, OLAP with the combination of
+multi agent system to find the knowledge from data in cloud computing. For
+this, I am also trying to explain one case study of online shopping of one
+Bakery Shop. May be we can increase the sale of items by using the model, which
+I am trying to represent.
+"
+21040,Matrix Completion from $O(n)$ Samples in Linear Time,"  We consider the problem of reconstructing a rank-$k$ $n \times n$ matrix $M$
+from a sampling of its entries. Under a certain incoherence assumption on $M$
+and for the case when both the rank and the condition number of $M$ are
+bounded, it was shown in \cite{CandesRecht2009, CandesTao2010, keshavan2010,
+Recht2011, Jain2012, Hardt2014} that $M$ can be recovered exactly or
+approximately (depending on some trade-off between accuracy and computational
+complexity) using $O(n \, \text{poly}(\log n))$ samples in super-linear time
+$O(n^{a} \, \text{poly}(\log n))$ for some constant $a \geq 1$.
+In this paper, we propose a new matrix completion algorithm using a novel
+sampling scheme based on a union of independent sparse random regular bipartite
+graphs. We show that under the same conditions w.h.p. our algorithm recovers an
+$\epsilon$-approximation of $M$ in terms of the Frobenius norm using $O(n
+\log^2(1/\epsilon))$ samples and in linear time $O(n \log^2(1/\epsilon))$. This
+provides the best known bounds both on the sample complexity and computational
+complexity for reconstructing (approximately) an unknown low-rank matrix.
+The novelty of our algorithm is two new steps of thresholding singular values
+and rescaling singular vectors in the application of the ""vanilla"" alternating
+minimization algorithm. The structure of sparse random regular graphs is used
+heavily for controlling the impact of these regularization steps.
+"
+21041,"Ingestion, Indexing and Retrieval of High-Velocity Multidimensional Sensor Data on a Single Node","  Multidimensional data are becoming more prevalent, partly due to the rise of
+the Internet of Things (IoT), and with that the need to ingest and analyze data
+streams at rates higher than before. Some industrial IoT applications require
+ingesting millions of records per second, while processing queries on recently
+ingested and historical data. Unfortunately, existing database systems suited
+to multidimensional data exhibit low per-node ingestion performance, and even
+if they can scale horizontally in distributed settings, they require large
+number of nodes to meet such ingest demands. For this reason, in this paper we
+evaluate a single-node multidimensional data store for high-velocity sensor
+data. Its design centers around a two-level indexing structure, wherein the
+global index is an in-memory R*-tree and the local indices are serialized
+kd-trees. This study is confined to records with numerical indexing fields and
+range queries, and covers ingest throughput, query response time, and storage
+footprint. We show that the adopted design streamlines data ingestion and
+offers ingress rates two orders of magnitude higher than those of Percona
+Server, SQLite, and Druid. Our prototype also reports query response times
+comparable to or better than those of Percona Server and Druid, and compares
+favorably in terms of storage footprint. In addition, we evaluate a kd-tree
+partitioning based scheme for grouping incoming streamed data records. Compared
+to a random scheme, this scheme produces less overlap between groups of
+streamed records, but contrary to what we expected, such reduced overlap does
+not translate into better query performance. By contrast, the local indices
+prove much more beneficial to query performance. We believe the experience
+reported in this paper is valuable to practitioners and researchers alike
+interested in building database systems for high-velocity multidimensional
+data.
+"
+21042,Electron and thermal transport via Variable Range Hopping in MoSe$_{2}$ single crystals,"  Bulk single crystal Molybdenum diselenide has been studied for its electronic
+and thermal transport properties. We perform resistivity measurements with
+current in-plane (CIP) and current perpendicular to plane (CPP) as a function
+of temperature. The CIP measurements exhibit metal to semiconductor transition
+at $\simeq 31$ K. In the semiconducting phase ($T > 31$ K), the transport is
+best explained by variable range hopping (VRH) model. Large magnitude of
+resistivity in CPP mode indicates strong structural anisotropy. Seebeck
+coefficient as a function of temperature measured in the range $90 - 300$ K,
+also agrees well with the VRH model. The room temperature Seebeck coefficient
+is found to be $139$ $\mu$V/K. VRH fittings of the resistivity and Seebeck
+coefficient data indicate high degree of localization.
+"
+21043,Ball in double hoop: demonstration model for numerical optimal control,"  Ball and hoop system is a well-known model for the education of linear
+control systems. In this paper, we have a look at this system from another
+perspective and show that it is also suitable for demonstration of more
+advanced control techniques. In contrast to the standard use, we describe the
+dynamics of the system at full length; in addition to the mode where the ball
+rolls on the (outer) hoop we also consider the mode where the ball drops out of
+the hoop and enters a free-fall mode. Furthermore, we add another (inner) hoop
+in the center upon which the ball can land from the free-fall mode. This
+constitutes another mode of the hybrid description of the system. We present
+two challenging tasks for this model and show how they can be solved by
+trajectory generation and stabilization. We also describe how such a model can
+be built and experimentally verify the validity of our approach solving the
+proposed tasks.
+"
+21044,Forecasting market states,"  We propose a novel methodology to define, analyse and forecast market states.
+In our approach market states are identified by a reference sparse precision
+matrix and a vector of expectation values. In our procedure each multivariate
+observation is associated to a given market state accordingly to a penalized
+likelihood maximization. The procedure is made computationally very efficient
+and can be used with a large number of assets. We demonstrate that this
+procedure successfully classifies different states of the markets in an
+unsupervised manner. In particular, we describe an experiment with one hundred
+log-returns and two states in which the methodology automatically associates
+one state to periods with average positive returns and the other state to
+periods with average negative returns, therefore discovering spontaneously the
+common classification of `bull' and `bear' markets. In another experiment, with
+again one hundred log-returns and two states, we demonstrate that this
+procedure can be efficiently used to forecast off-sample future market states
+with significant prediction accuracy. This methodology opens the way to a range
+of applications in risk management and trading strategies where the correlation
+structure plays a central role.
+"
+21045,Planetary Ring Dynamics -- The Streamline Formalism -- 2. Theory of Narrow Rings and Sharp Edges,"  The present material covers the features of large scale ring dynamics in
+perturbed flows that were not addressed in part 1 (astro-ph/1606.00759); this
+includes an extensive coverage of all kinds of ring modes dynamics (except
+density waves which have been covered in part 1), the origin of ring
+eccentricities and mode amplitudes, and the issue of ring/gap confinement. This
+still leaves aside a number of important dynamical issues relating to the ring
+small scale structure, most notably the dynamics of self-gravitational wakes,
+of local viscous overstabilities and of ballistic transport processes.
+As this material is designed to be self-contained, there is some 30% overlap
+with part 1. This work constitutes a preprint of Chapter 11 of the forthcoming
+Cambridge University book on rings (Planetary Ring Systems, Matt Tiscareno and
+Carl Murray, eds).
+"
+21046,Parallel Streaming Wasserstein Barycenters,"  Efficiently aggregating data from different sources is a challenging problem,
+particularly when samples from each source are distributed differently. These
+differences can be inherent to the inference task or present for other reasons:
+sensors in a sensor network may be placed far apart, affecting their individual
+measurements. Conversely, it is computationally advantageous to split Bayesian
+inference tasks across subsets of data, but data need not be identically
+distributed across subsets. One principled way to fuse probability
+distributions is via the lens of optimal transport: the Wasserstein barycenter
+is a single distribution that summarizes a collection of input measures while
+respecting their geometry. However, computing the barycenter scales poorly and
+requires discretization of all input distributions and the barycenter itself.
+Improving on this situation, we present a scalable, communication-efficient,
+parallel algorithm for computing the Wasserstein barycenter of arbitrary
+distributions. Our algorithm can operate directly on continuous input
+distributions and is optimized for streaming data. Our method is even robust to
+nonstationary input distributions and produces a barycenter estimate that
+tracks the input measures over time. The algorithm is semi-discrete, needing to
+discretize only the barycenter estimate. To the best of our knowledge, we also
+provide the first bounds on the quality of the approximate barycenter as the
+discretization becomes finer. Finally, we demonstrate the practical
+effectiveness of our method, both in tracking moving distributions on a sphere,
+as well as in a large-scale Bayesian inference task.
+"
+21047,Lecar's visual comparison method to assess the randomness of Bode's law: an answer,"  The usual main objection against any attempt in finding a physical cause for
+the planet distance distribution is based on the assumption that similar
+distance distribution could be obtained by sequences of random numbers. This
+assumption was stated by Lecar in an old paper (1973). We show here how this
+assumption is incorrect and how his visual comparison method is inappropriate.
+"
+21048,GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild,"  Facial landmark localization is a fundamental module for pose-invariant face
+recognition. The most common approach for facial landmark detection is cascaded
+regression, which is composed of two steps: feature extraction and facial shape
+regression. Recent methods employ deep convolutional networks to extract robust
+features for each step, while the whole system could be regarded as a deep
+cascaded regression architecture. In this work, instead of employing a deep
+regression network, a Globally Optimized Dual-Pathway (GoDP) deep architecture
+is proposed to identify the target pixels through solving a cascaded pixel
+labeling problem without resorting to high-level inference models or complex
+stacked architecture. The proposed end-to-end system relies on distance-aware
+softmax functions and dual-pathway proposal-refinement architecture. Results
+show that it outperforms the state-of-the-art cascaded regression-based methods
+on multiple in-the-wild face alignment databases. The model achieves 1.84
+normalized mean error (NME) on the AFLW database, which outperforms 3DDFA by
+61.8%. Experiments on face identification demonstrate that GoDP, coupled with
+DPM-headhunter, is able to improve rank-1 identification rate by 44.2% compared
+to Dlib toolbox on a challenging database.
+"
+21049,"A function with support of finite measure and ""small"" spectrum","  We construct a function on the real line supported on a set of finite measure
+whose spectrum has density zero.
+"
+21050,Cell growth rate dictates the onset of glass to fluid-like transition and long time super-diffusion in an evolving cell colony,"  Collective migration dominates many phenomena, from cell movement in living
+systems to abiotic self-propelling particles. Focusing on the early stages of
+tumor evolution, we enunciate the principles involved in cell dynamics and
+highlight their implications in understanding similar behavior in seemingly
+unrelated soft glassy materials and possibly chemokine-induced migration of
+CD8$^{+}$ T cells. We performed simulations of tumor invasion using a minimal
+three dimensional model, accounting for cell elasticity and adhesive cell-cell
+interactions as well as cell birth and death to establish that cell growth
+rate-dependent tumor expansion results in the emergence of distinct topological
+niches. Cells at the periphery move with higher velocity perpendicular to the
+tumor boundary, while motion of interior cells is slower and isotropic. The
+mean square displacement, $\Delta(t)$, of cells exhibits glassy behavior at
+times comparable to the cell cycle time, while exhibiting super-diffusive
+behavior, $\Delta (t) \approx t^{\alpha}$ ($\alpha > 1$), at longer times. We
+derive the value of $\alpha \approx 1.33$ using a field theoretic approach
+based on stochastic quantization. In the process we establish the universality
+of super-diffusion in a class of seemingly unrelated non-equilibrium systems.
+Super diffusion at long times arises only if there is an imbalance between cell
+birth and death rates. Our findings for the collective migration, which also
+suggests that tumor evolution occurs in a polarized manner, are in quantitative
+agreement with {\it in vitro} experiments. Although set in the context of tumor
+invasion the findings should also hold in describing collective motion in
+growing cells and in active systems where creation and annihilation of
+particles play a role.
+"
+21051,Distributed Average Tracking for Lipschitz-Type Nonlinear Dynamical Systems,"  In this paper, a distributed average tracking problem is studied for
+Lipschitz-type nonlinear dynamical systems. The objective is to design
+distributed average tracking algorithms for locally interactive agents to track
+the average of multiple reference signals. Here, in both the agents' and the
+reference signals' dynamics, there is a nonlinear term satisfying the
+Lipschitz-type condition. Three types of distributed average tracking
+algorithms are designed. First, based on state-dependent-gain designing
+approaches, a robust distributed average tracking algorithm is developed to
+solve distributed average tracking problems without requiring the same initial
+condition. Second, by using a gain adaption scheme, an adaptive distributed
+average tracking algorithm is proposed in this paper to remove the requirement
+that the Lipschitz constant is known for agents. Third, to reduce chattering
+and make the algorithms easier to implement, a continuous distributed average
+tracking algorithm based on a time-varying boundary layer is further designed
+as a continuous approximation of the previous discontinuous distributed average
+tracking algorithms.
+"
+21052,On the Möbius Function and Topology of General Pattern Posets,"  We introduce a formal definition of a pattern poset which encompasses several
+previously studied posets in the literature. Using this definition we present
+some general results on the Möbius function and topology of such pattern
+posets. We prove our results using a poset fibration based on the embeddings of
+the poset, where embeddings are representations of occurrences. We show that
+the Möbius function of these posets is intrinsically linked to the number of
+embeddings, and in particular to so called normal embeddings. We present
+results on when topological properties such as Cohen-Macaulayness and
+shellability are preserved by this fibration. Furthermore, we apply these
+results to some pattern posets and derive alternative proofs of existing
+results, such as Björner's results on subword order.
+"
+21053,A Potapov-type approach to a truncated matricial Stieltjes-type power moment problem,"  The paper gives a parametrization of the solution set of a matricial
+Stieltjes-type truncated power moment problem in the non-degenerate and
+degenerate cases. The key role plays the solution of the corresponding system
+of Potapov's fundamental matrix inequalities.
+"
+21054,The scaling limit of the KPZ equation in space dimension 3 and higher,"  We study in the present article the Kardar-Parisi-Zhang (KPZ) equation $$
+\partial_t h(t,x)=\nu\Delta h(t,x)+\lambda |\nabla h(t,x)|^2 +\sqrt{D}\,
+\eta(t,x), \qquad (t,x)\in\mathbb{R}_+\times\mathbb{R}^d $$ in $d\ge 3$
+dimensions in the perturbative regime, i.e. for $\lambda>0$ small enough and a
+smooth, bounded, integrable initial condition $h_0=h(t=0,\cdot)$. The forcing
+term $\eta$ in the right-hand side is a regularized space-time white noise. The
+exponential of $h$ -- its so-called Cole-Hopf transform -- is known to satisfy
+a linear PDE with multiplicative noise. We prove a large-scale diffusive limit
+for the solution, in particular a time-integrated heat-kernel behavior for the
+covariance in a parabolic scaling.
+The proof is based on a rigorous implementation of K. Wilson's
+renormalization group scheme. A double cluster/momentum-decoupling expansion
+allows for perturbative estimates of the bare resolvent of the Cole-Hopf linear
+PDE in the small-field region where the noise is not too large, following the
+broad lines of Iagolnitzer-Magnen. Standard large deviation estimates for
+$\eta$ make it possible to extend the above estimates to the large-field
+region. Finally, we show, by resumming all the by-products of the expansion,
+that the solution $h$ may be written in the large-scale limit (after a suitable
+Galilei transformation) as a small perturbation of the solution of the
+underlying linear Edwards-Wilkinson model ($\lambda=0$) with renormalized
+coefficients $\nu_{eff}=\nu+O(\lambda^2),D_{eff}=D+O(\lambda^2)$.
+"
+21055,Beyond Free Riding: Quality of Indicators for Assessing Participation in Information Sharing for Threat Intelligence,"  Threat intelligence sharing has become a growing concept, whereby entities
+can exchange patterns of threats with each other, in the form of indicators, to
+a community of trust for threat analysis and incident response. However,
+sharing threat-related information have posed various risks to an organization
+that pertains to its security, privacy, and competitiveness. Given the
+coinciding benefits and risks of threat information sharing, some entities have
+adopted an elusive behavior of ""free-riding"" so that they can acquire the
+benefits of sharing without contributing much to the community. So far,
+understanding the effectiveness of sharing has been viewed from the perspective
+of the amount of information exchanged as opposed to its quality. In this
+paper, we introduce the notion of quality of indicators (\qoi) for the
+assessment of the level of contribution by participants in information sharing
+for threat intelligence. We exemplify this notion through various metrics,
+including correctness, relevance, utility, and uniqueness of indicators. In
+order to realize the notion of \qoi, we conducted an empirical study and taken
+a benchmark approach to define quality metrics, then we obtained a reference
+dataset and utilized tools from the machine learning literature for quality
+assessment. We compared these results against a model that only considers the
+volume of information as a metric for contribution, and unveiled various
+interesting observations, including the ability to spot low quality
+contributions that are synonym to free riding in threat information sharing.
+"
+21056,On the Design of LQR Kernels for Efficient Controller Learning,"  Finding optimal feedback controllers for nonlinear dynamic systems from data
+is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful
+framework for direct controller tuning from experimental trials. For selecting
+the next query point and finding the global optimum, BO relies on a
+probabilistic description of the latent objective function, typically a
+Gaussian process (GP). As is shown herein, GPs with a common kernel choice can,
+however, lead to poor learning outcomes on standard quadratic control problems.
+For a first-order system, we construct two kernels that specifically leverage
+the structure of the well-known Linear Quadratic Regulator (LQR), yet retain
+the flexibility of Bayesian nonparametric learning. Simulations of uncertain
+linear and nonlinear systems demonstrate that the LQR kernels yield superior
+learning performance.
+"
+21057,How does propaganda influence the opinion dynamics of a population ?,"  The evolution of opinions in a population of individuals who constantly
+interact with a common source of user-generated content (i.e. the internet) and
+are also subject to propaganda is analyzed using computer simulations. The
+model is based on the bounded confidence approach. In the absence of
+propaganda, computer simulations show that the online population as a whole is
+either fragmented, polarized or in perfect harmony on a certain issue or
+ideology depending on the uncertainty of individuals in accepting opinions not
+closer to theirs. On applying the model to simulate radicalization, a
+proportion of the online population, subject to extremist propaganda radicalize
+depending on their pre-conceived opinions and opinion uncertainty. It is found
+that an optimal counter propaganda that prevents radicalization is not
+necessarily centrist.
+"
+21058,Explicit Solution for Constrained Stochastic Linear-Quadratic Control with Multiplicative Noise,"  We study in this paper a class of constrained linear-quadratic (LQ) optimal
+control problem formulations for the scalar-state stochastic system with
+multiplicative noise, which has various applications, especially in the
+financial risk management. The linear constraint on both the control and state
+variables considered in our model destroys the elegant structure of the
+conventional LQ formulation and has blocked the derivation of an explicit
+control policy so far in the literature. We successfully derive in this paper
+the analytical control policy for such a class of problems by utilizing the
+state separation property induced from its structure. We reveal that the
+optimal control policy is a piece-wise affine function of the state and can be
+computed off-line efficiently by solving two coupled Riccati equations. Under
+some mild conditions, we also obtain the stationary control policy for infinite
+time horizon. We demonstrate the implementation of our method via some
+illustrative examples and show how to calibrate our model to solve dynamic
+constrained portfolio optimization problems.
+"
+21059,Viewpoint Selection for Photographing Architectures,"  This paper studies the problem of how to choose good viewpoints for taking
+photographs of architectures. We achieve this by learning from professional
+photographs of world famous landmarks that are available on the Internet.
+Unlike previous efforts devoted to photo quality assessment which mainly rely
+on 2D image features, we show in this paper combining 2D image features
+extracted from images with 3D geometric features computed on the 3D models can
+result in more reliable evaluation of viewpoint quality. Specifically, we
+collect a set of photographs for each of 15 world famous architectures as well
+as their 3D models from the Internet. Viewpoint recovery for images is carried
+out through an image-model registration process, after which a newly proposed
+viewpoint clustering strategy is exploited to validate users' viewpoint
+preferences when photographing landmarks. Finally, we extract a number of 2D
+and 3D features for each image based on multiple visual and geometric cues and
+perform viewpoint recommendation by learning from both 2D and 3D features using
+a specifically designed SVM-2K multi-view learner, achieving superior
+performance over using solely 2D or 3D features. We show the effectiveness of
+the proposed approach through extensive experiments. The experiments also
+demonstrate that our system can be used to recommend viewpoints for rendering
+textured 3D models of buildings for the use of architectural design, in
+addition to viewpoint evaluation of photographs and recommendation of
+viewpoints for photographing architectures in practice.
+"
+21060,Geophysical tests for habitability in ice-covered ocean worlds,"  Geophysical measurements can reveal the structure of icy ocean worlds and
+cycling of volatiles. The associated density, temperature, sound speed, and
+electrical conductivity of such worlds thus characterizes their habitability.
+To explore the variability and correlation of these parameters, and to provide
+tools for planning and data analyses, we develop 1-D calculations of internal
+structure, which use available constraints on the thermodynamics of aqueous
+MgSO$_4$, NaCl (as seawater), and NH$_3$, water ices, and silicate content.
+Limits in available thermodynamic data narrow the parameter space that can be
+explored: insufficient coverage in pressure, temperature, and composition for
+end-member salinities of MgSO$_4$ and NaCl, and for relevant water ices; and a
+dearth of suitable data for aqueous mixtures of Na-Mg-Cl-SO$_4$-NH$_3$. For
+Europa, ocean compositions that are oxidized and dominated by MgSO$_4$, vs
+reduced (NaCl), illustrate these gaps, but also show the potential for
+diagnostic and measurable combinations of geophysical parameters. The
+low-density rocky core of Enceladus may comprise hydrated minerals, or anydrous
+minerals with high porosity comparable to Earth's upper mantle. Titan's ocean
+must be dense, but not necessarily saline, as previously noted, and may have
+little or no high-pressure ice at its base. Ganymede's silicious interior is
+deepest among all known ocean worlds, and may contain multiple phases of
+high-pressure ice, which will become buoyant if the ocean is sufficiently
+salty. Callisto's likely near-eutectic ocean cannot be adequately modeled using
+available data. Callisto may also lack high-pressure ices, but this cannot be
+confirmed due to uncertainty in its moment of inertia.
+"
+21061,A fast speed planning algorithm for robotic manipulators,"  We consider the speed planning problem for a robotic manipulator. In
+particular, we present an algorithm for finding the time-optimal speed law
+along an assigned path that satisfies velocity and acceleration constraints and
+respects the maximum forces and torques allowed by the actuators. The addressed
+optimization problem is a finite dimensional reformulation of the
+continuous-time speed optimization problem, obtained by discretizing the speed
+profile with N points. The proposed algorithm has linear complexity with
+respect to N and to the number of degrees of freedom. Such complexity is the
+best possible for this problem. Numerical tests show that the proposed
+algorithm is significantly faster than algorithms already existing in
+literature.
+"
+21062,Linearly convergent stochastic heavy ball method for minimizing generalization error,"  In this work we establish the first linear convergence result for the
+stochastic heavy ball method. The method performs SGD steps with a fixed
+stepsize, amended by a heavy ball momentum term. In the analysis, we focus on
+minimizing the expected loss and not on finite-sum minimization, which is
+typically a much harder problem. While in the analysis we constrain ourselves
+to quadratic loss, the overall objective is not necessarily strongly convex.
+"
+21063,Applying the Spacecraft with a Solar Sail to Form the Climate on a Mars Base,"  This article is devoted to research the application of the spacecraft with a
+solar sail to support the certain climatic conditions in an area of the Mars
+surface. Authors propose principles of functioning of the spacecraft, intended
+to create a light and thermal light spot in a predetermined area of the Martian
+surface. The mathematical motion model in such condition of the solar sail's
+orientation is considered and used for motion simulation session. Moreover, the
+analysis of this motion is performed. Thus, were obtained parameters of the
+stationary orbit of the spacecraft with a solar sail and were given
+recommendations for further applying spacecrafts to reflect the sunlight on a
+planet's surface.
+"
+21064,Data processing pipeline for Herschel HIFI,"  {Context}. The HIFI instrument on the Herschel Space Observatory performed
+over 9100 astronomical observations, almost 900 of which were calibration
+observations in the course of the nearly four-year Herschel mission. The data
+from each observation had to be converted from raw telemetry into calibrated
+products and were included in the Herschel Science Archive. {Aims}. The HIFI
+pipeline was designed to provide robust conversion from raw telemetry into
+calibrated data throughout all phases of the HIFI missions. Pre-launch
+laboratory testing was supported as were routine mission operations. {Methods}.
+A modular software design allowed components to be easily added, removed,
+amended and/or extended as the understanding of the HIFI data developed during
+and after mission operations. {Results}. The HIFI pipeline processed data from
+all HIFI observing modes within the Herschel automated processing environment
+as well as within an interactive environment. The same software can be used by
+the general astronomical community to reprocess any standard HIFI observation.
+The pipeline also recorded the consistency of processing results and provided
+automated quality reports. Many pipeline modules were in use since the HIFI
+pre-launch instrument level testing. {Conclusions}. Processing in steps
+facilitated data analysis to discover and address instrument artefacts and
+uncertainties. The availability of the same pipeline components from pre-launch
+throughout the mission made for well-understood, tested, and stable processing.
+A smooth transition from one phase to the next significantly enhanced
+processing reliability and robustness.
+"
+21065,Regret Analysis for Continuous Dueling Bandit,"  The dueling bandit is a learning framework wherein the feedback information
+in the learning process is restricted to a noisy comparison between a pair of
+actions. In this research, we address a dueling bandit problem based on a cost
+function over a continuous space. We propose a stochastic mirror descent
+algorithm and show that the algorithm achieves an $O(\sqrt{T\log T})$-regret
+bound under strong convexity and smoothness assumptions for the cost function.
+Subsequently, we clarify the equivalence between regret minimization in dueling
+bandit and convex optimization for the cost function. Moreover, when
+considering a lower bound in convex optimization, our algorithm is shown to
+achieve the optimal convergence rate in convex optimization and the optimal
+regret in dueling bandit except for a logarithmic factor.
+"
+21066,X-ray induced deuterium enrichment on N-rich organics in protoplanetary disks: an experimental investigation using synchrotron light,"  The deuterium enrichment of organics in the interstellar medium,
+protoplanetary disks and meteorites has been proposed to be the result of
+ionizing radiation. The goal of this study is to quantify the effects of soft
+X-rays (0.1 - 2 keV), a component of stellar radiation fields illuminating
+protoplanetary disks, on the refractory organics present in the disks. We
+prepared tholins, nitrogen-rich complex organics, via plasma deposition and
+used synchrotron radiation to simulate X-ray fluences in protoplanetary disks.
+Controlled irradiation experiments at 0.5 and 1.3 keV were performed at the
+SEXTANTS beam line of the SOLEIL synchrotron, and were followed by ex-situ
+infrared, Raman and isotopic diagnostics. Infrared spectroscopy revealed the
+loss of singly-bonded groups (N-H, C-H and R-N$\equiv$C) and the formation of
+sp$^3$ carbon defects. Raman analysis revealed the introduction of defects and
+structural amorphization. Finally, tholins were measured via secondary ion mass
+spectrometry (SIMS), revealing that significant D-enrichment is induced by
+X-ray irradiation. Our results are compared to previous experimental studies
+involving the thermal degradation and electron irradiation of organics. The
+penetration depth of soft X-rays in $\mu$m-sized tholins leads to volume rather
+than surface modifications: lower energy X-rays (0.5 keV) induce a larger
+D-enrichment than 1.3 keV X-rays, reaching a plateau for doses larger than 5
+$\times$ 10$^{27}$ eV cm$^{-3}$. Our work provides experimental evidence of a
+new non-thermal pathway to deuterium fractionation of organic matter.
+"
+21067,Parisian ruin of Brownian motion risk model over an infinite-time horizon,"  Let $B(t), t\in \mathbb{R}$ be a standard Brownian motion. In this paper, we
+derive the exact asymptotics of the probability of Parisian ruin on infinite
+time horizon for the following risk process \begin{align}\label{Rudef}
+R_u^{\delta}(t)=e^{\delta t}\left(u+c\int^{t}_{0}e^{-\delta v}d
+v-\sigma\int_{0}^{t}e^{-\delta v}d B(v)\right),\quad t\geq0, \end{align} where
+$u\geq 0$ is the initial reserve, $\delta\geq0$ is the force of interest, $c>0$
+is the rate of premium and $\sigma>0$ is a volatility factor.
+Further, we show the asymptotics of the Parisian ruin time of this risk
+process.
+"
+21068,"Exogeneity tests, incomplete models, weak identification and non-Gaussian distributions: invariance and finite-sample distributional theory","  We study the distribution of Durbin-Wu-Hausman (DWH) and Revankar-Hartley
+(RH) tests for exogeneity from a finite-sample viewpoint, under the null and
+alternative hypotheses. We consider linear structural models with possibly
+non-Gaussian errors, where structural parameters may not be identified and
+where reduced forms can be incompletely specified (or nonparametric). On level
+control, we characterize the null distributions of all the test statistics.
+Through conditioning and invariance arguments, we show that these distributions
+do not involve nuisance parameters. In particular, this applies to several test
+statistics for which no finite-sample distributional theory is yet available,
+such as the standard statistic proposed by Hausman (1978). The distributions of
+the test statistics may be non-standard -- so corrections to usual asymptotic
+critical values are needed -- but the characterizations are sufficiently
+explicit to yield finite-sample (Monte-Carlo) tests of the exogeneity
+hypothesis. The procedures so obtained are robust to weak identification,
+missing instruments or misspecified reduced forms, and can easily be adapted to
+allow for parametric non-Gaussian error distributions. We give a general
+invariance result (block triangular invariance) for exogeneity test statistics.
+This property yields a convenient exogeneity canonical form and a parsimonious
+reduction of the parameters on which power depends. In the extreme case where
+no structural parameter is identified, the distributions under the alternative
+hypothesis and the null hypothesis are identical, so the power function is
+flat, for all the exogeneity statistics. However, as soon as identification
+does not fail completely, this phenomenon typically disappears.
+"
+21069,Pair formation of hard core bosons in flat band systems,"  Hard core bosons in a large class of one or two dimensional flat band systems
+have an upper critical density, below which the ground states can be described
+completely. At the critical density, the ground states are Wigner crystals. If
+one adds a particle to the system at the critical density, the ground state and
+the low lying multi particle states of the system can be described as a Wigner
+crystal with an additional pair of particles. The energy band for the pair is
+separated from the rest of the multi-particle spectrum. The proofs use a
+Gerschgorin type of argument for block diagonally dominant matrices. In certain
+one-dimensional or tree-like structures one can show that the pair is
+localised, for example in the chequerboard chain. For this one-dimensional
+system with periodic boundary condition the energy band for the pair is flat,
+the pair is localised.
+"
+21070,"A Note on the Relationship Between Conditional and Unconditional Independence, and its Extensions for Markov Kernels","  Two known results on the relationship between conditional and unconditional
+independence are obtained as a consequence of the main result of this paper, a
+theorem that uses independence of Markov kernels to obtain a minimal condition
+which added to conditional independence implies independence. Some
+counterexamples and representation results are provided to clarify the concepts
+introduced and the propositions of the statement of the main theorem. Moreover,
+conditional independence and the mentioned results are extended to the
+framework of Markov kernels.
+"
+21071,The Effect of Temperature on Amdahl Law in 3D Multicore Era,"  This work studies the influence of temperature on performance and scalability
+of 3D Chip Multiprocessors (CMP) from Amdahl law perspective. We find that 3D
+CMP may reach its thermal limit before reaching its maximum power. We show that
+a high level of parallelism may lead to high peak temperatures even in small
+scale 3D CMPs, thus limiting 3D CMP scalability and calling for different,
+in-memory computing architectures.
+"
+21072,Video Object Segmentation using Supervoxel-Based Gerrymandering,"  Pixels operate locally. Superpixels have some potential to collect
+information across many pixels; supervoxels have more potential by implicitly
+operating across time. In this paper, we explore this well established notion
+thoroughly analyzing how supervoxels can be used in place of and in conjunction
+with other means of aggregating information across space-time. Focusing on the
+problem of strictly unsupervised video object segmentation, we devise a method
+called supervoxel gerrymandering that links masks of foregroundness and
+backgroundness via local and non-local consensus measures. We pose and answer a
+series of critical questions about the ability of supervoxels to adequately
+sway local voting; the questions regard type and scale of supervoxels as well
+as local versus non-local consensus, and the questions are posed in a general
+way so as to impact the broader knowledge of the use of supervoxels in video
+understanding. We work with the DAVIS dataset and find that our analysis yields
+an unsupervised method that outperforms all other known unsupervised methods
+and even many supervised ones.
+"
+21073,The Absent-Minded Driver Problem Redux,"  This paper reconsiders the problem of the absent-minded driver who must
+choose between alternatives with different payoff with imperfect recall and
+varying degrees of knowledge of the system. The classical absent-minded driver
+problem represents the case with limited information and it has bearing on the
+general area of communication and learning, social choice, mechanism design,
+auctions, theories of knowledge, belief, and rational agency. Within the
+framework of extensive games, this problem has applications to many artificial
+intelligence scenarios. It is obvious that the performance of the agent
+improves as information available increases. It is shown that a non-uniform
+assignment strategy for successive choices does better than a fixed probability
+strategy. We consider both classical and quantum approaches to the problem. We
+argue that the superior performance of quantum decisions with access to
+entanglement cannot be fairly compared to a classical algorithm. If the
+cognitive systems of agents are taken to have access to quantum resources, or
+have a quantum mechanical basis, then that can be leveraged into superior
+performance.
+"
+21074,Real-time Acceleration-continuous Path-constrained Trajectory Planning With Built-in Tradability Between Cruise and Time-optimal Motions,"  In this paper, a novel real-time acceleration-continuous path-constrained
+trajectory planning algorithm is proposed with an appealing built-in
+tradability mechanism between cruise motion and time-optimal motion. Different
+from existing approaches, the proposed approach smoothens time-optimal
+trajectories with bang-bang input structures to generate
+acceleration-continuous trajectories while preserving the completeness
+property. More importantly, a novel built-in tradability mechanism is proposed
+and embedded into the trajectory planning framework, so that the proportion of
+the cruise motion and time-optimal motion can be flexibly adjusted by changing
+a user-specified functional parameter. Thus, the user can easily apply the
+trajectory planning algorithm for various tasks with different requirements on
+motion efficiency and cruise proportion. Moreover, it is shown that feasible
+trajectories are computed more quickly than optimal trajectories. Rigorous
+mathematical analysis and proofs are provided for these aforementioned results.
+Comparative simulation and experimental results on omnidirectional wheeled
+mobile robots demonstrate the capability of the proposed algorithm in terms of
+flexible tunning between cruise and time-optimal motions, as well as higher
+computational efficiency.
+"
+21075,Multi-locus data distinguishes between population growth and multiple merger coalescents,"  We introduce a low dimensional function of the site frequency spectrum that
+is tailor-made for distinguishing coalescent models with multiple mergers from
+Kingman coalescent models with population growth, and use this function to
+construct a hypothesis test between these model classes. The null and
+alternative sampling distributions of the statistic are intractable, but its
+low dimensionality renders them amenable to Monte Carlo estimation. We
+construct kernel density estimates of the sampling distributions based on
+simulated data, and show that the resulting hypothesis test dramatically
+improves on the statistical power of a current state-of-the-art method. A key
+reason for this improvement is the use of multi-locus data, in particular
+averaging observed site frequency spectra across unlinked loci to reduce
+sampling variance. We also demonstrate the robustness of our method to nuisance
+and tuning parameters. Finally we show that the same kernel density estimates
+can be used to conduct parameter estimation, and argue that our method is
+readily generalisable for applications in model selection, parameter inference
+and experimental design.
+"
+21076,Ramsey interferometry of Rydberg ensembles inside microwave cavities,"  We study ensembles of Rydberg atoms in a confined electromagnetic environment
+such as provided by a microwave cavity. The competition between standard free
+space Ising type and cavity-mediated interactions leads to the emergence of
+different regimes where the particle-particle couplings range from the typical
+van der Waals $r^{-6}$ behavior to $r^{-3}$ and to $r$-independence. We apply a
+Ramsey spectroscopic technique to map the two-body interactions into a
+characteristic signal such as intensity and contrast decay curves. As opposed
+to previous treatments requiring high-densities for considerable contrast and
+phase decay, the cavity scenario can exhibit similar behavior at much lower
+densities.
+"
+21077,Impurity self-energy in the strongly-correlated Bose systems,"  We proposed the non-perturbative scheme for calculation of the impurity
+spectrum in the Bose system at zero temperature. The method is based on the
+path-integral formulation and describes an impurity as a zero-density ideal
+Fermi gas interacting with Bose system for which the action is written in terms
+of density fluctuations. On the example of the $^3$He atom immersed in the
+liquid helium-4 a good consistency with experimental data and results of Monte
+Carlo simulations is shown.
+"
+21078,$k$-step correction for mixed integer linear programming: a new approach for instrumental variable quantile regressions and related problems,"  This paper proposes a new framework for estimating instrumental variable (IV)
+quantile models. The first part of our proposal can be cast as a mixed integer
+linear program (MILP), which allows us to capitalize on recent progress in
+mixed integer optimization. The computational advantage of the proposed method
+makes it an attractive alternative to existing estimators in the presence of
+multiple endogenous regressors. This is a situation that arises naturally when
+one endogenous variable is interacted with several other variables in a
+regression equation. In our simulations, the proposed method using MILP with a
+random starting point can reliably estimate regressions for a sample size of
+500 with 20 endogenous variables in 5 seconds. Theoretical results for early
+termination of MILP are also provided. The second part of our proposal is a
+$k$-step correction framework, which is proved to be able to convert any point
+within a small but fixed neighborhood of the true parameter value into an
+estimate that is asymptotically equivalent to GMM. Our result does not require
+the initial estimate to be consistent and only $2\log n$ iterations are needed.
+Since the $k$-step correction does not require any optimization, applying the
+$k$-step correction to MILP estimate provides a computationally attractive way
+of obtaining efficient estimators. When dealing with very large data sets, we
+can run the MILP algorithm on only a small subsample and our theoretical
+results guarantee that the resulting estimator from the $k$-step correction is
+equivalent to computing GMM on the full sample. As a result, we can handle
+massive datasets of millions of observations within seconds. As an empirical
+illustration, we examine the heterogeneous treatment effect of Job Training
+Partnership Act (JTPA) using a regression with 13 interaction terms of the
+treatment variable.
+"
+21079,An MCMC Algorithm for Estimating the Reduced RUM,"  The RRUM is a model that is frequently seen in language assessment studies.
+The objective of this research is to advance an MCMC algorithm for the Bayesian
+RRUM. The algorithm starts with estimating correlated attributes. Using a
+saturated model and a binary decimal conversion, the algorithm transforms
+possible attribute patterns to a Multinomial distribution. Along with the
+likelihood of an attribute pattern, a Dirichlet distribution is used as the
+prior to sample from the posterior. The Dirichlet distribution is constructed
+using Gamma distributions. Correlated attributes of examinees are generated
+using the inverse transform sampling. Model parameters are estimated using the
+Metropolis within Gibbs sampler sequentially. Two simulation studies are
+conducted to evaluate the performance of the algorithm. The first simulation
+uses a complete and balanced Q-matrix that measures 5 attributes. Comprised of
+28 items and 9 attributes, the Q-matrix for the second simulation is incomplete
+and imbalanced. The empirical study uses the ECPE data obtained from the CDM R
+package. Parameter estimates from the MCMC algorithm and from the CDM R package
+are presented and compared. The algorithm developed in this research is
+implemented in R.
+"
+21080,Stretching p-wave molecules by transverse confinements,"  We revisit the confinement-induced p-wave resonance in quasi-one-dimensional
+(quasi-1D) atomic gases and study the induced molecules near resonance. We
+derive the reduced 1D interaction parameters and show that they can well
+predict the binding energy of shallow molecules in quasi-1D system.
+Importantly, these shallow molecules are found to be much more spatially
+extended compared to those in three dimensions (3D) without transverse
+confinement. Our results strongly indicate that a p-wave interacting atomic gas
+can be much more stable in quasi-1D near the induced p-wave resonance, where
+most weight of the molecule lies outside the short-range regime and thus the
+atom loss could be suppressed.
+"
+21081,A Conceptual Framework for Supporting a Rapid Design of Web Applications for Data Analysis of Electrical Quality Assurance Data for the LHC,"  The Large Hadron Collider (LHC) is one of the most complex machines ever
+build. It is composed of many components which constitute a large system. The
+tunnel and the accelerator is just one of a very critical fraction of the whole
+LHC infrastructure. Hardware comissioning as one of the critical processes
+before running the LHC is implemented during the Long Shutdown (LS) states of
+the macine, where Electrical Quality Assurance (ELQA) is one of its key
+components. Here a huge data is collected when implementing various ELQA
+electrical tests. In this paper we present a conceptual framework for
+supporting a rapid design of web applications for ELQA data analysis. We show a
+framework's main components, their possible integration with other systems and
+machine learning algorithms and a simple use case of prototyping an application
+for Electrical Quality Assurance of the LHC.
+"
+21082,The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations,"  The Parallel Meaning Bank is a corpus of translations annotated with shared,
+formal meaning representations comprising over 11 million words divided over
+four languages (English, German, Italian, and Dutch). Our approach is based on
+cross-lingual projection: automatically produced (and manually corrected)
+semantic annotations for English sentences are mapped onto their word-aligned
+translations, assuming that the translations are meaning-preserving. The
+semantic annotation consists of five main steps: (i) segmentation of the text
+in sentences and lexical items; (ii) syntactic parsing with Combinatory
+Categorial Grammar; (iii) universal semantic tagging; (iv) symbolization; and
+(v) compositional semantic analysis based on Discourse Representation Theory.
+These steps are performed using statistical models trained in a semi-supervised
+manner. The employed annotation models are all language-neutral. Our first
+results are promising.
+"
+21083,A nonparametric copula approach to conditional Value-at-Risk,"  Value-at-Risk and its conditional allegory, which takes into account the
+available information about the economic environment, form the centrepiece of
+the Basel framework for the evaluation of market risk in the banking sector. In
+this paper, a new nonparametric framework for estimating this conditional
+Value-at-Risk is presented. A nonparametric approach is particularly pertinent
+as the traditionally used parametric distributions have been shown to be
+insufficiently robust and flexible in most of the equity-return data sets
+observed in practice. The method extracts the quantile of the conditional
+distribution of interest, whose estimation is based on a novel estimator of the
+density of the copula describing the dynamic dependence observed in the series
+of returns. Real-world back-testing analyses demonstrate the potential of the
+approach, whose performance may be superior to its industry counterparts.
+"
+21084,Towards High-quality Visualization of Superfluid Vortices,"  Superfluidity is a special state of matter exhibiting macroscopic quantum
+phenomena and acting like a fluid with zero viscosity. In such a state,
+superfluid vortices exist as phase singularities of the model equation with
+unique distributions. This paper presents novel techniques to aid the visual
+understanding of superfluid vortices based on the state-of-the-art non-linear
+Klein-Gordon equation, which evolves a complex scalar field, giving rise to
+special vortex lattice/ring structures with dynamic vortex formation,
+reconnection, and Kelvin waves, etc. By formulating a numerical model with
+theoretical physicists in superfluid research, we obtain high-quality
+superfluid flow data sets without noise-like waves, suitable for vortex
+visualization. By further exploring superfluid vortex properties, we develop a
+new vortex identification and visualization method: a novel mechanism with
+velocity circulation to overcome phase singularity and an orthogonal-plane
+strategy to avoid ambiguity. Hence, our visualizations can help reveal various
+superfluid vortex structures and enable domain experts for related visual
+analysis, such as the steady vortex lattice/ring structures, dynamic vortex
+string interactions with reconnections and energy radiations, where the famous
+Kelvin waves and decaying vortex tangle were clearly observed. These
+visualizations have assisted physicists to verify the superfluid model, and
+further explore its dynamic behavior more intuitively.
+"
+21085,Nonrepetitive edge-colorings of trees,"  A repetition is a sequence of symbols in which the first half is the same as
+the second half. An edge-coloring of a graph is repetition-free or
+nonrepetitive if there is no path with a color pattern that is a repetition.
+The minimum number of colors so that a graph has a nonrepetitive edge-coloring
+is called its Thue edge-chromatic number.
+We improve on the best known general upper bound of $4\Delta-4$ for the Thue
+edge-chromatic number of trees of maximum degree $\Delta$ due to Alon,
+Grytczuk, Ha{\l}uszczak and Riordan (2002) by providing a simple nonrepetitive
+edge-coloring with $3\Delta-2$ colors.
+"
+21086,On the new wave behavior to the longitudinal wave quation in a magneto-electro-elastic circular rod,"  With the aid of the symbolic computations software; Wolfram Mathematica 9,
+the powerful sine-Gordon expansion method is used in examining the analytical
+solution of the longitudinal wave equation in a magneto-electro-elastic
+circular rod. Sine-Gordon expansion method is based on the well-known
+sine-Gordon equation and a wave transformation. The longitudinal wave equation
+is an equation that arises in mathematical physics with dispersion caused by
+the transverse Poisson's effect in a magneto-electro-elastic circular rod. We
+successfully get some solutions with the complex, trigonometric and hyperbolic
+function structure. We present the numerical simulations of all the obtained
+solutions by choosing appropriate values of the parameters. We give the
+physical meanings of some of the obtained analytical solutions which
+significantly explain some practical physical problems.
+"
+21087,Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge,"  Multiple automakers have in development or in production automated driving
+systems (ADS) that offer freeway-pilot functions. This type of ADS is typically
+limited to restricted-access freeways only, that is, the transition from manual
+to automated modes takes place only after the ramp merging process is completed
+manually. One major challenge to extend the automation to ramp merging is that
+the automated vehicle needs to incorporate and optimize long-term objectives
+(e.g. successful and smooth merge) when near-term actions must be safely
+executed. Moreover, the merging process involves interactions with other
+vehicles whose behaviors are sometimes hard to predict but may influence the
+merging vehicle optimal actions. To tackle such a complicated control problem,
+we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an
+optimal driving policy by maximizing the long-term reward in an interactive
+environment. Specifically, we apply a Long Short-Term Memory (LSTM)
+architecture to model the interactive environment, from which an internal state
+containing historical driving information is conveyed to a Deep Q-Network
+(DQN). The DQN is used to approximate the Q-function, which takes the internal
+state as input and generates Q-values as output for action selection. With this
+DRL architecture, the historical impact of interactive environment on the
+long-term reward can be captured and taken into account for deciding the
+optimal control policy. The proposed architecture has the potential to be
+extended and applied to other autonomous driving scenarios such as driving
+through a complex intersection or changing lanes under varying traffic flow
+conditions.
+"
+21088,CDS Rate Construction Methods by Machine Learning Techniques,"  Regulators require financial institutions to estimate counterparty default
+risks from liquid CDS quotes for the valuation and risk management of OTC
+derivatives. However, the vast majority of counterparties do not have liquid
+CDS quotes and need proxy CDS rates. Existing methods cannot account for
+counterparty-specific default risks; we propose to construct proxy CDS rates by
+associating to illiquid counterparty liquid CDS Proxy based on Machine Learning
+Techniques. After testing 156 classifiers from 8 most popular classifier
+families, we found that some classifiers achieve highly satisfactory accuracy
+rates. Furthermore, we have rank-ordered the performances and investigated
+performance variations amongst and within the 8 classifier families. This paper
+is, to the best of our knowledge, the first systematic study of CDS Proxy
+construction by Machine Learning techniques, and the first systematic
+classifier comparison study based entirely on financial market data. Its
+findings both confirm and contrast existing classifier performance literature.
+Given the typically highly correlated nature of financial data, we investigated
+the impact of correlation on classifier performance. The techniques used in
+this paper should be of interest for financial institutions seeking a CDS Proxy
+method, and can serve for proxy construction for other financial variables.
+Some directions for future research are indicated.
+"
+21089,Eigentriads and Eigenprogressions on the Tonnetz,"  We introduce a new multidimensional representation, named eigenprogression
+transform, that characterizes some essential patterns of Western tonal harmony
+while being equivariant to time shifts and pitch transpositions. This
+representation is deep, multiscale, and convolutional in the piano-roll domain,
+yet incurs no prior training, and is thus suited to both supervised and
+unsupervised MIR tasks. The eigenprogression transform combines ideas from the
+spiral scattering transform, spectral graph theory, and wavelet shrinkage
+denoising. We report state-of-the-art results on a task of supervised composer
+recognition (Haydn vs. Mozart) from polyphonic music pieces in MIDI format.
+"
+21090,A generalization of the Log Lindley distribution -- its properties and applications,"  An extension of the two-parameter Log-Lindley distribution of Gomez et al.
+(2014) with support in (0, 1) is proposed. Its important properties like
+cumulative distribution function, moments, survival function, hazard rate
+function, Shannon entropy, stochastic n ordering and convexity (concavity)
+conditions are derived. An application in distorted premium principal is
+outlined and parameter estimation by method of maximum likelihood is also
+presented. We also consider use of a re-parameterized form of the proposed
+distribution in regression modeling for bounded responses by considering a
+modeling of real life data in comparison with beta regression and log-Lindley
+regression models.
+"
+21091,A Flux Conserving Meshfree Method for Conservation Laws,"  Lack of conservation has been the biggest drawback in meshfree generalized
+finite difference methods (GFDMs). In this paper, we present a novel
+modification of classical meshfree GFDMs to include local balances which
+produce an approximate conservation of numerical fluxes. This numerical flux
+conservation is done within the usual moving least squares framework. Unlike
+Finite Volume Methods, it is based on locally defined control cells, rather
+than a globally defined mesh. We present the application of this method to an
+advection diffusion equation and the incompressible Navier - Stokes equations.
+Our simulations show that the introduction of flux conservation significantly
+reduces the errors in conservation in meshfree GFDMs.
+"
+21092,Anomaly detection in the dynamics of web and social networks,"  In this work, we propose a new, fast and scalable method for anomaly
+detection in large time-evolving graphs. It may be a static graph with dynamic
+node attributes (e.g. time-series), or a graph evolving in time, such as a
+temporal network. We define an anomaly as a localized increase in temporal
+activity in a cluster of nodes. The algorithm is unsupervised. It is able to
+detect and track anomalous activity in a dynamic network despite the noise from
+multiple interfering sources. We use the Hopfield network model of memory to
+combine the graph and time information. We show that anomalies can be spotted
+with a good precision using a memory network. The presented approach is
+scalable and we provide a distributed implementation of the algorithm. To
+demonstrate its efficiency, we apply it to two datasets: Enron Email dataset
+and Wikipedia page views. We show that the anomalous spikes are triggered by
+the real-world events that impact the network dynamics. Besides, the structure
+of the clusters and the analysis of the time evolution associated with the
+detected events reveals interesting facts on how humans interact, exchange and
+search for information, opening the door to new quantitative studies on
+collective and social behavior on large and dynamic datasets.
+"
+21093,Local coefficients and the Herbert Formula,"  We discuss a generalisation of the Herbert formula for double points, when
+the normal bundle of an immersion admits an additional structure, and an
+application.
+"
+21094,Emission line ratios of Fe III as astrophysical plasma diagnostics,"  Recent state-of-the-art calculations of A-values and electron impact
+excitation rates for Fe III are used in conjunction with the Cloudy modeling
+code to derive emission line intensity ratios for optical transitions among the
+fine-structure levels of the 3d$^6$ configuration. A comparison of these with
+high resolution, high signal-to-noise spectra of gaseous nebulae reveals that
+previous discrepancies found between theory and observation are not fully
+resolved by the latest atomic data. Blending is ruled out as a likely cause of
+the discrepancies, because temperature- and density-independent ratios (arising
+from lines with common upper levels) match well with those predicted by theory.
+For a typical nebular plasma with electron temperature $T_{\rm e} = 9000$ K and
+electron density $\rm N_{e}=10^4 \, cm^{-3}$, cascading of electrons from the
+levels $\rm ^3G_5$, $\rm ^3G_4$ and $\rm ^3G_3$ plays an important role in
+determining the populations of lower levels, such as $\rm ^3F_4$, which provide
+the density diagnostic emission lines of Fe III, such as $\rm ^5D_4$ - $\rm
+^3F_4$ at 4658 \AA. Hence further work on the A-values for these transitions is
+recommended, ideally including measurements if possible. However, some Fe III
+ratios do provide reliable $N_{\rm e}$-diagnostics, such as 4986/4658. The Fe
+III cooling function calculated with Cloudy using the most recent atomic data
+is found to be significantly greater at $T_e$ $\simeq$ 30000 K than predicted
+with the existing Cloudy model. This is due to the presence of additional
+emission lines with the new data, particularly in the 1000--4000 \AA\
+wavelength region.
+"
+21095,"Warped cones, (non-)rigidity, and piecewise properties, with a joint appendix with Dawid Kielak","  We prove that if a quasi-isometry of warped cones is induced by a map between
+the base spaces of the cones, the actions must be conjugate by this map. The
+converse is false in general, conjugacy of actions is not sufficient for
+quasi-isometry of the respective warped cones. For a general quasi-isometry of
+warped cones, using the asymptotically faithful covering constructed in a
+previous work with Jianchao Wu, we deduce that the two groups are
+quasi-isometric after taking Cartesian products with suitable powers of the
+integers.
+Secondly, we characterise geometric properties of a group (coarse
+embeddability into Banach spaces, asymptotic dimension, property A) by
+properties of the warped cone over an action of this group. These results apply
+to arbitrary asymptotically faithful coverings, in particular to box spaces. As
+an application, we calculate the asymptotic dimension of a warped cone, improve
+bounds by Szabó, Wu, and Zacharias and by Bartels on the amenability
+dimension of actions of virtually nilpotent groups, and give a partial answer
+to a question of Willett about dynamic asymptotic dimension.
+In the appendix, we justify optimality of the aforementioned result on
+general quasi-isometries by showing that quasi-isometric warped cones need not
+come from quasi-isometric groups, contrary to the case of box spaces.
+"
+21096,Enhancing MapReduce Fault Recovery Through Binocular Speculation,"  MapReduce speculation plays an important role in finding potential task
+stragglers and failures. But a tacit dichotomy exists in MapReduce due to its
+inherent two-phase (map and reduce) management scheme in which map tasks and
+reduce tasks have distinctly different execution behaviors, yet reduce tasks
+are dependent on the results of map tasks. We reveal that speculation policies
+for fault handling in MapReduce do not recognize this dichotomy between map and
+reduce tasks, which leads to an issue of speculation myopia for MapReduce fault
+recovery. These issues cause significant performance degradation upon network
+and node failures. To address the speculation myopia caused by MapReduce
+dichotomy, we introduce a new scheme called binocular speculation to help
+MapReduce increase its assessment scope for speculation. As part of the scheme,
+we also design three component techniques including neighborhood glance,
+collective speculation and speculative rollback. Our evaluation shows that,
+with these techniques, binocular speculation can increase the coordination of
+map and reduce phases, and enhance the efficiency of MapReduce fault recovery.
+"
+21097,Generalized classes of continuous symmetries in two-mode Dicke models,"  As recently realized experimentally [Léonard et al., Nature 543, 87
+(2017)], one can engineer models with continuous symmetries by coupling two
+cavity modes to trapped atoms, via a Raman pumping geometry. Considering
+specifically cases where internal states of the atoms couple to the cavity, we
+show an extended range of parameters for which continuous symmetry breaking can
+occur, and we classify the distinct steady states and time-dependent states
+that arise for different points in this extended parameter regime.
+"
+21098,The strong Prikry property,"  I isolate a combinatorial property of a poset $\mathbb{P}$ that I call the
+strong Prikry property, which implies the existence of an ultrafilter on the
+complete Boolean algebra $\mathbb{B}$ of $\mathbb{P}$ such that one inclusion
+of the Boolean ultrapower version of the so-called \Bukovsky-Dehornoy
+phenomenon holds with respect to $\mathbb{B}$ and $U$. I show that in all cases
+that were previously studied, and for which it was shown that they come with a
+canonical iterated ultrapower construction whose limit can be described as a
+single Boolean ultrapower, the posets in question satisfy this property: Prikry
+forcing, Magidor forcing and generalized Prikry forcing.
+"
+21099,Accelerated Optimization in the PDE Framework: Formulations for the Manifold of Diffeomorphisms,"  We consider the problem of optimization of cost functionals on the
+infinite-dimensional manifold of diffeomorphisms. We present a new class of
+optimization methods, valid for any optimization problem setup on the space of
+diffeomorphisms by generalizing Nesterov accelerated optimization to the
+manifold of diffeomorphisms. While our framework is general for infinite
+dimensional manifolds, we specifically treat the case of diffeomorphisms,
+motivated by optical flow problems in computer vision. This is accomplished by
+building on a recent variational approach to a general class of accelerated
+optimization methods by Wibisono, Wilson and Jordan, which applies in finite
+dimensions. We generalize that approach to infinite dimensional manifolds. We
+derive the surprisingly simple continuum evolution equations, which are partial
+differential equations, for accelerated gradient descent, and relate it to
+simple mechanical principles from fluid mechanics. Our approach has natural
+connections to the optimal mass transport problem. This is because one can
+think of our approach as an evolution of an infinite number of particles
+endowed with mass (represented with a mass density) that moves in an energy
+landscape. The mass evolves with the optimization variable, and endows the
+particles with dynamics. This is different than the finite dimensional case
+where only a single particle moves and hence the dynamics does not depend on
+the mass. We derive the theory, compute the PDEs for accelerated optimization,
+and illustrate the behavior of these new accelerated optimization schemes.
+"
+21100,Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments,"  Learning from Demonstration (LfD) is a popular approach to endowing robots
+with skills without having to program them by hand. Typically, LfD relies on
+human demonstrations in clutter-free environments. This prevents the
+demonstrations from being affected by irrelevant objects, whose influence can
+obfuscate the true intention of the human or the constraints of the desired
+skill. However, it is unrealistic to assume that the robot's environment can
+always be restructured to remove clutter when capturing human demonstrations.
+To contend with this problem, we develop an importance weighted batch and
+incremental skill learning approach, building on a recent inference-based
+technique for skill representation and reproduction. Our approach reduces
+unwanted environmental influences on the learned skill, while still capturing
+the salient human behavior. We provide both batch and incremental versions of
+our approach and validate our algorithms on a 7-DOF JACO2 manipulator with
+reaching and placing skills.
+"
+21101,Women are slightly more cooperative than men (in one-shot Prisoner's dilemma games played online),"  Differences between men and women have intrigued generations of social
+scientists, who have found that the two sexes behave differently in settings
+requiring competition, risk taking, altruism, honesty, as well as many others.
+Yet, little is known about whether there are gender differences in cooperative
+behavior. Previous evidence is mixed and inconclusive. Here I shed light on
+this topic by analyzing the totality of studies that my research group has
+conducted since 2013. This is a dataset of 10,951 observations coming from
+7,322 men and women living in the US, recruited through Amazon Mechanical Turk,
+and who passed four comprehension questions to make sure they understand the
+cooperation problem (a one-shot prisoner's dilemma). The analysis demonstrates
+that women are more cooperative than men. The effect size is small (about 4
+percentage points, and this might explain why previous studies failed to detect
+it) but highly significant (p<.0001).
+"
+21102,Relaxation of Radiation-Driven Two-Level Systems Interacting with a Bose-Einstein Condensate Bath,"  We develop a microscopic theory for the relaxation dynamics of an optically
+pumped two-level system (TLS) coupled to a bath of weakly interacting Bose gas.
+Using Keldysh formalism and diagrammatic perturbation theory, expressions for
+the relaxation times of the TLS Rabi oscillations are derived when the boson
+bath is in the normal state and the Bose-Einstein condensate (BEC) state. We
+apply our general theory to consider an irradiated quantum dot coupled with a
+boson bath consisting of a two-dimensional dipolar exciton gas. When the bath
+is in the BEC regime, relaxation of the Rabi oscillations is due to both
+condensate and non-condensate fractions of the bath bosons for weak TLS-light
+coupling and dominantly due to the non-condensate fraction for strong TLS-light
+coupling. Our theory also shows that a phase transition of the bath from the
+normal to the BEC state strongly influences the relaxation rate of the TLS Rabi
+oscillations. The TLS relaxation rate is approximately independent of the pump
+field frequency and monotonically dependent on the field strength when the bath
+is in the low-temperature regime of the normal phase. Phase transition of the
+dipolar exciton gas leads to a non-monotonic dependence of the TLS relaxation
+rate on both the pump field frequency and field strength, providing a
+characteristic signature for the detection of BEC phase transition of the
+coupled dipolar exciton gas.
+"
+21103,On the approximation of convex bodies by ellipses with respect to the symmetric difference metric,"  Given a centrally symmetric convex body $K \subset \mathbb{R}^d$ and a
+positive number $\lambda$, we consider, among all ellipsoids $E \subset
+\mathbb{R}^d$ of volume $\lambda$, those that best approximate $K$ with respect
+to the symmetric difference metric, or equivalently that maximize the volume of
+$E\cap K$: these are the maximal intersection (MI) ellipsoids introduced by
+Artstein-Avidan and Katzin. The question of uniqueness of MI ellipsoids (under
+the obviously necessary assumption that $\lambda$ is between the volumes of the
+John and the Loewner ellipsoids of $K$) is open in general. We provide a
+positive answer to this question in dimension $d=2$. Therefore we obtain a
+continuous $1$-parameter family of ellipses interpolating between the John and
+the Loewner ellipses of $K$. In order to prove uniqueness, we show that the
+area $I_K(E)$ of the intersection $K \cap E$ is a strictly quasiconcave
+function of the ellipse $E$, with respect to the natural affine structure on
+the set of ellipses of area $\lambda$. The proof relies on smoothening $K$,
+putting it in general position, and obtaining uniform estimates for certain
+derivatives of the function $I_K(.)$. Finally, we provide a characterization of
+maximal intersection positions, that is, the situation where the MI ellipse of
+$K$ is the unit disk, under the assumption that the two boundaries are
+transverse.
+"
+21104,POLAMI: Polarimetric Monitoring of Active Galactic Nuclei at Millimetre Wavelengths. II. Widespread circular polarisation,"  We analyse the circular polarisation data accumulated in the first 7 years of
+the POLAMI project introduced in an accompanying paper (Agudo et al.). In the
+3mm wavelength band, we acquired more than 2600 observations, and all but one
+of our 37 sample sources were detected, most of them several times. For most
+sources, the observed distribution of the degree of circular polarisation is
+broader than that of unpolarised calibrators, indicating that weak (<0.5%)
+circular polarisation is present most of the time. Our detection rate and the
+maximum degree of polarisation found, 2.0%, are comparable to previous surveys,
+all made at much longer wavelengths. We argue that the process generating
+circular polarisation must not be strongly wavelength dependent, and we propose
+that the widespread presence of circular polarisation in our short wavelength
+sample dominated by blazars is mostly due to Faraday conversion of the linearly
+polarised synchrotron radiation in the helical magnetic field of the jet.
+Circular polarisation is variable, most notably on time scales comparable to or
+shorter than our median sampling interval <1 month. Longer time scales of about
+one year are occasionally detected, but severely limited by the weakness of the
+signal. At variance with some longer wavelength investigations we find that the
+sign of circular polarisation changes in most sources, while only 7 sources,
+including 3 already known, have a strong preference for one sign. The degrees
+of circular and linear polarisation do not show any systematic correlation. We
+do find however one particular event where the two polarisation degrees vary in
+synchronism during a time span of 0.9 years. The paper also describes a novel
+method for calibrating the sign of circular polarisation observations.
+"
+21105,Single Classifier-based Passive System for Source Printer Classification using Local Texture Features,"  An important aspect of examining printed documents for potential forgeries
+and copyright infringement is the identification of source printer as it can be
+helpful for ascertaining the leak and detecting forged documents. This paper
+proposes a system for classification of source printer from scanned images of
+printed documents using all the printed letters simultaneously. This system
+uses local texture patterns based features and a single classifier for
+classifying all the printed letters. Letters are extracted from scanned images
+using connected component analysis followed by morphological filtering without
+the need of using an OCR. Each letter is sub-divided into a flat region and an
+edge region, and local tetra patterns are estimated separately for these two
+regions. A strategically constructed pooling technique is used to extract the
+final feature vectors. The proposed method has been tested on both a publicly
+available dataset of 10 printers and a new dataset of 18 printers scanned at a
+resolution of 600 dpi as well as 300 dpi printed in four different fonts. The
+results indicate shape independence property in the proposed method as using a
+single classifier it outperforms existing handcrafted feature-based methods and
+needs much smaller number of training pages by using all the printed letters.
+"
+21106,Heat spreader with parallel microchannel configurations employing nanofluids for near active cooling of MEMS,"  While parallel microchannel based cooling systems have been around for quite
+a period of time, employing the same and incorporating them for near active
+cooling of microelectronic devices is yet to be implemented and the
+implications of the same on thermal mitigation to be understood. The present
+article focusses on a specific design of the PMCS such that it can be
+implemented at ease on the heat spreader of a modern microprocessor to obtain
+near active cooling. Extensive experimental and numerical studies have been
+carried out to comprehend the same and three different flow configurations of
+PMCS have been adopted for the present investigations. Additional to focussing
+on the thermofluidics due to flow configuration, nanofluids have also been
+employed to achieve the desired essentials of mitigation of overshoot
+temperatures and improving uniformity of cooling. Two modelling methods,
+Discrete Phase Modelling and Effective Property Modelling have been employed
+for numerical study to model nanofluids as working fluid in micro flow paths
+and the DPM predictions have been observed to match accurately with
+experiments. To quantify the thermal performance of PMCS, an appropriate Figure
+of Merit has been proposed. From the FoM It has been perceived that the Z
+configuration employing nanofluid is the best suitable solutions for uniform
+thermal loads to achieve uniform cooling as well as reducing maximum
+temperature produced with in the device. The present results are very promising
+and viable approach for futuristic thermal mitigation of microprocessor
+systems.
+"
+21107,A review of asymptotic theory of estimating functions,"  Asymptotic statistical theory for estimating functions is reviewed in a
+generality suitable for stochastic processes. Conditions concerning existence
+of a consistent estimator, uniqueness, rate of convergence, and the asymptotic
+distribution are treated separately. Our conditions are not minimal, but can be
+verified for many interesting stochastic process models. Several examples
+illustrate the wide applicability of the theory and why the generality is
+needed.
+"
+21108,Multiview Deep Learning for Predicting Twitter Users' Location,"  The problem of predicting the location of users on large social networks like
+Twitter has emerged from real-life applications such as social unrest detection
+and online marketing. Twitter user geolocation is a difficult and active
+research topic with a vast literature. Most of the proposed methods follow
+either a content-based or a network-based approach. The former exploits
+user-generated content while the latter utilizes the connection or interaction
+between Twitter users. In this paper, we introduce a novel method combining the
+strength of both approaches. Concretely, we propose a multi-entry neural
+network architecture named MENET leveraging the advances in deep learning and
+multiview learning. The generalizability of MENET enables the integration of
+multiple data representations. In the context of Twitter user geolocation, we
+realize MENET with textual, network, and metadata features. Considering the
+natural distribution of Twitter users across the concerned geographical area,
+we subdivide the surface of the earth into multi-scale cells and train MENET
+with the labels of the cells. We show that our method outperforms the state of
+the art by a large margin on three benchmark datasets.
+"
+21109,"Designing the Optimal Bit: Balancing Energetic Cost, Speed and Reliability","  We consider the technologically relevant costs of operating a reliable bit
+that can be erased rapidly. We find that both erasing and reliability times are
+non-monotonic in the underlying friction, leading to a trade-off between
+erasing speed and bit reliability. Fast erasure is possible at the expense of
+low reliability at moderate friction, and high reliability comes at the expense
+of slow erasure in the underdamped and overdamped limits. Within a given class
+of bit parameters and control strategies, we define ""optimal"" designs of bits
+that meet the desired reliability and erasing time requirements with the lowest
+operational work cost. We find that optimal designs always saturate the bound
+on the erasing time requirement, but can exceed the required reliability time
+if critically damped. The non-trivial geometry of the reliability and erasing
+time-scales allows us to exclude large regions of parameter space as
+sub-optimal. We find that optimal designs are either critically damped or close
+to critical damping under the erasing procedure.
+"
+21110,Economic Factors of Vulnerability Trade and Exploitation,"  Cybercrime markets support the development and diffusion of new attack
+technologies, vulnerability exploits, and malware. Whereas the revenue streams
+of cyber attackers have been studied multiple times in the literature, no
+quantitative account currently exists on the economics of attack acquisition
+and deployment. Yet, this understanding is critical to characterize the
+production of (traded) exploits, the economy that drives it, and its effects on
+the overall attack scenario. In this paper we provide an empirical
+investigation of the economics of vulnerability exploitation, and the effects
+of market factors on likelihood of exploit. Our data is collected
+first-handedly from a prominent Russian cybercrime market where the trading of
+the most active attack tools reported by the security industry happens. Our
+findings reveal that exploits in the underground are priced similarly or above
+vulnerabilities in legitimate bug-hunting programs, and that the refresh cycle
+of exploits is slower than currently often assumed. On the other hand,
+cybercriminals are becoming faster at introducing selected vulnerabilities, and
+the market is in clear expansion both in terms of players, traded exploits, and
+exploit pricing. We then evaluate the effects of these market variables on
+likelihood of attack realization, and find strong evidence of the correlation
+between market activity and exploit deployment. We discuss implications on
+vulnerability metrics, economics, and exploit measurement.
+"
+21111,Complete classification of generalized crossing changes between GOF-knots,"  We show that the monodromy for a genus one, fibered knot can have at most two
+monodromy equivalence classes of once-unclean arcs. We use this to classify all
+monodromies of genus one, fibered knots that possess once-unclean arcs, all
+manifolds containing genus one fibered knots with generalized crossing changes
+resulting in another genus one fibered knot, and all generalized crossing
+changes between two genus one, fibered knots.
+"
+21112,Strain Mode of General Flow: Characterization and Implications for Flow Pattern Structures,"  Understanding the mixing capability of mixing devices based on their
+geometric shape is an important issue both for predicting mixing processes and
+for designing new mixers. The flow patterns in mixers are directly connected
+with the modes of the local strain rate, which is generally a combination of
+elongational flow and planar shear flow. We develop a measure to characterize
+the modes of the strain rate for general flow occurring in mixers. The spatial
+distribution of the volumetric strain rate (or non-planar strain rate) in
+connection with the flow pattern plays an essential role in understanding
+distributive mixing. With our measure, flows with different types of screw
+elements in a twin-screw extruder are numerically analyzed. The difference in
+flow pattern structure between conveying screws and kneading disks is
+successfully characterized by the distribution of the volumetric strain rate.
+The results suggest that the distribution of the strain rate mode offers an
+essential and convenient way for characterization of the relation between flow
+pattern structure and the mixer geometry.
+"
+21113,$\mathbb{Z}^2$-algebras as noncommutative blow-ups,"  The goal of this note is to first prove that for a well behaved
+$\mathbb{Z}^2$-algebra $R$, the category $QGr(R) := Gr(R)/Tors(R)$ is
+equivalent to $QGr(R_\Delta)$ where $R_\Delta$ is a diagonal-like
+sub-$\mathbb{Z}$-algebra of $R$. Afterwards we use this result to prove that
+the $\mathbb{Z}^2$-algebras as introduced in [arXiv:1607.08383] are
+QGr-equivalent to a diagonal-like sub-$\mathbb{Z}$-algebra which is a
+simultaneous noncommutative blow-up of a quadratic and a cubic Sklyanin
+algebra. As such we link the noncommutative birational transformation and the
+associated $\mathbb{Z}^2$-algebras as appearing in the work of Van den Bergh
+and Presotto with the noncommutative blowups appearing in the work of Rogalski,
+Sierra and Stafford.
+"
+21114,Deep Convolutional Neural Networks for Anomaly Event Classification on Distributed Systems,"  The increasing popularity of server usage has brought a plenty of anomaly log
+events, which have threatened a vast collection of machines. Recognizing and
+categorizing the anomalous events thereby is a much salient work for our
+systems, especially the ones generate the massive amount of data and harness it
+for technology value creation and business development. To assist in focusing
+on the classification and the prediction of anomaly events, and gaining
+critical insights from system event records, we propose a novel log
+preprocessing method which is very effective to filter abundant information and
+retain critical characteristics. Additionally, a competitive approach for
+automated classification of anomalous events detected from the distributed
+system logs with the state-of-the-art deep (Convolutional Neural Network)
+architectures is proposed in this paper. We measure a series of deep CNN
+algorithms with varied hyper-parameter combinations by using standard
+evaluation metrics, the results of our study reveals the advantages and
+potential capabilities of the proposed deep CNN models for anomaly event
+classification tasks on real-world systems. The optimal classification
+precision of our approach is 98.14%, which surpasses the popular traditional
+machine learning methods.
+"
+21115,Group-sparse block PCA and explained variance,"  The paper addresses the simultneous determination of goup-sparse loadings by
+block optimization, and the correlated problem of defining explained variance
+for a set of non orthogonal components. We give in both cases a comprehensive
+mathematical presentation of the problem, which leads to propose i) a new
+formulation/algorithm for group-sparse block PCA and ii) a framework for the
+definition of explained variance with the analysis of five definitions. The
+numerical results i) confirm the superiority of block optimization over
+deflation for the determination of group-sparse loadings, and the importance of
+group information when available, and ii) show that ranking of algorithms
+according to explained variance is essentially independant of the definition of
+explained variance. These results lead to propose a new optimal variance as the
+definition of choice for explained variance.
+"
+21116,Multi-Frequency Phase Synchronization,"  We propose a novel formulation for phase synchronization -- the statistical
+problem of jointly estimating alignment angles from noisy pairwise comparisons
+-- as a nonconvex optimization problem that enforces consistency among the
+pairwise comparisons in multiple frequency channels. Inspired by harmonic
+retrieval in signal processing, we develop a simple yet efficient two-stage
+algorithm that leverages the multi-frequency information. We demonstrate in
+theory and practice that the proposed algorithm significantly outperforms
+state-of-the-art phase synchronization algorithms, at a mild computational
+costs incurred by using the extra frequency channels. We also extend our
+algorithmic framework to general synchronization problems over compact Lie
+groups.
+"
+21117,A Bennequin-type inequality and combinatorial bounds,"  In this paper we provide a new Bennequin-type inequality for the Rasmussen-
+Beliakova-Wehrli invariant, featuring the numerical transverse braid invariants
+(the c-invariants) introduced by the author. From the Bennequin
+type-inequality, and a combinatorial bound on the value of the c-invariants, we
+deduce a new computable bound on the Rasmussen invariant.
+"
+21118,Thermal field theory of bosonic gases with finite-range effective interaction,"  We study a dilute and ultracold Bose gas of interacting atoms by using an
+effective field theory which takes account finite-range effects of the
+inter-atomic potential. Within the formalism of functional integration from the
+grand canonical partition function we derive beyond-mean-field analytical
+results which depend on both scattering length and effective range of the
+interaction. In particular, we calculate the equation of state of the bosonic
+system as a function of these interaction parameters both at zero and finite
+temperature including one-loop Gaussian fluctuation. In the case of zero-range
+effective interaction we explicitly show that, due to quantum fluctuations, the
+bosonic system is thermodynamically stable only for very small values of the
+gas parameter. We find that a positive effective range above a critical
+threshold is necessary to remove the thermodynamical instability of the uniform
+configuration. Remarkably, also for relatively large values of the gas
+parameter, our finite-range results are in quite good agreement with recent
+zero-temperature Monte Carlo calculations obtained with hard-sphere bosons.
+"
+21119,LtFi: Cross-technology Communication for RRM between LTE-U and IEEE 802.11,"  Cross-technology communication (CTC) was proposed in recent literature as a
+way to exploit the opportunities of collaboration between heterogeneous
+wireless technologies. This paper presents LtFi, a system which enables to
+set-up a CTC between nodes of co-located LTE-U and WiFi networks. LtFi follows
+a two-step approach: using the air-interface LTE-U BSs are broadcasting
+connection and identification data to adjacent WiFi nodes, which is used to
+create a bi-directional control channel over the wired Internet. This way LtFi
+enables the development of advanced cross-technology interference and radio
+resource management schemes between heterogeneous WiFi and LTE-U networks.
+LtFi is of low complexity and fully compliant with LTE-U technology and works
+on WiFi side with COTS hardware. It was prototypically implemented and
+evaluated. Experimental results reveal that LtFi is able to reliably decoded
+the data transmitted over the LtFi air-interface in a crowded wireless
+environment at even very low LTE-U receive power levels of -92dBm. Moreover,
+results from system-level simulations show that LtFi is able to accurately
+estimate the set of interfering LTE-U BSs in a typical LTE-U multi-cell
+environment.
+"
+21120,Multi-step Off-policy Learning Without Importance Sampling Ratios,"  To estimate the value functions of policies from exploratory data, most
+model-free off-policy algorithms rely on importance sampling, where the use of
+importance sampling ratios often leads to estimates with severe variance. It is
+thus desirable to learn off-policy without using the ratios. However, such an
+algorithm does not exist for multi-step learning with function approximation.
+In this paper, we introduce the first such algorithm based on
+temporal-difference (TD) learning updates. We show that an explicit use of
+importance sampling ratios can be eliminated by varying the amount of
+bootstrapping in TD updates in an action-dependent manner. Our new algorithm
+achieves stability using a two-timescale gradient-based TD update. A prior
+algorithm based on lookup table representation called Tree Backup can also be
+retrieved using action-dependent bootstrapping, becoming a special case of our
+algorithm. In two challenging off-policy tasks, we demonstrate that our
+algorithm is stable, effectively avoids the large variance issue, and can
+perform substantially better than its state-of-the-art counterpart.
+"
+21121,Atomic Ferris wheel beams,"  We study the generation of atom vortex beams in the case where an atomic
+wave-packet, moving in free space, is diffracted from a properly tailored light
+mask with a spiral transverse profile. We show how such a diffraction scheme
+could lead to the production of an atomic Ferris wheel beam.
+"
+21122,On the Limits of Learning Representations with Label-Based Supervision,"  Advances in neural network based classifiers have transformed automatic
+feature learning from a pipe dream of stronger AI to a routine and expected
+property of practical systems. Since the emergence of AlexNet every winning
+submission of the ImageNet challenge has employed end-to-end representation
+learning, and due to the utility of good representations for transfer learning,
+representation learning has become as an important and distinct task from
+supervised learning. At present, this distinction is inconsequential, as
+supervised methods are state-of-the-art in learning transferable
+representations. But recent work has shown that generative models can also be
+powerful agents of representation learning. Will the representations learned
+from these generative methods ever rival the quality of those from their
+supervised competitors? In this work, we argue in the affirmative, that from an
+information theoretic perspective, generative models have greater potential for
+representation learning. Based on several experimentally validated assumptions,
+we show that supervised learning is upper bounded in its capacity for
+representation learning in ways that certain generative models, such as
+Generative Adversarial Networks (GANs) are not. We hope that our analysis will
+provide a rigorous motivation for further exploration of generative
+representation learning.
+"
+21123,Coset space construction and inverse Higgs phenomenon for the conformal group,"  It is shown that conformally invariant theories can be obtained within the
+framework of the coset space construction. The corresponding technique is
+applicable for the construction of representations of the unbroken conformal
+group, as well as of a spontaneously broken one. A special role of the
+""Nambu-Goldstone fields"" for special conformal transformations is clarified -
+they ensure self-consistency of a theory by guaranteeing that discrete
+symmetries are indeed symmetries of the theory. A generalization of the
+developed construction to a special class of symmetry groups with a non-linear
+realization of its discrete elements is given. Based on these results, the
+usage of the inverse Higgs constraints for the conformal group undergoing
+spontaneous symmetry breaking is questioned.
+"
+21124,"A spatial predictive model for Malaria resurgence in central Greece integrating entomological, environmental and Social data","  Malaria constitutes an important cause of human mortality. After 2009 Greece
+experienced a resurgence of malaria. Here, we develop a modelbased framework
+that integrates entomological, geographical, social and environmental evidence
+in order to guide the mosquito control efforts and apply this framework to data
+from an entomological survey study conducted in Central Greece. Our results
+indicate that malaria transmission risk in Greece is potentially substantial.
+In addition, specific districts such as seaside, lakeside and rice field
+regions appear to represent potential malaria hotspots in Central Greece. We
+found that appropriate maps depicting the basic reproduction number, R0 , are
+useful tools for informing policy makers on the risk of malaria resurgence and
+can serve as a guide to inform recommendations regarding control measures.
+"
+21125,Large-scale analysis of user exposure to online advertising in Facebook,"  Online advertising is the major source of income for a large portion of
+Internet Services. There exists a body of literature aiming at optimizing ads
+engagement, understanding the privacy and ethical implications of online
+advertising, etc. However, to the best of our knowledge, no previous work
+analyses at large scale the exposure of real users to online advertising. This
+paper performs a comprehensive analysis of the exposure of users to ads and
+advertisers using a dataset including more than 7M ads from 140K unique
+advertisers delivered to more than 5K users that was collected between October
+2016 and May 2018. The study focuses on Facebook, which is the second largest
+advertising platform only to Google in terms of revenue, and accounts for more
+than 2.2B monthly active users. Our analysis reveals that Facebook users are
+exposed (in median) to 70 ads per week, which come from 12 advertisers. Ads
+represent between 10% and 15% of all the information received in users'
+newsfeed. A small increment of 1% in the portion of ads in the newsfeed could
+roughly represent a revenue increase of 8.17M USD per week for Facebook.
+Finally, we also reveal that Facebook users are overprofiled since in the best
+case only 22.76% of the interests Facebook assigns to users for advertising
+purpose are actually related to the ads those users receive.
+"
+21126,Concept Formation and Dynamics of Repeated Inference in Deep Generative Models,"  Deep generative models are reported to be useful in broad applications
+including image generation. Repeated inference between data space and latent
+space in these models can denoise cluttered images and improve the quality of
+inferred results. However, previous studies only qualitatively evaluated image
+outputs in data space, and the mechanism behind the inference has not been
+investigated. The purpose of the current study is to numerically analyze
+changes in activity patterns of neurons in the latent space of a deep
+generative model called a ""variational auto-encoder"" (VAE). What kinds of
+inference dynamics the VAE demonstrates when noise is added to the input data
+are identified. The VAE embeds a dataset with clear cluster structures in the
+latent space and the center of each cluster of multiple correlated data points
+(memories) is referred as the concept. Our study demonstrated that transient
+dynamics of inference first approaches a concept, and then moves close to a
+memory. Moreover, the VAE revealed that the inference dynamics approaches a
+more abstract concept to the extent that the uncertainty of input data
+increases due to noise. It was demonstrated that by increasing the number of
+the latent variables, the trend of the inference dynamics to approach a concept
+can be enhanced, and the generalization ability of the VAE can be improved.
+"
+21127,Lp-estimates for the square root of elliptic systems with mixed boundary conditions,"  This article focuses on Lp-estimates for the square root of elliptic systems
+of second order in divergence form on a bounded domain. We treat complex
+bounded measurable coefficients and allow for mixed Dirichlet/Neumann boundary
+conditions on domains beyond the Lipschitz class. If there is an associated
+bounded semigroup on Lp0 , then we prove that the square root extends for all p
+$\in$ (p0, 2) to an isomorphism between a closed subspace of W1p carrying the
+boundary conditions and Lp. This result is sharp and extrapolates to exponents
+slightly above 2. As a byproduct, we obtain an optimal p-interval for the
+bounded H$\infty$-calculus on Lp. Estimates depend holomorphically on the
+coefficients, thereby making them applicable to questions of non-autonomous
+maximal regularity and optimal control. For completeness we also provide a
+short summary on the Kato square root problem in L2 for systems with lower
+order terms in our setting.
+"
+21128,Ensemble of Thermostatically Controlled Loads: Statistical Physics Approach,"  Thermostatically Controlled Loads (TCL), e.g. air-conditioners and heaters,
+are by far the most wide-spread consumers of electricity. Normally the devices
+are calibrated to provide the so-called bang-bang control of temperature --
+changing from on to off, and vice versa, depending on temperature. Aggregation
+of a large group of similar devices into a statistical ensemble is considered,
+where the devices operate following the same dynamics subject to stochastic
+perturbations and randomized, Poisson on/off switching policy. We analyze,
+using theoretical and computational tools of statistical physics, how the
+ensemble relaxes to a stationary distribution and establish relation between
+the relaxation and statistics of the probability flux, associated with devices'
+cycling in the mixed (discrete, switch on/off, and continuous, temperature)
+phase space. This allowed us to derive and analyze spectrum of the
+non-equilibrium (detailed balance broken) statistical system and uncover how
+switching policy affects oscillatory trend and speed of the relaxation.
+Relaxation of the ensemble is of a practical interest because it describes how
+the ensemble recovers from significant perturbations, e.g. forceful temporary
+switching off aimed at utilizing flexibility of the ensemble in providing
+""demand response"" services relieving consumption temporarily to balance larger
+power grid. We discuss how the statistical analysis can guide further
+development of the emerging demand response technology.
+"
+21129,Automatic Document Image Binarization using Bayesian Optimization,"  Document image binarization is often a challenging task due to various forms
+of degradation. Although there exist several binarization techniques in
+literature, the binarized image is typically sensitive to control parameter
+settings of the employed technique. This paper presents an automatic document
+image binarization algorithm to segment the text from heavily degraded document
+images. The proposed technique uses a two band-pass filtering approach for
+background noise removal, and Bayesian optimization for automatic
+hyperparameter selection for optimal results. The effectiveness of the proposed
+binarization technique is empirically demonstrated on the Document Image
+Binarization Competition (DIBCO) and the Handwritten Document Image
+Binarization Competition (H-DIBCO) datasets.
+"
+21130,Vibrationally resolved electronic spectra including vibrational pre-excitation: Theory and application to VIPER spectroscopy,"  Vibrationally resolved electronic absorption spectra including the effect of
+vibrational pre-excitation are computed in order to interpret and predict
+vibronic transitions that are probed in the Vibrationally Promoted Electronic
+Resonance (VIPER) experiment [L. J. G. W. van Wilderen et al., Angew. Chem.
+Int. Ed. 53, 2667 (2014)]. To this end, we employ time-independent and
+time-dependent methods based on the evaluation of Franck-Condon overlap
+integrals and Fourier transformation of time-domain wavepacket autocorrelation
+functions, respectively. The time-independent approach uses a generalized
+version of the FCclasses method [F. Santoro et al., J. Chem. Phys. 126, 084509
+(2007)]. In the time-dependent approach, autocorrelation functions are obtained
+by wavepacket propagation and by evaluation of analytic expressions, within the
+harmonic approximation including Duschinsky rotation effects. For several
+medium-sized polyatomic systems, it is shown that selective pre-excitation of
+particular vibrational modes leads to a red-shift of the low-frequency edge of
+the electronic absorption spectrum, which is a prerequisite for the VIPER
+experiment. This effect is typically most pronounced upon excitation of ring
+distortion modes within an aromatic pi-system. Theoretical predictions as to
+which modes show the strongest VIPER effect are found to be in excellent
+agreement with experiment.
+"
+21131,Modular Multi-Objective Deep Reinforcement Learning with Decision Values,"  In this work we present a method for using Deep Q-Networks (DQNs) in
+multi-objective environments. Deep Q-Networks provide remarkable performance in
+single objective problems learning from high-level visual state
+representations. However, in many scenarios (e.g in robotics, games), the agent
+needs to pursue multiple objectives simultaneously. We propose an architecture
+in which separate DQNs are used to control the agent's behaviour with respect
+to particular objectives. In this architecture we introduce decision values to
+improve the scalarization of multiple DQNs into a single action. Our
+architecture enables the decomposition of the agent's behaviour into
+controllable and replaceable sub-behaviours learned by distinct modules.
+Moreover, it allows to change the priorities of particular objectives
+post-learning, while preserving the overall performance of the agent. To
+evaluate our solution we used a game-like simulator in which an agent -
+provided with high-level visual input - pursues multiple objectives in a 2D
+world.
+"
+21132,Coupled conditional backward sampling particle filter,"  Unbiased estimation for hidden Markov models has been recently proposed by
+Jacob et al (to appear), using a coupling of two conditional particle filters
+(CPFs). Unbiased estimation has many advantages, such as enabling the
+construction of asymptotically exact confidence intervals and straightforward
+parallelisation. In this work we propose a new coupling of two CPFs, for
+unbiased estimation, that uses backward sampling steps, which is an important
+efficiency enhancing technique in particle filtering. We show that this coupled
+conditional backward sampling particle filter (CCBPF) algorithm has better
+stability properties, in the sense that with fixed number of particles, the
+coupling time in terms of iterations increases only linearly with respect to
+the time horizon under a general (strong mixing) condition. In contrast,
+current coupled CPFs require the particle number to increase with the horizon
+length. An important corollary of our results is a new quantitative bound for
+the convergence rate of the popular backward sampling conditional particle
+filter. Previous theoretical results have not been able to demonstrate the
+improvement brought by backward sampling to the CPF, whereas we provide rates
+showing that backward sampling ensures that the CPF can remain effective with a
+fixed number of particles independent of the time horizon.
+"
+21133,A Bayesian Evidence Synthesis Approach to Estimate Disease Prevalence in Hard-To-Reach Populations: Hepatitis C in New York City,"  Existing methods to estimate the prevalence of chronic hepatitis C (HCV) in
+New York City (NYC) are limited in scope and fail to assess hard-to-reach
+subpopulations with highest risk such as injecting drug users (IDUs). To
+address these limitations, we employ a Bayesian multi-parameter evidence
+synthesis model to systematically combine multiple sources of data, account for
+bias in certain data sources, and provide unbiased HCV prevalence estimates
+with associated uncertainty. Our approach improves on previous estimates by
+explicitly accounting for injecting drug use and including data from high-risk
+subpopulations such as the incarcerated, and is more inclusive, utilizing ten
+NYC data sources. In addition, we derive two new equations to allow age at
+first injecting drug use data for former and current IDUs to be incorporated
+into the Bayesian evidence synthesis, a first for this type of model. Our
+estimated overall HCV prevalence as of 2012 among NYC adults aged 20-59 years
+is 2.78% (95% CI 2.61-2.94%), which represents between 124,900 and 140,000
+chronic HCV cases. These estimates suggest that HCV prevalence in NYC is higher
+than previously indicated from household surveys (2.2%) and the surveillance
+system (2.37%), and that HCV transmission is increasing among young injecting
+adults in NYC. An ancillary benefit from our results is an estimate of current
+IDUs aged 20-59 in NYC: 0.58% or 27,600 individuals.
+"
+21134,"Galaxies in the Illustris simulation as seen by the Sloan Digital Sky Survey - I: Bulge+disc decompositions, methods, and biases","  We present an image-based method for comparing the structural properties of
+galaxies produced in hydrodynamical simulations to real galaxies in the Sloan
+Digital Sky Survey. The key feature of our work is the introduction of
+extensive observational realism, such as object crowding, noise and viewing
+angle, to the synthetic images of simulated galaxies, so that they can be
+fairly compared to real galaxy catalogs. We apply our methodology to the
+dust-free synthetic image catalog of galaxies from the Illustris simulation at
+$z=0$, which are then fit with bulge+disc models to obtain morphological
+parameters. In this first paper in a series, we detail our methods, quantify
+observational biases, and present publicly available bulge+disc decomposition
+catalogs. We find that our bulge+disc decompositions are largely robust to the
+observational biases that affect decompositions of real galaxies. However, we
+identify a significant population of galaxies (roughly 30\% of the full sample)
+in Illustris that are prone to internal segmentation, leading to systematically
+reduced flux estimates by up to a factor of 6, smaller half-light radii by up
+to a factor of $\sim$ 2, and generally erroneous bulge-to-total fractions of
+(B/T)=0.
+"
+21135,PubTree: A Hierarchical Search Tool for the MEDLINE Database,"  Keeping track of the ever-increasing body of scientific literature is an
+escalating challenge. We present PubTree a hierarchical search tool that
+efficiently searches the PubMed/MEDLINE dataset based upon a decision tree
+constructed using >26 million abstracts. The tool is implemented as a webpage,
+where users are asked a series of eighteen questions to locate pertinent
+articles. The implementation of this hierarchical search tool highlights issues
+endemic with document retrieval. However, the construction of this tree
+indicates that with future developments hierarchical search could become an
+effective tool (or adjunct) in the mining of biological literature.
+"
+21136,Dimensional crossover and incipient quantum size effects in superconducting niobium nanofilms,"  Superconducting and normal state properties of sputtered Niobium nanofilms
+have been systematically investigated, as a function of film thickness in a
+d=9-90 nm range, on different substrates. The width of the
+superconducting-to-normal transition for all films remained in few tens of mK,
+thus remarkably narrow, confirming their high quality. We found that the
+superconducting critical current density exhibits a pronounced maximum, three
+times larger than its bulk value, for film thickness around 25 nm, marking the
+3D-to-2D crossover. The extracted magnetic penetration depth shows a sizeable
+enhancement for the thinnest films, aside the usual demagnetization effects.
+Additional amplification effects of the superconducting properties have been
+obtained in the case of sapphire substrates or squeezing the lateral size of
+the nanofilms. For thickness close to 20 nm we also measured a doubled
+perpendicular critical magnetic field compared to its saturation value for d>33
+nm, indicating shortening of the correlation length and the formation of small
+Cooper pairs in the condensate. Our data analysis evidences an exciting
+interplay between quantum-size and proximity effects together with
+strong-coupling effects and importance of disorder in the thinnest films,
+locating the ones with optimally enhanced critical properties close to the
+BCS-BEC crossover regime.
+"
+21137,Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors,"  Restricted Boltzmann Machines are described by the Gibbs measure of a
+bipartite spin glass, which in turn corresponds to the one of a generalised
+Hopfield network. This equivalence allows us to characterise the state of these
+systems in terms of retrieval capabilities, both at low and high load. We study
+the paramagnetic-spin glass and the spin glass-retrieval phase transitions, as
+the pattern (i.e. weight) distribution and spin (i.e. unit) priors vary
+smoothly from Gaussian real variables to Boolean discrete variables. Our
+analysis shows that the presence of a retrieval phase is robust and not
+peculiar to the standard Hopfield model with Boolean patterns. The retrieval
+region is larger when the pattern entries and retrieval units get more peaked
+and, conversely, when the hidden units acquire a broader prior and therefore
+have a stronger response to high fields. Moreover, at low load retrieval always
+exists below some critical temperature, for every pattern distribution ranging
+from the Boolean to the Gaussian case.
+"
+21138,Causal nearest neighbor rules for optimal treatment regimes,"  The estimation of optimal treatment regimes is of considerable interest to
+precision medicine. In this work, we propose a causal $k$-nearest neighbor
+method to estimate the optimal treatment regime. The method roots in the
+framework of causal inference, and estimates the causal treatment effects
+within the nearest neighborhood. Although the method is simple, it possesses
+nice theoretical properties. We show that the causal $k$-nearest neighbor
+regime is universally consistent. That is, the causal $k$-nearest neighbor
+regime will eventually learn the optimal treatment regime as the sample size
+increases. We also establish its convergence rate. However, the causal
+$k$-nearest neighbor regime may suffer from the curse of dimensionality, i.e.
+performance deteriorates as dimensionality increases. To alleviate this
+problem, we develop an adaptive causal $k$-nearest neighbor method to perform
+metric selection and variable selection simultaneously. The performance of the
+proposed methods is illustrated in simulation studies and in an analysis of a
+chronic depression clinical trial.
+"
+21139,Definitions of solutions to the IBVP for multiD scalar balance laws,"  We consider four definitions of solution to the initial-boundary value
+problem for a scalar balance laws in several space dimensions. These
+definitions are generalised to the same most general framework and then
+compared. The first aim of this paper is to detail differences and analogies
+among them. We focus then on the ways the boundary conditions are fulfilled
+according to each definition, providing also connections among these various
+modes. The main result is the proof of the equivalence among the presented
+definitions of solution.
+"
+21140,Post-selection estimation and testing following aggregated association tests,"  The practice of pooling several individual test statistics to form aggregate
+tests is common in many statistical application where individual tests may be
+underpowered. While selection by aggregate tests can serve to increase power,
+the selection process invalidates the individual test-statistics, making it
+difficult to identify the ones that drive the signal in follow-up inference.
+Here, we develop a general approach for valid inference following selection by
+aggregate testing. We present novel powerful post-selection tests for the
+individual null hypotheses which are exact for the normal model and
+asymptotically justified otherwise. Our approach relies on the ability to
+characterize the distribution of the individual test statistics after
+conditioning on the event of selection. We provide efficient algorithms for
+estimation of the post-selection maximum-likelihood estimates and suggest
+confidence intervals which rely on a novel switching regime for good coverage
+guarantees. We validate our methods via comprehensive simulation studies and
+apply them to data from the Dallas Heart Study, demonstrating that single
+variant association discovery following selection by an aggregated test is
+indeed possible in practice.
+"
+21141,GANs for Medical Image Analysis,"  Generative Adversarial Networks (GANs) and their extensions have carved open
+many exciting ways to tackle well known and challenging medical image analysis
+problems such as medical image de-noising, reconstruction, segmentation, data
+simulation, detection or classification. Furthermore, their ability to
+synthesize images at unprecedented levels of realism also gives hope that the
+chronic scarcity of labeled data in the medical field can be resolved with the
+help of these generative models. In this review paper, a broad overview of
+recent literature on GANs for medical applications is given, the shortcomings
+and opportunities of the proposed methods are thoroughly discussed and
+potential future work is elaborated. We review the most relevant papers
+published until the submission date. For quick access, important details such
+as the underlying method, datasets and performance are tabulated. An
+interactive visualization categorizes all papers to keep the review alive.
+"
+21142,NFL Injuries Before and After the 2011 Collective Bargaining Agreement (CBA),"  The National Football League's (NFL) 2011 collective bargaining agreement
+(CBA) with its players placed a number of contact and quantity limitations on
+practices and workouts. Some coaches and others have expressed a concern that
+this has led to poor conditioning and a subsequent increase in injuries. We
+sought to assess whether the 2011 CBA's practice restrictions affected the
+number of overall, conditioning-dependent, and/or non-conditioning-dependent
+injuries in the NFL or the number of games missed due to those injuries. The
+study population was player-seasons from 2007-2016. We included regular season,
+non-illness, non-head, game-loss injuries. Injuries were identified using a
+database from Football Outsiders. The primary outcomes were overall,
+conditioning-dependent and non-conditioning-dependent injury counts by season.
+We examined time trends in injury counts before (2007-2010) and after
+(2011-2016) the CBA using a Poisson interrupted time series model. The number
+of game-loss regular season, non-head, non-illness injuries grew from 701 in
+2007 to 804 in 2016 (15% increase). The number of regular season weeks missed
+exhibited a similar increase. Conditioning-dependent injuries increased from
+197 in 2007 to 271 in 2011 (38% rise), but were lower and remained relatively
+unchanged at 220-240 injuries per season thereafter. Non-conditioning injuries
+decreased by 37% in the first three years of the new CBA before returning to
+historic levels in 2014-2016. Poisson models for all, conditioning-dependent,
+and non-conditioning-dependent game-loss injury counts did not show
+statistically significant or meaningful detrimental changes associated with the
+CBA. We did not observe an increase in injuries following the 2011 CBA. Other
+concurrent injury-related rule and regulation changes limit specific causal
+inferences about the practice restrictions, however.
+"
+21143,"Structured and Unstructured Outlier Identification for Robust PCA: A Non iterative, Parameter free Algorithm","  Robust PCA, the problem of PCA in the presence of outliers has been
+extensively investigated in the last few years. Here we focus on Robust PCA in
+the outlier model where each column of the data matrix is either an inlier or
+an outlier. Most of the existing methods for this model assumes either the
+knowledge of the dimension of the lower dimensional subspace or the fraction of
+outliers in the system. However in many applications knowledge of these
+parameters is not available. Motivated by this we propose a parameter free
+outlier identification method for robust PCA which a) does not require the
+knowledge of outlier fraction, b) does not require the knowledge of the
+dimension of the underlying subspace, c) is computationally simple and fast d)
+can handle structured and unstructured outliers. Further, analytical guarantees
+are derived for outlier identification and the performance of the algorithm is
+compared with the existing state of the art methods in both real and synthetic
+data for various outlier structures.
+"
+21144,Foveated Video Streaming for Cloud Gaming,"  Good user experience with interactive cloud-based multimedia applications,
+such as cloud gaming and cloud-based VR, requires low end-to-end latency and
+large amounts of downstream network bandwidth at the same time. In this paper,
+we present a foveated video streaming system for cloud gaming. The system
+adapts video stream quality by adjusting the encoding parameters on the fly to
+match the player's gaze position. We conduct measurements with a prototype that
+we developed for a cloud gaming system in conjunction with eye tracker
+hardware. Evaluation results suggest that such foveated streaming can reduce
+bandwidth requirements by even more than 50% depending on parametrization of
+the foveated video coding and that it is feasible from the latency perspective.
+"
+21145,Data-driven Analysis of Complex Networks and their Model-generated Counterparts,"  Data-driven analysis of complex networks has been in the focus of research
+for decades. An important question is to discover the relation between various
+network characteristics in real-world networks and how these relationships vary
+across network domains. A related research question is to study how well the
+network models can capture the observed relations between the graph metrics. In
+this paper, we apply statistical and machine learning techniques to answer the
+aforementioned questions. We study 400 real-world networks along with 2400
+networks generated by five frequently used network models with previously
+fitted parameters to make the generated graphs as similar to the real network
+as possible. We find that the correlation profiles of the structural measures
+significantly differ across network domains and the domain can be efficiently
+determined using a small selection of graph metrics. The goodness-of-fit of the
+network models and the best performing models themselves highly depend on the
+domains. Using machine learning techniques, it turned out to be relatively easy
+to decide if a network is real or model-generated. We also investigate what
+structural properties make it possible to achieve a good accuracy, i.e. what
+features the network models cannot capture.
+"
+21146,A Short Note on Collecting Dependently Typed Values,"  Within dependently typed languages, such as Idris, types can depend on
+values. This dependency, however, can limit the collection of items in standard
+containers: all elements must have the same type, and as such their types must
+contain the same values. We present two dependently typed data structures for
+collecting dependent types: \texttt{DList} and \texttt{PList}. Use of these new
+data structures allow for the creation of single succinct inductive ADT whose
+constructions were previously verbose and split across many data structures.
+"
+21147,Dynamic Bridge-Finding in $\tilde{O}(\log ^2 n)$ Amortized Time,"  We present a deterministic fully-dynamic data structure for maintaining
+information about the bridges in a graph. We support updates in
+$\tilde{O}((\log n)^2)$ amortized time, and can find a bridge in the component
+of any given vertex, or a bridge separating any two given vertices, in $O(\log
+n / \log \log n)$ worst case time. Our bounds match the current best for bounds
+for deterministic fully-dynamic connectivity up to $\log\log n$ factors. The
+previous best dynamic bridge finding was an $\tilde{O}((\log n)^3)$ amortized
+time algorithm by Thorup [STOC2000], which was a bittrick-based improvement on
+the $O((\log n)^4)$ amortized time algorithm by Holm et al.[STOC98, JACM2001].
+Our approach is based on a different and purely combinatorial improvement of
+the algorithm of Holm et al., which by itself gives a new combinatorial
+$\tilde{O}((\log n)^3)$ amortized time algorithm. Combining it with Thorup's
+bittrick, we get down to the claimed $\tilde{O}((\log n)^2)$ amortized time.
+Essentially the same new trick can be applied to the biconnectivity data
+structure from [STOC98, JACM2001], improving the amortized update time to
+$\tilde{O}((\log n)^3)$.
+We also offer improvements in space. We describe a general trick which
+applies to both of our new algorithms, and to the old ones, to get down to
+linear space, where the previous best use $O(m + n\log n\log\log n)$. Finally,
+we show how to obtain $O(\log n/\log \log n)$ query time, matching the optimal
+trade-off between update and query time.
+Our result yields an improved running time for deciding whether a unique
+perfect matching exists in a static graph.
+"
+21148,Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning,"  Bayesian neural networks with latent variables are scalable and flexible
+probabilistic models: They account for uncertainty in the estimation of the
+network weights and, by making use of latent variables, can capture complex
+noise patterns in the data. We show how to extract and decompose uncertainty
+into epistemic and aleatoric components for decision-making purposes. This
+allows us to successfully identify informative points for active learning of
+functions with heteroscedastic and bimodal noise. Using the decomposition we
+further define a novel risk-sensitive criterion for reinforcement learning to
+identify policies that balance expected cost, model-bias and noise aversion.
+"
+21149,Standard errors for regression on relational data with exchangeable errors,"  Relational arrays represent interactions or associations between pairs of
+actors, often in varied contexts or over time. Such data appear as, for
+example, trade flows between countries, financial transactions between
+individuals, contact frequencies between school children in classrooms, and
+dynamic protein-protein interactions. This paper proposes and evaluates a new
+class of parameter standard errors for models that represent elements of a
+relational array as a linear function of observable covariates. Uncertainty
+estimates for regression coefficients must account for both heterogeneity
+across actors and dependence arising from relations involving the same actor.
+Existing estimators of parameter standard errors that recognize such relational
+dependence rely on estimating extremely complex, heterogeneous structure across
+actors. Leveraging an exchangeability assumption, we derive parsimonious
+standard error estimators that pool information across actors and are
+substantially more accurate than existing estimators in a variety of settings.
+This exchangeability assumption is pervasive in network and array models in the
+statistics literature, but not previously considered when adjusting for
+dependence in a regression setting with relational data. We show that our
+estimator is consistent and demonstrate improvements in inference through
+simulation and a data set involving international trade.
+"
+21150,Calculating normal tissue complication probabilities and probabilities of complication-free tumour control from stochastic models of population dynamics,"  We use a stochastic birth-death model for a population of cells to estimate
+the normal tissue complication probability (NTCP) under a particular
+radiotherapy protocol. We specifically allow for interaction between cells, via
+a nonlinear logistic growth model. To capture some of the effects of intrinsic
+noise in the population we develop several approximations of NTCP, using
+Kramers-Moyal expansion techniques. These approaches provide an approximation
+to the first and second moments of a general first-passage time problem in the
+limit of large, but finite populations. We use this method to study NTCP in a
+simple model of normal cells and in a model of normal and damaged cells. We
+also study a combined model of normal tissue cells and tumour cells. Based on
+existing methods to calculate tumour control probabilities, and our procedure
+to approximate NTCP, we estimate the probability of complication free tumour
+control.
+"
+21151,Pathwise Derivatives Beyond the Reparameterization Trick,"  We observe that gradients computed via the reparameterization trick are in
+direct correspondence with solutions of the transport equation in the formalism
+of optimal transport. We use this perspective to compute (approximate) pathwise
+gradients for probability distributions not directly amenable to the
+reparameterization trick: Gamma, Beta, and Dirichlet. We further observe that
+when the reparameterization trick is applied to the Cholesky-factorized
+multivariate Normal distribution, the resulting gradients are suboptimal in the
+sense of optimal transport. We derive the optimal gradients and show that they
+have reduced variance in a Gaussian Process regression task. We demonstrate
+with a variety of synthetic experiments and stochastic variational inference
+tasks that our pathwise gradients are competitive with other methods.
+"
+21152,Network-size independent covering number bounds for deep networks,"  We give a covering number bound for deep learning networks that is
+independent of the size of the network. The key for the simple analysis is that
+for linear classifiers, rotating the data doesn't affect the covering number.
+Thus, we can ignore the rotation part of each layer's linear transformation,
+and get the covering number bound by concentrating on the scaling part.
+"
+21153,NIF: A Framework for Quantifying Neural Information Flow in Deep Networks,"  In this paper, we present a new approach to interpreting deep learning
+models. More precisely, by coupling mutual information with network science, we
+explore how information flows through feed forward networks. We show that
+efficiently approximating mutual information via the dual representation of
+Kullback-Leibler divergence allows us to create an information measure that
+quantifies how much information flows between any two neurons of a deep
+learning model. To that end, we propose NIF, Neural Information Flow, a new
+metric for codifying information flow which exposes the internals of a deep
+learning model while providing feature attributions.
+"
+21154,On functions given by algebraic power series over Henselian valued fields,"  This paper provides, over Henselian valued fields, some theorems on implicit
+function and of Artin--Mazur on algebraic power series. Also discussed are
+certain versions of the theorems of Abhyankar--Jung and Newton--Puiseux. The
+latter is used in analysis of functions of one variable, definable in the
+language of Denef--Pas, to obtain a theorem on existence of the limit, proven
+over rank one valued fields in one of our recent papers. This result along with
+the technique of fiber shrinking (developed there over rank one valued fields)
+were, in turn, two basic tools in the proof of the closedness theorem.
+"
+21155,What can one learn about material structure given a single first-principles calculation?,"  We extract a variable $X$ from electron orbitals $\Psi_{n\bf{k}}$ and
+energies $E_{n\bf{k}}$ in the parent high-symmetry structure of a wide range of
+complex oxides: perovskites, rutiles, pyrochlores, and cristobalites. Even
+though calculation was done only in the parent structure, with no distortions,
+we show that $X$ dictates material's true ground state structure. We propose
+using Wannier functions to extract concealed variables such as $X$ both for
+material structure prediction and for high-throughput approaches.
+"
+21156,xUnit: Learning a Spatial Activation Function for Efficient Image Restoration,"  In recent years, deep neural networks (DNNs) achieved unprecedented
+performance in many low-level vision tasks. However, state-of-the-art results
+are typically achieved by very deep networks, which can reach tens of layers
+with tens of millions of parameters. To make DNNs implementable on platforms
+with limited resources, it is necessary to weaken the tradeoff between
+performance and efficiency. In this paper, we propose a new activation unit,
+which is particularly suitable for image restoration problems. In contrast to
+the widespread per-pixel activation units, like ReLUs and sigmoids, our unit
+implements a learnable nonlinear function with spatial connections. This
+enables the net to capture much more complex features, thus requiring a
+significantly smaller number of layers in order to reach the same performance.
+We illustrate the effectiveness of our units through experiments with
+state-of-the-art nets for denoising, de-raining, and super resolution, which
+are already considered to be very small. With our approach, we are able to
+further reduce these models by nearly 50% without incurring any degradation in
+performance.
+"
+21157,"Multiwavelength study of VHE emission from Markarian 501 using TACTIC observations during April-May, 2012","  We have observed Markarian 501 in Very High Energy (VHE) gamma-ray wavelength
+band for 70.6 hours from 15 April to 30 May, 2012 using TACTIC telescope.
+Detailed analysis of $\sim$66.3 hours of clean data revealed the presence of a
+TeV $\gamma$-ray signal (686$\pm$77 $\gamma$-ray events) from the source
+direction with a statistical significance of 8.89$\sigma$ above 850 GeV.
+Further, a total of 375 $\pm$ 47 $\gamma$-ray like events were detected in 25.2
+hours of observation from 22 - 27 May, 2012 with a statistical significance of
+8.05$\sigma$ indicating that the source has possibly switched over to a
+relatively high gamma-ray emission state. We have derived time-averaged
+differential energy spectrum of the state in the energy range 850 GeV - 17.24
+TeV which fits well with a power law function of the form
+$dF/dE=f_{0}E^{-\Gamma}$ with $f_{0}= (2.27 \pm 0.38) \times 10 ^{-11} $
+photons cm$^{-2}$ s$^{-1}$ TeV$^{-1}$ and $\Gamma=2.57 \pm 0.15$. In order to
+investigate the source state, we have also used almost simultaneous
+multiwavelength observations viz: high energy data collected by
+$\it{Fermi}$-LAT, X-ray data collected by $\it{Swift}$-XRT and MAXI, optical
+and UV data collected by $\it{Swift}$-UVOT, and radio data collected by OVRO,
+and reconstructed broad-band Spectral Energy Distribution (SED). The obtained
+SED supports leptonic model (homogeneous single zone) for VHE gamma-ray
+emission involving synchrotron and synchrotron self Compton (SSC) processes.
+"
+21158,"Sparsity enforcing priors in inverse problems via Normal variance mixtures: model selection, algorithms and applications","  The sparse structure of the solution for an inverse problem can be modelled
+using different sparsity enforcing priors when the Bayesian approach is
+considered. Analytical expression for the unknowns of the model can be obtained
+by building hierarchical models based on sparsity enforcing distributions
+expressed via conjugate priors. We consider heavy tailed distributions with
+this property: the Student-t distribution, which is expressed as a Normal scale
+mixture, with the mixing distribution the Inverse Gamma distribution, the
+Laplace distribution, which can also be expressed as a Normal scale mixture,
+with the mixing distribution the Exponential distribution or can be expressed
+as a Normal inverse scale mixture, with the mixing distribution the Inverse
+Gamma distribution, the Hyperbolic distribution, the Variance-Gamma
+distribution, the Normal-Inverse Gaussian distribution, all three expressed via
+conjugate distributions using the Generalized Hyperbolic distribution. For all
+distributions iterative algorithms are derived based on hierarchical models
+that account for the uncertainties of the forward model. For estimation,
+Maximum A Posterior (MAP) and Posterior Mean (PM) via variational Bayesian
+approximation (VBA) are used. The performances of resulting algorithm are
+compared in applications in 3D computed tomography (3D-CT) and chronobiology.
+Finally, a theoretical study is developed for comparison between sparsity
+enforcing algorithms obtained via the Bayesian approach and the sparsity
+enforcing algorithms issued from regularization techniques, like LASSO and some
+others.
+"
+21159,Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition,"  Spectral decomposition of the Koopman operator is attracting attention as a
+tool for the analysis of nonlinear dynamical systems. Dynamic mode
+decomposition is a popular numerical algorithm for Koopman spectral analysis;
+however, we often need to prepare nonlinear observables manually according to
+the underlying dynamics, which is not always possible since we may not have any
+a priori knowledge about them. In this paper, we propose a fully data-driven
+method for Koopman spectral analysis based on the principle of learning Koopman
+invariant subspaces from observed data. To this end, we propose minimization of
+the residual sum of squares of linear least-squares regression to estimate a
+set of functions that transforms data into a form in which the linear
+regression fits well. We introduce an implementation with neural networks and
+evaluate performance empirically using nonlinear dynamical systems and
+applications.
+"
+21160,Structure and Content of the Visible Darknet,"  In this paper, we analyze the topology and the content found on the
+""darknet"", the set of websites accessible via Tor. We created a darknet spider
+and crawled the darknet starting from a bootstrap list by recursively following
+links. We explored the whole connected component of more than 34,000 hidden
+services, of which we found 10,000 to be online. Contrary to folklore belief,
+the visible part of the darknet is surprisingly well-connected through hub
+websites such as wikis and forums. We performed a comprehensive categorization
+of the content using supervised machine learning. We observe that about half of
+the visible dark web content is related to apparently licit activities based on
+our classifier. A significant amount of content pertains to software
+repositories, blogs, and activism-related websites. Among unlawful hidden
+services, most pertain to fraudulent websites, services selling counterfeit
+goods, and drug markets.
+"
+21161,Pro-arrhythmogenic effects of heterogeneous tissue curvature: A suggestion for role of left atrial appendage in atrial fibrillation,"  Background: The arrhythmogenic role of atrial complex morphology has not yet
+been clearly elucidated. We hypothesized that bumpy tissue geometry can induce
+action potential duration (APD) dispersion and wavebreak in atrial fibrillation
+(AF).
+Methods and Results: We simulated 2D-bumpy atrial model by varying the degree
+of bumpiness, and 3D-left atrial (LA) models integrated by LA computed
+tomographic (CT) images taken from 14 patients with persistent AF. We also
+analyzed wave-dynamic parameters with bipolar electrograms during AF and
+compared them with LA-CT geometry in 30 patients with persistent AF. In
+2D-bumpy model, APD dispersion increased (p<0.001) and wavebreak occurred
+spontaneously when the surface bumpiness was higher, showing phase
+transition-like behavior (p<0.001). Bumpiness gradient 2D-model showed that
+spiral wave drifted in the direction of higher bumpiness, and phase singularity
+(PS) points were mostly located in areas with higher bumpiness. In 3D-LA model,
+PS density was higher in LA appendage (LAA) compared to other LA parts
+(p<0.05). In 30 persistent AF patients, the surface bumpiness of LAA was
+5.8-times that of other LA parts (p<0.001), and exceeded critical bumpiness to
+induce wavebreak. Wave dynamics complexity parameters were consistently
+dominant in LAA (p<0.001).
+Conclusion: The bumpy tissue geometry promotes APD dispersion, wavebreak, and
+spiral wave drift in in silico human atrial tissue, and corresponds to clinical
+electro-anatomical maps.
+"
+21162,Fragmentation of phase-fluctuating condensates,"  We study zero-temperature quantum phase fluctuations in harmonically trapped
+one-dimensional interacting Bose gases, using the self-consistent
+multiconfigurational time-dependent Hartree method. In a regime of mesoscopic
+particle numbers and moderate contact couplings, it is shown that the
+phase-fluctuating condensate is properly described as a fragmented condensate.
+In addition, we demonstrate that the spatial dependence of the amplitude of
+phase fluctuations significantly deviates from what is obtained in Bogoliubov
+theory. Our results can be verified in currently available experiments. They
+therefore provide an opportunity both to experimentally benchmark the
+multiconfigurational time-dependent Hartree method, as well as to directly
+observe, for the first time, the quantum many-body phenomenon of fragmentation
+in single traps.
+"
+21163,Thermoelectric transport parallel to the planes in a multilayered Mott-Hubbard heterostructure,"  We present a theory for charge and heat transport parallel to the interfaces
+of a multilayer (ML) in which the interfacing gives rise the redistribution of
+the electronic charges. The ensuing electrical field couples self-consistently
+to the itinerant electrons, so that the properties of the ML crucially depend
+on an interplay between the on-site Coulomb forces and the long range
+electrostatic forces. The ML is described by the Falicov-Kimball model and the
+self-consistent solution is obtained by iterating simultaneously the DMFT and
+the Poisson equations. This yields the reconstructed charge profile, the
+electrical potential, the planar density of states, the transport function, and
+the transport coefficients of the device.
+We find that a heterostructure built of two Mott-Hubbard insulators exhibits,
+in a large temperature interval, a linear conductivity and a large
+temperature-independent thermopower. The charge and energy currents are
+confined to the central part of the ML. Our results indicate that correlated
+multilayers have the potential for applications; by tuning the band shift and
+the Coulomb correlation on the central planes, we can bring the chemical
+potential in the immediate proximity of the Mott-Hubbard gap edge and optimize
+the transport properties of the device. In such a heterostructure, a small gate
+voltage can easily induce a MI transition. This switching does not involve the
+diffusion of electrons over macroscopic distances and it is much faster than in
+ordinary semiconductors. Furthermore, the right combination of strongly
+correlated materials with small ZT can produce, theoretically at least, a
+heterostructure with a large ZT.
+"
+21164,Mining Device-Specific Apps Usage Patterns from Large-Scale Android Users,"  When smartphones, applications (a.k.a, apps), and app stores have been widely
+adopted by the billions, an interesting debate emerges: whether and to what
+extent do device models influence the behaviors of their users? The answer to
+this question is critical to almost every stakeholder in the smartphone app
+ecosystem, including app store operators, developers, end-users, and network
+providers. To approach this question, we collect a longitudinal data set of app
+usage through a leading Android app store in China, called Wandoujia. The data
+set covers the detailed behavioral profiles of 0.7 million (761,262) unique
+users who use 500 popular types of Android devices and about 0.2 million
+(228,144) apps, including their app management activities, daily network access
+time, and network traffic of apps. We present a comprehensive study on
+investigating how the choices of device models affect user behaviors such as
+the adoption of app stores, app selection and abandonment, data plan usage,
+online time length, the tendency to use paid/free apps, and the preferences to
+choosing competing apps. Some significant correlations between device models
+and app usage are derived, leading to important findings on the various user
+behaviors. For example, users owning different device models have a substantial
+diversity of selecting competing apps, and users owning lower-end devices spend
+more money to purchase apps and spend more time under cellular network.
+"
+21165,Homotopical Stable Ranks for Certain C*-algebras,"  We study the general and connected stable ranks for C*-algebras. We estimate
+these ranks for pullbacks of C*-algebras, and for tensor products by
+commutative C*-algebras. Finally, we apply these results to determine these
+ranks for certain commutative C*-algebras, and non-commutative CW-complexes.
+"
+21166,Covariantly functorial wrapped Floer theory on Liouville sectors,"  We introduce a class of Liouville manifolds with boundary which we call
+Liouville sectors. We define the wrapped Fukaya category, symplectic
+cohomology, and the open-closed map for Liouville sectors, and we show that
+these invariants are covariantly functorial with respect to inclusions of
+Liouville sectors. From this foundational setup, a local-to-global principle
+for Abouzaid's generation criterion follows.
+"
+21167,Robust Sparse Covariance Estimation by Thresholding Tyler's M-Estimator,"  Estimating a high-dimensional sparse covariance matrix from a limited number
+of samples is a fundamental problem in contemporary data analysis. Most
+proposals to date, however, are not robust to outliers or heavy tails. Towards
+bridging this gap, in this work we consider estimating a sparse shape matrix
+from $n$ samples following a possibly heavy tailed elliptical distribution. We
+propose estimators based on thresholding either Tyler's M-estimator or its
+regularized variant. We derive bounds on the difference in spectral norm
+between our estimators and the shape matrix in the joint limit as the dimension
+$p$ and sample size $n$ tend to infinity with $p/n\to\gamma>0$. These bounds
+are minimax rate-optimal. Results on simulated data support our theoretical
+analysis.
+"
+21168,Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction,"  As algorithms are increasingly used to make important decisions that affect
+human lives, ranging from social benefit assignment to predicting risk of
+criminal recidivism, concerns have been raised about the fairness of
+algorithmic decision making. Most prior works on algorithmic fairness
+normatively prescribe how fair decisions ought to be made. In contrast, here,
+we descriptively survey users for how they perceive and reason about fairness
+in algorithmic decision making.
+A key contribution of this work is the framework we propose to understand why
+people perceive certain features as fair or unfair to be used in algorithms.
+Our framework identifies eight properties of features, such as relevance,
+volitionality and reliability, as latent considerations that inform people's
+moral judgments about the fairness of feature use in decision-making
+algorithms. We validate our framework through a series of scenario-based
+surveys with 576 people. We find that, based on a person's assessment of the
+eight latent properties of a feature in our exemplar scenario, we can
+accurately (> 85%) predict if the person will judge the use of the feature as
+fair.
+Our findings have important implications. At a high-level, we show that
+people's unfairness concerns are multi-dimensional and argue that future
+studies need to address unfairness concerns beyond discrimination. At a
+low-level, we find considerable disagreements in people's fairness judgments.
+We identify root causes of the disagreements, and note possible pathways to
+resolve them.
+"
+21169,Genetic Algorithm Based Floor Planning System,"  Genetic Algorithms are widely used in many different optimization problems
+including layout design. The layout of the shelves play an important role in
+the total sales metrics for superstores since this affects the customers'
+shopping behaviour. This paper employed a genetic algorithm based approach to
+design shelf layout of superstores. The layout design problem was tackled by
+using a novel chromosome representation which takes many different parameters
+to prevent dead-ends and improve shelf visibility into consideration. Results
+show that the approach can produce reasonably good layout designs in very short
+amounts of time.
+"
+21170,"On SDEs with Lipschitz coefficients, driven by continuous, model-free price paths","  Using similar assumptions as in Revuz and Yor's book we prove the existence
+and uniqueness of the solutions of SDEs with Lipschitz coefficients, driven by
+continuous, model-free price paths. The main tool in our reasonings is a
+model-free version of the Burkholder-Davis-Gundy inequality for integrals
+driven by model-free, continuous price paths.
+"
+21171,Stability of receding traveling waves for a fourth order degenerate parabolic free boundary problem,"  Consider the thin-film equation $h_t + \left(h h_{yyy}\right)_y = 0$ with a
+zero contact angle at the free boundary, that is, at the triple junction where
+liquid, gas, and solid meet. Previous results on stability and well-posedness
+of this equation have focused on perturbations of equilibrium-stationary or
+self-similar profiles, the latter eventually wetting the whole surface. These
+solutions have their counterparts for the second-order porous-medium equation
+$h_t - (h^m)_{yy} = 0$, where $m > 1$ is a free parameter. Both porous-medium
+and thin-film equation degenerate as $h \searrow 0$, but the porous-medium
+equation additionally fulfills a comparison principle while the thin-film
+equation does not.
+In this note, we consider traveling waves $h = \frac V 6 x^3 + \nu x^2$ for
+$x \ge 0$, where $x = y-V t$ and $V, \nu \ge 0$ are free parameters. These
+traveling waves are receding and therefore describe de-wetting, a phenomenon
+genuinely linked to the fourth-order nature of the thin-film equation and not
+encountered in the porous-medium case as it violates the comparison principle.
+The linear stability analysis leads to a linear fourth-order
+degenerate-parabolic operator for which we prove maximal-regularity estimates
+to arbitrary orders of the expansion in $x$ in a right-neighborhood of the
+contact line $x = 0$. This leads to a well-posedness and stability result for
+the corresponding nonlinear equation. As the linearized evolution has different
+scaling as $x \searrow 0$ and $x \to \infty$, the analysis is more intricate
+than in related previous works. We anticipate that our approach is a natural
+step towards investigating other situations in which the comparison principle
+is violated, such as droplet rupture.
+"
+21172,Group Importance Sampling for Particle Filtering and MCMC,"  Bayesian methods and their implementations by means of sophisticated Monte
+Carlo techniques have become very popular in signal processing over the last
+years. Importance Sampling (IS) is a well-known Monte Carlo technique that
+approximates integrals involving a posterior distribution by means of weighted
+samples. In this work, we study the assignation of a single weighted sample
+which compresses the information contained in a population of weighted samples.
+Part of the theory that we present as Group Importance Sampling (GIS) has been
+employed implicitly in different works in the literature. The provided analysis
+yields several theoretical and practical consequences. For instance, we discuss
+the application of GIS into the Sequential Importance Resampling framework and
+show that Independent Multiple Try Metropolis schemes can be interpreted as a
+standard Metropolis-Hastings algorithm, following the GIS approach. We also
+introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS.
+The first one, named Group Metropolis Sampling method, produces a Markov chain
+of sets of weighted samples. All these sets are then employed for obtaining a
+unique global estimator. The second one is the Distributed Particle
+Metropolis-Hastings technique, where different parallel particle filters are
+jointly used to drive an MCMC algorithm. Different resampled trajectories are
+compared and then tested with a proper acceptance probability. The novel
+schemes are tested in different numerical experiments such as learning the
+hyperparameters of Gaussian Processes, two localization problems in a wireless
+sensor network (with synthetic and real data) and the tracking of vegetation
+parameters given satellite observations, where they are compared with several
+benchmark Monte Carlo techniques. Three illustrative Matlab demos are also
+provided.
+"
+21173,Double Temporal Sparsity Based Accelerated Reconstruction in Compressed Sensing fMRI,"  A number of reconstruction methods have been proposed recently for
+accelerated functional Magnetic Resonance Imaging (fMRI) data collection.
+However, existing methods suffer with the challenge of greater artifacts at
+high acceleration factors. This paper addresses the issue of accelerating fMRI
+collection via undersampled k-space measurements combined with the proposed
+Double Temporal Sparsity based Reconstruction (DTSR) method with the l1 -l1
+norm constraint. The robustness of the proposed DTSR method has been thoroughly
+evaluated both at the subject level and at the group level on real fMRI data.
+Results are presented at various acceleration factors. Quantitative analysis in
+terms of Peak Signal-to-Noise Ratio (PSNR) and other metrics, and qualitative
+analysis in terms of reproducibility of brain Resting State Networks (RSNs)
+demonstrate that the proposed method is accurate and robust. In addition, the
+proposed DTSR method preserves brain networks that are important for studying
+fMRI data. Compared to the existing accelerated fMRI reconstruction methods,
+the DTSR method shows promising potential with an improvement of 10-12dB in
+PSNR with acceleration factors upto 3.5. Simulation results on real data
+demonstrate that DTSR method can be used to acquire accelerated fMRI with
+accurate detection of RSNs.
+"
+21174,A continuum model for distributions of dislocations incorporating short-range interactions,"  Dislocations are the main carriers of the permanent deformation of crystals.
+For simulations of engineering applications, continuum models where material
+microstructures are represented by continuous density distributions of
+dislocations are preferred. It is challenging to capture in the continuum model
+the short-range dislocation interactions, which vanish after the standard
+averaging procedure from discrete dislocation models. In this study, we
+consider systems of parallel straight dislocation walls and develop continuum
+descriptions for the short-range interactions of dislocations by using
+asymptotic analysis. The obtained continuum short-range interaction formulas
+are incorporated in the continuum model for dislocation dynamics based on a
+pair of dislocation density potential functions that represent continuous
+distributions of dislocations. This derived continuum model is able to describe
+the anisotropic dislocation interaction and motion. Mathematically, these
+short-range interaction terms ensure strong stability property of the continuum
+model that is possessed by the discrete dislocation dynamics model. The derived
+continuum model is validated by comparisons with the discrete dislocation
+dynamical simulation results.
+"
+21175,Phase transition and power-law coarsening in Ising-doped voter model,"  We examine an opinion formation model, which is a mixture of Voter and Ising
+agents. Numerical simulations show that even a very small fraction ($\sim 1\%$)
+of the Ising agents drastically changes the behaviour of the Voter model. The
+Voter agents act as a medium, which correlates sparsely dispersed Ising agents,
+and the resulting ferromagnetic ordering persists up to a certain temperature.
+Upon addition of the Ising agents, a logarithmically slow coarsening of the
+Voter model ($d=2$), or its active steady state ($d=3$), change into an
+Ising-type power-law coarsening.
+"
+21176,A probabilistic data-driven model for planar pushing,"  This paper presents a data-driven approach to model planar pushing
+interaction to predict both the most likely outcome of a push and its expected
+variability. The learned models rely on a variation of Gaussian processes with
+input-dependent noise called Variational Heteroscedastic Gaussian processes
+(VHGP) that capture the mean and variance of a stochastic function. We show
+that we can learn accurate models that outperform analytical models after less
+than 100 samples and saturate in performance with less than 1000 samples. We
+validate the results against a collected dataset of repeated trajectories, and
+use the learned models to study questions such as the nature of the variability
+in pushing, and the validity of the quasi-static assumption.
+"
+21177,Dual constant-flux energy cascades to both large scales and small scales,"  In this paper, we present an overview of concepts and data concerning inverse
+cascades of excitation towards scales larger than the forcing scale in a
+variety of contexts, from two-dimensional fluids and wave turbulence, to
+geophysical flows in the presence of rotation and stratification. We briefly
+discuss the role of anisotropy in the occurrence and properties of such
+cascades. We then show that the cascade of some invariant, for example the
+total energy, may be transferred through nonlinear interactions to both the
+small scales and the large scales, with in each case a constant flux. This is
+in contrast to the classical picture, and we illustrate such a dual cascade in
+the context of atmospheric and oceanic observations, direct numerical
+simulations and modeling. We also show that this dual cascade of total energy
+can in fact be decomposed in some cases into separate cascades of the kinetic
+and potential energies, provided the Froude and Rossby numbers are small
+enough. In all cases, the potential energy flux remains small, of the order of
+10% or less relative to the kinetic energy flux. Finally, we demonstrate that,
+in the small-scale inertial range, approximate equipartition between potential
+and kinetic modes is obtained, leading to an energy ratio close to one, with
+strong departure at large scales due to the dominant kinetic energy inverse
+cascade and piling-up at the lowest spatial frequency, and at small scales due
+to unbalanced dissipation processes, even though the Prandtl number is equal to
+one.
+"
+21178,Worst-case Optimal Submodular Extensions for Marginal Estimation,"  Submodular extensions of an energy function can be used to efficiently
+compute approximate marginals via variational inference. The accuracy of the
+marginals depends crucially on the quality of the submodular extension. To
+identify the best possible extension, we show an equivalence between the
+submodular extensions of the energy and the objective functions of linear
+programming (LP) relaxations for the corresponding MAP estimation problem. This
+allows us to (i) establish the worst-case optimality of the submodular
+extension for Potts model used in the literature; (ii) identify the worst-case
+optimal submodular extension for the more general class of metric labeling; and
+(iii) efficiently compute the marginals for the widely used dense CRF model
+with the help of a recently proposed Gaussian filtering method. Using synthetic
+and real data, we show that our approach provides comparable upper bounds on
+the log-partition function to those obtained using tree-reweighted message
+passing (TRW) in cases where the latter is computationally feasible.
+Importantly, unlike TRW, our approach provides the first practical algorithm to
+compute an upper bound on the dense CRF model.
+"
+21179,Superconducting energy gap in $\rm Ba_{1-x}K_xBiO_3$: Temperature dependence,"  The superconducting energy gap of $\rm Ba_{1-x}K_xBiO_3$ has been measured by
+tunneling. Despite the fact that the sample was macroscopically single phase
+with very sharp superconducting transition $T_c$ at 32~$K$, some of the
+measured tunnel junctions made by point contacts between silver tip and single
+crystal of $\rm Ba_{1-x}K_xBiO_3$ had lower transition at 20~$K$. Local
+variation of the potassium concentration as well as oxygen deficiency in $\rm
+Ba_{1-x}K_xBiO_3$ at the place where the point contact is made can account for
+the change of $T_c$. The conductance curves of the tunnel junctions reveal the
+BCS behavior with a small broadening of the superconducting-gap structure. A
+value of the energy gap scales with $T_c$. The reduced gap amounts to
+$2\Delta/kT_c = 4÷4.3$ indicating a medium coupling strength. Temperature
+dependence of the energy gap follows the BCS prediction.
+"
+21180,Square functions and the Hamming cube: Duality,"  For $1<p\leq 2$, any $n\geq 1$ and any $f:\{-1,1\}^{n} \to \mathbb{R}$, we
+obtain $(\mathbb{E} |\nabla f|^{p})^{1/p} \geq C(p)(\mathbb{E}|f|^{p} -
+|\mathbb{E}f|^{p})^{1/p}$ where $C(p)$ is the smallest positive zero of the
+confluent hypergeometric function ${}_{1}F_{1}(\frac{p}{2(1-p)}, \frac{1}{2},
+\frac{x^{2}}{2})$. Our approach is based on a certain duality between the
+classical square function estimates on the Euclidean space and the gradient
+estimates on the Hamming cube.
+"
+21181,A Special Homotopy Continuation Method For A Class of Polynomial Systems,"  A special homotopy continuation method, as a combination of the polyhedral
+homotopy and the linear product homotopy, is proposed for computing all the
+isolated solutions to a special class of polynomial systems. The root number
+bound of this method is between the total degree bound and the mixed volume
+bound and can be easily computed. The new algorithm has been implemented as a
+program called LPH using C++. Our experiments show its efficiency compared to
+the polyhedral or other homotopies on such systems. As an application, the
+algorithm can be used to find witness points on each connected component of a
+real variety.
+"
+21182,Arbitrage Opportunities in CDS Term Structure: Theory and Implications for OTC Derivatives,"  Absence-of-Arbitrage (AoA) is the basic assumption underpinning derivatives
+pricing theory. As part of the OTC derivatives market, the CDS market not only
+provides a vehicle for participants to hedge and speculate on the default risks
+of corporate and sovereign entities, it also reveals important market-implied
+default-risk information concerning the counterparties with which financial
+institutions trade, and for which these financial institutions have to
+calculate various valuation adjustments (collectively referred to as XVA) as
+part of their pricing and risk management of OTC derivatives, to account for
+counterparty default risks. In this study, we derive No-arbitrage conditions
+for CDS term structures, first in a positive interest rate environment and then
+in an arbitrary one. Using an extensive CDS dataset which covers the 2007-09
+financial crisis, we present a catalogue of 2,416 pairs of anomalous CDS
+contracts which violate the above conditions. Finally, we show in an example
+that such anomalies in the CDS term structure can lead to persistent arbitrage
+profits and to nonsensical default probabilities. The paper is a first
+systematic study on CDS-term-structure arbitrage providing model-free AoA
+conditions supported by ample empirical evidence.
+"
+21183,Asymptotic behavior of metric spaces at infinity,"  A new sequential approach to investigations of structure of metric spaces at
+infinity is proposed. Criteria for finiteness and boundedness of metric spaces
+at infinity are found.
+"
+21184,Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics,"  Scaling end-to-end reinforcement learning to control real robots from vision
+presents a series of challenges, in particular in terms of sample efficiency.
+Against end-to-end learning, state representation learning can help learn a
+compact, efficient and relevant representation of states that speeds up policy
+learning, reducing the number of samples needed, and that is easier to
+interpret. We evaluate several state representation learning methods on goal
+based robotics tasks and propose a new unsupervised model that stacks
+representations and combines strengths of several of these approaches. This
+method encodes all the relevant features, performs on par or better than
+end-to-end learning, and is robust to hyper-parameters change.
+"
+21185,$q-$difference equations concerning with $q-$gamma function,"  We consider a family of solutions of $q-$difference Riccati equation, and
+prove the meromorphic solutions of $q-$difference Riccati equation and
+corresponding second order $q-$difference equation are concerning with
+$q-$gamma function. The growth and value distribution of differences on
+solutions of $q-$difference Riccati equation are also investigated.
+"
+21186,Structural and Magnetic Phase Transitions in Chromium Nitride Thin Films Grown by RF Nitrogen Plasma Molecular Beam Epitaxy,"  A magneto-structural phase transition is investigated in single crystal CrN
+thin films grown by rf plasma molecular beam epitaxy on MgO(001) substrates.
+While still within the vacuum environment following MBE growth, $\it in-situ$
+low-temperature scanning tunneling microscopy, and $\it in-situ$ variable
+low-temperature reflection high energy electron diffraction are applied,
+revealing an atomically smooth and metallic CrN(001) surface, and an $\it
+in-plane$ structural transition from 1$\times$1 (primitive CrN unit cell) to
+$\mathrm{\sqrt{2}\times\sqrt{2}-R45^\circ}$ with a transition temperature of
+$\sim$ 278 K, respectively. $\it Ex-situ$ temperature dependent measurements
+are also performed, including x-ray diffraction and neutron diffraction,
+looking at the structural peaks and likewise revealing a first-order structural
+transition along both [001] and [111] $\it out-of-plane$ directions, with
+transition temperatures of 256 K and 268 K, respectively. Turning to the
+magnetic peaks, neutron diffraction confirms a clear magnetic transition from
+paramagnetic at room temperature to antiferromagnetic at low temperatures with
+a sharp, first-order phase transition and a N$é$el temperature of 270 K or
+280 K for two different films. In addition to the experimental measurements of
+structural and magnetic ordering, we also discuss results from first-principles
+theoretical calculations which explore various possible magneto-structural
+models.
+"
+21187,Predicting Audience's Laughter Using Convolutional Neural Network,"  For the purpose of automatically evaluating speakers' humor usage, we build a
+presentation corpus containing humorous utterances based on TED talks. Compared
+to previous data resources supporting humor recognition research, ours has
+several advantages, including (a) both positive and negative instances coming
+from a homogeneous data set, (b) containing a large number of speakers, and (c)
+being open. Focusing on using lexical cues for humor recognition, we
+systematically compare a newly emerging text classification method based on
+Convolutional Neural Networks (CNNs) with a well-established conventional
+method using linguistic knowledge. The advantages of the CNN method are both
+getting higher detection accuracies and being able to learn essential features
+automatically.
+"
+21188,The Solar Orbiter Mission: an Energetic Particle Perspective,"  Solar Orbiter is a joint ESA-NASA mission planed for launch in October 2018.
+The science payload includes remote-sensing and in-situ instrumentation
+designed with the primary goal of understanding how the Sun creates and
+controls the heliosphere. The spacecraft will follow an elliptical orbit around
+the Sun, with perihelion as close as 0.28 AU. During the late orbit phase the
+orbital plane will reach inclinations above 30 degrees, allowing direct
+observations of the solar polar regions. The Energetic Particle Detector (EPD)
+is an instrument suite consisting of several sensors measuring electrons,
+protons and ions over a broad energy interval (2 keV to 15 MeV for electrons, 3
+keV to 100 MeV for protons and few tens of keV/nuc to 450 MeV/nuc for ions),
+providing composition, spectra, timing and anisotropy information. We present
+an overview of Solar Orbiter from the energetic particle perspective,
+summarizing the capabilities of EPD and the opportunities that these new
+observations will provide for understanding how energetic particles are
+accelerated during solar eruptions and how they propagate through the
+Heliosphere.
+"
+21189,Consistent feature attribution for tree ensembles,"  Note that a newer expanded version of this paper is now available at:
+arXiv:1802.03888
+It is critical in many applications to understand what features are important
+for a model, and why individual predictions were made. For tree ensemble
+methods these questions are usually answered by attributing importance values
+to input features, either globally or for a single prediction. Here we show
+that current feature attribution methods are inconsistent, which means changing
+the model to rely more on a given feature can actually decrease the importance
+assigned to that feature. To address this problem we develop fast exact
+solutions for SHAP (SHapley Additive exPlanation) values, which were recently
+shown to be the unique additive feature attribution method based on conditional
+expectations that is both consistent and locally accurate. We integrate these
+improvements into the latest version of XGBoost, demonstrate the
+inconsistencies of current methods, and show how using SHAP values results in
+significantly improved supervised clustering performance. Feature importance
+values are a key part of understanding widely used models such as gradient
+boosting trees and random forests, so improvements to them have broad practical
+implications.
+"
+21190,K-orbit closures and Barbasch-Evens-Magyar varieties,"  We define the Barbasch-Evens-Magyar variety. We show it is isomorphic to the
+smooth variety defined in [D. Barbasch-S. Evens '94] that maps finite-to-one to
+a symmetric orbit closure, thereby giving a resolution of singularities in
+certain cases. Our definition parallels [P. Magyar '98]'s construction of the
+Bott-Samelson variety [H. C. Hansen '73, M. Demazure '74]. From this
+alternative viewpoint, one deduces a graphical description in type A,
+stratification into closed subvarieties of the same kind, and determination of
+the torus-fixed points. Moreover, we explain how these manifolds inherit a
+natural symplectic structure with Hamiltonian torus action. We then prove that
+the moment polytope is expressed in terms of the moment polytope of a
+Bott-Samelson variety.
+"
+21191,"A Review of Internet of Things Architecture, Technologies and Analysis Smartphone-based Attacks Against 3D printers","  Human beings cannot be happy with any kind of tiredness based work, so they
+focused on machines to work on behalf of humans. The Internet-based latest
+technology provides the platforms for human beings to relax and unburden
+feeling. The Internet of Things (IoT) field efficiently helps human beings with
+smart decisions through Machine-to-Machine (M2M) communication all over the
+world. It has been difficult to ignore the importance of the IoT field with the
+new development of applications such as a smartphone in the present era. The
+IoT field sensor plays a vital role in sensing the intelligent object/things
+and making an intelligent decision after sensing the objects. The rapid
+development of new applications using smartphones in the world caused all users
+of the IoT community to be faced with one major challenge of security in the
+form of side channel attacks against highly intensive 3D printing systems. The
+smartphone formulated Intellectual property (IP) of side channel attacks
+investigate against 3D printer in the physical domain through reconstructed
+G-code file through primitive operations. The smartphone (Nexus 5) solved the
+main problems such as orientation fixing, model accuracy of frame size and
+validate the feasibility and effectiveness in real case studies against the 3D
+printer. The 3D printing estimated value reached 20.2 billion of dollars in
+2021. The thermal camera is used for exploring the side channel attacks after
+reconstructing the objects against 3D printers. The researcher analyzed IoT
+security relevant issues which were avoided in future by enhanced strong
+security mechanism strategy, encryption, and machine learning-based algorithms,
+latest technologies, schemes and protocols utilized in an efficient way.
+Keywords: - Internet of Things (IoT), Machine-to-Machine (M2M), Security, 3D
+printer, smartphone
+"
+21192,Computable structures on topological manifolds,"  We propose a definition of computable manifold by introducing computability
+as a structure that we impose to a given topological manifold, just in the same
+way as differentiability or piecewise linearity are defined for smooth and PL
+manifolds respectively. Using the framework of computable topology and Type-2
+theory of effectivity, we develop computable versions of all the basic concepts
+needed to define manifolds, like computable atlases and (computably) compatible
+computable atlases. We prove that given a computable atlas $\Phi$ defined on a
+set $M$, we can construct a computable topological space $(M, \tau_\Phi,
+\beta_\Phi, \nu_\Phi)$, where $\tau_\Phi$ is the topology on $M$ induced by
+$\Phi$ and that the equivalence class of this computable space characterizes
+the computable structure determined by $\Phi$. The concept of computable
+submanifold is also investigated. We show that any compact computable manifold
+which satisfies a computable version of the $T_2$-separation axiom, can be
+embedded as a computable submanifold of some euclidean space $\mathbb{R}^{q}$,
+with a computable embedding, where $\mathbb{R}^{q}$ is equipped with its usual
+topology and some canonical computable encoding of all open rational balls.
+"
+21193,Trace-Based Run-time Analysis of Message-Passing Go Programs,"  We consider the task of analyzing message-passing programs by observing their
+run-time behavior.
+We introduce a purely library-based instrumentation method to trace
+communication events during execution. A model of the dependencies among events
+can be constructed to identify potential bugs. Compared to the vector clock
+method, our approach is much simpler and has in general a significant lower
+run-time overhead.
+A further advantage is that we also trace events that could not commit. Thus,
+we can infer alternative communications. This provides the user with additional
+information to identify potential bugs.
+We have fully implemented our approach in the Go programming language and
+provide a number of examples to substantiate our claims.
+"
+21194,An efficient algorithm for global interval solution of nonlinear algebraic equations and its GPGPU implementation,"  Solving nonlinear algebraic equations is a classic mathematics problem, and
+common in scientific researches and engineering applications. There are many
+numeric, symbolic and numeric-symbolic methods of solving (real) solutions.
+Unlucky, these methods are constrained by some factors, e.g., high complexity,
+slow serial calculation, and the notorious intermediate expression expansion.
+Especially when the count of variables is larger than six, the efficiency is
+decreasing drastically. In this paper, according to the property of physical
+world, we pay attention to nonlinear algebraic equations whose variables are in
+fixed constraints, and get meaningful real solutions. Combining with
+parallelism of GPGPU, we present an efficient algorithm, by searching the
+solution space globally and solving the nonlinear algebraic equations with real
+interval solutions. Furthermore, we realize the Hansen-Sengupta method on
+GPGPU. The experiments show that our method can solve many nonlinear algebraic
+equations, and the results are accurate and more efficient compared to
+traditional serial methods.
+"
+21195,Efficient improvement of frequency-domain Kalman filter,"  The frequency-domain Kalman filter (FKF) has been utilized in many audio
+signal processing applications due to its fast convergence speed and
+robustness. However, the performance of the FKF in under-modeling situations
+has not been investigated. This paper presents an analysis of the steady-state
+behavior of the commonly used diagonalized FKF and reveals that it suffers from
+a biased solution in under-modeling scenarios. Two efficient improvements of
+the FKF are proposed, both having the benefits of the guaranteed optimal
+steady-state behavior at the cost of a very limited increase of the
+computational burden. The convergence behavior of the proposed algorithms is
+also compared analytically. Computer simulations are conducted to validate the
+improved performance of the proposed methods.
+"
+21196,"""Higgs"" Factory at the Greek-Turkish Border","  We would like to propose the construction of the photon collider based ""Higgs
+factory"" in the coming years at the Greek-Turkish border, starting from its
+test facility with a high energy photon beam. This proposal was among the
+contributions to the Open Symposium of the ESPG'12.
+"
+21197,Detection and numerical simulation of optoacoustic near- and farfield signals observed in PVA hydrogel phantoms,"  We present numerical simulations for modelling optoacoustic (OA) signals
+observed in PVA hydrogel tissue phantoms. We review the computational approach
+to model the underlying mechanisms, i.e. optical absorption of laser energy and
+acoustic propagation of mechanical stress, geared towards experiments that
+involve absorbing media only. We apply the numerical procedure to model signals
+observed in the acoustic near- and farfield in both, forward and backward
+detection mode, in PVA hydrogel tissue phantoms (i.e. an elastic solid).
+Further, we illustrate the computational approach by modeling OA signal for
+several experiments on dye solution (i.e. a liquid) reported in the literature,
+and benchmark the research code by comparing our fully 3D procedure to limiting
+cases described in terms of effectively 1D approaches.
+"
+21198,Application of Bitcoin Data-Structures & Design Principles to Supply Chain Management,"  Heretofore the concept of ""blockchain"" has not been precisely defined.
+Accordingly the potential useful applications of this technology have been
+largely inflated. This work sidesteps the question of what constitutes a
+blockchain as such and focuses on the architectural components of the Bitcoin
+cryptocurrency, insofar as possible, in isolation. We consider common problems
+inherent in the design of effective supply chain management systems. With each
+identified problem we propose a solution that utilizes one or more component
+aspects of Bitcoin. This culminates in five design principles for increased
+efficiency in supply chain management systems through the application of
+incentive mechanisms and data structures native to the Bitcoin cryptocurrency
+protocol.
+"
+21199,Measuring Comodules and Enrichment,"  We study the existence of universal measuring comodules Q(M,N) for a pair of
+modules M,N in a braided monoidal closed category, and the associated
+enrichment of the global category of modules over the monoidal global category
+of comodules. In the process, we use results for general fibred adjunctions
+encompassing the fibred structure of modules over monoids and the opfibred
+structure of comodules over comonoids. We also explore applications to the
+theory of Hopf modules.
+"
+21200,Bond Graph Representation of Chemical Reaction Networks,"  The Bond Graph approach and the Chemical Reaction Network approach to
+modelling biomolecular systems developed independently. This paper brings
+together the two approaches by providing a bond graph interpretation of the
+chemical reaction network concept of complexes. Both closed and open systems
+are discussed.
+The method is illustrated using a simple enzyme-catalysed reaction and a
+trans-membrane transporter.
+"
+21201,Skew Brownian motion with dry friction: The Pugachev-Sveshnikov approach,"  The Caughey-Dieness process, also known as the Brownian motion with two
+valued drift, is used in theoretical physics as an advanced model of the
+Brownian particle velocity if the resistant force is assumed to be dry
+friction. This process also appears in many other fields, such as applied
+physics, mechanics, astrophysics, and pure mathematics. In the present paper we
+are concerned with a more general process, skew Brownian motion with dry
+friction. The probability distribution of the process itself and of its
+occupation time on the positive half line are studied. The approach based on
+the Pugachev-Sveshnikov equation is used.
+"
+21202,Numerical modeling of laser-driven experiments aiming to demonstrate magnetic field amplification via turbulent dynamo,"  The universe is permeated by magnetic fields, with strengths ranging from a
+femtogauss in the voids between the filaments of galaxy clusters to several
+teragauss in black holes and neutron stars. The standard model behind
+cosmological magnetic fields is the nonlinear amplification of seed fields via
+turbulent dynamo to the values observed. We have conceived experiments that aim
+to demonstrate and study the turbulent dynamo mechanism in the laboratory. Here
+we describe the design of these experiments through simulation campaigns using
+FLASH, a highly capable radiation magnetohydrodynamics code that we have
+developed, and large-scale three-dimensional simulations on the Mira
+supercomputer at Argonne National Laboratory. The simulation results indicate
+that the experimental platform may be capable of reaching a turbulent plasma
+state and study dynamo amplification. We validate and compare our numerical
+results with a small subset of experimental data using synthetic diagnostics.
+"
+21203,The Minimum Distance Estimation with Multiple Integral in Panel Data,"  This paper studies the minimum distance estimation problem for panel data
+model. We propose the minimum distance estimators of regression parameters of
+the panel data model and investigate their asymptotic distributions. This paper
+contains two main contributions. First, the domain of application of the
+minimum distance estimation method is extended to the panel data model. Second,
+the proposed estimators are more efficient than other existing ones. Simulation
+studies compare performance of the proposed estimators with performance of
+others and demonstrate some superiority of our estimators.
+"
+21204,Towards Proving the Adversarial Robustness of Deep Neural Networks,"  Autonomous vehicles are highly complex systems, required to function reliably
+in a wide variety of situations. Manually crafting software controllers for
+these vehicles is difficult, but there has been some success in using deep
+neural networks generated using machine-learning. However, deep neural networks
+are opaque to human engineers, rendering their correctness very difficult to
+prove manually; and existing automated techniques, which were not designed to
+operate on neural networks, fail to scale to large systems. This paper focuses
+on proving the adversarial robustness of deep neural networks, i.e. proving
+that small perturbations to a correctly-classified input to the network cannot
+cause it to be misclassified. We describe some of our recent and ongoing work
+on verifying the adversarial robustness of networks, and discuss some of the
+open questions we have encountered and how they might be addressed.
+"
+21205,Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations,"  We propose a new algorithm for solving parabolic partial differential
+equations (PDEs) and backward stochastic differential equations (BSDEs) in high
+dimension, by making an analogy between the BSDE and reinforcement learning
+with the gradient of the solution playing the role of the policy function, and
+the loss function given by the error between the prescribed terminal condition
+and the solution of the BSDE. The policy function is then approximated by a
+neural network, as is done in deep reinforcement learning. Numerical results
+using TensorFlow illustrate the efficiency and accuracy of the proposed
+algorithms for several 100-dimensional nonlinear PDEs from physics and finance
+such as the Allen-Cahn equation, the Hamilton-Jacobi-Bellman equation, and a
+nonlinear pricing model for financial derivatives.
+"
+21206,Robust Fusion Methods for Big Data,"  We address one of the important problems in Big Data, namely how to combine
+estimators from different subsamples by robust fusion procedures, when we are
+unable to deal with the whole sample.
+"
+21207,Reconstruction of Galaxy Star Formation Histories through SED Fitting: The Dense Basis Approach,"  We introduce the Dense Basis method for Spectral Energy Distribution (SED)
+fitting. It accurately recovers traditional SED parameters, including M$_*$,
+SFR and dust attenuation, and reveals previously inaccessible information about
+the number and duration of star formation episodes and the timing of stellar
+mass assembly, as well as uncertainties in these quantities. This is done using
+basis Star Formation Histories (SFHs) chosen by comparing the goodness-of-fit
+of mock galaxy SEDs to the goodness-of-reconstruction of their SFHs. We train
+and validate the method using a sample of realistic SFHs at $z =1$ drawn from
+stochastic realisations, semi-analytic models, and a cosmological
+hydrodynamical galaxy formation simulation. The method is then applied to a
+sample of 1100 CANDELS GOODS-S galaxies at $1<z<1.5$ to illustrate its
+capabilities at moderate S/N with 15 photometric bands. Of the six
+parametrizations of SFHs considered, we adopt linear-exponential,
+bessel-exponential, lognormal and gaussian SFHs and reject the traditional
+parametrizations of constant (Top-Hat) and exponential SFHs. We quantify the
+bias and scatter of each parametrization. $15\%$ of galaxies in our CANDELS
+sample exhibit multiple episodes of star formation, with this fraction
+decreasing above $M_*>10^{9.5}M_\odot$. About $40\%$ of the CANDELS galaxies
+have SFHs whose maximum occurs at or near the epoch of observation. The Dense
+Basis method is scalable and offers a general approach to a broad class of
+data-science problems.
+"
+21208,Two-stage multipolar ordering in Pr(TM)$_2$Al$_{20}$ Kondo materials,"  Among heavy fermion materials, there is a set of rare-earth intermetallics
+with non-Kramers Pr$^{3+}$ $4f^2$ moments which exhibit a rich phase diagram
+with intertwined quadrupolar orders, superconductivity, and non-Fermi liquid
+behavior. However, more subtle broken symmetries such as multipolar orders in
+these Kondo materials remain poorly studied. Here, we argue that multi-spin
+interactions between local moments beyond the conventional two-spin exchange
+must play an important role in Kondo materials near the ordered to heavy Fermi
+liquid transition. We show that this drives a plethora of phases with
+coexisting multipolar orders and multiple thermal phase transitions, providing
+a natural framework for interpreting experiments on the Pr(TM)$_2$Al$_{20}$
+class of compounds.
+"
+21209,Spin-Momentum Locking in the Near Field of Metal Nanoparticles,"  Light carries both spin and momentum. Spin-orbit interactions of light come
+into play at the subwavelength scale of nano-optics and nano-photonics, where
+they determine the behaviour of light. These phenomena, in which the spin
+affects and controls the spatial degrees of freedom of light, are attracting
+rapidly growing interest. Here we present results on the spin-momentum locking
+in the near field of metal nanostructures supporting localized surface
+resonances. These systems can confine light to very small dimensions below the
+diffraction limit, leading to a striking near-field enhancement. In contrast to
+the propagating evanescent waves of surface plasmon-polariton modes, the
+electromagnetic near-field of localized surface resonances does not exhibit a
+definite position-independent momentum or polarization. Our results can be
+useful to investigate the spin-orbit interactions of light for complex
+evanescent fields. Note that the spin of the incident light can control the
+rotation direction of the canonical momentum.
+"
+21210,Persistence and extinction for stochastic ecological difference equations with feedbacks,"  Species' densities experience both internal frequency-dependent feedbacks due
+to population structure and external feedbacks from abiotic factors. These
+feedbacks can determine whether populations persist or go extinct, and may be
+subject to stochastic fluctuations. To provide a general mathematical framework
+for studying these effects, we develop theorems for stochastic persistence and
+exclusion for stochastic ecological difference equations accounting for
+feedbacks. Specifically, we use the stochastic analog of average Lyapunov
+functions to develop sufficient and necessary conditions for (i) all population
+densities spending little time at low densities i.e. stochastic persistence,
+and (ii) population trajectories asymptotically approaching the extinction set
+with positive probability. For (i) and (ii), respectively, we provide
+quantitative estimates on the fraction of time that the system is near the
+extinction set, and the probability of asymptotic extinction as a function of
+the initial state of the system. Furthermore, in the case of persistence, we
+provide lower bounds for the time to escape neighborhoods of the extinction
+set. To illustrate the applicability of our results, we analyze models of
+evolutionary games, stochastic Lotka-Volterra difference equations, trait
+evolution, and spatially structured disease dynamics. Our analysis of these
+models demonstrates environmental stochasticity facilitates coexistence in the
+hawk-dove game, but inhibits coexistence in the rock-paper-scissors game and
+Lotka-Volterra predator-prey model. Furthermore, environmental fluctuations
+with positive auto-correlations can promote persistence of evolving populations
+and disease persistence in patchy landscapes. While these results help close
+the gap between persistence theories for deterministic and stochastic systems,
+we conclude by highlighting several challenges for future research.
+"
+21211,Stochastic reachability of a target tube: Theory and computation,"  Given a discrete-time stochastic system and a time-varying sequence of target
+sets, we consider the problem of maximizing the probability of the state
+evolving within this tube under bounded control authority. This problem
+subsumes existing work on stochastic viability and terminal hitting-time
+stochastic reach-avoid problems. Of special interest is the stochastic reach
+set, the set of all initial states from which the probability of staying in the
+target tube is above a desired threshold. This set provides non-trivial
+information about the safety and the performance of the system. In this paper,
+we provide sufficient conditions under which the stochastic reach set is
+closed, compact, and convex. We also discuss an underapproximative
+interpolation technique for stochastic reach sets. Finally, we propose a
+scalable, grid-free, and anytime algorithm that computes a polytopic
+underapproximation of the stochastic reach set and synthesizes an open-loop
+controller using convex optimization. We demonstrate the efficacy and
+scalability of our approach over existing techniques using three numerical
+simulations --- stochastic viability of a chain of integrators, stochastic
+reach-avoid computation for a satellite rendezvous and docking problem, and
+stochastic reachability of a target tube for a Dubin's car with a known turning
+rate sequence.
+"
+21212,Bright soliton to quantum droplet transition in a mixture of Bose-Einstein condensates,"  Attractive Bose-Einstein condensates can host two types of macroscopic
+self-bound states of different nature: bright solitons and quantum liquid
+droplets. Here, we investigate the connection between them with a Bose-Bose
+mixture confined in an optical waveguide. We develop a simple theoretical model
+to show that, depending on atom number and interaction strength, solitons and
+droplets can be smoothly connected or remain distinct states coexisting only in
+a bi-stable region. We experimentally measure their spin composition, extract
+their density for a broad range of parameters and map out the boundary of the
+region separating solitons from droplets.
+"
+21213,Upper bounds for the spectral function on homogeneous spaces via volume growth,"  We use spectral embeddings to give upper bounds on the spectral function of
+the Laplace--Beltrami operator on homogeneous spaces in terms of the volume
+growth of balls. In the case of compact manifolds, our bounds extend the 1980
+lower bound of Peter Li for the smallest positive eigenvalue to all
+eigenvalues. We also improve Li's bound itself. Our bounds translate to
+explicit upper bounds on the heat kernel for both compact and noncompact
+homogeneous spaces.
+"
+21214,An impossibility theorem for gerrymandering,"  The U.S. Supreme Court is currently deliberating over whether a proposed
+mathematical formula should be used to detect unconstitutional partisan
+gerrymandering. We show that in some cases, this formula will only flag
+bizarrely shaped districts as potentially constitutional.
+"
+21215,Cooling-Rate Effects in Sodium Silicate Glasses: Bridging the Gap between Molecular Dynamics Simulations and Experiments,"  Although molecular dynamics (MD) simulations are commonly used to predict the
+structure and properties of glasses, they are intrinsically limited to short
+time scales, necessitating the use of fast cooling rates. It is therefore
+challenging to compare results from MD simulations to experimental results for
+glasses cooled on typical laboratory time scales. Based on MD simulations of a
+sodium silicate glass with varying cooling rate (from 0.01 to 100 K/ps), here
+we show that thermal history primarily affects the medium-range order
+structure, while the short-range order is largely unaffected over the range of
+cooling rates simulated. This results in a decoupling between the enthalpy and
+volume relaxation functions, where the enthalpy quickly plateaus as the cooling
+rate decreases, whereas density exhibits a slower relaxation. Finally, we
+demonstrate that the outcomes of MD simulations can be meaningfully compared to
+experimental values if properly extrapolated to slower cooling rates.
+"
+21216,Representation Learning for Scale-free Networks,"  Network embedding aims to learn the low-dimensional representations of
+vertexes in a network, while structure and inherent properties of the network
+is preserved. Existing network embedding works primarily focus on preserving
+the microscopic structure, such as the first- and second-order proximity of
+vertexes, while the macroscopic scale-free property is largely ignored.
+Scale-free property depicts the fact that vertex degrees follow a heavy-tailed
+distribution (i.e., only a few vertexes have high degrees) and is a critical
+property of real-world networks, such as social networks. In this paper, we
+study the problem of learning representations for scale-free networks. We first
+theoretically analyze the difficulty of embedding and reconstructing a
+scale-free network in the Euclidean space, by converting our problem to the
+sphere packing problem. Then, we propose the ""degree penalty"" principle for
+designing scale-free property preserving network embedding algorithm: punishing
+the proximity between high-degree vertexes. We introduce two implementations of
+our principle by utilizing the spectral techniques and a skip-gram model
+respectively. Extensive experiments on six datasets show that our algorithms
+are able to not only reconstruct heavy-tailed distributed degree distribution,
+but also outperform state-of-the-art embedding models in various network mining
+tasks, such as vertex classification and link prediction.
+"
+21217,"Extractive Summarization: Limits, Compression, Generalized Model and Heuristics","  Due to its promise to alleviate information overload, text summarization has
+attracted the attention of many researchers. However, it has remained a serious
+challenge. Here, we first prove empirical limits on the recall (and F1-scores)
+of extractive summarizers on the DUC datasets under ROUGE evaluation for both
+the single-document and multi-document summarization tasks. Next we define the
+concept of compressibility of a document and present a new model of
+summarization, which generalizes existing models in the literature and
+integrates several dimensions of the summarization, viz., abstractive versus
+extractive, single versus multi-document, and syntactic versus semantic.
+Finally, we examine some new and existing single-document summarization
+algorithms in a single framework and compare with state of the art summarizers
+on DUC data.
+"
+21218,Backprop as Functor: A compositional perspective on supervised learning,"  A supervised learning algorithm searches over a set of functions $A \to B$
+parametrised by a space $P$ to find the best approximation to some ideal
+function $f\colon A \to B$. It does this by taking examples $(a,f(a)) \in
+A\times B$, and updating the parameter according to some rule. We define a
+category where these update rules may be composed, and show that gradient
+descent---with respect to a fixed step size and an error function satisfying a
+certain property---defines a monoidal functor from a category of parametrised
+functions to this category of update rules. This provides a structural
+perspective on backpropagation, as well as a broad generalisation of neural
+networks.
+"
+21219,Entropic selection of concepts unveils hidden topics in documents corpora,"  The organization and evolution of science has recently become itself an
+object of scientific quantitative investigation, thanks to the wealth of
+information that can be extracted from scientific documents, such as citations
+between papers and co-authorship between researchers. However, only few studies
+have focused on the concepts that characterize full documents and that can be
+extracted and analyzed, revealing the deeper organization of scientific
+knowledge. Unfortunately, several concepts can be so common across documents
+that they hinder the emergence of the underlying topical structure of the
+document corpus, because they give rise to a large amount of spurious and
+trivial relations among documents. To identify and remove common concepts, we
+introduce a method to gauge their relevance according to an objective
+information-theoretic measure related to the statistics of their occurrence
+across the document corpus. After progressively removing concepts that,
+according to this metric, can be considered as generic, we find that the topic
+organization displays a correspondingly more refined structure.
+"
+21220,An Optimal Multi-layer Reinsurance Policy under Conditional Tail Expectation,"  A usual reinsurance policy for insurance companies admits one or two layers
+of the payment deductions. Under optimal criterion of minimizing the
+conditional tail expectation (CTE) risk measure of the insurer's total risk,
+this article generalized an optimal stop-loss reinsurance policy to an optimal
+multi-layer reinsurance policy. To achieve such optimal multi-layer reinsurance
+policy, this article starts from a given optimal stop-loss reinsurance policy
+$f(\cdot).$ In the first step, it cuts down an interval $[0,\infty)$ into two
+intervals $[0,M_1)$ and $[M_1,\infty).$ By shifting the origin of Cartesian
+coordinate system to $(M_{1},f(M_{1})),$ and showing that under the $CTE$
+criteria $f(x)I_{[0, M_1)}(x)+(f(M_1)+f(x-M_1))I_{[M_1,\infty)}(x)$ is, again,
+an optimal policy. This extension procedure can be repeated to obtain an
+optimal k-layer reinsurance policy. Finally, unknown parameters of the optimal
+multi-layer reinsurance policy are estimated using some additional appropriate
+criteria. Three simulation-based studies have been conducted to demonstrate:
+({\bf 1}) The practical applications of our findings and ({\bf 2}) How one may
+employ other appropriate criteria to estimate unknown parameters of an optimal
+multi-layer contract. The multi-layer reinsurance policy, similar to the
+original stop-loss reinsurance policy is optimal, in a same sense. Moreover it
+has some other optimal criteria which the original policy does not have. Under
+optimal criterion of minimizing general translative and monotone risk measure
+$\rho(\cdot)$ of {\it either} the insurer's total risk {\it or} both the
+insurer's and the reinsurer's total risks, this article (in its discussion)
+also extends a given optimal reinsurance contract $f(\cdot)$ to a multi-layer
+and continuous reinsurance policy.
+"
+21221,"Tusnády's problem, the transference principle, and non-uniform QMC sampling","  It is well-known that for every $N \geq 1$ and $d \geq 1$ there exist point
+sets $x_1, \dots, x_N \in [0,1]^d$ whose discrepancy with respect to the
+Lebesgue measure is of order at most $(\log N)^{d-1} N^{-1}$. In a more general
+setting, the first author proved together with Josef Dick that for any
+normalized measure $\mu$ on $[0,1]^d$ there exist points $x_1, \dots, x_N$
+whose discrepancy with respect to $\mu$ is of order at most $(\log
+N)^{(3d+1)/2} N^{-1}$. The proof used methods from combinatorial mathematics,
+and in particular a result of Banaszczyk on balancings of vectors. In the
+present note we use a version of the so-called transference principle together
+with recent results on the discrepancy of red-blue colorings to show that for
+any $\mu$ there even exist points having discrepancy of order at most $(\log
+N)^{d-\frac12} N^{-1}$, which is almost as good as the discrepancy bound in the
+case of the Lebesgue measure.
+"
+21222,HBT+: an improved code for finding subhalos and building merger trees in cosmological simulations,"  Dark matter subhalos are the remnants of (incomplete) halo mergers.
+Identifying them and establishing their evolutionary links in the form of
+merger trees is one of the most important applications of cosmological
+simulations. The Hierachical Bound-Tracing (HBT) code identifies halos as they
+form and tracks their evolution as they merge, simultaneously detecting
+subhalos and building their merger trees. Here we present a new implementation
+of this approach, HBT+, that is much faster, more user friendly, and more
+physically complete than the original code. Applying HBT+ to cosmological
+simulations we show that both the subhalo mass function and the peak-mass
+function are well fit by similar double-Schechter functions.The ratio between
+the two is highest at the high mass end, reflecting the resilience of massive
+subhalos that experience substantial dynamical friction but limited tidal
+stripping. The radial distribution of the most massive subhalos is more
+concentrated than the universal radial distribution of lower mass subhalos.
+Subhalo finders that work in configuration space tend to underestimate the
+masses of massive subhalos, an effect that is stronger in the host centre. This
+may explain, at least in part, the excess of massive subhalos in galaxy cluster
+centres inferred from recent lensing observations. We demonstrate that the
+peak-mass function is a powerful diagnostic of merger tree defects, and the
+merger trees constructed using HBT+ do not suffer from the missing or switched
+links that tend to afflict merger trees constructed from more conventional halo
+finders. We make the HBT+ code publicly available.
+"
+21223,On Statistical Non-Significance,"  Significance tests are probably the most extended form of inference in
+empirical research, and significance is often interpreted as providing greater
+informational content than non-significance. In this article we show, however,
+that rejection of a point null often carries very little information, while
+failure to reject may be highly informative. This is particularly true in
+empirical contexts where data sets are large and where there are rarely reasons
+to put substantial prior probability on a point null. Our results challenge the
+usual practice of conferring point null rejections a higher level of scientific
+significance than non-rejections. In consequence, we advocate a visible
+reporting and discussion of non-significant results in empirical practice.
+"
+21224,On the Interplay between Strong Regularity and Graph Densification,"  In this paper we analyze the practical implications of Szemerédi's
+regularity lemma in the preservation of metric information contained in large
+graphs. To this end, we present a heuristic algorithm to find regular
+partitions. Our experiments show that this method is quite robust to the
+natural sparsification of proximity graphs. In addition, this robustness can be
+enforced by graph densification.
+"
+21225,Nonabelian Landau-Ginzburg orbifolds and Calabi-Yau/Landau-Ginzburg correspondence,"  In this paper, we study the bigraded vector space structure of
+Landau-Ginzburg orbifolds. We prove the formula for the generating function of
+the Hodge numbers of possibly nonabelian Landau-Ginzburg orbifolds. As an
+application, we calculate the Hodge numbers for all nondegenerate quintic
+homogeneous polynomials with five variables. These results yield an evidence
+for the Calabi-Yau/Landau-Ginzburg correspondence between the Calabi-Yau
+geometries and the Landau-Ginzburg B-models.
+"
+21226,Setpoint Tracking with Partially Observed Loads,"  We use online convex optimization (OCO) for setpoint tracking with uncertain,
+flexible loads. We consider full feedback from the loads, bandit feedback, and
+two intermediate types of feedback: partial bandit where a subset of the loads
+are individually observed and the rest are observed in aggregate, and Bernoulli
+feedback where in each round the aggregator receives either full or bandit
+feedback according to a known probability. We give sublinear regret bounds in
+all cases. We numerically evaluate our algorithms on examples with
+thermostatically controlled loads and electric vehicles.
+"
+21227,Size effect in the ionization energy of PAH clusters,"  We report the first experimental measurement of the near-threshold
+photo-ionization spectra of polycyclic aromatic hydrocarbon clusters made of
+pyrene C16H10 and coronene C24H12, obtained using imaging photoelectron
+photoion coincidence spectrometry with a VUV synchrotron beamline. The
+experimental results of the ionization energy are confronted to calculated ones
+obtained from simulations using dedicated electronic structure treatment for
+large ionized molecular clusters. Experiment and theory consistently find a
+decrease of the ionization energy with cluster size. The inclusion of
+temperature effects in the simulations leads to a lowering of this energy and
+to a quantitative agreement with the experiment. In the case of pyrene, both
+theory and experiment show a discontinuity in the IE trend for the hexamer.
+"
+21228,Runaway Feedback Loops in Predictive Policing,"  Predictive policing systems are increasingly used to determine how to
+allocate police across a city in order to best prevent crime. Discovered crime
+data (e.g., arrest counts) are used to help update the model, and the process
+is repeated. Such systems have been empirically shown to be susceptible to
+runaway feedback loops, where police are repeatedly sent back to the same
+neighborhoods regardless of the true crime rate.
+In response, we develop a mathematical model of predictive policing that
+proves why this feedback loop occurs, show empirically that this model exhibits
+such problems, and demonstrate how to change the inputs to a predictive
+policing system (in a black-box manner) so the runaway feedback loop does not
+occur, allowing the true crime rate to be learned. Our results are
+quantitative: we can establish a link (in our model) between the degree to
+which runaway feedback causes problems and the disparity in crime rates between
+areas. Moreover, we can also demonstrate the way in which \emph{reported}
+incidents of crime (those reported by residents) and \emph{discovered}
+incidents of crime (i.e. those directly observed by police officers dispatched
+as a result of the predictive policing algorithm) interact: in brief, while
+reported incidents can attenuate the degree of runaway feedback, they cannot
+entirely remove it without the interventions we suggest.
+"
+21229,AWAKE-related benchmarking tests for simulation codes,"  Two tests are described that were developed for benchmarking and comparison
+of numerical codes in the context of AWAKE experiment.
+"
+21230,Ricci-flat metrics on the cone over $\mathbb{CP}^2 \# \overline{\mathbb{CP}^2}$,"  We describe a framework for constructing the Ricci-flat metrics on the total
+space of the canonical bundle over $\mathbb{CP}^2 \# \overline{\mathbb{CP}^2}$
+(the del Pezzo surface of rank one). We construct explicitly the first-order
+deformation of the so-called `orthotoric metric' on this manifold. We also show
+that the deformation of the corresponding conformal Killing-Yano form does not
+exist.
+"
+21231,Solving Zero-sum Games using Best Response Oracles with Applications to Search Games,"  We present efficient algorithms for computing optimal or approximately
+optimal strategies in a zero-sum game for which Player I has n pure strategies
+and Player II has an arbitrary number of pure strategies. We assume that for
+any given mixed strategy of Player I, a best response or ""approximate"" best
+response of Player II can be found by an oracle in time polynomial in n. We
+then show how our algorithms may be applied to several search games with
+applications to security and counter-terrorism. We evaluate our main algorithm
+experimentally on a prototypical search game. Our results show it performs well
+compared to an existing, well-known algorithm for solving zero-sum games that
+can also be used to solve search games, given a best response oracle.
+"
+21232,On the idea of a new artificial intelligence based optimization algorithm inspired from the nature of vortex,"  In this paper, the idea of a new artificial intelligence based optimization
+algorithm, which is inspired from the nature of vortex, has been provided
+briefly. As also a bio-inspired computation algorithm, the idea is generally
+focused on a typical vortex flow / behavior in nature and inspires from some
+dynamics that are occurred in the sense of vortex nature. Briefly, the
+algorithm is also a swarm-oriented evolutional problem solution approach;
+because it includes many methods related to elimination of weak swarm members
+and trying to improve the solution process by supporting the solution space via
+new swarm members. In order have better idea about success of the algorithm; it
+has been tested via some benchmark functions. At this point, the obtained
+results show that the algorithm can be an alternative to the literature in
+terms of single-objective optimization solution ways. Vortex Optimization
+Algorithm (VOA) is the name suggestion by the authors; for this new idea of
+intelligent optimization approach.
+"
+21233,Sulfur Hazes in Giant Exoplanet Atmospheres: Impacts on Reflected Light Spectra,"  Recent work has shown that sulfur hazes may arise in the atmospheres of some
+giant exoplanets due to the photolysis of H$_{2}$S. We investigate the impact
+such a haze would have on an exoplanet's geometric albedo spectrum and how it
+may affect the direct imaging results of WFIRST, a planned NASA space
+telescope. For temperate (250 K $<$ T$_{\rm eq}$ $<$ 700 K) Jupiter--mass
+planets, photochemical destruction of H$_{2}$S results in the production of
+$\sim$1 ppmv of \seight between 100 and 0.1 mbar, which, if cool enough, will
+condense to form a haze. Nominal haze masses are found to drastically alter a
+planet's geometric albedo spectrum: whereas a clear atmosphere is dark at
+wavelengths between 0.5 and 1 $\mu$m due to molecular absorption, the addition
+of a sulfur haze boosts the albedo there to $\sim$0.7 due to scattering. Strong
+absorption by the haze shortward of 0.4 $\mu$m results in albedos $<$0.1, in
+contrast to the high albedos produced by Rayleigh scattering in a clear
+atmosphere. As a result, the color of the planet shifts from blue to orange.
+The existence of a sulfur haze masks the molecular signatures of methane and
+water, thereby complicating the characterization of atmospheric composition.
+Detection of such a haze by WFIRST is possible, though discriminating between a
+sulfur haze and any other highly reflective, high altitude scatterer will
+require observations shortward of 0.4 $\mu$m, which is currently beyond
+WFIRST's design.
+"
+21234,Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD,"  Posttraumatic stress disorder (PTSD) is a chronic and disabling
+neuropsychiatric disorder characterized by insufficient top-down modulation of
+the amygdala activity by the prefrontal cortex. Real-time fMRI neurofeedback
+(rtfMRI-nf) is an emerging method with potential for modifying the
+amygdala-prefrontal interactions. We report the first controlled emotion
+self-regulation study in veterans with combat-related PTSD utilizing rtfMRI-nf
+of the amygdala activity. PTSD patients in the experimental group (EG, n=20)
+learned to upregulate BOLD activity of the left amygdala (LA) using rtfMRI-nf
+during a happy emotion induction task. PTSD patients in the control group (CG,
+n=11) were provided with a sham rtfMRI-nf. The study included three rtfMRI-nf
+training sessions, and EEG recordings were performed simultaneously with fMRI.
+PTSD severity was assessed using the Clinician-Administered PTSD Scale (CAPS).
+The EG participants showed a significant reduction in total CAPS ratings,
+including significant reductions in avoidance and hyperarousal symptoms.
+Overall, 80% of the EG participants demonstrated clinically meaningful
+reductions in CAPS ratings, compared to 38% in the CG. During the first
+session, fMRI connectivity of the LA with the orbitofrontal cortex and the
+dorsolateral prefrontal cortex (DLPFC) was progressively enhanced, and this
+enhancement significantly and positively correlated with initial CAPS ratings.
+Left-lateralized enhancement in upper alpha EEG coherence also exhibited a
+significant positive correlation with the initial CAPS. Reduction in PTSD
+severity between the first and last rtfMRI-nf sessions significantly correlated
+with enhancement in functional connectivity between the LA and the left DLPFC.
+Our results demonstrate that the rtfMRI-nf of the amygdala activity has the
+potential to correct the amygdala-prefrontal functional connectivity
+deficiencies specific to PTSD.
+"
+21235,Approximations from Anywhere and General Rough Sets,"  Not all approximations arise from information systems. The problem of fitting
+approximations, subjected to some rules (and related data), to information
+systems in a rough scheme of things is known as the \emph{inverse problem}. The
+inverse problem is more general than the duality (or abstract representation)
+problems and was introduced by the present author in her earlier papers. From
+the practical perspective, a few (as opposed to one) theoretical frameworks may
+be suitable for formulating the problem itself. \emph{Granular operator spaces}
+have been recently introduced and investigated by the present author in her
+recent work in the context of antichain based and dialectical semantics for
+general rough sets. The nature of the inverse problem is examined from
+number-theoretic and combinatorial perspectives in a higher order variant of
+granular operator spaces and some necessary conditions are proved. The results
+and the novel approach would be useful in a number of unsupervised and semi
+supervised learning contexts and algorithms.
+"
+21236,The minimal volume of simplices containing a convex body,"  Let $K \subset \mathbb R^n$ be a convex body with barycenter at the origin.
+We show there is a simplex $S \subset K$ having also barycenter at the origin
+such that $\left(\frac{vol(S)}{vol(K)}\right)^{1/n} \geq \frac{c}{\sqrt{n}},$
+where $c>0$ is an absolute constant. This is achieved using stochastic
+geometric techniques. Precisely, if $K$ is in isotropic position, we present a
+method to find centered simplices verifying the above bound that works with
+very high probability.
+As a consequence, we provide correct asymptotic estimates on an old problem
+in convex geometry. Namely, we show that the simplex $S_{min}(K)$ of minimal
+volume enclosing a given convex body $K \subset \mathbb R^n$, fulfills the
+following inequality $$\left(\frac{vol(S_{min}(K))}{vol(K)}\right)^{1/n} \leq d
+\sqrt{n},$$ for some absolute constant $d>0$. Up to the constant, the estimate
+cannot be lessened.
+"
+21237,Socioeconomic bias in influenza surveillance,"  Individuals in low socioeconomic brackets are considered at-risk for
+developing influenza-related complications and often exhibit higher than
+average influenza-related hospitalization rates. This disparity has been
+attributed to various factors, including restricted access to preventative and
+therapeutic health care, limited sick leave, and household structure. Adequate
+influenza surveillance in these at-risk populations is a critical precursor to
+accurate risk assessments and effective intervention. However, the United
+States of America's primary national influenza surveillance system (ILINet)
+monitors outpatient healthcare providers, which may be largely inaccessible to
+lower socioeconomic populations. Recent initiatives to incorporate
+internet-source and hospital electronic medical records data into surveillance
+systems seek to improve the timeliness, coverage, and accuracy of outbreak
+detection and situational awareness. Here, we use a flexible statistical
+framework for integrating multiple surveillance data sources to evaluate the
+adequacy of traditional (ILINet) and next generation (BioSense 2.0 and Google
+Flu Trends) data for situational awareness of influenza across poverty levels.
+We find that zip codes in the highest poverty quartile are a critical
+blind-spot for ILINet that the integration of next generation data fails to
+ameliorate.
+"
+21238,Java Code Analysis and Transformation into AWS Lambda Functions,"  Software developers are faced with the issue of either adapting their
+programming model to the execution model (e.g. cloud platforms) or finding
+appropriate tools to adapt the model and code automatically. A recent execution
+model which would benefit from automated enablement is Function-as-a-Service.
+Automating this process requires a pipeline which includes steps for code
+analysis, transformation and deployment. In this paper, we outline the design
+and runtime characteristics of Podilizer, a tool which implements the pipeline
+specifically for Java source code as input and AWS Lambda as output. We
+contribute technical and economic metrics about this concrete 'FaaSification'
+process by observing the behaviour of Podilizer with two representative Java
+software projects.
+"
+21239,Criteria for Finite Difference Groebner Bases of Normal Binomial Difference Ideals,"  In this paper, we give decision criteria for normal binomial difference
+polynomial ideals in the univariate difference polynomial ring F{y} to have
+finite difference Groebner bases and an algorithm to compute the finite
+difference Groebner bases if these criteria are satisfied. The novelty of these
+criteria lies in the fact that complicated properties about difference
+polynomial ideals are reduced to elementary properties of univariate
+polynomials in Z[x].
+"
+21240,$p$-Euler equations and $p$-Navier-Stokes equations,"  We propose in this work new systems of equations which we call $p$-Euler
+equations and $p$-Navier-Stokes equations. $p$-Euler equations are derived as
+the Euler-Lagrange equations for the action represented by the Benamou-Brenier
+characterization of Wasserstein-$p$ distances, with incompressibility
+constraint. $p$-Euler equations have similar structures with the usual Euler
+equations but the `momentum' is the signed ($p-1$)-th power of the velocity. In
+the 2D case, the $p$-Euler equations have streamfunction-vorticity formulation,
+where the vorticity is given by the $p$-Laplacian of the streamfunction. By
+adding diffusion presented by $\gamma$-Laplacian of the velocity, we obtain
+what we call $p$-Navier-Stokes equations. If $\gamma=p$, the {\it a priori}
+energy estimates for the velocity and momentum have dual symmetries. Using
+these energy estimates and a time-shift estimate, we show the global existence
+of weak solutions for the $p$-Navier-Stokes equations in $\mathbb{R}^d$ for
+$\gamma=p$ and $p\ge d\ge 2$ through a compactness criterion.
+"
+21241,Unconventional minimal subtraction and Bogoliubov-Parasyuk-Hepp-Zimmermann: massive scalar theory and critical exponents,"  We introduce a simpler although unconventional minimal subtraction
+renormalization procedure in the case of a massive scalar $\lambda \phi^{4}$
+theory in Euclidean space using dimensional regularization. We show that this
+method is very similar to its counterpart in massless field theory. In
+particular, the choice of using the bare mass at higher perturbative order
+instead of employing its tree-level counterpart eliminates all tadpole
+insertions at that order. As an application, we compute diagrammatically the
+critical exponents $\eta$ and $\nu$ at least up to two loops. We perform an
+explicit comparison with the Bogoliubov-Parasyuk-Hepp-Zimmermann ($BPHZ$)
+method at the same loop order, show that the proposed method requires fewer
+diagrams and establish a connection between the two approaches.
+"
+21242,A covering theorem for singular measures in the Euclidean space,"  We prove that for any singular measure $\mu$ on $\mathbb{R}^n$ it is possible
+to cover $\mu$-almost every point with $n$ families of Lipschitz slabs of
+arbitrarily small total width. More precisely, up to a rotation, for every
+$\delta>0$ there are $n$ countable families of $1$-Lipschitz functions
+$\{f_i^1\}_{i\in\mathbb{N}},\ldots, \{f_i^n\}_{i\in\mathbb{N}},$
+$f_i^j:\{x_j=0\}\subset\mathbb{R}^n\to\mathbb{R}$, and $n$ sequences of
+positive real numbers $\{\varepsilon_i^1\}_{i\in\mathbb{N}},\ldots,
+\{\varepsilon_i^n\}_{i\in\mathbb{N}}$ such that, denoting $\hat x_j$ the
+orthogonal projection of the point $x$ onto $\{x_j=0\}$ and
+$$I_i^j:=\{x=(x_1,\ldots,x_n)\in \mathbb{R}^n:f_i^j(\hat x_j)-\varepsilon_i^j<
+x_j< f_i^j(\hat x_j)+\varepsilon_i^j\},$$ it holds
+$\sum_{i,j}\varepsilon_i^j\leq \delta$ and
+$\mu(\mathbb{R}^n\setminus\bigcup_{i,j}I_i^j)=0.$
+We apply this result to show that, if $\mu$ is not absolutely continuous, it
+is possible to approximate the identity with a sequence $g_h$ of smooth
+equi-Lipschitz maps satisfying
+$$\limsup_{h\to\infty}\int_{\mathbb{R}^n}{\rm{det}}(\nabla g_h)
+d\mu<\mu(\mathbb{R}^n).$$ From this, we deduce a simple proof of the fact that
+every top-dimensional Ambrosio-Kirchheim metric current in $\mathbb{R}^n$ is a
+Federer-Fleming flat chain.
+"
+21243,Linear-Time Algorithms for Maximum-Weight Induced Matchings and Minimum Chain Covers in Convex Bipartite Graphs,"  A bipartite graph $G=(U,V,E)$ is convex if the vertices in $V$ can be
+linearly ordered such that for each vertex $u\in U$, the neighbors of $u$ are
+consecutive in the ordering of $V$. An induced matching $H$ of $G$ is a
+matching such that no edge of $E$ connects endpoints of two different edges of
+$H$. We show that in a convex bipartite graph with $n$ vertices and $m$
+weighted edges, an induced matching of maximum total weight can be computed in
+$O(n+m)$ time. An unweighted convex bipartite graph has a representation of
+size $O(n)$ that records for each vertex $u\in U$ the first and last neighbor
+in the ordering of $V$. Given such a compact representation, we compute an
+induced matching of maximum cardinality in $O(n)$ time.
+In convex bipartite graphs, maximum-cardinality induced matchings are dual to
+minimum chain covers. A chain cover is a covering of the edge set by chain
+subgraphs, that is, subgraphs that do not contain induced matchings of more
+than one edge. Given a compact representation, we compute a representation of a
+minimum chain cover in $O(n)$ time. If no compact representation is given, the
+cover can be computed in $O(n+m)$ time.
+All of our algorithms achieve optimal running time for the respective problem
+and model. Previous algorithms considered only the unweighted case, and the
+best algorithm for computing a maximum-cardinality induced matching or a
+minimum chain cover in a convex bipartite graph had a running time of $O(n^2)$.
+"
+21244,Enhanced steady-state dissolution flux in reactive convective dissolution,"  Chemical reactions can accelerate, slow down or even be at the very origin of
+the development of dissolution-driven convection in partially miscible
+stratifications, when they impact the density profile in the host fluid phase.
+We numerically analyze the dynamics of this reactive convective dissolution in
+the fully developed non-linear regime for a phase A dissolving into a host
+layer containing a dissolved reactant B. We show that for a general
+A+B$\rightarrow$C reaction in solution, the dynamics vary with the Rayleigh
+numbers of the chemical species, i.e. with the nature of the chemicals in the
+host phase. Depending on whether the reaction slows down, accelerates or is at
+the origin of the development of convection, the spatial distributions of
+species A, B or C, the dissolution flux and the reaction rate are different. We
+show that chemical reactions enhance the steady-state flux as they consume A
+and can induce more intense convection than in the absence of reactions. This
+result is important in the context of CO$_2$ geological sequestration where
+quantifying the storage rate of CO$_2$ dissolving into the host oil or aqueous
+phase is crucial to assess the efficiency and the safety of the project.
+"
+21245,Vapor Condensed and Supercooled Glassy Nanoclusters,"  We use molecular simulation to study the structural and dynamic properties of
+glassy nanoclusters formed both through the direct condensation of the vapor
+below the glass transition temperature, without the presence of a substrate,
+and \textit{via} the slow supercooling of unsupported liquid nanodroplets. An
+analysis of local structure using Voronoi polyhedra shows that the energetic
+stability of the clusters is characterized by a large, increasing fraction of
+bicapped square antiprism motifs. We also show that nanoclusters with similar
+inherent structure energies are structurally similar, independent of their
+history, which suggests the supercooled clusters access the same low energy
+regions of the potential energy landscape as the vapor condensed clusters
+despite their different methods of formation. By measuring the intermediate
+scattering function at different radii from the cluster center, we find that
+the relaxation dynamics of the clusters are inhomogeneous, with the core
+becoming glassy above the glass transition temperature while the surface
+remains mobile at low temperatures. This helps the clusters sample the highly
+stable, low energy structures on the potential energy surface. Our work
+suggests the nanocluster systems are structurally more stable than the
+ultra-stable glassy thin films, formed through vapor deposition onto a cold
+substrate, but the nanoclusters do not exhibit the superheating effects
+characteristic of the ultra-stable glass states.
+"
+21246,The Dayenu Boolean Function Is Almost Always True!,"  The Boolean function implicit in the famous Dayenu song, sung at the Passover
+meal, is expressed in full conjunctive normal form, and it is proved that if
+there are n miracles the number of truth-vectors satisfying it is $2^n -(n+1)$.
+"
+21247,Autocalibrating and Calibrationless Parallel Magnetic Resonance Imaging as a Bilinear Inverse Problem,"  Modern reconstruction methods for magnetic resonance imaging (MRI) exploit
+the spatially varying sensitivity profiles of receive-coil arrays as additional
+source of information. This allows to reduce the number of time-consuming
+Fourier-encoding steps by undersampling. The receive sensitivities are a priori
+unknown and influenced by geometry and electric properties of the (moving)
+subject. For optimal results, they need to be estimated jointly with the image
+from the same undersampled measurement data. Formulated as an inverse problem,
+this leads to a bilinear reconstruction problem related to multi-channel blind
+deconvolution. In this work, we will discuss some recently developed approaches
+for the solution of this problem.
+"
+21248,"Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators","  Machine learning advances have afforded an increase in algorithms capable of
+creating art, music, stories, games, and more. However, it is not yet
+well-understood how machine learning algorithms might best collaborate with
+people to support creative expression. To investigate how practicing designers
+perceive the role of AI in the creative process, we developed a game level
+design tool for Super Mario Bros.-style games with a built-in AI level
+designer. In this paper we discuss our design of the Morai Maker intelligent
+tool through two mixed-methods studies with a total of over one-hundred
+participants. Our findings are as follows: (1) level designers vary in their
+desired interactions with, and role of, the AI, (2) the AI prompted the level
+designers to alter their design practices, and (3) the level designers
+perceived the AI as having potential value in their design practice, varying
+based on their desired role for the AI.
+"
+21249,Approximation Algorithms for Barrier Sweep Coverage,"  Time-varying coverage, namely sweep coverage is a recent development in the
+area of wireless sensor networks, where a small number of mobile sensors sweep
+or monitor comparatively large number of locations periodically. In this
+article we study barrier sweep coverage with mobile sensors where the barrier
+is considered as a finite length continuous curve on a plane. The coverage at
+every point on the curve is time-variant. We propose an optimal solution for
+sweep coverage of a finite length continuous curve. Usually energy source of a
+mobile sensor is battery with limited power, so energy restricted sweep
+coverage is a challenging problem for long running applications. We propose an
+energy restricted sweep coverage problem where every mobile sensors must visit
+an energy source frequently to recharge or replace its battery. We propose a
+$\frac{13}{3}$-approximation algorithm for this problem. The proposed algorithm
+for multiple curves achieves the best possible approximation factor 2 for a
+special case. We propose a 5-approximation algorithm for the general problem.
+As an application of the barrier sweep coverage problem for a set of line
+segments, we formulate a data gathering problem. In this problem a set of
+mobile sensors is arbitrarily monitoring the line segments one for each. A set
+of data mules periodically collects the monitoring data from the set of mobile
+sensors. We prove that finding the minimum number of data mules to collect data
+periodically from every mobile sensor is NP-hard and propose a 3-approximation
+algorithm to solve it.
+"
+21250,Reinforcement Learning via Recurrent Convolutional Neural Networks,"  Deep Reinforcement Learning has enabled the learning of policies for complex
+tasks in partially observable environments, without explicitly learning the
+underlying model of the tasks. While such model-free methods achieve
+considerable performance, they often ignore the structure of task. We present a
+natural representation of to Reinforcement Learning (RL) problems using
+Recurrent Convolutional Neural Networks (RCNNs), to better exploit this
+inherent structure. We define 3 such RCNNs, whose forward passes execute an
+efficient Value Iteration, propagate beliefs of state in partially observable
+environments, and choose optimal actions respectively. Backpropagating
+gradients through these RCNNs allows the system to explicitly learn the
+Transition Model and Reward Function associated with the underlying MDP,
+serving as an elegant alternative to classical model-based RL. We evaluate the
+proposed algorithms in simulation, considering a robot planning problem. We
+demonstrate the capability of our framework to reduce the cost of replanning,
+learn accurate MDP models, and finally re-plan with learnt models to achieve
+near-optimal policies.
+"
+21251,Evaluating Deep Convolutional Neural Networks for Material Classification,"  Determining the material category of a surface from an image is a demanding
+task in perception that is drawing increasing attention. Following the recent
+remarkable results achieved for image classification and object detection
+utilising Convolutional Neural Networks (CNNs), we empirically study material
+classification of everyday objects employing these techniques. More
+specifically, we conduct a rigorous evaluation of how state-of-the art CNN
+architectures compare on a common ground over widely used material databases.
+Experimental results on three challenging material databases show that the best
+performing CNN architectures can achieve up to 94.99\% mean average precision
+when classifying materials.
+"
+21252,Localization of JPEG double compression through multi-domain convolutional neural networks,"  When an attacker wants to falsify an image, in most of cases she/he will
+perform a JPEG recompression. Different techniques have been developed based on
+diverse theoretical assumptions but very effective solutions have not been
+developed yet. Recently, machine learning based approaches have been started to
+appear in the field of image forensics to solve diverse tasks such as
+acquisition source identification and forgery detection. In this last case, the
+aim ahead would be to get a trained neural network able, given a to-be-checked
+image, to reliably localize the forged areas. With this in mind, our paper
+proposes a step forward in this direction by analyzing how a single or double
+JPEG compression can be revealed and localized using convolutional neural
+networks (CNNs). Different kinds of input to the CNN have been taken into
+consideration, and various experiments have been carried out trying also to
+evidence potential issues to be further investigated.
+"
+21253,From CDF to PDF --- A Density Estimation Method for High Dimensional Data,"  CDF2PDF is a method of PDF estimation by approximating CDF. The original idea
+of it was previously proposed in [1] called SIC. However, SIC requires
+additional hyper-parameter tunning, and no algorithms for computing higher
+order derivative from a trained NN are provided in [1]. CDF2PDF improves SIC by
+avoiding the time-consuming hyper-parameter tuning part and enabling higher
+order derivative computation to be done in polynomial time. Experiments of this
+method for one-dimensional data shows promising results.
+"
+21254,Spectrally-normalized margin bounds for neural networks,"  This paper presents a margin-based multiclass generalization bound for neural
+networks that scales with their margin-normalized ""spectral complexity"": their
+Lipschitz constant, meaning the product of the spectral norms of the weight
+matrices, times a certain correction factor. This bound is empirically
+investigated for a standard AlexNet network trained with SGD on the mnist and
+cifar10 datasets, with both original and random labels; the bound, the
+Lipschitz constants, and the excess risks are all in direct correlation,
+suggesting both that SGD selects predictors whose complexity scales with the
+difficulty of the learning task, and secondly that the presented bound is
+sensitive to this complexity.
+"
+21255,Fully Coupled Simulation of Cosmic Reionization. III. Stochastic Early Reionization by the Smallest Galaxies,"  Previously we identified a new class of early galaxy that we estimate
+contributes up to 30\% of the ionizing photons responsible for reionization.
+These are low mass halos in the range $M_h =10^{6.5}-10^{8} M_{\odot}$ that
+have been chemically enriched by supernova ejecta from prior Pop III star
+formation. Despite their low star formation rates, these Metal Cooling halos
+(MCs) are significant sources of ionizing radiation, especially at the onset of
+reionization, due to their high number density and ionizing escape fractions.
+Here we present a fully-coupled radiation hydrodynamic simulation of
+reionization that includes these MCs as well the more massive hydrogen atomic
+line cooling halos. Our method is novel: we perform halo finding inline with
+the radiation hydrodynamical simulation, and assign escaping ionizing fluxes to
+halos using a probability distribution function (PDF) measured from the
+galaxy-resolving Renaissance Simulations. The PDF captures the mass dependence
+of the ionizing escape fraction as well as the probability that a halo is
+actively forming stars. With MCs, reionization starts earlier than if only
+halos of $10^8 M_{\odot}$ and above are included, however the redshift when
+reionization completes is only marginally affected as this is driven by more
+massive galaxies. Because star formation is intermittent in MCs, the earliest
+phase of reionization exhibits a stochastic nature, with small H II regions
+forming and recombining. Only later, once halos of mass $\sim 10^9 M_{\odot}$
+and above begin to dominate the ionizing emissivity, does reionization proceed
+smoothly in the usual manner deduced from previous studies. This occurs at
+$z\approx 10$ in our simulation.
+"
+21256,Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens,"  This paper presents an evaluation of deep neural networks for recognition of
+digits entered by users on a smartphone touchscreen. A new large dataset of
+Arabic numerals was collected for training and evaluation of the network. The
+dataset consists of spatial and temporal touch data recorded for 80 digits
+entered by 260 users. Two neural network models were investigated. The first
+model was a 2D convolutional neural (ConvNet) network applied to bitmaps of the
+glpyhs created by interpolation of the sensed screen touches and its topology
+is similar to that of previously published models for offline handwriting
+recognition from scanned images. The second model used a 1D ConvNet
+architecture but was applied to the sequence of polar vectors connecting the
+touch points. The models were found to provide accuracies of 98.50% and 95.86%,
+respectively. The second model was much simpler, providing a reduction in the
+number of parameters from 1,663,370 to 287,690. The dataset has been made
+available to the community as an open source resource.
+"
+21257,On Counting Perfect Matchings in General Graphs,"  Counting perfect matchings has played a central role in the theory of
+counting problems. The permanent, corresponding to bipartite graphs, was shown
+to be #P-complete to compute exactly by Valiant (1979), and a fully polynomial
+randomized approximation scheme (FPRAS) was presented by Jerrum, Sinclair, and
+Vigoda (2004) using a Markov chain Monte Carlo (MCMC) approach. However, it has
+remained an open question whether there exists an FPRAS for counting perfect
+matchings in general graphs. In fact, it was unresolved whether the same Markov
+chain defined by JSV is rapidly mixing in general. In this paper, we show that
+it is not. We prove torpid mixing for any weighting scheme on hole patterns in
+the JSV chain. As a first step toward overcoming this obstacle, we introduce a
+new algorithm for counting matchings based on the Gallai-Edmonds decomposition
+of a graph, and give an FPRAS for counting matchings in graphs that are
+sufficiently close to bipartite. In particular, we obtain a fixed-parameter
+tractable algorithm for counting matchings in general graphs, parameterized by
+the greatest ""order"" of a factor-critical subgraph.
+"
+21258,Density and spin modes in imbalanced normal Fermi gases from collisionless to hydrodynamic regime,"  We study mass and population imbalance effect on density (in-phase) and spin
+(out-of-phase) collective modes in a two-component normal Fermi gas. By
+calculating eigenmodes of the linearized Boltzmann equation as well as the
+density/spin dynamic structure factor, we show that mass and population
+imbalance effects offer a variety of collective mode crossover behaviors from
+collisionless to hydrodynamic regimes. The mass imbalance effect shifts the
+crossover regime to the higher-temperature, and a significant peak of the spin
+dynamic structure factor emerges only in the collisionless regime. This is in
+contrast to the case of mass and population balanced normal Fermi gases, where
+the spin dynamic response is always absent. Although the population imbalance
+effect does not shift the crossover regime, the spin dynamic structure factor
+survives both in the collisionless and hydrodynamic regimes.
+"
+21259,Bounds on parameters of minimally non-linear patterns,"  Let $ex(n, P)$ be the maximum possible number of ones in any 0-1 matrix of
+dimensions $n \times n$ that avoids $P$. Matrix $P$ is called minimally
+non-linear if $ex(n, P) = \omega(n)$ but $ex(n, P') = O(n)$ for every strict
+subpattern $P'$ of $P$. We prove that the ratio between the length and width of
+any minimally non-linear 0-1 matrix is at most $4$, and that a minimally
+non-linear 0-1 matrix with $k$ rows has at most $5k-3$ ones. We also obtain an
+upper bound on the number of minimally non-linear 0-1 matrices with $k$ rows.
+In addition, we prove corresponding bounds for minimally non-linear ordered
+graphs. The minimal non-linearity that we investigate for ordered graphs is for
+the extremal function $ex_{<}(n, G)$, which is the maximum possible number of
+edges in any ordered graph on $n$ vertices with no ordered subgraph isomorphic
+to $G$.
+"
+21260,The sum of multidimensional divisor function over values of quadratic polynomial,"  Let $F({\bf x})={\bf x}^tQ_m{\bf x}+\mathbf{b}^t{\bf x}+c\in\mathbb{Z}[{\bf
+x}]$ be a quadratic polynomial in $\ell (\ge 3 )$ variables ${\bf x}
+=(x_{1},...,x_{\ell})$, where $F({\bf x})$ is positive when ${\bf
+x}\in\mathbb{R}_{\ge 1}^{\ell}$, $Q_m\in {\rm M}_{\ell}(\mathbb{Z})$ is an
+$\ell\times\ell$ matrix and its discriminant $\det\left(Q_m^t+Q_m\right)\neq
+0$. It gives explicit asymptotic formulas for the following sum \[
+T_{k,F}(X)=\sum_{{\bf x}\in
+[1,X]^{\ell}\cap\mathbb{Z}^{\ell}}\tau_{k}\left(F({\bf x})\right) \] with the
+help of the circle method. Here
+$\tau_{k}(n)=\#\{(x_1,x_2,...,x_{k})\in\mathbb{N}^{k}: n=x_1x_2...x_{k}\}$ with
+$k\in\mathbb{Z}_{\ge 2}$ is the multidimensional divisor function.
+"
+21261,Blazhko effect in the Galactic bulge fundamental mode RR~Lyrae stars I: Incidence rate and differences between modulated and non-modulated stars,"  We present the first paper of a series focused on the Blazhko effect in RR
+Lyrae type stars pulsating in the fundamental mode, that are located in the
+Galactic bulge. A~comprehensive overview about the incidence rate and
+light-curve characteristics of the Blazhko stars is given. We analysed 8\,282
+stars having the best quality data in the OGLE-IV survey, and found that at
+least $40.3$\,\% of stars show modulation of their light curves. The number of
+Blazhko stars we identified is 3\,341, which is the largest sample ever studied
+implying the most relevant statistical results currently available. Using
+combined data sets with OGLE-III observations, we found that 50\,\% of stars
+that show unresolved close peaks to the main component in OGLE-IV are actually
+Blazhko stars with extremely long periods. Blazhko stars with modulation occur
+preferentially among RR Lyrae stars with shorter pulsation periods in the
+Galactic bulge. Fourier amplitude and phase coefficients based on the mean
+light curves appear to be substantially lower for Blazhko stars than for stars
+with unmodulated light curve in average. We derived new relations for the
+compatibility parameter $D_{m}$ in $I$ passband and relations that allow for
+differentiating modulated and non-modulated stars easily on the basis of
+$R_{31}$, $\phi_{21}$ and $\phi_{31}$. Photometric metallicities, intrinsic
+colours and absolute magnitudes computed using empirical relations are the same
+for Blazhko and non-modulated stars in the Galactic bulge suggesting no
+correlation between the occurrence of the Blazhko effect and these parameters.
+"
+21262,Radiation damage and thermal shock response of carbon-fiber-reinforced materials to intense high-energy proton beams,"  A comprehensive study on the effects of energetic protons on carbon-fiber
+composites and compounds under consideration for use as low-Z pion production
+targets in future high-power accelerators and low-impedance collimating
+elements for intercepting TeV-level protons at the Large Hadron Collider has
+been undertaken addressing two key areas, namely, thermal shock absorption and
+resistance to irradiation damage.
+"
+21263,Common greedy wiring and rewiring heuristics do not guarantee maximum assortative graphs of given degree,"  We examine two greedy heuristics - wiring and rewiring - for constructing
+maximum assortative graphs over all simple connected graphs with a target
+degree sequence. Counterexamples show that natural greedy rewiring heuristics
+do not necessarily return a maximum assortative graph, even though it is known
+that the meta-graph of all simple connected graphs with given degree is
+connected under rewiring. Counterexamples show an elegant greedy graph wiring
+heuristic from the literature may fail to achieve the target degree sequence or
+may fail to wire a maximally assortative graph.
+"
+21264,Electromagnetically Induced Transparency with Superradiant and Subradiant states,"  We construct the electromagnetically induced transparency (EIT) by
+dynamically coupling a superradiant state with a subradiant state. The
+superradiant and subradiant states with enhanced and inhibited decay rates act
+as the excited and metastable states in EIT, respectively. Their energy
+difference determined by the distance between the atoms can be measured by the
+EIT spectra, which renders this method useful in subwavelength metrology. The
+scheme can also be applied to many atoms in nuclear quantum optics, where the
+transparency point due to counter-rotating wave terms can be observed.
+"
+21265,Highly efficient angularly resolving x-ray spectrometer optimized for absorption measurements with collimated sources,"  Highly collimated betatron radiation from a laser wakefield accelerator is a
+promising tool for spectroscopic measurements. Therefore there is a requirement
+to create spectrometers suited to the unique properties of such a source. We
+demonstrate a spectrometer which achieves an energy resolution of < 5 eV at 9
+keV and is angularly resolving the x-ray emission allowing the reference and
+spectrum to be recorded at the same time. The single photon analysis is used to
+significantly reduce the background noise. Theoretical performance of various
+configurations of the spectrometer is calculated by a ray-tracing algorithm.
+The properties and performance of the spectrometer including the angular and
+spectral resolution are demonstrated experimentally on absorption above the
+K-edge of a Cu foil backlit by laser-produced betatron radiation x-ray beam.
+"
+21266,Assessing the impact of non-vaccinators: quantifying the average length of infection chains in outbreaks of vaccine-preventable disease,"  Analytical expressions for the basic reproduction number, R0, have been
+obtained in the past for a wide variety of mathematical models for infectious
+disease spread, along with expressions for the expected final size of an
+outbreak. However, what has so far not been studied is the average number of
+infections that descend down the chains of infection begun by each of the
+individuals infected in an outbreak (we refer to this quantity as the ""average
+number of descendant infections"" per infectious individual, or ANDI). ANDI
+includes not only the number of people that an individual directly contacts and
+infects, but also the number of people that those go on to infect, and so on
+until that particular chain of infection dies out. Quantification of ANDI has
+relevance to the vaccination debate, since with ANDI one can calculate the
+probability that one or more people are hospitalised (or die) from a disease
+down an average chain of infection descending from an infected un-vaccinated
+individual. Here we obtain estimates of ANDI using both deterministic and
+stochastic modelling formalisms. With both formalisms we find that even for
+relatively small community sizes and under most scenarios for R0 and initial
+fraction vaccinated, ANDI can be surprisingly large when the effective
+reproduction number is >1, leading to high probabilities of adverse outcomes
+for one or more people down an average chain of infection in outbreaks of
+diseases like measles.
+"
+21267,A Framework and Comparative Analysis of Control Plane Security of SDN and Conventional Networks,"  Software defined networking implements the network control plane in an
+external entity, rather than in each individual device as in conventional
+networks. This architectural difference implies a different design for control
+functions necessary for essential network properties, e.g., loop prevention and
+link redundancy. We explore how such differences redefine the security
+weaknesses in the SDN control plane and provide a framework for comparative
+analysis which focuses on essential network properties required by typical
+production networks. This enables analysis of how these properties are
+delivered by the control planes of SDN and conventional networks, and to
+compare security risks and mitigations. Despite the architectural difference,
+we find similar, but not identical, exposures in control plane security if both
+network paradigms provide the same network properties and are analyzed under
+the same threat model. However, defenses vary; SDN cannot depend on edge based
+filtering to protect its control plane, while this is arguably the primary
+defense in conventional networks. Our concrete security analysis suggests that
+a distributed SDN architecture that supports fault tolerance and consistency
+checks is important for SDN control plane security. Our analysis methodology
+may be of independent interest for future security analysis of SDN and
+conventional networks.
+"
+21268,Relative homological algebra via truncations,"  To do homological algebra with unbounded chain complexes one needs to first
+find a way of constructing resolutions. Spaltenstein solved this problem for
+chain complexes of R-modules by truncating further and further to the left,
+resolving the pieces, and gluing back the partial resolutions. Our aim is to
+give a homotopy theoretical interpretation of this procedure, which may be
+extended to a relative setting. We work in an arbitrary abelian category A and
+fix a class I of ""injective objects"". We show that Spaltenstein's construction
+can be captured by a pair of adjoint functors between unbounded chain complexes
+and towers of non-positively graded ones. This pair of adjoint functors forms
+what we call a Quillen pair and the above process of truncations, partial
+resolutions, and gluing, gives a meaningful way to resolve complexes in a
+relative setting up to a split error term. In order to do homotopy theory, and
+in particular to construct a well behaved relative derived category D(A; I), we
+need more: the split error term must vanish. This is the case when I is the
+class of all injective R-modules but not in general, not even for certain
+classes of injectives modules over a Noetherian ring. The key property is a
+relative analogue of Roos's AB4*-n axiom for abelian categories. Various
+concrete examples such as Gorenstein homological algebra and purity are also
+discussed.
+"
+21269,Note: A pairwise form of the Ewald sum for non-neutral systems,"  Using an example of a mixed discrete-continuum representation of charges
+under the periodic boundary condition, we show that the exact pairwise form of
+the Ewald sum, which is well-defined even if the system is non-neutral,
+provides a natural starting point for deriving unambiguous Coulomb energies
+that must remove all spurious dependence on the choice of the Ewald screening
+factor.
+"
+21270,Model selection and model averaging in MACML-estimated MNP models,"  This paper provides a review of model selection and model averaging methods
+for multinomial probit models estimated using the MACML approach. The proposed
+approaches are partitioned into test based methods (mostly derived from the
+likelihood ratio paradigm), methods based on information criteria and model
+averaging methods.
+Many of the approaches first have been derived for models estimated using
+maximum likelihood and later adapted to the composite marginal likelihood
+framework. In this paper all approaches are applied to the MACML approach for
+estimation. The investigation lists advantages and disadvantages of the various
+methods in terms of asymptotic properties as well as computational aspects. We
+find that likelihood-ratio-type tests and information criteria have a spotty
+performance when applied to MACML models and instead propose the use of an
+empirical likelihood test.
+Furthermore, we show that model averaging is easily adaptable to CML
+estimation and has promising performance w.r.t to parameter recovery. Finally
+model averaging is applied to a real world example in order to demonstrate the
+feasibility of the method in real world sized problems.
+"
+21271,Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling,"  Traffic congestion is rapidly increasing in urban areas, particularly in mega
+cities. To date, there exist a few sensor network based systems to address this
+problem. However, these techniques are not suitable enough in terms of
+monitoring an entire transportation system and delivering emergency services
+when needed. These techniques require real-time data and intelligent ways to
+quickly determine traffic activity from useful information. In addition, these
+existing systems and websites on city transportation and travel rely on rating
+scores for different factors (e.g., safety, low crime rate, cleanliness, etc.).
+These rating scores are not efficient enough to deliver precise information,
+whereas reviews or tweets are significant, because they help travelers and
+transportation administrators to know about each aspect of the city. However,
+it is difficult for travelers to read, and for transportation systems to
+process, all reviews and tweets to obtain expressive sentiments regarding the
+needs of the city. The optimum solution for this kind of problem is analyzing
+the information available on social network platforms and performing sentiment
+analysis. On the other hand, crisp ontology-based frameworks cannot extract
+blurred information from tweets and reviews; therefore, they produce inadequate
+results. In this regard, this paper proposes fuzzy ontology-based sentiment
+analysis and SWRL rule-based decision-making to monitor transportation
+activities and to make a city- feature polarity map for travelers. This system
+retrieves reviews and tweets related to city features and transportation
+activities. The feature opinions are extracted from these retrieved data, and
+then fuzzy ontology is used to determine the transportation and city-feature
+polarity. A fuzzy ontology and an intelligent system prototype are developed by
+using Protégé OWL and Java, respectively.
+"
+21272,On the Gap Between Strict-Saddles and True Convexity: An Omega(log d) Lower Bound for Eigenvector Approximation,"  We prove a \emph{query complexity} lower bound on rank-one principal
+component analysis (PCA). We consider an oracle model where, given a symmetric
+matrix $M \in \mathbb{R}^{d \times d}$, an algorithm is allowed to make $T$
+\emph{exact} queries of the form $w^{(i)} = Mv^{(i)}$ for $i \in
+\{1,\dots,T\}$, where $v^{(i)}$ is drawn from a distribution which depends
+arbitrarily on the past queries and measurements $\{v^{(j)},w^{(j)}\}_{1 \le j
+\le i-1}$. We show that for a small constant $\epsilon$, any adaptive,
+randomized algorithm which can find a unit vector $\widehat{v}$ for which
+$\widehat{v}^{\top}M\widehat{v} \ge (1-\epsilon)\|M\|$, with even small
+probability, must make $T = \Omega(\log d)$ queries. In addition to settling a
+widely-held folk conjecture, this bound demonstrates a fundamental gap between
+convex optimization and ""strict-saddle"" non-convex optimization of which PCA is
+a canonical example: in the former, first-order methods can have dimension-free
+iteration complexity, whereas in PCA, the iteration complexity of
+gradient-based methods must necessarily grow with the dimension. Our argument
+proceeds via a reduction to estimating the rank-one spike in a deformed Wigner
+model. We establish lower bounds for this model by developing a ""truncated""
+analogue of the $\chi^2$ Bayes-risk lower bound of Chen et al.
+"
+21273,A Predictive Approach Using Deep Feature Learning for Electronic Medical Records: A Comparative Study,"  Massive amount of electronic medical records accumulating from patients and
+populations motivates clinicians and data scientists to collaborate for the
+advanced analytics to extract knowledge that is essential to address the
+extensive personalized insights needed for patients, clinicians, providers,
+scientists, and health policy makers. In this paper, we propose a new
+predictive approach based on feature representation using deep feature learning
+and word embedding techniques. Our method uses different deep architectures for
+feature representation in higher-level abstraction to obtain effective and more
+robust features from EMRs, and then build prediction models on the top of them.
+Our approach is particularly useful when the unlabeled data is abundant whereas
+labeled one is scarce. We investigate the performance of representation
+learning through a supervised approach. First, we apply our method on a small
+dataset related to a specific precision medicine problem, which focuses on
+prediction of left ventricular mass indexed to body surface area (LVMI) as an
+indicator of heart damage risk in a vulnerable demographic subgroup
+(African-Americans). Then we use two large datasets from eICU collaborative
+research database to predict the length of stay in Cardiac-ICU and Neuro-ICU
+based on high dimensional features. Finally we provide a comparative study and
+show that our predictive approach leads to better results in comparison with
+others.
+"
+21274,Gelfand numbers related to structured sparsity and Besov space embeddings with small mixed smoothness,"  We consider the problem of determining the asymptotic order of the Gelfand
+numbers of mixed-(quasi-)norm embeddings $\ell^b_p(\ell^d_q) \hookrightarrow
+\ell^b_r(\ell^d_u)$ given that $p \leq r$ and $q \leq u$, with emphasis on
+cases with $p\leq 1$ and/or $q\leq 1$. These cases turn out to be related to
+structured sparsity. We obtain sharp bounds in a number of interesting
+parameter constellations. Our new matching bounds for the Gelfand numbers of
+the embeddings of $\ell_1^b(\ell_2^d)$ and $\ell_2^b(\ell_1^d)$ into
+$\ell_2^b(\ell_2^d)$ imply optimality assertions for the recovery of
+block-sparse and sparse-in-levels vectors, respectively. In addition, we apply
+the sharp estimates for $\ell^b_p(\ell^d_q)$-spaces to obtain new two-sided
+estimates for the Gelfand numbers of multivariate Besov space embeddings in
+regimes of small mixed smoothness. It turns out that in some particular cases
+these estimates show the same asymptotic behaviour as in the univariate
+situation. In the remaining cases they differ at most by a $\log\log$ factor
+from the univariate bound.
+"
+21275,"Dust attenuation, bulge formation and inside-out cessation of star-formation in Star-Forming Main Sequence galaxies at z~2","  We derive two-dimensional dust attenuation maps at $\sim1~\mathrm{kpc}$
+resolution from the UV continuum for ten galaxies on the $z\sim2$ Star-Forming
+Main Sequence (SFMS). Comparison with IR data shows that 9 out of 10 galaxies
+do not require further obscuration in addition to the UV-based correction,
+though our sample does not include the most heavily obscured, massive galaxies.
+The individual rest-frame $V$-band dust attenuation (A$_{\rm V}$) radial
+profiles scatter around an average profile that gently decreases from $\sim1.8$
+mag in the center down to $\sim0.6$ mag at $\sim3-4$ half-mass radii. We use
+these maps to correct UV- and H$\alpha$-based star-formation rates (SFRs),
+which agree with each other. At masses $<10^{11}~M_{\rm sun}$, the
+dust-corrected specific SFR (sSFR) profiles are on average radially constant at
+a mass-doubling timescale of $\sim300~\mathrm{Myr}$, pointing at a synchronous
+growth of bulge and disk components. At masses $>10^{11}~M_{\rm sun}$, the sSFR
+profiles are typically centrally-suppressed by a factor of $\sim10$ relative to
+the galaxy outskirts. With total central obscuration disfavored, this indicates
+that at least a fraction of massive $z\sim2$ SFMS galaxies have started their
+inside-out star-formation quenching that will move them to the quenched
+sequence. In combination with other observations, galaxies above and below the
+ridge of the SFMS relation have respectively centrally-enhanced and
+centrally-suppressed sSFRs relative to their outskirts, supporting a picture
+where bulges are built due to gas `compaction' that leads to a high central SFR
+as galaxies move towards the upper envelope of SFMS.
+"
+21276,Shannon Shakes Hands with Chernoff: Big Data Viewpoint On Channel Information Measures,"  Shannon entropy is the most crucial foundation of Information Theory, which
+has been proven to be effective in many fields such as communications. Renyi
+entropy and Chernoff information are other two popular measures of information
+with wide applications. The mutual information is effective to measure the
+channel information for the fact that it reflects the relation between output
+variables and input variables. In this paper, we reexamine these channel
+information measures in big data viewpoint by means of ACE algorithm. The
+simulated results show us that decomposition results of Shannon and Chernoff
+mutual information with respect to channel parametersare almost the same. In
+this sense, Shannon shakes hands with Chernoff since they are different
+measures of the same information quantity. We also propose a conjecture that
+there is nature of channel information which is only decided by the channel
+parameters.
+"
+21277,A Morse theoretic description of the Goresky-Hingston product,"  The Goresky-Hingston coproduct was first introduced by D. Sullivan and later
+extended by M. Goresky and N. Hingston. In this article we give a Morse
+theoretic description of the coproduct. Using the description we prove homotopy
+invariance property of the coproduct. We describe a connection between our
+Morse theoretic coproduct and a coproduct on Floer homology of cotangent
+bundle.
+"
+21278,SoAx: A generic C++ Structure of Arrays for handling Particles in HPC Codes,"  The numerical study of physical problems often require integrating the
+dynamics of a large number of particles evolving according to a given set of
+equations. Particles are characterized by the information they are carrying
+such as an identity, a position other. There are generally speaking two
+different possibilities for handling particles in high performance computing
+(HPC) codes. The concept of an Array of Structures (AoS) is in the spirit of
+the object-oriented programming (OOP) paradigm in that the particle information
+is implemented as a structure. Here, an object (realization of the structure)
+represents one particle and a set of many particles is stored in an array. In
+contrast, using the concept of a Structure of Arrays (SoA), a single structure
+holds several arrays each representing one property (such as the identity) of
+the whole set of particles.
+The AoS approach is often implemented in HPC codes due to its handiness and
+flexibility. For a class of problems, however, it is know that the performance
+of SoA is much better than that of AoS. We confirm this observation for our
+particle problem. Using a benchmark we show that on modern Intel Xeon
+processors the SoA implementation is typically several times faster than the
+AoS one. On Intel's MIC co-processors the performance gap even attains a factor
+of ten. The same is true for GPU computing, using both computational and
+multi-purpose GPUs.
+Combining performance and handiness, we present the library SoAx that has
+optimal performance (on CPUs, MICs, and GPUs) while providing the same
+handiness as AoS. For this, SoAx uses modern C++ design techniques such
+template meta programming that allows to automatically generate code for user
+defined heterogeneous data structures.
+"
+21279,Multi-scenario deep learning for multi-speaker source separation,"  Research in deep learning for multi-speaker source separation has received a
+boost in the last years. However, most studies are restricted to mixtures of a
+specific number of speakers, called a specific scenario. While some works
+included experiments for different scenarios, research towards combining data
+of different scenarios or creating a single model for multiple scenarios have
+been very rare. In this work it is shown that data of a specific scenario is
+relevant for solving another scenario. Furthermore, it is concluded that a
+single model, trained on different scenarios is capable of matching performance
+of scenario specific models.
+"
+21280,Strongly mixed random errors in Mann's iteration algorithm for a contractive real function,"  This work deals with the Mann's stochastic iteration algorithm under strong
+mixing random errors. We establish the Fuk-Nagaev's inequalities that enable us
+to prove the almost complete convergence with its corresponding rate of
+convergence. Moreover, these inequalities give us the possibility of
+constructing a confidence interval for the unique fixed point. Finally, to
+check the feasibility and validity of our theoretical results, we consider some
+numerical examples, namely a classical example from astronomy.
+"
+21281,Attribution Modeling Increases Efficiency of Bidding in Display Advertising,"  Predicting click and conversion probabilities when bidding on ad exchanges is
+at the core of the programmatic advertising industry. Two separated lines of
+previous works respectively address i) the prediction of user conversion
+probability and ii) the attribution of these conversions to advertising events
+(such as clicks) after the fact. We argue that attribution modeling improves
+the efficiency of the bidding policy in the context of performance advertising.
+Firstly we explain the inefficiency of the standard bidding policy with respect
+to attribution. Secondly we learn and utilize an attribution model in the
+bidder itself and show how it modifies the average bid after a click. Finally
+we produce evidence of the effectiveness of the proposed method on both offline
+and online experiments with data spanning several weeks of real traffic from
+Criteo, a leader in performance advertising.
+"
+21282,Identifying high betweenness centrality nodes in large social networks,"  This paper proposes an alternative way to identify nodes with high
+betweenness centrality. It introduces a new metric, k-path centrality, and a
+randomized algorithm for estimating it, and shows empirically that nodes with
+high k-path centrality have high node betweenness centrality. The randomized
+algorithm runs in time $O(\kappa^{3}n^{2-2\alpha}\log n)$ and outputs, for each
+vertex v, an estimate of its k-path centrality up to additive error of $\pm
+n^{1/2+ \alpha}$ with probability $1-1/n^2$. Experimental evaluations on real
+and synthetic social networks show improved accuracy in detecting high
+betweenness centrality nodes and significantly reduced execution time when
+compared with existing randomized algorithms.
+"
+21283,A functional limit theorem for the sine-process,"  The main result of this paper is a functional limit theorem for the
+sine-process. In particular, we study the limit distribution, in the space of
+trajectories, for the number of particles in a growing interval. The
+sine-process has the Kolmogorov property and satisfies the Central Limit
+Theorem, but our functional limit theorem is very different from the Donsker
+Invariance Principle. We show that the time integral of our process can be
+approximated by the sum of a linear Gaussian process and independent Gaussian
+fluctuations whose covariance matrix is computed explicitly. We interpret these
+results in terms of the Gaussian Free Field convergence for the random matrix
+models. The proof relies on a general form of the multidimensional Central
+Limit Theorem under the sine-process for linear statistics of two types: those
+having growing variance and those with bounded variance corresponding to
+observables of Sobolev regularity $1/2$.
+"
+21284,Multi-scale Lipschitz percolation of increasing events for Poisson random walks,"  Consider the graph induced by $\mathbb{Z}^d$, equipped with uniformly
+elliptic random conductances. At time $0$, place a Poisson point process of
+particles on $\mathbb{Z}^d$ and let them perform independent simple random
+walks. Tessellate the graph into cubes indexed by $i\in\mathbb{Z}^d$ and
+tessellate time into intervals indexed by $\tau$. Given a local event
+$E(i,\tau)$ that depends only on the particles inside the space time region
+given by the cube $i$ and the time interval $\tau$, we prove the existence of a
+Lipschitz connected surface of cells $(i,\tau)$ that separates the origin from
+infinity on which $E(i,\tau)$ holds. This gives a directly applicable and
+robust framework for proving results in this setting that need a multi-scale
+argument. For example, this allows us to prove that an infection spreads with
+positive speed among the particles.
+"
+21285,The Planck numbers and the essence of gravitation: phenomenology,"  We introduce phenomenological understanding of the electromagnetic component
+of the physical vacuum, the EM vacuum, as a basic medium for all masses of the
+expanding Universe, and ""Casimir polarization"" of this medium arising in the
+vicinity of any material object in the Universe as a result of conjugation of
+the electric field components of the EM vacuum on both sides (""external"" and
+""internal"") of atomic nucleus boundary of the each mass with vacuum. It is
+shown that the gravitational attraction of two material objects in accordance
+with Newton's law of gravity arises as a result of overlapping of the domains
+of the EM vacuum Casimir polarization created by atomic nuclei of the objects,
+taking into account the long-range gravitational influence of all masses of the
+Universe on each nucleus of these objects (Mach's idea). Newton's law of
+gravitational attraction between two bodies is generalized to the case of
+gravitational interaction of a system of bodies when the center of mass of the
+pair of bodies shifted relative to the center of mass of the system. The unique
+smallness of gravitational interactions as compared with the fundamental
+nuclear (strong and weak) and electromagnetic ones is determined by the ratio
+of the characteristic size of the domain of EM vacuum Casimir polarization in
+the vicinity of atomic nuclei to the Hubble radius of the Universe.
+"
+21286,Low-Dimensional Spatial Embedding Method for Shape Uncertainty Quantification in Acoustic Scattering,"  This paper introduces a novel boundary integral approach of shape uncertainty
+quantification for the Helmholtz scattering problem in the framework of the
+so-called parametric method. The key idea is to construct an integration grid
+whose associated weight function encompasses the irregularities and
+nonsmoothness imposed by the random boundary. Thus, the solution can be
+evaluated accurately with relatively low number of grid points. The integration
+grid is obtained by employing a low-dimensional spatial embedding using the
+coarea formula. The proposed method can handle large variation as well as
+non-smoothness of the random boundary. For the ease of presentation the theory
+is restricted to star-shaped obstacles in low-dimensional setting. Higher
+spatial and parametric dimensional cases are discussed, though, not extensively
+explored in the current study.
+"
+21287,Updates on the background estimates for the X-IFU instrument onboard of the ATHENA mission,"  ATHENA, with a launch foreseen in 2028 towards the L2 orbit, addresses the
+science theme ""The Hot and Energetic Universe"", coupling a high-performance
+X-ray Telescope with two complementary focal-plane instruments. One of these,
+the X-ray Integral Field Unit (X-IFU) is a TES based kilo-pixel array providing
+spatially resolved high-resolution spectroscopy (2.5 eV at 6 keV) over a 5
+arcmin FoV. The background for this kind of detectors accounts for several
+components: the diffuse Cosmic X-ray Background, the low energy particles
+(<~100 keV) focalized by the mirrors and reaching the detector from inside the
+field of view, and the high energy particles (>~100 MeV) crossing the
+spacecraft and reaching the focal plane from every direction. Each one of these
+components is under study to reduce their impact on the instrumental
+performances. This task is particularly challenging, given the lack of data on
+the background of X-ray detectors in L2, the uncertainties on the particle
+environment to be expected in such orbit, and the reliability of the models
+used in the Monte Carlo background computations. As a consequence, the
+activities addressed by the group range from the reanalysis of the data of
+previous missions like XMM-Newton, to the characterization of the L2
+environment by data analysis of the particle monitors onboard of satellites
+present in the Earth magnetotail, to the characterization of solar events and
+their occurrence, and to the validation of the physical models involved in the
+Monte Carlo simulations. All these activities will allow to develop a set of
+reliable simulations to predict, analyze and find effective solutions to reduce
+the particle background experienced by the X-IFU, ultimately satisfying the
+scientific requirement that enables the science of ATHENA. While the activities
+are still ongoing, we present here some preliminary results already obtained by
+the group.
+"
+21288,Galaxy Zoo: star-formation versus spiral arm number,"  Spiral arms are common features in low-redshift disc galaxies, and are
+prominent sites of star-formation and dust obscuration. However, spiral
+structure can take many forms: from galaxies displaying two strong `grand
+design' arms, to those with many `flocculent' arms. We investigate how these
+different arm types are related to a galaxy's star-formation and gas properties
+by making use of visual spiral arm number measurements from Galaxy Zoo 2. We
+combine UV and mid-IR photometry from GALEX and WISE to measure the rates and
+relative fractions of obscured and unobscured star formation in a sample of
+low-redshift SDSS spirals. Total star formation rate has little dependence on
+spiral arm multiplicity, but two-armed spirals convert their gas to stars more
+efficiently. We find significant differences in the fraction of obscured
+star-formation: an additional $\sim 10$ per cent of star-formation in two-armed
+galaxies is identified via mid-IR dust emission, compared to that in many-armed
+galaxies. The latter are also significantly offset below the IRX-$\beta$
+relation for low-redshift star-forming galaxies. We present several
+explanations for these differences versus arm number: variations in the spatial
+distribution, sizes or clearing timescales of star-forming regions (i.e.,
+molecular clouds), or contrasting recent star-formation histories.
+"
+21289,Dense Associative Memory is Robust to Adversarial Inputs,"  Deep neural networks (DNN) trained in a supervised way suffer from two known
+problems. First, the minima of the objective function used in learning
+correspond to data points (also known as rubbish examples or fooling images)
+that lack semantic similarity with the training data. Second, a clean input can
+be changed by a small, and often imperceptible for human vision, perturbation,
+so that the resulting deformed input is misclassified by the network. These
+findings emphasize the differences between the ways DNN and humans classify
+patterns, and raise a question of designing learning algorithms that more
+accurately mimic human perception compared to the existing methods.
+Our paper examines these questions within the framework of Dense Associative
+Memory (DAM) models. These models are defined by the energy function, with
+higher order (higher than quadratic) interactions between the neurons. We show
+that in the limit when the power of the interaction vertex in the energy
+function is sufficiently large, these models have the following three
+properties. First, the minima of the objective function are free from rubbish
+images, so that each minimum is a semantically meaningful pattern. Second,
+artificial patterns poised precisely at the decision boundary look ambiguous to
+human subjects and share aspects of both classes that are separated by that
+decision boundary. Third, adversarial images constructed by models with small
+power of the interaction vertex, which are equivalent to DNN with rectified
+linear units (ReLU), fail to transfer to and fool the models with higher order
+interactions. This opens up a possibility to use higher order models for
+detecting and stopping malicious adversarial attacks. The presented results
+suggest that DAM with higher order energy functions are closer to human visual
+perception than DNN with ReLUs.
+"
+21290,The Nature of Turbulence in the LITTLE THINGS Dwarf Irregular Galaxies,"  We present probability density functions and higher order (skewness and
+kurtosis) analyses of the galaxy-wide and spatially-resolved HI column density
+distributions in the LITTLE THINGS sample of dwarf irregular galaxies. This
+analysis follows that of Burkhart et al. (2010) for the Small Magellanic Cloud.
+About 60% of our sample have galaxy-wide values of kurtosis that are similar to
+that found for the Small Magellanic Cloud, with a range up to much higher
+values, and kurtosis increases with integrated star formation rate. Kurtosis
+and skewness were calculated for radial annuli and for a grid of 32 pixel X 32
+pixel kernels across each galaxy. For most galaxies, kurtosis correlates with
+skewness. For about half of the galaxies, there is a trend of increasing
+kurtosis with radius. The range of kurtosis and skewness values is modeled by
+small variations in the Mach number close to the sonic limit and by conversion
+of HI to molecules at high column density. The maximum HI column densities
+decrease with increasing radius in a way that suggests molecules are forming in
+the weak field limit, where H_2 formation balances photodissociation in
+optically thin gas at the edges of clouds.
+"
+21291,Degeneration of Trigonal Curves and Solutions of the KP-Hierarchy,"  It is known that soliton solutions of the KP-hierarchy corresponds to
+singular rational curves with only ordinary double points. In this paper we
+study the degeneration of theta function solutions corresponding to certain
+trigonal curves. We show that, when the curves degenerate to singular rational
+curves with only ordinary triple points, the solutions tend to some
+intermediate solutions between solitons and rational solutions. They are
+considered as cerain limits of solitons. The Sato Grassmannian is extensively
+used here to study the degeneration of solutions, since it directly connects
+solutions of the KP-hierarchy to the defining equations of algebraic curves.We
+define a class of solutions in the Wronskian form which contains soliton
+solutions as a subclass and prove that, using the Sato Grassmannian, the
+degenerate trigonal solutions are connected to those solutions by certain gauge
+transformations
+"
+21292,Blow-up solutions for a Kirchhoff type elliptic equation with trapping potential,"  We study a generalized Kirchhoff type equation with trapping potential. The
+existence and blow-up behavior of solutions with normalized L2-norm for this
+problem are discussed.
+"
+21293,Representing Sentences as Low-Rank Subspaces,"  Sentences are important semantic units of natural language. A generic,
+distributional representation of sentences that can capture the latent
+semantics is beneficial to multiple downstream applications. We observe a
+simple geometry of sentences -- the word representations of a given sentence
+(on average 10.23 words in all SemEval datasets with a standard deviation 4.84)
+roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this
+observation, we represent a sentence by the low-rank subspace spanned by its
+word vectors. Such an unsupervised representation is empirically validated via
+semantic textual similarity tasks on 19 different datasets, where it
+outperforms the sophisticated neural network models, including skip-thought
+vectors, by 15% on average.
+"
+21294,Kinetic Energy Density Functionals by Axiomatic Approach,"  An axiomatic approach is herein used to determine the physically acceptable
+forms for general $D$-dimensional kinetic energy density functionals (KEDF).
+The resulted expansion captures most of the known forms of one-point KEDFs. By
+statistically training the KEDF forms on a model problem of non-interacting
+kinetic energy in 1D (6 terms only), the mean relative accuracy for 1000
+randomly generated potentials is found to be better than the standard KEDF by
+several orders of magnitudes. The accuracy improves with the number of occupied
+states and was found to be better than $10^{-4}$ for a system with four
+occupied states. Furthermore, we show that free fitting of the coefficients
+associated with known KEDFs approaches the exactly analytic values. The
+presented approach can open a new route to search for physically acceptable
+kinetic energy density functionals and provide an essential step towards more
+accurate large-scale orbital free density functional theory calculations.
+"
+21295,Locating Power Flow Solution Space Boundaries: A Numerical Polynomial Homotopy Approach,"  The solution space of any set of power flow equations may contain different
+number of real-valued solutions. The boundaries that separate these regions are
+referred to as power flow solution space boundaries. Knowledge of these
+boundaries is important as they provide a measure for voltage stability.
+Traditionally, continuation based methods have been employed to compute these
+boundaries on the basis of initial guesses for the solution. However, with
+rapid growth of renewable energy sources these boundaries will be increasingly
+affected by variable parameters such as penetration levels, locations of the
+renewable sources, and voltage set-points, making it difficult to generate an
+initial guess that can guarantee all feasible solutions for the power flow
+problem. In this paper we solve this problem by applying a numerical polynomial
+homotopy based continuation method. The proposed method guarantees to find all
+solution boundaries within a given parameter space up to a chosen level of
+discretization, independent of any initial guess. Power system operators can
+use this computational tool conveniently to plan the penetration levels of
+renewable sources at different buses. We illustrate the proposed method through
+simulations on 3-bus and 10-bus power system examples with renewable
+generation.
+"
+21296,First principles calculations of the interface properties of amorphous-Al2O3/MoS2 under non-strain and biaxial strain conditions,"  Al2O3 is a potential dielectric material for metal-oxide-semiconductor (MOS)
+devices. Al2O3 films deposited on semiconductors usually exhibit amorphous due
+to lattice mismatch. Compared to two-dimensional graphene, MoS2 is a typical
+semiconductor, therefore, it has more extensive application. The
+amorphous-Al2O3/MoS2 (a-Al2O3/MoS2) interface has attracted people's attention
+because of its unique properties. In this paper, the interface behaviors of
+a-Al2O3/MoS2 under non-strain and biaxial strain are investigated by first
+principles calculations based on density functional theory (DFT). First of all,
+the generation process of a-Al2O3 sample is described, which is calculated by
+molecular dynamics and geometric optimization. Then, we introduce the band
+alignment method, and calculate band offset of a-Al2O3/MoS2 interface. It is
+found that the valence band offset (VBO) and conduction band offset (CBO)
+change with the number of MoS2 layers. The dependence of leakage current on the
+band offset is also illustrated. At last, the band structure of monolayer MoS2
+under biaxial strain is discussed. The biaxial strain is set in the range from
+-6% to 6% with the interval of 2%. Impact of the biaxial strain on the band
+alignment is investigated.
+"
+21297,On cohomological Hall algebras of quivers : Yangians,"  We consider the cohomological Hall algebra Y of a Lagrangian substack of the
+moduli stack of representations of the preprojective algebra of an arbitrary
+quiver Q, and its actions on the cohomology of quiver varieties. We conjecture
+that Y is equal, after a suitable extension of scalars, to the Yangian
+introduced by Maulik and Okounkov, and we construct an embedding of Y in the
+Yangian, intertwining the respective actions of both algebras on the cohomology
+of quiver varieties.
+"
+21298,Are Continuum Predictions of Clustering Chaotic?,"  Gas-solid multiphase flows are prone to develop an instability known as
+clustering. Two-fluid models, which treat the particulate phase as a continuum,
+are known to reproduce the qualitative features of this instability, producing
+highly-dynamic, spatiotemporal patterns. However, it is unknown whether such
+simulations are truly aperiodic or a type of complex periodic behavior. By
+showing that the system possesses a sensitive dependence on initial conditions
+and a positive largest Lyapunov exponent, $\lambda_1 \approx 1/\tau$, we
+provide a tentative answer: continuum predictions of clustering are chaotic. We
+further demonstrate that the chaotic behavior is dimensionally dependent, a
+conclusion which unifies previous results and strongly suggests that the
+chaotic behavior is not a result of the fundamental kinematic instability, but
+of the secondary (inherently multidimensional) instability.
+"
+21299,Easing Tensions with Quartessence,"  Tensions between cosmic microwave background (CMB) observations and the
+growth of the large-scale structure (LSS) inferred from late-time probes pose a
+serious challenge to the concordance $\Lambda$CDM cosmological model.
+State-of-the-art CMB data from the Planck satellite predicts a higher rate of
+structure growth than what preferred by low-redshift observables. Such tension
+has hitherto eluded conclusive explanations in terms of straightforward
+modifications to $\Lambda$CDM, e.g. the inclusion of massive neutrinos or a
+dynamical dark energy component. Here, we investigate `quartessence' models,
+where a single dark component mimics both dark matter and dark energy. We show
+that such models greatly alleviate the tension between high and low redshift
+observations, thanks to the non-vanishing sound speed of quartessence that
+inhibits structure growth at late times on scales smaller than its
+corresponding Jeans' length. In particular, the $3.4\sigma$ tension between CMB
+and LSS observables is thoroughly reabsorbed. For this reason, we argue that
+quartessence deserves further investigation and may lead to a deeper
+understanding of the physics of the dark Universe.
+"
+21300,Stable high-pressure phases in the H-S system determined by chemically reacting hydrogen and sulfur,"  Synchrotron X-ray diffraction and Raman spectroscopy have been used to study
+chemical reactions of molecular hydrogen (H2) with sulfur (S) at high
+pressures. We find theoretically predicted Cccm and Im-3m H3S to be the
+reaction products at 50 and 140 GPa, respectively. Im-3m H3S is a stable
+crystalline phase above 140 GPa and it transforms to R3m H3S on pressure
+release below 140 GPa. The latter phase is (meta)stable down to at least 70 GPa
+where it transforms to Cccm H3S upon annealing (T<1300 K) to overcome the
+kinetic hindrance. Cccm H3S has an extended structure with symmetric hydrogen
+bonds at 50 GPa and upon decompression it experiences a transformation to a
+molecular mixed H2S-H2 structure below 40 GPa without any apparent change in
+the crystal symmetry.
+"
+21301,Learning Neurosymbolic Generative Models via Program Synthesis,"  Significant strides have been made toward designing better generative models
+in recent years. Despite this progress, however, state-of-the-art approaches
+are still largely unable to capture complex global structure in data. For
+example, images of buildings typically contain spatial patterns such as windows
+repeating at regular intervals; state-of-the-art generative methods can't
+easily reproduce these structures. We propose to address this problem by
+incorporating programs representing global structure into the generative
+model---e.g., a 2D for-loop may represent a configuration of windows.
+Furthermore, we propose a framework for learning these models by leveraging
+program synthesis to generate training data. On both synthetic and real-world
+data, we demonstrate that our approach is substantially better than the
+state-of-the-art at both generating and completing images that contain global
+structure.
+"
+21302,Collapsibility of marginal models for categorical data,"  We consider marginal log-linear models for parameterizing distributions on
+multidimensional contingency tables. These models generalize ordinary
+log-linear and multivariate logistic models, besides several others. First, we
+obtain some characteristic properties of marginal log-linear parameters. Then
+we define collapsibility and strict collapsibility of these parameters in a
+general sense. Several necessary and sufficient conditions for collapsibility
+and strict collapsibility are derived using the technique of Möbius
+inversion. These include results for an arbitrary set of marginal log-linear
+parameters having some common effects. The connections of collapsibility and
+strict collapsibility to various forms of independence of the variables are
+discussed. Finally, we establish a result on the relationship between
+parameters with the same effect but different margins, and use it to
+demonstrate smoothness of marginal log-linear models under collapsibility
+conditions thereby obtaining a curved exponential family.
+"
+21303,Tunable viscosity modification with diluted particles: When particles decrease the viscosity of complex fluids,"  While spherical particles are the most studied viscosity modifiers, they are
+well known only to increase viscosities, in particular at low concentrations.
+Extended studies and theories on non-spherical particles find a more
+complicated behavior, but still a steady increase. Involving platelets in
+combination with complex fluids displays an even more complex scenario that we
+analyze experimentally and theoretically as a function of platelet diameter, to
+find the underlying concepts. Using a broad toolbox of different techniques we
+were able to decrease the viscosity of crude oils although solid particles were
+added. This apparent contradiction could lead to a wider range of applications.
+"
+21304,Research Portfolio Analysis and Topic Prominence,"  Stakeholders in the science system need to decide where to place their bets.
+Example questions include: Which areas of research should get more funding? Who
+should we hire? Which projects should we abandon and which new projects should
+we start? Making informed choices requires knowledge about these research
+options. Unfortunately, to date research portfolio options have not been
+defined in a consistent, transparent and relevant manner. Furthermore, we don't
+know how to define demand for these options. In this article, we address the
+issues of consistency, transparency, relevance and demand by using a model of
+science consisting of 91,726 topics (or research options) that contain over 58
+million documents. We present a new indicator of topic prominence - a measure
+of visibility, momentum and, ultimately, demand. We assign over $203 billion of
+project-level funding data from STAR METRICS to individual topics in science,
+and show that the indicator of topic prominence, explains over one-third of the
+variance in current (or future) funding by topic. We also show that highly
+prominent topics receive far more funding per researcher than topics that are
+not prominent. Implications of these results for research planning and
+portfolio analysis by institutions and researchers are emphasized.
+"
+21305,Un Crit{È}Re Simple,"  In this short note, we mimic the proof of the simplicity of the theory ACFA
+of generic difference fields in order to provide a criterion, valid for certain
+theories of pure fields and fields equipped with operators, which shows that a
+complete theory is simple whenever its definable and algebraic closures are
+controlled by an underlying stable theory.
+"
+21306,Orbits of irreducible binary forms over GF$(p)$,"  In this note I give a formula for calculating the number of orbits of
+irreducible binary forms of degree $n$ over GF$(p)$ under the action of
+GL$(2,p)$. This formula has applications to the classification of class two
+groups of exponent $p$ with derived groups of order $p^2$.
+"
+21307,A Survey of Entorhinal Grid Cell Properties,"  About a decade ago grid cells were discovered in the medial entorhinal cortex
+of rat. Their peculiar firing patterns, which correlate with periodic locations
+in the environment, led to early hypothesis that grid cells may provide some
+form of metric for space. Subsequent research has since uncovered a wealth of
+new insights into the characteristics of grid cells and their neural
+neighborhood, the parahippocampal-hippocampal region, calling for a revision
+and refinement of earlier grid cell models. This survey paper aims to provide a
+comprehensive summary of grid cell research published in the past decade. It
+focuses on the functional characteristics of grid cells such as the influence
+of external cues or the alignment to environmental geometry, but also provides
+a basic overview of the underlying neural substrate.
+"
+21308,An Automata-based Abstract Semantics for String Manipulation Languages,"  In recent years, dynamic languages, such as JavaScript or Python, have faced
+an important increment of usage in a wide range of fields and applications.
+Their tricky and misunderstood behaviors pose a hard challenge for static
+analysis of these programming languages. A key aspect of any dynamic language
+program is the multiple usage of strings, since they can be implicitly
+converted to another type value, transformed by string-to-code primitives or
+used to access an object-property. Unfortunately, string analyses for dynamic
+languages still lack of precision and do not take into account some important
+string features. Moreover, string obfuscation is very popular in the context of
+dynamic language malicious code, for example, to hide code information inside
+strings and then to dynamically transform strings into executable code. In this
+scenario, more precise string analyses become a necessity. This paper proposes
+a new semantics for string analysis placing a first step for handling dynamic
+languages string features.
+"
+21309,Context encoders as a simple but powerful extension of word2vec,"  With a simple architecture and the ability to learn meaningful word
+embeddings efficiently from texts containing billions of words, word2vec
+remains one of the most popular neural language models used today. However, as
+only a single embedding is learned for every word in the vocabulary, the model
+fails to optimally represent words with multiple meanings. Additionally, it is
+not possible to create embeddings for new (out-of-vocabulary) words on the
+spot. Based on an intuitive interpretation of the continuous bag-of-words
+(CBOW) word2vec model's negative sampling training objective in terms of
+predicting context based similarities, we motivate an extension of the model we
+call context encoders (ConEc). By multiplying the matrix of trained word2vec
+embeddings with a word's average context vector, out-of-vocabulary (OOV)
+embeddings and representations for a word with multiple meanings can be created
+based on the word's local contexts. The benefits of this approach are
+illustrated by using these word embeddings as features in the CoNLL 2003 named
+entity recognition (NER) task.
+"
+21310,Sandwiches Missing Two Ingredients of Order Four,"  For a set ${\cal F}$ of graphs, an instance of the ${\cal F}$-{\sc free
+Sandwich Problem} is a pair $(G_1,G_2)$ consisting of two graphs $G_1$ and
+$G_2$ with the same vertex set such that $G_1$ is a subgraph of $G_2$, and the
+task is to determine an ${\cal F}$-free graph $G$ containing $G_1$ and
+contained in $G_2$, or to decide that such a graph does not exist. Initially
+motivated by the graph sandwich problem for trivially perfect graphs, which are
+the $\{ P_4,C_4\}$-free graphs, we study the complexity of the ${\cal F}$-{\sc
+free Sandwich Problem} for sets ${\cal F}$ containing two non-isomorphic graphs
+of order four. We show that if ${\cal F}$ is one of the sets $\left\{ {\rm
+diamond},K_4\right\}$, $\left\{ {\rm diamond},C_4\right\}$, $\left\{ {\rm
+diamond},{\rm paw}\right\}$, $\left\{ K_4,\overline{K_4}\right\}$, $\left\{
+P_4,C_4\right\}$, $\left\{ P_4,\overline{\rm claw}\right\}$, $\left\{
+P_4,\overline{\rm paw}\right\}$, $\left\{ P_4,\overline{\rm diamond}\right\}$,
+$\left\{ {\rm paw},C_4\right\}$, $\left\{ {\rm paw},{\rm claw}\right\}$,
+$\left\{ {\rm paw},\overline{\rm claw}\right\}$, $\left\{ {\rm
+paw},\overline{\rm paw}\right\}$, $\left\{ C_4,\overline{C_4}\right\}$,
+$\left\{ {\rm claw},\overline{\rm claw}\right\}$, and $\left\{ {\rm
+claw},\overline{C_4}\right\}$, then the ${\cal F}$-{\sc free Sandwich Problem}
+can be solved in polynomial time, and, if ${\cal F}$ is one of the sets
+$\left\{ C_4,K_4\right\}$, $\left\{ {\rm paw},K_4\right\}$, $\left\{ {\rm
+paw},\overline{K_4}\right\}$, $\left\{ {\rm paw},\overline{C_4}\right\}$,
+$\left\{ {\rm diamond},\overline{C_4}\right\}$, $\left\{ {\rm
+paw},\overline{\rm diamond}\right\}$, and $\left\{ {\rm diamond},\overline{\rm
+diamond}\right\}$, then the decision version of the ${\cal F}$-{\sc free
+Sandwich Problem} is NP-complete.
+"
+21311,Butterfly velocity and bulk causal structure,"  The butterfly velocity was recently proposed as a characteristic velocity of
+chaos propagation in a local system. Compared to the Lieb-Robinson velocity
+that bounds the propagation speed of all perturbations, the butterfly velocity,
+studied in thermal ensembles, is an ""effective"" Lieb-Robinson velocity for a
+subspace of the Hilbert space defined by the microcanonical ensemble. In this
+paper, we generalize the concept of butterfly velocity beyond the thermal case
+to a large class of other subspaces. Based on holographic duality, we consider
+the code subspace of low energy excitations on a classical background geometry.
+Using local reconstruction of bulk operators, we prove a general relation
+between the boundary butterfly velocities (of different operators) and the bulk
+causal structure. Our result has implications in both directions of the
+bulk-boundary correspondence. Starting from a boundary theory with a given
+Lieb-Robinson velocity, our result determines an upper bound of the bulk light
+cone starting from a given point. Starting from a bulk space-time geometry, the
+butterfly velocity can be explicitly calculated for all operators that are the
+local reconstructions of bulk local operators. If the bulk geometry satisfies
+Einstein equation and the null energy condition, for rotation symmetric
+geometries we prove that infrared operators always have a slower butterfly
+velocity that the ultraviolet one. For asymptotic AdS geometries, this also
+implies that the butterfly velocities of all operators are upper bounded by the
+speed of light. We further prove that the butterfly velocity is equal to the
+speed of light if the causal wedge of the boundary region coincides with its
+entanglement wedge. Finally, we discuss the implication of our result to
+geometries that are not asymptotically AdS, and in particular, obtain
+constraints that must be satisfied by a dual theory of flat space gravity.
+"
+21312,Correlated atomic wires on substrates. II. Application to Hubbard wires,"  In the first part of our theoretical study of correlated atomic wires on
+substrates, we introduced lattice models for a one-dimensional quantum wire on
+a three-dimensional substrate and their approximation by quasi-one-dimensional
+effective ladder models [arXiv:1704.07350]. In this second part, we apply this
+approach to the case of a correlated wire with a Hubbard-type electron-electron
+repulsion deposited on an insulating substrate. The ground-state and spectral
+properties are investigated numerically using the density-matrix
+renormalization group method and quantum Monte Carlo simulations. As a function
+of the model parameters, we observe various phases with quasi-one-dimensional
+low-energy excitations localized in the wire, namely paramagnetic Mott
+insulators, Luttinger liquids, and spin-$1/2$ Heisenberg chains. The validity
+of the effective ladder models is assessed by studying the convergence with the
+number of legs and comparing to the full three-dimensional model. We find that
+narrow ladder models accurately reproduce the quasi-one-dimensional excitations
+of the full three-dimensional model but predict only qualitatively whether
+excitations are localized around the wire or delocalized in the
+three-dimensional substrate.
+"
+21313,Distributed Bayesian Piecewise Sparse Linear Models,"  The importance of interpretability of machine learning models has been
+increasing due to emerging enterprise predictive analytics, threat of data
+privacy, accountability of artificial intelligence in society, and so on.
+Piecewise linear models have been actively studied to achieve both accuracy and
+interpretability. They often produce competitive accuracy against
+state-of-the-art non-linear methods. In addition, their representations (i.e.,
+rule-based segmentation plus sparse linear formula) are often preferred by
+domain experts. A disadvantage of such models, however, is high computational
+cost for simultaneous determinations of the number of ""pieces"" and cardinality
+of each linear predictor, which has restricted their applicability to
+middle-scale data sets. This paper proposes a distributed factorized asymptotic
+Bayesian (FAB) inference of learning piece-wise sparse linear models on
+distributed memory architectures. The distributed FAB inference solves the
+simultaneous model selection issue without communicating $O(N)$ data where N is
+the number of training samples and achieves linear scale-out against the number
+of CPU cores. Experimental results demonstrate that the distributed FAB
+inference achieves high prediction accuracy and performance scalability with
+both synthetic and benchmark data.
+"
+21314,Mercury's magnetic field in the MESSENGER era,"  MESSENGER magnetometer data show that Mercury's magnetic field is not only
+exceptionally weak but also has a unique geometry. The internal field resembles
+an axial dipole that is offset to the North by 20% of the planetary radius.
+This implies that the axial quadrupol is particularly strong while the dipole
+tilt is likely below 0.8 degree. The close proximity to the sun in combination
+with the weak internal field results in a very small and highly dynamic Hermean
+magnetosphere. We review the current understanding of Mercury's internal and
+external magnetic field and discuss possible explanations. Classical convection
+driven core dynamos have a hard time to reproduce the observations. Strong
+quadrupol contributions can be promoted by different measures, but they always
+go along with a large dipole tilt and generally rather small scale fields. A
+stably stratified outer core region seems required to explain not only the
+particular geometry but also the weakness of the Hermean magnetic field. New
+interior models suggest that Mercury's core likely hosts an iron snow zone
+underneath the core-mantle boundary. The positive radial sulfur gradient likely
+to develop in such a zone would indeed promote stable stratification. However,
+even dynamo models that include the stable layer show Mercury-like magnetic
+fields only for a fraction of the total simulation time. Large scale variations
+in the core-mantle boundary heat flux promise to yield more persistent results
+but are not compatible with the current understanding of Mercury's lower
+mantle.
+"
+21315,A two-stage approach for estimating the parameters of an age-group epidemic model from incidence data,"  Age-dependent dynamics is an important characteristic of many infectious
+diseases. Age-group epidemic models describe the infection dynamics in
+different age-groups by allowing to set distinct parameter values for each.
+However, such models are highly nonlinear and may have a large number of
+unknown parameters. Thus, parameter estimation of age-group models, while
+becoming a fundamental issue for both the scientific study and policy making in
+infectious diseases, is not a trivial task in practice. In this paper, we
+examine the estimation of the so called next-generation matrix using incidence
+data of a single entire outbreak, and extend the approach to deal with
+recurring outbreaks. Unlike previous studies, we do not assume any constraints
+regarding the structure of the matrix. A novel two-stage approach is developed,
+which allows for efficient parameter estimation from both statistical and
+computational perspectives. Simulation studies corroborate the ability to
+estimate accurately the parameters of the model for several realistic
+scenarios. The model and estimation method are applied to real data of
+influenza-like-illness in Israel. The parameter estimates of the key relevant
+epidemiological parameters and the recovered structure of the estimated
+next-generation matrix are in line with results obtained in previous studies.
+"
+21316,Sequential Double Robustness in Right-Censored Longitudinal Models,"  Consider estimating the G-formula for the counterfactual mean outcome under a
+given treatment regime in a longitudinal study. Bang and Robins provided an
+estimator for this quantity that relies on a sequential regression formulation
+of this parameter. This approach is doubly robust in that it is consistent if
+either the outcome regressions or the treatment mechanisms are consistently
+estimated. We define a stronger notion of double robustness, termed sequential
+double robustness, for estimators of the longitudinal G-formula. The definition
+emerges naturally from a more general definition of sequential double
+robustness for the outcome regression estimators. An outcome regression
+estimator is sequentially doubly robust (SDR) if, at each subsequent time
+point, either the outcome regression or the treatment mechanism is consistently
+estimated. This form of robustness is exactly what one would anticipate is
+attainable by studying the remainder term of a first-order expansion of the
+G-formula parameter. We show that a particular implementation of an existing
+procedure is SDR. We also introduce a novel SDR estimator, whose development
+involves a novel translation of ideas used in targeted minimum loss-based
+estimation to the infinite-dimensional setting.
+"
+21317,On the treewidth of triangulated 3-manifolds,"  In graph theory, as well as in 3-manifold topology, there exist several
+width-type parameters to describe how ""simple"" or ""thin"" a given graph or
+3-manifold is. These parameters, such as pathwidth or treewidth for graphs, or
+the concept of thin position for 3-manifolds, play an important role when
+studying algorithmic problems; in particular, there is a variety of problems in
+computational 3-manifold topology - some of them known to be computationally
+hard in general - that become solvable in polynomial time as soon as the dual
+graph of the input triangulation has bounded treewidth.
+In view of these algorithmic results, it is natural to ask whether every
+3-manifold admits a triangulation of bounded treewidth. We show that this is
+not the case, i.e., that there exists an infinite family of closed 3-manifolds
+not admitting triangulations of bounded pathwidth or treewidth (the latter
+implies the former, but we present two separate proofs).
+We derive these results from work of Agol, of Scharlemann and Thompson, and
+of Scharlemann, Schultens and Saito by exhibiting explicit connections between
+the topology of a 3-manifold M on the one hand and width-type parameters of the
+dual graphs of triangulations of M on the other hand, answering a question that
+had been raised repeatedly by researchers in computational 3-manifold topology.
+In particular, we show that if a closed, orientable, irreducible, non-Haken
+3-manifold M has a triangulation of treewidth (resp. pathwidth) k then the
+Heegaard genus of M is at most 24(k+1) (resp. 4(3k+1)).
+"
+21318,The Geometry of Large Tundra Lakes Observed in Historical Maps and Satellite Images,"  Tundra lakes are key components of the Arctic climate system because they
+represent a source of methane to the atmosphere. In this paper, we aim to
+analyze the geometry of the patterns formed by large ($>0.8$ km$^2$) tundra
+lakes in the Russian High Arctic. We have studied images of tundra lakes in
+historical maps from the State Hydrological Institute, Russia (date 1977; scale
+$0.21166$ km/pixel) and in Landsat satellite images derived from the Google
+Earth Engine (G.E.E.; date 2016; scale $0.1503$ km/pixel). The G.E.E. is a
+cloud-based platform for planetary-scale geospatial analysis on over four
+decades of Landsat data. We developed an image-processing algorithm to segment
+these maps and images, measure the area and perimeter of each lake, and compute
+the fractal dimension of the lakes in the images we have studied. Our results
+indicate that as lake size increases, their fractal dimension bifurcates. For
+lakes observed in historical maps, this bifurcation occurs among lakes larger
+than $100$ km$^2$ (fractal dimension $1.43$ to $1.87$). For lakes observed in
+satellite images this bifurcation occurs among lakes larger than $\sim$100
+km$^2$ (fractal dimension $1.31$ to $1.95$). Tundra lakes with a fractal
+dimension close to $2$ have a tendency to be self-similar with respect to their
+area--perimeter relationships. Area--perimeter measurements indicate that lakes
+with a length scale greater than $70$ km$^2$ are power-law distributed.
+Preliminary analysis of changes in lake size over time in paired lakes (lakes
+that were visually matched in both the historical map and the satellite
+imagery) indicate that some lakes in our study region have increased in size
+over time, whereas others have decreased in size over time. Lake size change
+during this 39-year time interval can be up to half the size of the lake as
+recorded in the historical map.
+"
+21319,Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery,"  There is a growing demand for accurate high-resolution land cover maps in
+many fields, e.g., in land-use planning and biodiversity conservation.
+Developing such maps has been performed using Object-Based Image Analysis
+(OBIA) methods, which usually reach good accuracies, but require a high human
+supervision and the best configuration for one image can hardly be extrapolated
+to a different image. Recently, the deep learning Convolutional Neural Networks
+(CNNs) have shown outstanding results in object recognition in the field of
+computer vision. However, they have not been fully explored yet in land cover
+mapping for detecting species of high biodiversity conservation interest. This
+paper analyzes the potential of CNNs-based methods for plant species detection
+using free high-resolution Google Earth T M images and provides an objective
+comparison with the state-of-the-art OBIA-methods. We consider as case study
+the detection of Ziziphus lotus shrubs, which are protected as a priority
+habitat under the European Union Habitats Directive. According to our results,
+compared to OBIA-based methods, the proposed CNN-based detection model, in
+combination with data-augmentation, transfer learning and pre-processing,
+achieves higher performance with less human intervention and the knowledge it
+acquires in the first image can be transferred to other images, which makes the
+detection process very fast. The provided methodology can be systematically
+reproduced for other species detection.
+"
+21320,Tight Semi-Nonnegative Matrix Factorization,"  The nonnegative matrix factorization is a widely used, flexible matrix
+decomposition, finding applications in biology, image and signal processing and
+information retrieval, among other areas. Here we present a related matrix
+factorization. A multi-objective optimization problem finds conical
+combinations of templates that approximate a given data matrix. The templates
+are chosen so that as far as possible only the initial data set can be
+represented this way. However, the templates are not required to be nonnegative
+nor convex combinations of the original data.
+"
+21321,Low Precision RNNs: Quantizing RNNs Without Losing Accuracy,"  Similar to convolution neural networks, recurrent neural networks (RNNs)
+typically suffer from over-parameterization. Quantizing bit-widths of weights
+and activations results in runtime efficiency on hardware, yet it often comes
+at the cost of reduced accuracy. This paper proposes a quantization approach
+that increases model size with bit-width reduction. This approach will allow
+networks to perform at their baseline accuracy while still maintaining the
+benefits of reduced precision and overall model size reduction.
+"
+21322,Unsupervised Representation Adversarial Learning Network: from Reconstruction to Generation,"  A good representation for arbitrarily complicated data should have the
+capability of semantic generation, clustering and reconstruction. Previous
+research has already achieved impressive performance on either one. This paper
+aims at learning a disentangled representation effective for all of them in an
+unsupervised way. To achieve all the three tasks together, we learn the forward
+and inverse mapping between data and representation on the basis of a symmetric
+adversarial process. In theory, we minimize the upper bound of the two
+conditional entropy loss between the latent variables and the observations
+together to achieve the cycle consistency. The newly proposed RepGAN is tested
+on MNIST, fashionMNIST, CelebA, and SVHN datasets to perform unsupervised or
+semi-supervised classification, generation and reconstruction tasks. The result
+demonstrates that RepGAN is able to learn a useful and competitive
+representation. To the author's knowledge, our work is the first one to achieve
+both a high unsupervised classification accuracy and low reconstruction error
+on MNIST.
+"
+21323,Quantitative Models of Imperfect Deception in Network Security using Signaling Games with Evidence,"  Deception plays a critical role in many interactions in communication and
+network security. Game-theoretic models called ""cheap talk signaling games""
+capture the dynamic and information asymmetric nature of deceptive
+interactions. But signaling games inherently model undetectable deception. In
+this paper, we investigate a model of signaling games in which the receiver can
+detect deception with some probability. This model nests traditional signaling
+games and complete information Stackelberg games as special cases. We present
+the pure strategy perfect Bayesian Nash equilibria of the game. Then we
+illustrate these analytical results with an application to active network
+defense. The presence of evidence forces majority-truthful behavior and
+eliminates some pure strategy equilibria. It always benefits the deceived
+player, but surprisingly sometimes also benefits the deceiving player.
+"
+21324,Drawbacks and alternatives to the numerical calculation of the base inertial parameters expressions for low mobility mechanisms,"  Base inertial parameters constitute a minimal inertial parametrization of
+mechanical systems that is of interest, for example, in parameter estimation
+and model reduction. Numerical and symbolic methods are available to determine
+their expressions. In this paper the problems associated with the numerical
+determination of the base inertial parameters expressions in the context of low
+mobility mechanisms are analyzed and discussed through and example. To
+circumvent these problems two alternatives are proposed: a variable precision
+arithmetic implementation of the customary numerical algorithm and the
+application of a general symbolic method. Finally, the advantages of both
+approaches compared to the numerical one are discussed in the context of the
+proposed low mobility example.
+"
+21325,Self-Paced Multitask Learning with Shared Knowledge,"  This paper introduces self-paced task selection to multitask learning, where
+instances from more closely related tasks are selected in a progression of
+easier-to-harder tasks, to emulate an effective human education strategy, but
+applied to multitask machine learning. We develop the mathematical foundation
+for the approach based on iterative selection of the most appropriate task,
+learning the task parameters, and updating the shared knowledge, optimizing a
+new bi-convex loss function. This proposed method applies quite generally,
+including to multitask feature learning, multitask learning with alternating
+structure optimization, etc. Results show that in each of the above
+formulations self-paced (easier-to-harder) task selection outperforms the
+baseline version of these methods in all the experiments.
+"
+21326,Morphometric analysis of polygonal cracking patterns in desiccated starch slurries,"  We investigate the geometry of two-dimensional polygonal cracking that forms
+on the air-exposed surface of dried starch slurries. Two different kinds of
+starches, made from potato and corn, exhibited distinguished crack evolution,
+and there were contrasting effects of slurry thickness on the probability
+distribution of the polygonal cell area. The experimental findings are believed
+to result from the difference in the shape and size of starch grains, which
+strongly influence the capillary transport of water and tensile stress field
+that drives the polygonal cracking.
+"
+21327,Parameterized Shifted Combinatorial Optimization,"  Shifted combinatorial optimization is a new nonlinear optimization framework
+which is a broad extension of standard combinatorial optimization, involving
+the choice of several feasible solutions at a time. This framework captures
+well studied and diverse problems ranging from so-called vulnerability problems
+to sharing and partitioning problems. In particular, every standard
+combinatorial optimization problem has its shifted counterpart, which is
+typically much harder. Already with explicitly given input set the shifted
+problem may be NP-hard. In this article we initiate a study of the
+parameterized complexity of this framework. First we show that shifting over an
+explicitly given set with its cardinality as the parameter may be in XP, FPT or
+P, depending on the objective function. Second, we study the shifted problem
+over sets definable in MSO logic (which includes, e.g., the well known MSO
+partitioning problems). Our main results here are that shifted combinatorial
+optimization over MSO definable sets is in XP with respect to the MSO formula
+and the treewidth (or more generally clique-width) of the input graph, and is
+W[1]-hard even under further severe restrictions.
+"
+21328,Mermin-Wagner at the Crossover Temperature,"  Mermin-Wagner excludes spontaneous (staggered) magnetization in isotropic
+ferromagnetic (antiferromagnetic) Heisenberg models at finite temperature in
+spatial dimensions $d \le 2$. While the proof relies on the Bogoliubov
+inequality, here we illuminate the theorem from an effective field theory point
+of view. We estimate the crossover temperature $T_c$ and show that, in weak
+external fields $H$, it tends to zero: $T_c \propto \sqrt{H}$ ($d=1$) and $T_c
+\propto 1/|\ln H|$ ($d=2$). Including the case $d$=3, we derive upper bounds
+for the (staggered) magnetization by combining microscopic and effective
+perspectives -- unfortunately, these bounds are not restrictive.
+"
+21329,The injectivity radius of Lie manifolds,"  We prove in a direct, geometric way that for any compatible Riemannian metric
+on a Lie manifold the injectivity radius is positive
+"
+21330,Inverse Risk-Sensitive Reinforcement Learning,"  We address the problem of inverse reinforcement learning in Markov decision
+processes where the agent is risk-sensitive. In particular, we model
+risk-sensitivity in a reinforcement learning framework by making use of models
+of human decision-making having their origins in behavioral psychology,
+behavioral economics, and neuroscience. We propose a gradient-based inverse
+reinforcement learning algorithm that minimizes a loss function defined on the
+observed behavior. We demonstrate the performance of the proposed technique on
+two examples, the first of which is the canonical Grid World example and the
+second of which is a Markov decision process modeling passengers' decisions
+regarding ride-sharing. In the latter, we use pricing and travel time data from
+a ride-sharing company to construct the transition probabilities and rewards of
+the Markov decision process.
+"
+21331,Correlations and confinement of excitations in an asymmetric Hubbard ladder,"  Correlation functions and low-energy excitations are investigated in the
+asymmetric two-leg ladder consisting of a Hubbard chain and a noninteracting
+tight-binding (Fermi) chain using the density matrix renormalization group
+method. The behavior of charge, spin and pairing correlations is discussed for
+the four phases found at half filling, namely, Luttinger liquid, Kondo-Mott
+insulator, spin-gapped Mott insulator and correlated band insulator.
+Quasi-long-range antiferromagnetic spin correlations are found in the Hubbard
+leg in the Luttinger liquid phase only. Pair-density-wave correlations are
+studied to understand the structure of bound pairs found in the Fermi leg of
+the spin-gapped Mott phase at half filling and at light doping but we find no
+enhanced pairing correlations. Low-energy excitations cause variations of spin
+and charge densities on both legs that demonstrate the confinement of the
+lowest charge excitations on the Fermi leg while the lowest spin excitations
+are localized on the Hubbard leg in the three insulating phases. The velocities
+of charge, spin, and single-particle excitations are investigated to clarify
+the confinement of elementary excitations in the Luttinger liquid phase. The
+observed spatial separation of elementary spin and charge excitations could
+facilitate the coexistence of different (quasi-)long-range orders in
+higher-dimensional extensions of the asymmetric Hubbard ladder.
+"
+21332,Notes about collision monochromatization in $e^+e^-$ colliders,"  The manuscript describes several monochromatization schemes starting from
+A.~Renieri \cite{ref:Renieri} proposal for head-on collisions based on
+correlation between particles transverse position and energy deviation. We
+briefly explain initial proposal and expand it for crossing angle collisions.
+Then we discuss new monochromatization scheme for crossing angle collisions
+based on correlation between particles longitudinal position and energy
+deviation.
+"
+21333,Combinatorial Cost Sharing,"  We introduce a combinatorial variant of the cost sharing problem: several
+services can be provided to each player and each player values every
+combination of services differently. A publicly known cost function specifies
+the cost of providing every possible combination of services. A combinatorial
+cost sharing mechanism is a protocol that decides which services each player
+gets and at what price. We look for dominant strategy mechanisms that are
+(economically) efficient and cover the cost, ideally without overcharging
+(i.e., budget balanced). Note that unlike the standard cost sharing setting,
+combinatorial cost sharing is a multi-parameter domain. This makes designing
+dominant strategy mechanisms with good guarantees a challenging task.
+We present the Potential Mechanism -- a combination of the VCG mechanism and
+a well-known tool from the theory of cooperative games: Hart and Mas-Colell's
+potential function. The potential mechanism is a dominant strategy mechanism
+that always covers the incurred cost. When the cost function is subadditive the
+same mechanism is also approximately efficient. Our main technical contribution
+shows that when the cost function is submodular the potential mechanism is
+approximately budget balanced in three settings: supermodular valuations,
+symmetric cost function and general symmetric valuations, and two players with
+general valuations.
+"
+21334,A new family of one-coincidence sets of sequences with dispersed elements for frequency hopping CDMA systems,"  We present a new family of one-coincidence sequence sets suitable for
+frequency hopping code division multiple access (FH-CDMA) systems with
+dispersed (low density) sequence elements. These sets are derived from
+one-coincidence prime sequence sets, such that for each one-coincidence prime
+sequence set there is a new one-coincidence set comprised of sequences with
+dispersed sequence elements, required in some circumstances, for FH-CDMA
+systems. Getting rid of crowdedness of sequence elements is achieved by
+doubling the size of the sequence element alphabet. In addition, this doubling
+process eases control over the distance between adjacent sequence elements.
+Properties of the new sets are discussed.
+"
+21335,Crowdsourcing with Tullock contests: A new perspective,"  Incentive mechanisms for crowdsourcing have been extensively studied under
+the framework of all-pay auctions. Along a distinct line, this paper proposes
+to use Tullock contests as an alternative tool to design incentive mechanisms
+for crowdsourcing. We are inspired by the conduciveness of Tullock contests to
+attracting user entry (yet not necessarily a higher revenue) in other domains.
+In this paper, we explore a new dimension in optimal Tullock contest design, by
+superseding the contest prize---which is fixed in conventional Tullock
+contests---with a prize function that is dependent on the (unknown) winner's
+contribution, in order to maximize the crowdsourcer's utility. We show that
+this approach leads to attractive practical advantages: (a) it is well-suited
+for rapid prototyping in fully distributed web agents and smartphone apps; (b)
+it overcomes the disincentive to participate caused by players' antagonism to
+an increasing number of rivals. Furthermore, we optimize conventional,
+fixed-prize Tullock contests to construct the most superior benchmark to
+compare against our mechanism. Through extensive evaluations, we show that our
+mechanism significantly outperforms the optimal benchmark, by over three folds
+on the crowdsourcer's utility cum profit and up to nine folds on the players'
+social welfare.
+"
+21336,Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent,"  Most distributed machine learning systems nowadays, including TensorFlow and
+CNTK, are built in a centralized fashion. One bottleneck of centralized
+algorithms lies on high communication cost on the central node. Motivated by
+this, we ask, can decentralized algorithms be faster than its centralized
+counterpart?
+Although decentralized PSGD (D-PSGD) algorithms have been studied by the
+control community, existing analysis and theory do not show any advantage over
+centralized PSGD (C-PSGD) algorithms, simply assuming the application scenario
+where only the decentralized network is available. In this paper, we study a
+D-PSGD algorithm and provide the first theoretical analysis that indicates a
+regime in which decentralized algorithms might outperform centralized
+algorithms for distributed stochastic gradient descent. This is because D-PSGD
+has comparable total computational complexities to C-PSGD but requires much
+less communication cost on the busiest node. We further conduct an empirical
+study to validate our theoretical analysis across multiple frameworks (CNTK and
+Torch), different network configurations, and computation platforms up to 112
+GPUs. On network configurations with low bandwidth or high latency, D-PSGD can
+be up to one order of magnitude faster than its well-optimized centralized
+counterparts.
+"
+21337,Learning User Intent from Action Sequences on Interactive Systems,"  Interactive systems have taken over the web and mobile space with increasing
+participation from users. Applications across every marketing domain can now be
+accessed through mobile or web where users can directly perform certain actions
+and reach a desired outcome. Actions of user on a system, though, can be
+representative of a certain intent. Ability to learn this intent through user's
+actions can help draw certain insight into the behavior of users on a system.
+In this paper, we present models to optimize interactive systems by learning
+and analyzing user intent through their actions on the system. We present a
+four phased model that uses time-series of interaction actions sequentially
+using a Long Short-Term Memory (LSTM) based sequence learning system that helps
+build a model for intent recognition. Our system then provides an objective
+specific maximization followed by analysis and contrasting methods in order to
+identify spaces of improvement in the interaction system. We discuss deployment
+scenarios for such a system and present results from evaluation on an online
+marketplace using user clickstream data.
+"
+21338,Laman Graphs are Generically Bearing Rigid in Arbitrary Dimensions,"  This paper addresses the problem of constructing bearing rigid networks in
+arbitrary dimensions. We first show that the bearing rigidity of a network is a
+generic property that is critically determined by the underlying graph of the
+network. A new notion termed generic bearing rigidity is defined for graphs. If
+the underlying graph of a network is generically bearing rigid, then the
+network is bearing rigid for almost all configurations; otherwise, the network
+is not bearing rigid for any configuration. As a result, the key to construct
+bearing rigid networks is to construct generically bearing rigid graphs. The
+main contribution of this paper is to prove that Laman graphs, which can be
+generated by the Henneberg construction, are generically bearing rigid in
+arbitrary dimensions. As a consequence, if the underlying graph of a network is
+Laman, the network is bearing rigid for almost all configurations in arbitrary
+dimensions.
+"
+21339,"Finding Dominating Induced Matchings in $(S_{2,2,3})$-Free Graphs in Polynomial Time","  Let $G=(V,E)$ be a finite undirected graph. An edge set $E' \subseteq E$ is a
+{\em dominating induced matching} ({\em d.i.m.}) in $G$ if every edge in $E$ is
+intersected by exactly one edge of $E'$. The \emph{Dominating Induced Matching}
+(\emph{DIM}) problem asks for the existence of a d.i.m.\ in $G$; this problem
+is also known as the \emph{Efficient Edge Domination} problem; it is the
+Efficient Domination problem for line graphs.
+The DIM problem is motivated by parallel resource allocation problems,
+encoding theory and network routing. It is \NP-complete even for very
+restricted graph classes such as planar bipartite graphs with maximum degree 3
+and is solvable in linear time for $P_7$-free graphs, and in polynomial time
+for $S_{1,2,4}$-free graphs as well as for $S_{2,2,2}$-free graphs. In this
+paper, combining two distinct approaches, we solve it in polynomial time for
+$S_{2,2,3}$-free graphs.
+"
+21340,Symmetry reduction and soliton-like solutions for the generalized Korteweg-de Vries equation,"  We analyze the gKdV equation, a generalized version of Korteweg-de Vries with
+an arbitrary function $f(u)$. In general, for a function $f(u)$ the Lie algebra
+of symmetries of gKdV is the $2$-dimensional Lie algebra of translations of the
+plane $xt$. This implies the existence of plane wave solutions. Indeed, for
+some specific values of $f(u)$ the equation gKdV admits a Lie algebra of
+symmetries of dimension grater than $2$. We compute the similarity reductions
+corresponding to these exceptional symmetries. We prove that the gKdV equation
+has soliton-like solutions under some general assumptions, and we find a closed
+formula for the plane wave solutions, that are of hyperbolic secant type.
+"
+21341,Existence theory for magma equations in dimension two and higher,"  We examine a degenerate, dispersive, nonlinear wave equation related to the
+evolution of partially molten rock in dimensions two and higher. This
+simplified model, for a scalar field capturing the melt fraction by volume, has
+been studied by direct numerical simulation where it has been observed to
+develop stable solitary waves. In this work, we prove local in time
+well-posedness results for the time dependent equation, on both the whole space
+and the torus, for dimensions two and higher. We also prove the existence of
+the solitary wave solutions in dimensions two and higher.
+"
+21342,A short guide through integration theorems of generalized distributions,"  The generalization of Frobenius' theorem to foliations with singularities is
+usually attributed to Stefan and Sussmann, for their simultaneous discovery
+around 1973. However, their result is often referred to without caring much on
+the precise statement, as some sort of magic spell. This may be explained by
+the fact that the literature is not consensual on a unique formulation of the
+theorem, and because the history of the research leading to this result has
+been flawed by many claims that turned to be refuted some years later. This,
+together with the difficulty of doing proof-reading on this topic, brought much
+confusion about the precise statement of Stefan-Sussmann's theorem. This paper
+is dedicated to bring some light on this subject, by investigating the
+different statements and arguments that were put forward in geometric control
+theory between 1962 and 1994 regarding the problem of integrability of
+generalized distributions. We will present the genealogy of the main ideas and
+show that many mathematicians that were involved in this field made some
+mistakes that were successfully refuted. Moreover, we want to address the
+prominent influence of Hermann on this topic, as well as the fact that some
+statements of Stefan and Sussmann turned to be wrong. In this paper, we intend
+to provide the reader with a deeper understanding of the problem of
+integrability of generalized distributions, and to reduce the confusion
+surrounding these difficult questions.
+"
+21343,Polarization induced interference within electromagnetically induced transparency for atoms of double-V linkage,"  People have been paying attention to the role of atoms' complex internal
+level structures in the research of electromagnetically induced transparency
+(EIT) for a long time, where the various degenerate Zeeman levels usually
+generate complex linkage patterns for the atomic transitions. It turns out,
+with special choices of the atomic states and the atomic transitions' linkage
+structure, clear signatures of quantum interference induced by the probe and
+coupling light's polarizations can emerge from a typical EIT phenomena. We
+propose to study a four state system with double-V linkage pattern for the
+transitions and analyze the polarization induced interference under the EIT
+condition. We show that such interference arises naturally under mild
+conditions on the optical field and atom manipulation. Its anticipated
+properties and its potential application of all optical switching in
+polarization degree of freedom are also discussed. Moreover, we construct a
+variation form of double-M linkage pattern where the polarization induced
+interference enables polarization-dependent cross-modulation between incident
+lights that can be effective even at the few-photon level. The theme is to gain
+more insight into the essential question: how can we build non-trivial optical
+medium where incident lights will induce polarization-dependent non-linear
+optical interactions, covering a wide range of the incidence intensity from the
+many-photon level to the few-photon level, respectively.
+"
+21344,A note on X-rays of permutations and a problem of Brualdi and Fritscher,"  The subject of this note is a challenging conjecture about X-rays of
+permutations which is a special case of a conjecture regarding Skolem
+sequences. In relation to this, Brualdi and Fritscher [Linear Algebra and its
+Applications, 2014] posed the following problem: Determine a bijection between
+extremal Skolem sets and binary Hankel X-rays of permutation matrices. We give
+such a bijection, along with some related observations.
+"
+21345,Differential Evolution and Bayesian Optimisation for Hyper-Parameter Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle Avoidance,"  The Lobula Giant Movement Detector (LGMD) is a an identified neuron of the
+locust that detects looming objects and triggers its escape responses.
+Understanding the neural principles and networks that lead to these fast and
+robust responses can lead to the design of efficient facilitate obstacle
+avoidance strategies in robotic applications. Here we present a neuromorphic
+spiking neural network model of the LGMD driven by the output of a neuromorphic
+Dynamic Vision Sensor (DVS), which has been optimised to produce robust and
+reliable responses in the face of the constraints and variability of its mixed
+signal analogue-digital circuits. As this LGMD model has many parameters, we
+use the Differential Evolution (DE) algorithm to optimise its parameter space.
+We also investigate the use of Self-Adaptive Differential Evolution (SADE)
+which has been shown to ameliorate the difficulties of finding appropriate
+input parameters for DE. We explore the use of two biological mechanisms:
+synaptic plasticity and membrane adaptivity in the LGMD. We apply DE and SADE
+to find parameters best suited for an obstacle avoidance system on an unmanned
+aerial vehicle (UAV), and show how it outperforms state-of-the-art Bayesian
+optimisation used for comparison.
+"
+21346,Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking,"  Methods that learn representations of nodes in a graph play a critical role
+in network analysis since they enable many downstream learning tasks. We
+propose Graph2Gauss - an approach that can efficiently learn versatile node
+embeddings on large scale (attributed) graphs that show strong performance on
+tasks such as link prediction and node classification. Unlike most approaches
+that represent nodes as point vectors in a low-dimensional continuous space, we
+embed each node as a Gaussian distribution, allowing us to capture uncertainty
+about the representation. Furthermore, we propose an unsupervised method that
+handles inductive learning scenarios and is applicable to different types of
+graphs: plain/attributed, directed/undirected. By leveraging both the network
+structure and the associated node attributes, we are able to generalize to
+unseen nodes without additional training. To learn the embeddings we adopt a
+personalized ranking formulation w.r.t. the node distances that exploits the
+natural ordering of the nodes imposed by the network structure. Experiments on
+real world networks demonstrate the high performance of our approach,
+outperforming state-of-the-art network embedding methods on several different
+tasks. Additionally, we demonstrate the benefits of modeling uncertainty - by
+analyzing it we can estimate neighborhood diversity and detect the intrinsic
+latent dimensionality of a graph.
+"
+21347,Explaining Anomalies in Groups with Characterizing Subspace Rules,"  Anomaly detection has numerous applications and has been studied vastly. We
+consider a complementary problem that has a much sparser literature: anomaly
+description. Interpretation of anomalies is crucial for practitioners for
+sense-making, troubleshooting, and planning actions. To this end, we present a
+new approach called x-PACS (for eXplaining Patterns of Anomalies with
+Characterizing Subspaces), which ""reverse-engineers"" the known anomalies by
+identifying (1) the groups (or patterns) that they form, and (2) the
+characterizing subspace and feature rules that separate each anomalous pattern
+from normal instances. Explaining anomalies in groups not only saves analyst
+time and gives insight into various types of anomalies, but also draws
+attention to potentially critical, repeating anomalies.
+In developing x-PACS, we first construct a desiderata for the anomaly
+description problem. From a descriptive data mining perspective, our method
+exhibits five desired properties in our desiderata. Namely, it can unearth
+anomalous patterns (i) of multiple different types, (ii) hidden in arbitrary
+subspaces of a high dimensional space, (iii) interpretable by the analysts,
+(iv) different from normal patterns of the data, and finally (v) succinct,
+providing the shortest data description. Furthermore, x-PACS is highly
+parallelizable and scales linearly in terms of data size.
+No existing work on anomaly description satisfies all of these properties
+simultaneously. While not our primary goal, the anomalous patterns we find
+serve as interpretable ""signatures"" and can be used for detection. We show the
+effectiveness of x-PACS in explanation as well as detection on real-world
+datasets as compared to state-of-the-art.
+"
+21348,VAMPnets: Deep learning of molecular kinetics,"  There is an increasing demand for computing the relevant structures,
+equilibria and long-timescale kinetics of biomolecular processes, such as
+protein-drug binding, from high-throughput molecular dynamics simulations.
+Current methods employ transformation of simulated coordinates into structural
+features, dimension reduction, clustering the dimension-reduced data, and
+estimation of a Markov state model or related model of the interconversion
+rates between molecular structures. This handcrafted approach demands a
+substantial amount of modeling expertise, as poor decisions at any step will
+lead to large modeling errors. Here we employ the variational approach for
+Markov processes (VAMP) to develop a deep learning framework for molecular
+kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire
+mapping from molecular coordinates to Markov states, thus combining the whole
+data processing pipeline in a single end-to-end framework. Our method performs
+equally or better than state-of-the art Markov modeling methods and provides
+easily interpretable few-state kinetic models.
+"
+21349,Toward deciphering developmental patterning with deep neural network,"  Dynamics of complex biological systems is driven by intricate networks, the
+current knowledge of which are often incomplete. The traditional systems
+biology modeling usually implements an ad hoc fixed set of differential
+equations with predefined function forms. Such an approach often suffers from
+overfitting or underfitting and thus inadequate predictive power, especially
+when dealing with systems of high complexity. This problem could be overcome by
+deep neuron network (DNN). Choosing pattern formation of the gap genes in
+Drosophila early embryogenesis as an example, we established a differential
+equation model whose synthesis term is expressed as a DNN. The model yields
+perfect fitting and impressively accurate predictions on mutant patterns. We
+further mapped the trained DNN into a simplified conventional regulation
+network, which is consistent with the existing body of knowledge. The DNN model
+could lay a foundation of ""in-silico-embryo"", which can regenerate a great
+variety of interesting phenomena, and on which one can perform all kinds of
+perturbations to discover underlying mechanisms. This approach can be readily
+applied to a variety of complex biological systems.
+"
+21350,Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks,"  Most methods of voice restoration for patients suffering from aphonia either
+produce whispered or monotone speech. Apart from intelligibility, this type of
+speech lacks expressiveness and naturalness due to the absence of pitch
+(whispered speech) or artificial generation of it (monotone speech). Existing
+techniques to restore prosodic information typically combine a vocoder, which
+parameterises the speech signal, with machine learning techniques that predict
+prosodic information. In contrast, this paper describes an end-to-end neural
+approach for estimating a fully-voiced speech waveform from whispered
+alaryngeal speech. By adapting our previous work in speech enhancement with
+generative adversarial networks, we develop a speaker-dependent model to
+perform whispered-to-voiced speech conversion. Preliminary qualitative results
+show effectiveness in re-generating voiced speech, with the creation of
+realistic pitch contours.
+"
+21351,A graph-theoretic description of scale-multiplicative semigroups of automorphisms,"  It is shown that a flat subgroup, $H$, of the totally disconnected, locally
+compact group $G$ decomposes into a finite number of subsemigroups on which the
+scale function is multiplicative. The image, $P$, of a multiplicative semigroup
+in the quotient, $H/H(1)$, of $H$ by its uniscalar subgroup has a unique
+minimal generating set which determines a natural Cayley graph structure on
+$P$. For each compact, open subgroup $U$ of $G$, a graph is defined and it is
+shown that if $P$ is multiplicative over $U$ then this graph is a regular,
+rooted, strongly simple $P$-graph. This extends to higher rank the result of R.
+Möller that $U$ is tidy for $x$ if and only if a certain graph is a regular,
+rooted tree.
+"
+21352,Half-Heusler alloy LiBaBi: A new topological semimetal with five-fold band degeneracy,"  Based on first-principles study, we report the finding of a new topological
+semimetal LiBaBi in half-Heusler phase. The remarkable feature of this
+nonmagnetic, inversion-symmetry-breaking material is that it consists of only
+simple $s$- and $p$-block elements. Interestingly, the material is ordinary
+insulator in the absence of spin-orbit coupling (SOC) and becomes nodal-surface
+topological semimetal showing drumhead states when SOC is included. This is in
+stark contrast to other nodal-line and nodal-surface semimetals, where the
+extended nodal structure is destroyed once SOC is included. Importantly, the
+linear band crossings host three-, four-, five- and six-fold degeneracies near
+the Fermi level, making this compound very attractive for the study of
+`unconventional' fermions. The band crossing points form a three-dimensional
+nodal structure around the zone center at the Fermi level. We identify the
+surface states responsible for the appearance of the drumhead states. The alloy
+also shows a phase transition from topological semimetal to a trivial insulator
+on application of pressure. In addition to revealing an intriguing effect of
+SOC on the nodal structure, our findings introduce a new half-Heusler alloy in
+the family of topological semimetals, thus creating more avenues for
+experimental exploration.
+"
+21353,A task-driven implementation of a simple numerical solver for hyperbolic conservation laws,"  This article describes the implementation of an all-in-one numerical
+procedure within the runtime StarPU. In order to limit the complexity of the
+method, for the sake of clarity of the presentation of the non-classical
+task-driven programming environnement, we have limited the numerics to first
+order in space and time. Results show that the task distribution is efficient
+if the tasks are numerous and individually large enough so that the task heap
+can be saturated by tasks which computational time covers the task management
+overhead. Next, we also see that even though they are mostly faster on graphic
+cards, not all the tasks are suitable for GPUs, which brings forward the
+importance of the task scheduler. Finally, we look at a more realistic system
+of conservation laws with an expensive source term, what allows us to conclude
+and open on future works involving higher local arithmetic intensity, by
+increasing the order of the numerical method or by enriching the model
+(increased number of parameters and therefore equations).
+"
+21354,Composition of PPT Maps,"  M. Christandl conjectured that the composition of any trace preserving PPT
+map with itself is entanglement breaking. We prove that Christandl's conjecture
+holds asymptotically by showing that the distance between the iterates of any
+unital or trace preserving PPT map and the set of entanglement breaking maps
+tends to zero. Finally, for every graph we define a one-parameter family of
+maps on matrices and determine the least value of the parameter such that the
+map is variously, positive, completely positive, PPT and entanglement breaking
+in terms of properties of the graph. Our estimates are sharp enough to conclude
+that Christandl's conjecture holds for these families.
+"
+21355,Multi-View Image Generation from a Single-View,"  This paper addresses a challenging problem -- how to generate multi-view
+cloth images from only a single view input. To generate realistic-looking
+images with different views from the input, we propose a new image generation
+model termed VariGANs that combines the strengths of the variational inference
+and the Generative Adversarial Networks (GANs). Our proposed VariGANs model
+generates the target image in a coarse-to-fine manner instead of a single pass
+which suffers from severe artifacts. It first performs variational inference to
+model global appearance of the object (e.g., shape and color) and produce a
+coarse image with a different view. Conditioned on the generated low resolution
+images, it then proceeds to perform adversarial learning to fill details and
+generate images of consistent details with the input. Extensive experiments
+conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that
+images of a novel view generated by our model are more plausible than those
+generated by existing approaches, in terms of more consistent global appearance
+as well as richer and sharper details.
+"
+21356,Equidistribution of Neumann data mass on simplices and a simple inverse problem,"  In this paper we study the behaviour of the Neumann data of Dirichlet
+eigenfunctions on simplices. We prove that the $L^2$ norm of the
+(semi-classical) Neumann data on each face is equal to $2/n$ times the
+$(n-1)$-dimensional volume of the face divided by the volume of the simplex.
+This is a generalization of \cite{Chr-tri} to higher dimensions. Again it is
+{\it not} an asymptotic, but an exact formula. The proof is by simple
+integrations by parts and linear algebra.
+We also consider the following inverse problem: do the {\it norms} of the
+Neumann data on a simplex determine a constant coefficient elliptic operator?
+The answer is yes in dimension 2 and no in higher dimensions.
+"
+21357,A machine learning approach for efficient uncertainty quantification using multiscale methods,"  Several multiscale methods account for sub-grid scale features using coarse
+scale basis functions. For example, in the Multiscale Finite Volume method the
+coarse scale basis functions are obtained by solving a set of local problems
+over dual-grid cells. We introduce a data-driven approach for the estimation of
+these coarse scale basis functions. Specifically, we employ a neural network
+predictor fitted using a set of solution samples from which it learns to
+generate subsequent basis functions at a lower computational cost than solving
+the local problems. The computational advantage of this approach is realized
+for uncertainty quantification tasks where a large number of realizations has
+to be evaluated. We attribute the ability to learn these basis functions to the
+modularity of the local problems and the redundancy of the permeability patches
+between samples. The proposed method is evaluated on elliptic problems yielding
+very promising results.
+"
+21358,Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes,"  In this work we study the quantitative relation between the recursive
+teaching dimension (RTD) and the VC dimension (VCD) of concept classes of
+finite sizes. The RTD of a concept class $\mathcal C \subseteq \{0, 1\}^n$,
+introduced by Zilles et al. (2011), is a combinatorial complexity measure
+characterized by the worst-case number of examples necessary to identify a
+concept in $\mathcal C$ according to the recursive teaching model.
+For any finite concept class $\mathcal C \subseteq \{0,1\}^n$ with
+$\mathrm{VCD}(\mathcal C)=d$, Simon & Zilles (2015) posed an open problem
+$\mathrm{RTD}(\mathcal C) = O(d)$, i.e., is RTD linearly upper bounded by VCD?
+Previously, the best known result is an exponential upper bound
+$\mathrm{RTD}(\mathcal C) = O(d \cdot 2^d)$, due to Chen et al. (2016). In this
+paper, we show a quadratic upper bound: $\mathrm{RTD}(\mathcal C) = O(d^2)$,
+much closer to an answer to the open problem. We also discuss the challenges in
+fully solving the problem.
+"
+21359,Joining Jolie to Docker - Orchestration of Microservices on a Containers-as-a-Service Layer,"  Cloud computing is steadily growing and, as IaaS vendors have started to
+offer pay-as-you-go billing policies, it is fundamental to achieve as much
+elasticity as possible, avoiding over-provisioning that would imply higher
+costs. In this paper, we briefly analyse the orchestration characteristics of
+PaaSSOA, a proposed architecture already implemented for Jolie microservices,
+and Kubernetes, one of the various orchestration plugins for Docker; then, we
+outline similarities and differences of the two approaches, with respect to
+their own domain of application. Furthermore, we investigate some ideas to
+achieve a federation of the two technologies, proposing an architectural
+composition of Jolie microservices on Docker Container-as-a-Service layer.
+"
+21360,On Calculation of Bounds for Greedy Algorithms when Applied to Sensor Selection Problems,"  We consider the problem of studying the performance of greedy algorithm on
+sensor selection problem for stable linear systems with Kalman Filter.
+Specifically, the objective is to find the system parameters that affects the
+performance of greedy algorithms and conditions where greedy algorithm always
+produces optimal solutions. In this paper, we developed an upper bound for
+performance ratio of greedy algorithm, which is based on the work of Dr.Zhang
+\cite{Sundaram} and offers valuable insight into the system parameters that
+affects the performance of greedy algorithm. We also proposes a set of
+conditions where greedy algorithm will always produce the optimal solution. We
+then show in simulations how the system parameters mentioned by the performance
+ratio bound derived in this work affects the performance of greedy algorithm.
+"
+21361,Generators of reductions of ideals in a local Noetherian ring with finite residue field,"  Let $(R,\mathfrak{m})$ be a local Noetherian ring with residue field $k$.
+While much is known about the generating sets of reductions of ideals of $R$ if
+$k$ is infinite, the case in which $k$ is finite is less well understood. We
+investigate the existence (or lack thereof) of proper reductions of an ideal of
+$R$ and the number of generators needed for a reduction in the case $k$ is a
+finite field. When $R$ is one-dimensional, we give a formula for the smallest
+integer $n$ for which every ideal has an $n$-generated reduction. It follows
+that in a one-dimensional local Noetherian ring every ideal has a principal
+reduction if and only if the number of maximal ideals in the normalization of
+the reduced quotient of $R$ is at most $|k|$. In higher dimensions, we show
+that for any positive integer, there exists an ideal of $R$ that does not have
+an $n$-generated reduction and that if $n \geq \dim R$ this ideal can be chosen
+to be $\mathfrak{m}$-primary. In the case where $R$ is a two-dimensional
+regular local ring, we construct an example of an integrally closed
+$\mathfrak{m}$-primary ideal that does not have a $2$-generated reduction and
+thus answer in the negative a question raised by Heinzer and Shannon.
+"
+21362,Particle-like Structure of Lie algebras,"  If a Lie algebra structure g on a vector space is the sum of a family of
+mutually compatible Lie algebra structures g_i's, we say that g is simply
+assembled from the g_i's. Repeating this procedure with a number of Lie
+algebras, themselves simply assembled from the g_i's, one obtains a Lie algebra
+assembled in two steps from the g_i's, and so on. We describe the process of
+modular disassembling of a Lie algebra into a unimodular and a non-unimodular
+part. We then study two inverse questions: which Lie algebras can be assembled
+from a given family of Lie algebras, and from which Lie algebras can a given
+Lie algebra be assembled? We develop some basic assembling and disassembling
+techniques that constitute the elements of a new approach to the general theory
+of Lie algebras. The main result of our theory is that any finite-dimensional
+Lie algebra over an algebraically closed field of characteristic zero or over R
+can be assembled in a finite number of steps from two elementary constituents,
+which we call dyons and triadons. Up to an abelian summand, a dyon is a Lie
+algebra structure isomorphic to the non-abelian 2-dimensional Lie algebra,
+while a triadon is isomorphic to the 3-dimensional Heisenberg Lie algebra. As
+an example, we describe constructions of classical Lie algebras from triadons.
+"
+21363,"Reversed Dickson polynomials of the $(k+1)$-th kind over finite fields, II","  Let $p$ be an odd prime. In this paper, we study the permutation behaviour of
+the reversed Dickson polynomials of the $(k+1)$-th kind $D_{n,k}(1,x)$ when
+$n=p^{l_1}+3$, $n=p^{l_1}+p^{l_2}+p^{l_3}$, and
+$n=p^{l_1}+p^{l_2}+p^{l_3}+p^{l_4}$, where $l_1, l_2$, $l_3$, and $l_4$ are
+non-negative integers. A generalization to $n=p^{l_1}+p^{l_2}+\cdots +p^{l_i}$
+is also shown. We find some conditions under which $D_{n,k}(1,x)$ is not a
+permutation polynomial over finite fields for certain values of $n$ and $k$. We
+also present a generalization of a recent result regarding $D_{p^l-1,1}(1,x)$
+and present some algebraic and arithmetic properties of $D_{n,k}(1,x)$.
+"
+21364,Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time,"  Recently, Recurrent Neural Networks (RNNs) have been applied to the task of
+session-based recommendation. These approaches use RNNs to predict the next
+item in a user session based on the previ- ously visited items. While some
+approaches consider additional item properties, we argue that item dwell time
+can be used as an implicit measure of user interest to improve session-based
+item recommen- dations. We propose an extension to existing RNN approaches that
+captures user dwell time in addition to the visited items and show that
+recommendation performance can be improved. Additionally, we investigate the
+usefulness of a single validation split for model selection in the case of
+minor improvements and find that in our case the best model is not selected and
+a fold-like study with different validation sets is necessary to ensure the
+selection of the best model.
+"
+21365,Universal Spatiotemporal Sampling Sets for Discrete Spatially Invariant Evolution Systems,"  Let $(I,+)$ be a finite abelian group and $\mathbf{A}$ be a circular
+convolution operator on $\ell^2(I)$. The problem under consideration is how to
+construct minimal $\Omega \subset I$ and $l_i$ such that $Y=\{\mathbf{e}_i,
+\mathbf{A}\mathbf{e}_i, \cdots, \mathbf{A}^{l_i}\mathbf{e}_i: i\in \Omega\}$ is
+a frame for $\ell^2(I)$, where $\{\mathbf{e}_i: i\in I\}$ is the canonical
+basis of $\ell^2(I)$. This problem is motivated by the spatiotemporal sampling
+problem in discrete spatially invariant evolution systems. We will show that
+the cardinality of $\Omega $ should be at least equal to the largest geometric
+multiplicity of eigenvalues of $\mathbf{A}$, and we consider the universal
+spatiotemporal sampling sets $(\Omega, l_i)$ for convolution operators
+$\mathbf{A}$ with eigenvalues subject to the same largest geometric
+multiplicity. We will give an algebraic characterization for such sampling sets
+and show how this problem is linked with sparse signal processing theory and
+polynomial interpolation theory.
+"
+21366,Self-Organizing Maps Classification with Application to Laptop's Adapters Magnetic Field,"  This paper presents an application of the Self-Organizing-Map classification
+method, which is used for classification of the extremely low frequency
+magnetic field emission in the near neighborhood of the laptop adapters. The
+experiment is performed on different laptop adapters of the same
+characteristics. After that, the Self-Organizing-Map classification on the
+obtained emission data is performed. The classification results establish the
+typical emission levels of the laptop adapters, which are far above the safety
+standards' limit. At the end, a discussion is carried out about the importance
+of using the classification as a possible solution for safely use the laptop
+adapters in order to reduce the negative effects of the magnetic field emission
+to the laptop users.
+"
+21367,Attention Models in Graphs: A Survey,"  Graph-structured data arise naturally in many different application domains.
+By representing data as graphs, we can capture entities (i.e., nodes) as well
+as their relationships (i.e., edges) with each other. Many useful insights can
+be derived from graph-structured data as demonstrated by an ever-growing body
+of work focused on graph mining. However, in the real-world, graphs can be both
+large - with many complex patterns - and noisy which can pose a problem for
+effective graph mining. An effective way to deal with this issue is to
+incorporate ""attention"" into graph mining solutions. An attention mechanism
+allows a method to focus on task-relevant parts of the graph, helping it to
+make better decisions. In this work, we conduct a comprehensive and focused
+survey of the literature on the emerging field of graph attention models. We
+introduce three intuitive taxonomies to group existing work. These are based on
+problem setting (type of input and output), the type of attention mechanism
+used, and the task (e.g., graph classification, link prediction, etc.). We
+motivate our taxonomies through detailed examples and use each to survey
+competing approaches from a unique standpoint. Finally, we highlight several
+challenges in the area and discuss promising directions for future work.
+"
+21368,Multiplexing 200 modes on a single digital hologram,"  The on-demand tailoring of light's spatial shape is of great relevance in a
+wide variety of research areas. Computer-controlled devices, such as Spatial
+Light Modulators (SLMs) or Digital Micromirror Devices (DMDs), offer a very
+accurate, flexible and fast holographic means to this end. Remarkably, digital
+holography affords the simultaneous generation of multiple beams
+(multiplexing), a tool with numerous applications in many fields. Here, we
+provide a self-contained tutorial on light beam multiplexing. Through the use
+of several examples, the readers will be guided step by step in the process of
+light beam shaping and multiplexing. Additionally, on the multiplexing
+capabilities of SLMs to provide a quantitative analysis on the maximum number
+of beams that can be multiplexed on a single SLM, showing approximately 200
+modes on a single hologram.
+"
+21369,Ranking influential spreaders is an ill-defined problem,"  Finding influential spreaders of information and disease in networks is an
+important theoretical problem, and one of considerable recent interest. It has
+been almost exclusively formulated as a node-ranking problem -- methods for
+identifying influential spreaders rank nodes according to how influential they
+are. In this work, we show that the ranking approach does not necessarily work:
+the set of most influential nodes depends on the number of nodes in the set.
+Therefore, the set of $n$ most important nodes to vaccinate does not need to
+have any node in common with the set of $n+1$ most important nodes. We propose
+a method for quantifying the extent and impact of this phenomenon, and show
+that it is common in both empirical and model networks.
+"
+21370,Fixing a Broken ELBO,"  Recent work in unsupervised representation learning has focused on learning
+deep directed latent-variable models. Fitting these models by maximizing the
+marginal likelihood or evidence is typically intractable, thus a common
+approximation is to maximize the evidence lower bound (ELBO) instead. However,
+maximum likelihood training (whether exact or approximate) does not necessarily
+result in a good latent representation, as we demonstrate both theoretically
+and empirically. In particular, we derive variational lower and upper bounds on
+the mutual information between the input and the latent variable, and use these
+bounds to derive a rate-distortion curve that characterizes the tradeoff
+between compression and reconstruction accuracy. Using this framework, we
+demonstrate that there is a family of models with identical ELBO, but different
+quantitative and qualitative characteristics. Our framework also suggests a
+simple new method to ensure that latent variable models with powerful
+stochastic decoders do not ignore their latent code.
+"
+21371,Wavelet Shrinkage and Thresholding based Robust Classification for Brain Computer Interface,"  A macaque monkey is trained to perform two different kinds of tasks, memory
+aided and visually aided. In each task, the monkey saccades to eight possible
+target locations. A classifier is proposed for direction decoding and task
+decoding based on local field potentials (LFP) collected from the prefrontal
+cortex. The LFP time-series data is modeled in a nonparametric regression
+framework, as a function corrupted by Gaussian noise. It is shown that if the
+function belongs to Besov bodies, then using the proposed wavelet shrinkage and
+thresholding based classifier is robust and consistent. The classifier is then
+applied to the LFP data to achieve high decoding performance. The proposed
+classifier is also quite general and can be applied for the classification of
+other types of time-series data as well, not necessarily brain data.
+"
+21372,SCALAR - Simultaneous Calibration of 2D Laser and Robot's Kinematic Parameters Using Three Planar Constraints,"  Industrial robots are increasingly used in various applications where the
+robot accuracy becomes very important, hence calibrations of the robot's
+kinematic parameters and the measurement system's extrinsic parameters are
+required. However, the existing calibration approaches are either too
+cumbersome or require another expensive external measurement system such as
+laser tracker or measurement spinarm. In this paper, we propose SCALAR, a
+calibration method to simultaneously improve the kinematic parameters of a
+6-DoF robot and the extrinsic parameters of a 2D Laser Range Finder (LRF) which
+is attached to the robot. Three flat planes are placed around the robot, and
+for each plane the robot moves to several poses such that the LRF's ray
+intersect the respective plane. Geometric planar constraints are then used to
+optimize the calibration parameters using Levenberg- Marquardt nonlinear
+optimization algorithm. We demonstrate through simulations that SCALAR can
+reduce the average position and orientation errors of the robot system from
+14.6mm and 4.05 degrees to 0.09mm and 0.02 degrees.
+"
+21373,"Twitter adoption, students perceptions, Big Five personality traits and learning outcome: Lessons learned from 3 case studies","  This study presents the results of the introduction of Twitter in the
+educational process. It examines the relationship of the tool s use with the
+participants learning outcome through a series of well-organized activities.
+Three studies were conducted in the context of 2 academic courses. The
+participation in the Twitter activity was voluntarily for the students. In all
+3 studies the students who participated in the process had a higher laboratory
+grade than the students who did not participated. Students Conscientiousness
+and Openness to experience were related to their activity in one study.
+However, no relationship between the students personality traits and their
+grade was unveiled. Moreover, the students interventions in the activities are
+examined as well as the variation in their attitudes towards social media use
+in learning. The implications of the conducted studies are discussed
+extensively and a comparison with other related studies is presented.
+"
+21374,Entropic theory of Gravitation,"  We construct a manifestly Machian theory of gravitation on the foundation
+that information in the universe cannot be destroyed (Landauer's principle). If
+no bit of information in the Universe is lost, than the sum of the entropies of
+the geometric and the matter fields should be conserved. We propose a local
+invariant expression for the entropy of the geometric field and formulate a
+variational principle on the entropic functional which produces entropic field
+equations. This information-theoretic approach implies that the geometric field
+does not exist in an empty of matter Universe, the material entropy is geometry
+dependent, matter can exchange information (entropy) with the geometric field
+and a quantum condensate can channel energy into the geometric field at a
+particular coherent state. The entropic field equations feature a non-intuitive
+direct coupling between the material fields and the geometric field, which acts
+as an entropy reservoir. Cosmological consequences such as the emergence of the
+cosmological constant as well as experimental consequences involving
+gravity-quantum condensate interaction are discussed. The energetic aspect of
+the theory restores the repertoire of the classical General Relativity up to a
+different coupling constant between the fields.
+"
+21375,Understanding International Migration using Tensor Factorization,"  Understanding human migration is of great interest to demographers and social
+scientists. User generated digital data has made it easier to study such
+patterns at a global scale. Geo coded Twitter data, in particular, has been
+shown to be a promising source to analyse large scale human migration. But
+given the scale of these datasets, a lot of manual effort has to be put into
+processing and getting actionable insights from this data.
+In this paper, we explore feasibility of using a new tool, tensor
+decomposition, to understand trends in global human migration. We model human
+migration as a three mode tensor, consisting of (origin country, destination
+country, time of migration) and apply CP decomposition to get meaningful low
+dimensional factors. Our experiments on a large Twitter dataset spanning 5
+years and over 100M tweets show that we can extract meaningful migration
+patterns.
+"
+21376,Fiber-dependent deautonomization of integrable 2D mappings and discrete Painlevé equations,"  It is well known that two-dimensional mappings preserving a rational elliptic
+fibration, like the Quispel-Roberts-Thompson mappings, can be deautonomized to
+discrete Painlevé equations. However, the dependence of this procedure on the
+choice of a particular elliptic fiber has not been sufficiently investigated.
+In this paper we establish a way of performing the deautonomization for a pair
+of an autonomous mapping and a fiber. %By choosing a particular Starting from a
+single autonomous mapping but varying the type of a chosen fiber, we obtain
+different types of discrete Painlevé equations using this deautonomization
+procedure. We also introduce a technique for reconstructing a mapping from the
+knowledge of its induced action on the Picard group and some additional
+geometric data. This technique allows us to obtain factorized expressions of
+discrete Painlevé equations, including the elliptic case. Further, by
+imposing certain restrictions on such non-autonomous mappings we obtain new and
+simple elliptic difference Painlevé equations, including examples whose
+symmetry groups do not appear explicitly in Sakai's classification.
+"
+21377,ZnO and ZnO$_{1-x}$ based thin film memristors: The effects of oxygen deficiency and thickness in resistive switching behavior,"  In this study, direct-current reactive sputtered ZnO and ZnO1-x based thin
+film (30 nm and 300 nm in thickness) memristor devices were produced and the
+effects of oxygen vacancies and thickness on the memristive characteristics
+were investigated. The oxygen deficiency of the ZnO1-x structure was confirmed
+by SIMS analyses. The memristive characteristics of both the ZnO and ZnO1-x
+devices were determined by time dependent current-voltage (I-V-t) measurements.
+The distinctive pinched hysteresis I-V loops of memristors were observed in all
+the fabricated devices. The typical homogeneous interface and filamentary types
+of memristive behaviors were compared. In addition, conduction mechanisms,
+on/off ratios and the compliance current were analyzed. The 30 nm ZnO based
+devices with native oxygen vacancies showed the best on/off ratio. All of the
+devices exhibited dominant Schottky emissions and weaker Poole-Frenkel
+conduction mechanisms. Results suggested that the oxygen deficiency was
+responsible for the Schottky emission mechanism. Moreover, the compliance
+currents of the devices were related to the decreasing power consumption as the
+oxygen vacancies increased.
+"
+21378,Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning,"  The UN Sustainable Development Goals allude to the importance of
+infrastructure quality in three of its seventeen goals. However, monitoring
+infrastructure quality in developing regions remains prohibitively expensive
+and impedes efforts to measure progress toward these goals. To this end, we
+investigate the use of widely available remote sensing data for the prediction
+of infrastructure quality in Africa. We train a convolutional neural network to
+predict ground truth labels from the Afrobarometer Round 6 survey using Landsat
+8 and Sentinel 1 satellite imagery.
+Our best models predict infrastructure quality with AUROC scores of 0.881 on
+Electricity, 0.862 on Sewerage, 0.739 on Piped Water, and 0.786 on Roads using
+Landsat 8. These performances are significantly better than models that
+leverage OpenStreetMap or nighttime light intensity on the same tasks. We also
+demonstrate that our trained model can accurately make predictions in an unseen
+country after fine-tuning on a small sample of images. Furthermore, the model
+can be deployed in regions with limited samples to predict infrastructure
+outcomes with higher performance than nearest neighbor spatial interpolation.
+"
+21379,Fréchet Means and Procrustes Analysis in Wasserstein Space,"  We consider two statistical problems at the intersection of functional and
+non-Euclidean data analysis: the determination of a Fréchet mean in the
+Wasserstein space of multivariate distributions; and the optimal registration
+of deformed random measures and point processes. We elucidate how the two
+problems are linked, each being in a sense dual to the other. We first study
+the finite sample version of the problem in the continuum. Exploiting the
+tangent bundle structure of Wasserstein space, we deduce the Fréchet mean via
+gradient descent. We show that this is equivalent to a Procrustes analysis for
+the registration maps, thus only requiring successive solutions to pairwise
+optimal coupling problems. We then study the population version of the problem,
+focussing on inference and stability: in practice, the data are i.i.d.
+realisations from a law on Wasserstein space, and indeed their observation is
+discrete, where one observes a proxy finite sample or point process. We
+construct regularised nonparametric estimators, and prove their consistency for
+the population mean, and uniform consistency for the population Procrustes
+registration maps.
+"
+21380,Sliding-Window Superposition Coding:Two-User Interference Channels,"  A low-complexity coding scheme is developed to achieve the rate region of
+maximum likelihood decoding for interference channels. As in the classical
+rate-splitting multiple access scheme by Grant, Rimoldi, Urbanke, and Whiting,
+the proposed coding scheme uses superposition of multiple codewords with
+successive cancellation decoding, which can be implemented using standard
+point-to-point encoders and decoders. Unlike rate-splitting multiple access,
+which is not rate-optimal for multiple receivers, the proposed coding scheme
+transmits codewords over multiple blocks in a staggered manner and recovers
+them successively over sliding decoding windows, achieving the single-stream
+optimal rate region as well as the more general Han--Kobayashi inner bound for
+the two-user interference channel. The feasibility of this scheme in practice
+is verified by implementing it using commercial channel codes over the two-user
+Gaussian interference channel.
+"
+21381,Introspective Generative Modeling: Decide Discriminatively,"  We study unsupervised learning by developing introspective generative
+modeling (IGM) that attains a generator using progressively learned deep
+convolutional neural networks. The generator is itself a discriminator, capable
+of introspection: being able to self-evaluate the difference between its
+generated samples and the given training data. When followed by repeated
+discriminative learning, desirable properties of modern discriminative
+classifiers are directly inherited by the generator. IGM learns a cascade of
+CNN classifiers using a synthesis-by-classification algorithm. In the
+experiments, we observe encouraging results on a number of applications
+including texture modeling, artistic style transferring, face modeling, and
+semi-supervised learning.
+"
+21382,Counting submodules of a module over a noetherian commutative ring,"  We count the number of submodules of an arbitrary module over a countable
+noetherian commutative ring. We give, along the way, a structural description
+of meager modules, which are defined as those that do not have the square of a
+simple module as subquotient, and deduce in particular a characterization of
+uniserial modules over commutative noetherian rings.
+"
+21383,Modelling the Influence of Cultural Information on Vision-Based Human Home Activity Recognition,"  Daily life activities, such as eating and sleeping, are deeply influenced by
+a person's culture, hence generating differences in the way a same activity is
+performed by individuals belonging to different cultures. We argue that taking
+cultural information into account can improve the performance of systems for
+the automated recognition of human activities. We propose four different
+solutions to the problem and present a system which uses a Naive Bayes model to
+associate cultural information with semantic information extracted from still
+images. Preliminary experiments with a dataset of images of individuals lying
+on the floor, sleeping on a futon and sleeping on a bed suggest that: i)
+solutions explicitly taking cultural information into account are more accurate
+than culture-unaware solutions; and ii) the proposed system is a promising
+starting point for the development of culture-aware Human Activity Recognition
+methods.
+"
+21384,Image classification using local tensor singular value decompositions,"  From linear classifiers to neural networks, image classification has been a
+widely explored topic in mathematics, and many algorithms have proven to be
+effective classifiers. However, the most accurate classifiers typically have
+significantly high storage costs, or require complicated procedures that may be
+computationally expensive. We present a novel (nonlinear) classification
+approach using truncation of local tensor singular value decompositions (tSVD)
+that robustly offers accurate results, while maintaining manageable storage
+costs. Our approach takes advantage of the optimality of the representation
+under the tensor algebra described to determine to which class an image
+belongs. We extend our approach to a method that can determine specific
+pairwise match scores, which could be useful in, for example, object
+recognition problems where pose/position are different. We demonstrate the
+promise of our new techniques on the MNIST data set.
+"
+21385,Short term unpredictability of high Reynolds number turbulence --- rough dependence on initial data,"  Short term unpredictability is discovered numerically for high Reynolds
+number fluid flows under periodic boundary conditions. Furthermore, the
+abundance of the short term unpredictability is also discovered. These
+discoveries support our theory that fully developed turbulence is constantly
+driven by such short term unpredictability.
+"
+21386,A Three-Dimensional Mathematical Model of Collagen Contraction,"  In this paper, we introduce a three-dimensional mathematical model of
+collagen contraction with microbuckling based on the two-dimensional model
+previously developed by the authors. The model both qualitatively and
+quantitatively replicates experimental data including lattice contraction over
+a time course of 40 hours for lattices with various cell densities, cell
+density profiles within contracted lattices, radial cut angles in lattices, and
+cell force propagation within a lattice. The importance of the model lattice
+formation and the crucial nature of its connectivity are discussed including
+differences with models which do not include microbuckling. The model suggests
+that most cells within contracting lattices are engaged in directed motion.
+"
+21387,"Freeze Casting: A Review of Processing, Microstructure and Properties via the Open Data Repository, FreezeCasting.net","  Freeze-casting produces materials with complex, three-dimensional pore
+structures which may be tuned during the solidification process. The range of
+potential applications of freeze-cast materials is vast, and includes:
+structural materials, biomaterials, filtration membranes, pharmaceuticals, and
+foodstuffs. Fabrication of materials with application-specific microstructures
+is possible via freeze casting, however, the templating process is highly
+complex and the underlying principles are only partially understood. Here, we
+report the creation of a freeze-casting experimental data repository, which
+contains data extracted from ~800 different freeze-casting papers (as of August
+2017). These data pertain to variables that link processing conditions to
+microstructural characteristics, and finally, mechanical properties. The aim of
+this work is to facilitate broad dissemination of relevant data to
+freeze-casting researchers, promote better informed experimental design, and
+encourage modeling efforts that relate processing conditions to microstructure
+formation and material properties. An initial, systematic analysis of these
+data is provided and key processing-structure-property relationships posited in
+the freeze-casting literature are discussed and tested against the database.
+Tools for data visualization and exploration available through the web
+interface are also provided.
+"
+21388,On existence and approximation of solution of nonlinear Hilfer fractional differential equation,"  This paper gives the existence and uniqueness results for solution of
+fractional differential equations with Hilfer derivative. Using some new
+techniques and generalizing the restrictive conditions imposed on considered
+function, the iterative scheme for uniformly approximating the solution is
+established.
+"
+21389,The congruence subgroup problem for a family of branch groups,"  We construct a family of groups which generalize the Hanoi towers group and
+study the congruence subgroup problem for the groups in this family. We show
+that unlike the Hanoi towers group, the groups in this generalization are just
+infinite and have trivial rigid kernel. We also put strict bounds on the branch
+kernel. Additionally, we show that these groups have subgroups of finite index
+with non-trivial rigid kernel. The only previously known group where this
+kernel is non-trivial is the Hanoi towers group and so this adds infinitely
+many new examples. Finally, we show that the topological closures of these
+groups have Hausdorff dimension arbitrarily close to 1.
+"
+21390,Character formulae in category $\mathcal O$ for exceptional Lie superalgebras $D(2|1;ζ)$,"  We establish character formulae for representations of the one-parameter
+family of simple Lie superalgebras $D(2|1;\zeta)$. We provide a complete
+description of the Verma flag multiplicities of the tilting modules and the
+projective modules in the BGG category $\mathcal O$ of $D(2|1;\zeta)$-modules
+of integral weights, for any complex parameter $\zeta$. The composition factors
+of all Verma modules in $\mathcal O$ are then obtained.
+"
+21391,The M33 Synoptic Stellar Survey. II. Mira Variables,"  We present the discovery of 1847 Mira candidates in the Local Group galaxy
+M33 using a novel semi-parametric periodogram technique coupled with a Random
+Forest classifier. The algorithms were applied to ~2.4x10^5 I-band light curves
+previously obtained by the M33 Synoptic Stellar Survey. We derive preliminary
+Period-Luminosity relations at optical, near- & mid-infrared wavelengths and
+compare them to the corresponding relations in the Large Magellanic Cloud.
+"
+21392,Photometry of the long period dwarf nova GY Hya,"  Although comparatively bright, the cataclysmic variable GY Hya has not
+attracted much attention in the past. As part of a project to better
+characterize such systems photometrically, we observed light curves in white
+light, each spanning several hours, at Bronberg Observatory, South Africa, in
+2004 and 2005, and at the Observatório do Pico dos Dias, Brazil, in 2014 and
+2016. These data permit to study orbital modulations and their variations from
+season to season. The orbital period, already known from spectroscopic
+observations of Peters & Thorstensen (2005), is confirmed through strong
+ellipsoidal variations of the mass donor star in the system and the presence of
+eclipses of both components. A refined period of 0.34723972~(6) days and
+revised ephemeris are derived. Seasonal changes in the average orbital light
+curve can qualitatively be explained by variations of the contribution of a hot
+spot to the system light together with changes of the disk radius. The
+amplitude of the ellipsoidal variations and the eclipse contact phases permit
+to put some constraints on the mass ratio, orbital inclination and the relative
+brightness of the primary and secondary components. There are some indications
+that the disk radius during quiescence, expressed in units of the component
+separation, is smaller than in other dwarf novae.
+"
+21393,Network Structure Explains the Impact of Attitudes on Voting Decisions,"  Attitudes can have a profound impact on socially relevant behaviours, such as
+voting. However, this effect is not uniform across situations or individuals,
+and it is at present difficult to predict whether attitudes will predict
+behaviour in any given circumstance. Using a network model, we demonstrate that
+(a) more strongly connected attitude networks have a stronger impact on
+behaviour, and (b) within any given attitude network, the most central attitude
+elements have the strongest impact. We test these hypotheses using data on
+voting and attitudes toward presidential candidates in the US presidential
+elections from 1980 to 2012. These analyses confirm that the predictive value
+of attitude networks depends almost entirely on their level of connectivity,
+with more central attitude elements having stronger impact. The impact of
+attitudes on voting behaviour can thus be reliably determined before elections
+take place by using network analyses.
+"
+21394,Causal inference for social network data,"  We extend recent work by van der Laan (2014) on causal inference for causally
+connected units to more general social network settings. Our asymptotic results
+allow for dependence of each observation on a growing number of other units as
+sample size increases. We are not aware of any previous methods for inference
+about network members in observational settings that allow the number of ties
+per node to increase as the network grows. While previous methods have
+generally implicitly focused on one of two possible sources of dependence among
+social network observations, we allow for both dependence due to contagion, or
+transmission of information across network ties, and for dependence due to
+latent similarities among nodes sharing ties. We describe estimation and
+inference for causal effects that are specifically of interest in social
+network settings.
+"
+21395,Differential equations and the algebra of confluent spherical functions on semisimple Lie groups,"  We consider the notion of a confluent spherical function on a connected
+semisimple Lie group, $G,$ with finite center and of real rank $1,$ and discuss
+the properties and relationship of its algebra with the well-known Schwartz
+algebra of spherical functions on $G.$
+"
+21396,AMORPH: A statistical program for characterizing amorphous materials by X-ray diffraction,"  AMORPH utilizes a new Bayesian statistical approach to interpreting X-ray
+diffraction results of samples with both crystalline and amorphous components.
+AMORPH fits X-ray diffraction patterns with a mixture of narrow and wide
+components, simultaneously inferring all of the model parameters and
+quantifying their uncertainties. The program simulates background patterns
+previously applied manually, providing reproducible results, and significantly
+reducing inter- and intra-user biases. This approach allows for the
+quantification of amorphous and crystalline materials and for the
+characterization of the amorphous component, including properties such as the
+centre of mass, width, skewness, and nongaussianity of the amorphous component.
+Results demonstrate the applicability of this program for calculating amorphous
+contents of volcanic materials and independently modeling their properties in
+compositionally variable materials.
+"
+21397,Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition,"  Unsupervised domain adaptation of speech signal aims at adapting a
+well-trained source-domain acoustic model to the unlabeled data from target
+domain. This can be achieved by adversarial training of deep neural network
+(DNN) acoustic models to learn an intermediate deep representation that is both
+senone-discriminative and domain-invariant. Specifically, the DNN is trained to
+jointly optimize the primary task of senone classification and the secondary
+task of domain classification with adversarial objective functions. In this
+work, instead of only focusing on learning a domain-invariant feature (i.e. the
+shared component between domains), we also characterize the difference between
+the source and target domain distributions by explicitly modeling the private
+component of each domain through a private component extractor DNN. The private
+component is trained to be orthogonal with the shared component and thus
+implicitly increases the degree of domain-invariance of the shared component. A
+reconstructor DNN is used to reconstruct the original speech feature from the
+private and shared components as a regularization. This domain separation
+framework is applied to the unsupervised environment adaptation task and
+achieved 11.08% relative WER reduction from the gradient reversal layer
+training, a representative adversarial training method, for automatic speech
+recognition on CHiME-3 dataset.
+"
+21398,Robust Power System Dynamic State Estimator with Non-Gaussian Measurement Noise: Part I--Theory,"  This paper develops the theoretical framework and the equations of a new
+robust Generalized Maximum-likelihood-type Unscented Kalman Filter (GM-UKF)
+that is able to suppress observation and innovation outliers while filtering
+out non-Gaussian measurement noise. Because the errors of the real and reactive
+power measurements calculated using Phasor Measurement Units (PMUs) follow
+long-tailed probability distributions, the conventional UKF provides strongly
+biased state estimates since it relies on the weighted least squares estimator.
+By contrast, the state estimates and residuals of our GM-UKF are proved to be
+roughly Gaussian, allowing the sigma points to reliably approximate the mean
+and the covariance matrices of the predicted and corrected state vectors. To
+develop our GM-UKF, we first derive a batch-mode regression form by processing
+the predictions and observations simultaneously, where the statistical
+linearization approach is used. We show that the set of equations so derived
+are equivalent to those of the unscented transformation. Then, a robust
+GM-estimator that minimizes a convex Huber cost function while using weights
+calculated via Projection Statistics (PS's) is proposed. The PS's are applied
+to a two-dimensional matrix that consists of serially correlated predicted
+state and innovation vectors to detect observation and innovation outliers.
+These outliers are suppressed by the GM-estimator using the iteratively
+reweighted least squares algorithm. Finally, the asymptotic error covariance
+matrix of the GM-UKF state estimates is derived from the total influence
+function. In the companion paper, extensive simulation results will be shown to
+verify the effectiveness and robustness of the proposed method.
+"
+21399,Equivariant Algebraic Index Theorem,"  We prove a {\Gamma}-equivariant version of the algebraic index theorem, where
+{\Gamma} is a discrete group of automorphisms of a formal deformation of a
+symplectic manifold. The particular cases of this result are the algebraic
+version of the transversal index theorem related to the theorem of A. Connes
+and H. Moscovici for hypoelliptic operators and the index theorem for the
+extension of the algebra of pseudodifferential operators by a group of
+diffeomorphisms of the underlying manifold due to A. Savin, B. Sternin, E.
+Schrohe and D. Perrot.
+"
+21400,Partially-averaged Navier-Stokes (PANS) Method for Turbulence Simulations: Near-wall Modeling and Smooth-surface Separation Computations,"  The goal of this dissertation is to investigate the PANS model capabilities
+in providing significant improvement over RANS predictions at slightly higher
+computational expense and producing LES quality results at significantly lower
+computational cost. The objectives of this study are: (i) investigate the model
+fidelity at a fixed level of scale resolution (Generation1-PANS/G1-PANS) for
+smooth surface separation, (ii) Derive the PANS closure model in regions of
+resolution variation (Generation2-PANS/G2-PANS), and (iii) Validate G2-PANS
+model for attached and separated flows. The separated flows considered in this
+study have been designated as critical benchmark flows by NASA CFD study group.
+The key contributions of this dissertation are summarized as follows. The
+turbulence closure model of varying resolution, G2-PANS, is developed by
+deriving mathematically-consistent commutation residues and using energy
+conservation principles. The log-layer recovery and accurate computation of
+Reynolds stress anisotropy is accomplished by transitioning from steady RANS to
+scaled resolved simulations using the G2-PANS model. Finally, several
+smooth-separation flows on the NASA turbulence website have been computed with
+high degree of accuracy at a significantly reduced computational effort over
+LES using the G1-PANS and G2-PANS models.
+"
+21401,Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the $O(1/T)$ Convergence Rate,"  Stochastic approximation (SA) is a classical approach for stochastic convex
+optimization. Previous studies have demonstrated that the convergence rate of
+SA can be improved by introducing either smoothness or strong convexity
+condition. In this paper, we make use of smoothness and strong convexity
+simultaneously to boost the convergence rate. Let $\lambda$ be the modulus of
+strong convexity, $\kappa$ be the condition number, $F_*$ be the minimal risk,
+and $\alpha>1$ be some small constant. First, we demonstrate that, in
+expectation, an $O(1/[\lambda T^\alpha] + \kappa F_*/T)$ risk bound is
+attainable when $T = \Omega(\kappa^\alpha)$. Thus, when $F_*$ is small, the
+convergence rate could be faster than $O(1/[\lambda T])$ and approaches
+$O(1/[\lambda T^\alpha])$ in the ideal case. Second, to further benefit from
+small risk, we show that, in expectation, an $O(1/2^{T/\kappa}+F_*)$ risk bound
+is achievable. Thus, the excess risk reduces exponentially until reaching
+$O(F_*)$, and if $F_*=0$, we obtain a global linear convergence. Finally, we
+emphasize that our proof is constructive and each risk bound is equipped with
+an efficient stochastic algorithm attaining that bound.
+"
+21402,A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure,"  We often seek to estimate the impact of an exposure naturally occurring or
+randomly assigned at the cluster-level. For example, the literature on
+neighborhood determinants of health continues to grow. Likewise, community
+randomized trials are applied to learn about real-world implementation,
+sustainability, and population effects of interventions with proven
+individual-level efficacy. In these settings, individual-level outcomes are
+correlated due to shared cluster-level factors, including the exposure, as well
+as social or biological interactions between individuals. To flexibly and
+efficiently estimate the effect of a cluster-level exposure, we present two
+targeted maximum likelihood estimators (TMLEs). The first TMLE is developed
+under a non-parametric causal model, which allows for arbitrary interactions
+between individuals within a cluster. These interactions include direct
+transmission of the outcome (i.e. contagion) and influence of one individual's
+covariates on another's outcome (i.e. covariate interference). The second TMLE
+is developed under a causal sub-model assuming the cluster-level and
+individual-specific covariates are sufficient to control for confounding.
+Simulations compare the alternative estimators and illustrate the potential
+gains from pairing individual-level risk factors and outcomes during
+estimation, while avoiding unwarranted assumptions. Our results suggest that
+estimation under the sub-model can result in bias and misleading inference in
+an observational setting. Incorporating working assumptions during estimation
+is more robust than assuming they hold in the underlying causal model. We
+illustrate our approach with an application to HIV prevention and treatment.
+"
+21403,Excitation spectrum and Density Matrix Renormalization Group iterations,"  We show that, in certain circumstances, exact excitation energies appear as
+locally site-independent (or flat) modes if one records the excitation spectrum
+of the effective Hamiltonian while sweeping through the lattice in the
+variational Matrix Product State formulation of the Density Matrix
+Renormalization Group (DMRG), a remarkable property since the effective
+Hamiltonian is only constructed to target the ground state. Conversely, modes
+that are very flat over several consecutive iterations are systematically found
+to correspond to faithful excitations. We suggest to use this property to
+extract accurate information about excited states using the standard ground
+state algorithm. The results are spectacular for critical systems, for which
+the low-energy conformal tower of states can be obtained very accurately at
+essentially no additional cost, as demonstrated by confirming the predictions
+of boundary conformal field theory for two simple minimal models - the
+transverse-field Ising model and the critical three-state Potts model. This
+approach is also very efficient to detect the quasi-degenerate low-energy
+excitations in topological phases, and to identify localized excitations in
+systems with impurities. Finally, using the variance of the Hamiltonian as a
+criterion, we assess the accuracy of the resulting Matrix Product State
+representations of the excited states.
+"
+21404,Minimax estimation in linear models with unknown finite alphabet design,"  We provide minimax theory for joint estimation of $F$ and $\omega$ in linear
+models $Y = F \omega + Z$ where the parameter matrix $\omega$ and the design
+matrix $F$ are unknown but the latter takes values in a known finite set. We
+show that this allows to separate $F$ and $\omega$ uniquely under weak
+identifiability conditions, a task which is not doable, in general. These
+assumptions are justified in a variety of applications, ranging from signal
+processing to cancer genetics. We then obtain in the noiseless case, that is,
+$Z = 0$, stable recovery of $F$ and $\omega$ in a neighborhood of $Y$. Based on
+this, we show for Gaussian error matrix $Z$ that the LSE attains minimax rates
+for both, prediction error of $F \omega$ and estimation error of $F$ and
+$\omega$, separately. Due to the finite alphabet, estimation of $F$ amounts to
+a classification problem, where we show that the classification error
+$P(\hat{F} \neq F)$ decreases exponentially in the dimension of one component
+of $Y$.
+"
+21405,Rooted trees with the same plucking polynomial,"  In this paper we give a sufficient and necessary condition for two rooted
+trees with the same plucking polynomial. Furthermore, we give a criteria for a
+sequence of non-negative integers to be realized as a rooted tree.
+"
+21406,CityPersons: A Diverse Dataset for Pedestrian Detection,"  Convnets have enabled significant progress in pedestrian detection recently,
+but there are still open questions regarding suitable architectures and
+training data. We revisit CNN design and point out key adaptations, enabling
+plain FasterRCNN to obtain state-of-the-art results on the Caltech dataset.
+To achieve further improvement from more and better data, we introduce
+CityPersons, a new set of person annotations on top of the Cityscapes dataset.
+The diversity of CityPersons allows us for the first time to train one single
+CNN model that generalizes well over multiple benchmarks. Moreover, with
+additional training with CityPersons, we obtain top results using FasterRCNN on
+Caltech, improving especially for more difficult cases (heavy occlusion and
+small scale) and providing higher localization quality.
+"
+21407,The Better Half of Selling Separately,"  Separate selling of two independent goods is shown to yield at least 62% of
+the optimal revenue, and at least 73% when the goods satisfy the Myerson
+regularity condition. This improves the 50% result of Hart and Nisan (2017,
+originally circulated in 2012).
+"
+21408,Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields,"  Generative adversarial networks (GANs) evolved into one of the most
+successful unsupervised techniques for generating realistic images. Even though
+it has recently been shown that GAN training converges, GAN models often end up
+in local Nash equilibria that are associated with mode collapse or otherwise
+fail to model the target distribution. We introduce Coulomb GANs, which pose
+the GAN learning problem as a potential field of charged particles, where
+generated samples are attracted to training set samples but repel each other.
+The discriminator learns a potential field while the generator decreases the
+energy by moving its samples along the vector (force) field determined by the
+gradient of the potential field. Through decreasing the energy, the GAN model
+learns to generate samples according to the whole target distribution and does
+not only cover some of its modes. We prove that Coulomb GANs possess only one
+Nash equilibrium which is optimal in the sense that the model distribution
+equals the target distribution. We show the efficacy of Coulomb GANs on a
+variety of image datasets. On LSUN and celebA, Coulomb GANs set a new state of
+the art and produce a previously unseen variety of different samples.
+"
+21409,Involution on pseudoisotopy spaces and the space of the nonnegatively curved metrics,"  We prove that certain involutions defined by Vogell and Burghelea-Fiedorowicz
+on the rational algebraic K-theory of spaces coincide. This gives a way to
+compute the positive and negative eigenspaces of the involution on rational
+homotopy groups of pseudoisotopy spaces from the involution on rational
+$S^{1}$--homology group of the free loop space of a simply-connected manifold.
+As an application, we give explicit dimensions of the open manifolds $V$ that
+appear in Belegradek-Farrell-Kapovitch's work for which the spaces of complete
+nonnegatively curved metrics on $V$ have nontrivial rational homotopy groups.
+"
+21410,A class of semisimple Hopf algebras acting on quantum polynomial algebras,"  We construct a class of non-commutative, non-cocommutative, semisimple Hopf
+algebras of dimension $2n^2$ and present conditions to define an inner faithful
+action of these Hopf algebras on quantum polynomial algebras, providing, in
+this way, more examples of semisimple Hopf actions which do not factor through
+group actions. Also, under certain condition, we classify the inner faithful
+Hopf actions of the Kac-Paljutkin Hopf algebra of dimension $8$, $H_8$, on the
+quantum plane.
+"
+21411,Fusion systems of blocks with nontrivial strongly closed subgroups,"  In this paper, we find some exotic fusion systems which have non-trivial
+strongly closed subgroups, and we prove these fusion systems are also not
+realizable by p-blocks of finite groups.
+"
+21412,Nonequilibrium steady states and transient dynamics of conventional superconductors under phonon driving,"  We perform a systematic analysis of the influence of phonon driving on the
+superconducting Holstein model coupled to heat baths by studying both the
+transient dynamics and the nonequilibrium steady state (NESS) in the weak and
+strong electron-phonon coupling regimes. Our study is based on the
+nonequilibrium dynamical mean-field theory, and for the NESS we present a
+Floquet formulation adapted to electron-phonon systems. The analysis of the
+phonon propagator suggests that the effective attractive interaction can be
+strongly enhanced in a parametric resonant regime because of the Floquet side
+bands of phonons. While this may be expected to enhance the superconductivity
+(SC), our fully self-consistent calculations, which include the effects of
+heating and nonthermal distributions, show that the parametric phonon driving
+generically results in a suppression or complete melting of the SC order. In
+the strong coupling regime, the NESS always shows a suppression of the SC gap,
+the SC order parameter and the superfluid density as a result of the driving,
+and this tendency is most prominent at the parametric resonance. Using the
+real-time nonequilibrium DMFT formalism, we also study the dynamics towards the
+NESS, which shows that the heating effect dominates the transient dynamics, and
+SC is weakened by the external modulations, in particular at the parametric
+resonance. In the weak coupling regime, we find that the SC fluctuations above
+the transition temperature are generally weakened under the driving. The
+strongest suppression occurs again around the parametric resonances because of
+the efficient energy absorption.
+"
+21413,Optimal Portfolio in Intraday Electricity Markets Modelled by Lévy-Ornstein-Uhlenbeck Processes,"  We study an optimal portfolio problem designed for an agent operating in
+intraday electricity markets. The investor is allowed to trade in a single
+risky asset modelling the continuously traded power and aims to maximize the
+expected terminal utility of his wealth. We assume a mean-reverting additive
+process to drive the power prices. In the case of logarithmic utility, we
+reduce the fully non-linear Hamilton-Jacobi-Bellman equation to a linear
+parabolic integro-differential equation, for which we explicitly exhibit a
+classical solution in two cases of modelling interest. The optimal strategy is
+given implicitly as the solution of an integral equation, which is possible to
+solve numerically as well as to describe analytically. An analysis of two
+different approximations for the optimal policy is provided. Finally, we
+perform a numerical test by adapting the parameters of a popular electricity
+spot price model.
+"
+21414,Topological phase transitions in small mesoscopic chiral p-wave superconductors,"  Spin-triplet chiral p-wave superconductivity is typically described by a
+two-component order parameter, and as such is prone to unique emergent effects
+when compared to the standard single-component superconductors. Here we present
+the equilibrium phase diagram for small mesoscopic chiral p-wave
+superconducting disks in the presence of magnetic field, obtained by solving
+the microscopic Bogoliubov-de Gennes equations self-consistently. In the
+ultra-small limit, the cylindrically-symmetric giant-vortex states are the
+ground state of the system. However, with increasing sample size, the
+cylindrical symmetry is broken as the two components of the order parameter
+segregate into domains, and the number of fragmented domain walls between them
+characterizes the resulting states. Such domain walls are topological defects
+unique for the p-wave order, and constitute a dominant phase in the mesoscopic
+regime. Moreover, we find two possible types of domain walls, identified by
+their chirality-dependent interaction with the edge states.
+"
+21415,Squeezing on momentum states for atom interferometry,"  We propose and analyse a method that allows for the production of squeezed
+states of the atomic center-of-mass motion that can be injected into an atom
+interferometer. Our scheme employs dispersive probing in a ring resonator on a
+narrow transition of strontium atoms in order to provide a collective
+measurement of the relative population of two momentum states. We show that
+this method is applicable to a Bragg diffraction-based atom interferometer with
+large diffraction orders. The applicability of this technique can be extended
+also to small diffraction orders and large atom numbers by inducing atomic
+transparency at the frequency of the probe field, reaching an interferometer
+phase resolution scaling $\Delta\phi\sim N^{-3/4}$, where $N$ is the atom
+number. We show that for realistic parameters it is possible to obtain a 20 dB
+gain in interferometer phase estimation compared to the Standard Quantum Limit.
+"
+21416,Synthesis of Near-regular Natural Textures,"  Texture synthesis is widely used in the field of computer graphics, vision,
+and image processing. In the present paper, a texture synthesis algorithm is
+proposed for near-regular natural textures with the help of a representative
+periodic pattern extracted from the input textures using distance matching
+function. Local texture statistics is then analyzed against global texture
+statistics for non-overlapping windows of size same as periodic pattern size
+and a representative periodic pattern is extracted from the image and used for
+texture synthesis, while preserving the global regularity and visual
+appearance. Validation of the algorithm based on experiments with synthetic
+textures whose periodic pattern sizes are known and containing camouflages /
+defects proves the strength of the algorithm for texture synthesis and its
+application in detection of camouflages / defects in textures.
+"
+21417,The Uncertainty Bellman Equation and Exploration,"  We consider the exploration/exploitation problem in reinforcement learning.
+For exploitation, it is well known that the Bellman equation connects the value
+at any time-step to the expected value at subsequent time-steps. In this paper
+we consider a similar \textit{uncertainty} Bellman equation (UBE), which
+connects the uncertainty at any time-step to the expected uncertainties at
+subsequent time-steps, thereby extending the potential exploratory benefit of a
+policy beyond individual time-steps. We prove that the unique fixed point of
+the UBE yields an upper bound on the variance of the posterior distribution of
+the Q-values induced by any policy. This bound can be much tighter than
+traditional count-based bonuses that compound standard deviation rather than
+variance. Importantly, and unlike several existing approaches to optimism, this
+method scales naturally to large systems with complex generalization.
+Substituting our UBE-exploration strategy for $\epsilon$-greedy improves DQN
+performance on 51 out of 57 games in the Atari suite.
+"
+21418,Syntax-Preserving Belief Change Operators for Logic Programs,"  Recent methods have adapted the well-established AGM and belief base
+frameworks for belief change to cover belief revision in logic programs. In
+this study here, we present two new sets of belief change operators for logic
+programs. They focus on preserving the explicit relationships expressed in the
+rules of a program, a feature that is missing in purely semantic approaches
+that consider programs only in their entirety. In particular, operators of the
+latter class fail to satisfy preservation and support, two important properties
+for belief change in logic programs required to ensure intuitive results.
+We address this shortcoming of existing approaches by introducing partial
+meet and ensconcement constructions for logic program belief change, which
+allow us to define syntax-preserving operators that satisfy preservation and
+support. Our work is novel in that our constructions not only preserve more
+information from a logic program during a change operation than existing ones,
+but they also facilitate natural definitions of contraction operators, the
+first in the field to the best of our knowledge.
+In order to evaluate the rationality of our operators, we translate the
+revision and contraction postulates from the AGM and belief base frameworks to
+the logic programming setting. We show that our operators fully comply with the
+belief base framework and formally state the interdefinability between our
+operators. We further propose an algorithm that is based on modularising a
+logic program to reduce partial meet and ensconcement revisions or contractions
+to performing the operation only on the relevant modules of that program.
+Finally, we compare our approach to two state-of-the-art logic program revision
+methods and demonstrate that our operators address the shortcomings of one and
+generalise the other method.
+"
+21419,Mapping degrees between spherical $3$-manifolds,"  Let $D(M,N)$ be the set of integers that can be realized as the degree of a
+map between two closed connected orientable manifolds $M$ and $N$ of the same
+dimension. For closed $3$-manifolds with $S^3$-geometry $M$ and $N$, every such
+degree $deg f\equiv \overline{deg}\psi$ $(|\pi_1(N)|)$ where $0\le
+\overline{deg}\psi <|\pi_1(N)|$ and $\overline{deg}\psi$ only depends on the
+induced homomorphism $\psi=f_{\pi}$ on the fundamental group. In this paper, we
+calculate explicitly the set $\{\overline{deg}\psi\}$ when $\psi$ is surjective
+and then we show how to determine $\overline{deg}(\psi)$ for arbitrary
+homomorphisms. This leads to the determination of the set $D(M,N)$.
+"
+21420,Estimating graph parameters with random walks,"  An algorithm observes the trajectories of random walks over an unknown graph
+$G$, starting from the same vertex $x$, as well as the degrees along the
+trajectories. For all finite connected graphs, one can estimate the number of
+edges $m$ up to a bounded factor in
+$O\left(t_{\mathrm{rel}}^{3/4}\sqrt{m/d}\right)$ steps, where
+$t_{\mathrm{rel}}$ is the relaxation time of the lazy random walk on $G$ and
+$d$ is the minimum degree in $G$. Alternatively, $m$ can be estimated in
+$O\left(t_{\mathrm{unif}} +t_{\mathrm{rel}}^{5/6}\sqrt{n}\right)$, where $n$ is
+the number of vertices and $t_{\mathrm{unif}}$ is the uniform mixing time on
+$G$. The number of vertices $n$ can then be estimated up to a bounded factor in
+an additional $O\left(t_{\mathrm{unif}}\frac{m}{n}\right)$ steps. Our
+algorithms are based on counting the number of intersections of random walk
+paths $X,Y$, i.e. the number of pairs $(t,s)$ such that $X_t=Y_s$. This
+improves on previous estimates which only consider collisions (i.e., times $t$
+with $X_t=Y_t$). We also show that the complexity of our algorithms is optimal,
+even when restricting to graphs with a prescribed relaxation time. Finally, we
+show that, given either $m$ or the mixing time of $G$, we can compute the
+""other parameter"" with a self-stopping algorithm.
+"
+21421,RoI-based Robotic Grasp Detection in Object Overlapping Scenes Using Convolutional Neural Network,"  Grasp detection is an essential skill for widespread use of robots. Recent
+works demonstrate the advanced performance of Convolutional Neural Network
+(CNN) on robotic grasp detection. However, a significant shortcoming of
+existing grasp detection algorithms is that they all ignore the affiliation
+between grasps and targets. In this paper, we propose a robotic grasp detection
+algorithm based on Region of Interest (RoI) to simultaneously detect targets
+and their grasps in object overlapping scenes. Our proposed algorithm uses
+Regions of Interest (RoIs) to detect grasps while doing classification and
+location regression of targets. To train the network, we contribute a much
+bigger multi-object grasp dataset than Cornell Grasp Dataset, which is based on
+Visual Manipulation Relationship Dataset. Experimental results demonstrate that
+our algorithm achieves 24.9% miss rate at 1FPPI and 68.2% mAP with grasp on our
+dataset. Robotic experiments demonstrate that our proposed algorithm can help
+robots grasp specified target in multi-object scenes at 84% success rate.
+"
+21422,Light Source Point Cluster Selection Based Atmosphere Light Estimation,"  Atmosphere light value is a highly critical parameter in defogging algorithms
+that are based on an atmosphere scattering model. Any error in atmosphere light
+value will produce a direct impact on the accuracy of scattering computation
+and thus bring chromatic distortion to restored images. To address this
+problem, this paper propose a method that relies on clustering statistics to
+estimate atmosphere light value. It starts by selecting in the original image
+some potential atmosphere light source points, which are grouped into point
+clusters by means of clustering technique. From these clusters, a number of
+clusters containing candidate atmosphere light source points are selected, the
+points are then analyzed statistically, and the cluster containing the most
+candidate points is used for estimating atmosphere light value. The mean
+brightness vector of the candidate atmosphere light points in the chosen point
+cluster is taken as the estimate of atmosphere light value, while their
+geometric center in the image is accepted as the location of atmosphere light.
+Experimental results suggest that this statistics clustering method produces
+more accurate atmosphere brightness vectors and light source locations. This
+accuracy translates to, from a subjective perspective, more natural defogging
+effect on the one hand and to the improvement in various objective image
+quality indicators on the other hand.
+"
+21423,Complete Submodularity Characterization in the Comparative Independent Cascade Model,"  We study the propagation of comparative ideas or items in social networks. A
+full characterization for submodularity in the comparative independent cascade
+(Com-IC) model of two-idea cascade is given, for competing ideas and
+complementary ideas respectively, with or without reconsideration. We further
+introduce One-Shot model where agents show less patience toward ideas, and show
+that in One-Shot model, only the strongest idea spreads with submodularity.
+"
+21424,Physics-guided probabilistic modeling of extreme precipitation under climate change,"  Earth System Models (ESMs) are the state of the art for projecting the
+effects of climate change. However, longstanding uncertainties in their ability
+to simulate regional and local precipitation extremes and related processes
+inhibit decision making. Stakeholders would be best supported by probabilistic
+projections of changes in extreme precipitation at relevant space-time scales.
+Here we propose an empirical Bayesian model that extends an existing skill and
+consensus based weighting framework and test the hypothesis that nontrivial,
+physics-guided measures of ESM skill can help produce reliable probabilistic
+characterization of climate extremes. Specifically, the model leverages
+knowledge of physical relationships between temperature, atmospheric moisture
+capacity, and extreme precipitation intensity to iteratively weight and combine
+ESMs and estimate probability distributions of return levels. Out-of-sample
+validation shows evidence that the Bayesian model is a sound method for
+deriving reliable probabilistic projections. Beyond precipitation extremes, the
+framework may be a basis for a generic, physics-guided approach to modeling
+probability distributions of climate variables in general, extremes or
+otherwise.
+"
+21425,LISA Detection of Binary Black Holes in the Milky Way Galaxy,"  Using the black hole merger rate inferred from LIGO, we calculate the
+abundance of tightly bound binary black holes in the Milky Way galaxy. Binaries
+with a small semimajor axis ($\lesssim 10 R_\odot$) originate at larger
+separations through conventional formation mechanisms and evolve as a result of
+gravitational wave emission. We find that LISA could detect them in the Milky
+Way. We also identify possible X-ray signatures of such binaries.
+"
+21426,Compacton solutions and (non)integrability for nonlinear evolutionary PDEs associated with a chain of prestressed granules,"  We present the results of study of a nonlinear evolutionary PDE (more
+precisely, a one-parameter family of PDEs) associated with the chain of
+pre-stressed granules. The PDE in question supports solitary waves of
+compression and rarefaction (bright and dark compactons) and can be written in
+Hamiltonian form. We investigate {\em inter alia} integrability properties of
+this PDE and its generalized symmetries and conservation laws.
+For the compacton solutions we perform a stability test followed by the
+numerical study. In particular, we simulate the temporal evolution of a single
+compacton, and the interactions of compacton pairs. The results of numerical
+simulations performed for our model are compared with the numerical evolution
+of corresponding Cauchy data for the discrete model of chain of pre-stressed
+elastic granules.
+"
+21427,FormuLog: Datalog for static analysis involving logical formulae,"  Datalog has become a popular language for writing static analyses. Because
+Datalog is very limited, some implementations of Datalog for static analysis
+have extended it with new language features. However, even with these features
+it is hard or impossible to express a large class of analyses because they use
+logical formulae to represent program state. FormuLog fills this gap by
+extending Datalog to represent, manipulate, and reason about logical formulae.
+We have used FormuLog to implement declarative versions of symbolic execution
+and abstract model checking, analyses previously out of the scope of
+Datalog-based languages. While this paper focuses on the design of FormuLog and
+one of the analyses we have implemented in it, it also touches on a prototype
+implementation of the language and identifies performance optimizations that we
+believe will be necessary to scale FormuLog to real-world static analysis
+problems.
+"
+21428,A Double Parametric Bootstrap Test for Topic Models,"  Non-negative matrix factorization (NMF) is a technique for finding latent
+representations of data. The method has been applied to corpora to construct
+topic models. However, NMF has likelihood assumptions which are often violated
+by real document corpora. We present a double parametric bootstrap test for
+evaluating the fit of an NMF-based topic model based on the duality of the KL
+divergence and Poisson maximum likelihood estimation. The test correctly
+identifies whether a topic model based on an NMF approach yields reliable
+results in simulated and real data.
+"
+21429,Heat flows inferred from a Parker's-like formula for stable or quasi-stable continents,"  Surface heat flow is a key parameter for the geothermal structure, rheology,
+and hence the dynamics of continents. However, the coverage of heat flow
+measurements is still poor in many continental areas. By transforming the
+stable nonlinear heat conduction equation into a Poisson's one, we develop a
+method to infer surface heat flow for a stable or quasi-stable continent from a
+Parker's-like formula. This formula provides the relationship between the
+Fourier transform of surface heat flow and the sum of the Fourier transform of
+the powers of geometry for the heat production (HP) interface in the
+continental lithosphere. Once the interface geometry is known, one to three
+dimensional distribution of the surface heat flow can be calculated accurately
+by this formula. As a case study, we estimate the three-dimensional surface
+heat flows for the Ordos geological block and its adjacent areas in China on a
+$1^\circ \times 1^\circ$ grid based on a simple layered constant HP model.
+Comparing to the measurements, most relative errors of the heat flows inferred
+are less than 20\%, showing this method is a favorable way to estimate surface
+heat flow for stable or quasi-stable continental regions where measurements are
+rare or absent.
+"
+21430,Real-time 3D Reconstruction on Construction Site using Visual SLAM and UAV,"  3D reconstruction can be used as a platform to monitor the performance of
+activities on construction site, such as construction progress monitoring,
+structure inspection and post-disaster rescue. Comparing to other sensors, RGB
+image has the advantages of low-cost, texture rich and easy to implement that
+has been used as the primary method for 3D reconstruction in construction
+industry. However, the image-based 3D reconstruction always requires extended
+time to acquire and/or to process the image data, which limits its application
+on time critical projects. Recent progress in Visual Simultaneous Localization
+and Mapping (SLAM) make it possible to reconstruct a 3D map of construction
+site in real-time. Integrated with Unmanned Aerial Vehicle (UAV), the obstacles
+areas that are inaccessible for the ground equipment can also be sensed.
+Despite these advantages of visual SLAM and UAV, until now, such technique has
+not been fully investigated on construction site. Therefore, the objective of
+this research is to present a pilot study of using visual SLAM and UAV for
+real-time construction site reconstruction. The system architecture and the
+experimental setup are introduced, and the preliminary results and the
+potential applications using Visual SLAM and UAV on construction site are
+discussed.
+"
+21431,The Space of Transferable Adversarial Examples,"  Adversarial examples are maliciously perturbed inputs designed to mislead
+machine learning (ML) models at test-time. They often transfer: the same
+adversarial example fools more than one model.
+In this work, we propose novel methods for estimating the previously unknown
+dimensionality of the space of adversarial inputs. We find that adversarial
+examples span a contiguous subspace of large (~25) dimensionality. Adversarial
+subspaces with higher dimensionality are more likely to intersect. We find that
+for two different models, a significant fraction of their subspaces is shared,
+thus enabling transferability.
+In the first quantitative analysis of the similarity of different models'
+decision boundaries, we show that these boundaries are actually close in
+arbitrary directions, whether adversarial or benign. We conclude by formally
+studying the limits of transferability. We derive (1) sufficient conditions on
+the data distribution that imply transferability for simple model classes and
+(2) examples of scenarios in which transfer does not occur. These findings
+indicate that it may be possible to design defenses against transfer-based
+attacks, even for models that are vulnerable to direct attacks.
+"
+21432,Sensitivity of the entanglement spectrum to boundary conditions as a characterization of the phase transition from delocalization to localization,"  Sensitivity of entanglement Hamiltonian spectrum to boundary conditions is
+considered as a phase detection parameter for delocalized-localized phase
+transition. By employing one-dimensional models that undergo
+delocalized-localized phase transition, we study the shift in the entanglement
+energies and the shift in the entanglement entropy when we change boundary
+conditions from periodic to anti-periodic. Specifically, we show that both
+these quantities show a change of several orders of magnitude at the transition
+point in the models considered. Therefore, this shift can be used to indicate
+the phase transition points in the models. We also show that both these
+quantities can be used to determine \emph{mobility edges} separating localized
+and delocalized states.
+"
+21433,"Radial metal abundance profiles in the intra-cluster medium of cool-core galaxy clusters, groups, and ellipticals","  The hot intra-cluster medium (ICM) permeating galaxy clusters and groups is
+not pristine, as it is continuously enriched by metals synthesised in Type Ia
+(SNIa) and core-collapse (SNcc) supernovae since the major epoch of star
+formation (z ~ 2-3). The cluster/group enrichment history and the mechanisms
+responsible for releasing and mixing the metals can be probed via the radial
+distribution of SNIa and SNcc products within the ICM. In this paper, we use
+deep XMM-Newton/EPIC observations from a sample of 44 nearby cool-core galaxy
+clusters, groups, and ellipticals (CHEERS) to constrain the average radial O,
+Mg, Si, S, Ar, Ca, Fe, and Ni abundance profiles. The radial distributions of
+all these elements, averaged over a large sample for the first time, represent
+the best constrained profiles available currently. We find an overall decrease
+of the Fe abundance with radius out to ~$0.9 r_{500}$ and ~$0.6 r_{500}$ for
+clusters and groups, respectively, in good agreement with predictions from the
+most recent hydrodynamical simulations. The average radial profiles of all the
+other elements (X) are also centrally peaked and, when rescaled to their
+average central X/Fe ratios, follow well the Fe profile out to at least
+~0.5$r_{500}$. Using two sets of SNIa and SNcc yield models reproducing well
+the X/Fe abundance pattern in the core, we find that, as predicted by recent
+simulations, the relative contribution of SNIa (SNcc) to the total ICM
+enrichment is consistent with being uniform at all radii, both for clusters and
+groups. In addition to implying that the central metal peak is balanced between
+SNIa and SNcc, our results suggest that the enriching SNIa and SNcc products
+must share the same origin, and that the delay between the bulk of the SNIa and
+SNcc explosions must be shorter than the timescale necessary to diffuse out the
+metals.
+"
+21434,Edge states in dynamical superlattices,"  We address edge states and rich localization regimes available in the
+one-dimensional (1D) dynamically modulated superlattices, both theoretically
+and numerically. In contrast to conventional lattices with straight waveguides,
+the quasi-energy band of infinite modulated superlattice is periodic not only
+in the transverse Bloch momentum, but it also changes periodically with
+increase of the coupling strength between waveguides. Due to collapse of
+quasi-energy bands dynamical superlattices admit known dynamical localization
+effect. If, however, such a lattice is truncated, periodic longitudinal
+modulation leads to appearance of specific edge states that exist within
+certain periodically spaced intervals of coupling constants. We discuss unusual
+transport properties of such truncated superlattices and illustrate different
+excitation regimes and enhanced robustness of edge states in them, that is
+associated with topology of the quasi-energy band.
+"
+21435,"On the Monitoring of Decentralized Specifications Semantics, Properties, Analysis, and Simulation","  We define two complementary approaches to monitor decentralized systems. The
+first relies on those with a centralized specification, i.e, when the
+specification is written for the behavior of the entire system. To do so, our
+approach introduces a data-structure that i) keeps track of the execution of an
+automaton, ii) has predictable parameters and size, and iii) guarantees strong
+eventual consistency. The second approach defines decentralized specifications
+wherein multiple specifications are provided for separate parts of the system.
+We study two properties of decentralized specifications pertaining to
+monitorability and compatibility between specification and architecture. We
+also present a general algorithm for monitoring decentralized specifications.
+We map three existing algorithms to our approaches and provide a framework for
+analyzing their behavior. Furthermore, we introduce THEMIS, a framework for
+designing such decentralized algorithms and simulating their behavior. We show
+the usage of THEMIS to compare multiple algorithms and verify the trends
+predicted by the analysis by studying two scenarios: a synthetic benchmark and
+a real example.
+"
+21436,Morphology and the Color-Mass Diagram as Clues to Galaxy Evolution at z~1,"  We study the significance of mergers in the quenching of star formation in
+galaxies at z~1 by examining their color-mass distributions for different
+morphology types. We perform two-dimensional light profile fits to GOODS iz
+images of ~5000 galaxies and X-ray selected active galactic nucleus (AGN) hosts
+in the CANDELS/GOODS-north and south fields in the redshift range 0.7<z<1.3.
+Distinguishing between bulge-dominated and disk-dominated morphologies, we find
+that disks and spheroids have distinct color-mass distributions, in agreement
+with studies at z~0. The smooth distribution across colors for the disk
+galaxies corresponds to a slow exhaustion of gas, with no fast quenching event.
+Meanwhile, blue spheroids most likely come from major mergers of star-forming
+disk galaxies, and the dearth of spheroids at intermediate green colors is
+suggestive of rapid quenching. The distribution of moderate luminosity X-ray
+AGN hosts is even across colors, in contrast, and we find similar numbers and
+distributions among the two morphology types with no apparent dependence on
+Eddington ratio. The high fraction of bulge-dominated galaxies that host an AGN
+in the blue cloud and green valley is consistent with the scenario in which the
+AGN is triggered after a major merger, and the host galaxy then quickly evolves
+into the green valley. This suggests AGN feedback may play a role in the
+quenching of star formation in the minority of galaxies that undergo major
+mergers.
+"
+21437,That's Mine! Learning Ownership Relations and Norms for Robots,"  The ability for autonomous agents to learn and conform to human norms is
+crucial for their safety and effectiveness in social environments. While recent
+work has led to frameworks for the representation and inference of simple
+social rules, research into norm learning remains at an exploratory stage.
+Here, we present a robotic system capable of representing, learning, and
+inferring ownership relations and norms. Ownership is represented as a graph of
+probabilistic relations between objects and their owners, along with a database
+of predicate-based norms that constrain the actions permissible on owned
+objects. To learn these norms and relations, our system integrates (i) a novel
+incremental norm learning algorithm capable of both one-shot learning and
+induction from specific examples, (ii) Bayesian inference of ownership
+relations in response to apparent rule violations, and (iii) percept-based
+prediction of an object's likely owners. Through a series of simulated and
+real-world experiments, we demonstrate the competence and flexibility of the
+system in performing object manipulation tasks that require a variety of norms
+to be followed, laying the groundwork for future research into the acquisition
+and application of social norms.
+"
+21438,Understanding liquid-jet atomization cascades via vortex dynamics,"  Temporal instabilities of a planar liquid jet are studied using direct
+numerical simulation (DNS) of the incompressible Navier-Stokes equations with
+level-set (LS) and volume-of-fluid (VoF) surface tracking methods. $\lambda_2$
+contours are used to relate the vortex dynamics to the surface dynamics at
+different stages of the jet breakup, namely, lobe formation, lobe perforation,
+ligament formation, stretching, and tearing. Three distinct breakup mechanisms
+are identified in the primary breakup, which are well categorized on the
+parameter space of gas Weber number ($We_g$) versus liquid Reynolds number
+($Re_l$). These mechanisms are analyzed here from a vortex dynamics
+perspective. Vortex dynamics explains the hairpin formation, and the
+interaction between the hairpins and the Kelvin-Helmholtz (KH) roller explains
+the perforation of the lobes, which is attributed to the streamwise overlapping
+of two oppositely-oriented hairpin vortices on top and bottom of the lobe. The
+formation of corrugations on the lobe front edge at high $Re_l$ is also related
+to the location and structure of the hairpins with respect to the KH vortex.
+The lobe perforation and corrugation formation are inhibited at low $Re_l$ and
+low $We_g$ due to the high surface tension and viscous forces, which damp the
+small scale corrugations and resist hole formation. Streamwise vorticity
+generation - resulting in three-dimensional instabilities - is mainly caused by
+vortex stretching and baroclinic torque at high and low density ratios,
+respectively. Generation of streamwise vortices and their interaction with
+spanwise vortices produce the liquid structures seen at various flow
+conditions. Understanding the liquid sheet breakup and the related vortex
+dynamics are crucial for controlling the droplet size distribution in primary
+atomization.
+"
+21439,Multi-kernel learning of deep convolutional features for action recognition,"  Image understanding using deep convolutional network has reached human-level
+performance, yet a closely related problem of video understanding especially,
+action recognition has not reached the requisite level of maturity. We combine
+multi-kernels based support-vector-machines (SVM) with a multi-stream deep
+convolutional neural network to achieve close to state-of-the-art performance
+on a 51-class activity recognition problem (HMDB-51 dataset); this specific
+dataset has proved to be particularly challenging for deep neural networks due
+to the heterogeneity in camera viewpoints, video quality, etc. The resulting
+architecture is named pillar networks as each (very) deep neural network acts
+as a pillar for the hierarchical classifiers. In addition, we illustrate that
+hand-crafted features such as improved dense trajectories (iDT) and Multi-skip
+Feature Stacking (MIFS), as additional pillars, can further supplement the
+performance.
+"
+21440,"Vector bundles for ""Matrix algebras converge to the sphere""","  In the high-energy quantum-physics literature one finds statements such as
+""matrix algebras converge to the sphere"". Earlier I provided a general precise
+setting for understanding such statements, in which the matrix algebras are
+viewed as quantum metric spaces, and convergence is with respect to a quantum
+Gromov-Hausdorff-type distance.
+But physicists want even more to treat structures on spheres (and other
+spaces), such as vector bundles, Yang-Mills functionals, Dirac operators, etc.,
+and they want to approximate these by corresponding structures on matrix
+algebras. In the present paper we treat this idea for vector bundles. We
+develop a general precise way for understanding how, for two compact quantum
+metric spaces that are close together, to a given vector bundle on one of them
+there can correspond in a natural way a unique vector bundle on the other. We
+then show explicitly how this works for the case of matrix algebras converging
+to the 2-sphere.
+"
+21441,Ehrlich-Schwoebel Effect on the Growth Dynamics of GaAs(111)A surfaces,"  We present a detailed characterization of the growth dynamics of
+Ga(Al)As(111)A surfaces. We develop a theoretical growth model that well
+describes the observed behavior on the growth parameters and underlines the
+Ehrlich-Schwoebel barrier as leading factor that determines the growth
+dynamics. On such basis we analyze the factors that lead to the huge observed
+roughness on such surface orientations and we identify the growth conditions
+that drive the typical three-dimensional growth of Ga(Al)As(111)A towards
+atomically flat surface. GaAs/AlGaAs quantum wells realized on optimized
+surface (<0.2 nm roughness) show a record low emission linewidth of 4.5 meV.
+"
+21442,Warnings and Caveats in Brain Controllability,"  In this work we challenge the main conclusions of Gu et al work
+(Controllability of structural brain networks. Nature communications 6, 8414,
+doi:10.1038/ncomms9414, 2015) on brain controllability. Using the same methods
+and analyses on four datasets we find that the minimum set of nodes to control
+brain networks is always larger than one. We also find that the relationships
+between the average/modal controllability and weighted degrees also hold for
+randomized data and the there are not specific roles played by Resting State
+Networks in controlling the brain. In conclusion, we show that there is no
+evidence that topology plays specific and unique roles in the controllability
+of brain networks. Accordingly, Gu et al. interpretation of their results, in
+particular in terms of translational applications (e.g. using single node
+controllability properties to define target region(s) for neurostimulation)
+should be revisited. Though theoretically intriguing, our understanding of the
+relationship between controllability and structural brain network remains
+elusive.
+"
+21443,Novelty and Foreseeing Research Trends; The Case of Astrophysics and Astronomy,"  Metrics based on reference lists of research articles or on keywords have
+been used to predict citation impact. The concept behind such metrics is that
+original ideas stem from the reconfiguration of the structure of past
+knowledge, and therefore atypical combinations in the reference lists,
+keywords, or classification codes indicate future high impact research. The
+current paper serves as an introduction to this line of research for
+astronomers and also addresses some methodological questions of this field of
+innovation studies. It is still not clear if the choice of particular indexes,
+such as references to journals, articles, or specific bibliometric
+classification codes would affect the relationship between atypical
+combinations and citation impact. To understand more aspects of the innovation
+process, a new metric has been devised to measure to what extent researchers
+are able to anticipate the changing combinatorial trends of the future. Results
+show that the variant of the latter anticipation scores that is based on paper
+combinations is a good predictor of future citation impact of scholarly works.
+The study also shows that the effect of tested indexes vary with the
+aggregation level that was used to construct them. A detailed analysis of
+combinatorial novelty in the field reveals that certain sub-fields of astronomy
+and astrophysics have different roles in the reconfiguration in past knowledge.
+"
+21444,A Covert Queueing Channel in Round Robin Schedulers,"  We study a covert queueing channel (CQC) between two users sharing a round
+robin scheduler. Such a covert channel can arise when users share a resource
+such as a computer processor or a router arbitrated by a round robin policy. We
+present an information-theoretic framework to model and derive the maximum
+reliable data transmission rate, i.e., the capacity of this channel, for both
+noiseless and noisy setups. Our results show that seemingly isolated users can
+communicate with a high rate over the covert channel and demonstrate the
+possibility of significant information leakage and privacy threats brought by
+CQCs in round robin schedulers. Moreover, we propose practical finite-length
+code constructions, which achieve the capacity limit.
+"
+21445,Relative periodic orbits form the backbone of turbulent pipe flow,"  Chaotic dynamics of low-dimensional systems, such as Lorenz or Rössler
+flows, is guided by the infinity of periodic orbits embedded in their strange
+attractors. Whether this also be the case for the infinite-dimensional dynamics
+of Navier--Stokes equations has long been speculated, and is a topic of ongoing
+study. Periodic and relative periodic solutions have been shown to be involved
+in transitions to turbulence. Their relevance to turbulent
+dynamics---specifically, whether periodic orbits play the same role in
+high-dimensional nonlinear systems like the Navier--Stokes equations as they do
+in lower-dimensional systems---is the focus of the present investigation. We
+perform here a detailed study of pipe flow relative periodic orbits with
+energies and mean dissipations close to turbulent values. We outline several
+approaches to reduction of the translational symmetry of the system. We study
+pipe flow in a minimal computational cell, and report a library of invariant
+solutions found with the aid of the method of slices. Detailed study of the
+unstable manifolds of a sample of these solutions is consistent with the
+picture that relative periodic orbits are embedded in the chaotic saddle and
+that they guide the turbulent dynamics.
+"
+21446,Tunable hybridization of Majorana bound states at the quantum spin Hall edge,"  Confinement at the helical edge of a topological insulator is possible in the
+presence of proximity-induced magnetic (F) or superconducting (S) order. The
+interplay of both phenomena leads to the formation of localized Majorana bound
+states (MBS) or likewise (under certain resonance conditions) the formation of
+ordinary Andreev bound states (ABS). We investigate the properties of bound
+states in junctions composed of alternating regions of F or S barriers.
+Interestingly, the direction of magnetization in F regions and the relative
+superconducting phase between S regions can be exploited to hybridize MBS or
+ABS at will. We show that the local properties of MBS translate into a
+particular nonlocal superconducting pairing amplitude. Remarkably, the symmetry
+of the pairing amplitude contains information about the nature of the bound
+state that it stems from. Hence, this symmetry can in principle be used to
+distinguish MBS from ABS, owing to the strong connection between local density
+of states and nonlocal pairing in our setup.
+"
+21447,Vanishing lines for modules over the motivic Steenrod algebra,"  We study criteria for freeness and for the existence of a vanishing line for
+modules over certain Hopf subalgebras of the motivic Steenrod algebra over
+$\mathrm{Spec}(\mathbb{C})$ at the prime 2. These turn out to be determined by
+the vanishing of certain Margolis homology groups in the quotient Hopf algebra
+$\mathcal{A}/\tau$.
+"
+21448,A 20-Channel Magnetoencephalography System Based on Optically Pumped Magnetometers,"  We describe a multichannel magnetoencephalography (MEG) system that uses
+optically pumped magnetometers (OPMs) to sense the magnetic fields of the human
+brain. The system consists of an array of 20 OPM channels conforming to the
+human subject's head, a person-sized magnetic shield containing the array and
+the human subject, a laser system to drive the OPM array, and various control
+and data acquisitions systems. We conducted two MEG experiments: auditory
+evoked magnetic field (AEF) and somatosensory evoked magnetic field (SEF), on
+three healthy male subjects, using both our OPM array and a 306-channel
+Elekta-Neuromag superconducting quantum interference device (SQUID) MEG system.
+The described OPM array measures the tangential components of the magnetic
+field as opposed to the radial component measured by all SQUID-based MEG
+systems. Herein we compare the results of the OPM- and SQUID-based MEG systems
+on the auditory and somatosensory data recorded in the same individuals on both
+systems.
+"
+21449,A universal negative group delay filter for the prediction of band-limited signals,"  A filter for universal real-time prediction of band-limited signals is
+presented. The filter consists of multiple time-delayed feedback terms in order
+to accomplish anticipatory coupling, which again leads to a negative group
+delay for frequencies in the baseband. The universality of the filter arises
+from its property that it does not rely on a specific model of the signal.
+Specifically, as long as the signal to be predicted is band-limited with a
+known cutoff frequency, the filter order, the only parameter of the filter,
+follows and the filter predicts the signal in real time up to a prediction
+horizon that depends on the cutoff frequency, too. It is worked out in detail
+how signal prediction arises from the negative group delay of the filter. Its
+properties, including stability, are investigated theoretically, by numerical
+simulations, and by application to a physiological signal. Possible control and
+signal processing applications of this filter are discussed.
+"
+21450,The odd primary order of the commutator on low rank Lie groups,"  Let $G$ be a simply-connected, compact, simple Lie group of low rank relative
+to a fixed prime $p$. After localization at $p$, there is a space $A$ which
+""generates"" $G$ in a certain sense. Assuming $G$ satisfies a homotopy
+nilpotency condition relative to $p$, we show that the Samelson product
+$\langle Id_G, Id_G\rangle$ of the identity of $G$ equals the order of the
+Samelson product $\langle\imath,\imath\rangle$ of the inclusion $\imath:A\to
+G$. Applying this result, we calculate the orders of $\langle Id_G,Id_G\rangle$
+for all $p$-regular Lie groups and give bounds on the orders of $\langle
+Id_G,Id_G\rangle$ for certain quasi-$p$-regular Lie groups.
+"
+21451,Communication Complexity of Estimating Correlations,"  We characterize the communication complexity of the following distributed
+estimation problem. Alice and Bob observe infinitely many iid copies of
+$\rho$-correlated unit-variance (Gaussian or $\pm1$ binary) random variables,
+with unknown $\rho\in[-1,1]$. By interactively exchanging $k$ bits, Bob wants
+to produce an estimate $\hat\rho$ of $\rho$. We show that the best possible
+performance (optimized over interaction protocol $\Pi$ and estimator $\hat
+\rho$) satisfies $\inf_{\Pi,\hat\rho}\sup_\rho \mathbb{E} [|\rho-\hat\rho|^2] =
+\Theta(\tfrac{1}{k})$. Furthermore, we show that the best possible unbiased
+estimator achieves performance of $1+o(1)\over {2k\ln 2}$. Curiously, thus,
+restricting communication to $k$ bits results in (order-wise) similar minimax
+estimation error as restricting to $k$ samples. Our results also imply an
+$\Omega(n)$ lower bound on the information complexity of the Gap-Hamming
+problem, for which we show a direct information-theoretic proof.
+Notably, the protocol achieving (almost) optimal performance is one-way
+(non-interactive). For one-way protocols we also prove the
+$\Omega(\tfrac{1}{k})$ bound even when $\rho$ is restricted to any small open
+sub-interval of $[-1,1]$ (i.e. a local minimax lower bound). %We do not know if
+this local behavior remains true in the interactive setting. Our proof
+techniques rely on symmetric strong data-processing inequalities, various
+tensorization techniques from information-theoretic interactive
+common-randomness extraction, and (for the local lower bound) on the
+Otto-Villani estimate for the Wasserstein-continuity of trajectories of the
+Ornstein-Uhlenbeck semigroup.
+"
+21452,"Postponement of raa and Glivenko's theorem, revisited (extended version)","  This article focuses on the technique of postponing the application of the
+reduction ad absurdum rule (raa) in classical natural deduction. First, it is
+shown how this technique is connected with two normalization strategies for
+classical logic: one given by Prawitz, and the other by Seldin. Secondly, a
+variant of Seldin's strategy for the postponement of raa is proposed, and the
+similarities with Prawitz's approach are investigated. In particular, it is
+shown that, as for Prawitz, it is possible to use this variant of Seldin's
+strategy in order to induce a negative translation from classical to
+intuitionistic and minimal logic, which is nothing but a variant of Kuroda's
+translation. Through this translation, Glivenko's theorem for intuitionistic
+and minimal logic is proven.
+"
+21453,Inference for the cross-covariance operator of stationary functional time series,"  When considering two or more time series of functions or curves, for instance
+those derived from densely observed intraday stock price data of several
+companies, the empirical cross-covariance operator is of fundamental importance
+due to its role in functional lagged regression and exploratory data analysis.
+Despite its relevance, statistical procedures for measuring the significance of
+such estimators are undeveloped. We present methodology based on a functional
+central limit theorem for conducting statistical inference for the
+cross-covariance operator estimated between two stationary, weakly dependent,
+functional time series. Specifically, we consider testing the null hypothesis
+that two series possess a specified cross-covariance structure at a given lag.
+Since this test assumes that the series are jointly stationary, we also develop
+a change-point detection procedure to validate this assumption, which is of
+independent interest. The most imposing technical hurdle in implementing the
+proposed tests involves estimating the spectrum of a high dimensional spectral
+density operator at frequency zero. We propose a simple dimension reduction
+procedure based on functional PCA to achieve this, which is shown to perform
+well in a small simulation study. We illustrate the proposed methodology with
+an application to densely observed intraday price data of stocks listed on the
+NYSE.
+"
+21454,Systematic Testing of Convolutional Neural Networks for Autonomous Driving,"  We present a framework to systematically analyze convolutional neural
+networks (CNNs) used in classification of cars in autonomous vehicles. Our
+analysis procedure comprises an image generator that produces synthetic
+pictures by sampling in a lower dimension image modification subspace and a
+suite of visualization tools. The image generator produces images which can be
+used to test the CNN and hence expose its vulnerabilities. The presented
+framework can be used to extract insights of the CNN classifier, compare across
+classification models, or generate training and validation datasets.
+"
+21455,Sufficient conditions for convergence of multiple Fourier series with $J_k$-lacunary sequence of rectangular partial sums in terms of Weyl multipliers,"  We obtain sufficient conditions for convergence (almost everywhere) of
+multiple trigonometric Fourier series of functions $f$ in $L_2$ in terms of
+Weyl multipliers. We consider the case where rectangular partial sums of
+Fourier series $S_n(x;f)$ have indices $n=(n_1,\dots,n_N) \in \mathbb Z^N$,
+$N\ge 3$, in which $k$ $(1\leq k\leq N-2)$ components on the places
+$\{j_1,\dots,j_k\}=J_k \subset \{1,\dots,N\} = M$ are elements of (single)
+lacunary sequences (i.e., we consider the, so called, multiple Fourier series
+with $J_k$-lacunary sequence of partial sums). We prove that for any sample
+$J_k\subset M$ the Weyl multiplier for convergence of these series has the form
+$W(\nu)=\prod \limits_{j=1}^{N-k} \log(|\nu_{{\alpha}_j}|+2)$, where
+$\alpha_j\in M\setminus J_k $, $\nu=(\nu_1,\dots,\nu_N)\in{\mathbb Z}^N$. So,
+the ""one-dimensional"" Weyl multiplier -- $\log(|\cdot|+2)$ -- presents in
+$W(\nu)$ only on the places of ""free"" (nonlacunary) components of the vector
+$\nu$. Earlier, in the case where $N-1$ components of the index $n$ are
+elements of lacunary sequences, convergence almost everywhere for multiple
+Fourier series was obtained in 1977 by M.Kojima in the classes $L_p$, $p>1$,
+and by D.K.Sanadze, Sh.V.Kheladze in Orlizc class. Note, that presence of two
+or more ""free"" components in the index $n$ (as follows from the results by
+Ch.Fefferman (1971)) does not guarantee the convergence almost everywhere of
+$S_n(x;f)$ for $N\geq 3$ even in the class of continuous functions.
+"
+21456,"Emergent phases in iron pnictides: Double-Q antiferromagnetism, charge order and enhanced nematic correlations","  Electron correlations produce a rich phase diagram in the iron pnictides.
+Earlier theoretical studies on the correlation effect demonstrated how quantum
+fluctuations weaken and concurrently suppress a $C_2$-symmetric single-Q
+antiferromagnetic order and a nematic order. Here we examine the emergent
+phases near the quantum phase transition. For a $C_4$-symmetric collinear
+double-Q antiferromagnetic order, we show that it is accompanied by both a
+charge order and an enhanced nematic susceptibility. Our results provide
+understanding for several intriguing recent experiments in hole-doped iron
+arsenides, and bring out common physics that underlies the different magnetic
+phases of various iron-based superconductors.
+"
+21457,Synergistic effects in threshold models on networks,"  Network structure can have significant effects on the propagation of
+diseases, memes, and information on social networks. Such effects depend on the
+specific type of dynamical process that affects the nodes and edges of a
+network, and it is important to develop tractable models of spreading processes
+on networks to explore how network structure affects dynamics. In this paper,
+we incorporate the idea of \emph{synergy} into a two-state (""active"" or
+""passive"") threshold model of social influence on networks. Our model's update
+rule is deterministic, and the influence of each meme-carrying (i.e., active)
+neighbor can --- depending on a parameter --- either be enhanced or inhibited
+by an amount that depends on the number of active neighbors of a node. Such a
+synergistic system models social behavior in which the willingness to adopt
+either accelerates or saturates depending on the number of neighbors who have
+adopted that behavior. We illustrate that the synergy parameter in our model
+has a crucial effect on system dynamics, as it determines whether degree-$k$
+nodes are possible or impossible to activate. We simulate synergistic meme
+spreading on both random-graph models and networks constructed from empirical
+data. Using a local-tree approximation, we examine the spreading of synergistic
+memes and find good agreement on all but one of the networks on which we
+simulate spreading. We find for any network and for a broad family of
+synergistic models that one can predict which synergy-parameter values allow
+degree-$k$ nodes to be activated.
+"
+21458,Bayesian stochastic blockmodeling,"  This chapter provides a self-contained introduction to the use of Bayesian
+inference to extract large-scale modular structures from network data, based on
+the stochastic blockmodel (SBM), as well as its degree-corrected and
+overlapping generalizations. We focus on nonparametric formulations that allow
+their inference in a manner that prevents overfitting, and enables model
+selection. We discuss aspects of the choice of priors, in particular how to
+avoid underfitting via increased Bayesian hierarchies, and we contrast the task
+of sampling network partitions from the posterior distribution with finding the
+single point estimate that maximizes it, while describing efficient algorithms
+to perform either one. We also show how inferring the SBM can be used to
+predict missing and spurious links, and shed light on the fundamental
+limitations of the detectability of modular structures in networks.
+"
+21459,Hardy Spaces ($0<p<\infty$) over Lipschitz Domains,"  Let $0<p<\infty$, $\Gamma$ be a Lipschitz curve on the complex
+plane~$\mathbb{C}$ and $\Omega_+$ is the domain above $\Gamma$, we define Hardy
+space $H^p(\Omega_+)$ as the set of analytic functions $F$ satisfying
+$\sup_{\tau>0}(\int_{\Gamma} |F(\zeta+\mathrm{i}\tau)|^p
+|\,\mathrm{d}\zeta|)^{\frac1p}< \infty$. We denote the conformal mapping from
+$\mathbb{C}_+$ onto $\Omega_+$ as $\Phi$, and prove that, $H^p(\Omega_+)$ is
+isomorphic to $H^p(\mathbb{C}_+)$, the classical Hardy space on the upper half
+plane~$\mathbb{C}_+$, under the mapping $T\colon F\to F(\Phi)\cdot
+(\Phi')^{\frac1p}$. Besides, $T$ and $T^{-1}$ are both bounded. We also prove
+that if $F(w)\in H^p(\Omega_+)$, then $F(w)$ has non-tangential boundary limit
+$F(\zeta)$ a.e. on $\Gamma$, and, if $1\leqslant p< \infty$, $F(w)$ is the
+Cauchy integral on $\Gamma$ of $F(\zeta)$.
+"
+21460,A Crevice on the Crane Beach: Finite-Degree Predicates,"  First-order logic (FO) over words is shown to be equiexpressive with FO
+equipped with a restricted set of numerical predicates, namely the order, a
+binary predicate MSB$_0$, and the finite-degree predicates: FO[Arb] = FO[<,
+MSB$_0$, Fin].
+The Crane Beach Property (CBP), introduced more than a decade ago, is true of
+a logic if all the expressible languages admitting a neutral letter are
+regular.
+Although it is known that FO[Arb] does not have the CBP, it is shown here
+that the (strong form of the) CBP holds for both FO[<, Fin] and FO[<, MSB$_0$].
+Thus FO[<, Fin] exhibits a form of locality and the CBP, and can still express
+a wide variety of languages, while being one simple predicate away from the
+expressive power of FO[Arb]. The counting ability of FO[<, Fin] is studied as
+an application.
+"
+21461,Dirichlet's and Thomson's principles for non-selfadjoint elliptic operators with application to non-reversible metastable diffusion processes,"  We present two variational formulae for the capacity in the context of
+non-selfadjoint elliptic operators. The minimizers of these variational
+problems are expressed as solutions of boundary-value elliptic equations. We
+use these principles to provide a sharp estimate for the transition times
+between two different wells for non-reversible diffusion processes. This
+estimate permits to describe the metastable behavior of the system.
+"
+21462,Real-space investigation of short-range magnetic correlations in fluoride pyrochlores NaCaCo$_2$F$_7$ and NaSrCo$_2$F$_7$ with magnetic pair distribution function analysis,"  We present time-of-flight neutron total scattering and polarized neutron
+scattering measurements of the magnetically frustrated compounds
+NaCaCo$_2$F$_7$ and NaSrCo$_2$F$_7$, which belong to a class of recently
+discovered pyrochlore compounds based on transition metals and fluorine. The
+magnetic pair distribution function (mPDF) technique is used to analyze and
+model the total scattering data in real space. We find that a
+previously-proposed model of short-range XY-like correlations with a length
+scale of 10-15 \AA, combined with nearest-neighbor collinear antiferromagnetic
+correlations, accurately describes the mPDF data at low temperature, confirming
+the magnetic ground state in these materials. This model is further verified by
+the polarized neutron scattering data. From an analysis of the temperature
+dependence of the mPDF and polarized neutron scattering data, we find that
+short-range correlations persist on the nearest-neighbor length scale up to 200
+K, approximately two orders of magnitude higher than the spin freezing
+temperatures of these compounds. These results highlight the opportunity
+presented by these new pyrochlore compounds to study the effects of geometric
+frustration at relatively high temperatures, while also advancing the mPDF
+technique and providing a novel opportunity to investigate a genuinely
+short-range-ordered magnetic ground state directly in real space.
+"
+21463,Embedding is not Cipher: Understanding the risk of embedding leakages,"  Machine Learning (ML) already has been integrated into all kinds of systems,
+helping developers to solve problems with even higher accuracy than human
+beings. However, when integrating ML models into a system, developers may
+accidentally take not enough care of the outputs of ML models, mainly because
+of their unfamiliarity with ML and AI, resulting in severe consequences like
+hurting data owners' privacy. In this work, we focus on understanding the risks
+of abusing embeddings of ML models, an important and popular way of using ML.
+To show the consequence, we reveal several kinds of channels in which
+embeddings are accidentally leaked. As our study shows, a face verification
+system deployed by a government organization leaking only distance to authentic
+users allows an attacker to exactly recover the embedding of the verifier's
+pre-installed photo. Further, as we discovered, with the leaked embedding,
+attackers can easily recover the input photo with negligible quality losses,
+indicating devastating consequences to users' privacy. This is achieved with
+our devised GAN-like structure model, which showed 93.65% success rate on
+popular face embedding model under black box assumption.
+"
+21464,Anatomy of an online misinformation network,"  Massive amounts of fake news and conspiratorial content have spread over
+social media before and after the 2016 US Presidential Elections despite
+intense fact-checking efforts. How do the spread of misinformation and
+fact-checking compete? What are the structural and dynamic characteristics of
+the core of the misinformation diffusion network, and who are its main
+purveyors? How to reduce the overall amount of misinformation? To explore these
+questions we built Hoaxy, an open platform that enables large-scale, systematic
+studies of how misinformation and fact-checking spread and compete on Twitter.
+Hoaxy filters public tweets that include links to unverified claims or
+fact-checking articles. We perform k-core decomposition on a diffusion network
+obtained from two million retweets produced by several hundred thousand
+accounts over the six months before the election. As we move from the periphery
+to the core of the network, fact-checking nearly disappears, while social bots
+proliferate. The number of users in the main core reaches equilibrium around
+the time of the election, with limited churn and increasingly dense
+connections. We conclude by quantifying how effectively the network can be
+disrupted by penalizing the most central nodes. These findings provide a first
+look at the anatomy of a massive online misinformation diffusion network.
+"
+21465,Injection Bucket Jitter Compensation Using Phase Lock System At Fermilab Booster,"  The extraction bucket position in the Fermilab Booster is controlled with a
+cogging process that involves the comparison of the Booster RF count and the
+Recycler Ring revolution marker. A one RF bucket jitter in the extraction
+bucket position results from the variability of the process that phase matches
+the Booster to the Recycler. However, the new slow phase lock process used to
+lock the frequency and phase of the Booster RF to the Recycler RF has been made
+digital and programmable and has been modified to correct the extraction notch
+position. The beam loss at the Recycler injection has been reduced by 20%. Beam
+studies and the phase lock system will be discussed in this paper.
+"
+21466,Collaborative Deep Learning in Fixed Topology Networks,"  There is significant recent interest to parallelize deep learning algorithms
+in order to handle the enormous growth in data and model sizes. While most
+advances focus on model parallelization and engaging multiple computing agents
+via using a central parameter server, aspect of data parallelization along with
+decentralized computation has not been explored sufficiently. In this context,
+this paper presents a new consensus-based distributed SGD (CDSGD) (and its
+momentum variant, CDMSGD) algorithm for collaborative deep learning over fixed
+topology networks that enables data parallelization as well as decentralized
+computation. Such a framework can be extremely useful for learning agents with
+access to only local/private data in a communication constrained environment.
+We analyze the convergence properties of the proposed algorithm with strongly
+convex and nonconvex objective functions with fixed and diminishing step sizes
+using concepts of Lyapunov function construction. We demonstrate the efficacy
+of our algorithms in comparison with the baseline centralized SGD and the
+recently proposed federated averaging algorithm (that also enables data
+parallelism) based on benchmark datasets such as MNIST, CIFAR-10 and CIFAR-100.
+"
+21467,Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling,"  We present a new algorithm for identifying the transition and emission
+probabilities of a hidden Markov model (HMM) from the emitted data.
+Expectation-maximization becomes computationally prohibitive for long
+observation records, which are often required for identification. The new
+algorithm is particularly suitable for cases where the available sample size is
+large enough to accurately estimate second-order output probabilities, but not
+higher-order ones. We show that if one is only able to obtain a reliable
+estimate of the pairwise co-occurrence probabilities of the emissions, it is
+still possible to uniquely identify the HMM if the emission probability is
+\emph{sufficiently scattered}. We apply our method to hidden topic Markov
+modeling, and demonstrate that we can learn topics with higher quality if
+documents are modeled as observations of HMMs sharing the same emission (topic)
+probability, compared to the simple but widely used bag-of-words model.
+"
+21468,An explicit formula for Szego kernels on the Heisenberg group,"  In this paper, we give an explicit formula for the Szego kernel for $(0, q)$
+forms on the Heisenberg group $H_{n+1}$.
+"
+21469,Necessary and sufficient conditions for consistent root reconstruction in Markov models on trees,"  We establish necessary and sufficient conditions for consistent root
+reconstruction in continuous-time Markov models with countable state space on
+bounded-height trees. Here a root state estimator is said to be consistent if
+the probability that it returns to the true root state converges to 1 as the
+number of leaves tends to infinity. We also derive quantitative bounds on the
+error of reconstruction. Our results answer a question of Gascuel and Steel and
+have implications for ancestral sequence reconstruction in a classical
+evolutionary model of nucleotide insertion and deletion.
+"
+21470,Perfectly Controllable Multi-Agent Networks,"  This note investigates how to design topology structures to ensure the
+controllability of multi-agent networks (MASs) under any selection of leaders.
+We put forward a concept of perfect controllability, which means that a
+multi-agent system is controllable with no matter how the leaders are chosen.
+In this situation, both the number and the locations of leader agents are
+arbitrary. A necessary and sufficient condition is derived for the perfect
+controllability. Moreover, a step-by-step design procedure is proposed by which
+topologies are constructed and are proved to be perfectly controllable. The
+principle of the proposed design method is interpreted by schematic diagrams
+along with the corresponding topology structures from simple to complex. We
+show that the results are valid for any number and any location of leaders.
+Both the construction process and the corresponding topology structures are
+clearly outlined.
+"
+21471,Origin of the fundamental plane of elliptical galaxies in the Coma Cluster without fine-tuning,"  After thirty years of the discovery of the fundamental plane, explanations to
+the tilt of the fundamental plane with respect to the virial plane still suffer
+from the need of fine-tuning. In this paper, we try to explore the origin of
+this tilt from the perspective of modified Newtonian dynamics (MOND) by
+applying the 16 Coma galaxies available in Thomas et al.[1]. Based on the mass
+models that can reproduce de Vaucouleurs' law closely, we find that the tilt of
+the traditional fundamental plane is naturally explained by the simple form of
+the MONDian interpolating function, if we assume a well motivated choice of
+anisotropic velocity distribution, and adopt the Kroupa or Salpeter stellar
+mass-to-light ratio. Our analysis does not necessarily rule out a varying
+stellar mass-to-light ratio.
+"
+21472,Dopants Promoting Ferroelectricity in Hafnia: Insights From A Comprehensive Chemical Space Exploration,"  Although dopants have been extensively employed to promote ferroelectricity
+in hafnia films, their role in stabilizing the responsible ferroelectric
+non-equilibrium Pca21 phase is not well understood. In this work, using first
+principles computations, we investigate the influence of nearly 40 dopants on
+the phase stability in bulk hafnia to identify dopants that can favor formation
+of the polar Pca21 phase. Although no dopant was found to stabilize this polar
+phase as the ground state, suggesting that dopants alone cannot induce
+ferroelectricity in hafnia, Ca, Sr, Ba, La, Y and Gd were found to
+significantly lower the energy of the polar phase with respect to the
+equilibrium monoclinic phase. These results are consistent with the empirical
+measurements of large remnant polarization in hafnia films doped with these
+elements. Additionally, clear chemical trends of dopants with larger ionic
+radii and lower electronegativity favoring the polar Pca21 phase in hafnia were
+identified. For this polar phase, an additional bond between the dopant cation
+and the 2nd nearest oxygen neighbor was identified as the root-cause of these
+trends. Further, trivalent dopants (Y, La, and Gd) were revealed to stabilize
+the polar Pca21 phase at lower strains when compared to divalent dopants (Sr
+and Ba). Based on these insights, we predict that the lanthanide series metals,
+the lower half of alkaline earth metals (Ca, Sr and Ba) and Y as the most
+suitable dopants to promote ferroelectricity in hafnia.
+"
+21473,Wave propagation with irregular dissipation and applications to acoustic problems and shallow waters,"  In this paper we consider an acoustic problem of wave propagation through a
+discontinuous medium. The problem is reduced to the dissipative wave equation
+with distributional dissipation. We show that this problem has a so-called very
+weak solution, we analyse its properties and illustrate the theoretical results
+through some numerical simulations by approximating the solutions to the full
+dissipative model for a particular synthetic piecewise continuous medium. In
+particular, we discover numerically a very interesting phenomenon of the
+appearance of a new wave at the singular point. For the acoustic problem this
+can be interpreted as an echo effect at the discontinuity interface of the
+medium.
+"
+21474,Superconductivity vs quantum criticality: effects of thermal fluctuations,"  We study the interplay between superconductivity and non-Fermi liquid
+behavior of a Fermi surface coupled to a massless $SU(N)$ matrix boson near the
+quantum critical point. The presence of thermal infrared singularities in both
+the fermionic self-energy and the gap equation invalidates the Eliashberg
+approximation, and makes the quantum-critical pairing problem qualitatively
+different from that at zero temperature. Taking the large $N$ limit, we solve
+the gap equation beyond the Eliashberg approximation, and obtain the
+superconducting temperature $T_c$ as a function of $N$. Our results show an
+anomalous scaling between the zero-temperature gap and $T_c$. For $N$ greater
+than a critical value, we find that $T_c$ vanishes with a
+Berezinskii-Kosterlitz-Thouless scaling behavior, and the system retains
+non-Fermi liquid behavior down to zero temperature. This confirms and extends
+previous renormalization-group analyses done at $T=0$, and provides a
+controlled example of a naked quantum critical point. We discuss the crucial
+role of thermal fluctuations in relating our results with earlier work where
+superconductivity always develops due to the special role of the first
+Matsubara frequency.
+"
+21475,Ensemble Framework for Real-time Decision Making,"  This paper introduces a new framework for real-time decision making in video
+games. An Ensemble agent is a compound agent composed of multiple agents, each
+with its own tasks or goals to achieve. Usually when dealing with real-time
+decision making, reactive agents are used; that is agents that return a
+decision based on the current state. While reactive agents are very fast, most
+games require more than just a rule-based agent to achieve good results.
+Deliberative agents---agents that use a forward model to search future
+states---are very useful in games with no hard time limit, such as Go or
+Backgammon, but generally take too long for real-time games. The Ensemble
+framework addresses this issue by allowing the agent to be both deliberative
+and reactive at the same time. This is achieved by breaking up the game-play
+into logical roles and having highly focused components for each role, with
+each component disregarding anything outwith its own role. Reactive agents can
+be used where a reactive agent is suited to the role, and where a deliberative
+approach is required, branching is kept to a minimum by the removal of all
+extraneous factors, enabling an informed decision to be made within a much
+smaller time-frame. An Arbiter is used to combine the component results,
+allowing high performing agents to be created from simple, efficient
+components.
+"
+21476,Reliability and Market Price of Energy in the Presence of Intermittent and Non-Dispatchable Renewable Energies,"  The intermittent nature of the renewable energies increases the operation
+costs of conventional generators. As the share of energy supplied by renewable
+sources increases, these costs also increase. In this paper, we quantify these
+costs by developing a market clearing price of energy in the presence of
+renewable energy and congestion constraints. We consider an electricity market
+where generators propose their asking price per unit of energy to an
+independent system operator (ISO). The ISO solve an optimization problem to
+dispatch energy from each generator to minimize the total cost of energy
+purchased on behalf of the consumers.
+To ensure that the generators are able to meet the load within a desired
+confidence level, we incorporate the notion of load variance using the
+Conditional Value-at-Risk (CVAR) measure in an electricity market and we derive
+the amount of committed power and market clearing price of energy as a function
+of CVAR. It is shown that a higher penetration of renewable energies may
+increase the committed power, market clearing price of energy and consumer cost
+of energy due to renewable generation uncertainties. We also obtain an
+upper-bound on the amount that congestion constraints can affect the committed
+power. We present descriptive simulations to illustrate the impact of renewable
+energy penetration and reliability levels on committed power by the
+non-renewable generators, difference between the dispatched and committed
+power, market price of energy and profit of renewable and non-renewable
+generators.
+"
+21477,Does Neural Machine Translation Benefit from Larger Context?,"  We propose a neural machine translation architecture that models the
+surrounding text in addition to the source sentence. These models lead to
+better performance, both in terms of general translation quality and pronoun
+prediction, when trained on small corpora, although this improvement largely
+disappears when trained with a larger corpus. We also discover that
+attention-based neural machine translation is well suited for pronoun
+prediction and compares favorably with other approaches that were specifically
+designed for this task.
+"
+21478,Heat asymptotics for nonminimal Laplace type operators and application to noncommutative tori,"  Let $P$ be a Laplace type operator acting on a smooth hermitean vector bundle
+$V$ of fiber $\mathbb{C}^N$ over a compact Riemannian manifold given locally by
+$P= - [g^{\mu\nu} u(x)\partial_\mu\partial_\nu + v^\nu(x)\partial_\nu + w(x)]$
+where $u,\,v^\nu,\,w$ are $M_N(\mathbb{C})$-valued functions with $u(x)$
+positive and invertible. For any $a \in \Gamma(\text{End}(V))$, we consider the
+asymptotics $\text{Tr} (a e^{-tP}) \underset{t \downarrow 0^+}{\sim}
+\,\sum_{r=0}^\infty a_r(a, P)\,t^{(r-d)/2}$ where the coefficients $a_r(a, P)$
+can be written locally as $a_r(a, P)(x) = \text{tr}[a(x) \mathcal{R}_r(x)]$.
+The computation of $\mathcal{R}_2$ is performed opening the opportunity to
+calculate the modular scalar curvature for noncommutative tori.
+"
+21479,DLVM: A modern compiler infrastructure for deep learning systems,"  Deep learning software demands reliability and performance. However, many of
+the existing deep learning frameworks are software libraries that act as an
+unsafe DSL in Python and a computation graph interpreter. We present DLVM, a
+design and implementation of a compiler infrastructure with a linear algebra
+intermediate representation, algorithmic differentiation by adjoint code
+generation, domain-specific optimizations and a code generator targeting GPU
+via LLVM. Designed as a modern compiler infrastructure inspired by LLVM, DLVM
+is more modular and more generic than existing deep learning compiler
+frameworks, and supports tensor DSLs with high expressivity. With our
+prototypical staged DSL embedded in Swift, we argue that the DLVM system
+enables a form of modular, safe and performant frameworks for deep learning.
+"
+21480,k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiography,"  Time-resolved angiography with interleaved stochastic trajectories (TWIST)
+has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve
+highly accelerated acquisitions, TWIST combines the periphery of the k-space
+data from several adjacent frames to reconstruct one temporal frame. However,
+this view-sharing scheme limits the true temporal resolution of TWIST.
+Moreover, the k-space sampling patterns have been specially designed for a
+specific generalized autocalibrating partial parallel acquisition (GRAPPA)
+factor so that it is not possible to reduce the number of view-sharing once the
+k-data is acquired. To address these issues, this paper proposes a novel
+k-space deep learning approach for parallel MRI. In particular, we have
+designed our neural network so that accurate k-space interpolations are
+performed simultaneously for multiple coils by exploiting the redundancies
+along the coils and images. Reconstruction results using in vivo TWIST data set
+confirm that the proposed method can immediately generate high-quality
+reconstruction results with various choices of view- sharing, allowing us to
+exploit the trade-off between spatial and temporal resolution in time-resolved
+MR angiography.
+"
+21481,The Effect of Collective Attention on Controversial Debates on Social Media,"  We study the evolution of long-lived controversial debates as manifested on
+Twitter from 2011 to 2016. Specifically, we explore how the structure of
+interactions and content of discussion varies with the level of collective
+attention, as evidenced by the number of users discussing a topic. Spikes in
+the volume of users typically correspond to external events that increase the
+public attention on the topic -- as, for instance, discussions about `gun
+control' often erupt after a mass shooting.
+This work is the first to study the dynamic evolution of polarized online
+debates at such scale. By employing a wide array of network and content
+analysis measures, we find consistent evidence that increased collective
+attention is associated with increased network polarization and network
+concentration within each side of the debate; and overall more uniform lexicon
+usage across all users.
+"
+21482,Between-Ride Routing for Private Transportation Services,"  Spurred by the growth of transportation network companies and increasing data
+capabilities, vehicle routing and ride-matching algorithms can improve the
+efficiency of private transportation services. However, existing routing
+solutions do not address where drivers should travel after dropping off a
+passenger and before receiving the next passenger ride request, i.e., during
+the between-ride period. We address this problem by developing an efficient
+algorithm to find the optimal policy for drivers between rides in order to
+maximize driver profits. We model the road network as a graph, and we show that
+the between-ride routing problem is equivalent to a stochastic shortest path
+problem, an infinite dynamic program with no discounting. We prove under
+reasonable assumptions that an optimal routing policy exists that avoids
+cycles; policies of this type can be efficiently found. We present an iterative
+approach to find an optimal routing policy. Our approach can account for
+various factors, including the frequency of passenger ride requests at
+different locations, traffic conditions, and surge pricing. We demonstrate the
+effectiveness of the approach by implementing it on road network data from
+Boston and New York City.
+"
+21483,Effective Spoken Language Labeling with Deep Recurrent Neural Networks,"  Understanding spoken language is a highly complex problem, which can be
+decomposed into several simpler tasks. In this paper, we focus on Spoken
+Language Understanding (SLU), the module of spoken dialog systems responsible
+for extracting a semantic interpretation from the user utterance. The task is
+treated as a labeling problem. In the past, SLU has been performed with a wide
+variety of probabilistic models. The rise of neural networks, in the last
+couple of years, has opened new interesting research directions in this domain.
+Recurrent Neural Networks (RNNs) in particular are able not only to represent
+several pieces of information as embeddings but also, thanks to their recurrent
+architecture, to encode as embeddings relatively long contexts. Such long
+contexts are in general out of reach for models previously used for SLU. In
+this paper we propose novel RNNs architectures for SLU which outperform
+previous ones. Starting from a published idea as base block, we design new deep
+RNNs achieving state-of-the-art results on two widely used corpora for SLU:
+ATIS (Air Traveling Information System), in English, and MEDIA (Hotel
+information and reservation in France), in French.
+"
+21484,Trust-based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop,"  Symbolic motion planning for robots is the process of specifying and planning
+robot tasks in a discrete space, then carrying them out in a continuous space
+in a manner that preserves the discrete-level task specifications. Despite
+progress in symbolic motion planning, many challenges remain, including
+addressing scalability for multi-robot systems and improving solutions by
+incorporating human intelligence. In this paper, distributed symbolic motion
+planning for multi-robot systems is developed to address scalability. More
+specifically, compositional reasoning approaches are developed to decompose the
+global planning problem, and atomic propositions for observation,
+communication, and control are proposed to address inter-robot collision
+avoidance. To improve solution quality and adaptability, a dynamic,
+quantitative, and probabilistic human-to-robot trust model is developed to aid
+this decomposition. Furthermore, a trust-based real-time switching framework is
+proposed to switch between autonomous and manual motion planning for tradeoffs
+between task safety and efficiency. Deadlock- and livelock-free algorithms are
+designed to guarantee reachability of goals with a human-in-the-loop. A set of
+non-trivial multi-robot simulations with direct human input and trust
+evaluation are provided demonstrating the successful implementation of the
+trust-based multi-robot symbolic motion planning methods.
+"
+21485,Investigation of Beam Emittance and Beam Transport Line Optics on Polarization,"  Effects of beam emittance, energy spread, optical parameters and magnet
+misalignment on beam polarization through particle transport systems are
+investigated. Particular emphasis will be placed on the beam lines being used
+at Fermilab for the development of the muon beam for the Muon g-2 experiment,
+including comparisons with the natural polarization resulting from pion decay,
+and comments on the development of systematic correlations among phase space
+variables.
+"
+21486,Metrics for Bengali Text Entry Research,"  With the intention of bringing uniformity to Bengali text entry research,
+here we present a new approach for calculating the most popular English text
+entry evaluation metrics for Bengali. To demonstrate our approach, we conducted
+a user study where we evaluated four popular Bengali text entry techniques.
+"
+21487,Annexing magic and tune-out wavelengths to the clock transitions of the alkaline-earth metal ions,"  We present additional magic wavelengths ($\lambda_{\rm{magic}}$) for the
+clock transitions in the alkaline-earth metal ions considering circular
+polarized light aside from our previously reported values in [J. Kaur et al.,
+Phys. Rev. A {\bf 92}, 031402(R) (2015)] for the linearly polarized light.
+Contributions from the vector component to the dynamic dipole polarizabilities
+($\alpha_d(\omega)$) of the atomic states associated with the clock transitions
+play major roles in the evaluation of these $\lambda_{\rm{magic}}$, hence
+facilitating in choosing circular polarization of lasers in the experiments.
+Moreover, the actual clock transitions in these ions are carried out among the
+hyperfine levels. The $\lambda_{\rm{magic}}$ values in these hyperfine
+transitions are estimated and found to be different from $\lambda_{\rm{magic}}$
+for the atomic transitions due to different contributions coming from the
+vector and tensor part of $\alpha_d(\omega)$. Importantly, we also present
+$\lambda_{\rm{magic}}$ values that depend only on the scalar component of
+$\alpha_d(\omega)$ for their uses in a specially designed trap geometry for
+these ions so that they can be used unambiguously among any hyperfine levels of
+the atomic states of the clock transitions. We also present $\alpha_d(\omega)$
+values explicitly at the 1064 nm for the atomic states associated with the
+clock transitions which may be useful for creating ""high-field seeking"" traps
+for the above ions using the Nd:YAG laser. The tune out wavelengths at which
+the states would be free from the Stark shifts are also presented. Accurate
+values of the electric dipole matrix elements required for these studies are
+given and trends of electron correlation effects in determining them are also
+highlighted.
+"
+21488,Graph Convolutional Neural Networks for Web-Scale Recommender Systems,"  Recent advancements in deep neural networks for graph-structured data have
+led to state-of-the-art performance on recommender system benchmarks. However,
+making these methods practical and scalable to web-scale recommendation tasks
+with billions of items and hundreds of millions of users remains a challenge.
+Here we describe a large-scale deep recommendation engine that we developed and
+deployed at Pinterest. We develop a data-efficient Graph Convolutional Network
+(GCN) algorithm PinSage, which combines efficient random walks and graph
+convolutions to generate embeddings of nodes (i.e., items) that incorporate
+both graph structure as well as node feature information. Compared to prior GCN
+approaches, we develop a novel method based on highly efficient random walks to
+structure the convolutions and design a novel training strategy that relies on
+harder-and-harder training examples to improve robustness and convergence of
+the model. We also develop an efficient MapReduce model inference algorithm to
+generate embeddings using a trained model. We deploy PinSage at Pinterest and
+train it on 7.5 billion examples on a graph with 3 billion nodes representing
+pins and boards, and 18 billion edges. According to offline metrics, user
+studies and A/B tests, PinSage generates higher-quality recommendations than
+comparable deep learning and graph-based alternatives. To our knowledge, this
+is the largest application of deep graph embeddings to date and paves the way
+for a new generation of web-scale recommender systems based on graph
+convolutional architectures.
+"
+21489,Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation,"  Image segmentation is a fundamental problem in biomedical image analysis.
+Recent advances in deep learning have achieved promising results on many
+biomedical image segmentation benchmarks. However, due to large variations in
+biomedical images (different modalities, image settings, objects, noise, etc),
+to utilize deep learning on a new application, it usually needs a new set of
+training data. This can incur a great deal of annotation effort and cost,
+because only biomedical experts can annotate effectively, and often there are
+too many instances in images (e.g., cells) to annotate. In this paper, we aim
+to address the following question: With limited effort (e.g., time) for
+annotation, what instances should be annotated in order to attain the best
+performance? We present a deep active learning framework that combines fully
+convolutional network (FCN) and active learning to significantly reduce
+annotation effort by making judicious suggestions on the most effective
+annotation areas. We utilize uncertainty and similarity information provided by
+FCN and formulate a generalized version of the maximum set cover problem to
+determine the most representative and uncertain areas for annotation. Extensive
+experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node
+ultrasound image segmentation dataset show that, using annotation suggestions
+by our method, state-of-the-art segmentation performance can be achieved by
+using only 50% of training data.
+"
+21490,Estimation for high-frequency data under parametric market microstructure noise,"  In this paper, we propose a general class of noise-robust estimators based on
+the existing estimators in the non-noisy high-frequency data literature. The
+market microstructure noise is a known parametric function of the limit order
+book. The noise-robust estimators are constructed as a plug-in version of their
+counterparts, where we replace the efficient price, which is non-observable in
+our framework, by an estimator based on the raw price and the limit order book
+data. We show that the technology can be directly applied to estimate
+volatility, high-frequency covariance, functionals of volatility and volatility
+of volatility in a general nonparametric framework where, depending on the
+problem at hand, price possibly includes infinite jump activity and sampling
+times encompass asynchronicity and endogeneity.
+"
+21491,Probably approximate Bayesian computation: nonasymptotic convergence of ABC under misspecification,"  Approximate Bayesian computation (ABC) is a widely used inference method in
+Bayesian statistics to bypass the point-wise computation of the likelihood. In
+this paper we develop theoretical bounds for the distance between the
+statistics used in ABC. We show that some versions of ABC are inherently robust
+to misspecification. The bounds are given in the form of oracle inequalities
+for a finite sample size. The dependence on the dimension of the parameter
+space and the number of statistics is made explicit. The results are shown to
+be amenable to oracle inequalities in parameter space. We apply our theoretical
+results to given prior distributions and data generating processes, including a
+non-parametric regression model. In a second part of the paper, we propose a
+sequential Monte Carlo (SMC) to sample from the pseudo-posterior, improving
+upon the state of the art samplers.
+"
+21492,Generation of soliton bubbles in a sine-Gordon system with localised inhomogeneities,"  Nonlinear wave propagation plays a crucial role in the functioning of many
+physical and biophysical systems. In the propagation regime, disturbances due
+to the presence of local external perturbations, such as localised defects or
+boundary interphase walls have gained great attention. In this article, the
+complex phenomena that occur when sine-Gordon line solitons collide with
+localised inhomogeneities are investigated. By a one-dimensional theory, it is
+shown that internal modes of two-dimensional sine-Gordon solitons can be
+activated depending on the topological properties of the inhomogeneities. Shape
+mode instabilities cause the formation of bubble-like and drop-like structures
+for both stationary and travelling line solitons. It is shown that such
+structures are formed and stabilised by arrays of localised inhomogeneities
+distributed in space. Implications of the observed phenomena in physical and
+biological systems are discussed.
+"
+21493,Development and Test of a uTPC Cluster Reconstruction for a Triple GEM Detector in Strong Magnetic Field,"  Performance of triple GEM prototypes has been evaluated by means of a muon
+beam at the H4 line of the SPS test area at CERN. The data from two planar
+prototypes have been reconstructed and analyzed offline with two clusterization
+methods: the enter of gravity of the charge distribution and the micro Time
+Projection Chamber (\muTPC). Concerning the spatial resolution, the charge
+centroid cluster reconstruction performs extremely well with no magnetic field:
+the resolution is well below 100 \mum . Increasing the magnetic field
+intensity, the resolution degrades almost linearly as effect of the Lorentz
+force that displaces, broadens and asymmetrizes the electron avalanche. Tuning
+the electric fields of the GEM prototype we could achieve the unprecedented
+spatial resolution of 190 \mum at 1 Tesla. In order to boost the spatial
+resolution with strong magnetic field and inclined tracks a \muTPC cluster
+reconstruction has been investigated. Such a readout mode exploits the good
+time resolution of the GEM detector and electronics to reconstruct the
+trajectory of the particle inside the conversion gap. Beside the improvement of
+the spatial resolution, information on the track angle can be also extracted.
+The new clustering algorithm has been tested with diagonal tracks with no
+magnetic field showing a resolution between 100 um and 150 um for the incident
+angle ranging from 10° to 45° . Studies show similar performance with
+1 Tesla magnetic field. This is the first use of a \muTPC readout with a triple
+GEM detector in magnetic field. This study has shown that a combined readout is
+capable to guarantee stable performance over a broad spectrum of particle
+momenta and incident angles, up to a 1 Tesla magnetic field.
+"
+21494,"An update to the EVEREST K2 pipeline: Short cadence, saturated stars, and Kepler-like photometry down to Kp = 15","  We present an update to the EVEREST K2 pipeline that addresses various
+limitations in the previous version and improves the photometric precision of
+the de-trended light curves. We develop a fast regularization scheme for third
+order pixel level decorrelation (PLD) and adapt the algorithm to include the
+PLD vectors of neighboring stars to enhance the predictive power of the model
+and minimize overfitting, particularly for faint stars. We also modify PLD to
+work for saturated stars and improve its performance on extremely variable
+stars. On average, EVEREST 2.0 light curves have 10-20% higher photometric
+precision than those in the previous version, yielding the highest precision
+light curves at all Kp magnitudes of any publicly available K2 catalog. For
+most K2 campaigns, we recover the original Kepler precision to at least Kp =
+14, and to at least Kp = 15 for campaigns 1, 5, and 6. We also de-trend all
+short cadence targets observed by K2, obtaining even higher photometric
+precision for these stars. All light curves for campaigns 0-8 are available
+online in the EVEREST catalog, which will be continuously updated with future
+campaigns. EVEREST 2.0 is open source and is coded in a general framework that
+can be applied to other photometric surveys, including Kepler and the upcoming
+TESS mission.
+"
+21495,Induction of tin pest for cleaning tin-drop contaminated optics,"  Tin pest, the allotropic {\beta} - {\alpha} phase transformation of tin, was
+examined for use in cleaning of tin-contaminated optics. Induction of change in
+material structure led to disintegration of tin samples into pieces and powder.
+The transition times were studied for tin drops of different purity grades,
+using inoculation with {\alpha}-Sn seed particles, also after prior mechanical
+deformation and surface oxide removal. For tin of very high purity levels fast
+nucleation within hours and full transformation within a day could be achieved
+during cooling at -24 °C, resulting in strong embrittlement of the
+material. Tin dripped onto samples of multilayer-coated optics as used in
+extreme ultraviolet lithography machines was made cleanable by phase transition
+after inoculation and cooling. The reflectance of multilayer-coated mirrors was
+found to decrease by no more than 1% with this cleaning method.
+"
+21496,Fukaya categories of plumbings and multiplicative preprojective algebras,"  Given an arbitrary graph $\Gamma$ and non-negative integers $g_v$ for each
+vertex $v$ of $\Gamma$, let $X_\Gamma$ be the Weinstein $4$-manifold obtained
+by plumbing copies of $T^*\Sigma_v$ according to this graph, where $\Sigma_v$
+is a surface of genus $g_v$. We compute the wrapped Fukaya category of
+$X_\Gamma$ (with bulk parameters) using Legendrian surgery extending our
+previous work arXiv:1502.07922 where it was assumed that $g_v=0$ for all $v$
+and $\Gamma$ was a tree. The resulting algebra is recognized as the (derived)
+multiplicative preprojective algebra (and its higher genus version) defined by
+Crawley-Boevey and Shaw arXiv:math/0404186. Along the way, we find a smaller
+model for the internal DG-algebra of Ekholm-Ng arXiv:1307.8436 associated to
+$1$-handles in the Legendrian surgery presentation of Weinstein $4$-manifolds
+which might be of independent interest.
+"
+21497,Ten Simple Rules When Considering Retirement,"  This is an article submitted to the Ten Simple Rules series of professional
+development articles published by PLOS Computational Biology.
+"
+21498,Secure Classification With Augmented Features,"  With the evolution of data collection ways, it is possible to produce
+abundant data described by multiple feature sets. Previous studies show that
+including more features does not necessarily bring positive effect. How to
+prevent the augmented features worsening classification performance is crucial
+but rarely studied. In this paper, we study this challenging problem by
+proposing a secure classification approach, whose accuracy is never degenerated
+when exploiting augmented features. We propose two ways to achieve the security
+of our method named as SEcure Classification (SEC). Firstly, to leverage
+augmented features, we learn various types of classifiers and adapt them by
+employing a specially designed robust loss. It provides various candidate
+classifiers to meet the following assumption of security operation. Secondly,
+we integrate all candidate classifiers by approximately maximizing the
+performance improvement. Under a mild assumption, the integrated classifier has
+theoretical security guarantee. Several new optimization methods have been
+developed to accommodate the problems with proved convergence. Besides
+evaluating SEC on 16 data sets, we also apply SEC in the application of
+diagnostic classification of schizophrenia since it has vast application
+potentiality. Experimental results demonstrate the effectiveness of SEC in both
+tackling security problem and discriminating schizophrenic patients from
+healthy controls.
+"
+21499,Deceiving Google's Cloud Video Intelligence API Built for Summarizing Videos,"  Despite the rapid progress of the techniques for image classification, video
+annotation has remained a challenging task. Automated video annotation would be
+a breakthrough technology, enabling users to search within the videos.
+Recently, Google introduced the Cloud Video Intelligence API for video
+analysis. As per the website, the system can be used to ""separate signal from
+noise, by retrieving relevant information at the video, shot or per frame""
+level. A demonstration website has been also launched, which allows anyone to
+select a video for annotation. The API then detects the video labels (objects
+within the video) as well as shot labels (description of the video events over
+time). In this paper, we examine the usability of the Google's Cloud Video
+Intelligence API in adversarial environments. In particular, we investigate
+whether an adversary can subtly manipulate a video in such a way that the API
+will return only the adversary-desired labels. For this, we select an image,
+which is different from the video content, and insert it, periodically and at a
+very low rate, into the video. We found that if we insert one image every two
+seconds, the API is deceived into annotating the video as if it only contained
+the inserted image. Note that the modification to the video is hardly
+noticeable as, for instance, for a typical frame rate of 25, we insert only one
+image per 50 video frames. We also found that, by inserting one image per
+second, all the shot labels returned by the API are related to the inserted
+image. We perform the experiments on the sample videos provided by the API
+demonstration website and show that our attack is successful with different
+videos and images.
+"
+21500,Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation,"  A recurrent neural network model of phonological pattern learning is
+proposed. The model is a relatively simple neural network with one recurrent
+layer, and displays biases in learning that mimic observed biases in human
+learning. Single-feature patterns are learned faster than two-feature patterns,
+and vowel or consonant-only patterns are learned faster than patterns involving
+vowels and consonants, mimicking the results of laboratory learning
+experiments. In non-recurrent models, capturing these biases requires the use
+of alpha features or some other representation of repeated features, but with a
+recurrent neural network, these elaborations are not necessary.
+"
+21501,A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data,"  Gene expression data represents a unique challenge in predictive model
+building, because of the small number of samples $(n)$ compared to the huge
+amount of features $(p)$. This ""$n<<p$"" property has hampered application of
+deep learning techniques for disease outcome classification. Sparse learning by
+incorporating external gene network information could be a potential solution
+to this issue. Still, the problem is very challenging because (1) there are
+tens of thousands of features and only hundreds of training samples, (2) the
+scale-free structure of the gene network is unfriendly to the setup of
+convolutional neural networks. To address these issues and build a robust
+classification model, we propose the Graph-Embedded Deep Feedforward Networks
+(GEDFN), to integrate external relational information of features into the deep
+neural network architecture. The method is able to achieve sparse connection
+between network layers to prevent overfitting. To validate the method's
+capability, we conducted both simulation experiments and a real data analysis
+using a breast cancer RNA-seq dataset from The Cancer Genome Atlas (TCGA). The
+resulting high classification accuracy and easily interpretable feature
+selection results suggest the method is a useful addition to the current
+classification models and feature selection procedures. The method is available
+at this https URL.
+"
+21502,Isometric embeddings of a class of separable metric spaces into Banach spaces,"  Let $(M,d)$ be a bounded countable metric space and $c>0$ a constant, such
+that $d(x,y)+d(y,z)-d(x,z) \ge c$, for any pairwise distinct points $x,y,z$ of
+$M$. For such metric spaces we prove that they can be isometrically embedded
+into any Banach space containing an isomorphic copy of $\ell_\infty$.
+"
+21503,On stable transitivity of finitely generated groups of volume preserving diffeomorphisms,"  In this paper, we provide a new criterion for the stable transitivity of
+volume preserving finite generated group on any compact Riemannian manifold. As
+one of our applications, we generalised a result of Dolgopyat and Krikorian in
+\cite{DK} and obtained stable transitivity for random rotations on the sphere
+in any dimension. As another application, we showed that for $\infty \geq r
+\geq 2$, any $C^r$ volume preserving partially hyperbolic diffeomorphism $g$ on
+any compact Riemannian manifold $M$ having sufficiently Hölder stable or
+unstable distribution, for any sufficiently large integer $K$, for any
+$(f_i)_{i=1}^{K}$ in a $C^1$ open $C^r$ dense subset of
+$\textnormal{\Diff}^r(M,m)^K$, the group generated by $g, f_1,\cdots, f_K$ acts
+transitively.
+"
+21504,Cellular Automata on Group Sets,"  We introduce and study cellular automata whose cell spaces are
+left-homogeneous spaces. Examples of left-homogeneous spaces are spheres,
+Euclidean spaces, as well as hyperbolic spaces acted on by isometries; uniform
+tilings acted on by symmetries; vertex-transitive graphs, in particular, Cayley
+graphs, acted on by automorphisms; groups acting on themselves by
+multiplication; and integer lattices acted on by translations. For such
+automata and spaces, we prove, in particular, generalisations of topological
+and uniform variants of the Curtis-Hedlund-Lyndon theorem, of the
+Tarski-F{\o}lner theorem, and of the Garden-of-Eden theorem on the full shift
+and certain subshifts. Moreover, we introduce signal machines that can handle
+accumulations of events and using such machines we present a time-optimal
+quasi-solution of the firing mob synchronisation problem on finite and
+connected graphs.
+"
+21505,Scaling from gauge and scalar radiation in Abelian Higgs string networks,"  We investigate cosmic string networks in the Abelian Higgs model using data
+from a campaign of large-scale numerical simulations on lattices of up to
+$4096^3$ grid points. We observe scaling or self-similarity of the networks
+over a wide range of scales, and estimate the asymptotic values of the mean
+string separation in horizon length units $\dot{\xi}$ and of the mean square
+string velocity $\bar v^2$ in the continuum and large time limits. The scaling
+occurs because the strings lose energy into classical radiation of the scalar
+and gauge fields of the Abelian Higgs model. We quantify the energy loss with a
+dimensionless radiative efficiency parameter, and show that it does not vary
+significantly with lattice spacing or string separation. This implies that the
+radiative energy loss underlying the scaling behaviour is not a lattice
+artefact, and justifies the extrapolation of measured network properties to
+large times for computations of cosmological perturbations. We also show that
+the core growth method, which increases the defect core width with time to
+extend the dynamic range of simulations, does not introduce significant
+systematic error. We compare $\dot{\xi}$ and $\bar v^2$ to values measured in
+simulations using the Nambu-Goto approximation, finding that the latter
+underestimate the mean string separation by about 25%, and overestimate $\bar
+v^2$ by about 10%. The scaling of the string separation implies that string
+loops decay by the emission of massive radiation within a Hubble time in field
+theory simulations, in contrast to the Nambu-Goto scenario which neglects this
+energy loss mechanism. String loops surviving for only one Hubble time emit
+much less gravitational radiation than in the Nambu-Goto scenario, and are
+consequently subject to much weaker gravitational wave constraints on their
+tension.
+"
+21506,An Almost Analytical Approach to Simulating 2D Electronic Spectra,"  We introduce an almost analytical method to simulate 2D electronic spectra as
+a double Fourier transform of the the non-linear response function (NRF)
+corresponding to a particular optical pulse sequence. We employ a unitary
+transformation to represent the total system Hamiltonian in a stationary basis
+that allows us to separate contributions from decoherence and phonon-mediated
+population relaxation to the NRF. Previously, one of us demonstrated the use of
+an analytic, cumulant expansion approach to calculate the decoherence term.
+Here, we extend this idea to obtain an accurate expression for the population
+relaxation term, a significant improvement over standard quantum master
+equation-based approximations. We numerically demonstrate the accuracy of our
+method by computing the photon echo spectrum of a two-level system coupled to a
+thermal bath, and we highlight the mechanistic insights obtained from our
+simulation.
+"
+21507,"Binary classification models with ""Uncertain"" predictions","  Binary classification models which can assign probabilities to categories
+such as ""the tissue is 75% likely to be tumorous"" or ""the chemical is 25%
+likely to be toxic"" are well understood statistically, but their utility as an
+input to decision making is less well explored. We argue that users need to
+know which is the most probable outcome, how likely that is to be true and, in
+addition, whether the model is capable enough to provide an answer. It is the
+last case, where the potential outcomes of the model explicitly include ""don't
+know"" that is addressed in this paper. Including this outcome would better
+separate those predictions that can lead directly to a decision from those
+where more data is needed. Where models produce an ""Uncertain"" answer similar
+to a human reply of ""don't know"" or ""50:50"" in the examples we refer to
+earlier, this would translate to actions such as ""operate on tumour"" or ""remove
+compound from use"" where the models give a ""more true than not"" answer. Where
+the models judge the result ""Uncertain"" the practical decision might be ""carry
+out more detailed laboratory testing of compound"" or ""commission new tissue
+analyses"". The paper presents several examples where we first analyse the
+effect of its introduction, then present a methodology for separating
+""Uncertain"" from binary predictions and finally, we provide arguments for its
+use in practice.
+"
+21508,SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to Guarantee Correctness,"  SafePredict is a novel meta-algorithm that works with any base prediction
+algorithm for online data to guarantee an arbitrarily chosen correctness rate,
+$1-\epsilon$, by allowing refusals. Allowing refusals means that the
+meta-algorithm may refuse to emit a prediction produced by the base algorithm
+on occasion so that the error rate on non-refused predictions does not exceed
+$\epsilon$. The SafePredict error bound does not rely on any assumptions on the
+data distribution or the base predictor. When the base predictor happens not to
+exceed the target error rate $\epsilon$, SafePredict refuses only a finite
+number of times. When the error rate of the base predictor changes through time
+SafePredict makes use of a weight-shifting heuristic that adapts to these
+changes without knowing when the changes occur yet still maintains the
+correctness guarantee. Empirical results show that (i) SafePredict compares
+favorably with state-of-the art confidence based refusal mechanisms which fail
+to offer robust error guarantees; and (ii) combining SafePredict with such
+refusal mechanisms can in many cases further reduce the number of refusals. Our
+software (currently in Python) is included in the supplementary material.
+"
+21509,Automated Crowdturfing Attacks and Defenses in Online Review Systems,"  Malicious crowdsourcing forums are gaining traction as sources of spreading
+misinformation online, but are limited by the costs of hiring and managing
+human workers. In this paper, we identify a new class of attacks that leverage
+deep learning language models (Recurrent Neural Networks or RNNs) to automate
+the generation of fake online reviews for products and services. Not only are
+these attacks cheap and therefore more scalable, but they can control rate of
+content output to eliminate the signature burstiness that makes crowdsourced
+campaigns easy to detect.
+Using Yelp reviews as an example platform, we show how a two phased review
+generation and customization attack can produce reviews that are
+indistinguishable by state-of-the-art statistical detectors. We conduct a
+survey-based user study to show these reviews not only evade human detection,
+but also score high on ""usefulness"" metrics by users. Finally, we develop novel
+automated defenses against these attacks, by leveraging the lossy
+transformation introduced by the RNN training and generation cycle. We consider
+countermeasures against our mechanisms, show that they produce unattractive
+cost-benefit tradeoffs for attackers, and that they can be further curtailed by
+simple constraints imposed by online service providers.
+"
+21510,Manipulating charge-density-wave in monolayer $1T$-TiSe$_2$ by strain and charge doping: A first-principles investigation,"  We investigate the effects of the in-plane biaxial strain and charge doping
+on the charge density wave (CDW) order of monolayer $1T$-TiSe$_2$ by using the
+first-principles calculations. Our results show that the tensile strain can
+significantly enhance the CDW order, while both compressive strain and charge
+doping (electrons and holes) suppress the CDW instability. The tensile strain
+may provide an effective method for obtaining higher CDW transition temperature
+on the basis of monolayer $1T$-TiSe$_2$. We also discuss the potential
+superconductivity in charge-doped monolayer $1T$-TiSe$_2$. Controllable
+electronic phase transition from CDW state to metallic state or even
+superconducting state can be realized in monolayer $1T$-TiSe$_2$, which makes
+$1T$-TiSe$_2$ possess a promising application in controllable switching
+electronic devices based on CDW.
+"
+21511,Automatic Program Synthesis of Long Programs with a Learned Garbage Collector,"  We consider the problem of generating automatic code given sample
+input-output pairs. We train a neural network to map from the current state and
+the outputs to the program's next statement. The neural network optimizes
+multiple tasks concurrently: the next operation out of a set of high level
+commands, the operands of the next statement, and which variables can be
+dropped from memory. Using our method we are able to create programs that are
+more than twice as long as existing state-of-the-art solutions, while improving
+the success rate for comparable lengths, and cutting the run-time by two orders
+of magnitude. Our code, including an implementation of various literature
+baselines, is publicly available at this https URL
+"
+21512,ExoMol molecular line lists - XXIII: spectra of PO and PS,"  Comprehensive line lists for phosphorus monoxide ($^{31}$P$^{16}$O) and
+phosphorus monosulphide ($^{31}$P$^{32}$S) in their $X$ $^2\Pi$ electronic
+ground state are presented. The line lists are based on new ab initio potential
+energy (PEC), spin-orbit (SOC) and dipole moment (DMC) curves computed using
+the MRCI+Q-r method with aug-cc-pwCV5Z and aug-cc-pV5Z basis sets. The nuclear
+motion equations (i.e. the rovibronic Schrödinger equations for each
+molecule) are solved using the program Duo. The PECs and SOCs are refined in
+least-squares fits to available experimental data. Partition functions, $Q(T)$,
+are computed up to $T=$ 5000 K, the range of validity of the line lists. These
+line lists are the most comprehensive available for either molecule. The
+characteristically sharp peak of the $Q$-branches from the spin-orbit split
+components give useful diagnostics for both PO and PS in spectra at infrared
+wavelengths. These line lists should prove useful for analysing observations
+and setting up models of environments such as brown dwarfs, low-mass stars,
+O-rich circumstellar regions and potentially for exoplanetary retrievals. Since
+PS is yet to be detected in space, the role of the two lowest excited
+electronic states ($a$ $^4\Pi$ and $B$ $^2\Pi$) are also considered. An
+approximate line list for the PS $X - B$ electronic transition, which predicts
+a number of sharp vibrational bands in the near ultraviolet, is also presented.
+he line lists are available from the CDS (cdsarc.u-strasbg.fr) and ExoMol
+(www.exomol.com) databases.
+"
+21513,Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models,"  Under Markovian assumptions, we leverage a Central Limit Theorem (CLT) for
+the empirical measure in the test statistic of the composite hypothesis
+Hoeffding test so as to establish weak convergence results for the test
+statistic, and, thereby, derive a new estimator for the threshold needed by the
+test. We first show the advantages of our estimator over an existing estimator
+by conducting extensive numerical experiments. We find that our estimator
+controls better for false alarms while maintaining satisfactory detection
+probabilities. We then apply the Hoeffding test with our threshold estimator to
+detecting anomalies in two distinct applications domains: one in communication
+networks and the other in transportation networks. The former application seeks
+to enhance cyber security and the latter aims at building smarter
+transportation systems in cities.
+"
+21514,A Convex Similarity Index for Sparse Recovery of Missing Image Samples,"  This paper investigates the problem of recovering missing samples using
+methods based on sparse representation adapted especially for image signals.
+Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality
+measure is used as the similarity criterion between the original and the
+reconstructed images. The proposed criterion called Convex SIMilarity (CSIM)
+index is a modified version of the Structural SIMilarity (SSIM) index, which
+despite its predecessor, is convex and uni-modal. We derive mathematical
+properties for the proposed index and show how to optimally choose the
+parameters of the proposed criterion, investigating the Restricted Isometry
+(RIP) and error-sensitivity properties. We also propose an iterative sparse
+recovery method based on a constrained $l_1$-norm minimization problem,
+incorporating CSIM as the fidelity criterion. The resulting convex optimization
+problem is solved via an algorithm based on Alternating Direction Method of
+Multipliers (ADMM). Taking advantage of the convexity of the CSIM index, we
+also prove the convergence of the algorithm to the globally optimal solution of
+the proposed optimization problem, starting from any arbitrary point.
+Simulation results confirm the performance of the new similarity index as well
+as the proposed algorithm for missing sample recovery of image patch signals.
+"
+21515,Image Subtraction Reduction of Open Clusters M35 & NGC 2158 In The K2 Campaign-0 Super-Stamp,"  Observations were made of the open clusters M35 and NGC 2158 during the
+initial K2 campaign (C0). Reducing these data to high-precision photometric
+time-series is challenging due to the wide point spread function (PSF) and the
+blending of stellar light in such dense regions. We developed an
+image-subtraction-based K2 reduction pipeline that is applicable to both
+crowded and sparse stellar fields. We applied our pipeline to the data-rich C0
+K2 super-stamp, containing the two open clusters, as well as to the neighboring
+postage stamps. In this paper, we present our image subtraction reduction
+pipeline and demonstrate that this technique achieves ultra-high photometric
+precision for sources in the C0 super-stamp. We extract the raw light curves of
+3960 stars taken from the UCAC4 and EPIC catalogs and de-trend them for
+systematic effects. We compare our photometric results with the prior
+reductions published in the literature. For detrended, TFA-corrected sources in
+the 12--12.25 $\rm K_{p}$ magnitude range, we achieve a best 6.5 hour window
+running rms of 35 ppm falling to 100 ppm for fainter stars in the 14--14.25 $
+\rm K_{p}$ magnitude range. For stars with $\rm K_{p}> 14$, our detrended and
+6.5 hour binned light curves achieve the highest photometric precision.
+Moreover, all our TFA-corrected sources have higher precision on all time
+scales investigated. This work represents the first published image subtraction
+analysis of a K2 super-stamp. This method will be particularly useful for
+analyzing the Galactic bulge observations carried out during K2 campaign 9. The
+raw light curves and the final results of our detrending processes are publicly
+available at \url{this http URL}.
+"
+21516,Fast random field generation with $H$-matrices,"  We use the $H$-matrix technology to compute the approximate square root of a
+covariance matrix in linear cost. This allows us to generate normal and
+log-normal random fields on general point sets with optimal cost. We derive
+rigorous error estimates which show convergence of the method. Our approach
+requires only mild assumptions on the covariance function and on the point set.
+Therefore, it might be also a nice alternative to the circulant embedding
+approach which applies only to regular grids and stationary covariance
+functions.
+"
+21517,Random Feature Stein Discrepancies,"  Computable Stein discrepancies have been deployed for a variety of
+applications, ranging from sampler selection in posterior inference to
+approximate Bayesian inference to goodness-of-fit testing. Existing
+convergence-determining Stein discrepancies admit strong theoretical guarantees
+but suffer from a computational cost that grows quadratically in the sample
+size. While linear-time Stein discrepancies have been proposed for
+goodness-of-fit testing, they exhibit avoidable degradations in testing
+power---even when power is explicitly optimized. To address these shortcomings,
+we introduce feature Stein discrepancies ($\Phi$SDs), a new family of quality
+measures that can be cheaply approximated using importance sampling. We show
+how to construct $\Phi$SDs that provably determine the convergence of a sample
+to its target and develop high-accuracy approximations---random $\Phi$SDs
+(R$\Phi$SDs)---which are computable in near-linear time. In our experiments
+with sampler selection for approximate posterior inference and goodness-of-fit
+testing, R$\Phi$SDs perform as well or better than quadratic-time KSDs while
+being orders of magnitude faster to compute.
+"
+21518,3x3 Singular Matrices of Linear Forms,"  We determine the irreducible components of the space of 3x3 matrices of
+linear forms with vanishing determinant. We show that there are four
+irreducible components and we identify them concretely. In particular, under
+elementary row and column operations with constant coefficients, a 3x3 matrix
+with vanishing determinant is equivalent to one of the following four: a matrix
+with a zero row, a zero column, a zero 2x2 square or an antisymmetric matrix.
+"
+21519,Continual Learning in Generative Adversarial Nets,"  Developments in deep generative models have allowed for tractable learning of
+high-dimensional data distributions. While the employed learning procedures
+typically assume that training data is drawn i.i.d. from the distribution of
+interest, it may be desirable to model distinct distributions which are
+observed sequentially, such as when different classes are encountered over
+time. Although conditional variations of deep generative models permit multiple
+distributions to be modeled by a single network in a disentangled fashion, they
+are susceptible to catastrophic forgetting when the distributions are
+encountered sequentially. In this paper, we adapt recent work in reducing
+catastrophic forgetting to the task of training generative adversarial networks
+on a sequence of distinct distributions, enabling continual generative
+modeling.
+"
+21520,Intrinsic Phase Diagram of Superconductivity in the BiCh2-based System Without In-plane Disorder,"  We have investigated the crystal structure and physical properties of
+LaO1-xFxBiSSe to reveal the intrinsic superconductivity phase diagram of the
+BiCh2-based layered compound family. From synchrotron X-ray diffraction and
+Rietveld refinements with anisotropic displacement parameters, we clearly found
+that the in-plane disorder in the BiSSe layer was fully suppressed for all x.
+In LaO1-xFxBiSSe, metallic conductivity and superconductivity are suddenly
+induced by electron doping even at x = 0.05 and 0.1 with a monoclinic
+structure. In addition, x (F concentration) dependence of the transition
+temperature (Tc) for x = 0.2-0.5 with a tetragonal structure shows an
+anomalously flat phase diagram. With these experimental facts, we have proposed
+the intrinsic phase diagram of the ideal BiCh2-based superconductors with less
+in-plane disorder.
+"
+21521,Growth dynamics of turbulent spots in plane Couette flow,"  We experimentally and numerically investigate the temporal aspects of
+turbulent spots spreading in a plane Couette flow for transitional Reynolds
+numbers between 300 and 450. Spot growth rate, spot advection rate and
+large-scale flow intensity are measured as a function of time and Reynolds
+number. All these quantities show similar dynamics clarifying the role played
+by large-scale flows in the advection of the turbulent spot. The contributions
+of each possible growth mechanism: growth induced by large scale advection or
+growth by destabilization, are discussed for the different stages of the spot
+growth. A scenario which gathers all these elements is providing a better
+understanding of the growth dynamics of turbulent spots in plane Couette flow
+that should possibly apply to other extended shear flows.
+"
+21522,On the Distribution of Massive White Dwarfs and its Implication for Accretion-Induced Collapse,"  A White Dwarf (WD) star and a main-sequence companion may interact through
+their different stellar evolution stages. This sort of binary population has
+historically helped us improve our understanding of binary formation and
+evolution scenarios. The data set used for the analysis consists of 115
+well-measured WD masses obtained by the Sloan Digital Sky Survey (SDSS). A
+substantial fraction of these systems could potentially evolve and reach the
+Chandrasekhar limit, and then undergo an Accretion-Induced Collapse (AIC) to
+produce millisecond pulsars (MSPs). I focus my attention mainly on the massive
+WDs (M_WD > 1M_sun), that are able to grow further by mass-transfer phase in
+stellar binary systems to reach the Chandrasekhar mass. A mean value of M ~
+1.15 +/- 0.2M_sun is being derived. In the framework of the AIC process, such
+systems are considered to be good candidates for the production of MSPs. The
+implications of the results presented here to our understanding of binary MSPs
+evolution are discussed. As a by-product of my work, I present an updated
+distribution of all known pulsars in Galactic coordinates pattern. Keywords:
+Stars; Neutron stars; White dwarfs; X-ray binaries; Fundamental parameters.
+"
+21523,DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings,"  We propose DeepBreath, a deep learning model which automatically recognises
+people's psychological stress level (mental overload) from their breathing
+patterns. Using a low cost thermal camera, we track a person's breathing
+patterns as temperature changes around his/her nostril. The paper's technical
+contribution is threefold. First of all, instead of creating hand-crafted
+features to capture aspects of the breathing patterns, we transform the
+uni-dimensional breathing signals into two dimensional respiration variability
+spectrogram (RVS) sequences. The spectrograms easily capture the complexity of
+the breathing dynamics. Second, a spatial pattern analysis based on a deep
+Convolutional Neural Network (CNN) is directly applied to the spectrogram
+sequences without the need of hand-crafting features. Finally, a data
+augmentation technique, inspired from solutions for over-fitting problems in
+deep learning, is applied to allow the CNN to learn with a small-scale dataset
+from short-term measurements (e.g., up to a few hours). The model is trained
+and tested with data collected from people exposed to two types of cognitive
+tasks (Stroop Colour Word Test, Mental Computation test) with sessions of
+different difficulty levels. Using normalised self-report as ground truth, the
+CNN reaches 84.59% accuracy in discriminating between two levels of stress and
+56.52% in discriminating between three levels. In addition, the CNN
+outperformed powerful shallow learning methods based on a single layer neural
+network. Finally, the dataset of labelled thermal images will be open to the
+community.
+"
+21524,Hamiltonian and Algebraic Theories of Gapped Boundaries in Topological Phases of Matter,"  We present an exactly solvable lattice Hamiltonian to realize gapped
+boundaries of Kitaev's quantum double models for Dijkgraaf-Witten theories. We
+classify the elementary excitations on the boundary, and systematically
+describe the bulk-to-boundary condensation procedure. We also present the
+parallel algebraic/categorical structure of gapped boundaries.
+"
+21525,Third-harmonic-generation of a diode laser for quantum control of beryllium ions,"  We generate coherent ultraviolet radiation at 313 nm as the third harmonic of
+an external-cavity diode laser. We use this radiation for laser cooling of
+trapped beryllium atomic ions and sympathetic cooling of co-trapped
+beryllium-hydride molecular ions. An LBO crystal in an enhancement cavity
+generates the second harmonic, and a BBO crystal in a doubly resonant
+enhancement cavity mixes this second harmonic with the fundamental to produce
+the third harmonic. Each enhancement cavity is preceded by a tapered amplifier
+to increase the fundamental light. The 36-mW output power of this
+all-semiconductor-gain system will enable quantum control of the beryllium
+ions' motion.
+"
+21526,Experimental Demonstration of the Sign Reversal of the Order Parameter in (Li1-xFex)OHFe1-yZnySe,"  Iron pnictides are the only known family of unconventional high-temperature
+superconductors besides cuprates. Until recently, it was widely accepted that
+superconductivity is spin-fluctuation driven and intimately related to their
+fermiology, specifically, hole and electron pockets separated by the same wave
+vector that characterizes the dominant spin fluctuations, and supporting order
+parameters (OP) of opposite signs. This picture was questioned after the
+discovery of a new family, based on the FeSe layers, either intercalated or in
+the monolayer form. The critical temperatures there reach ~40 K, the same as in
+optimally doped bulk FeSe - despite the fact that intercalation removes the
+hole pockets from the Fermi level and, seemingly, undermines the basis for the
+spin-fluctuation theory and the idea of a sign-changing OP. In this paper,
+using the recently proposed phase-sensitive quasiparticle interference
+technique, we show that in LiOH intercalated FeSe compound the OP does change
+sign, albeit within the electronic pockets, and not between the hole and
+electron ones. This result unifies the pairing mechanism of iron based
+superconductors with or without the hole Fermi pockets and supports the
+conclusion that spin fluctuations play the key role in electron pairing.
+"
+21527,Defending against Intrusion of Malicious UAVs with Networked UAV Defense Swarms,"  Nowadays, companies such as Amazon, Alibaba, and even pizza chains are
+pushing forward to use drones, also called UAVs (Unmanned Aerial Vehicles), for
+service provision, such as package and food delivery. As governments intend to
+use these immense economic benefits that UAVs have to offer, urban planners are
+moving forward to incorporate so-called UAV flight zones and UAV highways in
+their smart city designs. However, the high-speed mobility and behavior
+dynamics of UAVs need to be monitored to detect and, subsequently, to deal with
+intruders, rogue drones, and UAVs with a malicious intent. This paper proposes
+a UAV defense system for the purpose of intercepting and escorting a malicious
+UAV outside the flight zone. The proposed UAV defense system consists of a
+defense UAV swarm, which is capable to self-organize its defense formation in
+the event of intruder detection, and chase the malicious UAV as a networked
+swarm. Modular design principles have been used for our fully localized
+approach. We developed an innovative auto-balanced clustering process to
+realize the intercept- and capture-formation. As it turned out, the resulting
+networked defense UAV swarm is resilient against communication losses. Finally,
+a prototype UAV simulator has been implemented. Through extensive simulations,
+we show the feasibility and performance of our approach.
+"
+21528,Core filling and snaking instability of dark solitons in spin-imbalanced superfluid Fermi gases,"  We use the time-dependent Bogoliubov de Gennes equations to study dark
+solitons in three-dimensional spin-imbalanced superfluid Fermi gases. We
+explore how the shape and dynamics of dark solitons are altered by the presence
+of excess unpaired spins which fill their low-density core. The unpaired
+particles broaden the solitons and suppress the transverse snake instability.
+We discuss ways of observing these phenomena in cold atom experiments.
+"
+21529,Gaia-ESO Survey: global properties of clusters Trumpler 14 and 16 in the Carina Nebula,"  We present the first extensive spectroscopic study of the global population
+in star clusters Trumpler~16, Trumpler~14 and Collinder~232 in the Carina
+Nebula, using data from the Gaia-ESO Survey, down to solar-mass stars. In
+addition to the standard homogeneous Survey data reduction, a special
+processing was applied here because of the bright nebulosity surrounding Carina
+stars. We find about four hundred good candidate members ranging from OB types
+down to slightly sub-solar masses. About one-hundred heavily-reddened
+early-type Carina members found here were previously unrecognized or poorly
+classified, including two candidate O stars and several candidate Herbig Ae/Be
+stars. Their large brightness makes them useful tracers of the obscured Carina
+population. The spectroscopically-derived temperatures for nearly 300 low-mass
+members allows the inference of individual extinction values, and the study of
+the relative placement of stars along the line of sight. We find a complex
+spatial structure, with definite clustering of low-mass members around the most
+massive stars, and spatially-variable extinction. By combining the new data
+with existing X-ray data we obtain a more complete picture of the
+three-dimensional spatial structure of the Carina clusters, and of their
+connection to bright and dark nebulosity, and UV sources. The identification of
+tens of background giants enables us also to determine the total optical depth
+of the Carina nebula along many sightlines. We are also able to put constraints
+on the star-formation history of the region, with Trumpler~14 stars found to be
+systematically younger than stars in other sub-clusters. We find a large
+percentage of fast-rotating stars among Carina solar-mass members, which
+provide new constraints on the rotational evolution of pre-main-sequence stars
+in this mass range.
+"
+21530,Stochastic Deep Learning in Memristive Networks,"  We study the performance of stochastically trained deep neural networks
+(DNNs) whose synaptic weights are implemented using emerging memristive devices
+that exhibit limited dynamic range, resolution, and variability in their
+programming characteristics. We show that a key device parameter to optimize
+the learning efficiency of DNNs is the variability in its programming
+characteristics. DNNs with such memristive synapses, even with dynamic range as
+low as $15$ and only $32$ discrete levels, when trained based on stochastic
+updates suffer less than $3\%$ loss in accuracy compared to floating point
+software baseline. We also study the performance of stochastic memristive DNNs
+when used as inference engines with noise corrupted data and find that if the
+device variability can be minimized, the relative degradation in performance
+for the Stochastic DNN is better than that of the software baseline. Hence, our
+study presents a new optimization corner for memristive devices for building
+large noise-immune deep learning systems.
+"
+21531,Stepwise regression for unsupervised learning,"  I consider unsupervised extensions of the fast stepwise linear regression
+algorithm \cite{efroymson1960multiple}. These extensions allow one to
+efficiently identify highly-representative feature variable subsets within a
+given set of jointly distributed variables. This in turn allows for the
+efficient dimensional reduction of large data sets via the removal of redundant
+features. Fast search is effected here through the avoidance of repeat
+computations across trial fits, allowing for a full representative-importance
+ranking of a set of feature variables to be carried out in $O(n^2 m)$ time,
+where $n$ is the number of variables and $m$ is the number of data samples
+available. This runtime complexity matches that needed to carry out a single
+regression and is $O(n^2)$ faster than that of naive implementations. I present
+pseudocode suitable for efficient forward, reverse, and forward-reverse
+unsupervised feature selection. To illustrate the algorithm's application, I
+apply it to the problem of identifying representative stocks within a given
+financial market index -- a challenge relevant to the design of Exchange Traded
+Funds (ETFs). I also characterize the growth of numerical error with iteration
+step in these algorithms, and finally demonstrate and rationalize the
+observation that the forward and reverse algorithms return exactly inverted
+feature orderings in the weakly-correlated feature set regime.
+"
+21532,Stochastic equivalence for performance analysis of concurrent systems in dtsiPBC,"  We propose an extension with immediate multiactions of discrete time
+stochastic Petri Box Calculus (dtsPBC), presented by I.V. Tarasyuk. The
+resulting algebra dtsiPBC is a discrete time analogue of stochastic Petri Box
+Calculus (sPBC) with immediate multiactions, designed by H. Macià, V. Valero
+et al. within a continuous time domain. The step operational semantics is
+constructed via labeled probabilistic transition systems. The denotational
+semantics is based on labeled discrete time stochastic Petri nets with
+immediate transitions. To evaluate performance, the corresponding semi-Markov
+chains are analyzed. We define step stochastic bisimulation equivalence of
+expressions that is applied to reduce their transition systems and underlying
+semi-Markov chains while preserving the functionality and performance
+characteristics. We explain how this equivalence can be used to simplify
+performance analysis of the algebraic processes. In a case study, a method of
+modeling, performance evaluation and behaviour reduction for concurrent systems
+is outlined and applied to the shared memory system.
+"
+21533,$O(N)$ Iterative and $O(NlogN)$ Fast Direct Volume Integral Equation Solvers with a Minimal-Rank ${\cal H}^2$-Representation for Large-Scale $3$-D Electrodynamic Analysis,"  Linear complexity iterative and log-linear complexity direct solvers are
+developed for the volume integral equation (VIE) based general large-scale
+electrodynamic analysis. The dense VIE system matrix is first represented by a
+new cluster-based multilevel low-rank representation. In this representation,
+all the admissible blocks associated with a single cluster are grouped together
+and represented by a single low-rank block, whose rank is minimized based on
+prescribed accuracy. From such an initial representation, an efficient
+algorithm is developed to generate a minimal-rank ${\cal H}^2$-matrix
+representation. This representation facilitates faster computation, and ensures
+the same minimal rank's growth rate with electrical size as evaluated from
+singular value decomposition. Taking into account the rank's growth with
+electrical size, we develop linear-complexity ${\cal H}^2$-matrix-based storage
+and matrix-vector multiplication, and thereby an $O(N)$ iterative VIE solver
+regardless of electrical size. Moreover, we develop an $O(NlogN)$ matrix
+inversion, and hence a fast $O(NlogN)$ \emph{direct} VIE solver for large-scale
+electrodynamic analysis. Both theoretical analysis and numerical simulations of
+large-scale $1$-, $2$- and $3$-D structures on a single-core CPU, resulting in
+millions of unknowns, have demonstrated the low complexity and superior
+performance of the proposed VIE electrodynamic solvers. %The algorithms
+developed in this work are kernel-independent, and hence applicable to other IE
+operators as well.
+"
+21534,Optimizing Cost-Sensitive SVM for Imbalanced Data :Connecting Cluster to Classification,"  Class imbalance is one of the challenging problems for machine learning in
+many real-world applications, such as coal and gas burst accident monitoring:
+the burst premonition data is extreme smaller than the normal data, however,
+which is the highlight we truly focus on. Cost-sensitive adjustment approach is
+a typical algorithm-level method resisting the data set imbalance. For SVMs
+classifier, which is modified to incorporate varying penalty parameter(C) for
+each of considered groups of examples. However, the C value is determined
+empirically, or is calculated according to the evaluation metric, which need to
+be computed iteratively and time consuming. This paper presents a novel
+cost-sensitive SVM method whose penalty parameter C optimized on the basis of
+cluster probability density function(PDF) and the cluster PDF is estimated only
+according to similarity matrix and some predefined hyper-parameters.
+Experimental results on various standard benchmark data sets and real-world
+data with different ratios of imbalance show that the proposed method is
+effective in comparison with commonly used cost-sensitive techniques.
+"
+21535,Plain stopping time and conditional complexities revisited,"  In this paper we analyze the notion of ""stopping time complexity"", informally
+defined as the amount of information needed to specify when to stop while
+reading an infinite sequence. This notion was introduced by Vovk and Pavlovic
+(2016). It turns out that plain stopping time complexity of a binary string $x$
+could be equivalently defined as (a) the minimal plain complexity of a Turing
+machine that stops after reading $x$ on a one-directional input tape; (b) the
+minimal plain complexity of an algorithm that enumerates a prefix-free set
+containing $x$; (c)~the conditional complexity $C(x|x*)$ where $x$ in the
+condition is understood as a prefix of an infinite binary sequence while the
+first $x$ is understood as a terminated binary string; (d) as a minimal upper
+semicomputable function $K$ such that each binary sequence has at most $2^n$
+prefixes $z$ such that $K(z)<n$; (e) as $\max C^X(x)$ where $C^X(z)$ is plain
+Kolmogorov complexity of $z$ relative to oracle $X$ and the maximum is taken
+over all extensions $X$ of $x$.
+We also show that some of these equivalent definitions become non-equivalent
+in the more general setting where the condition $y$ and the object $x$ may
+differ. We also answer an open question from Chernov, Hutter and~Schmidhuber.
+"
+21536,21 Million Opportunities: A 19 Facility Investigation of Factors Affecting Hand Hygiene Compliance via Linear Predictive Models,"  This large-scale study, consisting of 21.3 million hand hygiene opportunities
+from 19 distinct facilities in 10 different states, uses linear predictive
+models to expose factors that may affect hand hygiene compliance. We examine
+the use of features such as temperature, relative humidity, influenza severity,
+day/night shift, federal holidays and the presence of new medical residents in
+predicting daily hand hygiene compliance; the investigation is undertaken using
+both a ""global"" model to glean general trends, and facility-specific models to
+elicit facility-specific insights. The results suggest that colder temperatures
+and federal holidays have an adverse effect on hand hygiene compliance rates,
+and that individual cultures and attitudes regarding hand hygiene exist among
+facilities.
+"
+21537,Conjugacy of one-dimensional one-sided cellular automata is undecidable,"  Two cellular automata are strongly conjugate if there exists a
+shift-commuting conjugacy between them. We prove that the following two sets of
+pairs $(F,G)$ of one-dimensional one-sided cellular automata over a full shift
+are recursively inseparable: (i) pairs where $F$ has strictly larger
+topological entropy than $G$, and (ii) pairs that are strongly conjugate and
+have zero topological entropy.
+Because there is no factor map from a lower entropy system to a higher
+entropy one, and there is no embedding of a higher entropy system into a lower
+entropy system, we also get as corollaries that the following decision problems
+are undecidable: Given two one-dimensional one-sided cellular automata $F$ and
+$G$ over a full shift: Are $F$ and $G$ conjugate? Is $F$ a factor of $G$? Is
+$F$ a subsystem of $G$? All of these are undecidable in both strong and weak
+variants (whether the homomorphism is required to commute with the shift or
+not, respectively). It also immediately follows that these results hold for
+one-dimensional two-sided cellular automata.
+"
+21538,On equations over Brandt semigroups,"  In this paper, we study equations over Brandt semigroup $B_n$. We compute
+that the number of unsolvable equations in one variable asymptotically equals
+$\frac{2}{n^2}$, and the average number of solutions of these equations
+asymptotically equals $n^2$.
+"
+21539,Domains and Event Structures for Fusions,"  Stable event structures, and their duality with prime algebraic domains
+(arising as partial orders of configurations), are a landmark of concurrency
+theory, providing a clear characterisation of causality in computations. They
+have been used for defining a concurrent semantics of several formalisms, from
+Petri nets to linear graph rewriting systems, which in turn lay at the basis of
+many visual frameworks. Stability however is restrictive for dealing with
+formalisms where a computational step can merge parts of the state, like graph
+rewriting systems with non-linear rules, which are needed to cover some
+relevant applications (such as the graphical encoding of calculi with name
+passing). We characterise, as a natural generalisation of prime algebraic
+domains, a class of domains that is well-suited to model the semantics of
+formalisms with fusions. We then identify a corresponding class of event
+structures, that we call connected event structures, via a duality result
+formalised as an equivalence of categories. Connected event structures are
+exactly the class of event structures the arise as the semantics of non-linear
+graph rewriting systems. Interestingly, the category of general unstable event
+structures coreflects into our category of domains, so that our result provides
+a characterisation of the partial orders of configurations of such event
+structures.
+"
+21540,Beyond Greedy Ranking: Slate Optimization via List-CVAE,"  The conventional approach to solving the recommendation problem is through
+greedy ranking by prediction scores for individual document candidates. However
+these methods fail to optimize the slate as a whole, and often struggle at
+capturing biases caused by the page layout and interdependencies between
+documents. The slate recommendation problem aims to find the optimal, ordered
+subset of documents, a.k.a. slate, given the page layout to serve users
+recommendations. Solving this problem is hard due to combinatorial explosion of
+document candidates and their display positions on the page. In this paper, we
+introduce List Conditional Variational Auto-Encoders (List-CVAE) to learn the
+joint distribution of documents on the slate conditional on user responses, and
+directly generate slates. Experiments on simulated and real-world data show
+that List-CVAE outperforms greedy ranking methods consistently on various
+scales of documents corpora.
+"
+21541,A General Class of Multifractional Processes and Stock Price Informativeness,"  We introduce a general class of stochastic processes driven by a
+multifractional Brownian motion (mBm) and study the estimation problems of
+their pointwise Hölder exponents (PHE) based on a new localized generalized
+quadratic variation approach (LGQV). By comparing our suggested approach with
+the other two existing benchmark estimation approaches (classic GQV and
+oscillation approach) through a simulation study, we show that our estimator
+has better performance in the case where the observed process is some unknown
+bivariate function of time and mBm. Such multifractional processes, whose PHEs
+are time-varying, can be used to model stock prices under various market
+conditions, that are both time-dependent and region-dependent. As an
+application to finance, an empirical study on modeling cross-listed stocks
+provides new evidence that the equity path's roughness varies via time and the
+stock price informativeness properties from global stock markets.
+"
+21542,Collective Rabi dynamics of electromagnetically-coupled quantum dot ensembles,"  Rabi oscillations typify the inherent nonlinearity of optical excitations in
+quantum dots. Using an integral kernel formulation to solve the 3D
+Maxwell-Bloch equations in ensembles of up to $10^4$ quantum dots, we observe
+features in Rabi oscillations due to the interplay of nonlinearity,
+non-equilibrium excitation, and electromagnetic coupling between the dots. This
+approach allows us to observe the dynamics of each dot in the ensemble without
+resorting to spatial averages. Our simulations predict synchronized multiplets
+of dots that exchange energy, dots that dynamically couple to screen the effect
+of incident external radiation, localization of the polarization due to
+randomness and interactions, as well as wavelength-scale regions of enhanced
+and suppressed polarization.
+"
+21543,Optimality in cellular storage via the Pontryagin Maximum Principle,"  We study an optimal control problem arising from a resource allocation
+problem in cellular metabolism. A minimalistic model that describes the
+production of enzymatic vs. non-enzymatic biomass components from a single
+nutrient source is introduced. The basic growth modes with this model are
+linear growth, where only the non-enzymatic component is produced, and
+exponential growth with only enzymatic components being produced. Using
+Pontryagin's maximum principle, we derive the optimal growth trajectory
+depending on the model's parameters. It turns out that depending on the
+parameters, either a single growth mode is optimal, or otherwise the optimal
+solution is a concatenation of exponential growth with linear growth.
+Importantly, on the short time scale, the choice of growth mode depends only on
+catalytic rate constants and biomass weights of the two component types,
+whereas on longer time scales, where the nutrient amount becomes limiting, also
+the yield coefficients play a role.
+"
+21544,A semidiscrete version of the Citti-Petitot-Sarti model as a plausible model for anthropomorphic image reconstruction and pattern recognition,"  In his beautiful book [66], Jean Petitot proposes a sub-Riemannian model for
+the primary visual cortex of mammals. This model is neurophysiologically
+justified. Further developments of this theory lead to efficient algorithms for
+image reconstruction, based upon the consideration of an associated
+hypoelliptic diffusion. The sub-Riemannian model of Petitot and Citti-Sarti (or
+certain of its improvements) is a left-invariant structure over the group
+$SE(2)$ of rototranslations of the plane. Here, we propose a semi-discrete
+version of this theory, leading to a left-invariant structure over the group
+$SE(2,N)$, restricting to a finite number of rotations. This apparently very
+simple group is in fact quite atypical: it is maximally almost periodic, which
+leads to much simpler harmonic analysis compared to $SE(2).$ Based upon this
+semi-discrete model, we improve on previous image-reconstruction algorithms and
+we develop a pattern-recognition theory that leads also to very efficient
+algorithms in practice.
+"
+21545,Latent Geometry Inspired Graph Dissimilarities Enhance Affinity Propagation Community Detection in Complex Networks,"  Affinity propagation is one of the most effective unsupervised pattern
+recognition algorithms for data clustering in high-dimensional feature space.
+However, the numerous attempts to test its performance for community detection
+in complex networks have been attaining results very far from the state of the
+art methods such as Infomap and Louvain. Yet, all these studies agreed that the
+crucial problem is to convert the unweighted network topology in a
+'smart-enough' node dissimilarity matrix that is able to properly address the
+message passing procedure behind affinity propagation clustering. Here we
+introduce a conceptual innovation and we discuss how to leverage network latent
+geometry notions in order to design dissimilarity matrices for affinity
+propagation community detection. Our results demonstrate that the latent
+geometry inspired dissimilarity measures we design bring affinity propagation
+to equal or outperform current state of the art methods for community
+detection. These findings are solidly proven considering both synthetic
+'realistic' networks (with known ground-truth communities) and real networks
+(with community metadata), even when the data structure is corrupted by noise
+artificially induced by missing or spurious connectivity.
+"
+21546,Free Will in the Theory of Everything,"  From what is known today about the elementary particles of matter, and the
+forces that control their behavior, it may be observed that still a host of
+obstacles must be overcome that are standing in the way of further progress of
+our understanding. Most researchers conclude that drastically new concepts must
+be investigated, new starting points are needed, older structures and theories,
+in spite of their successes, will have to be overthrown, and new,
+superintelligent questions will have to be asked and investigated. In short,
+they say that we shall need new physics. Here, we argue in a different manner.
+Today, no prototype, or toy model, of any so-called Theory of Everything
+exists, because the demands required of such a theory appear to be conflicting.
+The demands that we propose include locality, special and general relativity,
+together with a fundamental finiteness not only of the forces and amplitudes,
+but also of the set of Nature's dynamical variables. We claim that the two
+remaining ingredients that we have today, Quantum Field Theory and General
+Relativity, indeed are coming a long way towards satisfying such elementary
+requirements. Putting everything together in a Grand Synthesis is like solving
+a gigantic puzzle. We argue that we need the correct analytical tools to solve
+this puzzle. Finally, it seems to be obvious that this solution will give room
+neither for ""Divine Intervention"", nor for ""Free Will"", an observation that,
+all by itself, can be used as a clue. We claim that this reflects on our
+understanding of the deeper logic underlying quantum mechanics.
+"
+21547,WZ Sge: an eclipsing cataclysmic variable evolving towards the period minimum,"  We present the photometric results of the eclipsing cataclysmic variable (CV)
+WZ Sge near the period minimum ($P_{min}$). Eight new mid-eclipse times were
+determined and the orbital ephemeris was updated. Our result shows that the
+orbital period of WZ Sge is decreasing at a rate of
+$\dot{P}=-2.72(\pm0.23)\times{10^{-13}}\,s s^{-1}$. This secular decrease,
+coupled with previous detection of its donor, suggest that WZ Sge is a
+pre-bounce system. Further analysis indicates that the observed period decrease
+rate is about $1.53$ times higher than pure gravitational radiation (GR)
+driving. We constructed the evolutionary track of WZ Sge, which predicts that
+$P_{min}$ of WZ Sge is $\sim77.98 (\pm0.90)$ min. If the orbital period
+decreases at the current rate, WZ Sge will evolve past its $P_{min}$ after
+$\sim25.3$ Myr. Based on the period evolution equation we find
+$\dot{M}_{2}\simeq4.04(\pm0.10)\times10^{-11}M_{\odot}yr^{-1}$, which is
+compatible with the current concept of CV evolution at ultrashort orbital
+periods.
+"
+21548,Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors,"  Breast cancer has the highest incidence and second highest mortality rate for
+women in the US. Our study aims to utilize deep learning for benign/malignant
+classification of mammogram tumors using a subset of cases from the Digital
+Database of Screening Mammography (DDSM). Though it was a small dataset from
+the view of Deep Learning (about 1000 patients), we show that currently state
+of the art architectures of deep learning can find a robust signal, even when
+trained from scratch. Using convolutional neural networks (CNNs), we are able
+to achieve an accuracy of 85% and an ROC AUC of 0.91, while leading
+hand-crafted feature based methods are only able to achieve an accuracy of 71%.
+We investigate an amalgamation of architectures to show that our best result is
+reached with an ensemble of the lightweight GoogLe Nets tasked with
+interpreting both the coronal caudal view and the mediolateral oblique view,
+simply averaging the probability scores of both views to make the final
+prediction. In addition, we have created a novel method to visualize what
+features the neural network detects for the benign/malignant classification,
+and have correlated those features with well known radiological features, such
+as spiculation. Our algorithm significantly improves existing classification
+methods for mammography lesions and identifies features that correlate with
+established clinical markers.
+"
+21549,Boosting the kernelized shapelets: Theory and algorithms for local features,"  We consider binary classification problems using local features of objects.
+One of motivating applications is time-series classification, where features
+reflecting some local closeness measure between a time series and a pattern
+sequence called shapelet are useful. Despite the empirical success of such
+approaches using local features, the generalization ability of resulting
+hypotheses is not fully understood and previous work relies on a bunch of
+heuristics. In this paper, we formulate a class of hypotheses using local
+features, where the richness of features is controlled by kernels. We derive
+generalization bounds of sparse ensembles over the class which is exponentially
+better than a standard analysis in terms of the number of possible local
+features. The resulting optimization problem is well suited to the boosting
+approach and the weak learning problem is formulated as a DC program, for which
+practical algorithms exist. In preliminary experiments on time-series data
+sets, our method achieves competitive accuracy with the state-of-the-art
+algorithms with small parameter-tuning cost.
+"
+21550,NeuRoute: Predictive Dynamic Routing for Software-Defined Networks,"  This paper introduces NeuRoute, a dynamic routing framework for Software
+Defined Networks (SDN) entirely based on machine learning, specifically, Neural
+Networks. Current SDN/OpenFlow controllers use a default routing based on
+Dijkstra algorithm for shortest paths, and provide APIs to develop custom
+routing applications. NeuRoute is a controller-agnostic dynamic routing
+framework that (i) predicts traffic matrix in real time, (ii) uses a neural
+network to learn traffic characteristics and (iii) generates forwarding rules
+accordingly to optimize the network throughput. NeuRoute achieves the same
+results as the most efficient dynamic routing heuristic but in much less
+execution time.
+"
+21551,Improving the Benjamini-Hochberg Procedure for Discrete Tests,"  To find interesting items in genome-wide association studies or next
+generation sequencing data, a crucial point is to design powerful false
+discovery rate (FDR) controlling procedures that suitably combine discrete
+tests (typically binomial or Fisher tests). In particular, recent research has
+been striving for appropriate modifications of the classical Benjamini-Hochberg
+(BH) step-up procedure that accommodate discreteness. However, despite an
+important number of attempts, these procedures did not come with theoretical
+guarantees. The present paper contributes to fill the gap: it presents new
+modifications of the BH procedure that incorporate the discrete structure of
+the data and provably control the FDR for any fixed number of null hypotheses
+(under independence). Markedly, our FDR controlling methodology allows to
+incorporate simultaneously the discreteness and the quantity of signal of the
+data (corresponding therefore to a so-called $\pi\_0$-adaptive procedure). The
+power advantage of the new methods is demonstrated in a numerical experiment
+and for some appropriate real data sets.
+"
+21552,Emergent Supersymmetry in Local Equilibrium Systems,"  Many physical processes we observe in nature involve variations of
+macroscopic quantities over spatial and temporal scales much larger than
+microscopic molecular collision scales and can be considered as in local
+thermal equilibrium. In this paper we show that any classical statistical
+system in local thermal equilibrium has an emergent supersymmetry at low
+energies. We use the framework of non-equilibrium effective field theory for
+quantum many-body systems defined on a closed time path contour and consider
+its classical limit. Unitarity of time evolution requires introducing
+anti-commuting degrees of freedom and BRST symmetry which survive in the
+classical limit. The local equilibrium is realized through a $Z_2$ dynamical
+KMS symmetry. We show that supersymmetry is equivalent to the combination of
+BRST and a specific consequence of the dynamical KMS symmetry, to which we
+refer as the special dynamical KMS condition. In particular, we prove a theorem
+stating that a system satisfying the special dynamical KMS condition is always
+supersymmetrizable. We discuss a number of examples explicitly, including model
+A for dynamical critical phenomena, a hydrodynamic theory of nonlinear
+diffusion, and fluctuating hydrodynamics for relativistic charged fluids.
+"
+21553,Spectral identification of networks with inputs,"  We consider a network of interconnected dynamical systems. Spectral network
+identification consists in recovering the eigenvalues of the network Laplacian
+from the measurements of a very limited number (possibly one) of signals. These
+eigenvalues allow to deduce some global properties of the network, such as
+bounds on the node degree.
+Having recently introduced this approach for autonomous networks of nonlinear
+systems, we extend it here to treat networked systems with external inputs on
+the nodes, in the case of linear dynamics. This is more natural in several
+applications, and removes the need to sometimes use several independent
+trajectories. We illustrate our framework with several examples, where we
+estimate the mean, minimum, and maximum node degree in the network. Inferring
+some information on the leading Laplacian eigenvectors, we also use our
+framework in the context of network clustering.
+"
+21554,Asymptotic mapping class groups of closed surfaces punctured along Cantor sets,"  For $g\in \mathbb{N}\cup\{0\}$, we study a subgroup $\mathcal B_g
+<\operatorname{Mod}(\Sigma_g)$ of asymptotically rigid mapping classes, for
+$\Sigma_g$ a closed orientable surface of genus $g$ minus a Cantor set. The
+group $\mathcal B_g$ contains the mapping class group of every compact
+orientable surface of genus $g$ with non-empty boundary. We show that $\mathcal
+B_g$ is finitely presented and that its $k$-th homology group coincides with
+the stable homology of the mapping class group of genus $g$, for $k$ small
+enough with respct to $g$. We then proceed to prove that the family of groups
+so obtained enjoys a number of properties analogous to known results for
+finite-type mapping class groups. More concretely, there are no (weakly)
+injective maps $\mathcal B_h \to \mathcal B_g$ if $g<h$, every automorphism of
+$\mathcal B_g$ is geometric, and every homomorphism from a higher-rank lattice
+to $\mathcal B_g$ has finite image. In addition to the translation of classical
+results about finite-type mapping class groups, we will show that $\mathcal
+B_g$ is not linear, and does not have Kazhdan's Property (T). Finally, in
+appendix we will study the group $\mathcal H_g$ of {\em half-twists} of
+$\Sigma_g$, which sits between $\mathcal B_g$ and
+$\operatorname{Mod}(\Sigma_g)$. More concretely, we will prove that $\mathcal
+H_g$ is finitely presented, and that it is dense in
+$\operatorname{Mod}(\Sigma_g)$.
+"
+21555,Vector Bundles over Projectivoid Line,"  In this article we describe vector bundles over projectivoid line and show
+how it is similar to (and different) from Gorthendieck's classification of
+vector bundles over projective line.
+"
+21556,Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks,"  Collecting large training datasets, annotated with high-quality labels, is
+costly and time-consuming. This paper proposes a novel framework for training
+deep convolutional neural networks from noisy labeled datasets that can be
+obtained cheaply. The problem is formulated using an undirected graphical model
+that represents the relationship between noisy and clean labels, trained in a
+semi-supervised setting. In our formulation, the inference over latent clean
+labels is tractable and is regularized during training using auxiliary sources
+of information. The proposed model is applied to the image labeling problem and
+is shown to be effective in labeling unseen images as well as reducing label
+noise in training on CIFAR-10 and MS COCO datasets.
+"
+21557,Accurate estimation of microscopic diffusion anisotropy and its time dependence in the mouse brain,"  Microscopic diffusion anisotropy ({\mu}A) has been recently gaining
+increasing attention for its ability to decouple the average compartment
+anisotropy from orientation dispersion. Advanced diffusion MRI sequences, such
+as double diffusion encoding (DDE) and double oscillating diffusion encoding
+(DODE) have been used for mapping {\mu}A. However, the time-dependence of
+{\mu}A has not been investigated insofar, and furthermore, the accuracy of
+{\mu}A estimation vis-à-vis different b-values was not assessed. Here, we
+investigate both these concepts using theory, simulation, and experiments in
+the mouse brain. In the first part, simulations and experimental results show
+that the conventional estimation of microscopic anisotropy from the difference
+of D(O)DE sequences with parallel and orthogonal gradient directions yields
+values that highly depend on the choice of b-value. To mitigate this
+undesirable bias, we propose a multi-shell approach that harnesses a polynomial
+fit of the signal difference up to third order terms in b-value. In
+simulations, this approach yields more accurate {\mu}A metrics, which are
+similar to the ground truth values. The second part of this work uses the
+proposed multi-shell method to estimate the time/frequency dependence of
+{\mu}A. The data shows an increase of {\mu}A with frequency, both in white and
+gray matter regions. When comparing the experimental results with simulations,
+it emerges that simple geometric models such as infinite cylinders with either
+negligible or finite radii cannot replicate the measured trend, and more
+complex models, which, for example, incorporate restrictions along the fibre
+direction, are required. Thus, measuring the time dependence of microscopic
+anisotropy can provide valuable information for characterizing tissue
+microstructure.
+"
+21558,Baryogenesis from L-violating Higgs-doublet decay in the density-matrix formalism,"  We compute in the density-matrix formalism the baryon asymmetry generated by
+the decay of the Higgs doublet into a right-handed (RH) neutrino and a Standard
+Model lepton. The emphasis is put on the baryon asymmetry produced by the total
+lepton-number violating decay. From the derivation of the corresponding
+evolution equations, and from their integration, we find that this contribution
+is fully relevant for large parts of the parameter space. This confirms the
+results found recently in the CP-violating decay formalism with thermal
+corrections and shows in particular that the lepton-number violating processes
+are important not only for high-scale leptogenesis but also when the
+RH-neutrino masses are in the GeV range. For large values of the Yukawa
+couplings, we also find that the strong washout is generically much milder for
+this total lepton-number violating part than for the usual RH-neutrino
+oscillation flavour part.
+"
+21559,Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGs,"  Many causal processes in biomedicine contain cycles and evolve. However, most
+causal discovery algorithms assume that the underlying causal process follows a
+single directed acyclic graph (DAG) that does not change over time. The
+algorithms can therefore infer erroneous causal relations with high confidence
+when run on real biomedical data. In this paper, I relax the single DAG
+assumption by modeling causal processes using a mixture of DAGs so that the
+graph can change over time. I then describe a causal discovery algorithm called
+Causal Inference over Mixtures (CIM) to infer causal structure from a mixture
+of DAGs using longitudinal data. CIM improves the accuracy of causal discovery
+on both real and synthetic clinical datasets even when cycles,
+non-stationarity, non-linearity, latent variables and selection bias exist
+simultaneously.
+"
+21560,Replication or exploration? Sequential design for stochastic simulation experiments,"  We investigate the merits of replication, and provide methods for optimal
+design (including replicates), with the goal of obtaining globally accurate
+emulation of noisy computer simulation experiments. We first show that
+replication can be beneficial from both design and computational perspectives,
+in the context of Gaussian process surrogate modeling. We then develop a
+lookahead based sequential design scheme that can determine if a new run should
+be at an existing input location (i.e., replicate) or at a new one (explore).
+When paired with a newly developed heteroskedastic Gaussian process model, our
+dynamic design scheme facilitates learning of signal and noise relationships
+which can vary throughout the input space. We show that it does so efficiently,
+on both computational and statistical grounds. In addition to illustrative
+synthetic examples, we demonstrate performance on two challenging real-data
+simulation experiments, from inventory management and epidemiology.
+"
+21561,Instrumental variables as bias amplifiers with general outcome and confounding,"  Drawing causal inference with observational studies is the central pillar of
+many disciplines. One sufficient condition for identifying the causal effect is
+that the treatment-outcome relationship is unconfounded conditional on the
+observed covariates. It is often believed that the more covariates we condition
+on, the more plausible this unconfoundedness assumption is. This belief has had
+a huge impact on practical causal inference, suggesting that we should adjust
+for all pretreatment covariates. However, when there is unmeasured confounding
+between the treatment and outcome, estimators adjusting for some pretreatment
+covariate might have greater bias than estimators without adjusting for this
+covariate. This kind of covariate is called a bias amplifier, and includes
+instrumental variables that are independent of the confounder, and affect the
+outcome only through the treatment. Previously, theoretical results for this
+phenomenon have been established only for linear models. We fill in this gap in
+the literature by providing a general theory, showing that this phenomenon
+happens under a wide class of models satisfying certain monotonicity
+assumptions. We further show that when the treatment follows an additive or
+multiplicative model conditional on the instrumental variable and the
+confounder, these monotonicity assumptions can be interpreted as the signs of
+the arrows of the causal diagrams.
+"
+21562,The use of controlled vocabularies in requirements engineering activities: a protocol for a systematic literature review,"  Context: The Evidence-Based Software Engineering (EBSE) paradigm and the
+planning phase of a systematic literature review. Objective: A protocol to do a
+systematic literature review with detailed information about the processes
+suggested by several guidelines in the field of evidence-based software
+engineering. Method: An analisys of recent systematic literature reviews
+published in world leading journals, plus the use of two renowned guidelines
+and a textbook to sinthetise a formal plan (the protocol). Results: The
+validated protocol Conclusions: We found that most of the published systematic
+reviews lack on reporting the protocol, or it is weak. There is a lack of tool
+support to develop formal protocols. Although a protocol, like a plan, must
+have the flexibility to adapt to unforeseen situations, its objective is that
+the actual activities should resemble as far as possible of those already
+planned. Therefore, it is a difficult balance to achieve and, researchers must
+be careful not to introduce alterations that could become threats to the
+validity of the entire work.
+"
+21563,A Convex Framework for Fair Regression,"  We introduce a flexible family of fairness regularizers for (linear and
+logistic) regression problems. These regularizers all enjoy convexity,
+permitting fast optimization, and they span the rang from notions of group
+fairness to strong individual fairness. By varying the weight on the fairness
+regularizer, we can compute the efficient frontier of the accuracy-fairness
+trade-off on any given dataset, and we measure the severity of this trade-off
+via a numerical quantity we call the Price of Fairness (PoF). The centerpiece
+of our results is an extensive comparative study of the PoF across six
+different datasets in which fairness is a primary consideration.
+"
+21564,Identifying Growth-Patterns in Children by Applying Cluster analysis to Electronic Medical Records,"  Obesity is one of the leading health concerns in the United States.
+Researchers and health care providers are interested in understanding factors
+affecting obesity and detecting the likelihood of obesity as early as possible.
+In this paper, we set out to recognize children who have higher risk of obesity
+by identifying distinct growth patterns in them. This is done by using
+clustering methods, which group together children who share similar body
+measurements over a period of time. The measurements characterizing children
+within the same cluster are plotted as a function of age. We refer to these
+plots as growthpattern curves. We show that distinct growth-pattern curves are
+associated with different clusters and thus can be used to separate children
+into the topmost (heaviest), middle, or bottom-most cluster based on early
+growth measurements.
+"
+21565,Waveform Design for Wireless Power Transfer with Limited Feedback,"  Waveform design is a key technique to jointly exploit a beamforming gain, the
+channel frequency-selectivity and the rectifier nonlinearity, so as to enhance
+the end-to-end power transfer efficiency of Wireless Power Transfer (WPT).
+Those waveforms have been designed assuming perfect channel state information
+at the transmitter. This paper proposes two waveform strategies relying on
+limited feedback for multi-antenna multi-sine WPT over frequency-selective
+channels. In the waveform selection strategy, the Energy Transmitter (ET)
+transmits over multiple timeslots with every time a different waveform precoder
+within a codebook, and the Energy Receiver (ER) reports the index of the
+precoder in the codebook that leads to the largest harvested energy. In the
+waveform refinement strategy, the ET sequentially transmits two waveforms in
+each stage, and the ER reports one feedback bit indicating an increase/decrease
+in the harvested energy during this stage. Based on multiple one-bit feedback,
+the ET successively refines waveform precoders in a tree-structured codebook
+over multiple stages. By employing the framework of the generalized Lloyd's
+algorithm, novel algorithms are proposed for both strategies to optimize the
+codebooks in both space and frequency domains. The proposed limited
+feedback-based waveform strategies are shown to outperform a set of baselines,
+achieving higher harvested energy.
+"
+21566,Relay synchronization in multiplex networks,"  Relay (or remote) synchronization between two not directly connected
+oscillators in a network is an important feature allowing distant coordination.
+In this work, we report a systematic study of this phenomenon in multiplex
+networks, where inter-layer synchronization occurs between distant layers
+mediated by a relay layer that acts as a transmitter. We show that this
+transmission can be extended to higher order relay configurations, provided
+symmetry conditions are preserved. By first order perturbative analysis, we
+identify the dynamical and topological dependencies of relay synchronization in
+a multiplex. We find that the relay synchronization threshold is considerably
+reduced in a multiplex configuration, and that such synchronous state is mostly
+supported by the lower degree nodes of the outer layers, while hubs can be
+de-multiplexed without affecting overall coherence. Finally, we experimentally
+validated the analytical and numerical findings by means of a multiplex of
+three layers of electronic circuits.the analytical and numerical findings by
+means of a multiplex of three layers of electronic circuits.
+"
+21567,Classification and Retrieval of Digital Pathology Scans: A New Dataset,"  In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image
+classification and retrieval in digital pathology. We use the whole scan images
+of 24 different tissue textures to generate 1,325 test patches of size
+1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according
+to preferences of algorithm designer and can range from approximately 27,000 to
+over 50,000 patches if the preset parameters are adopted. We propose a compound
+patch-and-scan accuracy measurement that makes achieving high accuracies quite
+challenging. In addition, we set the benchmarking line by applying LBP,
+dictionary approach and convolutional neural nets (CNNs) and report their
+results. The highest accuracy was 41.80\% for CNN.
+"
+21568,Boundary Perimeter Bethe Ansatz,"  We study the partition function of the six-vertex model in the rational limit
+on arbitrary Baxter lattices with reflecting boundary. Every such lattice is
+interpreted as an invariant of the twisted Yangian. This identification allows
+us to relate the partition function of the vertex model to the Bethe wave
+function of an open spin chain. We obtain the partition function in terms of
+creation operators on a reference state from the algebraic Bethe ansatz and as
+a sum of permutations and reflections from the coordinate Bethe ansatz.
+"
+21569,Performance Modeling and Prediction for Dense Linear Algebra,"  This dissertation introduces measurement-based performance modeling and
+prediction techniques for dense linear algebra algorithms. As a core principle,
+these techniques avoid executions of such algorithms entirely, and instead
+predict their performance through runtime estimates for the underlying compute
+kernels. For a variety of operations, these predictions allow to quickly select
+the fastest algorithm configurations from available alternatives. We consider
+two scenarios that cover a wide range of computations:
+To predict the performance of blocked algorithms, we design
+algorithm-independent performance models for kernel operations that are
+generated automatically once per platform. For various matrix operations,
+instantaneous predictions based on such models both accurately identify the
+fastest algorithm, and select a near-optimal block size.
+For performance predictions of BLAS-based tensor contractions, we propose
+cache-aware micro-benchmarks that take advantage of the highly regular
+structure inherent to contraction algorithms. At merely a fraction of a
+contraction's runtime, predictions based on such micro-benchmarks identify the
+fastest combination of tensor traversal and compute kernel.
+"
+21570,On the Numerical Solution of Fourth-Order Linear Two-Point Boundary Value Problems,"  This paper introduces a fast and numerically stable algorithm for the
+solution of fourth-order linear boundary value problems on an interval. This
+type of equation arises in a variety of settings in physics and signal
+processing. However, current methods of solution involve discretizing the
+differential equation directly by finite elements or finite differences, and
+consequently suffer from the poor conditioning introduced by such schemes. Our
+new method instead reformulates the equation as a collection of second-kind
+integral equations defined on local subdomains. Each such equation can be
+stably discretized. The boundary values of these local solutions are matched by
+solving a banded linear system. The method of deferred corrections is then used
+to increase the accuracy of the scheme. Deferred corrections requires applying
+the integral operator to a function on the entire domain, for which we provide
+an algorithm with linear cost. We illustrate the performance of our method on
+several numerical examples.
+"
+21571,Bayesian estimation of thermonuclear reaction rates for deuterium+deuterium reactions,"  The study of d+d reactions is of major interest since their reaction rates
+affect the predicted abundances of D, 3He, and 7Li. In particular, recent
+measurements of primordial D/H ratios call for reduced uncertainties in the
+theoretical abundances predicted by big bang nucleosynthesis (BBN). Different
+authors have studied reactions involved in BBN by incorporating new
+experimental data and a careful treatment of systematic and probabilistic
+uncertainties. To analyze the experimental data, Coc et al. (2015) used results
+of ab initio models for the theoretical calculation of the energy dependence of
+S-factors in conjunction with traditional statistical methods based on Chi-2
+minimization. Bayesian methods have now spread to many scientific fields and
+provide numerous advantages in data analysis. Astrophysical S-factors and
+reaction rates using Bayesian statistics were calculated by Iliadis et al.
+(2016). Here we present a similar analysis for two d+d reactions, d(d,n)3He and
+d(d,p)3H, that has been translated into a total decrease of the predicted D/H
+value by 0.16%.
+"
+21572,Detecting and Explaining Crisis,"  Individuals on social media may reveal themselves to be in various states of
+crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis
+from social media text automatically and accurately can have profound
+consequences. However, detecting a general state of crisis without explaining
+why has limited applications. An explanation in this context is a coherent,
+concise subset of the text that rationalizes the crisis detection. We explore
+several methods to detect and explain crisis using a combination of neural and
+non-neural techniques. We evaluate these techniques on a unique data set
+obtained from Koko, an anonymous emotional support network available through
+various messaging applications. We annotate a small subset of the samples
+labeled with crisis with corresponding explanations. Our best technique
+significantly outperforms the baseline for detection and explanation.
+"
+21573,From functors between categories to homomorphisms of C-systems,"  We prove a theorem, motivated by a conjecture of Voevodsky on C-systems, that
+provides, under some assumptions, a lift of a functor with a C-system as domain
+and a category as codomain, to a homomorphism of C-systems.
+"
+21574,FiLM: Visual Reasoning with a General Conditioning Layer,"  We introduce a general-purpose conditioning method for neural networks called
+FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network
+computation via a simple, feature-wise affine transformation based on
+conditioning information. We show that FiLM layers are highly effective for
+visual reasoning - answering image-related questions which require a
+multi-step, high-level process - a task which has proven difficult for standard
+deep learning methods that do not explicitly model reasoning. Specifically, we
+show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error
+for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are
+robust to ablations and architectural modifications, and 4) generalize well to
+challenging, new data from few examples or even zero-shot.
+"
+21575,Stability of charge-stripe ordered La2-xSrxNiO4+delta at one third doping,"  The stability of charge ordered phases is doping dependent, with different
+materials having particularly stable ordered phases. In the half filled charge
+ordered phases of the cuprates this occurs at one eighth doping, whereas in
+charge-stripe ordered La2-xSrxNiO4+delta there is enhanced stability at one
+third doping. In this paper we discuss the known details of the charge-stripe
+order in La2-xSrxNiO4+delta, and how these properties lead to the one third
+doping stability.
+"
+21576,Primal-Dual $π$ Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problems,"  Consider the problem of approximating the optimal policy of a Markov decision
+process (MDP) by sampling state transitions. In contrast to existing
+reinforcement learning methods that are based on successive approximations to
+the nonlinear Bellman equation, we propose a Primal-Dual $\pi$ Learning method
+in light of the linear duality between the value and policy. The $\pi$ learning
+method is model-free and makes primal-dual updates to the policy and value
+vectors as new data are revealed. For infinite-horizon undiscounted Markov
+decision process with finite state space $S$ and finite action space $A$, the
+$\pi$ learning method finds an $\epsilon$-optimal policy using the following
+number of sample transitions $$ \tilde{O}( \frac{(\tau\cdot t^*_{mix})^2 |S|
+|A| }{\epsilon^2} ),$$ where $t^*_{mix}$ is an upper bound of mixing times
+across all policies and $\tau$ is a parameter characterizing the range of
+stationary distributions across policies. The $\pi$ learning method also
+applies to the computational problem of MDP where the transition probabilities
+and rewards are explicitly given as the input. In the case where each state
+transition can be sampled in $\tilde{O}(1)$ time, the $\pi$ learning method
+gives a sublinear-time algorithm for solving the averaged-reward MDP.
+"
+21577,Automorphisms of pure braid Groups,"  In this paper, we investigate the structure of the automorphism groups of
+pure braid groups. We prove that, for $n>3$, $\Aut(P_n)$ is generated by the
+subgroup $\Aut_c(P_n)$ of central automorphisms of $P_n$, the subgroup
+$\Aut(B_n)$ of restrictions of automorphisms of $B_n$ on $P_n$ and one extra
+automorphism $w_n$. We also investigate the lifting and extension problem for
+automorphisms of some well-known exact sequences arising from braid groups, and
+prove that that answers are negative in most cases. Specifically, we prove that
+no non-trivial central automorphism of $P_n$ can be extended to an automorphism
+of $B_n$.
+"
+21578,Spin phonon interactions and magneto-thermal transport behavior in p-Si,"  The spin-phonon interaction is the dominant process for spin relaxation in
+Si, and as thermal transport in Si is dominated by phonons, one would expect
+spin polarization to influence Si's thermal conductivity. Here we report the
+experimental evidence of just such a coupling. We have performed concurrent
+measurements of spin, charge, and phonon transport in p-doped Si across a wide
+range of temperatures. In an experimental system of a freestanding two um p-Si
+beam coated on one side with a thin (25 nm) ferromagnetic spin injection layer,
+we use the self-heating 3 omega method to measure changes in electrical and
+thermal conductivity under the influence of a magnetic field. These
+magneto-thermal transport measurements reveal signatures in the variation of
+electrical and thermal transport that are consistent with spin-phonon
+interaction. Raman spectroscopy measurements and first principle's calculations
+support that these variations are due to spin-phonon interaction. Spin
+polarization leads to softening of phonon modes, a reduction in the group
+velocity of acoustic modes, and a subsequent decrease in thermal conductivity
+at room temperature. Moreover, magneto-thermal transport measurements as a
+function of temperature indicate a change in the spin-phonon relaxation
+behavior at low temperature.
+"
+21579,Benchmark of dynamic electron correlation models for seniority-zero wavefunctions and their application to thermochemistry,"  Wavefunctions restricted to electron-pair states are promising models to
+describe static/nondynamic electron correlation effects encountered, for
+instance, in bond-dissociation processes and transition-metal and actinide
+chemistry. To reach spectroscopic accuracy, however, the missing dynamic
+electron correlation effects that cannot be described by electron-pair states
+need to be included \textit{a posteriori}. In this article, we extend the
+previously presented perturbation theory models with an Antisymmetric Product
+of 1-reference orbital Geminal (AP1roG) reference function that allow us to
+describe both static/nondynamic and dynamic electron correlation effects.
+Specifically, our perturbation theory models combine a diagonal and
+off-diagonal zero-order Hamiltonian, a single-reference and multi-reference
+dual state, and different excitation operators used to construct the projection
+manifold. We benchmark all proposed models as well as an \textit{a posteriori}
+linearized coupled cluster correction on top of AP1roG against CR-CCSD(T)
+reference data for reaction energies of several closed-shell molecules that are
+extrapolated to the basis set limit. Moreover, we test the performance of our
+new methods for multiple bond breaking processes in the N$_2$, C$_2$, and BN
+dimers against MRCI-SD and MRCI-SD+Q reference data. Our numerical results
+indicate that the best performance is obtained from a linearized coupled
+cluster correction as well as second-order perturbation theory corrections
+employing a diagonal and off-diagonal zero-order Hamiltonian and a
+single-determinant dual state. These dynamic corrections on top of AP1roG allow
+us to reliably model molecular systems dominated by static/nondynamic as well
+as dynamic electron correlation.
+"
+21580,Noncommutative resolutions of discriminants,"  We give an introduction to the McKay correspondence and its connection to
+quotients of $\mathbb{C}^n$ by finite reflection groups. This yields a natural
+construction of noncommutative resolutions of the discriminants of these
+reflection groups. This paper is an extended version of E.F.'s talk with the
+same title delivered at the ICRA.
+"
+21581,Machine Learning $\mathbb{Z}_{2}$ Quantum Spin Liquids with Quasi-particle Statistics,"  After decades of progress and effort, obtaining a phase diagram for a
+strongly-correlated topological system still remains a challenge. Although in
+principle one could turn to Wilson loops and long-range entanglement,
+evaluating these non-local observables at many points in phase space can be
+prohibitively costly. With growing excitement over topological quantum
+computation comes the need for an efficient approach for obtaining topological
+phase diagrams. Here we turn to machine learning using quantum loop topography
+(QLT), a notion we have recently introduced. Specifically, we propose a
+construction of QLT that is sensitive to quasi-particle statistics. We then use
+mutual statistics between the spinons and visons to detect a $\mathbb{Z}_{2}$
+quantum spin liquid in a multi-parameter phase space. We successfully obtain
+the quantum phase boundary between the topological and trivial phases using a
+simple feed-forward neural network. Furthermore, we demonstrate advantages of
+our approach for the evaluation of phase diagrams relating to speed and
+storage. Such statistics-based machine learning of topological phases opens new
+efficient routes to studying topological phase diagrams in strongly correlated
+systems.
+"
+21582,Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation,"  In Multimodal Neural Machine Translation (MNMT), a neural model generates a
+translated sentence that describes an image, given the image itself and one
+source descriptions in English. This is considered as the multimodal image
+caption translation task. The images are processed with Convolutional Neural
+Network (CNN) to extract visual features exploitable by the translation model.
+So far, the CNNs used are pre-trained on object detection and localization
+task. We hypothesize that richer architecture, such as dense captioning models,
+may be more suitable for MNMT and could lead to improved translations. We
+extend this intuition to the word-embeddings, where we compute both linguistic
+and visual representation for our corpus vocabulary. We combine and compare
+different confi
+"
+21583,Delay-time distribution of core-collapse supernovae with late events resulting from binary interaction,"  Most massive stars, the progenitors of core-collapse supernovae, are in close
+binary systems and may interact with their companion through mass transfer or
+merging. We undertake a population synthesis study to compute the delay-time
+distribution of core-collapse supernovae, that is, the supernova rate versus
+time following a starburst, taking into account binary interactions. We test
+the systematic robustness of our results by running various simulations to
+account for the uncertainties in our standard assumptions. We find that a
+significant fraction, $15^{+9}_{-8}$%, of core-collapse supernovae are `late',
+that is, they occur 50-200 Myrs after birth, when all massive single stars have
+already exploded. These late events originate predominantly from binary systems
+with at least one, or, in most cases, with both stars initially being of
+intermediate mass ($4-8M_{\odot}$). The main evolutionary channels that
+contribute often involve either the merging of the initially more massive
+primary star with its companion or the engulfment of the remaining core of the
+primary by the expanding secondary that has accreted mass at an earlier
+evolutionary stage. Also, the total number of core-collapse supernovae
+increases by $14^{+15}_{-14}$% because of binarity for the same initial stellar
+mass. The high rate implies that we should have already observed such late
+core-collapse supernovae, but have not recognized them as such. We argue that
+$\phi$ Persei is a likely progenitor and that eccentric neutron star - white
+dwarf systems are likely descendants. Late events can help explain the
+discrepancy in the delay-time distributions derived from supernova remnants in
+the Magellanic Clouds and extragalactic type Ia events, lowering the
+contribution of prompt Ia events. We discuss ways to test these predictions and
+speculate on the implications for supernova feedback in simulations of galaxy
+evolution.
+"
+21584,Ignore or Comply? On Breaking Symmetry in Consensus,"  We study consensus processes on the complete graph of $n$ nodes. Initially,
+each node supports one from up to n opinions. Nodes randomly and in parallel
+sample the opinions of constant many nodes. Based on these samples, they use an
+update rule to change their own opinion.
+The goal is to reach consensus, a configuration where all nodes support the
+same opinion. We compare two well-known update rules: 2-Choices and 3-Majority.
+In the former, each node samples two nodes and adopts their opinion if they
+agree. In the latter, each node samples three nodes: If an opinion is supported
+by at least two samples the node adopts it, otherwise it randomly adopts one of
+the sampled opinions. Known results for these update rules focus on initial
+configurations with a limited number of colors (say $n^{1/3}$ ), or typically
+assume a bias, where one opinion has a much larger support than any other. For
+such biased configurations, the time to reach consensus is roughly the same for
+2-Choices and 3-Majority.
+Interestingly, we prove that this is no longer true for configurations with a
+large number of initial colors. In particular, we show that 3-Majority reaches
+consensus with high probability in $O(n^{3/4}\log^{7/8}n)$ rounds, while
+2-Choices can need $\Omega(n/\log n)$ rounds. We thus get the first
+unconditional sublinear bound for 3-Majority and the first result separating
+the consensus time of these processes. Along the way, we develop a framework
+that allows a fine-grained comparison between consensus processes from a
+specific class. We believe that this framework might help to classify the
+performance of more consensus processes.
+"
+21585,End to End Vehicle Lateral Control Using a Single Fisheye Camera,"  Convolutional neural networks are commonly used to control the steering angle
+for autonomous cars. Most of the time, multiple long range cameras are used to
+generate lateral failure cases. In this paper we present a novel model to
+generate this data and label augmentation using only one short range fisheye
+camera. We present our simulator and how it can be used as a consistent metric
+for lateral end-to-end control evaluation. Experiments are conducted on a
+custom dataset corresponding to more than 10000 km and 200 hours of open road
+driving. Finally we evaluate this model on real world driving scenarios, open
+road and a custom test track with challenging obstacle avoidance and sharp
+turns. In our simulator based on real-world videos, the final model was capable
+of more than 99% autonomy on urban road
+"
+21586,Metrics for Deep Generative Models,"  Neural samplers such as variational autoencoders (VAEs) or generative
+adversarial networks (GANs) approximate distributions by transforming samples
+from a simple random source---the latent space---to samples from a more complex
+distribution represented by a dataset. While the manifold hypothesis implies
+that the density induced by a dataset contains large regions of low density,
+the training criterions of VAEs and GANs will make the latent space densely
+covered. Consequently points that are separated by low-density regions in
+observation space will be pushed together in latent space, making stationary
+distances poor proxies for similarity. We transfer ideas from Riemannian
+geometry to this setting, letting the distance between two points be the
+shortest path on a Riemannian manifold induced by the transformation. The
+method yields a principled distance measure, provides a tool for visual
+inspection of deep generative models, and an alternative to linear
+interpolation in latent space. In addition, it can be applied for robot
+movement generalization using previously learned skills. The method is
+evaluated on a synthetic dataset with known ground truth; on a simulated robot
+arm dataset; on human motion capture data; and on a generative model of
+handwritten digits.
+"
+21587,Posterior Inference for Sparse Hierarchical Non-stationary Models,"  Gaussian processes are valuable tools for non-parametric modelling, where
+typically an assumption of stationarity is employed. While removing this
+assumption can improve prediction, fitting such models is challenging. In this
+work, hierarchical models are constructed based on Gaussian Markov random
+fields with stochastic spatially varying parameters. Importantly, this allows
+for non-stationarity while also addressing the computational burden through a
+sparse representation of the precision matrix. The prior field is chosen to be
+Matérn, and two hyperpriors, for the spatially varying parameters, are
+considered. One hyperprior is Ornstein-Uhlenbeck, formulated through an
+autoregressive process. The other corresponds to the widely used squared
+exponential. In this setting, efficient Markov chain Monte Carlo (MCMC)
+sampling is challenging due to the strong coupling a posteriori of the
+parameters and hyperparameters. We develop and compare three MCMC schemes,
+which are adaptive and therefore free of parameter tuning. Furthermore, a novel
+extension to higher-dimensional settings is proposed through an additive
+structure that retains the flexibility and scalability of the model, while also
+inheriting interpretability from the additive approach. A thorough assessment
+of the ability of the methods to efficiently explore the posterior distribution
+and to account for non-stationarity is presented, in both simulated experiments
+and a real-world computer emulation problem.
+"
+21588,A Semiotics-inspired Domain-Specific Modeling Language for Complex Event Processing Rules,"  Complex Event Processing (CEP) is one technique used to the handling data
+flows. It allows pre-establishing conditions through rules and firing events
+when certain patterns are found in the data flows. Because the rules for
+defining such patterns are expressed with specific languages, users of these
+technologies must understand the underlying expression syntax. To reduce the
+complexity of writing CEP rules, some researchers are employing Domain Specific
+Modeling Language (DSML) to provide modelling through visual tools. However,
+existing approaches are ignoring some user design techniques that facilitate
+usability. Thus, resulting tools eventually has become more complexes for
+handling CEP than the conventional usage. Also, research on DSML tools
+targeting CEP does not present any evaluation around usability. This article
+proposes a DSML combined with visual notations techniques to create CEP rules
+with a more intuitive development model adapted for the non-expert user needs.
+The resulting tool was evaluated by non-expert users that were capable of
+easily creating CEP rules without prior knowledge of the underlying expression
+language.
+"
+21589,Fairly Dividing a Cake after Some Parts Were Burnt in the Oven,"  There is a heterogeneous resource that contains both good parts and bad
+parts, for example, a cake with some parts burnt, a land-estate with some parts
+heavily taxed, or a chore with some parts fun to do. The resource has to be
+divided fairly among $n$ agents with different preferences, each of whom has a
+personal value-density function on the resource. The value-density functions
+can accept any real value --- positive, negative or zero. Each agent should
+receive a connected piece and no agent should envy another agent. We prove that
+such a division exists for 3 agents and present preliminary positive results
+for larger numbers of agents.
+"
+21590,"Atomic vapor as a source of tunable, non-Gaussian self-reconstructing optical modes","  In this manuscript, we demonstrate the ability of nonlinear light-atom
+interactions to produce tunably non-Gaussian, partially self-healing optical
+modes. Gaussian spatial-mode light tuned near to the atomic resonances in hot
+rubidium vapor is shown to result in non-Gaussian output mode structures that
+may be controlled by varying either the input beam power or the temperature of
+the atomic vapor. We show that the output modes exhibit a degree of
+self-reconstruction after encountering an obstruction in the beam path. The
+resultant modes are similar to truncated Bessel-Gauss modes that exhibit the
+ability to self-reconstruct earlier upon propagation than Gaussian modes. The
+ability to generate tunable, self-reconstructing beams has potential
+applications to a variety of imaging and communication scenarios.
+"
+21591,Understanding Performance of Edge Content Caching for Mobile Video Streaming,"  Today's Internet has witnessed an increase in the popularity of mobile video
+streaming, which is expected to exceed 3/4 of the global mobile data traffic by
+2019. To satisfy the considerable amount of mobile video requests, video
+service providers have been pushing their content delivery infrastructure to
+edge networks--from regional CDN servers to peer CDN servers (e.g.,
+smartrouters in users' homes)--to cache content and serve users with storage
+and network resources nearby. Among the edge network content caching paradigms,
+Wi-Fi access point caching and cellular base station caching have become two
+mainstream solutions. Thus, understanding the effectiveness and performance of
+these solutions for large-scale mobile video delivery is important. However,
+the characteristics and request patterns of mobile video streaming are unclear
+in practical wireless network. In this paper, we use real-world datasets
+containing 50 million trace items of nearly 2 million users viewing more than
+0.3 million unique videos using mobile devices in a metropolis in China over 2
+weeks, not only to understand the request patterns and user behaviors in mobile
+video streaming, but also to evaluate the effectiveness of Wi-Fi and
+cellular-based edge content caching solutions. To understand performance of
+edge content caching for mobile video streaming, we first present temporal and
+spatial video request patterns, and we analyze their impacts on caching
+performance using frequency-domain and entropy analysis approaches. We then
+study the behaviors of mobile video users, including their mobility and
+geographical migration behaviors. Using trace-driven experiments, we compare
+strategies for edge content caching including LRU and LFU, in terms of
+supporting mobile video requests. Moreover, we design an efficient caching
+strategy based on the measurement insights and experimentally evaluate its
+performance.
+"
+21592,Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation,"  Image segmentation is the process of partitioning the image into significant
+regions easier to analyze. Nowadays, segmentation has become a necessity in
+many practical medical imaging methods as locating tumors and diseases. Hidden
+Markov Random Field model is one of several techniques used in image
+segmentation. It provides an elegant way to model the segmentation process.
+This modeling leads to the minimization of an objective function. Conjugate
+Gradient algorithm (CG) is one of the best known optimization techniques. This
+paper proposes the use of the Conjugate Gradient algorithm (CG) for image
+segmentation, based on the Hidden Markov Random Field. Since derivatives are
+not available for this expression, finite differences are used in the CG
+algorithm to approximate the first derivative. The approach is evaluated using
+a number of publicly available images, where ground truth is known. The Dice
+Coefficient is used as an objective criterion to measure the quality of
+segmentation. The results show that the proposed CG approach compares favorably
+with other variants of Hidden Markov Random Field segmentation algorithms.
+"
+21593,Layer-wise Relevance Propagation for Explainable Recommendations,"  In this paper, we tackle the problem of explanations in a deep-learning based
+model for recommendations by leveraging the technique of layer-wise relevance
+propagation. We use a Deep Convolutional Neural Network to extract relevant
+features from the input images before identifying similarity between the images
+in feature space. Relationships between the images are identified by the model
+and layer-wise relevance propagation is used to infer pixel-level details of
+the images that may have significantly informed the model's choice. We evaluate
+our method on an Amazon products dataset and demonstrate the efficacy of our
+approach.
+"
+21594,Compressed channeled spectropolarimetry,"  Channeled spectropolarimetry measures the spectrally resolved Stokes
+parameters. A key aspect of this technique is to accurately reconstruct the
+Stokes parameters from a modulated measurement of the channeled
+spectropolarimeter. The state-of-the-art reconstruction algorithm uses the
+Fourier transform to extract the Stokes parameters from channels in the Fourier
+domain. While this approach is straightforward, it can be sensitive to noise
+and channel cross-talk, and it imposes bandwidth limitations that cut off high
+frequency details. To overcome these drawbacks, we present a reconstruction
+method called compressed channeled spectropolarimetry. In our proposed
+framework, reconstruction in channeled spectropolarimetry is an underdetermined
+problem, where we take N measurements and solve for 3N unknown Stokes
+parameters. We formulate an optimization problem by creating a mathematical
+model of the channeled spectropolarimeter with inspiration from compressed
+sensing. We show that our approach offers greater noise robustness and
+reconstruction accuracy compared with the Fourier transform technique in
+simulations and experimental measurements. By demonstrating more accurate
+reconstructions, we push performance to the native resolution of the sensor,
+allowing more information to be recovered from a single measurement of a
+channeled spectropolarimeter.
+"
+21595,Limits for Rumor Spreading in stochastic populations,"  Biological systems can share and collectively process information to yield
+emergent effects, despite inherent noise in communication. While man-made
+systems often employ intricate structural solutions to overcome noise, the
+structure of many biological systems is more amorphous. It is not well
+understood how communication noise may affect the computational repertoire of
+such groups. To approach this question we consider the basic collective task of
+rumor spreading, in which information from few knowledgeable sources must
+reliably flow into the rest of the population.
+In order to study the effect of communication noise on the ability of groups
+that lack stable structures to efficiently solve this task, we consider a noisy
+version of the uniform PULL model. We prove a lower bound which implies that,
+in the presence of even moderate levels of noise that affect all facets of the
+communication, no scheme can significantly outperform the trivial one in which
+agents have to wait until directly interacting with the sources. Our results
+thus show an exponential separation between the uniform PUSH and PULL
+communication models in the presence of noise. Such separation may be
+interpreted as suggesting that, in order to achieve efficient rumor spreading,
+a system must exhibit either some degree of structural stability or,
+alternatively, some facet of the communication which is immune to noise.
+We corroborate our theoretical findings with a new analysis of experimental
+data regarding recruitment in Cataglyphis niger desert ants.
+"
+21596,Approximability of Discriminators Implies Diversity in GANs,"  While Generative Adversarial Networks (GANs) have empirically produced
+impressive results on learning complex real-world distributions, recent work
+has shown that they suffer from lack of diversity or mode collapse. The
+theoretical work of Arora et al. suggests a dilemma about GANs' statistical
+properties: powerful discriminators cause overfitting, whereas weak
+discriminators cannot detect mode collapse.
+In contrast, we show in this paper that GANs can in principle learn
+distributions in Wasserstein distance (or KL-divergence in many cases) with
+polynomial sample complexity, if the discriminator class has strong
+distinguishing power against the particular generator class (instead of against
+all possible generators). For various generator classes such as mixture of
+Gaussians, exponential families, and invertible neural networks generators, we
+design corresponding discriminators (which are often neural nets of specific
+architectures) such that the Integral Probability Metric (IPM) induced by the
+discriminators can provably approximate the Wasserstein distance and/or
+KL-divergence. This implies that if the training is successful, then the
+learned distribution is close to the true distribution in Wasserstein distance
+or KL divergence, and thus cannot drop modes. Our preliminary experiments show
+that on synthetic datasets the test IPM is well correlated with KL divergence,
+indicating that the lack of diversity may be caused by the sub-optimality in
+optimization instead of statistical inefficiency.
+"
+21597,Identifying Nonlinear 1-Step Causal Influences in Presence of Latent Variables,"  We propose an approach for learning the causal structure in stochastic
+dynamical systems with a $1$-step functional dependency in the presence of
+latent variables. We propose an information-theoretic approach that allows us
+to recover the causal relations among the observed variables as long as the
+latent variables evolve without exogenous noise. We further propose an
+efficient learning method based on linear regression for the special sub-case
+when the dynamics are restricted to be linear. We validate the performance of
+our approach via numerical simulations.
+"
+21598,The discovery of tidal tails around the globular cluster NGC 7492 with Pan-STARRS1,"  We report the discovery of tidal tails around the Galactic globular cluster
+NGC 7492, based on the Data Release 1 of the Pan-STARRS 1 survey. The tails
+were detected with a version of the matched filter technique applied to the
+$(g-r,r)$ and $(g-i,i)$ color-magnitude diagrams. Tidal tails emerging from the
+cluster extend at least $\sim$3.5 degrees in the North-East to South-East
+direction, equivalent to $\sim1.5$ kpc in projected length.
+"
+21599,Strong 2.t and Strong 3.t Transformations for Strong M-equivalence,"  Parikh matrices have been extensively investigated due to their usefulness in
+studying subword occurrences in words. Due to the dependency of Parikh matrices
+on the ordering of the alphabet, strong M-equivalence was proposed as an
+order-independent alternative to M-equivalence in studying words possessing the
+same Parikh matrix. This paper introduces and studies the notions of strong 2.t
+and strong 3.t transformations in determining when two ternary words are
+strongly M-equivalent. The irreducibility of strong 2.t transformations are
+then scrutinized, exemplified by a structural characterization of irreducible
+strong 2.2 transformations. The common limitation of these transformations in
+characterizing strong M-equivalence is then addressed.
+"
+21600,Integrals of eigenfunctions over curves in surfaces of nonpositive curvature,"  Let $(M,g)$ be a compact, 2-dimensional Riemannian manifold with nonpositive
+sectional curvature. Let $\Delta_g$ be the Laplace-Beltrami operator
+corresponding to the metric $g$ on $M$, and let $e_\lambda$ be $L^2$-normalized
+eigenfunctions of $\Delta_g$ with eigenvalue $\lambda$, i.e. \[ -\Delta_g
+e_\lambda = \lambda^2 e_\lambda. \] We prove \[ \left| \int_{\mathbb R} b(t)
+e_\lambda (\gamma(t)) \, dt \right| = o(1) \quad \text{ as } \lambda \to \infty
+\] where $b$ is a smooth, compactly supported function on $\mathbb R$ and
+$\gamma$ is a curve parametrized by arc-length whose geodesic curvature
+$\kappa(\gamma(t))$ avoids two critical curvatures $\mathbf
+k(\gamma'^\perp(t))$ and $\mathbf k(-\gamma'^{\perp}(t))$ for each $t \in
+\operatorname{supp} b$. $\mathbf k(v)$ denotes the curvature of a circle with
+center taken to infinity along the geodesic ray in direction $-v$.
+"
+21601,Wavenet based low rate speech coding,"  Traditional parametric coding of speech facilitates low rate but provides
+poor reconstruction quality because of the inadequacy of the model used. We
+describe how a WaveNet generative speech model can be used to generate high
+quality speech from the bit stream of a standard parametric coder operating at
+2.4 kb/s. We compare this parametric coder with a waveform coder based on the
+same generative model and show that approximating the signal waveform incurs a
+large rate penalty. Our experiments confirm the high performance of the WaveNet
+based coder and show that the speech produced by the system is able to
+additionally perform implicit bandwidth extension and does not significantly
+impair recognition of the original speaker for the human listener, even when
+that speaker has not been used during the training of the generative model.
+"
+21602,On Synchronization of Dynamical Systems over Directed Switching Topologies: An Algebraic and Geometric Perspective,"  In this paper, we aim to investigate the synchronization problem of dynamical
+systems, which can be of generic linear or Lipschitz nonlinear type,
+communicating over directed switching network topologies. A mild connectivity
+assumption on the switching topologies is imposed, which allows them to be
+directed and jointly connected. We propose a novel analysis framework from both
+algebraic and geometric perspectives to justify the attractiveness of the
+synchronization manifold. Specifically, it is proven that the complementary
+space of the synchronization manifold can be spanned by certain subspaces.
+These subspaces can be the eigenspaces of the nonzero eigenvalues of Laplacian
+matrices in linear case. They can also be subspaces in which the projection of
+the nonlinear self-dynamics still retains the Lipschitz property. This allows
+to project the states of the dynamical systems into these subspaces and
+transform the synchronization problem under consideration equivalently into a
+convergence one of the projected states in each subspace. Then, assuming the
+joint connectivity condition on the communication topologies, we are able to
+work out a simple yet effective and unified convergence analysis for both types
+of dynamical systems. More specifically, for partial-state coupled generic
+linear systems, it is proven that synchronization can be reached if an extra
+condition, which is easy to verify in several cases, on the system dynamics is
+satisfied. For Lipschitz-type nonlinear systems with positive-definite inner
+coupling matrix, synchronization is realized if the coupling strength is strong
+enough to stabilize the evolution of the projected states in each subspace
+under certain conditions.
+"
+21603,"Independence, Conditionality and Structure of Dempster-Shafer Belief Functions","  Several approaches of structuring (factorization, decomposition) of
+Dempster-Shafer joint belief functions from literature are reviewed with
+special emphasis on their capability to capture independence from the point of
+view of the claim that belief functions generalize bayes notion of probability.
+It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets'
+{Smets:93} directed acyclic graphs are unable to capture statistical
+dependence/independence of bayesian networks {Pearl:88}. On the other hand,
+though Shenoy and Shafer's hypergraphs can explicitly represent bayesian
+network factorization of bayesian belief functions, they disclaim any need for
+representation of independence of variables in belief functions.
+Cano et al. {Cano:93} reject the hypergraph representation of Shenoy and
+Shafer just on grounds of missing representation of variable independence, but
+in their frameworks some belief functions factorizable in Shenoy/Shafer
+framework cannot be factored.
+The approach in {Klopotek:93f} on the other hand combines the merits of both
+Cano et al. and of Shenoy/Shafer approach in that for Shenoy/Shafer approach no
+simpler factorization than that in {Klopotek:93f} approach exists and on the
+other hand all independences among variables captured in Cano et al. framework
+and many more are captured in {Klopotek:93f} approach.%
+"
+21604,Wave Manipulations by Coherent Perfect Channeling,"  We report experimental and theoretical investigations of coherent perfect
+channeling (CPC), a process that two incoming coherent waves in waveguides are
+completely channeled into one or two other waveguides with little energy
+dissipation via strong coherent interaction between the two waves mediated by a
+deep subwavelength dimension scatterer at the common junction of the
+waveguides. Two such scatterers for acoustic waves are discovered, one
+confirmed by experiments and the other predicted by theory, and their
+scattering matrices are formulated. Scatterers with other CPC scattering
+matrices are explored, and preliminary investigations of their properties are
+conducted. The scattering matrix formulism makes it possible to extend the
+applicable domain of CPC to other scalar waves, such as electromagnetic waves
+and quantum wavefunctions.
+"
+21605,The Coldest Place in the Universe: Probing the Ultra-Cold Outflow and Dusty Disk in the Boomerang Nebula,"  Our Cycle 0 ALMA observations confirmed that the Boomerang Nebula is the
+coldest known object in the Universe, with a massive high-speed outflow that
+has cooled significantly below the cosmic background temperature. Our new CO
+1-0 data reveal heretofore unseen distant regions of this ultra-cold outflow,
+out to $\gtrsim120,000$ AU. We find that in the ultra-cold outflow, the
+mass-loss rate (dM/dt) increases with radius, similar to its expansion velocity
+($V$) - taking $V\propto r$, we find $dM/dt \propto r^{0.9-2.2}$. The mass in
+the ultra-cold outflow is $\gtrsim3.3$ Msun, and the Boomerang's main-sequence
+progenitor mass is $\gtrsim4$ Msun. Our high angular resolution ($\sim$0"".3) CO
+J=3-2 map shows the inner bipolar nebula's precise, highly-collimated shape,
+and a dense central waist of size (FWHM) $\sim$1740 AU$\times275$ AU. The
+molecular gas and the dust as seen in scattered light via optical HST imaging
+show a detailed correspondence. The waist shows a compact core in thermal dust
+emission at 0.87-3.3 mm, which harbors $(4-7)\times10^{-4}$ Msun~of very large
+($\sim$mm-to-cm sized), cold ($\sim20-30$ K) grains. The central waist
+(assuming its outer regions to be expanding) and fast bipolar outflow have
+expansion ages of $\lesssim1925$ yr and $\le1050$ yr: the ""jet-lag"" (i.e.,
+torus age minus the fast-outflow age) in the Boomerang supports models in which
+the primary star interacts directly with a binary companion. We argue that this
+interaction resulted in a common-envelope configuration while the Boomerang's
+primary was an RGB or early-AGB star, with the companion finally merging into
+the primary's core, and ejecting the primary's envelope that now forms the
+ultra-cold outflow.
+"
+21606,The CARMENES search for exoplanets around M dwarfs: High-resolution optical and near-infrared spectroscopy of 324 survey stars,"  The CARMENES radial velocity (RV) survey is observing 324 M dwarfs to search
+for any orbiting planets. In this paper, we present the survey sample by
+publishing one CARMENES spectrum for each M dwarf. These spectra cover the
+wavelength range 520--1710nm at a resolution of at least $R > 80,000$, and we
+measure its RV, H$\alpha$ emission, and projected rotation velocity. We present
+an atlas of high-resolution M-dwarf spectra and compare the spectra to
+atmospheric models. To quantify the RV precision that can be achieved in
+low-mass stars over the CARMENES wavelength range, we analyze our empirical
+information on the RV precision from more than 6500 observations. We compare
+our high-resolution M-dwarf spectra to atmospheric models where we determine
+the spectroscopic RV information content, $Q$, and signal-to-noise ratio. We
+find that for all M-type dwarfs, the highest RV precision can be reached in the
+wavelength range 700--900nm. Observations at longer wavelengths are equally
+precise only at the very latest spectral types (M8 and M9). We demonstrate that
+in this spectroscopic range, the large amount of absorption features
+compensates for the intrinsic faintness of an M7 star. To reach an RV precision
+of 1ms$^{-1}$ in very low mass M dwarfs at longer wavelengths likely requires
+the use of a 10m class telescope. For spectral types M6 and earlier, the
+combination of a red visual and a near-infrared spectrograph is ideal to search
+for low-mass planets and to distinguish between planets and stellar
+variability. At a 4m class telescope, an instrument like CARMENES has the
+potential to push the RV precision well below the typical jitter level of
+3-4ms$^{-1}$.
+"
+21607,Convolutional Neural Knowledge Graph Learning,"  Previous models for learning entity and relationship embeddings of knowledge
+graphs such as TransE, TransH, and TransR aim to explore new links based on
+learned representations. However, these models interpret relationships as
+simple translations on entity embeddings. In this paper, we try to learn more
+complex connections between entities and relationships. In particular, we use a
+Convolutional Neural Network (CNN) to learn entity and relationship
+representations in knowledge graphs. In our model, we treat entities and
+relationships as one-dimensional numerical sequences with the same length.
+After that, we combine each triplet of head, relationship, and tail together as
+a matrix with height 3. CNN is applied to the triplets to get confidence
+scores. Positive and manually corrupted negative triplets are used to train the
+embeddings and the CNN model simultaneously. Experimental results on public
+benchmark datasets show that the proposed model outperforms state-of-the-art
+models on exploring unseen relationships, which proves that CNN is effective to
+learn complex interactive patterns between entities and relationships.
+"
+21608,Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent,"  In this paper, we propose a novel approach to automatically determine the
+batch size in stochastic gradient descent methods. The choice of the batch size
+induces a trade-off between the accuracy of the gradient estimate and the cost
+in terms of samples of each update. We propose to determine the batch size by
+optimizing the ratio between a lower bound to a linear or quadratic Taylor
+approximation of the expected improvement and the number of samples used to
+estimate the gradient. The performance of the proposed approach is empirically
+compared with related methods on popular classification tasks.
+The work was presented at the NIPS workshop on Optimizing the Optimizers.
+Barcelona, Spain, 2016.
+"
+21609,Existence and uniqueness of solution for a nonhomogeneous nonlocal problem,"  In this paper we investigate a class of elliptic problems involving a
+nonlocal Kirchhoff type operator with variable coefficients and data changing
+its sign. Under appropriated conditions on the coefficients, we have shown
+existence and uniqueness of solution.
+"
+21610,Large deviations theory approach to cosmic shear calculations: the one-point aperture mass,"  This paper presents a general formalism that allows the derivation of the
+cumulant generating function and one-point Probability Distribution Function
+(PDF) of the aperture mass ($\hat{M}_{ap}$), a common observable for cosmic
+shear observations. Our formalism is based on the Large Deviation Principle
+(LDP) applied, in such cosmological context, to an arbitrary set of densities
+in concentric cells. We show here that the LDP can indeed be used for a much
+larger family of observables than previously envisioned, such as those built
+from continuous and nonlinear functionals of the density profiles. The general
+expression of the observable aperture mass depends on reduced shear profile
+making it a rather involved function of the projected density field. Because of
+this difficulty, an approximation that is commonly employed consists in
+replacing the reduced shear by the shear in such a construction neglecting
+therefore non-linear effects. We were precisely able to quantify how this
+approximation affects the $\hat{M}_{ap}$ statistical properties. In particular
+we derive the corrective term for the skewness of the $\hat{M}_{ap}$ and
+reconstruct its one-point PDF.
+"
+21611,"Band Structure, Band Offsets, Substitutional Doping, and Schottky Barriers in InSe","  We present a comprehensive study of the electronic structure of the layered
+semiconductor InSe using density functional theory. We calculate the band
+structure of the monolayer and bulk material with the band gap corrected using
+hybrid functionals. The band gap of the monolayer is 2.4 eV. The band edge
+states are surprising isotropic. The electron affinities and band offsets are
+then calculated for heterostructures as would be used in tunnel field effect
+transistors (TFETs). The ionization potential of InSe is quite large, similar
+to that of HfSe2 or SnSe2, and so InSe is suitable to act as the drain in the
+TFET. The intrinsic defects are then calculated. For Se-rich layers, the Se
+adatom is the lowest energy defect, whereas for In-rich layers, the In adatom
+is most stable for Fermi energies across most of the gap. Both substitutional
+donors and acceptors are calculated to be shallow, and not reconstructed.
+Finally, the Schottky barriers of metals are found to be strongly pinned, with
+the Fermi level pinned by metal induced gap states about 0.5 eV above the
+valence band edge.
+"
+21612,Scalable on-chip quantum state tomography,"  Quantum information systems are on a path to vastly exceed the complexity of
+any classical device. The number of entangled qubits in quantum devices is
+rapidly increasing and the information required to fully describe these systems
+scales exponentially with qubit number. This scaling is the key benefit of
+quantum systems, however it also presents a severe challenge. To characterize
+such systems typically requires an exponentially long sequence of different
+measurements, becoming highly resource demanding for large numbers of qubits.
+Here we propose a novel and scalable method to characterize quantum systems,
+where the complexity of the measurement process only scales linearly with the
+number of qubits. We experimentally demonstrate an integrated photonic chip
+capable of measuring two- and three-photon quantum states with reconstruction
+fidelity of 99.67%.
+"
+21613,Graph Partition Neural Networks for Semi-Supervised Classification,"  We present graph partition neural networks (GPNN), an extension of graph
+neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate
+between locally propagating information between nodes in small subgraphs and
+globally propagating information between the subgraphs. To efficiently
+partition graphs, we experiment with several partitioning algorithms and also
+propose a novel variant for fast processing of large scale graphs. We
+extensively test our model on a variety of semi-supervised node classification
+tasks. Experimental results indicate that GPNNs are either superior or
+comparable to state-of-the-art methods on a wide variety of datasets for
+graph-based semi-supervised classification. We also show that GPNNs can achieve
+similar performance as standard GNNs with fewer propagation steps.
+"
+21614,Nonparametric competing risks analysis using Bayesian Additive Regression Trees (BART),"  Many time-to-event studies are complicated by the presence of competing
+risks. Such data are often analyzed using Cox models for the cause specific
+hazard function or Fine-Gray models for the subdistribution hazard. In practice
+regression relationships in competing risks data with either strategy are often
+complex and may include nonlinear functions of covariates, interactions,
+high-dimensional parameter spaces and nonproportional cause specific or
+subdistribution hazards. Model misspecification can lead to poor predictive
+performance. To address these issues, we propose a novel approach to flexible
+prediction modeling of competing risks data using Bayesian Additive Regression
+Trees (BART). We study the simulation performance in two-sample scenarios as
+well as a complex regression setting, and benchmark its performance against
+standard regression techniques as well as random survival forests. We
+illustrate the use of the proposed method on a recently published study of
+patients undergoing hematopoietic stem cell transplantation.
+"
+21615,Performance Bounds For Co-/Sparse Box Constrained Signal Recovery,"  The recovery of structured signals from a few linear measurements is a
+central point in both compressed sensing (CS) and discrete tomography. In CS
+the signal structure is described by means of a low complexity model e.g.
+co-/sparsity. The CS theory shows that any signal/image can be undersampled at
+a rate dependent on its intrinsic complexity. Moreover, in such undersampling
+regimes, the signal can be recovered by sparsity promoting convex
+regularization like $\ell_1$- or total variation (TV-) minimization. Precise
+relations between many low complexity measures and the sufficient number of
+random measurements are known for many sparsity promoting norms. However, a
+precise estimate of the undersampling rate for the TV seminorm is still
+lacking. We address this issue by: a) providing dual certificates testing
+uniqueness of a given cosparse signal with bounded signal values, b)
+approximating the undersampling rates via the statistical dimension of the TV
+descent cone and c) showing empirically that the provided rates also hold for
+tomographic measurements.
+"
+21616,On the proper treatment of improper distributions,"  The axiomatic foundation of probability theory presented by Kolmogorov has
+been the basis of modern theory for probability and statistics. In certain
+applications it is, however, necessary or convenient to allow improper
+(unbounded) distributions, which is often done without a theoretical
+foundation. The paper reviews a recent theory which includes improper
+distributions, and which is related to Renyi's theory of conditional
+probability spaces. It is in particular demonstrated how the theory leads to
+simple explanations of apparent paradoxes known from the Bayesian literature.
+Several examples from statistical practice with improper distributions are
+discussed in light of the given theoretical results, which also include a
+recent theory of convergence of proper distributions to improper ones.
+"
+21617,Traces on reduced group C*-algebras,"  In this short note we prove that the reduced group C*-algebra of a locally
+compact group admits a non-zero trace if and only if the amenable radical of
+the group is open. This completely answers a question raised by Forrest, Spronk
+and Wiersma.
+"
+21618,Rate of Convergence of General Phase Field Equations towards their Homogenized Limit,"  Over the last few decades, phase-field equations have found increasing
+applicability in a wide range of mathematical-scientific fields (e.g. geometric
+PDEs and mean curvature flow, materials science for the study of phase
+transitions) but also engineering ones (e.g. as a computational tool in
+chemical engineering for interfacial flow studies). Here, we focus on
+phase-field equations in strongly heterogeneous materials with perforations
+such as porous media. To the best of our knowledge, we provide the first
+derivation of error estimates for fourth order, homogenized, and nonlinear
+evolution equations. Our fourth order problem induces a slightly lower
+convergence rate, i.e., $\epsilon^{1/4}$, where $\epsilon$ denotes the
+material's specific heterogeneity, than established for second-order elliptic
+problems (e.g. \cite{Zhikov2006}) for the error between the effective
+macroscopic solution of the (new) upscaled formulation and the solution of the
+microscopic phase field problem. We hope that our study will motivate new
+modelling, analytic, and computational perspectives for interfacial transport
+and phase transformations in strongly heterogeneous environments.
+"
+21619,Spin-wave excitations in the SDW state of iron pnictides: a comparison between the roles of interaction parameters,"  We investigate the role of Hund's coupling in the spin-wave excitations of
+the ($\pi, 0$) ordered magnetic state within a five-orbital tight-binding model
+for iron pnictides. To differentiate between the roles of intraorbital Coulomb
+interaction and Hund's coupling, we focus on the self-consistently obtained
+mean-field SDW state with a fixed magnetic moment obtained by using different
+sets of interaction parameters. We find that the Hund's coupling is crucial for
+the description of various experimentally observed characteristics of the
+spin-wave excitations including the anisotropy, energy-dependent behavior, and
+spin-wave spectral weight distribution.
+"
+21620,Artificial Neural Networks that Learn to Satisfy Logic Constraints,"  Logic-based problems such as planning, theorem proving, or puzzles, typically
+involve combinatoric search and structured knowledge representation. Artificial
+neural networks are very successful statistical learners, however, for many
+years, they have been criticized for their weaknesses in representing and in
+processing complex structured knowledge which is crucial for combinatoric
+search and symbol manipulation. Two neural architectures are presented, which
+can encode structured relational knowledge in neural activation, and store
+bounded First Order Logic constraints in connection weights. Both architectures
+learn to search for a solution that satisfies the constraints. Learning is done
+by unsupervised practicing on problem instances from the same domain, in a way
+that improves the network-solving speed. No teacher exists to provide answers
+for the problem instances of the training and test sets. However, the domain
+constraints are provided as prior knowledge to a loss function that measures
+the degree of constraint violations. Iterations of activation calculation and
+learning are executed until a solution that maximally satisfies the constraints
+emerges on the output units. As a test case, block-world planning problems are
+used to train networks that learn to plan in that domain, but the techniques
+proposed could be used more generally as in integrating prior symbolic
+knowledge with statistical learning
+"
+21621,Evaluating gender portrayal in Bangladeshi TV,"  Computer Vision and machine learning methods were previously used to reveal
+screen presence of genders in TV and movies. In this work, using head pose,
+gender detection, and skin color estimation techniques, we demonstrate that the
+gender disparity in TV in a South Asian country such as Bangladesh exhibits
+unique characteristics and is sometimes counter-intuitive to popular
+perception. We demonstrate a noticeable discrepancy in female screen presence
+in Bangladeshi TV advertisements and political talk shows. Further, contrary to
+popular hypotheses, we demonstrate that lighter-toned skin colors are less
+prevalent than darker complexions, and additionally, quantifiable body language
+markers do not provide conclusive insights about gender dynamics. Overall,
+these gender portrayal parameters reveal the different layers of onscreen
+gender politics and can help direct incentives to address existing disparities
+in a nuanced and targeted manner.
+"
+21622,A Distributional Perspective on Reinforcement Learning,"  In this paper we argue for the fundamental importance of the value
+distribution: the distribution of the random return received by a reinforcement
+learning agent. This is in contrast to the common approach to reinforcement
+learning which models the expectation of this return, or value. Although there
+is an established body of literature studying the value distribution, thus far
+it has always been used for a specific purpose such as implementing risk-aware
+behaviour. We begin with theoretical results in both the policy evaluation and
+control settings, exposing a significant distributional instability in the
+latter. We then use the distributional perspective to design a new algorithm
+which applies Bellman's equation to the learning of approximate value
+distributions. We evaluate our algorithm using the suite of games from the
+Arcade Learning Environment. We obtain both state-of-the-art results and
+anecdotal evidence demonstrating the importance of the value distribution in
+approximate reinforcement learning. Finally, we combine theoretical and
+empirical evidence to highlight the ways in which the value distribution
+impacts learning in the approximate setting.
+"
+21623,Computing Individual Risks based on Family History in Genetic Disease in the Presence of Competing Risks,"  When considering a genetic disease with variable age at onset (ex: diabetes ,
+familial amyloid neuropathy, cancers, etc.), computing the individual risk of
+the disease based on family history (FH) is of critical interest both for
+clinicians and patients. Such a risk is very challenging to compute because: 1)
+the genotype X of the individual of interest is in general unknown; 2) the
+posterior distribution P(X|FH, T > t) changes with t (T is the age at disease
+onset for the targeted individual); 3) the competing risk of death is not
+negligible. In this work, we present a modeling of this problem using a
+Bayesian network mixed with (right-censored) survival outcomes where hazard
+rates only depend on the genotype of each individual. We explain how belief
+propagation can be used to obtain posterior distribution of genotypes given the
+FH, and how to obtain a time-dependent posterior hazard rate for any individual
+in the pedigree. Finally, we use this posterior hazard rate to compute
+individual risk, with or without the competing risk of death. Our method is
+illustrated using the Claus-Easton model for breast cancer (BC). This model
+assumes an autosomal dominant genetic risk factor such as non-carriers
+(genotype 00) have a BC hazard rate $\lambda$ 0 (t) while carriers (genotypes
+01, 10 and 11) have a (much greater) hazard rate $\lambda$ 1 (t). Both hazard
+rates are assumed to be piecewise constant with known values (cuts at 20, 30,.
+.. , 80 years). The competing risk of death is derived from the national French
+registry.
+"
+21624,The Impact of Feature Selection on Predicting the Number of Bugs,"  Bug prediction is the process of training a machine learning model on
+software metrics and fault information to predict bugs in software entities.
+While feature selection is an important step in building a robust prediction
+model, there is insufficient evidence about its impact on predicting the number
+of bugs in software systems. We study the impact of both correlation-based
+feature selection (CFS) filter methods and wrapper feature selection methods on
+five widely-used prediction models and demonstrate how these models perform
+with or without feature selection to predict the number of bugs in five
+different open source Java software systems. Our results show that wrappers
+outperform the CFS filter; they improve prediction accuracy by up to 33% while
+eliminating more than half of the features. We also observe that though the
+same feature selection method chooses different feature subsets in different
+projects, this subset always contains a mix of source code and change metrics.
+"
+21625,Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks,"  An important goal of computer vision is to build systems that learn visual
+representations over time that can be applied to many tasks. In this paper, we
+investigate a vision-language embedding as a core representation and show that
+it leads to better cross-task transfer than standard multi-task learning. In
+particular, the task of visual recognition is aligned to the task of visual
+question answering by forcing each to use the same word-region embeddings. We
+show this leads to greater inductive transfer from recognition to VQA than
+standard multitask learning. Visual recognition also improves, especially for
+categories that have relatively few recognition training labels but appear
+often in the VQA setting. Thus, our paper takes a small step towards creating
+more general vision systems by showing the benefit of interpretable, flexible,
+and trainable core representations.
+"
+21626,On the Dynamical Stability and Instability of Parker Problem,"  We investigate a perturbation problem for the three-dimensional compressible
+isentropic viscous magnetohydrodynamic system with zero resistivity in the
+presence of a modified gravitational force in a vertical strip domain in which
+the velocity of the fluid is non-slip on the boundary, and focus on the
+stabilizing effect of the (equilibrium) magnetic field through the non-slip
+boundary condition. We show that there is a discriminant $\Xi$, depending on
+the known physical parameters, for the stability/instability of the
+perturbation problem. More precisely, if $\Xi<0$, then the perturbation problem
+is unstable, i.e., the Parker instability occurs, while if $\Xi>0$ and the
+initial perturbation satisfies some relations, then there exists a global
+(perturbation) solution which decays algebraically to zero in time, i.e., the
+Parker instability does not happen. The stability results in this paper reveal
+the stabilizing effect of the magnetic field through the non-slip boundary
+condition and the importance of boundary conditions upon the Parker
+instability, and demonstrate that a sufficiently strong magnetic field can
+prevent the Parker instability from occurring. In addition, based on the
+instability results, we further rigorously verify the Parker instability under
+Schwarzschild's or Tserkovnikov's instability conditions in the sense of
+Hadamard for a horizontally periodic domain.
+"
+21627,Characterizing Transgender Health Issues in Twitter,"  Although there are millions of transgender people in the world, a lack of
+information exists about their health issues. This issue has consequences for
+the medical field, which only has a nascent understanding of how to identify
+and meet this population's health-related needs. Social media sites like
+Twitter provide new opportunities for transgender people to overcome these
+barriers by sharing their personal health experiences. Our research employs a
+computational framework to collect tweets from self-identified transgender
+users, detect those that are health-related, and identify their information
+needs. This framework is significant because it provides a macro-scale
+perspective on an issue that lacks investigation at national or demographic
+levels. Our findings identified 54 distinct health-related topics that we
+grouped into 7 broader categories. Further, we found both linguistic and
+topical differences in the health-related information shared by transgender men
+(TM) as com-pared to transgender women (TW). These findings can help inform
+medical and policy-based strategies for health interventions within transgender
+communities. Also, our proposed approach can inform the development of
+computational strategies to identify the health-related information needs of
+other marginalized populations.
+"
+21628,CR-Analogue of Siu-$\partial\bar{\partial}$-formula and Applications to Rigidity problem for pseudo-Hermitian harmonic maps,"  We give several versions of Siu's $\partial\bar{\partial}$-formula for maps
+from a strictly pseudoconvex pseudo-Hermitian manifold $(M^{2m+1}, \theta)$
+into a Kähler manifold $(N^n, g)$. We also define and study the notion of
+pseudo-Hermitian harmonicity for maps from $M$ into $N$. In particular, we
+prove a CR version of Siu Rigidity Theorem for pseudo-Hermitian harmonic maps
+from a pseudo-Hermitian manifold with vanishing Webster torsion into a Kähler
+manifold having strongly negative curvature.
+"
+21629,Speculations on homological mirror symmetry for hypersurfaces in $(\mathbb{C}^*)^n$,"  Given an algebraic hypersurface $H=f^{-1}(0)$ in $(\mathbb{C}^*)^n$,
+homological mirror symmetry relates the wrapped Fukaya category of $H$ to the
+derived category of singularities of the mirror Landau-Ginzburg model. We
+propose an enriched version of this picture which also features the wrapped
+Fukaya category of the complement $(\mathbb{C}^*)^n\setminus H$ and the
+Fukaya-Seidel category of the Landau-Ginzburg model $((\mathbb{C}^*)^n,f)$. We
+illustrate our speculations on simple examples, and sketch a proof of
+homological mirror symmetry for higher-dimensional pairs of pants.
+"
+21630,Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling,"  Stochastic gradient methods enable learning probabilistic models from large
+amounts of data. While large step-sizes (learning rates) have shown to be best
+for least-squares (e.g., Gaussian noise) once combined with parameter
+averaging, these are not leading to convergent algorithms in general. In this
+paper, we consider generalized linear models, that is, conditional models based
+on exponential families. We propose averaging moment parameters instead of
+natural parameters for constant-step-size stochastic gradient descent. For
+finite-dimensional models, we show that this can sometimes (and surprisingly)
+lead to better predictions than the best linear model. For infinite-dimensional
+models, we show that it always converges to optimal predictions, while
+averaging natural parameters never does. We illustrate our findings with
+simulations on synthetic data and classical benchmarks with many observations.
+"
+21631,Virtual and welded periods of classical knots,"  We show that any virtual or welded period of a classical knot $K$ can be
+realized as a classical period. A direct consequence is that a classical knot
+admits only finitely many virtual or welded periods.
+"
+21632,Optimal hybrid block bootstrap for sample quantiles under weak dependence,"  We establish a general theory of optimality for block bootstrap distribution
+estimation for sample quantiles under a mild strong mixing assumption. In
+contrast to existing results, we study the block bootstrap for varying numbers
+of blocks. This corresponds to a hybrid between the subsampling bootstrap and
+the moving block bootstrap (MBB), in which the number of blocks is somewhere
+between 1 and the ratio of sample size to block length. Our main theorem
+determines the optimal choice of the number of blocks and block length to
+achieve the best possible convergence rate for the block bootstrap distribution
+estimator for sample quantiles. As part of our analysis, we also prove an
+important lemma which gives the convergence rate of the block bootstrap
+distribution estimator, with implications even for the smooth function model.
+We propose an intuitive procedure for empirical selection of the optimal number
+and length of blocks. Relevant examples are presented which illustrate the
+benefits of optimally choosing the number of blocks.
+"
+21633,Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks,"  Understanding and predicting the popularity of online items is an important
+open problem in social media analysis. Considerable progress has been made
+recently in data-driven predictions, and in linking popularity to external
+promotions. However, the existing methods typically focus on a single source of
+external influence, whereas for many types of online content such as YouTube
+videos or news articles, attention is driven by multiple heterogeneous sources
+simultaneously - e.g. microblogs or traditional media coverage. Here, we
+propose RNN-MAS, a recurrent neural network for modeling asynchronous streams.
+It is a sequence generator that connects multiple streams of different
+granularity via joint inference. We show RNN-MAS not only to outperform the
+current state-of-the-art Youtube popularity prediction system by 17%, but also
+to capture complex dynamics, such as seasonal trends of unseen influence. We
+define two new metrics: promotion score quantifies the gain in popularity from
+one unit of promotion for a Youtube video; the loudness level captures the
+effects of a particular user tweeting about the video. We use the loudness
+level to compare the effects of a video being promoted by a single
+highly-followed user (in the top 1% most followed users) against being promoted
+by a group of mid-followed users. We find that results depend on the type of
+content being promoted: superusers are more successful in promoting Howto and
+Gaming videos, whereas the cohort of regular users are more influential for
+Activism videos. This work provides more accurate and explainable popularity
+predictions, as well as computational tools for content producers and marketers
+to allocate resources for promotion campaigns.
+"
+21634,A moduli stack of tropical curves,"  We contribute to the foundations of tropical geometry with a view towards
+formulating tropical moduli problems, and with the moduli space of curves as
+our main example. We propose a moduli functor for the moduli space of curves
+and show that it is representable by a geometric stack over the category of
+rational polyhedral cones. In this framework the natural forgetful morphisms
+between moduli spaces of curves with marked points function as universal
+curves. Our approach to tropical geometry permits tropical moduli
+problems---moduli of curves or otherwise---to be extended to logarithmic
+schemes. We use this to construct a smooth tropicalization morphism from the
+moduli space of algebraic curves to the moduli space of tropical curves, and we
+show that this morphism commutes with all of the tautological morphisms.
+"
+21635,Kernel entropy estimation for linear processes,"  Let $\{X_n: n\in \mathbb{N}\}$ be a linear process with bounded probability
+density function $f(x)$. We study the estimation of the quadratic functional
+$\int_{\mathbb{R}} f^2(x)\, dx$. With a Fourier transform on the kernel
+function and the projection method, it is shown that, under certain mild
+conditions, the estimator \[ \frac{2}{n(n-1)h_n} \sum_{1\le i<j\le
+n}K\left(\frac{X_i-X_j}{h_n}\right) \] has similar asymptotical properties as
+the i.i.d. case studied in Giné and Nickl (2008) if the linear process
+$\{X_n: n\in \mathbb{N}\}$ has the defined short range dependence. We also
+provide an application to $L^2_2$ divergence and the extension to multivariate
+linear processes. The simulation study for linear processes with Gaussian and
+$\alpha$-stable innovations confirms our theoretical results. As an
+illustration, we estimate the $L^2_2$ divergences among the density functions
+of average annual river flows for four rivers and obtain promising results.
+"
+21636,Non-colocated Time-Reversal MUSIC: High-SNR Distribution of Null Spectrum,"  We derive the asymptotic distribution of the null spectrum of the well-known
+Multiple Signal Classification (MUSIC) in its computational Time-Reversal (TR)
+form. The result pertains to a single-frequency non-colocated multistatic
+scenario and several TR-MUSIC variants are here investigated. The analysis
+builds upon the 1st-order perturbation of the singular value decomposition and
+allows a simple characterization of null-spectrum moments (up to the 2nd
+order). This enables a comparison in terms of spectrums stability. Finally, a
+numerical analysis is provided to confirm the theoretical findings.
+"
+21637,"Google Cardboard Dates Augmented Reality : Issues, Challenges and Future Opportunities","  The Google's frugal Cardboard solution for immersive Virtual Reality
+experiences has come a long way in the VR market. The Google Cardboard VR
+applications will support us in the fields such as education, virtual tourism,
+entertainment, gaming, design etc. Recently, Qualcomm's Vuforia SDK has
+introduced support for developing mixed reality applications for Google
+Cardboard which can combine Virtual and Augmented Reality to develop exciting
+and immersive experiences. In this work, we present a comprehensive review of
+Google Cardboard for AR and also highlight its technical and subjective
+limitations by conducting a feasibility study through the inspection of a
+Desktop computer use-case. Additionally, we recommend the future avenues for
+the Google Cardboard in AR. This work also serves as a guide for Android/iOS
+developers as there are no published scholarly articles or well documented
+studies exclusively on Google Cardboard with both user and developer's
+experience captured at one place.
+"
+21638,Multiplayer bandits without observing collision information,"  We study multiplayer stochastic multi-armed bandit problems in which the
+players cannot communicate, and if two or more players pull the same arm, a
+collision occurs and the involved players receive zero reward. We consider two
+feedback models: a model in which the players can observe whether a collision
+has occurred, and a more difficult setup when no collision information is
+available. We give the first theoretical guarantees for the second model: an
+algorithm with a logarithmic regret, and an algorithm with a square-root regret
+type that does not depend on the gaps between the means. For the first model,
+we give the first square-root regret bounds that do not depend on the gaps.
+Building on these ideas, we also give an algorithm for reaching approximate
+Nash equilibria quickly in stochastic anti-coordination games.
+"
+21639,Development of pediatric myeloid leukemia may be related to the repeatedbone-remodeling during bone-growth,"  Acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) are two major
+formsof leukemia developed from myeloid cells (MCs). To understand why AML and
+CML occurin children, we analyzed the causes and the mechanism of cell
+transformation of a MC. I. Forthe MCs in marrow cavity, repeated
+bone-remodeling during bone-growth may be a source ofcell injuries. II. As a
+type of blood cell, a MC may have higher survivability from DNAchanges and
+require obtaining fewer cancerous properties for cell transformation than a
+tissuecell. III. Point DNA mutations (PDMs) and chromosome changes (CCs) are
+the two majortypes of DNA changes. CCs have three subtypes by effects on a
+cell: great effect CCs(GECCs), mild-effect CCs (MECCs), and intermediate-effect
+CCs (IECCs). A GECC affectsone or more genes and can alone trigger cell
+transformation. PDMs/MECCs are mostly mildand can accumulate in cells. Some of
+the PDMs/MECCs contribute to cell transformation. AnIECC affects one or more
+genes and participates in cell transformation. IV. Based on II andIII, we
+hypothesize that a MC may have two pathways on transformation: a slow and
+anaccelerated. Slow pathway is driven by accumulation of PDMs/MECCs.
+Accelerated pathwayis driven by accumulation of PDMs/MECCs/IECC(s). A
+transformation via slow pathwayoccurs at old age; whereas that via accelerated
+pathway occurs at any age. Thus, CML andpediatric AML may develop via
+accelerated pathway, and adult AML may develop via bothpathways. In conclusion,
+pediatric AML and CML may develop as a result of transformationof a MC via
+accelerated pathway; and repeated bone-remodeling for bone-growth may be
+atrigger for the transformation of a MC in a child.
+"
+21640,Gradient Descent for Spiking Neural Networks,"  Much of studies on neural computation are based on network models of static
+neurons that produce analog output, despite the fact that information
+processing in the brain is predominantly carried out by dynamic neurons that
+produce discrete pulses called spikes. Research in spike-based computation has
+been impeded by the lack of efficient supervised learning algorithm for spiking
+networks. Here, we present a gradient descent method for optimizing spiking
+network models by introducing a differentiable formulation of spiking networks
+and deriving the exact gradient calculation. For demonstration, we trained
+recurrent spiking networks on two dynamic tasks: one that requires optimizing
+fast (~millisecond) spike-based interactions for efficient encoding of
+information, and a delayed memory XOR task over extended duration (~second).
+The results show that our method indeed optimizes the spiking network dynamics
+on the time scale of individual spikes as well as behavioral time scales. In
+conclusion, our result offers a general purpose supervised learning algorithm
+for spiking neural networks, thus advancing further investigations on
+spike-based computation.
+"
+21641,Implicit and Implicit-Explicit Strong Stability Preserving Runge-Kutta Methods with High Linear Order,"  When evolving in time the solution of a hyperbolic partial differential
+equation, it is often desirable to use high order strong stability preserving
+(SSP) time discretizations. These time discretizations preserve the
+monotonicity properties satisfied by the spatial discretization when coupled
+with the first order forward Euler, under a certain time-step restriction.
+While the allowable time-step depends on both the spatial and temporal
+discretizations, the contribution of the temporal discretization can be
+isolated by taking the ratio of the allowable time-step of the high order
+method to the forward Euler time-step. This ratio is called the strong
+stability coefficient. The search for high order strong stability time-stepping
+methods with high order and large allowable time-step had been an active area
+of research. It is known that implicit SSP Runge-Kutta methods exist only up to
+sixth order. However, if we restrict ourselves to solving only linear
+autonomous problems, the order conditions simplify and we can find implicit SSP
+Runge-Kutta methods of any linear order. In the current work we aim to find
+very high linear order implicit SSP Runge-Kutta methods that are optimal in
+terms of allowable time-step. Next, we formulate an optimization problem for
+implicit-explicit (IMEX) SSP Runge-Kutta methods and find implicit methods with
+large linear stability regions that pair with known explicit SSP Runge-Kutta
+methods of orders plin=3,4,6 as well as optimized IMEX SSP Runge-Kutta pairs
+that have high linear order and nonlinear orders p=2,3,4. These methods are
+then tested on sample problems to verify order of convergence and to
+demonstrate the sharpness of the SSP coefficient and the typical behavior of
+these methods on test problems.
+"
+21642,Kinetic energy of a trapped Fermi gas at finite temperature,"  We study the statistics of the kinetic (or equivalently potential) energy for
+$N$ non-interacting fermions in a $1d$ harmonic trap of frequency $\omega$, at
+finite temperature $T$. Remarkably, we find an exact solution for the full
+distribution of the kinetic energy, at any temperature $T$ and for any $N$,
+using a non-trivial mapping to an integrable Calogero-Moser-Sutherland model.
+As a function of temperature $T$, and for large $N$, we identify: (i) a quantum
+regime, for $T \sim \hbar \omega$, where quantum fluctuations dominate and (ii)
+a thermal regime, for $T \sim N \hbar \omega$, governed by thermal
+fluctuations. We show how the mean, the variance as well as the large deviation
+function associated with the distribution of the kinetic energy cross over from
+the quantum to the thermal regime as temperature increases.
+"
+21643,Multilingual Multi-modal Embeddings for Natural Language Processing,"  We propose a novel discriminative model that learns embeddings from
+multilingual and multi-modal data, meaning that our model can take advantage of
+images and descriptions in multiple languages to improve embedding quality. To
+that end, we introduce a modification of a pairwise contrastive estimation
+optimisation function as our training objective. We evaluate our embeddings on
+an image-sentence ranking (ISR), a semantic textual similarity (STS), and a
+neural machine translation (NMT) task. We find that the additional multilingual
+signals lead to improvements on both the ISR and STS tasks, and the
+discriminative cost can also be used in re-ranking $n$-best lists produced by
+NMT models, yielding strong improvements.
+"
+21644,Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification,"  Person re-identification (re-id) aims to match pedestrians observed by
+disjoint camera views. It attracts increasing attention in computer vision due
+to its importance to surveillance system. To combat the major challenge of
+cross-view visual variations, deep embedding approaches are proposed by
+learning a compact feature space from images such that the Euclidean distances
+correspond to their cross-view similarity metric. However, the global Euclidean
+distance cannot faithfully characterize the ideal similarity in a complex
+visual feature space because features of pedestrian images exhibit unknown
+distributions due to large variations in poses, illumination and occlusion.
+Moreover, intra-personal training samples within a local range are robust to
+guide deep embedding against uncontrolled variations, which however, cannot be
+captured by a global Euclidean distance. In this paper, we study the problem of
+person re-id by proposing a novel sampling to mine suitable \textit{positives}
+(i.e. intra-class) within a local range to improve the deep embedding in the
+context of large intra-class variations. Our method is capable of learning a
+deep similarity metric adaptive to local sample structure by minimizing each
+sample's local distances while propagating through the relationship between
+samples to attain the whole intra-class minimization. To this end, a novel
+objective function is proposed to jointly optimize similarity metric learning,
+local positive mining and robust deep embedding. This yields local
+discriminations by selecting local-ranged positive samples, and the learned
+features are robust to dramatic intra-class variations. Experiments on
+benchmarks show state-of-the-art results achieved by our method.
+"
+21645,Frequency violations from random disturbances: an MCMC approach,"  The frequency stability of power systems is increasingly challenged by
+various types of disturbances. In particular, the increasing penetration of
+renewable energy sources is increasing the variability of power generation and
+at the same time reducing system inertia against disturbances. In this paper we
+are particularly interested in understanding how rate of change of frequency
+(RoCoF) violations could arise from unusually large power disturbances. We
+devise a novel specialization, named ghost sampling, of the Metropolis-Hastings
+Markov Chain Monte Carlo method that is tailored to efficiently sample rare
+power disturbances leading to nodal frequency violations. Generating a
+representative random sample addresses important statistical questions such as
+""which generator is most likely to be disconnected due to a RoCoF violation?""
+or ""what is the probability of having simultaneous RoCoF violations, given that
+a violation occurs?"" Our method can perform conditional sampling from any joint
+distribution of power disturbances including, for instance, correlated and
+non-Gaussian disturbances, features which have both been recently shown to be
+significant in security analyses.
+"
+21646,"A randomized, efficient algorithm for 3SAT","  In this paper I present a 3SAT algorithm based on the randomized algorithm of
+Papadimitriou from 1991, and Schoning from 1991. We also present strong
+arguments that this algorithm finds a solution (if it exists) for a 3SAT
+problem with high probability in polynomial time.
+"
+21647,Photogravimagnetic assists of light sails: a mixed blessing for Breakthrough Starshot?,"  Upon entering a star system, light sails are subject to both gravitational
+forces and radiation pressure, and can use both in concert to modify their
+trajectory. Moreover, stars possess significant magnetic fields, and if the
+sail is in any way charged, it will feel the Lorentz force also.
+We investigate the dynamics of so-called ""photogravimagnetic assists"" of
+sailcraft around $\alpha$ Centauri A, a potential first destination en route to
+Proxima Centauri (the goal of the Breakthrough Starshot program). We find that
+a 10m$^2$ sail with a charge-to-mass-ratio of around 10 $\mu$C/g or higher will
+need to take account of magnetic field effects during orbital maneouvres. The
+magnetic field can provide an extra source of deceleration and deflection, and
+allow capture onto closer orbits around a target star.
+However, flipping the sign of the sailcraft's charge can radically change
+resulting trajectories, resulting in complex loop-de-loops around magnetic
+field lines and essentially random ejection from the star system. Even on
+well-behaved trajectories, the field can generate off-axis deflections at
+$\alpha$ Centauri that, while minor, can result in very poor targetting of the
+final destination (Proxima) post-assist.
+Fortunately for Breakthrough Starshot, nanosails are less prone to charging
+en route than their heavier counterparts, but can still accrue relatively high
+charge at both the origin and destination, when travelling at low speeds.
+Photogravimagnetic assists are highly non-trivial, and require careful course
+correction to mitigate against unwanted changes in trajectory.
+"
+21648,Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities,"  Voice conversion (VC) using sequence-to-sequence learning of context
+posterior probabilities is proposed. Conventional VC using shared context
+posterior probabilities predicts target speech parameters from the context
+posterior probabilities estimated from the source speech parameters. Although
+conventional VC can be built from non-parallel data, it is difficult to convert
+speaker individuality such as phonetic property and speaking rate contained in
+the posterior probabilities because the source posterior probabilities are
+directly used for predicting target speech parameters. In this work, we assume
+that the training data partly include parallel speech data and propose
+sequence-to-sequence learning between the source and target posterior
+probabilities. The conversion models perform non-linear and variable-length
+transformation from the source probability sequence to the target one. Further,
+we propose a joint training algorithm for the modules. In contrast to
+conventional VC, which separately trains the speech recognition that estimates
+posterior probabilities and the speech synthesis that predicts target speech
+parameters, our proposed method jointly trains these modules along with the
+proposed probability conversion modules. Experimental results demonstrate that
+our approach outperforms the conventional VC.
+"
+21649,Provable Self-Representation Based Outlier Detection in a Union of Subspaces,"  Many computer vision tasks involve processing large amounts of data
+contaminated by outliers, which need to be detected and rejected. While outlier
+detection methods based on robust statistics have existed for decades, only
+recently have methods based on sparse and low-rank representation been
+developed along with guarantees of correct outlier detection when the inliers
+lie in one or more low-dimensional subspaces. This paper proposes a new outlier
+detection method that combines tools from sparse representation with random
+walks on a graph. By exploiting the property that data points can be expressed
+as sparse linear combinations of each other, we obtain an asymmetric affinity
+matrix among data points, which we use to construct a weighted directed graph.
+By defining a suitable Markov Chain from this graph, we establish a connection
+between inliers/outliers and essential/inessential states of the Markov chain,
+which allows us to detect outliers by using random walks. We provide a
+theoretical analysis that justifies the correctness of our method under
+geometric and connectivity assumptions. Experimental results on image databases
+demonstrate its superiority with respect to state-of-the-art sparse and
+low-rank outlier detection methods.
+"
+21650,Micro and Macro Pedestrian Dynamics in Counterflow: the Impact of Social Groups,"  Although it is widely recognised that the presence of groups influences
+microscopic and aggregated pedestrian dynamics, a precise characterisation of
+the phenomenon still calls for evidences and insights. The present paper
+describes micro and macro level original analyses on data characterising
+pedestrian behaviour in presence of counter-flows and grouping, in particular
+dyads, acquired through controlled experiments. Results suggest that the
+presence of dyads and their tendency to walk in a line-abreast formation
+influences the formation of lanes and, in turn, aggregated observables, such as
+overall specific flow.
+"
+21651,Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning,"  Gaussian process regression is a machine learning approach which has been
+shown its power for estimation of unknown functions. However, Gaussian
+processes suffer from high computational complexity, as in a basic form they
+scale cubically with the number of observations. Several approaches based on
+inducing points were proposed to handle this problem in a static context. These
+methods though face challenges with real-time tasks and when the data is
+received sequentially over time. In this paper, a novel online algorithm for
+training sparse Gaussian process models is presented. It treats the mean and
+hyperparameters of the Gaussian process as the state and parameters of the
+ensemble Kalman filter, respectively. The online evaluation of the parameters
+and the state is performed on new upcoming samples of data. This procedure
+iteratively improves the accuracy of parameter estimates. The ensemble Kalman
+filter reduces the computational complexity required to obtain predictions with
+Gaussian processes preserving the accuracy level of these predictions. The
+performance of the proposed method is demonstrated on the synthetic dataset and
+real large dataset of UK house prices.
+"
+21652,Damage detection in a unidimensional truss using the firefly optimization algorithm and finite elements,"  In this paper, we investigate the damage detection of structures seen as an
+optimization problem, using modal characterization to evaluate the dynamic
+response of the structure given a damage model. We implemented the firefly
+optimization algorithm with a simple numerical damage model to assess the
+performance of the method and its advantages for structural health monitoring
+(SHM). We show some implementation details and discuss the obtained results.
+"
+21653,Surface-sampled simulations of turbulent flow at high Reynolds number,"  A new approach to turbulence simulation, based on a combination of large-eddy
+simulation (LES) for the whole flow and an array of non-space-filling
+quasi-direct numerical simulations (QDNS), which sample the response of
+near-wall turbulence to large-scale forcing, is proposed and evaluated. The
+technique overcomes some of the cost limitations of turbulence simulation,
+since the main flow is treated with a coarse-grid LES, with the equivalent of
+wall functions supplied by the near-wall sampled QDNS. Two cases are tested, at
+friction Reynolds number Re$_\tau$=4200 and 20,000. The total grid node count
+for the first case is less than half a million and less than two million for
+the second case, with the calculations only requiring a desktop computer. A
+good agreement with published DNS is found at Re$_\tau$=4200, both in terms of
+the mean velocity profile and the streamwise velocity fluctuation statistics,
+which correctly show a substantial increase in near-wall turbulence levels due
+to a modulation of near-wall streaks by large-scale structures. The trend
+continues at Re$_\tau$=20,000, in agreement with experiment, which represents
+one of the major achievements of the new approach. A number of detailed aspects
+of the model, including numerical resolution, LES-QDNS coupling strategy and
+sub-grid model are explored. A low level of grid sensitivity is demonstrated
+for both the QDNS and LES aspects. Since the method does not assume a law of
+the wall, it can in principle be applied to flows that are out of equilibrium.
+"
+21654,Moving Block and Tapered Block Bootstrap for Functional Time Series with an Application to the K-Sample Mean Problem,"  We consider infinite-dimensional Hilbert space-valued random variables that
+are assumed to be temporal dependent in a broad sense. We prove a central limit
+theorem for the moving block bootstrap and for the tapered block bootstrap, and
+show that these block bootstrap procedures also provide consistent estimators
+of the long run covariance operator. Furthermore, we consider block
+bootstrap-based procedures for fully functional testing of the equality of mean
+functions between several independent functional time series. We establish
+validity of the block bootstrap methods in approximating the distribution of
+the statistic of interest under the null and show consistency of the block
+bootstrap-based tests under the alternative. The finite sample behaviour of the
+procedures is investigated by means of simulations. An application to a
+real-life dataset is also discussed.
+"
+21655,"Competing spin density wave, collinear, and helical magnetism in Fe1+xTe","  The Fe1+xTe phase diagram consists of two distinct magnetic structures with
+collinear order present at low interstitial iron concentrations and a helical
+phase at large values of x with these phases separated by a Lifshitz point. We
+use unpolarized single crystal diffraction to confirm the helical phase for
+large interstitial iron concentrations and polarized single crystal diffraction
+to demonstrate the collinear order for the iron deficient side of the Fe1+xTe
+phase diagram. Polarized neutron inelastic scattering show that the
+fluctuations associated with this collinear order are predominately transverse
+at low energy transfers, consistent with a localized magnetic moment picture.
+We then apply neutron inelastic scattering and polarization analysis to
+investigate the dynamics and structure near the boundary between collinear and
+helical order in the Fe1+xTe phase diagram. We first show that the phase
+separating collinear and helical order is characterized by a spin-density wave
+with a single propagation wave vector of (~ 0.45, 0, 0.5). We do not observe
+harmonics or the presence of a charge density wave. The magnetic fluctuations
+associated with this wavevector are different from the collinear phase being
+strongly longitudinal in nature and correlated anisotropically in the (H,K)
+plane. The excitations preserve the C4 symmetry of the lattice, but display
+different widths in momentum along the two tetragonal directions at low energy
+transfers. While the low energy excitations and minimal magnetic phase diagram
+can be understood in terms of localized interactions, we suggest that the
+presence of density wave phase implies the importance of electronic and orbital
+properties.
+"
+21656,Technical Report: Sensor-Based Reactive Symbolic Planning in Partially Known Environments,"  This paper considers the problem of completing assemblies of passive objects
+in nonconvex environments, cluttered with convex obstacles of unknown position,
+shape and size that satisfy a specific separation assumption. A differential
+drive robot equipped with a gripper and a LIDAR sensor, capable of perceiving
+its environment only locally, is used to position the passive objects in a
+desired configuration. The method combines the virtues of a deliberative
+planner generating high-level, symbolic commands, with the formal guarantees of
+convergence and obstacle avoidance of a reactive planner that requires little
+onboard computation and is used online. The validity of the proposed method is
+verified both with formal proofs and numerical simulations.
+"
+21657,Resonances on regular tree graphs,"  We investigate the distribution of the resonances near spectral thresholds of
+Laplace operators on regular tree graphs with $k$-fold branching, $k \geq 1$,
+perturbed by nonself-adjoint exponentially decaying potentials. We establish
+results on the absence of resonances which in particular involve absence of
+discrete spectrum near some sectors of the essential spectrum of the operators.
+"
+21658,Spectral Methods in the Presence of Discontinuities,"  Spectral methods provide an elegant and efficient way of numerically solving
+differential equations of all kinds. For smooth problems, truncation error for
+spectral methods vanishes exponentially in the infinity norm and $L_2$-norm.
+However, for non-smooth problems, convergence is significantly worse---the
+$L_2$-norm of the error for a discontinuous problem will converge at a
+sub-linear rate and the infinity norm will not converge at all. We explore and
+improve upon a post-processing technique---optimally convergent mollifiers---to
+recover exponential convergence from a poorly-converging spectral
+reconstruction of non-smooth data. This is an important first step towards
+using these techniques for simulations of realistic systems.
+"
+21659,A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm,"  We consider the problem of learning a high-dimensional but low-rank matrix
+from a large-scale dataset distributed over several machines, where
+low-rankness is enforced by a convex trace norm constraint. We propose
+DFW-Trace, a distributed Frank-Wolfe algorithm which leverages the low-rank
+structure of its updates to achieve efficiency in time, memory and
+communication usage. The step at the heart of DFW-Trace is solved approximately
+using a distributed version of the power method. We provide a theoretical
+analysis of the convergence of DFW-Trace, showing that we can ensure sublinear
+convergence in expectation to an optimal solution with few power iterations per
+epoch. We implement DFW-Trace in the Apache Spark distributed programming
+framework and validate the usefulness of our approach on synthetic and real
+data, including the ImageNet dataset with high-dimensional features extracted
+from a deep neural network.
+"
+21660,Stability of the Malvinas Current,"  Deterministic and probabilistic tools from nonlinear dynamics are used to
+assess enduring near-surface Lagrangian aspects of the Malvinas Current. The
+deterministic tools are applied on a multi-year record of velocities derived
+from satellite altimetry data, revealing a resilient cross-stream transport
+barrier. This is composed of shearless-parabolic Lagrangian coherent structures
+(LCS), which, extracted over sliding time windows along the multi-year
+altimetry-derived velocity record, lie in near coincidental position. The
+probabilistic tools are applied on a large collection of historical
+satellite-tracked drifter trajectories, revealing weakly communicating flow
+regions on either side of the altimetry-derived barrier. Shearless-parabolic
+LCS are detected for the first time from altimetry data, and their significance
+is supported on satellite-derived ocean color data, which reveal shapes that
+quite closely resemble the peculiar V shapes, dubbed `chevrons,' that have
+recently confirmed the presence of similar LCS in the atmosphere of Jupiter.
+Finally, using in-situ velocity and hydrographic data, conditions for symmetric
+stability are found to be satisfied, suggesting a duality between Lagrangian
+and Eulerian stability for the Malvinas Current.
+"
+21661,Probabilistic Mid- and Long-Term Electricity Price Forecasting,"  The liberalization of electricity markets and the development of renewable
+energy sources has led to new challenges for decision makers. These challenges
+are accompanied by an increasing uncertainty about future electricity price
+movements. The increasing amount of papers, which aim to model and predict
+electricity prices for a short period of time provided new opportunities for
+market participants. However, the electricity price literature seem to be very
+scarce on the issue of medium- to long-term price forecasting, which is
+mandatory for investment and political decisions. Our paper closes this gap by
+introducing a new approach to simulate electricity prices with hourly
+resolution for several months up to three years. Considering the uncertainty of
+future events we are able to provide probabilistic forecasts which are able to
+detect probabilities for price spikes even in the long-run. As market we
+decided to use the EPEX day-ahead electricity market for Germany and Austria.
+Our model extends the X-Model which mainly utilizes the sale and purchase curve
+for electricity day-ahead auctions. By applying our procedure we are able to
+give probabilities for the due to the EEG practical relevant event of six
+consecutive hours of negative prices. We find that using the supply and demand
+curve based model in the long-run yields realistic patterns for the time series
+of electricity prices and leads to promising results considering common error
+measures.
+"
+21662,"The infinity Quillen functor, Maurer-Cartan elements and DGL realizations","  We show an alternative construction of the cosimplicial free complete
+diferential graded Lie algebra
+$\mathfrak{L}_\bullet=\widehat{\mathbb{L}}(s^{-1}\Delta^\bullet)$ based on a
+new Lie bracket formulae for Lie polynomials on a general tensor algebra. Based
+on it,we prove that for any complete differential graded Lie algebra $L$, its
+geometrical realization $\langle
+L\rangle=\text{Hom}_{\text{cdgl}}(\mathfrak{L}_\bullet,L)$ is isomorphic to its
+nerve $\gamma_\bullet(L)$, a deformation retract of the Getzler-Hinich
+realization $\text{MC}(\mathscr{A}_\bullet\widehat{\otimes} L)$.
+"
+21663,Time Domain Filtering of Resolved Images of Sgr A*,"  The goal of the Event Horizon Telescope (EHT) is to provide spatially
+resolved images of Sgr A*, the source associated with the Galactic Center black
+hole. Because Sgr A* varies on timescales short compared to an EHT observing
+campaign, it is interesting to ask whether variability contains information
+about the structure and dynamics of the accretion flow. In this paper, we
+introduce ""time-domain filtering"", a technique to filter time fluctuating
+images with specific temporal frequency ranges, and demonstrate the power and
+usage of the technique by applying it to mock millimeter wavelength images of
+Sgr A*. The mock image data is generated from General Relativistic
+Magnetohydrodynamic (GRMHD) simulation and general relativistic ray-tracing
+method. We show that the variability on each line of sight is tightly
+correlated with a typical radius of emission. This is because disk emissivity
+fluctuates on a timescale of order the local orbital period. Time-domain
+filtered images therefore reflect the model dependent emission radius
+distribution, which is not accessible in time-averaged images. We show that, in
+principle, filtered data have the power to distinguish between models with
+different black hole spins, different disk viewing angles, and different disk
+orientations in the sky.
+"
+21664,HCU400: An Annotated Dataset for Exploring Aural Phenomenology Through Causal Uncertainty,"  The way we perceive a sound depends on many aspects-- its ecological
+frequency, acoustic features, typicality, and most notably, its identified
+source. In this paper, we present the HCU400: a dataset of 402 sounds ranging
+from easily identifiable everyday sounds to intentionally obscured artificial
+ones. It aims to lower the barrier for the study of aural phenomenology as the
+largest available audio dataset to include an analysis of causal attribution.
+Each sample has been annotated with crowd-sourced descriptions, as well as
+familiarity, imageability, arousal, and valence ratings. We extend existing
+calculations of causal uncertainty, automating and generalizing them with word
+embeddings. Upon analysis we find that individuals will provide less polarized
+emotion ratings as a sound's source becomes increasingly ambiguous; individual
+ratings of familiarity and imageability, on the other hand, diverge as
+uncertainty increases despite a clear negative trend on average.
+"
+21665,Nonlinear Unknown Input Observability: The General Analytic Solution,"  This book provides the analytic solution of a fundamental open problem in
+control theory. It provides the general analytic criterion to obtain the state
+observability of nonlinear systems in presence of multiple unknown inputs. This
+problem, called the Unknown Input Observability problem, was introduced in the
+seventies. The analytic criterion is simple and can be easily and automatically
+applied to nonlinear systems driven by both known and unknown inputs,
+independently of their complexity and type of nonlinearity. Very surprisingly,
+the complexity of the overall analytic criterion is comparable to the
+complexity of the standard method to check the state observability in the case
+without unknown inputs (i.e., the observability rank condition). Given any
+nonlinear system characterized by any type of nonlinearity, driven by both
+known and unknown inputs, the state observability is obtained by following a
+simple systematic procedure (e.g., by the usage of a very simple code that uses
+symbolic computation). This is a fundamental practical (and unexpected)
+advantage. On the other hand, the analytic derivations and all the proofs
+necessary to analytically derive the algorithm and its convergence properties
+and to prove their general validity are very complex and they are extensively
+based on an ingenious analogy with the theory of General Relativity. In
+practice, these derivations largely use Ricci calculus with tensors. The
+analytic criterion is illustrated by checking the observability of several
+nonlinear systems ranging from planar robotics up to advanced nonlinear
+systems. In particular, the last applications are in the framework of
+visual-inertial sensor fusion. For this problem, the application of the
+analytic criterion provides a remarkable and amazing result, which could have
+relevance on the problem of visual-vestibular integration for self-motion
+perception in neuroscience.
+"
+21666,Valley-Polarized Exciton-Polaritons in a Monolayer Semiconductor,"  Single layers of transition metal dichalcogenides are two-dimensional direct
+bandgap semiconductors with degenerate, but inequivalent, `valleys' in the
+electronic structure that can be selectively excited by polarized light.
+Coherent superpositions of light and matter, exciton-polaritons, have been
+observed when these materials are strongly coupled to photons, but these hybrid
+quasiparticles do not harness the valley-sensitive excitations of monolayer
+transition metal dichalcogenides. Here, we demonstrate evidence for valley
+polarized exciton-polaritons in monolayers of MoS$_2$ embedded in a dielectric
+microcavity. Unlike traditional microcavity exciton-polaritons, these
+light-matter quasiparticles emit polarized light with spectral Rabi splitting.
+The interplay of cavity-modified exciton dynamics and intervalley relaxation in
+the high-cooperativity regime causes valley polarized exciton-polaritons to
+persist to room temperature, distinct from the vanishing polarization in bare
+monolayers. Achieving polarization-sensitive polaritonic devices operating at
+room temperature presents a pathway for manipulating novel valley degrees of
+freedom in coherent states of light and matter.
+"
+21667,Identifying Driver Behaviors using Trajectory Features for Vehicle Navigation,"  We present a novel approach to automatically identify driver behaviors from
+vehicle trajectories and use them for safe navigation of autonomous vehicles.
+We propose a novel set of features that can be easily extracted from car
+trajectories. We derive a data-driven mapping between these features and six
+driver behaviors using an elaborate web-based user study. We also compute a
+summarized score indicating a level of awareness that is needed while driving
+next to other vehicles. We also incorporate our algorithm into a vehicle
+navigation simulation system and demonstrate its benefits in terms of safer
+real-time navigation, while driving next to aggressive or dangerous drivers.
+"
+21668,Using Global Constraints and Reranking to Improve Cognates Detection,"  Global constraints and reranking have not been used in cognates detection
+research to date. We propose methods for using global constraints by performing
+rescoring of the score matrices produced by state of the art cognates detection
+systems. Using global constraints to perform rescoring is complementary to
+state of the art methods for performing cognates detection and results in
+significant performance improvements beyond current state of the art
+performance on publicly available datasets with different language pairs and
+various conditions such as different levels of baseline state of the art
+performance and different data size conditions, including with more realistic
+large data size conditions than have been evaluated with in the past.
+"
+21669,"An All-Memristor Deep Spiking Neural Computing System: A Step Towards Realizing the Low Power,Stochastic Brain","  Deep 'Analog Artificial Neural Networks' (ANNs) perform complex
+classification problems with remarkably high accuracy. However, they rely on
+humongous amount of power to perform the calculations, veiling the accuracy
+benefits. The biological brain on the other hand is significantly more powerful
+than such networks and consumes orders of magnitude less power, indicating us
+about some conceptual mismatch. Given that the biological neurons communicate
+using energy efficient trains of spikes, and the behavior is non-deterministic,
+incorporating these effects in Deep Artificial Neural Networks may drive us few
+steps towards a more realistic neuron. In this work, we propose how the
+inherent stochasticity of nano-scale resistive devices can be harnessed to
+emulate the functionality of a spiking neuron that can be incorporated in deep
+stochastic Spiking Neural Networks (SNN). At the algorithmic level, we propose
+how the training can be modified to convert an ANN to an SNN while supporting
+the stochastic activation function offered by these devices. We devise circuit
+architectures to incorporate stochastic memristive neurons along with
+memristive crossbars which perform the functionality of the synaptic weights.
+We tested the proposed All Memristor deep stochastic SNN for image
+classification and observed only about 1% degradation in accuracy with the ANN
+baseline after incorporating the circuit and device related non-idealities. We
+witnessed that the network is robust to certain variations and consumes ~ 6.4x
+less energy than its CMOS counterpart.
+"
+21670,Bridge spectra of cables of 2-bridge knots,"  We compute the bridge spectra of cables of 2-bridge knots. We also give some
+results about bridge spectra and distance of Montesinos knots.
+"
+21671,Optimizations of Management Algorithms for Multi-Level Memory Hierarchy,"  In the near future the SCM is predicted to modify the form of new programs,
+the access form to storage, and the way that storage devices themselves are
+built. Therefore, a combination between the SCM and a designated Memory
+Allocation Manager (MAM) that will allow the programmer to manually control the
+different memories in the memory hierarchy will be likely to achieve a new
+level of performance for memory-aware data structures. Although the manual MAM
+seems to be the optimal approach for multi-level memory hierarchy management,
+this technique is still very far from being realistic, and the chances that it
+would be implemented in current codes using High Performance Computing (HPC)
+platforms is quite low. This premise means that the most reasonable way to
+introduce the SCM into any usable and popular memory system would be by
+implementing an automated version of the MAM using the fundamentals of paging
+algorithms, as used for two-level memory hierarchy. Our hypothesis is that
+achieving appropriate transferability between memory levels may be possible
+using ideas of algorithms employed in current virtual memory systems, and that
+the adaptation of those algorithms from a two-level memory hierarchy to an
+N-level memory hierarchy is possible. In order to reach the conclusion that our
+hypothesis is correct, we investigated various paging algorithms, and found the
+ones that could be adapted successfully from two-level memory hierarchy to an
+N-level memory hierarchy. We discovered that using an adaptation of the Aging
+paging algorithm to an N-level memory hierarchy results in the best
+performances in terms of Hit/Miss ratio. In order to verify our hypothesis we
+build a simulator called ""DeMemory simulator"" for analyzing our algorithms as
+well as for other algorithms that will be devised in the future.
+"
+21672,Mining Smart Card Data for Travelers' Mini Activities,"  In the context of public transport modeling and simulation, we address the
+problem of mismatch between simulated transit trips and observed ones. We point
+to the weakness of the current travel demand modeling process; the trips it
+generates are over-optimistic and do not reflect the real passenger choices. We
+introduce the notion of mini activities the travelers do during the trips; they
+can explain the deviation of simulated trips from the observed trips. We
+propose to mine the smart card data to extract the mini activities. We develop
+a technique to integrate them in the generated trips and learn such an
+integration from two available sources, the trip history and trip planner
+recommendations. For an input travel demand, we build a Markov chain over the
+trip collection and apply the Monte Carlo Markov Chain algorithm to integrate
+mini activities in such a way that the selected characteristics converge to the
+desired distributions. We test our method in different settings on the
+passenger trip collection of Nancy, France. We report experimental results
+demonstrating a very important mismatch reduction.
+"
+21673,Multiple nonlinear resonances and frequency combs in bottle microresonators,"  We introduce the generalized Lugiato-Lefever equation describing nonlinear
+effects in the bottle microresonators. We demonstrate that the nonlinear modes
+of these resonators can form multiple coexisting and overlapping nonlinear
+resonances and that their instabilities lead to the generation of the low
+repetition rate frequency combs.
+"
+21674,Data-Driven Robust Control for Type 1 Diabetes Under Meal and Exercise Uncertainties,"  We present a fully closed-loop design for an artificial pancreas (AP) which
+regulates the delivery of insulin for the control of Type I diabetes. Our AP
+controller operates in a fully automated fashion, without requiring any manual
+interaction (e.g. in the form of meal announcements) with the patient. A major
+obstacle to achieving closed-loop insulin control is the uncertainty in those
+aspects of a patient's daily behavior that significantly affect blood glucose,
+especially in relation to meals and physical activity. To handle such
+uncertainties, we develop a data-driven robust model-predictive control
+framework, where we capture a wide range of individual meal and exercise
+patterns using uncertainty sets learned from historical data. These sets are
+then used in the controller and state estimator to achieve automated, precise,
+and personalized insulin therapy. We provide an extensive in silico evaluation
+of our robust AP design, demonstrating the potential of this approach, without
+explicit meal announcements, to support high carbohydrate disturbances and to
+regulate glucose levels in large clusters of virtual patients learned from
+population-wide survey data.
+"
+21675,"""Tell me and I forget, teach me and I may remember, involve me and I learn"": changing the approach of teaching Computer Organization","  Millennials are arriving to university sometimes uncomfortable with the
+methods of some courses. Ideas that worked with previous generations of
+students begin to fail when digital natives receive paper and pencil as tools.
+Courses must update from old paper-based methods to hands-on and computerized
+versions. The present work discusses about this update and comments on one
+implementation in the course Computer Organization of the Computer Science
+curriculum at Universidad de Buenos Aires. It also includes some metrics that
+show the effectiveness of the changes in attracting and engaging the digital
+generation.
+"
+21676,Biotechnology and the lifetime of technical civilizations,"  The number of people able to end Earth's technical civilization has
+heretofore been small. Emerging dual-use technologies, such as biotechnology,
+may give similar power to thousands or millions of individuals. To
+quantitatively investigate the ramifications of such a marked shift on the
+survival of both terrestrial and extraterrestrial technical civilizations, this
+paper presents a two-parameter model for civilizational lifespans, i.e. the
+quantity $L$ in Drake's equation for the number of communicating
+extraterrestrial civilizations. One parameter characterizes the population
+lethality of a civilization's biotechnology and the other characterizes the
+civilization's psychosociology. $L$ is demonstrated to be less than the inverse
+of the product of these two parameters. Using empiric data from Pubmed to
+inform the biotechnology parameter, the model predicts human civilization's
+median survival time as decades to centuries, even with optimistic
+psychosociological parameter values, thereby positioning biotechnology as a
+proximate threat to human civilization. For an ensemble of civilizations having
+some median calculated survival time, the model predicts that, after 80 times
+that duration, only one in $10^{24}$ civilizations will survive -- a tempo and
+degree of winnowing compatible with Hanson's ""Great Filter."" Thus, assuming
+that civilizations universally develop advanced biotechnology, before they
+become vigorous interstellar colonizers, the model provides a resolution to the
+Fermi paradox.
+"
+21677,Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain,"  Next-generation sequencing (NGS) to profile temporal changes in living
+systems is gaining more attention for deriving better insights into the
+underlying biological mechanisms compared to traditional static sequencing
+experiments. Nonetheless, the majority of existing statistical tools for
+analyzing NGS data lack the capability of exploiting the richer information
+embedded in temporal data. Several recent tools have been developed to analyze
+such data but they typically impose strict model assumptions, such as
+smoothness on gene expression dynamic changes. To capture a broader range of
+gene expression dynamic patterns, we develop the gamma Markov negative binomial
+(GMNB) model that integrates a gamma Markov chain into a negative binomial
+distribution model, allowing flexible temporal variation in NGS count data.
+Using Bayes factors, GMNB enables more powerful temporal gene differential
+expression analysis across different phenotypes or treatment conditions. In
+addition, it naturally handles the heterogeneity of sequencing depth in
+different samples, removing the need for ad-hoc normalization. Efficient Gibbs
+sampling inference of the GMNB model parameters is achieved by exploiting novel
+data augmentation techniques. Extensive experiments on both simulated and
+real-world RNA-seq data show that GMNB outperforms existing methods in both
+receiver operating characteristic (ROC) and precision-recall (PR) curves of
+differential expression analysis results.
+"
+21678,The locality of quantum subsystems III: A 'subsystem compatible' interpretation of quantum mechanics,"  A local description of quantum subsystems can be used to construct ontologies
+of the full quantum predictions. This paper communicates one possible way to do
+so. A retrocausal interpretation of quantum mechanics where the de Broglie-Bohm
+particles are the retrocausal agents is developed. This interpretation is
+constrained to be compatible with the existence of a local realist description
+of quantum subsystems.
+"
+21679,On the Oberlin affine curvature condition,"  In this paper we generalize the well-known notions of affine arclength and
+affine hypersurface measure to submanifolds of any dimension $d$ in $\mathbb
+R^n$ , $1 \leq d \leq n-1$. We show that a canonical affine invariant measure
+exists and that, modulo sufficient regularity assumptions on the submanifold,
+the measure satisfies the affine curvature condition of D. Oberlin with an
+exponent which is best possible. The proof combines aspects of Geometric
+Invariant Theory, convex geometry, and frame theory. A significant new element
+of the proof is a generalization to higher dimensions of an earlier result of
+the author concerning inequalities of reverse Sobolev type for polynomials on
+arbitrary measurable subsets of the real line.
+"
+21680,GDSP: A Graphical Perspective on the Distributed Storage Systems,"  The classical distributed storage problem can be modeled by a k-uniform {\it
+complete} hyper-graph where vertices represent servers and hyper-edges
+represent users. Hence each hyper-edge should be able to recover the full file
+using only the memories of the vertices associated with it. This paper
+considers the generalization of this problem to {\it arbitrary} hyper-graphs
+and to the case of multiple files, where each user is only interested in one, a
+problem we will refer to as the graphical distributed storage problem (GDSP).
+Specifically, we make progress in the analysis of minimum-storage codes for two
+main subproblems of the GDSP which extend the classical model in two
+independent directions: the case of an arbitrary graph with multiple files, and
+the case of an arbitrary hyper-graph with a single file.
+"
+21681,Projected Coupled Cluster Theory,"  Coupled cluster theory is the method of choice for weakly correlated systems.
+But in the strongly correlated regime, it faces a symmetry dilemma, where it
+either completely fails to describe the system, or has to artificially break
+certain symmetries. On the other hand, projected Hartree-Fock theory captures
+the essential physics of many kinds of strong correlations via symmetry
+breaking and restoration. In this work, we combine and try to retain the merits
+of these two methods by applying symmetry projection to broken symmetry coupled
+cluster wavefunctions. The non-orthogonal nature of states resulting from the
+application of symmetry projection operators furnishes particle-hole
+excitations to all orders, thus creating an obstacle for the exact evaluation
+of overlaps. Here we provide a solution via a disentanglement framework theory
+that can be approximated rigorously and systematically. Results of projected
+coupled cluster theory are presented for molecules and the Hubbard model,
+showing that spin projection significantly improves unrestricted coupled
+cluster theory while restoring good quantum numbers. The energy of projected
+coupled cluster theory reduces to the unprojected one in the thermodynamic
+limit, albeit at a much slower rate than projected Hartree-Fock.
+"
+21682,"Existence, Uniqueness and Structure of Second Order absolute minimisers","  Let $\Omega \subseteq \mathbb{R}^n$ be a bounded open $C^{1,1}$ set. In this
+paper we prove the existence of a unique second order absolute minimiser
+$u_\infty$ of the functional \[ \mathrm{E}_\infty (u,\mathcal{O})\, :=\, \|
+\mathrm{F}(\cdot, \Delta u) \|_{L^\infty( \mathcal{O} )}, \ \ \ \mathcal{O}
+\subseteq \Omega \text{ measurable}, \] with prescribed boundary conditions for
+$u$ and $\mathrm{D} u$ on $\partial \Omega$ and under natural assumptions on
+$\mathrm{F}$. We also show that $u_\infty$ is partially smooth and there exists
+a harmonic function $f_\infty \in L^1(\Omega)$ such that \[ \mathrm{F}(x,
+\Delta u_\infty(x)) \, =\, e_\infty\, \mathrm{sgn}\big(f_\infty(x)\big) \] for
+all $x \in \{f_\infty \neq 0\}$, where $e_\infty$ is the infimum of the global
+energy.
+"
+21683,Interpretation of crystallographic groups under Riemann's elliptic geometry,"  This paper is devoted to the problem of choosing the most suitable model of a
+geometrical system for describing the real crystallographic space. It has been
+shown that all 230 crystallographic groups used to describe the crystalline
+structures in a Euclidean space can be presented by elliptic motions in the
+closed space $V^3$. Based on these results, it is stated that a special
+geometric system---the crystallographic space of interpretation $R_E$,
+determined by a form of an interpretant (the surface of a torus $T^2$ can serve
+as this interpretant)---can serve as a geometrical model of the real
+crystallographic space. The compact model of the closed structure of a crystal
+has been proposed and ways of its treatment for visualizing the constructions
+of elements of symmetry of a crystalline lattice in the Euclidean space $E^3$
+have been determined. As a modeling space for describing the internal structure
+of a crystal, the closed space $V^3$ with the elliptic metrics and constant
+positive Gaussian curvature ($K=1$) has been offered. The properties of the
+internal space of a real crystal are naturally deduced from the properties of
+the modeling space.
+"
+21684,Safe Policy Improvement with Baseline Bootstrapping,"  A common goal in Reinforcement Learning is to derive a good strategy given a
+limited batch of data. In this paper, we adopt the safe policy improvement
+(SPI) approach: we compute a target policy guaranteed to perform at least as
+well as a given baseline policy, approximately and with high probability. Our
+SPI strategy, inspired by the knows-what-it-knows paradigm, consists in
+bootstrapping the target with the baseline when the target does not know. We
+develop two computationally efficient bootstrapping algorithms, one value-based
+and one policy-based, both accompanied by theoretical SPI bounds for the
+tabular case. We empirically show the limits of the existing algorithms on a
+small stochastic gridworld problem, and then demonstrate that our algorithms
+not only improve the worst-case scenario but also the mean performance.
+"
+21685,Efficient Preconditioning for Noisy Separable NMFs by Successive Projection Based Low-Rank Approximations,"  The successive projection algorithm (SPA) can quickly solve a nonnegative
+matrix factorization problem under a separability assumption. Even if noise is
+added to the problem, SPA is robust as long as the perturbations caused by the
+noise are small. In particular, robustness against noise should be high when
+handling the problems arising from real applications. The preconditioner
+proposed by Gillis and Vavasis (2015) makes it possible to enhance the noise
+robustness of SPA. Meanwhile, an additional computational cost is required. The
+construction of the preconditioner contains a step to compute the top-$k$
+truncated singular value decomposition of an input matrix. It is known that the
+decomposition provides the best rank-$k$ approximation to the input matrix; in
+other words, a matrix with the smallest approximation error among all matrices
+of rank less than $k$. This step is an obstacle to an efficient implementation
+of the preconditioned SPA.
+To address the cost issue, we propose a modification of the algorithm for
+constructing the preconditioner. Although the original algorithm uses the best
+rank-$k$ approximation, instead of it, our modification uses an alternative.
+Ideally, this alternative should have high approximation accuracy and low
+computational cost. To ensure this, our modification employs a rank-$k$
+approximation produced by an SPA based algorithm. We analyze the accuracy of
+the approximation and evaluate the computational cost of the algorithm. We then
+present an empirical study revealing the actual performance of the SPA based
+rank-$k$ approximation algorithm and the modified preconditioned SPA.
+"
+21686,Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments,"  The sampling based motion planning algorithm known as Rapidly-exploring
+Random Trees (RRT) has gained the attention of many researchers due to their
+computational efficiency and effectiveness. Recently, a variant of RRT called
+RRT* has been proposed that ensures asymptotic optimality. Subsequently its
+bidirectional version has also been introduced in the literature known as
+Bidirectional-RRT* (B-RRT*). We introduce a new variant called Intelligent
+Bidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT*
+and bidirectional version of RRT* (B-RRT*) algorithms and is specially designed
+for complex cluttered environments. IB-RRT* utilizes the bidirectional trees
+approach and introduces intelligent sample insertion heuristic for fast
+convergence to the optimal path solution using uniform sampling heuristics. The
+proposed algorithm is evaluated theoretically and experimental results are
+presented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimental
+results demonstrate the superior efficiency of IB-RRT* in comparison with RRT*
+and B-RRT in complex cluttered environments.
+"
+21687,Regularity in gradient constraint problem and thin film micromagnetics,"  We prove some C^{1,\alpha} regularity in some gradient constraint problem and
+application to Torsion problem and micromagnetic problem and variational
+inequality.
+"
+21688,Asymptotic Expansion with Boundary Layer Analysis for Strongly Anisotropic Elliptic Equations,"  In this article, we derive the asymptotic expansion, up to an arbitrary order
+in theory, for the solution of a two-dimensional elliptic equation with
+strongly anisotropic diffusion coefficients along different directions, subject
+to the Neumann boundary condition and the Dirichlet boundary condition on
+specific parts of the domain boundary, respectively. The ill-posedness arising
+from the Neumann boundary condition in the strongly anisotropic diffusion limit
+is handled by the decomposition of the solution into a mean part and a
+fluctuation part. The boundary layer analysis due to the Dirichlet boundary
+condition is conducted for each order in the expansion for the fluctuation
+part. Our results suggest that the leading order is the combination of the mean
+part and the composite approximation of the fluctuation part for the general
+Dirichlet boundary condition.
+"
+21689,Discovery and recognition of motion primitives in human activities,"  We present a novel framework for the automatic discovery and recognition of
+motion primitives in videos of human activities. Given the 3D pose of a human
+in a video, human motion primitives are discovered by optimizing the `motion
+flux', a quantity which captures the motion variation of a group of skeletal
+joints. A normalization of the primitives is proposed in order to make them
+invariant with respect to a subject anatomical variations and data sampling
+rate. The discovered primitives are unknown and unlabeled and are
+unsupervisedly collected into classes via a hierarchical non-parametric Bayes
+mixture model. Once classes are determined and labeled they are further
+analyzed for establishing models for recognizing discovered primitives. Each
+primitive model is defined by a set of learned parameters.
+Given new video data and given the estimated pose of the subject appearing on
+the video, the motion is segmented into primitives, which are recognized with a
+probability given according to the parameters of the learned models.
+Using our framework we build a publicly available dataset of human motion
+primitives, using sequences taken from well-known motion capture datasets. We
+expect that our framework, by providing an objective way for discovering and
+categorizing human motion, will be a useful tool in numerous research fields
+including video analysis, human inspired motion generation, learning by
+demonstration, intuitive human-robot interaction, and human behavior analysis.
+"
+21690,DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics,"  Recent developments in many-body potential energy representation via deep
+learning have brought new hopes to addressing the accuracy-versus-efficiency
+dilemma in molecular simulations. Here we describe DeePMD-kit, a package
+written in Python/C++ that has been designed to minimize the effort required to
+build deep learning based representation of potential energy and force field
+and to perform molecular dynamics. Potential applications of DeePMD-kit span
+from finite molecules to extended systems and from metallic systems to
+chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the
+most popular deep learning frameworks, making the training process highly
+automatic and efficient. On the other end, DeePMD-kit is interfaced with
+high-performance classical molecular dynamics and quantum (path-integral)
+molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus,
+upon training, the potential energy and force field models can be used to
+perform efficient molecular simulations for different purposes. As an example
+of the many potential applications of the package, we use DeePMD-kit to learn
+the interatomic potential energy and forces of a water model using data
+obtained from density functional theory. We demonstrate that the resulted
+molecular dynamics model reproduces accurately the structural information
+contained in the original model.
+"
+21691,"Small-depth Multilinear Formula Lower Bounds for Iterated Matrix Multiplication, with Applications","  In this paper, we study the algebraic formula complexity of multiplying $d$
+many $2\times 2$ matrices, denoted $\mathrm{IMM}_{d}$, and show that the
+well-known divide-and-conquer algorithm cannot be significantly improved at any
+depth, as long as the formulas are multilinear.
+Formally, for each depth $\Delta \leq \log d$, we show that any product-depth
+$\Delta$ multilinear formula for $\mathrm{IMM}_d$ must have size
+$\exp(\Omega(\Delta d^{1/\Delta})).$ It also follows from this that any
+multilinear circuit of product-depth $\Delta$ for the same polynomial of the
+above form must have a size of $\exp(\Omega(d^{1/\Delta})).$ In particular, any
+polynomial-sized multilinear formula for $\mathrm{IMM}_d$ must have depth
+$\Omega(\log d)$, and any polynomial-sized multilinear circuit for
+$\mathrm{IMM}_d$ must have depth $\Omega(\log d/\log \log d).$ Both these
+bounds are tight up to constant factors.
+1. Depth-reduction: A well-known result of Brent (JACM 1974) implies that any
+formula of size $s$ can be converted to one of size $s^{O(1)}$ and depth
+$O(\log s)$; further, this reduction continues to hold for multilinear
+formulas. Our lower bound implies that any depth-reduction in the multilinear
+setting cannot reduce the depth to $o(\log s)$ without a superpolynomial
+blow-up in size.
+2. Separations from general formulas: Our result, along with a non-trivial
+upper bound for $\mathrm{IMM}_{d}$ implied by a result of Gupta, Kamath, Kayal
+and Saptharishi (SICOMP 2016), shows that for any size $s$ and product-depth
+$\Delta = o(\log s),$ general formulas of size $s$ and product-depth $\Delta$
+cannot be converted to multilinear formulas of size $s^{\omega(1)}$ and
+product-depth $\Delta,$ when the underlying field has characteristic zero.
+"
+21692,1-Dimensional Intrinsic Persistence of Geodesic Spaces,"  Given a compact geodesic space $X$ we apply the fundamental group and
+alternatively the first homology group functor to the corresponding Rips or
+Čech filtration of $X$ to obtain what we call a persistence. This paper
+contains the theory describing such persistence: properties of the set of
+critical points, their precise relationship to the size of holes, the structure
+of persistence and the relationship between open and close, Rips and Čech
+induced persistences. Amongst other results we prove that a Rips critical point
+$c$ corresponds to an isometrically embedded circle of length $3c$, that a
+homology persistence of a locally contractible space with coefficients in a
+field encodes the lengths of the lexicographically smallest base and that Rips
+and Čech induced persistences are isomorphic up to a factor $3/4$. The
+theory describes geometric properties of the underlying space encoded and
+extractable from persistence.
+"
+21693,Kalman Filter and its Modern Extensions for the Continuous-time Nonlinear Filtering Problem,"  This paper is concerned with the filtering problem in continuous-time. Three
+algorithmic solution approaches for this problem are reviewed: (i) the
+classical Kalman-Bucy filter which provides an exact solution for the linear
+Gaussian problem, (ii) the ensemble Kalman-Bucy filter (EnKBF) which is an
+approximate filter and represents an extension of the Kalman-Bucy filter to
+nonlinear problems, and (iii) the feedback particle filter (FPF) which
+represents an extension of the EnKBF and furthermore provides for an consistent
+solution in the general nonlinear, non-Gaussian case. The common feature of the
+three algorithms is the gain times error formula to implement the update step
+(to account for conditioning due to the observations) in the filter. In
+contrast to the commonly used sequential Monte Carlo methods, the EnKBF and FPF
+avoid the resampling of the particles in the importance sampling update step.
+Moreover, the feedback control structure provides for error correction
+potentially leading to smaller simulation variance and improved stability
+properties. The paper also discusses the issue of non-uniqueness of the filter
+update formula and formulates a novel approximation algorithm based on ideas
+from optimal transport and coupling of measures. Performance of this and other
+algorithms is illustrated for a numerical example.
+"
+21694,Okounkov bodies and transfinite diameter,"  We present an explicit calculation of an Okounkov body associated to an
+algebraic variety. This is used to derive a formula for transfinite diameter on
+the variety. We relate this formula to a recent result of D. Witt Nystrom.
+"
+21695,Spin Structures on Affine Kac-Moody Symmetric Spaces,"  We construct certain Fréchet principal $K$-bundles over affine Kac-Moody
+symmetric spaces by means of the natural projection $G\rightarrow G/K$ where
+$G$ is a (possibly twisted) geometric affine Kac-Moody group and $K$ is a real
+form when $G$ is complex and a ""compact"" real form when $G$ is real.\ Then we
+present two methods by means of which one can find certain spin structures on
+affine Kac-Moody symmetric spaces of compact type.\ We also briefly sketch
+defining a Dirac-like operator on these spaces.
+"
+21696,Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change,"  It is generally believed that, when a linguistic item acquires a new meaning,
+its overall frequency of use in the language rises with time with an S-shaped
+growth curve. Yet, this claim has only been supported by a limited number of
+case studies. In this paper, we provide the first corpus-based quantitative
+confirmation of the genericity of the S-curve in language change. Moreover, we
+uncover another generic pattern, a latency phase of variable duration preceding
+the S-growth, during which the frequency of use of the semantically expanding
+word remains low and more or less constant. We also propose a usage-based model
+of language change supported by cognitive considerations, which predicts that
+both phases, the latency and the fast S-growth, take place. The driving
+mechanism is a stochastic dynamics, a random walk in the space of frequency of
+use. The underlying deterministic dynamics highlights the role of a control
+parameter, the strength of the cognitive impetus governing the onset of change,
+which tunes the system at the vicinity of a saddle-node bifurcation. In the
+neighborhood of the critical point, the latency phase corresponds to the
+diffusion time over the critical region, and the S-growth to the fast
+convergence that follows. The duration of the two phases is computed as
+specific first passage times of the random walk process, leading to
+distributions that fit well the ones extracted from our dataset. We argue that
+our results are not specific to the studied corpus, but apply to semantic
+change in general.
+"
+21697,On model fitting and estimation of strictly stationary processes,"  Stationary processes have been extensively studied in the literature. Their
+applications include modeling and forecasting numerous real life phenomena such
+as natural disasters, sales and market movements. When stationary processes are
+considered, modeling is traditionally based on fitting an autoregressive moving
+average (ARMA) process. However, we challenge this conventional approach.
+Instead of fitting an ARMA model, we apply an AR(1) characterization in
+modeling any strictly stationary processes. Moreover, we derive consistent and
+asymptotically normal estimators of the corresponding model parameter.
+"
+21698,Spatially and time-resolved imaging of transport of indirect excitons in high magnetic fields,"  We present the direct measurements of magnetoexciton transport. Excitons give
+the opportunity to realize the high magnetic field regime for composite bosons
+with magnetic fields of a few Tesla. Long lifetimes of indirect excitons allow
+the study kinetics of magnetoexciton transport with time-resolved optical
+imaging of exciton photoluminescence. We performed spatially, spectrally, and
+time-resolved optical imaging of transport of indirect excitons in high
+magnetic fields. We observed that increasing magnetic field slows down
+magnetoexciton transport. The time-resolved measurements of the magnetoexciton
+transport distance allowed for an experimental estimation of the magnetoexciton
+diffusion coefficient. An enhancement of the exciton photoluminescence energy
+at the laser excitation spot was found to anti-correlate with the exciton
+transport distance. A theoretical model of indirect magnetoexciton transport is
+presented and is in agreement with the experimental data.
+"
+21699,"Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination","  The MAP-Elites algorithm produces a set of high-performing solutions that
+vary according to features defined by the user. This technique has the
+potential to be a powerful tool for design space exploration, but is limited by
+the need for numerous evaluations. The Surrogate-Assisted Illumination
+algorithm (SAIL), introduced here, integrates approximative models and
+intelligent sampling of the objective function to minimize the number of
+evaluations required by MAP-Elites.
+The ability of SAIL to efficiently produce both accurate models and diverse
+high performing solutions is illustrated on a 2D airfoil design problem. The
+search space is divided into bins, each holding a design with a different
+combination of features. In each bin SAIL produces a better performing solution
+than MAP-Elites, and requires several orders of magnitude fewer evaluations.
+The CMA-ES algorithm was used to produce an optimal design in each bin: with
+the same number of evaluations required by CMA-ES to find a near-optimal
+solution in a single bin, SAIL finds solutions of similar quality in every bin.
+"
+21700,A simple proof of the discrete time geometric Pontryagin maximum principle,"  We establish a geometric Pontryagin maximum principle for discrete time
+optimal control problems on finite dimensional smooth manifolds under the
+following three types of constraints: a) constraints on the states pointwise in
+time, b) constraints on the control actions pointwise in time, c) constraints
+on the frequency spectrum of the optimal control trajectories. Our proof
+follows, in spirit, the path to establish geometric versions of the Pontryagin
+maximum principle on smooth manifolds indicated in [Cha11] in the context of
+continuous-time optimal control.
+"
+21701,On the Relationship between Conditional (CAR) and Simultaneous (SAR) Autoregressive Models,"  We clarify relationships between conditional (CAR) and simultaneous (SAR)
+autoregressive models. We review the literature on this topic and find that it
+is mostly incomplete. Our main result is that a SAR model can be written as a
+unique CAR model, and while a CAR model can be written as a SAR model, it is
+not unique. In fact, we show how any multivariate Gaussian distribution on a
+finite set of points with a positive-definite covariance matrix can be written
+as either a CAR or a SAR model. We illustrate how to obtain any number of SAR
+covariance matrices from a single CAR covariance matrix by using Givens
+rotation matrices on a simulated example. We also discuss sparseness in the
+original CAR construction, and for the resulting SAR weights matrix. For a real
+example, we use crime data in 49 neighborhoods from Columbus, Ohio, and show
+that a geostatistical model optimizes the likelihood much better than typical
+first-order CAR models. We then use the implied weights from the geostatistical
+model to estimate CAR model parameters that provides the best overall
+optimization.
+"
+21702,Natural Gradient Deep Q-learning,"  We present a novel algorithm to train a deep Q-learning agent using
+natural-gradient techniques. We compare the original deep Q-network (DQN)
+algorithm to its natural-gradient counterpart, which we refer to as NGDQN, on a
+collection of classic control domains. Without employing target networks, NGDQN
+significantly outperforms DQN without target networks, and performs no worse
+than DQN with target networks, suggesting that NGDQN stabilizes training and
+can help reduce the need for additional hyperparameter tuning. We also find
+that NGDQN is less sensitive to hyperparameter optimization relative to DQN.
+Together these results suggest that natural-gradient techniques can improve
+value-function optimization in deep reinforcement learning.
+"
+21703,Volatility Models Applied to Geophysics and High Frequency Financial Market Data,"  This work is devoted to the study of modeling geophysical and financial time
+series. A class of volatility models with time-varying parameters is presented
+to forecast the volatility of time series in a stationary environment. The
+modeling of stationary time series with consistent properties facilitates
+prediction with much certainty. Using the GARCH and stochastic volatility
+model, we forecast one-step-ahead suggested volatility with +/- 2 standard
+prediction errors, which is enacted via Maximum Likelihood Estimation. We
+compare the stochastic volatility model relying on the filtering technique as
+used in the conditional volatility with the GARCH model. We conclude that the
+stochastic volatility is a better forecasting tool than GARCH (1, 1), since it
+is less conditioned by autoregressive past information.
+"
+21704,The Klein-Gordon-Zakharov equations with the positive fractional power terms and their exact solutions,"  In this paper, the famous Klein-Gordon-Zakharov equations are firstly
+generalized, the new special types of Klein-Gordon-Zakharov equations with the
+positive fractional power terms (gKGZE) are presented. In order to derive the
+exact solutions of new special gKGZE, the subsidiary higher order ordinary
+differential equations (sub-ODEs) with the positive fractional power terms are
+introduced, and with the aids of the Sub-ODE, the exact solutions of three
+special types of the gKGZE are derived, which are the bell-type solitary wave
+solution, the algebraic solitary wave solution, the kink-type solitary wave
+solution and the sinusoidal traveling wave solution, provided that the
+coefficients of gKGZE satisfy certain constraint conditions.
+"
+21705,Knowledge Rich Natural Language Queries over Structured Biological Databases,"  Increasingly, keyword, natural language and NoSQL queries are being used for
+information retrieval from traditional as well as non-traditional databases
+such as web, document, image, GIS, legal, and health databases. While their
+popularity are undeniable for obvious reasons, their engineering is far from
+simple. In most part, semantics and intent preserving mapping of a well
+understood natural language query expressed over a structured database schema
+to a structured query language is still a difficult task, and research to tame
+the complexity is intense. In this paper, we propose a multi-level
+knowledge-based middleware to facilitate such mappings that separate the
+conceptual level from the physical level. We augment these multi-level
+abstractions with a concept reasoner and a query strategy engine to dynamically
+link arbitrary natural language querying to well defined structured queries. We
+demonstrate the feasibility of our approach by presenting a Datalog based
+prototype system, called BioSmart, that can compute responses to arbitrary
+natural language queries over arbitrary databases once a syntactic
+classification of the natural language query is made.
+"
+21706,Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?,"  The use of alluring headlines (clickbait) to tempt the readers has become a
+growing practice nowadays. For the sake of existence in the highly competitive
+media industry, most of the on-line media including the mainstream ones, have
+started following this practice. Although the wide-spread practice of clickbait
+makes the reader's reliability on media vulnerable, a large scale analysis to
+reveal this fact is still absent. In this paper, we analyze 1.67 million
+Facebook posts created by 153 media organizations to understand the extent of
+clickbait practice, its impact and user engagement by using our own developed
+clickbait detection model. The model uses distributed sub-word embeddings
+learned from a large corpus. The accuracy of the model is 98.3%. Powered with
+this model, we further study the distribution of topics in clickbait and
+non-clickbait contents.
+"
+21707,Symbolic Computations of First Integrals for Polynomial Vector Fields,"  In this article we show how to generalize to the Darbouxian, Liouvillian and
+Riccati case the extactic curve introduced by J. Pereira. With this approach,
+we get new algorithms for computing, if it exists, a rational, Darbouxian,
+Liouvillian or Riccati first integral with bounded degree of a polynomial
+planar vector field. We give probabilistic and deterministic algorithms. The
+arithmetic complexity of our probabilistic algorithm is in
+$\tilde{\mathcal{O}}(N^{\omega+1})$, where $N$ is the bound on the degree of a
+representation of the first integral and $\omega \in [2;3]$ is the exponent of
+linear algebra. This result improves previous algorithms. Our algorithms have
+been implemented in Maple and are available on authors' websites. In the last
+section, we give some examples showing the efficiency of these algorithms.
+"
+21708,Of Kernels and Queues: when network calculus meets analytic combinatorics,"  Stochastic network calculus is a tool for computing error bounds on the
+performance of queueing systems. However, deriving accurate bounds for networks
+consisting of several queues or subject to non-independent traffic inputs is
+challenging. In this paper, we investigate the relevance of the tools from
+analytic combinatorics, especially the kernel method, to tackle this problem.
+Applying the kernel method allows us to compute the generating functions of the
+queue state distributions in the stationary regime of the network. As a
+consequence, error bounds with an arbitrary precision can be computed. In this
+preliminary work, we focus on simple examples which are representative of the
+difficulties that the kernel method allows us to overcome.
+"
+21709,Montgomery curves and their arithmetic,"  Three decades ago, Montgomery introduced a new elliptic curve model for use
+in Lenstra's ECM factorization algorithm. Since then, his curves and the
+algorithms associated with them have become foundational in the implementation
+of elliptic curve cryptosystems. This article surveys the theory and
+cryptographic applications of Montgomery curves over non-binary finite fields,
+including Montgomery's x-only arithmetic and Ladder algorithm, x-only
+Diffie--Hellman, y-coordinate recovery, and 2-dimensional and Euclidean
+differential addition chains such as Montgomery's PRAC algorithm.
+"
+21710,Towards Interrogating Discriminative Machine Learning Models,"  It is oftentimes impossible to understand how machine learning models reach a
+decision. While recent research has proposed various technical approaches to
+provide some clues as to how a learning model makes individual decisions, they
+cannot provide users with ability to inspect a learning model as a complete
+entity. In this work, we propose a new technical approach that augments a
+Bayesian regression mixture model with multiple elastic nets. Using the
+enhanced mixture model, we extract explanations for a target model through
+global approximation. To demonstrate the utility of our approach, we evaluate
+it on different learning models covering the tasks of text mining and image
+recognition. Our results indicate that the proposed approach not only
+outperforms the state-of-the-art technique in explaining individual decisions
+but also provides users with an ability to discover the vulnerabilities of a
+learning model.
+"
+21711,Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control,"  Deep reinforcement learning has demonstrated increasing capabilities for
+continuous control problems, including agents that can move with skill and
+agility through their environment. An open problem in this setting is that of
+developing good strategies for integrating or merging policies for multiple
+skills, where each individual skill is a specialist in a specific skill and its
+associated state distribution. We extend policy distillation methods to the
+continuous action setting and leverage this technique to combine expert
+policies, as evaluated in the domain of simulated bipedal locomotion across
+different classes of terrain. We also introduce an input injection method for
+augmenting an existing policy network to exploit new input features. Lastly,
+our method uses transfer learning to assist in the efficient acquisition of new
+skills. The combination of these methods allows a policy to be incrementally
+augmented with new skills. We compare our progressive learning and integration
+via distillation (PLAID) method against three alternative baselines.
+"
+21712,Sparse Approximation by Semidefinite Programming,"  The problem of sparse approximation and the closely related compressed
+sensing have received tremendous attention in the past decade. Primarily
+studied from the viewpoint of applied harmonic analysis and signal processing,
+there have been two dominant algorithmic approaches to this problem: Greedy
+methods called the matching pursuit (MP) and the linear programming based
+approaches called the basis pursuit (BP). The aim of the current paper is to
+bring a fresh perspective to sparse approximation by treating it as a
+combinatorial optimization problem and providing an algorithm based on the
+powerful optimization technique semidefinite programming (SDP). In particular,
+we show that there is a randomized algorithm based on a semidefinite relaxation
+of the problem with performance guarantees depending on the coherence and the
+restricted isometry constant of the dictionary used. We then show a
+derandomization of the algorithm based on the method of conditional
+probabilities.
+"
+21713,A note on gradient Einstein-type manifolds,"  In this note we show that a nontrivial, compact, degenerate or nondegenerate,
+gradient Einstein-type manifold of constant scalar curvature is isometric to
+the standard sphere with a well defined potential function. Moreover, under
+some geometric assumptions the noncompact case is also treated. In this case,
+the main result is that a homogeneous, proper, noncompact, nondegenerate,
+gradient Einstein-type manifold is an Einstein manifold.
+"
+21714,Structural Learning and Integrative Decomposition of Multi-View Data,"  The increased availability of the multi-view data (data on the same samples
+from multiple sources) has led to strong interest in models based on low-rank
+matrix factorizations. These models represent each data view via shared and
+individual components, and have been successfully applied for exploratory
+dimension reduction, association analysis between the views, and further
+learning tasks such as consensus clustering. Despite these advances, there
+remain significant challenges in modeling partially-shared components, and
+identifying the number of components of each type
+(shared/partially-shared/individual). In this work, we formulate a novel linked
+component model that directly incorporates partially-shared structures. We call
+this model SLIDE for Structural Learning and Integrative DEcomposition of
+multi-view data. We prove the existence of SLIDE decomposition and explicitly
+characterize the identifiability conditions. The proposed model fitting and
+selection techniques allow for joint identification of the number of components
+of each type, in contrast to existing sequential approaches. In our empirical
+studies, SLIDE demonstrates excellent performance in both signal estimation and
+component selection. We further illustrate the methodology on the breast cancer
+data from The Cancer Genome Atlas repository.
+"
+21715,Decentralized High-Dimensional Bayesian Optimization with Factor Graphs,"  This paper presents a novel decentralized high-dimensional Bayesian
+optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms,
+can exploit the interdependent effects of various input components on the
+output of the unknown objective function f for boosting the BO performance and
+still preserve scalability in the number of input dimensions without requiring
+prior knowledge or the existence of a low (effective) dimension of the input
+space. To realize this, we propose a sparse yet rich factor graph
+representation of f to be exploited for designing an acquisition function that
+can be similarly represented by a sparse factor graph and hence be efficiently
+optimized in a decentralized manner using distributed message passing. Despite
+richly characterizing the interdependent effects of the input components on the
+output of f with a factor graph, DEC-HBO can still guarantee no-regret
+performance asymptotically. Empirical evaluation on synthetic and real-world
+experiments (e.g., sparse Gaussian process model with 1811 hyperparameters)
+shows that DEC-HBO outperforms the state-of-the-art HBO algorithms.
+"
+21716,Direct visualization of coexisting channels of interaction in CeSb,"  Our understanding of correlated electron systems is vexed by the complexity
+of their interactions. Heavy fermion compounds are archetypal examples of this
+physics, leading to exotic properties that weave together magnetism,
+superconductivity and strange metal behavior. The Kondo semimetal CeSb is an
+unusual example where different channels of interaction not only coexist, but
+their physical signatures are coincident, leading to decades of debate about
+the microscopic picture describing the interactions between the $f$ moments and
+the itinerant electron sea. Using angle-resolved photoemission spectroscopy, we
+resonantly enhance the response of the Ce$f$-electrons across the magnetic
+transitions of CeSb and find there are two distinct modes of interaction that
+are simultaneously active, but on different kinds of carriers. This study is a
+direct visualization of how correlated systems can reconcile the coexistence of
+different modes on interaction - by separating their action in momentum space,
+they allow their coexistence in real space.
+"
+21717,Asynchronous Stochastic Approximation Based Learning Algorithms for As-You-Go Deployment of Wireless Relay Networks along a Line,"  We are motivated by the need for impromptu (or as-you-go) deployment of
+multihop wireless networks, by human agents or robots; the agent moves along a
+line, makes wireless link quality measurements at regular intervals, and makes
+on-line placement decisions using these measurements. As a first step, we have
+formulated such deployment along a line as a sequential decision problem. In
+our earlier work, we proposed two possible deployment approaches: (i) the pure
+as-you-go approach where the deployment agent can only move forward, and (ii)
+the explore-forward approach where the deployment agent explores a few
+successive steps and then selects the best relay placement location. The latter
+was shown to provide better performance but at the expense of more measurements
+and deployment time, which makes explore-forward impractical for quick
+deployment by an energy constrained agent such as a UAV. Further, the
+deployment algorithm should not require prior knowledge of the parameters of
+the wireless propagation model. In [1] we, therefore, developed learning
+algorithms for the explore-forward approach.
+The current paper provides deploy-and-learn algorithms for the pure as-you-go
+approach. We formulate the sequential relay deployment problem as an average
+cost Markov decision process (MDP), which trades off among power consumption,
+link outage probabilities, and the number of deployed relay nodes. First we
+show structural results for the optimal policy. Next, by exploiting the special
+structure of the optimality equation and by using the theory of asynchronous
+stochastic approximation, we develop two learning algorithms that
+asymptotically converge to the set of optimal policies as deployment
+progresses. Numerical results show reasonably fast speed of convergence, and
+hence the model-free algorithms can be useful for practical, fast deployment of
+emergency wireless networks.
+"
+21718,Log-aesthetic Curves as Similarity Geometric Analogue of Euler's Elasticae,"  In this paper we consider the log-aesthetic curves and their generalization
+which are used in CAGD. We consider those curves under similarity geometry and
+characterize them as stationary integrable flow on plane curves which is
+governed by the Burgers equation. We propose a variational formulation of those
+curves whose Euler-Lagrange equation yields the stationary Burgers equation.
+Our result suggests that the log-aesthetic curves and their generalization can
+be regarded as the similarity geometric analogue of Euler's elasticae.
+"
+21719,Local and global existence of solutions to a fourth-order parabolic equation modeling kinetic roughening and coarsening in thin films,"  In this paper we study both the Cauchy problem and the initial boundary value
+problem for the equation $\partial_tu+\mbox{div}\left(\nabla\Delta u-{\bf
+g}(\nabla u)\right)=0$. This equation has been proposed as a continuum model
+for kinetic roughening and coarsening in thin films. In the Cauchy problem, we
+obtain that local existence of a weak solution is guaranteed as long as the
+vector-valued function ${\bf g}$ is continuous and the initial datum $u_0$ lies
+in $C^1(\mathbb{R}^N)$ with $\nabla u_0(x)$ being uniformly continuous and
+bounded on $\mathbb{R}^N$ and that the global existence assertion also holds
+true if we assume that ${\bf g}$ is locally Lipschitz and satisfies the growth
+condition $|{\bf g}(\xi) |\leq c|\xi|^\alpha$ for some $c>0, \alpha\in (2, 3)$,
+$\sup_{\mathbb{R}^N}|\nabla u_0|<\infty$, and the norm of $u_0$ in the space
+$L^{\frac{(\alpha-1)N}{3-\alpha}}(\mathbb{R}^N) $ is sufficiently small. This
+is done by exploring various properties of the biharmonic heat kernel. In the
+initial boundary value problem, we assume that ${\bf g}$ is continuous and
+satisfies the growth condition $|{\bf g}(\xi) |\leq c|\xi|^\alpha+c$ for some
+$c, \alpha\in (0,\infty)$. Our investigations reveal that if $\alpha\leq 1$ we
+have global existence of a weak solution, while if
+$1<\alpha<\frac{N^2+2N+4}{N^2}$ only a local existence theorem can be
+established. Our method here is based upon a new interpolation inequality,
+which may be of interest in its own right.
+"
+21720,Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls,"  Research in statistical relational learning has produced a number of methods
+for learning relational models from large-scale network data. While these
+methods have been successfully applied in various domains, they have been
+developed under the unrealistic assumption of full data access. In practice,
+however, the data are often collected by crawling the network, due to
+proprietary access, limited resources, and privacy concerns. Recently, we
+showed that the parameter estimates for relational Bayes classifiers computed
+from network samples collected by existing network crawlers can be quite
+inaccurate, and developed a crawl-aware estimation method for such models
+(Yang, Ribeiro, and Neville, 2017). In this work, we extend the methodology to
+learning relational logistic regression models via stochastic gradient descent
+from partial network crawls, and show that the proposed method yields accurate
+parameter estimates and confidence intervals.
+"
+21721,Social Discrete Choice Models,"  Human decision making underlies data generating process in multiple
+application areas, and models explaining and predicting choices made by
+individuals are in high demand. Discrete choice models are widely studied in
+economics and computational social sciences. As digital social networking
+facilitates information flow and spread of influence between individuals, new
+advances in modeling are needed to incorporate social information into these
+models in addition to characteristic features affecting individual choices. In
+this paper, we propose two novel models with scalable training algorithms:
+local logistics graph regularization (LLGR) and latent class graph
+regularization (LCGR) models. We add social regularization to represent
+similarity between friends, and we introduce latent classes to account for
+possible preference discrepancies between different social groups. Training of
+the LLGR model is performed using alternating direction method of multipliers
+(ADMM), and training of the LCGR model is performed using a specialized Monte
+Carlo expectation maximization (MCEM) algorithm. Scalability to large graphs is
+achieved by parallelizing computation in both the expectation and the
+maximization steps. The LCGR model is the first latent class classification
+model that incorporates social relationships among individuals represented by a
+given graph. To evaluate our two models, we consider three classes of data to
+illustrate a typical large-scale use case in internet and social media
+applications. We experiment on synthetic datasets to empirically explain when
+the proposed model is better than vanilla classification models that do not
+exploit graph structure. We also experiment on real-world data, including both
+small scale and large scale real-world datasets, to demonstrate on which types
+of datasets our model can be expected to outperform state-of-the-art models.
+"
+21722,Operator spreading and the emergence of dissipative hydrodynamics under unitary evolution with conservation laws,"  We study the scrambling of local quantum information in chaotic many-body
+systems in the presence of a locally conserved quantity like charge or energy
+that moves diffusively. The interplay between conservation laws and scrambling
+sheds light on the mechanism by which unitary quantum dynamics, which is
+reversible, gives rise to diffusive hydrodynamics, which is a dissipative
+process. We obtain our results in a random quantum circuit model that is
+constrained to have a conservation law. We find that a generic spreading
+operator consists of two parts: (i) a conserved part which comprises the weight
+of the spreading operator on the local conserved densities, whose dynamics is
+described by diffusive charge spreading. This conserved part also acts as a
+source that steadily emits a flux of (ii) non-conserved operators. This
+emission leads to dissipation in the operator hydrodynamics, with the
+dissipative process being the conversion of operator weight from local
+conserved operators to nonconserved, at a rate set by the local diffusion
+current. The emitted nonconserved parts then spread ballistically at a
+butterfly speed, thus becoming highly nonlocal and hence essentially
+non-observable, thereby acting as the ""reservoir"" that facilitates the
+dissipation. In addition, we find that the nonconserved component develops a
+power law tail behind its leading ballistic front due to the slow dynamics of
+the conserved components. This implies that the out-of-time-order commutator
+(OTOC) between two initially separated operators grows sharply upon the arrival
+of the ballistic front but, in contrast to systems with no conservation laws,
+it develops a diffusive tail and approaches its asymptotic late-time value only
+as a power of time instead of exponentially. We also derive these results
+within an effective hydrodynamic description which contains multiple coupled
+diffusion equations.
+"
+21723,Performance of New High-Precision Muon Tracking Detectors for the ATLAS Experiment,"  The goals of the ongoing and planned ATLAS muon detector upgrades are to
+increase the acceptance for precision muon momentum measurement and triggering
+and to improve the rate capability of the muon chambers in the high-background
+regions corresponding to the increasing LHC luminosity. Smalldiameter Muon
+Drift Tube (sMDT) chambers have been developed for these purposes. With half
+the drift-tube diameter of the current ATLAS Muon Drift Tube (MDT) chambers
+with 30 mm drift tube diameter and otherwise unchanged operating parameters,
+the sMDT chambers share all the advantages of the MDTs, but have an about an
+order of magnitude higher rate capability and can be installed in detector
+regions where MDT chambers do not fit in. The construction of twelve chambers
+for the feet regions of the ATLAS detector has been completed for the
+installation in the winter shutdown 2016/17 of the Large Hadron Collider. The
+purpose of this upgrade of the ATLAS muon spectrometer is to increase the
+acceptance for three-point muon track measurement which substantially improves
+the muon momentum resolution in the regions concerned.
+"
+21724,Exact electrodynamics versus standard optics for a slab of cold dense gas,"  We study light propagation through a slab of cold gas using both the standard
+electrodynamics of polarizable media, and massive atom-by-atom simulations of
+the electrodynamics. The main finding is that the predictions from the two
+methods may differ qualitatively when the density of the atomic sample $\rho$
+and the wavenumber of resonant light $k$ satisfy $\rho k^{-3}\gtrsim 1$. The
+reason is that the standard electrodynamics is a mean-field theory, whereas for
+sufficiently strong light-mediated dipole-dipole interactions the atomic sample
+becomes correlated. The deviations from mean-field theory appear to scale with
+the parameter $\rho k^{-3}$, and we demonstrate noticeable effects already at
+$\rho k^{-3} \simeq 10^{-2}$. In dilute gases and in gases with an added
+inhomogeneous broadening the simulations show shifts of the resonance lines in
+qualitative agreement with the predicted Lorentz-Lorenz shift and ""cooperative
+Lamb shift"", but the quantitative agreement is unsatisfactory. Our
+interpretation is that the microscopic basis for the local-field corrections in
+electrodynamics is not fully understood.
+"
+21725,Sparse Grid Discretizations based on a Discontinuous Galerkin Method,"  We examine and extend Sparse Grids as a discretization method for partial
+differential equations (PDEs). Solving a PDE in $D$ dimensions has a cost that
+grows as $O(N^D)$ with commonly used methods. Even for moderate $D$ (e.g.
+$D=3$), this quickly becomes prohibitively expensive for increasing problem
+size $N$. This effect is known as the Curse of Dimensionality. Sparse Grids
+offer an alternative discretization method with a much smaller cost of $O(N
+\log^{D-1}N)$. In this paper, we introduce the reader to Sparse Grids, and
+extend the method via a Discontinuous Galerkin approach. We then solve the
+scalar wave equation in up to $6+1$ dimensions, comparing cost and accuracy
+between full and sparse grids. Sparse Grids perform far superior, even in three
+dimensions. Our code is freely available as open source, and we encourage the
+reader to reproduce the results we show.
+"
+21726,Study of the one-way speed of light anisotropy with particle beams,"  Concepts of high precision studies of the one-way speed of light anisotropy
+are discussed. The high energy particle beam allows measurement of a one-way
+speed of light anisotropy (SOLA) via analysis of the beam momentum variation
+with sidereal phase without the use of synchronized clocks. High precision beam
+position monitors could provide accurate monitoring of the beam orbit and
+determination of the particle beam momentum with relative accuracy on the level
+of $10^{-10}$, which corresponds to a limit on SOLA of $10^{-18}$ with existing
+storage rings. A few additional versions of the experiment are also presented.
+"
+21727,Three-dimensional radiation dosimetry based on optically-stimulated luminescence,"  A new approach to three-dimensional (3D) dosimetry based on
+optically-stimulated luminescence (OSL) is presented. By embedding OSL-active
+particles into a transparent silicone matrix (PDMS), the well-established
+dosimetric properties of an OSL material are exploited in a 3D-OSL dosimeter.
+By investigating prototype dosimeters in standard cuvettes in combination with
+small test samples for OSL readers, it is shown that a sufficient transparency
+of the 3D-OSL material can be combined with an OSL response giving an estimated
+>10.000 detected photons in 1 second per 1mm3 voxel of the dosimeter at a dose
+of 1 Gy. The dose distribution in the 3D-OSL dosimeters can be directly read
+out optically without the need for subsequent reconstruction by computational
+inversion algorithms. The dosimeters carry the advantages known from
+personal-dosimetry use of OSL: the dose distribution following irradiation can
+be stored with minimal fading for extended periods of time, and dosimeters are
+reusable as they can be reset, e.g. by an intense (bleaching) light field.
+"
+21728,Stochastic Neighbor Embedding separates well-separated clusters,"  Stochastic Neighbor Embedding and its variants are widely used dimensionality
+reduction techniques -- despite their popularity, no theoretical results are
+known. We prove that the optimal SNE embedding of well-separated clusters from
+high dimensions to any Euclidean space R^d manages to successfully separate the
+clusters in a quantitative way. The result also applies to a larger family of
+methods including a variant of t-SNE.
+"
+21729,Redshifts for galaxies in radio continuum surveys from Bayesian model fitting of HI 21-cm lines,"  We introduce a new Bayesian HI spectral line fitting technique capable of
+obtaining spectroscopic redshifts for millions of galaxies in radio surveys
+with the Square Kilometere Array (SKA). This technique is especially
+well-suited to the low signal-to-noise regime that the redshifted 21-cm HI
+emission line is expected to be observed in, especially with SKA Phase 1,
+allowing for robust source detection. After selecting a set of continuum
+objects relevant to large, cosmological-scale surveys with the first phase of
+the SKA dish array (SKA1-MID), we simulate data corresponding to their HI line
+emission as observed by the same telescope. We then use the MultiNest nested
+sampling code to find the best-fitting parametrised line profile, providing us
+with a full joint posterior probability distribution for the galaxy properties,
+including redshift. This provides high quality redshifts, with redshift errors
+$\Delta z / z <10^{-5}$, from radio data alone for some 1.8 million galaxies in
+a representative 5000 square degree survey with the SKA1-MID instrument with
+up-to-date sensitivity profiles. Interestingly, we find that the SNR definition
+commonly used in forecast papers does not correlate well with the actual
+detectability of an HI line using our method. We further detail how our method
+could be improved with per-object priors and how it may be also used to give
+robust constraints on other observables such as the HI mass function. We also
+make our line fitting code publicly available for application to other data
+sets.
+"
+21730,New upper bounds for Ramanujan primes,"  For $n\ge 1$, the $n^{\rm th}$ Ramanujan prime is defined as the smallest
+positive integer $R_n$ such that for all $x\ge R_n$, the interval
+$(\frac{x}{2}, x]$ has at least $n$ primes. We show that for every
+$\epsilon>0$, there is a positive integer $N$ such that if
+$\alpha=2n\left(1+\dfrac{\log 2+\epsilon}{\log n+j(n)}\right)$, then $R_n<
+p_{[\alpha]}$ for all $n>N$, where $p_i$ is the $i^{\rm th}$ prime and $j(n)>0$
+is any function that satisfies $j(n)\to \infty$ and $nj'(n)\to 0$.
+"
+21731,Thermal phases of correlated lattice boson: a classical fluctuation theory,"  We present a method that generalises the standard mean field theory of
+correlated lattice bosons to include amplitude and phase fluctuations of the
+$U(1)$ field that induces onsite particle number mixing. This arises formally
+from an auxiliary field decomposition of the kinetic term in a Bose Hubbard
+model. We solve the resulting problem, initially, by using a classical
+approximation for the particle number mixing field and a Monte Carlo treatment
+of the resulting bosonic model. In two dimensions we obtain $T_c$ scales that
+dramatically improve on mean field theory and are within about 20% of full
+quantum Monte Carlo estimates. The `classical approximation' ground state,
+however, is still mean field, with an overestimate of the critical interaction,
+$U_c$, for the superfluid to Mott transition. By further including low order
+quantum fluctuations in the free energy functional we improve significantly on
+the $U_c$, and the overall thermal phase diagram. The classical approximation
+based method has a computational cost linear in system size. The methods
+readily generalise to multispecies bosons and the presence of traps.
+"
+21732,Analytic Center Cutting Plane Methods for Variational Inequalities over Convex Bodies,"  An analytic center cutting plane method is an iterative algorithm based on
+the computation of analytic centers. In this paper, we propose some analytic
+center cutting plane methods for solving quasimonotone or pseudomonotone
+variational inequalities whose domains are bounded or unbounded convex bodies.
+"
+21733,Does Viscosity turn inflation into the CMB and $Λ$,"  Consideration of the entropy production in the creation of the CMB leads to a
+simple model of the evolution of the universe during this period which suggests
+a connection between the small observed acceleration term and the early
+inflation of a closed universe. From this we find an unexpected relationship
+between the Omega's of cosmology and calculate the total volume of the
+universe.
+"
+21734,The Secrecy Capacity of Gaussian MIMO Channels with Finite Memory - Full Version,"  In this work we study the secrecy capacity of Gaussian multiple-input
+multiple-output (MIMO) wiretap channels (WTCs) with a finite memory, subject to
+a per-symbol average power constraint on the MIMO channel input. MIMO channels
+with finite memory are very common in wireless communications as well as in
+wireline communications (e.g., in communications over power lines). To derive
+the secrecy capacity of the Gaussian MIMO WTC with finite memory we first
+construct an asymptotically-equivalent block-memoryless MIMO WTC, which is then
+transformed into a set of parallel, independent, memoryless MIMO WTCs in the
+frequency domain. The secrecy capacity of the Gaussian MIMO WTC with finite
+memory is obtained as the secrecy capacity of the set of parallel independent
+memoryless MIMO WTCs, and is expressed as a maximization over the input
+covariance matrices in the frequency domain. Lastly, we detail two applications
+of our result: First, we show that the secrecy capacity of the Gaussian scalar
+WTC with finite memory can be achieved by waterfilling, and obtain a
+closed-form expression for this secrecy capacity. Then, we use our result to
+characterize the secrecy capacity of narrowband powerline channels, thereby
+resolving one of the major open issues for this channel model.
+"
+21735,"On the Algebro-Geometric Analysis of Meromorphic (1,0)-forms","  In this paper, we analyze the theory of meromorphic $(1,0)$-forms
+$\omega\in\mathcal{M}\Omega^{(1,0)}(\mathbb{CP}^1).$ Hence, we show that on a
+compact Riemann surface of genus $g=0,$ isomorphic to $\mathbb{CP}^1,$ every
+non-constant meromorphic function $f:X\to\mathbb{CP}^1$ has as many zeros as
+poles, where each is counted according to multiplicities. Such an analysis
+gives rise to the following result. Invoking the Riemann-Roch theorem for a
+compact Riemann $X$ with canonical divisor $K,$ it follows that $deg(f)=0$ for
+any principal divisor $(f):=D$ on $X.$ More precisely,
+$\ell(D)-\ell(K-D)=deg(D)+1=1$ or $\ell(D)-\ell(K-D)-1=0.$ Furthermore, for a
+diffeomorphism $\eta:X\to\mathbb{CP}^1$ of a certain kind, a multistep program
+is implemented to show $X$ is a compact algebraic variety of dimension one,
+i.e. a non-singular projective variety. Hence, we adopt a group-theoretic
+approach and provide a useful heuristic, that is, a set of technical conditions
+to facilitate the algebro-geometric analysis of simply connected Riemann
+surfaces $X.$
+"
+21736,Tool Supported Analysis of IoT,"  The design of IoT systems could benefit from the combination of two different
+analyses. We perform a first analysis to approximate how data flow across the
+system components, while the second analysis checks their communication
+soundness. We show how the combination of these two analyses yields further
+benefits hardly achievable by separately using each of them. We exploit two
+independently developed tools for the analyses.
+Firstly, we specify IoT systems in IoT-LySa, a simple specification language
+featuring asynchronous multicast communication of tuples. The values carried by
+the tuples are drawn from a term-algebra obtained by a parametric signature.
+The analysis of communication soundness is supported by ChorGram, a tool
+developed to verify the compatibility of communicating finite-state machines.
+In order to combine the analyses we implement an encoding of IoT-LySa processes
+into communicating machines. This encoding is not completely straightforward
+because IoT-LySa has multicast communications with data, while communication
+machines are based on point-to-point communications where only finitely many
+symbols can be exchanged. To highlight the benefits of our approach we appeal
+to a simple yet illustrative example.
+"
+21737,Deep Learning for Ontology Reasoning,"  In this work, we present a novel approach to ontology reasoning that is based
+on deep learning rather than logic-based formal reasoning. To this end, we
+introduce a new model for statistical relational learning that is built upon
+deep recursive neural networks, and give experimental evidence that it can
+easily compete with, or even outperform, existing logic-based reasoners on the
+task of ontology reasoning. More precisely, we compared our implemented system
+with one of the best logic-based ontology reasoners at present, RDFox, on a
+number of large standard benchmark datasets, and found that our system attained
+high reasoning quality, while being up to two orders of magnitude faster.
+"
+21738,Wide applicability of high $T_c$ pairing originating from coexisting wide and incipient narrow bands in quasi one dimension,"  We study superconductivity in the Hubbard model on various
+quasi-one-dimensional lattices with coexisting wide and narrow bands
+originating from multiple sites within a unit cell, where each site corresponds
+to a single orbital. The systems studied are the 2-leg and 3-leg ladders, the
+diamond chain, and the criss-cross ladder. These one-dimensional lattices are
+weakly coupled to form two-dimensional (quasi-one-dimensional) ones, and the
+fluctuation exchange approximation is adopted to study
+spin-fluctuation-mediated superconductivity. When one of the bands is perfectly
+flat, and the Fermi level, intersecting the wide band, is placed in the
+vicinity of the flat band, superconductivity arising from the interband
+scattering processes is found to be strongly enhanced owing to the combination
+of the light electron mass of the wide band and the strong pairing interaction
+due to the large density of states of the flat band. Even when the narrow band
+has finite band width, the pairing mechanism still works since the edge of the
+narrow band, due to its large density of states, plays the role of the flat
+band. The results indicate the generality of the high $T_c$ pairing mechanism
+due to coexisting wide and ""incipient"" narrow bands in quasi-one-dimensional
+systems.
+"
+21739,Analysis and design of Raptor codes using a multi-edge framework,"  The focus of this paper is on the analysis and design of Raptor codes using a
+multi-edge framework. In this regard, we first represent the Raptor code as a
+multi-edge type low-density parity-check (METLDPC) code. This MET
+representation gives a general framework to analyze and design Raptor codes
+over a binary input additive white Gaussian noise channel using MET density
+evolution (MET-DE). We consider a joint decoding scheme based on the belief
+propagation (BP) decoding for Raptor codes in the multi-edge framework, and
+analyze the convergence behavior of the BP decoder using MET-DE. In joint
+decoding of Raptor codes, the component codes correspond to inner code and
+precode are decoded in parallel and provide information to each other. We also
+derive an exact expression for the stability of Raptor codes with joint
+decoding. We then propose an efficient Raptor code design method using the
+multi-edge framework, where we simultaneously optimize the inner code and the
+precode. Finally we consider performance-complexity trade-offs of Raptor codes
+using the multi-edge framework. Through density evolution analysis we show that
+the designed Raptor codes using the multi-edge framework outperform the
+existing Raptor codes in literature in terms of the realized rate.
+"
+21740,Geometric structures and Lie algebroids,"  In this thesis we study geometric structures from Poisson and generalized
+complex geometry with mild singular behavior using Lie algebroids. The process
+of lifting such structures to their Lie algebroid version makes them less
+singular, as their singular behavior is incorporated in the anchor of the Lie
+algebroid. We develop a framework for this using the concept of a divisor,
+which encodes the singularities, and show when structures exhibiting such
+singularities can be lifted to a Lie algebroid built out of the divisor. Once
+one has successfully lifted the structure, it becomes possible to study it
+using more powerful techniques. In the case of Poisson structures one can turn
+to employing symplectic techniques. These lead for example to normal form
+results for the underlying Poisson structures around their singular loci. In
+this thesis we further adapt the methods of Gompf and Thurston for constructing
+symplectic structures out of fibration-like maps to their Lie algebroid
+counterparts. More precisely, we introduce the notion of a Lie algebroid
+Lefschetz fibration and show when these give rise to A-symplectic structures
+for a given Lie algebroid A. We then use this general result to show how
+log-symplectic structures arise out of achiral Lefschetz fibrations. Moreover,
+we introduce the concept of a boundary Lefschetz fibration and show when they
+allow their total space to be equipped with a stable generalized complex
+structure. Other results in this thesis include homotopical obstructions to the
+existence of A-symplectic structures using characteristic classes, and
+splitting results for A-Lie algebroids (i.e., Lie algebroids whose anchor
+factors through that of a fixed Lie algebroid A), around specific transversal
+submanifolds.
+"
+21741,Financial Trading as a Game: A Deep Reinforcement Learning Approach,"  An automatic program that generates constant profit from the financial market
+is lucrative for every market practitioner. Recent advance in deep
+reinforcement learning provides a framework toward end-to-end training of such
+trading agent. In this paper, we propose an Markov Decision Process (MDP) model
+suitable for the financial trading task and solve it with the state-of-the-art
+deep recurrent Q-network (DRQN) algorithm. We propose several modifications to
+the existing learning algorithm to make it more suitable under the financial
+trading setting, namely 1. We employ a substantially small replay memory (only
+a few hundreds in size) compared to ones used in modern deep reinforcement
+learning algorithms (often millions in size.) 2. We develop an action
+augmentation technique to mitigate the need for random exploration by providing
+extra feedback signals for all actions to the agent. This enables us to use
+greedy policy over the course of learning and shows strong empirical
+performance compared to more commonly used epsilon-greedy exploration. However,
+this technique is specific to financial trading under a few market assumptions.
+3. We sample a longer sequence for recurrent neural network training. A side
+product of this mechanism is that we can now train the agent for every T steps.
+This greatly reduces training time since the overall computation is down by a
+factor of T. We combine all of the above into a complete online learning
+algorithm and validate our approach on the spot foreign exchange market.
+"
+21742,The Confinement of Vortices in Nano-Superconducting Devices,"  We have investigated the confinement of 3-D vortices in specific cases of
+Type-II ($\kappa = 2$) nano-superconducting devices. The emergent pattern of
+vortices greatly depends on the orientation of an applied magnetic field
+(transverse or longitudinal), and the size of the devices (a few coherence
+lengths $\xi$). Herein, cylindrical geometries are examined. The surface
+barriers become very significant in these nano-systems, and hence the
+characteristics of the vortices become highly sensitive to the shape of the
+system and direction of an applied field. It is observed that nano-cylindrical
+superconductors, depending on their sizes, can display either first or second
+order phase transitions, under the influence of a longitudinal field. In the
+confined geometries, nucleation of a giant vortex state composed of a n-quanta
+emerges for the longitudinal magnetic field.
+"
+21743,Design and Implementation of Modified Fuzzy based CPU Scheduling Algorithm,"  CPU Scheduling is the base of multiprogramming. Scheduling is a process which
+decides order of task from a set of multiple tasks that are ready to execute.
+There are number of CPU scheduling algorithms available, but it is very
+difficult task to decide which one is better. This paper discusses the design
+and implementation of modified fuzzy based CPU scheduling algorithm. This paper
+present a new set of fuzzy rules. It demonstrates that scheduling done with new
+priority improves average waiting time and average turnaround time.
+"
+21744,Message-Passing Methods for Complex Contagions,"  Message-passing methods provide a powerful approach for calculating the
+expected size of cascades either on random networks (e.g., drawn from a
+configuration-model ensemble or its generalizations) asymptotically as the
+number $N$ of nodes becomes infinite or on specific finite-size networks. We
+review the message-passing approach and show how to derive it for
+configuration-model networks using the methods of (Dhar et al., 1997) and
+(Gleeson, 2008). Using this approach, we explain for such networks how to
+determine an analytical expression for a ""cascade condition"", which determines
+whether a global cascade will occur. We extend this approach to the
+message-passing methods for specific finite-size networks (Shrestha and Moore,
+2014; Lokhov et al., 2015), and we derive a generalized cascade condition.
+Throughout this chapter, we illustrate these ideas using the Watts threshold
+model.
+"
+21745,Phase Transitions of the Typical Algorithmic Complexity of the Random Satisfiability Problem Studied with Linear Programming,"  Here we study the NP-complete $K$-SAT problem. Although the worst-case
+complexity of NP-complete problems is conjectured to be exponential, there
+exist parametrized random ensembles of problems where solutions can typically
+be found in polynomial time for suitable ranges of the parameter. In fact,
+random $K$-SAT, with $\alpha=M/N $ as control parameter, can be solved quickly
+for small enough values of $\alpha$. It shows a phase transition between a
+satisfiable phase and an unsatisfiable phase. For branch and bound algorithms,
+which operate in the space of feasible Boolean configurations, the empirically
+hardest problems are located only close to this phase transition. Here we study
+$K$-SAT ($K=3,4$) and the related optimization problem MAX-SAT by a linear
+programming approach, which is widely used for practical problems and allows
+for polynomial run time. In contrast to branch and bound it operates outside
+the space of feasible configurations. On the other hand, finding a solution
+within polynomial time is not guaranteed. We investigated several variants like
+including artificial objective functions, so called cutting-plane approaches,
+and a mapping to the NP-complete vertex-cover problem. We observed several
+easy-hard transitions, from where the problems are typically solvable (in
+polynomial time) using the given algorithms, respectively, to where they are
+not solvable in polynomial time. For the related vertex-cover problem on random
+graphs these easy-hard transitions can be identified with structural properties
+of the graphs, like percolation transitions. For the present random $K$-SAT
+problem we have investigated numerous structural properties also exhibiting
+clear transitions, but they appear not be correlated to the here observed
+easy-hard transitions. This renders the behaviour of random $K$-SAT more
+complex than, e.g., the vertex-cover problem.
+"
+21746,Unconstrained and Curvature-Constrained Shortest-Path Distances and their Approximation,"  We study shortest paths and their distances on a subset of a Euclidean space,
+and their approximation by their equivalents in a neighborhood graph defined on
+a sample from that subset. In particular, we recover and extend the results of
+Bernstein et al. (2000). We do the same with curvature-constrained shortest
+paths and their distances, establishing what we believe are the first
+approximation bounds for them.
+"
+21747,Hecke module structure on first and top pro-$p$-Iwahori cohomology,"  Let $p\geq 5$ be a prime number, $G$ a split connected reductive group
+defined over a $p$-adic field, and $I_1$ a choice of pro-$p$-Iwahori subgroup.
+Let $C$ be an algebraically closed field of characteristic $p$ and
+$\mathcal{H}$ the pro-$p$-Iwahori--Hecke algebra over $C$ associated to $I_1$.
+In this note, we compute the action of $\mathcal{H}$ on $\textrm{H}^1(I_1,C)$
+and $\textrm{H}^{\textrm{top}}(I_1,C)$ when the root system of $G$ is
+irreducible. We also give some partial results in the general case.
+"
+21748,Sensitivity Properties of Intermittent Control,"  The sensitivity properties of intermittent control are analysed and the
+conditions for a limit cycle derived theoretically and verified by simulation.
+"
+21749,Algebra of distributions of quantum-field densities and space-time properties,"  In this paper we consider properties of the space-time manifold M caused by
+the proposition that, according to the scheme theory, the manifold M is locally
+isomorphic to the spectrum of the algebra A, M = Spec(A), where A is the
+commutative algebra of distributions of quantum-field densities. In order to
+determine the algebra A, it is necessary to define multiplication on densities
+and to eliminate those densities, which cannot be multiplied. This leads to
+essential restrictions imposed on densities and on space-time properties. It is
+found that the only possible case, when the commutative algebra A exists, is
+the case, when the quantum fields are in the spacetime manifold M with the
+structure group SO(3,1) (Lorentz group). The algebra A consists of
+distributions of densities with singularities in the closed future light cone
+subset. On account of the local isomorphism M = Spec(A), the quantum fields
+exist only in the space-time manifold with the one-dimensional arrow of time.
+In the fermion sector the restrictions caused by the possibility to define the
+multiplication on the densities of spinor fields can explain the chirality
+violation. It is found that for bosons in the Higgs sector the charge
+conjugation symmetry violation on the densities of states can be observed. This
+symmetry violation can explain the matter-antimatter imbalance.
+"
+21750,Minimal penalties and the slope heuristics: a survey,"  Birg{é} and Massart proposed in 2001 the slope heuristics as a way to
+choose optimally from data an unknown multiplicative constant in front of a
+penalty. It is built upon the notion of minimal penalty, and it has been
+generalized since to some 'minimal-penalty algorithms'. This paper reviews the
+theoretical results obtained for such algorithms, with a self-contained proof
+in the simplest framework, precise proof ideas for further generalizations, and
+a few new results. Explicit connections are made with residual-variance
+estimators-with an original contribution on this topic, showing that for this
+task the slope heuristics performs almost as well as a residual-based estimator
+with the best model choice-and some classical algorithms such as L-curve or
+elbow heuristics, Mallows' C p , and Akaike's FPE. Practical issues are also
+addressed, including two new practical definitions of minimal-penalty
+algorithms that are compared on synthetic data to previously-proposed
+definitions. Finally, several conjectures and open problems are suggested as
+future research directions.
+"
+21751,Fast and Robust Detection of Fallen People from a Mobile Robot,"  This paper deals with the problem of detecting fallen people lying on the
+floor by means of a mobile robot equipped with a 3D depth sensor. In the
+proposed algorithm, inspired by semantic segmentation techniques, the 3D scene
+is over-segmented into small patches. Fallen people are then detected by means
+of two SVM classifiers: the first one labels each patch, while the second one
+captures the spatial relations between them. This novel approach showed to be
+robust and fast. Indeed, thanks to the use of small patches, fallen people in
+real cluttered scenes with objects side by side are correctly detected.
+Moreover, the algorithm can be executed on a mobile robot fitted with a
+standard laptop making it possible to exploit the 2D environmental map built by
+the robot and the multiple points of view obtained during the robot navigation.
+Additionally, this algorithm is robust to illumination changes since it does
+not rely on RGB data but on depth data. All the methods have been thoroughly
+validated on the IASLAB-RGBD Fallen Person Dataset, which is published online
+as a further contribution. It consists of several static and dynamic sequences
+with 15 different people and 2 different environments.
+"
+21752,On residual and guided proposals for diffusion bridge simulation,"  Recently Whitaker et al. (2017) considered Bayesian estimation of diffusion
+driven mixed effects models using data-augmentation. The missing data,
+diffusion bridges connecting discrete time observations, are drawn using a
+""residual bridge construct"". In this paper we compare this construct (which we
+call residual proposal) with the guided proposals introduced in Schauer et al.
+2017. It is shown that both approaches are related, but use a different
+approximation to the intractable stochastic differential equation of the true
+diffusion bridge. It reveals that the computational complexity of both
+approaches is similar. Some examples are included to compare the ability of
+both proposals to capture local nonlinearities in the dynamics of the true
+bridge.
+"
+21753,CHEERS: The chemical evolution RGS sample,"  The chemical yields of supernovae and the metal enrichment of the hot
+intra-cluster medium (ICM) are not well understood. This paper introduces the
+CHEmical Enrichment RGS Sample (CHEERS), which is a sample of 44 bright local
+giant ellipticals, groups and clusters of galaxies observed with XMM-Newton.
+This paper focuses on the abundance measurements of O and Fe using the
+reflection grating spectrometer (RGS). The deep exposures and the size of the
+sample allow us to quantify the intrinsic scatter and the systematic
+uncertainties in the abundances using spectral modeling techniques. We report
+the oxygen and iron abundances as measured with RGS in the core regions of all
+objects in the sample. We do not find a significant trend of O/Fe as a function
+of cluster temperature, but we do find an intrinsic scatter in the O and Fe
+abundances from cluster to cluster. The level of systematic uncertainties in
+the O/Fe ratio is estimated to be around 20-30%, while the systematic
+uncertainties in the absolute O and Fe abundances can be as high as 50% in
+extreme cases. We were able to identify and correct a systematic bias in the
+oxygen abundance determination, which was due to an inaccuracy in the spectral
+model. The lack of dependence of O/Fe on temperature suggests that the
+enrichment of the ICM does not depend on cluster mass and that most of the
+enrichment likely took place before the ICM was formed. We find that the
+observed scatter in the O/Fe ratio is due to a combination of intrinsic scatter
+in the source and systematic uncertainties in the spectral fitting, which we
+are unable to disentangle. The astrophysical source of intrinsic scatter could
+be due to differences in AGN activity and ongoing star formation in the BCG.
+The systematic scatter is due to uncertainties in the spatial line broadening,
+absorption column, multi-temperature structure and the thermal plasma models.
+(Abbreviated).
+"
+21754,Learning to Search via Retrospective Imitation,"  We study the problem of learning a good search policy from demonstrations for
+combinatorial search spaces. We propose retrospective imitation learning,
+which, after initial training by an expert, improves itself by learning from
+its own retrospective solutions. That is, when the policy eventually reaches a
+feasible solution in a search tree after making mistakes and backtracks, it
+retrospectively constructs an improved search trace to the solution by removing
+backtracks, which is then used to further train the policy. A key feature of
+our approach is that it can iteratively scale up, or transfer, to larger
+problem sizes than the initial expert demonstrations, thus dramatically
+expanding its applicability beyond that of conventional imitation learning. We
+showcase the effectiveness of our approach on two tasks: synthetic maze
+solving, and integer program based risk-aware path planning.
+"
+21755,Electronic fitness function for screening semiconductors as thermoelectric materials,"  We introduce a simple but efficient electronic fitness function (EFF) that
+describes the electronic aspect of the thermoelectric performance. This EFF
+finds materials that overcome the inverse relationship between $\sigma$ and $S$
+based on the complexity of the electronic structures regardless of specific
+origin (e.g., isosurface corrugation, valley degeneracy, heavy-light bands
+mixture, valley anisotropy or reduced dimensionality). This function is well
+suited for application in high throughput screening. We applied this function
+to 75 different thermoelectric and potential thermoelectric materials including
+full- and half-Heuslers, binary semiconductors and Zintl phases. We find an
+efficient screening using this transport function. The EFF identifies known
+high performance $p$- and $n$-type Zintl phases and half-Heuslers. In addition,
+we find some previously unstudied phases with superior EFF.
+"
+21756,Density Functional Theory of doped superfluid liquid helium and nanodroplets,"  During the last decade, density function theory (DFT) in its static and
+dynamic time dependent forms, has emerged as a powerful tool to describe the
+structure and dynamics of doped liquid helium and droplets. In this review, we
+summarize the activity carried out in this field within the DFT framework since
+the publication of the previous review article on this subject [M. Barranco et
+al., J. Low Temp. Phys. 142, 1 (2006)]. Furthermore, a comprehensive
+presentation of the actual implementations of helium DFT is given, which have
+not been discussed in the individual articles or are scattered in the existing
+literature. This is an Accepted Manuscript of an article published on August 2,
+2017 by Taylor & Francis Group in Int. Rev. Phys. Chem. 36, 621 (2017),
+available online: this http URL
+"
+21757,Higher degree S-lemma and the stability of quadratic modules,"  In this work we will investigate a certain generalization of the so called
+S-lemma in higher degrees. The importance of this generalization is, that it is
+closely related to Hilbert's 1888 theorem about tenary quartics. In fact, if
+such a generalization exits, then one can state a Hilbert-like theorem, where
+positivity is only demanded on some semi-algebraic set. We will show that such
+a generalization is not possible, at least not without additional conditions.
+To prove this, we will use and generalize certain tools developed by Netzer
+([Ne]). These new tools will allow us to conclude that this generalization of
+the S-lemma is not possible because of geometric reasons. Furthermore, we are
+able to establish a link between geometric reasons and algebraic reasons. This
+will be accomplished within the framework of quadratic modules.
+"
+21758,Phantom Domain Walls,"  We consider a model with two real scalar fields which admits phantom domain
+wall solutions. We investigate the structure and evolution of these phantom
+domain walls in an expanding homogeneous and isotropic universe. In particular,
+we show that the increase of the tension of the domain walls with cosmic time,
+associated to the evolution of the phantom scalar field, is responsible for an
+additional damping term in their equations of motion. We describe the
+macroscopic dynamics of phantom domain walls, showing that extended phantom
+defects whose tension varies on a cosmological timescale cannot be the dark
+energy.
+"
+21759,Causally Regularized Learning with Agnostic Data Selection Bias,"  Most of previous machine learning algorithms are proposed based on the i.i.d.
+hypothesis. However, this ideal assumption is often violated in real
+applications, where selection bias may arise between training and testing
+process. Moreover, in many scenarios, the testing data is not even available
+during the training process, which makes the traditional methods like transfer
+learning infeasible due to their need on prior of test distribution. Therefore,
+how to address the agnostic selection bias for robust model learning is of
+paramount importance for both academic research and real applications. In this
+paper, under the assumption that causal relationships among variables are
+robust across domains, we incorporate causal technique into predictive modeling
+and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm
+by jointly optimize global confounder balancing and weighted logistic
+regression. Global confounder balancing helps to identify causal features,
+whose causal effect on outcome are stable across domains, then performing
+logistic regression on those causal features constructs a robust predictive
+model against the agnostic bias. To validate the effectiveness of our CRLR
+algorithm, we conduct comprehensive experiments on both synthetic and real
+world datasets. Experimental results clearly demonstrate that our CRLR
+algorithm outperforms the state-of-the-art methods, and the interpretability of
+our method can be fully depicted by the feature visualization.
+"
+21760,The K-inductive Structure of the Noncommutative Fourier Transform,"  The noncommutative Fourier transform of the irrational rotation C*-algebra is
+shown to have a K-inductive structure (at least for a large concrete class of
+irrational parameters, containing dense $G_\delta$'s). This is a structure for
+automorphisms that is analogous to Huaxin Lin's notion of tracially AF for
+C*-algebras, except that it requires more structure from the complementary
+projection.
+"
+21761,Rigidity of inversive distance circle packings revisited,"  Inversive distance circle packing metric was introduced by P Bowers and K
+Stephenson \cite{BS} as a generalization of Thurston's circle packing metric
+\cite{T1}. They conjectured that the inversive distance circle packings are
+rigid. For nonnegative inversive distance, Guo \cite{Guo} proved the
+infinitesimal rigidity and then Luo \cite{L3} proved the global rigidity. In
+this paper, based on an observation of Zhou \cite{Z}, we prove this conjecture
+for inversive distance in $(-1, +\infty)$ by variational principles. We also
+study the global rigidity of a combinatorial curvature introduced in
+\cite{GJ4,GX4,GX6} with respect to the inversive distance circle packing
+metrics where the inversive distance is in $(-1, +\infty)$.
+"
+21762,Continuous and discrete one dimensional autonomous fractional ODEs,"  In this paper, we study 1D autonomous fractional ODEs $D_c^{\gamma}u=f(u), 0<
+\gamma <1$, where $u: [0,\infty)\mapsto\mathbb{R}$ is the unknown function and
+$D_c^{\gamma}$ is the generalized Caputo derivative introduced by Li and Liu (
+arXiv:1612.05103). Based on the existence and uniqueness theorem and regularity
+results in previous work, we show the monotonicity of solutions to the
+autonomous fractional ODEs and several versions of comparison principles. We
+also perform a detailed discussion of the asymptotic behavior for $f(u)=Au^p$.
+In particular, based on an Osgood type blow-up criteria, we find relatively
+sharp bounds of the blow-up time in the case $A>0, p>1$. These bounds indicate
+that as the memory effect becomes stronger ($\gamma\to 0$), if the initial
+value is big, the blow-up time tends to zero while if the initial value is
+small, the blow-up time tends to infinity. In the case $A<0, p>1$, we show that
+the solution decays to zero more slowly compared with the usual derivative.
+Lastly, we show several comparison principles and Grönwall inequalities for
+discretized equations, and perform some numerical simulations to confirm our
+analysis.
+"
+21763,Learning Vector Autoregressive Models with Latent Processes,"  We study the problem of learning the support of transition matrix between
+random processes in a Vector Autoregressive (VAR) model from samples when a
+subset of the processes are latent. It is well known that ignoring the effect
+of the latent processes may lead to very different estimates of the influences
+among observed processes, and we are concerned with identifying the influences
+among the observed processes, those between the latent ones, and those from the
+latent to the observed ones. We show that the support of transition matrix
+among the observed processes and lengths of all latent paths between any two
+observed processes can be identified successfully under some conditions on the
+VAR model. From the lengths of latent paths, we reconstruct the latent subgraph
+(representing the influences among the latent processes) with a minimum number
+of variables uniquely if its topology is a directed tree. Furthermore, we
+propose an algorithm that finds all possible minimal latent graphs under some
+conditions on the lengths of latent paths. Our results apply to both
+non-Gaussian and Gaussian cases, and experimental results on various synthetic
+and real-world datasets validate our theoretical results.
+"
+21764,Development of Single-Shot Multi-Frame Imaging of Cylindrical Shock Waves in a Multi-Layered Assembly,"  We demonstrate single-shot multi-frame imaging of quasi-2D cylindrically
+converging shock waves as they propagate through a multi-layer target sample
+assembly. We visualize the shock with sequences of up to 16 images, using a
+Fabry-Perot cavity to generate a pulse train that can be used in various
+imaging configurations. We employ multi-frame shadowgraph and dark-field
+imaging to measure the amplitude and phase of the light transmitted through the
+shocked target. Single-shot multi-frame imaging tracks geometric distortion and
+additional features in our images that were not previously resolvable in this
+experimental geometry. Analysis of our images, in combination with simulations,
+shows that the additional image features are formed by a coupled wave structure
+resulting from interface effects in our targets. This technique presents a new
+capability for tabletop imaging of shock waves that can be easily extended to
+experiments at large-scale facilities.
+"
+21765,Design rules for modulation-doped AlAs quantum wells,"  Thanks to their multi-valley, anisotropic, energy band structure,
+two-dimensional electron systems (2DESs) in modulation-doped AlAs quantum wells
+(QWs) provide a unique platform to investigate electron interaction physics and
+ballistic transport. Indeed, a plethora of phenomena unseen in other 2DESs have
+been observed over the past decade. However, a foundation for sample design is
+still lacking for AlAs 2DESs, limiting the means to achieve optimal quality
+samples. Here we present a systematic study on the fabrication of
+modulation-doped AlAs and GaAs QWs over a wide range of AlxGa1-xAs barrier
+alloy compositions. Our data indicate clear similarities in modulation doping
+mechanisms for AlAs and GaAs, and provide guidelines for the fabrication of
+very high quality AlAs 2DESs. We highlight the unprecedented quality of the
+fabricated AlAs samples by presenting the magnetotransport data for low density
+(~1X1011 cm2) AlAs 2DESs that exhibit high-order fractional quantum Hall
+signatures.
+"
+21766,Study of Clear Sky Models for Singapore,"  The estimation of total solar irradiance falling on the earth's surface is
+important in the field of solar energy generation and forecasting. Several
+clear-sky solar radiation models have been developed over the last few decades.
+Most of these models are based on empirical distribution of various
+geographical parameters; while a few models consider various atmospheric
+effects in the solar energy estimation. In this paper, we perform a comparative
+analysis of several popular clear-sky models, in the tropical region of
+Singapore. This is important in countries like Singapore, where we are
+primarily focused on reliable and efficient solar energy generation. We analyze
+and compare three popular clear-sky models that are widely used in the
+literature. We validate our solar estimation results using actual solar
+irradiance measurements obtained from collocated weather stations. We finally
+conclude the most reliable clear sky model for Singapore, based on all clear
+sky days in a year.
+"
+21767,Multiplicatively closed Markov models must form Lie algebras,"  We prove that the probability substitution matrices obtained from a
+continuous-time Markov chain form a multiplicatively closed set if and only if
+the rate matrices associated to the chain form a linear space spanning a Lie
+algebra. The key original contribution we make is to overcome an obstruction,
+due to the presence of inequalities that are unavoidable in the probabilistic
+application, that prevents free manipulation of terms in the
+Baker-Campbell-Haursdorff formula.
+"
+21768,Polynomial upper bound on interior Steklov nodal sets,"  We study solutions of uniformly elliptic PDE with Lipschitz leading
+coefficients and bounded lower order coefficients. We extend previous results
+of A. Logunov concerning nodal sets of harmonic functions and, in particular,
+prove polynomial upper bounds on interior nodal sets of Steklov eigenfunctions
+in terms of the corresponding eigenvalue $ \lambda $.
+"
+21769,Multiplicative Normalizing Flows for Variational Bayesian Neural Networks,"  We reinterpret multiplicative noise in neural networks as auxiliary random
+variables that augment the approximate posterior in a variational setting for
+Bayesian neural networks. We show that through this interpretation it is both
+efficient and straightforward to improve the approximation by employing
+normalizing flows while still allowing for local reparametrizations and a
+tractable lower bound. In experiments we show that with this new approximation
+we can significantly improve upon classical mean field for Bayesian neural
+networks on both predictive accuracy as well as predictive uncertainty.
+"
+21770,A categorical semantics for causal structure,"  We present a categorical construction for modelling causal structures within
+a general class of process theories that include the theory of classical
+probabilistic processes as well as quantum theory. Unlike prior constructions
+within categorical quantum mechanics, the objects of this theory encode
+fine-grained causal relationships between subsystems and give a new method for
+expressing and deriving consequences for a broad class of causal structures. We
+show that this framework enables one to define families of processes which are
+consistent with arbitrary acyclic causal orderings. In particular, one can
+define one-way signalling (a.k.a. semi-causal) processes, non-signalling
+processes, and quantum $n$-combs. Furthermore, our framework is general enough
+to accommodate recently-proposed generalisations of classical and quantum
+theory where processes only need to have a fixed causal ordering locally, but
+globally allow indefinite causal ordering.
+To illustrate this point, we show that certain processes of this kind, such
+as the quantum switch, the process matrices of Oreshkov, Costa, and Brukner,
+and a classical three-party example due to Baumeler, Feix, and Wolf are all
+instances of a certain family of processes we refer to as $\textrm{SOC}_n$ in
+the appropriate category of higher-order causal processes. After defining these
+families of causal structures within our framework, we give derivations of
+their operational behaviour using simple, diagrammatic axioms.
+"
+21771,Progressive Learning for Systematic Design of Large Neural Networks,"  We develop an algorithm for systematic design of a large artificial neural
+network using a progression property. We find that some non-linear functions,
+such as the rectifier linear unit and its derivatives, hold the property. The
+systematic design addresses the choice of network size and regularization of
+parameters. The number of nodes and layers in network increases in progression
+with the objective of consistently reducing an appropriate cost. Each layer is
+optimized at a time, where appropriate parameters are learned using convex
+optimization. Regularization parameters for convex optimization do not need a
+significant manual effort for tuning. We also use random instances for some
+weight matrices, and that helps to reduce the number of parameters we learn.
+The developed network is expected to show good generalization power due to
+appropriate regularization and use of random weights in the layers. This
+expectation is verified by extensive experiments for classification and
+regression problems, using standard databases.
+"
+21772,Sequential Monte Carlo algorithms for a class of outer measures,"  Closed-form stochastic filtering equations can be derived in a general
+setting where probability distributions are replaced by some specific outer
+measures. In this article, we study how the principles of the sequential Monte
+Carlo method can be adapted for the purpose of practical implementation of
+these equations. In particular, we explore how sampling can be used to provide
+support points for the approximation of these outer measures. This step enables
+practical algorithms to be derived in the spirit of particle filters. The
+performance of the obtained algorithms is demonstrated in simulations and their
+versatility is illustrated through various examples.
+"
+21773,Charge-induced force-noise on free-falling test masses: results from LISA Pathfinder,"  We report on electrostatic measurements made on board the European Space
+Agency mission LISA Pathfinder. Detailed measurements of the charge-induced
+electrostatic forces exerted on free-falling test masses (TMs) inside the
+capacitive gravitational reference sensor are the first made in a relevant
+environment for a space-based gravitational wave detector. Employing a
+combination of charge control and electric-field compensation, we show that the
+level of charge-induced acceleration noise on a single TM can be maintained at
+a level close to 1.0 fm/s^2/sqrt(Hz) across the 0.1-100 mHz frequency band that
+is crucial to an observatory such as LISA. Using dedicated measurements that
+detect these effects in the differential acceleration between the two test
+masses, we resolve the stochastic nature of the TM charge build up due to
+interplanetary cosmic rays and the TM charge-to-force coupling through stray
+electric fields in the sensor. All our measurements are in good agreement with
+predictions based on a relatively simple electrostatic model of the LISA
+Pathfinder instrument.
+"
+21774,Pseudoholomorphic Maps Relative to Normal Crossings Symplectic Divisors: Compactification,"  Inspired by the log Gromov-Witten theory of Gross-Siebert/Abramovich-Chen, we
+introduce a geometric notion of log pseudoholomorphic map relative to simple
+normal crossings symplectic divisors defined in [9]. For certain almost complex
+structures, we show that the moduli space of stable log pseudoholomorphic maps
+of any fixed type is compact and metrizable with respect to an enhancement of
+the Gromov topology. In the case of smooth symplectic divisors, our
+compactification is often smaller than the relative compactification and there
+is a projection map from the former onto the latter. The latter is constructed
+via expanded degenerations of the target. Our construction does not need any
+modification of (or any extra structure on) the target. Unlike the classical
+moduli spaces of stable maps, these log moduli spaces are often virtually
+singular. We describe an explicit toric model for the normal cone to each
+stratum in terms of the defining combinatorial data of that stratum. In
+upcoming papers, we will define a natural Fredholm operator which gives us the
+deformation/obstruction spaces of each stratum and prove a gluing theorem for
+smoothing log maps in the normal direction to each stratum. With minor
+modifications to the theory of Kuranishi structures, the latter would allow us
+to construct a virtual fundamental class for every such log moduli space.
+"
+21775,Improving Efficiency in Convolutional Neural Network with Multilinear Filters,"  The excellent performance of deep neural networks has enabled us to solve
+several automatization problems, opening an era of autonomous devices. However,
+current deep net architectures are heavy with millions of parameters and
+require billions of floating point operations. Several works have been
+developed to compress a pre-trained deep network to reduce memory footprint
+and, possibly, computation. Instead of compressing a pre-trained network, in
+this work, we propose a generic neural network layer structure employing
+multilinear projection as the primary feature extractor. The proposed
+architecture requires several times less memory as compared to the traditional
+Convolutional Neural Networks (CNN), while inherits the similar design
+principles of a CNN. In addition, the proposed architecture is equipped with
+two computation schemes that enable computation reduction or scalability.
+Experimental results show the effectiveness of our compact projection that
+outperforms traditional CNN, while requiring far fewer parameters.
+"
+21776,Combinatorial Penalties: Which structures are preserved by convex relaxations?,"  We consider the homogeneous and the non-homogeneous convex relaxations for
+combinatorial penalty functions defined on support sets. Our study identifies
+key differences in the tightness of the resulting relaxations through the
+notion of the lower combinatorial envelope of a set-function along with new
+necessary conditions for support identification. We then propose a general
+adaptive estimator for convex monotone regularizers, and derive new sufficient
+conditions for support recovery in the asymptotic setting.
+"
+21777,Multispecies fruit flower detection using a refined semantic segmentation network,"  In fruit production, critical crop management decisions are guided by bloom
+intensity, i.e., the number of flowers present in an orchard. Despite its
+importance, bloom intensity is still typically estimated by means of human
+visual inspection. Existing automated computer vision systems for flower
+identification are based on hand-engineered techniques that work only under
+specific conditions and with limited performance. This work proposes an
+automated technique for flower identification that is robust to uncontrolled
+environments and applicable to different flower species. Our method relies on
+an end-to-end residual convolutional neural network (CNN) that represents the
+state-of-the-art in semantic segmentation. To enhance its sensitivity to
+flowers, we fine-tune this network using a single dataset of apple flower
+images. Since CNNs tend to produce coarse segmentations, we employ a refinement
+method to better distinguish between individual flower instances. Without any
+pre-processing or dataset-specific training, experimental results on images of
+apple, peach and pear flowers, acquired under different conditions demonstrate
+the robustness and broad applicability of our method.
+"
+21778,Mobile phone records to feed activity-based travel demand models: MATSim for studying a cordon toll policy in Barcelona,"  Activity-based models appeared as an answer to the limitations of the
+traditional trip-based and tour-based four-stage models. The fundamental
+assumption of activity-based models is that travel demand is originated from
+people performing their daily activities. This is why they include a consistent
+representation of time, of the persons and households, time-dependent routing,
+and microsimulation of travel demand and traffic. In spite of their potential
+to simulate traffic demand management policies, their practical application is
+still limited. One of the main reasons is that these models require a huge
+amount of very detailed input data hard to get with surveys. However, the
+pervasive use of mobile devices has brought a valuable new source of data. The
+work presented here has a twofold objective: first, to demonstrate the
+capability of mobile phone records to feed activity-based transport models,
+and, second, to assert the advantages of using activity-based models to
+estimate the effects of traffic demand management policies. Activity diaries
+for the metropolitan area of Barcelona are reconstructed from mobile phone
+records. This information is then employed as input for building a transport
+MATSim model of the city. The model calibration and validation process proves
+the quality of the activity diaries obtained. The possible impacts of a cordon
+toll policy applied to two different areas of the city and at different times
+of the day is then studied. Our results show the way in which the modal share
+is modified in each of the considered scenario. The possibility of evaluating
+the effects of the policy at both aggregated and traveller level, together with
+the ability of the model to capture policy impacts beyond the cordon toll area
+confirm the advantages of activity-based models for the evaluation of traffic
+demand management policies.
+"
+21779,Gate Activation Signal Analysis for Gated Recurrent Neural Networks and Its Correlation with Phoneme Boundaries,"  In this paper we analyze the gate activation signals inside the gated
+recurrent neural networks, and find the temporal structure of such signals is
+highly correlated with the phoneme boundaries. This correlation is further
+verified by a set of experiments for phoneme segmentation, in which better
+results compared to standard approaches were obtained.
+"
+21780,Fully Convolutional Grasp Detection Network with Oriented Anchor Box,"  In this paper, we present a real-time approach to predict multiple grasping
+poses for a parallel-plate robotic gripper using RGB images. A model with
+oriented anchor box mechanism is proposed and a new matching strategy is used
+during the training process. An end-to-end fully convolutional neural network
+is employed in our work. The network consists of two parts: the feature
+extractor and multi-grasp predictor. The feature extractor is a deep
+convolutional neural network. The multi-grasp predictor regresses grasp
+rectangles from predefined oriented rectangles, called oriented anchor boxes,
+and classifies the rectangles into graspable and ungraspable. On the standard
+Cornell Grasp Dataset, our model achieves an accuracy of 97.74% and 96.61% on
+image-wise split and object-wise split respectively, and outperforms the latest
+state-of-the-art approach by 1.74% on image-wise split and 0.51% on object-wise
+split.
+"
+21781,Wetting States of Two-Dimensional Drops under Gravity,"  An analytical model is proposed for the Young-Laplace equation of
+two-dimensional (2D) drops under gravity. Inspired by the pioneering work of
+Landau & Lifshitz (1987), we derive analytical expressions of the profile of
+drops on flat surfaces, for arbitrary contact angles and drop volume. We then
+extend our theory for drops on inclined surfaces and reveal that the contact
+line plays a key role on the wetting state of the drops: (1) when the contact
+line is completely pinning, the advancing and receding contact angles and the
+shape of the drop can be uniquely determined by the predefined droplet volume,
+sliding angle and contact area, which does not rely on the Young contact angle;
+(2) when the drop has a movable contact line, it would achieve a wetting state
+with a minimum free energy resulting from the competition between the surface
+tension and gravity. Our theory is in excellent agreement with numerical
+results.
+"
+21782,Polarity-tunable magnetic tunnel junctions based on ferromagnetism at oxide heterointerfaces,"  Complex oxide systems have attracted considerable attention because of their
+fascinating properties, including the magnetic ordering at the conducting
+interface between two band insulators, such as LaAlO3 (LAO) and SrTiO3 (STO).
+However, the manipulation of the spin degree of freedom at the LAO/STO
+heterointerface has remained elusive. Here, we have fabricated hybrid magnetic
+tunnel junctions consisting of Co and LAO/STO ferromagnets with the insertion
+of a Ti layer in between, which clearly exhibit magnetic switching and the
+tunnelling magnetoresistance (TMR) effect below 10 K. The magnitude and the of
+the TMR are strongly dependent on the direction of the rotational magnetic
+field parallel to the LAO/STO plane, which is attributed to a strong
+Rashba-type spin orbit coupling in the LAO/STO heterostructure. Our study
+provides a further support for the existence of the macroscopic ferromagnetism
+at LAO/STO heterointerfaces and opens a novel route to realize interfacial
+spintronics devices.
+"
+21783,Pair-breaking of multi-gap superconductivity under parallel magnetic fields in electric-field-induced surface metallic state,"  Roles of paramagnetic and diamagnetic pair-breaking effects in
+superconductivity in electric-field-induced surface metallic state are studied
+by Bogoliubov-de Gennes equation, when magnetic fields are applied parallel to
+the surface. The multi-gap states of sub-bands are related to the depth
+dependence and the magnetic field dependence of superconductivity. In the
+Fermi-energy density of states and the spin density, sub-band contributions
+successively appear from higher-level sub-bands with increasing magnetic
+fields. The characteristic magnetic field dependence may be a key feature to
+identify the multi-gap structure of the surface superconductivity.
+"
+21784,A Lottery Model for Center-type Problems With Outliers,"  In this paper, we give tight approximation algorithms for the $k$-center and
+matroid center problems with outliers. Unfairness arises naturally in this
+setting: certain clients could always be considered as outliers. To address
+this issue, we introduce a lottery model in which each client $j$ is allowed to
+submit a parameter $p_j \in [0,1]$ and we look for a random solution that
+covers every client $j$ with probability at least $p_j$. Our techniques include
+a randomized rounding procedure to round a point inside a matroid intersection
+polytope to a basis plus at most one extra item such that all marginal
+probabilities are preserved and such that a certain linear function of the
+variables does not decrease in the process with probability one.
+"
+21785,Exact solutions in two-dimensional topological superconductors: Hubbard interaction induced spontaneous symmetry breaking,"  We present an exactly solvable model of a spin-triplet $f$-wave topological
+superconductor on the honeycomb lattice in the presence of the Hubbard
+interaction for arbitrary interaction strength. First we show that the
+Kane-Mele model with the corresponding spin-triplet $f$-wave superconducting
+pairings becomes a full-gap topological superconductor possessing the
+time-reversal symmetry. We then introduce the Hubbard interaction. The exactly
+solvable condition is found to be the emergence of perfect flat bands at zero
+energy. They generate infinitely many conserved quantities. It is intriguing
+that the Hubbard interaction breaks the time-reversal symmetry spontaneously.
+As a result, the system turns into a trivial superconductor. We demonstrate
+this topological property based on the topological number and by analyzing the
+edge state in nanoribbon geometry.
+"
+21786,Seymour's second neighbourhood conjecture for quasi-transitive oriented graphs,"  Seymour's second neighbourhood conjecture asserts that every oriented graph
+has a vertex whose second out-neighbourhood is at least as large as its
+out-neighbourhood. In this paper, we prove that the conjecture holds for
+quasi-transitive oriented graphs, which is a superclass of tournaments and
+transitive acyclic digraphs. A digraph $D$ is called quasi-transitive is for
+every pair $xy,yz$ of arcs between distinct vertices $x,y,z$, $xz$ or $zx$
+(""or"" is inclusive here) is in $D$.
+"
+21787,Analyzing the network structure and gender differences among the members of the Networked Knowledge Organization Systems (NKOS) community,"  In this paper, we analyze a major part of the research output of the
+Networked Knowledge Organization Systems (NKOS) community in the period 2000 to
+2016 from a network analytical perspective. We focus on the papers presented at
+the European and U.S. NKOS workshops and in addition four special issues on
+NKOS in the last 16 years. For this purpose, we have generated an open dataset,
+the ""NKOS bibliography"" which covers the bibliographic information of the
+research output. We analyze the co-authorship network of this community which
+results in 123 papers with a sum of 256 distinct authors. We use standard
+network analytic measures such as degree, betweenness and closeness centrality
+to describe the co-authorship network of the NKOS dataset. First, we
+investigate global properties of the network over time. Second, we analyze the
+centrality of the authors in the NKOS network. Lastly, we investigate gender
+differences in collaboration behavior in this community. Our results show that
+apart from differences in centrality measures of the scholars, they have higher
+tendency to collaborate with those in the same institution or the same
+geographic proximity. We also find that homophily is higher among women in this
+community. Apart from small differences in closeness and clustering among men
+and women, we do not find any significant dissimilarities with respect to other
+centralities.
+"
+21788,On a new closed formula for the solution of second order linear difference equations and applications,"  In this note, we establish a new closed formula for the solution of
+homogeneous second-order linear difference equations with constant coefficients
+by using matrix theory. This, in turn, gives new closed formulas concerning all
+sequences of this type such as the Fibonacci and Lucas sequences. As
+applications; we show that Binet's formula, in this case, is valid for negative
+integers as well. Finally, we find new summation formulas relating the elements
+of such sequences.
+"
+21789,Flat bands in lattices with non-Hermitian coupling,"  We study non-Hermitian photonic lattices that exhibit competition between
+conservative and non-Hermitian (gain/loss) couplings. A bipartite sublattice
+symmetry enforces the existence of non-Hermitian flat bands, which are
+typically embedded in an auxiliary dispersive band and give rise to
+non-diffracting ""compact localized states"". Band crossings take the form of
+non-Hermitian degeneracies known as exceptional points. Excitations of the
+lattice can produce either diffracting or amplifying behaviors. If the
+non-Hermitian coupling is fine-tuned to generate an effective $\pi$ flux, the
+lattice spectrum becomes completely flat, a non-Hermitian analogue of
+Aharonov-Bohm caging in which the magnetic field is replaced by balanced gain
+and loss. When the effective flux is zero, the non-Hermitian band crossing
+points give rise to asymmetric diffraction and anomalous linear amplification.
+"
+21790,An ultra-sensitive and wideband magnetometer based on a superconducting quantum interference device,"  The magnetic field noise in superconducting quantum interference devices
+(SQUIDs) used for biomagnetic research such as magnetoencephalography or
+ultra-low-field nuclear magnetic resonance is usually limited by instrumental
+dewar noise. We constructed a wideband, ultra-low noise system with a 45 mm
+diameter superconducting pick-up coil inductively coupled to a current sensor
+SQUID. Thermal noise in the liquid helium dewar is minimized by using
+aluminized polyester fabric as superinsulation and aluminum oxide strips as
+heat shields, respectively. With a magnetometer pick-up coil in the center of
+the Berlin magneti- cally shielded room 2 (BMSR2) a noise level of around 150
+aT Hz$^{-1/2}$ is achieved in the white noise regime between about 20 kHz and
+the system bandwidth of about 2.5 MHz. At lower frequencies, the resolution is
+limited by magnetic field noise arising from the walls of the shielded room.
+Modeling the BMSR2 as a closed cube with continuous \mu-metal walls we can
+quantitatively reproduce its measured field noise.
+"
+21791,Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations,"  We introduce physics informed neural networks -- neural networks that are
+trained to solve supervised learning tasks while respecting any given law of
+physics described by general nonlinear partial differential equations. In this
+two part treatise, we present our developments in the context of solving two
+main classes of problems: data-driven solution and data-driven discovery of
+partial differential equations. Depending on the nature and arrangement of the
+available data, we devise two distinct classes of algorithms, namely continuous
+time and discrete time models. The resulting neural networks form a new class
+of data-efficient universal function approximators that naturally encode any
+underlying physical laws as prior information. In this first part, we
+demonstrate how these networks can be used to infer solutions to partial
+differential equations, and obtain physics-informed surrogate models that are
+fully differentiable with respect to all input coordinates and free parameters.
+"
+21792,Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017),"  The large scale of scholarly publications poses a challenge for scholars in
+information seeking and sensemaking. Bibliometrics, information retrieval (IR),
+text mining and NLP techniques could help in these search and look-up
+activities, but are not yet widely used. This workshop is intended to stimulate
+IR researchers and digital library professionals to elaborate on new approaches
+in natural language processing, information retrieval, scientometrics, text
+mining and recommendation techniques that can advance the state-of-the-art in
+scholarly document understanding, analysis, and retrieval at scale. The BIRNDL
+workshop at SIGIR 2017 will incorporate an invited talk, paper sessions and the
+third edition of the Computational Linguistics (CL) Scientific Summarization
+Shared Task.
+"
+21793,Dimension free $L^p$-bounds of maximal functions associated to products of Euclidean balls,"  A few years ago, Bourgain proved that the centered Hardy-Littlewood maximal
+function for the cube has dimension free $L^p$-bounds for $p>1$. We extend his
+result to products of Euclidean balls of different dimensions. In addition, we
+provide dimension free $L^p$-bounds for the maximal function associated to
+products of Euclidean spheres for $p > \frac{N}{N-1}$ and $N \ge 3$, where
+$N-1$ is the lowest occurring dimension of a single sphere. The aforementioned
+result is obtained from the latter one by applying the method of rotations from
+Stein's pioneering work on the spherical maximal function.
+"
+21794,Pulling in models of cell migration,"  There are numerous scenarios in which populations of cells migrate in crowded
+environments. Typical examples include wound healing, cancer growth and embryo
+development. In these crowded environments cells are able to interact with each
+other in a variety of ways. These include excluded volume interactions,
+adhesion, repulsion, cell signalling, pushing and pulling. One popular way to
+understand the behaviour of a group of interacting cells is through an
+agent-based model (ABM). A typical aim of modellers using such represtations is
+to elucidate how the microscopic interactions at the cell-level impact on the
+macroscopic behaviour of the population. The complex cell-cell interactions
+listed above have also been incorporated into such models; all apart from
+cell-cell pulling. In this paper we consider this under-represented cell-cell
+interaction, in which an active cell is able to `pull' a nearby neighbour as it
+moves. We incorporate a variety of potential cell-cell pulling mechanisms into
+on- and off-lattice agent-based volume exclusion models of cell movement. For
+each of these agent-based models we derive a continuum partial differential
+equation which describes the evolution of the cells at a population-level. We
+study the agreement between the ABMs and the continuum, population-based
+models, and compare and contrast a range of ABMs (accounting for the different
+pulling mechanisms) with each other. We find generally good agreement between
+the ABMs and the corresponding continuum models that worsens as the agent-based
+models become more complex. Interestingly, we observe that the partial
+differential equations that we derive differ significantly, depending on
+whether they were derived from on- or off-lattice ABMs of pulling. This hints
+that it is important employ the appropriate ABM when representing pulling
+cell-cell interactions.
+"
+21795,Spatially - resolved study of the Meissner effect in superconductors using NV-centers-in-diamond optical magnetometry,"  Non-invasive magnetic field sensing using optically - detected magnetic
+resonance of nitrogen-vacancy (NV) centers in diamond was used to study spatial
+distribution of the magnetic induction upon penetration and expulsion of weak
+magnetic fields in several representative superconductors. Vector magnetic
+fields were measured on the surface of conventional, Pb and Nb, and
+unconventional, LuNi$_2$B$_2$C, Ba$_{0.6}$K$_{0.4}$Fe$_2$As$_2$,
+Ba(Fe$_{0.93}$Co$_{0.07}$)$_2$As$_2$, and CaKFe$_4$As$_4$, superconductors,
+with diffraction - limited spatial resolution using variable - temperature
+confocal system. Magnetic induction profiles across the crystal edges were
+measured in zero-field-cooled (ZFC) and field-cooled (FC) conditions. While all
+superconductors show nearly perfect screening of magnetic fields applied after
+cooling to temperatures well below the superconducting transition, $T_c$, a
+range of very different behaviors was observed for Meissner expulsion upon
+cooling in static magnetic field from above $T_c$. Substantial conventional
+Meissner expulsion is found in LuNi$_2$B$_2$C, paramagnetic Meissner effect
+(PME) is found in Nb, and virtually no expulsion is observed in iron-based
+superconductors. In all cases, good correlation with macroscopic measurements
+of total magnetic moment is found. Our measurements of the spatial distribution
+of magnetic induction provide insight into microscopic physics of the Meissner
+effect.
+"
+21796,A magnetic skyrmion as a non-linear resistive element - a potential building block for reservoir computing,"  Inspired by the human brain, there is a strong effort to find alternative
+models of information processing capable of imitating the high energy
+efficiency of neuromorphic information processing. One possible realization of
+cognitive computing are reservoir computing networks. These networks are built
+out of non-linear resistive elements which are recursively connected. We
+propose that a skyrmion network embedded in frustrated magnetic films may
+provide a suitable physical implementation for reservoir computing
+applications. The significant key ingredient of such a network is a
+two-terminal device with non-linear voltage characteristics originating from
+single-layer magnetoresistive effects, like the anisotropic magnetoresistance
+or the recently discovered non-collinear magnetoresistance. The most basic
+element for a reservoir computing network built from ""skyrmion fabrics"" is a
+single skyrmion embedded in a ferromagnetic ribbon. In order to pave the way
+towards reservoir computing systems based on skyrmion fabrics, here we simulate
+and analyze i) the current flow through a single magnetic skyrmion due to the
+anisotropic magneto-resistive effect and ii) the combined physics of local
+pinning and the anisotropic magneto-resistive effect.
+"
+21797,Bayesian optimal designs for dose-response curves with common parameters,"  The issue of determining not only an adequate dose but also a dosing
+frequency of a drug arises frequently in Phase II clinical trials. This results
+in the comparison of models which have some parameters in common. Planning such
+studies based on Bayesian optimal designs offers robustness to our conclusions
+since these designs, unlike locally optimal designs, are efficient even if the
+parameters are misspecified. In this paper we develop approximate design theory
+for Bayesian $D$-optimality for nonlinear regression models with common
+parameters and investigate the cases of common location or common location and
+scale parameters separately. Analytical characterisations of saturated Bayesian
+$D$-optimal designs are derived for frequently used dose-response models and
+the advantages of our results are illustrated via a numerical investigation.
+"
+21798,3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation,"  We propose a method based on deep learning to perform cardiac segmentation on
+short axis MRI image stacks iteratively from the top slice (around the base) to
+the bottom slice (around the apex). At each iteration, a novel variant of U-net
+is applied to propagate the segmentation of a slice to the adjacent slice below
+it. In other words, the prediction of a segmentation of a slice is dependent
+upon the already existing segmentation of an adjacent slice. 3D-consistency is
+hence explicitly enforced. The method is trained on a large database of 3078
+cases from UK Biobank. It is then tested on 756 different cases from UK Biobank
+and three other state-of-the-art cohorts (ACDC with 100 cases, Sunnybrook with
+30 cases, RVSC with 16 cases). Results comparable or even better than the
+state-of-the-art in terms of distance measures are achieved. They also
+emphasize the assets of our method, namely enhanced spatial consistency
+(currently neither considered nor achieved by the state-of-the-art), and the
+generalization ability to unseen cases even from other databases.
+"
+21799,"A Fast Method to Calculate Kendall Correlations of Large, Sparse Spike Trains","  Despite the fact that the Kendall correlation is becoming recognized as an
+important tool in neuroscience, most standard algorithms to compute the Kendall
+correlation of large spike trains are computationally very slow. Here we
+present a new, significantly faster method for calculating the Kendall
+correlation of spike trains that takes advantage of their structure. We show
+that our method is more than 300 times faster than traditional approaches for
+particularly large, sparse spike trains, and still more than 10 times faster
+for large, but considerably less sparse, spike trains. A MATLAB function
+executing the method described here was made freely available on-line.
+"
+21800,On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation,"  Camera relocalisation is an important problem in computer vision, with
+applications in simultaneous localisation and mapping, virtual/augmented
+reality and navigation. Common techniques either match the current image
+against keyframes with known poses coming from a tracker, or establish 2D-to-3D
+correspondences between keypoints in the current image and points in the scene
+in order to estimate the camera pose. Recently, regression forests have become
+a popular alternative to establish such correspondences. They achieve accurate
+results, but must be trained offline on the target scene, preventing
+relocalisation in new environments. In this paper, we show how to circumvent
+this limitation by adapting a pre-trained forest to a new scene on the fly. Our
+adapted forests achieve relocalisation performance that is on par with that of
+offline forests, and our approach runs in under 150ms, making it desirable for
+real-time systems that require online relocalisation.
+"
+21801,High-Kinetic Inductance Additive Manufactured Superconducting Microwave Cavity,"  Investigations into the microwave surface impedance of superconducting
+resonators have led to the development of single photon counters that rely on
+kinetic inductance for their operation. While concurrent progress in additive
+manufacturing, `3D printing', opens up a previously inaccessible design space
+for waveguide resonators. In this manuscript, we present results from the first
+synthesis of these two technologies in a titanium, aluminum, vanadium
+(Ti-6Al-4V) superconducting radio frequency resonator which exploits a design
+unattainable through conventional fabrication means. We find that Ti-6Al-4V has
+two distinct superconducting transition temperatures observable in heat
+capacity measurements. The higher transition temperature is in agreement with
+DC resistance measurements. While the lower transition temperature, not
+previously known in literature, is consistent with the observed temperature
+dependence of the superconducting microwave surface impedance. From the surface
+reactance, we extract a London penetration depth of $8\pm3{\mu}$m - roughly an
+order of magnitude larger than other titanium alloys and several orders of
+magnitude larger than other conventional elemental superconductors. This large
+London penetration depth suggests that Ti-6Al-4V may be a suitable material for
+high kinetic inductance applications such as single photon counting or
+parametric amplification used in quantum computing.
+"
+21802,Known Boundary Emulation of Complex Computer Models,"  Computer models are now widely used across a range of scientific disciplines
+to describe various complex physical systems, however to perform full
+uncertainty quantification we often need to employ emulators. An emulator is a
+fast statistical construct that mimics the complex computer model, and greatly
+aids the vastly more computationally intensive uncertainty quantification
+calculations that a serious scientific analysis often requires. In some cases,
+the complex model can be solved far more efficiently for certain parameter
+settings, leading to boundaries or hyperplanes in the input parameter space
+where the model is essentially known. We show that for a large class of
+Gaussian process style emulators, multiple boundaries can be formally
+incorporated into the emulation process, by Bayesian updating of the emulators
+with respect to the boundaries, for trivial computational cost. The resulting
+updated emulator equations are given analytically. This leads to emulators that
+possess increased accuracy across large portions of the input parameter space.
+We also describe how a user can incorporate such boundaries within standard
+black box GP emulation packages that are currently available, without altering
+the core code. Appropriate designs of model runs in the presence of known
+boundaries are then analysed, with two kinds of general purpose designs
+proposed. We then apply the improved emulation and design methodology to an
+important systems biology model of hormonal crosstalk in Arabidopsis Thaliana.
+"
+21803,Investigating the role of musical genre in human perception of music stretching resistance,"  To stretch a music piece to a given length is a common demand in people's
+daily lives, e.g., in audio-video synchronization and animation production.
+However, it is not always guaranteed that the stretched music piece is
+acceptable for general audience since music stretching suffers from people's
+perceptual artefacts. Over-stretching a music piece will make it uncomfortable
+for human psychoacoustic hearing. The research on music stretching resistance
+attempts to estimate the maximum stretchability of music pieces to further
+avoid over-stretch. It has been observed that musical genres can significantly
+improve the accuracy of automatic estimation of music stretching resistance,
+but how musical genres are related to music stretching resistance has never
+been explained or studied in detail in the literature. In this paper, the
+characteristics of music stretching resistance are compared across different
+musical genres. It is found that music stretching resistance has strong
+intra-genre cohesiveness and inter-genre discrepancies in the experiments.
+Moreover, the ambiguity and the symmetry of music stretching resistance are
+also observed in the experimental analysis. These findings lead to a new
+measurement on the similarity between different musical genres based on their
+music stretching resistance. In addition, the analysis of variance (ANOVA) also
+supports the findings in this paper by verifying the significance of musical
+genre in shaping music stretching resistance.
+"
+21804,Emerging Market Corporate Bonds as First-to-Default Baskets,"  Emerging market hard-currency bonds are an asset class of growing importance,
+and contain exposure to an EM sovereign and the underlying industry. The
+authors investigate how to model this as a modification of the well-known
+first-to-default (FtD) basket, using the structural model, and find the
+approach feasible.
+"
+21805,Fabrication of plasmonic surface relief gratings for the application of band-pass filter in UV-Visible spectral range,"  The measured experimental results of optical diffraction of 10, 5 and 3.4
+micrometer period plasmonic surface relief grating are presented for the
+application of band-pass filter in visible spectral range. Conventional
+scanning electron microscopic (SEM) is used to fabricate the grating structures
+on the silver halide based film (substrate) by exposing the electron beam in
+raster scan fashion. Morphological characterization of the gratings is
+performed by atomic force microscopy (AFM) shows that the period, height and
+profile depends on the line per frame, beam spot, single line dwell time, beam
+current, and accelerating voltage of the electron beam. Optical transmission
+spectra of 10 micrometer period grating shows a well-defined localized surface
+plasmon resonance (LSPR) dip at ~366 nm wavelength corresponding to gelatin
+embedded silver nanoparticles of the grating structure. As the period of the
+grating reduces LSPR dip becomes prominent. The maximum first order diffraction
+efficiency (DE) and bandwidth for 10 micrometer period grating are observed as
+4% and 400 nm in 350 nm to 800 nm wavelength range respectively. The DE and
+bandwidth are reduced up to 0.03% and 100 nm for 3.4 micrometer period grating.
+The profile of DE is significantly flat within the diffraction bandwidth for
+each of the gratings. An assessment of the particular role of LSPR absorption
+and varied grating period in the development of the profile of first order DE
+v/s wavelength are studied. Fabrication of such nano-scale structures in a
+large area using conventional SEM and silver halide based films may provide the
+simple and efficient technique for various optical devices applications.
+"
+21806,Metrics On $S^2$ With Bounded $\|K_g\|_{L^1\log L^1}$ And Small $\|K_g-1\|_{L^1}$,"  In this short paper, we will study the convergence of a metric sequence $g_k$
+on $S^2$ with bounded $\int|K_{g_k}|\log(1+|K_{g_k}|)d\mu_{g_k}$ and small
+$\int|K(g_k)-1\|d\mu_{g_k}$. We will show that such a sequence is precompact.
+"
+21807,Light traffic behavior under the power-of-two load balancing strategy: The case of heterogeneous servers,"  We consider a multi-server queueing system under the power-of-two policy with
+Poisson job arrivals, heterogeneous servers and a general job requirement
+distribution; each server operates under the first-come first-serve policy and
+there are no buffer constraints. We analyze the performance of this system in
+light traffic by evaluating the first two light traffic derivatives of the
+average job response time. These expressions point to several interesting
+structural features associated with server heterogeneity in light traffic: For
+unequal capacities, the average job response time is seen to decrease for small
+values of the arrival rate, and the more diverse the server speeds, the greater
+the gain in performance. These theoretical findings are assessed through
+limited simulations.
+"
+21808,Deep Learning with Dynamic Computation Graphs,"  Neural networks that compute over graph structures are a natural fit for
+problems in a variety of domains, including natural language (parse trees) and
+cheminformatics (molecular graphs). However, since the computation graph has a
+different shape and size for every input, such networks do not directly support
+batched training or inference. They are also difficult to implement in popular
+deep learning libraries, which are based on static data-flow graphs. We
+introduce a technique called dynamic batching, which not only batches together
+operations between different input graphs of dissimilar shape, but also between
+different nodes within a single input graph. The technique allows us to create
+static graphs, using popular libraries, that emulate dynamic computation graphs
+of arbitrary shape and size. We further present a high-level library of
+compositional blocks that simplifies the creation of dynamic graph models.
+Using the library, we demonstrate concise and batch-wise parallel
+implementations for a variety of models from the literature.
+"
+21809,Requirements for Secure Clock Synchronization,"  This paper establishes a fundamental theory of secure clock synchronization.
+Accurate clock synchronization is the backbone of systems managing power
+distribution, financial transactions, telecommunication operations, database
+services, etc. Some clock synchronization (time transfer) systems, such as the
+Global Navigation Satellite Systems (GNSS), are based on one-way communication
+from a master to a slave clock. Others, such as the Network Transport Protocol
+(NTP), and the IEEE 1588 Precision Time Protocol (PTP), involve two-way
+communication between the master and slave. This paper shows that all one-way
+time transfer protocols are vulnerable to replay attacks that can potentially
+compromise timing information. A set of conditions for secure two-way clock
+synchronization is proposed and proved to be necessary and sufficient. It is
+shown that IEEE 1588 PTP, although a two-way synchronization protocol, is not
+compliant with these conditions, and is therefore insecure. Requirements for
+secure IEEE 1588 PTP are proposed, and a second example protocol is offered to
+illustrate the range of compliant systems.
+"
+21810,Stability of fractional-order nonlinear systems by Lyapunov direct method,"  In this paper, by using a characterization of functions having fractional
+derivative, we propose a rigorous fractional Lyapunov function candidate method
+to analyze stability of fractional-order nonlinear systems. First, we prove an
+inequality concerning the fractional derivatives of convex Lyapunov functions
+without the assumption on the existence of derivative of pseudo-states. Second,
+we establish fractional Lyapunov functions to fractional-order systems without
+the assumption on the global existence of solutions. Our theorems fill the gaps
+and strengthen results in some existing papers.
+"
+21811,Scaling laws during collapse of a homopolymer: Lattice versus off-lattice,"  We present comparative results from simulations of a lattice and an
+off-lattice model of a homopolymer, in the context of kinetics of the collapse
+transition. Scaling laws related to the collapse time, cluster coarsening and
+aging behavior are compared. Although in both models the cluster growth is
+independent of temperature, the related exponents turn out to be different.
+Conversely, the aging and associated scaling properties are found to be
+universal, with the nonequilibrium autocorrelation exponent obeying a recently
+derived bound.
+"
+21812,Efficient Convex Optimization with Membership Oracles,"  We consider the problem of minimizing a convex function over a convex set
+given access only to an evaluation oracle for the function and a membership
+oracle for the set. We give a simple algorithm which solves this problem with
+$\tilde{O}(n^2)$ oracle calls and $\tilde{O}(n^3)$ additional arithmetic
+operations. Using this result, we obtain more efficient reductions among the
+five basic oracles for convex sets and functions defined by Grötschel, Lovasz
+and Schrijver.
+"
+21813,On the Prime Graph Question for Integral Group Rings of Conway simple groups,"  The Prime Graph Question for integral group rings asks if it is true that if
+the normalized unit group of the integral group ring of a finite group $G$
+contains an element of order $pq$, for some primes $p$ and $q$, also $G$
+contains an element of that order. We answer this question for the three Conway
+sporadic simple groups after reducing it to a combinatorial question about
+Young tableaus and Littlewood-Richardson coefficients. This finishes work of V.
+Bovdi, A. Konovalov and S. Linton.
+"
+21814,Challenges in Data-to-Document Generation,"  Recent neural models have shown significant progress on the problem of
+generating short descriptive texts conditioned on a small number of database
+records. In this work, we suggest a slightly more difficult data-to-text
+generation task, and investigate how effective current approaches are on this
+task. In particular, we introduce a new, large-scale corpus of data records
+paired with descriptive documents, propose a series of extractive evaluation
+methods for analyzing performance, and obtain baseline results using current
+neural generation methods. Experiments show that these models produce fluent
+text, but fail to convincingly approximate human-generated documents. Moreover,
+even templated baselines exceed the performance of these neural models on some
+metrics, though copy- and reconstruction-based extensions lead to noticeable
+improvements.
+"
+21815,The covering type of closed surfaces and minimal triangulations,"  The notion of covering type was recently introduced by Karoubi and Weibel to
+measure the complexity of a topological space by means of good coverings. When
+X has the homotopy type of a finite CW-complex, its covering type coincides
+with the minimum possible number of vertices of a simplicial complex homotopy
+equivalent to X. In this article we compute the covering type of all closed
+surfaces. Our results completely settle a problem posed by Karoubi and Weibel,
+and shed more light on the relationship between the topology of surfaces and
+the number of vertices of minimal triangulations from a homotopy point of view.
+"
+21816,Relaxation heuristics for the set multicover problem with generalized upper bound constraints,"  We consider an extension of the set covering problem (SCP) introducing
+(i)~multicover and (ii)~generalized upper bound (GUB)~constraints. For the
+conventional SCP, the pricing method has been introduced to reduce the size of
+instances, and several efficient heuristic algorithms based on such reduction
+techniques have been developed to solve large-scale instances. However, GUB
+constraints often make the pricing method less effective, because they often
+prevent solutions from containing highly evaluated variables together. To
+overcome this problem, we develop heuristic algorithms to reduce the size of
+instances, in which new evaluation schemes of variables are introduced taking
+account of GUB constraints. We also develop an efficient implementation of a
+2-flip neighborhood local search algorithm that reduces the number of
+candidates in the neighborhood without sacrificing the solution quality. In
+order to guide the search to visit a wide variety of good solutions, we also
+introduce a path relinking method that generates new solutions by combining two
+or more solutions obtained so far. According to computational comparison on
+benchmark instances, the proposed method succeeds in selecting a small number
+of promising variables properly and performs quite effectively even for
+large-scale instances having hard GUB constraints.
+"
+21817,Optimal percolation on multiplex networks,"  Optimal percolation is the problem of finding the minimal set of nodes such
+that if the members of this set are removed from a network, the network is
+fragmented into non-extensive disconnected clusters. The solution of the
+optimal percolation problem has direct applicability in strategies of
+immunization in disease spreading processes, and influence maximization for
+certain classes of opinion dynamical models. In this paper, we consider the
+problem of optimal percolation on multiplex networks. The multiplex scenario
+serves to realistically model various technological, biological, and social
+networks. We find that the multilayer nature of these systems, and more
+precisely multiplex characteristics such as edge overlap and interlayer
+degree-degree correlation, profoundly changes the properties of the set of
+nodes identified as the solution of the optimal percolation problem.
+"
+21818,Nearly Spectral Spaces,"  We study some natural generalizations of the spectral spaces in the contexts
+of commutative rings and distributive lattices. We obtain a topological
+characterization for the spectra of commutative (not necessarily unitary) rings
+and we find spectral versions for the up-spectral and down-spectral spaces. We
+show that the duality between distributive lattices and Balbes-Dwinger spaces
+is the co-equivalence associated to a pair of contravariant right adjoint
+functors between suitable categories.
+"
+21819,Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations,"  We study the estimation of the latent variable Gaussian graphical model
+(LVGGM), where the precision matrix is the superposition of a sparse matrix and
+a low-rank matrix. In order to speed up the estimation of the sparse plus
+low-rank components, we propose a sparsity constrained maximum likelihood
+estimator based on matrix factorization, and an efficient alternating gradient
+descent algorithm with hard thresholding to solve it. Our algorithm is orders
+of magnitude faster than the convex relaxation based methods for LVGGM. In
+addition, we prove that our algorithm is guaranteed to linearly converge to the
+unknown sparse and low-rank components up to the optimal statistical precision.
+Experiments on both synthetic and genomic data demonstrate the superiority of
+our algorithm over the state-of-the-art algorithms and corroborate our theory.
+"
+21820,The third data release of the Kilo-Degree Survey and associated data products,"  The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey
+with the OmegaCAM camera at the VLT Survey Telescope. It aims to image 1500
+square degrees in four filters (ugri). The core science driver is mapping the
+large-scale matter distribution in the Universe, using weak lensing shear and
+photometric redshift measurements. Further science cases include galaxy
+evolution, Milky Way structure, detection of high-redshift clusters, and
+finding rare sources such as strong lenses and quasars. Here we present the
+third public data release (DR3) and several associated data products, adding
+further area, homogenized photometric calibration, photometric redshifts and
+weak lensing shear measurements to the first two releases. A dedicated pipeline
+embedded in the Astro-WISE information system is used for the production of the
+main release. Modifications with respect to earlier releases are described in
+detail. Photometric redshifts have been derived using both Bayesian template
+fitting, and machine-learning techniques. For the weak lensing measurements,
+optimized procedures based on the THELI data reduction and lensfit shear
+measurement packages are used. In DR3 stacked ugri images, weight maps, masks,
+and source lists for 292 new survey tiles (~300 sq.deg) are made available. The
+multi-band catalogue, including homogenized photometry and photometric
+redshifts, covers the combined DR1, DR2 and DR3 footprint of 440 survey tiles
+(447 sq.deg). Limiting magnitudes are typically 24.3, 25.1, 24.9, 23.8 (5 sigma
+in a 2 arcsec aperture) in ugri, respectively, and the typical r-band PSF size
+is less than 0.7 arcsec. The photometric homogenization scheme ensures accurate
+colors and an absolute calibration stable to ~2% for gri and ~3% in u.
+Separately released are a weak lensing shear catalogue and photometric
+redshifts based on two different machine-learning techniques.
+"
+21821,Hydrogen Line Observations of Cometary Spectra at 1420 Mhz,"  In 2016, the Center for Planetary Science proposed a hypothesis arguing a
+comet and/or its hydrogen cloud were a strong candidate for the source of the
+Wow! Signal. From 27 November 2016 to 24 February 2017, the Center for
+Planetary Science conducted 200 observations in the radio spectrum to validate
+the hypothesis. The investigation discovered that comet 266/P Christensen
+emitted a radio signal at 1420.25 MHz. The results of this investigation,
+therefore, conclude that cometary spectra are detectable at 1420 MHz and, more
+importantly, that the 1977 Wow! Signal was a natural phenomenon from a Solar
+System body.
+"
+21822,Pain-Free Random Differential Privacy with Sensitivity Sampling,"  Popular approaches to differential privacy, such as the Laplace and
+exponential mechanisms, calibrate randomised smoothing through global
+sensitivity of the target non-private function. Bounding such sensitivity is
+often a prohibitively complex analytic calculation. As an alternative, we
+propose a straightforward sampler for estimating sensitivity of non-private
+mechanisms. Since our sensitivity estimates hold with high probability, any
+mechanism that would be $(\epsilon,\delta)$-differentially private under
+bounded global sensitivity automatically achieves
+$(\epsilon,\delta,\gamma)$-random differential privacy (Hall et al., 2012),
+without any target-specific calculations required. We demonstrate on worked
+example learners how our usable approach adopts a naturally-relaxed privacy
+guarantee, while achieving more accurate releases even for non-private
+functions that are black-box computer programs.
+"
+21823,Extremal Kaehler-Einstein metric for two-dimensional convex bodies,"  Given a convex body $K \subset \mathbb{R}^n$ with the barycenter at the
+origin we consider the corresponding K{ä}hler-Einstein equation $e^{-\Phi} =
+\det D^2 \Phi$. If $K$ is a simplex, then the Ricci tensor of the Hessian
+metric $D^2 \Phi$ is constant and equals $\frac{n-1}{4(n+1)}$. We conjecture
+that the Ricci tensor of $D^2 \Phi$ for arbitrary $K$ is uniformly bounded by
+$\frac{n-1}{4(n+1)}$ and verify this conjecture in the two-dimensional case.
+The general case remains open.
+"
+21824,Socio-spatial Self-organizing Maps: Using Social Media to Assess Relevant Geographies for Exposure to Social Processes,"  Social media offers a unique window into attitudes like racism and
+homophobia, exposure to which are important, hard to measure and understudied
+social determinants of health. However, individual geo-located observations
+from social media are noisy and geographically inconsistent. Existing areas by
+which exposures are measured, like Zip codes, average over irrelevant
+administratively-defined boundaries. Hence, in order to enable studies of
+online social environmental measures like attitudes on social media and their
+possible relationship to health outcomes, first there is a need for a method to
+define the collective, underlying degree of social media attitudes by region.
+To address this, we create the Socio-spatial-Self organizing map, ""SS-SOM""
+pipeline to best identify regions by their latent social attitude from Twitter
+posts. SS-SOMs use neural embedding for text-classification, and augment
+traditional SOMs to generate a controlled number of non-overlapping,
+topologically-constrained and topically-similar clusters. We find that not only
+are SS-SOMs robust to missing data, the exposure of a cohort of men who are
+susceptible to multiple racism and homophobia-linked health outcomes, changes
+by up to 42% using SS-SOM measures as compared to using Zip code-based
+measures.
+"
+21825,A Large-Scale Study of Language Models for Chord Prediction,"  We conduct a large-scale study of language models for chord prediction.
+Specifically, we compare N-gram models to various flavours of recurrent neural
+networks on a comprehensive dataset comprising all publicly available datasets
+of annotated chords known to us. This large amount of data allows us to
+systematically explore hyper-parameter settings for the recurrent neural
+networks---a crucial step in achieving good results with this model class. Our
+results show not only a quantitative difference between the models, but also a
+qualitative one: in contrast to static N-gram models, certain RNN
+configurations adapt to the songs at test time. This finding constitutes a
+further step towards the development of chord recognition systems that are more
+aware of local musical context than what was previously possible.
+"
+21826,A Novel Method of Subgroup Identification by Combining Virtual Twins with GUIDE (VG) for Development of Precision Medicines,"  A lack of understanding of human biology creates a hurdle for the development
+of precision medicines. To overcome this hurdle we need to better understand
+the potential synergy between a given investigational treatment (vs. placebo or
+active control) and various demographic or genetic factors, disease history and
+severity, etc., with the goal of identifying those patients at increased risk
+of exhibiting clinically meaningful treatment benefit. For this reason, we
+propose the VG method, which combines the idea of an individual treatment
+effect (ITE) from Virtual Twins (Foster, et al., 2011) with the unbiased
+variable selection and cutoff value determination algorithm from GUIDE (Loh, et
+al., 2015). Simulation results show the VG method has less variable selection
+bias than Virtual Twins and higher statistical power than GUIDE Interaction in
+the presence of prognostic variables with strong treatment effects. Type I
+error and predictive performance of Virtual Twins, GUIDE and VG are compared
+through the use of simulation studies. Results obtained after retrospectively
+applying VG to data from a clinical trial also are discussed.
+"
+21827,Critical Slowing Down of Quadrupole and Hexadecapole Orderings in Iron Pnictide Superconductor,"  Ultrasonic measurements have been carried out to investigate the critical
+dynamics of structural and superconducting transitions due to degenerate
+orbital bands in iron pnictide compounds with the formula
+Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$. The attenuation coefficient
+$\alpha_{\mathrm{L}[110]}$ of the longitudinal ultrasonic wave for
+$(C_{11}+C_{12}+2C_{66})/2$ for $x = 0.036$ reveals the critical slowing down
+of the relaxation time around the structural transition at $T_\mathrm{s} = 65$
+K, which is caused by ferro-type ordering of the quadrupole $O_{x'^2-y'^2}$
+coupled to the strain $\varepsilon_{xy}$. The attenuation coefficient
+$\alpha_{66}$ of the transverse ultrasonic wave for $C_{66}$ for $x = 0.071$
+also exhibits the critical slowing down around the superconducting transition
+at $T_\mathrm{SC} = 23$ K, which is caused by ferro-type ordering of the
+hexadecapole $H_z^\alpha \bigl( \boldsymbol{r}_i, \boldsymbol{r}_j \bigr) =
+O_{x'y'}\bigl( \boldsymbol{r}_i \bigr) O_{x'^2 - y'^2}\bigl( \boldsymbol{r}_j
+\bigr)
++ O_{x'^2 - y'^2}\bigl( \boldsymbol{r}_i \bigr) O_{x'y'}\bigl(
+\boldsymbol{r}_j \bigr)$ of the bound two-electron state coupled to the
+rotation $\omega_{xy}$. It is proposed that the hexadecapole ordering
+associated with the superconductivity brings about spontaneous rotation of the
+macroscopic superconducting state with respect to the host tetragonal lattice.
+"
+21828,On the Interaction between Autonomous Mobility-on-Demand and Public Transportation Systems,"  In this paper we study models and coordination policies for intermodal
+Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles
+provides on-demand mobility jointly with public transit. Specifically, we first
+present a network flow model for intermodal AMoD, where we capture the coupling
+between AMoD and public transit and the goal is to maximize social welfare.
+Second, leveraging such a model, we design a pricing and tolling scheme that
+allows to achieve the social optimum under the assumption of a perfect market
+with selfish agents. Finally, we present a real-world case study for New York
+City. Our results show that the coordination between AMoD fleets and public
+transit can yield significant benefits compared to an AMoD system operating in
+isolation.
+"
+21829,"The class of second order quasilinear equations: models, solutions and background of classification","  The paper is concerned with the unsteady solutions to the model of mutually
+penetrating continua and quasilinear hyperbolic modification of the Burgers
+equation (QHMB). The studies were focused on the peculiar solutions of models
+in question. On the base of these models and their solutions, the ideas of
+second order quasilinear models classification were developed.
+"
+21830,Solving Almost all Systems of Random Quadratic Equations,"  This paper deals with finding an $n$-dimensional solution $x$ to a system of
+quadratic equations of the form $y_i=|\langle{a}_i,x\rangle|^2$ for $1\le i \le
+m$, which is also known as phase retrieval and is NP-hard in general. We put
+forth a novel procedure for minimizing the amplitude-based least-squares
+empirical loss, that starts with a weighted maximal correlation initialization
+obtainable with a few power or Lanczos iterations, followed by successive
+refinements based upon a sequence of iteratively reweighted (generalized)
+gradient iterations. The two (both the initialization and gradient flow) stages
+distinguish themselves from prior contributions by the inclusion of a fresh
+(re)weighting regularization technique. The overall algorithm is conceptually
+simple, numerically scalable, and easy-to-implement. For certain random
+measurement models, the novel procedure is shown capable of finding the true
+solution $x$ in time proportional to reading the data $\{(a_i;y_i)\}_{1\le i
+\le m}$. This holds with high probability and without extra assumption on the
+signal $x$ to be recovered, provided that the number $m$ of equations is some
+constant $c>0$ times the number $n$ of unknowns in the signal vector, namely,
+$m>cn$. Empirically, the upshots of this contribution are: i) (almost) $100\%$
+perfect signal recovery in the high-dimensional (say e.g., $n\ge 2,000$) regime
+given only an information-theoretic limit number of noiseless equations,
+namely, $m=2n-1$ in the real-valued Gaussian case; and, ii) (nearly) optimal
+statistical accuracy in the presence of additive noise of bounded support.
+Finally, substantial numerical tests using both synthetic data and real images
+corroborate markedly improved signal recovery performance and computational
+efficiency of our novel procedure relative to state-of-the-art approaches.
+"
+21831,Predicting the outcomes of fuel drop impact on heated surfaces using SPH simulation,"  The impact of liquid drops on a heated solid surface is of great importance
+in many engineering applications. This paper describes the simulation of the
+drop-wall interaction using the smoothed particle hydrodynamics (SPH) method.
+The SPH method is a Lagrangian mesh-free method that can be used to solve the
+fluid equations. A vaporization model based on the SPH formulation was also
+developed and implemented. A parametric study was conducted to characterize the
+effects of impact velocity and wall temperature on the impact outcome. The
+present numerical method was able to predict different outcomes, such as
+deposition, splash, breakup, and rebound (i.e., Leidenfrost phenomenon). The
+present numerical method was used to construct a regime diagram for describing
+the impact of an iso-octane drop on a heated surface at various Weber numbers
+and wall temperatures.
+"
+21832,Learning in the Machine: the Symmetries of the Deep Learning Channel,"  In a physical neural system, learning rules must be local both in space and
+time. In order for learning to occur, non-local information must be
+communicated to the deep synapses through a communication channel, the deep
+learning channel. We identify several possible architectures for this learning
+channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges:
+1) symmetry of architectures; 2) symmetry of weights; 3) symmetry of neurons;
+4) symmetry of derivatives; 5) symmetry of processing; and 6) symmetry of
+learning rules. Random backpropagation (RBP) addresses the second and third
+symmetry, and some of its variations, such as skipped RBP (SRBP) address the
+first and the fourth symmetry. Here we address the last two desirable
+symmetries showing through simulations that they can be achieved and that the
+learning channel is particularly robust to symmetry variations. Specifically,
+random backpropagation and its variations can be performed with the same
+non-linear neurons used in the main input-output forward channel, and the
+connections in the learning channel can be adapted using the same algorithm
+used in the forward channel, removing the need for any specialized hardware in
+the learning channel. Finally, we provide mathematical results in simple cases
+showing that the learning equations in the forward and backward channels
+converge to fixed points, for almost any initial conditions. In symmetric
+architectures, if the weights in both channels are small at initialization,
+adaptation in both channels leads to weights that are essentially symmetric
+during and after learning. Biological connections are discussed.
+"
+21833,Coset spaces of metrizable groups,"  We characterize coset spaces of topological groups which are coset spaces of
+(separable) metrizable groups and complete metrizable (Polish) groups. Besides,
+it is shown that for a $G$-space $X$ with a $d$-open action there is a
+topological group $H$ of weight and cardinality less than or equal to the
+weight of $X$ such that $H$ admits a $d$-open action on $X$. This is further
+applied to show that if $X$ is a separable metrizable coset space then $X$ has
+a Polish extension which is a coset space of a Polish group.
+"
+21834,A Minimum Reconfiguration Probability Routing Algorithm for RWA in All-Optical Networks,"  In this paper, we present a detailed study of Minimum Reconfiguration
+Probability Routing (MRPR) algorithm, and its performance evaluation in
+comparison with Adaptive unconstrained routing (AUR) and Least Loaded routing
+(LLR) algorithms. We have minimized the effects of failures on link and router
+failure in the network under changing load conditions, we assess the
+probability of service and number of light path failures due to link or route
+failure on Wavelength Interchange(WI) network. The computation complexity is
+reduced by using Kalman Filter(KF) techniques. The minimum reconfiguration
+probability routing (MRPR) algorithm selects most reliable routes and assign
+wavelengths to connections in a manner that utilizes the light path(LP)
+established efficiently considering all possible requests.
+"
+21835,Emergence of correlated proton tunneling in water ice,"  Several experimental and theoretical studies report instances of concerted or
+correlated multiple proton tunneling in solid phases of water. Here, we
+construct a pseudo-spin model for the quantum motion of protons in a hexameric
+H$_2$O ring and extend it to open system dynamics that takes environmental
+effects into account in the form of O$-$H stretch vibrations. We approach the
+problem of correlations in tunneling using quantum information theory in a
+departure from previous studies. Our formalism enables us to quantify the
+coherent proton mobility around the hexagonal ring by one of the principal
+measures of coherence, the $l_1$ norm of coherence. The nature of the pairwise
+pseudo-spin correlations underlying the overall mobility is further
+investigated within this formalism. We show that the classical correlations of
+the individual quantum tunneling events in long-time limit is sufficient to
+capture the behaviour of coherent proton mobility observed in low-temperature
+experiments. We conclude that long-range intra-ring interactions do not appear
+to be a necessary condition for correlated proton tunneling in water ice.
+"
+21836,Constrained path-finding and structure from acyclicity,"  This note presents several results in graph theory inspired by the author's
+work in the proof theory of linear logic; these results are purely
+combinatorial and do not involve logic.
+We show that trails avoiding forbidden transitions and rainbow paths for
+complete multipartite color classes can be found in linear time, whereas
+finding rainbow paths is NP-complete for any other restriction on color
+classes. For the tractable cases, we also state new structural properties
+equivalent to Kotzig's theorem on bridges in unique perfect matchings.
+We also exhibit a connection between blossoms and bridge deletion orders in
+unique perfect matchings.
+"
+21837,Constraints on the optical polarization source in the luminous non-blazar quasar 3C 323.1 (PG 1545+210) from the photometric and polarimetric variability,"  We examine the optical photometric and polarimetric variability of the
+luminous type 1 non-blazar quasar 3C 323.1 (PG 1545+210). Two optical
+spectro-polarimetric measurements taken during the periods 1996$-$98 and 2003
+combined with a $V$-band imaging polarimetric measurement taken in 2002 reveal
+that (1) as noted in the literature, the polarization of 3C 323.1 is confined
+only to the continuum emission, that is, the emission from the broad line
+region is unpolarized; (2) the polarized flux spectra show evidence of a
+time-variable broad absorption feature in the wavelength range of the Balmer
+continuum and other recombination lines; (3) weak variability in the
+polarization position angle ($PA$) of $\sim$ 4 deg over a time-scale of 4$-$6
+years is observed; and (4) the V-band total flux and the polarized flux show
+highly correlated variability over a time-scale of one year. Taking the
+above-mentioned photometric and polarimetric variability properties and the
+results from previous studies into consideration, we propose a geometrical
+model for the polarization source in 3C 323.1, in which an equatorial absorbing
+region and an axi-asymmetric equatorial electron-scattering region are assumed
+to be located between the accretion disc and the broad line region. The
+scattering/absorbing regions can perhaps be attributed to the accretion disc
+wind or flared disc surface, but further polarimetric monitoring observations
+for 3C~323.1 and other quasars with continuum-confined polarization are needed
+to probe the true physical origins of these regions.
+"
+21838,Cliff-Weiss Inequalities and the Zassenhaus Conjecture,"  Let $N$ be a nilpotent normal subgroup of the finite group $G$. Assume that
+$u$ is a unit of finite order in the integral group ring $\mathbb{Z} G$ of $G$
+which maps to the identity under the linear extension of the natural
+homomorphism $G \rightarrow G/N$. We show how a result of Cliff and Weiss can
+be used to derive linear inequalities on the partial augmentations of $u$ and
+apply this to the study of the Zassenhaus Conjecture. This conjecture states
+that any unit of finite order in $\mathbb{Z} G$ is conjugate in the rational
+group algebra of $G$ to an element in $\pm G$.
+"
+21839,Flow Navigation by Smart Microswimmers via Reinforcement Learning,"  Smart active particles can acquire some limited knowledge of the fluid
+environment from simple mechanical cues and exert a control on their preferred
+steering direction. Their goal is to learn the best way to navigate by
+exploiting the underlying flow whenever possible. As an example, we focus our
+attention on smart gravitactic swimmers. These are active particles whose task
+is to reach the highest altitude within some time horizon, given the
+constraints enforced by fluid mechanics. By means of numerical experiments, we
+show that swimmers indeed learn nearly optimal strategies just by experience. A
+reinforcement learning algorithm allows particles to learn effective strategies
+even in difficult situations when, in the absence of control, they would end up
+being trapped by flow structures. These strategies are highly nontrivial and
+cannot be easily guessed in advance. This Letter illustrates the potential of
+reinforcement learning algorithms to model adaptive behavior in complex flows
+and paves the way towards the engineering of smart microswimmers that solve
+difficult navigation problems.
+"
+21840,Synthesizing a Clock Signal with Reactions---Part II: Frequency Alteration Based on Gears,"  On a chassis of gear model, we have offered a quantitative description for
+our method to synthesize a chemical clock signal with various duty cycles in
+Part I. As Part II of the study, this paper devotes itself in proposing a
+design methodology to handle frequency alteration issues for the chemical
+clock, including both frequency division and frequency multiplication. Several
+interesting examples are provided for a better explanation of our contribution.
+All the simulation results verify and validate the correctness and efficiency
+of our proposal.
+"
+21841,A characterization of affinely regular polygons,"  In 1970, Coxeter gave a short and elegant geometric proof showing that if
+$p_1, p_2, \ldots, p_n$ are vertices of an $n$-gon $P$ in cyclic order, then
+$P$ is affinely regular if, and only if there is some $\lambda \geq 0$ such
+that $p_{j+2}-p_{j-1} = \lambda (p_{j+1}-p_j)$ for $j=1,2,\ldots, n$. The aim
+of this paper is to examine the properties of polygons whose vertices
+$p_1,p_2,\ldots,p_n \in \mathbb{C}$ satisfy the property that
+$p_{j+m_1}-p_{j+m_2} = w (p_{j+k}-p_j)$ for some $w \in \mathbb{C}$ and
+$m_1,m_2,k \in \mathbb{Z}$. In particular, we show that in `most' cases this
+implies that the polygon is affinely regular, but in some special cases there
+are polygons which satisfy this property but are not affinely regular. The
+proofs are based on the use of linear algebraic and number theoretic tools. In
+addition, we apply our method to characterize polytopes with certain symmetry
+groups.
+"
+21842,On detection and annihilation of spherical virus embedded in a fluid matrix at low and moderate Reynolds number,"  Effect of high and low Reynolds number is studied on low frequency
+vibrational modes of a spherical virus embedded in the aqueous medium. We have
+used an analytical approach based on fluid dynamic and classical Lamb theory to
+calculate the vibrational modes of a virus with material parameters of lysozyme
+crystal in water. The obvious size effect on the vibrational modes is observed.
+The estimated damping time which is of the order of picosecond varies with
+Reynolds number and shows a high value for a critical Reynolds number. The
+stationary eigenfrequency regions are observed for every quantum number l and n
+suggesting the most probable Re ranges for acoustic treatment of viruses in
+order to detect or annihilate the virus using corresponding viruswater
+configuration.
+"
+21843,Robust two-level system control by a detuned and chirped laser pulse,"  We propose and demonstrate a robust control scheme by ultrafast nonadiabatic
+chirped laser pulse, designed for targeting coherent superpositions of
+two-level systems. Robustness against power fluctuation is proved by our
+numerical study and a proof-of-principle experiment performed with femtosecond
+laser interaction on cold atoms. They exhibit for the final driven dynamics a
+cusp on the Bloch sphere, corresponding to a zero curvature of fidelity. This
+solution is particularly simple and thus applicable to a wide range of
+potential applications.
+"
+21844,Analyzing Diffusion and Flow-driven Instability using Semidefinite Programming,"  Diffusion and flow-driven instability, or transport-driven instability, is
+one of the central mechanisms to generate inhomogeneous gradient of
+concentrations in spatially distributed chemical systems. However, verifying
+the transport-driven instability of reaction-diffusion-advection systems
+requires checking the Jacobian eigenvalues of infinitely many Fourier modes,
+which is computationally intractable. To overcome this limitation, this paper
+proposes mathematical optimization algorithms that determine the
+stability/instability of reaction-diffusion-advection systems by finite steps
+of algebraic calculations. Specifically, the stability/instability analysis of
+Fourier modes is formulated as a sum-of-squares (SOS) optimization program,
+which is a class of convex optimization whose solvers are widely available as
+software packages. The optimization program is further extended for facile
+computation of the destabilizing spatial modes. This extension allows for
+predicting and designing the shape of concentration gradient without simulating
+the governing equations. The streamlined analysis process of self-organized
+pattern formation is demonstrated with a simple illustrative reaction model
+with diffusion and advection.
+"
+21845,Petrographic and geochemical evidence for multiphase formation of carbonates in the Martian orthopyroxenite Allan Hills 84001,"  Martian meteorites can provide valuable information about past environmental
+conditions on Mars. Allan Hills 84001 formed more than 4 Gyr ago, and owing to
+its age and long exposure to the Martian environment, this meteorite has
+features that may record early processes. These features include a highly
+fractured texture, gases trapped during one or more impact events or during
+formation of the rock, and spherical Fe-Mg-Ca carbonates. Here we have
+concentrated on providing new insights into the context of these carbonates
+using a range of techniques to explore whether they record multiple
+precipitation and shock events. The petrographic features and compositional
+properties of these carbonates indicate that at least two pulses of Mg- and
+Fe-rich solutions saturated the rock. Those two generations of carbonates can
+be distinguished by a very sharp change in compositions, from being rich in Mg
+and poor in Fe and Mn, to being poor in Mg and rich in Fe and Mn. Between these
+two generations of carbonate is evidence for fracturing and local corrosion.
+"
+21846,Distributed Random-Fixed Projected Algorithm for Constrained Optimization Over Digraphs,"  This paper is concerned with a constrained optimization problem over a
+directed graph (digraph) of nodes, in which the cost function is a sum of local
+objectives, and each node only knows its local objective and constraints. To
+collaboratively solve the optimization, most of the existing works require the
+interaction graph to be balanced or ""doubly-stochastic"", which is quite
+restrictive and not necessary as shown in this paper. We focus on an epigraph
+form of the original optimization to resolve the ""unbalanced"" problem, and
+design a novel two-step recursive algorithm with a simple structure. Under
+strongly connected digraphs, we prove that each node asymptotically converges
+to some common optimal solution. Finally, simulations are performed to
+illustrate the effectiveness of the proposed algorithms.
+"
+21847,Detecting Cancer Metastases on Gigapixel Pathology Images,"  Each year, the treatment decisions for more than 230,000 breast cancer
+patients in the U.S. hinge on whether the cancer has metastasized away from the
+breast. Metastasis detection is currently performed by pathologists reviewing
+large expanses of biological tissues. This process is labor intensive and
+error-prone. We present a framework to automatically detect and localize tumors
+as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x
+100,000 pixels. Our method leverages a convolutional neural network (CNN)
+architecture and obtains state-of-the-art results on the Camelyon16 dataset in
+the challenging lesion-level tumor detection task. At 8 false positives per
+image, we detect 92.4% of the tumors, relative to 82.7% by the previous best
+automated approach. For comparison, a human pathologist attempting exhaustive
+search achieved 73.2% sensitivity. We achieve image-level AUC scores above 97%
+on both the Camelyon16 test set and an independent set of 110 slides. In
+addition, we discover that two slides in the Camelyon16 training set were
+erroneously labeled normal. Our approach could considerably reduce false
+negative rates in metastasis detection.
+"
+21848,Edge insulating topological phases in a two-dimensional long-range superconductor,"  We study the zero-temperature phase diagram of a two dimensional square
+lattice loaded by spinless fermions, with nearest neighbor hopping and
+algebraically decaying pairing. We find that for sufficiently long-range
+pairing, new phases, not continuously connected with any short-range phase,
+occur, signaled by the violation of the area law for the Von Neumann entropy,
+by semi-integer Chern numbers, and by edge modes with nonzero mass. The latter
+feature results in the absence of single-fermion edge conductivity, present
+instead in the short- range limit. The definition of a topology in the bulk and
+the presence of a bulk-boundary correspondence is still suggested for the
+long-range phases. Recent experimental proposals and advances open the
+stimulating possibility to probe the described long-range effects in
+next-future realistic set-ups.
+"
+21849,Using mobile service for supply chain management : a survey and challenges,"  Efficient supply chain management calls for robust analytical and optimal
+models to automate its process. Therefore, information technology is an
+essential ingredient that integrates these tools in supply chain. With the
+emergence of wireless, the high technologies and the reliability of mobile
+devices, mobile web services draw a promising horizon facing economic
+challenges. They offer new personalized services to each actor in the supply
+chain on their mobile devices at anytime and anywhere. This paper presents a
+literature review of mobile web service implemented on the industry context
+based on the supply chain management approach. First, a large definition of
+mobile web service and some proposal architecture are exposed. Then the paper
+discuss some generic related work on mobile web service focusing on supply
+chain management. Finally some challenges on m-service oriented supply chain
+management are proposed.
+"
+21850,Statistical distribution of roots of a polynomial modulo primes,"  Let $f(x)=x^n+a_{n-1}x^{n-1}+\dots+a_0$ be an irreducible polynomial with
+integer coefficients. For a prime $p$ for which $f(x)$ is fully splitting
+modulo $ p$, we consider $n$ roots $r_i$ of $f(x)\equiv 0\bmod p$ with $0 \le
+r_1\le\dots\le r_n<p$ and propose several conjectures on the distribution of an
+integer $\lceil \sum_{i\in S} r_i/p\rceil$ for a subset $S$ of $\{1,\dots,n\}$
+when $p\to\infty$.
+"
+21851,The index of coincidence for the binomial distribution is log-convex,"  We consider the binomial distribution with parameters $n$ and $x$, and show
+that the sum of the squared probabilities is a log-convex function of $x$. This
+completes the proof of a conjecture formulated in 2014. Applications to
+Rényi and Tsallis entropies are given.
+"
+21852,"Elements of Consciousness and Cognition. Biology, Mathematic, Physics and Panpsychism: an Information Topology Perspective","  This review presents recent and older results on elementary quantitative and
+qualitative aspects of consciousness and cognition and tackles the question
+""What is consciousness?"" conjointly from biological, neuroscience-cognitive,
+physical and mathematical points of view. It proposes to unify various results
+and theories by means of information topology.
+The first chapter presents the postulates and results on elementary
+perception at various organizational scales of the nervous system and proposes
+the hypothesis of an electrodynamic intrinsic nature of consciousness which is
+sustained by an analogical code. It underlines the diversity of the learning
+mechanisms that sustain the dynamics of perception and consciousness, including
+adaptive and homeostatic processes on multiple scales.
+The second chapter investigates the logical aspects of cognition and
+consciousness and proposes an axiomatization based on measure and probability
+theory. Topos and constructive logic are presented as providing an intrinsic
+probabilistic logic, with the long-term aim of avoiding the paradoxical
+decomposition induced by the Axiom of Choice. We sketch an elementary procedure
+allowing an expression of the information of a mathematical formula a la Godel.
+We then present the formalism of information topology and propose that it
+provides a preliminary basis for synthesizing the main models of cognition and
+consciousness within a formal Gestalt theory. Information topology establishes
+a characterization of information theory functions, allowing for a precise
+expression of information structures and patterns. It provides a quantification
+of the structure of statistical interactions and their expression in terms of
+statistical physics and machine learning. Notably, those topological methods
+allow conciliation of some of the main theories of consciousness.
+"
+21853,The Error Probability of Random Fourier Features is Dimensionality Independent,"  We show that the error probability of reconstructing kernel matrices from
+Random Fourier Features for the Gaussian kernel function is at most
+$\mathcal{O}(R^{2/3} \exp(-D))$, where $D$ is the number of random features and
+$R$ is the diameter of the data domain. We also provide an
+information-theoretic method-independent lower bound of $\Omega((1-\exp(-R^2))
+\exp(-D))$. Compared to prior work, we are the first to show that the error
+probability for random Fourier features is independent of the dimensionality of
+data points. As applications of our theory, we obtain dimension-independent
+bounds for kernel ridge regression and support vector machines.
+"
+21854,Ultrafast carbon monoxide photolysis and heme spin-crossover in myoglobin via nonadiabatic quantum dynamics,"  Light absorption of myoglobin triggers diatomic ligand photolysis and spin
+crossover transition of iron(II) that initiate protein conformational change.
+The photolysis and spin crossover reactions happen concurrently on a
+femtosecond timescale. The microscopic origin of these reactions remains
+controversial. Here, we apply quantum wavepacket dynamics to elucidate the
+ultrafast photochemical mechanism for a heme--carbon monoxide (heme--CO)
+complex. We observe coherent oscillations of the Fe-CO bond distance with a
+period of 42 fs and an amplitude of $\sim$1 \AA{}. These nuclear motions induce
+pronounced geometric reorganization, which makes the CO dissociation
+irreversible. The reaction is initially dominated by symmetry breaking
+vibrations inducing an electron transfer from porphyrin to iron. Subsequently,
+the wavepacket relaxes to the triplet manifold in $\sim$75 fs and to the
+quintet manifold in $\sim$430 fs. Our results highlight the central role of
+nuclear vibrations at the origin of the ultrafast photodynamics of
+organometallic complexes.
+"
+21855,Single-cluster PHD filter methods for joint multi-object filtering and parameter estimation,"  Many multi-object estimation problems require additional estimation of model
+or sensor parameters that are either common to all objects or related to
+unknown characterisation of one or more sensors. Important examples of these
+include registration of multiple sensors, estimating clutter profiles, and
+robot localisation. Often these parameters are estimated separately to the
+multi-object estimation process, which can lead to systematic errors or
+overconfidence in the estimates. These parameters can be estimated jointly with
+the multi-object process based only on the sensor data using a single-cluster
+point process model. This paper presents novel results for joint parameter
+estimation and multi-object filtering based on a single-cluster second-order
+Probability Hypothesis Density (PHD) and Cardinalised PHD (CPHD) filter.
+Experiments provide a comparison between the discussed approaches using
+different likelihood functions.
+"
+21856,Source Code Properties of Defective Infrastructure as Code Scripts,"  Context: In continuous deployment, software and services are rapidly deployed
+to end-users using an automated deployment pipeline. Defects in infrastructure
+as code (IaC) scripts can hinder the reliability of the automated deployment
+pipeline. We hypothesize that certain properties of IaC source code such as
+lines of code and hard-coded strings used as configuration values, show
+correlation with defective IaC scripts. Objective: The objective of this paper
+is to help practitioners in increasing the quality of infrastructure as code
+(IaC) scripts through an empirical study that identifies source code properties
+of defective IaC scripts. Methodology: We apply qualitative analysis on
+defect-related commits mined from open source software repositories to identify
+source code properties that correlate with defective IaC scripts. Next, we
+survey practitioners to assess the practitioner's agreement level with the
+identified properties. We also construct defect prediction models using the
+identified properties for 2,439 scripts collected from four datasets. Results:
+We identify 10 source code properties that correlate with defective IaC
+scripts. Of the identified 10 properties we observe lines of code and
+hard-coded string to show the strongest correlation with defective IaC scripts.
+Hard-coded string is the property of specifying configuration value as
+hard-coded string. According to our survey analysis, majority of the
+practitioners show agreement for two properties: include, the property of
+executing external modules or scripts, and hard-coded string. Using the
+identified properties, our constructed defect prediction models show a
+precision of 0.70~0.78, and a recall of 0.54~0.67.
+"
+21857,First results from the IllustrisTNG simulations: matter and galaxy clustering,"  Hydrodynamical simulations of galaxy formation have now reached sufficient
+volume to make precision predictions for clustering on cosmologically relevant
+scales. Here we use our new IllustrisTNG simulations to study the non-linear
+correlation functions and power spectra of baryons, dark matter, galaxies and
+haloes over an exceptionally large range of scales. We find that baryonic
+effects increase the clustering of dark matter on small scales and damp the
+total matter power spectrum on scales up to k ~ 10 h/Mpc by 20%. The non-linear
+two-point correlation function of the stellar mass is close to a power-law over
+a wide range of scales and approximately invariant in time from very high
+redshift to the present. The two-point correlation function of the simulated
+galaxies agrees well with SDSS at its mean redshift z ~ 0.1, both as a function
+of stellar mass and when split according to galaxy colour, apart from a mild
+excess in the clustering of red galaxies in the stellar mass range 10^9-10^10
+Msun/h^2. Given this agreement, the TNG simulations can make valuable
+theoretical predictions for the clustering bias of different galaxy samples. We
+find that the clustering length of the galaxy auto-correlation function depends
+strongly on stellar mass and redshift. Its power-law slope gamma is nearly
+invariant with stellar mass, but declines from gamma ~ 1.8 at redshift z=0 to
+gamma ~ 1.6 at redshift z ~ 1, beyond which the slope steepens again. We detect
+significant scale-dependencies in the bias of different observational tracers
+of large-scale structure, extending well into the range of the baryonic
+acoustic oscillations and causing nominal (yet fortunately correctable) shifts
+of the acoustic peaks of around ~5%.
+"
+21858,Efficient Computation of the 8-point DCT via Summation by Parts,"  This paper introduces a new fast algorithm for the 8-point discrete cosine
+transform (DCT) based on the summation-by-parts formula. The proposed method
+converts the DCT matrix into an alternative transformation matrix that can be
+decomposed into sparse matrices of low multiplicative complexity. The method is
+capable of scaled and exact DCT computation and its associated fast algorithm
+achieves the theoretical minimal multiplicative complexity for the 8-point DCT.
+Depending on the nature of the input signal simplifications can be introduced
+and the overall complexity of the proposed algorithm can be further reduced.
+Several types of input signal are analyzed: arbitrary, null mean, accumulated,
+and null mean/accumulated signal. The proposed tool has potential application
+in harmonic detection, image enhancement, and feature extraction, where input
+signal DC level is discarded and/or the signal is required to be integrated.
+"
+21859,Further insights into the damping-induced self-recovery phenomenon,"  In a series of papers, D. E. Chang, et al., proved and experimentally
+demonstrated a phenomenon they termed ""damping-induced self-recovery"". However,
+these papers left a few questions concerning the observed phenomenon unanswered
+- in particular, the effect of the intervening lubricant-fluid and its
+viscosity on the recovery, the abrupt change in behaviour with the introduction
+of damping, a description of the energy dynamics, and the curious occurrence of
+overshoots and oscillations and its dependence on the control law. In this
+paper we attempt to answer these questions through theory. In particular, we
+derive an expression for the infinite-dimensional fluid-stool-wheel system,
+that approximates its dynamics to that of the better understood
+finite-dimensional case.
+"
+21860,Duality of channels and codes,"  For any given channel $W$ with classical inputs and possibly quantum outputs,
+a dual classical-input channel $W^\perp$ can be defined by embedding the
+original into a channel $\mathcal N$ with quantum inputs and outputs. Here we
+give new uncertainty relations for a general class of entropies that lead to
+very close relationships between the original channel and its dual. Moreover,
+we show that channel duality can be combined with duality of linear codes,
+whereupon the uncertainty relations imply that the performance of a given code
+over a given channel is entirely characterized by the performance of the dual
+code on the dual channel. This has several applications. In the context of
+polar codes, it implies that the rates of polarization to ideal and useless
+channels must be identical. Duality also relates the tasks of channel coding
+and privacy amplification, implying that the finite blocklength performance of
+extractors and codes is precisely linked, and that optimal rate extractors can
+be transformed into capacity-achieving codes, and vice versa. Finally, duality
+also extends to the EXIT function of any channel and code. Here it implies that
+for any channel family, if the EXIT function for a fixed code has a sharp
+transition, then it must be such that the rate of the code equals the capacity
+at the transition. This may give a different route to proving a code family
+achieves capacity by establishing sharp EXIT function transitions.
+"
+21861,BIOS ORAM: Improved Privacy-Preserving Data Access for Parameterized Outsourced Storage,"  Algorithms for oblivious random access machine (ORAM) simulation allow a
+client, Alice, to obfuscate a pattern of data accesses with a server, Bob, who
+is maintaining Alice's outsourced data while trying to learn information about
+her data. We present a novel ORAM scheme that improves the asymptotic I/O
+overhead of previous schemes for a wide range of size parameters for
+client-side private memory and message blocks, from logarithmic to polynomial.
+Our method achieves statistical security for hiding Alice's access pattern and,
+with high probability, achieves an I/O overhead that ranges from $O(1)$ to
+$O(\log^2 n/(\log\log n)^2)$, depending on these size parameters, where $n$ is
+the size of Alice's outsourced memory. Our scheme, which we call BIOS ORAM,
+combines multiple uses of B-trees with a reduction of ORAM simulation to
+isogrammic access sequences.
+"
+21862,SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties,"  Chemical databases store information in text representations, and the SMILES
+format is a universal standard used in many cheminformatics software. Encoded
+in each SMILES string is structural information that can be used to predict
+complex chemical properties. In this work, we develop SMILES2vec, a deep RNN
+that automatically learns features from SMILES to predict chemical properties,
+without the need for additional explicit feature engineering. Using Bayesian
+optimization methods to tune the network architecture, we show that an
+optimized SMILES2vec model can serve as a general-purpose neural network for
+predicting distinct chemical properties including toxicity, activity,
+solubility and solvation energy, while also outperforming contemporary MLP
+neural networks that uses engineered features. Furthermore, we demonstrate
+proof-of-concept of interpretability by developing an explanation mask that
+localizes on the most important characters used in making a prediction. When
+tested on the solubility dataset, it identified specific parts of a chemical
+that is consistent with established first-principles knowledge with an accuracy
+of 88%. Our work demonstrates that neural networks can learn technically
+accurate chemical concept and provide state-of-the-art accuracy, making
+interpretable deep neural networks a useful tool of relevance to the chemical
+industry.
+"
+21863,Gauss factorials of polynomials over finite fields,"  In this paper we initiate a study on Gauss factorials of polynomials over
+finite fields, which are the analogues of Gauss factorials of positive
+integers.
+"
+21864,"Topological Phases in Graphene Nanoribbons: Junction States, Spin Centers and Quantum Spin Chains","  Knowledge of the topology of the electronic ground state of materials has led
+to deep insights to novel phenomena such as the integer quantum Hall effect and
+fermion-number fractionalization, as well as other properties of matter.
+Joining two insulators of different topological classes produces fascinating
+boundary states in the band gap. Another exciting recent development is the
+bottom-up synthesis (from molecular precursors) of graphene nanoribbons (GNRs)
+with atomic precision control of their edge and width. Here we connect these
+two fields, and show for the first time that semiconducting GNRs of different
+width, edge, and end termination belong to different topological classes. The
+topology of GNRs is protected by spatial symmetries and dictated by the
+terminating unit cell. We have derived explicit formula for their topological
+invariants, and show that localized junction states developed between two GNRs
+of distinct topology may be tuned by lateral junction geometry. The topology of
+a GNR can be further modified by dopants, such as a periodic array of boron
+atoms. In a superlattice consisted of segments of doped and pristine GNRs, the
+junction states are stable spin centers, forming a Heisenberg antiferromagnetic
+spin 1/2 chain with tunable exchange interaction. The discoveries here are not
+only of scientific interest for studies of quasi one-dimensional systems, but
+also open a new path for design principles of future GNR-based devices through
+their topological characters.
+"
+21865,Antiferromagnetic magnons from fractionalized excitations,"  We develop an approach to describe antiferromagnetic magnons on a bipartite
+lattice supporting the Néel state using fractionalized degrees of freedom
+typically inherent to quantum spin liquids. In particular we consider a
+long-range magnetically ordered state of interacting two-dimensional quantum
+spin$-1/2$ models using the Chern-Simons (CS) fermion representation of
+interacting spins. The interaction leads to Cooper instability and pairing of
+CS fermions, and to CS superconductivity which spontaneously violates the
+continuous $\mathrm{U}(1)$ symmetry generating a linearly-dispersing gapless
+Nambu-Goldstone mode due to phase fluctuations. We evaluate this mode and show
+that it is in high-precision agreement with magnons of the corresponding
+Néel antiferromagnet irrespective to the lattice symmetry. Using the
+fermion formulation of a system with competing interactions, we show that the
+frustration gives raise to nontrivial long-range four, six, and higher-leg
+interaction vertices mediated by the CS gauge field, which are responsible for
+restoring of the continuous symmetry at sufficiently strong frustration. We
+identify these new interaction vertices and discuss their implications to
+unconventional phase transitions. We also apply the proposed theory to a model
+of anyons that can be tuned continuously from fermions to bosons.
+"
+21866,"Secular chaotic dynamics in hierarchical quadruple systems, with applications to hot Jupiters in stellar binaries and triples","  Hierarchical quadruple systems arise naturally in stellar binaries and
+triples that harbour planets. Examples are hot Jupiters (HJs) in stellar triple
+systems, and planetary companions to HJs in stellar binaries. The secular
+dynamical evolution of these systems is generally complex, with secular chaotic
+motion possible in certain parameter regimes. The latter can lead to extremely
+high eccentricities and, therefore, strong interactions such as efficient tidal
+evolution. These interactions are believed to play an important role in the
+formation of HJs through high-eccentricity migration. Nevertheless, a deeper
+understanding of the secular dynamics of these systems is still lacking. Here
+we study in detail the secular dynamics of a special case of hierarchical
+quadruple systems in either the `2+2' or `3+1' configurations. We show how the
+equations of motion can be cast in a form representing a perturbed hierarchical
+three-body system, in which the outer orbital angular momentum vector is
+precessing steadily around a fixed axis. In this case, we show that
+eccentricity excitation can be significantly enhanced when the precession
+period is comparable to the Lidov-Kozai oscillation time-scale of the inner
+orbit. This arises from an induced large mutual inclination between the inner
+and outer orbits driven by the precession of the outer orbit, even if the
+initial mutual inclination is small. We present a simplified semi-analytic
+model that describes the latter phenomenon.
+"
+21867,Cobordisms of graphs. A sliceness criterion for stably odd knots and related results on cobordisms,"  In [D.A. Fedoseev, V.O. Manturov, A sliceness criterion for odd free
+knots,arXiv:1707.04923], the authors proved a sliceness criterion for odd free
+knots: free knots with odd chords. In the present paper we give a similar
+criterion for stably odd free knots. Some additional results on knot sliceness
+and cobordism are given.
+"
+21868,Efficient Information Flow Maximization in Probabilistic Graphs,"  Reliable propagation of information through large networks, e.g.,
+communication networks, social networks or sensor networks is very important in
+many applications concerning marketing, social networks, and wireless sensor
+networks. However, social ties of friendship may be obsolete, and communication
+links may fail, inducing the notion of uncertainty in such networks. In this
+paper, we address the problem of optimizing information propagation in
+uncertain networks given a constrained budget of edges. We show that this
+problem requires to solve two NP-hard subproblems: the computation of expected
+information flow, and the optimal choice of edges. To compute the expected
+information flow to a source vertex, we propose the F-tree as a specialized
+data structure, that identifies independent components of the graph for which
+the information flow can either be computed analytically and efficiently, or
+for which traditional Monte-Carlo sampling can be applied independently of the
+remaining network. For the problem of finding the optimal edges, we propose a
+series of heuristics that exploit properties of this data structure. Our
+evaluation shows that these heuristics lead to high quality solutions, thus
+yielding high information flow, while maintaining low running time.
+"
+21869,Analysis Of Congestion Control In Data Channels With Frequent Frame Loss,"  Development of optimal control procedures for congested networks is a key
+factor in maintaining efficient network utilization. The absence of congestion
+control mechanism or its failure can lead to the lack of availability for
+certain network segments, and in severe cases -- for the entire network. The
+paper presents an analytical model describing the operation of the TCP Reno
+congestion control algorithm in terms of differential calculus and queuing
+systems. The purpose of this research is to explore the possibilities and ways
+of increasing the virtual channel capacity utilization efficiency in a lossy
+environment.
+"
+21870,Epidemic spreading with awareness and different time scales in multiplex networks,"  One of the major issues in the theoretical modelling of epidemic spreading is
+the development of methods to control the transmission of an infectious agent.
+Human behaviour plays a fundamental role in the spreading dynamics and can be
+used to stop a disease from spreading or to reduce its burden, as individuals
+aware of the presence of a disease can take measures to reduce their exposure
+to contagion. In this paper, we propose a mathematical model for the spread of
+diseases with awareness in complex networks. Unlike previous models, the
+information is propagated following a generalized Maki-Thompson rumour model.
+Flexibility on the time scale between information and disease spreading is also
+included. We verify that the velocity characterizing the diffusion of
+information awareness greatly influences the disease prevalence. We also show
+that a reduction in the fraction of unaware individuals does not always imply a
+decrease of the prevalence, as the relative time scale between disease and
+awareness spreadings plays a crucial role in the systems' dynamics. Our results
+provide a new view on how information influence disease spreading and can be
+used for the development of more efficient methods for disease control.
+"
+21871,Estimation Efficiency Under Privacy Constraints,"  We investigate the problem of estimating a random variable $Y\in \mathcal{Y}$
+under a privacy constraint dictated by another random variable $X\in
+\mathcal{X}$, where estimation efficiency and privacy are assessed in terms of
+two different loss functions. In the discrete case, we use the Hamming loss
+function and express the corresponding utility-privacy tradeoff in terms of the
+privacy-constrained guessing probability $h(P_{XY}, \epsilon)$, the maximum
+probability $\mathsf{P}_\mathsf{c}(Y|Z)$ of correctly guessing $Y$ given an
+auxiliary random variable $Z\in \mathcal{Z}$, where the maximization is taken
+over all $P_{Z|Y}$ ensuring that $\mathsf{P}_\mathsf{c}(X|Z)\leq \epsilon$ for
+a given privacy threshold $\epsilon \geq 0$. We prove that $h(P_{XY}, \cdot)$
+is concave and piecewise linear, which allows us to derive its expression in
+closed form for any $\epsilon$ when $X$ and $Y$ are binary. In the non-binary
+case, we derive $h(P_{XY}, \epsilon)$ in the high utility regime (i.e., for
+sufficiently large values of $\epsilon$) under the assumption that $Z$ takes
+values in $\mathcal{Y}$. We also analyze the privacy-constrained guessing
+probability for two binary vector scenarios. When $X$ and $Y$ are continuous
+random variables, we use the squared-error loss function and express the
+corresponding utility-privacy tradeoff in terms of $\mathsf{sENSR}(P_{XY},
+\epsilon)$, which is the smallest normalized minimum mean squared-error (mmse)
+incurred in estimating $Y$ from its Gaussian perturbation $Z$, such that the
+mmse of $f(X)$ given $Z$ is within $\epsilon$ of the variance of $f(X)$ for any
+non-constant real-valued function $f$. We derive tight upper and lower bounds
+for $\mathsf{sENSR}$ when $Y$ is Gaussian. We also obtain a tight lower bound
+for $\mathsf{sENSR}(P_{XY}, \epsilon)$ for general absolutely continuous random
+variables when $\epsilon$ is sufficiently small.
+"
+21872,The DTM-signature for a geometric comparison of metric-measure spaces from samples,"  In this paper, we introduce the notion of DTM-signature, a measure on R +
+that can be associated to any metric-measure space. This signature is based on
+the distance to a measure (DTM) introduced by Chazal, Cohen-Steiner and
+Mérigot. It leads to a pseudo-metric between metric-measure spaces,
+upper-bounded by the Gromov-Wasserstein distance. Under some geometric
+assumptions, we derive lower bounds for this pseudo-metric. Given two
+N-samples, we also build an asymptotic statistical test based on the
+DTM-signature, to reject the hypothesis of equality of the two underlying
+metric-measure spaces, up to a measure-preserving isometry. We give strong
+theoretical justifications for this test and propose an algorithm for its
+implementation.
+"
+21873,The Schwarz Lemma at the Boundary of the Symmetrized Bidisc,"  The symmetrized bidisc ${\textbf{G}}_{2}$ is defined by
+$${\textbf{G}}_{2}:=\{(z_1+z_2,z_1z_2)\in\mathbb{C}^2: |z_1|<1,|z_2|<1,\;
+z_1,z_2\in\mathbb{C}\}.$$ It is a bounded inhomogeneous pseudoconvex domain
+without $\mathcal{C}^1$ boundary, and especially the symmetrized bidisc hasn't
+any strongly pseudoconvex boundary point and the boundary behavior of both
+Carathéodory and Kobayashi metrics over the symmetrized bidisc is hard to
+describe precisely. In this paper, we study the boundary Schwarz lemma for
+holomorphic self-mappings of the symmetrized bidisc ${\textbf{G}}_2$, and our
+boundary Schwarz lemma in the paper differs greatly from the earlier related
+results.
+"
+21874,"Group Recommendations: Axioms, Impossibilities, and Random Walks","  We introduce an axiomatic approach to group recommendations, in line of
+previous work on the axiomatic treatment of trust-based recommendation systems,
+ranking systems, and other foundational work on the axiomatic approach to
+internet mechanisms in social choice settings. In group recommendations we wish
+to recommend to a group of agents, consisting of both opinionated and undecided
+members, a joint choice that would be acceptable to them. Such a system has
+many applications, such as choosing a movie or a restaurant to go to with a
+group of friends, recommending games for online game players, & other communal
+activities.
+Our method utilizes a given social graph to extract information on the
+undecided, relying on the agents influencing them. We first show that a set of
+fairly natural desired requirements (a.k.a axioms) leads to an impossibility,
+rendering mutual satisfaction of them unreachable. However, we also show a
+modified set of axioms that fully axiomatize a group variant of the random-walk
+recommendation system, expanding a previous result from the individual
+recommendation case.
+"
+21875,The conjugate gradient algorithm on well-conditioned Wishart matrices is almost deteriministic,"  We prove that the number of iterations required to solve a random positive
+definite linear system with the conjugate gradient algorithm is almost
+deterministic for large matrices. We treat the case of Wishart matrices.
+Precisely, we prove that for most choices of error tolerance, as the matrix
+increases in size, the probability that the iteration count deviates from an
+explicit deterministic value tends to zero. In addition, for a fixed iteration,
+we show that the norm of the error vector and the norm of the residual converge
+exponentially fast in probability, converge in mean, converge almost surely
+and, after centering and rescaling, converge in distribution.
+"
+21876,Feature Extraction for Machine Learning Based Crackle Detection in Lung Sounds from a Health Survey,"  In recent years, many innovative solutions for recording and viewing sounds
+from a stethoscope have become available. However, to fully utilize such
+devices, there is a need for an automated approach for detecting abnormal lung
+sounds, which is better than the existing methods that typically have been
+developed and evaluated using a small and non-diverse dataset.
+We propose a machine learning based approach for detecting crackles in lung
+sounds recorded using a stethoscope in a large health survey. Our method is
+trained and evaluated using 209 files with crackles classified by expert
+listeners. Our analysis pipeline is based on features extracted from small
+windows in audio files. We evaluated several feature extraction methods and
+classifiers. We evaluated the pipeline using a training set of 175 crackle
+windows and 208 normal windows. We did 100 cycles of cross validation where we
+shuffled training sets between cycles. For all the division between training
+and evaluation was 70%-30%.
+We found and evaluated a 5-dimenstional vector with four features from the
+time domain and one from the spectrum domain. We evaluated several classifiers
+and found SVM with a Radial Basis Function Kernel to perform best. Our approach
+had a precision of 86% and recall of 84% for classifying a crackle in a window,
+which is more accurate than found in studies of health personnel. The
+low-dimensional feature vector makes the SVM very fast. The model can be
+trained on a regular computer in 1.44 seconds, and 319 crackles can be
+classified in 1.08 seconds.
+Our approach detects and visualizes individual crackles in recorded audio
+files. It is accurate, fast, and has low resource requirements. It can be used
+to train health personnel or as part of a smartphone application for Bluetooth
+stethoscopes.
+"
+21877,Basic protocols in quantum reinforcement learning with superconducting circuits,"  Superconducting circuit technologies have recently achieved quantum protocols
+involving closed feedback loops. Quantum artificial intelligence and quantum
+machine learning are emerging fields inside quantum technologies which may
+enable quantum devices to acquire information from the outer world and improve
+themselves via a learning process. Here we propose the implementation of basic
+protocols in quantum reinforcement learning, with superconducting circuits
+employing feedback-loop control. We introduce diverse scenarios for
+proof-of-principle experiments with state-of-the-art superconducting circuit
+technologies and analyze their feasibility in presence of imperfections. The
+field of quantum artificial intelligence implemented with superconducting
+circuits paves the way for enhanced quantum control and quantum computation
+protocols.
+"
+21878,Feedback Techniques in Computer-Based Simulation Training: A Survey,"  Computer-based simulation training (CBST) is gaining popularity in a vast
+range of applications such as surgery, rehabilitation therapy, military
+applications, and driver/pilot training, as it offers a low-cost,
+easily-accessible and effective training environment. Typically, CBST systems
+comprise of two essential components: 1) a simulation environment that provides
+an immersive and interactive learning experience, and 2) a feedback
+intervention system that supports knowledge/skill acquisition and decision
+making. The simulation environment is created using technologies such as
+virtual or augmented reality, and this is an area which has gained much
+interest in recent years. The provision of automated feedback in CBST however,
+has not been investigated as much, and thus, is the focus of this paper.
+Feedback is an essential component in learning, and should be provided to the
+trainee during the training process in order to improve skills, to correct
+mistakes, and most importantly, to inspire reasoning and critical thinking. In
+CBST, feedback should be provided in a useful and timely manner, ideally in a
+way that mimics the advice of an experienced tutor. Here, we explore the
+provision of feedback in CBST from three perspectives: 1) types of feedback to
+be provided, 2) presentation modalities of feedback, and 3) methods for
+feedback extraction/learning. This review is aimed at providing insight into
+how feedback is extracted, organized, and delivered in current applications, to
+be used as a guide to the development of future feedback intervention systems
+in CBST applications.
+"
+21879,Robot_gym: accelerated robot training through simulation in the cloud with ROS and Gazebo,"  Rather than programming, training allows robots to achieve behaviors that
+generalize better and are capable to respond to real-world needs. However, such
+training requires a big amount of experimentation which is not always feasible
+for a physical robot. In this work, we present robot_gym, a framework to
+accelerate robot training through simulation in the cloud that makes use of
+roboticists' tools, simplifying the development and deployment processes on
+real robots. We unveil that, for simple tasks, simple 3DoF robots require more
+than 140 attempts to learn. For more complex, 6DoF robots, the number of
+attempts increases to more than 900 for the same task. We demonstrate that our
+framework, for simple tasks, accelerates the robot training time by more than
+33% while maintaining similar levels of accuracy and repeatability.
+"
+21880,Local Modules in Imperative Languages,"  We propose a notion of local modules for imperative langauges. To be
+specific, we introduce a new implication statement of the form $D \supset G$
+where $D$ is a module (i.e., a set of procedure declarations) and $G$ is a
+statement. This statement tells the machine to add $D$ to the program in the
+course of executing $G$. Thus, $D$ acts as a local module and will be discarded
+after executing $G$. It therefore provides efficient module management. We
+illustrate our idea via C^{mod}, an extension of the core C with the new
+statement. In addition, we describe a new constructive module language to
+improve code reuse. Finally, we describe a scheme which considerably improves
+the heap management in traditional languages.
+"
+21881,Light-induced switching to a Metastable Phase in a Layered Superconductor La$_{2-x}$Sr$_x$CuO$_4$ (x=0.15),"  We investigated transient optical responses in an optimally-doped high-Tc
+superconductor La2-xSrxCuO4 (x=0.15) by using 800-nm optical pump and terahertz
+probe spectroscopy. With increasing the photoexcitation intensities, the
+Josephson plasma resonance shows a gradual redshift, indicating the suppression
+of superconductivity by the photoexcitation. With further increasing the
+photoexcitation intensities, a new longitudinal mode in the loss function
+spectrum appears and grows from the high energy side, accompanied by a new
+transverse mode as manifested in the conductivity spectrum. The observed
+spectra are described by the multilayer model with alternating interlayer
+Josephson couplings. The new longitudinal and transverse modes sustain much
+longer than several hundred picoseconds after the photoexcitation, indicating
+that the new metastable phase with possessing alternating interlayer Josephson
+couplings is induced by the strong photoexcitation.
+"
+21882,Fluid Passwords - Mitigating the effects of password leaks at the user level,"  Password leaks have been frequently reported in recent years, with big
+companies like Sony, Amazon, LinkedIn, and Walmart falling victim to breaches
+involving the release of customer information. Even though passwords are
+usually stored in a salted hash, attackers still guess passwords because of
+insecure password choices and password reuse. However, the adverse effects of a
+password breach can be mitigated by changing users' passwords. We introduce a
+simple yet powerful algorithm to reset user account passwords automatically,
+while still allowing users to authenticate without any additional effort on
+their part. We implemented our algorithm as a Firefox add-on that automatically
+resets a user's password when they log in to their account, and stores the new
+password in the built-in Firefox password manager.
+"
+21883,Developer Prioritization in Bug Repositories,"  Developers build all the software artifacts in development. Existing work has
+studied the social behavior in software repositories. In one of the most
+important software repositories, a bug repository, developers create and update
+bug reports to support software development and maintenance. However, no prior
+work has considered the priorities of developers in bug repositories. In this
+paper, we address the problem of the developer prioritization, which aims to
+rank the contributions of developers. We mainly explore two aspects, namely
+modeling the developer prioritization in a bug repository and assisting
+predictive tasks with our model. First, we model how to assign the priorities
+of developers based on a social network technique. Three problems are
+investigated, including the developer rankings in products, the evolution over
+time, and the tolerance of noisy comments. Second, we consider leveraging the
+developer prioritization to improve three predicted tasks in bug repositories,
+i.e., bug triage, severity identification, and reopened bug prediction. We
+empirically investigate the performance of our model and its applications in
+bug repositories of Eclipse and Mozilla. The results indicate that the
+developer prioritization can provide the knowledge of developer priorities to
+assist software tasks, especially the task of bug triage.
+"
+21884,Efficient discontinuous Galerkin implementations and preconditioners for implicit unsteady compressible flow simulations,"  This work presents and compares efficient implementations of high-order
+discontinuous Galerkin methods: a modal matrix-free discontinuous Galerkin (DG)
+method, a hybridizable discontinuous Galerkin (HDG) method, and a primal
+formulation of HDG, applied to the implicit solution of unsteady compressible
+flows. The matrix-free implementation allows for a reduction of the memory
+footprint of the solver when dealing with implicit time-accurate
+discretizations. HDG reduces the number of globally-coupled degrees of freedom
+relative to DG, at high order, by statically condensing element-interior
+degrees of freedom from the system in favor of face unknowns. The primal
+formulation further reduces the element-interior degrees of freedom by
+eliminating the gradient as a separate unknown. This paper introduces a
+$p$-multigrid preconditioner implementation for these discretizations and
+presents results for various flow problems. Benefits of the $p$-multigrid
+strategy relative to simpler, less expensive, preconditioners are observed for
+stiff systems, such as those arising from low-Mach number flows at high-order
+approximation. The $p$-multigrid preconditioner also shows excellent
+scalability for parallel computations. Additional savings in both speed and
+memory occur with a matrix-free/reduced version of the preconditioner.
+"
+21885,Coarse cohomology theories,"  We propose the notion of a coarse cohomology theory and study the examples of
+coarse ordinary cohomology, coarse stable cohomotopy and coarse cohomology
+theories obtained by dualizing coarse homology theories. Our investigations of
+coarse stable cohomotopy lead to a solution of J. R. Klein's conjecture that
+the dualizing spectrum of a group is a coarse invariant. We further investigate
+coarse cohomological $K$-theory functors and explain why (an adaption of) the
+functor of Emerson--Meyer does not seem to fit into our setting.
+"
+21886,A Survey on the Security of Pervasive Online Social Networks (POSNs),"  Pervasive Online Social Networks (POSNs) are the extensions of Online Social
+Networks (OSNs) which facilitate connectivity irrespective of the domain and
+properties of users. POSNs have been accumulated with the convergence of a
+plethora of social networking platforms with a motivation of bridging their
+gap. Over the last decade, OSNs have visually perceived an altogether
+tremendous amount of advancement in terms of the number of users as well as
+technology enablers. A single OSN is the property of an organization, which
+ascertains smooth functioning of its accommodations for providing a quality
+experience to their users. However, with POSNs, multiple OSNs have coalesced
+through communities, circles, or only properties, which make
+service-provisioning tedious and arduous to sustain. Especially, challenges
+become rigorous when the focus is on the security perspective of cross-platform
+OSNs, which are an integral part of POSNs. Thus, it is of utmost paramountcy to
+highlight such a requirement and understand the current situation while
+discussing the available state-of-the-art. With the modernization of OSNs and
+convergence towards POSNs, it is compulsory to understand the impact and reach
+of current solutions for enhancing the security of users as well as associated
+services. This survey understands this requisite and fixates on different sets
+of studies presented over the last few years and surveys them for their
+applicability to POSNs...
+"
+21887,Higher-Order Topological Insulators,"  Three-dimensional topological (crystalline) insulators are materials with an
+insulating bulk, but conducting surface states which are topologically
+protected by time-reversal (or spatial) symmetries. Here, we extend the notion
+of three-dimensional topological insulators to systems that host no gapless
+surface states, but exhibit topologically protected gapless hinge states. Their
+topological character is protected by spatio-temporal symmetries, of which we
+present two cases: (1) Chiral higher-order topological insulators protected by
+the combination of time-reversal and a four-fold rotation symmetry. Their hinge
+states are chiral modes and the bulk topology is $\mathbb{Z}_2$-classified. (2)
+Helical higher-order topological insulators protected by time-reversal and
+mirror symmetries. Their hinge states come in Kramers pairs and the bulk
+topology is $\mathbb{Z}$-classified. We provide the topological invariants for
+both cases. Furthermore we show that SnTe as well as surface-modified
+Bi$_2$TeI, BiSe, and BiTe are helical higher-order topological insulators and
+propose a realistic experimental setup to detect the hinge states.
+"
+21888,Uncertainty Quantification for Maxwell's Eigenproblem using Isogeometric Analysis,"  The electromagnetic field distribution as well as the resonating frequency of
+various modes in superconducting cavities used in particle accelerators for
+example are sensitive to small geometry deformations. The occurring variations
+are motivated by measurements of an available set of resonators from which we
+propose to extract a small number of relevant and independent deformations by
+using a truncated Karhunen-Loève expansion. The random deformations are used
+in an expressive uncertainty quantification workflow to determine the
+sensitivity of the eigenmodes. For the propagation of uncertainty, a stochastic
+collocation method based on sparse grids is employed. It requires the repeated
+solution of Maxwell's eigenvalue problem at predefined collocation points,
+i.e., for cavities with perturbed geometry. The main contribution of the paper
+is ensuring the consistency of the solution, i.e., matching the eigenpairs,
+among the various eigenvalue problems at the stochastic collocation points. To
+this end, a classical eigenvalue tracking technique is proposed that is based
+on homotopies between collocation points and a Newton-based eigenvalue solver.
+The approach can be efficiently parallelized while tracking the eigenpairs. In
+this paper, we propose the application of isogeometric analysis since it allows
+for the exact description of the geometrical domains with respect to common
+computer-aided design kernels, for a straightforward and convenient way of
+handling geometrical variations and smooth solutions.
+"
+21889,Mortality data reliability in an internal model,"  In this paper, we discuss the impact of some mortality data anomalies on an
+internal model capturing longevity risk in the Solvency 2 framework. In
+particular, we are concerned with abnormal cohort effects such as those for
+generations 1919 and 1920, for which the period tables provided by the Human
+Mortality Database show particularly low and high mortality rates respectively.
+To provide corrected tables for the three countries of interest here (France,
+Italy and West Germany), we use the approach developed by Boumezoued (2016) for
+countries for which the method applies (France and Italy), and provide an
+extension of the method for West Germany as monthly fertility histories are not
+sufficient to cover the generations of interest. These mortality tables are
+crucial inputs to stochastic mortality models forecasting future scenarios,
+from which the extreme 0,5% longevity improvement can be extracted, allowing
+for the calculation of the Solvency Capital Requirement (SCR). More precisely,
+to assess the impact of such anomalies in the Solvency II framework, we use a
+simplified internal model based on three usual stochastic models to project
+mortality rates in the future combined with a closure table methodology for
+older ages. Correcting this bias obviously improves the data quality of the
+mortality inputs, which is of paramount importance today, and slightly
+decreases the capital requirement. Overall, the longevity risk assessment
+remains stable, as well as the selection of the stochastic mortality model. As
+a collateral gain of this data quality improvement, the more regular estimated
+parameters allow for new insights and a refined assessment regarding longevity
+risk.
+"
+21890,Modularisation of Sequent Calculi for Normal and Non-normal Modalities,"  In this work we explore the connections between (linear) nested sequent
+calculi and ordinary sequent calculi for normal and non-normal modal logics. By
+proposing local versions to ordinary sequent rules we obtain linear nested
+sequent calculi for a number of logics, including to our knowledge the first
+nested sequent calculi for a large class of simply dependent multimodal logics,
+and for many standard non-normal modal logics. The resulting systems are
+modular and have separate left and right introduction rules for the modalities,
+which makes them amenable to specification as bipole clauses. While this
+granulation of the sequent rules introduces more choices for proof search, we
+show how linear nested sequent calculi can be restricted to blocked
+derivations, which directly correspond to ordinary sequent derivations.
+"
+21891,A Probabilistic View of Neighborhood-based Recommendation Methods,"  Probabilistic graphic model is an elegant framework to compactly present
+complex real-world observations by modeling uncertainty and logical flow
+(conditionally independent factors). In this paper, we present a probabilistic
+framework of neighborhood-based recommendation methods (PNBM) in which
+similarity is regarded as an unobserved factor. Thus, PNBM leads the estimation
+of user preference to maximizing a posterior over similarity. We further
+introduce a novel multi-layer similarity descriptor which models and learns the
+joint influence of various features under PNBM, and name the new framework
+MPNBM. Empirical results on real-world datasets show that MPNBM allows very
+accurate estimation of user preferences.
+"
+21892,Magnetic Reconnection in Turbulent Diluted Plasmas,"  We study magnetic reconnection events in a turbulent plasma within the
+two-fluid theory. By identifying the diffusive regions, we measure the
+reconnection rates as function of the conductivity and current sheet thickness.
+We have found that the reconnection rate scales as the squared of the inverse
+of the current sheet's thickness and is independent of the aspect ratio of the
+diffusive region, in contrast to other analytical, e.g. the Sweet-Parker and
+Petscheck, and numerical models. Furthermore, while the reconnection rates are
+also proportional to the square inverse of the conductivity, the aspect ratios
+of the diffusive regions, which exhibit values in the range of $0.1-0.9$, are
+not correlated to the latter. Our findings suggest a new expression for the
+magnetic reconnection rate, which, after experimental verification, can provide
+a further understanding of the magnetic reconnection process.
+"
+21893,Intermittency of quantum turbulence with superfluid fractions from 0% to 96%,"  The intermittency of turbulent superfluid helium is explored systematically
+in a steady wake flow from 1.28 K up to T>2.18K using a local anemometer. This
+temperature range spans relative densities of superfluid from 96% down to 0%,
+allowing to test numerical predictions of enhancement or depletion of
+intermittency at intermediate superfluid fractions. Using the so-called
+extended self-similarity method, scaling exponents of structure functions have
+been calculated. No evidence of temperature dependence is found on these
+scaling exponents in the upper part of the inertial cascade, where turbulence
+is well developed and fully resolved by the probe. This result supports the
+picture of a profound analogy between classical and quantum turbulence in their
+inertial range, including the violation of self-similarities associated with
+inertial-range intermittency.
+"
+21894,Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees,"  Deep Reinforcement Learning (DRL) has achieved impressive success in many
+applications. A key component of many DRL models is a neural network
+representing a Q function, to estimate the expected cumulative reward following
+a state-action pair. The Q function neural network contains a lot of implicit
+knowledge about the RL problems, but often remains unexamined and
+uninterpreted. To our knowledge, this work develops the first mimic learning
+framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to
+approximate neural network predictions. An LMUT is learned using a novel
+on-line algorithm that is well-suited for an active play setting, where the
+mimic learner observes an ongoing interaction between the neural net and the
+environment. Empirical evaluation shows that an LMUT mimics a Q function
+substantially better than five baseline methods. The transparent tree structure
+of an LMUT facilitates understanding the network's learned knowledge by
+analyzing feature influence, extracting rules, and highlighting the
+super-pixels in image inputs.
+"
+21895,Solution to dynamic economic dispatch with prohibited operating zones via MILP,"  Dynamic economic dispatch (DED) problem considering prohibited operating
+zones (POZ), ramp rate constraints, transmission losses and spinning reserve
+constraints is a complicated non-linear problem which is difficult to solve
+efficiently. In this paper, a mixed integer linear programming (MILP) method is
+proposed to solve such a DED problem. Firstly, a novel MILP formulation for DED
+problem without considering the transmission losses, denoted by MILP-1, is
+presented by using perspective cut reformulation technique. When the
+transmission losses are considered, the quadratic terms in the transmission
+losses are replaced by their first order Taylor expansions, and then an MILP
+formulation for DED considering the transmission losses, denoted by MILP-2, is
+obtained. Based on MILP-1 and MILP-2, an MILP-iteration algorithm (MILP-IA) is
+proposed to solve the complicated DED problem. The effectiveness of the MILP-1
+and MILP-IA are assessed by several cases and the simulation results show that
+both of them can solve to competitive solutions in a short time.
+"
+21896,Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes,"  We present an approximate Bayesian inference approach for estimating the
+intensity of a inhomogeneous Poisson process, where the intensity function is
+modelled using a Gaussian process (GP) prior via a sigmoid link function.
+Augmenting the model using a latent marked Poisson process and Pólya--Gamma
+random variables we obtain a representation of the likelihood which is
+conjugate to the GP prior. We approximate the posterior using a free--form mean
+field approximation together with the framework of sparse GPs. Furthermore, as
+alternative approximation we suggest a sparse Laplace approximation of the
+posterior, for which an efficient expectation--maximisation algorithm is
+derived to find the posterior's mode. Results of both algorithms compare well
+with exact inference obtained by a Markov Chain Monte Carlo sampler and
+standard variational Gauss approach, while being one order of magnitude faster.
+"
+21897,Perturbation analysis of sub/super hedging problems,"  We investigate the links between various no-arbitrage conditions and the
+existence of pricing functionals in general markets, and prove the Fundamental
+Theorem of Asset Pricing therein. No-arbitrage conditions, either in this
+abstract setting or in the case of a market consisting of European Call
+options, give rise to duality properties of infinite-dimensional sub- and
+super-hedging problems. With a view towards applications, we show how duality
+is preserved when reducing these problems over finite-dimensional bases. We
+finally perform a rigorous perturbation analysis of those linear programming
+problems, and highlight, as a numerical example, the influence of smile
+extrapolation on the bounds of exotic options.
+"
+21898,Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks,"  Despite the advantages of all-weather and all-day high-resolution imaging,
+synthetic aperture radar (SAR) images are much less viewed and used by general
+people because human vision is not adapted to microwave scattering phenomenon.
+However, expert interpreters can be trained by comparing side-by-side SAR and
+optical images to learn the mapping rules from SAR to optical. This paper
+attempts to develop machine intelligence that are trainable with large-volume
+co-registered SAR and optical images to translate SAR image to optical version
+for assisted SAR image interpretation. Reciprocal SAR-Optical image translation
+is a challenging task because it is raw data translation between two physically
+very different sensing modalities. This paper proposes a novel reciprocal
+adversarial network scheme where cascaded residual connections and hybrid
+L1-GAN loss are employed. It is trained and tested on both spaceborne GF-3 and
+airborne UAVSAR images. Results are presented for datasets of different
+resolutions and polarizations and compared with other state-of-the-art methods.
+The FID is used to quantitatively evaluate the translation performance. The
+possibility of unsupervised learning with unpaired SAR and optical images is
+also explored. Results show that the proposed translation network works well
+under many scenarios and it could potentially be used for assisted SAR
+interpretation.
+"
+21899,Molecular Computing for Markov Chains,"  In this paper, it is presented a methodology for implementing arbitrarily
+constructed time-homogenous Markov chains with biochemical systems. Not only
+discrete but also continuous-time Markov chains are allowed to be computed. By
+employing chemical reaction networks (CRNs) as a programmable language,
+molecular concentrations serve to denote both input and output values. One
+reaction network is elaborately designed for each chain. The evolution of
+species' concentrations over time well matches the transient solutions of the
+target continuous-time Markov chain, while equilibrium concentrations can
+indicate the steady state probabilities. Additionally, second-order Markov
+chains are considered for implementation, with bimolecular reactions rather
+that unary ones. An original scheme is put forward to compile unimolecular
+systems to DNA strand displacement reactions for the sake of future physical
+implementations. Deterministic, stochastic and DNA simulations are provided to
+enhance correctness, validity and feasibility.
+"
+21900,Theory of ground states for classical Heisenberg spin systems III,"  We extend the theory of ground states of classical Heisenberg spin systems
+published previously by a closer investigation of the convex Gram set of $N$
+spins. This is relevant for the present purpose since the set of ground states
+of a given spin system corresponds to a face of Gram set. The investigation of
+the Gram set is facilitated by the determination of its symmetry group. The
+case of the general spin triangle is completely solved and illustrated by a
+couple of examples.
+"
+21901,Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks,"  Recurrent neural networks have gained widespread use in modeling sequence
+data across various domains. While many successful recurrent architectures
+employ a notion of gating, the exact mechanism that enables such remarkable
+performance is not well understood. We develop a theory for signal propagation
+in recurrent networks after random initialization using a combination of mean
+field theory and random matrix theory. To simplify our discussion, we introduce
+a new RNN cell with a simple gating mechanism that we call the minimalRNN and
+compare it with vanilla RNNs. Our theory allows us to define a maximum
+timescale over which RNNs can remember an input. We show that this theory
+predicts trainability for both recurrent architectures. We show that gated
+recurrent networks feature a much broader, more robust, trainable region than
+vanilla RNNs, which corroborates recent experimental findings. Finally, we
+develop a closed-form critical initialization scheme that achieves dynamical
+isometry in both vanilla RNNs and minimalRNNs. We show that this results in
+significantly improvement in training dynamics. Finally, we demonstrate that
+the minimalRNN achieves comparable performance to its more complex
+counterparts, such as LSTMs or GRUs, on a language modeling task.
+"
+21902,Foundation of relativistic astrophysics: Curvature of Riemannian Space versus Relativistic Quantum Field in Minkowski Space,"  The common basis for many high energy astrophysical phenomena is the theory
+of gravitation, for which in modern theoretical physics there are two main
+directions: Einstein's geometrical and Feynman's nonmetric field approaches for
+description of gravitational interaction. Though classical relativistic effects
+have the same values in both approaches, there are dramatically different
+effects predicted by GRT and FGT for relativistic astrophysics. Crucial
+observational tests which allow to test the physics of the gravitational
+interaction are discussed, including detection of gravitational waves by
+advanced LIGO-Virgo antennas and Event Horizon Telescope observations of active
+galactic nuclei. Forthcoming relativistic astrophysics can open new ways in
+understanding of gravitation and the unification of fundamental physical
+interaction.
+"
+21903,Consistency of survival tree and forest models: splitting bias and correction,"  Random survival forest and survival trees are popular models in statistics
+and machine learning. However, there is a lack of general understanding
+regarding consistency, splitting rules and influence of the censoring
+mechanism. In this paper, we investigate the statistical properties of existing
+methods from several interesting perspectives. First, we show that traditional
+splitting rules with censored outcomes rely on a biased estimation of the
+within-node failure distribution. To exactly quantify this bias, we develop a
+concentration bound of the within-node estimation based on non i.i.d. samples
+and apply it to the entire forest. Second, we analyze the entanglement between
+the failure and censoring distributions caused by univariate splits, and show
+that without correcting the bias at an internal node, survival tree and forest
+models can still enjoy consistency under suitable conditions. In particular, we
+demonstrate this property under two cases: a finite-dimensional case where the
+splitting variables and cutting points are chosen randomly, and a
+high-dimensional case where the covariates are weakly correlated. Our results
+can also degenerate into an independent covariate setting, which is commonly
+used in the random forest literature for high-dimensional sparse models.
+However, it may not be avoidable that the convergence rate depends on the total
+number of variables in the failure and censoring distributions. Third, we
+propose a new splitting rule that compares bias-corrected cumulative hazard
+functions at each internal node. We show that the rate of consistency of this
+new model depends only on the number of failure variables, which improves from
+non-bias-corrected versions. We perform simulation studies to confirm that this
+can substantially benefit the prediction error.
+"
+21904,Missing Data and Prediction,"  Missing data are a common problem for both the construction and
+implementation of a prediction algorithm. Pattern mixture kernel submodels
+(PMKS) - a series of submodels for every missing data pattern that are fit
+using only data from that pattern - are a computationally efficient remedy for
+both stages. Here we show that PMKS yield the most predictive algorithm among
+all standard missing data strategies. Specifically, we show that the expected
+loss of a forecasting algorithm is minimized when each pattern-specific loss is
+minimized. Simulations and a re-analysis of the SUPPORT study confirms that
+PMKS generally outperforms zero-imputation, mean-imputation, complete-case
+analysis, complete-case submodels, and even multiple imputation (MI). The
+degree of improvement is highly dependent on the missingness mechanism and the
+effect size of missing predictors. When the data are Missing at Random (MAR) MI
+can yield comparable forecasting performance but generally requires a larger
+computational cost. We see that predictions from the PMKS are equivalent to the
+limiting predictions for a MI procedure that uses a mean model dependent on
+missingness indicators (the MIMI model). Consequently, the MIMI model can be
+used to assess the MAR assumption in practice. The focus of this paper is on
+out-of-sample prediction behavior, implications for model inference are only
+briefly explored.
+"
+21905,Multidimensional Urban Segregation - Toward A Neural Network Measure,"  We introduce a multidimensional, neural-network approach to reveal and
+measure urban segregation phenomena, based on the Self-Organizing Map algorithm
+(SOM). The multidimensionality of SOM allows one to apprehend a large number of
+variables simultaneously, defined on census or other types of statistical
+blocks, and to perform clustering along them. Levels of segregation are then
+measured through correlations between distances on the neural network and
+distances on the actual geographical map. Further, the stochasticity of SOM
+enables one to quantify levels of heterogeneity across census blocks. We
+illustrate this new method on data available for the city of Paris.
+"
+21906,Reconstructing networks with unknown and heterogeneous errors,"  The vast majority of network datasets contains errors and omissions, although
+this is rarely incorporated in traditional network analysis. Recently, an
+increasing effort has been made to fill this methodological gap by developing
+network reconstruction approaches based on Bayesian inference. These
+approaches, however, rely on assumptions of uniform error rates and on direct
+estimations of the existence of each edge via repeated measurements, something
+that is currently unavailable for the majority of network data. Here we develop
+a Bayesian reconstruction approach that lifts these limitations by not only
+allowing for heterogeneous errors, but also for single edge measurements
+without direct error estimates. Our approach works by coupling the inference
+approach with structured generative network models, which enable the
+correlations between edges to be used as reliable uncertainty estimates.
+Although our approach is general, we focus on the stochastic block model as the
+basic generative process, from which efficient nonparametric inference can be
+performed, and yields a principled method to infer hierarchical community
+structure from noisy data. We demonstrate the efficacy of our approach with a
+variety of empirical and artificial networks.
+"
+21907,Distributed Active State Estimation with User-Specified Accuracy,"  In this paper, we address the problem of controlling a network of mobile
+sensors so that a set of hidden states are estimated up to a user-specified
+accuracy. The sensors take measurements and fuse them online using an
+Information Consensus Filter (ICF). At the same time, the local estimates guide
+the sensors to their next best configuration. This leads to an LMI-constrained
+optimization problem that we solve by means of a new distributed random
+approximate projections method. The new method is robust to the state
+disagreement errors that exist among the robots as the ICF fuses the collected
+measurements. Assuming that the noise corrupting the measurements is zero-mean
+and Gaussian and that the robots are self localized in the environment, the
+integrated system converges to the next best positions from where new
+observations will be taken. This process is repeated with the robots taking a
+sequence of observations until the hidden states are estimated up to the
+desired user-specified accuracy. We present simulations of sparse landmark
+localization, where the robotic team achieves the desired estimation tolerances
+while exhibiting interesting emergent behavior.
+"
+21908,The Generalized Nagell-Ljunggren Problem: Powers with Repetitive Representations,"  We consider a natural generalization of the Nagell-Ljunggren equation to the
+case where the qth power of an integer y, for q >= 2, has a base-b
+representation that consists of a length-l block of digits repeated n times,
+where n >= 2. Assuming the abc conjecture of Masser and Oesterlé, we
+completely characterize those triples (q, n, l) for which there are infinitely
+many solutions b. In all cases predicted by the abc conjecture, we are able
+(without any assumptions) to prove there are indeed infinitely many solutions.
+"
+21909,Observing the sky at extremely high energies with the Cherenkov Telescope Array: Status of the GCT project,"  The Cherenkov Telescope Array is the main global project of ground-based
+gamma-ray astronomy for the coming decades. Performance will be significantly
+improved relative to present instruments, allowing a new insight into the
+high-energy Universe [1]. The nominal CTA southern array will include a
+sub-array of seventy 4 m telescopes spread over a few square kilometers to
+study the sky at extremely high energies, with the opening of a new window in
+the multi-TeV energy range. The Gamma-ray Cherenkov Telescope (GCT) is one of
+the proposed telescope designs for that sub-array. The GCT prototype recorded
+its first Cherenkov light on sky in 2015. After an assessment phase in 2016,
+new observations have been performed successfully in 2017. The GCT
+collaboration plans to install its first telescopes and cameras on the CTA site
+in Chile in 2018-2019 and to contribute a number of telescopes to the
+subsequent CTA production phase.
+"
+21910,"Light bending, static dark energy and related uniqueness of Schwarzschild-de Sitter spacetime","  Since the Schwarzschild-de Sitter spacetime is static inside the cosmological
+event horizon, if the dark energy state parameter is sufficiently close to
+$-1$, apparently one could still expect an effectively static geometry, in the
+attraction dominated region inside the maximum turn around radius, $R_{\rm TA,
+max}$, of a cosmic structure. We take the first order metric derived recently
+assuming a static and ideal dark energy fluid with equation of state
+$P(r)=\alpha\rho(r)$ as a source in Ref. [1], which reproduced the expression
+for $R_{\rm TA, max}$ found earlier in the cosmological McVittie spacetime.
+Here we show that the equality originates from the equivalence of geodesic
+motion in these two backgrounds, in the non-relativistic regime. We extend this
+metric up to the third order and compute the bending of light using the
+Rindler-Ishak method. For $ \alpha\neq -1$, a dark energy dependent term
+appears in the bending equation, unlike the case of the cosmological constant,
+$\alpha=-1$. Due to this new term in particular, existing data for the light
+bending at galactic scales yields, $(1+\alpha)\lesssim {\cal O}(10^{-14})$,
+thereby practically ruling out any such static and inhomogeneous dark energy
+fluid we started with. Implication of this result pertaining the uniqueness of
+the Schwarzschild-de Sitter spacetime in such inhomogeneous dark energy
+background is discussed.
+"
+21911,Free multivariate w*-semicrossed products: reflexivity and the bicommutant property,"  We study w*-semicrossed products over actions of the free semigroup and the
+free abelian semigroup on (possibly non-selfadjoint) w*-closed algebras. We
+show that they are reflexive when the dynamics are implemented by uniformly
+bounded families of invertible row operators. Combining with results of Helmer
+we derive that w*-semicrossed products over factors (on a separable Hilbert
+space) are reflexive. Furthermore we show that w*-semicrossed products of
+automorphic actions on maximal abelian selfadjoint algebras are reflexive. In
+all cases we prove that the w*-semicrossed products have the bicommutant
+property if and only if so does the ambient algebra of the dynamics.
+"
+21912,Computation and theory of Euler sums of generalized hyperharmonic numbers,"  Recently, Dil and Boyadzhiev \cite{AD2015} proved an explicit formula for the
+sum of multiple harmonic numbers whose indices are the sequence $\left(
+{{\left\{ 0 \right\}_r},1} \right)$. In this paper we show that the sums of
+multiple harmonic numbers whose indices are the sequence $\left( {{\left\{ 0
+\right\}_r,1};{\left\{ 1 \right\}_{k-1}}} \right)$ can be expressed in terms
+of (multiple) zeta values, multiple harmonic numbers and Stirling numbers of
+the first kind, and give an explicit formula.
+"
+21913,A Lightweight Music Texture Transfer System,"  Deep learning researches on the transformation problems for image and text
+have raised great attention. However, present methods for music feature
+transfer using neural networks are far from practical application. In this
+paper, we initiate a novel system for transferring the texture of music, and
+release it as an open source project. Its core algorithm is composed of a
+converter which represents sounds as texture spectra, a corresponding
+reconstructor and a feed-forward transfer network. We evaluate this system from
+multiple perspectives, and experimental results reveal that it achieves
+convincing results in both sound effects and computational performance.
+"
+21914,Assessment of Bayesian Expected Power via Bayesian Bootstrap,"  The Bayesian expected power (BEP) has become increasingly popular in sample
+size determination and assessment of the probability of success (POS) for a
+future trial. The BEP takes into consideration the uncertainty around the
+parameters assumed by a power analysis and is thus more robust compared to the
+traditional power that assumes a single set of parameters. Current methods for
+assessing BEP are often based in a parametric framework by imposing a model on
+the pilot data to derive and sample from the posterior distributions of the
+parameters. Implementation of the model-based approaches can be analytically
+challenging and computationally costly especially for multivariate data sets;
+it also runs the risk of generating misleading BEP if the model is
+mis-specified. We propose an approach based on the Bayesian bootstrap technique
+(BBS) to simulate future trials in the presence of individual-level pilot data,
+based on which the empirical BEP can be calculated. The BBS approach is
+model-free with no assumptions about the distribution of the prior data and
+circumvents the analytical and computational complexity associated with
+obtaining the posterior distribution of the parameters. Information from
+multiple pilot studies is also straightforward to combine. We also propose the
+double bootstrap (BS2), a frequentist counterpart to the BBS, that shares
+similar properties and achieves the same goal as the BBS for BEP assessment.
+Simulation studies and case studies are presented to demonstrate the
+implementation of the BBS and BS2 techniques and to compare the BEP results
+with model-based approaches.
+"
+21915,The EBLM Project IV. Spectroscopic orbits of over 100 eclipsing M dwarfs masquerading as transiting hot-Jupiters,"  We present 2271 radial velocity measurements taken on 118 single-line binary
+stars, taken over eight years with the CORALIE spectrograph. The binaries
+consist of F/G/K primaries and M-dwarf secondaries. They were initially
+discovered photometrically by the WASP planet survey, as their shallow eclipses
+mimic a hot-Jupiter transit. The observations we present permit a precise
+characterisation of the binary orbital elements and mass function. With
+modelling of the primary star this mass function is converted to a mass of the
+secondary star. In the future, this spectroscopic work will be combined with
+precise photometric eclipses to draw an empirical mass/radius relation for the
+bottom of the mass sequence. This has applications in both stellar astrophysics
+and the growing number of exoplanet surveys around M-dwarfs. In particular, we
+have discovered 34 systems with a secondary mass below $0.2 M_\odot$, and so we
+will ultimately double the known number of very low-mass stars with well
+characterised mass and radii.
+We are able to detect eccentricities as small as 0.001 and orbital periods to
+sub-second precision. Our sample can revisit some earlier work on the tidal
+evolution of close binaries, extending it to low mass ratios. We find some
+binaries that are eccentric at orbital periods < 3 days, while our longest
+circular orbit has a period of 10.4 days.
+By collating the EBLM binaries with published WASP planets and brown dwarfs,
+we derive a mass spectrum with twice the resolution of previous work. We
+compare the WASP/EBLM sample of tightly-bound orbits with work in the
+literature on more distant companions up to 10 AU. We note that the brown dwarf
+desert appears wider, as it carves into the planetary domain for our
+short-period orbits. This would mean that a significantly reduced abundance of
+planets begins at $\sim 3M_{\rm Jup}$, well before the Deuterium-burning limit.
+[abridged]
+"
+21916,Causal Inference from Observational Studies with Clustered Interference,"  Inferring causal effects from an observational study is challenging because
+participants are not randomized to treatment. Observational studies in
+infectious disease research present the additional challenge that one
+participant's treatment may affect another participant's outcome, i.e., there
+may be interference. In this paper recent approaches to defining causal effects
+in the presence of interference are considered, and new causal estimands
+designed specifically for use with observational studies are proposed.
+Previously defined estimands target counterfactual scenarios in which
+individuals independently select treatment with equal probability. However, in
+settings where there is interference between individuals within clusters, it
+may be unlikely that treatment selection is independent between individuals in
+the same cluster. The proposed causal estimands instead describe counterfactual
+scenarios in which the treatment selection correlation structure is the same as
+in the observed data distribution, allowing for within-cluster dependence in
+the individual treatment selections. These estimands may be more relevant for
+policy-makers or public health officials who desire to quantify the effect of
+increasing the proportion of treated individuals in a population. Inverse
+probability-weighted estimators for these estimands are proposed. The
+large-sample properties of the estimators are derived, and a simulation study
+demonstrating the finite-sample performance of the estimators is presented. The
+proposed methods are illustrated by analyzing data from a study of cholera
+vaccination in over 100,000 individuals in Bangladesh.
+"
+21917,Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget,"  Deep neural network (DNN) based approaches hold significant potential for
+reinforcement learning (RL) and have already shown remarkable gains over
+state-of-art methods in a number of applications. The effectiveness of DNN
+methods can be attributed to leveraging the abundance of supervised data to
+learn value functions, Q-functions, and policy function approximations without
+the need for feature engineering. Nevertheless, the deployment of DNN-based
+predictors with very deep architectures can pose an issue due to computational
+and other resource constraints at test-time in a number of applications. We
+propose a novel approach for reducing the average latency by learning a
+computationally efficient gating function that is capable of recognizing states
+in a sequential decision process for which policy prescriptions of a shallow
+network suffices and deeper layers of the DNN have little marginal utility. The
+overall system is adaptive in that it dynamically switches control actions
+based on state-estimates in order to reduce average latency without sacrificing
+terminal performance. We experiment with a number of alternative loss-functions
+to train gating functions and shallow policies and show that in a number of
+applications a speed-up of up to almost 5X can be obtained with little loss in
+performance.
+"
+21918,Linear Representation of Symmetric Games,"  Using semi-tensor product of matrices, the structures of several kinds of
+symmetric games are investigated via the linear representation of symmetric
+group in the structure vector of games as its representation space. First of
+all, the symmetry, described as the action of symmetric group on payoff
+functions, is converted into the product of permutation matrices with structure
+vectors of payoff functions. Using the linear representation of the symmetric
+group in structure vectors, the algebraic conditions for the ordinary,
+weighted, renaming and name-irrelevant symmetries are obtained respectively as
+the invariance under the corresponding linear representations. Secondly, using
+the linear representations the relationship between symmetric games and
+potential games is investigated. This part is mainly focused on Boolean games.
+An alternative proof is given to show that ordinary, renaming and weighted
+symmetric Boolean games are also potential ones under our framework. The
+corresponding potential functions are also obtained. Finally, an example is
+given to show that some other Boolean games could also be potential games.
+"
+21919,Snorkel: Rapid Training Data Creation with Weak Supervision,"  Labeling training data is increasingly the largest bottleneck in deploying
+machine learning systems. We present Snorkel, a first-of-its-kind system that
+enables users to train state-of-the-art models without hand labeling any
+training data. Instead, users write labeling functions that express arbitrary
+heuristics, which can have unknown accuracies and correlations. Snorkel
+denoises their outputs without access to ground truth by incorporating the
+first end-to-end implementation of our recently proposed machine learning
+paradigm, data programming. We present a flexible interface layer for writing
+labeling functions based on our experience over the past year collaborating
+with companies, agencies, and research labs. In a user study, subject matter
+experts build models 2.8x faster and increase predictive performance an average
+45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in
+this new setting and propose an optimizer for automating tradeoff decisions
+that gives up to 1.8x speedup per pipeline execution. In two collaborations,
+with the U.S. Department of Veterans Affairs and the U.S. Food and Drug
+Administration, and on four open-source text and image data sets representative
+of other deployments, Snorkel provides 132% average improvements to predictive
+performance over prior heuristic approaches and comes within an average 3.60%
+of the predictive performance of large hand-curated training sets.
+"
+21920,Efficient assembly based on B-spline tailored quadrature rules for the IgA-SGBEM,"  This paper deals with the discrete counterpart of 2D elliptic model problems
+rewritten in terms of Boundary Integral Equations. The study is done within the
+framework of Isogeometric Analysis based on B-splines. In such a context, the
+problem of constructing appropriate, accurate and efficient quadrature rules
+for the Symmetric Galerkin Boundary Element Method is here investigated. The
+new integration schemes, together with row assembly and sum factorization, are
+used to build a more efficient strategy to derive the final linear system of
+equations. Key ingredients are weighted quadrature rules tailored for
+B--splines, that are constructed to be exact in the whole test space, also with
+respect to the singular kernel. Several simulations are presented and
+discussed, showing accurate evaluation of the involved integrals and outlining
+the superiority of the new approach in terms of computational cost and elapsed
+time with respect to the standard element-by-element assembly.
+"
+21921,Perceptual audio loss function for deep learning,"  PESQ and POLQA , are standards are standards for automated assessment of
+voice quality of speech as experienced by human beings. The predictions of
+those objective measures should come as close as possible to subjective quality
+scores as obtained in subjective listening tests. Wavenet is a deep neural
+network originally developed as a deep generative model of raw audio
+wave-forms. Wavenet architecture is based on dilated causal convolutions, which
+exhibit very large receptive fields. In this short paper we suggest using the
+Wavenet architecture, in particular its large receptive filed in order to learn
+PESQ algorithm. By doing so we can use it as a differentiable loss function for
+speech enhancement.
+"
+21922,From Complex Event Processing to Simple Event Processing,"  Many problems in Computer Science can be framed as the computation of queries
+over sequences, or ""streams"" of data units called events. The field of Complex
+Event Processing (CEP) relates to the techniques and tools developed to
+efficiently process these queries. However, most CEP systems developed so far
+have concentrated on relatively narrow types of queries, which consist of
+sliding windows, aggregation functions, and simple sequential patterns computed
+over events that have a fixed tuple structure. Many of them boast throughput,
+but in counterpart, they are difficult to setup and cumbersome to extend with
+user-defined elements.
+This paper describes a variety of use cases taken from real-world scenarios
+that present features seldom considered in classical CEP problems. It also
+provides a broad review of current solutions, that includes tools and
+techniques going beyond typical surveys on CEP. From a critical analysis of
+these solutions, design principles for a new type of event stream processing
+system are exposed. The paper proposes a simple, generic and extensible
+framework for the processing of event streams of diverse types; it describes in
+detail a stream processing engine, called BeepBeep, that implements these
+principles. BeepBeep's modular architecture, which borrows concepts from many
+other systems, is complemented with an extensible query language, called eSQL.
+The end result is an open, versatile, and reasonably efficient query engine
+that can be used in situations that go beyond the capabilities of existing
+systems.
+"
+21923,Differentially Private Empirical Risk Minimization with Input Perturbation,"  We propose a novel framework for the differentially private ERM, input
+perturbation. Existing differentially private ERM implicitly assumed that the
+data contributors submit their private data to a database expecting that the
+database invokes a differentially private mechanism for publication of the
+learned model. In input perturbation, each data contributor independently
+randomizes her/his data by itself and submits the perturbed data to the
+database. We show that the input perturbation framework theoretically
+guarantees that the model learned with the randomized data eventually satisfies
+differential privacy with the prescribed privacy parameters. At the same time,
+input perturbation guarantees that local differential privacy is guaranteed to
+the server. We also show that the excess risk bound of the model learned with
+input perturbation is $O(1/n)$ under a certain condition, where $n$ is the
+sample size. This is the same as the excess risk bound of the state-of-the-art.
+"
+21924,Empirical Distribution of Scaled Eigenvalues for Product of Matrices from the Spherical Ensemble,"  Consider the product of $m$ independent $n\times n$ random matrices from the
+spherical ensemble for $m\ge 1$. The empirical distribution based on the $n$
+eigenvalues of the product is called the empirical spectral distribution. Two
+recent papers by Götze, Kösters and Tikhomirov (2015) and Zeng (2016)
+obtain the limit of the empirical spectral distribution for the product when
+$m$ is a fixed integer. In this paper, we investigate the limiting empirical
+distribution of scaled eigenvalues for the product of $m$ independent matrices
+from the spherical ensemble in the case when $m$ changes with $n$, that is,
+$m=m_n$ is an arbitrary sequence of positive integers.
+"
+21925,Pulsed high magnetic field measurement via a Rubidium vapor sensor,"  We present a new technique to measure pulsed magnetic fields based on the use
+of Rubidium in gas phase as a metrological standard. We have therefore
+developed an instrument based on laser inducing transitions at about 780~nm (D2
+line) in a Rubidium gas contained in a mini-cell of 3~mm~x~3~mm cross section.
+To be able to insert such a cell in a standard high field pulsed magnet we have
+realized a fibred probe kept at a fixed temperature. Transition frequencies for
+both the $\pi$ (light polarization parallel to the magnetic field) and $\sigma$
+(light polarization perpendicular to the magnetic field) configurations are
+measured by a commercial wavemeter. One innovation of our sensor is that in
+addition of monitoring the light transmitted by the Rb cell, which is usual, we
+also monitor the fluorescence emission of the gas sample from a very small
+volume with the advantage of reducing the impact of the field inhomogeneity on
+the field measurement. Our sensor has been tested up to about 58~T.
+"
+21926,A Massive Shell of Supernova-formed Dust in SNR G54.1+0.3,"  While theoretical dust condensation models predict that most refractory
+elements produced in core-collapse supernovae (SNe) efficiently condense into
+dust, a large quantity of dust has so far only been observed in SN 1987A. We
+present the analysis of Spitzer Space Telescope, Herschel Space Observatory,
+Stratospheric Observatory for Infrared Astronomy (SOFIA), and AKARI
+observations of the infrared (IR) shell surrounding the pulsar wind nebula in
+the supernova remnant G54.1+0.3. We attribute a distinctive spectral feature at
+21 $\mu$m to a magnesium silicate grain species that has been invoked in
+modeling the ejecta-condensed dust in Cas A, which exhibits the same spectral
+signature. If this species is responsible for producing the observed spectral
+feature and accounts for a significant fraction of the observed IR continuum,
+we find that it would be the dominant constituent of the dust in G54.1+0.3,
+with possible secondary contributions from other compositions, such as carbon,
+silicate, or alumina grains. The smallest mass of SN-formed dust required by
+our models is 1.1 $\pm$ 0.8 $\rm M_{\odot}$. We discuss how these results may
+be affected by varying dust grain properties and self-consistent grain heating
+models. The spatial distribution of the dust mass and temperature in G54.1+0.3
+confirms the scenario in which the SN-formed dust has not yet been processed by
+the SN reverse shock and is being heated by stars belonging to a cluster in
+which the SN progenitor exploded. The dust mass and composition suggest a
+progenitor mass of 16$-$27 $\rm M_{\odot}$ and imply a high dust condensation
+efficiency, similar to that found for Cas A and SN 1987A. The study provides
+another example of significant dust formation in a Type IIP SN and sheds light
+on the properties of pristine SN-condensed dust.
+"
+21927,A Purely Functional Computer Algebra System Embedded in Haskell,"  We demonstrate how methods in Functional Programming can be used to implement
+a computer algebra system. As a proof-of-concept, we present the
+computational-algebra package. It is a computer algebra system implemented as
+an embedded domain-specific language in Haskell, a purely functional
+programming language. Utilising methods in functional programming and prominent
+features of Haskell, this library achieves safety, composability, and
+correctness at the same time. To demonstrate the advantages of our approach, we
+have implemented advanced Gröbner basis algorithms, such as Faugère's
+$F_4$ and $F_5$, in a composable way.
+"
+21928,Weight recursions for any rotation symmetric Boolean functions,"  Let $f_n(x_1, x_2, \ldots, x_n)$ denote the algebraic normal form (polynomial
+form) of a rotation symmetric Boolean function of degree $d$ in $n \geq d$
+variables and let $wt(f_n)$ denote the Hamming weight of this function. Let
+$(1, a_2, \ldots, a_d)_n$ denote the function $f_n$ of degree $d$ in $n$
+variables generated by the monomial $x_1x_{a_2} \cdots x_{a_d}.$ Such a
+function $f_n$ is called {\em monomial rotation symmetric} (MRS). It was proved
+in a $2012$ paper that for any MRS $f_n$ with $d=3,$ the sequence of weights
+$\{w_k = wt(f_k):~k = 3, 4, \ldots\}$ satisfies a homogeneous linear recursion
+with integer coefficients. In this paper it is proved that such recursions
+exist for any rotation symmetric function $f_n;$ such a function is generated
+by some sum of $t$ monomials of various degrees. The last section of the paper
+gives a Mathematica program which explicitly computes the homogeneous linear
+recursion for the weights, given any rotation symmetric $f_n.$ The reader who
+is only interested in finding some recursions can use the program and not be
+concerned with the details of the rather complicated proofs in this paper.
+"
+21929,Generalized forbidden subposet problems,"  A subfamily $\{F_1,F_2,\dots,F_{|P|}\}\subseteq {\cal F}$ of sets is a copy
+of a poset $P$ in ${\cal F}$ if there exists a bijection $\phi:P\rightarrow
+\{F_1,F_2,\dots,F_{|P|}\}$ such that whenever $x \le_P x'$ holds, then so does
+$\phi(x)\subseteq \phi(x')$. For a family ${\cal F}$ of sets, let $c(P,{\cal
+F})$ denote the number of copies of $P$ in ${\cal F}$, and we say that ${\cal
+F}$ is $P$-free if $c(P,{\cal F})=0$ holds. For any two posets $P,Q$ let us
+denote by $La(n,P,Q)$ the maximum number of copies of $Q$ over all $P$-free
+families ${\cal F} \subseteq 2^{[n]}$, i.e. $\max\{c(Q,{\cal F}): {\cal F}
+\subseteq 2^{[n]}, c(P,{\cal F})=0 \}$.
+This generalizes the well-studied parameter $La(n,P)=La(n,P,P_1)$ where $P_1$
+is the one element poset. The quantity $La(n,P)$ has been determined (precisely
+or asymptotically) for many posets $P$, and in all known cases an
+asymptotically best construction can be obtained by taking as many middle
+levels as possible without creating a copy of $P$.
+In this paper we consider the first instances of the problem of determining
+$La(n,P,Q)$. We find its value when $P$ and $Q$ are small posets, like chains,
+forks, the $N$ poset and diamonds. Already these special cases show that the
+extremal families are completely different from those in the original $P$-free
+cases: sometimes not middle or consecutive levels maximize $La(n,P,Q)$ and
+sometimes no asymptotically extremal family is the union of levels.
+Finally, we determine the maximum number of copies of complete multi-level
+posets in $k$-Sperner families. The main tools for this are the profile
+polytope method and two extremal set system problems that are of independent
+interest: we maximize the number of $r$-tuples $A_1,A_2,\dots, A_r \in {\cal
+A}$ over all antichains ${\cal A}\subseteq 2^{[n]}$ such that (i)
+$\cap_{i=1}^rA_i=\emptyset$, (ii) $\cap_{i=1}^rA_i=\emptyset$ and
+$\cup_{i=1}^rA_i=[n]$.
+"
+21930,Radiative Heat Transfer Between Core-Shell Nanoparticles,"  Radiative heat transfer in systems with core-shell nanoparticles may exhibit
+not only a combination of disparate physical properties of its components but
+also further enhanced properties that arise from the synergistic properties of
+the core and shell components. We study the thermal conductance between two
+core-shell nanoparticles (CSNPs). The contribution of electric and magnetic
+dipole moments to the thermal conductance depend sensitively on the core and
+shell materials, and adjustable by core size and shell thickness. We predict
+that the radiative heat transfer in a dimer of Au@SiO2 CSNPs (i.e.,
+silica-coated gold nanoparticles) could be enhanced several order of magnitude
+compared to bare Au nanoparticles. However, the reduction of several orders of
+magnitude in the heat transfer is possible between SiO2@Au CSNPs (i.e., silica
+as a core and gold as a shell) than that of uncoated SiO2 nanoparticles.
+"
+21931,Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models,"  Matching 3D rigid point clouds in complex environments robustly and
+accurately is still a core technique used in many applications. This paper
+proposes a new architecture combining error estimation from sample covariances
+and dual global probability alignment based on the convolution of adaptive
+Gaussian Mixture Models (GMM) from point clouds. Firstly, a novel adaptive GMM
+is defined using probability distributions from the corresponding points. Then
+rigid point cloud alignment is performed by maximizing the global probability
+from the convolution of dual adaptive GMMs in the whole 2D or 3D space, which
+can be efficiently optimized and has a large zone of accurate convergence.
+Thousands of trials have been conducted on 200 models from public 2D and 3D
+datasets to demonstrate superior robustness and accuracy in complex
+environments with unpredictable noise, outliers, occlusion, initial rotation,
+shape and missing points.
+"
+21932,Attitudes of Children with Autism towards Robots: An Exploratory Study,"  In this exploratory study we assessed how attitudes of children with autism
+spectrum disorder (ASD) towards robots together with children's autism-related
+social impairments are linked to indicators of children's preference of an
+interaction with a robot over an interaction with a person. We found that
+children with ASD have overall positive attitudes towards robots and that they
+often prefer interacting with a robot than with a person. Several of children's
+attitudes were linked to children's longer gazes towards a robot compared to a
+person. Autism-related social impairments were linked to more repetitive and
+stereotyped behaviors and to a shorter gaze duration in the interaction with
+the robot compared to the person. These preliminary results contribute to
+better understand factors that might help determine sub-groups of children with
+ASD for whom robots could be particularly useful.
+"
+21933,Electromagnetic dipole moments of charged baryons with bent crystals at the LHC,"  We propose a unique program of measurements of electric and magnetic dipole
+moments of charm, beauty and strange charged baryons at the LHC, based on the
+phenomenon of spin precession of channeled particles in bent crystals. Studies
+of crystal channeling and spin precession of positively- and negatively-charged
+particles are presented, along with feasibility studies and expected
+sensitivities for the proposed experiment using a layout based on the LHCb
+detector.
+"
+21934,Distributed Gauss-Newton Method for State Estimation Using Belief Propagation,"  We present a novel distributed Gauss-Newton method for the non-linear state
+estimation (SE) model based on a probabilistic inference method called belief
+propagation (BP). The main novelty of our work comes from applying BP
+sequentially over a sequence of linear approximations of the SE model, akin to
+what is done by the Gauss-Newton method. The resulting iterative Gauss-Newton
+belief propagation (GN-BP) algorithm can be interpreted as a distributed
+Gauss-Newton method with the same accuracy as the centralized SE, however,
+introducing a number of advantages of the BP framework. The paper provides
+extensive numerical study of the GN-BP algorithm, provides details on its
+convergence behavior, and gives a number of useful insights for its
+implementation.
+"
+21935,A two-dimensional index to quantify both scientific research impact and scope,"  Modern management of research is increasingly based on quantitative
+bibliometric indices. Nowadays, the h-index is a major measure of research
+output that has supplanted all other citation-based indices. In this context,
+indicators that complement the h-index by evaluating different facets of
+research achievement are compelling. As an additional bibliometric source that
+can be easily extracted from available databases, we propose to use the number
+of distinct journals Nj in which an individual's papers were published. We show
+that Nj is independent of citation counts, and argue that it is a relevant
+indicator of research scope, since it quantifies readership extent and
+scientific multidisciplinarity. Combining the h-index and Nj, we define a
+two-dimensional index (H,M) that measures both the output (through H) and the
+outreach (through M) of individual's research. In order to probe the relevance
+of this two dimensional index, we have analysed the scientific production of a
+panel of physicists belonging to the same Department but with different
+research themes. The analysis of bibliometric data confirms that the two
+indices are uncorrelated and shows that while H reliably ranks the impact of
+researchers, M accurately sorts multidisciplinary or readership aspects. We
+conclude that the two indices together offer a more complete picture of
+research performance and can be applied to either individuals, research groups
+or institutions.
+"
+21936,"Dynamic programming algorithms, efficient solution of the LP-relaxation and approximation schemes for the Penalized Knapsack Problem","  We consider the 0-1 Penalized Knapsack Problem (PKP). Each item has a profit,
+a weight and a penalty and the goal is to maximize the sum of the profits minus
+the greatest penalty value of the items included in a solution. We propose an
+exact approach relying on a procedure which narrows the relevant range of
+penalties, on the identification of a core problem and on dynamic programming.
+The proposed approach turns out to be very effective in solving hard instances
+of PKP and compares favorably both to commercial solver CPLEX 12.5 applied to
+the ILP formulation of the problem and to the best available exact algorithm in
+the literature. Then we present a general inapproximability result and
+investigate several relevant special cases which permit fully polynomial time
+approximation schemes (FPTASs).
+"
+21937,Non-reciprocal Light-harvesting Nanoantennae Made by Nature,"  From a point of view of classical electrodynamics, the performance of
+two-dimensional shape-simplified antennae is discussed based upon the shape of
+naturally designed systems to harvest light. We explain the reason that the
+function of the notch at the complex, the function of the PufX presented at the
+notch, the function of the special pair, the bacteriochlorophylls are
+dielectric instead of conductor, and a mechanism to prevent damages from excess
+sunlight. The non-heme iron at the reaction center, the toroidal shape of the
+light harvestor, the functional role played by the long-lasting spectrometric
+signal observed, and the photon anti-bunching observed suggest non-reciprocity.
+Our model has the required structural information automatically built in. We
+further comment about how our prediction might be verified experimentally.
+"
+21938,Analysis of a SIRI epidemic model with distributed delay and relapse,"  We investigate the global behaviour of a SIRI epidemic model with distributed
+delay and relapse. From the theory of functional differential equations with
+delay, we prove that the solution of the system is unique, bounded, and
+positive, for all time. The basic reproduction number $R_{0}$ for the model is
+computed. By means of the direct Lyapunov method and LaSalle invariance
+principle, we prove that the disease free equilibrium is globally
+asymptotically stable when $R_{0} < 1$. Moreover, we show that there is a
+unique endemic equilibrium, which is globally asymptotically stable, when
+$R_{0} > 1$.
+"
+21939,TensOrMachine: Probabilistic Boolean Tensor Decomposition,"  Boolean tensor decomposition approximates data of multi-way binary
+relationships as product of interpretable low-rank binary factors, following
+the rules of Boolean algebra. Here, we present its first probabilistic
+treatment. We facilitate scalable sampling-based posterior inference by
+exploitation of the combinatorial structure of the factor conditionals. Maximum
+a posteriori decompositions feature higher accuracies than existing techniques
+throughout a wide range of simulated conditions. Moreover, the probabilistic
+approach facilitates the treatment of missing data and enables model selection
+with much greater accuracy. We investigate three real-world data-sets. First,
+temporal interaction networks in a hospital ward and behavioural data of
+university students demonstrate the inference of instructive latent patterns.
+Next, we decompose a tensor with more than 10 billion data points, indicating
+relations of gene expression in cancer patients. Not only does this demonstrate
+scalability, it also provides an entirely novel perspective on relational
+properties of continuous data and, in the present example, on the molecular
+heterogeneity of cancer. Our implementation is available on GitHub:
+this https URL.
+"
+21940,Word Embeddings: A Survey,"  This work lists and describes the main recent strategies for building
+fixed-length, dense and distributed representations for words, based on the
+distributional hypothesis. These representations are now commonly called word
+embeddings and, in addition to encoding surprisingly good syntactic and
+semantic information, have been proven useful as extra features in many
+downstream NLP tasks.
+"
+21941,The State of Sustainable Research Software: Results from the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1),"  This article summarizes motivations, organization, and activities of the
+Workshop on Sustainable Software for Science: Practice and Experiences
+(WSSSPE5.1) held in Manchester, UK in September 2017. The WSSSPE series
+promotes sustainable research software by positively impacting principles and
+best practices, careers, learning, and credit. This article discusses the Code
+of Conduct, idea papers, position papers, experience papers, demos, and
+lightning talks presented during the workshop. The main part of the article
+discusses the speed-blogging groups that formed during the meeting, along with
+the outputs of those sessions.
+"
+21942,Complex Analysis of Real Functions III: Extended Fourier Theory,"  In the context of the complex-analytic structure within the unit disk
+centered at the origin of the complex plane, that was presented in a previous
+paper, we show that the complete Fourier theory of integrable real functions is
+contained within that structure, that is, within the structure of the space of
+inner analytic functions on the open unit disk. We then extend the Fourier
+theory beyond the realm of integrable real functions, to include for example
+singular Schwartz distributions, and possibly other objects.
+"
+21943,Topological density estimation,"  We introduce \emph{topological density estimation} (TDE), in which the
+multimodal structure of a probability density function is topologically
+inferred and subsequently used to perform bandwidth selection for kernel
+density estimation. We show that TDE has performance and runtime advantages
+over competing methods of kernel density estimation for highly multimodal
+probability density functions. We also show that TDE yields useful auxiliary
+information, that it can determine its own suitability for use, and we explain
+its performance.
+"
+21944,From Bayesian Sparsity to Gated Recurrent Nets,"  The iterations of many first-order algorithms, when applied to minimizing
+common regularized regression functions, often resemble neural network layers
+with pre-specified weights. This observation has prompted the development of
+learning-based approaches that purport to replace these iterations with
+enhanced surrogates forged as DNN models from available training data. For
+example, important NP-hard sparse estimation problems have recently benefitted
+from this genre of upgrade, with simple feedforward or recurrent networks
+ousting proximal gradient-based iterations. Analogously, this paper
+demonstrates that more powerful Bayesian algorithms for promoting sparsity,
+which rely on complex multi-loop majorization-minimization techniques, mirror
+the structure of more sophisticated long short-term memory (LSTM) networks, or
+alternative gated feedback networks previously designed for sequence
+prediction. As part of this development, we examine the parallels between
+latent variable trajectories operating across multiple time-scales during
+optimization, and the activations within deep network structures designed to
+adaptively model such characteristic sequences. The resulting insights lead to
+a novel sparse estimation system that, when granted training data, can estimate
+optimal solutions efficiently in regimes where other algorithms fail, including
+practical direction-of-arrival (DOA) and 3D geometry recovery problems. The
+underlying principles we expose are also suggestive of a learning process for a
+richer class of multi-loop algorithms in other domains.
+"
+21945,Unified estimation framework for unnormalized models with statistical efficiency,"  Parameter estimation of unnormalized models is a challenging problem because
+normalizing constants are not calculated explicitly and maximum likelihood
+estimation is computationally infeasible. Although some consistent estimators
+have been proposed earlier, the problem of statistical efficiency does remain.
+In this study, we propose a unified, statistically efficient estimation
+framework for unnormalized models and several novel efficient estimators with
+reasonable computational time regardless of whether the sample space is
+discrete or continuous. The loss functions of the proposed estimators are
+derived by combining the following two methods: (1) density-ratio matching
+using Bregman divergence, and (2) plugging-in nonparametric estimators. We also
+analyze the properties of the proposed estimators when the unnormalized model
+is misspecified. Finally, the experimental results demonstrate the advantages
+of our method over existing approaches.
+"
+21946,Spectrally tunable linear polarization rotation using stacked metallic metamaterials,"  We theoretically study the transmission properties of a stack of metallic
+metamaterials and show that is able to achieve a perfect transmission
+selectively exhibiting broadband ($Q<10$) or extremely narrowband ($Q>10^5$)
+polarization rotation. We especially highlight how the arrangement of the
+stacked structure, as well as the metamaterial unit cell geometry, can highly
+influence the transmission in the spectral domain. For this purpose, we use an
+extended analytical Jones formalism that allows us to get a rigorous and
+analytical expression of the transmission. Such versatile structures could find
+potential applications in polarimetry or in the control of the light
+polarization for THz waves.
+"
+21947,On the Computational Complexity of Variants of Combinatorial Voter Control in Elections,"  Voter control problems model situations in which an external agent tries
+toaffect the result of an election by adding or deleting the fewest number of
+voters. The goal of the agent is to make a specific candidate either win
+(\emph{constructive} control) or lose (\emph{destructive} control) the
+election. We study the constructive and destructive voter control problems
+whenadding and deleting voters have a \emph{combinatorial flavor}: If we add
+(resp.\ delete) a voter~$v$, we also add (resp.\ delete) a bundle~$\kappa(v) $
+of voters that are associated with~$v$. While the bundle~$\kappa(v)$ may have
+more than one voter, a voter may also be associated with more than one voter.
+We analyze the computational complexity of the four voter control problems for
+the Plurality rule. We obtain that, in general, making a candidate lose is
+computationally easier than making her win. In particular, if the bundling
+relation is symmetric (i.e.\ $\forall w\colon w \in \kappa(v) \Leftrightarrow v
+\in \kappa(w) $), and if each voter has at most two voters associated with him,
+then destructive control is polynomial-time solvable while the constructive
+variant remains $\NP$-hard. Even if the bundles are disjoint (i.e.\ $\forall
+w\colon w \in \kappa(v) \Leftrightarrow \kappa(v) = \kappa(w) $), the
+constructive problem variants remain intractable. Finally, the minimization
+variant of constructive control by adding voters does not admit an efficient
+approximation algorithm, unless P=NP.
+"
+21948,Stochastic Submodular Maximization: The Case of Coverage Functions,"  Stochastic optimization of continuous objectives is at the heart of modern
+machine learning. However, many important problems are of discrete nature and
+often involve submodular objectives. We seek to unleash the power of stochastic
+continuous optimization, namely stochastic gradient descent and its variants,
+to such discrete problems. We first introduce the problem of stochastic
+submodular optimization, where one needs to optimize a submodular objective
+which is given as an expectation. Our model captures situations where the
+discrete objective arises as an empirical risk (e.g., in the case of
+exemplar-based clustering), or is given as an explicit stochastic model (e.g.,
+in the case of influence maximization in social networks). By exploiting that
+common extensions act linearly on the class of submodular functions, we employ
+projected stochastic gradient ascent and its variants in the continuous domain,
+and perform rounding to obtain discrete solutions. We focus on the rich and
+widely used family of weighted coverage functions. We show that our approach
+yields solutions that are guaranteed to match the optimal approximation
+guarantees, while reducing the computational cost by several orders of
+magnitude, as we demonstrate empirically.
+"
+21949,A non-singular theory of dislocations in anisotropic crystals,"  We develop a non-singular theory of three-dimensional dislocation loops in a
+particular version of Mindlin's anisotropic gradient elasticity with up to six
+length scale parameters. The theory is systematically developed as a
+generalization of the classical anisotropic theory in the framework of
+linearized incompatible elasticity. The non-singular version of all key
+equations of anisotropic dislocation theory are derived as line integrals,
+including the Burgers displacement equation with isolated solid angle, the
+Peach-Koehler stress equation, the Mura-Willis equation for the elastic
+distortion, and the Peach-Koehler force. The expression for the interaction
+energy between two dislocation loops as a double line integral is obtained
+directly, without the use of a stress function. It is shown that all the
+elastic fields are non-singular, and that they converge to their classical
+counterparts a few characteristic lengths away from the dislocation core. In
+practice, the non-singular fields can be obtained from the classical ones by
+replacing the classical (singular) anisotropic Green's tensor with the
+non-singular anisotropic Green's tensor derived by \cite{Lazar:2015ja}. The
+elastic solution is valid for arbitrary anisotropic media. In addition to the
+classical anisotropic elastic constants, the non-singular Green's tensor
+depends on a second order symmetric tensor of length scale parameters modeling
+a weak non-locality, whose structure depends on the specific class of crystal
+symmetry. The anisotropic Helmholtz operator defined by such tensor admits a
+Green's function which is used as the spreading function for the Burgers vector
+density. As a consequence, the Burgers vector density spreads differently in
+different crystal structures.
+"
+21950,A Modularized Efficient Framework for Non-Markov Time Series Estimation,"  We present a compartmentalized approach to finding the maximum a-posteriori
+(MAP) estimate of a latent time series that obeys a dynamic stochastic model
+and is observed through noisy measurements. We specifically consider modern
+signal processing problems with non-Markov signal dynamics (e.g. group
+sparsity) and/or non-Gaussian measurement models (e.g. point process
+observation models used in neuroscience). Through the use of auxiliary
+variables in the MAP estimation problem, we show that a consensus formulation
+of the alternating direction method of multipliers (ADMM) enables iteratively
+computing separate estimates based on the likelihood and prior and subsequently
+""averaging"" them in an appropriate sense using a Kalman smoother. As such, this
+can be applied to a broad class of problem settings and only requires modular
+adjustments when interchanging various aspects of the statistical model. Under
+broad log-concavity assumptions, we show that the separate estimation problems
+are convex optimization problems and that the iterative algorithm converges to
+the MAP estimate. As such, this framework can capture non-Markov latent time
+series models and non-Gaussian measurement models. We provide example
+applications involving (i) group-sparsity priors, within the context of
+electrophysiologic specrotemporal estimation, and (ii) non-Gaussian measurement
+models, within the context of dynamic analyses of learning with neural spiking
+and behavioral observations.
+"
+21951,A holistic look at requirements engineering practices in the gaming industry,"  In this work we present an account of the status of requirements engineering
+in the gaming industry. Recent papers in the area were surveyed.
+Characterizations of the gaming industry were deliberated upon by portraying
+its relations with the market industry. Some research directions in the area of
+requirements engineering in the gaming industry were also mentioned.
+"
+21952,Hamilton paths with lasting separation,"  We determine the asymptotics of the largest cardinality of a set of Hamilton
+paths in the complete graph with vertex set [n] under the condition that for
+any two of the paths in the family there is a subpath of length k entirely
+contained in only one of them and edge{disjoint from the other one.
+"
+21953,2D SLAM Quality Evaluation Methods,"  SLAM (Simultaneous Localization and mapping) is one of the most challenging
+problems for mobile platforms and there is a huge amount of modern SLAM
+algorithms. The choice of the algorithm that might be used in every particular
+problem requires prior knowledge about advantages and disadvantages of each
+algorithm. This paper presents the approach for comparison of SLAM algorithms
+that allows to find the most accurate one. The accent of research is made on 2D
+SLAM algorithms and the focus of analysis is 2D map that is built after
+algorithm performance. Three metrics for evaluation of maps are presented in
+this paper
+"
+21954,Communication: Two-structure thermodynamics unifying all scenarios for water anomalies,"  Anomalous behavior of water in superooled region (namely decrease of density
+and sharp increase of response functions at atmospheric pressure) are manly
+associated with either existence of liquid-liquid criticality or re-entering
+vapor-liquid spinodal into positive pressure. Despite different origin both
+scenarios introduce divergence of the response functions. We articulate in the
+communication that the criticality is behind water anomalies, water has normal
+vapor-liquid spinodal and that the re-entering feature was predicted because of
+curved density surface as a consequence of existence of critical point. We can
+proof this by new two structure equation of state with criticality resulting
+from non-ideal mixing of two states and a background state being modeled by
+exactly the same equation of state which was deployed for predicting of
+vapor-liquid spinodal while formulating the re-entering scenario.
+"
+21955,The scattering problem for the $abcd$ Boussinesq system in the energy space,"  The Boussinesq $abcd$ system is a 4-parameter set of equations posed in
+$\mathbb{R}_t \times \mathbb{R}_x$, originally derived by Bona, Chen and Saut
+as first order 2-wave approximations of the incompressible and irrotational,
+two dimensional water wave equations in the shallow water wave regime, in the
+spirit of the original Boussinesq derivation. Among many particular regimes,
+depending each of them in terms of the value of the parameters $(a,b,c,d)$
+present in the equations, the ""generic"" regime is characterized by the setting
+$b,d>0$ and $a,c<0$. The system is hamiltonian if also $b=d$. The equations in
+this regime are globally well-posed in the energy space $H^1\times H^1$,
+provided one works with small solutions. In this paper, we investigate decay
+and the scattering problem in this regime, which is characterized as having
+(quadratic) long-range nonlinearities, very weak linear decay $O(t^{-1/3})$
+because of the one dimensional setting, and existence of non scattering
+solutions (solitary waves). We prove, among other results, that for a
+sufficiently dispersive $abcd$ systems (characterized only in terms of
+parameters $a, b$ and $c$), all small solutions must decay to zero, locally
+strongly in the energy space, in proper subset of the light cone $|x|\leq |t|$.
+We prove this result by constructing three suitable virial functionals in the
+spirit of works by Kowalczyk, Martel and the second author, and more recently
+by the last three authors (valid for the simpler scalar ""good Boussinesq""
+model), leading to global in time decay and control of all local $H^1\times
+H^1$ terms. No parity nor extra decay assumptions are needed to prove decay,
+only small solutions in the energy space.
+"
+21956,Directed clustering in weighted networks: a new perspective,"  In this paper, we consider the problem of assessing local clustering in
+complex networks. Various definitions for this measure have been proposed for
+the cases of networks having weighted edges, but less attention has been paid
+to both weighted and directed networks. We provide a new local clustering
+coefficient for this kind of networks, starting from those existing in the
+literature for the weighted and undirected case. Furthermore, we extract from
+our coefficient four specific components, in order to separately consider
+different link patterns of triangles. Empirical applications on several real
+networks from different frameworks and with different order are provided. The
+performance of our coefficient is also compared with that of existing
+coefficients in the literature.
+"
+21957,Deep Learning Can Reverse Photon Migration for Diffuse Optical Tomography,"  Can artificial intelligence (AI) learn complicated non-linear physics? Here
+we propose a novel deep learning approach that learns non-linear photon
+scattering physics and obtains accurate 3D distribution of optical anomalies.
+In contrast to the traditional black-box deep learning approaches to inverse
+problems, our deep network learns to invert the Lippmann-Schwinger integral
+equation which describes the essential physics of photon migration of diffuse
+near-infrared (NIR) photons in turbid media. As an example for clinical
+relevance, we applied the method to our prototype diffuse optical tomography
+(DOT). We show that our deep neural network, trained with only simulation data,
+can accurately recover the location of anomalies within biomimetic phantoms and
+live animals without the use of an exogenous contrast agent.
+"
+21958,A new extended Cardioid model: an application to wind data,"  The Cardioid distribution is a relevant model for circular data. However,
+this model is not suitable for scenarios were there is asymmetry or
+multimodality. In order to overcome this gap, an extended Cardioid model is
+proposed, which is called Exponentiated Cardioid (EC) distribution. Besides,
+some of its properties are derived, such as trigonometric moments, kurtosis and
+skewness. A discussion about the modality and and expressions for the quantiles
+through approximations of the studied model are also presented. To fit the EC
+model, two estimation methods are presented based on maximum likelihood and
+quantile least squares procedures. The performance of proposed estimators is
+evaluated in a Monte Carlo simulation study, adopting both bias and mean square
+error as comparison criteria. Finally, the proposed model is applied to a
+dataset in the wind direction context. Results indicate that the EC
+distribution may outperform Cardioid and the von Mises distributions.
+"
+21959,On the rank and the convergence rate towards the Sato-Tate measure,"  Let $A$ be an abelian variety defined over a number field and let $G$ denote
+its Sato-Tate group. Under the assumption of certain standard conjectures on
+$L$-functions attached to the irreducible representations of $G$, we study the
+convergence rate of any virtual selfdual character of $G$. We find that this
+convergence rate is dictated by several arithmetic invariants of $A$, such as
+its rank or its Sato-Tate group $G$. The results are consonant with some
+previous experimental observations, and we also provide additional numerical
+evidence consistent with them. The techniques that we use were introduced by
+Sarnak, in order to explain the bias in the sign of the Frobenius traces of an
+elliptic curve without complex multiplication defined over $\mathbb{Q}$. We
+show that the same methods can be adapted to study the convergence rate of the
+characters of its Sato-Tate group, and that they can also be employed in the
+more general case of abelian varieties over number fields. A key tool in our
+analysis is the existence of limiting distributions for automorphic
+$L$-functions, which is due to Akbary, Ng, and Shahabi.
+"
+21960,Adaptive Sampling for Convex Regression,"  In this paper, we introduce the first principled adaptive-sampling procedure
+for learning a convex function in the $L_\infty$ norm, a problem that arises
+often in the behavioral and social sciences. We present a function-specific
+measure of complexity and use it to prove that, for each convex function
+$f_{\star}$, our algorithm nearly attains the information-theoretically
+optimal, function-specific error rate. We also corroborate our theoretical
+contributions with numerical experiments, finding that our method substantially
+outperforms passive, uniform sampling for favorable synthetic and data-derived
+functions in low-noise settings with large sampling budgets. Our results also
+suggest an idealized ""oracle strategy"", which we use to gauge the potential
+advance of any adaptive-sampling strategy over passive sampling, for any given
+convex function.
+"
+21961,Time Accuracy Analysis of Post-Mediation Packet-Switched Charging Data Records for Urban Mobility Applications,"  Telecommunication data is being used increasingly in urban mobility
+applications around the world. Despite its ubiquity and usefulness, technical
+difficulties arise when using Packet-Switched Charging Data Records (CDR),
+since its main purpose was not intended for this kind of applications. Due to
+its particular nature, a trade-off must be considered between accessibility and
+time accuracy when using this data. On the one hand, to obtain highly accurate
+timestamps, huge amounts of network-level CDR must be extracted and stored.
+This task is very difficult and expensive since highly critical network node
+applications can be compromised in the data extraction and storage. On the
+other hand, post-mediation CDR can be easily accessed since no network node
+application is involved in its analysis. The pay-off is in the lower accurate
+timestamps recorded, since several aggregations and filtering is performed in
+previous steps of the charging pipelines. In this work, a detailed description
+of the timestamp error problem using post-mediation CDR is presented, together
+with a methodology to analyze error time series collected in each network cell.
+"
+21962,Multi-fidelity Bayesian Optimisation with Continuous Approximations,"  Bandit methods for black-box optimisation, such as Bayesian optimisation, are
+used in a variety of applications including hyper-parameter tuning and
+experiment design. Recently, \emph{multi-fidelity} methods have garnered
+considerable attention since function evaluations have become increasingly
+expensive in such applications. Multi-fidelity methods use cheap approximations
+to the function of interest to speed up the overall optimisation process.
+However, most multi-fidelity methods assume only a finite number of
+approximations. In many practical applications however, a continuous spectrum
+of approximations might be available. For instance, when tuning an expensive
+neural network, one might choose to approximate the cross validation
+performance using less data $N$ and/or few training iterations $T$. Here, the
+approximations are best viewed as arising out of a continuous two dimensional
+space $(N,T)$. In this work, we develop a Bayesian optimisation method, BOCA,
+for this setting. We characterise its theoretical properties and show that it
+achieves better regret than than strategies which ignore the approximations.
+BOCA outperforms several other baselines in synthetic and real experiments.
+"
+21963,X-rays writing/reading of Charge Density Waves in the CuO2 plane of a simple cuprate superconductor,"  It is now well established that superconductivity in cuprates competes with
+charge modulations giving electronic phase separation at a nanoscale. More
+specifically, superconducting electronic current takes root in the available
+free space left by electronic charge ordered domains, called charge density
+wave (CDW) puddles. This means that CDW domain arrangement plays a fundamental
+role in the mechanism of high temperature superconductivity in cuprates. Here
+we report about the possibility of controlling the population and spatial
+organization of the charge density wave puddles in a single crystal La2CuO4+y
+through X - ray illumination and thermal treatments. We apply a pump - probe
+method based on X - ray illumination as pump and X - ray diffraction as a probe
+setting a writing and reading procedure of CDW puddles. Our findings are
+expected to allow new routes for advanced design and manipulation of
+superconducting pathways in new electronics.
+"
+21964,Alexandrov's theorem revisited,"  We show that among sets of finite perimeter balls are the only
+volume-constrained critical points of the perimeter functional.
+"
+21965,Autonomous Multirobot Excavation for Lunar Applications,"  In this paper, a control approach called Artificial Neural Tissue (ANT) is
+applied to multirobot excavation for lunar base preparation tasks including
+clearing landing pads and burying of habitat modules. We show for the first
+time, a team of autonomous robots excavating a terrain to match a given 3D
+blueprint. Constructing mounds around landing pads will provide physical
+shielding from debris during launch/landing. Burying a human habitat modules
+under 0.5 m of lunar regolith is expected to provide both radiation shielding
+and maintain temperatures of -25 $^{o}$C. This minimizes base life-support
+complexity and reduces launch mass. ANT is compelling for a lunar mission
+because it doesn't require a team of astronauts for excavation and it requires
+minimal supervision. The robot teams are shown to autonomously interpret
+blueprints, excavate and prepare sites for a lunar base. Because little
+pre-programmed knowledge is provided, the controllers discover creative
+techniques. ANT evolves techniques such as slot-dozing that would otherwise
+require excavation experts. This is critical in making an excavation mission
+feasible when it is prohibitively expensive to send astronauts. The controllers
+evolve elaborate negotiation behaviors to work in close quarters. These and
+other techniques such as concurrent evolution of the controller and team size
+are shown to tackle problem of antagonism, when too many robots interfere
+reducing the overall efficiency or worse, resulting in gridlock. While many
+challenges remain with this technology our work shows a compelling pathway for
+field testing this approach.
+"
+21966,Optimal Context Aware Transmission Strategy for non-Orthogonal D2D Communications,"  The increasing traffic demand in cellular networks has recently led to the
+investigation of new strategies to save precious resources like spectrum and
+energy. A possible solution employs direct device-to-device (D2D)
+communications, which is particularly promising when the two terminals involved
+in the communications are located in close proximity. The D2D communications
+should coexist with other transmissions, so they must be careful scheduled in
+order to avoid harmful interference impacts. In this paper, we analyze how a
+distributed context-awareness, obtained by observing few local channel and
+topology parameters, can be used to adaptively exploit D2D communications. We
+develop a rigorous theoretical analysis to quantify the balance between the
+gain offered by a D2D transmission, and its impact on the other network
+communications. Based on this analysis, we derive two theorems that define the
+optimal strategy to be employed, in terms of throughput maximization, when a
+single or multiple transmit power levels are available for the D2D
+communications. We compare this strategy to the state-of-the-art in the same
+network scenario, showing how context awareness can be exploited to achieve a
+higher sum throughput and an improved fairness.
+"
+21967,Giant-spin nonlinear response theory of magnetic nanoparticle hyperthermia: a field dependence study,"  Understanding high-field amplitude electromagnetic heat loss phenomena is of
+great importance, in particular in the biomedical field, since the
+heat-delivery treatment plans might rely on analytical models that are only
+valid at low field amplitudes. Here, we develop a nonlinear response model
+valid for single- domain nanoparticles of larger particle sizes and higher
+field amplitudes in comparison to linear response theory. A nonlinear
+magnetization expression and a generalized heat loss power equation are
+obtained and compared with the exact solution of the stochastic
+Landau-Lifshitz-Gilbert equation assuming the giant-spin hypothesis. The model
+is valid within the hyperthermia therapeutic window and predicts a shift of
+optimum particle size and distinct heat loss field amplitude exponents.
+Experimental hyperthermia data with distinct ferrite-based nanoparticles, as
+well as third harmonic magnetization data supports the nonlinear model, which
+also has implications for magnetic particle imaging and magnetic thermometry.
+"
+21968,Anisotropy crossover in the frustrated Hubbard model on four-chain cylinders,"  Motivated by dimensional crossover in layered organic ${\kappa}$ salts, we
+determine the phase diagram of a system of four periodically coupled Hubbard
+chains with frustration at half filling as a function of the interchain hopping
+${t_{\perp}/t}$ and interaction strength ${U/t}$ at a fixed ratio of
+frustration and interchain hopping ${t'/t_{\perp}=-0.5}$. We cover the range
+from the one-dimensional limit of uncoupled chains (${t_{\perp}/t=0.0}$) to the
+isotropic model (${t_{\perp}/t=1.0}$). For strong ${U/t}$, we find an
+antiferromagnetic insulator; in the weak-to-moderate-interaction regime, the
+phase diagram features quasi-one-dimensional antiferromagnetic behavior, an
+incommensurate spin-density wave, and a metallic phase as ${t_{\perp}/t}$ is
+increased. We characterize the phases through their magnetic ordering,
+dielectric response, and dominant static correlations. Our analysis is based
+primarily on a variant of the density-matrix renormalization-group algorithm
+based on an efficient hybrid-real-momentum-space formulation, in which we can
+treat relatively large lattices albeit of a limited width. This is complemented
+by a variational cluster approximation study with a cluster geometry
+corresponding to the cylindrical lattice allowing us to directly compare the
+two methods for this geometry. As an outlook, we make contact with work
+studying dimensional crossover in the full two-dimensional system.
+"
+21969,On the concavity of a sum of elementary symmetric polynomials,"  We introduce a new problem on the elementary symmetric polynomials
+$\sigma_k$, stemming from the constraint equations of some modified gravity
+theory. For which coefficients is a linear combination of $\sigma_k$
+$1/p$-concave, with $0 \leq k \leq p$? We establish connections between the
+$1/p$-concavity and the real-rootedness of some polynomials built on the
+coefficients. We conjecture that if the restriction of the linear combination
+to the positive diagonal is a real-rooted polynomial, then the linear
+combination is $1/p$-concave. Using the theory of hyperbolic polynomials, we
+show that this would be implied by a short algebraic statement: if the
+polynomials $P$ and $Q$ of degree $n$ are real-rooted, then $\sum_{k=0}^n
+P^{(k)}Q^{(n-k)}$ is real-rooted as well. This is not proven yet. We conjecture
+more generally that the global $1/p$-concavity is equivalent to the
+$1/p$-concavity on the positive diagonal. We prove all our guessings for $p=2$.
+The way is open for further developments.
+"
+21970,MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation,"  For the foreseeble future, human beings will likely remain an integral part
+of the driving task, monitoring the AI system as it performs anywhere from just
+over 0% to just under 100% of the driving. The governing objectives of the MIT
+Autonomous Vehicle Technology (MIT-AVT) study are to (1) undertake large-scale
+real-world driving data collection that includes high-definition video to fuel
+the development of deep learning based internal and external perception
+systems, (2) gain a holistic understanding of how human beings interact with
+vehicle automation technology by integrating video data with vehicle state
+data, driver characteristics, mental models, and self-reported experiences with
+technology, and (3) identify how technology and other factors related to
+automation adoption and use can be improved in ways that save lives. In
+pursuing these objectives, we have instrumented 21 Tesla Model S and Model X
+vehicles, 2 Volvo S90 vehicles, 2 Range Rover Evoque, and 2 Cadillac CT6
+vehicles for both long-term (over a year per driver) and medium term (one month
+per driver) naturalistic driving data collection. Furthermore, we are
+continually developing new methods for analysis of the massive-scale dataset
+collected from the instrumented vehicle fleet. The recorded data streams
+include IMU, GPS, CAN messages, and high-definition video streams of the driver
+face, the driver cabin, the forward roadway, and the instrument cluster (on
+select vehicles). The study is on-going and growing. To date, we have 99
+participants, 11,846 days of participation, 405,807 miles, and 5.5 billion
+video frames. This paper presents the design of the study, the data collection
+hardware, the processing of the data, and the computer vision algorithms
+currently being used to extract actionable knowledge from the data.
+"
+21971,Propagation estimates in the one-commutator theory,"  In the abstract framework of Mourre theory, the propagation of states is
+understood in terms of a conjugate operator $A$. A powerful estimate has long
+been known for Hamiltonians having a good regularity with respect to $A$ thanks
+to the limiting absorption principle (LAP). We study the case where $H$ has
+less regularity with respect to $A$, specifically in a situation where the LAP
+and the absence of singularly continuous spectrum have not yet been
+established. We show that in this case the spectral measure of $H$ is a
+Rajchman measure and we derive some propagation estimates. One estimate is an
+application of minimal escape velocities, while the other estimate relies on an
+improved version of the RAGE formula. Based on several examples, including
+continuous and discrete Schrödinger operators, it appears that the latter
+propagation estimate is a new result for multi-dimensional Hamiltonians.
+"
+21972,Collocation Methods for Exploring Perturbations in Linear Stability Analysis,"  Eigenvalue analysis is a well-established tool for stability analysis of
+dynamical systems. However, there are situations where eigenvalues miss some
+important features of physical models. For example, in models of incompressible
+fluid dynamics, there are examples where linear stability analysis predicts
+stability but transient simulations exhibit significant growth of infinitesimal
+perturbations. This behavior can be predicted by pseudo-spectral analysis. In
+this study, we show that an approach similar to pseudo-spectral analysis can be
+performed inexpensively using stochastic collocation methods and the results
+can be used to provide quantitative information about instability. In addition,
+we demonstrate that the results of the perturbation analysis provide insight
+into the behavior of unsteady flow simulations.
+"
+21973,Outlier Robust Online Learning,"  We consider the problem of learning from noisy data in practical settings
+where the size of data is too large to store on a single machine. More
+challenging, the data coming from the wild may contain malicious outliers. To
+address the scalability and robustness issues, we present an online robust
+learning (ORL) approach. ORL is simple to implement and has provable robustness
+guarantee -- in stark contrast to existing online learning approaches that are
+generally fragile to outliers. We specialize the ORL approach for two concrete
+cases: online robust principal component analysis and online linear regression.
+We demonstrate the efficiency and robustness advantages of ORL through
+comprehensive simulations and predicting image tags on a large-scale data set.
+We also discuss extension of the ORL to distributed learning and provide
+experimental evaluations.
+"
+21974,Sparsity Regularization and feature selection in large dimensional data,"  Feature selection has evolved to be an important step in several machine
+learning paradigms. Especially in the domains of bio-informatics and text
+classification which involve data of high dimensions, feature selection can
+help in drastically reducing the feature space. In cases where it is difficult
+or infeasible to obtain sufficient number of training examples, feature
+selection helps overcome the curse of dimensionality which in turn helps
+improve performance of the classification algorithm. The focus of our research
+here are five embedded feature selection methods which use either the ridge
+regression, or Lasso regression, or a combination of the two in the
+regularization part of the optimization function. We evaluate five chosen
+methods on five large dimensional datasets and compare them on the parameters
+of sparsity and correlation in the datasets and their execution times.
+"
+21975,The Diverse Cohort Selection Problem,"  How should a firm allocate its limited interviewing resources to select the
+optimal cohort of new employees from a large set of job applicants? How should
+that firm allocate cheap but noisy resume screenings and expensive but in-depth
+in-person interviews? We view this problem through the lens of combinatorial
+pure exploration (CPE) in the multi-armed bandit setting, where a central
+learning agent performs costly exploration of a set of arms before selecting a
+final subset with some combinatorial structure. We generalize a recent CPE
+algorithm to the setting where arm pulls can have different costs, and return
+different levels of information, and prove theoretical upper bounds for a
+general class of arm-pulling strategies in this new setting. We then apply our
+general algorithm to a real-world problem with combinatorial structure:
+incorporating diversity into university admissions. We take real data from
+admissions at one of the largest US-based computer science graduate programs
+and show that a simulation of our algorithm produces more diverse student
+cohorts at low cost to individual student quality, spending comparable budget
+to the current admissions process at that university.
+"
+21976,Recurrent fast radio bursts from collisions of neutron stars in the evolved stellar clusters,"  We propose the model describing the observed multiple fast radio bursts due
+to the close encounters and collisions of neutron stars in the central clusters
+of the evolved galactic nuclei. The subsystem of neutron star cluster may
+originate in the dense galactic nucleus evolutionary in the combined processes
+of stellar and dynamical evolution. The neutron stars in the compact cluster
+can produce the short living binaries with the highly eccentric orbits, and
+finally collide after several orbital revolutions. Fast radio bursts may be
+produced during the close periastron approach and at the process of the final
+binary merging. In the sufficiently dense star cluster the neutron stars
+collisions can be very frequent. Therefore, this model can explain in principle
+the observed recurrent (multiple) fast radio bursts, analogous to the observed
+ones from the source FRB 121102. Among the possible observational signatures of
+the proposed model may be the registration of the gravitational wave bursts by
+the laser interferometers LIGO/VIRGO or by the next generation of gravitational
+wave detectors.
+"
+21977,Emotion Recognition From Speech With Recurrent Neural Networks,"  In this paper the task of emotion recognition from speech is considered.
+Proposed approach uses deep recurrent neural network trained on a sequence of
+acoustic features calculated over small speech intervals. At the same time
+special probabilistic-nature CTC loss function allows to consider long
+utterances containing both emotional and neutral parts. The effectiveness of
+such an approach is shown in two ways. Firstly, the comparison with recent
+advances in this field is carried out. Secondly, human performance on the same
+task is measured. Both criteria show the high quality of the proposed method.
+"
+21978,Intra- and intercycle interference of angle-resolved electron emission in laser assisted XUV atomic ionization,"  A theoretical study of ionization of the hydrogen atom due to an XUV pulse in
+the presence of an IR laser is presented. Well-established theories are usually
+used to describe the problem of laser assisted photoelectron effect. However,
+the well-known soft-photon approximation firstly posed by Maquet et al in
+Journal of Modern Optics 54, 1847 (2007) and Kazansky's theory in Phys. Rev. A
+82, 033420 (2010) completely fails to predict the electron emission
+prependicularly to the polarization direction. Making use of a simple
+semiclassical model, we study the angle-resolved energy distribution of
+photoelectrons for the case that both fields are linearly polarized in the same
+direction. We thoroughly analize and characterize two different emission
+regions in the angle-energy domain: (i) the parallel-like region with
+contribution of two classical trajectories per optical cycle and (ii) the
+perpendicular-like region with contribution of four classical trajectories per
+optical cycle. We show that our semiclassical model is able to asses the
+interference patterns of the angle-resolved photoelectron spectrum in the two
+different mentioned regions. Electron trajectories stemming from different
+optical laser cycles give rise to angle-independent intercycle interference
+known as sidebands. These sidebands are modulated by an angle-dependent
+coarse-grained structure coming from the intracycle interference of the
+electron trajectories born during the same optical cycle. We show the accuracy
+of our semiclassical model as a function of the time delay between the IR and
+the XUV pulses and also as a function of the laser intensity by comparing the
+semiclassical predictions of the angle-resolved photoelectron spectrum with the
+continuum-distorted wave strong field approximation and the ab initio solution
+of the time dependent Schrödinger equation.
+"
+21979,Inferring transport characteristics in a fractured rock aquifer by combining single-hole GPR reflection monitoring and tracer test data,"  Investigations of solute transport in fractured rock aquifers often rely on
+tracer test data acquired at a limited number of observation points. Such data
+do not, by themselves, allow detailed assessments of the spreading of the
+injected tracer plume. To better understand the transport behavior in a
+granitic aquifer, we combine tracer test data with single-hole
+ground-penetrating radar (GPR) reflection monitoring data. Five successful
+tracer tests were performed under various experimental conditions between two
+boreholes 6 m apart. For each experiment, saline tracer was injected into a
+previously identified packed-off transmissive fracture while repeatedly
+acquiring single-hole GPR reflection profiles together with electrical
+conductivity logs in the pumping borehole. By analyzing depth-migrated GPR
+difference images together with tracer breakthrough curves and associated
+simplified flow and transport modeling, we estimate (1) the number, the
+connectivity, and the geometry of fractures that contribute to tracer
+transport, (2) the velocity and the mass of tracer that was carried along each
+flow path, and (3) the effective transport parameters of the identified flow
+paths. We find a qualitative agreement when comparing the time evolution of GPR
+reflectivity strengths at strategic locations in the formation with those
+arising from simulated transport. The discrepancies are on the same order as
+those between observed and simulated breakthrough curves at the outflow
+locations. The rather subtle and repeatable GPR signals provide useful and
+complementary information to tracer test data acquired at the outflow locations
+and may help us to characterize transport phenomena in fractured rock aquifers.
+"
+21980,Robust Audio Watermarking Algorithm Based on Moving Average and DCT,"  Noise is often brought to host audio by common signal processing operation,
+and it usually changes the high-frequency component of an audio signal. So
+embedding watermark by adjusting low-frequency coefficient can improve the
+robustness of a watermark scheme. Moving Average sequence is a low-frequency
+feature of an audio signal. This work proposed a method which embedding
+watermark into the maximal coefficient in discrete cosine transform domain of a
+moving average sequence. Subjective and objective tests reveal that the
+proposed watermarking scheme maintains highly audio quality, and
+simultaneously, the algorithm is highly robust to common digital signal
+processing operations, including additive noise, sampling rate change, bit
+resolution transformation, MP3 compression, and random cropping, especially
+low-pass filtering.
+"
+21981,Resource-Efficient Common Randomness and Secret-Key Schemes,"  We study common randomness where two parties have access to i.i.d. samples
+from a known random source, and wish to generate a shared random key using
+limited (or no) communication with the largest possible probability of
+agreement. This problem is at the core of secret key generation in
+cryptography, with connections to communication under uncertainty and locality
+sensitive hashing. We take the approach of treating correlated sources as a
+critical resource, and ask whether common randomness can be generated
+resource-efficiently.
+We consider two notable sources in this setup arising from correlated bits
+and correlated Gaussians. We design the first explicit schemes that use only a
+polynomial number of samples (in the key length) so that the players can
+generate shared keys that agree with constant probability using optimal
+communication. The best previously known schemes were both non-constructive and
+used an exponential number of samples. In the amortized setting, we
+characterize the largest achievable ratio of key length to communication in
+terms of the external and internal information costs, two well-studied
+quantities in theoretical computer science. In the relaxed setting where the
+two parties merely wish to improve the correlation between the generated keys
+of length $k$, we show that there are no interactive protocols using $o(k)$
+bits of communication having agreement probability even as small as
+$2^{-o(k)}$. For the related communication problem where the players wish to
+compute a joint function $f$ of their inputs using i.i.d. samples from a known
+source, we give a zero-communication protocol using $2^{O(c)}$ bits where $c$
+is the interactive randomized public-coin communication complexity of $f$. This
+matches the lower bound shown previously while the best previously known upper
+bound was doubly exponential in $c$.
+"
+21982,Exploring Outliers in Crowdsourced Ranking for QoE,"  Outlier detection is a crucial part of robust evaluation for crowdsourceable
+assessment of Quality of Experience (QoE) and has attracted much attention in
+recent years. In this paper, we propose some simple and fast algorithms for
+outlier detection and robust QoE evaluation based on the nonconvex optimization
+principle. Several iterative procedures are designed with or without knowing
+the number of outliers in samples. Theoretical analysis is given to show that
+such procedures can reach statistically good estimates under mild conditions.
+Finally, experimental results with simulated and real-world crowdsourcing
+datasets show that the proposed algorithms could produce similar performance to
+Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up,
+without or with a prior knowledge on the sparsity size of outliers,
+respectively. Therefore the proposed methodology provides us a set of helpful
+tools for robust QoE evaluation with crowdsourcing data.
+"
+21983,The distribution of $G$-Weyl CM fields and the Colmez conjecture,"  Assuming a weak form of the upper bound in Malle's conjecture, we prove that
+the Colmez conjecture is true for $100\%$ of CM fields of any fixed degree,
+when ordered by discriminant. This weak form of the upper bound in Malle's
+conjecture is known in many cases, which allows us to produce infinitely many
+density-one families of non-abelian CM fields which satisfy the Colmez
+conjecture.
+"
+21984,Improving Grey-Box Fuzzing by Modeling Program Behavior,"  Grey-box fuzzers such as American Fuzzy Lop (AFL) are popular tools for
+finding bugs and potential vulnerabilities in programs. While these fuzzers
+have been able to find vulnerabilities in many widely used programs, they are
+not efficient; of the millions of inputs executed by AFL in a typical fuzzing
+run, only a handful discover unseen behavior or trigger a crash. The remaining
+inputs are redundant, exhibiting behavior that has already been observed. Here,
+we present an approach to increase the efficiency of fuzzers like AFL by
+applying machine learning to directly model how programs behave. We learn a
+forward prediction model that maps program inputs to execution traces, training
+on the thousands of inputs collected during standard fuzzing. This learned
+model guides exploration by focusing on fuzzing inputs on which our model is
+the most uncertain (measured via the entropy of the predicted execution trace
+distribution). By focusing on executing inputs our learned model is unsure
+about, and ignoring any input whose behavior our model is certain about, we
+show that we can significantly limit wasteful execution. Through testing our
+approach on a set of binaries released as part of the DARPA Cyber Grand
+Challenge, we show that our approach is able to find a set of inputs that
+result in more code coverage and discovered crashes than baseline fuzzers with
+significantly fewer executions.
+"
+21985,A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields,"  Classical methods for X-ray computed tomography are based on the assumption
+that the X-ray source intensity is known, but in practice, the intensity is
+measured and hence uncertain. Under normal operating conditions, when the
+exposure time is sufficiently high, this kind of uncertainty typically has a
+negligible effect on the reconstruction quality. However, in time- or
+dose-limited applications such as dynamic CT, this uncertainty may cause severe
+and systematic artifacts known as ring artifacts. By carefully modeling the
+measurement process and by taking uncertainties into account, we derive a new
+convex model that leads to improved reconstructions despite poor quality
+measurements. We demonstrate the effectiveness of the methodology based on
+simulated and real data sets.
+"
+21986,Mathematical Backdoors in Symmetric Encryption Systems - Proposal for a Backdoored AES-like Block Cipher,"  Recent years have shown that more than ever governments and intelligence
+agencies try to control and bypass the cryptographic means used for the
+protection of data. Backdooring encryption algorithms is considered as the best
+way to enforce cryptographic control. Until now, only implementation backdoors
+(at the protocol/implementation/management level) are generally considered. In
+this paper we propose to address the most critical issue of backdoors:
+mathematical backdoors or by-design backdoors, which are put directly at the
+mathematical design of the encryption algorithm. While the algorithm may be
+totally public, proving that there is a backdoor, identifying it and exploiting
+it, may be an intractable problem. We intend to explain that it is probably
+possible to design and put such backdoors. Considering a particular family
+(among all the possible ones), we present BEA-1, a block cipher algorithm which
+is similar to the AES and which contains a mathematical backdoor enabling an
+operational and effective cryptanalysis. The BEA-1 algorithm (80-bit block
+size, 120-bit key, 11 rounds) is designed to resist to linear and differential
+cryptanalyses. A challenge will be proposed to the cryptography community soon.
+Its aim is to assess whether our backdoor is easily detectable and exploitable
+or not.
+"
+21987,Kinetostatic analysis and solution classification of a class of planar tensegrity mechanisms,"  Tensegrity mechanisms are composed of rigid and tensile parts that are in
+equilibrium. They are interesting alternative designs for some applications,
+such as modelling musculo-skeleton systems. Tensegrity mechanisms are more
+difficult to analyze than classical mechanisms as the static equilibrium
+conditions that must be satisfied generally result in complex equations. A
+class of planar one-degree-of-freedom tensegrity mechanisms with three linear
+springs is analyzed in detail for the sake of systematic solution
+classifications. The kinetostatic equations are derived and solved under
+several loading and geometric conditions. It is shown that these mechanisms
+exhibit up to six equilibrium configurations, of which one or two are stable,
+depending on the geometric and loading conditions. Discriminant varieties and
+cylindrical algebraic decomposition combined with Groebner base elimination are
+used to classify solutions as a function of the geometric, loading and actuator
+input parameters.
+"
+21988,A Framework to Support the Trust Process in News and Social Media,"  Current society is heavily influenced by the spread of online information,
+containing all sorts of claims, commonly found in news stories, tweets, and
+social media postings. Depending on the user, they may be considered ""true"" or
+""false"", according to the agent's trust on the claim. In this paper, we discuss
+the concept of content trust and trust process, and propose a framework to
+describe the trust process, which can support various possible models of
+content trust.
+"
+21989,Optimal control of Rydberg lattice gases,"  We present optimal control protocols to prepare different many-body quantum
+states of Rydberg atoms in optical lattices. Specifically, we show how to
+prepare highly ordered many-body ground states, GHZ states as well as some
+superposition of symmetric excitation number Fock states, that inherit the
+translational symmetry from the Hamiltonian, within sufficiently short
+excitation times minimizing detrimental decoherence effects. For the GHZ
+states, we propose a two-step detection protocol to experimentally verify the
+optimal preparation of the target state based only on standard measurement
+techniques. Realistic experimental constraints and imperfections are taken into
+account by our optimization procedure making it applicable to ongoing
+experiments.
+"
+21990,Sharp resolvent bounds and resonance-free regions,"  In this note, we consider semiclassical scattering on a manifold which is
+Euclidean near infinity or asymptotically hyperbolic. We show that, if the
+cut-off resolvent satisfies polynomial estimates in a strip of size $O(h |\log
+h|^{-\alpha})$ below the real axis, for some $\alpha\geq 0$, then the cut-off
+resolvent is actually bounded by $O(|\log h|^{\alpha+1} h^{-1})$ in this strip.
+As an application, we improve slightly the estimates on the real axis given by
+Bourgain and Dyatlov in the case of convex co-compact surfaces.
+"
+21991,Automatic estimation of harmonic tension by distributed representation of chords,"  The buildup and release of a sense of tension is one of the most essential
+aspects of the process of listening to music. A veridical computational model
+of perceived musical tension would be an important ingredient for many music
+informatics applications. The present paper presents a new approach to
+modelling harmonic tension based on a distributed representation of chords. The
+starting hypothesis is that harmonic tension as perceived by human listeners is
+related, among other things, to the expectedness of harmonic units (chords) in
+their local harmonic context. We train a word2vec-type neural network to learn
+a vector space that captures contextual similarity and expectedness, and define
+a quantitative measure of harmonic tension on top of this. To assess the
+veridicality of the model, we compare its outputs on a number of well-defined
+chord classes and cadential contexts to results from pertinent empirical
+studies in music psychology. Statistical analysis shows that the model's
+predictions conform very well with empirical evidence obtained from human
+listeners.
+"
+21992,Online Natural Gradient as a Kalman Filter,"  We cast Amari's natural gradient in statistical learning as a specific case
+of Kalman filtering. Namely, applying an extended Kalman filter to estimate a
+fixed unknown parameter of a probabilistic model from a series of observations,
+is rigorously equivalent to estimating this parameter via an online stochastic
+natural gradient descent on the log-likelihood of the observations.
+In the i.i.d. case, this relation is a consequence of the ""information
+filter"" phrasing of the extended Kalman filter. In the recurrent (state space,
+non-i.i.d.) case, we prove that the joint Kalman filter over states and
+parameters is a natural gradient on top of real-time recurrent learning (RTRL),
+a classical algorithm to train recurrent models.
+This exact algebraic correspondence provides relevant interpretations for
+natural gradient hyperparameters such as learning rates or initialization and
+regularization of the Fisher information matrix.
+"
+21993,Cyclic period oscillation of the eclipsing dwarf nova DV UMa,"  DV UMa is an eclipsing dwarf nova with an orbital period of $\sim2.06$ h,
+which lies just at the bottom edge of the period gap. To detect its orbital
+period changes we present 12 new mid-eclipse times by using our CCD photometric
+data and archival data. Combining with the published mid-eclipse times in
+quiescence, spanning $\sim30$ yr, the latest version of the $O-C$ diagram was
+obtained and analyzed. The best fit to those available eclipse timings shows
+that the orbital period of DV UMa is undergoing a cyclic oscillation with a
+period of $17.58(\pm0.52)$ yr and an amplitude of $71.1(\pm6.7)$ s. The
+periodic variation most likely arises from the light-travel-time effect via the
+presence of a circumbinary object because the required energy to drive the
+Applegate mechanism is too high in this system. The mass of the unseen
+companion was derived as $M_{3}\sin{i'}=0.025(\pm0.004)M_{\odot}$. If the third
+body is in the orbital plane (i.e. $i'=i=82.9^{\circ}$) of the eclipsing pair,
+it would match to a brown dwarf. This hypothetical brown dwarf is orbiting its
+host star at a separation of $\sim8.6$ AU in an eccentric orbit ($e=0.44$).
+"
+21994,Shallow-water models for a vibrating fluid,"  We consider a layer of an inviscid fluid with free surface which is subject
+to vertical high-frequency vibrations. We derive three asymptotic systems of
+equations that describe slowly evolving (in comparison with the vibration
+frequency) free-surface waves. The first set of equations is obtained without
+assuming that the waves are long. These equations are as difficult to solve as
+the exact equations for irrotational water waves in a non-vibrating fluid. The
+other two models describe long waves. These models are obtained under two
+different assumptions about the amplitude of the vibration. Surprisingly, the
+governing equations have exactly the same form in both cases (up to
+interpretation of some constants). These equations reduce to the standard
+dispersionless shallow-water equations if the vibration is absent, and the
+vibration manifests itself via an additional term which makes the equations
+dispersive and, for small-amplitude waves, is similar to the term that would
+appear if surface tension were taken into account. We show that our dispersive
+shallow water equations have both solitary and periodic travelling waves
+solutions and discuss an analogy between these solutions and travelling
+capillary-gravity waves in a non-vibrating fluid.
+"
+21995,Electrically Tunable Optical Nonlinearities in Graphene-Covered SiN Waveguides Characterized by Four-Wave Mixing,"  We present a degenerate four-wave mixing experiment on a silicon nitride
+(SiN) waveguide covered with gated graphene. We observe strong dependencies on
+signal-pump detuning and Fermi energy, i.e. the optical nonlinearity is
+demonstrated to be electrically tunable. In the vicinity of the interband
+absorption edge ($2|E_F|\approx \hbar\omega$) a peak value of the waveguide
+nonlinear parameter of $\approx$ 6400 m$^{-1}$W$^{-1}$, corresponding to a
+graphene nonlinear sheet conductivity $|\sigma_s^{(3)}|\approx4.3\cdot
+10^{-19}$ A m$^2$V$^{-3}$ is measured.
+"
+21996,Binary superlattice design by controlling DNA-mediated interactions,"  Most binary superlattices created using DNA functionalization or other
+approaches rely on particle size differences to achieve compositional order and
+structural diversity. Here we study two-dimensional (2D) assembly of
+DNA-functionalized micron-sized particles (DFPs), and employ a strategy that
+leverages the tunable disparity in interparticle interactions, and thus
+enthalpic driving forces, to open new avenues for design of binary
+superlattices that do not rely on the ability to tune particle size (i.e.,
+entropic driving forces). Our strategy employs tailored blends of complementary
+strands of ssDNA to control interparticle interactions between micron-sized
+silica particles in a binary mixture to create compositionally diverse 2D
+lattices. We show that the particle arrangement can be further controlled by
+changing the stoichiometry of the binary mixture in certain cases. With this
+approach, we demonstrate the abil- ity to program the particle assembly into
+square, pentagonal, and hexagonal lattices. In addition, different particle
+types can be compositionally ordered in square checkerboard and hexagonal -
+alternating string, honeycomb, and Kagome arrangements.
+"
+21997,"A New Use of Douglas-Rachford Splitting and ADMM for Identifying Infeasible, Unbounded, and Pathological Conic Programs","  In this paper, we present a method for identifying infeasible, unbounded, and
+pathological conic programs based on Douglas-Rachford splitting, or
+equivalently ADMM. When an optimization program is infeasible, unbounded, or
+pathological, the iterates of Douglas-Rachford splitting diverge. Somewhat
+surprisingly, such divergent iterates still provide useful information, which
+our method uses for identification. In addition, for strongly infeasible
+problems the method produces a separating hyperplane and informs the user on
+how to minimally modify the given problem to achieve strong feasibility. As a
+first-order method, the proposed algorithm relies on simple subroutines, and
+therefore is simple to implement and has low per-iteration cost.
+"
+21998,A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics,"  We study the Stochastic Gradient Langevin Dynamics (SGLD) algorithm for
+non-convex optimization. The algorithm performs stochastic gradient descent,
+where in each step it injects appropriately scaled Gaussian noise to the
+update. We analyze the algorithm's hitting time to an arbitrary subset of the
+parameter space. Two results follow from our general theory: First, we prove
+that for empirical risk minimization, if the empirical risk is point-wise close
+to the (smooth) population risk, then the algorithm achieves an approximate
+local minimum of the population risk in polynomial time, escaping suboptimal
+local minima that only exist in the empirical risk. Second, we show that SGLD
+improves on one of the best known learnability results for learning linear
+classifiers under the zero-one loss.
+"
+21999,Shallow Water Dynamics on Linear Shear Flows and Plane Beaches,"  Long waves in shallow water propagating over a background shear flow towards
+a sloping beach are being investigated. The classical shallow-water equations
+are extended to incorporate both a background shear flow and a linear beach
+profile, resulting in a non-reducible hyperbolic system. Nevertheless, it is
+shown how several changes of variables based on the hodograph transform may be
+used to transform the system into a linear equation which may be solved exactly
+using the method of separation of variables. This method can be used to
+investigate the run-up of a long wave on a planar beach including the
+development of the shoreline.
+"
+22000,p-Multigrid matrix-free discontinuous Galerkin solution strategies for the under-resolved simulation of incompressible turbulent flows,"  In recent years several research efforts focused on the development of
+high-order discontinuous Galerkin (dG) methods for scale resolving simulations
+of turbulent flows. Nevertheless, in the context of incompressible flow
+computations, the computational expense of solving large scale equation systems
+characterized by indefinite Jacobian matrices has often prevented from dealing
+with industrially-relevant computations. In this work we seek to improve the
+efficiency of Rosenbrock-type linearly-implicit Runge-Kutta methods by devising
+robust, scalable and memory-lean solution strategies. In particular, we
+combined p-multigrid preconditioners with matrix-free Krylov iterative solvers:
+the p-multigrid preconditioner relies on specifically crafted
+rescaled-inherited coarse operators and cheap block-diagonal smoother's
+preconditioners to obtain satisfactory convergence rates and a low memory
+occupation. Extensive numerical validation is performed. The rescaled-inherited
+p-multigrid algorithm for the BR2 dG discretization is firstly validated
+solving Poisson problems. The Rosenbrock formulation is then applied to test
+cases of growing complexity: the laminar unsteady flow around a two-dimensional
+cylinder at Re=200 and around a sphere at Re=300, and finally the transitional
+T3L1 flow problem of the ERCOFTAC test case suite with different levels of
+free-stream turbulence. For the latter good agreement with experimental data is
+documented, moreover, strong memory savings and execution time gains with
+respect to state-of-the art preconditioners are reported.
+"
+22001,Internet - assisted risk assessment of infectious diseases in women sexual and reproductive health,"  We develop open source infection risk calculators for patients and healthcare
+professionals as apps for hospital acquired infections (during child-delivery)
+and sexually transmitted infections (like HIV). Advanced versions of ehealth in
+non-communicable diseases do not apply to epidemiology much. There is, however,
+no infection risk calculator in the Polish Internet so far, despite the
+existence of data that may be applied to create such a tool.
+The algorithms involve data from Information Systems (like HIS in hospitals)
+and surveys by applying mathematical modelling, Bayesian inference, logistic
+regressions, covariance analysis and social network analysis. Finally, user may
+fill or import data from Information System to obtain risk assessment and test
+different settings to learn overall risk.
+The most promising risk calculator is developed for Healthcare-associated
+infections in modes for patient hospital sanitary inspection. The most extended
+version for hospital epidemiologists may include many layers of hospital
+interactions by agent-based modeling. Simplified version of calculator is
+dedicated to patients that require personalized hospitalization history of
+pregnancy described by questions represented by quantitative and qualitative
+variables. Patients receive risk assessment from interactive web application
+with additional description about modifiable risk factors.
+We also provide solution for sexually transmitted infections like HIV. The
+results of calculations with meaningful description and percentage chances are
+presented in real-time to interested users. Finally, user fills the form to
+obtain risk assessment for given settings.
+"
+22002,Neural Program Meta-Induction,"  Most recently proposed methods for Neural Program Induction work under the
+assumption of having a large set of input/output (I/O) examples for learning
+any underlying input-output mapping. This paper aims to address the problem of
+data and computation efficiency of program induction by leveraging information
+from related tasks. Specifically, we propose two approaches for cross-task
+knowledge transfer to improve program induction in limited-data scenarios. In
+our first proposal, portfolio adaptation, a set of induction models is
+pretrained on a set of related tasks, and the best model is adapted towards the
+new task using transfer learning. In our second approach, meta program
+induction, a $k$-shot learning approach is used to make a model generalize to
+new tasks without additional training. To test the efficacy of our methods, we
+constructed a new benchmark of programs written in the Karel programming
+language. Using an extensive experimental evaluation on the Karel benchmark, we
+demonstrate that our proposals dramatically outperform the baseline induction
+method that does not use knowledge transfer. We also analyze the relative
+performance of the two approaches and study conditions in which they perform
+best. In particular, meta induction outperforms all existing approaches under
+extreme data sparsity (when a very small number of examples are available),
+i.e., fewer than ten. As the number of available I/O examples increase (i.e. a
+thousand or more), portfolio adapted program induction becomes the best
+approach. For intermediate data sizes, we demonstrate that the combined method
+of adapted meta program induction has the strongest performance.
+"
+22003,Model Selection for Anomaly Detection,"  Anomaly detection based on one-class classification algorithms is broadly
+used in many applied domains like image processing (e.g. detection of whether a
+patient is ""cancerous"" or ""healthy"" from mammography image), network intrusion
+detection, etc. Performance of an anomaly detection algorithm crucially depends
+on a kernel, used to measure similarity in a feature space. The standard
+approaches (e.g. cross-validation) for kernel selection, used in two-class
+classification problems, can not be used directly due to the specific nature of
+a data (absence of a second, abnormal, class data). In this paper we generalize
+several kernel selection methods from binary-class case to the case of
+one-class classification and perform extensive comparison of these approaches
+using both synthetic and real-world data.
+"
+22004,Lines of descent in the deterministic mutation-selection model with pairwise interaction,"  We consider the mutation-selection differential equation with pairwise
+interaction and establish the corresponding ancestral process, which is a
+specific random tree and a variant of the ancestral selection graph. To make
+this object tractable, we prune the tree upon mutation, thus reducing it to its
+informative parts. The hierarchies inherent in the tree are encoded
+systematically via ternary trees with weighted leaves; this leads to the
+stratified ancestral selection graph. The latter is dual to the
+mutation-selection equation and provides a stochastic representation of its
+solution. It also allows to reveal the genealogical structures inherent in the
+bifurcations of the equilibria of the differential equation. Furthermore, we
+establish constructions, again based on the stratified ancestral selection
+graph, that allow to trace back the ancestral lines into the distant past and
+obtain explicit results about the ancestral population in the case of
+unidirectional mutation.
+"
+22005,"On the challenges of learning with inference networks on sparse, high-dimensional data","  We study parameter estimation in Nonlinear Factor Analysis (NFA) where the
+generative model is parameterized by a deep neural network. Recent work has
+focused on learning such models using inference (or recognition) networks; we
+identify a crucial problem when modeling large, sparse, high-dimensional
+datasets -- underfitting. We study the extent of underfitting, highlighting
+that its severity increases with the sparsity of the data. We propose methods
+to tackle it via iterative optimization inspired by stochastic variational
+inference \citep{hoffman2013stochastic} and improvements in the sparse data
+representation used for inference. The proposed techniques drastically improve
+the ability of these powerful models to fit sparse data, achieving
+state-of-the-art results on a benchmark text-count dataset and excellent
+results on the task of top-N recommendation.
+"
+22006,Circumcenter extension of Moebius maps to CAT(-1) spaces,"  Given a Moebius homeomorphism $f : \partial X \to \partial Y$ between
+boundaries of proper, geodesically complete CAT(-1) spaces $X,Y$, we describe
+an extension $\hat{f} : X \to Y$ of $f$, called the circumcenter map of $f$,
+which is constructed using circumcenters of expanding sets. The extension
+$\hat{f}$ is shown to coincide with the $(1, \log 2)$-quasi-isometric extension
+constructed in [biswas3], and is locally $1/2$-Holder continuous. When $X,Y$
+are complete, simply connected manifolds with sectional curvatures $K$
+satisfying $-b^2 \leq K \leq -1$ for some $b \geq 1$ then the extension
+$\hat{f} : X \to Y$ is a $(1, (1 - \frac{1}{b})\log 2)$-quasi-isometry.
+Circumcenter extension of Moebius maps is natural with respect to composition
+with isometries.
+"
+22007,A Comparison Study of Two High Accuracy Numerical Methods for a Parabolic System in Air Pollution Modelling,"  We present two approaches for enhancing the accuracy of second order finite
+difference approximations of two-dimensional semilinear parabolic systems.
+These are the fourth order compact difference scheme and the fourth order
+scheme based on Richardson extrapolation. Our interest is concentrated on a
+system of ten parabolic partial differential equations in air pollution
+modeling. We analyze numerical experiments to compare the two approaches with
+respect to accuracy, computational complexity, non-negativity preserving and
+etc. Sixth-order approximation based on the fourth-order compact difference
+scheme combined with Richardson extrapolation is also discussed numerically.
+"
+22008,Transition from electromagnetically induced transparency to Autler-Townes splitting in cold cesium atoms,"  Electromagnetically induced transparency (EIT) and Aulter-Townes splitting
+(ATS) are two similar yet distinct phenomena that modify the transmission of a
+weak probe field through an absorption medium in the presence of a coupling
+field, featured in a variety of three-level atomic systems. In many
+applications it is important to distinguish EIT from ATS splitting. We present
+EIT and ATS spectra in a cold-atom three-level cascade system, involving the
+35$S_{1/2}$ Rydberg state of cesium. The EIT linewidth, $\gamma_{EIT}$, defined
+as the full width at half maximum (FWHM), and the ATS splitting,
+$\gamma_{ATS}$, defined as the peak-to-peak distance between AT peak pairs, are
+used to delineate the EIT and ATS regimes and to characterize the transition
+between the regimes. In the cold-atom medium, in the weak-coupler (EIT) regime
+$\gamma_{EIT}$ $\approx$ A + B($\Omega_{c}^2$ + $\Omega_{p}^2)/\Gamma_{eg}$,
+where $\Omega_{c}$ and $\Omega_{p}$ are the coupler and probe Rabi frequencies,
+$\Gamma_{eg}$ is the spontaneous decay rate of the intermediate 6P$_{3/2}$
+level, and parameters $A$ and $B$ that depend on the laser linewidth. We
+explore the transition into the strong-coupler (ATS) regime, which is
+characterized by the linear relation $\gamma_{ATS}$ $\approx$ $\Omega_{c}$. The
+experiments are in agreement with numerical solutions of the Master equation.
+"
+22009,SIMCom: Statistical Sniffing of Inter-Module Communications for Run-time Hardware Trojan Detection,"  Timely detection of Hardware Trojans (HT) has become a major challenge for
+secure integrated circuits. We present a run-time methodology for HT detection
+that employs a multi-parameter statistical traffic modeling of the
+communication channel in a given System-on-Chip (SoC). Towards this, it
+leverages the Hurst exponent, the standard deviation of the injection
+distribution and hop distribution jointly to accurately identify HT-based
+online anomalies. At design time, our methodology employs a property
+specification language to define and embed assertions in the RTL, specifying
+the correct communication behavior of a given SoC. At runtime, it monitors the
+anomalies in the communication behavior by checking the execution patterns
+against these assertions. We evaluate our methodology for detecting HTs in
+MC8051 microcontrollers. The experimental results show that with the combined
+analysis of multiple statistical parameters, our methodology is able to detect
+all the benchmark Trojans (available on trust-hub) inserted in MC8051, which
+directly or indirectly affect the communication-channels in SoC.
+"
+22010,Sum of Square Proof for Brascamp-Lieb Type Inequality,"  Brascamp-Lieb inequality is an important mathematical tool in analysis,
+geometry and information theory. There are various ways to prove Brascamp-Lieb
+inequality such as heat flow method, Brownian motion and subadditivity of the
+entropy. While Brascamp-Lieb inequality is originally stated in Euclidean
+Space, discussed Brascamp-Lieb inequality for discrete Abelian group and
+discussed Brascamp-Lieb inequality for Markov semigroups.
+Many mathematical inequalities can be formulated as algebraic inequalities
+which asserts some given polynomial is nonnegative. In 1927, Artin proved that
+any non- negative polynomial can be represented as a sum of squares of rational
+functions, which can be further formulated as a polynomial certificate of the
+nonnegativity of the polynomial. This is a Sum of Square proof of the
+inequality. Take the degree of the polynomial certificate as the degree of Sum
+of Square proof. The degree of an Sum of Square proof determines the complexity
+of generating such proof by Sum of Square algorithm which is a powerful tool
+for optimization and computer aided proof.
+In this paper, we give a Sum of Square proof for some special settings of
+Brascamp- Lieb inequality following and and discuss some applications of
+Brascamp-Lieb inequality on Abelian group and Euclidean Sphere. If the original
+description of the inequality has constant degree and d is constant, the degree
+of the proof is also constant. Therefore, low degree sum of square algorithm
+can fully capture the power of low degree finite Brascamp-Lieb inequality.
+"
+22011,Robust Classification of Financial Risk,"  Algorithms are increasingly common components of high-impact decision-making,
+and a growing body of literature on adversarial examples in laboratory settings
+indicates that standard machine learning models are not robust. This suggests
+that real-world systems are also susceptible to manipulation or
+misclassification, which especially poses a challenge to machine learning
+models used in financial services. We use the loan grade classification problem
+to explore how machine learning models are sensitive to small changes in
+user-reported data, using adversarial attacks documented in the literature and
+an original, domain-specific attack. Our work shows that a robust optimization
+algorithm can build models for financial services that are resistant to
+misclassification on perturbations. To the best of our knowledge, this is the
+first study of adversarial attacks and defenses for deep learning in financial
+services.
+"
+22012,The deformed Hermitian-Yang-Mills equation in geometry and physics,"  We provide an introduction to the mathematics and physics of the deformed
+Hermitian-Yang-Mills equation, a fully nonlinear geometric PDE on Kahler
+manifolds which plays an important role in mirror symmetry. We discuss the
+physical origin of the equation, and some recent progress towards its solution.
+In dimension 3 we prove a new Chern number inequality and discuss the
+relationship with algebraic stability conditions.
+"
+22013,How deep learning works --The geometry of deep learning,"  Why and how that deep learning works well on different tasks remains a
+mystery from a theoretical perspective. In this paper we draw a geometric
+picture of the deep learning system by finding its analogies with two existing
+geometric structures, the geometry of quantum computations and the geometry of
+the diffeomorphic template matching. In this framework, we give the geometric
+structures of different deep learning systems including convolutional neural
+networks, residual networks, recursive neural networks, recurrent neural
+networks and the equilibrium prapagation framework. We can also analysis the
+relationship between the geometrical structures and their performance of
+different networks in an algorithmic level so that the geometric framework may
+guide the design of the structures and algorithms of deep learning systems.
+"
+22014,Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials,"  We consider goodness-of-fit tests with i.i.d. samples generated from a
+categorical distribution $(p_1,...,p_k)$. For a given $(q_1,...,q_k)$, we test
+the null hypothesis whether $p_j=q_{\pi(j)}$ for some label permutation $\pi$.
+The uncertainty of label permutation implies that the null hypothesis is
+composite instead of being singular. In this paper, we construct a testing
+procedure using statistics that are defined as indefinite integrals of some
+symmetric polynomials. This method is aimed directly at the invariance of the
+problem, and avoids the need of matching the unknown labels. The asymptotic
+distribution of the testing statistic is shown to be chi-squared, and its power
+is proved to be nearly optimal under a local alternative hypothesis. Various
+degenerate structures of the null hypothesis are carefully analyzed in the
+paper. A two-sample version of the test is also studied.
+"
+22015,Analysis of the non-Markovianity for electron transfer reactions in an oligothiophene-fullerene heterojunction,"  The non-Markovianity of the electron transfer in an oligothiophene-fullerene
+heterojunction described by a spin-boson model is analyzed using the time
+dependent decoherence canonical rates and the volume of accessible states in
+the Bloch sphere. The dynamical map of the reduced electronic system is
+computed by the hierarchical equations of motion methodology (HEOM) providing
+an exact dynamics. Transitory witness of non-Markovianity is linked to the bath
+dynamics analyzed from the HEOM auxiliary matrices. The signature of the
+collective bath mode detected from HEOM in each electronic state is compared
+with predictions of the effective mode extracted from the spectral density. We
+show that including this main reaction coordinate in a one-dimensional vibronic
+system coupled to a residual bath satisfactorily describes the electron
+transfer by a simple Markovian Redfield equation. Non-Markovianity is computed
+for three inter fragment distances and compared with a priori criterion based
+on the system and bath characteristic timescales.
+"
+22016,On Milne-Barbier-Unsöld relationships,"  This short review aims to clarify upon the origins of the so-called
+Eddington-Barbier relationships, which relate the emergent specific intensity
+and the flux to the photospheric source function at specific optical depths.
+Here we discuss about the assumptions behind the original derivation of Barbier
+(1943). We also point to the fact that Milne had already formulated these two
+relations in 1921.
+"
+22017,Block-diagonal Hessian-free Optimization for Training Neural Networks,"  Second-order methods for neural network optimization have several advantages
+over methods based on first-order gradient descent, including better scaling to
+large mini-batch sizes and fewer updates needed for convergence. But they are
+rarely applied to deep learning in practice because of high computational cost
+and the need for model-dependent algorithmic variations. We introduce a variant
+of the Hessian-free method that leverages a block-diagonal approximation of the
+generalized Gauss-Newton matrix. Our method computes the curvature
+approximation matrix only for pairs of parameters from the same layer or block
+of the neural network and performs conjugate gradient updates independently for
+each block. Experiments on deep autoencoders, deep convolutional networks, and
+multilayer LSTMs demonstrate better convergence and generalization compared to
+the original Hessian-free approach and the Adam method.
+"
+22018,Steklov eigenvalues of submanifolds with prescribed boundary in Euclidean space,"  We obtain upper and lower bounds for Steklov eigenvalues of submanifolds with
+prescribed boundary in Euclidean space. A very general upper bound is proved,
+which depends only on the geometry of the fixed boundary and on the measure of
+the interior. Sharp lower bounds are given for hypersurfaces of revolution with
+connected boundary: we prove that each eigenvalue is uniquely minimized by the
+ball. We also observe that each surface of revolution with connected boundary
+is isospectral to the disk.
+"
+22019,Theoretical and Experimental Analysis of the Canadian Traveler Problem,"  Devising an optimal strategy for navigation in a partially observable
+environment is one of the key objectives in AI. One of the problem in this
+context is the Canadian Traveler Problem (CTP). CTP is a navigation problem
+where an agent is tasked to travel from source to target in a partially
+observable weighted graph, whose edge might be blocked with a certain
+probability and observing such blockage occurs only when reaching upon one of
+the edges end points. The goal is to find a strategy that minimizes the
+expected travel cost. The problem is known to be P$\#$ hard. In this work we
+study the CTP theoretically and empirically. First, we study the Dep-CTP, a CTP
+variant we introduce which assumes dependencies between the edges status. We
+show that Dep-CTP is intractable, and further we analyze two of its subclasses
+on disjoint paths graph. Second, we develop a general algorithm Gen-PAO that
+optimally solve the CTP. Gen-PAO is capable of solving two other types of CTP
+called Sensing-CTP and Expensive-Edges CTP. Since the CTP is intractable,
+Gen-PAO use some pruning methods to reduce the space search for the optimal
+solution. We also define some variants of Gen-PAO, compare their performance
+and show some benefits of Gen-PAO over existing work.
+"
+22020,Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons,"  Crowdsourcing has become a popular method for collecting labeled training
+data. However, in many practical scenarios traditional labeling can be
+difficult for crowdworkers (for example, if the data is high-dimensional or
+unintuitive, or the labels are continuous).
+In this work, we develop a novel model for crowdsourcing that can complement
+standard practices by exploiting people's intuitions about groups and relations
+between them. We employ a recent machine learning setting, called Ballpark
+Learning, that can estimate individual labels given only coarse, aggregated
+signal over groups of data points. To address the important case of continuous
+labels, we extend the Ballpark setting (which focused on classification) to
+regression problems. We formulate the problem as a convex optimization problem
+and propose fast, simple methods with an innate robustness to outliers.
+We evaluate our methods on real-world datasets, demonstrating how useful
+constraints about groups can be harnessed from a crowd of non-experts. Our
+methods can rival supervised models trained on many true labels, and can obtain
+considerably better results from the crowd than a standard label-collection
+process (for a lower price). By collecting rough guesses on groups of instances
+and using machine learning to infer the individual labels, our lightweight
+framework is able to address core crowdsourcing challenges and train machine
+learning models in a cost-effective way.
+"
+22021,"How a dissimilar-chain system is splitting. Quasi-static, subsonic and supersonic regimes","  We consider parallel splitting of a strip composed of two different chains.
+As a waveguide, the dissimilar-chain structure radically differs from the
+well-studied identical-chain system. It is characterized by three speeds of the
+long waves, two for the separate chains, and one for the intact strip where the
+chains are connected. Accordingly, there exist three ranges, the subsonic for
+both chains, the intersonic and the supersonic. The speed in the latter range
+is supersonic for the intact strip and at the same time, it is subsonic for the
+separate higher-speed chain. This fact allows the splitting wave to propagate
+in the strip supersonically. We derive steady-state analytical solutions and
+find that the splitting can propagate steadily only in two of these speed
+ranges, the subsonic and the supersonic, whereas the intersonic regime is
+forbidden. In the case of considerable difference in the chain stiffness, the
+lowest dynamic threshold corresponds to the supersonic regime. The peculiarity
+of the supersonic mode is that the supersonic energy delivery channel, being
+initially absent, is opening with the moving splitting point. Based on the
+discrete and related continuous models we find which regime can be implemented
+depending on the structure parameters and loading conditions. The analysis
+allows us to represent the characteristics of such processes and to demonstrate
+strengths and weaknesses of different formulations, quasi-static, dynamic,
+discrete or continuous. Analytical solutions for steady-state regimes are
+obtained and analyzed in detail. We find the force-speed relations and show the
+difference between the static and dynamic thresholds. The parameters and energy
+of waves radiated by the propagating splitting are determined. We calculate the
+strain distribution ahead of the transition point and check whether the
+steady-state solutions are admissible.
+"
+22022,Room-temperature solid state quantum emitters in the telecom range,"  On demand single photon emitters (SPEs) play a key role across a broad range
+of quantum technologies, including quantum computation, quantum simulation,
+quantum metrology and quantum communications. In quantum networks and quantum
+key distribution protocols, where photons are employed as flying qubits,
+telecom wavelength operation is preferred due to the reduced fibre loss.
+However, despite the tremendous efforts to develop various triggered SPE
+platforms, a robust source of triggered SPEs operating at room temperature and
+the telecom wavelength is still missing. Here we report a triggered, optically
+stable, room temperature solid state SPE operating at telecom wavelengths. The
+emitters exhibit high photon purity (~ 5% multiphoton events) and a record-high
+brightness of ~ 1.5 MHz. The emission is attributed to localized defects in a
+gallium nitride (GaN) crystal. The high performance SPEs embedded in a
+technologically mature semiconductor are promising for on-chip quantum
+simulators and practical quantum communication technologies.
+"
+22023,Join-completions of ordered algebras,"  We present a systematic study of join-extensions and join-completions of
+ordered algebras, which naturally leads to a refined and simplified treatment
+of fundamental results and constructions in the theory of ordered structures
+ranging from properties of the Dedekind-MacNeille completion to the proof of
+the finite embeddability property for a number of varieties of ordered
+algebras.
+"
+22024,SNR Wall for Cooperative Spectrum Sensing Using Generalized Energy Detector,"  Cognitive radio (CR) is a promising scheme to improve the spectrum
+utilization. Spectrum sensing (SS) is one of the main tasks of CR. Cooperative
+spectrum sensing (CSS) is used in CR to improve detection capability. Due to
+its simplicity and low complexity, sensing based on energy detection known as
+conventional energy detection (CED) is widely adopted. CED can be generalized
+by changing the squaring operation of the amplitude of received samples by an
+arbitrary positive power p which is referred to as the generalized energy
+detector (GED). The performance of GED degrades when there exists noise
+uncertainty (NU). In this paper, we investigate the performance of CSS by
+considering the noise NU when all the secondary users (SUs) employ GED. We
+derive the signal to noise ratio (SNR) wall for CSS for both hard and soft
+decision combining. All the derived expressions are validated using Monte Carlo
+(MC) simulations.
+"
+22025,Automatic Breast Ultrasound Image Segmentation: A Survey,"  Breast cancer is one of the leading causes of cancer death among women
+worldwide. In clinical routine, automatic breast ultrasound (BUS) image
+segmentation is very challenging and essential for cancer diagnosis and
+treatment planning. Many BUS segmentation approaches have been studied in the
+last two decades, and have been proved to be effective on private datasets.
+Currently, the advancement of BUS image segmentation seems to meet its
+bottleneck. The improvement of the performance is increasingly challenging, and
+only few new approaches were published in the last several years. It is the
+time to look at the field by reviewing previous approaches comprehensively and
+to investigate the future directions. In this paper, we study the basic ideas,
+theories, pros and cons of the approaches, group them into categories, and
+extensively review each category in depth by discussing the principles,
+application issues, and advantages/disadvantages.
+"
+22026,Global stability and uniform persistence of the reaction-convection-diffusion cholera epidemic model,"  We study the global stability issue of the reaction-convection-diffusion
+cholera epidemic PDE model and show that the basic reproduction number serves
+as a threshold parameter that predicts whether cholera will persist or become
+globally extinct. Specifically, when the basic reproduction number is beneath
+one, we show that the disease-free-equilibrium is globally attractive. On the
+other hand, when the basic reproduction number exceeds one, if the infectious
+hosts or the concentration of bacteria in the contaminated water are not
+initially identically zero, we prove the uniform persistence result and that
+there exists at least one positive steady state.
+"
+22027,Learning Disordered Topological Phases by Statistical Recovery of Symmetry,"  In this letter, we apply the artificial neural network in a supervised manner
+to map out the quantum phase diagram of disordered topological superconductor
+in class DIII. Given the disorder that keeps the discrete symmetries of the
+ensemble as a whole, translational symmetry which is broken in the
+quasiparticle distribution individually is recovered statistically by taking an
+ensemble average. By using this, we classify the phases by the artificial
+neural network that learned the quasiparticle distribution in the clean limit,
+and show that the result is totally consistent with the calculation by the
+transfer matrix method or noncommutative geometry approach. If all three
+phases, namely the $\mathbb{Z}_2$, trivial, and the thermal metal phases appear
+in the clean limit, the machine can classify them with high confidence over the
+entire phase diagram. If only the former two phases are present, we find that
+the machine remains confused in the certain region, leading us to conclude the
+detection of the unknown phase which is eventually identified as the thermal
+metal phase. In our method, only the first moment of the quasiparticle
+distribution is used for input, but application to a wider variety of systems
+is expected by the inclusion of higher moments.
+"
+22028,Stability of iterated function systems on the circle,"  We prove that any Iterated Function System of circle homeomorphisms with at
+least one of them having dense orbit, is asymptotically stable. The
+corresponding Perron-Frobenius operator is shown to satisfy the e-property,
+that is, for any continuous function its iterates are equicontinuous. The
+Strong Law of Large Numbers for trajectories starting from an arbitrary point
+for such function systems is also proved.
+"
+22029,On labeling Android malware signatures using minhashing and further classification with Structural Equation Models,"  Multi-scanner Antivirus systems provide insightful information on the nature
+of a suspect application; however there is often a lack of consensus and
+consistency between different Anti-Virus engines. In this article, we analyze
+more than 250 thousand malware signatures generated by 61 different Anti-Virus
+engines after analyzing 82 thousand different Android malware applications. We
+identify 41 different malware classes grouped into three major categories,
+namely Adware, Harmful Threats and Unknown or Generic signatures. We further
+investigate the relationships between such 41 classes using community detection
+algorithms from graph theory to identify similarities between them; and we
+finally propose a Structure Equation Model to identify which Anti-Virus engines
+are more powerful at detecting each macro-category. As an application, we show
+how such models can help in identifying whether Unknown malware applications
+are more likely to be of Harmful or Adware type.
+"
+22030,Proceedings 3rd International Workshop on Symbolic and Numerical Methods for Reachability Analysis,"  Hybrid systems are complex dynamical systems that combine discrete and
+continuous components. Reachability questions, regarding whether a system can
+run into a certain subset of its state space, stand at the core of verification
+and synthesis problems for hybrid systems. This volume contains papers
+describing new developments in this area, which were presented at the 3rd
+International Workshop on Symbolic and Numerical Methods for Reachability
+Analysis.
+"
+22031,Building Prior Knowledge: A Markov Based Pedestrian Prediction Model Using Urban Environmental Data,"  Autonomous Vehicles navigating in urban areas have a need to understand and
+predict future pedestrian behavior for safer navigation. This high level of
+situational awareness requires observing pedestrian behavior and extrapolating
+their positions to know future positions. While some work has been done in this
+field using Hidden Markov Models (HMMs), one of the few observed drawbacks of
+the method is the need for informed priors for learning behavior. In this work,
+an extension to the Growing Hidden Markov Model (GHMM) method is proposed to
+solve some of these drawbacks. This is achieved by building on existing work
+using potential cost maps and the principle of Natural Vision. As a
+consequence, the proposed model is able to predict pedestrian positions more
+precisely over a longer horizon compared to the state of the art. The method is
+tested over ""legal"" and ""illegal"" behavior of pedestrians, having trained the
+model with sparse observations and partial trajectories. The method, with no
+training data, is compared against a trained state of the art model. It is
+observed that the proposed method is robust even in new, previously unseen
+areas.
+"
+22032,Group Sparsity Residual Constraint for Image Denoising,"  Group-based sparse representation has shown great potential in image
+denoising. However, most existing methods only consider the nonlocal
+self-similarity (NSS) prior of noisy input image. That is, the similar patches
+are collected only from degraded input, which makes the quality of image
+denoising largely depend on the input itself. However, such methods often
+suffer from a common drawback that the denoising performance may degrade
+quickly with increasing noise levels. In this paper we propose a new prior
+model, called group sparsity residual constraint (GSRC). Unlike the
+conventional group-based sparse representation denoising methods, two kinds of
+prior, namely, the NSS priors of noisy and pre-filtered images, are used in
+GSRC. In particular, we integrate these two NSS priors through the mechanism of
+sparsity residual, and thus, the task of image denoising is converted to the
+problem of reducing the group sparsity residual. To this end, we first obtain a
+good estimation of the group sparse coefficients of the original image by
+pre-filtering, and then the group sparse coefficients of the noisy image are
+used to approximate this estimation. To improve the accuracy of the nonlocal
+similar patch selection, an adaptive patch search scheme is designed.
+Furthermore, to fuse these two NSS prior better, an effective iterative
+shrinkage algorithm is developed to solve the proposed GSRC model. Experimental
+results demonstrate that the proposed GSRC modeling outperforms many
+state-of-the-art denoising methods in terms of the objective and the perceptual
+metrics.
+"
+22033,Scaling Text with the Class Affinity Model,"  Probabilistic methods for classifying text form a rich tradition in machine
+learning and natural language processing. For many important problems, however,
+class prediction is uninteresting because the class is known, and instead the
+focus shifts to estimating latent quantities related to the text, such as
+affect or ideology. We focus on one such problem of interest, estimating the
+ideological positions of 55 Irish legislators in the 1991 Dáil confidence
+vote. To solve the Dáil scaling problem and others like it, we develop a text
+modeling framework that allows actors to take latent positions on a ""gray""
+spectrum between ""black"" and ""white"" polar opposites. We are able to validate
+results from this model by measuring the influences exhibited by individual
+words, and we are able to quantify the uncertainty in the scaling estimates by
+using a sentence-level block bootstrap. Applying our method to the Dáil
+debate, we are able to scale the legislators between extreme pro-government and
+pro-opposition in a way that reveals nuances in their speeches not captured by
+their votes or party affiliations.
+"
+22034,Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships,"  Machine learning (ML) of quantum mechanical properties shows promise for
+accelerating chemical discovery. For transition metal chemistry where accurate
+calculations are computationally costly and available training data sets are
+small, the molecular representation becomes a critical ingredient in ML model
+predictive accuracy. We introduce a series of revised autocorrelation functions
+(RACs) that encode relationships between the heuristic atomic properties (e.g.,
+size, connectivity, and electronegativity) on a molecular graph. We alter the
+starting point, scope, and nature of the quantities evaluated in standard ACs
+to make these RACs amenable to inorganic chemistry. On an organic molecule set,
+we first demonstrate superior standard AC performance to other
+presently-available topological descriptors for ML model training, with mean
+unsigned errors (MUEs) for atomization energies on set-aside test molecules as
+low as 6 kcal/mol. For inorganic chemistry, our RACs yield 1 kcal/mol ML MUEs
+on set-aside test molecules in spin-state splitting in comparison to 15-20x
+higher errors from feature sets that encode whole-molecule structural
+information. Systematic feature selection methods including univariate
+filtering, recursive feature elimination, and direct optimization (e.g., random
+forest and LASSO) are compared. Random-forest- or LASSO-selected subsets 4-5x
+smaller than RAC-155 produce sub- to 1-kcal/mol spin-splitting MUEs, with good
+transferability to metal-ligand bond length prediction (0.004-5 {\AA} MUE) and
+redox potential on a smaller data set (0.2-0.3 eV MUE). Evaluation of feature
+selection results across property sets reveals the relative importance of
+local, electronic descriptors (e.g., electronegativity, atomic number) in
+spin-splitting and distal, steric effects in redox potential and bond lengths.
+"
+22035,"Bipartite Graphs, On-Shell Diagrams, and Bipartite Field Theories: a Computational Package in Mathematica","  We present bipartiteSUSY, a Mathematica package designed to perform
+calculations for physical theories based on bipartite graphs. In particular,
+the package can employ the recently developed arsenal of techniques surrounding
+on-shell diagrams in N=4 SYM scattering amplitudes, including those for
+non-planar diagrams, with particular attention to computational speed. It also
+contains a host of tools for computations in N=1 Bipartite Field Theories,
+which utilize the same bipartite graphs. Through the use of an interactive
+graphical tool, it is possible to draw the desired diagrams on the screen and
+compute commonly sought-after features. The package should be easily accessible
+to users with little or no previous experience in dealing with bipartite graphs
+and their combinatorial descriptions.
+"
+22036,Elucidating the role of Sn-substitution and Pb-$\Box$ in regulating stability and carrier concentration in CH$_3$NH$_3$Pb$_{1-X-Y}$Sn$_X$$\Box_Y$I$_3$,"  We address the role of Sn-substitution and Pb-vacancy (Pb-$\Box$) in
+regulating stability and carrier concentration of
+CH$_3$NH$_3$Pb$_{1-X-Y}$Sn$_X$$\Box_Y$I$_3$ perovskite using density functional
+theory, where the performance of the exchange-correlation functional is
+carefully analyzed, and validated w.r.t. available experimental results. We
+find the most stable configuration does not prefer any Pb at 50\% concentration
+of Sn. However, the Pb-$\Box$s become unfavourable above 250K due to the
+reduced linearity of Sn-I bonds. For n-type host the Sn substitution is more
+preferable than Pb-$\Box$ formation, while for p-type host the trend is exactly
+opposite. The charge states of both Sn and Pb-$\Box$ are found to be dependent
+on the Sn concentration, which in turn alters the perovskite from n-type to
+p-type with increasing $X$ ($>$0.5).
+"
+22037,Semi-Analytic Method for SINS Attitude and Parameters Online Estimation,"  In this note, the attitude and inertial sensors drift biases estimation for
+Strapdown inertial navigation system is investigated. A semi-analytic method is
+proposed, which contains two interlaced solution procedures. Specifically, the
+attitude encoding the body frame changes and gyroscopes drift biases are
+estimated through attitude estimation while the attitude encoding the constant
+value at the very start and accelerometers drift biases are determined through
+online optimization.
+"
+22038,"Comment on ""Nucleon spin-averaged forward virtual Compton tensor at large Q^2""","  In recent work, Hill and Paz apply the operator product expansion to forward
+doubly virtual Compton scattering. The resulting large-$Q^2$ form of the
+amplitude $W_1(0,Q^2)$ is compatible with the one we obtain by extrapolation of
+low-$Q^2$ results from a chiral effective field theory, providing support for
+our approach. That paper also presents a result for the two-photon contribution
+to the Lamb shift in muonic hydrogen that has a much larger uncertainty than in
+previous work. We show that this an overestimate arising from the inclusion of
+the proton pole term in the subtracted dispersion relation for $W_1$.
+"
+22039,Graph Theoretic Properties of the Darkweb,"  We collect and analyze the darkweb (a.k.a. the ""onionweb"") hyperlink graph.
+We find properties highly dissimilar to the well-studied world wide web
+hyperlink graph; for example, our analysis finds that >87% of darkweb sites
+never link to another site. We compare our results to prior work on
+world-wide-web and speculate about reasons for their differences. We conclude
+that in the term ""darkweb"", the word ""web"" is a connectivity misnomer. Instead,
+it is more accurate to view the darkweb as a set of largely isolated dark
+silos.
+"
+22040,A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks,"  We study the optimal convergence rates for distributed convex optimization
+problems over networks, where the objective is to minimize the sum
+$\sum_{i=1}^{m}f_i(z)$ of local functions of the nodes in the network. We
+provide optimal complexity bounds for four different cases, namely: the case
+when each function $f_i$ is strongly convex and smooth, the cases when it is
+either strongly convex or smooth and the case when it is convex but neither
+strongly convex nor smooth. Our approach is based on the dual of an
+appropriately formulated primal problem, which includes the underlying static
+graph that models the communication restrictions. Our results show distributed
+algorithms that achieve the same optimal rates as their centralized
+counterparts (up to constant and logarithmic factors), with an additional cost
+related to the spectral gap of the interaction matrix that captures the local
+communications of the nodes in the network.
+"
+22041,Maximizing the Conditional Expected Reward for Reaching the Goal,"  The paper addresses the problem of computing maximal conditional expected
+accumulated rewards until reaching a target state (briefly called maximal
+conditional expectations) in finite-state Markov decision processes where the
+condition is given as a reachability constraint. Conditional expectations of
+this type can, e.g., stand for the maximal expected termination time of
+probabilistic programs with non-determinism, under the condition that the
+program eventually terminates, or for the worst-case expected penalty to be
+paid, assuming that at least three deadlines are missed. The main results of
+the paper are (i) a polynomial-time algorithm to check the finiteness of
+maximal conditional expectations, (ii) PSPACE-completeness for the threshold
+problem in acyclic Markov decision processes where the task is to check whether
+the maximal conditional expectation exceeds a given threshold, (iii) a
+pseudo-polynomial-time algorithm for the threshold problem in the general
+(cyclic) case, and (iv) an exponential-time algorithm for computing the maximal
+conditional expectation and an optimal scheduler.
+"
+22042,Gender differences in lying in sender-receiver games: A meta-analysis,"  Whether there are gender differences in lying has been largely debated in the
+past decade. Previous studies found mixed results. To shed light on this topic,
+here I report a meta-analysis of 8,728 distinct observations, collected in 65
+Sender-Receiver game treatments, by 14 research groups. Following previous work
+and theoretical considerations, I distinguish three types of lies: black lies,
+that benefit the liar at a cost for another person; altruistic white lies, that
+benefit another person at a cost for the liar; Pareto white lies, that benefit
+both the liar and another person. The results show that gender differences in
+lying significantly depend on the consequences of lying. Specifically: (i)
+males are significantly more likely than females to tell black lies (N=4,161);
+(ii) males are significantly more likely than females to tell altruistic white
+(N=2,940); (iii) results are inconclusive in the case of Pareto white lies
+(N=1,627).
+"
+22043,Möbius disjointness along ergodic sequences for uniquely ergodic actions,"  We show that there are an irrational rotation $Tx=x+\alpha$ on the circle
+$\mathbb{T}$ and a continuous $\varphi\colon\mathbb{T}\to\mathbb{R}$ such that
+for each (continuous) uniquely ergodic flow
+$\mathcal{S}=(S_t)_{t\in\mathbb{R}}$ acting on a compact metric space $Y$, the
+automorphism $T_{\varphi,\mathcal{S}}$ acting on $(X\times Y,\mu\otimes\nu)$ by
+the formula $T_{\varphi,\mathcal{S}}(x,y)=(Tx,S_{\varphi(x)}(y))$, where $\mu$
+stands for Lebesgue measure on $\mathbb{T}$ and $\nu$ denotes the unique
+$\mathcal{S}$-invariant measure, has the property of asymptotically orthogonal
+powers. This gives a class of relatively weakly mixing extensions of irrational
+rotations for which Sarnak's conjecture on Möbius disjointness holds for all
+uniquely ergodic models of $T_{\varphi,\mathcal{S}}$. Moreover, we obtain a
+class of ""random"" ergodic sequences $(c_n)\subset\mathbb{Z}$ such that if
+$\boldsymbol{\mu}$ denotes the Möbius function, then $$
+\lim_{N\to\infty}\frac1N\sum_{n\leq N}g(S_{c_n}y)\boldsymbol{\mu}(n)=0 $$ for
+all (continuous) uniquely ergodic flows $\mathcal{S}$, all $g\in C(Y)$ and
+$y\in Y$.
+"
+22044,Towards Self-organized Large-Scale Shape Formation: A Cognitive Agent-Based Computing Approach,"  Swarm robotic systems are currently being used to address many real-world
+problems. One interesting application of swarm robotics is the self-organized
+formation of structures and shapes. Some of the key challenges in the swarm
+robotic systems include swarm size constraint, random motion, coordination
+among robots, localization, and adaptability in a decentralized environment.
+Rubenstein et al. presented a system (""Programmable self-assembly in a
+thousand-robot swarm"", Science, 2014) for thousand-robot swarm able to form
+only solid shapes with the robots in aggregated form by applying the collective
+behavior algorithm. Even though agent-based approaches have been presented in
+various studies for self-organized formation, however these studies lack
+agent-based modeling (ABM) approach along with the constraints in term of
+structure complexity and heterogeneity in large swarms with dynamic
+localization. The cognitive agent-based computing (CABC) approach is capable of
+modeling such self-organization based multi-agents systems (MAS). In this
+paper, we develop a simulation model using ABM under CABC approach for
+self-organized shape formation in swarm robots. We propose a shape formation
+algorithm for validating our model and perform simulation-based experiments for
+six different shapes including hole-based shapes. We also demonstrate the
+formal specification for our model. The simulation result shows the robustness
+of the proposed approach having the emergent behavior of robots for the
+self-organized shape formation. The performance of the proposed approach is
+evaluated by robots convergence rate.
+"
+22045,A Learning Framework for Robust Bin Picking by Customized Grippers,"  Customized grippers have specifically designed fingers to increase the
+contact area with the workpieces and improve the grasp robustness. However,
+grasp planning for customized grippers is challenging due to the object
+variations, surface contacts and structural constraints of the grippers. In
+this paper, we propose a learning framework to plan robust grasps for
+customized grippers in real-time. The learning framework contains a low-level
+optimization-based planner to search for optimal grasps locally under object
+shape variations, and a high-level learning-based explorer to learn the grasp
+exploration based on previous grasp experience. The optimization-based planner
+uses an iterative surface fitting (ISF) to simultaneously search for optimal
+gripper transformation and finger displacement by minimizing the surface
+fitting error. The high-level learning-based explorer trains a region-based
+convolutional neural network (R-CNN) to propose good optimization regions,
+which avoids ISF getting stuck in bad local optima and improves the collision
+avoidance performance. The proposed learning framework with RCNN-ISF is able to
+consider the structural constraints of the gripper, learn grasp exploration
+strategy from previous experience, and plan optimal grasps in clutter
+environment in real-time. The effectiveness of the algorithm is verified by
+experiments.
+"
+22046,Optimal encoding in stochastic latent-variable Models,"  We examine the problem of optimal sparse encoding of sensory stimuli by
+latent variables in stochastic models. Analyzing restricted Boltzmann machines
+with a communications theory approach, we search for the minimal model size
+that correctly conveys the correlations in stimulus patterns in an
+information-theoretic sense. We show that the Fisher information Matrix (FIM)
+reveals the optimal model size. In larger models the FIM reveals that
+irrelevant parameters are associated with individual latent variables,
+displaying a surprising amount of order. For well-fit models, we observe the
+emergence of statistical criticality as diverging generalized susceptibility of
+the model. In this case, an encoding strategy is adopted where highly
+informative, but rare stimuli selectively suppress variability in the encoding
+units. The information content of the encoded stimuli acts as an unobserved
+variable leading to criticality. Together, these results can explain the
+stimulus-dependent variability suppression observed in sensory systems, and
+suggest a simple, correlation-based measure to reduce the size of artificial
+neural networks.
+"
+22047,Completely Uncoupled Algorithms for Network Utility Maximization,"  In this paper, we present two completely uncoupled algorithms for utility
+maximization. In the first part, we present an algorithm that can be applied
+for general non-concave utilities. We show that this algorithm induces a
+perturbed (by $\epsilon$) Markov chain, whose stochastically stable states are
+the set of actions that maximize the sum utility. In the second part, we
+present an approximate sub-gradient algorithm for concave utilities which is
+considerably faster and requires lesser memory. We study the performance of the
+sub-gradient algorithm for decreasing and fixed step sizes. We show that, for
+decreasing step sizes, the Cesaro averages of the utilities converges to a
+neighbourhood of the optimal sum utility. For constant step size, we show that
+the time average utility converges to a neighbourhood of the optimal sum
+utility. Our main contribution is the expansion of the achievable rate region,
+which has been not considered in the prior literature on completely uncoupled
+algorithms for utility maximization. This expansion aids in allocating a fair
+share of resources to the nodes which is important in applications like channel
+selection, user association and power control.
+"
+22048,"Sobolev-Slobodeckij Spaces on Compact Manifolds, Revisited","  In this article we present a coherent rigorous overview of the main
+properties of Sobolev-Slobodeckij spaces of sections of vector bundles on
+compact manifolds; results of this type are scattered through the literature
+and can be difficult to find. A special emphasis has been put on spaces with
+noninteger smoothness order, and a special attention has been paid to the
+peculiar fact that for a general nonsmooth domain U in Rn, 0<t<1, and 1<p<oo,
+it is not necessarily true that W(1,p)(U) is continuously embedded in
+W(t,p)(U). This has dire consequences in the multiplication properties of
+Sobolev-Slobodeckij spaces and subsequently in the study of Sobolev spaces on
+manifolds. To the authors' knowledge, some of the proofs, especially those that
+are pertinent to the properties of Sobolev-Slobodeckij spaces of sections of
+general vector bundles, cannot be found in the literature in the generality
+appearing here.
+"
+22049,An LP-based hyperparameter optimization model for language modeling,"  In order to find hyperparameters for a machine learning model, algorithms
+such as grid search or random search are used over the space of possible values
+of the models hyperparameters. These search algorithms opt the solution that
+minimizes a specific cost function. In language models, perplexity is one of
+the most popular cost functions. In this study, we propose a fractional
+nonlinear programming model that finds the optimal perplexity value. The
+special structure of the model allows us to approximate it by a linear
+programming model that can be solved using the well-known simplex algorithm. To
+the best of our knowledge, this is the first attempt to use optimization
+techniques to find perplexity values in the language modeling literature. We
+apply our model to find hyperparameters of a language model and compare it to
+the grid search algorithm. Furthermore, we illustrating that it results in
+lower perplexity values. We perform this experiment on a real-world dataset
+from SwiftKey to validate our proposed approach.
+"
+22050,Ordered FePdCu nanoisland arrays made by templated solid-state dewetting,"  Ordered FePdCu nanoisland arrays were formed by annealing at 600oC, which
+caused solid state dewetting of [Cu/Fe/Pd] multilayers deposited on
+self-assembled SiO2 nanospheres with size of 100 nm. A single FePdCu island was
+formed on the top of each SiO2 nanosphere. The structure of the obtained system
+was studied by x-ray diffraction (XRD), while its magnetic properties by SQUID
+magnetometry. Partially ordered L10 alloy appeared in the annealed films,
+leading to magnetic hardening of the material. The paper presents the influence
+of the patterning on the system properties. It is shown that templated
+dewetting is a method providing nanoislands with well controlled sizes and
+positions. The role of copper admixture in controlling the structural and
+magnetic properties is also discussed.
+"
+22051,Adversary Detection in Neural Networks via Persistent Homology,"  We outline a detection method for adversarial inputs to deep neural networks.
+By viewing neural network computations as graphs upon which information flows
+from input space to out- put distribution, we compare the differences in graphs
+induced by different inputs. Specifically, by applying persistent homology to
+these induced graphs, we observe that the structure of the most persistent
+subgraphs which generate the first homology group differ between adversarial
+and unperturbed inputs. Based on this observation, we build a detection
+algorithm that depends only on the topological information extracted during
+training. We test our algorithm on MNIST and achieve 98% detection adversary
+accuracy with F1-score 0.98.
+"
+22052,Tensor network subspace identification of polynomial state space models,"  This article introduces a tensor network subspace algorithm for the
+identification of specific polynomial state space models. The polynomial
+nonlinearity in the state space model is completely written in terms of a
+tensor network, thus avoiding the curse of dimensionality. We also prove how
+the block Hankel data matrices in the subspace method can be exactly
+represented by low rank tensor networks, reducing the computational and storage
+complexity significantly. The performance and accuracy of our subspace
+identification algorithm are illustrated by numerical experiments, showing that
+our tensor network implementation is around 20 times faster than the standard
+matrix implementation before the latter fails due to insufficient memory, is
+robust with respect to noise and can model real-world systems.
+"
+22053,"Phase-space mixing in dynamically unstable, integrable few-mode quantum systems","  Quenches in isolated quantum systems are currently a subject of intense
+study. Here, we consider quantum few-mode systems that are integrable in their
+classical mean-field limit and become dynamically unstable after a quench of a
+system parameter. Specifically, we study a Bose-Einstein condensate (BEC) in a
+double-well potential and an antiferromagnetic spinor BEC constrained to a
+single spatial mode. We study the time dynamics after the quench within the
+truncated Wigner approximation (TWA) and find that system relaxes to a steady
+state due to phase-space mixing. Using the action-angle formalism and a
+pendulum as an illustration, we derive general analytical expressions for the
+time evolution of expectation values of observables and their long-time limits.
+We find that the deviation of the long-time expectation value from its
+classical value scales as $1/O(\ln N )$, where $N$ is the number of atoms in
+the condensate. Furthermore, the relaxation of an observable to its steady
+state value is a damped oscillation and the damping is Gaussian in time. We
+confirm our results with numerical TWA simulations.
+"
+22054,Quantum gates implementation by X-ray single-photons around rotating black holes,"  The curvature and twisting of spacetime rotate the angle of polarization and
+imprint orbital angular momentum to photons emitted by the accretion disk near
+rotating black holes. Considering polarization and orbital angular momentum as
+two degrees of freedom of single-photons that can encode quantum information,
+we emphasize that the particular shape of spacetime around rotating black holes
+implements quantum gates and simple quantum circuits. Consequently, we
+demonstrate the implementation of some elementary quantum gates, like Hadamard
+or C-NOT, and simple quantum circuits, like Bell states, with photons in the
+presence of spinning black holes. Detection and measurement of quantum
+information encoded in photons emitted in the accretion disk around rotating
+black holes may be performed by actual quantum information technology.
+"
+22055,Watch Your Step: Learning Node Embeddings via Graph Attention,"  Graph embedding methods represent nodes in a continuous vector space,
+preserving information from the graph (e.g. by sampling random walks). There
+are many hyper-parameters to these methods (such as random walk length) which
+have to be manually tuned for every graph. In this paper, we replace random
+walk hyper-parameters with trainable parameters that we automatically learn via
+backpropagation. In particular, we learn a novel attention model on the power
+series of the transition matrix, which guides the random walk to optimize an
+upstream objective. Unlike previous approaches to attention models, the method
+that we propose utilizes attention parameters exclusively on the data (e.g. on
+the random walk), and not used by the model for inference. We experiment on
+link prediction tasks, as we aim to produce embeddings that best-preserve the
+graph structure, generalizing to unseen information. We improve
+state-of-the-art on a comprehensive suite of real world datasets including
+social, collaboration, and biological networks. Adding attention to random
+walks can reduce the error by 20% to 45% on datasets we attempted. Further, our
+learned attention parameters are different for every graph, and our
+automatically-found values agree with the optimal choice of hyper-parameter if
+we manually tune existing methods.
+"
+22056,Guided Deep Reinforcement Learning for Swarm Systems,"  In this paper, we investigate how to learn to control a group of cooperative
+agents with limited sensing capabilities such as robot swarms. The agents have
+only very basic sensor capabilities, yet in a group they can accomplish
+sophisticated tasks, such as distributed assembly or search and rescue tasks.
+Learning a policy for a group of agents is difficult due to distributed partial
+observability of the state. Here, we follow a guided approach where a critic
+has central access to the global state during learning, which simplifies the
+policy evaluation problem from a reinforcement learning point of view. For
+example, we can get the positions of all robots of the swarm using a camera
+image of a scene. This camera image is only available to the critic and not to
+the control policies of the robots. We follow an actor-critic approach, where
+the actors base their decisions only on locally sensed information. In
+contrast, the critic is learned based on the true global state. Our algorithm
+uses deep reinforcement learning to approximate both the Q-function and the
+policy. The performance of the algorithm is evaluated on two tasks with simple
+simulated 2D agents: 1) finding and maintaining a certain distance to each
+others and 2) locating a target.
+"
+22057,In Defense of the Triplet Loss for Visual Recognition,"  We employ triplet loss as a space embedding regularizer to boost
+classification performance. Standard architectures, like ResNet and DesneNet,
+are extended to support both losses with minimal hyper-parameter tuning. This
+promotes generality while fine-tuning pretrained networks. Triplet loss is a
+powerful surrogate for recently proposed embedding regularizers. Yet, it is
+avoided for large batch-size requirement and high computational cost. Through
+our experiments, we re-assess these assumptions.
+During inference, our network supports both classification and embedding
+tasks without any computational overhead. Quantitative evaluation highlights
+how our approach compares favorably to the existing state of the art on
+multiple fine-grained recognition datasets. Further evaluation on an imbalanced
+video dataset achieves significant improvement (>7%). Beyond boosting
+efficiency, triplet loss brings retrieval and interpretability to
+classification models.
+"
+22058,Suboptimum Low Complexity Joint Multi-target Detection and Localization for Noncoherent MIMO Radar with Widely Separated Antennas,"  In this paper, the problems of simultaneously detecting and localizing
+multiple targets are considered for noncoherent multiple-input multiple-output
+(MIMO) radar with widely separated antennas. By assuming a prior knowledge of
+target number, an optimal solution to this problem is presented first. It is
+essentially a maximum-likelihood (ML) estimator searching parameters of
+interest in a high dimensional space. However, the complexity of this method
+increases exponentially with the number G of targets.Besides, without the prior
+information of the number of targets, a multi-hypothesis testing strategy to
+determine the number of targets is required, which further complicates this
+method. Therefore, we split the joint maximization into G disjoint optimization
+problems by clearing the interference from previously declared targets. In this
+way, we derive two fast and robust suboptimal solutions which allow trading
+performance for a much lower implementation complexity which is almost
+independent of the number of targets. In addition, the multi-hypothesis testing
+is no longer required when target number is unknown. Simulation results show
+the proposed algorithms can correctly detect and accurately localize multiple
+targets even when targets share common range bins in some paths.
+"
+22059,Graph Convolutional Encoders for Syntax-aware Neural Machine Translation,"  We present a simple and effective approach to incorporating syntactic
+structure into neural attention-based encoder-decoder models for machine
+translation. We rely on graph-convolutional networks (GCNs), a recent class of
+neural networks developed for modeling graph-structured data. Our GCNs use
+predicted syntactic dependency trees of source sentences to produce
+representations of words (i.e. hidden states of the encoder) that are sensitive
+to their syntactic neighborhoods. GCNs take word representations as input and
+produce word representations as output, so they can easily be incorporated as
+layers into standard encoders (e.g., on top of bidirectional RNNs or
+convolutional neural networks). We evaluate their effectiveness with
+English-German and English-Czech translation experiments for different types of
+encoders and observe substantial improvements over their syntax-agnostic
+versions in all the considered setups.
+"
+22060,Realization of Biquadratic Impedance as Five-Element Bridge Networks,"  This report includes the original manuscript and the supplementary material
+of ""Realization of Biquadratic Impedance as Five-Element Bridge Networks"".
+"
+22061,Towards understanding feedback from supermassive black holes using convolutional neural networks,"  Supermassive black holes at centers of clusters of galaxies strongly interact
+with their host environment via AGN feedback. Key tracers of such activity are
+X-ray cavities -- regions of lower X-ray brightness within the cluster. We
+present an automatic method for detecting, and characterizing X-ray cavities in
+noisy, low-resolution X-ray images. We simulate clusters of galaxies, insert
+cavities into them, and produce realistic low-quality images comparable to
+observations at high redshifts. We then train a custom-built convolutional
+neural network to generate pixel-wise analysis of presence of cavities in a
+cluster. A ResNet architecture is then used to decode radii of cavities from
+the pixel-wise predictions. We surpass the accuracy, stability, and speed of
+current visual inspection based methods on simulated data.
+"
+22062,Integration of 5G Technologies in LEO Mega-Constellations,"  3GPP is finalising the first release of the 5G New Radio physical layer. To
+cope with the demanding 5G requirements on global connectivity and large
+throughput, Satellite Communications might be a valuable resource to extend and
+complement terrestrial networks. In this context, we introduce an integrated
+architecture for 5G-based LEO mega-constellations and assess the impact of
+large Doppler shifts and delays on both the 5G waveform and the PHY/MAC layer
+procedures.
+"
+22063,First search for gravitational waves from known pulsars with Advanced LIGO,"  We present the result of searches for gravitational waves from 200 pulsars
+using data from the first observing run of the Advanced LIGO detectors. We find
+no significant evidence for a gravitational-wave signal from any of these
+pulsars, but we are able to set the most constraining upper limits yet on their
+gravitational-wave amplitudes and ellipticities. For eight of these pulsars,
+our upper limits give bounds that are improvements over the indirect spin-down
+limit values. For another 32, we are within a factor of 10 of the spin-down
+limit, and it is likely that some of these will be reachable in future runs of
+the advanced detector. Taken as a whole, these new results improve on previous
+limits by more than a factor of two.
+"
+22064,Observation of Various and Spontaneous Magnetic Skyrmionic Bubbles at Room-Temperature in a Frustrated Kagome Magnet with Uniaxial Magnetic Anisotropy,"  Various and spontaneous magnetic skyrmionic bubbles are experimentally
+observed for the first time, at room temperature in a frustrated kagome magnet
+Fe3Sn2 with unixial magnetic anisotropy. The magnetization dynamics were
+investigated using in-situ Lorentz transmission electron microscopy, revealing
+that the transformation between different magnetic bubbles and domains are via
+the motion of Bloch lines driven by applied external magnetic field. The
+results demonstrate that Fe3Sn2 facilitates a unique magnetic control of
+topological spin textures at room temperature, making it a promising candidate
+for further skyrmion-based spintronic devices.
+"
+22065,Quantum Correlations in Nonlocal BosonSampling,"  Determination of the quantum nature of correlations between two spatially
+separated systems plays a crucial role in quantum information science. Of
+particular interest is the questions of if and how these correlations enable
+quantum information protocols to be more powerful. Here, we report on a
+distributed quantum computation protocol in which the input and output quantum
+states are considered to be classically correlated in quantum informatics.
+Nevertheless, we show that the correlations between the outcomes of the
+measurements on the output state cannot be efficiently simulated using
+classical algorithms. Crucially, at the same time, local measurement outcomes
+can be efficiently simulated on classical computers. We show that the only
+known classicality criterion violated by the input and output states in our
+protocol is the one used in quantum optics, namely, phase-space
+nonclassicality. As a result, we argue that the global phase-space
+nonclassicality inherent within the output state of our protocol represents
+true quantum correlations.
+"
+22066,A precise deuterium abundance: Re-measurement of the z=3.572 absorption system towards the quasar PKS1937-101,"  The primordial deuterium abundance probes fundamental physics during the Big
+Bang Nucleosynthesis and can be used to infer cosmological parameters.
+Observationally, the abundance can be measured using absorbing clouds along the
+lines of sight to distant quasars. Observations of the quasar PKS1937--101
+contain two absorbers for which the deuterium abundance has previously been
+determined. Here we focus on the higher redshift one at $z_{abs} = 3.572$. We
+present new observations with significantly increased signal-to-noise ratio
+which enable a far more precise and robust measurement of the deuterium to
+hydrogen column density ratio, resulting in D/H = $2.62\pm0.05\times10^{-5}$.
+This particular measurement is of interest because it is among the most precise
+assessments to date and it has been derived from the second lowest
+column-density absorber [N(HI) $=17.9\mathrm{cm}^{-2}$] that has so-far been
+utilised for deuterium abundance measurements. The majority of existing
+high-precision measurements were obtained from considerably higher column
+density systems [i.e. N(HI) $>19.4\mathrm{cm}^{-2}$]. This bodes well for
+future observations as low column density systems are more common.
+"
+22067,Effect of Marangoni stress on the bulk rheology of a dilute emulsion of surfactant-laden deformable droplets in linear flows,"  In the present study we analytically investigate the deformation and bulk
+rheology of a dilute emulsion of surfactant-laden droplets suspended in a
+linear flow. We use an asymptotic approach to predict the effect of surfactant
+distribution on the deformation of a single droplet as well as the effective
+shear and extensional viscosity for the dilute emulsion. The non-uniform
+distribution of surfactants due to the bulk flow results in the generation of a
+Marangoni stress which affects both the deformation as well as the bulk
+rheology of the suspension. The present analysis is done for the limiting case
+when the surfactant transport is dominated by the surface diffusion relative to
+surface convection. As an example, we have used two commonly encountered bulk
+flows, namely, uniaxial extensional flow and simple shear flow. With the
+assumption of negligible inertial forces present in either of the phases, we
+are able to show that both the surfactant concentration on the droplet surface
+as well as the ratio of viscosity of the droplet phase with respect to the
+suspending fluid has a significant effect on the droplet deformation as well as
+the bulk rheology. It is seen that increase in the non-uniformity in surfactant
+distribution on the droplet surface results in a higher droplet deformation and
+a higher effective viscosity for either of linear flows considered. For the
+case of simple shear flow, surfactant distribution is found to have no effect
+on the inclination angle, however, a higher viscosity ratio predicts the
+droplet to be more aligned towards the direction of flow.
+"
+22068,Families of spherical surfaces and harmonic maps,"  We study singularities of constant positive Gaussian curvature surfaces and
+determine the way they bifurcate in generic 1-parameter families of such
+surfaces. We construct the bifurcations explicitly using loop group methods.
+Constant Gaussian curvature surfaces correspond to harmonic maps, and we
+examine the relationship between the two types of maps and their singularities.
+Finally, we determine which finitely A-determined map-germs from the plane to
+the plane can be represented by harmonic maps.
+"
+22069,Analyzing Privacy Breaches in the Aircraft Communications Addressing and Reporting System (ACARS),"  The manner in which Aircraft Communications, Addressing and Reporting System
+(ACARS) is being used has significantly changed over time. Whilst originally
+used by commercial airliners to track their flights and provide automated
+timekeeping on crew, today it serves as a multi-purpose air-ground data link
+for many aviation stakeholders including private jet owners, state actors and
+military. Since ACARS messages are still mostly sent in the clear over a
+wireless channel, any sensitive information sent with ACARS can potentially
+lead to a privacy breach for users. Naturally, different stakeholders consider
+different types of data sensitive. In this paper we propose a privacy framework
+matching aviation stakeholders to a range of sensitive information types and
+assess the impact for each. Based on more than one million ACARS messages,
+collected over several months, we then demonstrate that current ACARS usage
+systematically breaches privacy for all stakeholder groups. We further support
+our findings with a number of cases of significant privacy issues for each
+group and analyze the impact of such leaks. While it is well-known that ACARS
+messages are susceptible to eavesdropping attacks, this work is the first to
+quantify the extent and impact of privacy leakage in the real world for the
+relevant aviation stakeholders.
+"
+22070,Self-supporting structure design in additive manufacturing through explicit topology optimization,"  One of the challenging issues in additive manufacturing (AM) oriented
+topology optimization is how to design structures that are self-supportive in a
+manufacture process without introducing additional supporting materials. In the
+present contribution, it is intended to resolve this problem under an explicit
+topology optimization framework where optimal structural topology can be found
+by optimizing a set of explicit geometry parameters. Two solution approaches
+established based on the Moving Morphable Components (MMC) and Moving Morphable
+Voids (MMV) frameworks, respectively, are proposed and some theoretical issues
+associated with AM oriented topology optimization are also analyzed. Numerical
+examples provided demonstrate the effectiveness of the proposed methods.
+"
+22071,Calibration and performance studies of the balloon-borne hard X-ray polarimeter PoGO+,"  Polarimetric observations of celestial sources in the hard X-ray band stand
+to provide new information on emission mechanisms and source geometries. PoGO+
+is a Compton scattering polarimeter (20-150 keV) optimised for the observation
+of the Crab (pulsar and wind nebula) and Cygnus X-1 (black hole binary), from a
+stratospheric balloon-borne platform launched from the Esrange Space Centre in
+summer 2016. Prior to flight, the response of the polarimeter has been studied
+with polarised and unpolarised X-rays allowing a Geant4-based simulation model
+to be validated. The expected modulation factor for Crab observations is found
+to be $M_{\mathrm{Crab}}=(41.75\pm0.85)\%$, resulting in an expected Minimum
+Detectable Polarisation (MDP) of $7.3\%$ for a 7 day flight. This will allow a
+measurement of the Crab polarisation parameters with at least $5\sigma$
+statistical significance assuming a polarisation fraction $\sim20\%$ $-$ a
+significant improvement over the PoGOLite Pathfinder mission which flew in 2013
+and from which the PoGO+ design is developed.
+"
+22072,The size of the last merger and time reversal in $Λ$-coalescents,"  We consider the number of blocks involved in the last merger of a
+$\Lambda$-coalescent started with $n$ blocks. We give conditions under which,
+as $n \to \infty$, the sequence of these random variables a) is tight, b)
+converges in distribution to a finite random variable or c) converges to
+infinity in probability. Our conditions are optimal for $\Lambda$-coalescents
+that have a dust component. For general $\Lambda$, we relate the three cases to
+the existence, uniqueness and non-existence of quasi-invariant measures for the
+dynamics of the block-counting process, and in case b) investigate the
+time-reversal of the block-counting process back from the time of the last
+merger.
+"
+22073,Security Consideration For Deep Learning-Based Image Forensics,"  Recently, image forensics community has paied attention to the research on
+the design of effective algorithms based on deep learning technology and facts
+proved that combining the domain knowledge of image forensics and deep learning
+would achieve more robust and better performance than the traditional schemes.
+Instead of improving it, in this paper, the safety of deep learning based
+methods in the field of image forensics is taken into account. To the best of
+our knowledge, this is a first work focusing on this topic. Specifically, we
+experimentally find that the method using deep learning would fail when adding
+the slight noise into the images (adversarial images). Furthermore, two kinds
+of strategys are proposed to enforce security of deep learning-based method.
+Firstly, an extra penalty term to the loss function is added, which is referred
+to the 2-norm of the gradient of the loss with respect to the input images, and
+then an novel training method are adopt to train the model by fusing the normal
+and adversarial images. Experimental results show that the proposed algorithm
+can achieve good performance even in the case of adversarial images and provide
+a safety consideration for deep learning-based image forensics
+"
+22074,Dependent Microstructure Noise and Integrated Volatility Estimation from High-Frequency Data,"  In this paper, we develop econometric tools to analyze the integrated
+volatility of the efficient price and the dynamic properties of microstructure
+noise in high-frequency data under general dependent noise. We first develop
+consistent estimators of the variance and autocovariances of noise using a
+variant of realized volatility. Next, we employ these estimators to adapt the
+pre-averaging method and derive a consistent estimator of the integrated
+volatility, which converges stably to a mixed Gaussian distribution at the
+optimal rate $n^{1/4}$. To refine the finite sample performance, we propose a
+two-step approach that corrects the finite sample bias, which turns out to be
+crucial in applications. Our extensive simulation studies demonstrate the
+excellent performance of our two-step estimators. In an empirical study, we
+characterize the dependence structures of microstructure noise in several
+popular sampling schemes and provide intuitive economic interpretations; we
+also illustrate the importance of accounting for both the serial dependence in
+noise and the finite sample bias when estimating integrated volatility.
+"
+22075,Dynamically evolved community size and stability of random Lotka-Volterra ecosystems,"  We use dynamical generating functionals to study the stability and size of
+communities evolving in Lotka-Volterra systems with random interaction
+coefficients. The size of the eco-system is not set from the beginning.
+Instead, we start from a set of possible species, which may undergo extinction.
+How many species survive depends on the properties of the interaction matrix;
+the size of the resulting food web at stationarity is a property of the system
+itself in our model, and not a control parameter as in most studies based on
+random matrix theory. We find that prey-predator relations enhance stability,
+and that variability of species interactions promotes instability. Complexity
+of inter-species couplings leads to reduced sizes of ecological communities.
+Dynamically evolved community size and stability are hence positively
+correlated.
+"
+22076,Rate Constants for Fine-Structure Excitations in O-H Collisions with Error Bars Obtained by Machine Learning,"  We present an approach using a combination of coupled channel scattering
+calculations with a machine- learning technique based on Gaussian Process
+regression to determine the sensitivity of the rate constants for non-adiabatic
+transitions in inelastic atomic collisions to variations of the underlying
+adiabatic interaction potentials. Using this approach, we improve the previous
+computations of the rate constants for the fine-structure transitions in
+collisions of O(3Pj) with atomic H. We compute the error bars of the rate
+constants corresponding to 20 % variations of the ab initio potentials and show
+that this method can be used to determine which of the individual adiabatic
+potentials are more or less important for the outcome of different
+fine-structure changing collisions.
+"
+22077,DKN: Deep Knowledge-Aware Network for News Recommendation,"  Online news recommender systems aim to address the information explosion of
+news and make personalized recommendation for users. In general, news language
+is highly condensed, full of knowledge entities and common sense. However,
+existing methods are unaware of such external knowledge and cannot fully
+discover latent knowledge-level connections among news. The recommended results
+for a user are consequently limited to simple patterns and cannot be extended
+reasonably. Moreover, news recommendation also faces the challenges of high
+time-sensitivity of news and dynamic diversity of users' interests. To solve
+the above problems, in this paper, we propose a deep knowledge-aware network
+(DKN) that incorporates knowledge graph representation into news
+recommendation. DKN is a content-based deep recommendation framework for
+click-through rate prediction. The key component of DKN is a multi-channel and
+word-entity-aligned knowledge-aware convolutional neural network (KCNN) that
+fuses semantic-level and knowledge-level representations of news. KCNN treats
+words and entities as multiple channels, and explicitly keeps their alignment
+relationship during convolution. In addition, to address users' diverse
+interests, we also design an attention module in DKN to dynamically aggregate a
+user's history with respect to current candidate news. Through extensive
+experiments on a real online news platform, we demonstrate that DKN achieves
+substantial gains over state-of-the-art deep recommendation models. We also
+validate the efficacy of the usage of knowledge in DKN.
+"
+22078,Counting Roots of Polynomials over $\mathbb{Z}/p^2\mathbb{Z}$,"  Until recently, the only known method of finding the roots of polynomials
+over prime power rings, other than fields, was brute force. One reason for this
+is the lack of a division algorithm, obstructing the use of greatest common
+divisors. Fix a prime $p \in \mathbb{Z}$ and $f \in ( \mathbb{Z}/p^n \mathbb{Z}
+) [x]$ any nonzero polynomial of degree $d$ whose coefficients are not all
+divisible by $p$. For the case $n=2$, we prove a new efficient algorithm to
+count the roots of $f$ in $\mathbb{Z}/p^2\mathbb{Z}$ within time polynomial in
+$(d+\operatorname{size}(f)+\log{p})$, and record a concise formula for the
+number of roots, formulated by Cheng, Gao, Rojas, and Wan.
+"
+22079,High-harmonic generation in amorphous solids,"  High-order harmonic generation (HHG) in isolated atoms and molecules has been
+widely utilized in extreme ultraviolet (XUV) photonics and attosecond pulse
+metrology. Recently, HHG has also been observed in solids, which could lead to
+important applications such as all-optical methods to image valance charge
+density and reconstruction of electronic band structures, as well as compact
+XUV light sources. Previous HHG studies are confined on crystalline solids;
+therefore decoupling the respective roles of long-range periodicity and high
+density has been challenging. Here, we report the first observation of HHG from
+amorphous fused silica. We decouple the role of long-range periodicity by
+comparing with crystal quartz, which contains same atomic constituents but
+exhibits long-range periodicity. Our results advance current understanding of
+strong-field processes leading to high harmonic generation in solids with
+implications in robust and compact coherent XUV light sources.
+"
+22080,True Asymptotic Natural Gradient Optimization,"  We introduce a simple algorithm, True Asymptotic Natural Gradient
+Optimization (TANGO), that converges to a true natural gradient descent in the
+limit of small learning rates, without explicit Fisher matrix estimation.
+For quadratic models the algorithm is also an instance of averaged stochastic
+gradient, where the parameter is a moving average of a ""fast"", constant-rate
+gradient descent. TANGO appears as a particular de-linearization of averaged
+SGD, and is sometimes quite different on non-quadratic models. This further
+connects averaged SGD and natural gradient, both of which are arguably optimal
+asymptotically.
+In large dimension, small learning rates will be required to approximate the
+natural gradient well. Still, this shows it is possible to get arbitrarily
+close to exact natural gradient descent with a lightweight algorithm.
+"
+22081,Automated bird sound recognition in realistic settings,"  We evaluated the effectiveness of an automated bird sound identification
+system in a situation that emulates a realistic, typical application. We
+trained classification algorithms on a crowd-sourced collection of bird audio
+recording data and restricted our training methods to be completely free of
+manual intervention. The approach is hence directly applicable to the analysis
+of multiple species collections, with labelling provided by crowd-sourced
+collection. We evaluated the performance of the bird sound recognition system
+on a realistic number of candidate classes, corresponding to real conditions.
+We investigated the use of two canonical classification methods, chosen due to
+their widespread use and ease of interpretation, namely a k Nearest Neighbour
+(kNN) classifier with histogram-based features and a Support Vector Machine
+(SVM) with time-summarisation features. We further investigated the use of a
+certainty measure, derived from the output probabilities of the classifiers, to
+enhance the interpretability and reliability of the class decisions. Our
+results demonstrate that both identification methods achieved similar
+performance, but we argue that the use of the kNN classifier offers somewhat
+more flexibility. Furthermore, we show that employing an outcome certainty
+measure provides a valuable and consistent indicator of the reliability of
+classification results. Our use of generic training data and our investigation
+of probabilistic classification methodologies that can flexibly address the
+variable number of candidate species/classes that are expected to be
+encountered in the field, directly contribute to the development of a practical
+bird sound identification system with potentially global application. Further,
+we show that certainty measures associated with identification outcomes can
+significantly contribute to the practical usability of the overall system.
+"
+22082,Light-enhanced electron-phonon coupling from nonlinear electron-phonon coupling,"  We investigate an exact nonequilibrium solution of a two-site electron-phonon
+model, where an infrared-active phonon that is nonlinearly coupled to the
+electrons is driven by a laser field. The time-resolved electronic spectrum
+shows coherence-incoherence spectral weight transfer, a clear signature of
+light-enhanced electron-phonon coupling. The present study is motivated by
+recent evidence for enhanced electron-phonon coupling in pump-probe TeraHertz
+and angle-resolved photoemission spectroscopy in bilayer graphene when driven
+near resonance with an infrared-active phonon mode [E.~Pomarico et al.,
+Phys.~Rev.~B 95, 024304 (2017)], and by a theoretical study suggesting that
+transient electronic attraction arises from nonlinear electron-phonon coupling
+[D.~M.~Kennes et al., Nature Physics (2017), 10.1038/nphys4024]. We show that a
+linear scaling of light-enhanced electron-phonon coupling with the pump field
+intensity emerges, in accordance with a time-nonlocal self-energy based on a
+mean-field decoupling using quasi-classical phonon coherent states. Finally we
+demonstrate that this leads to enhanced double occupancies in accordance with
+an effective electron-electron attraction. Our results suggest that materials
+with strong phonon nonlinearities provide an ideal playground to achieve
+light-enhanced electron-phonon coupling and possibly light-induced
+superconductivity.
+"
+22083,New Results On Routing Via Matchings On Graphs,"  In this paper we present some new complexity results on the routing time of a
+graph under the \textit{routing via matching} model. This is a parallel routing
+model which was introduced by Alon et al\cite{alon1994routing}. The model can
+be viewed as a communication scheme on a distributed network. The nodes in the
+network can communicate via matchings (a step), where a node exchanges data
+(pebbles) with its matched partner. Let $G$ be a connected graph with vertices
+labeled from $\{1,...,n\}$ and the destination vertices of the pebbles are
+given by a permutation $\pi$. The problem is to find a minimum step routing
+scheme for the input permutation $\pi$. This is denoted as the routing time
+$rt(G,\pi)$ of $G$ given $\pi$. In this paper we characterize the complexity of
+some known problems under the routing via matching model and discuss their
+relationship to graph connectivity and clique number. We also introduce some
+new problems in this domain, which may be of independent interest.
+"
+22084,The solution of many electron problems arising when radiation pulses interact with closed or open shell multielectron atomic and molecular states,"  The paper reviews briefly elements from our work on the solution of various
+field induced, time independent or time dependent many electron problems, which
+has been developed and carried out within state and property specific
+frameworks. The discussion focuses on only a few items, and is presented as a
+commentary with explanations. The formal details and the numerical results can
+be found in the cited publications.
+"
+22085,Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization,"  Plain vanilla K-means clustering has proven to be successful in practice, yet
+it suffers from outlier sensitivity and may produce highly unbalanced clusters.
+To mitigate both shortcomings, we formulate a joint outlier detection and
+clustering problem, which assigns a prescribed number of datapoints to an
+auxiliary outlier cluster and performs cardinality-constrained K-means
+clustering on the residual dataset, treating the cluster cardinalities as a
+given input. We cast this problem as a mixed-integer linear program (MILP) that
+admits tractable semidefinite and linear programming relaxations. We propose
+deterministic rounding schemes that transform the relaxed solutions to feasible
+solutions for the MILP. We also prove that these solutions are optimal in the
+MILP if a cluster separation condition holds.
+"
+22086,Eigenvector Under Random Perturbation: A Nonasymptotic Rayleigh-Schrödinger Theory,"  Rayleigh-Schrödinger perturbation theory is a well-known theory in
+quantum mechanics and it offers useful characterization of eigenvectors of a
+perturbed matrix. Suppose $A$ and perturbation $E$ are both Hermitian matrices,
+$A^t = A + tE$, $\{\lambda_j\}_{j=1}^n$ are eigenvalues of $A$ in descending
+order, and $u_1, u^t_1$ are leading eigenvectors of $A$ and $A^t$.
+Rayleigh-Schrödinger theory shows asymptotically, $\langle u^t_1, u_j
+\rangle \propto t / (\lambda_1 - \lambda_j)$ where $ t = o(1)$. However, the
+asymptotic theory does not apply to larger $t$; in particular, it fails when $
+t \| E \|_2 > \lambda_1 - \lambda_2$. In this paper, we present a nonasymptotic
+theory with $E$ being a random matrix. We prove that, when $t = 1$ and $E$ has
+independent and centered subgaussian entries above its diagonal, with high
+probability, \begin{equation*} | \langle u^1_1, u_j \rangle | = O(\sqrt{\log n}
+/ (\lambda_1 - \lambda_j)), \end{equation*} for all $j>1$ simultaneously, under
+a condition on eigenvalues of $A$ that involves all gaps $\lambda_1 -
+\lambda_j$. This bound is valid, even in cases where $\| E \|_2 \gg \lambda_1 -
+\lambda_2$. The result is optimal, except for a log term. It also leads to an
+improvement of Davis-Kahan theorem.
+"
+22087,Effective Resistance Preserving Directed Graph Symmetrization,"  This work presents a new method for symmetrization of directed graphs that
+constructs an undirected graph with equivalent pairwise effective resistances
+as a given directed graph. Consequently a graph metric, square root of
+effective resistance, is preserved between the directed graph and its
+symmetrized version. It is shown that the preservation of this metric allows
+for interpretation of algebraic and spectral properties of the symmetrized
+graph in the context of the directed graph, due to the relationship between
+effective resistance and the Laplacian spectrum. Additionally, Lyapunov theory
+is used to demonstrate that the Laplacian matrix of a directed graph can be
+decomposed into the product of a projection matrix, a skew symmetric matrix,
+and the Laplacian matrix of the symmetrized graph. The application of effective
+resistance preserving graph symmetrization is discussed in the context of
+spectral graph partitioning and Kron reduction of directed graphs.
+"
+22088,Dual-label Deep LSTM Dereverberation For Speaker Verification,"  In this paper, we present a reverberation removal approach for speaker
+verification, utilizing dual-label deep neural networks (DNNs). The networks
+perform feature mapping between the spectral features of reverberant and clean
+speech. Long short term memory recurrent neural networks (LSTMs) are trained to
+map corrupted Mel filterbank (MFB) features to two sets of labels: i) the clean
+MFB features, and ii) either estimated pitch tracks or the fast Fourier
+transform (FFT) spectrogram of clean speech. The performance of reverberation
+removal is evaluated by equal error rates (EERs) of speaker verification
+experiments.
+"
+22089,Star Routing: Between Vehicle Routing and Vertex Cover,"  We consider an optimization problem posed by an actual newspaper company,
+which consists of computing a minimum length route for a delivery truck, such
+that the driver only stops at street crossings, each time delivering copies to
+all customers adjacent to the crossing. This can be modeled as an abstract
+problem that takes an unweighted simple graph $G = (V, E)$ and a subset of
+edges $X$ and asks for a shortest cycle, not necessarily simple, such that
+every edge of $X$ has an endpoint in the cycle.
+We show that the decision version of the problem is strongly NP-complete,
+even if $G$ is a grid graph. Regarding approximate solutions, we show that the
+general case of the problem is APX-hard, and thus no PTAS is possible unless P
+$=$ NP. Despite the hardness of approximation, we show that given any
+$\alpha$-approximation algorithm for metric TSP, we can build a
+$3\alpha$-approximation algorithm for our optimization problem, yielding a
+concrete $9/2$-approximation algorithm.
+The grid case is of particular importance, because it models a city map or
+some part of it. A usual scenario is having some neighborhood full of
+customers, which translates as an instance of the abstract problem where almost
+every edge of $G$ is in $X$. We model this property as $|E - X| = o(|E|)$, and
+for these instances we give a $(3/2 + \varepsilon)$-approximation algorithm,
+for any $\varepsilon > 0$, provided that the grid is sufficiently big.
+"
+22090,Additive manufacturing of magnetic shielding and ultra-high vacuum flange for cold atom sensors,"  Recent advances in the understanding and control of quantum technologies,
+such as those based on cold atoms, have resulted in devices with extraordinary
+metrological sensitivities. To realise this potential outside of a lab
+environment the size, weight and power consumption need to be reduced. Here we
+demonstrate the use of laser powder bed fusion, an additive manufacturing
+technique, as a production technique for the components that make up quantum
+sensors. As a demonstration we have constructed two key components using
+additive manufacturing, namely magnetic shielding and vacuum chambers. The
+initial prototypes for magnetic shields show shielding factors within a factor
+of 3 of conventional approaches. The vacuum demonstrator device shows that
+3D-printed titanium structures are suitable for use as vacuum chambers, with
+the test system reaching base pressures of $5 \pm 0.5 \times 10^{-10}$ mbar.
+These demonstrations show considerable promise for the use of additive
+manufacturing for cold atom based quantum technologies, in future enabling
+improved integrated structures, allowing for the reduction in size, weight and
+assembly complexity.
+"
+22091,EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals,"  Generative adversarial networks (GANs) are recently highly successful in
+generative applications involving images and start being applied to time series
+data. Here we describe EEG-GAN as a framework to generate
+electroencephalographic (EEG) brain signals. We introduce a modification to the
+improved training of Wasserstein GANs to stabilize training and investigate a
+range of architectural choices critical for time series generation (most
+notably up- and down-sampling). For evaluation we consider and compare
+different metrics such as Inception score, Frechet inception distance and
+sliced Wasserstein distance, together showing that our EEG-GAN framework
+generated naturalistic EEG examples. It thus opens up a range of new generative
+application scenarios in the neuroscientific and neurological context, such as
+data augmentation in brain-computer interfacing tasks, EEG super-sampling, or
+restoration of corrupted data segments. The possibility to generate signals of
+a certain class and/or with specific properties may also open a new avenue for
+research into the underlying structure of brain signals.
+"
+22092,Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification,"  Improving speech system performance in noisy environments remains a
+challenging task, and speech enhancement (SE) is one of the effective
+techniques to solve the problem. Motivated by the promising results of
+generative adversarial networks (GANs) in a variety of image processing tasks,
+we explore the potential of conditional GANs (cGANs) for SE, and in particular,
+we make use of the image processing framework proposed by Isola et al. [1] to
+learn a mapping from the spectrogram of noisy speech to an enhanced
+counterpart. The SE cGAN consists of two networks, trained in an adversarial
+manner: a generator that tries to enhance the input noisy spectrogram, and a
+discriminator that tries to distinguish between enhanced spectrograms provided
+by the generator and clean ones from the database using the noisy spectrogram
+as a condition. We evaluate the performance of the cGAN method in terms of
+perceptual evaluation of speech quality (PESQ), short-time objective
+intelligibility (STOI), and equal error rate (EER) of speaker verification (an
+example application). Experimental results show that the cGAN method overall
+outperforms the classical short-time spectral amplitude minimum mean square
+error (STSA-MMSE) SE algorithm, and is comparable to a deep neural
+network-based SE approach (DNN-SE).
+"
+22093,The (Un)reliability of saliency methods,"  Saliency methods aim to explain the predictions of deep neural networks.
+These methods lack reliability when the explanation is sensitive to factors
+that do not contribute to the model prediction. We use a simple and common
+pre-processing step ---adding a constant shift to the input data--- to show
+that a transformation with no effect on the model can cause numerous methods to
+incorrectly attribute. In order to guarantee reliability, we posit that methods
+should fulfill input invariance, the requirement that a saliency method mirror
+the sensitivity of the model with respect to transformations of the input. We
+show, through several examples, that saliency methods that do not satisfy input
+invariance result in misleading attribution.
+"
+22094,Exploiting Sparsity in the Coefficient Matching Conditions in Sum-of-Squares Programming using ADMM,"  This paper introduces an efficient first-order method based on the
+alternating direction method of multipliers (ADMM) to solve semidefinite
+programs (SDPs) arising from sum-of-squares (SOS) programming. We exploit the
+sparsity of the \emph{coefficient matching conditions} when SOS programs are
+formulated in the usual monomial basis to reduce the computational cost of the
+ADMM algorithm. Each iteration of our algorithm requires one projection onto
+the positive semidefinite cone and the solution of multiple quadratic programs
+with closed-form solutions free of any matrix inversion. Our techniques are
+implemented in the open-source MATLAB solver SOSADMM. Numerical experiments on
+SOS problems arising from unconstrained polynomial minimization and from
+Lyapunov stability analysis for polynomial systems show speed-ups compared to
+the interior-point solver SeDuMi, and the first-order solver CDCS.
+"
+22095,Time Stretch Inspired Computational Imaging,"  We show that dispersive propagation of light followed by phase detection has
+properties that can be exploited for extracting features from the waveforms.
+This discovery is spearheading development of a new class of physics-inspired
+algorithms for feature extraction from digital images with unique properties
+and superior dynamic range compared to conventional algorithms. In certain
+cases, these algorithms have the potential to be an energy efficient and
+scalable substitute to synthetically fashioned computational techniques in
+practice today.
+"
+22096,The ladder physics in the Spin Fermion model,"  A link is established between the spin-fermion (SF) model of the cuprates and
+the approach based on the analogy between the physics of doped Mott insulators
+in two dimensions and the physics of fermionic ladders. This enables one to use
+nonperturbative results derived for fermionic ladders to move beyond the
+large-N approximation in the SF model. It is shown that the paramagnon exchange
+postulated in the SF model has exactly the right form to facilitate the
+emergence of the fully gapped d-Mott state in the region of the Brillouin zone
+at the hot spots of the Fermi surface. Hence the SF model provides an adequate
+description of the pseudogap.
+"
+22097,A Bayesian framework for molecular strain identification from mixed diagnostic samples,"  We provide a mathematical formulation and develop a computational framework
+for identifying multiple strains of microorganisms from mixed samples of DNA.
+Our method is applicable in public health domains where efficient
+identification of pathogens is paramount, e.g., for the monitoring of disease
+outbreaks. We formulate strain identification as an inverse problem that aims
+at simultaneously estimating a binary matrix (encoding presence or absence of
+mutations in each strain) and a real-valued vector (representing the mixture of
+strains) such that their product is approximately equal to the measured data
+vector. The problem at hand has a similar structure to blind deconvolution,
+except for the presence of binary constraints, which we enforce in our
+approach. Following a Bayesian approach, we derive a posterior density. We
+present two computational methods for solving the non-convex maximum a
+posteriori estimation problem. The first one is a local optimization method
+that is made efficient and scalable by decoupling the problem into smaller
+independent subproblems, whereas the second one yields a global minimizer by
+converting the problem into a convex mixed-integer quadratic programming
+problem. The decoupling approach also provides an efficient way to integrate
+over the posterior. This provides useful information about the ambiguity of the
+underdetermined problem and, thus, the uncertainty associated with numerical
+solutions. We evaluate the potential and limitations of our framework in silico
+using synthetic and experimental data with available ground truths.
+"
+22098,Toward transient finite element simulation of thermal deformation of machine tools in real-time,"  Finite element models without simplifying assumptions can accurately describe
+the spatial and temporal distribution of heat in machine tools as well as the
+resulting deformation. In principle, this allows to correct for displacements
+of the Tool Centre Point and enables high precision manufacturing. However, the
+computational cost of FEM models and restriction to generic algorithms in
+commercial tools like ANSYS prevents their operational use since simulations
+have to run faster than real-time. For the case where heat diffusion is slow
+compared to machine movement, we introduce a tailored implicit-explicit
+multi-rate time stepping method of higher order based on spectral deferred
+corrections. Using the open-source FEM library DUNE, we show that fully coupled
+simulations of the temperature field are possible in real-time for a machine
+consisting of a stock sliding up and down on rails attached to a stand.
+"
+22099,Enhancing speed and scalability of the ParFlow simulation code,"  Regional hydrology studies are often supported by high resolution simulations
+of subsurface flow that require expensive and extensive computations. Efficient
+usage of the latest high performance parallel computing systems becomes a
+necessity. The simulation software ParFlow has been demonstrated to meet this
+requirement and shown to have excellent solver scalability for up to 16,384
+processes. In the present work we show that the code requires further
+enhancements in order to fully take advantage of current petascale machines. We
+identify ParFlow's way of parallelization of the computational mesh as a
+central bottleneck. We propose to reorganize this subsystem using fast mesh
+partition algorithms provided by the parallel adaptive mesh refinement library
+p4est. We realize this in a minimally invasive manner by modifying selected
+parts of the code to reinterpret the existing mesh data structures. We evaluate
+the scaling performance of the modified version of ParFlow, demonstrating good
+weak and strong scaling up to 458k cores of the Juqueen supercomputer, and test
+an example application at large scale.
+"
+22100,On the rank of universal quadratic forms over real quadratic fields,"  We study the minimal number of variables required by a totally positive
+definite diagonal universal quadratic form over a real quadratic field $\mathbb
+Q(\sqrt D)$ and obtain lower and upper bounds for it in terms of certain sums
+of coefficients of the associated continued fraction. We also estimate such
+sums in terms of $D$ and establish a link between continued fraction expansions
+and special values of $L$-functions in the spirit of Kronecker's limit formula.
+"
+22101,"Deep Learning for Medical Image Processing: Overview, Challenges and Future","  Healthcare sector is totally different from other industry. It is on high
+priority sector and people expect highest level of care and services regardless
+of cost. It did not achieve social expectation even though it consume huge
+percentage of budget. Mostly the interpretations of medical data is being done
+by medical expert. In terms of image interpretation by human expert, it is
+quite limited due to its subjectivity, the complexity of the image, extensive
+variations exist across different interpreters, and fatigue. After the success
+of deep learning in other real world application, it is also providing exciting
+solutions with good accuracy for medical imaging and is seen as a key method
+for future applications in health secotr. In this chapter, we discussed state
+of the art deep learning architecture and its optimization used for medical
+image segmentation and classification. In the last section, we have discussed
+the challenges deep learning based methods for medical imaging and open
+research issue.
+"
+22102,A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning,"  Automatic decision-making approaches, such as reinforcement learning (RL),
+have been applied to (partially) solve the resource allocation problem
+adaptively in the cloud computing system. However, a complete cloud resource
+allocation framework exhibits high dimensions in state and action spaces, which
+prohibit the usefulness of traditional RL techniques. In addition, high power
+consumption has become one of the critical concerns in design and control of
+cloud computing systems, which degrades system reliability and increases
+cooling cost. An effective dynamic power management (DPM) policy should
+minimize power consumption while maintaining performance degradation within an
+acceptable level. Thus, a joint virtual machine (VM) resource allocation and
+power management framework is critical to the overall cloud computing system.
+Moreover, novel solution framework is necessary to address the even higher
+dimensions in state and action spaces. In this paper, we propose a novel
+hierarchical framework for solving the overall resource allocation and power
+management problem in cloud computing systems. The proposed hierarchical
+framework comprises a global tier for VM resource allocation to the servers and
+a local tier for distributed power management of local servers. The emerging
+deep reinforcement learning (DRL) technique, which can deal with complicated
+control problems with large state space, is adopted to solve the global tier
+problem. Furthermore, an autoencoder and a novel weight sharing structure are
+adopted to handle the high-dimensional state space and accelerate the
+convergence speed. On the other hand, the local tier of distributed server
+power managements comprises an LSTM based workload predictor and a model-free
+RL based power manager, operating in a distributed manner.
+"
+22103,Structured signal recovery from quadratic measurements: Breaking sample complexity barriers via nonconvex optimization,"  This paper concerns the problem of recovering an unknown but structured
+signal $x \in R^n$ from $m$ quadratic measurements of the form
+$y_r=|<a_r,x>|^2$ for $r=1,2,...,m$. We focus on the under-determined setting
+where the number of measurements is significantly smaller than the dimension of
+the signal ($m<<n$). We formulate the recovery problem as a nonconvex
+optimization problem where prior structural information about the signal is
+enforced through constrains on the optimization variables. We prove that
+projected gradient descent, when initialized in a neighborhood of the desired
+signal, converges to the unknown signal at a linear rate. These results hold
+for any constraint set (convex or nonconvex) providing convergence guarantees
+to the global optimum even when the objective function and constraint set is
+nonconvex. Furthermore, these results hold with a number of measurements that
+is only a constant factor away from the minimal number of measurements required
+to uniquely identify the unknown signal. Our results provide the first provably
+tractable algorithm for this data-poor regime, breaking local sample complexity
+barriers that have emerged in recent literature. In a companion paper we
+demonstrate favorable properties for the optimization problem that may enable
+similar results to continue to hold more globally (over the entire ambient
+space). Collectively these two papers utilize and develop powerful tools for
+uniform convergence of empirical processes that may have broader implications
+for rigorous understanding of constrained nonconvex optimization heuristics.
+The mathematical results in this paper also pave the way for a new generation
+of data-driven phase-less imaging systems that can utilize prior information to
+significantly reduce acquisition time and enhance image reconstruction,
+enabling nano-scale imaging at unprecedented speeds and resolutions.
+"
+22104,Mining Electronic Health Records: A Survey,"  The continuously increasing cost of the US healthcare system has received
+significant attention. Central to the ideas aimed at curbing this trend is the
+use of technology, in the form of the mandate to implement electronic health
+records (EHRs). EHRs consist of patient information such as demographics,
+medications, laboratory test results, diagnosis codes and procedures. Mining
+EHRs could lead to improvement in patient health management as EHRs contain
+detailed information related to disease prognosis for large patient
+populations. In this manuscript, we provide a structured and comprehensive
+overview of data mining techniques for modeling EHR data. We first provide a
+detailed understanding of the major application areas to which EHR mining has
+been applied and then discuss the nature of EHR data and its accompanying
+challenges. Next, we describe major approaches used for EHR mining, the metrics
+associated with EHRs, and the various study designs. With this foundation, we
+then provide a systematic and methodological organization of existing data
+mining techniques used to model EHRs and discuss ideas for future research. We
+conclude this survey with a comprehensive summary of clinical data mining
+applications of EHR data, as illustrated in the online supplement.
+"
+22105,Strongly angle-dependent magnetoresistance in Weyl semimetals with long-range disorder,"  The chiral anomaly in Weyl semimetals states that the left- and right-handed
+Weyl fermions, constituting the low energy description, are not individually
+conserved, resulting, for example, in a negative magnetoresistance in such
+materials. Recent experiments see strong indications of such an anomalous
+resistance response; however, with a response that at strong fields is more
+sharply peaked for parallel magnetic and electric fields than expected from
+simple theoretical considerations. Here, we uncover a mechanism, arising from
+the interplay between the angle-dependent Landau level structure and long-range
+scalar disorder, that has the same phenomenology. In particular, we ana-
+lytically show, and numerically confirm, that the internode scattering time
+decreases exponentially with the angle between the magnetic field and the Weyl
+node separation in the large field limit, while it is insensitive to this angle
+at weak magnetic fields. Since, in the simplest approximation, the internode
+scattering time is proportional to the anomaly-related conductivity, this
+feature may be related to the experimental observations of a sharply peaked
+magnetoresistance.
+"
+22106,Fairness-aware machine learning: a perspective,"  Algorithms learned from data are increasingly used for deciding many aspects
+in our life: from movies we see, to prices we pay, or medicine we get. Yet
+there is growing evidence that decision making by inappropriately trained
+algorithms may unintentionally discriminate people. For example, in automated
+matching of candidate CVs with job descriptions, algorithms may capture and
+propagate ethnicity related biases. Several repairs for selected algorithms
+have already been proposed, but the underlying mechanisms how such
+discrimination happens from the computational perspective are not yet
+scientifically understood. We need to develop theoretical understanding how
+algorithms may become discriminatory, and establish fundamental machine
+learning principles for prevention. We need to analyze machine learning process
+as a whole to systematically explain the roots of discrimination occurrence,
+which will allow to devise global machine learning optimization criteria for
+guaranteed prevention, as opposed to pushing empirical constraints into
+existing algorithms case-by-case. As a result, the state-of-the-art will
+advance from heuristic repairing, to proactive and theoretically supported
+prevention. This is needed not only because law requires to protect vulnerable
+people. Penetration of big data initiatives will only increase, and computer
+science needs to provide solid explanations and accountability to the public,
+before public concerns lead to unnecessarily restrictive regulations against
+machine learning.
+"
+22107,Titanium oxynitride thin films with tunable double epsilon-near-zero behaviour,"  Titanium Oxynitride (TiOxNy) thin films are fabricated using reactive
+magnetron sputtering. The mechanism of their growth formation is explained and
+their optical properties are presented. The films grown when the level of
+residual Oxygen in the background vacuum was between 5E-9Torr to 20E-9Torr
+exhibit double Epsilon-Near-Zero (2-ENZ) behaviour with ENZ1 and ENZ2
+wavelengths tunable in the 700-850 nm and in the 1100-1350 nm spectral ranges,
+respectively. Samples fabricated when the level of residual Oxygen in the
+background vacuum was above 2E-8Torr exhibit non-metallic behaviour, while the
+layers deposited when the level of residual Oxygen in the background vacuum was
+below 5E-9Torr, show metallic behaviour with a single ENZ value. The double ENZ
+phenomenon is related to the level of residual Oxygen in the background vacuum
+and is attributed to the mixture of TiN and TiOxNy/TiOx phases in the films.
+Varying the partial pressure of nitrogen during the deposition can further
+control the amount of TiN, TiOx and TiOxNy compounds in the films and,
+therefore, tune the screened plasma wavelength. A good approximation of the
+ellipsometric behaviour is achieved with Maxwell-Garnett theory for a composite
+film formed by a mixture of TiO2 and TiN phases suggesting that double ENZ
+TiOxNy films are formed by inclusions of TiN within a TiO2 matrix. These
+oxynitride compounds could be considered as new materials exhibiting double ENZ
+in the visible and near-IR spectral ranges.
+"
+22108,Defect Line Terahertz Quantum Cascade Laser,"  We present terahertz quantum cascade lasers operating at a defect mode of a
+photonic crystal bandgap. This class of devices exhibits single mode emission
+and low threshold current compared to standard metal-metal lasers. The mode
+selectivity is an intrinsic property of the chosen fabrication design. The
+lower lasing threshold effect, already reported in photonic crystal quantum
+cascade lasers, is further enhanced in the ultra-flat-dispersion defect line.
+The presented results pave the way for integrated circuitry operating in the
+terahertz regime and have important applications in the field of quantum
+cascade lasers, spectroscopy and microcavity lasers.
+"
+22109,Design and Simulation of the IsoDAR RFQ Direct Injection System and Spiral Inflector,"  In this paper we present the development of a simulation code capable of
+optimizing the geometry of a spiral inflector designed for axial injection into
+a cyclotron. To do this, an electric field map of the device is generated by
+utilizing a boundary elements method and then used to track one or more
+particles. The information from the trajectories is then used to shorten the
+electrodes of the spiral inflector to adjust for fringing electric fields such
+that the particles end up on the mid-plane of the cyclotron. This method was
+also used to analyze the effects of a modified electrode geometry that can
+focus the beam as it travels through the device. Compared against commercial
+multiphysics software, the developed code produced similar results within a
+negligible margin of error attributed to differences in meshing and particle
+tracking algorithms.
+"
+22110,An Ensemble Generation Method Based on Instance Hardness,"  In Machine Learning, ensemble methods have been receiving a great deal of
+attention. Techniques such as Bagging and Boosting have been successfully
+applied to a variety of problems. Nevertheless, such techniques are still
+susceptible to the effects of noise and outliers in the training data. We
+propose a new method for the generation of pools of classifiers based on
+Bagging, in which the probability of an instance being selected during the
+resampling process is inversely proportional to its instance hardness, which
+can be understood as the likelihood of an instance being misclassified,
+regardless of the choice of classifier. The goal of the proposed method is to
+remove noisy data without sacrificing the hard instances which are likely to be
+found on class boundaries. We evaluate the performance of the method in
+nineteen public data sets, and compare it to the performance of the Bagging and
+Random Subspace algorithms. Our experiments show that in high noise scenarios
+the accuracy of our method is significantly better than that of Bagging.
+"
+22111,Comparison of Distances for Supervised Segmentation of White Matter Tractography,"  Tractograms are mathematical representations of the main paths of axons
+within the white matter of the brain, from diffusion MRI data. Such
+representations are in the form of polylines, called streamlines, and one
+streamline approximates the common path of tens of thousands of axons. The
+analysis of tractograms is a task of interest in multiple fields, like
+neurosurgery and neurology. A basic building block of many pipelines of
+analysis is the definition of a distance function between streamlines. Multiple
+distance functions have been proposed in the literature, and different authors
+use different distances, usually without a specific reason other than invoking
+the ""common practice"". To this end, in this work we want to test such common
+practices, in order to obtain factual reasons for choosing one distance over
+another. For these reasons, in this work we compare many streamline distance
+functions available in the literature. We focus on the common task of automatic
+bundle segmentation and we adopt the recent approach of supervised segmentation
+from expert-based examples. Using the HCP dataset, we compare several distances
+obtaining guidelines on the choice of which distance function one should use
+for supervised bundle segmentation.
+"
+22112,Study of Set-Membership Adaptive Kernel Algorithms,"  In the last decade, a considerable research effort has been devoted to
+developing adaptive algorithms based on kernel functions. One of the main
+features of these algorithms is that they form a family of universal
+approximation techniques, solving problems with nonlinearities elegantly. In
+this paper, we present data-selective adaptive kernel normalized least-mean
+square (KNLMS) algorithms that can increase their learning rate and reduce
+their computational complexity. In fact, these methods deal with kernel
+expansions, creating a growing structure also known as the dictionary, whose
+size depends on the number of observations and their innovation. The algorithms
+described herein use an adaptive step-size to accelerate the learning and can
+offer an excellent tradeoff between convergence speed and steady state, which
+allows them to solve nonlinear filtering and estimation problems with a large
+number of parameters without requiring a large computational cost. The
+data-selective update scheme also limits the number of operations performed and
+the size of the dictionary created by the kernel expansion, saving
+computational resources and dealing with one of the major problems of kernel
+adaptive algorithms. A statistical analysis is carried out along with a
+computational complexity analysis of the proposed algorithms. Simulations show
+that the proposed KNLMS algorithms outperform existing algorithms in examples
+of nonlinear system identification and prediction of a time series originating
+from a nonlinear difference equation.
+"
+22113,Localized dark solitons and vortices in defocusing media with spatially inhomogeneous nonlinearity,"  Recent studies have demonstrated that defocusing cubic nonlinearity with
+local strength growing from the center to the periphery faster than $r^{D}$, in
+space of dimension $D$ with radial coordinate $r$, supports a vast variety of
+robust bright solitons. In the framework of the same model, but with a weaker
+spatial-growth rate, $\sim r^{\alpha }$ with $\alpha \leq D$, we here test the
+possibility to create stable\textit{\ localized continuous waves} (LCWs) in
+one- and two-dimensional (1D and 2D) geometries, \textit{% localized dark
+solitons} (LDSs) in 1D, and \textit{localized dark vortices} (LDVs) in 2D,
+which are all realized as loosely confined states with a divergent norm.
+Asymptotic tails of the solutions, which determine the divergence of the norm,
+are constructed in a universal analytical form by means of the Thomas-Fermi
+approximation (TFA). Global approximations for the LCWs, LDSs, and LDVs are
+constructed on the basis of interpolations between analytical approximations
+available far from (TFA) and close to the center. In particular, the
+interpolations for the 1D LDS, as well as for the 2D LDVs, are based on a
+\textquotedblleft deformed-tanh"" expression, which is suggested by the usual 1D
+dark-soliton solution. In addition to the 1D fundamental LDSs with the single
+notch, and 2D vortices with $S=1$, higher-order LDSs with multiple notches are
+found too, as well as double LDVs, with $S=2$. Stability regions for the modes
+under the consideration are identified by means of systematic simulations, the
+LCWs being completely stable in 1D and 2D, as they are ground states in the
+corresponding settings. Basic evolution scenarios are identified for those
+vortices which are unstable. The settings considered in this work may be
+implemented in nonlinear optics and in Bose-Einstein condensates.
+"
+22114,Decoding of neural data using cohomological feature extraction,"  We introduce a novel data-driven approach to discover and decode features in
+the neural code coming from large population neural recordings with minimal
+assumptions, using cohomological feature extraction. We apply our approach to
+neural recordings of mice moving freely in a box, where we find a circular
+feature. We then observe that the decoded value corresponds well to the head
+direction of the mouse. Thus we capture head direction cells and decode the
+head direction from the neural population activity without having to process
+the behaviour of the mouse. Interestingly, the decoded values convey more
+information about the neural activity than the tracked head direction does,
+with differences that have some spatial organization. Finally, we note that the
+residual population activity, after the head direction has been accounted for,
+retains some low-dimensional structure which is correlated with the speed of
+the mouse.
+"
+22115,"Observations and light curve solutions of the W UMa binaries V796 Cep, V797 Cep, CSS J015341.9+381641 and NSVS 3853195","  Photometric observations in Sloan g' and i' bands of four W UMa binaries,
+V796 Cep, V797 Cep, CSS J015341.9+381641 and NSVS 3853195, are presented. Our
+observations showed that CSS J015404.1+382805 and NSVS 3853195 are the same
+star. We determined the initial epochs T_{0} of all targets and improved the
+period of NSVS 3853195. The light curve solutions of our data revealed that the
+components of each target are almost the same in mass, temperature, radius and
+luminosity. The stellar components are of G and K spectral types and undergo
+partial eclipses. All systems have barely-overcontact configurations and belong
+to H subtype W UMa binaries. We established that the relation between the
+luminosity ratio l_{2}/l_{1} and mass ratio q of our targets is approximately
+l_{2}/l_{1} = q^{1.5}.
+"
+22116,Short-Message Communication and FIR System Identification using Huffman Sequences,"  Providing short-message communication and simultaneous channel estimation for
+sporadic and fast fading scenarios is a challenge for future wireless networks.
+In this work we propose a novel blind communication and deconvolution scheme by
+using Huffman sequences, which allows to solve three important tasks in one
+step: (i) determination of the transmit power (ii) identification of the
+discrete-time FIR channel by providing a maximum delay of less than $L/2$ and
+(iii) simultaneously communicating $L-1$ bits of information. Our signal
+reconstruction uses a recent semi-definite program that can recover two unknown
+signals from their auto-correlations and cross-correlations. This convex
+algorithm is stable and operates fully deterministic without any further
+channel assumptions.
+"
+22117,Context Embedding Networks,"  Low dimensional embeddings that capture the main variations of interest in
+collections of data are important for many applications. One way to construct
+these embeddings is to acquire estimates of similarity from the crowd. However,
+similarity is a multi-dimensional concept that varies from individual to
+individual. Existing models for learning embeddings from the crowd typically
+make simplifying assumptions such as all individuals estimate similarity using
+the same criteria, the list of criteria is known in advance, or that the crowd
+workers are not influenced by the data that they see. To overcome these
+limitations we introduce Context Embedding Networks (CENs). In addition to
+learning interpretable embeddings from images, CENs also model worker biases
+for different attributes along with the visual context i.e. the visual
+attributes highlighted by a set of images. Experiments on two noisy crowd
+annotated datasets show that modeling both worker bias and visual context
+results in more interpretable embeddings compared to existing approaches.
+"
+22118,CERN: Confidence-Energy Recurrent Network for Group Activity Recognition,"  This work is about recognizing human activities occurring in videos at
+distinct semantic levels, including individual actions, interactions, and group
+activities. The recognition is realized using a two-level hierarchy of Long
+Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture,
+which can be trained end-to-end. In comparison with existing architectures of
+LSTMs, we make two key contributions giving the name to our approach as
+Confidence-Energy Recurrent Network -- CERN. First, instead of using the common
+softmax layer for prediction, we specify a novel energy layer (EL) for
+estimating the energy of our predictions. Second, rather than finding the
+common minimum-energy class assignment, which may be numerically unstable under
+uncertainty, we specify that the EL additionally computes the p-values of the
+solutions, and in this way estimates the most confident energy minimum. The
+evaluation on the Collective Activity and Volleyball datasets demonstrates: (i)
+advantages of our two contributions relative to the common softmax and
+energy-minimization formulations and (ii) a superior performance relative to
+the state-of-the-art approaches.
+"
+22119,Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms,"  We investigate the problem of estimating the causal effect of a treatment on
+individual subjects from observational data, this is a central problem in
+various application domains, including healthcare, social sciences, and online
+advertising. Within the Neyman Rubin potential outcomes model, we use the
+Kullback Leibler (KL) divergence between the estimated and true distributions
+as a measure of accuracy of the estimate, and we define the information rate of
+the Bayesian causal inference procedure as the (asymptotic equivalence class of
+the) expected value of the KL divergence between the estimated and true
+distributions as a function of the number of samples. Using Fano method, we
+establish a fundamental limit on the information rate that can be achieved by
+any Bayesian estimator, and show that this fundamental limit is independent of
+the selection bias in the observational data. We characterize the Bayesian
+priors on the potential (factual and counterfactual) outcomes that achieve the
+optimal information rate. As a consequence, we show that a particular class of
+priors that have been widely used in the causal inference literature cannot
+achieve the optimal information rate. On the other hand, a broader class of
+priors can achieve the optimal information rate. We go on to propose a prior
+adaptation procedure (which we call the information based empirical Bayes
+procedure) that optimizes the Bayesian prior by maximizing an information
+theoretic criterion on the recovered causal effects rather than maximizing the
+marginal likelihood of the observed (factual) data. Building on our analysis,
+we construct an information optimal Bayesian causal inference algorithm.
+"
+22120,Robust Empirical Bayes Small Area Estimation with Density Power Divergence,"  Empirical Bayes estimators are widely used to provide indirect and
+model-based estimates of means in small areas. The most common model is
+two-stage normal hierarchical model called Fay-Herriot model. However, due to
+the normality assumption, it can be highly influenced by the presence of
+outliers. In this article, we propose a simple modification of the conventional
+method by using density power divergence and derive a new robust empirical
+Bayes small area estimator. Based on some asymptotic properties of the robust
+estimator of the model parameters, we obtain an expression of second order
+approximation of the mean squared error of the proposed empirical Bayes
+estimator. We investigate some numerical performances of the proposed method
+through simulations and a real data application.
+"
+22121,Hardening Deep Neural Networks via Adversarial Model Cascades,"  Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an
+adversary to produce erroneous outputs. Works on securing neural networks
+against adversarial examples achieve high empirical robustness on simple
+datasets such as MNIST. However, these techniques are inadequate when
+empirically tested on complex data sets such as CIFAR-10 and SVHN. Further,
+existing techniques are designed to target specific attacks and fail to
+generalize across attacks. We propose the Adversarial Model Cascades (AMC) as a
+way to tackle the above inadequacies. Our approach trains a cascade of models
+sequentially where each model is optimized to be robust towards a mixture of
+multiple attacks. Ultimately, it yields a single model which is secure against
+a wide range of attacks; namely FGSM, Elastic, Virtual Adversarial
+Perturbations and Madry. On an average, AMC increases the model's empirical
+robustness against various attacks simultaneously, by a significant margin (of
+6.225% for MNIST, 5.075% for SVHN and 2.65% for CIFAR10). At the same time, the
+model's performance on non-adversarial inputs is comparable to the
+state-of-the-art models.
+"
+22122,EmotioNet Challenge: Recognition of facial expressions of emotion in the wild,"  This paper details the methodology and results of the EmotioNet challenge.
+This challenge is the first to test the ability of computer vision algorithms
+in the automatic analysis of a large number of images of facial expressions of
+emotion in the wild. The challenge was divided into two tracks. The first track
+tested the ability of current computer vision algorithms in the automatic
+detection of action units (AUs). Specifically, we tested the detection of 11
+AUs. The second track tested the algorithms' ability to recognize emotion
+categories in images of facial expressions. Specifically, we tested the
+recognition of 16 basic and compound emotion categories. The results of the
+challenge suggest that current computer vision and machine learning algorithms
+are unable to reliably solve these two tasks. The limitations of current
+algorithms are more apparent when trying to recognize emotion. We also show
+that current algorithms are not affected by mild resolution changes, small
+occluders, gender or age, but that 3D pose is a major limiting factor on
+performance. We provide an in-depth discussion of the points that need special
+attention moving forward.
+"
+22123,The Extreme Energy Events HECR array: status and perspectives,"  The Extreme Energy Events Project is a synchronous sparse array of 52
+tracking detectors for studying High Energy Cosmic Rays (HECR) and Cosmic
+Rays-related phenomena. The observatory is also meant to address Long Distance
+Correlation (LDC) phenomena: the network is deployed over a broad area covering
+10 degrees in latitude and 11 in longitude. An overview of a set of preliminary
+results is given, extending from the study of local muon flux dependance on
+solar activity to the investigation of the upward-going component of muon flux
+traversing the EEE stations; from the search for anisotropies at the sub-TeV
+scale to the hints for observations of km-scale Extensive Air Shower (EAS).
+"
+22124,A Warm Welcome Matters! The Link Between Social Feedback and Weight Loss in /r/loseit,"  Social feedback has long been recognized as an important element of
+successful health-related behavior change. However, most of the existing
+studies look at the effect that offline social feedback has. This paper fills
+gaps in the literature by proposing a framework to study the causal effect that
+receiving social support in the form of comments in an online weight loss
+community has on (i) the probability of the user to return to the forum, and,
+more importantly, on (ii) the weight loss reported by the user. Using a
+matching approach for causal inference we observe a difference of 9 lbs lost
+between users who do or do not receive comments. Surprisingly, this effect is
+mediated by neither an increase in lifetime in the community nor by an
+increased activity level of the user. Our results show the importance that a
+""warm welcome"" has when using online support forums to achieve health outcomes.
+"
+22125,Secure and Trustable Electronic Medical Records Sharing using Blockchain,"  Electronic medical records (EMRs) are critical, highly sensitive private
+information in healthcare, and need to be frequently shared among peers.
+Blockchain provides a shared, immutable and transparent history of all the
+transactions to build applications with trust, accountability and transparency.
+This provides a unique opportunity to develop a secure and trustable EMR data
+management and sharing system using blockchain. In this paper, we present our
+perspectives on blockchain based healthcare data management, in particular, for
+EMR data sharing between healthcare providers and for research studies. We
+propose a framework on managing and sharing EMR data for cancer patient care.
+In collaboration with Stony Brook University Hospital, we implemented our
+framework in a prototype that ensures privacy, security, availability, and
+fine-grained access control over EMR data. The proposed work can significantly
+reduce the turnaround time for EMR sharing, improve decision making for medical
+care, and reduce the overall cost
+"
+22126,Online Multilinear Dictionary Learning for Sequential Compressive Sensing,"  A method for online tensor dictionary learning is proposed. With the
+assumption of separable dictionaries, tensor contraction is used to diminish a
+$N$-way model of $\mathcal{O}\left(L^N\right)$ into a simple matrix equation of
+$\mathcal{O}\left(NL^2\right)$ with a real-time capability. To avoid numerical
+instability due to inversion of sparse matrix, a class of stochastic gradient
+with memory is formulated via a least-square solution to guarantee convergence
+and robustness. Both gradient descent with exact line search and Newton's
+method are discussed and realized. Extensions onto how to deal with bad
+initialization and outliers are also explained in detail. Experiments on two
+synthetic signals confirms an impressive performance of our proposed method.
+"
+22127,Election Predictions as Martingales: An Arbitrage Approach,"  We consider the estimation of binary election outcomes as martingales and
+propose an arbitrage pricing when one continuously updates estimates. We argue
+that the estimator needs to be priced as a binary option as the arbitrage
+valuation minimizes the conventionally used Brier score for tracking the
+accuracy of probability assessors.
+We create a dual martingale process $Y$, in $[L,H]$ from the standard
+arithmetic Brownian motion, $X$ in $(-\infty, \infty)$ and price elections
+accordingly. The dual process $Y$ can represent the numerical votes needed for
+success.
+We show the relationship between the volatility of the estimator in relation
+to that of the underlying variable. When there is a high uncertainty about the
+final outcome, 1) the arbitrage value of the binary gets closer to 50\%, 2) the
+estimate should not undergo large changes even if polls or other bases show
+significant variations.
+There are arbitrage relationships between 1) the binary value, 2) the
+estimation of $Y$, 3) the volatility of the estimation of $Y$ over the
+remaining time to expiration. We note that these arbitrage relationships were
+often violated by the various forecasting groups in the U.S. presidential
+elections of 2016, as well as the notion that all intermediate assessments of
+the success of a candidate need to be considered, not just the final one.
+"
+22128,Investigating the effect of social groups in uni-directional pedestrian flow,"  The influence of cohesion among members of dyads is investigated in scenarios
+characterized by uni-directional flow by means of a discrete model: a corridor
+and the egress from a room with a bottleneck of varying width are simulated.
+The model manages the dynamics of simulated group members with an adaptive
+mechanism, balancing the probability of movement according to the dispersion of
+the group; the cohesion mechanism is calibrated through the parameters
+$\kappa_c$ and $\delta$. All scenarios are simulated with two procedures:
+(Proc. 1) population composed of individual pedestrians, in order to validate
+the simulation model and to provide baseline data; (Proc. 2) population
+including dyads (50% of the simulated pedestrians), in order to verify their
+impact. In the corridor scenario, the presence of dyads causes a reduction of
+the velocities and specific flow at medium-high densities. Egress from a square
+room with a unique central exit produces results in line with recent studies in
+the literature, but also shows that the dyads negatively affect the dynamics,
+leading generally to a slower walking speed and a lower pedestrian flow.
+Ignoring the presence of dyads would lead to an overestimation of egress flows.
+"
+22129,The joint projected normal and skew-normal: a distribution for poly-cylindrical data,"  The contribution of this work is the introduction of a multivariate
+circular-linear (or poly- cylindrical) distribution obtained by combining the
+projected and the skew-normal. We show the flexibility of our proposal, its
+property of closure under marginalization and how to quantify multivariate
+dependence. Due to a non-identifiability issue that our proposal inherits from
+the projected normal, a compu- tational problem arises. We overcome it in a
+Bayesian framework, adding suitable latent variables and showing that posterior
+samples can be obtained with a post-processing of the estimation algo- rithm
+output. Under specific prior choices, this approach enables us to implement a
+Markov chain Monte Carlo algorithm relying only on Gibbs steps, where the
+updates of the parameters are done as if we were working with a multivariate
+normal likelihood. The proposed approach can be also used with the projected
+normal. As a proof of concept, on simulated examples we show the ability of our
+algorithm in recovering the parameters values and to solve the identification
+problem. Then the proposal is used in a real data example, where the
+turning-angles (circular variables) and the logarithm of the step-lengths
+(linear variables) of four zebras are jointly modelled.
+"
+22130,Weight Design of Distributed Approximate Newton Algorithms for Constrained Optimization,"  Motivated by economic dispatch and linearly-constrained resource allocation
+problems, this paper proposes a novel Distributed Approx-Newton algorithm that
+approximates the standard Newton optimization method. A main property of this
+distributed algorithm is that it only requires agents to exchange constant-size
+communication messages. The convergence of this algorithm is discussed and
+rigorously analyzed. In addition, we aim to address the problem of designing
+communication topologies and weightings that are optimal for second-order
+methods. To this end, we propose an effective approximation which is loosely
+based on completing the square to address the NP-hard bilinear optimization
+involved in the design. Simulations demonstrate that our proposed weight design
+applied to the Distributed Approx-Newton algorithm has a superior convergence
+property compared to existing weighted and distributed first-order gradient
+descent methods.
+"
+22131,Collections of hypersurfaces containing a curve,"  We consider the closed locus of $r$-tuples of hypersurfaces in $\mathbb{P}^r$
+with positive dimensional intersection, and show in a large range of degrees
+that its largest component is the locus of $r$-tuples of hypersurfaces whose
+intersection contains a line. We then apply our methods to obtain new results
+on the largest components of the locus of hypersurfaces with positive
+dimensional singular loci and the locus of smooth hypersurfaces with a higher
+dimensional family of lines through a point than expected.
+"
+22132,Robust antiferromagnetic spin waves across the metal-insulator transition in hole-doped BaMn$_{2}$As$_{2}$,"  BaMn$_{2}$As$_{2}$ is an antiferromagnetic insulator where a metal-insulator
+transition occurs with hole doping via the substitution of Ba with K. The
+metal-insulator transition causes only a small suppression of the Néel
+temperature ($T_\mathrm{N}$) and the ordered moment, suggesting that doped
+holes interact weakly with the Mn spin system. Powder inelastic neutron
+scattering measurements were performed on three different powder samples of
+Ba$_{1-x}$K$_{x}$Mn$_{2}$As$_{2}$ with $x=$0, 0.125 and 0.25 to study the
+effect of hole doping and metallization on the spin dynamics of these
+compounds. We compare the neutron intensities to a linear spin wave theory
+approximation to the $J_{1}-J_{2}-J_{c}$ Heisenberg model. Hole doping is found
+to introduce only minor modifications to the exchange energies and spin gap.
+The changes observed in the exchange constants are consistent with the small
+drop of $T_\mathrm{N}$ with doping.
+"
+22133,Dynamic Virtual Holonomic Constraints for Stabilization of Closed Orbits in Underactuated Mechanical Systems,"  This article investigates the problem of enforcing a virtual holonomic
+constraint (VHC) on a mechanical system with degree of underactuation one while
+simultaneously stabilizing a closed orbit on the constraint manifold. This
+problem, which to date is open, arises when designing controllers to induce
+complex repetitive motions in robots. In this paper, we propose a solution
+which relies on the parameterization of the VHC by the output of a
+double-integrator. While the original control inputs are used to enforce the
+VHC, the control input of the double-integrator is designed to asymptotically
+stabilize the closed orbit and make the state of the double-integrator converge
+to zero. The proposed design is applied to the problem of making a PVTOL
+aircraft follow a circle on the vertical plane with a desired speed profile,
+while guaranteeing that the aircraft does not roll over for suitable initial
+conditions.
+"
+22134,A Hierarchical WDM-based Scalable Data Center Network Architecture,"  Massive data centers are at the heart of the Internet. The rapid growth of
+Internet traffic and the abundance of rich data-driven applications have raised
+the need for enormous network bandwidth. Towards meeting this growing traffic
+demand, optical interconnects have gained significant attention, as they can
+provide high throughput, low latency, and scalability. In particular, optical
+Wavelength Division Multiplexing (WDM) provides the possibility to build data
+centers comprising of millions of servers, while providing hundreds of terabits
+per second bandwidth.
+In this paper, we propose a WDM-based Reconfigurable Hierarchical Optical
+Data Center Architecture (RHODA) that can satisfy future Internet traffic
+demands. To improve scalability, our DCN architecture is hierarchical, as it
+groups server racks into clusters. Cluster membership is reconfigurable through
+the use of optical switches. Each cluster enables heavy-traffic communication
+among the racks within. To support varying traffic patterns, the inter-cluster
+network topology and link capacities are also reconfigurable, which is achieved
+through the use of optical space switches and Wavelength Selective Switches
+(WSSs). Our simulation results demonstrate that in terms of average hop
+distance, RHODA outperforms OSA, FatTree and WaveCube by up to 81%, 66% and
+60%, respectively.
+"
+22135,"Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques","  This article provides an introduction to and review of the field of
+computer-aided molecular design (CAMD). It is intended to be approachable for
+the absolute beginner as well as useful to the seasoned CAMD practitioner. We
+begin by discussing various quantitative structure-property relationships
+(QSPRs) which have been demonstrated to work well with CAMD problems. The
+methods discussed in this article are (1) group contribution methods, (2)
+topological indices, and (3) signature descriptors. Next, we present general
+optimization formulations for various forms of the CAMD problem. Common design
+constraints are discussed and structural feasibility constraints are provided
+for the three types of QSPRs addressed. We then detail useful techniques for
+approaching CAMD optimization problems, including decomposition methods,
+heuristic approaches, and mathematical programming strategies. Finally, we
+discuss many applications that have been addressed using CAMD.
+"
+22136,Bolt: Accelerated Data Mining with Fast Vector Compression,"  Vectors of data are at the heart of machine learning and data mining.
+Recently, vector quantization methods have shown great promise in reducing both
+the time and space costs of operating on vectors. We introduce a vector
+quantization algorithm that can compress vectors over 12x faster than existing
+techniques while also accelerating approximate vector operations such as
+distance and dot product computations by up to 10x. Because it can encode over
+2GB of vectors per second, it makes vector quantization cheap enough to employ
+in many more circumstances. For example, using our technique to compute
+approximate dot products in a nested loop can multiply matrices faster than a
+state-of-the-art BLAS implementation, even when our algorithm must first
+compress the matrices.
+In addition to showing the above speedups, we demonstrate that our approach
+can accelerate nearest neighbor search and maximum inner product search by over
+100x compared to floating point operations and up to 10x compared to other
+vector quantization methods. Our approximate Euclidean distance and dot product
+computations are not only faster than those of related algorithms with slower
+encodings, but also faster than Hamming distance computations, which have
+direct hardware support on the tested platforms. We also assess the errors of
+our algorithm's approximate distances and dot products, and find that it is
+competitive with existing, slower vector quantization algorithms.
+"
+22137,Generalisation in humans and deep neural networks,"  We compare the robustness of humans and current convolutional deep neural
+networks (DNNs) on object recognition under twelve different types of image
+degradations. First, using three well known DNNs (ResNet-152, VGG-19,
+GoogLeNet) we find the human visual system to be more robust to nearly all of
+the tested image manipulations, and we observe progressively diverging
+classification error-patterns between humans and DNNs when the signal gets
+weaker. Secondly, we show that DNNs trained directly on distorted images
+consistently surpass human performance on the exact distortion types they were
+trained on, yet they display extremely poor generalisation abilities when
+tested on other distortion types. For example, training on salt-and-pepper
+noise does not imply robustness on uniform white noise and vice versa. Thus,
+changes in the noise distribution between training and testing constitutes a
+crucial challenge to deep learning vision systems that can be systematically
+addressed in a lifelong machine learning approach. Our new dataset consisting
+of 83K carefully measured human psychophysical trials provide a useful
+reference for lifelong robustness against image degradations set by the human
+visual system.
+"
+22138,On extensions of characters of affine pro-$p$ Iwahori--Hecke algebra,"  Let $K$ be a non-discrete non-Archimedean local field with residue
+characteristic $p$. Let $G$ be the group of $K$ rational points of a algebraic
+connected reductive group defined over $K$. In this article we compute the
+extensions between characters of affine pro-$p$ Iwahori--Hecke algebra
+$\mathcal{H}^{\text{aff}}$ over an algebraically closed field $R$ of
+characteristic $p$. In rank one case we deduce the relation between the blocks
+and $L$-packets.
+"
+22139,A posteriori noise estimation in variable data sets,"  Most physical data sets contain a stochastic contribution produced by
+measurement noise or other random sources along with the signal. Usually,
+neither the signal nor the noise are accurately known prior to the measurement
+so that both have to be estimated a posteriori. We have studied a procedure to
+estimate the standard deviation of the stochastic contribution assuming
+normality and independence, requiring a sufficiently well-sampled data set to
+yield reliable results. This procedure is based on estimating the standard
+deviation in a sample of weighted sums of arbitrarily sampled data points and
+is identical to the so-called DER_SNR algorithm for specific parameter
+settings. To demonstrate the applicability of our procedure, we present
+applications to synthetic data, high-resolution spectra, and a large sample of
+space-based light curves and, finally, give guidelines to apply the procedure
+in situation not explicitly considered here to promote its adoption in data
+analysis.
+"
+22140,HPSLPred: An Ensemble Multi-label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source,"  Predicting the subcellular localization of proteins is an important and
+challenging problem. Traditional experimental approaches are often expensive
+and time-consuming. Consequently, a growing number of research efforts employ a
+series of machine learning approaches to predict the subcellular location of
+proteins. There are two main challenges among the state-of-the-art prediction
+methods. First, most of the existing techniques are designed to deal with
+multi-class rather than multi-label classification, which ignores connections
+between multiple labels. In reality, multiple locations of particular proteins
+implies that there are vital and unique biological significances that deserve
+special focus and cannot be ignored. Second, techniques for handling imbalanced
+data in multi-label classification problems are necessary, but never employed.
+For solving these two issues, we have developed an ensemble multi-label
+classifier called HPSLPred, which can be applied for multi-label classification
+with an imbalanced protein source. For convenience, a user-friendly webserver
+has been established at this http URL.
+"
+22141,Bias and high-dimensional adjustment in observational studies of peer effects,"  Peer effects, in which the behavior of an individual is affected by the
+behavior of their peers, are posited by multiple theories in the social
+sciences. Other processes can also produce behaviors that are correlated in
+networks and groups, thereby generating debate about the credibility of
+observational (i.e. nonexperimental) studies of peer effects. Randomized field
+experiments that identify peer effects, however, are often expensive or
+infeasible. Thus, many studies of peer effects use observational data, and
+prior evaluations of causal inference methods for adjusting observational data
+to estimate peer effects have lacked an experimental ""gold standard"" for
+comparison. Here we show, in the context of information and media diffusion on
+Facebook, that high-dimensional adjustment of a nonexperimental control group
+(677 million observations) using propensity score models produces estimates of
+peer effects statistically indistinguishable from those from using a large
+randomized experiment (220 million observations). Naive observational
+estimators overstate peer effects by 320% and commonly used variables (e.g.,
+demographics) offer little bias reduction, but adjusting for a measure of prior
+behaviors closely related to the focal behavior reduces bias by 91%.
+High-dimensional models adjusting for over 3,700 past behaviors provide
+additional bias reduction, such that the full model reduces bias by over 97%.
+This experimental evaluation demonstrates that detailed records of individuals'
+past behavior can improve studies of social influence, information diffusion,
+and imitation; these results are encouraging for the credibility of some
+studies but also cautionary for studies of rare or new behaviors. More
+generally, these results show how large, high-dimensional data sets and
+statistical learning techniques can be used to improve causal inference in the
+behavioral sciences.
+"
+22142,The geometric structure of symplectic contraction,"  We show that the symplectic contraction map of Hilgert-Manon-Martens -- a
+symplectic version of Popov's horospherical contraction -- is simply the
+quotient of a Hamiltonian manifold $M$ by a ""stratified null foliation"" that is
+determined by the group action and moment map. We also show that the quotient
+differential structure on the symplectic contraction of $M$ supports a Poisson
+bracket. We end by proving a very general description of the topology of fibers
+of Gelfand-Zeitlin systems on multiplicity free Hamiltonian $U(n)$ and $SO(n)$
+manifolds.
+"
+22143,Accretion powered AGN feedback in the cores of galaxy clusters,"  Detection of the copious amount of X-ray emission from the dilute hot plasma
+in galaxy clusters suggests that a substantial fraction of the central
+intracluster medium (ICM) is cooling radiatively on a time scale much faster
+than the Hubble time. Theoretical models predict the cooling rate as high as
+about few hundred to few thousand solar mass per year, which would be then made
+available for the formation of new stars in the core of these clusters.
+However, systematic studies of the cores of such clusters failed to detect the
+expected reservoirs of cooled gas. Thus, the gas in the cores of galaxy
+clusters is losing substantial amount of energy in the form of X-rays but is
+not cooling. This in turn point towards the famous cooling flow paradox and
+hence demands some intermittent heating to balance the cooling over such a long
+period. Several sources have been suggested to counteract on the cooling of the
+ICM, however, the AGN feedback appeared to be the most promising and enough
+energetic source to resist cooling of the ICM in the cores of such clusters. In
+this presentation I will provide a brief overview on the feedback processes
+that are involved in the cores of the galaxy clusters with an emphasis on the
+AGN feedback and its observable signatures.
+"
+22144,Tensor rank is not multiplicative under the tensor product,"  The tensor rank of a tensor t is the smallest number r such that t can be
+decomposed as a sum of r simple tensors. Let s be a k-tensor and let t be an
+l-tensor. The tensor product of s and t is a (k + l)-tensor. Tensor rank is
+sub-multiplicative under the tensor product. We revisit the connection between
+restrictions and degenerations. A result of our study is that tensor rank is
+not in general multiplicative under the tensor product. This answers a question
+of Draisma and Saptharishi. Specifically, if a tensor t has border rank
+strictly smaller than its rank, then the tensor rank of t is not multiplicative
+under taking a sufficiently hight tensor product power. The ""tensor Kronecker
+product"" from algebraic complexity theory is related to our tensor product but
+different, namely it multiplies two k-tensors to get a k-tensor.
+Nonmultiplicativity of the tensor Kronecker product has been known since the
+work of Strassen.
+It remains an open question whether border rank and asymptotic rank are
+multiplicative under the tensor product. Interestingly, lower bounds on border
+rank obtained from generalised flattenings (including Young flattenings)
+multiply under the tensor product.
+"
+22145,A Partially Supervised Bayesian Image Classification Model with Applications in Diagnosis of Sentinel Lymph Node Metastases in Breast Cancer,"  A method has been developed for the analysis of images of sentinel lymph
+nodes generated by a spectral scanning device. The aim is to classify the
+nodes, excised during surgery for breast cancer, as normal or metastatic. The
+data from one node constitute spectra at 86 wavelengths for each pixel of a
+20*20 grid. For the analysis, the spectra are reduced to scores on two factors,
+one derived externally from a linear discriminant analysis using spectra taken
+manually from known normal and metastatic tissue, and one derived from the node
+under investigation to capture variability orthogonal to the external factor.
+Then a three-group mixture model (normal, metastatic, non-nodal background)
+using multivariate t distributions is fitted to the scores, with external data
+being used to specify informative prior distributions for the parameters of the
+three distributions. A Markov random field prior imposes smoothness on the
+image generated by the model. Finally, the node is classified as metastatic if
+any one pixel in this smoothed image is classified as metastatic. The model
+parameters were tuned on a training set of nodes, and then the tuned model was
+tested on a separate validation set of nodes, achieving satisfactory
+sensitivity and specificity. The aim in developing the analysis was to allow
+flexibility in the way each node is modelled whilst still using external
+information. The Bayesian framework employed is ideal for this.
+"
+22146,D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling,"  Exponential growth of social media consumption demands effective user
+interests-social contents modeling for more personalized recommendation and
+social media summarization. However, due to the heterogeneous nature of social
+contents, traditional approaches lack the ability of capturing the hidden
+semantic correlations across these multi-modal data, which leads to semantic
+gaps between social content understanding and user interests. To effectively
+bridge the semantic gaps, we propose a novel deep learning framework for user
+interests-social contents modeling. We first mine and parse data, i.e. textual
+content, visual content, social context and social relation, from heterogeneous
+social media feeds. Then, we design a two-branch network to map the social
+contents and users into a same latent space. Particularly, the network is
+trained by a large margin objective that combines a cross-instance distance
+constraint with a within-instance semantic-preserving constraint in an end-to-
+end manner. At last, a Deep Semantic-Preserving Embedding (D-Sempre) is
+learned, and the ranking results can be given by calculating distances between
+social contents and users. To demonstrate the effectiveness of D-Sempre in user
+interests-social contents modeling, we construct a Twitter dataset and conduct
+extensive experiments on it. As a result, D-Sempre effectively integrates the
+multi-modal data from heterogeneous social media feeds and captures the hidden
+semantic correlations between users' interests and social contents.
+"
+22147,Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularization,"  This article describes an implementation of a nonparametric Bayesian approach
+to solving binary classification problems on graphs. We consider a hierarchical
+Bayesian approach with a prior that is constructed by truncating a series
+expansion of the soft label function using the graph Laplacian eigenfunctions
+as basis functions. We compare our truncated prior to the untruncated Laplacian
+based prior in simulated and real data examples to illustrate the improved
+scalability in terms of size of the underlying graph.
+"
+22148,Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems,"  Designing an e-commerce recommender system that serves hundreds of millions
+of active users is a daunting challenge. From a human vision perspective,
+there're two key factors that affect users' behaviors: items' attractiveness
+and their matching degree with users' interests. This paper proposes Telepath,
+a vision-based bionic recommender system model, which understands users from
+such perspective. Telepath is a combination of a convolutional neural network
+(CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its
+CNN subnetwork simulates the human vision system to extract key visual signals
+of items' attractiveness and generate corresponding activations. Its RNN and
+DNN subnetworks simulate cerebral cortex to understand users' interest based on
+the activations generated from browsed items. In practice, the Telepath model
+has been launched to JD's recommender system and advertising system. For one of
+the major item recommendation blocks on the JD app, click-through rate (CTR),
+gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71%
+respectively. For several major ads publishers of JD demand-side platform, CTR,
+GMV and return on investment have increased 6.58%, 61.72% and 65.57%
+respectively by the first launch, and further increased 2.95%, 41.75% and
+41.37% respectively by the second launch.
+"
+22149,Weighted isoperimetric inequalities in warped product manifolds,"  We prove some isoperimetric type inequalities in warped product manifolds, or
+more generally, multiply warped product manifolds. We then relate them to
+inequalities involving the higher order mean-curvature integrals. We also apply
+our results to obtain sharp eigenvalue estimates and some sharp geometric
+inequalities in space forms.
+"
+22150,Approximation algorithms for TSP with neighborhoods in the plane,"  In the Euclidean TSP with neighborhoods (TSPN), we are given a collection of
+n regions (neighborhoods) and we seek a shortest tour that visits each region.
+As a generalization of the classical Euclidean TSP, TSPN is also NP-hard. In
+this paper, we present new approximation results for the TSPN, including (1) a
+constant-factor approximation algorithm for the case of arbitrary connected
+neighborhoods having comparable diameters; and (2) a PTAS for the important
+special case of disjoint unit disk neighborhoods (or nearly disjoint,
+nearly-unit disks). Our methods also yield improved approximation ratios for
+various special classes of neighborhoods, which have previously been studied.
+Further, we give a linear-time O(1)-approximation algorithm for the case of
+neighborhoods that are (infinite) straight lines.
+"
+22151,Phase-sensitive evidence for the sign-reversal s+- symmetry of the order parameter in an iron-pnictide superconductor using Nb/Ba1-xNaxFe2As2 Josephson junctions,"  Josephson current between two superconductors provides a phase sensitive tool
+for probing their pairing symmetries. Here we fabricate and study
+experimentally high-quality Josephson junctions between a conventional s-wave
+superconductor Nb and a multi-band iron-pnictide Ba$_{1-x}$Na$_x$Fe$_2$As$_2$.
+Junctions exhibit a large enough critical current density to preclude the
+d-wave symmetry of the order parameter in the pnictide. However, the $I_cR_n$
+product is very small $\simeq 3~\mu$V, which is not consistent with the
+sign-preserving $s_{++}$ symmetry either. We argue that the small $I_cR_n$
+value along with its unusual temperature dependence provide evidence for the
+$s_{\pm}$ symmetry of the order parameter in Ba$_{1-x}$Na$_x$Fe$_2$As$_2$. We
+conclude that it is the phase sensitivity of our junctions that leads to an
+almost complete (bellow a sub-percent) cancellation of opposite supercurrents
+from the sign-reversal $s_{\pm}$ bands in the pnictide.
+"
+22152,Total Variation Regularized Tensor-on-scalar Regression,"  In this paper, we propose Total Variation Regularized Tensor-on-scalar
+Regression(TVTR), a novel method for estimating the association between a
+tensor outcome (a one dimensional or multidimensional array) and scalar
+predictors. While the statistical developments proposed here were motivated by
+the brain mapping and activity tracking, the methodology is designed and
+presented in generality and is applicable to many other areas of scientific
+research. The estimator is the solution of a penalized regression problem where
+the objective is the sum of square error plus a total variation (TV)
+regularization on the predicted mean across all subjects. We propose an
+algorithm for the parameter estimation, which is efficient and scalable in
+distributed computing platform. Proof of the algorithm convergence is provided
+and the statistical consistency of the estimator is presented via an oracle
+inequality. We presented 1D and 2D simulation results, and demonstrate that
+TVTR outperforms existing methods in most cases. We also demonstrate the
+general applicability of the method by two real data examples including the
+analysis of the 1D accelerometry subsample of a large community-based study for
+mood disorders and the analysis of the 3D MRI data from the attention
+deficient/hyperactive deficient (ADHD) 200 consortium.
+"
+22153,Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science,"  We propose Marve, a system for extracting measurement values, units, and
+related words from natural language text. Marve uses conditional random fields
+(CRF) to identify measurement values and units, followed by a rule-based system
+to find related entities, descriptors and modifiers within a sentence. Sentence
+tokens are represented by an undirected graphical model, and rules are based on
+part-of-speech and word dependency patterns connecting values and units to
+contextual words. Marve is unique in its focus on measurement context and early
+experimentation demonstrates Marve's ability to generate high-precision
+extractions with strong recall. We also discuss Marve's role in refining
+measurement requirements for NASA's proposed HyspIRI mission, a hyperspectral
+infrared imaging satellite that will study the world's ecosystems. In general,
+our work with HyspIRI demonstrates the value of semantic measurement
+extractions in characterizing quantitative discussion contained in large
+corpuses of natural language text. These extractions accelerate broad,
+cross-cutting research and expose scientists new algorithmic approaches and
+experimental nuances. They also facilitate identification of scientific
+opportunities enabled by HyspIRI leading to more efficient scientific
+investment and research.
+"
+22154,Paley--Wiener theorems on the Siegel upper half-space,"  In this paper we study spaces of holomorphic functions on the Siegel upper
+half-space $\mathcal U$ and prove Paley-Wiener type theorems for such spaces.
+The boundary of $\mathcal U$ can be identified with the Heisenberg group
+$\mathbb H_n$. Using the group Fourier transform on $\mathbb H_n$, Ogden-Vagi
+proved a Paley-Wiener theorem for the Hardy space $H^2(\mathcal U)$.
+We consider a scale of Hilbert spaces on $\mathcal U$ that includes the Hardy
+space, the weighted Bergman spaces, the weighted Dirichlet spaces, and in
+particular the Drury-Arveson space, and the Dirichlet space $\mathcal D$. For
+each of these spaces, we prove a Paley-Wiener theorem, some structure theorems,
+and provide some applications. In particular we prove that the norm of the
+Dirichlet space modulo constants $\dot{\mathcal D}$ is the unique Hilbert space
+norm that is invariant under the action of the group of automorphisms of
+$\mathcal U$.
+"
+22155,BUOCA: Budget-Optimized Crowd Worker Allocation,"  Due to concerns about human error in crowdsourcing, it is standard practice
+to collect labels for the same data point from multiple internet workers. We
+here show that the resulting budget can be used more effectively with a
+flexible worker assignment strategy that asks fewer workers to analyze
+easy-to-label data and more workers to analyze data that requires extra
+scrutiny. Our main contribution is to show how the allocations of the number of
+workers to a task can be computed optimally based on task features alone,
+without using worker profiles. Our target tasks are delineating cells in
+microscopy images and analyzing the sentiment toward the 2016 U.S. presidential
+candidates in tweets. We first propose an algorithm that computes
+budget-optimized crowd worker allocation (BUOCA). We next train a machine
+learning system (BUOCA-ML) that predicts an optimal number of crowd workers
+needed to maximize the accuracy of the labeling. We show that the computed
+allocation can yield large savings in the crowdsourcing budget (up to 49
+percent points) while maintaining labeling accuracy. Finally, we envisage a
+human-machine system for performing budget-optimized data analysis at a scale
+beyond the feasibility of crowdsourcing.
+"
+22156,Performance bounds for optimal feedback control in networks,"  Many important complex networks, including critical infrastructure and
+emerging industrial automation systems, are becoming increasingly intricate
+webs of interacting feedback control loops. A fundamental concern is to
+quantify the control properties and performance limitations of the network as a
+function of its dynamical structure and control architecture. We study
+performance bounds for networks in terms of optimal feedback control costs. We
+provide a set of complementary bounds as a function of the system dynamics and
+actuator structure. For unstable network dynamics, we characterize a tradeoff
+between feedback control performance and the number of control inputs, in
+particular showing that optimal cost can increase exponentially with the size
+of the network. We also derive a bound on the performance of the worst-case
+actuator subset for stable networks, providing insight into dynamics properties
+that affect the potential efficacy of actuator selection. We illustrate our
+results with numerical experiments that analyze performance in regular and
+random networks.
+"
+22157,Spatially Controlled Relay Beamforming: $2$-Stage Optimal Policies,"  The problem of enhancing Quality-of-Service (QoS) in power constrained,
+mobile relay beamforming networks, by optimally and dynamically controlling the
+motion of the relaying nodes, is considered, in a dynamic channel environment.
+We assume a time slotted system, where the relays update their positions before
+the beginning of each time slot. Modeling the wireless channel as a Gaussian
+spatiotemporal stochastic field, we propose a novel $2$-stage stochastic
+programming problem formulation for optimally specifying the positions of the
+relays at each time slot, such that the expected QoS of the network is
+maximized, based on causal Channel State Information (CSI) and under a total
+relay transmit power budget. This results in a schema where, at each time slot,
+the relays, apart from optimally beamforming to the destination, also
+optimally, predictively decide their positions at the next time slot, based on
+causally accumulated experience. Exploiting either the Method of Statistical
+Differentials, or the multidimensional Gauss-Hermite Quadrature Rule, the
+stochastic program considered is shown to be approximately equivalent to a set
+of simple subproblems, which are solved in a distributed fashion, one at each
+relay. Optimality and performance of the proposed spatially controlled system
+are also effectively assessed, under a rigorous technical framework; strict
+optimality is rigorously demonstrated via the development of a version of the
+Fundamental Lemma of Stochastic Control, and, performance-wise, it is shown
+that, quite interestingly, the optimal average network QoS exhibits an
+increasing trend across time slots, despite our myopic problem formulation.
+Numerical simulations are presented, experimentally corroborating the success
+of the proposed approach and the validity of our theoretical predictions.
+"
+22158,Entropy-Preserving Coupling Conditions for One-dimensional Euler Systems at Junctions,"  This paper is concerned with a set of novel coupling conditions for the
+$3\times 3$ one-dimensional Euler system with source terms at a junction of
+pipes with possibly different cross-sectional areas. Beside conservation of
+mass, we require the equality of the total enthalpy at the junction and that
+the specific entropy for pipes with outgoing flow equals the convex combination
+of all entropies that belong to pipes with incoming flow. Previously used
+coupling conditions include equality of pressure or dynamic pressure. They are
+restricted to the special case of a junction having only one pipe with outgoing
+flow direction. Recently, Reigstad [SIAM J. Appl. Math., 75:679--702, 2015]
+showed that such pressure-based coupling conditions can produce non-physical
+solutions for isothermal flows through the production of mechanical energy. Our
+new coupling conditions ensure energy as well as entropy conservation and also
+apply to junctions connecting an arbitrary number of pipes with flexible flow
+directions. We prove the existence and uniqueness of solutions to the
+generalised Riemann problem at a junction in the neighbourhood of constant
+stationary states which belong to the subsonic region. This provides the basis
+for the well-posedness of the homogeneous and inhomogeneous Cauchy problems for
+initial data with sufficiently small total variation.
+"
+22159,Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks,"  Early detection of pulmonary cancer is the most promising way to enhance a
+patient's chance for survival. Accurate pulmonary nodule detection in computed
+tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In
+this paper, inspired by the successful use of deep convolutional neural
+networks (DCNNs) in natural image recognition, we propose a novel pulmonary
+nodule detection approach based on DCNNs. We first introduce a deconvolutional
+structure to Faster Region-based Convolutional Neural Network (Faster R-CNN)
+for candidate detection on axial slices. Then, a three-dimensional DCNN is
+presented for the subsequent false positive reduction. Experimental results of
+the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior
+detection performance of the proposed approach on nodule detection(average
+FROC-score of 0.891, ranking the 1st place over all submitted results).
+"
+22160,Free Finite Group Actions on Rational Homology 3--Spheres,"  We use methods from the cohomology of groups to describe the finite groups
+which can act freely and homologically trivially on closed 3-manifolds which
+are rational homology spheres.
+"
+22161,An Isometry Theorem for Generalized Persistence Modules,"  In recent work, generalized persistence modules have proved useful in
+distinguishing noise from the legitimate topological features of a data set.
+Algebraically, generalized persistence modules can be viewed as representations
+for the poset algebra. The interplay between various metrics on persistence
+modules has been of wide interest, most notably, the isometry theorem of Bauer
+and Lesnick for (one-dimensional) persistence modules. The interleaving metric
+of Bubenik, de Silva and Scott endows the collection of representations of a
+poset with values in any category with the structure of a metric space. This
+metric makes sense for any poset, and has the advantage that post-composition
+by any functor is a contraction. In this paper, we prove an isometry theorem
+using this interleaving metric on a full subcategory of generalized persistence
+modules for a large class of posets.
+"
+22162,Spatial Embedding Imposes Constraints on the Network Architectures of Neural Systems,"  A fundamental understanding of the network architecture of the brain is
+necessary for the further development of theories explicating circuit function.
+Perhaps as a derivative of its initial application to abstract informational
+systems, network science provides many methods and summary statistics that
+address the network's topological characteristics with little or no thought to
+its physical instantiation. Recent progress has capitalized on quantitative
+tools from network science to parsimoniously describe and predict neural
+activity and connectivity across multiple spatial and temporal scales. Yet, for
+embedded systems, physical laws can directly constrain processes of network
+growth, development, and function, and an appreciation of those physical laws
+is therefore critical to an understanding of the system. Recent evidence
+demonstrates that the constraints imposed by the physical shape of the brain,
+and by the mechanical forces at play in its development, have marked effects on
+the observed network topology and function. Here, we review the rules imposed
+by space on the development of neural networks and show that these rules give
+rise to a specific set of complex topologies. We present evidence that these
+fundamental wiring rules affect the repertoire of neural dynamics that can
+emerge from the system, and thereby inform our understanding of network
+dysfunction in disease. We also discuss several computational tools,
+mathematical models, and algorithms that have proven useful in delineating the
+effects of spatial embedding on a given networked system and are important
+considerations for addressing future problems in network neuroscience. Finally,
+we outline several open questions regarding the network architectures that
+support circuit function, the answers to which will require a thorough and
+honest appraisal of the role of physical space in brain network anatomy and
+physiology.
+"
+22163,Not a Technology Person: Motivating Older Adults Toward the Use of Mobile Technology,"  Older users population is rapidly increasing all over the World. Presently,
+we observe efforts in the human-computer interaction domain aiming to improve
+life quality of age 65 and over through the use of mobile apps. Nonetheless,
+these efforts focus primary on interface and interaction de- sign. Little work
+has focused on the study of motivation to use and adherence to, of elderly to
+technology. Developing specific design guidelines for this population is
+relevant, however it should be parallel to the study of desire of elderly to
+embrace specific technology in their life. Designers should not be limited to
+technology design but consider as well how to fully convey the value that
+technology can bring to the lives of the users and motivate adoption. This
+position paper discusses techniques that might nudge elderly towards the use of
+new technology.
+"
+22164,A high-order study of the quantum critical behavior of a frustrated spin-$\frac{1}{2}$ antiferromagnet on a stacked honeycomb bilayer,"  We study a frustrated spin-$\frac{1}{2}$
+$J_{1}$--$J_{2}$--$J_{3}$--$J_{1}^{\perp}$ Heisenberg antiferromagnet on an
+$AA$-stacked bilayer honeycomb lattice. In each layer we consider
+nearest-neighbor (NN), next-nearest-neighbor, and next-next-nearest-neighbor
+antiferromagnetic (AFM) exchange couplings $J_{1}$, $J_{2}$, and $J_{3}$,
+respectively. The two layers are coupled with an AFM NN exchange coupling
+$J_{1}^{\perp}\equiv\delta J_{1}$. The model is studied for arbitrary values of
+$\delta$ along the line $J_{3}=J_{2}\equiv\alpha J_{1}$ that includes the most
+highly frustrated point at $\alpha=\frac{1}{2}$, where the classical ground
+state is macroscopically degenerate. The coupled cluster method is used at high
+orders of approximation to calculate the magnetic order parameter and the
+triplet spin gap. We are thereby able to give an accurate description of the
+quantum phase diagram of the model in the $\alpha\delta$ plane in the window $0
+\leq \alpha \leq 1$, $0 \leq \delta \leq 1$. This includes two AFM phases with
+Néel and striped order, and an intermediate gapped paramagnetic phase that
+exhibits various forms of valence-bond crystalline order. We obtain accurate
+estimations of the two phase boundaries, $\delta = \delta_{c_{i}}(\alpha)$, or
+equivalently, $\alpha = \alpha_{c_{i}}(\delta)$, with $i=1$ (Néel) and 2
+(striped). The two boundaries exhibit an ""avoided crossing"" behavior with both
+curves being reentrant.
+"
+22165,"Chabauty Limits of Subgroups of $SL(n, \mathbb{Q}_p)$","  We study the Chabauty compactification of two families of closed subgroups of
+$SL(n,\mathbb{Q}_p)$. The first family is the set of all parahoric subgroups of
+$SL(n,\mathbb{Q}_p)$. Although the Chabauty compactification of parahoric
+subgroups is well studied, we give a different and more geometric proof using
+various Levi decompositions of $SL(n,\mathbb{Q}_p)$. Let $C$ be the subgroup of
+diagonal matrices in $SL(n, \mathbb{Q}_p)$. The second family is the set of all
+$SL(n,\mathbb{Q}_p)$-conjugates of $C$. We give a classification of the
+Chabauty limits of conjugates of $C$ using the action of $SL(n,\mathbb{Q}_p)$
+on its associated Bruhat--Tits building and compute all of the limits for
+$n\leq 4$ (up to conjugacy). In contrast, for $n\geq 7$ we prove there are
+infinitely many $SL(n,\mathbb{Q}_p)$-nonconjugate Chabauty limits of conjugates
+of $C$. Along the way we construct an explicit homeomorphism between the
+Chabauty compactification in $\mathfrak{sl}(n, \mathbb{Q}_p)$ of
+$SL(n,\mathbb{Q}_p)$-conjugates of the $p$-adic Lie algebra of $C$ and the
+Chabauty compactification of $SL(n,\mathbb{Q}_p)$-conjugates of $C$.
+"
+22166,The autofeat Python Library for Automatic Feature Engineering and Selection,"  This paper describes the autofeat Python library, which provides a
+scikit-learn style linear regression model with automatic feature engineering
+and selection capabilities. Complex non-linear machine learning models such as
+neural networks are in practice often difficult to train and even harder to
+explain to non-statisticians, who require transparent analysis results as a
+basis for important business decisions. While linear models are efficient and
+intuitive, they generally provide lower prediction accuracies. Our library
+provides a multi-step feature engineering and selection process, where first a
+large pool of non-linear features is generated, from which then a small and
+robust set of meaningful features is selected, which improve the prediction
+accuracy of a linear model while retaining its interpretability.
+"
+22167,Provably Optimal Algorithms for Generalized Linear Contextual Bandits,"  Contextual bandits are widely used in Internet services from news
+recommendation to advertising, and to Web search. Generalized linear models
+(logistical regression in particular) have demonstrated stronger performance
+than linear models in many applications where rewards are binary. However, most
+theoretical analyses on contextual bandits so far are on linear bandits. In
+this work, we propose an upper confidence bound based algorithm for generalized
+linear contextual bandits, which achieves an $\tilde{O}(\sqrt{dT})$ regret over
+$T$ rounds with $d$ dimensional feature vectors. This regret matches the
+minimax lower bound, up to logarithmic terms, and improves on the best previous
+result by a $\sqrt{d}$ factor, assuming the number of arms is fixed. A key
+component in our analysis is to establish a new, sharp finite-sample confidence
+bound for maximum-likelihood estimates in generalized linear models, which may
+be of independent interest. We also analyze a simpler upper confidence bound
+algorithm, which is useful in practice, and prove it to have optimal regret for
+certain cases.
+"
+22168,The Social Benefits of Balancing Creativity and Imitation: Evidence from an Agent-based Model,"  Although creativity is encouraged in the abstract it is often discouraged in
+educational and workplace settings. Using an agent-based model of cultural
+evolution, we investigated the idea that tempering the novelty-generating
+effects of creativity with the novelty-preserving effects of imitation is
+beneficial for society. In Experiment One we systematically introduced
+individual differences in creativity, and observed a trade-off between the
+ratio of creators to imitators, and how creative the creators were. Excess
+creativity was detrimental because creators invested in unproven ideas at the
+expense of propagating proven ones. Experiment Two tested the hypothesis that
+society as a whole benefits if individuals adjust how creative they are in
+accordance with their creative success. When effective creators created more,
+and ineffective creators created less (social regulation), the agents
+segregated into creators and imitators, and the mean fitness of outputs was
+temporarily higher. We hypothesized that the temporary nature of the effect was
+due to a ceiling on output fitness. In Experiment Three we made the space of
+possible outputs open-ended by giving agents the capacity to chain simple
+outputs into arbitrarily complex ones such that fitter outputs were always
+possible. With the capacity for chained outputs, the effect of social
+regulation could indeed be maintained indefinitely. The results are discussed
+in light of empirical data.
+"
+22169,Distributed Domination on Graph Classes of Bounded Expansion,"  We provide a new constant factor approximation algorithm for the (connected)
+distance-$r$ dominating set problem on graph classes of bounded expansion.
+Classes of bounded expansion include many familiar classes of sparse graphs
+such as planar graphs and graphs with excluded (topological) minors, and
+notably, these classes form the most general subgraph closed classes of graphs
+for which a sequential constant factor approximation algorithm for the
+distance-$r$ dominating set problem is currently known. Our algorithm can be
+implemented in the \congestbc model of distributed computing and uses
+$\mathcal{O}(r^2 \log n)$ communication rounds.
+Our techniques, which may be of independent interest, are based on a
+distributed computation of sparse neighborhood covers of small radius on
+bounded expansion classes. We show how to compute an $r$-neighborhood cover of
+radius~$2r$ and overlap $f(r)$ on every class of bounded expansion in
+$\mathcal{O}(r^2 \log n)$ communication rounds for some function~$f$.% in the
+$\mathcal{CONGEST}_{\mathrm{BC}}$ model.
+Finally, we show how to use the greater power of the $\mathcal{LOCAL}$ model
+to turn any distance-$r$ dominating set into a constantly larger connected
+distance-$r$ dominating set in $3r+1$ rounds on any class of bounded expansion.
+Combining this algorithm, e.g., with the constant factor approximation
+algorithm for dominating sets on planar graphs of Lenzen et al.\ gives a
+constant factor approximation algorithm for connected dominating sets on planar
+graphs in a constant number of rounds in the $\mathcal{LOCAL}$ model, where the
+approximation ratio is only $6$ times larger than that of Lenzen et al.'s
+algorithm.
+"
+22170,Active Learning for Graph Embedding,"  Graph embedding provides an efficient solution for graph analysis by
+converting the graph into a low-dimensional space which preserves the structure
+information. In contrast to the graph structure data, the i.i.d. node embedding
+can be processed efficiently in terms of both time and space. Current
+semi-supervised graph embedding algorithms assume the labelled nodes are given,
+which may not be always true in the real world. While manually label all
+training data is inapplicable, how to select the subset of training data to
+label so as to maximize the graph analysis task performance is of great
+importance. This motivates our proposed active graph embedding (AGE) framework,
+in which we design a general active learning query strategy for any
+semi-supervised graph embedding algorithm. AGE selects the most informative
+nodes as the training labelled nodes based on the graphical information (i.e.,
+node centrality) as well as the learnt node embedding (i.e., node
+classification uncertainty and node embedding representativeness). Different
+query criteria are combined with the time-sensitive parameters which shift the
+focus from graph based query criteria to embedding based criteria as the
+learning progresses. Experiments have been conducted on three public data sets
+and the results verified the effectiveness of each component of our query
+strategy and the power of combining them using time-sensitive parameters. Our
+code is available online at: this https URL.
+"
+22171,A Parametric Investigation on the Cyclotron Maser Instability Driven by Ring-beam Electrons with Intrinsic Alfvén Waves,"  The electron-cyclotron maser is a process that generates intense and coherent
+radio emission in plasma. In this paper, we present a comprehensive parametric
+investigation on the electron-cyclotron-maser instability driven by non-thermal
+ring-beam electrons with intrinsic Alfvén waves which pervade the solar
+atmosphere and interplanetary space. It is found that both forward propagating
+and backward propagating waves can be excited in the fast ordinary (O) and
+extraordinary (X) electromagnetic modes. The growth rates of X1 mode are almost
+always weakened by Alfvén waves. The average pitch-angle $\phi_0$ of
+electrons is a key parameter for the effect of Alfvén waves on the growth
+rate of modes O1, O2 and X2. For a beam-dominated electron distribution
+($\phi_0 \lesssim 30^\circ$ ), the growth rates of the maser instability for
+O1, O2 and X2 modes are enhanced with the increase of Alfvén wave energy
+density. In other conditions, the growth rates of O1, O2 and X2 modes weakened
+with increasing Alfvén wave intensity, except that the growth of O1 mode may
+also be enhanced by Alfvén waves for a ring distribution. The results may be
+important for us in analyzing the mechanism of radio bursts with various fine
+structures observed in space and astrophysical plasmas.
+"
+22172,A finiteness theorem via the mean curvature flow with surgery,"  In this article, we use the recently developed mean curvature flow with
+surgery for 2 convex hypersurfaces to prove several isotopy existence and
+finally extrinsic finiteness results (in the spirit of Cheeger's compactness
+theorem) for the space of 2 convex hypersurfaces in $\mathbb{R}^{n+1}$.
+"
+22173,Accelerating gravitational microlensing simulations using the Xeon Phi coprocessor,"  Recently Graphics Processing Units (GPUs) have been used to speed up very
+CPU-intensive gravitational microlensing simulations. In this work, we use the
+Xeon Phi coprocessor to accelerate such simulations and compare its performance
+on a microlensing code with that of NVIDIA's GPUs. For the selected set of
+parameters evaluated in our experiment, we find that the speedup by Intel's
+Knights Corner coprocessor is comparable to that by NVIDIA's Fermi family of
+GPUs with compute capability 2.0, but less significant than GPUs with higher
+compute capabilities such as the Kepler. However, the very recently released
+second generation Xeon Phi, Knights Landing, is about 5.8 times faster than the
+Knights Corner, and about 2.9 times faster than the Kepler GPU used in our
+simulations. We conclude that the Xeon Phi is a very promising alternative to
+GPUs for modern high performance microlensing simulations.
+"
+22174,Comment on LiH as a Li+ and H- ion provider,"  A first-principle study of the formation and migration of native defects in
+LiH, a material of interest in hydrogen storage and lithium-ion batteries, has
+recently been published. Their results are found here to be of key-importance
+to deduce useful results for the pressure dependence of the ionic conductivity
+of LiH.
+"
+22175,An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation,"  Rehabilitation robotics combined with video game technology provides a means
+of assisting in the rehabilitation of patients with neuromuscular disorders by
+performing various facilitation movements. The current work presents ReHabGame,
+a serious game using a fusion of implemented technologies that can be easily
+used by patients and therapists to assess and enhance sensorimotor performance
+and also increase the activities in the daily lives of patients. The game
+allows a player to control avatar movements through a Kinect Xbox, Myo armband
+and rudder foot pedal, and involves a series of reach-grasp-collect tasks whose
+difficulty levels are learnt by a fuzzy interface. The orientation, angular
+velocity, head and spine tilts and other data generated by the player are
+monitored and saved, whilst the task completion is calculated by solving an
+inverse kinematics algorithm which orientates the upper limb joints of the
+avatar. The different values in upper body quantities of movement provide fuzzy
+input from which crisp output is determined and used to generate an appropriate
+subsequent rehabilitation game level. The system can thus provide personalised,
+autonomously-learnt rehabilitation programmes for patients with neuromuscular
+disorders with superior predictions to guide the development of improved
+clinical protocols compared to traditional theraputic activities.
+"
+22176,Quantum walks with an anisotropic coin I: spectral theory,"  We perform the spectral analysis of the evolution operator U of quantum walks
+with an anisotropic coin, which include one-defect models, two-phase quantum
+walks, and topological phase quantum walks as special cases. In particular, we
+determine the essential spectrum of U, we show the existence of locally
+U-smooth operators, we prove the discreteness of the eigenvalues of U outside
+the thresholds, and we prove the absence of singular continuous spectrum for U.
+Our analysis is based on new commutator methods for unitary operators in a
+two-Hilbert spaces setting, which are of independent interest.
+"
+22177,On the cohomology ring and upper characteristic rank of Grassmannian of oriented $3$-planes,"  In this paper we study the mod $2$ cohomology ring of the Grasmannian
+$\widetilde{G}_{n,3}$ of oriented $3$-planes in $\mathbb{R}^n$. We determine
+the degrees of the indecomposable elements in the cohomology ring. We also
+obtain an almost complete description of the cohomology ring. This partial
+description allows us to provide lower and upper bounds on the cup length of
+$\widetilde{G}_{n,3}$. As another application, we show that the upper
+characteristic rank of $\widetilde{G}_{n,3}$ equals the characteristic rank of
+$\widetilde{\gamma}_{n,3}$, the oriented tautological bundle over
+$\widetilde{G}_{n,3}$.
+"
+22178,Biquandle Virtual Brackets,"  We introduce an infinite family of quantum enhancements of the biquandle
+counting invariant we call biquandle virtual brackets. Defined in terms of
+skein invariants of biquandle colored oriented knot and link diagrams with
+values in a commutative ring $R$ using virtual crossings as smoothings, these
+invariants take the form of multisets of elements of $R$ and can be written in
+a ""polynomial"" form for convenience. The family of invariants defined herein
+includes as special cases all quandle and biquandle 2-cocycle invariants, all
+classical skein invariants (Alexander-Conway, Jones, HOMFLYPT and Kauffman
+polynomials) and all biquandle bracket invariants defined in previous work as
+well as new invariants defined using virtual crossings in a fundamental way,
+without an obvious purely classical definition.
+"
+22179,Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications,"  In this paper, we propose a hybrid analog-digital beamforming architecture
+with resolution-adaptive ADCs for millimeter wave (mmWave) receivers with large
+antenna arrays. We adopt array response vectors for the analog combiners and
+derive ADC bit-allocation (BA) solutions in closed form. The BA solutions
+reveal that the optimal number of ADC bits is logarithmically proportional to
+the RF chain's signal-to-noise ratio raised to the 1/3 power. Using the
+solutions, two proposed BA algorithms minimize the mean square quantization
+error of received analog signals under a total ADC power constraint.
+Contributions of this paper include 1) ADC bit-allocation algorithms to improve
+communication performance of a hybrid MIMO receiver, 2) approximation of the
+capacity with the BA algorithm as a function of channels, and 3) a worst-case
+analysis of the ergodic rate of the proposed MIMO receiver that quantifies
+system tradeoffs and serves as the lower bound. Simulation results demonstrate
+that the BA algorithms outperform a fixed-ADC approach in both spectral and
+energy efficiency, and validate the capacity and ergodic rate formula. For a
+power constraint equivalent to that of fixed 4-bit ADCs, the revised BA
+algorithm makes the quantization error negligible while achieving 22% better
+energy efficiency. Having negligible quantization error allows existing
+state-of-the-art digital beamformers to be readily applied to the proposed
+system.
+"
+22180,Computing by nowhere increasing complexity,"  A cellular automaton is presented whose governing rule is that the Kolmogorov
+complexity of a cell's neighborhood may not increase when the cell's present
+value is substituted for its future value. Using an approximation of this
+two-dimensional Kolmogorov complexity the underlying automaton is shown to be
+capable of simulating logic circuits. It is also shown to capture trianry logic
+described by a quandle, a non-associative algebraic structure. A similar
+automaton whose rule permits at times the increase of a cell's neighborhood
+complexity is shown to produce animated entities which can be used as
+information carriers akin to gliders in Conway's game of life.
+"
+22181,The spectra of the unitary marix of a 2-tessellable staggered quantum walk on a graph,"  Recently, the staggered quantum walk (SQW) on a graph is discussed as a
+generalization of coined quantum walks on graphs and Szegedy walks. We present
+a formula for the time evolution matrix of a 2-tessellable SQW on a graph, and
+so directly give its spectra. Furthermore, we present a formula for the Szegedy
+matrix of a bipartite graph by the same method, and so give its spectra. As an
+application, we present a formula for the characteristic polynomial of the
+modified Szegedy matrix in the quantum search problem on a graph, and give its
+spectra.
+"
+22182,Analytical derivative coupling for multistate CASPT2 theory,"  The probability of non-radiative transitions in photochemical dynamics is
+determined by the derivative couplings, the couplings between different
+electronic states through the nuclear degrees of freedom. Efficient and
+accurate evaluation of the derivative couplings is, therefore, of central
+importance to realize reliable computer simulations of photochemical reactions.
+In this work, the derivative couplings for multistate multireference
+second-order perturbation theory (MS-CASPT2) and its 'extended' variant
+(XMS-CASPT2) are studied, in which we present an algorithm for their analytical
+evaluation. The computational costs for evaluating the derivative couplings are
+essentially the same as those for calculating the nuclear energy gradients. The
+geometries and energies calculated with XMS-CASPT2 for small molecules at
+minimum energy conical intersections (MECIs) are in good agreement with those
+computed by multireference configuration interaction. As numerical examples,
+MECIs are optimized using XMS-CASPT2 for stilbene and a GFP model chromophore
+(the 4-para-hydroxybenzylidene-1,2-dimethyl-imidazolin-5-one anion).
+"
+22183,Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis,"  The past decade has seen an explosion in the amount of digital information
+stored in electronic health records (EHR). While primarily designed for
+archiving patient clinical information and administrative healthcare tasks,
+many researchers have found secondary use of these records for various clinical
+informatics tasks. Over the same period, the machine learning community has
+seen widespread advances in deep learning techniques, which also have been
+successfully applied to the vast amount of EHR data. In this paper, we review
+these deep EHR systems, examining architectures, technical aspects, and
+clinical applications. We also identify shortcomings of current techniques and
+discuss avenues of future research for EHR-based deep learning.
+"
+22184,Specializing trees and answer to a question of Williams,"  We show that if $cf(2^{\aleph_0})=\aleph_1,$ then any non-trivial
+$\aleph_1$-closed forcing notion of size $\leq 2^{\aleph_0}$ is forcing
+equivalent to $Add(\aleph_1, 1),$ the Cohen forcing for adding a new Cohen
+subset of $\omega_1.$ We also produce, relative to the existence of suitable
+large cardinals, a model of $ZFC$ in which $2^{\aleph_0}=\aleph_2$ and all
+$\aleph_1$-closed forcing notion of size $\leq 2^{\aleph_0}$ collapse
+$\aleph_2,$ and hence are forcing equivalent to $Add(\aleph_1, 1).$ These
+results answer a question of Scott Williams from 1978. We also extend a result
+of Todorcevic and Foreman-Magidor-Shelah by showing that it is consistent that
+every partial order which adds a new subset of $\aleph_2,$ collapses $\aleph_2$
+or $\aleph_3.$
+"
+22185,Discrete series of odd general spin groups,"  We obtain a classification of discrete series representations of odd general
+spin groups, generalizing the M{\oe}glin-Tadić classification for classical
+groups. Using mostly algebraic methods, available in both classical and the odd
+general spin case, we provide a simplified and shorter version of the
+classification of non-cuspidal discrete series in terms of the admissible
+triples. The presented classification can potentially be directly applied to
+many other reductive $p$-adic groups.
+"
+22186,PixelSNAIL: An Improved Autoregressive Generative Model,"  Autoregressive generative models consistently achieve the best results in
+density estimation tasks involving high dimensional data, such as images or
+audio. They pose density estimation as a sequence modeling task, where a
+recurrent neural network (RNN) models the conditional distribution over the
+next element conditioned on all previous elements. In this paradigm, the
+bottleneck is the extent to which the RNN can model long-range dependencies,
+and the most successful approaches rely on causal convolutions, which offer
+better access to earlier parts of the sequence than conventional RNNs. Taking
+inspiration from recent work in meta reinforcement learning, where dealing with
+long-range dependencies is also essential, we introduce a new generative model
+architecture that combines causal convolutions with self attention. In this
+note, we describe the resulting model and present state-of-the-art
+log-likelihood results on CIFAR-10 (2.85 bits per dim) and $32 \times 32$
+ImageNet (3.80 bits per dim). Our implementation is available at
+this https URL
+"
+22187,A Brief Introduction to the Temporal Group LASSO and its Potential Applications in Healthcare,"  The Temporal Group LASSO is an example of a multi-task, regularized
+regression approach for the prediction of response variables that vary over
+time. The aim of this work is to introduce the reader to the concepts behind
+the Temporal Group LASSO and its related methods, as well as to the type of
+potential applications in a healthcare setting that the method has. We argue
+that the method is attractive because of its ability to reduce overfitting,
+select predictors, learn smooth effect patterns over time, and finally, its
+simplicity
+"
+22188,Scalable Approximation Algorithm for Network Immunization,"  The problem of identifying important players in a given network is of pivotal
+importance for viral marketing, public health management, network security and
+various other fields of social network analysis. In this work we find the most
+important vertices in a graph G = (V,E) to immunize so as the chances of an
+epidemic outbreak is minimized. This problem is directly relevant to minimizing
+the impact of a contagion spread (e.g. flu virus, computer virus and rumor) in
+a graph (e.g. social network, computer network) with a limited budget (e.g. the
+number of available vaccines, antivirus software, filters). It is well known
+that this problem is computationally intractable (it is NP-hard). In this work
+we reformulate the problem as a budgeted combinational optimization problem and
+use techniques from spectral graph theory to design an efficient greedy
+algorithm to find a subset of vertices to be immunized. We show that our
+algorithm takes less time compared to the state of the art algorithm. Thus our
+algorithm is scalable to networks of much larger sizes than best known
+solutions proposed earlier. We also give analytical bounds on the quality of
+our algorithm. Furthermore, we evaluate the efficacy of our algorithm on a
+number of real world networks and demonstrate that the empirical performance of
+algorithm supplements the theoretical bounds we present, both in terms of
+approximation guarantees and computational efficiency.
+"
+22189,Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks,"  Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is
+difficult since many cases of AF are usually clinically silent and undiagnosed.
+In particular paroxysmal AF is a form of AF that occurs occasionally, and has a
+higher probability of being undetected. In this work, we present an attention
+based deep learning framework for detection of paroxysmal AF episodes from a
+sequence of windows. Time-frequency representation of 30 seconds recording
+windows, over a 10 minute data segment, are fed sequentially into a deep
+convolutional neural network for image-based feature extraction, which are then
+presented to a bidirectional recurrent neural network with an attention layer
+for AF detection. To demonstrate the effectiveness of the proposed framework
+for transient AF detection, we use a database of 24 hour Holter
+Electrocardiogram (ECG) recordings acquired from 2850 patients at the
+University of Virginia heart station. The algorithm achieves an AUC of 0.94 on
+the testing set, which exceeds the performance of baseline models. We also
+demonstrate the cross-domain generalizablity of the approach by adapting the
+learned model parameters from one recording modality (ECG) to another
+(photoplethysmogram) with improved AF detection performance. The proposed high
+accuracy, low false alarm algorithm for detecting paroxysmal AF has potential
+applications in long-term monitoring using wearable sensors.
+"
+22190,Hindsight Experience Replay,"  Dealing with sparse rewards is one of the biggest challenges in Reinforcement
+Learning (RL). We present a novel technique called Hindsight Experience Replay
+which allows sample-efficient learning from rewards which are sparse and binary
+and therefore avoid the need for complicated reward engineering. It can be
+combined with an arbitrary off-policy RL algorithm and may be seen as a form of
+implicit curriculum.
+We demonstrate our approach on the task of manipulating objects with a
+robotic arm. In particular, we run experiments on three different tasks:
+pushing, sliding, and pick-and-place, in each case using only binary rewards
+indicating whether or not the task is completed. Our ablation studies show that
+Hindsight Experience Replay is a crucial ingredient which makes training
+possible in these challenging environments. We show that our policies trained
+on a physics simulation can be deployed on a physical robot and successfully
+complete the task.
+"
+22191,Coordination and Control of Multiple Climbing Robots in Transport of Heavy Loads through Extreme Terrain,"  The discovery of ice deposits in the permanently shadowed craters of the
+lunar North and South Pole Moon presents an important opportunity for In-Situ
+Resource Utilization. These ice deposits maybe the source for sustaining a
+lunar base or for enabling an interplanetary refueling station. These ice
+deposits also preserve a unique record of the geology and environment of their
+hosts, both in terms of impact history and the supply of volatile compounds,
+and so are of immense scientific interest. To date, these ice deposits have
+been studied indirectly and by remote active radar, but they need to be
+analyzed in-situ by robotic systems that can study the depths of the deposits,
+their purity and composition. However, these shadowed craters never see
+sunlight and are one of the coldest places in the solar system. NASA JPL
+proposed use of solar reflectors mounted on crater rims to project sunlight
+into the crater depths for use by ground robots. The solar reflectors would
+heat the crater base and vehicles positioned at the base sufficiently to
+survive the cold-temperatures. Our approach analyzes part of the logistics of
+the approach, with teams of robots climbing up and down to the crater to access
+the ice deposits. The mission will require robots to climb down extreme
+environments and carry large structures, including instruments and
+communication devices.
+"
+22192,Emergent Chiral Spin State in the Mott Phase of a Bosonic Kane-Mele-Hubbard Model,"  Recently, the frustrated XY model for spins-1/2 on the honeycomb lattice has
+attracted a lot of attention in relation with the possibility to realize a
+chiral spin liquid state. This model is relevant to the physics of some quantum
+magnets. Using the flexibility of ultra-cold atoms setups, we propose an
+alternative way to realize this model through the Mott regime of the bosonic
+Kane-Mele-Hubbard model. The phase diagram of this model is derived using the
+bosonic dynamical mean-field theory. Focussing on the Mott phase, we
+investigate its magnetic properties as a function of frustration. We do find an
+emergent chiral spin state in the intermediate frustration regime. Using exact
+diagonalization we study more closely the physics of the effective frustrated
+XY model and the properties of the chiral spin state. This gapped phase
+displays a chiral order, breaking time-reversal and parity symmetry, but is not
+topologically ordered (the Chern number is zero).
+"
+22193,Nonlinear geometric analysis on Finsler manifolds,"  This is a survey article on recent progress of comparison geometry and
+geometric analysis on Finsler manifolds of weighted Ricci curvature bounded
+below. Our purpose is two-fold: Give a concise and geometric review on the
+birth of weighted Ricci curvature and its applications; Explain recent results
+from a nonlinear analogue of the $\Gamma$-calculus based on the Bochner
+inequality. In the latter we discuss some gradient estimates, functional
+inequalities, and isoperimetric inequalities.
+"
+22194,Functional Continuum Regression,"  Functional principal component regression (PCR) can fail to provide good
+prediction if the response is highly correlated with some excluded functional
+principal component(s). This situation is common since the construction of
+functional principal components never involves the response. Aiming at this
+shortcoming, we develop functional continuum regression (CR). The framework of
+functional CR includes, as special cases, both functional PCR and functional
+partial least squares (PLS). Functional CR is expected to own a better accuracy
+than functional PCR and functional PLS both in estimation and prediction;
+evidence for this is provided through simulation and numerical case studies.
+Also, we demonstrate the consistency of estimators given by functional CR.
+"
+22195,A simulation comparison of tournament designs for the World Men's Handball Championships,"  The study aims to compare different designs for World Men's Handball
+Championships. This event, organised in every two years, has adopted four
+hybrid formats consisting of knockout and round-robin stages in recent decades,
+including a change of design between the two recent championships in 2017 and
+2019. They are evaluated under two seeding policies with respect to various
+outcome measures through Monte-Carlo simulations. We find that the ability to
+select the strongest competitors as the winner is not necessarily a monotonic
+function of the number of matches played, the most frugal format is the second
+best with respect to efficacy, making it a good compromise in this unavoidable
+trade-off. A possible error is identified in a particular design. The relative
+performance of the formats is independent of the seeding rules and the
+competitive balance of the teams. It is shown that the recent reform has
+increased the probability of winning for the top teams. Our results have useful
+implications for organisers of hybrid tournaments.
+"
+22196,Tverberg partitions as weak epsilon-nets,"  We prove a Tverberg-type theorem using the probabilistic method. Given
+$\varepsilon >0$, we find the smallest number of partitions of a set $X$ in
+$R^d$ into $r$ parts needed in order to induce at least one Tverberg partition
+on every subset of $X$ with at least $\varepsilon |X|$ elements. This
+generalizes known results about Tverberg's theorem with tolerance.
+"
+22197,Self-organizing maps and generalization: an algorithmic description of Numerosity and Variability Effects,"  Category, or property generalization is a central function in the human
+cognition. It plays a crucial role in a variety of domains, such as learning,
+everyday reasoning, specialized reasoning, and decision making. Judging the
+content of a dish as edible, a hormone level as healthy, a building as
+belonging to the same architectural style as previously seen buildings, are
+examples of category generalization. In this paper, we propose self-organizing
+maps as candidates to explain the psychological mechanisms underlying category
+generalization. Self-organizing maps are psychologically and biologically
+plausible neural network models that learn after limited exposure to positive
+category examples, without any need of contrastive information. Just like
+humans. They reproduce human behavior in category generalization, in particular
+for what concerns the well-known Numerosity and Variability effects, which are
+usually explained with Bayesian tools. Where category generalization is
+concerned, self-organizing maps are good candidates to bridge the gap between
+the computational level of analysis in Marr's hierarchy (where Bayesian models
+are situated) and the algorithmic level of aanalysis in Marr's hierarchy (where
+Bayesian models are situated) and the algorithmic level of analysis in which
+plausible mechanisms are described.
+"
+22198,"It All Matters: Reporting Accuracy, Inference Time and Power Consumption for Face Emotion Recognition on Embedded Systems","  While several approaches to face emotion recognition task are proposed in
+literature, none of them reports on power consumption nor inference time
+required to run the system in an embedded environment. Without adequate
+knowledge about these factors it is not clear whether we are actually able to
+provide accurate face emotion recognition in the embedded environment or not,
+and if not, how far we are from making it feasible and what are the biggest
+bottlenecks we face.
+The main goal of this paper is to answer these questions and to convey the
+message that instead of reporting only detection accuracy also power
+consumption and inference time should be reported as real usability of the
+proposed systems and their adoption in human computer interaction strongly
+depends on it. In this paper, we identify the state-of-the art face emotion
+recognition methods that are potentially suitable for embedded environment and
+the most frequently used datasets for this task. Our study shows that most of
+the performed experiments use datasets with posed expressions or in a
+particular experimental setup with special conditions for image collection.
+Since our goal is to evaluate the performance of the identified promising
+methods in the realistic scenario, we collect a new dataset with
+non-exaggerated emotions and we use it, in addition to the publicly available
+datasets, for the evaluation of detection accuracy, power consumption and
+inference time on three frequently used embedded devices with different
+computational capabilities. Our results show that gray images are still more
+suitable for embedded environment than color ones and that for most of the
+analyzed systems either inference time or energy consumption or both are
+limiting factor for their adoption in real-life embedded applications.
+"
+22199,Stable cosmology in ghost-free quasidilaton theory,"  We present a novel cosmological solution in the framework of extended
+quasidilaton theory which underwent scrutiny recently. We only consider terms
+that do not generate the Boulware-Deser degree of freedom, hence the
+""ghost-free"" quasidilaton theory, and show three new branches of cosmological
+evolution therein. One of the solutions passes the perturbative stability
+tests. This new solution exhibits a late time self-acceleration and all
+graviton polarizations acquire masses that converge to a constant in the
+asymptotic future. Moreover, all modes propagate at the speed of light. We
+propose that this solution can be used as a benchmark model for future
+phenomenological studies.
+"
+22200,Droplets in isotropic turbulence: deformation and breakup statistics,"  The statistics of the deformation and breakup of neutrally buoyant
+sub-Kolmogorov ellipsoidal drops is investigated via Lagrangian simulations of
+homogeneous isotropic turbulence. The mean lifetime of a drop is also studied
+as a function of the initial drop size and the capillary number. A vector model
+of drop previously introduced by Olbricht, Rallison and Leal [J. Non-Newtonian
+Fluid Mech. $\mathbf{10}$, 291 (1982)] is used to predict the behaviour of the
+above quantities analytically.
+"
+22201,Superconductivity Emerging from Excitonic Mott insulator - Theory of Alkaline Doped Fullerene,"  A three-orbital model derived from the two-dimensional projection of the $ab$
+$initio$ Hamiltonian for alkaline doped fullerene A$_3$C$_{60}$ with A=Cs,Rb,K
+is studied by a variational Monte Carlo method. We correctly reproduce the
+experimental isotropic s-wave superconductivity around the $ab$ $initio$
+parameters. With narrowing the bandwidth, the transition to an insulator is
+also reproduced, where orbital symmetry is found to be spontaneously broken
+with emergence of an excitonic Mott insulator for two orbitals and an
+antiferromagnetic insulator nearly degenerate with a spin liquid for the third
+orbital. The superconductivity is a consequence of exciton melting.
+"
+22202,Probability-Scale Residuals in HIV/AIDS Research: Diagnostics and Inference,"  The probability-scale residual (PSR) is well defined across a wide variety of
+variable types and models, making it useful for studies of HIV/AIDS. In this
+manuscript, we highlight some of the properties of the PSR and illustrate its
+application with HIV data. As a residual, it can be useful for model
+diagnostics; we demonstrate its use with ordered categorical data and
+semiparametric transformation models. The PSR can also be used to construct
+tests of residual correlation. In fact, partial Spearman's rank correlation
+between $X$ and $Y$ while adjusting for covariates $Z$ can be constructed as
+the correlation between PSRs from models of $Y$ on $Z$ and of $X$ on $Z$. The
+covariance of PSRs is also useful in some settings. We apply these methods to a
+variety of HIV datasets including 1) a study examining risk factors for more
+severe forms of cervical lesions among 145 women living with HIV in Zambia, 2)
+a study investigating the association between 21 metabolomic biomarkers among
+70 HIV-positive patients in the southeastern United States, and 3) a genome
+wide association study investigating the association between single nucleotide
+polymorphisms and tenofovir clearance among 501 HIV-positive persons
+participating in a multi-site randomized clinical trial.
+"
+22203,Prediction of amino acid side chain conformation using a deep neural network,"  A deep neural network based architecture was constructed to predict amino
+acid side chain conformation with unprecedented accuracy. Amino acid side chain
+conformation prediction is essential for protein homology modeling and protein
+design. Current widely-adopted methods use physics-based energy functions to
+evaluate side chain conformation. Here, using a deep neural network
+architecture without physics-based assumptions, we have demonstrated that side
+chain conformation prediction accuracy can be improved by more than 25%,
+especially for aromatic residues compared with current standard methods. More
+strikingly, the prediction method presented here is robust enough to identify
+individual conformational outliers from high resolution structures in a protein
+data bank without providing its structural factors. We envisage that our amino
+acid side chain predictor could be used as a quality check step for future
+protein structure model validation and many other potential applications such
+as side chain assignment in Cryo-electron microscopy, crystallography model
+auto-building, protein folding and small molecule ligand docking.
+"
+22204,Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix,"  Unsupervised single-channel blind source separation is a long standing signal
+processing challenge. Many methods were proposed to solve this task utilizing
+multiple signal priors such as low rank, sparsity, temporal continuity etc. The
+recent advance of generative adversarial models presented new opportunities in
+prior-free signal regression tasks. The power of adversarial training however
+has not yet been realized for unsupervised source separation tasks. In this
+work, we propose a novel method for unsupervised blind source separation (BSS)
+using adversarial methods. We rely on the independence of sources for creating
+adversarial constraints on pairs of approximately separated sources, which
+ensure good separation. Experiments are carried out on image sources validating
+the good performance of our approach, and presenting our method as a promising
+approach for solving unsupervised BSS for general signals.
+"
+22205,Non-Adaptive Group Testing Framework based on Concatenation Code,"  We consider an efficiently decodable non-adaptive group testing (NAGT)
+problem that meets theoretical bounds. The problem is to find a few specific
+items (at most $d$) satisfying certain characteristics in a colossal number of
+$N$ items as quickly as possible. Those $d$ specific items are called
+\textit{defective items}. The idea of NAGT is to pool a group of items, which
+is called \textit{a test}, then run a test on them. If the test outcome is
+\textit{positive}, there exists at least one defective item in the test, and if
+it is \textit{negative}, there exists no defective items. Formally, a binary $t
+\times N$ measurement matrix $\mathcal{M} = (m_{ij})$ is the representation for
+$t$ tests where row $i$ stands for test $i$ and $m_{ij} = 1$ if and only if
+item $j$ belongs to test $i$.
+There are three main objectives in NAGT: minimize the number of tests $t$,
+construct matrix $\mathcal{M}$, and identify defective items as quickly as
+possible. In this paper, we present a strongly explicit construction of
+$\mathcal{M}$ for when the number of defective items is at most 2, with the
+number of tests $t \simeq 16 \log{N} = O(\log{N})$. In particular, we need only
+$K \simeq N \times 16\log{N} = O(N\log{N})$ bits to construct such matrices,
+which is optimal. Furthermore, given these $K$ bits, any entry in the matrix
+can be constructed in time $O \left(\ln{N}/ \ln{\ln{N}} \right)$. Moreover,
+$\mathcal{M}$ can be decoded with high probability in time $O\left(
+\frac{\ln^2{N}}{\ln^2{\ln{N}}} \right)$. When the number of defective items is
+greater than 2, we present a scheme that can identify at least $(1-\epsilon)d$
+defective items with $t \simeq 32 C(\epsilon) d \log{N} = O(d \log{N})$ in time
+$O \left( d \frac{\ln^2{N}}{\ln^2{\ln{N}}} \right)$ for any close-to-zero
+$\epsilon$, where $C(\epsilon)$ is a constant that depends only on $\epsilon$.
+"
+22206,Adversarial Multi-Criteria Learning for Chinese Word Segmentation,"  Different linguistic perspectives causes many diverse segmentation criteria
+for Chinese word segmentation (CWS). Most existing methods focus on improve the
+performance for each single criterion. However, it is interesting to exploit
+these different criteria and mining their common underlying knowledge. In this
+paper, we propose adversarial multi-criteria learning for CWS by integrating
+shared knowledge from multiple heterogeneous segmentation criteria. Experiments
+on eight corpora with heterogeneous segmentation criteria show that the
+performance of each corpus obtains a significant improvement, compared to
+single-criterion learning. Source codes of this paper are available on Github.
+"
+22207,Polynomials Whose Coefficients Coincide with Their Zeros,"  In this paper we consider monic polynomials such that their coefficients
+coincide with their zeros. These polynomials were first introduced by S. Ulam.
+We combine methods of algebraic geometry and dynamical systems to prove several
+results. We obtain estimates on the number of Ulam polynomials of degree $N$.
+We provide additional methods to obtain algebraic identities satisfied by the
+zeros of Ulam polynomials, beyond the straightforward comparison of their zeros
+and coefficients. To address the question about existence of orthogonal Ulam
+polynomial sequences, we show that the only Ulam polynomial eigenfunctions of
+hypergeometric type differential operators are the trivial Ulam polynomials
+$\{x^N\}_{N=0}^\infty$. We propose a family of solvable $N$-body problems such
+that their stable equilibria are the zeros of certain Ulam polynomials.
+"
+22208,Theoretical prediction of the Fine and Hyperfine structure of heavy muonic atoms,"  Precision calculations of the fine and hyperfine structure of muonic atoms
+are performed in a relativistic approach and results for muonic 205 Bi, 147 Sm,
+and 89 Zr are presented. The hyperfine structure due to magnetic dipole and
+electric quadrupole splitting is calculated in first order perturbation theory,
+using extended nuclear charge and current distributions. The leading correction
+from quantum electrodynamics, namely vacuum polarization in Uehling
+approximation, is included as a potential directly in the Dirac equation. Also,
+an effective screening potential due to the surrounding electrons is
+calculated, and the leading relativistic recoil correction is estimated.
+"
+22209,A predictive pan-European economic and production dispatch model for the energy transition in the electricity sector,"  The energy transition is well underway in most European countries. It has a
+growing impact on electric power systems as it dramatically modifies the way
+electricity is produced. To ensure a safe and smooth transition towards a
+pan-European electricity production dominated by renewable sources, it is of
+paramount importance to anticipate how production dispatches will evolve, to
+understand how increased fluctuations in power generations can be absorbed at
+the pan-European level and to evaluate where the resulting changes in power
+flows will require significant grid upgrades. To address these issues, we
+construct an aggregated model of the pan-European transmission network which we
+couple to an optimized, few-parameter dispatch algorithm to obtain time- and
+geographically-resolved production profiles. We demonstrate the validity of our
+dispatch algorithm by reproducing historical production time series for all
+power productions in fifteen different European countries. Having calibrated
+our model in this way, we investigate future production profiles at later
+stages of the energy transition - determined by planned future production
+capacities - and the resulting interregional power flows. We find that large
+power fluctuations from increasing penetrations of renewable sources can be
+absorbed at the pan-European level via significantly increased electricity
+exchanges between different countries. We identify where these increased
+exchanges will require additional power transfer capacities. We finally
+introduce a physically-based economic indicator which allows to predict future
+financial conditions in the electricity market. We anticipate new economic
+opportunities for dam hydroelectricity and pumped-storage plants.
+"
+22210,Exponentially Consistent Kernel Two-Sample Tests,"  Given two sets of independent samples from unknown distributions $P$ and $Q$,
+a two-sample test decides whether to reject the null hypothesis that $P=Q$.
+Recent attention has focused on kernel two-sample tests as the test statistics
+are easy to compute, converge fast, and have low bias with their finite sample
+estimates. However, there still lacks an exact characterization on the
+asymptotic performance of such tests, and in particular, the rate at which the
+type-II error probability decays to zero in the large sample limit. In this
+work, we establish that a class of kernel two-sample tests are exponentially
+consistent with Polish, locally compact Hausdorff sample space, e.g., $\mathbb
+R^d$. The obtained exponential decay rate is further shown to be optimal among
+all two-sample tests satisfying the level constraint, and is independent of
+particular kernels provided that they are bounded continuous and
+characteristic. Our results gain new insights into related issues such as fair
+alternative for testing and kernel selection strategy. Finally, as an
+application, we show that a kernel based test achieves the optimal detection
+for off-line change detection in the nonparametric setting.
+"
+22211,Deep Transfer Learning for Error Decoding from Non-Invasive EEG,"  We recorded high-density EEG in a flanker task experiment (31 subjects) and
+an online BCI control paradigm (4 subjects). On these datasets, we evaluated
+the use of transfer learning for error decoding with deep convolutional neural
+networks (deep ConvNets). In comparison with a regularized linear discriminant
+analysis (rLDA) classifier, ConvNets were significantly better in both intra-
+and inter-subject decoding, achieving an average accuracy of 84.1 % within
+subject and 81.7 % on unknown subjects (flanker task). Neither method was,
+however, able to generalize reliably between paradigms. Visualization of
+features the ConvNets learned from the data showed plausible patterns of brain
+activity, revealing both similarities and differences between the different
+kinds of errors. Our findings indicate that deep learning techniques are useful
+to infer information about the correctness of action in BCI applications,
+particularly for the transfer of pre-trained classifiers to new recording
+sessions or subjects.
+"
+22212,Radiative deflection of a BaF molecular beam from the optical cycling,"  We demonstrate a quasi optical cycling for the $X(v=0)\to A(v'=0)$ transition
+and a radiative force induced deflection on the buffer-gas cooled BaF molecular
+beam. The laser induced fluorescence enhancement with additional sidebands and
+a polarization modulation scheme indicates that the hyperfine states and the
+Zeeman sublevels are closed. The quasi optical cycling by repumping the
+$X(v=1)\to A(v'=0)$ leads to a $\sim$ 0.8 mm deflection of the beam via
+scattering $\sim$ 150 photons per molecule, in good agreement with the
+predictions from our multi-level rate equation model. Further improvement by
+closing the leakage $X(v=2)$ and $\Delta$ state allows scattering thousands of
+photons, and laser cooling and slowing of BaF.
+"
+22213,Sustainable Fair Division,"  In this paper, I summarize our work on online fair division. In particular, I
+present two models for online fair division: (1) one existing model for fair
+division in food banks and (2) one new model for fair division of deceased
+organs to patients. I further discuss simple mechanisms for these models that
+allocate the resources as they arrive to agents. In practice, agents are often
+risk-averse having imperfect information. Within this assumption, I report
+several interesting axiomatic and complexity results for these mechanisms and
+conclude with future work.
+"
+22214,"A Dozen Problems, Questions and Conjectures about Positive Scalar Curvature","  We collect a few guesses on possible implications of a lower bound on the
+scalar curvature of a Riemannian manifold on the size and shape of this
+manifold.
+"
+22215,A Stability Theorem for Matchings in Tripartite 3-Graphs,"  It follows from known results that every regular tripartite hypergraph of
+positive degree, with $n$ vertices in each class, has matching number at least
+$n/2$. This bound is best possible, and the extremal configuration is unique.
+Here we prove a stability version of this statement, establishing that every
+regular tripartite hypergraph with matching number at most $(1 +
+\varepsilon)n/2$ is close in structure to the extremal configuration, where
+""closeness"" is measured by an explicit function of $\varepsilon$. We also
+answer a question of Aharoni, Kotlar and Ziv about matchings in hypergraphs
+with a more general degree condition.
+"
+22216,"Far Ultraviolet Spectroscopy of Old Novae II: RR Pic, V533 Her and DI Lac","  The old novae V533 Her (Nova Her 1963), DI Lac (Nova Lac 1910) and RR Pic
+(Nova Pic 1891) are in (or near) their quiescent stage following their nova
+explosions and continue to accrete at a high rate in the aftermath of their
+explosions. They exhibit continua that are steeply rising into the FUV as well
+as absorption lines and emission lines of uncertain origin. All three have FUSE
+spectra which offer not only higher spectral resolution but also wavelength
+coverage extending down to the Lyman Limit. For DI Lac, we have matched these
+FUSE spectra with existing archival IUE spectral coverage to broaden the FUV
+wavelength coverage. We adopted the newly determined interstellar reddening
+corrections of Selvelli and Gilmozzi (2013). The dereddened FUV spectra have
+been modeled with our grids of optically thick accretion disks and hot, NLTE
+white dwarf photospheres.
+The results of our modeling analysis indicate that the hot component in RR
+Pic and V533 Her is likely to be the accretion disk with a mass accretion of
+$10^{-8}M_{\odot}$/yr and $10^{-9}M_{\odot}$/yr respectively. However, the disk
+cannot produce the observed absorption lines. For the WD to be the source of
+the absorption lines in these two systems, it must be very hot with a radius
+several times its expected size (since the WD in these systems is massive, it
+has a smaller radius). For DI Lac we find the best fit to be a disk with
+$\dot{M}=10^{-10}M_{\odot}$/yr with a 30,000K WD.
+"
+22217,Mock modularity of the $M_d$-rank of overpartitions,"  We investigate the modular properties of a new partition rank, the $M_d$-rank
+of overpartitions. In fact this is an infinite family of ranks, indexed by the
+positive integer $d$, that gives both the Dyson rank of overpartitions and the
+overpartition $M_2$-rank as special cases. The $M_d$-rank of overpartitions is
+the holomorphic part of a certain harmonic Maass form of weight $\frac{1}{2}$.
+We give the exact transformation of this harmonic Maass form along with a few
+identities for the $M_d$-rank.
+"
+22218,"An Infinite C*-algebra with a Dense, Stably Finite *-Subalgebra","  We construct a unital pre-C*-algebra $A_0$ which is stably finite, in the
+sense that every left invertible square matrix over $A_0$ is right invertible,
+while the C*-completion of $A_0$ contains a non-unitary isometry, and so it is
+infinite.
+"
+22219,Quantum Potential induced UV-IR coupling in Analogue Hawking radiation: From Bose-Einstein Condensates to canonical acoustic black holes,"  Arising out of a Non-local non-relativistic BEC, we present an Analogue
+gravity model upto $\mathcal{O}(\xi^{2})$ accuracy in the presence of the
+quantum potential term for a canonical acoustic BH in $(3+1)$-d spacetime where
+the series solution of the free minimally coupled KG equation for the large
+length scale massive scalar modes is derived. We systematically address the
+issues of the presence of the quantum potential term being the root cause of a
+UV-IR coupling between short wavelength `primary' modes which are supposedly
+Hawking radiated through the sonic horizon and the large wavelength `secondary'
+modes. In the quantum gravity experiments of analogue Hawking radiation in the
+laboratory, this UV-IR coupling is inevitable and one can not get rid of these
+large wavelength excitations which would grow over space by gaining energy from
+the short wavelength Hawking radiated modes. We identify the characteristic
+feature in the growth rate(s) that would distinguish these primary and
+secondary modes.
+"
+22220,Rank-Favorable Bounds for Rational Points on Superelliptic Curves of Small Rank,"  Let $C$ be a curve of genus at least three defined over a number field, and
+let $r$ be the rank of the rational points of its Jacobian. Under mild
+hypotheses on $r$, recent results by Katz, Rabinoff, Zureick-Brown, and Stoll
+bound the number of rational points on $C$ by a constant that depends only on
+its genus. Yet one expects an even stronger bound that depends favorably on
+$r$: when $r$ is small, there should be fewer points on $C$. In a 2013 paper,
+Stoll established such a ""rank-favorable"" bound for hyperelliptic curves using
+Chabauty's method. In the present work we extend Stoll's results to
+superelliptic curves, noting in the process some differences that ought to
+inform uniformity conjectures for general curves. Our results have stark
+implications for bounding numbers of rational points, since $r$ is expected to
+be small for ""most"" curves.
+"
+22221,Approximating Constrained Minimum Cost Input-Output Selection for Generic Arbitrary Pole Placement in Structured Systems,"  This paper is about minimum cost constrained selection of inputs and outputs
+for generic arbitrary pole placement. The input-output set is constrained in
+the sense that the set of states that each input can influence and the set of
+states that each output can sense is pre-specified. Our goal is to optimally
+select an input-output set that the system has no structurally fixed modes.
+Polynomial algorithms do not exist for solving this problem unless P=NP. To
+this end, we propose an approximation algorithm by splitting the problem in to
+three sub-problems: a) minimum cost accessibility problem, b) minimum cost
+sensability problem and c) minimum cost disjoint cycle problem. We prove that
+problems a) and b) are equivalent to a suitably defined weighted set cover
+problems. We also show that problem c) is equivalent to a minimum cost perfect
+matching problem. Using these we give an approximation algorithm which solves
+the minimum cost generic arbitrary pole placement problem. The proposed
+algorithm incorporates an approximation algorithm to solve the weighted set
+cover problem for solving a) and b) and a minimum cost perfect matching
+algorithm to solve c). Further, we show that the algorithm is polynomial time
+an gives an order optimal solution to the minimum cost input-output selection
+for generic arbitrary pole placement problem.
+"
+22222,Laser map aided visual inertial localization in changing environment,"  Long-term visual localization in outdoor environment is a challenging
+problem, especially faced with the cross-seasonal, bi-directional tasks and
+changing environment. In this paper we propose a novel visual inertial
+localization framework that localizes against the LiDAR-built map. Based on the
+geometry information of the laser map, a hybrid bundle adjustment framework is
+proposed, which estimates the poses of the cameras with respect to the prior
+laser map as well as optimizes the state variables of the online visual
+inertial odometry system simultaneously. For more accurate cross-modal data
+association, the laser map is optimized using multi-session laser and visual
+data to extract the salient and stable subset for localization. To validate the
+efficiency of the proposed method, we collect data in south part of our campus
+in different seasons, along the same and opposite-direction route. In all
+sessions of localization data, our proposed method gives satisfactory results,
+and shows the superiority of the hybrid bundle adjustment and map optimization.
+"
+22223,Cofinal Extensions and Coded Sets,"  Let M be a model of Peano Arithmetic that is countably generated over an
+exponentially closed cut I. We characterize those sets X of subsets of I for
+which there is a finitely (or countably) generated cofinal extension N of M
+such that I is the Greatest Common Initial Segment of M and N and X is the set
+of subsets of I coded by the extension. We also characterize such X for which
+the extension N can be, in addition, one of the following: non-filling;
+filling; n-filling.
+"
+22224,Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants,"  In multi-agent path finding (MAPF) the task is to find non-conflicting paths
+for multiple agents. In this paper we focus on finding suboptimal solutions for
+MAPF for the sum-of-costs variant. Recently, a SAT-based approached was
+developed to solve this problem and proved beneficial in many cases when
+compared to other search-based solvers. In this paper, we present SAT-based
+unbounded- and bounded-suboptimal algorithms and compare them to relevant
+algorithms. Experimental results show that in many case the SAT-based solver
+significantly outperforms the search-based solvers.
+"
+22225,The one comparing narrative social network extraction techniques,"  Analysing narratives through their social networks is an expanding field in
+quantitative literary studies. Manually extracting a social network from any
+narrative can be time consuming, so automatic extraction methods of varying
+complexity have been developed. However, the effect of different extraction
+methods on the analysis is unknown. Here we model and compare three extraction
+methods for social networks in narratives: manual extraction, co-occurrence
+automated extraction and automated extraction using machine learning. Although
+the manual extraction method produces more precise results in the network
+analysis, it is much more time consuming and the automatic extraction methods
+yield comparable conclusions for density, centrality measures and edge weights.
+Our results provide evidence that social networks extracted automatically are
+reliable for many analyses. We also describe which aspects of analysis are not
+reliable with such a social network. We anticipate that our findings will make
+it easier to analyse more narratives, which help us improve our understanding
+of how stories are written and evolve, and how people interact with each other.
+"
+22226,Differences between Doppler velocities of ions and neutral atoms in a solar prominence,"  In astrophysical systems with partially ionized plasma the motion of ions is
+governed by the magnetic field while the neutral particles can only feel the
+magnetic field's Lorentz force indirectly through collisions with ions. The
+drift in the velocity between ionized and neutral species plays a key role in
+modifying important physical processes like magnetic reconnection, damping of
+magnetohydrodynamic waves, transport of angular momentum in plasma through the
+magnetic field, and heating. This paper investigates the differences between
+Doppler velocities of calcium ions and neutral hydrogen in a solar prominence
+to look for velocity differences between the neutral and ionized species. We
+simultaneously observed spectra of a prominence over an active region in H I
+397 nm, H I 434 nm, Ca II 397 nm, and Ca II 854 nm using a high dispersion
+spectrograph of the Domeless Solar Telescope at Hida observatory, and compared
+the Doppler velocities, derived from the shift of the peak of the spectral
+lines presumably emitted from optically-thin plasma. There are instances when
+the difference in velocities between neutral atoms and ions is significant,
+e.g. 1433 events (~ 3 % of sets of compared profiles) with a difference in
+velocity between neutral hydrogen atoms and calcium ions greater than 3sigma of
+the measurement error. However, we also found significant differences between
+the Doppler velocities of two spectral lines emitted from the same species, and
+the probability density functions of velocity difference between the same
+species is not significantly different from those between neutral atoms and
+ions. We interpreted the difference of Doppler velocities as a result of
+motions of different components in the prominence along the line of sight,
+rather than the decoupling of neutral atoms from plasma.
+"
+22227,Mathematical modelling of the vitamin C clock reaction,"  Chemical clock reactions are characterised by a relatively long induction
+period followed by a rapid `switchover' during which the concentration of a
+clock chemical rises rapidly. In addition to their interest in chemistry
+education, these reactions are relevant to industrial and biochemical
+applications. A substrate-depletive, non-autocatalytic clock reaction involving
+household chemicals (vitamin C, iodine, hydrogen peroxide and starch) is
+modelled mathematically via a system of nonlinear ordinary differential
+equations. Following dimensional analysis the model is analysed in the phase
+plane and via matched asymptotic expansions. Asymptotic approximations are
+found to agree closely with numerical solutions in the appropriate time
+regions. Asymptotic analysis also yields an approximate formula for the
+dependence of switchover time on initial concentrations and the rate of the
+slow reaction. This formula is tested via `kitchen sink chemistry' experiments,
+and is found to enable a good single-parameter fit to experimental series
+varying in initial concentrations of both iodine and vitamin C. The vitamin C
+clock reaction provides an accessible model system for mathematical chemistry.
+"
+22228,Solar activity forcing of terrestrial hydrological phenomena,"  Recently, the study of the influence of solar activity on the Earth's climate
+received strong attention, mainly due to the possibility, proposed by several
+authors, that global warming is not anthropogenic, but is due to an increase in
+solar activity. Although this possibility has been ruled out, there are strong
+evidences that solar variability has an influence on Earth's climate, in
+regional scales.
+Here we review some of these evidences, focusing in a particular aspect of
+climate: atmospheric moisture and related quantities like precipitation. In
+particular, we studied the influence of activity on South American
+precipitations during centuries. First, we analyzed the stream flow of the
+Paraná and other rivers of the region, and found a very strong correlation
+with Sunspot Number in decadal time scales. We found a similar correlation
+between Sunspot Number and tree-ring chronologies, which allows us to extend
+our study to cover the last two centuries.
+"
+22229,The Condorcet Principle for Multiwinner Elections: From Shortlisting to Proportionality,"  We study two notions of stability in multiwinner elections that are based on
+the Condorcet criterion. The first notion was introduced by Gehrlein: A
+committee is stable if each committee member is preferred to each non-member by
+a (possibly weak) majority of voters. The second notion is called local
+stability (introduced in this paper): A size-$k$ committee is locally stable in
+an election with $n$ voters if there is no candidate $c$ and no group of more
+than $\frac{n}{k+1}$ voters such that each voter in this group prefers $c$ to
+each committee member. We argue that Gehrlein-stable committees are appropriate
+for shortlisting tasks, and that locally stable committees are better suited
+for applications that require proportional representation. The goal of this
+paper is to analyze these notions in detail, explore their compatibility with
+notions of proportionality, and investigate the computational complexity of
+related algorithmic tasks.
+"
+22230,Performance Analysis of Dense Small Cell Networks with Generalized Fading,"  In this paper, we propose a unified framework to analyze the performance of
+dense small cell networks (SCNs) in terms of the coverage probability and the
+area spectral efficiency (ASE). In our analysis, we consider a practical path
+loss model that accounts for both non-line-of-sight (NLOS) and line-of-sight
+(LOS) transmissions. Furthermore, we adopt a generalized fading model, in which
+Rayleigh fading, Rician fading and Nakagami-m fading can be treated in a
+unified framework. The analytical results of the coverage probability and the
+ASE are derived, using a generalized stochastic geometry analysis. Different
+from existing work that does not differentiate NLOS and LOS transmissions, our
+results show that NLOS and LOS transmissions have a significant impact on the
+coverage probability and the ASE performance, particularly when the SCNs grow
+dense. Furthermore, our results establish for the first time that the
+performance of the SCNs can be divided into four regimes, according to the
+intensity (aka density) of BSs, where in each regime the performance is
+dominated by different factors.
+"
+22231,"On expansions of $(\mathbf{Z},+,0)$","  Call a (strictly increasing) sequence $R=(r_{n})$ of natural numbers regular
+if it satisfies the following condition: $r_{n+1}/r_{n}\to
+\theta\in\mathbf{R}^{>1}\cup\{\infty\}$ and, if $\theta$ is algebraic, then $R$
+satisfies a recurrence relation whose characteristic polynomial is the minimal
+polynomial of $\theta$. Our main result states that
+$\mathcal{Z}_{R}=(\mathbf{Z},+,0,R)$ is superstable whenever $R$ is a regular
+sequence. We provide two proofs of this result. One relies on a result of E.
+Casanovas and M. Ziegler and the other on a quantifier elimination result. Both
+proofs share an important ingredient: for a regular sequence, the set of
+solutions of homogeneous equations is either finite or controlled by (finitely
+many) recurrence relations. This property esembles the Mann property defined by
+L. van den Dries and A. Günayd{\i}n in their work on expansions of fields by
+subgroups. Inspired by their work and the proofs of our main result, we show
+that when $M$ is the domain of a multiplicative monoid of integers with the
+Mann property, then $\mathcal{Z}_{M}$ is also superstable.
+"
+22232,Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization,"  Parallel computing has played an important role in speeding up convex
+optimization methods for big data analytics and large-scale machine learning
+(ML). However, the scalability of these optimization methods is inhibited by
+the cost of communicating and synchronizing processors in a parallel setting.
+Iterative ML methods are particularly sensitive to communication cost since
+they often require communication every iteration. In this work, we extend
+well-known techniques from Communication-Avoiding Krylov subspace methods to
+first-order, block coordinate descent methods for Support Vector Machines and
+Proximal Least-Squares problems. Our Synchronization-Avoiding (SA) variants
+reduce the latency cost by a tunable factor of $s$ at the expense of a factor
+of $s$ increase in flops and bandwidth costs. We show that the SA-variants are
+numerically stable and can attain large speedups of up to $5.1\times$ on a Cray
+XC30 supercomputer.
+"
+22233,Dipole Incompatibility Related Artifacts in Quantitative Susceptibility Mapping,"  Artifacts in quantitative susceptibility mapping (QSM) are analyzed to
+establish an optimal design criterion for QSM inversion algorithms. The
+magnetic field data is decomposed into two parts, dipole compatible and
+incompatible parts. The dipole compatible part generates the desired
+non-streaking solution. The dipole incompatible part, including noise,
+discretization error, and anisotropy sources, generates artifacts defined by a
+wave propagator with the B0 field direction as the time axis. Granular noise
+causes streaking and contiguous error causes both streaking and shadow
+artifacts. As an example, truncation-based QSM algorithms are inherently prone
+to streaking artifacts because of the deviation of the dipole kernel during
+truncation. Through numerical phantom and imaging experiments, several QSM
+methods, including a Bayesian regularization method and the k-space truncation
+methods, were compared to show that the Bayesian approach with a structural
+prior provides advantages in terms of both image quality and quantitative
+measures.
+"
+22234,A sharp multiplier theorem for a perturbation-invariant class of Grushin operators of arbitrary step,"  We prove a multiplier theorem of Mihlin--Hörmander type for operators of
+the form $-\Delta_x - V(x) \Delta_y$ on $\mathbb{R}^{d_1}_x \times
+\mathbb{R}^{d_2}_y$, where $V(x) = \sum_{j=1}^{d_1} V_j(x_j)$, the $V_j$ are
+perturbations of the power law $t \mapsto |t|^{2\sigma}$, and $\sigma \in
+(1/2,\infty)$. The result is sharp whenever $d_1 \geq (1+\sigma) d_2$. The
+proof hinges on precise estimates for eigenvalues and eigenfunctions of
+one-dimensional Schrödinger operators, which are stable under perturbations
+of the potential.
+"
+22235,The ghost stairs stabilize to sharp symplectic embedding obstructions,"  In determining when a four-dimensional ellipsoid can be symplectically
+embedded into a ball, McDuff and Schlenk found an infinite sequence of ""ghost""
+obstructions that generate an infinite ""ghost staircase"" determined by the even
+index Fibonacci numbers. The ghost obstructions are not visible for the
+four-dimensional embedding problem because strictly stronger obstructions also
+exist. We show that in contrast, the embedding constraints associated to the
+ghost obstructions are sharp for the stabilized problem; moreover, the
+corresponding optimal embeddings are given by symplectic folding. The proof
+introduces several ideas of independent interest, namely: (i) an improved
+version of the index inequality familiar from the theory of embedded contact
+homology (ECH), (ii) new applications of relative intersection theory in the
+context of neck stretching analysis, (iii) a new approach to estimating the ECH
+grading of multiply covered elliptic orbits in terms of areas and continued
+fractions, and (iv) a new technique for understanding the ECH of ellipsoids by
+constructing explicit bijections between certain sets of lattice points.
+"
+22236,A Phase Change scenario for Galaxy Formation,"  The 5 independent correlations between the properties of galaxies observed
+outside big clusters must set such strong constraints on any theory of galaxy
+formation that it is hard to imagine any but the right one passing muster;
+certainly the standard model of Hierarchical Formation does not. Furthermore
+those global correlations imply that such galaxies must be remarkably simple,
+i.e. have virtually all of their variance attributable to a single Principal
+Component. That being so the correlations ought to give strong hints as to the
+process of formation. Two in particular: all galaxies have the same
+Luminosity-Density, while Luminosity and Dynamical Mass are everywhere tightly
+correlated, hint that formation took place at the epoch of Recombination.
+Halving of particle numbers then will set up strong pressure-forces which could
+fragment the medium and halt the expansion of neutral fragments, if they are
+not too large. We find that fragments between $10^{11} $ and $10^{6} $ solar
+masses (baryonic) will collapse gravitationally upon themselves, mostly
+flattening in one dimension, to form galaxies between 10 kpc. and 0.3 kpc. in
+size, and in two distinct classes. Those below $10^{8}$ solar masses will
+remain transparent and cool, eventually sub-fragmenting into stars with a warm,
+spheroidal distribution, while those above will become opaque and thus too hot
+to form stars until after they have radiated away most of their axial binding
+energy. They will then settle into thin discs with a low axial velocity
+dispersion. This simple scenario generates most of the properties of the
+galaxies but not flat rotation-curves. Finally we attempt to weigh up its pros
+and cons as a viable theory of galaxy formation and conclude that it is much
+superior to the Hierarchical model when it comes to fitting most observations.
+At the very least it deserves further critical evaluation.
+"
+22237,Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs,"  Training recurrent neural networks (RNNs) on long sequence tasks is plagued
+with difficulties arising from the exponential explosion or vanishing of
+signals as they propagate forward or backward through the network. Many
+techniques have been proposed to ameliorate these issues, including various
+algorithmic and architectural modifications. Two of the most successful RNN
+architectures, the LSTM and the GRU, do exhibit modest improvements over
+vanilla RNN cells, but they still suffer from instabilities when trained on
+very long sequences. In this work, we develop a mean field theory of signal
+propagation in LSTMs and GRUs that enables us to calculate the time scales for
+signal propagation as well as the spectral properties of the state-to-state
+Jacobians. By optimizing these quantities in terms of the initialization
+hyperparameters, we derive a novel initialization scheme that eliminates or
+reduces training instabilities. We demonstrate the efficacy of our
+initialization scheme on multiple sequence tasks, on which it enables
+successful training while a standard initialization either fails completely or
+is orders of magnitude slower. We also observe a beneficial effect on
+generalization performance using this new initialization.
+"
+22238,Security Code Smells in Android ICC,"  Android Inter-Component Communication (ICC) is complex, largely
+unconstrained, and hard for developers to understand. As a consequence, ICC is
+a common source of security vulnerability in Android apps. To promote secure
+programming practices, we have reviewed related research, and identified
+avoidable ICC vulnerabilities in Android-run devices and the security code
+smells that indicate their presence. We explain the vulnerabilities and their
+corresponding smells, and we discuss how they can be eliminated or mitigated
+during development. We present a lightweight static analysis tool on top of
+Android Lint that analyzes the code under development and provides just-in-time
+feedback within the IDE about the presence of such smells in the code.
+Moreover, with the help of this tool we study the prevalence of security code
+smells in more than 700 open-source apps, and manually inspect around 15% of
+the apps to assess the extent to which identifying such smells uncovers ICC
+security vulnerabilities.
+"
+22239,Normal approximation for stabilizing functionals,"  We establish presumably optimal rates of normal convergence with respect to
+the Kolmogorov distance for a large class of geometric functionals of marked
+Poisson and binomial point processes on general metric spaces. The rates are
+valid whenever the geometric functional is expressible as a sum of
+exponentially stabilizing score functions satisfying a moment condition. By
+incorporating stabilization methods into the Malliavin-Stein theory, we obtain
+rates of normal approximation for sums of stabilizing score functions which
+either improve upon existing rates or are the first of their kind. Our general
+rates hold for functionals of marked input on spaces more general than
+full-dimensional subsets of $\mathbb{R}^d$, including $m$-dimensional
+Riemannian manifolds, $m\leq d$. We use the general results to deduce improved
+and new rates of normal convergence for several functionals in stochastic
+geometry, including those whose variances re-scale as the volume or the surface
+area of an underlying set. In particular, we improve upon rates of normal
+convergence for the $k$-face and $i$th intrinsic volume functionals of the
+convex hull of Poisson and binomial random samples in a smooth convex body in
+dimension $d\geq 2$. We also provide improved rates of normal convergence for
+statistics of nearest neighbors graphs and high-dimensional data sets, the
+number of maximal points in a random sample, estimators of surface area and
+volume arising in set approximation via Voronoi tessellations, and clique
+counts in generalized random geometric graphs.
+"
+22240,Commutator width in the first Grigorchuk group,"  Let $G$ be the first Grigorchuk group. We show that the commutator width of
+$G$ is $2$: every element $g\in [G,G]$ is a product of two commutators, and
+also of six conjugates of $a$. Furthermore, we show that every finitely
+generated subgroup $H\leq G$ has finite commutator width, which however can be
+arbitrarily large, and that $G$ contains a subgroup of infinite commutator
+width. The proofs were assisted by the computer algebra system GAP.
+"
+22241,A General Approach for Cure Models in Survival Analysis,"  In survival analysis it often happens that some subjects under study do not
+experience the event of interest; they are considered to be `cured'. The
+population is thus a mixture of two subpopulations: the one of cured subjects,
+and the one of `susceptible' subjects. When covariates are present, a so-called
+mixture cure model can be used to model the conditional survival function of
+the population. It depends on two components: the probability of being cured
+and the conditional survival function of the susceptible subjects. In this
+paper we propose a novel approach to estimate a mixture cure model when the
+data are subject to random right censoring. We work with a parametric model for
+the cure proportion (like e.g. a logistic model), while the conditional
+survival function of the uncured subjects is unspecified. The approach is based
+on an inversion which allows to write the survival function as a function of
+the distribution of the observable random variables. This leads to a very
+general class of models, which allows a flexible and rich modeling of the
+conditional survival function. We show the identifiability of the proposed
+model, as well as the weak consistency and the asymptotic normality of the
+model parameters. We also consider in more detail the case where kernel
+estimators are used for the nonparametric part of the model. The new estimators
+are compared with the estimators from a Cox mixture cure model via finite
+sample simulations. Finally, we apply the new model and estimation procedure on
+two medical data sets.
+"
+22242,Some Algebraic Properties Of Linear Synchronous Cellular Automata,"  Relation between global transition function and local transition function of
+a homogeneous one dimensional cellular automaton (CA) is investigated for some
+standard transition functions. It could be shown that left shift and right
+shift CA are invertible. The final result of this paper states that the set of
+all left and right shift CA together with the identity CA on the same set forms
+an abelian group.
+"
+22243,An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions,"  Increasing technological sophistication and widespread use of smartphones and
+wearable devices provide opportunities for innovative and highly personalized
+health interventions. A Just-In-Time Adaptive Intervention (JITAI) uses
+real-time data collection and communication capabilities of modern mobile
+devices to deliver interventions in real-time that are adapted to the
+in-the-moment needs of the user. The lack of methodological guidance in
+constructing data-based JITAIs remains a hurdle in advancing JITAI research
+despite the increasing popularity of JITAIs among clinical scientists. In this
+article, we make a first attempt to bridge this methodological gap by
+formulating the task of tailoring interventions in real-time as a contextual
+bandit problem. Interpretability requirements in the domain of mobile health
+lead us to formulate the problem differently from existing formulations
+intended for web applications such as ad or news article placement. Under the
+assumption of linear reward function, we choose the reward function (the
+""critic"") parameterization separately from a lower dimensional parameterization
+of stochastic policies (the ""actor""). We provide an online actor-critic
+algorithm that guides the construction and refinement of a JITAI. Asymptotic
+properties of the actor-critic algorithm are developed and backed up by
+numerical experiments. Additional numerical experiments are conducted to test
+the robustness of the algorithm when idealized assumptions used in the analysis
+of contextual bandit algorithm are breached.
+"
+22244,1-skeletons of the spanning tree problems with additional constraints,"  We consider the polyhedral properties of two spanning tree problems with
+additional constraints. In the first problem, it is required to find a tree
+with a minimum sum of edge weights among all spanning trees with the number of
+leaves less or equal a given value. In the second problem, an additional
+constraint is the assumption that the degree of all vertices of the spanning
+tree does not exceed a given value. The decision versions of both problems are
+NP-complete.
+We consider the polytopes of these problems and their 1-skeletons. We prove
+that in both cases it is a NP-complete problem to determine whether the
+vertices of 1-skeleton are adjacent. Although it is possible to obtain a
+superpolynomial lower bounds on the clique numbers of these graphs. These
+values characterize the time complexity in a broad class of algorithms based on
+linear comparisons. The results indicate a fundamental difference in
+combinatorial and geometric properties between the considered problems and the
+classical minimum spanning tree problem.
+"
+22245,Towards Automatic Generation of Short Summaries of Commits,"  Committing to a version control system means submitting a software change to
+the system. Each commit can have a message to describe the submission. Several
+approaches have been proposed to automatically generate the content of such
+messages. However, the quality of the automatically generated messages falls
+far short of what humans write. In studying the differences between
+auto-generated and human-written messages, we found that 82% of the
+human-written messages have only one sentence, while the automatically
+generated messages often have multiple lines. Furthermore, we found that the
+commit messages often begin with a verb followed by an direct object. This
+finding inspired us to use a ""verb+object"" format in this paper to generate
+short commit summaries. We split the approach into two parts: verb generation
+and object generation. As our first try, we trained a classifier to classify a
+diff to a verb. We are seeking feedback from the community before we continue
+to work on generating direct objects for the commits.
+"
+22246,Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach,"  We propose a statistical framework for clustering multiple time series that
+exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups that
+each comprise time series with similar dynamics. Our motivation comes from
+neuroscience where an important problem is to identify, within a large assembly
+of neurons, sub-groups that respond similarly to a stimulus or contingency. In
+the neural setting, conditioned on cluster membership and the parameters
+governing the dynamics, time series within a cluster are assumed independent
+and generated according to a nonlinear binomial state-space model. We derive a
+Metropolis-within-Gibbs algorithm for full Bayesian inference that alternates
+between sampling of cluster membership and sampling of parameters of interest.
+The Metropolis step is a PMMH iteration that requires an unbiased, low variance
+estimate of the likelihood function of a nonlinear state-space model. We
+leverage recent results on controlled sequential Monte Carlo to estimate
+likelihood functions more efficiently compared to the bootstrap particle
+filter. We apply the framework to time series acquired from the prefrontal
+cortex of mice in an experiment designed to characterize the neural
+underpinnings of fear.
+"
+22247,Observation of forbidden phonons and dark excitons by resonance Raman scattering in few-layer WS$_2$,"  The optical properties of the two-dimensional (2D) crystals are dominated by
+tightly bound electron-hole pairs (excitons) and lattice vibration modes
+(phonons). The exciton-phonon interaction is fundamentally important to
+understand the optical properties of 2D materials and thus help develop
+emerging 2D crystal based optoelectronic devices. Here, we presented the
+excitonic resonant Raman scattering (RRS) spectra of few-layer WS$_2$ excited
+by 11 lasers lines covered all of A, B and C exciton transition energies at
+different sample temperatures from 4 to 300 K. As a result, we are not only
+able to probe the forbidden phonon modes unobserved in ordinary Raman
+scattering, but also can determine the bright and dark state fine structures of
+1s A exciton. In particular, we also observed the quantum interference between
+low-energy discrete phonon and exciton continuum under resonant excitation. Our
+works pave a way to understand the exciton-phonon coupling and many-body
+effects in 2D materials.
+"
+22248,Adaptive Restart of the Optimized Gradient Method for Convex Optimization,"  First-order methods with momentum such as Nesterov's fast gradient method are
+very useful for convex optimization problems, but can exhibit undesirable
+oscillations yielding slow convergence rates for some applications. An adaptive
+restarting scheme can improve the convergence rate of the fast gradient method,
+when the parameter of a strongly convex cost function is unknown or when the
+iterates of the algorithm enter a locally strongly convex region. Recently, we
+introduced the optimized gradient method, a first-order algorithm that has an
+inexpensive per-iteration computational cost similar to that of the fast
+gradient method, yet has a worst-case cost function rate that is twice faster
+than that of the fast gradient method and that is optimal for large-dimensional
+smooth convex problems. Building upon the success of accelerating the fast
+gradient method using adaptive restart, this paper investigates similar
+heuristic acceleration of the optimized gradient method. We first derive a new
+first-order method that resembles the optimized gradient method for strongly
+convex quadratic problems with known function parameters, yielding a linear
+convergence rate that is faster than that of the analogous version of the fast
+gradient method. We then provide a heuristic analysis and numerical experiments
+that illustrate that adaptive restart can accelerate the convergence of the
+optimized gradient method. Numerical results also illustrate that adaptive
+restart is helpful for a proximal version of the optimized gradient method for
+nonsmooth composite convex functions.
+"
+22249,Few-body model approach to the lowest bound S-state of non-symmetric exotic few-body systems,"  Lowest bound S-state energy of Coulomb three-body systems ($N^{Z+}\mu^-e^-$)
+having a positively charged nucleus of charge number Z ($N^{Z+}$), a negatively
+charged muon ($\mu^-$) and an electron ($e^-$), is investigated in the
+framework of hyperspherical harmonics expansion method. A Yukawa type Coulomb
+potential with an adjustable screening parameter ($\lambda$) is chosen for the
+2-body subsystems. In the resulting Schrödinger equation (SE), the three-body
+relative wave function is expanded in the complete set of hyperspherical
+harmonics (HH). Thereafter use of orthonormality of HH in the SE, led to a set
+of coupled differential equations which are solved numerically to get the
+energy (E) of the systems investigated.
+"
+22250,On surface completion and image inpainting by biharmonic functions: Numerical aspects,"  Numerical experiments with smooth surface extension and image inpainting
+using harmonic and biharmonic functions are carried out. The boundary data used
+for constructing biharmonic functions are the values of the Laplacian and
+normal derivatives of the functions on the boundary. Finite difference schemes
+for solving these harmonic functions are discussed in detail.
+"
+22251,Recursive formulas for the motivic Milnor basis,"  We give recursive formulas for the generating elements in the Milnor basis of
+the mod 2 motivic Steenrod algebra.
+"
+22252,Density Fluctuations in the Solar Wind Driven by Alfvén Wave Parametric Decay,"  Measurements and simulations of inertial compressive turbulence in the solar
+wind are characterized by anti-correlated magnetic fluctuations parallel to the
+mean field and density structures. This signature has been interpreted as
+observational evidence for non-propagating pressure balanced structures (PBS),
+kinetic ion acoustic waves, as well as the MHD slow-mode. Given the high
+damping rates of parallel propagating compressive fluctuations, their ubiquity
+in satellite observations is surprising, and suggestive of a local driving
+process. One possible candidate for the generation of compressive fluctuations
+in the solar wind is Alfvén wave parametric instability. Here we test the
+parametric decay process as a source of compressive waves in the solar wind by
+comparing the collisionless damping rates of compressive fluctuations with the
+growth rates of the parametric decay instability daughter waves. Our results
+suggest that generation of compressive waves through parametric decay is
+overdamped at 1 AU, but that the presence of slow-mode like density
+fluctuations is correlated with the parametric decay of Alfvén waves.
+"
+22253,Realization of Quantum Anomalous Hall Effect in Graphene from \textit{n}-\textit{p} Codoping Induced Stable Atomic-Adsorption,"  Using first-principles calculation methods, we study the possibility of
+realizing quantum anomalous Hall effect in graphene from stable
+3\textit{d}-atomic adsorption via charge-compensated \textit{n}-\textit{p}
+codoping scheme. As concrete examples, we show that long-range ferromagnetism
+can be established by codoping 3\textit{d} transition metal and boron atoms,
+but only the Ni codopants can open up a global bulk gap to harbour the quantum
+anomalous Hall effect. Our estimated ferromagnetic Curie transition temperature
+can reach over 10 Kelvin for various codoping concentrations.
+"
+22254,Low-energy Fock-space localization for attractive hard-core particles in disorder,"  We study a one-dimensional quantum system with an arbitrary number of
+hard-core particles on the lattice, which are subject to a deterministic
+attractive interaction as well as a random potential. Our choice of interaction
+is suggested by the spectral analysis of the XXZ quantum spin chain. The main
+result concerns a version of high-disorder Fock-space localization expressed
+here in the configuration space of hard-core particles. The proof relies on an
+energetically motivated Combes-Thomas estimate and an effective one-particle
+analysis. As an application, we show the exponential decay of the two-point
+function in the infinite system uniformly in the particle number.
+"
+22255,What Stands-in for a Missing Tool? A Prototypical Grounded Knowledge-based Approach to Tool Substitution,"  When a robot is operating in a dynamic environment, it cannot be assumed that
+a tool required to solve a given task will always be available. In case of a
+missing tool, an ideal response would be to find a substitute to complete the
+task. In this paper, we present a proof of concept of a grounded
+knowledge-based approach to tool substitution. In order to validate the
+suitability of a substitute, we conducted experiments involving 22 substitution
+scenarios. The substitutes computed by the proposed approach were validated on
+the basis of the experts' choices for each scenario. Our evaluation showed, in
+20 out of 22 scenarios (91%), the approach identified the same substitutes as
+experts.
+"
+22256,A comparison of C-shaped and brush armature performance,"  The most important part of a railgun launch package is the armature where the
+electromagnetic force is generated leading to the acceleration of the launch
+package. In case of metal armatures, the most commonly used armature types are
+the C-shape and the multi-fiber brush technology. However, rarely both armature
+types were systematically compared under similar experimental conditions. That
+is why we constructed launch packages based on the C-shaped and brush armature
+technology with comparable armature and payload mass. With these launch
+packages a series of experiments were performed in an energy range between 0.8
+MJ and 1.13 MJ corresponding to a speed range between 950 m/s and 1400 m/s. The
+results of the experiments were then analyzed qualitatively and quantitatively.
+On the one hand our results show that the total losses are higher for the
+C-shaped armature technology than for the brush aramture technology. On the
+other hand our results show that launch packages based on the C-shaped
+technology convert better electrical energy into kinetic energy.
+"
+22257,A QM/MM equation-of-motion coupled-cluster approach for predicting semiconductor color-center structure and emission frequencies,"  Valence excitation spectra are computed for all deep-center silicon-vacancy
+defect types in 3C, 4H, and 6H silicon carbide (SiC) and comparisons are made
+with literature photoluminescence measurements. Nuclear geometries surrounding
+the defect centers are optimized within a Gaussian basis-set framework using
+many-body perturbation theory or density functional theory (DFT) methods, with
+computational expenses minimized by a QM/MM technique called SIMOMM. Vertical
+excitation energies are subsequently obtained by applying excitation-energy,
+electron-attached, and ionized equation-of-motion coupled-cluster (EOMCC)
+methods, where appropriate, as well as time-dependent (TD) DFT, to small models
+including only a few atoms adjacent to the defect center. We consider the
+relative quality of various EOMCC and TD-DFT methods for (i) energy-ordering
+potential ground states differing incrementally in charge and multiplicity,
+(ii) accurately reproducing experimentally measured photoluminescence peaks,
+and (iii) energy-ordering defects of different types occurring within a given
+polytype. The extensibility of this approach to transition-metal defects is
+also tested by applying it to silicon-substitutional chromium defects in SiC
+and comparing with measurements. It is demonstrated that, when used in
+conjunction with SIMOMM-optimized geometries, EOMCC-based methods can provide a
+reliable prediction of the ground-state charge and multiplicity, while also
+giving a quantitative description of the photoluminescence spectra, accurate to
+within 0.1 eV of measurement in all cases considered.
+"
+22258,Fredholm conditions on non-compact manifolds: theory and examples,"  We give explicit Fredholm conditions for classes of pseudodifferential
+operators on suitable singular and non-compact spaces. In particular, we
+include a ""user's guide"" to Fredholm conditions on particular classes of
+manifolds including asymptotically hyperbolic manifolds, asymptotically
+Euclidean (or conic) manifolds, and manifolds with poly-cylindrical ends. The
+reader interested in applications should be able read right away the results
+related to those examples, beginning with Section 5. Our general, theoretical
+results are that an operator adapted to the geometry is Fredholm if, and only
+if, it is elliptic and all its limit operators, in a sense to be made precise,
+are invertible. Central to our theoretical results is the concept of a Fredholm
+groupoid, which is the class of groupoids for which this characterization of
+the Fredholm condition is valid. We use the notions of exhaustive and strictly
+spectral families of representations to obtain a general characterization of
+Fredholm groupoids. In particular, we introduce the class of the so-called
+groupoids with Exel's property as the groupoids for which the regular
+representations are exhaustive. We show that the class of ""stratified
+submersion groupoids"" has Exel's property, where stratified submersion
+groupoids are defined by glueing fibered pull-backs of bundles of Lie groups.
+We prove that a stratified submersion groupoid is Fredholm whenever its
+isotropy groups are amenable. Many groupoids, and hence many pseudodifferential
+operators appearing in practice, fit into this framework. This fact is explored
+to yield Fredholm conditions not only in the above mentioned classes, but also
+on manifolds that are obtained by desingularization or by blow-up of singular
+sets.
+"
+22259,Overcoming catastrophic forgetting with hard attention to the task,"  Catastrophic forgetting occurs when a neural network loses the information
+learned in a previous task after training on subsequent tasks. This problem
+remains a hurdle for artificial intelligence systems with sequential learning
+capabilities. In this paper, we propose a task-based hard attention mechanism
+that preserves previous tasks' information without affecting the current task's
+learning. A hard attention mask is learned concurrently to every task, through
+stochastic gradient descent, and previous masks are exploited to condition such
+learning. We show that the proposed mechanism is effective for reducing
+catastrophic forgetting, cutting current rates by 45 to 80%. We also show that
+it is robust to different hyperparameter choices, and that it offers a number
+of monitoring capabilities. The approach features the possibility to control
+both the stability and compactness of the learned knowledge, which we believe
+makes it also attractive for online learning or network compression
+applications.
+"
+22260,Distributed Synthesis for Parameterized Temporal Logics,"  We consider the synthesis of distributed implementations for specifications
+in parameterized temporal logics such as PROMPT-LTL, which extends LTL by
+temporal operators equipped with parameters that bound their scope. For single
+process synthesis it is well-established that such parametric extensions do not
+increase worst-case complexities. For synchronous distributed systems we show
+that, despite being more powerful, the realizability problem for PROMPT-LTL is
+not harder than its LTL counterpart. For asynchronous systems we have to
+express scheduling assumptions and therefore consider an assume-guarantee
+synthesis problem. As asynchronous distributed synthesis is already undecidable
+for LTL, we give a semi-decision procedure for the PROMPT-LTL assume-guarantee
+synthesis problem based on bounded synthesis. Finally, we show that our results
+extend to the stronger logics PLTL and PLDL.
+"
+22261,The Performance of Largest Caliper Matching: A Monte Carlo Simulation Approach,"  The paper presents an investigation of estimating treatment effect using
+different matching methods. The study proposed a new method which is
+computationally efficient and convenient in implication-'largest caliper
+matching' and compared the performance with other five popular matching methods
+by simulation. The bias, empirical standard deviation and the mean square error
+of the estimates in the simulation are checked under different treatment
+prevalence and different distributions of covariates. A Monte Carlo simulation
+study and a real data example are employed to measure the performance of these
+methods. It is shown that matched samples improve estimation of the population
+treatment effect in a wide range of settings. It reduces the bias if the data
+contains the selection on observables and treatment imbalances. Also, findings
+about the relative performance of the different matching methods are provided
+to help practitioners determine which method should be used under certain
+situations.
+"
+22262,Learning how to explain neural networks: PatternNet and PatternAttribution,"  DeConvNet, Guided BackProp, LRP, were invented to better understand deep
+neural networks. We show that these methods do not produce the theoretically
+correct explanation for a linear model. Yet they are used on multi-layer
+networks with millions of parameters. This is a cause for concern since linear
+models are simple neural networks. We argue that explanation methods for neural
+nets should work reliably in the limit of simplicity, the linear models. Based
+on our analysis of linear models we propose a generalization that yields two
+explanation techniques (PatternNet and PatternAttribution) that are
+theoretically sound for linear models and produce improved explanations for
+deep networks.
+"
+22263,"On $W^{1,p}$-regularity estimate for a class of generalized Stokes systems and its applications to the Navier-Stokes equations","  This paper establishes global weighted Calderón-Zygmund type regularity
+estimates for weak solutions of a class of generalized Stokes systems in
+divergence form. The focus of the paper is on the case that the coefficients in
+the divergence-form Stokes operator consist of symmetric and skew-symmetric
+parts, which are both discontinuous. Moreover, the skew-symmetric part is not
+required to be bounded and therefore it could be singular. Sufficient
+conditions on the coefficients are provided to ensure the global weighted
+$W^{1,p}$-regularity estimates for weak solutions of the systems. As a direct
+application, we show that our $W^{1,p}$-regularity results give some criteria
+in critical spaces for the global regularity of weak Leray-Hopf solutions of
+the Navier-Stokes system of equation
+"
+22264,Microfluidic study of effects of flow velocity and nutrient concentration on biofilm accumulation and adhesive strength in a microchannel,"  Biofilm accumulation in the porous media can cause plugging and change many
+physical properties of porous media. Targeted bioplugging may have significant
+applications for industrial processes. A deeper understanding of the relative
+influences of hydrodynamic conditions including flow velocity and nutrient
+concentration, on biofilm growth and detachment is necessary to plan and
+analyze bioplugging experiments and field trials. The experimental results by
+means of microscopic imaging over a T-shape microchannel show that increase in
+fluid velocity could facilitate biofilm growth, but that above a velocity
+threshold, biofilm detachment and inhibition of biofilm formation due to high
+shear stress were observed. High nutrient concentration prompts the biofilm
+growth, but was accompanied by a relatively weak adhesive strength. This letter
+provides an overview of biofilm development in a hydrodynamic environment for
+better predicting and modelling the bioplugging associated with porous system
+in petroleum industry, hydrogeology, and water purification.
+"
+22265,Concurrent interactive visualization and handling of molecular structures over the Internet in web browsers,"  This preprint presents a web app (essentially a web page-based program) with
+which two or more users (peers) can view and handle 3D molecular structures in
+a concurrent, interactive way through their web browsers. This means they can
+share orientation and zoom level, commands and other operations in almost real
+time over the Internet through standard web pages. This web app, open source
+and built with the open source components JSmol for molecular visualization and
+Peer.js for WebRTC connection, provides a practical tool for online
+collaboration and teaching at a distance. More broadly, it illustrates the
+strong integrability of technologies for client-side web programming, and paves
+the way for similar apps for concurrent work in other disciplines. Web app is
+available at:
+this http URL
+"
+22266,A new notion of majorization with applications to the comparison of extreme order statistics,"  In this paper, we use a new partial order, called the f-majorization order.
+The new order includes as special cases the majorization , the reciprocal
+majorization and the p-larger orders. We provide a comprehensive account of the
+mathematical properties of the f-majorization order and give applications of
+this order in the context of stochastic comparison for extreme order statistics
+of independent samples following the Frechet distribution and scale model. We
+discuss stochastic comparisons of series systems with independent heterogeneous
+exponentiated scale components in terms of the usual stochastic order and the
+hazard rate order. We also derive new result on the usual stochastic order for
+the largest order statistics of samples having exponentiated scale marginals
+and Archimedean copula structure.
+"
+22267,Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging,"  The ability to monitor respiratory rate is extremely important for medical
+treatment, healthcare and fitness sectors. In many situations, mobile methods,
+which allow users to undertake every day activities, are required. However,
+current monitoring systems can be obtrusive, requiring users to wear
+respiration belts or nasal probes. Recent advances in thermographic systems
+have shrunk their size, weight and cost, to the point where it is possible to
+create smart-phone based respiration rate monitoring devices that are not
+affected by lighting conditions. However, mobile thermal imaging is challenged
+in scenes with high thermal dynamic ranges. This challenge is further amplified
+by general problems such as motion artifacts and low spatial resolution,
+leading to unreliable breathing signals. In this paper, we propose a novel and
+robust approach for respiration tracking which compensates for the negative
+effects of variations in the ambient temperature and motion artifacts and can
+accurately extract breathing rates in highly dynamic thermal scenes. It has
+three main contributions. The first is a novel Optimal Quantization technique
+which adaptively constructs a color mapping of absolute temperature to improve
+segmentation, classification and tracking. The second is the Thermal Gradient
+Flow method that computes thermal gradient magnitude maps to enhance accuracy
+of the nostril region tracking. Finally, we introduce the Thermal Voxel method
+to increase the reliability of the captured respiration signals compared to the
+traditional averaging method. We demonstrate the extreme robustness of our
+system to track the nostril-region and measure the respiratory rate in high
+dynamic range scenes.
+"
+22268,Compact Factorization of Matrices Using Generalized Round-Rank,"  Matrix factorization is a well-studied task in machine learning for compactly
+representing large, noisy data. In our approach, instead of using the
+traditional concept of matrix rank, we define a new notion of link-rank based
+on a non-linear link function used within factorization. In particular, by
+applying the round function on a factorization to obtain ordinal-valued
+matrices, we introduce generalized round-rank (GRR). We show that not only are
+there many full-rank matrices that are low GRR, but further, that these
+matrices cannot be approximated well by low-rank linear factorization. We
+provide uniqueness conditions of this formulation and provide gradient
+descent-based algorithms. Finally, we present experiments on real-world
+datasets to demonstrate that the GRR-based factorization is significantly more
+accurate than linear factorization, while converging faster and using lower
+rank representations.
+"
+22269,Recurrent Estimation of Distributions,"  This paper presents the recurrent estimation of distributions (RED) for
+modeling real-valued data in a semiparametric fashion. RED models make two
+novel uses of recurrent neural networks (RNNs) for density estimation of
+general real-valued data. First, RNNs are used to transform input covariates
+into a latent space to better capture conditional dependencies in inputs.
+After, an RNN is used to compute the conditional distributions of the latent
+covariates. The resulting model is efficient to train, compute, and sample
+from, whilst producing normalized pdfs. The effectiveness of RED is shown via
+several real-world data experiments. Our results show that RED models achieve a
+lower held-out negative log-likelihood than other neural network approaches
+across multiple dataset sizes and dimensionalities. Further context of the
+efficacy of RED is provided by considering anomaly detection tasks, where we
+also observe better performance over alternative models.
+"
+22270,Decoding Superposed LoRa Signals,"  Long-range low-power wireless communications, such as LoRa, are used in many
+IoT and environmental monitoring applications. They typically increase the
+communication range to several kilometers, at the cost of reducing the bitrate
+to a few bits per seconds. Collisions further reduce the performance of these
+communications. In this paper, we propose two algorithms to decode colliding
+signals: one algorithm requires the transmitters to be slightly desynchronized,
+and the other requires the transmitters to be synchronized. To do so, we use
+the timing information to match the correct symbols to the correct
+transmitters. We show that our algorithms are able to significantly improve the
+overall throughput of LoRa.
+"
+22271,Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning,"  This work presents a novel objective function for the unsupervised training
+of neural network sentence encoders. It exploits signals from paragraph-level
+discourse coherence to train these models to understand text. Our objective is
+purely discriminative, allowing us to train models many times faster than was
+possible under prior methods, and it yields models which perform well in
+extrinsic evaluations.
+"
+22272,Non-Gaussian Error Distributions of Galactic Rotation Speed Measurements,"  We construct the error distributions for the galactic rotation speed
+($\Theta_0$) using 137 data points from measurements compiled in De Grijs et
+al. (arXiv:1709.02501), with all observations normalized to the galactocentric
+distance of 8.3 kpc. We then checked (using the same procedures as in works by
+Ratra et al) if the errors constructed using the weighted mean and the median
+as the estimate, obey Gaussian statistics. We find using both these estimates
+that they have much wider tails than a Gaussian distribution. We also tried to
+fit the data to three other distributions: Cauchy, double-exponential, and
+Students-t. The best fit is obtained using the Students-$t$ distribution for
+$n=2$ using the median value as the central estimate, corresponding to a
+$p$-value of 0.1. We also calculate the median value of $\Theta_0$ using all
+the data as well as using the median of each set of measurements based on the
+tracer population used. Because of the non-gaussianity of the residuals, we
+point out that the subgroup median value, given by $\Theta_{med}=219.65$ km/sec
+should be used as the central estimate for $\Theta_0$.
+"
+22273,Counting Curves on a Weierstrass Model,"  Let $X\to \mathbb P^2$ be the elliptic Calabi-Yau threefold given by a
+general Weierstrass equation. We answer the enumerative question of how many
+discrete rational curves lie over lines in the base, proving part of a
+conjecture by Huang, Katz, and Klemm. The key inputs are a modularity theorem
+of Kudla and Millson for locally symmetric spaces of orthogonal type and the
+deformation theory of $A_n$ singularities.
+"
+22274,Mixed-criticality Scheduling with Dynamic Redistribution of Shared Cache,"  The design of mixed-criticality systems often involvespainful tradeoffs
+between safety guarantees and performance.However, the use of more detailed
+architectural modelsin the design and analysis of scheduling arrangements for
+mixedcriticalitysystems can provide greater confidence in the analysis,but also
+opportunities for better performance. Motivated by thisview, we propose an
+extension of Vestal 19s model for mixedcriticalitymulticore systems that (i)
+accounts for the per-taskpartitioning of the last-level cache and (ii) supports
+the dynamicreassignment, for better schedulability, of cache portions
+initiallyreserved for lower-criticality tasks to the higher-criticalitytasks,
+when the system switches to high-criticality mode. Tothis model, we apply
+partitioned EDF scheduling with Ekbergand Yi 19s deadline-scaling technique.
+Our schedulability analysisand scalefactor calculation is cognisant of the
+cache resourcesassigned to each task, by using WCET estimates that take
+intoaccount these resources. It is hence able to leverage the
+dynamicreconfiguration of the cache partitioning, at mode change, forbetter
+performance, in terms of provable schedulability. We alsopropose heuristics for
+partitioning the cache in low- and highcriticalitymode, that promote
+schedulability. Our experimentswith synthetic task sets, indicate tangible
+improvements inschedulability compared to a baseline cache-aware
+arrangementwhere there is no redistribution of cache resources from low-
+tohigh-criticality tasks in the event of a mode change.
+"
+22275,On Distinguishing Interstellar Objects Like `Oumuamua From Products of Solar System Scattering,"  Schneider (2018) explored the possibility that 'Oumuamua is a Solar System
+object, and concluded that if it is, it must have been scattered by ""another,
+yet unknown planet."" I provide an extremely conservative upper limit on
+post-scattering velocities in the Solar System to show that 'Oumuamua is moving
+far to quickly to be the result of any hypothetical single scattering event
+between any bound Solar System objects within 21 au (a distance within which
+our understanding of objects capable of scattering 'Oumuamua is presumably
+complete).
+"
+22276,Large-scale pedestrian flow experiments under high-density conditions,"  Despite the vast amount of studies on pedestrian flow, the data concerning
+high densities are still very inadequate. We organize one large-scale
+pedestrian flow experiment on a ring corridor. With 278 participants, the
+density as high as 9 m^(-2) is reached. In the uni-directional flow, four
+different states are observed, including the free flow, congested state,
+over-congested state and hyper-congested state. The features of the
+hyper-congested state are similar to the ""crowd turbulence"" reported in the
+empirical data of Helbing et al., and the transition between the stopped state
+and the moving state can be found. The flow rates in the over-congested state
+are nearly constant, due to the downstream propagation of pedestrian clusters.
+In the bi-directional flow, three different types of lane formations are
+observed in the experiment: (1) three lanes are directly formed ; (2) two lanes
+are directly formed; (3) firstly three lanes are formed, and then they transit
+into two lanes. After the lane formation, some interesting phenomena have been
+observed, including the inhomogeneous distribution of pedestrians across the
+lanes, and the formation and dissipation of localized crowd. Our study is
+expected to help for better understanding and modeling the dynamics of high
+density pedestrian flow.
+"
+22277,The energy landscape of a simple neural network,"  We explore the energy landscape of a simple neural network. In particular, we
+expand upon previous work demonstrating that the empirical complexity of fitted
+neural networks is vastly less than a naive parameter count would suggest and
+that this implicit regularization is actually beneficial for generalization
+from fitted models.
+"
+22278,Semi-Implicit Variational Inference,"  Semi-implicit variational inference (SIVI) is introduced to expand the
+commonly used analytic variational distribution family, by mixing the
+variational parameter with a flexible distribution. This mixing distribution
+can assume any density function, explicit or not, as long as independent random
+samples can be generated via reparameterization. Not only does SIVI expand the
+variational family to incorporate highly flexible variational distributions,
+including implicit ones that have no analytic density functions, but also
+sandwiches the evidence lower bound (ELBO) between a lower bound and an upper
+bound, and further derives an asymptotically exact surrogate ELBO that is
+amenable to optimization via stochastic gradient ascent. With a substantially
+expanded variational family and a novel optimization algorithm, SIVI is shown
+to closely match the accuracy of MCMC in inferring the posterior in a variety
+of Bayesian inference tasks.
+"
+22279,Classical molecular dynamics simulations of fusion and fragmentation in fullerene-fullerene collisions,"  We present the results of classical molecular dynamics simulations of
+collision-induced fusion and fragmentation of C$_{60}$ fullerenes, performed by
+means of the MBN Explorer software package. The simulations provide information
+on structural differences of the fused compound depending on kinematics of the
+collision process. The analysis of fragmentation dynamics at different initial
+conditions shows that the size distributions of produced molecular fragments
+are peaked for dimers, which is in agreement with a well-established mechanism
+of C$_{60}$ fragmentation via preferential C$_2$ emission. Atomic trajectories
+of the colliding particles are analyzed and different fragmentation patterns
+are observed and discussed. On the basis of the performed simulations,
+characteristic time of C$_2$ emission is estimated as a function of collision
+energy. The results are compared with experimental time-of-flight distributions
+of molecular fragments and with earlier theoretical studies. Considering the
+widely explored case study of C$_{60}$--C$_{60}$ collisions, we demonstrate
+broad capabilities of the MBN Explorer software, which can be utilized for
+studying collisions of a broad variety of nanoscale and biomolecular systems by
+means of classical molecular dynamics.
+"
+22280,On the global and local magnetic fields in flare stars. Study of YZ CMi and OT Ser,"  Global magnetic fields of flare stars can evolve rapidly, in time scale of
+hundreds or dozens of days. We believe, that such changes result from rapid
+superposition of local magnetic fields generated by differential rotation of
+those stars. We discuss possible mechanisms of generation and dissipation of
+local and global magnetic fields in sample flare stars OT Ser and YZ CMi. We
+propose mechanism of magnetic braking of these stars, in which differential
+rotation generates local magnetic fields, and eventually energy accumulated in
+local fields is radiated away by flares. We obtained estimates of the
+rotational energy and the energy of the global magnetic field of OT Ser and YZ
+CMi. We also show that the energy of the local magnetic fields dissipated
+during superflare of YZ CMi on 9 February 2008 (UT 20:22:00) did not influence
+the global magnetic field of this star.
+"
+22281,Machine learning for neural decoding,"  Despite rapid advances in machine learning tools, the majority of neural
+decoding approaches still use traditional methods. Improving the performance of
+neural decoding algorithms allows us to better understand the information
+contained in a neural population, and can help advance engineering applications
+such as brain machine interfaces. Here, we apply modern machine learning
+techniques, including neural networks and gradient boosting, to decode from
+spiking activity in 1) motor cortex, 2) somatosensory cortex, and 3)
+hippocampus. We compare the predictive ability of these modern methods with
+traditional decoding methods such as Wiener and Kalman filters. Modern methods,
+in particular neural networks and ensembles, significantly outperformed the
+traditional approaches. For instance, for all of the three brain areas, an LSTM
+decoder explained over 40% of the unexplained variance from a Wiener filter.
+These results suggest that modern machine learning techniques should become the
+standard methodology for neural decoding. We provide a tutorial and code to
+facilitate wider implementation of these methods.
+"
+22282,Transfer Learning and Organic Computing for Autonomous Vehicles,"  Autonomous Vehicles(AV) are one of the brightest promises of the future which
+would help cut down fatalities and improve travel time while working in
+harmony. Autonomous vehicles will face with challenging situations and
+experiences not seen before. These experiences should be converted to knowledge
+and help the vehicle prepare better in the future. Online Transfer Learning
+will help transferring prior knowledge to a new task and also keep the
+knowledge updated as the task evolves. This paper presents the different
+methods of transfer learning, online transfer learning and organic computing
+that could be adapted to the domain of autonomous vehicles.
+"
+22283,DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks,"  Deep neural networks have become widely used, obtaining remarkable results in
+domains such as computer vision, speech recognition, natural language
+processing, audio recognition, social network filtering, machine translation,
+and bio-informatics, where they have produced results comparable to human
+experts. However, these networks can be easily fooled by adversarial
+perturbations: minimal changes to correctly-classified inputs, that cause the
+network to mis-classify them. This phenomenon represents a concern for both
+safety and security, but it is currently unclear how to measure a network's
+robustness against such perturbations. Existing techniques are limited to
+checking robustness around a few individual input points, providing only very
+limited guarantees. We propose a novel approach for automatically identifying
+safe regions of the input space, within which the network is robust against
+adversarial perturbations. The approach is data-guided, relying on clustering
+to identify well-defined geometric regions as candidate safe regions. We then
+utilize verification techniques to confirm that these regions are safe or to
+provide counter-examples showing that they are not safe. We also introduce the
+notion of targeted robustness which, for a given target label and region,
+ensures that a NN does not map any input in the region to the target label. We
+evaluated our technique on the MNIST dataset and on a neural network
+implementation of a controller for the next-generation Airborne Collision
+Avoidance System for unmanned aircraft (ACAS Xu). For these networks, our
+approach identified multiple regions which were completely safe as well as some
+which were only safe for specific labels. It also discovered several
+adversarial perturbations of interest.
+"
+22284,TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning,"  Combining deep model-free reinforcement learning with on-line planning is a
+promising approach to building on the successes of deep RL. On-line planning
+with look-ahead trees has proven successful in environments where transition
+models are known a priori. However, in complex environments where transition
+models need to be learned from data, the deficiencies of learned models have
+limited their utility for planning. To address these challenges, we propose
+TreeQN, a differentiable, recursive, tree-structured model that serves as a
+drop-in replacement for any value function network in deep RL with discrete
+actions. TreeQN dynamically constructs a tree by recursively applying a
+transition model in a learned abstract state space and then aggregating
+predicted rewards and state-values using a tree backup to estimate Q-values. We
+also propose ATreeC, an actor-critic variant that augments TreeQN with a
+softmax layer to form a stochastic policy network. Both approaches are trained
+end-to-end, such that the learned model is optimised for its actual use in the
+tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a
+box-pushing task, as well as n-step DQN and value prediction networks (Oh et
+al. 2017) on multiple Atari games. Furthermore, we present ablation studies
+that demonstrate the effect of different auxiliary losses on learning
+transition models.
+"
+22285,Quantification of spin accumulation causing spin-orbit torque in Pt/Co/Ta stack,"  Spin accumulation induced by spin-orbit coupling is experimentally quantified
+in stack with in-plane magnetic anisotropy via the contribution of spin
+accumulation to Hall resistances. Using a biasing direct current the spin
+accumulation within the structure can be tuned, enabling quantification.
+Quantification shows the spin accumulation can be more than ten percentage of
+local magnetization, when the electric current is 1E11 A/m*m. The spin
+accumulation is dependent of the thickness of Ta layer, the trend agrees with
+that of spin Hall angle indicating the capability of Ta and Pt in generating
+spins.
+"
+22286,Challenges of Multi-Factor Authentication for Securing Advanced IoT (A-IoT) Applications,"  The unprecedented proliferation of smart devices together with novel
+communication, computing, and control technologies have paved the way for the
+Advanced Internet of Things~(A-IoT). This development involves new categories
+of capable devices, such as high-end wearables, smart vehicles, and consumer
+drones aiming to enable efficient and collaborative utilization within the
+Smart City paradigm. While massive deployments of these objects may enrich
+people's lives, unauthorized access to the said equipment is potentially
+dangerous. Hence, highly-secure human authentication mechanisms have to be
+designed. At the same time, human beings desire comfortable interaction with
+their owned devices on a daily basis, thus demanding the authentication
+procedures to be seamless and user-friendly, mindful of the contemporary urban
+dynamics. In response to these unique challenges, this work advocates for the
+adoption of multi-factor authentication for A-IoT, such that multiple
+heterogeneous methods - both well-established and emerging - are combined
+intelligently to grant or deny access reliably. We thus discuss the pros and
+cons of various solutions as well as introduce tools to combine the
+authentication factors, with an emphasis on challenging Smart City
+environments. We finally outline the open questions to shape future research
+efforts in this emerging field.
+"
+22287,Student Success Prediction in MOOCs,"  Predictive models of student success in Massive Open Online Courses (MOOCs)
+are a critical component of effective content personalization and adaptive
+interventions. In this article we review the state of the art in predictive
+models of student success in MOOCs and present a categorization of MOOC
+research according to the predictors (features), prediction (outcomes), and
+underlying theoretical model. We critically survey work across each category,
+providing data on the raw data source, feature engineering, statistical model,
+evaluation method, prediction architecture, and other aspects of these
+experiments. Such a review is particularly useful given the rapid expansion of
+predictive modeling research in MOOCs since the emergence of major MOOC
+platforms in 2012. This survey reveals several key methodological gaps, which
+include extensive filtering of experimental subpopulations, ineffective student
+model evaluation, and the use of experimental data which would be unavailable
+for real-world student success prediction and intervention, which is the
+ultimate goal of such models. Finally, we highlight opportunities for future
+research, which include temporal modeling, research bridging predictive and
+explanatory student models, work which contributes to learning theory, and
+evaluating long-term learner success in MOOCs.
+"
+22288,Unsupervised Basis Function Adaptation for Reinforcement Learning,"  When using reinforcement learning (RL) algorithms to evaluate a policy it is
+common, given a large state space, to introduce some form of approximation
+architecture for the value function (VF). The exact form of this architecture
+can have a significant effect on the accuracy of the VF estimate, however, and
+determining a suitable approximation architecture can often be a highly complex
+task. Consequently there is a large amount of interest in the potential for
+allowing RL algorithms to adaptively generate approximation architectures.
+We investigate a method of adapting approximation architectures which uses
+feedback regarding the frequency with which an agent has visited certain states
+to guide which areas of the state space to approximate with greater detail.
+This method is ""unsupervised"" in the sense that it makes no direct reference to
+reward or the VF estimate. We introduce an algorithm based upon this idea which
+adapts a state aggregation approximation architecture on-line.
+A common method of scoring a VF estimate is to weight the squared Bellman
+error of each state-action by the probability of that state-action occurring.
+Adopting this scoring method, and assuming $S$ states, we demonstrate
+theoretically that - provided (1) the number of cells $X$ in the state
+aggregation architecture is of order $\sqrt{S}\log_2{S}\ln{S}$ or greater, (2)
+the policy and transition function are close to deterministic, and (3) the
+prior for the transition function is uniformly distributed - our algorithm,
+used in conjunction with a suitable RL algorithm, can guarantee a score which
+is arbitrarily close to zero as $S$ becomes large. It is able to do this
+despite having only $O(X \log_2S)$ space complexity and negligible time
+complexity. The results take advantage of certain properties of the stationary
+distributions of Markov chains.
+"
+22289,Infinitesimal finiteness obstructions,"  Does a space enjoying good finiteness properties admit an algebraic model
+with commensurable finiteness properties? In this note, we provide a rational
+homotopy obstruction for this to happen. As an application, we show that the
+maximal metabelian quotient of a very large, finitely generated group is not
+finitely presented. Using the theory of 1-minimal models, we also show that a
+finitely generated group $\pi$ admits a connected 1-model with
+finite-dimensional degree 1 piece if and only if the Malcev Lie algebra
+$\mathfrak{m}(\pi)$ is the lower central series completion of a finitely
+presented Lie algebra.
+"
+22290,Regularization approaches for support vector machines with applications to biomedical data,"  The support vector machine (SVM) is a widely used machine learning tool for
+classification based on statistical learning theory. Given a set of training
+data, the SVM finds a hyperplane that separates two different classes of data
+points by the largest distance. While the standard form of SVM uses L2-norm
+regularization, other regularization approaches are particularly attractive for
+biomedical datasets where, for example, sparsity and interpretability of the
+classifier's coefficient values are highly desired features. Therefore, in this
+paper we consider different types of regularization approaches for SVMs, and
+explore them in both synthetic and real biomedical datasets.
+"
+22291,Dimension functions for spherical fibrations,"  Given a spherical fibration $\xi$ over the classifying space $BG$ of a finite
+group we define a dimension function for the $m-$fold fiber join of $\xi$ where
+$m$ is some large positive integer. We show that the dimension functions
+satisfy the Borel-Smith conditions when $m$ is large enough. As an application
+we prove that there exists no spherical fibration over the classifying space of
+$\text{Qd}(p)= (\mathbb{Z}/p)^2\rtimes\text{SL}_2(\mathbb{Z}/p)$ with
+$p-$effective Euler class, generalizing the result of Özgün Ünlü about
+group actions on finite complexes homotopy equivalent to a sphere. We have been
+informed that this result will also appear in a future paper as a corollary of
+a previously announced program on homotopy group actions due to Jesper Grodal.
+"
+22292,A generalization of the Lomnitz logarithmic creep law via Hadamard fractional calculus,"  We present a new approach based on linear integro-differential operators with
+logarithmic kernel related to the Hadamard fractional calculus in order to
+generalize, by a parameter $\nu \in (0,1]$, the logarithmic creep law known in
+rheology as Lomnitz law (obtained for $\nu=1$). We derive the constitutive
+stress-strain relation of this generalized model in a form that couples memory
+effects and time-varying viscosity. Then, based on the hereditary theory of
+linear viscoelasticity, we also derive the corresponding relaxation function by
+solving numerically a Volterra integral equation of the second kind. So doing
+we provide a full characterization of the new model both in creep and in
+relaxation representation, where the slow varying functions of logarithmic type
+play a fundamental role as required in processes of ultra slow kinetics.
+"
+22293,Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors,"  Thompson sampling has impressive empirical performance for many multi-armed
+bandit problems. But current algorithms for Thompson sampling only work for the
+case of conjugate priors since these algorithms require to infer the posterior,
+which is often computationally intractable when the prior is not conjugate. In
+this paper, we propose a novel algorithm for Thompson sampling which only
+requires to draw samples from a tractable distribution, so our algorithm is
+efficient even when the prior is non-conjugate. To do this, we reformulate
+Thompson sampling as an optimization problem via the Gumbel-Max trick. After
+that we construct a set of random variables and our goal is to identify the one
+with highest mean. Finally, we solve it with techniques in best arm
+identification.
+"
+22294,Nonlinear electric and thermoelectric Andreev transport through a hybrid quantum dot coupled to ferromagnetic and superconducting leads,"  We discuss the nonlinear Andreev current of an interacting quantum dot
+coupled to spin-polarized and superconducting reservoirs when voltage and
+temperature biases are applied across the nanostructure. Due to the
+particle-hole symmetry introduced by the superconducting (S) lead, the subgap
+spin current vanishes identically. Nevertheless, the Andreev charge current
+depends on the degree of polarization in the ferromagnetic (F) contact since
+the shift of electrostatic internal potential of the conductor depends on spin
+orientation of the charge carrier. This spin-dependent potential shift
+characterizes nonlinear responses in our device. We show how the subgap current
+versus the bias voltage or temperature difference depends on the lead
+polarization in two cases, namely (i) S-dominant case, when the
+dot-superconductor tunneling rate ($\Gamma_R$) is much higher than the
+ferromagnet-dot tunnel coupling ($\Gamma_L$), and (ii) F-dominant case, when
+$\Gamma_L\gg \Gamma_R$. For the ferromagnetic dominant case the spin-dependent
+potential shows a nonmonotonic behavior as the dot level is detuned. Thus the
+subgap current can also exhibit interesting behaviors such as current
+rectification and the maximization of thermocurrents with smaller thermal
+biases when the lead polarization and the quantum dot level are adjusted.
+"
+22295,Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games,"  Esports has emerged as a popular genre for players as well as spectators,
+supporting a global entertainment industry. Esports analytics has evolved to
+address the requirement for data-driven feedback, and is focused on
+cyber-athlete evaluation, strategy and prediction. Towards the latter, previous
+work has used match data from a variety of player ranks from hobbyist to
+professional players. However, professional players have been shown to behave
+differently than lower ranked players. Given the comparatively limited supply
+of professional data, a key question is thus whether mixed-rank match datasets
+can be used to create data-driven models which predict winners in professional
+matches and provide a simple in-game statistic for viewers and broadcasters.
+Here we show that, although there is a slightly reduced accuracy, mixed-rank
+datasets can be used to predict the outcome of professional matches, with
+suitably optimized configurations.
+"
+22296,Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms,"  A multiple instance dictionary learning approach, Dictionary Learning using
+Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat
+heart rate estimation and to characterize heartbeat signatures from
+ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI
+estimates a ""heartbeat concept"" that represents an individual's personal
+ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection
+and heartbeat characterization as a multiple instance learning problem to
+address the uncertainty inherent in aligning BCG signals with ground truth
+during training. Experimental results show that the estimated heartbeat concept
+found by DL-FUMI is an effective heartbeat prototype and achieves superior
+performance over comparison algorithms.
+"
+22297,Experimental certification of millions of genuinely entangled atoms in a solid,"  Quantum theory predicts that entanglement can also persist in macroscopic
+physical systems, albeit difficulties to demonstrate it experimentally remain.
+Recently, significant progress has been achieved and genuine entanglement
+between up to 2900 atoms was reported. Here we demonstrate 16 million genuinely
+entangled atoms in a solid-state quantum memory prepared by the heralded
+absorption of a single photon. We develop an entanglement witness for
+quantifying the number of genuinely entangled particles based on the collective
+effect of directed emission combined with the nonclassical nature of the
+emitted light. The method is applicable to a wide range of physical systems and
+is effective even in situations with significant losses. Our results clarify
+the role of multipartite entanglement in ensemble-based quantum memories as a
+necessary prerequisite to achieve a high single-photon process fidelity crucial
+for future quantum networks. On a more fundamental level, our results reveal
+the robustness of certain classes of multipartite entangled states, contrary
+to, e.g., Schrödinger-cat states, and that the depth of entanglement can be
+experimentally certified at unprecedented scales.
+"
+22298,Observation of Zero Resistance in As-Electrodeposited FeSe,"  Superconducting FeSe films were electrochemically deposited on
+rolling-assisted biaxially textured substrate (RABiTS) tape. We observed zero
+resistivity in the as-electrodeposited FeSe film prepared on the RABiTS when
+the appropriate voltage was applied while it was dipped into the solution. When
+the RABiTS tape was dipped in the solution without applying voltage, a thin Se
+film was deposited on the substrate. The compositional ratio of the FeSe film
+got closer to the stoichiometric ratio with decreasing the dipping time before
+applying voltage.
+"
+22299,Cyber-Physical War Gaming,"  This paper presents general strategies for cyber war gaming of Cyber-Physical
+Systems (CPSs) that are used for cyber security research at the U.S. Army
+Research Laboratory (ARL). Since Supervisory Control and Data Acquisition
+(SCADA) and other CPSs are operational systems, it is difficult or impossible
+to perform security experiments on actual systems. The authors describe how
+table-top strategy sessions and realistic, live CPS war games are conducted at
+ARL. They also discuss how the recorded actions of the war game activity can be
+used to test and validate cyber-defence models, such as game-theoretic security
+models.
+"
+22300,Combating Human Trafficking with Deep Multimodal Models,"  Human trafficking is a global epidemic affecting millions of people across
+the planet. Sex trafficking, the dominant form of human trafficking, has seen a
+significant rise mostly due to the abundance of escort websites, where human
+traffickers can openly advertise among at-will escort advertisements. In this
+paper, we take a major step in the automatic detection of advertisements
+suspected to pertain to human trafficking. We present a novel dataset called
+Trafficking-10k, with more than 10,000 advertisements annotated for this task.
+The dataset contains two sources of information per advertisement: text and
+images. For the accurate detection of trafficking advertisements, we designed
+and trained a deep multimodal model called the Human Trafficking Deep Network
+(HTDN).
+"
+22301,Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes,"  Often in machine learning, data are collected as a combination of multiple
+conditions, e.g., the voice recordings of multiple persons, each labeled with
+an ID. How could we build a model that captures the latent information related
+to these conditions and generalize to a new one with few data? We present a new
+model called Latent Variable Multiple Output Gaussian Processes (LVMOGP) and
+that allows to jointly model multiple conditions for regression and generalize
+to a new condition with a few data points at test time. LVMOGP infers the
+posteriors of Gaussian processes together with a latent space representing the
+information about different conditions. We derive an efficient variational
+inference method for LVMOGP, of which the computational complexity is as low as
+sparse Gaussian processes. We show that LVMOGP significantly outperforms
+related Gaussian process methods on various tasks with both synthetic and real
+data.
+"
+22302,On the origins of spectral broadening of incoherent waves:Catastrophic process of coherence degradation,"  We revisit the mechanisms underlying the process of spectral broadening of
+incoherent optical waves propagating in nonlinear media on the basis of
+nonequilibrium thermodynamic considerations. A simple analysis reveals that a
+prerequisite for the existence of a significant spectral broadening of the
+waves is that the linear part of the energy (Hamiltonian) has different
+contributions of opposite signs. It turns out that, at variance with the
+expected soliton turbulence scenario, an increase of the amount of disorder
+(incoherence) in the system does not require the generation of a coherent
+soliton structure. We illustrate the idea by considering the propagation of two
+wave components in an optical fiber with opposite dispersion coefficients. A
+wave turbulence approach of the problem reveals that the increase of kinetic
+energy in one component is offset by the negative reduction in the other
+component, so that the waves exhibit, as a general rule, a virtually unlimited
+spectral broadening. More precisely, a self-similar solution of the kinetic
+equations reveals that the spectra of the incoherent waves tend to relax toward
+a homogeneous distribution in the wake of a front that propagates in frequency
+space with a decelerating velocity. We discuss this catastrophic process of
+spectral broadening in the light of different important phenomena, in
+particular supercontinuum generation, soliton turbulence, wave condensation,
+and the runaway motion of mechanical systems composed of positive and negative
+masses.
+"
+22303,Data-driven discovery of Koopman eigenfunctions for control,"  Data-driven transformations that reformulate nonlinear systems in a linear
+framework have the potential to enable the prediction, estimation, and control
+of strongly nonlinear dynamics using linear systems theory. The Koopman
+operator has emerged as a principled linear embedding of nonlinear dynamics,
+and its eigenfunctions establish intrinsic coordinates along which the dynamics
+behave linearly. In this work, we demonstrate a data-driven control
+architecture, termed Koopman Reduced Order Nonlinear Identification and Control
+(KRONIC), that utilizes Koopman eigenfunctions to manipulate nonlinear systems
+using linear systems theory. We approximate these eigenfunctions with
+data-driven regression and power series expansions, based on the partial
+differential equation governing the infinitesimal generator of the Koopman
+operator. Although previous regression-based methods may identify spurious
+dynamics, we show that lightly damped eigenfunctions may be faithfully
+extracted using sparse regression. These lightly damped eigenfunctions are
+particularly relevant for control, as they correspond to nearly conserved
+quantities that are associated with persistent dynamics, such as the
+Hamiltonian. We derive the form of control in these intrinsic eigenfunction
+coordinates and design nonlinear controllers using standard linear control
+theory. KRONIC is then demonstrated on a number of relevant examples, including
+1) a nonlinear system with a known linear embedding, 2) a variety of
+Hamiltonian systems, and 3) a high-dimensional double-gyre model for ocean
+mixing.
+"
+22304,Sparse Regularization in Marketing and Economics,"  Sparse alpha-norm regularization has many data-rich applications in Marketing
+and Economics. Alpha-norm, in contrast to lasso and ridge regularization, jumps
+to a sparse solution. This feature is attractive for ultra high-dimensional
+problems that occur in demand estimation and forecasting. The alpha-norm
+objective is nonconvex and requires coordinate descent and proximal operators
+to find the sparse solution. We study a typical marketing demand forecasting
+problem, grocery store sales for salty snacks, that has many dummy variables as
+controls. The key predictors of demand include price, equivalized volume,
+promotion, flavor, scent, and brand effects. By comparing with many commonly
+used machine learning methods, alpha-norm regularization achieves its goal of
+providing accurate out-of-sample estimates for the promotion lift effects.
+Finally, we conclude with directions for future research.
+"
+22305,Recruiting from the network: discovering Twitter users who can help combat Zika epidemics,"  Tropical diseases like \textit{Chikungunya} and \textit{Zika} have come to
+prominence in recent years as the cause of serious, long-lasting,
+population-wide health problems. In large countries like Brasil, traditional
+disease prevention programs led by health authorities have not been
+particularly effective. We explore the hypothesis that monitoring and analysis
+of social media content streams may effectively complement such efforts.
+Specifically, we aim to identify selected members of the public who are likely
+to be sensitive to virus combat initiatives that are organised in local
+communities. Focusing on Twitter and on the topic of Zika, our approach
+involves (i) training a classifier to select topic-relevant tweets from the
+Twitter feed, and (ii) discovering the top users who are actively posting
+relevant content about the topic. We may then recommend these users as the
+prime candidates for direct engagement within their community. In this short
+paper we describe our analytical approach and prototype architecture, discuss
+the challenges of dealing with noisy and sparse signal, and present encouraging
+preliminary results.
+"
+22306,Scalable Privacy-Compliant Virality Prediction on Twitter,"  The digital town hall of Twitter becomes a preferred medium of communication
+for individuals and organizations across the globe. Some of them reach
+audiences of millions, while others struggle to get noticed. Given the impact
+of social media, the question remains more relevant than ever: how to model the
+dynamics of attention in Twitter. Researchers around the world turn to machine
+learning to predict the most influential tweets and authors, navigating the
+volume, velocity, and variety of social big data, with many compromises. In
+this paper, we revisit content popularity prediction on Twitter. We argue that
+strict alignment of data acquisition, storage and analysis algorithms is
+necessary to avoid the common trade-offs between scalability, accuracy and
+privacy compliance. We propose a new framework for the rapid acquisition of
+large-scale datasets, high accuracy supervisory signal and multilanguage
+sentiment prediction while respecting every privacy request applicable. We then
+apply a novel gradient boosting framework to achieve state-of-the-art results
+in virality ranking, already before including tweet's visual or propagation
+features. Our Gradient Boosted Regression Tree is the first to offer
+explainable, strong ranking performance on benchmark datasets. Since the
+analysis focused on features available early, the model is immediately
+applicable to incoming tweets in 18 languages.
+"
+22307,Hourly-Similarity Based Solar Forecasting Using Multi-Model Machine Learning Blending,"  With the increasing penetration of solar power into power systems,
+forecasting becomes critical in power system operations. In this paper, an
+hourly-similarity (HS) based method is developed for 1-hour-ahead (1HA) global
+horizontal irradiance (GHI) forecasting. This developed method utilizes diurnal
+patterns, statistical distinctions between different hours, and hourly
+similarities in solar data to improve the forecasting accuracy. The HS-based
+method is built by training multiple two-layer multi-model forecasting
+framework (MMFF) models independently with the same-hour subsets. The final
+optimal model is a combination of MMFF models with the best-performed blending
+algorithm at every hour. At the forecasting stage, the most suitable model is
+selected to perform the forecasting subtask of a certain hour. The HS-based
+method is validated by 1-year data with six solar features collected by the
+National Renewable Energy Laboratory (NREL). Results show that the HS-based
+method outperforms the non-HS (all-in-one) method significantly with the same
+MMFF architecture, wherein the optimal HS- based method outperforms the best
+all-in-one method by 10.94% and 7.74% based on the normalized mean absolute
+error and normalized root mean square error, respectively.
+"
+22308,Is the quartic Diophantine equation $A^4+hB^4=C^4+hD^4$ solvable for any integer $h$?,"  The Diophantine equation $A^4+hB^4=C^4+hD^4$, where $h$ is a fixed arbitrary
+positive integer, has been investigated by some authors. Currently, by computer
+search, the integer solutions of this equation are known for all positive
+integer values of $h \le 5000$ and $A, B, C, D \le 100000$, except for some
+numbers, while a solution of this Diophantine equation is not known for
+arbitrary positive integer values of $h$. Gerardin and Piezas found solutions
+of this equation when $h$ is given by polynomials of degrees $5$ and $2$
+respectively. Also Choudhry presented some new solutions of this equation when
+$h$ is given by polynomials of degrees $2$, $3$, and $4$.
+In this paper, by using the elliptic curves theory, we study this Diophantine
+equation, where $h$ is a fixed arbitrary rational number. We work out some
+solutions of the Diophantine equation for certain values of $h$, in particular
+for the values which has not already been found a solution in the range where
+$A, B, C, D \le 100000$ by computer search. Also we present some new parametric
+solutions for the Diophantine equation when $h$ is given by polynomials of
+degrees $3$, $4$. Finally We present two conjectures such that if one of them
+is correct, then we may solve the above Diophantine equation for arbitrary
+rational number $h$.
+"
+22309,Multiple Changepoint Estimation in High-Dimensional Gaussian Graphical Models,"  We consider the consistency properties of a regularised estimator for the
+simultaneous identification of both changepoints and graphical dependency
+structure in multivariate time-series. Traditionally, estimation of Gaussian
+Graphical Models (GGM) is performed in an i.i.d setting. More recently, such
+models have been extended to allow for changes in the distribution, but only
+where changepoints are known a-priori. In this work, we study the Group-Fused
+Graphical Lasso (GFGL) which penalises partial-correlations with an L1 penalty
+while simultaneously inducing block-wise smoothness over time to detect
+multiple changepoints. We present a proof of consistency for the estimator,
+both in terms of changepoints, and the structure of the graphical models in
+each segment.
+"
+22310,PVC Polyhedra,"  We describe how to construct a dodecahedron, tetrahedron, cube, and
+octahedron out of pvc pipes using standard fittings.
+"
+22311,Quadratic genetic modifications: a streamlined route to cosmological simulations with controlled merger history,"  Recent work has studied the interplay between a galaxy's history and its
+observable properties using ""genetically modified"" cosmological zoom
+simulations. The approach systematically generates alternative histories for a
+halo, while keeping its cosmological environment fixed. Applications to date
+altered linear properties of the initial conditions such as the mean
+overdensity of specified regions; we extend the formulation to include
+quadratic features such as local variance, which determines the overall
+importance of smooth accretion relative to mergers in a galaxy's history. We
+introduce an efficient algorithm for this new class of modification and
+demonstrate its ability to control the variance of a region in a
+one-dimensional toy model. Outcomes of this work are two-fold: (i) a
+clarification of the formulation of genetic modifications and (ii) a proof of
+concept for quadratic modifications leading the way to a forthcoming
+implementation in cosmological simulations.
+"
+22312,"A Framework for Validating Models of Evasion Attacks on Machine Learning, with Application to PDF Malware Detection","  Machine learning (ML) techniques are increasingly common in security
+applications, such as malware and intrusion detection. However, there is
+increasing evidence that machine learning models are susceptible to evasion
+attacks, in which an adversary makes small changes to the input (such as
+malware) in order to cause erroneous predictions (for example, to avoid being
+detected). Evasion attacks on ML fall into two broad categories: 1) those which
+generate actual malicious instances and demonstrate both evasion of ML and
+efficacy of attack (we call these problem space attacks), and 2) attacks which
+directly manipulate features used by ML, abstracting efficacy of attack into a
+mathematical cost function (we call these feature space attacks). Central to
+our inquiry is the following fundamental question: are feature space models of
+attacks useful proxies for real attacks? In the process of answering this
+question, we make two major contributions: 1) a general methodology for
+evaluating validity of mathematical models of ML evasion attacks, and 2) an
+application of this methodology as a systematic hypothesis-driven evaluation of
+feature space evasion attacks on ML-based PDF malware detectors. Specific to
+our case study, we find that a) feature space evasion models are in general not
+adequate in representing real attacks, b) such models can be significantly
+improved by identifying conserved features (features that are invariant in real
+attacks) whenever these exist, and c) ML hardened using the improved feature
+space models remains robust to alternative attacks, in contrast to ML hardened
+using a very powerful class of problem space attacks, which does not.
+"
+22313,Signature of topological phase transition in the RKKY interaction of silicene,"  Silicene offers an ideal platform for exploring the phase transition due to
+strong spin-orbit interaction and its unique structure with strong tunability.
+With applied electric field and circularly polarized light, siliccene is
+predicted to exhibit rich phases. We propose that these intricate phase
+transitions can be detected by measuring the bulk Ruderman-Kittel-Kasuya-Yosida
+(RKKY) interaction. We have in detail analyzed the dependence of RKKY
+interaction on phase parameters for different impurity configurations along
+zigzag direction. Importantly, we present an interesting comparison between
+different terms of RKKY interaction with phase diagram. It is found that the
+in-plane and out-of-plane terms can exhibit the local extreme value or change
+of sign at the phase critical point and remarkable difference in magnitude for
+different phase regions. Consequently, the magnetic measurement provides
+unambiguous signatures to identify various types of phase transition
+simultaneously, which can be carried out with present technique.
+"
+22314,Hermitian and skew hermitian forms over local rings,"  We study the classification problem of possibly degenerate hermitian and skew
+hermitian bilinear forms over local rings where 2 is a unit.
+"
+22315,A survey and a new selection criterion for statistical home range estimation,"  The home range of a specific animal describes the geographic area where this
+individual spends most of the time while carrying out its usual activities
+(eating, resting, reproduction, ...). Although a well-established definition of
+this concept is lacking, there is a variety of home range estimators. The first
+objective of this work is to review and categorize the statistical
+methodologies proposed in the literature to approximate the home range of an
+animal, based on a sample of observed locations. The second aim is to address
+the open question of choosing the ""best"" home range from a collection of them
+based on the same sample. We introduce a numerical index, based on a
+penalization criterion, to rank the estimated home ranges. The key idea is to
+balance the excess area covered by the estimator (with respect to the original
+sample) and a shape descriptor measuring the over-adjustment of the home range
+to the data. To our knowledge, apart from computing the home range area, our
+ranking procedure is the first one which is both applicable to real data and to
+any type of home range estimator. Further, the optimization of the selection
+index provides in fact a way to select the smoothing parameter for the kernel
+home range estimator. For clarity of exposition, we have applied all the
+estimation procedures and our selection proposal to a set of real locations of
+a Mongolian wolf using R as the statistical software. As a byproduct, this
+review contains a thorough revision of the implementation of home range
+estimators in the R language.
+"
+22316,Reflection $K$-matrices for a nineteen vertex model with $U_{q}[\mathrm{osp}\left(2|2\right)^{\left(2\right)}]$ symmetry,"  We derive the solutions of the boundary Yang-Baxter equation associated with
+a supersymmetric nineteen vertex model constructed from the three-dimensional
+representation of the twisted quantum affine Lie superalgebra
+$U_{q}[\mathrm{osp}\left(2|2\right)^{\left(2\right)}]\simeq
+U_{q}[C\left(2\right)^{\left(2\right)}]$. We found three classes of solutions.
+The type I solution is characterized by three boundary free-parameters and all
+elements of the corresponding reflection $K$-matrix are different from zero. In
+the type II solution, the reflection $K$-matrix is even (every element of the
+$K$-matrix with an odd parity is null) and it has only one boundary
+free-parameter. Finally, the type III solution corresponds to a diagonal
+reflection $K$-matrix with two boundary free-parameters.
+"
+22317,Characterizing Diseases and disorders in Gay Users' tweets,"  A lack of information exists about the health issues of lesbian, gay,
+bisexual, transgender, and queer (LGBTQ) people who are often excluded from
+national demographic assessments, health studies, and clinical trials. As a
+result, medical experts and researchers lack a holistic understanding of the
+health disparities facing these populations. Fortunately, publicly available
+social media data such as Twitter data can be utilized to support the decisions
+of public health policy makers and managers with respect to LGBTQ people. This
+research employs a computational approach to collect tweets from gay users on
+health-related topics and model these topics. To determine the nature of
+health-related information shared by men who have sex with men on Twitter, we
+collected thousands of tweets from 177 active users. We sampled these tweets
+using a framework that can be applied to other LGBTQ sub-populations in future
+research. We found 11 diseases in 7 categories based on ICD 10 that are in line
+with the published studies and official reports.
+"
+22318,Travelling waves of density for a fourth-gradient model of fluids,"  In mean-field theory, the non-local state of fluid molecules can be taken
+into account using a statistical method. The molecular model combined with a
+density expansion in Taylor series of the fourth order yields an internal
+energy value relevant to the fourth-gradient model, and the equation of
+isother-mal motions takes then density's spatial derivatives into account for
+waves travelling in both liquid and vapour phases. At equilibrium, the equation
+of the density profile across interfaces is more precise than the Cahn and
+Hilliard equation, and near the fluid's critical-point, the density profile
+verifies an Extended Fisher-Kolmogorov equation, allowing kinks, which
+converges towards the Cahn-Hillard equation when approaching the critical
+point. Nonetheless, we also get pulse waves oscillating and generating critical
+opalescence.
+"
+22319,Solving the Conjugacy Decision Problem via Machine Learning,"  Machine learning and pattern recognition techniques have been successfully
+applied to algorithmic problems in free groups. In this paper, we seek to
+extend these techniques to finitely presented non-free groups, with a
+particular emphasis on polycyclic and metabelian groups that are of interest to
+non-commutative cryptography.
+As a prototypical example, we utilize supervised learning methods to
+construct classifiers that can solve the conjugacy decision problem, i.e.,
+determine whether or not a pair of elements from a specified group are
+conjugate. The accuracies of classifiers created using decision trees, random
+forests, and N-tuple neural network models are evaluated for several non-free
+groups. The very high accuracy of these classifiers suggests an underlying
+mathematical relationship with respect to conjugacy in the tested groups.
+"
+22320,Optimal Scaling of the MALA algorithm with Irreversible Proposals for Gaussian targets,"  It is well known in many settings that reversible Langevin diffusions in
+confining potentials converge to equilibrium exponentially fast. Adding
+irreversible perturbations to the drift of a Langevin diffusion that maintain
+the same invariant measure accelerates its convergence to stationarity. Many
+existing works thus advocate the use of such non-reversible dynamics for
+sampling. When implementing Markov Chain Monte Carlo algorithms (MCMC) using
+time discretisations of such Stochastic Differential Equations (SDEs), one can
+append the discretization with the usual Metropolis-Hastings accept-reject step
+and this is often done in practice because the accept--reject step eliminates
+bias. On the other hand, such a step makes the resulting chain reversible. It
+is not known whether adding the accept-reject step preserves the faster mixing
+properties of the non-reversible dynamics. In this paper, we address this gap
+between theory and practice by analyzing the optimal scaling of MCMC algorithms
+constructed from proposal moves that are time-step Euler discretisations of an
+irreversible SDE, for high dimensional Gaussian target measures. We call the
+resulting algorithm the ipMALA, in comparison to the classical MALA algorithm
+(here ip is for irreversible proposal). In order to quantify how the cost of
+the algorithm scales with the dimension $N$, we prove invariance principles for
+the appropriately rescaled chain. In contrast to the usual MALA algorithm, we
+show that there could be two regimes asymptotically: (i) a diffusive regime, as
+in the MALA algorithm and (ii) a ""fluid"" regime where the limit is an ordinary
+differential equation. We provide concrete examples where the limit is a
+diffusion, as in the standard MALA, but with provably higher limiting
+acceptance probabilities. Numerical results are also given corroborating the
+theory.
+"
+22321,NetGAN: Generating Graphs via Random Walks,"  We propose NetGAN - the first implicit generative model for graphs able to
+mimic real-world networks. We pose the problem of graph generation as learning
+the distribution of biased random walks over the input graph. The proposed
+model is based on a stochastic neural network that generates discrete output
+samples and is trained using the Wasserstein GAN objective. NetGAN is able to
+produce graphs that exhibit well-known network patterns without explicitly
+specifying them in the model definition. At the same time, our model exhibits
+strong generalization properties, as highlighted by its competitive link
+prediction performance, despite not being trained specifically for this task.
+Being the first approach to combine both of these desirable properties, NetGAN
+opens exciting avenues for further research.
+"
+22322,Deep Learning Classification in Asteroseismology,"  In the power spectra of oscillating red giants, there are visually distinct
+features defining stars ascending the red giant branch from those that have
+commenced helium core burning. We train a one-dimensional convolutional neural
+network by supervised learning to automatically learn these visual features
+from images of folded oscillation spectra. By training and testing on
+\textit{Kepler} red giants, we achieve an accuracy of up to 99\% in separating
+helium-burning red giants from those ascending the red giant branch. The
+convolutional neural network additionally shows capability in accurately
+predicting the evolutionary states of 5379 previously unclassified
+\textit{Kepler} red giants, by which we now have greatly increased the number
+of classified stars.
+"
+22323,On the matrix form of second-order linear difference equations,"  Results of research of possibility of transformation of a difference equation
+into a system of the first-order difference equation are presented. In contrast
+to the method used previously, an unknown grid function is split into two new
+auxiliary functions, which have definite properties. Several examples show that
+proposed approach can be useful in solving different physical problems.
+"
+22324,Accounting for hidden common causes when inferring cause and effect from observational data,"  Identifying causal relationships from observation data is difficult, in large
+part, due to the presence of hidden common causes. In some cases, where just
+the right patterns of conditional independence and dependence lie in the
+data---for example, Y-structures---it is possible to identify cause and effect.
+In other cases, the analyst deliberately makes an uncertain assumption that
+hidden common causes are absent, and infers putative causal relationships to be
+tested in a randomized trial. Here, we consider a third approach, where there
+are sufficient clues in the data such that hidden common causes can be
+inferred.
+"
+22325,Electricity Theft Detection using Machine Learning,"  Non-technical losses (NTL) in electric power grids arise through electricity
+theft, broken electric meters or billing errors. They can harm the power
+supplier as well as the whole economy of a country through losses of up to 40%
+of the total power distribution. For NTL detection, researchers use artificial
+intelligence to analyse data. This work is about improving the extraction of
+more meaningful features from a data set. With these features, the prediction
+quality will increase.
+"
+22326,DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework,"  As one of most fascinating machine learning techniques, deep neural network
+(DNN) has demonstrated excellent performance in various intelligent tasks such
+as image classification. DNN achieves such performance, to a large extent, by
+performing expensive training over huge volumes of training data. To reduce the
+data storage and transfer overhead in smart resource-limited Internet-of-Thing
+(IoT) systems, effective data compression is a ""must-have"" feature before
+transferring real-time produced dataset for training or classification. While
+there have been many well-known image compression approaches (such as JPEG), we
+for the first time find that a human-visual based image compression approach
+such as JPEG compression is not an optimized solution for DNN systems,
+especially with high compression ratios. To this end, we develop an image
+compression framework tailored for DNN applications, named ""DeepN-JPEG"", to
+embrace the nature of deep cascaded information process mechanism of DNN
+architecture. Extensive experiments, based on ""ImageNet"" dataset with various
+state-of-the-art DNNs, show that ""DeepN-JPEG"" can achieve ~3.5x higher
+compression rate over the popular JPEG solution while maintaining the same
+accuracy level for image recognition, demonstrating its great potential of
+storage and power efficiency in DNN-based smart IoT system design.
+"
+22327,Fine properties of the curvature of arbitrary closed sets,"  Given an arbitrary closed set $ A $ of $ \mathbf{R}^{n} $, we establish the
+relation between the eigenvalues of the approximate differential of the nearest
+point projection onto $ A $ and the principal curvatures of $ A $ introduced by
+Hug-Last-Weil, thus extending a well known relation for sets of positive reach
+contained the classical works of Federer and Zähle. Then we provide for every
+$ m = 1, \ldots , n-1 $ an integral representation for the support measure $
+\mu_{m} $ of $ A $ with respect to the $ m $ dimensional Hausdorff measure.
+Finally we study the relation with the notion of approximate curvature of
+second order rectifiable sets introduced in a previous work of the author.
+"
+22328,TableQA: Question Answering on Tabular Data,"  Tabular data is difficult to analyze and to search through, yielding for new
+tools and interfaces that would allow even non tech-savvy users to gain
+insights from open datasets without resorting to specialized data analysis
+tools or even without having to fully understand the dataset structure. The
+goal of our demonstration is to showcase answering natural language questions
+from tabular data, and to discuss related system configuration and model
+training aspects. Our prototype is publicly available and open-sourced (see
+this https URL).
+"
+22329,Group Invariant Bounded Linear Functions on Dedekind Complete Totally Ordered Riesz Spaces,"  In this paper we consider the set of all bounded subsets of totally ordered
+Dedekind complete Riesz spaces, equipped with the order topology. We show the
+existence of bounded linear functions on this set, that are invariant under
+group actions of the symmetric group of it.
+"
+22330,Faraday waves by resonant triad interaction of surface--compression waves,"  The propagation of wave disturbances over a vertically oscillating liquid may
+form standing waves, known as Faraday waves. Here we present an alternative
+description of the generation and evolution of Faraday waves by nonlinear
+resonant triad interactions, accounting for gravity surface effects and the
+slight compressibility of the liquid. To this end, we analyse a triad
+comprising an infinitely long-crested compression mode and two oppositely
+propagating subharmonic surface waves.
+"
+22331,Statistical Analysis of $σ$-Holes: A Novel Complementary View on Halogen Bonding,"  To contribute to the understanding of noncovalent binding of halogenated
+molecules with a biological activity, electrostatic potential (ESP) maps of
+more than 2,500 compounds were thoroughly analysed. A peculiar region of
+positive ESP, called $\sigma$-hole, is a concept of central importance for
+halogen bonding. We aim at simplifying the view on $\sigma$-holes and provide
+general trends in organic drug-like molecules. The results are in fair
+agreement with crystallographic surveys of small molecules as well as of
+biomolecular complexes and attempt to improve the intuition of chemists when
+dealing with halogenated compounds.
+"
+22332,Algorithms for the solution of systems of linear equations in commutative ring,"  Solution methods for linear equation systems in a commutative ring are
+discussed. Four methods are compared, in the setting of several different
+rings: Dodgson's method [1], Bareiss's method [2] and two methods of the author
+- method by forward and back-up procedures [3] and a one-pass method [4]. We
+show that for the number of coefficient operations, or for the number of
+operations in the finite rings, or for modular computation in the polynomial
+rings the one-pass method [4] is the best. The method of forward and back-up
+procedures [3] is the best for the polynomial rings when we make use of
+classical algorithms for polynomial operations.
+"
+22333,Rigidity of outermost MOTS - the initial data version,"  In [5], a rigidity result was obtained for outermost marginally outer trapped
+surfaces (MOTSs) that do not admit metrics of positive scalar curvature. This
+allowed one to treat the ""borderline case"" in the author's work with R. Schoen
+concerning the topology of higher dimensional black holes [8]. The proof of
+this rigidity result involved bending the initial data manifold in the vicinity
+of the MOTS within the ambient spacetime. In this note we show how to
+circumvent this step, and thereby obtain a pure initial data version of this
+rigidity result and its consequence concerning the topology of black holes.
+"
+22334,On linear complementary-dual multinegacirculant codes,"  Linear codes with complementary-duals (LCD) are linear codes that intersect
+with their dual trivially. Multinegacirculant codes of index $2$ that are LCD
+are characterized algebraically and some good codes are found in this family.
+Exact enumeration is performed for indices 2 and 3, and for all indices $t$ for
+a special case of the co-index by using their concatenated structure.
+Asymptotic existence results are derived for the special class of such codes
+that are one-generator and have co-index a power of two by means of Dickson
+polynomials. This shows that there are infinite families of LCD
+multinegacirculant codes with relative distance satisfying a modified
+Varshamov-Gilbert bound.
+"
+22335,Accelerated Variance Reduced Stochastic ADMM,"  Recently, many variance reduced stochastic alternating direction method of
+multipliers (ADMM) methods (e.g.\ SAG-ADMM, SDCA-ADMM and SVRG-ADMM) have made
+exciting progress such as linear convergence rates for strongly convex
+problems. However, the best known convergence rate for general convex problems
+is O(1/T) as opposed to O(1/T^2) of accelerated batch algorithms, where $T$ is
+the number of iterations. Thus, there still remains a gap in convergence rates
+between existing stochastic ADMM and batch algorithms. To bridge this gap, we
+introduce the momentum acceleration trick for batch optimization into the
+stochastic variance reduced gradient based ADMM (SVRG-ADMM), which leads to an
+accelerated (ASVRG-ADMM) method. Then we design two different momentum term
+update rules for strongly convex and general convex cases. We prove that
+ASVRG-ADMM converges linearly for strongly convex problems. Besides having a
+low per-iteration complexity as existing stochastic ADMM methods, ASVRG-ADMM
+improves the convergence rate on general convex problems from O(1/T) to
+O(1/T^2). Our experimental results show the effectiveness of ASVRG-ADMM.
+"
+22336,Exploring the Statistics of Magnetic Reconnection X-points in Kinetic Particle-in-Cell (PIC) Turbulence,"  Magnetic reconnection is a ubiquitous phenomenon in turbulent plasmas. It is
+an important part of the turbulent dynamics and heating of space and
+astrophysical plasmas. We examine the statistics of magnetic reconnection using
+a quantitative local analysis of the magnetic vector potential, previously used
+in magnetohydrodynamics simulations, and now generalized to fully kinetic PIC
+simulations. Different ways of reducing the particle noise for analysis
+purposes including multiple smoothing techniques are explored. We find that a
+Fourier filter applied at the Debye scale is an optimal choice for analyzing
+PIC data. Finlay, we find a broader distribution of normalized reconnection
+rates compared to the MHD limit with rates as large as 0.5 but with an average
+of approximately 0.1.
+"
+22337,Global well-posedness and long-time dynamics for a higher order Quasi-Geostrophic type equation,"  In this paper we study a higher order viscous quasi-geostrophic type
+equation. This equation was derived in [11] as the limit dynamics of a
+singularly perturbed Navier-Stokes-Korteweg system with Coriolis force, when
+the Mach, Rossby and Weber numbers go to zero at the same rate. The scope of
+the present paper is twofold. First of all, we investigate well-posedness of
+such a model on the whole space $\R^2$: we prove that it is well-posed in $H^s$
+for any $s\geq3$, globally in time. Interestingly enough, we show that this
+equation owns two levels of energy estimates, for which one gets existence and
+uniqueness of weak solutions with different regularities (namely, $H^3$ and
+$H^4$ regularities); this fact can be viewed as a remainder of the so called
+BD-entropy structure of the original system. In the second part of the paper we
+investigate the long-time behaviour of these solutions. We show that they
+converge to the solution of the corresponding linear parabolic type equation,
+with same initial datum and external force. Our proof is based on dispersive
+estimates both for the solutions to the linear and non-linear problems.
+"
+22338,Minimization Solutions to Conservation Laws with Non-smooth and Non-strictly Convex Flux,"  Conservation laws are usually studied in the context of sufficient regularity
+conditions imposed on the flux function, usually $C^{2}$ and uniform convexity.
+Some results are proven with the aid of variational methods and a unique
+minimizer such as Hopf-Lax and Lax-Oleinik. We show that many of these
+classical results can be extended to a flux function that is not necessarily
+smooth or uniformly or strictly convex. Although uniqueness a.e. of the
+minimizer will generally no longer hold, by considering the greatest (or
+supremum, where applicable) of all possible minimizers, we can successfully
+extend the results. One specific nonlinear case is that of a piecewise linear
+flux function, for which we prove existence and uniqueness results. We also
+approximate it by a smoothed, superlinearized version parameterized by
+$\varepsilon$ and consider the characterization of the minimizers for the
+smooth version and limiting behavior as $\varepsilon\downarrow0$ to that of the
+sharp, polygonal problem. In proving a key result for the solution in terms of
+the value of the initial condition, we provide a stepping stone to analyzing
+the system under stochastic processes, which will be explored further in a
+future paper.
+"
+22339,Self Configuration in Machine Learning,"  In this paper we first present a class of algorithms for training multi-level
+neural networks with a quadratic cost function one layer at a time starting
+from the input layer. The algorithm is based on the fact that for any layer to
+be trained, the effect of a direct connection to an optimized linear output
+layer can be computed without the connection being made. Thus, starting from
+the input layer, we can train each layer in succession in isolation from the
+other layers. Once trained, the weights are kept fixed and the outputs of the
+trained layer then serve as the inputs to the next layer to be trained. The
+result is a very fast algorithm. The simplicity of this training arrangement
+allows the activation function and step size in weight adjustment to be
+adaptive and self-adjusting. Furthermore, the stability of the training process
+allows relatively large steps to be taken and thereby achieving in even greater
+speeds. Finally, in our context configuring the network means determining the
+number of outputs for each layer. By decomposing the overall cost function into
+separate components related to approximation and estimation, we obtain an
+optimization formula for determining the number of outputs for each layer. With
+the ability to self-configure and set parameters, we now have more than a fast
+training algorithm, but the ability to build automatically a fully trained deep
+neural network starting with nothing more than data.
+"
+22340,Buckling of elastic filaments by discrete magnetic moments,"  We study the buckling of an idealized, semiflexible filament along whose
+contour magnetic moments are placed. {We give analytic expressions for the
+critical stiffness of the filament below which it buckles due to the magnetic
+compression. For this, we consider various scenarios of the attachment of the
+magnetic particles to the filament. One possible application for this model are
+the magnetosome chains of magnetotactic bacteria. An estimate of the critical
+bending stiffness indicates that buckling may occur within the range of
+biologically relevant parameters and suggests a role for the bending stiffness
+of the filament to stabilize the filament against buckling, which would
+compromise the functional relevance of the bending stiffness of the used
+filament.
+"
+22341,Two-stage robust optimization for orienteering problem with stochastic weights,"  In this paper, the two-stage orienteering problem with stochastic weights
+(OPSW) is considered, where the first-stage problem is to plan a path under the
+uncertain environment and the second-stage problem is recourse action to make
+sure that the length constraint is satisfied after the uncertainty is realized.
+Two recourse models are introduced based on two different uncertainty
+realization ways, one is based on sequentially realized weights which leads to
+the recourse model proposed by Evers et al. (2014) and the other is based on
+concurrently realized weights which leads to a new recourse model with less
+variables and less constraints and is computationally more efficient.
+Subsequently two two-stage robust models are introduced for OPSW based on the
+two different recourse models, and the relationships between the two-stage
+robust models and their corresponding static robsut models are investigated.
+Theoretical conclusions are drawn which show that the two-stage robust models
+are equivalent to their corresponding static robust models with the box
+uncertainty set defined, and the two two-stage robust models are also
+equivalent to each other even though they are based on different recourse
+models. A case study is presented by comparing the two-stage robust models with
+an one-stage robust model for OPSW. The numerical results of the comparative
+studies show the effectiveness and superiority of the proposed two-stage robust
+models for dealing with the two-stage OPSW.
+"
+22342,Diffusion geometry unravels the emergence of functional clusters in collective phenomena,"  Collective phenomena emerge from the interaction of natural or artificial
+units with a complex organization. The interplay between structural patterns
+and dynamics might induce functional clusters that, in general, are different
+from topological ones. In biological systems, like the human brain, the overall
+functionality is often favored by the interplay between connectivity and
+synchronization dynamics, with functional clusters that do not coincide with
+anatomical modules in most cases. In social, socio-technical and engineering
+systems, the quest for consensus favors the emergence of clusters.
+Despite the unquestionable evidence for mesoscale organization of many
+complex systems and the heterogeneity of their inter-connectivity, a way to
+predict and identify the emergence of functional modules in collective
+phenomena continues to elude us. Here, we propose an approach based on random
+walk dynamics to define the diffusion distance between any pair of units in a
+networked system. Such a metric allows to exploit the underlying diffusion
+geometry to provide a unifying framework for the intimate relationship between
+metastable synchronization, consensus and random search dynamics in complex
+networks, pinpointing the functional mesoscale organization of synthetic and
+biological systems.
+"
+22343,Cache Hierarchy Optimization,"  Power consumption, off-chip memory bandwidth, chip area and Network on Chip
+(NoC) capacity are among main chip resources limiting the scalability of Chip
+Multiprocessors (CMP). A closed form analytical solution for optimizing the CMP
+cache hierarchy and optimally allocating area among hierarchy levels under such
+constrained resources is developed. The optimization framework is extended by
+incorporating the impact of data sharing on cache miss rate. An analytical
+model for cache access time as a function of cache size is proposed and
+verified using CACTI simulation.
+"
+22344,Noisy Spins and the Richardson-Gaudin Model,"  We study a system of spins (qubits) coupled to a common noisy environment,
+each precessing at its own frequency. The correlated noise experienced by the
+spins implies long-lived correlations that relax only due to the differing
+frequencies. We use a mapping to a non-Hermitian integrable Richardson-Gaudin
+model to find the exact spectrum of the quantum master equation in the
+high-temperature limit, and hence determine the decay rate. Our solution can be
+used to evaluate the effect of inhomogeneous splittings on a system of qubits
+coupled to a common bath.
+"
+22345,Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization,"  This paper introduces a novel approach to robust surface reconstruction from
+photometric stereo normal vector maps that is particularly well-suited for
+reconstructing surfaces from noisy gradients. Specifically, we propose an
+adaptive dictionary learning based approach that attempts to simultaneously
+integrate the gradient fields while sparsely representing the spatial patches
+of the reconstructed surface in an adaptive dictionary domain. We show that our
+formulation learns the underlying structure of the surface, effectively acting
+as an adaptive regularizer that enforces a smoothness constraint on the
+reconstructed surface. Our method is general and may be coupled with many
+existing approaches in the literature to improve the integrity of the
+reconstructed surfaces. We demonstrate the performance of our method on
+synthetic data as well as real photometric stereo data and evaluate its
+robustness to noise.
+"
+22346,Anonymization of System Logs for Privacy and Storage Benefits,"  System logs constitute valuable information for analysis and diagnosis of
+system behavior. The size of parallel computing systems and the number of their
+components steadily increase. The volume of generated logs by the system is in
+proportion to this increase. Hence, long-term collection and storage of system
+logs is challenging. The analysis of system logs requires advanced text
+processing techniques. For very large volumes of logs, the analysis is highly
+time-consuming and requires a high level of expertise. For many parallel
+computing centers, outsourcing the analysis of system logs to third parties is
+the only affordable option. The existence of sensitive data within system log
+entries obstructs, however, the transmission of system logs to third parties.
+Moreover, the analytical tools for processing system logs and the solutions
+provided by such tools are highly system specific. Achieving a more general
+solution is only possible through the access and analysis system of logs of
+multiple computing systems. The privacy concerns impede, however, the sharing
+of system logs across institutions as well as in the public domain. This work
+proposes a new method for the anonymization of the information within system
+logs that employs de-identification and encoding to provide sharable system
+logs, with the highest possible data quality and of reduced size. The results
+presented in this work indicate that apart from eliminating the sensitive data
+within system logs and converting them into shareable data, the proposed
+anonymization method provides 25% performance improvement in post-processing of
+the anonymized system logs, and more than 50% reduction in their required
+storage space.
+"
+22347,"On the derivation of radial velocities of SB2 components: a ""CCF vs TODCOR"" comparison","  The radial velocity (RV) of a single star is easily obtained from
+cross-correlation of the spectrum with a template, but the treatment of
+double-lined spectroscopic binaries (SB2s) is more difficult. Two different
+approaches were applied to a set of SB2s: the fit of the cross-correlation
+function with two normal distributions, and the cross-correlation with two
+templates, derived with the TODCOR code. It appears that the minimum masses
+obtained through the two methods are sometimes rather different, although their
+estimated uncertainties are roughly equal. Moreover, both methods induce a
+shift in the zero point of the secondary RVs, but it is less pronounced for
+TODCOR. All-in-all the comparison between the two methods is in favour of
+TODCOR.
+"
+22348,Water Permeation through Layered Graphene-based Membranes: A Fully Atomistic Molecular Dynamics Investigation,"  Graphene-based membranes have been investigated as promising candidates for
+water filtration and gas separation applications. Experimental evidences have
+shown that graphene oxide can be impermeable to liquids, vapors and gases,
+while allowing a fast permeation of water molecules. This phenomenon has been
+attributed to the formation of a network of nano capillaries that allow nearly
+frictionless water flow while blocking other molecules by steric hindrance
+effects. It is supposed that water molecules are transported through the
+percolated two-dimensional channels formed between graphene-based sheets.
+Although these channels allow fast water permeation in such materials, the flow
+rates are strongly dependent on how the membranes are fabricated. Also, some
+fundamental issues regarding the nanoscale mechanisms of water permeation are
+still not fully understood and their interpretation remains controversial. In
+this work, we have investigated the dynamics of water permeation through
+pristine graphene and graphene oxide model membranes. We have carried out fully
+atomistic classical molecular dynamics simulations of systems composed of
+multiple layered graphene-based sheets into contact with a water reservoir
+under controlled thermodynamics conditions (e. g., by varying temperature and
+pressure values). We have systematically analyzed how the transport dynamics of
+the confined nanofluids depend on the interlayer distances and the role of the
+oxide functional groups. Our results show the water flux is much more effective
+for graphene than for graphene oxide membranes. These results are attributed to
+the H-bonds formation between oxide functional groups and water, which traps
+the water molecules and precludes ultrafast water transport through the
+nanochannels.
+"
+22349,Pressure-induced superconductivity and topological quantum phase transitions in a quasi-one-dimensional topological insulator: Bi4I4,"  Superconductivity and topological quantum states are two frontier fields of
+research in modern condensed matter physics. The realization of
+superconductivity in topological materials is highly desired, however,
+superconductivity in such materials is typically limited to two- or
+three-dimensional materials and is far from being thoroughly investigated. In
+this work, we boost the electronic properties of the quasi-one-dimensional
+topological insulator bismuth iodide \b{eta}-Bi4I4 by applying high pressure.
+Superconductivity is observed in \b{eta}-Bi4I4 for pressures where the
+temperature dependence of the resistivity changes from a semiconducting-like
+behavior to that of a normal metal. The superconducting transition temperature
+Tc increases with applied pressure and reaches a maximum value of 6 K at 23
+GPa, followed by a slow decrease. Our theoretical calculations suggest the
+presence of multiple pressure-induced topological quantum phase transitions as
+well as a structural-electronic instability.
+"
+22350,Tractable Post-Selection Maximum Likelihood Inference for the Lasso,"  Applying standard statistical methods after model selection may yield
+inefficient estimators and hypothesis tests that fail to achieve nominal type-I
+error rates. The main issue is the fact that the post-selection distribution of
+the data differs from the original distribution. In particular, the observed
+data is constrained to lie in a subset of the original sample space that is
+determined by the selected model. This often makes the post-selection
+likelihood of the observed data intractable and maximum likelihood inference
+difficult. In this work, we get around the intractable likelihood by generating
+noisy unbiased estimates of the post-selection score function and using them in
+a stochastic ascent algorithm that yields correct post-selection maximum
+likelihood estimates. We apply the proposed technique to the problem of
+estimating linear models selected by the lasso. In an asymptotic analysis the
+resulting estimates are shown to be consistent for the selected parameters and
+to have a limiting truncated normal distribution. Confidence intervals
+constructed based on the asymptotic distribution obtain close to nominal
+coverage rates in all simulation settings considered, and the point estimates
+are shown to be superior to the lasso estimates when the true model is sparse.
+"
+22351,Spatial Oversampling in LOS MIMO Systems with 1-Bit Quantization at the Receiver,"  In this paper we investigate the achievable rate of LOS MIMO systems that use
+1-bit quantization and spatial oversampling at the receiver. We propose that by
+using additional antennas at the receiver, the loss incurred due to the strong
+limitation of the quantization can be decreased. Mutual information results
+show that considerable rate gains can be achieved depending on the number and
+arrangement of the antennas. In some of the cases, even the full available rate
+from the transmitter can be attained. Furthermore, the results also reveal that
+two-dimensional antenna arrays can benefit more from spatial oversampling than
+one-dimensional arrays, when using 1-bit quantization in the LOS MIMO scenario.
+"
+22352,Using $k$-way Co-occurrences for Learning Word Embeddings,"  Co-occurrences between two words provide useful insights into the semantics
+of those words. Consequently, numerous prior work on word embedding learning
+have used co-occurrences between two words as the training signal for learning
+word embeddings. However, in natural language texts it is common for multiple
+words to be related and co-occurring in the same context. We extend the notion
+of co-occurrences to cover $k(\geq\!\!2)$-way co-occurrences among a set of
+$k$-words. Specifically, we prove a theoretical relationship between the joint
+probability of $k(\geq\!\!2)$ words, and the sum of $\ell_2$ norms of their
+embeddings. Next, we propose a learning objective motivated by our theoretical
+result that utilises $k$-way co-occurrences for learning word embeddings. Our
+experimental results show that the derived theoretical relationship does indeed
+hold empirically, and despite data sparsity, for some smaller $k$ values,
+$k$-way embeddings perform comparably or better than $2$-way embeddings in a
+range of tasks.
+"
+22353,Amplitude limits and nonlinear damping of shear-Alfvén waves in high-beta low-collisionality plasmas,"  This work, which extends Squire et al. [ApJL, 830 L25 (2016)], explores the
+effect of self-generated pressure anisotropy on linearly polarized
+shear-Alfvén fluctuations in low-collisionality plasmas. Such anisotropies
+lead to stringent limits on the amplitude of magnetic perturbations in
+high-beta plasmas, above which a fluctuation can destabilize itself through the
+parallel firehose instability. This causes the wave frequency to approach zero,
+""interrupting"" the wave and stopping its oscillation. These effects are
+explored in detail in the collisionless and weakly collisional ""Braginskii""
+regime, for both standing and traveling waves. The focus is on simplified
+models in one dimension, on scales much larger than the ion gyroradius. The
+effect has interesting implications for the physics of magnetized turbulence in
+the high-beta conditions that are prevalent in many astrophysical plasmas.
+"
+22354,Structural Identifiability of Cyclic Graphical Models of Biological Networks with Latent Variables,"  An efficient structural identifiability analysis algorithm is developed in
+this study for a broad range of network structures. The proposed method adopts
+the Wright's path coefficient method to generate identifiability equations in
+forms of symbolic polynomials, and then converts these symbolic equations to
+binary matrices (called identifiability matrix). Several matrix operations are
+introduced for identifiability matrix reduction with system equivalency
+maintained. Based on the reduced identifiability matrices, the structural
+identifiability of each parameter is determined. A number of benchmark models
+are used to verify the validity of the proposed approach. Finally, the network
+module for influenza A virus replication is employed as a real example to
+illustrate the application of the proposed approach in practice. The proposed
+approach can deal with cyclic networks with latent variables. The key advantage
+is that it intentionally avoids symbolic computation and is thus highly
+efficient. Also, this method is capable of determining the identifiability of
+each single parameter and is thus of higher resolution in comparison with many
+existing approaches. Overall, this study provides a basis for systematic
+examination and refinement of graphical models of biological networks from the
+identifiability point of view, and it has a significant potential to be
+extended to more complex network structures or high-dimensional systems.
+"
+22355,Satellites testing general relativity: residuals versus perturbations,"  Laser ranging satellites have proved their efficiency for high precision
+testing of the effect of frame-dragging, one of remarkable predictions of the
+General Relativity. The analysis of the randomness properties of the residuals
+of LAGEOS and LAGEOS 2 satellites reveals the role of the thermal thrust --
+Yarkovsky effect -- on the satellite which was in the orbit for longer period
+(LAGEOS). We also compute Earth's tidal modes affecting the satellite LARES.
+The recently obtained 5\% accuracy limit reached for the frame dragging effect
+based on the 3.5 year data of LARES analysed together with those of LAGEOS
+satellites and using the Earth gravity model of GRACE satellite, is also
+represented.
+"
+22356,Coupled Deep Learning for Heterogeneous Face Recognition,"  Heterogeneous face matching is a challenge issue in face recognition due to
+large domain difference as well as insufficient pairwise images in different
+modalities during training. This paper proposes a coupled deep learning (CDL)
+approach for the heterogeneous face matching. CDL seeks a shared feature space
+in which the heterogeneous face matching problem can be approximately treated
+as a homogeneous face matching problem. The objective function of CDL mainly
+includes two parts. The first part contains a trace norm and a block-diagonal
+prior as relevance constraints, which not only make unpaired images from
+multiple modalities be clustered and correlated, but also regularize the
+parameters to alleviate overfitting. An approximate variational formulation is
+introduced to deal with the difficulties of optimizing low-rank constraint
+directly. The second part contains a cross modal ranking among triplet domain
+specific images to maximize the margin for different identities and increase
+data for a small amount of training samples. Besides, an alternating
+minimization method is employed to iteratively update the parameters of CDL.
+Experimental results show that CDL achieves better performance on the
+challenging CASIA NIR-VIS 2.0 face recognition database, the IIIT-D Sketch
+database, the CUHK Face Sketch (CUFS), and the CUHK Face Sketch FERET (CUFSF),
+which significantly outperforms state-of-the-art heterogeneous face recognition
+methods.
+"
+22357,Atomic energy levels population kinetics in dense plasmas - plasma ions electric microfield mixing dynamics effect (I): the general theory,"  In the present paper we introduce a full and closed system of atomic kinetic
+equations on fractions of atomic energy levels taking into account the effect
+of the plasma ions electric microfield mixing dynamics. The system of equations
+that we present is deduced from a full quantum multi-atomic states density
+matrix master equation. The analytical derivation of this atomic kinetic system
+of equations is based on the introduction of a new hierarchy chain of equations
+involving a new set of statistical atomic variables. The final system of atomic
+kinetic equations is obtained by introducing relation de fermeture to truncate
+the hierarchy chain of equations. We show that the final system of equations
+contains a new plasma ions electric microfield dependent atomic rates related
+to the process of mixing dynamics of atomic energy states. The analytical
+expression of this rate is given in this paper. We discuss the importance of
+the present analysis in view of the radiation emission originating from dense
+plasmas created by the interaction of the X-ray Free Electron Laser (XFEL) with
+solid density matter.
+"
+22358,Digital Forensic Approaches for Amazon Alexa Ecosystem,"  Internet of Things devices such as the Amazon Echo are undoubtedly great
+sources of potential digital evidence due to their ubiquitous use and their
+always on mode of operation, constituting a human life black box. The Amazon
+Echo in particular plays a centric role for the cloud based intelligent virtual
+assistant Alexa developed by Amazon Lab126. The Alexa enabled wireless smart
+speaker is the gateway for all voice commands submitted to Alexa. Moreover, the
+IVA interacts with a plethora of compatible IoT devices and third party
+applications that leverage cloud resources. Understanding the complex cloud
+ecosystem that allows ubiquitous use of Alexa is paramount on supporting
+digital investigations when need raises. This paper discusses methods for
+digital forensics pertaining to the IVA Alexa ecosystem. The primary
+contribution of this paper consists of a new efficient approach of combining
+cloud native forensics with client side forensics, to support practical digital
+investigations. Based on a deep understanding of the targeted ecosystem, we
+propose a proof of concept tool, CIFT, that supports identification,
+acquisition and analysis of both native artifacts from the cloud and client
+centric artifacts from local devices.
+"
+22359,Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects,"  We propose a hybrid architecture for systematically computing robust visual
+explanation(s) encompassing hypothesis formation, belief revision, and default
+reasoning with video data. The architecture consists of two tightly integrated
+synergistic components: (1) (functional) answer set programming based abductive
+reasoning with space-time tracklets as native entities; and (2) a visual
+processing pipeline for detection based object tracking and motion analysis.
+We present the formal framework, its general implementation as a
+(declarative) method in answer set programming, and an example application and
+evaluation based on two diverse video datasets: the MOTChallenge benchmark
+developed by the vision community, and a recently developed Movie Dataset.
+"
+22360,Low Dose CT Image Reconstruction With Learned Sparsifying Transform,"  A major challenge in computed tomography (CT) is to reduce X-ray dose to a
+low or even ultra-low level while maintaining the high quality of reconstructed
+images. We propose a new method for CT reconstruction that combines penalized
+weighted-least squares reconstruction (PWLS) with regularization based on a
+sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images.
+We adopt an alternating algorithm to optimize the PWLS-ST cost function that
+alternates between a CT image update step and a sparse coding step. We adopt a
+relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed
+OS-LALM) to accelerate the CT image update step by reducing the number of
+forward and backward projections. Numerical experiments on the XCAT phantom
+show that for low dose levels, the proposed PWLS-ST method dramatically
+improves the quality of reconstructed images compared to PWLS reconstruction
+with a nonadaptive edge-preserving regularizer (PWLS-EP).
+"
+22361,On the Local Correctabilities of Projective Reed-Muller Codes,"  In this paper, we show that the projective Reed-Muller~(PRM) codes form a
+family of locally correctable codes~(LCC) in the regime of low query
+complexities. A PRM code is specified by the alphabet size $q$, the number of
+variables $m$, and the degree $d$. When $d\leq q-1$, we present a perfectly
+smooth local decoder to recover a symbol by accessing $\gamma\leq q$ symbols to
+the coordinates fall on a line. There are three major parameters considered in
+LCCs, namely the query complexity, the message length and the code length. This
+paper shows that PRM codes are shorter than generalized Reed-Muller~(GRM) codes
+in LCCs. Precisely, given a GRM code over a field of size $q$, there exists a
+class of shorter codes over a field of size $q-1$, while maintaining the same
+values on the query complexities and the message lengths.
+"
+22362,k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge,"  This abstract briefly describes a segmentation algorithm developed for the
+ISIC 2017 Skin Lesion Detection Competition hosted at [ref]. The objective of
+the competition is to perform a segmentation (in the form of a binary mask
+image) of skin lesions in dermoscopic images as close as possible to a
+segmentation performed by trained clinicians, which is taken as ground truth.
+This project only takes part in the segmentation phase of the challenge. The
+other phases of the competition (feature extraction and lesion identification)
+are not considered.
+The proposed algorithm consists of 4 steps: (1) lesion image preprocessing,
+(2) image segmentation using k-means clustering of pixel colors, (3)
+calculation of a set of features describing the properties of each segmented
+region, and (4) calculation of a final score for each region, representing the
+likelihood of corresponding to a suitable lesion segmentation. The scores in
+step (4) are obtained by averaging the results of 2 different regression models
+using the scores of each region as input. Before using the algorithm these
+regression models must be trained using the training set of images and ground
+truth masks provided by the Competition. Steps 2 to 4 are repeated with an
+increasing number of clusters (and therefore the image is segmented into more
+regions) until there is no further improvement of the calculated scores.
+"
+22363,Online Multi-Armed Bandit,"  We introduce a novel variant of the multi-armed bandit problem, in which
+bandits are streamed one at a time to the player, and at each point, the player
+can either choose to pull the current bandit or move on to the next bandit.
+Once a player has moved on from a bandit, they may never visit it again, which
+is a crucial difference between our problem and classic multi-armed bandit
+problems. In this online context, we study Bernoulli bandits (bandits with
+payout Ber($p_i$) for some underlying mean $p_i$) with underlying means drawn
+i.i.d. from various distributions, including the uniform distribution, and in
+general, all distributions that have a CDF satisfying certain differentiability
+conditions near zero. In all cases, we suggest several strategies and
+investigate their expected performance. Furthermore, we bound the performance
+of any optimal strategy and show that the strategies we have suggested are
+indeed optimal up to a constant factor. We also investigate the case where the
+distribution from which the underlying means are drawn is not known ahead of
+time. We again, are able to suggest algorithms that are optimal up to a
+constant factor for this case, given certain mild conditions on the universe of
+distributions.
+"
+22364,CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices,"  Large-scale deep neural networks (DNNs) are both compute and memory
+intensive. As the size of DNNs continues to grow, it is critical to improve the
+energy efficiency and performance while maintaining accuracy. For DNNs, the
+model size is an important factor affecting performance, scalability and energy
+efficiency. Weight pruning achieves good compression ratios but suffers from
+three drawbacks: 1) the irregular network structure after pruning; 2) the
+increased training complexity; and 3) the lack of rigorous guarantee of
+compression ratio and inference accuracy. To overcome these limitations, this
+paper proposes CirCNN, a principled approach to represent weights and process
+neural networks using block-circulant matrices. CirCNN utilizes the Fast
+Fourier Transform (FFT)-based fast multiplication, simultaneously reducing the
+computational complexity (both in inference and training) from O(n2) to
+O(nlogn) and the storage complexity from O(n2) to O(n), with negligible
+accuracy loss. Compared to other approaches, CirCNN is distinct due to its
+mathematical rigor: it can converge to the same effectiveness as DNNs without
+compression. The CirCNN architecture, a universal DNN inference engine that can
+be implemented on various hardware/software platforms with configurable network
+architecture. To demonstrate the performance and energy efficiency, we test
+CirCNN in FPGA, ASIC and embedded processors. Our results show that CirCNN
+architecture achieves very high energy efficiency and performance with a small
+hardware footprint. Based on the FPGA implementation and ASIC synthesis
+results, CirCNN achieves 6-102X energy efficiency improvements compared with
+the best state-of-the-art results.
+"
+22365,Electron-Phonon Coupling Constant of Metallic Overlayers from Specular He-Atom Scattering,"  He atom scattering has been shown to be a sensitive probe of electron-phonon
+interaction properties at surfaces. Here it is shown that measurements of the
+thermal attenuation of the specular He atom diffraction peak (the Debye-Waller
+effect) can determine the electron-phonon coupling constant $\lambda$ for
+ultrathin films of metal overlayers on various close-packed metal substrates.
+Values of $\lambda$ obtained for single and multiple monolayers of alkali
+metals, and for Pb layers on Cu(111), extrapolated to large thicknesses, agree
+favorably with known bulk values. This demonstrates that He atom scattering can
+measure the electron-phonon coupling strength as a function of film thickness
+on a layer-by-layer basis.
+"
+22366,Neutrally Expandable Models of Arithmetic,"  A subset of a model of ${\sf PA}$ is called neutral if it does not change the
+$\mathrm{dcl}$ relation. A model with undefinable neutral classes is called
+neutrally expandable. We study the existence and non-existence of neutral sets
+in various models of ${\sf PA}$. We show that cofinal extensions of prime
+models are neutrally expandable, and $\omega_1$-like neutrally expandable
+models exist, while no recursively saturated model is neutrally expandable. We
+also show that neutrality is not a first-order property. In the last section,
+we study a local version of neutral expandability.
+"
+22367,One-Class Semi-Supervised Learning: Detecting Linearly Separable Class by its Mean,"  In this paper, we presented a novel semi-supervised one-class classification
+algorithm which assumes that class is linearly separable from other elements.
+We proved theoretically that class is linearly separable if and only if it is
+maximal by probability within the sets with the same mean. Furthermore, we
+presented an algorithm for identifying such linearly separable class utilizing
+linear programming. We described three application cases including an
+assumption of linear separability, Gaussian distribution, and the case of
+linear separability in transformed space of kernel functions. Finally, we
+demonstrated the work of the proposed algorithm on the USPS dataset and
+analyzed the relationship of the performance of the algorithm and the size of
+the initially labeled sample.
+"
+22368,Asymptotic distribution of least square estimators for linear models with dependent errors : regular designs,"  In this paper, we consider the usual linear regression model in the case
+where the error process is assumed strictly stationary. We use a result from
+Hannan, who proved a Central Limit Theorem for the usual least square estimator
+under general conditions on the design and on the error process. We show that
+for a large class of designs, the asymptotic covariance matrix is as simple as
+the i.i.d. case. We then estimate the covariance matrix using an estimator of
+the spectral density whose consistency is proved under very mild conditions. As
+an application, we show how to modify the usual Fisher tests in this dependent
+context, in such a way that the type-$I$ error rate remains asymptotically
+correct, and we illustrate the performance of this procedure through different
+sets of simulations.
+"
+22369,Contextual Memory Trees,"  We design and study a Contextual Memory Tree (CMT), a learning memory
+controller that inserts new memories into an experience store of unbounded
+size. It is designed to efficiently query for memories from that store,
+supporting logarithmic time insertion and retrieval operations. Hence CMT can
+be integrated into existing statistical learning algorithms as an augmented
+memory unit without substantially increasing training and inference
+computation. We demonstrate the efficacy of CMT by augmenting existing
+multi-class and multi-label classification algorithms with CMT and observe
+statistical improvement. We also test CMT learning on several image-captioning
+tasks to demonstrate that it performs computationally better than a simple
+nearest neighbors memory system while benefitting from reward learning.
+"
+22370,On Tests for Complete Independence of Normal Random Vectors,"  Consider a random sample of $n$ independently and identically distributed
+$p$-dimensional normal random vectors. A test statistic for complete
+independence of high-dimensional normal distributions, proposed by Schott
+(2005), is defined as the sum of squared Pearson's correlation coefficients. A
+modified test statistic has been proposed by Mao (2014). Under the assumption
+of complete independence, both test statistics are asymptotically normal if the
+limit $\lim_{n\to\infty}p/n$ exists and is finite. In this paper, we
+investigate the limiting distributions for both Schott's and Mao's test
+statistics. We show that both test statistics, after suitably normalized,
+converge in distribution to the standard normal as long as both $n$ and $p$
+tend to infinity. Furthermore, we show that the distribution functions of the
+test statistics can be approximated very well by a chi-square distribution
+function with $p(p-1)/2$ degrees of freedom as $n$ tends to infinity regardless
+of how $p$ changes with $n$.
+"
+22371,Improving Value-at-Risk prediction under model uncertainty,"  Several well-established benchmark predictors exist for Value-at-Risk (VaR),
+a major instrument for financial risk management. Hybrid methods combining
+AR-GARCH filtering with skewed-$t$ residuals and the extreme value theory-based
+approach are particularly recommended. This study introduces yet another VaR
+predictor, G-VaR, which follows a novel methodology. Inspired by the recent
+mathematical theory of sublinear expectation, G-VaR is built upon the concept
+of model uncertainty, which in the present case signifies that the inherent
+volatility of financial returns cannot be characterized by a single
+distribution but rather by infinitely many statistical distributions. By
+considering the worst scenario among these potential distributions, the G-VaR
+predictor is precisely identified. Extensive experiments on both the NASDAQ
+Composite Index and S\&P500 Index demonstrate the excellent performance of the
+G-VaR predictor, which is superior to most existing benchmark VaR predictors.
+"
+22372,Electrochemical Capacitance of CO-terminated Pt(111) is Dominated by CO-Solvent Gap,"  The distribution of electric fields within the electrochemical double layer
+depends on both the electrode and electrolyte in complex ways. These fields
+strongly influence chemical dynamics in the electrode-electrolyte interface,
+but cannot be measured directly with sub-molecular resolution. We report
+experimental capacitance measurements for aqueous interfaces of CO-terminated
+Pt(111). By comparing these measurements with first-principles
+density-functional theory (DFT) calculations, we infer microscopic field
+distributions and decompose contributions to the inverse capacitance from
+various spatial regions of the interface. We find that the CO is strongly
+electronically coupled to the Pt, and that most of the interfacial potential
+difference appears across the gap between the terminating O and water, and not
+across the CO molecule as previously hypothesized. This `gap capacitance'
+resulting from hydrophobic termination lowers the overall capacitance of the
+aqueous Pt-CO interface, and makes it less sensitive to electrolyte
+concentration compared to the bare metal.
+"
+22373,Complexity of Linear Regions in Deep Networks,"  It is well-known that the expressivity of a neural network depends on its
+architecture, with deeper networks expressing more complex functions. In the
+case of networks that compute piecewise linear functions, such as those with
+ReLU activation, the number of distinct linear regions is a natural measure of
+expressivity. It is possible to construct networks for which the number of
+linear regions grows exponentially with depth, or with merely a single region;
+it is not clear where within this range most networks fall in practice, either
+before or after training. In this paper, we provide a mathematical framework to
+count the number of linear regions of a piecewise linear network and measure
+the volume of the boundaries between these regions. In particular, we prove
+that for networks at initialization, the average number of regions along any
+one-dimensional subspace grows linearly in the total number of neurons, far
+below the exponential upper bound. We also find that the average distance to
+the nearest region boundary at initialization scales like the inverse of the
+number of neurons. Our theory suggests that, even after training, the number of
+linear regions is far below exponential, an intuition that matches our
+empirical observations. We conclude that the practical expressivity of neural
+networks is likely far below that of the theoretical maximum, and that this gap
+can be quantified.
+"
+22374,Proximity-induced low-energy renormalization in hybrid semiconductor-superconductor Majorana structures,"  A minimal model for the hybrid superconductor-semiconductor nanowire Majorana
+platform is developed that fully captures the effects of the low-energy
+renormalization of the nanowire modes arising from the presence of the parent
+superconductor. In this model, the parent superconductor is an active component
+that participates explicitly in the low-energy physics, not just a passive
+partner that only provides proximity-induced Cooper pairs for the nanowire.
+This treatment on an equal footing of the superconductor and the semiconductor
+has become necessary in view of recent experiments, which do not allow a
+consistent interpretation based just on the bare semiconductor properties. The
+general theory involves the evaluation of the exact semiconductor Green
+function that includes a dynamical self-energy correction arising from the
+tunnel-coupled superconductor. General conditions for the emergence of
+topological superconductivity are worked out for single-band as well as
+multi-band nanowires and detailed numerical results are given for both infinite
+and finite wire cases. The topological quantum phase diagrams are provided
+numerically and the Majorana bound states are obtained along with their
+oscillatory energy splitting behaviors due to wavefunction overlap in finite
+wires. Renormalization effects are shown to be both qualitatively and
+quantitatively important in modifying the low-energy spectrum of the nanowire.
+The results of the theory are found to be in good qualitative agreement with
+Majorana nanowire experiments, leading to the conclusion that the
+proximity-induced low-energy renormalization of the nanowire modes by the
+parent superconductor is of fundamental importance in
+superconductor-semiconductor hybrid structures. Implications of the general
+theory for obtaining true zero energy topological Majorana modes are pointed
+out.
+"
+22375,Stochastic Separation Theorems,"  The problem of non-iterative one-shot and non-destructive correction of
+unavoidable mistakes arises in all Artificial Intelligence applications in the
+real world. Its solution requires robust separation of samples with errors from
+samples where the system works properly. We demonstrate that in (moderately)
+high dimension this separation could be achieved with probability close to one
+by linear discriminants. Surprisingly, separation of a new image from a very
+large set of known images is almost always possible even in moderately high
+dimensions by linear functionals, and coefficients of these functionals can be
+found explicitly. Based on fundamental properties of measure concentration, we
+show that for $M<a\exp(b{n})$ random $M$-element sets in $\mathbb{R}^n$ are
+linearly separable with probability $p$, $p>1-\vartheta$, where $1>\vartheta>0$
+is a given small constant. Exact values of $a,b>0$ depend on the probability
+distribution that determines how the random $M$-element sets are drawn, and on
+the constant $\vartheta$. These {\em stochastic separation theorems} provide a
+new instrument for the development, analysis, and assessment of machine
+learning methods and algorithms in high dimension. Theoretical statements are
+illustrated with numerical examples.
+"
+22376,A Discrepancy-Based Design for A/B Testing Experiments,"  The aim of this paper is to introduce a new design of experiment method for
+A/B tests in order to balance the covariate information in all treatment
+groups. A/B tests (or ""A/B/n tests"") refer to the experiments and the
+corresponding inference on the treatment effect(s) of a two-level or
+multi-level controllable experimental factor. The common practice is to use a
+randomized design and perform hypothesis tests on the estimates. However, such
+estimation and inference are not always accurate when covariate imbalance
+exists among the treatment groups. To overcome this issue, we propose a
+discrepancy-based criterion and show that the design minimizing this criterion
+significantly improves the accuracy of the treatment effect(s) estimates. The
+discrepancy-based criterion is model-free and thus makes the estimation of the
+treatment effect(s) robust to the model assumptions. More importantly, the
+proposed design is applicable to both continuous and categorical response
+measurements. We develop two efficient algorithms to construct the designs by
+optimizing the criterion for both offline and online A/B tests. Through
+simulation study and a real example, we show that the proposed design approach
+achieves good covariate balance and accurate estimation.
+"
+22377,Cosmic Growth Signatures of Modified Gravitational Strength,"  Cosmic growth of large scale structure probes the entire history of cosmic
+expansion and gravitational coupling. To get a clear picture of the effects of
+modification of gravity we consider a deviation in the coupling strength
+(effective Newton's constant) at different redshifts, with different durations
+and amplitudes. We derive, analytically and numerically, the impact on the
+growth rate and growth amplitude. Galaxy redshift surveys can measure a product
+of these through redshift space distortions and we connect the modified gravity
+to the observable in a way that may provide a useful parametrization of the
+ability of future surveys to test gravity. In particular, modifications during
+the matter dominated era can be treated by a single parameter, the ""area"" of
+the modification, to an accuracy of $\sim0.3\%$ in the observables. We project
+constraints on both early and late time gravity for the Dark Energy
+Spectroscopic Instrument and discuss what is needed for tightening tests of
+gravity to better than 5% uncertainty.
+"
+22378,Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems,"  We describe stochastic Newton and stochastic quasi-Newton approaches to
+efficiently solve large linear least-squares problems where the very large data
+sets present a significant computational burden (e.g., the size may exceed
+computer memory or data are collected in real-time). In our proposed framework,
+stochasticity is introduced in two different frameworks as a means to overcome
+these computational limitations, and probability distributions that can exploit
+structure and/or sparsity are considered. Theoretical results on consistency of
+the approximations for both the stochastic Newton and the stochastic
+quasi-Newton methods are provided. The results show, in particular, that
+stochastic Newton iterates, in contrast to stochastic quasi-Newton iterates,
+may not converge to the desired least-squares solution. Numerical examples,
+including an example from extreme learning machines, demonstrate the potential
+applications of these methods.
+"
+22379,AFiD-GPU: a versatile Navier-Stokes Solver for Wall-Bounded Turbulent Flows on GPU Clusters,"  The AFiD code, an open source solver for the incompressible Navier-Stokes
+equations ({\color{blue}\burl{this http URL}}), has been ported to GPU
+clusters to tackle large-scale wall-bounded turbulent flow simulations. The GPU
+porting has been carried out in CUDA Fortran with the extensive use of kernel
+loop directives (CUF kernels) in order to have a source code as close as
+possible to the original CPU version; just a few routines have been manually
+rewritten. A new transpose scheme, which is not limited to the GPU version only
+and can be generally applied to any CFD code that uses pencil distributed
+parallelization, has been devised to improve the scaling of the Poisson solver,
+the main bottleneck of incompressible solvers. The GPU version can reduce the
+wall clock time by an order of magnitude compared to the CPU version for large
+meshes. Due to the increased performance and efficient use of memory, the GPU
+version of AFiD can perform simulations in parameter ranges that are
+unprecedented in thermally-driven wall-bounded turbulence. To verify the
+accuracy of the code, turbulent Rayleigh-Bénard convection and plane Couette
+flow are simulated and the results are in good agreement with the experimental
+and computational data that published in previous literatures.
+"
+22380,Rasch Analysis of the Mathematics Self Concept Questionnaire,"  TIMSS & PIRLS International Study Center is a research center at Boston
+College that conducts a series of assessments in a number of countries to
+measure trends in mathematics and science achievement at the fourth and eighth
+grades. In general, TIMSS assessments include achievement tests as well as
+questionnaires for student, parent, teacher, school, and curricular. There are
+63 participating countries and 14 benchmarking participants in TIMSS 2011,
+including 608,641 students, 49,429 teachers, 19,612 school principals, and the
+National Research Coordinators of each country. TIMSS data are valuable for
+researchers and analysts from all over the world, especially for those from
+participating countries, to conduct related studies to improve education.
+"
+22381,Pomegranate: fast and flexible probabilistic modeling in python,"  We present pomegranate, an open source machine learning package for
+probabilistic modeling in Python. Probabilistic modeling encompasses a wide
+range of methods that explicitly describe uncertainty using probability
+distributions. Three widely used probabilistic models implemented in
+pomegranate are general mixture models, hidden Markov models, and Bayesian
+networks. A primary focus of pomegranate is to abstract away the complexities
+of training models from their definition. This allows users to focus on
+specifying the correct model for their application instead of being limited by
+their understanding of the underlying algorithms. An aspect of this focus
+involves the collection of additive sufficient statistics from data sets as a
+strategy for training models. This approach trivially enables many useful
+learning strategies, such as out-of-core learning, minibatch learning, and
+semi-supervised learning, without requiring the user to consider how to
+partition data or modify the algorithms to handle these tasks themselves.
+pomegranate is written in Cython to speed up calculations and releases the
+global interpreter lock to allow for built-in multithreaded parallelism, making
+it competitive with---or outperform---other implementations of similar
+algorithms. This paper presents an overview of the design choices in
+pomegranate, and how they have enabled complex features to be supported by
+simple code.
+"
+22382,Quasitrivial semigroups: characterizations and enumerations,"  We investigate the class of quasitrivial semigroups and provide various
+characterizations of the subclass of quasitrivial and commutative semigroups as
+well as the subclass of quasitrivial and order-preserving semigroups. We also
+determine explicitly the sizes of these classes when the semigroups are defined
+on finite sets. As a byproduct of these enumerations, we obtain several new
+integer sequences.
+"
+22383,Distributed inference for quantile regression processes,"  The increased availability of massive data sets provides a unique opportunity
+to discover subtle patterns in their distributions, but also imposes
+overwhelming computational challenges. To fully utilize the information
+contained in big data, we propose a two-step procedure: (i) estimate
+conditional quantile functions at different levels in a parallel computing
+environment; (ii) construct a conditional quantile regression process through
+projection based on these estimated quantile curves. Our general quantile
+regression framework covers both linear models with fixed or growing dimension
+and series approximation models. We prove that the proposed procedure does not
+sacrifice any statistical inferential accuracy provided that the number of
+distributed computing units and quantile levels are chosen properly. In
+particular, a sharp upper bound for the former and a sharp lower bound for the
+latter are derived to capture the minimal computational cost from a statistical
+perspective. As an important application, the statistical inference on
+conditional distribution functions is considered. Moreover, we propose
+computationally efficient approaches to conducting inference in the distributed
+estimation setting described above. Those approaches directly utilize the
+availability of estimators from sub-samples and can be carried out at almost no
+additional computational cost. Simulations confirm our statistical inferential
+theory.
+"
+22384,Manifold Alignment Determination: finding correspondences across different data views,"  We present Manifold Alignment Determination (MAD), an algorithm for learning
+alignments between data points from multiple views or modalities. The approach
+is capable of learning correspondences between views as well as correspondences
+between individual data-points. The proposed method requires only a few aligned
+examples from which it is capable to recover a global alignment through a
+probabilistic model. The strong, yet flexible regularization provided by the
+generative model is sufficient to align the views. We provide experiments on
+both synthetic and real data to highlight the benefit of the proposed approach.
+"
+22385,Residues modulo powers of two in the Young-Fibonacci lattice,"  We study the subgraph of the Young-Fibonacci graph induced by elements with
+odd $f$-statistic (the $f$-statistic of an element $w$ of a differential graded
+poset is the number of saturated chains from the minimal element of the poset
+to $w$). We show that this subgraph is a binary tree. Moreover, the odd
+residues of the $f$-statistics in a row of this tree equidistibute modulo any
+power two. This is equivalent to a purely number theoretic result about the
+equidistribution of residues modulo powers of two among the products of
+distinct odd numbers less than a fixed number.
+"
+22386,On a discretization of confocal quadrics. II. A geometric approach to general parametrizations,"  We propose a discretization of classical confocal coordinates. It is based on
+a novel characterization thereof as factorizable orthogonal coordinate systems.
+Our geometric discretization leads to factorizable discrete nets with a novel
+discrete analog of the orthogonality property. A discrete confocal coordinate
+system may be constructed geometrically via polarity with respect to a sequence
+of classical confocal quadrics. Various sequences correspond to various
+discrete parametrizations. The coordinate functions of discrete confocal
+quadrics are computed explicitly. The theory is illustrated with a variety of
+examples in two and three dimensions. These include confocal coordinate systems
+parametrized in terms of Jacobi elliptic functions. Connections with incircular
+(IC) nets and a generalized Euler-Poisson-Darboux system are established.
+"
+22387,Reliability and Fault-Tolerance by Choreographic Design,"  Distributed programs are hard to get right because they are required to be
+open, scalable, long-running, and tolerant to faults. In particular, the recent
+approaches to distributed software based on (micro-)services where different
+services are developed independently by disparate teams exacerbate the problem.
+In fact, services are meant to be composed together and run in open context
+where unpredictable behaviours can emerge. This makes it necessary to adopt
+suitable strategies for monitoring the execution and incorporate recovery and
+adaptation mechanisms so to make distributed programs more flexible and robust.
+The typical approach that is currently adopted is to embed such mechanisms in
+the program logic, which makes it hard to extract, compare and debug. We
+propose an approach that employs formal abstractions for specifying failure
+recovery and adaptation strategies. Although implementation agnostic, these
+abstractions would be amenable to algorithmic synthesis of code, monitoring and
+tests. We consider message-passing programs (a la Erlang, Go, or MPI) that are
+gaining momentum both in academia and industry. Our research agenda consists of
+(1) the definition of formal behavioural models encompassing failures, (2) the
+specification of the relevant properties of adaptation and recovery strategy,
+(3) the automatic generation of monitoring, recovery, and adaptation logic in
+target languages of interest.
+"
+22388,Oscillator versus prefundamental representations II. Arbitrary higher ranks,"  We find the $\ell$-weights and the $\ell$-weight vectors for the highest
+$\ell$-weight $q$-oscillator representations of the positive Borel subalgebra
+of the quantum loop algebra $U_q(\mathcal L(\mathfrak{sl}_{l+1}))$ for
+arbitrary values of $l$. Having this, we establish the explicit relationship
+between the $q$-oscillator and prefundamental representations. Our
+consideration allows us to conclude that the prefundamental representations can
+be obtained by tensoring $q$-oscillator representations.
+"
+22389,Likelihood test in permutations with bias. Premier League and La Liga: surprises during the last 25 seasons,"  In this paper, we introduce the models of permutations with bias, which are
+random permutations of a set, biased by some preference values. We present a
+new parametric test, together with an efficient way to calculate its p-value.
+The final tables of the English and Spanish major soccer leagues are tested
+according to this new procedure, to discover whether these results were aligned
+with expectations.
+"
+22390,The invasion speed of cell migration models with realistic cell cycle time distributions,"  Cell proliferation is typically incorporated into stochastic mathematical
+models of cell migration by assuming that cell divisions occur after an
+exponentially distributed waiting time. Experimental observations, however,
+show that this assumption is often far from the real cell cycle time
+distribution (CCTD). Recent studies have suggested an alternative approach to
+modelling cell proliferation based on a multi-stage representation of the CCTD.
+In order to validate and parametrise these models, it is important to connect
+them to experimentally measurable quantities. In this paper we investigate the
+connection between the CCTD and the speed of the collective invasion. We first
+state a result for a general CCTD, which allows the computation of the invasion
+speed using the Laplace transform of the CCTD. We use this to deduce the range
+of speeds for the general case. We then focus on the more realistic case of
+multi-stage models, using both a stochastic agent-based model and a set of
+reaction-diffusion equations for the cells' average density. By studying the
+corresponding travelling wave solutions, we obtain an analytical expression for
+the speed of invasion for a general N-stage model with identical transition
+rates, in which case the resulting cell cycle times are Erlang distributed. We
+show that, for a general N-stage model, the Erlang distribution and the
+exponential distribution lead to the minimum and maximum invasion speed,
+respectively. This result allows us to determine the range of possible invasion
+speeds in terms of the average proliferation time for any multi-stage model.
+"
+22391,Visualizing Co-Phylogenetic Reconciliations,"  We introduce a hybrid metaphor for the visualization of the reconciliations
+of co-phylogenetic trees, that are mappings among the nodes of two trees. The
+typical application is the visualization of the co-evolution of hosts and
+parasites in biology. Our strategy combines a space-filling and a node-link
+approach. Differently from traditional methods, it guarantees an unambiguous
+and `downward' representation whenever the reconciliation is time-consistent
+(i.e., meaningful). We address the problem of the minimization of the number of
+crossings in the representation, by giving a characterization of planar
+instances and by establishing the complexity of the problem. Finally, we
+propose heuristics for computing representations with few crossings.
+"
+22392,Comparison the optical properties for Bi2O3 and NiO ultrathin films deposited on different substrates by DC sputtering technique for transparent electronics,"  Bismuth and Nickel transparent oxides thin films were grown on glass and
+flexible polythelene terephalate (PET) substrates by DC sputtering technique at
+room temperature 300 K. The structures of Bi2O3 and NiO films were analyzed by
+X-ray diffraction (XRD) analyses and scanning electron microscopy (SEM). A
+profilometry is used to measure the thicknesses of the ultrathin films. These
+values were established by a spectro-ellipsometry technique with a new
+amorphous dispersion model in the range of 200 nm to 2200 nm with one nm
+increment. In addition, the optical constants of these films were investigated
+using a double beam UV-Vis-NIR spectrometer and a spectro-ellipsometry and
+compared using the same wavelength range. They present an excellent agreement.
+According to the optical transmittance spectra of the films a high average
+transmittance in the visible region of each sample deposited onto different
+substrates is measured. Moreover, the absorption coefficients and the optical
+energy gaps for four types of optical transitions (allowed direct, forbidden
+direct, allowed indirect, forbidden indirect) of the films were determined and
+compared with the two different techniques. Finally, the nature of the
+structure and morphology of Bi2O3 and NiO ultrathin films has been analyzed and
+compared.
+"
+22393,"On cogrowth, amenability and the spectral radius of a random walk on a semigroup","  We introduce two natural notions of cogrowth for finitely generated
+semigroups --- one local and one global --- and study their relationship with
+amenability and random walks. We establish the minimal and maximal possible
+values for cogrowth rates, and show that non-monogenic free semigroups are
+exactly characterised by minimal global cogrowth. We consider the relationship
+with cogrowth for groups and with amenability of semigroups. We also study the
+relationship with random walks on finitely generated semigroups, and in
+particular the spectral radius of the associated Markov operators (when
+defined) on $\ell_2$-spaces. We show that either of maximal global cogrowth or
+the weak F{\o}lner condition suffices for its spectral radius to be at least
+$1$, since left amenability implies the weak F{\o}lner condition, this
+represents a generalisation to semigroups of one implication of Kesten's
+Theorem for groups. By combining with known results about amenability, we are
+able to establish a number of new sufficient conditions for (left or right)
+amenability in broad classes of semigroups. In particular, maximal local
+cogrowth left implies amenability in any left reversible semigroup, while
+maximal global cogrowth (which is a much weaker property) suffices for left
+amenability in an extremely broad class of semigroups encompassing all inverse
+semigroups, left reversible left cancellative semigroups and left reversible
+regular semigroups.
+"
+22394,Kinetic temperature of massive star forming molecular clumps measured with formaldehyde. II. The Large Magellanic Cloud,"  The Large Magellanic Cloud (LMC), the closest star forming galaxy with low
+metallicity, provides an ideal laboratory to study star formation in such an
+environment. The classical dense molecular gas thermometer NH3 is rarely
+available in a low metallicity environment because of photoionization and a
+lack of nitrogen atoms. Our goal is to directly measure the gas kinetic
+temperature with formaldehyde toward six star-forming regions in the LMC. Three
+rotational transitions of para-H2CO near 218 GHz were observed with the APEX
+12m telescope toward six star forming regions in the LMC. Those data are
+complemented by C18O 2-1 spectra. Using non-LTE modeling with RADEX, we derive
+the gas kinetic temperature and spatial density, using as constraints the
+measured para-H2CO 321-220/303-202 and para-H2CO 303-202/C18O 2-1 ratios.
+Excluding the quiescent cloud N159S, where only one para-H2CO line could be
+detected, the gas kinetic temperatures derived from the preferred para-H2CO
+321-220/303-202 line ratios range from 35 to 63 K with an average of 47 K.
+Spatial densities of the gas derived from the paraH2CO 303-202/C18O 2-1 line
+ratios yield 0.4-2.9x10^5 cm^-3 with an average of 1.5x10^5 cm^-3. Temperatures
+derived from the para-H2CO line ratio are similar to those obtained with the
+same method from Galactic star forming regions and agree with results derived
+from CO in the dense regions of the LMC. A comparison of kinetic temperatures
+derived from para-H2CO with those from the dust also shows good agreement. This
+suggests that the dust and para-H2CO are well mixed in the studied star forming
+regions. A correlation between the gas kinetic temperatures derived from
+para-H2CO and infrared luminosity, represented by the 250um flux, suggests that
+the kinetic temperatures traced by para-H2CO are correlated with the ongoing
+massive star formation in the LMC.
+"
+22395,Symbolic Execution for Deep Neural Networks,"  Deep Neural Networks (DNN) are increasingly used in a variety of
+applications, many of them with substantial safety and security concerns. This
+paper introduces DeepCheck, a new approach for validating DNNs based on core
+ideas from program analysis, specifically from symbolic execution. The idea is
+to translate a DNN into an imperative program, thereby enabling program
+analysis to assist with DNN validation. A basic translation however creates
+programs that are very complex to analyze. DeepCheck introduces novel
+techniques for lightweight symbolic analysis of DNNs and applies them in the
+context of image classification to address two challenging problems in DNN
+analysis: 1) identification of important pixels (for attribution and
+adversarial generation); and 2) creation of 1-pixel and 2-pixel attacks.
+Experimental results using the MNIST data-set show that DeepCheck's lightweight
+symbolic analysis provides a valuable tool for DNN validation.
+"
+22396,The homotopy fibre of the inclusion $F\_n(M) \lhook\joinrel\longrightarrow \prod\_{1}^{n} M$ for $M$ either $\mathbb{S}^2$ or$\mathbb{R}P^2$ and orbit configuration spaces,"  Let $n\geq 1$, and let $\iota\_{n}\colon\thinspace F\_{n}(M) \longrightarrow
+\prod\_{1}^{n} M$ be the natural inclusion of the $n$th configuration space of
+$M$ in the $n$-fold Cartesian product of $M$ with itself. In this paper, we
+study the map $\iota\_{n}$, its homotopy fibre $I\_{n}$, and the induced
+homomorphisms $(\iota\_{n})\_{#k}$ on the $k$th homotopy groups of $F\_{n}(M)$
+and $\prod\_{1}^{n} M$ for $k\geq 1$ in the cases where $M$ is the $2$-sphere
+$\mathbb{S}^{2}$ or the real projective plane $\mathbb{R}P^{2}$. If $k\geq 2$,
+we show that the homomorphism $(\iota\_{n})\_{#k}$ is injective and diagonal,
+with the exception of the case $n=k=2$ and $M=\mathbb{S}^{2}$, where it is
+anti-diagonal. We then show that $I\_{n}$ has the homotopy type of
+$K(R\_{n-1},1) \times \Omega(\prod\_{1}^{n-1} \mathbb{S}^{2})$, where
+$R\_{n-1}$ is the $(n-1)$th Artin pure braid group if $M=\mathbb{S}^{2}$, and
+is the fundamental group $G\_{n-1}$ of the $(n-1)$th orbit configuration space
+of the open cylinder $\mathbb{S}^{2}\setminus \{\widetilde{z}\_{0},
+-\widetilde{z}\_{0}\}$ with respect to the action of the antipodal map of
+$\mathbb{S}^{2}$ if $M=\mathbb{R}P^{2}$, where $\widetilde{z}\_{0}\in
+\mathbb{S}^{2}$. This enables us to describe the long exact sequence in
+homotopy of the homotopy fibration $I\_{n} \longrightarrow F\_n(M)
+\stackrel{\iota\_{n}}{\longrightarrow} \prod\_{1}^{n} M$ in geometric terms,
+and notably the boundary homomorphism $\pi\_{k+1}(\prod\_{1}^{n}
+M)\longrightarrow \pi\_{k}(I\_{n})$. From this, if $M=\mathbb{R}P^{2}$ and
+$n\geq 2$, we show that $\ker{(\iota\_{n})\_{#1}}$ is isomorphic to the
+quotient of $G\_{n-1}$ by its centre, as well as to an iterated semi-direct
+product of free groups with the subgroup of order $2$ generated by the centre
+of $P\_{n}(\mathbb{R}P^{2})$ that is reminiscent of the combing operation for
+the Artin pure braid groups, as well as decompositions obtained in a previous
+paper.
+"
+22397,PES: Priority Edge Sampling in Streaming Triangle Estimation,"  The number of triangles ($\Delta$) is an important metric to analyze massive
+graphs. It is also used to compute clustering coefficient in networks. This
+paper proposes a new algorithm called PES (Priority Edge Sampling) to estimate
+triangles in the streaming model where we need to minimize the memory window.
+PES combines edge sampling and reservoir sampling. Compared with the
+state-of-the-art streaming algorithms, PES outperforms consistently. The
+results are verified extensively in 48 large real-world networks in different
+domains and structures. The performance ratio can be as large as 11. More
+importantly, the ratio grows with data size almost exponentially. This is
+especially important in the era of big data--while we can tolerate existing
+algorithms for smaller datasets, our method is indispensable in very large data
+sampling. In addition to empirical comparisons, we also proved that the
+estimator is unbiased, and derived the variance.
+"
+22398,On The Automated Planning And Design Of SMATV Systems,"  The paper presents some theoretical and practical considerations regarding
+the TV information distribution in local (small and medium) networks, using
+different technologies and architectures. The SMATV concept is chosen to be
+presented extensively. The most important design formulae are presented with a
+software package supporting the network planner to design and optimize the
+network. A case study is realized, using standard components in SMATV, for a 5
+floor building. The study proved that it is possible to design and optimize the
+entire network, without realizing first a costly experimental setup. It is also
+possible to run different architectures, optimizing also the costs of the final
+solution of network.
+"
+22399,A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization,"  The performance of many machine learning techniques depends on the choice of
+an appropriate similarity or distance measure on the input space. Similarity
+learning (or metric learning) aims at building such a measure from training
+data so that observations with the same (resp. different) label are as close
+(resp. far) as possible. In this paper, similarity learning is investigated
+from the perspective of pairwise bipartite ranking, where the goal is to rank
+the elements of a database by decreasing order of the probability that they
+share the same label with some query data point, based on the similarity
+scores. A natural performance criterion in this setting is pointwise ROC
+optimization: maximize the true positive rate under a fixed false positive
+rate. We study this novel perspective on similarity learning through a rigorous
+probabilistic framework. The empirical version of the problem gives rise to a
+constrained optimization formulation involving U-statistics, for which we
+derive universal learning rates as well as faster rates under a noise
+assumption on the data distribution. We also address the large-scale setting by
+analyzing the effect of sampling-based approximations. Our theoretical results
+are supported by illustrative numerical experiments.
+"
+22400,On the connection between turbulent motions and particle acceleration in galaxy clusters,"  Giant radio halos are Mpc-scale diffuse radio sources associated with the
+central regions of galaxy clusters. The most promising scenario to explain the
+origin of these sources is that of turbulent re-acceleration, in which MeV
+electrons injected throughout the formation history of galaxy clusters are
+accelerated to higher energies by turbulent motions mostly induced by cluster
+mergers. In this Letter, we use the amplitude of density fluctuations in the
+intracluster medium as a proxy for the turbulent velocity and apply this
+technique to a sample of 51 clusters with available radio data. Our results
+indicate a segregation in the turbulent velocity of radio halo and radio quiet
+clusters, with the turbulent velocity of the former being on average higher by
+about a factor of two. The velocity dispersion recovered with this technique
+correlates with the measured radio power through the relation $P_{\rm
+radio}\propto\sigma_v^{3.3\pm0.7}$, which implies that the radio power is
+nearly proportional to the turbulent energy rate. Our results provide an
+observational confirmation of a key prediction of the turbulent re-acceleration
+model and possibly shed light on the origin of radio halos.
+"
+22401,Optimality of orders one to three and beyond: characterization and evaluation complexity in constrained nonconvex optimization,"  Necessary conditions for high-order optimality in smooth nonlinear
+constrained optimization are explored and their inherent intricacy discussed. A
+two-phase minimization algorithm is proposed which can achieve approximate
+first-, second- and third-order criticality and its evaluation complexity is
+analyzed as a function of the choice (among existing methods) of an inner
+algorithm for solving subproblems in each of the two phases. The relation
+between high-order criticality and penalization techniques is finally
+considered, showing that standard algorithmic approaches will fail if
+approximate constrained high-order critical points are sought.
+"
+22402,Commissioning of ELLIE for SNO+,"  SNO+ is a neutrinoless double beta decay and low energy neutrino experiment
+located in Sudbury, Canada. To improve our understanding of the detector energy
+resolution and systematics, calibration systems have been developed to
+continuously monitor the optical properties of the detector, such as:
+absorption, re-emission, scattering and timing. A part of this in-situ optical
+calibration system is the Embedded LED/Laser Light Injection Entity (ELLIE). It
+consists of three subsystems: AMELLIE, SMELLIE, TELLIE. The attenuation module
+(AMELLIE) is designed to monitor the total optical attenuation, whereas the
+optical scattering over a wavelength range of 375nm -- 700nm will be
+characterized by the scattering module (SMELLIE). The timing module (TELLIE)
+aims to measure the timing characteristics of the photomultiplier tubes. We
+present the planned commissioning of these three systems, the running of which
+began early 2017.
+"
+22403,Magnetic field effects on dynamics of the ethylammonium nitrate ionic liquid confined between polar glass plates,"  Self-diffusion and NMR relaxation of ethylammonium (EA) cations were studied
+in the protic ionic liquid ethylammonium nitrate (EAN) confined between
+parallel polar glass plates separated by a few um as a function of time after
+placement in a magnetic field of 9.40 T. Immediately after sample placement,
+the diffusion coefficient of EA (D) increased by a factor of 2, while the
+transverse NMR relaxation of NH3 protons decreased by factors of up to 22 in
+comparison with bulk EAN, according to previously published data. Further
+exposure to the magnetic field leads to gradual changes of D, T1 and T2 with a
+time constant of 70 min and a total equilibration time of longer than 4 hours.
+This process does not depend on the orientation of the glass plates relative to
+the magnetic field. Removing the sample from the magnetic field and repeating
+the experiment demonstrated that complete recovery of the sample to the
+accelerated D and shortened T2 occurs in approximately 24 hours. Thus, EA
+cation dynamics is accelerated relative to bulk when the cation is confined
+between polar glass plates and stored in the absence of a strong magnetic
+field, but demonstrate a trend to change to bulk values of D and T2 after
+exposure to a magnetic field. Because the observed characteristic times of the
+change far exceed molecular process times in EAN, we relate this phenomenon to
+reversible phase transformations occurring with EAN between polar plates
+outside the strong magnetic field, and reverse change of the phases after
+placing the sample in the magnetic field. This conclusion was confirmed by data
+of Raman spectroscopy.
+"
+22404,Fractional derivatives of composite functions and the Cauchy problem for the nonlinear half wave equation,"  We show new results of wellposedness for the Cauchy problem for the half wave
+equation with power-type nonlinear terms. For the purpose, we propose two
+approaches on the basis of the contraction-mapping argument. One of them relies
+upon the $L_t^q L_x^\infty$ Strichartz-type estimate together with the chain
+rule of fairly general fractional orders. This chain rule has a significance of
+its own. Furthermore, in addition to the weighted fractional chain rule
+established in Hidano, Jiang, Lee, and Wang (arXiv:1605.06748v1 [math.AP]), the
+other approach uses weighted space-time $L^2$ estimates for the inhomogeneous
+equation which are recovered from those for the second-order wave equation. In
+particular, by the latter approach we settle the problem left open in
+Bellazzini, Georgiev, and Visciglia (arXiv:1611.04823v1 [math.AP]) concerning
+the local wellposedness in $H^{s}_{\rm rad}({\mathbb R}^n)$ with $s>1/2$.
+"
+22405,Ambient Backscatter Networking: A Novel Paradigm to Assist Wireless Powered Communications,"  Ambient backscatter communication technology has been introduced recently,
+and is then quickly becoming a promising choice for self-sustainable
+communication systems as an external power supply or a dedicated carrier
+emitter is not required. By leveraging existing RF signal resources, ambient
+backscatter technology can support sustainable and independent communications
+and consequently open up a whole new set of applications that facilitate
+Internet-of-Things (IoT). In this article, we study an integration of ambient
+backscatter with wireless powered communication networks (WPCNs). We first
+present an overview of backscatter communication systems with an emphasis on
+the emerging ambient backscatter technology. Then we propose a novel hybrid
+transmitter design by combining the advantages of both ambient backscatter and
+wireless powered communications. Furthermore, in the cognitive radio
+environment, we introduce a multiple access scheme to coordinate the hybrid
+data transmissions. The performance evaluation shows that the hybrid
+transmitter outperforms traditional designs. In addition, we discuss some open
+issues related to the ambient backscatter networking.
+"
+22406,Gyrokinetic projection of the divertor heat-flux width from present tokamaks to ITER,"  The XGC1 edge gyrokinetic code is used for a high fidelity prediction for the
+width of the heat-flux to divertor plates in attached plasma condition. The
+simulation results are validated against the empirical scaling $\lambda_q
+\propto B_P^{-\gamma}$ obtained from present tokamak devices, where $\lambda_q$
+is the divertor heat-flux width mapped to the outboard midplane and
+$\gamma_q=1.19$ as defined by T. Eich et al. [Nucl. Fusion 53 (2013) 093031],
+and $B_P$ is the magnitude of the poloidal magnetic field at outboard midplane
+separatrix surface. This empirical scaling predicts $\lambda_q \leq 1mm$ when
+extrapolated to ITER, which would require operation with very high separatrix
+densities $(n_{sep}/n_{Greenwald} > 0.6)$ in the Q=10 scenario to achieve
+semi-detached plasma operation and high radiative fractions leading to
+acceptable divertor power fluxes. XGC1 predicts, however, that $\lambda_q$ for
+ITER is over 5 mm, suggesting that operation in the ITER Q=10 scenario with
+acceptable divertor power loads could be obtained over a wider range of plasma
+separatrix densities and radiative fractions. The physics reason behind this
+difference is, according to the XGC1 results, that while the ion magnetic drift
+contribution to the divertor heat-flux width is wider in the present tokamaks,
+the turbulent electron contribution is wider in ITER. A high current C-Mod
+discharge is found to be in a mixed regime: While the heat-flux width by the
+ion neoclassical magnetic drift is still wider than the turbulent electron
+heat-flux width, the heat-flux magnitude is dominated by the narrower electron
+heat-flux.
+"
+22407,Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations,"  In our work, we bridge deep neural network design with numerical differential
+equations. We show that many effective networks, such as ResNet, PolyNet,
+FractalNet and RevNet, can be interpreted as different numerical
+discretizations of differential equations. This finding brings us a brand new
+perspective on the design of effective deep architectures. We can take
+advantage of the rich knowledge in numerical analysis to guide us in designing
+new and potentially more effective deep networks. As an example, we propose a
+linear multi-step architecture (LM-architecture) which is inspired by the
+linear multi-step method solving ordinary differential equations. The
+LM-architecture is an effective structure that can be used on any ResNet-like
+networks. In particular, we demonstrate that LM-ResNet and LM-ResNeXt (i.e. the
+networks obtained by applying the LM-architecture on ResNet and ResNeXt
+respectively) can achieve noticeably higher accuracy than ResNet and ResNeXt on
+both CIFAR and ImageNet with comparable numbers of trainable parameters. In
+particular, on both CIFAR and ImageNet, LM-ResNet/LM-ResNeXt can significantly
+compress ($>50$\%) the original networks while maintaining a similar
+performance. This can be explained mathematically using the concept of modified
+equation from numerical analysis. Last but not least, we also establish a
+connection between stochastic control and noise injection in the training
+process which helps to improve generalization of the networks. Furthermore, by
+relating stochastic training strategy with stochastic dynamic system, we can
+easily apply stochastic training to the networks with the LM-architecture. As
+an example, we introduced stochastic depth to LM-ResNet and achieve significant
+improvement over the original LM-ResNet on CIFAR10.
+"
+22408,Link Prediction Based on Graph Neural Networks,"  Link prediction is a key problem for network-structured data. Link prediction
+heuristics use some score functions, such as common neighbors and Katz index,
+to measure the likelihood of links. They have obtained wide practical uses due
+to their simplicity, interpretability, and for some of them, scalability.
+However, every heuristic has a strong assumption on when two nodes are likely
+to link, which limits their effectiveness on networks where these assumptions
+fail. In this regard, a more reasonable way should be learning a suitable
+heuristic from a given network instead of using predefined ones. By extracting
+a local subgraph around each target link, we aim to learn a function mapping
+the subgraph patterns to link existence, thus automatically learning a
+`heuristic' that suits the current network. In this paper, we study this
+heuristic learning paradigm for link prediction. First, we develop a novel
+$\gamma$-decaying heuristic theory. The theory unifies a wide range of
+heuristics in a single framework, and proves that all these heuristics can be
+well approximated from local subgraphs. Our results show that local subgraphs
+reserve rich information related to link existence. Second, based on the
+$\gamma$-decaying theory, we propose a new algorithm to learn heuristics from
+local subgraphs using a graph neural network (GNN). Its experimental results
+show unprecedented performance, working consistently well on a wide range of
+problems.
+"
+22409,LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos,"  Neuronal assemblies, loosely defined as subsets of neurons with reoccurring
+spatio-temporally coordinated activation patterns, or ""motifs"", are thought to
+be building blocks of neural representations and information processing. We
+here propose LeMoNADe, a new exploratory data analysis method that facilitates
+hunting for motifs in calcium imaging videos, the dominant microscopic
+functional imaging modality in neurophysiology. Our nonparametric method
+extracts motifs directly from videos, bypassing the difficult intermediate step
+of spike extraction. Our technique augments variational autoencoders with a
+discrete stochastic node, and we show in detail how a differentiable
+reparametrization and relaxation can be used. An evaluation on simulated data,
+with available ground truth, reveals excellent quantitative performance. In
+real video data acquired from brain slices, with no ground truth available,
+LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses
+for more focused biological investigations.
+"
+22410,Generalized stability for abstract homotopy theories,"  We show that a derivator is stable if and only if homotopy finite limits and
+homotopy finite colimits commute, if and only if homotopy finite limit functors
+have right adjoints, and if and only if homotopy finite colimit functors have
+left adjoints. These characterizations generalize to an abstract notion of
+""stability relative to a class of functors"", which includes in particular
+pointedness, semiadditivity, and ordinary stability. To prove them, we develop
+the theory of derivators enriched over monoidal left derivators and weighted
+homotopy limits and colimits therein.
+"
+22411,Distribution of Chern-Simons invariants,"  Let $M$ be a 3-manifold with a finite set $X(M)$ of conjugacy classes of
+representations $\rho:\pi_1(M)\to$SU$_2$. We study here the distribution of the
+values of the Chern-Simons function CS$:X(M)\to \mathbb{R}/2\pi\mathbb{Z}$. We
+observe in some examples that it resembles the distribution of quadratic
+residues. In particular for specific sequences of $3$-manifolds, the invariants
+tends to become equidistributed on the circle with white noise fluctuations of
+order $|X(M)|^{-1/2}$. We prove that for a manifold with toric boundary the
+Chern-Simons invariants of the Dehn fillings $M_{p/q}$ have the same behaviour
+when $p$ and $q$ go to infinity and compute fluctuations at first order.
+"
+22412,Near-Data Scheduling for Data Centers with Multiple Levels of Data Locality,"  Data locality is a fundamental issue for data-parallel applications.
+Considering MapReduce in Hadoop, the map task scheduling part requires an
+efficient algorithm which takes data locality into consideration; otherwise,
+the system may become unstable under loads inside the system's capacity region
+and jobs may experience longer completion times which are not of interest. The
+data chunk needed for any map task can be in memory, on a local disk, in a
+local rack, in the same cluster or even in another data center. Hence, unless
+there has been much work on improving the speed of data center networks,
+different levels of service rates still exist for a task depending on where its
+data chunk is saved and from which server it receives service. Most of the
+theoretical work on load balancing is for systems with two levels of data
+locality including the Pandas algorithm by Xie et al. and the JSQ-MW algorithm
+by Wang et al., where the former is both throughput and heavy-traffic optimal,
+while the latter is only throughput optimal, but heavy-traffic optimal in only
+a special traffic load. We show that an extension of the JSQ-MW algorithm for a
+system with thee levels of data locality is throughput optimal, but not
+heavy-traffic optimal for all loads, only for a special traffic scenario.
+Furthermore, we show that the Pandas algorithm is not even throughput optimal
+for a system with three levels of data locality. We then propose a novel
+algorithm, Balanced-Pandas, which is both throughput and heavy-traffic optimal.
+To the best of our knowledge, this is the first theoretical work on load
+balancing for a system with more than two levels of data locality. This is more
+challenging than two levels of data locality as a dilemma between performance
+and throughput emerges.
+"
+22413,s+is superconductivity with incipient bands: doping dependence and STM signatures,"  Motivated by the recent observations of small Fermi energies and
+comparatively large superconducting gaps, present also on bands not crossing
+the Fermi energy (incipient bands) in iron-based superconductors, we analyse
+the doping evolution of superconductivity in a four-band model across the
+Lifshitz transition including BCS-BEC crossover effects on the shallow bands.
+Similar to the BCS case we find that with hole doping the phase difference
+between superconducting order parameters of the hole bands changes from $0$ to
+$\pi$ through an intermediate $s+is$ state breaking time-reversal symmetry. The
+transition however occurs in the region where electron bands are incipient and
+chemical potential renormalization in the superconducting state leads to a
+significant broadening of the $s+is$ region. We further present the qualitative
+features of the $s+is$ state that can be observed in scanning tunnelling
+microscopy (STM) experiments also taking incipient bands into account.
+"
+22414,Studying the occupied and unoccupied electronic structure of LaCoO$_{3}$ by using DFT+embedded DMFT method with the calculated value of $\textit{U}$,"  In this work, we present a systematic study of the occupied and unoccupied
+electronic states of LaCoO$_{3}$ compound using DFT, DFT+$\textit{U}$ and
+DFT+embedded DMFT methods. The value of $\textit{U}$ used here is evaluated by
+using constrained DFT method and found to be $ \backsim $ 6.9 eV. It is found
+that DFT result has limitations with energy positions of PDOS peaks due to its
+inability of creating a hard gap although the DOS distribution appears to be
+fine with experimental attributes. The calculated value of $\textit{U}$ is not
+an appropriate value for carrying out DFT+$\textit{U}$ calculations as it has
+created an insulating gap of $ \backsim $ 1.8 eV with limitations in
+redistribution of DOS which is inconsistent with experimental spectral
+behaviour for the occupied states mainly. However, this value of $\textit{U}$
+is found to be an appropriate one for DFT+embedded DMFT method which creates a
+gap of $\backsim $ 1.1 eV. The calculated PDOS of Co 3$\textit{d}$, La
+5$\textit{d}$, La 4$\textit{f}$ and O 2$\textit{p}$ states are giving a
+remarkably good explanation for the occupied and unoccupied states of the
+experimental spectra in the energy range $\backsim $ -9.0 eV to $\backsim $
+12.0 eV.
+"
+22415,Axiomatic Foundations and Algorithms for Deciding Semantic Equivalences of SQL Queries,"  Deciding the equivalence of SQL queries is a fundamental problem in data
+management. As prior work has mainly focused on studying the theoretical
+limitations of the problem, very few implementations for checking such
+equivalences exist. In this paper, we present a new formalism and
+implementation for reasoning about the equivalences of SQL queries. Our
+formalism, U-semiring, extends SQL's semiring semantics with unbounded
+summation and duplicate elimination. U-semiring is defined using only very few
+axioms and can thus be easily implemented using proof assistants such as Coq
+for automated query reasoning. Yet, they are sufficient enough to enable us
+reason about sophisticated SQL queries that are evaluated over bags and sets,
+along with various integrity constraints. To evaluate the effectiveness of
+U-semiring, we have used it to formally verify 39 query rewrite rules from both
+classical data management research papers and real-world SQL engines, where
+many of them have never been proven correct before.
+"
+22416,A STEREO Survey of Magnetic Cloud Coronal Mass Ejections Observed at Earth in 2008--2012,"  We identify coronal mass ejections (CMEs) associated with magnetic clouds
+(MCs) observed near Earth by the Wind spacecraft from 2008 to mid-2012, a time
+period when the two STEREO spacecraft were well positioned to study
+Earth-directed CMEs. We find 31 out of 48 Wind MCs during this period can be
+clearly connected with a CME that is trackable in STEREO imagery all the way
+from the Sun to near 1 AU. For these events, we perform full 3-D
+reconstructions of the CME structure and kinematics, assuming a flux rope
+morphology for the CME shape, considering the full complement of STEREO and
+SOHO imaging constraints. We find that the flux rope orientations and sizes
+inferred from imaging are not well correlated with MC orientations and sizes
+inferred from the Wind data. However, velocities within the MC region are
+reproduced reasonably well by the image-based reconstruction. Our kinematic
+measurements are used to provide simple prescriptions for predicting CME
+arrival times at Earth, provided for a range of distances from the Sun where
+CME velocity measurements might be made. Finally, we discuss the differences in
+the morphology and kinematics of CME flux ropes associated with different
+surface phenomena (flares, filament eruptions, or no surface activity).
+"
+22417,Deep artifact learning for compressed sensing and parallel MRI,"  Purpose: Compressed sensing MRI (CS-MRI) from single and parallel coils is
+one of the powerful ways to reduce the scan time of MR imaging with performance
+guarantee. However, the computational costs are usually expensive. This paper
+aims to propose a computationally fast and accurate deep learning algorithm for
+the reconstruction of MR images from highly down-sampled k-space data.
+Theory: Based on the topological analysis, we show that the data manifold of
+the aliasing artifact is easier to learn from a uniform subsampling pattern
+with additional low-frequency k-space data. Thus, we develop deep aliasing
+artifact learning networks for the magnitude and phase images to estimate and
+remove the aliasing artifacts from highly accelerated MR acquisition.
+Methods: The aliasing artifacts are directly estimated from the distorted
+magnitude and phase images reconstructed from subsampled k-space data so that
+we can get an aliasing-free images by subtracting the estimated aliasing
+artifact from corrupted inputs. Moreover, to deal with the globally distributed
+aliasing artifact, we develop a multi-scale deep neural network with a large
+receptive field.
+Results: The experimental results confirm that the proposed deep artifact
+learning network effectively estimates and removes the aliasing artifacts.
+Compared to existing CS methods from single and multi-coli data, the proposed
+network shows minimal errors by removing the coherent aliasing artifacts.
+Furthermore, the computational time is by order of magnitude faster.
+Conclusion: As the proposed deep artifact learning network immediately
+generates accurate reconstruction, it has great potential for clinical
+applications.
+"
+22418,A complete equation of state for non-ideal condensed phase explosives,"  The objective of this work is to improve the robustness and accuracy of
+numerical simulations of both ideal and non-ideal explosives by introducing
+temperature dependence in mechanical equations of state for reactants and
+products. To this end, we modify existing mechanical equations of state to
+appropriately approximate the temperature in the reaction zone. Mechanical
+equations of state of Mie-Grüneisen form are developed with extensions,
+which allow the temperature to be evaluated appropriately, and the temperature
+equilibrium condition to be applied robustly. Furthermore the snow plow model
+is used to capture the effect of porosity on the reactants equation of state.
+We apply the methodology to predict the velocity of compliantly confined
+detonation waves. Once reaction rates are calibrated for unconfined detonation
+velocities, simulations of confined rate sticks and slabs are performed, and
+the experimental detonation velocities are matched without further parameter
+alteration, demonstrating the predictive capability of our simulations. We
+apply the same methodology to both ideal (PBX9502, a high explosive with
+principal ingredient TATB) and non-ideal (EM120D, an ANE or ammonium nitrate
+based emulsion) explosives.
+"
+22419,Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection,"  A significant challenge in energy system cyber security is the current
+inability to detect cyber-physical attacks targeting and originating from
+distributed grid-edge devices such as photovoltaics (PV) panels, smart flexible
+loads, and electric vehicles. We address this concern by designing and
+developing a distributed, multi-modal anomaly detection approach that can sense
+the health of the device and the electric power grid from the edge. This is
+realized by exploiting unsupervised machine learning algorithms on multiple
+sources of time-series data, fusing these multiple local observations and
+flagging anomalies when a deviation from the normal behavior is observed.
+We particularly focus on the cyber-physical threats to the distributed PVs
+that has the potential to cause local disturbances or grid instabilities by
+creating supply-demand mismatch, reverse power flow conditions etc. We use an
+open source power system simulation tool called GridLAB-D, loaded with real
+smart home and solar datasets to simulate the smart grid scenarios and to
+illustrate the impact of PV attacks on the power system. Various attacks
+targeting PV panels that create voltage fluctuations, reverse power flow etc
+were designed and performed. We observe that while individual unsupervised
+learning algorithms such as OCSVMs, Corrupt RF and PCA surpasses in identifying
+particular attack type, PCA with Convex Hull outperforms all algorithms in
+identifying all designed attacks with a true positive rate of 83.64% and an
+accuracy of 95.78%. Our key insight is that due to the heterogeneous nature of
+the distribution grid and the uncertainty in the type of the attack being
+launched, relying on single mode of information for defense can lead to
+increased false alarms and missed detection rates as one can design attacks to
+hide within those uncertainties and remain stealthy.
+"
+22420,A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization,"  We study the problem of domain adaptation for neural abstractive
+summarization. We make initial efforts in investigating what information can be
+transferred to a new domain. Experimental results on news stories and opinion
+articles indicate that neural summarization model benefits from pre-training
+based on extractive summaries. We also find that the combination of in-domain
+and out-of-domain setup yields better summaries when in-domain data is
+insufficient. Further analysis shows that, the model is capable to select
+salient content even trained on out-of-domain data, but requires in-domain data
+to capture the style for a target domain.
+"
+22421,WKB eigenmode construction for analytic Toeplitz operators,"  We provide almost eigenfunctions for Toeplitz operators with real-analytic
+symbols, at the bottom of non-degenerate wells. These almost eigenfunctions
+follow the WKB ansatz; the error is O(exp(--cN)), where c > 0 and N
+$\rightarrow$ +$\infty$ is the inverse semiclassical parameter.
+"
+22422,Study on Effects of Dipole-dipole Interactions in Nd_2Fe_{14}B thin film Based on Stochastic Cut-off Method with New Efficient Algorithm,"  We developed a new algorithm for the stochastic cutoff method, which is a
+Monte Carlo method for long-range interacting systems. The present new
+algorithm is efficient for complicated lattice structures, e.g., amorphous and
+also materials with complicated unit cell structure. In addition, it has an
+advantage in a high temperature region. With the present method, we
+investigated an atomistic model of the permanent magnet Nd 2 Fe 14 B with
+dipole-dipole interactions to study the effect of the anisotropy of the Fe ions
+on the magnetization structure in the thin film system. It has revealed that
+the anisotropy of the Fe ions plays an important role to maintain the uniaxial
+magnetization structure at high temperatures.
+"
+22423,Eigenvalues and Wiener index of the Zero Divisor graph $Γ[\mathbb {Z}_n]$,"  The Zero divisor Graph of a commutative ring $R$, denoted by $\Gamma[R]$, is
+a graph whose vertices are non-zero zero divisors of $R$ and two vertices are
+adjacent if their product is zero. In this paper, we consider the zero divisor
+graph $\Gamma[\mathbb{Z}_n]$ for $n=p^3$ and $n=p^2q$ with $p$ and $q$ primes.
+We discuss the adjacency matrix and eigenvalues of the zero divisor graph
+$\Gamma[\mathbb{Z}_n]$. We also calculate the energy of the graph
+$\Gamma[\mathbb{Z}_n]$.
+"
+22424,Rigidity of Circle Polyhedra in the 2-Sphere and of Hyperideal Polyhedra in Hyperbolic 3-Space,"  We generalize Cauchy's celebrated theorem on the global rigidity of convex
+polyhedra in Euclidean $3$-space $\mathbb{E}^{3}$ to the context of circle
+polyhedra in the $2$-sphere $\mathbb{S}^{2}$. We prove that any two convex and
+proper non-unitary c-polyhedra with Möbius-congruent faces that are
+consistently oriented are Möbius-congruent. Our result implies the global
+rigidity of convex inversive distance circle packings in the Riemann sphere as
+well as that of certain hyperideal hyperbolic polyhedra in $\mathbb{H}^{3}$.
+"
+22425,Bridging Network Embedding and Graph Summarization,"  An important reason behind the prevalence of node representation learning is
+their superiority in downstream machine learning tasks on graphs. However,
+storing the vector-based node representation of massive real-world graphs often
+requires space that is orders of magnitude larger. To alleviate this issue, we
+introduce the problem of latent network summarization that is complementary to
+the problem of network embedding, and propose a general framework called
+Multi-LENS. Instead of deriving dense node-wise representations, the goal of
+latent network summarization is to summarize the structural properties of the
+graph in a latent space with dimensionality that is independent of the nodes or
+edges in the input graph. The size-independent graph summary given by
+Multi-LENS consists of (i) a set of relational aggregators with their
+compositions (relational functions) that captures structural features of
+multi-order node-centric subgraphs, and (ii) the low-rank approximations to
+matrices that incorporate captured structural features. In addition, Multi-LENS
+is able to derive node embeddings on the fly from this latent summary due to
+its inductive properties. Multi-LENS bridges advantages brought by both
+embeddings and graph summarization, and applies to graphs with or without
+directionality, weights, attributes or labels. Extensive experiments on both
+synthetic and real-world graphs show that Multi-LENS achieves 2 - 89%
+improvement in AUC for link prediction, while requiring less than 79x space
+compared to existing representation learning approaches. We also show the
+effectiveness of Multi-LENS summaries in anomaly and event detection on two
+real-world graphs.
+"
+22426,Controlling a Robotic Stereo Camera Under Image Quantization Noise,"  In this paper, we address the problem of controlling a mobile stereo camera
+under image quantization noise. Assuming that a pair of images of a set of
+targets is available, the camera moves through a sequence of Next-Best-Views
+(NBVs), i.e., a sequence of views that minimize the trace of the targets'
+cumulative state covariance, constructed using a realistic model of the stereo
+rig that captures image quantization noise and a Kalman Filter (KF) that fuses
+the observation history with new information. The proposed algorithm decomposes
+control into two stages: first the NBV is computed in the camera relative
+coordinates, and then the camera moves to realize this view in the fixed global
+coordinate frame. This decomposition allows the camera to drive to a new pose
+that effectively realizes the NBV in camera coordinates while satisfying
+Field-of-View constraints in global coordinates, a task that is particularly
+challenging using complex sensing models. We provide simulations and real
+experiments that illustrate the ability of the proposed mobile camera system to
+accurately localize sets of targets. We also propose a novel data-driven
+technique to characterize unmodeled uncertainty, such as calibration errors, at
+the pixel level and show that this method ensures stability of the KF.
+"
+22427,Fractality and degree correlations in scale-free networks,"  Fractal scale-free networks are empirically known to exhibit disassortative
+degree mixing. It is, however, not obvious whether a negative degree
+correlation between nearest neighbor nodes makes a scale-free network fractal.
+Here we examine the possibility that disassortativity in complex networks is
+the origin of fractality. To this end, maximally disassortative (MD) networks
+are prepared by rewiring edges while keeping the degree sequence of an initial
+uncorrelated scale-free network that is guaranteed to become fractal by
+rewiring edges. Our results show that most of MD networks with different
+topologies are not fractal, which demonstrates that disassortativity does not
+cause the fractal property of networks. In addition, we suggest that fractality
+of scale-free networks requires a long-range repulsive correlation in similar
+degrees.
+"
+22428,DeepVel: deep learning for the estimation of horizontal velocities at the solar surface,"  Many phenomena taking place in the solar photosphere are controlled by plasma
+motions. Although the line-of-sight component of the velocity can be estimated
+using the Doppler effect, we do not have direct spectroscopic access to the
+components that are perpendicular to the line-of-sight. These components are
+typically estimated using methods based on local correlation tracking. We have
+designed DeepVel, an end-to-end deep neural network that produces an estimation
+of the velocity at every single pixel and at every time step and at three
+different heights in the atmosphere from just two consecutive continuum images.
+We confront DeepVel with local correlation tracking, pointing out that they
+give very similar results in the time- and spatially-averaged cases. We use the
+network to study the evolution in height of the horizontal velocity field in
+fragmenting granules, supporting the buoyancy-braking mechanism for the
+formation of integranular lanes in these granules. We also show that DeepVel
+can capture very small vortices, so that we can potentially expand the scaling
+cascade of vortices to very small sizes and durations.
+"
+22429,Analyticity of the Dirichlet-to-Neumann semigroup on continuous functions,"  Let $\Omega$ be a bounded open subset with $C^{1+\kappa}$-boundary for some
+$\kappa > 0$. Consider the Dirichlet-to-Neumann operator associated to the
+elliptic operator $- \sum \partial_l ( c_{kl} \, \partial_k ) + V$, where the
+$c_{kl} = c_{lk}$ are Hölder continuous and $V \in L_\infty(\Omega)$ are real
+valued. We prove that the Dirichlet-to-Neumann operator generates a
+$C_0$-semigroup on the space $C(\partial \Omega)$ which is in addition
+holomorphic with angle $\frac{\pi}{2}$. We also show that the kernel of the
+semigroup has Poisson bounds on the complex right half-plane. As a consequence
+we obtain an optimal holomorphic functional calculus and maximal regularity on
+$L_p(\Gamma)$ for all $p \in (1,\infty)$.
+"
+22430,Recurrent Ladder Networks,"  We propose a recurrent extension of the Ladder networks whose structure is
+motivated by the inference required in hierarchical latent variable models. We
+demonstrate that the recurrent Ladder is able to handle a wide variety of
+complex learning tasks that benefit from iterative inference and temporal
+modeling. The architecture shows close-to-optimal results on temporal modeling
+of video data, competitive results on music modeling, and improved perceptual
+grouping based on higher order abstractions, such as stochastic textures and
+motion cues. We present results for fully supervised, semi-supervised, and
+unsupervised tasks. The results suggest that the proposed architecture and
+principles are powerful tools for learning a hierarchy of abstractions,
+learning iterative inference and handling temporal information.
+"
+22431,Family-size variability grows with collapse rate in Birth-Death-Catastrophe model,"  Forest-fire and avalanche models support the notion that frequent
+catastrophes prevent the growth of very large populations and as such prevent
+rare large-scale catastrophes. We show that this notion is not universal. A new
+model class leads to a paradigm shift in the influence of catastrophes on the
+family-size distribution of sub-populations. We study a simple population
+dynamics model where individuals, as well as a whole family, may die with a
+constant probability, accompanied by a logistic population growth model. We
+compute the characteristics of the family-size distribution in steady-state and
+the phase diagram of the steady-state distribution, and show that the family
+and catastrophe size variances increase with the catastrophe frequency, which
+is the opposite of common intuition. Frequent catastrophes are balanced by a
+larger net-growth rate in surviving families, leading to the exponential growth
+of these families. When the catastrophe rate is further increased, a second
+phase transition to extinction occurs, when the rate of new families creations
+is lower than their destruction rate by catastrophes.
+"
+22432,Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention,"  This paper describes a novel text-to-speech (TTS) technique based on deep
+convolutional neural networks (CNN), without any recurrent units. Recurrent
+neural network (RNN) has been a standard technique to model sequential data
+recently, and this technique has been used in some cutting-edge neural TTS
+techniques. However, training RNN component often requires a very powerful
+computer, or very long time typically several days or weeks. Recent other
+studies, on the other hand, have shown that CNN-based sequence synthesis can be
+much faster than RNN-based techniques, because of high parallelizability. The
+objective of this paper is to show an alternative neural TTS system, based only
+on CNN, that can alleviate these economic costs of training. In our experiment,
+the proposed Deep Convolutional TTS can be sufficiently trained only in a night
+(15 hours), using an ordinary gaming PC equipped with two GPUs, while the
+quality of the synthesized speech was almost acceptable.
+"
+22433,Vanlearning: A Machine Learning SaaS Application for People Without Programming Backgrounds,"  Although we have tons of machine learning tools to analyze data, most of them
+require users have some programming backgrounds. Here we introduce a SaaS
+application which allows users analyze their data without any coding and even
+without any knowledge of machine learning. Users can upload, train, predict and
+download their data by simply clicks their mouses. Our system uses data
+pre-processor and validator to relieve the computational cost of our server.
+The simple architecture of Vanlearning helps developers can easily maintain and
+extend it.
+"
+22434,Grand-canonical Monte-Carlo simulation of solutions of salt mixtures: theory and implementation,"  A Grand-canonical Monte-Carlo simulation method extended to simulate a
+mixture of salts is presented. Due to charge neutrality requirement of
+electrolyte solutions, ions must be added to or removed from the system in
+groups. This leads to some complications compared to regular Grand Canonical
+simulation. Here, a recipe for simulation of electrolyte solution of salt
+mixture is presented. It is then implemented to simulate solution of 1:1, 2:1
+and 2:2 salts or their mixtures at different concentrations using the primitive
+ion model. The osmotic pressures of the electrolyte solutions are calculated
+and shown to depend linearly on the salt concentrations within the
+concentration range simulated. We also show that at the same concentration of
+divalent anions, the presence of divalent cations make it easier to insert
+monovalent cations into the system. This can explain some quantitative
+differences observed in experiments of the MgCl$_2$ salt mixture and MgSO$_4$
+salt mixture.
+"
+22435,On the EM-Tau algorithm: a new EM-style algorithm with partial E-steps,"  The EM algorithm is one of many important tools in the field of statistics.
+While often used for imputing missing data, its widespread applications include
+other common statistical tasks, such as clustering. In clustering, the EM
+algorithm assumes a parametric distribution for the clusters, whose parameters
+are estimated through a novel iterative procedure that is based on the theory
+of maximum likelihood. However, one major drawback of the EM algorithm, that
+renders it impractical especially when working with large datasets, is that it
+often requires several passes of the data before convergence. In this paper, we
+introduce a new EM-style algorithm that implements a novel policy for
+performing partial E-steps. We call the new algorithm the EM-Tau algorithm,
+which can approximate the traditional EM algorithm with high accuracy but with
+only a fraction of the running time.
+"
+22436,High-resolution computer meshes of the lower body bones of an adult human female derived from CT images,"  Background Computer-based geometrical meshes of bones are important for
+applications in computational biomechanics as well as clinical software. There
+is however a lack of freely available detailed bone meshes, especially related
+to the human female morphology.
+Methods & Results We provide high resolution bone meshes of the lower body,
+derived from CT images of a 59 year old female cadaver that were sourced from
+the Visible Human Data Set, Visible Human Project (NIH, USA). Important bone
+landmarks and joint rotation axes are identified from the extracted meshes. A
+script-based framework is developed to provide a graphical user interface that
+can visualize, resample and modify the meshes to fit different subject scales.
+Conclusion This open-data resource fills a gap in available data and is
+provided for free usage in research and other applications. The associated
+scripts allows users to easily transform the meshes to different laboratory and
+software setups. This resource may be accessed through the following web link:
+this https URL
+This document is the author's version of this article.
+"
+22437,Work extraction in an isolated quantum lattice system: Grand canonical and generalized Gibbs ensemble predictions,"  We study work extraction (defined as the difference between the initial and
+the final energy) in noninteracting and (effectively) weakly interacting
+isolated fermionic quantum lattice systems in one dimension, which undergo a
+sequence of quenches and equilibration. The systems are divided in two parts,
+which we identify as the subsystem of interest and the bath. We extract work by
+quenching the on-site potentials in the subsystem, letting the entire system
+equilibrate, and returning to the initial parameters in the subsystem using a
+quasi-static process (the bath is never acted upon). We select initial states
+that are direct products of thermal states of the subsystem and the bath, and
+consider equilibration to the generalized Gibbs ensemble (GGE, noninteracting
+case) and to the Gibbs ensemble (GE, weakly interacting case). We identify the
+class of quenches that, in the thermodynamic limit, results in GE and GGE
+entropies after the quench that are identical to the one in the initial state
+(quenches that do not produce entropy). Those quenches guarantee maximal work
+extraction when thermalization occurs. We show that the same remains true in
+the presence of integrable dynamics that results in equilibration to the GGE.
+"
+22438,London penetration depth and thermal fluctuations in the sulphur hydride 203 K superconductor,"  Recently, compressed H$_2$S has been shown to become superconducting at 203 K
+under a pressure of 155 GPa. One might expect fluctuations to dominate at such
+temperatures. Using the magnetisation critical current, we determine the
+ground-state London penetration depth, $\lambda_0$=189 nm, and the
+superconducting energy gap, $\Delta_0$=27.8 meV, and find these parameters are
+similar to those of cuprate superconductors. We also determine the fluctuation
+temperature scale, $T_{\textrm{fluc}}=1470$ K, which shows that, unlike the
+cuprates, $T_c$ of the hydride is not limited by fluctuations. This is due to
+its three dimensionality and suggests the search for better superconductors
+should refocus on three-dimensional systems where the inevitable thermal
+fluctuations are less likely to reduce the observed $T_c$.
+"
+22439,The cone topology on masures,"  Masures are generalizations of Bruhat--Tits buildings and the main examples
+are associated with almost split Kac--Moody groups G over non-Archimedean local
+fields. In this case, G acts strongly transitively on its corresponding masure
+$\Delta$ as well as on the building at infinity of $\Delta$, which is the twin
+building associated with G. The aim of this article is twofold: firstly, to
+introduce and study the cone topology on the twin building at infinity of a
+masure. It turns out that this topology has various favorable properties that
+are required in the literature as axioms for a topological twin building.
+Secondly, by making use of the cone topology, we study strongly transitive
+actions of a group G on a masure $\Delta$. Under some hypotheses, with respect
+to the masure and the group action of G, we prove that G acts strongly
+transitively on $\Delta$ if and only if it acts strongly transitively on the
+twin building at infinity $\partial$$\Delta$. Along the way a criterion for
+strong transitivity is given and the existence and good dynamical properties of
+strongly regular hyperbolic automorphisms of the masure are proven.
+"
+22440,Dual-horizon peridynamics: A stable solution to varying horizons,"  In this paper, we present a dual-horizon peridynamics formulation which
+allows for simulations with dual-horizon with minimal spurious wave reflection.
+We prove the general dual property for dual-horizon peridynamics, based on
+which the balance of momentum and angular momentum in PD are naturally
+satisfied. We also analyze the crack pattern of random point distribution and
+the multiple materials issue in peridynamics. For selected benchmark problems,
+we show that DH-PD is less sensitive to the spatial than the original PD
+formulation.
+"
+22441,Convolutional Neural Networks Via Node-Varying Graph Filters,"  Convolutional neural networks (CNNs) are being applied to an increasing
+number of problems and fields due to their superior performance in
+classification and regression tasks. Since two of the key operations that CNNs
+implement are convolution and pooling, this type of networks is implicitly
+designed to act on data described by regular structures such as images.
+Motivated by the recent interest in processing signals defined in irregular
+domains, we advocate a CNN architecture that operates on signals supported on
+graphs. The proposed design replaces the classical convolution not with a
+node-invariant graph filter (GF), which is the natural generalization of
+convolution to graph domains, but with a node-varying GF. This filter extracts
+different local features without increasing the output dimension of each layer
+and, as a result, bypasses the need for a pooling stage while involving only
+local operations. A second contribution is to replace the node-varying GF with
+a hybrid node-varying GF, which is a new type of GF introduced in this paper.
+While the alternative architecture can still be run locally without requiring a
+pooling stage, the number of trainable parameters is smaller and can be
+rendered independent of the data dimension. Tests are run on a synthetic source
+localization problem and on the 20NEWS dataset.
+"
+22442,"Scalar curvature, Kodaira dimension and $\hat A$-genus","  Let $(X,g)$ be a compact Riemannian manifold with quasi-positive Riemannian
+scalar curvature. If there exists a complex structure $J$ compatible with $g$,
+then the canonical bundle $K_X$ is not pseudo-effective and the Kodaira
+dimension $\kappa(X,J)=-\infty$. We also introduce the complex Yamabe number
+$\lambda_c(X)$ for compact complex manifold $X$, and show that if
+$\lambda_c(X)>0$, then $\kappa(X)=-\infty$; moreover, if $X$ is also spin, then
+the Hirzebruch $A$-hat genus $\hat A(X)=0$.
+"
+22443,Direct Numerical Simulations of pore competition in idealized micro-spall using the VOF method,"  Under shock loading, metals have been found to melt and with reflection of
+the shock wave from the material free surface, cavities nucleate and grow. This
+process is referred to as micro spall and has been studied experimentally with
+analytical models describing debris sizes. Measurements during the cavity
+growth phase are not possible at present and we present here the Direct
+Numerical Simulation of an idealized problem where we assume an inviscid,
+incompressible liquid subject to a constant expansion rate with cavities at a
+vanishing vapour pressure.
+In order to allow for a time-varying gas volume a free-surface interface
+condition has been implemented in an existing incompressible multiphase
+Navier-Stokes solver, PARIS Simulator, using a volume-of-fluid method. The gas
+flow remains unsolved and is instead assumed to have a fixed pressure which is
+applied to the liquid through a Dirichlet boundary condition on the arbitrary
+liquid-gas interface. Gas bubbles are tracked individually, allowing the gas
+pressure to be prescribed using a suitable equation of state.
+Simulations with hundreds of bubbles have been performed in a fixed domain
+under a constant rate of expansion. A bubble competition is observed: larger
+bubbles tend to expand more rapidly at the demise of smaller ones. The time
+scale of competition is shown to depend on a modified Weber number.
+"
+22444,Combined Mutiplicative-Heston Model for Stochastic Volatility,"  We consider a model of stochastic volatility which combines features of the
+multiplicative model for large volatilities and of the Heston model for small
+volatilities. The steady-state distribution in this model is a Beta Prime and
+is characterized by the power-law behavior at both large and small
+volatilities. We discuss the reasoning behind using this model as well as
+consequences for our recent analyses of distributions of stock returns and
+realized volatility.
+"
+22445,High-resolution optical spectroscopy with a buffer-gas-cooled beam of BaH molecules,"  Barium monohydride (BaH) is an attractive candidate for extending laser
+cooling and trapping techniques to diatomic hydrides. The apparatus and
+high-resolution optical spectroscopy presented here demonstrate progress toward
+this goal. A cryogenic buffer-gas-cooled molecular beam of BaH was constructed
+and characterized. Pulsed laser ablation into cryogenic helium buffer gas
+delivers $\sim1\times10^{10}$ molecules/sr/pulse in the X$^2\Sigma^+$
+($v''=0,N''=1$) state of primary interest. More than $1\times10^7$ of these
+molecules per pulse enter the downstream science region with forward velocities
+below 100 m/s and transverse temperature of 0.1 K. This molecular beam enabled
+high-resolution optical spectra of BaH in quantum states relevant to laser
+slowing and cooling. The reported measurements include hyperfine structure and
+magnetic $g$ factors in the X$^2\Sigma^+$, B$^2\Sigma^+$, and A$^2\Pi_{1/2}$
+states.
+"
+22446,D4M 3.0: Extended Database and Language Capabilities,"  The D4M tool was developed to address many of today's data needs. This tool
+is used by hundreds of researchers to perform complex analytics on unstructured
+data. Over the past few years, the D4M toolbox has evolved to support
+connectivity with a variety of new database engines, including SciDB.
+D4M-Graphulo provides the ability to do graph analytics in the Apache Accumulo
+database. Finally, an implementation using the Julia programming language is
+also now available. In this article, we describe some of our latest additions
+to the D4M toolbox and our upcoming D4M 3.0 release. We show through
+benchmarking and scaling results that we can achieve fast SciDB ingest using
+the D4M-SciDB connector, that using Graphulo can enable graph algorithms on
+scales that can be memory limited, and that the Julia implementation of D4M
+achieves comparable performance or exceeds that of the existing MATLAB(R)
+implementation.
+"
+22447,Predefined Sparseness in Recurrent Sequence Models,"  Inducing sparseness while training neural networks has been shown to yield
+models with a lower memory footprint but similar effectiveness to dense models.
+However, sparseness is typically induced starting from a dense model, and thus
+this advantage does not hold during training. We propose techniques to enforce
+sparseness upfront in recurrent sequence models for NLP applications, to also
+benefit training. First, in language modeling, we show how to increase hidden
+state sizes in recurrent layers without increasing the number of parameters,
+leading to more expressive models. Second, for sequence labeling, we show that
+word embeddings with predefined sparseness lead to similar performance as dense
+embeddings, at a fraction of the number of trainable parameters.
+"
+22448,Reconfigurable Topological Photonic Crystal,"  Topological insulators are materials that conduct on the surface and insulate
+in their interior due to non-trivial topological order. The edge states on the
+interface between topological (non-trivial) and conventional (trivial)
+insulators are topologically protected from scattering due to structural
+defects and disorders. Recently, it was shown that photonic crystals can serve
+as a platform for realizing a scatter-free propagation of light waves. In
+conventional photonic crystals, imperfections, structural disorders, and
+surface roughness lead to significant losses. The breakthrough in overcoming
+these problems is likely to come from the synergy of the topological photonic
+crystals and silicon-based photonics technology that enables high integration
+density, lossless propagation, and immunity to fabrication imperfections. For
+many applications, reconfigurability and capability to control the propagation
+of these non-trivial photonic edge states is essential. One way to facilitate
+such dynamic control is to use liquid crystals, which allow to modify the
+refractive index with external electric field. Here, we demonstrate dynamic
+control of topological edge states by modifying the refractive index of a
+liquid crystal background medium. Background index is changed depending on the
+orientation of a liquid crystal, while preserving the topological order of the
+system. This results in a change of the spectral position of the photonic
+bandgap and the topologically protected edge states. The proposed concept might
+be implemented using conventional semiconductor technology, and can be used for
+robust energy transport in integrated photonic devices, all-optical circuity,
+and optical communication systems.
+"
+22449,Mapping the Davis complex into the imaginary cone,"  The study of the set of limit roots associated to an infinite Coxeter group
+was initiated by Hohlweg, Labbé and Ripoll and further developed by Dyer,
+Hohlweg, Péaux and Ripoll. The Davis complex associated to a finitely
+generated Coxeter group $W$ is a piecewise Euclidean CAT(0) space on which $W$
+acts properly, cocompactly by isometries. The one skeleton of the Davis complex
+can be identified with the Cayley graph of $W$. In this paper we define a
+natural map from the Davis complex into the normalised imaginary cone of a
+based root system.
+"
+22450,"A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery","  Recognising individual trees within remotely sensed imagery has important
+applications in forest ecology and management. Several algorithms for tree
+delineation have been suggested, mostly based on locating local maxima or
+inverted basins in raster canopy height models (CHMs) derived from Light
+Detection And Ranging (LiDAR) data or photographs. However, these algorithms
+often lead to inaccurate estimates of forest stand characteristics due to the
+limited information content of raster CHMs. Here we develop a 3D tree
+delineation method which uses graph cut to delineate trees from the full 3D
+LiDAR point cloud, and also makes use of any optical imagery available
+(hyperspectral imagery in our case). First, conventional methods are used to
+locate local maxima in the CHM and generate an initial map of trees. Second, a
+graph is built from the LiDAR point cloud, fused with the hyperspectral data.
+For computational efficiency, the feature space of hyperspectral imagery is
+reduced using robust PCA. Third, a multi-class normalised cut is applied to the
+graph, using the initial map of trees to constrain the number of clusters and
+their locations. Finally, recursive normalised cut is used to subdivide, if
+necessary, each of the clusters identified by the initial analysis. We call
+this approach Multiclass Cut followed by Recursive Cut (MCRC). The
+effectiveness of MCRC was tested using three datasets: i) NewFor, ii) a
+coniferous forest in the Italian Alps, and iii) a deciduous woodland in the UK.
+The performance of MCRC was usually superior to that of other delineation
+methods, and was further improved by including high-resolution optical imagery.
+Since MCRC delineates the entire LiDAR point cloud in 3D, it allows individual
+crown characteristics to be measured. By making full use of the data available,
+graph cut has the potential to considerably improve the accuracy of tree
+delineation.
+"
+22451,Capacity Enhancement with Meta-Multiplexing,"  Multiplexing services as a key communication technique to effectively combine
+multiple signals into one signal and transmit over a shared medium.
+Multiplexing can increase the channel capacity by requiring more resources on
+the transmission medium. For instance, the space-division multiplexing
+accomplished through the multiple-input multiple-output (MIMO) scheme achieves
+significant capacity increase by the realized parallel channel, but it requires
+expensive hardware resources. Here, we present a novel multiplexing
+methodology, named meta-multiplexing, which allows ordinary modulated signals
+overlap together to form a set of ""artificial"" parallel channels, meanwhile, it
+only requires similar resources as ordinary modulation schemes. We prove the
+capacity law for the meta-multiplexing system and disclose that under broad
+conditions, the capacity of a single channel increases linearly with the signal
+to noise ratio (SNR), which breaks the conventional logarithmic growth of the
+capacity over SNR. Numerous simulation studies verify the capacity law and
+demonstrate the high efficiency of meta-multiplexing. Through proof-of-concept
+hardware experiments, we tested the proposed method in communication practices
+and achieved a spectral efficiency of 81.7 bits/s/Hz over a single channel,
+which is significantly higher than the efficiency of any existing communication
+system.
+"
+22452,A New CGAN Technique for Constrained Topology Design Optimization,"  This paper presents a new conditional GAN (named convex relaxing CGAN or
+crCGAN) to replicate the conventional constrained topology optimization
+algorithms in an extremely effective and efficient process. The proposed crCGAN
+consists of a generator and a discriminator, both of which are deep
+convolutional neural networks (CNN) and the topology design constraint can be
+conditionally set to both the generator and discriminator. In order to improve
+the training efficiency and accuracy due to the dependency between the training
+images and the condition, a variety of crCGAN formulation are introduced to
+relax the non-convex design space. These new formulations were evaluated and
+validated via a series of comprehensive experiments. Moreover, a minibatch
+discrimination technique was introduced in the crCGAN training process to
+stabilize the convergence and avoid the mode collapse problems. Additional
+verifications were conducted using the state-of-the-art MNIST digits and
+CIFAR-10 images conditioned by class labels. The experimental evaluations
+clearly reveal that the new objective formulation with the minibatch
+discrimination training provides not only the accuracy but also the consistency
+of the designs.
+"
+22453,Real-Time Grasp Planning for Multi-Fingered Hands by Finger Splitting,"  Grasp planning for multi-fingered hands is computationally expensive due to
+the joint-contact coupling, surface nonlinearities and high dimensionality,
+thus is generally not affordable for real-time implementations. Traditional
+planning methods by optimization, sampling or learning work well in planning
+for parallel grippers but remain challenging for multi-fingered hands. This
+paper proposes a strategy called finger splitting, to plan precision grasps for
+multi-fingered hands starting from optimal parallel grasps. The finger
+splitting is optimized by a dual-stage iterative optimization including a
+contact point optimization (CPO) and a palm pose optimization (PPO), to
+gradually split fingers and adjust both the contact points and the palm pose.
+The dual-stage optimization is able to consider both the object grasp quality
+and hand manipulability, address the nonlinearities and coupling, and achieve
+efficient convergence within one second. Simulation results demonstrate the
+effectiveness of the proposed approach. The simulation video is available at:
+this http URL\%7Eyongxiangfan/IROS2018/fingersplit.html
+"
+22454,Generalized Gaussian Kernel Adaptive Filtering,"  The present paper proposes generalized Gaussian kernel adaptive filtering,
+where the kernel parameters are adaptive and data-driven. The Gaussian kernel
+is parametrized by a center vector and a symmetric positive definite (SPD)
+precision matrix, which is regarded as a generalization of the scalar width
+parameter. These parameters are adaptively updated on the basis of a proposed
+least-square-type rule to minimize the estimation error. The main contribution
+of this paper is to establish update rules for precision matrices on the SPD
+manifold in order to keep their symmetric positive-definiteness. Different from
+conventional kernel adaptive filters, the proposed regressor is a superposition
+of Gaussian kernels with all different parameters, which makes such regressor
+more flexible. The kernel adaptive filtering algorithm is established together
+with a l1-regularized least squares to avoid overfitting and the increase of
+dimensionality of the dictionary. Experimental results confirm the validity of
+the proposed method.
+"
+22455,Generalized expressions for hyper-Rayleigh scattering from isotropic liquids,"  Generalized expressions for second harmonic scattering from isotropically
+distributed liquid molecules are derived for arbitrary scattering angles and
+polarization states.
+"
+22456,Persistent current in 2D topological superconductors,"  A junction between two boundaries of a topological superconductor (TSC),
+mediated by localized edge modes of Majorana fermions, is investigated. The
+tunneling of fermions across the junction depends on the magnetic flux. It
+breaks the time-reversal symmetry at the boundary of the sample. The persistent
+current is determined by the emergence of Majorana edge modes. The structure of
+the edge modes depends on the magnitude of the tunneling amplitude across the
+junction. It is shown that there are two different regimes, which correspond to
+strong and weak tunneling of Majorana fermions, distinctive in the persistent
+current behavior. In a strong tunneling regime, the fermion parity of edge
+modes is not conserved and the persistent current is a $2\pi$-periodic function
+of the magnetic flux. When the tunneling is weak the chiral Majorana states,
+which are propagating along the edges have the same fermion parity. They form a
+$4\pi$-phase periodic persistent current along the boundaries. The regions in
+the space of parameters, which correspond to the emergence of $2\pi$- and of
+$4\pi$-harmonics, are numerically determined. The peculiarities in the
+persistent current behavior are studied.
+"
+22457,Adversarial Transformation Networks: Learning to Generate Adversarial Examples,"  Multiple different approaches of generating adversarial examples have been
+proposed to attack deep neural networks. These approaches involve either
+directly computing gradients with respect to the image pixels, or directly
+solving an optimization on the image pixels. In this work, we present a
+fundamentally new method for generating adversarial examples that is fast to
+execute and provides exceptional diversity of output. We efficiently train
+feed-forward neural networks in a self-supervised manner to generate
+adversarial examples against a target network or set of networks. We call such
+a network an Adversarial Transformation Network (ATN). ATNs are trained to
+generate adversarial examples that minimally modify the classifier's outputs
+given the original input, while constraining the new classification to match an
+adversarial target class. We present methods to train ATNs and analyze their
+effectiveness targeting a variety of MNIST classifiers as well as the latest
+state-of-the-art ImageNet classifier Inception ResNet v2.
+"
+22458,Localized versions of function spaces and generic results,"  We consider generalizations of classical function spaces by requiring that a
+holomorphic in ${\Omega}$ function satisfies some property when we approach
+from ${\Omega}$, not the whole boundary, but only a part of it. These spaces
+endowed with their natural topology are Fr$é$chet spaces. We prove some
+generic non-extendability results in such spaces and generic nowhere
+differentiability on the corresponding part of ${\partial{\Omega}}$.
+"
+22459,A Geometric Approach to Spectral Analysis,"  Analyses of gamma-ray spectra, acquired through non-invasive techniques, have
+found applications in fields such as medicine, industry and homeland security.
+Constituent gamma-ray spectra of a chemical compound have been determined from
+its sole spectrum through a forward Monte Carlo simulation coupled with a least
+squares method (MCLLS). The method's limitations include its linearity
+assumption and its oversensitivity to correlated or noisy data, which render
+the method unfit to deal with such numerical ill conditioning. Recently this
+issue was tackled by iteratively reducing the condition number of the linear
+system of equations. Despite its superior results, it is not suitable for cases
+where there are missing libraries in the analysis. Our work introduces a novel
+framework that allows treating spectral analyses problems through geometrical
+insights. Based on this it was possible to propose solutions to three problems
+regarding the missing library: to find its photopeak, its most probable
+fraction, and an envelope around its spectrum. We successfully validated these
+on some Monte Carlo-generated radionuclide gamma-ray spectra.
+"
+22460,"About J-flow, J-balanced metrics, uniform J-stability and K-stability","  From the work of Dervan-Keller, there exists a quantization of the critical
+equation for the J-flow. This leads to the notion of J-balanced metrics. We
+prove that the existence of J-balanced metrics has a purely algebro-geometric
+characterization in terms of Chow stability, complementing the result of
+Dervan-Keller. We also obtain various criteria that imply uniform J-stability
+and uniform K-stability. Eventually, we discuss the case of Kähler classes
+that may not be integral over a compact manifold.
+"
+22461,DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers,"  With an abundance of research papers in deep learning, reproducibility or
+adoption of the existing works becomes a challenge. This is due to the lack of
+open source implementations provided by the authors. Further, re-implementing
+research papers in a different library is a daunting task. To address these
+challenges, we propose a novel extensible approach, DLPaper2Code, to extract
+and understand deep learning design flow diagrams and tables available in a
+research paper and convert them to an abstract computational graph. The
+extracted computational graph is then converted into execution ready source
+code in both Keras and Caffe, in real-time. An arXiv-like website is created
+where the automatically generated designs is made publicly available for 5,000
+research papers. The generated designs could be rated and edited using an
+intuitive drag-and-drop UI framework in a crowdsourced manner. To evaluate our
+approach, we create a simulated dataset with over 216,000 valid design
+visualizations using a manually defined grammar. Experiments on the simulated
+dataset show that the proposed framework provide more than $93\%$ accuracy in
+flow diagram content extraction.
+"
+22462,Beyond the rich-club: Properties of networks related to the better connected nodes,"  Many of the structural characteristics of a network depend on the
+connectivity with and within the hubs. These dependencies can be related to the
+degree of a node and the number of links that a node shares with nodes of
+higher degree. In here we revise and present new results showing how to
+construct network ensembles which give a good approximation to the
+degree-degree correlations, and hence to the projections of this correlation
+like the assortativity coefficient or the average neighbours degree. We present
+a new bound for the structural cut--off degree based on the connectivity within
+the hubs. Also we show that the connections with and within the hubs can be
+used to define different networks cores. Two of these cores are related to the
+spectral properties and walks of length one and two which contain at least on
+hub node, and they are related to the eigenvector centrality. We introduce a
+new centrality measured based on the connectivity with the hubs. In addition,
+as the ensembles and cores are related by the connectivity of the hubs, we show
+several examples how changes in the hubs linkage effects the degree--degree
+correlations and core properties.
+"
+22463,Local Equivalence and Intrinsic Metrics between Reeb Graphs,"  As graphical summaries for topological spaces and maps, Reeb graphs are
+common objects in the computer graphics or topological data analysis
+literature. Defining good metrics between these objects has become an important
+question for applications, where it matters to quantify the extent by which two
+given Reeb graphs differ. Recent contributions emphasize this aspect, proposing
+novel distances such as {\em functional distortion} or {\em interleaving} that
+are provably more discriminative than the so-called {\em bottleneck distance},
+being true metrics whereas the latter is only a pseudo-metric. Their main
+drawback compared to the bottleneck distance is to be comparatively hard (if at
+all possible) to evaluate. Here we take the opposite view on the problem and
+show that the bottleneck distance is in fact good enough {\em locally}, in the
+sense that it is able to discriminate a Reeb graph from any other Reeb graph in
+a small enough neighborhood, as efficiently as the other metrics do. This
+suggests considering the {\em intrinsic metrics} induced by these distances,
+which turn out to be all {\em globally} equivalent. This novel viewpoint on the
+study of Reeb graphs has a potential impact on applications, where one may not
+only be interested in discriminating between data but also in interpolating
+between them.
+"
+22464,Getting Reliable Annotations for Sarcasm in Online Dialogues,"  The language used in online forums differs in many ways from that of
+traditional language resources such as news. One difference is the use and
+frequency of nonliteral, subjective dialogue acts such as sarcasm. Whether the
+aim is to develop a theory of sarcasm in dialogue, or engineer automatic
+methods for reliably detecting sarcasm, a major challenge is simply the
+difficulty of getting enough reliably labelled examples. In this paper we
+describe our work on methods for achieving highly reliable sarcasm annotations
+from untrained annotators on Mechanical Turk. We explore the use of a number of
+common statistical reliability measures, such as Kappa, Karger's, Majority
+Class, and EM. We show that more sophisticated measures do not appear to yield
+better results for our data than simple measures such as assuming that the
+correct label is the one that a majority of Turkers apply.
+"
+22465,Towards visible soliton microcomb generation,"  Frequency combs have applications that extend from the ultra-violet into the
+mid-infrared bands. Microcombs, a miniature and often semiconductor-chip-based
+device, can potentially access most of these applications, but are currently
+more limited in spectral reach. Here, we demonstrate mode-locked silica
+microcombs with emission near the edge of the visible spectrum. By using both
+geometrical and mode-hybridization dispersion control, devices are engineered
+for soliton generation while also maintaining optical $Q$ factors as high as 80
+million. Electronics-bandwidth-compatible (20 GHz) soliton mode locking is
+achieved with low pumping powers (parametric oscillation threshold powers as
+low as 5.4 mW). These are the shortest wavelength soliton microcombs
+demonstrated to date and could be used in miniature optical clocks. The results
+should also extend to visible and potentially ultra-violet bands.
+"
+22466,Mean and Variance of Phylogenetic Trees,"  We describe the use of the Frechet mean and variance in the
+Billera-Holmes-Vogtmann (BHV) treespace to summarize and explore the diversity
+of a set of phylogenetic trees. We show that the Frechet mean is comparable to
+other summary methods, and, despite its stickiness property, is more likely to
+be binary than the majority-rules consensus tree. We show that the Frechet
+variance is faster and more precise than commonly used variance measures. The
+Frechet mean and variance are more theoretically justified, and more robust,
+than previous estimates of this type, and can be estimated reasonably
+efficiently, providing a foundation for building more advanced statistical
+methods and leading to applications such as mean hypothesis testing.
+"
+22467,Algorithmic learning of probability distributions from random data in the limit,"  We study the problem of identifying a probability distribution for some given
+randomly sampled data in the limit, in the context of algorithmic learning
+theory as proposed recently by Vinanyi and Chater. We show that there exists a
+computable partial learner for the computable probability measures, while by
+Bienvenu, Monin and Shen it is known that there is no computable learner for
+the computable probability measures. Our main result is the characterization of
+the oracles that compute explanatory learners for the computable (continuous)
+probability measures as the high oracles. This provides an analogue of a
+well-known result of Adleman and Blum in the context of learning computable
+probability distributions. We also discuss related learning notions such as
+behaviorally correct learning and orther variations of explanatory learning, in
+the context of learning probability distributions from data.
+"
+22468,"Double Q($σ$) and Q($σ, λ$): Unifying Reinforcement Learning Control Algorithms","  Temporal-difference (TD) learning is an important field in reinforcement
+learning. Sarsa and Q-Learning are among the most used TD algorithms. The
+Q($\sigma$) algorithm (Sutton and Barto (2017)) unifies both. This paper
+extends the Q($\sigma$) algorithm to an online multi-step algorithm Q($\sigma,
+\lambda$) using eligibility traces and introduces Double Q($\sigma$) as the
+extension of Q($\sigma$) to double learning. Experiments suggest that the new
+Q($\sigma, \lambda$) algorithm can outperform the classical TD control methods
+Sarsa($\lambda$), Q($\lambda$) and Q($\sigma$).
+"
+22469,"Categorical Logarithmic Hodge Theory, I","  We write down a new ""logarithmic"" quasicoherent category
+$\operatorname{Qcoh}_{log}(U, X, D)$ attached to a smooth open algebraic
+variety $U$ with toroidal compactification $X$ and boundary divisor $D$. This
+is a (large) symmetric monoidal Abelian category, which we argue can be thought
+of as the categorical substrate for logarithmic Hodge theory of $U$. We show
+that its Hochschild homology theory coincides with the theory of log-forms on
+$X$ with logarithmic structure induced by $D$, and in particular, that the
+noncommutative Hodge-to de Rham sequence on $\operatorname{Qcoh}_{log}(U, X,
+D)$ recovers known log Hodge structure on the de Rham cohomology of the open
+variety $U$. As an application, we compute the Hochschild homology of the
+category of coherent sheaves on the infinite root stack of Talpo and Vistoli in
+the toroidal setting. We prove a derived invariance result for this theory:
+namely, that strictly toroidal changes of compactification do not change the
+derived category of $\operatorname{Qcoh}_{log}(U, X, D)$. The definition is
+motivated by the coherent object appearing in the author's microlocal mirror
+symmetry result [20].
+In this paper, the first in a series, we work over an algebraically closed
+field of characteristic zero. The next installment will develop the
+characteristic p and mixed-characteristic theories.
+"
+22470,An analytically iterative method for solving problems of cosmic-ray modulation,"  The development of an analytically iterative method for solving steady-state
+as well as unsteady-state problems of cosmic-ray (CR) modulation is proposed.
+Iterations for obtaining the solutions are constructed for the spherically
+symmetric form of the CR propagation equation. The main solution of the
+considered problem consists of the zero-order solution that is obtained during
+the initial iteration and amendments that may be obtained by subsequent
+iterations. The finding of the zero-order solution is based on the CR isotropy
+during propagation in the space, whereas the anisotropy is taken into account
+when finding the next amendments. To begin with, the method is applied to solve
+the problem of CR modulation where the diffusion coefficient $\kappa$ and the
+solar wind speed $u$ are constants with an Local Interstellar Spectra (LIS)
+spectrum. The solution obtained with two iterations was compared with an
+analytical solution and with numerical solutions. Finally, solutions that have
+only one iteration for two problems of CR modulation with $u = constant$ and
+the same form of LIS spectrum were obtained and tested against numerical
+solutions. For the first problem, $\kappa$ is proportional to the momentum of
+the particle $p$, so it has the form $\kappa=k_0\eta$, where
+$\eta=\frac{p}{m_0c}$. For the second problem, the diffusion coefficient is
+given in the form $\kappa=k_0\beta\eta$, where $\beta=\frac{v}{c}$ is the
+particle speed relative to the speed of light. There was a good matching of the
+obtained solutions with the numerical solutions as well as with the analytical
+solution for the problem where $\kappa = constant$.
+"
+22471,Partial Fourier techniques in single-shot cross-term spatiotemporal encoded MRI,"  Purpose: Cross-term spatiotemporal encoding-xSPEN-is a single-shot imaging
+approach with exceptional resilience to field heterogeneities: its images do
+not require a priori information nor use post-acquisition corrections, to
+deliver faithfully the spatial distribution. xSPEN, however, suffers from SNR
+penalties due to its non-Fourier nature and due to diffusion losses- specially
+when desiring high resolution. This study explores partial Fourier transform
+approaches that acting along either the readout or the spatiotemporally-encoded
+dimensions, reduce these penalties.
+Methods: xSPEN uses an orthogonal gradient to read, in direct space, the
+lowbandwidth dimension. This changes substantially the nature of partial
+Fourier acquisitions vis-a-vis conventional imaging counterparts. A suitable
+theoretical analysis, however, allows one to implement these procedures along
+either the low-bandwidth or readout axes.
+Results: Partial Fourier single-shot xSPEN methods are illustrated on
+preclinical and human scanners. Owing to their reduction in the experimental
+acquisition times, these methods provide substantial sensitivity gains
+vis-a-vis previous implementations for a given targeted in-plane resolution.
+The magnitude of these gains is as expected.
+Conclusion: Partial Fourier approaches, particularly when implemented along
+the xSPEN axis, can provide substantial sensitivity advantages at minimal costs
+in the operation of the single-shot experiments.
+"
+22472,People Mover's Distance: Class level geometry using fast pairwise data adaptive transportation costs,"  We address the problem of defining a network graph on a large collection of
+classes. Each class is comprised of a collection of data points, sampled in a
+non i.i.d. way, from some unknown underlying distribution. The application we
+consider in this paper is a large scale high dimensional survey of people
+living in the US, and the question of how similar or different are the various
+counties in which these people live. We use a co-clustering diffusion metric to
+learn the underlying distribution of people, and build an approximate earth
+mover's distance algorithm using this data adaptive transportation cost.
+"
+22473,Sentiment Protocol: A Decentralized Protocol Leveraging Crowd Sourced Wisdom,"  The wisdom of the crowd is a valuable asset in today's society. It is not
+only important in predicting elections but also plays an essential role in
+marketing and the financial industry. Having a trustworthy source of opinion
+can make forecasts more accurate and markets predictable. Until now, a
+fundamental problem of surveys is the lack of incentives for participants to
+provide accurate information. Classical solutions like small monetary rewards
+or the chance of winning a prize are often not very attractive for
+participants. More attractive solutions, such as prediction markets, face the
+issue of illegality and are often unavailable. In this work, we present a
+solution that unites the advantages from classical polling and prediction
+markets via a customizable incentivization framework. Apart from predicting
+events, this framework can also be used to govern decentralized autonomous
+organizations.
+"
+22474,Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification,"  The aim of this paper was to compare soft confusion matrix approach and Bayes
+metaclassifier under the multi-label classification framework. Although the
+methods were successfully applied under the multi-label classification
+framework, they have not been compared directly thus far. Such comparison is of
+vital importance because both methods are quite similar as they are both based
+on the concept of randomized reference classifier. Since both algorithms were
+designed to deal with single-label problems, they are combined with the
+problem-transformation approach to multi-label classification. Present study
+included 29 benchmark datasets and four different base classifiers. The
+algorithms were compared in terms of 11 quality criteria and the results were
+subjected to statistical analysis.
+"
+22475,Continuity of weak solutions to rough infinitely degenerate equations,"  We obtain a generalization of the DeGiorgi Lemma to the infinitely degenerate
+regime and apply it to obtain continuity of weak solutions to certain
+infinitely degenerate equations. This reproduces the continuity result obtained
+in arXiv:1506.09203 via Moser iteration, but only for homogeneous equations.
+However, the proofs are much less technical and more transparent.
+"
+22476,Co-training for Demographic Classification Using Deep Learning from Label Proportions,"  Deep learning algorithms have recently produced state-of-the-art accuracy in
+many classification tasks, but this success is typically dependent on access to
+many annotated training examples. For domains without such data, an attractive
+alternative is to train models with light, or distant supervision. In this
+paper, we introduce a deep neural network for the Learning from Label
+Proportion (LLP) setting, in which the training data consist of bags of
+unlabeled instances with associated label distributions for each bag. We
+introduce a new regularization layer, Batch Averager, that can be appended to
+the last layer of any deep neural network to convert it from supervised
+learning to LLP. This layer can be implemented readily with existing deep
+learning packages. To further support domains in which the data consist of two
+conditionally independent feature views (e.g. image and text), we propose a
+co-training algorithm that iteratively generates pseudo bags and refits the
+deep LLP model to improve classification accuracy. We demonstrate our models on
+demographic attribute classification (gender and race/ethnicity), which has
+many applications in social media analysis, public health, and marketing. We
+conduct experiments to predict demographics of Twitter users based on their
+tweets and profile image, without requiring any user-level annotations for
+training. We find that the deep LLP approach outperforms baselines for both
+text and image features separately. Additionally, we find that co-training
+algorithm improves image and text classification by 4% and 8% absolute F1,
+respectively. Finally, an ensemble of text and image classifiers further
+improves the absolute F1 measure by 4% on average.
+"
+22477,Design and Analysis of the NIPS 2016 Review Process,"  Neural Information Processing Systems (NIPS) is a top-tier annual conference
+in machine learning. The 2016 edition of the conference comprised more than
+2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents
+a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and
+over 100% in terms of attendees as compared to the previous year. The massive
+scale as well as rapid growth of the conference calls for a thorough quality
+assessment of the peer-review process and novel means of improvement. In this
+paper, we analyze several aspects of the data collected during the review
+process, including an experiment investigating the efficacy of collecting
+ordinal rankings from reviewers. Our goal is to check the soundness of the
+review process, and provide insights that may be useful in the design of the
+review process of subsequent conferences.
+"
+22478,Theoretical Limits of Record Linkage and Microclustering,"  There has been substantial recent interest in record linkage, attempting to
+group the records pertaining to the same entities from a large database lacking
+unique identifiers. This can be viewed as a type of ""microclustering,"" with few
+observations per cluster and a very large number of clusters. A variety of
+methods have been proposed, but there is a lack of literature providing
+theoretical guarantees on performance. We show that the problem is
+fundamentally hard from a theoretical perspective, and even in idealized cases,
+accurate entity resolution is effectively impossible when the number of
+entities is small relative to the number of records and/or the separation among
+records from different entities is not extremely large. To characterize the
+fundamental difficulty, we focus on entity resolution based on multivariate
+Gaussian mixture models, but our conclusions apply broadly and are supported by
+simulation studies inspired by human rights applications. These results suggest
+conservatism in interpretation of the results of record linkage, support
+collection of additional data to more accurately disambiguate the entities, and
+motivate a focus on coarser inference. For example, results from a simulation
+study suggest that sometimes one may obtain accurate results for population
+size estimation even when fine scale entity resolution is inaccurate.
+"
+22479,On the Robustness of Coordinated Beamforming to Uncoordinated Interference and CSI Uncertainty,"  As network deployments become denser, interference arises as a dominant
+performance degradation factor. To confront with this trend, Long Term
+Evolution (LTE) incorporated features aiming at enabling cooperation among
+different base stations, a technique termed as Coordinated Multi Point (CoMP).
+Recent field trial results and theoretical studies of the performance of CoMP
+schemes revealed, however, that their gains are not as high as initially
+expected, despite their large coordination overhead. In this paper, we review
+recent advanced Coordinated Beamforming (CB) schemes, a special family of CoMP
+that reduces the coordination overhead through a joint choice of transmit and
+receive linear filters. We focus on assessing their resilience to uncoordinated
+interference and Channel State Information (CSI) imperfections, which both
+severely limit the performance gains of all CoMP schemes. We present a simple
+yet encompassing system model that aims at incorporating different parameters
+of interest in the relative interference power and CSI errors, and then utilize
+it for the performance evaluation of the state-of-the-art in CB schemes. It is
+shown that blindly applying CB in all system scenarios can indeed be
+counter-productive.
+"
+22480,What Does Explainable AI Really Mean? A New Conceptualization of Perspectives,"  We characterize three notions of explainable AI that cut across research
+fields: opaque systems that offer no insight into its algo- rithmic mechanisms;
+interpretable systems where users can mathemat- ically analyze its algorithmic
+mechanisms; and comprehensible systems that emit symbols enabling user-driven
+explanations of how a conclusion is reached. The paper is motivated by a corpus
+analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences
+in how work on explainable AI is positioned in various fields. We close by
+introducing a fourth notion: truly explainable systems, where automated
+reasoning is central to output crafted explanations without requiring human
+post processing as final step of the generative process.
+"
+22481,Ultra-Large Repair Search Space with Automatically Mined Templates: the Cardumen Mode of Astor,"  Astor is a program repair library which has different modes. In this paper,
+we present the Cardumen mode of Astor, a repair approach based mined templates
+that has an ultra-large search space. We evaluate the capacity of Cardumen to
+discover test-suite adequate patches (aka plausible patches) over the 356 real
+bugs from Defects4J. Cardumen finds 8935 patches over 77 bugs of Defects4J.
+This is the largest number of automatically synthesized patches ever reported,
+all patches being available in an open-science repository. Moreover, Cardumen
+identifies 8 unique patches, that are patches for Defects4J bugs that were
+never repaired in the whole history of program repair.
+"
+22482,Optimal Investment Strategies for Competing Camps in a Social Network: A Broad Framework,"  We study the problem of optimally investing in nodes of a social network in a
+competitive setting, wherein two camps aim to drive the average opinion of the
+population in their own favor. Using a well-established model of opinion
+dynamics, we formulate the problem as a zero-sum game with its players being
+the two camps. We derive optimal investment strategies for both camps, and show
+that a random investment strategy is optimal when the underlying network
+follows a popular class of weight distributions. We study a broad framework,
+where we consider various well-motivated settings of the problem, namely, when
+the influence of a camp on a node is a concave function of its investment on
+that node, when a camp aims at maximizing competitor's investment or deviation
+from its desired investment, and when one of the camps has uncertain
+information about the values of the model parameters. We also study a
+Stackelberg variant of this game under common coupled constraints on the
+combined investments by the camps and derive their equilibrium strategies, and
+hence quantify the first-mover advantage. For a quantitative and illustrative
+study, we conduct simulations on real-world datasets and provide results and
+insights.
+"
+22483,Joint auto-encoders: a flexible multi-task learning framework,"  The incorporation of prior knowledge into learning is essential in achieving
+good performance based on small noisy samples. Such knowledge is often
+incorporated through the availability of related data arising from domains and
+tasks similar to the one of current interest. Ideally one would like to allow
+both the data for the current task and for previous related tasks to
+self-organize the learning system in such a way that commonalities and
+differences between the tasks are learned in a data-driven fashion. We develop
+a framework for learning multiple tasks simultaneously, based on sharing
+features that are common to all tasks, achieved through the use of a modular
+deep feedforward neural network consisting of shared branches, dealing with the
+common features of all tasks, and private branches, learning the specific
+unique aspects of each task. Once an appropriate weight sharing architecture
+has been established, learning takes place through standard algorithms for
+feedforward networks, e.g., stochastic gradient descent and its variations. The
+method deals with domain adaptation and multi-task learning in a unified
+fashion, and can easily deal with data arising from different types of sources.
+Numerical experiments demonstrate the effectiveness of learning in domain
+adaptation and transfer learning setups, and provide evidence for the flexible
+and task-oriented representations arising in the network.
+"
+22484,Local Water Storage Control for the Developing World,"  Most cities in India do not have water distribution networks that provide
+water throughout the entire day. As a result, it is common for homes and
+apartment buildings to utilize water storage systems that are filled during a
+small window of time in the day when the water distribution network is active.
+However, these water storage systems do not have disinfection capabilities, and
+so long durations of storage (i.e., as few as four days) of the same water
+leads to substantial increases in the amount of bacteria and viruses in that
+water. This paper considers the stochastic control problem of deciding how much
+water to store each day in the system, as well as deciding when to completely
+empty the water system, in order to tradeoff: the financial costs of the water,
+the health costs implicit in long durations of storing the same water, the
+potential for a shortfall in the quantity of stored versus demanded water, and
+water wastage from emptying the system. To solve this problem, we develop a new
+Binary Dynamic Search (BiDS) algorithm that is able to use binary search in one
+dimension to compute the value function of stochastic optimal control problems
+with controlled resets to a single state and with constraints on the maximum
+time span in between resets of the system.
+"
+22485,Optimal interpolation and Compatible Relaxation in Classical Algebraic Multigrid,"  In this paper, we consider a classical form of optimal algebraic multigrid
+(AMG) interpolation that directly minimizes the two-grid convergence rate and
+compare it with the so-called ideal form that minimizes a certain weak
+approximation property of the coarse space. We study compatible relaxation type
+estimates for the quality of the coarse grid and derive a new sharp measure
+using optimal interpolation that provides a guaranteed lower bound on the
+convergence rate of the resulting two-grid method for a given grid. In
+addition, we design a generalized bootstrap algebraic multigrid setup algorithm
+that computes a sparse approximation to the optimal interpolation matrix. We
+demonstrate numerically that the BAMG method with sparse interpolation matrix
+(and spanning multiple levels) outperforms the two-grid method with the
+standard ideal interpolation (a dense matrix) for various scalar diffusion
+problems with highly varying diffusion coefficient.
+"
+22486,Automatic Heap Layout Manipulation for Exploitation,"  Heap layout manipulation is integral to exploiting heap-based memory
+corruption vulnerabilities. In this paper we present the first automatic
+approach to the problem, based on pseudo-random black-box search. Our approach
+searches for the inputs required to place the source of a heap-based buffer
+overflow or underflow next to heap-allocated objects that an exploit developer,
+or automatic exploit generation system, wishes to read or corrupt. We present a
+framework for benchmarking heap layout manipulation algorithms, and use it to
+evaluate our approach on several real-world allocators, showing that
+pseudo-random black box search can be highly effective. We then present SHRIKE,
+a novel system that can perform automatic heap layout manipulation on the PHP
+interpreter and can be used in the construction of control-flow hijacking
+exploits. Starting from PHP's regression tests, SHRIKE discovers fragments of
+PHP code that interact with the interpreter's heap in useful ways, such as
+making allocations and deallocations of particular sizes, or allocating objects
+containing sensitive data, such as pointers. SHRIKE then uses our search
+algorithm to piece together these fragments into programs, searching for one
+that achieves a desired heap layout. SHRIKE allows an exploit developer to
+focus on the higher level concepts in an exploit, and to defer the resolution
+of heap layout constraints to SHRIKE. We demonstrate this by using SHRIKE in
+the construction of a control-flow hijacking exploit for the PHP interpreter.
+"
+22487,Reliability Assessment for Large-Scale Molecular Dynamics Approximations,"  Molecular dynamics (MD) simulations are used in biochemistry, physics, and
+other fields to study the motions, thermodynamic properties, and the
+interactions between molecules. Computational limitations and the complexity of
+these problems, however, create the need for approximations to the standard MD
+methods and for uncertainty quantification and reliability assessment of those
+approximations. In this paper, we exploit the intrinsic two-scale nature of MD
+to construct a class of large-scale dynamics approximations. The reliability of
+these methods are evaluated here by measuring the differences between full,
+classical MD simulations and those based on these large-scale approximations.
+Molecular dynamics evolutions are non-linear and chaotic, so the complete
+details of molecular evolutions cannot be accurately predicted even using full,
+classical MD simulations. This paper provides numerical results that
+demonstrate the existence of computationally efficient large-scale MD
+approximations which accurately model certain large-scale properties of the
+molecules: the energy, the linear and angular momenta, and other macroscopic
+features of molecular motions.
+"
+22488,Identifying First-person Camera Wearers in Third-person Videos,"  We consider scenarios in which we wish to perform joint scene understanding,
+object tracking, activity recognition, and other tasks in environments in which
+multiple people are wearing body-worn cameras while a third-person static
+camera also captures the scene. To do this, we need to establish person-level
+correspondences across first- and third-person videos, which is challenging
+because the camera wearer is not visible from his/her own egocentric video,
+preventing the use of direct feature matching. In this paper, we propose a new
+semi-Siamese Convolutional Neural Network architecture to address this novel
+challenge. We formulate the problem as learning a joint embedding space for
+first- and third-person videos that considers both spatial- and motion-domain
+cues. A new triplet loss function is designed to minimize the distance between
+correct first- and third-person matches while maximizing the distance between
+incorrect ones. This end-to-end approach performs significantly better than
+several baselines, in part by learning the first- and third-person features
+optimized for matching jointly with the distance measure itself.
+"
+22489,Unleashing the Power of Hashtags in Tweet Analytics with Distributed Framework on Apache Storm,"  Twitter is a popular social network platform where users can interact and
+post texts of up to 280 characters called tweets. Hashtags, hyperlinked words
+in tweets, have increasingly become crucial for tweet retrieval and search.
+Using hashtags for tweet topic classification is a challenging problem because
+of context dependent among words, slangs, abbreviation and emoticons in a short
+tweet along with evolving use of hashtags. Since Twitter generates millions of
+tweets daily, tweet analytics is a fundamental problem of Big data stream that
+often requires a real-time Distributed processing. This paper proposes a
+distributed online approach to tweet topic classification with hashtags. Being
+implemented on Apache Storm, a distributed real time framework, our approach
+incrementally identifies and updates a set of strong predictors in the Naïve
+Bayes model for classifying each incoming tweet instance. Preliminary
+experiments show promising results with up to 97% accuracy and 37% increase in
+throughput on eight processors.
+"
+22490,Bayesian GAN,"  Generative adversarial networks (GANs) can implicitly learn rich
+distributions over images, audio, and data which are hard to model with an
+explicit likelihood. We present a practical Bayesian formulation for
+unsupervised and semi-supervised learning with GANs. Within this framework, we
+use stochastic gradient Hamiltonian Monte Carlo to marginalize the weights of
+the generator and discriminator networks. The resulting approach is
+straightforward and obtains good performance without any standard interventions
+such as feature matching, or mini-batch discrimination. By exploring an
+expressive posterior over the parameters of the generator, the Bayesian GAN
+avoids mode-collapse, produces interpretable and diverse candidate samples, and
+provides state-of-the-art quantitative results for semi-supervised learning on
+benchmarks including SVHN, CelebA, and CIFAR-10, outperforming DCGAN,
+Wasserstein GANs, and DCGAN ensembles.
+"
+22491,Discovery of a magnetic field in $ρ$ Pup,"  $\rho$ Pup is a $\delta$ Scuti F2 pulsator, known to host a main radial mode
+as well as non-radial pulsations, with chemical peculiarities typical of
+evolved Am stars. We present high-precision spectropolarimetric observations of
+this star, obtained with ESPaDOnS at the Canada France Hawaii Telescope (CFHT)
+in the frame of the BRITE spectropolarimetric survey. A magnetic field is
+clearly detected in $\rho$ Pup, with a longitudinal field strength below 1 G.
+This makes $\rho$ Pup the second known magnetic $\delta$ Scuti discovered,
+after HD 188774, and a possible cool evolved counterpart of the recently
+discovered ultra-weakly magnetic Am family.
+"
+22492,A recursive estimation approach to distributed identification of large-scale multi-input-single-output FIR systems,"  The problem of identifying single modules in multiple-input-single-output
+(MISO) systems is considered. A novel approach to distributed identification of
+MISO finite impulse response systems is presented. The distributed
+identification is discerned by the local estimation of local parameters, which
+correspond to a module in the MISO system. The local estimators are derived
+from the standard recursive least squares estimator and require limited
+information exchange. By Lyapunov's second method, sufficient conditions are
+derived for asymptotic convergence of the estimators to the true parameters in
+the absence of disturbances, which lead to asymptotic unbiasedness in the
+presence of additive output disturbances.
+"
+22493,Estimating Cosmological Parameters from the Dark Matter Distribution,"  A grand challenge of the 21st century cosmology is to accurately estimate the
+cosmological parameters of our Universe. A major approach to estimating the
+cosmological parameters is to use the large-scale matter distribution of the
+Universe. Galaxy surveys provide the means to map out cosmic large-scale
+structure in three dimensions. Information about galaxy locations is typically
+summarized in a ""single"" function of scale, such as the galaxy correlation
+function or power-spectrum. We show that it is possible to estimate these
+cosmological parameters directly from the distribution of matter. This paper
+presents the application of deep 3D convolutional networks to volumetric
+representation of dark-matter simulations as well as the results obtained using
+a recently proposed distribution regression framework, showing that machine
+learning techniques are comparable to, and can sometimes outperform,
+maximum-likelihood point estimates using ""cosmological models"". This opens the
+way to estimating the parameters of our Universe with higher accuracy.
+"
+22494,Evolving geometry of a vortex triangle,"  The motion of three interacting point vortices in the plane can be thought of
+as the motion of three geometrical points endowed with a dynamics. This motion
+can therefore be re-formulated in terms of dynamically evolving geometric
+quantities, viz. the circle that circumscribes the vortex triangle and the
+angles of the vortex triangle. In this study, we develop the equations of
+motion for the center, $Z$, and radius, $R$, of this circumcircle, and for the
+angles of the vortex triangle, $A$, $B$, and $C$. The equations of motion for
+$R$, $A$, $B$ and $C$ form an autonomous dynamical system. A number of known
+results in the three-vortex problem follow readily from the equations, giving a
+new geometrical perspective on the problem.
+"
+22495,The cosmic string as a channel for the massive particle teleportation,"  Here we prove the existence of a new type of the world-sheet string
+singularities - the cusps that are stable during the finite time. These
+singularities make the emission of the captured massive quantum particle
+possible in the frames of the author's model suggested earlier. In aggregate,
+we have a new mechanism of quantum teleportation of such particles at large
+distances.
+"
+22496,"A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation","  In statistical inference problems, we wish to obtain lower bounds on the
+minimax risk, that is to bound the performance of any possible estimator. A
+standard technique to obtain risk lower bounds involves the use of Fano's
+inequality. In an information-theoretic setting, it is known that Fano's
+inequality typically does not give a sharp converse result (error lower bound)
+for channel coding problems. Moreover, recent work has shown that an argument
+based on binary hypothesis testing gives tighter results. We adapt this
+technique to the statistical setting, and argue that Fano's inequality can
+always be replaced by this approach to obtain tighter lower bounds that can be
+easily computed and are asymptotically sharp. We illustrate our technique in
+three applications: density estimation, active learning of a binary classifier,
+and compressed sensing, obtaining tighter risk lower bounds in each case.
+"
+22497,Dissociating diatomic molecules in ultrafast and intense light,"  An $\mathit{ab\ initio}$ theory is devised for the quantum dynamics of
+molecules undergoing (multiple) ionization in ultrafast and intense light.
+Specifically, the intertwined problem of photoionization, radiative, and
+electronic transitions in the course of dissociation is addressed which arises,
+e.g., when molecules are exposed to XUV light or x rays from free electron
+lasers or attosecond light sources, but the approach is equally useful in
+optical strong-field physics. The coherent interaction of the molecule with the
+light in a specific charge state is also treated. I set out from an abstract
+formulation in terms of the quantum optical notion of system-reservoir
+interaction using a master equation in Lindblad form and analyze its short-time
+approximation. First, I express it in a direct sum rigged Hilbert space for an
+efficient solution with numerical methods for systems of differential
+equations. Second, I derive a treatment via quantum Monte Carlo wave packet
+(MCWP) propagation. The formalism is concretized to diatomic molecules in
+Born-Oppenheimer approximation whereby molecular rotation is disregarded. The
+numerical integration of the master equation is carried out with a suitably
+factored density matrix that exploits the locality of the Hamiltonian and the
+Lindblad superoperator with respect to the internuclear distance. The
+formulation of the MCWP for molecules requires a thorough analysis of the
+quantum jump process; namely, the dependence on the continuous distance renders
+a straight wave packet promotion useless and, instead, a projected outer
+product needs to be employed involving an integrated quantum jump operator.
+"
+22498,Adaptation of olfactory receptor abundances for efficient coding,"  Olfactory receptor usage is highly heterogeneous, with some receptor types
+being orders of magnitude more abundant than others. We propose an explanation
+for this striking fact: the receptor distribution is tuned to maximally
+represent information about the olfactory environment in a regime of efficient
+coding that is sensitive to the global context of correlated sensor responses.
+This model predicts that in mammals, where olfactory sensory neurons are
+replaced regularly, receptor abundances should continuously adapt to odor
+statistics. Experimentally, increased exposure to odorants leads variously, but
+reproducibly, to increased, decreased, or unchanged abundances of different
+activated receptors. We demonstrate that this diversity of effects is required
+for efficient coding when sensors are broadly correlated, and provide an
+algorithm for predicting which olfactory receptors should increase or decrease
+in abundance following specific environmental changes. Finally, we give simple
+dynamical rules for neural birth and death processes that might underlie this
+adaptation.
+"
+22499,On the Stanley depth of edge ideals of k-partite clutters,"  We give upper bounds for the Stanley depth of edge ideals of certain
+k-partite clutters. In particular, we generalize a result of Ishaq about the
+Stanley depth of the edge ideal of a complete bipartite graph. A result of
+Pournaki, Seyed Fakhari and Yassemi implies that the Stanley's conjecture holds
+for d-uniform complete d-partite clutters. Here we give a shorter and different
+proof of this fact.
+"
+22500,Projective cases for the restriction of the oscillator representation to dual pairs,"  We study here the restriction of the oscillator representation of the
+symplectic group $Sp(2p(m+n),\mathbb{R})$ to two different subgroups, namely
+$O(m,n)$ and $Sp(2p,\mathbb{R})$. We use the duality correspondence introduced
+by Howe to analyze these restrictions, and determine sufficient conditions on
+$m,n$ and $p$ so that the modules obtained are projective. The duality
+correspondence gives a description of the restriction in terms of lowest and
+highest modules, and we conclude by using gradings and filtrations to identify
+the modules.
+"
+22501,Near-Optimality of Linear Recovery from Indirect Observations,"  We consider the problem of recovering linear image $Bx$ of a signal $x$ known
+to belong to a given convex compact set ${\cal X}$ from indirect observation
+$\omega=Ax+\xi$ of $x$ corrupted by random noise $\xi$ with finite covariance
+matrix. It is shown that under some assumptions on ${\cal X}$ (satisfied, e.g.,
+when ${\cal X}$ is the intersection of $K$ concentric ellipsoids/elliptic
+cylinders, or the unit ball of the spectral norm in the space of matrices) and
+on the norm $\|\cdot\|$ used to measure the recovery error (satisfied, e.g., by
+$\|\cdot\|_p$-norms, $1\leq p\leq 2$, on ${\mathbf{R}}^m$ and by the nuclear
+norm on the space of matrices), one can build, in a computationally efficient
+manner, a ""presumably good"" linear in observations estimate, and that in the
+case of zero mean Gaussian observation noise, this estimate is near-optimal
+among all (linear and nonlinear) estimates in terms of its worst-case, over
+$x\in {\cal X}$, expected $\|\cdot\|$-loss. These results form an essential
+extension of those in our paper arXiv:1602.01355, where the assumptions on
+${\cal X}$ were more restrictive, and the norm $\|\cdot\|$ was assumed to be
+the Euclidean one. In addition, we develop near-optimal estimates for the case
+of ""uncertain-but-bounded"" noise, where all we know about $\xi$ is that it is
+bounded in a given norm by a given $\sigma$. Same as in arXiv:1602.01355, our
+results impose no restrictions on $A$ and $B$.
+"
+22502,NAM: Non-Adversarial Unsupervised Domain Mapping,"  Several methods were recently proposed for the task of translating images
+between domains without prior knowledge in the form of correspondences. The
+existing methods apply adversarial learning to ensure that the distribution of
+the mapped source domain is indistinguishable from the target domain, which
+suffers from known stability issues. In addition, most methods rely heavily on
+`cycle' relationships between the domains, which enforce a one-to-one mapping.
+In this work, we introduce an alternative method: Non-Adversarial Mapping
+(NAM), which separates the task of target domain generative modeling from the
+cross-domain mapping task. NAM relies on a pre-trained generative model of the
+target domain, and aligns each source image with an image synthesized from the
+target domain, while jointly optimizing the domain mapping function. It has
+several key advantages: higher quality and resolution image translations,
+simpler and more stable training and reusable target models. Extensive
+experiments are presented validating the advantages of our method.
+"
+22503,Deep-learning-based data page classification for holographic memory,"  We propose a deep-learning-based classification of data pages used in
+holographic memory. We numerically investigated the classification performance
+of a conventional multi-layer perceptron (MLP) and a deep neural network, under
+the condition that reconstructed page data are contaminated by some noise and
+are randomly laterally shifted. The MLP was found to have a classification
+accuracy of 91.58%, whereas the deep neural network was able to classify data
+pages at an accuracy of 99.98%. The accuracy of the deep neural network is two
+orders of magnitude better than the MLP.
+"
+22504,The Temple University Hospital Seizure Detection Corpus,"  We introduce the TUH EEG Seizure Corpus (TUSZ), which is the largest open
+source corpus of its type, and represents an accurate characterization of
+clinical conditions. In this paper, we describe the techniques used to develop
+TUSZ, evaluate their effectiveness, and present some descriptive statistics on
+the resulting corpus.
+"
+22505,Fractional Calculus Approach to Logistic Equation and its Application,"  In this paper, we propose a solution of fractional logistic equation by using
+properties of Mittag-Leffler function.
+"
+22506,qTorch: The Quantum Tensor Contraction Handler,"  Classical simulation of quantum computation is necessary for studying the
+numerical behavior of quantum algorithms, as there does not yet exist a large
+viable quantum computer on which to perform numerical tests. Tensor network
+(TN) contraction is an algorithmic method that can efficiently simulate some
+quantum circuits, often greatly reducing the computational cost over methods
+that simulate the full Hilbert space. In this study we implement a tensor
+network contraction program for simulating quantum circuits using multi-core
+compute nodes. We show simulation results for the Max-Cut problem on 3- through
+7-regular graphs using the quantum approximate optimization algorithm (QAOA),
+successfully simulating up to 100 qubits. We test two different methods for
+generating the ordering of tensor index contractions: one is based on the tree
+decomposition of the line graph, while the other generates ordering using a
+straight-forward stochastic scheme. Through studying instances of QAOA
+circuits, we show the expected result that as the treewidth of the quantum
+circuit's line graph decreases, TN contraction becomes significantly more
+efficient than simulating the whole Hilbert space. The results in this work
+suggest that tensor contraction methods are superior only when simulating
+Max-Cut/QAOA with graphs of regularities approximately five and below. Insight
+into this point of equal computational cost helps one determine which
+simulation method will be more efficient for a given quantum circuit. The
+stochastic contraction method outperforms the line graph based method only when
+the time to calculate a reasonable tree decomposition is prohibitively
+expensive. Finally, we release our software package, qTorch (Quantum TensOR
+Contraction Handler), intended for general quantum circuit simulation.
+"
+22507,The role of quantum correlations in Cop and Robber game,"  We introduce and study quantized versions of Cop and Robber game. We achieve
+this by using graph-preserving quantum operations, which are the quantum
+analogues of stochastic operations preserving the graph. We provide the tight
+bound for the number of operations required to reach the given state. By
+extending them to the controlled operations, we define a quantum-controlled Cop
+and Robber game, which expands the classical Cop and Robber game, as well as
+the classically controlled quantum Cop and Robber game. In contrast to the
+typical scheme for introducing quantum games, we assume that both parties can
+utilise full information about the opponent's strategy. We show that the
+utilisation of the full knowledge about the opponent's state does not provide
+the advantage. Moreover, the chances of catching the Robber decrease for
+classical cop-win graphs. This result does not depend on the chosen model of
+evolution. On the other hand, the possibility to execute controlled quantum
+operations allows catching the Robber on almost all classical cop-win graphs.
+By this, we demonstrate that it is necessary to enrich the structure of
+correlations between the players' systems to provide a non-trivial quantized
+Cop and Robber game. Thus the quantum controlled operations offer a significant
+advantage over the classically controlled quantum operations.
+"
+22508,Input Fast-Forwarding for Better Deep Learning,"  This paper introduces a new architectural framework, known as input
+fast-forwarding, that can enhance the performance of deep networks. The main
+idea is to incorporate a parallel path that sends representations of input
+values forward to deeper network layers. This scheme is substantially different
+from ""deep supervision"" in which the loss layer is re-introduced to earlier
+layers. The parallel path provided by fast-forwarding enhances the training
+process in two ways. First, it enables the individual layers to combine
+higher-level information (from the standard processing path) with lower-level
+information (from the fast-forward path). Second, this new architecture reduces
+the problem of vanishing gradients substantially because the fast-forwarding
+path provides a shorter route for gradient backpropagation. In order to
+evaluate the utility of the proposed technique, a Fast-Forward Network (FFNet),
+with 20 convolutional layers along with parallel fast-forward paths, has been
+created and tested. The paper presents empirical results that demonstrate
+improved learning capacity of FFNet due to fast-forwarding, as compared to
+GoogLeNet (with deep supervision) and CaffeNet, which are 4x and 18x larger in
+size, respectively. All of the source code and deep learning models described
+in this paper will be made available to the entire research community
+"
+22509,Improving the data quality in the research information systems,"  In order to introduce an integrated research information system, this will
+provide scientific institutions with the necessary information on research
+activities and research results in assured quality. Since data collection,
+duplication, missing values, incorrect formatting, inconsistencies, etc. can
+arise in the collection of research data in different research information
+systems, which can have a wide range of negative effects on data quality, the
+subject of data quality should be treated with better results. This paper
+examines the data quality problems in research information systems and presents
+the new techniques that enable organizations to improve their quality of
+research information.
+"
+22510,A4: Automatically Assisting Android API Migrations Using Code Examples,"  The fast-paced evolution of Android APIs has posed a challenging task for
+Android app developers. To leverage the newly and frequently released APIs from
+Android, developers often must spend considerable effort on API migrations.
+Prior research work and Android official documentation typically provide enough
+information to guide developers in identifying both the changed API calls that
+need to be migrated and the corresponding API calls in the new version of
+Android (what to migrate). However, the API migration task is still challenging
+since developers lack the knowledge of how to migrate the API calls. In
+addition to the official documentation, there exist code examples, such as
+Google Samples, that illustrate the usage of APIs. We posit that by analyzing
+the changes of API usage in code examples, we may be able to learn API
+migration patterns to assist developers with API migrations.
+In this paper, we propose an approach that automatically learns API migration
+patterns from code examples and applies these patterns to the source code of
+Android apps for API migration. To evaluate our approach, we migrate API calls
+in open source Android apps by learning API migration patterns from both public
+and manually generated code examples. We find that our approach can
+successfully learn API migration patterns and provide API migration assistance
+in 71 out of 80 cases. In particular, our approach can either automatically
+migrate API calls with little to no extra modifications needed or provide
+guidance to assist with the migrations. Our approach can be adopted by Android
+developers to reduce the effort they spend on regularly migrating Android APIs.
+"
+22511,The tilting-cotilting correspondence,"  To a big n-tilting object in a complete, cocomplete abelian category A with
+an injective cogenerator we assign a big n-cotilting object in a complete,
+cocomplete abelian category B with a projective generator, and vice versa. Then
+we construct an equivalence between the (conventional or absolute) derived
+categories of A and B. Under various assumptions on A, which cover a wide range
+of examples (for instance, if A is a module category or, more generally, a
+locally finitely presentable Grothendieck abelian category), we show that its
+counterpart B is the abelian category of contramodules over a topological ring
+and that the derived equivalences are realized by a contramodule-valued variant
+of the usual derived Hom-functor.
+"
+22512,Taylor term does not imply any nontrivial linear one-equality Maltsev condition,"  It is known that any finite idempotent algebra that satisfies a nontrivial
+Maltsev condition must satisfy the linear one-equality Maltsev condition (a
+variant of the term discovered by M. Siggers and refined by K. Kearnes, P.
+Marković, and R. McKenzie):
+\[
+t(r,a,r,e)\approx t(a,r,e,a).
+\] We show that if we drop the finiteness assumption, the $k$-ary weak near
+unanimity equations imply only trivial linear one-equality Maltsev conditions
+for every $k\geq 3$. From this it follows that there is no nontrivial linear
+one-equality condition that would hold in all idempotent algebras having Taylor
+terms.
+Miroslav Olšák has recently shown that there is a weakest nontrivial
+strong Maltsev condition for idempotent algebras. Olšák has found several
+such (mutually equivalent) conditions consisting of two or more equations. Our
+result shows that Olšák's equation systems can't be compressed into just
+one equation.
+"
+22513,Extensions between Verma modules for dihedral groups,"  Computing the extensions between Verma modules is in general a very difficult
+problem. Using Soergel bimodules, one can construct a graded version of the
+principal block of Category $\mathcal{O}$ for any finite coxeter group. In this
+setting, we compute the extensions between Verma modules for dihedral groups.
+"
+22514,Lectures on Randomized Numerical Linear Algebra,"  This chapter is based on lectures on Randomized Numerical Linear Algebra from
+the 2016 Park City Mathematics Institute summer school on The Mathematics of
+Data.
+"
+22515,Circular Statistics-based low complexity DOA estimation for hearing aid application,"  The proposed Circular statistics-based Inter-Microphone Phase difference
+estimation Localizer (CIMPL) method is tailored toward binaural hearing aid
+systems with microphone arrays in each unit. The method utilizes the circular
+statistics (circular mean and circular variance) of inter-microphone phase
+difference (IPD) across different microphone pairs. These IPDs are firstly
+mapped to time delays through a variance-weighted linear fit, then mapped to
+azimuth direction-of-arrival (DoA) and lastly information of different
+microphone pairs is combined. The variance is carried through the different
+transformations and acts as a reliability index of the estimated angle. Both
+the resulting angle and variance are fed into a wrapped Kalman filter, which
+provides a smoothed estimate of the DoA. The proposed method improves the
+accuracy of the tracked angle of a single moving source compared with the
+benchmark method provided by the LOCATA challenge, and it runs approximately 75
+times faster.
+"
+22516,The MacGyver Test - A Framework for Evaluating Machine Resourcefulness and Creative Problem Solving,"  Current measures of machine intelligence are either difficult to evaluate or
+lack the ability to test a robot's problem-solving capacity in open worlds. We
+propose a novel evaluation framework based on the formal notion of MacGyver
+Test which provides a practical way for assessing the resilience and
+resourcefulness of artificial agents.
+"
+22517,On a Penrose-like inequality in dimensions less than eight,"  On an asymptotically flat manifold $M^n$ with nonnegative scalar curvature,
+with outer minimizing boundary $\Sigma$, we prove a Penrose-like inequality in
+dimensions $ n < 8$, under suitable assumptions on the mean curvature and the
+scalar curvature of $ \Sigma$.
+"
+22518,Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods,"  The tremendous growth of positioning technologies and GPS enabled devices has
+produced huge volumes of tracking data during the recent years. This source of
+information constitutes a rich input for data analytics processes, either
+offline (e.g. cluster analysis, hot motion discovery) or online (e.g.
+short-term forecasting of forthcoming positions). This paper focuses on
+predictive analytics for moving objects (could be pedestrians, cars, vessels,
+planes, animals, etc.) and surveys the state-of-the-art in the context of
+future location and trajectory prediction. We provide an extensive review of
+over 50 works, also proposing a novel taxonomy of predictive algorithms over
+moving objects. We also list the properties of several real datasets used in
+the past for validation purposes of those works and, motivated by this, we
+discuss challenges that arise in the transition from conventional to Big Data
+applications.
+CCS Concepts: Information systems > Spatial-temporal systems; Information
+systems > Data analytics; Information systems > Data mining; Computing
+methodologies > Machine learning Additional Key Words and Phrases: mobility
+data, moving object trajectories, trajectory prediction, future location
+prediction.
+"
+22519,Supercoiling DNA locates mismatches,"  We present a method of detecting sequence defects by supercoiling DNA with
+magnetic tweezers. The method is sensitive to a single mismatched base pair in
+a DNA sequence of several thousand base pairs. We systematically compare DNA
+molecules with 0 to 16 adjacent mismatches at 1 M monovalent salt and 3.5 pN
+force and show that, under these conditions, a single plectoneme forms and is
+stably pinned at the defect. We use these measurements to estimate the energy
+and degree of end-loop kinking at defects. From this, we calculate the relative
+probability of plectoneme pinning at the mismatch under physiologically
+relevant conditions. Based on this estimate, we propose that DNA supercoiling
+could contribute to mismatch and damage sensing in vivo.
+"
+22520,Object-Based Audio Rendering,"  Apparatus and methods are disclosed for performing object-based audio
+rendering on a plurality of audio objects which define a sound scene, each
+audio object comprising at least one audio signal and associated metadata. The
+apparatus comprises: a plurality of renderers each capable of rendering one or
+more of the audio objects to output rendered audio data; and object adapting
+means for adapting one or more of the plurality of audio objects for a current
+reproduction scenario, the object adapting means being configured to send the
+adapted one or more audio objects to one or more of the plurality of renderers.
+"
+22521,An elementary direct proof that the Knaster-Kuratowski-Mazurkiewicz lemma implies Sperner's lemma,"  Three central results in economic theory --- Brouwer's fixed-point theorem,
+Sperner's lemma, and the Knaster-Kuratowski-Mazurkiewicz (KKM) lemma --- are
+known to be equivalent. In almost all cases, elementary direct proofs of one of
+these results using any of the others are easily found in the literature. This
+seems not to be the case for the claim that the KKM lemma implies Sperner's
+lemma. The goal of this note is to provide such a proof.
+"
+22522,Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams,"  Recently, there has been a growing interest in monitoring brain activity for
+individual recognition system. So far these works are mainly focussing on
+single channel data or fragment data collected by some advanced brain
+monitoring modalities. In this study we propose new individual recognition
+schemes based on spatio-temporal resting state Electroencephalography (EEG)
+data. Besides, instead of using features derived from artificially-designed
+procedures, modified deep learning architectures which aim to automatically
+extract an individual's unique features are developed to conduct
+classification. Our designed deep learning frameworks are proved of a small but
+consistent advantage of replacing the $softmax$ layer with Random Forest.
+Additionally, a voting layer is added at the top of designed neural networks in
+order to tackle the classification problem arisen from EEG streams. Lastly,
+various experiments are implemented to evaluate the performance of the designed
+deep learning architectures; Results indicate that the proposed EEG-based
+individual recognition scheme yields a high degree of classification accuracy:
+$81.6\%$ for characteristics in high risk (CHR) individuals, $96.7\%$ for
+clinically stable first episode patients with schizophrenia (FES) and $99.2\%$
+for healthy controls (HC).
+"
+22523,Computationally efficient inference for latent position network models,"  Latent position models are nowadays widely used for the analysis of networks
+in a variety of research fields. In fact, these models possess a number of
+desirable theoretical properties, and are particularly easy to interpret.
+However, statistical methodologies that infer latent position models generally
+require a computational cost which grows with the square of the number of nodes
+in the graph. This makes the analysis of large social networks impractical. In
+this paper, we propose a new method characterised by a linear computational
+complexity, which may be used to fit latent position models on networks with
+several tens of thousands of nodes. Our approach relies on an approximation of
+the likelihood function, where the amount of noise introduced can be
+arbitrarily reduced at the expense of computational efficiency. We establish
+several theoretical results that show how the likelihood error propagates to
+the invariant distribution of the Markov chain Monte Carlo sampler. In
+particular, we illustrate that one can achieve a substantial reduction in
+computing time and still obtain a reasonably good estimation of the latent
+structure. Finally, we propose applications of our method to simulated networks
+and to a large coauthorships network, demonstrating the usefulness of our
+approach.
+"
+22524,End-to-end optimization of goal-driven and visually grounded dialogue systems,"  End-to-end design of dialogue systems has recently become a popular research
+topic thanks to powerful tools such as encoder-decoder architectures for
+sequence-to-sequence learning. Yet, most current approaches cast human-machine
+dialogue management as a supervised learning problem, aiming at predicting the
+next utterance of a participant given the full history of the dialogue. This
+vision is too simplistic to render the intrinsic planning problem inherent to
+dialogue as well as its grounded nature, making the context of a dialogue
+larger than the sole history. This is why only chit-chat and question answering
+tasks have been addressed so far using end-to-end architectures. In this paper,
+we introduce a Deep Reinforcement Learning method to optimize visually grounded
+task-oriented dialogues, based on the policy gradient algorithm. This approach
+is tested on a dataset of 120k dialogues collected through Mechanical Turk and
+provides encouraging results at solving both the problem of generating natural
+dialogues and the task of discovering a specific object in a complex picture.
+"
+22525,Hemingway: Modeling Distributed Optimization Algorithms,"  Distributed optimization algorithms are widely used in many industrial
+machine learning applications. However choosing the appropriate algorithm and
+cluster size is often difficult for users as the performance and convergence
+rate of optimization algorithms vary with the size of the cluster. In this
+paper we make the case for an ML-optimizer that can select the appropriate
+algorithm and cluster size to use for a given problem. To do this we propose
+building two models: one that captures the system level characteristics of how
+computation, communication change as we increase cluster sizes and another that
+captures how convergence rates change with cluster sizes. We present
+preliminary results from our prototype implementation called Hemingway and
+discuss some of the challenges involved in developing such a system.
+"
+22526,The Kepler-19 system: a thick-envelope super-Earth with two Neptune-mass companions characterized using Radial Velocities and Transit Timing Variations,"  We report a detailed characterization of the Kepler-19 system. This star was
+previously known to host a transiting planet with a period of 9.29 days, a
+radius of 2.2 R$_\oplus$ and an upper limit on the mass of 20 M$_\oplus$. The
+presence of a second, non-transiting planet was inferred from the transit time
+variations (TTVs) of Kepler-19b, over 8 quarters of Kepler photometry, although
+neither mass nor period could be determined. By combining new TTVs measurements
+from all the Kepler quarters and 91 high-precision radial velocities obtained
+with the HARPS-N spectrograph, we measured through dynamical simulations a mass
+of $8.4 \pm 1.6$ M$_\oplus$ for Kepler-19b. From the same data, assuming system
+coplanarity, we determined an orbital period of 28.7 days and a mass of $13.1
+\pm 2.7$ M$_\oplus$ for Kepler-19c and discovered a Neptune-like planet with a
+mass of $20.3 \pm 3.4$ M$_\oplus$ on a 63 days orbit. By comparing dynamical
+simulations with non-interacting Keplerian orbits, we concluded that neglecting
+interactions between planets may lead to systematic errors that could hamper
+the precision in the orbital parameters when the dataset spans several years.
+With a density of $4.32 \pm 0.87$ g cm$^{-3}$ ($0.78 \pm 0.16$ $\rho_\oplus$)
+Kepler-19b belongs to the group of planets with a rocky core and a significant
+fraction of volatiles, in opposition to low-density planets characterized by
+transit-time variations only and the increasing number of rocky planets with
+Earth-like density. Kepler-19 joins the small number of systems that reconcile
+transit timing variation and radial velocity measurements.
+"
+22527,Impact of different time series aggregation methods on optimal energy system design,"  Modelling renewable energy systems is a computationally-demanding task due to
+the high fluctuation of supply and demand time series. To reduce the scale of
+these, this paper discusses different methods for their aggregation into
+typical periods. Each aggregation method is applied to a different type of
+energy system model, making the methods fairly incomparable. To overcome this,
+the different aggregation methods are first extended so that they can be
+applied to all types of multidimensional time series and then compared by
+applying them to different energy system configurations and analyzing their
+impact on the cost optimal design. It was found that regardless of the method,
+time series aggregation allows for significantly reduced computational
+resources. Nevertheless, averaged values lead to underestimation of the real
+system cost in comparison to the use of representative periods from the
+original time series. The aggregation method itself, e.g. k means clustering,
+plays a minor role. More significant is the system considered: Energy systems
+utilizing centralized resources require fewer typical periods for a feasible
+system design in comparison to systems with a higher share of renewable
+feed-in. Furthermore, for energy systems based on seasonal storage, currently
+existing models integration of typical periods is not suitable.
+"
+22528,Probing Leptophilic Dark Sectors with Hadronic Processes,"  We study vector portal dark matter models where the mediator couples only to
+leptons. In spite of the lack of tree-level couplings to colored states,
+radiative effects generate interactions with quark fields that could give rise
+to a signal in current and future experiments. We identify such experimental
+signatures: scattering of nuclei in dark matter direct detection; resonant
+production of lepton-antilepton pairs at the Large Hadron Collider; and
+hadronic final states in dark matter indirect searches. Furthermore, radiative
+effects also generate an irreducible mass mixing between the vector mediator
+and the $Z$ boson, severely bounded by ElectroWeak Precision Tests. We use
+current experimental results to put bounds on this class of models, accounting
+for both radiatively induced and tree-level processes. Remarkably, the former
+often overwhelm the latter.
+"
+22529,Computer activity learning from system call time series,"  Using a previously introduced similarity function for the stream of system
+calls generated by a computer, we engineer a program-in-execution classifier
+using deep learning methods. Tested on malware classification, it significantly
+outperforms current state of the art. We provide a series of performance
+measures and tests to demonstrate the capabilities, including measurements from
+production use. We show how the system scales linearly with the number of
+endpoints. With the system we estimate the total number of malware families
+created over the last 10 years as 3450, in line with reasonable economic
+constraints. The more limited rate for new malware families than previously
+acknowledged implies that machine learning malware classifiers risk being
+tested on their training set; we achieve F1 = 0.995 in a test carefully
+designed to mitigate this risk.
+"
+22530,Integrated Linear Reconstruction for Finite Volume Scheme on Arbitrary Unstructured Grids,"  In [L. Chen and R. Li, Journal of Scientific Computing, Vol. 68, pp.
+1172--1197, (2016)], an integrated linear reconstruction was proposed for
+finite volume methods on unstructured grids. However, the geometric hypothesis
+of the mesh to enforce a local maximum principle is too restrictive to be
+satisfied by, for example, locally refined meshes or distorted meshes generated
+by arbitrary Lagrangian-Eulerian methods in practical applications. In this
+paper, we propose an improved integrated linear reconstruction approach to get
+rid of the geometric hypothesis. The resulting optimization problem is a convex
+quadratic programming problem, and hence can be solved efficiently by classical
+active-set methods. The features of the improved integrated linear
+reconstruction include that i). the local maximum principle is fulfilled on
+arbitrary unstructured grids, ii). the reconstruction is parameter-free, and
+iii). the finite volume scheme is positivity-preserving when the reconstruction
+is generalized to the Euler equations. A variety of numerical experiments are
+presented to demonstrate the performance of this method.
+"
+22531,Minimum message length inference of the Poisson and geometric models using heavy-tailed prior distributions,"  Minimum message length is a general Bayesian principle for model selection
+and parameter estimation that is based on information theory. This paper
+applies the minimum message length principle to a small-sample model selection
+problem involving Poisson and geometric data models. Since MML is a Bayesian
+principle, it requires prior distributions for all model parameters. We
+introduce three candidate prior distributions for the unknown model parameters
+with both light- and heavy-tails. The performance of the MML methods is
+compared with objective Bayesian inference and minimum description length
+techniques based on the normalized maximum likelihood code. Simulations show
+that our MML approach with a heavy-tail prior distribution provides an
+excellent performance in all tests.
+"
+22532,A Geometric View of Optimal Transportation and Generative Model,"  In this work, we show the intrinsic relations between optimal transportation
+and convex geometry, especially the variational approach to solve Alexandrov
+problem: constructing a convex polytope with prescribed face normals and
+volumes. This leads to a geometric interpretation to generative models, and
+leads to a novel framework for generative models. By using the optimal
+transportation view of GAN model, we show that the discriminator computes the
+Kantorovich potential, the generator calculates the transportation map. For a
+large class of transportation costs, the Kantorovich potential can give the
+optimal transportation map by a close-form formula. Therefore, it is sufficient
+to solely optimize the discriminator. This shows the adversarial competition
+can be avoided, and the computational architecture can be simplified.
+Preliminary experimental results show the geometric method outperforms WGAN for
+approximating probability measures with multiple clusters in low dimensional
+space.
+"
+22533,Remarks on well-posedness of the generalized surface quasi-geostrophic equation,"  In this paper, we are concerned with the Cauchy problem of the generalized
+surface quasi-geostrophic (SQG) equation in which the velocity field is
+expressed as $u=K\ast\omega$, where $\omega=\omega(x,t)$ is an unknown function
+and $K(x)=\frac{x^\perp}{|x|^{2+2\alpha}}, 0\le\alpha\le \frac12.$ When
+$\alpha=0$, it is the two-dimensional Euler equations. When $\alpha=\frac 12$,
+it corresponds to the inviscid SQG. We will prove that if the existence
+interval of the smooth solution to the generalized SQG for some
+$0<\alpha_0\le\frac12$ is $[0,T]$, then under the same initial data, the
+existence interval of the generalized SQG with $\alpha$ which is close to
+$\alpha_0$ will keep on $[0,T]$. As a byproduct, our result implies that the
+construction of the possible singularity of the smooth solution of the Cauchy
+problem to the generalized SQG with $\alpha>0$ will be subtle, in comparison
+with the singularity presented in [Kiselev et al 2016]. To prove our main
+results, the difference between the two solutions and meanwhile the
+approximation of the singular integrals will be dealt with. Some new uniform
+estimates with respect to $\alpha$ on the singular integrals and commutator
+estimates will be shown in this paper.
+"
+22534,Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments,"  Decentralized systems are a subset of distributed systems where multiple
+authorities control different components and no authority is fully trusted by
+all. This implies that any component in a decentralized system is potentially
+adversarial. We revise fifteen years of research on decentralization and
+privacy, and provide an overview of key systems, as well as key insights for
+designers of future systems. We show that decentralized designs can enhance
+privacy, integrity, and availability but also require careful trade-offs in
+terms of system complexity, properties provided, and degree of
+decentralization. These trade-offs need to be understood and navigated by
+designers. We argue that a combination of insights from cryptography,
+distributed systems, and mechanism design, aligned with the development of
+adequate incentives, are necessary to build scalable and successful
+privacy-preserving decentralized systems.
+"
+22535,Modular Techniques For Noncommutative Gröbner Bases,"  In this note, we extend modular techniques for computing Gröbner bases from
+the commutative setting to the vast class of noncommutative $G$-algebras. As in
+the commutative case, an effective verification test is only known to us in the
+graded case. In the general case, our algorithm is probabilistic in the sense
+that the resulting Gröbner basis can only be expected to generate the given
+ideal, with high probability. We have implemented our algorithm in the computer
+algebra system {\sc{Singular}} and give timings to compare its performance with
+that of other instances of Buchberger's algorithm, testing examples from
+$D$-module theory as well as classical benchmark examples. A particular feature
+of the modular algorithm is that it allows parallel runs.
+"
+22536,Financial Risk and Returns Prediction with Modular Networked Learning,"  An artificial agent for financial risk and returns' prediction is built with
+a modular cognitive system comprised of interconnected recurrent neural
+networks, such that the agent learns to predict the financial returns, and
+learns to predict the squared deviation around these predicted returns. These
+two expectations are used to build a volatility-sensitive interval prediction
+for financial returns, which is evaluated on three major financial indices and
+shown to be able to predict financial returns with higher than 80% success rate
+in interval prediction in both training and testing, raising into question the
+Efficient Market Hypothesis. The agent is introduced as an example of a class
+of artificial intelligent systems that are equipped with a Modular Networked
+Learning cognitive system, defined as an integrated networked system of machine
+learning modules, where each module constitutes a functional unit that is
+trained for a given specific task that solves a subproblem of a complex main
+problem expressed as a network of linked subproblems. In the case of neural
+networks, these systems function as a form of an ""artificial brain"", where each
+module is like a specialized brain region comprised of a neural network with a
+specific architecture.
+"
+22537,Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences,"  The design of personalized incentives or recommendations to improve user
+engagement is gaining prominence as digital platform providers continually
+emerge. We propose a multi-armed bandit framework for matching incentives to
+users, whose preferences are unknown a priori and evolving dynamically in time,
+in a resource constrained environment. We design an algorithm that combines
+ideas from three distinct domains: (i) a greedy matching paradigm, (ii) the
+upper confidence bound algorithm (UCB) for bandits, and (iii) mixing times from
+the theory of Markov chains. For this algorithm, we provide theoretical bounds
+on the regret and demonstrate its performance via both synthetic and realistic
+(matching supply and demand in a bike-sharing platform) examples.
+"
+22538,Latent Parameter Estimation in Fusion Networks Using Separable Likelihoods,"  Multi-sensor state space models underpin fusion applications in networks of
+sensors. Estimation of latent parameters in these models has the potential to
+provide highly desirable capabilities such as network self-calibration.
+Conventional solutions to the problem pose difficulties in scaling with the
+number of sensors due to the joint multi-sensor filtering involved when
+evaluating the parameter likelihood. In this article, we propose a separable
+pseudo-likelihood which is a more accurate approximation compared to a
+previously proposed alternative under typical operating conditions. In
+addition, we consider using separable likelihoods in the presence of many
+objects and ambiguity in associating measurements with objects that originated
+them. To this end, we use a state space model with a hypothesis based
+parameterisation, and, develop an empirical Bayesian perspective in order to
+evaluate separable likelihoods on this model using local filtering. Bayesian
+inference with this likelihood is carried out using belief propagation on the
+associated pairwise Markov random field. We specify a particle algorithm for
+latent parameter estimation in a linear Gaussian state space model and
+demonstrate its efficacy for network self-calibration using measurements from
+non-cooperative targets in comparison with alternatives.
+"
+22539,"The Design, Implementation, and Deployment of a System to Transparently Compress Hundreds of Petabytes of Image Files for a File-Storage Service","  We report the design, implementation, and deployment of Lepton, a
+fault-tolerant system that losslessly compresses JPEG images to 77% of their
+original size on average. Lepton replaces the lowest layer of baseline JPEG
+compression-a Huffman code-with a parallelized arithmetic code, so that the
+exact bytes of the original JPEG file can be recovered quickly. Lepton matches
+the compression efficiency of the best prior work, while decoding more than
+nine times faster and in a streaming manner. Lepton has been released as
+open-source software and has been deployed for a year on the Dropbox
+file-storage backend. As of February 2017, it had compressed more than 203 PiB
+of user JPEG files, saving more than 46 PiB.
+"
+22540,Analyzing behavioral trends in community driven discussion platforms like Reddit,"  The aim of this paper is to present methods to systematically analyze
+individual and group behavioral patterns observed in community driven
+discussion platforms like Reddit where users exchange information and views on
+various topics of current interest. We conduct this study by analyzing the
+statistical behavior of posts and modeling user interactions around them. We
+have chosen Reddit as an example, since it has grown exponentially from a small
+community to one of the biggest social network platforms in the recent times.
+Due to its large user base and popularity, a variety of behavior is present
+among users in terms of their activity. Our study provides interesting insights
+about a large number of inactive posts which fail to gather attention despite
+their authors exhibiting Cyborg-like behavior to draw attention. We also
+present interesting insights about short-lived but extremely active posts
+emulating a phenomenon like Mayfly Buzz. Further, we present methods to find
+the nature of activity around highly active posts to determine the presence of
+Limelight hogging activity, if any. We analyzed over $2$ million posts and more
+than $7$ million user responses to them during entire 2008 and over $63$
+million posts and over $608$ million user responses to them from August 2014 to
+July 2015 amounting to two one-year periods, in order to understand how social
+media space has evolved over the years.
+"
+22541,Joint Strong and Weak Lensing Analysis of the Massive Cluster Field J0850+3604,"  We present a combined strong and weak lensing analysis of the
+J085007.6+360428 (J0850) field, which was selected by its high projected
+concentration of luminous red galaxies and contains the massive cluster Zwicky
+1953. Using Subaru/Suprime-Cam $BVR_{c}I_{c}i^{\prime}z^{\prime}$ imaging and
+MMT/Hectospec spectroscopy, we first perform a weak lensing shear analysis to
+constrain the mass distribution in this field, including the cluster at $z =
+0.3774$ and a smaller foreground halo at $z = 0.2713$. We then add a strong
+lensing constraint from a multiply-imaged galaxy in the imaging data with a
+photometric redshift of $z \approx 5.03$. Unlike previous cluster-scale lens
+analyses, our technique accounts for the full three-dimensional mass structure
+in the beam, including galaxies along the line of sight. In contrast with past
+cluster analyses that use only lensed image positions as constraints, we use
+the full surface brightness distribution of the images. This method predicts
+that the source galaxy crosses a lensing caustic such that one image is a
+highly-magnified ""fold arc"", which could be used to probe the source galaxy's
+structure at ultra-high spatial resolution ($< 30$ pc). We calculate the mass
+of the primary cluster to be $\mathrm{M_{vir}} = 2.93_{-0.65}^{+0.71} \times
+10^{15}~\mathrm{M_{\odot}}$ with a concentration of $\mathrm{c_{vir}} =
+3.46_{-0.59}^{+0.70}$, consistent with the mass-concentration relation of
+massive clusters at a similar redshift. The large mass of this cluster makes
+J0850 an excellent field for leveraging lensing magnification to search for
+high-redshift galaxies, competitive with and complementary to that of
+well-studied clusters such as the HST Frontier Fields.
+"
+22542,A new scope of penalized empirical likelihood with high-dimensional estimating equations,"  Statistical methods with empirical likelihood (EL) are appealing and
+effective especially in conjunction with estimating equations through which
+useful data information can be adaptively and flexibly incorporated. It is also
+known in the literature that EL approaches encounter difficulties when dealing
+with problems having high-dimensional model parameters and estimating
+equations. To overcome the challenges, we begin our study with a careful
+investigation on high-dimensional EL from a new scope targeting at estimating a
+high-dimensional sparse model parameters. We show that the new scope provides
+an opportunity for relaxing the stringent requirement on the dimensionality of
+the model parameter. Motivated by the new scope, we then propose a new
+penalized EL by applying two penalty functions respectively regularizing the
+model parameters and the associated Lagrange multipliers in the optimizations
+of EL. By penalizing the Lagrange multiplier to encourage its sparsity, we show
+that drastic dimension reduction in the number of estimating equations can be
+effectively achieved without compromising the validity and consistency of the
+resulting estimators. Most attractively, such a reduction in dimensionality of
+estimating equations is actually equivalent to a selection among those
+high-dimensional estimating equations, resulting in a highly parsimonious and
+effective device for high-dimensional sparse model parameters. Allowing both
+the dimensionalities of model parameters and estimating equations growing
+exponentially with the sample size, our theory demonstrates that the estimator
+from our new penalized EL is sparse and consistent with asymptotically normally
+distributed nonzero components. Numerical simulations and a real data analysis
+show that the proposed penalized EL works promisingly.
+"
+22543,Unsupervised Generative Modeling Using Matrix Product States,"  Generative modeling, which learns joint probability distribution from data
+and generates samples according to it, is an important task in machine learning
+and artificial intelligence. Inspired by probabilistic interpretation of
+quantum physics, we propose a generative model using matrix product states,
+which is a tensor network originally proposed for describing (particularly
+one-dimensional) entangled quantum states. Our model enjoys efficient learning
+analogous to the density matrix renormalization group method, which allows
+dynamically adjusting dimensions of the tensors and offers an efficient direct
+sampling approach for generative tasks. We apply our method to generative
+modeling of several standard datasets including the Bars and Stripes, random
+binary patterns and the MNIST handwritten digits to illustrate the abilities,
+features and drawbacks of our model over popular generative models such as
+Hopfield model, Boltzmann machines and generative adversarial networks. Our
+work sheds light on many interesting directions of future exploration on the
+development of quantum-inspired algorithms for unsupervised machine learning,
+which are promisingly possible to be realized on quantum devices.
+"
+22544,Global Method for Electron Correlation,"  The current work presents a new single-reference method for capturing at the
+same time the static and dynamic electron correlation. The starting-point is a
+determinant wavefunction formed with natural orbitals obtained from a new
+interacting-pair model. The latter leads to a natural orbital functional (NOF)
+capable of recovering the complete intra-pair, but only the static inter-pair
+correlation. Using the solution of the NOF, two new energy functionals are
+defined for both dynamic (Edyn) and static (Esta) correlation. Edyn is derived
+from a modified second-order Moller-Plesset perturbation theory (MP2), while
+Esta is obtained from the static component of the new NOF. Double counting is
+avoided by introducing the amount of static and dynamic correlation in each
+orbital as a function of its occupation. As a result, the total energy is
+represented by the sum Ehf + Edyn + Esta, where Ehf is the Hartree-Fock energy
+obtained with natural orbitals. The new procedure called NOF-MP2 scales
+formally as O(M^5)(M: number of basis functions), and is applied successfully
+to the homolytic dissociation of a selected set of diatomic molecules,
+paradigmatic cases of near-degeneracy effects. Its size-consistency has been
+numerically demonstrated for singlets. The values obtained are in good
+agreement with the experimental data.
+"
+22545,Algebras of quantum monodromy data and decorated character varieties,"  The Riemann-Hilbert correspondence is an isomorphism between the de Rham
+moduli space and the Betti moduli space, defined by associating to each
+Fuchsian system its monodromy representation class. In 1997 Hitchin proved that
+this map is a symplectomorphism. In this paper, we address the question of what
+happens to this theory if we extend the de Rham moduli space by allowing
+connections with higher order poles. In our previous paper arXiv:1511.03851,
+based on the idea of interpreting higher order poles in the connection as
+boundary components with bordered cusps (vertices of ideal triangles in the
+Poincaré metric) on the Riemann surface, we introduced the notion of
+decorated character variety to generalize the Betti moduli space. This
+decorated character variety is the quotient of the space of representations of
+the fundamental groupid of arcs by a product of unipotent Borel sub-groups (one
+per bordered cusp). Here we prove that this representation space is endowed
+with a Poisson structure induced by the Fock--Rosly bracket and show that the
+quotient by unipotent Borel subgroups giving rise to the decorated character
+variety is a Poisson reduction. We deal with the Poisson bracket and its
+quantization simultaneously, thus providing a quantisation of the decorated
+character variety. In the case of dimension 2, we also endow the representation
+space with explicit Darboux coordinates. We conclude with a conjecture on the
+extended Riemann-Hilbert correspondence in the case of higher order poles.
+"
+22546,Tropical Combinatorial Nullstellensatz and Sparse Polynomials,"  Tropical algebra emerges in many fields of mathematics such as algebraic
+geometry, mathematical physics and combinatorial optimization. In part, its
+importance is related to the fact that it makes various parameters of
+mathematical objects computationally accessible. Tropical polynomials play a
+fundamental role in this, especially for the case of algebraic geometry. On the
+other hand, many algebraic questions behind tropical polynomials remain open.
+In this paper we address four basic questions on tropical polynomials closely
+related to their computational properties:
+1. Given a polynomial with a certain support (set of monomials) and a
+(finite) set of inputs, when is it possible for the polynomial to vanish on all
+these inputs?
+2. A more precise question, given a polynomial with a certain support and a
+(finite) set of inputs, how many roots can polynomial have on this set of
+inputs?
+3. Given an integer $k$, for which $s$ there is a set of $s$ inputs such that
+any non-zero polynomial with at most $k$ monomials has a non-root among these
+inputs?
+4. How many integer roots can have a one variable polynomial given by an
+tropical algebraic circuit?
+In the classical algebra well-known results in the direction of these
+questions are Combinatorial Nullstellensatz due to N. Alon, J. Schwartz - R.
+Zippel Lemma and Universal Testing Set for sparse polynomials respectively. The
+classical analog of the last question is known as $\tau$-conjecture due to M.
+Shub - S. Smale. In this paper we provide results on these four questions for
+tropical polynomials.
+"
+22547,Boundary streaming by internal waves,"  Damped internal wave beams in stratified fluids have long been known to
+generate strong mean flows through a mechanism analogous to acoustic streaming.
+While the role of viscous boundary layers in acoustic streaming has thoroughly
+been addressed, it remains largely unexplored in the case of internal waves.
+Here we compute the mean flow generated close to an undulating wall that emits
+internal waves in a viscous, linearly stratified two-dimensional Boussinesq
+fluid. Using a quasi-linear approach, we demonstrate that the form of the
+boundary conditions dramatically impacts the generated boundary streaming. In
+the no-slip scenario, the early time Reynolds stress divergence within the
+viscous boundary layer is much stronger than within the bulk while also driving
+flow in the opposite direction. Whatever the boundary condition, boundary
+streaming is however dominated by bulk streaming at a larger time. Using a WKB
+approach, we investigate the consequences of adding boundary streaming effects
+to an idealised model of wave-mean flow interactions known to reproduce the
+salient features of the quasi-biennial oscillation. The presence of wave
+boundary layers has a quantitative impact on the flow reversals.
+"
+22548,Driving-induced metamorphosis of transport in arrays of coupled resonators,"  We propose a new driving scheme, when different parts of a system are driven
+with different, generally incommensurate, frequencies. Such driving provides a
+flexible handle to control various properties of the system and to obtain new
+types of effective (static) Hamiltonians with arbitrary static on-site
+potential, be it deterministic or random. This allows us to obtain
+reconfigurable changes in transport, from ballistic to localized (including
+sub- and super-diffusion), depending on the driving protocol. The versatile
+reconfigurability extends also to scattering from (locally) driven extended
+targets. We demonstrate our scheme using, an analytically solvable example, of
+an one-dimensional tight-binding chain with appropriately driven couplings
+between nearby sites.
+"
+22549,Conformal Bootstrap Analysis for Single and Branched Polymers,"  The determinant method in the conformal bootstrap is applied for the critical
+phenomena of a single polymer in arbitrary $D$ dimensions. The scale dimensions
+(critical exponents) of the polymer ($2< D \le 4$) and the branched polymer ($3
+< D \le 8$) are obtained from the small determinants. It is known that the
+dimensional reduction of the branched polymer in $D$ dimensions to Yang-Lee
+edge singularity in $D$-$2$ dimensions holds exactly. We examine this
+equivalence by the small determinant method.
+"
+22550,Cosmological constraints on the neutrino mass including systematic uncertainties,"  When combining cosmological and oscillations results to constrain the
+neutrino sector, the question of the propagation of systematic uncertainties is
+often raised. We address this issue in the context of the derivation of an
+upper bound on the sum of the neutrino masses ($\Sigma m_\nu$) with recent
+cosmological data. This work is performed within the
+${\mathrm{\Lambda{CDM}}}$ model extended to $\Sigma m_\nu$, for which we
+advocate the use of three mass-degenerate neutrinos. We focus on the study of
+systematic uncertainties linked to the foregrounds modelling in CMB data
+analysis, and on the impact of the present knowledge of the reionisation
+optical depth. This is done through the use of different likelihoods built from
+Planck data. Limits on $\Sigma m_\nu$ are derived with various combinations of
+data, including the latest Baryon Acoustic Oscillations (BAO) and Type Ia
+Supernovae (SN) results. We also discuss the impact of the preference for
+current CMB data for amplitudes of the gravitational lensing distortions higher
+than expected within the ${\mathrm{\Lambda{CDM}}}$ model, and add the Planck
+CMB lensing. We then derive a robust upper limit: $\Sigma m_\nu< 0.17\hbox{ eV
+at }95\% \hbox{CL}$, including 0.01 eV of foreground systematics. We also
+discuss the neutrino mass repartition and show that today's data do not allow
+one to disentangle normal from inverted hierarchy. The impact on the other
+cosmological parameters is also reported, for different assumptions on the
+neutrino mass repartition, and different high and low multipole CMB
+likelihoods.
+"
+22551,Parle: parallelizing stochastic gradient descent,"  We propose a new algorithm called Parle for parallel training of deep
+networks that converges 2-4x faster than a data-parallel implementation of SGD,
+while achieving significantly improved error rates that are nearly
+state-of-the-art on several benchmarks including CIFAR-10 and CIFAR-100,
+without introducing any additional hyper-parameters. We exploit the phenomenon
+of flat minima that has been shown to lead to improved generalization error for
+deep networks. Parle requires very infrequent communication with the parameter
+server and instead performs more computation on each client, which makes it
+well-suited to both single-machine, multi-GPU settings and distributed
+implementations.
+"
+22552,"A Hubble Space Telescope Survey for Novae in M87. II. Snuffing out the Maximum Magnitude - Rate of Decline Relation for Novae as a Non-Standard Candle, and a Prediction of the Existence of Ultrafast Novae","  The extensive grid of numerical simulations of nova eruptions of Yaron et
+al.(2005) first predicted that some classical novae might deviate significantly
+from the Maximum Magnitude - Rate of Decline (MMRD) relation, which purports to
+characterise novae as standard candles. Kasliwal et al. (2011) have announced
+the observational detection of an apparently new class of faint, fast classical
+novae in the Andromeda galaxy. These objects deviate strongly from the MMRD
+relationship, exactly as predicted by Yaron et al. (2005). Shara et al. (2016)
+recently reported the first detections of faint, fast novae in M87. These
+previously overlooked objects are as common in the giant elliptical galaxy M87
+as they are in the giant spiral M31; they comprise about 40% of all classical
+nova eruptions and greatly increase the observational scatter in the MMRD
+relation. We use the extensive grid of nova simulations of Yaron et al. (2005)
+to identify the underlying causes of the existence of faint, fast novae. These
+are systems which have accreted, and can thus eject, only very low mass
+envelopes, of order 10^-7 - 10^-8 Msun, on massive white dwarfs. Such binaries
+include, but are not limited to, the recurrent novae. These same models predict
+the existence of ultrafast novae which display decline times t2 as short as
+five hours. We outline a strategy for their future detection.
+"
+22553,Ferrofluids and magnetically guided superparamagnetic particles in flows: A review of simulations and modeling,"  Ferrofluids are typically suspensions of magnetite nanoparticles, and behave
+as a homogeneous continuum. The production of nanoparticles with a narrow size
+distribution and the achievement of colloidal stability are important
+technological issues. The ability of the ferrofluid to respond to an external
+magnetic field in a controllable manner has made it emerge as a smart material
+in a variety of applications, such as seals, lubricants, electronics cooling,
+shock absorbers and adaptive optics. Magnetic nanoparticle suspensions have
+also gained attraction recently in a range of biomedical applications, such as
+cell separation, hyperthermia, MRI, drug targeting and cancer diagnosis. In
+this review, we provide an introduction to mathematical modeling of three
+problems: motion of superparamagnetic nanoparticles in magnetic drug targeting,
+the motion of a ferrofluid drop consisting of chemically bound nanoparticles
+without a carrier fluid, and the breakage of a thin film of a ferrofluid.
+"
+22554,Irreducibility of Certain Subquotients of Spherical Principal Series Representations of Reductive Groups with Frobenius Maps,"  For infinite reductive groups with Frobenius maps, we show that certain
+subquotients of abstract representations of the groups induced from
+1-dimensional representations of Borel subgroups are irreducible.
+"
+22555,Illuminant Spectra-based Source Separation Using Flash Photography,"  Real-world lighting often consists of multiple illuminants with different
+spectra. Separating and manipulating these illuminants in post-process is a
+challenging problem that requires either significant manual input or calibrated
+scene geometry and lighting. In this work, we leverage a flash/no-flash image
+pair to analyze and edit scene illuminants based on their spectral differences.
+We derive a novel physics-based relationship between color variations in the
+observed flash/no-flash intensities and the spectra and surface shading
+corresponding to individual scene illuminants. Our technique uses this
+constraint to automatically separate an image into constituent images lit by
+each illuminant. This separation can be used to support applications like white
+balancing, lighting editing, and RGB photometric stereo, where we demonstrate
+results that outperform state-of-the-art techniques on a wide range of images.
+"
+22556,Temporal pattern recognition through analog molecular computation,"  Living cells communicate information about physiological conditions by
+producing signaling molecules in a specific timed manner. Different conditions
+can result in the same total amount of a signaling molecule, differing only in
+the pattern of the molecular concentration over time. Such temporally coded
+information can be completely invisible to even state-of-the-art molecular
+sensors with high chemical specificity that respond only to the total amount of
+the signaling molecule. Here, we demonstrate design principles for circuits
+with temporal specificity, that is, molecular circuits that respond to specific
+temporal patterns in a molecular concentration. We consider pulsatile patterns
+in a molecular concentration characterized by three fundamental temporal
+features - time period, duty fraction and number of pulses. We develop circuits
+that respond to each one of these features while being insensitive to the
+others. We demonstrate our design principles using abstract Chemical Reaction
+Networks and with explicit simulations of DNA strand displacement reactions. In
+this way, our work develops building blocks for temporal pattern recognition
+through molecular computation.
+"
+22557,Applications of Deep Learning and Reinforcement Learning to Biological Data,"  Rapid advances of hardware-based technologies during the past decades have
+opened up new possibilities for Life scientists to gather multimodal data in
+various application domains (e.g., Omics, Bioimaging, Medical Imaging, and
+[Brain/Body]-Machine Interfaces), thus generating novel opportunities for
+development of dedicated data intensive machine learning techniques. Overall,
+recent research in Deep learning (DL), Reinforcement learning (RL), and their
+combination (Deep RL) promise to revolutionize Artificial Intelligence. The
+growth in computational power accompanied by faster and increased data storage
+and declining computing costs have already allowed scientists in various fields
+to apply these techniques on datasets that were previously intractable for
+their size and complexity. This review article provides a comprehensive survey
+on the application of DL, RL, and Deep RL techniques in mining Biological data.
+In addition, we compare performances of DL techniques when applied to different
+datasets across various application domains. Finally, we outline open issues in
+this challenging research area and discuss future development perspectives.
+"
+22558,Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections,"  Nuclear segmentation is an important step for profiling aberrant regions of
+histology sections. However, segmentation is a complex problem as a result of
+variations in nuclear geometry (e.g., size, shape), nuclear type (e.g.,
+epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy).
+The problem is further complicated as a result of variations in sample
+preparation. It is shown and validated that fusion of very deep convolutional
+networks overcomes (i) complexities associated with multiple nuclear
+phenotypes, and (ii) separation of overlapping nuclei. The fusion relies on
+integrating of networks that learn region- and boundary-based representations.
+The system has been validated on a diverse set of nuclear phenotypes that
+correspond to the breast and brain histology sections.
+"
+22559,Intermediate Data Caching Optimization for Multi-Stage and Parallel Big Data Frameworks,"  In the era of big data and cloud computing, large amounts of data are
+generated from user applications and need to be processed in the datacenter.
+Data-parallel computing frameworks, such as Apache Spark, are widely used to
+perform such data processing at scale. Specifically, Spark leverages
+distributed memory to cache the intermediate results, represented as Resilient
+Distributed Datasets (RDDs). This gives Spark an advantage over other parallel
+frameworks for implementations of iterative machine learning and data mining
+algorithms, by avoiding repeated computation or hard disk accesses to retrieve
+RDDs. By default, caching decisions are left at the programmer's discretion,
+and the LRU policy is used for evicting RDDs when the cache is full. However,
+when the objective is to minimize total work, LRU is woefully inadequate,
+leading to arbitrarily suboptimal caching decisions. In this paper, we design
+an algorithm for multi-stage big data processing platforms to adaptively
+determine and cache the most valuable intermediate datasets that can be reused
+in the future. Our solution automates the decision of which RDDs to cache: this
+amounts to identifying nodes in a direct acyclic graph (DAG) representing
+computations whose outputs should persist in the memory. Our experiment results
+show that our proposed cache optimization solution can improve the performance
+of machine learning applications on Spark decreasing the total work to
+recompute RDDs by 12%.
+"
+22560,Directed random walks on polytopes with few facets,"  Let $P$ be a simple polytope with $n-d = 2$, where $d$ is the dimension and
+$n$ is the number of facets. The graph of such a polytope is also called a
+grid. It is known that the directed random walk along the edges of $P$
+terminates after $O(\log^2 n)$ steps, if the edges are oriented in a
+(pseudo-)linear fashion. We prove that the same bound holds for the more
+general unique sink orientations.
+"
+22561,Evolutionary dynamics of the cryptocurrency market,"  The cryptocurrency market surpassed the barrier of \$100 billion market
+capitalization in June 2017, after months of steady growth. Despite its
+increasing relevance in the financial world, however, a comprehensive analysis
+of the whole system is still lacking, as most studies have focused exclusively
+on the behaviour of one (Bitcoin) or few cryptocurrencies. Here, we consider
+the history of the entire market and analyse the behaviour of 1,469
+cryptocurrencies introduced between April 2013 and June 2017. We reveal that,
+while new cryptocurrencies appear and disappear continuously and their market
+capitalization is increasing (super-)exponentially, several statistical
+properties of the market have been stable for years. These include the number
+of active cryptocurrencies, the market share distribution and the turnover of
+cryptocurrencies. Adopting an ecological perspective, we show that the
+so-called neutral model of evolution is able to reproduce a number of key
+empirical observations, despite its simplicity and the assumption of no
+selective advantage of one cryptocurrency over another. Our results shed light
+on the properties of the cryptocurrency market and establish a first formal
+link between ecological modelling and the study of this growing system. We
+anticipate they will spark further research in this direction.
+"
+22562,On the algebras over equivariant little disks,"  We describe the structure present in algebras over the little disks operads
+for various representations of a finite group $G$, including those that are not
+necessarily universe or that do not contain trivial summands. We then spell out
+in more detail what happens for $G=C_{2}$, describing the structure on algebras
+over the little disks operad for the sign representation. Here we can also
+describe the resulting structure in Bredon homology. Finally, we produce a
+stable splitting of coinduced spaces analogous to the stable splitting of the
+product, and we use this to determine the homology of the signed James
+construction.
+"
+22563,Creating New Language and Voice Components for the Updated MaryTTS Text-to-Speech Synthesis Platform,"  We present a new workflow to create components for the MaryTTS text-to-speech
+synthesis platform, which is popular with researchers and developers, extending
+it to support new languages and custom synthetic voices. This workflow replaces
+the previous toolkit with an efficient, flexible process that leverages modern
+build automation and cloud-hosted infrastructure. Moreover, it is compatible
+with the updated MaryTTS architecture, enabling new features and
+state-of-the-art paradigms such as synthesis based on deep neural networks
+(DNNs). Like MaryTTS itself, the new tools are free, open source software
+(FOSS), and promote the use of open data.
+"
+22564,Markerless tracking of user-defined features with deep learning,"  Quantifying behavior is crucial for many applications in neuroscience.
+Videography provides easy methods for the observation and recording of animal
+behavior in diverse settings, yet extracting particular aspects of a behavior
+for further analysis can be highly time consuming. In motor control studies,
+humans or other animals are often marked with reflective markers to assist with
+computer-based tracking, yet markers are intrusive (especially for smaller
+animals), and the number and location of the markers must be determined a
+priori. Here, we present a highly efficient method for markerless tracking
+based on transfer learning with deep neural networks that achieves excellent
+results with minimal training data. We demonstrate the versatility of this
+framework by tracking various body parts in a broad collection of experimental
+settings: mice odor trail-tracking, egg-laying behavior in drosophila, and
+mouse hand articulation in a skilled forelimb task. For example, during the
+skilled reaching behavior, individual joints can be automatically tracked (and
+a confidence score is reported). Remarkably, even when a small number of frames
+are labeled ($\approx 200$), the algorithm achieves excellent tracking
+performance on test frames that is comparable to human accuracy.
+"
+22565,Paving the Way for Culturally Competent Robots: a Position Paper,"  Cultural competence is a well known requirement for an effective healthcare,
+widely investigated in the nursing literature. We claim that personal assistive
+robots should likewise be culturally competent, aware of general cultural
+characteristics and of the different forms they take in different individuals,
+and sensitive to cultural differences while perceiving, reasoning, and acting.
+Drawing inspiration from existing guidelines for culturally competent
+healthcare and the state-of-the-art in culturally competent robotics, we
+identify the key robot capabilities which enable culturally competent
+behaviours and discuss methodologies for their development and evaluation.
+"
+22566,Characterisation of the ionosphere above the Murchison Radio Observatory using the Murchison Widefield Array,"  We detail new techniques for analysing ionospheric activity, using Epoch of
+Reionisation (EoR) datasets obtained with the Murchison Widefield Array (MWA),
+calibrated by the `Real-Time System' (RTS). Using the high spatial- and
+temporal-resolution information of the ionosphere provided by the RTS
+calibration solutions over 19 nights of observing, we find four distinct types
+of ionospheric activity, and have developed a metric to provide an `at a
+glance' value for data quality under differing ionospheric conditions. For each
+ionospheric type, we analyse variations of this metric as we reduce the number
+of pierce points, revealing that a modest number of pierce points is required
+to identify the intensity of ionospheric activity; it is possible to calibrate
+in real-time, providing continuous information of the phase screen. We also
+analyse temporal correlations, determine diffractive scales, examine the
+relative fractions of time occupied by various types of ionospheric activity,
+and detail a method to reconstruct the total electron content responsible for
+the ionospheric data we observe. These techniques have been developed to be
+instrument agnostic, useful for application on LOFAR and SKA-Low.
+"
+22567,Adversarial Structured Prediction for Multivariate Measures,"  Many predicted structured objects (e.g., sequences, matchings, trees) are
+evaluated using the F-score, alignment error rate (AER), or other multivariate
+performance measures. Since inductively optimizing these measures using
+training data is typically computationally difficult, empirical risk
+minimization of surrogate losses is employed, using, e.g., the hinge loss for
+(structured) support vector machines. These approximations often introduce a
+mismatch between the learner's objective and the desired application
+performance, leading to inconsistency. We take a different approach:
+adversarially approximate training data while optimizing the exact F-score or
+AER. Structured predictions under this formulation result from solving zero-sum
+games between a predictor seeking the best performance and an adversary seeking
+the worst while required to (approximately) match certain structured properties
+of the training data. We explore this approach for word alignment (AER
+evaluation) and named entity recognition (F-score evaluation) with linear-chain
+constraints.
+"
+22568,Exozodiacal clouds: Hot and warm dust around main sequence stars,"  A warm/hot dust component (at temperature $>$ 300K) has been detected around
+$\sim$ 20% of stars. This component is called ""exozodiacal dust"" as it presents
+similarities with the zodiacal dust detected in our Solar System, even though
+its physical properties and spatial distribution can be significantly
+different. Understanding the origin and evolution of this dust is of crucial
+importance, not only because its presence could hamper future detections of
+Earth-like planets in their habitable zones, but also because it can provide
+invaluable information about the inner regions of planetary systems. In this
+review, we present a detailed overview of the observational techniques used in
+the detection and characterisation of exozodiacal dust clouds (""exozodis"") and
+the results they have yielded so far, in particular regarding the incidence
+rate of exozodis as a function of crucial parameters such as stellar type and
+age, or the presence of an outer cold debris disc. We also present the
+important constraints that have been obtained, on dust size distribution and
+spatial location, by using state-of-the-art radiation transfer models on some
+of these systems. Finally, we investigate the crucial issue of how to explain
+the presence of exozodiacal dust around so many stars (regardless of their
+ages) despite the fact that such dust so close to its host star should
+disappear rapidly due to the coupled effect of collisions and stellar radiation
+pressure. Several potential mechanisms have been proposed to solve this paradox
+and are reviewed in detail in this paper. The review finishes by presenting the
+future of this growing field.
+"
+22569,New exact relations for steady irrotational two-dimensional gravity and capillary surface waves,"  Steady two-dimensional surface capillary-gravity waves in irrotational motion
+are considered on constant depth. By exploiting the holomorphic properties in
+the physical plane and introducing some transformations of the boundary
+conditions at the free surface, new exact relations and equations for the free
+surface only are derived. In particular, a physical plane counterpart of the
+Babenko equation is obtained.
+"
+22570,The index of singular zeros of harmonic mappings of anti-analytic degree one,"  We study harmonic mappings of the form $f(z) = h(z) - \overline{z}$, where
+$h$ is an analytic function. In particular we are interested in the index (a
+generalized multiplicity) of the zeros of such functions. Outside the critical
+set of $f$, where the Jacobian of $f$ is non-vanishing, it is known that this
+index has similar properties as the classical multiplicity of zeros of analytic
+functions. Little is known about the index of zeros on the critical set, where
+the Jacobian vanishes; such zeros are called singular zeros. Our main result is
+a characterization of the index of singular zeros, which enables one to
+determine the index directly from the power series of $h$.
+"
+22571,Multi-view Metric Learning in Vector-valued Kernel Spaces,"  We consider the problem of metric learning for multi-view data and present a
+novel method for learning within-view as well as between-view metrics in
+vector-valued kernel spaces, as a way to capture multi-modal structure of the
+data. We formulate two convex optimization problems to jointly learn the metric
+and the classifier or regressor in kernel feature spaces. An iterative
+three-step multi-view metric learning algorithm is derived from the
+optimization problems. In order to scale the computation to large training
+sets, a block-wise Nystr{ö}m approximation of the multi-view kernel matrix is
+introduced. We justify our approach theoretically and experimentally, and show
+its performance on real-world datasets against relevant state-of-the-art
+methods.
+"
+22572,Distributed Estimation of Dynamic Fields over Multi-agent Networks,"  This work presents distributed algorithms for estimation of time-varying
+random fields over multi-agent/sensor networks. A network of sensors makes
+sparse and noisy local measurements of the dynamic field. Each sensor aims to
+obtain unbiased distributed estimates of the entire field with bounded
+mean-squared error (MSE) based on its own local observations and its neighbors'
+estimates. This work develops three novel distributed estimators:
+Pseudo-Innovations Kalman Filter (PIKF), Distributed Information Kalman Filter
+(DIKF) and Consensus+Innovations Kalman Filter (CIKF). We design the gain
+matrices such that the estimators achieve unbiased estimates with bounded MSE
+under minimal assumptions on the local observation and network communication
+models. This work establishes trade-offs between these three distributed
+estimators and demonstrates how they outperform existing solutions. We validate
+our results through extensive numerical evaluations.
+"
+22573,Knowledge Representation in Bicategories of Relations,"  We introduce the relational ontology log, or relational olog, a knowledge
+representation system based on the category of sets and relations. It is
+inspired by Spivak and Kent's olog, a recent categorical framework for
+knowledge representation. Relational ologs interpolate between ologs and
+description logic, the dominant formalism for knowledge representation today.
+In this paper, we investigate relational ologs both for their own sake and to
+gain insight into the relationship between the algebraic and logical approaches
+to knowledge representation. On a practical level, we show by example that
+relational ologs have a friendly and intuitive--yet fully precise--graphical
+syntax, derived from the string diagrams of monoidal categories. We explain
+several other useful features of relational ologs not possessed by most
+description logics, such as a type system and a rich, flexible notion of
+instance data. In a more theoretical vein, we draw on categorical logic to show
+how relational ologs can be translated to and from logical theories in a
+fragment of first-order logic. Although we make extensive use of categorical
+language, this paper is designed to be self-contained and has considerable
+expository content. The only prerequisites are knowledge of first-order logic
+and the rudiments of category theory.
+"
+22574,A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+,"  In school, a teacher plays an important role in various classroom teaching
+patterns. Likewise to this human learning activity, the learning using
+privileged information (LUPI) paradigm provides additional information
+generated by the teacher to 'teach' learning algorithms during the training
+stage. Therefore, this novel learning paradigm is a typical Teacher-Student
+Interaction mechanism. This paper is the first to present a random vector
+functional link network based on the LUPI paradigm, called RVFL+. Rather than
+simply combining two existing approaches, the newly-derived RVFL+ fills the gap
+between neural networks and the LUPI paradigm, which offers an alternative way
+to train RVFL networks. Moreover, the proposed RVFL+ can perform in conjunction
+with the kernel trick for highly complicated nonlinear feature learning, which
+is termed KRVFL+. Furthermore, the statistical property of the proposed RVFL+
+is investigated, and we derive a sharp and high-quality generalization error
+bound based on the Rademacher complexity. Competitive experimental results on
+14 real-world datasets illustrate the great effectiveness and efficiency of the
+novel RVFL+ and KRVFL+, which can achieve better generalization performance
+than state-of-the-art algorithms.
+"
+22575,Matrix integrals and Hurwitz numbers,"  We study multi-matrix models which may be viewed as integrals of products of
+tau functions which depend on the eigenvalues of products of random matrices.
+In the present paper we consider tau functions of the hierarchy the
+two-component KP (semiinfinite relativistic Toda lattice) and of hierarchy of
+the BKP introduced by Kac and van de Leur. Sometimes such integrals are tau
+functions themselves. We consider models which generate Hurwitz numbers
+$H^{e},f$, where $e$ is the Euler characteristic of the base surface and $f$ is
+the number of branch points. We show that in case the integrands contains the
+product of $n > 2$ matrices the integral generates Hurwitz numbers with $e\le
+2$ and $f\le n+2$, both numbers $e$ and $f$ depend both on $n$ and on the order
+of the multipliers in the matrix product. The Euler characteristic $ e $ can be
+either an even or an odd number, that is, match both orientable and
+nonorientable (Klein) base surfaces, depending on the presence of the tau
+function of the BKP hierarchy in the integrand. We study two cases: the
+products of complex and the products of unitary matrices.
+"
+22576,Learning Chained Deep Features and Classifiers for Cascade in Object Detection,"  Cascade is a widely used approach that rejects obvious negative samples at
+early stages for learning better classifier and faster inference. This paper
+presents chained cascade network (CC-Net). In this CC-Net, the cascaded
+classifier at a stage is aided by the classification scores in previous stages.
+Feature chaining is further proposed so that the feature learning for the
+current cascade stage uses the features in previous stages as the prior
+information. The chained ConvNet features and classifiers of multiple stages
+are jointly learned in an end-to-end network. In this way, features and
+classifiers at latter stages handle more difficult samples with the help of
+features and classifiers in previous stages. It yields consistent boost in
+detection performance on benchmarks like PASCAL VOC 2007 and ImageNet. Combined
+with better region proposal, CC-Net leads to state-of-the-art result of 81.1%
+mAP on PASCAL VOC 2007.
+"
+22577,When Fourth Moments Are Enough,"  This note concerns a somewhat innocent question motivated by an observation
+concerning the use of Chebyshev bounds on sample estimates of $p$ in the
+binomial distribution with parameters $n,p$. Namely, what moment order produces
+the best Chebyshev estimate of $p$? If $S_n(p)$ has a binomial distribution
+with parameters $n,p$, there it is readily observed that ${\rm argmax}_{0\le
+p\le 1}{\mathbb E}S_n^2(p) = {\rm argmax}_{0\le p\le 1}np(1-p) = \frac12,$ and
+${\mathbb E}S_n^2(\frac12) = \frac{n}{4}$. Rabi Bhattacharya observed that
+while the second moment Chebyshev sample size for a $95\%$ confidence estimate
+within $\pm 5$ percentage points is $n = 2000$, the fourth moment yields the
+substantially reduced polling requirement of $n = 775$. Why stop at fourth
+moment? Is the argmax achieved at $p = \frac12$ for higher order moments and,
+if so, does it help, and compute $\mathbb{E}S_n^{2m}(\frac12)$? As captured by
+the title of this note, answers to these questions lead to a simple rule of
+thumb for best choice of moments in terms of an effective sample size for
+Chebyshev concentration inequalities.
+"
+22578,Scaling the propulsive performance of heaving and pitching foils,"  Scaling laws for the propulsive performance of rigid foils undergoing
+oscillatory heaving and pitching motions are presented. Water tunnel
+experiments on a nominally two-dimensional flow validate the scaling laws, with
+the scaled data for thrust, power, and efficiency all showing excellent
+collapse. The analysis indicates that the behaviour of the foils depends on
+both Strouhal number and reduced frequency, but for motions where the viscous
+drag is small the thrust closely follows a linear dependence on reduced
+frequency. The scaling laws are also shown to be consistent with biological
+data on swimming aquatic animals.
+"
+22579,Nonparametric imputation by data depth,"  We present single imputation method for missing values which borrows the idea
+of data depth---a measure of centrality defined for an arbitrary point of a
+space with respect to a probability distribution or data cloud. This consists
+in iterative maximization of the depth of each observation with missing values,
+and can be employed with any properly defined statistical depth function. For
+each single iteration, imputation reverts to optimization of quadratic, linear,
+or quasiconcave functions that are solved analytically by linear programming or
+the Nelder-Mead method. As it accounts for the underlying data topology, the
+procedure is distribution free, allows imputation close to the data geometry,
+can make prediction in situations where local imputation (k-nearest neighbors,
+random forest) cannot, and has attractive robustness and asymptotic properties
+under elliptical symmetry. It is shown that a special case---when using the
+Mahalanobis depth---has direct connection to well-known methods for the
+multivariate normal model, such as iterated regression and regularized PCA. The
+methodology is extended to multiple imputation for data stemming from an
+elliptically symmetric distribution. Simulation and real data studies show good
+results compared with existing popular alternatives. The method has been
+implemented as an R-package. Supplementary materials for the article are
+available online.
+"
+22580,On the existence of a solution to a spectral estimation problem \emph{à la} Byrnes-Georgiou-Lindquist,"  A parametric spectral estimation problem in the style of Byrnes, Georgiou,
+and Lindquist was posed in \cite{FPZ-10}, but the existence of a solution was
+only proved in a special case. Based on their results, we show that a solution
+indeed exists given an arbitrary matrix-valued prior density. The main tool in
+our proof is the topological degree theory.
+"
+22581,Polynomial time estimates for #SAT,"  Limits on the number of satisfying assignments for CNS instances with n
+variables and m clauses are derived from various inequalities. Some bounds can
+be calculated in polynomial time, sharper bounds demand information about the
+distribution of the number of unsatisfied clauses. Quite generally, the number
+of satisfying assignments involve variance and mean of this distribution. For
+large formulae, m>>1, bounds vary with 2**n/n, so they may be of use only for
+instances with a large number of satisfying assignments.
+"
+22582,Reemergence of high-Tc superconductivity in the (Li1-xFex)OHFe1-ySe under high pressure,"  The pressure-induced reemergence of the second high-Tc superconducting phase
+(SC-II) in the alkali-metal intercalated AxFe2-ySe2 (A = K, Rb, Cs, Tl) remains
+an enigma and proper characterizations on the superconducting- and normal-state
+properties of the SC-II phase were hampered by the intrinsic inhomogeneity and
+phase separation. To elucidate this intriguing problem, we performed a detailed
+high-pressure magnetotransport study on the recently discovered
+(Li1-xFex)OHFe1-ySe single crystals, which have high Tc~40 K and share similar
+Fermi surface topology as AxFe2-ySe2, but are free from the sample
+complications. We found that the ambient-pressure Tc~41 K is suppressed
+gradually to below 2 K upon increasing pressure to Pc ~5 GPa, above which a
+SC-II phase with higher Tc emerges and the Tc increases progressively to above
+50 K up to 12.5 GPa. Interestingly, our high-precision resistivity data enable
+us to uncover the sharp transition of the normal state from a Fermi liquid for
+SC-I phase (0 < P < 5 GPa) to a non-Fermi-liquid for SC-II phase (P > 5GPa). In
+addition, the reemergence of high-Tc SC-II phase is found to accompany with a
+concurrent enhancement of electron carrier density. Since high-pressure
+structural study based on the synchrotron X-ray diffraction rules out the
+structural transition below 10 GPa, the observed SC-II phase with enhanced
+carrier density should be ascribed to an electronic origin associated with a
+pressure-induced Fermi surface reconstruction.
+"
+22583,Classifying Process Instances Using Recurrent Neural Networks,"  Process Mining consists of techniques where logs created by operative systems
+are transformed into process models. In process mining tools it is often
+desired to be able to classify ongoing process instances, e.g., to predict how
+long the process will still require to complete, or to classify process
+instances to different classes based only on the activities that have occurred
+in the process instance thus far. Recurrent neural networks and its subclasses,
+such as Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), have been
+demonstrated to be able to learn relevant temporal features for subsequent
+classification tasks. In this paper we apply recurrent neural networks to
+classifying process instances. The proposed model is trained in a supervised
+fashion using labeled process instances extracted from event log traces. This
+is the first time we know of GRU having been used in classifying business
+process instances. Our main experimental results shows that GRU outperforms
+LSTM remarkably in training time while giving almost identical accuracies to
+LSTM models. Additional contributions of our paper are improving the
+classification model training time by filtering infrequent activities, which is
+a technique commonly used, e.g., in Natural Language Processing (NLP).
+"
+22584,Shrinking of Rapidly Evaporating Water Microdroplets Reveals their Extreme Supercooling,"  The fast evaporative cooling of micrometer-sized water droplets in vacuum
+offers the appealing possibility to investigate supercooled water - below the
+melting point but still a liquid - at temperatures far beyond the
+state-of-the-art. However, it is challenging to obtain a reliable value of the
+droplet temperature under such extreme experimental conditions. Here, the
+observation of morphology-dependent resonances in the Raman scattering from a
+stream of perfectly uniform water droplets has allowed us to measure with an
+absolute precision of better than 0.2% the variation in droplet size resulting
+from evaporative mass losses. This finding proved crucial to an unambiguous
+determination of the droplet temperature. In particular, a fraction of water
+droplets with initial diameter of 6379$\pm$12 nm were found to remain liquid
+down to 230.6$\pm$0.6 K. Our results question temperature estimates reported
+recently for larger supercooled water droplets, and provide valuable
+information on the hydrogen-bond network in liquid water in the hard-to-access
+deeply supercooled regime.
+"
+22585,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,"  We propose a new algorithm for training generative adversarial networks that
+jointly learns latent codes for both identities (e.g. individual humans) and
+observations (e.g. specific photographs). By fixing the identity portion of the
+latent codes, we can generate diverse images of the same subject, and by fixing
+the observation portion, we can traverse the manifold of subjects while
+maintaining contingent aspects such as lighting and pose. Our algorithm
+features a pairwise training scheme in which each sample from the generator
+consists of two images with a common identity code. Corresponding samples from
+the real dataset consist of two distinct photographs of the same subject. In
+order to fool the discriminator, the generator must produce pairs that are
+photorealistic, distinct, and appear to depict the same individual. We augment
+both the DCGAN and BEGAN approaches with Siamese discriminators to facilitate
+pairwise training. Experiments with human judges and an off-the-shelf face
+verification system demonstrate our algorithm's ability to generate convincing,
+identity-matched photographs.
+"
+22586,Large-scale Analysis of Opioid Poisoning Related Hospital Visits in New York State,"  Opioid related deaths are increasing dramatically in recent years, and opioid
+epidemic is worsening in the United States. Combating opioid epidemic becomes a
+high priority for both the U.S. government and local governments such as New
+York State. Analyzing patient level opioid related hospital visits provides a
+data driven approach to discover both spatial and temporal patterns and
+identity potential causes of opioid related deaths, which provides essential
+knowledge for governments on decision making. In this paper, we analyzed opioid
+poisoning related hospital visits using New York State SPARCS data, which
+provides diagnoses of patients in hospital visits. We identified all patients
+with primary diagnosis as opioid poisoning from 2010-2014 for our main studies,
+and from 2003-2014 for temporal trend studies. We performed demographical based
+studies, and summarized the historical trends of opioid poisoning. We used
+frequent item mining to find co-occurrences of diagnoses for possible causes of
+poisoning or effects from poisoning. We provided zip code level spatial
+analysis to detect local spatial clusters, and studied potential correlations
+between opioid poisoning and demographic and social-economic factors.
+"
+22587,Universal consistency and minimax rates for online Mondrian Forests,"  We establish the consistency of an algorithm of Mondrian Forests, a
+randomized classification algorithm that can be implemented online. First, we
+amend the original Mondrian Forest algorithm, that considers a fixed lifetime
+parameter. Indeed, the fact that this parameter is fixed hinders the
+statistical consistency of the original procedure. Our modified Mondrian Forest
+algorithm grows trees with increasing lifetime parameters $\lambda_n$, and uses
+an alternative updating rule, allowing to work also in an online fashion.
+Second, we provide a theoretical analysis establishing simple conditions for
+consistency. Our theoretical analysis also exhibits a surprising fact: our
+algorithm achieves the minimax rate (optimal rate) for the estimation of a
+Lipschitz regression function, which is a strong extension of previous results
+to an arbitrary dimension.
+"
+22588,A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints,"  We develop a simple routine unifying the analysis of several important
+recently-developed stochastic optimization methods including SAGA, Finito, and
+stochastic dual coordinate ascent (SDCA). First, we show an intrinsic
+connection between stochastic optimization methods and dynamic jump systems,
+and propose a general jump system model for stochastic optimization methods.
+Our proposed model recovers SAGA, SDCA, Finito, and SAG as special cases. Then
+we combine jump system theory with several simple quadratic inequalities to
+derive sufficient conditions for convergence rate certifications of the
+proposed jump system model under various assumptions (with or without
+individual convexity, etc). The derived conditions are linear matrix
+inequalities (LMIs) whose sizes roughly scale with the size of the training
+set. We make use of the symmetry in the stochastic optimization methods and
+reduce these LMIs to some equivalent small LMIs whose sizes are at most 3 by 3.
+We solve these small LMIs to provide analytical proofs of new convergence rates
+for SAGA, Finito and SDCA (with or without individual convexity). We also
+explain why our proposed LMI fails in analyzing SAG. We reveal a key difference
+between SAG and other methods, and briefly discuss how to extend our LMI
+analysis for SAG. An advantage of our approach is that the proposed analysis
+can be automated for a large class of stochastic methods under various
+assumptions (with or without individual convexity, etc).
+"
+22589,One-Pass Error Bounded Trajectory Simplification,"  Nowadays, various sensors are collecting, storing and transmitting tremendous
+trajectory data, and it is known that raw trajectory data seriously wastes the
+storage, network band and computing resource. Line simplification (LS)
+algorithms are an effective approach to attacking this issue by compressing
+data points in a trajectory to a set of continuous line segments, and are
+commonly used in practice. However, existing LS algorithms are not sufficient
+for the needs of sensors in mobile devices. In this study, we first develop a
+one-pass error bounded trajectory simplification algorithm (OPERB), which scans
+each data point in a trajectory once and only once. We then propose an
+aggressive one-pass error bounded trajectory simplification algorithm
+(OPERB-A), which allows interpolating new data points into a trajectory under
+certain conditions. Finally, we experimentally verify that our approaches
+(OPERB and OPERB-A) are both efficient and effective, using four real-life
+trajectory datasets.
+"
+22590,"Endpoint Sobolev and BV Continuity for maximal operators, II","  In this paper we study some questions about the continuity of classical and
+fractional maximal operators in the Sobolev space $W^{1,1}$, in both continuous
+and discrete setting, giving a positive answer to two questions posed recently,
+one of them regarding the continuity of the map $f \mapsto \big(\widetilde
+M_{\beta}f\big)'$ from $W^{1,1}(\mathbb{R})$ to $L^q(\mathbb{R})$, for
+$q=\frac{1}{1-\beta}$. Here $\widetilde M_{\beta}$ denotes the non-centered
+fractional maximal operator on $\mathbb{R}$ with $\beta\in(0,1)$. The second
+one regarding the continuity of the discrete centered maximal operator in the
+space of functions of bounded variation BV$(\mathbb{Z})$, complementing some
+recent boundedness results.
+"
+22591,Emergent phases and novel critical behavior in a non-Markovian open quantum system,"  Open quantum systems exhibit a range of novel out-of-equilibrium behavior due
+to the interplay between coherent quantum dynamics and dissipation. Of
+particular interest in these systems are driven, dissipative transitions, the
+emergence of dynamical phases with novel broken symmetries, and critical
+behavior that lies beyond the conventional paradigms of Landau-Ginzburg
+phenomenology. Here, we consider a parametrically driven two-mode system in the
+presence of non-Markovian system-reservoir interactions. We show that
+non-Markovianity modifies the phase diagram of this system resulting in the
+emergence of a novel broken symmetry phase in a new universality class that has
+no counterpart in a Markovian or equilibrium system. Such reservoir-engineered
+dynamical phases can potentially shed light on universal aspects of dynamical
+phase transitions in a wide range of non-equilibrium systems, and aid in the
+development of techniques for the robust generation of entanglement and quantum
+correlations at finite temperatures with potential applications to quantum
+metrology.
+"
+22592,Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning,"  We introduce a method for following high-level navigation instructions by
+mapping directly from images, instructions and pose estimates to continuous
+low-level velocity commands for real-time control. The Grounded Semantic
+Mapping Network (GSMN) is a fully-differentiable neural network architecture
+that builds an explicit semantic map in the world reference frame by
+incorporating a pinhole camera projection model within the network. The
+information stored in the map is learned from experience, while the
+local-to-world transformation is computed explicitly. We train the model using
+DAggerFM, a modified variant of DAgger that trades tabular convergence
+guarantees for improved training speed and memory use. We test GSMN in virtual
+environments on a realistic quadcopter simulator and show that incorporating an
+explicit mapping and grounding modules allows GSMN to outperform strong neural
+baselines and almost reach an expert policy performance. Finally, we analyze
+the learned map representations and show that using an explicit map leads to an
+interpretable instruction-following model.
+"
+22593,Point-contact spectroscopy of superconducting YBaCuO single crystals of tetragonal modification,"  The point-contact spectra of tetragonal $\rm YBa_2Cu_3O_{7-\delta}$ with
+$T_c=50-60~K$ are studied at various temperatures. The single-electron energy
+($2\Delta_0/kT_c\simeq 17$) and its temperature dependence are determined. The
+characteristic energies of quasiparticle excitations interacting with
+conduction electrons in this high-$T_c$. superconductor are found to correlate
+with the characteristic phonon energies. The possibility of a pair gap, which
+increases with temperature, is revealed near the critical temperature.
+"
+22594,Schrodinger's Cat and World History: The Many Worlds Interpretation of Alternative Facts,"  I propose that much recent history can be explained by hypothesizing that
+sometime during the last quarter of 2016, the history of the world underwent a
+macroscopic quantum tunneling event, creating, according to the Many Worlds
+Interpretation, a new branch of the multiverse in which my consciousness and
+that of my readers is now trapped. The failure of much political polling is
+then understood by assuming that the particular branch we are on had very low
+amplitude in the quantum wave function of the multiverse. In this view, one
+must take a different attitude towards alternative facts than that proposed by
+the mainstream media. We know that quantum tunneling can change the low energy
+laws of physics in the different branches of the wave function. Alternative
+facts may simply be the reflection of the media's ignorance of the state of the
+world after a quantum transition of this magnitude.
+"
+22595,On sequences of polynomials arising from graph invariants,"  Graph polynomials are deemed useful if they give rise to algebraic
+characterizations of various graph properties, and their evaluations encode
+many other graph invariants. Algebraic: The complete graphs $K_n$ and the
+complete bipartite graphs $K_{n,n}$ can be characterized as those graphs whose
+matching polynomials satisfy a certain recurrence relations and are related to
+the Hermite and Laguerre polynomials. An encoded graph invariant: The absolute
+value of the chromatic polynomial $\chi(G,X)$ of a graph $G$ evaluated at $-1$
+counts the number of acyclic orientations of $G$.
+In this paper we prove a general theorem on graph families which are
+characterized by families of polynomials satisfying linear recurrence
+relations. This gives infinitely many instances similar to the characterization
+of $K_{n,n}$. We also show where to use, instead of the Hermite and Laguerre
+polynomials, linear recurrence relations where the coefficients do not depend
+on $n$.
+Finally, we discuss the distinctive power of graph polynomials in specific
+form.
+"
+22596,Constructing Metropolis-Hastings proposals using damped BFGS updates,"  The computation of Bayesian estimates of system parameters and functions of
+them on the basis of observed system performance data is a common problem
+within system identification. This is a previously studied issue where
+stochastic simulation approaches have been examined using the popular
+Metropolis--Hastings (MH) algorithm. This prior study has identified a
+recognised difficulty of tuning the {proposal distribution so that the MH
+method provides realisations with sufficient mixing to deliver efficient
+convergence. This paper proposes and empirically examines a method of tuning
+the proposal using ideas borrowed from the numerical optimisation literature
+around efficient computation of Hessians so that gradient and curvature
+information of the target posterior can be incorporated in the proposal.
+"
+22597,Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks,"  Traditionally, multi-layer neural networks use dot product between the output
+vector of previous layer and the incoming weight vector as the input to
+activation function. The result of dot product is unbounded, thus increases the
+risk of large variance. Large variance of neuron makes the model sensitive to
+the change of input distribution, thus results in poor generalization, and
+aggravates the internal covariate shift which slows down the training. To bound
+dot product and decrease the variance, we propose to use cosine similarity or
+centered cosine similarity (Pearson Correlation Coefficient) instead of dot
+product in neural networks, which we call cosine normalization. We compare
+cosine normalization with batch, weight and layer normalization in
+fully-connected neural networks as well as convolutional networks on the data
+sets of MNIST, 20NEWS GROUP, CIFAR-10/100 and SVHN. Experiments show that
+cosine normalization achieves better performance than other normalization
+techniques.
+"
+22598,TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild,"  Despite the numerous developments in object tracking, further development of
+current tracking algorithms is limited by small and mostly saturated datasets.
+As a matter of fact, data-hungry trackers based on deep-learning currently rely
+on object detection datasets due to the scarcity of dedicated large-scale
+tracking datasets. In this work, we present TrackingNet, the first large-scale
+dataset and benchmark for object tracking in the wild. We provide more than 30K
+videos with more than 14 million dense bounding box annotations. Our dataset
+covers a wide selection of object classes in broad and diverse context. By
+releasing such a large-scale dataset, we expect deep trackers to further
+improve and generalize. In addition, we introduce a new benchmark composed of
+500 novel videos, modeled with a distribution similar to our training dataset.
+By sequestering the annotation of the test set and providing an online
+evaluation server, we provide a fair benchmark for future development of object
+trackers. Deep trackers fine-tuned on a fraction of our dataset improve their
+performance by up to 1.6% on OTB100 and up to 1.7% on TrackingNet Test. We
+provide an extensive benchmark on TrackingNet by evaluating more than 20
+trackers. Our results suggest that object tracking in the wild is far from
+being solved.
+"
+22599,Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds (Preliminary Draft),"  This paper studies the application of machine learning in extracting the
+market implied features from historical risk neutral corporate bond yields. We
+consider the example of a hypothetical illiquid fixed income market. After
+choosing a surrogate liquid market, we apply the Denoising Autoencoder
+algorithm from the field of computer vision and pattern recognition to learn
+the features of the missing yield parameters from the historically implied data
+of the instruments traded in the chosen liquid market. The results of the
+trained machine learning algorithm are compared with the outputs of a point in-
+time 2 dimensional interpolation algorithm known as the Thin Plate Spline.
+Finally, the performances of the two algorithms are compared.
+"
+22600,The Mechanism of Kuznetsov-Ma Breather,"  We discuss how to understand the dynamical process of Kuznetsov-Ma breather,
+based on some basic physical mechanisms. It is shown that dynamical process of
+Kuznetsov-Ma breather involves at least two distinctive mechanisms:
+modulational instability, and the interference effects between a bright soliton
+and a plane wave background. Our analysis indicates that modulational
+instability plays dominant roles in mechanism of Kuznetsov-Ma breather
+admitting weak perturbations, and the interference effect plays dominant role
+for the Kuznetsov-Ma breather admitting strong perturbations. For intermediate
+cases, the two mechanisms are both involved greatly. These characters provide a
+possible way to understand the evolution of strong perturbations on a plane
+wave background.
+"
+22601,Convergence acceleration of alternating series,"  We propose a new simple convergence acceleration method for wide range class
+of convergent alternating series. It has some common features with Smith's and
+Ford's modification of Levin's and Weniger's sequence transformations, but its
+computational and memory cost is lower. We compare all three methods and give
+some common theoretical results. Numerical examples confirm a similar
+performance of all of them.
+"
+22602,The Marginal Value of Adaptive Gradient Methods in Machine Learning,"  Adaptive optimization methods, which perform local optimization with a metric
+constructed from the history of iterates, are becoming increasingly popular for
+training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We
+show that for simple overparameterized problems, adaptive methods often find
+drastically different solutions than gradient descent (GD) or stochastic
+gradient descent (SGD). We construct an illustrative binary classification
+problem where the data is linearly separable, GD and SGD achieve zero test
+error, and AdaGrad, Adam, and RMSProp attain test errors arbitrarily close to
+half. We additionally study the empirical generalization capability of adaptive
+methods on several state-of-the-art deep learning models. We observe that the
+solutions found by adaptive methods generalize worse (often significantly
+worse) than SGD, even when these solutions have better training performance.
+These results suggest that practitioners should reconsider the use of adaptive
+methods to train neural networks.
+"
+22603,A short history of frames and quantum designs,"  In this survey, we relate frame theory and quantum information theory,
+focusing on quantum 2-designs. These are arrangements of weighted subspaces
+which are in a specific sense optimal for quantum state tomography. After a
+brief introduction, we discuss the role of POVMs in quantum theory, developing
+the importance of quantum 2-designs. In the final section, we collect many if
+not most known examples of quantum-2 designs to date.
+"
+22604,Kinetic uncertainty relation,"  Relative fluctuations of observables in discrete stochastic systems are
+bounded at all times by the mean dynamical activity in the system, quantified
+by the mean number of jumps. This constitutes a kinetic uncertainty relation
+that is fundamentally different from the thermodynamic uncertainty relation
+recently discussed in the literature. The thermodynamic constraint is more
+relevant close to equilibrium while the kinetic constraint is the limiting
+factor of the precision of a observables in regimes far from equilibrium. This
+is visualized for paradigmatic simple systems and with an example of molecular
+motor dynamics. Our approach is based on the recent fluctuation response
+inequality by Dechant and Sasa [arXiv:1804.08250] and can be applied to generic
+Markov jump systems, which describe a wide class of phenomena and observables,
+including the irreversible predator-prey dynamics that we use as an
+illustration.
+"
+22605,Weighted Estimates for Rough Bilinear Singular Integrals via Sparse Domination,"  We prove weighted estimates for rough bilinear singular integral operators
+with kernel $$K(y_1, y_2) = \frac{\Omega((y_1,y_2)/|(y_1,y_2)|)}{|(y_1,
+y_2)|^{2d}},$$ where $y_i \in \mathbb{R}^{d}$ and $\Omega \in
+L^{\infty}(S^{2d-1})$ with $\int_{S^{2d-1}}\Omega d\sigma = 0.$ The argument is
+by sparse domination of rough bilinear operators, via an abstract theorem that
+is a multilinear generalization of recent work by Conde-Alonso, Culiuc, Di
+Plinio and Ou. We also use recent results due to Grafakos, He, and Honz\'{\i}k
+for the application to rough bilinear operators. In particular, since the
+weighted estimates are proved via sparse domination, we obtain some
+quantitative estimates in terms of the $A_{p}$ characteristics of the weights.
+The abstract theorem is also shown to apply to multilinear Calderón-Zygmund
+operators with a standard smoothness assumption. Due to the generality of the
+sparse domination theorem, future applications not considered in this paper are
+expected.
+"
+22606,Dense Disparity Estimation in Ego-motion Reduced Search Space,"  Depth estimation from stereo images remains a challenge even though studied
+for decades. The KITTI benchmark shows that the state-of-the-art solutions
+offer accurate depth estimation, but are still computationally complex and
+often require a GPU or FPGA implementation. In this paper we aim at increasing
+the accuracy of depth map estimation and reducing the computational complexity
+by using information from previous frames. We propose to transform the
+disparity map of the previous frame into the current frame, relying on the
+estimated ego-motion, and use this map as the prediction for the Kalman filter
+in the disparity space. Then, we update the predicted disparity map using the
+newly matched one. This way we reduce disparity search space and flickering
+between consecutive frames, thus increasing the computational efficiency of the
+algorithm. In the end, we validate the proposed approach on real-world data
+from the KITTI benchmark suite and show that the proposed algorithm yields more
+accurate results, while at the same time reducing the disparity search space.
+"
+22607,Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach,"  This study proposes a novel Graph Convolutional Neural Network with
+Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous
+pairwise correlations between stations to predict station-level hourly demand
+in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model
+are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the
+convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a
+recurrent block from the Long Short-term Memory neural network architecture to
+capture temporal dependencies in the bike-sharing demand series. Furthermore,
+four types of GCNN models are proposed whose adjacency matrices are based on
+various bike-sharing system data, including Spatial Distance matrix (SD),
+Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation
+matrix (DC). These six types of GCNN models and seven other benchmark models
+are built and compared on a Citi Bike dataset from New York City which includes
+272 stations and over 28 million transactions from 2013 to 2016. Results show
+that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error,
+the Mean Absolute Error and the coefficient of determination (R2), followed by
+the GCNNreg-DDGF. They outperform the other models. Through a more detailed
+graph network analysis based on the learned DDGF, insights are obtained on the
+black box of the GCNN-DDGF model. It is found to capture some information
+similar to details embedded in the SD, DE and DC matrices. More importantly, it
+also uncovers hidden heterogeneous pairwise correlations between stations that
+are not revealed by any of those matrices.
+"
+22608,Deciding How to Decide: Dynamic Routing in Artificial Neural Networks,"  We propose and systematically evaluate three strategies for training
+dynamically-routed artificial neural networks: graphs of learned
+transformations through which different input signals may take different paths.
+Though some approaches have advantages over others, the resulting networks are
+often qualitatively similar. We find that, in dynamically-routed networks
+trained to classify images, layers and branches become specialized to process
+distinct categories of images. Additionally, given a fixed computational
+budget, dynamically-routed networks tend to perform better than comparable
+statically-routed networks.
+"
+22609,Theoretical insights into the optimization landscape of over-parameterized shallow neural networks,"  In this paper we study the problem of learning a shallow artificial neural
+network that best fits a training data set. We study this problem in the
+over-parameterized regime where the number of observations are fewer than the
+number of parameters in the model. We show that with quadratic activations the
+optimization landscape of training such shallow neural networks has certain
+favorable characteristics that allow globally optimal models to be found
+efficiently using a variety of local search heuristics. This result holds for
+an arbitrary training data of input/output pairs. For differentiable activation
+functions we also show that gradient descent, when suitably initialized,
+converges at a linear rate to a globally optimal model. This result focuses on
+a realizable model where the inputs are chosen i.i.d. from a Gaussian
+distribution and the labels are generated according to planted weight
+coefficients.
+"
+22610,Electromagnetically induced transparency of ultralong-range Rydberg molecules,"  We study the impact of Rydberg molecule formation on the storage and
+retrieval of Rydberg polaritons in an ultracold atomic medium. We observe
+coherent revivals appearing in the retrieval efficiency of stored photons that
+originate from simultaneous excitation of Rydberg atoms and Rydberg molecules
+in the system with subsequent interference between the possible storage paths.
+We show that over a large range of principal quantum numbers the observed
+results can be described by a two-state model including only the atomic Rydberg
+state and the Rydberg dimer molecule state. At higher principal quantum numbers
+the influence of polyatomic molecules becomes relevant and the dynamics of the
+system undergoes a transition from coherent evolution of a few-state system to
+an effective dephasing into a continuum of molecular states.
+"
+22611,A Surface Density Perturbation in the TW Hydrae Disk at 95 au Traced by Molecular Emission,"  We present ALMA Cycle~2 observations at $0.5^{\prime\prime}$ resolution of TW
+Hya of CS $J=5-4$ emission. The radial profile of the integrated line emission
+displays oscillatory features outwards of $1.5^{\prime\prime}$ ($\approx 90$
+au). A dip-like feature at $1.6^{\prime\prime}$ is coincident in location,
+depth and width with features observed in dust scattered light at near-infrared
+wavelengths. Using a thermochemical model indicative of TW Hya, gas-grain
+chemical modelling and non-LTE radiative transfer, we demonstrate that such a
+feature can be reproduced with a surface density depression, consistent with
+the modelling performed for scattered light observations of TW Hya. We further
+demonstrate that a gap in the dust distribution and dust opacity only cannot
+reproduce the observed CS feature. The outer enhancement at
+$3.1^{\prime\prime}$ is identified as a region of intensified desorption due to
+enhanced penetration of the interstellar FUV radiation at the exponential edge
+of the disk surface density, which intensifies the photochemical processing of
+gas and ices.
+"
+22612,A Multimodal Classifier Generative Adversarial Network for Carry and Place Tasks from Ambiguous Language Instructions,"  This paper focuses on a multimodal language understanding method for
+carry-and-place tasks with domestic service robots. We address the case of
+ambiguous instructions, that is, when the target area is not specified. For
+instance ""put away the milk and cereal"" is a natural instruction where there is
+ambiguity regarding the target area, considering environments in daily life.
+Conventionally, this instruction can be disambiguated from a dialogue system,
+but at the cost of time and cumbersome interaction. Instead, we propose a
+multimodal approach, in which the instructions are disambiguated using the
+robot's state and environment context. We develop the Multi-Modal Classifier
+Generative Adversarial Network (MMC-GAN) to predict the likelihood of different
+target areas considering the robot's physical limitation and the target
+clutter. Our approach, MMC-GAN, significantly improves accuracy compared with
+baseline methods that use instructions only or simple deep neural networks.
+"
+22613,Near-infrared diffuse interstellar bands in APOGEE telluric standard star spectra: weak bands and comparisons with optical counterparts,"  Information on the existence and properties of diffuse interstellar bands
+(DIBs) outside the optical domain is still limited. Additional infra-red (IR)
+measurements and IR-optical correlative studies are needed to constrain DIB
+carriers and locate various absorbers in 3D maps of the interstellar matter. We
+extended our study of H-band DIBs in Apache Point Observatory Galactic
+Evolution Experiment (APOGEE) Telluric Standard Star (TSS) spectra. We used the
+strong 15273A band to select the most and least absorbed targets. We used
+individual spectra of the former subsample to extract weaker DIBs, and we
+searched the two stacked series for differences that could indicate additional
+bands. High-resolution NARVAL and SOPHIE optical spectra for a subsample of 55
+TSS targets were additionally recorded for NIR/optical correlative studies.
+From the TSS spectra we extract a catalog of measurements of the poorly studied
+15617, 15653, and 15673A DIBs in about 300 sightlines, we obtain a first
+accurate determination of their rest wavelength and constrained their intrinsic
+width and shape. In addition, we studied the relationship between these weak
+bands and the strong 15273A DIB. We provide a first or second confirmation of
+several other weak DIBs that have been proposed based on different instruments,
+and we add new constraints on their widths and locations. We finally propose
+two new DIB candidates. We compared the strength of the 15273A absorptions with
+their optical counterparts 5780, 5797, 6196, 6283, and 6614A. Using the
+5797-5780 ratio as a tracer of shielding against the radiation field, we showed
+that the 15273A DIB carrier is significantly more abundant in unshielded
+(sigma-type) clouds, and it responds even more strongly than the 5780A band
+carrier to the local ionizing field.
+"
+22614,Arbitrary-Oriented Scene Text Detection via Rotation Proposals,"  This paper introduces a novel rotation-based framework for arbitrary-oriented
+text detection in natural scene images. We present the Rotation Region Proposal
+Networks (RRPN), which are designed to generate inclined proposals with text
+orientation angle information. The angle information is then adapted for
+bounding box regression to make the proposals more accurately fit into the text
+region in terms of the orientation. The Rotation Region-of-Interest (RRoI)
+pooling layer is proposed to project arbitrary-oriented proposals to a feature
+map for a text region classifier. The whole framework is built upon a
+region-proposal-based architecture, which ensures the computational efficiency
+of the arbitrary-oriented text detection compared with previous text detection
+systems. We conduct experiments using the rotation-based framework on three
+real-world scene text detection datasets and demonstrate its superiority in
+terms of effectiveness and efficiency over previous approaches.
+"
+22615,Semi-supervised Bayesian Deep Multi-modal Emotion Recognition,"  In emotion recognition, it is difficult to recognize human's emotional states
+using just a single modality. Besides, the annotation of physiological
+emotional data is particularly expensive. These two aspects make the building
+of effective emotion recognition model challenging. In this paper, we first
+build a multi-view deep generative model to simulate the generative process of
+multi-modality emotional data. By imposing a mixture of Gaussians assumption on
+the posterior approximation of the latent variables, our model can learn the
+shared deep representation from multiple modalities. To solve the
+labeled-data-scarcity problem, we further extend our multi-view model to
+semi-supervised learning scenario by casting the semi-supervised classification
+problem as a specialized missing data imputation task. Our semi-supervised
+multi-view deep generative framework can leverage both labeled and unlabeled
+data from multiple modalities, where the weight factor for each modality can be
+learned automatically. Compared with previous emotion recognition methods, our
+method is more robust and flexible. The experiments conducted on two real
+multi-modal emotion datasets have demonstrated the superiority of our framework
+over a number of competitors.
+"
+22616,Mimicking cuprates: large orbital polarization in a metallic square-planar nickelate,"  High temperature cuprate superconductivity remains a defining problem in
+condensed matter physics. Among myriad approaches to addressing this problem
+has been the study of alternative transition metal oxides with similar
+structures and 3d electron count that are suggested as proxies for cuprate
+physics. None of these analogs has been superconducting, and few are even
+metallic. Here, we report that the low-valent, quasi-two-dimensional trilayer
+compound, Pr4Ni3O8 avoids a charge-stripe ordered phase previously reported for
+La4Ni3O8, leading to a metallic ground state. By combining x-ray absorption
+spectroscopy and density functional theory calculations, we further find that
+metallic Pr4Ni3O8 exhibits a low-spin configuration and significant orbital
+polarization of the unoccupied eg states with pronounced dx2-y2 character near
+the Fermi energy, both hallmarks of the cuprate superconductors. Belonging to a
+regime of 3d electron count found for hole-doped cuprates, Pr4Ni3O8 thus
+represents one of the closest analogies to cuprates yet reported and a
+singularly promising candidate for high-Tc superconductivity if appropriately
+doped.
+"
+22617,Community detection based on significance optimization in complex networks,"  Community structure is an important structural property that extensively
+exists in various complex networks. In the past decade, much attention has been
+paid to the design of community-detection methods, but analyzing the behaviors
+of the methods is also of interest in the theoretical research and real
+applications. Here, we focus on an important measure for community structure,
+significance [Sci. Rep. 3 (2013) 2930]. Specifically, we study the effect of
+various network parameters on this measure in detail, analyze the critical
+behaviors of it in partition transition, and analytically give the formula of
+the critical points and the phase diagrams. The results shows that the critical
+number of communities in partition transition increases dramatically with the
+difference between inter- and intra-community link densities, and thus
+significance optimization displays higher resolution in community detection
+than many other methods, but it is also easily to lead to the excessive
+splitting of communities. By Louvain algorithm for significance optimization,
+we confirmed the theoretical results on artificial and real-world networks, and
+give a series of comparisons with some classical methods.
+"
+22618,On Hyperparameter Search in Cluster Ensembles,"  Quality assessments of models in unsupervised learning and clustering
+verification in particular have been a long-standing problem in the machine
+learning research. The lack of robust and universally applicable cluster
+validity scores often makes the algorithm selection and hyperparameter
+evaluation a tough guess. In this paper, we show that cluster ensemble
+aggregation techniques such as consensus clustering may be used to evaluate
+clusterings and their hyperparameter configurations. We use normalized mutual
+information to compare individual objects of a clustering ensemble to the
+constructed consensus of the whole ensemble and show, that the resulting score
+can serve as an overall quality measure for clustering problems. This method is
+capable of highlighting the standout clustering and hyperparameter
+configuration in the ensemble even in the case of a distorted consensus. We
+apply this very general framework to various data sets and give possible
+directions for future research.
+"
+22619,Active Learning of Points-To Specifications,"  When analyzing programs, large libraries pose significant challenges to
+static points-to analysis. A popular solution is to have a human analyst
+provide points-to specifications that summarize relevant behaviors of library
+code, which can substantially improve precision and handle missing code such as
+native code. We propose ATLAS, a tool that automatically infers points-to
+specifications. ATLAS synthesizes unit tests that exercise the library code,
+and then infers points-to specifications based on observations from these
+executions. ATLAS automatically infers specifications for the Java standard
+library, and produces better results for a client static information flow
+analysis on a benchmark of 46 Android apps compared to using existing
+handwritten specifications.
+"
+22620,Fundamental groups of formal Legendrian and horizontal embedding spaces,"  We compute the fundamental group of each connected component of the space of
+formal Legendrian embeddings in R3. We use it to show that previous examples in
+the literature of non trivial loops of Legendrian embeddings are already non
+trivial at the formal level. Likewise, we compute the fundamental group of the
+different connected components of the space of formal horizontal embeddings
+into the standard Engel R4. We check that this computes the fundamental group
+of the space of horizontal embeddings as well.
+"
+22621,1D Modeling of Sensor Selection Problem for Weak Barrier Coverage and Gap Mending in Wireless Sensor Networks,"  In this paper, we first remodel the line coverage as a 1D discrete problem
+with co-linear targets. Then, an order-based greedy algorithm, called OGA, is
+proposed to solve the problem optimally. It will be shown that the existing
+order in the 1D modeling, and especially the resulted Markov property of the
+selected sensors can help design greedy algorithms such as OGA. These
+algorithms demonstrate optimal/efficient performance and have lower complexity
+compared to the state-of-the-art. Furthermore, it is demonstrated that the
+conventional continuous line coverage problem can be converted to an equivalent
+discrete problem and solved optimally by OGA. Next, we formulate the well-known
+weak barrier coverage problem as an instance of the continuous line coverage
+problem (i.e. a 1D problem) as opposed to the conventional 2D graph-based
+models. We demonstrate that the equivalent discrete version of this problem can
+be solved optimally and faster than the state-of-the-art methods using an
+extended version of OGA, called K-OGA. Moreover, an efficient local algorithm,
+called LOGM, is proposed to mend barrier gaps due to sensor failure. In the
+case of m gaps, LOGM is proved to select at most 2m-1 sensors more than the
+optimal while being local and implementable in distributed fashion. We
+demonstrate the optimal/efficient performance of the proposed algorithms via
+extensive simulations.
+"
+22622,Ambient temperature high-pressure-induced ferroelectric phase transition in CaMnTi$_2$O$_6$,"  The ferroelectric to paraelectric phase transition of multiferroic
+CaMnTi$_2$O$_6$ has been investigated at high pressures and ambient temperature
+by second harmonic generation (SHG), Raman spectroscopy, and powder and
+single-crystal x-ray diffraction. We have found that CaMnTi$_2$O$_6$ undergoes
+a pressure-induced structural phase transition ($P4_2mc \rightarrow P4_2/nmc$)
+at $\sim$7 GPa to the same paraelectric structure found at ambient pressure and
+$T_c$ = 630 K. The continuous linear decrease of the SHG intensity that
+disappears at 7 GPa and the existence of a Raman active mode at 244 cm$^{-1}$
+that first softens up to 7 GPa and then hardens with pressure, are used to
+discuss the nature of the phase transition of CaMnTi$_2$O$_6$ for which a
+d$T_c$/d$P = -48$ K/GPa has been found. Neither a volume contraction nor a
+change of the normalized pressure on the eulerian strain are observed across
+the phase transition with all the unit-cell volume data following a second
+order Birch-Murnaghan equation of state with a bulk modulus of $B_0$ =
+182.95(2) GPa.
+"
+22623,Optimal transport maps for distribution preserving operations on latent spaces of Generative Models,"  Generative models such as Variational Auto Encoders (VAEs) and Generative
+Adversarial Networks (GANs) are typically trained for a fixed prior
+distribution in the latent space, such as uniform or Gaussian. After a trained
+model is obtained, one can sample the Generator in various forms for
+exploration and understanding, such as interpolating between two samples,
+sampling in the vicinity of a sample or exploring differences between a pair of
+samples applied to a third sample. In this paper, we show that the latent space
+operations used in the literature so far induce a distribution mismatch between
+the resulting outputs and the prior distribution the model was trained on. To
+address this, we propose to use distribution matching transport maps to ensure
+that such latent space operations preserve the prior distribution, while
+minimally modifying the original operation. Our experimental results validate
+that the proposed operations give higher quality samples compared to the
+original operations.
+"
+22624,Spatial features of synaptic adaptation affecting learning performance,"  Recent studies have proposed that the diffusion of messenger molecules, such
+as monoamines, can mediate the plastic adaptation of synapses in supervised
+learning of neural networks. Based on these findings we developed a model for
+neural learning, where the signal for plastic adaptation is assumed to
+propagate through the extracellular space. We investigate the conditions
+allowing learning of Boolean rules in a neural network. Even fully excitatory
+networks show very good learning performances. Moreover, the investigation of
+the plastic adaptation features optimizing the performance suggests that
+learning is very sensitive to the extent of the plastic adaptation and the
+spatial range of synaptic connections.
+"
+22625,SGAN: An Alternative Training of Generative Adversarial Networks,"  The Generative Adversarial Networks (GANs) have demonstrated impressive
+performance for data synthesis, and are now used in a wide range of computer
+vision tasks. In spite of this success, they gained a reputation for being
+difficult to train, what results in a time-consuming and human-involved
+development process to use them.
+We consider an alternative training process, named SGAN, in which several
+adversarial ""local"" pairs of networks are trained independently so that a
+""global"" supervising pair of networks can be trained against them. The goal is
+to train the global pair with the corresponding ensemble opponent for improved
+performances in terms of mode coverage. This approach aims at increasing the
+chances that learning will not stop for the global pair, preventing both to be
+trapped in an unsatisfactory local minimum, or to face oscillations often
+observed in practice. To guarantee the latter, the global pair never affects
+the local ones.
+The rules of SGAN training are thus as follows: the global generator and
+discriminator are trained using the local discriminators and generators,
+respectively, whereas the local networks are trained with their fixed local
+opponent.
+Experimental results on both toy and real-world problems demonstrate that
+this approach outperforms standard training in terms of better mitigating mode
+collapse, stability while converging and that it surprisingly, increases the
+convergence speed as well.
+"
+22626,"See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content","  Humans have rich understanding of liquid containers and their contents; for
+example, we can effortlessly pour water from a pitcher to a cup. Doing so
+requires estimating the volume of the cup, approximating the amount of water in
+the pitcher, and predicting the behavior of water when we tilt the pitcher.
+Very little attention in computer vision has been made to liquids and their
+containers. In this paper, we study liquid containers and their contents, and
+propose methods to estimate the volume of containers, approximate the amount of
+liquid in them, and perform comparative volume estimations all from a single
+RGB image. Furthermore, we show the results of the proposed model for
+predicting the behavior of liquids inside containers when one tilts the
+containers. We also introduce a new dataset of Containers Of liQuid contEnt
+(COQE) that contains more than 5,000 images of 10,000 liquid containers in
+context labelled with volume, amount of content, bounding box annotation, and
+corresponding similar 3D CAD models.
+"
+22627,Brain Age from the Electroencephalogram of Sleep,"  The human electroencephalogram (EEG) of sleep undergoes profound changes with
+age. These changes can be conceptualized as ""brain age"", which can be compared
+to an age norm to reflect the deviation from normal aging process. Here, we
+develop an interpretable machine learning model to predict brain age based on
+two large sleep EEG datasets: the Massachusetts General Hospital sleep lab
+dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health
+Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean
+absolute deviation of 8.1 years between brain age and chronological age in the
+healthy participants in the MGH dataset. As validation, we analyze a subset of
+SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years
+difference in brain age. Participants with neurological and psychiatric
+diseases, as well as diabetes and hypertension medications show an older brain
+age compared to chronological age. The findings raise the prospect of using
+sleep EEG as a biomarker for healthy brain aging.
+"
+22628,Metal-insulator transition in the hybridized two-orbital Hubbard model revisited,"  In this work we study the two-orbital Hubbard model on a square lattice in
+the presence of hybridization between nearest-neighbor orbitals and a
+crystal-field splitting. We use a highly reliable numerical technique based on
+the density matrix renormalization group to solve the dynamical mean field
+theory self-consistent impurity problem. We find that the orbital mixing always
+leads to a finite local density states at the Fermi energy in both orbitals
+when at least one band is metallic. When one band is doped, and the chemical
+potential lies between the Hubbard bands in the other band, the coherent
+quasiparticle peak in this orbital has an exponential behavior with the Hubbard
+interaction $U$.
+"
+22629,Energy-Efficient Wireless Interconnection Framework for Multichip Systems with In-package Memory Stacks,"  Multichip systems with memory stacks and various processing chips are at the
+heart of platform based designs such as servers and embedded systems. Full
+utilization of the benefits of these integrated multichip systems need a
+seamless, and scalable in-package interconnection framework. However,
+state-of-the-art inter-chip communication requires long wireline channels which
+increases energy consumption and latency while decreasing data bandwidth. Here,
+we propose the design of an energy-efficient, seamless wireless interconnection
+network for multichip systems. We demonstrate with cycle-accurate simulations
+that such a design reduces the energy consumption and latency while increasing
+the bandwidth in comparison to modern multichip integration systems.
+"
+22630,On the Jordan structure of holomorphic matrices,"  Let $X$ be an open subset of $\Bbb C^N$, and let $A$ be an $n\times n$ matrix
+of holomorphic functions on $X$. We call a point $\xi\in X$ $\mathbf{Jordan}$
+$\mathbf{stable}$ for $A$ if $\xi$ is not a splitting point of the eigenvalues
+of $A$ and, moreover, there is a neighborhood $U$ of $\xi$ such that, for each
+$1\le k\le n$, the number of Jordan blocks of size $k$ in the Jordan normal
+forms of $A(\zeta)$ is the same for all $\zeta\in U$. H. Baumgärtel (Analytic
+perturbation theory for matrices and operators, Birkhäuser, 1985) proved that
+there is a nowhere dense closed analytic subset of $X$, which contains all
+points of $X$ which are not Jordan stable for $A$. We give a new proof of this
+result. This proof has the advantage that the result can be obtained in a more
+precise form, and with some estimates. Also, this proof applies to arbitrary,
+possibly non-smooth, complex spaces $X$.
+"
+22631,The Convergence of Least-Squares Progressive Iterative Approximation with Singular Iterative Matrix,"  Developed in [Deng and Lin, 2014], Least-Squares Progressive Iterative
+Approximation (LSPIA) is an efficient iterative method for solving B-spline
+curve and surface least-squares fitting systems. In [Deng and Lin 2014], it was
+shown that LSPIA is convergent when the iterative matrix is nonsingular. In
+this paper, we will show that LSPIA is still convergent even the iterative
+matrix is singular.
+"
+22632,Non-Asymptotic Uniform Rates of Consistency for k-NN Regression,"  We derive high-probability finite-sample uniform rates of consistency for
+$k$-NN regression that are optimal up to logarithmic factors under mild
+assumptions. We moreover show that $k$-NN regression adapts to an unknown lower
+intrinsic dimension automatically. We then apply the $k$-NN regression rates to
+establish new results about estimating the level sets and global maxima of a
+function from noisy observations.
+"
+22633,Structured sampling and fast reconstruction of smooth graph signals,"  This work concerns sampling of smooth signals on arbitrary graphs. We first
+study a structured sampling strategy for such smooth graph signals that
+consists of a random selection of few pre-defined groups of nodes. The number
+of groups to sample to stably embed the set of $k$-bandlimited signals is
+driven by a quantity called the \emph{group} graph cumulative coherence. For
+some optimised sampling distributions, we show that sampling $O(k\log(k))$
+groups is always sufficient to stably embed the set of $k$-bandlimited signals
+but that this number can be smaller -- down to $O(\log(k))$ -- depending on the
+structure of the groups of nodes. Fast methods to approximate these sampling
+distributions are detailed. Second, we consider $k$-bandlimited signals that
+are nearly piecewise constant over pre-defined groups of nodes. We show that it
+is possible to speed up the reconstruction of such signals by reducing
+drastically the dimension of the vectors to reconstruct. When combined with the
+proposed structured sampling procedure, we prove that the method provides
+stable and accurate reconstruction of the original signal. Finally, we present
+numerical experiments that illustrate our theoretical results and, as an
+example, show how to combine these methods for interactive object segmentation
+in an image using superpixels.
+"
+22634,"Dual Graph-Laplacian PCA: A Closed-Form Solution for Bi-clustering to Find ""Checkerboard"" Structures on Gene Expression Data","  In the context of cancer, internal ""checkerboard"" structures are normally
+found in the matrices of gene expression data, which correspond to genes that
+are significantly up- or down-regulated in patients with specific types of
+tumors. In this paper, we propose a novel method, called dual
+graph-regularization principal component analysis (DGPCA). The main innovation
+of this method is that it simultaneously considers the internal geometric
+structures of the condition manifold and the gene manifold. Specifically, we
+obtain principal components (PCs) to represent the data and approximate the
+cluster membership indicators through Laplacian embedding. This new method is
+endowed with internal geometric structures, such as the condition manifold and
+gene manifold, which are both suitable for bi-clustering. A closed-form
+solution is provided for DGPCA. We apply this new method to simultaneously
+cluster genes and conditions (e.g., different samples) with the aim of finding
+internal ""checkerboard"" structures on gene expression data, if they exist.
+Then, we use this new method to identify regulatory genes under the particular
+conditions and to compare the results with those of other state-of-the-art
+PCA-based methods. Promising results on gene expression data have been verified
+by extensive experiments
+"
+22635,Blood crystal: emergent order of red blood cells under wall-confined shear flow,"  Driven or active suspensions can display fascinating collective behavior,
+where coherent motions or structures arise on a scale much larger than that of
+the constituent particles. Here, we report experiments and numerical
+simulations revealing that red blood cells (RBCs) assemble into regular
+patterns in a confined shear flow. The order is of pure hydrodynamic and
+inertialess origin, and emerges from a subtle interplay between (i)
+hydrodynamic repulsion by the bounding walls which drives deformable cells
+towards the channel mid-plane and (ii) intercellular hydrodynamic interactions
+which can be attractive or repulsive depending on cell-cell separation. Various
+crystal-like structures arise depending on RBC concentration and confinement.
+Hardened RBCs in experiments and rigid particles in simulations remain
+disordered under the same conditions where deformable RBCs form regular
+patterns, highlighting the intimate link between particle deformability and the
+emergence of order. The difference in structuring ability of healthy
+(deformable) and diseased (stiff) RBCs creates a flow signature potentially
+exploitable for diagnosis of blood pathologies.
+"
+22636,"An Isolated Microlens Observed from K2, Spitzer and Earth","  We present the result of microlensing event MOA-2016-BLG-290, which received
+observations from the two-wheel Kepler (K2), Spitzer, as well as ground-based
+observatories. A joint analysis of data from K2 and the ground leads to two
+degenerate solutions of the lens mass and distance. This degeneracy is
+effectively broken once the (partial) Spitzer light curve is included.
+Altogether, the lens is found to be an extremely low-mass star located in the
+Galactic bulge. MOA-2016-BLG-290 is the first microlensing event for which we
+have signals from three well-separated ($\sim1$ AU) locations. It demonstrates
+the power of two-satellite microlensing experiment in reducing the ambiguity of
+lens properties, as pointed out independently by S. Refsdal and A. Gould
+several decades ago.
+"
+22637,"Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners","  This paper summarizes some recent advances on a set of tasks related to the
+processing of singing using state-of-the-art deep learning techniques. We
+discuss their achievements in terms of accuracy and sound quality, and the
+current challenges, such as availability of data and computing resources. We
+also discuss the impact that these advances do and will have on listeners and
+singers when they are integrated in commercial applications.
+"
+22638,How did the discussion go: Discourse act classification in social media conversations,"  We propose a novel attention based hierarchical LSTM model to classify
+discourse act sequences in social media conversations, aimed at mining data
+from online discussion using textual meanings beyond sentence level. The very
+uniqueness of the task is the complete categorization of possible pragmatic
+roles in informal textual discussions, contrary to extraction of
+question-answers, stance detection or sarcasm identification which are very
+much role specific tasks. Early attempt was made on a Reddit discussion
+dataset. We train our model on the same data, and present test results on two
+different datasets, one from Reddit and one from Facebook. Our proposed model
+outperformed the previous one in terms of domain independence; without using
+platform-dependent structural features, our hierarchical LSTM with word
+relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively
+to predict discourse roles of comments in Reddit and Facebook discussions.
+Efficiency of recurrent and convolutional architectures in order to learn
+discursive representation on the same task has been presented and analyzed,
+with different word and comment embedding schemes. Our attention mechanism
+enables us to inquire into relevance ordering of text segments according to
+their roles in discourse. We present a human annotator experiment to unveil
+important observations about modeling and data annotation. Equipped with our
+text-based discourse identification model, we inquire into how heterogeneous
+non-textual features like location, time, leaning of information etc. play
+their roles in charaterizing online discussions on Facebook.
+"
+22639,"TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure","  Predicting the structure of a protein from its sequence is a cornerstone task
+of molecular biology. Established methods in the field, such as homology
+modeling and fragment assembly, appeared to have reached their limit. However,
+this year saw the emergence of promising new approaches: end-to-end protein
+structure and dynamics models, as well as reinforcement learning applied to
+protein folding. For these approaches to be investigated on a larger scale, an
+efficient implementation of their key computational primitives is required. In
+this paper we present a library of differentiable mappings from two standard
+dihedral-angle representations of protein structure (full-atom representation
+""$\phi,\psi,\omega,\chi$"" and backbone-only representation
+""$\phi,\psi,\omega$"") to atomic Cartesian coordinates. The source code and
+documentation can be found at this https URL.
+"
+22640,On a characterization of path connected topological fields,"  The aim of this paper is to give a characterization of path connected
+topological fields, inspired by the classic Gelfand's correspondence between a
+compact Hausdorff topological space $X$ and the space of maximal ideals on the
+ring of real valued continuous functions $C(X,\mathbb{R})$. More explicitly,
+our motivation is the following question: What is the essential property of the
+topological field $F=\mathbb{R}$ that makes such correspondence valid for all
+compact Hausdorff spaces? It turns out that such perfect correspondence exists
+if and only if $F$ is a path connected topological field.
+"
+22641,A simple genome-wide association study algorithm,"  A computationally simple genome-wide association study (GWAS) algorithm for
+estimating the main and epistatic effects of markers or single nucleotide
+polymorphisms (SNPs) is proposed. It is based on the intuitive assumption that
+changes of alleles corresponding to important SNPs in a pair of individuals
+lead to large difference of phenotype values of these individuals. The
+algorithm is based on considering pairs of individuals instead of SNPs or pairs
+of SNPs. The main advantage of the algorithm is that it weakly depends on the
+number of SNPs in a genotype matrix. It mainly depends on the number of
+individuals, which is typically very small in comparison with the number of
+SNPs. Numerical experiments with real data sets illustrate the proposed
+algorithm.
+"
+22642,Classical and bayesian componentwise predictors for non-compact correlated ARH(1) processes,"  A special class of standard Gaussian Autoregressive Hilbertian processes of
+order one (Gaussian ARH(1) processes), with bounded linear autocorrelation
+operator, which does not satisfy the usual Hilbert-Schmidt assumption, is
+considered. To compensate the slow decay of the diagonal coefficients of the
+autocorrelation operator, a faster decay velocity of the eigenvalues of the
+trace autocovariance operator of the innovation process is assumed. As usual,
+the eigenvectors of the autocovariance operator of the ARH(1) process are
+considered for projection, since, here, they are assumed to be known. Diagonal
+componentwise classical and bayesian estimation of the autocorrelation operator
+is studied for prediction. The asymptotic efficiency and equivalence of both
+estimators is proved, as well as of their associated componentwise ARH(1)
+plugin predictors. A simulation study is undertaken to illustrate the
+theoretical results derived.
+"
+22643,Universality results for zeros of random holomorphic sections,"  In this work we prove an universality result regarding the equidistribution
+of zeros of random holomorphic sections associated to a sequence of singular
+Hermitian holomorphic line bundles on a compact Kähler complex space $X$.
+Namely, under mild moment assumptions, we show that the asymptotic distribution
+of zeros of random holomorphic sections is independent of the choice of the
+probability measure on the space of holomorphic sections. In the case when $X$
+is a compact Kähler manifold, we also prove an off-diagonal exponential decay
+estimate for the Bergman kernels of a sequence of positive line bundles on $X$.
+"
+22644,Explicit asymptotic expansions for tame supercuspidal characters,"  We combine the ideas of a Harish-Chandra--Howe local character expansion,
+which can be centred at an arbitrary semisimple element, and a Kim--Murnaghan
+asymptotic expansion, which so far has been considered only around the
+identity. We show that, for most smooth, irreducible representations (those
+containing a good, minimal K-type), Kim--Murnaghan-type asymptotic expansions
+are valid on explicitly defined neighbourhoods of nearly arbitrary semisimple
+elements. We then give an explicit, inductive recipe for computing the
+coefficients in an asymptotic expansion for a tame supercuspidal
+representation. The only additional information needed in the inductive step is
+a fourth root of unity, which we expect to be useful in proving stability and
+endoscopic-transfer identities.
+"
+22645,Error Feedback Fixes SignSGD and other Gradient Compression Schemes,"  Sign-based algorithms (e.g. signSGD) have been proposed as a biased gradient
+compression technique to alleviate the communication bottleneck in training
+large neural networks across multiple workers. We show simple convex
+counter-examples where signSGD does not converge to the optimum. Further, even
+when it does converge, signSGD may generalize poorly when compared with SGD.
+These issues arise because of the biased nature of the sign compression
+operator.
+We then show that using error-feedback, i.e. incorporating the error made by
+the compression operator into the next step, overcomes these issues. We prove
+that our algorithm EF-SGD achieves the same rate of convergence as SGD without
+any additional assumptions for arbitrary compression operators (including the
+sign operator), indicating that we get gradient compression for free. Our
+experiments thoroughly substantiate the theory showing the superiority of our
+algorithm.
+"
+22646,Partial Diffusion Kalman Filtering,"  In conventional distributed Kalman filtering, employing diffusion strategies,
+each node transmits its state estimate to all its direct neighbors in each
+iteration. In this paper we propose a partial diffusion Kalman filter (PDKF)
+for state estimation of linear dynamic systems. In the PDKF algorithm every
+node (agent) is allowed to share only a subset of its intermediate estimate
+vectors at each iteration among its neighbors, which reduces the amount of
+internode communications. We study the stability of the PDKF algorithm where
+our analysis reveals that the algorithm is stable and convergent in both mean
+and mean-square senses. We also investigate the steady-state mean-square
+deviation (MSD) of the PDKF algorithm and derive a closed-form expression that
+describes how the algorithm performs at the steady-state. Experimental results
+validate the effectiveness of PDKF algorithm and demonstrate that the proposed
+algorithm provides a trade-off between communication cost and estimation
+performance that is extremely profitable.
+"
+22647,Higher Cluster Categories and QFT Dualities,"  We present a unified mathematical framework that elegantly describes
+minimally SUSY gauge theories in even dimension, ranging from $6d$ to $0d$, and
+their dualities. This approach combines recent developments on graded quiver
+with potentials, higher Ginzburg algebras and higher cluster categories (also
+known as $m$-cluster categories). Quiver mutations studied in the context of
+mathematics precisely correspond to the order $(m+1)$ dualities of the gauge
+theories. Our work suggests that these equivalences of quiver gauge theories
+sit inside an infinite family of such generalized dualities, whose physical
+interpretation is yet to be understood.
+"
+22648,An Inexact Newton-like conditional gradient method for constrained nonlinear systems,"  In this paper, we propose an inexact Newton-like conditional gradient method
+for solving constrained systems of nonlinear equations. The local convergence
+of the new method as well as results on its rate are established by using a
+general majorant condition. Two applications of such condition are provided:
+one is for functions whose the derivative satisfies Holder-like condition and
+the other is for functions that satisfies a Smale condition, which includes a
+substantial class of analytic functions. Some preliminaries numerical
+experiments illustrating the applicability of the proposed method for medium
+and large problems are also presented.
+"
+22649,Forces and energetics of intermittent swimming,"  Experiments are reported on intermittent swimming motions. Water tunnel
+experiments on a nominally two-dimensional pitching foil show that the mean
+thrust and power scale linearly with the duty cycle, from a value of 0.2 all
+the way up to continuous motions, indicating that individual bursts of activity
+in intermittent motions are independent of each other. This conclusion is
+corroborated by PIV flow visualizations, which show that the main vortical
+structures in the wake do not change with duty cycle. The experimental data
+also demonstrate that intermittent motions are generally energetically
+advantageous over continuous motions. When metabolic energy losses are taken
+into account, this conclusion is maintained for metabolic power fractions less
+than 1.
+"
+22650,Texture to the Rescue: Practical Paper Fingerprinting based on Texture Patterns,"  In this paper, we propose a novel paper fingerprinting technique based on
+analyzing the translucent patterns revealed when a light source shines through
+the paper. These patterns represent the inherent texture of paper, formed by
+the random interleaving of wooden particles during the manufacturing process.
+We show these patterns can be easily captured by a commodity camera and
+condensed into to a compact 2048-bit fingerprint code. Prominent works in this
+area (Nature 2005, IEEE S&P 2009, CCS 2011) have all focused on fingerprinting
+paper based on the paper ""surface"". We are motivated by the observation that
+capturing the surface alone misses important distinctive features such as the
+non-even thickness, the random distribution of impurities, and different
+materials in the paper with varying opacities. Through experiments, we
+demonstrate that the embedded paper texture provides a more reliable source for
+fingerprinting than features on the surface. Based on the collected datasets,
+we achieve 0% false rejection and 0% false acceptance rates. We further report
+that our extracted fingerprints contain 807 degrees-of-freedom (DoF), which is
+much higher than the 249 DoF with iris codes (that have the same size of 2048
+bits). The high amount of DoF for texture-based fingerprints makes our method
+extremely scalable for recognition among very large databases; it also allows
+secure usage of the extracted fingerprint in privacy-preserving authentication
+schemes based on error correction techniques.
+"
+22651,Rule-Mining based classification: a benchmark study,"  This study proposed an exhaustive stable/reproducible rule-mining algorithm
+combined to a classifier to generate both accurate and interpretable models.
+Our method first extracts rules (i.e., a conjunction of conditions about the
+values of a small number of input features) with our exhaustive rule-mining
+algorithm, then constructs a new feature space based on the most relevant rules
+called ""local features"" and finally, builds a local predictive model by
+training a standard classifier on the new local feature space. This local
+feature space is easy interpretable by providing a human-understandable
+explanation under the explicit form of rules. Furthermore, our local predictive
+approach is as powerful as global classical ones like logistic regression (LR),
+support vector machine (SVM) and rules based methods like random forest (RF)
+and gradient boosted tree (GBT).
+"
+22652,Magnetic disorder in superconductors: Enhancement by mesoscopic fluctuations,"  We study the density of states (DOS) and the transition temperature $T_c$ in
+a dirty superconducting film with rare classical magnetic impurities of an
+arbitrary strength described by the Poissonian statistics. We take into account
+that the potential disorder is a source for mesoscopic fluctuations of the
+local DOS, and, consequently, for the effective strength of magnetic
+impurities. We find that these mesoscopic fluctuations result in a non-zero DOS
+for all energies in the region of the phase diagram where without this effect
+the DOS is zero within the standard mean-field theory. This mechanism can be
+more efficient in filling the mean-field superconducting gap than rare
+fluctuations of the potential disorder (instantons). Depending on the magnetic
+impurity strength, the suppression of $T_c$ by spin-flip scattering can be
+faster or slower than in the standard mean-field theory.
+"
+22653,A sample of metal-poor galaxies identified from the LAMOST spectral survey,"  We present a sample of 48 metal-poor galaxies at $ z < 0.14$ selected from
+92,510 galaxies in the LAMOST survey. These galaxies are identified for their
+detection of the auroral emission line \oiii$\lambda$4363 above $3\sigma$
+level, which allows a direct measurement of the electron temperature and the
+oxygen abundance. The emission line fluxes are corrected for internal dust
+extinction using Balmer decrement method. With electron temperature derived
+from \oiii$\lambda\lambda4959,5007/\oiii\lambda4363$ and electron density from
+$\sii\lambda6731/\sii\lambda6717$, we obtain the oxygen abundances in our
+sample which range from $\zoh= 7.63$ (0.09 $\Zsun$) to $8.46$ (0.6 $\Zsun$). We
+find an extremely metal-poor galaxy with $\zoh=7.63 \pm 0.01$. With multiband
+photometric data from FUV to NIR and $\ha$ measurements, we also determine the
+stellar masses and star formation rates, based on the spectral energy
+distribution fitting and $\ha$ luminosity, respectively. We find that our
+galaxies have low and intermediate stellar masses with $\rm 6.39 \le
+log(M/M_{\sun})\le 9.27$, and high star formation rates (SFRs) with $\rm -2.18
+\le log(SFR/M_{\sun} yr^{-1}) \le 1.95$. We also find that the metallicities of
+our galaxies are consistent with the local $T_e$-based mass-metallicity
+relation, while the scatter is about 0.28 dex. Additionally, assuming the
+coefficient of $\rm \alpha =0.66$, we find most of our galaxies follow the
+local mass-metallicity-SFR relation, while a scatter about 0.24 dex exists,
+suggesting the mass-metallicity relation is weakly dependent on SFR for those
+metal-poor galaxies.
+"
+22654,Characterization of Deterministic and Probabilistic Sampling Patterns for Finite Completability of Low Tensor-Train Rank Tensor,"  In this paper, we analyze the fundamental conditions for low-rank tensor
+completion given the separation or tensor-train (TT) rank, i.e., ranks of
+unfoldings. We exploit the algebraic structure of the TT decomposition to
+obtain the deterministic necessary and sufficient conditions on the locations
+of the samples to ensure finite completability. Specifically, we propose an
+algebraic geometric analysis on the TT manifold that can incorporate the whole
+rank vector simultaneously in contrast to the existing approach based on the
+Grassmannian manifold that can only incorporate one rank component. Our
+proposed technique characterizes the algebraic independence of a set of
+polynomials defined based on the sampling pattern and the TT decomposition,
+which is instrumental to obtaining the deterministic condition on the sampling
+pattern for finite completability. In addition, based on the proposed analysis,
+assuming that the entries of the tensor are sampled independently with
+probability $p$, we derive a lower bound on the sampling probability $p$, or
+equivalently, the number of sampled entries that ensures finite completability
+with high probability. Moreover, we also provide the deterministic and
+probabilistic conditions for unique completability.
+"
+22655,Starshape of the superlevel sets of solutions to equations involving the fractional Laplacian in starshaped rings,"  In the present work we study solutions of the problem $-(-\Delta)^{\alpha/2}u
+= f(x,u)$ in $D_0\setminus \overline{D}_1$, with exterior conditions $u = 0$ in
+$R^N \setminus D_0$ and $u = 1$ in $\overline{D}_1$, where $D_1, D_0 \subset
+R^N$ are open sets such that $\overline{D}_1 \subset D_0$, $\alpha \in (0,2)$,
+and $f$ is a nonlinearity. Under different assumptions on $f$ we prove that, if
+$D_0$ and $D_1$ are starshaped with respect to the same point $\bar{x} \in
+\overline{D}_1$, then the same occurs for every superlevel set of $u$.
+"
+22656,"Stratigraphy of Aeolis Dorsa, Mars: stratigraphic context of the great river deposits","  Unraveling the stratigraphic record is the key to understanding ancient
+climate and past climate changes on Mars. River deposits when placed in
+stratigraphic order could constrain the number, magnitudes, and durations of
+the wettest climates in Mars history. We establish the stratigraphic context of
+river deposits in Aeolis Dorsa sedimentary basin, 10E of Gale crater. Here,
+wind has exhumed a stratigraphic section of >=4 unconformity-bounded
+sedimentary rock packages, recording >=3 distinct episodes of surface runoff.
+Early deposits (>700m thick) are embayed by river deposits (>400m), which are
+in turn unconformably draped by fan-shaped deposits (<100m) which we interpret
+as alluvial fans. Yardang-forming deposits (>900 m) unconformably drape all
+previous deposits. River deposits embay a dissected sedimentary-rock landscape,
+and comprise >=2 distinguishable units. The total interval spanned by river
+deposits is >(1x10^6-2x10^7) yr; more if we include alluvial-fan deposits.
+Alluvial-fan deposits unconformably postdate thrust faults which crosscut river
+deposits. We infer a relatively dry interval of >4x10^7 yr after river deposits
+formed and before fan-shaped deposits formed. The time gap between the end of
+river deposition and the onset of yardang-forming deposits is constrained to
+>10^8 yr by the density of impact craters embedded at the unconformity. We
+correlate yardang-forming deposits to the upper layers of Gale crater's mound
+(Mt. Sharp/Aeolis Mons), and fan-shaped deposits to Peace Vallis fan.
+Alternations between periods of low vs. high mean obliquity may have modulated
+erosion-deposition cycling in Aeolis. This is consistent with results from an
+ensemble of simulations of Solar System orbital evolution and the resulting
+history of Mars obliquity. Almost all simulations yield intervals of
+continuously low mean Mars obliquity that are long enough to match our
+unconformity data.
+"
+22657,Scenario of the Birth of Hidden Attractors in the Chua Circuit,"  Recently it was shown that in the dynamical model of Chua circuit both the
+classical selfexcited and hidden chaotic attractors can be found. In this paper
+the dynamics of the Chua circuit is revisited. The scenario of the chaotic
+dynamics development and the birth of selfexcited and hidden attractors is
+studied. It is shown a pitchfork bifurcation in which a pair of symmetric
+attractors coexists and merges into one symmetric attractor through an
+attractormerging bifurcation and a splitting of a single attractor into two
+attractors. The scenario relating the subcritical Hopf bifurcation near
+equilibrium points and the birth of hidden attractors is discussed.
+"
+22658,EBIC: an open source software for high-dimensional and big data biclustering analyses,"  Motivation: In this paper we present the latest release of EBIC, a
+next-generation biclustering algorithm for mining genetic data. The major
+contribution of this paper is adding support for big data, making it possible
+to efficiently run large genomic data mining analyses. Additional enhancements
+include integration with R and Bioconductor and an option to remove influence
+of missing value on the final result.
+Results: EBIC was applied to datasets of different sizes, including a large
+DNA methylation dataset with 436,444 rows. For the largest dataset we observed
+over 6.6 fold speedup in computation time on a cluster of 8 GPUs compared to
+running the method on a single GPU. This proves high scalability of the
+algorithm.
+Availability: The latest version of EBIC could be downloaded from
+this http URL . Installation and usage instructions are
+also available online.
+"
+22659,Safe Triplet Screening for Distance Metric Learning,"  We study safe screening for metric learning. Distance metric learning can
+optimize a metric over a set of triplets, each one of which is defined by a
+pair of same class instances and an instance in a different class. However, the
+number of possible triplets is quite huge even for a small dataset. Our safe
+triplet screening identifies triplets which can be safely removed from the
+optimization problem without losing the optimality. Compared with existing safe
+screening studies, triplet screening is particularly significant because of (1)
+the huge number of possible triplets, and (2) the semi-definite constraint in
+the optimization. We derive several variants of screening rules, and analyze
+their relationships. Numerical experiments on benchmark datasets demonstrate
+the effectiveness of safe triplet screening.
+"
+22660,A reaction network scheme which implements the EM algorithm,"  A detailed algorithmic explanation is required for how a network of chemical
+reactions can generate the sophisticated behavior displayed by living cells.
+Though several previous works have shown that reaction networks are
+computationally universal and can in principle implement any algorithm, there
+is scope for constructions that map well onto biological reality, make
+efficient use of the computational potential of the native dynamics of reaction
+networks, and make contact with statistical mechanics. We describe a new
+reaction network scheme for solving a large class of statistical problems
+including the problem of how a cell would infer its environment from
+receptor-ligand bindings. Specifically we show how reaction networks can
+implement information projection, and consequently a generalized
+Expectation-Maximization algorithm, to solve maximum likelihood estimation
+problems in partially-observed exponential families on categorical data. Our
+scheme can be thought of as an algorithmic interpretation of E. T. Jaynes's
+vision of statistical mechanics as statistical inference.
+"
+22661,Lost in Diversification,"  As financial instruments grow in complexity more and more information is
+neglected by risk optimization practices. This brings down a curtain of opacity
+on the origination of risk, that has been one of the main culprits in the
+2007-2008 global financial crisis. We discuss how the loss of transparency may
+be quantified in bits, using information theoretic concepts. We find that {\em
+i)} financial transformations imply large information losses, {\em ii)}
+portfolios are more information sensitive than individual stocks only if
+fundamental analysis is sufficiently informative on the co-movement of assets,
+that {\em iii)} securitisation, in the relevant range of parameters, yields
+assets that are less information sensitive than the original stocks and that
+{\em iv)} when diversification (or securitisation) is at its best (i.e. when
+assets are uncorrelated) information losses are maximal. We also address the
+issue of whether pricing schemes can be introduced to deal with information
+losses. This is relevant for the transmission of incentives to gather
+information on the risk origination side. Within a simple mean variance scheme,
+we find that market incentives are not generally sufficient to make information
+harvesting sustainable.
+"
+22662,Exact Ground States of the Kaya-Berker Model,"  Here we study the two-dimensional Kaya-Berker model, with a site occupancy p
+of one sub lattice, by using a polynomial-time exact ground-state algorithm.
+Thus, we were able to obtain T=0 results in exact equilibrium for rather large
+system sizes up to 777^2 lattice sites. We obtained sub-lattice magnetization
+and the corresponding Binder parameter. We found a critical point p_c=0.6423(3)
+beyond which the sub-lattice magnetization vanishes.
+This is clearly smaller than previous results which were obtained by using
+non-exact approaches for much smaller systems. We also created for each
+realization minimum-energy domain walls from two ground-state calculations for
+periodic and anti-periodic boundary conditions, respectively. The analysis of
+the mean and the variance of the domain-wall distribution shows that there is
+no thermodynamic stable spin-glass phase, in contrast to previous claims about
+this model.
+"
+22663,Partitioned Paxos via the Network Data Plane,"  Consensus protocols are the foundation for building fault-tolerant,
+distributed systems, and services. They are also widely acknowledged as
+performance bottlenecks. Several recent systems have proposed accelerating
+these protocols using the network data plane. But, while network-accelerated
+consensus shows great promise, current systems suffer from an important
+limitation: they assume that the network hardware also accelerates the
+application itself. Consequently, they provide a specialized replicated
+service, rather than providing a general-purpose high-performance consensus
+that fits any off-the-shelf application.
+To address this problem, this paper proposes Partitioned Paxos, a novel
+approach to network-accelerated consensus. The key insight behind Partitioned
+Paxos is to separate the two aspects of Paxos, agreement, and execution, and
+optimize them separately. First, Partitioned Paxos uses the network forwarding
+plane to accelerate agreement. Then, it uses state partitioning and
+parallelization to accelerate execution at the replicas. Our experiments show
+that using this combination of data plane acceleration and parallelization,
+Partitioned Paxos is able to provide at least x3 latency improvement and x11
+throughput improvement for a replicated instance of a RocksDB key-value store.
+"
+22664,Asymmetry in crystal facet dynamics of homoepitaxy by a continuum model,"  In the absence of external material deposition, crystal surfaces usually
+relax to become flat by decreasing their free energy. We study an asymmetry in
+the relaxation of macroscopic plateaus, facets, of a periodic surface
+corrugation in 1+1 dimensions via a continuum model below the roughening
+transition temperature. The model invokes a highly degenerate parabolic partial
+differential equation (PDE) for surface diffusion, which is related to the
+weighted-$H^{-1}$ (nonlinear) gradient flow of a convex, singular surface free
+energy in homoepitaxy. The PDE is motivated both by an atomistic broken-bond
+model and a mesoscale model for steps. By constructing an explicit solution to
+the PDE, we demonstrate the lack of symmetry in the evolution of top and bottom
+facets in periodic surface profiles. Our explicit, analytical solution is
+compared to numerical simulations of the PDE via a regularized surface free
+energy.
+"
+22665,Hierarchical Classification using Binary Data,"  In classification problems, especially those that categorize data into a
+large number of classes, the classes often naturally follow a hierarchical
+structure. That is, some classes are likely to share similar structures and
+features. Those characteristics can be captured by considering a hierarchical
+relationship among the class labels. Here, we extend a recent simple
+classification approach on binary data in order to efficiently classify
+hierarchical data. In certain settings, specifically, when some classes are
+significantly easier to identify than others, we showcase computational and
+accuracy advantages.
+"
+22666,Ulrich bundles on non-special surfaces with $p_g=0$ and $q=1$,"  Let $S$ be a surface with $p_g(S)=0$, $q(S)=1$ and endowed with a very ample
+line bundle $\mathcal O_S(h)$ such that $h^1\big(S,\mathcal O_S(h)\big)=0$. We
+show that such an $S$ supports families of dimension $p$ of pairwise
+non-isomorphic, indecomposable, Ulrich bundles for arbitrary large $p$.
+Moreover, we show that $S$ supports stable Ulrich bundles of rank $2$ if the
+genus of the general element in $\vert h\vert$ is at least $2$.
+"
+22667,Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities,"  Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference
+algorithm used in most popular ""first-order differentiable"" Probabilistic
+Programming Languages (PPLs). However, the fact that HMC uses derivative
+information causes complications when the target distribution is
+non-differentiable with respect to one or more of the latent variables. In this
+paper, we show how to use extensions to HMC to perform inference in
+probabilistic programs that contain discontinuities. To do this, we design a
+Simple first-order Probabilistic Programming Language (SPPL) that contains a
+sufficient set of language restrictions together with a compilation scheme.
+This enables us to preserve both the statistical and syntactic interpretation
+of if-else statements in the probabilistic program, within the scope of
+first-order PPLs. We also provide a corresponding mathematical formalism that
+ensures any joint density denoted in such a language has a suitably low measure
+of discontinuities.
+"
+22668,Pixel Normalization from Numeric Data as Input to Neural Networks,"  Text to image transformation for input to neural networks requires
+intermediate steps. This paper attempts to present a new approach to pixel
+normalization so as to convert textual data into image, suitable as input for
+neural networks. This method can be further improved by its Graphics Processing
+Unit (GPU) implementation to provide significant speedup in computational time.
+"
+22669,Optimal spline spaces for $L^2$ $n$-width problems with boundary conditions,"  In this paper we show that, with respect to the $L^2$ norm, three classes of
+functions in $H^r(0,1)$, defined by certain boundary conditions, admit optimal
+spline spaces of all degrees $\geq r-1$, and all these spline spaces have
+uniform knots.
+"
+22670,The Role of Quality Assurance in Software Development Projects:Project Failures and Business Performance,"  In the product business still battles with the hard assignment of creating
+programming applications that meet quality gauges, and spending limitations.
+The requirement for programming to be without mistake remained a test to the IT
+business. Hence, the basic role of this study is to answer why officials
+hesitant to apportion assets to quality confirmation (QA) process amid the
+procedure of the framework advancement life cycle (SDLC)? This exploration
+utilized a quantitative study outline to research to what degree the inclusion
+of QA amid the SDLC procedure diminished programming venture disappointments.
+The information will be investigate utilizing inductive techniques and was
+expected to be summed up to the whole IT programming improvement populace. The
+exploration showed a feeling that incorporation of QA in all periods of SDLC
+was a great marvel. To gather the essential information, a proficient study
+will be led through survey structure will be utilized to take the criticisms
+and perspectives from various programming houses and industry specialists. The
+last stage will be the elucidating factual investigation by utilizing
+measurable techniques. Matlab programming will be utilized to gauge the
+fluctuation and execution by contrasting the execution parameters. As a
+consequence of this examination, the odds of the venture disappointment amid
+undertaking advancement will be declines and the business execution of the
+tasks will be expansions.
+"
+22671,Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research,"  The purpose of this technical report is two-fold. First of all, it introduces
+a suite of challenging continuous control tasks (integrated with OpenAI Gym)
+based on currently existing robotics hardware. The tasks include pushing,
+sliding and pick & place with a Fetch robotic arm as well as in-hand object
+manipulation with a Shadow Dexterous Hand. All tasks have sparse binary rewards
+and follow a Multi-Goal Reinforcement Learning (RL) framework in which an agent
+is told what to do using an additional input.
+The second part of the paper presents a set of concrete research ideas for
+improving RL algorithms, most of which are related to Multi-Goal RL and
+Hindsight Experience Replay.
+"
+22672,Multiple positive solutions for a class of p-Laplacian Neumann problems without growth conditions,"  For $1<p<\infty$, we consider the following problem $$ -\Delta_p u=f(u),\quad
+u>0\text{ in }\Omega,\quad\partial_\nu u=0\text{ on }\partial\Omega, $$ where
+$\Omega\subset\mathbb R^N$ is either a ball or an annulus. The nonlinearity $f$
+is possibly supercritical in the sense of Sobolev embeddings; in particular our
+assumptions allow to include the prototype nonlinearity $f(s)=-s^{p-1}+s^{q-1}$
+for every $q>p$. We use the shooting method to get existence and multiplicity
+of non-constant radial solutions. With the same technique, we also detect the
+oscillatory behavior of the solutions around the constant solution $u\equiv1$.
+In particular, we prove a conjecture proposed in [D. Bonheure, B. Noris, T.
+Weth, {\it Ann. Inst. H. Poincaré Anal. Non Lináire} vol. 29, pp. 573-588
+(2012)], that is to say, if $p=2$ and $f'(1)>\lambda_{k+1}^{rad}$, there exists
+a radial solution of the problem having exactly $k$ intersections with
+$u\equiv1$ for a large class of nonlinearities.
+"
+22673,"Calabi-Yau Structures, Spherical Functors, and Shifted Symplectic Structures","  A categorical formalism is introduced for studying various features of the
+symplectic geometry of Lefschetz fibrations and the algebraic geometry of
+Tyurin degenerations. This approach is informed by homological mirror symmetry,
+derived noncommutative geometry, and the theory of Fukaya categories with
+coefficients in a perverse Schober. The main technical results include (i) a
+comparison between the notion of relative Calabi-Yau structures and a certain
+refinement of the notion of a spherical functor, (ii) a local-to-global gluing
+principle for constructing Calabi-Yau structures, and (iii) the construction of
+shifted symplectic structures and Lagrangian structures on certain derived
+moduli spaces of branes. Potential applications to a theory of derived
+hyperkähler geometry are sketched.
+"
+22674,Method for determining optimal supercell representation of interfaces,"  The geometry and structure of an interface ultimately determines the behavior
+of devices at the nanoscale. We present a generic method to determine the
+possible lattice matches between two arbitrary surfaces and to calculate the
+strain of the corresponding matched interface. We apply this method to explore
+two relevant classes of interfaces for which accurate structural measurements
+of the interface are available: (i) the interface between pentacene crystals
+and the (111) surface of gold, and (ii) the interface between the semiconductor
+indium-arsenide and aluminum. For both systems, we demonstrate that the
+presented method predicts interface geometries in good agreement with those
+measured experimentally, which present nontrivial matching characteristics and
+would be difficult to guess without relying on automated structure-searching
+methods.
+"
+22675,Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets,"  With the rapid advance of the Internet, search engines (e.g., Google, Bing,
+Yahoo!) are used by billions of users for each day. The main function of a
+search engine is to locate the most relevant webpages corresponding to what the
+user requests. This report focuses on the core problem of information
+retrieval: how to learn the relevance between a document (very often webpage)
+and a query given by user. Our analysis consists of two parts: 1) we use
+standard statistical methods to select important features among 137 candidates
+given by information retrieval researchers from Microsoft. We find that not all
+the features are useful, and give interpretations on the top-selected features;
+2) we give baselines on prediction over the real-world dataset MSLR-WEB by
+using various learning algorithms. We find that models of boosting trees,
+random forest in general achieve the best performance of prediction. This
+agrees with the mainstream opinion in information retrieval community that
+tree-based algorithms outperform the other candidates for this problem.
+"
+22676,Sign-changing self-similar solutions of the nonlinear heat equation with positive initial value,"  We consider the nonlinear heat equation $u_t - \Delta u = |u|^\alpha u$ on
+${\mathbb R}^N$, where $\alpha >0$ and $N\ge 1$. We prove that in the range $0
+< \alpha <\frac {4} {N-2}$, for every $\mu >0$, there exist infinitely many
+sign-changing, self-similar solutions to the Cauchy problem with initial value
+$u_0 (x)= \mu |x|^{-\frac {2} {\alpha }}$. The construction is based on the
+analysis of the related inverted profile equation. In particular, we construct
+(sign-changing) self-similar solutions for positive initial values for which it
+is known that there does not exist any local, nonnegative solution.
+"
+22677,Facial structures of lattice path matroid polytopes,"  A lattice path matroid is a transversal matroid corresponding to a pair of
+lattice paths on the plane. A matroid base polytope is the polytope whose
+vertices are the incidence vectors of the bases of the given matroid. In this
+paper, we study facial structures of matroid base polytopes corresponding to
+lattice path matroids.
+"
+22678,Excellent extensions and homological conjectures,"  In this paper, we introduce the notion of excellent extension of rings. Let
+$\Gamma$ be an excellent extension of an artin algebra $\Lambda$, we prove that
+$\Lambda$ satisfies the Gorenstein symmetry conjecture (resp. finitistic
+dimension conjecture, Auslander-Gorenstein conjecture, Nakayama conjecture) if
+and only if so does $\Gamma$. As a special case of excellent extensions, if $G$
+is a finite group whose order is invertible in $\Lambda$ acting on $\Lambda$
+and $\Lambda$ is $G$-stable, we prove that if the skew group algebras $\Lambda
+G$ satisfies strong Nakayama conjecture (resp. generalized Nakayama
+conjecture), then so does $\Lambda$.
+"
+22679,Minimalist approach to the classification of symmetry protected topological phases,"  A number of proposals with differing predictions (e.g. Borel group
+cohomology, oriented cobordism, group supercohomology, spin cobordism, etc.)
+have been made for the classification of symmetry protected topological (SPT)
+phases. Here we treat various proposals on an equal footing and present
+rigorous, general results that are independent of which proposal is correct. We
+do so by formulating a minimalist Generalized Cohomology Hypothesis, which is
+satisfied by existing proposals and captures essential aspects of SPT
+classification. From this Hypothesis alone, formulas relating classifications
+in different dimensions and/or protected by different symmetry groups are
+derived. Our formalism is expected to work for fermionic as well as bosonic
+phases, Floquet as well as stationary phases, and spatial as well as on-site
+symmetries. As an application, we predict that the complete classification of
+3-dimensional bosonic SPT phases with space group symmetry $G$ is $H^4_{\rm
+Borel}\left(G;U(1)\right) \oplus H^1_{\rm group}\left(G;\mathbb Z\right)$,
+where the $H^1$ term classifies phases beyond the Borel group cohomology
+proposal.
+"
+22680,PCAS: Pruning Channels with Attention Statistics for Deep Network Compression,"  Compression techniques for deep neural networks are important for
+implementing them on small embedded devices. In particular, channel-pruning is
+a useful technique for realizing compact networks. However, many conventional
+methods require manual setting of compression ratios in each layer. It is
+difficult to analyze the relationships between all layers, especially for
+deeper models. To address these issues, we propose a simple channel-pruning
+technique based on attention statistics that enables to evaluate the importance
+of channels. We improved the method by means of a criterion for automatic
+channel selection, using a single compression ratio for the entire model in
+place of per-layer model analysis. The proposed approach achieved superior
+performance over conventional methods with respect to accuracy and the
+computational costs for various models and datasets. We provide analysis
+results for behavior of the proposed criterion on different datasets to
+demonstrate its favorable properties for channel pruning.
+"
+22681,FaceForensics++: Learning to Detect Manipulated Facial Images,"  The rapid progress in synthetic image generation and manipulation has now
+come to a point where it raises significant concerns on the implication on the
+society. At best, this leads to a loss of trust in digital content, but it
+might even cause further harm by spreading false information and the creation
+of fake news. In this paper, we examine the realism of state-of-the-art image
+manipulations, and how difficult it is to detect them - either automatically or
+by humans. In particular, we focus on DeepFakes, Face2Face, and FaceSwap as
+prominent representatives for facial manipulations. We create more than half a
+million manipulated images respectively for each approach. The resulting
+publicly available dataset is at least an order of magnitude larger than
+comparable alternatives and it enables us to train data-driven forgery
+detectors in a supervised fashion. We show that the use of additional domain
+specific knowledge improves forgery detection to an unprecedented accuracy,
+even in the presence of strong compression. By conducting a series of thorough
+experiments, we quantify the differences between classical approaches, novel
+deep learning approaches, and the performance of human observers.
+"
+22682,Gauss maps of the Ricci-mean curvature flow,"  In this paper, we investigate the Gauss maps of a Ricci-mean curvature flow.
+A Ricci-mean curvature flow is a coupled equation of a mean curvature flow and
+a Ricci flow on the ambient manifold. Ruh and Vilms proved that the Gauss map
+of a minimal submanifold in a Euclidean space is a harmonic map, and Wang
+extended this result to a mean curvature flow in a Euclidean space by proving
+its Gauss maps satisfy the harmonic map heat flow equation. In this paper, we
+deduce the evolution equation for the Gauss maps of a Ricci-mean curvature
+flow, and as a direct corollary we prove that the Gauss maps of a Ricci-mean
+curvature flow satisfy the vertically harmonic map heat flow equation when the
+codimension of submanifolds is 1.
+"
+22683,Climate change impacts on wind energy potential in the European domain,"  We may anticipate that climate change will bring changes to the intensity and
+variability of near surface winds, either through local effects or by altering
+the large-scale flow. The impact of climate change on European wind resources
+has been assessed using a single-model-ensemble of the latest regional climate
+model from the Rossby Centre, RCA4. These simulations used data from five of
+the global climate models in the contemporary Climate Model Intercomparison
+Project (CMIP5) as boundary conditions, and the results are publicly available
+under the COordinated Regional climate Downscaling EXperiment (CORDEX) project.
+Overall we find a consistent pattern of a decrease in the wind resources over
+the European domain under both the RCP 4.5 and RCP 8.5 scenarios, although
+there are some regions, principally North Africa and the Barents Sea, with
+projected increases in wind resources. The pattern of change is both robust
+across the choice of scenario, and persistent: there is a very similar pattern
+of change found in the latter part of the 21st century as in the earlier. A
+case study was chosen to assess the potential for offshore wind-farms in the
+Black Sea region. We developed a realistic methodology for extrapolating
+near-surface wind speeds up to hub-height using a time-varying roughness
+length, and determined the extractable wind power at hub-height using a
+realistic model of contemporary wind-turbine energy production. We demonstrate
+that, unlike much of the Mediterranean basin, there is no robust pattern of a
+negative climate change impact on wind resources in the studied regions of the
+Black Sea. Furthermore, the seasonality of wind resources, with a strong peak
+in the winter, matches well to the seasonality of energy-demand in the region,
+making offshore wind-farms in the Black Sea region a viable source of energy
+for neighbouring countries.
+"
+22684,Open Category Detection with PAC Guarantees,"  Open category detection is the problem of detecting ""alien"" test instances
+that belong to categories or classes that were not present in the training
+data. In many applications, reliably detecting such aliens is central to
+ensuring the safety and accuracy of test set predictions. Unfortunately, there
+are no algorithms that provide theoretical guarantees on their ability to
+detect aliens under general assumptions. Further, while there are algorithms
+for open category detection, there are few empirical results that directly
+report alien detection rates. Thus, there are significant theoretical and
+empirical gaps in our understanding of open category detection. In this paper,
+we take a step toward addressing this gap by studying a simple, but
+practically-relevant variant of open category detection. In our setting, we are
+provided with a ""clean"" training set that contains only the target categories
+of interest and an unlabeled ""contaminated"" training set that contains a
+fraction $\alpha$ of alien examples. Under the assumption that we know an upper
+bound on $\alpha$, we develop an algorithm with PAC-style guarantees on the
+alien detection rate, while aiming to minimize false alarms. Empirical results
+on synthetic and standard benchmark datasets demonstrate the regimes in which
+the algorithm can be effective and provide a baseline for further advancements.
+"
+22685,On the centers of cyclotomic quiver Hecke algebras,"  Let $n\in\mathbb{N}$ and $K$ be any field. For any symmetric generalized
+Cartan matrix $A$, any $\beta$ in the positive root lattice with height $n$ and
+any integral dominant weight $\Lambda$, one can associate a quiver Hecke
+algebras $R_{\beta}(K)$ and its cyclotomic quotient $R_{\beta}^{\Lambda}(K)$
+over $K$. It has been conjectured that the natural map from $R_{\beta}(K)$ to
+$R_{\beta}^{\Lambda}(K)$ maps the center of $R_{\beta}(K)$ surjectively onto
+the center of $R_{\beta}^{\Lambda}(K)$. A similar conjecture claims that the
+center of the affine Hecke algebra of type $A$ maps surjectively onto the
+center of its cyclotomic quotient---the cyclotomic Hecke algebra
+$H_n^{\Lambda}$ of type $G(\ell,1,n)$ over $K$. In this paper, we prove these
+two conjectures affirmatively. As a consequence, we show that the center of
+$H_n^{\Lambda}$ is stable under base change and it has dimension equal to the
+number of $\ell$-partitions of $n$. Finally, as a byproduct, we also verify a
+conjecture of Shan, Varagnolo and Vasserot on the grading structure of the
+center of $R_{\beta}^{\Lambda}(K)$.
+"
+22686,Hybrid correlation holography with a single pixel detector,"  The correlation holography reconstructs 3D objects as a distribution of
+two-point correlation of the random field detected by two dimensional detector
+arrays. Here, we describe a hybrid method, a combination of optical and
+computational channels, to reconstruct the objects from only a single pixel
+detector. An experimental arrangement is proposed and as a first step to
+realize the technique, we have simulated the experimental model for both scalar
+and vectorial objects. Proposed technique affords background free imaging with
+3D capability.
+"
+22687,Learning to Warm-Start Bayesian Hyperparameter Optimization,"  Hyperparameter optimization aims to find the optimal hyperparameter
+configuration of a machine learning model, which provides the best performance
+on a validation dataset. Manual search usually leads to get stuck in a local
+hyperparameter configuration, and heavily depends on human intuition and
+experience. A simple alternative of manual search is random/grid search on a
+space of hyperparameters, which still undergoes extensive evaluations of
+validation errors in order to find its best configuration. Bayesian
+optimization that is a global optimization method for black-box functions is
+now popular for hyperparameter optimization, since it greatly reduces the
+number of validation error evaluations required, compared to random/grid
+search. Bayesian optimization generally finds the best hyperparameter
+configuration from random initialization without any prior knowledge. This
+motivates us to let Bayesian optimization start from the configurations that
+were successful on similar datasets, which are able to remarkably minimize the
+number of evaluations. In this paper, we propose deep metric learning to learn
+meta-features over datasets such that the similarity over them is effectively
+measured by Euclidean distance between their associated meta-features. To this
+end, we introduce a Siamese network composed of deep feature and meta-feature
+extractors, where deep feature extractor provides a semantic representation of
+each instance in a dataset and meta-feature extractor aggregates a set of deep
+features to encode a single representation over a dataset. Then, our learned
+meta-features are used to select a few datasets similar to the new dataset, so
+that hyperparameters in similar datasets are adopted as initializations to
+warm-start Bayesian hyperparameter optimization.
+"
+22688,The pseudovariety of all nilpotent groups is tame,"  It has been shown that for every prime number $p$, the pseudovariety
+$\mathsf{G}_p$ of all finite $p$-groups is tame with respect to an implicit
+signature containing the canonical implicit signature. In this paper, we
+generalize this result and we show that the pseudovariety of all finite
+nilpotent groups is tame but it is not completely tame.
+"
+22689,Estimating Shortest Path Length Distributions via Random Walk Sampling,"  In a network, the shortest paths between nodes are of great importance as
+they allow the fastest and strongest interaction between nodes. However
+measuring the shortest paths between all nodes in a large network is
+computationally expensive. In this paper we propose a method to estimate the
+shortest path length (SPL) distribution of a network by random walk sampling.
+To deal with the unequal inclusion probabilities of dyads (pairs of nodes) in
+the sample, we generalize the usage of Hansen-Hurwitz estimator and
+Horvitz-Thompson estimator (and their ratio forms) and apply them to the
+sampled dyads. Based on theory of Markov chains we prove that the selection
+probability of a dyad is proportional to the product of the degrees of the two
+nodes. To approximate the actual SPL for a dyad, we use the observed SPL in the
+induced subgraph for networks with large degree variability, i.e., the standard
+deviation is at least two times of the mean, and for networks with small degree
+variability, estimate the SPL using landmarks for networks with small degree
+variability. By simulation studies and applications to real networks, we find
+that 1) for large networks, high estimation accuracy can be achieved by using a
+single random or multiple random walks with total number of steps equal to at
+least 20% of the nodes in the network; 2) the estimation performance increases
+as the network size increases but tends to stabilize when the network is large
+enough; 3) a single random walk performs as well as multiple random walks; 4)
+the Horvitz-Thompson ratio estimator performs best among the four estimators.
+"
+22690,A Kullback-Leibler divergence measure of intermittency: application to turbulence,"  For generic systems exhibiting power law behaviors, and hence multiscale
+dependencies, we propose a new, and yet simple, tool to analyze multifractality
+and intermittency, after noticing that these concepts are directly related to
+the deformation of a probability density function from Gaussian at large scales
+to non-Gaussian at smaller scales. Our framework is based on information
+theory, and uses Shannon entropy and Kullback-Leibler divergence. We propose an
+extensive application to three-dimensional fully developed turbulence, seen
+here as a paradigmatic complex system where intermittency was historically
+defined. Moreover, the concepts of scale invariance and multifractality were
+extensively studied in this field and, most importantly, benchmarked. We
+compute our measure on experimental Eulerian velocity measurements, as well as
+on synthetic processes and a phenomenological model of fluid turbulence.Our
+approach is very general and does not require any underlying model of the
+system, although it can probe the relevance of such a model.
+"
+22691,The Effect of Protoplanetary Disk Cooling Times on the Formation of Gas Giant Planets by Gravitational Instability,"  Observational evidence exists for the formation of gas giant planets on wide
+orbits around young stars by disk gravitational instability, but the roles of
+disk instability and core accretion for forming gas giants on shorter period
+orbits are less clear. The controversy extends to population synthesis models
+of exoplanet demographics and to hydrodynamical models of the fragmentation
+process. The latter refers largely to the handling of radiative transfer in
+three dimensional (3D) hydrodynamical models, which controls heating and
+cooling processes in gravitationally unstable disks, and hence dense clump
+formation. A suite of models using the $\beta$ cooling approximation is
+presented here. The initial disks have masses of 0.091 $M_\odot$ and extend
+from 4 to 20 AU around a 1 $M_\odot$ protostar. The initial minimum Toomre
+$Q_i$ values range from 1.3 to 2.7, while $\beta$ ranges from 1 to 100. We show
+that the choice of $Q_i$ is equal in importance to the $\beta$ value assumed:
+high $Q_i$ disks can be stable for small $\beta$, when the initial disk
+temperature is taken as a lower bound, while low $Q_i$ disks can fragment for
+high $\beta$. These results imply that the evolution of disks toward low $Q_i$
+must be taken into account in assessing disk fragmentation possibilities, at
+least in the inner disk, i.e., inside about 20 AU. The models suggest that if
+low $Q_i$ disks can form, there should be an as yet largely undetected
+population of gas giants orbiting G dwarfs between about 6 AU and 16 AU.
+"
+22692,"Discovery of a warm, dusty giant planet around HIP65426","  The SHINE program is a large high-contrast near-infrared survey of 600 young,
+nearby stars. It is aimed at searching for and characterizing new planetary
+systems using VLT/SPHERE's unprecedented high-contrast and high-angular
+resolution imaging capabilities. It also intends at placing statistical
+constraints on the occurrence and orbital properties of the giant planet
+population at large orbits as a function of the stellar host mass and age to
+test planet formation theories. We use the IRDIS dual-band imager and the IFS
+integral field spectrograph of SPHERE to acquire high-constrast coronagraphic
+differential near-infrared images and spectra of the young A2 star HIP65426. It
+is a member of the ~17 Myr old Lower Centaurus-Crux association. At a
+separation of 830 mas (92 au projected) from the star, we detect a faint red
+companion. Multi-epoch observations confirm that it shares common proper motion
+with HIP65426. Spectro-photometric measurements extracted with IFS and IRDIS
+between 0.95 and 2.2um indicate a warm, dusty atmosphere characteristic of
+young low surface-gravity L5-L7 dwarfs. Hot-start evolutionary models predict a
+luminosity consistent with a 6-12 MJup, Teff=1300-1600 K and R=1.5 RJup giant
+planet. Finally, the comparison with Exo-REM and PHOENIX BT-Settl synthetic
+atmosphere models gives consistent effective temperatures but with slightly
+higher surface gravity solutions of log(g)=4.0-5.0 with smaller radii (1.0-1.3
+RJup). Given its physical and spectral properties, HIP65426b occupies a rather
+unique placement in terms of age, mass and spectral-type among the currently
+known imaged planets. It represents a particularly interesting case to study
+the presence of clouds as a function of particle size, composition, and
+location in the atmosphere, to search for signatures of non-equilibrium
+chemistry, and finally to test the theory of planet formation and evolution.
+"
+22693,Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima,"  We consider the problem of learning a one-hidden-layer neural network with
+non-overlapping convolutional layer and ReLU activation, i.e., $f(\mathbf{Z},
+\mathbf{w}, \mathbf{a}) = \sum_j a_j\sigma(\mathbf{w}^T\mathbf{Z}_j)$, in which
+both the convolutional weights $\mathbf{w}$ and the output weights $\mathbf{a}$
+are parameters to be learned. When the labels are the outputs from a teacher
+network of the same architecture with fixed weights $(\mathbf{w}^*,
+\mathbf{a}^*)$, we prove that with Gaussian input $\mathbf{Z}$, there is a
+spurious local minimizer. Surprisingly, in the presence of the spurious local
+minimizer, gradient descent with weight normalization from randomly initialized
+weights can still be proven to recover the true parameters with constant
+probability, which can be boosted to probability $1$ with multiple restarts. We
+also show that with constant probability, the same procedure could also
+converge to the spurious local minimum, showing that the local minimum plays a
+non-trivial role in the dynamics of gradient descent. Furthermore, a
+quantitative analysis shows that the gradient descent dynamics has two phases:
+it starts off slow, but converges much faster after several iterations.
+"
+22694,Curvature of Hypergraphs via Multi-Marginal Optimal Transport,"  We introduce a novel definition of curvature for hypergraphs, a natural
+generalization of graphs, by introducing a multi-marginal optimal transport
+problem for a naturally defined random walk on the hypergraph. This curvature,
+termed \emph{coarse scalar curvature}, generalizes a recent definition of Ricci
+curvature for Markov chains on metric spaces by Ollivier [Journal of Functional
+Analysis 256 (2009) 810-864], and is related to the scalar curvature when the
+hypergraph arises naturally from a Riemannian manifold. We investigate basic
+properties of the coarse scalar curvature and obtain several bounds. Empirical
+experiments indicate that coarse scalar curvatures are capable of detecting
+""bridges"" across connected components in hypergraphs, suggesting it is an
+appropriate generalization of curvature on simple graphs.
+"
+22695,Computational mechanisms in genetic regulation by RNA,"  The evolution of the genome has led to very sophisticated and complex
+regulation. Because of the abundance of non-coding RNA (ncRNA) in the cell,
+different species will promiscuously associate with each other, suggesting
+collective dynamics similar to artificial neural networks. Here we present a
+simple mechanism allowing ncRNA to perform computations equivalent to neural
+network algorithms such as Boltzmann machines and the Hopfield model. The
+quantities analogous to the neural couplings are the equilibrium constants
+between different RNA species. The relatively rapid equilibration of RNA
+binding and unbinding is regulated by a slower process that degrades and
+creates new RNA. The model requires that the creation rate for each species be
+an increasing function of the ratio of total to unbound RNA. Similar mechanisms
+have already been found to exist experimentally for ncRNA regulation. With the
+overall concentration of RNA regulated, equilibrium constants can be chosen to
+store many different patterns, or many different input-output relations. The
+network is also quite insensitive to random mutations in equilibrium constants.
+Therefore one expects that this kind of mechanism will have a much higher
+mutation rate than ones typically regarded as being under evolutionary
+constraint.
+"
+22696,Switching from pyroelectric to ferroelectric order in Ni doped CaBaCo4O7,"  We report ferroelectric ordering in Ni substituted CaBaCo4O7. Magnetization
+showed ferrimagnetic transition at 60 K and an additional transition is found ~
+82 K, further, enhanced antiferromagnetic interactions and decrease in
+saturation magnetization are noticed with Ni substitution. The dielectric and
+pyroelectric measurements illustrate a strong coupling between spin and charge
+degrees of freedom; ferroelectric behavior is confirmed with enhanced ordering
+temperature (~82 K) and saturation polarization (250 muC/m2. Neutron
+diffraction has revealed an increase in c-lattice parameter in Ni sample and
+all the Co/Ni moments are reoriented in a- direction; evidently a non-collinear
+ferrimagnetic to collinear ferrimagnetic spin order is observed. The coupling
+between the triangular and Kagome layers weakens and leads to up-up-down-down
+AFM ordering in the Kagoma layer. This can be viewed as a 2D-collinear layer
+with unequal bond distances and most likely responsible for the switching of
+electric polarization.
+"
+22697,Equations for some nilpotent varieties,"  Let $\mathcal{O}$ be a Richardson nilpotent orbit in a simple Lie algebra
+$\mathfrak{g}$ over $\mathbb C$, induced from a Levi subalgebra whose simple
+roots are orthogonal short roots. The main result of the paper is a description
+of a minimal set of generators of the ideal defining $\overline{ \mathcal{O}}$
+in $S \mathfrak{g}^*$. In such cases, the ideal is generated by bases of at
+most two copies of the representation whose highest weight is the dominant
+short root, along with some fundamental invariants. This extends Broer's result
+for the subregular nilpotent orbit. Along the way we give another proof of
+Broer's result that $\overline{ \mathcal{O}}$ is normal. We also prove a result
+connecting a property of invariants related to flat bases to the question of
+when one copy of the adjoint representation is in the ideal in $S
+\mathfrak{g}^*$ generated by another copy of the adjoint representation and the
+fundamental invariants.
+"
+22698,On the Exponential Rate of Convergence of Fictitious Play in Potential Games,"  The paper studies fictitious play (FP) learning dynamics in continuous time.
+It is shown that in almost every potential game, and for almost every initial
+condition, the rate of convergence of FP is exponential. In particular, the
+paper focuses on studying the behavior of FP in potential games in which all
+equilibria of the game are regular, as introduced by Harsanyi. Such games are
+referred to as regular potential games. Recently it has been shown that almost
+all potential games (in the sense of the Lebesgue measure) are regular. In this
+paper it is shown that in any regular potential game (and hence, in almost
+every potential game), FP converges to the set of Nash equilibria at an
+exponential rate from almost every initial condition.
+"
+22699,Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features,"  Unsupervised learning of time series data, also known as temporal clustering,
+is a challenging problem in machine learning. Here we propose a novel
+algorithm, Deep Temporal Clustering (DTC), to naturally integrate
+dimensionality reduction and temporal clustering into a single end-to-end
+learning framework, fully unsupervised. The algorithm utilizes an autoencoder
+for temporal dimensionality reduction and a novel temporal clustering layer for
+cluster assignment. Then it jointly optimizes the clustering objective and the
+dimensionality reduction objec tive. Based on requirement and application, the
+temporal clustering layer can be customized with any temporal similarity
+metric. Several similarity metrics and state-of-the-art algorithms are
+considered and compared. To gain insight into temporal features that the
+network has learned for its clustering, we apply a visualization method that
+generates a region of interest heatmap for the time series. The viability of
+the algorithm is demonstrated using time series data from diverse domains,
+ranging from earthquakes to spacecraft sensor data. In each case, we show that
+the proposed algorithm outperforms traditional methods. The superior
+performance is attributed to the fully integrated temporal dimensionality
+reduction and clustering criterion.
+"
+22700,Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present,"  In a continuous-time setting where a risk-averse agent controls the drift of
+an output process driven by a Brownian motion, optimal contracts are linear in
+the terminal output; this result is well-known in a setting with moral hazard
+and -under stronger assumptions - adverse selection. We show that this result
+continues to hold when in addition reservation utilities are type-dependent.
+This type of problem occurs in the study of optimal compensation problems
+involving competing principals.
+"
+22701,Nonlinear Opinion Dynamics in Systems with Leadership Effect,"  Opinion dynamics of a group of individuals is the change in the members'
+opinions through mutual interaction with each other. The related literature
+contains works in which the dynamics is modeled as a continuous system, of
+which behavioral patterns are analyzed in regard to the parameters contained in
+the system. These models are constructed by the assumption that the individuals
+are independent. Besides, the decisions of the individuals are only affected by
+two forces: self-bias force and group influence force. In this work we consider
+a nonlinear dynamical system which models the evolution of the decision of a
+group under the existence of a leader. Bifurcation analysis of the system is
+performed to obtain stability results on the system.
+"
+22702,Projective and birational geometry of Grassmannians and other special varieties,"  In the first part of the thesis, we study a classical invariant of projective
+varieties, the secant defectivity. The second part is devoted to modern
+algebraic geometry, we study the birational geometry of blow-ups of
+Grassmannians at points.
+"
+22703,Remarks on the Gaudin model modulo $p$,"  We discuss the Bethe ansatz in the Gaudin model on the tensor product of
+finite-dimensional $sl_2$-modules over the field $F_p$ with $p$ elements, where
+$p$ is a prime number. We define the Bethe ansatz equations and show that if
+$(t^0_1,\dots,t^0_k)$ is a solution of the Bethe ansatz equations, then the
+corresponding Bethe vector is an eigenvector of the Gaudin Hamiltonians. We
+characterize solutions $(t^0_1,\dots,t^0_k)$ of the Bethe ansatz equations as
+certain two-dimensional subspaces of the space of polynomials $F_p[x]$. We
+consider the case when the number of parameters $k$ equals 1. In that case we
+show that the Bethe algebra, generated by the Gaudin Hamiltonians, is
+isomorphic to the algebra of functions on the scheme defined by the Bethe
+ansatz equation. If $k=1$ and in addition the tensor product is the product of
+vector representations, then the Bethe algebra is also isomorphic to the
+algebra of functions on the fiber of a suitable Wronski map.
+"
+22704,Three new infrared bands of the He-OCS complex,"  Three new infrared bands of the weakly-bound He-OCS complex are studied,
+using tunable lasers to probe a pulsed supersonic slit jet expansion. They
+correspond to the (0400) <-- (0000), (1001)<-- (0000), and (0401) <-- (0000)
+transitions of OCS at 2105, 2918, and 2937 cm-1, respectively. The latter band
+is about 7900 times weaker than the previously studied OCS nu1 fundamental.
+Vibrational shifts relative to the free OCS monomer are found to be additive.
+Since carbonyl sulfide has previously been shown to be a valuable probe of
+superfluid quantum solvation effects in helium clusters and droplets, the
+present results could be useful for future studies of vibrational effects in
+such systems.
+"
+22705,Tracking Multiple Vehicles Using a Variational Radar Model,"  High-resolution radar sensors are able to resolve multiple measurements per
+object and therefore provide valuable information for vehicle environment
+perception. For instance, multiple measurements allow to infer the size of an
+object or to more precisely measure the object's motion. Yet, the increased
+amount of data raises the demands on tracking modules: measurement models that
+are able to process multiple measurements for an object are necessary and
+measurement-to-object associations become more complex. This paper presents a
+new variational radar model for vehicles and demonstrates how this model can be
+incorporated in a Random-Finite-Set-based multi-object tracker. The measurement
+model is learned from actual data using variational Gaussian mixtures and
+avoids excessive manual engineering. In combination with the multi-object
+tracker, the entire process chain from the raw measurements to the resulting
+tracks is formulated probabilistically. The presented approach is evaluated on
+experimental data and it is demonstrated that data-driven measurement model
+outperforms a manually designed model.
+"
+22706,Application of Hilbert-Huang decomposition to reduce noise and characterize for NMR FID signal of proton precession magnetometer,"  The parameters in a nuclear magnetic resonance (NMR) free induction decay
+(FID) signal contain information that is useful in magnetic field measurement,
+magnetic resonance sounding (MRS) and other related applications. A real time
+sampled FID signal is well modeled as a finite mixture of exponential sequences
+plus noise. We propose to use the Hilbert-Huang Transform (HHT) for noise
+reduction and characterization, where the generalized Hilbert-Huang represents
+a way to decompose a signal into so-called intrinsic mode function (IMF) along
+with a trend, and obtain instantaneous frequency data. Moreover, the HHT for an
+FID signal's feature analysis is applied for the first time. First, acquiring
+the actual untuned FID signal by a developed prototype of proton magnetometer,
+and then the empirical mode decomposition (EMD) is performed to decompose the
+noise and original FID. Finally, the HHT is applied to the obtained IMFs to
+extract the Hilbert energy spectrum, to indicate the energy distribution of the
+signal on the frequency axis. By theory analysis and the testing of an actual
+FID signal, the results show that, compared with general noise reduction
+methods such as auto correlation and singular value decomposition (SVD),
+combined with the proposed method can further suppress the interfered signals
+effectively, and can obtain different components of FID signal, which can use
+to identify the magnetic anomaly, the existence of groundwater etc. This is a
+very important property since it can be exploited to separate the FID signal
+from noise and to estimate exponential sequence parameters of FID signal.
+"
+22707,Rough extreme learning machine: a new classification method based on uncertainty measure,"  Extreme learning machine (ELM) is a new single hidden layer feedback neural
+network. The weights of the input layer and the biases of neurons in hidden
+layer are randomly generated, the weights of the output layer can be
+analytically determined. ELM has been achieved good results for a large number
+of classification tasks. In this paper, a new extreme learning machine called
+rough extreme learning machine (RELM) was proposed. RELM uses rough set to
+divide data into upper approximation set and lower approximation set, and the
+two approximation sets are utilized to train upper approximation neurons and
+lower approximation neurons. In addition, an attribute reduction is executed in
+this algorithm to remove redundant attributes. The experimental results showed,
+comparing with the comparison algorithms, RELM can get a better accuracy and
+repeatability in most cases, RELM can not only maintain the advantages of fast
+speed, but also effectively cope with the classification task for
+high-dimensional data.
+"
+22708,Change of Measure in the Heston Model given a violated Feller Condition,"  When dealing with Heston's stochastic volatility model, the change of measure
+from the subjective measure P to the objective measure Q is usually
+investigated under the assumption that the Feller condition is satisfied. This
+paper closes this gap in the literature by deriving sufficient conditions for
+the existence of an equivalent (local) martingale measure in the Heston model
+when the Feller condition is violated. We also supplement the existing
+literature by the case of a finite lifetime of the Laplace transform of the
+integrated volatility process. Moreover, we deduce conditions for the stock
+price process in the Heston model being a true martingale, regardless if the
+Feller condition is satisfied or not.
+"
+22709,Self-replication and Borwein-like algorithms,"  Using a self-replicating method, we generalize with a free parameter some
+Borwein algorithms for the number $\pi$. This generalization includes values of
+the Gamma function like $\Gamma(1/3)$, $\Gamma(1/4)$ and of course
+$\Gamma(1/2)=\sqrt{\pi}$. In addition, we give new rapid algorithms for the
+perimeter of an ellipse.
+"
+22710,Nonsmooth Analysis and Optimization,"  These lecture notes for a graduate course cover generalized derivative
+concepts useful in deriving necessary optimality conditions and numerical
+algorithms for nondifferentiable optimization problems in inverse problems,
+imaging, and PDE-constrained optimization. Treated are convex functions and
+subdifferentials, Fenchel duality, monotone operators and resolvents,
+Moreau--Yosida regularization, proximal point and (some) first-order splitting
+methods, Clarke subdifferentials, and semismooth Newton methods. The required
+background from functional analysis and calculus of variations is also briefly
+summarized.
+"
+22711,Supressed splashing on elastic membranes,"  The dynamics of drop impact on solid surfaces can be changed significantly by
+tuning the elasticity of the solid. Most prominently, the substrate deformation
+causes an increase in the splashing threshold as compared to impact onto
+perfectly rigid surfaces, and can thus lead to splash suppression. Here, we
+experimentally determine the splashing threshold for impact on thin membranes
+as a function of the tension in the membrane and its elastic properties. The
+drop dynamics is correlated to the membrane deformation, which is
+simultaneously measured using a laser profilometry technique. The experimental
+results enable us to adapt current models for splashing, showing quantitatively
+how substrate deformation alters the splashing threshold.
+"
+22712,Poisson Cluster Process: Bridging the Gap Between PPP and 3GPP HetNet Models,"  The growing complexity of heterogeneous cellular networks (HetNets) has
+necessitated the need to consider variety of user and base station (BS)
+configurations for realistic performance evaluation and system design. This is
+directly reflected in the HetNet simulation models considered by
+standardization bodies, such as the third generation partnership project
+(3GPP). Complementary to these simulation models, stochastic geometry based
+approach modeling the user and BS locations as independent and homogeneous
+Poisson point processes (PPPs) has gained prominence in the past few years.
+Despite its success in revealing useful insights, this PPP-based model is not
+rich enough to capture all the spatial configurations that appear in real world
+HetNet deployments (on which 3GPP simulation models are based). In this paper,
+we bridge the gap between the 3GPP simulation models and the popular PPP-based
+analytical model by developing a new unified HetNet model in which a fraction
+of users and some BS tiers are modeled as Poisson cluster processes (PCPs).
+This model captures both non-uniformity and coupling in the BS and user
+locations. For this setup, we derive exact expression for downlink coverage
+probability under maximum signal-to-interference ratio (SIR) cell association
+model. As intermediate results, we define and evaluate sum-product functionals
+for PPP and PCP. Special instances of the proposed model are shown to closely
+resemble different configurations considered in 3GPP HetNet models. Our results
+concretely demonstrate that the performance trends are highly sensitive to the
+assumptions made on the user and SBS configurations.
+"
+22713,Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy,"  The paper proposes an on-line monitoring framework for continuous real-time
+safety/security in learning-based control systems (specifically application to
+a unmanned ground vehicle). We monitor validity of mappings from sensor inputs
+to actuator commands, controller-focused anomaly detection (CFAM), and from
+actuator commands to sensor inputs, system-focused anomaly detection (SFAM).
+CFAM is an image conditioned energy based generative adversarial network
+(EBGAN) in which the energy based discriminator distinguishes between proper
+and anomalous actuator commands. SFAM is based on an action condition video
+prediction framework to detect anomalies between predicted and observed
+temporal evolution of sensor data. We demonstrate the effectiveness of the
+approach on our autonomous ground vehicle for indoor environments and on
+Udacity dataset for outdoor environments.
+"
+22714,Sentiment Predictability for Stocks,"  In this work, we present our findings and experiments for stock-market
+prediction using various textual sentiment analysis tools, such as mood
+analysis and event extraction, as well as prediction models, such as LSTMs and
+specific convolutional architectures.
+"
+22715,Fair lending needs explainable models for responsible recommendation,"  The financial services industry has unique explainability and fairness
+challenges arising from compliance and ethical considerations in credit
+decisioning. These challenges complicate the use of model machine learning and
+artificial intelligence methods in business decision processes.
+"
+22716,Eliminating Variables in Boolean Equation Systems,"  Systems of Boolean equations of low degree arise in a natural way when
+analyzing block ciphers. The cipher's round functions relate the secret key to
+auxiliary variables that are introduced by each successive round. In algebraic
+cryptanalysis, the attacker attempts to solve the resulting equation system in
+order to extract the secret key. In this paper we study algorithms for
+eliminating the auxiliary variables from these systems of Boolean equations. It
+is known that elimination of variables in general increases the degree of the
+equations involved. In order to contain computational complexity and storage
+complexity, we present two new algorithms for performing elimination while
+bounding the degree at $3$, which is the lowest possible for elimination.
+Further we show that the new algorithms are related to the well known \emph{XL}
+algorithm. We apply the algorithms to a downscaled version of the LowMC cipher
+and to a toy cipher based on the Prince cipher, and report on experimental
+results pertaining to these examples.
+"
+22717,"Cleavage Tendency of Anisotropic Two Dimensional Materials: ReX2 (X=S, Se) and WTe2","  With unique distorted 1T structure and the associated in-plane anisotropic
+properties, mono- and few-layer ReX2 (X=S, Se) have recently attracted
+particular interest. Based on experiment and first-principles calculations, we
+investigate the fracture behavior of ReX2. We find that the cleaved edges of
+ReX2 flakes usually form an angle of ~120° or ~60°. In order to
+understand such phenomenon, we perform comprehensive investigations on the
+uniaxial tensile stress-strain relation of monolayer and multi-layer ReX2
+sheets. Our numerical calculation shows that the particular cleaved edges of
+ReX2 flakes are caused by unique anisotropic ultimate tensile strengths and
+critical strains. We also calculate the stress-strain relation of WTe2, which
+explains why their cleaved edges are not corresponding to the principle axes.
+Our proposed mechanism about the fracture angle has also been supported by the
+calculated cleavage energies and surface energies for different edge surfaces.
+"
+22718,Near-Optimal Compression for the Planar Graph Metric,"  The Planar Graph Metric Compression Problem is to compactly encode the
+distances among $k$ nodes in a planar graph of size $n$. Two naïve solutions
+are to store the graph using $O(n)$ bits, or to explicitly store the distance
+matrix with $O(k^2 \log{n})$ bits. The only lower bounds are from the seminal
+work of Gavoille, Peleg, Prennes, and Raz [SODA'01], who rule out compressions
+into a polynomially smaller number of bits, for {\em weighted} planar graphs,
+but leave a large gap for unweighted planar graphs. For example, when
+$k=\sqrt{n}$, the upper bound is $O(n)$ and their constructions imply an
+$\Omega(n^{3/4})$ lower bound. This gap is directly related to other major open
+questions in labelling schemes, dynamic algorithms, and compact routing.
+Our main result is a new compression of the planar graph metric into
+$\tilde{O}(\min (k^2 , \sqrt{k\cdot n}))$ bits, which is optimal up to log
+factors. Our data structure breaks an $\Omega(k^2)$ lower bound of Krauthgamer,
+Nguyen, and Zondiner [SICOMP'14] for compression using minors, and the lower
+bound of Gavoille et al. for compression of weighted planar graphs. This is an
+unexpected and decisive proof that weights can make planar graphs inherently
+more complex. Moreover, we design a new {\em Subset Distance Oracle} for planar
+graphs with $\tilde O(\sqrt{k\cdot n})$ space, and $\tilde O(n^{3/4})$ query
+time.
+Our work carries strong messages to related fields. In particular, the famous
+$O(n^{1/2})$ vs. $\Omega(n^{1/3})$ gap for distance labelling schemes in planar
+graphs {\em cannot} be resolved with the current lower bound techniques.
+"
+22719,Advanced Analytics for Connected Cars Cyber Security,"  The vehicular connectivity revolution is fueling the automotive industry's
+most significant transformation seen in decades. However, as modern vehicles
+become more connected, they also become much more vulnerable to cyber-attacks.
+In this paper, a fully working machine learning approach is proposed to protect
+connected vehicles (fleets and individuals) against such attacks. We present a
+system that monitors different vehicle's interfaces (Network, CAN and OS),
+extracts relevant information based on configurable rules and sends it to a
+trained generative model to detect deviations from normal behavior. Using
+configurable data collector, we provide a higher level of data abstraction as
+the model is trained based on events instead of raw data, which has a
+noise-filtering effect and eliminates the need to retrain the model whenever a
+protocol changes. We present a new approach for detecting anomalies, tailored
+to the temporal nature of our domain. Adapting the hybrid approach of Gutflaish
+et al. (2017) to the fully temporal setting, we first train a Hidden Markov
+Model to learn normal vehicle behavior, and then a regression model to
+calibrate the likelihood threshold for anomaly. Using this architecture, our
+method detects sophisticated and realistic anomalies, which are missed by other
+existing methods monitoring the CAN bus only. We also demonstrate the
+superiority of adaptive thresholds over static ones. Furthermore, our approach
+scales efficiently from monitoring individual cars to serving large fleets. We
+demonstrate the competitive advantage of our model via encouraging empirical
+results.
+"
+22720,Defect measures on graded lie groups,"  In this article, we define a generalisation of microlocal defect measures
+(also known as H-measures) to the setting of graded nilpotent Lie groups. This
+requires to develop the notions of homogeneous symbols and classical
+pseudo-differential calculus adapted to this setting and defined via the
+representations of the groups. Our method relies on the study of the C
+*-algebra of 0-homogeneous symbols. Then, we compute microlocal defect measures
+for concentrating and oscillating sequences, which also requires to investigate
+the notion of oscillating sequences in graded Lie groups. Finally, we discuss
+compacity compactness approaches in the context of graded nilpotent Lie groups.
+"
+22721,First-order logic with incomplete information,"  We develop first-order logic and some extensions for incomplete information
+scenarios and consider related complexity issues.
+"
+22722,Robust stability conditions for feedback interconnections of distributed-parameter negative imaginary systems,"  Sufficient and necessary conditions for the stability of positive feedback
+interconnections of negative imaginary systems are derived via an integral
+quadratic constraint (IQC) approach. The IQC framework accommodates
+distributed-parameter systems with irrational transfer function
+representations, while generalising existing results in the literature and
+allowing exploitation of flexibility at zero and infinite frequencies to reduce
+conservatism in the analysis. The main results manifest the important property
+that the negative imaginariness of systems gives rise to a certain form of IQCs
+on positive frequencies that are bounded away from zero and infinity. Two
+additional sets of IQCs on the DC and instantaneous gains of the systems are
+shown to be sufficient and necessary for closed-loop stability along a homotopy
+of systems.
+"
+22723,Reinforcement Learning under Model Mismatch,"  We study reinforcement learning under model misspecification, where we do not
+have access to the true environment but only to a reasonably close
+approximation to it. We address this problem by extending the framework of
+robust MDPs to the model-free Reinforcement Learning setting, where we do not
+have access to the model parameters, but can only sample states from it. We
+define robust versions of Q-learning, SARSA, and TD-learning and prove
+convergence to an approximately optimal robust policy and approximate value
+function respectively. We scale up the robust algorithms to large MDPs via
+function approximation and prove convergence under two different settings. We
+prove convergence of robust approximate policy iteration and robust approximate
+value iteration for linear architectures (under mild assumptions). We also
+define a robust loss function, the mean squared robust projected Bellman error
+and give stochastic gradient descent algorithms that are guaranteed to converge
+to a local minimum.
+"
+22724,An Upper Bound for the Depth of Some Classes of Polyhedra,"  K. Borsuk in the seventies introduced the notions of capacity and depth of
+compacta together with some relevant problems. In this paper, first, we
+introduce the concepts of the (strong) capacity and the (strong) depth of an
+object in an arbitrary category. Then in the category of groups, we compute the
+(strong) capacity and the (strong) depth of some well-known groups. Finally, we
+find an upper bound for the depth of some classes of finite polyhedra which
+generalizes a result of D. Kolodziejczyk in this subject.
+"
+22725,Weighted jump and variational inequalities for rough operators,"  In this paper, we systematically study weighted jump and variational
+inequalities for rough operators. More precisely, we show some weighted jump
+and variational inequalities for the families $\mathcal
+T:=\{T_\varepsilon\}_{\varepsilon>0}$ of truncated singular integrals and
+$\mathcal M_{\Omega}:=\{M_{\Omega,t}\}_{t>0}$ of averaging operators with rough
+kernels, which are defined respectively by $$ T_\varepsilon
+f(x)=\int_{|y|>\varepsilon}\frac{\Omega(y')}{|y|^n}f(x-y)dy$$ and
+$$M_{\Omega,t} f(x)=\frac1{t^n}\int_{|y|<t}\Omega(y')f(x-y)dy, $$ where the
+kernel $\Omega$ belongs to $L^q(\mathbf S^{n-1})$ for $q>1$.
+"
+22726,A generalized solution concept for the Keller-Segel system with logarithmic sensitivity: Global solvability for large nonradial data,"  The chemotaxis system \[ \left\{ \begin{array}{l} u_t = \Delta u - \chi\nabla
+\cdot (\frac{u}{v}\nabla v), v_t=\Delta v - v+u, \end{array} \right. \] is
+considered in a bounded domain $\Omega\subset \mathbb{R}^n$ with smooth
+boundary, where $\chi>0$. An apparently novel type of generalized solution
+framework is introduced within which an extension of previously known ranges
+for the key parameter $\chi$ with regard to global solvability is achieved. In
+particular, it is shown that under the hypothesis that\[ \chi < \left\{
+\begin{array}{ll} \infty \qquad & \mbox{if } n=2, \sqrt{8} \qquad & \mbox{if }
+n=3, \frac{n}{n-2} \qquad & \mbox{if } n\ge 4, \end{array} \right. \] for all
+initial data satisfying suitable assumptions on regularity and positivity, an
+associated no-flux initial-boundary value problem admits a globally defined
+generalized solution. This solution inter alia has the property that \[ u\in
+L^1_{loc}(\bar\Omega\times [0,\infty)). \]
+"
+22727,Space-efficient K-MER algorithm for generalized suffix tree,"  Suffix trees have emerged to be very fast for pattern searching yielding O
+(m) time, where m is the pattern size. Unfortunately their high memory
+requirements make it impractical to work with huge amounts of data. We present
+a memory efficient algorithm of a generalized suffix tree which reduces the
+space size by a factor of 10 when the size of the pattern is known beforehand.
+Experiments on the chromosomes and Pizza&Chili corpus show significant
+advantages of our algorithm over standard linear time suffix tree construction
+in terms of memory usage for pattern searching.
+"
+22728,MIDA: Multiple Imputation using Denoising Autoencoders,"  Missing data is a significant problem impacting all domains. State-of-the-art
+framework for minimizing missing data bias is multiple imputation, for which
+the choice of an imputation model remains nontrivial. We propose a multiple
+imputation model based on overcomplete deep denoising autoencoders. Our
+proposed model is capable of handling different data types, missingness
+patterns, missingness proportions and distributions. Evaluation on several real
+life datasets show our proposed model significantly outperforms current
+state-of-the-art methods under varying conditions while simultaneously
+improving end of the line analytics.
+"
+22729,Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks,"  Convolutional Neural Networks (CNNs) are extremely computationally demanding,
+presenting a large barrier to their deployment on resource-constrained devices.
+Since such systems are where some of their most useful applications lie (e.g.
+obstacle detection for mobile robots, vision-based medical assistive
+technology), significant bodies of work from both machine learning and systems
+communities have attempted to provide optimisations that will make CNNs
+available to edge devices. In this paper we unify the two viewpoints in a Deep
+Learning Inference Stack and take an across-stack approach by implementing and
+evaluating the most common neural network compression techniques (weight
+pruning, channel pruning, and quantisation) and optimising their parallel
+execution with a range of programming approaches (OpenMP, OpenCL) and hardware
+architectures (CPU, GPU). We provide comprehensive Pareto curves to instruct
+trade-offs under constraints of accuracy, execution time, and memory space.
+"
+22730,On Sampling Edges Almost Uniformly,"  We consider the problem of sampling an edge almost uniformly from an unknown
+graph, $G = (V, E)$. Access to the graph is provided via queries of the
+following types: (1) uniform vertex queries, (2) degree queries, and (3)
+neighbor queries. We describe an algorithm that returns a random edge $e \in E$
+using $\tilde{O}(n / \sqrt{\varepsilon m})$ queries in expectation, where $n =
+|V|$ is the number of vertices, and $m = |E|$ is the number of edges, such that
+each edge $e$ is sampled with probability $(1 \pm \varepsilon)/m$. We prove
+that our algorithm is optimal in the sense that any algorithm that samples an
+edge from an almost-uniform distribution must perform $\Omega(n / \sqrt{m})$
+queries.
+"
+22731,Modeling spatial social complex networks for dynamical processes,"  The study of social networks --- where people are located, geographically,
+and how they might be connected to one another --- is a current hot topic of
+interest, because of its immediate relevance to important applications, from
+devising efficient immunization techniques for the arrest of epidemics, to the
+design of better transportation and city planning paradigms, to the
+understanding of how rumors and opinions spread and take shape over time. We
+develop a spatial social complex network (SSCN) model that captures not only
+essential connectivity features of real-life social networks, including a
+heavy-tailed degree distribution and high clustering, but also the spatial
+location of individuals, reproducing Zipf's law for the distribution of city
+populations as well as other observed hallmarks. We then simulate Milgram's
+Small-World experiment on our SSCN model, obtaining good qualitative agreement
+with the known results and shedding light on the role played by various network
+attributes and the strategies used by the players in the game. This
+demonstrates the potential of the SSCN model for the simulation and study of
+the many social processes mentioned above, where both connectivity and
+geography play a role in the dynamics.
+"
+22732,Sentence Simplification with Deep Reinforcement Learning,"  Sentence simplification aims to make sentences easier to read and understand.
+Most recent approaches draw on insights from machine translation to learn
+simplification rewrites from monolingual corpora of complex and simple
+sentences. We address the simplification problem with an encoder-decoder model
+coupled with a deep reinforcement learning framework. Our model, which we call
+{\sc Dress} (as shorthand for {\bf D}eep {\bf RE}inforcement {\bf S}entence
+{\bf S}implification), explores the space of possible simplifications while
+learning to optimize a reward function that encourages outputs which are
+simple, fluent, and preserve the meaning of the input. Experiments on three
+datasets demonstrate that our model outperforms competitive simplification
+systems.
+"
+22733,Scalable Planning with Tensorflow for Hybrid Nonlinear Domains,"  Given recent deep learning results that demonstrate the ability to
+effectively optimize high-dimensional non-convex functions with gradient
+descent optimization on GPUs, we ask in this paper whether symbolic gradient
+optimization tools such as Tensorflow can be effective for planning in hybrid
+(mixed discrete and continuous) nonlinear domains with high dimensional state
+and action spaces? To this end, we demonstrate that hybrid planning with
+Tensorflow and RMSProp gradient descent is competitive with mixed integer
+linear program (MILP) based optimization on piecewise linear planning domains
+(where we can compute optimal solutions) and substantially outperforms
+state-of-the-art interior point methods for nonlinear planning domains.
+Furthermore, we remark that Tensorflow is highly scalable, converging to a
+strong plan on a large-scale concurrent domain with a total of 576,000
+continuous action parameters distributed over a horizon of 96 time steps and
+100 parallel instances in only 4 minutes. We provide a number of insights that
+clarify such strong performance including observations that despite long
+horizons, RMSProp avoids both the vanishing and exploding gradient problems.
+Together these results suggest a new frontier for highly scalable planning in
+nonlinear hybrid domains by leveraging GPUs and the power of recent advances in
+gradient descent with highly optimized toolkits like Tensorflow.
+"
+22734,Quantitative isoperimetry à la Levy-Gromov,"  On a Riemannian manifold with a positive lower bound on the Ricci tensor, the
+distance of isoperimetric sets from geodesic balls is quantitatively controlled
+in terms of the gap between the isoperimetric profile of the manifold and that
+of a round sphere of suitable radius. The deficit between the diameters of the
+manifold and of the corresponding sphere is bounded likewise. These results are
+actually obtained in the more general context of (possibly non-smooth) metric
+measure spaces with curvature-dimension conditions through a quantitative
+analysis of the transport-rays decompositions obtained by the localization
+method.
+"
+22735,A Simple Neural Attentive Meta-Learner,"  Deep neural networks excel in regimes with large amounts of data, but tend to
+struggle when data is scarce or when they need to adapt quickly to changes in
+the task. In response, recent work in meta-learning proposes training a
+meta-learner on a distribution of similar tasks, in the hopes of generalization
+to novel but related tasks by learning a high-level strategy that captures the
+essence of the problem it is asked to solve. However, many recent meta-learning
+approaches are extensively hand-designed, either using architectures
+specialized to a particular application, or hard-coding algorithmic components
+that constrain how the meta-learner solves the task. We propose a class of
+simple and generic meta-learner architectures that use a novel combination of
+temporal convolutions and soft attention; the former to aggregate information
+from past experience and the latter to pinpoint specific pieces of information.
+In the most extensive set of meta-learning experiments to date, we evaluate the
+resulting Simple Neural AttentIve Learner (or SNAIL) on several
+heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement
+learning, SNAIL attains state-of-the-art performance by significant margins.
+"
+22736,Field space parametrization in quantum gravity and the identification of a unitary conformal field theory at the heart of 2D Asymptotic Safety,"  In this thesis we investigate various fundamental aspects of asymptotically
+safe quantum gravity, in particular the compatibility of Asymptotic Safety with
+the requirements for background independence and unitarity. The first part
+contains a detailed analysis of the space of metrics. We unveil a novel,
+specifically designed connection on this space and use the corresponding
+geodesics to connect all metrics having the same signature. In the second part
+we study the consequences of different metric parametrizations, i.e. of
+different connections on the space of metrics, for the Asymptotic Safety
+scenario by exploring the respective properties of the decisive renormalization
+group fixed points. Based on a bimetric investigation we show that Asymptotic
+Safety can be reconciled with background independence. The third part of this
+work focuses on the 2D limit of quantum gravity. To this end, we show that the
+2D limit of the Einstein--Hilbert action at a nontrivial fixed point becomes
+Polyakov's induced gravity action which describes a conformal field theory. In
+this way, we can prove that the fixed point theory is unitary. The last part
+concerns the reconstruction of the functional integral out of a given effective
+theory, where we reconstruct the bare action for an Einstein--Hilbert-type
+effective average action and for a Liouville action.
+"
+22737,Source detection at 100 meter standoff with a time-encoded imaging system,"  We present the design, characterization, and testing of a laboratory
+prototype radiological search and localization system. The system, based on
+time-encoded imaging, uses the attenuation signature of neutrons in time,
+induced by the geometrical layout and motion of the system. We have
+demonstrated the ability to detect a ~1 mCi Cf-252 radiological source at 100 m
+standoff with 90% detection efficiency and 10% false positives against
+background in 12 min. This same detection efficiency is met at 15 s for a 40 m
+standoff, and 1.2 s for a 20 m standoff.
+"
+22738,Incremental Sparse Bayesian Ordinal Regression,"  Ordinal Regression (OR) aims to model the ordering information between
+different data categories, which is a crucial topic in multi-label learning. An
+important class of approaches to OR models the problem as a linear combination
+of basis functions that map features to a high dimensional non-linear space.
+However, most of the basis function-based algorithms are time consuming. We
+propose an incremental sparse Bayesian approach to OR tasks and introduce an
+algorithm to sequentially learn the relevant basis functions in the ordinal
+scenario. Our method, called Incremental Sparse Bayesian Ordinal Regression
+(ISBOR), automatically optimizes the hyper-parameters via the type-II maximum
+likelihood method. By exploiting fast marginal likelihood optimization, ISBOR
+can avoid big matrix inverses, which is the main bottleneck in applying basis
+function-based algorithms to OR tasks on large-scale datasets. We show that
+ISBOR can make accurate predictions with parsimonious basis functions while
+offering automatic estimates of the prediction uncertainty. Extensive
+experiments on synthetic and real word datasets demonstrate the efficiency and
+effectiveness of ISBOR compared to other basis function-based OR approaches.
+"
+22739,KELT-21b: A Hot Jupiter Transiting the Rapidly-Rotating Metal-Poor Late-A Primary of a Likely Hierarchical Triple System,"  We present the discovery of KELT-21b, a hot Jupiter transiting the $V=10.5$
+A8V star HD 332124. The planet has an orbital period of
+$P=3.6127647\pm0.0000033$ days and a radius of $1.586_{-0.040}^{+0.039}$ $R_J$.
+We set an upper limit on the planetary mass of $M_P<3.91$ $M_J$ at $3\sigma$
+confidence. We confirmed the planetary nature of the transiting companion using
+this mass limit and Doppler tomographic observations to verify that the
+companion transits HD 332124. These data also demonstrate that the planetary
+orbit is well-aligned with the stellar spin, with a sky-projected spin-orbit
+misalignment of $\lambda=-5.6_{-1.9}^{+1.7 \circ}$. The star has
+$T_{\mathrm{eff}}=7598_{-84}^{+81}$ K, $M_*=1.458_{-0.028}^{+0.029}$
+$M_{\odot}$, $R_*=1.638\pm0.034$ $R_{\odot}$, and $v\sin I_*=146$ km s$^{-1}$,
+the highest projected rotation velocity of any star known to host a transiting
+hot Jupiter. The star also appears to be somewhat metal-poor and
+$\alpha$-enhanced, with [Fe/H]$=-0.405_{-0.033}^{+0.032}$ and
+[$\alpha$/Fe]$=0.145 \pm 0.053$; these abundances are unusual, but not
+extraordinary, for a young star with thin-disk kinematics like KELT-21.
+High-resolution imaging observations revealed the presence of a pair of stellar
+companions to KELT-21, located at a separation of 1.2"" and with a combined
+contrast of $\Delta K_S=6.39 \pm 0.06$ with respect to the primary. Although
+these companions are most likely physically associated with KELT-21, we cannot
+confirm this with our current data. If associated, the candidate companions
+KELT-21 B and C would each have masses of $\sim0.12$ $M_{\odot}$, a projected
+mutual separation of $\sim20$ AU, and a projected separation of $\sim500$ AU
+from KELT-21. KELT-21b may be one of only a handful of known transiting planets
+in hierarchical triple stellar systems.
+"
+22740,Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity,"  Semantic similarity measures are an important part in Natural Language
+Processing tasks. However Semantic similarity measures built for general use do
+not perform well within specific domains. Therefore in this study we introduce
+a domain specific semantic similarity measure that was created by the
+synergistic union of word2vec, a word embedding method that is used for
+semantic similarity calculation and lexicon based (lexical) semantic similarity
+methods. We prove that this proposed methodology out performs word embedding
+methods trained on generic corpus and methods trained on domain specific corpus
+but do not use lexical semantic similarity methods to augment the results.
+Further, we prove that text lemmatization can improve the performance of word
+embedding methods.
+"
+22741,Classification of metric spaces with infinite asymptotic dimension,"  We introduce a geometric property complementary-finite asymptotic dimension
+(coas- dim). Similar with asymptotic dimension, we prove the corresponding
+coarse invariant theorem, union theorem and Hurewicz-type theorem.
+"
+22742,Verification & Validation of Agent Based Simulations using the VOMAS (Virtual Overlay Multi-agent System) approach,"  Agent Based Models are very popular in a number of different areas. For
+example, they have been used in a range of domains ranging from modeling of
+tumor growth, immune systems, molecules to models of social networks, crowds
+and computer and mobile self-organizing networks. One reason for their success
+is their intuitiveness and similarity to human cognition. However, with this
+power of abstraction, in spite of being easily applicable to such a wide number
+of domains, it is hard to validate agent-based models. In addition, building
+valid and credible simulations is not just a challenging task but also a
+crucial exercise to ensure that what we are modeling is, at some level of
+abstraction, a model of our conceptual system; the system that we have in mind.
+In this paper, we address this important area of validation of agent based
+models by presenting a novel technique which has broad applicability and can be
+applied to all kinds of agent-based models. We present a framework, where a
+virtual overlay multi-agent system can be used to validate simulation models.
+In addition, since agent-based models have been typically growing, in parallel,
+in multiple domains, to cater for all of these, we present a new single
+validation technique applicable to all agent based models. Our technique, which
+allows for the validation of agent based simulations uses VOMAS: a Virtual
+Overlay Multi-agent System. This overlay multi-agent system can comprise
+various types of agents, which form an overlay on top of the agent based
+simulation model that needs to be validated. Other than being able to watch and
+log, each of these agents contains clearly defined constraints, which, if
+violated, can be logged in real time. To demonstrate its effectiveness, we show
+its broad applicability in a wide variety of simulation models ranging from
+social sciences to computer networks in spatial and non-spatial conceptual
+models.
+"
+22743,Geometry of compact complex manifolds associated to generalized quasi-Fuchsian representations,"  We study the topology and geometry of compact complex manifolds associated to
+Anosov representations of surface groups and other hyperbolic groups in a
+complex semisimple Lie group $G$. These manifolds are obtained as quotients of
+the domains of discontinuity in generalized flag varieties $G/P$ constructed by
+Kapovich-Leeb-Porti (arXiv:1306.3837), and in some cases by Guichard-Wienhard
+(arXiv:1108.0733).
+For $G$-Fuchsian representations and their Anosov deformations, where $G$ is
+simple, we compute the homology of the domains of discontinuity and of the
+quotient manifolds. For $G$-Fuchsian and $G$-quasi-Fuchsian representations in
+simple $G$ of rank at least two, we show that the quotient manifolds are not
+Kähler. We also describe the Picard groups of these quotient manifolds,
+compute the cohomology of line bundles on them, and show that for $G$ of
+sufficiently large rank these manifolds admit nonconstant meromorphic
+functions.
+In a final section, we apply our topological results to several explicit
+families of domains and derive closed formulas for topological invariants in
+some cases. We also show that the quotient manifold for a $G$-Fuchsian
+representation in $\mathrm{PSL}_3(\mathbb{C})$ is a fiber bundle over a
+surface, and we conjecture that this holds for all simple $G$.
+"
+22744,Deconvolution and Restoration of Optical Endomicroscopy Images,"  Optical endomicroscopy (OEM) is an emerging technology platform with
+preclinical and clinical imaging applications. Pulmonary OEM via fibre bundles
+has the potential to provide in vivo, in situ molecular signatures of disease
+such as infection and inflammation. However, enhancing the quality of data
+acquired by this technique for better visualization and subsequent analysis
+remains a challenging problem. Cross coupling between fiber cores and sparse
+sampling by imaging fiber bundles are the main reasons for image degradation,
+and poor detection performance (i.e., inflammation, bacteria, etc.). In this
+work, we address the problem of deconvolution and restoration of OEM data. We
+propose a hierarchical Bayesian model to solve this problem and compare three
+estimation algorithms to exploit the resulting joint posterior distribution.
+The first method is based on Markov chain Monte Carlo (MCMC) methods, however,
+it exhibits a relatively long computational time. The second and third
+algorithms deal with this issue and are based on a variational Bayes (VB)
+approach and an alternating direction method of multipliers (ADMM) algorithm
+respectively. Results on both synthetic and real datasets illustrate the
+effectiveness of the proposed methods for restoration of OEM images.
+"
+22745,Learning to Play Pong using Policy Gradient Learning,"  Activities in reinforcement learning (RL) revolve around learning the Markov
+decision process (MDP) model, in particular, the following parameters: state
+values, V; state-action values, Q; and policy, pi. These parameters are
+commonly implemented as an array. Scaling up the problem means scaling up the
+size of the array and this will quickly lead to a computational bottleneck. To
+get around this, the RL problem is commonly formulated to learn a specific task
+using hand-crafted input features to curb the size of the array. In this
+report, we discuss an alternative end-to-end Deep Reinforcement Learning (DRL)
+approach where the DRL attempts to learn general task representations which in
+our context refers to learning to play the Pong game from a sequence of screen
+snapshots without game-specific hand-crafted features. We apply artificial
+neural networks (ANN) to approximate a policy of the RL model. The policy
+network, via Policy Gradients (PG) method, learns to play the Pong game from a
+sequence of frames without any extra semantics apart from the pixel information
+and the score. In contrast to the traditional tabular RL approach where the
+contents in the array have clear interpretations such as V or Q, the
+interpretation of knowledge content from the weights of the policy network is
+more illusive. In this work, we experiment with various Deep ANN architectures
+i.e., Feed forward ANN (FFNN), Convolution ANN (CNN) and Asynchronous Advantage
+Actor-Critic (A3C). We also examine the activation of hidden nodes and the
+weights between the input and the hidden layers, before and after the DRL has
+successfully learnt to play the Pong game. Insights into the internal learning
+mechanisms and future research directions are then discussed.
+"
+22746,Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet,"  Facial Key Points (FKPs) Detection is an important and challenging problem in
+the fields of computer vision and machine learning. It involves predicting the
+co-ordinates of the FKPs, e.g. nose tip, center of eyes, etc, for a given face.
+In this paper, we propose a LeNet adapted Deep CNN model - NaimishNet, to
+operate on facial key points data and compare our model's performance against
+existing state of the art approaches.
+"
+22747,Probing strong interactions in p-type few-layer WSe$_2$ by density-dependent Landau level crossing,"  Atomically thin transition metal dichalcogenides (TMDCs) such as MoS$_2$ and
+WSe$_2$ are emerging as a new platform for exploring many-body effects. Coulomb
+interactions are markedly enhanced in these materials because of the reduced
+screening and the large Wigner-Seitz radii. Although many-body excitonic
+effects in TMDCs have been extensively studied by optical means, not until
+recently did probing their strongly correlated electronic effects become
+possible in transport. Here, in p-type few-layer WSe$_2$ we observe highly
+density-dependent quantum Hall states of {\Gamma} valley holes below 12 T,
+whose predominant sequences alternate between odd- and even-integers. By
+tilting the magnetic field to induce Landau level crossings, we show that the
+strong Coulomb interaction enhances the Zeeman-to-cyclotron energy ratio from
+2.67 to 3.55 as the density is reduced from 5.7 to 4.0$\times$10$^{12}$
+cm$^{-2}$, giving rise to the even-odd alternation. Unprecedentedly, this
+indicates a 4.8 times enhancement of the g-factor over its band theory value at
+a density as high as 4.0$\times$10$^{12}$ cm$^{-2}$. Our findings unambiguously
+demonstrate that p-type few-layer WSe$_2$ is a superior platform for exploring
+strongly correlated electronic phenomena, opening a new perspective for
+realizing the elusive Wigner crystallization at a moderate density.
+"
+22748,Delocalization of charge and current in a chiral quasiparticle wave-packet,"  A chiral quasi-particle wave packet (c-QPWP) is defined as a conventional
+superposition of chiral quasi-particle states corresponding to an interacting
+electron system in two dimensions (2D) in the presence of Rashba spin-orbit
+coupling (RSOC). I investigate its internal structure via studying the charge
+and the current densities within the first order perturbation in the
+electron-electron interaction. It is found that the c-QPWP contains a localized
+charge which is less than the magnitude of the bare charge and the remaining
+charge resides at the system boundary. The amount of charge delocalized turns
+out to be inversely proportional to the degenerate Fermi velocity $v_0 ( =
+\sqrt{\alpha^2 + 2\mu / m})$ when RSOC (with strength $\alpha$) is weak, and
+therefore externally tunable. For strong RSOC, the magnitudes of both the
+delocalized charge and the current further strongly depend on the direction of
+propagation of the wave packet. Both the charge and the current densities
+consist of an anisotropic $r^{-2}$ tail away from the centre of the wave
+packet. Possible implications of such delocalizations in real systems
+corresponding to 2D semiconductor heterostructure are also discussed within the
+context of particle injection experiments.
+"
+22749,Localization and Eigenvalue Statistics for the Lattice Anderson model with Discrete Disorder,"  We prove localization and probabilistic bounds on the minimum level spacing
+for the Anderson tight-binding model on the lattice in any dimension, with
+single-site potential having a discrete distribution taking N values, with N
+large.
+"
+22750,Optimizing Timer-based Policies for General Cache Networks,"  Caching algorithms are usually described by the eviction method and analyzed
+using a metric of hit probability. Since contents have different importance
+(e.g. popularity), the utility of a high hit probability, and the cost of
+transmission can vary across contents. In this paper, we consider timer-based
+(TTL) policies across a cache network, where contents have differentiated
+timers over which we optimize. Each content is associated with a utility
+measured in terms of the corresponding hit probability. We start our analysis
+from a linear cache network: we propose a utility maximization problem where
+the objective is to maximize the sum of utilities and a cost minimization
+problem where the objective is to minimize the content transmission cost across
+the network. These frameworks enable us to design online algorithms for cache
+management, for which we prove achieving optimal performance. Informed by the
+results of our analysis, we formulate a non-convex optimization problem for a
+general cache network. We show that the duality gap is zero, hence we can
+develop a distributed iterative primal-dual algorithm for content management in
+the network. Numerical evaluations show that our algorithm significant
+outperforms path replication with traditional caching algorithms over some
+network topologies. Finally, we consider a direct application of our cache
+network model to content distribution.
+"
+22751,Transversality theorem in highly relative situations and its application,"  In this paper, we aim to provide a notion of ""relative objects"", i.e. objects
+equipped with some sort of subobjects, in differential topology. In spite of
+active researches relating them, e.g. knot theory or the theory of manifolds
+with corners, there seem to be poor general notions to deal with them.
+Moreover, we want even more direct differential calculus on relative objects
+and extension of classical notions and theories to relative situations; e.g.
+functions, vector fields, jet bundles, singularities, and so on. To establish
+this, the notion of arrangements of manifolds is introduced, which, for
+example, enables us to control behaviors of smooth maps on manifolds around
+corners. We construct jet bundles and prove a relative version of
+Transversality Theorem for some sorts of arrangements. Finally, embedding
+theorem of manifolds with faces into polyhedra is proved as an application.
+"
+22752,Emergent Network Modularity,"  We introduce a network growth model based on complete redirection: a new node
+randomly selects an existing target node, but attaches to a random neighbor of
+this target. For undirected networks, this simple growth rule generates
+unusual, highly modular networks. Individual network realizations typically
+contain multiple macrohubs---nodes whose degree scales linearly with the number
+of nodes $N$. The size of the network ""nucleus""---the set of nodes of degree
+greater than one---grows sublinearly with $N$ and thus constitutes a
+vanishingly small fraction of the network. The network therefore consists
+almost entirely of leaves (nodes of degree one) as $N\to\infty$.
+"
+22753,Loxodromic elements in the cyclic splitting complex and their centralizers,"  We show that an outer automorphism acts loxodromically on the cyclic
+splitting complex if and only if it has a filling lamination and no generic
+leaf of the lamination is carried by a vertex group of a cyclic splitting. This
+is the analog for the cyclic splitting complex of Handel-Mosher's theorem on
+loxodromics for the free splitting complex. We also show that such outer
+automorphisms have virtually cyclic centralizers.
+"
+22754,A Novel Use of Light Guides and Wavelength Shifting Plates for the Detection of Scintillation Photons in Large Liquid Argon Detectors,"  Scintillation light generated as charged particles traverse large liquid
+argon detectors adds valuable information to studies of weakly-interacting
+particles. This paper uses both laboratory measurements and cosmic ray data
+from the Blanche dewar facility at Fermilab to characterize the efficiency of
+the photon detector technology developed at Indiana University for the single
+phase far detector of DUNE. The efficiency of this technology was found to be
+0.48% at the readout end when the detector components were characterized with
+laboratory measurements. A second determination of the efficiency using cosmic
+ray tracks is in reasonable agreement with the laboratory determination. The
+agreement of these two efficiency determinations supports the result that
+minimum ionizing muons generate ${\mathcal N}_{phot} = 40,000$ photons/MeV as
+they cross the LAr volume.
+"
+22755,Con los juegos también se educa: un enfoque educativo de los juegos de la Oca y el Parchís,"  In this work, we have designed and implemented, based on traditional board
+games such as the game of the Goose or Parchis, an educational software that
+aim to reinforce the learning of children. The idea that we are going to
+develop to do this is very simple: the children play with the same rules as in
+the traditional game but we add the functionality that after throwing the dice
+the system asks a question randomly chosen from a predefined database that can
+be easily modified, and the game piece only moves in the case the question is
+answered correctly.
+"
+22756,A Model for Dissipation of Solar Wind Magnetic Turbulence by Kinetic Alfvén Waves at Electron Scales: Comparison with Observations,"  In hydrodynamic turbulence, it is well established that the length of the
+dissipation scale depends on the energy cascade rate, i.e., the larger the
+energy input rate per unit mass, the more the turbulent fluctuations need to be
+driven to increasingly smaller scales to dissipate the larger energy flux.
+Observations of magnetic spectral energy densities indicate that this intuitive
+picture is not valid in solar wind turbulence. Dissipation seems to set in at
+the same length scale for different solar wind conditions independently of the
+energy flux. To investigate this difference in more detail, we present an
+analytic dissipation model for solar wind turbulence at electron scales, which
+we compare with observed spectral densities. Our model combines the energy
+transport from large to small scales and collisionless damping, which removes
+energy from the magnetic fluctuations in the kinetic regime. We assume
+wave-particle interactions of kinetic Alfvén waves (KAW) to be the main
+damping process. Wave frequencies and damping rates of KAW are obtained from
+the hot plasma dispersion relation. Our model assumes a critically balanced
+turbulence, where larger energy cascade rates excite larger parallel
+wavenumbers for a certain perpendicular wavenumber. If the dissipation is
+additionally wave driven such that the dissipation rate is proportional to the
+parallel wavenumber - as with KAW - then an increase of the energy cascade rate
+is counter-balanced by an increased dissipation rate for the same perpendicular
+wavenumber leading to a dissipation length independent of the energy cascade
+rate.
+"
+22757,Optimal design problems for the first $p-$fractional eigenvalue with mixed boundary conditions,"  In this paper we study an optimal shape design problem for the first
+eigenvalue of the fractional $p-$laplacian with mixed boundary conditions. The
+optimization variable is the set where the Dirichlet condition is imposed (that
+is restricted to have measure equal than a prescribed quantity, $\alpha$). We
+show existence of an optimal design and analyze the asymptotic behavior when
+the fractional parameter $s\uparrow 1$ obtaining asymptotic bounds that are
+independent of $\alpha$.
+"
+22758,Isolating a Vertex via Lattices: Polytopes with Totally Unimodular Faces,"  We present a geometric approach towards derandomizing the Isolation Lemma by
+Mulmuley, Vazirani, and Vazirani. In particular, our approach produces a
+quasi-polynomial family of weights, where each weight is an integer and
+quasi-polynomially bounded, that can isolate a vertex in any 0/1 polytope for
+which each face lies in an affine space defined by a totally unimodular matrix.
+This includes the polytopes given by totally unimodular constraints and
+generalizes the recent derandomization of the Isolation Lemma for bipartite
+perfect matching and matroid intersection. We prove our result by associating a
+lattice to each face of the polytope and showing that if there is a totally
+unimodular kernel matrix for this lattice, then the number of vectors of length
+within 3/2 of the shortest vector in it is polynomially bounded. The proof of
+this latter geometric fact is combinatorial and follows from a polynomial bound
+on the number of circuits of size within 3/2 of the shortest circuit in a
+regular matroid. This is the technical core of the paper and relies on a
+variant of Seymour's decomposition theorem for regular matroids. It generalizes
+an influential result by Karger on the number of minimum cuts in a graph to
+regular matroids.
+"
+22759,Mira: A Framework for Static Performance Analysis,"  The performance model of an application can pro- vide understanding about its
+runtime behavior on particular hardware. Such information can be analyzed by
+developers for performance tuning. However, model building and analyzing is
+frequently ignored during software development until perfor- mance problems
+arise because they require significant expertise and can involve many
+time-consuming application runs. In this paper, we propose a fast, accurate,
+flexible and user-friendly tool, Mira, for generating performance models by
+applying static program analysis, targeting scientific applications running on
+supercomputers. We parse both the source code and binary to estimate
+performance attributes with better accuracy than considering just source or
+just binary code. Because our analysis is static, the target program does not
+need to be executed on the target architecture, which enables users to perform
+analysis on available machines instead of conducting expensive exper- iments on
+potentially expensive resources. Moreover, statically generated models enable
+performance prediction on non-existent or unavailable architectures. In
+addition to flexibility, because model generation time is significantly reduced
+compared to dynamic analysis approaches, our method is suitable for rapid
+application performance analysis and improvement. We present several scientific
+application validation results to demonstrate the current capabilities of our
+approach on small benchmarks and a mini application.
+"
+22760,Effect of doping on the characteristics of infrared photodetectors based on van der Waals~heterostructures with multiple graphene layers,"  We study the operation of infrared photodetectors based on van der Waals
+heterostructures with the multiple graphene layers (GLs) and n-type emitter and
+collector contacts. The operation of such GL infrared photodetectors (GLIPs) is
+associated with the photoassisted escape of electrons from the GLs into the
+continuum states in the conduction band of the barrier layers due to the
+interband photon absorption, the propagation of these electrons and the
+electrons injected from the emitter across the heterostructure and their
+collection by the collector contact. The space charge of the holes trapped in
+the GLs provides a relatively strong injection and large photoelectric gain. We
+calculate the GLIP responsivity and dark current detectivity as functions of
+the energy of incident infrared photons and the structural parameters. It is
+shown that both the periodic selective doping of the inter-GL barrier layers
+and the GL doping lead to a pronounced variation of the GLIP spectral
+characteristics, particularly near the interband absorption threshold, while
+the doping of GLs solely results in a substantial increase in the GLIP
+detectivity. The doping ""engineering"" opens wide opportunities for the
+optimization of GLIPs for operation in different parts of radiation spectrum
+from near infrared to terahertz.
+"
+22761,Extension of the non-parametric cluster-based time-frequency statistics to the full time windows and to single condition tests,"  Oscillatory processes are central for the understanding of the neural bases
+of cognition and behaviour. To analyse these processes, time-frequency (TF)
+decomposition methods are applied and non-parametric cluster-based statistical
+procedure are used for comparing two or more conditions. While this combination
+is a powerful method, it has two drawbacks. One the unreliable estimation of
+signals outside the cone-of-influence and the second relates to the length of
+the time frequency window used for the analysis. Both impose constrains on the
+non-parametric statistical procedure for inferring an effect in the TF domain.
+Here we extend the method to reliably infer oscillatory differences within the
+full TF map and to test single conditions. We show that it can be applied in
+small time windows irrespective of the cone-of-influence and we further develop
+its application to single-condition case for testing the hypothesis of the
+presence or not of time-varying signals. We present tests of this new method on
+real EEG and behavioural data and show that its sensitivity to single-condition
+tests is at least as good as classic Fourier analysis. Statistical inference in
+the full TF map is available and efficient in detecting differences between
+conditions as well as the presence of time-varying signal in single condition.
+"
+22762,How Do People Differ? A Social Media Approach,"  Research from a variety of fields including psychology and linguistics have
+found correlations and patterns in personal attributes and behavior, but
+efforts to understand the broader heterogeneity in human behavior have not yet
+integrated these approaches and perspectives with a cohesive methodology. Here
+we extract patterns in behavior and relate those patterns together in a
+high-dimensional picture. We use dimension reduction to analyze word usage in
+text data from the online discussion platform Reddit. We find that pronouns can
+be used to characterize the space of the two most prominent dimensions that
+capture the greatest differences in word usage, even though pronouns were not
+included in the determination of those dimensions. These patterns overlap with
+patterns of topics of discussion to reveal relationships between pronouns and
+topics that can describe the user population. This analysis corroborates
+findings from past research that have identified word use differences across
+populations and synthesizes them relative to one another. We believe this is a
+step toward understanding how differences between people are related to each
+other.
+"
+22763,Energy-Efficient Data Collection in Clustered Wireless Sensor Networks employing Distributed DCT,"  In this paper, a energy-efficient data collection method is proposed in which
+an integration between Discrete Cosine Transform (DCT) matrix and clustering in
+wireless sensor networks (WSNs) is exploited.Based on the fact that sensory
+data in WSNs is often highly correlated and is suitable to be transformed in
+DCT domain, we propose that each cluster from the networks only sends a small
+number of large DCT transformed coefficients to the base-station (BS) for data
+collection in two common ways, either directly or in multi-hop routing. All
+sensory data from the sensor network can be recovered based on the large
+coefficients received at the BS. We further analyze and formulate the
+communication cost as the power consumption for transmitting data in such
+networks based on stochastic problems. Some common clustering algorithms are
+applied and compared to verify the analysis and simulation results. Both noise
+and noiseless environments for the proposed method are considered.
+"
+22764,Asynchronous Early Output Dual-Bit Full Adders Based on Homogeneous and Heterogeneous Delay-Insensitive Data Encoding,"  This paper presents the designs of asynchronous early output dual-bit full
+adders without and with redundant logic (implicit) corresponding to homogeneous
+and heterogeneous delay-insensitive data encoding. For homogeneous
+delay-insensitive data encoding only dual-rail i.e. 1-of-2 code is used, and
+for heterogeneous delay-insensitive data encoding 1-of-2 and 1-of-4 codes are
+used. The 4-phase return-to-zero protocol is used for handshaking. To
+demonstrate the merits of the proposed dual-bit full adder designs, 32-bit
+ripple carry adders (RCAs) are constructed comprising dual-bit full adders. The
+proposed dual-bit full adders based 32-bit RCAs incorporating redundant logic
+feature reduced latency and area compared to their non-redundant counterparts
+with no accompanying power penalty. In comparison with the weakly indicating
+32-bit RCA constructed using homogeneously encoded dual-bit full adders
+containing redundant logic, the early output 32-bit RCA comprising the proposed
+homogeneously encoded dual-bit full adders with redundant logic reports
+corresponding reductions in latency and area by 22.2% and 15.1% with no
+associated power penalty. On the other hand, the early output 32-bit RCA
+constructed using the proposed heterogeneously encoded dual-bit full adder
+which incorporates redundant logic reports respective decreases in latency and
+area than the weakly indicating 32-bit RCA that consists of heterogeneously
+encoded dual-bit full adders with redundant logic by 21.5% and 21.3% with nil
+power overhead. The simulation results obtained are based on a 32/28nm CMOS
+process technology.
+"
+22765,Cu self-sputtering MD simulations for 0.1-5 keV ions at elevated temperatures,"  Self-sputtering of copper under high electric fields is considered to
+contribute to plasma buildup during a vacuum breakdown event frequently
+observed near metal surfaces, even in ultra high vacuum condition in different
+electric devices. In this study, by means of molecular dynamics simulations, we
+analyze the effect of surface temperature and morphology on the yield of
+self-sputtering of copper with ion energies of 0.1-5 keV. We analyze all three
+low-index surfaces of Cu, {100}, {110} and {111}, held at different
+temperatures, 300 K, 500 K and 1200 K. The surface roughness relief is studied
+by either varying the angle of incidence on flat surfaces, or by using
+arbitrary roughened surfaces, which result in a more natural distribution of
+surface relief variations. Our simulations provide detailed characterization of
+copper self-sputtering with respect to different material temperatures,
+crystallographic orientations, surface roughness, energies, and angles of ion
+incidence.
+"
+22766,Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks,"  Online media outlets, in a bid to expand their reach and subsequently
+increase revenue through ad monetisation, have begun adopting clickbait
+techniques to lure readers to click on articles. The article fails to fulfill
+the promise made by the headline. Traditional methods for clickbait detection
+have relied heavily on feature engineering which, in turn, is dependent on the
+dataset it is built for. The application of neural networks for this task has
+only been explored partially. We propose a novel approach considering all
+information found in a social media post. We train a bidirectional LSTM with an
+attention mechanism to learn the extent to which a word contributes to the
+post's clickbait score in a differential manner. We also employ a Siamese net
+to capture the similarity between source and target information. Information
+gleaned from images has not been considered in previous approaches. We learn
+image embeddings from large amounts of data using Convolutional Neural Networks
+to add another layer of complexity to our model. Finally, we concatenate the
+outputs from the three separate components, serving it as input to a fully
+connected layer. We conduct experiments over a test corpus of 19538 social
+media posts, attaining an F1 score of 65.37% on the dataset bettering the
+previous state-of-the-art, as well as other proposed approaches, feature
+engineering or otherwise.
+"
+22767,Squeezed Very Deep Convolutional Neural Networks for Text Classification,"  Most of the research in convolutional neural networks has focused on
+increasing network depth to improve accuracy, resulting in a massive number of
+parameters which restricts the trained network to platforms with memory and
+processing constraints. We propose to modify the structure of the Very Deep
+Convolutional Neural Networks (VDCNN) model to fit mobile platforms constraints
+and keep performance. In this paper, we evaluate the impact of Temporal
+Depthwise Separable Convolutions and Global Average Pooling in the network
+parameters, storage size, and latency. The squeezed model (SVDCNN) is between
+10x and 20x smaller, depending on the network depth, maintaining a maximum size
+of 6MB. Regarding accuracy, the network experiences a loss between 0.4% and
+1.3% and obtains lower latencies compared to the baseline model.
+"
+22768,Growing simplified vine copula trees: improving Dißmann's algorithm,"  Vine copulas are pair-copula constructions enabling multivariate dependence
+modeling in terms of bivariate building blocks. One of the main tasks of
+fitting a vine copula is the selection of a suitable tree structure. For this
+the prevalent method is a heuristic called Di{\ss}mann's algorithm. It
+sequentially constructs the vine's trees by maximizing dependence at each tree
+level, where dependence is measured in terms of absolute Kendall's $\tau$.
+However, the algorithm disregards any implications of the tree structure on the
+simplifying assumption that is usually made for vine copulas to keep inference
+tractable. We develop two new algorithms that select tree structures focused on
+producing simplified vine copulas for which the simplifying assumption is
+violated as little as possible. For this we make use of a recently developed
+statistical test of the simplifying assumption. In a simulation study we show
+that our proposed methods outperform the benchmark given by Di{\ss}mann's
+algorithm by a great margin. Several real data applications emphasize their
+practical relevance.
+"
+22769,Tighter Bounds for the Discrepancy of Boxes and Polytopes,"  Combinatorial discrepancy is a complexity measure of a collection of sets
+which quantifies how well the sets in the collection can be simultaneously
+balanced. More precisely, we are given an n-point set $P$, and a collection
+$\mathcal{F} = \{F_1, ..., F_m\}$ of subsets of $P$, and our goal is color $P$
+with two colors, red and blue, so that the maximum over the $F_i$ of the
+absolute difference between the number of red elements and the number of blue
+elements (the discrepancy) is minimized. Combinatorial discrepancy has many
+applications in mathematics and computer science, including constructions of
+uniformly distributed point sets, and lower bounds for data structures and
+private data analysis algorithms.
+We investigate the combinatorial discrepancy of geometrically defined
+systems, in which $P$ is an n-point set in $d$-dimensional space ,and
+$\mathcal{F}$ is the collection of subsets of $P$ induced by dilations and
+translations of a fixed convex polytope $B$. Such set systems include systems
+of sets induced by axis-aligned boxes, whose discrepancy is the subject of the
+well known Tusnady problem. We prove new discrepancy upper and lower bounds for
+such set systems by extending the approach based on factorization norms
+previously used by the author and Matousek. We improve the best known upper
+bound for the Tusnady problem by a logarithmic factor, using a result of
+Banaszczyk on signed series of vectors. We extend this improvement to any
+arbitrary convex polytope $B$ by using a decomposition due to Matousek. Using
+Fourier analytic techniques, we also prove a nearly matching discrepancy lower
+bound for sets induced by any fixed bounded polytope $B$ satisfying a certain
+technical condition.
+We also outline applications of our results to geometric discrepancy, data
+structure lower bounds, and differential privacy.
+"
+22770,Neuropeptide Y is up-regulated and induces antinociception in cancer-induced bone pain,"  Pain remains a major concern in patients suffering from metastatic cancer to
+the bone and more knowledge of the condition, as well as novel treatment
+avenues, are called for. Neuropeptide Y (NPY) is a highly conserved peptide
+that appears to play a central role in nociceptive signaling in inflammatory
+and neuropathic pain. However, little is known about the peptide in
+cancer-induced bone pain. Here, we evaluate the role of spinal NPY in the
+MRMT-1 rat model of cancer-induced bone pain. Our studies revealed an
+up-regulation of NPY-immunoreactivity in the dorsal horn of cancer-bearing rats
+17 days after inoculation, which could be a compensatory antinociceptive
+response. Consistent with this interpretation, intrathecal administration of
+NPY to rats with cancer-induced bone pain caused a reduction in nociceptive
+behaviors that lasted up to 150 min. This effect was diminished by both Y1
+(BIBO3304) and Y2 (BIIE0246) receptor antagonists, indicating that both
+receptors participate in mediating the antinociceptive effect of NPY. Y1 and Y2
+receptor binding in the spinal cord was unchanged in the cancer state as
+compared to sham-operated rats, consistent with the notion that increased NPY
+results in a net antinociceptive effect in the MRMT-1 model. In conclusion, the
+data indicate that NPY is involved in the spinal nociceptive signaling of
+cancer-induced bone pain and could be a new therapeutic target for patients
+with this condition.
+"
+22771,On noncommutative extensions of linear logic,"  In the seminal work ""Pomset logic: A noncommutative extension of classical
+linear logic"" Retoré introduced Pomset logic, an extension of linear logic
+with a self-dual noncommutative connective. Pomset logic is defined by means of
+proof-nets, later a deep inference system BV was designed for this extension,
+but equivalence of system has not been proven up to now. As for a sequent
+calculus formulation, it has not been known for either of these logics, and
+there are convincing arguments that such a sequent calculus in the usual sense
+simply does not exist for them.
+In an on-going work on semantics we discovered a system similar to Pomset
+logic, where a noncommutative connective is no longer self-dual. Pomset logic
+appears as a degeneration, when the class of models is restricted. This will be
+shown in a forthcoming paper.
+Motivated by these semantic considerations, in the current work we define a
+semicommutative multiplicative linear logic, which is multiplicative linear
+logic extended with two nonisomorphic noncommutative connectives (not to be
+confused with very different Abrusci-Ruet noncommutative logic). We develop a
+syntax of proof-nets and show how this logic degenerates to Pomset logic.
+However, a more important problem than just finding yet another
+noncommutative logic is finding a sequent calculus for this logic. We introduce
+decorated sequents, which are sequents equipped with an extra structure of a
+binary relation of reachability on formulas. We define a decorated sequent
+calculus for semicommutative logic and prove that it is cut-free, sound and
+complete. This is adapted to ""degenerate"" variations, including Pomset logic.
+Thus, in particular, we give a (sort of) sequent calculus formulation for
+Pomset logic, which is one of the key results of the paper.
+"
+22772,CRF Autoencoder for Unsupervised Dependency Parsing,"  Unsupervised dependency parsing, which tries to discover linguistic
+dependency structures from unannotated data, is a very challenging task. Almost
+all previous work on this task focuses on learning generative models. In this
+paper, we develop an unsupervised dependency parsing model based on the CRF
+autoencoder. The encoder part of our model is discriminative and globally
+normalized which allows us to use rich features as well as universal linguistic
+priors. We propose an exact algorithm for parsing as well as a tractable
+learning algorithm. We evaluated the performance of our model on eight
+multilingual treebanks and found that our model achieved comparable performance
+with state-of-the-art approaches.
+"
+22773,Open-ended Learning in Symmetric Zero-sum Games,"  Zero-sum games such as chess and poker are, abstractly, functions that
+evaluate pairs of agents, for example labeling them `winner' and `loser'. If
+the game is approximately transitive, then self-play generates sequences of
+agents of increasing strength. However, nontransitive games, such as
+rock-paper-scissors, can exhibit strategic cycles, and there is no longer a
+clear objective -- we want agents to increase in strength, but against whom is
+unclear. In this paper, we introduce a geometric framework for formulating
+agent objectives in zero-sum games, in order to construct adaptive sequences of
+objectives that yield open-ended learning. The framework allows us to reason
+about population performance in nontransitive games, and enables the
+development of a new algorithm (rectified Nash response, PSRO_rN) that uses
+game-theoretic niching to construct diverse populations of effective agents,
+producing a stronger set of agents than existing algorithms. We apply PSRO_rN
+to two highly nontransitive resource allocation games and find that PSRO_rN
+consistently outperforms the existing alternatives.
+"
+22774,Double Calorimetry System in JUNO,"  The Jiangmen Underground Neutrino Observatory (JUNO) is a multipurpose
+neutrino-oscillation experiment, with a 20 kiloton liquid scintillator detector
+of unprecedented 3\% energy resolution (at 1 MeV) at 700-meter deep
+underground. There are ~18,000 20-inch photomultiplier tubes (PMTs) in the
+central detector with an optical coverage greater than 75%. Control of the
+systematics of the energy response is crucial to archive the designed energy
+resolution as well as to reach 1% precision of the absolute energy scale. The
+detected number of photoelectrons in each PMT differs by two orders of
+magnitude in the reactor antineutrino energy range in such a large detector,
+which is a challenge to the single channel charge measurement. JUNO has
+approved a new small-PMT system, including 25,000 3-inch PMTs, installed
+alternately with 20-inch PMTs. The individual 3-inch PMT receives mostly single
+photoelectrons, which provides a unique way to calibrate the energy response of
+the 20-inch PMT system by a photon-counting technology. Besides, the small-PMT
+system naturally extends the dynamic range of the energy measurement to help
+the high-energy physics, such as cosmic muons and atmospheric neutrinos. We
+will present the physics concept of this double calorimetry, the design and
+implementation of the 3-inch PMT and its readout electronics system.
+"
+22775,Adaptive Basis Selection for Exponential Family Smoothing Splines with Application in Joint Modeling of Multiple Sequencing Samples,"  Second-generation sequencing technologies have replaced array-based
+technologies and become the default method for genomics and epigenomics
+analysis. Second-generation sequencing technologies sequence tens of millions
+of DNA/cDNA fragments in parallel. After the resulting sequences (short reads)
+are mapped to the genome, one gets a sequence of short read counts along the
+genome. Effective extraction of signals in these short read counts is the key
+to the success of sequencing technologies. Nonparametric methods, in particular
+smoothing splines, have been used extensively for modeling and processing
+single sequencing samples. However, nonparametric joint modeling of multiple
+second-generation sequencing samples is still lacking due to computational
+cost. In this article, we develop an adaptive basis selection method for
+efficient computation of exponential family smoothing splines for modeling
+multiple second-generation sequencing samples. Our adaptive basis selection
+gives a sparse approximation of smoothing splines, yielding a lower-dimensional
+effective model space for a more scalable computation. The asymptotic analysis
+shows that the effective model space is rich enough to retain essential
+features of the data. Moreover, exponential family smoothing spline models
+computed via adaptive basis selection are shown to have good statistical
+properties, e.g., convergence at the same rate as that of full basis
+exponential family smoothing splines. The empirical performance is demonstrated
+through simulation studies and two second-generation sequencing data examples.
+"
+22776,Change-point detection in a linear model by adaptive fused quantile method,"  A novel approach to quantile estimation in multivariate linear regression
+models with change-points is proposed. The change-point detection and the model
+estimation are both performed automatically, by adopting either the quantile
+fused penalty or the adaptive version of the quantile fused penalty. These two
+methods combine the idea of the check function used for the quantile estimation
+and the $L_1$ penalization principle known from the signal processing and,
+unlike some standard approaches, the presented methods go beyond typical
+assumptions usually required for the model errors, such as sub-Gaussian or
+Normal distribution. They can effectively handle heavy-tailed random error
+distributions, and, in general, they offer a more complex view on the data as
+one can obtain any conditional quantile of the target distribution, not just
+the conditional mean. Theoretical results are proved and proper convergence
+rates are derived. The empirical performance is investigated via an extensive
+comparative simulation study.
+"
+22777,The natural algorithmic approach of mixed trigonometric-polynomial problems,"  The aim of this paper is to present a new algorithm for proving mixed
+trigonometric-polynomial inequalities by reducing to polynomial inequalities.
+Finally, we show the great applicability of this algorithm and as examples, we
+use it to analyze some new rational (Pade) approximations of the function
+$\cos^2(x)$, and to improve a class of inequalities by Z.-H. Yang. The results
+of our analysis could be implemented by means of an automated proof assistant,
+so our work is a contribution to the library of automatic support tools for
+proving various analytic inequalities.
+"
+22778,The Dold-Whitney theorem and the Sato-Levine invariant,"  We use the Dold-Whitney theorem classifying $SO(3)$-bundles over a 4-complex
+to give a mod 4 obstruction to a 2-component link of trivial linking number
+being slice. It turns out that this coincides with the reduction of the
+Sato-Levine invariant.
+"
+22779,Analyzing Car Thefts and Recoveries with Connections to Modeling Origin-Destination Point Patterns,"  For a given region, we have a dataset composed of car theft locations along
+with a linked dataset of recovery locations which, due to partial recovery, is
+a relatively small subset of the set of theft locations. For an investigator
+seeking to understand the behavior of car thefts and recoveries in the region,
+several questions are addressed. Viewing the set of theft locations as a point
+pattern, can we propose useful models to explain the pattern? What types of
+predictive models can be built to learn about recovery location given theft
+location? Can the dependence between theft locations and recovery locations be
+formalized? Can the flow between theft sites and recovery sites be captured?
+Origin-destination modeling offers a natural framework for such problems.
+However, here the data is not for areal units but rather is a pair of point
+patterns, with the recovery point pattern only partially observed. We offer
+modeling approaches for investigating the questions above and apply the
+approaches to two datasets. One is small from the state of Neza in Mexico with
+areal covariate information regarding population features and crime type. A
+second, much larger one, is from Belo Horizonte in Brazil but lacks covariates.
+"
+22780,Local cocycle 3-Hom-Lie Bialgebras and 3-Lie Classical Hom-Yang-Baxter Equation,"  In this paper, we introduce 3-Hom-Lie bialgebras whose compatibility
+conditions between the multiplication and comultiplication are given by local
+cocycle conditions. We study a twisted 3-ary version of the Yang-Baxter
+Equation, called the 3-Lie classical HomYang-Baxter Equation (3-Lie CHYBE),
+which is a general form of 3-Lie classical YangBaxter Equation studied in [2]
+and prove that the bialgebras induced by the solutions of 3-Lie CHYBE induce
+the coboundary local cocycle 3-Hom-Lie bialgebras.
+"
+22781,Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO,"  The exquisite sensitivity of the advanced LIGO detectors has enabled the
+detection of multiple gravitational wave signals. The sophisticated design of
+these detectors mitigates the effect of most types of noise. However, advanced
+LIGO data streams are contaminated by numerous artifacts known as glitches:
+non-Gaussian noise transients with complex morphologies. Given their high rate
+of occurrence, glitches can lead to false coincident detections, obscure and
+even mimic gravitational wave signals. Therefore, successfully characterizing
+and removing glitches from advanced LIGO data is of utmost importance. Here, we
+present the first application of Deep Transfer Learning for glitch
+classification, showing that knowledge from deep learning algorithms trained
+for real-world object recognition can be transferred for classifying glitches
+in time-series based on their spectrogram images. Using the Gravity Spy
+dataset, containing hand-labeled, multi-duration spectrograms obtained from
+real LIGO data, we demonstrate that this method enables optimal use of very
+deep convolutional neural networks for classification given small training
+datasets, significantly reduces the time for training the networks, and
+achieves state-of-the-art accuracy above 98.8%, with perfect precision-recall
+on 8 out of 22 classes. Furthermore, new types of glitches can be classified
+accurately given few labeled examples with this technique. Once trained via
+transfer learning, we show that the convolutional neural networks can be
+truncated and used as excellent feature extractors for unsupervised clustering
+methods to identify new classes based on their morphology, without any labeled
+examples. Therefore, this provides a new framework for dynamic glitch
+classification for gravitational wave detectors, which are expected to
+encounter new types of noise as they undergo gradual improvements to attain
+design sensitivity.
+"
+22782,Deep Network Uncertainty Maps for Indoor Navigation,"  Estimating the uncertainty of predictions is a crucial ability for robots in
+unstructured environments. Most mobile robots for indoor use rely on 2D laser
+scanners for localization, mapping and navigation. These sensors, however,
+cannot detect transparent surfaces or measure the full occupancy of complex
+objects such as tables. Deep Neural Networks have recently been proposed to
+overcome this limitation by learning to estimate object occupancy. These
+estimates are nevertheless subject to noise, making the evaluation of their
+confidence an important issue. In this work we study uncertainty estimation in
+deep models, proposing a novel solution based on a fully convolutional
+autoencoder. The proposed architecture is not restricted by the assumption that
+the uncertainty follows a Gaussian model, as in the case of many popular
+solutions for deep model uncertainty estimation, e.g., MC Dropout. We present
+results showing that uncertainty over obstacle distances is actually better
+modeled with a Laplace distribution. As an example of application where
+uncertainty evaluation is crucial, we also present an algorithm to build a map
+that includes information over obstacle distance estimates while taking into
+account the level of uncertainty in each estimate. We finally show how the
+constructed map can be used to increase global navigation safety by planning
+trajectories which avoid areas of high uncertainty, enabling higher autonomy
+for mobile robots in indoor settings.
+"
+22783,Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data,"  Traditional stock market prediction methods commonly only utilize the
+historical trading data, ignoring the fact that stock market fluctuations can
+be impacted by various other information sources such as stock related events.
+Although some recent works propose event-driven prediction approaches by
+considering the event data, how to leverage the joint impacts of multiple data
+sources still remains an open research problem. In this work, we study how to
+explore multiple data sources to improve the performance of the stock
+prediction. We introduce an Extended Coupled Hidden Markov Model incorporating
+the news events with the historical trading data. To address the data sparsity
+issue of news events for each single stock, we further study the fluctuation
+correlations between the stocks and incorporate the correlations into the model
+to facilitate the prediction task. Evaluations on China A-share market data in
+2016 show the superior performance of our model against previous methods.
+"
+22784,Unfolding Hidden Barriers by Active Enhanced Sampling,"  Collective variable (CV) or order parameter based enhanced sampling
+algorithms have achieved great success due to their ability to efficiently
+explore the rough potential energy landscapes of complex systems. However, the
+degeneracy of microscopic configurations, originating from the orthogonal space
+perpendicular to the CVs, is likely to shadow ""hidden barriers"" and greatly
+reduce the efficiency of CV-based sampling. Here we demonstrate that systematic
+machine learning CV, through enhanced sampling, can iteratively lift such
+degeneracies on the fly. We introduce an active learning scheme that consists
+of a parametric CV learner based on deep neural network and a CV-based enhanced
+sampler. Our active enhanced sampling (AES) algorithm is capable of identifying
+the least informative regions based on a historical sample, forming a positive
+feedback loop between the CV learner and sampler. This approach is able to
+globally preserve kinetic characteristics by incrementally enhancing both
+sample completeness and CV quality.
+"
+22785,Weak comonotonicity,"  The classical notion of comonotonicity has played a pivotal role when solving
+diverse problems in economics, finance, and insurance. In various practical
+problems, however, this notion of extreme positive dependence structure is
+overly restrictive and sometimes unrealistic. In the present paper, we put
+forward a notion of weak comonotonicity, which contains the classical notion of
+comonotonicity as a special case, and gives rise to necessary and sufficient
+conditions for a number of optimization problems, such as those arising in
+portfolio diversification, risk aggregation, and premium calculation.
+"
+22786,Vertex isoperimetry and independent set stability for tensor powers of cliques,"  The tensor power of the clique on $t$ vertices (denoted by $K_t^n$) is the
+graph on vertex set $\{1, ..., t\}^n$ such that two vertices $x, y \in \{1,
+..., t\}^n$ are connected if and only if $x_i \neq y_i$ for all $i \in \{1,
+..., n\}$. Let the density of a subset $S$ of $K_t^n$ to be $\mu(S) :=
+\frac{|S|}{t^n}$, and let the vertex boundary of a set $S$ to be vertices which
+are incident to some vertex of $S$, perhaps including points of $S$. We
+investigate two similar problems on such graphs.
+First, we study the vertex isoperimetry problem. Given a density $\nu \in [0,
+1]$ what is the smallest possible density of the vertex boundary of a subset of
+$K_t^n$ of density $\nu$? Let $\Phi_t(\nu)$ be the infimum of these minimum
+densities as $n \to \infty$. We find a recursive relation allows one to compute
+$\Phi_t(\nu)$ in time polynomial to the number of desired bits of precision.
+Second, we study given an independent set $I \subseteq K_t^n$ of density
+$\mu(I) = \frac{1}{t}(1-\epsilon)$, how close it is to a maximum-sized
+independent set $J$ of density $\frac{1}{t}$. We show that this deviation
+(measured by $\mu(I \setminus J)$) is at most $4\epsilon^{\frac{\log t}{\log t
+- \log(t-1)}}$ as long as $\epsilon < 1 - \frac{3}{t} + \frac{2}{t^2}$. This
+substantially improves on results of Alon, Dinur, Friedgut, and Sudakov (2004)
+and Ghandehari and Hatami (2008) which had an $O(\epsilon)$ upper bound. We
+also show the exponent $\frac{\log t}{\log t - \log(t-1)}$ is optimal assuming
+$n$ tending to infinity and $\epsilon$ tending to $0$. The methods have
+similarity to recent work by Ellis, Keller, and Lifshitz (2016) in the context
+of Kneser graphs and other settings.
+The author hopes that these results have potential applications in hardness
+of approximation, particularly in approximate graph coloring and independent
+set problems.
+"
+22787,Online Learning with Randomized Feedback Graphs for Optimal PUE Attacks in Cognitive Radio Networks,"  In a cognitive radio network, a secondary user learns the spectrum
+environment and dynamically accesses the channel where the primary user is
+inactive. At the same time, a primary user emulation (PUE) attacker can send
+falsified primary user signals and prevent the secondary user from utilizing
+the available channel. The best attacking strategies that an attacker can apply
+have not been well studied. In this paper, for the first time, we study optimal
+PUE attack strategies by formulating an online learning problem where the
+attacker needs to dynamically decide the attacking channel in each time slot
+based on its attacking experience. The challenge in our problem is that since
+the PUE attack happens in the spectrum sensing phase, the attacker cannot
+observe the reward on the attacked channel. To address this challenge, we
+utilize the attacker's observation capability. We propose online learning-based
+attacking strategies based on the attacker's observation capabilities. Through
+our analysis, we show that with no observation within the attacking slot, the
+attacker loses on the regret order, and with the observation of at least one
+channel, there is a significant improvement on the attacking performance.
+Observation of multiple channels does not give additional benefit to the
+attacker (only a constant scaling) though it gives insight on the number of
+observations required to achieve the minimum constant factor. Our proposed
+algorithms are optimal in the sense that their regret upper bounds match their
+corresponding regret lower-bounds. We show consistency between simulation and
+analytical results under various system parameters.
+"
+22788,Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms,"  We consider empirical risk minimization of linear predictors with convex loss
+functions. Such problems can be reformulated as convex-concave saddle point
+problems, and thus are well suitable for primal-dual first-order algorithms.
+However, primal-dual algorithms often require explicit strongly convex
+regularization in order to obtain fast linear convergence, and the required
+dual proximal mapping may not admit closed-form or efficient solution. In this
+paper, we develop both batch and randomized primal-dual algorithms that can
+exploit strong convexity from data adaptively and are capable of achieving
+linear convergence even without regularization. We also present dual-free
+variants of the adaptive primal-dual algorithms that do not require computing
+the dual proximal mapping, which are especially suitable for logistic
+regression.
+"
+22789,Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes,"  In this paper, we introduce a novel task for machine learning in healthcare,
+namely personalized modeling of the female hormonal cycle. The motivation for
+this work is to model the hormonal cycle and predict its phases in time, both
+for healthy individuals and for those with disorders of the reproductive
+system. Because there are individual differences in the menstrual cycle, we are
+particularly interested in personalized models that can account for individual
+idiosyncracies, towards identifying phenotypes of menstrual cycles. As a first
+step, we consider the hormonal cycle as a set of observations through time. We
+use a previously validated mechanistic model to generate realistic hormonal
+patterns, and experiment with Gaussian process regression to estimate their
+values over time. Specifically, we are interested in the feasibility of
+predicting menstrual cycle phases under varying learning conditions: number of
+cycles used for training, hormonal measurement noise and sampling rates, and
+informed vs. agnostic sampling of hormonal measurements. Our results indicate
+that Gaussian processes can help model the female menstrual cycle. We discuss
+the implications of our experiments in the context of modeling the female
+menstrual cycle.
+"
+22790,Circle actions on almost complex manifolds with 4 fixed points,"  Let the circle act on a compact almost complex manifold $M$. In this paper,
+we classify the fixed point data of the action if there are 4 fixed points and
+the dimension of the manifold is at most 6. First, if $\dim M=2$, then $M$ is a
+disjoint union of rotations on two 2-spheres. Second, if $\dim M=4$, we prove
+that the action alikes a circle action on a Hirzebruch surface. Finally, if
+$\dim M=6$, we prove that six types occur for the fixed point data;
+$\mathbb{CP}^3$ type, complex quadric in $\mathbb{CP}^4$ type, Fano 3-fold
+type, $S^6 \cup S^6$ type, and two unknown types that might possibly be
+realized as blow ups of a manifold like $S^6$. When $\dim M=6$, we recover the
+result by Ahara in which the fixed point data is determined if furthermore
+$\mathrm{Todd}(M)=1$ and $c_1^3(M)[M] \neq 0$, and the result by Tolman in
+which the fixed point data is determined if furthermore the base manifold
+admits a symplectic structure and the action is Hamiltonian.
+"
+22791,Consistent Estimation of Mixed Memberships with Successive Projections,"  This paper considers the parameter estimation problem in Mixed Membership
+Stochastic Block Model (MMSB), which is a quite general instance of random
+graph model allowing for overlapping community structure. We present the new
+algorithm successive projection overlapping clustering (SPOC) which combines
+the ideas of spectral clustering and geometric approach for separable
+non-negative matrix factorization. The proposed algorithm is provably
+consistent under MMSB with general conditions on the parameters of the model.
+SPOC is also shown to perform well experimentally in comparison to other
+algorithms.
+"
+22792,On Second-Order Vibrational Lineshapes of the Air/Water Interface,"  We explore by means of modeling how absorptive-dispersive mixing between the
+second- and third-order terms modify the imaginary chi(2)total responses from
+air/water interfaces under conditions of varying charge densities and ionic
+strength. To do so, we use published Im(chi(2)) and chi(3) spectra of the neat
+air/water interface that were obtained either from computations or experiments.
+We find that the chi(2)total spectral lineshapes corresponding to
+experimentally measured spectra contain significant contributions from both
+interfacial chi(2) and bulk chi(3) terms at interfacial charge densities
+equivalent to less than 0.005% of a monolayer of water molecules, especially in
+the 3100 wavenumber to 3300 wavenumber frequency region. Additionally, the role
+of short-range static dipole potentials is examined under conditions mimicking
+brine. Our results indicate that surface potentials, if indeed present at the
+air/water interface, manifest themselves spectroscopically in the tightly
+bonded H-bond network observable in the 3200 wavenumber frequency range.
+"
+22793,A Game-Semantic Model of Computation,"  The present paper introduces a novel notion of `(effective) computability',
+called viability, of strategies in game semantics in an intrinsic (i.e.,
+without recourse to the standard Church-Turing computability), non-inductive
+and non-axiomatic manner, and shows, as a main technical achievement, that
+viable strategies are Turing complete. Consequently, we have given a
+mathematical foundation of computation in the same sense as Turing machines but
+beyond computation on natural numbers, e.g., higher-order computation, in a
+more abstract fashion. As immediate corollaries, some of the well-known
+theorems in computability theory such as the smn-theorem and the first
+recursion theorem are generalized. Notably, our game-semantic framework
+distinguishes `high-level' computational processes that operate directly on
+mathematical objects such as natural numbers (not on their symbolic
+representations) and their `symbolic implementations' that define their
+`computability', which sheds new light on the very concept of computation. This
+work is intended to be a stepping stone towards a new mathematical foundation
+of computation, intuitionistic logic and constructive mathematics.
+"
+22794,Exact Bloch oscillations in optical waveguide arrays with arbitrary long-range coupling,"  We find the exact Bloch oscillations in zigzag arrays of curved optical
+waveguides under the influence of arbitrary long-range coupling. The curvature
+induces a linear transverse potential gradient in the equations of the light
+evolution. In the case of arrays with second-order coupling, steady states can
+be obtained as linear combinations of Bessel functions of integer index. The
+corresponding eigenvalues are equally spaced and form the well-known
+Wannier-Stark ladder, the spacing being independent of the second-order
+coupling. We also solve exactly the wave packet dynamics and compare it with
+experimental results. Accordingly we find that a broad optical pulse performs
+Bloch oscillations. Frequency doubling of the fundamental Bloch frequency sets
+up at finite values of the second-order coupling. On the contrary when a single
+waveguide is initially excited, a breathing mode is activated with no signature
+of Bloch oscillations. We present a generalization of our results to waveguide
+arrays subject to long-range coupling. In the general case the centroid of the
+wave packet shows the occurrence of multiples of the Bloch frequency up to the
+order of the interaction.
+"
+22795,Geant4 Simulations Of A Wide-Angle X-Ray Focusing Telescope,"  The rapid development of X-ray astronomy has been made possible by widely
+deploying X-ray focusing telescopes on board many X-ray satellites. Geant4 is a
+very powerful toolkit for Monte Carlo simulations and has remarkable abilities
+to model complex geometrical configurations. However, the library of physical
+processes available in Geant4 lacks a description of the reflection of X-ray
+photons at a grazing incident angle which is the core physical process in the
+simulation of X-ray focusing telescopes. The scattering of low-energy charged
+particles from the mirror surfaces is another noteworthy process which is not
+yet incorporated into Geant4.
+Here we describe a Monte Carlo model of a simplified wide-angle X-ray
+focusing telescope adopting lobster-eye optics and a silicon detector using the
+Geant4 toolkit. With this model, we simulate the X-ray tracing, proton
+scattering and background detection. We find that: (1) the effective area
+obtained using Geant4 is in agreement with that obtained using Q software with
+an average difference of less than 3\%; (2) X-rays are the dominant background
+source below 10 keV; (3) the sensitivity of the telescope is better by at least
+one order of magnitude than that of a coded mask telescope with the same
+physical dimensions; (4) the number of protons passing through the optics and
+reaching the detector by Firsov scattering is about 2.5 times that of multiple
+scattering for the lobster-eye telescope.
+"
+22796,Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay,"  The Daya Bay experiment has observed correlations between reactor core fuel
+evolution and changes in the reactor antineutrino flux and energy spectrum.
+Four antineutrino detectors in two experimental halls were used to identify 2.2
+million inverse beta decays (IBDs) over 1230 days spanning multiple fuel cycles
+for each of six 2.9 GW$_{\textrm{th}}$ reactor cores at the Daya Bay and Ling
+Ao nuclear power plants. Using detector data spanning effective $^{239}$Pu
+fission fractions, $F_{239}$, from 0.25 to 0.35, Daya Bay measures an average
+IBD yield, $\bar{\sigma}_f$, of $(5.90 \pm 0.13) \times 10^{-43}$
+cm$^2$/fission and a fuel-dependent variation in the IBD yield,
+$d\sigma_f/dF_{239}$, of $(-1.86 \pm 0.18) \times 10^{-43}$ cm$^2$/fission.
+This observation rejects the hypothesis of a constant antineutrino flux as a
+function of the $^{239}$Pu fission fraction at 10 standard deviations. The
+variation in IBD yield was found to be energy-dependent, rejecting the
+hypothesis of a constant antineutrino energy spectrum at 5.1 standard
+deviations. While measurements of the evolution in the IBD spectrum show
+general agreement with predictions from recent reactor models, the measured
+evolution in total IBD yield disagrees with recent predictions at 3.1$\sigma$.
+This discrepancy indicates that an overall deficit in measured flux with
+respect to predictions does not result from equal fractional deficits from the
+primary fission isotopes $^{235}$U, $^{239}$Pu, $^{238}$U, and $^{241}$Pu.
+Based on measured IBD yield variations, yields of $(6.17 \pm 0.17)$ and $(4.27
+\pm 0.26) \times 10^{-43}$ cm$^2$/fission have been determined for the two
+dominant fission parent isotopes $^{235}$U and $^{239}$Pu. A 7.8% discrepancy
+between the observed and predicted $^{235}$U yield suggests that this isotope
+may be the primary contributor to the reactor antineutrino anomaly.
+"
+22797,On the conformal dimension of product measures,"  Given a compact set $E \subset \mathbb{R}^{d - 1}$, $d \geq 1$, write $K_{E}
+:= [0,1] \times E \subset \mathbb{R}^{d}$. A theorem of C. Bishop and J. Tyson
+states that any set of the form $K_{E}$ is minimal for conformal dimension: if
+$(X,d)$ is a metric space and $f \colon K_{E} \to (X,d)$ is a quasisymmetric
+homeomorphism, then $$\dim_{\mathrm{H}} f(K_{E}) \geq \dim_{\mathrm{H}}
+K_{E}.$$ We prove that the measure-theoretic analogue of the result is not
+true. For any $d \geq 2$ and $0 \leq s < d - 1$, there exist compact sets $E
+\subset \mathbb{R}^{d - 1}$ with $0 < \mathcal{H}^{s}(E) < \infty$ such that
+the conformal dimension of $\nu$, the restriction of the $(1 + s)$-dimensional
+Hausdorff measure on $K_{E}$, is zero. More precisely, for any $\epsilon > 0$,
+there exists a quasisymmetric embedding $F \colon K_{E} \to \mathbb{R}^{d}$
+such that $\dim_{\mathrm{H}} F_{\sharp}\nu < \epsilon$.
+"
+22798,Equilibration and Order in Quantum Floquet Matter,"  Equilibrium theormodynamics is characterized by two fundamental ideas:
+thermalisation--that systems approach a late time thermal state; and phase
+structure--that thermal states exhibit singular changes as various parameters
+characterizing the system are changed. We summarise recent progress that has
+established generalizations of these ideas to periodically driven, or Floquet,
+closed quantum systems. This has resulted in the discovery of entirely new
+phases which exist only out of equilibrium, such as the $\pi$-spin glass or
+Floquet time crystal.
+"
+22799,Magnetic Skyrmions for Cache Memory,"  Magnetic skyrmions (MS) are particle-like spin structures with whirling
+configuration, which are promising candidates for spin-based memory. MS
+contains alluring features including remarkably high stability, ultra low
+driving current density, and compact size. Due to their higher stability and
+lower drive current requirement for movement, skyrmions have great potential in
+energy efficient spintronic device applications. We propose a skyrmion-based
+cache memory where data can be stored in a long nanotrack as multiple bits.
+Write operation (formation of skyrmion) can be achieved by injecting spin
+polarized current in a magnetic nanotrack and subsequently shifting the MS in
+either direction along the nanotrack using charge current through a spin-Hall
+metal (SHM), underneath the magnetic layer. The presence of skyrmion can alter
+the resistance of a magnetic tunneling junction (MTJ) at the read port.
+Considering the read and write latency along the long nanotrack cache memory, a
+strategy of multiple read and write operations is discussed. Besides, the size
+of a skyrmion affects the packing density, current induced motion velocity, and
+readability (change of resistance while sensing the existence of a skyrmion).
+Design optimization to mitigate the above effects is also investigated.
+"
+22800,On the variance of radio interferometric calibration solutions: Quality-based Weighting Schemes,"  This paper investigates the possibility of improving radio interferometric
+images using an algorithm inspired by an optical method known as ""lucky
+imaging"", which would give more weight to the best-calibrated visibilities used
+to make a given image. A fundamental relationship between the statistics of
+interferometric calibration solution residuals and those of the image-plane
+pixels is derived in this paper. This relationship allows us to understand and
+describe the statistical properties of the residual image. In this framework,
+the noise-map can be described as the Fourier transform of the covariance
+between residual visibilities in a new differential Fourier plane. Image-plane
+artefacts can be seen as one realisation of the pixel covariance distribution,
+which can be estimated from the antenna gain statistics. Based on this
+relationship, we propose a means of improving images made with calibrated
+visibilities using weighting schemes. This improvement would occur after
+calibration, but before imaging - it is thus ideally used between major
+iterations of self-calibration loops. Applying the weighting scheme to
+simulated data improves the noise level in the final image at negligible
+computational cost.
+"
+22801,Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behavior,"  In this work, we propose a method for learning driver models that account for
+variables that cannot be observed directly. When trained on a synthetic
+dataset, our models are able to learn encodings for vehicle trajectories that
+distinguish between four distinct classes of driver behavior. Such encodings
+are learned without any knowledge of the number of driver classes or any
+objective that directly requires the models to learn encodings for each class.
+We show that driving policies trained with knowledge of latent variables are
+more effective than baseline methods at imitating the driver behavior that they
+are trained to replicate. Furthermore, we demonstrate that the actions chosen
+by our policy are heavily influenced by the latent variable settings that are
+provided to them.
+"
+22802,Finite sample change point inference and identification for high-dimensional mean vectors,"  Cumulative sum (CUSUM) statistics are widely used in the change point
+inference and identification. This paper studies two problems for
+high-dimensional mean vectors based on the $\ell^{\infty}$-norm of the CUSUM
+statistics. For the problem of testing for the existence of a change point in
+an independent sample generated from the mean-shift model, we introduce a
+Gaussian multiplier bootstrap to calibrate critical values of the CUSUM test
+statistics in high dimensions. The proposed bootstrap CUSUM test is fully
+data-dependent and it has strong theoretical guarantees under arbitrary
+dependence structures and mild moment conditions. Specifically, we show that
+with a boundary removal parameter the bootstrap CUSUM test enjoys the uniform
+validity in size under the null and it achieves the minimax separation rate
+under the sparse alternatives when the dimension $p$ can be larger than the
+sample size $n$.
+Once a change point is detected, we estimate the change point location by
+maximizing the $\ell^{\infty}$-norm of the generalized CUSUM statistics at two
+different weighting scales. The first estimator is based on the covariance
+stationary CUSUM statistics, and we prove its consistency in estimating the
+location at the nearly parametric rate $n^{-1/2}$ for sub-exponential
+observations. The second estimator is based on non-stationary CUSUM statistics,
+assigning less weights on the boundary data points. In the latter case, we show
+that it achieves the nearly best possible rate of convergence on the order
+$n^{-1}$. In both cases, dimension impacts the rate of convergence only through
+the logarithm factors, and therefore consistency of the CUSUM location
+estimators is possible when $p$ is much larger than $n$.
+"
+22803,Art Gallery Localization,"  We study the problem of placing a set $T$ of broadcast towers in a simple
+polygon $P$ in order for any point to locate itself in the interior of $P$. Let
+$V(p)$ denote the visibility polygon of a point $p$, as the set of all points
+$q \in P$ that are visible to $p$. For any point $p \in P$: for each tower $t
+\in T \cap V(p)$ the point $p$ receives the coordinates of $t$ and the
+Euclidean distance between $t$ and $p$. From this information $p$ can determine
+its coordinates. We show a tower-positioning algorithm that computes such a set
+$T$ of size at most $\lfloor 2n/3\rfloor$, where $n$ is the size of $P$. This
+improves the previous upper bound of $\lfloor 8n/9\rfloor$ towers. We also show
+that $\lfloor 2n/3\rfloor$ towers are sometimes necessary.
+"
+22804,Representations of pure symmetric automorphism groups of RAAGs,"  We study representations of the pure symmetric automorphism group
+$PAut(A_\Gamma)$ of a RAAG $A_\Gamma$ with defining graph $\Gamma$.
+We first construct a homomorphism from $PAut(A_\Gamma)$ to the direct product
+of a RAAG and a finite direct product of copies of $F_2 \times F_2$; moreover,
+the image of $PAut(A_\Gamma)$ under this homomorphism is surjective onto each
+factor. As a consequence, we obtain interesting actions of $PAut(A_\Gamma)$ on
+non-positively curved spaces
+We then exhibit, for connected $\Gamma$, a RAAG which property contains
+$Inn(A_\Gamma)$ and embeds as a normal subgroup of $PAut(A_\Gamma)$. We end
+with a discussion of the linearity problem for $PAut(A_\Gamma)$.
+"
+22805,Indecomposable modules of solvable Lie algebras,"  We classify all uniserial modules of the solvable Lie algebra
+$\mathfrak{g}=\langle x\rangle \ltimes V$, where $V$ is an abelian Lie algebra
+over an algebraically closed field of characteristic 0 and $x$ is an arbitrary
+automorphism of $V$.
+"
+22806,Usage Bibliometrics as a Tool to Measure Research Activity,"  Measures for research activity and impact have become an integral ingredient
+in the assessment of a wide range of entities (individual researchers,
+organizations, instruments, regions, disciplines). Traditional bibliometric
+indicators, like publication and citation based indicators, provide an
+essential part of this picture, but cannot describe the complete picture. Since
+reading scholarly publications is an essential part of the research life cycle,
+it is only natural to introduce measures for this activity in attempts to
+quantify the efficiency, productivity and impact of an entity. Citations and
+reads are significantly different signals, so taken together, they provide a
+more complete picture of research activity. Most scholarly publications are now
+accessed online, making the study of reads and their patterns possible.
+Click-stream logs allow us to follow information access by the entire research
+community, real-time. Publication and citation datasets just reflect activity
+by authors. In addition, download statistics will help us identify publications
+with significant impact, but which do not attract many citations. Click-stream
+signals are arguably more complex than, say, citation signals. For one, they
+are a superposition of different classes of readers. Systematic downloads by
+crawlers also contaminate the signal, as does browsing behavior. We discuss the
+complexities associated with clickstream data and how, with proper filtering,
+statistically significant relations and conclusions can be inferred from
+download statistics. We describe how download statistics can be used to
+describe research activity at different levels of aggregation, ranging from
+organizations to countries. These statistics show a correlation with
+socio-economic indicators. A comparison will be made with traditional
+bibliometric indicators. We will argue that astronomy is representative of more
+general trends.
+"
+22807,On the Khovanov Homology of Surgeries,"  We show a spectral sequence for the rational Khovanov homology of an oriented
+link in terms of the rational Khovanov complexes and homologies of the link
+surgeries along an admissible cut. As a non trivial corollary, we give an
+explicit splitting formula for the Jones polynomial.
+"
+22808,Sizes and albedos of Mars-crossing asteroids from WISE/NEOWISE data,"  Context. Mars-crossing asteroids (MCs) are a dynamically unstable group
+between the main belt and the near-Earth populations. Characterising the
+physical properties of a large sample of MCs can help to understand the
+original sources of many near-Earth asteroids, some of which may produce
+meteorites on Earth. Aims. Our aim is to provide diameters and albedos of MCs
+with available WISE/NEOWISE data. Methods. We used the near-Earth asteroid
+thermal model to find the best-fitting values of equivalent diameter and,
+whenever possible, the infrared beaming parameter. With the diameter and
+tabulated asteroid absolute magnitudes we also computed the visible geometric
+albedos. Results. We determined the diameters and beaming parameters of 404
+objects observed during the fully cryogenic phase of the WISE mission, most of
+which have not been published elsewhere. We also obtained 1572 diameters from
+data from the 3-Band and posterior non-cryogenic phases using a default value
+of beaming parameter. The average beaming parameter is 1.2 +/- 0.2 for objects
+smaller than 10 km, which constitute most of our sample. This is higher than
+the typical value of 1.0 found for the whole main belt and is possibly related
+to the fact that WISE is able to observe many more small objects at shorter
+heliocentric distances, i.e. at higher phase angles. We argue that this is a
+better default value for modelling Mars-crossing asteroids from the
+WISE/NEOWISE catalogue and discuss the effects of this choice on the diameter
+and albedo distributions. We find a double-peaked distribution for the visible
+geometric albedos, which is expected since this population is compositionally
+diverse and includes objects in the major spectral complexes. However, the
+distribution of beaming parameters is homogeneous for both low- and high-albedo
+objects.
+"
+22809,Hybridization-induced interface states in a topological insulator-magnetic metal heterostructure,"  Recent experiments demonstrating large spin-transfer torques in topological
+insulator (TI)-ferromagnetic metal (FM) bilayers have generated a great deal of
+excitement due to their potential applications in spintronics. The source of
+the observed spin-transfer torque, however, remains unclear. This is because
+the large charge transfer from the FM to TI layer would prevent the Dirac cone
+at the interface from being anywhere near the Fermi level to contribute to the
+observed spin-transfer torque. Moreover, there is yet little understanding of
+the impact on the Dirac cone at the interface from the metallic bands
+overlapping in energy and momentum, where strong hybridization could take
+place. Here, we build a simple microscopic model and perform
+first-principles-based simulations for such a TI-FM heterostructure,
+considering the strong hybridization and charge transfer effects. We find that
+the original Dirac cone is destroyed by the hybridization as expected. Instead,
+we find a new interface state which we dub 'descendent state' to form near the
+Fermi level due to the strong hybridization with the FM states at the same
+momentum. Such a `descendent state' carries a sizable weight of the original
+Dirac interface state, and thus inherits the localization at the interface and
+the same Rashba-type spin-momentum locking. We propose that the `descendent
+state' may be an important source of the experimentally observed large
+spin-transfer torque in the TI-FM heterostructure.
+"
+22810,Algebras of Information. A New and Extended Axiomatic Foundation,"  The basic idea behind information algebras is that information comes in
+pieces, each referring to a certain question, that these pieces can be combined
+or aggregated and that the part relating to a given question can be extracted.
+This algebraic structure can be given different forms. Questions were
+originally represented by subsets of variables. Pieces of information were then
+represented by valuations associated with the domains of variables. This leads
+to an algebraic structure called valuation algebras. The basic axiomatics of
+this algebraic structure was in essence proposed by Shenoy and Shafer. Here a
+much more general view of systems of questions is proposed and pieces of
+information are related to the elements of this system of questions. This leads
+to a new and extended system of axioms for information algebras. Classical
+valuation algebras are essentially a special case of this new system. A full
+discussion of the algebraic theory of this new information algebras is given,
+including local computation, duality between labeled and domain-free versions
+of the algebras, order of information, finiteness of information and
+approximation, compact and continuous information algebras. Finally a rather
+complete discussion of uncertain information, based on random maps into
+information algebras is presented. This is shown to represent a generalisation
+of classical Dempster-Shafer theory.
+"
+22811,Diffusion Variational Autoencoders,"  A standard Variational Autoencoder, with a Euclidean latent space, is
+structurally incapable of capturing topological properties of certain datasets.
+To remove topological obstructions, we introduce Diffusion Variational
+Autoencoders with arbitrary manifolds as a latent space. A Diffusion
+Variational Autoencoder uses transition kernels of Brownian motion on the
+manifold. In particular, it uses properties of the Brownian motion to implement
+the reparametrization trick and fast approximations to the KL divergence. We
+show that the Diffusion Variational Autoencoder is capable of capturing
+topological properties of synthetic datasets. Additionally, we train MNIST on
+spheres, tori, projective spaces, SO(3), and a torus embedded in R3. Although a
+natural dataset like MNIST does not have latent variables with a clear-cut
+topological structure, training it on a manifold can still highlight
+topological and geometrical properties.
+"
+22812,Experimental and Numerical Investigation of Phase Separation due to Multi-Component Mixing at High-Pressure Conditions,"  Experiments and numerical simulations were carried out in order to contribute
+to a better understanding and prediction of high-pressure injection into a
+gaseous environment. Specifically, the focus was put on the phase separation
+processes of an initially supercritical fluid due to the interaction with its
+surrounding. N-hexane was injected into a chamber filled with pure nitrogen at
+5 MPa and 293 K and three different test cases were selected such that they
+cover regimes in which the thermodynamic non-idealities, in particular the
+effects that stem from the potential phase separation, are significant.
+Simultaneous shadowgraphy and elastic light scattering experiments were
+conducted to capture both the flow structure as well as the phase separation.
+In addition, large-eddy simulations with a vapor-liquid equilibrium model were
+performed. Both experimental and numerical results show phase formation for the
+cases, where the a-priori calculation predicts two-phase flow. Moreover,
+qualitative characteristics of the formation process agree well between
+experiments and numerical simulations and the transition behaviour from a
+dense-gas to a spray-like jet was captured by both.
+"
+22813,Counting paths in perfect trees,"  We present some exact expressions for the number of paths of a given length
+in a perfect $m$-ary tree. We first count the paths in perfect rooted $m$-ary
+trees and then use the results to determine the number of paths in perfect
+unrooted $m$-ary trees, extending a known result for binary trees.
+"
+22814,A General Theory of Sample Complexity for Multi-Item Profit Maximization,"  The design of profit-maximizing multi-item mechanisms is a notoriously
+challenging problem with tremendous real-world impact. The mechanism designer's
+goal is to field a mechanism with high expected profit on the distribution over
+buyers' values. Unfortunately, if the set of mechanisms he optimizes over is
+complex, a mechanism may have high empirical profit over a small set of samples
+but low expected profit. This raises the question, how many samples are
+sufficient to ensure that the empirically optimal mechanism is nearly optimal
+in expectation? We uncover structure shared by a myriad of pricing, auction,
+and lottery mechanisms that allows us to prove strong sample complexity bounds:
+for any set of buyers' values, profit is a piecewise linear function of the
+mechanism's parameters. We prove new bounds for mechanism classes not yet
+studied in the sample-based mechanism design literature and match or improve
+over the best known guarantees for many classes. The profit functions we study
+are significantly different from well-understood functions in machine learning,
+so our analysis requires a sharp understanding of the interplay between
+mechanism parameters and buyer values. We strengthen our main results with
+data-dependent bounds when the distribution over buyers' values is
+""well-behaved."" Finally, we investigate a fundamental tradeoff in sample-based
+mechanism design: complex mechanisms often have higher profit than simple
+mechanisms, but more samples are required to ensure that empirical and expected
+profit are close. We provide techniques for optimizing this tradeoff.
+"
+22815,A Flat Inner Disk Model as an Alternative to the Kepler Dichotomy in the Q1 to Q16 Planet Population,"  We use simulated planetary systems to model the planet multiplicity of Kepler
+stars. Previous studies have underproduced single planet systems and invoked
+the so called Kepler dichotomy, where the planet forming ability of a Kepler
+star is dichotomous, producing either few or many transiting planets. In this
+paper we show that the Kepler dichotomy is only required when the inner part of
+planetary disks are just assumed to be flared. When the inner part of planetary
+disks are flat, we reproduce the observed planet multiplicity of Kepler stars
+without the need to invoke a dichotomy. We find that independent of the disk
+model assumed, the mean number of planets per star is approximately 2 for
+orbital periods between 3 and 200 days, and for planetary radii between 1 and 5
+Earth radii. This contrasts with the Solar System where no planets occupy the
+same parameter space.
+"
+22816,Deterministic Inequalities for Smooth M-estimators,"  Ever since the proof of asymptotic normality of maximum likelihood estimator
+by Cramer (1946), it has been understood that a basic technique of the Taylor
+series expansion suffices for asymptotics of $M$-estimators with
+smooth/differentiable loss function. Although the Taylor series expansion is a
+purely deterministic tool, the realization that the asymptotic normality
+results can also be made deterministic (and so finite sample) received far less
+attention. With the advent of big data and high-dimensional statistics, the
+need for finite sample results has increased. In this paper, we use the
+(well-known) Banach fixed point theorem to derive various deterministic
+inequalities that lead to the classical results when studied under randomness.
+In addition, we provide applications of these deterministic inequalities for
+crossvalidation/subsampling, marginal screening and uniform-in-submodel results
+that are very useful for post-selection inference and in the study of
+post-regularization estimators. Our results apply to many classical estimators,
+in particular, generalized linear models, non-linear regression and cox
+proportional hazards model. Extensions to non-smooth and constrained problems
+are also discussed.
+"
+22817,Deep Learning for Explicitly Modeling Optimization Landscapes,"  In all but the most trivial optimization problems, the structure of the
+solutions exhibit complex interdependencies between the input parameters.
+Decades of research with stochastic search techniques has shown the benefit of
+explicitly modeling the interactions between sets of parameters and the overall
+quality of the solutions discovered. We demonstrate a novel method, based on
+learning deep networks, to model the global landscapes of optimization
+problems. To represent the search space concisely and accurately, the deep
+networks must encode information about the underlying parameter interactions
+and their contributions to the quality of the solution. Once the networks are
+trained, the networks are probed to reveal parameter combinations with high
+expected performance with respect to the optimization task. These estimates are
+used to initialize fast, randomized, local search algorithms, which in turn
+expose more information about the search space that is subsequently used to
+refine the models. We demonstrate the technique on multiple optimization
+problems that have arisen in a variety of real-world domains, including:
+packing, graphics, job scheduling, layout and compression. The problems include
+combinatoric search spaces, discontinuous and highly non-linear spaces, and
+span binary, higher-cardinality discrete, as well as continuous parameters.
+Strengths, limitations, and extensions of the approach are extensively
+discussed and demonstrated.
+"
+22818,Magetostatic amplifier with tunable maximum by twisted-light plasma interactions,"  Laser beams with Laguerre-Gaussian (LG) mode carry orbital angular momentum
+(OAM), however when interacting with plasmas, the net OAM acquired by plasmas
+is basically zero after interaction. Here, we find when there exist a small
+magetostatic seed along laser propagation direction, the barrier would be
+broken, giving rise to dramatic OAM transfer from LG lasers to plasmas. Hence,
+the net OAM remained in plasmas system would continuously enhance the
+magetostatic field, until the corresponding Larmor frequency of electrons is
+comparable to the laser frequency in vacuum. Three-dimensional particle-in-cell
+(3D-PIC) simulations are performed to confirm our theory, producing
+space-uniform, time-stable and extremely intense magetostatic fields.
+"
+22819,A Comparative Study on Different Types of Approaches to Bengali document Categorization,"  Document categorization is a technique where the category of a document is
+determined. In this paper three well-known supervised learning techniques which
+are Support Vector Machine(SVM), Naïve Bayes(NB) and Stochastic Gradient
+Descent(SGD) compared for Bengali document categorization. Besides classifier,
+classification also depends on how feature is selected from dataset. For
+analyzing those classifier performances on predicting a document against twelve
+categories several feature selection techniques are also applied in this
+article namely Chi square distribution, normalized TFIDF (term
+frequency-inverse document frequency) with word analyzer. So, we attempt to
+explore the efficiency of those three-classification algorithms by using two
+different feature selection techniques in this article.
+"
+22820,Quantum Turing Machines Computations and Measurements,"  We propose a new formulation of Quantum Turing Ma- chines, as an extension of
+those proposed by Bernstein and Vazirani. For this new class of Quantum Turing
+Machines, both finite and infinite computations are meaningful -an infinite
+computation does not correspond trivially to a divergent function. Moreover, we
+propose a natural observation protocol for the new QTMs, that does not modify
+the probability of the possible outcomes of the machines. Finally, we use QTMs
+to define a class of quantum computable functions - any such a function is a
+mapping from a general quantum state to a distribution of probability of
+natural numbers.
+"
+22821,"Peculiarities of the SERS Spectra of 4,4-Bipyridine Molecule in a Single Molecule Detection Regime","  The paper briefly describes main statements of the theory of the SERS spectra
+with regards to the single molecule regime, when the enhancement achieves the
+values . Analysis of the spectra of 4,4-bypyridine, obtained on the dimer
+lattice of sharp nanoparticles points out that the observed enhancement is
+caused exclusively by a strong quadrupole light-molecule interaction, which
+manifests in the presence of lines, caused by vibrations with the unit
+irreducible representations of the and symmetry groups, which apparently
+describe the symmetry properties of the molecule. The study of the spectra,
+obtained by Tip enhanced spectroscopy demonstrates that the strong quadrupole
+light-molecule interaction still plays a leading role, however the strong
+dipole interaction still manifests in the existence of very weak forbidden
+lines. This result apparently is associated with another experimental geometry .
+"
+22822,On semisimple quasitriangular Hopf algebras of dimension $dq^n$,"  Let $q>2$ be a prime number, $d$ be an odd square-free natural number, and
+$n$ be a non-negative integer. We prove that a semisimple quasitriangular Hopf
+algebra of dimension $dq^n$ is solvable in the sense of Etingof, Nikshych and
+Ostrik. In particular, if $n\leq 3$ then it is either isomorphic to $k^G$ for
+some abelian group $G$, or twist equivalent to a Hopf algebra which fits into a
+cocentral abelian exact sequence.
+"
+22823,The effect of hardware-computed travel-time on localization accuracy in the inversion of experimental (acoustic) waveform data,"  This study aims to advance hardware-level computations for travel-time
+tomography applications in which the wavelength is close to the diameter of the
+information that has to be recovered. Such can be the case, for example, in the
+imaging applications of (1) biomedical physics, (2) astro-geophysics and (3)
+civil engineering. Our aim is to shed light on the effect of that preprocessing
+the digital waveform signal has on the inversion results and to find
+computational solutions that guarantee robust inversion when there are
+incomplete and/or noisy measurements. We describe a hardware-level
+implementation for integrated and thresholded travel-time computation (ITT and
+TTT). We compare the ITT and TTT approaches in inversion analysis with
+experimental acoustic travel-time data recorded using a ring geometry for the
+transmission and measurement points. The results obtained suggest that ITT is
+essential for maintaining the robustness of the inversion with imperfect signal
+digitization and sparsity. In order to ensure the relevance of the results, the
+specifications of the test setup were related to those of applications (1)-(3).
+"
+22824,Egocentric Spatial Memory,"  Egocentric spatial memory (ESM) defines a memory system with encoding,
+storing, recognizing and recalling the spatial information about the
+environment from an egocentric perspective. We introduce an integrated deep
+neural network architecture for modeling ESM. It learns to estimate the
+occupancy state of the world and progressively construct top-down 2D global
+maps from egocentric views in a spatially extended environment. During the
+exploration, our proposed ESM model updates belief of the global map based on
+local observations using a recurrent neural network. It also augments the local
+mapping with a novel external memory to encode and store latent representations
+of the visited places over long-term exploration in large environments which
+enables agents to perform place recognition and hence, loop closure. Our
+proposed ESM network contributes in the following aspects: (1) without feature
+engineering, our model predicts free space based on egocentric views
+efficiently in an end-to-end manner; (2) different from other deep
+learning-based mapping system, ESMN deals with continuous actions and states
+which is vitally important for robotic control in real applications. In the
+experiments, we demonstrate its accurate and robust global mapping capacities
+in 3D virtual mazes and realistic indoor environments by comparing with several
+competitive baselines.
+"
+22825,Reproducible research using biomodels,"  Like other types of computational research, modeling and simulation of
+biological processes (biomodels) is still largely communicated without
+sufficient detail to allow independent reproduction of results. But
+reproducibility in this area of research could easily be achieved by making use
+of existing resources, such as supplying models in standard formats and
+depositing code, models, and results in public repositories.
+"
+22826,Crime incidents embedding using restricted Boltzmann machines,"  We present a new approach for detecting related crime series, by unsupervised
+learning of the latent feature embeddings from narratives of crime record via
+the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a
+drastically different approach from prior work on crime analysis, which
+typically considers only time and location and at most category information.
+After the embedding, related cases are closer to each other in the Euclidean
+feature space, and the unrelated cases are far apart, which is a good property
+can enable subsequent analysis such as detection and clustering of related
+cases. Experiments over several series of related crime incidents hand labeled
+by the Atlanta Police Department reveal the promise of our embedding methods.
+"
+22827,A criterion for bubble merging in liquid metal: computational and experimental study,"  An innovative model is presented for merging of bubbles inside a liquid
+metal. The proposed model is based on forming a thin film (narrow channel)
+between merging bubbles during growth. Rupturing of the film occurs when an
+oscillation in velocity and pressure arises inside the channel followed by
+merging of the bubbles. The proposed model based on lattice Boltzmann Method is
+capable of simulating merging bubbles in micro, meso, and macro-scales with no
+limitation on the number of bubbles. Experimental studies reveal a good
+consistency between modeling results and real conditions.
+"
+22828,Sliced Wasserstein Generative Models,"  In the paper, we introduce a model of sliced optimal transport (SOT), which
+measures the distribution affinity with sliced Wasserstein distance (SWD).
+Since SWD enjoys the property of factorizing high-dimensional joint
+distributions into their multiple one-dimensional marginal distributions, its
+dual and primal forms can be approximated easier compared to Wasserstein
+distance (WD). Thus, we propose two types of differentiable SOT blocks to equip
+modern generative frameworks---Auto-Encoders (AEs) and Generative Adversarial
+Networks (GANs)---with the primal and dual forms of SWD. The superiority of our
+SWAE and SWGAN over the state-of-the-art generative models is studied both
+qualitatively and quantitatively on standard benchmarks.
+"
+22829,Binomial collisions and near collisions,"  We describe efficient algorithms to search for cases in which binomial
+coefficients are equal or almost equal, give a conjecturally complete list of
+all cases where two binomial coefficients differ by 1, and give some identities
+for binomial coefficients that seem to be new.
+"
+22830,Visualizing Sound Directivity via Smartphone Sensors,"  We present a fast, simple method for automated data acquisition and
+visualization of sound directivity, made convenient and accessible via a
+smartphone app, ""Polar Pattern Plotter."" The app synchronizes measurements of
+sound volume with the phone's angular orientation obtained from either compass,
+gyroscope or accelerometer sensors and produces a graph and exportable data
+file. It is generalizable to various sound sources and receivers via the use of
+an input-jack-adaptor to supplant the smartphone's (omnidirectional)
+microphone. Results provide both a visual and quantitative representation of
+sound fields and device responses, adequate for introductory physics
+experiments.
+"
+22831,Non-smooth Non-convex Bregman Minimization: Unification and new Algorithms,"  We propose a unifying algorithm for non-smooth non-convex optimization. The
+algorithm approximates the objective function by a convex model function and
+finds an approximate (Bregman) proximal point of the convex model. This
+approximate minimizer of the model function yields a descent direction, along
+which the next iterate is found. Complemented with an Armijo-like line search
+strategy, we obtain a flexible algorithm for which we prove (subsequential)
+convergence to a stationary point under weak assumptions on the growth of the
+model function error. Special instances of the algorithm with a Euclidean
+distance function are, for example, Gradient Descent, Forward--Backward
+Splitting, ProxDescent, without the common requirement of a ""Lipschitz
+continuous gradient"". In addition, we consider a broad class of Bregman
+distance functions (generated by Legendre functions) replacing the Euclidean
+distance. The algorithm has a wide range of applications including many linear
+and non-linear inverse problems in signal/image processing and machine
+learning.
+"
+22832,An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling,"  Mixture-of-experts (MoE) models are a powerful paradigm for modeling of data
+arising from complex data generating processes (DGPs). In this article, we
+demonstrate how different MoE models can be constructed to approximate the
+underlying DGPs of arbitrary types of data. Due to the probabilistic nature of
+MoE models, we propose the maximum quasi-likelihood (MQL) estimator as a method
+for estimating MoE model parameters from data, and we provide conditions under
+which MQL estimators are consistent and asymptotically normal. The blockwise
+minorization-maximizatoin (blockwise-MM) algorithm framework is proposed as an
+all-purpose method for constructing algorithms for obtaining MQL estimators. An
+example derivation of a blockwise-MM algorithm is provided. We then present a
+method for constructing information criteria for estimating the number of
+components in MoE models and provide justification for the classic Bayesian
+information criterion (BIC). We explain how MoE models can be used to conduct
+classification, clustering, and regression and we illustrate these applications
+via a pair of worked examples.
+"
+22833,Spectral bounds for singular indefinite Sturm-Liouville operators with $L^1$--potentials,"  The spectrum of the singular indefinite Sturm-Liouville operator
+$$A=\text{\rm sgn}(\cdot)\bigl(-\tfrac{d^2}{dx^2}+q\bigr)$$ with a real
+potential $q\in L^1(\mathbb R)$ covers the whole real line and, in addition,
+non-real eigenvalues may appear if the potential $q$ assumes negative values. A
+quantitative analysis of the non-real eigenvalues is a challenging problem, and
+so far only partial results in this direction were obtained. In this paper the
+bound $$|\lambda|\leq |q|_{L^1}^2$$ on the absolute values of the non-real
+eigenvalues $\lambda$ of $A$ is obtained. Furthermore, separate bounds on the
+imaginary parts and absolute values of these eigenvalues are proved in terms of
+the $L^1$-norm of the negative part of $q$.
+"
+22834,Data-driven Super-resolution on a Tactile Dome,"  While tactile sensor technology has made great strides over the past decades,
+applications in robotic manipulation are limited by aspects such as blind
+spots, difficult integration into hands, and low spatial resolution. We present
+a method for localizing contact with high accuracy over curved, three
+dimensional surfaces, with a low wire count and reduced integration complexity.
+To achieve this, we build a volume of soft material embedded with individual
+off-the-shelf pressure sensors. Using data driven techniques, we map the raw
+signals from these pressure sensors to known surface locations and indentation
+depths. Additionally, we show that a finite element model can be used to
+improve the placement of the pressure sensors inside the volume and to explore
+the design space in simulation. We validate our approach on physically
+implemented tactile domes which achieve high contact localization accuracy
+($1.1mm$ in the best case) over a large, curved sensing area ($1,300mm^2$
+hemisphere). We believe this approach can be used to deploy tactile sensing
+capabilities over three dimensional surfaces such as a robotic finger or palm.
+"
+22835,From State Estimation to Network Reconstruction,"  We develop methods to efficiently reconstruct the topology and line
+parameters of a power grid from the measurement of nodal variables. We propose
+two compressed sensing algorithms that minimize the amount of necessary
+measurement resources by exploiting network sparsity, symmetry of connections
+and potential prior knowledge about the connectivity. The algorithms are
+reciprocal to established state estimation methods, where nodal variables are
+estimated from few measurements given the network structure. Hence, they enable
+an advanced grid monitoring where both state and structure of a grid are
+subject to uncertainties or missing information.
+"
+22836,Kinetic models for topological nearest-neighbor interactions,"  We consider systems of agents interacting through topological interactions.
+These have been shown to play an important part in animal andhuman behavior.
+Precisely, the system consists of a finite number of particles characterized by
+their positions and velocities. At random times arandomly chosen particle, the
+follower, adopts the velocity of its closest neighbor, the leader. We study the
+limit of a system size going to infinityand, under the assumption of
+propagation of chaos, show that the limit kinetic equation is a non-standard
+spatial diffusion equation for the particle distribution function. We also
+study the case wherein the particles interact with their K closest neighbors
+and show that the correspondingkinetic equation is the same. Finally, we prove
+that these models can be seen as a singular limit of the smooth rank-based
+model previously studied in [10]. The proofs are based on a combinatorial
+interpretation of the rank as well as some concentration of measure arguments.
+"
+22837,Entropy degeneration of globally hyperbolic maximal compact anti-de Sitter structures,"  Using the parameterisation of the deformation space of GHMC anti-de Sitter
+structures on $S \times \mathbb{R}$ by the cotangent bundle of the
+Teichmüller space of $S$, we study how some geometric quantities, such as the
+Lorentzian Hausdorff dimension of the limit set, the width of the convex core
+and the Hölder exponent, degenerate along rays of quadratic differentials.
+"
+22838,The primitive equations as the small aspect ratio limit of the Navier-Stokes equations: rigorous justification of the hydrostatic approximation,"  An important feature of the planetary oceanic dynamics is that the aspect
+ratio (the ratio of the depth to horizontal width) is very small. As a result,
+the hydrostatic approximation (balance), derived by performing the formal small
+aspect ratio limit to the Navier-Stokes equations, is considered as a
+fundamental component in the primitive equations of the large-scale ocean. In
+this paper, we justify rigorously the small aspect ratio limit of the
+Navier-Stokes equations to the primitive equations. Specifically, we prove that
+the Navier-Stokes equations, after being scaled appropriately by the small
+aspect ratio parameter of the physical domain, converge strongly to the
+primitive equations, globally and uniformly in time, and the convergence rate
+is of the same order as the aspect ratio parameter. This result validates the
+hydrostatic approximation for the large-scale oceanic dynamics. Notably, only
+the weak convergence of this small aspect ratio limit was rigorously justified
+before.
+"
+22839,Linkbait: Active Link Obfuscation to Thwart Link-flooding attacks,"  Link-flooding attack (LFA) has emerged as a serious threat to Internet which
+cuts off connections between legitimate hosts and targeted servers by flooding
+only a few links (e.g., target links). Several mechanisms have been proposed to
+mitigate LFA, however, they can only mitigate LFA after target links have been
+compromised by adversaries. Based on the fact that adversaries rely on network
+linkmap to discover weakness of the network, in this paper, we propose an
+active LFA mitigation mechanism, called Linkbait, that actively and
+preventively mitigates LFA by providing a fake linkmap to adversaries. Inspired
+by Moving Target Defense (MTD), we propose a link obfuscation algorithm in
+Linkbait that selectively reroutes probing flows to hide target links from
+adversaries and mislead them to consider some bait links as target links. By
+providing the faked linkmap to adversaries, Linkbait can actively mitigate LFA
+even without identifying bots and does not affect flows from legitimate hosts.
+In order to further reduce the junk traffic generated by adversaries from
+entering the network, we propose a bot detection algorithm in Linkbait that
+extracts unique traffic patterns from LFA and leverages Support Vector Machine
+to accurately distinguish bots from legitimate hosts. Finally, we evaluate the
+feasibility of implementing Linkbait in real Internet, and evaluate its
+performance by using both a real-world testbed and large-scale simulations. The
+analyses and experiments results demonstrate the effectiveness of Linkbait.
+"
+22840,Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis,"  In this paper, we propose a model to analyze sentiment of online stock forum
+and use the information to predict the stock volatility in the Chinese market.
+We have labeled the sentiment of the online financial posts and make the
+dataset public available for research. By generating a sentimental dictionary
+based on financial terms, we develop a model to compute the sentimental score
+of each online post related to a particular stock. Such sentimental information
+is represented by two sentiment indicators, which are fused to market data for
+stock volatility prediction by using the Recurrent Neural Networks (RNNs).
+Empirical study shows that, comparing to using RNN only, the model performs
+significantly better with sentimental indicators.
+"
+22841,K-Geodetic Graphs and their Applications to Analysis across Different Scales of Dynamics of Complex Systems,"  This paper describes a new approach to the problem of the structural research
+of clusters based on the theory of geodetic and k-geodetic graphs. We firmly
+believe that this same approach can be used when solving problems of
+correlation between structural and spectral metrics in complex networks. So,
+what we want to point out is about the possibility of applying one theory of
+networks (the theory of k-geodetic networks) to the solution of problems in
+another type of networks (complex networks). The theory of geodetic graphs and
+their various modifications represents an important tool for the structural
+analysis of complex systems of transmission, processing, and analysis of
+information. In the case of large data sets, their stochastic dependence is
+described by large-dimensional correlation matrices. One of the problems of
+correlation analysis is the study of the structure of the correlation matrix.
+It is proved that such a structure is adequately described by geodetic graphs.
+The obtained structural data allow solving the choice problem of significant
+variables in multidimensional regression models.
+"
+22842,Testing a Saturation-Based Theorem Prover: Experiences and Challenges (Extended Version),"  This paper attempts to address the question of how best to assure the
+correctness of saturation-based automated theorem provers using our experience
+developing the theorem prover Vampire. We describe the techniques we currently
+employ to ensure that Vampire is correct and use this to motivate future
+challenges that need to be addressed to make this process more straightforward
+and to achieve better correctness guarantees.
+"
+22843,Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN,"  Video reviews are the natural evolution of written product reviews. In this
+paper we target this phenomenon and introduce the first dataset created from
+closed captions of YouTube product review videos as well as a new attention-RNN
+model for aspect extraction and joint aspect extraction and sentiment
+classification. Our model provides state-of-the-art performance on aspect
+extraction without requiring the usage of hand-crafted features on the SemEval
+ABSA corpus, while it outperforms the baseline on the joint task. In our
+dataset, the attention-RNN model outperforms the baseline for both tasks, but
+we observe important performance drops for all models in comparison to SemEval.
+These results, as well as further experiments on domain adaptation for aspect
+extraction, suggest that differences between speech and written text, which
+have been discussed extensively in the literature, also extend to the domain of
+product reviews, where they are relevant for fine-grained opinion mining.
+"
+22844,A Whole-Body Model Predictive Control Scheme Including External Contact Forces and CoM Height Variations,"  In this paper, we present an approach for generating a variety of whole-body
+motions for a humanoid robot. We extend the available Model Predictive Control
+(MPC) approaches for walking on flat terrain to plan for both vertical motion
+of the Center of Mass (CoM) and external contact forces consistent with a given
+task. The optimization problem is comprised of three stages, i. e. the CoM
+vertical motion, joint angles, and contact forces planning. The choice of
+external contact (e. g. hand contact with the object or environment) among all
+available locations and the appropriate time to reach and maintain a contact
+are all computed automatically within the algorithm. The presented algorithm
+benefits from the simplicity of the Linear Inverted Pendulum Model (LIPM),
+while it overcomes the common limitations of this model and enables us to
+generate a variety of whole-body motions through external contacts. Simulation
+and experimental implementation of several whole-body actions in multi-contact
+scenarios on a humanoid robot show the capability of the proposed algorithm.
+"
+22845,WISE data as a photometric redshift indicator for radio AGN,"  We show that mid-infrared data from the all-sky WISE survey can be used as a
+robust photometric redshift indicator for powerful radio AGN, in the absence of
+other spectroscopic or multi-band photometric information. Our work is
+motivated by a desire to extend the well-known K-z relation for radio galaxies
+to the wavelength range covered by the all-sky WISE mid-infrared survey. Using
+the LARGESS radio spectroscopic sample as a training set, and the mid-infrared
+colour information to classify radio sources, we generate a set of redshift
+probability distributions for the hosts of high-excitation and low-excitation
+radio AGN. We test the method using spectroscopic data from several other radio
+AGN studies, and find good agreement between our WISE-based redshift estimates
+and published spectroscopic redshifts out to z ~ 1 for galaxies and z ~ 3-4 for
+radio-loud QSOs. Our chosen method is also compared against other
+classification methods and found to perform reliably. This technique is likely
+to be particularly useful in the analysis of upcoming large-area radio surveys
+with SKA pathfinder telescopes, and our code is publicly available. As a
+consistency check, we show that our WISE-based redshift estimates for sources
+in the 843 MHz SUMSS survey reproduce the redshift distribution seen in the
+CENSORS study up to z ~ 2. We also discuss two specific applications of our
+technique for current and upcoming radio surveys; an interpretation of large
+scale HI absorption surveys, and a determination of whether low-frequency
+peaked spectrum sources lie at high redshift.
+"
+22846,Rate constants for the formation of SiO by radiative association,"  Accurate molecular data for the low-lying states of SiO are computed and used
+to calculate rate constants for radiative association of Si and O. Einstein
+A-coefficients are also calculated for transitions between all of the bound and
+quasi-bound levels for each molecular state. The radiative widths are used
+together with elastic tunneling widths to define effective radiative
+association rate constants which include both direct and indirect (inverse
+predissociation) formation processes. The indirect process is evaluated for two
+kinetic models which represent limiting cases for astrophysical environments.
+The first case scenario assumes an equilibrium distribution of quasi-bound
+states and would be applicable whenever collisional and/or radiative excitation
+mechanisms are able to maintain the population. The second case scenario
+assumes that no excitation mechanisms are available which corresponds to the
+limit of zero radiation temperature and zero atomic density. Rate constants for
+SiO formation in realistic astrophysical environments would presumably lie
+between these two limiting cases.
+"
+22847,autoBagging: Learning to Rank Bagging Workflows with Metalearning,"  Machine Learning (ML) has been successfully applied to a wide range of
+domains and applications. One of the techniques behind most of these successful
+applications is Ensemble Learning (EL), the field of ML that gave birth to
+methods such as Random Forests or Boosting. The complexity of applying these
+techniques together with the market scarcity on ML experts, has created the
+need for systems that enable a fast and easy drop-in replacement for ML
+libraries. Automated machine learning (autoML) is the field of ML that attempts
+to answers these needs. Typically, these systems rely on optimization
+techniques such as bayesian optimization to lead the search for the best model.
+Our approach differs from these systems by making use of the most recent
+advances on metalearning and a learning to rank approach to learn from
+metadata. We propose autoBagging, an autoML system that automatically ranks 63
+bagging workflows by exploiting past performance and dataset characterization.
+Results on 140 classification datasets from the OpenML platform show that
+autoBagging can yield better performance than the Average Rank method and
+achieve results that are not statistically different from an ideal model that
+systematically selects the best workflow for each dataset. For the purpose of
+reproducibility and generalizability, autoBagging is publicly available as an R
+package on CRAN.
+"
+22848,Extremes and gaps in sampling from a GEM random discrete distribution,"  We show that in a sample of size $n$ from a GEM$(0,\theta)$ random discrete
+distribution, the gaps $G_{i:n}:= X_{n-i+1:n} - X_{n-i:n}$ between order
+statistics $X_{1:n} \le \cdots \le X_{n:n}$ of the sample, with the convention
+$G_{n:n} := X_{1:n} - 1$, are distributed like the first $n$ terms of an
+infinite sequence of independent geometric$(i/(i+\theta))$ variables $G_i$.
+This extends a known result for the minimum $X_{1:n}$ to other gaps in the
+range of the sample, and implies that the maximum $X_{n:n}$ has the
+distribution of $1 + \sum_{i=1}^n G_i$, hence the known result that $X_{n:n}$
+grows like $\theta\log(n)$ as $n\to\infty$, with an asymptotically normal
+distribution. Other consequences include most known formulas for the exact
+distributions of GEM$(0,\theta)$ sampling statistics, including the Ewens and
+Donnelly--Tavaré sampling formulas. For the two-parameter
+GEM$(\alpha,\theta)$ distribution we show that the maximal value grows like a
+random multiple of $n^{\alpha/(1-\alpha)}$ and find the limit distribution of
+the multiplier.
+"
+22849,Performance of SiPMs in the nonlinear region,"  Silicon photomultipliers present saturation effects as they have a limited
+number of pixels and work in Geiger mode. Their response to light pulses in the
+nonlinear region is very complex for two reasons: pixel recharging after an
+avalanche affects the trigger probability and charge multiplication of
+subsequent avalanches, and non-trivial effects due to crosstalk and
+afterpulsing.
+A parametrization of the nonlinear response of silicon photomultipliers was
+developed where the above effects were readily accounted for. The model was
+tested on a setup of gamma-ray spectrometry using different combinations of
+scintillation crystals and detectors. The model parameters were interpreted in
+terms of fundamental characteristics of the setup (e.g., lifetime of the
+scintillation crystal and pixel recovery time). The proper conversion from
+signal resolution to energy resolution was provided.
+"
+22850,An Exact Reformulation of Feature-Vector-based Radial-Basis-Function Networks for Graph-based Observations,"  Radial-basis-function networks are traditionally defined for sets of
+vector-based observations. In this short paper, we reformulate such networks so
+that they can be applied to adjacency-matrix representations of weighted,
+directed graphs that represent the relationships between object pairs. We
+re-state the sum-of-squares objective function so that it is purely dependent
+on entries from the adjacency matrix. From this objective function, we derive a
+gradient descent update for the network weights. We also derive a gradient
+update that simulates the repositioning of the radial basis prototypes and
+changes in the radial basis prototype parameters. An important property of our
+radial basis function networks is that they are guaranteed to yield the same
+responses as conventional radial-basis networks trained on a corresponding
+vector realization of the relationships encoded by the adjacency-matrix. Such a
+vector realization only needs to provably exist for this property to hold,
+which occurs whenever the relationships correspond to distances from some
+arbitrary metric applied to a latent set of vectors. We therefore completely
+avoid needing to actually construct vectorial realizations via
+multi-dimensional scaling, which ensures that the underlying relationships are
+totally preserved.
+"
+22851,Deep Learning and Data Assimilation for Real-Time Production Prediction in Natural Gas Wells,"  The prediction of the gas production from mature gas wells, due to their
+complex end-of-life behavior, is challenging and crucial for operational
+decision making. In this paper, we apply a modified deep LSTM model for
+prediction of the gas flow rates in mature gas wells, including the
+uncertainties in input parameters. Additionally, due to changes in the system
+in time and in order to increase the accuracy and robustness of the prediction,
+the Ensemble Kalman Filter (EnKF) is used to update the flow rate predictions
+based on new observations. The developed approach was tested on the data from
+two mature gas production wells in which their production is highly dynamic and
+suffering from salt deposition. The results show that the flow predictions
+using the EnKF updated model leads to better Jeffreys' J-divergences than the
+predictions without the EnKF model updating scheme.
+"
+22852,A Review of Evaluation Techniques for Social Dialogue Systems,"  In contrast with goal-oriented dialogue, social dialogue has no clear measure
+of task success. Consequently, evaluation of these systems is notoriously hard.
+In this paper, we review current evaluation methods, focusing on automatic
+metrics. We conclude that turn-based metrics often ignore the context and do
+not account for the fact that several replies are valid, while end-of-dialogue
+rewards are mainly hand-crafted. Both lack grounding in human perceptions.
+"
+22853,Open Loop Hyperparameter Optimization and Determinantal Point Processes,"  Driven by the need for parallelizable hyperparameter optimization methods,
+this paper studies \emph{open loop} search methods: sequences that are
+predetermined and can be generated before a single configuration is evaluated.
+Examples include grid search, uniform random search, low discrepancy sequences,
+and other sampling distributions. In particular, we propose the use of
+$k$-determinantal point processes in hyperparameter optimization via random
+search. Compared to conventional uniform random search where hyperparameter
+settings are sampled independently, a $k$-DPP promotes diversity. We describe
+an approach that transforms hyperparameter search spaces for efficient use with
+a $k$-DPP. In addition, we introduce a novel Metropolis-Hastings algorithm
+which can sample from $k$-DPPs defined over any space from which uniform
+samples can be drawn, including spaces with a mixture of discrete and
+continuous dimensions or tree structure. Our experiments show significant
+benefits in realistic scenarios with a limited budget for training supervised
+learners, whether in serial or parallel.
+"
+22854,Direct Single Molecule Imaging of Enhanced Enzyme Diffusion,"  Recent experimental results have shown that active enzymes can diffuse faster
+when they are in the presence of their substrates. Fluorescence correlation
+spectroscopy (FCS), which relies on analyzing the fluctuations in fluorescence
+intensity signal to measure the diffusion coefficient of particles, has
+typically been employed in most of the prior studies. However, flaws in the FCS
+method, due to its high sensitivity to the environment, have recently been
+evaluated, calling the prior diffusion results into question. It behooves us to
+adopt complimentary and direct methods to measure the mobility of enzymes in
+solution. Herein, we use a novel technique of direct single-molecule imaging to
+observe the diffusion of single enzymes. This technique is less sensitive to
+intensity fluctuations and gives the diffusion coefficient directly based on
+the trajectory of the enzymes. Our measurements recapitulate that enzyme
+diffusion is enhanced in the presence of its substrate and find that the
+relative increase in diffusion of a single enzyme is even higher than those
+previously reported using FCS. We also use this complementary method to test if
+the total enzyme concentration affects the relative increase in diffusion and
+if enzyme oligomerization state changes during catalytic turnover. We find that
+the diffusion increase is independent of the total background concentration of
+enzyme and the catalysis of substrate does not change the oligomerization state
+of enzymes.
+"
+22855,Gradual Session Types,"  Session types are a rich type discipline, based on linear types, that lifts
+the sort of safety claims that come with type systems to communications.
+However, web-based applications and microservices are often written in a mix of
+languages, with type disciplines in a spectrum between static and dynamic
+typing. Gradual session types address this mixed setting by providing a
+framework which grants seamless transition between statically typed handling of
+sessions and any required degree of dynamic typing.
+We propose Gradual GV as a gradually typed extension of the functional
+session type system GV. Following a standard framework of gradual typing,
+Gradual GV consists of an external language, which relaxes the type system of
+GV using dynamic types, and an internal language with casts, for which
+operational semantics is given, and a cast-insertion translation from the
+former to the latter. We demonstrate type and communication safety as well as
+blame safety, thus extending previous results to functional languages with
+session-based communication. The interplay of linearity and dynamic types
+requires a novel approach to specifying the dynamics of the language.
+"
+22856,Busy beavers and Kolmogorov complexity,"  The idea to find the ""maximal number that can be named"" can be traced back to
+Archimedes (see his Psammit). From the viewpoint of computation theory the
+natural question is ""which number can be described by at most n bits""? This
+question led to the definition of the so-called ""busy beaver"" numbers
+(introduced by T. Rado). In this note we consider different versions of the
+busy beaver-like notions defined in terms of Kolmogorov complexity. We show
+that these versions differ depending on the version of complexity used (plain,
+prefix, or a priori complexities) and find out how these notions are related,
+providing matching lower and upper bounds.
+"
+22857,Free energy calculation of mechanically unstable but dynamically stabilized bcc titanium,"  The phase diagram of numerous materials of technological importance features
+high-symmetry high-temperature phases that exhibit phonon instabilities.
+Leading examples include shape-memory alloys, as well as ferroelectric,
+refractory, and structural materials. The thermodynamics of these phases have
+proven challenging to handle by atomistic computational thermodynamic
+techniques, due to the occurrence of constant anharmonicity-driven hopping
+between local low-symmetry distortions, while maintaining a high-symmetry
+time-averaged structure. To compute the free energy in such phases, we propose
+to explore the system's potential-energy surface by discrete sampling of local
+minima by means of a lattice gas Monte Carlo approach and by continuous
+sampling by means of a lattice dynamics approach in the vicinity of each local
+minimum. Given the proximity of the local minima, it is necessary to carefully
+partition phase space by using a Voronoi tessellation to constrain the domain
+of integration of the partition function, in order to avoid double-counting
+artifacts and enable an accurate harmonic treatment near each local minima. We
+consider the bcc phase of titanium as a prototypical examples to illustrate our
+approach.
+"
+22858,On the consistency of the spacings test for multivariate uniformity,"  We give a simple conceptual proof of the consistency of a test for
+multivariate uniformity in a bounded set $K \subset \mathbb{R}^d$ that is based
+on the maximal spacing generated by i.i.d. points $X_1, \ldots,X_n$ in $K$,
+i.e., the volume of the largest convex set of a given shape that is contained
+in $K$ and avoids each of these points. Since asymptotic results for the case
+$d > 1$ are only availabe under uniformity, a key element of the proof is a
+suitable coupling.
+"
+22859,Mixtures of Generalized Hyperbolic Distributions and Mixtures of Skew-t Distributions for Model-Based Clustering with Incomplete Data,"  Robust clustering from incomplete data is an important topic because, in many
+practical situations, real data sets are heavy-tailed, asymmetric, and/or have
+arbitrary patterns of missing observations. Flexible methods and algorithms for
+model-based clustering are presented via mixture of the generalized hyperbolic
+distributions and its limiting case, the mixture of multivariate skew-t
+distributions. An analytically feasible EM algorithm is formulated for
+parameter estimation and imputation of missing values for mixture models
+employing missing at random mechanisms. The proposed methodologies are
+investigated through a simulation study with varying proportions of synthetic
+missing values and illustrated using a real dataset. Comparisons are made with
+those obtained from the traditional mixture of generalized hyperbolic
+distribution counterparts by filling in the missing data using the mean
+imputation method.
+"
+22860,Active plasmon injection scheme for subdiffraction imaging with imperfect negative index flat lens,"  We present an active physical implementation of the recently introduced
+plasmon injection loss compensation scheme for Pendry's non-ideal negative
+index flat lens in the presence of realistic material losses and
+signal-dependent noise. In this active implementation, we propose to use a
+physically convolved external auxiliary source for signal amplification and
+suppression of the noise in the imaging system. In comparison with the previous
+passive implementations of the plasmon injection scheme for sub-diffraction
+limited imaging, where an inverse filter post-processing is used, the active
+implementation proposed here allows for deeper subwavelength imaging far beyond
+the passive post-processing scheme by extending the loss compensation to even
+higher spatial frequencies.
+"
+22861,Locally analytic representations in the moduli spaces of Lubin-Tate,"  Let $K$ be a finite extension of $\mathbb{Q}_{p}$ with ring of integers
+$\mathfrak{o}$, and let $H_{0}$ be a formal $\mathfrak{o}$-module of finite
+height over a separable closure of the residue class field of $K$. The
+Lubin-Tate moduli space $X_{m}$ classifies deformations of $H_{0}$ equipped
+with level-$m$-structure. In this article, we study a particular type of
+$p$-adic representations originating from the action of $\text{Aut}(H_{0})$ on
+certain equivariant vector bundles over the rigid analytic generic fibre of
+$X_{m}$. We show that, for arbitrary level $m$, the Fréchet space of the
+global sections of these vector bundles is dual to a locally $K$-analytic
+representation of $\text{Aut}(H_{0})$ generalizing previous results of J.
+Kohlhaase in the case $K=\mathbb{Q}_{p}$ and $m=0$. As a first step towards a
+better understanding of these representations, we compute their locally finite
+vectors. Essentially, all locally finite vectors arise from the global sections
+over the projective space via pullback along the Gross-Hopkins period map, and
+are in fact locally algebraic vectors.
+"
+22862,Virtualization technology for distributed time sensitive domains,"  This paper reports on the state of the art of virtualization technology for
+both general purpose domains as well as real-time domains. There exits no
+entirely instantaneous data transmission/transfer. There always exist a delay
+while transmitting data, either in the processing or in the medium itself.
+However most systems are designed to function appropriately with a delay
+tolerance. This delay, inevitably, is affected when operating with an extra
+layer, the virtualization. For real time systems it is crucial to know the
+temporal limits in order not to surpass them. Introducing virtualization in the
+real-time domain therefore requires deeper analysis by making use of techniques
+that will offer results with deterministic execution times. The study of time
+in systems and its behaviour under various possible circumstances is hence a
+key for properly assessing this technology applied to both domains, especially
+the real-time domain.
+"
+22863,Designs for estimating the treatment effect in networks with interference,"  In this paper we introduce new, easily implementable designs for drawing
+causal inference from randomized experiments on networks with interference.
+Inspired by the idea of matching in observational studies, we introduce the
+notion of considering a treatment assignment as a quasi-coloring"" on a graph.
+Our idea of a perfect quasi-coloring strives to match every treated unit on a
+given network with a distinct control unit that has identical number of treated
+and control neighbors. For a wide range of interference functions encountered
+in applications, we show both by theory and simulations that the classical
+Neymanian estimator for the direct effect has desirable properties for our
+designs. This further extends to settings where homophily is present in
+addition to interference.
+"
+22864,Quadruplet Network with One-Shot Learning for Fast Visual Object Tracking,"  In the same vein of discriminative one-shot learning, Siamese networks allow
+recognizing an object from a single exemplar with the same class label.
+However, they do not take advantage of the underlying structure of the data and
+the relationship among the multitude of samples as they only rely on pairs of
+instances for training. In this paper, we propose a new quadruplet deep network
+to examine the potential connections among the training instances, aiming to
+achieve a more powerful representation. We design four shared networks that
+receive multi-tuple of instances as inputs and are connected by a novel loss
+function consisting of pair-loss and triplet-loss. According to the similarity
+metric, we select the most similar and the most dissimilar instances as the
+positive and negative inputs of triplet loss from each multi-tuple. We show
+that this scheme improves the training performance. Furthermore, we introduce a
+new weight layer to automatically select suitable combination weights, which
+will avoid the conflict between triplet and pair loss leading to worse
+performance. We evaluate our quadruplet framework by model-free
+tracking-by-detection of objects from a single initial exemplar in several
+Visual Object Tracking benchmarks. Our extensive experimental analysis
+demonstrates that our tracker achieves superior performance with a real-time
+processing speed of 78 frames-per-second (fps).
+"
+22865,An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network,"  Polychronous neural groups are effective structures for the recognition of
+precise spike-timing patterns but the detection method is an inefficient
+multi-stage brute force process that works off-line on pre-recorded simulation
+data. This work presents a new model of polychronous patterns that can capture
+precise sequences of spikes directly in the neural simulation. In this scheme,
+each neuron is assigned a randomized code that is used to tag the post-synaptic
+neurons whenever a spike is transmitted. This creates a polychronous code that
+preserves the order of pre-synaptic activity and can be registered in a hash
+table when the post-synaptic neuron spikes. A polychronous code is a
+sub-component of a polychronous group that will occur, along with others, when
+the group is active. We demonstrate the representational and pattern
+recognition ability of polychronous codes on a direction selective visual task
+involving moving bars that is typical of a computation performed by simple
+cells in the cortex. The computational efficiency of the proposed algorithm far
+exceeds existing polychronous group detection methods and is well suited for
+online detection.
+"
+22866,Bayesian model averaging via mixture model estimation,"  A new approach for Bayesian model averaging (BMA) and selection is proposed,
+based on the mixture model approach for hypothesis testing in Kaniav et al.,
+2014. Inheriting from the good properties of this approach, it extends BMA to
+cases where improper priors are chosen for parameters that are common to all
+candidate models.
+From an algorithmic point of view, our approach consists in sampling from the
+posterior distribution of the single-datum mixture of all candidate models,
+weighted by their prior probabilities. We show that this posterior distribution
+is equal to the 'Bayesian-model averaged' posterior distribution over all
+candidate models, weighted by their posterior probability. From this BMA
+posterior sample, a simple Monte-Carlo estimate of each model's posterior
+probability is derived, as well as importance sampling estimates for
+expectations under each model's posterior distribution.
+"
+22867,Dynamical generation of superconducting order of different symmetries in hexagonal lattices,"  The growth of superconducting order after an interaction quench in a
+hexagonal lattice is studied. The cases of both time-reversal (TR) preserving
+graphene, as well as the TR broken Haldane model are explored. Spin singlet
+superconducting order is studied where the $s$, $d+id$, and $d-id$ wave orders
+are the irreducible representations of the hexagonal lattice. For small
+quenches, the $d$-wave order parameter grows the fastest, a result also
+expected when the system is in thermal equilibrium. For the TR symmetry
+preserving case, the growth rate of the two $d$-wave orders is identical, while
+the TR-broken case prefers one of the chiral $d$-wave orders over the other,
+leading to a TR broken topological superconductor. As the interaction quench
+becomes larger, a smooth crossover is found where eventually the growth rate of
+the $s$-wave becomes the largest. Thus for large interaction quenches, the
+$s$-wave is preferred over the $d$-wave for both TR preserving and TR broken
+systems. This result is explained in terms of the high energy quasi-particles
+responsible for the dynamics as the interaction quench amplitude grows. The
+results are relevant for time-resolved measurements that can probe the symmetry
+of the superconducting fluctuations in a transient regime.
+"
+22868,Temporal recompression through a scattering medium via a broadband transmission matrix,"  The transmission matrix is a unique tool to control light through a
+scattering medium. A monochromatic transmission matrix does not allow temporal
+control of broadband light. Conversely, measuring multiple transmission
+matrices with spectral resolution allows fine temporal control when a pulse is
+temporally broadened upon multiple scattering, but requires very long
+measurement time. Here, we show that a single linear operator, measured for a
+broadband pulse with a co-propagating reference, naturally allows for spatial
+focusing, and interestingly generates a two-fold temporal recompression at the
+focus, compared with the natural temporal broadening. This is particularly
+relevant for non-linear imaging techniques in biological tissues.
+"
+22869,Admissibility of invariant tests for means with covariates,"  For a multinormal distribution with a $p$-dimensional mean vector
+${\mbtheta}$ and an arbitrary unknown dispersion matrix ${\mbSigma}$, Rao ([9],
+[10]) proposed two tests for the problem of testing $ H_{0}:{\mbtheta}_{1} =
+{\bf 0}, {\mbtheta}_{2} = {\bf 0}, {\mbSigma}~
+\hbox{unspecified},~\hbox{versus}~H_{1}:{\mbtheta}_{1} \ne {\bf 0},
+{\mbtheta}_{2} ={\bf 0}, {\mbSigma}~\hbox{unspecified}$, where
+${\mbtheta}^{'}=({\mbtheta}^{'}_{1},{\mbtheta}^{'}_{2})$. These tests are
+referred to as Rao's $W$-test (likelihood ratio test) and Rao's $U$-test
+(union-intersection test), respectively. This work is inspired by the
+well-known work of Marden and Perlman [6] who claimed that Hotelling's
+$T^{2}$-test is admissible while Rao's $U$-test is inadmissible. Both Rao's
+$U$-test and Hotelling's $T^{2}$-test can be constructed by applying the
+union-intersection principle that incorporates the information
+${\mbtheta}_{2}={\bf 0}$ for Rao's $U$-test statistic but does not incorporate
+it for Hotelling's $T^{2}$-test statistic. Rao's $U$-test is believed to
+exhibit some optimal properties. Rao's $U$-test is shown to be admissible by
+fully incorporating the information ${\mbtheta}_{2}={\bf 0}$, but Hotelling's
+$T^{2}$-test is inadmissible.
+"
+22870,Enumeration of $2$-level polytopes,"  A (convex) polytope $P$ is said to be $2$-level if for every direction of
+hyperplanes which is facet-defining for $P$, the vertices of $P$ can be covered
+with two hyperplanes of that direction. The study of these polytopes is
+motivated by questions in combinatorial optimization and communication
+complexity, among others. In this paper, we present the first algorithm for
+enumerating all combinatorial types of $2$-level polytopes of a given dimension
+$d$, and provide complete experimental results for $d \leqslant 7$. Our
+approach is inductive: for each fixed $(d-1)$-dimensional $2$-level polytope
+$P_0$, we enumerate all $d$-dimensional $2$-level polytopes $P$ that have $P_0$
+as a facet. This relies on the enumeration of the closed sets of a closure
+operator over a finite ground set. By varying the prescribed facet $P_0$, we
+obtain all $2$-level polytopes in dimension $d$.
+"
+22871,Separable and Localized System Level Synthesis for Large-Scale Systems,"  A major challenge faced in the design of large-scale cyber-physical systems,
+such as power systems, the Internet of Things or intelligent transportation
+systems, is that traditional distributed optimal control methods do not scale
+gracefully, neither in controller synthesis nor in controller implementation,
+to systems composed of millions, billions or even trillions of interacting
+subsystems. This paper shows that this challenge can now be addressed by
+leveraging the recently introduced System Level Approach (SLA) to controller
+synthesis. In particular, in the context of the SLA, we define suitable notions
+of separability for control objective functions and system constraints such
+that the global optimization problem (or iterate update problems of a
+distributed optimization algorithm) can be decomposed into parallel
+subproblems. We then further show that if additional locality (i.e., sparsity)
+constraints are imposed, then these subproblems can be solved using local
+models and local decision variables. The SLA is essential to maintaining the
+convexity of the aforementioned problems under locality constraints. As a
+consequence, the resulting synthesis methods have $O(1)$ complexity relative to
+the size of the global system. We further show that many optimal control
+problems of interest, such as (localized) LQR and LQG, $\mathcal{H}_2$ optimal
+control with joint actuator and sensor regularization, and (localized) mixed
+$\mathcal{H}_2/\mathcal{L}_1$ optimal control problems, satisfy these notions
+of separability, and use these problems to explore tradeoffs in performance,
+actuator and sensing density, and average versus worst-case performance for a
+large-scale power inspired system.
+"
+22872,Disentangled Representations via Synergy Minimization,"  Scientists often seek simplified representations of complex systems to
+facilitate prediction and understanding. If the factors comprising a
+representation allow us to make accurate predictions about our system, but
+obscuring any subset of the factors destroys our ability to make predictions,
+we say that the representation exhibits informational synergy. We argue that
+synergy is an undesirable feature in learned representations and that
+explicitly minimizing synergy can help disentangle the true factors of
+variation underlying data. We explore different ways of quantifying synergy,
+deriving new closed-form expressions in some cases, and then show how to modify
+learning to produce representations that are minimally synergistic. We
+introduce a benchmark task to disentangle separate characters from images of
+words. We demonstrate that Minimally Synergistic (MinSyn) representations
+correctly disentangle characters while methods relying on statistical
+independence fail.
+"
+22873,"Solving the clique cover problem on (bull, $C_4$)-free graphs","  We give an $O(n^4)$ algorithm to find a minimum clique cover of a (bull,
+$C_4$)-free graph, or equivalently, a minimum colouring of a (bull,
+$2K_2$)-free graph, where $n$ is the number of vertices of the graphs.
+"
+22874,Intraday Seasonalities and Nonstationarity of Trading Volume in Financial Markets: Individual and Cross-Sectional Features,"  We study the intraday behaviour of the statistical moments of the trading
+volume of the blue chip equities that composed the Dow Jones Industrial Average
+index between 2003 and 2014. By splitting that time interval into semesters, we
+provide a quantitative account of the non-stationary nature of the intraday
+statistical properties as well. Explicitly, we prove the well-known U-shape
+exhibited by the average trading volume-as well as the volatility of the price
+fluctuations-experienced a significant change from 2008 (the year of the
+sub-prime financial crisis) onwards. That has resulted in a faster relaxation
+after the market opening and relates to a consistent decrease in the convexity
+of the average trading volume intraday profile. Simultaneously, the last part
+of the session has become steeper as well, a modification that is likely to
+have been triggered by the new short-selling rules that were introduced in 2007
+by the Securities and Exchange Commission. The combination of both results
+reveals that the has been turning into a t. Additionally, the analysis of
+higher-order cumulants namely the skewness and the kurtosis-shows that the
+morning and the afternoon parts of the trading session are each clearly
+associated with different statistical features and hence dynamical rules.
+Concretely, we claim that the large initial trading volume is due to wayward
+stocks whereas the large volume during the last part of the session hinges on a
+cohesive increase of the trading volume. That dissimilarity between the two
+parts of the trading session is stressed in periods of higher uproar in the
+market.
+"
+22875,On community structure validation in real networks,"  Community structure is a commonly observed feature of real networks. The term
+refers to the presence in a network of groups of nodes (communities) that
+feature high internal connectivity, but are poorly connected between each
+other. Whereas the issue of community detection has been addressed in several
+works, the problem of validating a partition of nodes as a good community
+structure for a real network has received considerably less attention and
+remains an open issue. We propose a set of indices for community structure
+validation of network partitions, which rely on concepts from network
+enrichment analysis. The proposed indices allow to compare the adequacy of
+different partitions of nodes as community structures. Moreover, they can be
+employed to assess whether two networks share the same or similar community
+structures, and to evaluate the performance of different network clustering
+algorithms.
+"
+22876,DCNN-GAN: Reconstructing Realistic Image from fMRI,"  Visualizing the perceptual content by analyzing human functional magnetic
+resonance imaging (fMRI) has been an active research area. However, due to its
+high dimensionality, complex dimensional structure, and small number of samples
+available, reconstructing realistic images from fMRI remains challenging.
+Recently with the development of convolutional neural network (CNN) and
+generative adversarial network (GAN), mapping multi-voxel fMRI data to complex,
+realistic images has been made possible. In this paper, we propose a model,
+DCNN-GAN, by combining a reconstruction network and GAN. We utilize the CNN for
+hierarchical feature extraction and the DCNN-GAN to reconstruct more realistic
+images. Extensive experiments have been conducted, showing that our method
+outperforms previous works, regarding reconstruction quality and computational
+cost.
+"
+22877,On Delay and Regret Determinization of Max-Plus Automata,"  Decidability of the determinization problem for weighted automata over the
+semiring $(\mathbb{Z} \cup {-\infty}, \max, +)$, WA for short, is a
+long-standing open question. We propose two ways of approaching it by
+constraining the search space of deterministic WA: k-delay and r-regret. A WA N
+is k-delay determinizable if there exists a deterministic automaton D that
+defines the same function as N and for all words {\alpha} in the language of N,
+the accepting run of D on {\alpha} is always at most k-away from a maximal
+accepting run of N on {\alpha}. That is, along all prefixes of the same length,
+the absolute difference between the running sums of weights of the two runs is
+at most k. A WA N is r-regret determinizable if for all words {\alpha} in its
+language, its non-determinism can be resolved on the fly to construct a run of
+N such that the absolute difference between its value and the value assigned to
+{\alpha} by N is at most r.
+We show that a WA is determinizable if and only if it is k-delay
+determinizable for some k. Hence deciding the existence of some k is as
+difficult as the general determinization problem. When k and r are given as
+input, the k-delay and r-regret determinization problems are shown to be
+EXPtime-complete. We also show that determining whether a WA is r-regret
+determinizable for some r is in EXPtime.
+"
+22878,Stateful characterization of resistive switching TiO2 with electron beam induced currents,"  Metal oxide resistive switches are increasingly important as possible
+artificial synapses in next generation neuromorphic networks. Nevertheless,
+there is still no codified set of tools for studying properties of the devices.
+To this end, we demonstrate electron beam induced current measurements as a
+powerful method to monitor the development of local resistive switching in TiO2
+based devices. By comparing beam-energy dependent electron beam induced
+currents with Monte Carlo simulations of the energy absorption in different
+device layers, it is possible to deconstruct the origins of filament image
+formation and relate this to both morphological changes and the state of the
+switch. By clarifying the contrast mechanisms in electron beam induced current
+microscopy it is possible to gain new insights into the scaling of the
+resistive switching phenomenon and observe the formation of a current leakage
+region around the switching filament. Additionally, analysis of symmetric
+device structures reveals propagating polarization domains.
+"
+22879,An approximate fractional Gaussian noise model with ${\mathcal O}(n)$ computational cost,"  Fractional Gaussian noise (fGn) is a stationary time series model with long
+memory properties applied in various fields like econometrics, hydrology and
+climatology. The computational cost in fitting an fGn model of length $n$ using
+a likelihood-based approach is ${\mathcal O}(n^{2})$, exploiting the Toeplitz
+structure of the covariance matrix. In most realistic cases, we do not observe
+the fGn process directly but only through indirect Gaussian observations, so
+the Toeplitz structure is easily lost and the computational cost increases to
+${\mathcal O}(n^{3})$. This paper presents an approximate fGn model of
+${\mathcal O}(n)$ computational cost, both with direct or indirect Gaussian
+observations, with or without conditioning. This is achieved by approximating
+fGn with a weighted sum of independent first-order autoregressive processes,
+fitting the parameters of the approximation to match the autocorrelation
+function of the fGn model. The resulting approximation is stationary despite
+being Markov and gives a remarkably accurate fit using only four components.
+The performance of the approximate fGn model is demonstrated in simulations and
+two real data examples.
+"
+22880,New method for torque magnetometry using a commercially available membrane-type surface-stress sensor,"  We present a new method for torque magnetometry by using a commercially
+available membrane-type surface-stress sensor (MSS). This sensor has a silicon
+membrane supported by four beams in which piezoresistive paths are integrated.
+Although originally developed as a gas sensor, it can be used for torque
+measurement by modifying its on-chip wiring. We demonstrate the magnetic-torque
+measurement of submillimeter-sized crystals at a low temperature and in strong
+magnetic fields. This MSS can observe de-Haas-van-Alphen oscillation, which
+confirms that it can be an alternative tool for self-sensitive
+microcantilevers.
+"
+22881,Almost split morphisms in subcategories of triangulated categories,"  For a suitable triangulated category $\mathcal{T}$ with a Serre functor $S$
+and a full precovering subcategory $\mathcal{C}$ closed under summands and
+extensions, an indecomposable object $C$ in $\mathcal{C}$ is called
+Ext-projective if Ext$^1(C,\mathcal{C})=0$. Then there is no Auslander-Reiten
+triangle in $\mathcal{C}$ with end term $C$. In this paper, we show that if,
+for such an object $C$, there is a minimal right almost split morphism
+$\beta:B\rightarrow C$ in $\mathcal{C}$, then $C$ appears in something very
+similar to an Auslander-Reiten triangle in $\mathcal{C}$: an essentially unique
+triangle in $\mathcal{T}$ of the form \begin{align*}
+\Delta= X\xrightarrow{\xi} B\xrightarrow{\beta} C\rightarrow \Sigma X,
+\end{align*} where $X$ is an indecomposable not in $\mathcal{C}$ and $\xi$ is a
+$\mathcal{C}$-envelope of $X$. Moreover, under some extra assumptions, we show
+that removing $C$ from $\mathcal{C}$ and replacing it with $X$ produces a new
+subcategory of $\mathcal{T}$ closed under extensions. We prove that this
+process coincides with the classic mutation of $\mathcal{C}$ with respect to
+the rigid subcategory of $\mathcal{C}$ generated by all the indecomposable
+Ext-projectives in $\mathcal{C}$ apart from $C$.
+When $\mathcal{T}$ is the cluster category of Dynkin type $A_n$ and
+$\mathcal{C}$ has the above properties, we give a full description of the
+triangles in $\mathcal{T}$ of the form $\Delta$ and show under which
+circumstances replacing $C$ by $X$ gives a new extension closed subcategory.
+"
+22882,Good Applications for Crummy Entity Linkers? The Case of Corpus Selection in Digital Humanities,"  Over the last decade we have made great progress in entity linking (EL)
+systems, but performance may vary depending on the context and, arguably, there
+are even principled limitations preventing a ""perfect"" EL system. This also
+suggests that there may be applications for which current ""imperfect"" EL is
+already very useful, and makes finding the ""right"" application as important as
+building the ""right"" EL system. We investigate the Digital Humanities use case,
+where scholars spend a considerable amount of time selecting relevant source
+texts. We developed WideNet; a semantically-enhanced search tool which
+leverages the strengths of (imperfect) EL without getting in the way of its
+expert users. We evaluate this tool in two historical case-studies aiming to
+collect a set of references to historical periods in parliamentary debates from
+the last two decades; the first targeted the Dutch Golden Age, and the second
+World War II. The case-studies conclude with a critical reflection on the
+utility of WideNet for this kind of research, after which we outline how such a
+real-world application can help to improve EL technology in general.
+"
+22883,Information Density as a Factor for Variation in the Embedding of Relative Clauses,"  In German, relative clauses can be positioned in-situ or extraposed. A
+potential factor for the variation might be information density. In this study,
+this hypothesis is tested with a corpus of 17th century German funeral sermons.
+For each referent in the relative clauses and their matrix clauses, the
+attention state was determined (first calculation). In a second calculation,
+for each word the surprisal values were determined, using a bi-gram language
+model. In a third calculation, the surprisal values were accommodated as to
+whether it is the first occurrence of the word in question or not. All three
+calculations pointed in the same direction: With in-situ relative clauses, the
+rate of new referents was lower and the average surprisal values were lower,
+especially the accommodated surprisal values, than with extraposed relative
+clauses. This indicated that in-formation density is a factor governing the
+choice between in-situ and extraposed relative clauses. The study also sheds
+light on the intrinsic relation-ship between the information theoretic concept
+of information density and in-formation structural concepts such as givenness
+which are used under a more linguistic perspective.
+"
+22884,Kaon-Scatter Introduced Backgrounds in the KOTO Experiment,"  The KOTO experiment is a particle physics experiment located in J-PARC,
+Japan, aiming to explore physics beyond the Standard Model by measuring the
+branching ratio of the $K_L\rightarrow\pi^{0}\nu\bar{\nu}$ decay. This decay
+has not yet been observed. The branching ratio predicted by the Standard Model
+of $(3.0\pm0.3)\times10^{-11}$ and the current experimental upper limit
+established by KEK E391a is $2.6\times10^{-8}$.
+The signal of $K_L\rightarrow\pi^{0}\nu\bar{\nu}$ decay has the signature of
+two photons on the calorimeter with no signal on the veto detectors. It also
+has a large transverse momentum due to missing neutrinos. Kaons that decay
+outside the beam line with final product of two photons, such as
+$K_L\rightarrow\gamma\gamma$ and $K_L\rightarrow\pi^{+}\pi^{-}\pi^{0}$, can
+appear to have large transverse momentum due to kaon scattering and beam
+interaction with the detectors. These off-axis kaon decay events can impact the
+upper limit of $K_L\rightarrow\pi^{0}\nu\bar{\nu}$ branching ratio. Aluminum
+targets located at the upstream of the KOTO detector and inside the
+decay-volume were used to study kaon beam profile, which provided off-axis kaon
+decay vertex information. The beam profile provided insights on background
+contributions to the signal. Studies on the kaon beam profile and background
+identification from kaon scattering were presented in this talk.
+"
+22885,Molecular near-infrared transitions determined with sub-kHz accuracy,"  Precise molecular transition frequencies are of great interest in the test of
+fundamental physics and also in various applications. Two well isolated
+ro-vibrational transitions of $^{12}$C$^{16}$O at 1.57 $\mu$m, R(9) and R(10)
+in the second overtone band molecule, were measured by a comb-locked cavity
+ring-down spectrometer. Despite the weakness of the lines (Einstein coefficient
+$A\simeq 0.008$ s$^{-1}$), Lamb-dip spectra were recorded with a
+signal-to-noise ratio over 1000, and the line positions were determined to be
+191 360 212 763.7 and 191 440 612 664.8 kHz respectively, with an uncertainty
+of 0.5 kHz ($\delta\nu/\nu=2.6\times 10^{-12}$). The present work demonstrated
+the possibility to explore extensive molecular lines in the near-infrared with
+sub-kHz accuracy.
+"
+22886,Generation of Flying Electromagnetic Donuts,"  Transverse electromagnetic plane waves are fundamental solutions of Maxwell's
+equations. It is less known that a radically different type of solutions has
+been described theoretically, but has never been realized experimentally, that
+exist only in the form of short burst of electromagnetic energy propagating in
+free-space at the speed of light. They are distinguished from transverse waves
+by a donut-like configuration of electric and magnetic fields with a strong
+field component along the propagation direction. Here, we report that such
+'Flying Donuts' can be generated from conventional pulses using a singular
+metamaterial converter designed to manipulate both the spatial and spectral
+structure of the input pulse. The ability to generate Flying Donuts is of
+fundamental interest, as they shall interact with matter in unique ways,
+including non-trivial field transformations upon reflection from interfaces and
+the excitation of toroidal response and anapole modes in matter, thus offering
+new opportunities for telecommunications, sensing, and spectroscopy.
+"
+22887,Effective Representations of Clinical Notes,"  Clinical notes are a rich source of information about patient state. However,
+using them to predict clinical events with machine learning models is
+challenging. They are very high dimensional, sparse and have complex structure.
+Furthermore, training data is often scarce because it is expensive to obtain
+reliable labels for many clinical events. These difficulties have traditionally
+been addressed by manual feature engineering encoding task specific domain
+knowledge. We explored the use of neural networks and transfer learning to
+learn representations of clinical notes that are useful for predicting future
+clinical events of interest, such as all causes mortality, inpatient
+admissions, and emergency room visits. Our data comprised 2.7 million notes and
+115 thousand patients at Stanford Hospital. We used the learned
+representations, along with commonly used bag of words and topic model
+representations, as features for predictive models of clinical events. We
+evaluated the effectiveness of these representations with respect to the
+performance of the models trained on small datasets. Models using the neural
+network derived representations performed significantly better than models
+using the baseline representations with small ($N < 1000$) training datasets.
+The learned representations offer significant performance gains over commonly
+used baseline representations for a range of predictive modeling tasks and
+cohort sizes, offering an effective alternative to task specific feature
+engineering when plentiful labeled training data is not available.
+"
+22888,FFT-Based Deep Learning Deployment in Embedded Systems,"  Deep learning has delivered its powerfulness in many application domains,
+especially in image and speech recognition. As the backbone of deep learning,
+deep neural networks (DNNs) consist of multiple layers of various types with
+hundreds to thousands of neurons. Embedded platforms are now becoming essential
+for deep learning deployment due to their portability, versatility, and energy
+efficiency. The large model size of DNNs, while providing excellent accuracy,
+also burdens the embedded platforms with intensive computation and storage.
+Researchers have investigated on reducing DNN model size with negligible
+accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN
+training and inference model suitable for embedded platforms with reduced
+asymptotic complexity of both computation and storage, making our approach
+distinguished from existing approaches. We develop the training and inference
+algorithms based on FFT as the computing kernel and deploy the FFT-based
+inference model on embedded platforms achieving extraordinary processing speed.
+"
+22889,A Boosting Framework of Factorization Machine,"  Recently, Factorization Machines (FM) has become more and more popular for
+recommendation systems, due to its effectiveness in finding informative
+interactions between features. Usually, the weights for the interactions is
+learnt as a low rank weight matrix, which is formulated as an inner product of
+two low rank matrices. This low rank can help improve the generalization
+ability of Factorization Machines. However, to choose the rank properly, it
+usually needs to run the algorithm for many times using different ranks, which
+clearly is inefficient for some large-scale datasets. To alleviate this issue,
+we propose an Adaptive Boosting framework of Factorization Machines (AdaFM),
+which can adaptively search for proper ranks for different datasets without
+re-training. Instead of using a fixed rank for FM, the proposed algorithm will
+adaptively gradually increases its rank according to its performance until the
+performance does not grow, using boosting strategy. To verify the performance
+of our proposed framework, we conduct an extensive set of experiments on many
+real-world datasets. Encouraging empirical results shows that the proposed
+algorithms are generally more effective than state-of-the-art other
+Factorization Machines.
+"
+22890,A Compressive Sensing Approach for Connected Vehicle Data Capture and Recovery and its Impact on Travel Time Estimation,"  Connected vehicles (CVs) can capture and transmit detailed data such as
+vehicle position and speed through vehicle-to-vehicle and
+vehicle-to-infrastructure communications. The wealth of CV data provides new
+opportunities to improve safety and mobility of transportation systems.
+However, it is likely to overburden storage and communication systems. To
+mitigate this issue, we propose a compressive sensing (CS) approach that allows
+CVs to capture and compress data in real-time and later recover the original
+data accurately and efficiently. The approach is evaluated using two case
+studies. In the first study, we use this approach to recapture 10 million CV
+Basic Safety Message (BSM) speed samples. It can recover the original speed
+data with root-mean-squared error as low as 0.05. We also explore recovery
+performance for other BSM variables. In the second study, a freeway traffic
+simulation model is built to evaluate the impact of this approach on travel
+time estimation. Multiple scenarios with various CV market penetration rates,
+On-board Unit (OBU) capacities, compression ratios, arrival rate patterns, and
+data capture rates are simulated. The results show that the approach provides
+more accurate estimation than conventional data collection methods, through up
+to 65% relative reduction in travel time estimation error. Even when the
+compression ratio is low, the approach can provide accurate estimation, thereby
+reducing OBU hardware costs. Further, it can improve accuracy of travel time
+estimation when CVs are in traffic congestion as it provides a broader
+spatial-temporal coverage of traffic conditions and can accurately and
+efficiently recover the original CV data.
+"
+22891,On the ground-state degeneracy and entropy in a double-tetrahedral chain formed by the localized Ising spins and mobile electrons,"  Ground-state properties of a hybrid double-tetrahedral chain, in which the
+localized Ising spins regularly alternate with triangular plaquettes occupied
+by a variable number of mobile electrons, are exactly investigated. We
+demonstrate that the zero-temperature phase diagram of the model involves
+several non-degenerate, two-fold degenerate and macroscopically degenerate
+chiral phases. Low-temperature dependencies of the entropy and specific heat
+are also examined in order to gain a deeper insight into the degeneracy of
+individual ground-state phases and phase transitions. It is shown that a
+diversity of the ground-state degeneracy manifests itself in multiple-peak
+structures of both thermodynamic quantities. A remarkable temperature
+dependencies of the specific heat with two and three Schottky-type maxima are
+discussed in detail.
+"
+22892,Generating carrier-envelope-phase stabilized few-cycle pulses from a free-electron laser oscillator,"  We propose a scheme to generate carrier-envelope-phase (CEP) stabilized
+few-cycle optical pulses from a free-electron laser oscillator. The CEP
+stabilization is realized by continuous injection of CEP-stabilized seed pulses
+from an external laser to the FEL oscillator whose cavity length is perfectly
+synchronized to the electron bunch repetition. Such CEP-stabilized few-cycle
+FEL pulses will be an efficient driver for exploring high-harmonic generation
+at energies 1-10~keV for attosecond and zeptosecond science, broad-band x-ray
+optical frequency combs and coherent control of chemical reaction.
+"
+22893,Fast and slow domino regimes in transient network dynamics,"  It is well known that the addition of noise to a multistable dynamical system
+can induce random transitions from one stable state to another. For low noise,
+the times between transitions have an exponential tail and Kramers' formula
+gives an expression for the mean escape time in the asymptotic limit. If a
+number of multistable systems are coupled into a network structure, a
+transition at one site may change the transition properties at other sites. We
+study the case of escape from a ""quiescent"" attractor to an ""active"" attractor
+in which transitions back can be ignored. There are qualitatively different
+regimes of transition, depending on coupling strength. For small coupling
+strengths the transition rates are simply modified but the transitions remain
+stochastic. For large coupling strengths transitions happen approximately in
+synchrony - we call this a ""fast domino"" regime. There is also an intermediate
+coupling regime some transitions happen inexorably but with a delay that may be
+arbitrarily long - we call this a ""slow domino"" regime. We characterise these
+regimes in the low noise limit in terms of bifurcations of the potential
+landscape of a coupled system. We demonstrate the effect of the coupling on the
+distribution of timings and (in general) the sequences of escapes of the
+system.
+"
+22894,Thieves can make sandwiches,"  We prove a common generalization of the Ham Sandwich theorem and Alon's
+Necklace Splitting theorem. Our main results show the existence of fair
+distributions of $m$ measures in $R^d$ among $r$ thieves using roughly $mr/d$
+convex pieces, even in the cases when $m$ is larger than the dimension. The
+main proof relies on a construction of a geometric realization of the
+topological join of two spaces of partitions of $R^d$ into convex parts, and
+the computation of the Fadell-Husseini ideal valued index of the resulting
+spaces.
+"
+22895,Joint Optimization of Power Splitting and Allocation for SWIPT in Interference Alignment Networks,"  Interference alignment (IA) is a promising solution for interference
+management in wireless networks. On the other hand, simultaneous wireless
+information and power transfer (SWIPT) has become an emerging technique.
+Although some works have been done on IA and SWIPT, these two important areas
+have traditionally been addressed separately in the literature. In this paper,
+we propose to use a common framework to jointly study IA and SWIPT. We analyze
+the performance of SWIPT in IA networks. Specifically, we derive the upper
+bound of the power that can be harvested in IA networks. In addition, we show
+that, to improve the performance of wireless power transfer and information
+transmission, users should be dynamically selected as energy harvesting (EH) or
+information decoding (ID) terminals. Furthermore, we design two
+easy-implemented SWIPT-user selection (SWIPT-US) algorithms in IA networks. To
+optimize the ID and EH performance of SWIPT in IA networks, a power-splitting
+optimization (PSO) algorithm is proposed when power splitters are available,
+and its closed-form optimal solutions are derived. Power allocation in the PSO
+algorithm is also studied to further optimize the performance. Simulation
+results are presented to show the effectiveness of the proposed algorithms.
+"
+22896,Deep Architectures for Automated Seizure Detection in Scalp EEGs,"  Automated seizure detection using clinical electroencephalograms is a
+challenging machine learning problem because the multichannel signal often has
+an extremely low signal to noise ratio. Events of interest such as seizures are
+easily confused with signal artifacts (e.g, eye movements) or benign variants
+(e.g., slowing). Commercially available systems suffer from unacceptably high
+false alarm rates. Deep learning algorithms that employ high dimensional models
+have not previously been effective due to the lack of big data resources. In
+this paper, we use the TUH EEG Seizure Corpus to evaluate a variety of hybrid
+deep structures including Convolutional Neural Networks and Long Short-Term
+Memory Networks. We introduce a novel recurrent convolutional architecture that
+delivers 30% sensitivity at 7 false alarms per 24 hours. We have also evaluated
+our system on a held-out evaluation set based on the Duke University Seizure
+Corpus and demonstrate that performance trends are similar to the TUH EEG
+Seizure Corpus. This is a significant finding because the Duke corpus was
+collected with different instrumentation and at different hospitals. Our work
+shows that deep learning architectures that integrate spatial and temporal
+contexts are critical to achieving state of the art performance and will enable
+a new generation of clinically-acceptable technology.
+"
+22897,3D Printing of Polymer Bonded Rare-Earth Magnets With a Variable Magnetic Compound Density for a Predefined Stray Field,"  Additive manufacturing of polymer bonded magnets is a recently developed
+technique, for single-unit production, and for structures that have been
+impossible to manufacture previously. Also new possibilities to create a
+specific stray field around the magnet are triggered. The current work presents
+a method to 3D print polymer bonded magnets with a variable magnetic compound
+density distribution. A low-cost, end-user 3D printer with a mixing extruder is
+used to mix permanent magnetic filaments with pure PA12 filaments. The magnetic
+filaments are compounded, extruded, and characterized for the printing process.
+To deduce the quality of the manufactured magnets with a variable compound
+density, an inverse stray field framework is used. The effectiveness of the
+printing process and the simulation method is shown. It can also be used to
+manufacture magnets that produce a predefined stray field in a given region.
+Examples for sensor applications are presented. This setup and simulation
+framework allows the design and manufacturing of polymer bonded permanent
+magnets which are impossible to create with conventional methods.
+"
+22898,Tensor-Structured Coupled Cluster Theory,"  We derive and implement a new way of solving coupled cluster equations with
+lower computational scaling. Our method is based on decomposition of both
+amplitudes and two electron integrals, using a combination of tensor
+hypercontraction and canonical polyadic decomposition. While the original
+theory scales as $O(N^6)$ with respect to the number of basis functions, we
+demonstrate numerically that we achieve sub-millihartree difference from the
+original theory with $O(N^4)$ scaling. This is accomplished by solving directly
+for the factors that decompose the cluster operator. The proposed scheme is
+quite general and can be easily extended to other many-body methods.
+"
+22899,Limited range multilinear extrapolation with applications to the bilinear Hilbert transform,"  We prove a limited range, off-diagonal extrapolation theorem that generalizes
+a number of results in the theory of Rubio de Francia extrapolation, and use
+this to prove a limited range, multilinear extrapolation theorem. We give two
+applications of this result to the bilinear Hilbert transform. First, we give
+sufficient conditions on a pair of weights $w_1,\,w_2$ for the bilinear Hilbert
+transform to satisfy weighted norm inequalities of the form \[ BH :
+L^{p_1}(w_1^{p_1}) \times L^{p_2}(w_2^{p_2}) \longrightarrow L^p(w^p), \] where
+$w=w_1w_2$ and $\frac{1}{p}=\frac{1}{p_1}+\frac{1}{p_2}<\frac{3}{2}$. This
+improves the recent results of Culiuc et al. by increasing the families of
+weights for which this inequality holds and by pushing the lower bound on $p$
+from $1$ down to $\frac{2}{3}$, the critical index from the unweighted theory
+of the bilinear Hilbert transform. Second, as an easy consequence of our method
+we obtain that the bilinear Hilbert transform satisfies some vector-valued
+inequalities with Muckenhoupt weights. This reproves and generalizes some of
+the vector-valued estimates obtained by Benea and Muscalu in the unweighted
+case. We also generalize recent results of Carando, et al. on
+Marcinkiewicz-Zygmund estimates for multilinear Calderón-Zygmund operators.
+"
+22900,The hyperbolic geometry of Markov's theorem on Diophantine approximation and quadratic forms,"  Markov's theorem classifies the worst irrational numbers with respect to
+rational approximation and the indefinite binary quadratic forms whose values
+for integer arguments stay farthest away from zero. The main purpose of this
+paper is to present a new proof of Markov's theorem using hyperbolic geometry.
+The main ingredients are a dictionary to translate between hyperbolic geometry
+and algebra/number theory, and some very basic tools borrowed from modern
+geometric Teichmüller theory. Simple closed geodesics and ideal
+triangulations of the modular torus play an important role, and so does the
+problem: How far can a straight line crossing a triangle stay away from the
+vertices? Definite binary quadratic forms are briefly discussed in the last
+section.
+"
+22901,Spectrum of the free rod under tension and compression,"  In this paper, we study the spectrum of the one-dimensional vibrating free
+rod equation $u^{(4)}-\tau u""=\mu u$ under tension $(\tau>0)$ or compression
+$(\tau<0)$. The eigenvalues $\mu$ as functions of the tension/compression
+parameter $\tau$ exhibit three distinct types of behavior. In particular,
+eigenvalue branches in the lower half-plane exhibit a cascading pattern of
+barely-avoided crossings.
+We provide a complete description of the eigenfunctions and eigenvalues by
+implicitly parameterizing the eigenvalue curves. We also establish properties
+of the eigenvalue curves such as monotonicity, crossings, asymptotic growth,
+cascading and phantom spectral lines.
+"
+22902,"Images of nowhere differentiable Lipschitz maps of $[0,1]$ into $L_1[0,1]$","  The main result: for every sequence $\{\omega_m\}_{m=1}^\infty$ of positive
+numbers ($\omega_m>0)$ there exists an isometric embedding $F:[0,1]\to
+L_1[0,1]$ which is nowhere differentiable, but for each $t\in [0,1]$ the image
+$F_t$ is infinitely differentiable on $[0,1]$ with bounds
+$\max_{x\in[0,1]}|F_t^{(m)}(x)|\le\omega_m$ and has an analytic extension to
+the complex plane which is an entire function.
+"
+22903,The radius of uniform convexity of Bessel functions,"  In this paper, we determine the radius of uniform convexity for three kinds
+of normalized Bessel functions of the first kind. In the mentioned cases the
+normalized Bessel functions are uniformly convex on the determined disks.
+Moreover, necessary and sufficient conditions are given for the parameters of
+the three normalized functions such that they to be uniformly convex in the
+open unit disk. The basic tool of this study is the development of Bessel
+functions in function series.
+"
+22904,GANGs: Generative Adversarial Network Games,"  Generative Adversarial Networks (GAN) have become one of the most successful
+frameworks for unsupervised generative modeling. As GANs are difficult to train
+much research has focused on this. However, very little of this research has
+directly exploited game-theoretic techniques. We introduce Generative
+Adversarial Network Games (GANGs), which explicitly model a finite zero-sum
+game between a generator ($G$) and classifier ($C$) that use mixed strategies.
+The size of these games precludes exact solution methods, therefore we define
+resource-bounded best responses (RBBRs), and a resource-bounded Nash
+Equilibrium (RB-NE) as a pair of mixed strategies such that neither $G$ or $C$
+can find a better RBBR. The RB-NE solution concept is richer than the notion of
+`local Nash equilibria' in that it captures not only failures of escaping local
+optima of gradient descent, but applies to any approximate best response
+computations, including methods with random restarts. To validate our approach,
+we solve GANGs with the Parallel Nash Memory algorithm, which provably
+monotonically converges to an RB-NE. We compare our results to standard GAN
+setups, and demonstrate that our method deals well with typical GAN problems
+such as mode collapse, partial mode coverage and forgetting.
+"
+22905,Non-commutative Clark measures for the free and abelian Toeplitz algebras,"  We construct a non-commutative Aleksandrov-Clark measure for any element in
+the operator-valued free Schur class, the closed unit ball of the free Toeplitz
+algebra of vector-valued full Fock space over $\mathbb{C} ^d$. Here, the free
+(analytic) Toeplitz algebra is the unital weak operator topology (WOT)-closed
+algebra generated by the component operators of the free shift, the row
+isometry of left creation operators. This defines a bijection between the free
+operator-valued Schur class and completely positive maps (non-commutative AC
+measures) on the operator system of the free disk algebra, the norm-closed
+algebra generated by the free shift. Identifying Drury-Arveson space with
+symmetric Fock space, we determine the relationship between the non-commutative
+AC measures for elements of the operator-valued commutative Schur class (the
+closed unit ball of the WOT-closed Toeplitz algebra generated by the Arveson
+shift) and the AC measures of their free liftings to the free Schur class.
+"
+22906,"We Don't Need Another Hero? The Impact of ""Heroes"" on Software Development","  A software project has ""Hero Developers"" when 80% of contributions are
+delivered by 20% of the developers. Are such heroes a good idea? Are too many
+heroes bad for software quality? Is it better to have more/less heroes for
+different kinds of projects? To answer these questions, we studied 661 open
+source projects from Public open source software (OSS) Github and 171 projects
+from an Enterprise Github.
+We find that hero projects are very common. In fact, as projects grow in
+size, nearly all project become hero projects. These findings motivated us to
+look more closely at the effects of heroes on software development. Analysis
+shows that the frequency to close issues and bugs are not significantly
+affected by the presence of project type (Public or Enterprise). Similarly, the
+time needed to resolve an issue/bug/enhancement is not affected by heroes or
+project type. This is a surprising result since, before looking at the data, we
+expected that increasing heroes on a project will slow down howfast that
+project reacts to change. However, we do find a statistically significant
+association between heroes, project types, and enhancement resolution rates.
+Heroes do not affect enhancement resolution rates in Public projects. However,
+in Enterprise projects, the more heroes increase the rate at which project
+complete enhancements.
+In summary, our empirical results call for a revision of a long-held truism
+in software engineering. Software heroes are far more common and valuable than
+suggested by the literature, particularly for medium to large Enterprise
+developments. Organizations should reflect on better ways to find and retain
+more of these heroes
+"
+22907,The ANTARES Collaboration: Contributions to ICRC 2017 Part I: Neutrino astronomy (diffuse fluxes and point sources),"  Papers on neutrino astronomy (diffuse fluxes and point sources, prepared for
+the 35th International Cosmic Ray Conference (ICRC 2017, Busan, South Korea) by
+the ANTARES Collaboration
+"
+22908,Manifestations of dynamical localization in the disordered XXZ spin chain,"  We study disordered XXZ spin chains in the Ising phase exhibiting droplet
+localization, a single cluster localization property we previously proved for
+random XXZ spin chains. It holds in an energy interval $I$ near the bottom of
+the spectrum, known as the droplet spectrum. We establish dynamical
+manifestations of localization in the energy window $I$, including
+non-spreading of information, zero-velocity Lieb-Robinson bounds, and general
+dynamical clustering. Our results do not rely on knowledge of the dynamical
+characteristics of the model outside the droplet spectrum. A byproduct of our
+analysis is that for random XXZ spin chains this droplet localization can
+happen only inside the droplet spectrum.
+"
+22909,Sub-cellular mRNA localization modulates the regulation of gene expression by small RNAs in bacteria,"  Small non-coding RNAs can exert significant regulatory activity on gene
+expression in bacteria. In recent years, substantial progress has been made in
+understanding bacterial gene expression by sRNAs. However, recent findings that
+demonstrate that families of mRNAs show non-trivial sub-cellular distributions
+raise the question of how localization may affect the regulatory activity of
+sRNAs. Here we address this question within a simple mathematical model. We
+show that the non-uniform spatial distributions of mRNA can alter the
+threshold-linear response that characterizes sRNAs that act stoichiometrically,
+and modulate the hierarchy among targets co-regulated by the same sRNA. We also
+identify conditions where the sub-cellular organization of cofactors in the
+sRNA pathway can induce spatial heterogeneity on sRNA targets. Our results
+suggest that under certain conditions, interpretation and modeling of natural
+and synthetic gene regulatory circuits need to take into account the spatial
+organization of the transcripts of participating genes.
+"
+22910,Parameter Transfer Unit for Deep Neural Networks,"  Parameters in deep neural networks which are trained on large-scale databases
+can generalize across multiple domains, which is referred as ""transferability"".
+Unfortunately, the transferability is usually defined as discrete states and it
+differs with domains and network architectures. Existing works usually
+heuristically apply parameter-sharing or fine-tuning, and there is no
+principled approach to learn a parameter transfer strategy. To address the gap,
+a parameter transfer unit (PTU) is proposed in this paper. The PTU learns a
+fine-grained nonlinear combination of activations from both the source and the
+target domain networks, and subsumes hand-crafted discrete transfer states. In
+the PTU, the transferability is controlled by two gates which are artificial
+neurons and can be learned from data. The PTU is a general and flexible module
+which can be used in both CNNs and RNNs. Experiments are conducted with various
+network architectures and multiple transfer domain pairs. Results demonstrate
+the effectiveness of the PTU as it outperforms heuristic parameter-sharing and
+fine-tuning in most settings.
+"
+22911,Learning from Demonstration for Hydraulic Manipulators,"  This paper presents, for the first time, a method for learning in-contact
+tasks from a teleoperated demonstration with a hydraulic manipulator. Due to
+the use of extremely powerful hydraulic manipulator, a force-reflected
+bilateral teleoperation is the most reasonable method of giving a human
+demonstration. An advanced subsystem-dynamic-based control design framework,
+virtual decomposition control (VDC), is used to design a stability-guaranteed
+controller for the teleoperation system, while taking into account the full
+nonlinear dynamics of the master and slave manipulators. The use of fragile
+force/ torque sensor at the tip of the hydraulic slave manipulator is avoided
+by estimating the contact forces from the manipulator actuators' chamber
+pressures. In the proposed learning method, it is observed that a
+surface-sliding tool has a friction-dependent range of directions (between the
+actual direction of motion and the contact force) from which the manipulator
+can apply force to produce the sliding motion. By this intuition, an
+intersection of these ranges can be taken over a motion to robustly find a
+desired direction for the motion from one or more demonstrations. The compliant
+axes required to reproduce the motion can be found by assuming that all motions
+outside the desired direction is caused by the environment, signalling the need
+for compliance. Finally, the learning method is incorporated to a novel
+VDC-based impedance control method to learn compliant behaviour from
+teleoperated human demonstrations. Experiments with 2-DOF hydraulic manipulator
+with a 475kg payload demonstrate the suitability and effectiveness of the
+proposed method to perform learning from demonstration (LfD) with heavy-duty
+hydraulic manipulators.
+"
+22912,A first guide to the character theory of finite groups of Lie type,"  This survey article is an introduction to some of Lusztig's work on the
+character theory of a finite group of Lie type $G(F_q)$, where $q$ is a power
+of a prime~$p$. It is partly based on two series of lectures given at the
+Centre Bernoulli (EPFL) in July 2016 and at a summer school in Les Diablerets
+in August 2015. Our focus here is on questions related to the parametrization
+of the irreducible characters and on results which hold without any assumption
+on~$p$ or~$q$.
+"
+22913,Quantum hydrodynamic modeling of edge modes in chiral Berry plasmons,"  A quantum hydrodynamic model is used to study the edge modes of chiral Berry
+plasmons. The transcendental equation of the dispersion relation is solved
+nonlinearly and semi-analytically. We predict a new one-way chiral edge state
+with the quantum effect compared to that without the quantum effect, at the
+both side of $q=0$. Indeed, the plasmon frequencies for positive and negative
+$q$, exhibit different limits for $q\rightarrow 0^{-}$ and $q\rightarrow
+0^{+}$. As a result, the quantum effect enhances the chirality in the vicinity
+of $q=0$. Both counterpropagating edge modes exhibit greater confinement to the
+edge with the quantum effect. In addition, new localized edge modes are found
+with increased Berry flux in both cases, i.e., without and with the quantum
+effect.
+"
+22914,On The Projection Operator to A Three-view Cardinality Constrained Set,"  The cardinality constraint is an intrinsic way to restrict the solution
+structure in many domains, for example, sparse learning, feature selection, and
+compressed sensing. To solve a cardinality constrained problem, the key
+challenge is to solve the projection onto the cardinality constraint set, which
+is NP-hard in general when there exist multiple overlapped cardinality
+constraints. In this paper, we consider the scenario where the overlapped
+cardinality constraints satisfy a Three-view Cardinality Structure (TVCS),
+which reflects the natural restriction in many applications, such as
+identification of gene regulatory networks and task-worker assignment problem.
+We cast the projection into a linear programming, and show that for TVCS, the
+vertex solution of this linear programming is the solution for the original
+projection problem. We further prove that such solution can be found with the
+complexity proportional to the number of variables and constraints. We finally
+use synthetic experiments and two interesting applications in bioinformatics
+and crowdsourcing to validate the proposed TVCS model and method.
+"
+22915,Wetting properties of structured interfaces composed of surface-attached spherical nanoparticles,"  The influence of the external pressure and surface energy on the wetting
+transition at nanotextured interfaces is studied using molecular dynamics and
+continuum simulations. The surface roughness of the composite interface is
+introduced via an array of spherical nanoparticles with controlled wettability.
+We find that in the absence of an external pressure, the liquid interface is
+flat and its location relative to the solid substrate is determined by the
+particle size and the local contact angle. With increasing pressure on the
+liquid film, the interface becomes more curved and the three-phase contact line
+is displaced along the spherical surface but remains stable due to re-entrant
+geometry. It is demonstrated that the results of molecular dynamics simulations
+for the critical pressure of the Cassie-Baxter wetting state agree well with
+the estimate of the critical pressure obtained by numerical minimization of the
+interfacial energy.
+"
+22916,Improving Language Modelling with Noise-contrastive estimation,"  Neural language models do not scale well when the vocabulary is large.
+Noise-contrastive estimation (NCE) is a sampling-based method that allows for
+fast learning with large vocabularies. Although NCE has shown promising
+performance in neural machine translation, it was considered to be an
+unsuccessful approach for language modelling. A sufficient investigation of the
+hyperparameters in the NCE-based neural language models was also missing. In
+this paper, we showed that NCE can be a successful approach in neural language
+modelling when the hyperparameters of a neural network are tuned appropriately.
+We introduced the 'search-then-converge' learning rate schedule for NCE and
+designed a heuristic that specifies how to use this schedule. The impact of the
+other important hyperparameters, such as the dropout rate and the weight
+initialisation range, was also demonstrated. We showed that appropriate tuning
+of NCE-based neural language models outperforms the state-of-the-art
+single-model methods on a popular benchmark.
+"
+22917,Reconsidering the gold open access citation advantage postulate in a multidisciplinary context: an analysis of the subject categories in the Web of Science database 2009-2014,"  Since Lawrence in 2001 proposed the open access (OA) citation advantage, the
+potential benefit of OA in relation to the citation impact has been discussed
+in depth. The methodology to test this postulate ranges from comparing the
+impact factors of OA journals versus traditional ones, to comparing citations
+of OA versus non-OA articles published in the same non-OA journals. However,
+conclusions are not entirely consistent among fields, and two possible
+explications have been suggested in those fields where a citation advantage has
+been observed for OA: the early view and the selection bias postulates. In this
+study, a longitudinal and multidisciplinary analysis of the gold OA citation
+advantage is developed. All research articles in all journals for all subject
+categories in the multidisciplinary database Web of Science are considered. A
+total of 1,137,634 articles - 86,712 OA articles (7.6%) and 1,050,922 non-OA
+articles (92.4%)- published in 2009 are analysed. The citation window
+considered goes from 2009 to 2014, and data are aggregated for the 249
+disciplines (subject categories). At journal level, we also study the evolution
+of journal impact factors for OA and non-OA journals in those disciplines whose
+OA prevalence is higher (top 36 subject categories). As the main conclusion,
+there is no generalizable gold OA citation advantage, neither at article nor at
+journal level.
+"
+22918,"Derived categories of K3 surfaces, O'Grady's filtration, and zero-cycles on holomorphic symplectic varieties","  Moduli spaces of stable objects in the derived category of a $K3$ surface
+provide a large class of holomorphic symplectic varieties. In this paper, we
+study the interplay between Chern classes of stable objects and zero-cycles on
+holomorphic symplectic varieties which arise as moduli spaces.
+First, we show that the second Chern class of any object in the derived
+category lies in a suitable piece of O'Grady's filtration on the
+$\mathrm{CH}_0$-group of the $K3$ surface. This solves a conjecture of O'Grady
+and improves on previous results of Huybrechts, O'Grady, and Voisin. Then we
+propose a candidate of the Beauville-Voisin filtration on the
+$\mathrm{CH}_0$-group of the moduli space of stable objects. We discuss its
+connection with Voisin's recent proposal via constant cycle subvarieties. In
+particular, we deduce the existence of algebraic coisotropic subvarieties in
+the moduli space.
+Further, for a generic cubic fourfold containing a plane, we establish a
+connection between zero-cycles on the Fano variety of lines and on the
+associated $K3$ surface.
+"
+22919,Existence and concentration of positive ground state solutions for nonlinear fractional Schrödinger-Poisson system with critical growth,"  In this paper, we study the following fractional Schrödinger-Poisson
+system involving competing potential functions \begin{equation*} \left\{
+\begin{array}{ll}
+\varepsilon^{2s}(-\Delta)^su+V(x)u+\phi u=K(x)f(u)+Q(x)|u|^{2_s^{\ast}-2}u, &
+\hbox{in $\mathbb{R}^3$,}
+\varepsilon^{2t}(-\Delta)^t\phi=u^2,& \hbox{in $\mathbb{R}^3$,}
+\end{array} \right. \end{equation*} where $\varepsilon>0$ is a small
+parameter, $f$ is a function of $C^1$ class, superlinear and subcritical
+nonlinearity, $2_s^{\ast}=\frac{6}{3-2s}$, $s>\frac{3}{4}$, $t\in(0,1)$, $V(x)$
+$K(x)$ and $Q(x)$ are positive continuous function. Under some suitable
+assumptions on $V$, $K$ and $Q$, we prove that there is a family of positive
+ground state solutions with polynomial growth for sufficiently small
+$\varepsilon>0$, of which it is concentrating on the set of minimal points of
+$V(x)$ and the sets of maximal points of $K(x)$ and $Q(x)$. The methods are
+based on the Nehari manifold, arguments of Brezis-Nirenberg and concentration
+compactness of P. L. Lions.
+"
+22920,Empirical forward price distribution from Bitcoin option prices,"  Report presents analysis of empirical distribution of future returns of
+bitcoin (BTC) from BTUSD inverse option prices. Logistic pdf is chosen as
+underlying distribution to fit option prices. The result is satisfactory and
+suggests that these prices can be described with just three or even one
+parameter. Fitted Logistic pdf matches forward price movements upto a scaling
+factor. Nevertheless, this observation stands alone and does not allow
+stochastic description of underlying prices with logistic pdf in similar
+fashion as it is done within Black-Scholes modelling framework. Put-call parity
+relationship is derived connecting prices of vanilla inverse options and
+futures.
+"
+22921,Order in Quantum Compass and Orbital $e_g$ Models,"  We investigate thermodynamic phase transitions in the compass model and in
+$e_g$ orbital model on an infinite square lattice by variational tensor network
+renormalization (VTNR) in imaginary time. The onset of nematic order in the
+quantum compass model is estimated at ${\cal T}_c/J=0.0606(4)$. For~the $e_g$
+orbital model one finds: ($i$) a very accurate estimate of ${\cal
+T}_c/J=0.3566\pm 0.0001$ and ($ii$)~the~critical exponents in the Ising
+universality class. Remarkably large difference in frustration results in so
+distinct values of ${\cal T}_c$, while entanglement influences the quality of
+${\cal T}_c$ estimation.
+"
+22922,Némethi's division algorithm for zeta-functions of plumbed 3-manifolds,"  A polynomial counterpart of the Seiberg-Witten invariant associated with a
+negative definite plumbed 3-manifold has been proposed by earlier work of the
+authors. It is provided by a special decomposition of the zeta-function defined
+by the combinatorics of the manifold. In this article we give an algorithm,
+based on multivariable Euclidean division of the zeta-function, for the
+explicit calculation of the polynomial, in particular for the Seiberg--Witten
+invariant.
+"
+22923,Preventing wireless deauthentication attacks over 802.11 Networks,"  The objective of this paper is to investigate a special Denial of Service
+(DoS) attack against 802.11 wireless networks. This attack is known as the
+Deauthentication / Disassociation attack which is launched against 802.11-based
+wireless networks. When a client needs to disconnect from the wireless access
+point, it sends special frames known as deauthentication or disassociation
+frames. Due to being encrypted, these frames do not require an authenticated
+user. Hence, an attacker can craft these frames and send them to the access
+point in such a way that the access point assumes the frames to be coming from
+the client and not the attacker. In this paper, an efficient solution is
+proposed to prevent deauthentication attack by using a session management
+system used to then verify deauthentication frames.
+"
+22924,Addition Theorems in Fp via the Polynomial Method,"  In this article, we use the Combinatorial Nullstellensatz to give new proofs
+of the Cauchy-Davenport, the Dias da Silva-Hamidoune and to generalize a
+previous addition theorem of the author. Precisely, this last result proves
+that for a set A $\subset$ Fp such that A $\cap$ (--A) = $\emptyset$ the
+cardinality of the set of subsums of at least $\alpha$ pairwise distinct
+elements of A is: |$\Sigma$$\alpha$(A)| $\ge$ min (p, |A|(|A| + 1)/2 --
+$\alpha$($\alpha$ + 1)/2 + 1) , the only cases previously known were $\alpha$
+$\in$ {0, 1}. The Combinatorial Nullstellensatz is used, for the first time, in
+a direct and in a reverse way. The direct (and usual) way states that if some
+coefficient of a polynomial is non zero then there is a solution or a
+contradiction. The reverse way relies on the coefficient formula (equivalent to
+the Combinatorial Nullstellensatz). This formula gives an expression for the
+coefficient as a sum over any cartesian product. For these three addition
+theorems, some arithmetical progressions (that reach the bounds) will allow to
+consider cartesian products such that the coefficient formula is a sum all of
+whose terms are zero but exactly one. Thus we can conclude the proofs without
+computing the appropriate coefficients.
+"
+22925,Key exchange with the help of a public ledger,"  Blockchains and other public ledger structures promise a new way to create
+globally consistent event logs and other records. We make use of this
+consistency property to detect and prevent man-in-the-middle attacks in a key
+exchange such as Diffie-Hellman or ECDH. Essentially, the MitM attack creates
+an inconsistency in the world views of the two honest parties, and they can
+detect it with the help of the ledger. Thus, there is no need for prior
+knowledge or trusted third parties apart from the distributed ledger. To
+prevent impersonation attacks, we require user interaction. It appears that, in
+some applications, the required user interaction is reduced in comparison to
+other user-assisted key-exchange protocols.
+"
+22926,Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization,"  We consider a wide range of regularized stochastic minimization problems with
+two regularization terms, one of which is composed with a linear function. This
+optimization model abstracts a number of important applications in artificial
+intelligence and machine learning, such as fused Lasso, fused logistic
+regression, and a class of graph-guided regularized minimization. The
+computational challenges of this model are in two folds. On one hand, the
+closed-form solution of the proximal mapping associated with the composed
+regularization term or the expected objective function is not available. On the
+other hand, the calculation of the full gradient of the expectation in the
+objective is very expensive when the number of input data samples is
+considerably large. To address these issues, we propose a stochastic variant of
+extra-gradient type methods, namely \textsf{Stochastic Primal-Dual Proximal
+ExtraGradient descent (SPDPEG)}, and analyze its convergence property for both
+convex and strongly convex objectives. For general convex objectives, the
+uniformly average iterates generated by \textsf{SPDPEG} converge in expectation
+with $O(1/\sqrt{t})$ rate. While for strongly convex objectives, the uniformly
+and non-uniformly average iterates generated by \textsf{SPDPEG} converge with
+$O(\log(t)/t)$ and $O(1/t)$ rates, respectively. The order of the rate of the
+proposed algorithm is known to match the best convergence rate for first-order
+stochastic algorithms. Experiments on fused logistic regression and
+graph-guided regularized logistic regression problems show that the proposed
+algorithm performs very efficiently and consistently outperforms other
+competing algorithms.
+"
+22927,On Flat Pseudo-Euclidean Nilpotent Lie Algebras,"  A flat pseudo-Euclidean Lie algebra is a real Lie algebra with a non
+degenerate symmetric bilinear form and a left symmetric product whose the
+commutator is the Lie bracket and such that the left multiplications are
+skew-symmetric. We show that the center of a flat pseudo-Euclidean nilpotent
+Lie algebra of signature $(2,n-2)$ must be degenerate and all flat
+pseudo-Euclidean nilpotent Lie algebras of signature $(2,n-2)$ can be obtained
+by using the double extension process from flat Lorentzian nilpotent Lie
+algebras. We show also that the center of a flat pseudo-Euclidean 2-step
+nilpotent Lie algebra is degenerate and all these Lie algebras are obtained by
+using a sequence of double extension from an abelian Lie algebra. In
+particular, we determine all flat pseudo-Euclidean 2-step nilpotent Lie
+algebras of signature $(2,n-2)$. The paper contains also some examples in low
+dimension.
+"
+22928,Evaluating the Usability of Automatically Generated Captions for People who are Deaf or Hard of Hearing,"  The accuracy of Automated Speech Recognition (ASR) technology has improved,
+but it is still imperfect in many settings. Researchers who evaluate ASR
+performance often focus on improving the Word Error Rate (WER) metric, but WER
+has been found to have little correlation with human-subject performance on
+many applications. We propose a new captioning-focused evaluation metric that
+better predicts the impact of ASR recognition errors on the usability of
+automatically generated captions for people who are Deaf or Hard of Hearing
+(DHH). Through a user study with 30 DHH users, we compared our new metric with
+the traditional WER metric on a caption usability evaluation task. In a
+side-by-side comparison of pairs of ASR text output (with identical WER), the
+texts preferred by our new metric were preferred by DHH participants. Further,
+our metric had significantly higher correlation with DHH participants'
+subjective scores on the usability of a caption, as compared to the correlation
+between WER metric and participant subjective scores. This new metric could be
+used to select ASR systems for captioning applications, and it may be a better
+metric for ASR researchers to consider when optimizing ASR systems.
+"
+22929,Orbital angular momentum mode selection by rotationally symmetric superposition of chiral states with application to electron vortex beams,"  A general orbital angular momentum (OAM) mode selection principle is put
+forward involving the rotationally symmetric superposition of chiral states.
+This principle is not only capable of explaining the operation of spiral zone
+plate holograms and suggesting that naturally occurring rotationally symmetric
+patterns could be inadvertent sources of vortex beams, but more importantly, it
+enables the systematic and flexible generation of structured OAM waves in
+general. This is demonstrated both experimentally and theoretically in the
+context of electron vortex beams using rotationally symmetric binary amplitude
+chiral sieve masks.
+"
+22930,Query-Efficient Black-Box Attack by Active Learning,"  Deep neural network (DNN) as a popular machine learning model is found to be
+vulnerable to adversarial attack. This attack constructs adversarial examples
+by adding small perturbations to the raw input, while appearing unmodified to
+human eyes but will be misclassified by a well-trained classifier. In this
+paper, we focus on the black-box attack setting where attackers have almost no
+access to the underlying models. To conduct black-box attack, a popular
+approach aims to train a substitute model based on the information queried from
+the target DNN. The substitute model can then be attacked using existing
+white-box attack approaches, and the generated adversarial examples will be
+used to attack the target DNN. Despite its encouraging results, this approach
+suffers from poor query efficiency, i.e., attackers usually needs to query a
+huge amount of times to collect enough information for training an accurate
+substitute model. To this end, we first utilize state-of-the-art white-box
+attack methods to generate samples for querying, and then introduce an active
+learning strategy to significantly reduce the number of queries needed.
+Besides, we also propose a diversity criterion to avoid the sampling bias. Our
+extensive experimental results on MNIST and CIFAR-10 show that the proposed
+method can reduce more than $90\%$ of queries while preserve attacking success
+rates and obtain an accurate substitute model which is more than $85\%$ similar
+with the target oracle.
+"
+22931,PassGAN: A Deep Learning Approach for Password Guessing,"  State-of-the-art password guessing tools, such as HashCat and John the
+Ripper, enable users to check billions of passwords per second against password
+hashes. In addition to performing straightforward dictionary attacks, these
+tools can expand password dictionaries using password generation rules, such as
+concatenation of words (e.g., ""password123456"") and leet speak (e.g.,
+""password"" becomes ""p4s5w0rd""). Although these rules work well in practice,
+expanding them to model further passwords is a laborious task that requires
+specialized expertise. To address this issue, in this paper we introduce
+PassGAN, a novel approach that replaces human-generated password rules with
+theory-grounded machine learning algorithms. Instead of relying on manual
+password analysis, PassGAN uses a Generative Adversarial Network (GAN) to
+autonomously learn the distribution of real passwords from actual password
+leaks, and to generate high-quality password guesses. Our experiments show that
+this approach is very promising. When we evaluated PassGAN on two large
+password datasets, we were able to surpass rule-based and state-of-the-art
+machine learning password guessing tools. However, in contrast with the other
+tools, PassGAN achieved this result without any a-priori knowledge on passwords
+or common password structures. Additionally, when we combined the output of
+PassGAN with the output of HashCat, we were able to match 51%-73% more
+passwords than with HashCat alone. This is remarkable, because it shows that
+PassGAN can autonomously extract a considerable number of password properties
+that current state-of-the art rules do not encode.
+"
+22932,Sum of previous inpatient serum creatinine measurements predicts acute kidney injury in rehospitalized patients,"  Acute Kidney Injury (AKI), the abrupt decline in kidney function due to
+temporary or permanent injury, is associated with increased mortality,
+morbidity, length of stay, and hospital cost. Sometimes, simple interventions
+such as medication review or hydration can prevent AKI. There is therefore
+interest in estimating risk of AKI at hospitalization. To gain insight into
+this task, we employ multilayer perceptron (MLP) and recurrent neural networks
+(RNNs) using serum creatinine (sCr) as a lone feature. We explore different
+feature input structures, including variable-length look-backs and a nested
+formulation for rehospitalized patients with previous sCr measurements.
+Experimental results show that the simplest model, MLP processing the sum of
+sCr, had best performance: AUROC 0.92 and AUPRC 0.70. Such a simple model could
+be easily integrated into an EHR. Preliminary results also suggest that
+inpatient data streams with missing outpatient measurements---common in the
+medical setting---might be best modeled with a tailored architecture.
+"
+22933,An intelligent household greenhouse system design based on Internet of Things,"  In order to combine indoor greenery conservation with Internet of Things
+(IOT) Technologies, this paper designs an intelligent household greenhouse
+project with the features of comprehensive sensing, reliable transmission and
+intelligent processing. Through the analysis of functional requirements of the
+intelligent household greenhouse system, an intelligent household greenhouse
+system is designed with the functions of greenhouse environmental data
+detection, greenhouse environmental control regulation, data remote
+transmission and human-computer interaction. Its sensor layer collects
+environmental data in real time based on the ZigBee wireless sensor network.
+The network layer STM32 intelligent gateway coordinates with network server, so
+as to exchange data from sensor layer to application layer, and solve the
+problems of non-blocking of data sending and receiving as well as concurrent
+requests of multiple mobile terminals. The application layer is designed into
+two types. One is a desktop management system as a data storage and analysis
+center, and the other is a mobile terminal APP. At the same time, we design a
+communication protocol that is applicable to the interaction of the three-layer
+structure of the Internet of Things, with the characteristics of simplicity,
+stability, readability, and scalability. It can avoid the mutual influence of
+multi-level data exchange and ensure the correctness of data circulation. In
+the design, the system sensor layer ensures stable transmission of various data
+and instructions, and the network layer has a high degree of concurrency and
+real time. And various measurement and control data of the sensor layer can
+interact with the data of mobile-terminal equipment of the application layer.
+The desktop management system and mobile terminal APP can monitor greenhouse
+data in real time and control various actuators in the greenhouse.
+"
+22934,A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning,"  Learning sparse combinations is a frequent theme in machine learning. In this
+paper, we study its associated optimization problem in the distributed setting
+where the elements to be combined are not centrally located but spread over a
+network. We address the key challenges of balancing communication costs and
+optimization errors. To this end, we propose a distributed Frank-Wolfe (dFW)
+algorithm. We obtain theoretical guarantees on the optimization error
+$\epsilon$ and communication cost that do not depend on the total number of
+combining elements. We further show that the communication cost of dFW is
+optimal by deriving a lower-bound on the communication cost required to
+construct an $\epsilon$-approximate solution. We validate our theoretical
+analysis with empirical studies on synthetic and real-world data, which
+demonstrate that dFW outperforms both baselines and competing methods. We also
+study the performance of dFW when the conditions of our analysis are relaxed,
+and show that dFW is fairly robust.
+"
+22935,"Bias Correction with Jackknife, Bootstrap, and Taylor Series","  We analyze the bias correction methods using jackknife, bootstrap, and Taylor
+series. We focus on the binomial model, and consider the problem of bias
+correction for estimating $f(p)$, where $f \in C[0,1]$ is arbitrary. We
+characterize the supremum norm of the bias of general jackknife and bootstrap
+estimators for any continuous functions, and demonstrate the in delete-$d$
+jackknife, different values of $d$ may lead to drastically different behavior
+in jackknife. We show that in the binomial model, iterating the bootstrap bias
+correction infinitely many times may lead to divergence of bias and variance,
+and demonstrate that the bias properties of the bootstrap bias corrected
+estimator after $r-1$ rounds is exactly the same as that of the $r$-jackknife
+estimator if a bounded coefficients condition is satisfied.
+"
+22936,More cat than cute? Interpretable Prediction of Adjective-Noun Pairs,"  The increasing availability of affect-rich multimedia resources has bolstered
+interest in understanding sentiment and emotions in and from visual content.
+Adjective-noun pairs (ANP) are a popular mid-level semantic construct for
+capturing affect via visually detectable concepts such as ""cute dog"" or
+""beautiful landscape"". Current state-of-the-art methods approach ANP prediction
+by considering each of these compound concepts as individual tokens, ignoring
+the underlying relationships in ANPs. This work aims at disentangling the
+contributions of the `adjectives' and `nouns' in the visual prediction of ANPs.
+Two specialised classifiers, one trained for detecting adjectives and another
+for nouns, are fused to predict 553 different ANPs. The resulting ANP
+prediction model is more interpretable as it allows us to study contributions
+of the adjective and noun components. Source code and models are available at
+this https URL .
+"
+22937,The Kazdan-Warner equation on canonically compactifiable graphs,"  We study the Kazdan-Warner equation on canonically compactifiable graphs.
+These graphs are distinguished as analytic properties of Laplacians on these
+graphs carry a strong resemblance to Laplacians on open pre-compact manifolds.
+"
+22938,Dynamical Quantum Anomalous Hall Effect in Strong Optical Fields,"  Topological insulators (TIs) are characterized by the quantum anomalous Hall
+effect (QAHE) on the topological surface states under time-reversal symmetry
+breaking. Motivated by recent experiments on the magneto-optical effects
+induced by the QAHE, we develop a theory for the dynamical Hall conductivity
+for subgap optical frequency and intense optical fields using the
+Keldysh-Floquet Green's function formalism. Our theory reveals a nonlinear
+regime in which the Hall conductivity remains close to $e^2/2h$ at low
+frequencies. At higher optical fields, we find that the subsequent collapse of
+the half quantization is accompanied by coherent oscillations of the dynamical
+Hall conductivity as a function of field strength, triggered by the formation
+of Floquet subbands and the concomitant inter-subband transitions.
+"
+22939,An Optimal Realization Algorithm for Bipartite Graphs with Degrees in Prescribed Intervals,"  We consider the problem of constructing a bipartite graph whose degrees lie
+in prescribed intervals. Necessary and sufficient conditions for the existence
+of such graphs are well-known. However, existing realization algorithms suffer
+from large running times. In this paper, we present a realization algorithm
+that constructs an appropriate bipartite graph G=(U,V,E) in O(|U| + |V| + |E|)
+time, which is asymptotically optimal. In addition, we show that our algorithm
+produces edge-minimal bipartite graphs and that it can easily be modified to
+construct edge-maximal graphs.
+"
+22940,An Early In-Situ Stress Signature of the AlN-Si Pre-growth Interface for Successful Integration of Nitrides with (111) Si,"  The integration of MOCVD grown group III-A nitride device stacks on Si (111)
+substrates is critically dependent on the quality of the first AlN buffer layer
+grown. A Si surface that is both oxide-free and smooth is a primary requirement
+for nucleating such layers. A single parameter, the AlN layer growth stress, is
+shown to be an early (within 50 nm), clear (<0.5 GPa versus >1 GPa) and
+fail-safe indicator of the pre-growth surface, and the AlN quality required for
+successful epitaxy. Grain coalescence model for stress generation is used to
+correlate growth stress, the AlN-Si interface and crystal quality.
+"
+22941,"Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features","  Music genres allow to categorize musical items that share common
+characteristics. Although these categories are not mutually exclusive, most
+related research is traditionally focused on classifying tracks into a single
+class. Furthermore, these categories (e.g., Pop, Rock) tend to be too broad for
+certain applications. In this work we aim to expand this task by categorizing
+musical items into multiple and fine-grained labels, using three different data
+modalities: audio, text, and images. To this end we present MuMu, a new dataset
+of more than 31k albums classified into 250 genre classes. For every album we
+have collected the cover image, text reviews, and audio tracks. Additionally,
+we propose an approach for multi-label genre classification based on the
+combination of feature embeddings learned with state-of-the-art deep learning
+methodologies. Experiments show major differences between modalities, which not
+only introduce new baselines for multi-label genre classification, but also
+suggest that combining them yields improved results.
+"
+22942,Towards Applying the OPRA Theory to Shape Similarity,"  The motivation for using qualitative shape descriptions is as follows:
+qualitative shape descriptions can implicitly act as a schema for measuring the
+similarity of shapes, which has the potential to be cognitively adequate. Then,
+shapes which are similar to each other would also be similar for a pattern
+recognition algorithm. There is substantial work in pattern recognition and
+computer vision dealing with shape similarity. Here with our approach to
+qualitative shape descriptions and shape similarity, the focus is on achieving
+a representation using only simple predicates that a human could even apply
+without computer support.
+"
+22943,Results in descriptive set theory on some represented spaces,"  Descriptive set theory was originally developed on Polish spaces. It was
+later extended to $\omega$-continuous domains [Selivanov 2004] and recently to
+quasi-Polish spaces [de Brecht 2013]. All these spaces are countably-based.
+Extending descriptive set theory and its effective counterpart to general
+represented spaces, including non-countably-based spaces has been started in
+[Pauly, de Brecht 2015].
+We study the spaces $\mathcal{O}(\mathbb{N}^\mathbb{N})$,
+$\mathcal{C}(\mathbb{N}^\mathbb{N},2)$ and the Kleene-Kreisel spaces
+$\mathbb{N}\langle\alpha\rangle$. We show that there is a $\Sigma^0_2$-subset
+of $\mathcal{O}(\mathbb{N}^\mathbb{N})$ which is not Borel. We show that the
+open subsets of $\mathbb{N}^{\mathbb{N}^\mathbb{N}}$ cannot be continuously
+indexed by elements of $\mathbb{N}^\mathbb{N}$ or even
+$\mathbb{N}^{\mathbb{N}^\mathbb{N}}$, and more generally that the open subsets
+of $\mathbb{N}\langle\alpha\rangle$ cannot be continuously indexed by elements
+of $\mathbb{N}\langle\alpha\rangle$. We also derive effective versions of these
+results.
+These results give answers to recent open questions on the classification of
+spaces in terms of their base-complexity, introduced in [de Brecht, Schröder,
+Selivanov 2016]. In order to obtain these results, we develop general
+techniques which are refinements of Cantor's diagonal argument involving
+multi-valued fixed-point free functions and that are interesting on their own
+right.
+"
+22944,Flagged Grothendieck polynomials,"  We show that the flagged Grothendieck polynomials defined as the generating
+functions of flagged set-valued tableaux of Knutson-Miller-Yong can be
+expressed by a Jacobi-Trudi type determinant formula generalizing the work of
+Hudson-Matsumura. We also introduce the flagged skew Grothendieck polynomials
+in these two expressions and show that they coincide.
+"
+22945,The effect of ram pressure on the molecular gas of galaxies: three case studies in the Virgo cluster,"  We present 12CO (2-1) data of three Virgo spirals - NGC 4330, NGC 4402 and
+NGC 4522 obtained using the Submillimeter Array. These three galaxies show
+clear evidence of ram pressure stripping due to the cluster medium as found in
+previous HI imaging studies. Using high-resolution CO data, we investigate how
+the properties of the inner molecular gas disc change while a galaxy is
+undergoing HI stripping in the cluster. At given sensitivity limits, we do not
+find any clear signs of molecular gas stripping. However, both its morphology
+and kinematics appear to be quite disturbed as those of HI. Morphological
+peculiarities present in the molecular and atomic gas are closely related with
+each other, suggesting that molecular gas can be also affected by strong ICM
+pressure even if it is not stripped. CO is found to be modestly enhanced along
+the upstream sides in these galaxies, which may change the local star formation
+activity in the disc. Indeed, the distribution of H$\alpha$ emission, a tracer
+of recent star formation, well coincides with that of the molecular gas,
+revealing enhancements near the local CO peak or along the CO compression. FUV
+and H$\alpha$ share some properties in common, but FUV is always more extended
+than CO/H$\alpha$ in the three galaxies, implying that the star-forming disc is
+rapidly shrinking as the molecular gas properties have changed. We discuss how
+ICM pressure affects dense molecular gas and hence star formation properties
+while diffuse atomic gas is being removed from a galaxy.
+"
+22946,Distance scaling of electric-field noise in a surface-electrode ion trap,"  We investigate anomalous ion-motional heating, a limitation to multi-qubit
+quantum-logic gate fidelity in trapped-ion systems, as a function of
+ion-electrode separation. Using a multi-zone surface-electrode trap in which
+ions can be held at five discrete distances from the metal electrodes, we
+measure power-law dependencies of the electric-field noise experienced by the
+ion on the ion-electrode distance $d$. We find a scaling of approximately
+$d^{-4}$ regardless of whether the electrodes are at room temperature or
+cryogenic temperature, despite the fact that the heating rates are
+approximately two orders of magnitude smaller in the latter case. Through
+auxiliary measurements using application of noise to the electrodes, we rule
+out technical limitations to the measured heating rates and scalings. We also
+measure frequency scaling of the inherent electric-field noise close to $1/f$
+at both temperatures. These measurements eliminate from consideration
+anomalous-heating models which do not have a $d^{-4}$ distance dependence,
+including several microscopic models of current interest.
+"
+22947,Lower bounds over Boolean inputs for deep neural networks with ReLU gates,"  Motivated by the resurgence of neural networks in being able to solve complex
+learning tasks we undertake a study of high depth networks using ReLU gates
+which implement the function $x \mapsto \max\{0,x\}$. We try to understand the
+role of depth in such neural networks by showing size lowerbounds against such
+network architectures in parameter regimes hitherto unexplored. In particular
+we show the following two main results about neural nets computing Boolean
+functions of input dimension $n$,
+1. We use the method of random restrictions to show almost linear,
+$\Omega(\epsilon^{2(1-\delta)}n^{1-\delta})$, lower bound for completely weight
+unrestricted LTF-of-ReLU circuits to match the Andreev function on at least
+$\frac{1}{2} +\epsilon$ fraction of the inputs for $\epsilon >
+\sqrt{2\frac{\log^{\frac {2}{2-\delta}}(n)}{n}}$ for any $\delta \in (0,\frac 1
+2)$
+2. We use the method of sign-rank to show exponential in dimension lower
+bounds for ReLU circuits ending in a LTF gate and of depths upto $O(n^{\xi})$
+with $\xi < \frac{1}{8}$ with some restrictions on the weights in the bottom
+most layer. All other weights in these circuits are kept unrestricted. This in
+turns also implies the same lowerbounds for LTF circuits with the same
+architecture and the same weight restrictions on their bottom most layer.
+Along the way we also show that there exists a $\mathbb{R}^ n\rightarrow
+\mathbb{R}$ Sum-of-ReLU-of-ReLU function which Sum-of-ReLU neural nets can
+never represent no matter how large they are allowed to be.
+"
+22948,$P-$state Positronium for Precision Physics: an Ultrafine Splitting at $α^6$,"  An ""ultrafine"" splitting in positronium between the $L=1$ spin-singlet state
+and the spin-average of the spin-triplet states is shown to arise only at order
+$\alpha^6$. The QED prediction for $n=2$ states is
+$\Delta_{2,P}=(683/172800)m\alpha^6=73.7(1.2)$ kHz. This represents the
+smallest leading-order QED splitting known. Current experimental efforts could
+observe this splitting, and its observation can constrain new ultralight
+interactions, such as axions or $Z'$.
+"
+22949,A Coordinate-wise Optimization Algorithm for Sparse Inverse Covariance Selection,"  Sparse inverse covariance selection is a fundamental problem for analyzing
+dependencies in high dimensional data. However, such a problem is difficult to
+solve since it is NP-hard. Existing solutions are primarily based on convex
+approximation and iterative hard thresholding, which only lead to sub-optimal
+solutions. In this work, we propose a coordinate-wise optimization algorithm to
+solve this problem which is guaranteed to converge to a coordinate-wise minimum
+point. The algorithm iteratively and greedily selects one variable or swaps two
+variables to identify the support set, and then solves a reduced convex
+optimization problem over the support set to achieve the greatest descent. As a
+side contribution of this paper, we propose a Newton-like algorithm to solve
+the reduced convex sub-problem, which is proven to always converge to the
+optimal solution with global linear convergence rate and local quadratic
+convergence rate. Finally, we demonstrate the efficacy of our method on
+synthetic data and real-world data sets. As a result, the proposed method
+consistently outperforms existing solutions in terms of accuracy.
+"
+22950,The IoT energy challenge: A software perspective,"  The Internet of Things (IoT) sparks a whole new world of embedded
+applications. Most of these applications are based on deeply embedded systems
+that have to operate on limited or unreliable sources of energy, such as
+batteries or energy harvesters. Meeting the energy requirements for such
+applications is a hard challenge, which threatens the future growth of the IoT.
+Software has the ultimate control over hardware. Therefore, its role is
+significant in optimizing the energy consumption of a system. Currently,
+programmers have no feedback on how their software affects the energy
+consumption of a system. Such feedback can be enabled by energy transparency, a
+concept that makes a program's energy consumption visible, from hardware to
+software. This paper discusses the need for energy transparency in software
+development and emphasizes on how such transparency can be realized to help
+tackling the IoT energy challenge.
+"
+22951,Volumes of hyperbolic three-manifolds associated to modular links,"  Periodic geodesics on the modular surface correspond to periodic orbits of
+the geodesic flow in its unit tangent bundle
+$\mathrm{PSL}_2(\mathbb{Z})\backslash\mathrm{PSL}_2(\mathbb{R})$. The
+complement of any finite number of orbits is a hyperbolic $3$-manifold, which
+thus has a well-defined volume. We present strong numerical evidence that, in
+the case of the set of geodesics corresponding to the ideal class group of a
+real quadratic field, the volume has linear asymptotics in terms of the total
+length of the geodesics. This is not the case for general sets of geodesics
+"
+22952,"Stiffnessometer, a magnetic-field-free superconducting stiffness meter and its application","  We developed a new method to measure superconducting stiffness $\rho_{s}$,
+critical current density $J^c$, and coherence length $\xi$ without subjecting
+the sample to magnetic field or attaching leads. The method is based on the
+London equation $\mathbf{J}=-\rho_{s}\mathbf{A}$, where ${\bf J}$ is the
+current density and ${\bf A}$ is the vector potential. Using a rotor free ${\bf
+A}$ and a measurement of ${\bf J}$ via the magnetic moment of a superconducting
+ring, we determine $\rho_{s}$. By increasing $\mathbf{A}$ until the London
+equation does not hold anymore we determine $J^c$ and $\xi$. The method is
+sensitive to very small stiffness, which translates to penetration depth
+$\lambda \lesssim 3$ mm. It is also sensitive to extremely low critical current
+density $J^c \sim 1$ Acm$^{-2}$ or long coherence length $\xi \sim 1\mu m$.
+Naturally, the method does not suffer from demagnetization factor
+complications, the presence of vortices, or out-of-equilibrium conditions.
+Therefore, the absolute values of the different parameters can be determined.
+We demonstrate the application of this method to La$_{2-x}$Sr$_{x}$CuO$_{4}$.
+"
+22953,Conceptual Domain Adaptation Using Deep Learning,"  Deep learning has recently been shown to be instrumental in the problem of
+domain adaptation, where the goal is to learn a model on a target domain using
+a similar --but not identical-- source domain. The rationale for coupling both
+techniques is the possibility of extracting common concepts across domains.
+Considering (strictly) local representations, traditional deep learning assumes
+common concepts must be captured in the same hidden units. We contend that
+jointly training a model with source and target data using a single deep
+network is prone to failure when there is inherently lower-level
+representational discrepancy between the two domains; such discrepancy leads to
+a misalignment of corresponding concepts in separate hidden units. We introduce
+a search framework to correctly align high-level representations when training
+deep networks; such framework leads to the notion of conceptual --as opposed to
+representational-- domain adaptation.
+"
+22954,On algorithms to calculate integer complexity,"  We consider a problem first proposed by Mahler and Popken in 1953 and later
+developed by Coppersmith, Erdős, Guy, Isbell, Selfridge, and others. Let
+$f(n)$ be the complexity of $n \in \mathbb{Z^{+}}$, where $f(n)$ is defined as
+the least number of $1$'s needed to represent $n$ in conjunction with an
+arbitrary number of $+$'s, $*$'s, and parentheses. Several algorithms have been
+developed to calculate the complexity of all integers up to $n$. Currently, the
+fastest known algorithm runs in time $\mathcal{O}(n^{1.230175})$ and was given
+by J. Arias de Reyna and J. van de Lune in 2014. This algorithm makes use of a
+recursive definition given by Guy and iterates through products, $f(d) +
+f\left(\frac{n}{d}\right)$, for $d \ |\ n$, and sums, $f(a) + f(n - a)$, for
+$a$ up to some function of $n$. The rate-limiting factor is iterating through
+the sums. We discuss potential improvements to this algorithm via a method that
+provides a strong uniform bound on the number of summands that must be
+calculated for almost all $n$. We also develop code to run J. Arias de Reyna
+and J. van de Lune's analysis in higher bases and thus reduce their runtime of
+$\mathcal{O}(n^{1.230175})$ to $\mathcal{O}(n^{1.222911236})$. All of our code
+can be found online at: this https URL.
+"
+22955,On the Existence and Structure of Mixed Nash Equilibria for In-Band Full-Duplex Wireless Networks,"  This article offers the first characterisation of mixed Nash equilibria (MNE)
+for a wireless system with full-duplex capable terminals that share a common
+channel via Aloha-based contention. Focussing on a simple grid topology, we
+prove that mixed MNE exist only if proportionality, driven by the accuracy of
+self-interference cancellation, is granted between the cost undergone for full-
+and half-duplex operations. The analysis shows how a proper selection of such
+costs allows MNE that are also optimal from a network viewpoint in terms of
+aggregate throughput. The sensitivity of system performance to costs is tackled
+and discussed considering the price of anarchy.
+"
+22956,Workflow description to dynamically model β-arrestin signaling networks,"  Dynamic models of signaling networks allow the formulation of hypotheses on
+the topology and kinetic rate laws characterizing a given molecular network,
+in-depth exploration and confrontation with kinetic biological data. Despite
+its standardization, dynamic modeling of signaling networks still requires
+successive technical steps that need to be carefully performed. Here, we detail
+these steps by going through the mathematical and statistical framework. We
+explain how it can be applied to the understanding of \beta-arrestin-dependent
+signaling networks. We illustrate our methodology through the modeling of
+\beta-arrestin recruitment kinetics at the Follicle Stimulating Hormone (FSH)
+receptor supported by in-house Bioluminescence Resonance Energy Transfer (BRET)
+data.
+"
+22957,Revisiting the decoupling effects in the running of the Cosmological Constant,"  We revisit the decoupling effects associated with heavy particles in the
+renormalization group running of the vacuum energy in a mass-dependent
+renormalization scheme. We find the running of the vacuum energy stemming from
+the Higgs condensate in the entire energy range and show that it behaves as
+expected from the simple dimensional arguments meaning that it exhibits the
+quadratic sensitivity to the mass of the heavy particles in the infrared
+regime. The consequence of such a running to the fine-tuning problem with the
+measured value of the Cosmological Constant is analyzed and the constraint on
+the mass spectrum of a given model is derived. We show that in the Standard
+Model (SM) this fine-tuning constraint is not satisfied while in the massless
+theories this constraint formally coincides with the well known Veltman
+condition. We also provide a remarkably simple extension of the SM where
+saturation of this constraint enables us to predict the radiative Higgs mass
+correctly. Generalization to constant curvature spaces is also given.
+"
+22958,Orthogonal Series Density Estimation for Complex Surveys,"  We propose an orthogonal series density estimator for complex surveys, where
+samples are neither independent nor identically distributed. The proposed
+estimator is proved to be design-unbiased and asymptotically design-consistent.
+The asymptotic normality is proved under both design and combined spaces. Two
+data driven estimators are proposed based on the proposed oracle estimator. We
+show the efficiency of the proposed estimators in simulation studies. A real
+survey data example is provided for an illustration.
+"
+22959,When tails wag the decision: The role of distributional tails on climate impacts on decision-relevant time-scales,"  Assessing and managing risks in a changing climate requires projections that
+account for decision-relevant uncertainties. These deep uncertainties are often
+approximated by ensembles of Earth-system model runs that sample only a subset
+of the known uncertainties. Here we demonstrate and quantify how this approach
+can cut off the tails of the distributions of projected climate variables such
+as sea-level rise. As a result, low-probability high-impact events that may
+drive risks can be under-represented. Neglecting the tails of this deep
+uncertainty may lead to overconfident projections and poor decisions when high
+reliabilities are important.
+"
+22960,Rates of Convergence of Spectral Methods for Graphon Estimation,"  This paper studies the problem of estimating the grahpon model - the
+underlying generating mechanism of a network. Graphon estimation arises in many
+applications such as predicting missing links in networks and learning user
+preferences in recommender systems. The graphon model deals with a random graph
+of $n$ vertices such that each pair of two vertices $i$ and $j$ are connected
+independently with probability $\rho \times f(x_i,x_j)$, where $x_i$ is the
+unknown $d$-dimensional label of vertex $i$, $f$ is an unknown symmetric
+function, and $\rho$ is a scaling parameter characterizing the graph sparsity.
+Recent studies have identified the minimax error rate of estimating the graphon
+from a single realization of the random graph. However, there exists a wide gap
+between the known error rates of computationally efficient estimation
+procedures and the minimax optimal error rate.
+Here we analyze a spectral method, namely universal singular value
+thresholding (USVT) algorithm, in the relatively sparse regime with the average
+vertex degree $n\rho=\Omega(\log n)$. When $f$ belongs to Hölder or Sobolev
+space with smoothness index $\alpha$, we show the error rate of USVT is at most
+$(n\rho)^{ -2 \alpha / (2\alpha+d)}$, approaching the minimax optimal error
+rate $\log (n\rho)/(n\rho)$ for $d=1$ as $\alpha$ increases. Furthermore, when
+$f$ is analytic, we show the error rate of USVT is at most $\log^d
+(n\rho)/(n\rho)$. In the special case of stochastic block model with $k$
+blocks, the error rate of USVT is at most $k/(n\rho)$, which is larger than the
+minimax optimal error rate by at most a multiplicative factor $k/\log k$. This
+coincides with the computational gap observed for community detection. A key
+step of our analysis is to derive the eigenvalue decaying rate of the edge
+probability matrix using piecewise polynomial approximations of the graphon
+function $f$.
+"
+22961,Conformal bootstrap to Rényi entropy in 2D Liouville and super-Liouville CFTs,"  The Rényi entanglement entropy (REE) of the states excited by local
+operators in two-dimensional irrational conformal field theories (CFTs),
+especially in Liouville field theory (LFT) and $\mathcal{N}=1$ super-Liouville
+field theory (SLFT), has been investigated. In particular, the excited states
+obtained by acting on the vacuum with primary operators were considered. {We
+start from evaluating the second REE in a compact $c=1$ free boson field theory
+at generic radius, which is an irrational CFT. Then we focus on the two special
+irrational CFTs, e.g., LFT and SLFT. In these theories, the second REE of such
+local excited states becomes divergent in early and late time limits. For
+simplicity, we study the memory effect of REE for the two classes of the local
+excited states in LFT and SLFT. In order to restore the quasiparticles picture,
+we define the difference of REE between target and reference states, which
+belong to the same class. The variation of the difference of REE between early
+and late time limits always coincides with the log of the ratio of the fusion
+matrix elements between target and reference states. Furthermore, the locally
+excited states by acting generic descendent operators on the vacuum have been
+also investigated. The variation of the difference of REE is the summation of
+the log of the ratio of the fusion matrix elements between the target and
+reference states, and an additional normalization factor. Since the identity
+operator (or vacuum state) does not live in the Hilbert space of LFT and SLFT
+and no discrete terms contribute to REE in the intermediate channel, the
+variation of the difference of REE between target and reference states is no
+longer the log of the quantum dimension which is shown in the 1+1-dimensional
+rational CFTs (RCFTs).
+"
+22962,Frobenius Divisibility and Hopf Centers,"  A classical theorem of I. Schur states that the degree of any irreducible
+complex representation of a finite group $G$ divides the order of
+$G/\mathscr{Z} G$, where $\mathscr{Z} G$ is the center $G$. This note discusses
+similar divisibility results for certain classes of Hopf algebras.
+"
+22963,The Cramer Distance as a Solution to Biased Wasserstein Gradients,"  The Wasserstein probability metric has received much attention from the
+machine learning community. Unlike the Kullback-Leibler divergence, which
+strictly measures change in probability, the Wasserstein metric reflects the
+underlying geometry between outcomes. The value of being sensitive to this
+geometry has been demonstrated, among others, in ordinal regression and
+generative modelling. In this paper we describe three natural properties of
+probability divergences that reflect requirements from machine learning: sum
+invariance, scale sensitivity, and unbiased sample gradients. The Wasserstein
+metric possesses the first two properties but, unlike the Kullback-Leibler
+divergence, does not possess the third. We provide empirical evidence
+suggesting that this is a serious issue in practice. Leveraging insights from
+probabilistic forecasting we propose an alternative to the Wasserstein metric,
+the Cramér distance. We show that the Cramér distance possesses all three
+desired properties, combining the best of the Wasserstein and Kullback-Leibler
+divergences. To illustrate the relevance of the Cramér distance in practice
+we design a new algorithm, the Cramér Generative Adversarial Network (GAN),
+and show that it performs significantly better than the related Wasserstein
+GAN.
+"
+22964,Time-consistent investment and consumption strategies under a general discount function,"  The paper [12] examines a concept of equilibrium policies instead of optimal
+controls in stochastic optimization to analyze a mean-variance portfolio
+selection problem. We follow the same approach in order to investigate the
+Merton portfolio management problem in the context of non-exponential
+discounting, a context that give rise to time-inconsistency of the decision
+maker. Equilibrium policies are characterized in this context by means of a
+variational method which leads to a stochastic system that consists of a flow
+of forward-backward stochastic differential equations and an equilibrium
+condition. An explicit representation of the equilibrium policies is provided
+for the special cases of power, logarithmic and exponential utility functions.
+"
+22965,"Photo-polarimetric characteristics of brown dwarfs, Part I: uniform cloud decks","  This work is a theoretical exploration for facilitating the interpretation of
+polarimetric observations in terms of cloudiness, rotational velocities and
+effective temperatures of brown dwarfs (BDs). An envelope of scatterers like
+free-electrons, atoms/molecules, or haze/clouds affects the Stokes-vector of
+radiation emitted by oblate bodies. Due to high rotation rates, BDs can be
+considerably oblate. We present a conics-based radiative transfer (RT) scheme
+for computing the disc-resolved and disc-integrated polarized emission of an
+oblate BD or extrasolar giant planet (EGP) bearing homogenous or patchy clouds.
+Assuming a uniform grey atmosphere, we theoretically examine the
+photopolarimetric sensitivity to its scattering properties like cloud optical
+thickness and grain-size, concurrently with BD properties, like oblateness,
+inclination and effective temperature, which are all treated as free
+parameters. Additionally, we examine the potential effects of gravitational
+darkening (GD), revealing that it could significantly amplify disc-integrated
+polarization. GD imparts a non-linear inverse temperature-dependence to the
+resulting polarization.
+Photopolarimetric observations are sensitive to oblateness and inclination.
+The degree-of-polarization (DoP) increases in response to both, making it
+potentially useful for assessing the spatial orientation of the BD. Under our
+model assumptions, increasing droplet size in optically thick clouds causes a
+blue-ward shift in near-infrared (NIR) colors of BDs --- interesting in view of
+the observed J-K brightening in L/T transition. For large cloud grains,
+polarization decreases sharply, while transmitted intensity shows a steady
+increase. BD polarization is thus a potential indicator not only of the
+presence of clouds but also provides information on cloud grain size.
+"
+22966,Supervisory Control of Discrete-event Systems under Attacks,"  We consider a multi-adversary version of the supervisory control problem for
+discrete-event systems, in which an adversary corrupts the observations
+available to the supervisor. The supervisor's goal is to enforce a specific
+language in spite of the opponent's actions and without knowing which adversary
+it is playing against. This problem is motivated by applications to computer
+security in which a cyber defense system must make decisions based on reports
+from sensors that may have been tampered with by an attacker. We start by
+showing that the problem has a solution if and only if the desired language is
+controllable (in the Discrete event system classical sense) and observable in a
+(novel) sense that takes the adversaries into account. For the particular case
+of attacks that insert symbols into or remove symbols from the sequence of
+sensor outputs, we show that testing the existence of a supervisor and building
+the supervisor can be done using tools developed for the classical DES
+supervisory control problem, by considering a family of automata with modified
+output maps, but without expanding the size of the state space and without
+incurring on exponential complexity on the number of attacks considered., we
+construct observers that are robust against attacks and lead to an automaton
+representation of the supervisor. We also develop a test for observability
+under such replacement-removal attacks by using the so-called product automata.
+"
+22967,Validation of the in-flight calibration procedures for the MICROSCOPE space mission,"  The MICROSCOPE space mission aims to test the Equivalence Principle with an
+accuracy of $10^{-15}$. The drag-free micro-satellite will orbit around the
+Earth and embark a differential electrostatic accelerometer including two
+cylindrical test masses submitted to the same gravitational field and made of
+different materials. The experience consists in testing the equality of the
+electrostatic acceleration applied to the masses to maintain them relatively
+motionless. The accuracy of the measurements exploited for the test of the
+Equivalence Principle is limited by our a priori knowledge of several physical
+parameters of the instrument. These parameters are partially estimated
+on-ground, but with an insufficient accuracy, and an in-orbit calibration is
+therefore required to correct the measurements. The calibration procedures have
+been defined and their analytical performances have been evaluated. In
+addition, a simulator software including the dynamics model of the instrument,
+the satellite drag-free system and the perturbing environment has been
+developed to numerically validate the analytical results. After an overall
+presentation of the MICROSCOPE mission, this paper will describe the
+calibration procedures and focus on the simulator. Such an in-flight
+calibration is mandatory for similar space missions taking advantage of a
+drag-free system.
+"
+22968,Linear Time Computation of Moments in Sum-Product Networks,"  Bayesian online algorithms for Sum-Product Networks (SPNs) need to update
+their posterior distribution after seeing one single additional instance. To do
+so, they must compute moments of the model parameters under this distribution.
+The best existing method for computing such moments scales quadratically in the
+size of the SPN, although it scales linearly for trees. This unfortunate
+scaling makes Bayesian online algorithms prohibitively expensive, except for
+small or tree-structured SPNs. We propose an optimal linear-time algorithm that
+works even when the SPN is a general directed acyclic graph (DAG), which
+significantly broadens the applicability of Bayesian online algorithms for
+SPNs. There are three key ingredients in the design and analysis of our
+algorithm: 1). For each edge in the graph, we construct a linear time reduction
+from the moment computation problem to a joint inference problem in SPNs. 2).
+Using the property that each SPN computes a multilinear polynomial, we give an
+efficient procedure for polynomial evaluation by differentiation without
+expanding the network that may contain exponentially many monomials. 3). We
+propose a dynamic programming method to further reduce the computation of the
+moments of all the edges in the graph from quadratic to linear. We demonstrate
+the usefulness of our linear time algorithm by applying it to develop a linear
+time assume density filter (ADF) for SPNs.
+"
+22969,Photons in the Quantum World,"  Einstein's photo-electric effect allows us to regard electromagnetic waves as
+massless particles. Then, how is the photon helicity translated into the
+electric and magnetic fields perpendicular to the direction of propagation?
+This is an issue of the internal space-time symmetries defined by Wigner's
+little group for massless particles. It is noted that there are three
+generators for the rotation group defining the spin of a particle at rest. The
+closed set of commutation relations is a direct consequence of Heisenberg's
+uncertainty relations. The rotation group can be generated by three two-by-two
+Pauli matrices for spin-half particles. This group of two-by-two matrices is
+called SU(2), with two-component spinors. The direct product of two spinors
+leads to four states leading to one spin-0 state and one spin-1 state with
+three sub-states. The SU(2) group can be expanded to another group of
+two-by-two matrices called SL(2,c), which serves as the covering group for the
+group of Lorentz transformations. In this Lorentz-covariant regime, it is
+possible to Lorentz-boost the particle at rest to its infinite-momentum or
+massless state. Also in this SL(2,c) regime, there are four spin states for
+each particle, as in the case of the Dirac equation. The direct product of two
+SL(2,c) spinors thus leads sixteen states. Among them, four of them can be used
+for the electromagnetic four-potential, and six for the Maxwell tensor. The
+gauge degree of freedom is shown to be a Lorentz-boosted rotation. The
+polarization of massless neutrinos is interpreted as a consequence of the gauge
+invariance.
+"
+22970,Field dynamics inference via spectral density estimation,"  Stochastic differential equations (SDEs) are of utmost importance in various
+scientific and industrial areas. They are the natural description of dynamical
+processes whose precise equations of motion are either not known or too
+expensive to solve, e.g., when modeling Brownian motion. In some cases, the
+equations governing the dynamics of a physical system on macroscopic scales
+occur to be unknown since they typically cannot be deduced from general
+principles. In this work, we describe how the underlying laws of a stochastic
+process can be approximated by the spectral density of the corresponding
+process. Furthermore, we show how the density can be inferred from possibly
+very noisy and incomplete measurements of the dynamical field. Generally,
+inverse problems like these can be tackled with the help of Information Field
+Theory (IFT). For now, we restrict to linear and autonomous processes. Though,
+this is a non-conceptual limitation that may be omitted in future work. To
+demonstrate its applicability we employ our reconstruction algorithm on a
+time-series and spatio-temporal processes.
+"
+22971,Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis,"  We provide a bridge between generative modeling in the Machine Learning
+community and simulated physical processes in High Energy Particle Physics by
+applying a novel Generative Adversarial Network (GAN) architecture to the
+production of jet images -- 2D representations of energy depositions from
+particles interacting with a calorimeter. We propose a simple architecture, the
+Location-Aware Generative Adversarial Network, that learns to produce realistic
+radiation patterns from simulated high energy particle collisions. The pixel
+intensities of GAN-generated images faithfully span over many orders of
+magnitude and exhibit the desired low-dimensional physical properties (i.e.,
+jet mass, n-subjettiness, etc.). We shed light on limitations, and provide a
+novel empirical validation of image quality and validity of GAN-produced
+simulations of the natural world. This work provides a base for further
+explorations of GANs for use in faster simulation in High Energy Particle
+Physics.
+"
+22972,"Aztec: A Platform to Render Biomedical Software Findable, Accessible, Interoperable, and Reusable","  Precision medicine and health requires the characterization and phenotyping
+of biological systems and patient datasets using a variety of data formats.
+This scenario mandates the centralization of various tools and resources in a
+unified platform to render them Findable, Accessible, Interoperable, and
+Reusable (FAIR Principles). Leveraging these principles, Aztec provides the
+scientific community with a new platform that promotes a long-term, sustainable
+ecosystem of biomedical research software. Aztec is available at
+this https URL and its source code is hosted at
+this https URL.
+"
+22973,Efficient semiparametric estimation in time-varying regression models,"  We study semiparametric inference in some linear regression models with
+time-varying coefficients, dependent regressors and dependent errors. This
+problem, which has been considered recently by Zhang and Wu (2012) under the
+functional dependence measure, is interesting for parsimony reasons or for
+testing stability of some coefficients in a linear regression model. In this
+paper, we propose a different procedure for estimating non time-varying
+parameters at the rate root n, in the spirit of the method introduced by
+Robinson (1988) for partially linear models. When the errors in the model are
+martingale differences, this approach can lead to more effcient estimates than
+the method considered in Zhang and Wu (2012). For a time-varying AR process
+with exogenous covariates and conditionally Gaussian errors, we derive a notion
+of efficient information matrix from a convolution theorem adapted to
+triangular arrays. For independent but non identically distributed Gaussian
+errors, we construct an asymptotically efficient estimator in a semiparametric
+sense.
+"
+22974,Two Error Bounds of Imperfect Binary Search,"  Suppose we know that an object is in a sorted table and we want to determine
+the index of that object. To achieve this goal we could perform a binary
+search. However, suppose it is time-consuming to determine the relative
+position of that object to any other objects in the table. In this scenario, we
+might want to resort to an incomplete solution: we could device an algorithm
+that quickly predicts the result of comparing two objects, and replace the
+actual comparison with this algorithm during a binary search. The question then
+is how far away are the results yielded by the imperfect binary search from the
+correct answers. We present two quick lemmas that answer this question.
+"
+22975,On frequency estimation for partially observed processes with small noise in observations,"  We consider the problem of frequency estimation of the periodic signal
+multiplied by a stationary Gaussian process (Ornstein-Uhlenbeck) and observed
+in the presence of the white Gaussian noise. We show the consistency and
+asymptotic normality of the maximum likelihood estimator in the asymptotics of
+small noise in observations. The model of observations is a linear
+nonhomogeneous partially observed system and the construction and study of the
+estimator is essentialy based on the asymptotics of the equations of
+Kalman-Bucy filtration.
+"
+22976,On volumes of quaternionic hyperbolic n-orbifolds,"  By use of H. C. Wang's bound on the radius of a ball embedded in the
+fundamental domain of a lattice of a semisimple Lie group, we construct an
+explicit lower bound for the volume of a quaternionic hyperbolic orbifold that
+depends only on dimension.
+"
+22977,A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm,"  Making high-quality decisions in strategic spatial planning is heavily
+dependent on extracting knowledge from vast amounts of data. Although many
+decision-making problems like developing urban areas require such perception
+and reasoning, existing methods in this field usually neglect the deep
+knowledge mined from geographic databases and are based on pure statistical
+methods. Due to the large volume of data gathered in spatial databases, and the
+uncertainty of spatial objects, mining association rules for high-level
+knowledge representation is a challenging task. Few algorithms manage
+geographical and non-geographical data using topological relations. In this
+paper, a novel approach for spatial data mining based on the MOSES evolutionary
+framework is presented which improves the classic genetic programming approach.
+A hybrid architecture called GGeo is proposed to apply the MOSES mining rules
+considering fuzzy topological relations from spatial data. The uncertainty and
+fuzziness aspects are addressed using an enriched model of topological
+relations by fuzzy region connection calculus. Moreover, to overcome the
+problem of time-consuming fuzzy topological relationships calculations, this a
+novel data pre-processing method is offered. GGeo analyses and learns from
+geographical and non-geographical data and uses topological and distance
+parameters, and returns a series of arithmetic-spatial formulas as
+classification rules. The proposed approach is resistant to noisy data, and all
+its stages run in parallel to increase speed. This approach may be used in
+different spatial data classification problems as well as representing an
+appropriate method of data analysis and economic policy making.
+"
+22978,Stochastic EM for Shuffled Linear Regression,"  We consider the problem of inference in a linear regression model in which
+the relative ordering of the input features and output labels is not known.
+Such datasets naturally arise from experiments in which the samples are
+shuffled or permuted during the protocol. In this work, we propose a framework
+that treats the unknown permutation as a latent variable. We maximize the
+likelihood of observations using a stochastic expectation-maximization (EM)
+approach. We compare this to the dominant approach in the literature, which
+corresponds to hard EM in our framework. We show on synthetic data that the
+stochastic EM algorithm we develop has several advantages, including lower
+parameter error, less sensitivity to the choice of initialization, and
+significantly better performance on datasets that are only partially shuffled.
+We conclude by performing two experiments on real datasets that have been
+partially shuffled, in which we show that the stochastic EM algorithm can
+recover the weights with modest error.
+"
+22979,A formula for the Entropy of the Convolution of Gibbs probabilities on the circle,"  Consider the transformation $T:S^1 \to S^1$, such that $T(x)=2\, x$ (mod 1),
+and where $S^1$ is the unitary circle. Suppose $J:S^1 \to \mathbb{R}$ is Holder
+continuous and positive, and moreover that, for any $y\in S^1$, we have that
+$\sum_{x\,\,\text{such that}\,\,\, T(x)= y} \, J(x)=1.$
+We say that $\rho$ is a Gibbs probability for the Holder continuous potential
+$\log J$, if $\mathcal{L}_{\log J}^* \,(\rho)=\rho ,$ where $\mathcal{L}_{\log
+J}$ is the Ruelle operator for $\log J$. We call $J$ the Jacobian of $\rho$.
+Suppose $\nu=\mu_1*\mu_2$ is the convolution of two Gibbs probabilities
+$\mu_1$ and $\mu_2$ associated, respectively, to $\log J_1$ and $\log J_2$. We
+show that $\nu$ is also Gibbs and its Jacobian $\tilde{J}$ is given by
+$\tilde{J}(u) = \int J_1(u-x) d \mu_2(x)$
+In this case, the entropy $h(\nu)$ is given by the expression $$ h(\nu) = -
+\int\,[\,\,\int\, \log \,(\,\int J_1(r+s-x) d \mu_2(x)\,) \, d \mu_2(r)\,\,
+]\,\,d \mu_1 (s).$$ For a fixed $\mu_2$ we consider differentiable variations
+$\mu_1^t$, $t \in (-\epsilon,\epsilon)$, of $\mu_1$ on the Banach manifold of
+Gibbs probabilities, where $\mu_1^0=\mu_1$, and we estimate the derivative of
+the entropy $h(\mu_1^t * \mu_2)$ at $t=0$.
+We also present an expression for the Jacobian of the convolution of a Gibbs
+probability $\rho$ with the invariant probability with support on a periodic
+orbit of period two. This expression is based on the Jacobian of $\rho$ and two
+Radon-Nidodym derivatives.
+"
+22980,An Extremal Optimization approach to parallel resonance constrained capacitor placement problem,"  Installation of capacitors in distribution networks is one of the most used
+procedure to compensate reactive power generated by loads and, consequently, to
+reduce technical losses. So, the problem consists in identifying the optimal
+placement and sizing of capacitors. This problem is known in the literature as
+optimal capacitor placement problem. Neverthless, depending on the location and
+size of the capacitor, it may become a harmonic source, allowing capacitor to
+enter into resonance with the distribution network, causing several undesired
+side effects. In this work we propose a parsimonious method to deal with the
+capacitor placement problem that incorporates resonance constraints, ensuring
+that every allocated capacitor will not act as a harmonic source. This proposed
+algorithm is based upon a physical inspired metaheuristic known as Extremal
+Optimization. The results achieved showed that this proposal has reached
+significant gains when compared with other proposals that attempt repair, in a
+post-optimization stage, already obtained solutions which violate resonance
+constraints.
+"
+22981,Generative Adversarial Forests for Better Conditioned Adversarial Learning,"  In recent times, many of the breakthroughs in various vision-related tasks
+have revolved around improving learning of deep models; these methods have
+ranged from network architectural improvements such as Residual Networks, to
+various forms of regularisation such as Batch Normalisation. In essence, many
+of these techniques revolve around better conditioning, allowing for deeper and
+deeper models to be successfully learned. In this paper, we look towards better
+conditioning Generative Adversarial Networks (GANs) in an unsupervised learning
+setting. Our method embeds the powerful discriminating capabilities of a
+decision forest into the discriminator of a GAN. This results in a better
+conditioned model which learns in an extremely stable way. We demonstrate
+empirical results which show both clear qualitative and quantitative evidence
+of the effectiveness of our approach, gaining significant performance
+improvements over several popular GAN-based approaches on the Oxford Flowers
+and Aligned Celebrity Faces datasets.
+"
+22982,Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective,"  Interactive model analysis, the process of understanding, diagnosing, and
+refining a machine learning model with the help of interactive visualization,
+is very important for users to efficiently solve real-world artificial
+intelligence and data mining problems. Dramatic advances in big data analytics
+has led to a wide variety of interactive model analysis tasks. In this paper,
+we present a comprehensive analysis and interpretation of this rapidly
+developing area. Specifically, we classify the relevant work into three
+categories: understanding, diagnosis, and refinement. Each category is
+exemplified by recent influential work. Possible future research opportunities
+are also explored and discussed.
+"
+22983,Capacity and Delay Scaling for Broadcast Transmission in Highly Mobile Wireless Networks,"  We study broadcast capacity and minimum delay scaling laws for highly mobile
+wireless networks, in which each node has to disseminate or broadcast packets
+to all other nodes in the network. In particular, we consider a cell
+partitioned network under the simplified independent and identically
+distributed (IID) mobility model, in which each node chooses a new cell at
+random every time slot. We derive scaling laws for broadcast capacity and
+minimum delay as a function of the cell size. We propose a simple
+first-come-first-serve (FCFS) flooding scheme that nearly achieves both
+capacity and minimum delay scaling. Our results show that high mobility does
+not improve broadcast capacity, and that both capacity and delay improve with
+increasing cell sizes. In contrast to what has been speculated in the
+literature we show that there is (nearly) no tradeoff between capacity and
+delay. Our analysis makes use of the theory of Markov Evolving Graphs (MEGs)
+and develops two new bounds on flooding time in MEGs by relaxing the previously
+required expander property assumption.
+"
+22984,Window Opening Model using Deep Learning Methods,"  Occupant behavior (OB) and in particular window openings need to be
+considered in building performance simulation (BPS), in order to realistically
+model the indoor climate and energy consumption for heating ventilation and air
+conditioning (HVAC). However, the proposed OB window opening models are often
+biased towards the over-represented class where windows remained closed. In
+addition, they require tuning for each occupant which can not be efficiently
+scaled to the increased number of occupants. This paper presents a window
+opening model for commercial buildings using deep learning methods. The model
+is trained using data from occupants from an office building in Germany. In
+total the model is evaluated using almost 20 mio. data points from 3
+independent buildings, located in Aachen, Frankfurt and Philadelphia.
+Eventually, the results of 3100 core hours of model development are summarized,
+which makes this study the largest of its kind in window states modeling.
+Additionally, the practical potential of the proposed model was tested by
+incorporating it in the Modelica-based thermal building simulation. The
+resulting evaluation accuracy and F1 scores on the office buildings ranged
+between 86-89 % and 0.53-0.65 respectively. The performance dropped around 15 %
+points in case of sparse input data, while the F1 score remained high.
+"
+22985,The automatic detection of the information operations event basis,"  The methodology of automatic detection of the event basis of information
+operations, reflected in thematic information flows, is described. The
+presented methodology is based on the technologies for identifying information
+operations, the formation of the terminological basis of the subject area, the
+application of cluster analysis with cluster centroids, determined by analyzing
+the terminology of the information flow. The clusters formed in this way
+reflect the main events occurring during the information operations and reveal
+the technique for their implementation.
+"
+22986,Effects of Load-Based Frequency Regulation on Distribution Network Operation,"  This paper examines the operation of distribution networks that have large
+aggregations of thermostatically controlled loads (TCLs) providing secondary
+frequency regulation to the bulk power system. Specifically, we assess the
+prevalence of distribution network constraint violations, such as over- or
+under-voltages and overloading of transformers. Our goal is to determine the
+set of constraints that are at increased risk of being violated when TCLs
+provide regulation. We compare network operation in two cases: first with TCLs
+operating freely, and second with TCLs controlled to track a regulation signal.
+Using GridLAB-D, we run power flow simulations of five real distribution
+networks. Our results indicate that voltage limits are at increased risk of
+violation when TCLs provide regulation because of increased voltage variation.
+Effects on transformer aging are more nuanced and depend on the method used for
+dispatching TCLs. We find that in many distribution networks it may only be
+necessary to consider voltage constraints when designing a TCL control strategy
+that protects the distribution network.
+"
+22987,Spurious Local Minima are Common in Two-Layer ReLU Neural Networks,"  We consider the optimization problem associated with training simple ReLU
+neural networks of the form $\mathbf{x}\mapsto
+\sum_{i=1}^{k}\max\{0,\mathbf{w}_i^\top \mathbf{x}\}$ with respect to the
+squared loss. We provide a computer-assisted proof that even if the input
+distribution is standard Gaussian, even if the dimension is arbitrarily large,
+and even if the target values are generated by such a network, with orthonormal
+parameter vectors, the problem can still have spurious local minima once $6\le
+k\le 20$. By a concentration of measure argument, this implies that in high
+input dimensions, \emph{nearly all} target networks of the relevant sizes lead
+to spurious local minima. Moreover, we conduct experiments which show that the
+probability of hitting such local minima is quite high, and increasing with the
+network size. On the positive side, mild over-parameterization appears to
+drastically reduce such local minima, indicating that an over-parameterization
+assumption is necessary to get a positive result in this setting.
+"
+22988,Knapsack Problems for Wreath Products,"  In recent years, knapsack problems for (in general non-commutative) groups
+have attracted attention. In this paper, the knapsack problem for wreath
+products is studied. It turns out that decidability of knapsack is not
+preserved under wreath product. On the other hand, the class of
+knapsack-semilinear groups, where solutions sets of knapsack equations are
+effectively semilinear, is closed under wreath product. As a consequence, we
+obtain the decidability of knapsack for free solvable groups. Finally, it is
+shown that for every non-trivial abelian group $G$, knapsack (as well as the
+related subset sum problem) for the wreath product $G \wr \mathbb{Z}$ is
+NP-complete.
+"
+22989,Digital Grid: Transforming the Electric Power Grid into an Innovation Engine for the United States,"  The electric power grid is one of the largest and most complex
+infrastructures ever built by mankind. Modern civilization depends on it for
+industry production, human mobility, and comfortable living. However, many
+critical technologies such as the 60 Hz transformers were developed at the
+beginning of the 20th century and have changed very little since then.1 The
+traditional unidirectional power from the generation to the customer through
+the transmission-distribution grid has also changed nominally, but it no longer
+meets the need of the 21st century market energy customers. On one hand, 128m
+US residential customers pay $15B/per month for their utility bill, yet they
+have no option to select their energy supplier. In a world of where many
+traditional industries are transformed by digital Internet technology (Amazon,
+Ebay, Uber, Airbnb), the traditional electric energy market is lagging
+significantly behind. A move towards a true digital grid is needed. Such a
+digital grid requires a tight integration of the physical layer (energy and
+power) with digital and cyber information to allow an open and real time market
+akin to the world of e-commerce. Another major factor that is pushing for this
+radical transformation are the rapidly changing patterns in energy resources
+ownership and load flow. Driven by the decreasing cost in distributed solar,
+energy storage, electric vehicle, on site generation and microgrids, the high
+penetration of Distributed Energy Resource (DER) is shifting challenges
+substantially towards the edge of grid from the control point of view. The
+envisioned Digital Grid must facilitate the open competition and open
+innovation needed to accelerate of the adoption of new DER technologies while
+satisfying challenges in grid stability, data explosion and cyber security.
+"
+22990,"Coevolution Maintains Diversity in the Stochastic ""Kill the Winner"" Model","  The ""Kill the Winner"" hypothesis is an attempt to address the problem of
+diversity in biology. It argues that host-specific predators control the
+population of each prey, preventing a winner from emerging and thus maintaining
+the coexistence of all species in the system. We develop a stochastic model for
+the ""Kill the Winner"" paradigm and show that the stable coexistence state of
+the deterministic ""Kill the Winner"" model is destroyed by demographic
+stochasticity, through a cascade of extinction events. We formulate an
+individual-level stochastic model in which predator-prey coevolution promotes
+high diversity of the ecosystem by generating a persistent population flux of
+species.
+"
+22991,Sensor Analytics in Basketball,"  A new approach in team sports analysis consists in studying positioning and
+movements of players during the game in relation to team performance. State of
+the art tracking systems produce spatio-temporal traces of players that have
+facilitated a variety of research aimed to extract insights from trajectories.
+Several methods borrowed from machine learning, network and complex systems,
+geographic information system, computer vision and statistics have been
+proposed. After having reviewed the state of the art in those niches of
+literature aiming to extract useful information to analysts and experts in
+terms of relation between players' trajectories and team performance, this
+paper presents preliminary results from analysing trajectories data and sheds
+light on potential future research in this field of study. In particular, using
+convex hulls, we find interesting regularities in players' movement patterns.
+"
+22992,Spectrum of the Wilson-Fisher conformal field theory on the torus,"  We study the finite-size spectrum of the O($N$) symmetric Wilson-Fisher
+conformal field theory (CFT) on the $d=2$ spatial-dimension torus using the
+expansion in $\epsilon=3-d$. This is done by deriving a set of universal
+effective Hamiltonians describing fluctuations of the zero momentum modes. The
+effective Hamiltonians take the form of $N$-dimensional quantum anharmonic
+oscillators, which are shown to be strongly coupled at the critical point for
+small $\epsilon$. The low-energy spectrum is solved numerically for $N =
+1,2,3,4$. Using exact diagonalization (ED), we also numerically study explicit
+lattice models known to be in the O($2$) and O($3$) universality class,
+obtaining estimates of the low-lying critical spectrum. The analytic and
+numerical results show excellent agreement and the critical low energy torus
+spectra are qualitatively different among the studied CFTs, identifying them as
+a useful fingerprint for detecting the universality class of a quantum critical
+point.
+"
+22993,Majorana algebras generated by a 2A algebra and one further axis,"  We consider Majorana algebras generated by three Majorana axes $a_0$, $a_1$
+and $a_2$ such that $a_0$ and $a_1$ generate a dihedral algebra of type 2A. We
+show that such an algebra must occur as a Majorana representation of one of 27
+groups. These 27 groups coincide with the subgroups of the Monster which are
+generated by three 2A-involutions $a$, $b$ and $c$ such that $ab$ is also a
+2A-involution, which were classified by S. P. Norton in 1985. Our work relies
+on that of S. Decelle and consists of showing that certain groups do not admit
+Majorana representations.
+"
+22994,A correspondence between thermodynamics and inference,"  We explore a natural analogy between Bayesian statistics and thermal physics
+in which sample size corresponds to inverse temperature. This analogy motivates
+the definition of two novel statistical quantities: a learning capacity and a
+Gibbs entropy. The analysis of the learning capacity, corresponding to the heat
+capacity in thermal physics, leads to a critical insight into why some models
+have anomalously good learning performance. The mechanism is a statistical
+analogue of the failure of the equipartition theorem formula for the heat
+capacity. We explore the properties of the learning capacity in a number of
+examples, including a sloppy model. We also propose that the Gibbs entropy
+provides a natural device for counting distinguishable distributions in the
+context of Bayesian inference. This insight results in a new solution to a
+long-standing problem in Bayesian inference: the definition of an objective or
+uninformative prior. We use the Gibbs entropy to define a generalized principle
+of indifference (GPI) in which every distinguishable model is assigned equal a
+priori probability. This approach both resolves a number of long standing
+inconsistencies in objective Bayesian inference and unifies several seemingly
+unrelated Bayesian methods with the information-based paradigm of inference.
+"
+22995,Stationary patterns in star networks of bistable units: Theory and application to chemical reactions,"  We present theoretical and experimental studies on pattern formation with
+bistable dynamical units coupled in a star network configuration. By applying a
+localized perturbation to the central or the peripheral elements, we
+demonstrate the subsequent spreading, pinning, or retraction of the
+activations; such analysis enables the characterization of the formation of
+stationary patterns of localized activity. The results are interpreted with a
+theoretical analysis of a simplified bistable reaction-diffusion model. Weak
+coupling results in trivial pinned states where the activation cannot
+propagate. At strong coupling, uniform state is expected with active or
+inactive elements at small or large degree networks respectively. Nontrivial
+stationary spatial pattern, corresponding to an activation pinning, is
+predicted to occur at intermediate number of peripheral elements and at
+intermediate coupling strengths, where the central activation of the network is
+pinned, but the peripheral activation propagates toward the center. The results
+are confirmed in experiments with star networks of bistable electrochemical
+reactions. The experiments confirm the existence of the stationary spatial
+patterns and the dependence of coupling strength on the number of peripheral
+elements for transitions between pinned and retreating or spreading fronts in
+forced network configurations (where the central or periphery elements are
+forced to maintain their states).
+"
+22996,Functional optimality of the sulcus pattern of the human brain,"  We develop a mathematical model of information transmission across the
+biological neural network of the human brain. The overall function of the brain
+consists of the emergent processes resulting from the spread of information
+through the neural network. The capacity of the brain is therefore related to
+the rate at which it can transmit information through the neural network. The
+particular transmission model under consideration allows for information to be
+transmitted along multiple paths between points of the cortex. The resulting
+transmission rates are governed by potential theory. According to this theory,
+the brain has preferred and quantized transmission modes that correspond to
+eigenfunctions of the classical Steklov eigenvalue problem, with the reciprocal
+eigenvalues quantifying the corresponding transmission rates. We take the model
+as a basis for testing the hypothesis that the sulcus pattern of the human
+brain has evolved to maximize the rate of transmission of information between
+points in the cerebral cortex. We show that the introduction of sulci, or cuts,
+in an otherwise smooth domain indeed increases the overall transmission rate.
+We demonstrate this result by means of numerical experiments concerned with a
+spherical domain with a varying number of slits on its surface.
+"
+22997,Externally controlled band gap in twisted bilayer graphene,"  We theoretically study the effects of electron-electron interaction in
+twisted bilayer graphene in applied transverse dc electric field. When the
+twist angle is not very small, the electronic spectrum of the bilayer consists
+of four Dirac cones inherited from each graphene layer. Applied bias voltage
+leads to the appearance of two hole-like and two electron-like Fermi surface
+sheets with perfect nesting among electron and hole components. Such a band
+structure is unstable with respect to exciton band gap opening due to the
+screened Coulomb interaction. The exciton order parameter is accompanied by the
+spin-density-wave order. The value of the gap depends on the twist angle. More
+importantly, it can be controlled by applied bias voltage which opens new
+directions in manufacturing of different nanoscale devices.
+"
+22998,Degree-optimal moving frames for rational curves,"  A $\mathit{\text{moving frame}}$ at a rational curve is a basis of vectors
+moving along the curve. When the rational curve is given parametrically by a
+row vector $\mathbf{a}$ of univariate polynomials, a moving frame with
+important algebraic properties can be defined by the columns of an invertible
+polynomial matrix $P$, such that $\mathbf{a} P=[\gcd(\mathbf{a}),0\ldots,0]$. A
+$\mathit{\text{degree-optimal moving frame}}$ has column-wise minimal degree,
+where the degree of a column is defined to be the maximum of the degrees of its
+components. Algebraic moving frames are closely related to the univariate
+versions of the celebrated Quillen-Suslin problem, effective Nullstellensatz
+problem, and syzygy module problem. However, this paper appears to be the first
+devoted to finding an efficient algorithm for constructing a degree-optimal
+moving frame, a property desirable in various applications. We compare our
+algorithm with other possible approaches, based on already available
+algorithms, and show that it is more efficient. We also establish several new
+theoretical results concerning the degrees of an optimal moving frame and its
+components. In addition, we show that any deterministic algorithm for computing
+a degree-optimal algebraic moving frame can be augmented so that it assigns a
+degree-optimal moving frame in a $GL_n(\mathbb{K})$-equivariant manner. This
+crucial property of classical geometric moving frames, in combination with the
+algebraic properties, can be exploited in various problems.
+"
+22999,Low-Rank Modifications of Riccati Factorizations for Model Predictive Control,"  In Model Predictive Control (MPC) the control input is computed by solving a
+constrained finite-time optimal control (CFTOC) problem at each sample in the
+control loop. The main computational effort is often spent on computing the
+search directions, which in MPC corresponds to solving unconstrained
+finite-time optimal control (UFTOC) problems. This is commonly performed using
+Riccati recursions or generic sparsity exploiting algorithms. In this work the
+focus is efficient search direction computations for active-set (AS) type
+methods. The system of equations to be solved at each AS iteration is changed
+only by a low-rank modification of the previous one, and exploiting this
+structured change is important for the performance of AS type solvers. In this
+paper, theory for how to exploit these low-rank changes by modifying the
+Riccati factorization between AS iterations in a structured way is presented. A
+numerical evaluation of the proposed algorithm shows that the computation time
+can be significantly reduced by modifying, instead of re-computing, the Riccati
+factorization. This speed-up can be important for AS type solvers used for
+linear, nonlinear and hybrid MPC.
+"
+23000,Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network,"  Efficient and high-fidelity prior sampling and inversion for complex
+geological media is still a largely unsolved challenge. Here, we use a deep
+neural network of the variational autoencoder type to construct a parametric
+low-dimensional base model parameterization of complex binary geological media.
+For inversion purposes, it has the attractive feature that random draws from an
+uncorrelated standard normal distribution yield model realizations with spatial
+characteristics that are in agreement with the training set. In comparison with
+the most commonly used parametric representations in probabilistic inversion,
+we find that our dimensionality reduction (DR) approach outperforms principle
+component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform
+(DCT) DR techniques for unconditional geostatistical simulation of a
+channelized prior model. For the considered examples, important compression
+ratios (200 - 500) are achieved. Given that the construction of our
+parameterization requires a training set of several tens of thousands of prior
+model realizations, our DR approach is more suited for probabilistic (or
+deterministic) inversion than for unconditional (or point-conditioned)
+geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D
+transient hydraulic tomography data are used to demonstrate the DR-based
+inversion. For the 2D case study, the performance is superior compared to
+current state-of-the-art multiple-point statistics inversion by sequential
+geostatistical resampling (SGR). Inversion results for the 3D application are
+also encouraging.
+"
+23001,A linear threshold for uniqueness of solutions to random jigsaw puzzles,"  We consider a problem introduced by Mossel and Ross [Shotgun assembly of
+labeled graphs, arXiv:1504.07682]. Suppose a random $n\times n$ jigsaw puzzle
+is constructed by independently and uniformly choosing the shape of each ""jig""
+from $q$ possibilities. We are given the shuffled pieces. Then, depending on
+$q$, what is the probability that we can reassemble the puzzle uniquely? We say
+that two solutions of a puzzle are similar if they only differ by permutation
+of duplicate pieces, and rotation of rotationally symmetric pieces. In this
+paper, we show that, with high probability, such a puzzle has at least two
+non-similar solutions when $2\leq q \leq \frac{2}{\sqrt{e}}n$, all solutions
+are similar when $q\geq (2+\varepsilon)n$, and the solution is unique when
+$q=\omega(n)$.
+"
+23002,Heterodiffusion coefficients in α-iron,"  The diffusion of tungsten in {\alpha}-iron is important for the application
+of ferritic-iron alloys to thermal power plants. These data, over a wide
+temperature range across the Curie temperature, have been recently reported. We
+show that these diffusion coefficients can be satisfactory reproduced in terms
+of the bulk elastic and expansivity data by means of a thermodynamical model
+that interconnects point defects parameters with bulk qualities.
+"
+23003,Role model detection using low rank similarity matrix,"  Computing meaningful clusters of nodes is crucial to analyse large networks.
+In this paper, we apply new clustering methods to improve the computational
+time. We use the properties of the adjacency matrix to obtain better role
+extraction. We also define a new non-recursive similarity measure and compare
+its results with the ones obtained with Browet's similarity measure. We will
+show the extraction of the different roles with a linear time complexity.
+Finally, we test our algorithm with real data structures and analyse the limit
+of our algorithm.
+"
+23004,Slowing down of particle beams in the dusty plasma with kappa-distributions,"  We study the slowing down of particle beams passing through the dusty plasma
+with power-law kappa-distributions. Three plasma components, electrons, ions
+and dust particles, can have different kappa-parameter. We derive the
+deceleration factor (the velocity moment equation) and the slowing down time of
+a test particle, and numerically study the slowing down properties of an
+electron beam, a proton beam and a dust particle beam, respectively, in the
+kappa-distributed dusty plasma. We show that the slowing down properties of
+particle beams depend strongly on the kappa-parameters of the plasma
+components, and the dust component plays a dominant role in the slowing down.
+And the slowing down also depends on mass and charge of the dust particles in
+the dusty plasma. More detailed results are shown in 17 numerical graphs.
+"
+23005,On the $FRS$-generic family of space cusps,"  We consider in this paper the $FRS$-deformations of a family of space curves
+with codimension $\leq 3$. Some geometric aspects of a space curve such as
+flattenings, vertices and twistings points has been studied.
+"
+23006,Estimating thresholding levels for random fields via Euler characteristics,"  We introduce Lipschitz-Killing curvature (LKC) regression, a new method to
+produce $(1-\alpha)$ thresholds for signal detection in random fields that does
+not require knowledge of the spatial correlation structure. The idea is to fit
+observed empirical Euler characteristics to the Gaussian kinematic formula via
+generalized least squares, which quickly and easily provides statistical
+estimates of the LKCs --- complex topological quantities that can be extremely
+challenging to compute, both theoretically and numerically. With these
+estimates, we can then make use of a powerful parametric approximation via
+Euler characteristics for Gaussian random fields to generate accurate
+$(1-\alpha)$ thresholds and $p$-values. The main features of our proposed LKC
+regression method are easy implementation, conceptual simplicity, and
+facilitated diagnostics, which we demonstrate in a variety of simulations and
+applications.
+"
+23007,An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics,"  We describe an ""embarrassingly parallel"" method for Bayesian phylogenetic
+inference, annealed Sequential Monte Carlo, based on recent advances in the
+Sequential Monte Carlo literature such as adaptive determination of annealing
+parameters. The algorithm provides an approximate posterior distribution over
+trees and evolutionary parameters as well as an unbiased estimator for the
+marginal likelihood. This unbiasedness property can be used for the purpose of
+testing the correctness of posterior simulation software. We evaluate the
+performance of phylogenetic annealed Sequential Monte Carlo by reviewing and
+comparing with other computational Bayesian phylogenetic methods, in
+particular, different marginal likelihood estimation methods. Unlike previous
+Sequential Monte Carlo methods in phylogenetics, our annealed method can
+utilize standard Markov chain Monte Carlo tree moves and hence benefit from the
+large inventory of such moves available in the literature. Consequently, the
+annealed Sequential Monte Carlo method should be relatively easy to incorporate
+into existing phylogenetic software packages based on Markov chain Monte Carlo
+algorithms. We illustrate our method using simulation studies and real data
+analysis.
+"
+23008,Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging,"  For effective treatment of Alzheimer disease (AD), it is important to
+identify subjects who are most likely to exhibit rapid cognitive decline.
+Herein, we developed a novel framework based on a deep convolutional neural
+network which can predict future cognitive decline in mild cognitive impairment
+(MCI) patients using flurodeoxyglucose and florbetapir positron emission
+tomography (PET). The architecture of the network only relies on baseline PET
+studies of AD and normal subjects as the training dataset. Feature extraction
+and complicated image preprocessing including nonlinear warping are unnecessary
+for our approach. Accuracy of prediction (84.2%) for conversion to AD in MCI
+patients outperformed conventional feature-based quantification approaches. ROC
+analyses revealed that performance of CNN-based approach was significantly
+higher than that of the conventional quantification methods (p < 0.05). Output
+scores of the network were strongly correlated with the longitudinal change in
+cognitive measurements. These results show the feasibility of deep learning as
+a tool for predicting disease outcome using brain images.
+"
+23009,A Mote in Andromeda's Disk: a Misidentified Periodic AGN Behind M31,"  We identify an object previously thought to be a star in the disk of M31,
+J0045+41, as a background $z\approx0.215$ AGN seen through a low-absorption
+region of M31. We present moderate resolution spectroscopy of J0045+41 obtained
+using GMOS at Gemini-North. The spectrum contains features attributable to the
+host galaxy. We model the spectrum to estimate the AGN contribution, from which
+we estimate the luminosity and virial mass of the central engine. Residuals to
+our fit reveal a blue-shifted component to the broad H$\alpha$ and H$\beta$ at
+a relative velocity of $\sim4800$ km s$^{-1}$. We also detect \ion{Na}{1}
+absorption in the Milky Way restframe. We search for evidence of periodicity
+using $g$-band photometry from the Palomar Transient Factory and find evidence
+for multiple periodicities ranging from $\sim80-350$ days. Two of the detected
+periods are in a 1:4 ratio, which is identical to the predictions of
+hydrodynamical simulations of binary supermassive black hole systems. If these
+signals arise due to such a system, J0045+41 is well within the gravitational
+wave regime. We calculate the time until inspiral due to gravitational
+radiation, assuming reasonable values of the mass ratio of the two black holes.
+We discuss the implications of our findings and forthcoming work to identify
+other such interlopers in the light of upcoming photometric surveys such as the
+Zwicky Transient Facility (ZTF) or the Large Synoptic Survey Telescope (LSST)
+projects.
+"
+23010,Characterization of Spherical and Plane Curves Using Rotation Minimizing Frames,"  In this work we furnish characterizations of spherical and plane curves using
+rotation minimizing frames. Due to their minimal twist, in many contexts these
+frames are preferable over the usual Frenet one, such as in motion design,
+sweep surface modeling, computer visualization, and in geometric considerations
+as well. Here, we first furnish an alternative proof for the characterization
+of spherical curves by using osculating spheres described in terms of a
+rotation minimizing frame. In addition, we show how to find the angle between
+the principal normal and a rotation minimizing vector for a spherical curve.
+This is done by conveniently writing the curvature and torsion for a curve on a
+sphere. Later, we extend these expressions for the curvature and torsion of a
+generic curve by studying its behavior near an osculating sphere, i.e., we
+describe them in terms of spherical analogs. Finally, we also address in this
+work the problem of characterizing those curves whose position vector, up to a
+translation, lies on a (moving) plane spanned by the unit tangent and a
+rotation minimizing vector field and prove that they are precisely the plane
+curves.
+"
+23011,SQL-Rank: A Listwise Approach to Collaborative Ranking,"  In this paper, we propose a listwise approach for constructing user-specific
+rankings in recommendation systems in a collaborative fashion. We contrast the
+listwise approach to previous pointwise and pairwise approaches, which are
+based on treating either each rating or each pairwise comparison as an
+independent instance respectively. By extending the work of (Cao et al. 2007),
+we cast listwise collaborative ranking as maximum likelihood under a
+permutation model which applies probability mass to permutations based on a low
+rank latent score matrix. We present a novel algorithm called SQL-Rank, which
+can accommodate ties and missing data and can run in linear time. We develop a
+theoretical framework for analyzing listwise ranking methods based on a novel
+representation theory for the permutation model. Applying this framework to
+collaborative ranking, we derive asymptotic statistical rates as the number of
+users and items grow together. We conclude by demonstrating that our SQL-Rank
+method often outperforms current state-of-the-art algorithms for implicit
+feedback such as Weighted-MF and BPR and achieve favorable results when
+compared to explicit feedback algorithms such as matrix factorization and
+collaborative ranking.
+"
+23012,Bernoulli Rank-$1$ Bandits for Click Feedback,"  The probability that a user will click a search result depends both on its
+relevance and its position on the results page. The position based model
+explains this behavior by ascribing to every item an attraction probability,
+and to every position an examination probability. To be clicked, a result must
+be both attractive and examined. The probabilities of an item-position pair
+being clicked thus form the entries of a rank-$1$ matrix. We propose the
+learning problem of a Bernoulli rank-$1$ bandit where at each step, the
+learning agent chooses a pair of row and column arms, and receives the product
+of their Bernoulli-distributed values as a reward. This is a special case of
+the stochastic rank-$1$ bandit problem considered in recent work that proposed
+an elimination based algorithm Rank1Elim, and showed that Rank1Elim's regret
+scales linearly with the number of rows and columns on ""benign"" instances.
+These are the instances where the minimum of the average row and column rewards
+$\mu$ is bounded away from zero. The issue with Rank1Elim is that it fails to
+be competitive with straightforward bandit strategies as $\mu \rightarrow 0$.
+In this paper we propose Rank1ElimKL which simply replaces the (crude)
+confidence intervals of Rank1Elim with confidence intervals based on
+Kullback-Leibler (KL) divergences, and with the help of a novel result
+concerning the scaling of KL divergences we prove that with this change, our
+algorithm will be competitive no matter the value of $\mu$. Experiments with
+synthetic data confirm that on benign instances the performance of Rank1ElimKL
+is significantly better than that of even Rank1Elim, while experiments with
+models derived from real data confirm that the improvements are significant
+across the board, regardless of whether the data is benign or not.
+"
+23013,Large Negative Flattened Dispersion over the S+C+L Band Using Highly Birefringent Photonic Crystal Fiber,"  Novel triangular lattice photonic crystal fiber with a very large negative
+flattened dispersion over the S+C+L band, and very high birefringence is
+offered here. To investigate different optical properties of the proposed
+fiber, finite element method (FEM) is deployed. The fiber presents a flattened
+negative dispersion of $-698.5\pm 5 ps/(nm-km)$ over the wavelength of $1440
+nm$ to $1600 nm$. Besides, the proposed photonic crystal fiber (PCF) exhibits a
+high birefringence of $1.886\times10^{-2}$ at the wavelength of $1550 nm$.
+Furthermore, the nonlinearity, single modeness, effective area etc. of the
+proposed PCF are thoroughly discussed. The fiber would have important
+applications in broadband residual dispersion compensation as well as
+polarization maintaining applications.
+"
+23014,Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise,"  Matrix decomposition is a popular and fundamental approach in machine
+learning and data mining. It has been successfully applied into various fields.
+Most matrix decomposition methods focus on decomposing a data matrix from one
+single source. However, it is common that data are from different sources with
+heterogeneous noise. A few of matrix decomposition methods have been extended
+for such multi-view data integration and pattern discovery. While only few
+methods were designed to consider the heterogeneity of noise in such multi-view
+data for data integration explicitly. To this end, we propose a joint matrix
+decomposition framework (BJMD), which models the heterogeneity of noise by
+Gaussian distribution in a Bayesian framework. We develop two algorithms to
+solve this model: one is a variational Bayesian inference algorithm, which
+makes full use of the posterior distribution; and another is a maximum a
+posterior algorithm, which is more scalable and can be easily paralleled.
+Extensive experiments on synthetic and real-world datasets demonstrate that
+BJMD considering the heterogeneity of noise is superior or competitive to the
+state-of-the-art methods.
+"
+23015,An Analysis of 1-to-First Matching in Iris Recognition,"  Iris recognition systems are a mature technology that is widely used
+throughout the world. In identification (as opposed to verification) mode, an
+iris to be recognized is typically matched against all N enrolled irises. This
+is the classic ""1-to-N search"". In order to improve the speed of large-scale
+identification, a modified ""1-to-First"" search has been used in some
+operational systems. A 1-to-First search terminates with the first
+below-threshold match that is found, whereas a 1-to-N search always finds the
+best match across all enrollments. We know of no previous studies that evaluate
+how the accuracy of 1-to-First search differs from that of 1-to-N search. Using
+a dataset of over 50,000 iris images from 2,800 different irises, we perform
+experiments to evaluate the relative accuracy of 1-to-First and 1-to-N search.
+We evaluate how the accuracy difference changes with larger numbers of enrolled
+irises, and with larger ranges of rotational difference allowed between iris
+images. We find that False Match error rate for 1-to-First is higher than for
+1-to-N, and the the difference grows with larger number of enrolled irises and
+with larger range of rotation.
+"
+23016,Identification of Credit Risk Based on Cluster Analysis of Account Behaviours,"  Assessment of risk levels for existing credit accounts is important to the
+implementation of bank policies and offering financial products. This paper
+uses cluster analysis of behaviour of credit card accounts to help assess
+credit risk level. Account behaviour is modelled parametrically and we then
+implement the behavioural cluster analysis using a recently proposed
+dissimilarity measure of statistical model parameters. The advantage of this
+new measure is the explicit exploitation of uncertainty associated with
+parameters estimated from statistical models. Interesting clusters of real
+credit card behaviours data are obtained, in addition to superior prediction
+and forecasting of account default based on the clustering outcomes.
+"
+23017,Boosting Black Box Variational Inference,"  Approximating a probability density in a tractable manner is a central task
+in Bayesian statistics. Variational Inference (VI) is a popular technique that
+achieves tractability by choosing a relatively simple variational family.
+Borrowing ideas from the classic boosting framework, recent approaches attempt
+to \emph{boost} VI by replacing the selection of a single density with a
+greedily constructed mixture of densities. In order to guarantee convergence,
+previous works impose stringent assumptions that require significant effort for
+practitioners. Specifically, they require a custom implementation of the greedy
+step (called the LMO) for every probabilistic model with respect to an
+unnatural variational family of truncated distributions. Our work fixes these
+issues with novel theoretical and algorithmic insights. On the theoretical
+side, we show that boosting VI satisfies a relaxed smoothness assumption which
+is sufficient for the convergence of the functional Frank-Wolfe (FW) algorithm.
+Furthermore, we rephrase the LMO problem and propose to maximize the Residual
+ELBO (RELBO) which replaces the standard ELBO optimization in VI. These
+theoretical enhancements allow for black box implementation of the boosting
+subroutine. Finally, we present a stopping criterion drawn from the duality gap
+in the classic FW analyses and exhaustive experiments to illustrate the
+usefulness of our theoretical and algorithmic contributions.
+"
+23018,Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network,"  We present an instance segmentation algorithm trained and applied to a CCTV
+recording of beef cattle during a winter finishing period. A fully
+convolutional network was transformed into an instance segmentation network
+that learns to label each instance of an animal separately. We introduce a
+conceptually simple framework that the network uses to output a single
+prediction for every animal. These results are a contribution towards behaviour
+analysis in winter finishing beef cattle for early detection of animal
+welfare-related problems.
+"
+23019,FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model,"  De novo protein structure prediction from amino acid sequence is one of the
+most challenging problems in computational biology. As one of the extensively
+explored mathematical models for protein folding, Hydrophobic-Polar (HP) model
+enables thorough investigation of protein structure formation and evolution.
+Although HP model discretizes the conformational space and simplifies the
+folding energy function, it has been proven to be an NP-complete problem. In
+this paper, we propose a novel protein folding framework FoldingZero,
+self-folding a de novo protein 2D HP structure from scratch based on deep
+reinforcement learning. FoldingZero features the coupled approach of a two-head
+(policy and value heads) deep convolutional neural network (HPNet) and a
+regularized Upper Confidence Bounds for Trees (R-UCT). It is trained solely by
+a reinforcement learning algorithm, which improves HPNet and R-UCT iteratively
+through iterative policy optimization. Without any supervision and domain
+knowledge, FoldingZero not only achieves comparable results, but also learns
+the latent folding knowledge to stabilize the structure. Without exponential
+computation, FoldingZero shows promising potential to be adopted for real-world
+protein properties prediction.
+"
+23020,Undecidability of Equations in Free Lie Algebras,"  In this paper we prove undecidability of finite systems of equations in free
+Lie algebras of rank at least three over an arbitrary field. We show that the
+ring of integers $\mathbb{Z}$ is interpretable by positive existential formulas
+in such free Lie algebras over a field of characteristic zero.
+"
+23021,Homotopy $4$-spheres associated to an infinite order loose cork,"  We show the homotopy spheres $\Sigma_{n} = -W\smile_{f^{n}}W$, formed by
+doubling the infinite order loose-cork $(W,f)$ by iterates of the cork
+diffeomorphism $f: \partial W \to \partial W$, is obtained by Gluck twistings
+of $S^4$; then by this we show how to cancel $3$-handles of $\Sigma_{n}$ and
+draw several handlebody pictures of $\Sigma_{n}$ without $3$-handles.
+"
+23022,High-dimensional classification by sparse logistic regression,"  We consider high-dimensional binary classification by sparse logistic
+regression. We propose a model/feature selection procedure based on penalized
+maximum likelihood with a complexity penalty on the model size and derive the
+non-asymptotic bounds for the resulting misclassification excess risk. The
+bounds can be reduced under the additional low-noise condition. The proposed
+complexity penalty is remarkably related to the VC-dimension of a set of sparse
+linear classifiers. Implementation of any complexity penalty-based criterion,
+however, requires a combinatorial search over all possible models. To find a
+model selection procedure computationally feasible for high-dimensional data,
+we extend the Slope estimator for logistic regression and show that under an
+additional weighted restricted eigenvalue condition it is rate-optimal in the
+minimax sense.
+"
+23023,Point-cloud-based place recognition using CNN feature extraction,"  This paper proposes a novel point-cloud-based place recognition system that
+adopts a deep learning approach for feature extraction. By using a
+convolutional neural network pre-trained on color images to extract features
+from a range image without fine-tuning on extra range images, significant
+improvement has been observed when compared to using hand-crafted features. The
+resulting system is illumination invariant, rotation invariant and robust
+against moving objects that are unrelated to the place identity. Apart from the
+system itself, we also bring to the community a new place recognition dataset
+containing both point cloud and grayscale images covering a full $360^\circ$
+environmental view. In addition, the dataset is organized in such a way that it
+facilitates experimental validation with respect to rotation invariance or
+robustness against unrelated moving objects separately.
+"
+23024,Stochastic k-Server: How Should Uber Work?,"  In this paper, we study a stochastic variant of the celebrated k-server
+problem. In the k-server problem, we are required to minimize the total
+movement of k servers that are serving an online sequence of t requests in a
+metric. In the stochastic setting we are given t independent distributions
+<P_1, P_2,..., P_t> in advance, and at every time step i a request is drawn
+from Pi. Designing the optimal online algorithm in such setting is NP-hard,
+therefore the emphasis of our work is on designing an approximately optimal
+online algorithm. We first show a structural characterization for a certain
+class of non-adaptive online algorithms. We prove that in general metrics, the
+best of such algorithms has a cost of no worse than three times that of the
+optimal online algorithm. Next, we present an integer program that finds the
+optimal algorithm of this class for any arbitrary metric. Finally, by rounding
+the solution of the linear relaxation of this program, we present an online
+algorithm for the stochastic k-server problem with the approximation factor of
+3 in the line and circle metrics and O(log n) in a general metric of size n.
+Moreover, we define the Uber problem, in which each demand consists of two
+endpoints, a source and a destination. We show that given an a-approximation
+algorithm for the k-server problem, we can obtain an (a+2)-approximation
+algorithm for the Uber problem. Motivated by the fact that demands are usually
+highly correlated with the time we study the stochastic Uber problem.
+Furthermore, we extend our results to the correlated setting where the
+probability of a request arriving at a certain point depends not only on the
+time step but also on the previously arrived requests.
+"
+23025,Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces,"  Reproducing kernel Hilbert spaces (RKHSs) play an important role in many
+statistics and machine learning applications ranging from support vector
+machines to Gaussian processes and kernel embeddings of distributions.
+Operators acting on such spaces are, for instance, required to embed
+conditional probability distributions in order to implement the kernel Bayes
+rule and build sequential data models. It was recently shown that transfer
+operators such as the Perron-Frobenius or Koopman operator can also be
+approximated in a similar fashion using covariance and cross-covariance
+operators and that eigenfunctions of these operators can be obtained by solving
+associated matrix eigenvalue problems. The goal of this paper is to provide a
+solid functional analytic foundation for the eigenvalue decomposition of RKHS
+operators and to extend the approach to the singular value decomposition. The
+results are illustrated with simple guiding examples.
+"
+23026,CARE-rCortex: a Matlab toolbox for the analysis of CArdio-REspiratory-related activity in the Cortex,"  Background: Although cardio-respiratory (CR) system is generally controlled
+by the autonomic nervous system, interactions between the cortex and these
+primary functions are receiving an increasing interest in neurosciences. New
+method: In general, the timing of such internally paced events (e.g. heartbeats
+or respiratory cycles) may display a large variability. For the analysis of
+such CR event-related EEG potentials, a baseline must be correctly associated
+to each cycle of detected events. The open-source toolbox CARE-rCortex provides
+an easy-to-use interface to detect CR events, define baselines, and analyse in
+time-frequency (TF) domain the CR-based EEG potentials. Results: CARE-rCortex
+provides some practical tools to detect and validate these CR events. Users can
+define baselines time-locked to a phase of respiratory or heart cycle. A
+statistical test has also been integrated to highlight significant points of
+the TF maps with respect to the baseline. We illustrate the use of CARE-rCortex
+with the analysis of two real cardio-respiratory datasets. Comparison with
+existing methods: Compared to other open-source toolboxes, CARE-rCortex allows
+users to automatically detect CR events, to define and check baselines for each
+detected event. Different baseline normalizations can be used in the TF
+analysis of EEG epochs. Conclusions: The analysis of CR-related EEG activities
+could provide valuable information about cognitive or pathological brain
+states. CARE-rCortex runs in Matlab as a plug-in of the EEGLAB software, and it
+is publicly available at this https URL.
+"
+23027,A Construction of Linear Codes and Their Complete Weight Enumerators,"  Recently, linear codes constructed from defining sets have been studied
+extensively. They may have nice parameters if the defining set is chosen
+properly. Let $ m >2$ be a positive integer. For an odd prime $ p $, let $
+r=p^m $ and $\text{Tr}$ be the absolute trace function from $\mathbb{F}_r$ onto
+$\mathbb{F}_p$. In this paper, we give a construction of linear codes by
+defining the code $ C_{D}=\{(\mathrm{Tr}(ax))_{x\in D}: a \in \mathbb{F}_{r}
+\}, $ where $ D =\left\{x\in \mathbb{F}_{r} : \mathrm{Tr}(x)=1,
+\mathrm{Tr}(x^2)=0 \right\}. $ Its complete weight enumerator and weight
+enumerator are determined explicitly by employing cyclotomic numbers and Gauss
+sums. In addition, we obtain several optimal linear codes with a few weights.
+They have higher rate compared with other codes, which enables them to have
+essential applications in areas such as association schemes and secret sharing
+schemes.
+"
+23028,Reinforced stochastic gradient descent for deep neural network learning,"  Stochastic gradient descent (SGD) is a standard optimization method to
+minimize a training error with respect to network parameters in modern neural
+network learning. However, it typically suffers from proliferation of saddle
+points in the high-dimensional parameter space. Therefore, it is highly
+desirable to design an efficient algorithm to escape from these saddle points
+and reach a parameter region of better generalization capabilities. Here, we
+propose a simple extension of SGD, namely reinforced SGD, which simply adds
+previous first-order gradients in a stochastic manner with a probability that
+increases with learning time. As verified in a simple synthetic dataset, this
+method significantly accelerates learning compared with the original SGD.
+Surprisingly, it dramatically reduces over-fitting effects, even compared with
+state-of-the-art adaptive learning algorithm---Adam. For a benchmark
+handwritten digits dataset, the learning performance is comparable to Adam, yet
+with an extra advantage of requiring one-fold less computer memory. The
+reinforced SGD is also compared with SGD with fixed or adaptive momentum
+parameter and Nesterov's momentum, which shows that the proposed framework is
+able to reach a similar generalization accuracy with less computational costs.
+Overall, our method introduces stochastic memory into gradients, which plays an
+important role in understanding how gradient-based training algorithms can work
+and its relationship with generalization abilities of deep networks.
+"
+23029,Expectation Learning for Adaptive Crossmodal Stimuli Association,"  The human brain is able to learn, generalize, and predict crossmodal stimuli.
+Learning by expectation fine-tunes crossmodal processing at different levels,
+thus enhancing our power of generalization and adaptation in highly dynamic
+environments. In this paper, we propose a deep neural architecture trained by
+using expectation learning accounting for unsupervised learning tasks. Our
+learning model exhibits a self-adaptable behavior, setting the first steps
+towards the development of deep learning architectures for crossmodal stimuli
+association.
+"
+23030,The Local Structure of Generalized Contact Bundles,"  Generalized contact bundles are odd dimensional analogues of generalized
+complex manifolds. They have been introduced recently and very little is known
+about them. In this paper we study their local structure. Specifically, we
+prove a local splitting theorem similar to those appearing in Poisson geometry.
+In particular, in a neighborhood of a regular point, a generalized contact
+bundle is either the product of a contact and a complex manifold or the product
+of a symplectic manifold and a manifold equipped with an integrable complex
+structure on the gauge algebroid of the trivial line bundle.
+"
+23031,Deep Gaussian Covariance Network,"  The correlation length-scale next to the noise variance are the most used
+hyperparameters for the Gaussian processes. Typically, stationary covariance
+functions are used, which are only dependent on the distances between input
+points and thus invariant to the translations in the input space. The
+optimization of the hyperparameters is commonly done by maximizing the log
+marginal likelihood. This works quite well, if the distances are uniform
+distributed. In the case of a locally adapted or even sparse input space, the
+prediction of a test point can be worse dependent of its position. A possible
+solution to this, is the usage of a non-stationary covariance function, where
+the hyperparameters are calculated by a deep neural network. So that the
+correlation length scales and possibly the noise variance are dependent on the
+test point. Furthermore, different types of covariance functions are trained
+simultaneously, so that the Gaussian process prediction is an additive overlay
+of different covariance matrices. The right covariance functions combination
+and its hyperparameters are learned by the deep neural network. Additional, the
+Gaussian process will be able to be trained by batches or online and so it can
+handle arbitrarily large data sets. We call this framework Deep Gaussian
+Covariance Network (DGCP). There are also further extensions to this framework
+possible, for example sequentially dependent problems like time series or the
+local mixture of experts. The basic framework and some extension possibilities
+will be presented in this work. Moreover, a comparison to some recent state of
+the art surrogate model methods will be performed, also for a time dependent
+problem.
+"
+23032,Optimal Posted Prices for Online Cloud Resource Allocation,"  We study online resource allocation in a cloud computing platform, through a
+posted pricing mechanism: The cloud provider publishes a unit price for each
+resource type, which may vary over time; upon arrival at the cloud system, a
+cloud user either takes the current prices, renting resources to execute its
+job, or refuses the prices without running its job there. We design pricing
+functions based on the current resource utilization ratios, in a wide array of
+demand-supply relationships and resource occupation durations, and prove
+worst-case competitive ratios of the pricing functions in terms of social
+welfare. In the basic case of a single-type, non-recycled resource (i.e.,
+allocated resources are not later released for reuse), we prove that our
+pricing function design is optimal, in that any other pricing function can only
+lead to a worse competitive ratio. Insights obtained from the basic cases are
+then used to generalize the pricing functions to more realistic cloud systems
+with multiple types of resources, where a job occupies allocated resources for
+a number of time slots till completion, upon which time the resources are
+returned back to the cloud resource pool.
+"
+23033,Antiferromagnetism with divalent Eu in EuNi$_5$As$_3$,"  We have successfully synthesized single crystals of EuNi$_5$As$_3$ using a
+flux method and we present a comprehensive study of the physical properties
+using magnetic susceptibility, specific heat, electrical resistivity,
+thermoelectric power and x-ray absorption spectroscopy (XAS) measurements.
+EuNi$_5$As$_3$ undergoes two close antiferromagnetic transitions at respective
+temperatures of $T_{N1}$ = 7.2 K and $T_{N2}$ = 6.4 K, which are associated
+with the Eu$^{2+}$ moments. Both transitions are suppressed upon applying a
+field and we map the temperature-field phase diagrams for fields applied
+parallel and perpendicular to the easy $a$ axis. XAS measurements reveal that
+the Eu is strongly divalent, with very little temperature dependence,
+indicating the localized Eu$^{2+}$ nature of EuNi$_5$As$_3$, with a lack of
+evidence for heavy fermion behavior.
+"
+23034,MULDEF: Multi-model-based Defense Against Adversarial Examples for Neural Networks,"  Despite being popularly used in many application domains such as image
+recognition and classification, neural network models have been found to be
+vulnerable to adversarial examples: given a model and an example correctly
+classified by the model, an adversarial example is a new example formed by
+applying small perturbation (imperceptible to human) on the given example so
+that the model misclassifies the new example. Adversarial examples can pose
+potential risks on safety or security in real-world applications. In recent
+years, given a vulnerable model, defense approaches, such as adversarial
+training and defensive distillation, improve the model to make it more robust
+against adversarial examples. However, based on the improved model, attackers
+can still generate adversarial examples to successfully attack the model. To
+address such limitation, we propose a new defense approach, named MULDEF, based
+on the design principle of diversity. Given a target model (as a seed model)
+and an attack approach to be defended against, MULDEF constructs additional
+models (from the seed model) together with the seed model to form a family of
+models, such that the models are complementary to each other to accomplish
+robustness diversity (i.e., one model's adversarial examples typically do not
+become other models' adversarial examples), while maintaining about the same
+accuracy for normal examples. At runtime, given an input example, MULDEF
+randomly selects a model from the family to be applied on the given example.
+The robustness diversity of the model family and the random selection of a
+model from the family together lower the success rate of attacks. Our
+evaluation results show that MULDEF substantially improves the target model's
+accuracy on adversarial examples by 35-50% and 2-10% in the white-box and
+black-box attack scenarios, respectively.
+"
+23035,Thermal Transients in District Heating Systems,"  Heat fluxes in a district heating pipeline systems need to be controlled on
+the scale from minutes to an hour to adjust to evolving demand. There are two
+principal ways to control the heat flux - keep temperature fixed but adjust
+velocity of the carrier (typically water) or keep the velocity flow steady but
+then adjust temperature at the heat producing source (heat plant). We study the
+latter scenario, commonly used for operations in Russia and Nordic countries,
+and analyze dynamics of the heat front as it propagates through the system.
+Steady velocity flows in the district heating pipelines are typically turbulent
+and incompressible. Changes in the heat, on either consumption or production
+sides, lead to slow transients which last from tens of minutes to hours. We
+classify relevant physical phenomena in a single pipe, e.g. turbulent spread of
+the turbulent front. We then explain how to describe dynamics of temperature
+and heat flux evolution over a network efficiently and illustrate the network
+solution on a simple example involving one producer and one consumer of heat
+connected by ""hot"" and ""cold"" pipes. We conclude the manuscript motivating
+future research directions.
+"
+23036,Monotone numerical methods for finite-state mean-field games,"  Here, we develop numerical methods for finite-state mean-field games (MFGs)
+that satisfy a monotonicity condition. MFGs are determined by a system of
+differential equations with initial and terminal boundary conditions. These
+non-standard conditions are the main difficulty in the numerical approximation
+of solutions. Using the monotonicity condition, we build a flow that is a
+contraction and whose fixed points solve the MFG, both for stationary and
+time-dependent problems. We illustrate our methods in a MFG modeling the
+paradigm-shift problem.
+"
+23037,Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality,"  In the modern era, each Internet user leaves enormous amounts of auxiliary
+digital residuals (footprints) by using a variety of on-line services. All this
+data is already collected and stored for many years. In recent works, it was
+demonstrated that it's possible to apply simple machine learning methods to
+analyze collected digital footprints and to create psycho-demographic profiles
+of individuals. However, while these works clearly demonstrated the
+applicability of machine learning methods for such an analysis, created simple
+prediction models still lacks accuracy necessary to be successfully applied for
+practical needs. We have assumed that using advanced deep machine learning
+methods may considerably increase the accuracy of predictions. We started with
+simple machine learning methods to estimate basic prediction performance and
+moved further by applying advanced methods based on shallow and deep neural
+networks. Then we compared prediction power of studied models and made
+conclusions about its performance. Finally, we made hypotheses how prediction
+accuracy can be further improved. As result of this work, we provide full
+source code used in the experiments for all interested researchers and
+practitioners in corresponding GitHub repository. We believe that applying deep
+machine learning for psycho-demographic profiling may have an enormous impact
+on the society (for good or worse) and provides means for Artificial
+Intelligence (AI) systems to better understand humans by creating their
+psychological profiles. Thus AI agents may achieve the human-like ability to
+participate in conversation (communication) flow by anticipating human
+opponents' reactions, expectations, and behavior.
+"
+23038,Prior Distributions for the Bradley-Terry Model of Paired Comparisons,"  The Bradley-Terry model assigns probabilities for the outcome of paired
+comparison experiments based on strength parameters associated with the objects
+being compared. We consider different proposed choices of prior parameter
+distributions for Bayesian inference of the strength parameters based on the
+paired comparison results. We evaluate them according to four desiderata
+motivated by the use of inferred Bradley-Terry parameters to rate teams on the
+basis of outcomes of a set of games: invariance under interchange of teams,
+invariance under interchange of winning and losing, normalizability and
+invariance under elimination of teams. We consider various proposals which fail
+to satisfy one or more of these desiderata, and illustrate two proposals which
+satisfy them. Both are one-parameter independent distributions for the
+logarithms of the team strengths: 1) Gaussian and 2) Type III generalized
+logistic.
+"
+23039,GPU-acceleration for Large-scale Tree Boosting,"  In this paper, we present a novel massively parallel algorithm for
+accelerating the decision tree building procedure on GPUs (Graphics Processing
+Units), which is a crucial step in Gradient Boosted Decision Tree (GBDT) and
+random forests training. Previous GPU based tree building algorithms are based
+on parallel multi-scan or radix sort to find the exact tree split, and thus
+suffer from scalability and performance issues. We show that using a histogram
+based algorithm to approximately find the best split is more efficient and
+scalable on GPU. By identifying the difference between classical GPU-based
+image histogram construction and the feature histogram construction in decision
+tree training, we develop a fast feature histogram building kernel on GPU with
+carefully designed computational and memory access sequence to reduce atomic
+update conflict and maximize GPU utilization. Our algorithm can be used as a
+drop-in replacement for histogram construction in popular tree boosting systems
+to improve their scalability. As an example, to train GBDT on epsilon dataset,
+our method using a main-stream GPU is 7-8 times faster than histogram based
+algorithm on CPU in LightGBM and 25 times faster than the exact-split finding
+algorithm in XGBoost on a dual-socket 28-core Xeon server, while achieving
+similar prediction accuracy.
+"
+23040,SPARQL over GraphX,"  The ability of the RDF data model to link data from heterogeneous domains has
+led to an explosive growth of RDF data. So, evaluating SPARQL queries over
+large RDF data has been crucial for the semantic web community. However, due to
+the graph nature of RDF data, evaluating SPARQL queries in relational databases
+and common data-parallel systems needs a lot of joins and is inefficient. On
+the other hand, the enormity of datasets that are graph in nature such as
+social network data, has led the database community to develop graph-parallel
+processing systems to support iterative graph computations efficiently. In this
+work we take advantage of the graph representation of RDF data and exploit
+GraphX, a new graph processing system based on Spark. We propose a subgraph
+matching algorithm, compatible with the GraphX programming model to evaluate
+SPARQL queries. Some experiments are performed to show the system scalability
+to handle large datasets.
+"
+23041,Transfer of Vertical Graphene Nanosheets onto Flexible Substrates towards Supercapacitor Application,"  Vertical graphene nanosheets (VGNs) are the material of choice for
+next-generation electronic device applications. The growing demand for flexible
+devices in electronic industry brings in restriction on growth temperature of
+the material of interest. However, VGNs with better structural quality is
+usually achieved at high growth temperatures. The difficulty associated with
+the direct growth on flexible substrates can overcome by adopting an effective
+strategy of transferring the well grown VGNs onto arbitrary flexible substrates
+through soft chemistry route. Hence, we demonstrated a simple, inexpensive and
+scalable technique for the transfer of VGNs onto arbitrary substrates without
+disrupting its morphology and structural properties. After transfer, the
+morphology, chemical structure and electronic properties are analyzed by
+scanning electron microscopy, Raman spectroscopy and four probe resistive
+methods, respectively. Associated characterization investigation indicates the
+retention of morphological, structural and electrical properties of transferred
+VGNs compared to as-grown one. Furthermore the storage capacity of the VGNs
+transferred onto flexible substrates is also examined. A very lower sheet
+resistance of 0.67 kOhm/sq. and excellent supercapacitance of 158
+micro-Farrad/cm2 with 91.4% retention after 2000 cycles confirms the great
+prospective of this damage-free transfer approach of VGNs for flexible
+nanoelectronic device applications
+"
+23042,First-principles study of spin texture and Fermi lines in Bi(111) multi-layer nanofilm,"  We have performed a fully relativistic first-principles density functional
+calculation examining the surface state of bismuth (Bi) (111) multi-layer
+nanofilm, with up to 20 Bi bilayers, and investigated the Rashba effect and
+spin texture on the Bi surfaces. We have revealed a giant out-of-plane spin
+states on the Fermi lines, and the maximum value of the out-of-plane spin
+component being approximately 40% of the magnitude of the total spin. We have
+also evaluated the Rashba parameter $\alpha_R \simeq 1.9 {\rm eV}\cdot$\AA
+using the surface state bands which is buried in the bulk state, at -0.32 eV
+below the Fermi energy.
+"
+23043,Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262,"  The use of machine learning (ML) is on the rise in many sectors of software
+development, and automotive software development is no different. In
+particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving
+Systems (ADS) are two areas where ML plays a significant role. In automotive
+development, safety is a critical objective, and the emergence of standards
+such as ISO 26262 has helped focus industry practices to address safety in a
+systematic and consistent way. Unfortunately, these standards were not designed
+to accommodate technologies such as ML or the type of functionality that is
+provided by an ADS and this has created a conflict between the need to innovate
+and the need to improve safety. In this report, we take steps to address this
+conflict by doing a detailed assessment and adaption of ISO 26262 for ML,
+specifically in the context of supervised learning. First we analyze the key
+factors that are the source of the conflict. Then we assess each software
+development process requirement (Part 6 of ISO 26262) for applicability to ML.
+Where there are gaps, we propose new requirements to address the gaps. Finally
+we discuss the application of this adapted and extended variant of Part 6 to ML
+development scenarios.
+"
+23044,Convex recovery of continuous domain piecewise constant images from non-uniform Fourier samples,"  We consider the recovery of a continuous domain piecewise constant image from
+its non-uniform Fourier samples using a convex matrix completion algorithm. We
+assume the discontinuities/edges of the image are localized to the zero
+levelset of a bandlimited function. This assumption induces linear dependencies
+between the Fourier coefficients of the image, which results in a two-fold
+block Toeplitz matrix constructed from the Fourier coefficients being low-rank.
+The proposed algorithm reformulates the recovery of the unknown Fourier
+coefficients as a structured low-rank matrix completion problem, where the
+nuclear norm of the matrix is minimized subject to structure and data
+constraints. We show that exact recovery is possible with high probability when
+the edge set of the image satisfies an incoherency property. We also show that
+the incoherency property is dependent on the geometry of the edge set curve,
+implying higher sampling burden for smaller curves. This paper generalizes
+recent work on the super-resolution recovery of isolated Diracs or signals with
+finite rate of innovation to the recovery of piecewise constant images.
+"
+23045,Improved Algorithms for Matrix Recovery from Rank-One Projections,"  We consider the problem of estimation of a low-rank matrix from a limited
+number of noisy rank-one projections. In particular, we propose two fast,
+non-convex \emph{proper} algorithms for matrix recovery and support them with
+rigorous theoretical analysis. We show that the proposed algorithms enjoy
+linear convergence and that their sample complexity is independent of the
+condition number of the unknown true low-rank matrix. By leveraging recent
+advances in low-rank matrix approximation techniques, we show that our
+algorithms achieve computational speed-ups over existing methods. Finally, we
+complement our theory with some numerical experiments.
+"
+23046,The Opacity Problem in Social Contagion,"  Fads, product adoption, mobs, rumors, memes, and emergent norms are diverse
+social contagions that have been modeled as network cascades. Empirical study
+of these cascades is vulnerable to what we describe as the ""opacity problem"":
+the inability to observe the critical level of peer influence required to
+trigger an individual's behavioral change. Even with maximal information,
+network cascades reveal intervals that bound critical levels of peer exposure,
+rather than critical values themselves. Existing practice uses interval maxima,
+which systematically over-estimates the social influence required for
+behavioral change. Simulations reveal that the over-estimation is likely common
+and large in magnitude. This is confirmed by an empirical study of hashtag
+cascades among 3.2 million Twitter users: one in five hashtag adoptions suffers
+critical value uncertainty due to the opacity problem. Different assumptions
+about these intervals lead to qualitatively different conclusions about the
+role of peer reinforcement in diffusion. We introduce a solution that combines
+identifying tightly bounded intervals with predicting uncertain critical values
+using node-level information.
+"
+23047,MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data,"  Big data repositories from online learning platforms such as Massive Open
+Online Courses (MOOCs) represent an unprecedented opportunity to advance
+research on education at scale and impact a global population of learners. To
+date, such research has been hindered by poor reproducibility and a lack of
+replication, largely due to three types of barriers: experimental, inferential,
+and data. We present a novel system for large-scale computational research, the
+MOOC Replication Framework (MORF), to jointly address these barriers. We
+discuss MORF's architecture, an open-source platform-as-a-service (PaaS) which
+includes a simple, flexible software API providing for multiple modes of
+research (predictive modeling or production rule analysis) integrated with a
+high-performance computing environment. All experiments conducted on MORF use
+executable Docker containers which ensure complete reproducibility while
+allowing for the use of any software or language which can be installed in the
+linux-based Docker container. Each experimental artifact is assigned a DOI and
+made publicly available. MORF has the potential to accelerate and democratize
+research on its massive data repository, which currently includes over 200
+MOOCs, as demonstrated by initial research conducted on the platform. We also
+highlight ways in which MORF represents a solution template to a more general
+class of problems faced by computational researchers in other domains.
+"
+23048,The Case for a Single System Image for Personal Devices,"  Computing technology has gotten cheaper and more powerful, allowing users to
+have a growing number of personal computing devices at their disposal. While
+this trend is beneficial for the user, it also creates a growing management
+burden for the user. Each device must be managed independently and users must
+repeat the same management tasks on the each device, such as updating software,
+changing configurations, backup, and replicating data for availability. To
+prevent the management burden from increasing with the number of devices, we
+propose that all devices run a single system image called a personal computing
+image. Personal computing images export a device-specific user interface on
+each device, but provide a consistent view of application and operating state
+across all devices. As a result, management tasks can be performed once on any
+device and will be automatically propagated to all other devices belonging to
+the user. We discuss evolutionary steps that can be taken to achieve personal
+computing images for devices and elaborate on challenges that we believe
+building such systems will face.
+"
+23049,A large-scale evaluation framework for EEG deep learning architectures,"  EEG is the most common signal source for noninvasive BCI applications. For
+such applications, the EEG signal needs to be decoded and translated into
+appropriate actions. A recently emerging EEG decoding approach is deep learning
+with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many
+different architectures already published. Here we present a novel framework
+for the large-scale evaluation of different deep-learning architectures on
+different EEG datasets. This framework comprises (i) a collection of EEG
+datasets currently including 100 examples (recording sessions) from six
+different classification problems, (ii) a collection of different EEG decoding
+algorithms, and (iii) a wrapper linking the decoders to the data as well as
+handling structured documentation of all settings and (hyper-) parameters and
+statistics, designed to ensure transparency and reproducibility. As an
+applications example we used our framework by comparing three publicly
+available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow
+ConvNet, and two versions of EEGNet. We also show how our framework can be used
+to study similarities and differences in the performance of different decoding
+methods across tasks. We argue that the deep learning EEG framework as
+described here could help to tap the full potential of deep learning for BCI
+applications.
+"
+23050,High significance detection of the tSZ effect relativistic corrections,"  The thermal Sunyaev-Zel'dovich (tSZ) effect is produced by the interaction of
+cosmic microwave background (CMB) photons with the hot (a few keV) and diffuse
+gas of electrons inside galaxy clusters integrated along the line of sight.
+This effect produces a distortion of CMB blackbody emission law. This
+distortion law depends on the electronic temperature of the intra-cluster hot
+gas, $T_{e}$, through the so-called tSZ relativistic corrections. In this work,
+we have performed a statistical analysis of the tSZ spectral distortion on
+large galaxy cluster samples. We performed a stacking analysis for several
+electronic temperature bins, using both spectroscopic measurements of X-ray
+temperatures and a scaling relation between X-ray luminosities and electronic
+temperatures. We report the first high significance detection of the
+relativistic tSZ at a significance of 5.3 $\sigma$. We also demonstrate that
+the observed tSZ relativistic corrections are consistent with X-ray deduced
+temperatures. This measurement of the tSZ spectral law demonstrates that tSZ
+effect spectral distorsion can be used as a probe to measure galaxy cluster
+temperatures.
+"
+23051,Robust Phase Retrieval via ADMM with Outliers,"  An outlier-resistance phase retrieval algorithm based on alternating
+direction method of multipliers (ADMM) is devised in this letter. Instead of
+the widely used least squares criterion that is only optimal for Gaussian noise
+environment, we adopt the least absolute deviation criterion to enhance the
+robustness against outliers. Considering both intensity- and amplitude-based
+observation models, the framework of ADMM is developed to solve the resulting
+non-differentiable optimization problems. It is demonstrated that the core
+subproblem of ADMM is the proximity operator of the L1-norm, which can be
+computed efficiently by soft-thresholding in each iteration. Simulation results
+are provided to validate the accuracy and efficiency of the proposed approach
+compared to the existing schemes.
+"
+23052,Fast and General Model Selection using Data Depth and Resampling,"  We present a technique using data depth functions and resampling to perform
+best subset variable selection for a wide range of statistical models. We do
+this by assigning a score, called an $e$-value, to a candidate model, and use a
+fast bootstrap method to approximate sample versions of these $e$-values. Under
+general conditions, $e$-values can separate statistical models that adequately
+explain properties of the data from those that do not. This results in a fast
+algorithm that fits only a single model and evaluates $p +1$ models, $p$ being
+the number of predictors under consideration, as opposed to the traditional
+requirement of fitting and evaluating $2^{p}$ models. We illustrate in
+simulation experiments that our proposed method typically performs better than
+an array of currently used methods for variable selection in linear models and
+fixed effect selection in linear mixed models. As a real data application, we
+use our procedure to elicit climatic drivers of Indian summer monsoon
+precipitation.
+"
+23053,Solitonic excitations in collisions of superfluid nuclei: a qualitatively new phenomenon distinct from the Josephson effect,"  Recently, we have reported a novel role of pairing in low-energy heavy ion
+reactions at energies above the Coulomb barrier, which may have a detectable
+impact on reaction outcomes, such as the kinetic energy of fragments and the
+fusion cross section [arXiv:1611.10261, arXiv:1702.00069]. The phenomenon
+mimics the one studied experimentally with ultracold atomic gases, where two
+clouds of fermionic superfluids with different phases of the pairing fields are
+forced to merge, inducing various excitation modes of the pairing field.
+Although it originates from the phase difference of the pairing fields, the
+physics behind it is markedly different from the so-called Josephson effect. In
+this short contribution, we will briefly outline the results discussed in our
+recent papers and explain relations with the field of ultracold atomic gases.
+"
+23054,Modeling and Multi-objective Optimization of a Kind of Teaching Manipulator,"  A new kind of six degree-of-freedom teaching manipulator without actuators is
+designed, for recording and conveniently setting a trajectory of an industrial
+robot. The device requires good gravity balance and operating force performance
+to ensure being controlled easily and fluently. In this paper, we propose a
+process for modeling the manipulator and then the model is used to formulate a
+multi-objective optimization problem to optimize the design of the testing
+manipulator. Three objectives, including total mass of the device, gravity
+balancing and operating force performance are analyzed and defined. A popular
+non-dominated sorting genetic algorithm (NSGA-II-CDP) is used to solve the
+optimization problem. The obtained solutions all outperform the design of a
+human expert. To extract design knowledge, an innovization study is performed
+to establish meaningful implicit relationship between the objective space and
+the decision space, which can be reused by the designer in future design
+process.
+"
+23055,On the Combination of the Bernays-Schönfinkel-Ramsey Fragment with Simple Linear Integer Arithmetic,"  In general, first-order predicate logic extended with linear integer
+arithmetic is undecidable. We show that the Bernays-Schönfinkel-Ramsey
+fragment ($\exists^* \forall^*$-sentences) extended with a restricted form of
+linear integer arithmetic is decidable via finite ground instantiation. The
+identified ground instances can be employed to restrict the search space of
+existing automated reasoning procedures considerably, e.g., when reasoning
+about quantified properties of array data structures formalized in Bradley,
+Manna, and Sipma's array property fragment. Typically, decision procedures for
+the array property fragment are based on an exhaustive instantiation of
+universally quantified array indices with all the ground index terms that occur
+in the formula at hand. Our results reveal that one can get along with
+significantly fewer instances.
+"
+23056,Matching Using Sufficient Dimension Reduction for Causal Inference,"  To estimate casual treatment effects, we propose a new matching approach
+based on the reduced covariates obtained from sufficient dimension reduction.
+Compared to the original covariates and the propensity score, which are
+commonly used for matching in the literature, the reduced covariates are
+estimable nonparametrically under a mild assumption on the original covariates,
+and are sufficient and effective in imputing the missing potential outcomes.
+Under the ignorability assumption, the consistency of the proposed approach
+requires a weaker common support condition. In addition, the researchers are
+allowed to use different reduced covariates to find matched subjects for
+different treatment groups. We develop relative asymptotic results, and conduct
+simulation studies as well as real data analysis to illustrate the usefulness
+of the proposed approach.
+"
+23057,Spatio-temporal Symmetry - Point Groups with Time Translations,"  Spatial symmetries occur in combination with temporal symmetries in a wide
+range of physical systems in nature, including time-periodic quantum systems
+typically described by the Floquet formalism. In this context, groups formed by
+three-dimensional point group symmetry operations in combination with time
+translation operations are discussed in this work. The derivation of these
+'spatio-temporal' groups from conventional point groups and their irreducible
+representations is outlined, followed by a complete listing. The groups are
+presented in a template similar to space group operations, and are visualized
+using a modified version of conventional stereographic projections. Simple
+examples of physical processes that simultaneously exhibit symmetry in space
+and time are identified and used to illustrate the application of
+spatio-temporal groups.
+"
+23058,Superconductivity and unexpected chemistry of germanium hydrides under pressure,"  Following the idea that hydrogen-rich compounds might be high-T$_c$
+superconductors at high pressures, and the very recent breakthrough in
+predicting and synthesizing hydrogen sulfide with record-high T$_c$ = 203 K, ab
+initio evolutionary algorithm for crystal structure prediction was employed to
+find stable germanium hydrides. In addition to the earlier structure of germane
+with space group Ama2, we propose a new C2/m structure, which is energetically
+more favorable at pressures above 278 GPa (with inclusion of zero point
+energy). Our calculations indicate metallicity of the new C2/m phase of germane
+with T$_c$ = 67 K at 280 GPa. Germane is found to exhibit thermodynamic
+instability to decomposition to hydrogen and the new compound Ge$_3$H$_{11}$ at
+pressures above 300 GPa. Ge$_3$H$_{11}$ with space group I$\bar{4}$m2 is found
+to become stable at above 285 GPa with T$_c$ = 43 K. We find that the
+pressure-induced phase stability of germanium hydrides is distinct from its
+analogous isoelectronic systems, e.g., Si-hydrides and Sn-hydrides.
+Superconductivity stems from large electron-phonon coupling associated with the
+wagging, bending and stretching intermediate-frequency modes derived mainly
+from hydrogen.
+"
+23059,The formation of rings and gaps in magnetically coupled disk-wind systems: ambipolar diffusion and reconnection,"  Radial substructures in circumstellar disks are now routinely observed by
+ALMA. There is also growing evidence that disk winds drive accretion in such
+disks. We show through 2D (axisymmetric) simulations that rings and gaps
+develop naturally in magnetically coupled disk-wind systems on the scale of
+tens of au, where ambipolar diffusion (AD) is the dominant non-ideal MHD
+effect. In simulations where the magnetic field and matter are moderately
+coupled, the disk remains relatively laminar with the radial electric current
+steepened by AD into a thin layer near the midplane. The toroidal magnetic
+field sharply reverses polarity in this layer, generating a large magnetic
+torque that drives fast accretion, which drags the poloidal field into a highly
+pinched radial configuration. The reconnection of this pinched field creates
+magnetic loops where the net poloidal magnetic flux (and thus the accretion
+rate) is reduced, yielding dense rings. Neighbouring regions with stronger
+poloidal magnetic fields accrete faster, forming gaps. In better magnetically
+coupled simulations, the so-called `avalanche accretion streams' develop
+continuously near the disk surface, rendering the disk-wind system more
+chaotic. Nevertheless, prominent rings and gaps are still produced, at least in
+part, by reconnection, which again enables the segregation of the poloidal
+field and the disk material similar to the more diffusive disks. However, the
+reconnection is now driven by the non-linear growth of MRI channel flows. The
+formation of rings and gaps in rapidly accreting yet laminar disks has
+interesting implications for dust settling and trapping, grain growth, and
+planet formation.
+"
+23060,Prediction of High Temperature Quantum Anomalous Hall Effect in Two Dimensional Transition-Metal Oxides,"  Quantum anomalous Hall (QAH) insulator is a topological phase which exhibits
+chiral edge states in the absence of magnetic field. The celebrated Haldane
+model is the first example of QAH effect, but difficult to realize. Here, we
+predict the two-dimensional single-atomic-layer V2O3 with a honeycomb-Kagome
+structure is a QAH insulator with a large band gap (large than 0.1 eV) and a
+high ferromagnetic Curie temperature (about 900 K). Combining the
+first-principle calculations with the effective Hamiltonian analysis, we find
+that the spin-majority dxy and dyz orbitals of V atoms on the honeycomb lattice
+form a massless Dirac cone near the Fermi level which becomes massive when the
+on-site spin-orbit coupling is included. Interestingly, we find that the large
+band gap is caused by a cooperative effect of electron correlation and
+spin-orbit coupling. Both first-principle calculations and the effective
+Hamiltonian analysis confirm that 2D V2O3 has a non-zero Chern number (i.e.,
+one). Our work paves a new direction towards realizing the QAH effect at room
+temperature.
+"
+23061,Controlling the morphology and outgrowth of nerve and neuroglial cells: The effect of surface topography,"  Unlike other tissue types, like epithelial tissue, which consist of cells
+with a much more homogeneous structure and function, the nervous tissue spans
+in a complex multilayer environment whose topographical features display a
+large spectrum of morphologies and size scales. Traditional cell cultures,
+which are based on two-dimensional cell-adhesive culture dishes or coverslips,
+are lacking topographical cues and mainly simulate the biochemical
+microenvironment of the cells. With the emergence of micro- and
+nano-fabrication techniques new types of cell culture platforms are developed,
+where the effect of various topographical cues on cellular morphology,
+proliferation and differentiation, can be studied. Different approaches
+(regarding the material, fabrication technique, topographical charactertistics,
+etc.) have been implemented. The present review paper aims at reviewing the
+existing body of literature on the use of artificial micro- and
+nano-topographical features to control neuronal morphology, outgrowth and
+neural network topology. The cell responses from phenomenology to investigation
+of the underlying mechanisms- on the different topographies, including both
+deterministic and random ones, are summarized.
+"
+23062,Non-universality in the erosion of tilted landscapes,"  The anisotropic model for landscapes erosion proposed by Pastor-Satorras and
+Rothman in [R. Pastor-Satorras and D. H. Rothman, Phys. Rev. Lett. 80, 4349
+(1998)] is believed to capture the physics of erosion at intermediate length
+scale ($\lesssim3$ km), and to account for the large value of the roughness
+exponent $\alpha$ observed in real data at this scale. Our study of this model
+-- conducted using the nonperturbative renormalization group (NPRG) --
+concludes on the nonuniversality of this exponent because of the existence of a
+line of fixed points. Thus the roughness exponent depends (weakly) on the
+details of the soil and the erosion mechanisms. We conjecture that this
+feature, while preserving the generic scaling observed in real data, could
+explain the wide spectrum of values of $\alpha$ measured for natural
+landscapes.
+"
+23063,Context-dependent architecture of brain state dynamics is explained by white matter connectivity and theories of network control,"  A diverse white matter network and finely tuned neuronal membrane properties
+allow the brain to transition seamlessly between cognitive states. However, it
+remains unclear how static structural connections guide the temporal
+progression of large-scale brain activity patterns in different cogni- tive
+states. Here, we deploy an unsupervised machine learning algorithm to define
+brain states as time point level activity patterns from functional magnetic
+resonance imaging data acquired dur- ing passive visual fixation (rest) and an
+n-back working memory task. We find that brain states are composed of
+interdigitated functional networks and exhibit context-dependent dynamics.
+Using diffusion-weighted imaging acquired from the same subjects, we show that
+structural connectivity constrains the temporal progression of brain states. We
+also combine tools from network control theory with geometrically conservative
+null models to demonstrate that brains are wired to sup- port states of high
+activity in default mode areas, while requiring relatively low energy. Finally,
+we show that brain state dynamics change throughout development and explain
+working mem- ory performance. Overall, these results elucidate the structural
+underpinnings of cognitively and developmentally relevant spatiotemporal brain
+dynamics.
+"
+23064,HOC simulation of Moffatt eddies and its flow topology in the triangular cavity flow,"  In this work, we present HOC simulation of vortices in the triangular cavity
+for slow viscous incompressible flows by using a recently proposed new paradigm
+approach for solving Navier-Stokes (N-S) equations. These vortices qualify as
+Moffatt vortices which are characterized by the computation of common ratios of
+their sizes and intensities. We further explore topological structures of
+Moffatt eddies by using crtical point theory which is one of the key concept in
+the field of topological fluid dynamics.
+"
+23065,The Pin Groups in General Relativity,"  Out of the eight possible Pin groups that can be used to describe the
+transformation behaviour of fermions under parity and time reversal, we show
+that only two are compatible with general relativity.
+"
+23066,On Two Theorems of Darboux,"  We provide precise formulations and proofs of two theorems from Darboux's
+lectures on orthogonal systems. These results provide local existence and
+uniqueness of solutions to certain types of first order PDE systems where each
+equation contains a single derivative for which it is solved: \[\frac{\partial
+u_i}{\partial x_j}(x)=f_{ij}(x,u(x)).\] The data prescribe values for the
+unknowns $u_i$ along certain hyperplanes through a given point $\bar x$.
+The first theorem applies to determined systems (the number of equations
+equals the number unknowns), and a unique, local solution is obtained via
+Picard iteration. While Darboux's statement of the theorem leaves unspecified
+""certaines conditions de continuité,"" it is clear from his proof that he
+assumes Lipschitz continuity of the maps $f_{ij}$. On the other hand, he did
+not address the regularity of the data. We provide a precise formulation and
+proof of his first theorem.
+The second theorem is more involved and applies to overdetermined systems of
+the same general form. Under the appropriate integrability conditions, Darboux
+used his first theorem to treat the cases with two and three independent
+variables. We provide a proof for any number of independent variables.
+While the systems are rather special, they do appear in applications; e.g.,
+the second theorem contains the classical Frobenius theorem on overdetermined
+systems as a special case. The key aspect of the proofs is that they apply to
+non-analytic situations. In an analytic setup the results are covered by the
+general Cartan-Kähler theorem.
+"
+23067,Electric vehicle charging during the day or at night: a perspective on carbon emissions,"  We propose an emission-oriented charging scheme to evaluate the emissions of
+electric vehicle (EV) charging from the electricity sector at the region of
+Electric Reliability Council of Texas (ERCOT). We investigate both day- and
+night-charging scenarios combined with realistic system load demand under the
+emission-oriented vs direct charging schemes. Our emission-oriented charging
+scheme reduces carbon emissions in the day by 13.8% on average. We also find
+that emission-oriented charging results in a significant CO2 reduction in 30%
+of the days in a year compared with direct charging. Apart from offering a flat
+rebate for EV owners, our analysis reveals that certain policy incentives (e.g.
+pricing) regarding EV charging should be taken into account in order to reflect
+the benefits of emissions reduction that haven't been incorporated in the
+current market of electricity transactions.
+"
+23068,Generators for a complex hyperbolic braid group,"  We give generators for a certain complex hyperbolic braid group. That is, we
+remove a hyperplane arrangement from complex hyperbolic $13$-space, take the
+quotient of the remaining space by a discrete group, and find generators for
+the orbifold fundamental group of the quotient. These generators have the most
+natural form: loops corresponding to the hyperplanes which come nearest the
+basepoint. Our results support the conjecture that motivated this study, the
+""monstrous proposal"", which posits a relationship between this braid group and
+the monster finite simple group.
+"
+23069,Halbach arrays at the nanoscale from chiral spin textures,"  Mallinson's idea that some spin textures in planar magnetic structures could
+produce an enhancement of the magnetic flux on one side of the plane at the
+expense of the other gave rise to permanent magnet configurations known as
+Halbach magnet arrays. Applications range from wiggler magnets in particle
+accelerators and free electron lasers, to motors, to magnetic levitation
+trains, but exploiting Halbach arrays in micro- or nanoscale spintronics
+devices requires solving the problem of fabrication and field metrology below
+100 {\mu}m size. In this work we show that a Halbach configuration of moments
+can be obtained over areas as small as 1 x 1 {\mu}m^2 in sputtered thin films
+with Néel-type domain walls of unique domain wall chirality, and we measure
+their stray field at a controlled probe-sample distance of 12.0 x 0.5 nm.
+Because here chirality is determined by the interfacial Dyzaloshinkii-Moriya
+interaction the field attenuation and amplification is an intrinsic property of
+this film, allowing for flexibility of design based on an appropriate
+definition of magnetic domains. 100 nm-wide skyrmions illustrate the smallest
+kind of such structures, for which our measurement of stray magnetic fields and
+mapping of the spin structure shows they funnel the field toward one specific
+side of the film given by the sign of the Dyzaloshinkii-Moriya interaction
+parameter D.
+"
+23070,Structural Resolution with Co-inductive Loop Detection,"  A way to combine co-SLD style loop detection with structural resolution was
+found and is introduced in this work, to extend structural resolution with
+co-induction. In particular, we present the operational semantics, called
+co-inductive structural resolution, of this novel combination and prove its
+soundness with respect to the greatest complete Herbrand model.
+"
+23071,FALKON: An Optimal Large Scale Kernel Method,"  Kernel methods provide a principled way to perform non linear, nonparametric
+learning. They rely on solid functional analytic foundations and enjoy optimal
+statistical properties. However, at least in their basic form, they have
+limited applicability in large scale scenarios because of stringent
+computational requirements in terms of time and especially memory. In this
+paper, we take a substantial step in scaling up kernel methods, proposing
+FALKON, a novel algorithm that allows to efficiently process millions of
+points. FALKON is derived combining several algorithmic principles, namely
+stochastic subsampling, iterative solvers and preconditioning. Our theoretical
+analysis shows that optimal statistical accuracy is achieved requiring
+essentially $O(n)$ memory and $O(n\sqrt{n})$ time. An extensive experimental
+analysis on large scale datasets shows that, even with a single machine, FALKON
+outperforms previous state of the art solutions, which exploit
+parallel/distributed architectures.
+"
+23072,On connections of the Liénard equation with some equations of Painlevé--Gambier type,"  The Liénard equation is used in various applications. Therefore,
+constructing general analytical solutions of this equation is an important
+problem. Here we study connections between the Liénard equation and some
+equations from the Painlevé--Gambier classification. We show that with the
+help of such connections one can construct general analytical solutions of the
+Liénard equation's subfamilies. In particular, we find three new integrable
+families of the Liénard equation. We also propose and discuss an approach for
+finding one--parameter families of closed--form analytical solutions of the
+Liénard equation.
+"
+23073,Transitions in large eddy simulation of box turbulence,"  One promising decomposition of turbulent dynamics is that into building
+blocks such as equilibrium and periodic solutions and orbits connecting these.
+While the numerical approximation of such building blocks is feasible for flows
+in small domains and at low Reynolds numbers, computations in developed
+turbulence are currently out of reach because of the large number of degrees of
+freedom necessary to represent Navier-Stokes flow on all relevant spatial
+scales. We mitigate this problem by applying large eddy simulation (LES), which
+aims to model, rather than resolve, motion on scales below the filter length,
+which is fixed by a model parameter. By considering a periodic spatial domain,
+we avoid complications that arise in LES modelling in the presence of boundary
+layers. We consider the motion of an LES fluid subject to a constant body force
+of the Taylor-Green type as the separation between the forcing length scale and
+the filter length is increased. In particular, we discuss the transition from
+laminar to weakly turbulent motion, regulated by simple invariant solution, on
+a grid of $32^3$ points.
+"
+23074,Functorial Semantics for Relational Theories,"  We introduce the concept of Frobenius theory as a generalisation of Lawvere's
+functorial semantics approach to categorical universal algebra. Whereas the
+universe for models of Lawvere theories is the category of sets and functions,
+or more generally cartesian categories, Frobenius theories take their models in
+the category of sets and relations, or more generally in cartesian
+bicategories.
+"
+23075,Energy Options for Future Humans on Titan,"  We review the possibilities for in situ energy resources on Titan for use by
+future humans, including chemical, nuclear, wind, solar, geothermal and
+hydropower. All of these options, with the possible exception of geothermal,
+represent effective sources of power. Combustion of methane (after electrolysis
+of the native water), in combination with another source of power such as
+nuclear, is a viable option; another chemical source of energy is the
+hydrogenation of acetylene. The large seas Kraken and Ligeia potentially
+represent effective sources of hydropower. Wind power, particularly at
+altitudes ~40 km, is expected to be productive. Despite the distance from the
+sun and the absorbing atmosphere, solar power is (as on Earth) an extremely
+efficient source of power on Titan.
+"
+23076,On Strong Determinacy of Countable Stochastic Games,"  We study 2-player turn-based perfect-information stochastic games with
+countably infinite state space. The players aim at maximizing/minimizing the
+probability of a given event (i.e., measurable set of infinite plays), such as
+reachability, Büchi, omega-regular or more general objectives.
+These games are known to be weakly determined, i.e., they have value.
+However, strong determinacy of threshold objectives (given by an event and a
+threshold $c \in [0,1]$) was open in many cases: is it always the case that the
+maximizer or the minimizer has a winning strategy, i.e., one that enforces,
+against all strategies of the other player, that the objective is satisfied
+with probability $\ge c$ (resp. $< c$)?
+We show that almost-sure objectives (where $c=1$) are strongly determined.
+This vastly generalizes a previous result on finite games with almost-sure tail
+objectives. On the other hand we show that $\ge 1/2$ (co-)Büchi objectives
+are not strongly determined, not even if the game is finitely branching.
+Moreover, for almost-sure reachability and almost-sure Büchi objectives in
+finitely branching games, we strengthen strong determinacy by showing that one
+of the players must have a memoryless deterministic (MD) winning strategy.
+"
+23077,Dark Solitons in Waveguide Polariton Fluids Shed Light on Interaction Constants,"  We study exciton-polariton nonlinear optical fluids in a high momentum regime
+for the first time. Defects in the fluid develop into dark solitons whose
+healing length decreases with increasing density. We deduce interaction
+constants for continuous wave polaritons an order of magnitude larger than with
+picosecond pulses. Time dependent measurements show a 100ps time for the
+buildup of the interaction strength suggesting a self-generated excitonic
+reservoir as the source of the extra nonlinearity. The experimental results
+agree well with a model of coupled photons, excitons and the reservoir.
+"
+23078,Spontaneous Hall effect induced by strain in Pr$_2$Ir$_2$O$_7$ epitaxial thin films,"  Strongly correlated iridate pyrochlores with geometrically frustrated spins
+have been recognized as a potentially interesting group of oxide materials
+where novel topological phases may appear. A particularly attractive system is
+the metallic Pr$_2$Ir$_2$O$_7$, as it is known as a Fermi node semimetal
+characterized by quadratic band touching at the Brillouin zone center,
+suggesting that the topology of its electronic states can be tuned by moderate
+lattice strain. In this work we report the growth of epitaxial
+Pr$_2$Ir$_2$O$_7$ thin films grown by solid-state epitaxy. We show that the
+strained parts of the films give rise to a spontaneous Hall effect that
+persists up to 50 K without having spontaneous magnetization within our
+experimental accuracy. This indicates that a macroscopic time reversal symmetry
+(TRS) breaking appears at a temperature scale that is too high for the
+magnetism to be due to Pr 4$f$ moments, and must thus be related to magnetic
+order of the iridium 5$d$ electrons. The magnetotransport and Hall analysis
+results are consistent with the formation of a Weyl semimetal state that is
+induced by a combination of TRS breaking and cubic symmetry breaking due to
+lattice strain.
+"
+23079,Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources,"  Our goal is to design architectures that retain the groundbreaking
+performance of CNNs for landmark localization and at the same time are
+lightweight, compact and suitable for applications with limited computational
+resources. To this end, we make the following contributions: (a) we are the
+first to study the effect of neural network binarization on localization tasks,
+namely human pose estimation and face alignment. We exhaustively evaluate
+various design choices, identify performance bottlenecks, and more importantly
+propose multiple orthogonal ways to boost performance. (b) Based on our
+analysis, we propose a novel hierarchical, parallel and multi-scale residual
+architecture that yields large performance improvement over the standard
+bottleneck block while having the same number of parameters, thus bridging the
+gap between the original network and its binarized counterpart. (c) We perform
+a large number of ablation studies that shed light on the properties and the
+performance of the proposed block. (d) We present results for experiments on
+the most challenging datasets for human pose estimation and face alignment,
+reporting in many cases state-of-the-art performance. Code can be downloaded
+from this https URL
+"
+23080,Unifying Local and Global Change Detection in Dynamic Networks,"  Many real-world networks are complex dynamical systems, where both local
+(e.g., changing node attributes) and global (e.g., changing network topology)
+processes unfold over time. Local dynamics may provoke global changes in the
+network, and the ability to detect such effects could have profound
+implications for a number of real-world problems. Most existing techniques
+focus individually on either local or global aspects of the problem or treat
+the two in isolation from each other. In this paper we propose a novel network
+model that simultaneously accounts for both local and global dynamics. To the
+best of our knowledge, this is the first attempt at modeling and detecting
+local and global change points on dynamic networks via a unified generative
+framework. Our model is built upon the popular mixed membership stochastic
+blockmodels (MMSB) with sparse co-evolving patterns. We derive an efficient
+stochastic gradient Langevin dynamics (SGLD) sampler for our proposed model,
+which allows it to scale to potentially very large networks. Finally, we
+validate our model on both synthetic and real-world data and demonstrate its
+superiority over several baselines.
+"
+23081,Search for low-mass Dark Matter with the CRESST Experiment,"  CRESST is a multi-stage experiment directly searching for dark matter (DM)
+using cryogenic $\mathrm{CaWO_4}$ crystals. Previous stages established leading
+limits for the spin-independent DM-nucleon cross section down to DM-particle
+masses $m_\mathrm{DM}$ below $1\,\mathrm{GeV/c^2}$. Furthermore, CRESST
+performed a dedicated search for dark photons (DP) which excludes new parameter
+space between DP masses $m_\mathrm{DP}$ of $300\,\mathrm{eV/c^2}$ to
+$700\,\mathrm{eV/c^2}$.
+In this contribution we will discuss the latest results based on the previous
+CRESST-II phase 2 and we will report on the status of the current CRESST-III
+phase 1: in this stage we have been operating 10 upgraded detectors with
+$24,\mathrm{g}$ target mass each and enhanced detector performance since summer
+2016. The improved detector design in terms of background suppression and
+reduction of the detection threshold will be discussed with respect to the
+previous stage. We will conclude with an outlook on the potential of the next
+stage, CRESST-III phase 2.
+"
+23082,Positive and negative results on the internal controllability of parabolic equations coupled by zero and first order terms,"  This paper is devoted to studying the null and approximate controllability of
+two linear coupled parabolic equations posed on a smooth domain of R^N (N>1)
+with coupling terms of zero and first orders and one control localized in some
+arbitrary nonempty open subset of the domain. We prove the null controllability
+under a new sufficient condition and we also provide the first example of a not
+approximately controllable system in the case where the support of one of the
+nontrivial first order coupling terms intersects the control domain.
+"
+23083,Quantum Monte Carlo study of the metal to insulator transition on a honeycomb lattice with 1/r interactions,"  Describing correlated electron systems near phase transitions has been a
+major challenge in computational condensed-matter physics. In this paper, we
+apply highly accurate fixed node quantum Monte Carlo techniques, which directly
+work with many body wave functions and simulate electron correlations, to
+investigate the metal to insulator transition of a correlated hydrogen lattice.
+By calculating spin and charge properties, and analyzing the low energy Hilbert
+space, we identify the transition point and identify order parameters that can
+be used to detect the transition. Our results provide a benchmark for density
+functional theories seeking to treat correlated electron systems.
+"
+23084,Complex-Valued Neural Networks for Privacy Protection,"  This paper proposes a generic method to revise traditional neural networks
+for privacy protection. Our method is designed to prevent inversion attacks,
+i.e., avoiding recovering private information from intermediate-layer features
+of a neural network. Our method transforms real-valued features of an
+intermediate layer into complex-valued features, in which private information
+is hidden in a random phase of the transformed features. To prevent the
+adversary from recovering the phase, we adopt an adversarial-learning algorithm
+to generate the complex-valued feature. More crucially, the transformed feature
+can be directly processed by the deep neural network, but without knowing the
+true phase, people cannot recover either the input information or the
+prediction result. Preliminary experiments with various neural networks
+(including the LeNet, the VGG, and residual networks) on different datasets
+have shown that our method can successfully defend feature inversion attacks
+while preserving learning accuracy.
+"
+23085,A Variable Step Size Implicit-Explicit Scheme for the Solution of the Poisson-Nernst-Planck Equations,"  The Poisson-Nernst-Planck equations with generalized Frumkin-Butler-Volmer
+boundary conditions (PNP-FBV) describe ion transport with Faradaic reactions
+and have applications in a wide variety of fields. In this article, we develop
+a variable step size implicit-explicit time stepping scheme for the solution of
+the PNP-FBV equations. We test our numerical scheme on a simplified ""toy""
+version of the PNP-FBV equations, paying special care to the treatment of the
+coupled nonlinear terms in the boundary condition. We evaluate various ways of
+incorporating the boundary condition into the scheme and a method based on
+ghost points is chosen for its favorable numerical properties compared to the
+alternatives. In fact, we observe that when the underlying dynamics is one that
+would have the solutions converge to a steady state solution, the numerical
+simulation does not result in the time-step sizes growing larger along with the
+expected convergence. We observe that the time-step sizes threshold at a
+particular step size. By performing an A-stability analysis we demonstrate that
+this thresholding does not appear to be due to a stability constraint. Using
+the developed numerical method, we are able to run simulations with a large
+range of parameters, including any value of the singular perturbation parameter
+epsilon.
+"
+23086,Inelastic e+Mg collision data and its impact on modelling stellar and supernova spectra,"  Results of calculations for inelastic e+Mg effective collision strengths for
+the lowest 25 physical states of Mg I (up to 3s6p 1P), and thus 300
+transitions, from the convergent close-coupling (CCC) and the B-spline R-matrix
+(BSR) methods are presented. At temperatures of interest, ~5000 K, the results
+of the two calculations differ on average by only 4%, with a scatter of 27%. As
+the methods are independent, this suggests that the calculations provide
+datasets for e+Mg collisions accurate to this level. Comparison with the
+commonly used dataset compiled by Mauas et al. (1988), covering 25 transitions
+among 12 states, suggests the Mauas et al. data are on average ~57% too low,
+and with a very large scatter of a factor of ~6.5. In particular the collision
+strength for the transition corresponding to the Mg I intercombination line at
+457 nm is significantly underestimated by Mauas et al., which has consequences
+for models that employ this dataset. In giant stars the new data leads to a
+stronger line compared to previous non-LTE calculations, and thus a reduction
+in the non-LTE abundance correction by ~0.1 dex (~25%). A non-LTE calculation
+in a supernova ejecta model shows this line becomes significantly stronger, by
+a factor of around two, alleviating the discrepancy where the 457 nm line in
+typical models with Mg/O ratios close to solar tended to be too weak compared
+to observations.
+"
+23087,A Batch Learning Framework for Scalable Personalized Ranking,"  In designing personalized ranking algorithms, it is desirable to encourage a
+high precision at the top of the ranked list. Existing methods either seek a
+smooth convex surrogate for a non-smooth ranking metric or directly modify
+updating procedures to encourage top accuracy. In this work we point out that
+these methods do not scale well to a large-scale setting, and this is partly
+due to the inaccurate pointwise or pairwise rank estimation. We propose a new
+framework for personalized ranking. It uses batch-based rank estimators and
+smooth rank-sensitive loss functions. This new batch learning framework leads
+to more stable and accurate rank approximations compared to previous work.
+Moreover, it enables explicit use of parallel computation to speed up training.
+We conduct empirical evaluation on three item recommendation tasks. Our method
+shows consistent accuracy improvements over state-of-the-art methods.
+Additionally, we observe time efficiency advantages when data scale increases.
+"
+23088,VENu: The Virtual Environment for Neutrinos,"  The Virtual Environment for Neutrinos (VENu) is a virtual reality-based
+visualisation of the MicroBooNE detector. MicroBooNE is a liquid-argon-based
+neutrino experiment, which is currently operating in Fermilab's Booster
+neutrino beam. The new VENu smartphone app provides informative explanations
+about neutrinos and uses real MicroBooNE neutrino data that can be visualised
+inside a virtual representation of the MicroBooNE detector. Available for both
+iOS and Android, the VENu app can be downloaded for free from the Apple and
+Google marketplaces. The app enables users to immerse themselves inside the
+MicroBooNE particle detector and to see particle tracks inside. This can be
+done in Virtual Reality mode, where the users can pair their smartphone with
+any consumer virtual reality headset and see the detector in 3D. To encourage
+learning in a fun environment, a game is also available, guiding users to learn
+about neutrinos and how to detect them. They can also try to ""catch""' neutrinos
+themselves in 3D mode. The app is currently being pursued for a QuarkNet
+neutrino master class and outreach events at several universities and labs
+worldwide.
+"
+23089,Breaking the Chains: Hot Super-Earth systems from migration and disruption of compact resonant chains,"  ""Hot super-Earths"" (or ""Mini-Neptunes"") between 1 and 4 times Earth's size
+with period shorter than 100 days orbit 30-50\% of Sun-like type stars. Their
+orbital configuration -- measured as the period ratio distribution of adjacent
+planets in multi-planet systems -- is a strong constraint for formation models.
+Here we use N-body simulations with synthetic forces from an underlying
+evolving gaseous disk to model the formation and long-term dynamical evolution
+of super-Earth systems. While the gas disk is present, planetary embryos grow
+and migrate inward to form a resonant chain anchored at the inner edge of the
+disk. These resonant chains are far more compact than the observed super-Earth
+systems. Once the gas dissipates resonant chains may become dynamically
+unstable. They undergo a phase of giant impacts that spreads the systems out.
+Disk turbulence has no measurable effect on the outcome. Our simulations match
+observations if a small fraction of resonant chains remain stable, while most
+super-Earths undergo a late dynamical instability. Our statistical analysis
+restricts the contribution of stable systems to less than $25\%$. Our results
+also suggest that the large fraction of observed single planet systems does not
+necessarily imply any dichotomy in the architecture of planetary systems.
+Finally, we use the low abundance of resonances in Kepler data to argue that,
+in reality, the survival of resonant chains happens likely only in $\sim 5\%$
+of the cases. This leads to a mystery: in our simulations only 50-60\% of
+resonant chains became unstable whereas at least 75\% (and probably 90-95\%)
+must be unstable to match observations.
+"
+23090,Asymptotic Bayesian Generalization Error in Topic Model and Stochastic Matrix Factorization,"  In this paper, we derive the asymptotic behavior of the Bayesian
+generalization error in the topic model. By theoretical analysis of the maximum
+pole of the zeta function (real log canonical threshold) of the topic model, we
+obtain an upper bound of the Bayesian generalization error and the free energy
+in the topic model and the stochastic matrix factorization (SMF; it can be
+regarded as a restriction of the non-negative matrix factorization). The
+results show that the generalization error in the topic model and SMF becomes
+smaller than regular statistical models if Bayesian inference is attained.
+"
+23091,Stochastic effects in autoimmune dynamics,"  Among various possible causes of autoimmune disease, an important role is
+played by infections that can result in a breakdown of immune tolerance,
+primarily through the mechanism of ""molecular mimicry"". In this paper we
+propose and analyse a stochastic model of immune response to a viral infection
+and subsequent autoimmunity, with account for the populations of T cells with
+different activation thresholds, regulatory T cells, and cytokines. We show
+analytically and numerically how stochasticity can result in sustained
+oscillations around deterministically stable steady states, and we also
+investigate stochastic dynamics in the regime of bi-stability. These results
+provide a possible explanation for experimentally observed variations in the
+progression of autoimmune disease. Computations of the variance of stochastic
+fluctuations provide practically important insights into how the size of these
+fluctuations depends on various biological parameters, and this also gives a
+headway for comparison with experimental data on variation in the observed
+numbers of T cells and organ cells affected by infection.
+"
+23092,Proprioceptive Sonomyographic Control: A novel method of intuitive proportional control of multiple degrees of freedom for upper-extremity amputees,"  Technological advances in multi-articulated prosthetic hands have outpaced
+the methods available to amputees to intuitively control these devices.
+Amputees often cite difficulty of use as a key contributing factor for
+abandoning their prosthesis, creating a pressing need for improved control
+technology. A major challenge of traditional myoelectric control strategies
+using surface electromyography electrodes has been the difficulty in achieving
+intuitive and robust proportional control of multiple degrees of freedom. In
+this paper, we describe a new control method, proprioceptive sonomyographic
+control that overcomes several limitations of myoelectric control. In
+sonomyography, muscle mechanical deformation is sensed using ultrasound, as
+compared to electrical activation, and therefore the resulting control signals
+can directly control the position of the end effector. Compared to myoelectric
+control which controls the velocity of the end-effector device, sonomyographic
+control is more congruent with residual proprioception in the residual limb. We
+tested our approach with 5 upper-extremity amputees and able-bodied subjects
+using a virtual target achievement and holding task. Amputees and able-bodied
+participants demonstrated the ability to achieve positional control for 5
+degrees of freedom with an hour of training. Our results demonstrate the
+potential of proprioceptive sonomyographic control for intuitive dexterous
+control of multiarticulated prostheses.
+"
+23093,Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting,"  We consider the optimal value of information (VoI) problem, where the goal is
+to sequentially select a set of tests with a minimal cost, so that one can
+efficiently make the best decision based on the observed outcomes. Existing
+algorithms are either heuristics with no guarantees, or scale poorly (with
+exponential run time in terms of the number of available tests). Moreover,
+these methods assume a known distribution over the test outcomes, which is
+often not the case in practice. We propose an efficient sampling-based online
+learning framework to address the above issues. First, assuming the
+distribution over hypotheses is known, we propose a dynamic hypothesis
+enumeration strategy, which allows efficient information gathering with strong
+theoretical guarantees. We show that with sufficient amount of samples, one can
+identify a near-optimal decision with high probability. Second, when the
+parameters of the hypotheses distribution are unknown, we propose an algorithm
+which learns the parameters progressively via posterior sampling in an online
+fashion. We further establish a rigorous bound on the expected regret. We
+demonstrate the effectiveness of our approach on a real-world interactive
+troubleshooting application and show that one can efficiently make high-quality
+decisions with low cost.
+"
+23094,Functional Pearl: Theorem Proving for All (Equational Reasoning in Liquid Haskell),"  Equational reasoning is one of the key features of pure functional languages
+such as Haskell. To date, however, such reasoning always took place externally
+to Haskell, either manually on paper, or mechanised in a theorem prover. This
+article shows how equational reasoning can be performed directly and seamlessly
+within Haskell itself, and be checked using Liquid Haskell. In particular,
+language learners --- to whom external theorem provers are out of reach --- can
+benefit from having their proofs mechanically checked. Concretely, we show how
+the equational proofs and derivations from Graham's textbook can be recast as
+proofs in Haskell (spoiler: they look essentially the same).
+"
+23095,Quadratic Chabauty for Modular Curves,"  Let $X/\mathbb{Q}$ be a curve of genus $g \ge 2$ with Jacobian $J$ and let
+$\ell$ be a prime of good reduction. Using Selmer varieties, Kim defines a
+decreasing sequence \[ X(\mathbb{Q}_\ell) \supseteq X(\mathbb{Q}_\ell)_1
+\supseteq X(\mathbb{Q}_\ell)_2 \supseteq \cdots \] all containing the rational
+points of $X$. Thanks to the work of Coleman, the `Chabauty set'
+$X(\mathbb{Q}_\ell)_1$ is known to be finite provided the Mordell--Weil rank of
+$J$ is smaller than $g$. In this case one has a practical strategy that often
+succeeds in computing the set of rational points of $X$. Balakrishnan and Dogra
+have recently shown that the `quadratic Chabauty set' $X(\mathbb{Q}_\ell)_2$ is
+finite provided the Mordell--Weil rank is less than $g + \rho-1$, where $\rho$
+is the Néron-Severi rank of $J/\mathbb{Q}$. In view of this it is interesting
+to give families of curves where $\rho \ge 2$ and where therefore quadratic
+Chabauty is more likely to succeed than classical Chabauty. In this note we
+show that this is indeed the case for all modular curves of genus at least 3.
+"
+23096,CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training,"  We propose an adversarial training procedure for learning a causal implicit
+generative model for a given causal graph. We show that adversarial training
+can be used to learn a generative model with true observational and
+interventional distributions if the generator architecture is consistent with
+the given causal graph. We consider the application of generating faces based
+on given binary labels where the dependency structure between the labels is
+preserved with a causal graph. This problem can be seen as learning a causal
+implicit generative model for the image and labels. We devise a two-stage
+procedure for this problem. First we train a causal implicit generative model
+over binary labels using a neural network consistent with a causal graph as the
+generator. We empirically show that WassersteinGAN can be used to output
+discrete labels. Later, we propose two new conditional GAN architectures, which
+we call CausalGAN and CausalBEGAN. We show that the optimal generator of the
+CausalGAN, given the labels, samples from the image distributions conditioned
+on these labels. The conditional GAN combined with a trained causal implicit
+generative model for the labels is then a causal implicit generative model over
+the labels and the generated image. We show that the proposed architectures can
+be used to sample from observational and interventional image distributions,
+even for interventions which do not naturally occur in the dataset.
+"
+23097,Dirac and Weyl fermions: from Gor'kov equations to Standard Model (in memory of Lev Petrovich Gorkov),"  Gor'kov theory of superconductivity opened the application of the methods of
+quantum field theory to condensed matter physics. Later the results became
+relevant to relativistic quantum fields.
+"
+23098,PSPACE hardness of approximating the capacity of Markoff channels with noiseless feedback,"  It will be proved that computing the capacity of a Markoff channel with
+noiseless feedback is PSPACE-hard.
+"
+23099,An Improved Oscillating-Error Classifier with Branching,"  This paper extends the earlier work on an oscillating error correction
+technique. Specifically, it extends the design to include further corrections,
+by adding new layers to the classifier through a branching method. This
+technique is still consistent with earlier work and also neural networks in
+general. With this extended design, the classifier can now achieve the high
+levels of accuracy reported previously.
+"
+23100,Asteroid (21) Lutetia: Disk-resolved Photometric Analysis of Baetica Region,"  (21) Lutetia has been visited by Rosetta mission on July 2010 and observed
+with a phase angle ranging from 0.15 to 156.8 degrees. The Baetica region,
+located at the north pole has been extensively observed by OSIRIS cameras
+system. Baetica encompass a region called North Pole Crater Cluster (NPCC),
+shows a cluster of superposed craters which presents signs of variegation at
+the small phase angle images. For studying the location, we used 187 images
+distributed throughout 14 filter recorded by the NAC (Narrow Angle Camera) and
+WAC (Wide Angle Camera) taken during the fly-by. We photometrically modeled the
+region using Minnaert disk-function and Akimov phase function to finally
+reconstruct a resolved spectral slope map at 5 and 20 degrees of phase angle.
+We observed a dichotomy between Gallicum and Danuvius-Sarnus Labes in the NPCC,
+but no significant phase reddening. In the next step, we applied the Hapke
+(2008, 2012) model for the NAC F82+F22 (649.2 nm), WAC F13 (375 nm) and WAC F17
+(631.6 nm), enabling us to compose the normal albedo and Hapke parameter maps
+for NAC F82+F22. On Baetica, the 649 nm global properties are: geometric albedo
+of 0.205+-0.005, the average single-scattering albedo of 0.181+-0.005, the
+average asymmetric factor of -0.342+-0.003, the average shadow-hiding
+opposition effect amplitude and width respectivelly of 0.824+-0.002 and
+0.040+-0.0007, the average roughness slope of 11.45+-3 deg. and the average
+porosity is 85+-0.2%. In the NPCC, the normal albedo variegation among the
+craters walls reach 8% brighter for Gallicum Labes and 2% fainter for Danuvius
+Labes. The Hapke parameter maps also show a dichotomy at the opposition effect
+coefficients, single-scattering albedo and asymmetric factor, that may be
+attributed to the maturation degree of the regolith or to compositonal
+variation.
+"
+23101,SPICE Simulation of tunnel FET aiming at 32 kHz crystal-oscillator operation,"  We numerically investigate the possibility of using Tunnel field-effect
+transistor (TFET) in a 32 kHz crystal oscillator circuit to reduce power
+consumption. A simulation using SPICE (Simulation Program with Integrated
+Circuit Emphasis) is carried out based on a conventional CMOS transistor model.
+It is shown that the power consumption of TFET is one-tenth that of
+conventional low-power CMOS.
+"
+23102,Properties of screw dislocation dynamics: time estimates on boundary and interior collisions,"  In this paper, the dynamics of a system of a finite number of screw
+dislocations is studied. Under the assumption of antiplane linear elasticity,
+the two-dimensional dynamics is determined by the renormalised energy. The
+interaction of one dislocation with the boundary and of two dislocations of
+opposite Burgers moduli are analysed in detail and estimates on the collision
+times are obtained. Some exactly solvable cases and numerical simulations show
+agreement with the estimates obtained.
+"
+23103,Closure of resource-bounded randomness notions under polynomial time permutations,"  An infinite bit sequence is called recursively random if no computable
+strategy betting along the sequence has unbounded capital. It is well-known
+that the property of recursive randomness is closed under computable
+permutations. We investigate analogous statements for randomness notions
+defined by betting strategies that are computable within resource bounds.
+Suppose that S is a polynomial time computable permutation of the set of
+strings over the unary alphabet (identified with N). If the inverse of S is not
+polynomially bounded, it is easy to build a polynomial time random bit sequence
+Z such that Z o S is not polynomial time random. So one should only consider
+permutations S satisfying the extra condition that the inverse is polynomially
+bounded. Now the closure depends on additional assumptions in complexity
+theory.
+Our first result shows that if BPP contains a superpolynomial deterministic
+time class then polynomial time randomness is not preserved by some permutation
+S such that in fact both S and its inverse are in P. Our second result shows
+that polynomial space randomness is preserved by polynomial time permutations
+with polynomially boun\-ded inverse, so if P=PSPACE then polynomial time
+randomness is preserved.
+"
+23104,Prediction in logarithmic distance,"  The metric properties of the set in which random variables take their values
+lead to relevant probabilistic concepts. For example, the mean of a random
+variable is a best predictor in that it minimizes the standard Euclidean
+distance or $L_2$ norm in an appropriate class of random variables. Similarly,
+the median is the same concept but when the distance is measured by the $L_1$
+norm. These two predictors stem from the fact that the mean and the median,
+minimize the distance to a given set of points when distances in $\mathbb{R}$
+or in $\mathbb{R}^n$ are measured in the aforementioned metrics.\\ It so
+happens that an interesting {\it logarithmic distance} can be defined on the
+cone of strictly positive vectors in $\mathbb{R}^n$ in such a way that the
+minimizer of the distance to a collection of points is their geometric mean.\\
+This distance on the base space leads to an interesting distance on the class
+of strictly positive random variables, which in turn leads to an interesting
+class of best predictors predictors and their estimators, as well as a
+corresponding notion of conditional expectation. The appropriate version of the
+Law of Large Numbers and the Central Limit Theorem, can also be obtained. We
+shall see that, for example, the lognormal variables are the analogue of the
+Gaussian variables for the modified version of the Central Limit Theorem.
+"
+23105,The free lunch of a scale-free metabolism,"  In this work it is shown that scale free tails in metabolic flux
+distributions inferred from realistic large scale models can be simply an
+artefact due to reactions involved in thermodynamically unfeasible cycles, that
+are unbounded by physical constraints and would be able to perform work without
+expenditure of free energy. After correcting for thermodynamics, the metabolic
+space scales meaningfully with the physical limiting factors, acquiring in turn
+a richer multimodal structure potentially leading to symmetry breaking while
+optimizing for objective functions.
+"
+23106,Project Overview of the Beijing-Arizona Sky Survey,"  The Beijing-Arizona Sky Survey (BASS) is a wide-field two-band photometric
+survey of the Northern Galactic Cap using the 90Prime imager on the 2.3 m Bok
+telescope at Kitt Peak. It is a four-year collaboration between the National
+Astronomical Observatory of China and Steward Observatory, the University of
+Arizona, serving as one of the three imaging surveys to provide photometric
+input catalogs for target selection of the Dark Energy Spectroscopic Instrument
+(DESI) project. BASS will take up to 240 dark/grey nights to cover an area of
+about 5400 deg$^2$ in the $g$ and $r$ bands. The 5$\sigma$ limiting AB
+magnitudes for point sources in the two bands, corrected for the Galactic
+extinction, are 24.0 and 23.4 mag, respectively. BASS, together with other DESI
+imaging surveys, will provide unique science opportunities that cover a wide
+range of topics in both Galactic and extragalactic astronomy.
+"
+23107,Effects of the Planetary Temperature on the Circumplanetary Disk and on the Gap,"  Circumplanetary disks regulate the late accretion to the giant planet and
+serve as the birthplace for satellites. Understanding their characteristics via
+simulations also helps to prepare for their observations. Here we study disks
+around 1, 3, 5, 10 $\mathrm{M_{Jup}}$ planets with three dimensional, global
+radiative hydrodynamic simulations with sub-planet peak resolution, and various
+planetary temperatures. We found that as the 1 $\mathrm{M_{Jup}}$ planet
+radiates away its formation heat, the circumplanetary envelope transitions to a
+disk between $T_p = 6000$ K and 4000 K. In the case of 3-10 $\mathrm{M_{Jup}}$
+planets a disk always forms. The temperature profile of the circumplanetary
+disks is very steep, the inner 1/6th is over the silicate condensation
+temperature and the entire disk is above water freezing point, making satellite
+formation impossible in this early stage ($<$1 Myr). Satellites might form much
+later and first in the outer parts of the disk migrating inwards later on. Our
+disk masses are $1, 7, 20, 40 \times 10^{-3}\mathrm{M_{Jup}}$ for the 1, 3, 5,
+10 $\mathrm{M_{Jup}}$ gas giants respectively, and we provide an empirical
+formula to estimate the subdisk masses based on the planet- and circumstellar
+disk mass. Our finding is that the cooler the planet, the lower the temperature
+of the subdisk, the higher the vertical influx velocities, and the planetary
+gap is both deeper and wider. We also show that the gaps in 2D and 3D are
+different. The subdisk eccentricity increases with planetary mass and violently
+interacts with the circumstellar disk, making satellite-formation less likely,
+if $\mathrm{M_p} \gtrsim 5 \mathrm{M_{Jup}}$.
+"
+23108,Context-modulation of hippocampal dynamics and deep convolutional networks,"  Complex architectures of biological neural circuits, such as parallel
+processing pathways, has been behaviorally implicated in many cognitive
+studies. However, the theoretical consequences of circuit complexity on neural
+computation have only been explored in limited cases. Here, we introduce a
+mechanism by which direct and indirect pathways from cortex to the CA3 region
+of the hippocampus can balance both contextual gating of memory formation and
+driving network activity. We implement this concept in a deep artificial neural
+network by enabling a context-sensitive bias. The motivation for this is to
+improve performance of a size-constrained network. Using direct knowledge of
+the superclass information in the CIFAR-100 and Fashion-MNIST datasets, we show
+a dramatic increase in performance without an increase in network size.
+"
+23109,Controllable Generative Adversarial Network,"  Recently introduced generative adversarial network (GAN) has been shown
+numerous promising results to generate realistic samples. The essential task of
+GAN is to control the features of samples generated from a random distribution.
+While the current GAN structures, such as conditional GAN, successfully
+generate samples with desired major features, they often fail to produce
+detailed features that bring specific differences among samples. To overcome
+this limitation, here we propose a controllable GAN (ControlGAN) structure. By
+separating a feature classifier from a discriminator, the generator of
+ControlGAN is designed to learn generating synthetic samples with the specific
+detailed features. Evaluated with multiple image datasets, ControlGAN shows a
+power to generate improved samples with well-controlled features. Furthermore,
+we demonstrate that ControlGAN can generate intermediate features and opposite
+features for interpolated and extrapolated input labels that are not used in
+the training process. It implies that ControlGAN can significantly contribute
+to the variety of generated samples.
+"
+23110,Integrated PV Charging of EV Fleet Based on Dynamic Energy Prices and Offer of Reserves,"  Workplace charging of electric vehicles (EV) from photovoltaic (PV) panels
+installed on an office building can provide several benefits. This includes the
+local production and use of PV energy for charging the EV and making use of
+dynamic tariffs from the grid to schedule the energy exchange with the grid.
+The long parking time of EV at the workplace provide the chance for the EV to
+support the grid via vehicle-to-grid technology, the use of a single EV charger
+for charging several EV by multiplexing and the offer of ancillary services to
+the grid for up and down regulation. Further, distribution network constraints
+can be considered to limit the power and prevent the overloading of the grid. A
+single MILP formulation that considers all the above applications has been
+proposed in this paper for a charging a fleet of EVs from PV. The MILP is
+implemented as a receding-horizon model predictive energy management system.
+Numerical simulation based on market and PV data in Austin, Texas have shown
+31% to 650% reduction in the cost of EV charging when compared to immediate and
+average rate charging policies.
+"
+23111,Improving particle beam acceleration in plasmas,"  The dynamics of wave-particle interactions in magnetized plasmas restricts
+the wave amplitude to moderate values for particle beam acceleration from rest
+energy. We analyze how a perturbing invariant robust barrier modifies the phase
+space of the system and enlarges the wave amplitude interval for particle
+acceleration. For low values of the wave amplitude, the acceleration becomes
+effective for particles with initial energy close to the rest energy. For
+higher values of the wave amplitude, the robust barrier controls chaos in the
+system and restores the acceleration process. We also determine the best
+position for the perturbing barrier in phase space in order to increase the
+final energy of the particles.
+"
+23112,Sensitivity Analysis of Expensive Black-Box Systems Using Metamodeling,"  Simulations are becoming ever more common as a tool for designing complex
+products. Sensitivity analysis techniques can be applied to these simulations
+to gain insight, or to reduce the complexity of the problem at hand. However,
+these simulators are often expensive to evaluate and sensitivity analysis
+typically requires a large amount of evaluations. Metamodeling has been
+successfully applied in the past to reduce the amount of required evaluations
+for design tasks such as optimization and design space exploration. In this
+paper, we propose a novel sensitivity analysis algorithm for variance and
+derivative based indices using sequential sampling and metamodeling. Several
+stopping criteria are proposed and investigated to keep the total number of
+evaluations minimal. The results show that both variance and derivative based
+techniques can be accurately computed with a minimal amount of evaluations
+using fast metamodels and FLOLA-Voronoi or density sequential sampling
+algorithms.
+"
+23113,Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks,"  In de novo drug design, computational strategies are used to generate novel
+molecules with good affinity to the desired biological target. In this work, we
+show that recurrent neural networks can be trained as generative models for
+molecular structures, similar to statistical language models in natural
+language processing. We demonstrate that the properties of the generated
+molecules correlate very well with the properties of the molecules used to
+train the model. In order to enrich libraries with molecules active towards a
+given biological target, we propose to fine-tune the model with small sets of
+molecules, which are known to be active against that target.
+Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test
+molecules that medicinal chemists designed, whereas against Plasmodium
+falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled
+with a scoring function, our model can perform the complete de novo drug design
+cycle to generate large sets of novel molecules for drug discovery.
+"
+23114,Computing Abelian regularities on RLE strings,"  Two strings x and y are said to be Abelian equivalent if x is a permutation
+of y, or vice versa. If a string z satisfies z = xy with x and y being Abelian
+equivalent, then z is said to be an Abelian square. If a string w can be
+factorized into a sequence v_1,...,v_s of strings such that v_1 ,..., v_{s-1}
+are all Abelian equivalent and vs is a substring of a permutation of v_1, then
+w is said to have a regular Abelian period (p,t) where p = |v_1| and t = |v_s|.
+If a substring w_1[i..i+l-1] of a string w_1 and a substring w_2[j..j+l-1] of
+another string w_2 are Abelian equivalent, then the substrings are said to be a
+common Abelian factor of w_1 and w_2 and if the length l is the maximum of such
+then the substrings are said to be a longest common Abelian factor of w_1 and
+w_2. We propose efficient algorithms which compute these Abelian regularities
+using the run length encoding (RLE) of strings. For a given string w of length
+n whose RLE is of size m, we propose algorithms which compute all Abelian
+squares occurring in w in O(mn) time, and all regular Abelian periods of w in
+O(mn) time. For two given strings w_1 and w_2 of total length n and of total
+RLE size m, we propose an algorithm which computes all longest common Abelian
+factors in O(m^2n) time.
+"
+23115,Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer,"  Three decades into the research seeking to derive the urban energy budget,
+the dynamics of the thermal exchange between the densely built infrastructure
+and the environment are still not well understood. We present a novel hybrid
+experimental-numerical approach for the analysis of the radiative heat transfer
+in New York City. The aim of this work is to contribute to the calculation of
+the urban energy budget, in particular the stored energy. Improved
+understanding of urban thermodynamics incorporating the interaction of the
+various bodies will have implications on energy conservation at the building
+scale, as well as human health and comfort at the urban scale. The platform
+presented is based on longwave hyperspectral imaging of nearly 100 blocks of
+Manhattan, and a geospatial radiosity model that describes the collective
+radiative heat exchange between multiple buildings. The close comparison of
+temperature values derived from measurements and the computed surface
+temperatures (including streets and roads) implies that this geospatial,
+thermodynamic numerical model applied to urban structures, is promising for
+accurate and high resolution analysis of urban surface temperatures.
+"
+23116,Safe and Complete Real-Time Planning and Exploration in Unknown Environments,"  We present a new framework for motion planning that wraps around existing
+kinodynamic planners and guarantees recursive feasibility when operating in a
+priori unknown, static environments. Our approach makes strong guarantees about
+overall safety and collision avoidance by utilizing a robust controller derived
+from reachability analysis. We ensure that motion plans never exit the safe
+backward reachable set of the initial state, while safely exploring the space.
+This preserves the safety of the initial state, and guarantees that that we
+will eventually find the goal if it is possible to do so while exploring
+safely. We implement our framework in the Robot Operating System (ROS) software
+environment and demonstrate it in a real-time simulation.
+"
+23117,Triangles with prime hypotenuse,"  The sequence $3, 5, 9, 11, 15, 19, 21, 25, 29, 35,\dots$ consists of odd legs
+in right triangles with integer side lengths and prime hypotenuse. We show that
+the upper density of this sequence is zero, with logarithmic decay. The same
+estimate holds for the sequence of even legs in such triangles. We expect our
+upper bound, which involves the Erdős--Ford--Tenenbaum constant, to be
+sharp up to a double-logarithmic factor. We also provide a nontrivial lower
+bound. Our techniques involve sieve methods, the distribution of Gaussian
+primes in narrow sectors, and the Hardy--Ramanujan inequality.
+"
+23118,Video Frame Interpolation via Adaptive Convolution,"  Video frame interpolation typically involves two steps: motion estimation and
+pixel synthesis. Such a two-step approach heavily depends on the quality of
+motion estimation. This paper presents a robust video frame interpolation
+method that combines these two steps into a single process. Specifically, our
+method considers pixel synthesis for the interpolated frame as local
+convolution over two input frames. The convolution kernel captures both the
+local motion between the input frames and the coefficients for pixel synthesis.
+Our method employs a deep fully convolutional neural network to estimate a
+spatially-adaptive convolution kernel for each pixel. This deep neural network
+can be directly trained end to end using widely available video data without
+any difficult-to-obtain ground-truth data like optical flow. Our experiments
+show that the formulation of video interpolation as a single convolution
+process allows our method to gracefully handle challenges like occlusion, blur,
+and abrupt brightness change and enables high-quality video frame
+interpolation.
+"
+23119,Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference,"  Probabilistic inference from real-time input data is becoming increasingly
+popular and may be one of the potential pathways at enabling cognitive
+intelligence. As a matter of fact, preliminary research has revealed that
+stochastic functionalities also underlie the spiking behavior of neurons in
+cortical microcircuits of the human brain. In tune with such observations,
+neuromorphic and other unconventional computing platforms have recently started
+adopting the usage of computational units that generate outputs
+probabilistically, depending on the magnitude of the input stimulus. In this
+work, we experimentally demonstrate a spintronic device that offers a direct
+mapping to the functionality of such a controllable stochastic switching
+element. We show that the probabilistic switching of Ta/CoFeB/MgO
+heterostructures in presence of spin-orbit torque and thermal noise can be
+harnessed to enable probabilistic inference in a plethora of unconventional
+computing scenarios. This work can potentially pave the way for hardware that
+directly mimics the computational units of Bayesian inference.
+"
+23120,Novel Crossover in Coupled Spin Ladders,"  We report a novel crossover behavior in the long-range-ordered phase of a
+prototypical spin-$1/2$ Heisenberg antiferromagnetic ladder compound
+$\mathrm{(C_7H_{10}N)_2CuBr_4}$. The staggered order was previously evidenced
+from a continuous and symmetric splitting of $^{14}$N NMR spectral lines on
+lowering temperature below $T_c\simeq 330$ mK, with a saturation towards
+$\simeq 150$ mK. Unexpectedly, the split lines begin to further separate away
+below $T^*\sim 100$ mK while the line width and shape remain completely
+invariable. This crossover behavior is further corroborated by the NMR
+relaxation rate $T_1^{-1}$ measurements. A very strong suppression reflecting
+the ordering, $T_1^{-1}\sim T^{5.5}$, observed above $T^*$, is replaced by
+$T_1^{-1}\sim T$ below $T^*$. These original NMR features are indicative of
+unconventional nature of the crossover, which may arise from a unique
+arrangement of the ladders into a spatially anisotropic and frustrated coupling
+network.
+"
+23121,An extended model of the quantum free-electron laser,"  Previous models of the quantum regime of operation of the Free Electron Laser
+(QFEL) have performed an averaging and the application of periodic boundary
+conditions to the coupled Maxwell - Schrodinger equations over short, resonant
+wavelength intervals of the interaction. Here, an extended, one-dimensional
+model of the QFEL interaction is presented in the absence of any such averaging
+or application of periodic boundary conditions, the absence of the latter
+allowing electron diffusion processes to be modeled throughout the pulse. The
+model is used to investigate how both the steady-state (CW) and pulsed regimes
+of QFEL operation are affected. In the steady-state regime it is found that the
+electrons are confined to evolve as a 2-level system, similar to the previous
+QFEL models. In the pulsed regime Coherent Spontaneous Emission (CSE) due to
+the shape of the electron pulse current distribution is shown to be present in
+the QFEL regime for the first time. However, unlike the classical case, CSE in
+the QFEL is damped by the effects of quantum diffusion of the electron
+wavefunction. Electron recoil from the QFEL interaction can also cause a
+diffusive drift between the recoiled and non-recoiled parts of the electron
+pulse wavefunction, effectively removing the recoiled part from the primary
+electron-radiation interaction.
+"
+23122,Network Science approach to Modelling Emergence and Topological Robustness of Supply Networks: A Review and Perspective,"  Due to the increasingly complex and interconnected nature of global supply
+chain networks (SCNs), a recent strand of research has applied network science
+methods to model SCN growth and subsequently analyse various topological
+features, such as robustness. This paper provides: (1) a comprehensive review
+of the methodologies adopted in literature for modelling the topology and
+robustness of SCNs; (2) a summary of topological features of the real world
+SCNs, as reported in various data driven studies; and (3) a discussion on the
+limitations of existing network growth models to realistically represent the
+observed topological characteristics of SCNs. Finally, a novel perspective is
+proposed to mimic the SCN topologies reported in empirical studies, through
+fitness based generative network models.
+"
+23123,"The Ages of the Thin Disk, Thick Disk, and the Halo from Nearby White Dwarfs","  We present a detailed analysis of the white dwarf luminosity functions
+derived from the local 40 pc sample and the deep proper motion catalog of Munn
+et al (2014, 2017). Many of the previous studies ignored the contribution of
+thick disk white dwarfs to the Galactic disk luminosity function, which results
+in an erronous age measurement. We demonstrate that the ratio of thick/thin
+disk white dwarfs is roughly 20\% in the local sample. Simultaneously fitting
+for both disk components, we derive ages of 6.8-7.0 Gyr for the thin disk and
+8.7 $\pm$ 0.1 Gyr for the thick disk from the local 40 pc sample. Similarly, we
+derive ages of 7.4-8.2 Gyr for the thin disk and 9.5-9.9 Gyr for the thick disk
+from the deep proper motion catalog, which shows no evidence of a deviation
+from a constant star formation rate in the past 2.5 Gyr. We constrain the time
+difference between the onset of star formation in the thin disk and the thick
+disk to be $1.6^{+0.3}_{-0.4}$ Gyr. The faint end of the luminosity function
+for the halo white dwarfs is less constrained, resulting in an age estimate of
+$12.5^{+1.4}_{-3.4}$ Gyr for the Galactic inner halo. This is the first time
+ages for all three major components of the Galaxy are obtained from a sample of
+field white dwarfs that is large enough to contain significant numbers of disk
+and halo objects. The resultant ages agree reasonably well with the age
+estimates for the oldest open and globular clusters.
+"
+23124,Model Selection for Nonnegative Matrix Factorization by Support Union Recovery,"  Nonnegative matrix factorization (NMF) has been widely used in machine
+learning and signal processing because of its non-subtractive, part-based
+property which enhances interpretability. It is often assumed that the latent
+dimensionality (or the number of components) is given. Despite the large amount
+of algorithms designed for NMF, there is little literature about automatic
+model selection for NMF with theoretical guarantees. In this paper, we propose
+an algorithm that first calculates an empirical second-order moment from the
+empirical fourth-order cumulant tensor, and then estimates the latent
+dimensionality by recovering the support union (the index set of non-zero rows)
+of a matrix related to the empirical second-order moment. By assuming a
+generative model of the data with additional mild conditions, our algorithm
+provably detects the true latent dimensionality. We show on synthetic examples
+that our proposed algorithm is able to find an approximately correct number of
+components.
+"
+23125,Real-Time Energy Disaggregation of a Distribution Feeder's Demand Using Online Learning,"  Though distribution system operators have been adding more sensors to their
+networks, they still often lack an accurate real-time picture of the behavior
+of distributed energy resources such as demand responsive electric loads and
+residential solar generation. Such information could improve system
+reliability, economic efficiency, and environmental impact. Rather than
+installing additional, costly sensing and communication infrastructure to
+obtain additional real-time information, it may be possible to use existing
+sensing capabilities and leverage knowledge about the system to reduce the need
+for new infrastructure. In this paper, we disaggregate a distribution feeder's
+demand measurements into: 1) the demand of a population of air conditioners,
+and 2) the demand of the remaining loads connected to the feeder. We use an
+online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time
+distribution feeder measurements as well as models generated from historical
+building- and device-level data. We develop two implementations of the
+algorithm and conduct case studies using real demand data from households and
+commercial buildings to investigate the effectiveness of the algorithm. The
+case studies demonstrate that DFS can effectively perform online disaggregation
+and the choice and construction of models included in the algorithm affects its
+accuracy, which is comparable to that of a set of Kalman filters.
+"
+23126,Hilfer-Katugampola fractional derivative,"  We propose a new fractional derivative, the Hilfer-Katugampola fractional
+derivative. Motivated by the Hilfer derivative this formulation interpolates
+the well-known fractional derivatives of Hilfer, Hilfer-Hadamard,
+Riemann-Liouville, Hadamard, Caputo, Caputo-Hadamard, Liouville, Weyl,
+generalized and Caputo-type. As an application, we consider a nonlinear
+fractional differential equation with an initial condition using this new
+formulation. We show that this equation is equivalent to a Volterra integral
+equation and demonstrate the existence and uniqueness of solution to the
+nonlinear initial value problem.
+"
+23127,VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View,"  The automatic parking is being massively developed by car manufacturers and
+providers. Until now, there are two problems with the automatic parking. First,
+there is no openly-available segmentation labels of parking slot on panoramic
+surround view (PSV) dataset. Second, how to detect parking slot and road
+structure robustly. Therefore, in this paper, we build up a public PSV dataset.
+At the same time, we proposed a highly fused convolutional network (HFCN) based
+segmentation method for parking slot and lane markings based on the PSV
+dataset. A surround-view image is made of four calibrated images captured from
+four fisheye cameras. We collect and label more than 4,200 surround view images
+for this task, which contain various illuminated scenes of different types of
+parking slots. A VH-HFCN network is proposed, which adopts an HFCN as the base,
+with an extra efficient VH-stage for better segmenting various markings. The
+VH-stage consists of two independent linear convolution paths with vertical and
+horizontal convolution kernels respectively. This modification enables the
+network to robustly and precisely extract linear features. We evaluated our
+model on the PSV dataset and the results showed outstanding performance in
+ground markings segmentation. Based on the segmented markings, parking slots
+and lanes are acquired by skeletonization, hough line transform and line
+arrangement.
+"
+23128,Nonconvex homogenization for one-dimensional controlled random walks in random potential,"  We consider a finite horizon stochastic optimal control problem for
+nearest-neighbor random walk $\{X_i\}$ on the set of integers. The cost
+function is the expectation of exponential of the path sum of a random
+stationary and ergodic bounded potential plus $\theta X_n$. The random walk
+policies are measurable with respect to the random potential, and are adapted,
+with their drifts uniformly bounded in magnitude by a parameter
+$\delta\in[0,1]$. Under natural conditions on the potential, we prove that the
+normalized logarithm of the optimal cost function converges. The proof is
+constructive in the sense that we identify asymptotically optimal policies
+given the value of the parameter $\delta$, as well as the law of the potential.
+It relies on correctors from large deviation theory as opposed to arguments
+based on subadditivity which do not seem to work except when $\delta = 0$.
+The Bellman equation associated to this control problem is a second-order
+Hamilton-Jacobi (HJ) stochastic partial difference equation with a separable
+random Hamiltonian which is nonconvex in $\theta$ unless $\delta = 0$. We prove
+that this equation homogenizes under linear initial data to a first-order HJ
+deterministic partial differential equation. When $\delta = 0$, the effective
+Hamiltonian is the tilted free energy of random walk in random potential and it
+is convex in $\theta$. In contrast, when $\delta = 1$, the effective
+Hamiltonian is piecewise linear and nonconvex in $\theta$. Finally, when
+$\delta \in (0,1)$, the effective Hamiltonian is expressed completely in terms
+of the tilted free energy for the $\delta=0$ case and its
+convexity/nonconvexity in $\theta$ is characterized by a simple inequality
+involving $\delta$ and the magnitude of the potential, thereby marking two
+qualitatively distinct control regimes.
+"
+23129,"A test of weak separability for multi-way functional data, with application to brain connectivity studies","  This paper concerns the modeling of multi-way functional data where double or
+multiple indices are involved. We introduce a concept of weak separability. The
+weakly separable structure supports the use of factorization methods that
+decompose the signal into its spatial and temporal components. The analysis
+reveals interesting connections to the usual strongly separable covariance
+structure, and provides insights into tensor methods for multi-way functional
+data. We propose a formal test for the weak separability hypothesis, where the
+asymptotic null distribution of the test statistic is a chi-square type
+mixture. The method is applied to study brain functional connectivity derived
+from source localized magnetoencephalography signals during motor tasks.
+"
+23130,Edge sampling using network local information,"  Edge sampling is an important topic in network analysis. It provides a
+natural way to reduce network size while retaining desired features of the
+original network. Sampling methods that only use local information are common
+in practice as they do not require access to the entire network and can be
+parallelized easily. Despite promising empirical performance, most of these
+methods are derived from heuristic considerations and therefore still lack
+theoretical justification. To address this issue, we study in this paper a
+simple edge sampling scheme that uses network local information. We show that
+when local connectivity is sufficiently strong, the sampled network satisfies a
+strong spectral property. We quantify the strength of local connectivity by a
+global parameter and relate it to more common network statistics such as
+clustering coefficient and Ricci curvature. Based on this result, we also
+derive a condition under which a hypergraph can be sampled and reduced to a
+weighted network.
+"
+23131,Fluid-loaded metasurfaces,"  We consider wave propagation along fluid-loaded structures which take the
+form of an elastic plate augmented by an array of resonators forming a
+metasurface, that is, a surface structured with sub-wavelength resonators. Such
+surfaces have had considerable recent success for the control of wave
+propagation in electromagnetism and acoustics, by combining the vision of
+sub-wavelength wave manipulation, with the design, fabrication and size
+advantages associated with surface excitation. We explore one aspect of recent
+interest in this field: graded metasurfaces, but within the context of
+fluid-loaded structures.
+Graded metasurfaces allow for selective spatial frequency separation and are
+often referred to as exhibiting rainbow trapping. Experiments, and theory, have
+been developed for acoustic, electromagnetic, and even elastic, rainbow devices
+but this has not been approached for fluid-loaded structures that support
+surface waves coupled with the acoustic field in a bulk fluid. This surface
+wave, coupled with the fluid, can be used to create an additional effect by
+designing a metasurface to mode convert from surface to bulk waves. We
+demonstrate that sub-wavelength control is possible and that one can create
+both rainbow trapping and mode conversion phenomena for a fluid-loaded elastic
+plate model.
+"
+23132,On semilinear Tricomi equations with critical exponents or in two space dimensions,"  This paper is a complement of our recent works on the semilinear Tricomi
+equations in [8] and[9].
+"
+23133,Utilization of Water Supply Networks for Harvesting Renewable Energy,"  Renewable surplus power is increasing due to the increasing penetration of
+these intermittent resources. In practice, electric grid operators either
+curtail the surplus energy resulting from renewable-based generations or
+utilize energy storage resources to absorb it. In this paper, we propose a
+framework for utilizing water pumps and tanks in water supply networks to
+absorb the surplus electrical energy resulting from renewable-based electricity
+generation resources in the electrical grid. We model water supply networks
+analytically, and propose a two-step procedure that utilizes the water tanks in
+the water supply network to harvest the surplus energy from an electrical grid.
+In each step, the water network operator needs to solve an optimization problem
+that is non-convex. To compute optimal pump schedules and water flows, we
+develop a second-order cone relaxation and an approximation technique that
+enable us to transform the proposed problems into mixed-integer second-order
+cone programs. We then provide the conditions under which the proposed
+relaxation is exact, and present an algorithm for constructing an exact
+solution to the original problem from a solution to the relaxed problem. We
+demonstrate the effectiveness of the proposed framework via numerical
+simulations.
+"
+23134,Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction,"  Today's densely instrumented world offers tremendous opportunities for
+continuous acquisition and analysis of multimodal sensor data providing
+temporal characterization of an individual's behaviors. Is it possible to
+efficiently couple such rich sensor data with predictive modeling techniques to
+provide contextual, and insightful assessments of individual performance and
+wellbeing? Prediction of different aspects of human behavior from these noisy,
+incomplete, and heterogeneous bio-behavioral temporal data is a challenging
+problem, beyond unsupervised discovery of latent structures. We propose a
+Supervised Tensor Embedding (STE) algorithm for high dimension multimodal data
+with join decomposition of input and target variable. Furthermore, we show that
+features selection will help to reduce the contamination in the prediction and
+increase the performance. The efficiently of the methods was tested via two
+different real world datasets.
+"
+23135,On the classification of prolongations up to Engel homotopy,"  In [CPPP] it was shown that Engel structures satisfy an existence
+$h$-principle, and the question of whether a full $h$-principle holds was left
+open. In this note we address the classification problem, up to Engel
+deformation, of Cartan and Lorentz prolongations. We show that it reduces to
+their formal data as soon as the turning number is large enough.
+Somewhat separately, we study the homotopy type of the space of Cartan
+prolongations, describing completely its connected components in the
+overtwisted case.
+"
+23136,Travel time tomography with adaptive dictionaries,"  We develop a 2D travel time tomography method which regularizes the inversion
+by modeling groups of slowness pixels from discrete slowness maps, called
+patches, as sparse linear combinations of atoms from a dictionary. We propose
+to use dictionary learning during the inversion to adapt dictionaries to
+specific slowness maps. This patch regularization, called the local model, is
+integrated into the overall slowness map, called the global model. The local
+model considers small-scale variations using a sparsity constraint and the
+global model considers larger-scale features constrained using $\ell_2$
+regularization. This strategy in a locally-sparse travel time tomography (LST)
+approach enables simultaneous modeling of smooth and discontinuous slowness
+features. This is in contrast to conventional tomography methods, which
+constrain models to be exclusively smooth or discontinuous. We develop a
+$\textit{maximum a posteriori}$ formulation for LST and exploit the sparsity of
+slowness patches using dictionary learning. The LST approach compares favorably
+with smoothness and total variation regularization methods on densely, but
+irregularly sampled synthetic slowness maps.
+"
+23137,Topological Entanglement Entropy of Fracton Stabilizer Codes,"  Entanglement entropy provides a powerful characterization of two-dimensional
+gapped topological phases of quantum matter, intimately tied to their
+description by topological quantum field theories (TQFTs). Fracton topological
+orders are three-dimensional gapped topologically ordered states of matter, but
+the existence of a TQFT description for these phases remains an open question.
+We show that three-dimensional fracton phases are nevertheless characterized,
+at least partially, by universal structure in the entanglement entropy of their
+ground state wave functions. We explicitly compute the entanglement entropy for
+two archetypal fracton models --- the `X-cube model' and `Haah's code' --- and
+demonstrate the existence of a topological contribution that scales linearly in
+subsystem size. We show via Schrieffer-Wolff transformations that the
+topological entanglement of fracton models is robust against arbitrary local
+perturbations of the Hamiltonian. Finally, we argue that these results may be
+extended to characterize localization-protected fracton topological order in
+excited states of disordered fracton models.
+"
+23138,Computing weight $q$-multiplicities for the representations of the simple Lie algebras,"  The multiplicity of a weight $\mu$ in an irreducible representation of a
+simple Lie algebra $\mathfrak{g}$ with highest weight $\lambda$ can be computed
+via the use of Kostant's weight multiplicity formula. This formula is an
+alternating sum over the Weyl group and involves the computation of a partition
+function. In this paper we consider a $q$-analog of Kostant's weight
+multiplicity and present a SageMath program to compute $q$-multiplicities for
+the simple Lie algebras.
+"
+23139,Cerebellar-Inspired Learning Rule for Gain Adaptation of Feedback Controllers,"  How does our nervous system successfully acquire feedback control strategies
+in spite of a wide spectrum of response dynamics from different
+musculo-skeletal systems? The cerebellum is a crucial brain structure in
+enabling precise motor control in animals. Recent advances suggest that
+synaptic plasticity of cerebellar Purkinje cells involves molecular mechanisms
+that mimic the dynamics of the efferent motor system that they control allowing
+them to match the timing of their learning rule to behavior. Counter-Factual
+Predictive Control (CFPC) is a cerebellum-based feed-forward control scheme
+that exploits that principle for acquiring anticipatory actions. CFPC extends
+the classical Widrow-Hoff/Least Mean Squares by inserting a forward model of
+the downstream closed-loop system in its learning rule. Here we apply that same
+insight to the problem of learning the gains of a feedback controller. To that
+end, we frame a Model-Reference Adaptive Control (MRAC) problem and derive an
+adaptive control scheme treating the gains of a feedback controller as if they
+were the weights of an adaptive linear unit. Our results demonstrate that
+rather than being exclusively confined to cerebellar learning, the approach of
+controlling plasticity with a forward model of the subsystem controlled, an
+approach that we term as Model-Enhanced Least Mean Squares (ME-LMS), can
+provide a solution to wide set of adaptive control problems.
+"
+23140,The information bottleneck and geometric clustering,"  The information bottleneck (IB) approach to clustering takes a joint
+distribution $P\!\left(X,Y\right)$ and maps the data $X$ to cluster labels $T$
+which retain maximal information about $Y$ (Tishby et al., 1999). This
+objective results in an algorithm that clusters data points based upon the
+similarity of their conditional distributions $P\!\left(Y\mid X\right)$. This
+is in contrast to classic ""geometric clustering"" algorithms such as $k$-means
+and gaussian mixture models (GMMs) which take a set of observed data points
+$\left\{ \mathbf{x}_{i}\right\}_{i=1:N}$ and cluster them based upon their
+geometric (typically Euclidean) distance from one another. Here, we show how to
+use the deterministic information bottleneck (DIB) (Strouse and Schwab, 2017),
+a variant of IB, to perform geometric clustering, by choosing cluster labels
+that preserve information about data point location on a smoothed dataset. We
+also introduce a novel intuitive method to choose the number of clusters, via
+kinks in the information curve. We apply this approach to a variety of simple
+clustering problems, showing that DIB with our model selection procedure
+recovers the generative cluster labels. We also show that, for one simple case,
+DIB interpolates between the cluster boundaries of GMMs and $k$-means in the
+large data limit. Thus, our IB approach to clustering also provides an
+information-theoretic perspective on these classic algorithms.
+"
+23141,Structural symmetry and membrane curvature sensing,"  Symmetry is closely intertwined with the function, genetics, and chemical
+properties of multiprotein complexes. Here, we explore the relation between
+structural symmetry and the ability of membrane proteins to sense and induce
+membrane curvature, which is a key factor for modulating the shape and
+organization of cell membranes. Using coarse-grained simulations and elasticity
+theory, we show that the potential for direction-dependent membrane curvature
+sensing is limited to asymmetric proteins, dimers, and tetramers, and argue
+that one should expect this anisotropy to be strongest for dimers. Odd and
+higher-order symmetries strongly suppress directional curvature sensing. This
+classification gives a new perspective on the structure-function relation for
+membrane proteins, and simplifies the task of translating between molecular
+sensing mechanisms and their large-scale cellular consequences.
+"
+23142,What is a noncommutative topos?,"  In 1702.04949 noncommutative frames were introduced, generalizing the usual
+notion of frames of open sets of a topological space. In this paper we extend
+this notion to noncommutative Grothendieck topologies and their associated
+noncommutative toposes of sheaves of sets.
+"
+23143,Feature Trajectory Dynamic Time Warping for Clustering of Speech Segments,"  Dynamic time warping (DTW) can be used to compute the similarity between two
+sequences of generally differing length. We propose a modification to DTW that
+performs individual and independent pairwise alignment of feature trajectories.
+The modified technique, termed feature trajectory dynamic time warping (FTDTW),
+is applied as a similarity measure in the agglomerative hierarchical clustering
+of speech segments. Experiments using MFCC and PLP parametrisations extracted
+from TIMIT and from the Spoken Arabic Digit Dataset (SADD) show consistent and
+statistically significant improvements in the quality of the resulting clusters
+in terms of F-measure and normalised mutual information (NMI).
+"
+23144,Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks,"  Automatically assessing emotional valence in human speech has historically
+been a difficult task for machine learning algorithms. The subtle changes in
+the voice of the speaker that are indicative of positive or negative emotional
+states are often ""overshadowed"" by voice characteristics relating to emotional
+intensity or emotional activation. In this work we explore a representation
+learning approach that automatically derives discriminative representations of
+emotional speech. In particular, we investigate two machine learning strategies
+to improve classifier performance: (1) utilization of unlabeled data using a
+deep convolutional generative adversarial network (DCGAN), and (2) multitask
+learning. Within our extensive experiments we leverage a multitask annotated
+emotional corpus as well as a large unlabeled meeting corpus (around 100
+hours). Our speaker-independent classification experiments show that in
+particular the use of unlabeled data in our investigations improves performance
+of the classifiers and both fully supervised baseline approaches are
+outperformed considerably. We improve the classification of emotional valence
+on a discrete 5-point scale to 43.88% and on a 3-point scale to 49.80%, which
+is competitive to state-of-the-art performance.
+"
+23145,The complete micromagnetic characterization of asymmetrically sandwiched ferromagnetic films,"  The magnetic properties of ferromagnetic thin films down to the nanoscale are
+ruled by the exchange stiffness, anisotropies and the effects of magnetic
+fields. As surfaces break inversion symmetry, an additional effective chiral
+exchange is omnipresent in any magnetic nanostructure. These so-called
+Dzyaloshinskii-Moriya interactions (DMI) affect all inhomogeneous magnetic
+states either subtly or spectacularly. E.g., DMIs cause a chirality selection
+of the rotation sense and can fix the local rotation axis for the magnetization
+in domain walls (DW). But, they can stabilize also entirely different twisted
+magnetic structures. The chiral skyrmions a two-dimensional particle-like
+topological soliton is the ultimately smallest of these objects, which
+currently is targetted as a possible information carrier in novel spintronic
+devices. Observation and quantification of the chiral exchange effects provide
+for the salient point in understanding magnetic properties in ultrathin films
+and other nanostructures. An easy and reliable method to determine the DMI
+constant as materials parameter of asymmetric thin films is the crucial
+problem. Here, we put forth an experimental approach for the determination of
+the complete set of the micromagnetic parameters. Quasi-static Kerr microcopy
+observations of DW creep motion and equilibrium sizes of circular magnetic
+objects in combination with standard magnetometry are used to derive a
+consistent set of these materials parameters in polycrystalline ultrathin film
+systems, namely CrOx/Co/Pt stacks. The quantified micromagnetic model for these
+films identifies the circular magnetic objects, as seen by the optical
+microscopy, as ordinary bubble domains with homochiral walls. From
+micromagnetic calculations, the chiral skyrmions stabilized by the DMI in these
+films are shown to have diameters in the range 40-200nm, too small to be
+observed by optical microscopy.
+"
+23146,GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest,"  Random forest and deep neural network are two schools of effective
+classification methods in machine learning. While the random forest is robust
+irrespective of the data domain, the deep neural network has advantages in
+handling high dimensional data. In view that a differentiable neural decision
+forest can be added to the neural network to fully exploit the benefits of both
+models, in our work, we further combine convolutional autoencoder with neural
+decision forest, where autoencoder has its advantages in finding the hidden
+representations of the input data. We develop a gradient boost module and embed
+it into the proposed convolutional autoencoder with neural decision forest to
+improve the performance. The idea of gradient boost is to learn and use the
+residual in the prediction. In addition, we design a structure to learn the
+parameters of the neural decision forest and gradient boost module at
+contiguous steps. The extensive experiments on several public datasets
+demonstrate that our proposed model achieves good efficiency and prediction
+performance compared with a series of baseline methods.
+"
+23147,Generalized Cosecant Numbers and the Hurwitz Zeta Function,"  This announcement paper summarises recent development concerning the
+generalized cosecant numbers $c_{\rho,k}$, which represent the coefficients of
+the power series expansion for the important fundamental function
+$z^{\rho}/\sin^{\rho} z$. These coefficients are obtained for all, including
+complex, values of $\rho$ via the partition method for a power series
+expansion, which is more versatile than the standard Taylor series approach,
+but yields the same results as the latter when both can be applied, though in a
+different form. Surprisingly, the generalized cosecant numbers are polynomials
+in $\rho$ of degree $k$, where $k$ is the power of $z$. General formulas for
+the coefficients of the highest order terms in the generalized cosecant numbers
+are presented. It is then shown how the generalized cosecant numbers are
+related to the specific symmetric polynomials from summing over quadratic
+powers of integers. Consequently, integral values of the Hurwitz zeta function
+for even powers are expressed for the first time ever in terms of ratios of the
+generalized cosecant numbers.
+"
+23148,Investigating accuracy of pitch-accent annotations in neural network-based speech synthesis and denoising effects,"  We investigated the impact of noisy linguistic features on the performance of
+a Japanese speech synthesis system based on neural network that uses WaveNet
+vocoder. We compared an ideal system that uses manually corrected linguistic
+features including phoneme and prosodic information in training and test sets
+against a few other systems that use corrupted linguistic features. Both
+subjective and objective results demonstrate that corrupted linguistic
+features, especially those in the test set, affected the ideal system's
+performance significantly in a statistical sense due to a mismatched condition
+between the training and test sets. Interestingly, while an utterance-level
+Turing test showed that listeners had a difficult time differentiating
+synthetic speech from natural speech, it further indicated that adding noise to
+the linguistic features in the training set can partially reduce the effect of
+the mismatch, regularize the model, and help the system perform better when
+linguistic features of the test set are noisy.
+"
+23149,Kennicutt-Schmidt relation variety and star-forming cloud fraction,"  The observationally derived Kennicutt-Schmidt (KS) relation slopes differ
+from study to study, ranging from sub-linear to super-linear. We investigate
+the KS-relation variety (slope and normalization) as a function of integrated
+intensity ratio, R31=CO(J=3-2)/CO(J=1-0) using spatially resolved CO(J=1-0),
+CO(J=3-2), HI, Ha and 24um data of three nearby spiral galaxies (NGC3627,
+NGC5055 and M83). We find that (1) the slopes for each subsample with a fixed
+R31 are shallower but the slope for all datasets combined becomes steeper, (2)
+normalizations for high R31 subsamples tend to be high, (3) R31 correlates with
+star-formation efficiency, thus the KS relation depends on the distribution in
+R31-Sigma_gas space of the samples: no Sigma_gas dependence of R31 results in a
+linear slope of the KS relation whereas a positive correlation between
+Sigma_gas and R31 results in a super-linear slope of the KS relation, and (4)
+R31-Sigma_gas distributions are different from galaxy to galaxy and within a
+galaxy: galaxies with prominent galactic structure tend to have large R31 and
+Sigma_gas. Our results suggest that the formation efficiency of star-forming
+cloud from molecular gas is different among galaxies as well as within a galaxy
+and is one of the key factors inducing the variety in galactic KS relation.
+"
+23150,Deploying Deep Ranking Models for Search Verticals,"  In this paper, we present an architecture executing a complex machine
+learning model such as a neural network capturing semantic similarity between a
+query and a document; and deploy to a real-world production system serving
+500M+users. We present the challenges that arise in a real-world system and how
+we solve them. We demonstrate that our architecture provides competitive
+modeling capability without any significant performance impact to the system in
+terms of latency. Our modular solution and insights can be used by other
+real-world search systems to realize and productionize recent gains in neural
+networks.
+"
+23151,Multigraded linear series and recollement,"  Given a scheme $Y$ equipped with a collection of globally generated vector
+bundles $E_1, \dots, E_n$, we study the universal morphism from $Y$ to a fine
+moduli space $\mathcal{M}(E)$ of cyclic modules over the endomorphism algebra
+of $E:=\mathcal{O}_Y\oplus E_1\oplus\cdots \oplus E_n$. This generalises the
+classical morphism to the linear series of a basepoint-free line bundle on a
+scheme. We describe the image of the morphism and present necessary and
+sufficient conditions for surjectivity in terms of a recollement of a module
+category. When the morphism is surjective, this gives a fine moduli space
+interpretation of the image, and as an application we show that for a small,
+finite subgroup $G\subset \text{GL}(2,k)$, every sub-minimal partial resolution
+of $\mathbb{A}^2_k/G$ is isomorphic to a fine moduli space $\mathcal{M}(E_C)$
+where $E_C$ is a summand of the bundle $E$ defining the reconstruction algebra.
+We also consider applications to Gorenstein affine threefolds, where Reid's
+recipe sheds some light on the classes of algebra from which one can
+reconstruct a given crepant resolution.
+"
+23152,Relations Between Greedy and Bit-Optimal LZ77 Encodings,"  This paper investigates the size in bits of the LZ77 encoding, which is the
+most popular and efficient variant of the Lempel-Ziv encodings used in data
+compression. We prove that, for a wide natural class of variable-length
+encoders for LZ77 phrases, the size of the greedily constructed LZ77 encoding
+on constant alphabets is within a factor $O(\frac{\log n}{\log\log\log n})$ of
+the optimal LZ77 encoding, where $n$ is the length of the processed string. We
+describe a series of examples showing that, surprisingly, this bound is tight,
+thus improving both the previously known upper and lower bounds. Further, we
+obtain a more detailed bound $O(\min\{z, \frac{\log n}{\log\log z}\})$, which
+uses the number $z$ of phrases in the greedy LZ77 encoding as a parameter, and
+construct a series of examples showing that this bound is tight even for binary
+alphabet. We then investigate the problem on non-constant alphabets: we show
+that the known $O(\log n)$ bound is tight even for alphabets of logarithmic
+size, and provide tight bounds for some other important cases.
+"
+23153,Private Incremental Regression,"  Data is continuously generated by modern data sources, and a recent challenge
+in machine learning has been to develop techniques that perform well in an
+incremental (streaming) setting. In this paper, we investigate the problem of
+private machine learning, where as common in practice, the data is not given at
+once, but rather arrives incrementally over time.
+We introduce the problems of private incremental ERM and private incremental
+regression where the general goal is to always maintain a good empirical risk
+minimizer for the history observed under differential privacy. Our first
+contribution is a generic transformation of private batch ERM mechanisms into
+private incremental ERM mechanisms, based on a simple idea of invoking the
+private batch ERM procedure at some regular time intervals. We take this
+construction as a baseline for comparison. We then provide two mechanisms for
+the private incremental regression problem. Our first mechanism is based on
+privately constructing a noisy incremental gradient function, which is then
+used in a modified projected gradient procedure at every timestep. This
+mechanism has an excess empirical risk of $\approx\sqrt{d}$, where $d$ is the
+dimensionality of the data. While from the results of [Bassily et al. 2014]
+this bound is tight in the worst-case, we show that certain geometric
+properties of the input and constraint set can be used to derive significantly
+better results for certain interesting regression problems.
+"
+23154,Maximally Entangled State and Bell's Inequality in Qubits,"  A maximally entangled state is a quantum state which has maximum von Neumann
+entropy for each bipartition. Through proposing a new method to classify
+quantum states by using concurrences of pure states of a region, one can apply
+Bell's inequality to study intensity of quantum entanglement of maximally
+entangled states. We use a class of seven-qubit quantum states to demonstrate
+the method, where we express all coefficients of the quantum states in terms of
+concurrences of pure states of a region. When a critical point of an upper
+bound of Bell's inequality occurs in our quantum states, one of the quantum
+state is a ground state of the toric code model on a disk manifold. Our result
+also implies that the maximally entangled states does not suggest local maximum
+quantum entanglement in our quantum states.
+"
+23155,Dynamic Discovery of Type Classes and Relations in Semantic Web Data,"  The continuing development of Semantic Web technologies and the increasing
+user adoption in the recent years have accelerated the progress incorporating
+explicit semantics with data on the Web. With the rapidly growing RDF (Resource
+Description Framework) data on the Semantic Web, processing large semantic
+graph data have become more challenging. Constructing a summary graph structure
+from the raw RDF can help obtain semantic type relations and reduce the
+computational complexity for graph processing purposes. In this paper, we
+addressed the problem of graph summarization in RDF graphs, and we proposed an
+approach for building summary graph structures automatically from RDF graph
+data. Moreover, we introduced a measure to help discover optimum class
+dissimilarity thresholds and an effective method to discover the type classes
+automatically. In future work, we plan to investigate further improvement
+options on the scalability of the proposed method.
+"
+23156,Springer fibers for the minimal and the minimal special nilpotent orbits,"  We describe Springer fibers corresponding to the minimal and minimal special
+nilpotent orbits of simple Lie algebras. As a result, we give an answer to the
+conjecture of Humphreys regarding some graphs attached to Springer fibers.
+"
+23157,Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning,"  Multi-task learning (MTL) is a supervised learning paradigm in which the
+prediction models for several related tasks are learned jointly to achieve
+better generalization performance. When there are only a few training examples
+per task, MTL considerably outperforms the traditional Single task learning
+(STL) in terms of prediction accuracy. In this work we develop an MTL based
+approach for classifying documents that are archived within dual concept
+hierarchies, namely, DMOZ and Wikipedia. We solve the multi-class
+classification problem by defining one-versus-rest binary classification tasks
+for each of the different classes across the two hierarchical datasets. Instead
+of learning a linear discriminant for each of the different tasks
+independently, we use a MTL approach with relationships between the different
+tasks across the datasets established using the non-parametric, lazy, nearest
+neighbor approach. We also develop and evaluate a transfer learning (TL)
+approach and compare the MTL (and TL) methods against the standard single task
+learning and semi-supervised learning approaches. Our empirical results
+demonstrate the strength of our developed methods that show an improvement
+especially when there are fewer number of training examples per classification
+task.
+"
+23158,North-south asymmetries in cold plasma density in the magnetotail lobes: Cluster observations,"  In this paper, we present observations of cold (0-70 eV) plasma density in
+the magnetotail lobes. The observations and results are based on 16 years of
+Cluster observation of spacecraft potential measurements converted into local
+plasma densities. Measurements from all four Cluster spacecraft have been used,
+and the survey indicates a persistent asymmetry in lobe density, with
+consistently higher cold plasma densities in the northern lobe. External
+influences, such as daily and seasonal variations in the Earth's tilt angle,
+can introduce temporary north-south asymmetries through asymmetric ionization
+of the two hemispheres. Likewise, external drivers, such as the orientation of
+the interplanetary magnetic field can set up additional spatial asymmetries in
+outflow and lobe filling. The persistent asymmetry reported in this paper is
+also influenced by these external factors but is mainly caused by differences
+in magnetic field configuration in the Northern and Southern Hemisphere
+ionospheres.
+"
+23159,Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition,"  Through this project, we researched on transfer learning methods and their
+applications on real world problems. By implementing and modifying various
+methods in transfer learning for our problem, we obtained an insight in the
+advantages and disadvantages of these methods, as well as experiences in
+developing neural network models for knowledge transfer. Due to time
+constraint, we only applied a representative method for each major approach in
+transfer learning. As pointed out in the literature review, each method has its
+own assumptions, strengths and shortcomings. Thus we believe that an
+ensemble-learning approach combining the different methods should yield a
+better performance, which can be our future research focus.
+"
+23160,Nonclassical light from large ensemble of trapped ions,"  The vast majority of physical objects we are dealing with are almost
+exclusively made of atoms. Due to their discrete level structure, single atoms
+have proved to be emitters of light which is incompatible with the classical
+description of electromagnetic waves. We demonstrate this incompatibility for
+atomic fluorescence when scaling up the size of the source ensemble, which
+consists of trapped atomic ions, by several orders of magnitude. The presented
+measurements of nonclassical statistics on light unconditionally emitted from
+ensembles containing up to more than a thousand ions promise further
+scalability to much larger emitter numbers. The methodology can be applied to a
+broad range of experimental platforms focusing on the bare nonclassical
+character of single isolated emitters.
+"
+23161,On weighted approximation with Jacobi weights,"  We obtain matching direct and inverse theorems for the degree of weighted
+$L_p$-approximation by polynomials with the Jacobi weights $(1-x)^\alpha
+(1+x)^\beta$. Combined, the estimates yield a constructive characterization of
+various smoothness classes of functions via the degree of their approximation
+by algebraic polynomials.
+"
+23162,Forward Thinking: Building Deep Random Forests,"  The success of deep neural networks has inspired many to wonder whether other
+learners could benefit from deep, layered architectures. We present a general
+framework called forward thinking for deep learning that generalizes the
+architectural flexibility and sophistication of deep neural networks while also
+allowing for (i) different types of learning functions in the network, other
+than neurons, and (ii) the ability to adaptively deepen the network as needed
+to improve results. This is done by training one layer at a time, and once a
+layer is trained, the input data are mapped forward through the layer to create
+a new learning problem. The process is then repeated, transforming the data
+through multiple layers, one at a time, rendering a new dataset, which is
+expected to be better behaved, and on which a final output layer can achieve
+good performance. In the case where the neurons of deep neural nets are
+replaced with decision trees, we call the result a Forward Thinking Deep Random
+Forest (FTDRF). We demonstrate a proof of concept by applying FTDRF on the
+MNIST dataset. We also provide a general mathematical formulation that allows
+for other types of deep learning problems to be considered.
+"
+23163,Electronic structure of ultralong-range Rydberg pentaatomic molecules with two polar diatomic molecules,"  We explore the electronic structure of ultralong-range pentaatomic Rydberg
+molecules from a merger of a Rydberg atom and two ground state heteronuclear
+diatomic molecules. Our focus is on the interaction of Rb($23s$) and Rb($n=20$,
+$l\ge 3$) Rydberg states with ground and rotationally excited KRb diatomic
+polar molecules. For symmetric and asymmetric configurations of the pentaatomic
+Rydberg molecule, we investigate the metamorphosis of the Born-Oppenheimer
+potential curves, essential for the binding of the molecule, with varying
+distance from the Rydberg core and analyze the alignment and orientation of the
+polar diatomic molecules.
+"
+23164,Cold collisions in a molecular synchrotron,"  We study collisions between neutral, deuterated ammonia molecules (ND$_3$)
+stored in a 50 cm diameter synchrotron and argon atoms in co-propagating
+supersonic beams. The advantages of using a synchrotron in collision studies
+are twofold: (i) By storing ammonia molecules many round-trips, the sensitivity
+to collisions is greatly enhanced; (ii) The collision partners move in the same
+direction as the stored molecules, resulting in low collision energies. We tune
+the collision energy in three different ways: by varying the velocity of the
+stored ammonia packets, by varying the temperature of the pulsed valve that
+releases the argon atoms, and by varying the timing between the supersonic
+argon beam and the stored ammonia packets. These give consistent results. We
+determine the relative, total, integrated cross-section for
+$\mathrm{ND}_3+\mathrm{Ar}$ collisions in the energy range of 40-140 cm$^{-1}$,
+with a resolution of 5-10 cm$^{-1}$ and an uncertainty of 7-15%. Our
+measurements are in good agreement with theoretical scattering calculations.
+"
+23165,Hierarchical Optimization for Whole-Body Control of Wheeled Inverted Pendulum Humanoids,"  In this paper, we present a whole-body control framework for Wheeled Inverted
+Pendulum (WIP) Humanoids. WIP Humanoids are redundant manipulators dynamically
+balancing themselves on wheels. Characterized by several degrees of freedom,
+they have the ability to perform several tasks simultaneously, such as
+balancing, maintaining a body pose, controlling the gaze, lifting a load or
+maintaining end-effector configuration in operation space. The problem of
+whole-body control is to enable simultaneous performance of these tasks with
+optimal participation of all degrees of freedom at specified priorities for
+each objective. The control also has to obey constraint of angle and torque
+limits on each joint. The proposed approach is hierarchical with a low level
+controller for body joints manipulation and a high-level controller that
+defines center of mass (CoM) targets for the low-level controller to control
+zero dynamics of the system driving the wheels. The low-level controller plans
+for shorter horizons while considering more complete dynamics of the system,
+while the high-level controller plans for longer horizon based on an
+approximate model of the robot for computational efficiency.
+"
+23166,Influence of periodic external fields in multiagent models with language dynamics,"  We investigate large-scale effects induced by external fields,
+phenomenologically interpreted as mass media, in multiagent models evolving
+with the microscopic dynamics of the binary naming game. In particular, we show
+that a single external field, broadcasting information at regular time
+intervals, can reverse the majority opinion of the population, provided the
+frequency and the effectiveness of the sent messages lie above well-defined
+thresholds. We study the phase structure of the model in the mean field
+approximation and in numerical simulations with several network topologies. We
+also investigate the influence on the agent dynamics of two competing external
+fields, periodically broadcasting different messages. In finite regions of the
+parameter space we observe periodic equilibrium states in which the average
+opinion densities are reversed with respect to naive expectations. Such
+equilibria occur in two cases: (i) when the frequencies of the competing
+messages are different but close to each other; (ii) when the frequencies are
+equal and the relative time shift of the messages does not exceed half a
+period. We interpret the observed phenomena as a result of the interplay
+between the external fields and the internal dynamics of the agents and
+conclude that, depending on the model parameters, the naming game is consistent
+with scenarios of first- or second-mover advantage (to borrow an expression
+from the jargon of business strategy).
+"
+23167,37 GHz observations of narrow-line Seyfert 1 galaxies,"  Observations at 37 GHz, performed at Metsähovi Radio Observatory, are
+presented for a sample of 78 radio-loud and radio-quiet narrow-line Seyfert 1
+(NLS1) galaxies, together with additional lower and higher frequency radio data
+from RATAN-600, Owens Valley Radio Observatory, and the Planck satellite. Most
+of the data have been gathered between February 2012 and April 2015 but for
+some sources even longer lightcurves exist. The detection rate at 37 GHz is
+around 19%, comparable to other populations of active galactic nuclei presumed
+to be faint at radio frequencies, such as BL Lac objects. Variability and
+spectral indices are determined for sources with enough detections. Based on
+the radio data, many NLS1 galaxies show a blazar-like radio spectra exhibiting
+significant variability. The spectra at a given time are often inverted or
+convex. The source of the high-frequency radio emission in NLS1 galaxies,
+detected at 37 GHz, is most probably a relativistic jet rather than star
+formation. Jets in NLS1 galaxies are therefore expected to be a much more
+common phenomenon than earlier assumed.
+"
+23168,Why are Big Data Matrices Approximately Low Rank?,"  Matrices of (approximate) low rank are pervasive in data science, appearing
+in recommender systems, movie preferences, topic models, medical records, and
+genomics. While there is a vast literature on how to exploit low rank structure
+in these datasets, there is less attention on explaining why the low rank
+structure appears in the first place. Here, we explain the effectiveness of low
+rank models in data science by considering a simple generative model for these
+matrices: we suppose that each row or column is associated to a (possibly high
+dimensional) bounded latent variable, and entries of the matrix are generated
+by applying a piecewise analytic function to these latent variables. These
+matrices are in general full rank. However, we show that we can approximate
+every entry of an $m \times n$ matrix drawn from this model to within a fixed
+absolute error by a low rank matrix whose rank grows as $\mathcal O(\log(m +
+n))$. Hence any sufficiently large matrix from such a latent variable model can
+be approximated, up to a small entrywise error, by a low rank matrix.
+"
+23169,MVDepthNet: Real-time Multiview Depth Estimation Neural Network,"  Although deep neural networks have been widely applied to computer vision
+problems, extending them into multiview depth estimation is non-trivial. In
+this paper, we present MVDepthNet, a convolutional network to solve the depth
+estimation problem given several image-pose pairs from a localized monocular
+camera in neighbor viewpoints. Multiview observations are encoded in a cost
+volume and then combined with the reference image to estimate the depth map
+using an encoder-decoder network. By encoding the information from multiview
+observations into the cost volume, our method achieves real-time performance
+and the flexibility of traditional methods that can be applied regardless of
+the camera intrinsic parameters and the number of images. Geometric data
+augmentation is used to train MVDepthNet. We further apply MVDepthNet in a
+monocular dense mapping system that continuously estimates depth maps using a
+single localized moving camera. Experiments show that our method can generate
+depth maps efficiently and precisely.
+"
+23170,Label Efficient Learning of Transferable Representations across Domains and Tasks,"  We propose a framework that learns a representation transferable across
+different domains and tasks in a label efficient manner. Our approach battles
+domain shift with a domain adversarial loss, and generalizes the embedding to
+novel task using a metric learning-based approach. Our model is simultaneously
+optimized on labeled source data and unlabeled or sparsely labeled data in the
+target domain. Our method shows compelling results on novel classes within a
+new domain even when only a few labeled examples per class are available,
+outperforming the prevalent fine-tuning approach. In addition, we demonstrate
+the effectiveness of our framework on the transfer learning task from image
+object recognition to video action recognition.
+"
+23171,Asteroid shapes and thermal properties from combined optical and mid-infrared photometry inversion,"  Optical lightcurves can be used for the shape and spin reconstruction of
+asteroids. Due to unknown albedo, these models are scale-free. When thermal
+infrared data are available, they can be used for scaling the shape models and
+for deriving thermophysical properties of the surface by applying a
+thermophysical model. We introduce a new method of simultaneous inversion of
+optical and thermal infrared data that allows the size of an asteroid to be
+derived along with its shape and spin state. The method optimizes all relevant
+parameters (shape and its size, spin state, light-scattering properties,
+thermal inertia, surface roughness) by gradient-based optimization. The thermal
+emission is computed by solving the 1-D heat diffusion equation. Calibrated
+optical photometry and thermal fluxes at different wavelengths are needed as
+input data. We demonstrate the reliability and test the accuracy of the method
+on selected targets with different amount and quality of data. Our results in
+general agree with those obtained by independent methods. Combining optical and
+thermal data into one inversion method opens a new possibility for processing
+photometry from large optical sky surveys with the data from WISE. It also
+provides more realistic estimates of errors of thermophysical parameters.
+"
+23172,High Dimensional Bayesian Optimization Using Dropout,"  Scaling Bayesian optimization to high dimensions is challenging task as the
+global optimization of high-dimensional acquisition function can be expensive
+and often infeasible. Existing methods depend either on limited active
+variables or the additive form of the objective function. We propose a new
+method for high-dimensional Bayesian optimization, that uses a dropout strategy
+to optimize only a subset of variables at each iteration. We derive theoretical
+bounds for the regret and show how it can inform the derivation of our
+algorithm. We demonstrate the efficacy of our algorithms for optimization on
+two benchmark functions and two real-world applications- training cascade
+classifiers and optimizing alloy composition.
+"
+23173,Visualizing and Exploring Dynamic High-Dimensional Datasets with LION-tSNE,"  T-distributed stochastic neighbor embedding (tSNE) is a popular and
+prize-winning approach for dimensionality reduction and visualizing
+high-dimensional data. However, tSNE is non-parametric: once visualization is
+built, tSNE is not designed to incorporate additional data into existing
+representation. It highly limits the applicability of tSNE to the scenarios
+where data are added or updated over time (like dashboards or series of data
+snapshots).
+In this paper we propose, analyze and evaluate LION-tSNE (Local Interpolation
+with Outlier coNtrol) - a novel approach for incorporating new data into tSNE
+representation. LION-tSNE is based on local interpolation in the vicinity of
+training data, outlier detection and a special outlier mapping algorithm. We
+show that LION-tSNE method is robust both to outliers and to new samples from
+existing clusters. We also discuss multiple possible improvements for special
+cases.
+We compare LION-tSNE to a comprehensive list of possible benchmark approaches
+that include multiple interpolation techniques, gradient descent for new data,
+and neural network approximation.
+"
+23174,Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpus,"  Every year at the United Nations, member states deliver statements during the
+General Debate discussing major issues in world politics. These speeches
+provide invaluable information on governments' perspectives and preferences on
+a wide range of issues, but have largely been overlooked in the study of
+international politics. This paper introduces a new dataset consisting of over
+7,701 English-language country statements from 1970-2016. We demonstrate how
+the UN General Debate Corpus (UNGDC) can be used to derive country positions on
+different policy dimensions using text analytic methods. The paper provides
+applications of these estimates, demonstrating the contribution the UNGDC can
+make to the study of international politics.
+"
+23175,Periodic and q-potent elements in rings,"  This paper, we consider some properties of rings via q-potent and periodic
+elements. In this paper we give some results of rings in which every element is
+a sum of an idempotent and a q-potent that commute; periodic rings and k-potent
+elements of algebras.
+"
+23176,Broadband DOA estimation using Convolutional neural networks trained with noise signals,"  A convolution neural network (CNN) based classification method for broadband
+DOA estimation is proposed, where the phase component of the short-time Fourier
+transform coefficients of the received microphone signals are directly fed into
+the CNN and the features required for DOA estimation are learnt during
+training. Since only the phase component of the input is used, the CNN can be
+trained with synthesized noise signals, thereby making the preparation of the
+training data set easier compared to using speech signals. Through experimental
+evaluation, the ability of the proposed noise trained CNN framework to
+generalize to speech sources is demonstrated. In addition, the robustness of
+the system to noise, small perturbations in microphone positions, as well as
+its ability to adapt to different acoustic conditions is investigated using
+experiments with simulated and real data.
+"
+23177,Spatio-Temporal Reference Frames as Geographic Objects,"  It is often desirable to analyse trajectory data in local coordinates
+relative to a reference location. Similarly, temporal data also needs to be
+transformed to be relative to an event. Together, temporal and spatial
+contextualisation permits comparative analysis of similar trajectories taken
+across multiple reference locations. To the GIS professional, the procedures to
+establish a reference frame at a location and reproject the data into local
+coordinates are well known, albeit tedious. However, GIS tools are now often
+used by subject matter experts who may not have the deep knowledge of
+coordinate frames and projections required to use these techniques effectively.
+We introduce a novel method for representing spatio-temporal reference frames
+using ordinary geographic objects available in GIS tools. We argue that our
+method both reduces the number of manual steps required to reproject data to a
+local reference frame, in addition to reducing the number of concepts a novice
+user would need to learn.
+"
+23178,The Effect of Data Marshalling on Computation Offloading Decisions,"  We conducted an extensive set of experiments with an offloading testbed to
+understand the impact that data marshalling techniques have on computation
+offloading decisions. We find that the popular JSON format to marshall data
+between client and server comes at a significant computational expense compared
+to a minimalistic raw data transfer. The computational time is significant in
+that it affects computation offloading decisions in a variety of conditions. We
+outline some of these conditions.
+"
+23179,"Linear Groups, Conjugacy Growth, and Classifying Spaces for Families of Subgroups","  Given a group $G$ and a family of subgroups $\mathcal{F}$, we consider its
+classifying space $E_{\mathcal F}G$ with respect to $\mathcal{F}$. When
+$\mathcal F = \mathcal{VC}yc$ is the family of virtually cyclic subgroups,
+Juan-Pineda and Leary conjectured that a group admits a finite model for this
+classifying space if and only if it is virtually cyclic. By establishing a
+connection to conjugacy growth we can show that this conjecture holds for
+linear groups. We investigate a similar question that was asked by
+Lück--Reich--Rognes--Varisco for the family of cyclic subgroups. Finally, we
+construct finitely generated groups that exhibit wild inner automorphims but
+which admit a model for $E_{\mathcal{VC}yc}(G)$ whose 0-skeleton is finite.
+"
+23180,Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture,"  Machine learning techniques have become increasingly popular in the field of
+resting state fMRI (functional magnetic resonance imaging) network based
+classification. However, the application of convolutional networks has been
+proposed only very recently and has remained largely unexplored. In this paper
+we describe a convolutional neural network architecture for functional
+connectome classification called connectome-convolutional neural network
+(CCNN). Our results on simulated datasets and a publicly available dataset for
+amnestic mild cognitive impairment classification demonstrate that our CCNN
+model can efficiently distinguish between subject groups. We also show that the
+connectome-convolutional network is capable to combine information from diverse
+functional connectivity metrics and that models using a combination of
+different connectivity descriptors are able to outperform classifiers using
+only one metric. From this flexibility follows that our proposed CCNN model can
+be easily adapted to a wide range of connectome based classification or
+regression tasks, by varying which connectivity descriptor combinations are
+used to train the network.
+"
+23181,On improved bound for measure of cluster structure in compact metric spaces,"  A compact metric space $(X, \rho)$ is given. Let $\mu$ be a Borel measure on
+$X$. By $r$-cluster we mean a measurable subset of $X$ with diameter at most
+$r$. A family of $k$ $2r$-clusters is called a $r$-cluster structure of order
+$k$ if any two clusters from the family are separated by a distance at least
+$r$. By measure of a cluster structure we mean a sum of clusters measures from
+the cluster structure. In our previous work we showed that under some
+parametric restrictions for distance distribution measure of maximal cluster
+structure $\mu(\mathcal{X})^*$ is close $\mu(X)$ and lower bound for
+$\mu(\mathcal{X})^*$ converges to $\mu(X)$ when corresponding parameters tend
+to 0. However, this bound asymptotically unimprovable. We propose an additional
+restriction for distance distribution that is responsible for balance of
+cluster's measure in cluster structure. This restriction allows to
+significantly improve previous bound in asymptotic sense.
+"
+23182,Converting water adsorption and capillary condensation in useable forces with simple porous inorganic thin films,"  This work reports an innovative humidity driven actuation concept based on
+Bangham effect using simple nanoporous sol-gel silica thin films as humidity
+responsive materials. Bilayer shaped actuators, consisting on a
+humidity-sensitive active nanostructured silica film deposited on a polymeric
+substrate (Kapton) were demonstrated as an original mean to convert water
+molecule adsorption and capillary condensation in useable mechanical work.
+Reversible silica surface energy modifications by water adsorption and the
+energy produced by the rigid silica film contraction, induced by water
+capillary condensation in mesopores, were finely controlled and used as the
+energy sources. The influence of the film nanostructure (microporosity,
+mesoporosity) and thickness, and of the polymeric support thickness, on the
+actuation force, on the movement speed, and on the amplitude of displacement
+are clearly evidenced and discussed. We show that the global mechanical
+response of such silica-based actuators can be easily adjusted to fabricate a
+humidity variation triggered tailor-made actuation systems. This first insight
+in hard ceramic stimulus responsive materials may open the door toward new
+generation of surface chemistry driven actuation systems.
+"
+23183,Predicting Audio Advertisement Quality,"  Online audio advertising is a particular form of advertising used abundantly
+in online music streaming services. In these platforms, which tend to host tens
+of thousands of unique audio advertisements (ads), providing high quality ads
+ensures a better user experience and results in longer user engagement.
+Therefore, the automatic assessment of these ads is an important step toward
+audio ads ranking and better audio ads creation. In this paper we propose one
+way to measure the quality of the audio ads using a proxy metric called Long
+Click Rate (LCR), which is defined by the amount of time a user engages with
+the follow-up display ad (that is shown while the audio ad is playing) divided
+by the impressions. We later focus on predicting the audio ad quality using
+only acoustic features such as harmony, rhythm, and timbre of the audio,
+extracted from the raw waveform. We discuss how the characteristics of the
+sound can be connected to concepts such as the clarity of the audio ad message,
+its trustworthiness, etc. Finally, we propose a new deep learning model for
+audio ad quality prediction, which outperforms the other discussed models
+trained on hand-crafted features. To the best of our knowledge, this is the
+first large-scale audio ad quality prediction study.
+"
+23184,The heat asymptotics on filtered manifolds,"  We extend the heat kernel expansion to formally selfadjoint non-negative
+Rockland differential operators on general closed filtered manifolds. Moreover,
+we work out the structure of the complex powers, and discuss the heat trace
+asymptotics, the zeta function, as well as Weyl's law for the eigenvalue
+asymptotics. We establish a McKean--Singer type formula for the index of
+Rockland differential operators, and describe the corresponding K-homology
+class. Furthermore, we construct a non-commutative residue on the algebra of
+Heisenberg pseudodifferential operators. We illustrate some of these results by
+providing a more explicit description of Weyl's law for Rumin--Seshadri
+operators associated with curved BGG sequences over 5-manifolds equipped with a
+rank two distribution of Cartan type.
+"
+23185,Estimation of Graphlet Statistics,"  Graphlets are induced subgraphs of a large network and are important for
+understanding and modeling complex networks. Despite their practical
+importance, graphlets have been severely limited to applications and domains
+with relatively small graphs. Most previous work has focused on exact
+algorithms, however, it is often too expensive to compute graphlets exactly in
+massive networks with billions of edges, and finding an approximate count is
+usually sufficient for many applications. In this work, we propose an unbiased
+graphlet estimation framework that is (a) fast with significant speedups
+compared to the state-of-the-art, (b) parallel with nearly linear-speedups, (c)
+accurate with <1% relative error, (d) scalable and space-efficient for massive
+networks with billions of edges, and (e) flexible for a variety of real-world
+settings, as well as estimating macro and micro-level graphlet statistics
+(e.g., counts) of both connected and disconnected graphlets. In addition, an
+adaptive approach is introduced that finds the smallest sample size required to
+obtain estimates within a given user-defined error bound. On 300 networks from
+20 domains, we obtain <1% relative error for all graphlets. This is
+significantly more accurate than existing methods while using less data.
+Moreover, it takes a few seconds on billion edge graphs (as opposed to
+days/weeks). These are by far the largest graphlet computations to date.
+"
+23186,Hyperparameters Optimization in Deep Convolutional Neural Network / Bayesian Approach with Gaussian Process Prior,"  Convolutional Neural Network is known as ConvNet have been extensively used
+in many complex machine learning tasks. However, hyperparameters optimization
+is one of a crucial step in developing ConvNet architectures, since the
+accuracy and performance are reliant on the hyperparameters. This multilayered
+architecture parameterized by a set of hyperparameters such as the number of
+convolutional layers, number of fully connected dense layers & neurons, the
+probability of dropout implementation, learning rate. Hence the searching the
+hyperparameter over the hyperparameter space are highly difficult to build such
+complex hierarchical architecture. Many methods have been proposed over the
+decade to explore the hyperparameter space and find the optimum set of
+hyperparameter values. Reportedly, Gird search and Random search are said to be
+inefficient and extremely expensive, due to a large number of hyperparameters
+of the architecture. Hence, Sequential model-based Bayesian Optimization is a
+promising alternative technique to address the extreme of the unknown cost
+function. The recent study on Bayesian Optimization by Snoek in nine
+convolutional network parameters is achieved the lowerest error report in the
+CIFAR-10 benchmark. This article is intended to provide the overview of the
+mathematical concept behind the Bayesian Optimization over a Gaussian prior.
+"
+23187,Sequential Local Learning for Latent Graphical Models,"  Learning parameters of latent graphical models (GM) is inherently much harder
+than that of no-latent ones since the latent variables make the corresponding
+log-likelihood non-concave. Nevertheless, expectation-maximization schemes are
+popularly used in practice, but they are typically stuck in local optima. In
+the recent years, the method of moments have provided a refreshing angle for
+resolving the non-convex issue, but it is applicable to a quite limited class
+of latent GMs. In this paper, we aim for enhancing its power via enlarging such
+a class of latent GMs. To this end, we introduce two novel concepts, coined
+marginalization and conditioning, which can reduce the problem of learning a
+larger GM to that of a smaller one. More importantly, they lead to a sequential
+learning framework that repeatedly increases the learning portion of given
+latent GM, and thus covers a significantly broader and more complicated class
+of loopy latent GMs which include convolutional and random regular models.
+"
+23188,Founsure 1.0: An Erasure Code Library with Efficient Repair and Update Features,"  Founsure is an open-source software library, distributed under LGPLv3 license
+and implements a multi-dimensional graph-based erasure coding entirely based on
+fast exclusive OR (XOR) logic. Its implementation utilizes compiler
+optimizations and the multi-threaded implementation to generate the right
+assembly code for the given multi-core CPU architectures with vector processing
+capabilities. Founsure (version 1.0) supports a variety of features that shall
+find interesting applications in modern data storage as well as communication
+and computer network systems which are becoming hungry in terms of network
+bandwidth, computational resources and average consumed power. In particular,
+Founsure library provides a three dimensional design space that consists of
+computation complexity, coding overhead and data/node repair bandwidth to meet
+different requirements of modern distributed data storage and processing
+systems in which the data needs to be protected against device, hardware and
+node failures. Unique features of Founsure include encoding, decoding,
+repairs/rebuilds and updates while the data and computation can be distributed
+across the network nodes.
+"
+23189,Infinite Mixtures of Infinite Factor Analysers,"  Factor-analytic Gaussian mixture models are often employed as a model-based
+approach to clustering high-dimensional data. Typically, the numbers of
+clusters and latent factors must be specified in advance of model fitting, and
+remain fixed. The pair which optimises some model selection criterion is then
+chosen. For computational reasons, models in which the number of latent factors
+is common across clusters are generally considered.
+Here the infinite mixture of infinite factor analysers (IMIFA) model is
+introduced. IMIFA employs a Poisson-Dirichlet process prior to facilitate
+automatic inference of the number of clusters using the stick-breaking
+construction and a slice sampler. Furthermore, IMIFA employs shrinkage priors
+to allow cluster-specific numbers of factors, automatically inferred via an
+adaptive Gibbs sampler. IMIFA is presented as the flagship of a family of
+factor-analytic mixture models, providing flexible approaches to clustering
+high-dimensional data.
+A simulation study and applications to a benchmark data set, metabolomic
+spectral data, and a manifold learning handwritten digit example illustrate the
+IMIFA model and its advantageous features: IMIFA obviates the need for model
+selection criteria, reduces model search and the associated computational
+burden, improves clustering performance by allowing cluster-specific numbers of
+factors, and quantifies uncertainty in the numbers of clusters and
+cluster-specific factors.
+"
+23190,RbEu(Fe$_{1-x}$Ni$_x$)$_4$As$_4$: From a ferromagnetic superconductor to a superconducting ferromagnet,"  The intrinsically hole-doped RbEuFe$_4$As$_4$ exhibits bulk superconductivity
+at $T_{\mathrm{sc}}=36.5$ K and ferromagnetic ordering in the Eu sublattice at
+$T_\mathrm{m}=15$ K. Here we present a hole-compensation study by introducing
+extra itinerant electrons via a Ni substitution in the ferromagnetic
+superconductor RbEuFe$_4$As$_4$ with $T_{\mathrm{sc}}>T_{\mathrm{m}}$. With the
+Ni doping, $T_{\mathrm{sc}}$ decreases rapidly, and the Eu-spin ferromagnetism
+and its $T_{\mathrm{m}}$ remain unchanged. Consequently, the system
+RbEu(Fe$_{1-x}$Ni$_x$)$_4$As$_4$ transforms into a superconducting ferromagnet
+with $T_{\mathrm{m}}>T_{\mathrm{sc}}$ for $0.07\leq x\leq0.08$. The occurrence
+of superconducting ferromagnets is attributed to the decoupling between
+Eu$^{2+}$ spins and superconducting Cooper pairs. The superconducting and
+magnetic phase diagram is established, which additionally includes a recovered
+yet suppressed spin-density-wave state.
+"
+23191,Safe and Efficient Intersection Control of Connected and Autonomous Intersection Traffic,"  In this dissertation, we address a problem of safe and efficient intersection
+crossing traffic management of autonomous and connected ground traffic. Toward
+this objective, an algorithm that is called the Discrete-time occupancies
+trajectory based Intersection traffic Coordination Algorithm (DICA) is
+proposed. All vehicles in the system are Connected and Autonomous Vehicles
+(CAVs) and capable of wireless Vehicle-to-Intersection communication. In the
+proposed framework, an intersection coordinates the motions of CAVs based on
+their proposed DTOTs to let them cross the intersection efficiently while
+avoiding collisions. In case when there is a collision between vehicles' DTOTs,
+the intersection modifies conflicting DTOTs to avoid the collision and requests
+CAVs to approach and cross the intersection according to the modified DTOTs. We
+then prove that the basic DICA is deadlock free and also starvation free. We
+also show that the basic DICA is conservative in computational complexity and
+improve it by several computational approaches.
+Next, we addressed the problem of evacuating emergency vehicles as quickly as
+possible through autonomous and connected intersection traffic in this
+dissertation. The proposed intersection control algorithm Reactive DICA aims to
+determine an efficient vehicle-passing sequence which allows the emergency
+vehicle to cross an intersection as soon as possible while the travel times of
+other vehicles are minimally affected. When there are no emergency vehicles
+within the intersection area, the vehicles are controlled by DICA. When there
+are emergency vehicles entering communication range, we prioritize emergency
+vehicles through optimal ordering of vehicles. A genetic algorithm is proposed
+to solve the optimization problem which finds the optimal vehicle sequence that
+gives the emergency vehicles the highest priority.
+"
+23192,Periodic groups saturated with finite simple groups of lie type rank 1,"  In the Kourovka notebook the following question is posed 14.101:
+Is it true that a periodic group saturated with finite simple groups of Lie
+type whose ranks are bounded in the aggregate, is itself a simple group of Lie
+type?
+In this paper we give a partial answer to this question for periodic groups
+saturated by finite simple groups of Lie type rank 1.
+"
+23193,On Inductive Abilities of Latent Factor Models for Relational Learning,"  Latent factor models are increasingly popular for modeling multi-relational
+knowledge graphs. By their vectorial nature, it is not only hard to interpret
+why this class of models works so well, but also to understand where they fail
+and how they might be improved. We conduct an experimental survey of
+state-of-the-art models, not towards a purely comparative end, but as a means
+to get insight about their inductive abilities. To assess the strengths and
+weaknesses of each model, we create simple tasks that exhibit first, atomic
+properties of binary relations, and then, common inter-relational inference
+through synthetic genealogies. Based on these experimental results, we propose
+new research directions to improve on existing models.
+"
+23194,Dance Dance Convolution,"  Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players
+perform steps on a dance platform in synchronization with music as directed by
+on-screen step charts. While many step charts are available in standardized
+packs, players may grow tired of existing charts, or wish to dance to a song
+for which no chart exists. We introduce the task of learning to choreograph.
+Given a raw audio track, the goal is to produce a new step chart. This task
+decomposes naturally into two subtasks: deciding when to place steps and
+deciding which steps to select. For the step placement task, we combine
+recurrent and convolutional neural networks to ingest spectrograms of low-level
+audio features to predict steps, conditioned on chart difficulty. For step
+selection, we present a conditional LSTM generative model that substantially
+outperforms n-gram and fixed-window approaches.
+"
+23195,A Robust Consensus Algorithm for Current Sharing and Voltage Regulation in DC Microgrids,"  In this paper a novel distributed control algorithm for current sharing and
+voltage regulation in Direct Current (DC) microgrids is proposed. The DC
+microgrid is composed of several Distributed Generation units (DGUs), including
+Buck converters and current loads. The considered model permits an arbitrary
+network topology and is affected by unknown load demand and modelling
+uncertainties. The proposed control strategy exploits a communication network
+to achieve proportional current sharing using a consensus-like algorithm.
+Voltage regulation is achieved by constraining the system to a suitable
+manifold. Two robust control strategies of Sliding Mode (SM) type are developed
+to reach the desired manifold in a finite time. The proposed control scheme is
+formally analyzed, proving the achievement of proportional current sharing,
+while guaranteeing that the weighted average voltage of the microgrid is
+identical to the weighted average of the voltage references.
+"
+23196,A Fixed-Parameter Perspective on #BIS,"  The problem of (approximately) counting the independent sets of a bipartite
+graph (#BIS) is the canonical approximate counting problem that is complete in
+the intermediate complexity class #RH\Pi_1. It is believed that #BIS does not
+have an efficient approximation algorithm but also that it is not NP-hard. We
+study the robustness of the intermediate complexity of #BIS by considering
+variants of the problem parameterised by the size of the independent set. We
+exhaustively map the complexity landscape for three problems, with respect to
+exact computation and approximation and with respect to conventional and
+parameterised complexity. The three problems are counting independent sets of a
+given size, counting independent sets with a given number of vertices in one
+vertex class and counting maximum independent sets amongst those with a given
+number of vertices in one vertex class. Among other things, we show that all of
+these problems are NP-hard to approximate within any polynomial ratio. (This is
+surprising because the corresponding problems without the size parameter are
+complete in #RH\Pi_1, and hence are not believed to be NP-hard.) We also show
+that the first problem is #W[1]-hard to solve exactly but admits an FPTRAS,
+whereas the other two are W[1]-hard to approximate even within any polynomial
+ratio. Finally, we show that, when restricted to graphs of bounded degree, all
+three problems have efficient exact fixed-parameter algorithms.
+"
+23197,Rational embeddings of hyperbolic groups,"  We prove that all Gromov hyperbolic groups embed into the asynchronous
+rational group defined by Grigorchuk, Nekrashevych and Sushchanskiĭ. The
+proof involves assigning a system of binary addresses to points in the Gromov
+boundary of $G$, and proving that elements of $G$ act on these addresses by
+transducers. These addresses derive from a certain self-similar tree of subsets
+of $G$, whose boundary is naturally homeomorphic to the horofunction boundary
+of $G$.
+"
+23198,Survival of the simplest: the cost of complexity in microbial evolution,"  The evolution of microbial and viral organisms often generates clonal
+interference, a mode of competition between genetic clades within a population.
+In this paper, we show that interference strongly constrains the genetic and
+phenotypic complexity of evolving systems. Our analysis uses biophysically
+grounded evolutionary models for an organism's quantitative molecular
+phenotypes, such as fold stability and enzymatic activity of genes. We find a
+generic mode of asexual evolution called phenotypic interference with strong
+implications for systems biology: it couples the stability and function of
+individual genes to the population's global speed of evolution. This mode
+occurs over a wide range of evolutionary parameters appropriate for microbial
+populations. It generates selection against genome complexity, because the
+fitness cost of mutations increases faster than linearly with the number of
+genes. Recombination can generate a distinct mode of sexual evolution that
+eliminates the superlinear cost. We show that positive selection can drive a
+transition from asexual to facultative sexual evolution, providing a specific,
+biophysically grounded scenario for the evolution of sex. In a broader context,
+our analysis suggests that the systems biology of microbial organisms is
+strongly intertwined with their mode of evolution.
+"
+23199,Bilinear endpoint estimates for Calderón commutator with rough kernel,"  In this paper, we establish some bilinear endpoint estimates of Calderón
+commutator $\mathcal{C}[\nabla A,f](x)$ with a homogeneous kernel when
+$\Omega\in L\log^+L(\mathbf{S}^{d-1})$. More precisely, we prove that
+$\mathcal{C}[\nabla A,f]$ maps $L^q(\mathbb{R}^d)\times L^1(\mathbb{R}^d)$ to
+$L^{r,\infty}(\mathbb{R}^d)$ if $q>d$ which improves previous result
+essentially. If $q=d$, we show that Calderón commutator maps
+$L^{d,1}(\mathbb{R}^d)\times L^1(\mathbb{R}^d)$ to $L^{r,\infty}(\mathbb{R}^d)$
+which is new even if the kernel is smooth. The novelty in the paper is that we
+prove a new endpoint estimate of the Mary Weiss maximal function which may have
+its own interest in the theory of singular integral.
+"
+23200,Submersions and curves of constant geodesic curvature,"  Considering Riemannian submersions, we find necessary and sufficient
+conditions for when sub-Riemannian normal geodesics project to curves of
+constant first geodesic curvature or constant first and vanishing second
+geodesic curvatures. We describe a canonical extension of the sub-Riemannian
+metric and study geometric properties of the obtained Riemannian manifold. This
+work contains several examples illustrating the results.
+"
+23201,Feedback Limits to Maximum Seed Masses of Black Holes,"  The most massive black holes observed in the Universe weigh up to $\sim
+10^{10} \, \mathrm{M_{\odot}}$, nearly independent of redshift. Reaching these
+final masses likely required copious accretion and several major mergers.
+Employing a dynamical approach, that rests on the role played by a new,
+relevant physical scale - the transition radius - we provide a theoretical
+calculation of the maximum mass achievable by a black hole seed that forms in
+an isolated halo, one that scarcely merged. Incorporating effects at the
+transition radius and their impact on the evolution of accretion in isolated
+haloes we are able to obtain new limits for permitted growth. We find that
+large black hole seeds ($M_{\bullet} \gtrsim 10^4 \, \mathrm{M_{\odot}}$)
+hosted in small isolated halos ($M_h \lesssim 10^9 \, \mathrm{M_{\odot}}$)
+accreting with relatively small radiative efficiencies ($\epsilon \lesssim
+0.1$) grow optimally in these circumstances. Moreover, we show that the
+standard $M_{\bullet}-\sigma$ relation observed at $z \sim 0$ cannot be
+established in isolated halos at high-$z$, but requires the occurrence of
+mergers. Since the average limiting mass of black holes formed at $z \gtrsim
+10$ is in the range $10^{4-6} \, \mathrm{M_{\odot}}$, we expect to observe them
+in local galaxies as intermediate-mass black holes, when hosted in the rare
+haloes that experienced only minor or no merging events. Such ancient black
+holes, formed in isolation with subsequent scant growth, could survive, almost
+unchanged, until present.
+"
+23202,Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract],"  Transfer learning which aims at utilizing knowledge learned from one problem
+(source domain) to solve another different but related problem (target domain)
+has attracted wide research attentions. However, the current transfer learning
+methods are mostly uninterpretable, especially to people without ML expertise.
+In this extended abstract, we brief introduce two knowledge graph (KG) based
+frameworks towards human understandable transfer learning explanation. The
+first one explains the transferability of features learned by Convolutional
+Neural Network (CNN) from one domain to another through pre-training and
+fine-tuning, while the second justifies the model of a target domain predicted
+by models from multiple source domains in zero-shot learning (ZSL). Both
+methods utilize KG and its reasoning capability to provide rich and human
+understandable explanations to the transfer procedure.
+"
+23203,The extension of holomorphic functions on a non-pluriharmonic locus,"  Let $n \geq 4$ and let $\Omega$ be a bounded hyperconvex domain in
+$\mathbb{C}^{n}$. Let $\varphi$ be a negative exhaustive smooth
+plurisubharmonic function on $\Omega$. We show that any holomorphic function
+defined on a connected open neighborhood of the support of $(i\partial
+\overline{\partial}\varphi)^{n-3}$ can be extended to the holomorphic function
+on $\Omega$.
+"
+23204,BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions,"  In this paper we introduce a new machine learning (ML) model for nonlinear
+regression called Boosting Smooth Transition Regression Trees (BooST). The main
+advantage of the BooST model is that it estimates the derivatives (partial
+effects) of very general nonlinear models, providing more interpretation about
+the mapping between the covariates and the dependent variable than other tree
+based models, such as Random Forests. We provide some asymptotic theory that
+shows consistency of the partial derivative estimates and we present some
+examples on both simulated and real data.
+"
+23205,An equivalence principle between polynomial and simultaneous Diophantine approximation,"  We show that Mahler's classification of real numbers $\zeta$ with respect to
+the growth of the sequence $(w_{n}(\zeta))_{n\geq 1}$ is equivalently induced
+by certain natural assumptions on the decay of the sequence
+$(\lambda_{n}(\zeta))_{n\geq 1}$ concerning simultaneous rational
+approximation. Thereby we obtain a much clearer picture on simultaneous
+approximation to successive powers of a real number in general. Another variant
+of the Mahler classification concerning uniform approximation by algebraic
+numbers is shown as well. Our method has several applications to classic
+exponents of Diophantine approximation and metric theory. We deduce estimates
+on the Hausdorff dimension of well-approximable vectors on the Veronese curve
+and refine the best known upper bound for the exponent
+$\widehat{\lambda}_{n}(\zeta)$ for even $n\geq 4$.
+"
+23206,Series of rational moduli components of stable rank 2 vector bundles on $\mathbb{P}^3$,"  We study the problem of rationality of an infinite series of components, the
+so-called Ein components, of the Gieseker-Maruyama moduli space $M(e,n)$ of
+rank 2 stable vector bundles with the first Chern class $e=0$ or -1 and all
+possible values of the second Chern class $n$ on the projective 3-space. The
+generalized null correlation bundles constituting open dense subsets of these
+components are defined as cohomology bundles of monads whose members are direct
+sums of line bundles of degrees depending on nonnegative integers $a,b,c$,
+where $b\ge a$ and $c>a+b$. We show that, in the wide range when $c>2a+b-e,\
+b>a,\ (e,a)\ne(0,0)$, the Ein components are rational, and in the remaining
+cases they are at least stably rational. As a consequence, the union of the
+spaces $M(e,n)$ over all $n\ge1$ contains an infinite series of rational
+components for both $e=0$ and $e=-1$. Explicit constructions of rationality of
+Ein components under the above conditions on $e,a,b,c$ and, respectively, of
+their stable rationality in the remaining cases, are given. In the case of
+rationality, we construct universal families of generalized null correlation
+bundles over certain open subsets of Ein components showing that these subsets
+are fine moduli spaces. As a by-product of our construction, for $c_1=0$ and
+$n$ even, they provide, perhaps the first known, examples of fine moduli spaces
+not satisfying the condition ""$n$ is odd"", which is a usual sufficient
+condition for fineness.
+"
+23207,Highly curved image sensors: a practical approach for improved optical performance,"  The significant optical and size benefits of using a curved focal surface for
+imaging systems have been well studied yet never brought to market for lack of
+a high-quality, mass-producible, curved image sensor. In this work we
+demonstrate that commercial silicon CMOS image sensors can be thinned and
+formed into accurate, highly curved optical surfaces with undiminished
+functionality. Our key development is a pneumatic forming process that avoids
+rigid mechanical constraints and suppresses wrinkling instabilities. A
+combination of forming-mold design, pressure membrane elastic properties, and
+controlled friction forces enables us to gradually contact the die at the
+corners and smoothly press the sensor into a spherical shape. Allowing the die
+to slide into the concave target shape enables a threefold increase in the
+spherical curvature over prior approaches having mechanical constraints that
+resist deformation, and create a high-stress, stretch-dominated state. Our
+process creates a bridge between the high precision and low-cost but planar
+CMOS process, and ideal non-planar component shapes such as spherical imagers
+for improved optical systems. We demonstrate these curved sensors in prototype
+cameras with custom lenses, measuring exceptional resolution of 3220
+line-widths per picture height at an aperture of f/1.2 and nearly 100% relative
+illumination across the field. Though we use a 1/2.3"" format image sensor in
+this report, we also show this process is generally compatible with many state
+of the art imaging sensor formats. By example, we report photogrammetry test
+data for an APS-C sized silicon die formed to a 30$^\circ$ subtended spherical
+angle. These gains in sharpness and relative illumination enable a new
+generation of ultra-high performance, manufacturable, digital imaging systems
+for scientific, industrial, and artistic use.
+"
+23208,Elliptic genus of singular algebraic varieties and quotients,"  We discuss the basic properties of various versions of two variable elliptic
+genus with special attention to the equivariant elliptic genus. The main
+applications are to the elliptic genera attached to non-compact GITs, including
+the elliptic genera of Witten's phases on $N=2$ theories.
+"
+23209,Moduli spaces of witch curves topologically realize the 2-associahedra,"  For $r \geq 1$ and $\mathbf{n} \in
+\mathbb{Z}_{\geq0}^r\setminus\{\mathbf{0}\}$, we construct the compactified
+moduli space $\overline{2\mathcal{M}}_{\mathbf{n}}$ of witch curves of type
+$\mathbf{n}$. We equip $\overline{2\mathcal{M}}_{\mathbf{n}}$ with a
+stratification by the 2-associahedron $W_{\mathbf{n}}$, and prove that
+$\overline{2\mathcal{M}}_{\mathbf{n}}$ is compact, second-countable, and
+metrizable. In addition, we show that the forgetful map
+$\overline{2\mathcal{M}}_{\mathbf{n}} \to \overline{\mathcal{M}}_r$ to the
+moduli space of stable disk trees is continuous and respects the
+stratifications.
+"
+23210,The Nilpotency Criterion for the Derived Subgroup of a Finite Group,"  It is proved that the derived subgroup of a finite group is nilpotent if and
+only if $|ab|\ge |a||b|$ for all primary commutators $a$ and $b$ of coprime
+orders.
+"
+23211,Fine-Grained Entity Typing with High-Multiplicity Assignments,"  As entity type systems become richer and more fine-grained, we expect the
+number of types assigned to a given entity to increase. However, most
+fine-grained typing work has focused on datasets that exhibit a low degree of
+type multiplicity. In this paper, we consider the high-multiplicity regime
+inherent in data sources such as Wikipedia that have semi-open type systems. We
+introduce a set-prediction approach to this problem and show that our model
+outperforms unstructured baselines on a new Wikipedia-based fine-grained typing
+corpus.
+"
+23212,Tropicalizing abelian covers of algebraic curves,"  In this thesis, we study the Berkovich skeleton of an algebraic curve over a
+discretely valued field $K$. We do this using coverings
+$C\rightarrow{\mathbb{P}^{1}}$ of the projective line. To study these
+coverings, we take the Galois closure of the corresponding injection of
+function fields $K(\mathbb{P}^{1})\rightarrow{K(C)}$, giving a Galois morphism
+$\overline{C}\rightarrow{\mathbb{P}^{1}}$. A theorem by Liu and Lorenzini tells
+us how to associate to this morphism a Galois morphism of semistable models
+$\mathcal{C}\rightarrow{\mathcal{D}}$. That is, we make the branch locus
+disjoint in the special fiber of $\mathcal{D}$ and remove any vertical
+ramification on the components of $\mathcal{D}_{s}$. This morphism
+$\mathcal{C}\rightarrow{\mathcal{D}}$ then gives rise to a morphism of
+intersection graphs $\Sigma(\mathcal{C})\rightarrow{\Sigma(\mathcal{D})}$. Our
+goal is to reconstruct $\Sigma(\mathcal{C})$ from $\Sigma(\mathcal{D})$ and we
+will do this by giving a set of covering and twisting data. These then give
+algorithms for finding the Berkovich skeleton of a curve $C$ whenever that
+curve has a morphism $\overline{C}\rightarrow{\mathbb{P}^{1}}$ with a solvable
+Galois group. In particular, this gives an algorithm for finding the Berkovich
+skeleton of any genus three curve. These coverings also give a new proof of a
+classical result on the semistable reduction type of an elliptic curve, saying
+that an elliptic curve has potential good reduction if and only if the
+valuation of the $j$-invariant is positive.
+"
+23213,Illinois Accelerator Research Center,"  The Illinois Accelerator Research Center (IARC) hosts a new accelerator
+development program at Fermi National Accelerator Laboratory. IARC provides
+access to Fermi's state-of-the-art facilities and technologies for research,
+development and industrialization of particle accelerator technology. In
+addition to facilitating access to available existing Fermi infrastructure, the
+IARC Campus has a dedicated 36,000 ft2 heavy assembly building (HAB) with all
+the infrastructure needed to develop, commission and operate new accelerators.
+Connected to the HAB is a 47,000 ft2 Office, Technology and Engineering (OTE)
+building, paid for by the state, that has office, meeting, and light technical
+space. The OTE building, which contains the Accelerator Physics Center, and
+nearby Accelerator and Technical divisions provide IARC collaborators with
+unique access to world class expertise in a wide array of accelerator
+technologies. At IARC scientists and engineers from Fermilab and academia work
+side by side with industrial partners to develop breakthroughs in accelerator
+science and translate them into applications for the nation's health, wealth
+and security.
+"
+23214,Randomized Rank-Revealing UZV Decomposition for Low-Rank Approximation of Matrices,"  Low-rank matrix approximation plays an increasingly important role in signal
+and image processing applications. This paper presents a new rank-revealing
+decomposition method called randomized rank-revealing UZV decomposition
+(RRR-UZVD). RRR-UZVD is powered by randomization to approximate a low-rank
+input matrix. Given a large and dense matrix ${\bf A} \in \mathbb R^{m \times
+n}$ whose numerical rank is $k$, where $k$ is much smaller than $m$ and $n$,
+RRR-UZVD constructs an approximation $\hat{\bf A}$ such as $\hat{\bf A}={\bf
+UZV}^T$, where ${\bf U}$ and ${\bf V}$ have orthonormal columns, the
+leading-diagonal block of ${\bf Z}$ reveals the rank of $\bf A$, and its
+off-diagonal blocks have small $\ell_2$-norms. RRR-UZVD is simple, accurate,
+and only requires a few passes through $\bf A$ with an arithmetic cost of
+$O(mnk)$ floating-point operations. To demonstrate the effectiveness of the
+proposed method, we conduct experiments on synthetic data, as well as real data
+in applications of image reconstruction and robust principal component
+analysis.
+"
+23215,$CPT$-symmetric coupler with intermodal dispersion,"  A dual-core waveguide with balanced gain and loss in different arms and with
+intermodal coupling is considered. The system is not invariant under the
+conventional $PT$ symmetry but obeys $CPT$ symmetry where an additional spatial
+inversion $C$ corresponds to swapping the coupler arms. We show that
+second-order dispersion of coupling allows for unbroken $CPT$ symmetry and
+supports propagation of stable vector solitons along the coupler.
+Small-amplitude solitons are found in explicit form. The combined effect of
+gain-and-loss and dispersive coupling results in several interesting features
+which include a separation between the components in different arms, nontrivial
+dependence of stability of a soliton on its velocity, and the existence of more
+complex stationary two-hump solutions. Unusual decay dynamics of unstable
+solitons is discussed too.
+"
+23216,Magnetization plateau as a result of the uniform and gradual electron doping in a coupled spin-electron double-tetrahedral chain,"  The double-tetrahedral chain in a longitudinal magnetic field, whose nodal
+lattice sites occupied by the localized Ising spins regularly alternate with
+triangular plaquettes with the dynamics described by the Hubbard model, is
+rigorously investigated. It is demonstrated that the uniform change of electron
+concentration controlled by the chemical potential in a combination with the
+competition between model parameters and the external magnetic field leads to
+the formation of one chiral and seven non-chiral phases at the absolute zero
+temperature. Rational plateaux at one-third and one-half of the saturation
+magnetization can also be identified in the low-temperature magnetization
+curves. On the other hand, the gradual electron doping results in eleven
+different ground-state regions which distinguish from each other by the
+evolution of the electron distribution during this process. Several
+doping-dependent magnetization plateaux are observed in the magnetization
+process as a result of the continuous change of electron content in the model.
+"
+23217,Machine Learning Approach for Detection of nonTor Traffic,"  Intrusion detection has attracted a considerable interest from researchers
+and industries. After many years of research the community still faces the
+problem of building reliable and efficient intrusion detection systems (IDS)
+capable of handling large quantities of data with changing patterns in real
+time situations. The Tor network is popular in providing privacy and security
+to end user by anonymising the identity of internet users connecting through a
+series of tunnels and nodes. This work focuses on the classification of Tor
+traffic and nonTor traffic to expose the activities within Tor traffic that
+minimizes the protection of users. A study to compare the reliability and
+efficiency of Artificial Neural Network and Support vector machine in detecting
+nonTor traffic in UNB-CIC Tor Network Traffic dataset is presented in this
+paper. The results are analysed based on the overall accuracy, detection rate
+and false positive rate of the two algorithms. Experimental results show that
+both algorithms could detect nonTor traffic in the dataset. A hybrid Artificial
+neural network proved a better classifier than SVM in detecting nonTor traffic
+in UNB-CIC Tor Network Traffic dataset.
+"
+23218,Conformal Field Theories as Scaling Limit of Anyonic Chains,"  We provide a mathematical definition of a low energy scaling limit of a
+sequence of general non-relativistic quantum theories in any dimension, and
+apply our formalism to anyonic chains. We formulate Conjecture 4.3 on
+conditions when a chiral unitary rational (1+1)-conformal field theory would
+arise as such a limit and verify the conjecture for the Ising minimal model
+$M(4,3)$ using Ising anyonic chains. Part of the conjecture is a precise
+relation between Temperley-Lieb generators $\{e_i\}$ and some finite stage
+operators of the Virasoro generators $\{L_m+L_{-m}\}$ and $\{i(L_m-L_{-m})\}$
+for unitary minimal models $M(k+2,k+1)$ in Conjecture 5.5. A similar earlier
+relation is known as the Koo-Saleur formula in the physics literature [39].
+Assuming Conjecture 4.3, most of our main results for the Ising minimal model
+$M(4,3)$ hold for unitary minimal models $M(k+2,k+1), k\geq 3$ as well. Our
+approach is inspired by an eventual application to an efficient simulation of
+conformal field theories by quantum computers, and supported by extensive
+numerical simulation and physical proofs in the physics literature.
+"
+23219,Equivalence of Palm measures for determinantal point processes governed by Bergman kernels,"  For a determinantal point process induced by the reproducing kernel of the
+weighted Bergman space $A^2(U, \omega)$ over a domain $U \subset \mathbb{C}^d$,
+we establish the mutual absolute continuity of reduced Palm measures of any
+order provided that the domain $U$ contains a non-constant bounded holomorphic
+function. The result holds in all dimensions. The argument uses the
+$H^\infty(U)$-module structure of $A^2(U, \omega)$. A corollary is the
+quasi-invariance of our determinantal point process under the natural action of
+the group of compactly supported diffeomorphisms of $U$.
+"
+23220,Cooperative Robot Localization Using Event-triggered Estimation,"  This paper describes a novel communication-spare cooperative localization
+algorithm for a team of mobile unmanned robotic vehicles. Exploiting an
+event-based estimation paradigm, robots only send measurements to neighbors
+when the expected innovation for state estimation is high. Since agents know
+the event-triggering condition for measurements to be sent, the lack of a
+measurement is thus also informative and fused into state estimates. The robots
+use a Covariance Intersection (CI) mechanism to occasionally synchronize their
+local estimates of the full network state. In addition, heuristic balancing
+dynamics on the robots' CI-triggering thresholds ensure that, in large diameter
+networks, the local error covariances remains below desired bounds across the
+network. Simulations on both linear and nonlinear dynamics/measurement models
+show that the event-triggering approach achieves nearly optimal state
+estimation performance in a wide range of operating conditions, even when using
+only a fraction of the communication cost required by conventional full data
+sharing. The robustness of the proposed approach to lossy communications, as
+well as the relationship between network topology and CI-based synchronization
+requirements, are also examined.
+"
+23221,Energy Propagation in Deep Convolutional Neural Networks,"  Many practical machine learning tasks employ very deep convolutional neural
+networks. Such large depths pose formidable computational challenges in
+training and operating the network. It is therefore important to understand how
+fast the energy contained in the propagated signals (a.k.a. feature maps)
+decays across layers. In addition, it is desirable that the feature extractor
+generated by the network be informative in the sense of the only signal mapping
+to the all-zeros feature vector being the zero input signal. This ""trivial
+null-set"" property can be accomplished by asking for ""energy conservation"" in
+the sense of the energy in the feature vector being proportional to that of the
+corresponding input signal. This paper establishes conditions for energy
+conservation (and thus for a trivial null-set) for a wide class of deep
+convolutional neural network-based feature extractors and characterizes
+corresponding feature map energy decay rates. Specifically, we consider general
+scattering networks employing the modulus non-linearity and we find that under
+mild analyticity and high-pass conditions on the filters (which encompass,
+inter alia, various constructions of Weyl-Heisenberg filters, wavelets,
+ridgelets, ($\alpha$)-curvelets, and shearlets) the feature map energy decays
+at least polynomially fast. For broad families of wavelets and Weyl-Heisenberg
+filters, the guaranteed decay rate is shown to be exponential. Moreover, we
+provide handy estimates of the number of layers needed to have at least
+$((1-\varepsilon)\cdot 100)\%$ of the input signal energy be contained in the
+feature vector.
+"
+23222,Black Box Submodular Maximization: Discrete and Continuous Settings,"  In this paper, we consider the problem of black box continuous submodular
+maximization where we only have access to the function values and no
+information about the derivatives is provided. For a monotone and continuous
+DR-submodular function, and subject to a bounded convex body constraint, we
+propose Black-box Continuous Greedy, a derivative-free algorithm that provably
+achieves the tight $[(1-1/e)OPT-\epsilon]$ approximation guarantee with
+$O(d/\epsilon^3)$ function evaluations. We then extend our result to the
+stochastic setting where function values are subject to stochastic zero-mean
+noise. It is through this stochastic generalization that we revisit the
+discrete submodular maximization problem and use the multi-linear extension as
+a bridge between discrete and continuous settings. Finally, we extensively
+evaluate the performance of our algorithm on continuous and discrete submodular
+objective functions using both synthetic and real data.
+"
+23223,The extended Northern ROSAT Galaxy Cluster Survey (NORAS II) I. Survey Construction and First Results,"  As the largest, clearly defined building blocks of our Universe, galaxy
+clusters are interesting astrophysical laboratories and important probes for
+cosmology. X-ray surveys for galaxy clusters provide one of the best ways to
+characterise the population of galaxy clusters. We provide a description of the
+construction of the NORAS II galaxy cluster survey based on X-ray data from the
+northern part of the ROSAT All-Sky Survey. NORAS II extends the NORAS survey
+down to a flux limit of 1.8 x 10^(-12) erg s^-1 cm^-2 (0.1 - 2.4 keV)
+increasing the sample size by about a factor of two. The NORAS II cluster
+survey now reaches the same quality and depth of its counterpart, the Southern
+REFLEX II survey, allowing us to combine the two complementary surveys. The
+paper provides information on the determination of the cluster X-ray
+parameters, the identification process of the X-ray sources, the statistics of
+the survey, and the construction of the survey selection function, which we
+provide in numerical format. Currently NORAS II contains 860 clusters with a
+median redshift of z = 0.102. We provide a number of statistical functions
+including the logN-logS and the X-ray luminosity function and compare these to
+the results from the complementary REFLEX II survey. Using the NORAS II sample
+to constrain the cosmological parameters, sigma_8 and Omega_m, yields results
+perfectly consistent with those of REFLEX II. Overall, the results show that
+the two hemisphere samples, NORAS II and REFLEX II, can be combined without
+problems to an all-sky sample, just excluding the Zone-of-Avoidance.
+"
+23224,Language modeling with Neural trans-dimensional random fields,"  Trans-dimensional random field language models (TRF LMs) have recently been
+introduced, where sentences are modeled as a collection of random fields. The
+TRF approach has been shown to have the advantages of being computationally
+more efficient in inference than LSTM LMs with close performance and being able
+to flexibly integrating rich features. In this paper we propose neural TRFs,
+beyond of the previous discrete TRFs that only use linear potentials with
+discrete features. The idea is to use nonlinear potentials with continuous
+features, implemented by neural networks (NNs), in the TRF framework. Neural
+TRFs combine the advantages of both NNs and TRFs. The benefits of word
+embedding, nonlinear feature learning and larger context modeling are inherited
+from the use of NNs. At the same time, the strength of efficient inference by
+avoiding expensive softmax is preserved. A number of technical contributions,
+including employing deep convolutional neural networks (CNNs) to define the
+potentials and incorporating the joint stochastic approximation (JSA) strategy
+in the training algorithm, are developed in this work, which enable us to
+successfully train neural TRF LMs. Various LMs are evaluated in terms of speech
+recognition WERs by rescoring the 1000-best lists of WSJ'92 test data. The
+results show that neural TRF LMs not only improve over discrete TRF LMs, but
+also perform slightly better than LSTM LMs with only one fifth of parameters
+and 16x faster inference efficiency.
+"
+23225,Familia: An Open-Source Toolkit for Industrial Topic Modeling,"  Familia is an open-source toolkit for pragmatic topic modeling in industry.
+Familia abstracts the utilities of topic modeling in industry as two paradigms:
+semantic representation and semantic matching. Efficient implementations of the
+two paradigms are made publicly available for the first time. Furthermore, we
+provide off-the-shelf topic models trained on large-scale industrial corpora,
+including Latent Dirichlet Allocation (LDA), SentenceLDA and Topical Word
+Embedding (TWE). We further describe typical applications which are
+successfully powered by topic modeling, in order to ease the confusions and
+difficulties of software engineers during topic model selection and
+utilization.
+"
+23226,A de Montessus Type Convergence Study for a Vector-Valued Rational Interpolation Procedure of Epsilon Class,"  In a series of recent publications of the author, three interpolation
+procedures, denoted IMPE, IMMPE, and ITEA, were proposed for vector-valued
+functions $F(z)$, where $F : \C \to\C^N$, and their algebraic properties were
+studied. The convergence studies of two of the methods, namely, IMPE and IMMPE,
+were also carried out as these methods are being applied to meromorphic
+functions with simple poles, and de Montessus and König type theorems for
+them were proved. In the present work, we concentrate on ITEA. We study its
+convergence properties as it is applied to meromorphic functions with simple
+poles, and prove de Montessus and König type theorems analogous to those
+obtained for IMPE and IMMPE.
+"
+23227,A Projection Decoding of a Binary Extremal Self-Dual Code of Length $40$,"  As far as we know, there is no decoding algorithm of any binary self-dual
+$[40, 20, 8]$ code except for the syndrome decoding applied to the code
+directly. This syndrome decoding for a binary self-dual $[40,20,8]$ code is not
+efficient in the sense that it cannot be done by hand due to a large syndrome
+table. The purpose of this paper is to give two new efficient decoding
+algorithms for an extremal binary doubly-even self-dual $[40,20, 8]$ code
+$C_{40,1}^{DE}$ by hand with the help of a Hermitian self-dual $[10,5,4]$ code
+$E_{10}$ over $GF(4)$. The main idea of this decoding is to project codewords
+of $C_{40,1}^{DE}$ onto $E_{10}$ so that it reduces the complexity of the
+decoding of $C_{40,1}^{DE}$. The first algorithm is called the representation
+decoding algorithm. It is based on the pattern of codewords of $E_{10}$. Using
+certain automorphisms of $E_{10}$, we show that only eight types of codewords
+of $E_{10}$ can produce all the codewords of $E_{10}$. The second algorithm is
+called the syndrome decoding algorithm based on $E_{10}$. It first solves the
+syndrome equation in $E_{10}$ and finds a corresponding binary codeword of
+$C_{40,1}^{DE}$.
+"
+23228,Random gradient extrapolation for distributed and stochastic optimization,"  In this paper, we consider a class of finite-sum convex optimization problems
+defined over a distributed multiagent network with $m$ agents connected to a
+central server. In particular, the objective function consists of the average
+of $m$ ($\ge 1$) smooth components associated with each network agent together
+with a strongly convex term. Our major contribution is to develop a new
+randomized incremental gradient algorithm, namely random gradient extrapolation
+method (RGEM), which does not require any exact gradient evaluation even for
+the initial point, but can achieve the optimal ${\cal O}(\log(1/\epsilon))$
+complexity bound in terms of the total number of gradient evaluations of
+component functions to solve the finite-sum problems. Furthermore, we
+demonstrate that for stochastic finite-sum optimization problems, RGEM
+maintains the optimal ${\cal O}(1/\epsilon)$ complexity (up to a certain
+logarithmic factor) in terms of the number of stochastic gradient computations,
+but attains an ${\cal O}(\log(1/\epsilon))$ complexity in terms of
+communication rounds (each round involves only one agent). It is worth noting
+that the former bound is independent of the number of agents $m$, while the
+latter one only linearly depends on $m$ or even $\sqrt m$ for ill-conditioned
+problems. To the best of our knowledge, this is the first time that these
+complexity bounds have been obtained for distributed and stochastic
+optimization problems. Moreover, our algorithms were developed based on a novel
+dual perspective of Nesterov's accelerated gradient method.
+"
+23229,In Defense of Product Quantization,"  Despite their widespread adoption, Product Quantization techniques were
+recently shown to be inferior to other hashing techniques. In this work, we
+present an improved Deep Product Quantization (DPQ) technique that leads to
+more accurate retrieval and classification than the latest state of the art
+methods, while having similar computational complexity and memory footprint as
+the Product Quantization method. To our knowledge, this is the first work to
+introduce a representation that is inspired by Product Quantization and which
+is learned end-to-end, and thus benefits from the supervised signal. DPQ
+explicitly learns soft and hard representations to enable an efficient and
+accurate asymmetric search, by using a straight-through estimator. A novel loss
+function, Joint Central Loss, is introduced, which both improves the retrieval
+performance, and decreases the discrepancy between the soft and the hard
+representations. Finally, by using a normalization technique, we improve the
+results for cross-domain category retrieval.
+"
+23230,Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage,"  This paper concerns a fundamental class of convex matrix optimization
+problems. It presents the first algorithm that uses optimal storage and
+provably computes a low-rank approximation of a solution. In particular, when
+all solutions have low rank, the algorithm converges to a solution. This
+algorithm, SketchyCGM, modifies a standard convex optimization scheme, the
+conditional gradient method, to store only a small randomized sketch of the
+matrix variable. After the optimization terminates, the algorithm extracts a
+low-rank approximation of the solution from the sketch. In contrast to
+nonconvex heuristics, the guarantees for SketchyCGM do not rely on statistical
+models for the problem data. Numerical work demonstrates the benefits of
+SketchyCGM over heuristics.
+"
+23231,Polarized single crystal neutron diffraction study of the zero-magnetization ferromagnet Sm$_{1-x}$Gd$_x$Al$_2$ (x = 0.024),"  We have determined the temperature evolution of the spin and orbital moments
+in the zero magnetization ferromagnet Sm$_{1-x}$Gd$_x$Al$_2$ (x = 0.024) by
+combining polarized and unpolarized single crystal neutron diffraction data.
+The sensitivity of the polarized neutron technique has allowed the moment
+values to be determined with a precision of $\approx 0.1$~\mub. Our results
+clearly demonstrate that, when magnetised by a field of 8T, the spin and
+orbital moments in Sm$_{1-x}$Gd$_x$Al$_2$ are oppositely directed so that the
+net magnetization is very small. Below 60 K the contributions from spin and
+orbital motions are both about 2\mub\ with that due to orbital motion being
+slightly larger than that due to spin. Between 60 and 65 K the contributions of
+each to the magnetization fall rapidly and change sign at \Tcomp\ $\approx 67$K
+above which the aligned moments recover but with the orbital magnetization
+still slightly higher than the spin one. These results imply that above \Tcomp\
+the small resultant magnetization of the \smion\ ion is oppositely directed to
+the magnetizing field. It is suggested that this anomaly is due to polarization
+of conduction electron spin associated with the doping Gd$^{3+}$ ions.
+"
+23232,Unknowable Manipulators: Social Network Curator Algorithms,"  For a social networking service to acquire and retain users, it must find
+ways to keep them engaged. By accurately gauging their preferences, it is able
+to serve them with the subset of available content that maximises revenue for
+the site. Without the constraints of an appropriate regulatory framework, we
+argue that a sufficiently sophisticated curator algorithm tasked with
+performing this process may choose to explore curation strategies that are
+detrimental to users. In particular, we suggest that such an algorithm is
+capable of learning to manipulate its users, for several qualitative reasons:
+1. Access to vast quantities of user data combined with ongoing breakthroughs
+in the field of machine learning are leading to powerful but uninterpretable
+strategies for decision making at scale. 2. The availability of an effective
+feedback mechanism for assessing the short and long term user responses to
+curation strategies. 3. Techniques from reinforcement learning have allowed
+machines to learn automated and highly successful strategies at an abstract
+level, often resulting in non-intuitive yet nonetheless highly appropriate
+action selection. In this work, we consider the form that these strategies for
+user manipulation might take and scrutinise the role that regulation should
+play in the design of such systems.
+"
+23233,Temperature Dependence of the Yield Strength of Aluminum Thin Films: Multiscale Modeling Approach,"  Modeling deformation at elevated temperatures using discrete dislocation
+dynamics (DDD) is a recent area of high interest. However, the literature
+dedicated to this subject fails to address the variations of DDD parameters
+with temperature. This study aims to investigate the effect of temperature on
+the yield strength of aluminum thin films in two-dimensional DDD simulations.
+To this end, the temperature dependence of DDD parameters has been studied
+using molecular dynamics, three-dimensional DDD simulations, and the existing
+experimental results. Based on these calculations, we observed 18% decrease in
+the yield strength when temperature was increased from 100 K to 600 K.
+"
+23234,Multi-fidelity Bayesian Optimization with Max-value Entropy Search,"  Bayesian optimization (BO) is an effective tool for black-box optimization in
+which objective function evaluation is usually quite expensive. In practice,
+lower fidelity approximations of the objective function are often available.
+Recently, multi-fidelity Bayesian optimization (MFBO) has attracted
+considerable attention because it can dramatically accelerate the optimization
+process by using those cheaper observations. We propose a novel information
+theoretic approach to MFBO. Information-based approaches are popular and
+empirically successful in BO, but existing studies for information-based MFBO
+are plagued by difficulty for accurately estimating the information gain. Our
+approach is based on a variant of information-based BO called max-value entropy
+search (MES), which greatly facilitates evaluation of the information gain in
+MFBO. In fact, computations of our acquisition function is written analytically
+except for one dimensional integral and sampling, which can be calculated
+efficiently and accurately. We demonstrate effectiveness of our approach by
+using synthetic and benchmark datasets, and further we show a real-world
+application to materials science data.
+"
+23235,Native Language Identification using i-vector,"  The task of determining a speaker's native language based only on his
+speeches in a second language is known as Native Language Identification or
+NLI. Due to its increasing applications in various domains of speech signal
+processing, this has emerged as an important research area in recent times. In
+this paper we have proposed an i-vector based approach to develop an automatic
+NLI system using MFCC and GFCC features. For evaluation of our approach, we
+have tested our framework on the 2016 ComParE Native language sub-challenge
+dataset which has English language speakers from 11 different native language
+backgrounds. Our proposed method outperforms the baseline system with an
+improvement in accuracy by 21.95% for the MFCC feature based i-vector framework
+and 22.81% for the GFCC feature based i-vector framework.
+"
+23236,Concordance of knots in $S^1\times S^2$,"  We establish a number of results about smooth and topological concordance of
+knots in $S^1\times S^2$. The winding number of a knot in $S^1\times S^2$ is
+defined to be its class in $H_1(S^1\times S^2;\mathbb{Z})\cong \mathbb{Z}$. We
+show that there is a unique smooth concordance class of knots with winding
+number one. This improves the corresponding result of Friedl-Nagel-Orson-Powell
+in the topological category. We say a knot in $S^1\times S^2$ is slice (resp.
+topologically slice) if it bounds a smooth (resp. locally flat) disk in
+$D^2\times S^2$. We show that there are infinitely many topological concordance
+classes of non-slice knots, and moreover, for any winding number other than
+$\pm 1$, there are infinitely many topological concordance classes even within
+the collection of slice knots. Additionally we demonstrate the distinction
+between the smooth and topological categories by constructing infinite families
+of slice knots that are topologically but not smoothly concordant, as well as
+non-slice knots that are topologically slice and topologically concordant, but
+not smoothly concordant.
+"
+23237,Networks as Proxies: a relational approach towards economic complexity in the Roman period,"  Based on the assumption that economic complexity is characterised by the
+interactions of economic agents (who) constantly change their actions and
+strategies in response to the outcome they mutually create, this paper presents
+how network models can be used a proxies for the mapping, quantification and
+analysis of Roman economic complexity. Network analysis provides tools to
+visualise and analyse the inherent complexity of various types of data and
+their combination (archaeological, geographical, textual) or even of a single
+piece of evidence. Equally, the relational approach invites to a structural and
+quantitative comparison between periods, regions and the economic systems of
+polities and empires. An increasing number of proxies of this kind may allow us
+to capture the trajectories of economic complexity beyond metaphors.
+"
+23238,Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD,"  Distributed Stochastic Gradient Descent (SGD) when run in a synchronous
+manner, suffers from delays in waiting for the slowest learners (stragglers).
+Asynchronous methods can alleviate stragglers, but cause gradient staleness
+that can adversely affect convergence. In this work we present a novel
+theoretical characterization of the speed-up offered by asynchronous methods by
+analyzing the trade-off between the error in the trained model and the actual
+training runtime (wallclock time). The novelty in our work is that our runtime
+analysis considers random straggler delays, which helps us design and compare
+distributed SGD algorithms that strike a balance between stragglers and
+staleness. We also present a new convergence analysis of asynchronous SGD
+variants without bounded or exponential delay assumptions, and a novel learning
+rate schedule to compensate for gradient staleness.
+"
+23239,"The distortion principle for insurance pricing: properties, identification and robustness","  Distortion (Denneberg 1990) is a well known premium calculation principle for
+insurance contracts. In this paper, we study sensitivity properties of
+distortion functionals w.r.t. the assumptions for risk aversion as well as
+robustness w.r.t. ambiguity of the loss distribution. Ambiguity is measured by
+the Wasserstein distance. We study variances of distances for probability
+models and identify some worst case distributions. In addition to the direct
+problem we also investigate the inverse problem, that is how to identify the
+distortion density on the basis of observations of insurance premia.
+"
+23240,Bayesian Learning of Clique Tree Structure,"  The problem of categorical data analysis in high dimensions is considered. A
+discussion of the fundamental difficulties of probability modeling is provided,
+and a solution to the derivation of high dimensional probability distributions
+based on Bayesian learning of clique tree decomposition is presented. The main
+contributions of this paper are an automated determination of the optimal
+clique tree structure for probability modeling, the resulting derived
+probability distribution, and a corresponding unified approach to clustering
+and anomaly detection based on the probability distribution.
+"
+23241,On Classification of Distorted Images with Deep Convolutional Neural Networks,"  Image blur and image noise are common distortions during image acquisition.
+In this paper, we systematically study the effect of image distortions on the
+deep neural network (DNN) image classifiers. First, we examine the DNN
+classifier performance under four types of distortions. Second, we propose two
+approaches to alleviate the effect of image distortion: re-training and
+fine-tuning with noisy images. Our results suggest that, under certain
+conditions, fine-tuning with noisy images can alleviate much effect due to
+distorted inputs, and is more practical than re-training.
+"
+23242,Generalizing the Minkowski Question Mark Function to a Family of Multidimensional Continued Fractions,"  The Minkowski question mark function, maping the unit interval to itself, is
+a continuous, strictly increasing, one-to-one and onto function that has
+derivative zero almost everywhere. Key to these facts are the basic properties
+of continued fractions. Thus the question mark function is a naturally
+occurring number theoretic singular function. This paper generalizes the
+question mark function to the 216 triangle partition (TRIP) maps. These are
+multidimensional continued fractions which generate a family of almost all
+known multidimensional continued fractions. We show for each TRIP map that
+there is a natural candidate for its analog of the Minkowski question mark
+function. We then show that the analog is singular for 96 of the TRIP maps and
+show that 60 more are singular under an assumption of ergodicity.
+"
+23243,dna2vec: Consistent vector representations of variable-length k-mers,"  One of the ubiquitous representation of long DNA sequence is dividing it into
+shorter k-mer components. Unfortunately, the straightforward vector encoding of
+k-mer as a one-hot vector is vulnerable to the curse of dimensionality. Worse
+yet, the distance between any pair of one-hot vectors is equidistant. This is
+particularly problematic when applying the latest machine learning algorithms
+to solve problems in biological sequence analysis. In this paper, we propose a
+novel method to train distributed representations of variable-length k-mers.
+Our method is based on the popular word embedding model word2vec, which is
+trained on a shallow two-layer neural network. Our experiments provide evidence
+that the summing of dna2vec vectors is akin to nucleotides concatenation. We
+also demonstrate that there is correlation between Needleman-Wunsch similarity
+score and cosine similarity of dna2vec vectors.
+"
+23244,An asymptotic analysis of distributed nonparametric methods,"  We investigate and compare the fundamental performance of several distributed
+learning methods that have been proposed recently. We do this in the context of
+a distributed version of the classical signal-in-Gaussian-white-noise model,
+which serves as a benchmark model for studying performance in this setting. The
+results show how the design and tuning of a distributed method can have great
+impact on convergence rates and validity of uncertainty quantification.
+Moreover, we highlight the difficulty of designing nonparametric distributed
+procedures that automatically adapt to smoothness.
+"
+23245,Conflict diagnostics for evidence synthesis in a multiple testing framework,"  Evidence synthesis models that combine multiple datasets of varying design,
+to estimate quantities that cannot be directly observed, require the
+formulation of complex probabilistic models that can be expressed as graphical
+models. An assessment of whether the different datasets synthesised contribute
+information that is consistent with each other, and in a Bayesian context, with
+the prior distribution, is a crucial component of the model criticism process.
+However, a systematic assessment of conflict suffers from the multiple testing
+problem, through testing for conflict at multiple locations in a model. We
+demonstrate the systematic use of conflict diagnostics, while accounting for
+the multiple hypothesis tests of no conflict at each location in the graphical
+model. The method is illustrated by a network meta-analysis to estimate
+treatment effects in smoking cessation programs and an evidence synthesis to
+estimate HIV prevalence in Poland.
+"
+23246,Settling Payments Fast and Private: Efficient Decentralized Routing for Path-Based Transactions,"  Path-based transaction (PBT) networks, which settle payments from one user to
+another via a path of intermediaries, are a growing area of research. They
+overcome the scalability and privacy issues in cryptocurrencies like Bitcoin
+and Ethereum by replacing expensive and slow on-chain blockchain operations
+with inexpensive and fast off-chain transfers. In the form of credit networks
+such as Ripple and Stellar, they also enable low-price real-time gross
+settlements across different currencies. For example, SilentWhsipers is a
+recently proposed fully distributed credit network relying on path-based
+transactions for secure and in particular private payments without a public
+ledger. At the core of a decentralized PBT network is a routing algorithm that
+discovers transaction paths between payer and payee. During the last year, a
+number of routing algorithms have been proposed. However, the existing ad hoc
+efforts lack either efficiency or privacy. In this work, we first identify
+several efficiency concerns in SilentWhsipers. Armed with this knowledge, we
+design and evaluate SpeedyMurmurs, a novel routing algorithm for decentralized
+PBT networks using efficient and flexible embedding-based path discovery and
+on-demand efficient stabilization to handle the dynamics of a PBT network. Our
+simulation study, based on real-world data from the currently deployed Ripple
+credit network, indicates that SpeedyMurmurs reduces the overhead of
+stabilization by up to two orders of magnitude and the overhead of routing a
+transaction by more than a factor of two. Furthermore, using SpeedyMurmurs
+maintains at least the same success ratio as decentralized landmark routing,
+while providing lower delays. Finally, SpeedyMurmurs achieves key privacy goals
+for routing in PBT networks.
+"
+23247,Learning-aided Stochastic Network Optimization with Imperfect State Prediction,"  We investigate the problem of stochastic network optimization in the presence
+of imperfect state prediction and non-stationarity. Based on a novel
+distribution-accuracy curve prediction model, we develop the predictive
+learning-aided control (PLC) algorithm, which jointly utilizes historic and
+predicted network state information for decision making. PLC is an online
+algorithm that requires zero a-prior system statistical information, and
+consists of three key components, namely sequential distribution estimation and
+change detection, dual learning, and online queue-based control.
+Specifically, we show that PLC simultaneously achieves good long-term
+performance, short-term queue size reduction, accurate change detection, and
+fast algorithm convergence. In particular, for stationary networks, PLC
+achieves a near-optimal $[O(\epsilon)$, $O(\log(1/\epsilon)^2)]$ utility-delay
+tradeoff. For non-stationary networks, \plc{} obtains an $[O(\epsilon),
+O(\log^2(1/\epsilon)$ $+ \min(\epsilon^{c/2-1}, e_w/\epsilon))]$
+utility-backlog tradeoff for distributions that last
+$\Theta(\frac{\max(\epsilon^{-c}, e_w^{-2})}{\epsilon^{1+a}})$ time, where
+$e_w$ is the prediction accuracy and $a=\Theta(1)>0$ is a constant (the
+Backpressue algorithm \cite{neelynowbook} requires an $O(\epsilon^{-2})$ length
+for the same utility performance with a larger backlog). Moreover, PLC detects
+distribution change $O(w)$ slots faster with high probability ($w$ is the
+prediction size) and achieves an $O(\min(\epsilon^{-1+c/2},
+e_w/\epsilon)+\log^2(1/\epsilon))$ convergence time. Our results demonstrate
+that state prediction (even imperfect) can help (i) achieve faster detection
+and convergence, and (ii) obtain better utility-delay tradeoffs.
+"
+23248,Signature functions of knots,"  The signature function of a knot is an integer-valued step function on the
+unit circle in the complex plane. Necessary and sufficient conditions for a
+function to be the signature function of a knot are presented.
+"
+23249,Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play,"  Mobile network that millions of people use every day is one of the most
+complex systems in the world. Optimization of mobile network to meet exploding
+customer demand and reduce capital/operation expenditures poses great
+challenges. Despite recent progress, application of deep reinforcement learning
+(DRL) to complex real world problem still remains unsolved, given data
+scarcity, partial observability, risk and complex rules/dynamics in real world,
+as well as the huge reality gap between simulation and real world. To bridge
+the reality gap, we introduce a Sim-to-Real framework to directly transfer
+learning from simulation to real world via graph convolutional neural network
+(CNN) - by abstracting partially observable mobile network into graph, then
+distilling domain-variant irregular graph into domain-invariant tensor in
+locally Euclidean space as input to CNN -, domain randomization and multi-task
+learning. We use a novel self-play mechanism to encourage competition among DRL
+agents for best record on multiple tasks via simulated annealing, just like
+athletes compete for world record in decathlon. We also propose a decentralized
+multi-agent, competitive and cooperative DRL method to coordinate the actions
+of multi-cells to maximize global reward and minimize negative impact to
+neighbor cells. Using 6 field trials on commercial mobile networks, we
+demonstrate for the first time that a DRL agent can successfully transfer
+learning from simulation to complex real world problem with imperfect
+information, complex rules/dynamics, huge state/action space, and multi-agent
+interactions, without any training in the real world.
+"
+23250,Localization and self-trapping in driven-dissipative polariton condensates,"  We study driven-dissipative Bose-Einstein condensates in a two-mode Josephson
+system, such as a double-well potential, with asymmetrical pumping. We
+investigate nonlinear effects on the condensate populations and mode
+transitions. The generalized Gross-Pitaevskii equations are modified in order
+to treat pumping of only a single mode. We characterize the steady-state
+solutions in such a system as well as criteria for potential trapping of a
+condensate mode. There are many possible steady-states, with different density
+and/or phase profiles. Transitions between different condensate modes can be
+induced by varying the parameters of the junction or the initial conditions, or
+by applying external fields.
+"
+23251,Learning Graph-Level Representation for Drug Discovery,"  Predicating macroscopic influences of drugs on human body, like efficacy and
+toxicity, is a central problem of small-molecule based drug discovery.
+Molecules can be represented as an undirected graph, and we can utilize graph
+convolution networks to predication molecular properties. However, graph
+convolutional networks and other graph neural networks all focus on learning
+node-level representation rather than graph-level representation. Previous
+works simply sum all feature vectors for all nodes in the graph to obtain the
+graph feature vector for drug predication. In this paper, we introduce a dummy
+super node that is connected with all nodes in the graph by a directed edge as
+the representation of the graph and modify the graph operation to help the
+dummy super node learn graph-level feature. Thus, we can handle graph-level
+classification and regression in the same way as node-level classification and
+regression. In addition, we apply focal loss to address class imbalance in drug
+datasets. The experiments on MoleculeNet show that our method can effectively
+improve the performance of molecular properties predication.
+"
+23252,On-chip Face Recognition System Design with Memristive Hierarchical Temporal Memory,"  Hierarchical Temporal Memory is a new machine learning algorithm intended to
+mimic the working principle of neocortex, part of the human brain, which is
+responsible for learning, classification, and making predictions. Although many
+works illustrate its effectiveness as a software algorithm, hardware design for
+HTM remains an open research problem. Hence, this work proposes an architecture
+for HTM Spatial Pooler and Temporal Memory with learning mechanism, which
+creates a single image for each class based on important and unimportant
+features of all images in the training set. In turn, the reduction in the
+number of templates within database reduces the memory requirements and
+increases the processing speed. Moreover, face recognition analysis indicates
+that for a large number of training images, the proposed design provides higher
+accuracy results (83.5\%) compared to only Spatial Pooler design presented in
+the previous works.
+"
+23253,Low-Rank Matrix Approximation in the Infinity Norm,"  The low-rank matrix approximation problem with respect to the entry-wise
+$\ell_{\infty}$-norm is the following: given a matrix $M$ and a factorization
+rank $r$, find a matrix $X$ whose rank is at most $r$ and that minimizes
+$\max_{i,j} |M_{ij} - X_{ij}|$. In this paper, we prove that the decision
+variant of this problem for $r=1$ is NP-complete using a reduction from the
+problem `not all equal 3SAT'. We also analyze several cases when the problem
+can be solved in polynomial time, and propose a simple practical heuristic
+algorithm which we apply on the problem of the recovery of a quantized low-rank
+matrix.
+"
+23254,Fast Approximate Spectral Clustering for Dynamic Networks,"  Spectral clustering is a widely studied problem, yet its complexity is
+prohibitive for dynamic graphs of even modest size. We claim that it is
+possible to reuse information of past cluster assignments to expedite
+computation. Our approach builds on a recent idea of sidestepping the main
+bottleneck of spectral clustering, i.e., computing the graph eigenvectors, by
+using fast Chebyshev graph filtering of random signals. We show that the
+proposed algorithm achieves clustering assignments with quality approximating
+that of spectral clustering and that it can yield significant complexity
+benefits when the graph dynamics are appropriately bounded.
+"
+23255,Object Tracking based on Quantum Particle Swarm Optimization,"  In Computer Vision domain, moving Object Tracking considered as one of the
+toughest problem.As there so many factors associated like illumination of
+light, noise, occlusion, sudden start and stop of moving object, shading which
+makes tracking even harder problem not only for dynamic background but also for
+static background.In this paper we present a new object tracking algorithm
+based on Dominant points on tracked object using Quantum particle swarm
+optimization (QPSO) which is a new different version of PSO based on Quantum
+theory. The novelty in our approach is that it can be successfully applicable
+in variable background as well as static background and application of quantum
+PSO makes the algorithm runs lot faster where other basic PSO algorithm failed
+to do so due to heavy computation.In our approach firstly dominants points of
+tracked objects detected, then a group of particles form a swarm are
+initialized randomly over the image search space and then start searching the
+curvature connected between two consecutive dominant points until they satisfy
+fitness criteria. Obviously it is a Multi-Swarm approach as there are multiple
+dominant points, as they moves, the curvature moves and the curvature movement
+is tracked by the swarm throughout the video and eventually when the swarm
+reaches optimal solution , a bounding box drawn based on particles final
+position.Experimental results demonstrate this proposed QPSO based method work
+efficiently and effectively in visual object tracking in both dynamic and
+static environments and run time shows that it runs closely 90% faster than
+basic PSO.in our approach we also apply parallelism using MatLab Parfor command
+to show how very less number of iteration and swarm size will enable us to
+successfully track object.
+"
+23256,L$^3$-SVMs: Landmarks-based Linear Local Support Vectors Machines,"  For their ability to capture non-linearities in the data and to scale to
+large training sets, local Support Vector Machines (SVMs) have received a
+special attention during the past decade. In this paper, we introduce a new
+local SVM method, called L$^3$-SVMs, which clusters the input space, carries
+out dimensionality reduction by projecting the data on landmarks, and jointly
+learns a linear combination of local models. Simple and effective, our
+algorithm is also theoretically well-founded. Using the framework of Uniform
+Stability, we show that our SVM formulation comes with generalization
+guarantees on the true risk. The experiments based on the simplest
+configuration of our model (i.e. landmarks randomly selected, linear
+projection, linear kernel) show that L$^3$-SVMs is very competitive w.r.t. the
+state of the art and opens the door to new exciting lines of research.
+"
+23257,Quantum group symmetries and completeness for A_{2n}^(2) open spin chains,"  We argue that the Hamiltonians for A_{2n}^(2) open quantum spin chains
+corresponding to two choices of integrable boundary conditions have the
+symmetries U_q(B_n) and U_q(C_n), respectively. We find a formula for the
+Dynkin labels of the Bethe states (which determine the degeneracies of the
+corresponding eigenvalues) in terms of the numbers of Bethe roots of each type.
+With the help of this formula, we verify numerically (for a generic value of
+the anisotropy parameter) that the degeneracies and multiplicities of the
+spectra implied by the quantum group symmetries are completely described by the
+Bethe ansatz.
+"
+23258,A Hele-Shaw-Cahn-Hilliard model for incompressible two-phase flows with different densities,"  Topology changes in multi-phase fluid flows are difficult to model within a
+traditional sharp interface theory. Diffuse interface models turn out to be an
+attractive alternative to model two-phase flows. Based on a
+Cahn-Hilliard-Navier-Stokes model introduced by Abels, Garcke and Grün
+(Math. Models Methods Appl. Sci. 2012), which uses a volume averaged velocity,
+we derive a diffuse interface model in a Hele-Shaw geometry, which in the case
+of non-matched densities, simplifies an earlier model of Lee, Lowengrub and
+Goodman (Phys. Fluids 2002). We recover the classical Hele-Shaw model as a
+sharp interface limit of the diffuse interface model. Furthermore, we show the
+existence of weak solutions and present several numerical computations
+including situations with rising bubbles and fingering instabilities.
+"
+23259,Observation of new magnetic ground state in frustrated quantum antiferromagnet spin liquid system Cs2CuCl4,"  Cs2CuCl4 is known to possess a quantum spin liquid phase with
+antiferromagnetic interaction below 2.8 K. We report the observation of a new
+metastable magnetic phase of the triangular frustrated quantum spin system
+Cs2CuCl4 induced by the application of hydrostatic pressure. We measured the
+magnetic properties of Cs2CuCl4 following the application and release of
+pressure after 3 days. We observed a previously unknown ordered magnetic phase
+with a transition temperature of 9 K. Furthermore, the recovered sample with
+new magnetic ground state possesses an equivalent crystal structure to the
+uncompressed one with antiferromagnetic quantum spin liquid phase.
+"
+23260,Arithmetic universes and classifying toposes,"  Reasoning in the 2-category Con of contexts, certain sketches for arithmetic
+universes (i.e. list arithmetic pretoposes; AUs), is shown to give rise to
+base-independent results of Grothendieck toposes, provided the base elementary
+topos has a natural numbers object.
+Categories of strict models of contexts $T$ in AUs are acted on strictly on
+the left by non-strict AU-functors and strictly on the right by context maps,
+and the actions combine in a strict action of a Gray tensor product.
+Any context extension $T_0 \subset T_1$ gives rise to a bundle. For each
+point of $T_0$ - a model $M$ of $T_0$ in an elementary topos $S$ with nno - its
+fibre is a generalized space, the classifying topos $S[T_1/M]$ for the
+geometric theory $T_1/M$ of $T_1$-models restricting to $M$. This construction
+is ""geometric"" in the sense that for any geometric morphism $f: S' \to S$, the
+classifier $S'[T_1/f^\ast M]$ is got by pseudopullback of $S[T_1/M]$ along $f$.
+This is treated in a fibrational way by considering a 2-category GTop of
+Grothendieck toposes (bounded geometric morphisms) fibred (as bicategory) over
+a 2-category of elementary toposes with nno, geometric morphisms, and natural
+isomorphisms. The notion of classifying topos as representing object for a
+split fibration is then fibred over variable base using fibrations ""locally
+representable"" over a second fibration.
+"
+23261,Automatically Assessing Vulnerabilities Discovered by Compositional Analysis,"  Testing is the most widely employed method to find vulnerabilities in
+real-world software programs. Compositional analysis, based on symbolic
+execution, is an automated testing method to find vulnerabilities in medium- to
+large-scale programs consisting of many interacting components. However,
+existing compositional analysis frameworks do not assess the severity of
+reported vulnerabilities. In this paper, we present a framework to analyze
+vulnerabilities discovered by an existing compositional analysis tool and
+assign CVSS3 (Common Vulnerability Scoring System v3.0) scores to them, based
+on various heuristics such as interaction with related components, ease of
+reachability, complexity of design and likelihood of accepting unsanitized
+input. By analyzing vulnerabilities reported with CVSS3 scores in the past, we
+train simple machine learning models. By presenting our interactive framework
+to developers of popular open-source software and other security experts, we
+gather feedback on our trained models and further improve the features to
+increase the accuracy of our predictions. By providing qualitative (based on
+community feedback) and quantitative (based on prediction accuracy) evidence
+from 21 open-source programs, we show that our severity prediction framework
+can effectively assist developers with assessing vulnerabilities.
+"
+23262,Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data,"  What people buy is an important aspect or view of lifestyles. Studying
+people's shopping patterns in different urban regions can not only provide
+valuable information for various commercial opportunities, but also enable a
+better understanding about urban infrastructure and urban lifestyle. In this
+paper, we aim to predict city-wide shopping patterns. This is a challenging
+task due to the sparsity of the available data -- over 60% of the city regions
+are unknown for their shopping records. To address this problem, we incorporate
+another important view of human lifestyles, namely mobility patterns. With
+information on ""where people go"", we infer ""what people buy"". Moreover, to
+model the relations between regions, we exploit spatial interactions in our
+method. To that end, Collective Matrix Factorization (CMF) with an interaction
+regularization model is applied to fuse the data from multiple views or
+sources. Our experimental results have shown that our model outperforms the
+baseline methods on two standard metrics. Our prediction results on multiple
+shopping patterns reveal the divergent demands in different urban regions, and
+thus reflect key functional characteristics of a city. Furthermore, we are able
+to extract the connection between the two views of lifestyles, and achieve a
+better or novel understanding of urban lifestyles.
+"
+23263,Phase transition of ultracold atoms immersed in a BEC vortex lattice,"  We investigate the quantum phases of ultracold atoms trapped in a vortex
+lattice using a mixture of two bosonic species (A and B), in the presence of an
+artificial gauge field. Heavy atoms of species B are confined in the array of
+vortices generated in species A, and they are described through a Bose-Hubbard
+model. In contrast to the optical-lattice setups, the vortex lattice has an
+intrinsic dynamics, given by its Tkachenko modes. Including these quantum
+fluctuations in the effective model for B atoms yields an extended Bose-Hubbard
+model, with an additional ""phonon""-mediated long-range attraction. The
+ground-state phase diagram of this model is computed through a variational
+ansatz and the quantum Monte Carlo technique. When compared with the ordinary
+Bose-Hubbard case, the long-range interatomic attraction causes a shift and
+resizing of the Mott-insulator regions. Finally, we discuss the experimental
+feasibility of the proposed scheme, which relies on the proper choice of the
+atomic species and on a large control of physical parameters, like the
+scattering lengths and the vorticity.
+"
+23264,Personalized Influence Estimation Technique,"  Customer Satisfaction is the most important factors in the industry
+irrespective of domain. Key Driver Analysis is a common practice in data
+science to help the business to evaluate the same. Understanding key features,
+which influence the outcome or dependent feature, is highly important in
+statistical model building. This helps to eliminate not so important factors
+from the model to minimize noise coming from the features, which does not
+contribute significantly enough to explain the behavior of the dependent
+feature, which we want to predict. Personalized Influence Estimation is a
+technique introduced in this paper, which can estimate key factor influence for
+individual observations, which contribute most for each observations behavior
+pattern based on the dependent class or estimate. Observations can come from
+multiple business problem i.e. customers related to satisfaction study,
+customer related to Fraud Detection, network devices for Fault detection etc.
+It is highly important to understand the cause of issue at each observation
+level to take appropriate Individualized action at customer level or device
+level etc. This technique is based on joint behavior of the feature dimension
+for the specific observation, and relative importance of the feature to
+estimate impact. The technique mentioned in this paper is aimed to help
+organizations to understand each respondents or observations individual key
+contributing factor of Influence. Result of the experiment is really
+encouraging and able to justify key reasons for churn for majority of the
+sample appropriately
+"
+23265,A Model of the H$α$ and Na Transmission Spectrum of HD 189733b,"  This paper presents a detailed hydrostatic model of the upper atmosphere of
+HD 189733b, with the goal of constraining its temperature, particle densities,
+and radiation field over the pressure range $10^{-4}-10\, \mu \rm bar$, where
+the observed H$\alpha$ transmission spectrum is produced. The atomic hydrogen
+level population is computed including both collisional and radiative
+transition rates. The Ly$\alpha$ resonant scattering is computed using a
+Monte-Carlo simulation. The model transmission spectra are in broad agreement
+with the data. Excitation of the H(2$\ell$) population is mainly by Ly$\alpha$
+radiative excitation due to the large Ly$\alpha$ intensity. The density of
+H(2$\ell$) is nearly flat over two decades in pressure, and is optically thick
+to H$\alpha$. Additional models computed for a range of the stellar Lyman
+continuum (LyC) flux suggest that the variability in H$\alpha$ transit depth
+may be due to the variability in the stellar LyC. Since metal lines provide the
+dominant cooling of this part of the atmosphere, the atmosphere structure is
+sensitive to the density of species such as Mg and Na which may themselves be
+constrained by observations. Since the H$\alpha$ and Na D lines have comparable
+absorption depths, we argue that the center of the Na D lines are also formed
+in the atomic layer where the H$\alpha$ line is formed.
+"
+23266,Towards Deep Modeling of Music Semantics using EEG Regularizers,"  Modeling of music audio semantics has been previously tackled through
+learning of mappings from audio data to high-level tags or latent unsupervised
+spaces. The resulting semantic spaces are theoretically limited, either because
+the chosen high-level tags do not cover all of music semantics or because audio
+data itself is not enough to determine music semantics. In this paper, we
+propose a generic framework for semantics modeling that focuses on the
+perception of the listener, through EEG data, in addition to audio data. We
+implement this framework using a novel end-to-end 2-view Neural Network (NN)
+architecture and a Deep Canonical Correlation Analysis (DCCA) loss function
+that forces the semantic embedding spaces of both views to be maximally
+correlated. We also detail how the EEG dataset was collected and use it to
+train our proposed model. We evaluate the learned semantic space in a transfer
+learning context, by using it as an audio feature extractor in an independent
+dataset and proxy task: music audio-lyrics cross-modal retrieval. We show that
+our embedding model outperforms Spotify features and performs comparably to a
+state-of-the-art embedding model that was trained on 700 times more data. We
+further discuss improvements to the model that are likely to improve its
+performance.
+"
+23267,"Model-based Iterative Reconstruction for Flat-Panel Cone-Beam CT with Focal Spot Blur, Detector Blur, and Correlated Noise","  While model-based reconstruction methods have been successfully applied to
+flat-panel cone-beam CT (FP-CBCT) systems, typical implementations ignore both
+spatial correlations in the projection data as well as system blurs due to the
+detector and focal spot in the x-ray source. In this work, we develop a forward
+model for flat-panel-based systems that includes blur and noise correlation
+associated with finite focal spot size and an indirect detector (e.g.,
+scintillator). This forward model is used to develop a staged reconstruction
+framework where projection data are deconvolved and log-transformed, followed
+by a generalized least-squares reconstruction that utilizes a non-diagonal
+statistical weighting to account for the correlation that arises from the
+acquisition and data processing chain. We investigate the performance of this
+novel reconstruction approach in both simulated data and in CBCT test-bench
+data. In comparison to traditional filtered backprojection and model-based
+methods that ignore noise correlation, the proposed approach yields a superior
+noise-resolution tradeoff. For example, for a system with 0.34 mm FWHM
+scintillator blur and 0.70 FWHM focal spot blur, using the a correlated noise
+model instead of an uncorrelated noise model increased resolution by 42% (with
+variance matched at 6.9 x 10^-8 mm^-2 ). While this advantage holds across a
+wide range of systems with differing blur characteristics, the improvements are
+greatest for systems where source blur is larger than detector blur.
+"
+23268,New method to study ion-molecule reactions at low temperatures and application to the H$_2^+$ + H$_2$ $\rightarrow$ H$_3^+$ + H reaction,"  Studies of ion-molecule reactions at low temperatures are difficult because
+stray electric fields in the reaction volume affect the kinetic energy of
+charged reaction partners. We describe a new experimental approach to study
+ion-molecule reactions at low temperatures and present, as example, a
+measurement of the ${\rm H}_2^+ + {\rm H}_2\rightarrow {\rm H}_3^+ + {\rm H}$
+reaction with the ${\rm H}_2^+$ ion prepared in a single rovibrational state at
+collision energies in the range $E_{\rm col}/k_{\rm B} = 5$-60 K. To reach such
+low collision energies, we use a merged-beam approach and observe the reaction
+within the orbit of a Rydberg electron, which shields the ions from stray
+fields. The first beam is a supersonic beam of pure ground-state H$_2$
+molecules and the second is a supersonic beam of H$_2$ molecules excited to
+Rydberg-Stark states of principal quantum number $n$ selected in the range
+20-40. Initially, the two beams propagate along axes separated by an angle of
+10$^\circ$. To merge the two beams, the Rydberg molecules in the latter beam
+are deflected using a surface-electrode Rydberg-Stark deflector. The collision
+energies of the merged beams are determined by measuring the velocity
+distributions of the two beams and they are adjusted by changing the
+temperature of the pulsed valve used to generate the ground-state ${\rm H}_2$
+beam and by adapting the electric-potential functions to the electrodes of the
+deflector. The collision energy is varied down to below $E_{\rm col}/k_{\rm B}=
+10$ K, i.e., below $E_{\rm col}\approx 1$ meV, with an energy resolution of 100
+$\mu$eV. We demonstrate that the Rydberg electron acts as a spectator and does
+not affect the cross sections, which are found to closely follow a
+classical-Langevin-capture model in the collision-energy range investigated.
+Because all neutral atoms and molecules can be excited to Rydberg states, this
+method of studying
+"
+23269,Transverse Anderson localization of surface plasmon polaritons,"  We investigate the effect of disorder on the propagation of surface plasmon
+polaritons in arrays of evanescently coupled dielectric loaded surface plasmon
+polariton waveguides. Diagonal disorder is implemented by randomly varying the
+heights of the waveguides. Real-space as well as Fourier-space images of the
+surface plasmon polariton intensity distribution in the waveguide arrays are
+recorded by leakage radiation microscopy. With these techniques we
+experimentally demonstrate the transverse localization of surface plasmon
+polaritons with increasing disorder.
+"
+23270,Variance Based Moving K-Means Algorithm,"  Clustering is a useful data exploratory method with its wide applicability in
+multiple fields. However, data clustering greatly relies on initialization of
+cluster centers that can result in large intra-cluster variance and dead
+centers, therefore leading to sub-optimal solutions. This paper proposes a
+novel variance based version of the conventional Moving K-Means (MKM) algorithm
+called Variance Based Moving K-Means (VMKM) that can partition data into
+optimal homogeneous clusters, irrespective of cluster initialization. The
+algorithm utilizes a novel distance metric and a unique data element selection
+criteria to transfer the selected elements between clusters to achieve low
+intra-cluster variance and subsequently avoid dead centers. Quantitative and
+qualitative comparison with various clustering techniques is performed on four
+datasets selected from image processing, bioinformatics, remote sensing and the
+stock market respectively. An extensive analysis highlights the superior
+performance of the proposed method over other techniques.
+"
+23271,Real-time 3D Pose Estimation with a Monocular Camera Using Deep Learning and Object Priors On an Autonomous Racecar,"  We propose a complete pipeline that allows object detection and
+simultaneously estimate the pose of these multiple object instances using just
+a single image. A novel ""keypoint regression"" scheme with a cross-ratio term is
+introduced that exploits prior information about the object's shape and size to
+regress and find specific feature points. Further, a priori 3D information
+about the object is used to match 2D-3D correspondences and accurately estimate
+object positions up to a distance of 15m. A detailed discussion of the results
+and an in-depth analysis of the pipeline is presented. The pipeline runs
+efficiently on a low-powered Jetson TX2 and is deployed as part of the
+perception pipeline on a real-time autonomous vehicle cruising at a top speed
+of 54 km/hr.
+"
+23272,On Connecting Stochastic Gradient MCMC and Differential Privacy,"  Significant success has been realized recently on applying machine learning
+to real-world applications. There have also been corresponding concerns on the
+privacy of training data, which relates to data security and confidentiality
+issues. Differential privacy provides a principled and rigorous privacy
+guarantee on machine learning models. While it is common to design a model
+satisfying a required differential-privacy property by injecting noise, it is
+generally hard to balance the trade-off between privacy and utility. We show
+that stochastic gradient Markov chain Monte Carlo (SG-MCMC) -- a class of
+scalable Bayesian posterior sampling algorithms proposed recently -- satisfies
+strong differential privacy with carefully chosen step sizes. We develop theory
+on the performance of the proposed differentially-private SG-MCMC method. We
+conduct experiments to support our analysis and show that a standard SG-MCMC
+sampler without any modification (under a default setting) can reach
+state-of-the-art performance in terms of both privacy and utility on Bayesian
+learning.
+"
+23273,Exploring Low Internal Reorganization Energies for Silicene Nanoclusters,"  High-performance materials rely on small reorganization energies to
+facilitate both charge separation and charge transport. Here, we performed DFT
+calculations to predict small reorganization energies of rectangular silicene
+nanoclusters with hydrogen-passivated edges denoted by H-SiNC. We observe that
+across all geometries, H-SiNCs feature large electron affinities and highly
+stabilized anionic states, indicating their potential as n-type materials. Our
+findings suggest that fine-tuning the size of H-SiNCs along the zigzag and
+armchair directions may permit the design of novel n-type electronic materials
+and spinctronics devices that incorporate both high electron affinities and
+very low internal reorganization energies.
+"
+23274,Mixing Spectrum in Reduced Phase Spaces of Stochastic Differential Equations. Part II: Stochastic Hopf Bifurcation,"  The spectrum of the Markov semigroup of a diffusion process, referred to as
+the mixing spectrum, provides a detailed characterization of the dynamics of
+statistics such as the correlation function and the power spectrum. Stochastic
+analysis techniques for the study of the mixing spectrum and a rigorous
+reduction method have been presented in the first part (Chekroun et al. 2017)
+of this contribution. This framework is now applied to the study of a
+stochastic Hopf bifurcation, to characterize the statistical properties of
+nonlinear oscillators perturbed by noise, depending on their stability. In
+light of the Hörmander theorem, it is first shown that the geometry of the
+unperturbed limit cycle, in particular its isochrons, i.e., the leaves of the
+stable manifold of the limit cycle generalizing the notion of phase, is
+essential to understand the effect of the noise and the phenomenon of phase
+diffusion. In addition, it is shown that the mixing spectrum has a spectral
+gap, even at the bifurcation point, and that correlations decay exponentially
+fast. Small-noise expansions of the eigenvalues and eigenfunctions are then
+obtained, away from the bifurcation point, based on the knowledge of the
+linearized deterministic dynamics and the characteristics of the noise. These
+formulas allow to understand how the interaction of the noise with the
+deterministic dynamics affect the decay of correlations. Numerical results
+complement the study of the mixing spectrum at the bifurcation point, revealing
+interesting scaling laws. The analysis of the Markov semigroup for stochastic
+bifurcations is thus promising in providing a complementary approach to the
+more geometric random dynamical systems. This approach is not limited to
+low-dimensional systems and the reduction method presented in part I will be
+applied to stochastic models relevant to climate dynamics in Part III.
+"
+23275,Collective modes of Dirac and Weyl semimetals in strong magnetic fields,"  Dirac and Weyl semimetals provide a new example of three-dimensional electron
+gases which are sensitive to strong magnetic fields. In this paper we address
+their collective excitations in the extreme quantum limit in which the
+Hamiltonian can be projected to the $N=0$ Landau level. We show that the energy
+of the plasmon collective mode is not shifted by interactions, even though the
+quasiparticle bands are strongly renormalized, and that both optical and
+acoustic magnetoplasmons emerge from the particle-hole continuum when the
+chemical potential is not at the band-crossing point. The rare presence of a
+sharp acoustic magnetoplasmon excitation can be understood as a consequence of
+the unusual particle-hole excitation phase space of these pseudo-1D systems,
+and could prove valuable as a sample characterization tool. We comment on the
+relationship between the collective mode dispersion and the non-Fermi-liquid
+character of these interacting electron systems.
+"
+23276,Domain Generalization via Conditional Invariant Representation,"  Domain generalization aims to apply knowledge gained from multiple labeled
+source domains to unseen target domains. The main difficulty comes from the
+dataset bias: training data and test data have different distributions, and the
+training set contains heterogeneous samples from different distributions. Let
+$X$ denote the features, and $Y$ be the class labels. Existing domain
+generalization methods address the dataset bias problem by learning a
+domain-invariant representation $h(X)$ that has the same marginal distribution
+$\mathbb{P}(h(X))$ across multiple source domains. The functional relationship
+encoded in $\mathbb{P}(Y|X)$ is usually assumed to be stable across domains
+such that $\mathbb{P}(Y|h(X))$ is also invariant. However, it is unclear
+whether this assumption holds in practical problems. In this paper, we consider
+the general situation where both $\mathbb{P}(X)$ and $\mathbb{P}(Y|X)$ can
+change across all domains. We propose to learn a feature representation which
+has domain-invariant class conditional distributions $\mathbb{P}(h(X)|Y)$. With
+the conditional invariant representation, the invariance of the joint
+distribution $\mathbb{P}(h(X),Y)$ can be guaranteed if the class prior
+$\mathbb{P}(Y)$ does not change across training and test domains. Extensive
+experiments on both synthetic and real data demonstrate the effectiveness of
+the proposed method.
+"
+23277,A Coordinate-Free Construction of Scalable Natural Gradient,"  Most neural networks are trained using first-order optimization methods,
+which are sensitive to the parameterization of the model. Natural gradient
+descent is invariant to smooth reparameterizations because it is defined in a
+coordinate-free way, but tractable approximations are typically defined in
+terms of coordinate systems, and hence may lose the invariance properties. We
+analyze the invariance properties of the Kronecker-Factored Approximate
+Curvature (K-FAC) algorithm by constructing the algorithm in a coordinate-free
+way. We explicitly construct a Riemannian metric under which the natural
+gradient matches the K-FAC update; invariance to affine transformations of the
+activations follows immediately. We extend our framework to analyze the
+invariance properties of K-FAC applied to convolutional networks and recurrent
+neural networks, as well as metrics other than the usual Fisher metric.
+"
+23278,"Multiplicity, regularity and blow-spherical equivalence of complex analytic sets","  This paper is devoted to study multiplicity and regularity as well as to
+present some classifications of complex analytic sets. We present an
+equivalence for complex analytical sets, namely blow-spherical equivalence and
+we receive several applications with this new approach. For example, we reduce
+to homogeneous complex algebraic sets a version of Zariski's multiplicity
+conjecture in the case of blow-spherical homeomorphism, we give some partial
+answers to the Zariski's multiplicity conjecture, we show that a blow-spherical
+regular complex analytic set is smooth and we give a complete classification of
+complex analytic curves.
+"
+23279,Entangled de Sitter from Stringy Axionic Bell pair I: An analysis using Bunch Davies vacuum,"  In this work, we study the quantum entanglement and compute entanglement
+entropy in de Sitter space for a bipartite quantum field theory driven by axion
+originating from ${\bf Type~ IIB}$ string compactification on a Calabi Yau
+three fold (${\bf CY^3}$) and in presence of ${\bf NS5}$ brane. For this
+compuation, we consider a spherical surface ${\bf S}^2$, which divide the
+spatial slice of de Sitter (${\bf dS_4}$) into exterior and interior sub
+regions. We also consider the initial choice of vaccum to be Bunch Davies
+state. First we derive the solution of the wave function of axion in a
+hyperbolic open chart by constructing a suitable basis for Bunch Davies vacuum
+state using Bogoliubov transformation. We then, derive the expression for
+density matrix by tracing over the exterior region. This allows us to compute
+entanglement entropy and R$\acute{e}$nyi entropy in $3+1$ dimension. Further we
+quantify the UV finite contribution of entanglement entropy which contain the
+physics of long range quantum correlations of our expanding universe. Finally,
+our analysis compliments the necessary condition for the violation of Bell's
+inequality in primordial cosmology due to the non vanishing entanglement
+entropy for axionic Bell pair.
+"
+23280,"X-ray surface brightness observations of galaxy clusters, cosmic opacity and the limits on the matter density parameter","  In this work, we use two gas mass fraction samples of galaxy clusters
+obtained from their X-ray surface brightness observations jointly with recent
+$H(z)$ data in a flat $\Lambda$CDM framework to impose limits on cosmic
+opacity. It is assumed that the galaxy clusters are in hydrostatic equilibrium
+and their gas mass fraction measurement is constant with redshift. We show that
+the current limits on the matter density parameter obtained from X-ray gas mass
+fraction test are strongly dependent on the cosmic transparency assumption even
+for a flat scenario. Our results are consistent with a transparent universe
+within $1\sigma$ c.l. in full agreement with other analyses which used type Ia
+supernovae, gamma ray burst and $H(z)$ data.
+"
+23281,Learnable pooling with Context Gating for video classification,"  Current methods for video analysis often extract frame-level features using
+pre-trained convolutional neural networks (CNNs). Such features are then
+aggregated over time e.g., by simple temporal averaging or more sophisticated
+recurrent neural networks such as long short-term memory (LSTM) or gated
+recurrent units (GRU). In this work we revise existing video representations
+and study alternative methods for temporal aggregation. We first explore
+clustering-based aggregation layers and propose a two-stream architecture
+aggregating audio and visual features. We then introduce a learnable non-linear
+unit, named Context Gating, aiming to model interdependencies among network
+activations. Our experimental results show the advantage of both improvements
+for the task of video classification. In particular, we evaluate our method on
+the large-scale multi-modal Youtube-8M v2 dataset and outperform all other
+methods in the Youtube 8M Large-Scale Video Understanding challenge.
+"
+23282,Strichartz estimates for the Schrödinger flow on compact Lie groups,"  Every connected compact Lie group is the quotient by a finite central
+subgroup of a product of circles and compact simply connected simple Lie
+groups. Let each of the circles be equipped with a constant metric, and each of
+the simple groups be equipped a metric that is a constant multiple of the
+negative of the Cartan-Killing form, under the requirement that the periods of
+the Schrödinger flow on each component are rational multiples of each other.
+This metric on the covering group is pushed down to a metric on the original
+group, which is called a rational metric. This paper establishes scaling
+invariant Strichartz estimates for the Schrödinger flow on compact Lie groups
+equipped with rational metrics. The highlights of this paper include a Weyl
+differencing technique for rational lattices, the different decompositions of
+the Schrödinger kernel that accommodate different positions of the variable
+inside the maximal torus relative to the Weyl chamber walls, and the
+application of the BGG-Demazure operators to the estimate of the difference
+between characters.
+"
+23283,Plasmons in topological insulator cylindrical nanowires,"  We present a theoretical analysis of Dirac magneto-plasmons in topological
+insulator nanowires. We discuss a cylindrical geometry where Berry phase
+effects induce the opening of a gap at the neutrality point. By taking into
+account surface electron wave functions introduced in previous papers and
+within the random phase approximation, we provide an analytical form of the
+dynamic structure factor. Dispersions and spectral weights of Dirac plasmons
+are studied with varying the radius of the cylinder, the surface doping, and
+the strength of an external magnetic field. We show that, at zero surface
+doping, inter-band damped plasmon-like excitations form at the surface and
+survive at low electron surface dopings ($\sim 10^{10} cm^{-2} $). Then, we
+point out that the plasmon excitations are sensitive to the Berry phase gap
+closure when an external magnetic field close to half quantum flux is
+introduced. Indeed, a well-defined magneto-plasmon peak is observed at lower
+energies upon the application of the magnetic field. Finally, the increase of
+the surface doping induces a crossover from damped inter-band to sharp
+intra-band magneto-plasmons which, as expected for large radii and dopings
+($\sim 10^{12} cm^{-2}$), approach the proper limit of a two-dimensional
+surface.
+"
+23284,On the Magnetic Structure of Density Matrices,"  The spin structure of wave functions is reflected in the magnetic structure
+of the one-particle density matrix. Indeed, for single determinants we can use
+either one to determine the other. In this work we discuss how one can simply
+examine the one-particle density matrix to faithfully determine whether the
+spin magnetization density vector field is collinear, coplanar, or noncoplanar.
+For single determinants, this test suffices to distinguish collinear
+determinants which are eigenfunctions of $\hat{S}_{\hat{n}}$ from noncollinear
+determinants which are not. We also point out the close relationship between
+noncoplanar magnetism on the one hand and complex conjugation symmetry breaking
+on the other. Finally, we use these ideas to classify the various ways single
+determinant wave functions break and respect symmetries of the Hamiltonian in
+terms of their one-particle density matrix.
+"
+23285,A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network,"  In this paper, a time delay neural network (TDNN) based acoustic model is
+proposed to implement a fast-converged acoustic modeling for Korean speech
+recognition. The TDNN has an advantage in fast-convergence where the amount of
+training data is limited, due to subsampling which excludes duplicated weights.
+The TDNN showed an absolute improvement of 2.12% in terms of character error
+rate compared to feed forward neural network (FFNN) based modelling for Korean
+speech corpora. The proposed model converged 1.67 times faster than a
+FFNN-based model did.
+"
+23286,Velocity-selective spectroscopy measurements of Rydberg fine structure states in a hot vapor cell,"  A velocity-selective spectroscopy technique for studying the spectra from hot
+Rydberg gases is presented. This method provides high-resolution measurement of
+the spectrum interval. Based on this method, the velocity-selective hyperfine
+splitting of intermediate states is measured, as well as the Doppler-free
+fine-structure splitting of the Rydberg states via two-photon Rydberg
+excitation in a room-temperature 133Cs vapor cell. The experiment data and
+theoretical predictions show excellent agreement.
+"
+23287,"Stable pairs, flat connections and Gopakumar-Vafa invariants","  Using the interpretation of certain generalised Donaldson-Thomas invariants,
+including stable pairs curve counts, as the monodromy of a flat connection on a
+formal principal bundle, we show that the conjectural Gopakumar-Vafa
+contributions of all genera to the Gromov-Witten partition function appear in
+the asymptotics of the corresponding flat sections. The Fourier-Laplace
+integrals used to produce flat sections lead naturally to the GW/DT change of
+variable -q = e^{i u}.
+"
+23288,Random Spatial Network Models with Core-Periphery Structure,"  Core-periphery structure is a common property of complex networks, which is a
+composition of tightly connected groups of core vertices and sparsely connected
+periphery vertices. This structure frequently emerges in traffic systems,
+biology, and social networks via underlying spatial positioning of the
+vertices. While core-periphery structure is ubiquitous, there have been limited
+attempts at modeling network data with this structure. Here, we develop a
+generative, random network model with core-periphery structure that jointly
+accounts for topological and spatial information by ""core scores"" of vertices.
+Our model achieves substantially higher likelihood than existing generative
+models of core-periphery structure, and we demonstrate how the core scores can
+be used in downstream data mining tasks, such as predicting airline traffic and
+classifying fungal networks. We also develop nearly linear time algorithms for
+learning model parameters and network sampling by using a method akin to the
+fast multipole method, a technique traditional to computational physics, which
+allow us to scale to networks with millions of vertices with minor tradeoffs in
+accuracy.
+"
+23289,Adversarial Sets for Regularising Neural Link Predictors,"  In adversarial training, a set of models learn together by pursuing competing
+goals, usually defined on single data instances. However, in relational
+learning and other non-i.i.d domains, goals can also be defined over sets of
+instances. For example, a link predictor for the is-a relation needs to be
+consistent with the transitivity property: if is-a(x_1, x_2) and is-a(x_2, x_3)
+hold, is-a(x_1, x_3) needs to hold as well. Here we use such assumptions for
+deriving an inconsistency loss, measuring the degree to which the model
+violates the assumptions on an adversarially-generated set of examples. The
+training objective is defined as a minimax problem, where an adversary finds
+the most offending adversarial examples by maximising the inconsistency loss,
+and the model is trained by jointly minimising a supervised loss and the
+inconsistency loss on the adversarial examples. This yields the first method
+that can use function-free Horn clauses (as in Datalog) to regularise any
+neural link predictor, with complexity independent of the domain size. We show
+that for several link prediction models, the optimisation problem faced by the
+adversary has efficient closed-form solutions. Experiments on link prediction
+benchmarks indicate that given suitable prior knowledge, our method can
+significantly improve neural link predictors on all relevant metrics.
+"
+23290,Some topics in differential geometry of normed spaces,"  For a surface immersed in a three-dimensional space endowed with a norm
+instead of an inner product, one can define analogous concepts of curvature and
+metric. With these concepts in mind, various questions immediately appear. The
+aim of this paper is to propose and answer some of those questions. In this
+framework we prove several characterizations of minimal surfaces, and analogues
+of some global theorems (e.g., Hadamard-type theorems) are also derived. A
+result on the curvature of surfaces of constant Minkowski width is also given.
+Finally, we study the ambient metric induced on the surface, proving an
+extension of the classical Bonnet theorem.
+"
+23291,Randomization for the direct effect of an infectious disease intervention in a clustered study population,"  Randomized trials of infectious disease interventions often focus on
+populations of connected or potentially interacting individuals. When the
+pathogen of interest is contagious (transmissible) between study subjects,
+infection outcomes may exhibit dependence, and it can be challenging to define
+and estimate the causal ""direct effect"" of the intervention on individuals who
+receive it. Two very different paradigms guide researchers in conceptualizing
+this effect: a randomization design-based perspective defines the direct effect
+as a contrast between the infection risk of a given individual under treatment
+versus no treatment, averaged over the conditional distribution of randomized
+assignments to other individuals in the cluster; a structural model-based
+perspective defines the direct effect as a contrast between the infection risk
+of a given individual under treatment versus no treatment, with exposure to
+infectiousness held constant. In this paper, we show that the design- and
+model-based definitions of the causal direct effect are incompatible under some
+randomization designs when the outcome is contagious. In particular,
+design-based average risk differences may not recover the sign of the true
+individualistic effect of the intervention, even in large samples. The results
+suggest that widely recommended randomization designs and estimators may
+provide misleading inferences about the direct effect of an intervention - such
+as a vaccine - when outcomes are contagious.
+"
+23292,An Auto-tuning Framework for Autonomous Vehicles,"  Many autonomous driving motion planners generate trajectories by optimizing a
+reward/cost functional. Designing and tuning a high-performance reward/cost
+functional for Level-4 autonomous driving vehicles with exposure to different
+driving conditions is challenging. Traditionally, reward/cost functional tuning
+involves substantial human effort and time spent on both simulations and road
+tests. As the scenario becomes more complicated, tuning to improve the motion
+planner performance becomes increasingly difficult. To systematically solve
+this issue, we develop a data-driven auto-tuning framework based on the Apollo
+autonomous driving framework. The framework includes a novel rank-based
+conditional inverse reinforcement learning algorithm, an offline training
+strategy and an automatic method of collecting and labeling data. Our
+auto-tuning framework has the following advantages that make it suitable for
+tuning an autonomous driving motion planner. First, compared to that of most
+inverse reinforcement learning algorithms, our algorithm training is efficient
+and capable of being applied to different scenarios. Second, the offline
+training strategy offers a safe way to adjust the parameters before public road
+testing. Third, the expert driving data and information about the surrounding
+environment are collected and automatically labeled, which considerably reduces
+the manual effort. Finally, the motion planner tuned by the framework is
+examined via both simulation and public road testing and is shown to achieve
+good performance.
+"
+23293,Preconditioned warm-started Newton-Krylov methods for MPC with discontinuous control,"  We present Newton-Krylov methods for efficient numerical solution of optimal
+control problems arising in model predictive control, where the optimal control
+is discontinuous. As in our earlier work, preconditioned GMRES practically
+results in an optimal $O(N)$ complexity, where $N$ is a discrete horizon
+length. Effects of a warm-start, shifting along the predictive horizon, are
+numerically investigated. The~method is tested on a classical double integrator
+example of a minimum-time problem with a known bang-bang optimal control.
+"
+23294,Lower bounds for the first eigenvalue of the magnetic Laplacian,"  We consider a Riemannian cylinder endowed with a closed potential 1-form A
+and study the magnetic Laplacian with magnetic Neumann boundary conditions
+associated with those data. We establish a sharp lower bound for the first
+eigenvalue and show that the equality characterizes the situation where the
+metric is a product. We then look at the case of a planar domain bounded by two
+closed curves and obtain an explicit lower bound in terms of the geometry of
+the domain. We finally discuss sharpness of this last estimate.
+"
+23295,Constraints on Bygone Nucleosynthesis of Accreting Neutron Stars,"  Nuclear burning near the surface of an accreting neutron star produces ashes
+that, when compressed deeper by further accretion, alter the star's thermal and
+compositional structure. Bygone nucleosynthesis can be constrained by the
+impact of compressed ashes on the thermal relaxation of quiescent neutron star
+transients. In particular, Urca cooling nuclei pairs in nuclear burning ashes,
+which cool the neutron star crust via neutrino emission from
+electron-capture/beta-decay cycles, provide signatures of prior nuclear burning
+over the ~century timescales it takes to accrete to the electron-capture depth
+of the strongest cooling pairs. Using crust cooling models of the accreting
+neutron star transient MAXI J0556-332, we show that this source likely lacked
+Type I X-ray bursts and superbursts >120 years ago. Reduced nuclear physics
+uncertainties in rp-process reaction rates and electron-capture ft-values for
+low-lying transitions will improve nucleosynthesis constraints using this
+technique.
+"
+23296,"Imaginary triangles, Pythagorean theorems, and algebraic geometry","  We extend the notion of triangle to ""imaginary triangles"" with complex valued
+sides and angles, and parametrize families of such triangles by plane algebraic
+curves. We study in detail families of triangles with two commensurable angles,
+and apply the theory of plane Cremona transformations to find ""Pythagorean
+theorems"" for them, which are interpreted as the implicit equations of their
+parametrizing curves.
+"
+23297,Hodge numbers of a hypothetical complex structure on $S^6$,"  These are the notes for the talk ""Hodge numbers of a hypothetical complex
+structure on $S^6$"" given by the author at the MAM1 ""(Non)-existence of complex
+structures on $S^6$"" held in Marburg in March 2017. They are based on [A. Gray,
+A property of a hypothetical complex structure on the six sphere, Boll. Un.
+Mat. Ital. B (7)} $\textbf{11}$ (1997), Suppl. fasc. 2, 251--255.] and [L.
+Ugarte, Hodge numbers of a hypothetical complex structure on the six sphere,
+Geom. Dedicata $\textbf{81}$ (2000), no. 1-3, 173--179.], where Hodge numbers
+and the dimensions of the succesive pages of the Frölicher spectral sequence
+for $S^6$ endowed with a hypothetical complex structure are investigated. We
+also add results from [Andrew McHugh, Narrowing cohomologies on complex $S^6$,
+Eur. J. Pure Appl. Math. $\textbf{10}$ (2017), no. 3, 440--454.], where the
+Bott-Chern cohomology of hypothetical complex structures on $S^6$ is studied.
+The material is not intended to be original.
+"
+23298,Quantum Brownian motion at strong dissipation probed by superconducting tunnel junctions,"  We have studied the temporal evolution of a quantum system subjected to
+strong dissipation at ultra-low temperatures where the system-bath interaction
+represents the leading energy scale. In this regime, theory predicts the time
+evolution of the system to follow a generalization of the classical
+Smoluchowski description, the quantum Smoluchowski equation, thus, exhibiting
+quantum Brownian motion characteristics. For this purpose, we have investigated
+the phase dynamics of a superconducting tunnel junction in the presence of high
+damping. We performed current-biased measurements on the small-capacitance
+Josephson junction of a scanning tunneling microscope placed in a low impedance
+environment at milli-Kelvin temperatures. We can describe our experimental
+findings by a quantum diffusion model with high accuracy in agreement with
+theoretical predications based on the quantum Smoluchowski equation. In this
+way we experimentally demonstrate that quantum systems subjected to strong
+dissipation follow quasi-classical dynamics with significant quantum effects as
+the leading corrections.
+"
+23299,Impact of Screw and Edge Dislocation on the Thermal Conductivity of Nanowires and Bulk GaN,"  We report on thermal transport properties of wurtzite GaN in the presence of
+dislocations, by using molecular dynamics simulations. A variety of isolated
+dislocations in a nanowire configuration were analyzed and found to reduce
+considerably the thermal conductivity while impacting its temperature
+dependence in a different manner. We demonstrate that isolated screw
+dislocations reduce the thermal conductivity by a factor of two, while the
+influence of edge dislocations is less pronounced. The relative reduction of
+thermal conductivity is correlated with the strain energy of each of the five
+studied types of dislocations and the nature of the bonds around the
+dislocation core. The temperature dependence of the thermal conductivity
+follows a physical law described by a T$^{-1}$ variation in combination with an
+exponent factor which depends on the material's nature, the type and the
+structural characteristics of the dislocation's core. Furthermore, the impact
+of the dislocations density on the thermal conductivity of bulk GaN is
+examined. The variation and even the absolute values of the total thermal
+conductivity as a function of the dislocation density is similar for both types
+of dislocations. The thermal conductivity tensors along the parallel and
+perpendicular directions to the dislocation lines are analyzed. The discrepancy
+of the anisotropy of the thermal conductivity grows in increasing the density
+of dislocations and it is more pronounced for the systems with edge
+dislocations.
+"
+23300,Machine learning of molecular properties: locality and active learning,"  In recent years the machine learning techniques have shown a great potential
+in various problems from a multitude of disciplines, including materials design
+and drug discovery. The high computational speed on the one hand and the
+accuracy comparable to that of DFT on another hand make machine learning
+algorithms efficient for high-throughput screening through chemical and
+configurational space. However, the machine learning algorithms available in
+the literature require large training datasets to reach the chemical accuracy
+and also show large errors for the so-called outliers - the out-of-sample
+molecules, not well-represented in the training set. In the present paper we
+propose a new machine learning algorithm for predicting molecular properties
+that addresses these two issues: it is based on a local model of interatomic
+interactions providing high accuracy when trained on relatively small training
+sets and an active learning algorithm of optimally choosing the training set
+that significantly reduces the errors for the outliers. We compare our model to
+the other state-of-the-art algorithms from the literature on the widely used
+benchmark tests.
+"
+23301,Meta-Learning via Feature-Label Memory Network,"  Deep learning typically requires training a very capable architecture using
+large datasets. However, many important learning problems demand an ability to
+draw valid inferences from small size datasets, and such problems pose a
+particular challenge for deep learning. In this regard, various researches on
+""meta-learning"" are being actively conducted. Recent work has suggested a
+Memory Augmented Neural Network (MANN) for meta-learning. MANN is an
+implementation of a Neural Turing Machine (NTM) with the ability to rapidly
+assimilate new data in its memory, and use this data to make accurate
+predictions. In models such as MANN, the input data samples and their
+appropriate labels from previous step are bound together in the same memory
+locations. This often leads to memory interference when performing a task as
+these models have to retrieve a feature of an input from a certain memory
+location and read only the label information bound to that location. In this
+paper, we tried to address this issue by presenting a more robust MANN. We
+revisited the idea of meta-learning and proposed a new memory augmented neural
+network by explicitly splitting the external memory into feature and label
+memories. The feature memory is used to store the features of input data
+samples and the label memory stores their labels. Hence, when predicting the
+label of a given input, our model uses its feature memory unit as a reference
+to extract the stored feature of the input, and based on that feature, it
+retrieves the label information of the input from the label memory unit. In
+order for the network to function in this framework, a new memory-writingmodule
+to encode label information into the label memory in accordance with the
+meta-learning task structure is designed. Here, we demonstrate that our model
+outperforms MANN by a large margin in supervised one-shot classification tasks
+using Omniglot and MNIST datasets.
+"
+23302,A Computationally Surveyable Proof of the Group Properties of an Elliptic Curve,"  We present an elementary proof of the group properties of the elliptic curve
+known as ""Curve25519"", as a component of a comprehensive proof of correctness
+of a hardware implementation of the associated Diffie-Hellman key agreement
+algorithm. The entire proof has been formalized and mechanically verified with
+ACL2, and is computationally surveyable in the sense that all steps that
+require mechanical support are presented in such a way that they may readily
+reproduced in any suitable programming language.
+"
+23303,Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information,"  We study the misclassification error for community detection in general
+heterogeneous stochastic block models (SBM) with noisy or partial label
+information. We establish a connection between the misclassification rate and
+the notion of minimum energy on the local neighborhood of the SBM. We develop
+an optimally weighted message passing algorithm to reconstruct labels for SBM
+based on the minimum energy flow and the eigenvectors of a certain Markov
+transition matrix. The general SBM considered in this paper allows for
+unequal-size communities, degree heterogeneity, and different connection
+probabilities among blocks. We focus on how to optimally weigh the message
+passing to improve misclassification.
+"
+23304,Growing Linear Consensus Networks Endowed by Spectral Systemic Performance Measures,"  We propose an axiomatic approach for design and performance analysis of noisy
+linear consensus networks by introducing a notion of systemic performance
+measure. This class of measures are spectral functions of Laplacian eigenvalues
+of the network that are monotone, convex, and orthogonally invariant with
+respect to the Laplacian matrix of the network. It is shown that several
+existing gold-standard and widely used performance measures in the literature
+belong to this new class of measures. We build upon this new notion and
+investigate a general form of combinatorial problem of growing a linear
+consensus network via minimizing a given systemic performance measure. Two
+efficient polynomial-time approximation algorithms are devised to tackle this
+network synthesis problem: a linearization-based method and a simple greedy
+algorithm based on rank-one updates. Several theoretical fundamental limits on
+the best achievable performance for the combinatorial problem is derived that
+assist us to evaluate optimality gaps of our proposed algorithms. A detailed
+complexity analysis confirms the effectiveness and viability of our algorithms
+to handle large-scale consensus networks.
+"
+23305,Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models,"  Geostatistical modeling of petrophysical properties is a key step in modern
+integrated oil and gas reservoir studies. Recently, generative adversarial
+networks (GAN) have been shown to be a successful method for generating
+unconditional simulations of pore- and reservoir-scale models. This
+contribution leverages the differentiable nature of neural networks to extend
+GANs to the conditional simulation of three-dimensional pore- and
+reservoir-scale models. Based on the previous work of Yeh et al. (2016), we use
+a content loss to constrain to the conditioning data and a perceptual loss
+obtained from the evaluation of the GAN discriminator network. The technique is
+tested on the generation of three-dimensional micro-CT images of a Ketton
+limestone constrained by two-dimensional cross-sections, and on the simulation
+of the Maules Creek alluvial aquifer constrained by one-dimensional sections.
+Our results show that GANs represent a powerful method for sampling conditioned
+pore and reservoir samples for stochastic reservoir evaluation workflows.
+"
+23306,Time-domain Brillouin scattering for the determination of laser-induced temperature gradients in liquids,"  We present an optical technique based on ultrafast photoacoustics to
+precisely determine the local temperature distribution profile in liquid
+samples in contact with a laser heated optical transducer. This ultrafast
+pump-probe experiment uses time-domain Brillouin scattering (TDBS) to locally
+determine the light scattering frequency shift. As the temperature influences
+the Brillouin scattering frequency, the TDBS signal probes the local
+laser-induced temperature distribution in the liquid. We demonstrate the
+relevance and the sensitivity of this technique for the measurement of the
+absolute laser-induced temperature gradient of a glass forming liquid
+prototype, glycerol, at different laser pump powers - i.e. different steady
+state background temperatures. Complementarily, our experiments illustrate how
+this TDBS technique can be applied to measure thermal diffusion in complex
+multilayer systems in contact to a surrounding liquid.
+"
+23307,A Hybrid Architecture for Multi-Party Conversational Systems,"  Multi-party Conversational Systems are systems with natural language
+interaction between one or more people or systems. From the moment that an
+utterance is sent to a group, to the moment that it is replied in the group by
+a member, several activities must be done by the system: utterance
+understanding, information search, reasoning, among others. In this paper we
+present the challenges of designing and building multi-party conversational
+systems, the state of the art, our proposed hybrid architecture using both
+rules and machine learning and some insights after implementing and evaluating
+one on the finance domain.
+"
+23308,Unramified Godement-Jacquet theory for the spin similitude group,"  Suppose $F$ is a non-archimedean local field. The classical Godement-Jacquet
+theory is that one can use Schwartz-Bruhat functions on $n \times n$ matrices
+$M_n(F)$ to define the local standard $L$-functions on $\mathrm{GL}_n$. The
+purpose of this partly expository note is to give evidence that there is an
+analogous and useful ""approximate"" Godement-Jacquet theory for the standard
+$L$-functions on the special orthogonal groups $\mathrm{SO}(V)$: One replaces
+$\mathrm{GL}_n(F)$ with $\mathrm{GSpin}(V)(F)$ and $M_n(F)$ with
+$\mathrm{Clif}(V)(F)$, the Clifford algebra of $V$. More precisely, we explain
+how a few different local unramified calculations for standard $L$-functions on
+$\mathrm{SO}(V)$ can be done easily using Schwartz-Bruhat functions on
+$\mathrm{Clif}(V)(F)$. We do not attempt any of the ramified or global theory
+of $L$-functions on $\mathrm{SO}(V)$ using Schwartz-Bruhat functions on
+$\mathrm{Clif}(V)$.
+"
+23309,Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization,"  In this paper, we propose a novel sufficient decrease technique for
+stochastic variance reduced gradient descent methods such as SVRG and SAGA. In
+order to make sufficient decrease for stochastic optimization, we design a new
+sufficient decrease criterion, which yields sufficient decrease versions of
+stochastic variance reduction algorithms such as SVRG-SD and SAGA-SD as a
+byproduct. We introduce a coefficient to scale current iterate and to satisfy
+the sufficient decrease property, which takes the decisions to shrink, expand
+or even move in the opposite direction, and then give two specific update rules
+of the coefficient for Lasso and ridge regression. Moreover, we analyze the
+convergence properties of our algorithms for strongly convex problems, which
+show that our algorithms attain linear convergence rates. We also provide the
+convergence guarantees of our algorithms for non-strongly convex problems. Our
+experimental results further verify that our algorithms achieve significantly
+better performance than their counterparts.
+"
+23310,Toward the design principles of molecular machines,"  Living things avoid equilibrium using molecular machines. Such microscopic
+soft-matter objects encounter relatively large friction and fluctuations. We
+discuss design principles for effective molecular machine operation in this
+unfamiliar context.
+"
+23311,Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies,"  We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm. By
+proposing a new framework for the convergence analysis, we prove improved
+convergence rates and computational complexities of the stochastic L-BFGS
+algorithms compared to previous works. In addition, we propose several
+practical acceleration strategies to speed up the empirical performance of such
+algorithms. We also provide theoretical analyses for most of the strategies.
+Experiments on large-scale logistic and ridge regression problems demonstrate
+that our proposed strategies yield significant improvements vis-à-vis
+competing state-of-the-art algorithms.
+"
+23312,Oxygen budget in low-mass protostars: the NGC1333-IRAS4A R1 shock observed in [OI] at 63 um with SOFIA-GREAT,"  In molecular outflows from forming low-mass protostars, most oxygen is
+expected to be locked up in water. However, Herschel observations have shown
+that typically an order of magnitude or more of the oxygen is still unaccounted
+for. To test if the oxygen is instead in atomic form, SOFIA-GREAT observed the
+R1 position of the bright molecular outflow from NGC1333-IRAS4A. The [OI] 63 um
+line is detected and spectrally resolved. From an intensity peak at +15 km/s,
+the intensity decreases until +50 km/s. The profile is similar to that of
+high-velocity (HV) H2O and CO 16-15, the latter observed simultaneously with
+[OI]. A radiative transfer analysis suggests that ~15% of the oxygen is in
+atomic form toward this shock position. The CO abundance is inferred to be
+~10^-4 by a similar analysis, suggesting that this is the dominant oxygen
+carrier in the HV component. These results demonstrate that a large portion of
+the observed [OI] emission is part of the outflow. Further observations are
+required to verify whether this is a general trend.
+"
+23313,Interface enhanced spin-orbit torques and current-induced magnetization switching of Pd/Co/AlO$_x$ layers,"  Magnetic heterostructures that combine large spin-orbit torque efficiency,
+perpendicular magnetic anisotropy, and low resistivity are key to develop
+electrically-controlled memory and logic devices. Here we report on vector
+measurements of the current-induced spin orbit torques and magnetization
+switching in perpendicularly magnetized Pd/Co/AlO$_x$ layers as a function of
+Pd thickness. We find sizeable damping-like (DL) and field-like (FL) torques,
+of the order of 1~mT per $10^7$~A/cm$^2$, which have different thickness and
+magnetization angle dependence. The analysis of the DL torque efficiency per
+unit current density and electric field using drift-diffusion theory leads to
+an effective spin Hall angle and spin diffusion length of Pd larger than 0.03
+and 7~nm, respectively. The FL SOT includes a significant interface
+contribution, is larger than estimated using drift-diffusion parameters, and is
+further strongly enhanced upon rotation of the magnetization from the
+out-of-plane to the in-plane direction. Finally, taking advantage of the large
+spin-orbit torques in this system, we demonstrate bipolar magnetization
+switching of Pd/Co/AlO$_x$ layers with similar current density as used for
+Pt/Co layers with comparable perpendicular magnetic anisotropy.
+"
+23314,High-pressure versus isoelectronic doping effect on the honeycomb iridate Na$_2$IrO$_3$,"  We study the effect of isoelectronic doping and external pressure in tuning
+the ground state of the honeycomb iridate Na$_2$IrO$_3$ by combining optical
+spectroscopy with synchrotron x-ray diffraction measurements on single
+crystals. The obtained optical conductivity of Na$_2$IrO$_3$ is discussed in
+terms of a Mott insulating picture versus the formation of quasimolecular
+orbitals and in terms of Kitaev-interactions. With increasing Li content $x$,
+(Na$_{1-x}$Li$_x$)$_2$IrO$_3$ moves deeper into the Mott insulating regime and
+there are indications that up to a doping level of 24\% the compound comes
+closer to the Kitaev-limit. The optical conductivity spectrum of single
+crystalline $\alpha$-Li$_2$IrO$_3$ does not follow the trends observed for the
+series up to $x=0.24$. There are strong indications that $\alpha$-Li$_2$IrO$_3$
+is less close to the Kitaev-limit compared to Na$_2$IrO$_3$ and closer to the
+quasimolecular orbital picture. Except for the pressure-induced hardening of
+the phonon modes, the optical properties of Na$_2$IrO$_3$ seem to be robust
+against external pressure. Possible explanations of the unexpected evolution of
+the optical conductivity with isolectronic doping and the drastic change
+between $x=0.24$ and $x=1$ are given by comparing the pressure-induced changes
+of lattice parameters and the optical conductivity with the corresponding
+changes induced by doping.
+"
+23315,Thermoelectric properties of a strongly correlated layer,"  In this paper we investigate the effect of strong electronic interactions on
+the thermoelectric properties of a simple generic system, consisting of a
+single correlated layer sandwiched between two metallic leads. Results will be
+given for the linear response regime as well as beyond, for which a full
+nonequilibrium many-body calculation is performed, based on nonequilibrium
+dynamical mean-field theory (DMFT). As impurity solver we use the auxiliary
+master equation approach, which addresses the impurity problem within a finite
+auxiliary system consisting of a correlated impurity, a small number of
+uncorrelated bath sites and Markovian environments. For the linear response
+regime, results will be presented for the Seebeck coefficient, the electrical
+conductance and the electronic contribution to the thermal conductance. Beyond
+linear response, i.e. for finite differences in the temperatures and/or the
+bias voltages in the leads, we study the dependence of the current on various
+model parameters, such as gate voltage and Hubbard interaction of the central
+layer. We will give a detailed parameter study as far as the thermoelectric
+efficiency is concerned. We find that strong correlations can indeed increase
+the thermopower of the device. In addition, some general theoretical
+requirements for an efficient thermoelectric device will be given.
+"
+23316,Smoothness-constrained model for nonparametric item response theory,"  This paper is concerned with the nonparametric item response theory (NIRT)
+for estimating item characteristic curves (ICCs) and latent abilities of
+examinees on educational and psychological tests. In contrast to parametric
+models, NIRT models can estimate various forms of ICCs under mild shape
+restrictions, such as the constraints of monotone homogeneity and double
+monotonicity. However, NIRT models frequently suffer from estimation
+instability because of the great flexibility of nonparametric ICCs, especially
+when there is only a small amount of item-response data. To improve the
+estimation accuracy, we propose a novel NIRT model constrained by monotone
+homogeneity and smoothness based on ordered latent classes. Our smoothness
+constraints avoid overfitting of nonparametric ICCs by keeping them close to
+logistic curves. We also implement a tailored expectation--maximization
+algorithm to calibrate our smoothness-constrained NIRT model efficiently. We
+conducted computational experiments to assess the effectiveness of our
+smoothness-constrained model in comparison with the common two-parameter
+logistic model and the monotone-homogeneity model. The computational results
+demonstrate that our model obtained more accurate estimation results than did
+the two-parameter logistic model when the latent abilities of examinees for
+some test items followed bimodal distributions. Moreover, our model
+outperformed the monotone-homogeneity model because of the effect of the
+smoothness constraints.
+"
+23317,Red shift of spectral lines of hydrogen atoms caused by its electrical polarization,"  A system of Lorentz oscillators is considered to interpret of spectral lines
+of hydrogen atoms. The dielectric permittivity of this system, which takes into
+account its electric polarization, is considered. The substance is examined in
+the gas state or in the form of small dust of matter. It is shown that the
+considering of the electric polarization results in a redshift of spectral
+lines of the substance and the appearance of dip of curve of spectrum. This dip
+takes place in the blue side with respect to the spectral position of the
+already shifted line. The magnitude of the red shift of spectral line and the
+width of this dip in the spectrum depend strongly on the concentration of
+hydrogen atoms that create this spectrum and on the spectral position of line
+that not shifted.
+"
+23318,Lower Bounds on the Number of Writing Operations by ILIFC with Inversion Cells,"  Index-less Indexed Flash Code (ILIFC) is a coding scheme for flash memories,
+in which one bit of a data sequence is stored in a slice consisting of several
+cells but the index of the bit is stored implicitly. Although several modified
+ILIFC schemes have been proposed, in this research we consider an ILIFC with
+inversion cells(I-ILIFC). The I-ILIFC reduces the total number of cell level
+changes at each writing request. Computer simulation is used to show that the
+I-ILIFC improves the average performance of the ILIFC in many cases. This paper
+presents our derivation of the lower bounds on the number of writing operations
+by I-ILIFC and shows that the worst-case performance of the I-ILIFC is better
+than that of the ILIFC if the code length is sufficiently large. Additionally,
+we consider the tight lower bounds thereon. The results show that the threshold
+of the code length that determines whether the I-ILIFC improves the worst-case
+performance of the ILIFC is smaller than that in the first lower bounds.
+"
+23319,Uniform in $N$ Global Well-posedness of the Time-Dependent Hartree-Fock-Bogoliubov Equations in $\mathbb{R}^{1+1}$,"  In this article, we prove the global well-posedness of the time-dependent
+Hartree-Fock-Bogoliubov (TDHFB) equations in $\mathbb{R}^{1+1}$ with two-body
+interaction potentials of the form $N^{-1}v_N(x) = N^{\beta-1} v(N^\beta x)$
+where $v$ is a sufficiently regular radial function $v \in L^1(\mathbb{R})\cap
+C^\infty(\mathbb{R})$. In particular, using methods of dispersive PDEs similar
+to the ones used in Grillakis and Machedon, Comm. PDEs., (2017), we are able to
+show for any scaling parameter $\beta>0$ the TDHFB equations are globally
+well-posed in some Strichartz-type spaces independent of $N$, cf. (Bach et al.
+in arXiv:1602.05171).
+"
+23320,Unobstructed symplectic packing by ellipsoids for tori and hyperkahler manifolds,"  Let M be a closed symplectic manifold of volume V. We say that the symplectic
+packings of M by ellipsoids are unobstructed if any collection of disjoint
+symplectic ellipsoids (possibly of different sizes) of total volume less than V
+admits a symplectic embedding to M. We show that the symplectic packings by
+ellipsoids are unobstructed for all even-dimensional tori equipped with Kahler
+symplectic forms and all closed hyperkahler manifolds of maximal holonomy, or,
+more generally, for closed Campana simple manifolds (that is, Kahler manifolds
+that are not unions of their complex subvarieties), as well as for any closed
+Kahler manifold which is a limit of Campana simple manifolds in a smooth
+deformation. The proof involves the construction of a Kahler resolution of a
+Kahler orbifold with isolated singularities and relies on the results of
+Demailly-Paun and Miyaoka on Kahler cohomology classes.
+"
+23321,Performance of the EDELWEISS-III experiment for direct dark matter searches,"  We present the results of measurements demonstrating the efficiency of the
+EDELWEISS-III array of cryogenic germanium detectors for direct dark matter
+searches. The experimental setup and the FID (Fully Inter-Digitized) detector
+array is described, as well as the efficiency of the double measurement of heat
+and ionization signals in background rejection. For the whole set of 24 FID
+detectors used for coincidence studies, the baseline resolutions for the
+fiducial ionization energy are mainly below 0.7 keV$_{ee}$ (FHWM) whereas the
+baseline resolutions for heat energies are mainly below 1.5 keV$_{ee}$ (FWHM).
+The response to nuclear recoils as well as the very good discrimination
+capability of the FID design has been measured with an AmBe source. The surface
+$\beta$- and $\alpha$-decay rejection power of $R_{\rm surf} < 4 \times
+10^{-5}$ per $\alpha$ at 90% C.L. has been determined with a $^{210}$Pb source,
+the rejection of bulk $\gamma$-ray events has been demonstrated using
+$\gamma$-calibrations with $^{133}$Ba sources leading to a value of
+$R_{\gamma{\rm -mis-fid}} < 2.5 \times 10^{-6}$ at 90% C.L.. The current levels
+of natural radioactivity measured in the detector array are shown as the rate
+of single $\gamma$ background. The fiducial volume fraction of the FID
+detectors has been measured to a weighted average value of $(74.6 \pm 0.4)\%$
+using the cosmogenic activation of the $^{65}$Zn and $^{68,71}$Ge isotopes. The
+stability and uniformity of the detector response is also discussed. The
+achieved resolutions, thresholds and background levels of the upgraded
+EDELWEISS-III detectors in their setup are thus well suited to the direct
+search of WIMP dark matter over a large mass range.
+"
+23322,On the Capacity of a Class of Signal-Dependent Noise Channels,"  In some applications, the variance of additive measurement noise depends on
+the signal that we aim to measure. For instance, additive Gaussian
+signal-dependent noise (AGSDN) channel models are used in molecular and optical
+communication. Herein we provide lower and upper bounds on the capacity of
+additive signal-dependent noise (ASDN) channels. The idea of the first lower
+bound is the extension of the majorization inequality, and for the second one,
+it uses some calculations based on the fact that $h(Y) > h (Y|Z)$. Both of them
+are valid for all additive signal-dependent noise (ASDN) channels defined in
+the paper. The upper bound is based on a previous idea of the authors
+(""symmetric relative entropy"") and is used for the additive Gaussian
+signal-dependent noise (AGSDN) channels. These bounds indicate that in ASDN
+channels (unlike the classical AWGN channels), the capacity does not
+necessarily become larger by making the variance function of the noise smaller.
+We also provide sufficient conditions under which the capacity becomes
+infinity. This is complemented by a number of conditions that imply capacity is
+finite and a unique capacity achieving measure exists (in the sense of the
+output measure).
+"
+23323,Entropy-based Pruning for Learning Bayesian Networks using BIC,"  For decomposable score-based structure learning of Bayesian networks,
+existing approaches first compute a collection of candidate parent sets for
+each variable and then optimize over this collection by choosing one parent set
+for each variable without creating directed cycles while maximizing the total
+score. We target the task of constructing the collection of candidate parent
+sets when the score of choice is the Bayesian Information Criterion (BIC). We
+provide new non-trivial results that can be used to prune the search space of
+candidate parent sets of each node. We analyze how these new results relate to
+previous ideas in the literature both theoretically and empirically. We show in
+experiments with UCI data sets that gains can be significant. Since the new
+pruning rules are easy to implement and have low computational costs, they can
+be promptly integrated into all state-of-the-art methods for structure learning
+of Bayesian networks.
+"
+23324,Effect of Core Cooling on the Radius of Sub-Neptune Planets,"  Sub-Neptune planets are very common in our galaxy and show a large diversity
+in their mass-radius relation. In sub-Neptunes most of the planet mass is in
+the rocky part (hereafter core) which is surrounded by a modest hydrogen-helium
+envelope. As a result, the total initial heat content of such a planet is
+dominated by that of the core. Nonetheless, most studies contend that the core
+cooling will only have a minor effect on the radius evolution of the gaseous
+envelope, because the core's cooling is in sync with the envelope, i.e., most
+of the initial heat is released early on timescales of about 10-100 Myr. In
+this Letter we examine the importance of the core cooling rate for the thermal
+evolution of the envelope. Thus, we relax the early core cooling assumption and
+present a model where the core is characterized by two parameters: the initial
+temperature and the cooling time. We find that core cooling can significantly
+enhance the radius of the planet when it operates on a timescale similar to the
+observed age, i.e. several Gyr. Consequently, the interpretation of
+sub-Neptunes' mass-radius observations depends on the assumed core thermal
+properties and the uncertainty therein. The degeneracy of composition and core
+thermal properties can be reduced by obtaining better estimates of the planet
+ages (in addition to their radii and masses) as envisioned by future
+observations.
+"
+23325,Roadmap on Atto-Joule per Bit Modulators,"  Electrooptic modulation performs the conversion between the electrical and
+optical domain with applications in data communication for optical
+interconnects, but also for novel optical compute algorithms such as providing
+nonlinearity at the output stage of optical perceptrons in neuromorphic
+analogue optical computing. Since the clock frequency for photonics on chip has
+a power overhead sweet slot around 10s of GHz, ultrafast modulation may only be
+required in long distance communication, but not for short onchip links. Here
+we show a roadmap towards atto Joule per bit efficient modulators on chip as
+well as some experimental demonstrations of novel plasmon modulators with sub
+1fJ per bit efficiencies. We then discuss the first experimental demonstration
+of a photon plasmon-hybrid Graphene-based electroabsorption modulator on
+silicon. The device shows a sub 1V steep switching enabled by near ideal
+electrostatics delivering a high 0.05dB per V um performance requiring only 110
+aJ per bit. Improving on this design, we discuss a plasmonic slot based
+Graphene modulator design, where the polarization of the plasmonic mode matches
+with Graphenes inplane dimension. Here a push pull dual gating scheme enables
+2dB per V um efficient modulation allowing the device to be just 770 nm short
+for 3dB small signal modulation. This in turn allows for a device-enabled two
+orders of magnitude improvement of electrical optical co integrated network on
+chips over electronic only architectures. The latter opens technological
+opportunities in in cognitive computing, dynamic data-driven applications
+system, and optical analogue compute engines to include neuromorphic photonic
+computing.
+"
+23326,AGN Activity in Nucleated Galaxies as Measured by Chandra,"  Motivated by theoretical expectations that Nuclear Star Clusters (NSCs) in
+galactic centers may provide a favorable environment for super-massive black
+holes to form and/or efficiently grow, we set out to measure the fraction of
+nearby nucleated galaxies that also host an Active Galactic Nucleus (AGN). We
+targeted a distance-limited sample of 98 objects with the Chandra X-ray
+Telescope, down to a uniform X-ray luminosity threshold of $\sim$10$^{38}$ erg
+s$^{-1}$. The sample is composed of 47 late-types and 51 early-types, enabling
+us to further investigate the active fraction as a function of galactic
+morphology. After correcting for contamination to the nuclear X-ray signal from
+bright X-ray binaries, we measure an active fraction $f$=11.2$\%^{+7.4}_{-4.9}$
+(1$\sigma$ C.L.) across the whole sample, in agreement with previous estimates
+based on an heterogeneous combination of optical, X-ray and radio diagnostics,
+by Seth et al. (2008). After accounting for the different stellar mass
+distributions in our samples, we find no statistically significant difference
+in the active fraction of early- vs. late-type nucleated galaxies, with
+$f$=10.6$\%^{+11.9}_{-4.9}$ and 10.8$\%^{+11.3}_{-6.3}$, respectively. For the
+early-type nucleated galaxies, we are able to carry out a controlled comparison
+with a parent sample of non-nucleated galaxies covering the same stellar mass
+range, finding again no statistically significant difference in the active
+fraction. Taken at face value, our findings suggest that the presence of a NSC
+does not facilitate nor enhance accretion-powered emission from a nuclear
+super-massive black hole. This is true even for late-type nucleated galaxies,
+home to bluer NSCs and arguably larger gas reservoirs.
+"
+23327,Connected numbers and the embedded topology of plane curves,"  The splitting number of a plane irreducible curve for a Galois cover is
+effective to distinguish the embedded topologies of plane curves. In this
+paper, we define a connected number of any plane curve for a Galois cover whose
+branch divisor has no common components with the plane curve, which is similar
+to the splitting number. We classify the embedded topology of Artal
+arrangements of degree $b\geq 4$ by the connected number, where an Artal
+arrangement of degree $b$ is a plane curve consisting of one smooth curve of
+dgree $b$ and three total inflectional tangents.
+"
+23328,"The dual actions, autoequivalences and stable tilting objects","  For a finite abelian group action on a linear category, we study the dual
+action given by the character group acting on the category of equivariant
+objects. We prove that the groups of equivariant autoequivalences on these two
+categories are isomorphic. In the triangulated situation, this isomorphism
+implies that the classifications of stable tilting objects for these two
+categories are in a natural bijection. We apply these results to stable tilting
+complexes on weighted projective lines of tubular type.
+"
+23329,"Characterization and formation of NV centers in 3C, 4H and 6H SiC: an \emph{ab initio} study","  Fluorescent paramagnetic defects in solids have become attractive systems for
+quantum information processing in the recent years. One of the leading
+contenders is the negatively charged nitrogen-vacancy defect in diamond with
+visible emission but alternative solution in technologically mature host is an
+immediate quest for many applications in this field. It has been recently found
+that various polytypes of silicon carbide (SiC), that are standard
+semiconductors with wafer scale technology, can host nitrogen-vacancy defect
+(NV) that could be an alternative qubit candidate with emission in the near
+infrared region. However, it is much less known about this defect than its
+counterpart in diamond. The inequivalent sites within a polytype and the
+polytype variations offer a family of NV defects. However, there is an
+insufficient knowledge on the magneto-optical properties of these
+configurations. Here we carry out density functional theory calculations, in
+order to characterize the numerous forms of NV defects in the most common
+polytypes of SiC including 3C, 4H and 6H, and we also provide new experimental
+data in 4H SiC. Our calculations mediate the identification of individual NV
+qubits in SiC polytypes. In addition, we discuss the formation of NV defects in
+SiC with providing detailed ionization energies of NV defect in SiC which
+reveals the critical optical excitation energies for ionizing this qubits in
+SiC. Our calculations unravel the challenges to produce NV defects in SiC with
+a desirable spin bath.
+"
+23330,Waveform to Single Sinusoid Regression to Estimate the F0 Contour from Noisy Speech Using Recurrent Deep Neural Networks,"  The fundamental frequency (F0) represents pitch in speech that determines
+prosodic characteristics of speech and is needed in various tasks for speech
+analysis and synthesis. Despite decades of research on this topic, F0
+estimation at low signal-to-noise ratios (SNRs) in unexpected noise conditions
+remains difficult. This work proposes a new approach to noise robust F0
+estimation using a recurrent neural network (RNN) trained in a supervised
+manner. Recent studies employ deep neural networks (DNNs) for F0 tracking as a
+frame-by-frame classification task into quantised frequency states but we
+propose waveform-to-sinusoid regression instead to achieve both noise
+robustness and accurate estimation with increased frequency resolution.
+Experimental results with PTDB-TUG corpus contaminated by additive noise
+(NOISEX-92) demonstrate that the proposed method improves gross pitch error
+(GPE) rate and fine pitch error (FPE) by more than 35 % at SNRs between -10 dB
+and +10 dB compared with well-known noise robust F0 tracker, PEFAC.
+Furthermore, the proposed method also outperforms state-of-the-art DNN-based
+approaches by more than 15 % in terms of both FPE and GPE rate over the
+preceding SNR range.
+"
+23331,CHAM: action recognition using convolutional hierarchical attention model,"  Recently, the soft attention mechanism, which was originally proposed in
+language processing, has been applied in computer vision tasks like image
+captioning. This paper presents improvements to the soft attention model by
+combining a convolutional LSTM with a hierarchical system architecture to
+recognize action categories in videos. We call this model the Convolutional
+Hierarchical Attention Model (CHAM). The model applies a convolutional
+operation inside the LSTM cell and an attention map generation process to
+recognize actions. The hierarchical architecture of this model is able to
+explicitly reason on multi-granularities of action categories. The proposed
+architecture achieved improved results on three publicly available datasets:
+the UCF sports dataset, the Olympic sports dataset and the HMDB51 dataset.
+"
+23332,Science with the Cherenkov Telescope Array,"  The Cherenkov Telescope Array, CTA, will be the major global observatory for
+very high energy gamma-ray astronomy over the next decade and beyond. The
+scientific potential of CTA is extremely broad: from understanding the role of
+relativistic cosmic particles to the search for dark matter. CTA is an explorer
+of the extreme universe, probing environments from the immediate neighbourhood
+of black holes to cosmic voids on the largest scales. Covering a huge range in
+photon energy from 20 GeV to 300 TeV, CTA will improve on all aspects of
+performance with respect to current instruments.
+The observatory will operate arrays on sites in both hemispheres to provide
+full sky coverage and will hence maximize the potential for the rarest
+phenomena such as very nearby supernovae, gamma-ray bursts or gravitational
+wave transients. With 99 telescopes on the southern site and 19 telescopes on
+the northern site, flexible operation will be possible, with sub-arrays
+available for specific tasks. CTA will have important synergies with many of
+the new generation of major astronomical and astroparticle observatories.
+Multi-wavelength and multi-messenger approaches combining CTA data with those
+from other instruments will lead to a deeper understanding of the broad-band
+non-thermal properties of target sources.
+The CTA Observatory will be operated as an open, proposal-driven observatory,
+with all data available on a public archive after a pre-defined proprietary
+period. Scientists from institutions worldwide have combined together to form
+the CTA Consortium. This Consortium has prepared a proposal for a Core
+Programme of highly motivated observations. The programme, encompassing
+approximately 40% of the available observing time over the first ten years of
+CTA operation, is made up of individual Key Science Projects (KSPs), which are
+presented in this document.
+"
+23333,Joint Screening Tests for LASSO,"  This paper focusses on ""safe"" screening techniques for the LASSO problem.
+Motivated by the need for low-complexity algorithms, we propose a new approach,
+dubbed ""joint"" screening test, allowing to screen a set of atoms by carrying
+out one single test. The approach is particularized to two different sets of
+atoms, respectively expressed as sphere and dome regions. After presenting the
+mathematical derivations of the tests, we elaborate on their relative
+effectiveness and discuss the practical use of such procedures.
+"
+23334,Optimal Ballistic Transport and Hopf-Lax Formulae on Wasserstein Space,"  We investigate the optimal mass transport problem associated to the following
+""ballistic"" cost functional on phase space $M\times M^*$, $$ b_T(v,
+x):=\inf\{\langle v, \gamma (0)\rangle +\int_0^TL(\gamma (t), {\dot
+\gamma}(t))\, dt, \gamma \in C^1([0, T), M), \gamma(T)=x\}, $$ where
+$M=\mathbb{R}^d$, $T>0$, and $L:M\times M \to \mathbb{R}$ is a Lagrangian that
+is jointly convex in both variables. Under suitable conditions on the initial
+and final probability measures, we use convex duality à la Bolza and
+Monge-Kantorovich theory to lift classical Hopf-Lax formulae from state space
+to Wasserstein space. This allows us to relate optimal transport maps for the
+ballistic cost to those associated with the fixed-end cost defined on $M\times
+M$ by $$ c_T(x,y):=\inf\{\int_0^TL(\gamma(t), {\dot \gamma}(t))\, dt, \gamma\in
+C^1([0, T), M), \gamma(0)=x, \gamma(T)=y\}. $$ We also point to links with the
+theory of mean field games.
+"
+23335,Multi-scale statistics of turbulence motorized by active matter,"  A number of micro-scale biological flows are characterized by spatio-temporal
+chaos. These include dense suspensions of swimming bacteria, microtubule
+bundles driven by motor proteins, and dividing and migrating confluent layers
+of cells. A characteristic common to all of these systems is that they are
+laden with active matter, which transforms free energy in the fluid into
+kinetic energy. Because of collective effects, the active matter induces
+multi-scale flow motions that bear strong visual resemblance to turbulence. In
+this study, multi-scale statistical tools are employed to analyze direct
+numerical simulations (DNS) of periodic two- (2D) and three-dimensional (3D)
+active flows and compare them to classic turbulent flows. Statistical
+descriptions of the flows and their variations with activity levels are
+provided in physical and spectral spaces. A scale-dependent intermittency
+analysis is performed using wavelets. The results demonstrate fundamental
+differences between active and high-Reynolds number turbulence; for instance,
+the intermittency is smaller and less energetic in active flows, and the work
+of the active stress is spectrally exerted near the integral scales and
+dissipated mostly locally by viscosity, with convection playing a minor role in
+momentum transport across scales.
+"
+23336,Unlocking of predicate: application to non-anticipating selections,"  We consider an approach to constructing a non-anticipating selection of a
+multivalued mapping; such a problem arises in control theory under conditions
+of uncertainty. The approach is called ""unlocking of predicate"" and consists in
+the reduction of finding the truth set of a predicate to searching fixed points
+of some mappings. Unlocking of predicate gives an extra opportunity to analyze
+the truth set and to build its elements with desired properties.
+In this article, we outline how to build ""unlocking mappings"" for some
+general types of predicates: we give a formal definition of the predicate
+unlocking operation, the rules for the construction and calculation of
+""unlocking mappings"" and their basic properties. As an illustration, we
+routinely construct two unlocking mappings for the predicate ""be
+non-anticipating mapping"" and then on this base we provide the expression for
+the greatest non-anticipating selection of a given multifunction.
+"
+23337,Topological spin Hall effect in antiferromagnetic skyrmions,"  The topological Hall effect (THE), as one of the primary manifestations of
+non-trivial topology of chiral skyrmions, is traditionally used to detect the
+emergence of skyrmion lattices with locally ferromagnetic order. In this work
+we demonstrate that the appearance of non-trivial two-dimensional chiral
+textures with locally {\it anti}-ferromagnetic order can be detected through
+the spin version of the THE $-$ the topological spin Hall effect (TSHE).
+Utilizing the semiclassical formalism, here used to combine chiral
+antiferromagnetic textures with a density functional theory description of the
+collinear, degenerate electronic structure, we follow the real-space real-time
+evolution of electronic SU(2) wavepackets in an external electric field to
+demonstrate the emergence of sizeable transverse pure spin current in synthetic
+antiferromagnets of the Fe/Cu/Fe trilayer type. We further unravel the extreme
+sensitivity of the TSHE to the details of the electronic structure, suggesting
+that the magnitude and sign of the TSHE in transition-metal synthetic
+antiferromagnets can be engineered by tuning such parameters as the thickness
+or band filling. Besides being an important step in our understanding of the
+topological properties of ever more complex skyrmionic systems, our results
+bear great potential in stimulating the discovery of antiferromagnetic
+skyrmions.
+"
+23338,An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service,"  In this paper, we present machine learning approaches for characterizing and
+forecasting the short-term demand for on-demand ride-hailing services. We
+propose the spatio-temporal estimation of the demand that is a function of
+variable effects related to traffic, pricing and weather conditions. With
+respect to the methodology, a single decision tree, bootstrap-aggregated
+(bagged) decision trees, random forest, boosted decision trees, and artificial
+neural network for regression have been adapted and systematically compared
+using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and
+slope. To better assess the quality of the models, they have been tested on a
+real case study using the data of DiDi Chuxing, the main on-demand ride hailing
+service provider in China. In the current study, 199,584 time-slots describing
+the spatio-temporal ride-hailing demand has been extracted with an
+aggregated-time interval of 10 mins. All the methods are trained and validated
+on the basis of two independent samples from this dataset. The results revealed
+that boosted decision trees provide the best prediction accuracy (RMSE=16.41),
+while avoiding the risk of over-fitting, followed by artificial neural network
+(20.09), random forest (23.50), bagged decision trees (24.29) and single
+decision tree (33.55).
+"
+23339,Efficient Detection of Points of Interest from Georeferenced Visual Content,"  Many people take photos and videos with smartphones and more recently with
+360-degree cameras at popular places and events, and share them in social
+media. Such visual content is produced in large volumes in urban areas, and it
+is a source of information that online users could exploit to learn what has
+got the interest of the general public on the streets of the cities where they
+live or plan to visit. A key step to providing users with that information is
+to identify the most popular k spots in specified areas. In this paper, we
+propose a clustering and incremental sampling (C&IS) approach that trades off
+accuracy of top-k results for detection speed. It uses clustering to determine
+areas with high density of visual content, and incremental sampling, controlled
+by stopping criteria, to limit the amount of computational work. It leverages
+spatial metadata, which represent the scenes in the visual content, to rapidly
+detect the hotspots, and uses a recently proposed Gaussian probability model to
+describe the capture intention distribution in the query area. We evaluate the
+approach with metadata, derived from a non-synthetic, user-generated dataset,
+for regular mobile and 360-degree visual content. Our results show that the
+C&IS approach offers 2.8x-19x reductions in processing time over an optimized
+baseline, while in most cases correctly identifying 4 out of 5 top locations.
+"
+23340,Symbolic and Numerical Analysis in General Relativity with Open Source Computer Algebra Systems,"  We study three computer algebra systems, namely SageMath (with SageManifolds
+package), Maxima (with ctensor package) and Python language (with GraviPy
+module), which allow tensor manipulation for general relativity calculations
+along with general algebraic calculations. We present a benchmark of these
+systems using simple examples. After the general analysis, we focus on the
+SageMath and SageManifolds system to derive, analyze and visualize the
+solutions of the massless Klein-Gordon equation and geodesic motion with
+Hamilton-Jacobi formalism. We compare our numerical result of the Klein-Gordon
+equation with the asymptotic form of the analytical solution to see that they
+agree.
+"
+23341,Towards compact Free Electron Laser based on laser plasma accelerators,"  The laser invention more than fifty years ago was a major scientific
+revolution. Among the different possible gain media, the Free Electron Lasers
+(FEL) uses free electrons in the periodic permanent magnetic field of an
+undulator, covering wavelengths from far infrared to X-ray, with easy
+tuneability and high peak power. Nowadays, the advent of tuneable intense (mJ
+level) short pulse FELs with record peak power (GW level) in the X-ray domain
+sets a major step in laser development, and enables to explore new scientific
+areas, such as deciphering molecular reactions in real time, understanding
+functions of proteins. Besides, lasers have also been considered for driving
+plasma electron acceleration. A high-power femtosecond laser is focused into a
+gas target and resonantly drives a nonlinear plasma wave in which plasma
+electrons are trapped and accelerated with high energy gain of GeV/m. Nowadays,
+laser wakefield acceleration became reality and electron beams with multi-GeV
+energies, hundreds of pC charge, sub-percent energy spread and sub-milliradian
+divergence can be produced. It is relevant to consider a FEL application to
+quality these laser plasma produced electrons. After having described the FEL
+panorama, the strategies towards laser plasma based acceleration based FELs
+will be discussed, including the mitigation of the large energy spread and
+divergence of these beams should be mitigated.
+"
+23342,Lattice paths with catastrophes,"  In queuing theory, it is usual to have some models with a ""reset"" of the
+queue. In terms of lattice paths, it is like having the possibility of jumping
+from any altitude to zero. These objects have the interesting feature that they
+do not have the same intuitive probabilistic behaviour as classical Dyck paths
+(the typical properties of which are strongly related to Brownian motion
+theory), and this article quantifies some relations between these two types of
+paths. We give a bijection with some other lattice paths and a link with a
+continued fraction expansion. Furthermore, we prove several formulae for
+related combinatorial structures conjectured in the On-Line Encyclopedia of
+Integer Sequences. Thanks to the kernel method and via analytic combinatorics,
+we provide the enumeration and limit laws of these ""lattice paths with
+catastrophes"" for any finite set of jumps. We end with an algorithm to generate
+such lattice paths uniformly at random.
+"
+23343,On the splitting of the Kummer exact sequence,"  We prove the splitting of the Kummer exact sequence and related exact
+sequences in arithmetic geometry.
+"
+23344,Exciton-phonon cooperative mechanism of the triple-$q$ charge-density-wave and antiferroelectric electron polarization in TiSe$_2$,"  We investigate the microscopic mechanisms of the charge-density-wave (CDW)
+formation in a monolayer TiSe$_2$ using a realistic multiorbital $d$-$p$ model
+with electron-phonon coupling and intersite Coulomb (excitonic) interactions.
+First, we estimate the tight-binding bands of Ti $3d$ and Se $4p$ orbitals in
+the monolayer TiSe$_2$ on the basis of the first-principles band structure
+calculations. We thereby show orbital textures of the undistorted band
+structure near the Fermi level. Next, we derive the electron-phonon coupling
+using the tight-binding approximation and show that the softening occurs in the
+transverse phonon mode at the M point of the Brillouin zone. The stability of
+the triple-$q$ CDW state is thus examined to show that the transverse phonon
+modes at the M$_1$, M$_2$, and M$_3$ points are frozen simultaneously. Then, we
+introduce the intersite Coulomb interactions between the nearest-neighbor Ti
+and Se atoms that lead to the excitonic instability between the valence Se $4p$
+and conduction Ti $3d$ bands. Treating the intersite Coulomb interactions in
+the mean-field approximation, we show that the electron-phonon and excitonic
+interactions cooperatively stabilize the triple-$q$ CDW state in TiSe$_2$. We
+also calculate a single-particle spectrum in the CDW state and reproduce the
+band folding spectra observed in photoemission spectroscopies. Finally, to
+clarify the nature of the CDW state, we examine the electronic charge density
+distribution and show that the CDW state in TiSe$_2$ is of a bond-type and
+induces a vortex-like antiferroelectric polarization in the kagome network of
+Ti atoms.
+"
+23345,The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation,"  Deep reinforcement learning (DRL) demonstrates its potential in learning a
+model-free navigation policy for robot visual navigation. However, the
+data-demanding algorithm relies on a large number of navigation trajectories in
+training. Existing datasets supporting training such robot navigation
+algorithms consist of either 3D synthetic scenes or reconstructed scenes.
+Synthetic data suffers from domain gap to the real-world scenes while visual
+inputs rendered from 3D reconstructed scenes have undesired holes and
+artifacts. In this paper, we present a new dataset collected in real-world to
+facilitate the research in DRL based visual navigation. Our dataset includes 3D
+reconstruction for real-world scenes as well as densely captured real 2D images
+from the scenes. It provides high-quality visual inputs with real-world scene
+complexity to the robot at dense grid locations. We further study and benchmark
+one recent DRL based navigation algorithm and present our attempts and thoughts
+on improving its generalizability to unseen test targets in the scenes.
+"
+23346,Multilevel Monte Carlo for Smoothing via Transport Methods,"  In this article we consider recursive approximations of the smoothing
+distribution associated to partially observed stochastic differential equations
+(SDEs), which are observed discretely in time. Such models appear in a wide
+variety of applications including econometrics, finance and engineering. This
+problem is notoriously challenging, as the smoother is not available
+analytically and hence require numerical approximation. This usually consists
+by applying a time-discretization to the SDE, for instance the Euler method,
+and then applying a numerical (e.g. Monte Carlo) method to approximate the
+smoother. This has lead to a vast literature on methodology for solving such
+problems, perhaps the most popular of which is based upon the particle filter
+(PF) e.g. [9]. In the context of filtering for this class of problems, it is
+well-known that the particle filter can be improved upon in terms of cost to
+achieve a given mean squared error (MSE) for estimates. This in the sense that
+the computational effort can be reduced to achieve this target MSE, by using
+multilevel (ML) methods [12, 13, 18], via the multilevel particle filter (MLPF)
+[16, 20, 21]. For instance, to obtain a MSE of $\mathcal{O}(\epsilon^2)$ for
+some $\epsilon > 0$ when approximating filtering distributions associated with
+Euler-discretized diffusions with constant diffusion coefficients, the cost of
+the PF is $\mathcal{O}(\epsilon^{-3})$ while the cost of the MLPF is
+$\mathcal{O}(\epsilon^{-2}\log(\epsilon)^2)$. In this article we consider a new
+approach to replace the particle filter, using transport methods in [27]. In
+the context of filtering, one expects that the proposed method improves upon
+the MLPF by yielding, under assumptions, a MSE of $\mathcal{O}(\epsilon^2)$ for
+a cost of $\mathcal{O}(\epsilon^{-2})$. This is established theoretically in an
+""ideal"" example and numerically in numerous examples.
+"
+23347,On the Simulation of Polynomial NARMAX Models,"  In this paper, we show that the common approach for simulation non-linear
+stochastic models, commonly used in system identification, via setting the
+noise contributions to zero results in a biased response. We also demonstrate
+that to achieve unbiased simulation of finite order NARMAX models, in general,
+we require infinite order simulation models. The main contributions of the
+paper are two-fold. Firstly, an alternate representation of polynomial NARMAX
+models, based on Hermite polynomials, is proposed. The proposed representation
+provides a convenient way to translate a polynomial NARMAX model to a
+corresponding simulation model by simply setting certain terms to zero. This
+translation is exact when the simulation model can be written as an NFIR model.
+Secondly, a parameterized approximation method is proposed to curtail infinite
+order simulation models to a finite order. The proposed approximation can be
+viewed as a trade-off between the conventional approach of setting noise
+contributions to zero and the approach of incorporating the bias introduced by
+higher-order moments of the noise distribution. Simulation studies are provided
+to illustrate the utility of the proposed representation and approximation
+method.
+"
+23348,A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation,"  In this article, we propose a novel ensemble technique with a multi-scheme
+weighting based on a technique called coopetitive soft gating. This technique
+combines both, ensemble member competition and cooperation, in order to
+maximize the overall forecasting accuracy of the ensemble. The proposed
+algorithm combines the ideas of multiple ensemble paradigms (power forecasting
+model ensemble, weather forecasting model ensemble, and lagged ensemble) in a
+hierarchical structure. The technique is designed to be used in a flexible
+manner on single and multiple weather forecasting models, and for a variety of
+lead times. We compare the technique to other power forecasting models and
+ensemble techniques with a flexible number of weather forecasting models, which
+can have the same, or varying forecasting horizons. It is shown that the model
+is able to outperform those models on a number of publicly available data sets.
+The article closes with a discussion of properties of the proposed model which
+are relevant in its application.
+"
+23349,Thermodynamics of frustrated ferromagnetic spin-$1/2$ Heisenberg chains: The role of inter-chain coupling,"  The thermodynamics of coupled frustrated ferromagnetic chains is studied
+within a spin-rotation-invariant Green's function approach. We consider an
+isotropic Heisenberg spin-half system with a ferromagnetic in-chain coupling
+$J_1<0$ between nearest neighbors and a frustrating antiferromagnetic
+next-nearest neighbor in-chain coupling $J_2>0$. We focus on moderate strength
+of frustration $J_2 < |J_1|/4$ such that the in-chain spin-spin correlations
+are predominantly ferromagnetic. We consider two inter-chain couplings (ICs)
+$J_{\perp,y}$ and $J_{\perp,z}$, corresponding to the two axis perpendicular to
+the chain, where ferromagnetic as well as antiferromagnetic ICs are taken into
+account. We discuss the influence of frustration on the ground-state properties
+for antiferromagnetic ICs, where the ground state is of quantum nature. The
+major part of our study is devoted to the finite-temperature properties. We
+calculate the critical temperature $T_{c}$ as a function of the competing
+exchange couplings $J_{2},J_{\perp,y}, J_{\perp,z}$. We find that for fixed ICs
+$T_c$ monotonically decreases with increasing frustration $J_2$, where as $J_2
+\to |J_1|/4$ the $T_c(J_2)$-curve drops down rapidly. To characterize the
+magnetic ordering below and above $T_c$ we calculate the spin-spin correlation
+functions $\langle {\bf S}_0 {\bf S}_{\bf R} \rangle$, the magnetic order
+parameter $M$, the uniform static susceptibility $\chi_0$ as well as the
+correlation length $\xi$. Moreover, we discuss the specific heat $C_V$ and the
+temperature dependence of the excitation spectrum $\omega_{\mathbf{q}}$. As
+$J_2 \to |J_1|/4$ some unusual frustration-induced features were found, such as
+an increase of the in-chain spin stiffness (in case of ferromagnetic ICs) or of
+the in-chain spin-wave velocity (in case of antiferromagnetic ICs) with growing
+temperature.
+"
+23350,Effect of turbulence on collisional growth of cloud droplets,"  We investigate the effect of turbulence on the collisional growth of um-sized
+droplets through high- resolution numerical simulations with well resolved
+Kolmogorov scales, assuming a collision and coalescence efficiency of unity.
+The droplet dynamics and collisions are approximated using a superparticle
+approach. In the absence of gravity, we show that the time evolution of the
+shape of the droplet-size distribution due to turbulence-induced collisions
+depends strongly on the turbulent energy-dissipation rate, but only weakly on
+the Reynolds number. This can be explained through the energy dissipation rate
+dependence of the mean collision rate described by the Saffman-Turner collision
+model. Consistent with the Saffman-Turner collision model and its extensions,
+the collision rate increases as the square root of the energy dissipation rate
+even when coalescence is invoked. The size distribution exhibits power law
+behavior with a slope of -3.7 between a maximum at approximately 10 um up to
+about 40 um. When gravity is invoked, turbulence is found to dominate the time
+evolution of an initially monodisperse droplet distribution at early times. At
+later times, however, gravity takes over and dominates the collisional growth.
+We find that the formation of large droplets is very sensitive to the turbulent
+energy dissipation rate. This is due to the fact that turbulence enhances the
+collisional growth between similar sized droplets at the early stage of
+raindrop formation. The mean collision rate grows exponentially, which is
+consistent with the theoretical prediction of the continuous collisional growth
+even when turbulence-generated collisions are invoked. This consistency only
+reflects the mean effect of turbulence on collisional growth.
+"
+23351,Assessing the Utility of Weather Data for Photovoltaic Power Prediction,"  Photovoltaic systems have been widely deployed in recent times to meet the
+increased electricity demand as an environmental-friendly energy source. The
+major challenge for integrating photovoltaic systems in power systems is the
+unpredictability of the solar power generated. In this paper, we analyze the
+impact of having access to weather information for solar power generation
+prediction and find weather information that can help best predict photovoltaic
+power.
+"
+23352,"Trimming the Hill estimator: robustness, optimality and adaptivity","  We introduce a trimmed version of the Hill estimator for the index of a
+heavy-tailed distribution, which is robust to perturbations in the extreme
+order statistics. In the ideal Pareto setting, the estimator is essentially
+finite-sample efficient among all unbiased estimators with a given strict upper
+break-down point. For general heavy-tailed models, we establish the asymptotic
+normality of the estimator under second order conditions and discuss its
+minimax optimal rate in the Hall class. We introduce the so-called trimmed Hill
+plot, which can be used to select the number of top order statistics to trim.
+We also develop an automatic, data-driven procedure for the choice of trimming.
+This results in a new type of robust estimator that can {\em adapt} to the
+unknown level of contamination in the extremes. As a by-product we also obtain
+a methodology for identifying extreme outliers in heavy tailed data. The
+competitive performance of the trimmed Hill and adaptive trimmed Hill
+estimators is illustrated with simulations.
+"
+23353,Swarming for Faster Convergence in Stochastic Optimization,"  We study a distributed framework for stochastic optimization which is
+inspired by models of collective motion found in nature (e.g., swarming) with
+mild communication requirements. Specifically, we analyze a scheme in which
+each one of $N > 1$ independent threads, implements in a distributed and
+unsynchronized fashion, a stochastic gradient-descent algorithm which is
+perturbed by a swarming potential. Assuming the overhead caused by
+synchronization is not negligible, we show the swarming-based approach exhibits
+better performance than a centralized algorithm (based upon the average of $N$
+observations) in terms of (real-time) convergence speed. We also derive an
+error bound that is monotone decreasing in network size and connectivity. We
+characterize the scheme's finite-time performances for both convex and
+non-convex objective functions.
+"
+23354,ALMA Maps of Dust and Warm Dense Gas Emission in the Starburst Galaxy IC 5179$^\star$,"  We present our high-resolution
+($0^{\prime\prime}.15\times0^{\prime\prime}.13$, $\sim$34 pc) observations of
+the CO(6-5) line emission, which probes the warm and dense molecular gas, and
+the 434 $\mu$m dust continuum emission in the nuclear region of the starburst
+galaxy IC 5179, conducted with the Atacama Large Millimeter Array (ALMA). The
+CO(6-5) emission is spatially distributed in filamentary structures with many
+dense cores and shows a velocity field that is characteristic of a
+circum-nuclear rotating gas disk, with 90% of the rotation speed arising within
+a radius of $\lesssim150$ pc. At the scale of our spatial resolution, the
+CO(6-5) and dust emission peaks do not always coincide, with their surface
+brightness ratio varying by a factor of $\sim$10. This result suggests that
+their excitation mechanisms are likely different, as further evidenced by the
+Southwest to Northeast spatial gradient of both CO-to-dust continuum ratio and
+Pa-$\alpha$ equivalent width. Within the nuclear region (radius$\sim$300 pc)
+and with a resolution of $\sim$34 pc, the CO line flux (dust flux density)
+detected in our ALMA observations is $180\pm18$ Jy km/s ($71\pm7$ mJy), which
+account for 22% (2.4%) of the total value measured by Herschel.
+"
+23355,Fractional derivative in spaces of continuous functions,"  This is a first version of a paper concerning abstract evolution equation
+with fractional time derivatives. Maximal regularity results in spaces of
+continuous and Hoelder continuous functions are described.
+"
+23356,Dynamic Models of Wasserstein-1-Type Unbalanced Transport,"  We consider a class of convex optimization problems modelling temporal mass
+transport and mass change between two given mass distributions (the so-called
+dynamic formulation of unbalanced transport), where we focus on those models
+for which transport costs are proportional to transport distance. For those
+models we derive an equivalent, computationally more efficient static
+formulation, we perform a detailed analysis of the model optimizers and the
+associated optimal mass change and transport, and we examine which static
+models are generated by a corresponding equivalent dynamic one. Alongside we
+discuss thoroughly how the employed model formulations relate to other
+formulations found in the literature.
+"
+23357,Optimizing the fractional power in a model with stochastic PDE constraints,"  We study an optimization problem with SPDE constraints, which has the
+peculiarity that the control parameter $s$ is the $s$-th power of the diffusion
+operator in the state equation. Well-posedness of the state equation and
+differentiability properties with respect to the fractional parameter $s$ are
+established. We show that under certain conditions on the noise, optimality
+conditions for the control problem can be established.
+"
+23358,Designing Privacy-aware Internet of Things Applications,"  Internet of Things (IoT) applications typically collect and analyse personal
+data that can be used to derive sensitive information about individuals.
+However, thus far, privacy concerns have not been explicitly considered in
+software engineering processes when designing IoT applications. In this paper,
+we explore how a Privacy-by-Design (PbD) framework, formulated as a set of
+guidelines, can help software engineers to design privacy-aware IoT
+applications. We studied the utility of our proposed PbD framework by studying
+how software engineers use it to design IoT applications. We also explore the
+challenges in using set of guidelines to influence the IoT applications design
+process. This paper also highlights the benefits of providing a framework that
+helps software engineers explicitly consider privacy for IoT applications and
+also surfaced a number of challenges associated with our approach. Our studies
+show that PbD framework significantly increase both novice and expert software
+engineers' ability to design privacy aware IoT applications.
+"
+23359,Thermal Pressure in the Cold Neutral Medium of Nearby Galaxies,"  Dynamic and thermal processes regulate the structure of the multi-phase
+interstellar medium (ISM), and ultimately establish how galaxies evolve through
+star formation. Thus, to constrain ISM models and better understand the
+interplay of these processes, it is of great interest to measure the thermal
+pressure ($P_{\rm th}$) of the diffuse, neutral gas. By combining [C II] 158
+$\mu$m, HI, and CO data from 31 galaxies selected from the Herschel KINGFISH
+sample, we have measured thermal pressures in 534 predominantly atomic regions
+with typical sizes of $\sim$1 kiloparsec. We find a distribution of thermal
+pressures in the $P_{\rm th}/k\sim10^3-10^5$ K cm$^{-3}$ range. For a
+sub-sample of regions with conditions similar to those of the diffuse, neutral
+gas in the Galactic plane, we find thermal pressures that follow a log-normal
+distribution with a median value of $P_{\rm th}/k\approx3600$ K cm$^{-3}$.
+These results are consistent with thermal pressure measurements using other
+observational methods. We find that $P_{\rm th}$ increases with radiation field
+strength and star formation activity, as expected from the close link between
+the heating of the gas and the star formation rate. Our thermal pressure
+measurements fall in the regime where a two-phase ISM with cold and warm
+neutral medium could exist in pressure equilibrium. Finally, we find that the
+midplane thermal pressure of the diffuse gas is about $\sim30$% of the vertical
+weight of the overlying ISM, consistent with results from hydrodynamical
+simulations of self-regulated star formation in galactic disks.
+"
+23360,Topological Chern-Simons/Matter Theories,"  We propose a new partially topological theory in three dimensions which
+couples Chern-Simons theory to matter. The 3-manifolds needed for this
+construction admit transverse holomorphic foliation (THF). The theory depends
+only on the choice of such a structure, but not on a choice of metric and in
+this sense, it is topological. We argue that this theory arises in topological
+A-model string theory on Lagrangian 3-branes in the presence of additional
+parallel coisotropic 5-branes. The theory obtained in this way is equivalent to
+an N=2 supersymmetric Chern-Simons matter theory on the same 3-manifold, which
+also only depends on the THF structure. The theory is a realization of a
+topological theory of class H, which allows splitting of a temporal direction
+from spatial directions. We briefly discuss potential condensed matter
+applications.
+"
+23361,Learning like humans with Deep Symbolic Networks,"  We introduce the Deep Symbolic Network (DSN) model, which aims at becoming
+the white-box version of Deep Neural Networks (DNN). The DSN model provides a
+simple, universal yet powerful structure, similar to DNN, to represent any
+knowledge of the world, which is transparent to humans. The conjecture behind
+the DSN model is that any type of real world objects sharing enough common
+features are mapped into human brains as a symbol. Those symbols are connected
+by links, representing the composition, correlation, causality, or other
+relationships between them, forming a deep, hierarchical symbolic network
+structure. Powered by such a structure, the DSN model is expected to learn like
+humans, because of its unique characteristics. First, it is universal, using
+the same structure to store any knowledge. Second, it can learn symbols from
+the world and construct the deep symbolic networks automatically, by utilizing
+the fact that real world objects have been naturally separated by
+singularities. Third, it is symbolic, with the capacity of performing causal
+deduction and generalization. Fourth, the symbols and the links between them
+are transparent to us, and thus we will know what it has learned or not - which
+is the key for the security of an AI system. Fifth, its transparency enables it
+to learn with relatively small data. Sixth, its knowledge can be accumulated.
+Last but not least, it is more friendly to unsupervised learning than DNN. We
+present the details of the model, the algorithm powering its automatic learning
+ability, and describe its usefulness in different use cases. The purpose of
+this paper is to generate broad interest to develop it within an open source
+project centered on the Deep Symbolic Network (DSN) model towards the
+development of general AI.
+"
+23362,The Crab pulsar at VHE,"  The last six years have witnessed major revisions of our knowledge about the
+Crab Pulsar. The consensus scenario for the origin of the high-energy pulsed
+emission has been challenged with the discovery of a very-high-energy power law
+tail extending up to 400 GeV, above the expected spectral cut off at a few GeV.
+Now, new measurements obtained by the MAGIC collaboration extend the energy
+spectrum of the Crab Pulsar even further, on the TeV regime. Above 400 GeV the
+pulsed emission comes mainly from the inter-pulse, which becomes more prominent
+with energy due to a harder spectral index. These findings require gamma-ray
+production via inverse Compton scattering close to or beyond the light cylinder
+radius by an underlying particle population with Lorentz factors greater than 5
+times 106. We will present those new results and discuss the implications in
+our current knowledge concerning pulsar environments.
+"
+23363,Boundary-bulk relation in topological orders,"  In this paper, we study the relation between an anomaly-free $n+$1D
+topological order, which are often called $n+$1D topological order in physics
+literature, and its $n$D gapped boundary phases. We argue that the $n+$1D bulk
+anomaly-free topological order for a given $n$D gapped boundary phase is
+unique. This uniqueness defines the notion of the ""bulk"" for a given gapped
+boundary phase. In this paper, we show that the $n+$1D ""bulk"" phase is given by
+the ""center"" of the $n$D boundary phase. In other words, the geometric notion
+of the ""bulk"" corresponds precisely to the algebraic notion of the ""center"". We
+achieve this by first introducing the notion of a morphism between two
+(potentially anomalous) topological orders of the same dimension, then proving
+that the notion of the ""bulk"" satisfies the same universal property as that of
+the ""center"" of an algebra in mathematics, i.e. ""bulk = center"". The entire
+argument does not require us to know the precise mathematical description of a
+(potentially anomalous) topological order. This result leads to concrete
+physical predictions.
+"
+23364,Optical properties of periodic systems within the current-current response framework: pitfalls and remedies,"  We compare the optical absorption of extended systems using the
+density-density and current-current linear response functions calculated within
+many-body perturbation theory. The two approaches are formally equivalent for a
+finite momentum $\mathbf{q}$ of the external perturbation. At
+$\mathbf{q}=\mathbf{0}$, however, the equivalence is maintained only if a small
+$q$ expansion of the density-density response function is used. Moreover, in
+practical calculations this equivalence can be lost if one naively extends the
+strategies usually employed in the density-based approach to the current-based
+approach. Specifically we discuss the use of a smearing parameter or of the
+quasiparticle lifetimes to describe the finite width of the spectral peaks and
+the inclusion of electron-hole interaction. In those instances we show that the
+incorrect definition of the velocity operator and the violation of the
+conductivity sum rule introduce unphysical features in the optical absorption
+spectra of three paradigmatic systems: silicon (semiconductor), copper (metal)
+and lithium fluoride (insulator). We then demonstrate how to correctly
+introduce lifetime effects and electron-hole interactions within the
+current-based approach.
+"
+23365,Radiative Transfer in a Translucent Cloud Illuminated by an Extended Background Source,"  We discuss the radiative transfer theory for translucent clouds illuminated
+by an extended background source. First we derive a rigorous solution based on
+the assumption that multiple scattering produce an isotropic flux. Then we
+derive a more manageable analytic approximation showing that it nicely matches
+the results of the rigorous approach. To validate our model, we compare our
+predictions with accurate laboratory measurements for various types of well
+characterized grains, including purely dielectric and strongly absorbing
+materials representative of astronomical icy and metallic grains, respectively,
+finding excellent agreement without the need of adding free parameters. We use
+our model to explore the behavior of an astrophysical cloud illuminated by a
+diffuse source with dust grains having parameters typical of the classic ISM
+grains of Draine & Lee (1984) and protoplanetary disks, with an application to
+the dark silhouette disk 114-426 in Orion Nebula. We find that the scattering
+term modifies the transmitted radiation, both in terms of intensity
+(extinction) and shape (reddening) of the spectral distribution. In particular,
+for small optical thickness our results show that scattering makes reddening
+almost negligible at visible wavelengths. Once the optical thickness increases
+enough and the probability of scattering events become close to or larger than
+1, reddening becomes present but appreciably modified with respect to the
+standard expression for line-of-sight absorption. Moreover, variations of the
+grain refractive index, in particular the amount of absorption, also play an
+important role changing the shape of the spectral transmission curve, with
+dielectric grain showing the minimum amount of reddening.
+"
+23366,Utilitarians Without Utilities: Maximizing Social Welfare for Graph Problems using only Ordinal Preferences - Full Version,"  We consider ordinal approximation algorithms for a broad class of utility
+maximization problems for multi-agent systems. In these problems, agents have
+utilities for connecting to each other, and the goal is to compute a
+maximum-utility solution subject to a set of constraints. We represent these as
+a class of graph optimization problems, including matching, spanning tree
+problems, TSP, maximum weight planar subgraph, and many others. We study these
+problems in the ordinal setting: latent numerical utilities exist, but we only
+have access to ordinal preference information, i.e., every agent specifies an
+ordering over the other agents by preference. We prove that for the large class
+of graph problems we identify, ordinal information is enough to compute
+solutions which are close to optimal, thus demonstrating there is no need to
+know the underlying numerical utilities. For example, for problems in this
+class with bounded degree $b$ a simple ordinal greedy algorithm always produces
+a ($b+1$)-approximation; we also quantify how the quality of ordinal
+approximation depends on the sparsity of the resulting graphs. In particular,
+our results imply that ordinal information is enough to obtain a
+2-approximation for Maximum Spanning Tree; a 4-approximation for Max Weight
+Planar Subgraph; a 2-approximation for Max-TSP; and a 2-approximation for
+various Matching problems.
+"
+23367,MiniOS: an instructional platform for teaching operating systems labs,"  Delivering hands-on practice laboratories for introductory courses on
+operating systems is a difficult task. One of the main sources of the
+difficulty is the sheer size and complexity of the operating systems software.
+Consequently, some of the solutions adopted in the literature to teach
+operating systems laboratory consider smaller and simpler systems, generally
+referred to as instructional operating systems. This work continues in the same
+direction and is threefold. First, it considers a simpler hardware platform.
+Second, it argues that a minimal operating system is a viable option for
+delivering laboratories. Third, it presents a laboratory teaching platform,
+whereby students build a minimal operating system for an embedded hardware
+platform. The proposed platform is called MiniOS. An important aspect of MiniOS
+is that it is sufficiently supported with additional technical and pedagogic
+material. Finally, the effectiveness of the proposed approach to teach
+operating systems laboratories is illustrated through the experience of using
+it to deliver laboratory projects in the Operating Systems course at the
+University of Northern British Columbia. Finally, we discuss experimental
+research in computing education and considered the qualitative results of this
+work as part of a larger research endeavour.
+"
+23368,A Class of Control Certificates to Ensure Reach-While-Stay for Switched Systems,"  In this article, we consider the problem of synthesizing switching
+controllers for temporal properties through the composition of simple primitive
+reach-while-stay (RWS) properties. Reach-while-stay properties specify that the
+system states starting from an initial set I, must reach a goal (target) set G
+in finite time, while remaining inside a safe set S. Our approach synthesizes
+switched controllers that select between finitely many modes to satisfy the
+given RWS specification. To do so, we consider control certificates, which are
+Lyapunov-like functions that represent control strategies to achieve the
+desired specification. However, for RWS problems, a control Lyapunov-like
+function is often hard to synthesize in a simple polynomial form. Therefore, we
+combine control barrier and Lyapunov functions with an additional compatibility
+condition between them. Using this approach, the controller synthesis problem
+reduces to one of solving quantified nonlinear constrained problems that are
+handled using a combination of SMT solvers. The synthesis of controllers is
+demonstrated through a set of interesting numerical examples drawn from the
+related work, and compared with the state-of-the-art tool SCOTS. Our evaluation
+suggests that our approach is computationally feasible, and adds to the growing
+body of formal approaches to controller synthesis.
+"
+23369,Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?,"  Submodular functions are a broad class of set functions, which naturally
+arise in diverse areas. Many algorithms have been suggested for the
+maximization of these functions. Unfortunately, once the function deviates from
+submodularity, the known algorithms may perform arbitrarily poorly. Amending
+this issue, by obtaining approximation results for set functions generalizing
+submodular functions, has been the focus of recent works.
+One such class, known as weakly submodular functions, has received a lot of
+attention. A key result proved by Das and Kempe (2011) showed that the
+approximation ratio of the greedy algorithm for weakly submodular maximization
+subject to a cardinality constraint degrades smoothly with the distance from
+submodularity. However, no results have been obtained for maximization subject
+to constraints beyond cardinality. In particular, it is not known whether the
+greedy algorithm achieves any non-trivial approximation ratio for such
+constraints.
+In this paper, we prove that a randomized version of the greedy algorithm
+(previously used by Buchbinder et al. (2014) for a different problem) achieves
+an approximation ratio of $(1 + 1/\gamma)^{-2}$ for the maximization of a
+weakly submodular function subject to a general matroid constraint, where
+$\gamma$ is a parameter measuring the distance of the function from
+submodularity. Moreover, we also experimentally compare the performance of this
+version of the greedy algorithm on real world problems against natural
+benchmarks, and show that the algorithm we study performs well also in
+practice. To the best of our knowledge, this is the first algorithm with a
+non-trivial approximation guarantee for maximizing a weakly submodular function
+subject to a constraint other than the simple cardinality constraint. In
+particular, it is the first algorithm with such a guarantee for the important
+and broad class of matroid constraints.
+"
+23370,$i$QIST v0.7: An open source continuous-time quantum Monte Carlo impurity solver toolkit,"  In this paper, we present a new version of the $i$QIST software package,
+which is capable of solving various quantum impurity models by using the
+hybridization expansion (or strong coupling expansion) continuous-time quantum
+Monte Carlo algorithm. In the revised version, the software architecture is
+completely redesigned. New basis (intermediate representation or singular value
+decomposition representation) for the single-particle and two-particle Green's
+functions is introduced. A lot of useful physical observables are added, such
+as the charge susceptibility, fidelity susceptibility, Binder cumulant, and
+autocorrelation time. Especially, we optimize measurement for the two-particle
+Green's functions. Both the particle-hole and particle-particle channels are
+supported. In addition, the block structure of the two-particle Green's
+functions is exploited to accelerate the calculation. Finally, we fix some
+known bugs and limitations. The computational efficiency of the code is greatly
+enhanced.
+"
+23371,Spectral Statistics for Anderson Model with sporadic potentials,"  In this paper we consider Anderson model with a large number of sites with
+zero interaction. For such models we study the spectral statistics in the
+region of complete localization. We show that Poisson statistics holds for such
+energies, by proving the Minami estimate.
+"
+23372,On squares of cyclic codes,"  The square $C^{*2}$ of a linear error correcting code $C$ is the linear code
+spanned by the component-wise products of every pair of (non-necessarily
+distinct) words in $C$. Squares of codes have gained attention for several
+applications mainly in the area of cryptography, and typically in those
+applications one is concerned about some of the parameters (dimension, minimum
+distance) of both $C^{*2}$ and $C$. In this paper, motivated mostly by the
+study of this problem in the case of linear codes defined over the binary
+field, squares of cyclic codes are considered. General results on the minimum
+distance of the squares of cyclic codes are obtained and constructions of
+cyclic codes $C$ with relatively large dimension of $C$ and minimum distance of
+the square $C^{*2}$ are discussed. In some cases, the constructions lead to
+codes $C$ such that both $C$ and $C^{*2}$ simultaneously have the largest
+possible minimum distances for their length and dimensions.
+"
+23373,Origin of magnetic frustration in Bi$_3$Mn$_4$O$_{12}$(NO$_3$),"  Bi$_3$Mn$_4$O$_{12}$(NO$_3$) (BMNO) is a honeycomb bilayers anti-ferromagnet,
+not showing any ordering down to very low temperatures despite having a
+relatively large Curie-Weiss temperature. Using ab initio density functional
+theory, we extract an effective spin Hamiltonian for this compound. The
+proposed spin Hamiltonian consists of anti-ferrimagnetic Heisenberg terms with
+coupling constants ranging up to third intra-layer and fourth inter-layer
+neighbors. Performing Monte Carlo simulation, we obtain the temperature
+dependence of magnetic susceptibility and so the Curie-Weiss temperature and
+find the coupling constants which best matches with the experimental value. We
+discover that depending on the strength of the interlayer exchange couplings,
+two collinear spin configurations compete with each other in this system. Both
+states have in plane N{é}el character, however, at small interlayer coupling
+spin directions in the two layers are antiparallel (N$_1$ state) and
+discontinuously transform to parallel (N$_2$ state) by enlarging the interlayer
+couplings at a first order transition point. Classical Monte Carlo simulation
+and density matrix renormalization group calculations confirm that exchange
+couplings obtained for BMNO are in such a way that put this material {at the
+phase boundary of a first order phase transition}, where the trading between
+these two collinear spin states prevents it from setting in a magnetically
+ordered state.
+"
+23374,"Optimization Based Solutions for Control and State Estimation in Non-holonomic Mobile Robots: Stability, Distributed Control, and Relative Localization","  Interest in designing, manufacturing, and using autonomous robots has been
+rapidly growing during the most recent decade. The main motivation for this
+interest is the wide range of potential applications these autonomous systems
+can serve in. The applications include, but are not limited to, area coverage,
+patrolling missions, perimeter surveillance, search and rescue missions, and
+situational awareness. In this thesis, the area of control and state estimation
+in non-holonomic mobile robots is tackled. Herein, optimization based solutions
+for control and state estimation are designed, analyzed, and implemented to
+such systems. One of the main motivations for considering such solutions is
+their ability of handling constrained and nonlinear systems such as
+non-holonomic mobile robots. Moreover, the recent developments in dynamic
+optimization algorithms as well as in computer processing facilitated the
+real-time implementation of such optimization based methods in embedded
+computer systems.
+"
+23375,Wavelets and spectral triples for higher-rank graphs,"  In this paper, we present two new ways to associate a spectral triple to a
+higher-rank graph $\Lambda$. Moreover, we prove that these spectral triples are
+intimately connected to the wavelet decomposition of the infinite path space of
+$\Lambda$ which was introduced by Farsi, Gillaspy, Kang, and Packer in 2015. We
+first introduce the concept of stationary $k$-Bratteli diagrams, to associate a
+family of ultrametric Cantor sets to a finite, strongly connected higher-rank
+graph $\Lambda$. Then we show that under mild hypotheses, the
+Pearson-Bellissard spectral triples of such Cantor sets have a regular
+$\zeta$-function, whose abscissa of convergence agrees with the Hausdorff
+dimension of the Cantor set, and that the measure $\mu$ induced by the
+associated Dixmier trace agrees with the measure $M$ on the infinite path space
+$\Lambda^\infty$ of $\Lambda$ which was introduced by an Huef, Laca, Raeburn,
+and Sims. Furthermore, we prove that $\mu = M$ is a rescaled version of the
+Hausdorff measure of the ultrametric Cantor set. From work of Julien and
+Savinien, we know that for $\zeta$-regular Pearson-Bellissard spectral triples,
+the eigenspaces of the associated Laplace-Beltrami operator constitute an
+orthogonal decomposition of $L^2(\Lambda^\infty, \mu)$; we show that this
+orthogonal decomposition refines the wavelet decomposition of Farsi et al. In
+addition, we generalize a spectral triple of Consani and Marcolli from
+Cuntz-Krieger algebras to higher-rank graph $C^*$-algebras, and prove that the
+wavelet decomposition of Farsi et al.~describes the eigenspaces of its Dirac
+operator.
+"
+23376,Causality Refined Diagnostic Prediction,"  Applying machine learning in the health care domain has shown promising
+results in recent years. Interpretable outputs from learning algorithms are
+desirable for decision making by health care personnel. In this work, we
+explore the possibility of utilizing causal relationships to refine diagnostic
+prediction. We focus on the task of diagnostic prediction using discomfort
+drawings, and explore two ways to employ causal identification to improve the
+diagnostic results. Firstly, we use causal identification to infer the causal
+relationships among diagnostic labels which, by itself, provides interpretable
+results to aid the decision making and training of health care personnel.
+Secondly, we suggest a post-processing approach where the inferred causal
+relationships are used to refine the prediction accuracy of a multi-view
+probabilistic model. Experimental results show firstly that causal
+identification is capable of detecting the causal relationships among
+diagnostic labels correctly, and secondly that there is potential for improving
+pain diagnostics prediction accuracy using the causal relationships.
+"
+23377,The Shock Development Problem,"  The subject of this work is the shock development problem in fluid mechanics.
+A shock originates from an acoustically spacelike surface in spacetime at which
+the 1st derivatives of the physical variables blow up. The solution requires
+the construction of a hypersurface in spacetime which is acoustically timelike
+as viewed from its future, acoustically spacelike as viewed from its past, the
+shock hypersurface, across which the physical variables suffer discontinuities
+obeying jump conditions in accordance with the integral form of the particle
+and energy-momentum conservation laws. Mathematically, this is a free boundary
+problem, with nonlinear conditions at the free boundary, for a 1st order
+quasilinear hyperbolic system of p.d.e., with characteristic initial data which
+are singular at the past boundary of the initial characteristic hypersurface,
+that boundary being the surface of origin. This work solves, in any number of
+spatial dimensions, a restricted form of the problem which retains the
+essential difficulties due to the singular nature of the surface of origin. The
+solution is accomplished through the introduction of new geometric and analytic
+methods.
+"
+23378,Statistical Modelling of Computer Network Traffic Event Times,"  This paper introduces a statistical model for the arrival times of connection
+events in a computer network. Edges between nodes in a network can be
+interpreted and modelled as point processes where events in the process
+indicate information being sent along that edge. A model of normal behaviour
+can be constructed for each edge in the network by identifying key network user
+features such as seasonality and self-exciting behaviour, where events
+typically arise in bursts at particular times of day. When monitoring the
+network in real time, unusual patterns of activity could indicate the presence
+of a malicious actor. Four different models for self-exciting behaviour are
+introduced and compared using data collected from the Imperial College and Los
+Alamos National Laboratory computer networks.
+"
+23379,Representation theory of W-algebras and Higgs branch conjecture,"  We survey a number of results regarding the representation theory of
+$W$-algebras and their connection with the resent development of the four
+dimensional $N=2$ superconformal field theories in physics.
+"
+23380,Bulk-boundary correspondance for Sturmian Kohmoto like models,"  We consider one dimensional tight binding models on $\ell^2(\mathbb Z)$ whose
+spatial structure is encoded by a Sturmian sequence $(\xi_n)_n\in
+\{a,b\}^\mathbb Z$. An example is the Kohmoto Hamiltonian, which is given by
+the discrete Laplacian plus an onsite potential $v_n$ taking value $0$ or $1$
+according to whether $\xi_n$ is $a$ or $b$. The only non-trivial topological
+invariants of such a model are its gap-labels. The bulk-boundary correspondence
+we establish here states that there is a correspondence between the gap label
+and a winding number associated to the edge states, which arises if the system
+is augmented and compressed onto half space $\ell^2(\mathbb N)$. This has been
+experimentally observed with polaritonic waveguides. A correct theoretical
+explanation requires, however, first a smoothing out of the atomic motion via
+phason flips. With such an interpretation at hand, the winding number
+corresponds to the mechanical work through a cycle which the atomic motion
+exhibits on the edge states.
+"
+23381,Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings,"  A method to produce personalized classification models to automatically
+review online dating profiles on Tinder is proposed, based on the user's
+historical preference. The method takes advantage of a FaceNet facial
+classification model to extract features which may be related to facial
+attractiveness. The embeddings from a FaceNet model were used as the features
+to describe an individual's face. A user reviewed 8,545 online dating profiles.
+For each reviewed online dating profile, a feature set was constructed from the
+profile images which contained just one face. Two approaches are presented to
+go from the set of features for each face, to a set of profile features. A
+simple logistic regression trained on the embeddings from just 20 profiles
+could obtain a 65% validation accuracy. A point of diminishing marginal returns
+was identified to occur around 80 profiles, at which the model accuracy of 73%
+would only improve marginally after reviewing a significant number of
+additional profiles.
+"
+23382,Two-dimensional grating magneto-optical trap,"  We demonstrate a two-dimensional grating magneto-optical trap (2D GMOT) with
+a single input cooling laser beam and a planar diffraction grating using
+$^{87}$Rb. This configuration increases experimental access when compared with
+a traditional 2D MOT. As described in the paper, the output flux is several
+hundred million rubidium atoms/s at a mean velocity of $16.5(9)$ m/s and a
+velocity distribution of $4(3)$ m/s standard deviation. We use the atomic beam
+from the 2D GMOT to demonstrate loading of a three dimensional grating MOT (3D
+GMOT) with $2.46(7)\times 10^8$ atoms. Methods to improve output flux are
+discussed.
+"
+23383,Autonomous Braking System via Deep Reinforcement Learning,"  In this paper, we propose a new autonomous braking system based on deep
+reinforcement learning. The proposed autonomous braking system automatically
+decides whether to apply the brake at each time step when confronting the risk
+of collision using the information on the obstacle obtained by the sensors. The
+problem of designing brake control is formulated as searching for the optimal
+policy in Markov decision process (MDP) model where the state is given by the
+relative position of the obstacle and the vehicle's speed, and the action space
+is defined as whether brake is stepped or not. The policy used for brake
+control is learned through computer simulations using the deep reinforcement
+learning method called deep Q-network (DQN). In order to derive desirable
+braking policy, we propose the reward function which balances the damage
+imposed to the obstacle in case of accident and the reward achieved when the
+vehicle runs out of risk as soon as possible. DQN is trained for the scenario
+where a vehicle is encountered with a pedestrian crossing the urban road.
+Experiments show that the control agent exhibits desirable control behavior and
+avoids collision without any mistake in various uncertain environments.
+"
+23384,Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction,"  Cross-project defect prediction (CPDP) aims to predict defects of projects
+lacking training data by using prediction models trained on historical defect
+data from other projects. However, since the distribution differences between
+datasets from different projects, it is still a challenge to build high-quality
+CPDP models. Unfortunately, class imbalanced nature of software defect datasets
+further increases the difficulty. In this paper, we propose a transferlearning
+oriented minority over-sampling technique (TOMO) based feature weighting
+transfer naive Bayes (FWTNB) approach (TOMOFWTNB) for CPDP by considering both
+classimbalance and feature importance problems. Differing from traditional
+over-sampling techniques, TOMO not only can balance the data but reduce the
+distribution difference. And then FWTNB is used to further increase the
+similarity of two distributions. Experiments are performed on 11 public defect
+datasets. The experimental results show that (1) TOMO improves the average
+G-Measure by 23.7\%$\sim$41.8\%, and the average MCC by 54.2\%$\sim$77.8\%. (2)
+feature weighting (FW) strategy improves the average G-Measure by 11\%, and the
+average MCC by 29.2\%. (3) TOMOFWTNB improves the average G-Measure value by at
+least 27.8\%, and the average MCC value by at least 71.5\%, compared with
+existing state-of-theart CPDP approaches. It can be concluded that (1) TOMO is
+very effective for addressing class-imbalance problem in CPDP scenario; (2) our
+FW strategy is helpful for CPDP; (3) TOMOFWTNB outperforms previous
+state-of-the-art CPDP approaches.
+"
+23385,Learned in Translation: Contextualized Word Vectors,"  Computer vision has benefited from initializing multiple deep layers with
+weights pretrained on large supervised training sets like ImageNet. Natural
+language processing (NLP) typically sees initialization of only the lowest
+layer of deep models with pretrained word vectors. In this paper, we use a deep
+LSTM encoder from an attentional sequence-to-sequence model trained for machine
+translation (MT) to contextualize word vectors. We show that adding these
+context vectors (CoVe) improves performance over using only unsupervised word
+and character vectors on a wide variety of common NLP tasks: sentiment analysis
+(SST, IMDb), question classification (TREC), entailment (SNLI), and question
+answering (SQuAD). For fine-grained sentiment analysis and entailment, CoVe
+improves performance of our baseline models to the state of the art.
+"
+23386,Measuring Contextual Relationships in Temporal Social Networks by Circle Link,"  Network science has released its talents in social network analysis based on
+the information of static topologies. In reality social contacts are dynamic
+and evolve concurrently in time. Nowadays they can be recorded by ubiquitous
+information technologies, and generated into temporal social networks to
+provide new sights in social reality mining. Here, we define \emph{circle link}
+to measure contextual relationships in three empirical social temporal
+networks, and find that the tendency of friends having frequent continuous
+interactions with their common friend prefer to be close, which can be
+considered as the extension of Granovetter's hypothesis in temporal social
+networks. Finally, we present a heuristic method based on circle link to
+predict relationships and acquire acceptable results.
+"
+23387,Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience,"  In line with the sensorimotor contingency theory, we investigate the problem
+of the perception of space from a fundamental sensorimotor perspective. Despite
+its pervasive nature in our perception of the world, the origin of the concept
+of space remains largely mysterious. For example in the context of artificial
+perception, this issue is usually circumvented by having engineers pre-define
+the spatial structure of the problem the agent has to face. We here show that
+the structure of space can be autonomously discovered by a naive agent in the
+form of sensorimotor regularities, that correspond to so called compensable
+sensory experiences: these are experiences that can be generated either by the
+agent or its environment. By detecting such compensable experiences the agent
+can infer the topological and metric structure of the external space in which
+its body is moving. We propose a theoretical description of the nature of these
+regularities and illustrate the approach on a simulated robotic arm equipped
+with an eye-like sensor, and which interacts with an object. Finally we show
+how these regularities can be used to build an internal representation of the
+sensor's external spatial configuration.
+"
+23388,Chimera at the phase-flip transition of an ensemble of identical nonlinear oscillators,"  A complex collective emerging behavior characterized by coexisting coherent
+and incoherent do- mains is termed as a chimera state. We bring out the
+existence of a new type of chimera in a nonlocally coupled ensemble of
+identical oscillators driven by a common dynamic environment. The latter
+facilitates the onset of phase-flip bifurcation/transitions among the coupled
+oscillators of the ensemble, while the nonlocal coupling induces a partial
+asynchronization among the out-of-phase synchronized oscillators at this onset.
+This leads to the manifestation of coexisting out-of-phase synchronized
+coherent domains interspersed by asynchronous incoherent domains elucidating
+the existence of a different type of chimera state. In addition to this, a rich
+variety of other collective behaviors such as clusters with phase-flip
+transition, conventional chimera, solitary state and com- plete synchronized
+state which have been reported using different coupling architectures are found
+to be induced by the employed couplings for appropriate coupling strengths. The
+robustness of the resulting dynamics is demonstrated in ensembles of two
+paradigmatic models, namely Rössler oscillators and Stuart-Landau
+oscillators.
+"
+23389,RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs,"  Power and reproducibility are key to enabling refined scientific discoveries
+in contemporary big data applications with general high-dimensional nonlinear
+models. In this paper, we provide theoretical foundations on the power and
+robustness for the model-free knockoffs procedure introduced recently in
+Candès, Fan, Janson and Lv (2016) in high-dimensional setting when the
+covariate distribution is characterized by Gaussian graphical model. We
+establish that under mild regularity conditions, the power of the oracle
+knockoffs procedure with known covariate distribution in high-dimensional
+linear models is asymptotically one as sample size goes to infinity. When
+moving away from the ideal case, we suggest the modified model-free knockoffs
+method called graphical nonlinear knockoffs (RANK) to accommodate the unknown
+covariate distribution. We provide theoretical justifications on the robustness
+of our modified procedure by showing that the false discovery rate (FDR) is
+asymptotically controlled at the target level and the power is asymptotically
+one with the estimated covariate distribution. To the best of our knowledge,
+this is the first formal theoretical result on the power for the knockoffs
+procedure. Simulation results demonstrate that compared to existing approaches,
+our method performs competitively in both FDR control and power. A real data
+set is analyzed to further assess the performance of the suggested knockoffs
+procedure.
+"
+23390,An Issue in the Martingale Analysis of the Influence Maximization Algorithm IMM,"  This paper explains a subtle issue in the martingale analysis of the IMM
+algorithm, a state-of-the-art influence maximization algorithm. Two workarounds
+are proposed to fix the issue, both requiring minor changes on the algorithm
+and incurring a slight penalty on the running time of the algorithm.
+"
+23391,Statistical inference on random dot product graphs: a survey,"  The random dot product graph (RDPG) is an independent-edge random graph that
+is analytically tractable and, simultaneously, either encompasses or can
+successfully approximate a wide range of random graphs, from relatively simple
+stochastic block models to complex latent position graphs. In this survey
+paper, we describe a comprehensive paradigm for statistical inference on random
+dot product graphs, a paradigm centered on spectral embeddings of adjacency and
+Laplacian matrices. We examine the analogues, in graph inference, of several
+canonical tenets of classical Euclidean inference: in particular, we summarize
+a body of existing results on the consistency and asymptotic normality of the
+adjacency and Laplacian spectral embeddings, and the role these spectral
+embeddings can play in the construction of single- and multi-sample hypothesis
+tests for graph data. We investigate several real-world applications, including
+community detection and classification in large social networks and the
+determination of functional and biologically relevant network properties from
+an exploratory data analysis of the Drosophila connectome. We outline requisite
+background and current open problems in spectral graph inference.
+"
+23392,LIGER: mock relativistic light-cones from Newtonian simulations,"  We introduce a method to create mock galaxy catalogues in redshift space
+including general relativistic effects to linear order in the cosmological
+perturbations. We dub our method LIGER, short for `light cones with general
+relativity'. LIGER takes a (N-body or hydrodynamic) Newtonian simulation as an
+input and outputs the distribution of galaxies in comoving redshift space. This
+result is achieved making use of a coordinate transformation and simultaneously
+accounting for lensing magnification. The calculation includes both local
+corrections and terms that have been integrated along the line of sight. Our
+fast implementation allows the production of many realizations that can be used
+to forecast the performance of forthcoming wide-angle surveys and to estimate
+the covariance matrix of the observables. To facilitate this use, we also
+present a variant of LIGER designed for large-volume simulations with low mass
+resolution. In this case, the galaxy distribution on large scales is obtained
+by biasing the matter-density field. Finally, we present two sample
+applications of LIGER. First, we discuss the impact of weak gravitational
+lensing onto the angular clustering of galaxies in a Euclid-like survey. In
+agreement with previous analytical studies, we find that magnification bias can
+be measured with high confidence. Second, we focus on two generally neglected
+Doppler-induced effects: magnification and the change of number counts with
+redshift. We show that the corresponding redshift-space distortions can be
+detected at 5.5$\sigma$ significance with the completed Square Kilometre Array.
+"
+23393,Vector bundles on Symmetric product of curves,"  This is a survey article on recent results on vector bundles on symmetric
+product of non-singular projective curves.
+"
+23394,Whitham modulation equations and application to small dispersion asymptotics and long time asymptotics of nonlinear dispersive equations,"  We review the theory of modulation equations or Whitham equations for the
+travelling wave solution of KdV. We then apply the Whitham modulation equations
+to describe the long-time asymptotics and small dispersion asymptotics of the
+KdV solution
+"
+23395,Rotation number of integrable symplectic mappings of the plane,"  Symplectic mappings are discrete-time analogs of Hamiltonian systems. They
+appear in many areas of physics, including, for example, accelerators, plasma,
+and fluids. Integrable mappings, a subclass of symplectic mappings, are
+equivalent to a Twist map, with a rotation number, constant along the phase
+trajectory. In this letter, we propose a succinct expression to determine the
+rotation number and present two examples. Similar to the period of the bounded
+motion in Hamiltonian systems, the rotation number is the most fundamental
+property of integrable maps and it provides a way to analyze the phase-space
+dynamics.
+"
+23396,Uniformly recurrent subgroups and simple $C^*$-algebras,"  We study uniformly recurrent subgroups (URS) introduced by Glasner and Weiss
+\cite{GW}. Answering their query we show that any URS $Z$ of a finitely
+generated group is the stability system of a minimal $Z$-proper action. We also
+show that for any sofic $URS$ $Z$ there is a $Z$-proper action admitting an
+invariant measure. We prove that for a $URS$ $Z$ all $Z$-proper actions admits
+an invariant measure if and only if $Z$ is coamenable. In the second part of
+the paper we study the separable $\C^*$-algebras associated to URS's. We prove
+that if an URS is generic then its $\C^*$-algebra is simple. We give various
+examples of generic URS's with exact and nuclear $\C^*$-algebras and an example
+of a URS $Z$ for which the associated simple $\C^*$-algebra is not exact and
+not even locally reflexive, in particular, it admits both a uniformly amenable
+trace and a nonuniformly amenable trace.
+"
+23397,Left-symmetric bialgebroids and their corresponding Manin triples,"  In this paper, we introduce the notion of a left-symmetric bialgebroid as a
+geometric generalization of a left-symmetric bialgebra and construct a
+left-symmetric bialgebroid from a pseudo-Hessian manifold. We also introduce
+the notion of a Manin triple for left-symmetric algebroids, which is equivalent
+to a left-symmetric bialgebroid. The corresponding double structure is a
+pre-symplectic algebroid rather than a left-symmetric algebroid. In particular,
+we establish a relation between Maurer-Cartan type equations and Dirac
+structures of the pre-symplectic algebroid which is the corresponding double
+structure for a left-symmetric bialgebroid.
+"
+23398,"Congruences between Hilbert modular forms of weight $2$, and special values of their $L$-functions","  The purpose of this paper is to show how a congruence between (the Fourier
+coefficients of) a Hilbert cusp form and a Hilbert Eisenstein series of
+parallel weight $2$ gives rise to congruences between algebraic parts of
+critical values of their $L$-functions. This is a generalization of a result of
+V. Vatsal.
+"
+23399,The asymptotic distribution of the isotonic regression estimator over a general countable pre-ordered set,"  We study the isotonic regression estimator over a general countable
+pre-ordered set. We obtain the limiting distribution of the estimator and study
+its properties. It is proved that, under some general assumptions, the limiting
+distribution of the isotonized estimator is given by the concatenation of the
+separate isotonic regressions of the certain subvectors of an unrestrecred
+estimator's asymptotic distribution. Also, we show that the isotonization
+preserves the rate of convergence of the underlying estimator. We apply these
+results to the problems of estimation of a bimonotone regression function and
+estimation of a bimonotone probability mass function.
+"
+23400,Magnetotransport in a model of a disordered strange metal,"  Despite much theoretical effort, there is no complete theory of the 'strange'
+metal state of the high temperature superconductors, and its
+linear-in-temperature, $T$, resistivity. Recent experiments showing an
+unexpected linear-in-field, $B$, magnetoresistivity have deepened the puzzle.
+We propose a simple model of itinerant electrons, interacting via random
+couplings with electrons localized on a lattice of quantum 'dots' or 'islands'.
+This model is solvable in a large-$N$ limit, and can reproduce observed
+behavior. The key feature of our model is that the electrons in each quantum
+dot are described by a Sachdev-Ye-Kitaev model describing electrons without
+quasiparticle excitations. For a particular choice of the interaction between
+the itinerant and localized electrons, this model realizes a controlled
+description of a diffusive marginal-Fermi liquid (MFL) without momentum
+conservation, which has a linear-in-$T$ resistivity and a $T \ln T$ specific
+heat as $T\rightarrow 0$. By tuning the strength of this interaction relative
+to the bandwidth of the itinerant electrons, we can additionally obtain a
+finite-$T$ crossover to a fully incoherent regime that also has a linear-in-$T$
+resistivity. We show that the MFL regime has conductivities which scale as a
+function of $B/T$; however, its magnetoresistance saturates at large $B$. We
+then consider a macroscopically disordered sample with domains of MFLs with
+varying densities of electrons. Using an effective-medium approximation, we
+obtain a macroscopic electrical resistance that scales linearly in the magnetic
+field $B$ applied perpendicular to the plane of the sample, at large $B$. The
+resistance also scales linearly in $T$ at small $B$, and as $T f(B/T)$ at
+intermediate $B$. We consider implications for recent experiments reporting
+linear transverse magnetoresistance in the strange metal phases of the
+pnictides and cuprates.
+"
+23401,Deep learning from crowds,"  Over the last few years, deep learning has revolutionized the field of
+machine learning by dramatically improving the state-of-the-art in various
+domains. However, as the size of supervised artificial neural networks grows,
+typically so does the need for larger labeled datasets. Recently, crowdsourcing
+has established itself as an efficient and cost-effective solution for labeling
+large sets of data in a scalable manner, but it often requires aggregating
+labels from multiple noisy contributors with different levels of expertise. In
+this paper, we address the problem of learning deep neural networks from
+crowds. We begin by describing an EM algorithm for jointly learning the
+parameters of the network and the reliabilities of the annotators. Then, a
+novel general-purpose crowd layer is proposed, which allows us to train deep
+neural networks end-to-end, directly from the noisy labels of multiple
+annotators, using only backpropagation. We empirically show that the proposed
+approach is able to internally capture the reliability and biases of different
+annotators and achieve new state-of-the-art results for various crowdsourced
+datasets across different settings, namely classification, regression and
+sequence labeling.
+"
+23402,Mean Field Games with Singular Controls of Bounded Velocity,"  This paper studies a class of mean field games (MFGs) with singular controls
+of bounded velocity. By relaxing the absolute continuity of the control
+process, it generalizes the MFG framework of Lasry and Lions [36] and Huang,
+Malhamé, and Caines [29]. It provides a unique solution to the MFG with
+explicit optimal control policies and establishes the $\epsilon$-Nash
+equilibrium of the corresponding $N$-player game. Finally, it analyzes a
+particular MFG with explicit solutions in a systemic risk model originally
+formulated by Carmona, Fouque, and Sun [17] in a regular control setting.
+"
+23403,New Variants of Pattern Matching with Constants and Variables,"  Given a text and a pattern over two types of symbols called constants and
+variables, the parameterized pattern matching problem is to find all
+occurrences of substrings of the text that the pattern matches by substituting
+a variable in the text for each variable in the pattern, where the substitution
+should be injective. The function matching problem is a variant of it that
+lifts the injection constraint. In this paper, we discuss variants of those
+problems, where one can substitute a constant or a variable for each variable
+of the pattern. We give two kinds of algorithms for both problems, a
+convolution-based method and an extended KMP-based method, and analyze their
+complexity.
+"
+23404,Conformal volume and eigenvalue problems,"  We prove inequalities for Laplace eigenvalues on Riemannian manifolds
+generalising to higher eigenvalues two classical inequalities for the first
+Laplace eigenvalue - the inequality in terms of the $L^2$-norm of mean
+curvature, due to Reilly in 1977, and the inequality in terms of conformal
+volume, due to Li and Yau in 1982, and El Soufi and Ilias in 1986. We also
+obtain bounds for the number of negative eigenvalues of Schrödinger
+operators, and in particular, index bounds for minimal hypersurfaces in
+spheres.
+"
+23405,SentRNA: Improving computational RNA design by incorporating a prior of human design strategies,"  Designing RNA sequences that fold into specific structures and perform
+desired biological functions is an emerging field in bioengineering with broad
+applications from intracellular chemical catalysis to cancer therapy via
+selective gene silencing. Effective RNA design requires first solving the
+inverse folding problem: given a target structure, propose a sequence that
+folds into that structure. Although significant progress has been made in
+developing computational algorithms for this purpose, current approaches are
+ineffective at designing sequences for complex targets, limiting their utility
+in real-world applications. However, an alternative that has shown
+significantly higher performance are human players of the online RNA design
+game EteRNA. Through many rounds of gameplay, these players have developed a
+collective library of ""human"" rules and strategies for RNA design that have
+proven to be more effective than current computational approaches, especially
+for complex targets. Here, we present an RNA design agent, SentRNA, which
+consists of a fully-connected neural network trained using the $eternasolves$
+dataset, a set of $1.8 x 10^4$ player-submitted sequences across 724 unique
+targets. The agent first predicts an initial sequence for a target using the
+trained network, and then refines that solution if necessary using a short
+adaptive walk utilizing a canon of standard design moves. Through this
+approach, we observe SentRNA can learn and apply human-like design strategies
+to solve several complex targets previously unsolvable by any computational
+approach. We thus demonstrate that incorporating a prior of human design
+strategies into a computational agent can significantly boost its performance,
+and suggests a new paradigm for machine-based RNA design.
+"
+23406,A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem,"  Disturbance noises are always bounded in a practical system, while fusion
+estimation is to best utilize multiple sensor data containing noises for the
+purpose of estimating a quantity--a parameter or process. However, few results
+are focused on the information fusion estimation problem under bounded noises.
+In this paper, we study the distributed fusion estimation problem for linear
+time-varying systems and nonlinear systems with bounded noises, where the
+addressed noises do not provide any statistical information, and are unknown
+but bounded. When considering linear time-varying fusion systems with bounded
+noises, a new local Kalman-like estimator is designed such that the square
+error of the estimator is bounded as time goes to $\infty$. A novel
+constructive method is proposed to find an upper bound of fusion estimation
+error, then a convex optimization problem on the design of an optimal weighting
+fusion criterion is established in terms of linear matrix inequalities, which
+can be solved by standard software packages. Furthermore, according to the
+design method of linear time-varying fusion systems, each local nonlinear
+estimator is derived for nonlinear systems with bounded noises by using Taylor
+series expansion, and a corresponding distributed fusion criterion is obtained
+by solving a convex optimization problem. Finally, target tracking system and
+localization of a mobile robot are given to show the advantages and
+effectiveness of the proposed methods.
+"
+23407,Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques,"  The IoT is a network of interconnected everyday objects called things that
+have been augmented with a small measure of computing capabilities. Lately, the
+IoT has been affected by a variety of different botnet activities. As botnets
+have been the cause of serious security risks and financial damage over the
+years, existing Network forensic techniques cannot identify and track current
+sophisticated methods of botnets. This is because commercial tools mainly
+depend on signature-based approaches that cannot discover new forms of botnet.
+In literature, several studies have conducted the use of Machine Learning ML
+techniques in order to train and validate a model for defining such attacks,
+but they still produce high false alarm rates with the challenge of
+investigating the tracks of botnets. This paper investigates the role of ML
+techniques for developing a Network forensic mechanism based on network flow
+identifiers that can track suspicious activities of botnets. The experimental
+results using the UNSW-NB15 dataset revealed that ML techniques with flow
+identifiers can effectively and efficiently detect botnets attacks and their
+tracks.
+"
+23408,Modeling of Information Diffusion on Social Networks with Applications to WeChat,"  Traces of user activities recorded in online social networks such as the
+creation, viewing and forwarding/sharing of information over time open new
+possibilities to quantitatively and systematically understand the information
+diffusion process on social networks. From an online social network like
+WeChat, we could collect a large number of information cascade trees, each of
+which tells the spreading trajectory of a message/information such as which
+user creates the information and which users view or forward the information
+shared by which neighbors. In this work, we propose two heterogeneous
+non-linear models. Both models are validated by the WeChat data in reproducing
+and explaining key features of cascade trees. Specifically, we firstly apply
+the Random Recursive Tree (RRT) to model the cascade tree topologies, capturing
+key features, i.e. the average path length and degree variance of a cascade
+tree in relation to the number of nodes (size) of the tree. The RRT model with
+a single parameter $\theta$ describes the growth mechanism of a tree, where a
+node in the existing tree has a probability $d_i^{\theta}$ of being connected
+to a newly added node that depends on the degree $d_i$ of the existing node.
+The identified parameter $\theta$ quantifies the relative depth or broadness of
+the cascade trees, indicating that information propagates via a star-like
+broadcasting or viral-like hop by hop spreading. The RRT model explains the
+appearance of hubs, thus a possibly smaller average path length as the cascade
+size increases, as observed in WeChat. We further propose the stochastic
+Susceptible View Forward Removed (SVFR) model to depict the dynamic user
+behaviors including creating, viewing, forwarding and ignoring a message on a
+given social network.
+"
+23409,Unstable standard candles. Periodic light curve modulation in fundamental mode classical Cepheids,"  We report the discovery of periodic modulation of pulsation in 51 fundamental
+mode classical Cepheids of the Magellanic Clouds observed by the Optical
+Gravitational Lensing Experiment. Although the overall incidence rate is very
+low, about 1 per cent in each of the Magellanic Clouds, in the case of the SMC
+and pulsation periods between 12 and 16d the incidence rate is nearly 40 per
+cent. On the other hand, in the LMC the highest incidence rate is 5 per cent
+for pulsation periods between 8 and 14d, and the overall amplitude of the
+effect is smaller. It indicates that the phenomenon is metallicity dependent.
+Typical modulation periods are between 70 and 300d. In nearly all stars the
+mean brightness is modulated, which, in principle, may influence the use of
+classical Cepheids for distance determination. Fortunately, the modulation of
+mean brightness does not exceed 0.01 mag in all but one star. Also, the effect
+averages out in typical observations spanning a long time base. Consequently,
+the effect of modulation on the determination of the distance moduli is
+negligible. The relative modulation amplitude of the fundamental mode is also
+low and, with one exception, it does not exceed 6 per cent. The origin of the
+modulation is unknown. We draw a hypothesis that the modulation is caused by
+the 2:1 resonance between the fundamental mode and the second overtone that
+shapes the famous Hertzsprung bump progression.
+"
+23410,Finding Streams in Knowledge Graphs to Support Fact Checking,"  The volume and velocity of information that gets generated online limits
+current journalistic practices to fact-check claims at the same rate.
+Computational approaches for fact checking may be the key to help mitigate the
+risks of massive misinformation spread. Such approaches can be designed to not
+only be scalable and effective at assessing veracity of dubious claims, but
+also to boost a human fact checker's productivity by surfacing relevant facts
+and patterns to aid their analysis. To this end, we present a novel,
+unsupervised network-flow based approach to determine the truthfulness of a
+statement of fact expressed in the form of a (subject, predicate, object)
+triple. We view a knowledge graph of background information about real-world
+entities as a flow network, and knowledge as a fluid, abstract commodity. We
+show that computational fact checking of such a triple then amounts to finding
+a ""knowledge stream"" that emanates from the subject node and flows toward the
+object node through paths connecting them. Evaluation on a range of real-world
+and hand-crafted datasets of facts related to entertainment, business, sports,
+geography and more reveals that this network-flow model can be very effective
+in discerning true statements from false ones, outperforming existing
+algorithms on many test cases. Moreover, the model is expressive in its ability
+to automatically discover several useful path patterns and surface relevant
+facts that may help a human fact checker corroborate or refute a claim.
+"
+23411,Total Haskell is Reasonable Coq,"  We would like to use the Coq proof assistant to mechanically verify
+properties of Haskell programs. To that end, we present a tool, named
+hs-to-coq, that translates total Haskell programs into Coq programs via a
+shallow embedding. We apply our tool in three case studies -- a lawful Monad
+instance, ""Hutton's razor"", and an existing data structure library -- and prove
+their correctness. These examples show that this approach is viable: both that
+hs-to-coq applies to existing Haskell code, and that the output it produces is
+amenable to verification.
+"
+23412,On the growth of Sobolev norms for a class of linear Schrödinger equations on the torus with superlinear dispersion,"  In this paper we consider time dependent Schrödinger equations on the
+one-dimensional torus $\T := \R /(2 \pi \Z)$ of the form $\partial_t u = \ii
+{\cal V}(t)[u]$ where ${\cal V}(t)$ is a time dependent, self-adjoint
+pseudo-differential operator of the form ${\cal V}(t) = V(t, x) |D|^M + {\cal
+W}(t)$, $M > 1$, $|D| := \sqrt{- \partial_{xx}}$, $V$ is a smooth function
+uniformly bounded from below and ${\cal W}$ is a time-dependent
+pseudo-differential operator of order strictly smaller than $M$. We prove that
+the solutions of the Schrödinger equation $\partial_t u = \ii {\cal V}(t)[u]$
+grow at most as $t^\e$, $t \to + \infty$ for any $\e > 0$. The proof is based
+on a reduction to constant coefficients up to smoothing remainders of the
+vector field $\ii {\cal V}(t)$ which uses Egorov type theorems and
+pseudo-differential calculus.
+"
+23413,Stem-ming the Tide: Predicting STEM attrition using student transcript data,"  Science, technology, engineering, and math (STEM) fields play growing roles
+in national and international economies by driving innovation and generating
+high salary jobs. Yet, the US is lagging behind other highly industrialized
+nations in terms of STEM education and training. Furthermore, many economic
+forecasts predict a rising shortage of domestic STEM-trained professions in the
+US for years to come. One potential solution to this deficit is to decrease the
+rates at which students leave STEM-related fields in higher education, as
+currently over half of all students intending to graduate with a STEM degree
+eventually attrite. However, little quantitative research at scale has looked
+at causes of STEM attrition, let alone the use of machine learning to examine
+how well this phenomenon can be predicted. In this paper, we detail our efforts
+to model and predict dropout from STEM fields using one of the largest known
+datasets used for research on students at a traditional campus setting. Our
+results suggest that attrition from STEM fields can be accurately predicted
+with data that is routinely collected at universities using only information on
+students' first academic year. We also propose a method to model student STEM
+intentions for each academic term to better understand the timing of STEM
+attrition events. We believe these results show great promise in using machine
+learning to improve STEM retention in traditional and non-traditional campus
+settings.
+"
+23414,AMAT: Medial Axis Transform for Natural Images,"  We introduce Appearance-MAT (AMAT), a generalization of the medial axis
+transform for natural images, that is framed as a weighted geometric set cover
+problem. We make the following contributions: i) we extend previous medial
+point detection methods for color images, by associating each medial point with
+a local scale; ii) inspired by the invertibility property of the binary MAT, we
+also associate each medial point with a local encoding that allows us to invert
+the AMAT, reconstructing the input image; iii) we describe a clustering scheme
+that takes advantage of the additional scale and appearance information to
+group individual points into medial branches, providing a shape decomposition
+of the underlying image regions. In our experiments, we show state-of-the-art
+performance in medial point detection on Berkeley Medial AXes (BMAX500), a new
+dataset of medial axes based on the BSDS500 database, and good generalization
+on the SK506 and WH-SYMMAX datasets. We also measure the quality of
+reconstructed images from BMAX500, obtained by inverting their computed AMAT.
+Our approach delivers significantly better reconstruction quality with respect
+to three baselines, using just 10% of the image pixels. Our code and
+annotations are available at this https URL .
+"
+23415,On the viscoplastic squeeze flow between two identical infinite circular cylinders,"  Direct numerical simulations of closely interacting infinite circular
+cylinders in a Bingham fluid are presented, and results compared to asymptotic
+solutions based on lubrication theory in the gap. Unlike for a Newtonian fluid,
+the macroscopic flow outside of the gap between the cylinders is shown to have
+a large effect on the pressure profile within the gap and the resulting
+lubrication force on the cylinders. The presented results indicate that the
+asymptotic lubrication solution can be used to predict the lubrication pressure
+only if the surrounding viscoplastic matrix is yielded by a macroscopic flow.
+This has implications for the use of sub-grid-scale lubrication models in
+simulations of non-colloidal particulate suspensions in viscoplastic fluids.
+"
+23416,The search for radio emission from exoplanets using LOFAR low-frequency beam-formed observations: Data pipeline and preliminary results for the 55 Cnc system,"  Detection of radio emission from exoplanets can provide information on the
+star-planet system that is difficult to study otherwise, such as the planetary
+magnetic field, magnetosphere, rotation period, interior structure, atmospheric
+dynamics and escape, and any star-planet interactions. Such a detection in the
+radio domain would open up a whole new field in the study of exoplanets.
+However, currently there are no confirmed detections of an exoplanet at radio
+frequencies. In this study, we search for non-thermal radio emission from the
+55 Cnc system which has 5 known exoplanets. According to theoretical
+predictions 55 Cnc e, the innermost planet, is among the best targets for this
+search. We observed for 18 hours with the Low-Frequency Array (LOFAR) Low Band
+Antenna in the frequency range 26-73 MHz with full-polarization and covered 85%
+of the orbital phase of 55 Cnc e. During the observations four digital beams
+within the station beam were recorded simultaneously on 55 Cnc, nearby ""empty""
+sky, a bright radio source, and a pulsar. A pipeline was created to
+automatically find and mask radio frequency interference, calibrate the
+time-frequency response of the telescope, and to search for bursty planetary
+radio signals in our data. Extensive tests and verifications were carried out
+on the pipeline. Analysis of the first 4 hours of these observations do not
+contain any exoplanet signal from 55 Cnc but we can confirm that our setup is
+adequate to detect faint astrophysical signals. We find a 3$\sigma$ upper limit
+for 55 Cnc of 230 mJy using the pulsar to estimate the sensitivity of the
+observations and 2.6 Jy using the time-series difference between the target and
+sky beam. The full data set is still under-going analysis. In the near future
+we will apply our observational technique and pipeline to the most promising
+exoplanet candidates for which LOFAR observations have already been obtained.
+"
+23417,Long-term behaviour of Hertzian chains between fixed walls is really equilibrium,"  We examine the long-term behaviour of non-integrable, energy-conserved, 1D
+systems of macroscopic grains interacting via a contact-only generalized Hertz
+potential and held between stationary walls. Existing dynamical studies showed
+the absence of energy equipartitioning in such systems, hence their long-term
+dynamics was described as quasi-equilibrium. Here we show that these systems do
+in fact reach thermal equilibrium at sufficiently long times, as indicated by
+the calculated heat capacity. This phase is described by equilibrium
+statistical mechanics, opening up the possibility that the machinery of
+non-equilibrium statistical mechanics may be used to understand the behaviour
+of these systems away from equilibrium.
+"
+23418,The effect of RNA stiffness on the self-assembly of virus particles,"  Under many in vitro conditions, some small viruses spontaneously encapsidate
+a single stranded (ss) RNA into a protein shell called the capsid. While viral
+RNAs are found to be compact and highly branched because of long distance
+base-pairing between nucleotides, recent experiments reveal that in a
+head-to-head competition between a ssRNA with no secondary or higher order
+structure and a viral RNA, the capsid proteins preferentially encapsulate the
+linear polymer! In this paper, we study the impact of genome stiffness on the
+encapsidation free energy of the complex of RNA and capsid proteins. We show
+that an increase in effective chain stiffness because of base-pairing could be
+the reason why under certain conditions linear chains have an advantage over
+branched chains when it comes to encapsidation efficiency. While branching
+makes the genome more compact, RNA base-pairing increases the effective Kuhn
+length of the RNA molecule, which could result in an increase of the free
+energy of RNA confinement, that is, the work required to encapsidate RNA, and
+thus less efficient packaging.
+"
+23419,Radiative age mapping of the remnant radio galaxy B2 0924+30: the LOFAR perspective,"  We have observed the steep spectrum radio source B2 0924+30 using the LOw
+Frequency ARray (LOFAR) telescope. Hosted by a z=0.026 elliptical galaxy, it
+has a relatively large angular size of 12' (corresponding to 360 kpc projected
+linear size) and a morphology reminiscent of a remnant Fanaroff-Riley type II
+(FRII) radio galaxy. We combine LOFAR and archival images to study the spectral
+properties at a spatial resolution of 1'. We derive low frequency spectral
+index maps and use synchrotron ageing models to infer ages for different
+regions of the source. Using radiative ageing model fitting we show that the
+AGN activity ceased around 50 Myr ago. We note that the outer regions of the
+lobes are younger than the inner regions which is interpreted as a sign that
+those regions are remnant hotspots. We demonstrate the usefulness of maps of
+AGN radio remnants taken at low frequencies and suggest caution over the
+interpretation of spectral ages derived from integrated flux density
+measurements versus age mapping. The spectral index properties as well as the
+derived ages of B2 0924+30 are consistent with it being an FRII AGN radio
+remnant. LOFAR data are proving to be instrumental in extending our studies to
+the lowest radio frequencies and enabling analysis of the oldest source
+regions.
+"
+23420,Parametric identification of parallel Wiener-Hammerstein systems,"  Block-oriented nonlinear models are popular in nonlinear modeling because of
+their advantages to be quite simple to understand and easy to use. To increase
+the flexibility of single branch block-oriented models, such as Hammerstein,
+Wiener, and Wiener-Hammerstein models, parallel block-oriented models can be
+considered. This paper presents a method to identify parallel
+Wiener-Hammerstein systems starting from input-output data only. In the first
+step, the best linear approximation is estimated for different input excitation
+levels. In the second step, the dynamics are decomposed over a number of
+parallel orthogonal branches. Next, the dynamics of each branch are partitioned
+into a linear time invariant subsystem at the input and a linear time invariant
+subsystem at the output. This is repeated for each branch of the model. The
+static nonlinear part of the model is also estimated during this step. The
+consistency of the proposed initialization procedure is proven. The method is
+validated on real-world measurements using a custom built parallel
+Wiener-Hammerstein test system.
+"
+23421,Existence of mild solution to stochastic extended Korteweg - de Vries equation,"  In the paper we consider stochastic Korteweg - de Vries - type equation. We
+give sufficient conditions for the existence and uniqueness of local mild
+solution to the equation with additive noise. We discuss possibility of
+globalization of mild solution, as well.
+"
+23422,Network models in neuroscience,"  From interacting cellular components to networks of neurons and neural
+systems, interconnected units comprise a fundamental organizing principle of
+the nervous system. Understanding how their patterns of connections and
+interactions give rise to the many functions of the nervous system is a primary
+goal of neuroscience. Recently, this pursuit has begun to benefit from the
+development of new mathematical tools that can relate a system's architecture
+to its dynamics and function. These tools, which are known collectively as
+network science, have been used with increasing success to build models of
+neural systems across spatial scales and species. Here we discuss the nature of
+network models in neuroscience. We begin with a review of model theory from a
+philosophical perspective to inform our view of networks as models of complex
+systems in general, and of the brain in particular. We then summarize the types
+of models that are frequently studied in network neuroscience along three
+primary dimensions: from data representations to first-principles theory, from
+biophysical realism to functional phenomenology, and from elementary
+descriptions to coarse-grained approximations. We then consider ways to
+validate these models, focusing on approaches that perturb a system to probe
+its function. We close with a description of important frontiers in the
+construction of network models and their relevance for understanding
+increasingly complex functions of neural systems.
+"
+23423,Detecting and monitoring foodborne illness outbreaks: Twitter communications and the 2015 U.S. Salmonella outbreak linked to imported cucumbers,"  This research uses Twitter, as a social media device, to track communications
+related to the 2015 U.S. foodborne illness outbreak linked to Salmonella in
+imported cucumbers from Mexico. The relevant Twitter data are analyzed in light
+of the timeline of the official announcements made by the Centers for Disease
+Control and Prevention (CDC). The largest number of registered tweets is
+associated with the period immediately following the CDC initial announcement
+and the official release of the first recall of cucumbers.
+"
+23424,Pseudo-simple heteroclinic cycles in $R^4$,"  We study pseudo-simple heteroclinic cycles for a $\Gamma$-equivariant system
+in $R^4$ with finite $\Gamma\subset O(4)$, and their nearby dynamics. In
+particular, in a first step towards a full classification - analogous to that
+which exists already for the class of simple cycles - we identify all finite
+subgroups of $O(4)$ admitting pseudo-simple cycles. To this end we introduce a
+constructive method to build equivariant dynamical systems possessing a robust
+heteroclinic cycle. Extending a previous study we also investigate the
+existence of periodic orbits close to a pseudo-simple cycle, which depends on
+the symmetry groups of equilibria in the cycle. Moreover, we identify subgroups
+$\Gamma\subset O(4)$, $\Gamma\not\subset SO(4)$, admitting fragmentarily
+asymptotically stable pseudo-simple heteroclinic cycles. (It has been
+previously shown that for $\Gamma\subset SO(4)$ pseudo-simple cycles
+generically are completely unstable.) Finally, we study a generalized
+heteroclinic cycle, which involves a pseudo-simple cycle as a subset.
+"
+23425,Structural and dielectric characterization of Sm2MgMnO6,"  The polycrystalline Sm2MgMnO6 (SMMO) was synthesized at 1173K by means of
+sol-gel technique. Rietveld refine-ment of X-ray diffraction (XRD) pattern
+confirmed the formation of a single phase monoclinic structure with space group
+P21/n. The band gap achieved from UV-vis spectra shows the semiconducting
+nature of the material. To observe the effect of grains and grain-boundaries in
+the conduction process and dielectric relaxation measurements are carried out
+on SMMO sample at different frequencies between 313 K and 673 K. An electrical
+equivalent circuit consisting of the resistance and constant phase element is
+used to clarify the impedance data.
+"
+23426,Non-commutative Courant algebroids and Quiver algebras,"  In this paper, we develop a differential-graded symplectic
+(Batalin-Vilkovisky) version of the framework of Crawley-Boevey, Etingof and
+Ginzburg on noncommutative differential geometry based on double derivations to
+construct non-commutative analogues of the Courant algebroids introduced by
+Liu, Weinstein and Xu. Adapting geometric constructions of Ševera and
+Roytenberg for (commutative) graded symplectic supermanifolds, we express the
+BRST charge, given in our framework by a `homological double derivation', in
+terms of Van den Bergh's double Poisson algebras for graded bi-symplectic
+non-commutative 2-forms of weight 1, and in terms of our non-commutative
+Courant algebroids for graded bi-symplectic non-commutative 2-forms of weight 2
+(here, the grading, or ghost degree, is called weight). We then apply our
+formalism to obtain examples of exact non-commutative Courant algebroids, using
+appropriate graded quivers equipped with bi-symplectic forms of weight 2, with
+a possible twist by a closed Karoubi-de Rham non-commutative differential
+3-form.
+"
+23427,Appearance invariance in convolutional networks with neighborhood similarity,"  We present a neighborhood similarity layer (NSL) which induces appearance
+invariance in a network when used in conjunction with convolutional layers. We
+are motivated by the observation that, even though convolutional networks have
+low generalization error, their generalization capability does not extend to
+samples which are not represented by the training data. For instance, while
+novel appearances of learned concepts pose no problem for the human visual
+system, feedforward convolutional networks are generally not successful in such
+situations. Motivated by the Gestalt principle of grouping with respect to
+similarity, the proposed NSL transforms its input feature map using the feature
+vectors at each pixel as a frame of reference, i.e. center of attention, for
+its surrounding neighborhood. This transformation is spatially varying, hence
+not a convolution. It is differentiable; therefore, networks including the
+proposed layer can be trained in an end-to-end manner. We analyze the
+invariance of NSL to significant changes in appearance that are not represented
+in the training data. We also demonstrate its advantages for digit recognition,
+semantic labeling and cell detection problems.
+"
+23428,Simultaneous confidence sets for ranks using the partitioning principle - Technical report,"  Ranking institutions such as medical centers or universities is based on an
+indicator accompanied with an uncertainty measure such as a standard deviation,
+and confidence intervals should be calculated to assess the quality of these
+ranks. We consider the problem of constructing simultaneous confidence
+intervals for the ranks of centers based on an observed sample. We present in
+this paper a novel method based on multiple testing which uses the partitioning
+principle and employs the likelihood ratio (LR) test on the partitions. The
+complexity of the algorithm is super exponential. We present several ways and
+shortcuts to reduce this complexity. We provide also a polynomial algorithm
+which produces a very good bracketing for the multiple testing by linearizing
+the critical value of the LR test. We show that Tukey's Honest Significant
+Difference (HSD) test can be written as a partitioning procedure. The new
+methodology has promising properties in the sens that it opens the door in a
+simple and easy way to construct new methods which may trade the exponential
+complexity with power of the test or vice versa. In comparison to Tukey's HSD
+test, the LR test seems to give better results when the centers are close to
+each others or the uncertainty in the data is high which is confirmed during a
+simulation study.
+"
+23429,From Symmetry to Monotonicity,"  We offer an alternative and shorter proof to a result by Jan J.Ub{\o}e about
+monotonicity properties of a one-dimensional function that appeared in the
+Mathematical Intelligencer in 2015. Our proof is based on reducing the problem
+to symmetry properties of a two-dimensional surface.
+"
+23430,Derivation of the dipolar Gross-Pitaevskii energy,"  We consider N trapped bosons in R 3 interacting via a pair potential w which
+has a long range of dipolar type. We show the convergence of the energy and of
+the minimizers for the many-body problem towards those of the dipolar
+Gross-Pitaevskii functional, when N tends to infinity. In addition to the usual
+cubic interaction term, the latter has the long range dipolar interaction. Our
+results hold under the assumption that the two-particle interaction is scaled
+in the form N 3$\beta$--1 w(N $\beta$ x) for some 0 $\le$ $\beta$ \textless{}
+$\beta$max with $\beta$max = 1/3 + s/(45 + 42s) where s is related to the
+growth of the trapping potential.
+"
+23431,Analytically calculated post-Keplerian range and range-rate perturbations: the solar Lense-Thirring effect and BepiColombo,"  We analytically calculate the time series for the perturbations
+$\Delta\rho(t),~\Delta\dot\rho(t)$ induced by a general disturbing acceleration
+$\boldsymbol{A}$ on the mutual range $\rho$ and range-rate $\dot\rho$ of two
+test particles $\textrm{A},~\textrm{B}$ orbiting the same spinning body. We
+apply it to the general relativistic Lense-Thirring effect, due to the
+primary's spin $\boldsymbol{S}$, and the classical perturbation arising from
+its quadrupole mass moment $J_2$ for arbitrary orbital geometries and
+orientation of the source's symmetry axis $\boldsymbol{\hat{S}}$. The
+Earth-Mercury range and range-rate are nominally affected by the Sun's
+gravitomagnetic field to the $10~\textrm{m},~10^{-3}~\textrm{cm s}^{-1}$ level,
+respectively, during the extended phase (2026-2028) of the forthcoming
+BepiColombo mission to Mercury whose expected tracking accuracy is of the order
+of $\simeq 0.1~\textrm{m},~2\times 10^{-4}~\textrm{cm s}^{-1}$. The competing
+signatures due to the solar quadrupole $J_2^\odot$, if modelled at the
+$\sigma_{J_2^\odot}\simeq 10^{-9}$ level of the latest planetary ephemerides
+INPOP17a, are nearly 10 times smaller than the relativistic gravitomagnetic
+effects. The position and velocity vectors $\mathbf{r},~\mathbf{v}$ of Mercury
+and Earth are changed by the solar Lense-Thirring effect by about
+$10~\textrm{m},~1.5~\textrm{m}$ and $10^{-3}~\textrm{cm
+s}^{-1},~10^{-5}~\textrm{cm s}^{-1}$, respectively, over 2 yr; neglecting such
+shifts may have an impact on long-term integrations of the inner solar system
+dynamics over $\sim\textrm{Gyr}$ timescales.
+"
+23432,Learning Non-overlapping Convolutional Neural Networks with Multiple Kernels,"  In this paper, we consider parameter recovery for non-overlapping
+convolutional neural networks (CNNs) with multiple kernels. We show that when
+the inputs follow Gaussian distribution and the sample size is sufficiently
+large, the squared loss of such CNNs is $\mathit{~locally~strongly~convex}$ in
+a basin of attraction near the global optima for most popular activation
+functions, like ReLU, Leaky ReLU, Squared ReLU, Sigmoid and Tanh. The required
+sample complexity is proportional to the dimension of the input and polynomial
+in the number of kernels and a condition number of the parameters. We also show
+that tensor methods are able to initialize the parameters to the local strong
+convex region. Hence, for most smooth activations, gradient descent following
+tensor initialization is guaranteed to converge to the global optimal with time
+that is linear in input dimension, logarithmic in precision and polynomial in
+other factors. To the best of our knowledge, this is the first work that
+provides recovery guarantees for CNNs with multiple kernels under polynomial
+sample and computational complexities.
+"
+23433,Aerial Drop of Robots and Sensors for Optimal Area Coverage,"  The problem of rapid optimal coverage through the distribution a team of
+robots or static sensors via means of aerial drop is the topic of this work.
+Considering a nonholonomic (fixed-wing) aerial robot that corresponds to the
+carrier of a set of small holonomic (rotorcraft) aerial robots as well as
+static modules that are all equipped with a camera sensor, we address the
+problem of selecting optimal aerial drop times and configurations while the
+motion capabilities of the small aerial robots are also exploited to further
+survey their area of responsibility until they hit the ground. The overall
+solution framework consists of lightweight path-planning algorithms that can
+run on virtually any processing unit that might be available on-board.
+Evaluation studies in simulation as well as a set of elementary experiments
+that prove the validity of important assumptions illustrate the potential of
+the approach.
+"
+23434,A Collaborative Approach to Angel and Venture Capital Investment Recommendations,"  Matrix factorization was used to generate investment recommendations for
+investors. An iterative conjugate gradient method was used to optimize the
+regularized squared-error loss function. The number of latent factors, number
+of iterations, and regularization values were explored. Overfitting can be
+addressed by either early stopping or regularization parameter tuning. The
+model achieved the highest average prediction accuracy of 13.3%. With a similar
+model, the same dataset was used to generate investor recommendations for
+companies undergoing fundraising, which achieved highest prediction accuracy of
+11.1%.
+"
+23435,On The Compound MIMO Wiretap Channel with Mean Feedback,"  Compound MIMO wiretap channel with double sided uncertainty is considered
+under channel mean information model. In mean information model, channel
+variations are centered around its mean value which is fed back to the
+transmitter. We show that the worst case main channel is anti-parallel to the
+channel mean information resulting in an overall unit rank channel. Further,
+the worst eavesdropper channel is shown to be isotropic around its mean
+information. Accordingly, we provide the capacity achieving beamforming
+direction. We show that the saddle point property holds under mean information
+model, and thus, compound secrecy capacity equals to the worst case capacity
+over the class of uncertainty. Moreover, capacity achieving beamforming
+direction is found to require matrix inversion, thus, we derive the null
+steering (NS) beamforming as an alternative suboptimal solution that does not
+require matrix inversion. NS beamformer is in the direction orthogonal to the
+eavesdropper mean channel that maintains the maximum possible gain in mean main
+channel direction. Extensive computer simulation reveals that NS performs very
+close to the optimal solution. It also verifies that, NS beamforming
+outperforms both maximum ratio transmission (MRT) and zero forcing (ZF)
+beamforming approaches over the entire SNR range. Finally, An equivalence
+relation with MIMO wiretap channel in Rician fading environment is established.
+"
+23436,A complex network analysis of ethnic conflicts and human rights violations,"  News reports in media contain records of a wide range of socio-economic and
+political events in time. Using a publicly available, large digital database of
+news records, and aggregating them over time, we study the network of ethnic
+conflicts and human rights violations. Complex network analyses of the events
+and the involved actors provide important insights on the engaging actors,
+groups, establishments and sometimes nations, pointing at their long range
+effect over space and time. We find power law decays in distributions of actor
+mentions, co-actor mentions and degrees and dominance of influential actors and
+groups. Most influential actors or groups form a giant connected component
+which grows in time, and is expected to encompass all actors globally in the
+long run. We demonstrate how targeted removal of actors may help stop spreading
+unruly events. We study the cause-effect relation between types of events, and
+our quantitative analysis confirm that ethnic conflicts lead to human rights
+violations, while it does not support the converse.
+"
+23437,Causal inference with confounders missing not at random,"  It is important to draw causal inference from observational studies, which,
+however, becomes challenging if the confounders have missing values. Generally,
+causal effects are not identifiable if the confounders are missing not at
+random. We propose a novel framework to nonparametrically identify causal
+effects with confounders subject to an outcome-independent missingness, that
+is, the missing data mechanism is independent of the outcome, given the
+treatment and possibly missing confounders. We then propose a nonparametric
+two-stage least squares estimator and a parametric estimator for causal
+effects.
+"
+23438,Data-driven Discovery of Closure Models,"  Derivation of reduced order representations of dynamical systems requires the
+modeling of the truncated dynamics on the retained dynamics. In its most
+general form, this so-called closure model has to account for memory effects.
+In this work, we present a framework of operator inference to extract the
+governing dynamics of closure from data in a compact, non-Markovian form. We
+employ sparse polynomial regression and artificial neural networks to extract
+the underlying operator. For a special class of non-linear systems,
+observability of the closure in terms of the resolved dynamics is analyzed and
+theoretical results are presented on the compactness of the memory. The
+proposed framework is evaluated on examples consisting of linear to nonlinear
+systems with and without chaotic dynamics, with an emphasis on predictive
+performance on unseen data.
+"
+23439,What is the Maximum Mass of a Population III Galaxy?,"  We utilize cosmological hydrodynamic simulations to study the formation of
+Population III (Pop III) stars in dark matter halos exposed to strong ionizing
+radiation. We simulate the formation of three halos subjected to a wide range
+of ionizing fluxes, and find that for high flux, ionization and photoheating
+can delay gas collapse and star formation up to halo masses significantly
+larger than the atomic cooling threshold. The threshold halo mass at which gas
+first collapses and cools increases with ionizing flux for intermediate values,
+and saturates at a value approximately an order of magnitude above the atomic
+cooling threshold for extremely high flux (e.g. $\approx 5 \times 10^8 ~
+M_\odot$ at $z\approx6$). This behavior can be understood in terms of
+photoheating, ionization/recombination, and Ly$\alpha$ cooling in the
+pressure-supported, self-shielded gas core at the center of the growing dark
+matter halo. We examine the spherically-averaged radial velocity profiles of
+collapsing gas and find that a gas mass of up to $\approx 10^{6}~ M_\odot$ can
+reach the central regions within $3~{\rm Myr}$, providing an upper limit on the
+amount of massive Pop III stars that can form. The ionizing radiation increases
+this limit by a factor of a few compared to strong Lyman-Werner (LW) radiation
+alone. We conclude that the bright HeII 1640 \AA\ emission recently observed
+from the high-redshift galaxy CR7 cannot be explained by Pop III stars alone.
+However, in some halos, a sufficient number of Pop III stars may form to be
+detectable with future telescopes such as the James Webb Space Telescope
+(JWST).
+"
+23440,Transferring Category-based Functional Grasping Skills by Latent Space Non-Rigid Registration,"  Objects within a category are often similar in their shape and usage. When
+we---as humans---want to grasp something, we transfer our knowledge from past
+experiences and adapt it to novel objects. In this paper, we propose a new
+approach for transferring grasping skills that accumulates grasping knowledge
+into a category-level canonical model. Grasping motions for novel instances of
+the category are inferred from geometric deformations between the observed
+instance and the canonical shape. Correspondences between the shapes are
+established by means of a non-rigid registration method that combines the
+Coherent Point Drift approach with subspace methods. By incorporating
+category-level information into the registration, we avoid unlikely shapes and
+focus on deformations actually observed within the category. Control poses for
+generating grasping motions are accumulated in the canonical model from
+grasping definitions of known objects. According to the estimated shape
+parameters of a novel instance, the control poses are transformed towards it.
+The category-level model makes our method particularly relevant for on-line
+grasping, where fully-observed objects are not easily available. This is
+demonstrated through experiments in which objects with occluded handles are
+successfully grasped.
+"
+23441,A Tight Lower Bound for Counting Hamiltonian Cycles via Matrix Rank,"  For even $k$, the matchings connectivity matrix $\mathbf{M}_k$ encodes which
+pairs of perfect matchings on $k$ vertices form a single cycle. Cygan et al.
+(STOC 2013) showed that the rank of $\mathbf{M}_k$ over $\mathbb{Z}_2$ is
+$\Theta(\sqrt 2^k)$ and used this to give an $O^*((2+\sqrt{2})^{\mathsf{pw}})$
+time algorithm for counting Hamiltonian cycles modulo $2$ on graphs of
+pathwidth $\mathsf{pw}$. The same authors complemented their algorithm by an
+essentially tight lower bound under the Strong Exponential Time Hypothesis
+(SETH). This bound crucially relied on a large permutation submatrix within
+$\mathbf{M}_k$, which enabled a ""pattern propagation"" commonly used in previous
+related lower bounds, as initiated by Lokshtanov et al. (SODA 2011).
+We present a new technique for a similar pattern propagation when only a
+black-box lower bound on the asymptotic rank of $\mathbf{M}_k$ is given; no
+stronger structural insights such as the existence of large permutation
+submatrices in $\mathbf{M}_k$ are needed. Given appropriate rank bounds, our
+technique yields lower bounds for counting Hamiltonian cycles (also modulo
+fixed primes $p$) parameterized by pathwidth.
+To apply this technique, we prove that the rank of $\mathbf{M}_k$ over the
+rationals is $4^k / \mathrm{poly}(k)$. We also show that the rank of
+$\mathbf{M}_k$ over $\mathbb{Z}_p$ is $\Omega(1.97^k)$ for any prime $p\neq 2$
+and even $\Omega(2.15^k)$ for some primes.
+As a consequence, we obtain that Hamiltonian cycles cannot be counted in time
+$O^*((6-\epsilon)^{\mathsf{pw}})$ for any $\epsilon>0$ unless SETH fails. This
+bound is tight due to a $O^*(6^{\mathsf{pw}})$ time algorithm by Bodlaender et
+al. (ICALP 2013). Under SETH, we also obtain that Hamiltonian cycles cannot be
+counted modulo primes $p\neq 2$ in time $O^*(3.97^\mathsf{pw})$, indicating
+that the modulus can affect the complexity in intricate ways.
+"
+23442,The kinematic Sunyaev-Zel'dovich effect of the large-scale structure (I): dependence on neutrino mass,"  The study of neutrinos in astrophysics requires the combination of different
+observational probes. The temperature anisotropies of the cosmic microwave
+background induced via the kinematic Sunyaev-Zel'dovich (kSZ) effect may
+provide interesting information since they are expected to receive significant
+contribution from high-redshift plasma. We present a set of cosmological
+hydrodynamical simulations that include a treatment of the neutrino component
+considering four different sum of neutrino masses: $\Sigma
+m_\nu=(0,0.15,0.3,0.6)$ eV. Using their outputs, we modelled the kSZ effect due
+to the large-scale structure after the reionization by producing mock maps,
+then computed the kSZ power spectrum and studied how it depends on $z_{\rm re}$
+and $\Sigma m_\nu$. We also run a set of four simulations to study and correct
+possible systematics due to resolution, finite box size and astrophysics. With
+massless neutrinos we obtain $\mathcal{D}^{\rm kSZ}_{3000}=4.0$ $\mu {\rm K}^2$
+($z_{\rm re}$=8.8), enough to account for all of the kSZ signal of
+$\mathcal{D}^{\rm kSZ}_{3000}=(2.9\pm1.3)$ $\mu {\rm K}^2$ measured with the
+South Pole Telescope. This translates into an upper limit on the kSZ effect due
+to patchy reionization of $\mathcal{D}^{\rm kSZ,patchy}_{3000}<1.0$ $\mu {\rm
+K}^2$ (95 per cent confidence level). Massive neutrinos induce a damping of kSZ
+effect power of about 8, 12 and 40 per cent for $\Sigma m_\nu=(0.15,0.3,0.6)$
+eV, respectively. We study the dependence of the kSZ signal with $z_{\rm re}$
+and the neutrino mass fraction, $f_\nu$, and obtain $\mathcal{D}^{\rm
+kSZ}_{3000}\propto z_{\rm re}^{0.26}(1-f_\nu)^{14.3}$. Interestingly, the
+scaling with $f_\nu$ is significantly shallower with respect to the equivalent
+thermal SZ effect, and may be used to break the degeneracy with other
+cosmological parameters.
+"
+23443,On the diagonalizability of the Atkin U-operator for Drinfeld cusp forms,"  We study the diagonalizability of the Atkin $U$-operator acting on Drinfeld
+cusp forms for $\Gamma_1(t)$ and $\Gamma(t)$ using Teitelbaum's interpretation
+as harmonic cocycles. We prove $U$ is diagonalizable for small weights and
+explicitly compute the eigenvalues. We also formulate a conjecture, supported
+by numerical search and proofs in some special cases, about non
+diagonalizability of $U$ in even characteristic.
+"
+23444,Unbiased Online Recurrent Optimization,"  The novel Unbiased Online Recurrent Optimization (UORO) algorithm allows for
+online learning of general recurrent computational graphs such as recurrent
+network models. It works in a streaming fashion and avoids backtracking through
+past activations and inputs. UORO is computationally as costly as Truncated
+Backpropagation Through Time (truncated BPTT), a widespread algorithm for
+online learning of recurrent networks. UORO is a modification of NoBackTrack
+that bypasses the need for model sparsity and makes implementation easy in
+current deep learning frameworks, even for complex models.
+Like NoBackTrack, UORO provides unbiased gradient estimates; unbiasedness is
+the core hypothesis in stochastic gradient descent theory, without which
+convergence to a local optimum is not guaranteed. On the contrary, truncated
+BPTT does not provide this property, leading to possible divergence.
+On synthetic tasks where truncated BPTT is shown to diverge, UORO converges.
+For instance, when a parameter has a positive short-term but negative long-term
+influence, truncated BPTT diverges unless the truncation span is very
+significantly longer than the intrinsic temporal range of the interactions,
+while UORO performs well thanks to the unbiasedness of its gradients.
+"
+23445,CacheZoom: How SGX Amplifies The Power of Cache Attacks,"  In modern computing environments, hardware resources are commonly shared, and
+parallel computation is widely used. Parallel tasks can cause privacy and
+security problems if proper isolation is not enforced. Intel proposed SGX to
+create a trusted execution environment within the processor. SGX relies on the
+hardware, and claims runtime protection even if the OS and other software
+components are malicious. However, SGX disregards side-channel attacks. We
+introduce a powerful cache side-channel attack that provides system adversaries
+a high resolution channel. Our attack tool named CacheZoom is able to virtually
+track all memory accesses of SGX enclaves with high spatial and temporal
+precision. As proof of concept, we demonstrate AES key recovery attacks on
+commonly used implementations including those that were believed to be
+resistant in previous scenarios. Our results show that SGX cannot protect
+critical data sensitive computations, and efficient AES key recovery is
+possible in a practical environment. In contrast to previous works which
+require hundreds of measurements, this is the first cache side-channel attack
+on a real system that can recover AES keys with a minimal number of
+measurements. We can successfully recover AES keys from T-Table based
+implementations with as few as ten measurements.
+"
+23446,Some extensions of theorems of Knörrer and Herzog-Popescu,"  A construction due to Knörrer shows that if $N$ is a maximal Cohen-Macaulay
+module over a hypersurface defined by $f+y^2$, then the first syzygy of $N/yN$
+decomposes as the direct sum of $N$ and its own first syzygy. This was extended
+by Herzog-Popescu to hypersurfaces $f+y^n$, replacing $N/yN$ by $N/y^{n-1}N$.
+We show, in the same setting as Herzog-Popescu, that the first syzygy of
+$N/y^{k}N$ is always an extension of $N$ by its first syzygy, and moreover that
+this extension has useful approximation properties. We give two applications.
+First, we construct a ring $\Lambda^\#$ over which every finitely generated
+module has an eventually $2$-periodic projective resolution, prompting us to
+call it a ""non-commutative hypersurface ring"". Second, we give upper bounds on
+the dimension of the stable module category (a.k.a. the singularity category)
+of a hypersurface defined by a polynomial of the form $x_1^{a_1} + \dots +
+x_d^{a_d}$.
+"
+23447,On the local uniqueness of steady states for the Vlasov-Poisson system,"  Motivated by recent results of Lemou-Méhats-Räphael and Lemou concerning
+the quatitative stability of some suitable steady states for the Vlasov-Poisson
+system, we investigate the local uniqueness of steady states near these one.
+This research is inspired by analogous results of Couffrut and Šverák in
+the context of the 2D Euler equations.
+"
+23448,Decentralized Collision-Free Control of Multiple Robots in 2D and 3D Spaces,"  Decentralized control of robots has attracted huge research interests.
+However, some of the research used unrealistic assumptions without collision
+avoidance. This report focuses on the collision-free control for multiple
+robots in both complete coverage and search tasks in 2D and 3D areas which are
+arbitrary unknown. All algorithms are decentralized as robots have limited
+abilities and they are mathematically proved.
+The report starts with the grid selection in the two tasks. Grid patterns
+simplify the representation of the area and robots only need to move straightly
+between neighbor vertices. For the 100% complete 2D coverage, the equilateral
+triangular grid is proposed. For the complete coverage ignoring the boundary
+effect, the grid with the fewest vertices is calculated in every situation for
+both 2D and 3D areas.
+The second part is for the complete coverage in 2D and 3D areas. A
+decentralized collision-free algorithm with the above selected grid is
+presented driving robots to sections which are furthest from the reference
+point. The area can be static or expanding, and the algorithm is simulated in
+MATLAB.
+Thirdly, three grid-based decentralized random algorithms with collision
+avoidance are provided to search targets in 2D or 3D areas. The number of
+targets can be known or unknown. In the first algorithm, robots choose vacant
+neighbors randomly with priorities on unvisited ones while the second one adds
+the repulsive force to disperse robots if they are close. In the third
+algorithm, if surrounded by visited vertices, the robot will use the
+breadth-first search algorithm to go to one of the nearest unvisited vertices
+via the grid. The second search algorithm is verified on Pioneer 3-DX robots.
+The general way to generate the formula to estimate the search time is
+demonstrated. Algorithms are compared with five other algorithms in MATLAB to
+show their effectiveness.
+"
+23449,The k-tacnode process,"  The tacnode process is a universal behavior arising in nonintersecting
+particle systems and tiling problems. For Dyson Brownian bridges, the tacnode
+process describes the grazing collision of two packets of walkers. We consider
+such a Dyson sea on the unit circle with drift. For any integer k, we show that
+an appropriate double scaling of the drift and return time leads to a
+generalization of the tacnode process in which k particles are expected to wrap
+around the circle. We derive winding number probabilities and an expression for
+the correlation kernel in terms of functions related to the generalized
+Hastings-McLeod solutions to the inhomogeneous Painleve-II equation. The method
+of proof is asymptotic analysis of discrete orthogonal polynomials with a
+complex weight.
+"
+23450,Effects of layer thickness on the mechanical behavior of oxidation-strengthened Zr/Nb nanoscale multilayers,"  The mechanical behaviour and deformation mechanisms of magnetron-sputtered
+Zr/Nb nanoscale multilayers were analysed as a function of the periodicity (L =
+10 and 75 nm) and annealing time at 350C (2-336 hrs). The strength of the
+as-deposited multilayers was independent of the layer thickness and it was
+controlled by the co-deformation of the Zr and Nb layers. Annealing led to
+transformation of the Zr layers into ZrO2 after a few hours, while the Nb
+layers oxidised at a much slower rate. The volumetric expansion associated with
+the oxidation led to the formation of cracks at the interfaces and within the
+ZrO2 layers for the multilayers with L = 75 nm but not in the case of L = 10
+nm. The nanoindentation hardness increased significantly after annealing due to
+the contribution of the residual stresses associated with the volume increase
+due to oxidation and to the higher strength of the oxides. The strength
+increase after annealing, as measured by micropillar compression tests, was
+smaller than that measured by nanoindentation, as it did not include the
+contribution of the residual stresses, which were relieved during micropillar
+fabrication. The nanolaminate with L = 10 nm presented the highest strength and
+toughness as damage during oxidation was suppressed, and a deformation
+mechanism controlled by the formation of shear bands.
+"
+23451,Quantified Derandomization of Linear Threshold Circuits,"  One of the prominent current challenges in complexity theory is the attempt
+to prove lower bounds for $TC^0$, the class of constant-depth, polynomial-size
+circuits with majority gates. Relying on the results of Williams (2013), an
+appealing approach to prove such lower bounds is to construct a non-trivial
+derandomization algorithm for $TC^0$. In this work we take a first step towards
+the latter goal, by proving the first positive results regarding the
+derandomization of $TC^0$ circuits of depth $d>2$.
+Our first main result is a quantified derandomization algorithm for $TC^0$
+circuits with a super-linear number of wires. Specifically, we construct an
+algorithm that gets as input a $TC^0$ circuit $C$ over $n$ input bits with
+depth $d$ and $n^{1+\exp(-d)}$ wires, runs in almost-polynomial-time, and
+distinguishes between the case that $C$ rejects at most $2^{n^{1-1/5d}}$ inputs
+and the case that $C$ accepts at most $2^{n^{1-1/5d}}$ inputs. In fact, our
+algorithm works even when the circuit $C$ is a linear threshold circuit, rather
+than just a $TC^0$ circuit (i.e., $C$ is a circuit with linear threshold gates,
+which are stronger than majority gates).
+Our second main result is that even a modest improvement of our quantified
+derandomization algorithm would yield a non-trivial algorithm for standard
+derandomization of all of $TC^0$, and would consequently imply that
+$NEXP\not\subseteq TC^0$. Specifically, if there exists a quantified
+derandomization algorithm that gets as input a $TC^0$ circuit with depth $d$
+and $n^{1+O(1/d)}$ wires (rather than $n^{1+\exp(-d)}$ wires), runs in time at
+most $2^{n^{\exp(-d)}}$, and distinguishes between the case that $C$ rejects at
+most $2^{n^{1-1/5d}}$ inputs and the case that $C$ accepts at most
+$2^{n^{1-1/5d}}$ inputs, then there exists an algorithm with running time
+$2^{n^{1-\Omega(1)}}$ for standard derandomization of $TC^0$.
+"
+23452,On tope graphs of complexes of oriented matroids,"  We give two graph theoretical characterizations of tope graphs of (complexes
+of) oriented matroids. The first is in terms of excluded partial cube minors,
+the second is that all antipodal subgraphs are gated. A direct consequence is a
+third characterization in terms of zone graphs of tope graphs.
+Further corollaries include a characterization of topes of oriented matroids
+due to da Silva, another one of Handa, a characterization of lopsided systems
+due to Lawrence, and an intrinsic characterization of tope graphs of affine
+oriented matroids. Furthermore, we obtain polynomial time recognition
+algorithms for tope graphs of the above and a finite list of excluded partial
+cube minors for the bounded rank case. In particular, this answers a relatively
+long-standing open question in oriented matroids.
+"
+23453,Improved Human Emotion Recognition Using Symmetry of Facial Key Points with Dihedral Group,"  This article describes how to deploy dihedral group theory to detect Facial
+Key Points (FKP) symmetry to recognize emotions. The method can be applied in
+many other areas which those have the same data texture.
+"
+23454,Optical Spectroscopy of Candidates for Quasars at 3<z<5.5 from the XMM-Newton X-ray survey. A distant X-ray Quasar at z=5.08,"  We present the results of optical spectroscopy for 19 quasar candidates at
+photometric redshifts $\zphot \gtrsim 3$, \Nobs \ of which enter into the
+Khorunzhev et al.~(2016) catalog (K16). This is a catalog of quasar candidates
+and known type 1 quasars selected among the X-ray sources of the
+\textit{3XMM-DR4}catalog of the XMM-Newton serendipitous survey. We have
+performed spectroscopy for a quasi-random sample of new candidates at the 1.6-m
+\Azt \ telescope of the Sayan Solar Observatory and the 6-m BTA telescope of
+the Special Astrophysical Observatory. The spectra at \Azt \ were taken with
+the new low- and medium-resolution ADAM spectrograph that was produced and
+installed on the telescope in 2015. Fourteen of the \Nobs \ candidates actually
+have turned out to be quasars; 10 of them are at spectroscopic redshifts z > 3.
+The high purity of the sample of new candidates suggests that the purity of the
+entire K16 catalog of quasars is probably 70--80\%. One of the most distant
+($\zspec=5.08$) optically bright ($i^\prime\lesssim 21$) quasars ever detected
+in X-ray surveys has been discovered.
+"
+23455,Unsupervised Creation of Parameterized Avatars,"  We study the problem of mapping an input image to a tied pair consisting of a
+vector of parameters and an image that is created using a graphical engine from
+the vector of parameters. The mapping's objective is to have the output image
+as similar as possible to the input image. During training, no supervision is
+given in the form of matching inputs and outputs.
+This learning problem extends two literature problems: unsupervised domain
+adaptation and cross domain transfer. We define a generalization bound that is
+based on discrepancy, and employ a GAN to implement a network solution that
+corresponds to this bound. Experimentally, our method is shown to solve the
+problem of automatically creating avatars.
+"
+23456,Parameter-space study of kinetic-impactor mission design,"  While almost all potentially hazardous asteroids (PHAs) with a size larger
+than one kilometre have been discovered, it is well-known that the vast
+majority of the smaller ones are in fact yet to be found. There is therefore an
+excellent motivation to consider at once all possible Earth-crossing orbits,
+and to undertake a systematic study of mitigation missions for the entire
+parameter space of orbital elements. It is shown that the whole parameter space
+can be reduced, without loss of generality, to only three relevant
+dimensionless parameters: the eccentricity and inclination of the asteroid
+orbit, and the asteroid true anomaly at impact. Ballistic kinetic-impactor
+mitigation missions are studied for the entire parameter space, considering
+critical feasibility constraints such as the launcher performance and the
+illumination conditions at deflection. Different classes of optimal solutions
+are found to exist and can be directly linked to asteroid orbital properties.
+The aim of this work is to help identify an appropriate response to the
+potential threat of a collision of a near-Earth object with our planet, to
+provide a preliminary mission design, and to determine in which parts of
+parameter space difficulties may arise. (abridged)
+"
+23457,Was Planet 9 captured in the Sun's natal star-forming region?,"  The presence of an unseen `Planet 9' on the outskirts of the Solar system has
+been invoked to explain the unexpected clustering of the orbits of several
+Edgeworth--Kuiper Belt Objects. We use $N$-body simulations to investigate the
+probability that Planet 9 was a free-floating planet (FFLOP) that was captured
+by the Sun in its birth star-formation environment. We find that only 1 - 6 per
+cent of FFLOPs are ensnared by stars, even with the most optimal initial
+conditions for capture in star-forming regions (one FFLOP per star, and highly
+correlated stellar velocities to facilitate capture). Depending on the initial
+conditions of the star-forming regions, only 5 - 10 of 10000 planets are
+captured onto orbits that lie within the constraints for Planet 9. When we
+apply an additional environmental constraint for Solar system formation -
+namely the injection of short-lived radioisotopes into the Sun's protoplanetary
+disc from supernovae - we find that the probability for the capture of Planet 9
+to be almost zero.
+"
+23458,Multiqubit and multilevel quantum reinforcement learning with quantum technologies,"  We propose a protocol to perform quantum reinforcement learning with quantum
+technologies. At variance with recent results on quantum reinforcement learning
+with superconducting circuits, in our current protocol coherent feedback during
+the learning process is not required, enabling its implementation in a wide
+variety of quantum systems. We consider diverse possible scenarios for an
+agent, an environment, and a register that connects them, involving multiqubit
+and multilevel systems, as well as open-system dynamics. We finally propose
+possible implementations of this protocol in trapped ions and superconducting
+circuits. The field of quantum reinforcement learning with quantum technologies
+will enable enhanced quantum control, as well as more efficient machine
+learning calculations.
+"
+23459,Highly Efficient Hierarchical Online Nonlinear Regression Using Second Order Methods,"  We introduce highly efficient online nonlinear regression algorithms that are
+suitable for real life applications. We process the data in a truly online
+manner such that no storage is needed, i.e., the data is discarded after being
+used. For nonlinear modeling we use a hierarchical piecewise linear approach
+based on the notion of decision trees where the space of the regressor vectors
+is adaptively partitioned based on the performance. As the first time in the
+literature, we learn both the piecewise linear partitioning of the regressor
+space as well as the linear models in each region using highly effective second
+order methods, i.e., Newton-Raphson Methods. Hence, we avoid the well known
+over fitting issues by using piecewise linear models, however, since both the
+region boundaries as well as the linear models in each region are trained using
+the second order methods, we achieve substantial performance compared to the
+state of the art. We demonstrate our gains over the well known benchmark data
+sets and provide performance results in an individual sequence manner
+guaranteed to hold without any statistical assumptions. Hence, the introduced
+algorithms address computational complexity issues widely encountered in real
+life applications while providing superior guaranteed performance in a strong
+deterministic sense.
+"
+23460,Nonequilibrium-induced enhancement of dynamical quantum coherence and entanglement of spin arrays,"  The random magnetic field produced by nuclear spins has long been viewed as
+the dominating source of decoherence in the quantum-dot based spins. Here we
+obtain in both exact and analytical manner the dynamics of spin qubits coupled
+to nuclear spin environments via the hyperfine interaction, going beyond the
+weak system-bath interaction and Markovian approximation. We predict that the
+detailed-balance breaking produced by chemical potential gradient in nuclear
+baths leads to the rapid oscillations of populations, quantum coherence and
+entanglement, which are absent in the conventional case (i.e., Overhauser
+noise). This is attributed to the nonequilibrium feature of the system as shown
+in the relation between the oscillation period and the chemical potential
+imbalance. Our results reveal the essentiality of nonequilibriumness with
+detailed-balance breaking for enhancing the dynamical coherence and
+entanglement of spin qubits. Moreover, our exact solution explicitly
+demonstrates that the non-Markovian bath comprised by nuclear spins can
+preserve the collective quantum state, due to the recovery of coherence.
+Finally, we propose an experiment using ultracold trapped ions to observe these
+nonequilibrium and memory effects.
+"
+23461,REAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing,"  Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most
+previous studies on co-design of incentive mechanism and privacy preservation
+assume a trustworthy fusion center (FC). Very recent work has taken steps to
+relax the assumption on trustworthy FC and allows participatory users (PUs) to
+add well calibrated noise to their raw sensing data before reporting them,
+whereas the focus is on the equilibrium behavior of data subjects with binary
+data. Making a paradigm shift, this paper aim to quantify the privacy
+compensation for continuous data sensing while allowing FC to directly control
+PUs. There are two conflicting objectives in such scenario: FC desires better
+quality data in order to achieve higher aggregation accuracy whereas PUs prefer
+adding larger noise for higher privacy-preserving levels (PPLs). To achieve a
+good balance therein, we design an efficient incentive mechanism to REconcile
+FC's Aggregation accuracy and individual PU's data Privacy (REAP).
+Specifically, we adopt the celebrated notion of differential privacy to measure
+PUs' PPLs and quantify their impacts on FC's aggregation accuracy. Then,
+appealing to Contract Theory, we design an incentive mechanism to maximize FC's
+aggregation accuracy under a given budget. The proposed incentive mechanism
+offers different contracts to PUs with different privacy preferences, by which
+FC can directly control PUs. It can further overcome the information asymmetry,
+i.e., the FC typically does not know each PU's precise privacy preference. We
+derive closed-form solutions for the optimal contracts in both complete
+information and incomplete information scenarios. Further, the results are
+generalized to the continuous case where PUs' privacy preferences take values
+in a continuous domain. Extensive simulations are provided to validate the
+feasibility and advantages of our proposed incentive mechanism.
+"
+23462,Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks,"  Convolutional Neural Networks (CNNs) are effective models for reducing
+spectral variations and modeling spectral correlations in acoustic features for
+automatic speech recognition (ASR). Hybrid speech recognition systems
+incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models
+(HMMs/GMMs) have achieved the state-of-the-art in various benchmarks.
+Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural
+Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it
+feasible to train an end-to-end speech recognition system instead of hybrid
+settings. However, RNNs are computationally expensive and sometimes difficult
+to train. In this paper, inspired by the advantages of both CNNs and the CTC
+approach, we propose an end-to-end speech framework for sequence labeling, by
+combining hierarchical CNNs with CTC directly without recurrent connections. By
+evaluating the approach on the TIMIT phoneme recognition task, we show that the
+proposed model is not only computationally efficient, but also competitive with
+the existing baseline systems. Moreover, we argue that CNNs have the capability
+to model temporal correlations with appropriate context information.
+"
+23463,Fixed point theorems in $G$-metric space,"  In this article, we present a new type of fixed point for single valed
+mapping in a $G$-complete $G$-metric space.
+"
+23464,Robust Structure-based Shape Correspondence,"  We present a robust method to find region-level correspondences between
+shapes, which are invariant to changes in geometry and applicable across
+multiple shape representations. We generate simplified shape graphs by jointly
+decomposing the shapes, and devise an adapted graph-matching technique, from
+which we infer correspondences between shape regions. The simplified shape
+graphs are designed to primarily capture the overall structure of the shapes,
+without reflecting precise information about the geometry of each region, which
+enables us to find correspondences between shapes that might have significant
+geometric differences. Moreover, due to the special care we take to ensure the
+robustness of each part of our pipeline, our method can find correspondences
+between shapes with different representations, such as triangular meshes and
+point clouds. We demonstrate that the region-wise matching that we obtain can
+be used to find correspondences between feature points, reveal the intrinsic
+self-similarities of each shape, and even construct point-to-point maps across
+shapes. Our method is both time and space efficient, leading to a pipeline that
+is significantly faster than comparable approaches. We demonstrate the
+performance of our approach through an extensive quantitative and qualitative
+evaluation on several benchmarks where we achieve comparable or superior
+performance to existing methods.
+"
+23465,The ($n+1$)-Lipschitz homotopy group of the Heisenberg group $\mathbb{H}^n$,"  We prove that for $n\geq 2$, the Lipschitz homotopy group $\pi_{n+1}^{\rm
+Lip}(\mathbb{H}^n)\neq 0$ of the Heisenberg group $\mathbb{H}^n$ is nontrivial.
+"
+23466,Online Convex Optimization with Stochastic Constraints,"  This paper considers online convex optimization (OCO) with stochastic
+constraints, which generalizes Zinkevich's OCO over a known simple fixed set by
+introducing multiple stochastic functional constraints that are i.i.d.
+generated at each round and are disclosed to the decision maker only after the
+decision is made. This formulation arises naturally when decisions are
+restricted by stochastic environments or deterministic environments with noisy
+observations. It also includes many important problems as special cases, such
+as OCO with long term constraints, stochastic constrained convex optimization,
+and deterministic constrained convex optimization. To solve this problem, this
+paper proposes a new algorithm that achieves $O(\sqrt{T})$ expected regret and
+constraint violations and $O(\sqrt{T}\log(T))$ high probability regret and
+constraint violations. Experiments on a real-world data center scheduling
+problem further verify the performance of the new algorithm.
+"
+23467,Wave Function and Emergent SU(2) Symmetry in $ν_T=1$ Quantum Hall Bilayer,"  We propose a trial wave function for the quantum Hall bilayer system of total
+filling factor $\nu_T=1$ at a layer distance $d$ to magnetic length $\ell$
+ratio $d/\ell=\kappa_{c1}\approx1.1$, where the lowest charged excitation is
+known to have a level crossing. The wave function has two-particle correlations
+which fit well with those in previous numerical studies, and can be viewed as a
+Bose-Einstein condensate of free excitons formed by composite bosons and
+anti-composite bosons in different layers. We show the free nature of these
+excitons indicating an emergent SU(2) symmetry for the composite bosons at
+$d/\ell=\kappa_{c1}$, which leads to the level crossing in low-lying charged
+excitations. We further show the overlap between the trial wave function and
+the ground state of a small size exact diagonalization is peaked near
+$d/\ell=\kappa_{c1}$, which supports our theory.
+"
+23468,Deep Character-Level Click-Through Rate Prediction for Sponsored Search,"  Predicting the click-through rate of an advertisement is a critical component
+of online advertising platforms. In sponsored search, the click-through rate
+estimates the probability that a displayed advertisement is clicked by a user
+after she submits a query to the search engine. Commercial search engines
+typically rely on machine learning models trained with a large number of
+features to make such predictions. This is inevitably requires a lot of
+engineering efforts to define, compute, and select the appropriate features. In
+this paper, we propose two novel approaches (one working at character level and
+the other working at word level) that use deep convolutional neural networks to
+predict the click-through rate of a query-advertisement pair. Specially, the
+proposed architectures only consider the textual content appearing in a
+query-advertisement pair as input, and produce as output a click-through rate
+prediction. By comparing the character-level model with the word-level model,
+we show that language representation can be learnt from scratch at character
+level when trained on enough data. Through extensive experiments using billions
+of query-advertisement pairs of a popular commercial search engine, we
+demonstrate that both approaches significantly outperform a baseline model
+built on well-selected text features and a state-of-the-art word2vec-based
+approach. Finally, by combining the predictions of the deep models introduced
+in this study with the prediction of the model in production of the same
+commercial search engine, we significantly improve the accuracy and the
+calibration of the click-through rate prediction of the production system.
+"
+23469,Quantum Communication-Query Tradeoffs,"  For any function $f: X \times Y \to Z$, we prove that $Q^{*\text{cc}}(f)
+\cdot Q^{\text{OIP}}(f) \cdot (\log Q^{\text{OIP}}(f) + \log |Z|) \geq
+\Omega(\log |X|)$. Here, $Q^{*\text{cc}}(f)$ denotes the bounded-error
+communication complexity of $f$ using an entanglement-assisted two-way qubit
+channel, and $Q^{\text{OIP}}(f)$ denotes the number of quantum queries needed
+to learn $x$ with high probability given oracle access to the function $f_x(y)
+\stackrel{\text{def}}{=} f(x, y)$. We show that this tradeoff is close to the
+best possible. We also give a generalization of this tradeoff for
+distributional query complexity.
+As an application, we prove an optimal $\Omega(\log q)$ lower bound on the
+$Q^{*\text{cc}}$ complexity of determining whether $x + y$ is a perfect square,
+where Alice holds $x \in \mathbf{F}_q$, Bob holds $y \in \mathbf{F}_q$, and
+$\mathbf{F}_q$ is a finite field of odd characteristic. As another application,
+we give a new, simpler proof that searching an ordered size-$N$ database
+requires $\Omega(\log N / \log \log N)$ quantum queries. (It was already known
+that $\Theta(\log N)$ queries are required.)
+"
+23470,Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities,"  One of the major hurdles preventing the full exploitation of information from
+online communities is the widespread concern regarding the quality and
+credibility of user-contributed content. Prior works in this domain operate on
+a static snapshot of the community, making strong assumptions about the
+structure of the data (e.g., relational tables), or consider only shallow
+features for text classification.
+To address the above limitations, we propose probabilistic graphical models
+that can leverage the joint interplay between multiple factors in online
+communities --- like user interactions, community dynamics, and textual content
+--- to automatically assess the credibility of user-contributed online content,
+and the expertise of users and their evolution with user-interpretable
+explanation. To this end, we devise new models based on Conditional Random
+Fields for different settings like incorporating partial expert knowledge for
+semi-supervised learning, and handling discrete labels as well as numeric
+ratings for fine-grained analysis. This enables applications such as extracting
+reliable side-effects of drugs from user-contributed posts in healthforums, and
+identifying credible content in news communities.
+Online communities are dynamic, as users join and leave, adapt to evolving
+trends, and mature over time. To capture this dynamics, we propose generative
+models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian
+Motion to trace the continuous evolution of user expertise and their language
+model over time. This allows us to identify expert users and credible content
+jointly over time, improving state-of-the-art recommender systems by explicitly
+considering the maturity of users. This also enables applications such as
+identifying helpful product reviews, and detecting fake and anomalous reviews
+with limited information.
+"
+23471,Toward A Scalable Exploratory Framework for Complex High-Dimensional Phenomics Data,"  Phenomics is an emerging branch of modern biology, which uses high throughput
+phenotyping tools to capture multiple environment and phenotypic trait
+measurements, at a massive scale. The resulting high dimensional data sets
+represent a treasure trove of information for providing an indepth
+understanding of how multiple factors interact and contribute to control the
+growth and behavior of different plant crop genotypes. However, computational
+tools that can parse through such high dimensional data sets and aid in
+extracting plausible hypothesis are currently lacking. In this paper, we
+present a new algorithmic approach to effectively decode and characterize the
+role of environment on phenotypic traits, from complex phenomic data. To the
+best of our knowledge, this effort represents the first application of
+topological data analysis on phenomics data.
+We applied this novel algorithmic approach on a real-world maize data set.
+Our results demonstrate the ability of our approach to delineate emergent
+behavior among subpopulations, as dictated by one or more environmental
+factors; notably, our approach shows how the environment plays a key role in
+determining the phenotypic behavior of one of the two genotypes.
+Source code for our implementation and test data are freely available with
+detailed instructions at this https URL
+"
+23472,Detection and Mitigation of Classes of Attacks in Supervisory Control Systems,"  The deployment of control systems with network-connected components has made
+feedback control systems vulnerable to attacks over the network. This paper
+considers the problem of intrusion detection and mitigation in supervisory
+control systems, where the attacker has the ability to enable or disable
+vulnerable actuator commands and erase or insert vulnerable sensor readings. We
+present a mathematical model for the system under certain classes of actuator
+enablement attacks, sensor erasure attacks, or sensor insertion attacks. We
+then propose a defense strategy that aims to detect such attacks online and
+disables all controllable events after an attack is detected. We develop an
+algorithmic procedure for verifying whether the system can prevent damage from
+the attacks considered with the proposed defense strategy, where damage is
+modeled as the reachability of a pre-defined set of unsafe system states.The
+technical condition of interest that is necessary and sufficient in this
+context, termed ""GF-safe controllability"", is characterized. We show that the
+verification of GF-safe controllability can be performed using diagnoser or
+verifier automata. Finally, we illustrate the methodology with a traffic
+control system example.
+"
+23473,Personalized Dynamics Models for Adaptive Assistive Navigation Systems,"  Consider an assistive system that guides visually impaired users through
+speech and haptic feedback to their destination. Existing robotic and
+ubiquitous navigation technologies (e.g., portable, ground, or wearable
+systems) often operate in a generic, user-agnostic manner. However, to minimize
+confusion and navigation errors, our real-world analysis reveals a crucial need
+to adapt the instructional guidance across different end-users with diverse
+mobility skills. To address this practical issue in scalable system design, we
+propose a novel model-based reinforcement learning framework for personalizing
+the system-user interaction experience. When incrementally adapting the system
+to new users, we propose to use a weighted experts model for addressing
+data-efficiency limitations in transfer learning with deep models. A real-world
+dataset of navigation by blind users is used to show that the proposed approach
+allows for (1) more accurate long-term human behavior prediction (up to 20
+seconds into the future) through improved reasoning over personal mobility
+characteristics, interaction with surrounding obstacles, and the current
+navigation goal, and (2) quick adaptation at the onset of learning, when data
+is limited.
+"
+23474,Concurrent Channel Probing and Data Transmission in Full-duplex MIMO Systems,"  An essential step for achieving multiplexing gain in MIMO downlink systems is
+to collect accurate channel state information (CSI) from the users.
+Traditionally, CSIs have to be collected before any data can be transmitted.
+Such a sequential scheme incurs a large feedback overhead, which substantially
+limits the multiplexing gain especially in a network with a large number of
+users. In this paper, we propose a novel approach to mitigate the feedback
+overhead by leveraging the recently developed Full-duplex radios. Our approach
+is based on the key observation that using Full-duplex radios, when the
+base-station (BS) is collecting CSI of one user through the uplink channel, it
+can use the downlink channel to simultaneously transmit data to other
+(non-interfering) users for which CSIs are already known. By allowing
+concurrent channel probing and data transmission, our scheme can potentially
+achieve a higher throughput compared to traditional schemes using Half-duplex
+radios. The new flexibility introduced by our scheme, however, also leads to
+fundamental challenges in achieving throughout optimal scheduling. In this
+paper, we make an initial effort to this important problem by considering a
+simplified group interference model. We develop a throughput optimal scheduling
+policy with complexity $O((N/I)^I)$, where $N$ is the number of users and $I$
+is the number of user groups. To further reduce the complexity, we propose a
+greedy policy with complexity $O(N\log N)$ that not only achieves at least 2/3
+of the optimal throughput region, but also outperforms any feasible Half-duplex
+solutions. We derive the throughput gain offered by Full-duplex under different
+system parameters and show the advantage of our algorithms through numerical
+studies.
+"
+23475,Two proposals to protect a qubit using CQED techniques: inequality between atomic velocity dispersion and losses of a quantum memory,"  We present in this work an analysis of the damage imposed by the atom on the
+field state inside a lossy superconducting cavity. To access such effects, we
+propose two procedures to preserve a qubit of the decay effects of an imperfect
+quantum memory: the first by means of an quasi-instantaneous phase kick applied
+in the atom, and the second by means of controlled resonant and dispersive
+interactions. We immediately demonstrate that, in both procedures, the dwell
+time of the qubit in the cavity increases significantly, being expressively
+higher for the second. A relation between the inaccuracy of the preparation of
+the atomic beam and the quality of the cavity arises naturally from our
+calculations for each procedure. This result is unprecedented, and sets out the
+rules to increase the dwell time of the qubit.
+"
+23476,"Multi-point Gaussian states, quadratic-exponential cost functionals, and large deviations estimates for linear quantum stochastic systems","  This paper is concerned with risk-sensitive performance analysis for linear
+quantum stochastic systems interacting with external bosonic fields. We
+consider a cost functional in the form of the exponential moment of the
+integral of a quadratic polynomial of the system variables over a bounded time
+interval. An integro-differential equation is obtained for the time evolution
+of this quadratic-exponential functional, which is compared with the original
+quantum risk-sensitive performance criterion employed previously for
+measurement-based quantum control and filtering problems. Using multi-point
+Gaussian quantum states for the past history of the system variables and their
+first four moments, we discuss a quartic approximation of the cost functional
+and its infinite-horizon asymptotic behaviour. The computation of the
+asymptotic growth rate of this approximation is reduced to solving two
+algebraic Lyapunov equations. We also outline further approximations of the
+cost functional, based on higher-order cumulants and their growth rates,
+together with large deviations estimates. For comparison, an auxiliary
+classical Gaussian Markov diffusion process is considered in a complex
+Euclidean space which reproduces the quantum system variables at the level of
+covariances but has different higher-order moments relevant to the
+risk-sensitive criteria. The results of the paper are also demonstrated by a
+numerical example and may find applications to coherent quantum risk-sensitive
+control problems, where the plant and controller form a fully quantum
+closed-loop system, and other settings with nonquadratic cost functionals.
+"
+23477,Tools for higher-order network analysis,"  Networks are a fundamental model of complex systems throughout the sciences,
+and network datasets are typically analyzed through lower-order connectivity
+patterns described at the level of individual nodes and edges. However,
+higher-order connectivity patterns captured by small subgraphs, also called
+network motifs, describe the fundamental structures that control and mediate
+the behavior of many complex systems. We develop three tools for network
+analysis that use higher-order connectivity patterns to gain new insights into
+network datasets: (1) a framework to cluster nodes into modules based on joint
+participation in network motifs; (2) a generalization of the clustering
+coefficient measurement to investigate higher-order closure patterns; and (3) a
+definition of network motifs for temporal networks and fast algorithms for
+counting them. Using these tools, we analyze data from biology, ecology,
+economics, neuroscience, online social networks, scientific collaborations,
+telecommunications, transportation, and the World Wide Web.
+"
+23478,Harvesting the decay energy of $^{26}$Al to drive lightning discharge in protoplanetary discs,"  Chondrules in primitive meteorites likely formed by recrystallisation of dust
+aggregates that were flash-heated to nearly complete melting. Chondrules may
+represent the building blocks of rocky planetesimals and protoplanets in the
+inner regions of protoplanetary discs, but the source of ubiquitous thermal
+processing of their dust aggregate precursors remains elusive. Here we
+demonstrate that escape of positrons released in the decay of the short-lived
+radionuclide $^{26}$Al leads to a large-scale charging of dense pebble
+structures, resulting in neutralisation by lightning discharge and
+flash-heating of dust and pebbles. This charging mechanism is similar to a
+nuclear battery where a radioactive source charges a capacitor. We show that
+the nuclear battery effect operates in circumplanetesimal pebble discs. The
+extremely high pebble densities in such discs are consistent with conditions
+during chondrule heating inferred from the high abundance of sodium within
+chondrules. The sedimented mid-plane layer of the protoplanetary disc may also
+be prone to charging by the emission of positrons, if the mass density of small
+dust there is at least an order of magnitude above the gas density. Our results
+imply that the decay energy of $^{26}$Al can be harvested to drive intense
+lightning activity in protoplanetary discs. The total energy stored in positron
+emission is comparable to the energy needed to melt all solids in the
+protoplanetary disc. The efficiency of transferring the positron energy to the
+electric field nevertheless depends on the relatively unknown distribution and
+scale-dependence of pebble density gradients in circumplanetesimal pebble discs
+and in the protoplanetary disc mid-plane layer.
+"
+23479,Close entrainment of massive molecular gas flows by radio bubbles in the central galaxy of Abell 1795,"  We present new ALMA observations tracing the morphology and velocity
+structure of the molecular gas in the central galaxy of the cluster Abell 1795.
+The molecular gas lies in two filaments that extend 5 - 7 kpc to the N and S
+from the nucleus and project exclusively around the outer edges of two inner
+radio bubbles. Radio jets launched by the central AGN have inflated bubbles
+filled with relativistic plasma into the hot atmosphere surrounding the central
+galaxy. The N filament has a smoothly increasing velocity gradient along its
+length from the central galaxy's systemic velocity at the nucleus to -370 km/s,
+the average velocity of the surrounding galaxies, at the furthest extent. The S
+filament has a similarly smooth but shallower velocity gradient and appears to
+have partially collapsed in a burst of star formation. The close spatial
+association with the radio lobes, together with the ordered velocity gradients
+and narrow velocity dispersions, show that the molecular filaments are gas
+flows entrained by the expanding radio bubbles. Assuming a Galactic
+$X_{\mathrm{CO}}$ factor, the total molecular gas mass is
+$3.2\pm0.2\times10^{9}$M$_{\odot}$. More than half lies above the N radio
+bubble. Lifting the molecular clouds appears to require an infeasibly efficient
+coupling between the molecular gas and the radio bubble. The energy required
+also exceeds the mechanical power of the N radio bubble by a factor of two.
+Stimulated feedback, where the radio bubbles lift low entropy X-ray gas that
+becomes thermally unstable and rapidly cools in situ, provides a plausible
+model. Multiple generations of radio bubbles are required to lift this
+substantial gas mass. The close morphological association then indicates that
+the cold gas either moulds the newly expanding bubbles or is itself pushed
+aside and shaped as they inflate.
+"
+23480,Scalable computation for optimal control of cascade systems with constraints,"  A method is devised for numerically solving a class of finite-horizon optimal
+control problems subject to cascade linear discrete-time dynamics. It is
+assumed that the linear state and input inequality constraints, and the
+quadratic measure of performance, are all separable with respect to the spatial
+dimension of the underlying cascade of sub-systems, as well as the temporal
+dimension of the dynamics. By virtue of this structure, the computation cost of
+an interior-point method for an equivalent quadratic programming formulation of
+the optimal control problem can be made to scale linearly with the number of
+sub-systems. However, the complexity of this approach grows cubically with the
+time horizon. As such, computational advantage becomes apparent in situations
+where the number of sub-systems is relatively large. In any case, the method is
+amenable to distributed computation with low communication overhead and only
+immediate upstream neighbour sharing of partial model data among processing
+agents. An example is presented to illustrate an application of the main
+results to model data for the cascade dynamics of an automated irrigation
+channel.
+"
+23481,NP-completeness of anti-Kekulé and matching preclusion problems,"  Anti-Kekulé problem is a concept of chemical graph theory precluding the
+Kekulé structure of molecules. Matching preclusion and conditional matching
+preclusion were proposed as measures of robustness in the event of edge failure
+in interconnection networks. It is known that matching preclusion problem on
+bipartite graphs is NP-complete. In this paper, we mainly prove that
+anti-Kekulé problem on bipartite graphs is NP-complete. As an extension to
+(conditional) matching preclusion problem, we propose the concept of
+$s$-restricted matching preclusion problem, and prove that such problem on
+bipartite graphs is also NP-complete. Finally, we determine that $s$-restricted
+matching preclusion number of $Q_n$ ($n\geq3$) is $2n-2$.
+"
+23482,On the combinatorics of gentle algebras,"  For $A$ a gentle algebra, and $X$ and $Y$ string modules, we construct a
+combinatorial basis for Hom($X,\tau Y$). We use this to describe support
+$\tau$-tilting modules for $A$. We give a combinatorial realization of maps in
+both directions realizing the bijection between support $\tau$-tilting modules
+and functorially finite torsion classes. We give an explicit basis of
+Ext$^1(Y,X)$ as short exact sequences. We analyze several constructions given
+in a more restricted, combinatorial setting by McConville, showing that many
+but not all of them can be extended to general gentle algebras.
+"
+23483,A symplectic perspective on constrained eigenvalue problems,"  The Maslov index is a powerful tool for computing spectra of selfadjoint,
+elliptic boundary value problems. This is done by counting intersections of a
+fixed Lagrangian subspace, which designates the boundary condition, with the
+set of Cauchy data for the differential operator. We apply this methodology to
+constrained eigenvalue problems, in which the operator is restricted to a (not
+necessarily invariant) subspace. The Maslov index is defined and used to
+compute the Morse index of the constrained operator. We then prove a
+constrained Morse index theorem, which says that the Morse index of the
+constrained problem equals the number of constrained conjugate points, counted
+with multiplicity, and give an application to the nonlinear Schrödinger
+equation.
+"
+23484,Single Anchor Localization and Orientation Performance Limits using Massive Arrays: MIMO vs. Beamforming,"  Next generation cellular networks will experience the combination of
+femtocells, millimeter-wave (mm-wave) communications and massive antenna
+arrays. Thanks to the beamforming capability as well as the high angular
+resolution provided by massive arrays, only one single access point (AP) acting
+as an anchor node could be used for localization estimation, thus avoiding
+over-sized infrastructures dedicated to positioning. In this context, our paper
+aims at investigating the localization and orientation performance limits
+employing massive arrays both at the AP and mobile side. Thus, we first
+asymptotically demonstrate the tightness of the Cramer-Rao bound (CRB) in
+massive array regime, and in the presence or not of multipath. Successively, we
+propose a comparison between MIMO and beamforming in terms of array structure,
+time synchronization error and multipath components. Among different array
+configurations, we consider also random weighting as a trade-off between the
+high diversity gain of MIMO and the high directivity guaranteed by phased
+arrays. By evaluating the CRB for the different array configurations, results
+show the interplay between diversity and beamforming gain as well as the
+benefits achievable by varying the number of array elements in terms of
+localization accuracy.
+"
+23485,A dynamic resource allocation decision model for IT security,"  Today, with the continued growth in using information and communication
+technologies (ICT) for business purposes, business organizations become
+increasingly dependent on their information systems. Thus, they need to protect
+them from the different attacks exploiting their vulnerabilities. To do so, the
+organization has to use security technologies, which may be proactive or
+reactive ones. Each security technology has a relative cost and addresses
+specific vulnerabilities. Therefore, the organization has to put in place the
+appropriate security technologies set that minimizes the information system s
+vulnerabilities with a minimal cost. This bi objective problem will be
+considered as a resources allocation problem (RAP) where security technologies
+represent the resources to be allocated. However, the set of vulnerabilities
+may change, periodically, with the continual appearance of new ones. Therefore,
+the security technologies set should be flexible to face these changes, in real
+time, and the problem becomes a dynamic one. In this paper, we propose a
+harmony search based algorithm to solve the bi objective dynamic resource
+allocation decision model. This approach was compared to a genetic algorithm
+and provided good results.
+"
+23486,Dissipativity Theory for Nesterov's Accelerated Method,"  In this paper, we adapt the control theoretic concept of dissipativity theory
+to provide a natural understanding of Nesterov's accelerated method. Our theory
+ties rigorous convergence rate analysis to the physically intuitive notion of
+energy dissipation. Moreover, dissipativity allows one to efficiently construct
+Lyapunov functions (either numerically or analytically) by solving a small
+semidefinite program. Using novel supply rate functions, we show how to recover
+known rate bounds for Nesterov's method and we generalize the approach to
+certify both linear and sublinear rates in a variety of settings. Finally, we
+link the continuous-time version of dissipativity to recent works on algorithm
+analysis that use discretizations of ordinary differential equations.
+"
+23487,An LSTM-Based Dynamic Customer Model for Fashion Recommendation,"  Online fashion sales present a challenging use case for personalized
+recommendation: Stores offer a huge variety of items in multiple sizes. Small
+stocks, high return rates, seasonality, and changing trends cause continuous
+turnover of articles for sale on all time scales. Customers tend to shop
+rarely, but often buy multiple items at once. We report on backtest experiments
+with sales data of 100k frequent shoppers at Zalando, Europe's leading online
+fashion platform. To model changing customer and store environments, our
+recommendation method employs a pair of neural networks: To overcome the cold
+start problem, a feedforward network generates article embeddings in ""fashion
+space,"" which serve as input to a recurrent neural network that predicts a
+style vector in this space for each client, based on their past purchase
+sequence. We compare our results with a static collaborative filtering
+approach, and a popularity ranking baseline.
+"
+23488,UVC LEDs on Bulk AlN Substrates Using Silicon Nanomembranes for Efficient Hole Injection,"  As UV LEDs are explored at shorter wavelengths (< 280 nm) into the UVC
+spectral range, the crystalline quality of epitaxial AlGaN films with high Al
+compositions and inefficient hole injection from p-type AlGaN severely limit
+the LED performance and development. In this work, we report on 237 nm light
+emission with a record light output power of 265 uW from AlN/Al0.72Ga028N
+multiple quantum well UVC LEDs using bulk AlN substrates and p-type silicon
+nanomembrane contact layers for significantly improved AlGaN film quality and
+hole injection, respectively. No intensity degradation or efficiency droop was
+observed up to a current density of 245 A/cm2, which is attributed to the low
+dislocation density within AlGaN films, the large concentration of holes from
+p-Si, and efficient hole-transport to the active region. Additionally, the
+emission peak at 237 nm is dominant across the electroluminescence spectrum
+with no significant parasitic emissions observed. This study demonstrates the
+feasibility of using p-Si as a hole injector for UVC LEDs, which can be
+extended to even shorter wavelengths where hole injection from chemically doped
+AlGaN layers is not feasible.
+"
+23489,Pseudo-Goldstone magnons in the frustrated S=3/2 Heisenberg helimagnet ZnCr2Se4 with a pyrochlore magnetic sublattice,"  Low-energy spin excitations in any long-range ordered magnetic system in the
+absence of magnetocrystalline anisotropy are gapless Goldstone modes emanating
+from the ordering wave vectors. In helimagnets, these modes hybridize into the
+so-called helimagnon excitations. Here we employ neutron spectroscopy supported
+by theoretical calculations to investigate the magnetic excitation spectrum of
+the isotropic Heisenberg helimagnet ZnCr2Se4 with a cubic spinel structure, in
+which spin-3/2 magnetic Cr3+ ions are arranged in a geometrically frustrated
+pyrochlore sublattice. Apart from the conventional Goldstone mode emanating
+from the (0 0 q) ordering vector, low-energy magnetic excitations in the
+single-domain proper-screw spiral phase show soft helimagnon modes with a small
+energy gap of ~0.17 meV, emerging from two orthogonal wave vectors (q 0 0) and
+(0 q 0) where no magnetic Bragg peaks are present. We term them
+pseudo-Goldstone magnons, as they appear gapless within linear spin-wave theory
+and only acquire a finite gap due to higher-order quantum-fluctuation
+corrections. Our results are likely universal for a broad class of symmetric
+helimagnets, opening up a new way of studying weak magnon-magnon interactions
+with accessible spectroscopic methods.
+"
+23490,Quasi-diagonal Inhomogeneous Closure for Classical and Quantum Statistical Dynamics,"  The Quasi-diagonal Direct Interaction Approximation (QDIA) closure equations
+are formulated for inhomogeneous classical and quantum fields interacting
+through dynamical equations with quadratic nonlinearity and with first or
+second order time derivatives. Associated more complex inhomogeneous DIA and
+Self-Energy closure equations are expounded as part of the derivation. The QDIA
+employs a bare vertex approximation and is only a few times more
+computationally intensive than the homogeneous DIA. Examples of applications to
+turbulent classical geophysical and Navier Stokes fluids, including
+non-Gaussian noise, to classical and quantum Klein Gordon equations with g
+phi^3 Lagrangian interaction, and to coupled field-auxiliary field equations
+associated lambda phi^4 Lagrangian interaction, are presented.
+"
+23491,Generalized Gelation Theory describes Human Online Aggregation in support of Extremism,"  Though many aggregation theories exist for physical, chemical and biological
+systems, they do not account for the significant heterogeneity found, for
+example, in populations of living objects. This is unfortunate since
+understanding how heterogeneous individuals come together in support of an
+extremist cause, for example, represents an urgent societal problem. Here we
+develop such a theory and show that the intrinsic population heterogeneity can
+significantly delay the gel transition point and change the gel's growth rate.
+We apply our theory to examine how humans aggregate online in support of a
+particular extremist cause. We show that the theory provides an accurate
+description of the online extremist support for ISIS (so-called Islamic State)
+which started in late 2014.
+"
+23492,Concordances from connected sums of torus knots to L-space knots,"  If a knot is a nontrivial connected sum of positive torus knots, then it is
+not concordant to an L-space knot.
+"
+23493,"Analysis of eight binaries in Lyncis constellation: RV Lyn, AA Lyn, AH Lyn, CD Lyn, CF Lyn, DR Lyn, EK Lyn, and FS Lyn","  The available photometry mainly from the WASP database was used for the first
+light curve analysis of eight eclipsing binary systems located in the Lyncis
+constellation: RV Lyn, AA Lyn, AH Lyn, CD Lyn, CF Lyn, DR Lyn, EK Lyn, and FS
+Lyn. Most of these eclipsing stars are detached ones, having the orbital
+periods from 0.54 to 2.3 days. For the systems AA Lyn and CF Lyn a
+non-negligible third light was detected during the light curve solution.
+Moreover, 284 new times of minima for these binaries were derived, trying to
+identify the period variations. For the system CD Lyn a hypothetical third body
+was detected with the period of about 59 yr.
+"
+23494,KdV-type equations linked via Baecklund transformations: remarks and perspectives,"  Third order nonlinear evolution equations, that is the Korteweg-deVries
+(KdV), modified Korteweg-deVries (mKdV) equation and other ones are considered:
+they all are connected via Baecklund transformations. These links can be
+depicted in a wide Baecklund Chart} which further extends the previous one
+constructed in [22]. In particular, the Baecklund transformation which links
+the mKdV equation to the KdV singularity manifold equation is reconsidered and
+the nonlinear equation for the KdV eigenfunction is shown to be linked to all
+the equations in the previously constructed Baecklund Chart. That is, such a
+Baecklund Chart is expanded to encompass the nonlinear equation for the KdV
+eigenfunctions [30], which finds its origin in the early days of the study of
+Inverse scattering Transform method, when the Lax pair for the KdV equation was
+constructed. The nonlinear equation for the KdV eigenfunctions is proved to
+enjoy a nontrivial invariance property. Furthermore, the hereditary recursion
+operator it admits [30 is recovered via a different method. Then, the results
+are extended to the whole hierarchy of nonlinear evolution equations it
+generates. Notably, the established links allow to show that also the nonlinear
+equation for the KdV eigenfunction is connected to the Dym equation since both
+such equations appear in the same Baecklund chart.
+"
+23495,Anisotropy Driven Transition from Moore-Read State to Quantum Hall Stripes,"  We investigate the nature of the quantum Hall liquid in a half-filled second
+Landau level ($n=1$) as a function of band mass anisotropy using numerical
+exact diagonalization (ED) and density matrix renormalization group (DMRG)
+methods. We find increasing the mass anisotropy induces a quantum phase
+transition from the Moore-Read state to a charge density wave state. By
+analyzing the energy spectrum, guiding center structure factors and by adding
+weak pinning potentials, we show that this charge density wave is a
+uni-directional quantum Hall stripe, which has a periodicity of a few magnetic
+lengths and survives in the thermodynamic limit. We find smooth profiles for
+the guiding center occupation function that reveal the strong coupling nature
+of the array of chiral Luttinger liquids residing at the stripe edges.
+"
+23496,Improving Gibbs Sampler Scan Quality with DoGS,"  The pairwise influence matrix of Dobrushin has long been used as an
+analytical tool to bound the rate of convergence of Gibbs sampling. In this
+work, we use Dobrushin influence as the basis of a practical tool to certify
+and efficiently improve the quality of a discrete Gibbs sampler. Our
+Dobrushin-optimized Gibbs samplers (DoGS) offer customized variable selection
+orders for a given sampling budget and variable subset of interest, explicit
+bounds on total variation distance to stationarity, and certifiable
+improvements over the standard systematic and uniform random scan Gibbs
+samplers. In our experiments with joint image segmentation and object
+recognition, Markov chain Monte Carlo maximum likelihood estimation, and Ising
+model inference, DoGS consistently deliver higher-quality inferences with
+significantly smaller sampling budgets than standard Gibbs samplers.
+"
+23497,Finite orbits of the pure braid group on the monodromy of the $2$-variable Garnier system,"  In this paper we show that the $SL_{2}(\mathbb C)$ character variety of the
+Riemann sphere $\Sigma_5$ with five boundary components is a $5$-parameter
+family of affine varieties of dimension $4$. We endow this family of affine
+varieties with an action of the braid group and classify exceptional finite
+orbits. This action represents the nonlinear monodromy of the $2$ variable
+Garnier system and finite orbits correspond to algebraic solutions.
+"
+23498,Coalitional game based cost optimization of energy portfolio in smart grid communities,"  In this paper we propose two novel coalitional game theory based optimization
+methods for minimizing the cost of electricity consumed by households from a
+smart community. Some households in the community may own renewable energy
+systems (RESs) conjoined with energy storing systems (ESSs). Some other
+residences own ESSs only, while the remaining households are simple energy
+consumers. We first propose a coalitional cost optimization method in which
+RESs and ESSs owners exchange energy and share their renewable energy and
+storage spaces. We show that by participating in the proposed game these
+households may considerably reduce their costs in comparison to performing
+individual cost optimization. We further propose another coalitional
+optimization model in which RESs and ESSs owning households not only share
+their resources, but also sell energy to simple energy consuming households. We
+show that through this energy trade the RESs and ESSs owners can further reduce
+their costs, while the simple energy consumers also gain cost savings. The
+monetary revenues gained by the coalition are distributed among its members
+according to the Shapley value. Simulation examples show that the proposed
+coalitional optimization methods may reduce the electricity costs for the RESs
+and ESSs owning households by 20%, while the sole energy consumers may reduce
+their costs by 5%.
+"
+23499,Simulations of the Galaxy Cluster CIZA J2242.8+5301 I: Thermal Model and Shock Properties,"  The giant radio relic in CIZA J2242.8+5301 is likely evidence of a Mpc sized
+shock in a massive merging galaxy cluster. However, the exact shock properties
+are still not clearly determined. In particular, the Mach number derived from
+the integrated radio spectrum exceeds the Mach number derived from the X-ray
+temperature jump by a factor of two. We present here a numerical study, aiming
+for a model that is consistent with the majority of observations of this galaxy
+cluster. We first show that in the northern shock upstream X-ray temperature
+and radio data are consistent with each other. We then derive progenitor masses
+for the system using standard density profiles, X-ray properties and the
+assumption of hydrostatic equilibrium. We find a class of models that is
+roughly consistent with weak lensing data, radio data and some of the X-ray
+data. Assuming a cool-core versus non-cool-core merger, we find a fiducial
+model with a total mass of $1.6 \times 10^{15}\,M_\odot$, a mass ratio of 1.76
+and a Mach number that is consistent with estimates from the radio spectrum. We
+are not able to match X-ray derived Mach numbers, because even low mass models
+over-predict the X-ray derived shock speeds. We argue that deep X-ray
+observations of CIZA J2242.8+5301 will be able to test our model and
+potentially reconcile X-ray and radio derived Mach numbers in relics.
+"
+23500,Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid,"  Deep neural networks have been widely adopted in recent years, exhibiting
+impressive performances in several application domains. It has however been
+shown that they can be fooled by adversarial examples, i.e., images altered by
+a barely-perceivable adversarial noise, carefully crafted to mislead
+classification. In this work, we aim to evaluate the extent to which
+robot-vision systems embodying deep-learning algorithms are vulnerable to
+adversarial examples, and propose a computationally efficient countermeasure to
+mitigate this threat, based on rejecting classification of anomalous inputs. We
+then provide a clearer understanding of the safety properties of deep networks
+through an intuitive empirical analysis, showing that the mapping learned by
+such networks essentially violates the smoothness assumption of learning
+algorithms. We finally discuss the main limitations of this work, including the
+creation of real-world adversarial examples, and sketch promising research
+directions.
+"
+23501,Free energy-based reinforcement learning using a quantum processor,"  Recent theoretical and experimental results suggest the possibility of using
+current and near-future quantum hardware in challenging sampling tasks. In this
+paper, we introduce free energy-based reinforcement learning (FERL) as an
+application of quantum hardware. We propose a method for processing a quantum
+annealer's measured qubit spin configurations in approximating the free energy
+of a quantum Boltzmann machine (QBM). We then apply this method to perform
+reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum
+annealer. The experimental results show that our technique is a promising
+method for harnessing the power of quantum sampling in reinforcement learning
+tasks.
+"
+23502,General scheme for preparation of different topological states on the cluster states,"  Although it is well known that all quantum states can be produced by single
+qubit measurements on the cluster states, it is not a simple task to explicitly
+find which measurement patterns on the cluster states can generate different
+quantum states. In this paper we introduce a general scheme to find measurement
+patterns corresponding to different topological CSS states containing Kitaev's
+toric code states and color code states on different lattices and in different
+dimensions. Furthermore, we find a measurement pattern for generating non
+abelian anyons where measurement induced defects on a toric code state play the
+role of Ising anyons. We also support our scheme by a graphical notation where,
+by following a few simple graphical transformations, one will be able to
+convert a CSS topological state to a cluster state. Our scheme can be also used
+for experimental realization of anyons on cluster states.
+"
+23503,Performance Control of Tendon-Driven Endoscopic Surgical Robots With Friction and Hysteresis,"  In this study, a new position control scheme for the tendon-sheath mechanism
+(TSM) which is used in flexible medical devices is presented. TSM is widely
+used in dexterous robotic applications because it can flexibly work in limited
+space, in constrained environments, and provides efficient power transmission
+from the external actuator to the distal joint. However, nonlinearities from
+friction and backlash hysteresis between the tendon and the sheath pose
+challenges in achieving precise position controls of the end effector. Previous
+studies on the TSM only address the control problem under the assumptions of
+known tendon-sheath configuration and known model parameters of the backlash
+hysteresis nonlinearity. These approaches can have adverse impact and
+limitations on the overall system performances and practical implementation.
+This paper presents a new approach to model and control the TSM-driven flexible
+robotic systems. The designed controller does not require exact knowledge of
+nonlinear friction and backlash hysteresis parameters, only their bounds are
+online estimated. Simulation and experimental validation results show that the
+proposed control scheme can significantly improve the tracking performances
+without the presence of the exact knowledge of the model parameters and the
+sheath configuration.
+"
+23504,Static field limit of excitation probabilities in laser-atom interactions,"  We consider the interaction of atomic hydrogen, in its ground state, with an
+electromagnetic pulse whose duration is fixed in terms of the number of optical
+cycles. We study the probability of excitation of the atom in the static field
+limit i.e. for field frequencies going to zero. Despite the fact that the well
+known Born-Fock adiabatic theorem is valid only for a system whose energy
+spectrum is discrete, we show that it is still possible to use this theorem to
+derive, in the low frequency limit, an analytical formula which gives the
+probability of transition to any excited state of the atom as a function of the
+field intensity, the carrier envelope phase and the number of optical cycles
+within the pulse. The results for the probability of excitation to low-lying
+excited states, obtained with this formula, agree with those we get by solving
+the time-dependent Schroedinger equation. The domain of validity is discussed
+in detail.
+"
+23505,Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees,"  This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An
+HFIT produces an optimum treelike structure, i.e., a natural hierarchical
+structure that accommodates simplicity by combining several low-dimensional
+fuzzy inference systems (FISs). Such a natural hierarchical structure provides
+a high degree of approximation accuracy. The construction of HFIT takes place
+in two phases. Firstly, a nondominated sorting based multiobjective genetic
+programming (MOGP) is applied to obtain a simple tree structure (a low
+complexity model) with a high accuracy. Secondly, the differential evolution
+algorithm is applied to optimize the obtained tree's parameters. In the derived
+tree, each node acquires a different input's combination, where the
+evolutionary process governs the input's combination. Hence, HFIT nodes are
+heterogeneous in nature, which leads to a high diversity among the rules
+generated by the HFIT. Additionally, the HFIT provides an automatic feature
+selection because it uses MOGP for the tree's structural optimization that
+accepts inputs only relevant to the knowledge contained in data. The HFIT was
+studied in the context of both type-1 and type-2 FISs, and its performance was
+evaluated through six application problems. Moreover, the proposed
+multiobjective HFIT was compared both theoretically and empirically with
+recently proposed FISs methods from the literature, such as McIT2FIS,
+TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the
+obtained results, it was found that the HFIT provided less complex and highly
+accurate models compared to the models produced by the most of other methods.
+Hence, the proposed HFIT is an efficient and competitive alternative to the
+other FISs for function approximation and feature selection.
+"
+23506,Logarithmic Coefficients and a Coefficient Conjecture for Univalent Functions,"  Let ${\mathcal U}(\lambda)$ denote the family of analytic functions $f(z)$,
+$f(0)=0=f'(0)-1$, in the unit disk $\ID$, which satisfy the condition $\big
+|\big (z/f(z)\big )^{2}f'(z)-1\big |<\lambda $ for some $0<\lambda \leq 1$. The
+logarithmic coefficients $\gamma_n$ of $f$ are defined by the formula
+$\log(f(z)/z)=2\sum_{n=1}^\infty \gamma_nz^n$. In a recent paper, the present
+authors proposed a conjecture that if $f\in {\mathcal U}(\lambda)$ for some
+$0<\lambda \leq 1$, then $|a_n|\leq \sum_{k=0}^{n-1}\lambda ^k$ for $n\geq 2$
+and provided a new proof for the case $n=2$. One of the aims of this article is
+to present a proof of this conjecture for $n=3, 4$ and an elegant proof of the
+inequality for $n=2$, with equality for $f(z)=z/[(1+z)(1+\lambda z)]$. In
+addition, the authors prove the following sharp inequality for $f\in{\mathcal
+U}(\lambda)$: $$\sum_{n=1}^{\infty}|\gamma_{n}|^{2} \leq
+\frac{1}{4}\left(\frac{\pi^{2}}{6}+2{\rm Li\,}_{2}(\lambda)+{\rm
+Li\,}_{2}(\lambda^{2})\right), $$ where ${\rm Li}_2$ denotes the dilogarithm
+function. Furthermore, the authors prove two such new inequalities satisfied by
+the corresponding logarithmic coefficients of some other subfamilies of
+$\mathcal S$.
+"
+23507,Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting,"  Timely accurate traffic forecast is crucial for urban traffic control and
+guidance. Due to the high nonlinearity and complexity of traffic flow,
+traditional methods cannot satisfy the requirements of mid-and-long term
+prediction tasks and often neglect spatial and temporal dependencies. In this
+paper, we propose a novel deep learning framework, Spatio-Temporal Graph
+Convolutional Networks (STGCN), to tackle the time series prediction problem in
+traffic domain. Instead of applying regular convolutional and recurrent units,
+we formulate the problem on graphs and build the model with complete
+convolutional structures, which enable much faster training speed with fewer
+parameters. Experiments show that our model STGCN effectively captures
+comprehensive spatio-temporal correlations through modeling multi-scale traffic
+networks and consistently outperforms state-of-the-art baselines on various
+real-world traffic datasets.
+"
+23508,Robust Near-Field 3D Localization of an Unaligned Single-Coil Agent Using Unobtrusive Anchors,"  The magnetic near-field provides a suitable means for indoor localization,
+due to its insensitivity to the environment and strong spatial gradients. We
+consider indoor localization setups consisting of flat coils, allowing for
+convenient integration of the agent coil into a mobile device (e.g., a smart
+phone or wristband) and flush mounting of the anchor coils to walls. In order
+to study such setups systematically, we first express the Cramér-Rao lower
+bound (CRLB) on the position error for unknown orientation and evaluate its
+distribution within a square room of variable size, using 15 x 10cm anchor
+coils and a commercial NFC antenna at the agent. Thereby, we find cm-accuracy
+being achievable in a room of 10 x 10 x 3 meters with 12 flat wall-mounted
+anchors and with 10mW used for the generation of magnetic fields. Practically
+achieving such estimation performance is, however, difficult because of the
+non-convex 5D likelihood function. To that end, we propose a fast and accurate
+weighted least squares (WLS) algorithm which is insensitive to initialization.
+This is enabled by effectively eliminating the orientation nuisance parameter
+in a rigorous fashion and scaling the individual anchor observations, leading
+to a smoothed 3D cost function. Using WLS estimates to initialize a
+maximum-likelihood (ML) solver yields accuracy near the theoretical limit in up
+to 98% of cases, thus enabling robust indoor localization with unobtrusive
+infrastructure, with a computational efficiency suitable for real-time
+processing.
+"
+23509,Virtual Borders: Accurate Definition of a Mobile Robot's Workspace Using Augmented Reality,"  We address the problem of interactively controlling the workspace of a mobile
+robot to ensure a human-aware navigation. This is especially of relevance for
+non-expert users living in human-robot shared spaces, e.g. home environments,
+since they want to keep the control of their mobile robots, such as vacuum
+cleaning or companion robots. Therefore, we introduce virtual borders that are
+respected by a robot while performing its tasks. For this purpose, we employ a
+RGB-D Google Tango tablet as human-robot interface in combination with an
+augmented reality application to flexibly define virtual borders. We evaluated
+our system with 15 non-expert users concerning accuracy, teaching time and
+correctness and compared the results with other baseline methods based on
+visual markers and a laser pointer. The experimental results show that our
+method features an equally high accuracy while reducing the teaching time
+significantly compared to the baseline methods. This holds for different border
+lengths, shapes and variations in the teaching process. Finally, we
+demonstrated the correctness of the approach, i.e. the mobile robot changes its
+navigational behavior according to the user-defined virtual borders.
+"
+23510,Energy scale calibration of KEDR detector tagging system,"  The tagging system of the KEDR detector is a symmetrical focusing magnetic
+spectrometer for electrons and positrons scattered at small angles; it is
+embedded into the lattice of the VEPP-4M collider and intended for two-photon
+processes study. It measures scattered electron energy with resolution $\Delta
+E/E_0=0.03\%...0.6\%$ ($E_0$ is the beam energy). For precise energy scale
+calibration two methods are used: tagging of bremsstrahlung electron/positron
+by the photon energy measured by BGO calorimeter, and direct calibration using
+Compton backscattering spectrum edge. This report covers design and current
+status of the calibration system.
+"
+23511,Restriction of Hecke eigenforms to horocycles,"  We prove a sharp upper bound on the $L^2$-norm of Hecke eigenforms restricted
+to a horocycle, as the weight tends to infinity.
+"
+23512,Stability of Topic Modeling via Matrix Factorization,"  Topic models can provide us with an insight into the underlying latent
+structure of a large corpus of documents. A range of methods have been proposed
+in the literature, including probabilistic topic models and techniques based on
+matrix factorization. However, in both cases, standard implementations rely on
+stochastic elements in their initialization phase, which can potentially lead
+to different results being generated on the same corpus when using the same
+parameter values. This corresponds to the concept of ""instability"" which has
+previously been studied in the context of $k$-means clustering. In many
+applications of topic modeling, this problem of instability is not considered
+and topic models are treated as being definitive, even though the results may
+change considerably if the initialization process is altered. In this paper we
+demonstrate the inherent instability of popular topic modeling approaches,
+using a number of new measures to assess stability. To address this issue in
+the context of matrix factorization for topic modeling, we propose the use of
+ensemble learning strategies. Based on experiments performed on annotated text
+corpora, we show that a K-Fold ensemble strategy, combining both ensembles and
+structured initialization, can significantly reduce instability, while
+simultaneously yielding more accurate topic models.
+"
+23513,Degree counting for Toda system with simple singularity : one point blow up,"  In this paper, we study the degree counting formula of the rank two Toda
+system with simple singular source when $\rho_1\in(0,4\pi)\cup(4\pi,8\pi)$ and
+$\rho_2\notin 4\pi\mathbb{N}.$ The key step is to derive the degree formula of
+the shadow system, which arises from the bubbling solutions as $\rho_1$ tends
+to $4\pi$. In order to compute the topological degree of the shadow system, we
+need to find some suitable deformation. During this deformation, we shall deal
+with \textit{new} difficulty arising from the new phenomena: blow up does not
+necessarily imply concentration of mass. This phenomena occurs due to the
+collapsing of singularities. This is a continuation of the previous work Lee,
+Lin, Wei and Yang.
+"
+23514,Ring Oscillators for Clocking Reversible Superconducting Circuits and Fabrication Process Benchmarking,"  Existing concepts of reversible superconducting circuits as well as
+demonstrated adiabatic circuits require three-phase bias/clock signals
+generated by room temperature sources. A while ago, we suggested that a
+multi-phase bias/clock could be provided by a local Josephson junction-based
+generator. The generator looks like a long annular Josephson junction, only
+composed of discreet elements - junctions and inductors, and closed into a ring
+via a flux pump to inject any required number of vortices into the ring. A
+steady motion of the vortices forced by a uniformly distributed dc bias current
+applied to the ring is accompanied by a nearly harmonic ac currents flowing via
+the Josephson junctions (JJs) connected in series with small inductors. These
+ac currents serve as multi-phase bias/clock for nSQUID-based circuitry. To
+verify this concept and compare the dissipated energy with kBTln2 threshold, we
+developed a ring composed of 256 unshunted JJs with 20 {\mu}A target critical
+current, Ic. We investigated the behavior of the ring oscillator at each vortex
+count from 0 to 256. The measured critical current of the ring with vortices
+was about 0.1 {\mu}A per one JJ, which can be explained by unavoidable
+nonuniformity of the ring components and the influence of fluxes frozen near
+the ring. The corresponding energy dissipation, about 10kBT per passage of one
+vortex through one JJ, should be reduced further for prospective experiments
+with reversible circuits. However, obtained I-V characteristics could be of
+interest for scientists working with long Josephson junctions. Superiority of
+the fabrication process used in this work is demonstrated by the obtained about
+200 times reduction of Ic of the ring with vortices with respect to a single
+comprising JJ, much larger than in any previously described case.
+"
+23515,Optimal Input Design for Parameter Estimation in AR(1) with Dependent Stationary Noise,"  This paper focus on the asymptotical input and the asymptotical properties of
+the MLE of the drift parameter in the Autoregressive of order 1 (AR(1)) driven
+by an regular stationary noises (with dependence). The Laplace Transform
+computations will be the main tool for our analysis.
+"
+23516,Multiharmonic frequency-chirped transducers for surface-acoustic-wave optomechanics,"  Wide passband interdigital transducers are employed to establish a stable
+phase-lock between a train of laser pulses emitted by a mode-locked laser and a
+surface acoustic wave generated electrically by the transducer. The transducer
+design is based on a multi-harmonic split-finger architecture for the
+excitation of a fundamental surface acoustic wave and a discrete number of its
+overtones. Simply by introducing a variation of the transducer's periodicity
+$p$, a frequency chirp is added. This combination results in wide frequency
+bands for each harmonic. The transducer's conversion efficiency from the
+electrical to the acoustic domain was characterized optomechanically using
+single quantum dots acting as nanoscale pressure sensors. The ability to
+generate surface acoustic waves over a wide band of frequencies enables
+advanced acousto-optic spectroscopy using mode-locked lasers with fixed
+repetition rate. Stable phase-locking between the electrically generated
+acoustic wave and the train of laser pulses was confirmed by performing
+stroboscopic spectroscopy on a single quantum dot at a frequency of 320 MHz.
+Finally, the dynamic spectral modulation of the quantum dot was directly
+monitored in the time domain combining stable phase-locked optical excitation
+and time-correlated single photon counting. The demonstrated scheme will be
+particularly useful for the experimental implementation of surface acoustic
+wave-driven quantum gates of optically addressable qubits or collective quantum
+states or for multi-component Fourier synthesis of tailored nanomechanical
+waveforms.
+"
+23517,Machine learning approach for early detection of autism by combining questionnaire and home video screening,"  Existing screening tools for early detection of autism are expensive,
+cumbersome, time-intensive, and sometimes fall short in predictive value. In
+this work, we apply Machine Learning (ML) to gold standard clinical data
+obtained across thousands of children at risk for autism spectrum disorders to
+create a low-cost, quick, and easy to apply autism screening tool that performs
+as well or better than most widely used standardized instruments. This new tool
+combines two screening methods into a single assessment, one based on short,
+structured parent-report questionnaires and the other on tagging key behaviors
+from short, semi-structured home videos of children. To overcome the scarcity,
+sparsity, and imbalance of training data, we apply creative feature selection,
+feature engineering, and novel feature encoding techniques. We allow for
+inconclusive determination where appropriate in order to boost screening
+accuracy when conclusive. We demonstrate a significant accuracy improvement
+over standard screening tools in a clinical study sample of 162 children.
+"
+23518,Model-order selection in statistical shape models,"  Statistical shape models enhance machine learning algorithms providing prior
+information about deformation. A Point Distribution Model (PDM) is a popular
+landmark-based statistical shape model for segmentation. It requires choosing a
+model order, which determines how much of the variation seen in the training
+data is accounted for by the PDM. A good choice of the model order depends on
+the number of training samples and the noise level in the training data set.
+Yet the most common approach for choosing the model order simply keeps a
+predetermined percentage of the total shape variation. In this paper, we
+present a technique for choosing the model order based on information-theoretic
+criteria, and we show empirical evidence that the model order chosen by this
+technique provides a good trade-off between over- and underfitting.
+"
+23519,Column subset selection is NP-complete,"  Let $M$ be a real $r\times c$ matrix and let $k$ be a positive integer. In
+the column subset selection problem (CSSP), we need to minimize the quantity
+$\|M-SA\|$, where $A$ can be an arbitrary $k\times c$ matrix, and $S$ runs over
+all $r\times k$ submatrices of $M$. This problem and its applications in
+numerical linear algebra are being discussed for several decades, but its
+algorithmic complexity remained an open issue. We show that CSSP is
+NP-complete.
+"
+23520,The hidden traits of endemic illiteracy in cities,"  In spite of the considerable progress towards reducing illiteracy rates, many
+countries, including developed ones, have encountered difficulty achieving
+further reduction in these rates. This is worrying because illiteracy has been
+related to numerous health, social, and economic problems. Here, we show that
+the spatial patterns of illiteracy in urban systems have several features
+analogous to the spread of diseases such as dengue and obesity. Our results
+reveal that illiteracy rates are spatially long-range correlated, displaying
+non-trivial clustering structures characterized by percolation-like transitions
+and fractality. These patterns can be described in the context of percolation
+theory of long-range correlated systems at criticality. Together, these results
+provide evidence that the illiteracy incidence can be related to a
+transmissible process, in which the lack of access to minimal education
+propagates in a population in a similar fashion to endemic diseases.
+"
+23521,Supersymmetric Schrödinger Operators with Applications to Morse Theory,"  In 1981 Edward Witten proved a remarkable result where he derived the
+classical Morse Inequalities using ideas from Supersymmetric (SUSY) Quantum
+Mechanics. In this regard, one has an example where a Physical Theory has
+something to say about the underlying Mathematical Structure. The objective of
+this essay is to understand this classical result from the perspective of
+Schrödinger Operators. The essay will be divided in four parts. The first
+part will revisit the classical theory of Morse and recall some of its
+fundamental results. In the second part, we consider the underlying physical
+motivations by considering Quantum Mechanics and 0-Dimensional SUSY. The third
+part will focus on Schrödinger Operators and highlight some of their basic
+properties. Finally in the last section we will put everything together and
+present Witten's proof of the Morse Inequalities. Even here we must be
+completely honest and say that we only follow Witten in proving the Weak Morse
+Inequalities. The Strong Morse Inequalities are derived using related ideas
+from Supersymmetry, but mention is made of the techniques used by Witten to get
+at the Strong Morse Inequalities.
+"
+23522,"Dimensionality, nematicity and Superconductivity in Fe-based systems","  Study of Fe based compounds have drawn much attention due to the discovery of
+superconductivity as well as many other exotic electronic properties. Here, we
+review some of our works in these materials carried out employing density
+functional theory and angle resolved photoemission spectroscopy. The results
+presented here indicate that the dimensionality of the underlying electronic
+structure plays important role in deriving their interesting electronic
+properties. The nematicity found in most of these materials appears to be
+related to the magnetic long range order. We argue that the exoticity in the
+electronic properties are related to the subtlety in competing structural and
+magnetic instabilities present in these materials.
+"
+23523,A Radio Astronomy Search for Cold Dark Matter Axions,"  The search for axions has gained ground in recent years, with laboratory
+searches for cold dark matter (CDM) axions, relativistic solar axions and
+ultra-light axions the subject of extensive literature. In particular, the
+interest in axions as a CDM candidate has been motivated by its potential to
+account for all of the inferred value of $\Omega_{DM} \sim 0.26$ in the
+standard $\Lambda CDM$ model. Indeed, the value of $\Omega_{DM} \sim 0.26$
+could be provided by a light axion. We investigate the possibility of
+complementing existing axion search experiments with radio telescope
+observations in an attempt to detect axion conversion in astrophysical magnetic
+fields. Searching for a CDM axion signal from a large-scale astrophysical
+environment provides new challenges, with the magnetic field structure playing
+a crucial role in both the rate of interaction and the properties of the
+observed photon. However, with a predicted frequency in the radio band (200MHz
+- 200GHz) and a distinguishable spectral profile, next generation radio
+telescopes may offer new opportunities for detection. The SKA-mid telescope has
+a planned frequency range of 0.4 - 13.8GHz with optimal sensitivity in the
+range $\sim$ 2 - 7 GHz. Considering observations at $\sim 500$MHz, the limiting
+sensitivity is expected to be $\sim 0.04$mK based on a 24 hour integration
+time. This compares with a predicted CDM axion all-sky signal temperature of
+$\sim 0.04$mK using SKA Phase 1 telescopes and up to $\sim 1.17$mK using a
+collecting area of (1km)$^2$ as planned for Phase 2.
+"
+23524,Madness and regularity properties,"  Starting from an inaccessible cardinal, we construct a model of $ZF+DC$ where
+there exists a mad family and all sets of reals are $\mathbb Q$-measurable for
+$\omega^{\omega}$-bounding sufficiently absolute forcing notions $\mathbb Q$.
+"
+23525,Automated Scalable Bayesian Inference via Hilbert Coresets,"  The automation of posterior inference in Bayesian data analysis has enabled
+experts and nonexperts alike to use more sophisticated models, engage in faster
+exploratory modeling and analysis, and ensure experimental reproducibility.
+However, standard automated posterior inference algorithms are not tractable at
+the scale of massive modern datasets, and modifications to make them so are
+typically model-specific, require expert tuning, and can break theoretical
+guarantees on inferential quality. Building on the Bayesian coresets framework,
+this work instead takes advantage of data redundancy to shrink the dataset
+itself as a preprocessing step, providing fully-automated, scalable Bayesian
+inference with theoretical guarantees. We begin with an intuitive reformulation
+of Bayesian coreset construction as sparse vector sum approximation, and
+demonstrate that its automation and performance-based shortcomings arise from
+the use of the supremum norm. To address these shortcomings we develop Hilbert
+coresets, i.e., Bayesian coresets constructed under a norm induced by an
+inner-product on the log-likelihood function space. We propose two Hilbert
+coreset construction algorithms---one based on importance sampling, and one
+based on the Frank-Wolfe algorithm---along with theoretical guarantees on
+approximation quality as a function of coreset size. Since the exact
+computation of the proposed inner-products is model-specific, we automate the
+construction with a random finite-dimensional projection of the log-likelihood
+functions. The resulting automated coreset construction algorithm is simple to
+implement, and experiments on a variety of models with real and synthetic
+datasets show that it provides high-quality posterior approximations and a
+significant reduction in the computational cost of inference.
+"
+23526,Positive projectively flat manifolds are locally conformally flat-Kähler Hopf manifolds,"  We define a partition of the space of projectively flat metrics in three
+classes according to the sign of the Chern scalar curvature; we prove that the
+class of negative projectively flat metrics is empty, and that the class of
+positive projectively flat metrics consists precisely of locally conformally
+flat-Kähler metrics on Hopf manifolds, explicitly characterized by Vaisman
+[vaisman]. Finally, we review the properties of zero projectively flat metrics.
+As applications, we refine a list of possible projectively flat metrics by Li,
+Yau, and Zheng [Theorem 1]; moreover we prove that projectively flat
+astheno-Kähler metrics are in fact Kähler and globally conformally flat.
+"
+23527,Extended T-process Regression Models,"  Gaussian process regression (GPR) model has been widely used to fit data when
+the regression function is unknown and its nice properties have been well
+established. In this article, we introduce an extended t-process regression
+(eTPR) model, a nonlinear model which allows a robust best linear unbiased
+predictor (BLUP). Owing to its succinct construction, it inherits many
+attractive properties from the GPR model, such as having closed forms of
+marginal and predictive distributions to give an explicit form for robust
+procedures, and easy to cope with large dimensional covariates with an
+efficient implementation. Properties of the robustness are studied. Simulation
+studies and real data applications show that the eTPR model gives a robust fit
+in the presence of outliers in both input and output spaces and has a good
+performance in prediction, compared with other existed methods.
+"
+23528,Optimized Data Pre-Processing for Discrimination Prevention,"  Non-discrimination is a recognized objective in algorithmic decision making.
+In this paper, we introduce a novel probabilistic formulation of data
+pre-processing for reducing discrimination. We propose a convex optimization
+for learning a data transformation with three goals: controlling
+discrimination, limiting distortion in individual data samples, and preserving
+utility. We characterize the impact of limited sample size in accomplishing
+this objective, and apply two instances of the proposed optimization to
+datasets, including one on real-world criminal recidivism. The results
+demonstrate that all three criteria can be simultaneously achieved and also
+reveal interesting patterns of bias in American society.
+"
+23529,Cheeger Inequalities for Submodular Transformations,"  The Cheeger inequality for undirected graphs, which relates the conductance
+of an undirected graph and the second smallest eigenvalue of its normalized
+Laplacian, is a cornerstone of spectral graph theory. The Cheeger inequality
+has been extended to directed graphs and hypergraphs using normalized
+Laplacians for those, that are no longer linear but piecewise linear
+transformations.
+In this paper, we introduce the notion of a submodular transformation
+$F:\{0,1\}^n \to \mathbb{R}^m$, which applies $m$ submodular functions to the
+$n$-dimensional input vector, and then introduce the notions of its Laplacian
+and normalized Laplacian. With these notions, we unify and generalize the
+existing Cheeger inequalities by showing a Cheeger inequality for submodular
+transformations, which relates the conductance of a submodular transformation
+and the smallest non-trivial eigenvalue of its normalized Laplacian. This
+result recovers the Cheeger inequalities for undirected graphs, directed
+graphs, and hypergraphs, and derives novel Cheeger inequalities for mutual
+information and directed information.
+Computing the smallest non-trivial eigenvalue of a normalized Laplacian of a
+submodular transformation is NP-hard under the small set expansion hypothesis.
+In this paper, we present a polynomial-time $O(\log n)$-approximation algorithm
+for the symmetric case, which is tight, and a polynomial-time $O(\log^2n+\log n
+\cdot \log m)$-approximation algorithm for the general case.
+We expect the algebra concerned with submodular transformations, or
+\emph{submodular algebra}, to be useful in the future not only for generalizing
+spectral graph theory but also for analyzing other problems that involve
+piecewise linear transformations, e.g., deep learning.
+"
+23530,Entire holomorphic curves into projective plane intersecting few generic algebraic curves,"  For $q\leq 3$ smooth plane algebraic curves $\mathcal{C}_i$ having simple
+normal crossings, if the invariant logarithmic $2$-jet differential bundle
+associated to $(\mathbb{P}^2(\mathbb{C}), \sum_{i=1}^q \mathcal{C}_i)$ has a
+nonzero section vanishing on some ample divisor, then, for every algebraically
+nondegenerate entire holomorphic curve
+$f\colon\mathbb{C}\rightarrow\mathbb{P}^2(\mathbb{C})$, we have a Second Main
+Theorem type estimate: \[ T_f(r) \leq c\sum_{i=1}^q\,N_f^{[1]}(r,\mathcal{C}_i)
++ o\big(T_f(r) \big)\parallel, \] where $T_f(r)$ and $N_f^{[1]}(r,C_i)$ stand
+for the order function and the $1$--truncated counting functions in the
+Nevanlinna theory, and where the constant $c=c(q,d_i)>0$ can be computed
+explicitly. In particular, our result includes the case of $3$ conics in
+$\mathbb{P}^2(\mathbb{C})$. Moreover, we provide some new results concerning
+the algebraic degeneracy of certain complex surfaces, e.g., the complex
+hyperbolicity of a very generic surface of degree $\geq 15$ in
+$\mathbb{P}^3(\mathbb{C})$.
+"
+23531,Quantitative and Qualitative Analysis of Editor Behavior through Potentially Coercive Citations,"  How much is the h-index of an editor of a well ranked journal improved due to
+citations which occur after his or her appointment? Scientific recognition
+within academia is widely measured nowadays by the number of citations or
+h-index. Our dataset is based on a sample of four editors from a well ranked
+journal (impact factor - IF - greater than 2). The target group consists of two
+editors who seem to benefit by their position through an increased citation
+number (and subsequently h-index) within journal. The total amount of citations
+for the target group is bigger than 600. The control group is formed by another
+set of two editors from the same journal whose relations between their
+positions and their citation records remain neutral. The total amount of
+citations for the control group is more than 1200. The timespan for which
+pattern of citations has been studied is 1975-2015. Previous coercive citations
+for a journal benefit (increase its IF) has been signaled. To the best of our
+knowledge, this is a pioneering work on coercive citations for personal (or
+editors) benefit. Editorial teams should be aware about this type of
+potentially unethical behavior and act accordingly.
+"
+23532,Exploring Factors that Influence Connected Drivers to (Not) Use or Follow Recommended Optimal Routes,"  Navigation applications are becoming ubiquitous in our daily navigation
+experiences. With the intention to circumnavigate congested roads, their route
+guidance always follows the basic assumption that drivers always want the
+fastest route. However, it is unclear how their recommendations are followed
+and what factors affect their adoption. We present the results of a
+semi-structured qualitative study with 17 drivers, mostly from the Philippines
+and Japan. We recorded their daily commutes and occasional trips, and inquired
+into their navigation practices, route choices and on-the-fly decision-making.
+We found that while drivers choose a recommended route in urgent situations,
+many still preferred to follow familiar routes. Drivers deviated because of a
+recommendation's use of unfamiliar roads, lack of local context, perceived
+driving unsuitability, and inconsistencies with realized navigation
+experiences. Our findings and implications emphasize their personalization
+needs, and how the right amount of algorithmic sophistication can encourage
+behavioral adaptation.
+"
+23533,Magnetic Proximity Effects in Transition-Metal Dichalcogenides: Converting Excitons,"  The two-dimensional character and reduced screening in monolayer
+transition-metal dichalcogenides (TMDs) lead to the ubiquitous formation of
+robust excitons with binding energies orders of magnitude larger than in bulk
+semiconductors. Focusing on neutral excitons, bound electron-hole pairs, that
+dominate the optical response in TMDs, it is shown that they can provide
+fingerprints for magnetic proximity effects in magnetic heterostructures. These
+proximity effects cannot be described by the widely used single-particle
+description, but instead reveal the possibility of a conversion between
+optically inactive and active excitons by rotating the magnetization of the
+magnetic substrate. With recent breakthroughs in fabricating Mo- and W-based
+magnetic TMD-heterostructures, this emergent optical response can be directly
+tested experimentally.
+"
+23534,Multiple domination models for placement of electric vehicle charging stations in road networks,"  Electric and hybrid vehicles play an increasing role in the road transport
+networks. Despite their advantages, they have a relatively limited cruising
+range in comparison to traditional diesel/petrol vehicles, and require
+significant battery charging time. We propose to model the facility location
+problem of the placement of charging stations in road networks as a multiple
+domination problem on reachability graphs. This model takes into consideration
+natural assumptions such as a threshold for remaining battery load, and
+provides some minimal choice for a travel direction to recharge the battery.
+Experimental evaluation and simulations for the proposed facility location
+model are presented in the case of real road networks corresponding to the
+cities of Boston and Dublin.
+"
+23535,Exceptions in Business Processes in Relation to Operational Performance,"  Business process models describe the way of working in an organization.
+Typically, business process models distinguish between the normal flow of work
+and exceptions to that normal flow. However, they often present an idealized
+view. This means that unexpected exceptions - exceptions that are not modelled
+in the business process model - can also occur in practice. This has an effect
+on the efficiency of the organization, because information systems are not
+developed to handle unexpected exceptions. This paper studies the relation
+between the occurrence of exceptions and operational performance. It does this
+by analyzing the execution logs of business processes from five organizations,
+classifying execution paths as normal or exceptional. Subsequently, it analyzes
+the differences between normal and exceptional paths. The results show that
+exceptions are related to worse operational performance in terms of a longer
+throughput time and that unexpected exceptions relate to a stronger increase in
+throughput time than expected exceptions.
+"
+23536,Generating Sentences by Editing Prototypes,"  We propose a new generative model of sentences that first samples a prototype
+sentence from the training corpus and then edits it into a new sentence.
+Compared to traditional models that generate from scratch either left-to-right
+or by first sampling a latent sentence vector, our prototype-then-edit model
+improves perplexity on language modeling and generates higher quality outputs
+according to human evaluation. Furthermore, the model gives rise to a latent
+edit vector that captures interpretable semantics such as sentence similarity
+and sentence-level analogies.
+"
+23537,Lifting endo-$p$-permutation modules,"  We prove that all endo-$p$-permutation modules for a finite group are
+liftable from characteristic $p>0$ to characteristic $0$.
+"
+23538,Stochastic Linear Bandits with Hidden Low Rank Structure,"  High-dimensional representations often have a lower dimensional underlying
+structure. This is particularly the case in many decision making settings. For
+example, when the representation of actions is generated from a deep neural
+network, it is reasonable to expect a low-rank structure whereas conventional
+structures like sparsity are not valid anymore. Subspace recovery methods, such
+as Principle Component Analysis (PCA) can find the underlying low-rank
+structures in the feature space and reduce the complexity of the learning
+tasks. In this work, we propose Projected Stochastic Linear Bandit (PSLB), an
+algorithm for high dimensional stochastic linear bandits (SLB) when the
+representation of actions has an underlying low-dimensional subspace structure.
+PSLB deploys PCA based projection to iteratively find the low rank structure in
+SLBs. We show that deploying projection methods assures dimensionality
+reduction and results in a tighter regret upper bound that is in terms of the
+dimensionality of the subspace and its properties, rather than the
+dimensionality of the ambient space. We modify the image classification task
+into the SLB setting and empirically show that, when a pre-trained DNN provides
+the high dimensional feature representations, deploying PSLB results in
+significant reduction of regret and faster convergence to an accurate model
+compared to state-of-art algorithm.
+"
+23539,Numerical Study of Quantum Hall Bilayers at Total Filling $ν_T=1$: A New Phase at Intermediate Layer Distances,"  We study the phase diagram of quantum Hall bilayer systems with total filing
+$\nu_T=1/2+1/2$ of the lowest Landau level as a function of layer distances
+$d$. Based on numerical exact diagonalization calculations, we obtain three
+distinct phases, including an exciton superfluid phase with spontaneous
+interlayer coherence at small $d$, a composite Fermi liquid at large $d$, and
+an intermediate phase for $1.1<d/l_B<1.8$ ($l_B$ is the magnetic length). The
+transition from the exciton superfluid to the intermediate phase is identified
+by (i) a dramatic change in the Berry curvature of the ground state under
+twisted boundary conditions on the two layers; (ii) an energy level crossing of
+the first excited state. The transition from the intermediate phase to the
+composite Fermi liquid is identified by the vanishing of the exciton superfluid
+stiffness. Furthermore, from our finite-size study, the energy cost of
+transferring one electron between the layers shows an even-odd effect and
+possibly extrapolates to a finite value in the thermodynamic limit, indicating
+the enhanced intralayer correlation. Our identification of an intermediate
+phase and its distinctive features shed new light on the theoretical
+understanding of the quantum Hall bilayer system at total filling $\nu_T=1$.
+"
+23540,Density Functional study on the transesterification of triacetin assisted by cooperative weak interactions via a gold heterogeneous catalyst: Insights into Biodiesel production mechanisms,"  A Density Functional study predicting a heterogeneous-catalyzed reaction
+giving rise to biodiesel, was performed. Triacetin was used as a model of
+triglyceride in the presence of an Au(111) surface as the heterogeneous
+catalyst substrate. Explicit methoxy molecules were implemented as an alcohol
+solvent to understand the reaction trajectory along the well known three-step
+transesterification process. It was found that the reactants and products in
+the three-step processes are adsorbed to the Au substrate through non-covalent
+interactions of the electrostatic-type, which are also mediated by a van der
+Waals attraction. The density of states indicate that the electronic structure
+nature of Au is preserved after the interaction with the organic moieties. This
+may be addressed to an enhanced stability of the Au(111) catalyst through the
+overall reaction. A charge transfer analysis also reveals that the Au surface
+oxidation is also responsible for the transesterification of triacetin and
+evidences that molecular gold plays an important role in this catalytic
+process. Such results may provide fundamental insights into the design of
+heterogeneous catalysts for biodiesel production.
+"
+23541,Convergence Rate of Riemannian Hamiltonian Monte Carlo and Faster Polytope Volume Computation,"  We give the first rigorous proof of the convergence of Riemannian Hamiltonian
+Monte Carlo, a general (and practical) method for sampling Gibbs distributions.
+Our analysis shows that the rate of convergence is bounded in terms of natural
+smoothness parameters of an associated Riemannian manifold. We then apply the
+method with the manifold defined by the log barrier function to the problems of
+(1) uniformly sampling a polytope and (2) computing its volume, the latter by
+extending Gaussian cooling to the manifold setting. In both cases, the total
+number of steps needed is O^{*}(mn^{\frac{2}{3}}), improving the state of the
+art. A key ingredient of our analysis is a proof of an analog of the KLS
+conjecture for Gibbs distributions over manifolds.
+"
+23542,An involutive upsilon knot invariant,"  Using the theory of involutive Heegaard Floer knot theory developed by
+Hendricks-Manolescu, we define two involutive analogs of the Upsilon knot
+concordance invariant of Ozsvath-Stipsicz-Szabo. These involutive invariants
+are piecewise linear functions defined on the interval [0,2]. Each is a
+concordance invariant and provides bounds on the three-genus of a knot.
+"
+23543,Competition and partnership between conformity and payoff-based imitations in social dilemmas,"  Learning from a partner who collects higher payoff is a frequently used
+working hypothesis in evolutionary game theory. One of the alternative
+dynamical rules is when the focal player prefers to follow the strategy choice
+of the majority in the local neighborhood, which is often called as
+conformity-driven strategy update. In this work we assume that both strategy
+learning methods are present and compete for space within the framework of a
+coevolutionary model. Our results reveal that the presence of payoff-driven
+strategy learning method becomes exclusive for high succer's payoff and/or high
+temptation values that represent a snowdrift game dilemma situation. In
+general, however, the competition of the mentioned strategy learning methods
+could be useful to enlarge the parameter space where only cooperators prevail.
+This success of cooperation is based on the enforced coordination of cooperator
+players which reveals the benefit of the latter strategy. Interestingly, the
+payoff-based and the conformity-based cooperator players can form an effective
+alliance against defectors that can also extend the parameter space of full
+cooperator solution in the stag-hunt game region. Our work highlights that the
+coevolution of strategies and individual features such as learning method can
+provide novel type of pattern formation mechanism that cannot be observed in a
+static model, hence remains hidden in traditional models.
+"
+23544,On Levi-Malcev theorem for Leibniz algebras,"  The present paper is devoted to provide conditions for the Levi--Malcev
+theorem to hold or not to hold (i.e. for two Levi subalgebras to be or not
+conjugate by an inner automorphism) in the context of finite-dimensional
+Leibniz algebras over a field of characteristic zero. Particularly, in the case
+of the field $\mathbb{C}$ of complex numbers, we consider all possible cases in
+which Levi subalgebras are conjugate and not conjugate.
+"
+23545,On the linear independence of shifted powers,"  We call shifted power a polynomial of the form $(x-a)^e$. The main goal of
+this paper is to obtain broadly applicable criteria ensuring that the elements
+of a finite family $F$ of shifted powers are linearly independent or, failing
+that, to give a lower bound on the dimension of the space of polynomials
+spanned by $F$. In particular, we give simple criteria ensuring that the
+dimension of the span of $F$ is at least $c.|F|$ for some absolute constant
+$c<1$. We also propose conjectures implying the linear independence of the
+elements of $F$. These conjectures are known to be true for the field of real
+numbers, but not for the field of complex numbers.
+"
+23546,Are Erdös- Rényi Random Graphs Topologically Random?,"  No. We prove that Erdos- Renyi Random Graphs are not topologically random.
+"
+23547,Deep Neural Network Approximation using Tensor Sketching,"  Deep neural networks are powerful learning models that achieve
+state-of-the-art performance on many computer vision, speech, and language
+processing tasks. In this paper, we study a fundamental question that arises
+when designing deep network architectures: Given a target network architecture
+can we design a smaller network architecture that approximates the operation of
+the target network? The question is, in part, motivated by the challenge of
+parameter reduction (compression) in modern deep neural networks, as the ever
+increasing storage and memory requirements of these networks pose a problem in
+resource constrained environments.
+In this work, we focus on deep convolutional neural network architectures,
+and propose a novel randomized tensor sketching technique that we utilize to
+develop a unified framework for approximating the operation of both the
+convolutional and fully connected layers. By applying the sketching technique
+along different tensor dimensions, we design changes to the convolutional and
+fully connected layers that substantially reduce the number of effective
+parameters in a network. We show that the resulting smaller network can be
+trained directly, and has a classification accuracy that is comparable to the
+original network.
+"
+23548,Improved Adaptive Resolution Molecular Dynamics Simulation,"  -Molecular simulations allow the study of properties and interactions of
+molecular systems. This article presents an improved version of the Adaptive
+Resolution Scheme that links two systems having atomistic (also called
+fine-grained) and coarse-grained resolutions using a force interpolation
+scheme. Interactions forces are obtained based on the Hamiltonian derivation
+for a given molecular system. The new algorithm was implemented in GROMACS
+molecular dynamics software package and tested on a butane system. The MARTINI
+coarse-grained force field is applied between the coarse-grained particles of
+the butane system. The molecular dynamics package GROMACS and the Message
+Passing Interface allow the simulation of such a system in a reasonable amount
+of time.
+"
+23549,Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs,"  Cryo-electron microscopy (cryoEM) is an increasingly popular method for
+protein structure determination. However, identifying a sufficient number of
+particles for analysis (often >100,000) can take months of manual effort.
+Current computational approaches are limited by high false positive rates and
+require significant ad-hoc post-processing, especially for unusually shaped
+particles. To address this shortcoming, we develop Topaz, an efficient and
+accurate particle picking pipeline using neural networks trained with few
+labeled particles by newly leveraging the remaining unlabeled particles through
+the framework of positive-unlabeled (PU) learning. Remarkably, despite using
+minimal labeled particles, Topaz allows us to improve reconstruction resolution
+by up to 0.15 {\AA} over published particles on three public cryoEM datasets
+without any post-processing. Furthermore, we show that our novel
+generalized-expectation criteria approach to PU learning outperforms existing
+general PU learning approaches when applied to particle detection, especially
+for challenging datasets of non-globular proteins. We expect Topaz to be an
+essential component of cryoEM analysis.
+"
+23550,SolarisNet: A Deep Regression Network for Solar Radiation Prediction,"  Effective utilization of photovoltaic (PV) plants requires weather
+variability robust global solar radiation (GSR) forecasting models. Random
+weather turbulence phenomena coupled with assumptions of clear sky model as
+suggested by Hottel pose significant challenges to parametric & non-parametric
+models in GSR conversion rate estimation. Also, a decent GSR estimate requires
+costly high-tech radiometer and expert dependent instrument handling and
+measurements, which are subjective. As such, a computer aided monitoring (CAM)
+system to evaluate PV plant operation feasibility by employing smart grid past
+data analytics and deep learning is developed. Our algorithm, SolarisNet is a
+6-layer deep neural network trained on data collected at two weather stations
+located near Kalyani metrological site, West Bengal, India. The daily GSR
+prediction performance using SolarisNet outperforms the existing state of art
+and its efficacy in inferring past GSR data insights to comprehend daily and
+seasonal GSR variability along with its competence for short term forecasting
+is discussed.
+"
+23551,$Σ^μ_2$ is decidable for $Π^μ_2$,"  Given a $\Pi^{\mu}_2$ formula of the modal $\mu$ calculus, it is decidable
+whether it is equivalent to a $\Sigma^{\mu}_2$ formula.
+"
+23552,Fast initial guess estimation for digital image correlation,"  Digital image correlation (DIC) is a widely used optical metrology for
+quantitative deformation measurement due to its non-contact, low-cost, highly
+precise feature. DIC relies on nonlinear optimization algorithm. Thus it is
+quite important to efficiently obtain a reliable initial guess. The most widely
+used method for obtaining initial guess is reliability-guided digital image
+correlation (RG-DIC) method, which is reliable but path-dependent. This
+path-dependent method limits the further improvement of computation speed of
+DIC using parallel computing technology, and error of calculation may be spread
+out along the calculation path. Therefore, a reliable and path-independent
+algorithm which is able to provide reliable initial guess is desirable to reach
+full potential of the ability of parallel computing. In this paper, an
+algorithm used for initial guess estimation is proposed. Numerical and real
+experiments show that the proposed algorithm, adaptive incremental
+dissimilarity approximations algorithm (A-IDA), has the following
+characteristics: 1) Compared with inverse compositional Gauss-Newton (IC-GN)
+sub-pixel registration algorithm, the computational time required by A-IDA
+algorithm is negligible, especially when subset size is relatively large; 2)
+the efficiency of A-IDA algorithm is less influenced by search range; 3) the
+efficiency is less influenced by subset size; 4) it is easy to select the
+threshold for the proposed algorithm.
+"
+23553,A general extension theorem for cohomology classes on non reduced analytic spaces,"  The main purpose of this paper is to generalize the celebrated L${}^2$
+extension theorem of Ohsawa-Takegoshi in several directions : the holomorphic
+sections to extend are taken in a possibly singular hermitian line bundle, the
+subvariety from which the extension is performed may be non reduced, the
+ambient manifold is K{ä}hler and holomorphically convex, but not necessarily
+compact.
+"
+23554,Generative learning for deep networks,"  Learning, taking into account full distribution of the data, referred to as
+generative, is not feasible with deep neural networks (DNNs) because they model
+only the conditional distribution of the outputs given the inputs. Current
+solutions are either based on joint probability models facing difficult
+estimation problems or learn two separate networks, mapping inputs to outputs
+(recognition) and vice-versa (generation). We propose an intermediate approach.
+First, we show that forward computation in DNNs with logistic sigmoid
+activations corresponds to a simplified approximate Bayesian inference in a
+directed probabilistic multi-layer model. This connection allows to interpret
+DNN as a probabilistic model of the output and all hidden units given the
+input. Second, we propose that in order for the recognition and generation
+networks to be more consistent with the joint model of the data, weights of the
+recognition and generator network should be related by transposition. We
+demonstrate in a tentative experiment that such a coupled pair can be learned
+generatively, modelling the full distribution of the data, and has enough
+capacity to perform well in both recognition and generation.
+"
+23555,Drop impact experiments of non-Newtonian liquids on micro-structured surfaces,"  The spreading dynamics of Newtonian liquids have been extensively studied in
+hydrophilic and hydrophobic surfaces and its behavior has been extensively
+explored over the last years. However, drop impact of Non-Newtonian liquids
+still needs further study. Luu and Forterre (J. Fluid Mech., 632, 2009)
+successfully found scaling laws for yield-stress fluids on hydrophilic
+surfaces. They also uncovered interesting and yet unexplained regimes when the
+impact was performed on a superhydrophobic surface. In this work, we perform
+drop impact experiments on micro-patterned surfaces with two types of
+non-Newtonian liquids: one showing shear-thickening behavior and another one
+showing shear-thinning. Our results show that a typical shear-thickening liquid
+as cornstarch -at least a the relatively low concentration of 30\%w/w- spreads
+according to the scaling laws of Newtonian liquids, whereas visco-elastic
+liquids as Carbopol behave as predicted by Luu and Forterre for impact on
+hydrophilic surfaces, but show different scaling laws when they impact on
+superhydrophobic surfaces.
+"
+23556,"Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data","  Although there is no shortage of clustering algorithms proposed in the
+literature, the question of the most relevant strategy for clustering
+compositional data (i.e., data made up of profiles, whose rows belong to the
+simplex) remains largely unexplored in cases where the observed value of an
+observation is equal or close to zero for one or more samples. This work is
+motivated by the analysis of two sets of compositional data, both focused on
+the categorization of profiles but arising from considerably different
+applications: (1) identifying groups of co-expressed genes from high-throughput
+RNA sequencing data, in which a given gene may be completely silent in one or
+more experimental conditions; and (2) finding patterns in the usage of stations
+over the course of one week in the Velib' bicycle sharing system in Paris,
+France. For both of these applications, we focus on the use of appropriately
+chosen data transformations, including the Centered Log Ratio and a novel
+extension we propose called the Log Centered Log Ratio, in conjunction with the
+K-means algorithm. We use a nonasymptotic penalized criterion, whose penalty is
+calibrated with the slope heuristics, to select the number of clusters present
+in the data. Finally, we illustrate the performance of this clustering
+strategy, which is implemented in the Bioconductor package coseq, on both the
+gene expression and bicycle sharing system data.
+"
+23557,The Faint Young Sun and Faint Young Stars Paradox,"  The purpose of this paper is to explore a resolution for the Faint Young Sun
+Paradox that has been mostly rejected by the community, namely the possibility
+of a somewhat more massive young Sun with a large mass loss rate sustained for
+two to three billion years. This would make the young Sun bright enough to keep
+both the terrestrial and Martian oceans from freezing, and thus resolve the
+paradox. It is found that a large and sustained mass loss is consistent with
+the well observed spin-down rate of Sun-like stars, and indeed may be required
+for it. It is concluded that a more massive young Sun must be considered a
+plausible hypothesis.
+"
+23558,Impact of Land Use on the DOM Composition in Different Seasons in a Subtropical River Flowing through the Region Undergoing Rapid Urbanization,"  The dissolved organic matter (DOM) composition in river ecosystems could
+reflect the human impacts on the river ecosystem, and plays an important role
+in the carbon cycling process. We collected water and phytoplankton samples at
+107 sites in the Dongjiang River in two seasons to assess the impact of the
+sub-catchments land use structure on the DOM composition. The results showed
+that (1) the forested sub-catchments had higher humic-like C1 (16.45%) and C2
+(25.04%) and lower protein-like C3 (22.57%) and C4 (35.95%) than urbanized and
+mixed forest-agriculture sub-catchments, while the urbanized sub-catchments
+showed an inverse trend (4.54%, 15.51%, 33.97% and 45.98%, respectively). (2)
+The significant variation in the proportion of C1 and C4 between the dry and
+rainy seasons was recorded in both the forested and the mixed
+forest-agriculture sub-catchments (p<0.01), but only C4 showed an obvious
+seasonal variation in the urbanized sub-catchments (p<0.01). While the DOM
+composition was mainly related to the proportion of urbanized land and forested
+land year-round (p<0.01), it had stronger correlation with forested land in the
+dry season and urbanized land in the rainy season. (3) No significant
+correlation between the DOM composition and the agricultural land proportion
+was found in either season (p>0.05). Our findings indicated that the DOM
+composition was strongly dependent on the land use structure of the
+sub-catchments and varied seasonally, but the seasonal variation pattern could
+be disturbed by human activities in the extensively urbanized catchments.
+Notably, the higher C4 proportion compared with those in temperate rivers
+implied a larger amount of CO2 released from the subtropical rivers into the
+atmosphere when considering bioavailability.
+"
+23559,Estimating the Reach of a Manifold,"  Various problems in manifold estimation make use of a quantity called the
+reach, denoted by $\tau\_M$, which is a measure of the regularity of the
+manifold. This paper is the first investigation into the problem of how to
+estimate the reach. First, we study the geometry of the reach through an
+approximation perspective. We derive new geometric results on the reach for
+submanifolds without boundary. An estimator $\hat{\tau}$ of $\tau\_{M}$ is
+proposed in a framework where tangent spaces are known, and bounds assessing
+its efficiency are derived. In the case of i.i.d. random point cloud
+$\mathbb{X}\_{n}$, $\hat{\tau}(\mathbb{X}\_{n})$ is showed to achieve uniform
+expected loss bounds over a $\mathcal{C}^3$-like model. Finally, we obtain
+upper and lower bounds on the minimax rate for estimating the reach.
+"
+23560,Optimal Rates and Tradeoffs in Multiple Testing,"  Multiple hypothesis testing is a central topic in statistics, but despite
+abundant work on the false discovery rate (FDR) and the corresponding Type-II
+error concept known as the false non-discovery rate (FNR), a fine-grained
+understanding of the fundamental limits of multiple testing has not been
+developed. Our main contribution is to derive a precise non-asymptotic tradeoff
+between FNR and FDR for a variant of the generalized Gaussian sequence model.
+Our analysis is flexible enough to permit analyses of settings where the
+problem parameters vary with the number of hypotheses $n$, including various
+sparse and dense regimes (with $o(n)$ and $\mathcal{O}(n)$ signals). Moreover,
+we prove that the Benjamini-Hochberg algorithm as well as the Barber-Candès
+algorithm are both rate-optimal up to constants across these regimes.
+"
+23561,Prescribing Gaussian curvature on closed Riemann surface with conical singularity in the negative case,"  The problem of prescribing Gaussian curvature on Riemann surface with conical
+singularity is considered. Let $(\Sigma,\beta)$ be a closed Riemann surface
+with a divisor $\beta$, and $K_\lambda=K+\lambda$, where
+$K:\Sigma\rightarrow\mathbb{R}$ is a Hölder continuous function satisfying
+$\max_\Sigma K= 0$, $K\not\equiv 0$, and $\lambda\in\mathbb{R}$. If the Euler
+characteristic $\chi(\Sigma,\beta)$ is negative, then by a variational method,
+it is proved that there exists a constant $\lambda^\ast>0$ such that for any
+$\lambda\leq 0$, there is a unique conformal metric with the Gaussian curvature
+$K_\lambda$; for any $\lambda$, $0<\lambda<\lambda^\ast$, there are at least
+two conformal metrics having $K_\lambda$ its Gaussian curvature; for
+$\lambda=\lambda^\ast$, there is at least one conformal metric with the
+Gaussian curvature $K_{\lambda^\ast}$; for any $\lambda>\lambda^\ast$, there is
+no certain conformal metric having $K_{\lambda}$ its Gaussian curvature. This
+result is an analog of that of Ding and Liu \cite{Ding-Liu}, partly resembles
+that of Borer, Galimberti and Struwe \cite{B-G-Stru}, and generalizes that of
+Troyanov \cite{Troyanov} in the negative case.
+"
+23562,The $N$-Body Problem in Spaces with Uniformly Varying Curvature,"  We generalize the curved $N$-body problem to spheres and hyperbolic spheres
+whose curvature $\kappa$ varies in time. Unlike in the particular case when the
+curvature is constant, the equations of motion are non-autonomous. We first
+briefly consider the analogue of the Kepler problem and then investigate the
+homographic orbits for any number of bodies, proving the existence of several
+such classes of solutions on spheres. Allowing the curvature to vary in time
+offers some insight into the effect of an expanding universe, in the context
+the curved $N$-body problem, when $\kappa$ satisfies Hubble's law. The study of
+these equations also opens the possibility of finding new connections between
+classical mechanics and general relativity.
+"
+23563,Evolving Influence Maximization,"  Influence Maximization (IM) aims to maximize the number of people that become
+aware of a product by finding the `best' set of `seed' users to initiate the
+product advertisement. Unlike prior arts on static social networks containing
+fixed number of users, we undertake the first study of IM in more realistic
+evolving networks with temporally growing topology. The task of evolving IM
+({\bfseries EIM}), however, is far more challenging over static cases in the
+sense that seed selection should consider its impact on future users and the
+probabilities that users influence one another also evolve over time.
+We address the challenges through $\mathbb{EIM}$, a newly proposed
+bandit-based framework that alternates between seed nodes selection and
+knowledge (i.e., nodes' growing speed and evolving influences) learning during
+network evolution. Remarkably, $\mathbb{EIM}$ involves three novel components
+to handle the uncertainties brought by evolution:
+"
+23564,Fast and Continuous Foothold Adaptation for Dynamic Locomotion through Convolutional Neural Networks,"  Legged robots can outperform wheeled machines for most navigation tasks
+across unknown and rough terrains. For such tasks, visual feedback is a
+fundamental asset to provide robots with terrain-awareness. However, robust
+dynamic locomotion on difficult terrains with real-time performance guarantees
+remains a challenge. Indeed, the computational effort demanded by visual
+processing limits the potential for realtime control and planning strategies.
+In this paper, we present a real-time, dynamic foothold adaptation strategy
+based on visual feedback. Our method adjusts the landing position of the feet
+in a fully reactive manner, using only on-board computers and sensors. The
+correction is computed and executed continuously along the swing phase
+trajectory of each leg. To efficiently adapt the landing position, we implement
+a self-supervised foothold classifier based on a Convolutional Neural Network
+(CNN). The training set is automatically generated by a heuristic algorithm
+that jointly evaluates terrain morphology, kinematics, and leg collisions. Our
+method results in an up to 200 times faster computation with respect to the
+full-blown heuristics. Our goal is to react to visual stimuli from the
+environment, bridging the gap between blind reactive locomotion and purely
+vision-based planning strategies. We assess the performance of our method on
+the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds
+up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe
+foothold adaptation is clearly demonstrated by the overall robot behavior.
+"
+23565,The Roman harmonic numbers revisited,"  Two decades ago, Steven Roman, Daniel E. Loeb and Gian-Carlo Rota introduced
+a family of harmonic numbers in their study of harmonic logarithms. We propose
+to refer to those numbers as {\it Roman harmonic numbers}. With the purpose of
+revitalizing the study of these mathematical objects, we recall here their
+known properties and unveil additional ones. An integral representation,
+several generating relations, and a collection of sum rules involving those
+numbers are presented. It is also shown that higher derivatives of the
+Pochhammer and reciprocal Pochhammer symbols are easily expressed in terms of
+Roman harmonic numbers.
+"
+23566,Minutia-pair spectral representations for fingerprint template protection,"  We introduce a new fixed-length representation of fingerprint minutiae, for
+use in template protection. It is similar to the `spectral minutiae'
+representation of Xu et al. but is based on coordinate differences between
+pairs of minutiae. Our technique has the advantage that it does not discard the
+phase information of the spectral functions. We show that the fingerprint
+matching performance (Equal Error Rate) is comparable to that of the original
+spectral minutiae representation, while the speed is improved.
+"
+23567,Z2 topological order and Majorana doubling in Kitaev Chain Model,"  The Kitaev chain model exhibits topological order which manifests as
+topological degeneracy, Majorana edge modes and $Z_{2}$ topological invariant
+of bulk spectrum. This model can be obtained from a transverse field Ising
+model(TFIM) using the Jordan-Wigner transformation. TFIM has neither
+topological degeneracy nor any edge modes. These two models are related to each
+other via duality transformation which maps local observables in spin model to
+non-local observables in fermionic model keeping the $Z2$ symmetry intact. In
+this paper we asked the question of how topological order arises in the
+fermionic models, particularly Majorana fermion models. We find that for the
+fermionic models, there are emergent symmetries which protect the topological
+order in the model. We explore the full Clifford algebra of Majorana fermions
+and show that the high order products are also the symmetries of the
+Hamiltonian. For the case of odd number of Majorana fermions, there is always a
+special operator which behaves like a Majorana fermion and has been called
+emergent Majorana fermion. Emergent Majorana fermions are also the Majorana
+mode operators and these operators are the clear signature of the topological
+order in the Hamiltonian. We also show that Majorana fermion Hamiltonians also
+have supersymmetry which is tied to the existence of fermionic mode operators
+and hence to the topological order. We also explore the implications of these
+emergent symmetries for the braid group representations of Majorana fermions.
+We explore how the quantum entanglement of topologically ordered phases is
+related to topological entanglement of their braid group representations. We
+notice that braid group representations arise only in the topological phase of
+the Hamiltonian and hence can be used to characterize topological phases of the
+lattice models.
+"
+23568,The Delta Game,"  We introduce a game related to the $I_{3322}$ game and analyze a constrained
+value function for this game over various families of synchronous quantum
+probability densities.
+"
+23569,QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation,"  In this paper, we study the problem of learning vision-based dynamic
+manipulation skills using a scalable reinforcement learning approach. We study
+this problem in the context of grasping, a longstanding challenge in robotic
+manipulation. In contrast to static learning behaviors that choose a grasp
+point and then execute the desired grasp, our method enables closed-loop
+vision-based control, whereby the robot continuously updates its grasp strategy
+based on the most recent observations to optimize long-horizon grasp success.
+To that end, we introduce QT-Opt, a scalable self-supervised vision-based
+reinforcement learning framework that can leverage over 580k real-world grasp
+attempts to train a deep neural network Q-function with over 1.2M parameters to
+perform closed-loop, real-world grasping that generalizes to 96% grasp success
+on unseen objects. Aside from attaining a very high success rate, our method
+exhibits behaviors that are quite distinct from more standard grasping systems:
+using only RGB vision-based perception from an over-the-shoulder camera, our
+method automatically learns regrasping strategies, probes objects to find the
+most effective grasps, learns to reposition objects and perform other
+non-prehensile pre-grasp manipulations, and responds dynamically to
+disturbances and perturbations.
+"
+23570,Saliency-based Sequential Image Attention with Multiset Prediction,"  Humans process visual scenes selectively and sequentially using attention.
+Central to models of human visual attention is the saliency map. We propose a
+hierarchical visual architecture that operates on a saliency map and uses a
+novel attention mechanism to sequentially focus on salient regions and take
+additional glimpses within those regions. The architecture is motivated by
+human visual attention, and is used for multi-label image classification on a
+novel multiset task, demonstrating that it achieves high precision and recall
+while localizing objects with its attention. Unlike conventional multi-label
+image classification models, the model supports multiset prediction due to a
+reinforcement-learning based training process that allows for arbitrary label
+permutation and multiple instances per label.
+"
+23571,An evidential Markov decision making model,"  The sure thing principle and the law of total probability are basic laws in
+classic probability theory. A disjunction fallacy leads to the violation of
+these two classical laws. In this paper, an Evidential Markov (EM) decision
+making model based on Dempster-Shafer (D-S) evidence theory and Markov
+modelling is proposed to address this issue and model the real human
+decision-making process. In an evidential framework, the states are extended by
+introducing an uncertain state which represents the hesitance of a decision
+maker. The classical Markov model can not produce the disjunction effect, which
+assumes that a decision has to be certain at one time. However, the state is
+allowed to be uncertain in the EM model before the final decision is made. An
+extra uncertainty degree parameter is defined by a belief entropy, named Deng
+entropy, to assignment the basic probability assignment of the uncertain state,
+which is the key to predict the disjunction effect. A classical categorization
+decision-making experiment is used to illustrate the effectiveness and validity
+of EM model. The disjunction effect can be well predicted and the free
+parameters are less compared with the existing models.
+"
+23572,Degenerations of $\mathbf{C}^n$ and Calabi-Yau metrics,"  We construct infinitely many complete Calabi-Yau metrics on $\mathbf{C}^n$
+for $n \geq 3$, with maximal volume growth, and singular tangent cones at
+infinity. In addition we construct Calabi-Yau metrics in neighborhoods of
+certain isolated singularities whose tangent cones have singular cross section,
+generalizing work of Hein-Naber.
+"
+23573,Filming non-radiative population transfer: Time-resolved x-ray scattering near an avoided crossing,"  We show that time-resolved x-ray scattering from molecules prepared in a
+superposition of electronic states moving through an avoided crossing has new
+features not found in diffraction from the corresponding classical mixed state.
+Photoabsorption in molecular iodine at 520 nm produces a superposition of two
+dipole-allowed nearly degenerate electronic states, which interact due to
+non-adiabatic coupling. We show experimental evidence that the mixing of the
+nuclear wavepackets from the two electronic states at the avoided crossing
+leads to ultrafast changes in the angular composition of the scattering
+pattern. This provides a novel means to study transitions in excited molecular
+systems. We reconstruct a movie of the nuclear probability density arising from
+this interference.
+"
+23574,On Learning Invariant Representation for Domain Adaptation,"  Due to the ability of deep neural nets to learn rich representations, recent
+advances in unsupervised domain adaptation have focused on learning
+domain-invariant features that achieve a small error on the source domain. The
+hope is that the learnt representation, together with the hypothesis learnt
+from the source domain, can generalize to the target domain. In this paper, we
+first construct a simple counterexample showing that, contrary to common
+belief, the above conditions are not sufficient to guarantee successful domain
+adaptation. In particular, the counterexample (Fig. 1) exhibits
+\emph{conditional shift}: the class-conditional distributions of input features
+change between source and target domains. To give a sufficient condition for
+domain adaptation, we propose a natural and interpretable generalization upper
+bound that explicitly takes into account the aforementioned shift. Moreover, we
+shed new light on the problem by proving an information-theoretic lower bound
+on the joint error of \emph{any} domain adaptation method that attempts to
+learn invariant representations. Our result characterizes a fundamental
+tradeoff between learning invariant representations and achieving small joint
+error on both domains when the marginal label distributions differ from source
+to target. Finally, we conduct experiments on real-world datasets that
+corroborate our theoretical findings. We believe these insights are helpful in
+guiding the future design of domain adaptation and representation learning
+algorithms.
+"
+23575,Influence of grafting on the glass transition temperature of PS thin films,"  We present an investigation of the effect of the interaction between a thin
+polystyrene film and its supporting substrate on its glass transition
+temperature (Tg). We modulate this interaction by depositing the film on
+end-tethered polystyrene grafted layers of controlled molecular parameters. By
+comparing Tg measurements versus film thickness for films deposited on
+different grafted layers and films deposited directly on a silicon substrate,
+we can conclude that there is no important effect of the film-subtrate
+interaction. Our interpretation of these results is that local orientation and
+dynamic effects substantial enough to influence Tg cannot readily be obtained
+by grafting prepolymerized chains to a surface, due to intrinsic limitation of
+the surface grafting density.
+"
+23576,Constraints on physical reality arising from a formalization of knowledge,"  There are (at least) four ways that an agent can acquire information
+concerning the state of the universe: via observation, control, prediction, or
+via retrodiction, i.e., memory. Each of these four ways of acquiring
+information seems to rely on a different kind of physical device (resp., an
+observation device, a control device, etc.). However it turns out that certain
+mathematical structure is common to those four types of device. Any device that
+possesses a certain subset of that structure is known as an ""inference device""
+(ID).
+Here I review some of the properties of IDs, including their relation with
+Turing machines, and (more loosely) quantum mechanics. I also review the bounds
+of the joint abilities of any set of IDs to know facts about the physical
+universe that contains them. These bounds constrain the possible properties of
+any universe that contains agents who can acquire information concerning that
+universe.
+I then extend this previous work on IDs, by adding to the definition of IDs
+some of the other mathematical structure that is common to the four ways of
+acquiring information about the universe but is not captured in the (minimal)
+definition of IDs. I discuss these extensions of IDs in the context of
+epistemic logic (especially possible worlds formalisms like Kripke structures
+and Aumann structures). In particular, I show that these extensions of IDs are
+not subject to the problem of logical omniscience that plagues many previously
+studied forms of epistemic logic.
+"
+23577,On the thinness and proper thinness of a graph,"  Graphs with bounded thinness were defined in 2007 as a generalization of
+interval graphs. In this paper we introduce the concept of proper thinness,
+such that graphs with bounded proper thinness generalize proper interval
+graphs. We study the complexity of problems related to the computation of these
+parameters, describe the behavior of the thinness and proper thinness under
+three graph operations, and relate thinness and proper thinness with other
+graph invariants in the literature. Finally, we describe a wide family of
+problems that can be solved in polynomial time for graphs with bounded
+thinness, generalizing for example list matrix partition problems with bounded
+size matrix, and enlarge this family of problems for graphs with bounded proper
+thinness, including domination problems.
+"
+23578,On the postulation of lines and a fat line,"  In this note we show that the union of $r$ general lines and one fat line in
+${\mathbb P}^3$ imposes independent conditions on forms of sufficiently high
+degree $d$, where the bound on $d$ is independent of the number of lines. This
+extends former results of Hartshorne and Hirschowitz on unions of general
+lines, and of Aladpoosh on unions of general lines and one double line.
+"
+23579,Low-rank computation of posterior covariance matrices in Bayesian inverse problems,"  We consider the problem of estimating the uncertainty in statistical inverse
+problems using Bayesian inference. When the probability density of the noise
+and the prior are Gaussian, the solution of such a statistical inverse problem
+is also Gaussian. Therefore, the underlying solution is characterized by the
+mean and covariance matrix of the posterior probability density. However, the
+covariance matrix of the posterior probability density is full and large.
+Hence, the computation of such a matrix is impossible for large dimensional
+parameter spaces. It is shown that for many ill-posed problems, the Hessian
+matrix of the data misfit part has low numerical rank and it is therefore
+possible to perform a low-rank approach to approximate the posterior covariance
+matrix. For such a low-rank approximation, one needs to solve a forward partial
+differential equation (PDE) and the adjoint PDE in both space and time. This in
+turn gives $\mathcal{O}(n_x n_t)$ complexity for both, computation and storage,
+where $n_x$ is the dimension of the spatial domain and $n_t$ is the dimension
+of the time domain. Such computations and storage demand are infeasible for
+large problems. To overcome this obstacle, we develop a new approach that
+utilizes a recently developed low-rank in time algorithm together with the
+low-rank Hessian method. We reduce both the computational complexity and
+storage requirement from $\mathcal{O}(n_x n_t)$ to $\mathcal{O}(n_x + n_t)$. We
+use numerical experiments to illustrate the advantages of our approach.
+"
+23580,An Optical Catalog of Galaxy Clusters Obtained from an Adaptive Matched Filter Finder Applied to SDSS DR9 Data,"  We present a new galaxy cluster catalog constructed from SDSS DR9 using an
+Adaptive Matched Filter technique. Our main catalog has 46,479 galaxy clusters
+with richness $\Lambda_{200} > 20$ in the redshift range 0.045 $\le z <$ 0.641
+in $\sim$11,500 $deg^{2}$ of the sky. Angular position, richness, core and
+virial radii and redshift estimates for these clusters, as well as their error
+analysis are provided. We also provide an extended version with a lower
+richness cut, containing 79,368 clusters. This version, in addition to the
+clusters in the main catalog, also contains those clusters (with richness
+$<20$) which have a one-to-one match in the DR8 catalog developed by Wen et al
+(WHL). We obtain probabilities for cluster membership for each galaxy and
+implement several procedures for the identification and removal of false
+cluster detections.
+We compare our catalog with other SDSS-based ones such as the redMaPPer
+(26,350 clusters) and the WHL (132,684 clusters) in the same area of the sky
+and in the overlapping redshift range. We match 97$\%$ of the richest Abell
+clusters, the same as WHL, while redMaPPer matches $\sim 90\%$ of these
+clusters. Considering AMF DR9 richness bins, redMaPPer consistently does not
+possess one-to-one matches for $\sim$20$\%$ AMF DR9 clusters with
+$\Lambda_{200}>40$, while WHL matches $\geq$70$\%$ of these missed clusters on
+average. We also match the AMF catalog with the X-ray cluster catalogs BAX,
+MCXC and a combined catalog from NORAS and REFLEX. We consistently obtain a
+greater number of one-to-one matches for X-ray clusters across higher
+luminosity bins ($L_x>6 \times 10^{44}$ ergs/sec) than redMaPPer while WHL
+matches the most clusters overall. For the most luminous clusters ($L_x>8$),
+our catalog performs equivalently to WHL. This new catalog provides a wider
+sample than redMaPPer while retaining many fewer objects than WHL.
+"
+23581,Astrometry with the WFIRST Wide-Field Imager,"  The Wide-Field InfraRed Space Telescope (WFIRST) will be capable of
+delivering precise astrometry for faint sources over the enormous field of view
+of its main camera, the Wide-Field Imager (WFI). This unprecedented combination
+will be transformative for the many scientific questions that require precise
+positions, distances, and velocities of stars. We describe the expectations for
+the astrometric precision of the WFIRST WFI in different scenarios, illustrate
+how a broad range of science cases will see significant advances with such
+data, and identify aspects of WFIRST's design where small adjustments could
+greatly improve its power as an astrometric instrument.
+"
+23582,Initial-boundary value problems in a half-strip for two-dimensional Zakharov-Kuznetsov equation,"  Initial-boundary value problems in a half-strip with different types of
+boundary conditions for two-dimensional Zakharov-Kuznetsov equation are
+considered. Results on global existence, uniqueness and long-time decay of weak
+and regular solutions are establishrd.
+"
+23583,Robust Online Matrix Factorization for Dynamic Background Subtraction,"  We propose an effective online background subtraction method, which can be
+robustly applied to practical videos that have variations in both foreground
+and background. Different from previous methods which often model the
+foreground as Gaussian or Laplacian distributions, we model the foreground for
+each frame with a specific mixture of Gaussians (MoG) distribution, which is
+updated online frame by frame. Particularly, our MoG model in each frame is
+regularized by the learned foreground/background knowledge in previous frames.
+This makes our online MoG model highly robust, stable and adaptive to practical
+foreground and background variations. The proposed model can be formulated as a
+concise probabilistic MAP model, which can be readily solved by EM algorithm.
+We further embed an affine transformation operator into the proposed model,
+which can be automatically adjusted to fit a wide range of video background
+transformations and make the method more robust to camera movements. With using
+the sub-sampling technique, the proposed method can be accelerated to execute
+more than 250 frames per second on average, meeting the requirement of
+real-time background subtraction for practical video processing tasks. The
+superiority of the proposed method is substantiated by extensive experiments
+implemented on synthetic and real videos, as compared with state-of-the-art
+online and offline background subtraction methods.
+"
+23584,Rigidity of length-angle spectrum for closed hyperbolic surfaces,"  The rigidity of marked length spectrum for closed hyperbolic surfaces due to
+Fricke-Klein [7] has been the motivation of many different rigidity results,
+specially for manifolds of negative curvature. From the works of Vigneras [18],
+Sunada [17] and many other authors this result is far from being true for the
+unmarked length spectrum. The purpose of this paper is to introduce a closely a
+related unmarked spectrum, the length-angle spectrum, and show that it
+determines the surface uniquely.
+"
+23585,"Nature of the spiral state, electric polarisation and magnetic transitions in Sr-doped YBaCuFeO$_5$: A first-principles study","  Contradictory results on the ferroelectric response of type II multiferroic
+YBaCuFeO$_{5}$, in its incommensurate phase, has of late, opened up a lively
+debate. There are ambiguous reports on the nature of the spiral magnetic state.
+Using first-principles DFT calculations for the parent compound within
+LSDA+U+SO approximation, the multiferroic response and the nature of spiral
+state is revealed. The helical spiral is found to be more stable below the
+transition temperature as spins prefer to lie in ab plane. The
+Dzyaloshinskii-Moriya (DM) interaction and the spin current mechanism were
+earlier invoked to account for the electric polarisation in this system.
+However, the DM interaction is found to be absent, spin current mechanism is
+not valid in the helical spiral state and there is no electric polarisation
+thereof. These results are in good agreement with the recent single-crystal
+data. We also investigate the magnetic transitions in
+YBa$_{1-x}$Sr$_x$CuFeO$_5$ for the entire range $0\le x\le 1$ of doping. The
+exchange interactions are estimated as a function of doping and a quantum Monte
+Carlo (QMC) calculation on an effective spin Hamiltonian shows that the
+paramagnetic to commensurate phase transition temperature increases with doping
+till $x=0.5$ and decreases beyond. Our observations are consistent with
+experimental findings.
+"
+23586,Improving Stock Market Prediction via Heterogeneous Information Fusion,"  Traditional stock market prediction approaches commonly utilize the
+historical price-related data of the stocks to forecast their future trends. As
+the Web information grows, recently some works try to explore financial news to
+improve the prediction. Effective indicators, e.g., the events related to the
+stocks and the people's sentiments towards the market and stocks, have been
+proved to play important roles in the stocks' volatility, and are extracted to
+feed into the prediction models for improving the prediction accuracy. However,
+a major limitation of previous methods is that the indicators are obtained from
+only a single source whose reliability might be low, or from several data
+sources but their interactions and correlations among the multi-sourced data
+are largely ignored.
+In this work, we extract the events from Web news and the users' sentiments
+from social media, and investigate their joint impacts on the stock price
+movements via a coupled matrix and tensor factorization framework.
+Specifically, a tensor is firstly constructed to fuse heterogeneous data and
+capture the intrinsic relations among the events and the investors' sentiments.
+Due to the sparsity of the tensor, two auxiliary matrices, the stock
+quantitative feature matrix and the stock correlation matrix, are constructed
+and incorporated to assist the tensor decomposition. The intuition behind is
+that stocks that are highly correlated with each other tend to be affected by
+the same event. Thus, instead of conducting each stock prediction task
+separately and independently, we predict multiple correlated stocks
+simultaneously through their commonalities, which are enabled via sharing the
+collaboratively factorized low rank matrices between matrices and the tensor.
+Evaluations on the China A-share stock data and the HK stock data in the year
+2015 demonstrate the effectiveness of the proposed model.
+"
+23587,Hypergeometric special solutions for $d$-Painlevé equations,"  We have studied Padé interpolation problems on an additive grid, related to
+additive difference ($d$-) Painlevé equations of type $E_7^{(1)}$,
+$E_6^{(1)}$, $D_4^{(1)}$ and $A_3^{(1)}$. By solving those problems, we can
+derive time evolution equations, Lax pairs of scalar type and determinant
+formulae of special solutions, for the corresponding $d$-Painlevé equations.
+We show how the special solutions are expressed as determinant formulae by
+using terminating (generalized) hypergeometric functions.
+"
+23588,Ising Processing Units: Potential and Challenges for Discrete Optimization,"  The recent emergence of novel computational devices, such as adiabatic
+quantum computers, CMOS annealers, and optical parametric oscillators, presents
+new opportunities for hybrid-optimization algorithms that leverage these kinds
+of specialized hardware. In this work, we propose the idea of an Ising
+processing unit as a computational abstraction for these emerging tools.
+Challenges involved in using and benchmarking these devices are presented, and
+open-source software tools are proposed to address some of these challenges.
+The proposed benchmarking tools and methodology are demonstrated by conducting
+a baseline study of established solution methods to a D-Wave 2X adiabatic
+quantum computer, one example of a commercially available Ising processing
+unit.
+"
+23589,Guiding waves along an infinitesimal line between impedance surfaces,"  We present a new electromagnetic mode that forms at the interface between two
+planar surfaces laid side by side in free space, effectively guiding energy
+along an infinitesimal, one-dimensional line. It is shown that this mode occurs
+when the boundaries have complementary surface impedances, and it is possible
+to control the mode confinement by altering their values correspondingly. The
+mode exhibits singular field enhancement, broad bandwidth, direction-dependent
+polarization, and robustness to certain defects. As a proof-of-concept,
+experimental results in the microwave regime are provided using patterned
+conducting sheets. Our proposed effective-medium-based approach is general,
+however, thus allowing for potential implementation up to optical frequencies.
+Our system is promising for applications including integrated photonics,
+sensing, switching, chiral quantum coupling, and reconfigurable waveguides.
+"
+23590,Automated Design of CubeSats and Small Spacecrafts,"  The miniaturization of electronics, sensors and actuators has enabled the
+growing use of CubeSats and sub-20 kg spacecraft. Their reduced mass and volume
+has the potential to translate into significant reductions in required
+propellant and launch mass for interplanetary missions, earth observation and
+for astrophysics applications. There is an important need to optimize the
+design of these spacecraft to better ascertain their maximal capabilities by
+finding optimized solution, where mass, volume and power is a premium. Current
+spacecraft design methods require a team of experts, who use their engineering
+experience and judgement to develop a spacecraft design. Such an approach can
+miss innovative designs not thought of by a human design team. In this work we
+present a compelling alternative approach that extends the capabilities of a
+spacecraft engineering design team to search for and identify near-optimal
+solutions using machine learning. The approach enables automated design of a
+spacecraft that requires specifying quantitative goals, requiring reaching a
+target location or operating at a predetermined orbit for a required time. Next
+a virtual warehouse of components is specified that be selected to produce a
+candidate design. Candidate designs are produced using an artificial Darwinian
+approach, where fittest design survives and reproduce, while unfit individuals
+are culled off. Our past work in space robotic has produced systems designs and
+controllers that are human competitive. Finding a near-optimal solution
+presents vast improvements over a solution obtained through engineering
+judgment and point design alone. The approach shows a credible pathway to
+identify and evaluate many more candidate designs than it would be otherwise
+possible with a human design team alone.
+"
+23591,Talent Flow Analytics in Online Professional Network,"  Analyzing job hopping behavior is important for understanding job preference
+and career progression of working individuals. When analyzed at the workforce
+population level, job hop analysis helps to gain insights of talent flow among
+different jobs and organizations. Traditionally, surveys are conducted on job
+seekers and employers to study job hop behavior. Beyond surveys, job hop
+behavior can also be studied in a highly scalable and timely manner using a
+data driven approach in response to fast-changing job landscape. Fortunately,
+the advent of online professional networks (OPNs) has made it possible to
+perform a large-scale analysis of talent flow. In this paper, we present a new
+data analytics framework to analyze the talent flow patterns of close to 1
+million working professionals from three different countries/regions using
+their publicly-accessible profiles in an established OPN. As OPN data are
+originally generated for professional networking applications, our proposed
+framework re-purposes the same data for a different analytics task. Prior to
+performing job hop analysis, we devise a job title normalization procedure to
+mitigate the amount of noise in the OPN data. We then devise several metrics to
+measure the amount of work experience required to take up a job, to determine
+that existence duration of the job (also known as the job age), and the
+correlation between the above metric and propensity of hopping. We also study
+how job hop behavior is related to job promotion/demotion. Lastly, we perform
+connectivity analysis at job and organization levels to derive insights on
+talent flow as well as job and organizational competitiveness.
+"
+23592,Estimation of Vertex Degrees in a Sampled Network,"  The need to produce accurate estimates of vertex degree in a large network,
+based on observation of a subnetwork, arises in a number of practical settings.
+We study a formalized version of this problem, wherein the goal is, given a
+randomly sampled subnetwork from a large parent network, to estimate the actual
+degree of the sampled nodes. Depending on the sampling scheme, trivial method
+of moments estimators (MMEs) can be used. However, the MME is not expected, in
+general, to use all relevant network information. In this study, we propose a
+handful of novel estimators derived from a risk-theoretic perspective, which
+make more sophisticated use of the information in the sampled network.
+Theoretical assessment of the new estimators characterizes under what
+conditions they can offer improvement over the MME, while numerical comparisons
+show that when such improvement obtains, it can be substantial. Illustration is
+provided on a human trafficking network.
+"
+23593,On the Complexity of ATL and ATL* Module Checking,"  Module checking has been introduced in late 1990s to verify open systems,
+i.e., systems whose behavior depends on the continuous interaction with the
+environment. Classically, module checking has been investigated with respect to
+specifications given as CTL and CTL* formulas. Recently, it has been shown that
+CTL (resp., CTL*) module checking offers a distinctly different perspective
+from the better-known problem of ATL (resp., ATL*) model checking. In
+particular, ATL (resp., ATL*) module checking strictly enhances the
+expressiveness of both CTL (resp., CTL*) module checking and ATL (resp. ATL*)
+model checking. In this paper, we provide asymptotically optimal bounds on the
+computational cost of module checking against ATL and ATL*, whose upper bounds
+are based on an automata-theoretic approach. We show that module-checking for
+ATL is EXPTIME-complete, which is the same complexity of module checking
+against CTL. On the other hand, ATL* module checking turns out to be
+3EXPTIME-complete, hence exponentially harder than CTL* module checking.
+"
+23594,Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models,"  The restricted maximum likelihood method enhances popularity of maximum
+likelihood methods for variance component analysis on large scale unbalanced
+data. As the high throughput biological data sets and the emerged science on
+uncertainty quantification, such a method receives increasing attention.
+Estimating the unknown variance parameters with restricted maximum likelihood
+method usually requires an nonlinear iterative method. Therefore proper
+formulae for the log-likelihood function and its derivatives play an essential
+role in practical algorithm design. It is our aim to provide a mathematical
+introduction to this method, and supply a self-contained derivation on some
+available formulae used in practical algorithms. Some new proof are supplied.
+"
+23595,Deep Hashing Network for Unsupervised Domain Adaptation,"  In recent years, deep neural networks have emerged as a dominant machine
+learning tool for a wide variety of application domains. However, training a
+deep neural network requires a large amount of labeled data, which is an
+expensive process in terms of time, labor and human expertise. Domain
+adaptation or transfer learning algorithms address this challenge by leveraging
+labeled data in a different, but related source domain, to develop a model for
+the target domain. Further, the explosive growth of digital data has posed a
+fundamental challenge concerning its storage and retrieval. Due to its storage
+and retrieval efficiency, recent years have witnessed a wide application of
+hashing in a variety of computer vision applications. In this paper, we first
+introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms.
+The dataset contains images of a variety of everyday objects from multiple
+domains. We then propose a novel deep learning framework that can exploit
+labeled source data and unlabeled target data to learn informative hash codes,
+to accurately classify unseen target data. To the best of our knowledge, this
+is the first research effort to exploit the feature learning capabilities of
+deep neural networks to learn representative hash codes to address the domain
+adaptation problem. Our extensive empirical studies on multiple transfer tasks
+corroborate the usefulness of the framework in learning efficient hash codes
+which outperform existing competitive baselines for unsupervised domain
+adaptation.
+"
+23596,RICERCANDO: Data Mining Toolkit for Mobile Broadband Measurements,"  Increasing reliance on mobile broadband (MBB) networks for communication,
+vehicle navigation, healthcare, and other critical purposes calls for improved
+monitoring and troubleshooting of such networks. While recent advances in
+monitoring with crowdsourced as well as network infrastructure-based methods
+allow us to tap into a number of performance metrics from all layers of
+networking, huge swaths of data remain poorly or completely unexplored due to a
+lack of tools suitable for rapid, interactive, and rigorous MBB data analysis.
+In this paper we present RICERCANDO, a MBB data mining toolkit developed in a
+unique collaboration of networking and data mining experts. RICERCANDO consists
+of a preprocessing module that ensures that time-series data is stored in the
+most appropriate form for mining, a rapid exploration module that enables
+iterative analysis of time-series and geomobile data, so that anomalies are
+detected and singled out, and the advanced mining module that lets the analyst
+deduce root causes of observed anomalies. We implement and release RICERCANDO
+as open-source software, and validate its usability on case studies from MONROE
+pan-European MBB measurement testbed.
+"
+23597,Ultra-low background mass spectrometry for rare-event searches,"  Inductively Coupled Plasma Mass Spectrometry (ICP-MS) allows for rapid,
+high-sensitivity determination of trace impurities, notably the primordial
+radioisotopes $^{238}$U and $^{232}$Th, in candidate materials for
+low-background rare-event search experiments. We describe the setup and
+characterisation of a dedicated low-background screening facility at University
+College London where we operate an Agilent 7900 ICP-MS. The impact of reagent
+and carrier gas purity is evaluated and we show that twice-distilled
+ROMIL-SpA-grade nitric acid and zero-grade Ar gas delivers similar sensitivity
+to ROMIL-UpA-grade acid and research grade gas. A straightforward procedure for
+sample digestion and analysis of materials with U/Th concentrations down to 10
+ppt g/g is presented. This includes the use of $^{233}$U and $^{230}$Th spikes
+to correct for signal loss from a range of sources and verification of
+$^{238}$U and $^{232}$Th recovery through digestion and analysis of a certified
+reference material with a complex sample matrix. Finally, we demonstrate assays
+and present results from two sample preparation and assay methods: a
+high-sensitivity measurement of ultra-pure Ti using open digestion techniques,
+and a closed vessel microwave digestion of a nickel-chromium-alloy using a
+multi-acid mixture.
+"
+23598,A 3D DLM/FD method for simulating the motion of spheres in a bounded shear flow of Oldroyd-B fluids,"  We present a novel distributed Lagrange multiplier/fictitious domain (DLM/FD)
+method for simulating fluid-particle interaction in Oldroyd-B fluids under
+creeping conditions. The results concerning two ball interaction in a three
+dimensional (3D) bounded shear flow are obtained for Weissenberg numbers up to
+1 . The pass and return trajectories of the two ball mass centers are similar
+to those in a Newtonian fluid; but they lose the symmetry due to the effect of
+elastic force arising from viscoelastic fluids. A tumbling chain of two balls
+(a dipole) may occur, depending on the value of the Weissenberg number and the
+initial vertical displacement of the ball mass center to the middle plane
+between two walls.
+"
+23599,Practical Locally Private Heavy Hitters,"  We present new practical local differentially private heavy hitters
+algorithms achieving optimal or near-optimal worst-case error and running time
+-- TreeHist and Bitstogram. In both algorithms, server running time is $\tilde
+O(n)$ and user running time is $\tilde O(1)$, hence improving on the prior
+state-of-the-art result of Bassily and Smith [STOC 2015] requiring $O(n^{5/2})$
+server time and $O(n^{3/2})$ user time. With a typically large number of
+participants in local algorithms ($n$ in the millions), this reduction in time
+complexity, in particular at the user side, is crucial for making locally
+private heavy hitters algorithms usable in practice. We implemented Algorithm
+TreeHist to verify our theoretical analysis and compared its performance with
+the performance of Google's RAPPOR code.
+"
+23600,On outer-connected domination for graph products,"  An outer-connected dominating set for an arbitrary graph $G$ is a set
+$\tilde{D} \subseteq V$ such that $\tilde{D}$ is a dominating set and the
+induced subgraph $G [V \setminus \tilde{D}]$ be connected. In this paper, we
+focus on the outer-connected domination number of the product of graphs. We
+investigate the existence of outer-connected dominating set in lexicographic
+product and Corona of two arbitrary graphs, and we present upper bounds for
+outer-connected domination number in lexicographic and Cartesian product of
+graphs. Also, we establish an equivalent form of the Vizing's conjecture for
+outer-connected domination number in lexicographic and Cartesian product as
+$\tilde{\gamma_c}(G \circ K)\tilde{\gamma_c}(H \circ K) \leq
+\tilde{\gamma_c}(G\Box H)\circ K$. Furthermore, we study the outer-connected
+domination number of the direct product of finitely many complete graphs.
+"
+23601,First principles predictions of magneto-optical data for semiconductor defects: the case of divacancy defects in 4H-SiC,"  Study and design of magneto-optically active single point defects in
+semiconductors are rapidly growing fields due to their potential in quantum bit
+and single photon emitter applications. Detailed understanding of the
+properties of candidate defects is essential for these applications, and
+requires the identification of the defects microscopic configuration and
+electronic structure. Multi-component semiconductors often host two or more
+non-equivalent configurations of point defects. These configurations generally
+exhibit similar electronic structure and basic functionalities, however, they
+differ in details that are of great importance whenever single defect
+applications are considered. Identification of non-equivalent configurations of
+point defects is thus essential for successful single defect manipulation and
+application. A promising way to identify defects is via comparison of
+experimental measurements and results of first-principle calculations. We
+investigate a possibility to produce accurate ab initio data for zero-phonon
+lines and hyperfine coupling parameters that are required for systematic
+quantum bit search. We focus on properties relevant for the possible use of the
+divacancy defect in quantum bits in 4H-SiC. We provide a decisive
+identification of divacancy configurations in 4H-SiC and clarify differences in
+prior predictions of 4H-SiC divacancy zero-phonon photoluminescence lines.
+"
+23602,Bypassing dynamical systems : A simple way to get the box-counting dimension of the graph of the Weierstrass function,"  In the following, bypassing dynamical systems tools, we propose a simple
+means of computing the box dimension of the graph of the classical Weierstrass
+function defined, for any real number~$x$, by~$ {\cal W}(x)=\displaystyle
+\sum_{n=0}^{+\infty} \lambda^n\,\cos \left ( 2\, \pi\,N_b^n\,x \right)
+$, where~$\lambda$ and~$N_b$ are two real numbers such that~\mbox{$0
+<\lambda<1$},~\mbox{$ N_b\,\in\,\N$} and~$ \lambda\,N_b > 1 $, using a sequence
+a graphs that approximate the studied one.
+"
+23603,Elliptic double affine Hecke algebras,"  We give a construction of an affine Hecke algebra associated to any Coxeter
+group acting on an abelian variety by reflections; in the case of an affine
+Weyl group, the result is an elliptic analogue of the usual double affine Hecke
+algebra. As an application, we use a variant of the $\tilde{C}_n$ version of
+the construction to construct a flat noncommutative deformation of the $n$th
+symmetric power of any rational surface with a smooth anticanonical curve, and
+give a further construction which conjecturally is a corresponding deformation
+of the Hilbert scheme of points.
+"
+23604,Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding,"  As adversarial attacks pose a serious threat to the security of AI system in
+practice, such attacks have been extensively studied in the context of computer
+vision applications. However, few attentions have been paid to the adversarial
+research on automatic path finding. In this paper, we show dominant adversarial
+examples are effective when targeting A3C path finding, and design a Common
+Dominant Adversarial Examples Generation Method (CDG) to generate dominant
+adversarial examples against any given map. In addition, we propose Gradient
+Band-based Adversarial Training, which trained with a single randomly choose
+dominant adversarial example without taking any modification, to realize the
+""1:N"" attack immunity for generalized dominant adversarial examples. Extensive
+experimental results show that, the lowest generation precision for CDG
+algorithm is 91.91%, and the lowest immune precision for Gradient Band-based
+Adversarial Training is 93.89%, which can prove that our method can realize the
+generalized attack immunity of A3C path finding with a high confidence.
+"
+23605,Convergence to the Self-similar Solutions to the Homogeneous Boltzmann Equation,"  The Boltzmann H-theorem implies that the solution to the Boltzmann equation
+tends to an equilibrium, that is, a Maxwellian when time tends to infinity.
+This has been proved in varies settings when the initial energy is finite.
+However, when the initial energy is infinite, the time asymptotic state is no
+longer described by a Maxwellian, but a self-similar solution obtained by
+Bobylev-Cercignani. The purpose of this paper is to rigorously justify this for
+the spatially homogeneous problem with Maxwellian molecule type cross section
+without angular cutoff.
+"
+23606,Singular equivalences of commutative noetherian rings and reconstruction of singular loci,"  Two left noetherian rings $R$ and $S$ are said to be {\it singularly
+equivalent} if their singularity categories are equivalent as triangulated
+categories. The aim of this paper is to give a necessary condition for two
+commutative noetherian rings to be singularly equivalent. To do this, we
+develop the support theory for triangulated categories without tensor
+structure.
+"
+23607,Macroscopic irreversibility and decay to kinetic equilibrium for classical hard-sphere systems,"  In this paper the conditions are investigated for the occurrence of the
+so-called macroscopic irreversibility property and the related phenomenon of
+decay to kinetic equilibrium which may characterize the $1-$body probability
+density function (PDF) associated with hard-sphere systems. The problem is set
+in the framework of the axiomatic ""ab initio"" approach to classical statistical
+mechanics recently developed [Tessarotto \textit{et al}., 2013-2017] and the
+related establishment of an exact kinetic equation realized by Master equation
+for the same kinetic PDF. As shown in the paper the task involves the
+introduction of a suitable functional of the $1-$body PDF here identified with
+the \textit{Master kinetic information}. The goal is to show that, provided the
+same PDF is realized in terms of an arbitrary suitably-smooth particular
+solution of the Master kinetic equation the two properties indicated above are
+indeed realized and that the same functional is unrelated either with the
+Boltzmann-Shannon entropy and the Fisher information.
+"
+23608,Inconsistent Node Flattening for Improving Top-down Hierarchical Classification,"  Large-scale classification of data where classes are structurally organized
+in a hierarchy is an important area of research. Top-down approaches that
+exploit the hierarchy during the learning and prediction phase are efficient
+for large scale hierarchical classification. However, accuracy of top-down
+approaches is poor due to error propagation i.e., prediction errors made at
+higher levels in the hierarchy cannot be corrected at lower levels. One of the
+main reason behind errors at the higher levels is the presence of inconsistent
+nodes that are introduced due to the arbitrary process of creating these
+hierarchies by domain experts. In this paper, we propose two different
+data-driven approaches (local and global) for hierarchical structure
+modification that identifies and flattens inconsistent nodes present within the
+hierarchy. Our extensive empirical evaluation of the proposed approaches on
+several image and text datasets with varying distribution of features, classes
+and training instances per class shows improved classification performance over
+competing hierarchical modification approaches. Specifically, we see an
+improvement upto 7% in Macro-F1 score with our approach over best TD baseline.
+SOURCE CODE: this http URL
+"
+23609,Planning in Dynamic Environments with Conditional Autoregressive Models,"  We demonstrate the use of conditional autoregressive generative models (van
+den Oord et al., 2016a) over a discrete latent space (van den Oord et al.,
+2017b) for forward planning with MCTS. In order to test this method, we
+introduce a new environment featuring varying difficulty levels, along with
+moving goals and obstacles. The combination of high-quality frame generation
+and classical planning approaches nearly matches true environment performance
+for our task, demonstrating the usefulness of this method for model-based
+planning in dynamic environments.
+"
+23610,Mean-field scaling of the superfluid to Mott insulator transition in a 2D optical superlattice,"  The mean-field treatment of the Bose-Hubbard model predicts properties of
+lattice-trapped gases to be insensitive to the specific lattice geometry once
+system energies are scaled by the lattice coordination number $z$. We test this
+scaling directly by comparing coherence properties of $^{87}$Rb gases that are
+driven across the superfluid to Mott insulator transition within optical
+lattices of either the kagome ($z=4$) or the triangular ($z=6$) geometries. The
+coherent fraction measured for atoms in the kagome lattice is lower than for
+those in a triangular lattice with the same interaction and tunneling energies.
+A comparison of measurements from both lattices agrees quantitatively with the
+scaling prediction. We also study the response of the gas to a change in
+lattice geometry, and observe the dynamics as a strongly interacting
+kagome-lattice gas is suddenly ""hole-doped"" by introducing the additional sites
+of the triangular lattice.
+"
+23611,Query Combinators,"  We introduce Rabbit, a combinator-based query language. Rabbit is designed to
+let data analysts and other accidental programmers query complex structured
+data.
+We combine the functional data model and the categorical semantics of
+computations to develop denotational semantics of database queries. In Rabbit,
+a query is modeled as a Kleisli arrow for a monadic container determined by the
+query cardinality. In this model, monadic composition can be used to navigate
+the database, while other query combinators can aggregate, filter, sort and
+paginate data; construct compound data; connect self-referential data; and
+reorganize data with grouping and data cube operations. A context-aware query
+model, with the input context represented as a comonadic container, can express
+query parameters and window functions. Rabbit semantics enables pipeline
+notation, encouraging its users to construct database queries as a series of
+distinct steps, each individually crafted and tested. We believe that Rabbit
+can serve as a practical tool for data analytics.
+"
+23612,"False Discovery Control for Pairwise Comparisons - An Asymptotic Solution to Williams, Jones and Tukey's Conjecture","  Under weak moment and asymptotic conditions, we offer an affirmative answer
+to whether the BH procedure (Benjamini and Hochberg, 1995) can control the
+false discovery rate in testing pairwise comparisons of means under a one-way
+ANOVA layout. Specifically, despite the fact that the two sample t-statistics
+do not exhibit positive regression dependency (Benjamini and Yekutieli, 2001),
+our result shows that the BH procedure can asymptotically control the
+directional false discovery rate as conjectured by Williams, Jones, and Tukey
+(1999). Such a result is useful for most general situations when the number of
+variables is moderately large and/or when idealistic assumptions such as
+normality and a balanced design are violated.
+"
+23613,"Parallel-in-Space-and-Time Simulation of the Three-Dimensional, Unsteady Navier-Stokes Equations for Incompressible Flow","  In this paper we combine the Parareal parallel-in-time method together with
+spatial parallelization and investigate this space-time parallel scheme by
+means of solving the three-dimensional incompressible Navier-Stokes equations.
+Parallelization of time stepping provides a new direction of parallelization
+and allows to employ additional cores to further speed up simulations after
+spatial parallelization has saturated. We report on numerical experiments
+performed on a Cray XE6, simulating a driven cavity flow with and without
+obstacles. Distributed memory parallelization is used in both space and time,
+featuring up to 2,048 cores in total. It is confirmed that the
+space-time-parallel method can provide speedup beyond the saturation of the
+spatial parallelization.
+"
+23614,Adaptive estimation and noise detection for an ergodic diffusion with observation noises,"  We research adaptive maximum likelihood-type estimation for an ergodic
+diffusion process where the observation is contaminated by noise. This
+methodology leads to the asymptotic independence of the estimators for the
+variance of observation noise, the diffusion parameter and the drift one of the
+latent diffusion process. Moreover, it can lessen the computational burden
+compared to simultaneous maximum likelihood-type estimation. In addition to
+adaptive estimation, we propose a test to see if noise exists or not, and
+analyse real data as the example such that data contains observation noise with
+statistical significance.
+"
+23615,A path-integral formulation of the run and tumble motion and chemotaxis in Escherichia coli,"  Bacteria such as Escherichia coli move about in a series of runs and tumbles:
+while a run state (straight motion) entails all the flagellar motors spinning
+in counterclockwise mode, a tumble is caused by a shift in the state of one or
+more motors to clockwise spinning mode. In the presence of an attractant
+gradient in the environment, runs in the favourable direction are extended, and
+this results in a net drift of the organism in the direction of the gradient.
+The underlying signal transduction mechanism produces directed motion through a
+bi-lobed response function which relates the clockwise bias of the flagellar
+motor to temporal changes in the attractant concentration. The two lobes
+(positive and negative) of the response function are separated by a time
+interval of $\sim 1$s, such that the bacterium effectively compares the
+concentration at two different positions in space and responds accordingly. We
+present here a novel path-integral method which allows us to address this
+problem in the most general way possible, including multi-step CW-CCW
+transitions, directional persistence and power-law waiting time distributions.
+The method allows us to calculate quantities such as the effective diffusion
+coefficient and drift velocity, in a power series expansion in the attractant
+gradient. Explicit results in the lowest order in the expansion are presented
+for specific models, which, wherever applicable, agree with the known results.
+New results for gamma-distributed run interval distributions are also
+presented.
+"
+23616,Continuation of homoclinic orbits in the suspension bridge equation: a computer-assisted proof,"  In this paper, we prove existence of symmetric homoclinic orbits for the
+suspension bridge equation $u""""+\beta u"" + e^u-1=0$ for all parameter values
+$\beta \in [0.5,1.9]$. For each $\beta$, a parameterization of the stable
+manifold is computed and the symmetric homoclinic orbits are obtained by
+solving a projected boundary value problem using Chebyshev series. The proof is
+computer-assisted and combines the uniform contraction theorem and the radii
+polynomial approach, which provides an efficient means of determining a set,
+centered at a numerical approximation of a solution, on which a Newton-like
+operator is a contraction.
+"
+23617,A construction of hyperkähler metrics through Riemann-Hilbert problems I,"  In 2009 Gaiotto, Moore and Neitzke presented a new construction of
+hyperkähler metrics on the total spaces of certain complex integrable
+systems, represented as a torus fibration $\mathcal{M}$ over a base space
+$\mathcal{B}$, except for a divisor $D$ in $\mathcal{B}$, in which the torus
+fiber degenerates into a nodal torus. The hyperkähler metric $g$ is
+obtained via solutions $\mathcal{X}_\gamma$ of a Riemann-Hilbert problem. We
+interpret the Kontsevich-Soibelman Wall Crossing Formula as an isomonodromic
+deformation of a family of RH problems, therefore guaranteeing continuity of
+$\mathcal{X}_\gamma$ at the walls of marginal stability. The technical details
+about solving the different classes of Riemann-Hilbert problems that arise here
+are left to a second article. To extend this construction to singular fibers,
+we use the Ooguri-Vafa case as our model and choose a suitable gauge
+transformation that allow us to define an integral equation defined at the
+degenerate fiber, whose solutions are the desired Darboux coordinates
+$\mathcal{X}_\gamma$. We show that these functions yield a holomorphic
+symplectic form $\varpi(\zeta)$, which, by Hitchin's twistor construction,
+constructs the desired hyperkähler metric.
+"
+23618,On the rate of convergence of the $p$-curve shortening flow,"  In this paper we give rates of convergence for the $p$-curve shortening flow
+for $p\geq 1$ an integer, which improves on the known estimates and which are
+probably sharp.
+"
+23619,Nonlinear spectral analysis: A local Gaussian approach,"  The spectral distribution $f(\omega)$ of a stationary time series
+$\{Y_t\}_{t\in\mathbb{Z}}$ can be used to investigate whether or not periodic
+structures are present in $\{Y_t\}_{t\in\mathbb{Z}}$, but $f(\omega)$ has some
+limitations due to its dependence on the autocovariances $\gamma(h)$. For
+example, $f(\omega)$ can not distinguish white i.i.d. noise from GARCH-type
+models (whose terms are dependent, but uncorrelated), which implies that
+$f(\omega)$ can be an inadequate tool when $\{Y_t\}_{t\in\mathbb{Z}}$ contains
+asymmetries and nonlinear dependencies.
+Asymmetries between the upper and lower tails of a time series can be
+investigated by means of the local Gaussian autocorrelations $\gamma_{v}(h)$
+introduced in Tj{\o}stheim and Hufthammer (2013), and these local measures of
+dependence can be used to construct the local Gaussian spectral density
+$f_{v}(\omega)$ that is presented in this paper. A key feature of
+$f_{v}(\omega)$ is that it coincides with $f(\omega)$ for Gaussian time series,
+which implies that $f_{v}(\omega)$ can be used to detect non-Gaussian traits in
+the time series under investigation. In particular, if $f(\omega)$ is flat,
+then peaks and troughs of $f_{v}(\omega)$ can indicate nonlinear traits, which
+potentially might discover local periodic phenomena that goes undetected in an
+ordinary spectral analysis.
+"
+23620,Orthogonal direct sum of Lie groups,"  By this short preface we show the main idea and we will bring some
+definitions and concepts in each section.
+"
+23621,Inequalities for the modified k-Bessel function,"  The article considers the generalized k-Bessel functions and represents it as
+Wright functions. Then we study the monotonicity properties of the ratio of two
+different orders k- Bessel functions, and the ratio of the k-Bessel and the
+m-Bessel functions. The log-convexity with respect to the order of the k-Bessel
+also given. An investigation regarding the monotonicity of the ratio of the
+k-Bessel and k-confluent hypergeometric functions are discussed.
+"
+23622,Commentary: Intentional Observer Effects on Quantum Randomness: A Bayesian Analysis Reveals Evidence Against Micro-Psychokinesis,"  The paper titled `Intentional Observer Effects on Quantum Randomness: A
+Bayesian Analysis Reveals Evidence Against Micro-Psychokinesis', published in
+Frontiers of Psychology in March 2018, reports on a mind-matter experiment with
+the main result of strong evidence against Micro-Psychokinesis. Despite this
+conclusion, the authors interpret the observed pattern in their data as
+possible evidence for Micro-Psychokinesis, albeit of a different kind.
+Suggesting a connection to some existing models, the authors put forward the
+hypothesis that a higher frequency of slow data variations can be observed in
+their experiment data than in a set of control data. This commentary analyses
+this claim and concludes that the variation in the data motivating this
+hypothesis would show up just by chance with a probability of p=0.328 under a
+null hypothesis. Therefore, there is no evidence for the hypothesis of faster
+data variations, and thus for this kind of suggested Micro-Psychokinesis in
+this experiment.
+"
+23623,Speeding up finite-time consensus via minimal polynomial of a weighted graph - a numerical approach,"  Reaching consensus among states of a multi-agent system is a key requirement
+for many distributed control/optimization problems. Such a consensus is often
+achieved using the standard Laplacian matrix (for continuous system) or Perron
+matrix (for discrete-time system). Recent interest in speeding up consensus
+sees the development of finite-time consensus algorithms. This work proposes an
+approach to speed up finite-time consensus algorithm using the weights of a
+weighted Laplacian matrix. The approach is an iterative procedure that finds a
+low-order minimal polynomial that is consistent with the topology of the
+underlying graph. In general, the lowest-order minimal polynomial achievable
+for a network system is an open research problem. This work proposes a
+numerical approach that searches for the lowest order minimal polynomial via a
+rank minimization problem using a two-step approach: the first being an
+optimization problem involving the nuclear norm and the second a correction
+step. Several examples are provided to illustrate the effectiveness of the
+approach.
+"
+23624,"Volume minimization and Conformally Kähler, Einstein-Maxwell geometry","  Let $M$ be a compact complex manifold admitting a Kähler structure. A
+conformally Kähler, Einstein-Maxwell metric (cKEM metric for short) is a
+Hermitian metric $\tilde{g}$ on $M$ with constant scalar curvature such that
+there is a positive smooth function $f$ with $g = f^2 \tilde{g}$ being a
+Kähler metric and $f$ being a Killing Hamiltonian potential with respect to
+$g$. Fixing a Kähler class, we characterize such Killing vector fields whose
+Hamiltonian function $f$ with respect to some Kähler metric $g$ in the fixed
+Kähler class gives a cKEM metric $\tilde{g} = f^{-2}g$. The characterization
+is described in terms of critical points of certain volume functional. The
+conceptual idea is similar to the cases of Kähler-Ricci solitons and
+Sasaki-Einstein metrics in that the derivative of the volume functional gives
+rise to a natural obstruction to the existence of cKEM metrics. However, unlike
+the Kähler-Ricci soliton case and Sasaki-Einstein case, the functional is
+neither convex nor proper in general, and often has more than one critical
+points. The last observation matches well with the ambitoric examples studied
+earlier by LeBrun and Apostolov-Maschler.
+"
+23625,The Algebraic View of Computation,"  We argue that computation is an abstract algebraic concept, and a computer is
+a result of a morphism (a structure preserving map) from a finite universal
+semigroup.
+"
+23626,Predictors of Re-admission for Homeless Families in New York City: The Case of the Win Shelter Network,"  New York City faces the challenge of an ever-increasing homeless population
+with almost 60,000 people currently living in city shelters. In 2015,
+approximately 25% of families stayed longer than 9 months in a shelter, and 17%
+of families with children that exited a homeless shelter returned to the
+shelter system within 30 days of leaving. This suggests that ""long-term""
+shelter residents and those that re-enter shelters contribute significantly to
+the rise of the homeless population living in city shelters and indicate
+systemic challenges to finding adequate permanent housing. Women in Need (Win)
+is a non-profit agency that provides shelter to almost 10,000 homeless women
+and children (10% of all homeless families of NYC), and is the largest homeless
+shelter provider in the City. This paper focuses on our preliminary work with
+Win to understand the factors that affect the rate of readmission of homeless
+families at Win shelters, and to predict the likelihood of re-entry into the
+shelter system on exit. These insights will enable improved service delivery
+and operational efficiencies at these shelters. This paper describes our recent
+efforts to integrate Win datasets with city records to create a unified,
+comprehensive database of the homeless population being served by Win shelters.
+A preliminary classification model is developed to predict the odds of
+readmission and length of shelter stay based on the demographic and
+socioeconomic characteristics of the homeless population served by Win. This
+work is intended to form the basis for establishing a network of ""smart
+shelters"" through the use of data science and data technologies.
+"
+23627,From $S^1$-fixed points to $\mathcal{W}$-algebra representations,"  We study a set $\mathcal{M}_{K,N}$ parameterizing filtered $SL(K)$-Higgs
+bundles over $\mathbb{CP}^1$ with an irregular singularity at $z = \infty$,
+such that the eigenvalues of the Higgs field grow like $\lvert \lambda \rvert
+\sim \lvert z ^{N/K} \mathrm{d} z \rvert$, where $K$ and $N$ are coprime.
+$\mathcal{M}_{K,N}$ carries a $\mathbb{C}^\times$-action analogous to the
+famous $\mathbb{C}^\times$-action introduced by Hitchin on the moduli spaces of
+Higgs bundles over compact curves. The construction of this
+$\mathbb{C}^\times$-action on $\mathcal{M}_{K,N}$ involves the rotation
+automorphism of the base $\mathbb{CP}^1$. We classify the fixed points of this
+$\mathbb{C}^\times$-action, and exhibit a curious $1$-$1$ correspondence
+between these fixed points and certain representations of the vertex algebra
+$\mathcal{W}_K$; in particular we have the relation $\mu = \frac{1}{12} \left(K
+- 1 - c_{\mathrm{eff}} \right)$, where $\mu$ is a regulated version of the
+$L^2$ norm of the Higgs field, and $c_{\mathrm{eff}}$ is the effective Virasoro
+central charge of the corresponding $W$-algebra representation. We also discuss
+a Bialynicki-Birula-type stratification of $\mathcal{M}_{K,N}$, where the
+strata are labeled by isomorphism classes of the underlying filtered vector
+bundles.
+"
+23628,Most Websites Don't Need to Vibrate: A Cost-Benefit Approach to Improving Browser Security,"  Modern web browsers have accrued an incredibly broad set of features since
+being invented for hypermedia dissemination in 1990. Many of these features
+benefit users by enabling new types of web applications. However, some features
+also bring risk to users' privacy and security, whether through implementation
+error, unexpected composition, or unintended use. Currently there is no general
+methodology for weighing these costs and benefits. Restricting access to only
+the features which are necessary for delivering desired functionality on a
+given website would allow users to enforce the principle of lease privilege on
+use of the myriad APIs present in the modern web browser.
+However, security benefits gained by increasing restrictions must be balanced
+against the risk of breaking existing websites. This work addresses this
+problem with a methodology for weighing the costs and benefits of giving
+websites default access to each browser feature. We model the benefit as the
+number of websites that require the feature for some user-visible benefit, and
+the cost as the number of CVEs, lines of code, and academic attacks related to
+the functionality. We then apply this methodology to 74 Web API standards
+implemented in modern browsers. We find that allowing websites default access
+to large parts of the Web API poses significant security and privacy risks,
+with little corresponding benefit.
+We also introduce a configurable browser extension that allows users to
+selectively restrict access to low-benefit, high-risk features on a per site
+basis. We evaluated our extension with two hardened browser configurations, and
+found that blocking 15 of the 74 standards avoids 52.0% of code paths related
+to previous CVEs, and 50.0% of implementation code identified by our metric,
+without affecting the functionality of 94.7% of measured websites.
+"
+23629,Analysis and modeling of localized invariant solutions in pipe flow,"  Turbulent spots surrounded by laminar flow are a landmark of transitional
+shear flows, but the dependence of their kinematic properties on spatial
+structure is poorly understood. We here investigate this dependence in pipe
+flow for Reynolds numbers between 1500 and 5000. We compute spatially localized
+relative periodic orbits in long pipes and show that their upstream and
+downstream fronts decay exponentially towards the laminar profile. This allows
+to model the fronts by employing the linearized Navier-Stokes equations, and
+the resulting model yields the spatial decay rate and the front velocity
+profiles of the periodic orbits as a function of Reynolds number, azimuthal
+wave number and propagation speed. In addition, when applied to a localized
+turbulent puff, the model is shown to accurately approximate the spatial decay
+rate of its upstream and downstream tails. Our study provides insight into the
+relationship between the kinematics and spatial structure of localized
+turbulence and more generally into the physics of localization.
+"
+23630,The Dynamical Manin-Mumford Conjecture and the Dynamical Bogomolov Conjecture for endomorphisms of (P^1)^n,"  We prove Zhang's Dynamical Manin-Mumford Conjecture and Dynamical Bogomolov
+Conjecture for dominant endomorphisms of (P^1)^n. We use the equidistribution
+theorem for points of small height with respect to an algebraic dynamical
+system, combined with an analysis of the symmetries of the Julia set for a
+rational function.
+"
+23631,Data science is science's second chance to get causal inference right: A classification of data science tasks,"  Causal inference from observational data is the goal of many data analyses in
+the health and social sciences. However, academic statistics has often frowned
+upon data analyses with a causal objective. The introduction of the term ""data
+science"" provides a historic opportunity to redefine data analysis in such a
+way that it naturally accommodates causal inference from observational data.
+Like others before, we organize the scientific contributions of data science
+into three classes of tasks: description, prediction, and causal inference. An
+explicit classification of data science tasks is necessary to discuss the data,
+assumptions, and analytics required to successfully accomplish each task. We
+argue that a failure to adequately describe the role of subject-matter expert
+knowledge in data analysis is a source of widespread misunderstandings about
+data science. Specifically, causal analyses typically require not only good
+data and algorithms, but also domain expert knowledge. We discuss the
+implications for the use of data science to guide decision-making in the real
+world and to train data scientists.
+"
+23632,Time-frequency representations of autoionization dynamics in helium,"  Autoionization, which results from the interference between direct
+photoionization and photoexcitation to a discrete state decaying to the
+continuum by configuration interaction, is a well known example of the
+important role of electron correlation in light-matter interaction. Information
+on this process can be obtained by studying the spectral, or equivalently,
+temporal complex amplitude of the ionized electron wavepacket. Using an
+energy-resolved interferometric technique, we measure the spectral amplitude
+and phase of autoionized wavepackets emitted via the sp2+ and sp3+ resonances
+in helium. These measurements allow us to reconstruct the corresponding
+temporal profiles by Fourier transform. In addition, applying various
+time-frequency representations, we observe the build up of the wavepackets in
+the continuum, monitor the instantaneous frequencies emitted at any time and
+disentangle the dynamics of the direct and resonant ionization channels.
+"
+23633,Theory of epithelial cell shape transitions induced by mechanoactive chemical gradients,"  Cell shape is determined by a balance of intrinsic properties of the cell as
+well as its mechanochemical environment. Inhomogeneous shape changes underly
+many morphogenetic events and involve spatial gradients in active cellular
+forces induced by complex chemical signaling. Here, we introduce a
+mechanochemical model based on the notion that cell shape changes may be
+induced by external diffusible biomolecules that influence cellular
+contractility (or equivalently, adhesions) in a concentration-dependent manner
+-- and whose spatial profile in turn is affected by cell shape. We map out
+theoretically the possible interplay between chemical concentration and
+cellular structure. Besides providing a direct route to spatial gradients in
+cell shape profiles in tissues, our results indicate that the dependence on
+cell shape helps create robust mechanochemical gradients.
+"
+23634,Subset sum problem in polycyclic groups,"  We consider a group-theoretic analogue of the classic subset sum problem. It
+is known that every virtually nilpotent group has polynomial time decidable
+subset sum problem. In this paper we use subgroup distortion to show that every
+polycyclic non-virtually-nilpotent group has NP-complete subset sum problem.
+"
+23635,Automated Cloud Provisioning on AWS using Deep Reinforcement Learning,"  As the use of cloud computing continues to rise, controlling cost becomes
+increasingly important. Yet there is evidence that 30\% - 45\% of cloud spend
+is wasted. Existing tools for cloud provisioning typically rely on highly
+trained human experts to specify what to monitor, thresholds for triggering
+action, and actions. In this paper we explore the use of reinforcement learning
+(RL) to acquire policies to balance performance and spend, allowing humans to
+specify what they want as opposed to how to do it, minimizing the need for
+cloud expertise. Empirical results with tabular, deep, and dueling double deep
+Q-learning with the CloudSim simulator show the utility of RL and the relative
+merits of the approaches. We also demonstrate effective policy transfer
+learning from an extremely simple simulator to CloudSim, with the next step
+being transfer from CloudSim to an Amazon Web Services physical environment.
+"
+23636,Lie-Bäcklund symmetry and non-invariant solutions of nonlinear evolution equations,"  We study the symmetry reduction of nonlinear partial differential equations
+which are used for describing diffusion processes in nonhomogeneous medium. We
+find ansatzes reducing partial differential equations to systems of ordinary
+differential equations. The ansatzes are constructed by using operators of
+Lie-Bäcklund symmetry of third order ordinary differential equation. The
+method gives the possibility to find solutions which cannot be obtained by
+virtue of classical Lie method. Such solutions have been constructed for
+nonlinear diffusion equations which are invariant with respect to
+one-parameter, two-parameter and three-parameter Lie groups of point
+transformations.
+"
+23637,A Layered Architecture for Erasure-Coded Consistent Distributed Storage,"  Motivated by emerging applications to the edge computing paradigm, we
+introduce a two-layer erasure-coded fault-tolerant distributed storage system
+offering atomic access for read and write operations. In edge computing,
+clients interact with an edge-layer of servers that is geographically near; the
+edge-layer in turn interacts with a back-end layer of servers. The edge-layer
+provides low latency access and temporary storage for client operations, and
+uses the back-end layer for persistent storage. Our algorithm, termed Layered
+Data Storage (LDS) algorithm, offers several features suitable for
+edge-computing systems, works under asynchronous message-passing environments,
+supports multiple readers and writers, and can tolerate $f_1 < n_1/2$ and $f_2
+< n_2/3$ crash failures in the two layers having $n_1$ and $n_2$ servers,
+respectively. We use a class of erasure codes known as regenerating codes for
+storage of data in the back-end layer. The choice of regenerating codes,
+instead of popular choices like Reed-Solomon codes, not only optimizes the cost
+of back-end storage, but also helps in optimizing communication cost of read
+operations, when the value needs to be recreated all the way from the back-end.
+The two-layer architecture permits a modular implementation of atomicity and
+erasure-code protocols; the implementation of erasure-codes is mostly limited
+to interaction between the two layers. We prove liveness and atomicity of LDS,
+and also compute performance costs associated with read and write operations.
+Further, in a multi-object system running $N$ independent instances of LDS,
+where only a small fraction of the objects undergo concurrent accesses at any
+point during the execution, the overall storage cost is dominated by that of
+persistent storage in the back-end layer, and is given by $\Theta(N)$.
+"
+23638,Architecture of processing and analysis system for big astronomical data,"  This work explores the use of big data technologies deployed in the cloud for
+processing of astronomical data. We have applied Hadoop and Spark to the task
+of co-adding astronomical images. We compared the overhead and execution time
+of these frameworks. We conclude that performance of both frameworks is
+generally on par. The Spark API is more flexible, which allows one to easily
+construct astronomical data processing pipelines.
+"
+23639,Hydro-Responsive Curling of the Resurrection Plant Selaginella lepidophylla,"  The spirally arranged stems of the spikemoss Selaginella lepidophylla, an
+ancient resurrection plant, compactly curl into a nest-ball shape upon
+dehydration. Due to its spiral phyllotaxy, older outer stems on the plant
+interlace and envelope the younger inner stems forming the plant centre. Stem
+curling is a morphological mechanism that limits photoinhibitory and thermal
+damages the plant might experience in arid environments. Here, we investigate
+the distinct conformational changes of outer and inner stems of S. lepidophylla
+triggered by dehydration. Outer stems bend into circular rings in a relatively
+short period of desiccation, whereas inner stems curl slowly into spirals due
+to hydro-actuated strain gradient along their length. This arrangement eases
+both the tight packing of the plant during desiccation and its fast opening
+upon rehydration. The insights gained from this work shed light on the
+hydro-responsive movements in plants and might contribute to the development of
+deployable structures with remarkable shape transformations in response to
+environmental stimuli.
+"
+23640,Stochastic Zeroth-order Optimization in High Dimensions,"  We consider the problem of optimizing a high-dimensional convex function
+using stochastic zeroth-order queries. Under sparsity assumptions on the
+gradients or function values, we present two algorithms: a successive
+component/feature selection algorithm and a noisy mirror descent algorithm
+using Lasso gradient estimates, and show that both algorithms have convergence
+rates that de- pend only logarithmically on the ambient dimension of the
+problem. Empirical results confirm our theoretical findings and show that the
+algorithms we design outperform classical zeroth-order optimization methods in
+the high-dimensional setting.
+"
+23641,Convenient Interface to Inverse Ising (ConIII): A Python package for solving maximum entropy models,"  ConIII (pronounced CON-ee) is an open-source Python project providing a
+simple interface to solving maximum entropy models, with a focus on the Ising
+model. We describe the maximum entropy problem and give an overview of the
+algorithms that are implemented as part of ConIII
+(this https URL) including Monte Carlo histogram,
+pseudolikelihood, minimum probability flow, a regularized mean field method,
+and a cluster expansion method. We briefly explain how one should approach and
+validate maxent models from the perspective of model selection. Our goal is to
+make a variety of maximum entropy techniques accessible to those unfamiliar
+with the techniques and accelerate workflow for users.
+"
+23642,"Note: Derivative divide, a method for the analysis of broadband ferromagnetic resonance in the frequency domain","  Broadband ferromagnetic resonance (bbFMR) spectroscopy is an established
+experimental tool to quantify magnetic properties. Due to frequency-dependent
+transmission of the microwave setup, bbFMR measurements in the frequency domain
+require a suitable background removal method. Here, we present a measurement
+and data analysis protocol that allows to perform quantitative frequency-swept
+bbFMR measurements without the need for a calibration of the microwave setup.
+The method, its limitations and advantages are described in detail. Finally the
+method is applied to evaluate FMR spectra of a permalloy thin film. The
+extracted material parameters are in very good agreement with those obtained
+using a conventional analysis in field-space.
+"
+23643,Abaqus2Matlab: A suitable tool for finite element post-processing,"  A suitable piece of software is presented to connect Abaqus, a sophisticated
+finite element package, with Matlab, the most comprehensive program for
+mathematical analysis. This interface between these well-known codes not only
+benefits from the image processing and the integrated graph-plotting features
+of Matlab but also opens up new opportunities in results post-processing,
+statistical analysis and mathematical optimization, among many other
+possibilities. The software architecture and usage are appropriately described
+and two problems of particular engineering significance are addressed to
+demonstrate its capabilities. Firstly, the software is employed to assess
+cleavage fracture through a novel 3-parameter Weibull probabilistic framework.
+Then, its potential to create and train neural networks is used to identify
+damage parameters through a hybrid experimental-numerical scheme, and model
+crack propagation in structural materials by means of a cohesive zone approach.
+The source code, detailed documentation and a large number of tutorials can be
+freely downloaded from www.abaqus2matlab.com.
+"
+23644,Formation Maneuvering Control of Multiple Nonholonomic Robotic Vehicles: Theory and Experimentation,"  In this paper, we present a new leader-follower type solution to the
+formation maneuvering problem for multiple, nonholonomic wheeled mobile robots.
+The solution is based on the graph that models the coordination among the
+robots being a spanning tree. Our decentralized control law ensures, in the
+least squares sense, that the robots globally acquire a given planar formation
+while the formation as a whole globally tracks a desired trajectory. The
+control law is first designed at the kinematic level and then extended to the
+dynamic level. In the latter, we consider that parametric uncertainty exists in
+the equations of motion. These uncertainties are accounted for by employing an
+adaptive control scheme. The proposed formation maneuvering controls are
+demonstrated experimentally and numerically.
+"
+23645,"Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture","  We introduce a dynamic artificial neural network-based (ANN) adaptive
+inference process, which learns temporal predictive models of dynamical
+systems. We term the process REPRISE, a REtrospective and PRospective Inference
+SchEme. REPRISE infers the unobservable contextual state that best explains its
+recently encountered sensorimotor experiences as well as accompanying,
+context-dependent temporal predictive models retrospectively. Meanwhile, it
+executes prospective inference, optimizing upcoming motor activities in a
+goal-directed manner. In a first implementation, a recurrent neural network
+(RNN) is trained to learn a temporal forward model, which predicts the
+sensorimotor contingencies of different simulated dynamic vehicles. The RNN is
+augmented with contextual neurons, which enable the compact encoding of
+distinct, but related sensorimotor dynamics. We show that REPRISE is able to
+concurrently learn to separate and approximate the encountered sensorimotor
+dynamics. Moreover, we show that REPRISE can exploit the learned model to
+induce goal-directed, model-predictive control, that is, approximate active
+inference: Given a goal state, the system imagines a motor command sequence
+optimizing it with the prospective objective to minimize the distance to a
+given goal. Meanwhile, the system evaluates the encountered sensorimotor
+contingencies retrospectively, adapting its neural hidden states for
+maintaining model coherence. The RNN activities thus continuously imagine the
+upcoming future and reflect on the recent past, optimizing both, hidden state
+and motor activities. In conclusion, the combination of temporal predictive
+structures with modulatory, generative encodings offers a way to develop
+compact event codes, which selectively activate particular types of
+sensorimotor event-specific dynamics.
+"
+23646,Doppler Synthetic Aperture Radar Interferometry: A Novel SAR Interferometry for Height Mapping using Ultra-Narrowband Waveforms,"  This paper introduces a new and novel radar interferometry based on Doppler
+synthetic aperture radar (Doppler-SAR) paradigm. Conventional SAR
+interferometry relies on wideband transmitted waveforms to obtain high range
+resolution. Topography of a surface is directly related to the range difference
+between two antennas configured at different positions. Doppler-SAR is a novel
+imaging modality that uses ultra-narrowband continuous waves (UNCW). It takes
+advantage of high resolution Doppler information provided by UNCWs to form high
+resolution SAR images.
+We introduced the theory of Doppler-SAR interferometry. We derived
+interferometric phase model and develop the equations of height mapping. Unlike
+conventional SAR interferometry, we show that the topography of a scene is
+related to the difference in Doppler between two antennas configured at
+different velocities. While the conventional SAR interferometry uses range,
+Doppler and Doppler due to interferometric phase in height mapping, Doppler-SAR
+interferometry uses Doppler, Doppler-rate and Doppler-rate due to
+interferometric phase in height mapping. We demonstrate our theory in numerical
+simulations.
+Doppler-SAR interferometry offers the advantages of long-range, robust,
+environmentally friendly operations; low-power, low-cost, lightweight systems
+suitable for low-payload platforms, such as micro-satellites; and passive
+applications using sources of opportunity transmitting UNCW.
+"
+23647,Transformation-Optics-Based Design of a Metamaterial Radome for Extending the Scanning Angle of a Phased Array Antenna,"  We apply the transformation-optics approach to the design of a metamaterial
+radome that can extend the scanning angle of a phased-array antenna. For
+moderate enhancement of the scanning angle, via suitable parameterization and
+optimization of the coordinate transformation, we obtain a design that admits a
+technologically viable, robust and potentially broadband implementation in
+terms of thin-metallic-plate inclusions. Our results, validated via
+finite-element-based numerical simulations, indicate an alternative route to
+the design of metamaterial radomes which does not require negative-valued
+and/or extreme constitutive parameters.
+"
+23648,Cooperative Robust Output Regulation Problem for Discrete-Time Linear Time-Delay Multi-Agent Systems,"  In this paper, we study the cooperative robust output regulation problem for
+discrete-time linear multi-agent systems with both communication and input
+delays by distributed internal model approach. We first introduce the
+distributed internal model for discrete-time multi-agent systems with both
+communication and input delays. Then, we define so-called auxiliary system and
+auxiliary augmented system. Finally, we solve our problem by showing, under
+some standard assumptions, that if a distributed state feedback control or a
+distributed output feedback control solves the robust output regulation problem
+of the auxiliary system, then the same control law solves the cooperative
+robust output regulation problem of the original multi-agent systems.
+"
+23649,Effect of one-dimensional superlattice potentials on the band gap of two-dimensional materials,"  Using the tight-binding approach, we analyze the effect of a one-dimensional
+superlattice (1DSL) potential on the electronic structure of black phosphorene
+and transition metal dichalcogenides. We observe that the 1DSL potential
+results in a decrease of the energy band gap of the two-dimensional (2D)
+materials. An analytical model is presented to relate the decrease in the
+direct-band gap to the different orbital characters between the valence band
+top and conduction band bottom of the 2D materials. The direct-to-indirect gap
+transition, which occurs under a 1DSL potential with an unequal barrier width,
+is also discussed.
+"
+23650,RR Lyrae stars in NGC 6362,"  We present the analysis of the top-quality photometry of RR Lyrae stars in
+the globular cluster NGC 6362, gathered over 11 observing seasons by the CASE
+project. 16 stars are fundamental mode pulsators (RRab stars) and 16 are first
+overtone pulsators (RRc stars). In two stars, previously identified as RRab, V3
+and V34, we detect additional periodicity identified as radial first overtone
+mode. Lower than usual period ratios (0.730 and 0.728), dominant pulsation in
+the radial fundamental mode and presence of a long-period modulation indicate,
+that these two variables are not classical RRd stars, but are new members of
+the recently identified class of anomalous RRd variables. In a significant
+fraction of RRc stars, 63 per cent, we detect additional shorter-period
+variability in the (0.60, 0.65)$P_1$ range. This form of double-periodic
+pulsation must be common in first overtone RR Lyr stars, as space observations
+indicate. The incidence rate we find in NGC 6362, is the highest in
+ground-based observations reported so far. We study the properties of these
+stars in detail; in particular we confirm that in the colour-magnitude diagram,
+this group is adjacent to the interface between RRab and RRc stars, as first
+reported in the analysis of M3 observations by Jurcsik et al. The incidence
+rate of the Blazhko effect is also very high: we observe it in 69 per cent of
+RRab stars and in 19 per cent of RRc stars. Rare, double-periodic modulation is
+reported in one RRab and in one RRc star. Finally we discuss V37 - a peculiar
+variable in which we detect two close high-amplitude periodicities and
+modulation. Its previous classification as RRc must be treated as tentative.
+"
+23651,Efficient Synchronous Byzantine Consensus,"  We present new protocols for Byzantine state machine replication and
+Byzantine agreement in the synchronous and authenticated setting. The
+celebrated PBFT state machine replication protocol tolerates $f$ Byzantine
+faults in an asynchronous setting using $3f+1$ replicas, and has since been
+studied or deployed by numerous works. In this work, we improve the Byzantine
+fault tolerance threshold to $n=2f+1$ by utilizing a relaxed synchrony
+assumption. We present a synchronous state machine replication protocol that
+commits a decision every 3 rounds in the common case. The key challenge is to
+ensure quorum intersection at one honest replica. Our solution is to rely on
+the synchrony assumption to form a post-commit quorum of size $2f+1$, which
+intersects at $f+1$ replicas with any pre-commit quorums of size $f+1$. Our
+protocol also solves synchronous authenticated Byzantine agreement in expected
+8 rounds. The best previous solution (Katz and Koo, 2006) requires expected 24
+rounds. Our protocols may be applied to build Byzantine fault tolerant systems
+or improve cryptographic protocols such as cryptocurrencies when synchrony can
+be assumed.
+"
+23652,Generalized Picard-Vessiot extensions and differential Galois cohomology,"  In an earlier paper it was proved that if a differential field $(K,\delta)$
+is algebraically closed and closed under Picard-Vessiot extensions then every
+differential algebraic principal homogeneous space over K for a linear
+differential algebraic group G over K has a K-rational point (in fact if and
+only if). This paper explores whether and if so, how, this can be extended to
+(a) several derivations, (b) one automorphism. Under a natural notion of
+""generalized Picard-Vessiot extension"" (in the case of several derivations), we
+give a counterexample. We also have a counterexample in the case of one
+automorphism. We also formulate and prove some positive statements in the case
+of several derivations.
+"
+23653,Generalizing Across Domains via Cross-Gradient Training,"  We present CROSSGRAD, a method to use multi-domain training data to learn a
+classifier that generalizes to new domains. CROSSGRAD does not need an
+adaptation phase via labeled or unlabeled data, or domain features in the new
+domain. Most existing domain adaptation methods attempt to erase domain signals
+using techniques like domain adversarial training. In contrast, CROSSGRAD is
+free to use domain signals for predicting labels, if it can prevent overfitting
+on training domains. We conceptualize the task in a Bayesian setting, in which
+a sampling step is implemented as data augmentation, based on domain-guided
+perturbations of input instances. CROSSGRAD parallelly trains a label and a
+domain classifier on examples perturbed by loss gradients of each other's
+objectives. This enables us to directly perturb inputs, without separating and
+re-mixing domain signals while making various distributional assumptions.
+Empirical evaluation on three different applications where this setting is
+natural establishes that (1) domain-guided perturbation provides consistently
+better generalization to unseen domains, compared to generic instance
+perturbation methods, and that (2) data augmentation is a more stable and
+accurate method than domain adversarial training.
+"
+23654,Robot Vision: Calibration of Wide-Angle Lens Cameras Using Collinearity Condition and K-Nearest Neighbour Regression,"  Visual perception is regularly used by humans and robots for navigation. By
+either implicitly or explicitly mapping the environment, ego-motion can be
+determined and a path of actions can be planned. The process of mapping and
+navigation are delicately intertwined; therefore, improving one can often lead
+to an improvement of the other. Both processes are sensitive to the interior
+orientation parameters of the camera system and mathematically modelling these
+systematic errors can often improve the precision and accuracy of the overall
+solution. This paper presents an automatic camera calibration method suitable
+for any lens, without having prior knowledge about the sensor. Statistical
+inference is performed to map the environment and localize the camera
+simultaneously. K-nearest neighbour regression is used to model the geometric
+distortions of the images. A normal-angle lens Nikon camera and wide-angle lens
+GoPro camera were calibrated using the proposed method, as well as the
+conventional bundle adjustment with self-calibration method (for comparison).
+Results showed that the mapping error was reduced from an average of 14.9 mm to
+1.2 mm (i.e. a 92% improvement) and 66.6 mm to 1.5 mm (i.e. a 98% improvement)
+using the proposed method for the Nikon and GoPro cameras, respectively. In
+contrast, the conventional approach achieved an average 3D error of 0.9 mm
+(i.e. 94% improvement) and 3.3 mm (i.e. 95% improvement) for the Nikon and
+GoPro cameras, respectively. Thus, the proposed method performs well
+irrespective of the lens/sensor used: it yields results that are comparable to
+the conventional approach for normal-angle lens cameras, and it has the
+additional benefit of improving calibration results for wide-angle lens
+cameras.
+"
+23655,A software framework for pipelined arithmetic algorithms in field programmable gate arrays,"  Pipelined algorithms implemented in field programmable gate arrays are being
+extensively used for hardware triggers in the modern experimental high energy
+physics field and the complexity of such algorithms are increases rapidly. For
+development of such hardware triggers, algorithms are developed in
+$\texttt{C++}$, ported to hardware description language for synthesizing
+firmware, and then ported back to $\texttt{C++}$ for simulating the firmware
+response down to the single bit level. We present a $\texttt{C++}$ software
+framework which automatically simulates and generates hardware description
+language code for pipelined arithmetic algorithms.
+"
+23656,Modelling community formation driven by the status of individual in a society,"  In human societies, people's willingness to compete and strive for better
+social status as well as being envious of those perceived in some way superior
+lead to social structures that are intrinsically hierarchical. Here we propose
+an agent-based, network model to mimic the ranking behaviour of individuals and
+its possible repercussions in human society. The main ingredient of the model
+is the assumption that the relevant feature of social interactions is each
+individual's keenness to maximise his or her status relative to others. The
+social networks produced by the model are homophilous and assortative, as
+frequently observed in human communities and most of the network properties
+seem quite independent of its size. However, it is seen that for small number
+of agents the resulting network consists of disjoint weakly connected
+communities while being highly assortative and homophilic. On the other hand
+larger networks turn out to be more cohesive with larger communities but less
+homophilic. We find that the reason for these changes is that larger network
+size allows agents to use new strategies for maximizing their social status
+allowing for more diverse links between them.
+"
+23657,"Parity Games, Imperfect Information and Structural Complexity","  We address the problem of solving parity games with imperfect information on
+finite graphs of bounded structural complexity. It is a major open problem
+whether parity games with perfect information can be solved in PTIME.
+Restricting the structural complexity of the game arenas, however, often leads
+to efficient algorithms for parity games. Such results are known for graph
+classes of bounded tree-width, DAG-width, directed path-width, and
+entanglement, which we describe in terms of cops and robber games. Conversely,
+the introduction of imperfect information makes the problem more difficult, it
+becomes EXPTIME-hard. We analyse the interaction of both approaches.
+We use a simple method to measure the amount of ""unawareness""' of a player,
+the amount of imperfect information. It turns out that if it is unbounded, low
+structural complexity does not make the problem simpler. It remains
+EXPTIME-hard or PSPACE-hard even on very simple graphs.
+For games with bounded imperfect information we analyse the powerset
+construction, which is commonly used to convert a game of imperfect information
+into an equivalent game with perfect information. This construction preserves
+boundedness of directed path-width and DAG-width, but not of entanglement or of
+tree-width. Hence, if directed path-width or DAG-width are bounded, parity
+games with bounded imperfect information can be solved in PTIME. For DAG-width
+we follow two approaches. One leads to a generalization of the known fact that
+perfect information parity games are in PTIME if DAG-width is bounded. We prove
+this theorem for non-monotone DAG-width. The other approach introduces a cops
+and robbers game (with multiple robbers) on directed graphs, considered
+Richerby and Thilikos forundirected graphs. We show a tight linear bound for
+the number of additional cops needed to capture an additional robber.
+"
+23658,Using SVDD in SimpleMKL for 3D-Shapes Filtering,"  This paper proposes the adaptation of Support Vector Data Description (SVDD)
+to the multiple kernel case (MK-SVDD), based on SimpleMKL. It also introduces a
+variant called Slim-MK-SVDD that is able to produce a tighter frontier around
+the data. For the sake of comparison, the equivalent methods are also developed
+for One-Class SVM, known to be very similar to SVDD for certain shapes of
+kernels.
+Those algorithms are illustrated in the context of 3D-shapes filtering and
+outliers detection. For the 3D-shapes problem, the objective is to be able to
+select a sub-category of 3D-shapes, each sub-category being learned with our
+algorithm in order to create a filter. For outliers detection, we apply the
+proposed algorithms for unsupervised outliers detection as well as for the
+supervised case.
+"
+23659,Software Engineering Modeling Applied to English Verb Classification (and Poetry),"  In requirements specification, software engineers create a textual
+description of the envisioned system as well as develop conceptual models using
+such tools as Universal Modeling Language (UML) and System Modeling Language
+(SysML). One such tool, called FM, has recently been developed as an extension
+of the INPUT-PROCESS-OUTPUT (IPO) model. IPO has been used extensively in many
+interdisciplinary applications and is described as one of the most fundamental
+and important of all descriptive tools. This paper is an attempt to
+understanding the PROCESS in IPO. The fundamental way to describe PROCESS is in
+verbs. This use of language has an important implication for systems modeling
+since verbs express the vast range of actions and movements of all things. It
+is clear that modeling needs to examine verbs. Accordingly, this paper involves
+a study of English verbs as a bridge to learn about processes, not as
+linguistic analysis but rather to reveal the semantics of processes,
+particularly the five verbs that form the basis of FM states: create, process,
+receive, release, and transfer. The paper focuses on verb classification, and
+specifically on how to model the action of verbs diagrammatically. From the
+linguistics point of view, according to some researchers, further exploration
+of the notion of verb classes is needed for real-world tasks such as machine
+translation, language generation, and document classification. Accordingly,
+this non-linguistics study may benefit linguistics.
+"
+23660,Contrasting magnetoelectric behavior in multiferroic hexaferrites as understood by crystal symmetry analyses,"  Magnetoelectric (ME) properties under rotating magnetic field H are
+comparatively investigated in two representative hexaferrites Y-type
+Ba0.5Sr1.5Zn2(Fe0.92Al0.08)12O22 and Z-type Ba0.52Sr2.48Co2Fe24O41, both of
+which have exhibited a similar transverse conical spin structure and giant ME
+coupling near room temperature. When the external H is rotated clockwise by
+2pi, in-plane P vector is rotated clockwise by 2pi in the Y-type hexaferrite
+and counterclockwise by 4pi in the Z-type hexaferrite. A symmetry-based
+analysis reveals that the faster and opposite rotation of P vector in the
+Z-type hexaferrite is associated with the existence of a mirror plane
+perpendicular to c-axis. Moreover, such a peculiar crystal symmetry also
+results in contrasting microscopic origins for the spin-driven
+ferroelectricity; only the inverse DM interaction is responsible for the Y-type
+hexaferrite while additional p-d hybridization becomes more important in the
+Z-type hexaferrite. This work demonstrates the importance of the crystal
+symmetry in the determination of ME properties in the hexaferrites and provides
+a fundamental framework for understanding and applying the giant ME coupling in
+various ferrites with hexagonal crystal structure.
+"
+23661,A Nonparametric Bayesian Methodology for Regression Discontinuity Designs,"  One of the most popular methodologies for estimating the average treatment
+effect at the threshold in a regression discontinuity design is local linear
+regression (LLR), which places larger weight on units closer to the threshold.
+We propose a Gaussian process regression methodology that acts as a Bayesian
+analog to LLR for regression discontinuity designs. Our methodology provides a
+flexible fit for treatment and control responses by placing a general prior on
+the mean response functions. Furthermore, unlike LLR, our methodology can
+incorporate uncertainty in how units are weighted when estimating the treatment
+effect. We prove our method is consistent in estimating the average treatment
+effect at the threshold. Furthermore, we find via simulation that our method
+exhibits promising coverage, interval length, and mean squared error properties
+compared to standard LLR and state-of-the-art LLR methodologies. Finally, we
+explore the performance of our method on a real-world example by studying the
+impact of being a first-round draft pick on the performance and playing time of
+basketball players in the National Basketball Association.
+"
+23662,Lisco: A Continuous Approach in LiDAR Point-cloud Clustering,"  The light detection and ranging (LiDAR) technology allows to sense
+surrounding objects with fine-grained resolution in a large areas. Their data
+(aka point clouds), generated continuously at very high rates, can provide
+information to support automated functionality in cyberphysical systems.
+Clustering of point clouds is a key problem to extract this type of
+information. Methods for solving the problem in a continuous fashion can
+facilitate improved processing in e.g. fog architectures, allowing continuous,
+streaming processing of data close to the sources. We propose Lisco, a
+single-pass continuous Euclidean-distance-based clustering of LiDAR point
+clouds, that maximizes the granularity of the data processing pipeline. Besides
+its algorithmic analysis, we provide a thorough experimental evaluation and
+highlight its up to 3x improvements and its scalability benefits compared to
+the baseline, using both real-world datasets as well as synthetic ones to fully
+explore the worst-cases.
+"
+23663,Adversarial Meta-Learning,"  Meta-learning enables a model to learn from very limited data to undertake a
+new task. In this paper, we study the general meta-learning with adversarial
+samples. We present a meta-learning algorithm, ADML (ADversarial Meta-Learner),
+which leverages clean and adversarial samples to optimize the initialization of
+a learning model in an adversarial manner. ADML leads to the following
+desirable properties: 1) it turns out to be very effective even in the cases
+with only clean samples; 2) it is model-agnostic, i.e., it is compatible with
+any learning model that can be trained with gradient descent; and most
+importantly, 3) it is robust to adversarial samples, i.e., unlike other
+meta-learning methods, it only leads to a minor performance degradation when
+there are adversarial samples. We show via extensive experiments that ADML
+delivers the state-of-the-art performance on two widely-used image datasets,
+MiniImageNet and CIFAR100, in terms of both accuracy and robustness.
+"
+23664,"No, This is not a Circle","  A popular curve shown in introductory maths textbooks, seems like a circle.
+But it is actually a different curve. This paper discusses some elementary
+approaches to identify the geometric object, including novel technological
+means by using GeoGebra. We demonstrate two ways to refute the false
+impression, two suggestions to find a correct conjecture, and four ways to
+confirm the result by proving it rigorously.
+All of the discussed approaches can be introduced in classrooms at various
+levels from middle school to high school.
+"
+23665,The dynamics of conservative peakons in the NLS hierarchy,"  Using the tri-hamiltonian splitting method, the authors of [Anco and
+Mobasheramini, Physica D, 355:1--23, 2017] derived two $U(1)$-invariant
+nonlinear PDEs that arise from the hierarchy of the nonlinear Schrödinger
+equation and admit peakons ($non-smooth\ solitons$). In the present paper,
+these two peakon PDEs are generalized to a family of $U(1)$-invariant peakon
+PDEs parametrized by the real projective line $ \mathbf{RP} ^1$. All equations
+in this family are shown to posses $conservative\ peakon \ solutions$ (whose
+Sobolev $H^1(\mathbf{R})$ norm is time invariant). The Hamiltonian structure
+for the sector of conservative peakons is identified and the peakon ODEs are
+shown to be Hamiltonian with respect to several Poisson structures. It is shown
+that the resulting Hamilonian peakon flows in the case of the two peakon
+equations derived in [Anco and Mobasheramini, Physica D, 355:1--23, 2017] form
+orthogonal families, while in general the Hamiltonian peakon flows for two
+different equations in the general family intersect at a fixed angle equal to
+the angle between two lines in $ \mathbf{RP} ^1$ parametrizing those two
+equations. Moreover, it is shown that inverse spectral methods allow one to
+solve explicitly the dynamics of conservative peakons using explicit solutions
+to a certain interpolation problem. The graphs of multipeakon solutions confirm
+the existence of multipeakon breathers as well as asymptotic formation of pairs
+of two peakon bound states in the non-periodic time domain.
+"
+23666,Upgrade of compact neutron spectrometer for high flux environments,"  In this paper a new version of Li6-based neutron spectrometer for high flux
+environments is described. The new spectrometer was built with commercial
+single crystal Chemical Vapour Deposition diamonds of electronic grade. These
+crystals feature better charge collection as well as higher radiation hardness.
+Ohmic metal contacts were deposited on the diamonds suppressing build-up of
+space charge observed in the previous prototypes. New passive preamplification
+of signal at detector side was implemented to improve the resolution. This
+preamplification is based on RF transformer not sensitive to high neutron flux.
+Compact mechanical design allowed to reduce detector size to a tube of 1 cm
+diameter and 13 cm long. The spectrometer was tested in thermal column of TRIGA
+reactor and at DD neutron generator. The test results indicate an energy
+resolution of 72 keV (RMS) and coincidence timing resolution of 68 ps (RMS).
+The measured data are in agreement with Geant4 simulations except for larger
+energy loss tail presumably related to imperfections of metal contacts and glue
+expansion.
+"
+23667,ChemTS: An Efficient Python Library for de novo Molecular Generation,"  Automatic design of organic materials requires black-box optimization in a
+vast chemical space. In conventional molecular design algorithms, a molecule is
+built as a combination of predetermined fragments. Recently, deep neural
+network models such as variational auto encoders (VAEs) and recurrent neural
+networks (RNNs) are shown to be effective in de novo design of molecules
+without any predetermined fragments. This paper presents a novel python library
+ChemTS that explores the chemical space by combining Monte Carlo tree search
+(MCTS) and an RNN. In a benchmarking problem of optimizing the octanol-water
+partition coefficient and synthesizability, our algorithm showed superior
+efficiency in finding high-scoring molecules. ChemTS is available at
+this https URL.
+"
+23668,"Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions","  Traditional power grids are being transformed into Smart Grids (SGs) to solve
+the problems of uni-directional information flow, energy wastage, growing
+energy demand, reliability and security. SGs offer bi-directional energy flow
+between service providers and consumers, involving power generation,
+transmission, distribution and utilization systems. SGs employ various devices
+for the monitoring, analysis and control of the grid, deployed at power plants,
+distribution centers and in consumers' premises in a very large number. Hence,
+an SG requires connectivity, automation and the tracking of such devices. This
+is achieved with the help of Internet of Things (IoT). IoT helps SG systems to
+support various network functions throughout the generation, transmission,
+distribution and consumption of energy by incorporating IoT devices (such as
+sensors, actuators and smart meters), as well as by providing the connectivity,
+automation and tracking for such devices. In this paper, we provide the first
+comprehensive survey on IoT-aided SG systems, which includes the existing
+architectures, applications and prototypes of IoT-aided SG systems. This survey
+also highlights the open issues, challenges and future research directions for
+IoT-aided SG systems.
+"
+23669,Anisotropies of the magnetic field fluctuations at kinetic scales in the solar wind : Cluster observations,"  We present the first statistical study of the anisotropy of the magnetic
+field turbulence in the solar wind between 1 and 200Hz, i.e. from proton to
+sub-electron scales. We consider 93 10-minute intervals of Cluster/STAFF
+measurements. We find that the fluctuations $\delta B_{\perp}^2$ are not
+gyrotropic at a given frequency $f$, a property already observed at larger
+scales ($\parallel$/$\perp$ mean parallel/perpendicular to the average magnetic
+${\bf B_0}$). This non-gyrotropy gives indications on the angular distribution
+of the wave vectors ${\bf k}$: at $f <$ 10Hz, we find that $k_{\perp}\gg
+k_{\parallel}$, mainly in the fast wind; at $f >$ 10Hz, fluctuations with a
+non-negligible $k_{\parallel}$ are also present. We then consider the
+anisotropy ratio $\delta B_{\parallel}^2/\delta B_{\perp}^2$, which is a
+measure of the magnetic compressibility of the fluctuations. This ratio, always
+smaller than 1, increases with $f.$ It reaches a value showing that the
+fluctuations are more or less isotropic at electron scales, for $f \geq 50$Hz.
+From 1 to 15-20Hz, there is a strong correlation between the observed
+compressibility and the one expected for the kinetic Alfvén waves (KAW),
+which only depends on the total plasma $\beta$. For $f > $ 15-20Hz, the
+observed compressibility is larger than expected for KAW; and it is stronger in
+the slow wind: this could be an indication of the presence of a slow-ion
+acoustic mode of fluctuations, which is more compressive and is favored by the
+larger values of the electron to proton temperature ratio generally observed in
+the slow wind.
+"
+23670,An Optimal Choice Dictionary,"  A choice dictionary is a data structure that can be initialized with a
+parameter $n\in\{1,2,\ldots\}$ and subsequently maintains an initially empty
+subset $S$ of $\{1,\ldots,n\}$ under insertion, deletion, membership queries
+and an operation 'choice' that returns an arbitrary element of $S$. The choice
+dictionary is fundamental in space-efficient computing and has numerous
+applications. The best previous choice dictionary can be initialized with $n$
+and a second parameter $t\in\{1,2,\ldots\}$ in constant time and subsequently
+executes all operations in $O(t)$ time and occupies $n+O(n({t/w})^t+\log n)$
+bits on a word RAM with a word length of $w=\Omega(\log n)$ bits. We describe a
+new choice dictionary that, following a constant-time initialization, executes
+all operations in constant time and, in addition to the space needed to store
+$n$, occupies only $n+1$ bits, which is shown to be optimal if $w=o(n)$.
+Allowed $\lceil\log_2(n+1)\rceil$ bits of additional space, the new data
+structure also supports iteration over the set $S$ in constant time per
+element.
+"
+23671,There Are (super)Giants in the Sky: Searching for Misidentified Massive Stars in Algorithmically-Selected Quasar Catalogs,"  Thanks to incredible advances in instrumentation, surveys like the Sloan
+Digital Sky Survey have been able to find and catalog billions of objects,
+ranging from local M dwarfs to distant quasars. Machine learning algorithms
+have greatly aided in the effort to classify these objects; however, there are
+regimes where these algorithms fail, where interesting oddities may be found.
+We present here an X-ray bright quasar misidentified as a red supergiant/X-ray
+binary, and a subsequent search of the SDSS quasar catalog for X-ray bright
+stars misidentified as quasars.
+"
+23672,dynnode2vec: Scalable Dynamic Network Embedding,"  Network representation learning in low dimensional vector space has attracted
+considerable attention in both academic and industrial domains. Most real-world
+networks are dynamic with addition/deletion of nodes and edges. The existing
+graph embedding methods are designed for static networks and they cannot
+capture evolving patterns in a large dynamic network. In this paper, we propose
+a dynamic embedding method, dynnode2vec, based on the well-known graph
+embedding method node2vec. Node2vec is a random walk based embedding method for
+static networks. Applying static network embedding in dynamic settings has two
+crucial problems: 1) Generating random walks for every time step is time
+consuming 2) Embedding vector spaces in each timestamp are different. In order
+to tackle these challenges, dynnode2vec uses evolving random walks and
+initializes the current graph embedding with previous embedding vectors. We
+demonstrate the advantages of the proposed dynamic network embedding by
+conducting empirical evaluations on several large dynamic network datasets.
+"
+23673,Two- and three-body problem with Floquet-driven zero-range interactions,"  We study the two-body scattering problem in the zero-range approximation with
+a sinusoidally driven scattering length and calculate the relation between the
+mean value and amplitude of the drive for which the effective scattering
+amplitude is resonantly enhanced. In this manner we arrive at a family of
+curves along which the effective scattering length diverges but the nature of
+the corresponding Floquet-induced resonance changes from narrow to wide.
+Remarkably, on these curves the driving does not induce heating. In order to
+study the effect of these resonances on the three-body problem we consider one
+light and two heavy particles with driven heavy-light interaction in the
+Born-Oppenheimer approximation and find that the Floquet driving can be used to
+tune the three-body and inelasticity parameters.
+"
+23674,Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data,"  Real-world sensors suffer from noise, blur, and other imperfections that make
+high-level computer vision tasks like scene segmentation, tracking, and scene
+understanding difficult. Making high-level computer vision networks robust is
+imperative for real-world applications like autonomous driving, robotics, and
+surveillance. We propose a novel end-to-end differentiable architecture for
+joint denoising, deblurring, and classification that makes classification
+robust to realistic noise and blur. The proposed architecture dramatically
+improves the accuracy of a classification network in low light and other
+challenging conditions, outperforming alternative approaches such as retraining
+the network on noisy and blurry images and preprocessing raw sensor inputs with
+conventional denoising and deblurring algorithms. The architecture learns
+denoising and deblurring pipelines optimized for classification whose outputs
+differ markedly from those of state-of-the-art denoising and deblurring
+methods, preserving fine detail at the cost of more noise and artifacts. Our
+results suggest that the best low-level image processing for computer vision is
+different from existing algorithms designed to produce visually pleasing
+images. The principles used to design the proposed architecture easily extend
+to other high-level computer vision tasks and image formation models, providing
+a general framework for integrating low-level and high-level image processing.
+"
+23675,Code loops in dimension at most 8,"  Code loops are certain Moufang $2$-loops constructed from doubly even binary
+codes that play an important role in the construction of local subgroups of
+sporadic groups. More precisely, code loops are central extensions of the group
+of order $2$ by an elementary abelian $2$-group $V$ in the variety of loops
+such that their squaring map, commutator map and associator map are related by
+combinatorial polarization and the associator map is a trilinear alternating
+form.
+Using existing classifications of trilinear alternating forms over the field
+of $2$ elements, we enumerate code loops of dimension $d=\mathrm{dim}(V)\le 8$
+(equivalently, of order $2^{d+1}\le 512$) up to isomorphism. There are $767$
+code loops of order $128$, and $80826$ of order $256$, and $937791557$ of order
+$512$.
+"
+23676,Laplace deconvolution in the presence of indirect long-memory data,"  We investigate the problem of estimating a function $f$ based on observations
+from its noisy convolution when the noise exhibits long-range dependence. We
+construct an adaptive estimator based on the kernel method, derive minimax
+lower bound for the $L^2$-risk when $f$ belongs to Sobolev space and show that
+such estimator attains optimal rates that deteriorate as the LRD worsens.
+"
+23677,Datenqualität in Regressionsproblemen,"  Regression models are increasingly built using datasets which do not follow a
+design of experiment. Instead, the data is e.g. gathered by an automated
+monitoring of a technical system. As a consequence, already the input data
+represents phenomena of the system and violates statistical assumptions of
+distributions. The input data can show correlations, clusters or other
+patterns. Further, the distribution of input data influences the reliability of
+regression models. We propose criteria to quantify typical phenomena of input
+data for regression and show their suitability with simulated benchmark
+datasets.
+-----
+Regressionen werden zunehmend auf Datensätzen angewendet, deren
+Eingangsvektoren nicht durch eine statistische Versuchsplanung festgelegt
+wurden. Stattdessen werden die Daten beispielsweise durch die passive
+Beobachtung technischer Systeme gesammelt. Damit bilden bereits die
+Eingangsdaten Phänomene des Systems ab und widersprechen statistischen
+Verteilungsannahmen. Die Verteilung der Eingangsdaten hat Einfluss auf die
+Zuverlässigkeit eines Regressionsmodells. Wir stellen deshalb
+Bewertungskriterien für einige typische Phänomene in Eingangsdaten von
+Regressionen vor und zeigen ihre Funktionalität anhand simulierter
+Benchmarkdatensätze.
+"
+23678,Thermal effects of late accretion to the crust and mantle of Mercury,"  Impact bombardment on Mercury in the solar system late accretion phase (ca.
+4.4 to 3.8 Ga) caused considerable mechanical, chemical and thermal reworking
+of its silicate reservoirs (crust and mantle). Depending on the frequency, size
+and velocity of such impactors, effects included regional and global scale
+crustal melting, and thermal perturbations of the mercurian mantle. We use a 3D
+transient heating model to test the effects of two bombardment scenarios on
+early (pre Tolstojan) Mercury mantle and crust. Results show that rare impacts
+by the largest (greater than 100 km diameter) bodies deliver sufficient heat to
+the shallow mercurian mantle producing high-temperature ultra-magnesian
+(komatiitic s.s.) melts. Impact heating leading to effusive (flood) volcanism
+can account for eponymous High-Magnesium Region (HMR) observed during the
+MErcury Surface, Space Environment, GEochemistry Ranging (MESSENGER) mission.
+We find that late accretion to Mercury induced volumetrically significant
+crustal melting (less than 58 vol.percent), mantle heating and melt production,
+which, combined with extensive resurfacing (less than 100 percent), also
+explains why its oldest cratering record was effectively erased, consistent
+with crater-counting statistics.
+"
+23679,Quadratic Frobenius probable prime tests costing two selfridges,"  By an elementary observation about the computation of the difference of
+squares for large in- tegers, deterministic quadratic Frobenius probable prime
+tests are given with running times of approximately 2 selfridges.
+"
+23680,Understanding Flash Crash Contagion and Systemic Risk: A Micro-Macro Agent-Based Approach,"  The purpose of this paper is to advance the understanding of the conditions
+that give rise to flash crash contagion, particularly with respect to
+overlapping asset portfolio crowding. To this end, we designed, implemented,
+and assessed a hybrid micro-macro agent-based model, where price impact arises
+endogenously through the limit order placement activity of algorithmic traders.
+Our novel hybrid microscopic and macroscopic model allows us to quantify
+systemic risk not just in terms of system stability, but also in terms of the
+speed of financial distress propagation over intraday timescales. We find that
+systemic risk is strongly dependent on the behaviour of algorithmic traders, on
+leverage management practices, and on network topology. Our results demonstrate
+that, for high-crowding regimes, contagion speed is a non-monotone function of
+portfolio diversification. We also find the surprising result that, in certain
+circumstances, increased portfolio crowding is beneficial to systemic
+stability. We are not aware of previous studies that have exhibited this
+phenomenon, and our results establish the importance of considering non-uniform
+asset allocations in future studies. Finally, we characterise the time window
+available for regulatory interventions during the propagation of flash crash
+distress, with results suggesting ex ante precautions may have higher efficacy
+than ex post reactions.
+"
+23681,European Option Pricing with Stochastic Volatility models under Parameter Uncertainty,"  We consider stochastic volatility models under parameter uncertainty and
+investigate how model derived prices of European options are affected. We let
+the pricing parameters evolve dynamically in time within a specified region,
+and formalise the problem as a control problem where the control acts on the
+parameters to maximise/minimise the option value. Through a dual representation
+with backward stochastic differential equations, we obtain explicit equations
+for Heston's model and investigate several numerical solutions thereof. In an
+empirical study, we apply our results to market data from the S&P 500 index
+where the model is estimated to historical asset prices. We find that the
+conservative model-prices cover 98% of the considered market-prices for a set
+of European call options.
+"
+23682,Improved Probabilistic Principal Component Analysis for Application to Reduced Order Modeling,"  In our previous work, a reduced order model (ROM) for a stochastic system was
+made, where noisy data was projected onto principal component analysis
+(PCA)-derived basis vectors to obtain an accurate reconstruction of the
+noise-free data. That work used techniques designed for deterministic data, PCA
+was used for the basis function generation and $L_2$ projection was used to
+create the reconstructions. In this work, probabilistic approaches are used.
+The probabilistic PCA (PPCA) is used to generate the basis, which then allows
+the noise in the training data to be estimated. PPCA has also been improved so
+that the derived basis vectors are orthonormal and the variance of the basis
+expansion coefficients over the training data set can be estimated. The
+standard approach assumes a unit variance for these coefficients. Based on the
+results of the PPCA, model selection criteria are applied to automatically
+choose the dimension of the ROM. In our previous work, a heuristic approach was
+used to pick the dimension. Lastly, a new statistical approach is used for the
+projection step where the variance information obtained from the improved PPCA
+is used as a prior to improve the projection. This gives improved accuracy over
+$L_2$ projection when the projected data is noisy. In addition, the noise
+statistics for the projected data are not assumed to be the same as that of the
+training data, but are estimated in the projection process. The entire approach
+gives a fully stochastic method for computing a ROM from noisy training data,
+determining ideal model selection, and projecting noisy test data, thus
+enabling accurate predictions of noise-free data from data that is dominated by
+noise.
+"
+23683,Nonbacktracking Bounds on the Influence in Independent Cascade Models,"  This paper develops upper and lower bounds on the influence measure in a
+network, more precisely, the expected number of nodes that a seed set can
+influence in the independent cascade model. In particular, our bounds exploit
+nonbacktracking walks, Fortuin-Kasteleyn-Ginibre (FKG) type inequalities, and
+are computed by message passing implementation. Nonbacktracking walks have
+recently allowed for headways in community detection, and this paper shows that
+their use can also impact the influence computation. Further, we provide a knob
+to control the trade-off between the efficiency and the accuracy of the bounds.
+Finally, the tightness of the bounds is illustrated with simulations on various
+network models.
+"
+23684,The neural and cognitive architecture for learning from a small sample,"  Artificial intelligence algorithms are capable of fantastic exploits, yet
+they are still grossly inefficient compared with the brain's ability to learn
+from few exemplars or solve problems that have not been explicitly defined.
+What is the secret that the evolution of human intelligence has unlocked?
+Generalization is one answer, but there is more to it. The brain does not
+directly solve difficult problems, it is able to recast them into new and more
+tractable problems. Here we propose a model whereby higher cognitive functions
+profoundly interact with reinforcement learning to drastically reduce the
+degrees of freedom of the search space, simplifying complex problems and
+fostering more efficient learning.
+"
+23685,Unstable points for torus actions on flag varieties,"  In this paper, we will look at actions on complex flag varieties $G/P$ of the
+torus $\hat{T}=\bigcap\limits_{\alpha\in\Delta\setminus\Delta_P}\ker(\alpha)$,
+and under reasonable assumptions, we will give a description of the set
+$X^{us}$ of unstable points for $\hat{T}$-linearized invertible sheaves. We
+will investigate the case where $P$ is a maximal parabolic subgroup, and show
+that $X^{us}$ can be written as a disjoint union of a Schubert variety and an
+opposite Schubert variety, and we deduce the vanishing of cohomology groups
+$H^i(Y,{\cal M})$ for invertible sheaves ${\cal M}$ on the quotient variety $Y$
+for $i$ in a range given by the codimension of $X^{us}$.
+"
+23686,Linking Sequences of Events with Sparse or No Common Occurrence across Data Sets,"  Data of practical interest - such as personal records, transaction logs, and
+medical histories - are sequential collections of events relevant to a
+particular source entity. Recent studies have attempted to link sequences that
+represent a common entity across data sets to allow more comprehensive
+statistical analyses and to identify potential privacy failures. Yet, current
+approaches remain tailored to their specific domains of application, and they
+fail when co-referent sequences in different data sets contain sparse or no
+common events, which occurs frequently in many cases.
+To address this, we formalize the general problem of ""sequence linkage"" and
+describe ""LDA-Link,"" a generic solution that is applicable even when
+co-referent event sequences contain no common items at all. LDA-Link is built
+upon ""Split-Document"" model, a new mixed-membership probabilistic model for the
+generation of event sequence collections. It detects the latent similarity of
+sequences and thus achieves robustness particularly when co-referent sequences
+share sparse or no event overlap. We apply LDA-Link in the context of social
+media profile reconciliation where users make no common posts across platforms,
+comparing to the state-of-the-art generic solution to sequence linkage.
+"
+23687,On Yau-Tian-Donaldson conjecture for singular Fano varieties,"  We prove the Yau-Tian-Donaldson's conjecture for any $\mathbb{Q}$-Fano
+variety that has a log smooth resolution of singularities such that the
+discrepancies of all exceptional divisors are non-positive. In other words, if
+such a Fano variety is K-polystable, then it admits a Kähler-Einstein
+metric. This extends the previous result for smooth Fano varieties to this
+class of singular $\mathbb{Q}$-Fano varieties, which include those admitting
+crepant log resolutions.
+"
+23688,Two Recursively Inseparable Problems for Probabilistic Automata,"  This paper introduces and investigates decision problems for numberless
+probabilistic automata, i.e. probabilistic automata where the support of each
+probabilistic transitions is specified, but the exact values of the
+probabilities are not. A numberless probabilistic automaton can be instantiated
+into a probabilistic automaton by specifying the exact values of the non-zero
+probabilistic transitions.
+We show that the two following properties of numberless probabilistic
+automata are recursively inseparable: - all instances of the numberless
+automaton have value 1, - no instance of the numberless automaton has value 1.
+"
+23689,Modelling Irregular Spatial Patterns using Graph Convolutional Neural Networks,"  The understanding of geographical reality is a process of data representation
+and pattern discovery. Former studies mainly adopted continuous-field models to
+represent spatial variables and to investigate the underlying spatial
+continuity/heterogeneity in the regular spatial domain. In this article, we
+introduce a more generalized model based on graph convolutional neural networks
+(GCNs) that can capture the complex parameters of spatial patterns underlying
+graph-structured spatial data, which generally contain both Euclidean spatial
+information and non-Euclidean feature information. A trainable semi-supervised
+prediction framework is proposed to model the spatial distribution patterns of
+intra-urban points of interest(POI) check-ins. This work demonstrates the
+feasibility of GCNs in complex geographic decision problems and provides a
+promising tool to analyze irregular spatial data.
+"
+23690,Results from a 2016 Pilot Survey on Math Post-docs,"  A pilot survey was sent to chairs of 14 doctoral math departments asking for
+three types of data: (1) category on job-placements for research post-docs
+leaving their department in three recent years; (2) category of jobs from which
+their new faculty hires came in two recent years and two years a decade
+earlier; and (3) preparation for future careers offered by their department to
+their research post-docs. Eleven departments submitted data on post-docs. Of
+the 162 departing post-docs for whom data was supplied, 25% obtained
+tenure-track jobs in doctoral departments; 22% took another post-doc; and 18%
+were reported as ""unknown/other"". The remaining 35% were evenly divided among
+tenure-track in non-doctoral departments, full-time non-tenure-track, academic
+outside the US, and business-industry-government. Eight departments gave
+complete responses to (2): From the early 2000's to the early 2010's tenure
+track hiring increased by about 35% (from 18 to 25). The changes across this
+period as strikingly larger for other ranks: 233%(from 21 to 70) for research
+post-docs and 211% (from 18 to 56)full-time non-tenure-track doctoral teaching
+faculty. In section (3), all departments reported observing post-docs' teaching
+and providing feedback. However, relatively few provided explicit preparation
+for teaching or guidance for communicating with audiences other than research
+specialists.
+"
+23691,Explicit size distributions of failure cascades redefine systemic risk on finite networks,"  How big is the risk that a few initial failures of nodes in a network amplify
+to large cascades that span a substantial share of all nodes? Predicting the
+final cascade size is critical to ensure the functioning of a system as a
+whole. Yet, this task is hampered by uncertain or changing parameters and
+missing information. In infinitely large networks, the average cascade size can
+often be well estimated by established approaches building on local tree
+approximations and mean field approximations. Yet, as we demonstrate, in finite
+networks, this average does not even need to be a likely outcome. Instead, we
+find broad and even bimodal cascade size distributions. This phenomenon
+persists for system sizes up to $10^{7}$ and different cascade models, i.e. it
+is relevant for most real systems. To show this, we derive explicit closed-form
+solutions for the full probability distribution of the final cascade size. We
+focus on two topological limit cases, the complete network representing a dense
+network with a very narrow degree distribution, and the star network
+representing a sparse network with a inhomogeneous degree distribution. Those
+topologies are of great interest, as they either minimize or maximize the
+average cascade size and are common motifs in many real world networks.
+"
+23692,Positive Semidefinite Univariate Matrix Polynomials,"  We study sum-of-squares representations of symmetric univariate real matrix
+polynomials that are positive semidefinite along the real line. We give a new
+proof of the fact that every positive semidefinite univariate matrix polynomial
+of size $n\times n$ can be written as a sum of squares $M=Q^TQ$, where $Q$ has
+size $(n+1)\times n$, which was recently proved by
+Blekherman-Plaumann-Sinn-Vinzant. Our new approach using the theory of
+quadratic forms allows us to prove the conjecture made by these authors that
+these minimal representations $M=Q^TQ$ are generically in one-to-one
+correspondence with the representations of the nonnegative univariate
+polynomial $\det(M)$ as sums of two squares.
+In parallel, we will use our methods to prove the more elementary hermitian
+analogue that every hermitian univariate matrix polynomial $M$ that is positive
+semidefinite along the real line, is a square, which is known as the matrix
+Fejér-Riesz Theorem.
+"
+23693,"Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach","  This paper presents a real-time, object-independent grasp synthesis method
+which can be used for closed-loop grasping. Our proposed Generative Grasping
+Convolutional Neural Network (GG-CNN) predicts the quality and pose of grasps
+at every pixel. This one-to-one mapping from a depth image overcomes
+limitations of current deep-learning grasping techniques by avoiding discrete
+sampling of grasp candidates and long computation times. Additionally, our
+GG-CNN is orders of magnitude smaller while detecting stable grasps with
+equivalent performance to current state-of-the-art techniques. The light-weight
+and single-pass generative nature of our GG-CNN allows for closed-loop control
+at up to 50Hz, enabling accurate grasping in non-static environments where
+objects move and in the presence of robot control inaccuracies. In our
+real-world tests, we achieve an 83% grasp success rate on a set of previously
+unseen objects with adversarial geometry and 88% on a set of household objects
+that are moved during the grasp attempt. We also achieve 81% accuracy when
+grasping in dynamic clutter.
+"
+23694,A Comparative Study of Cohesive Zone Models for Predicting Delamination Behaviors of Arterial Wall,"  Arterial tissue delamination, manifested as the failure between arterial
+layers, is a critical process in the rupture of atherosclerotic plaque, leading
+to potential life-threatening clinical consequences. Numerous models have been
+used to characterize the arterial tissue delamination. Few has investigated the
+effect of Cohesive Zone Model (CZM) shapes on predicting the delamination
+behavior of the arterial wall. In this study, four types of cohesive zone
+models (triangular, trapezoidal, linear-exponential and exponential-linear)
+were investigated to compare their predictability of the arterial wall failure.
+The Holzapfel-Gasser-Ogden (HGO) model was adopted for modelling the mechanical
+behavior of the aortic bulk material. The simulation results using CZM on the
+aortic media delamination were also compared with the results on mouse plaque
+delamination and human fibrous cap delamination. The results show that: 1) the
+simulation results based on the four shapes of CZMs match well with the
+experimental results, 2) the triangular and exponential-linear CZMs are in good
+agreement with the experimental force-displacement curves of mouse plaque
+delamination, 3) considering the viscoelastic effect of the arterial tissue,
+the triangular and exponential-linear CZMs match well with the experimental
+force-displacement curves of human fibrous cap delamination. Thus, triangular
+and exponential-linear CZMs can capture the arterial tissue failure response
+well.
+"
+23695,Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing,"  While Monte Carlo Tree Search and closely related methods have dominated
+General Video Game Playing, recent research has demonstrated the promise of
+Rolling Horizon Evolutionary Algorithms as an interesting alternative. However,
+there is little attention paid to population initialization techniques in the
+setting of general real-time video games. Therefore, this paper proposes the
+use of population seeding to improve the performance of Rolling Horizon
+Evolution and presents the results of two methods, One Step Look Ahead and
+Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus
+with multiple evolution parameter values (population size and individual
+length). An in-depth analysis is carried out between the results of the seeding
+methods and the vanilla Rolling Horizon Evolution. In addition, the paper
+presents a comparison to a Monte Carlo Tree Search algorithm. The results are
+promising, with seeding able to boost performance significantly over baseline
+evolution and even match the high level of play obtained by the Monte Carlo
+Tree Search.
+"
+23696,Persistent Stochastic Non-Interference,"  In this paper we present an information flow security property for
+stochastic, cooperating, processes expressed as terms of the Performance
+Evaluation Process Algebra (PEPA). We introduce the notion of Persistent
+Stochastic Non-Interference (PSNI) based on the idea that every state reachable
+by a process satisfies a basic Stochastic Non-Interference (SNI) property. The
+structural operational semantics of PEPA allows us to give two
+characterizations of PSNI: the first involves a single bisimulation-like
+equivalence check, while the second is formulated in terms of unwinding
+conditions. The observation equivalence at the base of our definition relies on
+the notion of lumpability and ensures that, for a secure process P, the steady
+state probability of observing the system being in a specific state P' is
+independent from its possible high level interactions.
+"
+23697,Generative and Discriminative Text Classification with Recurrent Neural Networks,"  We empirically characterize the performance of discriminative and generative
+LSTM models for text classification. We find that although RNN-based generative
+models are more powerful than their bag-of-words ancestors (e.g., they account
+for conditional dependencies across words in a document), they have higher
+asymptotic error rates than discriminatively trained RNN models. However we
+also find that generative models approach their asymptotic error rate more
+rapidly than their discriminative counterparts---the same pattern that Ng &
+Jordan (2001) proved holds for linear classification models that make more
+naive conditional independence assumptions. Building on this finding, we
+hypothesize that RNN-based generative classification models will be more robust
+to shifts in the data distribution. This hypothesis is confirmed in a series of
+experiments in zero-shot and continual learning settings that show that
+generative models substantially outperform discriminative models.
+"
+23698,"Weakest-link control of invasive species: Impacts of memory, bounded rationality and network structure in repeated cooperative games","  The nature of dispersal of many invasive pests and pathogens in agricultural
+and forestry makes it necessary to consider how the actions of one manager
+affect neighbouring properties. In addition to the direct effects of a
+potential spread of a pest and the resulting economic loss, there are also
+indirect consequences that affect whole regions and that require coordinated
+actions to manage and/or to eradicate it (like movement restrictions). In this
+paper we address the emergence and stability of cooperation among agents who
+respond to a threat of an invasive pest or disease. The model, based on the
+weakest-link paradigm, uses repeated multi-participant coordination games where
+players' pay-offs depend on management decisions to prevent the invasion on
+their own land as well as of their neighbours on a network. We show that for
+the basic cooperation game agents select the risk-dominant strategy of a Stag
+hunt game over the pay-off dominant strategy of implementing control measures.
+However, cooperation can be achieved by the social planner offering a
+biosecurity payment. The critical level of this payment depends on the details
+of the decision-making process, with higher trust (based on a reputation of
+other agents reflecting their past performance) allowing a significant
+reduction in necessary payments and slowing down decay in cooperation when the
+payment is low. We also find that allowing for uncertainty in decision-making
+process can enhance cooperation for low levels of payments. Finally, we show
+the importance of industry structure to the emergence of cooperation, with
+increase in the average coordination number of network nodes leading to
+increase in the critical biosecurity payment.
+"
+23699,A strategy and a new operator to generate covariants in small characteristic,"  We present some new results about covariants in small characteristic. In
+section 1, we give a method to construct covariants using an approach similar
+to Sturmfels. We apply our method to find a separating system of covariants for
+binary quartic in characteristic 3. In the second section, we construct a new
+operator on covariants when the characteristic is small compared to the degree
+of the form.
+"
+23700,Solutions of Quadratic First-Order ODEs applied to Computer Vision Problems,"  This article is a study about the existence and the uniqueness of solutions
+of a specific quadratic first-order ODE that frequently appears in multiple
+reconstruction problems. It is called the \emph{planar-perspective equation}
+due to the duality with the geometric problem of reconstruction of
+planar-perspective curves from their modulus. Solutions of the
+\emph{planar-perspective equation} are related with planar curves parametrized
+with perspective parametrization due to this geometric interpretation. The
+article proves the existence of only two local solutions to the \emph{initial
+value problem} with \emph{regular initial conditions} and a maximum of two
+analytic solutions with \emph{critical initial conditions}. The article also
+gives theorems to extend the local definition domain where the existence of
+both solutions are guaranteed. It introduces the \emph{maximal depth function}
+as a function that upper-bound all possible solutions of the
+\emph{planar-perspective equation} and contains all its possible \emph{critical
+points}. Finally, the article describes the \emph{maximal-depth solution
+problem} that consists of finding the solution of the referred equation that
+has maximum the depth and proves its uniqueness. It is an important problem as
+it does not need initial conditions to obtain the unique solution and its the
+frequent solution that practical algorithms of the state-of-the-art give.
+"
+23701,Approximate Backbone Based Multilevel Algorithm for Next Release Problem,"  The next release problem (NRP) aims to effectively select software
+requirements in order to acquire maximum customer profits. As an NP-hard
+problem in software requirement engineering, NRP lacks efficient approximate
+algorithms for large scale instances. The backbone is a new tool for tackling
+large scale NP-hard problems in recent years. In this paper, we employ the
+backbone to design high performance approximate algorithms for large scale NRP
+instances. Firstly we show that it is NP-hard to obtain the backbone of NRP.
+Then, we illustrate by fitness landscape analysis that the backbone can be well
+approximated by the shared common parts of local optimal solutions. Therefore,
+we propose an approximate backbone based multilevel algorithm (ABMA) to solve
+large scale NRP instances. This algorithm iteratively explores the search
+spaces by multilevel reductions and refinements. Experimental results
+demonstrate that ABMA outperforms existing algorithms on large instances in
+terms of solution quality and running time.
+"
+23702,Distributed Nonparametric Regression under Communication Constraints,"  This paper studies the problem of nonparametric estimation of a smooth
+function with data distributed across multiple machines. We assume an
+independent sample from a white noise model is collected at each machine, and
+an estimator of the underlying true function needs to be constructed at a
+central machine. We place limits on the number of bits that each machine can
+use to transmit information to the central machine. Our results give both
+asymptotic lower bounds and matching upper bounds on the statistical risk under
+various settings. We identify three regimes, depending on the relationship
+among the number of machines, the size of the data available at each machine,
+and the communication budget. When the communication budget is small, the
+statistical risk depends solely on this communication bottleneck, regardless of
+the sample size. In the regime where the communication budget is large, the
+classic minimax risk in the non-distributed estimation setting is recovered. In
+an intermediate regime, the statistical risk depends on both the sample size
+and the communication budget.
+"
+23703,The correlation of co-located hydrogen masers,"  The correlation of co-located hydrogen masers (H-masers) is difficult to
+measure because their common-mode noise induced by the environment will be
+cancelled out during the comparison measurement. With the development of
+fibre-based high-precision time and frequency transfer technique, the
+correlation of co-located hydrogen masers can be directly measured with the
+help of remote H-masers. Recently, a fiber-based frequency synchronization
+network was constructed in the Beijing region by connecting 5 H-masers from 4
+institutions. The correlation coefficient of atomic clocks is defined and the
+correlation between two co-located H-masers is measured using both experimental
+and simulative methods. The results show that the correlation is not prominent
+until the averaging time is larger than $\sim10^3$s; then, the coefficient
+grows rapidly for averaging times ranging from $\sim10^3$s to $\sim10^5$s and
+decreases beyond $\sim10^5$s up to 5 days.
+"
+23704,Holonomic Gradient Method-Based CDF Evaluation for the Largest Eigenvalue of a Complex Noncentral Wishart Matrix,"  The outage probability of maximal-ratio combining (MRC) for a multiple-input
+multiple-output (MIMO) wireless communications system under Rician fading is
+given by the cumulative distribution function (CDF) for the largest eigenvalue
+of a complex noncentral Wishart matrix. This CDF has previously been expressed
+as a determinant whose elements are integrals of a confluent hypergeometric
+function. For the determinant elements, conventional evaluation approaches,
+e.g., truncation of infinite series ensuing from the hypergeometric function or
+numerical integration, can be unreliable and slow even for moderate antenna
+numbers and Rician $ K $-factor values. Therefore, herein, we derive by hand
+and by computer algebra also differential equations that are then solved from
+initial conditions computed by conventional approaches. This is the holonomic
+gradient method (HGM). Previous HGM-based evaluations of MIMO relied on
+differential equations that were not theoretically guaranteed to converge, and,
+thus, yielded reliable results only for few antennas or moderate $ K $. Herein,
+we reveal that gauge transformations can yield differential equations that are
+{\emph{stabile}}, i.e., guarantee HGM convergence. The ensuing HGM-based CDF
+evaluation is demonstrated reliable, accurate, and expeditious in computing the
+MRC outage probability even for very large antenna numbers and values of $ K $.
+"
+23705,Sparse reconstruction of the merging A520 cluster system,"  Merging galaxy clusters present a unique opportunity to study the properties
+of dark matter in an astrophysical context. These are rare and extreme cosmic
+events in which the bulk of the baryonic matter becomes displaced from the dark
+matter halos of the colliding subclusters. Since all mass bends light, weak
+gravitational lensing is a primary tool to study the total mass distribution in
+such systems. Combined with X-ray and optical analyses, mass maps of cluster
+mergers reconstructed from weak-lensing observations have been used to
+constrain the self-interaction cross-section of dark matter. The dynamically
+complex Abell 520 (A520) cluster is an exceptional case, even among merging
+systems: multi-wavelength observations have revealed a surprising high
+mass-to-light concentration of dark mass, the interpretation of which is
+difficult under the standard assumption of effectively collisionless dark
+matter. We revisit A520 using a new sparsity-based mass-mapping algorithm to
+independently assess the presence of the puzzling dark core. We obtain
+high-resolution mass reconstructions from two separate galaxy shape catalogs
+derived from Hubble Space Telescope observations of the system. Our mass maps
+agree well overall with the results of previous studies, but we find important
+differences. In particular, although we are able to identify the dark core at a
+certain level in both data sets, it is at much lower significance than has been
+reported before using the same data. As we cannot confirm the detection in our
+analysis, we do not consider A520 as posing a significant challenge to the
+collisionless dark matter scenario.
+"
+23706,An Introduction to Deep Visual Explanation,"  The practical impact of deep learning on complex supervised learning problems
+has been significant, so much so that almost every Artificial Intelligence
+problem, or at least a portion thereof, has been somehow recast as a deep
+learning problem. The applications appeal is significant, but this appeal is
+increasingly challenged by what some call the challenge of explainability, or
+more generally the more traditional challenge of debuggability: if the outcomes
+of a deep learning process produce unexpected results (e.g., less than expected
+performance of a classifier), then there is little available in the way of
+theories or tools to help investigate the potential causes of such unexpected
+behavior, especially when this behavior could impact people's lives. We
+describe a preliminary framework to help address this issue, which we call
+""deep visual explanation"" (DVE). ""Deep,"" because it is the development and
+performance of deep neural network models that we want to understand. ""Visual,""
+because we believe that the most rapid insight into a complex multi-dimensional
+model is provided by appropriate visualization techniques, and ""Explanation,""
+because in the spectrum from instrumentation by inserting print statements to
+the abductive inference of explanatory hypotheses, we believe that the key to
+understanding deep learning relies on the identification and exposure of
+hypotheses about the performance behavior of a learned deep model. In the
+exposition of our preliminary framework, we use relatively straightforward
+image classification examples and a variety of choices on initial configuration
+of a deep model building scenario. By careful but not complicated
+instrumentation, we expose classification outcomes of deep models using
+visualization, and also show initial results for one potential application of
+interpretability.
+"
+23707,Scattering theory from artificial piezoelectric-like meta-atoms and molecules,"  Inspired by the natural piezoelectric effect, we introduce hybrid-wave
+electromechanical meta-atoms and meta-molecules that consist of coupled
+electrical and mechanical oscillators with similar resonance frequencies. We
+propose an analytical model for the linearized electromechanical scattering
+process, and explore its properties based on first principles. We demonstrate
+that by exploiting the linearized hybrid-wave interaction, one may enable
+functionalities that are forbidden otherwise, going beyond the limits of
+today's metamaterials. As an example we show an electrically deep
+sub-wavelength dimer of meta-atoms with extremely sensitive response to the
+direction-of-arrival of an impinging electromagnetic wave. This scheme of
+meta-atoms and molecules may open ways for metamaterials with a plethora of
+exciting dynamics and phenomena that have not been studied before with
+potential technological implications in radio-frequencies and acoustics.
+"
+23708,Unipotent elements forcing irreducibility in linear algebraic groups,"  Let $G$ be a simple algebraic group over an algebraically closed field $K$ of
+characteristic $p > 0$. We consider connected reductive subgroups $X$ of $G$
+that contain a given distinguished unipotent element $u$ of $G$. A result of
+Testerman and Zalesski (Proc. Amer. Math. Soc., 2013) shows that if $u$ is a
+regular unipotent element, then $X$ cannot be contained in a proper parabolic
+subgroup of $G$. We generalize their result and show that if $u$ has order $p$,
+then except for two known examples which occur in the case $(G, p) = (C_2, 2)$,
+the subgroup $X$ cannot be contained in a proper parabolic subgroup of $G$. In
+the case where $u$ has order $> p$, we also present further examples arising
+from indecomposable tilting modules with quasi-minuscule highest weight.
+"
+23709,3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose Estimation,"  The conventional pose estimation of a 3D object usually requires the
+knowledge of the 3D model of the object. Even with the recent development in
+convolutional neural networks (CNNs), a 3D model is often necessary in the
+final estimation. In this paper, we propose a two-stage pipeline that takes in
+raw colored point cloud data and estimates an object's translation and rotation
+by running 3D convolutions on voxels. The pipeline is simple yet highly
+accurate: translation error is reduced to the voxel resolution (around 1 cm)
+and rotation error is around 5 degrees. The pipeline is also put to actual
+robotic grasping tests where it achieves above 90% success rate for test
+objects. Another innovation is that a motion capture system is used to
+automatically label the point cloud samples which makes it possible to rapidly
+collect a large amount of highly accurate real data for training the neural
+networks.
+"
+23710,An Ising Model for Metal-Organic Frameworks,"  We present a three-dimensional Ising model where lines of equal spins are
+frozen in such that they form an ordered framework structure. The frame spins
+impose an external field on the rest of the spins (active spins). We
+demonstrate that this ""porous Ising model"" can be seen as a minimal model for
+condensation transitions of gas molecules in metal-organic frameworks. Using
+Monte Carlo simulation techniques, we compare the phase behavior of a porous
+Ising model with that of a particle-based model for the condensation of methane
+(CH$_4$) in the isoreticular metal-organic framework IRMOF-16. For both models,
+we find a line of first-order phase transitions that end in a critical point.
+We show that the critical behavior in both cases belongs to the 3D Ising
+universality class, in contrast to other phase transitions in confinement such
+as capillary condensation.
+"
+23711,Deep uncertainties in sea-level rise and storm surge projections: Implications for coastal flood risk management,"  Sea-levels are rising in many areas around the world, posing risks to coastal
+communities and infrastructures. Strategies for managing these flood risks
+present decision challenges that require a combination of geophysical,
+economic, and infrastructure models. Previous studies have broken important new
+ground on the considerable tensions between the costs of upgrading
+infrastructure and the damages that could result from extreme flood events.
+However, many risk-based adaptation strategies remain silent on certain
+potentially important uncertainties, as well as the trade-offs between
+competing objectives. Here, we implement and improve on a classic
+decision-analytical model (van Dantzig 1956) to: (i) capture trade-offs across
+conflicting stakeholder objectives, (ii) demonstrate the consequences of
+structural uncertainties in the sea-level rise and storm surge models, and
+(iii) identify the parametric uncertainties that most strongly influence each
+objective using global sensitivity analysis. We find that the flood adaptation
+model produces potentially myopic solutions when formulated using traditional
+mean-centric decision theory. Moving from a single-objective problem
+formulation to one with multi-objective trade-offs dramatically expands the
+decision space, and highlights the need for compromise solutions to address
+stakeholder preferences. We find deep structural uncertainties that have large
+effects on the model outcome, with the storm surge parameters accounting for
+the greatest impacts. Global sensitivity analysis effectively identifies
+important parameter interactions that local methods overlook, and which could
+have critical implications for flood adaptation strategies.
+"
+23712,Classification with Costly Features using Deep Reinforcement Learning,"  We study a classification problem where each feature can be acquired for a
+cost and the goal is to optimize a trade-off between the expected
+classification error and the feature cost. We revisit a former approach that
+has framed the problem as a sequential decision-making problem and solved it by
+Q-learning with a linear approximation, where individual actions are either
+requests for feature values or terminate the episode by providing a
+classification decision. On a set of eight problems, we demonstrate that by
+replacing the linear approximation with neural networks the approach becomes
+comparable to the state-of-the-art algorithms developed specifically for this
+problem. The approach is flexible, as it can be improved with any new
+reinforcement learning enhancement, it allows inclusion of pre-trained
+high-performance classifier, and unlike prior art, its performance is robust
+across all evaluated datasets.
+"
+23713,Integrals of motion from quantum toroidal algebras,"  We identify the Taylor coefficients of the transfer matrices corresponding to
+quantum toroidal algebras with the elliptic local and non-local integrals of
+motion introduced by Kojima, Shiraishi, Watanabe, and one of the authors.
+That allows us to prove the Litvinov conjectures on the Intermediate Long
+Wave model.
+We also discuss the (gl(m),gl(n)) duality of XXZ models in quantum toroidal
+setting and the implications for the quantum KdV model. In particular, we
+conjecture that the spectrum of non-local integrals of motion of Bazhanov,
+Lukyanov, and Zamolodchikov is described by Gaudin Bethe ansatz equations
+associated to affine sl(2).
+"
+23714,Weighted-SVD: Matrix Factorization with Weights on the Latent Factors,"  The Matrix Factorization models, sometimes called the latent factor models,
+are a family of methods in the recommender system research area to (1) generate
+the latent factors for the users and the items and (2) predict users' ratings
+on items based on their latent factors. However, current Matrix Factorization
+models presume that all the latent factors are equally weighted, which may not
+always be a reasonable assumption in practice. In this paper, we propose a new
+model, called Weighted-SVD, to integrate the linear regression model with the
+SVD model such that each latent factor accompanies with a corresponding weight
+parameter. This mechanism allows the latent factors have different weights to
+influence the final ratings. The complexity of the Weighted-SVD model is
+slightly larger than the SVD model but much smaller than the SVD++ model. We
+compared the Weighted-SVD model with several latent factor models on five
+public datasets based on the Root-Mean-Squared-Errors (RMSEs). The results show
+that the Weighted-SVD model outperforms the baseline methods in all the
+experimental datasets under almost all settings.
+"
+23715,Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model,"  A new Bayesian modeling method is proposed by combining the maximization of
+the marginal likelihood with a momentum-space renormalization group
+transformation for Gaussian graphical models. Moreover, we present a scheme for
+computint the statistical averages of hyperparameters and mean square errors in
+our proposed method based on a momentumspace renormalization transformation.
+"
+23716,"Mediated behavioural change in human-machine networks: exploring network characteristics, trust and motivation","  Human-machine networks pervade much of contemporary life. Network change is
+the product of structural modifications along with differences in participant
+be-havior. If we assume that behavioural change in a human-machine network is
+the result of changing the attitudes of participants in the network, then the
+question arises whether network structure can affect participant attitude.
+Taking citizen par-ticipation as an example, engagement with relevant
+stakeholders reveals trust and motivation to be the major objectives for the
+network. Using a typology to de-scribe network state based on multiple
+characteristic or dimensions, we can pre-dict possible behavioural outcomes in
+the network. However, this has to be medi-ated via attitude change. Motivation
+for the citizen participation network can only increase in line with enhanced
+trust. The focus for changing network dynamics, therefore, shifts to the
+dimensional changes needed to encourage increased trust. It turns out that the
+coordinated manipulation of multiple dimensions is needed to bring about the
+desired shift in attitude.
+"
+23717,A 3D Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts,"  Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the
+National Airspace System (NAS) is a complex problem which is required to be
+addressed by simulation studies before allowing the routine access of UAS into
+the NAS. This paper focuses on providing a 3-dimensional (3D) simulation
+framework using a game theoretical methodology to evaluate integration concepts
+using scenarios where manned and unmanned air vehicles co-exist. In the
+proposed method, human pilot interactive decision making process is
+incorporated into airspace models which can fill the gap in the literature
+where the pilot behavior is generally assumed to be known a priori. The
+proposed human pilot behavior is modeled using dynamic level-k reasoning
+concept and approximate reinforcement learning. The level-k reasoning concept
+is a notion in game theory and is based on the assumption that humans have
+various levels of decision making. In the conventional ""static"" approach, each
+agent makes assumptions about his or her opponents and chooses his or her
+actions accordingly. On the other hand, in the dynamic level-k reasoning,
+agents can update their beliefs about their opponents and revise their level-k
+rule. In this study, Neural Fitted Q Iteration, which is an approximate
+reinforcement learning method, is used to model time-extended decisions of
+pilots with 3D maneuvers. An analysis of UAS integration is conducted using an
+example 3D scenario in the presence of manned aircraft and fully autonomous UAS
+equipped with sense and avoid algorithms.
+"
+23718,Numerically Stable Variants of the Communication-hiding Pipelined Conjugate Gradients Algorithm for the Parallel Solution of Large Scale Symmetric Linear Systems,"  By reducing the number of global synchronization bottlenecks per iteration
+and hiding communication behind useful computational work, pipelined Krylov
+subspace methods achieve significantly improved parallel scalability on
+present-day HPC hardware. However, this typically comes at the cost of a
+reduced maximal attainable accuracy. This paper presents and compares several
+stabilized versions of the communication-hiding pipelined Conjugate Gradients
+method. The main novel contribution of this work is the reformulation of the
+multi-term recurrence pipelined CG algorithm by introducing shifts in the
+recursions for specific auxiliary variables. These shifts reduce the
+amplification of local rounding errors on the residual. The stability analysis
+presented in this work provides a rigorous method for selection of the optimal
+shift value in practice. It is shown that, given a proper choice for the shift
+parameter, the resulting shifted pipelined CG algorithm restores the attainable
+accuracy and displays nearly identical robustness to local rounding error
+propagation compared to classical CG. Numerical results on a variety of SPD
+benchmark problems compare different stabilization techniques for the pipelined
+CG algorithm, showing that the shifted pipelined CG algorithm is able to attain
+a high accuracy while displaying excellent parallel performance.
+"
+23719,"StreetGen : In base city scale procedural generation of streets: road network, road surface and street objects","  Streets are large, diverse, and used for several (and possibly conflicting)
+transport modalities as well as social and cultural activities. Proper planning
+is essential and requires data. Manually fabricating data that represent
+streets (street reconstruction) is error-prone and time consuming. Automatising
+street reconstruction is a challenge because of the diversity, size, and scale
+of the details (few centimetres for cornerstone) required. The state-of-the-art
+focuses on roads (no context, no urban features) and is strongly determined by
+each application (simulation, visualisation, planning). We propose a unified
+framework that works on real Geographic Information System (GIS) data and uses
+a strong, yet simple modelling hypothesis when possible to robustly model
+streets at the city level or street level. Our method produces a coherent
+street-network model containing topological traffic information, road surface
+and street objects. We demonstrate the robustness and genericity of our method
+by reconstructing the entire city of Paris streets and exploring other similar
+reconstruction (airport driveway).
+"
+23720,3D Common-Refinement Method for Non-Matching Meshes in Partitioned Variational Fluid-Structure Analysis,"  We present a three-dimensional (3D) common-refinement method for non-matching
+meshes between discrete non-overlapping subdomains of incompressible fluid and
+nonlinear hyperelastic structure. To begin, we first investigate the accuracy
+of common-refinement method (CRM) to satisfy traction equilibrium condition
+along the fluid-elastic interface with non-matching meshes. We systematically
+assess the accuracy of CRM against the matching grid solution by varying grid
+mismatch between the fluid and solid meshes over a cylindrical tubular elastic
+body. We demonstrate second-order accuracy of CRM through uniform refinements
+of fluid and solid meshes along the interface. We then extend the error
+analysis to transient data transfer across non-matching meshes between fluid
+and solid solvers. We show that the common-refinement discretization across
+non-matching fluid-structure grids yields accurate transfer of the physical
+quantities across the fluid-solid interface. We next solve a 3D benchmark
+problem of a cantilevered hyperelastic plate behind a circular bluff body and
+verify the accuracy of coupled solutions with respect to the available solution
+in the literature. By varying the solid interface resolution, we generate
+various non-matching grid ratios and quantify the accuracy of CRM for the
+nonlinear structure interacting with a laminar flow. We illustrate that the CRM
+with the partitioned NIFC treatment is stable for low solid-to-fluid density
+ratio and non-matching meshes. Finally, we demonstrate the 3D parallel
+implementation of common-refinement with NIFC scheme for a realistic
+engineering problem of drilling riser undergoing complex vortex-induced
+vibration with strong added mass effects.
+"
+23721,Charge Fractionalization in a Kondo Device,"  We study nonequilibrium transport through a charge Kondo device realizing the
+two-channel Kondo critical point in a recent experiment by Iftikhar et al. By
+computing the current and shot noise at low voltages near the critical point,
+we obtain a universal Fano factor $e^*/e=1/2$. We identify elementary transport
+processes as weak scattering of emergent fermions carrying half-integer charge
+quantum numbers. This forms an experimental fingerprint for fractionalization
+in a non-Fermi liquid, which, compared to spin-Kondo devices, could be observed
+at elevated temperatures.
+"
+23722,Bayesian active learning for choice models with deep Gaussian processes,"  In this paper, we propose an active learning algorithm and models which can
+gradually learn individual's preference through pairwise comparisons. The
+active learning scheme aims at finding individual's most preferred choice with
+minimized number of pairwise comparisons. The pairwise comparisons are encoded
+into probabilistic models based on assumptions of choice models and deep
+Gaussian processes. The next-to-compare decision is determined by a novel
+acquisition function. We benchmark the proposed algorithm and models using
+functions with multiple local optima and one public airline itinerary dataset.
+The experiments indicate the effectiveness of our active learning algorithm and
+models.
+"
+23723,"The Inverse Weighted Lindley Distribution: Properties, Estimation and an Application on a Failure Time Data","  In this paper a new distribution is proposed. This new model provides more
+flexibility to modeling data with upside-down bathtub hazard rate function. A
+significant account of mathematical properties of the new distribution is
+presented. The maximum likelihood estimators for the parameters in the presence
+of complete and censored data are presented. Two corrective approaches are
+considered to derive modified estimators that are bias-free to second order. A
+numerical simulation is carried out to examine the efficiency of the bias
+correction. Finally, an application using a real data set is presented in order
+to illustrate our proposed distribution.
+"
+23724,Gradient and Newton Boosting for Classification and Regression,"  Boosting algorithms enjoy large popularity due to their high predictive
+accuracy on a wide array of datasets. In this article, we argue that it is
+important to distinguish between three types of statistical boosting
+algorithms: gradient and Newton boosting as well as a hybrid variant of the
+two. To date, both researchers and practitioners often do not discriminate
+between these boosting variants. We compare the different boosting algorithms
+on a wide range of real and simulated datasets for various choices of loss
+functions using trees as base learners. In addition, we introduce a novel
+tuning parameter for Newton boosting. We find that Newton boosting performs
+substantially better than the other boosting variants for classification, and
+that the novel tuning parameter is important for predictive accuracy
+"
+23725,Pricing and Revenue Sharing for Secondary Data Market,"  Recent advances in technology enable public or commercial establishments and
+individual data plan subscribers to operate as Wi-Fi providers, offering
+Internet access. However, the model of a monthly flat service fee charged by
+ISP to establishment or individual WFPs offers very little incentives for ISP
+to provide additional bandwidth to WFPs customers. Thus, these users may
+experience insufficient bandwidth when there is high demand for bandwidth. In
+addition, ISP even discourages individual subscribers to provide Internet
+access through their smartphone because such practices may cause market
+saturation. To address this, in this paper we propose a dynamic pricing scheme
+and a revenue sharing mechanism that provide incentives for ISP to support
+establishment and individual Wi-Fi providers offering Internet access to users.
+Our revenue sharing model is based on Shapley value mechanism. Importantly, our
+proposed revenue sharing mechanism captures the power negotiation between ISP
+and Wi-Fi providers, and how shifts in the power balance between the two
+entities influence revenue division. Specifically, the model assures that the
+party who contributes more receives a higher portion of the revenue. In
+addition to that, the division of revenue eventually converges to a percentage
+value.
+"
+23726,Lasso Regularization Paths for NARMAX Models via Coordinate Descent,"  We propose a new algorithm for estimating NARMAX models with $L_1$
+regularization for models represented as a linear combination of basis
+functions. Due to the $L_1$-norm penalty the Lasso estimation tends to produce
+some coefficients that are exactly zero and hence gives interpretable models.
+The novelty of the contribution is the inclusion of error regressors in the
+Lasso estimation (which yields a nonlinear regression problem). The proposed
+algorithm uses cyclical coordinate descent to compute the parameters of the
+NARMAX models for the entire regularization path. It deals with the error terms
+by updating the regressor matrix along with the parameter vector. In
+comparative timings we find that the modification does not reduce the
+computational efficiency of the original algorithm and can provide the most
+important regressors in very few inexpensive iterations. The method is
+illustrated for linear and polynomial models by means of two examples.
+"
+23727,Deep Gaussian Mixture Models,"  Deep learning is a hierarchical inference method formed by subsequent
+multiple layers of learning able to more efficiently describe complex
+relationships. In this work, Deep Gaussian Mixture Models are introduced and
+discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers
+of latent variables, where, at each layer, the variables follow a mixture of
+Gaussian distributions. Thus, the deep mixture model consists of a set of
+nested mixtures of linear models, which globally provide a nonlinear model able
+to describe the data in a very flexible way. In order to avoid
+overparameterized solutions, dimension reduction by factor models can be
+applied at each layer of the architecture thus resulting in deep mixtures of
+factor analysers.
+"
+23728,Taming the heavy-tailed features by shrinkage and clipping,"  In this paper, we consider the generalized linear models (GLM) with
+heavy-tailed features and corruptions. Besides clipping the response, we
+propose to shrink the feature vector by its $\ell_4$-norm under the low
+dimensional regime and clip each entry of the feature vector in the
+high-dimensional regime. Under bounded fourth moment assumptions, we show that
+the MLE based on shrunk or clipped data enjoys nearly the minimax optimal rate
+with exponential deviation bound. Simulations demonstrate significant
+improvement in statistical performance by feature shrinkage and clipping in
+linear regression with heavy-tailed noise and logistic regression with noisy
+labels. We also apply shrinkage to deep features of MNIST images and find that
+classifiers trained by shrunk deep features are fairly robust to noisy labels:
+it achieves $0.9\%$ testing error in the presence of $40\%$ mislabeled data.
+"
+23729,Non-iterative triples excitations in equation-of-motion coupled-cluster theory for electron attachment with applications to bound and temporary anions,"  The impact of residual electron correlation beyond the equation-of-motion
+coupled-cluster singles and doubles approximation (EOM-CCSD) on positions and
+widths of electronic resonances is investigated. To establish a method that
+accomplishes this task in an economical manner, several approaches proposed for
+the approximate treatment of triples excitations are reviewed with respect to
+their performance in the electron attachment (EA) variant of EOM-CC theory.
+The recently introduced EOM-CCSD(T)(a)* method, which includes non-iterative
+corrections to the reference and the target states, reliably reproduces
+vertical attachment energies from EOM-EA-CC calculations with singles, doubles,
+and full triples excitations in contrast to schemes in which non-iterative
+corrections are applied only to the target states. Applications of
+EOM-EA-CCSD(T)(a)* augmented by a complex absorbing potential (CAP) to several
+temporary anions illustrate that shape resonances are well described by
+EOM-EA-CCSD, but that residual electron correlation often makes a
+non-negligible impact on their positions and widths. The positions of Feshbach
+resonances, on the other hand, are significantly improved when going from
+CAP-EOM-EA-CCSD to CAP-EOM-EA-CCSD(T)(a)*, but the correct energetic order of
+the relevant electronic states is still not achieved.
+"
+23730,Optimization of thin n-in-p planar pixel modules for the ATLAS upgrade at HL-LHC,"  The ATLAS experiment will undergo around the year 2025 a replacement of the
+tracker system in view of the high luminosity phase of the LHC (HL-LHC) with a
+new 5-layer pixel system. Thin planar pixel sensors are promising candidates to
+instrument the innermost region of the new pixel system, thanks to the reduced
+contribution to the material budget and their high charge collection efficiency
+after irradiation. The sensors of 50-150 $\mu$m thickness, interconnected to
+FE-I4 read-out chips, have been characterized with radioactive sources and beam
+tests. In particular active edge sensors have been investigated. The
+performance of two different versions of edge designs are compared: the first
+with a bias ring, and the second one where only a floating guard ring has been
+implemented. The hit efficiency at the edge has also been studied after
+irradiation at a fluence of $10^{15}$ \neqcm. Highly segmented sensors will
+represent a challenge for the tracking in the forward region of the pixel
+system at HL-LHC. In order to reproduce the performance of 50x50 $\mu$m$^2$
+pixels at high pseudo-rapidity values, FE-I4 compatible planar pixel sensors
+have been studied before and after irradiation in beam tests at high incidence
+angles with respect to the short pixel direction. Results on the hit efficiency
+in this configuration are discussed for different sensor thicknesses.
+"
+23731,"Quantum groups, property (T), and weak mixing","  For second countable discrete quantum groups, and more generally second
+countable locally compact quantum groups with trivial scaling group, we show
+that property (T) is equivalent to every weakly mixing unitary representation
+not having almost invariant vectors. This is a generalization of a theorem of
+Bekka and Valette from the group setting and was previously established in the
+case of low dual by Daws, Skalsi, and Viselter. Our approach uses spectral
+techniques and is completely different from those of Bekka--Valette and
+Daws--Skalski--Viselter. By a separate argument we furthermore extend the
+result to second countable nonunimodular locally compact quantum groups, which
+are shown in particular not to have property (T), generalizing a theorem of
+Fima from the discrete setting. We also obtain quantum group versions of
+characterizations of property (T) of Kerr and Pichot in terms of the Baire
+category theory of weak mixing representations and of Connes and Weiss in term
+of the prevalence of strongly ergodic actions.
+"
+23732,Enhancing Robustness of Machine Learning Systems via Data Transformations,"  We propose the use of data transformations as a defense against evasion
+attacks on ML classifiers. We present and investigate strategies for
+incorporating a variety of data transformations including dimensionality
+reduction via Principal Component Analysis and data `anti-whitening' to enhance
+the resilience of machine learning, targeting both the classification and the
+training phase. We empirically evaluate and demonstrate the feasibility of
+linear transformations of data as a defense mechanism against evasion attacks
+using multiple real-world datasets. Our key findings are that the defense is
+(i) effective against the best known evasion attacks from the literature,
+resulting in a two-fold increase in the resources required by a white-box
+adversary with knowledge of the defense for a successful attack, (ii)
+applicable across a range of ML classifiers, including Support Vector Machines
+and Deep Neural Networks, and (iii) generalizable to multiple application
+domains, including image classification and human activity classification.
+"
+23733,Relating virtual knot invariants to links in $\mathbb{S}^{3}$,"  Geometric interpretations of some virtual knot invariants are given in terms
+of invariants of links in $\mathbb{S}^3$. Alexander polynomials of almost
+classical knots are shown to be specializations of the multi-variable Alexander
+polynomial of certain two-component boundary links of the form $J \sqcup K$
+with $J$ a fibered knot. The index of a crossing, a common ingredient in the
+construction of virtual knot invariants, is related to the Milnor triple
+linking number of certain three-component links $J \sqcup K_1 \sqcup K_2$ with
+$J$ a connected sum of trefoils or figure-eights. Our main technical tool is
+virtual covers. This technique, due to Manturov and the first author,
+associates a virtual knot $\upsilon$ to a link $J \sqcup K$, where $J$ is
+fibered and $\text{lk}(J,K)=0$. Here we extend virtual covers to all
+multicomponent links $L=J \sqcup K$, with $K$ a knot. It is shown that an
+unknotted component $J_0$ can be added to $L$ so that $J_0 \sqcup J$ is fibered
+and $K$ has algebraic intersection number zero with a fiber of $J_0 \sqcup J$.
+This is called fiber stabilization. It provides an avenue for studying all
+links with virtual knots.
+"
+23734,On the controllability of the Navier-Stokes equation in spite of boundary layers,"  In this proceeding we expose a particular case of a recent result obtained by
+the authors regarding the incompressible Navier-Stokes equations in a smooth
+bounded and simply connected bounded domain, either in 2D or in 3D, with a
+Navier slip-with-friction boundary condition except on a part of the boundary.
+This under-determination encodes that one has control over the remaining part
+of the boundary. We prove that for any initial data, for any positive time,
+there exists a weak Leray solution which vanishes at this given time.
+"
+23735,Direct observation of self energy signatures of the resonant collective mode in Bi2Sr2CaCu2O8,"  We use high resolution ARPES to study the resonant, collective excitation
+mode in the superconducting state of Bi2212. By collecting very high quality
+data we found new features in the self energy in the antinodal region, where
+the interaction of electrons with the mode is the strongest. This interaction
+leads to pronounced peak in the scattering rate and we demonstrate that this
+feature is directly responsible for well known hump-dip-peak structure in
+cuprates. By studying how the weight of this peak changes with temperature we
+unequivocally demonstrate that interaction of electrons with resonant mode in
+cuprates vanishes at Tc and is very much localized in the momentum space close
+to the antinode. These findings present a consistent picture of line shape and
+self energy signatures of the electron-boson coupling in cuprates and resolve
+long standing controversy surrounding this issue. The momentum dependence of
+the strength of electron-mode interaction enables development of quantitative
+theory of this phenomenon in cuprates.
+"
+23736,Neural Domain Adaptation for Biomedical Question Answering,"  Factoid question answering (QA) has recently benefited from the development
+of deep learning (DL) systems. Neural network models outperform traditional
+approaches in domains where large datasets exist, such as SQuAD (ca. 100,000
+questions) for Wikipedia articles. However, these systems have not yet been
+applied to QA in more specific domains, such as biomedicine, because datasets
+are generally too small to train a DL system from scratch. For example, the
+BioASQ dataset for biomedical QA comprises less then 900 factoid (single
+answer) and list (multiple answers) QA instances. In this work, we adapt a
+neural QA system trained on a large open-domain dataset (SQuAD, source) to a
+biomedical dataset (BioASQ, target) by employing various transfer learning
+techniques. Our network architecture is based on a state-of-the-art QA system,
+extended with biomedical word embeddings and a novel mechanism to answer list
+questions. In contrast to existing biomedical QA systems, our system does not
+rely on domain-specific ontologies, parsers or entity taggers, which are
+expensive to create. Despite this fact, our systems achieve state-of-the-art
+results on factoid questions and competitive results on list questions.
+"
+23737,Gaussian Lower Bound for the Information Bottleneck Limit,"  The Information Bottleneck (IB) is a conceptual method for extracting the
+most compact, yet informative, representation of a set of variables, with
+respect to the target. It generalizes the notion of minimal sufficient
+statistics from classical parametric statistics to a broader
+information-theoretic sense. The IB curve defines the optimal trade-off between
+representation complexity and its predictive power. Specifically, it is
+achieved by minimizing the level of mutual information (MI) between the
+representation and the original variables, subject to a minimal level of MI
+between the representation and the target. This problem is shown to be in
+general NP hard. One important exception is the multivariate Gaussian case, for
+which the Gaussian IB (GIB) is known to obtain an analytical closed form
+solution, similar to Canonical Correlation Analysis (CCA). In this work we
+introduce a Gaussian lower bound to the IB curve; we find an embedding of the
+data which maximizes its ""Gaussian part"", on which we apply the GIB. This
+embedding provides an efficient (and practical) representation of any arbitrary
+data-set (in the IB sense), which in addition holds the favorable properties of
+a Gaussian distribution. Importantly, we show that the optimal Gaussian
+embedding is bounded from above by non-linear CCA. This allows a fundamental
+limit for our ability to Gaussianize arbitrary data-sets and solve complex
+problems by linear methods.
+"
+23738,A Kind of Magic,"  We introduce the extended Freudenthal-Rosenfeld-Tits magic square based on
+six algebras: the reals $\mathbb{R}$, complexes $\mathbb{C}$, ternions
+$\mathbb{T}$, quaternions $\mathbb{H}$, sextonions $\mathbb{S}$ and octonions
+$\mathbb{O}$. The ternionic and sextonionic rows/columns of the magic square
+yield non-reductive Lie algebras, including
+$\mathfrak{e}_{7\scriptscriptstyle{\frac{1}{2}}}$. It is demonstrated that the
+algebras of the extended magic square appear quite naturally as the symmetries
+of supergravity Lagrangians. The sextonionic row (for appropriate choices of
+real forms) gives the non-compact global symmetries of the Lagrangian for the
+$D=3$ maximal $\mathcal{N}=16$, magic $\mathcal{N}=4$ and magic
+non-supersymmetric theories, obtained by dimensionally reducing the $D=4$
+parent theories on a circle, with the graviphoton left undualised. In
+particular, the extremal intermediate non-reductive Lie algebra
+$\tilde{\mathfrak{e}}_{7(7)\scriptscriptstyle{\frac{1}{2}}}$ (which is not a
+subalgebra of $\mathfrak{e}_{8(8)}$) is the non-compact global symmetry algebra
+of $D=3$, $\mathcal{N}=16$ supergravity as obtained by dimensionally reducing
+$D=4$, $\mathcal{N}=8$ supergravity with $\mathfrak{e}_{7(7)}$ symmetry on a
+circle. The ternionic row (for appropriate choices of real forms) gives the
+non-compact global symmetries of the Lagrangian for the $D=4$ maximal
+$\mathcal{N}=8$, magic $\mathcal{N}=2$ and magic non-supersymmetric theories
+obtained by dimensionally reducing the parent $D=5$ theories on a circle. In
+particular, the Kantor-Koecher-Tits intermediate non-reductive Lie algebra
+$\mathfrak{e}_{6(6)\scriptscriptstyle{\frac{1}{4}}}$ is the non-compact global
+symmetry algebra of $D=4$, $\mathcal{N}=8$ supergravity as obtained by
+dimensionally reducing $D=5$, $\mathcal{N}=8$ supergravity with
+$\mathfrak{e}_{6(6)}$ symmetry on a circle.
+"
+23739,Log-DenseNet: How to Sparsify a DenseNet,"  Skip connections are increasingly utilized by deep neural networks to improve
+accuracy and cost-efficiency. In particular, the recent DenseNet is efficient
+in computation and parameters, and achieves state-of-the-art predictions by
+directly connecting each feature layer to all previous ones. However,
+DenseNet's extreme connectivity pattern may hinder its scalability to high
+depths, and in applications like fully convolutional networks, full DenseNet
+connections are prohibitively expensive. This work first experimentally shows
+that one key advantage of skip connections is to have short distances among
+feature layers during backpropagation. Specifically, using a fixed number of
+skip connections, the connection patterns with shorter backpropagation distance
+among layers have more accurate predictions. Following this insight, we propose
+a connection template, Log-DenseNet, which, in comparison to DenseNet, only
+slightly increases the backpropagation distances among layers from 1 to ($1 +
+\log_2 L$), but uses only $L\log_2 L$ total connections instead of $O(L^2)$.
+Hence, Log-DenseNets are easier than DenseNets to implement and to scale. We
+demonstrate the effectiveness of our design principle by showing better
+performance than DenseNets on tabula rasa semantic segmentation, and
+competitive results on visual recognition.
+"
+23740,Arithmetic monodromy actions on pro-metabelian fundamental groups of once-punctured elliptic curves,"  We prove structure theorems for the moduli stack of elliptic curves equipped
+with $G$-structures, where $G$ is a finite 2-generated metabelian group. In
+particular, we show that if $G$ has exponent $e$, then there is a subgroup
+$H\le GL_2(\mathbb{Z}/e)$ such that $G$-structures on elliptic curves $E$ are
+equivalent to ""congruence structures of level $H$"". Our methods are almost
+entirely group theoretic. Let $\widehat{M}$ denote the free profinite
+metabelian group of rank 2, then along the way we prove a decomposition of
+$Out(\widehat{M})$ as an internal semi-direct product of the subgroup of
+""braid-like outer automorphisms"" with the subgroup of ""IA"" outer automorphisms
+which induce the identity on the abelianization. We also show a surprising
+result that all IA-automorphisms leave every open normal subgroup stable.
+"
+23741,A computational continuum model of poroelastic beds,"  Despite the ubiquity of fluid flows interacting with porous and elastic
+materials, we lack a validated non-empirical macroscale method for
+characterizing the flow over and through a poroelastic medium. We propose a
+computational tool to describe such configurations by deriving and validating a
+continuum model for the poroelastic bed and its interface with the above free
+fluid. We show that, using stress continuity condition and slip velocity
+condition at the interface, the effective model captures the effects of small
+changes in the microstructure anisotropy correctly and predicts the overall
+behaviour in a physically consistent and controllable manner. Moreover, we show
+that the performance of the effective model is accurate by validating with
+fully microscopic resolved simulations. The proposed computational tool can be
+used in investigations in a wide range of fields, including mechanical
+engineering, bio-engineering and geophysics.
+"
+23742,One-to-one Mapping between Stimulus and Neural State: Memory and Classification,"  Synaptic strength can be seen as probability to propagate impulse, and
+according to synaptic plasticity, function could exist from propagation
+activity to synaptic strength. If the function satisfies constraints such as
+continuity and monotonicity, neural network under external stimulus will always
+go to fixed point, and there could be one-to-one mapping between external
+stimulus and synaptic strength at fixed point. In other words, neural network
+""memorizes"" external stimulus in its synapses. A biological classifier is
+proposed to utilize this mapping.
+"
+23743,Learning Independently-Obtainable Reward Functions,"  We present a novel method for learning a set of disentangled reward functions
+that sum to the original environment reward and are constrained to be
+independently achievable. We define independent achievability in terms of value
+functions with respect to achieving one learned reward while pursuing another
+learned reward. Empirically, we illustrate that our method can learn meaningful
+reward decompositions in a variety of domains and that these decompositions
+exhibit some form of generalization performance when the environment's reward
+is modified. Theoretically, we derive results about the effect of maximizing
+our method's objective on the resulting reward functions and their
+corresponding optimal policies.
+"
+23744,Ultra-compact low-loss broadband waveguide taper in silicon-on-insulator,"  A novel design of large bandwidth, fabrication tolerant, CMOS-compatible
+compact tapers (15 um) have been proposed and experimentally demonstrated in
+silicon-on-insulator. The proposed taper along with linear grating couplers for
+spot-size conversion exhibits no degradation in the coupling efficiency
+compared to a standard focusing grating in 1550 nm band. A single taper design
+has a broadband operation over 600 nm that can be used in O, C and L-band. The
+proposed compact taper is highly tolerant to fabrication variations; 80 nm
+change in the taper width and 200 nm in end waveguide width varies the taper
+transmission by <0.4 dB. The footprint of the device i.e. taper along with the
+linear gratings is ~ 250 {\mu}m2; this is 20X smaller than the adiabatic taper
+and 2X smaller than the focusing grating coupler.
+"
+23745,On $\varepsilon$-Admissibility in High Dimension and Nonparametrics,"  In this paper, we discuss the use of $\varepsilon$-admissibility for
+estimation in high-dimensional and nonparametric statistical models. The
+minimax rate of convergence is widely used to compare the performance of
+estimators in high-dimensional and nonparametric models. However, it often
+works poorly as a criterion of comparison. In such cases, the addition of
+comparison by $\varepsilon$-admissibility provides a better outcome. We
+demonstrate the usefulness of $\varepsilon$-admissibility through
+high-dimensional Poisson model and Gaussian infinite sequence model, and
+present noble results.
+"
+23746,Data-dependent Generalization Bounds for Multi-class Classification,"  In this paper, we study data-dependent generalization error bounds exhibiting
+a mild dependency on the number of classes, making them suitable for
+multi-class learning with a large number of label classes. The bounds generally
+hold for empirical multi-class risk minimization algorithms using an arbitrary
+norm as regularizer. Key to our analysis are new structural results for
+multi-class Gaussian complexities and empirical $\ell_\infty$-norm covering
+numbers, which exploit the Lipschitz continuity of the loss function with
+respect to the $\ell_2$- and $\ell_\infty$-norm, respectively. We establish
+data-dependent error bounds in terms of complexities of a linear function class
+defined on a finite set induced by training examples, for which we show tight
+lower and upper bounds. We apply the results to several prominent multi-class
+learning machines, exhibiting a tighter dependency on the number of classes
+than the state of the art. For instance, for the multi-class SVM by Crammer and
+Singer (2002), we obtain a data-dependent bound with a logarithmic dependency
+which significantly improves the previous square-root dependency. Experimental
+results are reported to verify the effectiveness of our theoretical findings.
+"
+23747,New Subquadratic Approximation Algorithms for the Girth,"  We consider the problem of approximating the girth, $g$, of an unweighted and
+undirected graph $G=(V,E)$ with $n$ nodes and $m$ edges. A seminal result of
+Itai and Rodeh [SICOMP'78] gave an additive $1$-approximation in $O(n^2)$ time,
+and the main open question is thus how well we can do in subquadratic time.
+In this paper we present two main results. The first is a
+$(1+\varepsilon,O(1))$-approximation in truly subquadratic time. Specifically,
+for any $k\ge 2$ our algorithm returns a cycle of length $2\lceil
+g/2\rceil+2\left\lceil\frac{g}{2(k-1)}\right\rceil$ in $\tilde{O}(n^{2-1/k})$
+time. This generalizes the results of Lingas and Lundell [IPL'09] who showed it
+for the special case of $k=2$ and Roditty and Vassilevska Williams [SODA'12]
+who showed it for $k=3$. Our second result is to present an
+$O(1)$-approximation running in $O(n^{1+\varepsilon})$ time for any
+$\varepsilon > 0$. Prior to this work the fastest constant-factor approximation
+was the $\tilde{O}(n^{3/2})$ time $8/3$-approximation of Lingas and Lundell
+[IPL'09] using the algorithm corresponding to the special case $k=2$ of our
+first result.
+"
+23748,SLAM with Objects using a Nonparametric Pose Graph,"  Mapping and self-localization in unknown environments are fundamental
+capabilities in many robotic applications. These tasks typically involve the
+identification of objects as unique features or landmarks, which requires the
+objects both to be detected and then assigned a unique identifier that can be
+maintained when viewed from different perspectives and in different images. The
+\textit{data association} and \textit{simultaneous localization and mapping}
+(SLAM) problems are, individually, well-studied in the literature. But these
+two problems are inherently tightly coupled, and that has not been
+well-addressed. Without accurate SLAM, possible data associations are
+combinatorial and become intractable easily. Without accurate data association,
+the error of SLAM algorithms diverge easily. This paper proposes a novel
+nonparametric pose graph that models data association and SLAM in a single
+framework. An algorithm is further introduced to alternate between inferring
+data association and performing SLAM. Experimental results show that our
+approach has the new capability of associating object detections and localizing
+objects at the same time, leading to significantly better performance on both
+the data association and SLAM problems than achieved by considering only one
+and ignoring imperfections in the other.
+"
+23749,Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion,"  Most of the traditional convolutional neural networks (CNNs) implements
+bottom-up approach (feed-forward) for image classifications. However, many
+scientific studies demonstrate that visual perception in primates rely on both
+bottom-up and top-down connections. Therefore, in this work, we propose a CNN
+network with feedback structure for Solar power plant detection on
+middle-resolution satellite images. To express the strength of the top-down
+connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model
+used for solar power plant classification on multi-spectral satellite data.
+Moreover, we introduce a method to improve class activation mapping (CAM) to
+our FB-Net, which takes advantage of multi-channel pulse coupled neural network
+(m-PCNN) for weakly-supervised localization of the solar power plants from the
+features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN,
+experimental results demonstrated promising results on both solar-power plant
+image classification and detection task.
+"
+23750,Post trimming of silicon photonics microresonators by nanoscale flash memory technology,"  Flash memory technology is widely common in modern microelectronics, and is
+essentially affecting our daily life. Considering the recent progress in
+photonic circuitry, and in particular silicon photonics circuitry, there is now
+an opportunity to embed the flash memory technology in photonic applications. A
+particularly promising candidate that can benefit from such integration is the
+photonic resonator. As of today, chip scale resonators are essential building
+blocks in modern silicon photonic platform. However, their properties, and in
+particular their resonance frequencies deviate from their designed values due
+to unavoidable fabrication imperfections, imposing a stringent limitation on
+the applicability of such devices. Here we present a solution for this major
+obstacle and demonstrate electrical approach for post trimming of such
+resonators. This is achieved by integrating the well-established flash memory
+technology into the photonic circuitry. More specifically, we use the nanoscale
+Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) structure in order to trap charges
+in the thin silicon nitride layer, which is located in close proximity to the
+silicon device layer. This enables the accumulation of charges in the silicon,
+modifying the effective index of the optical mode and consequently the
+resonance frequency. By doing so, we provide a robust and elegant CMOS
+compatible memory solution, which can be easily manufactured and
+commercialized. We expect such an approach to pave the way for even more
+efficient utilization of resonators and interferometers in chip scale photonic
+and electro optic systems, with wide range of applications such as filters,
+modulators, sensors, and lasers, to name a few.
+"
+23751,Opinion formation on dynamic networks: identifying conditions for the emergence of partisan echo chambers,"  Modern political interaction is characterized by strong partisanship and a
+lack of interest in information sharing and agreement across party lines. It
+remains largely unclear how such partisan echo chambers arise and how they
+coevolve with opinion formation. Here we explore the emergence of these
+structures through the lens of coevolutionary games. In our model, the payoff
+of an individual is determined jointly by the magnitude of their opinion, their
+degree of conformity with their social neighbors, and the benefit of having
+social connections. Each individual can simultaneously adjust their opinion as
+well as the weights of their social connections. We present and validate the
+conditions for the emergence of partisan echo chambers, characterizing the
+transition from cohesive communities with consensus to divisive networks with
+splitting opinions. Moreover, we apply our model to voting records of the
+United States House of Representatives over a timespan of decades in order to
+understand the influence of underlying psychological and social factors on
+increasing partisanship in recent years. Our work helps elucidate how the
+division of today has come to be and how cohesion and unity could otherwise be
+attained on a variety of political and social issues.
+"
+23752,"A Stabilised Nodal Spectral Element Method for Fully Nonlinear Water Waves, Part 2: Wave-body interaction","  We present a new stabilised and efficient high-order nodal spectral element
+method based on the Mixed Eulerian Lagrangian (MEL) method for general-purpose
+simulation of fully nonlinear water waves and wave-body interactions. In this
+MEL formulation a standard Laplace formulation is used to handle arbitrary body
+shapes using unstructured - possibly hybrid - meshes consisting of high-order
+curvilinear iso-parametric quadrilateral/triangular elements to represent the
+body surfaces and for the evolving free surface. Importantly, our numerical
+analysis highlights that a single top layer of quadrilaterals elements resolves
+temporal instabilities in the numerical MEL scheme that are known to be
+associated with mesh topology containing asymmetric element orderings. The
+'surface variable only' free surface formulation based on introducing a
+particle-following (Lagrangian) reference frame contains quartic nonlinear
+terms that require proper treatment by numerical discretisation due to the
+possibility of strong aliasing effects. We demonstrate how to stabilise this
+nonlinear MEL scheme using an efficient combination of (i) global L2 projection
+without quadrature errors, (ii) mild nonlinear spectral filtering and (iii)
+re-meshing techniques. Numerical experiments revisiting known benchmarks are
+presented, and highlights that modelling using a high-order spectral element
+method provides excellent accuracy in prediction of nonlinear and dispersive
+wave propagation, and of nonlinear wave-induced loads on fixed submerged and
+surface-piercing bodies.
+"
+23753,Deep Burst Denoising,"  Noise is an inherent issue of low-light image capture, one which is
+exacerbated on mobile devices due to their narrow apertures and small sensors.
+One strategy for mitigating noise in a low-light situation is to increase the
+shutter time of the camera, thus allowing each photosite to integrate more
+light and decrease noise variance. However, there are two downsides of long
+exposures: (a) bright regions can exceed the sensor range, and (b) camera and
+scene motion will result in blurred images. Another way of gathering more light
+is to capture multiple short (thus noisy) frames in a ""burst"" and intelligently
+integrate the content, thus avoiding the above downsides. In this paper, we use
+the burst-capture strategy and implement the intelligent integration via a
+recurrent fully convolutional deep neural net (CNN). We build our novel,
+multiframe architecture to be a simple addition to any single frame denoising
+model, and design to handle an arbitrary number of noisy input frames. We show
+that it achieves state of the art denoising results on our burst dataset,
+improving on the best published multi-frame techniques, such as VBM4D and
+FlexISP. Finally, we explore other applications of image enhancement by
+integrating content from multiple frames and demonstrate that our DNN
+architecture generalizes well to image super-resolution.
+"
+23754,On conservation laws for the supersymmetric sigma model,"  We derive conservation laws for Dirac-harmonic maps and their extensions to
+manifolds that have isometries, where we mostly focus on the spherical case. In
+addition, we discuss several geometric and analytic applications of the latter.
+"
+23755,Dependence of Inferred Climate Sensitivity on the Discrepancy Model,"  We consider the effect of different temporal error structures on the
+inference of equilibrium climate sensitivity\footnote{ECS is defined as the
+realized equilibrium surface warming---globally-averaged surface air
+temperature---for a doubling of CO$_2$}(ECS), in the context of an energy
+balance model (EBM) that is commonly employed in analyzing earth system models
+(ESM) and observations. We consider error structures ranging from uncorrelated
+(IID normal) to AR(1) to Gaussian correlation (Gaussian Process GP) to analyze
+the abrupt 4xCO$_2$ CMIP5 experiment in twenty-one different ESMs. For seven of
+the ESMs, the posterior distribution of ECS is seen to depend rather weakly on
+the discrepancy model used suggesting that the discrepancies were largely
+uncorrelated. However, large differences for four, and moderate differences for
+the rest of the ESMs, leads us to suggest that AR(1) is an appropriate
+discrepancy correlation structure to use in situations such as the one
+considered in this article. Other significant findings include: (a) When
+estimates of ECS (mode) were differrent, estimates using IID were higher (b)
+For four of the ESMs, uncertainty in the inference of ECS was higher with the
+IID discrepancy structure than with the other correlated structures, and (c)
+Uncertainty in the estimation of GP parameters were much higher than with the
+estimation of IID or AR(1) parameters, possibly due to identifiability issues.
+They need to be investigated further.
+"
+23756,Information Theoretic Optimal Learning of Gaussian Graphical Models,"  What is the optimal number of independent observations from which a sparse
+Gaussian Graphical Model can be correctly recovered? Information-theoretic
+arguments provide a lower bound on the minimum number of samples necessary to
+perfectly identify the support of any multivariate normal distribution as a
+function of model parameters. For a model defined on a sparse graph with $p$
+nodes, a maximum degree $d$ and minimum normalized edge strength $\kappa$, this
+necessary number of samples scales at least as $d \log p/\kappa^2$. The sample
+complexity requirements of existing methods for perfect graph reconstruction
+exhibit dependency on additional parameters that do not enter in the lower
+bound. The question of whether the lower bound is tight and achievable by a
+polynomial time algorithm remains open. In this paper, we constructively answer
+this question and propose an algorithm, termed DICE, whose sample complexity
+matches the information-theoretic lower bound up to a universal constant
+factor. We also propose a related algorithm SLICE that has a slightly higher
+sample complexity, but can be implemented as a mixed integer quadratic program
+which makes it attractive in practice. Importantly, SLICE retains a critical
+advantage of DICE in that its sample complexity only depends on quantities
+present in the information theoretic lower bound. We anticipate that this
+result will stimulate future search of computationally efficient sample-optimal
+algorithms.
+"
+23757,Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations,"  Users of social media sites like Facebook and Twitter rely on crowdsourced
+content recommendation systems (e.g., Trending Topics) to retrieve important
+and useful information. Contents selected for recommendation indirectly give
+the initial users who promoted (by liking or posting) the content an
+opportunity to propagate their messages to a wider audience. Hence, it is
+important to understand the demographics of people who make a content worthy of
+recommendation, and explore whether they are representative of the media site's
+overall population. In this work, using extensive data collected from Twitter,
+we make the first attempt to quantify and explore the demographic biases in the
+crowdsourced recommendations. Our analysis, focusing on the selection of
+trending topics, finds that a large fraction of trends are promoted by crowds
+whose demographics are significantly different from the overall Twitter
+population. More worryingly, we find that certain demographic groups are
+systematically under-represented among the promoters of the trending topics. To
+make the demographic biases in Twitter trends more transparent, we developed
+and deployed a Web-based service 'Who-Makes-Trends' at
+twitter-app.mpi-sws.org/who-makes-trends.
+"
+23758,Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information,"  In this paper, we provide an approach to clustering relational matrices whose
+entries correspond to either similarities or dissimilarities between objects.
+Our approach is based on the value of information, a parameterized,
+information-theoretic criterion that measures the change in costs associated
+with changes in information. Optimizing the value of information yields a
+deterministic annealing style of clustering with many benefits. For instance,
+investigators avoid needing to a priori specify the number of clusters, as the
+partitions naturally undergo phase changes, during the annealing process,
+whereby the number of clusters changes in a data-driven fashion. The
+global-best partition can also often be identified.
+"
+23759,Exploiting the Structure via Sketched Gradient Algorithms,"  Sketched gradient algorithms have been recently introduced for efficiently
+solving the large-scale constrained Least-squares regressions. In this paper we
+provide novel convergence analysis for the basic method {\it Gradient
+Projection Classical Sketch} (GPCS) to reveal the fast linear convergence rate
+of GPCS towards a vicinity of the solution thanks to the intrinsic
+low-dimensional geometric structure of the solution prompted by constraint set.
+Similar to our analysis we observe computational and sketch size trade-offs in
+numerical experiments. Hence we justify that the combination of gradient
+methods and the sketching technique is a way of designing efficient algorithms
+which can actively exploit the low-dimensional structure to accelerate
+computation in large scale data regression and signal processing applications.
+"
+23760,Treo: Textual Syntax for Reo Connectors,"  Reo is an interaction-centric model of concurrency for compositional
+specification of communication and coordination protocols. Formal verification
+tools exist to ensure correctness and compliance of protocols specified in Reo,
+which can readily be (re)used in different applications, or composed into more
+complex protocols. Recent benchmarks show that compiling such high-level Reo
+specifications produces executable code that can compete with or even beat the
+performance of hand-crafted programs written in languages such as C or Java
+using conventional concurrency constructs.
+The original declarative graphical syntax of Reo does not support intuitive
+constructs for parameter passing, iteration, recursion, or conditional
+specification. This shortcoming hinders Reo's uptake in large-scale practical
+applications. Although a number of Reo-inspired syntax alternatives have
+appeared in the past, none of them follows the primary design principles of
+Reo: a) declarative specification; b) all channel types and their sorts are
+user-defined; and c) channels compose via shared nodes. In this paper, we offer
+a textual syntax for Reo that respects these principles and supports flexible
+parameter passing, iteration, recursion, and conditional specification. In
+on-going work, we use this textual syntax to compile Reo into target languages
+such as Java, Promela, and Maude.
+"
+23761,Joint Discovery of Object States and Manipulation Actions,"  Many human activities involve object manipulations aiming to modify the
+object state. Examples of common state changes include full/empty bottle,
+open/closed door, and attached/detached car wheel. In this work, we seek to
+automatically discover the states of objects and the associated manipulation
+actions. Given a set of videos for a particular task, we propose a joint model
+that learns to identify object states and to localize state-modifying actions.
+Our model is formulated as a discriminative clustering cost with constraints.
+We assume a consistent temporal order for the changes in object states and
+manipulation actions, and introduce new optimization techniques to learn model
+parameters without additional supervision. We demonstrate successful discovery
+of seven manipulation actions and corresponding object states on a new dataset
+of videos depicting real-life object manipulations. We show that our joint
+formulation results in an improvement of object state discovery by action
+recognition and vice versa.
+"
+23762,Generating Steganographic Images via Adversarial Training,"  Adversarial training was recently shown to be competitive against supervised
+learning methods on computer vision tasks, however, studies have mainly been
+confined to generative tasks such as image synthesis. In this paper, we apply
+adversarial training techniques to the discriminative task of learning a
+steganographic algorithm. Steganography is a collection of techniques for
+concealing information by embedding it within a non-secret medium, such as
+cover texts or images. We show that adversarial training can produce robust
+steganographic techniques: our unsupervised training scheme produces a
+steganographic algorithm that competes with state-of-the-art steganographic
+techniques, and produces a robust steganalyzer, which performs the
+discriminative task of deciding if an image contains secret information. We
+define a game between three parties, Alice, Bob and Eve, in order to
+simultaneously train both a steganographic algorithm and a steganalyzer. Alice
+and Bob attempt to communicate a secret message contained within an image,
+while Eve eavesdrops on their conversation and attempts to determine if secret
+information is embedded within the image. We represent Alice, Bob and Eve by
+neural networks, and validate our scheme on two independent image datasets,
+showing our novel method of studying steganographic problems is surprisingly
+competitive against established steganographic techniques.
+"
+23763,Different dust and gas radial extents in protoplanetary disks: consistent models of grain growth and CO emission,"  ALMA observations of protoplanetary disks confirm earlier indications that
+there is a clear difference between the dust and gas radial extents. The origin
+of this difference is still debated, with both radial drift of the dust and
+optical depth effects suggested in the literature. In this work, the feedback
+of realistic dust particle distributions onto the gas chemistry and molecular
+emissivity is investigated, with a particular focus on CO isotopologues. The
+radial dust grain size distribution is determined using dust evolution models
+that include growth, fragmentation and radial drift. A new version of the code
+DALI is used to take into account how dust surface area and density influence
+the disk thermal structure, molecular abundances and excitation. The difference
+of dust and gas radial sizes is largely due to differences in the optical depth
+of CO lines and millimeter continuum, without the need to invoke radial drift.
+The effect of radial drift is primarily visible in the sharp outer edge of the
+continuum intensity profile. The gas outer radius probed by $^{12}$CO emission
+can easily differ by a factor of $\sim 2$ between the models for a turbulent
+$\alpha$ ranging between typical values. Grain growth and settling concur in
+thermally decoupling the gas and dust components, due to the low collision rate
+with large grains. As a result, the gas can be much colder than the dust at
+intermediate heights, reducing the CO excitation and emission, especially for
+low turbulence values. Also, due to disk mid-plane shadowing, a second CO
+thermal desorption (rather than photodesorption) front can occur in the warmer
+outer mid-plane disk. The models are compared to ALMA observations of HD 163296
+as a test case. In order to reproduce the observed CO snowline of the system, a
+binding energy for CO typical of ice mixtures needs to be used rather than the
+lower pure CO value.
+"
+23764,Protecting User Privacy: An Approach for Untraceable Web Browsing History and Unambiguous User Profiles,"  The overturning of the Internet Privacy Rules by the Federal Communications
+Commissions (FCC) in late March 2017 allows Internet Service Providers (ISPs)
+to collect, share and sell their customers' Web browsing data without their
+consent. With third-party trackers embedded on Web pages, this new rule has put
+user privacy under more risk. The need arises for users on their own to protect
+their Web browsing history from any potential adversaries. Although some
+available solutions such as Tor, VPN, and HTTPS can help users conceal their
+online activities, their use can also significantly hamper personalized online
+services, i.e., degraded utility. In this paper, we design an effective Web
+browsing history anonymization scheme, PBooster, aiming to protect users'
+privacy while retaining the utility of their Web browsing history. The proposed
+model pollutes users' Web browsing history by automatically inferring how many
+and what links should be added to the history while addressing the
+utility-privacy trade-off challenge. We conduct experiments to validate the
+quality of the manipulated Web browsing history and examine the robustness of
+the proposed approach for user privacy protection.
+"
+23765,"An explicit theory of $π_{1}^{\un,\crys}(\mathbb{P}^{1} - \{0,μ_{N},\infty\})$ - V-1 : The Frobenius extended to $π_{1}^{\un,\DR}(\mathbb{P}^{1} - \{0,μ_{p^αN},\infty\})$","  Let $p$ a prime number. For all $N \in \mathbb{N}^{\ast}$ prime to $p$, let
+$k_{N}$ be a finite field of characteristic $p$ containing a primitive $N$-th
+root of unity. Let $X_{k_{N},N}=\text{ }\mathbb{P}^{1} - (\{0,\infty\} \cup
+\mu_{N})\text{ }/\text{ }k_{N}$. This work is an explicit theory of the
+crystalline pro-unipotent fundamental groupoid $(\pi_{1}^{\un,\crys})$ of
+$X_{k_{N},N}$. In the parts I to IV, we have considered each possible value of
+$N$ separately. The purpose of part V is to study the role of the morphisms
+relating $\pi_{1}^{\un}(\mathbb{P}^{1} - \{0,\mu_{N_{1}},\infty\})$ and
+$\pi_{1}^{\un}(\mathbb{P}^{1} - \{0,\mu_{N_{2}},\infty\})$ when $N_{1}$ divides
+$N_{2}$. In V-1, we specify this question to the theme of part I, the
+computation of the Frobenius. For any $N \in \mathbb{N}^{\ast}$, let
+$K_{N}=\mathbb{Q}_{p}(\xi_{N})$ where $\xi_{N}\in \overline{\mathbb{Q}_{p}}$ is
+a primitive $N$-th root of unity, and $X_{K_{N},N} = \mathbb{P}^{1} -
+(\{0,\infty\} \cup \mu_{N})\text{ }/\text{ }K_{N}$. For $N$ prime to $p$, we
+are used to view the Frobenius of $\pi_{1}^{\un,\crys}(X_{k_{N},N})$ as a
+structure on $\pi_{1}^{\un,\DR}(X_{K_{N},N})$. In V-1, we show that the
+Frobenius of $\pi_{1}^{\un,\DR}(X_{K_{N},N})$, iterated $\alpha \in
+\mathbb{N}^{\ast}$ times, can be extended canonically as a structure of
+$\pi_{1}^{\un,\DR}(X_{K_{p^{\alpha}N},p^{\alpha}N})$. This allows to define
+generalizations of adjoint $p$-adic multiple zeta values associated with roots
+of unity of order $p^{\alpha}N$, and several related objects. This also gives a
+canonical framework to relate to each other the direct method of computation of
+the Frobenius of I-1 and the indirect methods of computation of the Frobenius
+of I-2 and I-3.
+"
+23766,Extracting and Analyzing Hidden Graphs from Relational Databases,"  Analyzing interconnection structures among underlying entities or objects in
+a dataset through the use of graph analytics has been shown to provide
+tremendous value in many application domains. However, graphs are not the
+primary representation choice for storing most data today, and in order to have
+access to these analyses, users are forced to extract data from their data
+stores, construct the requisite graphs, and then load them into some graph
+engine in order to execute their graph analysis task. Moreover, these graphs
+can be significantly larger than the initial input stored in the database,
+making it infeasible to construct or analyze such graphs in memory. In this
+paper we address both of these challenges by building a system that enables
+users to declaratively specify graph extraction tasks over a relational
+database schema and then execute graph algorithms on the extracted graphs. We
+propose a declarative domain-specific language for this purpose, and pair it up
+with a novel condensed, in-memory representation that significantly reduces the
+memory footprint of these graphs, permitting analysis of larger-than-memory
+graphs. We present a general algorithm for creating this condensed
+representation for a large class of graph extraction queries against arbitrary
+schemas. We observe that the condensed representation suffers from a
+duplication issue, that results in inaccuracies for most graph algorithms. We
+then present a suite of in-memory representations that handle this duplication
+in different ways and allow trading off the memory required and the
+computational cost for executing different graph algorithms. We introduce novel
+deduplication algorithms for removing this duplication in the graph, which are
+of independent interest for graph compression, and provide a comprehensive
+experimental evaluation over several real-world and synthetic datasets
+illustrating these trade-offs.
+"
+23767,Visual Sensor Network Reconfiguration with Deep Reinforcement Learning,"  We present an approach for reconfiguration of dynamic visual sensor networks
+with deep reinforcement learning (RL). Our RL agent uses a modified
+asynchronous advantage actor-critic framework and the recently proposed
+Relational Network module at the foundation of its network architecture. To
+address the issue of sample inefficiency in current approaches to model-free
+reinforcement learning, we train our system in an abstract simulation
+environment that represents inputs from a dynamic scene. Our system is
+validated using inputs from a real-world scenario and preexisting object
+detection and tracking algorithms.
+"
+23768,SDSS IV MaNGA - Rotation Velocity Lags in the Extraplanar Ionized Gas from MaNGA Observations of Edge-on Galaxies,"  We present a study of the kinematics of the extraplanar ionized gas around
+several dozen galaxies observed by the Mapping of Nearby Galaxies at the Apache
+Point Observatory (MaNGA) survey. We considered a sample of 67 edge-on galaxies
+out of more than 1400 extragalactic targets observed by MaNGA, in which we
+found 25 galaxies (or 37%) with regular lagging of the rotation curve at large
+distances from the galactic midplane. We model the observed $H\alpha$ emission
+velocity fields in the galaxies, taking projection effects and a simple model
+for the dust extinction into the account. We show that the vertical lag of the
+rotation curve is necessary in the modeling, and estimate the lag amplitude in
+the galaxies. We find no correlation between the lag and the star formation
+rate in the galaxies. At the same time, we report a correlation between the lag
+and the galactic stellar mass, central stellar velocity dispersion, and axial
+ratio of the light distribution. These correlations suggest a possible higher
+ratio of infalling-to-local gas in early-type disk galaxies or a connection
+between lags and the possible presence of hot gaseous halos, which may be more
+prevalent in more massive galaxies. These results again demonstrate that
+observations of extraplanar gas can serve as a potential probe for accretion of
+gas.
+"
+23769,Gaia: on the road to DR2,"  The second Gaia data release (DR2) is scheduled for April 2018. While Gaia
+DR1 had increased the number of stars with parallaxes by a factor 20 with
+respect to the Hipparcos catalogue, Gaia DR2 will bring another factor 500
+increase, with parallaxes (and proper motions) for more than a billion stars.
+In addition, Gaia DR2 will deliver improved accuracy and precision for the
+astrometric and photometric data, $G$, $G_\mathrm{BP}$, $G_\mathrm{RP}$
+magnitudes, radial velocities, identification and characterisation of variable
+stars and asteroids as well as stellar parameters for stars down to $G = 17$
+mag. On behalf of the teams of the Gaia-DPAC consortium, these proceedings give
+a foretaste of Gaia DR2, 6 months before the release.
+"
+23770,The history of chemical enrichment in the intracluster medium from cosmological simulations,"  The distribution of metals in the intracluster medium (ICM) of galaxy
+clusters provides valuable information on their formation and evolution, on the
+connection with the cosmic star formation and on the effects of different gas
+processes. By analyzing a sample of simulated galaxy clusters, we study the
+chemical enrichment of the ICM, its evolution, and its relation with the
+physical processes included in the simulation and with the thermal properties
+of the core. These simulations, consisting of re-simulations of 29 Lagrangian
+regions performed with an upgraded version of the SPH GADGET-3 code, have been
+run including two different sets of baryonic physics: one accounts for
+radiative cooling, star formation, metal enrichment and supernova (SN)
+feedback, and the other one further includes the effects of feedback from
+active galactic nuclei (AGN). In agreement with observations, we find an
+anti-correlation between entropy and metallicity in cluster cores, and similar
+radial distributions of heavy-element abundances and abundance ratios out to
+large cluster-centric distances (~R180). In the outskirts, namely outside of
+~0.2R180, we find a remarkably homogeneous metallicity distribution, with
+almost flat profiles of the elements produced by either SNIa or SNII. We
+investigated the origin of this phenomenon and discovered that it is due to the
+widespread displacement of metal-rich gas by early (z>2-3) AGN powerful bursts,
+acting on small high-redshift haloes. Our results also indicate that the
+intrinsic metallicity of the hot gas for this sample is on average consistent
+with no evolution between z=2 and z=0, across the entire radial range.
+"
+23771,A Brief Comparison of Two Enterprise-Class RDBMSs,"  This paper is an extended version of a report from a student-developed study
+to compare Microsoft SQL Server and PostgreSQL, two widely-used
+enterprise-class relational database management systems (RDBMSs). The study
+followed an introductory undergraduate course in relational systems and was
+designed to help gain practical understanding of specific DBMSs. During this
+study, we implemented three non-trivial schemas in each system, identified 26
+common database design, development, and administration activities while
+implementing the schemas, and compared the support each system offers to carry
+out the identified activities. Where relevant, we also compared each system
+against the SQL standard.
+In this report, we present a summary of the similarities and differences we
+found between the two systems, and we provide a quantitative measure ranking
+both systems' implementations of the 26 activities. We also briefly discuss the
+""technical suitability"" of PostgreSQL to enterprise applications. Although this
+report is not comprehensive and is too general to comment on the suitability of
+either system to a specific enterprise application, it can nevertheless provide
+an initial set of considerations and criteria to choose a system for most
+enterprise applications.
+"
+23772,Maximum likelihood estimation of the Latent Class Model through model boundary decomposition,"  The Expectation-Maximization (EM) algorithm is routinely used for the maximum
+likelihood estimation in the latent class analysis. However, the EM algorithm
+comes with no guarantees of reaching the global optimum. We study the geometry
+of the latent class model in order to understand the behavior of the maximum
+likelihood estimator. In particular, we characterize the boundary
+stratification of the binary latent class model with a binary hidden variable.
+For small models, such as for three binary observed variables, we show that
+this stratification allows exact computation of the maximum likelihood
+estimator. In this case we use simulations to study the maximum likelihood
+estimation attraction basins of the various strata. Our theoretical study is
+complemented with a careful analysis of the EM fixed point ideal which provides
+an alternative method of studying the boundary stratification and maximizing
+the likelihood function. In particular, we compute the minimal primes of this
+ideal in the case of a binary latent class model with a binary or ternary
+hidden random variable.
+"
+23773,Dissipative Kerr solitons and Cherenkov radiation in optical microresonators with third order dispersion,"  We present the results of asymptotic and numerical analysis of dissipative
+Kerr solitons in whispering gallery mode microresonators influenced by higher
+order dispersive terms leading to the appearance of a dispersive wave
+(Cherenkov radiation). Combining direct perturbation method with the method of
+moments we find expressions for the frequency, strength, spectral width of the
+dispersive wave and soliton velocity. Mutual influence of the soliton and
+dispersive wave was studied. The formation of the dispersive wave leads to a
+shift of the soliton spectrum maximum from the pump frequency (spectral
+recoil), while the soliton displaces the dispersive wave spectral peak from the
+zero dispersion point.
+"
+23774,On Noisy Negative Curvature Descent: Competing with Gradient Descent for Faster Non-convex Optimization,"  The Hessian-vector product has been utilized to find a second-order
+stationary solution with strong complexity guarantee (e.g., almost linear time
+complexity in the problem's dimensionality). In this paper, we propose to
+further reduce the number of Hessian-vector products for faster non-convex
+optimization. Previous algorithms need to approximate the smallest eigen-value
+with a sufficient precision (e.g., $\epsilon_2\ll 1$) in order to achieve a
+sufficiently accurate second-order stationary solution (i.e.,
+$\lambda_{\min}(\nabla^2 f(\x))\geq -\epsilon_2)$. In contrast, the proposed
+algorithms only need to compute the smallest eigen-vector approximating the
+corresponding eigen-value up to a small power of current gradient's norm. As a
+result, it can dramatically reduce the number of Hessian-vector products during
+the course of optimization before reaching first-order stationary points (e.g.,
+saddle points). The key building block of the proposed algorithms is a novel
+updating step named the NCG step, which lets a noisy negative curvature descent
+compete with the gradient descent. We show that the worst-case time complexity
+of the proposed algorithms with their favorable prescribed accuracy
+requirements can match the best in literature for achieving a second-order
+stationary point but with an arguably smaller per-iteration cost. We also show
+that the proposed algorithms can benefit from inexact Hessian by developing
+their variants accepting inexact Hessian under a mild condition for achieving
+the same goal. Moreover, we develop a stochastic algorithm for a finite or
+infinite sum non-convex optimization problem. To the best of our knowledge, the
+proposed stochastic algorithm is the first one that converges to a second-order
+stationary point in {\it high probability} with a time complexity independent
+of the sample size and almost linear in dimensionality.
+"
+23775,Why Modern Open Source Projects Fail,"  Open source is experiencing a renaissance period, due to the appearance of
+modern platforms and workflows for developing and maintaining public code. As a
+result, developers are creating open source software at speeds never seen
+before. Consequently, these projects are also facing unprecedented mortality
+rates. To better understand the reasons for the failure of modern open source
+projects, this paper describes the results of a survey with the maintainers of
+104 popular GitHub systems that have been deprecated. We provide a set of nine
+reasons for the failure of these open source projects. We also show that some
+maintenance practices -- specifically the adoption of contributing guidelines
+and continuous integration -- have an important association with a project
+failure or success. Finally, we discuss and reveal the principal strategies
+developers have tried to overcome the failure of the studied projects.
+"
+23776,The inversion of motion of bristle bots: analytical and experimental analysis,"  Bristle bots are vibration-driven robots actuated by the motion of an
+internal oscillating mass. Vibrations are translated into directed locomotion
+due to the alternating friction resistance between robots' bristles and the
+substrate during oscillations. Bristle bots are, in general, unidirectional
+locomotion systems. In this paper we demonstrate that motion direction of
+vertically vibrated bristle systems can be controlled by tuning the frequency
+of their oscillatory actuation. We report theoretical and experimental results
+obtained by studying an equivalent system, consisting of an inactive robot
+placed on a vertically vibrating substrate.
+"
+23777,IOTA-based Directed Acyclic Graphs without Orphans,"  Directed Acylic Graphs (DAGs) are emerging as an attractive alternative to
+traditional blockchain architectures for distributed ledger technology (DLT).
+In particular DAG ledgers with stochastic attachment mechanisms potentially
+offer many advantages over blockchain, including scalability and faster
+transaction speeds. However, the random nature of the attachment mechanism
+coupled with the requirement of protection against double-spend transactions
+leaves open the possibility that not all transactions will be eventually
+validated. Such transactions are said to be orphaned, and will never be
+validated. Our principal contribution is to propose a simple modification to
+the attachment mechanism for the Tangle (the IOTA DAG architecture). This
+modification ensures that all transactions are validated in finite time, and
+preserves essential features of the popular Monte-Carlo selection algorithm. In
+order to demonstrate these results we derive a fluid approximation for the
+Tangle (in the limit of infinite arrival rate) and prove that this fluid model
+exhibits the desired behavior. We also present simulations which validate the
+results for finite arrival rates.
+"
+23778,"On the truncated matricial Stieltjes moment problem $\mathsf{M}[[α,\infty);(s_j)_{j=0}^m,\leq]$","  This paper gives via Stieltjes transform a complete description of the
+solution set of a matricial truncated Stieltjes-type power moment problem in
+the non-degenerate and degenerate cases. The approach is based on the Schur
+type algorithm which was worked out in the papers [arXiv:1604.07240,
+arXiv:1604.07629]. Furthermore, the subset of parameters is determined which
+corresponds to another truncated matricial Stieltjes-type moment problem.
+"
+23779,Zero reflection and transmission in graded index media,"  Graded index media whose electric susceptibility satisfies the spatial
+Kramers-Kronig relations are known to be one-way reflectionless to
+electromagnetic radiation, for all angles of incidence. We demonstrate how a
+family of these media, in addition to being reflectionless, also have
+negligible transmission. To this end, we discuss how the transmission
+coefficient for the propagation of waves through a medium whose permittivity is
+built from poles in the complex position plane, with residues that sum to
+infinity, can be controlled by tuning the positions and residues of the poles.
+In particular, we have shown how to make the transmission arbitrarily small,
+and hence maximise the absorption of the wave's energy. This behaviour is
+confirmed by numerical simulations.
+"
+23780,CFI CaRE: Hardware-supported Call and Return Enforcement for Commercial Microcontrollers,"  With the increasing scale of deployment of Internet of Things (IoT), concerns
+about IoT security have become more urgent. In particular, memory corruption
+attacks play a predominant role as they allow remote compromise of IoT devices.
+Control-flow integrity (CFI) is a promising and generic defense technique
+against these attacks. However, given the nature of IoT deployments, existing
+protection mechanisms for traditional computing environments (including CFI)
+need to be adapted to the IoT setting. In this paper, we describe the
+challenges of enabling CFI on microcontroller (MCU) based IoT devices. We then
+present CaRE, the first interrupt-aware CFI scheme for low-end MCUs. CaRE uses
+a novel way of protecting the CFI metadata by leveraging TrustZone-M security
+extensions introduced in the ARMv8-M architecture. Its binary instrumentation
+approach preserves the memory layout of the target MCU software, allowing
+pre-built bare-metal binary code to be protected by CaRE. We describe our
+implementation on a Cortex-M Prototyping System and demonstrate that CaRE is
+secure while imposing acceptable performance and memory impact.
+"
+23781,Spin-dependent thermoelectric phenomena in a quantum dot attached to ferromagnetic and superconducting electrodes,"  We investigate transport and thermoelectric properties of hybrid systems
+based on a single-level quantum dot and one superconducting lead. The other
+lead is generally normal-metallic ferromagnet. In the latter case
+single-particle transport is spin-polarized. Our main interest is in the
+interplay of Andreev tunneling of Cooper pairs and single-particle tunneling.
+The latter is responsible for relatively large thermopower and figure of merit
+due to a diverging density of single-particle states at the superconducting gap
+edges. System with ferromagnetic and superconducting leads can also reveal spin
+thermoelectric phenomena. Finite superconducting gap is considered within the
+BCS theory, and the thermoelectric coefficients are calculated by means of
+nonequilibrium Green's function technique within Hartree-Fock like
+approximation with respect to the intradot Coulomb interaction.
+"
+23782,The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems,"  We propose a deep learning based method, the Deep Ritz Method, for
+numerically solving variational problems, particularly the ones that arise from
+partial differential equations. The Deep Ritz method is naturally nonlinear,
+naturally adaptive and has the potential to work in rather high dimensions. The
+framework is quite simple and fits well with the stochastic gradient descent
+method used in deep learning. We illustrate the method on several problems
+including some eigenvalue problems.
+"
+23783,Group Sequential Clinical Trial Designs for Normally Distributed Outcome Variables,"  In a group sequential clinical trial, accumulated data are analysed at
+numerous time-points in order to allow early decisions about a hypothesis of
+interest. These designs have historically been recommended for their ethical,
+administrative and economic benefits. In this work, we discuss a collection of
+new Stata commands for computing the stopping boundaries and required group
+size of various classical group sequential designs, assuming a normally
+distributed outcome variable. Following this, we demonstrate how the
+performance of several designs can be compared graphically.
+"
+23784,Superconductivity in $p$-Terphenyl,"  Motivated by the exploration of bipolaronic superconductivity in conducting
+polymers, we examine such a possibility in the starting member of
+$p$-oligophenyls $-$ $p$-terphenyl with three phenyl rings, belonging to the
+family of conducting polymer polyparaphenylene. The formation of bipolarons is
+identified from Raman scattering measurements. Both the dc and ac magnetic
+susceptibility measurements reveal that $p$-terphenyl is a type-II
+superconductor with a critical temperature of 7.2 K upon doping potassium. The
+electron-lattice interaction, manifested by pronounced bipolaronic bands, is
+suggested to account for the observed superconductivity. Conducting polymers
+are thus demonstrated to have the potential for the development of new
+superconducting technologies and devices.
+"
+23785,Symmetric Synthesis,"  We study the problem of determining whether a given temporal specification
+can be implemented by a symmetric system, i.e., a system composed from
+identical components. Symmetry is an important goal in the design of
+distributed systems, because systems that are composed from identical
+components are easier to build and maintain. We show that for the class of
+rotation-symmetric architectures, i.e., multi-process architectures where all
+processes have access to all system inputs, but see different rotations of the
+inputs, the symmetric synthesis problem is EXPTIME-complete in the number of
+processes. In architectures where the processes do not have access to all input
+variables, the symmetric synthesis problem becomes undecidable, even in cases
+where the standard distributed synthesis problem is decidable.
+"
+23786,Electronic structure of FeSe monolayer superconductors: shallow bands and correlations,"  Electronic spectra of typical single FeSe layer superconductors obtained from
+ARPES data reveal several puzzles: what is the origin of shallow and the so
+called ""replica"" bands near M-point and why the hole-like Fermi surfaces near
+$\Gamma$-point are absent. Our extensive LDA+DMFT calculations show that
+correlation effects on Fe-3d states can almost quantitatively reproduce rather
+complicated band structure, which is observed in ARPES, in close vicinity of
+the Fermi level for FeSe/STO and K$_x$Fe$_{2-y}$Se$_{2}$. Rather unusual
+shallow electron-like bands around the M(X)-point in the Brillouin zone are
+well reproduced. However, in FeSe/STO correlation effects are apparently
+insufficient to eliminate the hole-like Fermi surfaces around the
+$\Gamma$-point, which are not observed in most ARPES experiments. Detailed
+analysis of the theoretical and experimental quasiparticle bands with respect
+to their origin and orbital composition is performed. It is shown that for
+FeSe/STO system the LDA calculated Fe-3d$_{xy}$ band, renormalized by
+electronic correlations within DMFT gives the quasiparticle band almost exactly
+in the energy region of the experimentally observed ""replica"" quasiparticle
+band at the M-point. For the case of K$_x$Fe$_{2-y}$Se$_{2}$ most bands
+observed in ARPES can also be understood as correlation renormalized Fe-3d LDA
+calculated bands, with overall semi-quantitative agreement with our LDA+DMFT
+calculations. Thus the shallow bands near the M-point are common feature for
+FeSe-based systems, not just FeSe/STO. We also present some simple estimates of
+""forward scattering"" electron-optical phonon interaction at FeSe/STO interface,
+showing that it is apparently irrelevant for the formation of ""replica"" band in
+this system and significant increase of superconducting $T_c$.
+"
+23787,Visual guide to optical tweezers,"  It is common to introduce optical tweezers using either geometric optics for
+large particles or the Rayleigh approximation for very small particles. These
+approaches are successful at conveying the key ideas behind optical tweezers in
+their respective regimes. However, they are insufficient for modelling
+particles of intermediate size and large particles with small features. For
+this, a full field approach provides greater insight into the mechanisms
+involved in trapping. The advances in computational capability over the last
+decade has led to better modelling and understanding of optical tweezers.
+Problems that were previously difficult to model computationally can now be
+solved using a variety of methods on modern systems. These advances in
+computational power allow for full field solutions to be visualised, leading to
+increased understanding of the fields and behaviour in various scenarios. In
+this paper we describe the operation of optical tweezers using full field
+simulations calculated using the finite difference time domain method. We use
+these simulations to visually illustrate various situations relevant to optical
+tweezers, from the basic operation of optical tweezers, to engineered particles
+and evanescent fields.
+"
+23788,Distributed Solution of Large-Scale Linear Systems via Accelerated Projection-Based Consensus,"  Solving a large-scale system of linear equations is a key step at the heart
+of many algorithms in machine learning, scientific computing, and beyond. When
+the problem dimension is large, computational and/or memory constraints make it
+desirable, or even necessary, to perform the task in a distributed fashion. In
+this paper, we consider a common scenario in which a taskmaster intends to
+solve a large-scale system of linear equations by distributing subsets of the
+equations among a number of computing machines/cores. We propose an accelerated
+distributed consensus algorithm, in which at each iteration every machine
+updates its solution by adding a scaled version of the projection of an error
+signal onto the nullspace of its system of equations, and where the taskmaster
+conducts an averaging over the solutions with momentum. The convergence
+behavior of the proposed algorithm is analyzed in detail and analytically shown
+to compare favorably with the convergence rate of alternative distributed
+methods, namely distributed gradient descent, distributed versions of
+Nesterov's accelerated gradient descent and heavy-ball method, the block
+Cimmino method, and ADMM. On randomly chosen linear systems, as well as on
+real-world data sets, the proposed method offers significant speed-up relative
+to all the aforementioned methods. Finally, our analysis suggests a novel
+variation of the distributed heavy-ball method, which employs a particular
+distributed preconditioning, and which achieves the same theoretical
+convergence rate as the proposed consensus-based method.
+"
+23789,Finite temperature dynamics of the Mott insulating Hubbard chain,"  We study the dynamical response of the half-filled one-dimensional(1d)
+Hubbard model for a range of interaction strengths $U$ and temperatures $T$ by
+a combination of numerical and analytical techniques. Using time-dependent
+density matrix renormalization group (tDMRG) computations we find that the
+single-particle spectral function undergoes a crossover to a spin-incoherent
+Luttinger liquid regime at temperatures $T \sim J=4t^2/U$ for sufficiently
+large $U > 4t$. At smaller values of $U$ and elevated temperatures the spectral
+function is found to exhibit two thermally broadened bands of excitations,
+reminiscent of what is found in the Hubbard-I approximation. The dynamical
+density-density response function is shown to exhibit a finite temperature
+resonance at low frequencies inside the Mott gap, with a physical origin
+similar to the Villain mode in gapped quantum spin chains. We complement our
+numerical computations by developing an analytic strong-coupling approach to
+the low-temperature dynamics in the spin-incoherent regime.
+"
+23790,Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units,"  Emotion being a subjective thing, leveraging knowledge and science behind
+labeled data and extracting the components that constitute it, has been a
+challenging problem in the industry for many years. With the evolution of deep
+learning in computer vision, emotion recognition has become a widely-tackled
+research problem. In this work, we propose two independent methods for this
+very task. The first method uses autoencoders to construct a unique
+representation of each emotion, while the second method is an 8-layer
+convolutional neural network (CNN). These methods were trained on the
+posed-emotion dataset (JAFFE), and to test their robustness, both the models
+were also tested on 100 random images from the Labeled Faces in the Wild (LFW)
+dataset, which consists of images that are candid than posed. The results show
+that with more fine-tuning and depth, our CNN model can outperform the
+state-of-the-art methods for emotion recognition. We also propose some exciting
+ideas for expanding the concept of representational autoencoders to improve
+their performance.
+"
+23791,OBTAIN: Real-Time Beat Tracking in Audio Signals,"  In this paper, we design a system in order to perform the real-time beat
+tracking for an audio signal. We use Onset Strength Signal (OSS) to detect the
+onsets and estimate the tempos. Then, we form Cumulative Beat Strength Signal
+(CBSS) by taking advantage of OSS and estimated tempos. Next, we perform peak
+detection by extracting the periodic sequence of beats among all CBSS peaks. In
+simulations, we can see that our proposed algorithm, Online Beat TrAckINg
+(OBTAIN), outperforms state-of-art results in terms of prediction accuracy
+while maintaining comparable and practical computational complexity. The
+real-time performance is tractable visually as illustrated in the simulations.
+"
+23792,Monitoring physical function in patients with knee osteoarthritis using data from wearable activity monitors,"  Currently used clinical assessments for physical function do not objectively
+quantify daily activities in routine living. Wearable activity monitors enable
+objective measurement of routine daily activities, but do not map to clinically
+measured physical performance measures. We represent physical function as a
+daily activity profile derived from minute-level activity data obtained via a
+wearable activity monitor. We construct daily activity profiles representing
+average time spent in a set of activity classes over consecutive days using the
+Osteoarthritis Initiative (OAI) data. Using the daily activity profile as
+input, we trained statistical models that classify subjects into quartiles of
+objective measurements of physical function as measured via the 400m walk test,
+the 20m walk test and 5 times sit stand test. We evaluated model performance on
+held out data from the same calendar year as that used to train the models as
+well as on activity data two years into the future. The daily activity profile
+predicts physical performance as measured via clinical assessments. Using held
+out data, the AUC obtained in classifying performance values in the 1st
+quartile was 0.79, 0.78 and 0.72, for the 400m walk, the 20m walk and 5 times
+sit stand tests. For classifying performance values in the 4th quartile, the
+AUC obtained was 0.77, 0.66 and 0.73 respectively. Evaluated on data from two
+years into the future, for the 20m pace test and the 5 times sit stand tests,
+the highest AUC obtained was 0.77 and 0.68 for the 1st quartile and 0.75 and
+0.70 for the 4th quartile respectively. We can construct activity profiles
+representing actual physical function as demonstrated by the relationship
+between the activity profiles and the clinically measured physical performance
+measures. Measurement of physical performance via the activity profile as
+described can enable remote functional monitoring of patients.
+"
+23793,Holographic Floquet states II: Floquet condensation of vector mesons in nonequilibrium phase diagram,"  With the aim to reveal universal features of hadronic matter and correlated
+Dirac insulators in strong AC-electric fields, we study the $\mathcal{N}=2$
+supersymmetric QCD with a finite quark mass driven by a rotating electric field
+$\mathcal{E}_x+i\mathcal{E}_y= E e^{i\Omega t}$. The analysis is done in the
+holographically dual D3/D7 system in the co-rotating frame, effectively. The
+nonequilibrium phase diagram is determined from the threshold electric field at
+which the insulator phase breaks down to a conductive phase due to the AC
+version of the Schwinger mechanism. The external field induces a rotating
+current $\mathcal{J}_x + i \mathcal{J}_y = J e^{i\Omega t}$ originating from
+vacuum polarization and dissipative current in the insulating and conductive
+phases respectively. Intriguing features are observed as the frequency $\Omega$
+approaches resonance with the meson excitation energy $\Omega_{\rm meson}$.
+There, the threshold minimizes and a condensate of vector mesons with
+oscillating current exists even in the zero driving field limit. This state,
+which we call Floquet condensate of vector mesons, is expected to be
+dynamically stable realizing a non-thermal fixed point that breaks time
+translational and reversal symmetries. Our finding has many similarities with
+exciton BEC discussed in solid state systems, where the semiconductor is to be
+replaced by materials hosting gapped Dirac electrons, e.g. 3D topological
+insulators or bismuth. Vector meson Floquet condensate may also have
+implications in the pre-thermalized dynamics in heavy ion collision
+experiments.
+"
+23794,Analyzing Exoplanet Phase Curve Information Content: Toward Optimized Observing Strategies,"  Secondary eclipses and phase curves reveal information about the reflectivity
+and heat distribution in exoplanet atmospheres. The phase curve is composed of
+a combination of reflected, and thermally emitted light from the planet, and
+for circular orbits the phase curve peaks during the secondary eclipse or at an
+orbital phase of 0.5. Physical mechanisms have been discovered which shift the
+phase curve maximum of tidally locked close in planets to the right, or left,
+of the secondary eclipse. These mechanisms include cloud formations, and
+atmospheric superrotation, both of which serve to shift the thermally bright
+hot-spot, or highly reflective bright spot, of the atmosphere away from the
+sub-stellar point. Here we present a methodology for optimizing observing
+strategies for both secondary eclipses and phase curves with the goal of
+maximizing the information gained about the planetary atmosphere while
+minimizing the (assumed) continuous observation time. We show that we can
+increase the duty cycle of observations aimed at the measurements of phase
+curve characteristics (Amplitude, Phase offset) by up to $50\%$ for future
+platforms such as CHEOPS and JWST. We apply this methodology to the test cases
+of the Spitzer phase curve of 55-Cancri-e, which displays an eastward shift in
+its phase curve maximum, as well as model-generated observations of an
+ultra-short period planet observed with CHEOPS.
+"
+23795,Sparse Weighted Canonical Correlation Analysis,"  Given two data matrices $X$ and $Y$, sparse canonical correlation analysis
+(SCCA) is to seek two sparse canonical vectors $u$ and $v$ to maximize the
+correlation between $Xu$ and $Yv$. However, classical and sparse CCA models
+consider the contribution of all the samples of data matrices and thus cannot
+identify an underlying specific subset of samples. To this end, we propose a
+novel sparse weighted canonical correlation analysis (SWCCA), where weights are
+used for regularizing different samples. We solve the $L_0$-regularized SWCCA
+($L_0$-SWCCA) using an alternating iterative algorithm. We apply $L_0$-SWCCA to
+synthetic data and real-world data to demonstrate its effectiveness and
+superiority compared to related methods. Lastly, we consider also SWCCA with
+different penalties like LASSO (Least absolute shrinkage and selection
+operator) and Group LASSO, and extend it for integrating more than three data
+matrices.
+"
+23796,Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration,"  The amount of unstructured text-based data is growing every day. Querying,
+clustering, and classifying this big data requires similarity computations
+across large sets of documents. Whereas low-complexity similarity metrics are
+available, attention has been shifting towards more complex methods that
+achieve a higher accuracy. In particular, the Word Mover's Distance (WMD)
+method proposed by Kusner et al. is a promising new approach, but its time
+complexity grows cubically with the number of unique words in the documents.
+The Relaxed Word Mover's Distance (RWMD) method, again proposed by Kusner et
+al., reduces the time complexity from qubic to quadratic and results in a
+limited loss in accuracy compared with WMD. Our work contributes a
+low-complexity implementation of the RWMD that reduces the average time
+complexity to linear when operating on large sets of documents. Our
+linear-complexity RWMD implementation, henceforth referred to as LC-RWMD, maps
+well onto GPUs and can be efficiently distributed across a cluster of GPUs. Our
+experiments on real-life datasets demonstrate 1) a performance improvement of
+two orders of magnitude with respect to our GPU-based distributed
+implementation of the quadratic RWMD, and 2) a performance improvement of three
+to four orders of magnitude with respect to our distributed WMD implementation
+that uses GPU-based RWMD for pruning.
+"
+23797,A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data,"  High-dimensional data of discrete and skewed nature is commonly encountered
+in high-throughput sequencing studies. Analyzing the network itself or the
+interplay between genes in this type of data continues to present many
+challenges. As data visualization techniques become cumbersome for higher
+dimensions and unconvincing when there is no clear separation between
+homogeneous subgroups within the data, cluster analysis provides an intuitive
+alternative. The aim of applying mixture model-based clustering in this context
+is to discover groups of co-expressed genes, which can shed light on biological
+functions and pathways of gene products. A mixture of multivariate Poisson-Log
+Normal (MPLN) model is proposed for clustering of high-throughput transcriptome
+sequencing data. The MPLN model is able to fit a wide range of correlation and
+overdispersion situations, and is ideal for modeling multivariate count data
+from RNA sequencing studies. Parameter estimation is carried out via a Markov
+chain Monte Carlo expectation-maximization algorithm (MCMC-EM), and information
+criteria are used for model selection.
+"
+23798,Peculiar modules for 4-ended tangles,"  With a 4-ended tangle $T$, we associate a Heegaard Floer invariant
+$\operatorname{CFT^\partial}(T)$, the peculiar module of $T$. We classify
+peculiar modules in terms of immersed curves on the 4-punctured sphere, based
+on an algorithm due to Hanselman, Rasmussen and Watson. We deduce that the
+peculiar module of any tangle $T$ can be computed combinatorially using nice
+diagrams and illustrate this result by computing the peculiar modules of the
+2-stranded pretzel tangles $T_{2n,-(2m+1)}$ for $n,m>0$.
+Based on Zarev's bordered sutured Heegaard Floer theory, we prove a glueing
+formula for peculiar modules which recovers link Floer homology
+$\operatorname{\widehat{HFL}}$. We then study some applications: Firstly, we
+show that peculiar modules detect rational tangles. Secondly, we give short
+proofs of various skein exact triangles. Finally, we observe certain symmetries
+in the peculiar modules of the pretzel tangles $T_{2n,-(2m+1)}$ and deduce that
+mutation of those tangles preserves $\delta$-graded, and for some orientations
+even bigraded link Floer homology.
+"
+23799,A geometer's view of the the Cramér-Rao bound on estimator variance,"  The classical Cramér-Rao inequality gives a lower bound for the variance of
+a unbiased estimator of an unknown parameter, in some statistical model of a
+random process. In this note we rewrite the statment and proof of the bound
+using contemporary geometric language.
+"
+23800,Ultrashort Mn-Mn Bonds in Organometallic Complexes,"  Manganese metallocenes larger than the experimentally produced sandwiched
+MnBz$_2$ compound are studied using several density functional theory methods.
+First, we show that the lowest energy structures have Mn clusters surrounded by
+benzene molecules, in so-called rice-ball structures. We then find a strikingly
+short bond length of 1.8 {\AA} between pairs of Mn atoms, accompanied by
+magnetism depletion. The ultrashort bond lengths are related to Bz molecules
+caging a pair of Mn atoms, leading to a Mn-Mn triple bond. This effect is also
+found when replacing benzenes by other molecules such as borazine or
+cyclopentadiene. The stability of the Mn-Mn bond for Mn$_2$Bz$_2$ is further
+investigated using dissociation energy curves. For each spin configuration, the
+energy versus distance plot shows different spin minima with barriers, which
+must be overcome to synthesize larger Mn-Bz complexes.
+"
+23801,Shift Aggregate Extract Networks,"  We introduce an architecture based on deep hierarchical decompositions to
+learn effective representations of large graphs. Our framework extends classic
+R-decompositions used in kernel methods, enabling nested ""part-of-part""
+relations. Unlike recursive neural networks, which unroll a template on input
+graphs directly, we unroll a neural network template over the decomposition
+hierarchy, allowing us to deal with the high degree variability that typically
+characterize social network graphs. Deep hierarchical decompositions are also
+amenable to domain compression, a technique that reduces both space and time
+complexity by exploiting symmetries. We show empirically that our approach is
+competitive with current state-of-the-art graph classification methods,
+particularly when dealing with social network datasets.
+"
+23802,What About Applied Fairness?,"  Machine learning practitioners are often ambivalent about the ethical aspects
+of their products. We believe anything that gets us from that current state to
+one in which our systems are achieving some degree of fairness is an
+improvement that should be welcomed. This is true even when that progress does
+not get us 100% of the way to the goal of ""complete"" fairness or perfectly
+align with our personal belief on which measure of fairness is used. Some
+measure of fairness being built would still put us in a better position than
+the status quo. Impediments to getting fairness and ethical concerns applied in
+real applications, whether they are abstruse philosophical debates or technical
+overhead such as the introduction of ever more hyper-parameters, should be
+avoided. In this paper we further elaborate on our argument for this viewpoint
+and its importance.
+"
+23803,AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning,"  Clinical prognostic models derived from largescale healthcare data can inform
+critical diagnostic and therapeutic decisions. To enable off-theshelf usage of
+machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a
+system for automating the design of predictive modeling pipelines tailored for
+clinical prognosis. AUTOPROGNOSIS optimizes ensembles of pipeline
+configurations efficiently using a novel batched Bayesian optimization (BO)
+algorithm that learns a low-dimensional decomposition of the pipelines
+high-dimensional hyperparameter space in concurrence with the BO procedure.
+This is achieved by modeling the pipelines performances as a black-box function
+with a Gaussian process prior, and modeling the similarities between the
+pipelines baseline algorithms via a sparse additive kernel with a Dirichlet
+prior. Meta-learning is used to warmstart BO with external data from similar
+patient cohorts by calibrating the priors using an algorithm that mimics the
+empirical Bayes method. The system automatically explains its predictions by
+presenting the clinicians with logical association rules that link patients
+features to predicted risk strata. We demonstrate the utility of AUTOPROGNOSIS
+using 10 major patient cohorts representing various aspects of cardiovascular
+patient care.
+"
+23804,Efficient regularization with wavelet sparsity constraints in PAT,"  In this paper we consider the reconstruction problem of photoacoustic
+tomography (PAT) with a flat observation surface. We develop a direct
+reconstruction method that employs regularization with wavelet sparsity
+constraints. To that end, we derive a wavelet-vaguelette decomposition (WVD)
+for the PAT forward operator and a corresponding explicit reconstruction
+formula in the case of exact data. In the case of noisy data, we combine the
+WVD reconstruction formula with soft-thresholding which yields a spatially
+adaptive estimation method. We demonstrate that our method is statistically
+optimal for white random noise if the unknown function is assumed to lie in any
+Besov-ball. We present generalizations of this approach and, in particular, we
+discuss the combination of vaguelette soft-thresholding with a TV prior. We
+also provide an efficient implementation of the vaguelette transform that leads
+to fast image reconstruction algorithms supported by numerical results.
+"
+23805,A Fourier-invariant method for locating point-masses and computing their attributes,"  Motivated by the interest of observing the growth of cancer cells among
+normal living cells and exploring how galaxies and stars are truly formed, the
+objective of this paper is to introduce a rigorous and effective method for
+counting point-masses, determining their spatial locations, and computing their
+attributes. Based on computation of Hermite moments that are Fourier-invariant,
+our approach facilitates the processing of both spatial and Fourier data in any
+dimension.
+"
+23806,Hack Weeks as a model for Data Science Education and Collaboration,"  Across almost all scientific disciplines, the instruments that record our
+experimental data and the methods required for storage and data analysis are
+rapidly increasing in complexity. This gives rise to the need for scientific
+communities to adapt on shorter time scales than traditional university
+curricula allow for, and therefore requires new modes of knowledge transfer.
+The universal applicability of data science tools to a broad range of problems
+has generated new opportunities to foster exchange of ideas and computational
+workflows across disciplines. In recent years, hack weeks have emerged as an
+effective tool for fostering these exchanges by providing training in modern
+data analysis workflows. While there are variations in hack week
+implementation, all events consist of a common core of three components:
+tutorials in state-of-the-art methodology, peer-learning and project work in a
+collaborative environment. In this paper, we present the concept of a hack week
+in the larger context of scientific meetings and point out similarities and
+differences to traditional conferences. We motivate the need for such an event
+and present in detail its strengths and challenges. We find that hack weeks are
+successful at cultivating collaboration and the exchange of knowledge.
+Participants self-report that these events help them both in their day-to-day
+research as well as their careers. Based on our results, we conclude that hack
+weeks present an effective, easy-to-implement, fairly low-cost tool to
+positively impact data analysis literacy in academic disciplines, foster
+collaboration and cultivate best practices.
+"
+23807,Reconstruction of~3-D Rigid Smooth Curves Moving Free when Two Traceable Points Only are Available,"  This paper extends previous research in that sense that for orthogonal
+projections of rigid smooth (true-3D) curves moving totally free it reduces the
+number of required traceable points to two only (the best results known so far
+to the author are 3 points from free motion and 2 for motion restricted to
+rotation around a fixed direction and and 2 for motion restricted to influence
+of a homogeneous force field). The method used is exploitation of information
+on tangential projections. It discusses also possibility of simplification of
+reconstruction of flat curves moving free for prospective projections.
+"
+23808,Categorizing Variants of Goodhart's Law,"  There are several distinct failure modes for overoptimization of systems on
+the basis of metrics. This occurs when a metric which can be used to improve a
+system is used to an extent that further optimization is ineffective or
+harmful, and is sometimes termed Goodhart's Law. This class of failure is often
+poorly understood, partly because terminology for discussing them is ambiguous,
+and partly because discussion using this ambiguous terminology ignores
+distinctions between different failure modes of this general type. This paper
+expands on an earlier discussion by Garrabrant, which notes there are ""(at
+least) four different mechanisms"" that relate to Goodhart's Law. This paper is
+intended to explore these mechanisms further, and specify more clearly how they
+occur. This discussion should be helpful in better understanding these types of
+failures in economic regulation, in public policy, in machine learning, and in
+Artificial Intelligence alignment. The importance of Goodhart effects depends
+on the amount of power directed towards optimizing the proxy, and so the
+increased optimization power offered by artificial intelligence makes it
+especially critical for that field.
+"
+23809,The International Cosmic Day - An Outreach Event for Astroparticle Physics,"  The International Cosmic Day (ICD) is an astroparticle physics outreach event
+for high-school students and brings together students and different physics
+outreach projects from all over the world. Groups of scientists, teachers, and
+students meet for one day to learn about cosmic rays and perform an experiment
+with atmospheric muons. All participating groups investigate an identical
+question. The students are enabled to work together like in an international
+collaboration, discussing their results in joint video conferences. Analyzing
+data, comparing and discussing with other ""young scientists"" gives the students
+a glimpse of how professional scientific research works. Scientists join the
+video conferences and give lectures to provide an insight in current
+astroparticle physics research. Several participating research experiments
+analyze big science data tailored to the questions addressed by the students
+and present their results on equal terms with the students. To create a lasting
+event, proceedings with measurement results of all participating groups are
+published. Every participant receives a personal e-mail with his certificate
+and the proceedings booklet. Organized by DESY in cooperation with Netzwerk
+Teilchenwelt, IPPOG, QuarkNet, Fermilab, and national partners like INFN, the
+ICD is a growing event with more and more popularity. We present the
+organization of the event and the experience from five years of ICD.
+"
+23810,Higher-Order Analogs of Lie Algebroids via Vector Bundle Comorphisms,"  We introduce the concept of a higher algebroid, generalizing the notions of
+an algebroid and a higher tangent bundle. Our ideas are based on a description
+of (Lie) algebroids as vector bundle comorphisms - differential relations of a
+special kind. In our approach higher algebroids are vector bundle comorphism
+between graded-linear bundles satisfying natural axioms. We provide natural
+examples and discuss applications in geometric mechanics.
+"
+23811,Testing lowered isothermal models with direct N-body simulations of globular clusters - II: Multimass models,"  Lowered isothermal models, such as the multimass Michie-King models, have
+been successful in describing observational data of globular clusters. In this
+study we assess whether such models are able to describe the phase space
+properties of evolutionary $N$-body models. We compare the multimass models as
+implemented in (Gieles \& Zocchi) to $N$-body models of star clusters with
+different retention fractions for the black holes and neutron stars evolving in
+a tidal field. We find that multimass models successfully reproduce the density
+and velocity dispersion profiles of the different mass components in all
+evolutionary phases and for different remnants retention. We further use these
+results to study the evolution of global model parameters. We find that over
+the lifetime of clusters, radial anisotropy gradually evolves from the low-mass
+to the high-mass components and we identify features in the properties of
+observable stars that are indicative of the presence of stellar-mass black
+holes. We find that the model velocity scale depends on mass as $m^{-\delta}$,
+with $\delta\simeq0.5$ for almost all models, but the dependence of central
+velocity dispersion on $m$ can be shallower, depending on the dark remnant
+content, and agrees well with that of the $N$-body models. The reported model
+parameters, and correlations amongst them, can be used as theoretical priors
+when fitting these types of mass models to observational data.
+"
+23812,Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder,"  Recent neural networks such as WaveNet and sampleRNN that learn directly from
+speech waveform samples have achieved very high-quality synthetic speech in
+terms of both naturalness and speaker similarity even in multi-speaker
+text-to-speech synthesis systems. Such neural networks are being used as an
+alternative to vocoders and hence they are often called neural vocoders. The
+neural vocoder uses acoustic features as local condition parameters, and these
+parameters need to be accurately predicted by another acoustic model. However,
+it is not yet clear how to train this acoustic model, which is problematic
+because the final quality of synthetic speech is significantly affected by the
+performance of the acoustic model. Significant degradation happens, especially
+when predicted acoustic features have mismatched characteristics compared to
+natural ones. In order to reduce the mismatched characteristics between natural
+and generated acoustic features, we propose frameworks that incorporate either
+a conditional generative adversarial network (GAN) or its variant, Wasserstein
+GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that
+uses the WaveNet vocoder. We also extend the GAN frameworks and use the
+discretized mixture logistic loss of a well-trained WaveNet in addition to mean
+squared error and adversarial losses as parts of objective functions.
+Experimental results show that acoustic models trained using the WGAN-GP
+framework using back-propagated discretized-mixture-of-logistics (DML) loss
+achieves the highest subjective evaluation scores in terms of both quality and
+speaker similarity.
+"
+23813,Linear and Nonlinear Time- and Frequency-Domain Spectroscopy with Multiple Frequency Combs,"  Two techniques that employ equally spaced trains of optical pulses to map an
+optical high frequency into a low frequency modulation of the signal that can
+be detected in real time are compared. The development of phase-stable optical
+frequency combs has opened up new avenues to metrology and spectroscopy. The
+ability to generate a series of frequency spikes with precisely controlled
+separation permits a fast, highly accurate sampling of the material response.
+Recently, pairs of frequency combs with slightly different repetition rates
+have been utilized to down-convert material susceptibilities from the optical
+to microwave regime where they can be recorded in real time. We show how this
+one-dimensional dual comb technique can be extended to multiple dimensions by
+using several combs. We demonstrate how nonlinear susceptibilities can be
+quickly acquired using this technique. In a second class of techniques,
+sequences of ultrafast mode locked laser pulses are used to recover pathways of
+interactions contributing to nonlinear susceptibilities by using a
+photo-acoustic modulation varying along the sequences. We show that these
+techniques can be viewed as a time-domain analogue of the multiple frequency
+comb scheme. %We compare the two techniques: in both, an optical high frequency
+is mapped into a low frequency modulation of the signal that can be detected in
+real time.
+"
+23814,On-off Analog Beamforming for Massive MIMO,"  This paper investigates a new analog beamforming architecture for massive
+multiple-input multiple-output (MIMO) systems, where each of the multiple
+transmit antennas is switched to be on or off to form a beam according to the
+channel state information at transmitters. This on-off analog beamforming
+(OABF) scheme has the advantages in terms of both hardware complexities and
+algorithmic complexities. OABF can completely remove the high-cost,
+power-consuming and bulky analog phase-shifters that are extensively employed
+by traditional analog beamforming schemes; it only requires the deployment of
+low-cost analog switches that are easy to implement. Moreover, we show that the
+beams formed by such simple antenna on-off switch operations can achieve rather
+good performances with low-complexity beamforming algorithms. Specifically, we
+first propose two optimal signal-to-noise ratio maximization algorithms to
+determine the on-off state of each switch under the per-antenna power
+constraint and the total power constraint, respectively. After that, we
+theoretically prove that OABF can achieve the full diversity gain and the full
+array gain with complexities up to a polynomial order. Numerical results are
+consistent with our theoretical analysis. We believe that the simple structure
+of OABF makes massive MIMO much easier to implement in real systems.
+"
+23815,Compatible actions and non-abelian tensor products,"  For a pair of groups $G, H$ we study pairs of actions $G$ on $H$ and $H$ on
+$G$ such that these pairs are compatible and non-abelian tensor products $G
+\otimes H$ are defined.
+"
+23816,Automated Identification of Relevant Frontier Orbitals for Chemical Compounds and Processes,"  Quantum-chemical multi-configurational methods are required for a proper
+description of static electron correlation, a phenomenon inherent to the
+electronic structure of molecules with multiple (near-)degenerate frontier
+orbitals. Here, we review how a property of these frontier orbitals, namely the
+entanglement entropy is related to static electron correlation. A subset of
+orbitals, the so-called active orbital space, is an essential ingredient for
+all multi-configurational methods. We proposed an automated selection of this
+active orbital space, that would otherwise be a tedious and error prone manual
+procedure, based on entanglement measures. Here, we extend this scheme to
+demonstrate its capability for the selection of consistent active spaces for
+several excited states and along reaction coordinates.
+"
+23817,"Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative","  In this article, we mathematically study several GAN related topics,
+including Inception score, label smoothing, gradient vanishing and the
+-log(D(x)) alternative.
+---
+An advanced version is included in arXiv:1703.02000 ""Activation Maximization
+Generative Adversarial Nets"".
+Please refer Section 6 in 1703.02000 for detailed analysis on Inception
+Score, and refer its appendix for the discussions on Label Smoothing, Gradient
+Vanishing and -log(D(x)) Alternative.
+"
+23818,Perturbation Bootstrap in Adaptive Lasso,"  The Adaptive Lasso(Alasso) was proposed by Zou [\textit{J. Amer. Statist.
+Assoc. \textbf{101} (2006) 1418-1429}] as a modification of the Lasso for the
+purpose of simultaneous variable selection and estimation of the parameters in
+a linear regression model. Zou (2006) established that the Alasso estimator is
+variable-selection consistent as well as asymptotically Normal in the indices
+corresponding to the nonzero regression coefficients in certain
+fixed-dimensional settings. In an influential paper, Minnier, Tian and Cai
+[\textit{J. Amer. Statist. Assoc. \textbf{106} (2011) 1371-1382}] proposed a
+perturbation bootstrap method and established its distributional consistency
+for the Alasso estimator in the fixed-dimensional setting. In this paper,
+however, we show that this (naive) perturbation bootstrap fails to achieve
+second order correctness in approximating the distribution of the Alasso
+estimator. We propose a modification to the perturbation bootstrap objective
+function and show that a suitably studentized version of our modified
+perturbation bootstrap Alasso estimator achieves second-order correctness even
+when the dimension of the model is allowed to grow to infinity with the sample
+size. As a consequence, inferences based on the modified perturbation bootstrap
+will be more accurate than the inferences based on the oracle Normal
+approximation. We give simulation studies demonstrating good finite-sample
+properties of our modified perturbation bootstrap method as well as an
+illustration of our method on a real data set.
+"
+23819,Systemic risk governance in a dynamical model of a banking system,"  We consider the problem of governing systemic risk in a banking system model.
+The banking system model consists in an initial value problem for a system of
+stochastic differential equations whose dependent variables are the
+log-monetary reserves of the banks as functions of time. The banking system
+model considered generalizes previous models studied in [5], [4], [7] and
+describes an homogeneous population of banks. Two distinct mechanisms are used
+to model the cooperation among banks and the cooperation between banks and
+monetary authority. These mechanisms are regulated respectively by the
+parameters $\alpha$ and $\gamma$. A bank fails when its log-monetary reserves
+go below an assigned default level. We call systemic risk or systemic event in
+a bounded time interval the fact that in that time interval at least a given
+fraction of the banks fails. The probability of systemic risk in a bounded time
+interval is evaluated using statistical simulation. A method to govern the
+probability of systemic risk in a bounded time interval is presented. The goal
+of the governance is to keep the probability of systemic risk in a bounded time
+interval between two given thresholds. The governance is based on the choice of
+the log-monetary reserves of a kind of ""ideal bank"" as a function of time and
+on the solution of an optimal control problem for the mean field approximation
+of the banking system model. The solution of the optimal control problem
+determines the parameters $\alpha$ and $\gamma$ as functions of time, that is
+defines the rules of the borrowing and lending activity among banks and between
+banks and monetary authority. Some numerical examples are discussed. The
+systemic risk governance is tested in absence and in presence of positive and
+negative shocks acting on the banking system.
+"
+23820,"Linearization of nonlinear connections on vector and affine bundles, and some applications","  A linear connection is associated to a nonlinear connection on a vector
+bundle by a linearization procedure. Our definition is intrinsic in terms of
+vector fields on the bundle. For a connection on an affine bundle our procedure
+can be applied after homogenization and restriction. Several applications in
+Classical Mechanics are provided.
+"
+23821,Tensor Regression Networks with various Low-Rank Tensor Approximations,"  Tensor regression networks achieve high compression rate of neural networks
+while having slight impact on performances. They do so by imposing low tensor
+rank structure on the weight matrices of fully connected layers. In recent
+years, tensor regression networks have been investigated from the perspective
+of their compressive power, however, the regularization effect of enforcing
+low-rank tensor structure has not been investigated enough. We study tensor
+regression networks using various low-rank tensor approximations, aiming to
+compare the compressive and regularization power of different low-rank
+constraints. We evaluate the compressive and regularization performances of the
+proposed model with both deep and shallow convolutional neural networks. The
+outcome of our experiment suggests the superiority of Global Average Pooling
+Layer over Tensor Regression Layer when applied to deep convolutional neural
+network with CIFAR-10 dataset. On the contrary, shallow convolutional neural
+networks with tensor regression layer and dropout achieved lower test error
+than both Global Average Pooling and fully-connected layer with dropout
+function when trained with a small number of samples.
+"
+23822,"Comparative growth analysis of special type of differential polynomial generated by entire and meromorphic functions on the basis of their (p,q)-th order","  In this paper we aim to establish some results depending on the comparative
+growth properties of composite transcendental entire or meromorphic functions
+and some special type of differential polynomials generated by one of the
+factors on the basis of (p,q)-th order and (p,q)-th lower order where p,q are
+positive integers with p greater than or equal to q.
+"
+23823,Guided evolutionary strategies: escaping the curse of dimensionality in random search,"  Many applications in machine learning require optimizing a function whose
+true gradient is unknown, but where surrogate gradient information (directions
+that may be correlated with, but not necessarily identical to, the true
+gradient) is available instead. This arises when an approximate gradient is
+easier to compute than the full gradient (e.g. in meta-learning or unrolled
+optimization), or when a true gradient is intractable and is replaced with a
+surrogate (e.g. in certain reinforcement learning applications, or when using
+synthetic gradients). We propose Guided Evolutionary Strategies, a method for
+optimally using surrogate gradient directions along with random search. We
+define a search distribution for evolutionary strategies that is elongated
+along a guiding subspace spanned by the surrogate gradients. This allows us to
+estimate a descent direction which can then be passed to a first-order
+optimizer. We analytically and numerically characterize the tradeoffs that
+result from tuning how strongly the search distribution is stretched along the
+guiding subspace, and we use this to derive a setting of the hyperparameters
+that works well across problems. Finally, we apply our method to example
+problems, demonstrating an improvement over both standard evolutionary
+strategies and first-order methods (that directly follow the surrogate
+gradient). We provide a demo of Guided ES at
+this https URL
+"
+23824,A linear algebra approach to hybrid polynomial sequences,"  In this article, a new approach based on linear algebra is adopted to study a
+hybrid Sheffer polynomial sequences. The recurrence relations and differential
+equation for these polynomials are derived by using the properties and
+relationships between the Pascal functional matrices and the Wronskian
+matrices. The corresponding results for some mixed type Sheffer polynomials are
+also obtained.
+"
+23825,Pointwise estimate for the Bergman kernel of the weighted Bergman spaces with exponential type weights,"  Let $AL^{2}_{\phi}(\mathbb{D})$ denote the closed subspace of
+$L^{2}(\mathbb{D},e^{-2\phi}d\lambda)$ consisting of holomorphic functions in
+the unit disc ${\mathbb D}$. For certain class of subharmonic funcions $\phi :
+{\mathbb D}\rightarrow{\mathbb D}$, we prove upper pointwise estimate for the
+Bergman kernel for $AL^{2}_{\phi}(\mathbb{D})$
+"
+23826,Une alternative robuste au maximum de vraisemblance: la $ρ$-estimation,"  This paper is based on our personal notes for the short course we gave on
+January 5, 2017 at Institut Henri Poincaré, after an invitation of the SFdS.
+Our purpose is to give an overview of the method of $\rho$-estimation and of
+the optimality and robustness properties of the estimators built according to
+this procedure. This method can be viewed as the sequel of a long series of
+researches which were devoted to the construction of estimators with good
+properties in various statistical frameworks. We shall emphasize the connection
+between the $\rho$-estimators and the previous ones, in particular the maximum
+likelihood estimator, and we shall show, via some typical examples, that the
+$\rho$-estimators perform better from various points of view.
+------
+Cet article est fondé sur les notes du mini-cours que nous avons donné le
+5 janvier 2017 à l'Institut Henri Poincaré à l'occasion d'une journée
+organisée par la SFdS et consacrée à la Statistique Mathématique. Il
+vise à donner un aperçu de la méthode de $\rho$-estimation ainsi que
+des propriétés d'optimalité et de robustesse des estimateurs construits
+selon cette procédure. Cette méthode s'inscrit dans une longue lignée de
+recherches dont l'objectif a été de produire des estimateurs possédant de
+bonnes propriétés pour un ensemble de cadres statistiques aussi vaste que
+possible. Nous mettrons en lumière les liens forts qui existent entre les
+$\rho$-estimateurs et ces prédécesseurs, notamment les estimateurs du
+maximum de vraisemblance, mais montrerons également, au travers d'exemples
+choisis, que les $\rho$-estimateurs les surpassent sur bien des aspects.
+"
+23827,Maximal Area Triangles in a Convex Polygon,"  The widely known linear time algorithm for computing the maximum area
+triangle in a convex polygon was found incorrect recently by Keikha et.
+al.(arXiv:1705.11035). We present an alternative algorithm in this paper.
+Comparing to the only previously known correct solution, ours is much simpler
+and more efficient. More importantly, our new approach is powerful in solving
+related problems.
+"
+23828,Lifespan of strong solutions to the periodic derivative nonlinear Schrödinger equation,"  An explicit lifespan estimate is presented for the derivative Schrödinger
+equations with periodic boundary condition.
+"
+23829,A Test of the Neutron Star Hypothesis for Fomalhaut b,"  Fomalhaut b is a directly imaged object in the debris disk of the star
+Fomalhaut. It has been hypothesized to be a planet, however there are issues
+with the observed colours of the object that do not fit planetary models. An
+alternative hypothesis is that the object is a neutron star in the near fore-
+or background of Fomalhaut's disk. We test if Fomalhaut b could be a neutron
+star using X-ray observations with Chandra's HRC-I instrument in the energy
+range of 0.08-10 keV. We do not detect X-ray emission from either Fomalhaut b
+or the star Fomalhaut itself. Our nondetection corresponds to an upper limit on
+the X-ray flux of Fomalhaut b of F_X < 1.3e-14 erg/cm/s^2 in the energy range
+0.08-10 keV. For the A-type central star Fomalhaut, we derive an X-ray upper
+limit of L_X < 2e25 erg/s in the energy range 0.08-10 keV. Fomalhaut b's X-ray
+non-detection constrains the parameter space for a possible neutron star
+significantly, implying surface temperatures lower than 91000 K and distances
+closer than 13.3 pc to the solar system. In addition we find that reflected
+starlight from the central star fits the available optical detections of
+Fomalhaut b; a smaller planet with a large ring system might explain such a
+scenario.
+"
+23830,Domain Attentive Fusion for End-to-end Dialect Identification with Unknown Target Domain,"  End-to-end deep learning language or dialect identification systems operate
+on the spectrogram or other acoustic feature and directly generate
+identification scores for each class. An important issue for end-to-end systems
+is to have some knowledge of the application domain, because the system can be
+vulnerable to use cases that were not seen in the training phase; such a
+scenario is often referred to as a domain mismatched condition. In general, we
+assume that there is enough variation in the training dataset to expose the
+system to multiple domains. In this work, we study how to best make use a
+training dataset in order to have maximum effectiveness on unknown target
+domains. Our goal is to process the input without any knowledge of the target
+domain while preserving robust performance on other domains as well. To
+accomplish this objective, we propose a domain attentive fusion approach for
+end-to-end dialect/language identification systems. To help with
+experimentation, we collect a dataset from three different domains, and create
+experimental protocols for a domain mismatched condition. The results of our
+proposed approach, which were tested on a variety of broadcast and YouTube
+data, shows significant performance gain compared to traditional approaches,
+even without any prior target domain information.
+"
+23831,Comment on: Observation of the Wigner-Huntington transition to metallic hydrogen,"  In a recently published article [1], Ranga P. Dias & Isaac F. Silvera have
+reported the visual evidence of metallic hydrogen concomitantly with its
+characterization at a pressure of 495 GPa and low temperatures. We have
+expressed serious doubts of such a conclusion when interviewed to comment on
+this publication [2,3]. In the following comment, we would like to detail the
+reasons, based on experimental evidences obtained by us and by other groups
+worldwide that sustain our skepticism. We have identified two main flaws in
+this paper, as discussed in details below: the pressure is largely
+overestimated; the origin of the sample reflectivity and the analysis of the
+reflectance can be seriously questioned.
+"
+23832,Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree,"  With the advent of depth sensing technologies, the extraction of object
+contours in images---a common and important pre-processing step for later
+higher-level computer vision tasks like object detection and human action
+recognition---has become easier. However, acquisition noise in captured depth
+images means that detected contours suffer from unavoidable errors. In this
+paper, we propose to jointly denoise and compress detected contours in an image
+for bandwidth-constrained transmission to a client, who can then carry out
+aforementioned application-specific tasks using the decoded contours as input.
+We first prove theoretically that in general a joint denoising / compression
+approach can outperform a separate two-stage approach that first denoises then
+encodes contours lossily. Adopting a joint approach, we first propose a burst
+error model that models typical errors encountered in an observed string y of
+directional edges. We then formulate a rate-constrained maximum a posteriori
+(MAP) problem that trades off the posterior probability p(x'|y) of an estimated
+string x' given y with its code rate R(x'). We design a dynamic programming
+(DP) algorithm that solves the posed problem optimally, and propose a compact
+context representation called total suffix tree (TST) that can reduce
+complexity of the algorithm dramatically. Experimental results show that our
+joint denoising / compression scheme outperformed a competing separate scheme
+in rate-distortion performance noticeably.
+"
+23833,The first principle of neural circuit and the general Circuit-Probability theory,"  A new neural circuit is proposed by considering the myelin as an inductor.
+This new neural circuit can explain why the lump-parameter circuit used in
+previous C-P theory is valid. Meanwhile, it provides a new explanation of the
+biological function of myelin for neural signal propagation. Furthermore, a new
+model for magnetic nerve stimulation is built and all phenomena in magnetic
+nerve stimulation can be well explained. Based on this model, the coil
+structure can be optimized.
+"
+23834,Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts,"  Word embedding is a Natural Language Processing (NLP) technique that
+automatically maps words from a vocabulary to vectors of real numbers in an
+embedding space. It has been widely used in recent years to boost the
+performance of a vari-ety of NLP tasks such as Named Entity Recognition,
+Syntac-tic Parsing and Sentiment Analysis. Classic word embedding methods such
+as Word2Vec and GloVe work well when they are given a large text corpus. When
+the input texts are sparse as in many specialized domains (e.g.,
+cybersecurity), these methods often fail to produce high-quality vectors. In
+this pa-per, we describe a novel method to train domain-specificword embeddings
+from sparse texts. In addition to domain texts, our method also leverages
+diverse types of domain knowledge such as domain vocabulary and semantic
+relations. Specifi-cally, we first propose a general framework to encode
+diverse types of domain knowledge as text annotations. Then we de-velop a novel
+Word Annotation Embedding (WAE) algorithm to incorporate diverse types of text
+annotations in word em-bedding. We have evaluated our method on two
+cybersecurity text corpora: a malware description corpus and a Common
+Vulnerability and Exposure (CVE) corpus. Our evaluation re-sults have
+demonstrated the effectiveness of our method in learning domain-specific word
+embeddings.
+"
+23835,A distribution formula for Kashio's $p$-adic log-gamma function,"  We study a special case of Kashio's $p$-adic $\log\Gamma$-function, that we
+call ${\mathrm{Log}}\Gamma_{\mathit{p}}$, which combines these of Morita and
+Diamond. It agrees with each of these on large parts of its domain and has the
+advantage of being a locally analytic function. We prove a distribution formula
+for ${\mathrm{Log}}\Gamma_{\mathit{p}}$ which generalizes and links the known
+distribution formulas for Diamond's and Morita's functions.
+"
+23836,Stiffness: A New Perspective on Generalization in Neural Networks,"  We investigate neural network training and generalization using the concept
+of stiffness. We measure how stiff a network is by looking at how a small
+gradient step on one example affects the loss on another example. In
+particular, we study how stiffness varies with 1) class membership, 2) distance
+between data points (in the input space as well as in latent spaces), 3)
+training iteration, and 4) learning rate. We empirically study the evolution of
+stiffness on MNIST, FASHION MNIST, CIFAR-10 and CIFAR-100 using fully-connected
+and convolutional neural networks. Our results demonstrate that stiffness is a
+useful concept for diagnosing and characterizing generalization. We observe
+that small learning rates lead to initial learning of more specific features
+that do not translate well to improvements on inputs from all classes, whereas
+high learning rates initially benefit all classes at once. We measure stiffness
+as a function of distance between data points and observe that higher learning
+rates induce positive correlation between changes in loss further apart,
+pointing towards a regularization effect of learning rate. When training on
+CIFAR-100, the stiffness matrix exhibits a coarse-grained behavior suggestive
+of the model's awareness of super-class membership.
+"
+23837,Phase recovery and holographic image reconstruction using deep learning in neural networks,"  Phase recovery from intensity-only measurements forms the heart of coherent
+imaging techniques and holography. Here we demonstrate that a neural network
+can learn to perform phase recovery and holographic image reconstruction after
+appropriate training. This deep learning-based approach provides an entirely
+new framework to conduct holographic imaging by rapidly eliminating twin-image
+and self-interference related spatial artifacts. Compared to existing
+approaches, this neural network based method is significantly faster to
+compute, and reconstructs improved phase and amplitude images of the objects
+using only one hologram, i.e., requires less number of measurements in addition
+to being computationally faster. We validated this method by reconstructing
+phase and amplitude images of various samples, including blood and Pap smears,
+and tissue sections. These results are broadly applicable to any phase recovery
+problem, and highlight that through machine learning challenging problems in
+imaging science can be overcome, providing new avenues to design powerful
+computational imaging systems.
+"
+23838,Constant scalar curvature equation and the regularity of its weak solution,"  In this paper we study constant scalar curvature equation (CSCK), a nonlinear
+fourth order elliptic equation, and its weak solutions on Kähler manifolds.
+We first define a notion of weak solution of CSCK for an $L^\infty$ Kähler
+metric. The main result is to show that such a weak solution (with uniform
+$L^\infty$ bound) is smooth. As an application, this answers in part a
+conjecture of Chen regarding the regularity of $K$-energy minimizers. The new
+technical ingredient is a $W^{2, 2}$ regularity result for the Laplacian
+equation $\Delta_g u=f$ on Kähler manifolds, where the metric has only
+$L^\infty$ coefficients. It is well-known that such a $W^{2, 2}$ regularity
+($W^{2, p}$ regularity for any $p>1$) fails in general (except for dimension
+two) for uniform elliptic equations of the form $a^{ij}\partial^2_{ij}u=f$ for
+$a^{ij}\in L^\infty$, without certain smallness assumptions on the local
+oscillation of $a^{ij}$. We observe that the Kähler condition plays an
+essential role to obtain a $W^{2, 2}$ regularity for elliptic equations with
+only $L^\infty$ elliptic coefficients on compact manifolds.
+"
+23839,HJB equations with gradient constraint associated with controlled jump-diffusion processes,"  In this paper, we guarantee the existence and uniqueness (in the almost
+everywhere sense) of the solution to a Hamilton-Jacobi-Bellman (HJB) equation
+with gradient constraint and a partial integro-differential operator whose
+Lévy measure has bounded variation. This type of equation arises in a
+singular control problem, where the state process is a multidimensional
+jump-diffusion with jumps of finite variation and infinite activity. We verify,
+by means of {\epsilon}-penalized controls, that the value function associated
+with this problem satisfies the aforementioned HJB equation.
+"
+23840,A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex Sparsity-Regularized Rank Minimization,"  In this paper, we propose a convergent parallel best-response algorithm with
+the exact line search for the nondifferentiable nonconvex sparsity-regularized
+rank minimization problem. On the one hand, it exhibits a faster convergence
+than subgradient algorithms and block coordinate descent algorithms. On the
+other hand, its convergence to a stationary point is guaranteed, while ADMM
+algorithms only converge for convex problems. Furthermore, the exact line
+search procedure in the proposed algorithm is performed efficiently in
+closed-form to avoid the meticulous choice of stepsizes, which is however a
+common bottleneck in subgradient algorithms and successive convex approximation
+algorithms. Finally, the proposed algorithm is numerically tested.
+"
+23841,Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -,"  Recently, triggered by the impressive results in TV-games or game of Go by
+Google DeepMind, end-to-end reinforcement learning (RL) is collecting
+attentions. Although little is known, the author's group has propounded this
+framework for around 20 years and already has shown various functions that
+emerge in a neural network (NN) through RL. In this paper, they are introduced
+again at this timing.
+""Function Modularization"" approach is deeply penetrated subconsciously. The
+inputs and outputs for a learning system can be raw sensor signals and motor
+commands. ""State space"" or ""action space"" generally used in RL show the
+existence of functional modules. That has limited reinforcement learning to
+learning only for the action-planning module. In order to extend reinforcement
+learning to learning of the entire function on a huge degree of freedom of a
+massively parallel learning system and to explain or develop human-like
+intelligence, the author has believed that end-to-end RL from sensors to motors
+using a recurrent NN (RNN) becomes an essential key. Especially in the higher
+functions, this approach is very effective by being free from the need to
+decide their inputs and outputs.
+The functions that emerge, we have confirmed, through RL using a NN cover a
+broad range from real robot learning with raw camera pixel inputs to
+acquisition of dynamic functions in a RNN. Those are (1)image recognition,
+(2)color constancy (optical illusion), (3)sensor motion (active recognition),
+(4)hand-eye coordination and hand reaching movement, (5)explanation of brain
+activities, (6)communication, (7)knowledge transfer, (8)memory, (9)selective
+attention, (10)prediction, (11)exploration. The end-to-end RL enables the
+emergence of very flexible comprehensive functions that consider many things in
+parallel although it is difficult to give the boundary of each function
+clearly.
+"
+23842,On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective,"  We implement and benchmark parallel I/O methods for the fully-manycore driven
+particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as
+a major challenge for applications on today's and future HPC systems, we
+present a scaling law characterizing performance bottlenecks in
+state-of-the-art approaches for data reduction. Consequently, we propose,
+implement and verify multi-threaded data-transformations for the I/O library
+ADIOS as a feasible way to trade underutilized host-side compute potential on
+heterogeneous systems for reduced I/O latency.
+"
+23843,Community Identity and User Engagement in a Multi-Community Landscape,"  A community's identity defines and shapes its internal dynamics. Our current
+understanding of this interplay is mostly limited to glimpses gathered from
+isolated studies of individual communities. In this work we provide a
+systematic exploration of the nature of this relation across a wide variety of
+online communities. To this end we introduce a quantitative, language-based
+typology reflecting two key aspects of a community's identity: how distinctive,
+and how temporally dynamic it is. By mapping almost 300 Reddit communities into
+the landscape induced by this typology, we reveal regularities in how patterns
+of user engagement vary with the characteristics of a community.
+Our results suggest that the way new and existing users engage with a
+community depends strongly and systematically on the nature of the collective
+identity it fosters, in ways that are highly consequential to community
+maintainers. For example, communities with distinctive and highly dynamic
+identities are more likely to retain their users. However, such niche
+communities also exhibit much larger acculturation gaps between existing users
+and newcomers, which potentially hinder the integration of the latter.
+More generally, our methodology reveals differences in how various social
+phenomena manifest across communities, and shows that structuring the
+multi-community landscape can lead to a better understanding of the systematic
+nature of this diversity.
+"
+23844,Numerically evaluating the bispectrum in curved field-space - with PyTransport 2.0,"  We extend the transport framework for numerically evaluating the power
+spectrum and bispectrum in multi-field inflation to the case of a curved
+field-space metric. This method naturally accounts for all sub- and
+super-horizon tree level effects, including those induced by the curvature of
+the field-space. We present an open source implementation of our equations in
+an extension of the publicly available PyTransport code. Finally we illustrate
+how our technique is applied to examples of inflationary models with a
+non-trivial field-space metric.
+"
+23845,Automated Camera-Based Estimation of Rehabilitation Criteria Following ACL Reconstruction,"  Anterior cruciate ligament (ACL) reconstruction necessitates months of
+rehabilitation, during which a clinician evaluates whether a patient is ready
+to return to sports or occupation. Due to their time- and cost-intensive
+nature, these screenings to assess progress are unavailable to many. This paper
+introduces an automated, markerless, camera-based method for estimating
+rehabilitation criteria following ACL reconstruction. To evaluate the
+performance of this novel technique, data were collected weekly from 12
+subjects as they used a leg press over the course of a 12-week rehabilitation
+period. The proposed camera-based method for estimating displacement and force
+was compared to encoder and force plate measurements. The leg press
+displacement and force values were estimated with 89.7% and 85.3% accuracy,
+respectively. These values were then used to calculate lower-limb symmetry and
+to track patient progress over time.
+"
+23846,An Orthogonally Equivariant Estimator of the Covariance Matrix in High Dimensions and Small Sample Size,"  An orthogonally equivariant estimator for the covariance matrix is proposed
+that is valid when the dimension $p$ is larger than the sample size $n$.
+Equivariance under orthogonal transformations is a less restrictive assumption
+than structural assumptions on the true covariance matrix. It reduces the
+problem of estimation of the covariance matrix to that of estimation of its
+eigenvalues. In this paper, the eigenvalue estimates are obtained from an
+adjusted likelihood function derived by approximating the integral over the
+eigenvectors of the sample covariance matrix, which is a challenging problem in
+its own right. Comparisons with two well-known orthogonally equivariant
+estimators are given, which are based on Monte-Carlo risk estimates for
+simulated data and misclassification errors in a real data analysis.
+"
+23847,Modelling structure and predicting dynamics of discussion threads in online boards,"  Internet boards are platforms for online discussions about a variety of
+topics. On these boards, individuals may start a new thread on a specific
+matter, or leave comments in an existing discussion. The resulting collective
+process leads to the formation of `discussion trees', where nodes represent a
+post and comments, and an edge represents a `reply-to' relation. The structure
+of discussion trees has been analysed in previous works, but only from a static
+perspective. In this paper, we focus on their structural and dynamical
+properties by modelling their formation as a self-exciting Hawkes process. We
+first study a Reddit dataset to show that the structure of the trees resemble
+those produced by a Galton-Watson process with a special root offspring
+distribution. The dynamical aspect of the model is then used to predict future
+commenting activity and the final size of a discussion tree. We compare the
+efficiency of our approach with previous works and show its superiority for the
+prediction of the dynamics of discussions.
+"
+23848,"Network maps of student work with physics, other sciences, and math in an integrated science course","  In 2004 Denmark introduced a compulsory integrated science course the most
+popular upper secondary study program. One of the nation-wide course aims are
+for students to ""achieve knowledge about some of the central scientific issues
+and their social, ethical, and historical perspectives"". This is to be done via
+collaboration between the subjects, and often involves physics and another
+scientific subject. The official teaching plans further state that mathematics
+must be used for analysing data. We use network analysis to study six different
+implementations of the course in terms of the structure of different kinds of
+teaching/learning activities. By creating networks maps of each lesson, we show
+that teaching/learning activities in the course seldom tends to address how
+sciences can work together to solve a problem, but rather stages each natural
+science as a distinct and separate activity with a distinct identity.
+"
+23849,Multi-parameter mechanical and thermal sensing based on multi-mode planar photonic crystals,"  This paper proposes a novel multifunctional sensing platform based on
+multimode planar photonic crystals (PPCs). We analytically and numerically
+demonstrate that the reflection spectrum of PPCs exhibits multiple high-Q
+resonant modes, and the fundamental and higher-order modes respond
+distinctively to external mechanical and thermal perturbations, rendering the
+PPCs superior capability for detection and discrimination of multiple
+parameters. We further demonstrate simultaneous pressure and temperature
+sensing with a PPC sensor. Other advantages of PPC sensors include on-chip
+integration and wafer-scale fabrications.
+"
+23850,Nearly Commuting Matrices,"  We prove that the algebraic set of pairs of matrices with a diagonal
+commutator over a field of positive prime characteristic, its irreducible
+components, and their intersection are $F$-pure when the size of matrices is
+equal to 3. Furthermore, we show that this algebraic set is reduced and the
+intersection of its irreducible components is irreducible in any characteristic
+for pairs of matrices of any size. In addition, we discuss various conjectures
+on the singularities of these algebraic sets and the system of parameters on
+the corresponding coordinate rings.
+"
+23851,The nonparametric Fisher geometry and the chi-square process density prior,"  It is well known that the Fisher information induces a Riemannian geometry on
+parametric families of probability density functions. Following recent work, we
+consider the nonparametric generalization of the Fisher geometry. The resulting
+nonparametric Fisher geometry is shown to be equivalent to a familiar, albeit
+infinite-dimensional, geometric object---the sphere. By shifting focus away
+from density functions and toward \emph{square-root} density functions, one may
+calculate theoretical quantities of interest with ease. More importantly, the
+sphere of square-root densities is much more computationally tractable. This
+insight leads to a novel Bayesian nonparametric density estimation model. We
+construct the $\chi^2$-process density prior by modeling the square-root
+density with a restricted Gaussian process prior. Inference over square-root
+densities is fast, and the model retains the flexibility characteristic of
+Bayesian nonparametric models. Finally, we formalize the relationship between
+spherical HMC in the infinite-dimensional limit and standard Riemannian HMC.
+"
+23852,Magnetic and magnetotransport properties of ThCr2Si2-type Ce2O2Bi composed of conducting Bi2- square net and magnetic Ce-O layer,"  ThCr2Si2-type Ce2O2Bi epitaxial thin films were grown by recently developed
+multilayer solid phase epitaxy. The ionic state of Ce was confirmed to be 3+ by
+x-ray photoelectron spectroscopy, corresponding to the electronic configuration
+of [Xe]4f1. Electrical resistivity showed the nonmonotonic temperature
+dependence with a sharp resistivity maximum, concomitant with a magnetization
+kink at 10 K, suggesting antiferromagnetic transition. In addition,
+magnetoresistance showed a large angular-dependent magnetoresistance. These
+results imply that carrier transport in the Bi2- square net could be influenced
+by magnetic ordering in the Ce-O layer owing to its unique layered structure
+[Bi2-/(Ce2O2)2+], particularly in the form of epitaxial thin films.
+"
+23853,A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies,"  Learning effective visuomotor policies for robots purely from data is
+challenging, but also appealing since a learning-based system should not
+require manual tuning or calibration. In the case of a robot operating in a
+real environment the training process can be costly, time-consuming, and even
+dangerous since failures are common at the start of training. For this reason,
+it is desirable to be able to leverage \textit{simulation} and
+\textit{off-policy} data to the extent possible to train the robot. In this
+work, we introduce a robust framework that plans in simulation and transfers
+well to the real environment. Our model incorporates a gradient-descent based
+planning module, which, given the initial image and goal image, encodes the
+images to a lower dimensional latent state and plans a trajectory to reach the
+goal. The model, consisting of the encoder and planner modules, is trained
+through a meta-learning strategy in simulation first. We subsequently perform
+adversarial domain transfer on the encoder by using a bank of unlabelled but
+random images from the simulation and real environments to enable the encoder
+to map images from the real and simulated environments to a similarly
+distributed latent representation. By fine tuning the entire model (encoder +
+planner) with far fewer real world expert demonstrations, we show successful
+planning performances in different navigation tasks.
+"
+23854,Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks,"  Popular deep learning frameworks require users to fine-tune their memory
+usage so that the training data of a deep neural network (DNN) fits within the
+GPU physical memory. Prior work tries to address this restriction by
+virtualizing the memory usage of DNNs, enabling both CPU and GPU memory to be
+utilized for memory allocations. Despite its merits, virtualizing memory can
+incur significant performance overheads when the time needed to copy data back
+and forth from CPU memory is higher than the latency to perform the
+computations required for DNN forward and backward propagation. We introduce a
+high-performance virtualization strategy based on a ""compressing DMA engine""
+(cDMA) that drastically reduces the size of the data structures that are
+targeted for CPU-side allocations. The cDMA engine offers an average 2.6x
+(maximum 13.8x) compression ratio by exploiting the sparsity inherent in
+offloaded data, improving the performance of virtualized DNNs by an average 32%
+(maximum 61%).
+"
+23855,Linear stability analysis of detonations via numerical computation and dynamic mode decomposition,"  We introduce a new method to investigate linear stability of gaseous
+detonations that is based on an accurate shock-fitting numerical integration of
+the linearized reactive Euler equations with a subsequent analysis of the
+computed solution via the dynamic mode decomposition. The method is applied to
+the detonation models based on both the standard one-step Arrhenius kinetics
+and two-step exothermic-endothermic reaction kinetics. Stability spectra for
+all cases are computed and analyzed. The new approach is shown to be a viable
+alternative to the traditional normal-mode analysis used in detonation theory.
+"
+23856,New Wilson-like theorems arising from Dickson polynomials,"  Wilson's Theorem states that the product of all nonzero elements of a finite
+field ${\mathbb F}_q$ is $-1$. In this article, we define some natural subsets
+$S \subset {\mathbb F}_q^\times$ and find formulas for the product of the
+elements of $S$, denoted $\prod S$. These new formulas are appealing for the
+simple, natural description of the sets $S$, and for the simplicity of the
+product. An example is $\prod\left\{ a \in {\mathbb F}_q^\times : \text{$1-a$
+and $3+a$ are nonsquares} \right\} = 2$ if $q \equiv \pm 1 \pmod{12}$, or $-1$
+otherwise.
+"
+23857,$T$-$Ω$ formulation with higher order hierarchical basis functions for non simply connected conductors,"  This paper extends the $T$-$\Omega$ formulation for eddy currents based on
+higher order hierarchical basis functions so that it can deal with conductors
+of arbitrary topology. To this aim we supplement the classical hierarchical
+basis functions with non-local basis functions spanning the first de Rham
+cohomology group of the insulating region. Such non-local basis functions may
+be efficiently found in negligible time with the recently introduced
+D{\l}otko--Specogna (DS) algorithm.
+"
+23858,Extreme Learning Machine with Local Connections,"  This paper is concerned with the sparsification of the input-hidden weights
+of ELM (Extreme Learning Machine). For ordinary feedforward neural networks,
+the sparsification is usually done by introducing certain regularization
+technique into the learning process of the network. But this strategy can not
+be applied for ELM, since the input-hidden weights of ELM are supposed to be
+randomly chosen rather than to be learned. To this end, we propose a modified
+ELM, called ELM-LC (ELM with local connections), which is designed for the
+sparsification of the input-hidden weights as follows: The hidden nodes and the
+input nodes are divided respectively into several corresponding groups, and an
+input node group is fully connected with its corresponding hidden node group,
+but is not connected with any other hidden node group. As in the usual ELM, the
+hidden-input weights are randomly given, and the hidden-output weights are
+obtained through a least square learning. In the numerical simulations on some
+benchmark problems, the new ELM-CL behaves better than the traditional ELM.
+"
+23859,Calibration of the island effect: Experimental validation of closed-loop focal plane wavefront control on Subaru/SCExAO,"  Island effect (IE) aberrations are induced by differential pistons, tips, and
+tilts between neighboring pupil segments on ground-based telescopes, which
+severely limit the observations of circumstellar environments on the recently
+deployed exoplanet imagers (e.g., VLT/SPHERE, Gemini/GPI, Subaru/SCExAO) during
+the best observing conditions. Caused by air temperature gradients at the level
+of the telescope spiders, these aberrations were recently diagnosed with
+success on VLT/SPHERE, but so far no complete calibration has been performed to
+overcome this issue. We propose closed-loop focal plane wavefront control based
+on the asymmetric Fourier pupil wavefront sensor (APF-WFS) to calibrate these
+aberrations and improve the image quality of exoplanet high-contrast
+instruments in the presence of the IE. Assuming the archetypal four-quadrant
+aperture geometry in 8m class telescopes, we describe these aberrations as a
+sum of the independent modes of piston, tip, and tilt that are distributed in
+each quadrant of the telescope pupil. We calibrate these modes with the APF-WFS
+before introducing our wavefront control for closed-loop operation. We perform
+numerical simulations and then experimental tests on a real system using
+Subaru/SCExAO to validate our control loop in the laboratory and on-sky.
+Closed-loop operation with the APF-WFS enables the compensation for the IE in
+simulations and in the laboratory for the small aberration regime. Based on a
+calibration in the near infrared, we observe an improvement of the image
+quality in the visible range on the SCExAO/VAMPIRES module with a relative
+increase in the image Strehl ratio of 37%. Our first IE calibration paves the
+way for maximizing the science operations of the current exoplanet imagers.
+Such an approach and its results prove also very promising in light of the
+Extremely Large Telescopes (ELTs) and the presence of similar artifacts.
+"
+23860,Validity of Clusters Produced By kernel-$k$-means With Kernel-Trick,"  This paper corrects the proof of the Theorem 2 from the Gower's paper
+\cite[page 5]{Gower:1982} as well as corrects the Theorem 7 from Gower's paper
+\cite{Gower:1986}. The first correction is needed in order to establish the
+existence of the kernel function used commonly in the kernel trick e.g. for
+$k$-means clustering algorithm, on the grounds of distance matrix. The
+correction encompasses the missing if-part proof and dropping unnecessary
+conditions. The second correction deals with transformation of the kernel
+matrix into a one embeddable in Euclidean space.
+"
+23861,Optimal Control of Quantum Purity for $n=2$ Systems,"  The objective of this work is to study time-minimum and energy-minimum global
+optimal control for dissipative open quantum systems whose dynamics is governed
+by the Lindblad equation. The controls appear only in the Hamiltonian.
+Using recent results regarding the decoupling of such dissipative dynamics
+into intra- and inter-unitary orbits, we transform the control system into a
+bi-linear control system on the Bloch ball (the unitary sphere together with
+its interior). We then design a numerical algorithm to construct an optimal
+path to achieve a desired point given initial states close to the origin (the
+singular point) of the Bloch ball. This is done both for the minimum-time and
+minimum -energy control problems.
+"
+23862,Light-curve analysis of KOI 2700b: the second extrasolar planet with a comet-like tail,"  The Kepler object KOI 2700b (KIC 8639908b) was discovered recently as the
+second exoplanet with a comet-like tail. It exhibits a distinctly asymmetric
+transit profile, likely indicative of the emission of dusty effluents and
+reminiscent of KIC 12557548b, the first exoplanet with a comet-like tail.The
+scientific goal of this work is to verify the disintegrating-planet scenario of
+KOI 2700b by modeling its light curve and to put constraints on various tail
+and planet properties, as was done in the case of KIC 12557548b. We obtained
+the phase-folded and binned transit light curve of KOI 2700b, which we
+subsequently iteratively modeled using the radiative-transfer code SHELLSPEC.
+We modeled the comet-like tail as part of a ring around the parent star and we
+also included the solid body of the planet in the model. During the modeling we
+applied selected species and dust particle sizes. We confirmed the
+disintegrating-planet scenario of KOI 2700b. Furthermore, via modeling, we
+derived some interesting features of KOI 2700b and its comet-like tail.
+"
+23863,Semi-Supervised Active Clustering with Weak Oracles,"  Semi-supervised active clustering (SSAC) utilizes the knowledge of a domain
+expert to cluster data points by interactively making pairwise ""same-cluster""
+queries. However, it is impractical to ask human oracles to answer every
+pairwise query. In this paper, we study the influence of allowing ""not-sure""
+answers from a weak oracle and propose algorithms to efficiently handle
+uncertainties. Different types of model assumptions are analyzed to cover
+realistic scenarios of oracle abstraction. In the first model, random-weak
+oracle, an oracle randomly abstains with a certain probability. We also
+proposed two distance-weak oracle models which simulate the case of getting
+confused based on the distance between two points in a pairwise query. For each
+weak oracle model, we show that a small query complexity is adequate for the
+effective $k$ means clustering with high probability. Sufficient conditions for
+the guarantee include a $\gamma$-margin property of the data, and an existence
+of a point close to each cluster center. Furthermore, we provide a sample
+complexity with a reduced effect of the cluster's margin and only a logarithmic
+dependency on the data dimension. Our results allow significantly less number
+of same-cluster queries if the margin of the clusters is tight, i.e. $\gamma
+\approx 1$. Experimental results on synthetic data show the effective
+performance of our approach in overcoming uncertainties.
+"
+23864,Flexible Hall Sensors Based on Graphene,"  The excellent electronic and mechanical properties of graphene provide a
+perfect basis for high performance flexible electronic and sensor devices.
+Here, we present the fabrication and characterization of flexible graphene
+based Hall sensors. The Hall sensors are fabricated on 50 um thick flexible
+Kapton foil using large scale graphene grown by chemical vapor deposition
+technique on copper foil. Voltage and current normalized sensitivities of up to
+0.096 V/VT and 79 V/AT were measured, respectively. These values are comparable
+to the sensitivity of rigid silicon based Hall sensors and are the highest
+values reported so far for any flexible Hall sensor devices. The sensitivity of
+the Hall sensor shows no degradation after being bent to a minimum radius of 4
+mm, which corresponds to a tensile strain of 0.6%, and after 1,000 bending
+cycles to a radius of 5 mm.
+"
+23865,CactusNets: Layer Applicability as a Metric for Transfer Learning,"  Deep neural networks trained over large datasets learn features that are both
+generic to the whole dataset, and specific to individual classes in the
+dataset. Learned features tend towards generic in the lower layers and specific
+in the higher layers of a network. Methods like fine-tuning are made possible
+because of the ability for one filter to apply to multiple target classes. Much
+like the human brain this behavior, can also be used to cluster and separate
+classes. However, to the best of our knowledge there is no metric for how
+applicable learned features are to specific classes. In this paper we propose a
+definition and metric for measuring the applicability of learned features to
+individual classes, and use this applicability metric to estimate input
+applicability and produce a new method of unsupervised learning we call the
+CactusNet.
+"
+23866,"Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm","  NLP tasks are often limited by scarcity of manually annotated data. In social
+media sentiment analysis and related tasks, researchers have therefore used
+binarized emoticons and specific hashtags as forms of distant supervision. Our
+paper shows that by extending the distant supervision to a more diverse set of
+noisy labels, the models can learn richer representations. Through emoji
+prediction on a dataset of 1246 million tweets containing one of 64 common
+emojis we obtain state-of-the-art performance on 8 benchmark datasets within
+sentiment, emotion and sarcasm detection using a single pretrained model. Our
+analyses confirm that the diversity of our emotional labels yield a performance
+improvement over previous distant supervision approaches.
+"
+23867,Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization,"  Dynamic portfolio optimization is the process of sequentially allocating
+wealth to a collection of assets in some consecutive trading periods, based on
+investors' return-risk profile. Automating this process with machine learning
+remains a challenging problem. Here, we design a deep reinforcement learning
+(RL) architecture with an autonomous trading agent such that, investment
+decisions and actions are made periodically, based on a global objective, with
+autonomy. In particular, without relying on a purely model-free RL agent, we
+train our trading agent using a novel RL architecture consisting of an infused
+prediction module (IPM), a generative adversarial data augmentation module
+(DAM) and a behavior cloning module (BCM). Our model-based approach works with
+both on-policy or off-policy RL algorithms. We further design the back-testing
+and execution engine which interact with the RL agent in real time. Using
+historical {\em real} financial market data, we simulate trading with practical
+constraints, and demonstrate that our proposed model is robust, profitable and
+risk-sensitive, as compared to baseline trading strategies and model-free RL
+agents from prior work.
+"
+23868,Competing spin liquids and hidden nematic order in spin ice with frustrated transverse exchange,"  Frustration in magnetic interactions can give rise to disordered ground
+states with subtle and beautiful properties. The spin ices Ho2Ti2O7 and
+Dy2Ti2O7 exemplify this phenomenon, displaying a classical spin liquid state,
+with fractionalized magnetic--monopole excitations. Recently there has been
+great interest in closely-related ""quantum spin ice"" materials, following the
+realization that anisotropic exchange interactions could convert spin ice into
+a massively-entangled, quantum, spin liquid, where magnetic monopoles become
+the charges of an emergent quantum electrodynamics. Here we show that even the
+simplest model of a quantum spin ice, the XXZ model on the pyrochlore lattice,
+can realise a still-richer scenario. Using a combination of classical Monte
+Carlo simulation, semi--classical molecular--dynamics simulation, and analytic
+field theory, we explore the properties of this model for frustrated transverse
+exchange. We find not one, but three, competing forms of spin liquid, as well
+as a phase with hidden, spin-nematic, order. We explore the experimental
+signatures of each of these different states, making explicit predictions for
+inelastic neutron scattering. These results show an intriguing similarity to
+experiments on a range of pyrochlore oxides.
+"
+23869,Privacy-Preserving Personal Model Training,"  Many current Internet services rely on inferences from models trained on user
+data. Commonly, both the training and inference tasks are carried out using
+cloud resources fed by personal data collected at scale from users. Holding and
+using such large collections of personal data in the cloud creates privacy
+risks to the data subjects, but is currently required for users to benefit from
+such services. We explore how to provide for model training and inference in a
+system where computation is pushed to the data in preference to moving data to
+the cloud, obviating many current privacy risks. Specifically, we take an
+initial model learnt from a small set of users and retrain it locally using
+data from a single user. We evaluate on two tasks: one supervised learning
+task, using a neural network to recognise users' current activity from
+accelerometer traces; and one unsupervised learning task, identifying topics in
+a large set of documents. In both cases the accuracy is improved. We also
+analyse the robustness of our approach against adversarial attacks, as well as
+its feasibility by presenting a performance evaluation on a representative
+resource-constrained device (a Raspberry Pi).
+"
+23870,Minimal and maximal matrix convex sets,"  To every convex body $K \subseteq \mathbb{R}^d$, one may associate a minimal
+matrix convex set $\mathcal{W}^{\textrm{min}}(K)$, and a maximal matrix convex
+set $\mathcal{W}^{\textrm{max}}(K)$, which have $K$ as their ground level. The
+main question treated in this paper is: under what conditions on a given pair
+of convex bodies $K,L \subseteq \mathbb{R}^d$ does
+$\mathcal{W}^{\textrm{max}}(K) \subseteq \mathcal{W}^{\textrm{min}}(L)$ hold?
+For a convex body $K$, we aim to find the optimal constant $\theta(K)$ such
+that $\mathcal{W}^{\textrm{max}}(K) \subseteq \theta(K) \cdot
+\mathcal{W}^{\textrm{min}}(K)$; we achieve this goal for all the $\ell^p$ unit
+balls, as well as for other sets. For example, if $\overline{\mathbb{B}}_{p,d}$
+is the closed unit ball in $\mathbb{R}^d$ with the $\ell^p$ norm, then \[
+\theta(\overline{\mathbb{B}}_{p,d}) = d^{1-|1/p - 1/2|}. \] This constant is
+sharp, and it is new for all $p \neq 2$. Moreover, for some sets $K$ we find a
+minimal set $L$ for which $\mathcal{W}^{\textrm{max}}(K) \subseteq
+\mathcal{W}^{\textrm{min}}(L)$. In particular, we obtain that a convex body $K$
+satisfies $\mathcal{W}^{\textrm{max}}(K) = \mathcal{W}^{\textrm{min}}(K)$ if
+and only if $K$ is a simplex.
+These problems relate to dilation theory, convex geometry, operator systems,
+and completely positive maps. We discuss and exploit these connections as well.
+For example, our results show that every $d$-tuple of self-adjoint operators of
+norm less than or equal to $1$, can be dilated to a commuting family of
+self-adjoints, each of norm at most $\sqrt{d}$. We also introduce new explicit
+constructions of these (and other) dilations.
+"
+23871,Adaptive Queue Prediction Algorithm for an Edge Centric Cyber Physical System Platform in a Connected Vehicle Environment,"  In the early days of connected vehicles (CVs), data will be collected only
+from a limited number of CVs (i.e., low CV penetration rate) and not from other
+vehicles (i.e., non-connected vehicles). Moreover, the data loss rate in the
+wireless CV environment contributes to the unavailability of data from the
+limited number of CVs. Thus, it is very challenging to predict traffic
+behavior, which changes dynamically over time, with the limited CV data. The
+primary objective of this study was to develop an adaptive queue prediction
+algorithm to predict real-time queue status in the CV environment in an
+edge-centric cyber-physical system (CPS), which is a relatively new CPS
+concept. The adaptive queue prediction algorithm was developed using a machine
+learning algorithm with a real-time feedback system. The algorithm was
+evaluated using SUMO (i.e., Simulation of Urban Mobility) and ns3 (Network
+Simulator 3) simulation platforms to illustrate the efficacy of the algorithm
+on a roadway network in Clemson, South Carolina, USA. The performance of the
+adaptive queue prediction application was measured in terms of queue detection
+accuracy with varying CV penetration levels and data loss rates. The analyses
+revealed that the adaptive queue prediction algorithm with feedback system
+outperforms without feedback system algorithm.
+"
+23872,Higher order dispersive deformations of multidimensional Poisson brackets of hydrodynamic type,"  The theory of multidimensional Poisson vertex algebras (mPVAs) provides a
+completely algebraic formalism to study the Hamiltonian structure of PDEs, for
+any number of dependent and independent variables. In this paper, we compute
+the cohomology of the PVAs associated with two-dimensional, two-components
+Poisson brackets of hydrodynamic type at the third differential degree. This
+allows us to obtain their corresponding Poisson-Lichnerowicz cohomology, which
+is the main building block of the theory of their deformations. Such a
+cohomology is trivial neither in the second group, corresponding to the
+existence of a class of not equivalent infinitesimal deformation, nor in the
+third, corresponding to the obstruction to extend such deformations
+"
+23873,Superconducting cosmic strings as sources of cosmological fast radio bursts,"  In this paper we calculate the radio burst signals from three kinds of
+structures of superconducting cosmic strings. By taking into account the
+observational factors including scattering and relativistic effects, we derive
+the event rate of radio bursts as a function of redshift with the theoretical
+parameters $G\mu$ and $\mathcal{I}$ of superconducting strings. Our analyses
+show that cusps and kinks may have noticeable contributions to the event rate
+and in most cases cusps would dominate the contribution, while the kink-kink
+collisions tend to have secondary effects. By fitting theoretical predictions
+with the normalized data of fast radio bursts, we for the first time constrain
+the parameter space of superconducting strings and report that the parameter
+space of $G\mu \sim [10^{-14}, 10^{-12}]$ and $\mathcal{I} \sim [10^{-1},
+10^{2}] ~ \rm{GeV}$ fit the observation well although the statistic
+significance is low due to the lack of observational data. Moreover, we derive
+two types of best fittings, with one being dominated by cusps with a redshift
+$z = 1.3$, and the other dominated by kinks at the range of the maximal event
+rate.
+"
+23874,Ensemble Bayesian Decision Making with Redundant Deep Perceptual Control Policies,"  This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for
+control of safety-critical systems. Decision making for safety-critical systems
+is challenging due to performance requirements with significant consequences in
+the event of failure. In practice, failure of such systems can be avoided by
+introducing redundancies of control. Neural Networks (NNs) are generally not
+used for safety-critical systems as they can behave in unexpected ways in
+response to novel inputs. In addition, there may not be any indication as to
+when they will fail. BNNs have been recognized for their ability to produce not
+only viable outputs but also provide a measure of uncertainty in these outputs.
+This work combines the knowledge of prediction uncertainty obtained from BNNs
+and ensemble control for a redundant control methodology. Our technique is
+applied to an agile autonomous driving task. Multiple BNNs are trained to
+control a vehicle in an end-to-end fashion on different sensor inputs provided
+by the system. We show that an individual network is successful in maneuvering
+around the track but crashes in the presence of unforeseen input noise. Our
+proposed ensemble of BNNs shows successful task performance even in the event
+of multiple sensor failures.
+"
+23875,Marginals with finite repulsive cost,"  We consider a multimarginal transport problem with repulsive cost, where the
+marginals are all equal to a fixed probability $\rho \in
+\mathcal{P}(\mathbb{R}^d)$. We prove that, if the concentration of $\rho$ is
+less than $1/N$, then the problem has a solution of finite cost. The result is
+sharp, in the sense that there exists $\rho$ with concentration $1/N$ for which
+$C(\rho) = \infty$.
+"
+23876,Early kinetic decoupling of dark matter: when the standard way of calculating the thermal relic density fails,"  Calculating the abundance of thermally produced dark matter particles has
+become a standard procedure, with sophisticated methods guaranteeing a
+precision that matches the percent-level accuracy in the observational
+determination of the dark matter density. Here, we point out that one of the
+main assumptions in the commonly adopted formalism, namely local thermal
+equilibrium during the freeze-out of annihilating dark matter particles, does
+not have to be satisfied in general. We present two methods for how to deal
+with such situations, in which the kinetic decoupling of dark matter happens so
+early that it interferes with the chemical decoupling process: i) an
+approximate treatment in terms of a coupled system of differential equations
+for the leading momentum moments of the dark matter distribution, and ii) a
+full numerical solution of the Boltzmann equation in phase-space. For
+illustration, we apply these methods to the case of Scalar Singlet dark matter.
+We explicitly show that even in this simple model the prediction for the dark
+matter abundance can be affected by up to one order of magnitude compared to
+the traditional treatment.
+"
+23877,STARDATA: A StarCraft AI Research Dataset,"  We release a dataset of 65646 StarCraft replays that contains 1535 million
+frames and 496 million player actions. We provide full game state data along
+with the original replays that can be viewed in StarCraft. The game state data
+was recorded every 3 frames which ensures suitability for a wide variety of
+machine learning tasks such as strategy classification, inverse reinforcement
+learning, imitation learning, forward modeling, partial information extraction,
+and others. We use TorchCraft to extract and store the data, which standardizes
+the data format for both reading from replays and reading directly from the
+game. Furthermore, the data can be used on different operating systems and
+platforms. The dataset contains valid, non-corrupted replays only and its
+quality and diversity was ensured by a number of heuristics. We illustrate the
+diversity of the data with various statistics and provide examples of tasks
+that benefit from the dataset. We make the dataset available at
+this https URL . En Taro Adun!
+"
+23878,Holographic calculation of entanglement entropy in the presence of boundaries,"  When a spacetime has boundaries, the entangling surface does not have to be
+necessarily compact and it may have boundaries as well. Then there appear a
+new, boundary, contribution to the entanglement entropy due to the intersection
+of the entangling surface with the boundary of the spacetime. We study the
+boundary contribution to the logarithmic term in the entanglement entropy in
+dimensions $d=3$ and $d=4$ when the entangling surface is orthogonal to the
+boundary. In particular, we compute a boundary term in the entropy of
+${\mathcal{N}}=4$ super-gauge multiplet at weak coupling. This result is
+compared with the holographic calculation of the entropy based on the
+Ryu-Takayanagi proposal adapted appropriately to the present situation. We find
+a complete agreement between these two calculations provided the boundary
+conditions imposed on the gauge multiplet preserve $1/2$ of the supersymmetry
+and the extension of the boundary into the AdS bulk is a minimal hypersurface.
+"
+23879,A mirror theorem for genus two Gromov-Witten invariants of quintic threefolds,"  We derive a closed formula for the generating function of genus two
+Gromov-Witten invariants of quintic 3-folds and verify the corresponding mirror
+symmetry conjecture of Bershadsky, Cecotti, Ooguri and Vafa.
+"
+23880,Universal Absence of Walker Breakdown and Linear Current-Velocity Relation via Spin-Orbit Torques in Coupled and Single Domain Wall Motion,"  We consider theoretically domain wall motion driven by spin-orbit and spin
+Hall torques. We find that it is possible to achieve universal absence of
+Walker breakdown for all spin-orbit torques using experimentally relevant
+spin-orbit coupling strengths. For spin-orbit torques other than the pure
+Rashba spin-orbit torque, this gives a linear current-velocity relation instead
+of a saturation of the velocity at high current densities. The effect is very
+robust and is found in both soft and hard magnetic materials, as well as in the
+presence of the Dzyaloshinskii-Moriya interaction and in coupled domain walls
+in synthetic antiferromagnets, where it leads to very high domain wall
+velocities. Moreover, recent experiments have demonstrated that the switching
+of a synthetic antiferromagnet does not obey the usual spin Hall
+angle-dependence, but that domain expansion and contraction can be selectively
+controlled toggling only the applied in-plane magnetic field magnitude and not
+its sign. We show for the first time that the combination of spin Hall torques
+and interlayer exchange coupling produces the necessary relative velocities for
+this switching to occur.
+"
+23881,Fixup Initialization: Residual Learning Without Normalization,"  Normalization layers are a staple in state-of-the-art deep neural network
+architectures. They are widely believed to stabilize training, enable higher
+learning rate, accelerate convergence and improve generalization, though the
+reason for their effectiveness is still an active research topic. In this work,
+we challenge the commonly-held beliefs by showing that none of the perceived
+benefits is unique to normalization. Specifically, we propose fixed-update
+initialization (Fixup), an initialization motivated by solving the exploding
+and vanishing gradient problem at the beginning of training via properly
+rescaling a standard initialization. We find training residual networks with
+Fixup to be as stable as training with normalization -- even for networks with
+10,000 layers. Furthermore, with proper regularization, Fixup enables residual
+networks without normalization to achieve state-of-the-art performance in image
+classification and machine translation.
+"
+23882,Fundamental limit of resolving two point sources limited by an arbitrary point spread function,"  Estimating the angular separation between two incoherently radiating
+monochromatic point sources is a canonical toy problem to quantify spatial
+resolution in imaging. In recent work, Tsang {\em et al.} showed, using a
+Fisher Information analysis, that Rayleigh's resolution limit is just an
+artifact of the conventional wisdom of intensity measurement in the image
+plane. They showed that the optimal sensitivity of estimating the angle is only
+a function of the total photons collected during the camera's integration time
+but entirely independent of the angular separation itself no matter how small
+it is, and found the information-optimal mode basis, intensity detection in
+which achieves the aforesaid performance. We extend the above analysis, which
+was done for a Gaussian point spread function (PSF) to a hard-aperture pupil
+proving the information optimality of image-plane sinc-Bessel modes, and
+generalize the result further to an arbitrary PSF. We obtain new
+counterintuitive insights on energy vs. information content in spatial modes,
+and extend the Fisher Information analysis to exact calculations of minimum
+mean squared error, both for Gaussian and hard aperture pupils.
+"
+23883,Multifield Stochastic Particle Production: Beyond a Maximum Entropy Ansatz,"  We explore non-adiabatic particle production for $N_{\rm f}$ coupled scalar
+fields in a time-dependent background with stochastically varying effective
+masses, cross-couplings and intervals between interactions. Under the
+assumption of weak scattering per interaction, we provide a framework for
+calculating the typical particle production rates after a large number of
+interactions. After setting up the framework, for analytic tractability, we
+consider interactions (effective masses and cross couplings) characterized by
+series of Dirac-delta functions in time with amplitudes and locations drawn
+from different distributions. Without assuming that the fields are
+statistically equivalent, we present closed form results (up to quadratures)
+for the asymptotic particle production rates for the $N_{\rm f}=1$ and $N_{\rm
+f}=2$ cases. We also present results for the general $N_{\rm f} >2$ case, but
+with more restrictive assumptions. We find agreement between our analytic
+results and direct numerical calculations of the total occupation number of the
+produced particles, with departures that can be explained in terms of violation
+of our assumptions.
+We elucidate the precise connection between the maximum entropy ansatz (MEA)
+used in Amin & Baumann (2015) and the underlying statistical distribution of
+the self and cross couplings. We provide and justify a simple to use
+(MEA-inspired) expression for the particle production rate, which agrees with
+our more detailed treatment when the parameters characterizing the effective
+mass and cross-couplings between fields are all comparable to each other.
+However, deviations are seen when some parameters differ significantly from
+others. We show that such deviations become negligible for a broad range of
+parameters when $N_{\rm f}\gg 1$.
+"
+23884,A multigrid solver to the Helmholtz equation with a point source based on travel time and amplitude,"  The Helmholtz equation arises when modeling wave propagation in the frequency
+domain. The equation is discretized as an indefinite linear system, which is
+difficult to solve at high wave numbers. In many applications, the solution of
+the Helmholtz equation is required for a point source. In this case, it is
+possible to reformulate the equation as two separate equations: one for the
+travel time of the wave and one for its amplitude. The travel time is obtained
+by a solution of the factored eikonal equation, and the amplitude is obtained
+by solving a complex-valued advection-diffusion-reaction (ADR) equation. The
+reformulated equation is equivalent to the original Helmholtz equation, and the
+differences between the numerical solutions of these equations arise only from
+discretization errors. We develop an efficient multigrid solver for obtaining
+the amplitude given the travel time, which can be efficiently computed. This
+approach is advantageous because the amplitude is typically smooth in this
+case, and hence, more suitable for multigrid solvers than the standard
+Helmholtz discretization. We demonstrate that our second order ADR
+discretization is more accurate than the standard second order discretization
+at high wave numbers, as long as there are no reflections or caustics.
+Moreover, we show that using our approach, the problem can be solved more
+efficiently than using the common shifted Laplacian multigrid approach.
+"
+23885,Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles,"  This paper describes the development of a novel algorithm to tackle the
+problem of real-time video stabilization for unmanned aerial vehicles (UAVs).
+There are two main components in the algorithm: (1) By designing a suitable
+model for the global motion of UAV, the proposed algorithm avoids the necessity
+of estimating the most general motion model, projective transformation, and
+considers simpler motion models, such as rigid transformation and similarity
+transformation. (2) To achieve a high processing speed, optical-flow based
+tracking is employed in lieu of conventional tracking and matching methods used
+by state-of-the-art algorithms. These two new ideas resulted in a real-time
+stabilization algorithm, developed over two phases. Stage I considers
+processing the whole sequence of frames in the video while achieving an average
+processing speed of 50fps on several publicly available benchmark videos. Next,
+Stage II undertakes the task of real-time video stabilization using a
+multi-threading implementation of the algorithm designed in Stage I.
+"
+23886,Online Dictionary Learning Aided Target Recognition In Cognitive GPR,"  Sparse decomposition of ground penetration radar (GPR) signals facilitates
+the use of compressed sensing techniques for faster data acquisition and
+enhanced feature extraction for target classification. In this paper, we
+investigate the application of an online dictionary learning (ODL) technique in
+the context of GPR to bring down the learning time as well as improve
+identification of abandoned anti-personnel landmines. Our experimental results
+using real data from an L-band GPR for PMN/PMA2, ERA and T72 mines show that
+ODL reduces learning time by 94\% and increases clutter detection by 10\% over
+the classical K-SVD algorithm. Moreover, the proposed methodology could be
+helpful in cognitive operation of the GPR where the system adapts the range
+sampling based on the learned dictionary.
+"
+23887,Learning neural trans-dimensional random field language models with noise-contrastive estimation,"  Trans-dimensional random field language models (TRF LMs) where sentences are
+modeled as a collection of random fields, have shown close performance with
+LSTM LMs in speech recognition and are computationally more efficient in
+inference. However, the training efficiency of neural TRF LMs is not
+satisfactory, which limits the scalability of TRF LMs on large training corpus.
+In this paper, several techniques on both model formulation and parameter
+estimation are proposed to improve the training efficiency and the performance
+of neural TRF LMs. First, TRFs are reformulated in the form of exponential
+tilting of a reference distribution. Second, noise-contrastive estimation (NCE)
+is introduced to jointly estimate the model parameters and normalization
+constants. Third, we extend the neural TRF LMs by marrying the deep
+convolutional neural network (CNN) and the bidirectional LSTM into the
+potential function to extract the deep hierarchical features and
+bidirectionally sequential features. Utilizing all the above techniques enables
+the successful and efficient training of neural TRF LMs on a 40x larger
+training set with only 1/3 training time and further reduces the WER with
+relative reduction of 4.7% on top of a strong LSTM LM baseline.
+"
+23888,Energy-Efficient Low-Power Circuit Techniques for Wireless Energy and Data Transfer in IoT Sensor Nodes,"  In this paper, we present techniques and examples to reduce power consumption
+and increase energy efficiency of autonomous Wireless Sensor Nodes (WSNs) for
+the Internet of Things. We focus on the RF Energy Harvester (RFEH), the data
+receiver and the transmitter, all of which have a large impact on the device
+cost, lifetime and functionality. Co-design of the antenna and the electronics
+is explored to boost the power conversion efficiency of the RF-DC converter. As
+a proof of principle, a charge pump rectifier is designed, and its measurement
+results are presented. To boost the rectifier output voltage, a DC-DC converter
+that employs maximum power point tracking has been designed. A prototype
+circuit is also presented that can accommodate an input power level range of 1
+{\mu}W to 1 mW and offers peak efficiencies of 76.3% and 82% at 1 {\mu}W and 1
+mW, respectively. The co-design principle is also used at the receiver side
+where the antenna-electronics interface is optimized. It is shown how this
+technique allows improving the noise figure of the Low Noise Amplifier (LNA)
+without sacrificing power consumption. As a low power alternative to
+narrow-band wireless transmission, sub-GHz ultra-wideband is proposed. As a
+proof of principle, the design of a novel low-power sub-GHz
+Ultra-Wide-Bandwidth (UWB) transmitter which consumes only 0.28 mW is
+presented. Its working principle is verified by means of circuit simulations
+and measurements. The low power nature of the transmitter and receiver
+principles, combined with the power efficient RF-DC converter paves the way
+towards the continuous operation of a WSN.
+"
+23889,Designing a Finite-Time Mixer: Optimizing Stirring for Two-Dimensional Maps,"  Mixing of a passive scalar in a fluid flow results from a two part process in
+which large gradients are first created by advection and then smoothed by
+diffusion. We investigate methods of designing efficient stirrers to optimize
+mixing of a passive scalar in a two-dimensional nonautonomous, incompressible
+flow over a finite time interval. The flow is modeled by a sequence of
+area-preserving maps whose parameters change in time, defining a mixing
+protocol. Stirring efficiency is measured by a negative Sobolev seminorm; its
+decrease implies creation of fine scale structure. A Perron-Frobenius operator
+is used to numerically advect the scalar for two examples: compositions of
+Chirikov standard maps and of Harper maps. In the former case, we find that a
+protocol corresponding to a single vertical shear composed with horizontal
+shearing at all other steps is nearly optimal. For the Harper maps, we devise a
+predictive, one-step scheme to choose appropriate fixed point stabilities and
+to control the Fourier spectrum evolution to obtain a near optimal protocol.
+"
+23890,Remote Sensing Image Classification with Large Scale Gaussian Processes,"  Current remote sensing image classification problems have to deal with an
+unprecedented amount of heterogeneous and complex data sources. Upcoming
+missions will soon provide large data streams that will make land cover/use
+classification difficult. Machine learning classifiers can help at this, and
+many methods are currently available. A popular kernel classifier is the
+Gaussian process classifier (GPC), since it approaches the classification
+problem with a solid probabilistic treatment, thus yielding confidence
+intervals for the predictions as well as very competitive results to
+state-of-the-art neural networks and support vector machines. However, its
+computational cost is prohibitive for large scale applications, and constitutes
+the main obstacle precluding wide adoption. This paper tackles this problem by
+introducing two novel efficient methodologies for Gaussian Process (GP)
+classification. We first include the standard random Fourier features
+approximation into GPC, which largely decreases its computational cost and
+permits large scale remote sensing image classification. In addition, we
+propose a model which avoids randomly sampling a number of Fourier frequencies,
+and alternatively learns the optimal ones within a variational Bayes approach.
+The performance of the proposed methods is illustrated in complex problems of
+cloud detection from multispectral imagery and infrared sounding data.
+Excellent empirical results support the proposal in both computational cost and
+accuracy.
+"
+23891,Descent of Hilbert C*-modules,"  Let F be a right Hilbert C*-module over a C*-algebra B, and suppose that F is
+equipped with a left action, by compact operators, of a second C*-algebra A.
+Tensor product with F gives a functor from Hilbert C*-modules over A to Hilbert
+C*-modules over B. We prove that under certain conditions (which are always
+satisfied if, for instance, A is nuclear), the image of this functor can be
+described in terms of coactions of a certain coalgebra canonically associated
+to F. We then discuss several examples that fit into this framework: parabolic
+induction of tempered group representations; Hermitian connections on Hilbert
+C*-modules; Fourier (co)algebras of compact groups; and the maximal C*-dilation
+of operator modules over non-self-adjoint operator algebras.
+"
+23892,Model-Based Multiple Instance Learning,"  While Multiple Instance (MI) data are point patterns -- sets or multi-sets of
+unordered points -- appropriate statistical point pattern models have not been
+used in MI learning. This article proposes a framework for model-based MI
+learning using point process theory. Likelihood functions for point pattern
+data derived from point process theory enable principled yet conceptually
+transparent extensions of learning tasks, such as classification, novelty
+detection and clustering, to point pattern data. Furthermore, tractable point
+pattern models as well as solutions for learning and decision making from point
+pattern data are developed.
+"
+23893,The Navarro refinement of the McKay conjecture for finite groups of Lie type in defining characteristic,"  In this paper we verify Navarro's refinement of the McKay conjecture for
+quasi-simple groups of Lie type $G$ and the prime $p$, where $p$ is the
+defining characteristic of $G$. Navarro's refinement takes into account the
+action of specific Galois automorphisms on the characters present in the McKay
+conjecture. Our proof of this special case of the conjecture relies on a
+character correspondence which Maslowski constructed in the context of the
+inductive McKay conditions by Isaacs, Malle and Navarro.
+"
+23894,GLOBAL stability for SIRS epidemic models with general incidence rate and tranfer from infectious to susceptible,"  We study a class of SIRS epidemic dynamical models with a general non-linear
+incidence rate and transfer from infectious to susceptible. The incidence rate
+includes a wide range of monotonic, con- cave incidence rates and some
+non-monotonic or concave cases. We apply LaSalle's invariance principle and
+Lyapunov's direct method to prove that the disease-free equilibrium is globally
+asymptotically stable if the basic reproduction number R0 lesser or equal to 1,
+and the endemic equilibrium is globally asymptotically stable if R0 > 1, under
+some conditions imposed on the incidence function f(S; I).
+"
+23895,From spherical harmonics to a sharp multiplier theorem on the Grushin sphere,"  We prove a sharp multiplier theorem of Mihlin-Hörmander type for the
+Grushin operator on the unit sphere in $\mathbb{R}^3$, and a corresponding
+boundedness result for the associated Bochner-Riesz means. The proof hinges on
+precise pointwise bounds for spherical harmonics.
+"
+23896,Particle-hole symmetry reveals failed superconductivity in the metallic phase of two-dimensional superconducting films,"  Electrons confined to two dimensions display an unexpected diversity of
+behaviors as they are cooled to absolute zero. Noninteracting electrons are
+predicted to eventually ""localize"" into an insulating ground state, and it has
+long been supposed that electron correlations stabilize only one other phase:
+superconductivity. However, many two-dimensional (2D) superconducting materials
+have shown surprising evidence for metallic behavior, where the electrical
+resistivity saturates in the zero-temperature limit, the nature of this
+unexpected metallic state remains under intense scrutiny. We report electrical
+transport properties for two disordered 2D superconductors, indium oxide and
+tantalum nitride, and observe a magnetic field-tuned transition from a true
+superconductor to a metallic phase with saturated resistivity. This metallic
+phase is characterized by a vanishing Hall resistivity, suggesting that it
+retains particle-hole symmetry from the disrupted superconducting state.
+"
+23897,The galaxy group NGC2563,"  We present a $Chandra$ study of the hot intragroup medium (hIGM) of the
+galaxy group NCG2563. The $Chandra$ mosaic observations, with a total exposure
+time of ~430 ks, allow the gas density to be detected beyond $R_{200}$ and the
+gas temperature out to 0.75 $R_{200}$. This represents the first observational
+measurement of the physical properties of a poor groups beyond $R_{500}$. By
+capitalizing on the exquisite spatial resolution of $Chandra$ that is capable
+to remove unrelated emission from point sources and substructures, we are able
+to radially constrain the inhomogeneities of gas (""clumpiness""), gas fraction,
+temperature and entropy distribution. Although there is some uncertainty in the
+measurements, we find evidences of gas clumping in the virialization region,
+with clumping factor of about 2 - 3 at $R_{200}$. The gas clumping-corrected
+gas fraction is significantly lower than the cosmological baryon budget. These
+results may indicate a larger impact of the gas inhomogeneities with respect to
+the prediction from hydrodynamic numerical simulations, and we discuss possible
+explanations for our findings.
+"
+23898,Principal Component Analysis with Tensor Train Subspace,"  Tensor train is a hierarchical tensor network structure that helps alleviate
+the curse of dimensionality by parameterizing large-scale multidimensional data
+via a set of network of low-rank tensors. Associated with such a construction
+is a notion of Tensor Train subspace and in this paper we propose a TT-PCA
+algorithm for estimating this structured subspace from the given data. By
+maintaining low rank tensor structure, TT-PCA is more robust to noise comparing
+with PCA or Tucker-PCA. This is borne out numerically by testing the proposed
+approach on the Extended YaleFace Dataset B.
+"
+23899,On Darcy-and Brinkman-Type Models for Two-Phase Flow in Asymptotically Flat Domains,"  We study two-phase flow for Darcy and Brinkman regimes. To reduce the
+computational complexity for flow in vertical equilibrium various simplified
+models have been suggested. Examples are dimensional reduction by vertical
+integration, the multiscale model approach in [Guo et al., 2014] or the
+asymptotic approach in [Yortsos, 1995]. The latter approach uses a geometrical
+scaling. We show the efficiency of the approach in asymptotically flat domains
+for Darcy regimes. Moreover, we prove that it is vastly equivalent to the
+multiscale model approach.
+We apply then asymptotic analysis to the two-phase flow model in Brinkman
+regimes. The limit model is a single nonlocal evolution law with a
+pseudo-parabolic extension. Its computational efficiency is demonstrated using
+numerical examples. Finally, we show that the new limit model exhibits
+overshoot behaviour as it has been observed for dynamical capillarity laws
+[Hassanizadeh and Gray, 1993].
+"
+23900,Multi Resolution LSTM For Long Term Prediction In Neural Activity Video,"  Epileptic seizures are caused by abnormal, overly syn- chronized, electrical
+activity in the brain. The abnor- mal electrical activity manifests as waves,
+propagating across the brain. Accurate prediction of the propagation velocity
+and direction of these waves could enable real- time responsive brain
+stimulation to suppress or prevent the seizures entirely. However, this problem
+is very chal- lenging because the algorithm must be able to predict the neural
+signals in a sufficiently long time horizon to allow enough time for medical
+intervention. We consider how to accomplish long term prediction using a LSTM
+network. To alleviate the vanishing gradient problem, we propose two
+encoder-decoder-predictor structures, both using multi-resolution
+representation. The novel LSTM structure with multi-resolution layers could
+significantly outperform the single-resolution benchmark with similar number of
+parameters. To overcome the blurring effect associated with video prediction in
+the pixel domain using standard mean square error (MSE) loss, we use energy-
+based adversarial training to improve the long-term pre- diction. We
+demonstrate and analyze how a discriminative model with an encoder-decoder
+structure using 3D CNN model improves long term prediction.
+"
+23901,Semi-supervised cross-entropy clustering with information bottleneck constraint,"  In this paper, we propose a semi-supervised clustering method, CEC-IB, that
+models data with a set of Gaussian distributions and that retrieves clusters
+based on a partial labeling provided by the user (partition-level side
+information). By combining the ideas from cross-entropy clustering (CEC) with
+those from the information bottleneck method (IB), our method trades between
+three conflicting goals: the accuracy with which the data set is modeled, the
+simplicity of the model, and the consistency of the clustering with side
+information. Experiments demonstrate that CEC-IB has a performance comparable
+to Gaussian mixture models (GMM) in a classical semi-supervised scenario, but
+is faster, more robust to noisy labels, automatically determines the optimal
+number of clusters, and performs well when not all classes are present in the
+side information. Moreover, in contrast to other semi-supervised models, it can
+be successfully applied in discovering natural subgroups if the partition-level
+side information is derived from the top levels of a hierarchical clustering.
+"
+23902,Boundary Quotient C*-algebras of Products of Odometers,"  In this paper, we study the boundary quotient C*-algebras associated to
+products of odometers. One of our main results shows that the boundary quotient
+C*-algebra of the standard product of $k$ odometers over $n_i$-letter alphabets
+($1\le i\le k$) is always nuclear, and that it is a UCT Kirchberg algebra if
+and only if $\{\ln n_i: 1\le i\le k\}$ is rationally independent, if and only
+if the associated single-vertex $k$-graph C*-algebra is simple. To achieve
+this, one of our main steps is to construct a topological $k$-graph such that
+its associated Cuntz-Pimsner C*-algebra is isomorphic to the boundary quotient
+C*-algebra. Some relations between the boundary quotient C*-algebra and the
+C*-algebra $\Q_\bN$ introduced by Cuntz are also investigated. As an easy
+consequence of our main results, it settles a boundary quotient C*-algebra
+constructed by Brownlowe-Ramagge-Robertson-Whittaker.
+"
+23903,The Response of Conductive-Fiber Reinforced Composites to Electric Field,"  An analytical procedure, which couples electric, magnetic, thermal and
+mechanical effects is presented for the prediction of the response of
+unidirectional fiber-reinforced composites that are subjected to electric
+field, applied in the fibers' direction. It is assumed that at least one of the
+phases of the composite (e.g., the fibers) is electrically conductive, and that
+all phases are thermally conductive. The composite is assumed to occupy a
+finite, symmetric domain which is discretized into a double array of subcells.
+The governing equations, with the interfacial and boundary conditions, are
+satisfied in the integral sense. The externally applied field generates
+electric current, which induces a magnetic field as well as temperature
+increase. The mechanical deformation of the composite results from the combined
+effect of the ponderomotive force, which is created by the magnetic field, and
+the temperature distribution within the constituents. The purpose of the
+present paper is three-fold. (a) To perform quantitative analysis of the model
+for the ponderoromotive force in deformable media. (b) To present computational
+strong-form treatment of the magneto-mechanical boundary-value problem in a
+composite. (c) To suggest a computational apparatus for deriving the response
+of a sensor/actuator excited by an applied electric field or electric field
+gradient. Application is given, presenting the magnetic, thermal and mechanical
+field distributions, as well as the macroscopic (global) response of a
+composite, which consists, for simplicity, of two iron fibers embedded in an
+epoxy matrix.
+"
+23904,Multiobjective optimization to a TB-HIV/AIDS coinfection optimal control problem,"  We consider a recent coinfection model for Tuberculosis (TB), Human
+Immunodeficiency Virus (HIV) infection and Acquired Immunodeficiency Syndrome
+(AIDS) proposed in [Discrete Contin. Dyn. Syst. 35 (2015), no. 9, 4639--4663].
+We introduce and analyze a multiobjective formulation of an optimal control
+problem, where the two conflicting objectives are: minimization of the number
+of HIV infected individuals with AIDS clinical symptoms and coinfected with
+AIDS and active TB; and costs related to prevention and treatment of HIV and/or
+TB measures. The proposed approach eliminates some limitations of previous
+works. The results of the numerical study provide comprehensive insights about
+the optimal treatment policies and the population dynamics resulting from their
+implementation. Some nonintuitive conclusions are drawn. Overall, the
+simulation results demonstrate the usefulness and validity of the proposed
+approach.
+"
+23905,Small union with large set of centers,"  Let $T\subset{\mathbb R}^n$ be a fixed set. By a scaled copy of $T$ around
+$x\in{\mathbb R}^n$ we mean a set of the form $x+rT$ for some $r>0$.
+In this survey paper we study results about the following type of problems:
+How small can a set be if it contains a scaled copy of $T$ around every point
+of a set of given size? We will consider the cases when $T$ is circle or sphere
+centered at the origin, Cantor set in ${\mathbb R}$, the boundary of a square
+centered at the origin, or more generally the $k$-skeleton ($0\le k<n$) of an
+$n$-dimensional cube centered at the origin or the $k$-skeleton of a more
+general polytope of ${\mathbb R}^n$.
+We also study the case when we allow not only scaled copies but also scaled
+and rotated copies and also the case when we allow only rotated copies.
+"
+23906,Molecular Dynamics Studies of the Bufallo Prion Protein Structured Region at Higher Temperatures,"  Molecular dynamics (MD) studies of buffalo prion protein (BufPrP$^\text{C}$)
+(J Biomol Struct Dyn. 2016 Apr;34(4):762-77) showed that the structure of this
+protein is very stable at room temperature (whether under neutral pH or low pH
+environments). In order to understand the reason why buffalo is lowly
+susceptible to prion diseases and why BufPrP$^\text{C}$ is so stable at room
+temperature, this paper will prolong our MD running time at room temperature
+and extend our research to higher temperatures to study this BufPrP$^\text{C}$
+structure furthermore. We found an important reason why BufPrP$^\text{C}$ is so
+stable at room temperature and this might be a nice clue of drug discovery or
+drug design for the treatment of prion diseases.
+"
+23907,Automatic Generation of Bounds for Polynomial Systems with Application to the Lorenz System,"  This study covers an analytical approach to calculate positively invariant
+sets of dynamical systems. Using Lyapunov techniques and quantifier elimination
+methods, an automatic procedure for determining bounds in the state space as an
+enclosure of attractors is proposed. The available software tools permit an
+algorithmizable process, which normally requires a good insight into the
+systems dynamics and experience. As a result we get an estimation of the
+attractor, whose conservatism only results from the initial choice of the
+Lyapunov candidate function. The proposed approach is illustrated on the
+well-known Lorenz system.
+"
+23908,Kernel Scaling for Manifold Learning and Classification,"  Kernel methods play a critical role in many dimensionality reduction
+algorithms. They are useful in manifold learning, classification, clustering
+and other machine learning tasks. Setting the kernel's scale parameter, also
+referred as the kernel's bandwidth, highly affects the extracted
+low-dimensional representation. We propose to set a scale parameter that is
+tailored to the desired application such as classification and manifold
+learning. The scale computation for the manifold learning task enables that the
+dimension of the extracted embedding equals the intrinsic dimension estimation.
+Three methods are proposed for scale computation in a classification task. The
+proposed frameworks are simulated on artificial and real datasets. The results
+show a high correlation between optimal classification rates and the computed
+scaling.
+"
+23909,Inverse Gaussian quadrature and finite normal-mixture approximation of generalized hyperbolic distribution,"  In this study, a numerical quadrature for the generalized inverse Gaussian
+distribution is derived from the Gauss--Hermite quadrature by exploiting its
+relationship with the normal distribution. Unlike Gaussian quadrature, the
+proposed quadrature exactly evaluates both positive and negative moments, thus
+improving evaluation accuracy. The generalized hyperbolic distribution is
+efficiently approximated as a finite normal variance-mean mixture with the
+quadrature. Therefore, the expectations under the distribution, such as
+cumulative distribution function and option price, are accurately computed as
+weighted sums of those under normal distributions.
+"
+23910,AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning,"  The AMIDST Toolbox is a software for scalable probabilistic machine learning
+with a spe- cial focus on (massive) streaming data. The toolbox supports a
+flexible modeling language based on probabilistic graphical models with latent
+variables and temporal dependencies. The specified models can be learnt from
+large data sets using parallel or distributed implementa- tions of Bayesian
+learning algorithms for either streaming or batch data. These algorithms are
+based on a flexible variational message passing scheme, which supports discrete
+and continu- ous variables from a wide range of probability distributions.
+AMIDST also leverages existing functionality and algorithms by interfacing to
+software tools such as Flink, Spark, MOA, Weka, R and HUGIN. AMIDST is an open
+source toolbox written in Java and available at this http URL
+under the Apache Software License version 2.0.
+"
+23911,Sharp bounds on linear semigroup of Navier Stokes with boundary layer norms,"  In this paper, we derive sharp bounds on the semigroup of the linearized
+Navier-Stokes equations near a stationary boundary layer on the half space. The
+bounds are obtained uniformly in the inviscid limit. Delicate boundary layer
+norms are introduced in order to capture the true boundary layer behavior of
+vorticity near the boundary. As an immediate application, we construct an
+approximate solution which exhibits an $L^\infty$ instability of Prandtl's
+layers.
+"
+23912,Machine Learning Harnesses Molecular Dynamics to Discover New $μ$ Opioid Chemotypes,"  Computational chemists typically assay drug candidates by virtually screening
+compounds against crystal structures of a protein despite the fact that some
+targets, like the $\mu$ Opioid Receptor and other members of the GPCR family,
+traverse many non-crystallographic states. We discover new conformational
+states of $\mu OR$ with molecular dynamics simulation and then machine learn
+ligand-structure relationships to predict opioid ligand function. These
+artificial intelligence models identified a novel $\mu$ opioid chemotype.
+"
+23913,Remarks on Bayesian Control Charts,"  There is a considerable amount of ongoing research on the use of Bayesian
+control charts for detecting a shift from a good quality distribution to a bad
+quality distribution in univariate and multivariate processes. It is widely
+claimed that Bayesian control charts are economically optimal; see, for
+example, Calabrese (1995) [Bayesian process control for attributes. Management
+Science, DOI: 10.1287/mnsc.41.4.637] and Makis (2008) [Multivariate Bayesian
+control chart. Operations Research, DOI: 10.1287/opre.1070.0495]. Some
+researchers also generalize the optimality of controls defined based on
+posterior probabilities to the class of partially observable Markov decision
+processes. This note points out that the existing Bayesian control charts
+cannot generally be optimal because many years ago an analytical counterexample
+was provided by Taylor (1965) [Markovian sequential replacement processes. The
+Annals of Mathematical Statistics, DOI: 10.1214/aoms/1177699796].
+"
+23914,The Convergence of Markov chain Monte Carlo Methods: From the Metropolis method to Hamiltonian Monte Carlo,"  From its inception in the 1950s to the modern frontiers of applied
+statistics, Markov chain Monte Carlo has been one of the most ubiquitous and
+successful methods in statistical computing. In that time its development has
+been fueled by increasingly difficult problems and novel techniques from
+physics. In this article I will review the history of Markov chain Monte Carlo
+from its inception with the Metropolis method to today's state-of-the-art in
+Hamiltonian Monte Carlo. Along the way I will focus on the evolving interplay
+between the statistical and physical perspectives of the method.
+"
+23915,Trajectory Planning of Automated Vehicles in Tube-like Road Segments,"  This paper presents a method based on linear programming for trajectory
+planning of automated vehicles, combining obstacle avoidance, time scheduling
+for the reaching of waypoints and time-optimal traversal of tube-like road
+segments. System modeling is conducted entirely spatial-based. Kinematic
+vehicle dynamics as well as time are expressed in a road-aligned coordinate
+frame with path along the road centerline serving as the dependent variable. We
+elaborate on control rate constraints in the spatial domain. A vehicle
+dimension constraint heuristic is proposed to constrain vehicle dimensions
+inside road boundaries. It is outlined how friction constraints are accounted
+for. The discussion is extended to dynamic vehicle models. The benefits of the
+proposed method are illustrated by a comparison to a time-based method.
+"
+23916,Is Privacy Controllable?,"  One of the major views of privacy associates privacy with the control over
+information. This gives rise to the question how controllable privacy actually
+is. In this paper, we adapt certain formal methods of control theory and
+investigate the implications of a control theoretic analysis of privacy. We
+look at how control and feedback mechanisms have been studied in the privacy
+literature. Relying on the control theoretic framework, we develop a simplistic
+conceptual control model of privacy, formulate privacy controllability issues
+and suggest directions for possible research.
+"
+23917,Incremental Learning Through Deep Adaptation,"  Given an existing trained neural network, it is often desirable to learn new
+capabilities without hindering performance of those already learned. Existing
+approaches either learn sub-optimal solutions, require joint training, or incur
+a substantial increment in the number of parameters for each added domain,
+typically as many as the original network. We propose a method called
+\emph{Deep Adaptation Networks} (DAN) that constrains newly learned filters to
+be linear combinations of existing ones. DANs precisely preserve performance on
+the original domain, require a fraction (typically 13\%, dependent on network
+architecture) of the number of parameters compared to standard fine-tuning
+procedures and converge in less cycles of training to a comparable or better
+level of performance. When coupled with standard network quantization
+techniques, we further reduce the parameter cost to around 3\% of the original
+with negligible or no loss in accuracy. The learned architecture can be
+controlled to switch between various learned representations, enabling a single
+network to solve a task from multiple different domains. We conduct extensive
+experiments showing the effectiveness of our method on a range of image
+classification tasks and explore different aspects of its behavior.
+"
+23918,"Collet, Eckmann and the bifurcation measure","  The moduli space $\mathcal{M}_d$ of degree $d\geq2$ rational maps can
+naturally be endowed with a measure $\mu_\mathrm{bif}$ detecting maximal
+bifurcations, called the bifurcation measure. We prove that the support of the
+bifurcation measure $\mu_\mathrm{bif}$ has positive Lebesgue measure. To do so,
+we establish a general sufficient condition for the conjugacy class of a
+rational map to belong to the support of $\mu_\mathrm{bif}$ and we exhibit a
+large set of Collet-Eckmann rational maps which satisfy this condition. As a
+consequence, we get a set of Collet-Eckmann rational maps of positive Lebesgue
+measure which are approximated by hyperbolic rational maps.
+"
+23919,"Geometric properties of cones with applications on the Hellinger-Kantorovich space, and a new distance on the space of probability measures","  We study general geometric properties of cone spaces, and we apply them on
+the Hellinger--Kantorovich space $(\mathcal{M}(X),\mathsf{H\hspace{-0.25em}
+K}_{\alpha,\beta}).$ We exploit a two-parameter scaling property of the
+Hellinger-Kantorovich metric $ \mathsf{H\hspace{-0.25em} K}_{\alpha,\beta},$
+and we prove the existence of a distance $\mathsf{S\hspace{-0.18em}
+H\hspace{-0.25em} K}_{\alpha,\beta}$ on the space of Probability measures that
+turns the Hellinger--Kantorovich space
+$(\mathcal{M}(X),\mathsf{H\hspace{-0.25em} K}_{\alpha,\beta})$ into a cone
+space over the space of probabilities measures
+$(\mathcal{P}(X),\mathsf{S\hspace{-0.18em} H\hspace{-0.25em}
+K}_{\alpha,\beta}).$ We provide a two parameter rescaling of geodesics in
+$(\mathcal{M}(X),\mathsf{H\hspace{-0.25em} K}_{\alpha,\beta}),$ and for
+$(\mathcal{P}(X),\mathsf{S\hspace{-0.18em} H\hspace{-0.25em}
+K}_{\alpha,\beta})$ we obtain a full characterization of the geodesics. We
+finally prove finer geometric properties, including local-angle condition and
+partial $K$-semiconcavity of the squared distances, that will be used in a
+future paper to prove existence of gradient flows on both spaces.
+"
+23920,A self-consistent ground-state formulation of the first-principles Hubbard U parameter validated on one-electron self-interaction error,"  In electronic structure methods based on the correction of approximate
+density-functional theory (DFT) for systematic inaccuracies, Hubbard $U$
+parameters may be used to quantify and amend the self-interaction errors
+ascribed to selected subspaces. Here, in order to enable the accurate,
+computationally convenient calculation of $U$ by means of DFT algorithms that
+locate the ground-state by direct total-energy minimization, we introduce a
+reformulation of the successful linear-response method for $U$ in terms of the
+fully-relaxed constrained ground-state density. Defining $U$ as an implicit
+functional of the ground-state density implies the comparability of DFT +
+Hubbard $U$ (DFT+$U$) total-energies, and related properties, as external
+parameters such as ionic positions are varied together with their corresponding
+first-principles $U$ values. Our approach provides a framework in which to
+address the partially unresolved question of self-consistency over $U$, for
+which plausible schemes have been proposed, and to precisely define the energy
+associated with subspace many-body self-interaction error. We demonstrate that
+DFT+$U$ precisely corrects the total energy for self-interaction error under
+ideal conditions, but only if a simple self-consistency condition is applied.
+Such parameters also promote to first-principles a recently proposed DFT+$U$
+based method for enforcing Koopmans' theorem.
+"
+23921,Exposure: A White-Box Photo Post-Processing Framework,"  Retouching can significantly elevate the visual appeal of photos, but many
+casual photographers lack the expertise to do this well. To address this
+problem, previous works have proposed automatic retouching systems based on
+supervised learning from paired training images acquired before and after
+manual editing. As it is difficult for users to acquire paired images that
+reflect their retouching preferences, we present in this paper a deep learning
+approach that is instead trained on unpaired data, namely a set of photographs
+that exhibits a retouching style the user likes, which is much easier to
+collect. Our system is formulated using deep convolutional neural networks that
+learn to apply different retouching operations on an input image. Network
+training with respect to various types of edits is enabled by modeling these
+retouching operations in a unified manner as resolution-independent
+differentiable filters. To apply the filters in a proper sequence and with
+suitable parameters, we employ a deep reinforcement learning approach that
+learns to make decisions on what action to take next, given the current state
+of the image. In contrast to many deep learning systems, ours provides users
+with an understandable solution in the form of conventional retouching edits,
+rather than just a ""black-box"" result. Through quantitative comparisons and
+user studies, we show that this technique generates retouching results
+consistent with the provided photo set.
+"
+23922,Fast Low-Rank Matrix Estimation without the Condition Number,"  In this paper, we study the general problem of optimizing a convex function
+$F(L)$ over the set of $p \times p$ matrices, subject to rank constraints on
+$L$. However, existing first-order methods for solving such problems either are
+too slow to converge, or require multiple invocations of singular value
+decompositions. On the other hand, factorization-based non-convex algorithms,
+while being much faster, require stringent assumptions on the \emph{condition
+number} of the optimum. In this paper, we provide a novel algorithmic framework
+that achieves the best of both worlds: asymptotically as fast as factorization
+methods, while requiring no dependency on the condition number.
+We instantiate our general framework for three important matrix estimation
+problems that impact several practical applications; (i) a \emph{nonlinear}
+variant of affine rank minimization, (ii) logistic PCA, and (iii) precision
+matrix estimation in probabilistic graphical model learning. We then derive
+explicit bounds on the sample complexity as well as the running time of our
+approach, and show that it achieves the best possible bounds for both cases. We
+also provide an extensive range of experimental results, and demonstrate that
+our algorithm provides a very attractive tradeoff between estimation accuracy
+and running time.
+"
+23923,On Throughput Optimization and Bound Analysis in Cache-Enabled Fiber-Wireless Access Networks,"  The backhaul of fiber-wireless (FiWi) access networks is facing the bandwidth
+crunch due to the increasing demands for bandwidth-hungry applications. In this
+paper, we consider utilizing the caches in FiWi access networks to mitigate
+backhaul bottleneck and improve network throughput. As both the power budget in
+wireless access networks and the bandwidth of backhaul are constrained, it is
+challenging to allocate power for caching and wireless transmission properly to
+achieve superior system performance. Our goal is to maximize the downlink
+throughput by considering power allocation and caching strategy jointly. We
+first propose a volume-adjustable backhaul-constrained water-filling method
+(VABWF) to derive the optimal power allocation for wireless transmission. A
+dynamic programming algorithm is then designed by reformulating the problem as
+a multiple-choice knapsack problem (MCKP). To explore the potential of
+throughput in cache-enabled FiWi access networks, we derive a tractable
+expression of the ergodic capacity (EC) of wireless links and provide a
+theoretical upper bound of the throughput by utilizing the analytical
+properties of EC. We show that an appropriate allocation of transmission power
+and caching power is essential to achieve higher throughput. Numerical and
+simulation results validate our theoretical analysis and demonstrate the
+effectiveness of our proposed optimization method.
+"
+23924,Structure and regularity of the singular set in the obstacle problem for the fractional Laplacian,"  We study the singular part of the free boundary in the obstacle problem for
+the fractional Laplacian, \ $\min\bigl\{(-\Delta)^su,\,u-\varphi\bigr\}=0$ in
+$\mathbb R^n$, for general obstacles $\varphi$. Our main result establishes the
+complete structure and regularity of the singular set. To prove it, we
+construct new monotonicity formulas of Monneau-type that extend those in
+\cite{GP} to all $s\in(0,1)$.
+"
+23925,Josephine: Using JavaScript to safely manage the lifetimes of Rust data,"  This paper is about the interface between languages which use a garbage
+collector and those which use fancy types for safe manual memory management.
+Garbage collection is the traditional memory management scheme for functional
+languages, whereas type systems are now used for memory safety in imperative
+languages. We use existing techniques for linear capabilities to provide safe
+access to copyable references, but the application to languages with a tracing
+garbage collector, and to data with explicit lifetimes is new. This work is
+related to mixed linear/non-linear programming, but the languages being mixed
+are Rust and JavaScript.
+"
+23926,Fractional quantum Hall effects in graphene on a h-BN substrate,"  Fractional quantum Hall (FQH) effects in graphene are studied because of
+their relativistic characteristics and the valley degree of freedom. Recently
+FQH effects have been observed at various filling factors with graphene on a
+hexagonal boron nitride (h-BN) substrate. However, it is known that h-BN
+creates the mass term in the Dirac Hamiltonian that acts as the effective model
+of graphene. To understand recent experiments, we shall investigate many-body
+effects in the massive Dirac electron system. In this paper, we study the
+mass-term effects on the FQH states of Dirac electrons by exact
+diagonalization. We examine the ground state at filling factor 1/3 in the
+$n=\pm 1$ Landau level. Without the mass term, the ground state in the Laughlin
+state featuring valley degeneracy and the lowest excitation is characterized by
+the valley unpolarized state (known as the valley skyrmion state). Conversely,
+we find that the mass-term lifts the valley degeneracy due to the breaking of
+the inversion symmetry. We also demonstrate that the valley unpolarized
+excitation is suppressed and that the fully or partially polarized state
+appears in the lowest excitation by increasing the mass term. Finally, we
+discuss the stability of FQH states in the massive Dirac Hamiltonian in
+experimental situations. We find that our numerical results are in agreement
+with previous experimental results.
+"
+23927,Dynamic Tensor Clustering,"  Dynamic tensor data are becoming prevalent in numerous applications. Existing
+tensor clustering methods either fail to account for the dynamic nature of the
+data, or are inapplicable to a general-order tensor. Also there is often a gap
+between statistical guarantee and computational efficiency for existing tensor
+clustering solutions. In this article, we aim to bridge this gap by proposing a
+new dynamic tensor clustering method, which takes into account both sparsity
+and fusion structures, and enjoys strong statistical guarantees as well as high
+computational efficiency. Our proposal is based upon a new structured tensor
+factorization that encourages both sparsity and smoothness in parameters along
+the specified tensor modes. Computationally, we develop a highly efficient
+optimization algorithm that benefits from substantial dimension reduction. In
+theory, we first establish a non-asymptotic error bound for the estimator from
+the structured tensor factorization. Built upon this error bound, we then
+derive the rate of convergence of the estimated cluster centers, and show that
+the estimated clusters recover the true cluster structures with a high
+probability. Moreover, our proposed method can be naturally extended to
+co-clustering of multiple modes of the tensor data. The efficacy of our
+approach is illustrated via simulations and a brain dynamic functional
+connectivity analysis from an Autism spectrum disorder study.
+"
+23928,High-energy electron bursts in the inner Earth magnetosphere caused by precipitation from radiation belt,"  Orbital experiment ARINA on the board of Russian satellite Resurs-DK1
+launched in 2006 developed to study charged particle flux (electrons E ~ 3 -
+30MeV , protons E ~ 30 - 100MeV ) in near-Earth space, especially high-energy
+electron precipitation from the inner radiation belt caused by various
+geophysical and solar-magnetospheric phenomena. Precipitated electrons under
+certain conditions (energy, LB-coordinate) drifts around the Earth and can be
+detected as fast increase in count rate of satellite spectrometer (so called
+bursts). High-energy electron bursts can be caused by local geophysical
+phenomena (like earthquakes or thunderstorms). Such bursts have distinct
+features in their measured energy-time distribution. These features contains
+information about initial location of electron precipitation. Several methods
+(linear, robust regression) were used previously to find longitudinal distance
+between region of precipitation and burst registration location on the board of
+satellite. In this report, the new ensemble method was developed, it uses the
+combining results from several methods in dependence of burst registration
+conditions. Numerical simulation of local particles precipitations based on
+well-known equations of relativistic particle movement in Earth magnetosphere.
+In experimental data analysis, the results from ARINA experiment for 10 years
+was used. Several results based on burst experimental data are shown. Ensemble
+method shows better results than any single method alone.
+"
+23929,OpenMP GNU and Intel Fortran programs for solving the time-dependent Gross-Pitaevskii equation,"  We present Open Multi-Processing (OpenMP) version of Fortran 90 programs for
+solving the Gross-Pitaevskii (GP) equation for a Bose-Einstein condensate in
+one, two, and three spatial dimensions, optimized for use with GNU and Intel
+compilers. We use the split-step Crank-Nicolson algorithm for imaginary- and
+real-time propagation, which enables efficient calculation of stationary and
+non-stationary solutions, respectively. The present OpenMP programs are
+designed for computers with multi-core processors and optimized for compiling
+with both commercially-licensed Intel Fortran and popular free open-source GNU
+Fortran compiler. The programs are easy to use and are elaborated with helpful
+comments for the users. All input parameters are listed at the beginning of
+each program. Different output files provide physical quantities such as
+energy, chemical potential, root-mean-square sizes, densities, etc. We also
+present speedup test results for new versions of the programs.
+"
+23930,A New Formulation of the $3D$ Compressible Euler Equations With Dynamic Entropy: Remarkable Null Structures and Regularity Properties,"  We derive a new formulation of the $3D$ compressible Euler equations with
+dynamic entropy exhibiting remarkable null structures and regularity
+properties. Our results hold for an arbitrary equation of state (which yields
+the pressure in terms of the density and the entropy) in non-vacuum regions
+where the speed of sound is positive. Our work is an extension of our prior
+joint work with J. Luk, in which we derived a similar new formulation in the
+special case of a barotropic fluid, that is, when the equation of state depends
+only on the density. The new formulation comprises covariant wave equations for
+the Cartesian components of the velocity and the logarithmic density coupled to
+a transport equation for the specific vorticity (defined to be vorticity
+divided by density), a transport equation for the entropy, and some additional
+transport-divergence-curl-type equations involving special combinations of the
+derivatives of the solution variables. The good geometric structures in the
+equations allow one to use the full power of the vectorfield method in treating
+the ""wave part"" of the system. In a forthcoming application, we will use the
+new formulation to give a sharp, constructive proof of finite-time shock
+formation, tied to the intersection of acoustic ""wave"" characteristics, for
+solutions with nontrivial vorticity and entropy at the singularity. In the
+present article, we derive the new formulation and overview the central role
+that it plays in the proof of shock formation.
+"
+23931,A Perturbation Scheme for Passivity Verification and Enforcement of Parameterized Macromodels,"  This paper presents an algorithm for checking and enforcing passivity of
+behavioral reduced-order macromodels of LTI systems, whose frequency-domain
+(scattering) responses depend on external parameters. Such models, which are
+typically extracted from sampled input-output responses obtained from numerical
+solution of first-principle physical models, usually expressed as Partial
+Differential Equations, prove extremely useful in design flows, since they
+allow optimization, what-if or sensitivity analyses, and design centering.
+Starting from an implicit parameterization of both poles and residues of the
+model, as resulting from well-known model identification schemes based on the
+Generalized Sanathanan-Koerner iteration, we construct a parameter-dependent
+Skew-Hamiltonian/Hamiltonian matrix pencil. The iterative extraction of purely
+imaginary eigenvalues ot fhe pencil, combined with an adaptive sampling scheme
+in the parameter space, is able to identify all regions in the
+frequency-parameter plane where local passivity violations occur. Then, a
+singular value perturbation scheme is setup to iteratively correct the model
+coefficients, until all local passivity violations are eliminated. The final
+result is a corrected model, which is uniformly passive throughout the
+parameter range. Several numerical examples denomstrate the effectiveness of
+the proposed approach.
+"
+23932,On the Input-Degradedness and Input-Equivalence Between Channels,"  A channel $W$ is said to be input-degraded from another channel $W'$ if $W$
+can be simulated from $W'$ by randomization at the input. We provide a
+necessary and sufficient condition for a channel to be input-degraded from
+another one. We show that any decoder that is good for $W'$ is also good for
+$W$. We provide two characterizations for input-degradedness, one of which is
+similar to the Blackwell-Sherman-Stein theorem. We say that two channels are
+input-equivalent if they are input-degraded from each other. We study the
+topologies that can be constructed on the space of input-equivalent channels,
+and we investigate their properties. Moreover, we study the continuity of
+several channel parameters and operations under these topologies.
+"
+23933,An improved explicit scheme for whole-building hygrothermal simulation,"  Implicit schemes require important sub-iterations when dealing with highly
+nonlinear problems such as the combined heat and moisture transfer through
+porous building elements. The computational cost rises significantly when the
+whole-building is simulated, especially when there is important coupling among
+the building elements themselves with neighbouring zones and with HVAC (Heating
+Ventilation and Air Conditioning) systems. On the other hand, the classical
+Euler explicit scheme is generally not used because its stability condition
+imposes very fine time discretisation. Hence, this paper explores the use of an
+improved explicit approach - the Dufort-Frankel scheme - to overcome the
+disadvantage of the classical explicit one and to bring benefits that cannot be
+obtained by implicit methods. The Dufort-Frankel approach is first compared to
+the classical Euler implicit and explicit schemes to compute the solution of
+nonlinear heat and moisture transfer through porous materials. Then, the
+analysis of the Dufort-Frankel unconditionally stable explicit scheme is
+extended to the coupled heat and moisture balances on the scale of a one- and a
+two-zone building models. The Dufort-Frankel scheme has the benefits of being
+unconditionally stable, second-order accurate in time O(dt^2) and to compute
+explicitly the solution at each time step, avoiding costly sub-iterations. This
+approach may reduce the computational cost by twenty, as well as it may enable
+perfect synchronism for whole-building simulation and co-simulation.
+"
+23934,Improving Entity Retrieval on Structured Data,"  The increasing amount of data on the Web, in particular of Linked Data, has
+led to a diverse landscape of datasets, which make entity retrieval a
+challenging task. Explicit cross-dataset links, for instance to indicate
+co-references or related entities can significantly improve entity retrieval.
+However, only a small fraction of entities are interlinked through explicit
+statements. In this paper, we propose a two-fold entity retrieval approach. In
+a first, offline preprocessing step, we cluster entities based on the
+\emph{x--means} and \emph{spectral} clustering algorithms. In the second step,
+we propose an optimized retrieval model which takes advantage of our
+precomputed clusters. For a given set of entities retrieved by the BM25F
+retrieval approach and a given user query, we further expand the result set
+with relevant entities by considering features of the queries, entities and the
+precomputed clusters. Finally, we re-rank the expanded result set with respect
+to the relevance to the query. We perform a thorough experimental evaluation on
+the Billions Triple Challenge (BTC12) dataset. The proposed approach shows
+significant improvements compared to the baseline and state of the art
+approaches.
+"
+23935,Control of the Black-Scholes equation,"  There are hundreds of papers dealing with the Black-Scholes equation. Yet, no
+one seems to have ever used the scaling invariance coming from the heat
+equation. And there appears to be very few studies on the ""control aspect"" of
+the equation.
+In previous works, J.Y. Chemin and Cl. David obtained new results for the
+mass critical nonlinear Schrödinger equation, with a simple principle: one
+uses the fact that for this nonlinear equation, solutions of scales which are
+different enough almost do not interact. The nonlinearity of the original
+equation just required to determine a condition about the size of the scale
+which depends continuously on the data.
+The idea is to apply the building technique of the map described above,
+starting from the point that the Black-Scholes equation can be transformed into
+a linear heat equation, which has a natural scaling invariance, and that the
+linearity of the transformed equation enables one to obtain exact solutions,
+and, thus, yield interesting results. The aim of this paper is to present a new
+way to change either the Delta, or other greeks, by a given amount ; this
+technique appears thus as a new way to control the equation, without resorting
+to control functions. Indeed, one can change the greeks directly using the
+general solution of the Black-Scholes equation, nevertheless, our technique
+appears as an alternative and simple one to implement.
+"
+23936,Optimal and Learning Control for Autonomous Robots,"  Optimal and Learning Control for Autonomous Robots has been taught in the
+Robotics, Systems and Controls Masters at ETH Zurich with the aim to teach
+optimal control and reinforcement learning for closed loop control problems
+from a unified point of view. The starting point is the formulation of of an
+optimal control problem and deriving the different types of solutions and
+algorithms from there. These lecture notes aim at supporting this unified view
+with a unified notation wherever possible, and a bit of a translation help to
+compare the terminology and notation in the different fields. The course
+assumes basic knowledge of Control Theory, Linear Algebra and Stochastic
+Calculus.
+"
+23937,Controlled K-theory for groupoids and applications to Coarse Geometry,"  We develop a generalization of quantitative $K$-theory, which we call
+controlled $K$-theory. It is powerful enough to study the $K$-theory of crossed
+product of $C^*$-algebras by action of étale groupoids and discrete quantum
+groups. In this article, we will use it to study groupoids crossed products. We
+define controlled assembly maps, which factorize the Baum-Connes assembly maps,
+and define the controlled Baum-Connes conjecture. We relate the controlled
+conjecture for groupoids to the classical conjecture, and to the coarse
+Baum-Connes conjecture. This allows to give applications to Coarse Geometry. In
+particular, we can prove that the maximal version of the controlled coarse
+Baum-Connes conjecture is satisfied for a coarse space which admits a fibred
+coarse embedding, which is a stronger version of a result of M. Finn-Sell.
+"
+23938,Pressure-induced topological phase transition in LaSb: First-principles study,"  By using first-principles electronic structure calculations, we predict that
+the extreme magnetoresistance (XMR) material LaSb takes a topological phase
+transition without breaking any symmetry under a hydrostatic pressure applied
+between 3 and 4 GPa, meanwhile the electron-hole compensation remains in its
+electronic band structure. Thus LaSb provides an ideal platform for studying
+the individual role of topological property playing in the XMR phenomenon, in
+addition to the electron-hole compensation. This has general implication to the
+relationship of XMR effect and topological property in topological materials.
+"
+23939,Political Popularity Analysis in Social Media,"  Popularity is a critical success factor for a politician and her/his party to
+win in elections and implement their plans. Finding the reasons behind the
+popularity can provide a stable political movement. This research attempts to
+measure popularity in Twitter using a mixed method. In recent years, Twitter
+data has provided an excellent opportunity for exploring public opinions by
+analyzing a large number of tweets. This study has collected and examined 4.5
+million tweets related to a US politician, Senator Bernie Sanders. This study
+investigated eight economic reasons behind the senator's popularity in Twitter.
+This research has benefits for politicians, informatics experts, and
+policymakers to explore public opinion. The collected data will also be
+available for further investigation.
+"
+23940,"Critical exponents of normal subgroups, the spectrum of group extended transfer operators, and Kazhdan distance","  For a pinched Hadamard manifold $X$ and a discrete group of isometries
+$\Gamma$ of $X$, the critical exponent $\delta_\Gamma$ is the exponential
+growth rate of the orbit of a point in $X$ under the action of $\Gamma$. We
+show that the critical exponent for any family $\mathcal{N}$ of normal
+subgroups of $\Gamma_0$ has the same coarse behaviour as the Kazhdan distances
+for the right regular representations of the quotients $\Gamma_0/\Gamma$. The
+key tool is to analyse the spectrum of transfer operators associated to
+subshifts of finite type, for which we obtain a result of independent interest.
+"
+23941,Symbolic Models for Retarded Jump-Diffusion Systems,"  In this paper, we provide for the first time an automated,
+correct-by-construction, controller synthesis scheme for a class of infinite
+dimensional stochastic systems, namely, retarded jump-diffusion systems. First,
+we construct finite dimensional abstractions approximately bisimilar to
+original retarded jump-diffusion systems having some stability property,
+namely, incremental input-to-state stability. Second, we construct finite
+abstractions approximately bisimilar to constructed finite dimensional
+abstractions. Both types of abstractions are derived without any state-space
+discretization. By using the transitivity property of approximate bisimulation
+relations, we establish that the constructed finite abstractions are also
+approximately bisimilar to original retarded jump-diffusion systems with a
+precision that can be chosen a-priori. Given those finite abstractions, one can
+synthesize controllers for original systems satisfying high-level logic
+requirements in a systematic way. Moreover, we provide sufficient conditions
+for the proposed notion of incremental stability in terms of the existence of
+incremental Lyapunov functions which reduce to linear matrix inequalities (LMI)
+for the linear systems. Finally, the effectiveness of the results is
+illustrated by synthesizing a controller regulating the temperatures in a
+ten-room building modeled as a delayed jump-diffusion system.
+"
+23942,SAGA: A Submodular Greedy Algorithm For Group Recommendation,"  In this paper, we propose a unified framework and an algorithm for the
+problem of group recommendation where a fixed number of items or alternatives
+can be recommended to a group of users. The problem of group recommendation
+arises naturally in many real world contexts, and is closely related to the
+budgeted social choice problem studied in economics. We frame the group
+recommendation problem as choosing a subgraph with the largest group consensus
+score in a completely connected graph defined over the item affinity matrix. We
+propose a fast greedy algorithm with strong theoretical guarantees, and show
+that the proposed algorithm compares favorably to the state-of-the-art group
+recommendation algorithms according to commonly used relevance and coverage
+performance measures on benchmark dataset.
+"
+23943,Nature vs. Nurture in Discrete Spin Dynamics,"  The problem of predictability, or ""nature vs. nurture"", in both ordered and
+disordered Ising systems following a deep quench from infinite to zero
+temperature is reviewed. Two questions are addressed. The first deals with the
+nature of the final state: for an infinite system, does every spin flip
+infinitely often, or does every spin flip only finitely many times, or do some
+spins flip infinitely often and others finitely often? Once this question is
+determined, the evolution of the system from its initial state can be studied
+with attention to the issue of how much information contained in the final
+state depends on that contained in the initial state, and how much depends on
+the detailed history of the system. This problem has been addressed both
+analytically and numerically in several papers, and their main methods,
+results, and conclusions will be reviewed. The discussion closes with some open
+problems that remain to be addressed.
+"
+23944,Characterizing Moonshine Functions by Vertex-Operator-Algebraic Conditions,"  Given a holomorphic $C_2$-cofinite vertex operator algebra $V$ with graded
+dimension $j-744$, Borcherds's proof of the monstrous moonshine conjecture
+implies any finite order automorphism of $V$ has graded trace given by a
+""completely replicable function"", and by work of Cummins and Gannon, these
+functions are principal moduli of genus zero modular groups. The action of the
+monster simple group on the monster vertex operator algebra produces 171 such
+functions, known as the monstrous moonshine functions. We show that 154 of the
+157 non-monstrous completely replicable functions cannot possibly occur as
+trace functions on $V$.
+"
+23945,Cheshire: An Online Algorithm for Activity Maximization in Social Networks,"  User engagement in social networks depends critically on the number of online
+actions their users take in the network. Can we design an algorithm that finds
+when to incentivize users to take actions to maximize the overall activity in a
+social network? In this paper, we model the number of online actions over time
+using multidimensional Hawkes processes, derive an alternate representation of
+these processes based on stochastic differential equations (SDEs) with jumps
+and, exploiting this alternate representation, address the above question from
+the perspective of stochastic optimal control of SDEs with jumps. We find that
+the optimal level of incentivized actions depends linearly on the current level
+of overall actions. Moreover, the coefficients of this linear relationship can
+be found by solving a matrix Riccati differential equation, which can be solved
+efficiently, and a first order differential equation, which has a closed form
+solution. As a result, we are able to design an efficient online algorithm,
+Cheshire, to sample the optimal times of the users' incentivized actions.
+Experiments on both synthetic and real data gathered from Twitter show that our
+algorithm is able to consistently maximize the number of online actions more
+effectively than the state of the art.
+"
+23946,Higher Ramanujan equations II: periods of abelian varieties and transcendence questions,"  In the first part of this work, we have considered a moduli space $B_g$
+classifying principally polarized abelian varieties of dimension $g$ endowed
+with a symplectic-Hodge basis, and we have constructed the higher Ramanujan
+vector fields $(v_{kl})_{1\le k\le l \le g}$ on it. In this second part, we
+study these objects from a complex analytic viewpoint. We construct a
+holomorphic map $\varphi_g : \mathbf{H}_g \to B_g(\mathbf{C})$, where
+$\mathbf{H}_g$ denotes the Siegel upper half-space of genus $g$, satisfying the
+system of differential equations $\frac{1}{2\pi i}\frac{\partial
+\varphi_g}{\partial \tau_{kl}}=v_{kl}\circ \varphi_g$, $1\le k\le l \le g$.
+When $g=1$, we prove that $\varphi_1$ may be identified with the triple of
+Eisenstein series $(E_2,E_4,E_6)$, so that the previous differential equations
+coincide with Ramanujan's classical relations concerning Eisenstein series. We
+discuss the relation between the values of $\varphi_g$ and the fields of
+periods of abelian varieties, and we explain how this relates to Grothendieck's
+periods conjecture. Finally, we prove that every leaf of the holomorphic
+foliation on $B_g(\mathbf{C})$ induced by the vector fields $v_{kl}$ is
+Zariski-dense in $B_{g,\mathbf{C}}$. This last result implies a ""functional
+version"" of Grothendieck's periods conjecture for abelian varieties.
+"
+23947,Under the sea: Pulsing corals in ambient flow,"  While many organisms filter feed and exchange heat or nutrients in flow, few
+benthic organisms also actively pulse to enhance feeding and exchange. One
+example is the pulsing soft coral (Heteroxenia fuscescens). Pulsing corals live
+in colonies, where each polyp actively pulses through contraction and
+relaxation of their tentacles. The pulses are typically out of phase and
+without a clear pattern. These corals live in lagoons and bays found in the Red
+Sea and Indian Ocean where they at times experience strong ambient flows. In
+this paper, $3D$ fluid-structure interaction simulations are used to quantify
+the effects of ambient flow on the exchange currents produced by the active
+contraction of pulsing corals. We find a complex interaction between the flows
+produced by the coral and the background flow. The dynamics can either enhance
+or reduce the upward jet generated in a quiescent medium. The pulsing behavior
+also slows the average horizontal flow near the polyp when there is a strong
+background flow. The dynamics of these flows have implications for particle
+capture and nutrient exchange.
+"
+23948,Characterization of Invariant subspaces in the polydisc,"  We give a complete characterization of invariant subspaces for $(M_{z_1},
+\ldots, M_{z_n})$ on the Hardy space $H^2(\mathbb{D}^n)$ over the unit polydisc
+$\mathbb{D}^n$ in $\mathbb{C}^n$, $n >1$. In particular, this yields a complete
+set of unitary invariants for invariant subspaces for $(M_{z_1}, \ldots,
+M_{z_n})$ on $H^2(\mathbb{D}^n)$, $n > 1$. As a consequence, we classify a
+large class of $n$-tuples, $n > 1$, of commuting isometries. All of our results
+hold for vector-valued Hardy spaces over $\mathbb{D}^n$, $n > 1$. Our invariant
+subspace theorem solves the well-known open problem on characterizations of
+invariant subspaces of the Hardy space over the unit polydisc.
+"
+23949,A characterization of a class of convex log-Sobolev inequalities on the real line,"  We give a sufficient and necessary condition for a probability measure $\mu$
+on the real line to satisfy the logarithmic Sobolev inequality for convex
+functions. The condition is expressed in terms of the unique left-continuous
+and non-decreasing map transporting the symmetric exponential measure onto
+$\mu$. The main tool in the proof is the theory of weak transport costs. As a
+consequence, we obtain dimension-free concentration bounds for the lower and
+upper tails of convex functions of independent random variables which satisfy
+the convex log-Sobolev inequality.
+"
+23950,Assessment of long-range-corrected exchange-correlation kernels for solids: accurate exciton binding energies via an empirically scaled Bootstrap kernel,"  In time-dependent density-functional theory, a family of exchange-correlation
+kernels, known as long-range-corrected (LRC) kernels, have shown promise in the
+calculation of excitonic effects in solids. We perform a systematic assessment
+of existing static LRC kernels (empirical LRC, Bootstrap, and
+jellium-with-a-gap model) for a range of semiconductors and insulators,
+focusing on optical spectra and exciton binding energies. We find that no LRC
+kernel is capable of simultaneously producing good optical spectra and
+quantitatively accurate exciton binding energies for both semiconductors and
+insulators. We propose a simple and universal, empirically scaled Bootstrap
+kernel which yields accurate exciton binding energies for all materials under
+consideration, with low computational cost.
+"
+23951,Preferential Attachment Graphs with Planted Communities,"  A variation of the preferential attachment random graph model of Barabási
+and Albert is defined that incorporates planted communities. The graph is built
+progressively, with new vertices attaching to the existing ones one-by-one. At
+every step, the incoming vertex is randomly assigned a label, which represents
+a community it belongs to. This vertex then chooses certain vertices as its
+neighbors, with the choice of each vertex being proportional to the degree of
+the vertex multiplied by an affinity depending on the labels of the new vertex
+and a potential neighbor. It is shown that the fraction of half-edges attached
+to vertices with a given label converges almost surely for some classes of
+affinity matrices. In addition, the empirical degree distribution for the set
+of vertices with a given label converges to a heavy tailed distribution, such
+that the tail decay parameter can be different for different communities. Our
+proof method may be of independent interest, both for the classical Barabási
+-Albert model and for other possible extensions.
+"
+23952,Structural identities for generalized multiple zeta values,"  There has been an avalanche of recent research on multiple zeta values. We
+propose dividing identities for multiple zeta values into structural and
+specific types. Structural identities are valid for any generalized multiple
+zeta function, and we systematically investigate them through symmetric
+functions. Specific identities are only valid for a specific zeta function, and
+we show how these can be used in conjunction with structural identities to find
+closed form multiple zeta values. This allows us to interpret generalized
+multiple zeta values as the moments of a random variable, which we characterize
+in certain cases. We also evaluate certain multiple Bessel zeta values and
+multiple Hurwitz zeta values.
+"
+23953,Peaceful Coexistence: Examining Kent's Relativistic Solution to the Quantum Measurement Problem,"  Can there be `peaceful coexistence' between quantum theory and special
+relativity? Thirty years ago, Shimony hoped that isolating the culprit (i.e.
+the false assumption) in proofs of Bell inequalities as Outcome Independence
+would secure such peaceful coexistence: or, if not secure it, at least show a
+way---maybe the best or only way---to secure it.
+In this paper, I begin by being sceptical of Shimony's approach, urging that
+we need a relativistic solution to the quantum measurement problem (Section 2).
+Then I analyse Outcome Independence in Kent's realist one-world
+Lorentz-invariant interpretation of quantum theory (Sections 3 and 4). Then I
+consider Shimony's other condition, Parameter Independence, both in Kent's
+proposal and more generally, in the light of recent remarkable theorems by
+Colbeck, Renner and Leegwater (Section 5).
+For both Outcome Independence and Parameter Independence, there is a striking
+analogy with the situation in pilot-wave theory. Finally, I will suggest that
+these recent theorems make some kind of peaceful coexistence mandatory for
+someone who, like Shimony, endorses Parameter Independence.
+"
+23954,The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment,"  An original dataset for semantic segmentation, Ciona17, is introduced, which
+to the best of the authors' knowledge, is the first dataset of its kind with
+pixel-level annotations pertaining to invasive species in a marine environment.
+Diverse outdoor illumination, a range of object shapes, colour, and severe
+occlusion provide a significant real world challenge for the computer vision
+community. An accompanying ground-truthing tool for superpixel labeling, Truth
+and Crop, is also introduced. Finally, we provide a baseline using a variant of
+Fully Convolutional Networks, and report results in terms of the standard mean
+intersection over union (mIoU) metric.
+"
+23955,Automatic Response Category Combination in Multinomial Logistic Regression,"  We propose a penalized likelihood method that simultaneously fits the
+multinomial logistic regression model and combines subsets of the response
+categories. The penalty is non differentiable when pairs of columns in the
+optimization variable are equal. This encourages pairwise equality of these
+columns in the estimator, which corresponds to response category combination.
+We use an alternating direction method of multipliers algorithm to compute the
+estimator and we discuss the algorithm's convergence. Prediction and model
+selection are also addressed.
+"
+23956,Inverse Kinematics and Sensitivity Minimization of an n-Stack Stewart Platform,"  An autonomous system is presented to solve the problem of in space assembly,
+which can be used to further the NASA goal of deep space exploration. Of
+particular interest is the assembly of large truss structures, which requires
+precise and dexterous movement in a changing environment. A prototype of an
+autonomous manipulator called ""Assemblers"" was fabricated from an aggregation
+of Stewart Platform robots for the purpose of researching autonomous in space
+assembly capabilities. The forward kinematics for an Assembler is described by
+the set of translations and rotation angles for each component Stewart
+Platform, from which the position and orientation of the end effector are
+simple to calculate. However, selecting inverse kinematic poses, defined by the
+translations and rotation angles, for the Assembler requires coordination
+between each Stewart Platform and is an underconstrained non-linear
+optimization problem. For assembly tasks, it is ideal that the pose selected
+has the least sensitivity to disturbances possible. A method of sensitivity
+reduction is proposed by minimizing the Frobenius Norm (FN) of the Jacobian for
+the forward kinematics. The effectiveness of the FN method will be demonstrated
+through a Monte Carlo simulation method to model random motion internal to the
+structure.
+"
+23957,Relativistic electron beam acceleration in a zero-slippage terahertz-driven Inverse Free Electron Laser scheme,"  THz radiation promises breakthrough advances in compact advanced accelerators
+due to the high frequency and GV/m fields achievable, orders of magnitude
+larger than in conventional radiofrequency (RF) based accelerators. Compared to
+laser-driven schemes, the large phase acceptances of THz-driven accelerators
+are advantageous for operation with sizable charge. Despite burgeoning
+research, THz sources, particularly laser-based ones, cannot yet compete with
+the efficiency of RF amplifiers for high average current accelerators.
+Nevertheless, THz-based phase space manipulation is of immediate interest for a
+variety of applications, including generation and diagnostics of ultrashort
+bunches for electron diffraction/microscopy and compact free-electron laser
+applications.
+The challenge of maintaining overlap and synchronism between an electron beam
+and short laser-generated THz pulse has so far limited interactions to the few
+mm scale. We discuss a novel scheme for simultaneous group and phase velocity
+matching of nearly single-cycle THz radiation with a relativistic electron beam
+for meter-scale interaction. We demonstrate energy modulations of up to 150 keV
+using modest THz pulse energies (up to 1 uJ). We apply this large and efficient
+energy exchange for beam compression and time-stamping of a relativistic beam,
+paving the way towards realizing the unique opportunities enabled by
+laser-based THz accelerators.
+"
+23958,Nonclassical photon pairs from warm atomic vapor using a single driving laser,"  Generation of nonclassical light is an essential tool for quantum optics
+research and applications in quantum information technology. We present
+realization of the source of nonclassically correlated photon pairs based on
+the process of spontaneous four-wave-mixing in warm atomic vapor. Atoms are
+excited only by a single laser beam in retro-reflected configuration and
+narrowband frequency filtering is employed for selection of correlated photon
+pairs. Nonclassicality of generated light fields is proved by analysis of their
+statistical properties. Measured parameters of the presented source promise
+further applicability for efficient interaction with atomic ensembles.
+"
+23959,Properties of thermocapillary fluids and symmetrization of motion equations,"  The equations of fluid motions are considered in the case of internal energy
+depending on mass density, volume entropy and their spatial derivatives. The
+model corresponds to domains with large density gradients in which the
+temperature is not necessary uniform. In this new general representation writes
+in symmetric form with respect to the mass and entropy densities. For
+conservative motions of perfect thermocapillary fluids, Kelvin's circulation
+theorems are always valid. Dissipative cases are also considered; we obtain the
+balance of energy and we prove that equations are compatible with the second
+law of thermodynamics. The internal energy form allows to obtain a Legendre
+transformation inducing a quasi-linear system of conservation laws which can be
+written in a divergence form and the stability near equilibrium positions can
+be deduced. The result extends classical hyperbolicity theory for
+governing-equations' systems in hydrodynamics, but symmetric matrices are
+replaced by Hermitian matrices.
+"
+23960,Polyhedral Gauss-Bonnet theorems and valuations,"  The Gauss-Bonnet theorem for a polyhedron (a union of finitely many compact
+convex polytopes) in $n$-dimensional Euclidean space expresses the Euler
+characteristic of the polyhedron as a sum of certain curvatures, which are
+different from zero only at the vertices of the polyhedron. This note suggests
+a generalization of these polyhedral vertex curvatures, based on valuations,
+and thus obtains a variety of polyhedral Gauss-Bonnet theorems.
+"
+23961,M-Estimation Method Based Asymmetric Objective Function,"  The asymmetric objective function is proposed as an alternative to Huber
+objective function to model skewness and obtain robust estimators for the
+location, scale and skewness parameters. The robustness and asymptotic
+properties of the asymmetric M-estimators are explored. A simulation study and
+real data examples are given to illustrate the performance of proposed
+asymmetric M-estimation method over the symmetric M-estimation method. It is
+observed from the simulation results that the asymmetric M-estimators perform
+better than Huber M-estimators when the data have skewness. The application on
+regression is also considered.
+"
+23962,Photobleaching of randomly-rotating fluorescently-decorated particles,"  Randomly rotating particles that have been isotropically labeled with rigidly
+linked fluorophores will undergo non-isotropic (patchy) photobleaching under
+illumination due to the dipole coupling of fluorophores with light. For a
+rotational diffusion rate $D$ of the particle and a photobleaching timescale
+$\tau$ of the fluorophores, the dynamics of this process are characterized by
+the dimensionless combination $D \tau$. We find significant interparticle
+fluctuations at intermediate $D \tau$. These fluctuations vanish at both large
+and small $D \tau$, or at small or large elapsed times $t$. Associated with
+these fluctuations between particles, we also observe transient
+non-monotonicities of the brightness of individual particles. These
+non-monotonicities can be as much 20\% of the original brightness. We show that
+these novel photobleach-fluctuations dominate over variability of
+single-fluorophore orientation when there are at least $10^3$ fluorophores on
+individual particles.
+"
+23963,Tangent Cones to TT Varieties,"  As already done for the matrix case for example in [Joe Harris, Algebraic
+Geometry - A first course, p.256] we give a parametrization of the Bouligand
+tangent cone of the variety of tensors of bounded TT rank. We discuss how the
+proof generalizes to any binary hierarchical format. The parametrization can be
+rewritten as an orthogonal sum of TT tensors. Its retraction onto the variety
+is particularly easy to compose. We also give an implicit description of the
+tangent cone as the solution of a system of polynomial equations.
+"
+23964,Deformable Part Networks,"  In this paper we propose novel Deformable Part Networks (DPNs) to learn {\em
+pose-invariant} representations for 2D object recognition. In contrast to the
+state-of-the-art pose-aware networks such as CapsNet \cite{sabour2017dynamic}
+and STN \cite{jaderberg2015spatial}, DPNs can be naturally {\em interpreted} as
+an efficient solver for a challenging detection problem, namely Localized
+Deformable Part Models (LDPMs) where localization is introduced to DPMs as
+another latent variable for searching for the best poses of objects over all
+pixels and (predefined) scales. In particular we construct DPNs as sequences of
+such LDPM units to model the semantic and spatial relations among the
+deformable parts as hierarchical composition and spatial parsing trees.
+Empirically our 17-layer DPN can outperform both CapsNets and STNs
+significantly on affNIST \cite{sabour2017dynamic}, for instance, by 19.19\% and
+12.75\%, respectively, with better generalization and better tolerance to
+affine transformations.
+"
+23965,An effective model for the electronic and optical properties of stanene,"  The existence of several 2D materials with heavy atoms in their composition
+has been recently demonstrated. The electronic and optical properties of these
+materials can be accurately computed with numerically intensive density
+functional theory methods. However, it is desirable to have simple effective
+models that can accurately describe these properties at low energies. Here we
+present an effective model for stanene that is reliable for electronic and
+optical properties for photon energies up to 1.1 eV. For this material, we find
+that a quadratic model with respect to the lattice momentum is the best suited
+for calculations based on the bandstructure, even with respect to band warping.
+We also find that splitting the two spin-z subsectors is a good approximation,
+which indicates that the lattice buckling can be neglected in calculations
+based on the bandstructure. We illustrate the applicability of the model by
+computing the linear optical injection rates of carrier and spin densities in
+stanene. Our calculations indicate that an incident circularly polarized
+optical field only excites electrons with spin that matches its helicity.
+"
+23966,Temporary Capture of Asteroids by an Eccentric Planet,"  We have investigated the probability of temporary capture of asteroids in
+eccentric orbits by a planet in a circular or an eccentric orbit through
+analytical and numerical calculations. We found that in the limit of the
+circular orbit, the capture probability is $\sim 0.1\%$ of encounters to the
+planet's Hill sphere, independent of planetary mass and semimajor axis. In
+general, the temporary capture becomes more difficult as the planet's
+eccentricity ($e_{\rm p}$) increases. We found that the capture probability is
+almost independent of $e_{\rm p}$ until a critical value ($e_{\rm p}^{\rm c}$)
+that is given by $\simeq$ 5 times the Hill radius scaled by the planet's
+semimajor axis. For $e_{\rm p} > e_{\rm p}^{\rm c}$, the probability decreases
+approximately in proportion to $e_{\rm p}^{-1}$. The current orbital
+eccentricity of Mars is several times larger than $e_{\rm p}^{\rm c}$. However,
+since the range of secular change in Martian eccentricity overlaps $e_{\rm
+p}^{\rm c}$, the capture of minor bodies by the past Mars is not ruled out.
+"
+23967,Optimal rates of entropy estimation over Lipschitz balls,"  We consider the problem of minimax estimation of the entropy of a density
+over Lipschitz balls. Dropping the usual assumption that the density is bounded
+away from zero, we obtain the minimax rates $(n\ln n)^{-\frac{s}{s+d}} +
+n^{-1/2}$ for $0<s\leq 2$ for densities supported on $[0,1]^d$, where $s$ is
+the smoothness parameter and $n$ is the number of independent samples. We
+generalize the results to sub-Gaussian settings and show that for densities
+with a constant sub-Gaussian parameter, the minimax rates increase to $(n\ln
+n)^{-\frac{s}{s+d}} (\ln n)^{\frac{d}{2}\left( 1- \frac{d}{p(s+d)} \right)} +
+n^{-1/2}$, where $p$ is the norm parameter in the Lipschitz ball. We also show
+that plugging any kernel density estimator into the entropy function does not
+achieve the minimax rates, and characterize their worst case performances over
+the Lipschitz ball.
+One of the key steps in analyzing the bias relies on a novel application of
+the Hardy-Littlewood maximal inequality, which also leads to a new inequality
+on the Fisher information that could be of independent interest.
+"
+23968,On the extremal Betti numbers of the binomial edge ideal of closed graphs,"  We study the equality of the extremal Betti numbers of the binomial edge
+ideal $J_G$ and those of its initial ideal ${\rm in}(J_G)$ of a closed graph
+$G$. We prove that in some cases there is an unique extremal Betti number for
+${\rm in}(J_G)$ and as a consequence there is an unique extremal Betti number
+for $J_G$ and these extremal Betti numbers are equal
+"
+23969,GUIDES - Geospatial Urban Infrastructure Data Engineering Solutions,"  As the underground infrastructure systems of cities age, maintenance and
+repair become an increasing concern. Cities face difficulties in planning
+maintenance, predicting and responding to infrastructure related issues, and in
+realizing their vision to be a smart city due to their incomplete understanding
+of the existing state of the infrastructure. Only few cities have accurate and
+complete digital information on their underground infrastructure (e.g.,
+electricity, water, natural gas) systems, which poses problems to those
+planning and performing construction projects. To address these issues, we
+introduce GUIDES as a new data conversion and management framework for urban
+underground infrastructure systems that enable city administrators, workers,
+and contractors along with the general public and other users to query
+digitized and integrated data to make smarter decisions. This demo paper
+presents the GUIDES architecture and describes two of its central components:
+(i) mapping of underground infrastructure systems, and (ii) integration of
+heterogeneous geospatial data.
+"
+23970,Target assignment for robots constrained by limited communication range,"  This paper investigates the task assignment problem for multiple dispersed
+robots constrained by limited communication range. The robots are initially
+randomly distributed and need to visit several target locations while
+minimizing the total travel time. A centralized rendezvous-based algorithm is
+proposed, under which all the robots first move towards a rendezvous position
+until communication paths are established between every pair of robots either
+directly or through intermediate peers, and then one robot is chosen as the
+leader to make a centralized task assignment for the other robots. Furthermore,
+we propose a decentralized algorithm based on a single-traveling-salesman tour,
+which does not require all the robots to be connected through communication. We
+investigate the variation of the quality of the assignment solutions as the
+level of information sharing increases and as the communication range grows,
+respectively. The proposed algorithms are compared with a centralized algorithm
+with shared global information and a decentralized greedy algorithm
+respectively. Monte Carlo simulation results show the satisfying performance of
+the proposed algorithms.
+"
+23971,Revisiting the origin of satellites in core level photoemission of transparent conducting oxides: the case of $n$-doped SnO$_2$,"  The longstanding problem of interpretation of satellite structures in core
+level photoemission spectra of metallic systems with a low density of
+conduction electrons is addressed using the specific example of Sb-doped
+SnO$_2$. Comparison of {\it ab initio} many-body calculations with experimental
+hard X-ray photoemission spectra of the Sn 4$d$ states shows that strong
+satellites are produced by coupling of the Sn core hole to the plasma
+oscillations of the free electrons introduced by doping. Within the same
+theoretical framework, spectral changes of the valence band spectra are also
+related to dynamical screening effects. These results demonstrate that, for the
+interpretation of electron correlation features in the core level photoelectron
+spectra of such narrow-band materials, going beyond the homogeneous electron
+gas electron-plasmon coupling model is essential.
+"
+23972,Hidden Hamiltonian Cycle Recovery via Linear Programming,"  We introduce the problem of hidden Hamiltonian cycle recovery, where there is
+an unknown Hamiltonian cycle in an $n$-vertex complete graph that needs to be
+inferred from noisy edge measurements. The measurements are independent and
+distributed according to $\calP_n$ for edges in the cycle and $\calQ_n$
+otherwise. This formulation is motivated by a problem in genome assembly, where
+the goal is to order a set of contigs (genome subsequences) according to their
+positions on the genome using long-range linking measurements between the
+contigs. Computing the maximum likelihood estimate in this model reduces to a
+Traveling Salesman Problem (TSP). Despite the NP-hardness of TSP, we show that
+a simple linear programming (LP) relaxation, namely the fractional $2$-factor
+(F2F) LP, recovers the hidden Hamiltonian cycle with high probability as $n \to
+\infty$ provided that $\alpha_n - \log n \to \infty$, where $\alpha_n
+\triangleq -2 \log \int \sqrt{d P_n d Q_n}$ is the Rényi divergence of order
+$\frac{1}{2}$. This condition is information-theoretically optimal in the sense
+that, under mild distributional assumptions, $\alpha_n \geq (1+o(1)) \log n$ is
+necessary for any algorithm to succeed regardless of the computational cost.
+Departing from the usual proof techniques based on dual witness construction,
+the analysis relies on the combinatorial characterization (in particular, the
+half-integrality) of the extreme points of the F2F polytope. Represented as
+bicolored multi-graphs, these extreme points are further decomposed into
+simpler ""blossom-type"" structures for the large deviation analysis and counting
+arguments. Evaluation of the algorithm on real data shows improvements over
+existing approaches.
+"
+23973,A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data,"  This paper presents a non-parametric approach for segmenting trees from
+airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the
+approach collects crown information such as steepness and height on-the-fly to
+delineate crown boundaries, and most importantly, does not require a priori
+assumptions of crown shape and size. The approach segments trees iteratively
+starting from the tallest within a given area to the smallest until all trees
+have been segmented. To evaluate its performance, the approach was applied to
+the University of Kentucky Robinson Forest, a deciduous closed-canopy forest
+with complex terrain and vegetation conditions. The approach identified 94% of
+dominant and co-dominant trees with a false detection rate of 13%. About 62% of
+intermediate, overtopped, and dead trees were also detected with a false
+detection rate of 15%. The overall segmentation accuracy was 77%. Correlations
+of the segmentation scores of the proposed approach with local terrain and
+stand metrics was not significant, which is likely an indication of the
+robustness of the approach as results are not sensitive to the differences in
+terrain and stand structures.
+"
+23974,A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem,"  The multi-armed bandit problem has been extensively studied under the
+stationary assumption. However in reality, this assumption often does not hold
+because the distributions of rewards themselves may change over time. In this
+paper, we propose a change-detection (CD) based framework for multi-armed
+bandit problems under the piecewise-stationary setting, and study a class of
+change-detection based UCB (Upper Confidence Bound) policies, CD-UCB, that
+actively detects change points and restarts the UCB indices. We then develop
+CUSUM-UCB and PHT-UCB, that belong to the CD-UCB class and use cumulative sum
+(CUSUM) and Page-Hinkley Test (PHT) to detect changes. We show that CUSUM-UCB
+obtains the best known regret upper bound under mild assumptions. We also
+demonstrate the regret reduction of the CD-UCB policies over arbitrary
+Bernoulli rewards and Yahoo! datasets of webpage click-through rates.
+"
+23975,A General Sensitivity Analysis Approach for Demand Response Optimizations,"  It is well-known that demand response can improve the system efficiency as
+well as lower consumers' (prosumers') electricity bills. However, it is not
+clear how we can either qualitatively identify the prosumer with the most
+impact potential or quantitatively estimate each prosumer's contribution to the
+total social welfare improvement when additional resource capacity/flexibility
+is introduced to the system with demand response, such as allowing net-selling
+behavior. In this work, we build upon existing literature on the electricity
+market, which consists of price-taking prosumers each with various appliances,
+an electric utility company and a social welfare optimizing distribution system
+operator, to design a general sensitivity analysis approach (GSAA) that can
+estimate the potential of each consumer's contribution to the social welfare
+when given more resource capacity. GSAA is based on existence of an efficient
+competitive equilibrium, which we establish in the paper. When prosumers'
+utility functions are quadratic, GSAA can give closed forms characterization on
+social welfare improvement based on duality analysis. Furthermore, we extend
+GSAA to a general convex settings, i.e., utility functions with strong
+convexity and Lipschitz continuous gradient. Even without knowing the specific
+forms the utility functions, we can derive upper and lower bounds of the social
+welfare improvement potential of each prosumer, when extra resource is
+introduced. For both settings, several applications and numerical examples are
+provided: including extending AC comfort zone, ability of EV to discharge and
+net selling. The estimation results show that GSAA can be used to decide how to
+allocate potentially limited market resources in the most impactful way.
+"
+23976,Performance of Test Supermartingale Confidence Intervals for the Success Probability of Bernoulli Trials,"  Given a composite null hypothesis H, test supermartingales are non-negative
+supermartingales with respect to H with initial value 1. Large values of test
+supermartingales provide evidence against H. As a result, test supermartingales
+are an effective tool for rejecting H, particularly when the p-values obtained
+are very small and serve as certificates against the null hypothesis. Examples
+include the rejection of local realism as an explanation of Bell test
+experiments in the foundations of physics and the certification of entanglement
+in quantum information science. Test supermartingales have the advantage of
+being adaptable during an experiment and allowing for arbitrary stopping rules.
+By inversion of acceptance regions, they can also be used to determine
+confidence sets. We use an example to compare the performance of test
+supermartingales for computing p-values and confidence intervals to
+Chernoff-Hoeffding bounds and the ""exact"" p-value. The example is the problem
+of inferring the probability of success in a sequence of Bernoulli trials.
+There is a cost in using a technique that has no restriction on stopping rules,
+and for a particular test supermartingale, our study quantifies this cost.
+"
+23977,Weighted PBW degenerations and tropical flag varieties,"  We study algebraic, combinatorial and geometric aspects of weighted PBW-type
+degenerations of (partial) flag varieties in type $A$. These degenerations are
+labeled by degree functions lying in an explicitly defined polyhedral cone,
+which can be identified with a maximal cone in the tropical flag variety.
+Varying the degree function in the cone, we recover, for example, the classical
+flag variety, its abelian PBW degeneration, some of its linear degenerations
+and a particular toric degeneration.
+"
+23978,Chimera patterns in conservative systems and ultracold atoms with mediated nonlocal hopping,"  Chimera patterns, characterized by coexisting regions of phase coherence and
+incoherence, have so far been studied in non-conservative systems with
+dissipation. Here, we show that the formation of chimera patterns can also be
+observed in conservative Hamiltonian systems with nonlocal hopping in which
+both energy and particle number are conserved. Effective nonlocality can be
+realized in a physical system with only local coupling if different time scales
+exist, which can be illustrated by a minimal conservative model with an
+additional mediating channel. Finally, we show that the patterns should be
+observable in ultracold atomic systems. Nonlocal spatial hopping over up to
+tens of lattice sites with independently tunable hopping strength and on-site
+nonlinearity can be implemented in a two-component Bose-Einstein condensate
+with a spin-dependent optical lattice, where the untrapped component serves as
+the matter-wave mediating field. The present work highlights the connections
+between chimera patterns, nonlinear dynamics, condensed matter, and ultracold
+atoms.
+"
+23979,On Advisability of Designing Short Length QC-LDPC Codes Using Perfect Difference Families,"  A simple and general definition of quasi cyclic low density parity check (QC
+LDPC) codes which are constructed based on circulant permutation matrices (CPM)
+is proposed. As an special case of this definition, we first represent one type
+of so called combinatorially designed multiple edge protograph codes. The code
+construction is mainly based on perfect difference families (PDF) and is called
+Construction 1. Secondly, using the proposed Construction 1 along with a
+technique named as column dispersion technique (CDT), we design several types
+of multiple-edge CPM QC LDPC codes (codes with Construction 2) in a wide range
+of rates, lengths, girths and minimum distances. Parameters of some of these
+codes are reported in tables. Also included in this paper are the
+multiplicities of short simple cycles of length up to 10 in Tanner graph of our
+constructed codes. Our experimental results for short to moderate length codes
+show that although minimum distances of codes play an important role in
+waterfall region, the higher the number of short simple cycles is, the better
+(sharper) the waterfall is. The performances of many codes such as WiMAX, PEG,
+array, MacKay, algebraic and combinatorial, and also, symmetrical codes have
+compared with our constructed codes. Based on our numerical results and
+regardless of how a code is constructed, those with higher number of short
+simple cycles and higher minimum distances, have better waterfalls.
+"
+23980,Spectrum Estimation from a Few Entries,"  Singular values of a data in a matrix form provide insights on the structure
+of the data, the effective dimensionality, and the choice of hyper-parameters
+on higher-level data analysis tools. However, in many practical applications
+such as collaborative filtering and network analysis, we only get a partial
+observation. Under such scenarios, we consider the fundamental problem of
+recovering spectral properties of the underlying matrix from a sampling of its
+entries. We are particularly interested in directly recovering the spectrum,
+which is the set of singular values, and also in sample-efficient approaches
+for recovering a spectral sum function, which is an aggregate sum of the same
+function applied to each of the singular values. We propose first estimating
+the Schatten $k$-norms of a matrix, and then applying Chebyshev approximation
+to the spectral sum function or applying moment matching in Wasserstein
+distance to recover the singular values. The main technical challenge is in
+accurately estimating the Schatten norms from a sampling of a matrix. We
+introduce a novel unbiased estimator based on counting small structures in a
+graph and provide guarantees that match its empirical performance. Our
+theoretical analysis shows that Schatten norms can be recovered accurately from
+strictly smaller number of samples compared to what is needed to recover the
+underlying low-rank matrix. Numerical experiments suggest that we significantly
+improve upon a competing approach of using matrix completion methods.
+"
+23981,Scheduling Maintenance Jobs in Networks,"  We investigate the problem of scheduling the maintenance of edges in a
+network, motivated by the goal of minimizing outages in transportation or
+telecommunication networks. We focus on maintaining connectivity between two
+nodes over time; for the special case of path networks, this is related to the
+problem of minimizing the busy time of machines.
+We show that the problem can be solved in polynomial time in arbitrary
+networks if preemption is allowed. If preemption is restricted to integral time
+points, the problem is NP-hard and in the non-preemptive case we give strong
+non-approximability results. Furthermore, we give tight bounds on the power of
+preemption, that is, the maximum ratio of the values of non-preemptive and
+preemptive optimal solutions.
+Interestingly, the preemptive and the non-preemptive problem can be solved
+efficiently on paths, whereas we show that mixing both leads to a weakly
+NP-hard problem that allows for a simple 2-approximation.
+"
+23982,A Direct Sum Result for the Information Complexity of Learning,"  How many bits of information are required to PAC learn a class of hypotheses
+of VC dimension $d$? The mathematical setting we follow is that of Bassily et
+al. (2018), where the value of interest is the mutual information
+$\mathrm{I}(S;A(S))$ between the input sample $S$ and the hypothesis outputted
+by the learning algorithm $A$. We introduce a class of functions of VC
+dimension $d$ over the domain $\mathcal{X}$ with information complexity at
+least $\Omega\left(d\log \log \frac{|\mathcal{X}|}{d}\right)$ bits for any
+consistent and proper algorithm (deterministic or random). Bassily et al.
+proved a similar (but quantitatively weaker) result for the case $d=1$.
+The above result is in fact a special case of a more general phenomenon we
+explore. We define the notion of information complexity of a given class of
+functions $\mathcal{H}$. Intuitively, it is the minimum amount of information
+that an algorithm for $\mathcal{H}$ must retain about its input to ensure
+consistency and properness. We prove a direct sum result for information
+complexity in this context; roughly speaking, the information complexity sums
+when combining several classes.
+"
+23983,First order differential subordination for functions with positive real part,"  Sharp estimates on $\beta$ are determined so that an analytic function $p$
+defined on the open unit disk in the complex plane normalized by $p(0)=1$ is
+subordinate to some well known starlike functions with positive real part
+whenever $1+\beta z p'(z), \,\,1+\beta z p'(z)/p(z), \,\,\mbox{or}\,\,1+\beta z
+p'(z)/p^{2}(z)$ is subordinate to $\sqrt{1+z}$. Our results provide sharp
+version of previously known results.
+"
+23984,On Generalized Bellman Equations and Temporal-Difference Learning,"  We consider off-policy temporal-difference (TD) learning in discounted Markov
+decision processes, where the goal is to evaluate a policy in a model-free way
+by using observations of a state process generated without executing the
+policy. To curb the high variance issue in off-policy TD learning, we propose a
+new scheme of setting the $\lambda$-parameters of TD, based on generalized
+Bellman equations. Our scheme is to set $\lambda$ according to the eligibility
+trace iterates calculated in TD, thereby easily keeping these traces in a
+desired bounded range. Compared with prior work, this scheme is more direct and
+flexible, and allows much larger $\lambda$ values for off-policy TD learning
+with bounded traces. As to its soundness, using Markov chain theory, we prove
+the ergodicity of the joint state-trace process under nonrestrictive
+conditions, and we show that associated with our scheme is a generalized
+Bellman equation (for the policy to be evaluated) that depends on both the
+evolution of $\lambda$ and the unique invariant probability measure of the
+state-trace process. These results not only lead immediately to a
+characterization of the convergence behavior of least-squares based
+implementation of our scheme, but also prepare the ground for further analysis
+of gradient-based implementations.
+"
+23985,Genetic and Neuroanatomical Support for Functional Brain Network Dynamics in Epilepsy,"  Focal epilepsy is a devastating neurological disorder that affects an
+overwhelming number of patients worldwide, many of whom prove resistant to
+medication. The efficacy of current innovative technologies for the treatment
+of these patients has been stalled by the lack of accurate and effective
+methods to fuse multimodal neuroimaging data to map anatomical targets driving
+seizure dynamics. Here we propose a parsimonious model that explains how
+large-scale anatomical networks and shared genetic constraints shape
+inter-regional communication in focal epilepsy. In extensive ECoG recordings
+acquired from a group of patients with medically refractory focal-onset
+epilepsy, we find that ictal and preictal functional brain network dynamics can
+be accurately predicted from features of brain anatomy and geometry, patterns
+of white matter connectivity, and constraints complicit in patterns of gene
+coexpression, all of which are conserved across healthy adult populations.
+Moreover, we uncover evidence that markers of non-conserved architecture,
+potentially driven by idiosyncratic pathology of single subjects, are most
+prevalent in high frequency ictal dynamics and low frequency preictal dynamics.
+Finally, we find that ictal dynamics are better predicted by white matter
+features and more poorly predicted by geometry and genetic constraints than
+preictal dynamics, suggesting that the functional brain network dynamics
+manifest in seizures rely on - and may directly propagate along - underlying
+white matter structure that is largely conserved across humans. Broadly, our
+work offers insights into the generic architectural principles of the human
+brain that impact seizure dynamics, and could be extended to further our
+understanding, models, and predictions of subject-level pathology and response
+to intervention.
+"
+23986,Sparse Hierachical Extrapolated Parametric Methods for Cortical Data Analysis,"  Many neuroimaging studies focus on the cortex, in order to benefit from
+better signal to noise ratios and reduced computational burden. Cortical data
+are usually projected onto a reference mesh, where subsequent analyses are
+carried out. Several multiscale approaches have been proposed for analyzing
+these surface data, such as spherical harmonics and graph wavelets. As far as
+we know, however, the hierarchical structure of the template icosahedral meshes
+used by most neuroimaging software has never been exploited for cortical data
+factorization. In this paper, we demonstrate how the structure of the
+ubiquitous icosahedral meshes can be exploited by data factorization methods
+such as sparse dictionary learning, and we assess the optimization speed-up
+offered by extrapolation methods in this context. By testing different
+sparsity-inducing norms, extrapolation methods, and factorization schemes, we
+compare the performances of eleven methods for analyzing four datasets: two
+structural and two functional MRI datasets obtained by processing the data
+publicly available for the hundred unrelated subjects of the Human Connectome
+Project. Our results demonstrate that, depending on the level of details
+requested, a speedup of several orders of magnitudes can be obtained.
+"
+23987,Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning,"  In portable, three dimensional, and ultra-fast ultrasound imaging systems,
+there is an increasing demand for the reconstruction of high quality images
+from a limited number of radio-frequency (RF) measurements due to receiver (Rx)
+or transmit (Xmit) event sub-sampling. However, due to the presence of side
+lobe artifacts from RF sub-sampling, the standard beamformer often produces
+blurry images with less contrast, which are unsuitable for diagnostic purposes.
+Existing compressed sensing approaches often require either hardware changes or
+computationally expensive algorithms, but their quality improvements are
+limited. To address this problem, here we propose a novel deep learning
+approach that directly interpolates the missing RF data by utilizing redundancy
+in the Rx-Xmit plane. Our extensive experimental results using sub-sampled RF
+data from a multi-line acquisition B-mode system confirm that the proposed
+method can effectively reduce the data rate without sacrificing image quality.
+"
+23988,Statistical properties of 3D cell geometry from 2D slices,"  Although cell shape can reflect the mechanical and biochemical properties of
+the cell and its environment, quantification of 3D cell shapes within 3D
+tissues remains difficult, typically requiring digital reconstruction from a
+stack of 2D images. We investigate a simple alternative technique to extract
+information about the 3D shapes of cells in a tissue; this technique connects
+the ensemble of 3D shapes in the tissue with the distribution of 2D shapes
+observed in independent 2D slices. Using cell vertex model geometries, we find
+that the distribution of 2D shapes allows clear determination of the mean value
+of a 3D shape index. We analyze the errors that may arise in practice in the
+estimation of the mean 3D shape index from 2D imagery and find that typically
+only a few dozen cells in 2D imagery are required to reduce uncertainty below
+2\%. This framework could be naturally extended to estimate additional 3D
+geometric features and quantify their uncertainty in other materials.
+"
+23989,Combinational Q-Learning for Dou Di Zhu,"  Deep reinforcement learning (DRL) has gained a lot of attention in recent
+years, and has been proven to be able to play Atari games and Go at or above
+human levels. However, those games are assumed to have a small fixed number of
+actions and could be trained with a simple CNN network. In this paper, we study
+a special class of Asian popular card games called Dou Di Zhu, in which two
+adversarial groups of agents must consider numerous card combinations at each
+time step, leading to huge number of actions. We propose a novel method to
+handle combinatorial actions, which we call combinational Q-learning (CQL). We
+employ a two-stage network to reduce action space and also leverage
+order-invariant max-pooling operations to extract relationships between
+primitive actions. Results show that our method prevails over state-of-the art
+methods like naive Q-learning and A3C. We develop an easy-to-use card game
+environments and train all agents adversarially from sractch, with only
+knowledge of game rules and verify that our agents are comparative to humans.
+Our code to reproduce all reported results will be available online.
+"
+23990,Performance Analysis of Low Latency Multiple Full-Duplex Selective Decode and Forward Relays,"  In order to follow up with mission-critical applications, new features need
+to be carried to satisfy a reliable communication with reduced latency. With
+this regard, this paper proposes a low latency cooperative transmission scheme,
+where multiple full-duplex relays, simultaneously, assist the communication
+between a source node and a destination node. First, we present the
+communication model of the proposed transmission scheme. Then, we derive the
+outage probability closed-form for two cases: asynchronous transmission (where
+all relays have different processing delay) and synchronous transmissions
+(where all relays have the same processing delay). Finally, using simulations,
+we confirm the theoretical results and compare the proposed multi-relays
+transmission scheme with relay selection schemes.
+"
+23991,Social Connection Induces Cultural Contraction: Evidence from Hyperbolic Embeddings of Social and Semantic Networks,"  Research has repeatedly demonstrated the influence of social connection and
+communication on convergence in cultural tastes, opinions and ideas. Here we
+review recent studies and consider the implications of social connection on
+cultural, epistemological and ideological contraction, then formalize these
+intuitions within the language of information theory. To systematically examine
+connectivity and cultural diversity, we introduce new methods of manifold
+learning to map both social networks and topic combinations into comparable,
+two-dimensional hyperbolic spaces or Poincaré disks, which represent both
+hierarchy and diversity within a system. On a Poincaré disk, radius from
+center traces the position of an actor in a social hierarchy or an idea in a
+cultural hierarchy. The angle of the disk required to inscribe connected actors
+or ideas captures their diversity. Using this method in the epistemic culture
+of 21st Century physics, we empirically demonstrate that denser university
+collaborations systematically contract the space of topics discussed and ideas
+investigated more than shared topics drive collaboration, despite the extreme
+commitments academic physicists make to research programs over the course of
+their careers. Dense connections unleash flows of communication that contract
+otherwise fragmented semantic spaces into convergent hubs or polarized
+clusters. We theorize the dynamic interplay between structural expansion and
+cultural contraction and explore how this introduces an essential tension
+between the enjoyment and protection of difference.
+"
+23992,Tree substitutions and Rauzy fractals,"  We work with attracting subshifts generated by substitutions which are also
+irreducible parageometric automorphisms of free groups. For such a dynamical
+system, we construct a tree substitution to approximate the repelling real tree
+of the automorphism. We produce images of this tree inside the Rauzy fractal
+when the substitution is irreducible Pisot. We describe the contour of this
+tree and compute an interval exchange transformation of the circle covering the
+original substitution.
+"
+23993,There is no finitely isometric Krivine's theorem,"  We prove that for every $p\in(1,\infty)$, $p\ne 2$, there exist a Banach
+space $X$ isomorphic to $\ell_p$ and a finite subset $U$ in $\ell_p$, such that
+$U$ is not isometric to a subset of $X$. This result shows that the finite
+isometric version of the Krivine theorem (which would be a strengthening of the
+Krivine theorem (1976)) does not hold.
+"
+23994,CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior,"  We introduce a new deep convolutional neural network, CrescendoNet, by
+stacking simple building blocks without residual connections. Each Crescendo
+block contains independent convolution paths with increased depths. The numbers
+of convolution layers and parameters are only increased linearly in Crescendo
+blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all
+networks without residual connections on benchmark datasets, CIFAR10, CIFAR100,
+and SVHN. Given sufficient amount of data as in SVHN dataset, CrescendoNet with
+15 layers and 4.1M parameters can match the performance of DenseNet-BC with 250
+layers and 15.3M parameters. CrescendoNet provides a new way to construct high
+performance deep convolutional neural networks without residual connections.
+Moreover, through investigating the behavior and performance of subnetworks in
+CrescendoNet, we note that the high performance of CrescendoNet may come from
+its implicit ensemble behavior, which differs from the FractalNet that is also
+a deep convolutional neural network without residual connections. Furthermore,
+the independence between paths in CrescendoNet allows us to introduce a new
+path-wise training procedure, which can reduce the memory needed for training.
+"
+23995,Stability analysis of LPV systems with piecewise differentiable parameters,"  Linear Parameter-Varying (LPV) systems with piecewise differentiable
+parameters is a class of LPV systems for which no proper analysis conditions
+have been obtained so far. To fill this gap, we propose an approach based on
+the theory of hybrid systems. The underlying idea is to reformulate the
+considered LPV system as an equivalent hybrid system that will incorporate,
+through a suitable state augmentation, information on both the dynamics of the
+state of the system and the considered class of parameter trajectories. Then,
+using a result pertaining on the stability of hybrid systems, two stability
+conditions are established and shown to naturally generalize and unify the
+well-known quadratic and robust stability criteria together. The obtained
+conditions being infinite-dimensional, a relaxation approach based on sum of
+squares programming is used in order to obtain tractable finite-dimensional
+conditions. The approach is finally illustrated on two examples from the
+literature.
+"
+23996,"Providing Privacy, Safety, and Security in IoT-Based Transactive Energy Systems using Distributed Ledgers","  Power grids are undergoing major changes due to rapid growth in renewable
+energy resources and improvements in battery technology. While these changes
+enhance sustainability and efficiency, they also create significant management
+challenges as the complexity of power systems increases. To tackle these
+challenges, decentralized Internet-of-Things (IoT) solutions are emerging,
+which arrange local communities into transactive microgrids. Within a
+transactive microgrid, ""prosumers"" (i.e., consumers with energy generation and
+storage capabilities) can trade energy with each other, thereby smoothing the
+load on the main grid using local supply. It is hard, however, to provide
+security, safety, and privacy in a decentralized and transactive energy system.
+On the one hand, prosumers' personal information must be protected from their
+trade partners and the system operator. On the other hand, the system must be
+protected from careless or malicious trading, which could destabilize the
+entire grid. This paper describes Privacy-preserving Energy Transactions
+(PETra), which is a secure and safe solution for transactive microgrids that
+enables consumers to trade energy without sacrificing their privacy. PETra
+builds on distributed ledgers, such as blockchains, and provides anonymity for
+communication, bidding, and trading.
+"
+23997,Discovery of slow magnetic fluctuations and critical slowing down in the pseudogap phase of YBa$_2$Cu$_3$O$_y$,"  Evidence for intra-unit-cell (IUC) magnetic order in the pseudogap region of
+high-$T_c$ cuprates below a temperature $T^\ast$ is found in several studies,
+but NMR and $\mu$SR experiments do not observe the expected static local
+magnetic fields. It has been noted, however, that such fields could be averaged
+by fluctuations. Our measurements of muon spin relaxation rates in single
+crystals of YBa$_2$Cu$_3$O$_y$ reveal magnetic fluctuations of the expected
+order of magnitude that exhibit critical slowing down at $T^\ast$. These
+results are strong evidence for fluctuating IUC magnetic order in the pseudogap
+phase.
+"
+23998,Non-reduced moduli spaces of sheaves on multiple curves,"  Some coherent sheaves on projective varieties have a non reduced versal
+deformation space. For example, this is the case for most unstable rank 2
+vector bundles on ${\mathbb P}_2$. In particular, it may happen that some
+moduli spaces of stable sheaves are non reduced.
+We consider the case of some sheaves on ribbons (double structures on smooth
+projective curves): the quasi locally free sheaves of rigid type. Le $E$ be
+such a sheaf.
+-- Let ${\mathcal E}$ be a flat family of sheaves containing $E$. We find
+that it is a reduced deformation of $E$ when some canonical family associated
+to ${\mathcal E}$ is also flat.
+-- We consider a deformation of the ribbon to reduced projective curves with
+two components, and find that $E$ can be deformed in two distinct ways to
+sheaves on the reduced curves. In particular some components $\bf M$ of the
+moduli spaces of stable sheaves deform to two components of the moduli spaces
+of sheaves on the reduced curves, and $\bf M$ appears as the ""limit"" of
+varieties with two components, whence the non reduced structure of $\bf M$.
+"
+23999,Profinite detection of 3-manifold decompositions,"  The profinite completion of the fundamental group of a closed, orientable
+$3$-manifold determines the Kneser--Milnor decomposition. If $M$ is
+irreducible, then the profinite completion determines the
+Jaco--Shalen--Johannson decomposition of $M$.
+"
+24000,The State-of-the-Art HST Astro-Photometric Analysis of the core of $ω$ Centauri. I. The Catalog,"  We have constructed the most-comprehensive catalog of photometry and proper
+motions ever assembled for a globular cluster (GC). The core of $\omega$Cen has
+been imaged over 650 times through WFC3's UVIS and IR channels for the purpose
+of detector calibration. There exist from 4 to over 60 exposures through each
+of 26 filters, stretching continuously from F225W in the UV to F160W in the
+infrared. Furthermore, the 11-year baseline between these data and a 2002 ACS
+survey has allowed us to more than double the proper-motion accuracy and triple
+the number of well-measured stars compared to our previous groundbreaking
+effort. This totally unprecedented complete spectral coverage for over 470,000
+stars within the cluster's core, from the tip of the red-giant branch down to
+the white dwarfs, provides the best astro-photometric observational data base
+yet to understand the multiple-population phenomenon in any GC. In this first
+paper of the series we describe in detail the data-reduction processes and
+deliver the astro-photometric catalog to the astronomical community.
+"
+24001,Distributed Real-Time HVAC Control for Cost-Efficient Commercial Buildings under Smart Grid Environment,"  In this paper, we investigate the problem of minimizing the long-term total
+cost (i.e., the sum of energy cost and thermal discomfort cost) associated with
+a Heating, Ventilation, and Air Conditioning (HVAC) system of a multizone
+commercial building under smart grid environment. To be specific, we first
+formulate a stochastic program to minimize the time average expected total cost
+with the consideration of uncertainties in electricity price, outdoor
+temperature, the most comfortable temperature level, and external thermal
+disturbance. Due to the existence of temporally and spatially coupled
+constraints as well as unknown information about the future system parameters,
+it is very challenging to solve the formulated problem. To this end, we propose
+a realtime HVAC control algorithm based on the framework of Lyapunov
+optimization techniques without the need to predict any system parameters and
+know their stochastic information. The key idea of the proposed algorithm is to
+construct and stabilize virtual queues associated with indoor temperatures of
+all zones. Moreover, we provide a distributed implementation of the proposed
+realtime algorithm with the aim of protecting user privacy and enhancing
+algorithmic scalability. Extensive simulation results based on real-world
+traces show that the proposed algorithm could reduce energy cost effectively
+with small sacrifice in thermal comfort.
+"
+24002,Geometry and Dynamics for Markov Chain Monte Carlo,"  Markov Chain Monte Carlo methods have revolutionised mathematical computation
+and enabled statistical inference within many previously intractable models. In
+this context, Hamiltonian dynamics have been proposed as an efficient way of
+building chains which can explore probability densities efficiently. The method
+emerges from physics and geometry and these links have been extensively studied
+by a series of authors through the last thirty years. However, there is
+currently a gap between the intuitions and knowledge of users of the
+methodology and our deep understanding of these theoretical foundations. The
+aim of this review is to provide a comprehensive introduction to the geometric
+tools used in Hamiltonian Monte Carlo at a level accessible to statisticians,
+machine learners and other users of the methodology with only a basic
+understanding of Monte Carlo methods. This will be complemented with some
+discussion of the most recent advances in the field which we believe will
+become increasingly relevant to applied scientists.
+"
+24003,Selection dynamics in transient compartmentalization,"  Transient compartments have been recently shown to be able to maintain
+functional replicators in the context of prebiotic studies. Motivated by this
+experiment, we show that a broad class of selection dynamics is able to achieve
+this goal. We identify two key parameters, the relative amplification of
+non-active replicators (parasites) and the size of compartments. Since the
+basic ingredients of our model are the competition between a host and its
+parasite, and the diversity generated by small size compartments, our results
+are relevant to various phage-bacteria or virus-host ecology problems.
+"
+24004,Message Passing Graph Kernels,"  Graph kernels have recently emerged as a promising approach for tackling the
+graph similarity and learning tasks at the same time. In this paper, we propose
+a general framework for designing graph kernels. The proposed framework
+capitalizes on the well-known message passing scheme on graphs. The kernels
+derived from the framework consist of two components. The first component is a
+kernel between vertices, while the second component is a kernel between graphs.
+The main idea behind the proposed framework is that the representations of the
+vertices are implicitly updated using an iterative procedure. Then, these
+representations serve as the building blocks of a kernel that compares pairs of
+graphs. We derive four instances of the proposed framework, and show through
+extensive experiments that these instances are competitive with
+state-of-the-art methods in various tasks.
+"
+24005,On the diffusion approximation of nonconvex stochastic gradient descent,"  We study the Stochastic Gradient Descent (SGD) method in nonconvex
+optimization problems from the point of view of approximating diffusion
+processes. We prove rigorously that the diffusion process can approximate the
+SGD algorithm weakly using the weak form of master equation for probability
+evolution. In the small step size regime and the presence of omnidirectional
+noise, our weak approximating diffusion process suggests the following dynamics
+for the SGD iteration starting from a local minimizer (resp.~saddle point): it
+escapes in a number of iterations exponentially (resp.~almost linearly)
+dependent on the inverse stepsize. The results are obtained using the theory
+for random perturbations of dynamical systems (theory of large deviations for
+local minimizers and theory of exiting for unstable stationary points). In
+addition, we discuss the effects of batch size for the deep neural networks,
+and we find that small batch size is helpful for SGD algorithms to escape
+unstable stationary points and sharp minimizers. Our theory indicates that one
+should increase the batch size at later stage for the SGD to be trapped in flat
+minimizers for better generalization.
+"
+24006,Linear-time geometric algorithm for evaluating Bézier curves,"  New algorithm for evaluating a point on a Bézier curve and on a rational
+Bézier curve is given. The method has a~geometric interpretation and a~convex
+hull property. The computational complexity of the new algorithm is linear with
+respect to the degree of the considered curve, and its memory complexity is
+$O(1)$.
+"
+24007,Rayleigh Quotient Iteration with a Multigrid in Energy Preconditioner for Massively Parallel Neutron Transport,"  Three complementary methods have been implemented in the code Denovo that
+accelerate neutral particle transport calculations with methods that use
+leadership-class computers fully and effectively: a multigroup block (MG)
+Krylov solver, a Rayleigh quotient iteration (RQI) eigenvalue solver, and a
+multigrid in energy preconditioner. The multigroup Krylov solver converges more
+quickly than Gauss Seidel and enables energy decomposition such that Denovo can
+scale to hundreds of thousands of cores. The new multigrid in energy
+preconditioner reduces iteration count for many problem types and takes
+advantage of the new energy decomposition such that it can scale efficiently.
+These two tools are useful on their own, but together they enable the RQI
+eigenvalue solver to work. Each individual method has been described before,
+but this is the first time they have been demonstrated to work together
+effectively.
+RQI should converge in fewer iterations than power iteration (PI) for large
+and challenging problems. RQI creates shifted systems that would not be
+tractable without the MG Krylov solver. It also creates ill-conditioned
+matrices that cannot converge without the multigrid in energy preconditioner.
+Using these methods together, RQI converged in fewer iterations and in less
+time than all PI calculations for a full pressurized water reactor core. It
+also scaled reasonably well out to 275,968 cores.
+"
+24008,Recover Missing Sensor Data with Iterative Imputing Network,"  Sensor data has been playing an important role in machine learning tasks,
+complementary to the human-annotated data that is usually rather costly.
+However, due to systematic or accidental mis-operations, sensor data comes very
+often with a variety of missing values, resulting in considerable difficulties
+in the follow-up analysis and visualization. Previous work imputes the missing
+values by interpolating in the observational feature space, without consulting
+any latent (hidden) dynamics. In contrast, our model captures the latent
+complex temporal dynamics by summarizing each observation's context with a
+novel Iterative Imputing Network, thus significantly outperforms previous work
+on the benchmark Beijing air quality and meteorological dataset. Our model also
+yields consistent superiority over other methods in cases of different missing
+rates.
+"
+24009,Thermodynamic and kinetic modeling of Mn-Ni-Si precipitates in low-Cu reactor pressure vessel steels,"  Formation of large volume fractions of Mn-Ni-Si precipitates (MNSPs) causes
+excess irradiation embrittlement of reactor pressure vessel (RPV) steels at
+high, extended-life fluences. Thus, a new and unique, semi-empirical cluster
+dynamics model was developed to study the evolution of MNSPs in low- Cu RPV
+steels. The model is based on CALPHAD thermodynamics and radiation enhanced
+diffusion ki- netics. The thermodynamics dictates the compositional and
+temperature dependence of the free energy reductions that drive precipitation.
+The model treats both homogeneous and heterogeneous nucleation, where the
+latter occurs on cascade damage, like dislocation loops. The model has only
+four adjustable parameters that were fit to an atom probe tomography (APT)
+database. The model predictions are in semi-quantitative agreement with
+systematic Mn, Ni and Si composition variations in alloys character- ized by
+APT, including a sensitivity to local tip-to-tip variations even in the same
+steel. The model predicts that heterogeneous nucleation plays a critical role
+in MNSP formation in lower alloy Ni contents. Single variable assessments of
+compositional effects show that Ni plays a dominant role, while even small
+variations in irradiation temperature can have a large effect on theMNSP
+evolution. Within typical RPV steel ranges, Mn and Si have smaller effects. The
+delayed but then rapid growth of MNSPs to large volume fractions at high
+fluence is well predicted by the model. For purposes of illustration, the
+effect of MNSPs on transition temperature shifts are presented based on
+well-established microstructure-prop- erty and property-property models.
+"
+24010,Coloring graphs of various maximum degree from random lists,"  Let $G=G(n)$ be a graph on $n$ vertices with maximum degree
+$\Delta=\Delta(n)$. Assign to each vertex $v$ of $G$ a list $L(v)$ of colors by
+choosing each list independently and uniformly at random from all $k$-subsets
+of a color set $\mathcal{C}$ of size $\sigma= \sigma(n)$. Such a list
+assignment is called a \emph{random $(k,\mathcal{C})$-list assignment}. In this
+paper, we are interested in determining the asymptotic probability (as $n \to
+\infty$) of the existence of a proper coloring $\varphi$ of $G$, such that
+$\varphi(v) \in L(v)$ for every vertex $v$ of $G$, a so-called $L$-coloring. We
+give various lower bounds on $\sigma$, in terms of $n$, $k$ and $\Delta$, which
+ensures that with probability tending to 1 as $n \to \infty$ there is an
+$L$-coloring of $G$. In particular, we show, for all fixed $k$ and growing $n$,
+that if $\sigma(n) = \omega(n^{1/k^2} \Delta^{1/k})$ and
+$\Delta=O\left(n^{\frac{k-1}{k(k^3+ 2k^2 - k +1)}}\right)$, then the
+probability that $G$ has an $L$-coloring tends to 1 as $n \rightarrow \infty$.
+If $k\geq 2$ and $\Delta= \Omega(n^{1/2})$, then the same conclusion holds
+provided that $\sigma=\omega(\Delta)$. We also give related results for other
+bounds on $\Delta$, when $k$ is constant or a strictly increasing function of
+$n$.
+"
+24011,Sensitivity of anomalous Hall effect to disorder correlations,"  Both longitudinal and anomalous Hall conductivity are computed in the model
+of two-dimensional Dirac fermions with a mass in the presence of arbitrary
+correlated weak disorder. The anomalous Hall conductivity is shown to be highly
+sensitive to the correlation properties of the random potential, such as the
+correlation length, while it remains independent of the integral disorder
+strength. This property extends beyond the Dirac model making the anomalous
+Hall effect an interesting tool to probe disorder correlations.
+"
+24012,Global bounds on stable polynomials,"  A classical inequality of Szász bounds polynomials with no zeros in the
+upper half plane entirely in terms of their first few coefficients.
+Borcea-Brändén generalized this result to several variables as a piece of
+their characterization of linear maps on polynomials preserving stability. In
+this paper, we improve Szász's original inequality, use determinantal
+representations to prove Szász type inequalities in two variables, and then
+prove that one can use the two variable inequality to prove an inequality for
+several variables.
+"
+24013,Added value of morphological features to breast lesion diagnosis in ultrasound,"  Ultrasound imaging plays an important role in breast lesion differentiation.
+However, diagnostic accuracy depends on ultrasonographer experience. Various
+computer aided diagnosis systems has been developed to improve breast cancer
+detection and reduce the number of unnecessary biopsies. In this study, our aim
+was to improve breast lesion classification based on the BI-RADS (Breast
+Imaging - Reporting and Data System). This was accomplished by combining the
+BI-RADS with morphological features which assess lesion boundary. A dataset of
+214 lesion images was used for analysis. 30 morphological features were
+extracted and feature selection scheme was applied to find features which
+improve the BI-RADS classification performance. Additionally, the best
+performing morphological feature subset was indicated. We obtained a better
+classification by combining the BI-RADS with six morphological features. These
+features were the extent, overlap ratio, NRL entropy, circularity,
+elliptic-normalized circumference and the normalized residual value. The area
+under the receiver operating curve calculated with the use of the combined
+classifier was 0.986. The best performing morphological feature subset
+contained six features: the DWR, NRL entropy, normalized residual value,
+overlap ratio, extent and the morphological closing ratio. For this set, the
+area under the curve was 0.901. The combination of the radiologist's experience
+related to the BI-RADS and the morphological features leads to a more effective
+breast lesion classification.
+"
+24014,Pseudorandom number generator based on the Bernoulli map on cubic algebraic integers,"  We develop a method for generating pseudorandom binary sequences using the
+Bernoulli map on cubic algebraic integers. The distinguishing characteristic of
+our generator is that it generates chaotic true orbits of the Bernoulli map by
+exact computation. In particular, we clarify a way to properly prepare a set of
+initial points (i.e., seeds), which is needed when generating multiple
+pseudorandom sequences. With this seed selection method, we can distribute the
+initial points almost uniformly in the unit interval and can also guarantee
+that the orbits starting from them do not merge. We also report results of a
+large variety of tests indicating that the generated pseudorandom sequences
+have good statistical properties as well as an advantage over what is probably
+the most popular generator, the Mersenne Twister MT19937.
+"
+24015,End-to-end 3D shape inverse rendering of different classes of objects from a single input image,"  In this paper a semi-supervised deep framework is proposed for the problem of
+3D shape inverse rendering from a single 2D input image. The main structure of
+proposed framework consists of unsupervised pre-trained components which
+significantly reduce the need to labeled data for training the whole framework.
+using labeled data has the advantage of achieving to accurate results without
+the need to predefined assumptions about image formation process. Three main
+components are used in the proposed network: an encoder which maps 2D input
+image to a representation space, a 3D decoder which decodes a representation to
+a 3D structure and a mapping component in order to map 2D to 3D representation.
+The only part that needs label for training is the mapping part with not too
+many parameters. The other components in the network can be pre-trained
+unsupervised using only 2D images or 3D data in each case. The way of
+reconstructing 3D shapes in the decoder component, inspired by the model based
+methods for 3D reconstruction, maps a low dimensional representation to 3D
+shape space with the advantage of extracting the basis vectors of shape space
+from training data itself and is not restricted to a small set of examples as
+used in predefined models. Therefore, the proposed framework deals directly
+with coordinate values of the point cloud representation which leads to achieve
+dense 3D shapes in the output. The experimental results on several benchmark
+datasets of objects and human faces and comparing with recent similar methods
+shows the power of proposed network in recovering more details from single 2D
+images.
+"
+24016,Rapid-Rate: A Framework for Semi-supervised Real-time Sentiment Trend Detection in Unstructured Big Data,"  Commercial establishments like restaurants, service centres and retailers
+have several sources of customer feedback about products and services, most of
+which need not be as structured as rated reviews provided by services like
+Yelp, or Amazon, in terms of sentiment conveyed. For instance, Amazon provides
+a fine-grained score on a numeric scale for product reviews. Some sources,
+however, like social media (Twitter, Facebook), mailing lists (Google Groups)
+and forums (Quora) contain text data that is much more voluminous, but
+unstructured and unlabelled. It might be in the best interests of a business
+establishment to assess the general sentiment towards their brand on these
+platforms as well. This text could be pipelined into a system with a built-in
+prediction model, with the objective of generating real-time graphs on opinion
+and sentiment trends. Although such tasks like the one described about have
+been explored with respect to document classification problems in the past, the
+implementation described in this paper, by virtue of learning a continuous
+function rather than a discrete one, offers a lot more depth of insight as
+compared to document classification approaches. This study aims to explore the
+validity of such a continuous function predicting model to quantify sentiment
+about an entity, without the additional overhead of manual labelling, and
+computational preprocessing & feature extraction. This research project also
+aims to design and implement a re-usable document regression pipeline as a
+framework, Rapid-Rate, that can be used to predict document scores in
+real-time.
+"
+24017,Brück Conjecture with hyper-order less than one,"  In this paper we affirm Brück conjecture provided $f$ is of hyper-order
+less than one by studying the infinite hyper-order of solutions of a complex
+differential equation.
+"
+24018,Scalable Spectrum Allocation for Large Networks Based on Sparse Optimization,"  Joint allocation of spectrum and user association is considered for a large
+cellular network. The objective is to optimize a network utility function such
+as average delay given traffic statistics collected over a slow timescale. A
+key challenge is scalability: given $n$ Access Points (APs), there are $O(2^n)$
+ways in which the APs can share the spectrum. The number of variables is
+reduced from $O(2^n)$ to $O(nk)$, where $k$ is the number of users, by
+optimizing over local overlapping neighborhoods, defined by interference
+conditions, and by exploiting the existence of sparse solutions in which the
+spectrum is divided into $k+1$ segments. We reformulate the problem by
+optimizing the assignment of subsets of active APs to those segments. An
+$\ell_0$ constraint enforces a one-to-one mapping of subsets to spectrum, and
+an iterative (reweighted $\ell_1$) algorithm is used to find an approximate
+solution. Numerical results for a network with 100 APs serving several hundred
+users show the proposed method achieves a substantial increase in total
+throughput relative to benchmark schemes.
+"
+24019,Trading Cointegrated Assets with Price Impact,"  Executing a basket of co-integrated assets is an important task facing
+investors. Here, we show how to do this accounting for the informational
+advantage gained from assets within and outside the basket, as well as for the
+permanent price impact of market orders (MOs) from all market participants, and
+the temporary impact that the agent's MOs have on prices. The execution problem
+is posed as an optimal stochastic control problem and we demonstrate that,
+under some mild conditions, the value function admits a closed-form solution,
+and prove a verification theorem. Furthermore, we use data of five stocks
+traded in the Nasdaq exchange to estimate the model parameters and use
+simulations to illustrate the performance of the strategy. As an example, the
+agent liquidates a portfolio consisting of shares in Intel Corporation (INTC)
+and Market Vectors Semiconductor ETF (SMH). We show that including the
+information provided by three additional assets, FARO Technologies (FARO),
+NetApp (NTAP) and Oracle Corporation (ORCL), considerably improves the
+strategy's performance; for the portfolio we execute, it outperforms the
+multi-asset version of Almgren-Chriss by approximately 4 to 4.5 basis points.
+"
+24020,The 4-Dimensional Light Bulb Theorem,"  For embedded 2-spheres in a 4-manifold sharing the same embedded transverse
+sphere homotopy implies isotopy, provided the ambient 4-manifold has no
+$\BZ_2$-torsion in the fundamental group. This gives a generalization of the
+classical light bulb trick to 4-dimensions, the uniqueness of spanning discs
+for a simple closed curve in $S^4$ and $\pi_0(\Diff_0(S^2\times
+D^2)/\Diff_0(B^4))=1$. In manifolds with $\BZ_2$-torsion, one surface can be
+put into a normal form relative to the other.
+"
+24021,pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking for Bioinformatics Applications,"  Motivation: A major challenge in the development of machine learning based
+methods in computational biology is that data may not be accurately labeled due
+to the time and resources required for experimentally annotating properties of
+proteins and DNA sequences. Standard supervised learning algorithms assume
+accurate instance-level labeling of training data. Multiple instance learning
+is a paradigm for handling such labeling ambiguities. However, the widely used
+large-margin classification methods for multiple instance learning are
+heuristic in nature with high computational requirements. In this paper, we
+present stochastic sub-gradient optimization large margin algorithms for
+multiple instance classification and ranking, and provide them in a software
+suite called pyLEMMINGS.
+Results: We have tested pyLEMMINGS on a number of bioinformatics problems as
+well as benchmark datasets. pyLEMMINGS has successfully been able to identify
+functionally important segments of proteins: binding sites in Calmodulin
+binding proteins, prion forming regions, and amyloid cores. pyLEMMINGS achieves
+state-of-the-art performance in all these tasks, demonstrating the value of
+multiple instance learning. Furthermore, our method has shown more than
+100-fold improvement in terms of running time as compared to heuristic
+solutions with improved accuracy over benchmark datasets.
+Availability and Implementation: pyLEMMINGS python package is available for
+download at: this http URL.
+"
+24022,Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks,"  In this paper, a sequential probing method for interference constraint
+learning is proposed to allow a centralized Cognitive Radio Network (CRN)
+accessing the frequency band of a Primary User (PU) in an underlay cognitive
+scenario with a designed PU protection specification. The main idea is that the
+CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire
+the binary ACK/NACK packet. This feedback indicates whether the probing-induced
+interference is harmful or not and can be used to learn the PU interference
+constraint. The cognitive part of this sequential probing process is the
+selection of the power levels of the Secondary Users (SUs) which aims to learn
+the PU interference constraint with a minimum number of probing attempts while
+setting a limit on the number of harmful probing-induced interference events or
+equivalently of NACK packet observations over a time window. This constrained
+design problem is studied within the Active Learning (AL) framework and an
+optimal solution is derived and implemented with a sophisticated, accurate and
+fast Bayesian Learning method, the Expectation Propagation (EP). The
+performance of this solution is also demonstrated through numerical simulations
+and compared with modified versions of AL techniques we developed in earlier
+work.
+"
+24023,Magnetic fluctuations and spin-spirals in single-layer FeSe,"  The magnetic properties of monolayer FeSe films are investigated via
+first-principles spin-spiral calculations. Although the (pi,pi) collinear
+antiferromagnetic (CL-AFM) mode is lowest in energy, the spin-wave energy E(q)
+- which exhibits intrinsic non-Heisenberg behavior - is found to be extremely
+very flat over a large region of the two-dimensional Brillouin zone centered at
+the checkerboard antiferromagnetic (CB-AFM) q=0 configuration, giving rise to a
+sharp peak in the spin density of states. Considering the paramagnetic state as
+an incoherent average over spin-spiral states, we find that resulting
+electronic band states around the Fermi level closely resemble the bands of the
+CB-AFM configuration - not the CL-AFM one - and thus providing a natural
+explanation of the angle-resolved photoemission observations. The presence of
+the SrTiO3(001) substrate, both with and without interfacial oxygen vacancies,
+is found to reduce the energy difference between the CB-AFM and CL-AFM states
+and hence enhancing the CB-AFM-like fluctuations.
+"
+24024,Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks,"  Missing data is a ubiquitous problem. It is especially challenging in medical
+settings because many streams of measurements are collected at different - and
+often irregular - times. Accurate estimation of those missing measurements is
+critical for many reasons, including diagnosis, prognosis and treatment.
+Existing methods address this estimation problem by interpolating within data
+streams or imputing across data streams (both of which ignore important
+information) or ignoring the temporal aspect of the data and imposing strong
+assumptions about the nature of the data-generating process and/or the pattern
+of missing data (both of which are especially problematic for medical data). We
+propose a new approach, based on a novel deep learning architecture that we
+call a Multi-directional Recurrent Neural Network (M-RNN) that interpolates
+within data streams and imputes across data streams. We demonstrate the power
+of our approach by applying it to five real-world medical datasets. We show
+that it provides dramatically improved estimation of missing measurements in
+comparison to 11 state-of-the-art benchmarks (including Spline and Cubic
+Interpolations, MICE, MissForest, matrix completion and several RNN methods);
+typical improvements in Root Mean Square Error are between 35% - 50%.
+Additional experiments based on the same five datasets demonstrate that the
+improvements provided by our method are extremely robust.
+"
+24025,Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization,"  The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a popular
+method to deal with nonconvex and/or stochastic optimization problems when the
+gradient information is not available. Being based on the CMA-ES, the recently
+proposed Matrix Adaptation Evolution Strategy (MA-ES) provides a rather
+surprising result that the covariance matrix and all associated operations
+(e.g., potentially unstable eigendecomposition) can be replaced in the CMA-ES
+by a updated transformation matrix without any loss of performance. In order to
+further simplify MA-ES and reduce its $\mathcal{O}\big(n^2\big)$ time and
+storage complexity to $\mathcal{O}\big(n\log(n)\big)$, we present the
+Limited-Memory Matrix Adaptation Evolution Strategy (LM-MA-ES) for efficient
+zeroth order large-scale optimization. The algorithm demonstrates
+state-of-the-art performance on a set of established large-scale benchmarks. We
+explore the algorithm on the problem of generating adversarial inputs for a
+(non-smooth) random forest classifier, demonstrating a surprising vulnerability
+of the classifier.
+"
+24026,"iWinRNFL: A Simple, Interpretable & Well-Calibrated In-Game Win Probability Model for NFL","  During the last few sports seasons a lot of discussion has been generated for
+the several, high-profile, ""comebacks"" that were observed in almost all sports.
+The Cavaliers won the championship after being down 3-1 in the 2016 NBA finals'
+series against the Golden State Warriors, which was exactly the case for
+Chicago Cubs and the World Series. The Patriots won the Super Bowl in 2016 even
+though they were trailing by 25 points late in the third quarter, while FC
+Barcelona in the top-16 round of the 2016-17 Champions League scored 3 goals
+during the last 7 minutes of the game (including stoppage time) against PSG to
+advance in the tournament. This has brought the robustness and accuracy of the
+various probabilistic prediction models under high scrutiny. Many of these
+models are proprietary, which makes it hard to evaluate. In this paper, we
+build a simple and open, yet robust and well-calibrated, in-game probability
+model for predicting the winner in an NFL (iWinRNFL) game. In particular, we
+build a logistic regression model that utilizes a set of 10 variables to
+predict the running win probability for the home team. We train our model using
+detailed play-by-play data from the last 7 NFL seasons obtained through the
+league's API. Our results indicate that in 75% of the cases iWinRNFL provides
+an accurate winner projection, as compared to a 63% accuracy of a baseline
+pre-game win probability model. Most importantly the probabilities that
+iWinRNFL provides are well-calibrated. Finally, we have also evaluated more
+complex, non-linear, models using the same set of features, without any
+significant improvement in performance.
+"
+24027,Elastic-net regularized High-dimensional Negative Binomial Regression: Consistency and Weak Signals Detection,"  We study sparse high-dimensional negative binomial regression problem for
+count data regression by showing non-asymptotic merits of the Elastic-net
+regularized estimator. With the KKT conditions, we derive two types of
+non-asymptotic oracle inequalities for the elastic net estimates of negative
+binomial regression by utilizing Compatibility factor and Stabil Condition,
+respectively. Based on oracle inequalities we proposed, we firstly show the
+sign consistency property of the Elastic-net estimators provided that the
+non-zero components in sparse true vector are large than a proper choice of the
+weakest signal detection threshold, and the second application is that we give
+an oracle inequality for bounding the grouping effect with high probability,
+thirdly, under some assumptions of design matrix, we can recover the true
+variable set with high probability if the weakest signal detection threshold is
+large than 3 times the value of turning parameter, at last, we briefly discuss
+the de-biased Elastic-net estimator.
+"
+24028,NIMFA: A Python Library for Nonnegative Matrix Factorization,"  NIMFA is an open-source Python library that provides a unified interface to
+nonnegative matrix factorization algorithms. It includes implementations of
+state-of-the-art factorization methods, initialization approaches, and quality
+scoring. It supports both dense and sparse matrix representation. NIMFA's
+component-based implementation and hierarchical design should help the users to
+employ already implemented techniques or design and code new strategies for
+matrix factorization tasks.
+"
+24029,A microwave plasma source for VUV atmospheric photochemistry,"  Microwave plasma discharges working at low pressure are nowadays a
+well-developed technique mainly used to provide radiations at different
+wavelengths. The aim of this work is to show that those discharges are an
+efficient windowless VUV photon source for planetary atmospheric photochemistry
+experiments. To do this, we use a surfatron-type discharge with a neon gas flow
+in the mbar pressure range coupled to a photochemical reactor. Working in the
+VUV range allows to focus on nitrogen-dominated atmospheres ({\lambda}<100nm).
+The experimental setup makes sure that no other energy sources (electrons,
+metastable atoms) than the VUV photons interact with the reactive medium. Neon
+owns two resonance lines at 73.6 and 74.3 nm which behave differently regarding
+the pressure or power conditions. In parallel, the VUV photon flux emitted at
+73.6 nm has been experimentally estimated in different conditions of pressure
+and power and varies in a large range between 2x1013 ph.s-1.cm-2 and 4x1014
+ph.s-1.cm-2 which is comparable to a VUV synchrotron photon flux. Our first
+case study is the atmosphere of Titan and its N2-CH4 atmosphere. With this VUV
+source, the production of HCN and C2N2, two major Titan compounds, is detected,
+ensuring the suitability of the source for atmospheric photochemistry
+experiments.
+"
+24030,E-theory Spectra for graded C*-algebras,"  This paper brings together C*-algebras and algebraic topology in terms of
+viewing a C*-algebraic invariant in terms of a topological spectrum. E-theory,
+E(A,B), is a bivariant functor in the sense that is a cohomology functor in the
+first variable and a homology functor in the second variable but underlying
+goes from the category of separable C*-algebras and *-homomorphisms to the
+category of abelian groups and group homomorphisms. Here we create a
+generalisation of a orthogonal spectrum to quasi-topological spaces for
+E-theory. This includes a rich product structure in the context of graded
+separable C*-algebras.
+"
+24031,A pre-semantics for counterfactual conditionals and similar logics,"  The elegant Stalnaker/Lewis semantics for counterfactual conditonals works
+with distances between models. But human beings certainly have no tables of
+models and distances in their head. We begin here an investigation using a more
+realistic picture, based on findings in neuroscience. We call it a
+pre-semantics, as its meaning is not a description of the world, but of the
+brain, whose structure is (partly) determined by the world it reasons about. In
+the final section, we reconsider the components, and postulate that there are
+no atomic pictures, we can always look inside.
+"
+24032,General rogue waves in the nonlocal PT-symmetric nonlinear Schrodinger equation,"  Rogue waves in the nonlocal PT-symmetric nonlinear Schrodinger (NLS) equation
+are studied by Darboux transformation. Three types of rogue waves are derived,
+and their explicit expressions in terms of Schur polynomials are presented.
+These rogue waves show a much wider variety than those in the local NLS
+equation. For instance, the polynomial degrees of their denominators can be not
+only $n(n+1)$, but also $n(n-1)+1$ and $n^2$, where $n$ is an arbitrary
+positive integer. Dynamics of these rogue waves is also examined. It is shown
+that these rogue waves can be bounded for all space and time, or develop
+collapsing singularities, depending on their types as well as values of their
+free parameters. In addition, the solution dynamics exhibits rich patterns,
+most of which have no counterparts in the local NLS equation.
+"
+24033,Visual Translation Embedding Network for Visual Relation Detection,"  Visual relations, such as ""person ride bike"" and ""bike next to car"", offer a
+comprehensive scene understanding of an image, and have already shown their
+great utility in connecting computer vision and natural language. However, due
+to the challenging combinatorial complexity of modeling
+subject-predicate-object relation triplets, very little work has been done to
+localize and predict visual relations. Inspired by the recent advances in
+relational representation learning of knowledge bases and convolutional object
+detection networks, we propose a Visual Translation Embedding network (VTransE)
+for visual relation detection. VTransE places objects in a low-dimensional
+relation space where a relation can be modeled as a simple vector translation,
+i.e., subject + predicate $\approx$ object. We propose a novel feature
+extraction layer that enables object-relation knowledge transfer in a
+fully-convolutional fashion that supports training and inference in a single
+forward/backward pass. To the best of our knowledge, VTransE is the first
+end-to-end relation detection network. We demonstrate the effectiveness of
+VTransE over other state-of-the-art methods on two large-scale datasets: Visual
+Relationship and Visual Genome. Note that even though VTransE is a purely
+visual model, it is still competitive to the Lu's multi-modal model with
+language priors.
+"
+24034,A General Framework for Enhancing Sparsity of Generalized Polynomial Chaos Expansions,"  Compressive sensing has become a powerful addition to uncertainty
+quantification when only limited data is available. In this paper we provide a
+general framework to enhance the sparsity of the representation of uncertainty
+in the form of generalized polynomial chaos expansion. We use alternating
+direction method to identify new sets of random variables through iterative
+rotations such that the new representation of the uncertainty is sparser.
+Consequently, we increases both the efficiency and accuracy of the compressive
+sensing-based uncertainty quantification method. We demonstrate that the
+previously developed iterative method to enhance the sparsity of Hermite
+polynomial expansion is a special case of this general framework. Moreover, we
+use Legendre and Chebyshev polynomials expansions to demonstrate the
+effectiveness of this method with applications in solving stochastic partial
+differential equations and high-dimensional (O(100)) problems.
+"
+24035,Bayesian Conditional Generative Adverserial Networks,"  Traditional GANs use a deterministic generator function (typically a neural
+network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that
+the discriminator seeks to distinguish. We propose a new GAN called Bayesian
+Conditional Generative Adversarial Networks (BC-GANs) that use a random
+generator function to transform a deterministic input $y'$ to a sample
+$\mathbf{x}$. Our BC-GANs extend traditional GANs to a Bayesian framework, and
+naturally handle unsupervised learning, supervised learning, and
+semi-supervised learning problems. Experiments show that the proposed BC-GANs
+outperforms the state-of-the-arts.
+"
+24036,Two remarks on Wall's D2 problem,"  If a finite group $G$ is isomorphic to a subgroup of $SO(3)$, then $G$ has
+the D2-property. Let $X$ be a finite complex satisfying Wall's D2-conditions.
+If $\pi_1(X)=G$ is finite, and $\chi(X) \geq 1-Def(G)$, then $X \vee S^2$ is
+simple homotopy equivalent to a finite $2$-complex, whose simple homotopy type
+depends only on $G$ and $\chi(X)$.
+"
+24037,MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs,"  We introduce MURA, a large dataset of musculoskeletal radiographs containing
+40,561 images from 14,863 studies, where each study is manually labeled by
+radiologists as either normal or abnormal. To evaluate models robustly and to
+get an estimate of radiologist performance, we collect additional labels from
+six board-certified Stanford radiologists on the test set, consisting of 207
+musculoskeletal studies. On this test set, the majority vote of a group of
+three radiologists serves as gold standard. We train a 169-layer DenseNet
+baseline model to detect and localize abnormalities. Our model achieves an
+AUROC of 0.929, with an operating point of 0.815 sensitivity and 0.887
+specificity. We compare our model and radiologists on the Cohen's kappa
+statistic, which expresses the agreement of our model and of each radiologist
+with the gold standard. Model performance is comparable to the best radiologist
+performance in detecting abnormalities on finger and wrist studies. However,
+model performance is lower than best radiologist performance in detecting
+abnormalities on elbow, forearm, hand, humerus, and shoulder studies. We
+believe that the task is a good challenge for future research. To encourage
+advances, we have made our dataset freely available at
+this https URL .
+"
+24038,Simultaneous Confidence Bands for Functional Data Using the Gaussian Kinematic Formula,"  This article constructs simultaneous confidence bands (SCBs) for functional
+parameters using the Gaussian Kinematic formula of $t$-processes (tGKF).
+Although the tGKF relies on Gaussianity, we show that a central limit theorem
+(CLT) for the parameter of interest is enough to obtain asymptotically precise
+covering rates even for non-Gaussian processes. As a proof of concept we study
+the functional signal-plus-noise model and derive a CLT for an estimator of the
+Lipschitz-Killing curvatures, the only data dependent quantities in the tGKF
+SCBs. Extensions to discrete sampling with additive observation noise are
+discussed using scale space ideas from regression analysis. Here we provide
+sufficient conditions on the processes and kernels to obtain convergence of the
+functional scale space surface.
+The theoretical work is accompanied by a simulation study comparing different
+methods to construct SCBs for the population mean. We show that the tGKF works
+well even for small sample sizes and only a Rademacher multiplier-$t$ bootstrap
+performs similarily well. For larger sample sizes the tGKF often outperforms
+the bootstrap methods and is computational faster. We apply the method to
+diffusion tensor imaging (DTI) fibers using a scale space approach for the
+difference of population means. R code is available in our Rpackage SCBfda.
+"
+24039,Unique Information via Dependency Constraints,"  The partial information decomposition (PID) is perhaps the leading proposal
+for resolving information shared between a set of sources and a target into
+redundant, synergistic, and unique constituents. Unfortunately, the PID
+framework has been hindered by a lack of a generally agreed-upon, multivariate
+method of quantifying the constituents. Here, we take a step toward rectifying
+this by developing a decomposition based on a new method that quantifies unique
+information. We first develop a broadly applicable method---the dependency
+decomposition---that delineates how statistical dependencies influence the
+structure of a joint distribution. The dependency decomposition then allows us
+to define a measure of the information about a target that can be uniquely
+attributed to a particular source as the least amount which the source-target
+statistical dependency can influence the information shared between the sources
+and the target. The result is the first measure that satisfies the core axioms
+of the PID framework while not satisfying the Blackwell relation, which depends
+on a particular interpretation of how the variables are related. This makes a
+key step forward to a practical PID.
+"
+24040,Hydrodynamic limit of the Boltzmann-Monge-Ampere system,"  In this paper we investigate the hydrodynamic limit of the
+Boltzmann-Monge-Ampere system in the so-called quasineutral regime. We prove
+the convergence of the Boltzmann-Monge-Ampere system to the Euler equation by
+using the relative entropy method.
+"
+24041,Optimal experimental design that minimizes the width of simultaneous confidence bands,"  We propose an optimal experimental design for a curvilinear regression model
+that minimizes the band-width of simultaneous confidence bands. Simultaneous
+confidence bands for curvilinear regression are constructed by evaluating the
+volume of a tube about a curve that is defined as a trajectory of a regression
+basis vector (Naiman, 1986). The proposed criterion is constructed based on the
+volume of a tube, and the corresponding optimal design that minimizes the
+volume of tube is referred to as the tube-volume optimal (TV-optimal) design.
+For Fourier and weighted polynomial regressions, the problem is formalized as
+one of minimization over the cone of Hankel positive definite matrices, and the
+criterion to minimize is expressed as an elliptic integral. We show that the
+Möbius group keeps our problem invariant, and hence, minimization can be
+conducted over cross-sections of orbits. We demonstrate that for the weighted
+polynomial regression and the Fourier regression with three bases, the
+tube-volume optimal design forms an orbit of the Möbius group containing
+D-optimal designs as representative elements.
+"
+24042,Excuse me! Perception of Abrupt Direction Changes Using Body Cues and Paths on Mixed Reality Avatars,"  We evaluate two methods of signalling abrupt direction changes of a robotic
+platform using a Mixed Reality avatar. The ""Body"" method uses gaze, gesture and
+torso direction to point to upcoming waypoints. The ""Path"" method visualises
+the change in direction using an angled path on the ground. We compare these
+two methods using a controlled user study and show that each method has its
+strengths depending on the situation. Overall the ""Path"" method was slightly
+more accurate in communicating the direction change of the robot but
+participants overall preferred the ""Body"" method.
+"
+24043,Lens rigidity in scattering by non-trapping obstacles,"  We prove that if two non-trapping obstacles in $\mathbb{R}^n$ satisfy some
+rather weak non-degeneracy conditions and the scattering rays in their
+exteriors have (almost) the same travelling times or (almost) the same
+scattering length spectrum, then they coincide.
+"
+24044,Direct Monocular Odometry Using Points and Lines,"  Most visual odometry algorithm for a monocular camera focuses on points,
+either by feature matching, or direct alignment of pixel intensity, while
+ignoring a common but important geometry entity: edges. In this paper, we
+propose an odometry algorithm that combines points and edges to benefit from
+the advantages of both direct and feature based methods. It works better in
+texture-less environments and is also more robust to lighting changes and fast
+motion by increasing the convergence basin. We maintain a depth map for the
+keyframe then in the tracking part, the camera pose is recovered by minimizing
+both the photometric error and geometric error to the matched edge in a
+probabilistic framework. In the mapping part, edge is used to speed up and
+increase stereo matching accuracy. On various public datasets, our algorithm
+achieves better or comparable performance than state-of-the-art monocular
+odometry methods. In some challenging texture-less environments, our algorithm
+reduces the state estimation error over 50%.
+"
+24045,On the applicability of logarithmic tensor category theory,"  We give results and observations which allow the application of the
+logarithmic tensor category theory of Lepowsky, Zhang and the author
+([HLZ1]--[HLZ9]) to more general vertex (operator) algebras and their module
+categories than those studied in a paper by the author ([H3]).
+"
+24046,Conditioned limit theorems for products of positive random matrices,"  Inspired by a recent paper of I. Grama, E. Le Page and M. Peigné, we
+consider a sequence $(g_n)_{n \geq 1}$ of i.i.d. random $d\times d$-matrices
+with non-negative entries and study the fluctuations of the process $(\log
+\vert g_n\cdots g_1\cdot x\vert )_{n \geq 1}$ for any non-zero vector $x$ in
+$\mathbb R^d$ with non-negative coordinates. Our method involves approximating
+this process by a martingale and studying harmonic functions for its
+restriction to the upper half line. Under certain conditions, the probability
+for this process to stay in the upper half real line up to time $n$ decreases
+as $c \over \sqrt n$ for some positive constant $c$.
+"
+24047,Bayesian Decision Making in Groups is Hard,"  We address the computations that Bayesian agents in a network undertake in an
+opinion exchange model, where they repeatedly act on private information,
+taking myopic actions maximizing expected utility according to a fully rational
+posterior. We show that such computations are NP-hard for two natural utility
+functions, including the case where agents reveal their posteriors. Our results
+are robust in the sense that they show NP-hardness of distinguishing (and
+therefore also approximating) between posteriors that are concentrated on two
+distinct states of the world. We also describe a natural search algorithm that
+computes agents' actions, which we call iterated elimination of infeasible
+signals (IEIS), and show that if the network is transitive, the algorithm can
+be modified to run in polynomial-time.
+"
+24048,SiD Status and Plans,"  Recent work carried out in the SiD Consortium is reported. Results have been
+obtained with the Chronopix version 3 chip, intended for the SiD vertex
+detector. A test structure is being produced for KPiXM, a CMOS MAPS approach
+that could be used for the tracker and electromagnetic calorimeter. Tracker
+sensors are being tested and a new prototype sensor is being planned. A new
+effort on tracker support structures has begun and first prototypes produced.
+New studies of the pair background envelope in the beam pipe have been carried
+out. Electromagnetic calorimeter testbeam data has been analysed and plans are
+being developed for a full stack prototype. New comprehensive studies of
+high-rate backgrounds and muons from the beam delivery system have been carried
+out to determine aspects of the forward region layout. Work has started on
+alignment and calibration strategies; and progress has been made on a new
+simulation and reconstruction framework for SiD.
+"
+24049,Landmark Diffusion Maps (L-dMaps): Accelerated manifold learning out-of-sample extension,"  Diffusion maps are a nonlinear manifold learning technique based on harmonic
+analysis of a diffusion process over the data. Out-of-sample extensions with
+computational complexity $\mathcal{O}(N)$, where $N$ is the number of points
+comprising the manifold, frustrate applications to online learning applications
+requiring rapid embedding of high-dimensional data streams. We propose landmark
+diffusion maps (L-dMaps) to reduce the complexity to $\mathcal{O}(M)$, where $M
+\ll N$ is the number of landmark points selected using pruned spanning trees or
+k-medoids. Offering $(N/M)$ speedups in out-of-sample extension, L-dMaps
+enables the application of diffusion maps to high-volume and/or high-velocity
+streaming data. We illustrate our approach on three datasets: the Swiss roll,
+molecular simulations of a C$_{24}$H$_{50}$ polymer chain, and biomolecular
+simulations of alanine dipeptide. We demonstrate up to 50-fold speedups in
+out-of-sample extension for the molecular systems with less than 4% errors in
+manifold reconstruction fidelity relative to calculations over the full
+dataset.
+"
+24050,Experiments on Crowdsourcing Policy Assessment,"  Can Crowds serve as useful allies in policy design? How do non-expert Crowds
+perform relative to experts in the assessment of policy measures? Does the
+geographic location of non-expert Crowds, with relevance to the policy context,
+alter the performance of non-experts Crowds in the assessment of policy
+measures? In this work, we investigate these questions by undertaking
+experiments designed to replicate expert policy assessments with non-expert
+Crowds recruited from Virtual Labor Markets. We use a set of ninety-six climate
+change adaptation policy measures previously evaluated by experts in the
+Netherlands as our control condition to conduct experiments using two discrete
+sets of non-expert Crowds recruited from Virtual Labor Markets. We vary the
+composition of our non-expert Crowds along two conditions: participants
+recruited from a geographical location directly relevant to the policy context
+and participants recruited at-large. We discuss our research methods in detail
+and provide the findings of our experiments.
+"
+24051,Credimus,"  We believe that economic design and computational complexity---while already
+important to each other---should become even more important to each other with
+each passing year. But for that to happen, experts in on the one hand such
+areas as social choice, economics, and political science and on the other hand
+computational complexity will have to better understand each other's
+worldviews.
+This article, written by two complexity theorists who also work in
+computational social choice theory, focuses on one direction of that process by
+presenting a brief overview of how most computational complexity theorists view
+the world. Although our immediate motivation is to make the lens through which
+complexity theorists see the world be better understood by those in the social
+sciences, we also feel that even within computer science it is very important
+for nontheoreticians to understand how theoreticians think, just as it is
+equally important within computer science for theoreticians to understand how
+nontheoreticians think.
+"
+24052,First Detection of Mid-Infrared Variability from an Ultraluminous X-Ray Source Holmberg II X-1,"  We present mid-infrared (IR) light curves of the Ultraluminous X-ray Source
+(ULX) Holmberg II X-1 from observations taken between 2014 January 13 and 2017
+January 5 with the \textit{Spitzer Space Telescope} at 3.6 and 4.5 $\mu$m in
+the \textit{Spitzer} Infrared Intensive Transients Survey (SPIRITS). The mid-IR
+light curves, which reveal the first detection of mid-IR variability from a
+ULX, is determined to arise primarily from dust emission rather than from a jet
+or an accretion disk outflow. We derived the evolution of the dust temperature
+($T_\mathrm{d}\sim600 - 800$ K), IR luminosity ($L_\mathrm{IR}\sim3\times10^4$
+$\mathrm{L}_\odot$), mass ($M_\mathrm{d}\sim1-3\times10^{-6}$
+$\mathrm{M}_\odot$), and equilibrium temperature radius
+($R_\mathrm{eq}\sim10-20$ AU). A comparison of X-1 with a sample
+spectroscopically identified massive stars in the Large Magellanic Cloud on a
+mid-IR color-magnitude diagram suggests that the mass donor in X-1 is a
+supergiant (sg) B[e]-star. The sgB[e]-interpretation is consistent with the
+derived dust properties and the presence of the [Fe II] ($\lambda=1.644$
+$\mu$m) emission line revealed from previous near-IR studies of X-1. We
+attribute the mid-IR variability of X-1 to increased heating of dust located in
+a circumbinary torus. It is unclear what physical processes are responsible for
+the increased dust heating; however, it does not appear to be associated with
+the X-ray flux from the ULX given the constant X-ray luminosities provided by
+serendipitous, near-contemporaneous X-ray observations around the first mid-IR
+variability event in 2014. Our results highlight the importance of mid-IR
+observations of luminous X-ray sources traditionally studied at X-ray and radio
+wavelengths.
+"
+24053,The Loewner Equation for Multiple Hulls,"  Kager, Nienhuis, and Kadanoff conjectured that the hull generated from the
+Loewner equation driven by two constant functions with constant weights could
+be generated by a single rapidly and randomly oscillating function. We prove
+their conjecture and generalize to multiple continuous driving functions. In
+the process, we generalize to multiple hulls a result of Roth and Schleissinger
+that says multiple slits can be generated by constant weight functions. The
+proof gives a simulation method for hulls generated by the multiple Loewner
+equation.
+"
+24054,Cavity QED with hybrid nanocircuits: from atomic-like physics to condensed matter phenomena,"  Circuit QED techniques have been instrumental to manipulate and probe with
+exquisite sensitivity the quantum state of superconducting quantum bits coupled
+to microwave cavities. Recently, it has become possible to fabricate new
+devices where the superconducting quantum bits are replaced by hybrid
+mesoscopic circuits combining nanoconductors and metallic reservoirs. This
+mesoscopic QED provides a new experimental playground to study the light-matter
+interaction in electronic circuits. Here, we present the experimental state of
+the art of Mesoscopic QED and its theoretical description. A first class of
+experiments focuses on the artificial atom limit, where some quasiparticles are
+trapped in nanocircuit bound states. In this limit, the Circuit QED techniques
+can be used to manipulate and probe electronic degrees of freedom such as
+confined charges, spins, or Andreev pairs. A second class of experiments
+consists in using cavity photons to reveal the dynamics of electron tunneling
+between a nanoconductor and fermionic reservoirs. For instance, the Kondo
+effect, the charge relaxation caused by grounded metallic contacts, and the
+photo-emission caused by voltage-biased reservoirs have been studied. The
+tunnel coupling between nanoconductors and fermionic reservoirs also enable one
+to obtain split Cooper pairs, or Majorana bound states. Cavity photons
+represent a qualitatively new tool to study these exotic condensed matter
+states.
+"
+24055,Bounds for totally separable translative packings in the plane,"  A packing of translates of a convex domain in the Euclidean plane is said to
+be totally separable if any two packing elements can be separated by a line
+disjoint from the interior of every packing element. This notion was introduced
+by G. Fejes Tóth and L. Fejes Tóth (1973) and has attracted significant
+attention. In this paper we prove an analogue of Oler's inequality for totally
+separable translative packings of convex domains and then we derive from it
+some new results. This includes finding the largest density of totally
+separable translative packings of an arbitrary convex domain and finding the
+smallest area convex hull of totally separable packings (resp., totally
+separable soft packings) generated by given number of translates of a convex
+domain (resp., soft convex domain). Finally, we determine the largest covering
+ratio (that is, the largest fraction of the plane covered by the soft disks) of
+an arbitrary totally separable soft disk packing with given soft parameter.
+"
+24056,Differential imprints of distinct ENSO flavors in global extreme precipitation patterns,"  The specific impacts of El Niño's two flavors, East Pacific (EP) and
+Central Pacific (CP) El Niño, have been studied intensively in recent years,
+mostly by applying linear statistical or composite analyses. These techniques,
+however, focus on average spatio-temporal patterns of climate variability and
+do not allow for a specific assessment of related extreme impacts. Here, we use
+event coincidence analysis to study the differential imprints of EP and CP
+types of both, El Niño and La Niña on global extreme precipitation
+patterns. We provide a detailed statistical assessment of seasonal
+precipitation extremes related to the two flavors of both phases of the El
+Niño Southern Oscillation (ENSO) in terms of their likelihood and spatial
+extent. Our analysis recaptures previously reported interrelations and uncovers
+further regional extremes arising along with different ENSO phases that have
+not been reported so far.
+"
+24057,Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing,"  Low-energy random number generation is critical for many emerging computing
+schemes proposed to complement or replace von Neumann architectures. However,
+current random number generators are always associated with an energy cost that
+is prohibitive for these computing schemes. In this paper, we introduce random
+number bit generation based on specific nanodevices: superparamagnetic tunnel
+junctions. We experimentally demonstrate high quality random bit generation
+that represents orders-of-magnitude improvements in energy efficiency compared
+to current solutions. We show that the random generation speed improves with
+nanodevice scaling, and investigate the impact of temperature, magnetic field
+and crosstalk. Finally, we show how alternative computing schemes can be
+implemented using superparamagentic tunnel junctions as random number
+generators. These results open the way for fabricating efficient hardware
+computing devices leveraging stochasticity, and highlight a novel use for
+emerging nanodevices.
+"
+24058,Harmonic-Percussive Source Separation with Deep Neural Networks and Phase Recovery,"  Harmonic/percussive source separation (HPSS) consists in separating the
+pitched instruments from the percussive parts in a music mixture. In this
+paper, we propose to apply the recently introduced Masker-Denoiser with twin
+networks (MaD TwinNet) system to this task. MaD TwinNet is a deep learning
+architecture that has reached state-of-the-art results in monaural singing
+voice separation. Herein, we propose to apply it to HPSS by using it to
+estimate the magnitude spectrogram of the percussive source. Then, we retrieve
+the complex-valued short-time Fourier transform of the sources by means of a
+phase recovery algorithm, which minimizes the reconstruction error and enforces
+the phase of the harmonic part to follow a sinusoidal phase model. Experiments
+conducted on realistic music mixtures show that this novel separation system
+outperforms the previous state-of-the art kernel additive model approach.
+"
+24059,Sound-Word2Vec: Learning Word Representations Grounded in Sounds,"  To be able to interact better with humans, it is crucial for machines to
+understand sound - a primary modality of human perception. Previous works have
+used sound to learn embeddings for improved generic textual similarity
+assessment. In this work, we treat sound as a first-class citizen, studying
+downstream textual tasks which require aural grounding. To this end, we propose
+sound-word2vec - a new embedding scheme that learns specialized word embeddings
+grounded in sounds. For example, we learn that two seemingly (semantically)
+unrelated concepts, like leaves and paper are similar due to the similar
+rustling sounds they make. Our embeddings prove useful in textual tasks
+requiring aural reasoning like text-based sound retrieval and discovering foley
+sound effects (used in movies). Moreover, our embedding space captures
+interesting dependencies between words and onomatopoeia and outperforms prior
+work on aurally-relevant word relatedness datasets such as AMEN and ASLex.
+"
+24060,Modification of Moment-Based Tail Index Estimator: Sums versus Maxima,"  In this paper we continue the investigation of the SRCEN estimator of the
+extreme value index $\gamma$ (or the tail index $\alpha=1/\gamma$) proposed in
+\cite{MCE} for $\gamma>1/2$. We propose a new estimator based on the local
+maximum. This, in fact, is a modification of the SRCEN estimator to the case
+$\gamma>0$. We establish the consistency and asymptotic normality of the newly
+proposed estimator for i.i.d. data. Also, a short discussion on the comparison
+of the estimators is included.
+"
+24061,The Temporal Event Graph,"  Temporal networks are increasingly being used to model the interactions of
+complex systems. Most studies require the temporal aggregation of edges (or
+events) into discrete time steps to perform analysis. In this article we
+describe a static, lossless, and unique representation of a temporal network,
+the temporal event graph (TEG). The TEG describes the temporal network in terms
+of both the inter-event time and two-event temporal motif distributions. By
+considering these distributions in unison we provide a new method to
+characterise the behaviour of individuals and collectives in temporal networks
+as well as providing a natural decomposition of the network. We illustrate the
+utility of the TEG by providing examples on both synthetic and real temporal
+networks.
+"
+24062,Implementation of radiating aperture field distribution using tensorial metasurfaces,"  This paper deals with the design of tensorial modulated metasurfaces able to
+implement a general radiating aperture field distribution. A new aperture
+synthesis approach is introduced, based on local holography and variable
+impedance modulation. In particular, it is shown that tensorial metasurfaces
+can be used to generate general radiating distribution (phase and amplitude).
+In addition, a step by step algorithm is presented. In order to validate the
+method, several solutions are presented at 20 GHz which implement aperture
+distributions able to radiate different beams with general polarization.
+"
+24063,Logistic Ensemble Models,"  Predictive models that are developed in a regulated industry or a regulated
+application, like determination of credit worthiness, must be interpretable and
+rational (e.g., meaningful improvements in basic credit behavior must result in
+improved credit worthiness scores). Machine Learning technologies provide very
+good performance with minimal analyst intervention, making them well suited to
+a high volume analytic environment, but the majority are black box tools that
+provide very limited insight or interpretability into key drivers of model
+performance or predicted model output values. This paper presents a methodology
+that blends one of the most popular predictive statistical modeling methods for
+binary classification with a core model enhancement strategy found in machine
+learning. The resulting prediction methodology provides solid performance, from
+minimal analyst effort, while providing the interpretability and rationality
+required in regulated industries, as well as in other environments where
+interpretation of model parameters is required (e.g. businesses that require
+interpretation of models, to take action on them).
+"
+24064,MAGIX: Model Agnostic Globally Interpretable Explanations,"  Explaining the behavior of a black box machine learning model at the instance
+level is useful for building trust. However, it is also important to understand
+how the model behaves globally. Such an understanding provides insight into
+both the data on which the model was trained and the patterns that it learned.
+We present here an approach that learns if-then rules to globally explain the
+behavior of black box machine learning models that have been used to solve
+classification problems. The approach works by first extracting conditions that
+were important at the instance level and then evolving rules through a genetic
+algorithm with an appropriate fitness function. Collectively, these rules
+represent the patterns followed by the model for decisioning and are useful for
+understanding its behavior. We demonstrate the validity and usefulness of the
+approach by interpreting black box models created using publicly available data
+sets as well as a private digital marketing data set.
+"
+24065,cellGPU: massively parallel simulations of dynamic vertex models,"  Vertex models represent confluent tissue by polygonal or polyhedral tilings
+of space, with the individual cell interacting via force laws that depend on
+both the geometry of the cells and the topology of the tessellation. This
+dependence on the connectivity of the cellular network introduces several
+complications to performing molecular-dynamics-like simulations of vertex
+models, and in particular makes parallelizing the simulations difficult.
+cellGPU addresses this difficulty and lays the foundation for massively
+parallelized, GPU-based simulations of these models. This article discusses its
+implementation for a pair of two-dimensional models, and compares the typical
+performance that can be expected between running cellGPU entirely on the CPU
+versus its performance when running on a range of commercial and server-grade
+graphics cards. By implementing the calculation of topological changes and
+forces on cells in a highly parallelizable fashion, cellGPU enables researchers
+to simulate time- and length-scales previously inaccessible via existing
+single-threaded CPU implementations.
+"
+24066,Fractional div-curl quantities and applications to nonlocal geometric equations,"  We investigate a fractional notion of gradient and divergence operator. We
+generalize the div-curl estimate by Coifman-Lions-Meyer-Semmes to fractional
+div-curl quantities, obtaining, in particular, a nonlocal version of Wente's
+lemma.
+We demonstrate how these quantities appear naturally in nonlocal geometric
+equations, which can be used to obtain a theory for fractional harmonic maps
+analogous to the local theory. Firstly, regarding fractional harmonic maps into
+spheres, we obtain a conservation law analogous to Shatah's conservation law
+and give a new regularity proof analogous to Hélein's for harmonic maps into
+spheres.
+Secondly, we prove regularity for solutions to critical systems with nonlocal
+antisymmetric potentials on the right-hand side. Since the half-harmonic map
+equation into general target manifolds has this form, as a corollary, we obtain
+a new proof of the regularity of half-harmonic maps into general target
+manifolds following closely Rivière's celebrated argument in the local
+case.
+Lastly, the fractional div-curl quantities provide also a new, simpler, proof
+for Hölder continuity of $W^{s,n/s}$-harmonic maps into spheres and we extend
+this to an argument for $W^{s,n/s}$-harmonic maps into homogeneous targets.
+This is an analogue of Strzelecki's and Toro-Wang's proof for $n$-harmonic maps
+into spheres and homogeneous target manifolds, respectively.
+"
+24067,Making Defeating CAPTCHAs Harder for Bots,"  For a number of years, many websites have used CAPTCHAs to filter out
+interactions by bots. However, attackers have found ways to circumvent CAPTCHAs
+by programming bots to solve or bypass them, or even relay them for humans to
+solve. In order to reduce the chances of success of such attacks, CAPTCHAs can
+be strengthened by the addition of certain safeguards. In this paper, we
+discuss seven existing safeguards as well as five novel safeguards designed to
+make circumventing CAPTCHAs harder. These safeguards are not mutually exclusive
+and can add multiple layers of protection to a CAPTCHA. We further provide a
+high-level comparison of their effectiveness in addressing the threat posed by
+CAPTCHA-defeating techniques. In order to focus on safeguards that are usable,
+we restrict our attention to those which have minimal adverse effect on the
+user experience.
+"
+24068,Online linear optimization with the log-determinant regularizer,"  We consider online linear optimization over symmetric positive semi-definite
+matrices, which has various applications including the online collaborative
+filtering. The problem is formulated as a repeated game between the algorithm
+and the adversary, where in each round t the algorithm and the adversary choose
+matrices X_t and L_t, respectively, and then the algorithm suffers a loss given
+by the Frobenius inner product of X_t and L_t. The goal of the algorithm is to
+minimize the cumulative loss. We can employ a standard framework called Follow
+the Regularized Leader (FTRL) for designing algorithms, where we need to choose
+an appropriate regularization function to obtain a good performance guarantee.
+We show that the log-determinant regularization works better than other popular
+regularization functions in the case where the loss matrices L_t are all
+sparse. Using this property, we show that our algorithm achieves an optimal
+performance guarantee for the online collaborative filtering. The technical
+contribution of the paper is to develop a new technique of deriving performance
+bounds by exploiting the property of strong convexity of the log-determinant
+with respect to the loss matrices, while in the previous analysis the strong
+convexity is defined with respect to a norm. Intuitively, skipping the norm
+analysis results in the improved bound. Moreover, we apply our method to online
+linear optimization over vectors and show that the FTRL with the Burg entropy
+regularizer, which is the analogue of the log-determinant regularizer in the
+vector case, works well.
+"
+24069,Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation,"  In statistical dialogue management, the dialogue manager learns a policy that
+maps a belief state to an action for the system to perform. Efficient
+exploration is key to successful policy optimisation. Current deep
+reinforcement learning methods are very promising but rely on epsilon-greedy
+exploration, thus subjecting the user to a random choice of action during
+learning. Alternative approaches such as Gaussian Process SARSA (GPSARSA)
+estimate uncertainties and are sample efficient, leading to better user
+experience, but on the expense of a greater computational complexity. This
+paper examines approaches to extract uncertainty estimates from deep Q-networks
+(DQN) in the context of dialogue management. We perform an extensive benchmark
+of deep Bayesian methods to extract uncertainty estimates, namely
+Bayes-By-Backprop, dropout, its concrete variation, bootstrapped ensemble and
+alpha-divergences, combining it with DQN algorithm.
+"
+24070,Opportunistic Communication in Extreme Wireless Sensor Networks,"  Sensor networks can nowadays deliver 99.9% of their data with duty cycles
+below 1%. This remarkable performance is, however, dependent on some important
+underlying assumptions: low traffic rates, medium size densities and static
+nodes. In this thesis, we investigate the performance of these same
+resource-constrained devices, but under scenarios that present extreme
+conditions: high traffic rates, high densities and mobility: the so-called
+Extreme Wireless Sensor Networks (EWSNs).
+"
+24071,Lifting of curves with automorphisms,"  The lifting problem for curves with automorphisms asks whether we can lift a
+smooth projective characteristic p curve with a group G of automorphisms to
+characteristic zero. This was solved by Grothendieck when G acts with
+prime-to-p stabilizers, and there has been much progress over the last few
+decades in the wild case. We survey the techniques and obstructions for this
+lifting problem, aiming at a reader whose background is limited to scheme
+theory at the level of Hartshorne's book. Throughout, we include numerous
+examples and clarifying remarks. We also provide a list of open questions.
+"
+24072,Energy surface and lifetime of magnetic skyrmions,"  The stability of skyrmions in various environments is estimated by analyzing
+the multidimensional surface describing the energy of the system as a function
+of the directions of the magnetic moments in the system. The energy is given by
+a Heisenberg-like Hamiltonian that includes Dzyaloshinskii-Moriya interaction,
+anisotropy and external magnetic field. Local minima on this surface correspond
+to the ferromagnetic and skyrmion states. Minimum energy paths (MEP) between
+the minima are calculated using the geodesic nudged elastic band method. The
+maximum energy along an MEP corresponds to a first order saddle point on the
+energy surface and gives an estimate of the activation energy for the magnetic
+transition, such as creation and annihilation of a skyrmion. The
+pre-exponential factor in the Arrhenius law for the rate, the so-called attempt
+frequency, is estimated within harmonic transition state theory where the
+eigenvalues of the Hessian at the saddle point and the local minima are used to
+characterize the shape of the energy surface. For some degrees of freedom,
+so-called 'zero modes', the energy of the system remains invariant. They need
+to be treated separately and give rise to temperature dependence of the attempt
+frequency. As an example application of this general theory, the lifetime of a
+skyrmion in a track of finite width for a PdFe overlayer on a Ir(111) substrate
+is calculated as a function of track width and external magnetic field. Also,
+the effect of non-magnetic impurities is studied. Various MEPs for annihilation
+inside a track, via the boundary of a track and at an impurity are presented.
+The attempt frequency as well as the activation energy has been calculated for
+each mechanism to estimate the transition rate as a function of temperature.
+"
+24073,"Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions","  Scientific code is not production software. Scientific code participates in
+the evaluation of a scientific hypothesis. This imposes specific constraints on
+the code that are often overlooked in practice. We articulate, with a small
+example, five characteristics that a scientific code in computational science
+should possess: re-runnable, repeatable, reproducible, reusable and replicable.
+"
+24074,Standard Embeddings of Smooth Schubert Varieties in Rational Homogeneous Manifolds of Picard Number 1,"  Smooth Schubert varieties in rational homogeneous manifolds of Picard number
+1 are horospherical varieties. We characterize standard embeddings of smooth
+Schubert varieties in rational homogeneous manifolds of Picard number 1 by
+means of varieties of minimal rational tangents. In particular, we mainly
+consider nonhomogeneous smooth Schubert varieties in symplectic Grassmannians
+and in the 20-dimensional $F_4$-homogeneous manifold associated to a short
+simple root.
+"
+24075,Relaxation Oscillations and Ultrafast Emission Pulses in a Disordered Expanding Polariton Condensate,"  Semiconductor microcavities are often influenced by structural imperfections,
+which can disturb the flow and dynamics of exciton-polariton condensates.
+Additionally, in exciton-polariton condensates there is a variety of dynamical
+scenarios and instabilities, owing to the properties of the incoherent
+excitonic reservoir. We investigate the dynamics of an exciton-polariton
+condensate which emerges in semiconductor microcavity subject to disorder,
+which determines its spatial and temporal behaviour. Our experimental data
+revealed complex burst-like time evolution under non-resonant optical pulsed
+excitation. The temporal patterns of the condensate emission result from the
+intrinsic disorder and are driven by properties of the excitonic reservoir,
+which decay in time much slower with respect to the polariton condensate
+lifetime. This feature entails a relaxation oscillation in polariton condensate
+formation, resulting in ultrafast emission pulses of coherent polariton field.
+The experimental data can be well reproduced by numerical simulations, where
+the condensate is coupled to the excitonic reservoir described by a set of rate
+equations. Theory suggests the existence of slow reservoir temporarily emptied
+by stimulated scattering to the condensate, generating ultrashort pulses of the
+condensate emission.
+"
+24076,Graphene Terahertz Absorption,"  The unique terahertz properties of graphene has been identified for novel
+optoelectronic applications. In a graphene sample with bias voltage added,
+there is an enhanced absorption in the far infrared region and a diminished
+absorption in the infrared region. The strength of enhancement(diminishment)
+increases with the gate voltage, and the enhancement compensates the
+diminishment. We find that it is the coherence length of electrons in graphene
+that allows pure electronic transitions between states differing by small
+momentums and makes intraband transition possible, is responsible for the far
+infrared enhancement. Phonon assisted processes are not necessary and would not
+in any case contribute to a sum rule. This naturally leads to results obeying
+the general sum-rule in optical absorptions. Our prediction of the strength of
+enhancement(diminishment) in terms of the bias agrees with experiments. This is
+the first direct calculation we are aware of, since the prior phonon assisted
+model for indirect transition should not obey a sum rule.
+"
+24077,Bayesian State Space Modeling of Physical Processes in Industrial Hygiene,"  Exposure assessment models are deterministic models derived from
+physical-chemical laws. In real workplace settings, chemical concentration
+measurements can be noisy and indirectly measured. In addition, inference on
+important parameters such as generation and ventilation rates are usually of
+interest since they are difficult to obtain. In this paper we outline a
+flexible Bayesian framework for parameter inference and exposure prediction. In
+particular, we propose using Bayesian state space models by discretizing the
+differential equation models and incorporating information from observed
+measurements and expert prior knowledge. At each time point, a new measurement
+is available that contains some noise, so using the physical model and the
+available measurements, we try to obtain a more accurate state estimate, which
+can be called filtering. We consider Monte Carlo sampling methods for parameter
+estimation and inference under nonlinear and non-Gaussian assumptions. The
+performance of the different methods is studied on computer-simulated and
+controlled laboratory-generated data. We consider some commonly used exposure
+models representing different physical hypotheses.
+"
+24078,Multi-objective Contextual Bandit Problem with Similarity Information,"  In this paper we propose the multi-objective contextual bandit problem with
+similarity information. This problem extends the classical contextual bandit
+problem with similarity information by introducing multiple and possibly
+conflicting objectives. Since the best arm in each objective can be different
+given the context, learning the best arm based on a single objective can
+jeopardize the rewards obtained from the other objectives. In order to evaluate
+the performance of the learner in this setup, we use a performance metric
+called the contextual Pareto regret. Essentially, the contextual Pareto regret
+is the sum of the distances of the arms chosen by the learner to the context
+dependent Pareto front. For this problem, we develop a new online learning
+algorithm called Pareto Contextual Zooming (PCZ), which exploits the idea of
+contextual zooming to learn the arms that are close to the Pareto front for
+each observed context by adaptively partitioning the joint context-arm set
+according to the observed rewards and locations of the context-arm pairs
+selected in the past. Then, we prove that PCZ achieves $\tilde O
+(T^{(1+d_p)/(2+d_p)})$ Pareto regret where $d_p$ is the Pareto zooming
+dimension that depends on the size of the set of near-optimal context-arm
+pairs. Moreover, we show that this regret bound is nearly optimal by providing
+an almost matching $\Omega (T^{(1+d_p)/(2+d_p)})$ lower bound.
+"
+24079,Kinematics of Subluminous O and B Stars by Surface Helium Abundance,"  The majority of hot subdwarf stars are low-mass core-helium-burning stars.
+Their atmospheres are generally helium deficient; however a minority have
+extremely helium-rich surfaces. An additional fraction have an intermediate
+surface-helium abundance, occasionally accompanied by peculiar abundances of
+other elements. We have identified a sample of 88 hot subdwarfs including 38
+helium-deficient, 27 intermediate-helium and 23 extreme-helium stars for which
+radial-velocity and proper-motion measurements, together with distances, allow
+a calculation of Galactic space velocities. We have investigated the kinematics
+of these three groups to determine whether they belong to similar or different
+galactic populations. The majority of helium-deficient subdwarfs in our sample
+show a kinematic distribution similar to that of thick disk stars. Helium-rich
+sdBs show a more diverse kinematic distribution. Although the majority are
+probably disk stars, a minority show a much higher velocity dispersion
+consistent with membership of a Galactic halo population. Several of the halo
+subd- warfs are members of the class of ""heavy-metal"" subdwarfs discovered by
+Naslim et al. (2011, 2013).
+"
+24080,R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting,"  Multivariate time-series modeling and forecasting is an important problem
+with numerous applications. Traditional approaches such as VAR (vector
+auto-regressive) models and more recent approaches such as RNNs (recurrent
+neural networks) are indispensable tools in modeling time-series data. In many
+multivariate time series modeling problems, there is usually a significant
+linear dependency component, for which VARs are suitable, and a nonlinear
+component, for which RNNs are suitable. Modeling such times series with only
+VAR or only RNNs can lead to poor predictive performance or complex models with
+large training times. In this work, we propose a hybrid model called R2N2
+(Residual RNN), which first models the time series with a simple linear model
+(like VAR) and then models its residual errors using RNNs. R2N2s can be trained
+using existing algorithms for VARs and RNNs. Through an extensive empirical
+evaluation on two real world datasets (aviation and climate domains), we show
+that R2N2 is competitive, usually better than VAR or RNN, used alone. We also
+show that R2N2 is faster to train as compared to an RNN, while requiring less
+number of hidden units.
+"
+24081,General Backpropagation Algorithm for Training Second-order Neural Networks,"  The artificial neural network is a popular framework in machine learning. To
+empower individual neurons, we recently suggested that the current type of
+neurons could be upgraded to 2nd order counterparts, in which the linear
+operation between inputs to a neuron and the associated weights is replaced
+with a nonlinear quadratic operation. A single 2nd order neurons already has a
+strong nonlinear modeling ability, such as implementing basic fuzzy logic
+operations. In this paper, we develop a general backpropagation (BP) algorithm
+to train the network consisting of 2nd-order neurons. The numerical studies are
+performed to verify of the generalized BP algorithm.
+"
+24082,"A comparison of the most massive quiescent galaxies from $z \sim 3$ to the present: slow evolution in size, and spheroid-dominated","  We use Hubble Space Telescope imaging to study the structural properties of
+ten of the most massive ($M \geq 10^{11.25}$ Msun) quiescent galaxies (QGs) in
+the UKIDSS UDS at $2.5<z<3.0$. The low spatial density of these galaxies
+required targeted WFC3 $H_{160}$ imaging, as such systems are rare in existing
+surveys like CANDELS. We fit Sersic models to the 2D light profiles and find
+that the median half-light radius is $R_e \sim 3$ kpc, a factor of $\sim 3$
+smaller than QGs with similar masses at $z \sim 0$. Complementing our sample
+with similarly massive QGs at lower redshifts, we find that the median size
+evolves as $R_e \propto H(z)^{-0.85 \pm 0.12}$ (or alternatively, $R_e \propto
+(1+z)^{-0.90 \pm 0.12}$). This rate of evolution is slower than that for lower
+mass QGs. When compared to low redshift QGs, the axis ratio distribution for
+our high redshift massive QG sample is most consistent with those in which
+spheroids are dominant. These observations point to earlier size growth among
+massive QGs that also resulted in spheroidal systems. Finally, we measured
+residual-corrected surface brightness profiles for our sample. These show that
+the Sersic parameterization is generally representative out to several
+effective radii and does not miss excess low surface brightness light. The
+sizes inferred from the light profiles therefore confirm the compactness of
+these most massive high redshift QGs.
+"
+24083,"Assessment of toxic metals and hazardous substances in tattoo inks using Sy-XRF, AAS and Raman spectroscopy","  A set of tattoo inks from a top brand was analysed by means of
+Synchrotron-based X-ray Fluorescence spectrometry (Sy-XRF), Atomic Absorption
+spectrometry (AAS) and Raman Spectroscopy to investigate the presence of toxic
+metals and hazardous substances. Quantitative analysis shows that Cr, Cu and Pb
+content is above the maximum allowed levels established by the Council of
+Europe in the resolution ResAP(2008)1 on requirements and criteria for the
+safety of tattoos and permanent make-up. Raman analysis reveals the presence of
+pigments Blue 15, Green 7 and Violet 23 in blue, green and violet inks that
+belong to a list of prohibited substances mentioned in ResAP(2008)1. Besides,
+Pigment White 6, carbon black, Pigment Red 8 and a diazo yellow were also
+identified in white, black, red and yellow inks. The results presented in this
+study show the importance of regulating tattoo ink composition.
+"
+24084,Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network,"  In this paper, we propose a generic and simple strategy for utilizing
+stochastic gradient information in optimization. The technique essentially
+contains two consecutive steps in each iteration: 1) computing and normalizing
+each block (layer) of the mini-batch stochastic gradient; 2) selecting
+appropriate step size to update the decision variable (parameter) towards the
+negative of the block-normalized gradient. We conduct extensive empirical
+studies on various non-convex neural network optimization problems, including
+multi-layer perceptron, convolution neural networks and recurrent neural
+networks. The results indicate the block-normalized gradient can help
+accelerate the training of neural networks. In particular, we observe that the
+normalized gradient methods having constant step size with occasionally decay,
+such as SGD with momentum, have better performance in the deep convolution
+neural networks, while those with adaptive step sizes, such as Adam, perform
+better in recurrent neural networks. Besides, we also observe this line of
+methods can lead to solutions with better generalization properties, which is
+confirmed by the performance improvement over strong baselines.
+"
+24085,Learning Discrete Weights Using the Local Reparameterization Trick,"  Recent breakthroughs in computer vision make use of large deep neural
+networks, utilizing the substantial speedup offered by GPUs. For applications
+running on limited hardware, however, high precision real-time processing can
+still be a challenge. One approach to solving this problem is training networks
+with binary or ternary weights, thus removing the need to calculate
+multiplications and significantly reducing memory size. In this work, we
+introduce LR-nets (Local reparameterization networks), a new method for
+training neural networks with discrete weights using stochastic parameters. We
+show how a simple modification to the local reparameterization trick,
+previously used to train Gaussian distributed weights, enables the training of
+discrete weights. Using the proposed training we test both binary and ternary
+models on MNIST, CIFAR-10 and ImageNet benchmarks and reach state-of-the-art
+results on most experiments.
+"
+24086,KMS and ground states on ultragraph C*-algebras,"  We describe KMS and ground states arising from a generalized gauge action on
+ultragraph C*-algebras. We focus on ultragraphs that satisfy Condition~(RFUM),
+so that we can use the partial crossed product description of ultragraph
+C*-algebras recently described by the second author and Danilo Royer. In
+particular, for ultragraphs with no sinks, we generalize a recent result by
+Toke Carlsen and Nadia Larsen: Given a time evolution on the C*-algebra of an
+ultragraph, induced by a function on the edge set, we characterize the KMS
+states in five different ways and ground states in four different ways. In both
+cases we include a characterization given by maps on the set of generalized
+vertices of the ultragraph. We apply this last result to show the existence of
+KMS and ground states for the ultragraph C*-algebra that is neither an
+Exel-Laca nor a graph C*-algebra.
+"
+24087,Brewster anomaly in random anisotropic media,"  Anderson localization and the Brewster anomaly phenomenon, which is the
+delocalization of $p$-polarized waves at a special incident angle, in
+randomly-stratified anisotropic media are studied theoretically for two
+different random models. In the first case, the random parts of the transverse
+and longitudinal components of the dielectric tensor are assumed to be
+uncorrelated, while, in the second case, they are proportional to each other.
+The longitudinal component is the one in the stratification direction. We
+calculate the localization length numerically using the invariant imbedding
+method. In addition, we derive the existence condition for the Brewster anomaly
+and concise analytical expressions for the localization length, which are
+accurate in the weak disorder limit. We find that the Brewster anomaly can
+occur only in the second case with positive ratio of the random parts. The
+incident angle at which the anomaly occurs depends sensitively on the ratio of
+the random parts and the average values of the tensor components. In the cases
+where the critical angle of total reflection exists, the angle at which the
+anomaly occurs can be either bigger or smaller than the critical angle. When
+the transverse and longitudinal components are uncorrelated, localization is
+dominated by the the transverse component at small incident angles and by the
+longitudinal component at large incident angles.
+"
+24088,Hateminers : Detecting Hate speech against Women,"  With the online proliferation of hate speech, there is an urgent need for
+systems that can detect such harmful content. In this paper, We present the
+machine learning models developed for the Automatic Misogyny Identification
+(AMI) shared task at EVALITA 2018. We generate three types of features:
+Sentence Embeddings, TF-IDF Vectors, and BOW Vectors to represent each tweet.
+These features are then concatenated and fed into the machine learning models.
+Our model came First for the English Subtask A and Fifth for the English
+Subtask B. We release our winning model for public use and it's available at
+this https URL.
+"
+24089,Symmetrized topological complexity,"  We present upper and lower bounds for symmetrized topological complexity
+$TC^\Sigma(X)$ in the sense of Basabe-González-Rudyak-Tamaki. The upper bound
+comes from equivariant obstruction theory, and the lower bounds from the
+cohomology of the symmetric square $SP^2(X)$. We also show that symmetrized
+topological complexity coincides with its monoidal version, where the path from
+a point to itself is required to be constant. Using these results, we calculate
+the symmetrized topological complexity of all odd spheres.
+"
+24090,Kleene Algebra Modulo Theories,"  Kleene algebras with tests (KATs) offer sound, complete, and decidable
+equational reasoning about regularly structured programs. Since NetKAT
+demonstrated how well various extensions of KATs apply to computer networks,
+interest in KATs has increased greatly. Unfortunately, extending a KAT to a
+particular domain by adding custom primitives, proving its equational theory
+sound and complete, and coming up with efficient automata-theoretic
+implementations is still an expert's task.
+We present a general framework for deriving KATs we call Kleene algebra
+modulo theories: given primitives and notions of state, we can automatically
+derive a corresponding KAT's semantics, prove its equational theory sound and
+complete, and generate an automata-based implementation of equivalence
+checking. Our framework is based on pushback, a way of specifying how
+predicates and actions interact, first used in Temporal NetKAT. We offer
+several case studies, including theories for bitvectors, increasing natural
+numbers, unbounded sets and maps, temporal logic, and network protocols.
+Finally, we provide an OCaml implementation that closely matches the theory:
+with only a few declarations, users can automatically derive an
+automata-theoretic decision procedure for a KAT.
+"
+24091,Early Stopping without a Validation Set,"  Early stopping is a widely used technique to prevent poor generalization
+performance when training an over-expressive model by means of gradient-based
+optimization. To find a good point to halt the optimizer, a common practice is
+to split the dataset into a training and a smaller validation set to obtain an
+ongoing estimate of the generalization performance. We propose a novel early
+stopping criterion based on fast-to-compute local statistics of the computed
+gradients and entirely removes the need for a held-out validation set. Our
+experiments show that this is a viable approach in the setting of least-squares
+and logistic regression, as well as neural networks.
+"
+24092,Topological quantum phase transition in the Ising-like antiferromagnetic spin chain BaCo$_2$V$_2$O$_8$,"  Since the seminal ideas of Berezinskii, Kosterlitz and Thouless, topological
+excitations are at the heart of our understanding of a whole novel class of
+phase transitions. In most of the cases, those transitions are controlled by a
+single type of topological objects. There are however some situations, still
+poorly understood, where two dual topological excitations fight to control the
+phase diagram and the transition. Finding experimental realization of such
+cases is thus of considerable interest. We show here that this situation occurs
+in BaCo$_2$V$_2$O$_8$, a spin-1/2 Ising-like quasi-one dimensional
+antiferromagnet when subjected to a uniform magnetic field transverse to the
+Ising axis. Using neutron scattering experiments, we measure a drastic
+modification of the quantum excitations beyond a critical value of the magnetic
+field. This quantum phase transition is identified, through a comparison with
+theoretical calculations, to be a transition between two different types of
+solitonic topological objects, which are captured by different components of
+the dynamical structure factor.
+"
+24093,Food-bridging: a new network construction to unveil the principles of cooking,"  In this manuscript we propose, analyse, and discuss a possible new principle
+behind traditional cuisine: the Food-bridging hypothesis and its comparison
+with the food-pairing hypothesis using the same dataset and graphical models
+employed in the food-pairing study by Ahn et al. [Scientific Reports, 1:196
+(2011)].
+The Food-bridging hypothesis assumes that if two ingredients do not share a
+strong molecular or empirical affinity, they may become affine through a chain
+of pairwise affinities. That is, in a graphical model as employed by Ahn et
+al., a chain represents a path that joints the two ingredients, the shortest
+path represents the strongest pairwise chain of affinities between the two
+ingredients.
+Food-pairing and Food-bridging are different hypotheses that may describe
+possible mechanisms behind the recipes of traditional cuisines. Food-pairing
+intensifies flavour by mixing ingredients in a recipe with similar chemical
+compounds, and food-bridging smoothes contrast between ingredients. Both
+food-pairing and food-bridging are observed in traditional cuisines, as shown
+in this work.
+We observed four classes of cuisines according to food-pairing and
+food-bridging: (1) East Asian cuisines, at one extreme, tend to avoid
+food-pairing as well as food-bridging; and (4) Latin American cuisines, at the
+other extreme, follow both principles. For the two middle classes: (2)
+Southeastern Asian cuisines, avoid food-pairing and follow food-bridging; and
+(3) Western cuisines, follow food-pairing and avoid food-bridging.
+"
+24094,Josephson Flux Flow Oscillator: the Microscopic Tunneling Approach,"  We elaborate a theoretical description of large Josephson junctions which is
+based on the Werthamer's microscopic tunneling theory. The model naturally
+incorporates coupling of electromagnetic radiation to the tunnel currents and,
+therefore, is particularly suitable for description of the self-coupling effect
+in Josephson junction. In our numerical calculations we treat the arising
+integro-differential equation, which describes temporal evolution of the
+superconducting phase difference coupled to the electromagnetic field, by the
+Odintsov-Semenov-Zorin algorithm. This allows us to avoid evaluation of the
+time integrals at each time step while taking into account all the memory
+effects. To validate the obtained microscopic model of large Josephson junction
+we focus our attention on the Josephson flux flow oscillator. The proposed
+microscopic model of flux flow oscillator does not involve the phenomenological
+damping parameter, rather, the damping is taken into account naturally in the
+tunnel current amplitudes calculated at a given temperature. The theoretically
+calculated current-voltage characteristics is compared to our experimental
+results obtained for a set of fabricated flux flow oscillators of different
+lengths. Our theoretical calculation agrees well with the obtained experimental
+results, and, to our knowledge, is the first where theoretical description of
+Josephson flux flow oscillator is brought beyond the perturbed sine-Gordon
+equation.
+"
+24095,On applicability of differential mixing rules for statistically homogeneous and isotropic dispersions,"  The classical differential mixing rules are assumed to be independent
+effective-medium approaches, applicable to certain classes of systems. In the
+present work, the inconsistency of differential models for macroscopically
+homogeneous and isotropic systems is illustrated with a model for the effective
+permittivity of simple dielectric systems of impenetrable balls. The analysis
+is carried out in terms of the compact group approach reformulated in a way
+that allows one to analyze the role of different contributions to the
+permittivity distribution in the system. It is shown that the asymmetrical
+Bruggeman model (ABM) is physically inconsistent since the electromagnetic
+interaction between previously added constituents and those being added is
+replaced by the interaction of the latter with recursively formed effective
+medium. The overall changes in the effective permittivity due to addition of
+one constituent include the contributions from both constituents and depend on
+the system structure before the addition. Ignoring the contribution from one of
+the constituents, we obtain generalized versions of the original ABM mixing
+rules. They still remain applicable only in a certain concentration ranges, as
+is shown with the Hashin-Shtrikman bounds. The results obtained can be
+generalized to macroscopically homogeneous and isotropic systems with complex
+permittivities of constituents.
+"
+24096,Homogeneous manifolds whose geodesics are orbits. Recent results and some open problems,"  A homogeneous Riemannian manifold $(M=G/K, g)$ is called a space with
+homogeneous geodesics or a $G$-g.o. space if every geodesic $\gamma (t)$ of $M$
+is an orbit of a one-parameter subgroup of $G$, that is $\gamma(t) =
+\exp(tX)\cdot o$, for some non zero vector $X$ in the Lie algebra of $G$. We
+give an exposition on the subject, by presenting techniques that have been used
+so far and a wide selection of previous and recent results.
+We also present some open problems.
+"
+24097,Performance tradeoffs of dynamically controlled grid-connected inverters in low inertia power systems,"  Implementing frequency response using grid-connected inverters is one of the
+popular proposed alternatives to mitigate the dynamic degradation experienced
+in low inertia power systems. However, such solution faces several challenges
+as inverters do not intrinsically possess the natural response to power
+fluctuations that synchronous generators have. Thus, to synthetically generate
+this response, inverters need to take frequency measurements, which are usually
+noisy, and subsequently make changes in the output power, which are therefore
+delayed. This paper explores the system-wide performance tradeoffs that arise
+when measurement noise, power disturbances, and delayed actions are considered
+in the design of dynamic controllers for grid-connected inverters. Using a
+recently proposed dynamic droop (iDroop) control for grid-connected inverters,
+which is inspired by classical first order lead-lag compensation, we show that
+the sets of parameters that result in highest noise attenuation, power
+disturbance mitigation, and delay robustness do not necessarily have a common
+intersection. In particular, lead compensation is desired in systems where
+power disturbances are the predominant source of degradation, while lag
+compensation is a better alternative when the system is dominated by delays or
+frequency noise. Our analysis further shows that iDroop can outperform the
+standard droop alternative in both joint noise and disturbance mitigation, and
+delay robustness.
+"
+24098,Remarks on critical metrics of the scalar curvature and volume functionals on compact manifolds with boundary,"  We provide a general Böchner type formula which enables us to prove some
+rigidity results for $V$-static spaces. In particular, we show that an
+$n$-dimensional positive static triple with connected boundary and positive
+scalar curvature must be isometric to the standard hemisphere, provided that
+the metric has zero radial Weyl curvature and satisfies a suitable pinching
+condition. Moreover, we classify $V$-static spaces with non-negative sectional
+curvature.
+"
+24099,On Convergence of Moments for Approximating Processes and Applications to Surrogate Models,"  We study critera for a pair $ (\{ X_n \} $, $ \{ Y_n \}) $ of approximating
+processes which guarantee closeness of moments by generalizing known results
+for the special case that $ Y_n = Y $ for all $n$ and $ X_n $ converges to $Y$
+in probability. This problem especially arises when working with surrogate
+models, e.g. to enrich observed data by simulated data, where the surrogates
+$Y_n$'s are constructed to justify that they approximate the $ X_n $'s.
+The results of this paper deal with sequences of random variables. Since this
+framework does not cover many applications where surrogate models such as deep
+neural networks are used to approximate more general stochastic processes, we
+extend the results to the more general framework of random fields of stochastic
+processes. This framework especially covers image data and sequences of images.
+We show that uniform integrability is sufficient, and this holds even for the
+case of processes provided they satisfy a weak stationarity condition.
+"
+24100,On Preserving Observation Properties of the Reduced Supervisor in Discrete-Event Systems,"  Supervisor reduction procedure can be used to construct the reduced
+supervisor with a reduced number of states in discrete-event systems. The main
+concepts which are used in this procedure are control consistency of states,
+control cover, induced supervisor, and normality of the reduced supervisor
+w.r.t. the original supervisor. In this paper, it is proved that the reduced
+supervisor, constructed by the proposed method in [9], preserves the
+observation properties, i.e. normality and relative observability, by self
+looping corresponding unobservable events at some states of the reduced
+supervisor. This property can be applied to find a natural projection, under
+which the supervisor is relative observable.
+"
+24101,Local finiteness for Green's relations in semigroup varieties,"  A semigroup variety V is said to be locally K-finite, where K stands for any
+of Green's relations H, R, L, D, or J, if every finitely generated semigroup
+from V has only finitely many K-classes. We characterize locally K-finite
+varieties of finite axiomatic rank in the language of ""forbidden objects"".
+"
+24102,Voisin's Conjecture for Zero--cycles on Calabi--Yau Varieties and their Mirrors,"  We study a conjecture, due to Voisin, on 0-cycles on varieties with $p_g=1$.
+Using Kimura's finite dimensional motives and recent results of Vial's on the
+refined (Chow-)Künneth decomposition, we provide a general criterion for
+Calabi-Yau manifolds of dimension at most $5$ to verify Voisin's conjecture. We
+then check, using in most cases some cohomological computations on the mirror
+partners, that the criterion can be successfully applied to various examples in
+each dimension up to $5$.
+"
+24103,A Surface Hopping Gaussian Beam Method for High-Dimensional Transport Systems,"  We propose a surface hopping Gaussian beam method to numerically solve a
+class of high frequency linear transport systems in high spatial dimensions,
+based on asymptotic analysis. The stochastic surface hopping is combined with
+Gaussian beam method to deal with the multiple characteristic directions of the
+transport system in high dimensions. The Monte Carlo nature of the proposed
+algorithm makes it easy for parallel implementations. We validate the
+performance of the algorithms for applications on the quantum-classical
+Liouville equations.
+"
+24104,Robustness from structure: Inference with hierarchical spiking networks on analog neuromorphic hardware,"  How spiking networks are able to perform probabilistic inference is an
+intriguing question, not only for understanding information processing in the
+brain, but also for transferring these computational principles to neuromorphic
+silicon circuits. A number of computationally powerful spiking network models
+have been proposed, but most of them have only been tested, under ideal
+conditions, in software simulations. Any implementation in an analog, physical
+system, be it in vivo or in silico, will generally lead to distorted dynamics
+due to the physical properties of the underlying substrate. In this paper, we
+discuss several such distortive effects that are difficult or impossible to
+remove by classical calibration routines or parameter training. We then argue
+that hierarchical networks of leaky integrate-and-fire neurons can offer the
+required robustness for physical implementation and demonstrate this with both
+software simulations and emulation on an accelerated analog neuromorphic
+device.
+"
+24105,Workflow Complexity for Collaborative Interactions: Where are the Metrics? -- A Challenge,"  In this paper, we introduce the problem of denoting and deriving the
+complexity of workflows (plans, schedules) in collaborative, planner-assisted
+settings where humans and agents are trying to jointly solve a task. The
+interactions -- and hence the workflows that connect the human and the agents
+-- may differ according to the domain and the kind of agents. We adapt insights
+from prior work in human-agent teaming and workflow analysis to suggest metrics
+for workflow complexity. The main motivation behind this work is to highlight
+metrics for human comprehensibility of plans and schedules. The planning
+community has seen its fair share of work on the synthesis of plans that take
+diversity into account -- what value do such plans hold if their generation is
+not guided at least in part by metrics that reflect the ease of engaging with
+and using those plans?
+"
+24106,Lens elliptic gamma function solution of the Yang-Baxter equation at roots of unity,"  We study the root of unity limit of the lens elliptic gamma function solution
+of the star-triangle relation, for an integrable model with continuous and
+discrete spin variables. This limit involves taking an elliptic nome to a
+primitive $rN$-th root of unity, where $r$ is an existing integer parameter of
+the lens elliptic gamma function, and $N$ is an additional integer parameter.
+This is a singular limit of the star-triangle relation, and at subleading order
+of an asymptotic expansion, another star-triangle relation is obtained for a
+model with discrete spin variables in $\mathbb{Z}_{rN}$. Some special choices
+of solutions of equation of motion are shown to result in well-known discrete
+spin solutions of the star-triangle relation. The saddle point equations
+themselves are identified with three-leg forms of ""3D-consistent"" classical
+discrete integrable equations, known as $Q4$ and $Q3_{(\delta=0)}$. We also
+comment on the implications for supersymmetric gauge theories, and in
+particular comment on a close parallel with the works of Nekrasov and
+Shatashvili.
+"
+24107,The Boundary of the Future,"  We prove that the boundary of the future of a surface $K$ consists precisely
+of the points $p$ that lie on a null geodesic orthogonal to $K$ such that
+between $K$ and $p$ there are no points conjugate to $K$ nor intersections with
+another such geodesic. Our theorem has applications to holographic screens and
+their associated light sheets and in particular enters the proof that
+holographic screens satisfy an area law.
+"
+24108,A Note on the Inception Score,"  Deep generative models are powerful tools that have produced impressive
+results in recent years. These advances have been for the most part empirically
+driven, making it essential that we use high quality evaluation metrics. In
+this paper, we provide new insights into the Inception Score, a recently
+proposed and widely used evaluation metric for generative models, and
+demonstrate that it fails to provide useful guidance when comparing models. We
+discuss both suboptimalities of the metric itself and issues with its
+application. Finally, we call for researchers to be more systematic and careful
+when evaluating and comparing generative models, as the advancement of the
+field depends upon it.
+"
+24109,Dispersive shallow water wave modelling. Part IV: Numerical simulation on a globally spherical geometry,"  In the present manuscript, we consider the problem of dispersive wave
+simulation on a rotating globally spherical geometry. In this Part IV, we focus
+on numerical aspects while the model derivation was described in Part III. The
+algorithm we propose is based on the splitting approach. Namely, equations are
+decomposed on a uniformly elliptic equation for the dispersive pressure
+component and a hyperbolic part of shallow water equations (on a sphere) with
+source terms. This algorithm is implemented as a two-step predictor-corrector
+scheme. On every step, we solve separately elliptic and hyperbolic problems.
+Then, the performance of this algorithm is illustrated on model idealised
+situations with an even bottom, where we estimate the influence of sphericity
+and rotation effects on dispersive wave propagation. The dispersive effects are
+quantified depending on the propagation distance over the sphere and on the
+linear extent of generation region. Finally, the numerical method is applied to
+a couple of real-world events. Namely, we undertake simulations of the
+Bulgarian 2007 and Chilean 2010 tsunamis. Whenever the data is available, our
+computational results are confronted with real measurements.
+"
+24110,Adding a lot of random reals by adding a few,"  We study pairs $(V, V_1)$ of models of $ZFC$ such that adding $\kappa$-many
+random reals over $V_1$ adds $\lambda$-many random reals over $V$, for some
+$\lambda > \kappa.$
+"
+24111,The Partial Entropy Decomposition: Decomposing multivariate entropy and mutual information via pointwise common surprisal,"  Obtaining meaningful quantitative descriptions of the statistical dependence
+within multivariate systems is a difficult open problem. Recently, the Partial
+Information Decomposition (PID) was proposed to decompose mutual information
+(MI) about a target variable into components which are redundant, unique and
+synergistic within different subsets of predictor variables. Here, we propose
+to apply the elegant formalism of the PID to multivariate entropy, resulting in
+a Partial Entropy Decomposition (PED). We implement the PED with an entropy
+redundancy measure based on pointwise common surprisal; a natural definition
+which is closely related to the definition of MI. We show how this approach can
+reveal the dyadic vs triadic generative structure of multivariate systems that
+are indistinguishable with classical Shannon measures. The entropy perspective
+also shows that misinformation is synergistic entropy and hence that MI itself
+includes both redundant and synergistic effects. We show the relationships
+between the PED and MI in two predictors, and derive two alternative
+information decompositions which we illustrate on several example systems. This
+reveals that in entropy terms, univariate predictor MI is not a proper subset
+of the joint MI, and we suggest this previously unrecognised fact explains in
+part why obtaining a consistent PID has proven difficult. The PED also allows
+separate quantification of mechanistic redundancy (related to the function of
+the system) versus source redundancy (arising from dependencies between
+inputs); an important distinction which no existing methods can address. The
+new perspective provided by the PED helps to clarify some of the difficulties
+encountered with the PID approach and the resulting decompositions provide
+useful tools for practical data analysis across a wide range of application
+areas.
+"
+24112,$ν$-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters,"  Background: Cardiac MRI derived biventricular mass and function parameters,
+such as end-systolic volume (ESV), end-diastolic volume (EDV), ejection
+fraction (EF), stroke volume (SV), and ventricular mass (VM) are clinically
+well established. Image segmentation can be challenging and time-consuming, due
+to the complex anatomy of the human heart.
+Objectives: This study introduces $\nu$-net (/nju:n$\varepsilon$t/) -- a deep
+learning approach allowing for fully-automated high quality segmentation of
+right (RV) and left ventricular (LV) endocardium and epicardium for extraction
+of cardiac function parameters.
+Methods: A set consisting of 253 manually segmented cases has been used to
+train a deep neural network. Subsequently, the network has been evaluated on 4
+different multicenter data sets with a total of over 1000 cases.
+Results: For LV EF the intraclass correlation coefficient (ICC) is 98, 95,
+and 80 % (95 %), and for RV EF 96, and 87 % (80 %) on the respective data sets
+(human expert ICCs reported in parenthesis). The LV VM ICC is 95, and 94 % (84
+%), and the RV VM ICC is 83, and 83 % (54 %). This study proposes a simple
+adjustment procedure, allowing for the adaptation to distinct segmentation
+philosophies. $\nu$-net exhibits state of-the-art performance in terms of dice
+coefficient.
+Conclusions: Biventricular mass and function parameters can be determined
+reliably in high quality by applying a deep neural network for cardiac MRI
+segmentation, especially in the anatomically complex right ventricle. Adaption
+to individual segmentation styles by applying a simple adjustment procedure is
+viable, allowing for the processing of novel data without time-consuming
+additional training.
+"
+24113,QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM,"  In this paper, we use 2D object detections from multiple views to
+simultaneously estimate a 3D quadric surface for each object and localize the
+camera position. We derive a SLAM formulation that uses dual quadrics as 3D
+landmark representations, exploiting their ability to compactly represent the
+size, position and orientation of an object, and show how 2D object detections
+can directly constrain the quadric parameters via a novel geometric error
+formulation. We develop a sensor model for object detectors that addresses the
+challenge of partially visible objects, and demonstrate how to jointly estimate
+the camera pose and constrained dual quadric parameters in factor graph based
+SLAM with a general perspective camera.
+"
+24114,Zone clearance in an infinite TASEP with a step initial condition,"  The TASEP is a paradigmatic model of out-of-equilibrium statistical physics,
+for which many quantities have been computed, either exactly or by approximate
+methods. In this work we study two new kinds of observables that have some
+relevance in biological or traffic models. They represent the probability for a
+given clearance zone of the lattice to be empty (for the first time) at a given
+time, starting from a step density profile. Exact expressions are obtained for
+single-time quantities, while more involved history-dependent observables are
+studied by Monte Carlo simulation, and partially predicted by a
+phenomenological approach.
+"
+24115,Uncertainty Quantification of Water Distribution System Measurement Data based on a Least Squares Loop Flows State Estimator,"  This paper presents a novel algorithm for uncertainty quantification of water
+distribution system measurement data including nodal demands/consumptions as
+well as real pressure and flow measurements. This procedure, referred to as
+Confidence Limit Analysis (CLA), is concerned with a deployment of a Least
+Squares (LS) state estimator based on the loop corrective flows and the
+variation of nodal demands as independent variables. The confidence limits
+obtained for the nodal pressures and the inflows/outflows of a water network
+are determined with the novel algorithm called Error Maximization (EM) method
+and are evaluated with respect to two other more established CLA algorithms
+based on an Experimental Sensitivity Matrix (ESM) and on the sensitivity matrix
+method obtained with the LS nodal heads equations state estimator. The
+estimated confidence limits obtained for two real water networks show that the
+proposed EM algorithm is comparable to the other two CLA benchmark algorithms
+but due to its computational efficiency it is more suitable for online decision
+support applications in water distribution systems. Both ESM and EM methods
+work for any operating point, whether arbitrarily or randomly chosen, for any
+water network although EM method has the advantage of being computationally
+superior and working with any sets of measurements.
+"
+24116,FSITM: A Feature Similarity Index For Tone-Mapped Images,"  In this work, based on the local phase information of images, an objective
+index, called the feature similarity index for tone-mapped images (FSITM), is
+proposed. To evaluate a tone mapping operator (TMO), the proposed index
+compares the locally weighted mean phase angle map of an original high dynamic
+range (HDR) to that of its associated tone-mapped image calculated using the
+output of the TMO method. In experiments on two standard databases, it is shown
+that the proposed FSITM method outperforms the state-of-the-art index, the tone
+mapped quality index (TMQI). In addition, a higher performance is obtained by
+combining the FSITM and TMQI indices. The MATLAB source code of the proposed
+metric(s) is available at
+this https URL.
+"
+24117,Numerical studies on the link between radioisotopic signatures on Earth and the formation of the Local Bubble. I. 60Fe transport to the solar system by turbulent mixing of ejecta from nearby supernovae into a locally homogeneous ISM,"  The discovery of radionuclides like 60Fe with half-lives of million years in
+deep-sea crusts and sediments offers the unique possibility to date and locate
+nearby supernovae. We want to quantitatively establish that the 60Fe
+enhancement is the result of several supernovae which are also responsible for
+the formation of the Local Bubble, our Galactic habitat. We performed
+three-dimensional hydrodynamic adaptive mesh refinement simulations (with
+resolutions down to subparsec scale) of the Local Bubble and the neighbouring
+Loop I superbubble in different homogeneous, self-gravitating environments. For
+setting up the Local and Loop I superbubble, we took into account the time
+sequence and locations of the generating core-collapse supernova explosions,
+which were derived from the mass spectrum of the perished members of certain
+stellar moving groups. The release of 60Fe and its subsequent turbulent mixing
+process inside the superbubble cavities was followed via passive scalars, where
+the yields of the decaying radioisotope were adjusted according to recent
+stellar evolution calculations. The models are able to reproduce both the
+timing and the intensity of the 60Fe excess observed with rather high
+precision, provided that the external density does not exceed 0.3 cm-3 on
+average. Thus the two best-fit models presented here were obtained with
+background media mimicking the classical warm ionised and warm neutral medium.
+We also found that 60Fe (which is condensed onto dust grains) can be delivered
+to Earth via two physical mechanisms: either through individual fast-paced
+supernova blast waves, which cross the Earth's orbit sometimes even twice as a
+result of reflection from the Local Bubble's outer shell, or, alternatively,
+through the supershell of the Local Bubble itself, injecting the 60Fe content
+of all previous supernovae at once, but over a longer time range.
+"
+24118,Behavior of the Gaussian curvature of timelike minimal surfaces with singularities,"  In the 3-dimensional Lorentz-Minkowski space we prove that the sign of the
+Gaussian curvature of any timelike minimal surface is determined by the
+degeneracy and the orientations of the two null curves that generate the
+surface. Moreover, we also investigate the behavior of the Gaussian curvature
+near singular points of a timelike minimal surface which admits some kind of
+singular points.
+"
+24119,Monopole Operators in $U(1)$ Chern-Simons-Matter Theories,"  We study monopole operators at the infrared fixed points of $U(1)$
+Chern-Simons-matter theories (QED$_3$, scalar QED$_3$, ${\cal N} =1$ SQED$_3$,
+and ${\cal N} = 2$ SQED$_3$) with $N$ matter flavors and Chern-Simons level
+$k$. We work in the limit where both $N$ and $k$ are taken to be large with
+$\kappa = k/N$ fixed. In this limit, we extract information about the low-lying
+spectrum of monopole operators from evaluating the $S^2 \times S^1$ partition
+function in the sector where the $S^2$ is threaded by magnetic flux $4 \pi q$.
+At leading order in $N$, we find a large number of monopole operators with
+equal scaling dimensions and a wide range of spins and flavor symmetry
+irreducible representations. In two simple cases, we deduce how the degeneracy
+in the scaling dimensions is broken by the $1/N$ corrections. For QED$_3$ at
+$\kappa=0$, we provide conformal bootstrap evidence that this near-degeneracy
+is in fact maintained to small values of $N$. For ${\cal N} = 2$ SQED$_3$, we
+find that the lowest dimension monopole operator is generically non-BPS.
+"
+24120,Structure Learning in Motor Control:A Deep Reinforcement Learning Model,"  Motor adaptation displays a structure-learning effect: adaptation to a new
+perturbation occurs more quickly when the subject has prior exposure to
+perturbations with related structure. Although this `learning-to-learn' effect
+is well documented, its underlying computational mechanisms are poorly
+understood. We present a new model of motor structure learning, approaching it
+from the point of view of deep reinforcement learning. Previous work outside of
+motor control has shown how recurrent neural networks can account for
+learning-to-learn effects. We leverage this insight to address motor learning,
+by importing it into the setting of model-based reinforcement learning. We
+apply the resulting processing architecture to empirical findings from a
+landmark study of structure learning in target-directed reaching (Braun et al.,
+2009), and discuss its implications for a wider range of learning-to-learn
+phenomena.
+"
+24121,The dynamics of scattering in undulatory active collisions,"  Natural and artificial self-propelled systems must manage environmental
+interactions during movement. Such interactions, which we refer to as active
+collisions, are fundamentally different from momentum-conserving interactions
+studied in classical physics, largely because the internal driving of the
+locomotor can lead to persistent contact with heterogeneities. Here, we
+experimentally and numerically study the effects of active collisions on a
+laterally-undulating sensory-deprived robophysical model, whose dynamics are
+applicable to self-propelled systems across length scales and environments. The
+robot moves via spatial undulation of body segments, with a nearly-linear
+center-of-geometry trajectory. Interactions with a single rigid post scatter
+the robot, and these deflections are proportional to the head-post contact
+duration. The distribution of scattering angles is smooth and strongly-peaked
+directly behind the post. Interactions with a single row of evenly-spaced posts
+(with inter-post spacing $d$) produce distributions reminiscent of far-field
+diffraction patterns: as $d$ decreases, distinct secondary peaks emerge as
+large deflections become more likely. Surprisingly, we find that the presence
+of multiple posts does not change the nature of individual collisions; instead,
+multi-modal scattering patterns arise from multiple posts altering the
+likelihood of individual collisions to occur. As $d$ decreases, collisions near
+the leading edges of the posts become more probable, and we find that these
+interactions are associated with larger deflections. Our results, which
+highlight the surprising dynamics that can occur during active collisions of
+self-propelled systems, can inform control principles for locomotors in complex
+terrain and facilitate design of task-capable active matter.
+"
+24122,On the prime geodesic theorem for hyperbolic 3-manifolds,"  Through the Selberg zeta approach, we reduce the exponent in the error term
+of the prime geodesic theorem for cocompact Kleinian groups or Bianchi groups
+from Sarnak's $\frac{5}{3}$ to $\frac{3}{2}$. At the cost of excluding a set of
+finite logarithmic measure, the bound is further improved to $\frac{13}{9}$.
+"
+24123,Cellular approximations of p-local compact groups,"  Let A be the classifying space of an abelian p-torsion group. We compute
+A-cellular approximations (in the sense of Chachólski and Farjoun) of
+classifying spaces of p-local compact groups, with special emphasis in the
+cases which arise from honest compact Lie groups.
+"
+24124,An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees,"  We propose a parallel graph-based data clustering algorithm using CUDA GPU,
+based on exact clustering of the minimum spanning tree in terms of a minimum
+isoperimetric criteria. We also provide a comparative performance analysis of
+our algorithm with other related ones which demonstrates the general
+superiority of this parallel algorithm over other competing algorithms in terms
+of accuracy and speed.
+"
+24125,Extensions of tensor products of ${\mathbb Z}_p$-orbifold models of the lattice vertex operator algebra $V_{\sqrt{2}A_{p-1}}$,"  Let $p$ be an odd prime and let $\widehat{\sigma}$ be an order $p$
+automorphism of $V_{\sqrt{2}A_{p-1}}$ which is a lift of a $p$-cycle in the
+Weyl group ${\rm Weyl}(A_{p-1})\cong {\mathfrak S}_p$. We study a certain
+extension $V$ of a tensor product of finitely many copies of the orbifold model
+$V_{\sqrt{2}A_{p-1}}^{\langle \widehat{\sigma} \rangle}$ and give a criterion
+for $V$ that every irreducible $V$-module is a simple current.
+"
+24126,Prototypal Analysis and Prototypal Regression,"  Prototypal analysis is introduced to overcome two shortcomings of archetypal
+analysis: its sensitivity to outliers and its non-locality, which reduces its
+applicability as a learning tool. Same as archetypal analysis, prototypal
+analysis finds prototypes through convex combination of the data points and
+approximates the data through convex combination of the archetypes, but it adds
+a penalty for using prototypes distant from the data points for their
+reconstruction. Prototypal analysis can be extended---via kernel embedding---to
+probability distributions, since the convexity of the prototypes makes them
+interpretable as mixtures. Finally, prototypal regression is developed, a
+robust supervised procedure which allows the use of distributions as either
+features or labels.
+"
+24127,Is feature selection secure against training data poisoning?,"  Learning in adversarial settings is becoming an important task for
+application domains where attackers may inject malicious data into the training
+set to subvert normal operation of data-driven technologies. Feature selection
+has been widely used in machine learning for security applications to improve
+generalization and computational efficiency, although it is not clear whether
+its use may be beneficial or even counterproductive when training data are
+poisoned by intelligent attackers. In this work, we shed light on this issue by
+providing a framework to investigate the robustness of popular feature
+selection methods, including LASSO, ridge regression and the elastic net. Our
+results on malware detection show that feature selection methods can be
+significantly compromised under attack (we can reduce LASSO to almost random
+choices of feature sets by careful insertion of less than 5% poisoned training
+samples), highlighting the need for specific countermeasures.
+"
+24128,Triangle-free planar graphs with small independence number,"  Since planar triangle-free graphs are 3-colourable, such a graph with n
+vertices has an independent set of size at least n/3. We prove that unless the
+graph contains a certain obstruction, its independence number is at least
+n/(3-epsilon) for some fixed epsilon>0. We also provide a reduction rule for
+this obstruction, which enables us to transform any plane triangle-free graph G
+into a plane triangle-free graph G' such that alpha(G')-|G'|/3=alpha(G)-|G|/3
+and |G'|<=(alpha(G)-|G|/3)/epsilon. We derive a number of algorithmic
+consequences as well as a structural description of n-vertex plane
+triangle-free graphs whose independence number is close to n/3.
+"
+24129,Fluorescence spectroscopy enhancement on photonic nanoantennas,"  This document briefly introduces the key concepts to understand the
+phenomenon of fluorescence enhancement with optical nanostructures. The
+equations remain simple, with the major goal to illustrate the different
+physical effects at play. It also provides an overview of different recent
+approaches to enhance single molecule fluorescence with plasmonic and
+non-plasmonic nanoantennas, and describes three main biochemical applications.
+"
+24130,Effective and Extensible Feature Extraction Method Using Genetic Algorithm-Based Frequency-Domain Feature Search for Epileptic EEG Multi-classification,"  In this paper, a genetic algorithm-based frequency-domain feature search
+(GAFDS) method is proposed for the electroencephalogram (EEG) analysis of
+epilepsy. In this method, frequency-domain features are first searched and then
+combined with nonlinear features. Subsequently, these features are selected and
+optimized to classify EEG signals. The extracted features are analyzed
+experimentally. The features extracted by GAFDS show remarkable independence,
+and they are superior to the nonlinear features in terms of the ratio of
+inter-class distance and intra-class distance. Moreover, the proposed feature
+search method can additionally search for features of instantaneous frequency
+in a signal after Hilbert transformation. The classification results achieved
+using these features are reasonable, thus, GAFDS exhibits good extensibility.
+Multiple classic classifiers (i.e., $k$-nearest neighbor, linear discriminant
+analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes)
+achieve good results by using the features generated by GAFDS method and the
+optimized selection. Specifically, the accuracies for the two-classification
+and three-classification problems may reach up to 99% and 97%, respectively.
+Results of several cross-validation experiments illustrate that GAFDS is
+effective in feature extraction for EEG classification. Therefore, the proposed
+feature selection and optimization model can improve classification accuracy.
+"
+24131,"Lempel-Ziv: a ""one-bit catastrophe"" but not a tragedy","  The so-called ""one-bit catastrophe"" for the compression algorithm LZ'78 asks
+whether the compression ratio of an infinite word can change when a single bit
+is added in front of it. We answer positively this open question raised by Lutz
+and others: we show that there exists an infinite word $w$ such that
+$\rho_{sup}(w)=0$ but $\rho_{inf}(0w)>0$, where $\rho_{sup}$ and $\rho_{inf}$
+are respectively the $\limsup$ and the $\liminf$ of the compression ratios
+$\rho$ of the prefixes. To that purpose we explore the behaviour of LZ'78 on
+finite words and show the following results:
+- There is a constant $C>0$ such that, for any finite word $w$ and any letter
+$a$, $\rho(aw)\leq C\sqrt{\rho(w)\log|w|}$. Thus, sufficiently compressible
+words ($\rho(w)=o(1/\log|w|)$) remain compressible with a letter in front;
+- The previous result is tight up to a multiplicative constant for any
+compression ratio $\rho(w)=O(1/\log|w|)$. In particular, there are infinitely
+many words $w$ satisfying $\rho(w)=O(1/\log|w|)$ but $\rho(0w)=\Omega(1)$.
+"
+24132,Recovering Covariance from Functional Fragments,"  We consider nonparametric estimation of a covariance function on the unit
+square, given a sample of discretely observed fragments of functional data.
+When each sample path is only observed on a subinterval of length $\delta<1$,
+one has no statistical information on the unknown covariance outside a
+$\delta$-band around the diagonal. The problem seems unidentifiable without
+parametric assumptions, but we show that nonparametric estimation is feasible
+under suitable smoothness and rank conditions on the unknown covariance. This
+remains true even when observation is discrete, and we give precise
+deterministic conditions on how fine the observation grid needs to be relative
+to the rank and fragment length for identifiability to hold true. We show that
+our conditions translate the estimation problem to a low-rank matrix completion
+problem, construct a nonparametric estimator in this vein, and study its
+asymptotic properties. We illustrate the numerical performance of our method on
+real and simulated data.
+"
+24133,Collective neutrino oscillations and neutrino wave packets,"  Effects of decoherence by wave packet separation on collective neutrino
+oscillations in dense neutrino gases are considered. We estimate the length of
+the wave packets of neutrinos produced in core collapse supernovae and the
+expected neutrino coherence length, and then proceed to consider the
+decoherence effects within the density matrix formalism of neutrino flavour
+transitions. First, we demonstrate that for neutrino oscillations in vacuum the
+decoherence effects are described by a damping term in the equation of motion
+of the density matrix of a neutrino as a whole (as contrasted to that of the
+fixed-momentum components of the neutrino density matrix). Next, we consider
+neutrino oscillations in ordinary matter and dense neutrino backgrounds, both
+in the adiabatic and non-adiabatic regimes. In the latter case we study two
+specific models of adiabaticity violation -- one with short-term and another
+with extended non-adiabaticity. It is demonstrated that, while in the adiabatic
+case a damping term is present in the equation of motion of the neutrino
+density matrix (just like in the vacuum oscillation case), no such term in
+general appears in the non-adiabatic regime.
+"
+24134,Metamaterials from modified CPT-odd standard model extension and minimum length,"  Here we discuss the standard model extension in the presence of CPT-odd
+Lorentz violation (LV) sector and of a deformed Heisenberg algebra that leads
+to a non-commutative theory with minimum length (ML). We derive the set of
+Maxwell equations emerging from this theory and considered the consequences
+with respect to the usual effects of electromagnetic waves and material media.
+We then considered the set of modified equations in material media and
+investigate the metamaterial behaviour as a consequence of LV and ML. We show
+that a negative index refraction can be derived from the presence of a
+non-commutativity suitably tuned by the $\beta$ parameter, while in the
+presence of LV, we obtained the set of modified Maxwell equation in terms of
+the corresponding material fields with terms depending explicitly from the
+terms of interaction between the material fields depending on non-commutativity
+with the background field due to CPT-odd LV. We conclude that a new set of
+metamaterials can be derived as a consequence of CPT-odd LV and
+non-commutativity with minimum length.
+"
+24135,Exponential Decay Of Concentration Variance During Magma Mixing: Robustness Of A Volcanic Chronometer And Implications For The Homogenization Of Chemical Heterogeneities In Magmatic Systems,"  The mixing of magmas is a fundamental process in the Earth system causing
+extreme compositional variations in igneous rocks. This process can develop
+with different intensities both in space and time, making the interpretation of
+compositional patterns in igneous rocks a petrological challenge. As a
+time-dependent process, magma mixing has been suggested to preserve information
+about the time elapsed between the injection of a new magma into sub-volcanic
+magma chambers and eruptions. This allowed the use of magma mixing as an
+additional volcanological tool to infer the mixing-to-eruption timescales. In
+spite of the potential of magma mixing processes to provide information about
+the timing of volcanic eruptions its statistical robustness is not yet
+established. This represents a prerequisite to apply reliably this conceptual
+model. Here, new chaotic magma mixing experiments were performed at different
+times using natural melts. The degree of reproducibility of experimental
+results was tested repeating one experiment at the same starting conditions and
+comparing the compositional variability. We further tested the robustness of
+the statistical analysis by randomly removing from the analysed dataset a
+progressively increasing number of samples. Results highlight the robustness of
+the method to derive empirical relationships linking the efficiency of chemical
+exchanges and mixing time. These empirical relationships remain valid by
+removing up to 80% of the analytical determinations. Experimental results were
+applied to constrain the homogenization time of chemical heterogeneities in
+natural magmatic system during mixing. The calculations show that, when the
+mixing dynamics generate millimetre thick filaments, homogenization timescales
+of the order of a few minutes are to be expected.
+"
+24136,Lipschitz regularized Deep Neural Networks converge and generalize,"  Generalization of deep neural networks (DNNs) is an open problem which, if
+solved, could impact the reliability and verification of deep neural network
+architectures. In this paper, we show that if the usual fidelity term used in
+training DNNs is augmented by a Lipschitz regularization term, then the
+networks converge and generalize. The convergence is in the limit as the number
+of data points, $n\to \infty$, while also allowing the network to grow as
+needed to fit the data. Two regimes are identified: in the case of clean
+labels, we prove convergence to the label function which corresponds to zero
+loss, in the case of corrupted labels which we prove convergence to a
+regularized label function which is the solution of a limiting variational
+problem. In both cases, a convergence rate is also provided.
+"
+24137,Coalescent embedding in the hyperbolic space unsupervisedly discloses the hidden geometry of the brain,"  The human brain displays a complex network topology, whose structural
+organization is widely studied using diffusion tensor imaging. The original
+geometry from which emerges the network topology is known, as well as the
+localization of the network nodes in respect to the brain morphology and
+anatomy. One of the most challenging problems of current network science is to
+infer the latent geometry from the mere topology of a complex network. The
+human brain structural connectome represents the perfect benchmark to test
+algorithms aimed to solve this problem. Coalescent embedding was recently
+designed to map a complex network in the hyperbolic space, inferring the node
+angular coordinates. Here we show that this methodology is able to
+unsupervisedly reconstruct the latent geometry of the brain with an incredible
+accuracy and that the intrinsic geometry of the brain networks strongly relates
+to the lobes organization known in neuroanatomy. Furthermore, coalescent
+embedding allowed the detection of geometrical pathological changes in the
+connectomes of Parkinson's Disease patients. The present study represents the
+first evidence of brain networks' angular coalescence in the hyperbolic space,
+opening a completely new perspective, possibly towards the realization of
+latent geometry network markers for evaluation of brain disorders and
+pathologies.
+"
+24138,An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges,"  In many distributed learning problems, the heterogeneous loading of computing
+machines may harm the overall performance of synchronous strategies. In this
+paper, we propose an effective asynchronous distributed framework for the
+minimization of a sum of smooth functions, where each machine performs
+iterations in parallel on its local function and updates a shared parameter
+asynchronously. In this way, all machines can continuously work even though
+they do not have the latest version of the shared parameter. We prove the
+convergence of the consistency of this general distributed asynchronous method
+for gradient iterations then show its efficiency on the matrix factorization
+problem for recommender systems and on binary classification.
+"
+24139,Recurrent Latent Variable Networks for Session-Based Recommendation,"  In this work, we attempt to ameliorate the impact of data sparsity in the
+context of session-based recommendation. Specifically, we seek to devise a
+machine learning mechanism capable of extracting subtle and complex underlying
+temporal dynamics in the observed session data, so as to inform the
+recommendation algorithm. To this end, we improve upon systems that utilize
+deep learning techniques with recurrently connected units; we do so by adopting
+concepts from the field of Bayesian statistics, namely variational inference.
+Our proposed approach consists in treating the network recurrent units as
+stochastic latent variables with a prior distribution imposed over them. On
+this basis, we proceed to infer corresponding posteriors; these can be used for
+prediction and recommendation generation, in a way that accounts for the
+uncertainty in the available sparse training data. To allow for our approach to
+easily scale to large real-world datasets, we perform inference under an
+approximate amortized variational inference (AVI) setup, whereby the learned
+posteriors are parameterized via (conventional) neural networks. We perform an
+extensive experimental evaluation of our approach using challenging benchmark
+datasets, and illustrate its superiority over existing state-of-the-art
+techniques.
+"
+24140,"Some perverse equivalences of $SL(2,q)$ in its defining characteristic","  In this article, we study the modular representations of the special linear
+group of degree two over a finite field in defining characteristic. In
+particular, we study the automorphisms of derived category of representations.
+We have been able to obtain a new type of autoequivalence. This autoequivalence
+has some uncommon features. It is more conveniently conceived and proved using
+the representation theory of its Brauer correspondence but at the same time it
+can be very neatly described, using a type of derived equivalence called
+perverse equivalence, in global settings.
+"
+24141,Helium ignition in rotating magnetized CO white dwarfs leading to fast and faint rather than classical type Ia supernovae,"  Based mostly on stellar models which do not include rotation, CO white dwarfs
+which accrete helium at rates of about $\sim
+10^{-8}~\mathrm{M}_\odot/\mathrm{yr}$ have been put forward as candidate
+progenitors for a number of transient astrophysical phenomena, including
+supernovae of Type Ia, and the peculiar and fainter Type Iax supernovae. Here
+we study the impact of accretion-induced spin-up including the subsequent
+magnetic field generation, angular momentum transport, and viscous heating on
+the white dwarf evolution up to the point of helium ignition. We resolve the
+structure of the helium accreting white dwarf models with a one dimensional
+Langrangian hydrodynamic code, modified to include rotational and magnetic
+effects. We find magnetic angular momentum transport, which leads to quasi
+solid-body rotation, profoundly impacts the evolution of the white dwarf
+models. Our rotating lower mass ($0.54$ and $0.82~\mathrm{M}_\odot$) models
+accrete up to 50\% more mass up to ignition compared to the non-rotating case
+and the opposite for our more massive models. Furthermore, we find that
+rotation leads to up to 10-times smaller helium ignition densities, except for
+the lowest adopted initial white dwarf mass. Ignition densities of order
+$10^6\,\mathrm{g/cm}^3$ are only found for the lowest accretion rates and for
+large amounts of accreted helium $\gtrsim 0.4\,\mathrm{M}_\odot$. However,
+correspondingly massive donor stars would transfer mass at much higher rates.
+We expect explosive He-shell burning to mostly occur as deflagrations and at
+$\dot{M}>2\cdot10^{-8}~\mathrm{M}_\odot/\mathrm{yr}$, regardless of white dwarf
+mass. Our results imply that helium accretion onto CO white dwarfs at the
+considered rates is unlikely to lead to explosions of the CO core or to
+classical Type,Ia supernovae, but may instead produce events like faint and
+fast hydrogen-free supernovae.
+"
+24142,Structural and metal-insulator transitions in rhenium based double perovskites via orbital ordering,"  Re-based double perovskites (DPs) exhibit a complex interplay of structural
+and metal-insulator transitions. Here we systematically study the ground state
+electronic and structural properties for a family of Re-based DPs $A_2B$ReO$_6$
+($A$=Sr, Ca and $B$=Cr, Fe), which are related by a common low energy
+Hamiltonian, using density functional theory + $U$ calculations. We show that
+the on-site interaction $U$ of Re induces orbital ordering (denoted COO), with
+each Re site having an occupied $d_{xy}$ orbital and a C-type alternation among
+$d_{xz}/d_{yz}$, resulting in an insulating state consistent with
+experimentally determined insulators Sr$_2$CrReO$_6$, Ca$_2$CrReO$_6$, and
+Ca$_2$FeReO$_6$. The threshold value of $U_{Re}$ for orbital ordering is
+reduced by inducing $E_g$ octahedral distortions of the same C-type wavelength
+(denoted COD), which serves as a structural signature of the orbital ordering;
+octahedral tilting also reduces the threshold. The COO, and the concomitant
+COD, are a spontaneously broken symmetry for the Sr based materials (i.e.
+$a^0a^0c^-$ tilt pattern), while not for the Ca based systems (i.e. $a^-a^-b^+$
+tilt pattern). Spin-orbit coupling does not qualitatively change the physics of
+the COO/COD, but can induce relevant quantitative changes. We prove that a
+single set of $U_{Cr}$,$U_{Fe}$,$U_{Re}$ capture the experimentally observed
+metallic state in Sr$_2$FeReO$_6$ and insulating states in the other three
+systems. We predict that the COO is the origin of the insulating state in
+Sr$_2$CrReO$_6$, and that the concomitant COD may be experimentally observed at
+sufficiently low temperatures (ie. space group P$4_2/m$). Additionally, given
+our prescribed values of $U$, we show that the COO induced insulating state in
+Ca$_2$CrReO$_6$ will survive even if the COD amplitude is suppressed (e.g. due
+to thermal fluctuations).
+"
+24143,Testing independence with high-dimensional correlated samples,"  Testing independence among a number of (ultra) high-dimensional random
+samples is a fundamental and challenging problem. By arranging $n$ identically
+distributed $p$-dimensional random vectors into a $p \times n$ data matrix, we
+investigate the problem of testing independence among columns under the
+matrix-variate normal modeling of data. We propose a computationally simple and
+tuning-free test statistic, characterize its limiting null distribution,
+analyze the statistical power and prove its minimax optimality. As an important
+by-product of the test statistic, a ratio-consistent estimator for the
+quadratic functional of a covariance matrix from correlated samples is
+developed. We further study the effect of correlation among samples to an
+important high-dimensional inference problem --- large-scale multiple testing
+of Pearson's correlation coefficients. Indeed, blindly using classical
+inference results based on the assumed independence of samples will lead to
+many false discoveries, which suggests the need for conducting independence
+testing before applying existing methods. To address the challenge arising from
+correlation among samples, we propose a ""sandwich estimator"" of Pearson's
+correlation coefficient by de-correlating the samples. Based on this approach,
+the resulting multiple testing procedure asymptotically controls the overall
+false discovery rate at the nominal level while maintaining good statistical
+power. Both simulated and real data experiments are carried out to demonstrate
+the advantages of the proposed methods.
+"
+24144,"Conditional Belief, Knowledge and Probability","  A natural way to represent beliefs and the process of updating beliefs is
+presented by Bayesian probability theory, where belief of an agent a in P can
+be interpreted as a considering that P is more probable than not P. This paper
+attempts to get at the core logical notion underlying this.
+The paper presents a sound and complete neighbourhood logic for conditional
+belief and knowledge, and traces the connections with probabilistic logics of
+belief and knowledge. The key notion in this paper is that of an agent a
+believing P conditionally on having information Q, where it is assumed that Q
+is compatible with what a knows.
+Conditional neighbourhood logic can be viewed as a core system for reasoning
+about subjective plausibility that is not yet committed to an interpretation in
+terms of numerical probability. Indeed, every weighted Kripke model gives rise
+to a conditional neighbourhood model, but not vice versa. We show that our
+calculus for conditional neighbourhood logic is sound but not complete for
+weighted Kripke models. Next, we show how to extend the calculus to get
+completeness for the class of weighted Kripke models.
+Neighbourhood models for conditional belief are closed under model
+restriction (public announcement update), while earlier neighbourhood models
+for belief as `willingness to bet' were not. Therefore the logic we present
+improves on earlier neighbourhood logics for belief and knowledge. We present
+complete calculi for public announcement and for publicly revealing the truth
+value of propositions using reduction axioms. The reductions show that adding
+these announcement operators to the language does not increase expressive
+power.
+"
+24145,Density waves in strongly correlated quantum chains,"  We review exact numerical results for one-dimensional quantum systems with
+half-filled bands. The topics covered include Peierls transitions in Holstein,
+Fröhlich, Su-Schrieffer-Heeger, and Heisenberg models with quantum phonons,
+competing fermion-boson and fermion-fermion interactions, as well as
+symmetry-protected topological states in fermion and anyon models.
+"
+24146,Statistics of optimal information flow in ensembles of regulatory motifs,"  Genetic regulatory circuits universally cope with different sources of noise
+that limit their ability to coordinate input and output signals. In many cases,
+optimal regulatory performance can be thought to correspond to configurations
+of variables and parameters that maximize the mutual information between inputs
+and outputs. Such optima have been well characterized in several biologically
+relevant cases over the past decade. Here we use methods of statistical field
+theory to calculate the statistics of the maximal mutual information (the
+`capacity') achievable by tuning the input variable only in an ensemble of
+regulatory motifs, such that a single controller regulates N targets. Assuming
+(i) sufficiently large N, (ii) quenched random kinetic parameters, and (iii)
+small noise affecting the input-output channels, we can accurately reproduce
+numerical simulations both for the mean capacity and for the whole
+distribution. Our results provide insight into the inherent variability in
+effectiveness occurring in regulatory systems with heterogeneous kinetic
+parameters.
+"
+24147,Technology Aware Training in Memristive Neuromorphic Systems based on non-ideal Synaptic Crossbars,"  The advances in the field of machine learning using neuromorphic systems have
+paved the pathway for extensive research on possibilities of hardware
+implementations of neural networks. Various memristive technologies such as
+oxide-based devices, spintronics and phase change materials have been explored
+to implement the core functional units of neuromorphic systems, namely the
+synaptic network, and the neuronal functionality, in a fast and energy
+efficient manner. However, various non-idealities in the crossbar
+implementations of the synaptic arrays can significantly degrade performance of
+neural networks and hence, impose restrictions on feasible crossbar sizes. In
+this work, we build mathematical models of various non-idealities that occur in
+crossbar implementations such as source resistance, neuron resistance and
+chip-to-chip device variations and analyze their impact on the classification
+accuracy of a fully connected network (FCN) and convolutional neural network
+(CNN) trained with standard training algorithm. We show that a network trained
+under ideal conditions can suffer accuracy degradation as large as 59.84% for
+FCNs and 62.4% for CNNs when implemented on non-ideal crossbars for relevant
+non-ideality ranges. This severely constrains the sizes for crossbars. As a
+solution, we propose a technology aware training algorithm which incorporates
+the mathematical models of the non-idealities in the standard training
+algorithm. We demonstrate that our proposed methodology achieves significant
+recovery of testing accuracy within 1.9% of the ideal accuracy for FCNs and
+1.5% for CNNs. We further show that our proposed training algorithm can
+potentially allow the use of significantly larger crossbar arrays of sizes
+784$\times$500 for FCNs and 4096$\times$512 for CNNs with a minor or no
+trade-off in accuracy
+"
+24148,Efficient Approximate Solutions to Mutual Information Based Global Feature Selection,"  Mutual Information (MI) is often used for feature selection when developing
+classifier models. Estimating the MI for a subset of features is often
+intractable. We demonstrate, that under the assumptions of conditional
+independence, MI between a subset of features can be expressed as the
+Conditional Mutual Information (CMI) between pairs of features. But selecting
+features with the highest CMI turns out to be a hard combinatorial problem. In
+this work, we have applied two unique global methods, Truncated Power Method
+(TPower) and Low Rank Bilinear Approximation (LowRank), to solve the feature
+selection problem. These algorithms provide very good approximations to the
+NP-hard CMI based feature selection problem. We experimentally demonstrate the
+effectiveness of these procedures across multiple datasets and compare them
+with existing MI based global and iterative feature selection procedures.
+"
+24149,A meromorphic extension of the 3D Index,"  Using the locally compact abelian group $\BT \times \BZ$, we assign a
+meromorphic function to each ideal triangulation of a 3-manifold with torus
+boundary components. The function is invariant under all 2--3 Pachner moves,
+and thus is a topological invariant of the underlying manifold. If the ideal
+triangulation has a strict angle structure, our meromorphic function can be
+expanded into a Laurent power series whose coefficients are formal power series
+in $q$ with integer coefficients that coincide with the 3D index of
+\cite{DGG2}. Our meromorphic function can be computed explicitly from the
+matrix of the gluing equations of a triangulation, and we illustrate this with
+several examples.
+"
+24150,A Partial Solution to Continuous Blotto,"  This paper analyzes the structure of mixed-strategy equilibria for Colonel
+Blotto games, where the outcome on each battlefield is a polynomial function of
+the difference between the two players' allocations. This paper severely
+reduces the set of strategies that needs to be searched to find a Nash
+equilibrium. It finds that there exists a Nash equilibrium where both players'
+mixed strategies are discrete distributions, and it places an upper bound on
+the number of points in the supports of these discrete distributions.
+"
+24151,Neural Variational Inference and Learning in Undirected Graphical Models,"  Many problems in machine learning are naturally expressed in the language of
+undirected graphical models. Here, we propose black-box learning and inference
+algorithms for undirected models that optimize a variational approximation to
+the log-likelihood of the model. Central to our approach is an upper bound on
+the log-partition function parametrized by a function q that we express as a
+flexible neural network. Our bound makes it possible to track the partition
+function during learning, to speed-up sampling, and to train a broad class of
+hybrid directed/undirected models via a unified variational inference
+framework. We empirically demonstrate the effectiveness of our method on
+several popular generative modeling datasets.
+"
+24152,Regularization via Mass Transportation,"  The goal of regression and classification methods in supervised learning is
+to minimize the empirical risk, that is, the expectation of some loss function
+quantifying the prediction error under the empirical distribution. When facing
+scarce training data, overfitting is typically mitigated by adding
+regularization terms to the objective that penalize hypothesis complexity. In
+this paper we introduce new regularization techniques using ideas from
+distributionally robust optimization, and we give new probabilistic
+interpretations to existing techniques. Specifically, we propose to minimize
+the worst-case expected loss, where the worst case is taken over the ball of
+all (continuous or discrete) distributions that have a bounded transportation
+distance from the (discrete) empirical distribution. By choosing the radius of
+this ball judiciously, we can guarantee that the worst-case expected loss
+provides an upper confidence bound on the loss on test data, thus offering new
+generalization bounds. We prove that the resulting regularized learning
+problems are tractable and can be tractably kernelized for many popular loss
+functions. We validate our theoretical out-of-sample guarantees through
+simulated and empirical experiments.
+"
+24153,Wasserstein GAN,"  We introduce a new algorithm named WGAN, an alternative to traditional GAN
+training. In this new model, we show that we can improve the stability of
+learning, get rid of problems like mode collapse, and provide meaningful
+learning curves useful for debugging and hyperparameter searches. Furthermore,
+we show that the corresponding optimization problem is sound, and provide
+extensive theoretical work highlighting the deep connections to other distances
+between distributions.
+"
+24154,Clustering Signed Networks with the Geometric Mean of Laplacians,"  Signed networks allow to model positive and negative relationships. We
+analyze existing extensions of spectral clustering to signed networks. It turns
+out that existing approaches do not recover the ground truth clustering in
+several situations where either the positive or the negative network structures
+contain no noise. Our analysis shows that these problems arise as existing
+approaches take some form of arithmetic mean of the Laplacians of the positive
+and negative part. As a solution we propose to use the geometric mean of the
+Laplacians of positive and negative part and show that it outperforms the
+existing approaches. While the geometric mean of matrices is computationally
+expensive, we show that eigenvectors of the geometric mean can be computed
+efficiently, leading to a numerical scheme for sparse matrices which is of
+independent interest.
+"
+24155,Stochastic parameterization identification using ensemble Kalman filtering combined with expectation-maximization and Newton-Raphson maximum likelihood methods,"  For modelling geophysical systems, large-scale processes are described
+through a set of coarse-grained dynamical equations while small-scale processes
+are represented via parameterizations. This work proposes a method for
+identifying the best possible stochastic parameterization from noisy data.
+State-the-art sequential estimation methods such as Kalman and particle filters
+do not achieve this goal succesfully because both suffer from the collapse of
+the parameter posterior distribution. To overcome this intrinsic limitation, we
+propose two statistical learning methods. They are based on the combination of
+two methodologies: the maximization of the likelihood via
+Expectation-Maximization (EM) and Newton-Raphson (NR) algorithms which are
+mainly applied in the statistic and machine learning communities, and the
+ensemble Kalman filter (EnKF). The methods are derived using a Bayesian
+approach for a hidden Markov model. They are applied to infer deterministic and
+stochastic physical parameters from noisy observations in coarse-grained
+dynamical models. Numerical experiments are conducted using the Lorenz-96
+dynamical system with one and two scales as a proof-of-concept. The imperfect
+coarse-grained model is modelled through a one-scale Lorenz-96 system in which
+a stochastic parameterization is incorpored to represent the small-scale
+dynamics. The algorithms are able to identify an optimal stochastic
+parameterization with a good accuracy under moderate observational noise. The
+proposed EnKF-EM and EnKF-NR are promising statistical learning methods for
+developing stochastic parameterizations in high-dimensional geophysical models.
+"
+24156,Are GANs Created Equal? A Large-Scale Study,"  Generative adversarial networks (GAN) are a powerful subclass of generative
+models. Despite a very rich research activity leading to numerous interesting
+GAN algorithms, it is still very hard to assess which algorithm(s) perform
+better than others. We conduct a neutral, multi-faceted large-scale empirical
+study on state-of-the art models and evaluation measures. We find that most
+models can reach similar scores with enough hyperparameter optimization and
+random restarts. This suggests that improvements can arise from a higher
+computational budget and tuning more than fundamental algorithmic changes. To
+overcome some limitations of the current metrics, we also propose several data
+sets on which precision and recall can be computed. Our experimental results
+suggest that future GAN research should be based on more systematic and
+objective evaluation procedures. Finally, we did not find evidence that any of
+the tested algorithms consistently outperforms the non-saturating GAN
+introduced in \cite{goodfellow2014generative}.
+"
+24157,What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing,"  Driven by new software development processes and testing in clouds, system
+and integration testing nowadays tends to produce enormous number of alarms.
+Such test alarms lay an almost unbearable burden on software testing engineers
+who have to manually analyze the causes of these alarms. The causes are
+critical because they decide which stakeholders are responsible to fix the bugs
+detected during the testing. In this paper, we present a novel approach that
+aims to relieve the burden by automating the procedure. Our approach, called
+Cause Analysis Model, exploits information retrieval techniques to efficiently
+infer test alarm causes based on test logs. We have developed a prototype and
+evaluated our tool on two industrial datasets with more than 14,000 test
+alarms. Experiments on the two datasets show that our tool achieves an accuracy
+of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by
+up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per
+cause analysis. Due to the attractive experimental results, our industrial
+partner, a leading information and communication technology company in the
+world, has deployed the tool and it achieves an average accuracy of 72% after
+two months of running, nearly three times more accurate than a previous
+strategy based on regular expressions.
+"
+24158,On Ulam type stability for nonlinear implicit fractional differential equations,"  In present paper, we establish sufficient conditions for existence and
+stability of solutions for system of nonlinear implicit fractional differential
+equations. The main techniques are based on method of successive
+approximations. Finally, an illustrative example is given to show the
+applicability of our theoretical results.
+"
+24159,Alignment of dynamic networks,"  Networks can model real-world systems in a variety of domains. Network
+alignment (NA) aims to find a node mapping that conserves similar regions
+between compared networks. NA is applicable to many fields, including
+computational biology, where NA can guide the transfer of biological knowledge
+from well- to poorly-studied species across aligned network regions. Existing
+NA methods can only align static networks. However, most complex real-world
+systems evolve over time and should thus be modeled as dynamic networks. We
+hypothesize that aligning dynamic network representations of evolving systems
+will produce superior alignments compared to aligning the systems' static
+network representations, as is currently done. For this purpose, we introduce
+the first ever dynamic NA method, DynaMAGNA++. This proof-of-concept dynamic NA
+method is an extension of a state-of-the-art static NA method, MAGNA++. Even
+though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation
+across the aligned networks, MAGNA++ conserves static edges and similarity
+between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges
+(events) and similarity between evolving node neighborhoods. For this purpose,
+we introduce the first ever measure of dynamic edge conservation and rely on
+our recent measure of dynamic node conservation. Importantly, the two dynamic
+conservation measures can be optimized using any state-of-the-art NA method and
+not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to
+static NA, under fair comparison conditions, on synthetic and real-world
+networks, in computational biology and social network domains. DynaMAGNA++ is
+parallelized and it includes a user-friendly graphical interface.
+"
+24160,Constraints on the Obliquities of Kepler Planet-Hosting Stars,"  Stars with hot Jupiters have obliquities ranging from 0-180 degrees, but
+relatively little is known about the obliquities of stars with smaller planets.
+Using data from the California-Kepler Survey, we investigate the obliquities of
+stars with planets spanning a wide range of sizes, most of which are smaller
+than Neptune. First, we identify 156 planet hosts for which measurements of the
+projected rotation velocity (vsini) and rotation period are both available. By
+combining estimates of v and vsini, we find nearly all the stars to be
+compatible with high inclination, and hence, low obliquity (less than about 20
+degrees). Second, we focus on a sample of 159 hot stars (> 6000K) for which
+vsini is available but not necessarily the rotation period. We find 6 stars for
+which vsini is anomalously low, an indicator of high obliquity. Half of these
+have hot Jupiters, even though only 3% of the stars that were searched have hot
+Jupiters. We also compare the vsini distribution of the hot stars with planets
+to that of 83 control stars selected without prior knowledge of planets. The
+mean vsini of the control stars is lower than that of the planet hosts by a
+factor of approximately pi/4, as one would expect if the planet hosts have low
+obliquities. All these findings suggest that the Kepler planet-hosting stars
+generally have low obliquities, with the exception of hot stars with hot
+Jupiters.
+"
+24161,Downlink Coverage Analysis for a Finite 3D Wireless Network of Unmanned Aerial Vehicles,"  In this paper, we consider a finite network of unmanned aerial vehicles
+(UAVs) serving a given region. Modeling this network as a uniform binomial
+point process (BPP), we derive the downlink coverage probability of a reference
+receiver located at an arbitrary position on the ground assuming Nakagami-$m$
+fading for all wireless links. The reference receiver is assumed to connect to
+its closest transmitting node as is usually the case in cellular systems. After
+deriving the distribution of distances from the reference receiver to the
+serving and interfering nodes, we derive an exact expression for downlink
+coverage probability in terms of the derivative of Laplace transform of
+interference power distribution. In the downlink of this system, it is not
+unusual to encounter scenarios in which the line-of-sight (LOS) component is
+significantly stronger than the reflected multipath components. To emulate such
+scenarios, we also derive the coverage probability in the absence of fading
+from the results of Nakagami-$m$ fading by taking the limit $m \to \infty$.
+Using asymptotic expansion of incomplete gamma function, we concretely show
+that this limit reduces to a redundant condition. Consequently, we derive an
+accurate coverage probability approximation for this case using dominant
+interferer-based approach in which the effect of dominant interferer is exactly
+captured and the residual interference from other interferers is carefully
+approximated. We then derive the bounds of the approximate coverage probability
+using Berry-Esseen theorem. Our analyses reveal several useful trends in
+coverage probability as a function of height of the transmitting nodes and the
+location of reference receiver on the ground.
+"
+24162,Source Code Verification for Embedded Systems using Prolog,"  System relevant embedded software needs to be reliable and, therefore, well
+tested, especially for aerospace systems. A common technique to verify programs
+is the analysis of their abstract syntax tree (AST). Tree structures can be
+elegantly analyzed with the logic programming language Prolog. Moreover, Prolog
+offers further advantages for a thorough analysis: On the one hand, it natively
+provides versatile options to efficiently process tree or graph data
+structures. On the other hand, Prolog's non-determinism and backtracking eases
+tests of different variations of the program flow without big effort. A
+rule-based approach with Prolog allows to characterize the verification goals
+in a concise and declarative way.
+In this paper, we describe our approach to verify the source code of a flash
+file system with the help of Prolog. The flash file system is written in C++
+and has been developed particularly for the use in satellites. We transform a
+given abstract syntax tree of C++ source code into Prolog facts and derive the
+call graph and the execution sequence (tree), which then are further tested
+against verification goals. The different program flow branching due to control
+structures is derived by backtracking as subtrees of the full execution
+sequence. Finally, these subtrees are verified in Prolog.
+We illustrate our approach with a case study, where we search for incorrect
+applications of semaphores in embedded software using the real-time operating
+system RODOS. We rely on computation tree logic (CTL) and have designed an
+embedded domain specific language (DSL) in Prolog to express the verification
+goals.
+"
+24163,Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis,"  The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a
+unifying model for representing a wide range of combinatorial optimization
+problems, and for linking a variety of disciplines that face these problems. A
+new class of quantum annealing computer that maps QUBO onto a physical qubit
+network structure with specific size and edge density restrictions is
+generating a growing interest in ways to transform the underlying QUBO
+structure into an equivalent graph having fewer nodes and edges. In this paper
+we present rules for reducing the size of the QUBO matrix by identifying
+variables whose value at optimality can be predetermined. We verify that the
+reductions improve both solution quality and time to solution and, in the case
+of metaheuristic methods where optimal solutions cannot be guaranteed, the
+quality of solutions obtained within reasonable time limits.
+We discuss the general QUBO structural characteristics that can take
+advantage of these reduction techniques and perform careful experimental design
+and analysis to identify and quantify the specific characteristics most
+affecting reduction. The rules make it possible to dramatically improve
+solution times on a new set of problems using both the exact Cplex solver and a
+tabu search metaheuristic.
+"
+24164,Two-Planar Graphs Are Quasiplanar,"  It is shown that every 2-planar graph is quasiplanar, that is, if a simple
+graph admits a drawing in the plane such that every edge is crossed at most
+twice, then it also admits a drawing in which no three edges pairwise cross. We
+further show that quasiplanarity is witnessed by a simple topological drawing,
+that is, any two edges cross at most once and adjacent edges do not cross.
+"
+24165,Domination in intersecting hypergraphs,"  A matching in a hypergraph $H$ is a set of pairwise disjoint hyperedges. The
+matching number $\alpha'(H)$ of $H$ is the size of a maximum matching in $H$. A
+subset $D$ of vertices of $H$ is a dominating set of $H$ if for every $v\in
+V\setminus D$ there exists $u\in D$ such that $u$ and $v$ lie in an hyperedge
+of $H$. The cardinality of a minimum dominating set of $H$ is called the
+domination number of $H$, denoted by $\gamma(H)$. It is known that for a
+intersecting hypergraph $H$ with rank $r$, $\gamma(H)\leq r-1$. In this paper
+we present structural properties on intersecting hypergraphs with rank $r$
+satisfying the equality $\gamma(H)=r-1$. By applying the properties we show
+that all linear intersecting hypergraphs $H$ with rank $4$ satisfying
+$\gamma(H)=r-1$ can be constructed by the well-known Fano plane.
+"
+24166,Single-View 3D Reconstruction of Correlated Gamma-Neutron Sources,"  We describe a new method of 3D image reconstruction of neutron sources that
+emit correlated gammas (e.g. Cf-252, Am-Be). This category includes a vast
+majority of neutron sources important in nuclear threat search, safeguards and
+non-proliferation. Rather than requiring multiple views of the source this
+technique relies on the source's intrinsic property of coincidence gamma and
+neutron emission. As a result only a single-view measurement of the source is
+required to perform the 3D reconstruction. In principle, any scatter camera
+sensitive to gammas and neutrons with adequate timing and interaction location
+resolution can perform this reconstruction. Using a neutron double scatter
+technique, we can calculate a conical surface of possible source locations. By
+including the time to a correlated gamma we further constrain the source
+location in three-dimensions by solving for the source-to-detector distance
+along the surface of said cone. As a proof of concept we applied these
+reconstruction techniques on measurements taken with the the Mobile Imager of
+Neutrons for Emergency Responders (MINER). Two Cf-252 sources measured at 50
+and 60 cm from the center of the detector were resolved in their varying depth
+with average radial distance relative resolution of 26%. To demonstrate the
+technique's potential with an optimized system we simulated the measurement in
+MCNPX-PoliMi assuming timing resolution of 200 ps (from 2 ns in the current
+system) and source interaction location resolution of 5 mm (from 3 cm). These
+simulated improvements in scatter camera performance resulted in radial
+distance relative resolution decreasing to an average of 11%.
+"
+24167,Pharmacophore and ligand-guided screening of antibacterial leads targeting antibiotic resistance factor in Gram-negative bacteria,"  Pathogenic Gram-negative bacteria have developed resistance to antibiotics
+due to their ability in creating an envelope on the outer layer of
+lipooligosaccharides (LOS). The cationic phosphoethanolamine (PEA) decoration
+of LOS lipid A is regulated by lipid A-PEA transferase (EptA) which may serve
+as a prominent target for developing new antibiotics. The structural
+characterization of Neisserial EptA has provided a structural basis to its
+catalytic mechanisms and ligand recognition that are crucial for inhibitor
+development. In this study, a combination of pharmacophore- and ligand-based
+approach has been employed to explore novel potent EptA inhibitors among
+millions of commercially-available compounds and approved drugs. A total of
+8166 hit molecules obtained from ZincPharmer pharmacophore-based screening and
+PubMed ligand similarity search were further examined through individual
+two-step semi-flexible docking simulation performed in MOE. Best hits were
+therefore selected based on their docking score and consensus of the two
+docking validations. Free energy of binding calculation suggests that the best
+20 consensus compounds have a stronger binding affinity than EptA natural
+substrate PEA. Further interaction analyses of selected eight ligands
+demonstrate that these ligands have overall more effective interactions with
+catalytically-essential residues and metal cofactors of EptA. Selected hits can
+be further analyzed in vitro and examined through a pre-clinical trial. This
+study provides an insight into drug repurposing which may serve as an initial
+step to develop novel potent EptA inhibitors to combat the virulence of
+multi-drug resistant Gram-negative bacteria.
+"
+24168,Numerical results on the short-range spin correlation functions in the ground state of the two-dimensional Hubbard model,"  Optical lattice experiments with ultracold fermion atoms and quantum gas
+microscopy have recently realized direct measurements of magnetic correlations
+at the site-resolved level. We calculate the short-range spin correlation
+functions in the ground state of the two-dimensional repulsive Hubbard model
+with the auxiliary-field Quantum Monte Carlo (AFQMC) method. The results are
+numerically exact at half filling where the fermion sign problem is absent.
+Away from half-filling, we employ the constrained path AFQMC approach to
+eliminate the exponential computational scaling from the sign problem. The
+constraint employs unrestricted Hartree-Fock trial wave-functions with an
+effective interaction strength U , which is optimized self-consistently within
+AFQMC. Large supercells are studied, with twist averaged boundary conditions as
+needed, to reach the thermodynamic limit. We find that the nearest-neighbor
+spin correlation always increases with the interaction strength U , contrary to
+the finite-temperature behavior where a maximum is reached at a finite U value.
+We also observe a change of sign in the next nearest neighbor spin correlation
+with increasing density, which is a consequence of the buildup of the
+long-range anti-ferromagnetic correlation. We expect the results presented in
+this work to serve as a benchmark as lower temperatures are reached in
+ultracold atom experiments.
+"
+24169,Rigidity of the Spacetime Positive Mass Theorem,"  We prove that if an asymptotically flat initial data set satisfies the
+dominant energy condition and has $E=|P|$, then $E=|P|=0$, where $(E, P)$ is
+the ADM energy-momentum vector. Previously the result was only known for spin
+manifolds [8, 14]. We consider a variational approach to the Regge-Teitelboim
+Hamiltonian, except that we use a modified constraint operator introduced by
+the first named author and J. Corvino [15] in place of the usual constraint
+operator. The spacetime positive mass inequality implies that an initial data
+set satisfying $E=|P|$ must locally minimize the modified Regge-Teitelboim
+Hamiltonian among initial data sets with the same modified constraints. The
+Lagrange multipliers corresponding to this constrained minimizer give rise to
+asymptotically vacuum Killing initial data which is also asymptotically
+translational. Earlier work of R. Beig and P. Chruściel [8] then implies
+that the ADM energy-momentum vector must be zero. Since the variational
+formalism takes place in the space of initial data sets of low regularity, we
+prove a spacetime positive mass inequality $E\ge |P|$ in the setting of
+weighted Sobolev spaces, which may be of independent interest.
+"
+24170,Provable Estimation of the Number of Blocks in Block Models,"  Community detection is a fundamental unsupervised learning problem for
+unlabeled networks which has a broad range of applications. Many community
+detection algorithms assume that the number of clusters $r$ is known apriori.
+In this paper, we propose an approach based on semi-definite relaxations, which
+does not require prior knowledge of model parameters like many existing convex
+relaxation methods and recovers the number of clusters and the clustering
+matrix exactly under a broad parameter regime, with probability tending to one.
+On a variety of simulated and real data experiments, we show that the proposed
+method often outperforms state-of-the-art techniques for estimating the number
+of clusters.
+"
+24171,MapReduce Scheduler: A 360-degree view,"  Undoubtedly, the MapReduce is the most powerful programming paradigm in
+distributed computing. The enhancement of the MapReduce is essential and it can
+lead the computing faster. Therefore, here are many scheduling algorithms to
+discuss based on their characteristics. Moreover, there are many shortcoming to
+discover in this field. In this article, we present the state-of-the-art
+scheduling algorithm to enhance the understanding of the algorithms. The
+algorithms are presented systematically such that there can be many future
+possibilities in scheduling algorithm through this article. In this paper, we
+provide in-depth insight on the MapReduce scheduling algorithm. In addition, we
+discuss various issues of MapReduce scheduler developed for large-scale
+computing as well as heterogeneous environment.
+"
+24172,A Traffic Model for Machine-Type Communications Using Spatial Point Processes,"  A source traffic model for machine-to-machine communications is presented in
+this paper. We consider a model in which devices operate in a regular mode
+until they are triggered into an alarm mode by an alarm event. The positions of
+devices and events are modeled by means of Poisson point processes, where the
+generated traffic by a given device depends on its position and event
+positions. We first consider the case where devices and events are static and
+devices generate traffic according to a Bernoulli process, where we derive the
+total rate from the devices at the base station. We then extend the model by
+defining a two-state Markov chain for each device, which allows for devices to
+stay in alarm mode for a geometrically distributed holding time. The temporal
+characteristics of this model are analyzed via the autocovariance function,
+where the effect of event density and mean holding time are shown.
+"
+24173,Comments on the proof of adaptive submodular function minimization,"  We point out an issue with Theorem 5 appearing in ""Group-based active query
+selection for rapid diagnosis in time-critical situations"". Theorem 5 bounds
+the expected number of queries for a greedy algorithm to identify the class of
+an item within a constant factor of optimal. The Theorem is based on
+correctness of a result on minimization of adaptive submodular functions. We
+present an example that shows that a critical step in Theorem A.11 of ""Adaptive
+Submodularity: Theory and Applications in Active Learning and Stochastic
+Optimization"" is incorrect.
+"
+24174,Topological states at the (001) surface of SrTiO3,"  Defect-free SrTiO3 (STO) is a band insulator but Angle Resolved Photoemission
+Spectroscopy (ARPES) experiments have demonstrated the existence of a nanometer
+thin two-dimensional electron liquid (2DEG) at the (001) oriented surface of
+this compound. The bulk is a trivial insulator, but our theoretical study
+reveals that the parity of electronic wavefunctions in this 2DEG is inverted in
+the vicinity of special points in reciprocal space where the low-energy
+dispersion consists of four gapped Dirac cones with a tilted and anisotropic
+shape. This gives rise to linearly dispersing topological edge states at the
+one-dimensional boundary. We propose to probe these modes by measuring the
+Josephson radiation from gapless bound Andreev states in STO based junctions,
+as it is predicted that they display distinctive signatures of topology.
+"
+24175,Summary Analysis of the 2017 GitHub Open Source Survey,"  This report is a high-level summary analysis of the 2017 GitHub Open Source
+Survey dataset, presenting frequency counts, proportions, and frequency or
+proportion bar plots for every question asked in the survey.
+"
+24176,Multi-scale Spectrum Sensing in Small-Cell mm-Wave Cognitive Wireless Networks,"  In this paper, a multi-scale approach to spectrum sensing in cognitive
+cellular networks is proposed. In order to overcome the huge cost incurred in
+the acquisition of full network state information, a hierarchical scheme is
+proposed, based on which local state estimates are aggregated up the hierarchy
+to obtain aggregate state information at multiple scales, which are then sent
+back to each cell for local decision making. Thus, each cell obtains
+fine-grained estimates of the channel occupancies of nearby cells, but
+coarse-grained estimates of those of distant cells. The performance of the
+aggregation scheme is studied in terms of the trade-off between the throughput
+achievable by secondary users and the interference generated by the activity of
+these secondary users to primary users. In order to account for the irregular
+structure of interference patterns arising from path loss, shadowing, and
+blockages, which are especially relevant in millimeter wave networks, a greedy
+algorithm is proposed to find a multi-scale aggregation tree to optimize the
+performance. It is shown numerically that this tailored hierarchy outperforms a
+regular tree construction by 60%.
+"
+24177,Vision-based Robotic Arm Imitation by Human Gesture,"  One of the most efficient ways for a learning-based robotic arm to learn to
+process complex tasks as human, is to directly learn from observing how human
+complete those tasks, and then imitate. Our idea is based on success of Deep
+Q-Learning (DQN) algorithm according to reinforcement learning, and then extend
+to Deep Deterministic Policy Gradient (DDPG) algorithm. We developed a
+learning-based method, combining modified DDPG and visual imitation network.
+Our approach acquires frames only from a monocular camera, and no need to
+either construct a 3D environment or generate actual points. The result we
+expected during training, was that robot would be able to move as almost the
+same as how human hands did.
+"
+24178,The Nonlinear Schrodinger equation with a potential in dimension 1,"  We consider the cubic nonlinear Schrodinger equation with a potential in one
+space dimension. Under the assumptions that the potential is generic,
+sufficiently localized, and does not have bound states, we obtain the long time
+asymptotic behavior of small solutions. In particular, we prove that, as time
+goes to infinity, solutions exhibit nonlinear phase corrections that depend on
+the scattering matrix associated to the potential. The proof of our result is
+based on the use of the distorted Fourier transform - the so-called
+Weyl-Kodaira-Titchmarsh theory -, a precise understanding of the ""nonlinear
+spectral measure"" associated to the equation, and nonlinear stationary phase
+arguments as well as multilinear estimates in this distorted setting.
+"
+24179,On the efficiency of the de-biased Lasso,"  We consider the high-dimensional linear regression model $Y = X \beta^0 +
+\epsilon$ with Gaussian noise $\epsilon$ and Gaussian random design $X$. We
+assume that $\Sigma:= E X^T X / n$ is non-singular and write its inverse as
+$\Theta := \Sigma^{-1}$. The parameter of interest is the first component
+$\beta_1^0$ of $\beta^0$. We show that in the high-dimensional case the
+asymptotic variance of a debiased Lasso estimator can be smaller than
+$\Theta_{1,1}$. For some special such cases we establish asymptotic efficiency.
+The conditions include $\beta^0$ being sparse and the first column $\Theta_1$
+of $\Theta$ being not sparse. These conditions depend on whether $\Sigma$ is
+known or not.
+"
+24180,Wavefunctions for large electronic systems,"  Wavefunctions for large electron numbers suffer from an exponential growth of
+the Hilbert space which is required for their description. In fact, as pointed
+out by W. Kohn, for electron numbers $N > N_0$ where $N_0 \approx 10^3$ they
+become meaningless (exponential wall problem). Nevertheless, despite of the
+enormous successes of density functional theory, one would also like to develop
+electronic structure calculations for large systems based on wavefunctions.
+This is possible if one defines the latter in Liouville space with a cumulant
+metric rather than in Hilbert space. The cluster expansion of the free energy
+of a classical monoatomic gas makes it plausible that cumulants are a proper
+tool for electronic structure calculations.
+"
+24181,Extensions of Morse-Smale Regression with Application to Actuarial Science,"  The problem of subgroups is ubiquitous in scientific research (ex. disease
+heterogeneity, spatial distributions in ecology...), and piecewise regression
+is one way to deal with this phenomenon. Morse-Smale regression offers a way to
+partition the regression function based on level sets of a defined function and
+that function's basins of attraction. This topologically-based piecewise
+regression algorithm has shown promise in its initial applications, but the
+current implementation in the literature has been limited to elastic net and
+generalized linear regression. It is possible that nonparametric methods, such
+as random forest or conditional inference trees, may provide better prediction
+and insight through modeling interaction terms and other nonlinear
+relationships between predictors and a given outcome.
+This study explores the use of several machine learning algorithms within a
+Morse-Smale piecewise regression framework, including boosted regression with
+linear baselearners, homotopy-based LASSO, conditional inference trees, random
+forest, and a wide neural network framework called extreme learning machines.
+Simulations on Tweedie regression problems with varying Tweedie parameter and
+dispersion suggest that many machine learning approaches to Morse-Smale
+piecewise regression improve the original algorithm's performance, particularly
+for outcomes with lower dispersion and linear or a mix of linear and nonlinear
+predictor relationships. On a real actuarial problem, several of these new
+algorithms perform as good as or better than the original Morse-Smale
+regression algorithm, and most provide information on the nature of predictor
+relationships within each partition to provide insight into differences between
+dataset partitions.
+"
+24182,A Survey on Homomorphic Encryption Schemes: Theory and Implementation,"  Legacy encryption systems depend on sharing a key (public or private) among
+the peers involved in exchanging an encrypted message. However, this approach
+poses privacy concerns. Especially with popular cloud services, the control
+over the privacy of the sensitive data is lost. Even when the keys are not
+shared, the encrypted material is shared with a third party that does not
+necessarily need to access the content. Moreover, untrusted servers, providers,
+and cloud operators can keep identifying elements of users long after users end
+the relationship with the services. Indeed, Homomorphic Encryption (HE), a
+special kind of encryption scheme, can address these concerns as it allows any
+third party to operate on the encrypted data without decrypting it in advance.
+Although this extremely useful feature of the HE scheme has been known for over
+30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE)
+scheme, which allows any computable function to perform on the encrypted data,
+was introduced by Craig Gentry in 2009. Even though this was a major
+achievement, different implementations so far demonstrated that FHE still needs
+to be improved significantly to be practical on every platform. First, we
+present the basics of HE and the details of the well-known Partially
+Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which
+are important pillars of achieving FHE. Then, the main FHE families, which have
+become the base for the other follow-up FHE schemes are presented. Furthermore,
+the implementations and recent improvements in Gentry-type FHE schemes are also
+surveyed. Finally, further research directions are discussed. This survey is
+intended to give a clear knowledge and foundation to researchers and
+practitioners interested in knowing, applying, as well as extending the state
+of the art HE, PHE, SWHE, and FHE systems.
+"
+24183,Rapid Sampling of Stochastic Displacements in Brownian Dynamics Simulations with Stresslet Constraints,"  Brownian Dynamics simulations are an important tool for modeling the dynamics
+of soft matter. However, accurate and rapid computations of the hydrodynamic
+interactions between suspended, microscopic components in a soft material is a
+significant computational challenge. Here, we present a new method for Brownian
+Dynamics simulations of suspended colloidal scale particles subject to an
+important class of hydrodynamic constraints with practically linear scaling of
+the computational cost with the number of particles modeled. Specifically, we
+consider the ""stresslet"" constraint for which suspended particles resist local
+deformation. The presented method is an extension of the recently reported
+positively-split formulation for Ewald summation of the Rotne-Prager-Yamakawa
+(RPY) mobility tensor to higher order (dipole) terms in the hydrodynamic
+scattering series [Andrew M. Fiore et al. The Journal of Chemical Physics,
+146(12):124116, 2017]. The hydrodynamic mobility tensor, which is proportional
+to the covariance of particle Brownian displacements, is constructed as an
+Ewald sum in a novel way that guarantees the real-space and wave-space sums are
+independently positive-definite for all possible particle configurations. This
+property is leveraged to rapidly sample the Brownian displacements from a
+superposition of statistically independent processes with the wave-space and
+real-space contributions as respective covariances. The cost of computing the
+Brownian displacements in this way is comparable to the cost of computing the
+deterministic displacements. Addition of a stresslet constraint to the
+over-damped particle equations of motion leads to a stochastic differential
+algebraic equation (SDAE) of index 1, which is integrated in time using a
+mid-point integration scheme that implicitly produces displacements consistent
+with the fluctuation-dissipation theorem for the constrained system.
+"
+24184,X-ray Spectro-polarimetry with Photoelectric Polarimeters,"  We derive a generalization of forward fitting for X-ray spectroscopy to
+include linear polarization of X-ray sources, appropriate for the anticipated
+next generation of space-based photoelectric polarimeters. We show that the
+inclusion of polarization sensitivity requires joint fitting to three observed
+spectra, one for each of the Stoke's parameters, I(E), U(E), and Q(E). The
+equations for Stoke's I(E) (the total intensity spectrum) are identical to the
+familiar case with no polarization sensitivity, and for which the
+model-predicted spectrum is obtained by a convolution of the source spectrum,
+F(E'), with the familiar energy response function, e(E')*R(E', E), where e(E')
+and R(E', E) are the effective area and energy redistribution matrix,
+respectively. In addition to the energy spectrum, the two new relations for
+U(E) and Q(E) include the source polarization fraction and position angle
+versus energy, a(E'), and psi'_0(E'), respectively, and the model-predicted
+spectra for these relations are obtained by a convolution with the ""modulated""
+energy response function, m(E')*e(E')R(E, E'), where m(E') is the
+energy-dependent modulation fraction that quantifies a polarimeter's angular
+response to 100% polarized radiation. We present results of simulations with
+response parameters appropriate for the proposed PRAXyS Small Explorer
+observatory to illustrate the procedures and methods, and we discuss some
+aspects of photoelectric polarimeters with relevance to understanding their
+calibration and operation.
+"
+24185,Tune: A Research Platform for Distributed Model Selection and Training,"  Modern machine learning algorithms are increasingly computationally
+demanding, requiring specialized hardware and distributed computation to
+achieve high performance in a reasonable time frame. Many hyperparameter search
+algorithms have been proposed for improving the efficiency of model selection,
+however their adaptation to the distributed compute environment is often
+ad-hoc. We propose Tune, a unified framework for model selection and training
+that provides a narrow-waist interface between training scripts and search
+algorithms. We show that this interface meets the requirements for a broad
+range of hyperparameter search algorithms, allows straightforward scaling of
+search to large clusters, and simplifies algorithm implementation. We
+demonstrate the implementation of several state-of-the-art hyperparameter
+search algorithms in Tune. Tune is available at
+this http URL.
+"
+24186,keV sterile neutrino Dark Matter,"  We give an overview of the current status of keV sterile neutrino Dark
+Matter. After a short introduction, we start by a general discussion of
+non-thermal production of Dark Matter, which applies to the three most commonly
+discussed mechanisms to produce sterile neutrino Dark Matter in the Universe:
+non-resonant, resonant, and decay production. The main goal in each case is to
+compute the momentum distribution function $f(p,t)$, which incorporates the
+full information about the Dark Matter setting under consideration, at least in
+what concerns its cosmological aspects. While some difficulties lie in the
+actual computation of this quantity, it is decisive to obtain bounds from
+cosmic structure formation, which turn out to be the most crucial ones to
+distinguish different types of production. We will introduce these bounds and
+we put the resulting limits into a proper context, thereby illustrating that a
+significant amount of relevant parameter space is available, part of which is
+testable in particular by Lyman-$\alpha$ data.
+"
+24187,Node Authentication Using BLS Signature in Distributed PKI Based MANETS,"  Authenticating a node in mobile ad-hoc networks is a challenging task due to
+their dynamic and resource constraint infrastructure. For this purpose, MANETS
+adopt two kinds of approaches Public key cryptography and identity-based
+cryptography. In Public Key Infrastructure (PKI), Certificate Authority (CA) is
+responsible for key management. In order to adapt it to MANET, the job of the
+CA must be distributed. The master secret key is shared among the nodes of the
+MANET, to self-organize the network without a central authority.The key is
+shared based on Shamir secret sharing scheme with bivariate polynomial to make
+the MANET fully self-managed by nodes.In this paper, we considered PKI based
+scenario and proposed a new scheme to authenticate a node using BLS signature
+which is light weight compared to the existing schemes thus making it suitable
+for MANET.
+"
+24188,An Instability in Variational Inference for Topic Models,"  Topic models are Bayesian models that are frequently used to capture the
+latent structure of certain corpora of documents or images. Each data element
+in such a corpus (for instance each item in a collection of scientific
+articles) is regarded as a convex combination of a small number of vectors
+corresponding to `topics' or `components'. The weights are assumed to have a
+Dirichlet prior distribution. The standard approach towards approximating the
+posterior is to use variational inference algorithms, and in particular a mean
+field approximation.
+We show that this approach suffers from an instability that can produce
+misleading conclusions. Namely, for certain regimes of the model parameters,
+variational inference outputs a non-trivial decomposition into topics. However
+--for the same parameter values-- the data contain no actual information about
+the true decomposition, and hence the output of the algorithm is uncorrelated
+with the true topic decomposition. Among other consequences, the estimated
+posterior mean is significantly wrong, and estimated Bayesian credible regions
+do not achieve the nominal coverage. We discuss how this instability is
+remedied by more accurate mean field approximations.
+"
+24189,Robust causal structure learning with some hidden variables,"  We introduce a new method to estimate the Markov equivalence class of a
+directed acyclic graph (DAG) in the presence of hidden variables, in settings
+where the underlying DAG among the observed variables is sparse, and there are
+a few hidden variables that have a direct effect on many of the observed ones.
+Building on the so-called low rank plus sparse framework, we suggest a
+two-stage approach which first removes the effect of the hidden variables, and
+then estimates the Markov equivalence class of the underlying DAG under the
+assumption that there are no remaining hidden variables. This approach is
+consistent in certain high-dimensional regimes and performs favourably when
+compared to the state of the art, both in terms of graphical structure recovery
+and total causal effect estimation.
+"
+24190,Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization,"  Patch-based sparse representation modeling has shown great potential in image
+compressive sensing (CS) reconstruction. However, this model usually suffers
+from some limits, such as dictionary learning with great computational
+complexity, neglecting the relationship among similar patches. In this paper, a
+group-based sparse representation method with non-convex regularization
+(GSR-NCR) for image CS reconstruction is proposed. In GSR-NCR, the local
+sparsity and nonlocal self-similarity of images is simultaneously considered in
+a unified framework. Different from the previous methods based on
+sparsity-promoting convex regularization, we extend the non-convex weighted Lp
+(0 < p < 1) penalty function on group sparse coefficients of the data matrix,
+rather than conventional L1-based regularization. To reduce the computational
+complexity, instead of learning the dictionary with a high computational
+complexity from natural images, we learn the principle component analysis (PCA)
+based dictionary for each group. Moreover, to make the proposed scheme
+tractable and robust, we have developed an efficient iterative
+shrinkage/thresholding algorithm to solve the non-convex optimization problem.
+Experimental results demonstrate that the proposed method outperforms many
+state-of-the-art techniques for image CS reconstruction.
+"
+24191,Mathematics of Market Microstructure under Asymmetric Information,"  These are the lecture notes for the summer course given for 2018 Mathematical
+Finance Summer School at Shandong Unversity. It contains a brief introduction
+to the Kyle model and the related topics in filtering, enlargement of
+filtrations and Markov bridges.
+"
+24192,$G_2$-structures and quantization of non-geometric M-theory backgrounds,"  We describe the quantization of a four-dimensional locally non-geometric
+M-theory background dual to a twisted three-torus by deriving a phase space
+star product for deformation quantization of quasi-Poisson brackets related to
+the nonassociative algebra of octonions. The construction is based on a choice
+of $G_2$-structure which defines a nonassociative deformation of the addition
+law on the seven-dimensional vector space of Fourier momenta. We demonstrate
+explicitly that this star product reduces to that of the three-dimensional
+parabolic constant $R$-flux model in the contraction of M-theory to string
+theory, and use it to derive quantum phase space uncertainty relations as well
+as triproducts for the nonassociative geometry of the four-dimensional
+configuration space. By extending the $G_2$-structure to a $Spin(7)$-structure,
+we propose a 3-algebra structure on the full eight-dimensional M2-brane phase
+space which reduces to the quasi-Poisson algebra after imposing a particular
+gauge constraint, and whose deformation quantisation simultaneously encompasses
+both the phase space star products and the configuration space triproducts. We
+demonstrate how these structures naturally fit in with previous occurences of
+3-algebras in M-theory.
+"
+24193,Radon Mitigation for the SuperCDMS SNOLAB Dark Matter Experiment,"  A potential background for the SuperCDMS SNOLAB dark matter experiment is
+from radon daughters that have plated out onto detector surfaces. To reach
+desired backgrounds, understanding plate-out rates during detector fabrication
+as well as mitigating radon in surrounding air is critical. A radon mitigated
+cleanroom planned at SNOLAB builds upon a system commissioned at the South
+Dakota School of Mines & Technology (SD Mines). The ultra-low radon cleanroom
+at SD Mines has air supplied by a vacuum-swing-adsorption radon mitigation
+system that has achieved $>$1000$\times$ reduction for a cleanroom activity
+consistent with zero and $<0.067\,$Bq$\,$m$^{-3}$ at 90% confidence. Our
+simulation of this system, validated against calibration data, provides
+opportunity for increased understanding and optimization for this and future
+systems.
+"
+24194,"Ordeal Mechanisms, Information, and the Cost-Effectiveness of Subsidies: Evidence from Subsidized Eyeglasses in Rural China","  The cost-effectiveness of policies providing subsidized goods is often
+compromised by limited use of the goods provided. Through a randomized trial,
+we test two approaches to improve the cost-effectiveness of a program
+distributing free eyeglasses to myopic children in rural China. Requiring
+recipients to undergo an ordeal better targeted eyeglasses to those who used
+them without reducing usage relative to free delivery. An information campaign
+increased use when eyeglasses were freely delivered but not under an ordeal.
+Free delivery plus information was determined to be the most socially
+cost-effective approach and obtained the highest rate of eyeglass use.
+"
+24195,Optical Properties of Bogoliubov Quasiparticles,"  We calculated the optical conductivity $\sigma(T,\Omega)$ of a gas of
+Bogoliubov quasiparticles (BQP) from their Green's function and the Kubo
+formula. We compare with corresponding normal state (N) and superconducting
+state (SC) results. The superconducting case includes the dynamic response of
+the condensate through additional contributions to the Kubo formula involving
+the Gor'kov anomalous Green's function. The differences in the optical
+scattering rate are largest just above the optical gap and become progressively
+smaller as the photon energy is increased or the temperature is raised. Our
+results are compared with those obtained using a recently advocated
+phenomenological procedure for eliminating the effect of the condensate [1].
+The $\delta$-function contribution at zero photon energy, proportional to the
+superfluid density, is dropped in the real part of the conductivity
+$[\sigma_1(T,\Omega)]$ and its Kramers-Kronig transform is subtracted from the
+imaginary part $\sigma_2(T,\Omega)$. This results in deviations from our BQP
+and superconducting state optical scattering rates even in the region where
+these have merged and are, in addition, close to the normal state result.
+"
+24196,Simple expressions for the holed torus relations,"  In the mapping class group of a $k$-holed torus with $0 \leq k \leq 9$, one
+can factorize the boundary multi-twist (or the identity when $k=0$) as the
+product of twelve right-handed Dehn twists. Such factorizations were explicitly
+given by Korkmaz and Ozbagci for each $k \leq 9$ and an alternative one for
+$k=8$ by Tanaka. In this note, we simplify their expressions for the $k$-holed
+torus relations.
+"
+24197,Customising Agent Based Analysis Towards Analysis of Disaster Management Knowledge,"  In developed countries such as Australia, for recurring disasters (e.g.
+floods), there are dedicated document repositories of Disaster Management Plans
+(DISPLANs), and supporting doctrine and processes that are used to prepare
+organisations and communities for disasters. They are maintained on an ongoing
+cyclical basis and form a key information source for community education,
+engagement and awareness programme in the preparation for and mitigation of
+disasters. DISPLANS, generally in semi-structured text document format, are
+then accessed and activated during the response and recovery to incidents to
+coordinate emergency service and community safety actions. However, accessing
+the appropriate plan and the specific knowledge within the text document from
+across its conceptual areas in a timely manner and sharing activities between
+stakeholders requires intimate domain knowledge of the plan contents and its
+development. This paper describes progress on an ongoing project with NSW State
+Emergency Service (NSW SES) to convert DISPLANs into a collection of knowledge
+units that can be stored in a unified repository with the goal to form the
+basis of a future knowledge sharing capability. All Australian emergency
+services covering a wide range of hazards develop DISPLANs of various structure
+and intent, in general the plans are created as instances of a template, for
+example those which are developed centrally by the NSW and Victorian SESs State
+planning policies. In this paper, we illustrate how by using selected templates
+as part of an elaborate agent-based process, we can apply agent-oriented
+analysis more efficiently to convert extant DISPLANs into a centralised
+repository. The repository is structured as a layered abstraction according to
+Meta Object Facility (MOF). The work is illustrated using DISPLANs along the
+flood-prone Murrumbidgee River in central NSW.
+"
+24198,A Penalty Method for Rank Minimization Problems in Symmetric Matrices,"  The problem of minimizing the rank of a symmetric positive semidefinite
+matrix subject to constraints can be cast equivalently as a semidefinite
+program with complementarity constraints (SDCMPCC). The formulation requires
+two positive semidefinite matrices to be complementary. This is a continuous
+and nonconvex reformulation of the rank minimization problem. We investigate
+calmness of locally optimal solutions to the SDCMPCC formulation and hence show
+that any locally optimal solution is a KKT point.
+We develop a penalty formulation of the problem. We present calmness results
+for locally optimal solutions to the penalty formulation. We also develop a
+proximal alternating linearized minimization (PALM) scheme for the penalty
+formulation, and investigate the incorporation of a momentum term into the
+algorithm. Computational results are presented.
+"
+24199,Mutualistic network maximizing species abundance under exploitative competition,"  Nutrients from a flowering plant are shared by its pollinators, giving rise
+to competition in the latter. Such exploitative competition of pollinators can
+limit their abundance and affect the global organization of the mutualistic
+partnership in the plant-pollinator mutualistic community. Here we generalize a
+model of the mutualistic network evolution towards increasing the species
+abundance [Suweis et al., Nature 500, 449 (2013)] by including the exploitative
+competition between pollinators, in addition to mutualism, in the abundance
+dynamics. Simulations show different emergent network characteristics between
+plants and animals; hub plants connected to many pollinators are very rare
+while a few super-hub pollinators appear with the exploitative competition
+included, in contrast to equally many hubs of both types without the
+exploitative competition. More interestingly, the abundance of plant species
+increases with increasing the exploitative competition strength. The inverse of
+the generalized interaction matrix analyzed in the weak-interaction limit
+allows us to identify the leading structural factors relevant to the species
+abundance, which are shown to be instrumental in optimizing the network
+structure to increase the mutualistic benefit and lower the cost of
+exploitative competition.
+"
+24200,"Mutual Information, Neural Networks and the Renormalization Group","  Physical systems differring in their microscopic details often display
+strikingly similar behaviour when probed at macroscopic scales. Those universal
+properties, largely determining their physical characteristics, are revealed by
+the powerful renormalization group (RG) procedure, which systematically retains
+""slow"" degrees of freedom and integrates out the rest. However, the important
+degrees of freedom may be difficult to identify. Here we demonstrate a machine
+learning algorithm capable of identifying the relevant degrees of freedom and
+executing RG steps iteratively without any prior knowledge about the system. We
+introduce an artificial neural network based on a model-independent,
+information-theoretic characterization of a real-space RG procedure, performing
+this task. We apply the algorithm to classical statistical physics problems in
+one and two dimensions. We demonstrate RG flow and extract the Ising critical
+exponent. Our results demonstrate that machine learning techniques can extract
+abstract physical concepts and consequently become an integral part of theory-
+and model-building.
+"
+24201,"Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor","  Using supporting backchannel (BC) cues can make human-computer interaction
+more social. BCs provide a feedback from the listener to the speaker indicating
+to the speaker that he is still listened to. BCs can be expressed in different
+ways, depending on the modality of the interaction, for example as gestures or
+acoustic cues. In this work, we only considered acoustic cues. We are proposing
+an approach towards detecting BC opportunities based on acoustic input features
+like power and pitch. While other works in the field rely on the use of a
+hand-written rule set or specialized features, we made use of artificial neural
+networks. They are capable of deriving higher order features from input
+features themselves. In our setup, we first used a fully connected feed-forward
+network to establish an updated baseline in comparison to our previously
+proposed setup. We also extended this setup by the use of Long Short-Term
+Memory (LSTM) networks which have shown to outperform feed-forward based setups
+on various tasks. Our best system achieved an F1-Score of 0.37 using power and
+pitch features. Adding linguistic information using word2vec, the score
+increased to 0.39.
+"
+24202,Adversarial Generalized Method of Moments,"  We provide an approach for learning deep neural net representations of models
+described via conditional moment restrictions. Conditional moment restrictions
+are widely used, as they are the language by which social scientists describe
+the assumptions they make to enable causal inference. We formulate the problem
+of estimating the underling model as a zero-sum game between a modeler and an
+adversary and apply adversarial training. Our approach is similar in nature to
+Generative Adversarial Networks (GAN), though here the modeler is learning a
+representation of a function that satisfies a continuum of moment conditions
+and the adversary is identifying violating moments. We outline ways of
+constructing effective adversaries in practice, including kernels centered by
+k-means clustering, and random forests. We examine the practical performance of
+our approach in the setting of non-parametric instrumental variable regression.
+"
+24203,Generalized Pascal triangle for binomial coefficients of words,"  We introduce a generalization of Pascal triangle based on binomial
+coefficients of finite words. These coefficients count the number of times a
+word appears as a subsequence of another finite word. Similarly to the
+Sierpiński gasket that can be built as the limit set, for the Hausdorff
+distance, of a convergent sequence of normalized compact blocks extracted from
+Pascal triangle modulo $2$, we describe and study the first properties of the
+subset of $[0, 1] \times [0, 1]$ associated with this extended Pascal triangle
+modulo a prime $p$.
+"
+24204,Computing optimal strategy for cop in the game of Cop v.s. Gambler,"  We present two efficient algorithms that compute the optimal strategy for cop
+in the game of Cop v.s. Gambler where the gambler's strategy is not optimal but
+known to the cop. The first algorithm is analogous to Bellman-Ford algorithm
+for single source shortest path problem and runs in $O(|V(G)||E(G)|)$ time. The
+second is analogous to Dijkstra's algorithm and runs in $O(|E(G)|+|V(G)|\log
+|V(G)|)$ time. Compared with each other, they are more suitable for sparse and
+dense graphs, respectively.
+"
+24205,"Diffusiophoresis in Cells: a General Non-Equilibrium, Non-Motor Mechanism for the Metabolism-Dependent Transport of Particles in Cells","  The more we learn about the cytoplasm of cells, the more we realise that the
+cytoplasm is not uniform but instead is highly inhomogeneous. In any
+inhomogeneous solution, there are concentration gradients, and particles move
+either up or down these gradients due to a mechanism called diffusiophoresis. I
+estimate that inside metabolically active cells, the dynamics of particles can
+be strongly accelerated by diffusiophoresis, provided that they are at least
+tens of nanometres across. The dynamics of smaller objects, such as single
+proteins are largely unaffected.
+"
+24206,Minimax density estimation for growing dimension,"  This paper presents minimax rates for density estimation when the data
+dimension $d$ is allowed to grow with the number of observations $n$ rather
+than remaining fixed as in previous analyses. We prove a non-asymptotic lower
+bound which gives the worst-case rate over standard classes of smooth
+densities, and we show that kernel density estimators achieve this rate. We
+also give oracle choices for the bandwidth and derive the fastest rate $d$ can
+grow with $n$ to maintain estimation consistency.
+"
+24207,On the Role of Caustic in Solar Gravitational Lens Imaging,"  We consider scattering of electromagnetic waves from a distant point source
+by the gravitational field of the sun, taking the field oblateness due to the
+quadrupole moment of the sun into account. Effects of the field oblateness can
+play an important role in the high resolution solar gravitational lens imaging
+in the sub-micrometer wavelength range of the electromagnetic spectrum.
+"
+24208,Fast nonparametric near-maximum likelihood estimation of a mixing density,"  Mixture models are regularly used in density estimation applications, but the
+problem of estimating the mixing distribution remains a challenge.
+Nonparametric maximum likelihood produce estimates of the mixing distribution
+that are discrete, and these may be hard to interpret when the true mixing
+distribution is believed to have a smooth density. In this paper, we
+investigate an algorithm that produces a sequence of smooth estimates that has
+been conjectured to converge to the nonparametric maximum likelihood estimator.
+Here we give a rigorous proof of this conjecture, and propose a new data-driven
+stopping rule that produces smooth near-maximum likelihood estimates of the
+mixing density, and simulations demonstrate the quality empirical performance
+of this estimator.
+"
+24209,Design of Quantum Annealing Machine for Prime Factoring,"  We propose a prime factoring machine operated in a frame work of quantum
+annealing (QA). The idea is inverse operation of a quantum-mechanically
+reversible multiplier implemented with QA-based Boolean logic circuits. We
+designed the QA machine on an application-specific-annealing-computing
+architecture which efficiently increases available hardware budgets at the cost
+of restricted functionality. The circuits are to be implemented and fabricated
+by using superconducting integrated circuit technology. We propose a
+three-dimensional packaging scheme of a qubit-chip / interposer /
+package-substrate structure for realizing practically-large scale QA systems.
+"
+24210,Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case,"  This paper advocates Riemannian multi-manifold modeling in the context of
+network-wide non-stationary time-series analysis. Time-series data, collected
+sequentially over time and across a network, yield features which are viewed as
+points in or close to a union of multiple submanifolds of a Riemannian
+manifold, and distinguishing disparate time series amounts to clustering
+multiple Riemannian submanifolds. To support the claim that exploiting the
+latent Riemannian geometry behind many statistical features of time series is
+beneficial to learning from network data, this paper focuses on brain networks
+and puts forth two feature-generation schemes for network-wide dynamic time
+series. The first is motivated by Granger-causality arguments and uses an
+auto-regressive moving average model to map low-rank linear vector subspaces,
+spanned by column vectors of appropriately defined observability matrices, to
+points into the Grassmann manifold. The second utilizes (non-linear)
+dependencies among network nodes by introducing kernel-based partial
+correlations to generate points in the manifold of positive-definite matrices.
+Capitilizing on recently developed research on clustering Riemannian
+submanifolds, an algorithm is provided for distinguishing time series based on
+their geometrical properties, revealed within Riemannian feature spaces.
+Extensive numerical tests demonstrate that the proposed framework outperforms
+classical and state-of-the-art techniques in clustering brain-network
+states/structures hidden beneath synthetic fMRI time series and brain-activity
+signals generated from real brain-network structural connectivity matrices.
+"
+24211,Evolution of linear wave dark matter perturbations in the radiation-dominant era,"  Linear perturbations of the wave dark matter, or $\psi$ dark matter
+($\psi$DM), of particle mass $\sim 10^{-22}$eV in the radiation-dominant era
+are analyzed, and the matter power spectrum at the photon-matter equality is
+obtained. We identify four phases of evolution for $\psi$DM perturbations,
+where the dynamics can be vastly different from the counterparts of cold dark
+matter (CDM). While in late stages after mass oscillation long-wave $\psi$DM
+perturbations are almost identical to CDM perturbations, some subtle
+differences remain, let alone intermediate-to-short waves that bear no
+resemblance with those of CDM throughout the whole evolutionary history. The
+dissimilarity is due to quantum mechanical effects which lead to severe mode
+suppression. We also discuss the axion model with a cosine field potential. The
+power spectrum of axion models are generally almost identical to those of
+$\psi$DM, but in the extreme case when the initial axion angle is near the
+field potential top, this axion model predict a power excess over a range of
+wave number and a higher spectral cutoff than $\psi$DM as if $\psi$DM had a
+higher particle mass.
+"
+24212,"Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module","  We introduce ScannerBit, the statistics and sampling module of the public,
+open-source global fitting framework GAMBIT. ScannerBit provides a standardised
+interface to different sampling algorithms, enabling the use and comparison of
+multiple computational methods for inferring profile likelihoods, Bayesian
+posteriors, and other statistical quantities. The current version offers
+random, grid, raster, nested sampling, differential evolution, Markov Chain
+Monte Carlo (MCMC) and ensemble Monte Carlo samplers. We also announce the
+release of a new standalone differential evolution sampler, Diver, and describe
+its design, usage and interface to ScannerBit. We subject Diver and three other
+samplers (the nested sampler MultiNest, the MCMC GreAT, and the native
+ScannerBit implementation of the ensemble Monte Carlo algorithm T-Walk) to a
+battery of statistical tests. For this we use a realistic physical likelihood
+function, based on the scalar singlet model of dark matter. We examine the
+performance of each sampler as a function of its adjustable settings, and the
+dimensionality of the sampling problem. We evaluate performance on four
+metrics: optimality of the best fit found, completeness in exploring the
+best-fit region, number of likelihood evaluations, and total runtime. For
+Bayesian posterior estimation at high resolution, T-Walk provides the most
+accurate and timely mapping of the full parameter space. For profile likelihood
+analysis in less than about ten dimensions, we find that Diver and MultiNest
+score similarly in terms of best fit and speed, outperforming GreAT and T-Walk;
+in ten or more dimensions, Diver substantially outperforms the other three
+samplers on all metrics.
+"
+24213,Home-Box based collaborative caching strategy: An asset for Content Delivery Networks,"  -- Within the Future Internet, a new trend is foreseen with the creation of
+overlay networks composed of residential gateways (i.e. Home-Box), leveraging
+their storage and upload capacity in order to achieve scalable and
+cost-efficient content distribution. In this paper, we highlight an
+architecture of such a home-box overlay for Video On Demand (VOD) services, in
+cooperation with a network-aware request redirection and content caching
+strategy that optimize the resource usage at both network and client side to
+reduce the overall distribution cost. The proposed system is compared to
+existing solutions through comprehensive simulations. The results demonstrate
+the strong advantage of introducing such a network-aware and popularity-based
+caching strategy in terms of cost reduction for VOD services, especially for
+Content Delivery Networks.
+"
+24214,On Affine and Conjugate Nonparametric Regression,"  Suppose the nonparametric regression function of a response variable $Y$ on
+covariates $X$ and $Z$ is an affine function of $X$ such that the slope $\beta$
+and the intercept $\alpha$ are real valued measurable functions on the range of
+the completely arbitrary random element $Z$. Assume that $X$ has a finite
+moment of order greater than or equal to $2$, $Y$ has a finite moment of
+conjugate order, and $\alpha\left(Z\right)$ and $\alpha\left(Z\right)X$ have
+finite first moments. Then, the nonparametric regression function equals the
+least squares linear regression function of $Y$ on $X$ with all the moments
+that appear in the expression of the linear regression function calculated
+conditional on $Z$. Consequently, conditional mean independence implies zero
+conditional covariance and a degenerate version of the aforesaid affine form
+for the nonparametric regression function, whereas the aforesaid affine form
+and zero conditional covariance imply conditional mean independence. Further,
+it turns out that the nonparametric regression function has the aforesaid
+affine form if $X$ is Bernoulli, and since $1$ is the conjugate exponent of
+$\infty$, the least squares linear regression formula for the nonparametric
+regression function holds when $Y$ has only a finite first moment and $Z$ is
+completely arbitrary.
+"
+24215,Three-dimensional flow in cavity with elevated helicity driven by parallel walls,"  The proposed flow in a 3-D cubic cavity is driven by its parallel walls
+moving in perpendicular directions to create a genuinely three-dimensional
+highly separated vortical flow yet having simple single-block cubical geometry
+of computational domain. The elevated level of helicity is caused by motion of
+a wall in the direction of axis of primary vortex created by a parallel wall.
+The velocity vector field is obtained numerically by using second-order upwind
+scheme and 200^3 grid. Helicity, magnitude of normalized helicity and kinematic
+vorticity number are evaluated for Reynolds numbers ranging from 100 to 1000.
+Formation of two primary vortices with their axis oriented perpendicularly and
+patterns of secondary vortices are discussed. Computational results are
+compared to the well-known 3-D recirculating cavity flow case where the lid
+moves in the direction parallel to the cavity side walls. Also results are
+compared to the diagonally top-driven cavity and to cavity flow driven by
+moving top and side walls. The streamlines for the proposed flow show that the
+particles emerging from top and bottom of cavity do mix well. Quantitative
+evaluation of mixing of two fluids in the proposed cavity flow confirms that
+the mixing occurs faster than in the benchmark case.
+"
+24216,Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks,"  We describe a novel spiking neural network (SNN) for automated, real-time
+handwritten digit classification and its implementation on a GP-GPU platform.
+Information processing within the network, from feature extraction to
+classification is implemented by mimicking the basic aspects of neuronal spike
+initiation and propagation in the brain. The feature extraction layer of the
+SNN uses fixed synaptic weight maps to extract the key features of the image
+and the classifier layer uses the recently developed NormAD approximate
+gradient descent based supervised learning algorithm for spiking neural
+networks to adjust the synaptic weights. On the standard MNIST database images
+of handwritten digits, our network achieves an accuracy of 99.80% on the
+training set and 98.06% on the test set, with nearly 7x fewer parameters
+compared to the state-of-the-art spiking networks. We further use this network
+in a GPU based user-interface system demonstrating real-time SNN simulation to
+infer digits written by different users. On a test set of 500 such images, this
+real-time platform achieves an accuracy exceeding 97% while making a prediction
+within an SNN emulation time of less than 100ms.
+"
+24217,Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model,"  Predicting the price correlation of two assets for future time periods is
+important in portfolio optimization. We apply LSTM recurrent neural networks
+(RNN) in predicting the stock price correlation coefficient of two individual
+stocks. RNNs are competent in understanding temporal dependencies. The use of
+LSTM cells further enhances its long term predictive properties. To encompass
+both linearity and nonlinearity in the model, we adopt the ARIMA model as well.
+The ARIMA model filters linear tendencies in the data and passes on the
+residual value to the LSTM model. The ARIMA LSTM hybrid model is tested against
+other traditional predictive financial models such as the full historical
+model, constant correlation model, single index model and the multi group
+model. In our empirical study, the predictive ability of the ARIMA-LSTM model
+turned out superior to all other financial models by a significant scale. Our
+work implies that it is worth considering the ARIMA LSTM model to forecast
+correlation coefficient for portfolio optimization.
+"
+24218,Matched Filters for Noisy Induced Subgraph Detection,"  The problem of finding the vertex correspondence between two noisy graphs
+with different number of vertices where the smaller graph is still large has
+many applications in social networks, neuroscience, and computer vision. We
+propose a solution to this problem via a graph matching matched filter:
+centering and padding the smaller adjacency matrix and applying graph matching
+methods to align it to the larger network. The centering and padding schemes
+can be incorporated into any algorithm that matches using adjacency matrices.
+Under a statistical model for correlated pairs of graphs, which yields a noisy
+copy of the small graph within the larger graph, the resulting optimization
+problem can be guaranteed to recover the true vertex correspondence between the
+networks.
+However, there are currently no efficient algorithms for solving this
+problem. To illustrate the possibilities and challenges of such problems, we
+use an algorithm that can exploit a partially known correspondence and show via
+varied simulations and applications to {\it Drosophila} and human connectomes
+that this approach can achieve good performance.
+"
+24219,Global stability in a mathematical model of de-radicalization,"  Radicalization is the process by which people come to adopt increasingly
+extreme political, social or religious ideologies. When radicalization leads to
+violence, radical thinking becomes a threat to national security.
+De-radicalization programs are part of an effort to combat violent extremism
+and terrorism. This type of initiatives attempt to alter violent extremists
+radical beliefs and violent behavior with the aim to reintegrate them into
+society. In this paper we introduce a simple compartmental model suitable to
+describe de-radicalization programs. The population is divided into four
+compartments: $ (S) $ susceptible, $ (E) $ extremists, $ (R) $ recruiters, and
+$ (T) $ treatment. We calculate the basic reproduction number $ \mathcal{R}_0
+$. For $ \mathcal{R}_0< 1 $ the system has one globally asymptotically stable
+equilibrium where no extremist or recruiters are present. For $ \mathcal{R}_0
+>1 $ the system has an additional equilibrium where extremists and recruiters
+are endemic to the population. A Lyapunov function is used to show that, for $
+\mathcal{R}_0 >1 $, the endemic equilibrium is globally asymptotically stable.
+We use numerical simulations to support our analytical results. Based on our
+model we asses strategies to counter violent extremism.
+"
+24220,Behavior of self-propelled acetone droplets in a Leidenfrost state on liquid substrates,"  It is demonstrated that non-coalescent droplets of acetone can be formed on
+liquid substrates. The fluid flows around and in an acetone droplet hovering on
+water are recorded to shed light on the mechanisms which might lead to
+non-coalescence. For sufficiently low impact velocities, droplets undergo a
+damped oscillation on the surface of the liquid substrate but at higher
+velocities clean bounce-off occurs. Comparisons of experimentally observed
+static configurations of floating droplets to predictions from a theoretical
+model for a small non-wetting rigid sphere resting on a liquid substrate are
+made and a tentative strategy for determining the thickness of the vapor layer
+under a small droplet on a liquid is proposed. This strategy is based on the
+notion of effective surface tension. The droplets show self-propulsion in
+straight line trajectories in a manner which can be ascribed to a Marangoni
+effect. Surprisingly, self-propelled droplets can become immersed beneath the
+undisturbed water surface. This phenomenon is reasoned to be drag-inducing and
+might provide a basis for refining observations in previous work.
+"
+24221,Rules and mechanisms governing octahedral tilts in perovskites under pressure,"  The rotation of octahedra (octahedral tilting) is common in ABO3 perovskites
+and relevant to many physical phenomena, ranging from electronic and magnetic
+properties, metal-insulator transitions to improper ferroelectricity.
+Hydrostatic pressure is an efficient way to tune and control octahedral
+tiltings. However, the pressure behavior of such tiltings can dramatically
+differ from one material to another, with the origins of such differences
+remaining controversial. In this work, we discover several new mechanisms and
+formulate a set of simple rules that allow to understand how pressure affects
+oxygen octahedral tiltings, via the use and analysis of first-principles
+results for a variety of compounds. Besides the known A-O interactions, we
+reveal that the interactions between specific B-ions and oxygen ions contribute
+to the tilting instability. We explain the previously reported trend that the
+derivative of the oxygen octahedral tilting with respect to pressure (dR/dP)
+usually decreases with both the tolerance factor and the ionization state of
+the A-ion, by illustrating the key role of A-O interactions and their change
+under pressure. Furthermore, three new mechanisms/rules are discovered. We
+further predict that the polarization associated with the so-called hybrid
+improper ferroelectricity could be manipulated by hydrostatic pressure, by
+indirectly controlling the amplitude of octahedral rotations.
+"
+24222,The effect of the stellar absorption line centre-to-limb variation on exoplanet transmission spectrum observations,"  Transit spectroscopy is one of the most commonly used techniques for
+exoplanet atmosphere characterisation. This technique has been used to detect
+ionized and neutral species in exoplanet atmospheres by comparing the observed
+stellar lines in and out of transit. The centre-to-limb variation (CLV) of the
+stellar lines across the stellar disk is an important effect for transmission
+spectroscopy, since it results in a change of stellar line depth when the
+planet transits different parts of the stellar disk. We reanalyse the transit
+data of HD 189733b taken with the HARPS spectrograph to study the CLV effect
+during transit. The transmission light curve of the Na i D line so obtained
+shows a clear imprint of the CLV effect. We use a one-dimensional non-LTE
+stellar spectral model to simulate the CLV effect. After applying the
+correction, the measurement of the Na i absorption in the atmosphere of HD
+189733b becomes better determined. We compare the CLV effect of HD 189733b to
+that of HD 209458b. The CLV effects are different for these two benchmark
+planetary systems and this is attributed to their different stellar effective
+temperatures and transit impact parameters. We then explore the general CLV
+effect that occurs during exoplanet transits. Normally, a star with a lower
+effective temperature exhibits a stronger CLV effect and its CLV feature
+extends over a relatively broad wavelength range. The transit impact parameter
+(b) describes the transit trajectory on the stellar disk and thus determines
+the actual manifestation of the CLV effect. We introduce a b-diagram which
+describes the behavior of the CLV effect as the function of different impact
+parameters. With improving observational precision, a careful modeling and
+correction of the CLV effect is necessary for exoplanet atmosphere
+characterisation using transit spectroscopy.
+"
+24223,Quantum Communication and Quantum Multivariate Polynomial Interpolation,"  The paper is devoted to the problem of multivariate polynomial interpolation
+and its application to quantum secret sharing. We show that using quantum
+Fourier transform one can produce the protocol for quantum secret sharing
+distribution.
+"
+24224,First passage properties in a crowded environment,"  We develop a model to compute the first-passage time of a random walker in a
+crowded environment. Hard-core particles with the same size and diffusion
+coefficient than the tracer diffuse, and the model allows to compute the first
+passage time of the tracer on euclidian lattices. The result is compared to
+classical Nakazato-Kitahara model, and extends previous results obtained for
+persistent random walker. The crowding in a confined media acts as a memory
+effect, and thus lead to a persistent-like behavior.
+"
+24225,Goodness of fit test under progressive Type-I interval censoring,"  In this paper, we propose several statistics for testing uniformity under
+progressive Type-I interval censoring. We obtain the critical points of these
+statistics and study the power of the proposed tests against a representative
+set of alternatives via simulation. Finally, we generalize our methods for
+continuous and completely specified distributions.
+"
+24226,The irreversible thermodynamics of curved lipid membranes,"  The theory of irreversible thermodynamics for arbitrarily curved lipid
+membranes is presented here. The coupling between elastic bending and
+irreversible processes such as intra-membrane lipid flow, intra-membrane phase
+transitions, and protein binding and diffusion is studied. The forms of the
+entropy production for the irreversible processes are obtained, and the
+corresponding thermodynamic forces and fluxes are identified. Employing the
+linear irreversible thermodynamic framework, the governing equations of motion
+along with appropriate boundary conditions are provided.
+"
+24227,An approximate Bayesian inference on propensity score estimation under unit nonresponse,"  Nonresponse weighting adjustment using the response propensity score is a
+popular tool for handling unit nonresponse. Statistical inference after the
+nonresponse weighting adjustment is complicated because the effect of
+estimating the propensity model parameter needs to be incorporated. In this
+paper, we propose an approximate Bayesian approach to handle unit nonresponse
+with parametric model assumptions on the response probability, but without
+model assumptions for the outcome variable. The proposed Bayesian method is
+calibrated to the frequentist inference in that the credible region obtained
+from the posterior distribution asymptotically matches to the frequentist
+confidence interval obtained from the Taylor linearization method. Unlike the
+frequentist approach, however, the proposed method does not involve Taylor
+linearization. The proposed method can be extended to handle over-identified
+cases in which there are more estimating equations than the parameters.
+Besides, the proposed method can also be modified to handle nonignorable
+nonresponse. Results from two simulation studies confirm the validity of the
+proposed methods, which are then applied to data from a Korean longitudinal
+survey.
+"
+24228,Generic Zero-Cost Reuse for Dependent Types,"  Dependently typed languages are well known for having a problem with code
+reuse. Traditional non-indexed algebraic datatypes (e.g. lists) appear
+alongside a plethora of indexed variations (e.g. vectors). Functions are often
+rewritten for both non-indexed and indexed versions of essentially the same
+datatype, which is a source of code duplication.
+We work in a Curry-style dependent type theory, where the same untyped term
+may be classified as both the non-indexed and indexed versions of a datatype.
+Many solutions have been proposed for the problem of dependently typed reuse,
+but we exploit Curry-style type theory in our solution to not only reuse data
+and programs, but do so at zero-cost (without a runtime penalty). Our work is
+an exercise in dependently typed generic programming, and internalizes the
+process of zero-cost reuse as the identity function in a Curry-style theory.
+"
+24229,Hyporeductive and Pseudoreductive Hopf algebras,"  In his generalization of reductive homogeneous spaces, Lev Sabinin showed
+that Lie's fundamental theorems hold for local analytic hyporeductive and
+pseudoreductive loops. We derive Sabinin's results in an algebraic context in
+terms of non-associative Hopf algebras that satisfy the analog of the
+hyporeductive and pseudoreductive identities for loops.
+"
+24230,Binomial edge ideals of bipartite graphs,"  We classify the bipartite graphs $G$ whose binomial edge ideal $J_G$ is
+Cohen-Macaulay. The connected components of such graphs can be obtained by
+gluing a finite number of basic blocks with two operations. In this context we
+prove the converse of a well-known result due to Hartshorne, showing that the
+Cohen-Macaulayness of these ideals is equivalent to the connectedness of their
+dual graphs. We study interesting properties also for non-bipartite graphs and
+in the unmixed case, constructing classes of bipartite graphs with $J_G$
+unmixed and not Cohen-Macaulay.
+"
+24231,Adaptive Sampling for Rapidly Matching Histograms,"  In exploratory data analysis, analysts often have a need to identify
+histograms that possess a specific distribution, among a large class of
+candidate histograms, e.g., find countries whose income distribution is most
+similar to that of Greece. This distribution could be a new one that the user
+is curious about, or a known distribution from an existing histogram
+visualization. At present, this process of identification is brute-force,
+requiring the manual generation and evaluation of a large number of histograms.
+We present FastMatch: an end-to-end approach for interactively retrieving the
+histogram visualizations most similar to a user-specified target, from a large
+collection of histograms. The primary technical contribution underlying
+FastMatch is a probabilistic algorithm, HistSim, a theoretically sound
+sampling-based approach to identify the top-$k$ closest histograms under
+$\ell_1$ distance. While HistSim can be used independently, within FastMatch we
+couple HistSim with a novel system architecture that is aware of practical
+considerations, employing asynchronous block-based sampling policies, building
+on lightweight sampling engines developed in recent work. FastMatch obtains
+near-perfect accuracy with up to $35\times$ speedup over approaches that do not
+use sampling on several real-world datasets.
+"
+24232,Bivariate density estimation using normal-gamma kernel with application to astronomy,"  We consider the problem of estimation of a bivariate density function with
+support $\Re\times[0,\infty)$, where a classical bivariate kernel estimator
+causes boundary bias due to the non-negative variable. To overcome this
+problem, we propose four kernel density estimators and compare their
+performances in terms of the mean integrated squared error. Simulation study
+shows that the estimator based on the proposed normal-gamma kernel performs
+best. Two astronomical data sets are used to demonstrate the applicability of
+this estimator.
+"
+24233,Factors of sums involving $q$-binomial coefficients and powers of $q$-integers,"  We show that, for all positive integers $n_1, \ldots, n_m$, $n_{m+1}=n_1$,
+and any non-negative integers $j$ and $r$ with $j\leqslant m$, the expression
+$$ \frac{1}{[n_1]}{n_1+n_{m}\brack n_1}^{-1}
+\sum_{k=1}^{n_1}[2k][k]^{2r}q^{jk^2-(r+1)k}\prod_{i=1}^{m} {n_i+n_{i+1}\brack
+n_i+k} $$ is a Laurent polynomial in $q$ with integer cofficients, where
+$[n]=1+q+\cdots+q^{n-1}$ and ${n\brack k}=\prod_{i=1}^k(1-q^{n-i+1})/(1-q^i)$.
+This gives a $q$-analogue of a divisibility result on the Catalan triangle
+obtained by the first author and Zeng, and also confirms a conjecture of the
+first author and Zeng. We further propose several related conjectures.
+"
+24234,BRST Symmetry for Torus Knot,"  We develop BRST symmetry for the first time for a particle on the surface of
+a torus knot by analyzing the constraints of the system. The theory contains
+$2^{nd}$ class constraints and has been extended by introducing Wess-Zumino
+term to the convert it into a theory with first class constraints. BFV analysis
+of the extended theory is performed to construct BRST/anti-BRST symmetries for
+the particle on torus knot. The nilpotent BRST/anti-BRST charges which generate
+such symmetries are constructed explicitly. The states annihilated by these
+nilpotent charges consist the physical Hilbert space. We indicate how various
+effective theories on the surface of the torus knot are related through the
+generalized version of BRST transformation with finite field dependent
+parameters.
+"
+24235,An elliptic theory of indicial weights and applications to non-linear geometry problems,"  Given an elliptic operator $P$ on a non-compact manifold (with proper
+asymptotic conditions), there is a discrete set of numbers called indicial
+roots. It's known that $P$ is Fredholm between weighted Sobolev spaces if and
+only if the weight is not indicial. We show that an elliptic theory exists even
+when the weight is indicial. We also discuss some simple applications to
+Yang-Mills theory and minimal surfaces.
+"
+24236,Counting fundamental solutions to the Pell equation with prescribed size,"  The cardinality of the set of $D\leqslant x$ for which the fundamental
+solution of the Pell equation $t^2-Du^2=1$ is less than
+$D^{\frac{1}{2}+\alpha}$ with $\alpha\in[\frac{1}{2},1]$ is studied and certain
+lower bounds are obtained, improving previous results of Fouvry by introducing
+the $q$-analogue of van der Corput method to algebraic exponential sums with
+smooth moduli.
+"
+24237,Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach,"  Distributed computing systems are well-known to suffer from the problem of
+slow or failed nodes; these are referred to as stragglers. Straggler mitigation
+(for distributed matrix computations) has recently been investigated from the
+standpoint of erasure coding in several works. In this work we present a
+strategy for distributed matrix-vector multiplication based on convolutional
+coding. Our scheme can be decoded using a low-complexity peeling decoder. The
+recovery process enjoys excellent numerical stability as compared to
+Reed-Solomon coding based approaches (which exhibit significant problems owing
+their badly conditioned decoding matrices). Finally, our schemes are better
+matched to the practically important case of sparse matrix-vector
+multiplication as compared to many previous schemes. Extensive simulation
+results corroborate our findings.
+"
+24238,Scaling Properties of Human Brain Functional Networks,"  We investigate scaling properties of human brain functional networks in the
+resting-state. Analyzing network degree distributions, we statistically test
+whether their tails scale as power-law or not. Initial studies, based on
+least-squares fitting, were shown to be inadequate for precise estimation of
+power-law distributions. Subsequently, methods based on maximum-likelihood
+estimators have been proposed and applied to address this question.
+Nevertheless, no clear consensus has emerged, mainly because results have shown
+substantial variability depending on the data-set used or its resolution. In
+this study, we work with high-resolution data (10K nodes) from the Human
+Connectome Project and take into account network weights. We test for the
+power-law, exponential, log-normal and generalized Pareto distributions. Our
+results show that the statistics generally do not support a power-law, but
+instead these degree distributions tend towards the thin-tail limit of the
+generalized Pareto model. This may have implications for the number of hubs in
+human brain functional networks.
+"
+24239,On semilattices and lattices for families of theories,"  We define and study semilattices and lattices for $E$-closed families of
+theories. Properties of these semilattices and lattices are investigated. It is
+shown that lattices for families of theories with least generating sets are
+distributive.
+"
+24240,A new minimal expansion of the integers,"  We consider the structure $(\mathbb{Z},+,0,|_{p_{1}},...,|_{p_{n}})$, where
+$x|_{p}y$ means $v_{p}(x)\leq v_{p}(y)$. We prove that its theory has QE in the
+language $\{+,-,0,1,(D_{m})_{m\geq1},|_{p_{1}},...,|_{p_{n}})$, and that it has
+dp-rank $n$. In addition, we prove that a first order structure with universe
+$\mathbb{Z}$ which is an expansion of $(\mathbb{Z},+,0)$ and a reduct of
+$(\mathbb{Z},+,0,|_{p})$ must be interdefinable with one of them. We also give
+an alternative proof for Conant's analogous result about $(\mathbb{Z},+,0,<)$.
+"
+24241,The General Subclasses of the Analytic Functions and Their Various Properties,"  The object of the present paper is to introduce and investigate two new
+general subclasses ${{S}^{*}}C(\alpha ,\beta ;\gamma )$ and $T{{S}^{*}}C(\alpha
+,\beta ;\gamma )~~(\alpha, \beta \in [0,1),~\gamma \in [0,1])$ of the analytic
+functions. Here, we give sufficient conditions as well as necessary and
+sufficient conditions for the functions belonging to the classes.
+"
+24242,Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques,"  Slow waves (SWs) occur both during natural sleep and anesthesia and are
+universal across species. Even though electrophysiological recordings have been
+largely used to characterize brain states, they are limited in the spatial
+resolution and cannot target specific neuronal population. Recently,
+large-scale optical imaging techniques coupled with functional indicators
+overcame these restrictions. Here we combined wide-field fluorescence
+microscopy and a transgenic mouse model expressing a calcium indicator
+(GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale
+under ketamine anesthesia. We developed ""de novo"" a versatile analysis pipeline
+to quantify the spatio-temporal propagation of the SWs in the frequency band
+[0.5, 4] Hz. Moreover, we designed a computational simulator based on a simple
+theoretical model, which takes into account the statistics of neural activity,
+the response of fluorescence proteins and the slow waves dynamics. The
+simulator was capable of synthesizing artificial signals that could reliably
+reproduce several features of the SWs observed in vivo. Comparison of
+preliminary experimental and simulated data shows the robustness of the
+analysis tools and its potential to uncover mechanistic insights of the Slow
+Wave Activity (SWA).
+"
+24243,Special homogeneous almost complex structures on symplectic manifolds,"  Homogeneous compatible almost complex structures on symplectic manifolds are
+studied, focusing on those which are special, meaning that their Chern-Ricci
+form is a multiple of the symplectic form. Non Chern-Ricci flat ones are proven
+to be covered by co-adjoint orbits. Conversely, compact isotropy co-adjoint
+orbits of semi-simple Lie groups are shown to admit special compatible almost
+complex structures whenever they satisfy a necessary topological condition.
+Some classes of examples including twistor spaces of hyperbolic manifolds and
+discrete quotients of Griffiths period domains of weight two are discussed.
+"
+24244,Context Aware Query Image Representation for Particular Object Retrieval,"  The current models of image representation based on Convolutional Neural
+Networks (CNN) have shown tremendous performance in image retrieval. Such
+models are inspired by the information flow along the visual pathway in the
+human visual cortex. We propose that in the field of particular object
+retrieval, the process of extracting CNN representations from query images with
+a given region of interest (ROI) can also be modelled by taking inspiration
+from human vision. Particularly, we show that by making the CNN pay attention
+on the ROI while extracting query image representation leads to significant
+improvement over the baseline methods on challenging Oxford5k and Paris6k
+datasets. Furthermore, we propose an extension to a recently introduced
+encoding method for CNN representations, regional maximum activations of
+convolutions (R-MAC). The proposed extension weights the regional
+representations using a novel saliency measure prior to aggregation. This leads
+to further improvement in retrieval accuracy.
+"
+24245,Some Results on Circuit Lower Bounds and Derandomization of Arthur-Merlin Problems,"  We prove a downward separation for $\mathsf{\Sigma}_2$-time classes.
+Specifically, we prove that if $\Sigma_2$E does not have polynomial size
+non-deterministic circuits, then $\Sigma_2$SubEXP does not have \textit{fixed}
+polynomial size non-deterministic circuits. To achieve this result, we use
+Santhanam's technique on augmented Arthur-Merlin protocols defined by
+Aydinlioğlu and van Melkebeek. We show that augmented Arthur-Merlin
+protocols with one bit of advice do not have fixed polynomial size
+non-deterministic circuits. We also prove a weak unconditional derandomization
+of a certain type of promise Arthur-Merlin protocols. Using Williams' easy
+hitting set technique, we show that $\Sigma_2$-promise AM problems can be
+decided in $\Sigma_2$SubEXP with $n^c$ advice, for some fixed constant $c$.
+"
+24246,Dependence of sunspot photospheric waves on the depth of the source of solar $p-$modes,"  Photospheric waves in sunspots moving radially outwards at speeds faster than
+the characteristic wave velocities have been recently detected. It has been
+suggested that they are the visual pattern of $p-$modes excited around 5 Mm
+beneath the sunspot. Using numerical simulations, we have performed a
+parametric study of the waves observed at the photosphere and higher layers
+produced by sources located at different depths. The observational measurements
+are consistent with waves driven between approximately 1 Mm and 5 Mm below the
+sunspot surface.
+"
+24247,Improving the Performance of WLANs by Reducing Unnecessary Active Scans,"  We consider the problem of excessive and unnecessary active scans in heavily
+utilized WLANs during which low rate probe requests and responses are
+broadcast. These management frames severely impact the goodput. Our analysis of
+two production WLANs reveals that lesser number of non-overlapping channels in
+$2.4$ GHz makes it more prone to the effects of increased probe frames than $5$
+GHz. We find that not only up to $90$% of probe responses carry redundant
+information but the probe traffic can be as high as $60$\% of the management
+traffic. Furthermore, active scanning severely impacts real-time applications
+at a client as it increases the latency by $91$ times.
+We present a detailed analysis of the impact of active scans on an individual
+client and the whole network. We discuss three ways to control the probe
+traffic in production WLANs -- access point configurations, network planning,
+and client modification. Our proposals for access point configuration are in
+line with current WLAN deployments, better network planning is device agnostic
+in nature, and client modification reduces the average number of probe requests
+per client by up to $50$% without hampering the ongoing WiFi connection.
+"
+24248,Temporal evolution of notoriety of Wikipedia pages with origin in social networks,"  The Wikipedia is a web portal created by users and its simplicity, references
+and also the inclusion as insets introductory paragraphs for their pages in
+Google search results have made it the go-to place to find out about current
+events or people featured in them. Besides, its open application programming
+interface (API) allows any user to know about the number of visits some
+particular page has. In this paper, after certain events that made Copernicus a
+viral meme in Spain, we study the intensity and duration of the increment of
+visits to his page and other pages related to the event. Using pages related to
+other persons as a comparison, we try to establish a typical duration of
+notoriety achieved through social networks, mainly comparing with visits in
+previous years in the same dates. We conclude that a 7 day duration is the
+statistical mode and that this duration is relatively independent of the
+initial increase in the number of visits.
+"
+24249,Goodness-of-Fit Tests for Large Datasets,"  Nowadays, data analysis in the world of Big Data is connected typically to
+data mining, descriptive or exploratory statistics, e.~g.\ cluster analysis,
+classification or regression analysis. Aside these techniques there is a huge
+area of methods from inferential statistics that are rarely considered in
+connection with Big Data. Nevertheless, inferential methods are also of use for
+Big Data analysis, especially for quantifying uncertainty. The article at hand
+will provide some insights to methodological and technical issues referring
+inferential methods in the Big Data area in order to bring together Big Data
+and inferential statistics, as it comes along with its difficulties. We present
+an approach that allows testing goodness-of-fit without model assumptions and
+relying on the empirical distribution. Especially, the method is able to
+utilize information from large datasets. Thereby, the approach is based on a
+clear theoretical background. We concentrate on the widely-used
+Kolmogorov-Smirnov test that is applied for testing goodness-of-fit in
+statistics. Our approach can be parallelized easily, which makes it applicable
+to distributed datasets particularly on a compute cluster. By this
+contribution, we turn to an audience that is interested in the technical and
+methodological backgrounds while implementing especially inferential
+statistical methods with Big Data tools as e. g. Spark.
+"
+24250,The Effect of Color Space Selection on Detectability and Discriminability of Colored Objects,"  In this paper, we investigate the effect of color space selection on
+detectability and discriminability of colored objects under various conditions.
+20 color spaces from the literature are evaluated on a large dataset of
+simulated and real images. We measure the suitability of color spaces from two
+different perspectives: detectability and discriminability of various color
+groups. Through experimental evaluation, we found that there is no single
+optimal color space suitable for all color groups. The color spaces have
+different levels of sensitivity to different color groups and they are useful
+depending on the color of the sought object. Overall, the best results were
+achieved in both simulated and real images using color spaces C1C2C3, UVW and
+XYZ. In addition, using a simulated environment, we show a practical
+application of color space selection in the context of top-down control in
+active visual search. The results indicate that on average color space C1C2C3
+followed by HSI and XYZ achieve the best time in searching for objects of
+various colors. Here, the right choice of color space can improve time of
+search on average by 20%. As part of our contribution, we also introduce a
+large dataset of simulated 3D objects
+"
+24251,Simulating the effect of high column density absorbers on the one-dimensional Lyman-alpha forest flux power spectrum,"  We measure the effect of high column density absorbing systems of neutral
+hydrogen (HI) on the one-dimensional (1D) Lyman-alpha forest flux power
+spectrum using cosmological hydrodynamical simulations from the Illustris
+project. High column density absorbers (which we define to be those with HI
+column densities $N(\mathrm{HI}) > 1.6 \times
+10^{17}\,\mathrm{atoms}\,\mathrm{cm}^{-2}$) cause broadened absorption lines
+with characteristic damping wings. These damping wings bias the 1D Lyman-alpha
+forest flux power spectrum by causing absorption in quasar spectra away from
+the location of the absorber itself. We investigate the effect of high column
+density absorbers on the Lyman-alpha forest using hydrodynamical simulations
+for the first time. We provide templates as a function of column density and
+redshift, allowing the flexibility to accurately model residual contamination,
+i.e., if an analysis selectively clips out the largest damping wings. This
+flexibility will improve cosmological parameter estimation, e.g., allowing more
+accurate measurement of the shape of the power spectrum, with implications for
+cosmological models containing massive neutrinos or a running of the spectral
+index. We provide fitting functions to reproduce these results so that they can
+be incorporated straightforwardly into a data analysis pipeline.
+"
+24252,Multiple Context-Free Tree Grammars: Lexicalization and Characterization,"  Multiple (simple) context-free tree grammars are investigated, where ""simple""
+means ""linear and nondeleting"". Every multiple context-free tree grammar that
+is finitely ambiguous can be lexicalized; i.e., it can be transformed into an
+equivalent one (generating the same tree language) in which each rule of the
+grammar contains a lexical symbol. Due to this transformation, the rank of the
+nonterminals increases at most by 1, and the multiplicity (or fan-out) of the
+grammar increases at most by the maximal rank of the lexical symbols; in
+particular, the multiplicity does not increase when all lexical symbols have
+rank 0. Multiple context-free tree grammars have the same tree generating power
+as multi-component tree adjoining grammars (provided the latter can use a
+root-marker). Moreover, every multi-component tree adjoining grammar that is
+finitely ambiguous can be lexicalized. Multiple context-free tree grammars have
+the same string generating power as multiple context-free (string) grammars and
+polynomial time parsing algorithms. A tree language can be generated by a
+multiple context-free tree grammar if and only if it is the image of a regular
+tree language under a deterministic finite-copying macro tree transducer.
+Multiple context-free tree grammars can be used as a synchronous translation
+device.
+"
+24253,An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses,"  Several supermodular losses have been shown to improve the perceptual quality
+of image segmentation in a discriminative framework such as a structured output
+support vector machine (SVM). These loss functions do not necessarily have the
+same structure as the one used by the segmentation inference algorithm, and in
+general, we may have to resort to generic submodular minimization algorithms
+for loss augmented inference. Although these come with polynomial time
+guarantees, they are not practical to apply to image scale data. Many
+supermodular losses come with strong optimization guarantees, but are not
+readily incorporated in a loss augmented graph cuts procedure. This motivates
+our strategy of employing the alternating direction method of multipliers
+(ADMM) decomposition for loss augmented inference. In doing so, we create a new
+API for the structured SVM that separates the maximum a posteriori (MAP)
+inference of the model from the loss augmentation during training. In this way,
+we gain computational efficiency, making new choices of loss functions
+practical for the first time, while simultaneously making the inference
+algorithm employed during training closer to the test time procedure. We show
+improvement both in accuracy and computational performance on the Microsoft
+Research Grabcut database and a brain structure segmentation task, empirically
+validating the use of several supermodular loss functions during training, and
+the improved computational properties of the proposed ADMM approach over the
+Fujishige-Wolfe minimum norm point algorithm.
+"
+24254,Photonic ring resonator filters for astronomical OH suppression,"  Ring resonators provide a means of filtering specific wavelengths from a
+waveguide, and optionally dropping the filtered wavelengths into a second
+waveguide. Both of these features are potentially useful for astronomical
+instruments.
+In this paper we focus on their use as notch filters to remove the signal
+from atmospheric OH emission lines from astronomical spectra, however we also
+briefly discuss their use as frequency combs for wavelength calibration and as
+drop filters for Doppler planet searches.
+We derive the design requirements for ring resonators for OH suppression from
+theory and finite difference time domain simulations. We find that rings with
+small radii (<10 microns) are required to provide an adequate free spectral
+range, leading to high index contrast materials such as Si and Si$_{3}$N$_{4}$.
+Critically coupled rings with high self-coupling coefficients should provide
+the necessary Q factors, suppression depth, and throughput for efficient OH
+suppression.
+We report on our progress in fabricating both Si and Si$_{3}$N$_{4}$ rings
+for OH suppression, and give results from preliminary laboratory tests. Our
+early devices show good control over the free spectral range and wavelength
+separation of multi-ring devices. The self-coupling coefficients are high
+(>0.9), but further optimisation is required to achieve higher Q and deeper
+notches, with current devices having $Q \approx 4000$ and $\approx 10$ dB
+suppression. The overall prospects for the use of ring resonators in
+astronomical instruments is promising, provided efficient fibre-chip coupling
+can be achieved.
+"
+24255,Molecular Kondo effect in flat-band lattices,"  The Kondo effect of a single magnetic impurity embedded in the Lieb lattice
+is studied by the numerical renormalization group. When the band flatness is
+present in the local density of states, it quenches the participation of all
+dispersive electrons in the Kondo singlet formation, and reduces the many-body
+Kondo problem to a two-electron molecular Kondo problem. The two-electron
+analog of the many-body Kondo singlet, a quantum entanglement of two spins, is
+stable at low temperature, and the impurity contributions to thermodynamical
+and dynamical quantities are qualitatively different from that obtained in the
+conventional Kondo effect in systems without flat bands. The existence
+conditions of the molecular Kondo effect in narrow band systems are also
+discussed.
+"
+24256,Inviscid scaling laws of a self-propelled pitching airfoil,"  Inviscid computational results are presented on a self-propelled virtual body
+combined with an airfoil undergoing pitch oscillations about its leading-edge.
+The scaling trends of the time-averaged thrust forces are shown to be predicted
+accurately by Garrick's theory. However, the scaling of the time-averaged power
+for finite amplitude motions is shown to deviate from the theory. Novel
+time-averaged power scalings are presented that account for a contribution from
+added-mass forces, from the large-amplitude separating shear layer at the
+trailing-edge, and from the proximity of the trailing-edge vortex. Scaling laws
+for the self-propelled speed, efficiency and cost of transport ($CoT$) are
+subsequently derived. Using these scaling relations the self-propelled metrics
+can be predicted to within 5% of their full-scale values by using parameters
+known a priori. The relations may be used to drastically speed-up the design
+phase of bio-inspired propulsion systems by offering a direct link between
+design parameters and the expected $CoT$. The scaling relations also offer one
+of the first mechanistic rationales for the scaling of the energetics of
+self-propelled swimming. Specifically, the cost of transport is shown to scale
+predominately with the added mass power. This suggests that the $CoT$ of
+organisms or vehicles using unsteady propulsion will scale with their mass as
+$CoT \propto m^{-1/3}$, which is indeed shown to be consistent with existing
+biological data.
+"
+24257,The Post-Pericenter Evolution of the Galactic Center Source G2,"  In early 2014 the fast-moving near-infrared source G2 reached its closest
+approach to the supermassive black hole Sgr A* in the Galactic Center. We
+report on the evolution of the ionized gaseous component and the dusty
+component of G2 immediately after this event, revealed by new observations
+obtained in 2015 and 2016 with the SINFONI integral field spectrograph and the
+NACO imager at the ESO VLT. The spatially resolved dynamics of the Br$\gamma$
+line emission can be accounted for by the ballistic motion and tidal shearing
+of a test-particle cloud that has followed a highly eccentric Keplerian orbit
+around the black hole for the last 12 years. The non-detection of a drag force
+or any strong hydrodynamic interaction with the hot gas in the inner accretion
+zone limits the ambient density to less than a few 10$^3$ cm$^{-3}$ at the
+distance of closest approach (1500 $R_s$), assuming G2 is a spherical cloud
+moving through a stationary and homogeneous atmosphere. The dust continuum
+emission is unresolved in L'-band, but stays consistent with the location of
+the Br$\gamma$ emission. The total luminosity of the Br$\gamma$ and L' emission
+has remained constant to within the measurement uncertainty. The nature and
+origin of G2 are likely related to that of the precursor source G1, since their
+orbital evolution is similar, though not identical. Both object are also likely
+related to a trailing tail structure, which is continuously connected to G2
+over a large range in position and radial velocity.
+"
+24258,Rainbow Trapping in a Chirped Three-Dimensional Photonic Crystal,"  Light localization and intensity enhancement in a woodpile layer-by-layer
+photonic crystal, whose interlayer distance along the propagation direction is
+gradually varied, has been theoretically predicted and experimentally
+demonstrated. The phenomenon is shown to be related to the progressive slowing
+down and stopping of the incoming wave, as a result of the gradual variation of
+the local dispersion. The light localization is chromatically resolved, since
+every frequency component is stopped and reflected back at different spatial
+positions. It has been further discussed that the peculiar relation between the
+stopping distance and the wave vector distribution can substantially increase
+the enhancement factor to more than two orders of magnitude. Compared to
+previously reported one- and two-dimensional photonic crystal configurations,
+the proposed scheme has the advantage of reducing the propagation losses by
+providing a three-dimensional photonic bandgap confinement in all directions.
+The slowing down and localization of waves inside photonic media can be
+exploited in many applications that requires enhanced interaction of light and
+matter.
+"
+24259,ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching,"  We investigate the non-identifiability issues associated with bidirectional
+adversarial training for joint distribution matching. Within a framework of
+conditional entropy, we propose both adversarial and non-adversarial approaches
+to learn desirable matched joint distributions for unsupervised and supervised
+tasks. We unify a broad family of adversarial models as joint distribution
+matching problems. Our approach stabilizes learning of unsupervised
+bidirectional adversarial learning methods. Further, we introduce an extension
+for semi-supervised learning tasks. Theoretical results are validated in
+synthetic data and real-world applications.
+"
+24260,A GPP algorithm for hippocampal interneuron characterization,"  Correctly identifying neuronal subsets is critical to multiple downstream
+methods in several areas of neuroscience research. The hippocampal interneuron
+characterization technology has achieved rapid development in recent years.
+However, capturing true neuronal features for accurate interneuron
+characterization and segmentation has remained elusive. In the current study, a
+novel global preserving estimate algorithm is used to capture the non-linearity
+in the features of hippocampal interneurons after factor Algorithm. Our results
+provide evidence for the effective integration of the original linear and
+nonlinear neuronal features and achieves better characterization performance on
+multiple hippocampal interneuron databases through array matching.
+"
+24261,Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks,"  Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That
+is, adversarial examples, obtained by adding delicately crafted distortions
+onto original legal inputs, can mislead a DNN to classify them as any target
+labels. This work provides a solution to hardening DNNs under adversarial
+attacks through defensive dropout. Besides using dropout during training for
+the best test accuracy, we propose to use dropout also at test time to achieve
+strong defense effects. We consider the problem of building robust DNNs as an
+attacker-defender two-player game, where the attacker and the defender know
+each others' strategies and try to optimize their own strategies towards an
+equilibrium. Based on the observations of the effect of test dropout rate on
+test accuracy and attack success rate, we propose a defensive dropout algorithm
+to determine an optimal test dropout rate given the neural network model and
+the attacker's strategy for generating adversarial examples.We also investigate
+the mechanism behind the outstanding defense effects achieved by the proposed
+defensive dropout. Comparing with stochastic activation pruning (SAP), another
+defense method through introducing randomness into the DNN model, we find that
+our defensive dropout achieves much larger variances of the gradients, which is
+the key for the improved defense effects (much lower attack success rate). For
+example, our defensive dropout can reduce the attack success rate from 100% to
+13.89% under the currently strongest attack i.e., C&W attack on MNIST dataset.
+"
+24262,A Least Squares Radial Basis Function Partition of Unity Method for Solving PDEs,"  Recently, collocation based radial basis function (RBF) partition of unity
+methods (PUM) for solving partial differential equations have been formulated
+and investigated numerically and theoretically. When combined with stable
+evaluation methods such as the RBF-QR method, high order convergence rates can
+be achieved and sustained under refinement. However, some numerical issues
+remain. The method is sensitive to the node layout, and condition numbers
+increase with the refinement level. Here, we propose a modified formulation
+based on least squares approximation. We show that the sensitivity to node
+layout is removed and that conditioning can be controlled through oversampling.
+We derive theoretical error estimates both for the collocation and least
+squares RBF-PUM. Numerical experiments are performed for the Poisson equation
+in two and three space dimensions for regular and irregular geometries. The
+convergence experiments confirm the theoretical estimates, and the least
+squares formulation is shown to be 5-10 times faster than the collocation
+formulation for the same accuracy.
+"
+24263,Enhancing diamond fluorescence via optimized nanorod dimer configurations,"  Optical response of silicon (SiV) and nitrogen (NV) vacancy diamond color
+centers coupled to silver and gold nanorod dimers was numerically inspected.
+Optimization of the coupled emitter - nanorod dimer configurations was
+performed to attain the highest possible fluorescence enhancement by enhancing
+the excitation and emission of color centers simultaneously through plasmonic
+antenna resonances. To minimize losses conditional optimization was realized by
+setting a criterion regarding the minimum quantum efficiency of the coupled
+system (cQE). Restricted symmetric and allowed asymmetric antenna designs were
+also inspected to prove the potential advantages of asymmetric configurations
+tuneability. Among all inspected systems the highest 2.59*10^8 fluorescence
+enhancement with 46.08% cQE was achieved in case of NV color center coupled to
+asymmetric silver nanoantenna dimer. This is 3.17-times larger than the
+enhancement in corresponding symmetric configuration, which has larger 68.52%
+cQE. In case of SiV color center the highest 1.04*10^8 fluorescence enhancement
+with 37.83% cQE was achieved via asymmetric silver nanoantenna dimer. This is
+1.06-times larger than the enhancement in the corresponding symmetric
+configuration, which has larger 57.46% cQE. The highest fluorescence
+enhancement achieved by gold nanorods is 4.75*10^4 with 21.8% cQE, which was
+shown in case of SiV color center coupled to asymmetric dimer. The attained
+enhancement is 8.48- (92.42-) times larger than the fluorescence enhancement
+achievable via symmetric (asymmetric) gold nanorod dimer coupled to SiV (NV)
+color center.
+"
+24264,Hydrodynamic approach to electronic transport in graphene,"  The last few years have seen an explosion of interest in hydrodynamic effects
+in interacting electron systems in ultra-pure materials. In this paper we
+briefly review the recent advances, both theoretical and experimental, in the
+hydrodynamic approach to electronic transport in graphene, focusing on viscous
+phenomena, Coulomb drag, non-local transport measurements, and possibilities
+for observing nonlinear effects.
+"
+24265,Loss of vimentin intermediate filaments decreases peri-nuclear stiffness and enhances cell motility through confined spaces,"  The migration of cells through tight constricting spaces or along fibrous
+tracks in tissues is important for biological processes, such as embryogenesis,
+wound healing, and cancer metastasis, and depends on the mechanical properties
+of the cytoskeleton. Migratory cells often express and upregulate the
+intermediate filament protein vimentin. The viscoelasticity of vimentin
+networks in shear deformation has been documented, but its role in motility is
+largely unexplored. Here, we studied the effects of vimentin on cell motility
+and stiffness using mouse embryo fibroblasts derived from wild-type and
+vimentin-null mice. We find that loss of vimentin increases motility through
+small pores and along thin capillaries. Atomic force microscopy measurements
+reveal that the presence of vimentin enhances the perinuclear stiffness of the
+cell, to an extent that depends on surface ligand presentation and therefore
+signaling from extracellular matrix receptors. Together, our results indicate
+that vimentin hinders three-dimensional motility by providing mechanical
+resistance against large strains and may thereby protect the structural
+integrity of cells.
+"
+24266,Time quasi-periodic gravity water waves in finite depth,"  We prove the existence and the linear stability of Cantor families of small
+amplitude time quasi-periodic standing water wave solutions - namely periodic
+and even in the space variable x - of a bi-dimensional ocean with finite depth
+under the action of pure gravity. Such a result holds for all the values of the
+depth parameter in a Borel set of asymptotically full measure. This is a small
+divisor problem. The main difficulties are the quasi-linear nature of the
+gravity water waves equations and the fact that the linear frequencies grow
+just in a sublinear way at infinity. We overcome these problems by first
+reducing the linearized operators obtained at each approximate quasi-periodic
+solution along the Nash-Moser iteration to constant coefficients up to
+smoothing operators, using pseudo-differential changes of variables that are
+quasi-periodic in time. Then we apply a KAM reducibility scheme which requires
+very weak Melnikov non-resonance conditions (losing derivatives both in time
+and space), which we are able to verify for most values of the depth parameter
+using degenerate KAM theory arguments.
+"
+24267,Visual Imitation Learning with Recurrent Siamese Networks,"  People solve the difficult problem of understanding the salient features of
+both observations of others and the relationship to their own state when
+learning to imitate specific tasks. In this work, we train a comparator network
+which is used to compute distances between motions. Given a desired motion the
+comparator can provide a reward signal to the agent via the distance between
+the desired motion and the agent's motion. We train an RNN-based comparator
+model to compute distances in space and time between motion clips while
+training an RL policy to minimize this distance. Furthermore, we examine a
+challenging form of this problem where a single \demonstrationText is provided
+for a given task. We demonstrate our approach in the setting of deep learning
+based control for physical simulation of humanoid walking in both 2D with $10$
+degrees of freedom (DoF) and 3D with $38$ DoF.
+"
+24268,The Rise and Fall of Network Stars: Analyzing 2.5 million graphs to reveal how high-degree vertices emerge over time,"  Trends change rapidly in today's world, prompting this key question: What is
+the mechanism behind the emergence of new trends? By representing real-world
+dynamic systems as complex networks, the emergence of new trends can be
+symbolized by vertices that ""shine."" That is, at a specific time interval in a
+network's life, certain vertices become increasingly connected to other
+vertices. This process creates new high-degree vertices, i.e., network stars.
+Thus, to study trends, we must look at how networks evolve over time and
+determine how the stars behave. In our research, we constructed the largest
+publicly available network evolution dataset to date, which contains 38,000
+real-world networks and 2.5 million graphs. Then, we performed the first
+precise wide-scale analysis of the evolution of networks with various scales.
+Three primary observations resulted: (a) links are most prevalent among
+vertices that join a network at a similar time; (b) the rate that new vertices
+join a network is a central factor in molding a network's topology; and (c) the
+emergence of network stars (high-degree vertices) is correlated with
+fast-growing networks. We applied our learnings to develop a flexible
+network-generation model based on large-scale, real-world data. This model
+gives a better understanding of how stars rise and fall within networks, and is
+applicable to dynamic systems both in nature and society.
+"
+24269,Sketching for Kronecker Product Regression and P-splines,"  TensorSketch is an oblivious linear sketch introduced in Pagh'13 and later
+used in Pham, Pagh'13 in the context of SVMs for polynomial kernels. It was
+shown in Avron, Nguyen, Woodruff'14 that TensorSketch provides a subspace
+embedding, and therefore can be used for canonical correlation analysis, low
+rank approximation, and principal component regression for the polynomial
+kernel. We take TensorSketch outside of the context of polynomials kernels, and
+show its utility in applications in which the underlying design matrix is a
+Kronecker product of smaller matrices. This allows us to solve Kronecker
+product regression and non-negative Kronecker product regression, as well as
+regularized spline regression. Our main technical result is then in extending
+TensorSketch to other norms. That is, TensorSketch only provides input sparsity
+time for Kronecker product regression with respect to the $2$-norm. We show how
+to solve Kronecker product regression with respect to the $1$-norm in time
+sublinear in the time required for computing the Kronecker product, as well as
+for more general $p$-norms.
+"
+24270,Singularity categories of representations of quivers over local rings,"  Let $\Lambda$ be a finite-dimensional algebra with finite global dimension,
+$R_k=K[X]/(X^k)$ be the $\mathbb{Z}$-graded local ring with $k\geq1$, and
+$\Lambda_k=\Lambda\otimes_K R_k$. We consider the singularity category
+$D_{sg}(\mod^{\mathbb{Z}}(\Lambda_k))$ of the graded modules over $\Lambda_k$.
+It is showed that there is a tilting object in
+$D_{sg}(\mod^{\mathbb{Z}}(\Lambda_k))$ such that its endomorphism algebra is
+isomorphic to the triangular matrix algebra $T_{k-1}(\Lambda)$ with
+coefficients in $\Lambda$ and there is a triangle equivalence between
+$D_{sg}(\mod^{\mathbb{Z}/k\mathbb{Z}}(\Lambda))$ and the root category of
+$T_{k-1}(\Lambda)$. Finally, a classification of $\Lambda_k$ up to the
+Cohen-Macaulay representation type is given.
+"
+24271,Effect of Dynamic Surface Polarization on the Oxidative Stability of Solvents in Nonaqueous Li-O$_2$ Batteries,"  Polarization-induced renormalization of the frontier energy levels of
+interacting molecules and surfaces can cause significant shifts in the
+excitation and transport behavior of electrons. This phenomenon is crucial in
+determining the oxidative stability of nonaqeous electrolytes in high energy
+density electrochemical systems such as the Li-O$_2$ battery. On the basis of
+partially self-consistent first-principles ScGW0 calculations, we
+systematically study how the electronic energy levels of four commonly used
+solvent molecules, namely dimethylsulfoxide (DMSO), dimethoxyethane (DME),
+tetrahydrofuran (THF) and acetonitrile (ACN), renormalize when physisorbed on
+the different stable surfaces of Li$_2$O$_2$, the main discharge product. Using
+band level alignment arguments, we propose that the difference between the
+solvent's highest occupied molecular orbital (HOMO) level and the surface's
+valence band maximum (VBM) is a refined metric of oxidative stability. This
+metric and a previously used descriptor, solvent's gas phase HOMO level, agree
+quite well for physisorbed cases on pristine surfaces where ACN is oxidatively
+most stable followed by DME, THF and DMSO. However, this effect is
+intrinsically linked to the surface chemistry of solvent's interaction with the
+surfaces states and defects, and depends strongly on their nature. We
+conclusively show that the propensity of solvent molecules to oxidize will be
+significantly higher on Li$_2$O$_2$ surfaces with defects as compared to
+pristine surfaces. This suggests that the oxidatively stability of solvent is
+dynamic and is a strong function of surface electronic properties. Thus, while
+gas phase HOMO levels could be used for preliminary solvent candidate
+screening, a more refined picture of solvent stability requires mapping out the
+solvent stability as a function of the state of the surface under the operating
+conditions.
+"
+24272,Insights into Entity Name Evolution on Wikipedia,"  Working with Web archives raises a number of issues caused by their temporal
+characteristics. Depending on the age of the content, additional knowledge
+might be needed to find and understand older texts. Especially facts about
+entities are subject to change. Most severe in terms of information retrieval
+are name changes. In order to find entities that have changed their name over
+time, search engines need to be aware of this evolution. We tackle this problem
+by analyzing Wikipedia in terms of entity evolutions mentioned in articles
+regardless the structural elements. We gathered statistics and automatically
+extracted minimum excerpts covering name changes by incorporating lists
+dedicated to that subject. In future work, these excerpts are going to be used
+to discover patterns and detect changes in other sources. In this work we
+investigate whether or not Wikipedia is a suitable source for extracting the
+required knowledge.
+"
+24273,Integrable discretization of recursion operators and unified bilinear forms to soliton hierarchies,"  In this paper, we give a procedure of how to discretize the recursion
+operators by considering unified bilinear forms of integrable hierarchies. As
+two illustrative examples, the unified bilinear forms of the AKNS hierarchy and
+the KdV hierarchy are presented from their recursion operators. Via the
+compatibility between soliton equations and their auto-Bäcklund
+transformations, the bilinear integrable hierarchies are discretized and the
+discrete recursion operators are obtained. The discrete recursion operators
+converge to the original continuous forms after a standard limit.
+"
+24274,Combining Method of Alternating Projections and Augmented Lagrangian for Task Constrained Trajectory Optimization,"  Motion planning for manipulators under task space constraints is difficult as
+it constrains the joint configurations to always lie on an implicitly defined
+manifold. It is possible to view task constrained motion planning as an
+optimization problem with non-linear equality constraints which can be solved
+by general non-linear optimization techniques. In this paper, we present a
+novel custom optimizer which exploits the underlying structure present in many
+task constraints.
+At the core of our approach are some simple reformulations, which when
+coupled with the \emph{method of alternating projection}, leads to an efficient
+convex optimization based routine for computing a feasible solution to the task
+constraints. We subsequently build on this result and use the concept of
+Augmented Lagrangian to guide the feasible solutions towards those which also
+minimize the user defined cost function. We show that the proposed optimizer is
+fully distributive and thus, can be easily parallelized. We validate our
+formulation on some common robotic benchmark problems. In particular, we show
+that the proposed optimizer achieves cyclic motion in the joint space
+corresponding to a similar nature trajectory in the task space. Furthermore, as
+a baseline, we compare the proposed optimizer with an off-the-shelf non-linear
+solver provide in open source package SciPy. We show that for similar task
+constraint residuals and smoothness cost, it can be upto more than three times
+faster than the SciPy alternative.
+"
+24275,Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination,"  Despite the extreme diversity of T cell repertoires, many identical T-cell
+receptor (TCR) sequences are found in a large number of individual mice and
+humans. These widely-shared sequences, often referred to as `public', have been
+suggested to be over-represented due to their potential immune functionality or
+their ease of generation by V(D)J recombination. Here we show that even for
+large cohorts the observed degree of sharing of TCR sequences between
+individuals is well predicted by a model accounting for by the known
+quantitative statistical biases in the generation process, together with a
+simple model of thymic selection. Whether a sequence is shared by many
+individuals is predicted to depend on the number of queried individuals and the
+sampling depth, as well as on the sequence itself, in agreement with the data.
+We introduce the degree of publicness conditional on the queried cohort size
+and the size of the sampled repertoires. Based on these observations we propose
+a public/private sequence classifier, `PUBLIC' (Public Universal Binary
+Likelihood Inference Classifier), based on the generation probability, which
+performs very well even for small cohort sizes.
+"
+24276,The possible role of van Hove singularities in the high $T_c$ of superconducting H$_3$S,"  We observe that H$_3$S has a bcc structure and, with nearest neighbour
+hopping only, a strong singularity occurs at zero energy. This singularity is
+accompanied with a highly nested Fermi surface, which is {\it not} conducive to
+a stable superconducting instability. Introduction of next-nearest-neighbour
+hopping removes the singularity, but a `robust' peak remains in the electron
+density of states. Solution of the BCS equations shows an enhanced
+superconducting $T_c$ due to this peak. Furthermore, nesting is no longer
+present, so other instabilities will not compete effectively with
+superconductivity. We find high critical temperatures are possible, even with
+very modest coupling strengths. We also examine a limit of the $T=0$ equations
+(in an Appendix) where an analytical solution is possible over the entire range
+of coupling strengths, and therefore the BCS-BEC crossover is fully covered.
+"
+24277,HT-eQTL: Integrative Expression Quantitative Trait Loci Analysis in a Large Number of Human Tissues,"  Expression quantitative trait loci (eQTL) analysis identifies genetic markers
+associated with the expression of a gene. Most existing eQTL analyses and
+methods investigate association in a single, readily available tissue, such as
+blood. Joint analysis of eQTL in multiple tissues has the potential to improve,
+and expand the scope of, single-tissue analyses. Large-scale collaborative
+efforts such as the Genotype-Tissue Expression (GTEx) program are currently
+generating high quality data in a large number of tissues. However,
+computational constraints limit genome-wide multi-tissue eQTL analysis. We
+develop an integrative method under a hierarchical Bayesian framework for eQTL
+analysis in a large number of tissues. The model fitting procedure is highly
+scalable, and the computing time is a polynomial function of the number of
+tissues. Multi-tissue eQTLs are identified through a local false discovery rate
+approach, which rigorously controls the false discovery rate. Using simulation
+and GTEx real data studies, we show that the proposed method has superior
+performance to existing methods in terms of computing time and the power of
+eQTL discovery. We provide a scalable method for eQTL analysis in a large
+number of tissues. The method enables the identification of eQTL with different
+configurations and facilitates the characterization of tissue specificity.
+"
+24278,Removing Algorithmic Discrimination (With Minimal Individual Error),"  We address the problem of correcting group discriminations within a score
+function, while minimizing the individual error. Each group is described by a
+probability density function on the set of profiles. We first solve the problem
+analytically in the case of two populations, with a uniform bonus-malus on the
+zones where each population is a majority. We then address the general case of
+n populations, where the entanglement of populations does not allow a similar
+analytical solution. We show that an approximate solution with an arbitrarily
+high level of precision can be computed with linear programming. Finally, we
+address the inverse problem where the error should not go beyond a certain
+value and we seek to minimize the discrimination.
+"
+24279,Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration,"  We study the combinatorial pure exploration problem Best-Set in stochastic
+multi-armed bandits. In a Best-Set instance, we are given $n$ arms with unknown
+reward distributions, as well as a family $\mathcal{F}$ of feasible subsets
+over the arms. Our goal is to identify the feasible subset in $\mathcal{F}$
+with the maximum total mean using as few samples as possible. The problem
+generalizes the classical best arm identification problem and the top-$k$ arm
+identification problem, both of which have attracted significant attention in
+recent years. We provide a novel instance-wise lower bound for the sample
+complexity of the problem, as well as a nontrivial sampling algorithm, matching
+the lower bound up to a factor of $\ln|\mathcal{F}|$. For an important class of
+combinatorial families, we also provide polynomial time implementation of the
+sampling algorithm, using the equivalence of separation and optimization for
+convex program, and approximate Pareto curves in multi-objective optimization.
+We also show that the $\ln|\mathcal{F}|$ factor is inevitable in general
+through a nontrivial lower bound construction. Our results significantly
+improve several previous results for several important combinatorial
+constraints, and provide a tighter understanding of the general Best-Set
+problem.
+We further introduce an even more general problem, formulated in geometric
+terms. We are given $n$ Gaussian arms with unknown means and unit variance.
+Consider the $n$-dimensional Euclidean space $\mathbb{R}^n$, and a collection
+$\mathcal{O}$ of disjoint subsets. Our goal is to determine the subset in
+$\mathcal{O}$ that contains the $n$-dimensional vector of the means. The
+problem generalizes most pure exploration bandit problems studied in the
+literature. We provide the first nearly optimal sample complexity upper and
+lower bounds for the problem.
+"
+24280,On Continuity Properties for Infinite Rectangle Packing,"  By rectangle packing we mean putting a set of rectangles into an enclosing
+rectangle, without any overlapping. We begin with perfect rectangle packing
+problems, then prove two continuity properties for parallel rectangle packing
+problems, and discuss how they might be used to obtain negative results for
+perfect rectangle packing problems.
+"
+24281,On the utility of Metropolis-Hastings with asymmetric acceptance ratio,"  The Metropolis-Hastings algorithm allows one to sample asymptotically from
+any probability distribution $\pi$. There has been recently much work devoted
+to the development of variants of the MH update which can handle scenarios
+where such an evaluation is impossible, and yet are guaranteed to sample from
+$\pi$ asymptotically. The most popular approach to have emerged is arguably the
+pseudo-marginal MH algorithm which substitutes an unbiased estimate of an
+unnormalised version of $\pi$ for $\pi$. Alternative pseudo-marginal algorithms
+relying instead on unbiased estimates of the MH acceptance ratio have also been
+proposed. These algorithms can have better properties than standard PM
+algorithms. Convergence properties of both classes of algorithms are known to
+depend on the variability of the estimators involved and reduced variability is
+guaranteed to decrease the asymptotic variance of ergodic averages and will
+shorten the burn-in period, or convergence to equilibrium, in most scenarios of
+interest. A simple approach to reduce variability, amenable to parallel
+computations, consists of averaging independent estimators. However, while
+averaging estimators of $\pi$ in a pseudo-marginal algorithm retains the
+guarantee of sampling from $\pi$ asymptotically, naive averaging of acceptance
+ratio estimates breaks detailed balance, leading to incorrect results. We
+propose an original methodology which allows for a correct implementation of
+this idea. We establish theoretical properties which parallel those available
+for standard PM algorithms and discussed above. We demonstrate the interest of
+the approach on various inference problems. In particular we show that
+convergence to equilibrium can be significantly shortened, therefore offering
+the possibility to reduce a user's waiting time in a generic fashion when a
+parallel computing architecture is available.
+"
+24282,Generalized deconvolution procedure for structural modeling of turbulence,"  Approximate deconvolution forms a mathematical framework for the structural
+modeling of turbulence. The sub-filter scale flow quantities are typically
+recovered by using the Van Cittert iterative procedure. In this paper, however,
+we put forth a generalized approach for the iterative deconvolution process of
+sub-filter scale recovery of turbulent flows by introducing Krylov space
+iterative methods. Their accuracy and efficiency are demonstrated through a
+systematic a-priori analysis of solving the Kraichnan and Kolmogorov
+homogeneous isotropic turbulence problems in two- and three-dimensional
+domains, respectively. Our numerical assessments show that the conjugate
+gradient based iterative techniques lead to significantly improved performance
+over the Van Cittert procedure and offer great promise for approximate
+deconvolution turbulence models. In fact, our energy spectra analysis
+illustrates that a substantially longer inertial range can be recovered by
+using the proposed procedure equipped with the BiCGSTAB iterative scheme. This
+trend is also confirmed by capturing tails of the probability density function
+of turbulent flow quantities.
+"
+24283,Geometry Processing of Conventionally Produced Mouse Brain Slice Images,"  Brain mapping research in most neuroanatomical laboratories relies on
+conventional processing techniques, which often introduce histological
+artifacts such as tissue tears and tissue loss. In this paper we present
+techniques and algorithms for automatic registration and 3D reconstruction of
+conventionally produced mouse brain slices in a standardized atlas space. This
+is achieved first by constructing a virtual 3D mouse brain model from annotated
+slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed
+model generates ARA-based slice images corresponding to the microscopic images
+of histological brain sections. These image pairs are aligned using a geometric
+approach through contour images. Histological artifacts in the microscopic
+images are detected and removed using Constrained Delaunay Triangulation before
+performing global alignment. Finally, non-linear registration is performed by
+solving Laplace's equation with Dirichlet boundary conditions. Our methods
+provide significant improvements over previously reported registration
+techniques for the tested slices in 3D space, especially on slices with
+significant histological artifacts. Further, as an application we count the
+number of neurons in various anatomical regions using a dataset of 51
+microscopic slices from a single mouse brain. This work represents a
+significant contribution to this subfield of neuroscience as it provides tools
+to neuroanatomist for analyzing and processing histological data.
+"
+24284,Incorporating Prior Information in Compressive Online Robust Principal Component Analysis,"  We consider an online version of the robust Principle Component Analysis
+(PCA), which arises naturally in time-varying source separations such as video
+foreground-background separation. This paper proposes a compressive online
+robust PCA with prior information for recursively separating a sequences of
+frames into sparse and low-rank components from a small set of measurements. In
+contrast to conventional batch-based PCA, which processes all the frames
+directly, the proposed method processes measurements taken from each frame.
+Moreover, this method can efficiently incorporate multiple prior information,
+namely previous reconstructed frames, to improve the separation and thereafter,
+update the prior information for the next frame. We utilize multiple prior
+information by solving $n\text{-}\ell_{1}$ minimization for incorporating the
+previous sparse components and using incremental singular value decomposition
+($\mathrm{SVD}$) for exploiting the previous low-rank components. We also
+establish theoretical bounds on the number of measurements required to
+guarantee successful separation under assumptions of static or slowly-changing
+low-rank components. Using numerical experiments, we evaluate our bounds and
+the performance of the proposed algorithm. In addition, we apply the proposed
+algorithm to online video foreground and background separation from compressive
+measurements. Experimental results show that the proposed method outperforms
+the existing methods.
+"
+24285,Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning,"  Recommender systems aim to find an accurate and efficient mapping from
+historic data of user-preferred items to a new item that is to be liked by a
+user. Towards this goal, energy-based sequence generative adversarial nets
+(EB-SeqGANs) are adopted for recommendation by learning a generative model for
+the time series of user-preferred items. By recasting the energy function as
+the feature function, the proposed EB-SeqGANs is interpreted as an instance of
+maximum-entropy imitation learning.
+"
+24286,Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces,"  Reinforcement learning consists of finding policies that maximize an expected
+cumulative long-term reward in a Markov decision process with unknown
+transition probabilities and instantaneous rewards. In this paper, we consider
+the problem of finding such optimal policies while assuming they are continuous
+functions belonging to a reproducing kernel Hilbert space (RKHS). To learn the
+optimal policy we introduce a stochastic policy gradient ascent algorithm with
+three unique novel features: (i) The stochastic estimates of policy gradients
+are unbiased. (ii) The variance of stochastic gradients is reduced by drawing
+on ideas from numerical differentiation. (iii) Policy complexity is controlled
+using sparse RKHS representations. Novel feature (i) is instrumental in proving
+convergence to a stationary point of the expected cumulative reward. Novel
+feature (ii) facilitates reasonable convergence times. Novel feature (iii) is a
+necessity in practical implementations which we show can be done in a way that
+does not eliminate convergence guarantees. Numerical examples in standard
+problems illustrate successful learning of policies with low complexity
+representations which are close to stationary points of the expected cumulative
+reward.
+"
+24287,Extended Dijkstra algorithm and Moore-Bellman-Ford algorithm,"  Study the general single-source shortest path problem. Firstly, define a path
+function on a set of some path with same source on a graph, and develop a kind
+of general single-source shortest path problem (GSSSP) on the defined path
+function. Secondly, following respectively the approaches of the well known
+Dijkstra's algorithm and Moore-Bellman-Ford algorithm, design an extended
+Dijkstra's algorithm (EDA) and an extended Moore-Bellman-Ford algorithm (EMBFA)
+to solve the problem GSSSP under certain given conditions. Thirdly, introduce a
+few concepts, such as order-preserving in last road (OPLR) of path function,
+and so on. And under the assumption that the value of related path function for
+any path can be obtained in $M(n)$ time, prove respectively the algorithm EDA
+solving the problem GSSSP in $O(n^2)M(n)$ time and the algorithm EMBFA solving
+the problem GSSSP in $O(mn)M(n)$ time. Finally, some applications of the
+designed algorithms are shown with a few examples. What we done can improve
+both the researchers and the applications of the shortest path theory.
+"
+24288,An efficient tight-binding mode-space NEGF model enabling up to million atoms III-V nanowire MOSFETs and TFETs simulations,"  We report the capability to simulate in a quantum mechanical tight-binding
+(TB) atomistic fashion NW devices featuring several hundred to millions of
+atoms and diameter up to 18 nm. Such simulations go far beyond what is
+typically affordable with today's supercomputers using a traditional real space
+(RS) TB Hamiltonian technique. We have employed an innovative TB mode space
+(MS) technique instead and demonstrate large speedup (up to 10,000x) while
+keeping good accuracy (error smaller than 1 percent) compared to the RS NEGF
+method. Such technique and capability open new avenues to explore and
+understand the physics of nanoscale and mesoscopic devices dominated by quantum
+effects. In particular, our method addresses in an unprecedented way the
+technological relevant case of band-to-band tunneling (BTBT) in III-V nanowire
+MOSFETs and broken gap heterojunction tunnel-FETs (TFETs). We demonstrate an
+accurate match of simulated BTBT currents to experimental measurements in a
+[111] InAs NW having a 12 nm diameter and a 300 nm long channel. We apply the
+predictivity of our TB MS simulations and report an in-depth atomistic study of
+the scaling potential of III-V GAA nanowire heterojunction n and pTFETs
+quantifying the benefits of this technology for low-power, low-voltage CMOS
+application. At VDD = 0.3 V and IOFF = 50 pA/um, the on-current (Ion) and
+energy-delay product (ETP) gain over a Si NW GAA MOSFET are 58x and 56x
+respectively.
+"
+24289,Lattice models with exactly solvable topological hinge and corner states,"  We devise a generic recipe for constructing $D$-dimensional lattice models
+whose $d$-dimensional boundary states, located on surfaces, hinges, corners,
+and so forth, can be obtained exactly. The solvability is rooted in the
+underlying lattice structure and as such does not depend on fine tuning,
+allowing us to track their evolution throughout various phases and across phase
+transitions. Most saliently, our models provide ""boundary solvable"" examples of
+the recently introduced higher-order topological phases. We apply our general
+approach to breathing and anisotropic kagome and pyrochlore lattices for which
+we obtain exact corner eigenstates, and to periodically driven two-dimensional
+models as well as to three-dimensional lattices where we present exact
+solutions corresponding to one-dimensional chiral states at the hinges of the
+lattice. We relate the higher-order topological nature of these models to
+reflection symmetries in combination with their provenance from
+lower-dimensional conventional topological phases.
+"
+24290,Geodesic Clustering in Deep Generative Models,"  Deep generative models are tremendously successful in learning
+low-dimensional latent representations that well-describe the data. These
+representations, however, tend to much distort relationships between points,
+i.e. pairwise distances tend to not reflect semantic similarities well. This
+renders unsupervised tasks, such as clustering, difficult when working with the
+latent representations. We demonstrate that taking the geometry of the
+generative model into account is sufficient to make simple clustering
+algorithms work well over latent representations. Leaning on the recent finding
+that deep generative models constitute stochastically immersed Riemannian
+manifolds, we propose an efficient algorithm for computing geodesics (shortest
+paths) and computing distances in the latent space, while taking its distortion
+into account. We further propose a new architecture for modeling uncertainty in
+variational autoencoders, which is essential for understanding the geometry of
+deep generative models. Experiments show that the geodesic distance is very
+likely to reflect the internal structure of the data.
+"
+24291,SHARP - IV. An apparent flux ratio anomaly resolved by the edge-on disc in B0712+472,"  Flux ratio anomalies in quasar lenses can be attributed to dark matter
+substructure surrounding the lensing galaxy and, thus, used to constrain the
+substructure mass fraction. Previous applications of this approach infer a
+substructure abundance that potentially in tension with the predictions of a
+$\Lambda$CDM cosmology. However, the assumption that all flux ratio anomalies
+are due to substructure is a strong one, and alternative explanations have not
+been fully investigated. Here, we use new high-resolution near-IR Keck~II
+adaptive optics imaging for the lens system CLASS B0712+472 to perform
+pixel-based lens modelling for this system and, in combination with new VLBA
+radio observations, show that the inclusion of the disc in the lens model can
+explain the flux ratio anomalies without the need for dark matter
+substructures. The projected disc mass comprises 16% of the total lensing mass
+within the Einstein radius and the total disc mass is $1.79 \times 10^{10}
+M_{sun}$. The case of B0712+472 adds to the evidence that not all flux ratio
+anomalies are due to dark subhaloes, and highlights the importance of taking
+the effects of baryonic structures more fully into account in order to obtain
+an accurate measure of the substructure mass fraction.
+"
+24292,Regret Bounds and Regimes of Optimality for User-User and Item-Item Collaborative Filtering,"  We consider an online model for recommendation systems, with each user being
+recommended an item at each time-step and providing 'like' or 'dislike'
+feedback. A latent variable model specifies the user preferences: both users
+and items are clustered into types. All users of a given type have identical
+preferences for the items, and similarly, items of a given type are either all
+liked or all disliked by a given user. The model captures structure in both the
+item and user spaces, and in this paper, we assume that the type preference
+matrix is randomly generated. We describe two algorithms inspired by user-user
+and item-item collaborative filtering (CF), modified to explicitly make
+exploratory recommendations, and prove performance guarantees in terms of their
+expected regret. For two regimes of model parameters, with structure only in
+item space or only in user space, we prove information-theoretic lower bounds
+on regret that match our upper bounds up to logarithmic factors. Our analysis
+elucidates system operating regimes in which existing CF algorithms are nearly
+optimal.
+"
+24293,The Probability of Causation,"  Many legal cases require decisions about causality, responsibility or blame,
+and these may be based on statistical data. However, causal inferences from
+such data are beset by subtle conceptual and practical difficulties, and in
+general it is, at best, possible to identify the ""probability of causation"" as
+lying between certain empirically informed limits. These limits can be refined
+and improved if we can obtain additional information, from statistical or
+scientific data, relating to the internal workings of the causal processes. In
+this paper we review and extend recent work in this area, where additional
+information may be available on covariate and/or mediating variables.
+"
+24294,Integrable RCS as a proposed replacement for Fermilab Booster,"  Integrable optics is an innovation in particle accelerator design that
+potentially enables a greater betatron tune spread and damps collective
+instabilities. An integrable rapid-cycling synchrotron (RCS) would be an
+effective replacement for the Fermilab Booster, as part of a plan to reach
+multi-MW beam power at 120 GeV for the Fermilab high-energy neutrino program.
+We provide an example integrable lattice with features of a modern RCS -
+dispersion-free drifts, low momentum compaction factor, superperiodicity,
+chromaticity correction, bounded beta functions, and separate-function magnets.
+"
+24295,Active Learning for Cost-Sensitive Classification,"  We design an active learning algorithm for cost-sensitive multiclass
+classification: problems where different errors have different costs. Our
+algorithm, COAL, makes predictions by regressing to each label's cost and
+predicting the smallest. On a new example, it uses a set of regressors that
+perform well on past data to estimate possible costs for each label. It queries
+only the labels that could be the best, ignoring the sure losers. We prove COAL
+can be efficiently implemented for any regression family that admits squared
+loss optimization; it also enjoys strong guarantees with respect to predictive
+performance and labeling effort. We empirically compare COAL to passive
+learning and several active learning baselines, showing significant
+improvements in labeling effort and test cost on real-world datasets.
+"
+24296,Modularity-based Dynamic Community Detection,"  In this project, we designed a modularity-based dynamic community detection
+method, which keeps tracking the community structure of a dynamic network with
+very low computing complexity in updating the community structure while the
+network changing dynamically, and we implemented our algorithm using Java.
+Furthermore, we generated random dynamic networks that have the community
+structures with different parameters as our experiment dataset. Extensive
+experiments have been taken to evaluate our method in terms of accuracy and
+efficiency. The experiment result shows that, comparing with Louvain algorithm,
+our method could greatly enhance the efficiency without jeopardizing much of
+the accuracy. Although we got a pretty good results from our experiments, we
+only implemented our method on our own generated dataset. For future work, we
+might want to test our method on real public dataset. In the real public
+dynamic networks, there might be more specific dynamic change cases other than
+the cases covered by our method, so that we might need to dig into more
+specific other dynamic cases and find out the solutions.
+"
+24297,Tunable X-band optoelectronic oscillators based on external-cavity semiconductor lasers,"  Laser diodes with optical feedback can exhibit periodic intensity
+oscillations at or near the relaxation-oscillation frequency. We demonstrate
+optoelectronic oscillators based on external-cavity semiconductor lasers in a
+periodic dynamical regime tunable over the entire X-band. Moreover, unlike
+standard optoelectronic oscillators, we need not employ the time-dependent
+optical intensity incident on a photodiode to generate the microwave signal,
+but rather have the option of generating the electrical microwave signal
+directly as a voltage $V(t)$ at the laser-diode injection terminals under
+constant current operation; no photodiode need be involved, thus circumventing
+optical-to-electrical conversion. We achieve a timing jitter of $\lesssim 10$
+ps and a quality factor of $\gtrsim 2\times 10^5$ across the entire X-band,
+that ranges from 6.79 GHz to 11.48 GHz. Tuning is achieved by varying the
+injection current $J$.
+"
+24298,Post-Keplerian perturbations of the orbital time shift in binary pulsars: an analytical formulation with applications to the Galactic Center,"  We develop a general approach to analytically calculate the perturbations
+$\Delta\delta\tau_\textrm{p}$ of the orbital component of the change
+$\delta\tau_\textrm{p}$ of the times of arrival of the pulses emitted by a
+binary pulsar p induced by the post-Keplerian accelerations due to the mass
+quadrupole $Q_2$, and the post-Newtonian gravitoelectric (GE) and
+Lense-Thirring (LT) fields. We apply our results to the so-far still
+hypothetical scenario involving a pulsar orbiting the Supermassive Black Hole
+in in the Galactic Center at Sgr A$^\ast$. We also evaluate the gravitomagnetic
+and quadrupolar Shapiro-like propagation delays $\delta\tau_\textrm{prop}$. By
+assuming the orbit of the existing S2 main sequence star and a time span as
+long as its orbital period $P_\textrm{b}$, we obtain
+$\left|\Delta\delta\tau_\textrm{p}^\textrm{GE}\right|\lesssim
+10^3~\textrm{s},~\left|\Delta\delta\tau_\textrm{p}^\textrm{LT}\right|\lesssim
+0.6~\textrm{s},\left|\Delta\delta\tau_\textrm{p}^{Q_2}\right|\lesssim
+0.04~\textrm{s}$. Faster $\left(P_\textrm{b} = 5~\textrm{yr}\right)$ and more
+eccentric $\left(e=0.97\right)$ orbits would imply net shifts per revolution as
+large as
+$\left|\left\langle\Delta\delta\tau_\textrm{p}^\textrm{GE}\right\rangle\right|\lesssim
+10~\textrm{Ms},~\left|\left\langle\Delta\delta\tau_\textrm{p}^\textrm{LT}\right\rangle\right|\lesssim
+400~\textrm{s},\left|\left\langle\Delta\delta\tau_\textrm{p}^{Q_2}\right\rangle\right|\lesssim
+10^3~\textrm{s}$, depending on the other orbital parameters and the initial
+epoch. For the propagation delays, we have
+$\left|\delta\tau_\textrm{prop}^\textrm{LT}\right|\lesssim
+0.02~\textrm{s},~\left|\delta\tau_\textrm{prop}^{Q_2}\right|\lesssim
+1~\mu\textrm{s}$. The expected precision in pulsar timing in Sgr A$^\ast$ is of
+the order of $100~\mu\textrm{s}$, or, perhaps, even $1-10~\mu\textrm{s}$.
+"
+24299,Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN,"  The recently proposed distributional approach to reinforcement learning
+(DiRL) is centered on learning the distribution of the reward-to-go, often
+referred to as the value distribution. In this work, we show that the
+distributional Bellman equation, which drives DiRL methods, is equivalent to a
+generative adversarial network (GAN) model. In this formulation, DiRL can be
+seen as learning a deep generative model of the value distribution, driven by
+the discrepancy between the distribution of the current value, and the
+distribution of the sum of current reward and next value. We use this insight
+to propose a GAN-based approach to DiRL, which leverages the strengths of GANs
+in learning distributions of high-dimensional data. In particular, we show that
+our GAN approach can be used for DiRL with multivariate rewards, an important
+setting which cannot be tackled with prior methods. The multivariate setting
+also allows us to unify learning the distribution of values and state
+transitions, and we exploit this idea to devise a novel exploration method that
+is driven by the discrepancy in estimating both values and states.
+"
+24300,Blockchain based trust & authentication for decentralized sensor networks,"  Sensor networks and Wireless Sensor Networks (WSN) are key components for the
+development of the Internet of Things. These networks are subject of two kinds
+of constraints. Adaptability by the mean of mutability and evolutivity, and
+constrained node resources such as energy consumption, computational complexity
+or memory usage. In this context, none of the existing protocols and models
+allows reliable peer authentication and trust level management. In the field of
+virtual economic transactions, Bitcoin has proposed a new decentralized and
+evolutive way to model and acknowledge trust and data validity in a peer
+network by the mean of the blockchain. We propose a new security model and its
+protocol based on the blockchain technology to ensure validity and integrity of
+cryptographic authentication data and associate peer trust level, from the
+beginning to the end of the sensor network lifetime.
+"
+24301,Transitivity Demolition and the Falls of Social Networks,"  In this paper, we study crucial elements of a complex network, namely its
+nodes and connections, which play a key role in maintaining the network's
+structure and function under unexpected structural perturbations of nodes and
+edges removal. Specifically, we want to identify vital nodes and edges whose
+failure (either random or intentional) will break the most number of connected
+triples (or triangles) in the network. This problem is extremely important
+because connected triples form the foundation of strong connections in many
+real-world systems, such as mutual relationships in social networks, reliable
+data transmission in communication networks, and stable routing strategies in
+mobile networks. Disconnected triples, analog to broken mutual connections, can
+greatly affect the network's structure and disrupt its normal function, which
+can further lead to the corruption of the entire system. The analysis of such
+crucial elements will shed light on key factors behind the resilience and
+robustness of many complex systems in practice.
+We formulate the analysis under multiple optimization problems and show their
+intractability. We next propose efficient approximation algorithms, namely
+DAK-n and DAK-e, which guarantee an $(1-1/e)$-approximate ratio (compared to
+the overall optimal solutions) while having the same time complexity as the
+best triangle counting and listing algorithm on power-law networks. This
+advantage makes our algorithms scale extremely well even for very large
+networks. In an application perspective, we perform comprehensive experiments
+on real social traces with millions of nodes and billions of edges. These
+empirical experiments indicate that our approaches achieve comparably better
+results while are up to 100x faster than current state-of-the-art methods.
+"
+24302,"pH sensing properties of flexible, bias-free graphene microelectrodes in complex fluids: from phosphate buffer solution to human serum","  Advances in techniques for monitoring pH in complex fluids could have
+significant impact on analytical and biomedical applications ranging from water
+quality assessment to in vivo diagnostics. We developed flexible graphene
+microelectrodes (GEs) for rapid (< 5 seconds), very low power (femtowatt)
+detection of the pH of complex biofluids. The method is based on real-time
+measurement of Faradaic charge transfer between the GE and a solution at zero
+electrical bias. For an idealized sample of phosphate buffer solution (PBS),
+the Faradaic current varied monotonically and systematically with the pH with
+resolution of ~0.2 pH unit. The current-pH dependence was well described by a
+hybrid analytical-computational model where the electric double layer derives
+from an intrinsic, pH-independent (positive) charge associated with the
+graphene-water interface and ionizable (negative) charged groups described by
+the Langmuir-Freundlich adsorption isotherm. We also tested the GEs in more
+complex bio-solutions. In the case of a ferritin solution, the relative
+Faradaic current, defined as the difference between the measured current
+response and a baseline response due to PBS, showed a strong signal associated
+with the disassembly of the ferritin and the release of ferric ions at pH ~
+2.0. For samples of human serum, the Faradaic current showed a reproducible
+rapid (<20s) response to pH. By combining the Faradaic current and real time
+current variation, the methodology is potentially suitable for use to detect
+tumor-induced changes in extracellular pH.
+"
+24303,A Class of Multirate Infinitesimal GARK Methods,"  Differential equations arising in many practical applications are
+characterized by multiple time scales. Multirate time integration seeks to
+solve them efficiently by discretizing each scale with a different, appropriate
+time step, while ensuring the overall accuracy and stability of the numerical
+solution. In a seminal paper Knoth and Wolke (APNUM, 1998) proposed a hybrid
+solution approach: discretize the slow component with an explicit Runge-Kutta
+method, and advance the fast component via a modified fast differential
+equation. The idea led to the development of multirate infinitesimal step (MIS)
+methods by Wensch et al. (BIT, 2009.)Günther and Sandu (BIT, 2016)
+explained MIS schemes as a particular case of multirate General-structure
+Additive Runge-Kutta (MR-GARK) methods. The hybrid approach offers extreme
+flexibility in the choice of the numerical solution process for the fast
+component.
+This work constructs a family of multirate infinitesimal GARK schemes
+(MRI-GARK) that extends the hybrid dynamics approachin multiple ways. Order
+conditions theory and stability analyses are developed, and practical explicit
+and implicit methods of up to order four are constructed. Numerical results
+confirm the theoretical findings. We expect the new MRI-GARK family to be most
+useful for systems of equations with widely disparate time scales, where the
+fast process is dispersive, and where the influence of the fast component on
+the slow dynamics is weak.
+"
+24304,Noncommutativity and relativity,"  In this paper, we endeavour to show that from the noncommutative nature of
+spacetime one can deduce the concept of relativity in the sense that the
+velocity cannot be infinite as in the case of Galilean relativity.
+"
+24305,Useful redundancy in parameter and time delay estimation for continuous-time models,"  This paper proposes an algorithm to estimate the parameters, including time
+delay, of continuous time systems based on instrumental variable identification
+methods. To overcome the multiple local minima of the cost function associated
+with the estimation of a time delay system, we utilise the useful redundancy
+technique. Specifically, the cost function is filtered through a set of
+low-pass filters to improve convexity with the useful redundancy technique
+exploited to achieve convergence to the global minimum of the optimization
+problem. Numerical examples are presented to demonstrate the effectiveness of
+the proposed algorithm.
+"
+24306,A note on spaces of continuous functions on compact scattered spaces,"  In 1959, Pelczynski and Semadeni proved a theorem in which they gave some
+equivalent conditions for a compact Hausdorff space to be scattered. The
+purpose of the current note is that to clarify the meaning of the subtle term
+""conditionally weakly sequentially compact"" they used as the basis for the
+proof of their theorem. Unfortunately, the term now is taken over by a similar
+but subtle concept that may cause a serious problem.
+"
+24307,New approaches to coding information using inverse scattering transform,"  Remarkable mathematical properties of the integrable nonlinear Schrödinger
+equation (NLSE) can offer advanced solutions for the mitigation of nonlinear
+signal distortions in optical fibre links. Fundamental optical soliton,
+continuous and discrete eigenvalues of the nonlinear spectrum have already been
+considered for transmission of information in fibre-optic channels. Here we
+propose to apply signal modulation to the kernel of the
+Gelfand-Levitan-Marchenko equations (GLME) that offers the advantage of a
+relatively simple decoder design. First, we describe an approach based on
+exploiting the general N-soliton solution of the NLSE for simultaneous coding
+of $N$ symbols involving $4\times N$ coding parameters. As a specific elegant
+sub-class of the general schemes we introduce a soliton orthogonal frequency
+division multiplexing (SOFDM) method. This method is based on the choice of
+identical imaginary parts of the N-soliton solution eigenvalues, corresponding
+to equidistant soliton frequencies making it similar to the conventional OFDM
+scheme, thus, allowing use of the efficient fast Fourier transform algorithm to
+recover the data. Then, we demonstrate how to use this new approach to control
+signal parameters in the case of the continuous spectrum.
+"
+24308,Bifurcation analysis of Wilson-Cowan model with singular impulses,"  The paper concerns with Wilson-Cowan neural model with impulses. The main
+novelty of the study is that besides the traditional singularity of the model,
+we consider singular impulses. A new technique of analysis of the phenomenon is
+suggested. This allows to consider the existence of solutions of the model and
+bifurcation in ultimate neural behavior is observed through numerical
+simulations. The bifurcations are reasoned by impulses and singularity in the
+model and they concern the structure of attractors, which consist of newly
+introduced sets in the phase space such that medusas and rings.
+"
+24309,An SLE approach to four dimensional black hole microstate entropy,"  In this note, we model the Bekenstein-Hawking entropy of a four dimensional
+extremal black hole in terms of classifying particles moving in its near
+horizon AdS_2 geometry. We use the framework of SLE curves in AdS_2 to classify
+these particle trajectories in terms of their boundary conditions. These turn
+out to be related to singular vectors in two-dimensional conformal quantum
+gravity theory in AdS_2 and the dynamics of these particles are governed by the
+Hamiltonians of the integrable Calogero-like models, for these boundary
+conditions. We use this classification to count the leading order
+Bekenstein-Hawking entropy of the black hole and arrive at a first principle
+microscopic computation of black hole degeneracy.
+"
+24310,Si Orientation Dependent Release of SiO2 Microcantilevers by Wet Chemical Etching Method,"  Micro-cantilevers (MCs) are widely used in sensing, probing and a variety of
+other purposes. Apart from Si, the most versatile material in the micro domain,
+SiO2 MCs have attracted much attention lately. In this study, fabrication of
+SiO2 MCs using wet anisotropic etching technique is reported. Thermally grown
+SiO2 (300 nm) layer on single crystalline(c-) Si is utilized for the
+micro-fabrication. Standard lithographic procedure was followed and the
+exposure of the photo-resist was performed by mask-less direct laser writer.
+Various orientations on the c-Si wafer, their properties and implications on
+the structure and integrity of the released SiO2 MC are discussed. The 110
+direction was found to be much time consuming and sticking of the released
+structures was almost evident. On the other hand 100 and 410 directions showed
+lesser release time and higher stability of the released structures due to
+under-etching along the width of the MCs.
+"
+24311,Calibration of the Space-borne Compton Polarimeter POLAR flight model with 100% polarized X-ray beams,"  POLAR is space-borne detector designed for a precise measurement of gamma-ray
+polarization of the prompt emissions of Gamma-Ray Bursts in the energy range 50
+keV - 500 keV. POLAR is a compact Compton polarimeter consisting of 40$\times$
+40 plastic scintillator bars read out by 25 multi-anode PMTs. In May 2015, we
+performed a series of tests of the POLAR flight model with 100\% polarized
+x-rays beams at the European Synchrotron Radiation Facility beam-line ID11
+aming to study thresholds, crosstalk between channels and responses of POLAR
+flight model to polarized X-ray beams. In this paper we present the data
+analysis method and some analysis results. According the results, POLAR FM has
+good polarimetric capabilities.
+"
+24312,Thermal ratchet effect in confining geometries,"  Stochastic model of the Feynman-Smoluchowski ratchet is proposed and solved
+using generalization of the Fick-Jacobs theory. The theory fully captures
+nonlinear response of the ratchet to the difference of heat bath temperatures.
+The ratchet performance is discussed using the mean velocity, the average heat
+flow between the two heat reservoirs and the figure of merit, which quantifies
+energetic cost for attaining a certain mean velocity. Limits of the theory are
+tested comparing its predictions to numerics. We also demonstrate connection
+between the ratchet effect emerging in the model and rotations of the
+probability current and explain direction of the mean velocity using simple
+discrete analogue of the model.
+"
+24313,Ultrafast Optical Signal Processing with Bragg Structures,"  The phase, amplitude, speed, and polarization, in addition to many other
+properties of light, can be modulated by photonic Bragg structures. In
+conjunction with nonlinearity and quantum effects, a variety of ensuing micro-
+or nano-photonic applications can be realized. This paper reviews various
+optical phenomena in several exemplary 1D Bragg gratings. Important examples
+are resonantly absorbing photonic structures, chirped Bragg grating, and
+cholesteric liquid crystals; their unique operation capabilities and key issues
+are considered in detail. These Bragg structures are expected to be used in
+wide-spread applications involving light field modulations, especially in the
+rapidly advancing field of ultrafast optical signal processing.
+"
+24314,Multilevel Clustering via Wasserstein Means,"  We propose a novel approach to the problem of multilevel clustering, which
+aims to simultaneously partition data in each group and discover grouping
+patterns among groups in a potentially large hierarchically structured corpus
+of data. Our method involves a joint optimization formulation over several
+spaces of discrete probability measures, which are endowed with Wasserstein
+distance metrics. We propose a number of variants of this problem, which admit
+fast optimization algorithms, by exploiting the connection to the problem of
+finding Wasserstein barycenters. Consistency properties are established for the
+estimates of both local and global clusters. Finally, experiment results with
+both synthetic and real data are presented to demonstrate the flexibility and
+scalability of the proposed approach.
+"
+24315,Heterogeneous and rate-dependent streptavidin-biotin unbinding revealed by high-speed force spectroscopy and atomistic simulations,"  Receptor-ligand interactions are essential for biological function and their
+binding strength is commonly explained in terms of static lock-and-key models
+based on molecular complementarity. However, detailed information of the full
+unbinding pathway is often lacking due, in part, to the static nature of atomic
+structures and ensemble averaging inherent to bulk biophysics approaches. Here
+we combine molecular dynamics and high-speed force spectroscopy on the
+streptavidin-biotin complex to determine the binding strength and unbinding
+pathways over the widest dynamic range. Experiment and simulation show
+excellent agreement at overlapping velocities and provided evidence of the
+unbinding mechanisms. During unbinding, biotin crosses multiple energy barriers
+and visits various intermediate states far from the binding pocket while
+streptavidin undergoes transient induced fits, all varying with loading rate.
+This multistate process slows down the transition to the unbound state and
+favors rebinding, thus explaining the long lifetime of the complex. We provide
+an atomistic, dynamic picture of the unbinding process, replacing a simple
+two-state picture with one that involves many routes to the lock and
+rate-dependent induced-fit motions for intermediates, which might be relevant
+for other receptor-ligand bonds.
+"
+24316,Geodesic triangulations exist for cusped Platonic manifolds,"  We show that if a cusped hyperbolic manifold is Platonic, i.e., can be
+decomposed into isometric Platonic solids, it can also be decomposed into
+geodesic ideal tetrahedra.
+"
+24317,"The symplectic fermion ribbon quasi-Hopf algebra and the SL(2,Z)-action on its centre","  We introduce a family of factorisable ribbon quasi-Hopf algebras $Q(N)$ for
+$N$ a positive integer: as an algebra, $Q(N)$ is the semidirect product of
+$\mathbb{C}\mathbb{Z}_2$ with the direct sum of a Grassmann and a Clifford
+algebra in $2N$ generators. We show that $Rep Q(N)$ is ribbon equivalent to the
+symplectic fermion category $SF(N)$ that was computed by the third author from
+conformal blocks of the corresponding logarithmic conformal field theory. The
+latter category in turn is conjecturally ribbon equivalent to representations
+of $V_{ev}$, the even part of the symplectic fermion vertex operator super
+algebra.
+Using the formalism developed in our previous paper we compute the projective
+$SL(2,\mathbb{Z})$-action on the centre of $Q(N)$ as obtained from
+Lyubashenko's general theory of mapping class group actions for factorisable
+finite ribbon categories. This allows us to test a conjectural non-semisimple
+version of the modular Verlinde formula: we verify that the
+$SL(2,\mathbb{Z})$-action computed from $Q(N)$ agrees projectively with that on
+pseudo trace functions of $V_{ev}$.
+"
+24318,Dancing disclinations in confined active nematics,"  The spontaneous emergence of collective flows is a generic property of active
+fluids and often leads to chaotic flow patterns characterised by swirls, jets,
+and topological disclinations in their orientation field. However, the ability
+to achieve structured flows and ordered disclinations is of particular
+importance in the design and control of active systems. By confining an active
+nematic fluid within a channel, we find a regular motion of disclinations, in
+conjunction with a well defined and dynamic vortex lattice. As pairs of moving
+disclinations travel through the channel, they continually exchange partners
+producing a dynamic ordered state, reminiscent of Ceilidh dancing. We
+anticipate that this biomimetic ability to self-assemble organised topological
+disclinations and dynamically structured flow fields in engineered geometries
+will pave the road towards establishing new active topological microfluidic
+devices.
+"
+24319,Demand Shaping in Cellular Networks,"  Demand shaping is a promising way to mitigate the wireless cellular capacity
+shortfall in the presence of ever-increasing wireless data demand. In this
+paper, we formulate demand shaping as an optimization problem that minimizes
+the variation in aggregate traffic. We design a distributed and randomized
+offline demand shaping algorithm under complete traffic information and prove
+its almost surely convergence. We further consider a more realistic setting
+where the traffic information is incomplete but the future traffic can be
+predicted to a certain degree of accuracy. We design an online demand shaping
+algorithm that updates the schedules of deferrable applications (DAs) each time
+when new information is available, based on solving at each timeslot an
+optimization problem over a shrinking horizon from the current time to the end
+of the day. We compare the performance of the online algorithm against the
+optimal offline algorithm, and provide numerical examples to complement the
+theoretical analysis.
+"
+24320,Deep linear neural networks with arbitrary loss: All local minima are global,"  We consider deep linear networks with arbitrary convex differentiable loss.
+We provide a short and elementary proof of the fact that all local minima are
+global minima if the hidden layers are either 1) at least as wide as the input
+layer, or 2) at least as wide as the output layer. This result is the strongest
+possible in the following sense: If the loss is convex and Lipschitz but not
+differentiable then deep linear networks can have sub-optimal local minima.
+"
+24321,PSF field learning based on Optimal Transport Distances,"  Context: in astronomy, observing large fractions of the sky within a
+reasonable amount of time implies using large field-of-view (fov) optical
+instruments that typically have a spatially varying Point Spread Function
+(PSF). Depending on the scientific goals, galaxies images need to be corrected
+for the PSF whereas no direct measurement of the PSF is available. Aims: given
+a set of PSFs observed at random locations, we want to estimate the PSFs at
+galaxies locations for shapes measurements correction. Contributions: we
+propose an interpolation framework based on Sliced Optimal Transport. A
+non-linear dimension reduction is first performed based on local pairwise
+approximated Wasserstein distances. A low dimensional representation of the
+unknown PSFs is then estimated, which in turn is used to derive representations
+of those PSFs in the Wasserstein metric. Finally, the interpolated PSFs are
+calculated as approximated Wasserstein barycenters. Results: the proposed
+method was tested on simulated monochromatic PSFs of the Euclid space mission
+telescope (to be launched in 2020). It achieves a remarkable accuracy in terms
+of pixels values and shape compared to standard methods such as Inverse
+Distance Weighting or Radial Basis Function based interpolation methods.
+"
+24322,On the Complexity of Chore Division,"  We study the proportional chore division problem where a protocol wants to
+divide an undesirable object, called chore, among $n$ different players. The
+goal is to find an allocation such that the cost of the chore assigned to each
+player be at most $1/n$ of the total cost. This problem is the dual variant of
+the cake cutting problem in which we want to allocate a desirable object.
+Edmonds and Pruhs showed that any protocol for the proportional cake cutting
+must use at least $\Omega(n \log n)$ queries in the worst case, however,
+finding a lower bound for the proportional chore division remained an
+interesting open problem. We show that chore division and cake cutting problems
+are closely related to each other and provide an $\Omega(n \log n)$ lower bound
+for chore division.
+"
+24323,Random Walks on Finite Quantum Groups: Diaconis-Shahshahani Theory for Quantum Groups,"  The concept of a random walk on a finite group converging to random - and a
+way of measuring the distance to random after $k$ transitions - is generalised
+from the classical case to the case of random walks on finite quantum groups.
+A central tool in the study of ergodic random walks on finite groups is the
+Upper Bound Lemma of Diaconis and Shahshahani. The Upper Bound Lemma uses the
+representation theory of the group to generate upper bounds for the distance to
+random and thus can be used to determine convergence rates for ergodic walks.
+The representation theory of quantum groups is very well understood and is
+remarkably similar to the representation theory of classical groups. This
+allows for a generalisation of the Upper Bound Lemma to an Upper Bound Lemma
+for quantum groups.
+The Quantum Diaconis--Shahshahani Upper Bound Lemma is used to study the
+convergence of ergodic random walks on classical groups $\mathbb{Z}_n$,
+$\mathbb{Z}_2^n$, the dual group $\widehat{S_n}$ as well as the `truly' quantum
+groups of Kac & Paljutkin, and Sekine.
+"
+24324,Cleaning the correlation matrix with a denoising autoencoder,"  In this paper, we use an adjusted autoencoder to estimate the true
+eigenvalues of the population correlation matrix from the sample correlation
+matrix when the number of samples is small. We show that the model outperforms
+the Rotational Invariant Estimator (Bouchaud) which is the optimal estimator in
+the sample eigenvectors basis when the dimension goes to infinity.
+"
+24325,Exact Nonlinear Model Reduction for a von Karman beam: Slow-Fast Decomposition and Spectral Submanifolds,"  We apply two recently formulated mathematical techniques, Slow-Fast
+Decomposition (SFD) and Spectral Submanifold (SSM) reduction, to a von Karman
+beam with geometric nonlinearities and viscoelastic damping. SFD identifies a
+global slow manifold in the full system which attracts solutions at rates
+faster than typical rates within the manifold. An SSM, the smoothest nonlinear
+continuation of a linear modal subspace, is then used to further reduce the
+beam equations within the slow manifold. This two-stage, mathematically exact
+procedure results in a drastic reduction of the finite-element beam model to a
+one-degree-of freedom nonlinear oscillator. We also introduce the technique of
+spectral quotient analysis, which gives the number of modes relevant for
+reduction as output rather than input to the reduction process.
+"
+24326,The Influence of Social Ties on Performance in Team-based Online Games,"  Social ties are the invisible glue that keeps together human ecosystems.
+Despite the massive amount of research studying the role of social ties in
+communities (groups, teams, etc.) and society at large, little attention has
+been devoted to study their interplay with other human behavioral dynamics. Of
+particular interest is the influence that social ties have on human performance
+in collaborative team-based settings. Our research aims to elucidate the
+influence of social ties on individual and team performance dynamics. We will
+focus on a popular Multiplayer Online Battle Arena (MOBA) collaborative
+team-based game, Defense of the Ancients 2 (Dota 2), a rich dataset with
+millions of players and matches. Our research reveals that, when playing with
+their friends, individuals are systematically more active in the game as
+opposed to taking part in a team of strangers. However, we find that increased
+activity does not homogeneously lead to an improvement in players' performance.
+Despite being beneficial to low skill players, playing with friends negatively
+affects performance of high skill players. Our findings shed light on the mixed
+influence of social ties on performance, and can inform new perspectives on
+virtual team management and on behavioral incentives.
+"
+24327,Infrastructure for Usable Machine Learning: The Stanford DAWN Project,"  Despite incredible recent advances in machine learning, building machine
+learning applications remains prohibitively time-consuming and expensive for
+all but the best-trained, best-funded engineering organizations. This expense
+comes not from a need for new and improved statistical models but instead from
+a lack of systems and tools for supporting end-to-end machine learning
+application development, from data preparation and labeling to
+productionization and monitoring. In this document, we outline opportunities
+for infrastructure supporting usable, end-to-end machine learning applications
+in the context of the nascent DAWN (Data Analytics for What's Next) project at
+Stanford.
+"
+24328,Frequency pulling and mixing of relaxation oscillations in superconducting nanowires,"  Many superconducting technologies such as rapid single flux quantum computing
+(RSFQ) and superconducting quantum interference devices (SQUIDs) rely on the
+modulation of nonlinear dynamics in Josephson junctions for functionality. More
+recently, however, superconducting devices have been developed based on the
+switching and thermal heating of nanowires for use in fields such as single
+photon detection and digital logic. In this paper, we use resistive shunting to
+control the nonlinear heating of a superconducting nanowire and compare the
+resulting dynamics to those observed in Josephson junctions. We show that
+interaction of the hotspot growth with the external shunt produces high
+frequency relaxation oscillations with similar behavior as observed in
+Josephson junctions due to their rapid time constants and ability to be
+modulated by a weak periodic signal. In particular, we use a microwave drive to
+pull and mix the oscillation frequency, resulting in phase locked features that
+resemble the AC Josephson effect. New nanowire devices based on these
+conclusions have promising applications in fields such as parametric
+amplification and frequency multiplexing.
+"
+24329,PCOT: Cache Oblivious Tiling of Polyhedral Programs,"  This paper studies two variants of tiling: iteration space tiling (or loop
+blocking) and cache-oblivious methods that recursively split the iteration
+space with divide-and-conquer. The key question to answer is when we should be
+using one over the other. The answer to this question is complicated for modern
+architecture due to a number of reasons. In this paper, we present a detailed
+empirical study to answer this question for a range of kernels that fit the
+polyhedral model. Our study is based on a generalized cache oblivious code
+generator that support this class, which is a superset of those supported by
+existing tools. The conclusion is that cache oblivious code is most useful when
+the aim is to have reduced off-chip memory accesses, e.g., lower energy, albeit
+certain situations that diminish its effectiveness exist.
+"
+24330,Dynamic Sharing Through the ADMM,"  In this paper, we study a dynamic version of the sharing problem, in which a
+dynamic system cost function composed of time-variant local costs of subsystems
+and a shared time-variant cost of the whole system is minimized. A dynamic
+alternating direction method of multipliers (ADMM) is proposed to track the
+varying optimal points of the dynamic optimization problem in an online manner.
+We analyze the convergence properties of the dynamic ADMM and show that, under
+several standard technical assumptions, the iterations of the dynamic ADMM
+converge linearly to some neighborhoods of the time-varying optimal points. The
+sizes of these neighborhoods depend on the drifts of the dynamic objective
+functions: the more drastically the dynamic objective function evolves across
+time, the larger the sizes of these neighborhoods. We also investigate the
+impact of the drifts on the steady state convergence behaviors of the dynamic
+ADMM. Finally, two numerical examples, namely a dynamic sharing problem and the
+dynamic least absolute shrinkage and selection operator (LASSO), are presented
+to corroborate the effectiveness of the proposed dynamic ADMM. It is observed
+that the dynamic ADMM can track the time-varying optimal points quickly and
+accurately. For the dynamic LASSO, the dynamic ADMM has competitive performance
+compared to the benchmark offline optimizor while the former possesses
+significant computational advantage over the latter.
+"
+24331,Isotope shift and search for metastable superheavy elements in astrophysical data,"  Spectral lines belonging to the short-lifetime heavy radioactive elements up
+to Es ($Z$=99) have been found in the spectra of the Przybylski's star. We
+suggest that these unstable elements may be decay products of a ""magic""
+metastable nucleus belonging to the the island of stability where the nuclei
+have a magic number of neutrons $N=184$. The laboratory-produced nuclei have a
+significantly smaller number of neutrons. To identify spectra of the $N=184$
+isotopes of these nuclei and their neutron-reach superheavy decay products in
+astrophysical data we calculate the isotope shift which should be added to the
+laboratory - measured wavelenghs. The results for the isotopic shifts in the
+strongest optical electromagnetic transitions in No, Lr, Nh, Fl,and $Z$=120
+elements are presented.
+"
+24332,Pollution State Modeling for Mexico City,"  Ground-level ozone and particulate matter pollutants are associated with a
+variety of health issues and increased mortality. For this reason, Mexican
+environmental agencies regulate pollutant levels. In addition, Mexico City
+defines pollution emergencies using thresholds that rely on regional maxima for
+ozone and particulate matter with diameter less than 10 micrometers
+($\text{PM}_{10}$). To predict local pollution emergencies and to assess
+compliance to Mexican ambient air quality standards, we analyze hourly ozone
+and $\text{PM}_{10}$ measurements from 24 stations across Mexico City from 2017
+using a bivariate spatiotemporal model. Using this model, we predict future
+pollutant levels using current weather conditions and recent pollutant
+concentrations. Using hourly pollutant projections, we predict regional maxima
+needed to estimate the probability of future pollution emergencies. We discuss
+how predicted compliance to legislated pollution limits varies across regions
+within Mexico City in 2017. We find that predicted probability of pollution
+emergencies is limited to a few time periods. In contrast, we show that
+predicted exceedance of Mexican ambient air quality standards is a common,
+nearly daily occurrence.
+"
+24333,"Optical properties of periodic, quasi-periodic, and disordered one-dimensional photonic structures","  Photonic crystals are characterized by a spatial modulation of the dielectric
+constant on the length scale of the wavelength of light giving rise to energy
+ranges where light cannot propagate through the crystal - the photonic band
+gap. While mostly photonic crystals are referred to as periodic arrangements,
+in this review we aim to highlight as well how aperiodicity and disorder
+affects light modulation. In this review article, we introduce the concepts of
+periodicity, quasi-periodicity, and disorder in photonic crystals, focussing on
+the one-dimensional case. We discuss in detail the physical peculiarities, the
+fabrication techniques, and the applications of periodic, quasi-periodic, and
+disorder photonic structures, highlighting how the degree of crystallinity
+matters in the manipulation of light. We report different types of disorder in
+1D photonic structures and we discuss their properties in terms of light
+transmission. We discuss the relationship between the average total
+transmission, in a range of wavelengths around the photonic band gap of the
+corresponding photonic crystal, and the homogeneity of the photonic structures,
+quantified by the Shannon index. Then we discuss the light transmission in
+structures in which the high refractive index layers are aggregated in clusters
+following a power law distribution. Finally, in the case of structures in which
+the high refractive index layers are aggregated in clusters with a truncated
+uniform distribution, we discuss: i) how different refractive index contrast
+tailors the total light transmission; ii) how the total light transmission is
+affected by the introduction of defects made with a third material.
+"
+24334,Reinforced dynamics for enhanced sampling in large atomic and molecular systems,"  A new approach for efficiently exploring the configuration space and
+computing the free energy of large atomic and molecular systems is proposed,
+motivated by an analogy with reinforcement learning. There are two major
+components in this new approach. Like metadynamics, it allows for an efficient
+exploration of the configuration space by adding an adaptively computed biasing
+potential to the original dynamics. Like deep reinforcement learning, this
+biasing potential is trained on the fly using deep neural networks, with data
+collected judiciously from the exploration and an uncertainty indicator from
+the neural network model playing the role of the reward function.
+Parameterization using neural networks makes it feasible to handle cases with a
+large set of collective variables. This has the potential advantage that
+selecting precisely the right set of collective variables has now become less
+critical for capturing the structural transformations of the system. The method
+is illustrated by studying the full-atom, explicit solvent models of alanine
+dipeptide and tripeptide, as well as the system of a polyalanine-10 molecule
+with 20 collective variables.
+"
+24335,Constraints on hydrodynamics from many-body quantum chaos,"  Is the hydrodynamics of an interacting many-body system fundamentally limited
+by basic principles of quantum mechanics? Starting with the conjecture that
+viscosity is at least as large as entropy density (as measured in fundamental
+units), there has been a long search for a precise answer to this question. In
+this work, we identify a simple relationship between hydrodynamics and
+many-body quantum chaos in a broad class of experimentally realizable systems.
+Consistency with the quantum butterfly effect leads to upper bounds on the
+speed of sound and diffusion constants of hydrodynamics. These bounds link two
+very different theories of quantum many-body dynamics, clarify the relationship
+between classical hydrodynamics and quantum information loss, and provide a
+simple way to constrain theories of thermalization and quantum chaos in
+experiments.
+"
+24336,A defense of using resting state fMRI as null data for estimating false positive rates,"  A recent Editorial by Slotnick (2017) reconsiders the findings of our paper
+on the accuracy of false positive rate control with cluster inference in fMRI
+(Eklund et al, 2016), in particular criticising our use of resting state fMRI
+data as a source for null data in the evaluation of task fMRI methods. We
+defend this use of resting fMRI data, as while there is much structure in this
+data, we argue it is representative of task data noise and as such analysis
+software should be able to accommodate this noise. We also discuss a potential
+problem with Slotnick's own method.
+"
+24337,"Transmitter and Receiver Architectures for Molecular Communications: A Survey on Physical Design with Modulation, Coding and Detection Techniques","  Inspired by Nature, molecular communications (MC), i.e., use of molecules to
+encode, transmit and receive information, stands as the most promising
+communication paradigm to realize nanonetworks. Even though there has been
+extensive theoretical research towards nanoscale MC, there are no examples of
+implemented nanoscale MC networks. The main reason for this lies in the
+peculiarities of nanoscale physics, challenges in nanoscale fabrication and
+highly stochastic nature of biochemical domain of envisioned nanonetwork
+applications. This mandates developing novel device architectures and
+communication methods compatible with MC constraints. To that end, various
+transmitter and receiver designs for MC have been proposed in literature
+together with numerable modulation, coding and detection techniques. However,
+these works fall into domains of a very wide spectrum of disciplines, including
+but not limited to information and communication theory, quantum physics,
+materials science, nanofabrication, physiology and synthetic biology.
+Therefore, we believe it is imperative for the progress of the field that, an
+organized exposition of cumulative knowledge on subject matter be compiled.
+Thus, to fill this gap, in this comprehensive survey we review the existing
+literature on transmitter and receiver architectures towards realizing MC
+amongst nanomaterial-based nanomachines and/or biological entities, and provide
+a complete overview of modulation, coding and detection techniques employed for
+MC. Moreover, we identify the most significant shortcomings and challenges in
+all these research areas, and propose potential solutions to overcome some of
+them.
+"
+24338,Two Results on Slime Mold Computations,"  We present two results on slime mold computations. In wet-lab experiments
+(Nature'00) by Nakagaki et al. the slime mold Physarum polycephalum
+demonstrated its ability to solve shortest path problems. Biologists proposed a
+mathematical model, a system of differential equations, for the slime's
+adaption process (J. Theoretical Biology'07). It was shown that the process
+convergences to the shortest path (J. Theoretical Biology'12) for all graphs.
+We show that the dynamics actually converges for a much wider class of
+problems, namely undirected linear programs with a non-negative cost vector.
+Combinatorial optimization researchers took the dynamics describing slime
+behavior as an inspiration for an optimization method and showed that its
+discretization can $\varepsilon$-approximately solve linear programs with
+positive cost vector (ITCS'16). Their analysis requires a feasible starting
+point, a step size depending linearly on $\varepsilon$, and a number of steps
+with quartic dependence on $\mathrm{opt}/(\varepsilon\Phi)$, where $\Phi$ is
+the difference between the smallest cost of a non-optimal basic feasible
+solution and the optimal cost ($\mathrm{opt}$).
+We give a refined analysis showing that the dynamics initialized with any
+strongly dominating point converges to the set of optimal solutions. Moreover,
+we strengthen the convergence rate bounds and prove that the step size is
+independent of $\varepsilon$, and the number of steps depends logarithmically
+on $1/\varepsilon$ and quadratically on $\mathrm{opt}/\Phi$.
+"
+24339,Meta-QSAR: a large-scale application of meta-learning to drug design and discovery,"  We investigate the learning of quantitative structure activity relationships
+(QSARs) as a case-study of meta-learning. This application area is of the
+highest societal importance, as it is a key step in the development of new
+medicines. The standard QSAR learning problem is: given a target (usually a
+protein) and a set of chemical compounds (small molecules) with associated
+bioactivities (e.g. inhibition of the target), learn a predictive mapping from
+molecular representation to activity. Although almost every type of machine
+learning method has been applied to QSAR learning there is no agreed single
+best way of learning QSARs, and therefore the problem area is well-suited to
+meta-learning. We first carried out the most comprehensive ever comparison of
+machine learning methods for QSAR learning: 18 regression methods, 6 molecular
+representations, applied to more than 2,700 QSAR problems. (These results have
+been made publicly available on OpenML and represent a valuable resource for
+testing novel meta-learning methods.) We then investigated the utility of
+algorithm selection for QSAR problems. We found that this meta-learning
+approach outperformed the best individual QSAR learning method (random forests
+using a molecular fingerprint representation) by up to 13%, on average. We
+conclude that meta-learning outperforms base-learning methods for QSAR
+learning, and as this investigation is one of the most extensive ever
+comparisons of base and meta-learning methods ever made, it provides evidence
+for the general effectiveness of meta-learning over base-learning.
+"
+24340,Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach,"  One of the current issues in Brain-Computer Interface is how to deal with
+noisy Electroencephalography measurements organized as multidimensional
+datasets. On the other hand, recently, significant advances have been made in
+multidimensional signal completion algorithms that exploit tensor decomposition
+models to capture the intricate relationship among entries in a
+multidimensional signal. We propose to use tensor completion applied to EEG
+data for improving the classification performance in a motor imagery BCI system
+with corrupted measurements. Noisy measurements are considered as unknowns that
+are inferred from a tensor decomposition model. We evaluate the performance of
+four recently proposed tensor completion algorithms plus a simple interpolation
+strategy, first with random missing entries and then with missing samples
+constrained to have a specific structure (random missing channels), which is a
+more realistic assumption in BCI Applications. We measured the ability of these
+algorithms to reconstruct the tensor from observed data. Then, we tested the
+classification accuracy of imagined movement in a BCI experiment with missing
+samples. We show that for random missing entries, all tensor completion
+algorithms can recover missing samples increasing the classification
+performance compared to a simple interpolation approach. For the random missing
+channels case, we show that tensor completion algorithms help to reconstruct
+missing channels, significantly improving the accuracy in the classification of
+motor imagery, however, not at the same level as clean data. Tensor completion
+algorithms are useful in real BCI applications. The proposed strategy could
+allow using motor imagery BCI systems even when EEG data is highly affected by
+missing channels and/or samples, avoiding the need of new acquisitions in the
+calibration stage.
+"
+24341,Poor Peering: a reflexion about a RIXP,"  Since more than twenty years, Internet evolves as well as users' needs. and
+the users' needs too. The need of bandwidth growths every day with the new
+usages. In this context, with megabit per second count. There is a real
+business behind our free access to web site. From a group of island, the
+economy of bandwidth can be a major challenge. Installing a Regional Internet
+eXchange Point (RIXP) can be a solution to keep regional Internet traffic. This
+paper brings a reflexion about where to install a RIXP in the Indian Ocean Area
+(IOA). As there is a lot of different countries and stakes, we (stay) only on
+an scientist approach.
+"
+24342,"Functional Calculus on BMO-type Spaces of Bourgain, Brezis and Mironescu","  A nonlinear superposition operator $T_g$ related to a Borel measurable
+function $g:\ {\mathbb C}\to {\mathbb C}$ is defined via $T_g(f):=g\circ f$ for
+any complex-valued function $f$ on ${\mathbb R^n}$. This article is devoted to
+investigating the mapping properties of $T_g$ on a new BMO type space recently
+introduced by Bourgain, Brezis and Mironescu [J. Eur. Math. Soc. (JEMS) 17
+(2015), 2083-2101], as well as its VMO and CMO type subspaces. Some sufficient
+and necessary conditions for the inclusion result and the continuity property
+of $T_g$ on these spaces are obtained.
+"
+24343,Active Preference Learning for Personalized Portfolio Construction,"  In financial asset management, choosing a portfolio requires balancing
+returns, risk, exposure, liquidity, volatility and other factors. These
+concerns are difficult to compare explicitly, with many asset managers using an
+intuitive or implicit sense of their interaction. We propose a mechanism for
+learning someone's sense of distinctness between portfolios with the goal of
+being able to identify portfolios which are predicted to perform well but are
+distinct from the perspective of the user. This identification occurs, e.g., in
+the context of Bayesian optimization of a backtested performance metric.
+Numerical experiments are presented which show the impact of personal beliefs
+in informing the development of a diverse and high-performing portfolio.
+"
+24344,French SKA White Book - The French Community towards the Square Kilometre Array,"  The ""Square Kilometre Array"" (SKA) is a large international radio telescope
+project characterised, as suggested by its name, by a total collecting area of
+approximately one square kilometre, and consisting of several interferometric
+arrays to observe at metric and centimetric wavelengths. The deployment of the
+SKA will take place in two sites, in South Africa and Australia, and in two
+successive phases. From its Phase 1, the SKA will be one of the most formidable
+scientific machines ever deployed by mankind, and by far the most impressive in
+terms of data throughput and required computing power. With the participation
+of almost 200 authors from forty research institutes and six private companies,
+the publication of this French SKA white paper illustrates the strong
+involvement in the SKA project of the French astronomical community and of a
+rapidly growing number of major scientific and technological players in the
+fields of Big Data, high performance computing, energy production and storage,
+as well as system integration.
+"
+24345,Integrability of Stochastic Birth-Death processes via Differential Galois Theory,"  Stochastic birth-death processes are described as continuous-time Markov
+processes in models of population dynamics. A system of infinite, coupled
+ordinary differential equations (the so-called master equation) describes the
+time-dependence of the probability of each system state. Using a generating
+function, the master equation can be transformed into a partial differential
+equation. In this contribution we analyze the integrability of two types of
+stochastic birth-death processes (with polynomial birth and death rates) using
+standard differential Galois theory. We discuss the integrability of the PDE
+via a Laplace transform acting over the temporal variable. We show that the PDE
+is not integrable except for the (trivial) case in which rates are linear
+functions of the number of individuals.
+"
+24346,"Computation of real basis functions for the 3-D rotational polyhedral groups $T$, $O$, and $I$","  Basis functions which are invariant under the operations of a rotational
+polyhedral group $G$ are able to describe any 3-D object which exhibits the
+rotational symmetry of the corresponding Platonic solid. However, in order to
+characterize the spatial statistics of an ensemble of objects in which each
+object is different but the statistics exhibit the symmetry, a larger set of
+basis functions is required. In particular, for each irreducible representation
+(irrep) of $G$, it is necessary to include basis functions that transform
+according to that irrep. This larger set of basis functions is a basis for
+square-integrable functions on the surface of the sphere in 3-D. Because the
+objects are real-valued, it is convenient to have real-valued basis functions.
+In this paper the existence of such real-valued bases is proven and an
+algorithm for their computation is provided for the icosahedral $I$ and the
+octahedral $O$ symmetries. Furthermore, it is proven that such a real-valued
+basis cannot exist for the tetrahedral $T$ symmetry because some irreps of $T$
+are essentially complex. The importance of these basis functions to
+computations in single-particle cryo electron microscopy is described.
+"
+24347,Secure SWIPT Networks Based on a Non-linear Energy Harvesting Model,"  We optimize resource allocation to enable communication security in
+simultaneous wireless information and power transfer (SWIPT) for
+internet-of-things (IoT) networks. The resource allocation algorithm design is
+formulated as a non-convex optimization problem. We aim at maximizing the total
+harvested power at energy harvesting (EH) receivers via the joint optimization
+of transmit beamforming vectors and the covariance matrix of the artificial
+noise injected to facilitate secrecy provisioning. The proposed problem
+formulation takes into account the non-linearity of energy harvesting circuits
+and the quality of service requirements for secure communication.
+To obtain a globally optimal solution of the resource allocation problem, we
+first transform the resulting non-convex sum-of-ratios objective function into
+an equivalent objective function in parametric subtractive form, which
+facilitates the design of a novel iterative resource allocation algorithm. In
+each iteration, the semidefinite programming (SDP) relaxation approach is
+adopted to solve a rank-constrained optimization problem optimally. Numerical
+results reveal that the proposed algorithm can guarantee communication security
+and provide a significant performance gain in terms of the harvested energy
+compared to existing designs which are based on the traditional linear EH
+model.
+"
+24348,Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions,"  This paper demonstrates the use of genetic algorithms for evolving a
+grandmaster-level evaluation function for a chess program. This is achieved by
+combining supervised and unsupervised learning. In the supervised learning
+phase the organisms are evolved to mimic the behavior of human grandmasters,
+and in the unsupervised learning phase these evolved organisms are further
+improved upon by means of coevolution.
+While past attempts succeeded in creating a grandmaster-level program by
+mimicking the behavior of existing computer chess programs, this paper presents
+the first successful attempt at evolving a state-of-the-art evaluation function
+by learning only from databases of games played by humans. Our results
+demonstrate that the evolved program outperforms a two-time World Computer
+Chess Champion.
+"
+24349,Eigenvector spatial filtering for large data sets: fixed and random effects approaches,"  Eigenvector spatial filtering (ESF) is a spatial modeling approach, which has
+been applied in urban and regional studies, ecological studies, and so on.
+However, it is computationally demanding, and may not be suitable for large
+data modeling. The objective of this study is developing fast ESF and random
+effects ESF (RE-ESF), which are capable of handling very large samples. To
+achieve it, we accelerate eigen-decomposition and parameter estimation, which
+make ESF and RE-ESF slow. The former is accelerated by utilizing the Nystrom
+extension, whereas the latter is by small matrix tricks. The resulting fast ESF
+and fast RE-ESF are compared with non-approximated ESF and RE-ESF in Monte
+Carlo simulation experiments. The result shows that, while ESF and RE-ESF are
+slow for several thousand samples, fast ESF and RE-ESF require only several
+seconds for the samples. They also suggest that the proposed approaches
+effectively remove positive spatial dependence in the residuals with very small
+approximation errors when the number of eigenvectors considered is 200 or more.
+Note that these approaches cannot deal with negative spatial dependence. The
+proposed approaches are implemented in an R package ""spmoran.""
+"
+24350,A characterization of Linearizable instances of the Quadratic Traveling Salesman Problem,"  We consider the linearization problem associated with the quadratic traveling
+salesman problem (QTSP). Necessary and sufficient conditions are given for a
+cost matrix $Q$ of QTSP to be linearizable. It is shown that these conditions
+can be verified in $O(n^5)$ time. Some simpler sufficient conditions for
+linearization are also given along with related open problems.
+"
+24351,Revisiting revisits in trajectory recommendation,"  Trajectory recommendation is the problem of recommending a sequence of places
+in a city for a tourist to visit. It is strongly desirable for the recommended
+sequence to avoid loops, as tourists typically would not wish to revisit the
+same location. Given some learned model that scores sequences, how can we then
+find the highest-scoring sequence that is loop-free? This paper studies this
+problem, with three contributions. First, we detail three distinct approaches
+to the problem -- graph-based heuristics, integer linear programming, and list
+extensions of the Viterbi algorithm -- and qualitatively summarise their
+strengths and weaknesses. Second, we explicate how two ostensibly different
+approaches to the list Viterbi algorithm are in fact fundamentally identical.
+Third, we conduct experiments on real-world trajectory recommendation datasets
+to identify the tradeoffs imposed by each of the three approaches. Overall, our
+results indicate that a greedy graph-based heuristic offer excellent
+performance and runtime, leading us to recommend its use for removing loops at
+prediction time.
+"
+24352,Comparative Analysis and Framework Evaluating Mimicry-Resistant and Invisible Web Authentication Schemes,"  In web authentication, the many password alternatives proposed over the
+years, despite having different designs and objectives, all predominantly rely
+on an element of secrecy. This motivates us, herein, to provide the first
+detailed exploration of the integration of a fundamentally different element of
+defense into the design of web authentication schemes: a mimicry resistance
+dimension. We analyze web authentication mechanisms with respect to new
+properties related to mimicry-resistance, and in particular evaluate invisible
+techniques that provide some mimicry-resistance (unlike those relying solely on
+static secrets), including device fingerprinting schemes, PUFs (physically
+unclonable functions), and a subset of Internet geolocation mechanisms.
+"
+24353,Charge Fractionalization in Artificial Tomonaga-Luttinger Liquids with Controlled Interaction Strength,"  We investigate charge fractionalizations in artificial Tomonaga-Luttinger
+liquids (TLLs) composed of two capacitively coupled quantum Hall edge channels
+(ECs) in graphene. The interaction strength of the artificial TLLs can be
+controlled through distance W between the ECs. We show that the
+fractionalization ratio r and the TLL mode velocity v vary with W. The
+experimentally obtained relation between v and r follows a unique function
+predicted by the TLL theory. We also show that charged wavepackets are
+reflected back and forth multiple times at both ends of the TLL region.
+"
+24354,Dark Energy Induced Anisotropy in Cosmic Expansion,"  In order to understand the nature of the accelerating expansion of the
+late-time universe, it is important to experimentally determine whether dark
+energy is cosmological constant or dynamical in nature. If dark energy already
+exists prior to inflation, which is a reasonable assumption, then one expects
+that a dynamical dark energy would leave some footprint in the anisotropy of
+the late-time accelerated expansion. We invoke the quintessence field, one of
+the simplest dynamical dark energy models, to investigate the effects of its
+quantum fluctuations (fully correlated with curvature perturbations) during
+inflation and estimate the anisotropy of the cosmic expansion so induced. For
+that we calculate the perturbed luminosity distance and its power spectrum,
+which is an estimator of anisotropicity of late-time accelerated expansion. We
+find that the gravitational potential at large scales and late times is less
+decayed in QCDM compared to that in $ \Lambda $CDM so that the smaller the
+redshift and multipole, the more deficit of power in QCDM. These features may
+help to distinguish the cosmological constant from a dynamical dark energy. In
+this regard, the apparent (but not yet statistically significant) power deficit
+in CMB low multipoles based on $ \Lambda $CDM may even be a smoking gun for
+QCDM.
+"
+24355,Fast Similarity Sketching,"  We consider the Similarity Sketching problem: Given a universe $[u]=
+\{0,\ldots,u-1\}$ we want a random function $S$ mapping subsets $A\subseteq
+[u]$ into vectors $S(A)$ of size $t$, such that similarity is preserved. More
+precisely: Given sets $A,B\subseteq [u]$, define $X_i=[S(A)[i]= S(B)[i]]$ and
+$X=\sum_{i\in [t]}X_i$. We want to have $E[X]=t\cdot J(A,B)$, where
+$J(A,B)=|A\cap B|/|A\cup B|$ and furthermore to have strong concentration
+guarantees (i.e. Chernoff-style bounds) for $X$. This is a fundamental problem
+which has found numerous applications in data mining, large-scale
+classification, computer vision, similarity search, etc. via the classic
+MinHash algorithm. The vectors $S(A)$ are also called sketches.
+The seminal $t\times$MinHash algorithm uses $t$ random hash functions
+$h_1,\ldots, h_t$, and stores $\left(\min_{a\in A}h_1(A),\ldots, \min_{a\in
+A}h_t(A)\right)$ as the sketch of $A$. The main drawback of MinHash is,
+however, its $O(t\cdot |A|)$ running time, and finding a sketch with similar
+properties and faster running time has been the subject of several papers.
+Addressing this, Li et al. [NIPS'12] introduced one permutation hashing (OPH),
+which creates a sketch of size $t$ in $O(t + |A|)$ time, but with the drawback
+that possibly some of the $t$ entries are ""empty"" when $|A| = O(t)$. One could
+argue that sketching is not necessary in this case, however the desire in most
+applications is to have one sketching procedure that works for sets of all
+sizes. Therefore, filling out these empty entries is the subject of several
+follow-up papers initiated by Shrivastava and Li [ICML'14]. However, these
+""densification"" schemes fail to provide good concentration bounds exactly in
+the case $|A| = O(t)$, where they are needed. (continued...)
+"
+24356,Non-affine lattice dynamics of defective fcc crystals,"  The mechanical, thermal and vibrational properties of defective crystals are
+important in many different contexts, from metallurgy and solid-state physics
+to, more recently, soft matter and colloidal physics. Here we study two
+different models of disordered fcc crystal lattices, with randomly-removed
+bonds and with vacancies, respectively, within the framework of non-affine
+lattice dynamics. We find that both systems feature the same scaling of the
+shear modulus with the newly defined inversion-symmetry breaking (ISB)
+parameter, which shows that local inversion-symmetry breaking around defects is
+the universal root source of the non-affine softening of the shear modulus.
+This finding allows us to derive analytical relations for the non-affine
+(zero-frequency) shear modulus as a function of vacancy concentration in
+excellent agreement with numerical simulations. Nevertheless, due to the
+different microstructural disorder, the spatial fluctuations of the local ISB
+parameter are different in the vacancy and bond-depleted case. The vacancy fcc
+exhibits comparatively a more heterogenous microstructural disorder (due to the
+broader distribution of coordination number $Z$), which is reflected in a
+different scaling relation between boson peak frequency in the DOS and the
+average $\bar{Z}$. These differences are less important at low vacancy
+concentrations, where the numerical DOS of the vacancy fcc can be well
+described theoretically by coherent-potential approximation, presented here for
+the bond-depleted fcc lattice in 3d.
+"
+24357,Overcoming model simplifications when quantifying predictive uncertainty,"  It is generally accepted that all models are wrong -- the difficulty is
+determining which are useful. Here, a useful model is considered as one that is
+capable of combining data and expert knowledge, through an inversion or
+calibration process, to adequately characterize the uncertainty in predictions
+of interest. This paper derives conditions that specify which simplified models
+are useful and how they should be calibrated. To start, the notion of an
+optimal simplification is defined. This relates the model simplifications to
+the nature of the data and predictions, and determines when a standard
+probabilistic calibration scheme is capable of accurately characterizing
+uncertainty. Furthermore, two additional conditions are defined for suboptimal
+models that determine when the simplifications can be safely ignored. The first
+allows a suboptimally simplified model to be used in a way that replicates the
+performance of an optimal model. This is achieved through the judicial
+selection of a prior term for the calibration process that explicitly includes
+the nature of the data, predictions and modelling simplifications. The second
+considers the dependency structure between the predictions and the available
+data to gain insights into when the simplifications can be overcome by using
+the right calibration data. Furthermore, the derived conditions are related to
+the commonly used calibration schemes based on Tikhonov and subspace
+regularization. To allow concrete insights to be obtained, the analysis is
+performed under a linear expansion of the model equations and where the
+predictive uncertainty is characterized via second order moments only.
+"
+24358,Asymptotic distribution of nodal intersections for arithmetic random waves,"  We study the nodal intersections number of random Gaussian toral Laplace
+eigenfunctions (""arithmetic random waves"") against a fixed smooth reference
+curve. The expected intersection number is proportional to the the square root
+of the eigenvalue times the length of curve, independent of its geometry. The
+asymptotic behaviour of the variance was addressed by Rudnick-Wigman; they
+found a precise asymptotic law for ""generic"" curves with nowhere vanishing
+curvature, depending on both its geometry and the angular distribution of
+lattice points lying on circles corresponding to the Laplace eigenvalue. They
+also discovered that there exist peculiar ""static"" curves, with variance of
+smaller order of magnitude, though did not prescribe what the true asymptotic
+behaviour is in this case. In this paper we study the finer aspects of the
+limit distribution of the nodal intersections number. For ""generic"" curves we
+prove the Central Limit Theorem (at least, for ""most"" of the energies). For the
+aforementioned static curves we establish a non-Gaussian limit theorem for the
+distribution of nodal intersections, and on the way find the true asymptotic
+behaviour of their fluctuations, under the well-separatedness assumption on the
+corresponding lattice points, satisfied by most of the eigenvalues.
+"
+24359,On Efficient Domination for Some Classes of $H$-Free Chordal Graphs,"  A vertex set $D$ in a finite undirected graph $G$ is an efficient dominating
+set (e.d.s. for short) of $G$ if every vertex of $G$ is dominated by exactly
+one vertex of $D$. The Efficient Domination (ED) problem, which asks for the
+existence of an e.d.s.\ in $G$, is known to be \NP-complete even for very
+restricted graph classes such as for $2P_3$-free chordal graphs while it is
+solvable in polynomial time for $P_6$-free chordal graphs (and even for
+$P_6$-free graphs). A standard reduction from the \NP-complete Exact Cover
+problem shows that ED is \NP-complete for a very special subclass of chordal
+graphs generalizing split graphs. The reduction implies that ED is \NP-complete
+e.g.\ for double-gem-free chordal graphs while it is solvable in linear time
+for gem-free chordal graphs (by various reasons such as bounded clique-width,
+distance-hereditary graphs, chordal square etc.), and ED is \NP-complete for
+butterfly-free chordal graphs while it is solvable in linear time for
+$2P_2$-free graphs.
+We show that (weighted) ED can be solved in polynomial time for $H$-free
+chordal graphs when $H$ is net, extended gem, or $S_{1,2,3}$.
+"
+24360,Multi-level hypothesis testing for populations of heterogeneous networks,"  In this work, we consider hypothesis testing and anomaly detection on
+datasets where each observation is a weighted network. Examples of such data
+include brain connectivity networks from fMRI flow data, or word co-occurrence
+counts for populations of individuals. Current approaches to hypothesis testing
+for weighted networks typically requires thresholding the edge-weights, to
+transform the data to binary networks. This results in a loss of information,
+and outcomes are sensitivity to choice of threshold levels. Our work avoids
+this, and we consider weighted-graph observations in two situations, 1) where
+each graph belongs to one of two populations, and 2) where entities belong to
+one of two populations, with each entity possessing multiple graphs (indexed
+e.g. by time). Specifically, we propose a hierarchical Bayesian hypothesis
+testing framework that models each population with a mixture of latent space
+models for weighted networks, and then tests populations of networks for
+differences in distribution over components. Our framework is capable of
+population-level, entity-specific, as well as edge-specific hypothesis testing.
+We apply it to synthetic data and three real-world datasets: two social media
+datasets involving word co-occurrences from discussions on Twitter of the
+political unrest in Brazil, and on Instagram concerning Attention Deficit
+Hyperactivity Disorder (ADHD) and depression drugs, and one medical dataset
+involving fMRI brain-scans of human subjects. The results show that our
+proposed method has lower Type I error and higher statistical power compared to
+alternatives that need to threshold the edge weights. Moreover, they show our
+proposed method is better suited to deal with highly heterogeneous datasets.
+"
+24361,Oscillator-based Ising Machine,"  Many combinatorial optimization problems can be mapped to finding the ground
+states of the corresponding Ising Hamiltonians. The physical systems that can
+solve optimization problems in this way, namely Ising machines, have been
+attracting more and more attention recently. Our work shows that Ising machines
+can be realized using almost any nonlinear self-sustaining oscillators with
+logic values encoded in their phases. Many types of such oscillators are
+readily available for large-scale integration, with potentials in high-speed
+and low-power operation. In this paper, we describe the operation and mechanism
+of oscillator-based Ising machines. The feasibility of our scheme is
+demonstrated through several examples in simulation and hardware, among which a
+simulation study reports average solutions exceeding those from state-of-art
+Ising machines on a benchmark combinatorial optimization problem of size 2000.
+"
+24362,Unbiased Estimation and Sensitivity Analysis for Network-Specific Spillover Effects: Application to An Online Network Experiment,"  Online network experiments have become widely-used methods to estimate
+spillover effects and study online social influence. In such experiments, the
+spillover effects often arise not only through an online network of interest
+but also through a face-to-face network. Social scientists are therefore often
+interested in measuring the importance of online human interactions by
+estimating the spillover effects specific to the online network, separately
+from spillover effects attributable to the offline network. However, the
+unbiased estimation of these network-specific spillover effects requires an
+often-violated assumption that researchers observe all relevant networks; they
+cannot observe the offline network in many applications. In this paper, we
+derive an exact expression of bias due to unobserved networks. Unlike the
+conventional omitted variable bias, this bias for the network-specific
+spillover effect remains even when treatment assignment is randomized and when
+unobserved networks and the network of interest are independently generated. By
+incorporating this bias into the estimation, we also develop parametric and
+nonparametric sensitivity analysis methods, with which researchers can examine
+the robustness of empirical findings to the possible existence of unmeasured
+networks. We analyze a political mobilization experiment on the Twitter network
+and find that an estimate of the Twitter-specific spillover effect is sensitive
+to assumptions about an unobserved face-to-face network.
+"
+24363,Progress on syzygies of algebraic curves,"  These lectures discuss recent advances on syzygies on algebraic curves,
+especially concerning the Green, the Prym-Green and the Green-Lazarsfeld Secant
+Conjectures. The methods used are largely geometric and variational, with a
+special emphasis on examples and explicit calculations. The notes are based on
+series of lectures given in Daejeon (March 2013), Rome (November-December 2015)
+and Guanajuato (February 2016).
+"
+24364,Gram-scale cryogenic calorimeters for rare-event searches,"  The energy threshold of a cryogenic calorimeter can be lowered by reducing
+its size. This is of importance since the resulting increase in signal rate
+enables new approaches in rare-event searches, including the detection of MeV
+mass dark matter and coherent scattering of reactor or solar neutrinos. A
+scaling law for energy threshold vs. detector size is given. We analyze the
+possibility of lowering the threshold of a gram-scale cryogenic calorimeter to
+the few eV regime. A prototype 0.5 g Al$_2$O$_3$ device achieved an energy
+threshold of (${19.7\pm0.1}$) eV, the lowest value reported for a macroscopic
+calorimeter.
+"
+24365,High-frequency backreaction for the Einstein equations under polarized $\mathbb U(1)$ symmetry,"  Known examples in plane symmetry or Gowdy symmetry show that given a
+$1$-parameter family of solutions to the vacuum Einstein equations, it may have
+a weak limit which does not satisfy the vacuum equations, but instead has a
+non-trivial stress-energy-momentum tensor. We consider this phenomenon under
+polarized $\mathbb U(1)$ symmetry - a much weaker symmetry than most of the
+known examples - such that the stress-energy-momentum tensor can be identified
+with that of multiple families of null dust propagating in distinct directions.
+We prove that any generic local-in-time small-data polarized-$\mathbb
+U(1)$-symmetric solution to the Einstein-multiple null dust system can be
+achieved as a weak limit of vacuum solutions. Our construction allows the
+number of families to be arbitrarily large, and appears to be the first
+construction of such examples with more than two families.
+"
+24366,Confidentiality enforcement by hybrid control of information flows,"  An information owner, possessing diverse data sources, might want to offer
+information services based on these sources to cooperation partners and to this
+end interact with these partners by receiving and sending messages, which the
+owner on his part generates by program execution. Independently from data
+representation or its physical storage, information release to a partner might
+be restricted by the owner's confidentiality policy on an integrated, unified
+view of the sources. Such a policy should even be enforced if the partner as an
+intelligent and only semi-honest attacker attempts to infer hidden information
+from message data, also employing background knowledge. For this problem of
+inference control, we present a framework for a unified, holistic control of
+information flow induced by program-based processing of the data sources to
+messages sent to a cooperation partner. Our framework expands on and combines
+established concepts for confidentiality enforcement and its verification and
+is instantiated in a Java environment. More specifically, as a hybrid control
+we combine gradual release of information via declassification, enforced by
+static program analysis using a security type system, with a dynamic monitoring
+approach. The dynamic monitoring employs flow tracking for generalizing values
+to be declassified under confidentiality policy compliance.
+"
+24367,The effects of large climate fluctuations on forests as a Big Data approach,"  In the present study it is shown that significant impacts on forests can lead
+to large climate fluctuations at global level. This research takes into account
+various scenarios for the reconstruction of forest areas using the
+geo-ecological information-modeling system. This tool is described as a
+possible improvement of the Big Data approach. Its structure includes 24 blocks
+that combine a series of models and algorithms for the global processing of
+large data and their analysis. The key block of this tool is the global model
+of the climate-nature-society system.
+"
+24368,Kinematic Basis of Emergent Energetic Descriptions of General Stochastic Dynamics,"  We show a stochastic, kinematic description of a dynamics has a hidden
+energetic and thermodynamic structure. An energy function $\varphi(x)$ emerges
+as the limit of the generalized free energy of the stochastic dynamics with
+vanishing noise. In terms of the $\varphi$ and its orthogonal field
+$\gamma(x)\perp\nabla\varphi$, a general vector field $b(x)$ is decomposed into
+$-D(x)\nabla\varphi+\gamma$, where
+$\nabla\cdot\big(\omega(x)\gamma(x)\big)=-\nabla\omega D(x)\nabla\varphi$,
+$D(x)$ and $\omega(x)$ represent the local geometry and density in the state
+space at $x$. $\varphi(x)$ and $\omega(x)$ are interpreted as the emergent
+energy and degeneracy of the motion, with energy balance equation
+$d\varphi(x(t))/dt=\gamma D^{-1}\gamma-b D^{-1} b$. The partition function and
+J. W. Gibbs' method of statistical ensemble change naturally arise. The present
+theory provides a mathematical basis for P. W. Anderson's emergent behavior in
+the hierarchical structure of science.
+"
+24369,Indecomposable branched coverings over the projective plane by surfaces $M$ with $χ(M) \leq 0$,"  In this work we study the decomposability property of branched coverings of
+degree $d$ odd, over the projective plane, where the covering surface has Euler
+characteristic $\leq 0$. The latter condition is equivalent to say that the
+defect of the covering is greater than $d$. We show that, given a datum
+$\mathscr{D}=\{D_{1},\dots,D_{s}\}$ with an even defect greater than $d$, it is
+realizable by an indecomposable branched covering over the projective plane.
+The case when $d$ is even is known.
+"
+24370,CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi,"  Deep learning is an important component of big-data analytic tools and
+intelligent applications, such as, self-driving cars, computer vision, speech
+recognition, or precision medicine. However, the training process is
+computationally intensive, and often requires a large amount of time if
+performed sequentially. Modern parallel computing systems provide the
+capability to reduce the required training time of deep neural networks. In
+this paper, we present our parallelization scheme for training convolutional
+neural networks (CNN) named Controlled Hogwild with Arbitrary Order of
+Synchronization (CHAOS). Major features of CHAOS include the support for thread
+and vector parallelism, non-instant updates of weight parameters during
+back-propagation without a significant delay, and implicit synchronization in
+arbitrary order. CHAOS is tailored for parallel computing systems that are
+accelerated with the Intel Xeon Phi. We evaluate our parallelization approach
+empirically using measurement techniques and performance modeling for various
+numbers of threads and CNN architectures. Experimental results for the MNIST
+dataset of handwritten digits using the total number of threads on the Xeon Phi
+show speedups of up to 103x compared to the execution on one thread of the Xeon
+Phi, 14x compared to the sequential execution on Intel Xeon E5, and 58x
+compared to the sequential execution on Intel Core i5.
+"
+24371,Network Simplex Algorithm associated with the Maximum Flow Problem,"  In the present paper, we apply the network simplex algorithm for solving the
+minimum cost flow problem, to the maximum flow problem. Then we prove that the
+cycling phenomenon which causes the infinite loop in the algorithm, does not
+occur in the network simplex algorithm associated with the maximum flow
+problem.
+"
+24372,Subpicosecond $X$ rotations of atomic clock states,"  We demonstrate subpicosecond-time-scale population transfer between the pair
+of hyperfine ground states of atomic rubidium using a single laser-pulse. Our
+scheme utilizes the geometric and dynamic phases induced during Rabi
+oscillation through the fine-structure excited state in order to construct an
+$X$ rotation gate for the hyperfine-state qubit system. Experiment performed
+with a femtosecond laser and cold rubidium atoms, in a magneto-optical trap,
+shows over 98\% maximal population transfer between the clock states.
+"
+24373,A Churn for the Better: Localizing Censorship using Network-level Path Churn and Network Tomography,"  Recent years have seen the Internet become a key vehicle for citizens around
+the globe to express political opinions and organize protests. This fact has
+not gone unnoticed, with countries around the world repurposing network
+management tools (e.g., URL filtering products) and protocols (e.g., BGP, DNS)
+for censorship. However, repurposing these products can have unintended
+international impact, which we refer to as ""censorship leakage"". While there
+have been anecdotal reports of censorship leakage, there has yet to be a
+systematic study of censorship leakage at a global scale. In this paper, we
+combine a global censorship measurement platform (ICLab) with a general-purpose
+technique -- boolean network tomography -- to identify which AS on a network
+path is performing censorship. At a high-level, our approach exploits BGP churn
+to narrow down the set of potential censoring ASes by over 95%. We exactly
+identify 65 censoring ASes and find that the anomalies introduced by 24 of the
+65 censoring ASes have an impact on users located in regions outside the
+jurisdiction of the censoring AS, resulting in the leaking of regional
+censorship policies.
+"
+24374,"Comment on ""Observation of the Wigner-Huntington transition to metallic hydrogen""","  Dias and Silvera (Letters, p. 715, 2017) claim the observation of the
+Wigner-Huntington transition to metallic hydrogen at 495 GPa. We show that
+neither the claims of the record pressure or the phase transition to a metallic
+state are supported by any data and contradict the authors' own unconfirmed
+previous results.
+"
+24375,Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning,"  In this paper, we focus on developing a novel mechanism to preserve
+differential privacy in deep neural networks, such that: (1) The privacy budget
+consumption is totally independent of the number of training steps; (2) It has
+the ability to adaptively inject noise into features based on the contribution
+of each to the output; and (3) It could be applied in a variety of different
+deep neural networks. To achieve this, we figure out a way to perturb affine
+transformations of neurons, and loss functions used in deep neural networks. In
+addition, our mechanism intentionally adds ""more noise"" into features which are
+""less relevant"" to the model output, and vice-versa. Our theoretical analysis
+further derives the sensitivities and error bounds of our mechanism. Rigorous
+experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is
+highly effective and outperforms existing solutions.
+"
+24376,Control of eigenfunctions on hyperbolic surfaces: an application of fractal uncertainty principle,"  This expository article, written for the proceedings of the Journées EDP
+(Roscoff, June 2017), presents recent work joint with Jean Bourgain
+[arXiv:1612.09040] and Long Jin [arXiv:1705.05019]. We in particular show that
+eigenfunctions of the Laplacian on hyperbolic surfaces are bounded from below
+in $L^2$ norm on each nonempty open set, by a constant depending on the set but
+not on the eigenvalue.
+"
+24377,Dual Horadam Octonions,"  In this study, we investigate Horadam sequence as generalization of linear
+recurrence equations of order two. By the aid of this sequence we obtain a new
+generalization for sequences of dual quaternions and dual octonions. Moreover,
+we derive some important identities such as Binet formula, generating function,
+Cassini identity, sum formula and norm formula by their Binet forms. We
+generalize all studied linear second order recurrence relations over dual
+octonions and quaternions in a single formula.
+"
+24378,Collective emission of matter-wave jets from driven Bose-Einstein condensates,"  Scattering is an elementary probe for matter and its interactions in all
+areas of physics. Ultracold atomic gases provide a powerful platform in which
+control over pair-wise interactions empowers us to investigate scattering in
+quantum many-body systems. Past experiments on colliding Bose-Einstein
+condensates have revealed many important features, including matter-wave
+interference, halos of scattered atoms, four-wave mixing, and correlations
+between counter-propagating pairs. However, a regime with strong stimulation of
+spontaneous collisions analogous to superradiance has proven elusive. Here we
+access that regime, finding that runaway stimulated collisions in condensates
+with modulated interaction strength cause the emission of matter-wave jets
+which resemble fireworks. Jets appear only above a threshold modulation
+amplitude and their correlations are invariant even as the ejected atom number
+grows exponentially. Hence, we show that the structures and occupations of the
+jets stem from the quantum fluctuations of the condensate. Our findings
+demonstrate the conditions for runaway stimulated collisions and reveal the
+quantum nature of the matter-wave emission.
+"
+24379,"Load-Flow in Multiphase Distribution Networks: Existence, Uniqueness, Non-Singularity and Linear Models","  This paper considers unbalanced multiphase distribution systems with generic
+topology and different load models, and extends the Z-bus iterative load-flow
+algorithm based on a fixed-point interpretation of the AC load-flow equations.
+Explicit conditions for existence and uniqueness of load-flow solutions are
+presented. These conditions also guarantee convergence of the load-flow
+algorithm to the unique solution. The proposed methodology is applicable to
+generic systems featuring (i) wye connections; (ii) ungrounded delta
+connections; (iii) a combination of wye-connected and delta-connected
+sources/loads; and, (iv) a combination of line-to-line and
+line-to-grounded-neutral devices at the secondary of distribution transformers.
+Further, a sufficient condition for the non-singularity of the load-flow
+Jacobian is proposed. Finally, linear load-flow models are derived, and their
+approximation accuracy is analyzed. Theoretical results are corroborated
+through experiments on IEEE test feeders.
+"
+24380,"Thermal, Structural, and Optical Analysis of a Balloon-Based Imaging System","  The Subarcsecond Telescope And BaLloon Experiment, STABLE, is the fine stage
+of a guidance system for a high-altitude ballooning platform designed to
+demonstrate subarcsecond pointing stability, over one minute using relatively
+dim guide stars in the visible spectrum. The STABLE system uses an attitude
+rate sensor and the motion of the guide star on a detector to control a Fast
+Steering Mirror in order to stabilize the image. The characteristics of the
+thermal-optical-mechanical elements in the system directly affect the quality
+of the point spread function of the guide star on the detector, and so, a
+series of thermal, structural, and optical models were built to simulate system
+performance and ultimately inform the final pointing stability predictions.
+This paper describes the modeling techniques employed in each of these
+subsystems. The results from those models are discussed in detail, highlighting
+the development of the worst-case cold and hot cases, the optical metrics
+generated from the finite element model, and the expected STABLE residual
+wavefront error and decenter. Finally, the paper concludes with the predicted
+sensitivities in the STABLE system, which show that thermal deadbanding,
+structural preloading and self-deflection under different loading conditions,
+and the speed of individual optical elements were particularly important to the
+resulting STABLE optical performance.
+"
+24381,An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition,"  Complex activity recognition is challenging due to the inherent uncertainty
+and diversity of performing a complex activity. Normally, each instance of a
+complex activity has its own configuration of atomic actions and their temporal
+dependencies. We propose in this paper an atomic action-based Bayesian model
+that constructs Allen's interval relation networks to characterize complex
+activities with structural varieties in a probabilistic generative way: By
+introducing latent variables from the Chinese restaurant process, our approach
+is able to capture all possible styles of a particular complex activity as a
+unique set of distributions over atomic actions and relations. We also show
+that local temporal dependencies can be retained and are globally consistent in
+the resulting interval network. Moreover, network structure can be learned from
+empirical data. A new dataset of complex hand activities has been constructed
+and made publicly available, which is much larger in size than any existing
+datasets. Empirical evaluations on benchmark datasets as well as our in-house
+dataset demonstrate the competitiveness of our approach.
+"
+24382,On cell surface deformation during an action potential,"  The excitation of many cells and tissues is associated with cell mechanical
+changes. The evidence presented herein corroborates that single cells deform
+during an action potential (AP). It is demonstrated that excitation of plant
+cells (Chara braunii internodes) is accompanied by out-of-plane displacements
+of the cell surface in the micrometer range (1-10 micron). The onset of
+cellular deformation coincides with the depolarization phase of the AP. The
+mechanical pulse (i) propagates with the same velocity as the electrical pulse
+(within experimental accuracy; 10 mm/s), (ii) is reversible, (iii) in most
+cases of biphasic nature (109 out of 152 experiments) and (iv) presumably
+independent of actin-myosin-motility. The existence of transient mechanical
+changes in the cell cortex is confirmed by micropipette aspiration experiments.
+A theoretical analysis demonstrates that this observation can be explained by a
+reversible change in the mechanical properties of the cell surface
+(transmembrane pressure, surface tension and bending rigidity). Taken together,
+these findings contribute to the ongoing debate about the physical nature of
+cellular excitability.
+"
+24383,Transformations of Asymptotically AdS Hyperbolic Initial Data and Associated Geometric Inequalities,"  We construct transformations which take asymptotically AdS hyperbolic initial
+data into asymptotically flat initial data, and which preserve relevant
+physical quantities. This is used to derive geometric inequalities in the
+asymptotically AdS hyperbolic setting from counterparts in the asymptotically
+flat realm, whenever a geometrically motivated system of elliptic equations
+admits a solution. The inequalities treated here relate mass, angular momentum,
+charge, and horizon area.
+"
+24384,Generative Poisoning Attack Method Against Neural Networks,"  Poisoning attack is identified as a severe security threat to machine
+learning algorithms. In many applications, for example, deep neural network
+(DNN) models collect public data as the inputs to perform re-training, where
+the input data can be poisoned. Although poisoning attack against support
+vector machines (SVM) has been extensively studied before, there is still very
+limited knowledge about how such attack can be implemented on neural networks
+(NN), especially DNNs. In this work, we first examine the possibility of
+applying traditional gradient-based method (named as the direct gradient
+method) to generate poisoned data against NNs by leveraging the gradient of the
+target model w.r.t. the normal data. We then propose a generative method to
+accelerate the generation rate of the poisoned data: an auto-encoder
+(generator) used to generate poisoned data is updated by a reward function of
+the loss, and the target NN model (discriminator) receives the poisoned data to
+calculate the loss w.r.t. the normal data. Our experiment results show that the
+generative method can speed up the poisoned data generation rate by up to
+239.38x compared with the direct gradient method, with slightly lower model
+accuracy degradation. A countermeasure is also designed to detect such
+poisoning attack methods by checking the loss of the target model.
+"
+24385,Completeness of the Six Vertex Model with Reflecting Boundary Conditions,"  In this note, we prove the completeness of Bethe vectors for the six vertex
+model with diagonal reflecting boundary conditions. We show that as
+inhomogeneity parameters get sent to infinity in a successive order the Bethe
+vectors give a complete basis of the space of states.
+"
+24386,Statistical Cost Sharing,"  We study the cost sharing problem for cooperative games in situations where
+the cost function $C$ is not available via oracle queries, but must instead be
+derived from data, represented as tuples $(S, C(S))$, for different subsets $S$
+of players. We formalize this approach, which we call statistical cost sharing,
+and consider the computation of the core and the Shapley value, when the tuples
+are drawn from some distribution $\mathcal{D}$.
+Previous work by Balcan et al. in this setting showed how to compute cost
+shares that satisfy the core property with high probability for limited classes
+of functions. We expand on their work and give an algorithm that computes such
+cost shares for any function with a non-empty core. We complement these results
+by proving an inapproximability lower bound for a weaker relaxation.
+We then turn our attention to the Shapley value. We first show that when cost
+functions come from the family of submodular functions with bounded curvature,
+$\kappa$, the Shapley value can be approximated from samples up to a $\sqrt{1 -
+\kappa}$ factor, and that the bound is tight. We then define statistical
+analogues of the Shapley axioms, and derive a notion of statistical Shapley
+value. We show that these can always be approximated arbitrarily well for
+general functions over any distribution $\mathcal{D}$.
+"
+24387,Optimal Strategies for Disjunctive Sensing and Control,"  A disjunctive sensing and actuation problem is considered in which the
+actuators and sensors are prevented from operating together over any given time
+step. This problem is motivated by practical applications in the area of
+spacecraft control. Assuming a linear system model with stochastic process
+disturbance and measurement noise, a procedure to construct a periodic sequence
+that ensures bounded states and estimation error covariance is described along
+with supporting analysis results. The procedure is also extended to ensure
+eventual satisfaction of probabilistic chance constraints on the state. The
+proposed scheme demonstrates good performance in simulations for spacecraft
+relative motion control.
+"
+24388,Entropy and quasimorphisms,"  Let $S$ be a compact oriented surface. We construct homogeneous
+quasimorphisms on $Diff(S, area)$, on $Diff_0(S, area)$ and on $Ham(S)$
+generalizing the constructions of Gambaudo-Ghys and Polterovich.
+We prove that there are infinitely many linearly independent homogeneous
+quasimorphisms on $Diff(S, area)$, on $Diff_0(S, area)$ and on $Ham(S)$ whose
+absolute values bound from below the topological entropy. In case when $S$ has
+a positive genus, the quasimorphisms we construct on $Ham(S)$ are
+$C^0$-continuous.
+We define a bi-invariant metric on these groups, called the entropy metric,
+and show that it is unbounded. In particular, we reprove the fact that the
+autonomous metric on $Ham(S)$ is unbounded.
+"
+24389,Optimality conditions for minimizers at infinity in polynomial programming,"  In this paper we study necessary optimality conditions for the optimization
+problem $$\textrm{infimum}f_0(x) \quad \textrm{ subject to } \quad x \in S,$$
+where $f_0 \colon \mathbb{R}^n \rightarrow \mathbb{R}$ is a polynomial function
+and $S \subset \mathbb{R}^n$ is a set defined by polynomial inequalities.
+Assume that the problem is bounded below and has the Mangasarian--Fromovitz
+property at infinity. We first show that if the problem does {\em not} have an
+optimal solution, then a version at infinity of the Fritz-John optimality
+conditions holds. From this we derive a version at infinity of the
+Karush--Kuhn--Tucker optimality conditions. As applications, we obtain a
+Frank--Wolfe type theorem which states that the optimal solution set of the
+problem is nonempty provided the objective function $f_0$ is convenient.
+Finally, in the unconstrained case, we show that the optimal value of the
+problem is the smallest critical value of some polynomial. All the results are
+presented in terms of the Newton polyhedra of the polynomials defining the
+problem.
+"
+24390,Hall universal group has ample generic automorphisms,"  We show that the automorphism group of Philip Hall's universal locally finite
+group has ample generics,that is, it admits comeager diagonal conjugacy classes
+in all dimensions.Consequently, it has the small index property, is not the
+union of a countable chain of non-open subgroups, and has the automatic
+continuity property. Also, we discuss some algebraic and topological properties
+of the automorphism group of Hall universal group. For example, we show that
+every generic automorphism of Hall universal group is conjugate to all of its
+powers, and hence has roots of all orders.
+"
+24391,Electronically highly cubic conditions for Ru in alpha-RuCl3,"  We studied the local Ru 4d electronic structure of alpha-RuCl3 by means of
+polarization dependent x-ray absorption spectroscopy at the Ru-L2,3 edges. We
+observed a vanishingly small linear dichroism indicating that electronically
+the Ru 4d local symmetry is highly cubic. Using full multiplet cluster
+calculations we were able to reproduce the spectra excellently and to extract
+that the trigonal splitting of the t2g orbitals is -12 $\pm10$ meV, i.e.
+negligible as compared to the Ru 4d spin-orbit coupling constant. Consistent
+with our magnetic circular dichroism measurements, we found that the ratio of
+the orbital and spin moments is 2.0, the value expected for a Jeff = 1/2 ground
+state. We have thus shown that as far as the Ru 4d local properties are
+concerned, alpha-RuCl3 is an ideal candidate for the realization of Kitaev
+physics.
+"
+24392,Can Network Analysis Techniques help to Predict Design Dependencies? An Initial Study,"  The degree of dependencies among the modules of a software system is a key
+attribute to characterize its design structure and its ability to evolve over
+time. Several design problems are often correlated with undesired dependencies
+among modules. Being able to anticipate those problems is important for
+developers, so they can plan early for maintenance and refactoring efforts.
+However, existing tools are limited to detecting undesired dependencies once
+they appeared in the system. In this work, we investigate whether module
+dependencies can be predicted (before they actually appear). Since the module
+structure can be regarded as a network, i.e, a dependency graph, we leverage on
+network features to analyze the dynamics of such a structure. In particular, we
+apply link prediction techniques for this task. We conducted an evaluation on
+two Java projects across several versions, using link prediction and machine
+learning techniques, and assessed their performance for identifying new
+dependencies from a project version to the next one. The results, although
+preliminary, show that the link prediction approach is feasible for package
+dependencies. Also, this work opens opportunities for further development of
+software-specific strategies for dependency prediction.
+"
+24393,IHT dies hard: Provable accelerated Iterative Hard Thresholding,"  We study --both in theory and practice-- the use of momentum motions in
+classic iterative hard thresholding (IHT) methods. By simply modifying plain
+IHT, we investigate its convergence behavior on convex optimization criteria
+with non-convex constraints, under standard assumptions. In diverse scenaria,
+we observe that acceleration in IHT leads to significant improvements, compared
+to state of the art projected gradient descent and Frank-Wolfe variants. As a
+byproduct of our inspection, we study the impact of selecting the momentum
+parameter: similar to convex settings, two modes of behavior are observed
+--""rippling"" and linear-- depending on the level of momentum.
+"
+24394,Spin-flip reflection at the normal metal-spin superconductor interface,"  We study spin transport through a normal metal-spin superconductor junction.
+A spin-flip reflection is demonstrated at the interface, where a spin-up
+electron incident from the normal metal can be reflected as a spin-down
+electron and the spin $2\times \hbar/2$ will be injected into the spin
+superconductor. When the (spin) voltage is smaller than the gap of the spin
+superconductor, the spin-flip reflection determines the transport properties of
+the junction. We consider both graphene-based (linear-dispersion-relation) and
+quadratic-dispersion-relation normal metal-spin superconductor junctions in
+detail. For the two-dimensional graphene-based junction, the spin-flip
+reflected electron can be along the specular direction (retro-direction) when
+the incident and reflected electron locates in the same band (different bands).
+A perfect spin-flip reflection can occur when the incident electron is normal
+to the interface, and the reflection coefficient is slightly suppressed for the
+oblique incident case. As a comparison, for the one-dimensional
+quadratic-dispersion-relation junction, the spin-flip reflection coefficient
+can reach 1 at certain incident energies. In addition, both the charge current
+and the spin current under a charge (spin) voltage are studied. The spin
+conductance is proportional to the spin-flip reflection coefficient when the
+spin voltage is less than the gap of the spin superconductor. These results
+will help us get a better understanding of spin transport through the normal
+metal-spin superconductor junction.
+"
+24395,Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds,"  Although there exist plentiful theories of empirical risk minimization (ERM)
+for supervised learning, current theoretical understandings of ERM for a
+related problem---stochastic convex optimization (SCO), are limited. In this
+work, we strengthen the realm of ERM for SCO by exploiting smoothness and
+strong convexity conditions to improve the risk bounds. First, we establish an
+$\widetilde{O}(d/n + \sqrt{F_*/n})$ risk bound when the random function is
+nonnegative, convex and smooth, and the expected function is Lipschitz
+continuous, where $d$ is the dimensionality of the problem, $n$ is the number
+of samples, and $F_*$ is the minimal risk. Thus, when $F_*$ is small we obtain
+an $\widetilde{O}(d/n)$ risk bound, which is analogous to the
+$\widetilde{O}(1/n)$ optimistic rate of ERM for supervised learning. Second, if
+the objective function is also $\lambda$-strongly convex, we prove an
+$\widetilde{O}(d/n + \kappa F_*/n )$ risk bound where $\kappa$ is the condition
+number, and improve it to $O(1/[\lambda n^2] + \kappa F_*/n)$ when
+$n=\widetilde{\Omega}(\kappa d)$. As a result, we obtain an $O(\kappa/n^2)$
+risk bound under the condition that $n$ is large and $F_*$ is small, which to
+the best of our knowledge, is the first $O(1/n^2)$-type of risk bound of ERM.
+Third, we stress that the above results are established in a unified framework,
+which allows us to derive new risk bounds under weaker conditions, e.g.,
+without convexity of the random function and Lipschitz continuity of the
+expected function. Finally, we demonstrate that to achieve an $O(1/[\lambda
+n^2] + \kappa F_*/n)$ risk bound for supervised learning, the
+$\widetilde{\Omega}(\kappa d)$ requirement on $n$ can be replaced with
+$\Omega(\kappa^2)$, which is dimensionality-independent.
+"
+24396,Integration Methods and Accelerated Optimization Algorithms,"  We show that accelerated optimization methods can be seen as particular
+instances of multi-step integration schemes from numerical analysis, applied to
+the gradient flow equation. In comparison with recent advances in this vein,
+the differential equation considered here is the basic gradient flow and we
+show that multi-step schemes allow integration of this differential equation
+using larger step sizes, thus intuitively explaining acceleration results.
+"
+24397,Computing a tree having a small vertex cover,"  We consider a new Steiner tree problem, called vertex-cover-weighted Steiner
+tree problem. This problem defines the weight of a Steiner tree as the minimum
+weight of vertex covers in the tree, and seeks a minimum-weight Steiner tree in
+a given vertex-weighted undirected graph. Since it is included by the Steiner
+tree activation problem, the problem admits an O(log n)-approximation algorithm
+in general graphs with n vertices. This approximation factor is tight up to a
+constant because it is NP-hard to achieve an o(log n)-approximation for the
+vertex-cover-weighted Steiner tree problem on general graphs even if the given
+vertex weights are uniform and a spanning tree is required instead of a Steiner
+tree. In this paper, we present constant-factor approximation algorithms for
+the problem with unit disk graphs and with graphs excluding a fixed minor. For
+the latter graph class, our algorithm can be also applied for the Steiner tree
+activation problem.
+"
+24398,Tropical Sufficient Statistics for Persistent Homology,"  We show that an embedding in Euclidean space based on tropical geometry
+generates stable sufficient statistics for barcodes. In topological data
+analysis, barcodes are multiscale summaries of algebraic topological
+characteristics that capture the `shape' of data; however, in practice, they
+have complex structures which make them difficult to use in statistical
+settings. The sufficiency result presented in this work allows for classical
+probability distributions to be assumed on the tropical geometric
+representation of barcodes. This makes a variety of parametric statistical
+inference methods amenable to barcodes, all while maintaining their initial
+interpretations. More specifically, we show that exponential family
+distributions may be assumed, and that likelihood functions for persistent
+homology may be constructed. We conceptually demonstrate sufficiency and
+illustrate its utility in persistent homology dimensions 0 and 1 with concrete
+parametric applications to HIV and avian influenza data.
+"
+24399,The Spingroup and its actions in discrete Clifford analysis,"  Recently, it has been established that the discrete star Laplace and the
+discrete Dirac operator, i.e. the discrete versions of their continuous
+counterparts when working on the standard grid, are rotation-invariant. This
+was done starting from the Lie algebra so(m, C) corresponding to the special
+orthogonal Lie group SO(m), considering its representation in the discrete
+Clifford algebra setting and proving that these operators are symmetries of the
+Dirac and Laplace operators. This set-up showed in an abstract way that
+representation-theoretically the discrete setting mirrors the Euclidean
+Clifford analysis setting. However from a practical point of view, the
+group-action remains indispensable for actual calculations. In this paper, we
+define the discrete Spingroup, which is a double cover of SO(m), and consider
+its actions on discrete functions. We show that this group-action makes the
+spaces Hk and Mk into Spin(m)-representations. We will often consider the
+compliance of our results to the results under the so(m, C)- action.
+"
+24400,Low Complexity Receiver for Uplink SCMA System via Expectation Propagation,"  Sparse code multiple access (SCMA) scheme is considered to be one promising
+non-orthogonal multiple access technology for the future fifth generation (5G)
+communications. Due to the sparse nature, message passing algorithm (MPA) has
+been used as the receiver to achieve close to maximum likelihood (ML) detection
+performance with much lower complexity. However, the complexity order of MPA is
+still exponential with the size of codebook and the degree of signal
+superposition on a given resource element. In this paper, we propose a novel
+low complexity iterative receiver based on expectation propagation algorithm
+(EPA), which reduces the complexity order from exponential to linear.
+Simulation results demonstrate that the proposed EPA receiver achieves nearly
+the same block error rate (BLER) performance as the conventional message
+passing algorithm (MPA) receiver with orders less complexity.
+"
+24401,Shadowing of non-transversal heteroclinic chains,"  We present a new result about the shadowing of nontransversal chain of
+heteroclinic connections based on the idea of dropping dimensions. We
+illustrate this new mechanism with several examples. As an application we
+discuss this mechanism in a simplification of a toy model system derived by
+Colliander et al. in the context of cubic defocusing nonlinear Schrödinger
+equation.
+"
+24402,Recalling a Witness: Foundations and Applications of Monotonic State,"  We provide a way to ease the verification of programs whose state evolves
+monotonically. The main idea is that a property witnessed in a prior state can
+be soundly recalled in the current state, provided (1) state evolves according
+to a given preorder, and (2) the property is preserved by this preorder. In
+many scenarios, such monotonic reasoning yields concise modular proofs, saving
+the need for explicit program invariants. We distill our approach into the
+monotonic-state monad, a general yet compact interface for Hoare-style
+reasoning about monotonic state in a dependently typed language. We prove the
+soundness of the monotonic-state monad and use it as a unified foundation for
+reasoning about monotonic state in the F* verification system. Based on this
+foundation, we build libraries for various mutable data structures like
+monotonic references and apply these libraries at scale to the verification of
+several distributed applications.
+"
+24403,Some classifications of biharmonic hypersurfaces with constant scalar curvature,"  We give some classifications of biharmonic hypersurfaces with constant scalar
+curvature. These include biharmonic Einstein hypersurfaces in space forms,
+compact biharmonic hypersurfaces with constant scalar curvature in a sphere,
+and some complete biharmonic hypersurfaces of constant scalar curvature in
+space forms and in a non-positively curved Einstein space. Our results provide
+additional cases (Theorem 2.3 and Proposition 2.8) that supports the conjecture
+that a biharmonic submanifold in a sphere has constant mean curvature, and two
+more cases that support Chen's conjecture on biharmonic hypersurfaces
+(Corollaries 2.2,2.7).
+"
+24404,Towards Impedance Characterization of Carbon-Carbon Ultrasonically Absorptive Cavities via the Inverse Helmholtz Problem,"  We present a numerical method to determine the complex acoustic impedance at
+the open surface of an arbitrarily shaped cavity, associated to an impinging
+planar acoustic wave with a given wavenumber vector and frequency. We have
+achieved this by developing the first inverse Helmholtz Solver (iHS), which
+implicitly reconstructs the complex acoustic waveform--at a given frequency--up
+to the unknown impedance boundary, hereby providing the spatial distribution of
+impedance as a result of the calculation for that given frequency. We show that
+the algebraic closure conditions required by the inverse Helmholtz problem are
+physically related to the assignment of the spatial distribution of the
+pressure phase over the unknown impedance boundary. The iHS is embarrassingly
+parallelizable over the frequency domain allowing for the straightforward
+determination of the full broadband impedance at every point of the target
+boundary. In this paper, we restrict our analysis to two-dimensions only. We
+first validate our results against Rott's quasi one-dimensional thermoacoustic
+theory for viscid and inviscid constant-area rectangular ducts, test our iHS in
+a fully unstructured fashion with a geometrically complex cavity, and finally,
+present a simplified, two-dimensional analysis of a sample of carbon-carbon
+ultrasonically absorptive coatings (C/C UACs) manufactured in DLR-Stuttgart,
+and used in the hypersonic transition delay experiments by Wagner et al. in
+AIAA 2012-5865. The final goal is to model C/C UACs with multi-oscillator Time
+Domain Impedance Boundary Conditions (TDIBC) developed by Lin et al. in JFM
+(2016) to be applied in direct numerical simulations (DNS) focused on the
+overlying boundary layer, eliminating the need to simultaneously resolve the
+microscopic porous structure of the C/C UACs.
+"
+24405,"Cocktails, but no party: multipath-enabled private audio","  We describe a private audio messaging system that uses echoes to unscramble
+messages at a few predetermined locations in a room. The system works by
+splitting the audio into short chunks and emitting them from different
+loudspeakers. The chunks are filtered so that as they echo around the room,
+they sum to noise everywhere except at a few chosen focusing spots where they
+exactly reproduce the intended messages. Unlike in the case of standard
+personal audio zones, the proposed method renders sound outside the focusing
+spots unintelligible. Our method essentially depends on echoes: the room acts
+as a mixing system such that at given points we get the desired output.
+Finally, we only require a modest number of loudspeakers and only a few impulse
+response measurements at points where the messages should be delivered. We
+demonstrate the effectiveness of the proposed method via objective quantitative
+metrics as well as informal listening experiments in a real room.
+"
+24406,Negating van Enk-Pike's assertion on quantum games OR Is the essence of a quantum game captured completely in the original classical game?,"  S. J. van Enk and R. Pike in PRA 66, 024306 (2002), argue that the
+equilibrium solution to a quantum game isn't unique but is already present in
+the classical game itself. In this work, we debunk this assertion by showing
+that a random strategy in a particular quantum (Hawk-Dove) game is unique to
+the quantum game. In other words the equilibrium solution of the quantum
+Hawk-Dove game can not be obtained in the classical Hawk-Dove game. Moreover,
+we provide an analytical solution to the quantum $2\times2$ strategic form
+Hawk-Dove game using random mixed strategies. The random strategies which we
+describe are evolutionary stable implying both Pareto optimality and Nash
+equilibrium with their payoff's classically unobtainable.
+"
+24407,Origin of the Galactic Diffuse X-ray Emission: Iron K-shell Line Diagnostics,"  This paper reports detailed K-shell line profiles of iron (Fe) and nickel
+(Ni) of the Galactic Center X-ray Emission (GCXE), Galactic Bulge X-ray
+Emission (GBXE), Galactic Ridge X-ray Emission (GRXE), magnetic Cataclysmic
+Variables (mCVs), non-magnetic Cataclysmic Variables (non-mCVs), and coronally
+Active Binaries (ABs). For the study of the origin of the GCXE, GBXE, and GRXE,
+the spectral analysis is focused on equivalent widths of the Fe K$\alpha$, Fe
+He$\alpha$, and Fe Ly$\alpha$ lines. The global spectrum of the GBXE is
+reproduced by a combination of the mCVs, non-mCVs, and ABs spectra. On the
+other hand, the GRXE spectrum shows significant data excesses at the Fe
+K$\alpha$ and Fe He$\alpha$ line energies. This means that additional
+components other than mCVs, non-mCVs, and ABs are required, which have
+symbiotic phenomena of cold gas and very high-temperature plasma. The GCXE
+spectrum shows larger excesses than those found in the GRXE spectrum at all the
+K-shell lines of iron and nickel. Among them the largest ones are the Fe
+K$\alpha$, Fe He$\alpha$, Fe Ly$\alpha$, and Fe Ly$\beta$ lines. Together with
+the fact that the scale heights of the Fe K$\alpha$, Fe He$\alpha$, and Fe
+Ly$\alpha$ lines are similar to that of the central molecular zone (CMZ), the
+excess components would be related to high-energy activity in the extreme
+envelopment of the CMZ.
+"
+24408,DeepDrum: An Adaptive Conditional Neural Network,"  Considering music as a sequence of events with multiple complex dependencies,
+the Long Short-Term Memory (LSTM) architecture has proven very efficient in
+learning and reproducing musical styles. However, the generation of rhythms
+requires additional information regarding musical structure and accompanying
+instruments. In this paper we present DeepDrum, an adaptive Neural Network
+capable of generating drum rhythms under constraints imposed by Feed-Forward
+(Conditional) Layers which contain musical parameters along with given
+instrumentation information (e.g. bass and guitar notes). Results on generated
+drum sequences are presented indicating that DeepDrum is effective in producing
+rhythms that resemble the learned style, while at the same time conforming to
+given constraints that were unknown during the training process.
+"
+24409,Nanodiamond arrays on glass for quantification and fluorescence characterisation,"  Quantifying the variation in emission properties of fluorescent nanodiamonds
+is important for developing their wide-ranging applicability. Directed
+self-assembly techniques show promise for positioning nanodiamonds precisely
+enabling such quantification. Here we show an approach for depositing
+nanodiamonds in pre-determined arrays which are used to gather statistical
+information about fluorescent lifetimes. The arrays were created via a layer of
+photoresist patterned with grids of apertures using electron beam lithography
+and then drop-cast with nanodiamonds. Electron microscopy revealed a 90%
+average deposition yield across 3,376 populated array sites, with an average of
+20 nanodiamonds per site. Confocal microscopy, optimised for nitrogen vacancy
+fluorescence collection, revealed a broad distribution of fluorescent lifetimes
+in agreement with literature. This method for statistically quantifying
+fluorescent nanoparticles provides a step towards fabrication of hybrid
+photonic devices for applications from quantum cryptography to sensing.
+"
+24410,Miyachi's Theorem for the Quaternion Fourier Transform,"  The quaternion Fourier transform (QFT) satisfies some uncertainty principles
+similar to the Euclidean Fourier transform. In this paper, we establish
+Miyachi's theorem for this transform.
+"
+24411,A generalized framework for the estimation of edge infection probabilities,"  Modeling the spread of infections on networks is a well-studied and important
+field of research. Most infection and diffusion models require a real value or
+probability on the edges of the network as an input, but this is rarely
+available in real-life applications. Our goal in this paper is to develop a
+general framework for this task. The general model works with the most widely
+used infection models and is able to handle an arbitrary number of observations
+on such processes. The model is defined as a general optimization task and a
+Particle Swarm heuristic is proposed to solve it. We evaluate the accuracy and
+speed of the proposed method on a high variety of realistic infection
+scenarios.
+"
+24412,Maximizing Diversity of Opinion in Social Networks,"  We study the problem of maximizing opinion diversity in a social network that
+includes opinion leaders with binary opposing opinions. The members of the
+network who are not leaders form their opinions using the French-DeGroot model
+of opinion dynamics. To quantify the diversity of such a system, we adapt two
+diversity measures from ecology to our setting, the Simpson Diversity Index and
+the Shannon Index. Using these two measures, we formalize the problem of how to
+place a single leader with opinion 1, given a network with a leader with
+opinion 0, so as to maximize the opinion diversity. We give analytical
+solutions to these problems for paths, cycles, and trees, and we highlight our
+results through a numerical example.
+"
+24413,Spectrum Sharing between DSRC and WiFi: A Repeated Game Approach,"  We model the coexistence of Dedicated Short Range Communications (DSRC) and
+WiFi networks, when they access a shared wireless medium, as a repeated game.
+Both the networks use a similar CSMA/CA like medium access mechanism. However,
+while a DSRC network cares for the freshness of status updates and desires to
+minimize the age of received updates, a WiFi network would like to maximize
+throughput. We define the stage game, which is parameterized by the average age
+of the DSRC network at the beginning of the stage, and derive its mixed
+strategy Nash equilibrium. We study the evolution of the equilibrium strategies
+over time and the resulting average discounted payoffs of the networks. It
+turns out that DSRC's requirement of timely updates works in favour of WiFi,
+making spectrum sharing with DSRC favorable for a WiFi network in comparison to
+sharing with another WiFi network. Specifically, as we show, the equilibrium
+strategy has the DSRC network, given its requirement of timeliness,
+occasionally refrain from transmitting during a stage. Such stages allow the
+WiFi network competition free access to the medium.
+"
+24414,Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game,"  Fog computing, which provides low-latency computing services at the network
+edge, is an enabler for the emerging Internet of Things (IoT) systems. In this
+paper, we study the allocation of fog computing resources to the IoT users in a
+hierarchical computing paradigm including fog and remote cloud computing
+services. We formulate a computation offloading game to model the competition
+between IoT users and allocate the limited processing power of fog nodes
+efficiently. Each user aims to maximize its own quality of experience (QoE),
+which reflects its satisfaction of using computing services in terms of the
+reduction in computation energy and delay. Utilizing a potential game approach,
+we prove the existence of a pure Nash equilibrium and provide an upper bound
+for the price of anarchy. Since the time complexity to reach the equilibrium
+increases exponentially in the number of users, we further propose a
+near-optimal resource allocation mechanism and prove that in a system with $N$
+IoT users, it can achieve an $\epsilon$-Nash equilibrium in $O(N/\epsilon)$
+time. Through numerical studies, we evaluate the users' QoE as well as the
+equilibrium efficiency. Our results reveal that by utilizing the proposed
+mechanism, more users benefit from computing services in comparison to an
+existing offloading mechanism. We further show that our proposed mechanism
+significantly reduces the computation delay and enables low-latency fog
+computing services for delay-sensitive IoT applications.
+"
+24415,Pulse-coupled model of excitable elements on heterogeneous sparse networks,"  We study a pulse-coupled dynamics of excitable elements in uncorrelated
+scale-free networks. Regimes of self-sustained activity are found for
+homogeneous and inhomogeneous couplings, in which the system displays a wide
+variety of behaviors, including periodic and irregular global spiking signals,
+as well as coherent oscillations, an unexpected form of synchronization. Our
+numerical results also show that the properties of the population firing rate
+depend on the size of the system, particularly its structure and average value
+over time. However, a few straightforward dynamical and topological strategies
+can be introduced to enhance or hinder these global behaviors, rendering a
+scenario where signal control is attainable, which incorporates a basic
+mechanism to turn off the dynamics permanently. As our main result, here we
+present a framework to estimate, in the stationary state, the mean firing rate
+over a long time window and to decompose the global dynamics into average
+values of the inter-spike-interval of each connectivity group. Our approach
+provides accurate predictions of these average quantities when the network
+exhibits high heterogeneity, a remarkable finding that is not restricted
+exclusively to the scale-free topology.
+"
+24416,Deep Counterfactual Networks with Propensity-Dropout,"  We propose a novel approach for inferring the individualized causal effects
+of a treatment (intervention) from observational data. Our approach
+conceptualizes causal inference as a multitask learning problem; we model a
+subject's potential outcomes using a deep multitask network with a set of
+shared layers among the factual and counterfactual outcomes, and a set of
+outcome-specific layers. The impact of selection bias in the observational data
+is alleviated via a propensity-dropout regularization scheme, in which the
+network is thinned for every training example via a dropout probability that
+depends on the associated propensity score. The network is trained in
+alternating phases, where in each phase we use the training examples of one of
+the two potential outcomes (treated and control populations) to update the
+weights of the shared layers and the respective outcome-specific layers.
+Experiments conducted on data based on a real-world observational study show
+that our algorithm outperforms the state-of-the-art.
+"
+24417,Attributed Network Embedding for Learning in a Dynamic Environment,"  Network embedding leverages the node proximity manifested to learn a
+low-dimensional node vector representation for each node in the network. The
+learned embeddings could advance various learning tasks such as node
+classification, network clustering, and link prediction. Most, if not all, of
+the existing works, are overwhelmingly performed in the context of plain and
+static networks. Nonetheless, in reality, network structure often evolves over
+time with addition/deletion of links and nodes. Also, a vast majority of
+real-world networks are associated with a rich set of node attributes, and
+their attribute values are also naturally changing, with the emerging of new
+content patterns and the fading of old content patterns. These changing
+characteristics motivate us to seek an effective embedding representation to
+capture network and attribute evolving patterns, which is of fundamental
+importance for learning in a dynamic environment. To our best knowledge, we are
+the first to tackle this problem with the following two challenges: (1) the
+inherently correlated network and node attributes could be noisy and
+incomplete, it necessitates a robust consensus representation to capture their
+individual properties and correlations; (2) the embedding learning needs to be
+performed in an online fashion to adapt to the changes accordingly. In this
+paper, we tackle this problem by proposing a novel dynamic attributed network
+embedding framework - DANE. In particular, DANE first provides an offline
+method for a consensus embedding and then leverages matrix perturbation theory
+to maintain the freshness of the end embedding results in an online manner. We
+perform extensive experiments on both synthetic and real attributed networks to
+corroborate the effectiveness and efficiency of the proposed framework.
+"
+24418,Sampling and Frequency Warping,"  Optimal sampling of non band-limited functions is an issue of great
+importance that has attracted considerable attention. We propose to tackle this
+problem through the use of a frequency warping: First, by a nonlinear shrinking
+of frequencies, the function is transformed into a band-limited one. One may
+then perform a decomposition in Fourier series. This process gives rise to new
+orthonormal bases of the Sobolev spaces H^alpha. When alpha is an integer,
+these orthonormal bases can be expressed in terms of Laguerre functions. We
+study the reconstruction and speed of convergence properties of the
+warping-based sampling. Besides theoretical considerations, numerical
+experiments are performed.
+"
+24419,SGXIO: Generic Trusted I/O Path for Intel SGX,"  Application security traditionally strongly relies upon security of the
+underlying operating system. However, operating systems often fall victim to
+software attacks, compromising security of applications as well. To overcome
+this dependency, Intel introduced SGX, which allows to protect application code
+against a subverted or malicious OS by running it in a hardware-protected
+enclave. However, SGX lacks support for generic trusted I/O paths to protect
+user input and output between enclaves and I/O devices.
+This work presents SGXIO, a generic trusted path architecture for SGX,
+allowing user applications to run securely on top of an untrusted OS, while at
+the same time supporting trusted paths to generic I/O devices. To achieve this,
+SGXIO combines the benefits of SGX's easy programming model with traditional
+hypervisor-based trusted path architectures. Moreover, SGXIO can tweak insecure
+debug enclaves to behave like secure production enclaves. SGXIO surpasses
+traditional use cases in cloud computing and makes SGX technology usable for
+protecting user-centric, local applications against kernel-level keyloggers and
+likewise. It is compatible to unmodified operating systems and works on a
+modern commodity notebook out of the box. Hence, SGXIO is particularly
+promising for the broad x86 community to which SGX is readily available.
+"
+24420,On human motion prediction using recurrent neural networks,"  Human motion modelling is a classical problem at the intersection of graphics
+and computer vision, with applications spanning human-computer interaction,
+motion synthesis, and motion prediction for virtual and augmented reality.
+Following the success of deep learning methods in several computer vision
+tasks, recent work has focused on using deep recurrent neural networks (RNNs)
+to model human motion, with the goal of learning time-dependent representations
+that perform tasks such as short-term motion prediction and long-term human
+motion synthesis. We examine recent work, with a focus on the evaluation
+methodologies commonly used in the literature, and show that, surprisingly,
+state-of-the-art performance can be achieved by a simple baseline that does not
+attempt to model motion at all. We investigate this result, and analyze recent
+RNN methods by looking at the architectures, loss functions, and training
+procedures used in state-of-the-art approaches. We propose three changes to the
+standard RNN models typically used for human motion, which result in a simple
+and scalable RNN architecture that obtains state-of-the-art performance on
+human motion prediction.
+"
+24421,Controlled Tactile Exploration and Haptic Object Recognition,"  In this paper we propose a novel method for in-hand object recognition. The
+method is composed of a grasp stabilization controller and two exploratory
+behaviours to capture the shape and the softness of an object. Grasp
+stabilization plays an important role in recognizing objects. First, it
+prevents the object from slipping and facilitates the exploration of the
+object. Second, reaching a stable and repeatable position adds robustness to
+the learning algorithm and increases invariance with respect to the way in
+which the robot grasps the object. The stable poses are estimated using a
+Gaussian mixture model (GMM). We present experimental results showing that
+using our method the classifier can successfully distinguish 30 objects.We also
+compare our method with a benchmark experiment, in which the grasp
+stabilization is disabled. We show, with statistical significance, that our
+method outperforms the benchmark method.
+"
+24422,Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway,"  The development of Smart Grid in Norway in specific and Europe/US in general
+will shortly lead to the availability of massive amount of fine-grained
+spatio-temporal consumption data from domestic households. This enables the
+application of data mining techniques for traditional problems in power system.
+Clustering customers into appropriate groups is extremely useful for operators
+or retailers to address each group differently through dedicated tariffs or
+customer-tailored services. Currently, the task is done based on demographic
+data collected through questionnaire, which is error-prone. In this paper, we
+used three different clustering techniques (together with their variants) to
+automatically segment electricity consumers based on their consumption
+patterns. We also proposed a good way to extract consumption patterns for each
+consumer. The grouping results were assessed using four common internal
+validity indexes. We found that the combination of Self Organizing Map (SOM)
+and k-means algorithms produce the most insightful and useful grouping. We also
+discovered that grouping quality cannot be measured effectively by automatic
+indicators, which goes against common suggestions in literature.
+"
+24423,Z-Forcing: Training Stochastic Recurrent Networks,"  Many efforts have been devoted to training generative latent variable models
+with autoregressive decoders, such as recurrent neural networks (RNN).
+Stochastic recurrent models have been successful in capturing the variability
+observed in natural sequential data such as speech. We unify successful ideas
+from recently proposed architectures into a stochastic recurrent model: each
+step in the sequence is associated with a latent variable that is used to
+condition the recurrent dynamics for future steps. Training is performed with
+amortized variational inference where the approximate posterior is augmented
+with a RNN that runs backward through the sequence. In addition to maximizing
+the variational lower bound, we ease training of the latent variables by adding
+an auxiliary cost which forces them to reconstruct the state of the backward
+recurrent network. This provides the latent variables with a task-independent
+objective that enhances the performance of the overall model. We found this
+strategy to perform better than alternative approaches such as KL annealing.
+Although being conceptually simple, our model achieves state-of-the-art results
+on standard speech benchmarks such as TIMIT and Blizzard and competitive
+performance on sequential MNIST. Finally, we apply our model to language
+modeling on the IMDB dataset where the auxiliary cost helps in learning
+interpretable latent variables. Source Code:
+\url{this https URL}
+"
+24424,Online Continuous Submodular Maximization,"  In this paper, we consider an online optimization process, where the
+objective functions are not convex (nor concave) but instead belong to a broad
+class of continuous submodular functions. We first propose a variant of the
+Frank-Wolfe algorithm that has access to the full gradient of the objective
+functions. We show that it achieves a regret bound of $O(\sqrt{T})$ (where $T$
+is the horizon of the online optimization problem) against a
+$(1-1/e)$-approximation to the best feasible solution in hindsight. However, in
+many scenarios, only an unbiased estimate of the gradients are available. For
+such settings, we then propose an online stochastic gradient ascent algorithm
+that also achieves a regret bound of $O(\sqrt{T})$ regret, albeit against a
+weaker $1/2$-approximation to the best feasible solution in hindsight. We also
+generalize our results to $\gamma$-weakly submodular functions and prove the
+same sublinear regret bounds. Finally, we demonstrate the efficiency of our
+algorithms on a few problem instances, including non-convex/non-concave
+quadratic programs, multilinear extensions of submodular set functions, and
+D-optimal design.
+"
+24425,Paraphrasing verbal metonymy through computational methods,"  Verbal metonymy has received relatively scarce attention in the field of
+computational linguistics despite the fact that a model to accurately
+paraphrase metonymy has applications both in academia and the technology
+sector. The method described in this paper makes use of data from the British
+National Corpus in order to create word vectors, find instances of verbal
+metonymy and generate potential paraphrases. Two different ways of creating
+word vectors are evaluated in this study: Continuous bag of words and
+Skip-grams. Skip-grams are found to outperform the Continuous bag of words
+approach. Furthermore, the Skip-gram model is found to operate with
+better-than-chance accuracy and there is a strong positive relationship (phi
+coefficient = 0.61) between the model's classification and human judgement of
+the ranked paraphrases. This study lends credence to the viability of modelling
+verbal metonymy through computational methods based on distributional
+semantics.
+"
+24426,Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning,"  Understanding physical phenomena is a key component of human intelligence and
+enables physical interaction with previously unseen environments. In this
+paper, we study how an artificial agent can autonomously acquire this intuition
+through interaction with the environment. We created a synthetic block stacking
+environment with physics simulation in which the agent can learn a policy
+end-to-end through trial and error. Thereby, we bypass to explicitly model
+physical knowledge within the policy. We are specifically interested in tasks
+that require the agent to reach a given goal state that may be different for
+every new trial. To this end, we propose a deep reinforcement learning
+framework that learns policies which are parametrized by a goal. We validated
+the model on a toy example navigating in a grid world with different target
+positions and in a block stacking task with different target structures of the
+final tower. In contrast to prior work, our policies show better generalization
+across different goals.
+"
+24427,Multi-Mode Inference Engine for Convolutional Neural Networks,"  During the past few years, interest in convolutional neural networks (CNNs)
+has risen constantly, thanks to their excellent performance on a wide range of
+recognition and classification tasks. However, they suffer from the high level
+of complexity imposed by the high-dimensional convolutions in convolutional
+layers. Within scenarios with limited hardware resources and tight power and
+latency constraints, the high computational complexity of CNNs makes them
+difficult to be exploited. Hardware solutions have striven to reduce the power
+consumption using low-power techniques, and to limit the processing time by
+increasing the number of processing elements (PEs). While most of ASIC designs
+claim a peak performance of a few hundred giga operations per seconds, their
+average performance is substantially lower when applied to state-of-the-art
+CNNs such as AlexNet, VGGNet and ResNet, leading to low resource utilization.
+Their performance efficiency is limited to less than 55% on average, which
+leads to unnecessarily high processing latency and silicon area. In this paper,
+we propose a dataflow which enables to perform both the fully-connected and
+convolutional computations for any filter/layer size using the same PEs. We
+then introduce a multi-mode inference engine (MMIE) based on the proposed
+dataflow. Finally, we show that the proposed MMIE achieves a performance
+efficiency of more than 84% when performing the computations of the three
+renown CNNs (i.e., AlexNet, VGGNet and ResNet), outperforming the best
+architecture in the state-of-the-art in terms of energy consumption, processing
+latency and silicon area.
+"
+24428,Experiments on barotropic-baroclinic conversion and the applicability of linear n-layer internal wave theories,"  Interfacial internal waves in a stratified fluid excited by periodic
+free-surface perturbations in a closed tank are studied experimentally.
+Barotropic-baroclinic energy conversion is induced by the presence of a bottom
+obstacle. The connection between horizontal surface velocities and internal
+wave amplitudes is investigated, the developing flow patterns are described
+qualitatively, and the wave speeds of internal waves are systematically
+analyzed and compared to linear 2- and 3-layer theories. We find that, despite
+of the fact that the observed internal waves can have considerable amplitudes,
+a linear 3-layer approximation still gives fairly good agreement with the
+experimental results.
+"
+24429,Diffusion-controlled growth and microstructural evolution of aluminide coatings,"  The diffusion-controlled growth and microstructural evolution at the
+interface of aluminide coatings and different substrates such as Ni-base
+superalloys and steel are reviewed. Quantitative diffusion analysis indicates
+that the diffusion rates of components in the beta-NiAl phase increases with
+the addition of Pt. This directly reflects on the growth rate of the
+interdiffusion zone. The thickness and formation of precipitates between the
+bond coat and the superalloys increase significantly with the Pt addition.
+Mainly Fe2Al5 phase grows during hot dip aluminization of steel along with few
+other phases with very thin layer. Chemical vapor deposition process is being
+established for a better control of the composition of the Fe-aluminide coating
+on steel.
+"
+24430,Cross-connections and variants of the full transformation semigroup,"  Cross-connection theory propounded by K. S. S. Nambooripad describes the
+ideal structure of a regular semigroup using the categories of principal left
+(right) ideals. A variant $\mathscr{T}_X^\theta$ of the full transformation
+semigroup $(\mathscr{T}_X,\cdot)$ for an arbitrary $\theta \in \mathscr{T}_X$
+is the semigroup $\mathscr{T}_X^\theta= (\mathscr{T}_X,\ast)$ with the binary
+operation $\alpha \ast \beta = \alpha\cdot\theta\cdot\beta$ where $\alpha,
+\beta \in \mathscr{T}_X$. In this article, we describe the ideal structure of
+the regular part $Reg(\mathscr{T}_X^\theta)$ of the variant of the full
+transformation semigroup using cross-connections. We characterize the
+constituent categories of $Reg(\mathscr{T}_X^\theta)$ and describe how they are
+\emph{cross-connected} by a functor induced by the sandwich transformation
+$\theta$. This lead us to a structure theorem for the semigroup and give the
+representation of $Reg(\mathscr{T}_X^\theta)$ as a cross-connection semigroup.
+Using this, we give a description of the biordered set and the sandwich sets of
+the semigroup.
+"
+24431,Coded Computation over Heterogeneous Clusters,"  In large-scale distributed computing clusters, such as Amazon EC2, there are
+several types of ""system noise"" that can result in major degradation of
+performance: bottlenecks due to limited communication bandwidth, latency due to
+straggler nodes, etc. On the other hand, these systems enjoy abundance of
+redundancy - a vast number of computing nodes and large storage capacity. There
+have been recent results that demonstrate the impact of coding for efficient
+utilization of computation and storage redundancy to alleviate the effect of
+stragglers and communication bottlenecks in homogeneous clusters. In this
+paper, we focus on general heterogeneous distributed computing clusters
+consisting of a variety of computing machines with different capabilities. We
+propose a coding framework for speeding up distributed computing in
+heterogeneous clusters by trading redundancy for reducing the latency of
+computation. In particular, we propose Heterogeneous Coded Matrix
+Multiplication (HCMM) algorithm for performing distributed matrix
+multiplication over heterogeneous clusters that is provably asymptotically
+optimal for a broad class of processing time distributions. Moreover, we show
+that HCMM is unboundedly faster than any uncoded scheme. To demonstrate
+practicality of HCMM, we carry out experiments over Amazon EC2 clusters where
+HCMM is found to be up to $61\%$, $46\%$ and $36\%$ respectively faster than
+three benchmark load allocation schemes - Uniform Uncoded, Load-balanced
+Uncoded, and Uniform Coded. Additionally, we provide a generalization to the
+problem of optimal load allocation in heterogeneous settings, where we take
+into account the monetary costs associated with the clusters. We argue that
+HCMM is asymptotically optimal for budget-constrained scenarios as well, and we
+develop a heuristic algorithm for (HCMM) load allocation for budget-limited
+computation tasks.
+"
+24432,Generalised Magnetic Polarizability Tensors,"  We present a new complete asymptotic expansion for the low frequency
+time-harmonic magnetic field perturbation caused by the presence of a
+conducting (permeable) object as its size tends to zero for the eddy current
+regime of Maxwell's equations. The new asymptotic expansion allows the
+characterisation of the shape and material properties of such objects by a new
+class of generalised magnetic polarizability tensors and we provide an explicit
+formula for their calculation. Our result will have important implications for
+metal detectors since it will improve small object discrimination and, for
+situations where the background field varies over the inclusion, this
+information will be useable, and indeed useful, in identifying their shape and
+material properties. Thus, improving the ability of metal detectors to locate
+landmines and unexploded ordnance, sort metals in recycling processes, ensure
+food safety as well as enhancing security screening at airports and public
+events.
+"
+24433,Unsupervised Multi Class Segmentation of 3D Images with Intensity Inhomogeneities,"  Intensity inhomogeneities in images constitute a considerable challenge in
+image segmentation. In this paper we propose a novel biconvex variational model
+to tackle this task. We combine a total variation approach for multi class
+segmentation with a multiplicative model to handle the inhomogeneities. Our
+method assumes that the image intensity is the product of a smoothly varying
+part and a component which resembles important image structures such as edges.
+Therefore, we penalize in addition to the total variation of the label
+assignment matrix a quadratic difference term to cope with the smoothly varying
+factor. A critical point of our biconvex functional is computed by a modified
+proximal alternating linearized minimization method (PALM). We show that the
+assumptions for the convergence of the algorithm are fulfilled by our model.
+Various numerical examples demonstrate the very good performance of our method.
+Particular attention is paid to the segmentation of 3D FIB tomographical images
+which was indeed the motivation of our work.
+"
+24434,Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding,"  The widespread availability of electronic health records (EHRs) promises to
+usher in the era of personalized medicine. However, the problem of extracting
+useful clinical representations from longitudinal EHR data remains challenging.
+In this paper, we explore deep neural network models with learned medical
+feature embedding to deal with the problems of high dimensionality and
+temporality. Specifically, we use a multi-layer convolutional neural network
+(CNN) to parameterize the model and is thus able to capture complex non-linear
+longitudinal evolution of EHRs. Our model can effectively capture local/short
+temporal dependency in EHRs, which is beneficial for risk prediction. To
+account for high dimensionality, we use the embedding medical features in the
+CNN model which hold the natural medical concepts. Our initial experiments
+produce promising results and demonstrate the effectiveness of both the medical
+feature embedding and the proposed convolutional neural network in risk
+prediction on cohorts of congestive heart failure and diabetes patients
+compared with several strong baselines.
+"
+24435,Multistage Electronic Nematic Transitions in Cuprate Superconductors: Functional-Renormalization-Group Analysis,"  Recently, complex phase transitions accompanied by the rotational symmetry
+breaking have been discovered experimentally in cuprate superconductors. To
+find the realized order parameters, we study various charge susceptibilities in
+an unbiased way, by applying the functional-renormalization-group method to the
+realistic $d$-$p$ Hubbard model. Without assuming the wavevector of the order
+parameter, we reveal that the most dominant instability is the uniform ($q =
+0$) charge modulation on the $p_x$ and $p_y$ orbitals, which possesses the
+d-symmetry. This uniform nematic order triggers another nematic p-orbital
+density wave along the axial (Cu-Cu) direction at $Q_a = (\pi/2,0)$. It is
+predicted that uniform nematic order is driven by the spin fluctuations in the
+pseudogap region, and another nematic density-wave order at $q = Q_a$ is
+triggered by the uniform order. The predicted multistage nematic transitions
+are caused by the Aslamazov-Larkin-type fluctuation-exchange processes.
+"
+24436,Adversarial Learning: A Critical Review and Active Learning Study,"  This papers consists of two parts. The first is a critical review of prior
+art on adversarial learning, identifying some significant limitations of
+previous works. The second part is an experimental study considering
+adversarial active learning and an investigation of the efficacy of a mixed
+sample selection strategy for combating an adversary who attempts to disrupt
+the classifier learning.
+"
+24437,Local Information with Feedback Perturbation Suffices for Dictionary Learning in Neural Circuits,"  While the sparse coding principle can successfully model information
+processing in sensory neural systems, it remains unclear how learning can be
+accomplished under neural architectural constraints. Feasible learning rules
+must rely solely on synaptically local information in order to be implemented
+on spatially distributed neurons. We describe a neural network with spiking
+neurons that can address the aforementioned fundamental challenge and solve the
+L1-minimizing dictionary learning problem, representing the first model able to
+do so. Our major innovation is to introduce feedback synapses to create a
+pathway to turn the seemingly non-local information into local ones. The
+resulting network encodes the error signal needed for learning as the change of
+network steady states caused by feedback, and operates akin to the classical
+stochastic gradient descent method.
+"
+24438,LazyFP: Leaking FPU Register State using Microarchitectural Side-Channels,"  Modern processors utilize an increasingly large register set to facilitate
+efficient floating point and SIMD computation. This large register set is a
+burden for operating systems, as its content needs to be saved and restored
+when the operating system context switches between tasks. As an optimization,
+the operating system can defer the context switch of the FPU and SIMD register
+set until the first instruction is executed that needs access to these
+registers. Meanwhile, the old content is left in place with the hope that the
+current task might not use these registers at all. This optimization is
+commonly called lazy FPU context switching. To make it possible, a processor
+offers the ability to toggle the availability of instructions utilizing
+floating point and SIMD registers. If the instructions are turned off, any
+attempt of executing them will generate a fault.
+In this paper, we present an attack that exploits lazy FPU context switching
+and allows an adversary to recover the FPU and SIMD register set of arbitrary
+processes or VMs. The attack works on processors that transiently execute FPU
+or SIMD instructions that follow an instruction generating the fault indicating
+the first use of FPU or SIMD instructions. On operating systems using lazy FPU
+context switching, the FPU and SIMD register content of other processes or
+virtual machines can then be reconstructed via cache side effects.
+With SIMD registers not only being used for cryptographic computation, but
+also increasingly for simple operations, such as copying memory, we argue that
+lazy FPU context switching is a dangerous optimization that needs to be turned
+off in all operating systems, if there is a chance that they run on affected
+processors.
+"
+24439,An Exploration of Heterogeneity in Supernova Type Ia Samples,"  We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to check
+their consistency using cosmology blind statistical analyses as well as
+cosmological parameter fitting. We find that Panstarrs dataset is the most
+stable of the three to changes, although it does not, at the moment, go to high
+enough redshifts to tightly constrain the dark energy equation of state, $w$.
+Union2.1, drawn from many different sources, appears somewhat susceptible to
+changes within the dataset. JLA reconstructs well for a smaller number of
+cosmological parameters. At higher degrees of freedom, the dependence of its
+errors on redshift can lead to varying results between subsets. Panstarrs is
+inconsistent with the other two at about $2\sigma$, and JLA and Union2.1 are
+about $1\sigma$ away from each other. For the $\Omega_{0m}-w$ cosmological
+reconstruction, the $1\sigma$ range of values in $w$ for selected subsets of
+each dataset is two times larger for JLA and Union2.1 as compared to Panstarrs.
+The range in $\Omega_{0m}$ for the same subsets remains approximately similar
+for all three datasets. Although there are differences in the fitting and
+correction techniques used in the different samples, the most important
+criterion is SNe selection, a slightly different SNe selection can lead to
+noticeably different results both in the purely statistical analysis and
+cosmological reconstruction. We note that a single, high quality low redshift
+sample could help decrease the uncertainties in the result. We also note that
+lack of homogeneity in the magnitude errors may bias the results and should
+either be modeled, or its effect neutralized by using other, complementary
+datasets. A supernova sample with high quality data at both high and low
+redshifts, constructed from a few surveys to avoid heterogeneity in the sample,
+and with homogeneous errors, would result in a more robust cosmological
+reconstruction.
+"
+24440,Structure of optimal strategies for remote estimation over Gilbert-Elliott channel with feedback,"  We investigate remote estimation over a Gilbert-Elliot channel with feedback.
+We assume that the channel state is observed by the receiver and fed back to
+the transmitter with one unit delay. In addition, the transmitter gets ACK/NACK
+feedback for successful/unsuccessful transmission. Using ideas from team
+theory, we establish the structure of optimal transmission and estimation
+strategies and identify a dynamic program to determine optimal strategies with
+that structure. We then consider first-order autoregressive sources where the
+noise process has unimodal and symmetric distribution. Using ideas from
+majorization theory, we show that the optimal transmission strategy has a
+threshold structure and the optimal estimation strategy is Kalman-like.
+"
+24441,Modeling and control of a cable-driven series elastic actuator,"  Series elastic actuators (SEA) are playing an increasingly important role in
+the fields of physical human-robot interaction. This paper focuses on the
+modeling and control of a cable-driven SEA. First, the scheme of the
+cable-driven SEA has been proposed, and a velocity controlled DC motor has been
+used as its power source. Based on this, the model of the cable-driven SEA has
+been built up. Further, a two degrees of freedom (2-DOF) control approach has
+been employed to control the output torque. Simulation results have shown that
+the 2-DOF method has achieved better robust performance than the PD method.
+"
+24442,Resilient Energy Allocation Model for Supply Shortage Outages,"  Supply Shortage Outages are a major concern during peak demand for developing
+countries. In the Philippines, commercial loads have unused backup generation
+of up to 3000 MW, at the same time there are shortages of as much as 700 MW
+during peak demand. This gives utilities the incentive to implement Demand
+Response programs to minimize this shortage. But when considering Demand
+Response from a modeling perspective, social welfare through profit is always
+the major objective for program implementation. That isn't always the case
+during an emergency situation as there can be a trade-off between grid
+resilience and cost of electricity.
+The question is how the Distribution Utility (DU) shall optimally allocate
+the unused generation to meet the shortage when this trade-off exists. We
+formulate a combined multi-objective optimal dispatch model where we can make a
+direct comparison between the least-cost and resilience objectives.
+We find that this trade-off is due to the monotonically increasing nature of
+energy cost functions. If the supply is larger than the demand, the DU can
+perform a least-cost approach in the optimal dispatch since maximizing the
+energy generated in this case can lead to multiple solutions. We also find in
+our simulation that in cases where the supply of energy from the customers is
+less than shortage quantity, the DU must prioritize maximizing the generated
+energy rather than minimizing cost.
+"
+24443,Leveraging Sparse and Dense Feature Combinations for Sentiment Classification,"  Neural networks are one of the most popular approaches for many natural
+language processing tasks such as sentiment analysis. They often outperform
+traditional machine learning models and achieve the state-of-art results on
+most tasks. However, many existing deep learning models are complex, difficult
+to train and provide a limited improvement over simpler methods. We propose a
+simple, robust and powerful model for sentiment classification. This model
+outperforms many deep learning models and achieves comparable results to other
+deep learning models with complex architectures on sentiment analysis datasets.
+We publish the code online.
+"
+24444,Improved measurements of turbulence in the hot gaseous atmospheres of nearby giant elliptical galaxies,"  We present significantly improved measurements of turbulent velocities in the
+hot gaseous halos of nearby giant elliptical galaxies. Using deep XMM-Newton
+Reflection Grating Spectrometer (RGS ) observations and a combination of
+resonance scattering and direct line broadening methods, we obtain well bounded
+constraints for 13 galaxies. Assuming that the turbulence is isotropic, we
+obtain a best fit mean 1D turbulent velocity of ~110 km/s. This implies a
+typical 3D Mach number ~0.45 and a typical non-thermal pressure contribution of
+~6 per cent in the cores of nearby massive galaxies. The intrinsic scatter
+around these values is modest - consistent with zero, albeit with large
+statistical uncertainty - hinting at a common and quasi-continuous mechanism
+sourcing the velocity structure in these objects. Using conservative estimates
+of the spatial scales associated with the observed turbulent motions, we find
+that turbulent heating can be sufficient to offset radiative cooling in the
+inner regions of these galaxies (<10 kpc, typically 2-3 kpc). The full
+potential of our analysis methods will be enabled by future X-ray
+microcalorimeter observations.
+"
+24445,"Projective, Sparse, and Learnable Latent Position Network Models","  When modeling network data using a latent position model, it is typical to
+assume that all nodes' latent positions are independently and identically
+distributed. However, this assumption implies the average node degree grows
+linearly with the number of nodes, which is inappropriate when the graph is
+thought to be sparse. We propose an alternative assumption--- that the latent
+positions are generated according to a Poisson point process--- and show that
+it is compatible with various levels of sparsity. Unlike other sparse latent
+position models, our approach also defines a projective family of probability
+distributions, ensuring statistical inference and prediction are well-defined
+for networks of different sizes. We establish conditions for consistently
+inferring the latent positions in a latent position network model, and compare
+our results to those of existing frameworks for modeling sparse graphs.
+"
+24446,Space-time convex functions and sectional curvature,"  We show that in Lorentzian manifolds, sectional curvature bounds of the form
+$\mathcal{R}\le K\,$, as defined by Andersson and Howard, are closely tied to
+space-time convex and $\lambda$-convex ($\lambda>0$) functions, as defined by
+Gibbons and Ishibashi. Among the consequences are a natural construction of
+such functions, and an analogue, that applies to domains of a new type, of a
+theorem of Alías, Bessa and deLira ruling out trapped submanifolds.
+"
+24447,Interacting Conceptual Spaces I : Grammatical Composition of Concepts,"  The categorical compositional approach to meaning has been successfully
+applied in natural language processing, outperforming other models in
+mainstream empirical language processing tasks. We show how this approach can
+be generalized to conceptual space models of cognition. In order to do this,
+first we introduce the category of convex relations as a new setting for
+categorical compositional semantics, emphasizing the convex structure important
+to conceptual space applications. We then show how to construct conceptual
+spaces for various types such as nouns, adjectives and verbs. Finally we show
+by means of examples how concepts can be systematically combined to establish
+the meanings of composite phrases from the meanings of their constituent parts.
+This provides the mathematical underpinnings of a new compositional approach to
+cognition.
+"
+24448,Exploring Increasing-Chord Paths and Trees,"  A straight-line drawing $\Gamma$ of a graph $G=(V,E)$ is a drawing of $G$ in
+the Euclidean plane, where every vertex in $G$ is mapped to a distinct point,
+and every edge in $G$ is mapped to a straight line segment between their
+endpoints. A path $P$ in $\Gamma$ is called increasing-chord if for every four
+points (not necessarily vertices) $a,b,c,d$ on $P$ in this order, the Euclidean
+distance between $b,c$ is at most the Euclidean distance between $a,d$. A
+spanning tree $T$ rooted at some vertex $r$ in $\Gamma$ is called
+increasing-chord if $T$ contains an increasing-chord path from $r$ to every
+vertex in $T$. In this paper we prove that given a vertex $r$ in a
+straight-line drawing $\Gamma$, it is NP-complete to determine whether $\Gamma$
+contains an increasing-chord spanning tree rooted at $r$. We conjecture that
+finding an increasing-chord path between a pair of vertices in $\Gamma$, which
+is an intriguing open problem posed by Alamdari et al., is also NP-complete,
+and show a (non-polynomial) reduction from the 3-SAT problem.
+"
+24449,On the Expressive Power of Overlapping Architectures of Deep Learning,"  Expressive efficiency refers to the relation between two architectures A and
+B, whereby any function realized by B could be replicated by A, but there
+exists functions realized by A, which cannot be replicated by B unless its size
+grows significantly larger. For example, it is known that deep networks are
+exponentially efficient with respect to shallow networks, in the sense that a
+shallow network must grow exponentially large in order to approximate the
+functions represented by a deep network of polynomial size. In this work, we
+extend the study of expressive efficiency to the attribute of network
+connectivity and in particular to the effect of ""overlaps"" in the convolutional
+process, i.e., when the stride of the convolution is smaller than its filter
+size (receptive field). To theoretically analyze this aspect of network's
+design, we focus on a well-established surrogate for ConvNets called
+Convolutional Arithmetic Circuits (ConvACs), and then demonstrate empirically
+that our results hold for standard ConvNets as well. Specifically, our analysis
+shows that having overlapping local receptive fields, and more broadly denser
+connectivity, results in an exponential increase in the expressive capacity of
+neural networks. Moreover, while denser connectivity can increase the
+expressive capacity, we show that the most common types of modern architectures
+already exhibit exponential increase in expressivity, without relying on
+fully-connected layers.
+"
+24450,Event-Clock Nested Automata,"  In this paper we introduce and study Event-Clock Nested Automata (ECNA), a
+formalism that combines Event Clock Automata (ECA) and Visibly Pushdown
+Automata (VPA). ECNA allow to express real-time properties over non-regular
+patterns of recursive programs. We prove that ECNA retain the same closure and
+decidability properties of ECA and VPA being closed under Boolean operations
+and having a decidable language-inclusion problem. In particular, we prove that
+emptiness, universality, and language-inclusion for ECNA are EXPTIME-complete
+problems. As for the expressiveness, we have that ECNA properly extend any
+previous attempt in the literature of combining ECA and VPA.
+"
+24451,Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels,"  We introduce $\textit{semi-unsupervised learning}$, an extreme case of
+semi-supervised learning with ultra-sparse categorisation where some classes
+have no labels in the training set. That is, in the training data some classes
+are sparsely labelled and other classes appear only as unlabelled data. Many
+real-world datasets are conceivably of this type. We demonstrate that effective
+learning in this regime is only possible when a model is capable of capturing
+both semi-supervised and unsupervised learning. We develop two deep generative
+models for classification in this regime that extend previous deep generative
+models designed for semi-supervised learning. By changing their probabilistic
+structure to contain a mixture of Gaussians in their continuous latent space,
+these new models can learn in both unsupervised and semi-unsupervised
+paradigms. We demonstrate their performance both for semi-unsupervised and
+unsupervised learning on various standard datasets. We show that our models can
+learn in an semi-unsupervised manner on Fashion-MNIST. Here we artificially
+mask out all labels for half of the classes of data and keep $2\%$ of labels
+for the remaining classes. Our model is able to learn effectively, obtaining a
+trained classifier with $(77.2\pm1.3)\%$ test set accuracy. We also can train
+on Fashion-MNIST unsupervised, obtaining $(75.2\pm1.5)\%$ test set accuracy.
+Additionally, doing the same for MNIST unsupervised we get $(96.3\pm0.9)\%$
+test set accuracy, which is state-of-the art for fully probabilistic deep
+generative models.
+"
+24452,Variational Information Bottleneck on Vector Quantized Autoencoders,"  In this paper, we provide an information-theoretic interpretation of the
+Vector Quantized-Variational Autoencoder (VQ-VAE). We show that the loss
+function of the original VQ-VAE can be derived from the variational
+deterministic information bottleneck (VDIB) principle. On the other hand, the
+VQ-VAE trained by the Expectation Maximization (EM) algorithm can be viewed as
+an approximation to the variational information bottleneck(VIB) principle.
+"
+24453,Aggregated Wasserstein Metric and State Registration for Hidden Markov Models,"  We propose a framework, named Aggregated Wasserstein, for computing a
+dissimilarity measure or distance between two Hidden Markov Models with state
+conditional distributions being Gaussian. For such HMMs, the marginal
+distribution at any time position follows a Gaussian mixture distribution, a
+fact exploited to softly match, aka register, the states in two HMMs. We refer
+to such HMMs as Gaussian mixture model-HMM (GMM-HMM). The registration of
+states is inspired by the intrinsic relationship of optimal transport and the
+Wasserstein metric between distributions. Specifically, the components of the
+marginal GMMs are matched by solving an optimal transport problem where the
+cost between components is the Wasserstein metric for Gaussian distributions.
+The solution of the optimization problem is a fast approximation to the
+Wasserstein metric between two GMMs. The new Aggregated Wasserstein distance is
+a semi-metric and can be computed without generating Monte Carlo samples. It is
+invariant to relabeling or permutation of states. The distance is defined
+meaningfully even for two HMMs that are estimated from data of different
+dimensionality, a situation that can arise due to missing variables. This
+distance quantifies the dissimilarity of GMM-HMMs by measuring both the
+difference between the two marginal GMMs and that between the two transition
+matrices. Our new distance is tested on tasks of retrieval, classification, and
+t-SNE visualization of time series. Experiments on both synthetic and real data
+have demonstrated its advantages in terms of accuracy as well as efficiency in
+comparison with existing distances based on the Kullback-Leibler divergence.
+"
+24454,Compact Descriptors for Video Analysis: the Emerging MPEG Standard,"  This paper provides an overview of the on-going compact descriptors for video
+analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG).
+MPEG-CDVA targets at defining a standardized bitstream syntax to enable
+interoperability in the context of video analysis applications. During the
+developments of MPEGCDVA, a series of techniques aiming to reduce the
+descriptor size and improve the video representation ability have been
+proposed. This article describes the new standard that is being developed and
+reports the performance of these key technical contributions.
+"
+24455,Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative Machine Learning approaches,"  Chemical multisensor devices need calibration algorithms to estimate gas
+concentrations. Their possible adoption as indicative air quality measurements
+devices poses new challenges due to the need to operate in continuous
+monitoring modes in uncontrolled environments. Several issues, including slow
+dynamics, continue to affect their real world performances. At the same time,
+the need for estimating pollutant concentrations on board the devices, espe-
+cially for wearables and IoT deployments, is becoming highly desirable. In this
+framework, several calibration approaches have been proposed and tested on a
+variety of proprietary devices and datasets; still, no thorough comparison is
+available to researchers. This work attempts a benchmarking of the most
+promising calibration algorithms according to recent literature with a focus on
+machine learning approaches. We test the techniques against absolute and
+dynamic performances, generalization capabilities and computational/storage
+needs using three different datasets sharing continuous monitoring operation
+methodology. Our results can guide researchers and engineers in the choice of
+optimal strategy. They show that non-linear multivariate techniques yield
+reproducible results, outperforming lin- ear approaches. Specifically, the
+Support Vector Regression method consistently shows good performances in all
+the considered scenarios. We highlight the enhanced suitability of shallow
+neural networks in a trade-off between performance and computational/storage
+needs. We confirm, on a much wider basis, the advantages of dynamic approaches
+with respect to static ones that only rely on instantaneous sensor array
+response. The latter have been shown to be best choice whenever prompt and
+precise response is needed.
+"
+24456,Uncertainty Quantification in Three Dimensional Natural Convection using Polynomial Chaos Expansion and Deep Neural Networks,"  This paper analyzes the effects of input uncertainties on the outputs of a
+three dimensional natural convection problem in a differentially heated cubical
+enclosure. Two different cases are considered for parameter uncertainty
+propagation and global sensitivity analysis. In case A, stochastic variation is
+introduced in the two non-dimensional parameters (Rayleigh and Prandtl numbers)
+with an assumption that the boundary temperature is uniform. Being a two
+dimensional stochastic problem, the polynomial chaos expansion (PCE) method is
+used as a surrogate model. Case B deals with non-uniform stochasticity in the
+boundary temperature. Instead of the traditional Gaussian process model with
+the Karhunen-Lo$\grave{e}$ve expansion, a novel approach is successfully
+implemented to model uncertainty in the boundary condition. The boundary is
+divided into multiple domains and the temperature imposed on each domain is
+assumed to be an independent and identically distributed (i.i.d) random
+variable. Deep neural networks are trained with the boundary temperatures as
+inputs and Nusselt number, internal temperature or velocities as outputs. The
+number of domains which is essentially the stochastic dimension is 4, 8, 16 or
+32. Rigorous training and testing process shows that the neural network is able
+to approximate the outputs to a reasonable accuracy. For a high stochastic
+dimension such as 32, it is computationally expensive to fit the PCE. This
+paper demonstrates a novel way of using the deep neural network as a surrogate
+modeling method for uncertainty quantification with the number of simulations
+much fewer than that required for fitting the PCE, thus, saving the
+computational cost.
+"
+24457,What Automated Planning can do for Business Process Management,"  Business Process Management (BPM) is a central element of today
+organizations. Despite over the years its main focus has been the support of
+processes in highly controlled domains, nowadays many domains of interest to
+the BPM community are characterized by ever-changing requirements,
+unpredictable environments and increasing amounts of data that influence the
+execution of process instances. Under such dynamic conditions, BPM systems must
+increase their level of automation to provide the reactivity and flexibility
+necessary for process management. On the other hand, the Artificial
+Intelligence (AI) community has concentrated its efforts on investigating
+dynamic domains that involve active control of computational entities and
+physical devices (e.g., robots, software agents, etc.). In this context,
+Automated Planning, which is one of the oldest areas in AI, is conceived as a
+model-based approach to synthesize autonomous behaviours in automated way from
+a model. In this paper, we discuss how automated planning techniques can be
+leveraged to enable new levels of automation and support for business
+processing, and we show some concrete examples of their successful application
+to the different stages of the BPM life cycle.
+"
+24458,Quantum-kinetic perspective on photovoltaic device operation in nanostructure-based solar cells,"  The implementation of a wide range of novel concepts for next-generation
+high-efficiency solar cells is based on nanostructures with
+configuration-tunable optoelectronic properties. On the other hand, effective
+nano-optical light-trapping concepts enable the use of ultra-thin absorber
+architectures. In both cases, the local density of electronic and optical
+states deviates strongly from that in a homogeneous bulk material. At the same
+time, non-local and coherent phenomena like tunneling or ballistic transport
+become increasingly relevant. As a consequence, the semi-classical, diffusive
+bulk picture conventionally assumed may no longer be appropriate to describe
+the physical processes of generation, transport, and recombination governing
+the photovoltaic operation of such devices. In this review, we provide a
+quantum-kinetic perspective on photovoltaic device operation that reaches
+beyond the limits of the standard simulation models for bulk solar cells.
+Deviations from bulk physics are assessed in ultra-thin film and
+nanostructure-based solar cell architectures by comparing the predictions of
+the semi-classical models for key physical quantities such as absorption
+coefficients, emission spectra, generation and recombination rates as well as
+potentials, densities and currents with the corresponding properties as given
+by a more fundamental description based on non-equilibrium quantum statistical
+mechanics. This advanced approach, while paving the way to a comprehensive
+quantum theory of photovoltaics, bridges simulations at microscopic material
+and macroscopic device levels by providing the charge carrier dynamics at the
+mesoscale.
+"
+24459,On unitarizability in the case of classical p-adic groups,"  In the introduction of this paper we discuss a possible approach to the
+unitarizability problem for classical p-adic groups. In this paper we give some
+very limited support that such approach is not without chance. In a forthcoming
+paper we shall give additional evidence in generalized cuspidal rank (up to)
+three.
+"
+24460,The S66 noncovalent interactions benchmark reconsidered using explicitly correlated methods near the basis set limit,"  The S66 benchmark for noncovalent interactions has been re-evaluated using
+explicitly correlated methods with basis sets near the one-particle basis set
+limit. It is found that post-MP2 ""high-level corrections"" are treated
+adequately well using a combination of CCSD(F12*) with (aug-)cc-pVTZ-F12 basis
+sets on the one hand, and (T) extrapolated from conventional
+CCSD(T)/heavy-aug-cc-pV{D,T}Z on the other hand. Implications for earlier
+benchmarks on the larger S66x8 problem set in particular, and for accurate
+calculations on noncovalent interactions in general, are discussed. At a slight
+cost in accuracy, (T) can be considerably accelerated by using sano-V{D,T}Z+
+basis sets, while half-counterpoise CCSD(F12*)(T)/cc-pVDZ-F12 offers the best
+compromise between accuracy and computational cost.
+"
+24461,Confident Multiple Choice Learning,"  Ensemble methods are arguably the most trustworthy techniques for boosting
+the performance of machine learning models. Popular independent ensembles (IE)
+relying on naive averaging/voting scheme have been of typical choice for most
+applications involving deep neural networks, but they do not consider advanced
+collaboration among ensemble models. In this paper, we propose new ensemble
+methods specialized for deep neural networks, called confident multiple choice
+learning (CMCL): it is a variant of multiple choice learning (MCL) via
+addressing its overconfidence issue.In particular, the proposed major
+components of CMCL beyond the original MCL scheme are (i) new loss, i.e.,
+confident oracle loss, (ii) new architecture, i.e., feature sharing and (iii)
+new training method, i.e., stochastic labeling. We demonstrate the effect of
+CMCL via experiments on the image classification on CIFAR and SVHN, and the
+foreground-background segmentation on the iCoseg. In particular, CMCL using 5
+residual networks provides 14.05% and 6.60% relative reductions in the top-1
+error rates from the corresponding IE scheme for the classification task on
+CIFAR and SVHN, respectively.
+"
+24462,Study of the homogeneous Bethe-Salpeter equation in the 2+1 Minkowski space,"  The problem of a relativistic bound-state system consisting of two scalar
+bosons interacting through the exchange of another scalar boson, in 2+1
+space-time dimensions, has been studied. The Bethe-Salpeter equation (BSE) was
+solved by adopting the Nakanishi integral representation (NIR) and the
+Light-Front projection. The NIR allows us to solve the BSE in Minkowski space,
+which is a big and important challenge, since most of non-perturbative
+calculations are done in Euclidean space, e.g. Lattice and Schwinger-Dyson
+calculations. We have in this work adopted an interaction kernel containing the
+ladder and cross-ladder exchanges. In order to check that the NIR is also a
+good representation in 2+1, the coupling constants and Wick-rotated amplitudes
+have been computed and compared with calculations performed in Euclidean space.
+Very good agreement between the calculations performed in the Minkowski and
+Euclidean spaces has been found. This is an important consistence test that
+allows Minkowski calculations with the Nakanishi representation in 2+1
+dimensions. This relativistic approach will allow us to perform applications in
+condensed matter problems in a near future.
+"
+24463,Evolution of Ego-networks in Social Media with Link Recommendations,"  Ego-networks are fundamental structures in social graphs, yet the process of
+their evolution is still widely unexplored. In an online context, a key
+question is how link recommender systems may skew the growth of these networks,
+possibly restraining diversity. To shed light on this matter, we analyze the
+complete temporal evolution of 170M ego-networks extracted from Flickr and
+Tumblr, comparing links that are created spontaneously with those that have
+been algorithmically recommended. We find that the evolution of ego-networks is
+bursty, community-driven, and characterized by subsequent phases of explosive
+diameter increase, slight shrinking, and stabilization. Recommendations favor
+popular and well-connected nodes, limiting the diameter expansion. With a
+matching experiment aimed at detecting causal relationships from observational
+data, we find that the bias introduced by the recommendations fosters global
+diversity in the process of neighbor selection. Last, with two link prediction
+experiments, we show how insights from our analysis can be used to improve the
+effectiveness of social recommender systems.
+"
+24464,Proper Functors and Fixed Points for Finite Behaviour,"  The rational fixed point of a set functor is well-known to capture the
+behaviour of finite coalgebras. In this paper we consider functors on algebraic
+categories. For them the rational fixed point may no longer be fully abstract,
+i.e. a subcoalgebra of the final coalgebra. Inspired by Ésik and Maletti's
+notion of a proper semiring, we introduce the notion of a proper functor. We
+show that for proper functors the rational fixed point is determined as the
+colimit of all coalgebras with a free finitely generated algebra as carrier and
+it is a subcoalgebra of the final coalgebra. Moreover, we prove that a functor
+is proper if and only if that colimit is a subcoalgebra of the final coalgebra.
+These results serve as technical tools for soundness and completeness proofs
+for coalgebraic regular expression calculi, e.g. for weighted automata.
+"
+24465,Orthogonality preserving property for pairs of operators on Hilbert $C^*$-modules,"  We investigate the orthogonality preserving property for pairs of mappings on
+inner product $C^*$-modules extending existing results for a single
+orthogonality-preserving mapping. Guided by the point of view that the
+$C^*$-valued inner product structure of a Hilbert $C^*$-module is determined
+essentially by the module structure and by the orthogonality structure, pairs
+of linear and local orthogonality-preserving mappings are investigated, not a
+priori bounded. The intuition is that most often $C^*$-linearity and
+boundedness can be derived from the settings under consideration. In
+particular, we obtain that if $\mathscr{A}$ is a $C^{*}$-algebra and $T,
+S:\mathscr{E}\longrightarrow \mathscr{F}$ are two bounded ${\mathscr A}$-linear
+mappings between full Hilbert $\mathscr{A}$-modules, then $\langle x, y\rangle
+= 0$ implies $\langle T(x), S(y)\rangle = 0$ for all $x, y\in \mathscr{E}$ if
+and only if there exists an element $\gamma$ of the center $Z(M({\mathscr A}))$
+of the multiplier algebra $M({\mathscr A})$ of ${\mathscr A}$ such that
+$\langle T(x), S(y)\rangle = \gamma \langle x, y\rangle$ for all $x, y\in
+\mathscr{E}$. In addition, we give some applications.
+"
+24466,Modeling CMB Lensing Cross Correlations with {\sc CLEFT},"  A new generation of surveys will soon map large fractions of sky to ever
+greater depths and their science goals can be enhanced by exploiting cross
+correlations between them. In this paper we study cross correlations between
+the lensing of the CMB and biased tracers of large-scale structure at high $z$.
+We motivate the need for more sophisticated bias models for modeling
+increasingly biased tracers at these redshifts and propose the use of
+perturbation theories, specifically Convolution Lagrangian Effective Field
+Theory ({\sc CLEFT}). Since such signals reside at large scales and redshifts,
+they can be well described by perturbative approaches. We compare our model
+with the current approach of using scale independent bias coupled with fitting
+functions for non-linear matter power spectra, showing that the latter will not
+be sufficient for upcoming surveys. We illustrate our ideas by estimating
+$\sigma_8$ from the auto- and cross-spectra of mock surveys, finding that {\sc
+CLEFT} returns accurate and unbiased results at high $z$. We discuss
+uncertainties due to the redshift distribution of the tracers, and several
+avenues for future development.
+"
+24467,Two-channel Kondo physics in a periodically driven single-impurity Anderson model,"  We investigate a quantum dot (Anderson impurity) coupled to metallic leads,
+with a time-periodic voltage bias across the device. Using a time-dependent
+Schrieffer-Wolff transformation, we show that the Floquet Hamiltonian of the
+model can be mapped onto a two-channel Kondo model, in which the impurity is
+screened by separate conduction bands corresponding to parity-even and odd
+superpositions of the metallic leads. By changing the frequency and amplitude
+of the perturbation, one can tune the system to a quantum critical point with
+symmetric coupling of the impurity to both channels. For the understanding of
+the driven state, energy absorption from the drive must be considered: Although
+the absorption at the impurity is balanced by the energy flow into the
+conduction band, locally it leads to non-thermal distribution functions which
+can have a detrimental effect on the Kondo physics. However, a numerical
+simulation demonstrates that the absorption can be systematically suppressed at
+a fixed value of the induced couplings by increasing the frequency, so that the
+time-averaged dynamics in the driven one-channel Anderson model can be used to
+study the critical behavior of the two-channel model.
+"
+24468,When is an automatic set an additive basis?,"  We characterize those $k$-automatic sets $S$ of natural numbers that form an
+additive basis for the natural numbers, and we show that this characterization
+is effective. In addition, we give an algorithm to determine the smallest $j$
+such that $S$ forms an additive basis of order $j$, if it exists.
+"
+24469,Coherence number as a discrete quantum resource,"  We introduce a new discrete coherence monotone named the \emph{coherence
+number}, which is a generalization of the coherence rank to mixed states. After
+defining the coherence number in a similar manner to the Schmidt number in
+entanglement theory, we present a necessary and sufficient condition of the
+coherence number for a coherent state to be converted to an entangled state of
+nonzero $k$-concurrence (a member of the generalized concurrence family with
+$2\le k \le d$). It also turns out that the coherence number is a useful
+measure to understand the process of Grover search algorithm of $N$ items. We
+show that the coherence number remains $N$ and falls abruptly when the success
+probability of the searching process becomes maximal. This phenomenon motivates
+us to analyze the depletion pattern of $C_c^{(N)}$ (the last member of the
+generalized coherence concurrence, nonzero when the coherence number is $N$),
+which turns out to be an optimal resource for the process since it is
+completely consumed to finish the searching task.
+"
+24470,Serverless Protocols for Inventory and Tracking with a UAV,"  It is widely acknowledged that the proliferation of Unmanned Aerial Vehicles
+(UAVs) may lead to serious concerns regarding avionics safety, particularly
+when end-users are not adhering to air safety regulations. There are, however,
+domains in which UAVs may help to increase the safety of airplanes and the
+management of flights and airport resources that often require substantial
+human resources. For instance, Paris Charles de Gaulle airport (CDG) has more
+than 7,000 staff and supports 30,000 direct jobs for more than 60 million
+passengers per year (as of 2016). Indeed, these new systems can be used
+beneficially for several purposes, even in sensitive areas like airports. Among
+the considered applications are those that suggest using UAVs to enhance safety
+of on-ground airplanes; for instance, by collecting (once the aircraft has
+landed) data recorded by different systems during the flight (like the sensors
+of the Aircraft Data Networks - ADN) or by examining the state of airplane
+structure. In this paper, our proposal is to use UAVs, under the control of the
+airport authorities, to inventory and track various tagged assets, such as
+luggage, supplies required for the flights, and maintenance tools. The aim of
+our proposal is to make airport management systems more efficient for
+operations requiring inventory and tracking, along with increasing safety
+(sensitive assets such as refueling tanks, or sensitive pieces of luggage can
+be tracked), thus raising financial profit.
+"
+24471,Discovering the Graph Structure in the Clustering Results,"  In a standard cluster analysis, such as k-means, in addition to clusters
+locations and distances between them, it's important to know if they are
+connected or well separated from each other. The main focus of this paper is
+discovering the relations between the resulting clusters. We propose a new
+method which is based on pairwise overlapping k-means clustering, that in
+addition to means of clusters provides the graph structure of their relations.
+The proposed method has a set of parameters that can be tuned in order to
+control the sensitivity of the model and the desired relative size of the
+pairwise overlapping interval between means of two adjacent clusters, i.e.,
+level of overlapping. We present the exact formula for calculating that
+parameter. The empirical study presented in the paper demonstrates that our
+approach works well not only on toy data but also compliments standard
+clustering results with a reasonable graph structure on real datasets, such as
+financial indices and restaurants.
+"
+24472,$\star$-Liftings for Differential Privacy,"  Recent developments in formal verification have identified approximate
+liftings (also known as approximate couplings) as a clean, compositional
+abstraction for proving differential privacy. This construction can be defined
+in two styles. Earlier definitions require the existence of one or more witness
+distributions, while a recent definition by Sato uses universal quantification
+over all sets of samples. These notions have each have their own strengths: the
+universal version is more general than the existential ones, while existential
+liftings are known to satisfy more precise composition principles.
+We propose a novel, existential version of approximate lifting, called
+$\star$-lifting, and show that it is equivalent to Sato's construction for
+discrete probability measures. Our work unifies all known notions of
+approximate lifting, yielding cleaner properties, more general constructions,
+and more precise composition theorems for both styles of lifting, enabling
+richer proofs of differential privacy. We also clarify the relation between
+existing definitions of approximate lifting, and consider more general
+approximate liftings based on $f$-divergences.
+"
+24473,A Data-Oriented Model of Literary Language,"  We consider the task of predicting how literary a text is, with a gold
+standard from human ratings. Aside from a standard bigram baseline, we apply
+rich syntactic tree fragments, mined from the training set, and a series of
+hand-picked features. Our model is the first to distinguish degrees of highly
+and less literary novels using a variety of lexical and syntactic features, and
+explains 76.0 % of the variation in literary ratings.
+"
+24474,Knowledge Integrated Classifier Design Based on Utility Optimization,"  This paper proposes a systematic framework to design a classification model
+that yields a classifier which optimizes a utility function based on prior
+knowledge. Specifically, as the data size grows, we prove that the produced
+classifier asymptotically converges to the optimal classifier, an extended
+version of the Bayes rule, which maximizes the utility function. Therefore, we
+provide a meaningful theoretical interpretation for modeling with the knowledge
+incorporated. Our knowledge incorporation method allows domain experts to guide
+the classifier towards correctly classifying data that they think to be more
+significant.
+"
+24475,Involutive filters of pseudo-hoops,"  In this this paper we introduce the notion of involutive filters of
+pseudo-hoops, and we emphasize their role in the probability theory on these
+structures. A characterization of involutive pseudo-hoops is given and their
+properties are investigated. We give characterizations of involutive filters of
+a bounded pseudo-hoop and we prove that in the case of bounded Wajsberg
+pseudo-hoops the notions of fantastic and involutive filters coincide. One of
+main results consists of proving that a normal filter $F$ of a bounded
+pseudo-hoop $A$ is involutive if and only if $A/F$ is an involutive
+pseudo-hoop. It is also proved that any Boolean filter of a bounded Wajsberg
+pseudo-hoop is involutive. The notions of state operators and state-morphism
+operators on pseudo-hoops are introduced and the relationship between these
+operators are investigated. For a bounded Wajsberg pseudo-hoop we prove that
+the kernel of any state operator is an involutive filter.
+"
+24476,Structured Embedding Models for Grouped Data,"  Word embeddings are a powerful approach for analyzing language, and
+exponential family embeddings (EFE) extend them to other types of data. Here we
+develop structured exponential family embeddings (S-EFE), a method for
+discovering embeddings that vary across related groups of data. We study how
+the word usage of U.S. Congressional speeches varies across states and party
+affiliation, how words are used differently across sections of the ArXiv, and
+how the co-purchase patterns of groceries can vary across seasons. Key to the
+success of our method is that the groups share statistical information. We
+develop two sharing strategies: hierarchical modeling and amortization. We
+demonstrate the benefits of this approach in empirical studies of speeches,
+abstracts, and shopping baskets. We show how S-EFE enables group-specific
+interpretation of word usage, and outperforms EFE in predicting held-out data.
+"
+24477,Feasibility of Principal Component Analysis in hand gesture recognition system,"  Nowadays actions are increasingly being handled in electronic ways, instead
+of physical interaction. From earlier times biometrics is used in the
+authentication of a person. It recognizes a person by using a human trait
+associated with it like eyes (by calculating the distance between the eyes) and
+using hand gestures, fingerprint detection, face detection etc. Advantages of
+using these traits for identification are that they uniquely identify a person
+and cannot be forgotten or lost. These are unique features of a human being
+which are being used widely to make the human life simpler. Hand gesture
+recognition system is a powerful tool that supports efficient interaction
+between the user and the computer. The main moto of hand gesture recognition
+research is to create a system which can recognise specific hand gestures and
+use them to convey useful information for device control. This paper presents
+an experimental study over the feasibility of principal component analysis in
+hand gesture recognition system. PCA is a powerful tool for analyzing data. The
+primary goal of PCA is dimensionality reduction. Frames are extracted from the
+Sheffield KInect Gesture (SKIG) dataset. The implementation is done by creating
+a training set and then training the recognizer. It uses Eigen space by
+processing the eigenvalues and eigenvectors of the images in training set.
+Euclidean distance with the threshold value is used as similarity metric to
+recognize the gestures. The experimental results show that PCA is feasible to
+be used for hand gesture recognition system.
+"
+24478,Left-symmetric algebras and homogeneous improper affine spheres,"  The nonzero level sets in $n$-dimensional flat affine space of a
+translationally homogeneous function are improper affine spheres if and only if
+the Hessian determinant of the function is equal to a nonzero constant multiple
+of the $n$th power of the function. The exponentials of the characteristic
+polynomials of certain left-symmetric algebras yield examples of such functions
+whose level sets are analogues of the generalized Cayley hypersurface of
+Eastwood-Ezhov. There are found purely algebraic conditions sufficient for the
+characteristic polynomial of the left-symmetric algebra to have the desired
+properties. Precisely, it suffices that the algebra has triangularizable left
+multiplication operators and the trace of the right multiplication is a Koszul
+form for which right multiplication by the dual idempotent is projection along
+its kernel, which equals the derived Lie subalgebra of the left-symmetric
+algebra.
+"
+24479,Evolutionary Game for Mining Pool Selection in Blockchain Networks,"  In blockchain networks adopting the proof-of-work schemes, the monetary
+incentive is introduced by the Nakamoto consensus protocol to guide the
+behaviors of the full nodes (i.e., block miners) in the process of maintaining
+the consensus about the blockchain state. The block miners have to devote their
+computation power measured in hash rate in a crypto-puzzle solving competition
+to win the reward of publishing (a.k.a., mining) new blocks. Due to the
+exponentially increasing difficulty of the crypto-puzzle, individual block
+miners tends to join mining pools, i.e., the coalitions of miners, in order to
+reduce the income variance and earn stable profits. In this paper, we study the
+dynamics of mining pool selection in a blockchain network, where mining pools
+may choose arbitrary block mining strategies. We identify the hash rate and the
+block propagation delay as two major factors determining the outcomes of mining
+competition, and then model the strategy evolution of the individual miners as
+an evolutionary game. We provide the theoretical analysis of the evolutionary
+stability for the pool selection dynamics in a case study of two mining pools.
+The numerical simulations provide the evidence to support our theoretical
+discoveries as well as demonstrating the stability in the evolution of miners'
+strategies in a general case.
+"
+24480,Complexity of Scheduling Charging in the Smart Grid,"  In the smart grid, the intent is to use flexibility in demand, both to
+balance demand and supply as well as to resolve potential congestion. A first
+prominent example of such flexible demand is the charging of electric vehicles,
+which do not necessarily need to be charged as soon as they are plugged in. The
+problem of optimally scheduling the charging demand of electric vehicles within
+the constraints of the electricity infrastructure is called the charge
+scheduling problem. The models of the charging speed, horizon, and charging
+demand determine the computational complexity of the charge scheduling problem.
+For about 20 variants, we show, using a dynamic programming approach, that the
+problem is either in P or weakly NP-hard. We also show that about 10 variants
+of the problem are strongly NP-hard, presenting a potentially significant
+obstacle to their use in practical situations of scale.
+"
+24481,Lengths of closed geodesics on random surfaces of large genus,"  We prove Poisson approximation results for the bottom part of the length
+spectrum of a random closed hyperbolic surface of large genus. Here, a random
+hyperbolic surface is a surface picked at random using the Weil-Petersson
+volume form on the corresponding moduli space. As an application of our result,
+we compute the large genus limit of the expected systole.
+"
+24482,Numerically Stable Evaluation of Moments of Random Gram Matrices with Applications,"  This paper is focuses on the computation of the positive moments of one-side
+correlated random Gram matrices. Closed-form expressions for the moments can be
+obtained easily, but numerical evaluation thereof is prone to numerical
+stability, especially in high-dimensional settings. This letter provides a
+numerically stable method that efficiently computes the positive moments in
+closed-form. The developed expressions are more accurate and can lead to higher
+accuracy levels when fed to moment based-approaches. As an application, we show
+how the obtained moments can be used to approximate the marginal distribution
+of the eigenvalues of random Gram matrices.
+"
+24483,Phase transition in relaxation paths in allosteric molecules: enzymatic kinetically constrained model,"  A hierarchy of timescales is ubiquitous in biological systems, where
+enzymatic reactions play an important role because they can hasten the
+relaxation to equilibrium. We introduced a statistical physics model of
+interacting spins that also incorporates enzymatic reactions to extend the
+classic model for allosteric regulation. Through Monte Carlo simulations and
+theoretical analysis, we found that the relaxation dynamics are much slower
+than the elementary reactions and are logarithmic in time with several
+plateaus, as is commonly observed for glasses. This is because of the kinetic
+constraints from the cooperativity via the competition for enzymes, which tend
+to bind to molecules with limited structure. This inhibits the progress of the
+modification reaction. Our model showed symmetry breaking in the relaxation
+trajectories that led to inherently kinetic phase transitions without any
+correspondence to the equilibrium state. In this paper, we discuss the
+relevance of these results for diverse responses in biology.
+"
+24484,Spatial social networks identified from urban group travel,"  While the individual travel implicates the trace of individual mobility
+decision, group travels signify the possible social relationship behind these
+traces. Different from online social network, spatial interaction between
+individuals is a critical yet unknown dimension to understand the collective
+behaviors in a city. In this paper, based on over 127 million trips in Beijing
+metro network, we develop a method to distinguish the group travel of friends
+from the encounter travel of familiar strangers. We find travels of friends are
+among the most predictable groups. These identified friendships are interwoven
+and form a friendship network, with structural properties different from
+encounter network. The topological role of individuals in this network is found
+strongly correlated with her travel predictability. The overall time savings of
+about 34190 minutes after redistribution of inefficient group traveler with
+flexible travel purposes shows the potential of designing specific traffic
+fares for group travel. Our identification and understanding of group travel
+may help to develop and organize new traffic mode in the future smart
+transportation.
+"
+24485,Mapping higher-order network flows in memory and multilayer networks with Infomap,"  Comprehending complex systems by simplifying and highlighting important
+dynamical patterns requires modeling and mapping higher-order network flows.
+However, complex systems come in many forms and demand a range of
+representations, including memory and multilayer networks, which in turn call
+for versatile community-detection algorithms to reveal important modular
+regularities in the flows. Here we show that various forms of higher-order
+network flows can be represented in a unified way with networks that
+distinguish physical nodes for representing a~complex system's objects from
+state nodes for describing flows between the objects. Moreover, these so-called
+sparse memory networks allow the information-theoretic community detection
+method known as the map equation to identify overlapping and nested flow
+modules in data from a range of~different higher-order interactions such as
+multistep, multi-source, and temporal data. We derive the map equation applied
+to sparse memory networks and describe its search algorithm Infomap, which can
+exploit the flexibility of sparse memory networks. Together they provide a
+general solution to reveal overlapping modular patterns in higher-order flows
+through complex systems.
+"
+24486,"Existence, nonexistence, and asymptotics of deep water solitary waves with localized vorticity","  In this paper, we study solitary waves propagating along the surface of an
+infinitely deep body of water in two or three dimensions. The waves are acted
+upon by gravity and capillary effects are allowed --- but not required --- on
+the interface. We assume that the vorticity is localized in the sense that it
+satisfies certain moment conditions, and we permit there to be finitely many
+point vortices in the bulk of the fluid in two dimensions. We also consider a
+two-fluid model with a vortex sheet.
+Under mild decay assumptions, we obtain precise asymptotics for the velocity
+field and free surface, and relate this to global properties of the wave. For
+instance, we rule out the existence of waves whose free surface elevations have
+a single sign and of vortex sheets with finite angular momentum. Building on
+the work of Shatah, Walsh, and Zeng, we also prove the existence of families of
+two-dimensional capillary-gravity waves with compactly supported vorticity
+satisfying the above assumptions. For these waves, we further show that the
+free surface is positive in a neighborhood of infinity, and that the
+asymptotics at infinity are linked to the net vorticity.
+"
+24487,SO-MRS: a multi-robot system architecture based on the SOA paradigm and ontology,"  A generic architecture for a class of distributed robotic systems is
+presented. The architecture supports openness and heterogeneity, i.e.
+heterogeneous components may be joined and removed from the systems without
+affecting its basic functionality. The architecture is based on the paradigm of
+Service Oriented Architecture (SOA), and a generic representation (ontology) of
+the environment. A device (e.g. robot) is seen as a collection of its
+capabilities exposed as services. Generic protocols for publishing,
+discovering, arranging services are proposed for creating composite services
+that can accomplish complex tasks in an automatic way. Also generic protocols
+for execution of composite services are proposed along with simple protocols
+for monitoring the executions, and for recovery from failures. A software
+platform built on a multi-robot system (according to the proposed architecture)
+is a multi-agent system.
+"
+24488,A minimal HIV-AIDS infection model with general incidence rate and application to Morocco data,"  We study the global dynamics of a SICA infection model with general incidence
+rate. The proposed model is calibrated with cumulative cases of infection by
+HIV-AIDS in Morocco from 1986 to 2015. We first prove that our model is
+biologically and mathematically well-posed. Stability analysis of different
+steady states is performed and threshold parameters are identified where the
+model exhibits clearance of infection or maintenance of a chronic infection.
+Furthermore, we examine the robustness of the model to some parameter values by
+examining the sensitivity of the basic reproduction number. Finally, using
+numerical simulations with real data from Morocco, we show that the model
+predicts well such reality.
+"
+24489,Exoplanet Predictions Based on Harmonic Orbit Resonances,"  The current exoplanet database includes 5454 confirmed planets and candidate
+planets observed with the KEPLER mission. We find 932 planet pairs from which
+we extract distance and orbital period ratios. While earlier studies used the
+Titius-Bode law or a generalized version with logarithmic spacing, which both
+lack a physical model, we employ here the theory of harmonic orbit resonances,
+which contains quantized ratios instead, to explain the observed planet
+distance ratios and to predict undetected exoplanets. We find that the most
+prevailing harmonic ratios are (2:1), (3:2), and (5:3), in 73\% of the cases,
+while alternative harmonic ratios of (5:4), (4:3), (5:2), (3:1) occur in 27\%
+of the other cases. Our orbital predictions includes 171 exoplanets, 2 Jupiter
+moons, one Saturn moon, 3 Uranus moons, and 4 Neptune moons. The accuracy of
+the predicted planet distances amounts to a few percent, which fits the data
+significantly better than the Titius-Bode law or a logarithmic spacing. This
+information may be useful for targeted exoplanet searches with Kepler data and
+to estimate the number of live-carrying planets in habitable zones.
+"
+24490,Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals,"  To estimate direct and indirect effects of an exposure on an outcome from
+observed data strong assumptions about unconfoundedness are required. Since
+these assumptions cannot be tested using the observed data, a mediation
+analysis should always be accompanied by a sensitivity analysis of the
+resulting estimates. In this article we propose a sensitivity analysis method
+for parametric estimation of direct and indirect effects when the exposure,
+mediator and outcome are all binary. The sensitivity parameters consist of the
+correlation between the error terms of the mediator and outcome models, the
+correlation between the error terms of the mediator model and the model for the
+exposure assignment mechanism, and the correlation between the error terms of
+the exposure assignment and outcome models. These correlations are incorporated
+into the estimation of the model parameters and identification sets are then
+obtained for the direct and indirect effects for a range of plausible
+correlation values. We take the sampling variability into account through the
+construction of uncertainty intervals. The proposed method is able to assess
+sensitivity to both mediator-outcome confounding and confounding involving the
+exposure. To illustrate the method we apply it to a mediation study based on
+data from the Swedish Stroke Register (Riksstroke).
+"
+24491,High-power femtosecond pulses without a modelocked laser,"  We demonstrate a fiber system which amplifies and compresses pulses from a
+gain-switched diode. A Mamyshev regenerator shortens the pulses and improves
+their coherence, enabling subsequent amplification by parabolic pre-shaping. As
+a result, we are able to control nonlinear effects and generate nearly
+transform-limited, 140-fs pulses with 13-MW peak power---an order-of-magnitude
+improvement over previous gain-switched diode sources. Seeding with a
+gain-switched diode results in random fluctuations of 2% in the pulse energy,
+which future work using known techniques may ameliorate. Further development
+may allow such systems to compete directly with sources based on modelocked
+oscillators in some applications while enjoying unparalleled robustness and
+repetition rate control.
+"
+24492,Amenability of groups and $G$-sets,"  This text surveys classical and recent results in the field of amenability of
+groups, from a combinatorial standpoint. It has served as the support of
+courses at the University of Göttingen and the École Normale Supérieure.
+The goals of the text are (1) to be as self-contained as possible, so as to
+serve as a good introduction for newcomers to the field; (2) to stress the use
+of combinatorial tools, in collaboration with functional analysis, probability
+etc., with discrete groups in focus; (3) to consider from the beginning the
+more general notion of amenable actions; (4) to describe recent classes of
+examples, and in particular groups acting on Cantor sets and topological full
+groups.
+"
+24493,Initialising Kernel Adaptive Filters via Probabilistic Inference,"  We present a probabilistic framework for both (i) determining the initial
+settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive
+KAFs whereby in addition to weights and dictionaries, kernel parameters are
+learnt sequentially. This is achieved by formulating the estimator as a
+probabilistic model and defining dedicated prior distributions over the kernel
+parameters, weights and dictionary, enforcing desired properties such as
+sparsity. The model can then be trained using a subset of data to initialise
+standard KAFs or updated sequentially each time a new observation becomes
+available. Due to the nonlinear/non-Gaussian properties of the model, learning
+and inference is achieved using gradient-based maximum-a-posteriori
+optimisation and Markov chain Monte Carlo methods, and can be confidently used
+to compute predictions. The proposed framework was validated on nonlinear time
+series of both synthetic and real-world nature, where it outperformed standard
+KAFs in terms of mean square error and the sparsity of the learnt dictionaries.
+"
+24494,Some observations on the logical foundations of inductive theorem proving,"  In this paper we study the logical foundations of automated inductive theorem
+proving. To that aim we first develop a theoretical model that is centered
+around the difficulty of finding induction axioms which are sufficient for
+proving a goal.
+Based on this model, we then analyze the following aspects: the choice of a
+proof shape, the choice of an induction rule and the language of the induction
+formula. In particular, using model-theoretic techniques, we clarify the
+relationship between notions of inductiveness that have been considered in the
+literature on automated inductive theorem proving. This is a corrected version
+of the paper arXiv:1704.01930v5 published originally on Nov.~16, 2017.
+"
+24495,Dimension free bounds for the vector-valued Hardy-Littlewood maximal operator,"  In this article, Fefferman-Stein inequalities in $L^p(\mathbb R^d;\ell^q)$
+withbounds independent of the dimension $d$ are proved, for all $1 \textless{}
+p, q \textless{} + \infty.$This result generalizes in a vector-valued setting
+the famous one by Steinfor the standard Hardy-Littlewood maximal operator. We
+then extendour result by replacing $\ell^q$ with an arbitrary UMD Banach
+lattice. Finally,we prove similar dimensionless inequalities in the setting of
+the Grushinoperators.
+"
+24496,CRSTIP - An Assessment Scheme for Security Assessment Processes,"  Complex networked systems are an integral part of today's support
+infrastructures. Due to their importance, these systems become more and more
+the target for cyber-attacks, suffering a notable number of security incidents.
+Also, they are subject to regulation by national and international legislation.
+An operator of such an infrastructure or system is responsible for ensuring its
+security and correct functioning in order to satisfy customers. In addition,
+the entire process of risk and quality control needs to be efficient and
+manageable. This short paper introduces the Compliance, Risk Assessment and
+Security Testing Improvement Profiling (CRSTIP) scheme. CRSTIP is an evaluation
+scheme that enables assessing the maturity of security assessment processes,
+taking into consideration systematic use of formalisms, integration and
+tool-support in the areas of compliance assessment, security risk assessment
+and security testing. The paper describes the elements of the scheme and their
+application to one of the case studies of the RASEN research project.
+"
+24497,Context-aware Cluster Based Device-to-Device Communication to Serve Machine Type Communications,"  Billions of Machine Type Communication (MTC) devices are foreseen to be
+deployed in next ten years and therefore potentially open a new market for next
+generation wireless network. However, MTC applications have different
+characteristics and requirements compared with the services provided by legacy
+cellular networks. For instance, an MTC device sporadically requires to
+transmit a small data packet containing information generated by sensors. At
+the same time, due to the massive deployment of MTC devices, it is inefficient
+to charge their batteries manually and thus a long battery life is required for
+MTC devices. In this sense, legacy networks designed to serve human-driven
+traffics in real time can not support MTC efficiently. In order to improve the
+availability and battery life of MTC devices, context-aware device-to-device
+(D2D) communication is exploited in this paper. By applying D2D communication,
+some MTC users can serve as relays for other MTC users who experience bad
+channel conditions. Moreover, signaling schemes are also designed to enable the
+collection of context information and support the proposed D2D communication
+scheme. Last but not least, a system level simulator is implemented to evaluate
+the system performance of the proposed technologies and a large performance
+gain is shown by the numerical results.
+"
+24498,RefCurv: A Software for the Construction of Pediatric Reference Curves,"  In medicine, reference curves serve as an important tool for everyday
+clinical practice. Pediatricians assess the growth process of children with the
+help of percentile curves serving as norm references. The mathematical methods
+for the construction of these reference curves are sophisticated and often
+require technical knowledge beyond the scope of physicians. An easy-to-use
+software for life scientists and physicians is missing. As a consequence, most
+medical publications do not document the construction properly. This project
+aims to develop a software that enables non-technical users to apply modern
+statistical methods to create and analyze reference curves. In this paper, we
+present RefCurv, a software that facilitates the construction of reference
+curves. The software comprises functionalities to select and visualize data.
+Users can fit models to the data and graphically present them as percentile
+curves. Furthermore, the software provides features to highlight possible
+outliers, perform model selection, and analyze the sensitivity. RefCurv is an
+open-source software with a graphical user interface (GUI) written in Python.
+It uses R and the gamlss add-on package (Rigby and Stasinopoulos (2005)) as the
+underlying statistical engine. In summary, RefCurv is the first software based
+on the gamlss package, which enables practitioners to construct and analyze
+reference curves in a user-friendly GUI. In broader terms, the software brings
+together the fields of statistical learning and medical application.
+Consequently, RefCurv can help to establish the construction of reference
+curves in other medical fields.
+"
+24499,Light tunneling anomaly in interlaced metallic wire meshes,"  For long wavelengths three-dimensional connected metallic wire meshes are
+impenetrable by light and have an electromagnetic response similar to that of
+an electron gas below the plasma frequency. Surprisingly, here it is shown that
+when two opaque metallic meshes are spatially-interlaced the combined structure
+enables an anomalous light tunneling in the long wavelength regime. The effect
+is due to the destructive interference of the waves scattered by the two wire
+meshes, which leads to a Fano-type resonance.
+"
+24500,"Hidden momentum of electrons, nuclei, atoms and molecules","  We consider the positions and velocities of electrons and spinning nuclei and
+demonstrate that these particles harbour hidden momentum when located in an
+electromagnetic field. This hidden momentum is present in all atoms and
+molecules, however it is ultimately cancelled by the momentum of the
+electromagnetic field. We point out that an electron vortex in an electric
+field might harbour a comparatively large hidden momentum and recognise the
+phenomenon of 'hidden hidden momentum'.
+"
+24501,"On a $(p,q)$-Laplacian problem with parametric concave term and asymmetric perturbation","  A Dirichlet problem driven by the $(p,q)$-Laplace operator and an asymmetric
+concave reaction with positive parameter is investigated. Four nontrivial
+smooth solutions (two positive, one negative, and the remaining nodal) are
+obtained once the parameter turns out to be sufficiently small. Under a oddness
+condition near the origin for the perturbation, a whole sequence of
+sign-changing solutions, which converges to zero, is produced.
+"
+24502,Degeneracies in quasi-categories,"  In this note we show that a semisimplicial set with the weak Kan condition
+admits a simplicial structure, provided any object allows an idempotent
+self-equivalence. Moreover, any two choices of simplicial structures give rise
+to equivalent quasi-categories. The method is purely combinatorial and extends
+to semisimplicial objects in other categories; in particular to semi-simplicial
+spaces satisfying the Segal condition (semi-Segal spaces).
+"
+24503,Gradient Coding via the Stochastic Block Model,"  Gradient descent and its many variants, including mini-batch stochastic
+gradient descent, form the algorithmic foundation of modern large-scale machine
+learning. Due to the size and scale of modern data, gradient computations are
+often distributed across multiple compute nodes. Unfortunately, such
+distributed implementations can face significant delays caused by straggler
+nodes, i.e., nodes that are much slower than average. Gradient coding is a new
+technique for mitigating the effect of stragglers via algorithmic redundancy.
+While effective, previously proposed gradient codes can be computationally
+expensive to construct, inaccurate, or susceptible to adversarial stragglers.
+In this work, we present the stochastic block code (SBC), a gradient code based
+on the stochastic block model. We show that SBCs are efficient, accurate, and
+that under certain settings, adversarial straggler selection becomes as hard as
+detecting a community structure in the multiple community, block stochastic
+graph model.
+"
+24504,"State-space analysis of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons","  In this study, we analyzed the activity of monkey V1 neurons responding to
+grating stimuli of different orientations using inference methods for a
+time-dependent Ising model. The method provides optimal estimation of
+time-dependent neural interactions with credible intervals according to the
+sequential Bayes estimation algorithm. Furthermore, it allows us to trace
+dynamics of macroscopic network properties such as entropy, sparseness, and
+fluctuation. Here we report that, in all examined stimulus conditions, pairwise
+interactions contribute to increasing sparseness and fluctuation. We then
+demonstrate that the orientation of the grating stimulus is in part encoded in
+the pairwise interactions of the neural populations. These results demonstrate
+the utility of the state-space Ising model in assessing contributions of neural
+interactions during stimulus processing.
+"
+24505,Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes,"  In this paper, we present a label transfer model from texts to images for
+image classification tasks. The problem of image classification is often much
+more challenging than text classification. On one hand, labeled text data is
+more widely available than the labeled images for classification tasks. On the
+other hand, text data tends to have natural semantic interpretability, and they
+are often more directly related to class labels. On the contrary, the image
+features are not directly related to concepts inherent in class labels. One of
+our goals in this paper is to develop a model for revealing the functional
+relationships between text and image features as to directly transfer
+intermodal and intramodal labels to annotate the images. This is implemented by
+learning a transfer function as a bridge to propagate the labels between two
+multimodal spaces. However, the intermodal label transfers could be undermined
+by blindly transferring the labels of noisy texts to annotate images. To
+mitigate this problem, we present an intramodal label transfer process, which
+complements the intermodal label transfer by transferring the image labels
+instead when relevant text is absent from the source corpus. In addition, we
+generalize the inter-modal label transfer to zero-shot learning scenario where
+there are only text examples available to label unseen classes of images
+without any positive image examples. We evaluate our algorithm on an image
+classification task and show the effectiveness with respect to the other
+compared algorithms.
+"
+24506,"Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications","  Markov random fields area popular model for high-dimensional probability
+distributions. Over the years, many mathematical, statistical and algorithmic
+problems on them have been studied. Until recently, the only known algorithms
+for provably learning them relied on exhaustive search, correlation decay or
+various incoherence assumptions. Bresler gave an algorithm for learning general
+Ising models on bounded degree graphs. His approach was based on a structural
+result about mutual information in Ising models.
+Here we take a more conceptual approach to proving lower bounds on the mutual
+information through setting up an appropriate zero-sum game. Our proof
+generalizes well beyond Ising models, to arbitrary Markov random fields with
+higher order interactions. As an application, we obtain algorithms for learning
+Markov random fields on bounded degree graphs on $n$ nodes with $r$-order
+interactions in $n^r$ time and $\log n$ sample complexity. The sample
+complexity is information theoretically optimal up to the dependence on the
+maximum degree. The running time is nearly optimal under standard conjectures
+about the hardness of learning parity with noise.
+"
+24507,Statistical characterization of discrete conservative systems: The web map,"  We numerically study the two-dimensional, area preserving, web map. When the
+map is governed by ergodic behavior, it is, as expected, correctly described by
+Boltzmann-Gibbs statistics, based on the additive entropic functional
+$S_{BG}[p(x)] = -k\int dx\,p(x) \ln p(x)$. In contrast, possible ergodicity
+breakdown and transitory sticky dynamical behavior drag the map into the realm
+of generalized $q$-statistics, based on the nonadditive entropic functional
+$S_q[p(x)]=k\frac{1-\int dx\,[p(x)]^q}{q-1}$ ($q \in {\cal R}; S_1=S_{BG}$). We
+statistically describe the system (probability distribution of the sum of
+successive iterates, sensitivity to the initial condition, and entropy
+production per unit time) for typical values of the parameter that controls the
+ergodicity of the map. For small (large) values of the external parameter $K$,
+we observe $q$-Gaussian distributions with $q=1.935\dots$ (Gaussian
+distributions), like for the standard map. In contrast, for intermediate values
+of $K$, we observe a different scenario, due to the fractal structure of the
+trajectories embedded in the chaotic sea. Long-standing non-Gaussian
+distributions are characterized in terms of the kurtosis and the box-counting
+dimension of chaotic sea.
+"
+24508,Shuffled equi-n-squares,"  A formal n-square is the set of positions in an square matrix of size n. A
+shuffle of a formal n-square consists of independent rotations of each row and
+of each column. A key result turns out to be valid at least for n <= 34 and n =
+37: Each set of n positions can be mapped with one shuffle onto a transversal
+of the columns. We consider two applications to equi-n-squares (i.e.,
+n-matrices filled with digits 0, .., n - 1 in equal amounts).
+First, a shuffled equi-n-square can be seen as a torus with n colors and two
+orthogonal layers of n rings that can be rotated. Unlike Rubik's cube, each
+permutation of colored cells can be implemented with shuffles. An upper bound
+of $3*(-1)^{n-1} + 6n$ shuffles is derived from the key result.
+Our second application invokes column transversals and a process of
+indirection to produce theoretically unpredictable sequences of integers in
+shuffled equi-n-squares.
+Our proof of the key result involves optimizing position sets, averaging,
+computations based on number partitions, rotating subsets of a regular $n$-gon
+apart, and the use of cyclotomic polynomials. A few intermediate results need
+computer assistence. These efforts also generated a variety of (partially)
+unsolved problems. We selected eight of these for a brief discussion based on
+the available theoretical and computer evidence.
+"
+24509,A concordant scenario to explain FU Ori from deep centimeter and millimeter interferometric observations,"  We resolved FU Ori at 29-37 GHz using the JVLA with $\sim$0$''$.07
+resolution, and performed the complementary JVLA 8-10 GHz observations, the SMA
+224 GHz and 272 GHz observations, and compared with archival ALMA 346 GHz
+observations to obtain the SEDs. Our 8-10 GHz observations do not find evidence
+for the presence of thermal radio jets, and constrain the radio jet/wind flux
+to at least 90 times lower than the expected value from the previously reported
+bolometric luminosity-radio luminosity correlation. The emission at $>$29 GHz
+may be dominated by the two spatially unresolved sources, which are located
+immediately around FU Ori and its companion FU Ori S, respectively. Their
+deconvolved radii at 33 GHz are only a few au. The 8-346 GHz SEDs of FU Ori and
+FU Ori S cannot be fit by constant spectral indices (over frequency). The more
+sophisticated models for SEDs suggest that the $>$29 GHz emission is
+contributed by a combination of free-free emission from ionized gas, and
+thermal emission from optically thick and optically thin dust components. We
+hypothesize that dust in the innermost parts of the disks ($\lesssim$0.1 au)
+has been sublimated, and thus the disks are no more well shielded against the
+ionizing photons. The estimated overall gas and dust mass based on SED
+modeling, can be as high as a fraction of a solar mass, which is adequate for
+developing disk gravitational instability. Our present explanation for the
+observational data is that the massive inflow of gas and dust due to disk
+gravitational instability or interaction with a companion/intruder, was piled
+up at the few au scale due to the development of a deadzone with negligible
+ionization. The piled up material subsequently triggered the thermal and the
+MRI instabilities when the ionization fraction in the inner sub-au scale region
+exceeded a threshold value, leading to the high protostellar accretion rate.
+"
+24510,Offline Handwritten Recognition of Malayalam District Name - A Holistic Approach,"  Various machine learning methods for writer independent recognition of
+Malayalam handwritten district names are discussed in this paper. Data
+collected from 56 different writers are used for the experiments. The proposed
+work can be used for the recognition of district in the address written in
+Malayalam. Different methods for Dimensionality reduction are discussed.
+Features consider for the recognition are Histogram of Oriented Gradient
+descriptor, Number of Black Pixels in the upper half and lower half, length of
+image. Classifiers used in this work are Neural Network, SVM and RandomForest.
+"
+24511,Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation,"  Transfer learning using deep neural networks as feature extractors has become
+increasingly popular over the past few years. It allows to obtain
+state-of-the-art accuracy on datasets too small to train a deep neural network
+on its own, and it provides cutting edge descriptors that, combined with
+nonparametric learning methods, allow rapid and flexible deployment of
+performing solutions in computationally restricted settings. In this paper, we
+are interested in showing that the features extracted using deep neural
+networks have specific properties which can be used to improve accuracy of
+downstream nonparametric learning methods. Namely, we demonstrate that for some
+distributions where information is embedded in a few coordinates, segmenting
+feature vectors can lead to better accuracy. We show how this model can be
+applied to real datasets by performing experiments using three mainstream deep
+neural network feature extractors and four databases, in vision and audio.
+"
+24512,Estimation of Cusp Location of Stochastic Processes: a Survey,"  We present a review of some recent results on estimation of location
+parameter for several models of observations with cusp-type singularity at the
+change point. We suppose that the cusp-type models fit better to the real
+phenomena described usually by change point models. The list of models includes
+Gaussian, inhomogeneous Poisson, ergodic diffusion processes, time series and
+the classical case of i.i.d. observations. We describe the properties of the
+maximum likelihood and Bayes estimators under some asymptotic assumptions. The
+asymptotic efficiency of estimators are discussed as well and the results of
+some numerical simulations are presented. We provide some heuristic arguments
+which demonstrate the convergence of log-likelihood ratios in the models under
+consideration to the fractional Brownian motion.
+"
+24513,Mind the Gap: A Study in Global Development through Persistent Homology,"  The Gapminder project set out to use statistics to dispel simplistic notions
+about global development. In the same spirit, we use persistent homology, a
+technique from computational algebraic topology, to explore the relationship
+between country development and geography. For each country, four indicators,
+gross domestic product per capita; average life expectancy; infant mortality;
+and gross national income per capita, were used to quantify the development.
+Two analyses were performed. The first considers clusters of the countries
+based on these indicators, and the second uncovers cycles in the data when
+combined with geographic border structure. Our analysis is a multi-scale
+approach that reveals similarities and connections among countries at a variety
+of levels. We discover localized development patterns that are invisible in
+standard statistical methods.
+"
+24514,Access Strategy in Super WiFi Network Powered by Solar Energy Harvesting: A POMDP Method,"  The recently announced Super Wi-Fi Network proposal in United States is
+aiming to enable Internet access in a nation-wide area. As traditional
+cable-connected power supply system becomes impractical or costly for a wide
+range wireless network, new infrastructure deployment for Super Wi-Fi is
+required. The fast developing Energy Harvesting (EH) techniques receive global
+attentions for their potential of solving the above power supply problem. It is
+a critical issue, from the user's perspective, how to make efficient network
+selection and access strategies. Unlike traditional wireless networks, the
+battery charge state and tendency in EH based networks have to be taken into
+account when making network selection and access, which has not been well
+investigated. In this paper, we propose a practical and efficient framework for
+multiple base stations access strategy in an EH powered Super Wi-Fi network. We
+consider the access strategy from the user's perspective, who exploits downlink
+transmission opportunities from one base station. To formulate the problem, we
+used Partially Observable Markov Decision Process (POMDP) to model users'
+observations on the base stations' battery situation and decisions on the base
+station selection and access. Simulation results show that our methods are
+efficacious and significantly outperform the traditional widely used CSMA
+method.
+"
+24515,Euler totient of subfactor planar algebras,"  We extend the Euler's totient function (from arithmetic) to any irreducible
+subfactor planar algebra, using the Mobius function of its biprojection
+lattice, as Hall did for the finite groups. We prove that if it is nonzero then
+there is a minimal 2-box projection generating the identity biprojection. We
+explain a relation with a problem of K.S. Brown. As an application, we define
+the dual Euler totient of a finite group and we show that if it is nonzero then
+the group admits a faithful irreducible complex representation. We also get an
+analogous result at depth 2, involving the central biprojection lattice.
+"
+24516,Unitary Representations with Dirac cohomology: a finiteness result,"  Let $G$ be a connected complex simple Lie group, and let
+$\widehat{G}^{\mathrm{d}}$ be the set of all equivalence classes of irreducible
+unitary representations with non-vanishing Dirac cohomology. We show that
+$\widehat{G}^{\mathrm{d}}$ consists of two parts: finitely many scattered
+representations, and finitely many strings of representations. Moreover, the
+strings of $\widehat{G}^{\mathrm{d}}$ come from $\widehat{L}^{\mathrm{d}}$ via
+cohomological induction and they are all in the good range. Here $L$ runs over
+the $\theta$-stable proper Levi subgroups of $G$. It follows that figuring out
+$\widehat{G}^{\mathrm{d}}$ requires a finite calculation in total. As an
+application, we report a complete description of $\widehat{F}_4^{\mathrm{d}}$.
+"
+24517,Memory matrix theory of the dc resistivity of a disordered antiferromagnetic metal with an effective composite operator,"  We perform the calculation of the dc resistivity as a function of temperature
+of the ""strange-metal"" state that emerges in the vicinity of a
+spin-density-wave phase transition in the presence of weak disorder. This
+scenario is relevant to the phenomenology of many important correlated
+materials, such as, e.g., the pnictides, the heavy-fermion compounds and the
+cuprates. To accomplish this task, we implement the memory-matrix approach that
+allows the calculation of the transport coefficients of the model beyond the
+quasiparticle paradigm. Our computation is also inspired by the $\epsilon=3-d$
+expansion in a hot-spot model embedded in $d$-space dimensions recently put
+forth by Sur and Lee [Phys. Rev. B 91, 125136 (2015)], in which they find a new
+low-energy non-Fermi liquid fixed point that is perturbatively accessible near
+three dimensions. As a consequence, we are able to establish here the
+temperature and doping dependence of the electrical resistivity at intermediate
+temperatures of a two-dimensional disordered antiferromagnetic metallic model
+with a composite operator that couples the order-parameter fluctuations to the
+entire Fermi surface. We argue that our present theory provides a good basis in
+order to unify the experimental transport data, e.g., in the cuprates and the
+pnictide superconductors, within a wide range of doping regimes.
+"
+24518,Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes,"  In diffusion MRI (dMRI), a good sampling scheme is important for efficient
+acquisition and robust reconstruction. Diffusion weighted signal is normally
+acquired on single or multiple shells in q-space. Signal samples are typically
+distributed uniformly on different shells to make them invariant to the
+orientation of structures within tissue, or the laboratory coordinate frame.
+The Electrostatic Energy Minimization (EEM) method, originally proposed for
+single shell sampling scheme in dMRI, was recently generalized to multi-shell
+schemes, called Generalized EEM (GEEM). GEEM has been successfully used in the
+Human Connectome Project (HCP). However, EEM does not directly address the goal
+of optimal sampling, i.e., achieving large angular separation between sampling
+points. In this paper, we propose a more natural formulation, called Spherical
+Code (SC), to directly maximize the minimal angle between different samples in
+single or multiple shells. We consider not only continuous problems to design
+single or multiple shell sampling schemes, but also discrete problems to
+uniformly extract sub-sampled schemes from an existing single or multiple shell
+scheme, and to order samples in an existing scheme. We propose five algorithms
+to solve the above problems, including an incremental SC (ISC), a sophisticated
+greedy algorithm called Iterative Maximum Overlap Construction (IMOC), an 1-Opt
+greedy method, a Mixed Integer Linear Programming (MILP) method, and a
+Constrained Non-Linear Optimization (CNLO) method. To our knowledge, this is
+the first work to use the SC formulation for single or multiple shell sampling
+schemes in dMRI. Experimental results indicate that SC methods obtain larger
+angular separation and better rotational invariance than the state-of-the-art
+EEM and GEEM. The related codes and a tutorial have been released in DMRITool.
+"
+24519,Random Ensembles of Lattices from Generalized Reductions,"  We propose a general framework to study constructions of Euclidean lattices
+from linear codes over finite fields. In particular, we prove general
+conditions for an ensemble constructed using linear codes to contain dense
+lattices (i.e., with packing density comparable to the Minkowski-Hlawka lower
+bound). Specializing to number field lattices, we obtain a number of
+interesting corollaries - for instance, the best known packing density of ideal
+lattices, and an elementary coding-theoretic construction of asymptotically
+dense Hurwitz lattices. All results are algorithmically effective, in the sense
+that, for any dimension, a finite family containing dense lattices is
+exhibited. For suitable constructions based on Craig's lattices, this family is
+significantly smaller, in terms of alphabet-size, than previous ones in the
+literature.
+"
+24520,Guided Labeling using Convolutional Neural Networks,"  Over the last couple of years, deep learning and especially convolutional
+neural networks have become one of the work horses of computer vision. One
+limiting factor for the applicability of supervised deep learning to more areas
+is the need for large, manually labeled datasets. In this paper we propose an
+easy to implement method we call guided labeling, which automatically
+determines which samples from an unlabeled dataset should be labeled. We show
+that using this procedure, the amount of samples that need to be labeled is
+reduced considerably in comparison to labeling images arbitrarily.
+"
+24521,Epidemic Extinction Paths in Complex Networks,"  We study the extinction of long-lived epidemics on finite complex networks
+induced by intrinsic noise. Applying analytical techniques to the stochastic
+Susceptible-Infected-Susceptible model, we predict the distribution of large
+fluctuations, the most probable, or optimal path through a network that leads
+to a disease-free state from an endemic state, and the average extinction time
+in general configurations. Our predictions agree with Monte-Carlo simulations
+on several networks, including synthetic weighted and degree-distributed
+networks with degree correlations, and an empirical high school contact
+network. In addition, our approach quantifies characteristic scaling patterns
+for the optimal path and distribution of large fluctuations, both near and away
+from the epidemic threshold, in networks with heterogeneous eigenvector
+centrality and degree distributions.
+"
+24522,Tree sums of maximal connected spaces,"  A topology $\tau$ on a set $X$ is called maximal connected if it is
+connected, but no strictly finer topology $\tau^* > \tau$ is connected. We
+consider a construction of so-called tree sums of topological spaces, and we
+show how this construction preserves maximal connectedness and also related
+properties of strong connectedness and essential connectedness.
+We also recall the characterization of finitely generated maximal connected
+spaces and reformulate it in the language of specialization preorder and
+graphs, from which it is clear that finitely generated maximal connected spaces
+are precisely $T_\frac{1}{2}$ tree sums of copies of the Sierpiński space.
+"
+24523,Pseudo-Prikry sequences,"  We generalize results of Gitik, Dzamonja-Shelah, and Magidor-Sinapova on the
+existence of pseudo-Prikry sequences, which are sequences that approximate the
+behavior of the generic objects introduced by Prikry-type forcings, in outer
+models of set theory. Such sequences play an important role in the study of
+singular cardinal combinatorics by placing restrictions on the type of behavior
+that can consistently be obtained in outer models. In addition, we provide
+results about the existence of diagonal pseudo-Prikry sequences, which
+approximate the behavior of the generic objects introduced by diagonal
+Prikry-type forcings. Our proof techniques are substantially different from
+those of previous results and rely on an analysis of PCF-theoretic objects in
+the outer model.
+"
+24524,Existence and Uniqueness of Weak Homotopy Moment Maps,"  In this paper we show that the classical results on the existence and
+uniqueness of moment maps in symplectic geometry generalize directly to weak
+homotopy moment maps in multisym- plectic geometry. In particular, we show that
+their existence and uniqueness is governed by a Lie algebra cohomology complex
+which reduces to the Chevalley-Eilenberg complex in the symplectic setup
+"
+24525,Pixel Deconvolutional Networks,"  Deconvolutional layers have been widely used in a variety of deep models for
+up-sampling, including encoder-decoder networks for semantic segmentation and
+deep generative models for unsupervised learning. One of the key limitations of
+deconvolutional operations is that they result in the so-called checkerboard
+problem. This is caused by the fact that no direct relationship exists among
+adjacent pixels on the output feature map. To address this problem, we propose
+the pixel deconvolutional layer (PixelDCL) to establish direct relationships
+among adjacent pixels on the up-sampled feature map. Our method is based on a
+fresh interpretation of the regular deconvolution operation. The resulting
+PixelDCL can be used to replace any deconvolutional layer in a plug-and-play
+manner without compromising the fully trainable capabilities of original
+models. The proposed PixelDCL may result in slight decrease in efficiency, but
+this can be overcome by an implementation trick. Experimental results on
+semantic segmentation demonstrate that PixelDCL can consider spatial features
+such as edges and shapes and yields more accurate segmentation outputs than
+deconvolutional layers. When used in image generation tasks, our PixelDCL can
+largely overcome the checkerboard problem suffered by regular deconvolution
+operations.
+"
+24526,On strongly indefinite systems involving fractional elliptic operators,"  In this paper we discuss the existence and regularity of solutions of
+strongly indefinite systems involving fractional elliptic operators on a smooth
+bounded domain $\Omega$ in $\R^n$.
+"
+24527,Large Magellanic Cloud Near-Infrared Synoptic Survey. IV. Leavitt Laws for Type II Cepheid Variables,"  We present time-series observations of Population \rom{2} Cepheids in the
+Large Magellanic Cloud at near-infrared ($JHK_s$) wavelengths. Our sample
+consists of 81 variables with accurate periods and optical ($VI$) magnitudes
+from the OGLE survey, covering various subtypes of pulsators (BL Herculis, W
+Virginis and RV Tauri). We generate light curve templates using high-quality
+$I$-band data in the LMC from OGLE and $K_s$-band data in the Galactic Bulge
+from VVV and use them to obtain robust mean magnitudes. We derive
+Period-Luminosity (P-L) relations in the near-infrared and Period-Wesenheit
+(P-W) relations by combining optical and near-infrared data. Our P-L and P-W
+relations are consistent with published work when excluding long-period RV
+Tauris. We find that Pop II Cepheids and RR Lyraes follow the same P-L
+relations in the LMC. Therefore, we use trigonometric parallax from the {\it
+Gaia DR1} for VY~Pyx and the {\it Hubble Space Telescope} parallaxes for
+$k$~Pav and 5 RR Lyrae variables to obtain an absolute calibration of the
+Galactic $K_s$-band P-L relation, resulting in a distance modulus to the LMC of
+$\mu_{\rm LMC} = 18.54\pm0.08$~mag. We update the mean magnitudes of Pop~II
+Cepheids in Galactic globular clusters using our light curve templates and
+obtain distance estimates to those systems, anchored to a precise late-type
+eclipsing binary distance to the LMC. We find the distances to these globular
+clusters based on Pop~II Cepheids are consistent (within $2\sigma$) with
+estimates based on the $M_V-[\rm{Fe}/\rm{H}]$ relation for horizontal branch
+stars.
+"
+24528,The role of spin and angular momentum in strong-field ionization,"  The spin polarization of electrons from multiphoton ionization of Xe by 395
+nm circularly polarized laser pulses at $6\cdot10^{13}$ W/cm$^2$ has been
+measured. At this photon energy of 3.14 eV the above threshold ionization peaks
+connected to Xe$^+$ ions in the ground state ($J=3/2$, ionization potential
+$I_p=12.1$ eV) and the first exicted state ($J=1/2$, $I_p=13.4$ eV) are clearly
+separated in the electron energy distribution. These two combs of ATI peaks
+show opposite spin polarizations. The magnitude of the spin polarization is a
+factor of two higher for the $J=1/2$ than for the $J=3/2$ final ionic state. In
+turn the data show that the ionization probability is strongly dependent on the
+sign of the magnetic quantum number.
+"
+24529,"Liftings, Young measures, and lower semicontinuity","  This work introduces liftings and their associated Young measures as new
+tools to study the asymptotic behaviour of sequences of pairs $(u_j,Du_j)j$ for
+$(u_j)_j \in \mathrm{BV}(\Omega;\mathbb{R}^m)$ under weak* convergence. These
+tools are then used to prove an integral representation theorem for the
+relaxation of the functional
+\[ \mathcal{F}\colon u\to\int_\Omega f(x,u(x),\nabla u(x))
+\;\mathrm{dx},\quad u\in\mathrm{W}^{1,1}({\Omega};\mathbb{R}^m),\quad
+{\Omega}\in\mathbb{R}^d\text{ open}, \] to the space $\mathrm{BV}(\Omega;
+\mathbb{R}^m)$. Lower semicontinuity results of this type were first obtained
+by Fonseca and Müller [Arch. Ration. Mech. Anal. 123 (1993), 1-49] and later
+improved by a number of authors, but our theorem is valid under more natural,
+essentially optimal, hypotheses than those currently present in the literature,
+requiring principally that $f$ be Carathéodory and quasiconvex in the final
+variable. The key idea is that liftings provide the right way of localising
+$\mathcal{F}$ in the $x$ and $u$ variables simultaneously under weak*
+convergence. As a consequence, we are able to implement an optimal
+measure-theoretic blow-up procedure.
+"
+24530,Central intersections of element centralisers,"  In 1970 R. Schmidt gave a structural classification for CA-groups. In this
+paper we consider a condition upon the intersection of element centralisers
+which turns out to be equivalent to the definition of a CA-group. We then
+weaken which centralisers we chose to intersect and structurally classify this
+new family of groups. Furthermore we apply a similar weakening to the class of
+F-groups introduced by Ito in 1953 and classified by Rebmann in 1971.
+"
+24531,Graph networks as learnable physics engines for inference and control,"  Understanding and interacting with everyday physical scenes requires rich
+knowledge about the structure of the world, represented either implicitly in a
+value or policy function, or explicitly in a transition model. Here we
+introduce a new class of learnable models--based on graph networks--which
+implement an inductive bias for object- and relation-centric representations of
+complex, dynamical systems. Our results show that as a forward model, our
+approach supports accurate predictions from real and simulated data, and
+surprisingly strong and efficient generalization, across eight distinct
+physical systems which we varied parametrically and structurally. We also found
+that our inference model can perform system identification. Our models are also
+differentiable, and support online planning via gradient-based trajectory
+optimization, as well as offline policy optimization. Our framework offers new
+opportunities for harnessing and exploiting rich knowledge about the world, and
+takes a key step toward building machines with more human-like representations
+of the world.
+"
+24532,Heterogeneity Preserving Upscaling for Heat Transport in Fractured Geothermal Reservoirs,"  In simulation of fluid injection in fractured geothermal reservoirs, the
+characteristics of the physical processes are severely affected by the local
+occurence of connected fractures. To resolve these structurally dominated
+processes, there is a need to develop discretization strategies that also limit
+computational effort. In this paper we present an upscaling methodology for
+geothermal heat transport with fractures represented explicitly in the
+computational grid. The heat transport is modeled by an advection-conduction
+equation for the temperature, and solved on a highly irregular coarse grid that
+preserves the fracture heterogeneity. The upscaling is based on different
+strategies for the advective term and the conductive term, respectively. The
+coarse scale advective term is constructed from sums of fine scale fluxes,
+whereas the coarse scale conductive term is constructed based on numerically
+computed basis functions. The method naturally incorporates a coupling between
+the matrix and the fractures via the discretization, so that explicit transfer
+terms that couple solution variables in the fractures and the matrix are
+avoided. Numerical results show that the upscaling methodology performs well,
+in particular for large upscaling ratios, and that it is applicable also to
+highly complex fracture networks.
+"
+24533,"WiFi based trajectory alignment, calibration and easy site survey using smart phones and foot-mounted IMUs","  Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts
+and unknown initial states in real applications, which renders the estimated
+trajectories inaccurate and not obtained in a well defined coordinate system
+for matching trajectories of different users. In this paper, an approach
+adopting received signal strength (RSS) measurements for Wifi access points
+(APs) are proposed to align and calibrate the trajectories estimated from foot
+mounted inertial measurement units (IMUs). A crowd-sourced radio map (RM) can
+be built subsequently and can be used for fingerprinting based Wifi indoor
+positioning (FWIP). The foundation of the proposed approach is graph based
+simultaneously localization and mapping (SLAM). The nodes in the graph denote
+users poses and the edges denote the pairwise constrains between the nodes. The
+constrains are derived from: (1) inertial estimated trajectories; (2) vicinity
+in the RSS space. With these constrains, an error functions is defined. By
+minimizing the error function, the graph is optimized and the
+aligned/calibrated trajectories along with the RM are acquired. The
+experimental results have corroborated the effectiveness of the approach for
+trajectory alignment, calibration as well as RM construction.
+"
+24534,A non-stable C*-algebra with an elementary essential composition series,"  A C*-algebra $A$ is said to be stable if it is isomorphic to $A \otimes
+K(\ell_2)$. Hjelmborg and R\o rdam have shown that countable inductive limits
+of separable stable C*-algebras are stable. We show that this is no longer true
+in the nonseparable context even for the most natural case of an uncountable
+inductive limit of an increasing chain of separable stable and AF ideals: we
+construct a GCR, AF (in fact, scattered) subalgebra $A$ of $B(\ell_2)$, which
+is the inductive limit of length $\omega_1$ of its separable stable ideals
+$I_\alpha$ ($\alpha<\omega_1$) satisfying $I_{\alpha+1}/I_\alpha\cong
+K(\ell_2)$ for each $\alpha<\omega_1$, while $A$ is not stable.
+The sequence $(I_\alpha)_{\alpha\leq\omega_1}$ is the GCR composition series
+of $A$ which in this case coincides with the Cantor-Bendixson composition
+series as a scattered C*-algebra. $A$ has the property that all of its proper
+two-sided ideals are listed as $I_\alpha$s for some $\alpha<\omega_1$ and
+therefore the family of stable ideals of $A$ has no maximal element.
+By taking $A'=A\otimes K(\ell_2)$ we obtain a stable C*-algebra with
+analogous composition series $(J_\alpha)_{\alpha<\omega_1}$ whose ideals
+$J_\alpha$s are isomorphic to $I_\alpha$s for each $\alpha<\omega_1$. In
+particular, there are nonisomorphic scattered C*-algebras whose GCR composition
+series $(I_\alpha)_{\alpha\leq\omega_1}$ satisfy $I_{\alpha+1}/I_\alpha\cong
+K(\ell_2)$ for all $\alpha<\omega_1$, for which the composition series differ
+first at $\alpha=\omega_1$.
+"
+24535,Extreme wave phenomena in down-stream running modulated waves,"  Modulational, Benjamin-Feir, instability is studied for the down-stream
+evolution of surface gravity waves. An explicit solution, the soliton on finite
+background, of the NLS equation in physical space is used to study various
+phenomena in detail. It is shown that for sufficiently long modulation lengths,
+at a unique position where the largest waves appear, phase singularities are
+present in the time signal. These singularities are related to wave
+dislocations and lead to a discrimination between successive `extreme' waves
+and much smaller intermittent waves. Energy flow in opposite directions through
+successive dislocations at which waves merge and split, causes the large
+amplitude difference. The envelope of the time signal at that point is shown to
+have a simple phase plane representation, and will be described by a symmetry
+breaking unfolding of the steady state solutions of NLS. The results are used
+together with the maximal temporal amplitude MTA, to design a strategy for the
+generation of extreme (freak, rogue) waves in hydrodynamic laboratories.
+"
+24536,Spatial statistics of single-quantum detection,"  In a single-particle detection experiment, a wavefront impinges on a detector
+but observers only see a point response. The extent of the wavefront becomes
+evident only in statistical accumulation of many independent detections, with
+probability given by the Born rule. Drawing on concepts from quantum optics, we
+analyze a simple model to reverse-engineer how this behavior can come about in
+terms of wave mechanics alone without a measurement axiom. The model detector
+consists of many molecules, each of which can be resonantly excited by the
+incoming particle and then emit a detection signature (e.g., localized flash of
+light). Different molecules have different resonant energies because local
+conditions (proximity of other molecules, Doppler shifts, etc.) vary. The
+detector is thus a quasi-continuum, and the incoming particle preferentially
+excites the molecule that it matches most closely in energy. (In actuality,
+molecules can be so numerous that many could closely match the incoming
+particle in energy; but in that case only one will be the first, and there will
+be nothing left for the others by the time the first match resonates and then
+emits. The model does not explicitly take into account the temporal advance of
+the particle wave packet through the detector medium.) The excited molecule can
+emit a detection signature, but that process competes with fluctuation-driven
+dephasing. We estimate the probability that a given molecule is resonantly
+excited, and we also estimate the probability that a detection signature is
+produced before being overwhelmed by fluctuations in the detector medium. The
+product of these two probabilities is proportional to the absolute-square of
+the incoming wavefunction at the molecule in question, i.e. the Born rule. We
+discuss ways to probe these mechanisms experimentally, and check the model with
+numbers from a neutron interference experiment.
+"
+24537,Trisections of 3-manifold bundles over $S^1$,"  Let $X$ be a bundle over $S^1$ with fiber a 3--manifold $M$ and with
+monodromy $\varphi$. Gay and Kirby showed that if $\varphi$ fixes a genus $g$
+Heegaard splitting of $M$ then $X$ has a genus $6g+1$ trisection. Genus $3g+1$
+trisections have been found in certain special cases, such as the case where
+$\varphi$ is trivial, and it is known that trisections of genus lower than
+$3g+1$ cannot exist in general. We generalize these results to prove that there
+exists a trisection of genus $3g+1$ whenever $\varphi$ fixes a genus $g$
+Heegaard surface of $M$. This means that $\varphi$ can be nontrivial, and can
+preserve or switch the two handlebodies of the Heegaard splitting. We
+additionally describe an algorithm to draw a diagram for such a trisection
+given a Heegaard diagram for $M$ and a description of $\varphi$.
+"
+24538,"Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective","  The free energy is a key quantity of interest in Ising models, but
+unfortunately, computing it in general is computationally intractable. Two
+popular (variational) approximation schemes for estimating the free energy of
+general Ising models (in particular, even in regimes where correlation decay
+does not hold) are: (i) the mean-field approximation with roots in statistical
+physics, which estimates the free energy from below, and (ii) hierarchies of
+convex relaxations with roots in theoretical computer science, which estimate
+the free energy from above. We show, surprisingly, that the tight regime for
+both methods to compute the free energy to leading order is identical.
+More precisely, we show that the mean-field approximation is within
+$O((n\|J\|_{F})^{2/3})$ of the free energy, where $\|J\|_F$ denotes the
+Frobenius norm of the interaction matrix of the Ising model. This
+simultaneously subsumes both the breakthrough work of Basak and Mukherjee, who
+showed the tight result that the mean-field approximation is within $o(n)$
+whenever $\|J\|_{F} = o(\sqrt{n})$, as well as the work of Jain, Koehler, and
+Mossel, who gave the previously best known non-asymptotic bound of
+$O((n\|J\|_{F})^{2/3}\log^{1/3}(n\|J\|_{F}))$. We give a simple, algorithmic
+proof of this result using a convex relaxation proposed by Risteski based on
+the Sherali-Adams hierarchy, automatically giving sub-exponential time
+approximation schemes for the free energy in this entire regime. Our
+algorithmic result is tight under Gap-ETH.
+We furthermore combine our techniques with spin glass theory to prove (in a
+strong sense) the optimality of correlation rounding, refuting a recent
+conjecture of Allen, O'Donnell, and Zhou. Finally, we give the tight
+generalization of all of these results to $k$-MRFs, capturing as a special case
+previous work on approximating MAX-$k$-CSP.
+"
+24539,"Self-Sustainability of Energy Harvesting Systems: Concept, Analysis, and Design","  Ambient energy harvesting is touted as a low cost solution to prolong the
+life of low-powered devices, reduce the carbon footprint, and make the system
+self-sustainable. Most research to date have focused either on the physical
+aspects of energy conversion process or on optimal consumption policy of the
+harvested energy at the system level. However, although intuitively understood,
+to the best of our knowledge, the idea of self-sustainability is yet to be made
+precise and studied as a performance metric. In this paper, we provide a
+mathematical definition of the concept of self-sustainability of an energy
+harvesting system, based on the complementary idea of eventual outage. In
+particular, we analyze the harvest-store-consume system with infinite battery
+capacity, stochastic energy arrivals, and fixed energy consumption rate. Using
+the random walk theory, we identify the necessary condition for the system to
+be self-sustainable. General formulas are given for the self-sustainability
+probability in the form of integral equations. Since these integral equations
+are difficult to solve analytically, an exponential upper bound for eventual
+outage probability is given using martingales. This bound guarantees that the
+eventual outage probability can be made arbitrarily small simply by increasing
+the initial battery energy. We also give an asymptotic formula for eventual
+outage. For the special case when the energy arrival follows a Poisson process,
+we are able to find the exact formulas for the eventual outage probability. We
+also show that the harvest-store-consume system is mathematically equivalent to
+a $GI/G/1$ queueing system, which allows us to easily find the outage
+probability, in case the necessary condition for self-sustainability is
+violated. Monte-Carlo simulations verify our analysis.
+"
+24540,Addressing the statistical mechanics of planet orbits in the solar system,"  The chaotic nature of planet dynamics in the solar system suggests the
+relevance of a statistical approach to planetary orbits. In such a statistical
+description, the time-dependent position and velocity of the planets are
+replaced by the probability density function (PDF) of their orbital elements.
+It is quite natural to set up this kind of approach in the framework of
+statistical mechanics. In the present paper I focus on the collisionless
+excitation of eccentricities and inclinations by gravitational interactions in
+a planetary system, the prototype of such a dynamics being the future planet
+trajectories in the solar system. I thus address the statistical mechanics of
+the planetary orbits in the solar system and try to reproduce the PDFs
+numerically constructed by Laskar (2008). I show that the microcanonical
+ensemble of the Laplace-Lagrange theory accurately reproduce the statistics of
+the giant planet orbits. To model the inner planets I then investigate the
+ansatz of equiprobability in the phase space constrained by the secular
+integrals of motion. The eccentricity and inclination PDFs of Earth and Venus
+are reproduced with no free parameters. Within the limitations of a stationary
+model, the predictions also show a reasonable agreement with Mars PDFs and that
+of Mercury inclination. The eccentricity of Mercury demands in contrast a
+deeper analysis. I finally revisit Laskar's random walk approach to the time
+dependence of the inner planet PDFs. Such a statistical theory could be
+combined with direct numerical simulations of planet trajectories in the
+context of planet formation, which is likely to be a chaotic process.
+"
+24541,Better estimates from binned income data: Interpolated CDFs and mean-matching,"  Researchers often estimate income statistics from summaries that report the
+number of incomes in bins such as \$0-10,000, \$10,001-20,000,...,\$200,000+.
+Some analysts assign incomes to bin midpoints, but this treats income as
+discrete. Other analysts fit a continuous parametric distribution, but the
+distribution may not fit well.
+We fit nonparametric continuous distributions that reproduce the bin counts
+perfectly by interpolating the cumulative distribution function (CDF). We also
+show how both midpoints and interpolated CDFs can be constrained to reproduce
+the mean of income when it is known.
+We compare the methods' accuracy in estimating the Gini coefficients of all
+3,221 US counties. Fitting parametric distributions is very slow. Fitting
+interpolated CDFs is much faster and slightly more accurate. Both interpolated
+CDFs and midpoints give dramatically better estimates if constrained to match a
+known mean.
+We have implemented interpolated CDFs in the binsmooth package for R. We have
+implemented the midpoint method in the rpme command for Stata. Both
+implementations can be constrained to match a known mean.
+"
+24542,QFUN: Towards Machine Learning in QBF,"  This paper reports on the QBF solver QFUN that has won the non-CNF track in
+the recent QBF evaluation. The solver is motivated by the fact that it is easy
+to construct Quantified Boolean Formulas (QBFs) with short winning strategies
+(Skolem/Herbrand functions) but are hard to solve by nowadays solvers. This
+paper argues that a solver benefits from generalizing a set of individual wins
+into a strategy. This idea is realized on top of the competitive RAReQS
+algorithm by utilizing machine learning. The results of the implemented
+prototype are highly encouraging.
+"
+24543,An Examination of Rotational Effects on Large Wind Turbine Blades,"  CFD studies have been performed to demonstrate the effects of rotation on
+large wind turbine blades. The 10 MW blade of the Advanced Aerodynamic Tools
+for Large Rotors (AVATAR) project was selected in the present investigation. In
+order to demonstrate the rotational augmentation, lift coefficients of the
+blade sections were compared with the two-dimensional simulations at consistent
+inflow conditions. The increase of the lift coefficient was observed at the
+inboard section of the blade, showing the effect of rotation. A significant
+delay of separation point as well as a massive reduction of the wake size occur
+at the inboard blade section. The results of the present manuscript provide
+more information of the rotational augmentation occurring in the near hub
+region of wind turbine blades.
+"
+24544,Unperturbed: spectral analysis beyond Davis-Kahan,"  Classical matrix perturbation results, such as Weyl's theorem for eigenvalues
+and the Davis-Kahan theorem for eigenvectors, are general purpose. These
+classical bounds are tight in the worst case, but in many settings sub-optimal
+in the typical case. In this paper, we present perturbation bounds which
+consider the nature of the perturbation and its interaction with the
+unperturbed structure in order to obtain significant improvements over the
+classical theory in many scenarios, such as when the perturbation is random. We
+demonstrate the utility of these new results by analyzing perturbations in the
+stochastic blockmodel where we derive much tighter bounds than provided by the
+classical theory. We use our new perturbation theory to show that a very simple
+and natural clustering algorithm -- whose analysis was difficult using the
+classical tools -- nevertheless recovers the communities of the blockmodel
+exactly even in very sparse graphs.
+"
+24545,A random matrix analysis and improvement of semi-supervised learning for large dimensional data,"  This article provides an original understanding of the behavior of a class of
+graph-oriented semi-supervised learning algorithms in the limit of large and
+numerous data. It is demonstrated that the intuition at the root of these
+methods collapses in this limit and that, as a result, most of them become
+inconsistent. Corrective measures and a new data-driven parametrization scheme
+are proposed along with a theoretical analysis of the asymptotic performances
+of the resulting approach. A surprisingly close behavior between theoretical
+performances on Gaussian mixture models and on real datasets is also
+illustrated throughout the article, thereby suggesting the importance of the
+proposed analysis for dealing with practical data. As a result, significant
+performance gains are observed on practical data classification using the
+proposed parametrization.
+"
+24546,A Short Note on Proximity-based Scoring of Documents with Multiple Fields,"  The BM25 ranking function is one of the most well known query relevance
+document scoring functions and many variations of it are proposed. The BM25F
+function is one of its adaptations designed for modeling documents with
+multiple fields. The Expanded Span method extends a BM25-like function by
+taking into considerations of the proximity between term occurrences. In this
+note, we combine these two variations into one scoring method in view of
+proximity-based scoring of documents with multiple fields.
+"
+24547,Dark-ages reionization and galaxy formation simulation XI: Clustering and halo masses of high redshift galaxies,"  We investigate the clustering properties of Lyman-break galaxies (LBGs) at
+$z\sim6$ - $8$. Using the semi-analytical model {\scshape Meraxes} constructed
+as part of the Dark-ages Reionization And Galaxy-formation Observables from
+Numerical Simulation (DRAGONS) project, we predict the angular correlation
+function (ACF) of LBGs at $z\sim6$ - $8$. Overall, we find that the predicted
+ACFs are in good agreement with recent measurements at $z\sim 6$ and $z\sim
+7.2$ from observations consisting of the Hubble eXtreme Deep Field (XDF), the
+Hubble Ultra-Deep Field (HUDF) and Cosmic Assembly Near-infrared Deep
+Extragalactic Legacy Survey (CANDELS) field. We confirm the dependence of
+clustering on luminosity, with more massive dark matter haloes hosting brighter
+galaxies, remains valid at high redshift. The predicted galaxy bias at fixed
+luminosity is found to increase with redshift, in agreement with observations.
+We find that LBGs of magnitude $M_{\rm AB(1600)} < -19.4$ at $6\lesssim z
+\lesssim 8$ reside in dark matter haloes of mean mass $\sim 10^{11.0}$-
+$10^{11.5} M_{\rm \odot}$, and this dark matter halo mass does not evolve
+significantly during reionisation.
+"
+24548,Explanation and exact formula of Zipfs law evaluated from rank-share combinatorics,"  This work proves that ranks and shares are statistically dependent on one
+another, based on simple combinatorics. It presents a formula for rank-share
+distribution and illustrates that Zipfs law, is descended from expected values
+of various ranks in the new distribution. All conclusions, formulas and charts
+presented here were tested against publicly available statistical data in
+different areas. The correlation coefficient between the calculated values and
+statistical numbers provided by Bureau of Labor Statistics was 0.99899.
+Monte-Carlo simulations were performed as additional evidence.
+"
+24549,Sample average approximation with heavier tails I: non-asymptotic bounds with weak assumptions and stochastic constraints,"  We derive exponential finite-sample nonasymptotic deviation inequalities for
+the sample average approximation (SAA) estimator's near-optimal solution set
+and optimal value. In that respect, we give three contributions. First, our
+bounds do not require \emph{sub-Gaussian} assumptions as in previous literature
+of stochastic optimization (SO). Instead, we just assume random Hölder
+continuity and a \emph{heavy-tailed} distribution with finite 2nd moments, a
+framework more suited for risk-averse portfolio optimization. Second, we derive
+new deviation inequalities for SO problems with \emph{expected-valued
+stochastic constraints} which guarantee \emph{joint} near feasibility and
+optimality in terms of the original problem. Unlike previous works, we do not
+assume a metric regular (MR) \emph{solution set}, strong growth conditions on
+the objective nor an indirect problem reformulation (such as penalization or
+first order conditions, both of which are often necessary but not sufficient
+conditions). Instead, we just assume a MR \emph{feasible set}, making our
+analysis general enough for many classes of problems. A finite-sample near
+feasibility and optimality deviation is established for MR sets which are
+nonconvex or which are convex and \emph{non}-strictly feasible. For strictly
+feasible convex sets, we obtain \emph{finite-sample} \emph{exact} feasibility
+and near optimality guarantees. The feasible set's MR constant is an additional
+condition number. For convex sets, we present \emph{localized} feasibility
+guarantees in terms of smaller metric entropies. Third, we obtain a general
+uniform concentration inequality for heavy-tailed Hölder continuous functions
+using empirical process theory. This is the main tool in our analysis but it is
+also a result of independent interest.
+"
+24550,A General Approximation Method for Bicriteria Minimization Problems,"  We present a general technique for approximating bicriteria minimization
+problems with positive-valued, polynomially computable objective functions.
+Given $0<\epsilon\leq1$ and a polynomial-time $\alpha$-approximation algorithm
+for the corresponding weighted sum problem, we show how to obtain a bicriteria
+$(\alpha\cdot(1+2\epsilon),\alpha\cdot(1+\frac{2}{\epsilon}))$-approximation
+algorithm for the budget-constrained problem whose running time is polynomial
+in the encoding length of the input and linear in $\frac{1}{\epsilon}$.
+Moreover, we show that our method can be extended to compute an
+$(\alpha\cdot(1+2\epsilon),\alpha\cdot(1+\frac{2}{\epsilon}))$-approximate
+Pareto curve under the same assumptions. Our technique applies to many
+minimization problems to which most previous algorithms for computing
+approximate Pareto curves cannot be applied because the corresponding gap
+problem is $\textsf{NP}$-hard to solve. For maximization problems, however, we
+show that approximation results similar to the ones presented here for
+minimization problems are impossible to obtain in polynomial time unless
+$\textsf{P}=\textsf{NP}$.
+"
+24551,Reconstruction of three-dimensional porous media using generative adversarial neural networks,"  To evaluate the variability of multi-phase flow properties of porous media at
+the pore scale, it is necessary to acquire a number of representative samples
+of the void-solid structure. While modern x-ray computer tomography has made it
+possible to extract three-dimensional images of the pore space, assessment of
+the variability in the inherent material properties is often experimentally not
+feasible. We present a novel method to reconstruct the solid-void structure of
+porous media by applying a generative neural network that allows an implicit
+description of the probability distribution represented by three-dimensional
+image datasets. We show, by using an adversarial learning approach for neural
+networks, that this method of unsupervised learning is able to generate
+representative samples of porous media that honor their statistics. We
+successfully compare measures of pore morphology, such as the Euler
+characteristic, two-point statistics and directional single-phase permeability
+of synthetic realizations with the calculated properties of a bead pack, Berea
+sandstone, and Ketton limestone. Results show that GANs can be used to
+reconstruct high-resolution three-dimensional images of porous media at
+different scales that are representative of the morphology of the images used
+to train the neural network. The fully convolutional nature of the trained
+neural network allows the generation of large samples while maintaining
+computational efficiency. Compared to classical stochastic methods of image
+reconstruction, the implicit representation of the learned data distribution
+can be stored and reused to generate multiple realizations of the pore
+structure very rapidly.
+"
+24552,Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network,"  A deep learning network was used to predict future blood glucose levels, as
+this can permit diabetes patients to take action before imminent hyperglycaemia
+and hypoglycaemia. A sequential model with one long-short-term memory (LSTM)
+layer, one bidirectional LSTM layer and several fully connected layers was used
+to predict blood glucose levels for different prediction horizons. The method
+was trained and tested on 26 datasets from 20 real patients. The proposed
+network outperforms the baseline methods in terms of all evaluation criteria.
+"
+24553,"""Dave...I can assure you...that it's going to be all right..."" -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships","  People who design, use, and are affected by autonomous artificially
+intelligent agents want to be able to \emph{trust} such agents -- that is, to
+know that these agents will perform correctly, to understand the reasoning
+behind their actions, and to know how to use them appropriately. Many
+techniques have been devised to assess and influence human trust in
+artificially intelligent agents. However, these approaches are typically ad
+hoc, and have not been formally related to each other or to formal trust
+models. This paper presents a survey of \emph{algorithmic assurances}, i.e.
+programmed components of agent operation that are expressly designed to
+calibrate user trust in artificially intelligent agents. Algorithmic assurances
+are first formally defined and classified from the perspective of formally
+modeled human-artificially intelligent agent trust relationships. Building on
+these definitions, a synthesis of research across communities such as machine
+learning, human-computer interaction, robotics, e-commerce, and others reveals
+that assurance algorithms naturally fall along a spectrum in terms of their
+impact on an agent's core functionality, with seven notable classes ranging
+from integral assurances (which impact an agent's core functionality) to
+supplemental assurances (which have no direct effect on agent performance).
+Common approaches within each of these classes are identified and discussed;
+benefits and drawbacks of different approaches are also investigated.
+"
+24554,Interpretable Explanations of Black Boxes by Meaningful Perturbation,"  As machine learning algorithms are increasingly applied to high impact yet
+high risk tasks, such as medical diagnosis or autonomous driving, it is
+critical that researchers can explain how such algorithms arrived at their
+predictions. In recent years, a number of image saliency methods have been
+developed to summarize where highly complex neural networks ""look"" in an image
+for evidence for their predictions. However, these techniques are limited by
+their heuristic nature and architectural constraints. In this paper, we make
+two main contributions: First, we propose a general framework for learning
+different kinds of explanations for any black box algorithm. Second, we
+specialise the framework to find the part of an image most responsible for a
+classifier decision. Unlike previous works, our method is model-agnostic and
+testable because it is grounded in explicit and interpretable image
+perturbations.
+"
+24555,The Prandtl Plus Scaling Failure and its Remedy,"  The Prandtl Plus scaling parameters are reexamined theoretically. The flow
+governing equations approach is used to examine similarity issues of the inner
+region of wall-bounded turbulent flows as well as laminar flow cases. It is
+found that the Prandtl Plus parameters are in fact similarity scaling
+parameters for laminar sink flow but NOT the more general laminar Falkner-Skan
+boundary layer flows. For turbulent boundary layer flows along a wall, it is
+found that only turbulent sink flows show similarity with the Prandtl Plus
+scaling parameters. This is diametrically opposed to the accepted notion that
+the Prandtl Plus parameters work for every wall-bounded turbulent flow. To
+correct the problem with the Prandtl Plus parameters, we introduce a new set of
+scaling parameters that work for the Falkner-Skan boundary layer flows and
+satisfy the relevant part of the flow governing equations approach to
+similarity.
+"
+24556,Multi-messenger Astronomy: a Bayesian approach,"  After the discovery of the gravitational waves and the observation of
+neutrinos of cosmic origin, we have entered a new and exciting era where cosmic
+rays, neutrinos, photons and gravitational waves will be used simultaneously to
+study the highest energy phenomena in the Universe. Here we present a fully
+Bayesian approach to the challenge of combining and comparing the wealth of
+measurements from existing and upcoming experimental facilities. We discuss the
+procedure from a theoretical point of view and using simulations, we also
+demonstrate the feasibility of the method by incorporating the use of
+information provided by different theoretical models and different experimental
+measurements.
+"
+24557,Coding for Distributed Fog Computing,"  Redundancy is abundant in Fog networks (i.e., many computing and storage
+points) and grows linearly with network size. We demonstrate the
+transformational role of coding in Fog computing for leveraging such redundancy
+to substantially reduce the bandwidth consumption and latency of computing. In
+particular, we discuss two recently proposed coding concepts, namely Minimum
+Bandwidth Codes and Minimum Latency Codes, and illustrate their impacts in Fog
+computing. We also review a unified coding framework that includes the above
+two coding techniques as special cases, and enables a tradeoff between
+computation latency and communication load to optimize system performance. At
+the end, we will discuss several open problems and future research directions.
+"
+24558,Light breeze in the local Universe,"  We analyze a complete spectroscopic sample of galaxies ($\sim$600,000 ) drawn
+from Sloan Digital Sky Survey (SDSS, DR7) to look for evidence of galactic
+winds in the local Universe. We focus on the shape of the [OIII]$\lambda$5007
+emission line as a tracer of ionizing gas outflows. We stack our spectra in a
+fine grid of star formation rate (SFR) and stellar mass to analyze the
+dependence of winds on the position of galaxies in the SFR versus mass diagram.
+We do not find any significant evidence of broad and shifted
+[OIII]$\lambda$5007 emission line which we interpret as no evidence of
+outflowing ionized gas in the global population. We have also classified these
+galaxies as star-forming or AGN dominated according to their position in the
+standard BPT diagram. We show how the average [OIII]$\lambda$5007 profile
+changes as function of nature of the dominant ionizing source. We find that in
+the star-forming dominated source the oxygen line is symmetric and governed by
+the gravitational potential well. The AGN or composite
+AGN$\setminus$star-formation activity objects, in contrast, display a prominent
+and asymmetric profile that can be well described by a broad gaussian component
+that is blue-shifted from a narrow symmetric core. In particular, we find that
+the blue wings of the average [OIII]$\lambda$5007 profiles are increasingly
+prominent in the LINERs and Seyfert galaxies. We conclude that, in the
+low-redshift Universe, ""pure"" star-formation activity does not seem capable of
+driving ionized-gas outflows, while, the presence of optically selected AGN
+seems to play a primary role to drive such winds. We discuss the implications
+of these results for the role of the quenching mechanism in the present day
+Universe.
+"
+24559,Choosing the number of groups in a latent stochastic block model for dynamic networks,"  Latent stochastic block models are flexible statistical models that are
+widely used in social network analysis. In recent years, efforts have been made
+to extend these models to temporal dynamic networks, whereby the connections
+between nodes are observed at a number of different times. In this paper we
+extend the original stochastic block model by using a Markovian property to
+describe the evolution of nodes' cluster memberships over time. We recast the
+problem of clustering the nodes of the network into a model-based context, and
+show that the integrated completed likelihood can be evaluated analytically for
+a number of likelihood models. Then, we propose a scalable greedy algorithm to
+maximise this quantity, thereby estimating both the optimal partition and the
+ideal number of groups in a single inferential framework. Finally we propose
+applications of our methodology to both real and artificial datasets.
+"
+24560,SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization,"  Computer vision is experiencing an AI renaissance, in which machine learning
+models are expediting important breakthroughs in academic research and
+commercial applications. Effectively training these models, however, is not
+trivial due in part to hyperparameters: user-configured values that control a
+model's ability to learn from data. Existing hyperparameter optimization
+methods are highly parallel but make no effort to balance the search across
+heterogeneous hardware or to prioritize searching high-impact spaces. In this
+paper, we introduce a framework for massively Scalable Hardware-Aware
+Distributed Hyperparameter Optimization (SHADHO). Our framework calculates the
+relative complexity of each search space and monitors performance on the
+learning task over all trials. These metrics are then used as heuristics to
+assign hyperparameters to distributed workers based on their hardware. We first
+demonstrate that our framework achieves double the throughput of a standard
+distributed hyperparameter optimization framework by optimizing SVM for MNIST
+using 150 distributed workers. We then conduct model search with SHADHO over
+the course of one week using 74 GPUs across two compute clusters to optimize
+U-Net for a cell segmentation task, discovering 515 models that achieve a lower
+validation loss than standard U-Net.
+"
+24561,Energy decay of a viscoelastic wave equation with supercritical nonlinearities,"  This paper presents a study of the asymptotic behavior of the solutions for
+the history value problem of a viscoelastic wave equation which features a
+fading memory term as well as a supercritical source term and a frictional
+damping term: \begin{align*} \begin{cases} u_{tt}- k(0) \Delta u -
+\int_0^{\infty} k'(s) \Delta u(t-s) ds +|u_t|^{m-1}u_t =|u|^{p-1}u, \quad
+\text{ in } \Omega \times (0,T), \\ u(x,t)=u_0(x,t), \quad \text{ in } \Omega
+\times (-\infty,0], \end{cases} \end{align*} where $\Omega$ is a bounded domain
+in $\mathbb R^3$ with a Dirichlét boundary condition and $u_0$ represents the
+history value. A suitable notion of a potential well is introduced for the
+system, and global existence of solutions is justified provided that the
+history value $u_0$ is taken from a subset of the potential well. Also, uniform
+energy decay rate is obtained which depends on the relaxation kernel $-k'(s)$
+as well as the growth rate of the damping term. This manuscript complements our
+previous work [Guo et al. in J Differ Equ 257, 3778-3812(2014), J Differ Equ
+262, 1956-1979(2017)] where Hadamard well-posedness and the singularity
+formulation have been studied for the system. It is worth stressing the special
+features of the model, namely the source term here has a supercritical growth
+rate and the memory term accounts to the full past history that goes back to
+$-\infty$.
+"
+24562,A Neural Network Based on Synchronized Pairs of Nano-Oscillators,"  Artificial neural networks are intensively used to perform cognitive tasks
+such as image classification on traditional computers. With the end of CMOS
+scaling and increasing demand for efficient neural networks, alternative
+architectures implementing neural functions efficiently are being studied. This
+study leverages the demonstrated frequency tuning capabilities of compact
+nano-oscillators and their synchronization dynamics to implement a neuron using
+a pair of synchronized oscillators, and which features an unconventional
+response curve. We show that this compact neuron can naturally implement
+generic logic gates, including XOR. A simulated oscillator-based neural network
+is then shown to achieve results equivalent to standard approaches on two
+reference classification tasks. Finally, the performance of the system is
+evaluated in the presence of oscillator phase noise, an important issue of
+oscillating nanodevices. These results open the way for the design of
+alternative architectures adapted to efficient neural network execution.
+"
+24563,Third-order Limiting for Hyperbolic Conservation Laws applied to Adaptive Mesh Refinement and Non-Uniform 2D Grids,"  In this paper we extend the recently developed third-order limiter function
+$H_{3\text{L}}^{(c)}$ [J. Sci. Comput., (2016), 68(2), pp.~624--652] to make it
+applicable for more elaborate test cases in the context of finite volume
+schemes. This work covers the generalization to non-uniform grids in one and
+two space dimensions, as well as two-dimensional Cartesian grids with adaptive
+mesh refinement (AMR). The extension to 2D is obtained by the common approach
+of dimensional splitting. In order to apply this technique without loss of
+third-order accuracy, the order-fix developed by Buchmüller and Helzel [J.
+Sci. Comput., (2014), 61(2), pp.~343--368] is incorporated into the scheme.
+Several numerical examples on different grid configurations show that the
+limiter function $H_{3\text{L}}^{(c)}$ maintains the optimal third-order
+accuracy on smooth profiles and avoids oscillations in case of discontinuous
+solutions.
+"
+24564,Non-iterative recomputation of dense layers for performance improvement of DCNN,"  An iterative method of learning has become a paradigm for training deep
+convolutional neural networks (DCNN). However, utilizing a non-iterative
+learning strategy can accelerate the training process of the DCNN and
+surprisingly such approach has been rarely explored by the deep learning (DL)
+community. It motivates this paper to introduce a non-iterative learning
+strategy that eliminates the backpropagation (BP) at the top dense or fully
+connected (FC) layers of DCNN, resulting in, lower training time and higher
+performance. The proposed method exploits the Moore-Penrose Inverse to pull
+back the current residual error to each FC layer, generating well-generalized
+features. Then using the recomputed features, i.e., the new generalized
+features the weights of each FC layer is computed according to the
+Moore-Penrose Inverse. We evaluate the proposed approach on six widely accepted
+object recognition benchmark datasets: Scene-15, CIFAR-10, CIFAR-100, SUN-397,
+Places365, and ImageNet. The experimental results show that the proposed method
+obtains significant improvements over 30 state-of-the-art methods.
+Interestingly, it also indicates that any DCNN with the proposed method can
+provide better performance than the same network with its original training
+based on BP.
+"
+24565,On the migration-induced formation of the 9:7 mean motion resonance,"  We study formation of 9:7 mean motion resonance (MMR) as a result of
+convergent migration of two planets embedded in a disc. Depending on the
+migration parameters, initial orbits and planets' masses the system may pass
+through the resonance or enter it (permanently or temporarily). We illustrate
+that a stable equilibrium of the averaged system (a periodic orbit of the
+N-body model) is surrounded by the region of permanent resonance capture, whose
+size depends on the migration parameters and the planets mass ratio. A system
+located inside this region tends towards the equilibrium (the capture is
+permanent), while a system located outside the region evolves away from the
+equilibrium and leaves the resonance. We verify recent results of Delisle et
+al. and Xu & Lai where they show that for $m_1 \lesssim m_2$ ($m_1, m_2$ are
+the inner and outer planets' masses) the equilibrium is unstable when the
+migration is added, so the capture cannot be permanent. We show that for
+particular migration parameters the situation may be reversed (the equilibria
+are unstable for $m_1 \gtrsim m_2$). We illustrate that 9:7 MMR consists of two
+modes separated with a separatrix. The inner one is centred at the equilibrium
+and the outer one has no equilibrium in its centre. A system located outside
+the region of stable capture evolves from the inner into the outer mode. The
+evolution occurs along families of periodic orbits of the averaged system, that
+play a crucial role in the dynamics after the resonance capture.
+"
+24566,A generalized Schmidt subspace theorem for closed subschemes,"  We prove a generalized version of Schmidt's subspace theorem for closed
+subschemes in general position in terms of suitably defined Seshadri constants
+with respect to a fixed ample divisor. Our proof builds on previous work by
+Evertse and Ferretti, Corvaja and Zannier, and others, and uses standard
+techniques from algebraic geometry such as notions of positivity, blowing-ups
+and direct image sheaves. As an application, we recover a higher-dimensional
+Diophantine approximation theorem of K.F. Roth-type due to D. McKinnon and M.
+Roth with a significantly shortened proof, while simultaneously extending the
+scope of the use of Seshadri constants in this context in a natural way.
+"
+24567,Balanced Policy Evaluation and Learning,"  We present a new approach to the problems of evaluating and learning
+personalized decision policies from observational data of past contexts,
+decisions, and outcomes. Only the outcome of the enacted decision is available
+and the historical policy is unknown. These problems arise in personalized
+medicine using electronic health records and in internet advertising. Existing
+approaches use inverse propensity weighting (or, doubly robust versions) to
+make historical outcome (or, residual) data look like it were generated by a
+new policy being evaluated or learned. But this relies on a plug-in approach
+that rejects data points with a decision that disagrees with the new policy,
+leading to high variance estimates and ineffective learning. We propose a new,
+balance-based approach that too makes the data look like the new policy but
+does so directly by finding weights that optimize for balance between the
+weighted data and the target policy in the given, finite sample, which is
+equivalent to minimizing worst-case or posterior conditional mean square error.
+Our policy learner proceeds as a two-level optimization problem over policies
+and weights. We demonstrate that this approach markedly outperforms existing
+ones both in evaluation and learning, which is unsurprising given the wider
+support of balance-based weights. We establish extensive theoretical
+consistency guarantees and regret bounds that support this empirical success.
+"
+24568,ChemKED: a human- and machine-readable data standard for chemical kinetics experiments,"  Fundamental experimental measurements of quantities such as ignition delay
+times, laminar flame speeds, and species profiles (among others) serve
+important roles in understanding fuel chemistry and validating chemical kinetic
+models. However, despite both the importance and abundance of such information
+in the literature, the community lacks a widely adopted standard format for
+this data. This impedes both sharing and wide use by the community. Here we
+introduce a new chemical kinetics experimental data format, ChemKED, and the
+related Python-based package for validating and working with ChemKED-formatted
+files called PyKED. We also review past and related efforts, and motivate the
+need for a new solution. ChemKED currently supports the representation of
+autoignition delay time measurements from shock tubes and rapid compression
+machines. ChemKED-formatted files contain all of the information needed to
+simulate experimental data points, including the uncertainty of the data.
+ChemKED is based on the YAML data serialization language, and is intended as a
+human- and machine-readable standard for easy creation and automated use.
+Development of ChemKED and PyKED occurs openly on GitHub under the BSD 3-clause
+license, and contributions from the community are welcome. Plans for future
+development include support for experimental data from laminar flame, jet
+stirred reactor, and speciation measurements.
+"
+24569,How likely are two independent recurrent events to occur simultaneously during a given time?,"  We determine the probability $P$ of two independent events $A$ and $B$, which
+occur randomly $n_A$ and $n_B$ times during a total time $T$ and last for $t_A$
+and $t_B$, to occur simultaneously at some point during $T$. Therefore we first
+prove the precise equation \begin{equation*} P^* = \dfrac{t_A+t_B}{T} -
+\dfrac{t_A^2+t_B^2}{2T^2} \end{equation*} for the case $n_A = n_B = 1$ and
+continue to establish a simple approximation equation \begin{equation*} P
+\approx 1 - \left( 1 - n_A \dfrac{t_A + t_B}{T} \right)^{n_B} \end{equation*}
+for any given value of $n_A$ and $n_B$. Finally we prove the more complex
+universal equation \begin{equation*} P = 1 - \dfrac{ \left( T^+ - t_A n_A - t_B
+n_B \right)^{n_A + n_B} }{ \left( T^+ - t_A n_A \right)^{n_A} \left( T^+ - t_B
+n_B \right)^{n_B} } \pm E^\pm, \end{equation*} which yields the probability for
+$A$ and $B$ to overlap at some point for any given parameter, with $T^+ := T +
+\frac{t_A + t_B}{2}$ and a small error term $E^\pm$.
+"
+24570,Segmenting Dynamic Network Data,"  Networks and graphs arise naturally in many complex systems, often exhibiting
+dynamic behavior that can be modeled using dynamic networks. Two major research
+problems in dynamic networks are (1) community detection, which aims to find
+specific sub-structures within the networks, and (2) change point detection,
+which tries to find the time points at which sub-structures change. This paper
+proposes a new methodology to solve both problems simultaneously, using a model
+selection framework in which the Minimum Description Length Principle (MDL) is
+utilized as minimizing objective criterion. The derived detection algorithm is
+compatible with many existing methods, and is supported by empirical results
+and data analysis.
+"
+24571,"Observation of Dirac surface states in the hexagonal PtBi2, a possible origin of the linear magnetoresistance","  The nonmagnetic compounds showing extremely large magnetoresistance are
+attracting a great deal of research interests due to their potential
+applications in the field of spintronics. PtBi$_2$ is one of such interesting
+compounds showing large linear magnetoresistance (MR) in its both the hexagonal
+and pyrite crystal structure. We use angle-resolved photoelectron spectroscopy
+(ARPES) and density functional theory (DFT) calculations to understand the
+mechanism of liner MR observed in the hexagonal PtBi$_2$. Our results uncover
+for the first time linear dispersive surface Dirac states at the
+$\bar{\Gamma}$-point, crossing Fermi level with node at a binding energy of
+$\approx$ 900 meV, in addition to the previously reported Dirac states at the
+$\bar{M}$-point in the same compound. We further notice from our dichroic
+measurements that these surface states show an asymmetric spectral intensity
+when measured with left and right circularly polarized light, hinting at a
+substantial spin polarization of the bands. Following these observations, we
+suggest that the linear dispersive Dirac states at the $\bar{\Gamma}$ and
+$\bar{M}$-points are likely to play a crucial role for the linear field
+dependent magnetoresistance recorded in this compound.
+"
+24572,Elastic Splines II: unicity of optimal s-curves and $G^2$ regularity of splines,"  Given points $P_1,P_2,\ldots,P_m$ in the complex plane, we are concerned with
+the problem of finding an interpolating curve with minimal bending energy
+(i.e., an optimal interpolating curve). It was shown previously that existence
+is assured if one requires that the pieces of the interpolating curve be
+s-curves. In the present article we also impose the restriction that these
+s-curves have chord angles not exceeding $\pi/2$ in magnitude. With this setup,
+we have identified a sufficient condition for the $G^2$ regularity of optimal
+interpolating curves. This sufficient condition relates to the stencil angles
+$\{\psi_j\}$, where $\psi_j$ is defined as the angular change in direction from
+segment $[P_{j-1},P_j]$ to segment $[P_j,P_{j+1}]$. A distinguished angle
+$\Psi$ ($\approx 37^\circ$) is identified, and we show that if the stencil
+angles satisfy $|\psi_j|<\Psi$, then optimal interpolating curves are globally
+$G^2$.
+As with the previous article, most of our effort is concerned with the
+geometric Hermite interpolation problem of finding an optimal s-curve which
+connects $P_1$ to $P_2$ with prescribed chord angles $(\alpha,\beta)$. Whereas
+existence was previously shown, and sometimes uniqueness, the present article
+begins by establishing uniqueness when $|\alpha |,|\beta |\leq\pi/2$ and
+$|\alpha -\beta|<\pi$.
+"
+24573,Automatic Detection of Indoor and Outdoor Scenarios using NMEA Message Data from GPS Receivers,"  Detection of indoor and outdoor scenarios is an important resource for many
+types of activities such as multisensor navigation and location-based services.
+This research presents the use of NMEA data provided by GPS receivers to
+characterize different types of scenarios automatically. A set of static tests
+was performed to evaluate metrics such as number of satellites, positioning
+solution geometry and carrier-to-receiver noise-density ratio values to detect
+possible patterns to determine indoor and outdoor scenarios. Subsequently,
+validation tests are applied to verify that parameters obtained are adequate.
+"
+24574,Formalization of some central theorems in combinatorics of finite sets,"  We present fully formalized proofs of some central theorems from
+combinatorics. These are Dilworth's decomposition theorem, Mirsky's theorem,
+Hall's marriage theorem and the Erdős-Szekeres theorem. Dilworth's
+decomposition theorem is the key result among these. It states that in any
+finite partially ordered set (poset), the size of a smallest chain cover and a
+largest antichain are the same. Mirsky's theorem is a dual of Dilworth's
+decomposition theorem, which states that in any finite poset, the size of a
+smallest antichain cover and a largest chain are the same. We use Dilworth's
+theorem in the proofs of Hall's Marriage theorem and the Erdős-Szekeres
+theorem. The combinatorial objects involved in these theorems are sets and
+sequences. All the proofs are formalized in the Coq proof assistant. We develop
+a library of definitions and facts that can be used as a framework for
+formalizing other theorems on finite posets.
+"
+24575,Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions,"  With more than 300 million people depressed worldwide, depression is a global
+problem. Due to access barriers such as social stigma, cost, and treatment
+availability, 60% of mentally-ill adults do not receive any mental health
+services. Effective and efficient diagnosis relies on detecting clinical
+symptoms of depression. Automatic detection of depressive symptoms would
+potentially improve diagnostic accuracy and availability, leading to faster
+intervention. In this work, we present a machine learning method for measuring
+the severity of depressive symptoms. Our multi-modal method uses 3D facial
+expressions and spoken language, commonly available from modern cell phones. It
+demonstrates an average error of 3.67 points (15.3% relative) on the
+clinically-validated Patient Health Questionnaire (PHQ) scale. For detecting
+major depressive disorder, our model demonstrates 83.3% sensitivity and 82.6%
+specificity. Overall, this paper shows how speech recognition, computer vision,
+and natural language processing can be combined to assist mental health
+patients and practitioners. This technology could be deployed to cell phones
+worldwide and facilitate low-cost universal access to mental health care.
+"
+24576,Power Systems Data Fusion based on Belief Propagation,"  The increasing complexity of the power grid, due to higher penetration of
+distributed resources and the growing availability of interconnected,
+distributed metering devices re- quires novel tools for providing a unified and
+consistent view of the system. A computational framework for power systems data
+fusion, based on probabilistic graphical models, capable of combining
+heterogeneous data sources with classical state estimation nodes and other
+customised computational nodes, is proposed. The framework allows flexible
+extension of the notion of grid state beyond the view of flows and injection in
+bus-branch models, and an efficient, naturally distributed inference algorithm
+can be derived. An application of the data fusion model to the quantification
+of distributed solar energy is proposed through numerical examples based on
+semi-synthetic simulations of the standard IEEE 14-bus test case.
+"
+24577,Spectral data for simply-periodic solutions of the sinh-Gordon equation,"  This note summarizes results that were obtained by the author in his
+habilitation thesis (arXiv:1607.08792) concerning the development of a spectral
+theory for simply periodic, 2-dimensional, complex-valued solutions of the
+sinh-Gordon equation. Spectral data for such solutions are defined for periodic
+Cauchy data on a line (following Hitchin and Bobenko) and the space of spectral
+data is described by an asymptotic characterization. Using methods of
+asymptotic estimates, the inverse problem for the spectral data of such Cauchy
+data is answered. Finally a Jacobi variety for the spectral curve is
+constructed, and this is used to study the asymptotic behavior of the spectral
+data corresponding to actual simply periodic solutions of the sinh-Gordon
+equation on strips of positive height.
+"
+24578,A Passivity-Based Distributed Reference Governor for Constrained Robotic Networks,"  This paper focuses on a passivity-based distributed reference governor (RG)
+applied to a pre-stabilized mobile robotic network. The novelty of this paper
+lies in the method used to solve the RG problem, where a passivity-based
+distributed optimization scheme is proposed. In particular, the gradient
+descent method minimizes the global objective function while the dual ascent
+method maximizes the Hamiltonian. To make the agents converge to the agreed
+optimal solution, a proportional-integral consensus estimator is used. This
+paper proves the convergence of the state estimates of the RG to the optimal
+solution through passivity arguments, considering the physical system static.
+Then, the effectiveness of the scheme considering the dynamics of the physical
+system is demonstrated through simulations and experiments.
+"
+24579,Training Feedforward Neural Networks with Standard Logistic Activations is Feasible,"  Training feedforward neural networks with standard logistic activations is
+considered difficult because of the intrinsic properties of these sigmoidal
+functions. This work aims at showing that these networks can be trained to
+achieve generalization performance comparable to those based on hyperbolic
+tangent activations. The solution consists on applying a set of conditions in
+parameter initialization, which have been derived from the study of the
+properties of a single neuron from an information-theoretic perspective. The
+proposed initialization is validated through an extensive experimental
+analysis.
+"
+24580,Adaptive multiclass nearest neighbor classifier,"  We consider a problem of multiclass classification, where the training sample
+$S_n = \{(X_i, Y_i)\}_{i=1}^n$ is generated from the model $\mathbb P(Y = m | X
+= x) = \eta_m(x)$, $1 \leq m \leq M$, and $\eta_1(x), \dots, \eta_M(x)$ are
+unknown $\alpha$-Holder continuous functions.Given a test point $X$, our goal
+is to predict its label. A widely used $\mathsf k$-nearest-neighbors classifier
+constructs estimates of $\eta_1(X), \dots, \eta_M(X)$ and uses a plug-in rule
+for the prediction. However, it requires a proper choice of the smoothing
+parameter $\mathsf k$, which may become tricky in some situations. In our
+solution, we fix several integers $n_1, \dots, n_K$, compute corresponding
+$n_k$-nearest-neighbor estimates for each $m$ and each $n_k$ and apply an
+aggregation procedure. We study an algorithm, which constructs a convex
+combination of these estimates such that the aggregated estimate behaves
+approximately as well as an oracle choice. We also provide a non-asymptotic
+analysis of the procedure, prove its adaptation to the unknown smoothness
+parameter $\alpha$ and to the margin and establish rates of convergence under
+mild assumptions.
+"
+24581,Boundary conformal field theory and symmetry protected topological phases in $2+1$ dimensions,"  We propose a diagnostic tool for detecting non-trivial symmetry protected
+topological (SPT) phases protected by a symmetry group $G$ in 2+1 dimensions.
+Our method is based on directly studying the 1+1-dimensional anomalous edge
+conformal field theory (CFT) of SPT phases. We claim that if the CFT is the
+edge theory of an SPT phase, then there must be an obstruction to cutting it
+open. This obstruction manifests in the in-existence of boundary states that
+preserves both the conformal symmetry and the global symmetry $G$. We discuss
+the relation between edgeability, the ability to find a consistent boundary
+state, and gappability, the ability to gap out a CFT, in the presence of $G$.
+We study several cases including time-reversal symmetric topological
+insulators, $\mathbb{Z}_N$ symmetric bosonic SPTs, and $\mathbb{Z}_2 \times
+\mathbb{Z}_2$ symmetric topological superconductors.
+"
+24582,Fixation in Fluctuating Populations,"  We investigate the dynamics of the voter model in which the population itself
+changes endogenously via the birth-death process. There are two species of
+voters, labeled A and B, and the population of each species can grow or shrink
+by the birth-death process at equal rates $b$. Individuals of opposite species
+also undergo voter model dynamics in which an AB pair can equiprobably become
+AA or BB with rate $v$---neutral evolution. In the limit $b/v\to\infty$, the
+distribution of consensus times varies as $t^{-3}$ and the probability that the
+population size equals $n$ at the moment of consensus varies as $n^{-3}$. As
+the birth/death rate $b$ is increased, fixation occurs more more quickly; that
+is, population fluctuations promote consensus.
+"
+24583,On the interplay between Babai and Cerny's conjectures,"  Motivated by the Babai conjecture and the Cerny conjecture, we study the
+reset thresholds of automata with the transition monoid equal to the full
+monoid of transformations of the state set. For automata with $n$ states in
+this class, we prove that the reset thresholds are upper-bounded by $2n^2-6n+5$
+and can attain the value $\tfrac{n(n-1)}{2}$. In addition, we study diameters
+of the pair digraphs of permutation automata and construct $n$-state
+permutation automata with diameter $\tfrac{n^2}{4} + o(n^2)$.
+"
+24584,Achievement and Friends: Key Factors of Player Retention Vary Across Player Levels in Online Multiplayer Games,"  Retaining players over an extended period of time is a long-standing
+challenge in game industry. Significant effort has been paid to understanding
+what motivates players enjoy games. While individuals may have varying reasons
+to play or abandon a game at different stages within the game, previous studies
+have looked at the retention problem from a snapshot view. This study, by
+analyzing in-game logs of 51,104 distinct individuals in an online multiplayer
+game, uniquely offers a multifaceted view of the retention problem over the
+players' virtual life phases. We find that key indicators of longevity change
+with the game level. Achievement features are important for players at the
+initial to the advanced phases, yet social features become the most predictive
+of longevity once players reach the highest level offered by the game. These
+findings have theoretical and practical implications for designing online games
+that are adaptive to meeting the players' needs.
+"
+24585,Particle conservation in numerical models of the tokamak plasma edge,"  The test particle Monte-Carlo models for neutral particles are often used in
+the tokamak edge modelling codes. The drawback of this approach is that the
+self-consistent solution suffers from random error introduced by the
+statistical method. A particular case where the onset of nonphysical solutions
+can be clearly identified is violation of the global particle balance due to
+non-converged residuals. There are techniques which can reduce the residuals -
+such as internal iterations in the code B2-EIRENE - but they may pose severe
+restrictions on the time-step and slow down the computations. Numerical
+diagnostics described in the paper can be used to unambiguously identify when
+the too large error in the global particle balance is due to finite-volume
+residuals, and their reduction is absolutely necessary. Algorithms which reduce
+the error while allowing large time-step are also discussed.
+"
+24586,Probing the Efimov discrete scaling in atom-molecule collision,"  The discrete Efimov scaling behavior, well-known in the low-energy spectrum
+of three-body bound systems for large scattering lengths (unitary limit), is
+identified in the energy dependence of atom-molecule elastic cross-section in
+mass imbalanced systems. That happens in the collision of a heavy atom with
+mass $m_H$ with a weakly-bound dimer formed by the heavy atom and a lighter one
+with mass $m_L \ll m_H$. Approaching the heavy-light unitary limit the $s-$wave
+elastic cross-section $\sigma$ will present a sequence of zeros/minima at
+collision energies following closely the Efimov geometrical law. Our results
+open a new perspective to detect the discrete scaling behavior from low-energy
+scattering data, which is timely in view of the ongoing experiments with
+ultra-cold binary mixtures having strong mass asymmetries, such as Lithium and
+Caesium or Lithium and Ytterbium.
+"
+24587,Bootstrap for change point detection,"  In Change point detection task Likelihood Ratio Test (LRT) is sequentially
+applied in a sliding window procedure. Its high values indicate changes of
+parametric distribution in the data sequence. Correspondingly LRT values
+require predefined bound for their maximum. The maximum value has unknown
+distribution and may be calibrated with multiplier bootstrap. Bootstrap
+procedure convolves independent components of the Likelihood function with
+random weights, that enables to estimate empirically LRT distribution. For this
+empirical distribution of the LRT we show convergence rates to the real maximum
+value distribution.
+"
+24588,High resolution resonant inelastic EUV scattering from orbital excitations in a Heisenberg antiferromagnet,"  We report the high resolution resonant inelastic EUV scattering study of
+quantum Heisenberg antiferromagnet KCoF3. By tuning the EUV photon energy to
+cobalt M23 edge, a complete set of low energy 3d spin-orbital excitations is
+revealed. These low-lying electronic excitations are modeled using an extended
+multiplet-based mean field calculation to identify the roles of lattice and
+magnetic degrees of freedom in modifying the RIXS spectral lineshape. We have
+demonstrated that the temperature dependence of RIXS features upon the
+antiferromagnetic ordering transition enables us to probe the energetics of
+short-range spin correlations in this material.
+"
+24589,Co-axial monodromy,"  For Riemannian metrics of constant positive curvature on a punctured sphere
+with conic singularities at the punctures and co-axial monodromy of the
+developing map, possible angles at the singularities are completely described.
+This completes the recent result of Mondello and Panov. The related problem of
+describing possible multiplicities of critical points of logarithmic potentials
+of finitely many charges is also solved.
+"
+24590,Field induced cell proliferation and death in a thick epithelium,"  We study the dynamics of a thick polar epithelium subjected to the action of
+both an electric and a flow field in a planar geometry. We develop a
+generalized continuum hydrodynamic description and describe the tissue as a two
+component fluid system. The cells and the interstitial fluid are the two
+components and we keep all terms allowed by symmetry. In particular we keep
+track of the cell pumping activity for both solvent flow and electric current
+and discuss the corresponding orders of magnitude. We study the growth dynamics
+of tissue slabs, their steady states and obtain the dependence of the cell
+velocity, net cell division rate, and cell stress on the flow strength and the
+applied electric field. We find that finite thickness tissue slabs exist only
+in a restricted region of phase space and that relatively modest electric
+fields or imposed external flows can induce either proliferation or death.
+"
+24591,Bonnet's type theorems in the relative differential geometry of the 4-dimensional space,"  We deal with hypersurfaces in the framework of the relative differential
+geometry in $\mathbb{R}^4$. We consider a hypersurface $\varPhi$ in
+$\mathbb{R}^4$ with position vector field $\vect{x}$ which is relatively
+normalized by a relative normalization $\vect{y}$. Then $\vect{y}$ is also a
+relative normalization of every member of the one-parameter family
+$\mathcal{F}$ of hypersurfaces $\varPhi_\mu$ with position vector field
+$\vect{x}_\mu = \vect{x} + \mu \, \vect{y}$, where $\mu$ is a real constant. We
+call every hypersurface $\varPhi_\mu \in \mathcal{F}$ relatively parallel to
+$\varPhi$. This consideration includes both Euclidean and Blaschke
+hypersurfaces of the affine differential geometry. In this paper we express the
+relative mean curvature's functions of a hypersurface $\varPhi_\mu$ relatively
+parallel to $\varPhi$ by means of the ones of $\varPhi$ and the ""relative
+distance"" $\mu$. Then we prove several Bonnet's type theorems. More precisely,
+we show that if two relative mean curvature's functions of $\varPhi$ are
+constant, then there exists at least one relatively parallel hypersurface with
+a constant relative mean curvature's function.
+"
+24592,"Orbital superconductivity, defects and pinned nematic fluctuations in the doped iron chalcogenide FeSe$_{0.45}$Te$_{0.55}$","  We demonstrate that the differential conductance, $dI/dV$, measured via
+spectroscopic imaging scanning tunneling microscopy in the doped iron
+chalcogenide FeSe$_{0.45}$Te$_{0.55}$, possesses a series of characteristic
+features that allow one to extract the orbital structure of the superconducting
+gaps. This yields nearly isotropic superconducting gaps on the two hole-like
+Fermi surfaces, and a strongly anisotropic gap on the electron-like Fermi
+surface. Moreover, we show that the pinning of nematic fluctuations by defects
+can give rise to a dumbbell-like spatial structure of the induced impurity
+bound states, and explains the related $C_2$-symmetry in the Fourier
+transformed differential conductance.
+"
+24593,"Words, Hausdorff dimension and randomly free groups","  We study fibers of word maps in finite, profinite, and residually finite
+groups. Our main result is that, for any word w in the free group on d
+generators, there exists $\epsilon > 0$ such that if G is a residually finite
+group with infinitely many non-isomorphic non-abelian upper composition
+factors, then all fibers of the word map $w\colon G^d \to G$ have Hausdorff
+dimension at most $d-\epsilon$.
+We conclude that the profinite completion of a group G as above satisfies no
+probabilistic identity. It is therefore randomly free; namely, for any d > 0,
+the probability that d randomly chosen elements freely generate a free subgroup
+of G is 1. This solves an open problem of Dixon, Pyber, Seress, and Shalev.
+Additional applications and related results are also established. For
+example, combining our results with recent results of Bors, we conclude that a
+profinite group in which the set of elements of finite odd order has positive
+measure has an open prosolvable subgroup. This may be regarded as a
+probabilistic version of the Feit-Thompson theorem.
+"
+24594,Low temperature magnetic structure of CeRhIn$_5$ by neutron diffraction on absorption-optimized samples,"  Two aspects of the ambient pressure magnetic structure of heavy fermion
+material CeRhIn$_5$ have remained under some debate since its discovery:
+whether the structure is indeed an incommensurate helix or a spin density wave,
+and what is the precise magnitude of the ordered magnetic moment. By using a
+single crystal sample optimized for hot neutrons to minimize neutron absorption
+by Rh and In, here we report an ordered moment of $m=0.54(2)~\mu_B$. In
+addition, by using spherical neutron polarimetry measurements on a similar
+single crystal sample, we have confirmed the helical nature of the magnetic
+structure, and identified a single chiral domain.
+"
+24595,Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM,"  Nowadays, most of the objective speech quality assessment tools (e.g.,
+perceptual evaluation of speech quality (PESQ)) are based on the comparison of
+the degraded/processed speech with its clean counterpart. The need of a
+""golden"" reference considerably restricts the practicality of such assessment
+tools in real-world scenarios since the clean reference usually cannot be
+accessed. On the other hand, human beings can readily evaluate the speech
+quality without any reference (e.g., mean opinion score (MOS) tests), implying
+the existence of an objective and non-intrusive (no clean reference needed)
+quality assessment mechanism. In this study, we propose a novel end-to-end,
+non-intrusive speech quality evaluation model, termed Quality-Net, based on
+bidirectional long short-term memory. The evaluation of utterance-level quality
+in Quality-Net is based on the frame-level assessment. Frame constraints and
+sensible initializations of forget gate biases are applied to learn meaningful
+frame-level quality assessment from the utterance-level quality label.
+Experimental results show that Quality-Net can yield high correlation to PESQ
+(0.9 for the noisy speech and 0.84 for the speech processed by speech
+enhancement). We believe that Quality-Net has potential to be used in a wide
+variety of applications of speech signal processing.
+"
+24596,Can Computers overcome Humans? Consciousness interaction and its implications,"  Can computers overcome human capabilities? This is a paradoxical and
+controversial question, particularly because there are many hidden assumptions.
+This article focuses on that issue putting on evidence some misconception
+related with future generations of machines and the understanding of the brain.
+It will be discussed to what extent computers might reach human capabilities,
+and how it could be possible only if the computer is a conscious machine.
+However, it will be shown that if the computer is conscious, an interference
+process due to consciousness would affect the information processing of the
+system. Therefore, it might be possible to make conscious machines to overcome
+human capabilities, which will have limitations as well as humans. In other
+words, trying to overcome human capabilities with computers implies the
+paradoxical conclusion that a computer will never overcome human capabilities
+at all, or if the computer does, it should not be considered as a computer
+anymore.
+"
+24597,"A variational inequality framework for network games: Existence, uniqueness, convergence and sensitivity analysis","  We provide a unified variational inequality framework for the study of
+fundamental properties of the Nash equilibrium in network games. We identify
+several conditions on the underlying network (in terms of spectral norm,
+infinity norm and minimum eigenvalue of its adjacency matrix) that guarantee
+existence, uniqueness, convergence and continuity of equilibrium in general
+network games with multidimensional and possibly constrained strategy sets. We
+delineate the relations between these conditions and characterize classes of
+networks that satisfy each of these conditions.
+"
+24598,Spectral asymptotics for $δ$-interactions on sharp cones,"  We investigate the spectrum of three-dimensional Schrödinger operators
+with $\delta$-interactions of constant strength supported on circular cones. As
+shown in earlier works, such operators have infinitely many eigenvalues below
+the threshold of the essential spectrum. We focus on spectral properties for
+sharp cones, that is when the cone aperture goes to zero, and we describe the
+asymptotic behavior of the eigenvalues and of the eigenvalue counting function.
+A part of the results are given in terms of numerical constants appearing as
+solutions of transcendental equations involving modified Bessel functions.
+"
+24599,Time dilation and length contraction without relativity: The Bohr atom and the semi-classical hydrogen molecule ion,"  Recently it was shown that classical ""relativistic"" particle dynamics was
+implicit in physics going back to Maxwell. The demonstration utilized a simple
+modification of a 1906 thought experiment by which Einstein established the
+mass equivalence of energy, independently of the relativity postulates. The
+modified thought experiment leads directly to correct expressions for the
+momentum and kinetic energy of a particle at high speed. In the present paper,
+one additional ingredient is added, namely, the Bohr quantization rule of the
+old quantum theory. It is found that a semi-classical model of a hydrogen atom
+and a hydrogen molecule ion exhibit, respectively, time dilation and length
+contraction.
+"
+24600,Inferential results for a new measure of inequality,"  In this paper we derive inferential results for a new index of inequality,
+specifically defined for capturing significant changes observed both in the
+left and in the right tail of the income distributions. The latter shifts are
+an apparent fact for many countries like US, Germany, UK, and France in the
+last decades, and are a concern for many policy makers. We propose two
+empirical estimators for the index, and show that they are asymptotically
+equivalent. Afterwards, we adopt one estimator and prove its consistency and
+asymptotic normality. Finally we introduce an empirical estimator for its
+variance and provide conditions to show its convergence to the finite
+theoretical value. An analysis of real data on net income from the Bank of
+Italy Survey of Income and Wealth is also presented, on the base of the
+obtained inferential results.
+"
+24601,Stellar structure and compact objects before 1940: Towards relativistic astrophysics,"  Since the mid-1920s, different strands of research used stars as ""physics
+laboratories"" for investigating the nature of matter under extreme densities
+and pressures, impossible to realize on Earth. To trace this process this paper
+is following the evolution of the concept of a dense core in stars, which was
+important both for an understanding of stellar evolution and as a testing
+ground for the fast-evolving field of nuclear physics. In spite of the divide
+between physicists and astrophysicists, some key actors working in the
+cross-fertilized soil of overlapping but different scientific cultures
+formulated models and tentative theories that gradually evolved into more
+realistic and structured astrophysical objects. These investigations culminated
+in the first contact with general relativity in 1939, when J. Robert
+Oppenheimer and his students George Volkoff and Hartland Snyder systematically
+applied the theory to the dense core of a collapsing neutron star. This
+pioneering application of Einstein's theory to an astrophysical compact object
+can be regarded as a milestone in the path eventually leading to the emergence
+of relativistic astrophysics in the early 1960s.
+"
+24602,Solving the Schwarzian via the Conformal Bootstrap,"  We obtain exact expressions for a general class of correlation functions in
+the 1D quantum mechanical model described by the Schwarzian action, that arises
+as the low energy limit of the SYK model. The answer takes the form of an
+integral of a momentum space amplitude obtained via a simple set of
+diagrammatic rules. The derivation relies on the precise equivalence between
+the 1D Schwarzian theory and a suitable large $c$ limit of 2D Virasoro CFT. The
+mapping from the 1D to the 2D theory is similar to the construction of
+kinematic space. We also compute the out-of-time ordered four point function.
+The momentum space amplitude in this case contains an extra factor in the form
+of a crossing kernel, or R-matrix, given by a 6j-symbol of SU(1,1). We argue
+that the R-matrix describes the gravitational scattering amplitude near the
+horizon of an AdS${}_2$ black hole. Finally, we discuss the generalization of
+some of our results to ${\cal N}=1$ and ${\cal N}=2$ supersymmetric Schwarzian
+QM.
+"
+24603,Insights into Analogy Completion from the Biomedical Domain,"  Analogy completion has been a popular task in recent years for evaluating the
+semantic properties of word embeddings, but the standard methodology makes a
+number of assumptions about analogies that do not always hold, either in recent
+benchmark datasets or when expanding into other domains. Through an analysis of
+analogies in the biomedical domain, we identify three assumptions: that of a
+Single Answer for any given analogy, that the pairs involved describe the Same
+Relationship, and that each pair is Informative with respect to the other. We
+propose modifying the standard methodology to relax these assumptions by
+allowing for multiple correct answers, reporting MAP and MRR in addition to
+accuracy, and using multiple example pairs. We further present BMASS, a novel
+dataset for evaluating linguistic regularities in biomedical embeddings, and
+demonstrate that the relationships described in the dataset pose significant
+semantic challenges to current word embedding methods.
+"
+24604,Classical affine W-algebras and the associated integrable Hamiltonian hierarchies for classical Lie algebras,"  We prove that any classical affine W-algebra W(g,f), where g is a classical
+Lie algebra and f is an arbitrary nilpotent element of g, carries an integrable
+Hamiltonian hierarchy of Lax type equations. This is based on the theories of
+generalized Adler type operators and of generalized quasideterminants, which we
+develop in the paper. Moreover, we show that under certain conditions, the
+product of two generalized Adler type operators is a Lax type operator. We use
+this fact to construct a large number of integrable Hamiltonian systems,
+recovering, as a special case, all KdV type hierarchies constructed by Drinfeld
+and Sokolov.
+"
+24605,Programming Not Only by Example,"  In recent years, there has been tremendous progress in automated synthesis
+techniques that are able to automatically generate code based on some intent
+expressed by the programmer. A major challenge for the adoption of synthesis
+remains in having the programmer communicate their intent. When the expressed
+intent is coarse-grained (for example, restriction on the expected type of an
+expression), the synthesizer often produces a long list of results for the
+programmer to choose from, shifting the heavy-lifting to the user. An
+alternative approach, successfully used in end-user synthesis is programming by
+example (PBE), where the user leverages examples to interactively and
+iteratively refine the intent. However, using only examples is not expressive
+enough for programmers, who can observe the generated program and refine the
+intent by directly relating to parts of the generated program.
+We present a novel approach to interacting with a synthesizer using a
+granular interaction model. Our approach employs a rich interaction model where
+(i) the synthesizer decorates a candidate program with debug information that
+assists in understanding the program and identifying good or bad parts, and
+(ii) the user is allowed to provide feedback not only on the expected output of
+a program, but also on the underlying program itself. That is, when the user
+identifies a program as (partially) correct or incorrect, they can also
+explicitly indicate the good or bad parts, to allow the synthesizer to accept
+or discard parts of the program instead of discarding the program as a whole.
+We show the value of our approach in a controlled user study. Our study shows
+that participants have strong preference to using granular feedback instead of
+examples, and are able to provide granular feedback much faster.
+"
+24606,Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering,"  Matrix factorization is one of the best approaches for collaborative
+filtering, because of its high accuracy in presenting users and items latent
+factors. The main disadvantages of matrix factorization are its complexity, and
+being very hard to be parallelized, specially with very large matrices. In this
+paper, we introduce a new method for collaborative filtering based on Matrix
+Factorization by combining simulated annealing with levy distribution. By using
+this method, good solutions are achieved in acceptable time with low
+computations, compared to other methods like stochastic gradient descent,
+alternating least squares, and weighted non-negative matrix factorization.
+"
+24607,Astronomical verification of a stabilized frequency reference transfer system for the Square Kilometre Array,"  In order to meet its cutting-edge scientific objectives, the Square Kilometre
+Array (SKA) telescope requires high-precision frequency references to be
+distributed to each of its antennas. The frequency references are distributed
+via fiber-optic links and must be actively stabilized to compensate for
+phase-noise imposed on the signals by environmental perturbations on the links.
+SKA engineering requirements demand that any proposed frequency reference
+distribution system be proved in ""astronomical verification"" tests. We present
+results of the astronomical verification of a stabilized frequency reference
+transfer system proposed for SKA-mid. The dual-receiver architecture of the
+Australia Telescope Compact Array was exploited to subtract the phase-noise of
+the sky signal from the data, allowing the phase-noise of observations
+performed using a standard frequency reference, as well as the stabilized
+frequency reference transfer system transmitting over 77 km of fiber-optic
+cable, to be directly compared. Results are presented for the fractional
+frequency stability and phase-drift of the stabilized frequency reference
+transfer system for celestial calibrator observations at 5 GHz and 25 GHz.
+These observations plus additional laboratory results for the transferred
+signal stability over a 166 km metropolitan fiber-optic link are used to show
+that the stabilized transfer system under test exceeds all SKA phase-stability
+requirements under a broad range of observing conditions. Furthermore, we have
+shown that alternative reference dissemination systems that use multiple
+synthesizers to supply reference signals to sub-sections of an array may limit
+the imaging capability of the telescope.
+"
+24608,Multifractality without fine-tuning in a Floquet quasiperiodic chain,"  Periodically driven, or Floquet, disordered quantum systems have generated
+many unexpected discoveries of late, such as the anomalous Floquet Anderson
+insulator and the discrete time crystal. Here, we report the emergence of an
+entire band of multifractal wavefunctions in a periodically driven chain of
+non-interacting particles subject to spatially quasiperiodic disorder.
+Remarkably, this multifractality is robust in that it does not require any
+fine-tuning of the model parameters, which sets it apart from the known
+multifractality of $critical$ wavefunctions. The multifractality arises as the
+periodic drive hybridises the localised and delocalised sectors of the undriven
+spectrum. We account for this phenomenon in a simple random matrix based
+theory. Finally, we discuss dynamical signatures of the multifractal states,
+which should betray their presence in cold atom experiments. Such a simple yet
+robust realisation of multifractality could advance this so far elusive
+phenomenon towards applications, such as the proposed disorder-induced
+enhancement of a superfluid transition.
+"
+24609,Pose-Normalized Image Generation for Person Re-identification,"  Person Re-identification (re-id) faces two major challenges: the lack of
+cross-view paired training data and learning discriminative identity-sensitive
+and view-invariant features in the presence of large pose variations. In this
+work, we address both problems by proposing a novel deep person image
+generation model for synthesizing realistic person images conditional on the
+pose. The model is based on a generative adversarial network (GAN) designed
+specifically for pose normalization in re-id, thus termed pose-normalization
+GAN (PN-GAN). With the synthesized images, we can learn a new type of deep
+re-id feature free of the influence of pose variations. We show that this
+feature is strong on its own and complementary to features learned with the
+original images. Importantly, under the transfer learning setting, we show that
+our model generalizes well to any new re-id dataset without the need for
+collecting any training data for model fine-tuning. The model thus has the
+potential to make re-id model truly scalable.
+"
+24610,Diffusion-based nonlinear filtering for multimodal data fusion with application to sleep stage assessment,"  The problem of information fusion from multiple data-sets acquired by
+multimodal sensors has drawn significant research attention over the years. In
+this paper, we focus on a particular problem setting consisting of a physical
+phenomenon or a system of interest observed by multiple sensors. We assume that
+all sensors measure some aspects of the system of interest with additional
+sensor-specific and irrelevant components. Our goal is to recover the variables
+relevant to the observed system and to filter out the nuisance effects of the
+sensor-specific variables. We propose an approach based on manifold learning,
+which is particularly suitable for problems with multiple modalities, since it
+aims to capture the intrinsic structure of the data and relies on minimal prior
+model knowledge. Specifically, we propose a nonlinear filtering scheme, which
+extracts the hidden sources of variability captured by two or more sensors,
+that are independent of the sensor-specific components. In addition to
+presenting a theoretical analysis, we demonstrate our technique on real
+measured data for the purpose of sleep stage assessment based on multiple,
+multimodal sensor measurements. We show that without prior knowledge on the
+different modalities and on the measured system, our method gives rise to a
+data-driven representation that is well correlated with the underlying sleep
+process and is robust to noise and sensor-specific effects.
+"
+24611,A Radial Time Projection Chamber for $α$ detection in CLAS at JLab,"  A new Radial Time Projection Chamber (RTPC) was developed at the Jefferson
+Laboratory to track low-energy nuclear recoils for the purpose of measuring
+exclusive nuclear reactions, such as coherent Deeply Virtual Compton Scattering
+and coherent meson production off $^4$He. In such processes, the $^4$He nucleus
+remains intact in the final state, however the CEBAF Large Acceptance
+Spectrometer (CLAS) cannot track the low energy $\alpha$ particles. In 2009, we
+carried out measurements using the CLAS spectrometer supplemented by the RTPC
+positioned directly around a gaseous $^4$He target, allowing a detection
+threshold as low as 12$\sim$MeV for $^4$He. This article discusses the design,
+principle of operation, calibration methods and the performances of this RTPC.
+"
+24612,Variance Reduction for Reinforcement Learning in Input-Driven Environments,"  We consider reinforcement learning in input-driven environments, where an
+exogenous, stochastic input process affects the dynamics of the system. Input
+processes arise in many applications, including queuing systems, robotics
+control with disturbances, and object tracking. Since the state dynamics and
+rewards depend on the input process, the state alone provides limited
+information for the expected future returns. Therefore, policy gradient methods
+with standard state-dependent baselines suffer high variance during training.
+We derive a bias-free, input-dependent baseline to reduce this variance, and
+analytically show its benefits over state-dependent baselines. We then propose
+a meta-learning approach to overcome the complexity of learning a baseline that
+depends on a long sequence of inputs. Our experimental results show that across
+environments from queuing systems, computer networks, and MuJoCo robotic
+locomotion, input-dependent baselines consistently improve training stability
+and result in better eventual policies.
+"
+24613,Ferromagnetism in chiral multilayer 2D semimetals,"  We calculate the temperature dependent long-range magnetic coupling in the
+presence of dilute concentrations of random magnetic impurities in chiral
+multilayer two-dimensional semimetals, i.e., undoped intrinsic multilayer
+graphene. Assuming a carrier-mediated indirect RKKY exchange interaction among
+the well-separated magnetic impurities with the itinerant carriers mediating
+the magnetic interaction between the impurities, we investigate the magnetic
+properties of intrinsic multilayer graphene using an effective chiral
+Hamiltonian model. We find that due to the enhanced density of states in the
+rhombohedral stacking sequence of graphene layers, the magnetic ordering of
+multilayer graphene is ferromagnetic in the continuum limit. The ferromagnetic
+transition temperature is calculated using a finite-temperature self-consistent
+field approximation and found to be within the experimentally accessible range
+for reasonable values of the impurity-carrier coupling.
+"
+24614,Perturbed Dissipative Solitons: A Variational Approach,"  We adopt a variational technique to study the dynamics of perturbed
+dissipative solitons, whose evolution is governed by a Ginzburg--Landau
+equation (GLE). As a specific example of such solitons, we consider a
+silicon-based active waveguide in which free carriers are generated through
+two-photon absorption. In this case, dissipative solitons are perturbed by
+physical processes such as third-order dispersion, intrapulse Raman scattering,
+self-steepening, and free-carrier generation. To solve the variational problem,
+we adopt the Pereira--Stenflo soliton as an ansatz since this soliton is the
+exact solution of the unperturbed GLE. With this ansatz, we derive a set of six
+coupled differential equations exhibiting the dynamics of various pulse
+parameters. This set of equations provides considerable physical insight in the
+complex behavior of perturbed dissipative solitons. Its predictions are found
+to be in good agreement with direct numerical simulations of the GLE. More
+specifically, the spectral and temporal shifts of the chirped soliton induced
+by free carriers and intrapulse Raman scattering are predicted quite
+accurately. We also provide simple analytic expressions of these shifts by
+making suitable approximations. Our semi-analytic treatment is useful for
+gaining physical insight into complex soliton-evolution processes.
+"
+24615,Combined Task and Motion Planning as Classical AI Planning,"  Planning in robotics is often split into task and motion planning. The
+high-level, symbolic task planner decides what needs to be done, while the
+motion planner checks feasibility and fills up geometric detail. It is known
+however that such a decomposition is not effective in general as the symbolic
+and geometrical components are not independent. In this work, we show that it
+is possible to compile task and motion planning problems into classical AI
+planning problems; i.e., planning problems over finite and discrete state
+spaces with a known initial state, deterministic actions, and goal states to be
+reached. The compilation is sound, meaning that classical plans are valid robot
+plans, and probabilistically complete, meaning that valid robot plans are
+classical plans when a sufficient number of configurations is sampled. In this
+approach, motion planners and collision checkers are used for the compilation,
+but not at planning time. The key elements that make the approach effective are
+1) expressive classical AI planning languages for representing the compiled
+problems in compact form, that unlike PDDL make use of functions and state
+constraints, and 2) general width-based search algorithms capable of finding
+plans over huge combinatorial spaces using weak heuristics only. Empirical
+results are presented for a PR2 robot manipulating tens of objects, for which
+long plans are required.
+"
+24616,Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network,"  We revisit fuzzy neural network with a cornerstone notion of generalized
+hamming distance, which provides a novel and theoretically justified framework
+to re-interpret many useful neural network techniques in terms of fuzzy logic.
+In particular, we conjecture and empirically illustrate that, the celebrated
+batch normalization (BN) technique actually adapts the normalized bias such
+that it approximates the rightful bias induced by the generalized hamming
+distance. Once the due bias is enforced analytically, neither the optimization
+of bias terms nor the sophisticated batch normalization is needed. Also in the
+light of generalized hamming distance, the popular rectified linear units
+(ReLU) can be treated as setting a minimal hamming distance threshold between
+network inputs and weights. This thresholding scheme, on the one hand, can be
+improved by introducing double thresholding on both extremes of neuron outputs.
+On the other hand, ReLUs turn out to be non-essential and can be removed from
+networks trained for simple tasks like MNIST classification. The proposed
+generalized hamming network (GHN) as such not only lends itself to rigorous
+analysis and interpretation within the fuzzy logic theory but also demonstrates
+fast learning speed, well-controlled behaviour and state-of-the-art
+performances on a variety of learning tasks.
+"
+24617,Lattice-based designs with quasi-uniform projections,"  We provide a theoretical framework to study the projective uniformity of
+lattice-based designs and give new magic rotation matrices. Utilizing these
+matrices, we propose an algorithm to generate densest packing-based maximum
+projection designs, a type of lattice-based designs that process asymptotically
+optimal separation distance and good projective uniformity. In particular, in
+two, three, four, six, and eight dimensions, they process asymptotically
+optimal order of separation and fill distances on every univariate projection,
+while in four and eight dimensions, they also process asymptotically optimal
+order of separation distance on multivariate projections. Numerical results
+imply that these designs are useful when there are likely more than one active
+variables and more striking if most variables are active.
+"
+24618,An Experimental Study of Deep Convolutional Features For Iris Recognition,"  Iris is one of the popular biometrics that is widely used for identity
+authentication. Different features have been used to perform iris recognition
+in the past. Most of them are based on hand-crafted features designed by
+biometrics experts. Due to tremendous success of deep learning in computer
+vision problems, there has been a lot of interest in applying features learned
+by convolutional neural networks on general image recognition to other tasks
+such as segmentation, face recognition, and object detection. In this paper, we
+have investigated the application of deep features extracted from VGG-Net for
+iris recognition. The proposed scheme has been tested on two well-known iris
+databases, and has shown promising results with the best accuracy rate of
+99.4\%, which outperforms the previous best result.
+"
+24619,Soft decision decoding of Reed-Muller codes: recursive lists,"  Recursive list decoding is considered for Reed-Muller (RM) codes. The
+algorithm repeatedly relegates itself to the shorter RM codes by recalculating
+the posterior probabilities of their symbols. Intermediate decodings are only
+performed when these recalculations reach the trivial RM codes. In turn, the
+updated lists of most plausible codewords are used in subsequent decodings. The
+algorithm is further improved by using permutation techniques on code positions
+and by eliminating the most error-prone information bits. Simulation results
+show that for all RM codes of length 256 and many subcodes of length 512, these
+algorithms approach maximum-likelihood (ML) performance within a margin of 0.1
+dB. As a result, we present tight experimental bounds on ML performance for
+these codes.
+"
+24620,Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent,"  Nesterov's accelerated gradient descent (AGD), an instance of the general
+family of ""momentum methods"", provably achieves faster convergence rate than
+gradient descent (GD) in the convex setting. However, whether these methods are
+superior to GD in the nonconvex setting remains open. This paper studies a
+simple variant of AGD, and shows that it escapes saddle points and finds a
+second-order stationary point in $\tilde{O}(1/\epsilon^{7/4})$ iterations,
+faster than the $\tilde{O}(1/\epsilon^{2})$ iterations required by GD. To the
+best of our knowledge, this is the first Hessian-free algorithm to find a
+second-order stationary point faster than GD, and also the first single-loop
+algorithm with a faster rate than GD even in the setting of finding a
+first-order stationary point. Our analysis is based on two key ideas: (1) the
+use of a simple Hamiltonian function, inspired by a continuous-time
+perspective, which AGD monotonically decreases per step even for nonconvex
+functions, and (2) a novel framework called improve or localize, which is
+useful for tracking the long-term behavior of gradient-based optimization
+algorithms. We believe that these techniques may deepen our understanding of
+both acceleration algorithms and nonconvex optimization.
+"
+24621,The particle background of the X-IFU instrument,"  In this paper we are going to review the latest estimates for the particle
+background expected on the X-IFU instrument onboard of the ATHENA mission. The
+particle background is induced by two different particle populations: the so
+called ""soft protons"" and the Cosmic rays. The first component is composed of
+low energy particles (< 100s keV) that get funnelled by the mirrors towards the
+focal plane, losing part of their energy inside the filters and inducing
+background counts inside the instrument sensitivity band. The latter component
+is induced by high energy particles (> 100 MeV) that possess enough energy to
+cross the spacecraft and reach the detector from any direction, depositing a
+small fraction of their energy inside the instrument. Both these components are
+estimated using Monte Carlo simulations and the latest results are presented
+here.
+"
+24622,Efficiency of quantum versus classical annealing in non-convex learning problems,"  Quantum annealers aim at solving non-convex optimization problems by
+exploiting cooperative tunneling effects to escape local minima. The underlying
+idea consists in designing a classical energy function whose ground states are
+the sought optimal solutions of the original optimization problem and add a
+controllable quantum transverse field to generate tunneling processes. A key
+challenge is to identify classes of non-convex optimization problems for which
+quantum annealing remains efficient while thermal annealing fails. We show that
+this happens for a wide class of problems which are central to machine
+learning. Their energy landscapes is dominated by local minima that cause
+exponential slow down of classical thermal annealers while simulated quantum
+annealing converges efficiently to rare dense regions of optimal solutions.
+"
+24623,Optimal Universal Controllers for Roll Damping,"  Roll damping is an important problem of ship motion control since excessive
+roll motion may cause motion sickness of human occupants and damage fragile
+cargo. Actuators used for roll damping (fins, rudders and thrusters) inevitably
+create a rotating yaw moment, interfering thus with the vessel's autopilot
+(heading control system). To reach and maintain the ""trade-off"" between the
+concurrent goals of accurate vessel steering and roll damping, an optimization
+procedure in general needs to take place where the cost functional penalizes
+the roll angle, the steering error and the control effort. Since the vessel's
+motion is influenced by the uncertain wave disturbance, the optimal value of
+this functional and the resulting optimal process are also uncertain. Standard
+approaches, prevailing in the literature, approximate the wave disturbance by
+the ""colored noise"" with a known spectral density, reducing the optimization
+problem to conventional loop-shaping, LQG or $\mathcal{H}_\infty$ control. In
+this paper, we propose a novel approach to optimal roll damping, approximating
+the disturbance by a \emph{polyharmonic} signal with known frequencies yet
+uncertain amplitudes and phase shifts. For this class of external disturbances,
+an optimal \emph{universal} controller (OUC) can be found, delivering the
+optimal solution \emph{for any} uncertain parameters of the signal. Using
+numerical simulations, we compare our design method with classical approaches
+to optimal roll damping and show that OUC controllers exhibit better
+performance.
+"
+24624,\$1 Today or \$2 Tomorrow? The Answer is in Your Facebook Likes,"  In economics and psychology, delay discounting is often used to characterize
+how individuals choose between a smaller immediate reward and a larger delayed
+reward. People with higher delay discounting rate (DDR) often choose smaller
+but more immediate rewards (a ""today person""). In contrast, people with a lower
+discounting rate often choose a larger future rewards (a ""tomorrow person"").
+Since the ability to modulate the desire of immediate gratification for long
+term rewards plays an important role in our decision-making, the lower
+discounting rate often predicts better social, academic and health outcomes. In
+contrast, the higher discounting rate is often associated with problematic
+behaviors such as alcohol/drug abuse, pathological gambling and credit card
+default. Thus, research on understanding and moderating delay discounting has
+the potential to produce substantial societal benefits.
+"
+24625,Analyzing Reconstruction Artifacts from Arbitrary Incomplete X-ray CT Data,"  This article provides a mathematical analysis of singular (nonsmooth)
+artifacts added to reconstructions by filtered backprojection (FBP) type
+algorithms for X-ray CT with arbitrary incomplete data. We prove that these
+singular artifacts arise from points at the boundary of the data set. Our
+results show that, depending on the geometry of this boundary, two types of
+artifacts can arise: object-dependent and object-independent artifacts.
+Object-dependent artifacts are generated by singularities of the object being
+scanned and these artifacts can extend along lines. They generalize the streak
+artifacts observed in limited-angle tomography. Object-independent artifacts,
+on the other hand, are essentially independent of the object and take one of
+two forms: streaks on lines if the boundary of the data set is not smooth at a
+point and curved artifacts if the boundary is smooth locally. We prove that
+these streak and curve artifacts are the only singular artifacts that can occur
+for FBP in the continuous case. In addition to the geometric description of
+artifacts, the article provides characterizations of their strength in Sobolev
+scale in certain cases. The results of this article apply to the well-known
+incomplete data problems, including limited-angle and region-of-interest
+tomography, as well as to unconventional X-ray CT imaging setups that arise in
+new practical applications. Reconstructions from simulated and real data are
+analyzed to illustrate our theorems, including the reconstruction that
+motivated this work---a synchrotron data set in which artifacts appear on lines
+that have no relation to the object.
+"
+24626,Invariant Solutions for the Einstein Field Equation,"  In this paper we provide a method capable of producing an infinite number of
+solutions for Einstein's equation on static spacetimes with perfect fluid as a
+matter field. All spacetimes of this type which are symmetric with respect to a
+given group of translations and whose spatial factor is conformally flat, are
+characterized. We use this method to give some exact solutions of the referred
+equation.
+"
+24627,How Robust is 3D Human Pose Estimation to Occlusion?,"  Occlusion is commonplace in realistic human-robot shared environments, yet
+its effects are not considered in standard 3D human pose estimation benchmarks.
+This leaves the question open: how robust are state-of-the-art 3D pose
+estimation methods against partial occlusions? We study several types of
+synthetic occlusions over the Human3.6M dataset and find a method with
+state-of-the-art benchmark performance to be sensitive even to low amounts of
+occlusion. Addressing this issue is key to progress in applications such as
+collaborative and service robotics. We take a first step in this direction by
+improving occlusion-robustness through training data augmentation with
+synthetic occlusions. This also turns out to be an effective regularizer that
+is beneficial even for non-occluded test cases.
+"
+24628,Online Reweighted Least Squares Algorithm for Sparse Recovery and Application to Short-Wave Infrared Imaging,"  We address the problem of sparse recovery in an online setting, where random
+linear measurements of a sparse signal are revealed sequentially and the
+objective is to recover the underlying signal. We propose a reweighted least
+squares (RLS) algorithm to solve the problem of online sparse reconstruction,
+wherein a system of linear equations is solved using conjugate gradient with
+the arrival of every new measurement. The proposed online algorithm is useful
+in a setting where one seeks to design a progressive decoding strategy to
+reconstruct a sparse signal from linear measurements so that one does not have
+to wait until all measurements are acquired. Moreover, the proposed algorithm
+is also useful in applications where it is infeasible to process all the
+measurements using a batch algorithm, owing to computational and storage
+constraints. It is not needed a priori to collect a fixed number of
+measurements; rather one can keep collecting measurements until the quality of
+reconstruction is satisfactory and stop taking further measurements once the
+reconstruction is sufficiently accurate. We provide a proof-of-concept by
+comparing the performance of our algorithm with the RLS-based batch
+reconstruction strategy, known as iteratively reweighted least squares (IRLS),
+on natural images. Experiments on a recently proposed focal plane array-based
+imaging setup show up to 1 dB improvement in output peak signal-to-noise ratio
+as compared with the total variation-based reconstruction.
+"
+24629,A thermally-driven differential mutation approach for the structural optimization of large atomic systems,"  A computational method is presented which is capable to obtain low lying
+energy structures of topological amorphous systems. The method merges a
+differential mutation genetic algorithm with simulated annealing. This is done
+by incorporating a thermal selection criterion, which makes it possible to
+reliably obtain low lying minima with just a small population size and is
+suitable for multimodal structural optimization. The method is tested on the
+structural optimization of amorphous graphene from unbiased atomic starting
+configurations. With just a population size of six systems, energetically very
+low structures are obtained. While each of the structures represents a
+distinctly different arrangement of the atoms, their properties, such as
+energy, distribution of rings, radial distribution function, coordination
+number and distribution of bond angles, are very similar.
+"
+24630,Epidemic spread on one-way circular-coupled networks,"  Real epidemic spreading networks often composed of several kinds of networks
+interconnected with each other, and the interrelated networks have the
+different topologies and epidemic dynamics. Moreover, most human diseases are
+derived from animals, and the zoonotic infections always spread on
+interconnected networks. In this paper, we consider the epidemic spreading on
+one-way circular-coupled network consist of three interconnected subnetworks.
+Here, two one-way three-layer circular interactive networks are established by
+introducing the heterogeneous mean-field approach method, then we get the basic
+reproduction numbers and prove the global stability of the disease-free
+equilibrium and endemic equilibrium of the models. Through mathematical
+analysis and numerical simulations, it is found that the basic reproduction
+numbers $R_0$ of the two models are dependent on the infection rates, infection
+periods, average degrees and degree ratios. In the first model, the network
+structures of the inner contact patterns have a bigger impact on $R_0$ than
+that of the cross contact patterns. Under the same contact pattern, the
+internal infection rates have greater influence on $R_0$ than the
+cross-infection rates. In the second model, the disease prevails in a
+heterogeneous network has a greater impact on $R_0$ than the disease from a
+homogeneous network, and the infections among the three subnetworks all play a
+important role in the propagation process. Numerical examples verify and expand
+these theoretical results very well.
+"
+24631,Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records,"  The rapid growth of Electronic Health Records (EHRs), as well as the
+accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting
+widespread interests and attentions. Recent progress in the design and
+applications of deep learning methods has shown promising results and is
+forcing massive changes in healthcare academia and industry, but most of these
+methods rely on massive labeled data. In this work, we propose a general deep
+learning framework which is able to boost risk prediction performance with
+limited EHR data. Our model takes a modified generative adversarial network
+namely ehrGAN, which can provide plausible labeled EHR data by mimicking real
+patient records, to augment the training dataset in a semi-supervised learning
+manner. We use this generative model together with a convolutional neural
+network (CNN) based prediction model to improve the onset prediction
+performance. Experiments on two real healthcare datasets demonstrate that our
+proposed framework produces realistic data samples and achieves significant
+improvements on classification tasks with the generated data over several
+stat-of-the-art baselines.
+"
+24632,A Spiral Spin State with Open Boundary Conditions in a Magnetic Field,"  In order to model a spiral spin state in a thin film, we study a classical
+Heisenberg model with open boundary conditions. With magnetic field applied in
+the plane of the film, the spin state becomes ferromagnetic above a critical
+field that increases with thickness $N$. For a given $N$, the spiral passes
+through states with $n= n_0$ up to 0 complete periods in steps of 1. These
+numerical results agree with earlier analytic results in the continuum limit
+and help explain the susceptibility jumps observed in thin films.
+"
+24633,"Survey of Water and Ammonia in Nearby galaxies (SWAN): Resolved Ammonia Thermometry, Water and Methanol Masers in the Nuclear Starburst of NGC 253","  We present Karl G Jansky Very Large Array molecular line observations of the
+nearby starburst galaxy NGC 253, from SWAN: ""Survey of Water and Ammonia in
+Nearby galaxies"". SWAN is a molecular line survey at centimeter wavelengths
+designed to reveal the physical conditions of star forming gas over a range of
+star forming galaxies. NGC 253 has been observed in four 1GHz bands from 21 to
+36 GHz at 6"" ($\sim100$pc) spatial and 3.5 km s$^{-1}$ spectral resolution. In
+total we detect 19 transitions from seven molecular and atomic species. We have
+targeted the metastable inversion transitions of ammonia (NH$_{3}$) from (1,1)
+to (5,5) and the (9,9) line, the 22.2 GHz water (H$_2$O) ($6_{16}-5_{23}$)
+maser, and the 36.1 GHz methanol (CH$_3$OH) ($4_{-1}-3_{0}$) maser. Utilizing
+NH$_{3}$ as a thermometer, we present evidence for uniform heating over the
+central kpc of NGC 253. The molecular gas is best described by a two kinetic
+temperature model with a warm 130K and a cooler 57K component. A comparison of
+these observations with previous ALMA results suggests that the molecular gas
+is not heated in photon dominated regions or shocks. It is possible that the
+gas is heated by turbulence or cosmic rays. In the galaxy center we find
+evidence for NH$_{3}$(3,3) masers. Furthermore we present velocities and
+luminosities of three water maser features related to the nuclear starburst. We
+partially resolve CH$_3$OH masers seen at the edges of the bright molecular
+emission, which coincides with expanding molecular superbubbles. This suggests
+that the masers are pumped by weak shocks in the bubble surfaces.
+"
+24634,Conservation laws and symmetries of a generalized Kawahara equation,"  The generalized Kawahara equation $u_t=a(t) u_{xxxxx} +b(t)u_{xxx} +c(t)f(u)
+u_x$ appears in many physical applications. A complete classification of
+low-order conservation laws and point symmetries is obtained for this equation,
+which includes as a special case the usual Kawahara equation $u_t = \alpha u
+u_x+\beta u^2u_x +\gamma u_{xxx}+\mu u_{xxxxx}$. A general connection between
+conservation laws and symmetries for the generalized Kawahara equation is
+derived through the Hamiltonian structure of this equation and its relationship
+to Noether's theorem using a potential formulation.
+"
+24635,Robustness of symmetry-protected topological states against time-periodic perturbations,"  The existence of gapless boundary states is a key attribute of any
+topological insulator. Topological band theory predicts that these states are
+robust against static perturbations that preserve the relevant symmetries. In
+this article, using Floquet theory, we examine how chiral symmetry-protection
+extends also to states subject to time-periodic perturbations $-$ in
+one-dimensional Floquet topological insulators as well as in ordinary
+one-dimensional time-independent topological insulators. It is found that, in
+the case of the latter, the edge modes are resistant to a much larger class of
+time-periodic symmetry-preserving perturbations than in Floquet topological
+insulators. Notably, boundary states in chiral time-independent topological
+insulators also exhibit an unexpected resilience against a certain type of
+symmetry-breaking time-periodic perturbations. We argue that this is a generic
+property for topological phases protected by chiral symmetry. Implications for
+experiments are discussed.
+"
+24636,A Sharp Lower Bound for Mixed-membership Estimation,"  Consider an undirected network with $n$ nodes and $K$ perceivable
+communities, where some nodes may have mixed memberships. We assume that for
+each node $1 \leq i \leq n$, there is a probability mass function $\pi_i$
+defined over $\{1, 2, \ldots, K\}$ such that \[ \pi_i(k) = \mbox{the weight of
+node $i$ on community $k$}, \qquad 1 \leq k \leq K. \] The goal is to estimate
+$\{\pi_i, 1 \leq i \leq n\}$ (i.e., membership estimation).
+We model the network with the {\it degree-corrected mixed membership (DCMM)}
+model \cite{Mixed-SCORE}. Since for many natural networks, the degrees have an
+approximate power-law tail, we allow {\it severe degree heterogeneity} in our
+model.
+For any membership estimation $\{\hat{\pi}_i, 1 \leq i \leq n\}$, since each
+$\pi_i$ is a probability mass function, it is natural to measure the errors by
+the average $\ell^1$-norm \[ \frac{1}{n} \sum_{i = 1}^n \| \hat{\pi}_i -
+\pi_i\|_1. \] We also consider a variant of the $\ell^1$-loss, where each
+$\|\hat{\pi}_i - \pi_i\|_1$ is re-weighted by the degree parameter $\theta_i$
+in DCMM (to be introduced).
+We present a sharp lower bound. We also show that such a lower bound is
+achievable under a broad situation. More discussion in this vein is continued
+in our forthcoming manuscript.
+The results are very different from those on community detection. For
+community detection, the focus is on the special case where all $\pi_i$ are
+degenerate; the goal is clustering, so Hamming distance is the natural choice
+of loss function, and the rate can be exponentially fast. The setting here is
+broader and more difficult: it is more natural to use the $\ell^1$-loss, and
+the rate is only polynomially fast.
+"
+24637,Deep $k$-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions,"  The current trend of pushing CNNs deeper with convolutions has created a
+pressing demand to achieve higher compression gains on CNNs where convolutions
+dominate the computation and parameter amount (e.g., GoogLeNet, ResNet and Wide
+ResNet). Further, the high energy consumption of convolutions limits its
+deployment on mobile devices. To this end, we proposed a simple yet effective
+scheme for compressing convolutions though applying k-means clustering on the
+weights, compression is achieved through weight-sharing, by only recording $K$
+cluster centers and weight assignment indexes. We then introduced a novel
+spectrally relaxed $k$-means regularization, which tends to make hard
+assignments of convolutional layer weights to $K$ learned cluster centers
+during re-training. We additionally propose an improved set of metrics to
+estimate energy consumption of CNN hardware implementations, whose estimation
+results are verified to be consistent with previously proposed energy
+estimation tool extrapolated from actual hardware measurements. We finally
+evaluated Deep $k$-Means across several CNN models in terms of both compression
+ratio and energy consumption reduction, observing promising results without
+incurring accuracy loss. The code is available at
+this https URL
+"
+24638,Improved Generalized Periods estimates on Riemannian Surfaces with Nonpositive Curvature,"  We show that on compact Riemann surfaces of negative curvature, the
+generalized periods, i.e. the $\nu$-th order Fourier coefficient of
+eigenfunctions $e_\lambda$ over a period geodesic $\gamma$ goes to 0 at the
+rate of $O((\log\lambda)^{-1/2})$, if $0<\nu<c_0\lambda$, given any $0<c_0<1$.
+No such result is possible for the sphere $S^2$ or the flat torus $\mathbb
+T^2$. Combined with the quantum ergodic restriction result of Toth and
+Zelditch, our results imply that for a generic closed geodesic $\gamma$ on a
+compact hyperbolic surface, the restriction $e_{\lambda_j}|_\gamma$ of an
+orthonormal basis $\{e_{\lambda_j}\}$ has a full density subsequence that goes
+to zero weakly in $L^2(\gamma)$. Our proof consists of a further refinement of
+a recent paper by Sogge, Xi and Zhang on the geodesic period integrals
+($\nu=0$), which featured the Gauss-Bonnet Theorem as a key quantitative tool
+to avoid geodesic rectangles on the universal cover of $M$. In contrast, we
+shall employ the Gauss-Bonnet Theorem to quantitatively avoid geodesic
+parallelograms. The use of Gauss-Bonnet also enables us to weaken our curvature
+condition, by allowing the curvature to vanish at an averaged rate of finite
+type.
+"
+24639,A test for k sample Behrens-Fisher problem in high dimensional data,"  In this paper, the $k$ sample Behrens-Fisher problem is investigated in high
+dimensional setting. We propose a new test statistic and demonstrate that the
+proposed test is expected to have more powers than some existing test
+especially when sample sizes are unbalanced. We provide theoretical
+investigation as well as numerical studies on both sizes and powers of the
+proposed tests and existing test. Both theoretical comparison of the asymptotic
+power functions and numerical studies show that the proposed test tends to have
+more powers than existing test in many cases of unbalanced sample sizes.
+"
+24640,Transfer Learning for Named-Entity Recognition with Neural Networks,"  Recent approaches based on artificial neural networks (ANNs) have shown
+promising results for named-entity recognition (NER). In order to achieve high
+performances, ANNs need to be trained on a large labeled dataset. However,
+labels might be difficult to obtain for the dataset on which the user wants to
+perform NER: label scarcity is particularly pronounced for patient note
+de-identification, which is an instance of NER. In this work, we analyze to
+what extent transfer learning may address this issue. In particular, we
+demonstrate that transferring an ANN model trained on a large labeled dataset
+to another dataset with a limited number of labels improves upon the
+state-of-the-art results on two different datasets for patient note
+de-identification.
+"
+24641,Optimal graphon estimation in cut distance,"  Consider the twin problems of estimating the connection probability matrix of
+an inhomogeneous random graph and the graphon of a W-random graph. We establish
+the minimax estimation rates with respect to the cut metric for classes of
+block constant matrices and step function graphons. Surprisingly, our results
+imply that, from the minimax point of view, the raw data, that is, the
+adjacency matrix of the observed graph, is already optimal and more involved
+procedures cannot improve the convergence rates for this metric. This
+phenomenon contrasts with optimal rates of convergence with respect to other
+classical distances for graphons such as the l 1 or l 2 metrics.
+"
+24642,Generalised Mermin-type non-locality arguments,"  We broadly generalise Mermin-type arguments on GHZ states, and we provide
+exact group-theoretic conditions for non-locality to be achieved. Our results
+are of interest in quantum foundations, where they yield a new hierarchy of
+quantum-realisable All-vs-Nothing arguments. They are also of interest to
+quantum protocols, where they find immediate application to a non-trivial
+extension of the hybrid quantum-classical secret sharing scheme of Hillery,
+Bužek and Berthiaume (HBB). Our proofs are carried out in the graphical
+language of string diagrams for dagger compact categories, and their validity
+extends beyond quantum theory to any theory featuring the relevant algebraic
+structures.
+"
+24643,Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding,"  In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention
+mechanism for sentence embedding. We design DSA by modifying dynamic routing in
+capsule network (Sabouretal.,2017) for natural language processing. DSA attends
+to informative words with a dynamic weight vector. We achieve new
+state-of-the-art results among sentence encoding methods in Stanford Natural
+Language Inference (SNLI) dataset with the least number of parameters, while
+showing comparative results in Stanford Sentiment Treebank (SST) dataset.
+"
+24644,A Notebook Format for the Holistic Design of Embedded Systems (Tool Paper),"  This paper proposes the use of notebooks for the design documentation and
+tool interaction in the rigorous design of embedded systems. Conventionally, a
+notebook is a sequence of cells alternating between (textual) code and prose to
+form a document that is meant to be read from top to bottom, in the spirit of
+literate programming. We extend the use of notebooks to embedded systems
+specified by pCharts. The charts are visually edited in cells inline. Other
+cells can contain statements that generate code and analyze the charts
+qualitatively and quantitatively; in addition, notebook cells can contain other
+instructions to build the product from the generated code. This allows a
+notebook to be replayed to re-analyze the design and re-build the product, like
+a script, but also allows the notebook to be used for presentations, as for
+this paper, and for the inspection of the design. The interaction with the
+notebook is done through a web browser that connects to a local or remote
+server, thus allowing a computationally intensive analysis to run remotely if
+needed. The pState notebooks are implemented as an extension to Jupyter. The
+underlying software architecture is described and the issue of proper placement
+of transition labels in charts embedded in notebooks is discussed.
+"
+24645,Minimal and Canonical Images,"  We describe a family of new algorithms for finding the canonical image of a
+set of points under the action of a permutation group. This family of
+algorithms makes use of the orbit structure of the group, and a chain of
+subgroups of the group, to efficiently reduce the amount of search which must
+be performed to find a canonical image.
+We present both a formal proof of correctness of our algorithms and
+experiments on different permutation groups, which compare our algorithms with
+the previous state of the art.
+"
+24646,Exploring the Regularity of Sparse Structure in Convolutional Neural Networks,"  Sparsity helps reduce the computational complexity of deep neural networks by
+skipping zeros. Taking advantage of sparsity is listed as a high priority in
+next generation DNN accelerators such as TPU. The structure of sparsity, i.e.,
+the granularity of pruning, affects the efficiency of hardware accelerator
+design as well as the prediction accuracy. Coarse-grained pruning creates
+regular sparsity patterns, making it more amenable for hardware acceleration
+but more challenging to maintain the same accuracy. In this paper we
+quantitatively measure the trade-off between sparsity regularity and prediction
+accuracy, providing insights in how to maintain accuracy while having more a
+more structured sparsity pattern. Our experimental results show that
+coarse-grained pruning can achieve a sparsity ratio similar to unstructured
+pruning without loss of accuracy. Moreover, due to the index saving effect,
+coarse-grained pruning is able to obtain a better compression ratio than
+fine-grained sparsity at the same accuracy threshold. Based on the recent
+sparse convolutional neural network accelerator (SCNN), our experiments further
+demonstrate that coarse-grained sparsity saves about 2x the memory references
+compared to fine-grained sparsity. Since memory reference is more than two
+orders of magnitude more expensive than arithmetic operations, the regularity
+of sparse structure leads to more efficient hardware design.
+"
+24647,Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions,"  We consider the problem of learning causal DAGs in the setting where both
+observational and interventional data is available. This setting is common in
+biology, where gene regulatory networks can be intervened on using chemical
+reagents or gene deletions. Hauser and Bühlmann (2012) previously
+characterized the identifiability of causal DAGs under perfect interventions,
+which eliminate dependencies between targeted variables and their direct
+causes. In this paper, we extend these identifiability results to general
+interventions, which may modify the dependencies between targeted variables and
+their causes without eliminating them. We define and characterize the
+interventional Markov equivalence class that can be identified from general
+(not necessarily perfect) intervention experiments. We also propose the first
+provably consistent algorithm for learning DAGs in this setting and evaluate
+our algorithm on simulated and biological datasets.
+"
+24648,Theory and Practice of Data Citation,"  Citations are the cornerstone of knowledge propagation and the primary means
+of assessing the quality of research, as well as directing investments in
+science. Science is increasingly becoming ""data-intensive"", where large volumes
+of data are collected and analyzed to discover complex patterns through
+simulations and experiments, and most scientific reference works have been
+replaced by online curated datasets. Yet, given a dataset, there is no
+quantitative, consistent and established way of knowing how it has been used
+over time, who contributed to its curation, what results have been yielded or
+what value it has.
+The development of a theory and practice of data citation is fundamental for
+considering data as first-class research objects with the same relevance and
+centrality of traditional scientific products. Many works in recent years have
+discussed data citation from different viewpoints: illustrating why data
+citation is needed, defining the principles and outlining recommendations for
+data citation systems, and providing computational methods for addressing
+specific issues of data citation.
+The current panorama is many-faceted and an overall view that brings together
+diverse aspects of this topic is still missing. Therefore, this paper aims to
+describe the lay of the land for data citation, both from the theoretical (the
+why and what) and the practical (the how) angle.
+"
+24649,The general case on the order of appearance of product of consecutive Fibonacci and Lucas numbers,"  Let $F_{n}$ and $L_n$ be the $n$th Fibonacci and Lucas number, respectively.
+For each positive integer $m$, the order of appearance of $m$ in the Fibonacci
+sequence, denoted by $z(m)$, is the smallest positive integer $k$ such that $m$
+divides $F_k$. Recently, D. Marques has obtained a formula for
+$z(F_{n}F_{n+1})$, $z(F_{n}F_{n+1}F_{n+2})$, and
+$z(F_{n}F_{n+1}F_{n+2}F_{n+3})$. In this paper, we extend Marques' result to
+the case $z(F_{n}F_{n+1}\cdots F_{n+k})$ for every $4\leq k \leq 6$. We also
+give a formula for $z(L_nL_{n+1}\cdots L_{n+k})$ when $k = 5,6$ which extends
+the recent result of Marques and Trojovský. Our method gives a general idea
+on how to obtain the formulas for $z(F_nF_{n+1}\cdots F_{n+k})$ and
+$z(L_nL_{n+1}\cdots L_{n+k})$ for every $k\geq 1$.
+"
+24650,Efficient and Robust Recovery of Sparse Signal and Image Using Generalized Nonconvex Regularization,"  This work addresses the robust reconstruction problem of a sparse signal from
+compressed measurements. We propose a robust formulation for sparse
+reconstruction which employs the $\ell_1$-norm as the loss function for the
+residual error and utilizes a generalized nonconvex penalty for sparsity
+inducing. The $\ell_1$-loss is less sensitive to outliers in the measurements
+than the popular $\ell_2$-loss, while the nonconvex penalty has the capability
+of ameliorating the bias problem of the popular convex LASSO penalty and thus
+can yield more accurate recovery. To solve this nonconvex and nonsmooth
+minimization formulation efficiently, we propose a first-order algorithm based
+on alternating direction method of multipliers (ADMM). A smoothing strategy on
+the $\ell_1$-loss function has been used in deriving the new algorithm to make
+it convergent. Further, a sufficient condition for the convergence of the new
+algorithm has been provided for generalized nonconvex regularization. In
+comparison with several state-of-the-art algorithms, the new algorithm showed
+better performance in numerical experiments in recovering sparse signals and
+compressible images. The new algorithm scales well for large-scale problems, as
+often encountered in image processing.
+"
+24651,Input to State Stability of Bipedal Walking Robots: Application to DURUS,"  Bipedal robots are a prime example of systems which exhibit highly nonlinear
+dynamics, underactuation, and undergo complex dissipative impacts. This paper
+discusses methods used to overcome a wide variety of uncertainties, with the
+end result being stable bipedal walking. The principal contribution of this
+paper is to establish sufficiency conditions for yielding input to state stable
+(ISS) hybrid periodic orbits, i.e., stable walking gaits under model-based and
+phase-based uncertainties. In particular, it will be shown formally that
+exponential input to state stabilization (e-ISS) of the continuous dynamics,
+and hybrid invariance conditions are enough to realize stable walking in the
+23-DOF bipedal robot DURUS. This main result will be supported through
+successful and sustained walking of the bipedal robot DURUS in a laboratory
+environment.
+"
+24652,Counting coarse subsets of a countable group,"  For every countable group $G$, there are $2^{\omega}$ distinct classes of
+coarsely equivalent subsets of $G$.
+"
+24653,A new and improved algorithm for online bin packing,"  We revisit the classic online bin packing problem. In this problem, items of
+positive sizes no larger than 1 are presented one by one to be packed into
+subsets called ""bins"" of total sizes no larger than 1, such that every item is
+assigned to a bin before the next item is presented. We use online partitioning
+of items into classes based on sizes, as in previous work, but we also apply a
+new method where items of one class can be packed into more than two types of
+bins, where a bin type is defined according to the number of such items grouped
+together. Additionally, we allow the smallest class of items to be packed in
+multiple kinds of bins, and not only into their own bins. We combine this with
+the approach of packing of sufficiently big items according to their exact
+sizes. Finally, we simplify the analysis of such algorithms, allowing the
+analysis to be based on the most standard weight functions. This simplified
+analysis allows us to study the algorithm which we defined based on all these
+ideas. This leads us to the design and analysis of the first algorithm of
+asymptotic competitive ratio strictly below 1.58, specifically, we break this
+barrier and provide an algorithm AH (Advanced Harmonic) whose asymptotic
+competitive ratio does not exceed 1.5783.
+"
+24654,An Extended Laplace Approximation Method for Bayesian Inference of Self-Exciting Spatial-Temporal Models of Count Data,"  Self-Exciting models are statistical models of count data where the
+probability of an event occurring is influenced by the history of the process.
+In particular, self-exciting spatio-temporal models allow for spatial
+dependence as well as temporal self-excitation. For large spatial or temporal
+regions, however, the model leads to an intractable likelihood. An increasingly
+common method for dealing with large spatio-temporal models is by using Laplace
+approximations (LA). This method is convenient as it can easily be applied and
+is quickly implemented. However, as we will demonstrate in this manuscript,
+when applied to self-exciting Poisson spatial-temporal models, Laplace
+Approximations result in a significant bias in estimating some parameters. Due
+to this bias, we propose using up to sixth-order corrections to the LA for
+fitting these models. We will demonstrate how to do this in a Bayesian setting
+for Self-Exciting Spatio-Temporal models. We will further show there is a
+limited parameter space where the extended LA method still has bias. In these
+uncommon instances we will demonstrate how a more computationally intensive
+fully Bayesian approach using the Stan software program is possible in those
+rare instances. The performance of the extended LA method is illustrated with
+both simulation and real-world data.
+"
+24655,Local Euler Obstruction and Chern-Mather classes of Determinantal Varieties,"  For $m\geq n$, Let $K$ be an algebraic closed base field, and define
+$\tau_{m,n,k}$ to be the set of $m\times n$ matrices over $K$ with kernel
+dimension $\geq k$. This is a projective subvariety of $\mathbb{P}^{mn-1}$, and
+is usually called determinantal variety. In most cases $\tau_{m,n,k}$ is
+singular with singular locus $\tau_{m,n,k+1}$. In this paper we compute the
+local Euler obstruction of $\tau_{m,n,k}$, and we prove that the characteristic
+cycle of the intersection cohomology complex of $\tau_{m,n,k}$ is irreducible.
+We also give an explicit formula for the Chern-Mather class of $\tau_{m,n,k}$
+as a class in projective space. The irreducibility of the intersection
+cohomology characteristic cycle follows from the explicit computation of the
+local Euler obstruction, a study of the `Tjurina transforms' of determinantal
+varieties, and the Kashiwara-Dubson's microlocal index theorem.
+Our explicit formulas are based on calculations of degrees of certain Chern
+classes of the universal bundles over the Grassmannian. We use The Schubert 2
+package in Macaulay2 to exhibit examples of the Chern-Mather class and the
+class of the characteristic cycle of $\tau_{m,n,k}$ for some small values of
+$m,n,k$.
+Over the complex numbers, the local Euler obstruction of $\tau_{m,n,k}$ was
+recently computed by N.~Grulha, T.~Gaffney and M.~Ruas by methods in complex
+geometry.
+"
+24656,On Picard Value Problem of Some Difference Polynomials,"  In this paper, we study the value distribution of zeros of certain nonlinear
+difference polynomials of entire functions of finite order.
+"
+24657,Evidence for the role of the magnon energy relaxation length in the Spin Seebeck Effect,"  Temperature-dependent spin-Seebeck effect data on Pt|YIG
+(Y$_3$Fe$_5$O$_{12}$)|GGG (Gd$_3$Ga$_5$O$_{12}$) are reported for YIG films of
+various thicknesses. The effect is reported as a spin-Seebeck resistivity
+(SSR), the inverse spin-Hall field divided by the heat flux, to circumvent
+uncertainties about temperature gradients inside the films. The SSR is a
+non-monotonic function of YIG thickness. A diffusive model for magnon transport
+demonstrates how these data give evidence for the existence of two distinct
+length scales in thermal spin transport, a spin diffusion length and a magnon
+energy relaxation length.
+"
+24658,Context based Analysis of Lexical Semantics for Hindi Language,"  A word having multiple senses in a text introduces the lexical semantic task
+to find out which particular sense is appropriate for the given context. One
+such task is Word sense disambiguation which refers to the identification of
+the most appropriate meaning of the polysemous word in a given context using
+computational algorithms. The language processing research in Hindi, the
+official language of India, and other Indian languages is restricted by
+unavailability of the standard corpus. For Hindi word sense disambiguation
+also, the large corpus is not available. In this work, we prepared the text
+containing new senses of certain words leading to the enrichment of the
+sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed
+two novel lexical associations for Hindi word sense disambiguation based on the
+contextual features of the polysemous word. The evaluation of these methods is
+carried out over learning algorithms and favorable results are achieved.
+"
+24659,A congruence sum and rational approximations,"  We give a reciprocity formula for a two-variable sum where the variables
+satisfy a linear congruence condition. We also prove that such sum is a measure
+of how well a rational is approximable from below and show that the reciprocity
+formula is a simple consequence of this fact.
+"
+24660,Multilabel Classification with R Package mlr,"  We implemented several multilabel classification algorithms in the machine
+learning package mlr. The implemented methods are binary relevance, classifier
+chains, nested stacking, dependent binary relevance and stacking, which can be
+used with any base learner that is accessible in mlr. Moreover, there is access
+to the multilabel classification versions of randomForestSRC and rFerns. All
+these methods can be easily compared by different implemented multilabel
+performance measures and resampling methods in the standardized mlr framework.
+In a benchmark experiment with several multilabel datasets, the performance of
+the different methods is evaluated.
+"
+24661,e-QRAQ: A Multi-turn Reasoning Dataset and Simulator with Explanations,"  In this paper we present a new dataset and user simulator e-QRAQ (explainable
+Query, Reason, and Answer Question) which tests an Agent's ability to read an
+ambiguous text; ask questions until it can answer a challenge question; and
+explain the reasoning behind its questions and answer. The User simulator
+provides the Agent with a short, ambiguous story and a challenge question about
+the story. The story is ambiguous because some of the entities have been
+replaced by variables. At each turn the Agent may ask for the value of a
+variable or try to answer the challenge question. In response the User
+simulator provides a natural language explanation of why the Agent's query or
+answer was useful in narrowing down the set of possible answers, or not. To
+demonstrate one potential application of the e-QRAQ dataset, we train a new
+neural architecture based on End-to-End Memory Networks to successfully
+generate both predictions and partial explanations of its current understanding
+of the problem. We observe a strong correlation between the quality of the
+prediction and explanation.
+"
+24662,An Alternative Globalization Strategy for Unconstrained Optimization,"  We propose a new globalization strategy that can be used in unconstrained
+optimization algorithms to support rapid convergence from remote starting
+points. Our approach is based on using multiple points at each iteration to
+build a representative model of the objective function. Using the new
+information gathered from those multiple points, a local step is gradually
+improved by updating its direction as well as its length. We give a global
+convergence result and also provide parallel implementation details accompanied
+with a numerical study. Our numerical study shows that the proposed algorithm
+is a promising alternative as a globalization strategy.
+"
+24663,Darboux Transformation for the Nonisospectral and Variable-coefficient KdV Equation,"  With the nonuniform media taken into account, the nonisospectral and
+variable-coefficient Korteweg-de Vries equation, which describes various
+physical situations such as fluid dynamics and plasma, is under investigation
+in this paper. With appropriate selection of wave functions, the Darboux
+transformation is constructed, by which the multi-soliton solutions are derived
+and graphs are presented. The spectral parameters, coefficients and initial
+phase are discussed analytically and numerically to demonstrate their
+respective effect on the soliton dynamics, which plays a role in achieving the
+feasible soliton management with explicit conditions taken into account.
+"
+24664,Mutual information between reflected and transmitted speckle images,"  We study theoretically the mutual information between reflected and
+transmitted speckle patterns produced by wave scattering from disordered media.
+The mutual information between the two speckle images recorded on an array of N
+detection points (pixels) takes the form of long-range intensity correlation
+loops, that we evaluate explicitly as a function of the disorder strength and
+the Thouless number g. Our analysis, supported by extensive numerical
+simulations, reveals a competing effect of cross-sample and surface spatial
+correlations. An optimal distance between pixels is proven to exist, that
+enhances the mutual information by a factor Ng compared to the single-pixel
+scenario.
+"
+24665,Review on Modeling of Mechanical and Thermal Properties of Nano- and Micro-Composites,"  This article deals with the prediction of thermomechanical properties of
+fiber reinforced composites using several micromechanics models. These include
+strength of material approach, Halpin-Tsai equations, multi-phase mechanics of
+materials approaches, multi-phase Mori-Tanaka models, composite cylindrical
+assemblage model, Voigt-Reuss models, modified mixture rule, Cox model,
+effective medium approach and method of cells. Several composite systems
+reinforced with short and long, aligned, random and wavy reinforcements were
+considered. In addition, different aspects such as fiber-matrix interphase,
+fiber-matrix interfacial thermal resistance, fiber geometry, and multiple types
+of reinforcements were considered to model the composites systems. The current
+study also presents some important preliminary concepts and application of
+developed micromechanics models to advanced nanocomposites such as carbon
+nanotube reinforced composite. Main contribution of the current work is the
+investigation of several analytical micromechanical models, while most of the
+existing studies on the subject deal with only one or two approaches
+considering few aspects.
+"
+24666,A cascaded multiple-speaker localization and tracking system,"  This paper presents an online multiple-speaker localization and tracking
+method, as the INRIA-Perception contribution to the LOCATA Challenge 2018.
+First, the recursive least-square method is used to adaptively estimate the
+direct-path relative transfer function as an interchannel localization feature.
+The feature is assumed to associate with a single speaker at each
+time-frequency bin. Second, a complex Gaussian mixture model (CGMM) is used as
+a generative model of the features. The weight of each CGMM component
+represents the probability that this component corresponds to an active
+speaker, and is adaptively estimated with an online optimization algorithm.
+Finally, taking the CGMM component weights as observations, a Bayesian
+multiple-speaker tracking method based on the variational expectation
+maximization algorithm is used. The tracker accounts for the variation of
+active speakers and the localization miss measurements, by introducing speaker
+birth and sleeping processes. The experiments carried out on the development
+dataset of the challenge are reported.
+"
+24667,Lectures on Feynman categories,"  These are expanded lecture notes from lectures given at the Workshop on
+higher structures at MATRIX Melbourne. These notes give an introduction to
+Feynman categories and their applications.
+Feynman categories give a universal categorical way to encode operations and
+relations. This includes the aspects of operad--like theories such as PROPs,
+modular operads, twisted (modular) operads, properads, hyperoperads and their
+colored versions. There is more depth to the general theory as it applies as
+well to algebras over operads and an abundance of other related structures,
+such as crossed simplicial groups, the augmented simplicial category or
+FI--modules. Through decorations and transformations the theory is also related
+to the geometry of moduli spaces. Furthermore the morphisms in a Feynman
+category give rise to Hopf-- and bi--algebras with examples coming from
+topology, number theory and quantum field theory. All these aspects are
+covered.
+"
+24668,On the Basel Liquidity Formula for Elliptical Distributions,"  A justification of the Basel liquidity formula for risk capital in the
+trading book is given under the assumption that market risk-factor changes form
+a Gaussian white noise process over 10-day time steps and changes to P&L are
+linear in the risk-factor changes. A generalization of the formula is derived
+under the more general assumption that risk-factor changes are multivariate
+elliptical. It is shown that the Basel formula tends to be conservative when
+the elliptical distributions are from the heavier-tailed generalized hyperbolic
+family. As a by-product of the analysis a Fourier approach to calculating
+expected shortfall for general symmetric loss distributions is developed.
+"
+24669,Toward an Epistemic-Logical Theory of Categorization,"  Categorization systems are widely studied in psychology, sociology, and
+organization theory as information-structuring devices which are critical to
+decision-making processes. In the present paper, we introduce a sound and
+complete epistemic logic of categories and agents' categorical perception. The
+Kripke-style semantics of this logic is given in terms of data structures based
+on two domains: one domain representing objects (e.g. market products) and one
+domain representing the features of the objects which are relevant to the
+agents' decision-making. We use this framework to discuss and propose
+logic-based formalizations of some core concepts from psychological,
+sociological, and organizational research in categorization theory.
+"
+24670,Semiclassics in a system without classical limit: The few-body spectrum of two interacting bosons in one dimension,"  We present a semiclassical study of the spectrum of a few-body system
+consisting of two short-range interacting bosonic particles in one dimension, a
+particular case of a general class of integrable many-body systems where the
+energy spectrum is given by the solution of algebraic transcendental equations.
+By an exact mapping between $\delta$-potentials and boundary conditions on the
+few-body wave functions, we are able to extend previous semiclassical results
+for single-particle systems with mixed boundary conditions to the two-body
+problem. The semiclassical approach allows us to derive explicit analytical
+results for the smooth part of the two-body density of states that are in
+excellent agreement with numerical calculations. It further enables us to
+include the effect of bound states in the attractive case. Remarkably, for the
+particular case of two particles in one dimension, the discrete energy levels
+obtained through a requantization condition of the smooth density of states are
+essentially in perfect agreement with the exact ones.
+"
+24671,Local Correlation and Gap Statistics under Dyson Brownian Motion for Covariance Matrices,"  This paper is the third chapter of three of the author's undergraduate
+thesis. In this paper, we study the convergence of local bulk statistics for
+linearized covariance matrices under Dyson's Brownian motion. We consider
+deterministic initial data $V$ approximate the Dyson Brownian motion for
+linearized covariance matrices by the Wigner flow. Using universality results
+for the Wigner flow, we deduce universality for the linearized covariance
+matrices. We deduce bulk universality of averaged bulk correlation functions
+for both biregular bipartite graphs and honest covariance matrices. We also
+deduce a weak level repulsion estimate for the Dyson Brownian motion of
+linearized covariance matrices.
+"
+24672,Cherednik operators and Ruijsenaars-Schneider model at infinity,"  Heckman introduced $N$ operators on the space of polynomials in $N$
+variables, such that these operators form a covariant set relative to
+permutations of the operators and variables, and such that Jack symmetric
+polynomials are eigenfunctions of the power sums of these operators. We
+introduce the analogues of these $N$ operators for Macdonald symmetric
+polynomials, by using Cherednik operators. The latter operators pairwise
+commute, and Macdonald polynomials are eigenfunctions of their power sums. We
+compute the limits of our operators at $N\to\infty$. These limits yield the
+same Lax operator for Macdonald symmetric functions as constructed in our
+previous work.
+"
+24673,Relief-Based Feature Selection: Introduction and Review,"  Feature selection plays a critical role in biomedical data mining, driven by
+increasing feature dimensionality in target problems and growing interest in
+advanced but computationally expensive methodologies able to model complex
+associations. Specifically, there is a need for feature selection methods that
+are computationally efficient, yet sensitive to complex patterns of
+association, e.g. interactions, so that informative features are not mistakenly
+eliminated prior to downstream modeling. This paper focuses on Relief-based
+algorithms (RBAs), a unique family of filter-style feature selection algorithms
+that have gained appeal by striking an effective balance between these
+objectives while flexibly adapting to various data characteristics, e.g.
+classification vs. regression. First, this work broadly examines types of
+feature selection and defines RBAs within that context. Next, we introduce the
+original Relief algorithm and associated concepts, emphasizing the intuition
+behind how it works, how feature weights generated by the algorithm can be
+interpreted, and why it is sensitive to feature interactions without evaluating
+combinations of features. Lastly, we include an expansive review of RBA
+methodological research beyond Relief and its popular descendant, ReliefF. In
+particular, we characterize branches of RBA research, and provide comparative
+summaries of RBA algorithms including contributions, strategies, functionality,
+time complexity, adaptation to key data characteristics, and software
+availability.
+"
+24674,Notions of the ergodic hierarchy for curved statistical manifolds,"  We present an extension of the ergodic, mixing, and Bernoulli levels of the
+ergodic hierarchy for statistical models on curved manifolds, making use of
+elements of the information geometry. This extension focuses on the notion of
+statistical independence between the microscopical variables of the system.
+Moreover, we establish an intimately relationship between statistical models
+and family of probability distributions belonging to the canonical ensemble,
+which for the case of the quadratic Hamiltonian systems provides a closed form
+for the correlations between the microvariables in terms of the temperature of
+the heat bath as a power law. From this we obtain an information geometric
+method for studying Hamiltonian dynamics in the canonical ensemble. We
+illustrate the results with two examples: a pair of interacting harmonic
+oscillators presenting phase transitions and the 2x2 Gaussian ensembles. In
+both examples the scalar curvature results a global indicator of the dynamics.
+"
+24675,Data analysis recipes: Using Markov Chain Monte Carlo,"  Markov Chain Monte Carlo (MCMC) methods for sampling probability density
+functions (combined with abundant computational resources) have transformed the
+sciences, especially in performing probabilistic inferences, or fitting models
+to data. In this primarily pedagogical contribution, we give a brief overview
+of the most basic MCMC method and some practical advice for the use of MCMC in
+real inference problems. We give advice on method choice, tuning for
+performance, methods for initialization, tests of convergence, troubleshooting,
+and use of the chain output to produce or report parameter estimates with
+associated uncertainties. We argue that autocorrelation time is the most
+important test for convergence, as it directly connects to the uncertainty on
+the sampling estimate of any quantity of interest. We emphasize that sampling
+is a method for doing integrals; this guides our thinking about how MCMC output
+is best used.
+"
+24676,Anomalous vibrational properties in the continuum limit of glasses,"  The low-temperature thermal properties of glasses are anomalous with respect
+to those of crystals. These thermal anomalies indicate that the low-frequency
+vibrational properties of glasses differ from those of crystals. Recent studies
+revealed that, in the simplest model of glasses, i.e., the harmonic potential
+system, phonon modes coexist with soft localized modes in the low-frequency
+(continuum) limit. However, the nature of low-frequency vibrational modes of
+more realistic models is still controversial. In the present work, we study the
+Lennard-Jones (LJ) system using large-scale molecular-dynamics (MD) simulation
+and establish that the vibrational property of the LJ glass converges to
+coexistence of the phonon modes and the soft localized modes in the continuum
+limit as in the case of the harmonic potential system. Importantly, we find
+that the low-frequency vibrations are rather sensitive to the numerical scheme
+of potential truncation which is usually implemented in the MD simulation, and
+this is the reason why contradictory arguments have been reported by previous
+works. We also discuss the physical origin of this sensitiveness by means of a
+linear stability analysis.
+"
+24677,$p$-adic Mahler measure and $\mathbb{Z}$-covers of links,"  Let $p$ be a prime number. We develop a theory of $p$-adic Mahler measure of
+polynomials and apply it to the study of $\mathbb{Z}$-covers of rational
+homology 3-spheres branched over links. We obtain a $p$-adic analogue of the
+asymptotic formula of the torsion homology growth and a balance formula among
+the leading coefficient of the Alexander polynomial, the $p$-adic entropy, and
+the Iwasawa $\mu_p$-invariant. We also apply the purely $p$-adic theory of
+Besser--Deninger to $\mathbb{Z}$-covers of links. In addition, we study the
+entropies of profinite cyclic covers of links. We examine various examples
+throughout the paper.
+"
+24678,Efficient Bayesian Experimental Design for Implicit Models,"  Bayesian experimental design involves the optimal allocation of resources in
+an experiment, with the aim of optimising cost and performance. For implicit
+models, where the likelihood is intractable but sampling from the model is
+possible, this task is particularly difficult and therefore largely unexplored.
+This is mainly due to technical difficulties associated with approximating
+posterior distributions and utility functions. We devise a novel experimental
+design framework for implicit models that improves upon previous work in two
+ways. First, we use the mutual information between parameters and data as the
+utility function, which has previously not been feasible. We achieve this by
+utilising Likelihood-Free Inference by Ratio Estimation (LFIRE) to approximate
+posterior distributions, instead of the traditional approximate Bayesian
+computation or synthetic likelihood methods. Secondly, we use Bayesian
+optimisation in order to solve the optimal design problem, as opposed to the
+typically used grid search. We find that this increases efficiency and allows
+us to consider higher design dimensions.
+"
+24679,Strength of forensic evidence for composite hypotheses: An empirical Bayes view with a fixed prior quantile,"  Motivated by the forensic problem of determining the strength of evidence of
+a continuously distributed measurement of evidence, in the situation of
+composite hypotheses of the prosecutor and the defence concerning a parameter
+of a parametric model, we consider empirical Bayes methods with a prescribed
+quantile value for the prior distribution.
+Firstly we derive the strength of evidence for nonparametric priors. It turns
+out that we get the by now more or less accepted strength of evidence as the
+ratio of two suprema,
+$\sup_{\theta\geq\theta_0}f(x|\theta)/\sup_{\theta<\theta_0}f(x|\theta)$. Here
+the hypotheses of the prosecutor and defence are given by $H_p: \theta\geq
+\theta_0$ and $H_d:\theta<\theta_0$. The evidence is seen as a measurement $x$
+which is a realization of a random variable with a density $f(x|\theta)$.
+Secondly we consider a similar parametric empirical Bayes method with a
+quantile restriction on the prior where the prior distribution is assumed to be
+normal. Some interesting strength of evidence functions are derived for this
+situation.
+"
+24680,Joint Bayesian Estimation of Quasar Continua and the Lyman-Alpha Forest Flux Probability Distribution Function,"  We present a new Bayesian algorithm making use of Markov Chain Monte Carlo
+sampling that allows us to simultaneously estimate the unknown continuum level
+of each quasar in an ensemble of high-resolution spectra, as well as their
+common probability distribution function (PDF) for the transmitted Ly$\alpha$
+forest flux. This fully automated PDF regulated continuum fitting method models
+the unknown quasar continuum with a linear Principal Component Analysis (PCA)
+basis, with the PCA coefficients treated as nuisance parameters. The method
+allows one to estimate parameters governing the thermal state of the
+intergalactic medium (IGM), such as the slope of the temperature-density
+relation $\gamma-1$, while marginalizing out continuum uncertainties in a fully
+Bayesian way. Using realistic mock quasar spectra created from a simplified
+semi-numerical model of the IGM, we show that this method recovers the
+underlying quasar continua to a precision of $\simeq7\%$ and $\simeq10\%$ at
+$z=3$ and $z=5$, respectively. Given the number of principal component spectra,
+this is comparable to the underlying accuracy of the PCA model itself. Most
+importantly, we show that we can achieve a nearly unbiased estimate of the
+slope $\gamma-1$ of the IGM temperature-density relation with a precision of
+$\pm8.6\%$ at $z=3$, $\pm6.1\%$ at $z=5$, for an ensemble of ten mock
+high-resolution quasar spectra. Applying this method to real quasar spectra and
+comparing to a more realistic IGM model from hydrodynamical simulations would
+enable precise measurements of the thermal and cosmological parameters
+governing the IGM, albeit with somewhat larger uncertainties given the
+increased flexibility of the model.
+"
+24681,ParaExp using Leapfrog as Integrator for High-Frequency Electromagnetic Simulations,"  Recently, ParaExp was proposed for the time integration of linear hyperbolic
+problems. It splits the time interval of interest into sub-intervals and
+computes the solution on each sub-interval in parallel. The overall solution is
+decomposed into a particular solution defined on each sub-interval with zero
+initial conditions and a homogeneous solution propagated by the matrix
+exponential applied to the initial conditions. The efficiency of the method
+depends on fast approximations of this matrix exponential based on recent
+results from numerical linear algebra. This paper deals with the application of
+ParaExp in combination with Leapfrog to electromagnetic wave problems in
+time-domain. Numerical tests are carried out for a simple toy problem and a
+realistic spiral inductor model discretized by the Finite Integration
+Technique.
+"
+24682,Coulomb-corrected quantum interference in above-threshold ionization: Working towards multi-trajectory electron holography,"  Using the recently developed Coulomb Quantum Orbit Strong-Field Approximation
+(CQSFA), we perform a systematic analysis of several features encountered in
+above-threshold ionization (ATI) photoelectron angle-resolved distributions
+(PADs), such as side lobes, and intra- and intercycle interference patterns.
+The latter include not only the well-known intra-cycle rings and the
+near-threshold fan-shaped structure, but also previously overlooked patterns.
+We provide a direct account of how the Coulomb potential distorts different
+types of interfering trajectories and changes the corresponding phase
+differences, and show that these patterns may be viewed as generalized
+holographic structures formed by up to three types of trajectories. We also
+derive analytical interference conditions and estimates valid in the presence
+or absence of the residual potential, and assess the range of validity of
+Coulomb-corrected interference conditions provided in the literature.
+"
+24683,A General Framework for Multi-vehicle Cooperative Localization Using Pose Graph,"  When a vehicle observes another one, the two vehicles' poses are correlated
+by this spatial relative observation, which can be used in cooperative
+localization for further increasing localization accuracy and precision. To use
+spatial relative observations, we propose to add them into a pose graph for
+optimal pose estimation. Before adding them, we need to know the identities of
+the observed vehicles. The vehicle identification is formulated as a linear
+assignment problem, which can be solved efficiently. By using pose graph
+techniques and the start-of-the-art factor composition/decomposition method,
+our cooperative localization algorithm is robust against communication delay,
+packet loss, and out-of-sequence packet reception. We demonstrate the usability
+of our framework and effectiveness of our algorithm through both simulations
+and real-world experiments using three vehicles on the road.
+"
+24684,Efficient Differentiable Programming in a Functional Array-Processing Language,"  We present a system for the automatic differentiation of a higher-order
+functional array-processing language. The core functional language underlying
+this system simultaneously supports both source-to-source automatic
+differentiation and global optimizations such as loop transformations. Thanks
+to this feature, we demonstrate how for some real-world machine learning and
+computer vision benchmarks, the system outperforms the state-of-the-art
+automatic differentiation tools.
+"
+24685,Using transmission Kikuchi diffraction to characterise α variants in an α + β titanium alloy,"  Two phase titanium alloys are important for high performance engineering
+components, such as aeroengine discs. The microstructures of these alloys are
+tailored during thermomechanical processing to precisely control phase
+factions, morphology and crystallographic orientations. In bimodal two phase
+({\alpha} + {\beta}) Ti-6Al-2Sn-4Zr-2Mo (Ti-6242) alloys there are often three
+microstructural lengthscales to consider: large (~10 {\mu}m) equiaxed primary
+{\alpha}; >200 nm thick plate {\alpha} with a basketweave morphology; and very
+fine scaled (>50 nm plate thickness) secondary {\alpha} that grows between the
+larger {\alpha} plates surrounded by retained {\beta}. In this work, we utilise
+high spatial resolution transmission Kikuchi diffraction (TKD, also known as
+transmission based electron backscatter diffraction, t-EBSD) and scanning
+electron microscopy (SEM) based forward scattering electron imaging to resolve
+the structures and orientations of basketweave and secondary {\alpha} in
+Ti-6242. We analyse the {\alpha} variants formed within one prior {\beta}
+grain, and test whether existing theories of habit planes of the phase
+transformation are upheld. Our analysis is important in understanding both the
+thermomechanical processing strategy of new bimodal two-phase titanium alloys,
+as well the ultimate performance of these alloys in complex loading regimes
+such as dwell fatigue. Our paper champions the significant increase in spatial
+resolution afforded using transmission techniques, combined with the ease of
+SEM based analysis using conventional electron backscatter diffraction (EBSD)
+systems and forescatter detector (FSD) imaging, to study the nanostructure of
+real-world engineering alloys.
+"
+24686,Twisted Burnside-Frobenius theory for endomorphisms of polycyclic groups,"  Let $R(\phi)$ be the number of $\phi$-conjugacy (or Reidemeister) classes of
+an endomorphism $\phi$ of a group $G$. We prove for several classes of groups
+(including polycyclic) that the number $R(\phi)$ is equal to the number of
+fixed points of the induced map of an appropriate subspace of the unitary dual
+space $\widehat G$, when $R(\phi)<\infty$. Applying the result to iterations of
+$\phi$ we obtain Gauss congruences for Reidemeister numbers.
+In contrast with the case of automorphisms, studied previously, we have a
+plenty of examples having the above finiteness condition, even among groups
+with $R_\infty$ property.
+"
+24687,From orbital measures to Littlewood-Richardson coefficients and hive polytopes,"  The volume of the hive polytope (or polytope of honeycombs) associated with a
+Littlewood- Richardson coefficient of SU(n), or with a given admissible triple
+of highest weights, is expressed, in the generic case, in terms of the Fourier
+transform of a convolution product of orbital measures. Several properties of
+this function -- a function of three non-necessarily integral weights or of
+three multiplets of real eigenvalues for the associated Horn problem-- are
+already known. In the integral case it can be thought of as a semi-classical
+approximation of Littlewood-Richardson coefficients. We prove that it may be
+expressed as a local average of a finite number of such coefficients. We also
+relate this function to the Littlewood-Richardson polynomials (stretching
+polynomials) i.e., to the Ehrhart polynomials of the relevant hive polytopes.
+Several SU(n) examples, for n=2,3,...,6, are explicitly worked out.
+"
+24688,Error modeling for surrogates of dynamical systems using machine learning,"  A machine-learning-based framework for modeling the error introduced by
+surrogate models of parameterized dynamical systems is proposed. The framework
+entails the use of high-dimensional regression techniques (e.g., random
+forests, LASSO) to map a large set of inexpensively computed `error indicators'
+(i.e., features) produced by the surrogate model at a given time instance to a
+prediction of the surrogate-model error in a quantity of interest (QoI). This
+eliminates the need for the user to hand-select a small number of informative
+features. The methodology requires a training set of parameter instances at
+which the time-dependent surrogate-model error is computed by simulating both
+the high-fidelity and surrogate models. Using these training data, the method
+first determines regression-model locality (via classification or clustering),
+and subsequently constructs a `local' regression model to predict the
+time-instantaneous error within each identified region of feature space. We
+consider two uses for the resulting error model: (1) as a correction to the
+surrogate-model QoI prediction at each time instance, and (2) as a way to
+statistically model arbitrary functions of the time-dependent surrogate-model
+error (e.g., time-integrated errors). We apply the proposed framework to model
+errors in reduced-order models of nonlinear oil--water subsurface flow
+simulations. The reduced-order models used in this work entail application of
+trajectory piecewise linearization with proper orthogonal decomposition. When
+the first use of the method is considered, numerical experiments demonstrate
+consistent improvement in accuracy in the time-instantaneous QoI prediction
+relative to the original surrogate model, across a large number of test cases.
+When the second use is considered, results show that the proposed method
+provides accurate statistical predictions of the time- and well-averaged
+errors.
+"
+24689,Wave model of the Sturm-Liouville operator on the half-line,"  The notion of the wave spectrum of a semi-bounded symmetric operator was
+introduced by one of the authors in 2013. The wave spectrum is a topological
+space determined by the operator in a canonical way. The definition uses a
+dynamical system associated with the operator: the wave spectrum is constructed
+from its reachable sets. In the paper we give a description of the wave
+spectrum of the operator $L_0=-\frac{d^2}{dx^2}+q$ which acts in the space
+$L_2(0,\infty)$ and has defect indices $(1,1)$. We construct a functional
+(wave) model of the operator $L_0^*$ in which the elements of the original
+$L_2(0,\infty)$ are realized as functions on the wave spectrum. It turns out to
+be identical to the original $L_0^*$. The latter is fundamental in solving
+inverse problems: the wave model is determined by their data, which allows for
+reconstruction of the original.
+"
+24690,Applications of the Ohsawa-Takegoshi Extension Theorem to Direct Image Problems,"  In the first part of the paper, we study a Fujita-type conjecture by Popa and
+Schnell, and give an effective bound on the generic global generation of the
+direct image of the twisted pluricanonical bundle. We also point out the
+relation between the Seshadri constant and the optimal bound. In the second
+part, we give an affirmative answer to a question by
+Demailly-Peternell-Schneider in a more general setting. As an application, we
+generalize the theorems by Fujino and Gongyo on images of weak Fano manifolds
+to the Kawamata log terminal cases, and refine a result by Broustet and
+Pacienza on the rational connectedness of the image.
+"
+24691,Addressing the missing matter problem in galaxies through a new fundamental gravitational radius,"  We demonstrate that the existence of a Noether symmetry in $f(R)$ theories of
+gravity gives rise to a further gravitational radius, besides the standard
+Schwarzschild one, determining the dynamics at galactic scales. By this
+feature, it is possible to explain the baryonic Tully-Fisher relation and the
+rotation curve of gas-rich galaxies without the dark matter hypothesis.
+"
+24692,The Role of Data Analysis in Uncertainty Quantification: Case Studies for Materials Modeling,"  In computational materials science, mechanical properties are typically
+extracted from simulations by means of analysis routines that seek to mimic
+their experimental counterparts. However, simulated data often exhibit
+uncertainties that can propagate into final predictions in unexpected ways.
+Thus, modelers require data analysis tools that (i) address the problems posed
+by simulated data, and (ii) facilitate uncertainty quantification. In this
+manuscript, we discuss three case studies in materials modeling where careful
+data analysis can be leveraged to address specific instances of these issues.
+As a unifying theme, we highlight the idea that attention to physical and
+mathematical constraints surrounding the generation of computational data can
+significantly enhance its analysis.
+"
+24693,Understanding NaI(Tl) crystal background for dark matter searches,"  We have developed ultra-low-background NaI(Tl) crystals to reproduce the DAMA
+results with the ultimate goal of achieving purity levels that are comparable
+to or better than those of the DAMA/LIBRA crystals. Even though the achieved
+background level does not approach that of DAMA/LIBRA, it is crucial to have a
+quantitative understanding of the backgrounds. We have studied background
+simulations toward a deeper understanding of the backgrounds and developed
+background models for a 9.16-kg NaI(Tl) crystal used in the test arrangement.
+In this paper we describe the contributions of background sources
+quantitatively by performing Geant4 Monte Carlo simulations that are fitted to
+the measured data to quantify the unknown fractions of the background
+compositions. In the fitted results, the overall simulated background spectrum
+well describes the measured data with a 9.16-kg NaI(Tl) crystal and shows that
+the background sources are dominated by surface $^{210}$Pb and internal
+$^{40}$K in the 2 to 6-keV energy interval, which produce 2.4 counts/day/keV/kg
+(dru) and 0.5 dru, respectively.
+"
+24694,The Importance of Generation Order in Language Modeling,"  Neural language models are a critical component of state-of-the-art systems
+for machine translation, summarization, audio transcription, and other tasks.
+These language models are almost universally autoregressive in nature,
+generating sentences one token at a time from left to right. This paper studies
+the influence of token generation order on model quality via a novel two-pass
+language model that produces partially-filled sentence ""templates"" and then
+fills in missing tokens. We compare various strategies for structuring these
+two passes and observe a surprisingly large variation in model quality. We find
+the most effective strategy generates function words in the first pass followed
+by content words in the second. We believe these experimental results justify a
+more extensive investigation of generation order for neural language models.
+"
+24695,A Case Study of the 2016 Korean Cyber Command Compromise,"  On October 2016 the South Korean cyber military unit was the victim of a
+successful cyber attack that allowed access to internal networks. Per usual
+with large scale attacks against South Korean entities, the hack was
+immediately attributed to North Korea. Also, per other large-scale cyber
+security incidents, the same types of 'evidence' were used for attribution
+purposes. Disclosed methods of attribution provide weak evidence, and the
+procedure Korean organizations tend to use for information disclosure lead many
+to question any conclusions. We will analyze and discuss a number of issues
+with the current way that South Korean organizations disclose cyber attack
+information to the public. A time line of events and disclosures will be
+constructed and analyzed in the context of appropriate measures for cyber
+warfare. Finally, we will examine the South Korean cyber military attack in
+terms previously proposed cyber warfare response guidelines. Specifically,
+whether any of the guidelines can be applied to this real-world case, and if
+so, is South Korea justified in declaring war based on the most recent cyber
+attack.
+"
+24696,On the Use of Context in Recommending Exception Handling Code Examples,"  Studies show that software developers often either misuse exception handling
+features or use them inefficiently, and such a practice may lead an undergoing
+software project to a fragile, insecure and non-robust application system. In
+this paper, we propose a context-aware code recommendation approach that
+recommends exception handling code examples from a number of popular open
+source code repositories hosted at GitHub. It collects the code examples
+exploiting GitHub code search API, and then analyzes, filters and ranks them
+against the code under development in the IDE by leveraging not only the
+structural (i.e., graph-based) and lexical features but also the heuristic
+quality measures of exception handlers in the examples. Experiments with 4,400
+code examples and 65 exception handling scenarios as well as comparisons with
+four existing approaches show that the proposed approach is highly promising.
+"
+24697,Admissible endpoints of gaps in the Lagrange spectrum,"  We call a positive real number $\lambda$ admissible if it belongs to the
+Lagrange spectrum and there exists an irrational number $\alpha$ such that
+$\mu(\alpha)=\lambda$. Here $\mu(\alpha)$ denotes the Lagrange constant of
+$\alpha$ - maximal real number $c$ such that $\forall \varepsilon>0$ the
+inequality $|\alpha-\frac{p}{q}|\le\frac{1}{(c-\varepsilon)q^2}$ has infinitely
+many solutions for relatively prime $p$ and $q$. In this paper we establish a
+necessary and sufficient condition of admissibility of the Lagrange spectrum
+element and construct an infinite series of not admissible numbers.
+"
+24698,Uniqueness of planar tangent maps in the modified Ericksen model,"  We prove the uniqueness of homogeneous blow-up limits of maps minimizing the
+modified Ericksen energy for nematic liquid crystals in a planar domain. The
+proof is based on the Weiss monotonicity formula, and a blow-up argument,
+originally due to Allard and Almgren \cite{AA} for minimal surfaces, and L.
+Simon \cite{SL} for energy-minimizing maps into analytic targets, which
+exploits the integrability of certain Jacobi fields.
+"
+24699,Exomoon Habitability and Tidal Evolution in Low-Mass Star Systems,"  Discoveries of extrasolar planets in the habitable zone (HZ) of their parent
+star lead to questions about the habitability of massive moons orbiting planets
+in the HZ. Around low-mass stars, the HZ is much closer to the star than for
+Sun-like stars. For a planet-moon binary in such a HZ, the proximity of the
+star forces a close orbit for the moon to remain gravitationally bound to the
+planet. Under these conditions the effects of tidal heating, distortion
+torques, and stellar perturbations become important considerations for exomoon
+habitability.
+Utilizing a model that considers both dynamical and tidal interactions
+simultaneously, we performed a computational investigation into exomoon
+evolution for systems in the HZ of low-mass stars ($\lesssim 0.6\ M_{\odot}$).
+We show that dwarf stars with masses $\lesssim 0.2\ M_{\odot}$ cannot host
+habitable exomoons within the stellar HZ due to extreme tidal heating in the
+moon. Perturbations from a central star may continue to have deleterious
+effects in the HZ up to $\approx 0.5\ M_{\odot}$, depending on the host
+planet's mass and its location in the HZ, amongst others. In addition to
+heating concerns, torques due to tidal and spin distortion can lead to the
+relatively rapid inward spiraling of a moon. Therefore, moons of giant planets
+in HZs around the most abundant type of star are unlikely to have habitable
+surfaces. In cases with lower intensity tidal heating the stellar perturbations
+may have a positive influence on exomoon habitability by promoting long-term
+heating and possibly extending the HZ for exomoons.
+"
+24700,Closed Form Analytical Model for Airflow around 2-Dimensional Composite Airfoil Via Conformal Mapping,"  This paper presents a method of computing section lift characteristics for a
+2-dimensional airfoil with a second 2-dimensional object at a position at or
+ahead of the leading edge of the airfoil. Since both objects are 2-dimensional,
+the analysis yields a closed form solution to calculation of the airflow over
+the airfoil and second object, using conformal mapping of analytically closed
+form airflow velocity vector past two circular shaped objects in initial
+complex plane, using a standard air flow model for each object individually.
+The combined airflow velocity vector is obtained by linear superposition of the
+velocity vector for the two objects, computed individually. The lift
+characteristics are obtained from the circulation around the airfoil and second
+object which is computed from the combined closed form velocity vector and the
+geometry along the contour integral for circulation. The illustrative example
+considered in this paper shows that the second object which is essentially a
+cylinder whose diameter is approximately 9% of the chord length of the airfoil
+reduces the section lift coefficient by approximately 6:3% from that of the
+airfoil alone. 1.
+"
+24701,Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery,"  Given a target prior information, our goal is to propose a method for
+automatically separating targets of interests from the background in
+hyperspectral imagery. More precisely, we regard the given hyperspectral image
+(HSI) as being made up of the sum of low-rank background HSI and a sparse
+target HSI that contains the targets based on a pre-learned target dictionary
+constructed from some online spectral libraries. Based on the proposed method,
+two strategies are briefly outlined and evaluated to realize the target
+detection on both synthetic and real experiments.
+"
+24702,Realizing Uncertainty-Aware Timing Stack in Embedded Operating System,"  Time awareness is critical to a broad range of emerging applications -- in
+Cyber-Physical Systems and Internet of Things -- running on commodity platforms
+and operating systems. Traditionally, time is synchronized across devices
+through a best-effort background service whose performance is neither
+observable nor controllable, thus consuming system resources independently of
+application needs while not allowing the applications and OS services to adapt
+to changes in uncertainty in system time. We advocate for rethinking how time
+is managed in a system stack. In this paper, we propose a new clock model that
+characterizes various sources of timing uncertainties in true time. We then
+present a Kalman filter based time synchronization protocol that adapts to the
+uncertainties exposed by the clock model. Our realization of a
+uncertainty-aware clock model and synchronization protocol is based on a
+standard embedded Linux platform.
+"
+24703,An explicit inversion formula for the $p$-adic Whittaker transform on $\text{GL}_n(\mathbb{Q}_p)$,"  Whittaker functions on non-archimedean fields were first introduced in the
+work of Jacquet, and they were characterized explicitly by Shintani. We obtain
+an explicit inversion formula and a Plancherel formula for the $p$-adic
+Whittaker transform on $\text{GL}_n(\mathbb{Q}_p)$. As an application, integral
+representations are obtained for the local factors of certain symmetric power
+$L$-functions.
+"
+24704,On the Approximability and Hardness of the Minimum Connected Dominating Set with Routing Cost Constraint,"  In the problem of minimum connected dominating set with routing cost
+constraint, we are given a graph $G=(V,E)$, and the goal is to find the
+smallest connected dominating set $D$ of $G$ such that, for any two
+non-adjacent vertices $u$ and $v$ in $G$, the number of internal nodes on the
+shortest path between $u$ and $v$ in the subgraph of $G$ induced by $D \cup
+\{u,v\}$ is at most $\alpha$ times that in $G$. For general graphs, the only
+known previous approximability result is an $O(\log n)$-approximation algorithm
+($n=|V|$) for $\alpha = 1$ by Ding et al. For any constant $\alpha > 1$, we
+give an $O(n^{1-\frac{1}{\alpha}}(\log n)^{\frac{1}{\alpha}})$-approximation
+algorithm. When $\alpha \geq 5$, we give an $O(\sqrt{n}\log n)$-approximation
+algorithm. Finally, we prove that, when $\alpha =2$, unless $NP \subseteq
+DTIME(n^{poly\log n})$, for any constant $\epsilon > 0$, the problem admits no
+polynomial-time $2^{\log^{1-\epsilon}n}$-approximation algorithm, improving
+upon the $\Omega(\log n)$ bound by Du et al. (albeit under a stronger hardness
+assumption).
+"
+24705,Decomposition of elliptic multiple zeta values and iterated Eisenstein integrals,"  We describe a decomposition algorithm for elliptic multiple zeta values,
+which amounts to the construction of an injective map $\psi$ from the algebra
+of elliptic multiple zeta values to a space of iterated Eisenstein integrals.
+We give many examples of this decomposition, and conclude with a short
+discussion about the image of $\psi$. It turns out that the failure of
+surjectivity of $\psi$ is in some sense governed by period polynomials of
+modular forms.
+"
+24706,Objective evaluation metrics for automatic classification of EEG events,"  The evaluation of machine learning algorithms in biomedical fields for
+applications involving sequential data lacks standardization. Common
+quantitative scalar evaluation metrics such as sensitivity and specificity can
+often be misleading depending on the requirements of the application.
+Evaluation metrics must ultimately reflect the needs of users yet be
+sufficiently sensitive to guide algorithm development. Feedback from critical
+care clinicians who use automated event detection software in clinical
+applications has been overwhelmingly emphatic that a low false alarm rate,
+typically measured in units of the number of errors per 24 hours, is the single
+most important criterion for user acceptance. Though using a single metric is
+not often as insightful as examining performance over a range of operating
+conditions, there is a need for a single scalar figure of merit. In this paper,
+we discuss the deficiencies of existing metrics for a seizure detection task
+and propose several new metrics that offer a more balanced view of performance.
+We demonstrate these metrics on a seizure detection task based on the TUH EEG
+Corpus. We show that two promising metrics are a measure based on a concept
+borrowed from the spoken term detection literature, Actual Term-Weighted Value,
+and a new metric, Time-Aligned Event Scoring (TAES), that accounts for the
+temporal alignment of the hypothesis to the reference annotation. We also
+demonstrate that state of the art technology based on deep learning, though
+impressive in its performance, still needs significant improvement before it
+will meet very strict user acceptance guidelines.
+"
+24707,Synthesizing facial photometries and corresponding geometries using generative adversarial networks,"  Artificial data synthesis is currently a well studied topic with useful
+applications in data science, computer vision, graphics and many other fields.
+Generating realistic data is especially challenging since human perception is
+highly sensitive to non realistic appearance. In recent times, new levels of
+realism have been achieved by advances in GAN training procedures and
+architectures. These successful models, however, are tuned mostly for use with
+regularly sampled data such as images, audio and video. Despite the successful
+application of the architecture on these types of media, applying the same
+tools to geometric data poses a far greater challenge. The study of geometric
+deep learning is still a debated issue within the academic community as the
+lack of intrinsic parametrization inherent to geometric objects prohibits the
+direct use of convolutional filters, a main building block of today's machine
+learning systems. In this paper we propose a new method for generating
+realistic human facial geometries coupled with overlayed textures. We
+circumvent the parametrization issue by imposing a global mapping from our data
+to the unit rectangle. We further discuss how to design such a mapping to
+control the mapping distortion and conserve area within the mapped image. By
+representing geometric textures and geometries as images, we are able to use
+advanced GAN methodologies to generate new geometries. We address the often
+neglected topic of relation between texture and geometry and propose to use
+this correlation to match between generated textures and their corresponding
+geometries. We offer a new method for training GAN models on partially
+corrupted data. Finally, we provide empirical evidence demonstrating our
+generative model's ability to produce examples of new identities independent
+from the training data while maintaining a high level of realism, two traits
+that are often at odds.
+"
+24708,Generative Models for Stochastic Processes Using Convolutional Neural Networks,"  The present paper aims to demonstrate the usage of Convolutional Neural
+Networks as a generative model for stochastic processes, enabling researchers
+from a wide range of fields (such as quantitative finance and physics) to
+develop a general tool for forecasts and simulations without the need to
+identify/assume a specific system structure or estimate its parameters.
+"
+24709,Learning Determinantal Point Processes with Moments and Cycles,"  Determinantal Point Processes (DPPs) are a family of probabilistic models
+that have a repulsive behavior, and lend themselves naturally to many tasks in
+machine learning where returning a diverse set of objects is important. While
+there are fast algorithms for sampling, marginalization and conditioning, much
+less is known about learning the parameters of a DPP. Our contribution is
+twofold: (i) we establish the optimal sample complexity achievable in this
+problem and show that it is governed by a natural parameter, which we call the
+\emph{cycle sparsity}; (ii) we propose a provably fast combinatorial algorithm
+that implements the method of moments efficiently and achieves optimal sample
+complexity. Finally, we give experimental results that confirm our theoretical
+findings.
+"
+24710,Width Provably Matters in Optimization for Deep Linear Neural Networks,"  We prove that for an $L$-layer fully-connected linear neural network, if the
+width of every hidden layer is $\tilde\Omega (L \cdot r \cdot d_{\mathrm{out}}
+\cdot \kappa^3 )$, where $r$ and $\kappa$ are the rank and the condition number
+of the input data, and $d_{\mathrm{out}}$ is the output dimension, then
+gradient descent with Gaussian random initialization converges to a global
+minimum at a linear rate. The number of iterations to find an
+$\epsilon$-suboptimal solution is $O(\kappa \log(\frac{1}{\epsilon}))$. Our
+polynomial upper bound on the total running time for wide deep linear networks
+and the $\exp\left(\Omega\left(L\right)\right)$ lower bound for narrow deep
+linear neural networks [Shamir, 2018] together demonstrate that wide layers are
+necessary for optimizing deep models.
+"
+24711,Fujiki relations and fibrations of irreducible symplectic varieties,"  This paper concerns singular, complex projective, irreducible symplectic
+varieties. The generalized Beauville-Bogomolov form satisfies the Fujiki
+relations and has the same rank as in the smooth case. As a consequence, we see
+that these varieties admit only very special fibrations. Our results give new
+evidence that the correct definition of singular analogs of irreducible
+symplectic manifolds is the one from Greb-Kebekus-Peternell.
+"
+24712,Wave-Turbulence Theory of four-wave nonlinear interactions,"  The Sagdeev-Zaslavski (SZ) equation for wave turbulence is analytically
+derived, both in terms of generating function and of multi-point pdf, for
+weakly interacting waves with initial random phases. When also initial
+amplitudes are random, the one-point pdf equation is derived. Such analytical
+calculations remarkably agree with results obtained in totally different
+fashions. Numerical investigations of the two-dimensional nonlinear
+Schroedinger equation (NLSE) and of a vibrating plate prove that: (i) generic
+Hamiltonian 4-wave systems rapidly attain a random distribution of phases
+independently of the slower dynamics of the amplitudes, vindicating the
+hypothesis of initially random phases; (ii) relaxation of the Fourier
+amplitudes to the predicted stationary distribution (exponential) happens on a
+faster timescale than relaxation of the spectrum (Rayleigh-Jeans distribution);
+(iii) the pdf equation correctly describes dynamics under different forcings:
+the NLSE has an exponential pdf corresponding to a quasi-gaussian solution,
+like the vibrating plates, that also show some intermittency at very strong
+forcings.
+"
+24713,A sharp nonlinear Hausdorff-Young inequality for small potentials,"  The nonlinear Hausdorff-Young inequality follows from the work of Christ and
+Kiselev. Later Muscalu, Tao, and Thiele asked if the constants can be chosen
+independently of the exponent. We show that the nonlinear Hausdorff-Young
+quotient admits an even better upper bound than the linear one, provided that
+the function is sufficiently small in the $L^1$ norm. The proof combines
+perturbative techniques with the sharpened version of the linear
+Hausdorff-Young inequality due to Christ.
+"
+24714,Symmetric minimally entangled typical thermal states for canonical and grand-canonical ensembles,"  Based on the density matrix renormalization group (DMRG), strongly correlated
+quantum many-body systems at finite temperatures can be simulated by sampling
+over a certain class of pure matrix product states (MPS) called minimally
+entangled typical thermal states (METTS). When a system features symmetries,
+these can be utilized to substantially reduce MPS computation costs. It is
+conceptually straightforward to simulate canonical ensembles using symmetric
+METTS. In practice, it is important to alternate between different symmetric
+collapse bases to decrease autocorrelations in the Markov chain of METTS. To
+this purpose, we introduce symmetric Fourier and Haar-random block bases that
+are efficiently mixing. We also show how grand-canonical ensembles can be
+simulated efficiently with symmetric METTS. We demonstrate these approaches for
+spin-1/2 XXZ chains and discuss how the choice of the collapse bases influences
+autocorrelations as well as the distribution of measurement values and, hence,
+convergence speeds.
+"
+24715,"Energy demands of diverse spiking cells from the neocortex, hippocampus, and thalamus","  It has long been known that neurons in the brain are not physiologically
+homogeneous. In response to current stimulus, they can fire several distinct
+patterns of action potentials that are associated with different physiological
+classes ranging from regular-spiking cells, fast-spiking cells, intrinsically
+bursting cells, and low-threshold cells. In this work we show that the high
+degree of variability in firing characteristics of action potentials among
+these cells is accompanied with a significant variability in the energy demands
+required to restore the concentration gradients after an action potential. The
+values of the metabolic energy were calculated for a wide range of cell
+temperatures and stimulus intensities following two different approaches. The
+first one is based on the amount of Na+ load crossing the membrane during a
+single action potential, while the second one focuses on the electrochemical
+energy functions deduced from the dynamics of the computational neuron models.
+The results show that the thalamocortical relay neuron is the most
+energy-efficient cell consuming between 7 and 18 nJ/cm2 for each spike
+generated, while both the regular and fast spiking cells from somatosensory
+cortex and the intrinsically-bursting cell from a cat visual cortex are the
+least energy-efficient, and can consume up to 100 nJ/cm2 per spike. The lowest
+values of these energy demands were achieved at higher temperatures and high
+external stimuli.
+"
+24716,Large time behavior of solutions to 3-D MHD system with initial data near equilibrium,"  In \cite{ChCa}, Califano and Chiuderi conjectured that the energy of
+incompressible Magnetic hydrodynamical system is dissipated at a rate that is
+independent of the ohmic resistivity. The goal of this paper is to
+mathematically justify this conjecture in three space dimension provided that
+the initial magnetic field and velocity is a small perturbation of the
+equilibrium state $(e_3,0).$ In particular, we prove that for such data, 3-D
+incompressible MHD system without magnetic diffusion has a unique global
+solution. Furthermore, the velocity field and the difference between the
+magnetic field and $e_3$ decay to zero in both $L^\infty$ and $L^2$ norms with
+explicit rates. We point out that the decay rate in the $L^2$ norm is optimal
+in sense that this rate coincides with that of the linear system. The main idea
+of the proof is to exploit H$\ddot{o}$rmander's version of Nash-Moser iteration
+scheme, which is very much motivated by the seminar papers \cite{Kl80, Kl82,
+Kl84} by Klainerman on the long time behavior to the evolution equations.
+"
+24717,Online Energy Management for a Sustainable Smart Home with an HVAC Load and Random Occupancy,"  In this paper, we investigate the problem of minimizing the sum of energy
+cost and thermal discomfort cost in a long-term time horizon for a sustainable
+smart home with a Heating, Ventilation, and Air Conditioning (HVAC) load.
+Specifically, we first formulate a stochastic program to minimize the time
+average expected total cost with the consideration of uncertainties in
+electricity price, outdoor temperature, renewable generation output, electrical
+demand, the most comfortable temperature level, and home occupancy state. Then,
+we propose an online energy management algorithm based on the framework of
+Lyapunov optimization techniques without the need to predict any system
+parameters. The key idea of the proposed algorithm is to construct and
+stabilize four queues associated with indoor temperature, electric vehicle
+charging, and energy storage. Moreover, we theoretically analyze the
+feasibility and performance guarantee of the proposed algorithm. Extensive
+simulations based on real-world traces show the effectiveness of the proposed
+algorithm.
+"
+24718,Aerial Grasping: Modeling and Control of a Flying Hand,"  In this paper, we present the design, simulation and experimental validation
+of a control architecture for a flying hand, i.e., a system made of an unmanned
+aerial vehicle, a robotic manipulator and a gripper, which is grasping an
+object fixed on a vertical wall. The goal of this work is to show that the
+overall control allows the flying hand to approach the wall, to dock on the
+object by means of the gripper, take the object and fly away. The control
+strategy has been implemented and validated in the simulated model and in
+experiments on the complete flying hand system.
+"
+24719,A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations,"  Human-in-the-loop data analysis applications necessitate greater transparency
+in machine learning models for experts to understand and trust their decisions.
+To this end, we propose a visual analytics workflow to help data scientists and
+domain experts explore, diagnose, and understand the decisions made by a binary
+classifier. The approach leverages ""instance-level explanations"", measures of
+local feature relevance that explain single instances, and uses them to build a
+set of visual representations that guide the users in their investigation. The
+workflow is based on three main visual representations and steps: one based on
+aggregate statistics to see how data distributes across correct / incorrect
+decisions; one based on explanations to understand which features are used to
+make these decisions; and one based on raw data, to derive insights on
+potential root causes for the observed patterns. The workflow is derived from a
+long-term collaboration with a group of machine learning and healthcare
+professionals who used our method to make sense of machine learning models they
+developed. The case study from this collaboration demonstrates that the
+proposed workflow helps experts derive useful knowledge about the model and the
+phenomena it describes, thus experts can generate useful hypotheses on how a
+model can be improved.
+"
+24720,Monitoring Informed Testing for IoT,"  Internet of Things (IoT) systems continuously collect a large amount of data
+from heterogeneous ""smart objects"" through standardised service interfaces. A
+key challenge is how to use these data and relevant event logs to construct
+continuously adapted usage profiles and apply them to enhance testing methods,
+i.e., prioritization of tests for the testing of continuous integration of an
+IoT system. In addition, these usage profiles provide relevance weightings to
+analyse architecture and behaviour of the system. Based on the analysis,
+testing methods can predict specific system locations that are susceptible to
+error, and therefore suggest where expanded runtime monitoring is necessary.
+Furthermore, IoT aims to connect billions of ""smart devices"" over the network.
+Testing even a small IoT system connecting a few dozens of smart devices would
+require a network of test Virtual Machines (VMs) possibly spreading across the
+Fog and the Cloud. In this paper we propose a framework for testing of each IoT
+layer in a separate VM environment, and discuss potential difficulties with
+optimal VM allocation.
+"
+24721,Dynamic Steerable Blocks in Deep Residual Networks,"  Filters in convolutional networks are typically parameterized in a pixel
+basis, that does not take prior knowledge about the visual world into account.
+We investigate the generalized notion of frames designed with image properties
+in mind, as alternatives to this parametrization. We show that frame-based
+ResNets and Densenets can improve performance on Cifar-10+ consistently, while
+having additional pleasant properties like steerability. By exploiting these
+transformation properties explicitly, we arrive at dynamic steerable blocks.
+They are an extension of residual blocks, that are able to seamlessly transform
+filters under pre-defined transformations, conditioned on the input at training
+and inference time. Dynamic steerable blocks learn the degree of invariance
+from data and locally adapt filters, allowing them to apply a different
+geometrical variant of the same filter to each location of the feature map.
+When evaluated on the Berkeley Segmentation contour detection dataset, our
+approach outperforms all competing approaches that do not utilize pre-training.
+Our results highlight the benefits of image-based regularization to deep
+networks.
+"
+24722,On the Calogero-Moser space associated with dihedral groups,"  Using the geometry of the associated Calogero-Moser space, R. Rouquier and
+the author have attached to any finite complex reflection group $W$ several
+notions (Calogero-Moser left, right or two-sided cells, Calogero-Moser cellular
+characters), completing the notion of Calogero-Moser families defined by
+Gordon. If moreover $W$ is a Coxeter group, they conjectured that these notions
+coincide with the analogous notions defined using the Hecke algebra by Kazhdan
+and Lusztig (or Lusztig in the unequal parameters case). In the present paper,
+we aim to investigate these conjectures whenever $W$ is a dihedral group.
+"
+24723,"A Monte Carlo study of critical properties of strongly diluted magnetic semiconductor (Ga,Mn)As","  Within a Monte Carlo technique we examine critical properties of diluted bulk
+magnetic semiconductor (Ga,Mn)As modeled by a strongly diluted ferromagnetic
+Heisenberg spin-$\frac{5}{2}$ system on a face centered cubic lattice. We
+assumed that 5\% of Ga atoms is substituted by Mn atoms and the interaction
+between them is of the RKKY-type. The considered system is randomly quenched
+and a double average was performed: firstly, over the Boltzmann probability
+distribution and secondly - over 2048 configurations related to the quenched
+disorder. We estimated the critical temperature: $T_c=97\pm6$ K, which is in
+agreement with the experiment. The calculated high value of critical exponent
+$\nu$ seems to point to a possibility of non-universal critical behavior.
+"
+24724,Dynamical dark energy in light of the latest observations,"  A flat Friedman-Roberson-Walker universe dominated by a cosmological constant
+($\Lambda$) and cold dark matter (CDM) has been the working model preferred by
+cosmologists since the discovery of cosmic acceleration. However, tensions of
+various degrees of significance are known to be present among existing datasets
+within the $\Lambda$CDM framework. In particular, the Lyman-$\alpha$ forest
+measurement of the Baryon Acoustic Oscillations (BAO) by the Baryon Oscillation
+Spectroscopic Survey (BOSS) prefers a smaller value of the matter density
+fraction $\Omega_{\rm M}$ compared to the value preferred by cosmic microwave
+background (CMB). Also, the recently measured value of the Hubble constant,
+$H_0=73.24\pm1.74 \ {\rm km}\ {\rm s}^{-1} \ {\rm Mpc}^{-1}$, is $3.4\sigma$
+higher than $66.93\pm0.62 \ {\rm km}\ {\rm s}^{-1} \ {\rm Mpc}^{-1}$ inferred
+from the Planck CMB data. In this work, we investigate if these tensions can be
+interpreted as evidence for a non-constant dynamical dark energy (DE). Using
+the Kullback-Leibler (KL) divergence to quantify the tension between datasets,
+we find that the tensions are relieved by an evolving DE, with the dynamical DE
+model preferred at a $3.5\sigma$ significance level based on the improvement in
+the fit alone. While, at present, the Bayesian evidence for the dynamical DE is
+insufficient to favour it over $\Lambda$CDM, we show that, if the current best
+fit DE happened to be the true model, it would be decisively detected by the
+upcoming DESI survey.
+"
+24725,Simple Recurrent Units for Highly Parallelizable Recurrence,"  Common recurrent neural architectures scale poorly due to the intrinsic
+difficulty in parallelizing their state computations. In this work, we propose
+the Simple Recurrent Unit (SRU), a light recurrent unit that balances model
+capacity and scalability. SRU is designed to provide expressive recurrence,
+enable highly parallelized implementation, and comes with careful
+initialization to facilitate training of deep models. We demonstrate the
+effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over
+cuDNN-optimized LSTM on classification and question answering datasets, and
+delivers stronger results than LSTM and convolutional models. We also obtain an
+average of 0.7 BLEU improvement over the Transformer model on translation by
+incorporating SRU into the architecture.
+"
+24726,Dirac operators with Lorentz scalar shell interactions,"  This paper deals with the massive three-dimensional Dirac operator coupled
+with a Lorentz scalar shell interaction supported on a compact smooth surface.
+The rigorous definition of the operator involves suitable transmission
+conditions along the surface. After showing the self-adjointness of the
+resulting operator we switch to the investigation of its spectral properties,
+in particular, to the existence and non-existence of eigenvalues. In the case
+of an attractive coupling, we study the eigenvalue asymptotics as the mass
+becomes large and show that the behavior of the individual eigenvalues and
+their total number are governed by an effective Schrödinger operator on the
+boundary with an external Yang-Mills potential and a curvature-induced
+potential.
+"
+24727,AutoEncoder by Forest,"  Auto-encoding is an important task which is typically realized by deep neural
+networks (DNNs) such as convolutional neural networks (CNN). In this paper, we
+propose EncoderForest (abbrv. eForest), the first tree ensemble based
+auto-encoder. We present a procedure for enabling forests to do backward
+reconstruction by utilizing the equivalent classes defined by decision paths of
+the trees, and demonstrate its usage in both supervised and unsupervised
+setting. Experiments show that, compared with DNN autoencoders, eForest is able
+to obtain lower reconstruction error with fast training speed, while the model
+itself is reusable and damage-tolerable.
+"
+24728,Non-convex Optimization for Machine Learning,"  A vast majority of machine learning algorithms train their models and perform
+inference by solving optimization problems. In order to capture the learning
+and prediction problems accurately, structural constraints such as sparsity or
+low rank are frequently imposed or else the objective itself is designed to be
+a non-convex function. This is especially true of algorithms that operate in
+high-dimensional spaces or that train non-linear models such as tensor models
+and deep networks.
+The freedom to express the learning problem as a non-convex optimization
+problem gives immense modeling power to the algorithm designer, but often such
+problems are NP-hard to solve. A popular workaround to this has been to relax
+non-convex problems to convex ones and use traditional methods to solve the
+(convex) relaxed optimization problems. However this approach may be lossy and
+nevertheless presents significant challenges for large scale optimization.
+On the other hand, direct approaches to non-convex optimization have met with
+resounding success in several domains and remain the methods of choice for the
+practitioner, as they frequently outperform relaxation-based techniques -
+popular heuristics include projected gradient descent and alternating
+minimization. However, these are often poorly understood in terms of their
+convergence and other properties.
+This monograph presents a selection of recent advances that bridge a
+long-standing gap in our understanding of these heuristics. The monograph will
+lead the reader through several widely used non-convex optimization techniques,
+as well as applications thereof. The goal of this monograph is to both,
+introduce the rich literature in this area, as well as equip the reader with
+the tools and techniques needed to analyze these simple procedures for
+non-convex problems.
+"
+24729,Observation of two-dimensional Fermi surface and Dirac dispersion in YbMnSb$_2$,"  We present the crystal structure, electronic structure, and transport
+properties of the material YbMnSb$_2$, a candidate system for the investigation
+of Dirac physics in the presence of magnetic order. Our measurements reveal
+that this system is a low-carrier-density semimetal with a 2D Fermi surface
+arising from a Dirac dispersion, consistent with the predictions of density
+functional theory calculations of the antiferromagnetic system. The low
+temperature resistivity is very large, suggesting scattering in this system is
+highly efficient at dissipating momentum despite its Dirac-like nature.
+"
+24730,Controlled Experiments with Student Participants in Software Engineering: Preliminary Results from a Systematic Mapping Study,"  [Context] In software engineering research, emphasis is given to sound
+evaluations of new approaches. While industry surveys or industrial case
+studies are preferred to evaluate industrial applicability, controlled
+experiments with student participants are commonly used to determine
+measurements such as effectiveness and efficiency of a proposed approach.
+[Objectives] In this paper, we elaborate on the current state of the art of
+controlled experiments using student participants. As student participants are
+commonly only reluctantly accepted in scientific communities and threats
+regarding the generalizability are quite obvious, we want to determine how
+widespread controlled experiments with student participants are and in which
+settings they are used. [Methods] This paper reports on a systematic mapping
+study using high-quality journals and conferences from the software engineering
+field as data sources. We scanned all papers published between 2010 and 2014
+and investigated all papers reporting student experiments in detail. [Results]
+From 2788 papers under investigation 175 report results from controlled
+experiments. 109 (62.29%) of these controlled experiments have been conducted
+with student participants. Most experiments used undergraduate student
+participants, recruited students on a voluntary basis, and set them tasks to
+measure their comprehension. However, many experiments lack information
+regarding the students' recruitment and other important factors. [Conclusions]
+In conclusion, student participation in software engineering experiments can be
+seen as a common evaluation approach. In contrast, there seems to be little
+knowledge about the threats to validity in student experiments, as major
+drivers such as the recruitment are not reported at all.
+"
+24731,Eichler-Shimura isomorphism and group cohomology on arithmetic groups,"  The Eichler-Shimura isomorphism realizes the automorphic representation
+generated by an automorphic newform in certain cohomology of an arithmetic
+group. In this short note, we give a cohomological interpretation of the
+Eichler-Shimura isomorphism as a connection morphism of certain exact sequence
+of G-modules, for the multiplicative group G of any quaternion algebra over a
+totally real field.
+"
+24732,"Measures by means, means by measures","  We construct measure which determines an ordinary mean in a very natural way.
+Using that measure we can extend the mean to infinite sets as well. E.g. we can
+calculate the geometric mean of any set with positive Lebesgue measure. We also
+study the properties and behavior of such generalized means that are obtained
+by a measure.
+"
+24733,On Discrete Gibbs Measure Approximation to Runs,"  In this paper, some erroneous results for a dependent setup arising from
+independent sequence of Bernoulli trials are corrected. Next, a Stein operator
+for discrete Gibbs measure is derived using PGF approach. Also, an operator for
+dependent setup is derived and shown as perturbation of the Stein operator for
+discrete Gibbs measure. Finally, using perturbation technique and explicit form
+of distributions from discrete Gibbs measure, new error bounds between the
+dependent setup and Poisson, pseudo-binomial and negative binomial
+distributions are obtained by matching up to first two moments.
+"
+24734,Optimal diversification strategies in the networks of related products and of related research areas,"  Countries and cities are likely to enter economic activities that are related
+to those that are already present in them. Yet, while these path dependencies
+are universally acknowledged, we lack an understanding of the diversification
+strategies that can optimally balance the development of related and unrelated
+activities. Here, we develop algorithms to identify the activities that are
+optimal to target at each time step. We find that the strategies that minimize
+the total time needed to diversify an economy target highly connected
+activities during a narrow and specific time window. We compare the strategies
+suggested by our model with the strategies followed by countries in the
+diversification of their exports and research activities, finding that
+countries follow strategies that are close to the ones suggested by the model.
+These findings add to our understanding of economic diversification and also to
+our general understanding of diffusion in networks.
+"
+24735,MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings,"  E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell
+billions of products. Machine learning (ML) algorithms involving products are
+often used to improve the customer experience and increase revenue, e.g.,
+product similarity, recommendation, and price estimation. The products are
+required to be represented as features before training an ML algorithm. In this
+paper, we propose an approach called MRNet-Product2Vec for creating generic
+embeddings of products within an e-commerce ecosystem. We learn a dense and
+low-dimensional embedding where a diverse set of signals related to a product
+are explicitly injected into its representation. We train a Discriminative
+Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a
+product title fed through a Bidirectional RNN and at the output, product labels
+corresponding to fifteen different tasks are predicted. The task set includes
+several intrinsic characteristics about a product such as price, weight, size,
+color, popularity, and material. We evaluate the proposed embedding
+quantitatively and qualitatively. We demonstrate that they are almost as good
+as sparse and extremely high-dimensional TF-IDF representation in spite of
+having less than 3% of the TF-IDF dimension. We also use a multimodal
+autoencoder for comparing products from different language-regions and show
+preliminary yet promising qualitative results.
+"
+24736,Nonlinear Model Predictive Guidance for Fixed-wing UAVs Using Identified Control Augmented Dynamics,"  As off-the-shelf (OTS) autopilots become more widely available and
+user-friendly and the drone market expands, safer, more efficient, and more
+complex motion planning and control will become necessary for fixed-wing aerial
+robotic platforms. Considering typical low-level attitude stabilization
+available on OTS flight controllers, this paper first develops an approach for
+modeling and identification of the control augmented dynamics for a small
+fixed-wing Unmanned Aerial Vehicle (UAV). A high-level Nonlinear Model
+Predictive Controller (NMPC) is subsequently formulated for simultaneous
+airspeed stabilization, path following, and soft constraint handling, using the
+identified model for horizon propagation. The approach is explored in several
+exemplary flight experiments including path following of helix and connected
+Dubins Aircraft segments in high winds as well as a motor failure scenario. The
+cost function, insights on its weighting, and additional soft constraints used
+throughout the experimentation are discussed.
+"
+24737,Thermodynamics of Two-impurity Anderson Model with Dzyaloshinskii-Moriya Interaction,"  In this work, we use the numerical renormalization group (NRG) theory to
+study the thermodynamics of the two-impurity Anderson model. Two different
+methods are used to estimate the effect of the Dzyaloshiskii-Moriya (DM)
+interaction on the variation of the Kondo temperature. When the
+Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction is vanishing, the two
+different estimations give different tendency. If we use the peak of the
+specific heat to identify the variation of the Kondo temperature versus the
+Dzyaloshiskii-Moriya interaction, we get an almost linear function. However, if
+we use the low temperature universal curve of the impurity entropy, we get a
+quadratic function. These results indicate that the previous debates about the
+influence of the spin-orbit coupling on the Kondo temperature may come from the
+different definitions of the Kondo temperature. When the RKKY interaction is
+ferromagnetic, there are two stages of the Kondo screening. Both the two
+estimations demonstrate that the second stage of the Kondo temperature is
+exponentially dependent on the DM interaction. There results are dramatically
+different from those calculated via perturbation theory.
+"
+24738,Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives,"  Planning robust executions under uncertainty is a fundamental challenge for
+building autonomous robots. Partially Observable Markov Decision Processes
+(POMDPs) provide a standard framework for modeling uncertainty in many
+applications. In this work, we study POMDPs with safe-reachability objectives,
+which require that with a probability above some threshold, a goal state is
+eventually reached while keeping the probability of visiting unsafe states
+below some threshold. This POMDP formulation is different from the traditional
+POMDP models with optimality objectives and we show that in some cases, POMDPs
+with safe-reachability objectives can provide a better guarantee of both safety
+and reachability than the existing POMDP models through an example. A key
+algorithmic problem for POMDPs is policy synthesis, which requires reasoning
+over a vast space of beliefs (probability distributions). To address this
+challenge, we introduce the notion of a goal-constrained belief space, which
+only contains beliefs reachable from the initial belief under desired
+executions that can achieve the given safe-reachability objective. Our method
+compactly represents this space over a bounded horizon using symbolic
+constraints, and employs an incremental Satisfiability Modulo Theories (SMT)
+solver to efficiently search for a valid policy over it. We evaluate our method
+using a case study involving a partially observable robotic domain with
+uncertain obstacles. The results show that our method can synthesize policies
+over large belief spaces with a small number of SMT solver calls by focusing on
+the goal-constrained belief space.
+"
+24739,Learning Multiplication-free Linear Transformations,"  In this paper, we propose several dictionary learning algorithms for sparse
+representations that also impose specific structures on the learned
+dictionaries such that they are numerically efficient to use: reduced number of
+addition/multiplications and even avoiding multiplications altogether. We base
+our work on factorizations of the dictionary in highly structured basic
+building blocks (binary orthonormal, scaling and shear transformations) for
+which we can write closed-form solutions to the optimization problems that we
+consider. We show the effectiveness of our methods on image data where we can
+compare against well-known numerically efficient transforms such as the fast
+Fourier and the fast discrete cosine transforms.
+"
+24740,JVLA Observations of Young Brown Dwarfs,"  We present sensitive 3.0 cm JVLA radio continuum observations of six regions
+of low-mass star formation that include twelve young brown dwarfs and four
+young brown dwarf candidates. We detect a total of 49 compact radio sources in
+the fields observed, of which 24 have no reported counterparts and are
+considered new detections. Twelve of the radio sources show variability in
+timescales of weeks to months, suggesting gyrosynchrotron emission produced in
+active magnetospheres. Only one of the target brown dwarfs, FU Tau A, was
+detected. However, we detected radio emission associated with two of the brown
+dwarf candidates, WL 20S and CHLT 2. The radio flux densities of the sources
+associated with these brown dwarf candidates are more than an order of
+magnitude larger than expected for a brown dwarf and suggest a revision of
+their classification. In contrast, FU Tau A falls on the well-known correlation
+between radio luminosity and bolometric luminosity, suggesting that the
+emission comes from a thermal jet and that this brown dwarf seems to be forming
+as a scaled-down version of low-mass stars.
+"
+24741,Mix-point property in quasi-pseudometric spaces,"  In this article, we give new results in the startpoint theory for
+quasi-pseudometric spaces. The results we present provide us with the existence
+of startpoint (endpoint, fixed point) for multi-valued maps defined on a
+bicomplete quasi-pseudometric space. We characterise the existence of
+startpoint and endpoint by the so-called \textit{mix-point property}. The
+present results extend known ones in the area.
+"
+24742,Predicting drug response of tumors from integrated genomic profiles by deep neural networks,"  The study of high-throughput genomic profiles from a pharmacogenomics
+viewpoint has provided unprecedented insights into the oncogenic features
+modulating drug response. A recent screening of ~1,000 cancer cell lines to a
+collection of anti-cancer drugs illuminated the link between genotypes and
+vulnerability. However, due to essential differences between cell lines and
+tumors, the translation into predicting drug response in tumors remains
+challenging. Here we proposed a DNN model to predict drug response based on
+mutation and expression profiles of a cancer cell or a tumor. The model
+contains a mutation and an expression encoders pre-trained using a large
+pan-cancer dataset to abstract core representations of high-dimension data,
+followed by a drug response predictor network. Given a pair of mutation and
+expression profiles, the model predicts IC50 values of 265 drugs. We trained
+and tested the model on a dataset of 622 cancer cell lines and achieved an
+overall prediction performance of mean squared error at 1.96 (log-scale IC50
+values). The performance was superior in prediction error or stability than two
+classical methods and four analog DNNs of our model. We then applied the model
+to predict drug response of 9,059 tumors of 33 cancer types. The model
+predicted both known, including EGFR inhibitors in non-small cell lung cancer
+and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive
+analysis further revealed the molecular mechanisms underlying the resistance to
+a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer
+potential of a novel agent, CX-5461, in treating gliomas and hematopoietic
+malignancies. Overall, our model and findings improve the prediction of drug
+response and the identification of novel therapeutic options.
+"
+24743,AGN wind scaling relations and the co-evolution of black holes and galaxies,"  Feedback from accreting SMBHs is often identified as the main mechanism
+responsible for regulating star-formation in AGN host galaxies. However, the
+relationships between AGN activity, radiation, winds, and star-formation are
+complex and still far from being understood. We study scaling relations between
+AGN properties, host galaxy properties and AGN winds. We then evaluate the wind
+mean impact on the global star-formation history, taking into account the short
+AGN duty cycle with respect to that of star-formation. We first collect AGN
+wind observations for 94 AGN with detected massive winds at sub-pc to kpc
+spatial scales. We then fold AGN wind scaling relations with AGN luminosity
+functions, to evaluate the average AGN wind mass-loading factor as a function
+of cosmic time. We find strong correlations between the AGN molecular and
+ionised wind mass outflow rates and the AGN bolometric luminosity. The power
+law scaling is steeper for ionised winds (slope 1.29+/-0.38) than for molecular
+winds (0.76+/-0.06), meaning that the two rates converge at high bolometric
+luminosities. The molecular gas depletion timescale and the molecular gas
+fraction of galaxies hosting powerful AGN winds are 3-10 times shorter and
+smaller than those of main-sequence galaxies with similar SFR, stellar mass and
+redshift. These findings suggest that, at high AGN bolometric luminosity, the
+reduced molecular gas fraction may be due to the destruction of molecules by
+the wind, leading to a larger fraction of gas in the atomic ionised phase. The
+AGN wind mass-loading factor $\eta=\dot M_{OF}/SFR$ is systematically higher
+than that of starburst driven winds. Our analysis shows that AGN winds are, on
+average, powerful enough to clean galaxies from their molecular gas only in
+massive systems at z<=2, i.e. a strong form of co-evolution between SMBHs and
+galaxies appears to break down for the least massive galaxies.
+"
+24744,HomotopyContinuation.jl: A package for homotopy continuation in Julia,"  We present the Julia package HomotopyContinuation.jl, which provides an
+algorithmic framework for solving polynomial systems by numerical homotopy
+continuation. We introduce the basic capabilities of the package and
+demonstrate the software on an illustrative example. We motivate our choice of
+Julia and how its features allow us to improve upon existing software packages
+with respect to usability, modularity and performance. Furthermore, we compare
+the performance of HomotopyContinuation.jl to the existing packages Bertini and
+PHCpack.
+"
+24745,"A Proof of the Riemann hypothesis The new version contains a change in Definition 3.4, resulting in simpler proofs of theorems in Sections 3 and 4. Also, important proof in Sec.5 has been modified, for completeness and clarity","  The function $G(z) = \int_0^\infty \xi^{z-1}(1+\exp(\xi))^{-1} \, d\xi$ is
+analytic and has the same zeros as the Riemann zeta function in the critical
+strip $D = \{z \in {\mathbf C} : 0 < \Re z < 1\}$. This paper combines some
+novel methods about indefinite integration, indefinite convolutions and
+inversions of Fourier transforms with numerical ranges of operators to prove
+the Riemann hypothesis.
+"
+24746,Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-way Fixed Effects,"  We present a fast and memory efficient algorithm for the estimation of
+generalized linear models with an additive separable k-way error component. The
+brute force approach uses dummy variables to account for the unobserved
+heterogeneity, but quickly faces computational limits. Thus, we show how a
+weighted version of the Frisch-Waugh-Lovell theorem combined with the method of
+alternating projections can be incorporated into a Newton-Raphson algorithm to
+dramatically reduce the computational costs. The algorithm is especially useful
+in situations, where generalized linear models with k-way fixed effects based
+on dummy variables are computationally demanding or even infeasible due to time
+or memory limitations. In a simulation study and an empirical application we
+demonstrate the performance of our algorithm.
+"
+24747,Accelerated Inference for Latent Variable Models,"  Inference of latent feature models in the Bayesian nonparametric setting is
+generally difficult, especially in high dimensional settings, because it
+usually requires proposing features from some prior distribution. In special
+cases, where the integration is tractable, we could sample feature assignments
+according to a predictive likelihood. However, this still may not be efficient
+in high dimensions. We present a novel method to accelerate the mixing of
+latent variable model inference by proposing feature locations from the data,
+as opposed to the prior. This sampling method is efficient for proper mixing of
+the Markov chain Monte Carlo sampler, computationally attractive because this
+method can be performed in parallel, and is theoretically guaranteed to
+converge to the posterior distribution as its limiting distribution.
+"
+24748,Thickness dependence of the resistivity of Platinum group metal thin films,"  We report on the thin film resistivity of several platinum-group metals (Ru,
+Pd, Ir, Pt). Platinum-group thin films show comparable or lower resistivities
+than Cu for film thicknesses below about 5\,nm due to a weaker thickness
+dependence of the resistivity. Based on experimentally determined mean linear
+distances between grain boundaries as well as ab initio calculations of the
+electron mean free path, the data for Ru, Ir, and Cu were modeled within the
+semiclassical Mayadas--Shatzkes model [Phys. Rev. B 1, 1382 (1970)] to assess
+the combined contributions of surface and grain boundary scattering to the
+resistivity. For Ru, the modeling results indicated that surface scattering was
+strongly dependent on the surrounding material with nearly specular scattering
+at interfaces with SiO2 or air but with diffuse scattering at interfaces with
+TaN. The dependence of the thin film resistivity on the mean free path is also
+discussed within the Mayadas--Shatzkes model in consideration of the
+experimental findings.
+"
+24749,A Tutorial on Bridge Sampling,"  The marginal likelihood plays an important role in many areas of Bayesian
+statistics such as parameter estimation, model comparison, and model averaging.
+In most applications, however, the marginal likelihood is not analytically
+tractable and must be approximated using numerical methods. Here we provide a
+tutorial on bridge sampling (Bennett, 1976; Meng & Wong, 1996), a reliable and
+relatively straightforward sampling method that allows researchers to obtain
+the marginal likelihood for models of varying complexity. First, we introduce
+bridge sampling and three related sampling methods using the beta-binomial
+model as a running example. We then apply bridge sampling to estimate the
+marginal likelihood for the Expectancy Valence (EV) model---a popular model for
+reinforcement learning. Our results indicate that bridge sampling provides
+accurate estimates for both a single participant and a hierarchical version of
+the EV model. We conclude that bridge sampling is an attractive method for
+mathematical psychologists who typically aim to approximate the marginal
+likelihood for a limited set of possibly high-dimensional models.
+"
+24750,On the Effectiveness of Least Squares Generative Adversarial Networks,"  Unsupervised learning with generative adversarial networks (GANs) has proven
+to be hugely successful. Regular GANs hypothesize the discriminator as a
+classifier with the sigmoid cross entropy loss function. However, we found that
+this loss function may lead to the vanishing gradients problem during the
+learning process. To overcome such a problem, we propose in this paper the
+Least Squares Generative Adversarial Networks (LSGANs) which adopt the least
+squares loss for both the discriminator and the generator. We show that
+minimizing the objective function of LSGAN yields minimizing the Pearson
+$\chi^2$ divergence. We also show that the derived objective function that
+yields minimizing the Pearson $\chi^2$ divergence performs better than the
+classical one of using least squares for classification. There are two benefits
+of LSGANs over regular GANs. First, LSGANs are able to generate higher quality
+images than regular GANs. Second, LSGANs perform more stably during the
+learning process. For evaluating the image quality, we conduct both qualitative
+and quantitative experiments, and the experimental results show that LSGANs can
+generate higher quality images than regular GANs. Furthermore, we evaluate the
+stability of LSGANs in two groups. One is to compare between LSGANs and regular
+GANs without gradient penalty. We conduct three experiments, including Gaussian
+mixture distribution, difficult architectures, and a newly proposed method ---
+datasets with small variability, to illustrate the stability of LSGANs. The
+other one is to compare between LSGANs with gradient penalty (LSGANs-GP) and
+WGANs with gradient penalty (WGANs-GP). The experimental results show that
+LSGANs-GP succeed in training for all the difficult architectures used in
+WGANs-GP, including 101-layer ResNet.
+"
+24751,Deep learning with convolutional neural networks for decoding and visualization of EEG pathology,"  We apply convolutional neural networks (ConvNets) to the task of
+distinguishing pathological from normal EEG recordings in the Temple University
+Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet
+architectures recently shown to decode task-related information from EEG at
+least as well as established algorithms designed for this purpose. In decoding
+EEG pathology, both ConvNets reached substantially better accuracies (about 6%
+better, ~85% vs. ~79%) than the only published result for this dataset, and
+were still better when using only 1 minute of each recording for training and
+only six seconds of each recording for testing. We used automated methods to
+optimize architectural hyperparameters and found intriguingly different ConvNet
+architectures, e.g., with max pooling as the only nonlinearity. Visualizations
+of the ConvNet decoding behavior showed that they used spectral power changes
+in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside
+other features, consistent with expectations derived from spectral analysis of
+the EEG data and from the textual medical reports. Analysis of the textual
+medical reports also highlighted the potential for accuracy increases by
+integrating contextual information, such as the age of subjects. In summary,
+the ConvNets and visualization techniques used in this study constitute a next
+step towards clinically useful automated EEG diagnosis and establish a new
+baseline for future work on this topic.
+"
+24752,Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming,"  Machine learning has been gaining traction in recent years to meet the demand
+for tools that can efficiently analyze and make sense of the ever-growing
+databases of biomedical data in health care systems around the world. However,
+effectively using machine learning methods requires considerable domain
+expertise, which can be a barrier of entry for bioinformaticians new to
+computational data science methods. Therefore, off-the-shelf tools that make
+machine learning more accessible can prove invaluable for bioinformaticians. To
+this end, we have developed an open source pipeline optimization tool
+(TPOT-MDR) that uses genetic programming to automatically design machine
+learning pipelines for bioinformatics studies. In TPOT-MDR, we implement
+Multifactor Dimensionality Reduction (MDR) as a feature construction method for
+modeling higher-order feature interactions, and combine it with a new expert
+knowledge-guided feature selector for large biomedical data sets. We
+demonstrate TPOT-MDR's capabilities using a combination of simulated and real
+world data sets from human genetics and find that TPOT-MDR significantly
+outperforms modern machine learning methods such as logistic regression and
+eXtreme Gradient Boosting (XGBoost). We further analyze the best pipeline
+discovered by TPOT-MDR for a real world problem and highlight TPOT-MDR's
+ability to produce a high-accuracy solution that is also easily interpretable.
+"
+24753,Doping the holographic Mott insulator,"  Mott insulators form because of strong electron repulsions, being at the
+heart of strongly correlated electron physics. Conventionally these are
+understood as classical ""traffic jams"" of electrons described by a short-ranged
+entangled product ground state. Exploiting the holographic duality, which maps
+the physics of densely entangled matter onto gravitational black hole physics,
+we show how Mott-insulators can be constructed departing from entangled
+non-Fermi liquid metallic states, such as the strange metals found in cuprate
+superconductors. These ""entangled Mott insulators"" have traits in common with
+the ""classical"" Mott insulators, such as the formation of Mott gap in the
+optical conductivity, super-exchange-like interactions, and form ""stripes"" when
+doped. They also exhibit new properties: the ordering wave vectors are detached
+from the number of electrons in the unit cell, and the DC resistivity diverges
+algebraically instead of exponentially as function of temperature. These
+results may shed light on the mysterious ordering phenomena observed in
+underdoped cuprates.
+"
+24754,GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning,"  Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibitively
+large number of training samples for learning complex tasks. Many recent works
+on speeding up Deep RL have focused on distributed training and simulation.
+While distributed training is often done on the GPU, simulation is not. In this
+work, we propose using GPU-accelerated RL simulations as an alternative to CPU
+ones. Using NVIDIA Flex, a GPU-based physics engine, we show promising
+speed-ups of learning various continuous-control, locomotion tasks. With one
+GPU and CPU core, we are able to train the Humanoid running task in less than
+20 minutes, using 10-1000x fewer CPU cores than previous works. We also
+demonstrate the scalability of our simulator to multi-GPU settings to train
+more challenging locomotion tasks.
+"
+24755,Nematic Bond Theory of Heisenberg Helimagnets,"  We study classical two-dimensional frustrated Heisenberg models with
+generically incommensurate groundstates. A new theory for the spin-nematic
+""order by disorder"" transition is developed based on the self-consistent
+determination of the effective exchange coupling bonds. In our approach,
+fluctuations of the constraint field imposing conservation of the local
+magnetic moment drive nematicity at low temperatures. The critical temperature
+is found to be highly sensitive to the peak helimagnetic wavevector, and
+vanishes continuously when approaching rotation symmetric Lifshitz points.
+Transitions between symmetry distinct nematic orders may occur by tuning the
+exchange parameters, leading to lines of bicritical points.
+"
+24756,DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization,"  In recent years, optimization theory has been greatly impacted by the advent
+of sum of squares (SOS) optimization. The reliance of this technique on
+large-scale semidefinite programs however, has limited the scale of problems to
+which it can be applied. In this paper, we introduce DSOS and SDSOS
+optimization as linear programming and second-order cone programming-based
+alternatives to sum of squares optimization that allow one to trade off
+computation time with solution quality. These are optimization problems over
+certain subsets of sum of squares polynomials (or equivalently subsets of
+positive semidefinite matrices), which can be of interest in general
+applications of semidefinite programming where scalability is a limitation. We
+show that some basic theorems from SOS optimization which rely on results from
+real algebraic geometry are still valid for DSOS and SDSOS optimization.
+Furthermore, we show with numerical experiments from diverse application
+areas---polynomial optimization, statistics and machine learning, derivative
+pricing, and control theory---that with reasonable tradeoffs in accuracy, we
+can handle problems at scales that are currently significantly beyond the reach
+of traditional sum of squares approaches. Finally, we provide a review of
+recent techniques that bridge the gap between our DSOS/SDSOS approach and the
+SOS approach at the expense of additional running time. The Supplementary
+Material of the paper introduces an accompanying MATLAB package for DSOS and
+SDSOS optimization.
+"
+24757,Deep Learning for Tumor Classification in Imaging Mass Spectrometry,"  Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data
+has a high potential for future applications in pathology. Due to the
+complexity and size of the data, automated feature extraction and
+classification steps are required to fully process the data. Deep learning
+offers an approach to learn feature extraction and classification combined in a
+single model. Commonly these steps are handled separately in IMS data analysis,
+hence deep learning offers an alternative strategy worthwhile to explore.
+Results: Methodologically, we propose an adapted architecture based on deep
+convolutional networks to handle the characteristics of mass spectrometry data,
+as well as a strategy to interpret the learned model in the spectral domain
+based on a sensitivity analysis. The proposed methods are evaluated on two
+challenging tumor classification tasks and compared to a baseline approach.
+Competitiveness of the proposed methods are shown on both tasks by studying the
+performance via cross-validation. Moreover, the learned models are analyzed by
+the proposed sensitivity analysis revealing biologically plausible effects as
+well as confounding factors of the considered task. Thus, this study may serve
+as a starting point for further development of deep learning approaches in IMS
+classification tasks.
+"
+24758,Rank Pruning for Dominance Queries in CP-Nets,"  Conditional preference networks (CP-nets) are a graphical representation of a
+person's (conditional) preferences over a set of discrete variables. In this
+paper, we introduce a novel method of quantifying preference for any given
+outcome based on a CP-net representation of a user's preferences. We
+demonstrate that these values are useful for reasoning about user preferences.
+In particular, they allow us to order (any subset of) the possible outcomes in
+accordance with the user's preferences. Further, these values can be used to
+improve the efficiency of outcome dominance testing. That is, given a pair of
+outcomes, we can determine which the user prefers more efficiently. Through
+experimental results, we show that this method is more effective than existing
+techniques for improving dominance testing efficiency. We show that the above
+results also hold for CP-nets that express indifference between variable
+values.
+"
+24759,Dynamical Generation of Topological Masses in Dirac Fermions,"  We report discovery of a topological Mott insulator in strongly-correlated
+Dirac semimetals. Such an interaction-driven topological state has been
+theoretically proposed but not yet observed with unbiased large scale numerical
+simulations. In our model, interactions between electrons are mediated by Ising
+spins in a transverse field. The results indicate that the topological mass
+term is dynamically generated and the resulting quantum phase transition
+belongs to the (2+1)D $N=8$ chiral Ising universality class. These conclusions
+stem from large scale sign free quantum Monte Carlo simulations.
+"
+24760,Analytical study of mode degeneracy in non-Hermitian photonic crystals with TM-like polarization,"  We present an analytical study of the mode degeneracy in non-Hermitian
+photonic crystals (PC) with $C_{4v}$ symmetry, from the perspective of the
+coupled-wave-theory (CWT). The wave couplings and leakages within the
+non-Hermitian PCs are depicted, and the condition of accidental triple
+degeneracy is derived which leads to a Dirac-cone like dispersion. We prove
+that, similar to the real Dirac-cone, the Dirac-cone like band in non-Hermitian
+PC possesses good linearity and isotropy in the vicinity of the $\Gamma$ point.
+Moreover, some topological transit of the band may occur when the parameters
+adiabatically evolve. However, the Berry phase remains zero at the $\Gamma$
+point, indicated the Dirac-like-cone dosen't obey Dirac equation and it is only
+Dirac-cone.
+"
+24761,How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility,"  Recommendation systems are ubiquitous and impact many domains; they have the
+potential to influence product consumption, individuals' perceptions of the
+world, and life-altering decisions. These systems are often evaluated or
+trained with data from users already exposed to algorithmic recommendations;
+this creates a pernicious feedback loop. Using simulations, we demonstrate how
+using data confounded in this way homogenizes user behavior without increasing
+utility.
+"
+24762,Beltrami's theorem via parabolic geometry,"  We use Beltrami's theorem as an excuse to present some arguments from
+parabolic differential geometry without any of the parabolic machinery.
+"
+24763,Multi-Label Annotation Aggregation in Crowdsourcing,"  As a means of human-based computation, crowdsourcing has been widely used to
+annotate large-scale unlabeled datasets. One of the obvious challenges is how
+to aggregate these possibly noisy labels provided by a set of heterogeneous
+annotators. Another challenge stems from the difficulty in evaluating the
+annotator reliability without even knowing the ground truth, which can be used
+to build incentive mechanisms in crowdsourcing platforms. When each instance is
+associated with many possible labels simultaneously, the problem becomes even
+harder because of its combinatorial nature. In this paper, we present new
+flexible Bayesian models and efficient inference algorithms for multi-label
+annotation aggregation by taking both annotator reliability and label
+dependency into account. Extensive experiments on real-world datasets confirm
+that the proposed methods outperform other competitive alternatives, and the
+model can recover the type of the annotators with high accuracy. Besides, we
+empirically find that the mixture of multiple independent Bernoulli
+distribution is able to accurately capture label dependency in this
+unsupervised multi-label annotation aggregation scenario.
+"
+24764,A gapped $Z_{2}$ spin liquid phase with a $U(1)$ mean field ansatz: a Bosonic resonating valence-bond description,"  Gapped $Z_{2}$ spin liquid as the simplest spin liquid has been proved to be
+the most difficult to realize in realistic models. Here we show that the
+frustration from a third-neighbor exchange $J_{3}$ on the spin-$\frac{1}{2}$
+$J_{1}-J_{2}$ model on the square lattice may serve to stabilize such a
+long-sought state. We argue that a Bosonic RVB description is more appropriate
+than a Fermonic RVB description for such a gapped spin liquid phase. We show
+that while the mean field ansatz of the proposed state has a $U(1)$ gauge
+symmetry, the gauge fluctuation spectrum on it is actually gapped and the state
+should be understood rather as a $Z_{2}$ spin liquid state with topological
+order. The state is thus locally stable with respect to gauge fluctuation and
+can emerge continuously from a collinear Neel ordered phase.
+"
+24765,Fast and Highly Sensitive Ionic Polymer Gated WS$_2$-Graphene Photodetectors,"  The combination of graphene with semiconductor materials in heterostructure
+photodetectors, has enabled amplified detection of femtowatt light signals
+using micron-scale electronic devices. Presently, the speed of such detectors
+is limited by long-lived charge traps and impractical strategies, e.g. the use
+of large gate voltage pulses, have been employed to achieve bandwidths suitable
+for applications, such as video-frame-rate imaging. Here, we report atomically
+thin graphene-WS$_2$ heterostructure photodetectors encapsulated in an ionic
+polymer, which are uniquely able to operate at bandwidths up to 1.5 kHz, whilst
+maintaining internal gain as large as $10^6$. Highly mobile ions and a
+nanometre scale Debye length of the ionic polymer are used to screen charge
+traps and tune the Fermi level of graphene over an unprecedented range at the
+interface with WS$_2$. We observe a responsivity $R=10^6$ A W$^{-1}$ and
+detectivity $D^*=3.8\times10^{11}$ Jones, approaching that of single photon
+counters. The combination of both high responsivity and fast response times
+makes these photodetectors suitable for video-frame-rate imaging applications.
+"
+24766,Disorder-induced half-integer quantized conductance plateau in quantum anomalous Hall insulator-superconductor structures,"  A weak superconducting proximity effect in the vicinity of the topological
+transition of a quantum anomalous Hall system has been proposed as a venue to
+realize a topological superconductor (TSC) with chiral Majorana edge modes
+(CMEMs). A recent experiment [Science 357, 294 (2017)] claimed to have observed
+such CMEMs in the form of a half-integer quantized conductance plateau in the
+two-terminal transport measurement of a quantum anomalous Hall-superconductor
+junction. Although the presence of a superconducting proximity effect
+generically splits the quantum Hall transition into two phase transitions with
+a gapped TSC in between, in this Rapid Communication we propose that a nearly
+flat conductance plateau, similar to that expected from CMEMs, can also arise
+from the percolation of quantum Hall edges well before the onset of the TSC or
+at temperatures much above the TSC gap. Our Rapid Communication, therefore,
+suggests that, in order to confirm the TSC, it is necessary to supplement the
+observation of the half-quantized conductance plateau with a hard
+superconducting gap (which is unlikely for a disordered system) from the
+conductance measurements or the heat transport measurement of the transport
+gap. Alternatively, the half-quantized thermal conductance would also serve as
+a smoking-gun signature of the TSC.
+"
+24767,"A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination","  Much has been learned about plasticity of biological synapses from empirical
+studies. Hebbian plasticity is driven by correlated activity of presynaptic and
+postsynaptic neurons. Synapses that converge onto the same neuron often behave
+as if they compete for a fixed resource; some survive the competition while
+others are eliminated. To provide computational interpretations of these
+aspects of synaptic plasticity, we formulate unsupervised learning as a
+zero-sum game between Hebbian excitation and anti-Hebbian inhibition in a
+neural network model. The game formalizes the intuition that Hebbian excitation
+tries to maximize correlations of neurons with their inputs, while anti-Hebbian
+inhibition tries to decorrelate neurons from each other. We further include a
+model of synaptic competition, which enables a neuron to eliminate all
+connections except those from its most strongly correlated inputs. Through
+empirical studies, we show that this facilitates the learning of sensory
+features that resemble parts of objects.
+"
+24768,Non-ideal magnetohydrodynamics on a moving mesh,"  In certain astrophysical systems the commonly employed ideal
+magnetohydrodynamics (MHD) approximation breaks down. Here, we introduce novel
+explicit and implicit numerical schemes of ohmic resistivity terms in the
+moving-mesh code AREPO. We include these non-ideal terms for two MHD
+techniques: the Powell 8-wave formalism and a constrained transport scheme,
+which evolves the cell-centred magnetic vector potential. We test our
+implementation against problems of increasing complexity, such as one- and
+two-dimensional diffusion problems, and the evolution of progressive and
+stationary Alfvén waves. On these test problems, our implementation recovers
+the analytic solutions to second-order accuracy. As first applications, we
+investigate the tearing instability in magnetized plasmas and the gravitational
+collapse of a rotating magnetized gas cloud. In both systems, resistivity plays
+a key role. In the former case, it allows for the development of the tearing
+instability through reconnection of the magnetic field lines. In the latter,
+the adopted (constant) value of ohmic resistivity has an impact on both the gas
+distribution around the emerging protostar and the mass loading of magnetically
+driven outflows. Our new non-ideal MHD implementation opens up the possibility
+to study magneto-hydrodynamical systems on a moving mesh beyond the ideal MHD
+approximation.
+"
+24769,On slopes of $L$-functions of $\mathbb{Z}_p$-covers over the projective line,"  Let $\mathcal{P}: \cdots \rightarrow C_2\rightarrow C_1\rightarrow {\mathbb
+P}^1$ be a $\mathbb{Z}_p$-cover of the projective line over a finite field of
+cardinality $q$ and characteristic $p$ which ramifies at exactly one rational
+point, and is unramified at other points. In this paper, we study the $q$-adic
+valuations of the reciprocal roots in $\mathbb{C}_p$ of $L$-functions
+associated to characters of the Galois group of $\mathcal{P}$. We show that for
+all covers $\mathcal{P}$ such that the genus of $C_n$ is a quadratic polynomial
+in $p^n$ for $n$ large, the valuations of these reciprocal roots are uniformly
+distributed in the interval $[0,1]$. Furthermore, we show that for a large
+class of such covers $\mathcal{P}$, the valuations of the reciprocal roots in
+fact form a finite union of arithmetic progressions.
+"
+24770,Universal Components of Real-world Diffusion Dynamics based on Point Processes,"  Bursts in human and natural activities are highly clustered in time,
+suggesting that these activities are influenced by previous events within the
+social or natural system. Bursty behavior in the real world conveys information
+of underlying diffusion processes, which have been the focus of diverse
+scientific communities from online social media to criminology and
+epidemiology. However, universal components of real-world diffusion dynamics
+that cut across disciplines remain unexplored. Here, we introduce a wide range
+of diffusion processes across disciplines and propose universal components of
+diffusion frameworks. We apply these components to diffusion-based studies of
+human disease spread, through a case study of the vector-borne disease dengue.
+The proposed universality of diffusion can motivate transdisciplinary research
+and provide a fundamental framework for diffusion models.
+"
+24771,A Short-term Intervention for Long-term Fairness in the Labor Market,"  The persistence of racial inequality in the U.S. labor market against a
+general backdrop of formal equality of opportunity is a troubling phenomenon
+that has significant ramifications on the design of hiring policies. In this
+paper, we show that current group disparate outcomes may be immovable even when
+hiring decisions are bound by an input-output notion of ""individual fairness.""
+Instead, we construct a dynamic reputational model of the labor market that
+illustrates the reinforcing nature of asymmetric outcomes resulting from
+groups' divergent accesses to resources and as a result, investment choices. To
+address these disparities, we adopt a dual labor market composed of a Temporary
+Labor Market (TLM), in which firms' hiring strategies are constrained to ensure
+statistical parity of workers granted entry into the pipeline, and a Permanent
+Labor Market (PLM), in which firms hire top performers as desired. Individual
+worker reputations produce externalities for their group; the corresponding
+feedback loop raises the collective reputation of the initially disadvantaged
+group via a TLM fairness intervention that need not be permanent. We show that
+such a restriction on hiring practices induces an equilibrium that, under
+particular market conditions, Pareto-dominates those arising from strategies
+that statistically discriminate or employ a ""group-blind"" criterion. The
+enduring nature of equilibria that are both inequitable and Pareto suboptimal
+suggests that fairness interventions beyond procedural checks of hiring
+decisions will be of critical importance in a world where machines play a
+greater role in the employment process.
+"
+24772,FabULous Interoperability for ML and a Linear Language,"  Instead of a monolithic programming language trying to cover all features of
+interest, some programming systems are designed by combining together simpler
+languages that cooperate to cover the same feature space. This can improve
+usability by making each part simpler than the whole, but there is a risk of
+abstraction leaks from one language to another that would break expectations of
+the users familiar with only one or some of the involved languages.
+We propose a formal specification for what it means for a given language in a
+multi-language system to be usable without leaks: it should embed into the
+multi-language in a fully abstract way, that is, its contextual equivalence
+should be unchanged in the larger system.
+To demonstrate our proposed design principle and formal specification
+criterion, we design a multi-language programming system that combines an
+ML-like statically typed functional language and another language with linear
+types and linear state. Our goal is to cover a good part of the expressiveness
+of languages that mix functional programming and linear state (ownership), at
+only a fraction of the complexity. We prove that the embedding of ML into the
+multi-language system is fully abstract: functional programmers should not fear
+abstraction leaks. We show examples of combined programs demonstrating in-place
+memory updates and safe resource handling, and an implementation extending
+OCaml with our linear language.
+"
+24773,Characterizing health informatics journals by subject-level dependencies: a citation network analysis,"  Citation network analysis has become one of methods to study how scientific
+knowledge flows from one domain to another. Health informatics is a
+multidisciplinary field that includes social science, software engineering,
+behavioral science, medical science and others. In this study, we perform an
+analysis of citation statistics from health informatics journals using data set
+extracted from CrossRef. For each health informatics journal, we extract the
+number of citations from/to studies related to computer science,
+medicine/clinical medicine and other fields, including the number of
+self-citations from the health informatics journal. With a similar number of
+articles used in our analysis, we show that the Journal of the American Medical
+Informatics Association (JAMIA) has more in-citations than the Journal of
+Medical Internet Research (JMIR); while JMIR has a higher number of
+out-citations and self-citations. We also show that JMIR cites more articles
+from health informatics journals and medicine related journals. In addition,
+the Journal of Medical Systems (JMS) cites more articles from computer science
+journals compared with other health informatics journals included in our
+analysis.
+"
+24774,Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks,"  A network embedding consists of a vector representation for each node in the
+network. Network embeddings have shown their usefulness in node classification
+and visualization in many real-world application domains, such as social
+networks and web networks. Directed networks with text associated with each
+node, such as citation networks and software package dependency networks, are
+commonplace. However, to the best of our knowledge, their embeddings have not
+been specifically studied. In this paper, we create PCTADW-1 and PCTADW-2, two
+algorithms based on NNs that learn embeddings of directed networks with text
+associated with each node. We create two new labeled directed networks with
+text-associated node: The package dependency networks in two popular GNU/Linux
+distributions, Debian and Fedora. We experimentally demonstrate that the
+embeddings produced by our NNs resulted in node classification with better
+quality than those of various baselines on these two networks. We observe that
+there exist systematic presence of analogies (similar to those in word
+embeddings) in the network embeddings of software package dependency networks.
+To the best of our knowledge, this is the first time that such a systematic
+presence of analogies is observed in network and document embeddings. This may
+potentially open up a new venue for better understanding networks and documents
+algorithmically using their embeddings as well as for better human
+understanding of network and document embeddings.
+"
+24775,The Do's and Don'ts for CNN-based Face Verification,"  While the research community appears to have developed a consensus on the
+methods of acquiring annotated data, design and training of CNNs, many
+questions still remain to be answered. In this paper, we explore the following
+questions that are critical to face recognition research: (i) Can we train on
+still images and expect the systems to work on videos? (ii) Are deeper datasets
+better than wider datasets? (iii) Does adding label noise lead to improvement
+in performance of deep networks? (iv) Is alignment needed for face recognition?
+We address these questions by training CNNs using CASIA-WebFace, UMDFaces, and
+a new video dataset and testing on YouTube- Faces, IJB-A and a disjoint portion
+of UMDFaces datasets. Our new data set, which will be made publicly available,
+has 22,075 videos and 3,735,476 human annotated frames extracted from them.
+"
+24776,Feature Extraction and Feature Selection: Reducing Data Complexity with Apache Spark,"  Feature extraction and feature selection are the first tasks in
+pre-processing of input logs in order to detect cyber security threats and
+attacks while utilizing machine learning. When it comes to the analysis of
+heterogeneous data derived from different sources, these tasks are found to be
+time-consuming and difficult to be managed efficiently. In this paper, we
+present an approach for handling feature extraction and feature selection for
+security analytics of heterogeneous data derived from different network
+sensors. The approach is implemented in Apache Spark, using its python API,
+named pyspark.
+"
+24777,Superconductivity model for a spin-vortex checkerboard,"  We introduce a microscopic model aimed at describing superconductivity that
+can possibly exist in the background of a magnetic texture called ""spin-vortex
+checkerboard"". This texture was proposed previously as a possible alternative
+to stripes to interpret the experimental phenomenology of spin and charge
+modulations in 1/8-doped lanthanum cuprates. The model involves two kinds of
+interacting fermionic excitations residing in spin-rich and spin-poor regions
+of the modulated structure. It is a generalization of another model developed
+earlier for the so-called ""grid checkerboard"". We present the mean-field
+solution of the model, from which we obtain model's predictions for the
+temperature evolution of the superconducting gap, compare these predictions
+with available experiments on high-$T_c$ cuprate superconductors and find a
+good overall agreement.
+"
+24778,Review of Inductive Pulsed Power Generators for Railguns,"  This literature review addresses inductive pulsed power generators and their
+major components. Different inductive storage designs like solenoids, toroids
+and force-balanced coils are briefly presented and their advantages and
+disadvantages are mentioned. Special emphasis is given to inductive circuit
+topologies which have been investigated in railgun research such as the XRAM,
+meat grinder or pulse transformer topologies. One section deals with opening
+switches as they are indispensable for inductive storages and another one deals
+briefly with SMES for pulsed power applications. In the end, the most relevant
+inductor systems which were realized in respect to railgun research are
+summarized in a table, together with its main characteristics.
+"
+24779,Practical Data-Dependent Metric Compression with Provable Guarantees,"  We introduce a new distance-preserving compact representation of
+multi-dimensional point-sets. Given $n$ points in a $d$-dimensional space where
+each coordinate is represented using $B$ bits (i.e., $dB$ bits per point), it
+produces a representation of size $O( d \log(d B/\epsilon) + \log n)$ bits per
+point from which one can approximate the distances up to a factor of $1 \pm
+\epsilon$. Our algorithm almost matches the recent bound
+of~\cite{indyk2017near} while being much simpler. We compare our algorithm to
+Product Quantization (PQ)~\cite{jegou2011product}, a state of the art heuristic
+metric compression method. We evaluate both algorithms on several data sets:
+SIFT (used in \cite{jegou2011product}), MNIST~\cite{lecun1998mnist}, New York
+City taxi time series~\cite{guha2016robust} and a synthetic one-dimensional
+data set embedded in a high-dimensional space. With appropriately tuned
+parameters, our algorithm produces representations that are comparable to or
+better than those produced by PQ, while having provable guarantees on its
+performance.
+"
+24780,Hierarchy of the low-lying excitations for the $(2+1)$-dimensional $q=3$ Potts model in the ordered phase,"  The $(2+1)$-dimensional $q=3$ Potts model was simulated with the exact
+diagonalization method. In the ordered phase, the elementary excitations
+(magnons) are attractive, forming a series of bound states in the low-energy
+spectrum. We investigate the low-lying spectrum through a dynamical
+susceptibility, which is readily tractable with the exact diagonalization
+method via the continued-fraction expansion. As a result, we estimate the
+series of (scaled) mass gaps, $m_{2,3,4}/m_1$ ($m_1$: single-magnon mass), in
+proximity to the transition point.
+"
+24781,Afivo: a framework for quadtree/octree AMR with shared-memory parallelization and geometric multigrid methods,"  Afivo is a framework for simulations with adaptive mesh refinement (AMR) on
+quadtree (2D) and octree (3D) grids. The framework comes with a geometric
+multigrid solver, shared-memory (OpenMP) parallelism and it supports output in
+Silo and VTK file formats. Afivo can be used to efficiently simulate AMR
+problems with up to about $10^{8}$ unknowns on desktops, workstations or single
+compute nodes. For larger problems, existing distributed-memory frameworks are
+better suited. The framework has no built-in functionality for specific physics
+applications, so users have to implement their own numerical methods. The
+included multigrid solver can be used to efficiently solve elliptic partial
+differential equations such as Poisson's equation. Afivo's design was kept
+simple, which in combination with the shared-memory parallelism facilitates
+modification and experimentation with AMR algorithms. The framework was already
+used to perform 3D simulations of streamer discharges, which required tens of
+millions of cells.
+"
+24782,Concentration of tempered posteriors and of their variational approximations,"  While Bayesian methods are extremely popular in statistics and machine
+learning, their application to massive datasets is often challenging, when
+possible at all. Indeed, the classical MCMC algorithms are prohibitively slow
+when both the model dimension and the sample size are large. Variational
+Bayesian methods aim at approximating the posterior by a distribution in a
+tractable family. Thus, MCMC are replaced by an optimization algorithm which is
+orders of magnitude faster. VB methods have been applied in such
+computationally demanding applications as including collaborative filtering,
+image and video processing, NLP and text processing... However, despite very
+nice results in practice, the theoretical properties of these approximations
+are usually not known. In this paper, we propose a general approach to prove
+the concentration of variational approximations of fractional posteriors. We
+apply our theory to two examples: matrix completion, and Gaussian VB.
+"
+24783,The power of deeper networks for expressing natural functions,"  It is well-known that neural networks are universal approximators, but that
+deeper networks tend in practice to be more powerful than shallower ones. We
+shed light on this by proving that the total number of neurons $m$ required to
+approximate natural classes of multivariate polynomials of $n$ variables grows
+only linearly with $n$ for deep neural networks, but grows exponentially when
+merely a single hidden layer is allowed. We also provide evidence that when the
+number of hidden layers is increased from $1$ to $k$, the neuron requirement
+grows exponentially not with $n$ but with $n^{1/k}$, suggesting that the
+minimum number of layers required for practical expressibility grows only
+logarithmically with $n$.
+"
+24784,Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization,"  Camera relocalization plays a vital role in many robotics and computer vision
+tasks, such as global localization, recovery from tracking failure and loop
+closure detection. Recent random forests based methods exploit randomly sampled
+pixel comparison features to predict 3D world locations for 2D image locations
+to guide the camera pose optimization. However, these image features are only
+sampled randomly in the images, without considering the spatial structures or
+geometric information, leading to large errors or failure cases with the
+existence of poorly textured areas or in motion blur. Line segment features are
+more robust in these environments. In this work, we propose to jointly exploit
+points and lines within the framework of uncertainty driven regression forests.
+The proposed approach is thoroughly evaluated on three publicly available
+datasets against several strong state-of-the-art baselines in terms of several
+different error metrics. Experimental results prove the efficacy of our method,
+showing superior or on-par state-of-the-art performance.
+"
+24785,Recent results of quantum ergodicity on graphs and further investigation,"  We outline some recent proofs of quantum ergodicity on large graphs and give
+new applications in the context of irregular graphs. We also discuss some
+remaining questions.
+"
+24786,WristAuthen: A Dynamic Time Wrapping Approach for User Authentication by Hand-Interaction through Wrist-Worn Devices,"  The growing trend of using wearable devices for context-aware computing and
+pervasive sensing systems has raised its potentials for quick and reliable
+authentication techniques. Since personal writing habitats differ from each
+other, it is possible to realize user authentication through writing. This is
+of great significance as sensible information is easily collected by these
+devices. This paper presents a novel user authentication system through
+wrist-worn devices by analyzing the interaction behavior with users, which is
+both accurate and efficient for future usage. The key feature of our approach
+lies in using much more effective Savitzky-Golay filter and Dynamic Time
+Wrapping method to obtain fine-grained writing metrics for user authentication.
+These new metrics are relatively unique from person to person and independent
+of the computing platform. Analyses are conducted on the wristband-interaction
+data collected from 50 users with diversity in gender, age, and height.
+Extensive experimental results show that the proposed approach can identify
+users in a timely and accurate manner, with a false-negative rate of 1.78\%,
+false-positive rate of 6.7\%, and Area Under ROC Curve of 0.983 . Additional
+examination on robustness to various mimic attacks, tolerance to training data,
+and comparisons to further analyze the applicability.
+"
+24787,"Average Consensus by Graph Filtering: New Approach, Explicit Convergence Rate and Optimal Design","  This paper revisits the problem of multi-agent consensus from a graph signal
+processing perspective. Describing a consensus protocol as a graph spectrum
+filter, we present an effective new approach to the analysis and design of
+consensus protocols in the graph spectrum domain for the uncertain networks,
+which are difficult to handle by the existing time-domain methods. This novel
+approach has led to the following new results in this paper: 1) New necessary
+and sufficient conditions for both finite-time and asymptotic average consensus
+of multi-agent systems. 2) Direct link between the consensus convergence rate
+and the periodic consensus protocols. 3) Conversion of the fast consensus
+problem to the problem of polynomial design of graph spectrum filter. 4) A
+Lagrange polynomial interpolation method and a worst-case optimal interpolation
+method for the design of periodic consensus protocols for the MASs on uncertain
+graphs. 5) Explicit formulas for the convergence rate of the designed
+protocols. Several numerical examples are given to demonstrate the validity,
+effectiveness and advantages of these results.
+"
+24788,The autophoretic torus,"  Phoretic swimmers provide new avenues to study non-equilibrium statistical
+physics and are also hailed as a promising technology for bioengineering at the
+cellular scale. Exact solutions for the locomotion of such swimmers have been
+restricted so far to spheroidal shapes. In this paper we solve for the flow
+induced by the canonical non-simply connected shape, namely an axisymmetric
+phoretic torus. The analytical solution takes the form of an infinite series
+solution, which we validate against boundary element computations. For a torus
+of uniform chemical activity, confinement effects in the hole allow the torus
+to act as a pump, which we optimize subject to fixed particle surface area.
+Under the same constraint, we next characterize the fastest swimming Janus
+torus for a variety of assumptions on the surface chemistry. Perhaps
+surprisingly, none of the optimal tori occur in the limit where the central
+hole vanishes.
+"
+24789,A confidence index for forecasting of meteor showers,"  The forecasting of meteor showers is currently very good at predicting the
+timing of meteor outbursts, but still needs further work regarding the level of
+a given shower. Moreover, uncertainties are rarely provided, leaving the end
+user (scientist, space agency or the public) with no way to evaluate how much
+the prediction is trustworthy. A confidence index for the forecasting of meteor
+showers is presented. It allows one to better understand how a specific
+forecasting has been performed. In particular, it underlines the role of our
+current knowledge of the parent body, its past orbit and past activity. The
+role of close encounters with planets for the time period considered is
+quantified as well. This confidence index is a first step towards better
+constrained forecasting of future meteor showers.
+"
+24790,BAGAN: Data Augmentation with Balancing GAN,"  Image classification datasets are often imbalanced, characteristic that
+negatively affects the accuracy of deep-learning classifiers. In this work we
+propose balancing GAN (BAGAN) as an augmentation tool to restore balance in
+imbalanced datasets. This is challenging because the few minority-class images
+may not be enough to train a GAN. We overcome this issue by including during
+the adversarial training all available images of majority and minority classes.
+The generative model learns useful features from majority classes and uses
+these to generate images for minority classes. We apply class conditioning in
+the latent space to drive the generation process towards a target class. The
+generator in the GAN is initialized with the encoder module of an autoencoder
+that enables us to learn an accurate class-conditioning in the latent space. We
+compare the proposed methodology with state-of-the-art GANs and demonstrate
+that BAGAN generates images of superior quality when trained with an imbalanced
+dataset.
+"
+24791,Audio-based identification of beehive states,"  The absence of the queen in a beehive is a very strong indicator of the need
+for beekeeper intervention. Manually searching for the queen is an arduous
+recurrent task for beekeepers that disrupts the normal life cycle of the
+beehive and can be a source of stress for bees. Sound is an indicator for
+signalling different states of the beehive, including the absence of the queen
+bee. In this work, we apply machine learning methods to automatically recognise
+different states in a beehive using audio as input. % The system is built on
+top of a method for beehive sound recognition in order to detect bee sounds
+from other external sounds. We investigate both support vector machines and
+convolutional neural networks for beehive state recognition, using audio data
+of beehives collected from the NU-Hive project. Results indicate the potential
+of machine learning methods as well as the challenges of generalizing the
+system to new hives.
+"
+24792,Optimality of Approximate Inference Algorithms on Stable Instances,"  Approximate algorithms for structured prediction problems---such as LP
+relaxations and the popular alpha-expansion algorithm (Boykov et al.
+2001)---typically far exceed their theoretical performance guarantees on
+real-world instances. These algorithms often find solutions that are very close
+to optimal. The goal of this paper is to partially explain the performance of
+alpha-expansion and an LP relaxation algorithm on MAP inference in
+Ferromagnetic Potts models (FPMs). Our main results give stability conditions
+under which these two algorithms provably recover the optimal MAP solution.
+These theoretical results complement numerous empirical observations of good
+performance.
+"
+24793,An algorithm for the rapid numerical evaluation of Bessel functions of real orders and arguments,"  We describe a method for the rapid numerical evaluation of the Bessel
+functions of the first and second kinds of nonnegative real orders and positive
+arguments. Our algorithm makes use of the well-known observation that although
+the Bessel functions themselves are expensive to represent via piecewise
+polynomial expansions, the logarithms of certain solutions of Bessel's equation
+are not. We exploit this observation by numerically precomputing the logarithms
+of carefully chosen Bessel functions and representing them with piecewise
+bivariate Chebyshev expansions. Our scheme is able to evaluate Bessel functions
+of orders between $0$ and $1\sep,000\sep,000\sep,000$ at essentially any
+positive real argument. In that regime, it is competitive with existing methods
+for the rapid evaluation of Bessel functions and has several advantages over
+them. First, our approach is quite general and can be readily applied to many
+other special functions which satisfy second order ordinary differential
+equations. Second, by calculating the logarithms of the Bessel functions rather
+than the Bessel functions themselves, we avoid many issues which arise from
+numerical overflow and underflow. Third, in the oscillatory regime, our
+algorithm calculates the values of a nonoscillatory phase function for Bessel's
+differential equation and its derivative. These quantities are useful for
+computing the zeros of Bessel functions, as well as for rapidly applying the
+Fourier-Bessel transform. The results of extensive numerical experiments
+demonstrating the efficacy of our algorithm are presented. A Fortran package
+which includes our code for evaluating the Bessel functions as well as our code
+for all of the numerical experiments described here is publically available.
+"
+24794,"Boundedness of singular integrals on $C^{1,α}$ intrinsic graphs in the Heisenberg group","  We study singular integral operators induced by $3$-dimensional
+Calderón-Zygmund kernels in the Heisenberg group. We show that if such an
+operator is $L^{2}$ bounded on vertical planes, with uniform constants, then it
+is also $L^{2}$ bounded on all intrinsic graphs of compactly supported
+$C^{1,\alpha}$ functions over vertical planes.
+In particular, the result applies to the operator $\mathcal{R}$ induced by
+the kernel $$\mathcal{K}(z) = \nabla_{\mathbb{H}} \| z \|^{-2}, \quad z \in
+\mathbb{H} \setminus \{0\},$$ the horizontal gradient of the fundamental
+solution of the sub-Laplacian. The $L^{2}$ boundedness of $\mathcal{R}$ is
+connected with the question of removability for Lipschitz harmonic functions.
+As a corollary of our result, we infer that the intrinsic graphs mentioned
+above are non-removable. Apart from subsets of vertical planes, these are the
+first known examples of non-removable sets with positive and locally finite
+$3$-dimensional measure.
+"
+24795,On resolution of 1-dimensional foliations on 3-manifolds,"  We prove a sharp resolution theorem for an important class of singularities
+of foliations containing in particular all singularities of complete
+holomorphic vector fields on complex manifolds of dimension 3. Roughly speaking
+our result relies on the combination of classical results about asymptotic
+expansions for solutions of differential equations with recent results on
+resolution of singularities for general foliations in dimension 3. With respect
+to the latter, starting from a by-product of Cano-Roche-Spivakovsky we provide
+an otherwise self-contained elementary proof of a result paralleling
+McQuillan-Panazzolo which is particularly well adapted to our needs.
+"
+24796,Learning Robotic Manipulation of Granular Media,"  In this paper, we examine the problem of robotic manipulation of granular
+media. We evaluate multiple predictive models used to infer the dynamics of
+scooping and dumping actions. These models are evaluated on a task that
+involves manipulating the media in order to deform it into a desired shape. Our
+best performing model is based on a highly-tailored convolutional network
+architecture with domain-specific optimizations, which we show accurately
+models the physical interaction of the robotic scoop with the underlying media.
+We empirically demonstrate that explicitly predicting physical mechanics
+results in a policy that out-performs both a hand-crafted dynamics baseline,
+and a ""value-network"", which must otherwise implicitly predict the same
+mechanics in order to produce accurate value estimates.
+"
+24797,Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure,"  We focus on the problem of estimating the change in the dependency structures
+of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for
+sparse change estimation in GGMs involve expensive and difficult non-smooth
+optimization. We propose a novel method, DIFFEE for estimating DIFFerential
+networks via an Elementary Estimator under a high-dimensional situation. DIFFEE
+is solved through a faster and closed form solution that enables it to work in
+large-scale settings. We conduct a rigorous statistical analysis showing that
+surprisingly DIFFEE achieves the same asymptotic convergence rates as the
+state-of-the-art estimators that are much more difficult to compute. Our
+experimental results on multiple synthetic datasets and one real-world data
+about brain connectivity show strong performance improvements over baselines,
+as well as significant computational benefits.
+"
+24798,Nonlinear dust-acoustic solitary waves and shocks in dusty plasmas with a pair of trapped ions,"  The propagation characteristics of small-amplitude dust-acoustic (DA)
+solitary waves (SWs) and shocks are studied in an unmagnetized dusty plasma
+with a pair of trapped positive and negative ions. Using the standard reductive
+perturbation technique with two different scaling of stretched coordinates, the
+evolution equations for DA SWs and shocks are derived in the forms of complex
+Korteweg-de Vries (KdV) and complex Burgers' equations. The effects of dust
+charge variation, the dust thermal pressure, and the ratios of positive to
+negative ion number densities as well as the free to trapped ion temperatures
+on the profiles of SWs and shocks are analysed and discussed.
+"
+24799,The Theoretical Proof for GLHUA EM Invisible Double Layer Cloak By Using GL No Scattering Modeling and Inversion,"  In this paper, we proposed the GLHUA double layer cloak; proved the
+properties of GLHUA double layer cloak; Using GL no scattering inversion and
+the pre cloak condition 6.1 to 6.4 in paper [1], we create GLHUA outer layer
+cloak radial relative parameter and angular relative parameter theoretically.
+We proved theorem 4.1 to theorem 4.4 that the phase velocity of the
+electromagnetic wave in GLHUA outer layer cloak is less than light speed and
+tends to zero at the boundary $r=R_1$, the EM wave tends to zero at $r=R1$;
+When source $r_s > R_2$ in the outside of the cloak and observer $r_o < R_o$ in
+the concealment, then $\vec E(\vec r_o)=0$ and $\vec H(\vec r_o)=0$ that is
+proved in theorem 5.1 and 5.3. We prove that the EM wave excited in outside of
+the cloak can not propagation penetrate into the concealment. In theorem 5.4 to
+5.6, we rigorously proved the EM wave excited in outside of the cloak can not
+be disturbed by the cloak. In theorem 6.1 to theorem 6.6, we prove that the EM
+wave excited in the concealment can not propagate to outside of GLHUA inner
+cloak. we prove that the EM wave excited in the concealment can not be
+disturbed by the cloak. We theoretically prove that the GLHUA double cloak is
+invisible cloak with concealment and with relative parameter not less than 1;
+the GLHUA double layer cloak is practicable. In December of 2016, we submitted
+3 papers to arXiv for GLHUA double layer cloak with relative parameters not
+less than 1. This is the third paper. The task and content of the three paper
+are different from each other. The second paper [1] and this Paper are
+theoretical base and proof of the subjects in paper arXiv:1612.02857. We find
+an exact analytical EM wave field of Maxwell EM equation in GLHUA cloak and
+mirage bridge wave. Patent of the GLHUA EM cloaks,GLHUA sphere and GL modeling
+and inversion methods are reserved by authors in GL Geophysical Laboratory.
+"
+24800,Additive structure of totally positive quadratic integers,"  Let $K=\mathbb Q(\sqrt D)$ be a real quadratic field. We obtain a
+presentation of the additive semigroup $\mathcal O_K^+(+)$ of totally positive
+integers in $K$; its generators (indecomposable integers) and relations can be
+nicely described in terms of the periodic continued fraction for $\sqrt D$. We
+also characterize all uniquely decomposable integers in $K$ and estimate their
+norms. Using these results, we prove that the semigroup $\mathcal O_K^+(+)$
+completely determines the real quadratic field $K$.
+"
+24801,Contextualizing selection bias in Mendelian randomization: how bad is it likely to be?,"  Selection bias affects Mendelian randomization investigations when selection
+into the study sample depends on a collider between the genetic variant and
+confounders of the risk factor-outcome association. However, the relative
+importance of selection bias for Mendelian randomization compared to other
+potential biases is unclear. We performed an extensive simulation study to
+assess the impact of selection bias on a typical Mendelian randomization
+investigation. Selection bias had a severe impact on bias and Type 1 error
+rates in our simulation study, but only when selection effects were large. For
+moderate effects of the risk factor on selection, bias was generally small and
+Type 1 error rate inflation was not considerable. The magnitude of bias was
+also affected by the strength of confounder-risk factor and confounder-outcome
+associations, the structure of the causal diagram and selection frequency. The
+use of inverse probability weighting ameliorated bias when the selection model
+was correctly specified, but increased bias when selection bias was moderate
+and the model was misspecified. Finally, we investigated whether selection bias
+may explain a recently reported finding that lipoprotein(a) is not a causal
+risk factor for cardiovascular mortality in individuals with previous coronary
+heart disease.
+"
+24802,Scaling-up Split-Merge MCMC with Locality Sensitive Sampling (LSS),"  Split-Merge MCMC (Monte Carlo Markov Chain) is one of the essential and
+popular variants of MCMC for problems when an MCMC state consists of an unknown
+number of components. It is well known that state-of-the-art methods for
+split-merge MCMC do not scale well. Strategies for rapid mixing requires smart
+and informative proposals to reduce the rejection rate. However, all known
+smart proposals involve expensive operations to suggest informative
+transitions. As a result, the cost of each iteration is prohibitive for massive
+scale datasets. It is further known that uninformative but computationally
+efficient proposals, such as random split-merge, leads to extremely slow
+convergence. This tradeoff between mixing time and per update cost seems hard
+to get around.
+In this paper, we show a sweet spot. We leverage some unique properties of
+weighted MinHash, which is a popular LSH, to design a novel class of
+split-merge proposals which are significantly more informative than random
+sampling but at the same time efficient to compute. Overall, we obtain a
+superior tradeoff between convergence and per update cost. As a direct
+consequence, our proposals are around 6X faster than the state-of-the-art
+sampling methods on two large real datasets KDDCUP and PubMed with several
+millions of entities and thousands of clusters.
+"
+24803,Influence Maximization for Fixed Heterogeneous Thresholds,"  Influence Maximization is a NP-hard problem of selecting the optimal set of
+influencers in a network. Here, we propose two new approaches to influence
+maximization based on two very different metrics. The first metric, termed
+Balanced Index (BI), is fast to compute and assigns top values to two kinds of
+nodes: those with high resistance to adoption, and those with large out-degree.
+This is done by linearly combining three properties of a node: its degree,
+susceptibility to new opinions, and the impact its activation will have on its
+neighborhood. Controlling the weights between those three terms has a huge
+impact on performance. The second metric, termed Group Performance Index (GPI),
+measures performance of each node as an initiator when it is a part of randomly
+selected initiator set. In each such selection, the score assigned to each
+teammate is inversely proportional to the number of initiators causing the
+desired spread. These two metrics are applicable to various cascade models;
+here we test them on the Linear Threshold Model with fixed and known
+thresholds. Furthermore, we study the impact of network degree assortativity
+and threshold distribution on the cascade size for metrics including ours. The
+results demonstrate our two metrics deliver strong performance for influence
+maximization.
+"
+24804,Confidence Intervals for Finite Difference Solutions,"  Although applications of Bayesian analysis for numerical quadrature problems
+have been considered before, it's only very recently that statisticians have
+focused on the connections between statistics and numerical analysis of
+differential equations. In line with this very recent trend, we show how
+certain commonly used finite difference schemes for numerical solutions of
+ordinary and partial differential equations can be considered in a regression
+setting. Focusing on this regression framework, we apply a simple Bayesian
+strategy to obtain confidence intervals for the finite difference solutions. We
+apply this framework on several examples to show how the confidence intervals
+are related to truncation error and illustrate the utility of the confidence
+intervals for the examples considered.
+"
+24805,Accumulation-layer Surface Plasmons,"  A rigorous analytical study of the eigenmodes supported by a charge
+accumulation layer within a transparent conductive oxide (TCO) is presented.
+The new class of surface plasmons termed Accumulation-Layer Surface Plasmon
+(ASP) is introduced. Near resonance ASP are tightly bound and display a vast
+effective index tunability that could be of great practical interest. The
+suppression of ASP in the presence of epsilon-near zero regions is discussed.
+"
+24806,On the connective eccentricity index of two types of trees,"  The connective eccentricity index $\xi^{ce}=\sum^{}_{u\in
+V}\frac{d(u)}{\varepsilon(u)}$, where
+$\varepsilon(u)$ and $d(u)$ denote the eccentricity and the degree of the
+vertex $u$, respectively. In this paper, we first determine the extremal trees
+which minimize and maximize the connective eccentricity index among all trees
+with a given degree sequence, and then determine the extremal trees which
+minimize and maximize the connective eccentricity index among all trees with a
+given number of branching vertices.
+"
+24807,What is the next innovation after the internet of things?,"  The world had witnessed several generations of the Internet. Starting with
+the Fixed Internet, then the Mobile Internet, scientists now focus on many
+types of research related to the ""Thing"" Internet (or Internet of Things). The
+question is ""what is the next Internet generation after the Thing Internet?""
+This paper envisions about the Tactile Internet which could be the next
+Internet generation in the near future. The paper will introduce what is the
+tactile internet, why it could be the next future Internet, as well as the
+impact and its application in the future society. Furthermore, some challenges
+and the requirements are presented to guide further research in this near
+future field.
+"
+24808,Dark Energy from pNGB Mediated Dirac Neutrino Condensate,"  We consider an extension of the Standard Model that provide an unified
+description of eV scale neutrino mass and dark energy. An explicit model is
+presented by augmenting the Standard Model with an $SU(2)_L$ doublet scalar, a
+singlet scalar and right handed neutrinos where all of them are assumed to be
+charged under a global $U(1)_X$ symmetry. A light pseudo-Nambu-Goldstone Boson,
+associated with the spontaneously broken $U(1)_{X}$ symmetry, acts as a
+mediator of an attractive force leading to a Dirac neutrino condensate, with
+large correlation length, and a non-zero gap in the right range providing a
+cosmologically feasible dark energy scenario. The neutrino mass is generated
+through the usual Dirac seesaw mechanism. Parameter space, reproducing viable
+dark energy scenario while having neutrino mass in the right ballpark, is
+presented.
+"
+24809,"Large gauge transformation, Soft theorem, and Infrared divergence in inflationary spacetime","  It is widely known that the primordial curvature perturbation $\zeta$ has
+several universal properties in the infrared (IR) such as the soft theorem,
+which is also known as the consistency relation, and the conservation in time.
+They are valid in rather general single clock models of inflation. It has been
+argued that these universal properties are deeply related to the large gauge
+transformations in inflationary spacetime. However, the invariance under the
+large gauge transformations is not sufficient to show these IR properties. In
+this paper, we show that the locality condition is crucial to show the
+consistency relation and the conservation of $\zeta$. This argument also can
+apply to an interacting system with the inflaton and heavy fields which have
+arbitrary integer spins, including higher spin fields, which may be motivated
+from string theory. We will also show that the locality condition guarantees
+the cancellation of the IR divergences in a certain class of variables whose
+correlation functions resemble cosmologically observable quantities.
+"
+24810,Unified Bayesian estimator of EEG reference at infinity: rREST,"  The choice of reference for electroencephalogram (EEG) is a long-lasting
+unsolved issue resulting in inconsistent usages and endless debates. Currently,
+both average reference (AR) and reference electrode standardization technique
+(REST) are two primary, irreconcilable contenders. We propose a theoretical
+framework to resolve this issue by formulating both a) estimation of potentials
+at infinity, and, b) determination of the reference, as a unified Bayesian
+linear inverse problem. We find that AR and REST are very particular cases of
+this unified framework: AR results from biophysically non-informative prior;
+while REST utilizes the prior of EEG generative model. We develop the
+regularized versions of AR and REST, named rAR, and rREST, respectively. Both
+depend on a regularization parameter that is the noise to signal ratio.
+Traditional and new estimators are evaluated with this framework, by both
+simulations and analysis of real EEGs. Generated artificial EEGs, show that
+relative error in estimating the EEG potentials at infinity is lowest for
+rREST. It also reveals that realistic volume conductor models improve the
+performances of REST and rREST. For practical applications, it is shown that
+average lead field gives the results comparable to the individual lead field.
+Finally, it is shown that the selection of the regularization parameter with
+Generalized Cross-Validation (GCV) is close to the 'oracle' choice based on the
+ground truth. When evaluated with the real 89 resting state EEGs, rREST
+consistently yields the lowest GCV. This study provides a novel perspective on
+the EEG reference problem by means of a unified inverse solution framework. It
+may allow additional principled theoretical formulations and numerical
+evaluation of performance.
+"
+24811,Heavy-Traffic Insensitive Bounds for Weighted Proportionally Fair Bandwidth Sharing Policies,"  We consider a connection-level model proposed by Massoulié and Roberts
+for bandwidth sharing among file transfer flows in a communication network, and
+we study weighted proportionally fair sharing policies where the weights
+represent the relative importance of flows on different routes. We are
+interested in characterizing performance in the heavy-traffic regime. Existing
+work on this problem has focused on diffusion approximations, which were first
+studied by Kang et al. (2009). However, except for the case where the weights
+of all the routes are equal, the steady-state distribution of the limiting
+diffusion process is unknown and thus there are no explicit-form
+characterizations, even when exponential file size distributions are assumed.
+For more general file size distributions, the diffusion approximation was
+derived for the equal-weights case by Vlasiou, Zhang and Zwart (2014), but an
+interchange-of-limits result was lacking.
+We take a Lyapunov-drift-based approach that is different from the diffusion
+approximation approach, where we directly analyze the steady state of the
+system. We first establish a state-space collapse result in steady state, and
+then obtain explicit-form bounds on the weighted sum of the expected numbers of
+flows on different routes, where the weights are the same as those used in the
+weighted proportionally fair sharing policy. Our bounds hold for a class of
+phase-type file size distributions; i.e., the bounds are heavy-traffic
+insensitive to the distributions in this class. For the equal-weights case, the
+upper and lower bounds coincide, which implies the heavy-traffic insensitivity
+of the expectation of the total number of flows. Furthermore, our state-space
+collapse result implies an interchange of limits as a by-product for the
+diffusion approximation by Vlasiou, Zhang and Zwart (2014).
+"
+24812,Our Practice Of Using Machine Learning To Recognize Species By Voice,"  As the technology is advancing, audio recognition in machine learning is
+improved as well. Research in audio recognition has traditionally focused on
+speech. Living creatures (especially the small ones) are part of the whole
+ecosystem, monitoring as well as maintaining them are important tasks. Species
+such as animals and birds are tending to change their activities as well as
+their habitats due to the adverse effects on the environment or due to other
+natural or man-made calamities. For those in far deserted areas, we will not
+have any idea about their existence until we can continuously monitor them.
+Continuous monitoring will take a lot of hard work and labor. If there is no
+continuous monitoring, then there might be instances where endangered species
+may encounter dangerous situations. The best way to monitor those species are
+through audio recognition. Classifying sound can be a difficult task even for
+humans. Powerful audio signals and their processing techniques make it possible
+to detect audio of various species. There might be many ways wherein audio
+recognition can be done. We can train machines either by pre-recorded audio
+files or by recording them live and detecting them. The audio of species can be
+detected by removing all the background noise and echoes. Smallest sound is
+considered as a syllable. Extracting various syllables is the process we are
+focusing on which is known as audio recognition in terms of Machine Learning
+(ML).
+"
+24813,Black Hole Binaries Dynamically Formed in Globular Clusters,"  We investigate properties of black hole (BH) binaries formed in globular
+clusters via dynamical processes, using direct N-body simulations. We pay
+attention to effects of BH mass function on the total mass and mass ratio
+distributions of BH binaries ejected from clusters. Firstly, we consider BH
+populations with two different masses in order to learn basic differences from
+models with single-mass BHs only. Secondly, we consider continuous BH mass
+functions adapted from recent studies on massive star evolution in a low
+metallicity environment, where globular clusters are formed. In this work, we
+consider only binaries that are formed by three-body processes and ignore
+stellar evolution and primordial binaries for simplicity. Our results imply
+that most BH binary mergers take place after they get ejected from the cluster.
+Also, mass ratios of dynamically formed binaries should be close to one or
+likely to be less than 2:1. Since the binary formation efficiency is larger for
+higher-mass BHs, it is likely that a BH mass function sampled by
+gravitational-wave observations would be weighed toward higher masses than the
+mass function of single BHs for a dynamically formed population. Applying
+conservative assumptions regarding globular cluster populations such as small
+BH mass fraction and no primordial binaries, the merger rate of BH binaries
+originated from globular clusters is estimated to be at least 6.5 per yr per
+Gpc^3. Actual rate can be up to more than several times of our conservative
+estimate.
+"
+24814,Convergence of quasi-Fuchsian groups using critical exponent,"  We prove that a sequence of quasi-Fuchsian representations for which the
+critical exponent converges to the topological dimension of the boundary of the
+group (larger than 2), converges up to subsequence and conjugacy to a totally
+geodesic representation.
+"
+24815,Fluctuation of Dynamical Robustness in a Networked Oscillators System,"  In this work, we study the dynamical robustness in a system consisting of
+both active and inactive oscillators. We analytically show that the dynamical
+robustness of such system is determined by the cross link density between
+active and inactive subpopulations, which depends on the specific process of
+inactivation. It is the multi-valued dependence of the cross link density on
+the control parameter, i.e., the ratio of inactive oscillators in the system,
+that leads to the fluctuation of the critical points. We further investigate
+how different network topologies and inactivation strategies affect the
+fluctuation. Our results explain why the fluctuation is more obvious in
+heterogeneous networks than in homogeneous ones, and why the low-degree nodes
+are crucial in terms of dynamical robustness. The analytical results are
+supported by numerical verifications.
+"
+24816,The mean value theorems and a Nagumo-type uniqueness theorem for Caputo's fractional calculus (Corrected Version),"  We generalize the classical mean value theorem of differential calculus by
+allowing the use of a Caputo-type fractional derivative instead of the commonly
+used first-order derivative. Similarly, we generalize the classical mean value
+theorem for integrals by allowing the corresponding fractional integral, viz.\
+the Riemann-Liouville operator, instead of a classical (first-order) integral.
+As an application of the former result we then prove a uniqueness theorem for
+initial value problems involving Caputo-type fractional differential operators.
+This theorem generalizes the classical Nagumo theorem for first-order
+differential equations.
+"
+24817,A Numerical Solution of the Time-Dependent Neutron Transport Equation Using the Characteristic Method. Applications to ICF and to Hybrid Fission-Fusion Systems,"  In this work we present a solution of the one-dimensional spherical symmetric
+time-dependent neutron transport equation (written for a moving system in
+lagrangian coordinates) by using the characteristic method. One of the
+objectives is to overcome the negative flux problem that arises when the system
+is very opaque and the angular neutron flux can become negative when it is
+extrapolated in spatial meshes --- as, for example, in diamond scheme adopted
+in many codes. Although there are recipes to overcome this problem, it can
+completely degrade the numerical solution if repeated many times.
+The solution presented here can be easily coupled to radiation-hydrodynamics
+equations, but it is necessary an additional term to maintain neutron
+conservation in a moving system in lagrangian coordinates. Energy multigroup
+method and a former SN method to deal with the angular variable are used, with
+the assumption of isotropic scattering and the transport cross sections
+approximation. An artifice is employed for emulating the neutron upscattering
+when neutron energy is lower than the temperature of the medium. The
+consistency of the numerical solution is checked by making at each time-step
+the balance of neutrons in the system.
+Two examples of applications are shown using a
+neutronic-radiation-hydrodynamic code to which the solution here presented was
+incorporated: one consists of a heterogeneous pellet of DT (deuterium-tritium)
+tamped by an highly-enriched uranium or plutonium (a symbiotic fusion-fission
+system); and the other is a very complex and also a symbiotic
+fission-fusion-fission system composed by layers of the thermonuclear fuels
+LiDT, LiD and a highly-supercritical fission fuel. The last is considered an
+extreme case for testing the time-dependent neutron transport solution
+presented here.
+"
+24818,Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents,"  The Arcade Learning Environment (ALE) is an evaluation platform that poses
+the challenge of building AI agents with general competency across dozens of
+Atari 2600 games. It supports a variety of different problem settings and it
+has been receiving increasing attention from the scientific community, leading
+to some high-profile success stories such as the much publicized Deep
+Q-Networks (DQN). In this article we take a big picture look at how the ALE is
+being used by the research community. We show how diverse the evaluation
+methodologies in the ALE have become with time, and highlight some key concerns
+when evaluating agents in the ALE. We use this discussion to present some
+methodological best practices and provide new benchmark results using these
+best practices. To further the progress in the field, we introduce a new
+version of the ALE that supports multiple game modes and provides a form of
+stochasticity we call sticky actions. We conclude this big picture look by
+revisiting challenges posed when the ALE was introduced, summarizing the
+state-of-the-art in various problems and highlighting problems that remain
+open.
+"
+24819,Robust Optimization for Non-Convex Objectives,"  We consider robust optimization problems, where the goal is to optimize in
+the worst case over a class of objective functions. We develop a reduction from
+robust improper optimization to Bayesian optimization: given an oracle that
+returns $\alpha$-approximate solutions for distributions over objectives, we
+compute a distribution over solutions that is $\alpha$-approximate in the worst
+case. We show that de-randomizing this solution is NP-hard in general, but can
+be done for a broad class of statistical learning tasks. We apply our results
+to robust neural network training and submodular optimization. We evaluate our
+approach experimentally on corrupted character classification, and robust
+influence maximization in networks.
+"
+24820,Magnetic Spiral Arms in Galaxy Halos,"  We seek the conditions for a {\it steady} mean field galactic dynamo. The
+parameter set is reduced to those appearing in the $\alpha^2$ and
+$\alpha/\omega$ dynamo, namely velocity amplitudes, and the ratio of sub-scale
+helicity to diffusivity. The parameters can be allowed to vary on conical
+spirals. We analyze the mean field dynamo equations in terms of scale invariant
+logarithmic spiral modes and special exact solutions. Compatible scale
+invariant gravitational spiral arms are introduced and illustrated in an
+appendix, but the detailed dynamical interaction with the magnetic field is
+left for another work. As a result of planar magnetic spirals `lifting' into
+the halo, multiple sign changes in average rotation measures forming a regular
+pattern on each side of the galactic minor axis, are predicted. Such changes
+have recently been detected in the CHANG-ES survey.
+"
+24821,Identity and Granularity of Events in Text,"  In this paper we describe a method to detect event descrip- tions in
+different news articles and to model the semantics of events and their
+components using RDF representations. We compare these descriptions to solve a
+cross-document event coreference task. Our com- ponent approach to event
+semantics defines identity and granularity of events at different levels. It
+performs close to state-of-the-art approaches on the cross-document event
+coreference task, while outperforming other works when assuming similar quality
+of event detection. We demonstrate how granularity and identity are
+interconnected and we discuss how se- mantic anomaly could be used to define
+differences between coreference, subevent and topical relations.
+"
+24822,Higher-dimension Tensor Completion via Low-rank Tensor Ring Decomposition,"  The problem of incomplete data is common in signal processing and machine
+learning. Tensor completion algorithms aim to recover the incomplete data from
+its partially observed entries. In this paper, taking advantages of high
+compressibility and flexibility of recently proposed tensor ring (TR)
+decomposition, we propose a new tensor completion approach named tensor ring
+weighted optimization (TR-WOPT). It finds the latent factors of the incomplete
+tensor by gradient descent algorithm, then the latent factors are employed to
+predict the missing entries of the tensor. We conduct various tensor completion
+experiments on synthetic data and real-world data. The simulation results show
+that TR-WOPT performs well in various high-dimension tensors. Furthermore,
+image completion results show that our proposed algorithm outperforms the
+state-of-the-art algorithms in many situations. Especially when the missing
+rate of the test images is high (e.g., over 0.9), the performance of our
+TR-WOPT is significantly better than the compared algorithms.
+"
+24823,The 1223 new periodic orbits of planar three-body problem with unequal mass and zero angular momentum,"  We present 1349 families of Newtonian periodic planar three-body orbits with
+unequal mass and zero angular momentum and the initial conditions in case of
+isosceles collinear configurations. These 1349 families of the periodic
+collisionless orbits can be divided into seven classes according to their
+geometric and algebraic symmetries. Among these 1349 families, 1223 families
+are entirely new, to the best of our knowledge. Furthermore, some periodic
+orbits have the same free group element for different mass. We find that the
+scale-invariant average period linearly increases with the mass of one body
+when the masses of the other two are constant. It is suggested that the masses
+of bodies may play an important role in periodic three-body systems. The movies
+of these periodic orbits are given on the website
+\url{this http URL} .
+"
+24824,MK-fuzzy Automata and MSO Logics,"  We introduce MK-fuzzy automata over a bimonoid K which is related to the
+fuzzification of the McCarthy-Kleene logic. Our automata are inspired by, and
+intend to contribute to, practical applications being in development in a
+project on runtime network monitoring based on predicate logic. We investigate
+closure properties of the class of recognizable MK-fuzzy languages accepted by
+MK-fuzzy automata as well as of deterministically recognizable MK-fuzzy
+languages accepted by their deterministic counterparts. Moreover, we establish
+a Nivat-like result for recognizable MK-fuzzy languages. We introduce an
+MK-fuzzy MSO logic and show the expressive equivalence of a fragment of this
+logic with MK-fuzzy automata, i.e., a Büchi type theorem.
+"
+24825,Model topoi and motivic homotopy theory,"  Given a small simplicial category $\C$ whose underlying ordinary category is
+equipped with a Grothendieck topology $\tau$, we construct a model structure on
+the category of simplicially enriched presheaves on $\C$ where the weak
+equivalences are the local weak equivalences of the underlying (non-enriched)
+simplicial presheaves. We show that this model category is a $t$-complete model
+topos and describe the Grothendieck topology $[\tau]$ on the homotopy category
+of $\C$ that corresponds to this model topos. After we first review a proof
+showing that the motivic homotopy theory is not a model topos, we specialize
+this construction to the category of smooth schemes of finite type, which is
+simplicially enriched using the standard algebraic cosimplicial object, and
+compare the result with the motivic homotopy theory. We also collect some
+partial positive results on the exactness properties of the motivic
+localization functor.
+"
+24826,Maximal distant entanglement in Kitaev tube,"  We study the Kitaev model on a finite-size square lattice with periodic
+boundary conditions in one direction and open boundary conditions in the other.
+Based on the fact that the Majorana representation of Kitaev model is
+equivalent to a brick wall model under the condition $t=\Delta =\mu $, this
+system is shown to support perfect Majorana bound states which is in strong
+localization limit. By introducing edge-mode fermionic operator and pseudo-spin
+representation, we find that such edge modes are always associated with maximal
+entanglement between two edges of the tube, which is independent of the size of
+the system.
+"
+24827,Incorporating Posterior Model Discrepancy into a Hierarchical Framework to Facilitate Out-of-the-Box MCMC Sampling for Geothermal Inverse Problems and Uncertainty Quantification,"  We consider geothermal inverse problems and uncertainty quantification from a
+Bayesian perspective. Our goal is to make standard, 'out-of-the-box' Markov
+chain Monte Carlo (MCMC) sampling more feasible for complex simulation models.
+To do this, we first show how to pose the inverse and prediction problems in a
+hierarchical Bayesian framework. We then show how to incorporate so-called
+posterior model approximation error into this hierarchical framework, using a
+modified form of the Bayesian approximation error (BAE) approach. This enables
+the use of a 'coarse', approximate model in place of a finer, more expensive
+model, while also accounting for the additional uncertainty and potential bias
+that this can introduce. Our method requires only simple probability modelling
+and only modifies the target posterior - the same standard MCMC sampling
+algorithm can be used to sample the new target posterior. We show that our
+approach can achieve significant computational speed-ups on a geothermal test
+problem. A problem which would take around a year to carry out full MCMC
+sampling for, now only takes around a day or so using our approach. We also
+demonstrate the dangers of naively using coarse, approximate models in place of
+finer models, without accounting for model discrepancies. The naive approach
+tends to give overly confident and biased posteriors, while incorporating BAE
+into our hierarchical framework corrects for this while maintaining
+computational efficiency and ease-of-use.
+"
+24828,PolarBear: A 28-nm FD-SOI ASIC for Decoding of Polar Codes,"  Polar codes are a recently proposed class of block codes that provably
+achieve the capacity of various communication channels. They received a lot of
+attention as they can do so with low-complexity encoding and decoding
+algorithms, and they have an explicit construction. Their recent inclusion in a
+5G communication standard will only spur more research. However, only a couple
+of ASICs featuring decoders for polar codes were fabricated, and none of them
+implements a list-based decoding algorithm. In this paper, we present ASIC
+measurement results for a fabricated 28 nm CMOS chip that implements two
+different decoders: the first decoder is tailored toward error-correction
+performance and flexibility. It supports any code rate as well as three
+different decoding algorithms: successive cancellation (SC), SC flip and SC
+list (SCL). The flexible decoder can also decode both non-systematic and
+systematic polar codes. The second decoder targets speed and energy efficiency.
+We present measurement results for the first silicon-proven SCL decoder, where
+its coded throughput is shown to be of 306.8 Mbps with a latency of 3.34 us and
+an energy per bit of 418.3 pJ/bit at a clock frequency of 721 MHz for a supply
+of 1.3 V. The energy per bit drops down to 178.1 pJ/bit with a more modest
+clock frequency of 308 MHz, lower throughput of 130.9 Mbps and a reduced supply
+voltage of 0.9 V. For the other two operating modes, the energy per bit is
+shown to be of approximately 95 pJ/bit. The less flexible high-throughput
+unrolled decoder can achieve a coded throughput of 9.2 Gbps and a latency of
+628 ns for a measured energy per bit of 1.15 pJ/bit at 451 MHz.
+"
+24829,"REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models","  Learning in models with discrete latent variables is challenging due to high
+variance gradient estimators. Generally, approaches have relied on control
+variates to reduce the variance of the REINFORCE estimator. Recent work (Jang
+et al. 2016, Maddison et al. 2016) has taken a different approach, introducing
+a continuous relaxation of discrete variables to produce low-variance, but
+biased, gradient estimates. In this work, we combine the two approaches through
+a novel control variate that produces low-variance, \emph{unbiased} gradient
+estimates. Then, we introduce a modification to the continuous relaxation and
+show that the tightness of the relaxation can be adapted online, removing it as
+a hyperparameter. We show state-of-the-art variance reduction on several
+benchmark generative modeling tasks, generally leading to faster convergence to
+a better final log-likelihood.
+"
+24830,Modeling joint probability distribution of yield curve parameters,"  US Yield curve has recently collapsed to its most flattened level since
+subprime crisis and is close to the inversion. This fact has gathered attention
+of investors around the world and revived the discussion of proper modeling and
+forecasting yield curve, since changes in interest rate structure are believed
+to represent investors expectations about the future state of economy and have
+foreshadowed recessions in the United States. While changes in term structure
+of interest rates are relatively easy to interpret they are however very
+difficult to model and forecast due to no proper economic theory underlying
+such events. Yield curves are usually represented by multivariate sparse time
+series, at any point in time infinite dimensional curve is portrayed via
+relatively few points in a multivariate space of data and as a consequence
+multimodal statistical dependencies behind these curves are relatively hard to
+extract and forecast via typical multivariate statistical methods.We propose to
+model yield curves via reconstruction of joint probability distribution of
+parameters in functional space as a high degree polynomial. Thanks to adoption
+of an orthonormal basis, the MSE estimation of coefficients of a given function
+is an average over a data sample in the space of functions. Since such
+polynomial coefficients are independent and have cumulant-like interpretation
+ie.describe corresponding perturbation from an uniform joint distribution, our
+approach can also be extended to any d-dimensional space of yield curve
+parameters (also in neighboring times) due to controllable accuracy. We believe
+that this approach to modeling of local behavior of a sparse multivariate
+curved time series can complement prediction from standard models like ARIMA,
+that are using long range dependencies, but provide only inaccurate prediction
+of probability distribution, often as just Gaussian with constant width.
+"
+24831,Strong convergence of two--dimensional Vilenkin-Fourier series,"  We prove that certain means of the quadratical partial sums of the
+two-dimensional Vilenkin-Fourier series are uniformly bounded operators from
+the Hardy space $H_{p}$ to the space $L_{p}$ for $0<p\leq 1.$ We also prove
+that the sequence in the denominator cannot be improved.
+"
+24832,A linear streaming algorithm for community detection in very large networks,"  In this paper, we introduce a novel community detection algorithm in graphs,
+called SCoDA (Streaming Community Detection Algorithm), based on an edge
+streaming setting. This algorithm has an extremely low memory footprint and a
+lightning-fast execution time as it only stores two integers per node and
+processes each edge strictly once. The approach is based on the following
+simple observation: if we pick an edge uniformly at random in the network, this
+edge is more likely to connect two nodes of the same community than two nodes
+of distinct communities. We exploit this idea to build communities by local
+changes at each edge arrival. Using theoretical arguments, we relate the
+ability of SCoDA to detect communities to usual quality metrics of these
+communities like the conductance. Experimental results performed on massive
+real-life networks ranging from one million to more than one billion edges
+shows that SCoDA runs more than ten times faster than existing algorithms and
+leads to similar or better detection scores on the largest graphs.
+"
+24833,Spectral Resolution Clustering for Brain Parcellation,"  We take an image science perspective on the problem of determining brain
+network connectivity given functional activity. But adapting the concept of
+image resolution to this problem, we provide a new perspective on network
+partitioning for individual brain parcellation. The typical goal here is to
+determine densely-interconnected subnetworks within a larger network by
+choosing the best edges to cut. We instead define these subnetworks as
+resolution cells, where highly-correlated activity within the cells makes edge
+weights difficult to determine from the data. Subdividing the resolution
+estimates into disjoint resolution cells via clustering yields a new variation,
+and new perspective, on spectral clustering. This provides insight and
+strategies for open questions such as the selection of model order and the
+optimal choice of preprocessing steps for functional imaging data. The approach
+is demonstrated using functional imaging data, where we find the proposed
+approach produces parcellations which are more predictive across multiple scans
+versus conventional methods, as well as versus alternative forms of spectral
+clustering.
+"
+24834,Well-posedness of the Ericksen-Leslie System for the Oseen-Frank Model in L^3_{uloc}(\mathbb{R}^3),"  We investigate the Ericksen-Leslie system for the Oseen-Frank model with
+unequal Frank elastic constants in $\mathbb{R}^3$. To generalize the result of
+Hineman-Wang \cite{HW}, we prove existence of solutions to the Ericksen-Leslie
+system with initial data having small $L^3_{uloc}$-norm. In particular, we use
+a new idea to obtain a local $L^3$-estimate through interpolation inequalities
+and a covering argument, which is different from the one in \cite{HW}.
+Moreover, for uniqueness of solutions, we find a new way to remove the
+restriction on the Frank elastic constants by using the rotation invariant
+property of the Oseen-Frank density. We combine this with a method of
+Li-Titi-Xin \cite{LTX} to prove uniqueness of the $L^3_{uloc}$-solutions of the
+Ericksen-Leslie system assuming that the initial data has a finite energy.
+"
+24835,The moduli space of points in quaternionic projective space,"  Let $\mathcal{M}(n,m;\F \bp^n)$ be the configuration space of $m$-tuples of
+pairwise distinct points in $\F \bp^n$, that is, the quotient of the set of
+$m$-tuples of pairwise distinct points in $\F \bp^n$ with respect to the
+diagonal action of ${\rm PU}(1,n;\F)$ equipped with the quotient topology. It
+is an important problem in hyperbolic geometry to parameterize
+$\mathcal{M}(n,m;\F \bp^n)$ and study the geometric and topological structures
+on the associated parameter space. In this paper, by mainly using the
+rotation-normalized and block-normalized algorithms, we construct the parameter
+spaces of both $\mathcal{M}(n,m; \bhq)$ and $\mathcal{M}(n,m;\bp(V_+))$,
+respectively.
+"
+24836,An Empirical Unravelling of Lord's Paradox,"  Lord's Paradox occurs when a continuous covariate is statistically controlled
+for and the relationship between a continuous outcome and group status
+indicator changes in both magnitude and direction. This phenomenon poses a
+challenge to the notion of evidence-based policy, where data are supposed to be
+self-evident. We examined 50 effect size estimates from 34 large-scale
+educational interventions, and found that impact estimates are affected in
+magnitude, with or without reversal in sign, when there is substantial baseline
+imbalance. We also demonstrated that multilevel modelling can ameliorate the
+divergence in sign and/or magnitude of effect estimation, which, together with
+project specific knowledge, promises to help those who are presented with
+conflicting or confusing evidence in decision making.
+"
+24837,Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm,"  The classification of time series data is a well-studied problem with
+numerous practical applications, such as medical diagnosis and speech
+recognition. A popular and effective approach is to classify new time series in
+the same way as their nearest neighbours, whereby proximity is defined using
+Dynamic Time Warping (DTW) distance, a measure analogous to sequence alignment
+in bioinformatics. However, practitioners are not only interested in accurate
+classification, they are also interested in why a time series is classified a
+certain way. To this end, we introduce here the problem of finding a minimum
+length subsequence of a time series, the removal of which changes the outcome
+of the classification under the nearest neighbour algorithm with DTW distance.
+Informally, such a subsequence is expected to be relevant for the
+classification and can be helpful for practitioners in interpreting the
+outcome. We describe a simple but optimized implementation for detecting these
+subsequences and define an accompanying measure to quantify the relevance of
+every time point in the time series for the classification. In tests on
+electrocardiogram data we show that the algorithm allows discovery of important
+subsequences and can be helpful in detecting abnormalities in cardiac rhythms
+distinguishing sick from healthy patients.
+"
+24838,Revenue Loss in Shrinking Markets,"  We analyze the revenue loss due to market shrinkage. Specifically, consider a
+simple market with one item for sale and $n$ bidders whose values are drawn
+from some joint distribution. Suppose that the market shrinks as a single
+bidder retires from the market. Suppose furthermore that the value of this
+retiring bidder is fixed and always strictly smaller than the values of the
+other players. We show that even this slight decrease in competition might
+cause a significant fall of a multiplicative factor of
+$\frac{1}{e+1}\approx0.268$ in the revenue that can be obtained by a dominant
+strategy ex-post individually rational mechanism.
+In particular, our results imply a solution to an open question that was
+posed by Dobzinski, Fu, and Kleinberg [STOC'11].
+"
+24839,Haga's theorems in paper folding and related theorems in Wasan geometry Part 1,"  Haga's fold in paper folding is generalized. Recent generalization of Haga's
+theorems and problems in Wasan geometry involving Haga's fold are also
+generalized.
+"
+24840,Barrier Enabled IO Stack for Flash Storage,"  This work is dedicated to eliminating the overhead of guaranteeing the
+storage order in modern IO stack. The existing block device adopts
+prohibitively expensive resort in ensuring the storage order among write
+requests: interleaving successive write requests with transfer and flush.
+Exploiting the cache barrier command for the Flash storage, we overhaul the IO
+scheduler, the dispatch module and the filesystem so that these layers are
+orchestrated to preserve the ordering condition imposed by the application can
+be delivered to the storage. Key ingredients of Barrier Enabled IO stack are
+Epoch based IO scheduling, Order Preserving Dispatch, and Dual Mode Journaling.
+Barrier enabled IO stack successfully eliminates the root cause of excessive
+overhead in enforcing the storage order. Dual Mode Journaling in BarrierFS
+dedicates the separate threads to effectively decouple the control plane and
+data plane of the journal commit. We implement Barrier Enabled IO Stack in
+server as well as in mobile platform. SQLite performance increases by 270% and
+75%, in server and in smartphone, respectively. Relaxing the durability of a
+transaction, SQLite performance and MySQL performance increases as much as by
+73X and by 43X, respectively, in server storage.
+"
+24841,An algorithm to compute the Teichmueller polynomial from matrices,"  In their precedent work, the authors constructed closed oriented hyperbolic
+surfaces with pseudo-Anosov homeomorphisms from certain class of integral
+matrices. In this paper, we present a very simple algorithm to compute the
+Teichmueller polynomial corresponding to those surface homeomorphisms by first
+constructing an invariant track whose first homology group can be naturally
+identified with the first homology group of the surface, and computing its
+Alexander polynomial.
+"
+24842,A Quasi-Polynomial Approximation for the Restricted Assignment Problem,"  Scheduling jobs on unrelated machines and minimizing the makespan is a
+classical problem in combinatorial optimization. In this problem a job $j$ has
+a processing time $p_{ij}$ for every machine $i$. The best polynomial algorithm
+known goes back to Lenstra et al. and has an approximation ratio of $2$. In
+this paper we study the Restricted Assignment problem, which is the special
+case where $p_{ij}\in\{p_j,\infty\}$. We present an algorithm for this problem
+with an approximation ratio of $11/6 + \epsilon$ and quasi-polynomial running
+time $n^{\mathcal O(1/\epsilon \log(n))}$ for every $\epsilon > 0$. This closes
+the gap to the best estimation algorithm known for the problem with regard to
+quasi-polynomial running time.
+"
+24843,Bayesian forecasting of many count-valued time series,"  This paper develops forecasting methodology and application of new classes of
+dynamic models for time series of non-negative counts. Novel univariate models
+synthesise dynamic generalized linear models for binary and conditionally
+Poisson time series, with dynamic random effects for over-dispersion. These
+models allow use of dynamic covariates in both binary and non-zero count
+components. Sequential Bayesian analysis allows fast, parallel analysis of sets
+of decoupled time series. New multivariate models then enable information
+sharing in contexts when data at a more highly aggregated level provide more
+incisive inferences on shared patterns such as trends and seasonality. A novel
+multi-scale approach-- one new example of the concept of decouple/recouple in
+time series-- enables information sharing across series. This incorporates
+cross-series linkages while insulating parallel estimation of univariate
+models, hence enables scalability in the number of series. The major motivating
+context is supermarket sales forecasting. Detailed examples drawn from a case
+study in multi-step forecasting of sales of a number of related items showcase
+forecasting of multiple series, with discussion of forecast accuracy metrics
+and broader questions of probabilistic forecast accuracy assessment.
+"
+24844,PrivacyScore: Analyse von Webseiten auf Sicherheits- und Privatheitsprobleme -- Konzept und rechtliche Zulässigkeit,"  PrivacyScore ist ein öffentliches Web-Portal, mit dem automatisiert
+überprüft werden kann, ob Webseiten gängige Mechanismen zum Schutz von
+Sicherheit und Privatheit korrekt implementieren. Im Gegensatz zu existierenden
+Diensten ermöglicht PrivacyScore, mehrere Webseiten in Benchmarks miteinander
+zu vergleichen, die Ergebnisse differenziert und im Zeitverlauf zu analysieren
+sowie nutzerdefinierte Kriterien für die Auswertung zu definieren.
+PrivacyScore verbessert dadurch nicht nur die Transparenz für Endanwender,
+sondern erleichtert auch die Arbeit der Datenschutz-Aufsichtsbehörden. In
+diesem Beitrag stellen wir das Konzept des Dienstes vor und wir erörtern,
+unter welchen Umständen das automatische Scannen und öffentliche
+""Anprangern"" von Schwächen aus rechtlicher Sicht zulässig ist.
+--
+This German article describes the technical and legal considerations
+surrounding PrivacyScore, a public web portal that allows automatic scans of
+websites for privacy and security problems. For an English article discussing
+the same system in more technical detail, but lacking the legal interpretation,
+see arXiv:1705.05139.
+"
+24845,Neural Networks Trained to Solve Differential Equations Learn General Representations,"  We introduce a technique based on the singular vector canonical correlation
+analysis (SVCCA) for measuring the generality of neural network layers across a
+continuously-parametrized set of tasks. We illustrate this method by studying
+generality in neural networks trained to solve parametrized boundary value
+problems based on the Poisson partial differential equation. We find that the
+first hidden layer is general, and that deeper layers are successively more
+specific. Next, we validate our method against an existing technique that
+measures layer generality using transfer learning experiments. We find
+excellent agreement between the two methods, and note that our method is much
+faster, particularly for continuously-parametrized problems. Finally, we
+visualize the general representations of the first layers, and interpret them
+as generalized coordinates over the input domain.
+"
+24846,Sketching Word Vectors Through Hashing,"  We propose a new fast word embedding technique using hash functions. The
+method is a derandomization of a new type of random projections: By
+disregarding the classic constraint used in designing random projections (i.e.,
+preserving pairwise distances in a particular normed space), our solution
+exploits extremely sparse non-negative random projections. Our experiments show
+that the proposed method can achieve competitive results, comparable to neural
+embedding learning techniques, however, with only a fraction of the
+computational complexity of these methods. While the proposed derandomization
+enhances the computational and space complexity of our method, the possibility
+of applying weighting methods such as positive pointwise mutual information
+(PPMI) to our models after their construction (and at a reduced dimensionality)
+imparts a high discriminatory power to the resulting embeddings. Obviously,
+this method comes with other known benefits of random projection-based
+techniques such as ease of update.
+"
+24847,Tensorial and bipartite block models for link prediction in layered networks and temporal networks,"  Many real-world complex systems are well represented as multilayer networks;
+predicting interactions in those systems is one of the most pressing problems
+in predictive network science. To address this challenge, we introduce two
+stochastic block models for multilayer and temporal networks; one of them uses
+nodes as its fundamental unit, whereas the other focuses on links. We also
+develop scalable algorithms for inferring the parameters of these models.
+Because our models describe all layers simultaneously, our approach takes full
+advantage of the information contained in the whole network when making
+predictions about any particular layer. We illustrate the potential of our
+approach by analyzing two empirical datasets---a temporal network of email
+communications, and a network of drug interactions for treating different
+cancer types. We find that modeling all layers simultaneously does result, in
+general, in more accurate link prediction. However, the most predictive model
+depends on the dataset under consideration; whereas the node-based model is
+more appropriate for predicting drug interactions, the link-based model is more
+appropriate for predicting email communication.
+"
+24848,FraudDroid: Automated Ad Fraud Detection for Android Apps,"  Although mobile ad frauds have been widespread, state-of-the-art approaches
+in the literature have mainly focused on detecting the so-called static
+placement frauds, where only a single UI state is involved and can be
+identified based on static information such as the size or location of ad
+views. Other types of fraud exist that involve multiple UI states and are
+performed dynamically while users interact with the app. Such dynamic
+interaction frauds, although now widely spread in apps, have not yet been
+explored nor addressed in the literature. In this work, we investigate a wide
+range of mobile ad frauds to provide a comprehensive taxonomy to the research
+community. We then propose, FraudDroid, a novel hybrid approach to detect ad
+frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI
+state transition graphs and collects their associated runtime network traffics,
+which are then leveraged to check against a set of heuristic-based rules for
+identifying ad fraudulent behaviours. We show empirically that FraudDroid
+detects ad frauds with a high precision (93%) and recall (92%). Experimental
+results further show that FraudDroid is capable of detecting ad frauds across
+the spectrum of fraud types. By analysing 12,000 ad-supported Android apps,
+FraudDroid identified 335 cases of fraud associated with 20 ad networks that
+are further confirmed to be true positive results and are shared with our
+fellow researchers to promote advanced ad fraud detection
+"
+24849,Multi-second magnetic coherence in a single domain spinor Bose-Einstein condensate,"  We describe a compact, robust and versatile system for studying magnetic
+dynamics in a spinor Bose-Einstein condensate. Condensates of 87 Rb are
+produced by all-optical evaporation in a 1560 nm optical dipole trap, using a
+non-standard loading sequence that employs an auxiliary 1529 nm beam for
+partial compensation of the strong differential light shift induced by the
+dipole trap itself. We use near-resonance Faraday rotation probing to
+non-destructively track the condensate magnetization, and demonstrate
+few-Larmor-cycle tracking with no detectable degradation of the spin
+polarization. In the ferromagnetic F = 1 ground state, we observe magnetic T1
+and T2$^*$ coherence times limited only by the several-second residence time of
+the atoms in the trap.
+"
+24850,Intermolecular Interactions in Radial-Contour Mode Microring Resonators,"  This research is on the dynamics of electrostatically actuated radial-contour
+mode microring resonators. The governing equation of motion is derived by the
+minimization of the Hamiltonian and generalized to include the viscous damping
+effect. The Galerkin method is used to discretize the distributed-parameter
+model of the considered ring resonator. The influences of intermolecular forces
+such as van der Waals and Casimir on the dynamic behavior of the resonator are
+investigated. The natural frequencies and mode shapes of the ring are
+calculated for various values of ratio of radii (\b{eta}). The effect of the
+design parameters including ring radius, electrostatic voltage and quality
+factor on the dynamic responses, is discussed. The results of present study can
+be used in the design of novel MEMS resonators, RF filters and channelizers.
+"
+24851,Autoencoders Learn Generative Linear Models,"  Recent progress in learning theory has led to the emergence of provable
+algorithms for training certain families of neural networks. Under the
+assumption that the training data is sampled from a suitable generative model,
+the weights of the trained networks obtained by these algorithms recover
+(either exactly or approximately) the generative model parameters. However, the
+large majority of these results are only applicable to supervised learning
+architectures. In this paper, we complement this line of work by providing a
+series of results for unsupervised learning with neural networks. Specifically,
+we study the familiar setting of shallow autoencoder architectures with shared
+weights. We focus on three generative models for the data: (i) the
+mixture-of-gaussians model, (ii) the sparse coding model, and (iii) the
+non-negative sparsity model. All three models are widely studied in the machine
+learning literature. For each of these models, we rigorously prove that under
+suitable choices of hyperparameters, architectures, and initialization, the
+autoencoder weights learned by gradient descent % -based training can
+successfully recover the parameters of the corresponding model. To our
+knowledge, this is the first result that rigorously studies the dynamics of
+gradient descent for weight-sharing autoencoders. Our analysis can be viewed as
+theoretical evidence that shallow autoencoder modules indeed can be used as
+unsupervised feature training mechanisms for a wide range of datasets, and may
+shed insight on how to train larger stacked architectures with autoencoders as
+basic building blocks.
+"
+24852,Engineering fast multilevel support vector machines,"  The computational complexity of solving nonlinear support vector machine
+(SVM) is prohibitive on large-scale data. In particular, this issue becomes
+very sensitive when the data represents additional difficulties such as highly
+imbalanced class sizes. Typically, nonlinear kernels produce significantly
+higher classification quality to linear kernels but introduce extra kernel and
+model parameters which computationally expensive fitting. This increases the
+quality but also reduces the performance dramatically. We introduce a
+generalized fast multilevel framework for regular and weighted SVM and discuss
+several versions of its algorithmic components that lead to a good trade-off
+between quality and time. Our framework is implemented using PETSc which allows
+an easy integration with scientific computing tasks. The experimental results
+demonstrate significant speedup compared to the state-of-the-art nonlinear SVM
+libraries.
+"
+24853,Self-Organization and Chaos in the Metabolism of Hemostasis in a Blood Vessel,"  A mathematical model of the metabolic process of formation of the hemostasis
+in a blood-carrying vessel is constructed. As distinct from the earlier
+developed model of the multienzyme prostacyclin-thromboxane system of blood,
+this model includes, for the first time, the influence of the level of ""bad
+cholesterol"", i.e., low-density lipoproteins (LDLs), on the hemostasis. The
+conditions, under which the self-organization of the system appears, and the
+modes of autooscillations and chaos in the metabolic process, which affects the
+formation of hemostasis and the development of thrombophilia, are found. With
+the aid of a phase-parametric diagram, the scenario of their appearance is
+studied. The bifurcations of doubling of a period and the transition to chaotic
+oscillations as a result of the intermittence are found. The obtained strange
+attractors are formed due to a mixing funnel. The full spectra of Lyapunov's
+indices, KS-entropies, ""predictability horizons"", and Lyapunov's dimensions of
+strange attractors are calculated. The reasons for a change in the cyclicity of
+the given metabolic process, its stability, and the physiological manifestation
+in the blood-carrying system are discussed. The role of physiologically active
+substances in a decrease in the level of cholesterol in blood vessels is
+estimated.
+"
+24854,SensX: About Sensing and Assessment of Complex Human Motion,"  The great success of wearables and smartphone apps for provision of extensive
+physical workout instructions boosts a whole industry dealing with consumer
+oriented sensors and sports equipment. But with these opportunities there are
+also new challenges emerging. The unregulated distribution of instructions
+about ambitious exercises enables unexperienced users to undertake demanding
+workouts without professional supervision which may lead to suboptimal training
+success or even serious injuries. We believe, that automated supervision and
+realtime feedback during a workout may help to solve these issues. Therefore we
+introduce four fundamental steps for complex human motion assessment and
+present SensX, a sensor-based architecture for monitoring, recording, and
+analyzing complex and multi-dimensional motion chains. We provide the results
+of our preliminary study encompassing 8 different body weight exercises, 20
+participants, and more than 9,220 recorded exercise repetitions. Furthermore,
+insights into SensXs classification capabilities and the impact of specific
+sensor configurations onto the analysis process are given.
+"
+24855,"Guidance, Navigation and Control of Multirobot Systems in Cooperative Cliff Climbing","  The application of GNC devices on small robots is a game-changer that enables
+these robots to be mobile on low-gravity planetary surfaces and small bodies.
+Use of reaction wheels enables these robots to roll, hop, summersault and rest
+on precarious/sloped surfaces that would otherwise not be possible with
+conven-tional wheeled robots. We are extending this technology to enable robots
+to climb off-world canyons, cliffs and caves. A single robot may slip and fall,
+however, a multirobot system can work cooperatively by being interlinked using
+spring-tethers and work much like a team of mountaineers to systematically
+climb a slope. A multirobot system as we will show in this paper can climb
+sur-faces not possible with a single robot alone. We consider a team of four
+robots that are interlinked with tethers in an 'x' configuration. Each robot
+secures itself to a slope using spiny gripping actuators, and one by one each
+robot moves up-wards by crawling, rolling or hopping up the slope. If any one
+of the robots loses grip, slips or falls, the remaining robots will be holding
+it up as they are anchored. This distributed controls approach to cliff
+climbing enables the system to reconfigure itself where possible and avoid
+getting stuck at one hard to reach location. Instead, the risk is distributed
+and through close cooperation, the robots can identify multiple trajectories to
+climb a cliff or rugged surface. The benefits can also be realized on
+milligravity surfaces such as asteroids. Too fast a jump can result in the
+robot flying off the surface into space. Having multiple robots anchored to the
+surface keeps the entire system secure. Our work combines dynamics and control
+simulation to evaluate the feasibility of our approach. The simulation results
+show a promising pathway towards advanced development of this technology on a
+team of real robots.
+"
+24856,K2-114b and K2-115b: Two Transiting Warm Jupiters,"  We report the first results from a search for transiting warm Jupiter
+exoplanets - gas giant planets receiving stellar irradiation below about $10^8$
+erg s$^{-1}$ cm$^{-2}$, equivalent to orbital periods beyond about 10 days
+around Sun-like stars. We have discovered two transiting warm Jupiter
+exoplanets initially identified as transiting candidates in ${\it K2}$
+photometry. K2-114b has a mass of $1.85^{+0.23}_{-0.22}\ M_J$, a radius of
+$0.942^{+0.032}_{-0.020}\ R_J$, and an orbital period of 11.4 days. K2-115b has
+a mass of $0.84^{+0.18}_{-0.20}\ M_J$, a radius of $1.115^{+0.057}_{-0.061}\
+R_J$, and an orbital period of 20.3 days. Both planets are among the longest
+period transiting gas giant planets with a measured mass, and they are orbiting
+relatively old host stars. Both planets are not inflated as their radii are
+consistent with theoretical expectations. Their position in the planet radius -
+stellar irradiation diagram is consistent with the scenario where the radius -
+irradiation correlation levels off below about 10$^8$ erg s$^{-1}$ cm$^{-2}$,
+suggesting that for warm Jupiters the stellar irradiation does not play a
+significant role in determining the planet radius. We also report our
+identification of another ${\it K2}$ transiting warm Jupiter candidate, EPIC
+212504617, as a false positive.
+"
+24857,Quotient groups of IA-automorphisms of a free group of rank 3,"  We prove that, for any positive integer $c$, the quotient group
+$\gamma_{c}(M_{3})/\gamma_{c+1}(M_{3})$ of the lower central series of the
+McCool group $M_{3}$ is isomorphic to two copies of the quotient group
+$\gamma_{c}(F_{3})/\gamma_{c+1}(F_{3})$ of the lower central series of a free
+group $F_{3}$ of rank $3$ as $\mathbb{Z}$-modules. Furthermore, we give a
+necessary and sufficient condition whether the associated graded Lie algebra
+${\rm gr}(M_{3})$ of $M_3$ is naturally embedded into the Johnson Lie algebra
+${\cal L}({\rm IA}(F_{3}))$ of the IA-automorphisms of $F_{3}$.
+"
+24858,"An analog of the Deligne-Lusztig duality for $(\mathfrak{g},K)$-modules","  Drinfeld suggested the definition of a certain endo-functor, called the
+pseudo-identity functor, on the category of D-modules on an algebraic stack. We
+extend this definition to an arbitrary DG category, and show that if certain
+finiteness conditions are satisfied, this functor is the inverse of the Serre
+functor. We apply this to the category of (g,K)-modules, and we stipulate that
+the pseudo-identity functor is the analog of the Deligne-Lusztig functor. In
+order to support this, we show that the pseudo-identity functor for
+(g,K)-modules is isomorphic to the composition of cohomological and
+contragredient dualities, which is parallel to the corresponding assertion for
+p-adic groups in [BBK].
+"
+24859,Active Self-Tracking of Subjective Experience with a One-Button Wearable: A Case Study in Military PTSD,"  We describe a case study with the participation of a Danish veteran suffering
+from post-traumatic stress disorder (PTSD). As part of psychotherapeutic
+treatment the participant and therapist have used our novel technique for
+instrumenting self-tracking of select aspects of subjective experience using a
+one-button wearable device. The instrumentation system is described along with
+the specific self-track- ing protocol which defined the participant's
+self-tracking of a single symptom, namely the occurrences of a bodily
+experienced precursor to hyperarousal. Results from the case study demonstrate
+how self-tracking data on a single symptom collected by a patient can provide
+valuable input to the therapeutic process. Specifically, it facilitated
+identification of crucial details otherwise unavailable from the clinical
+assessment and even became decisive in disentangling different symptoms and
+their causes.
+"
+24860,Three Phase-Grating Moire Neutron Interferometer for Large Interferometer Area Applications,"  We demonstrate a three phase-grating neutron interferometer as a robust
+candidate for large area interferometry applications and characterization of
+materials. This novel far-field moire technique allows for broad wavelength
+acceptance and relaxed requirements related to fabrication and alignment,
+circumventing the main obstacles associated with perfect crystal neutron
+interferometry. Interference fringes were observed with a total interferometer
+length of four meters, and the effects of an aluminum 6061 alloy sample on the
+coherence of the system was examined. Experiments to measure the
+autocorrelation length of samples and the universal gravitational constant are
+proposed and discussed.
+"
+24861,On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches,"  The recent, remarkable growth of machine learning has led to intense interest
+in the privacy of the data on which machine learning relies, and to new
+techniques for preserving privacy. However, older ideas about privacy may well
+remain valid and useful. This note reviews two recent works on privacy in the
+light of the wisdom of some of the early literature, in particular the
+principles distilled by Saltzer and Schroeder in the 1970s.
+"
+24862,Complete classification of pseudo $H$-type algebras: II,"  We classify a class of 2-step nilpotent Lie algebras related to the
+representations of the Clifford algebras in the following way. Let $J\colon
+\Cl(\mathbb R^{r,s})\toU$ be a representation of the Clifford algebra
+$\Cl(\mathbb R^{r,s})$ generated by the pseudo Euclidean vector space $\mathbb
+R^{r,s}$. Assume that the Clifford module $U$ is endowed with a bilinear
+symmetric non-degenerate real form $\la\cdot\,,\cdot\ra_U$ making the linear
+map $J_z$ skew symmetric for any $z\in\mathbb R^{r,s}$. The Lie algebras and
+the Clifford algebras are related by $\la J_zv,w\ra_U=\la z,[v,w]\ra_{\mathbb
+R^{r,s}}$, $z\in \mathbb R^{r,s}$, $v,w\in U$. We detect the isomorphic and
+non-isomorphic Lie algebras according to the dimension of $U$ and the range of
+the non-negative integers~$r,s$.
+"
+24863,Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty,"  We suggest a general approach to quantification of different forms of
+aleatoric uncertainty in regression tasks performed by artificial neural
+networks. It is based on the simultaneous training of two neural networks with
+a joint loss function and a specific hyperparameter $\lambda>0$ that allows for
+automatically detecting noisy and clean regions in the input space and
+controlling their {\em relative contribution} to the loss and its gradients.
+After the model has been trained, one of the networks performs predictions and
+the other quantifies the uncertainty of these predictions by estimating the
+locally averaged loss of the first one. Unlike in many classical uncertainty
+quantification methods, we do not assume any a priori knowledge of the ground
+truth probability distribution, neither do we, in general, maximize the
+likelihood of a chosen parametric family of distributions. We analyze the
+learning process and the influence of clean and noisy regions of the input
+space on the loss surface, depending on $\lambda$. In particular, we show that
+small values of $\lambda$ increase the relative contribution of clean regions
+to the loss and its gradients. This explains why choosing small $\lambda$
+allows for better predictions compared with neural networks without uncertainty
+counterparts and those based on classical likelihood maximization. Finally, we
+demonstrate that one can naturally form ensembles of pairs of our networks and
+thus capture both aleatoric and epistemic uncertainty and avoid overfitting.
+"
+24864,Function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions,"  A zonal function (ZF) network on the $q$ dimensional sphere $\mathbb{S}^q$ is
+a network of the form $\mathbf{x}\mapsto \sum_{k=1}^n
+a_k\phi(\mathbf{x}\cdot\mathbf{x}_k)$ where $\phi :[-1,1]\to\mathbf{R}$ is the
+activation function, $\mathbf{x}_k\in\mathbb{S}^q$ are the centers, and
+$a_k\in\mathbb{R}$. While the approximation properties of such networks are
+well studied in the context of positive definite activation functions, recent
+interest in deep and shallow networks motivate the study of activation
+functions of the form $\phi(t)=|t|$, which are not positive definite. In this
+paper, we define an appropriate smoothess class and establish approximation
+properties of such networks for functions in this class. The centers can be
+chosen independently of the target function, and the coefficients are linear
+combinations of the training data. The constructions preserve rotational
+symmetries.
+"
+24865,An Onsager Singularity Theorem for Turbulent Solutions of Compressible Euler Equations,"  We prove that bounded weak solutions of the compressible Euler equations will
+conserve thermodynamic entropy unless the solution fields have sufficiently low
+space-time Besov regularity. A quantity measuring kinetic energy cascade will
+also vanish for such Euler solutions, unless the same singularity conditions
+are satisfied. It is shown furthermore that strong limits of solutions of
+compressible Navier-Stokes equations that are bounded and exhibit anomalous
+dissipation are weak Euler solutions. These inviscid limit solutions have
+non-negative anomalous entropy production and kinetic energy dissipation, with
+both vanishing when solutions are above the critical degree of Besov
+regularity. Stationary, planar shocks in Euclidean space with an ideal-gas
+equation of state provide simple examples that satisfy the conditions of our
+theorems and which demonstrate sharpness of our $L^3$-based conditions. These
+conditions involve space-time Besov regularity, but we show that they are
+satisfied by Euler solutions that possess similar space regularity uniformly in
+time.
+"
+24866,The physical and chemical structure of Sagittarius B2. II. Continuum millimeter emission of SgrB2(M) and SgrB2(N) with ALMA,"  The high-mass star forming sites SgrB2(M) and SgrB2(N) have been the target
+of numerous studies, revealing e.g. a rich chemistry. We want to characterize
+their physical and chemical structure using ALMA high-angular resolution
+observations at mm wavelengths, reaching spatial scales of about 4000 au, and
+covering the whole band 6 (from 211 to 275 GHz). In order to determine the
+continuum emission in line-rich sources, we use a new statistical method:
+STATCONT. We detect 27 continuum sources in SgrB2(M) and 20 in SgrB2(N). We
+study the continuum emission across the ALMA band 6, and compare it with
+previous SMA 345 GHz and VLA 40 GHz observations, to study the nature of the
+sources detected. The brightest sources are dominated by (partially optically
+thick) dust emission, while there is an important degree of contamination from
+ionized gas free-free emission in weaker sources. While the total mass in
+SgrB2(M) is distributed in many fragments, most of the mass in SgrB2(N) arises
+from a single object, with filamentary-like structures converging towards the
+center. There seems to be a lack of low-mass dense cores in both regions. We
+determine H2 volume densities for the cores of about 10^5-10^7 Msun pc^-3, one
+to two orders of magnitude higher than the stellar densities of super star
+clusters. In general, SgrB2(N) is chemically richer than SgrB2(M). There seems
+to be a correlation between the chemical richness and the mass of the
+fragments, with more massive clumps being more chemically rich. Both SgrB2(N)
+and SgrB2(M) harbour a cluster of hot molecular cores. We compare the continuum
+images with predictions from a detailed 3D radiative transfer model that
+reproduces the structure of SgrB2 from 45 pc down to 100 au. This dataset,
+together with ongoing projects in the range 5 to 200 GHz, better constrain the
+3D structure of SgrB2, and allow us to understand its physical and chemical
+structure.
+"
+24867,Localized electron wave packet description of chemical bond and excitation: Floating and breathing Gaussian with valence-bond coupling,"  A model of localized electron wave packets (WPs) with variable position and
+width (floating and breathing) that are spin-coupled as per the valence-bond
+theory is presented. It produces accurate potential energy curves of LiH in the
+ground singlet and triplet states. Quantization in a mean-field approximation
+of the motion of a WP that corresponds to the Li 2s electron generates
+semi-quantitative potential energy curves of low energy excited states.
+Real-time semiquantal dynamics of the WP induced by an intense laser pulse
+gives high-harmonic generation spectra that capture qualitative features of a
+higher-level wave function calculation.
+"
+24868,Quasi-Monte Carlo integration for twice differentiable functions over a triangle,"  We study quasi-Monte Carlo integration for twice differentiable functions
+defined over a triangle. We provide an explicit construction of infinite
+sequences of points including one by Basu and Owen (2015) as a special case,
+which achieves the integration error of order $N^{-1}(\log N)^3$ for any $N\geq
+2$. Since a lower bound of order $N^{-1}$ on the integration error holds for
+any linear quadrature rule, the upper bound we obtain is best possible apart
+from the $\log N$ factor. The major ingredient in our proof of the upper bound
+is the dyadic Walsh analysis of twice differentiable functions over a triangle
+under a suitable recursive partitioning.
+"
+24869,Improving Exoplanet Detection Power: Multivariate Gaussian Process Models for Stellar Activity,"  The radial velocity method is one of the most successful techniques for
+detecting exoplanets. It works by detecting the velocity of a host star induced
+by the gravitational effect of an orbiting planet, specifically the velocity
+along our line of sight, which is called the radial velocity of the star. As
+astronomical instrumentation has improved, radial velocity surveys have become
+sensitive to low-mass planets that cause their host star to move with radial
+velocities of 1 m/s or less. While analysis of a time series of stellar spectra
+can in theory reveal such small radial velocities, in practice intrinsic
+stellar variability (e.g., star spots, convective motion, pulsations) affects
+the spectra and often mimics a radial velocity signal. This signal
+contamination makes it difficult to reliably detect low mass planets and
+planets orbiting magnetically active stars. A principled approach to recovering
+planet radial velocity signals in the presence of stellar activity was proposed
+by Rajpaul et al. (2015) and involves the use of a multivariate Gaussian
+process model to jointly capture time series of the apparent radial velocity
+and multiple indicators of stellar activity. We build on this work in two ways:
+(i) we propose using dimension reduction techniques to construct more
+informative stellar activity indicators that make use of a larger portion of
+the stellar spectrum; (ii) we extend the Rajpaul et al. (2015) model to a
+larger class of models and use a model comparison procedure to select the best
+model for the particular stellar activity indicators at hand. By combining our
+high-information stellar activity indicators, Gaussian process models, and
+model selection procedure, we achieve substantially improved planet detection
+power compared to previous state-of-the-art approaches.
+"
+24870,Adaptive Performance Assessment For Drivers Through Behavioral Advantage,"  The potential positive impact of autonomous driving and driver assistance
+technolo- gies have been a major impetus over the last decade. On the flip
+side, it has been a challenging problem to analyze the performance of human
+drivers or autonomous driving agents quantitatively. In this work, we propose a
+generic method that compares the performance of drivers or autonomous driving
+agents even if the environmental conditions are different, by using the driver
+behavioral advantage instead of absolute metrics, which efficiently removes the
+environmental factors. A concrete application of the method is also presented,
+where the performance of more than 100 truck drivers was evaluated and ranked
+in terms of fuel efficiency, covering more than 90,000 trips spanning an
+average of 300 miles in a variety of driving conditions and environments.
+"
+24871,Fast Single-Class Classification and the Principle of Logit Separation,"  We consider neural network training, in applications in which there are many
+possible classes, but at test-time, the task is a binary classification task of
+determining whether the given example belongs to a specific class, where the
+class of interest can be different each time the classifier is applied. For
+instance, this is the case for real-time image search. We define the Single
+Logit Classification (SLC) task: training the network so that at test-time, it
+would be possible to accurately identify whether the example belongs to a given
+class in a computationally efficient manner, based only on the output logit for
+this class. We propose a natural principle, the Principle of Logit Separation,
+as a guideline for choosing and designing losses suitable for the SLC. We show
+that the cross-entropy loss function is not aligned with the Principle of Logit
+Separation. In contrast, there are known loss functions, as well as novel batch
+loss functions that we propose, which are aligned with this principle. In
+total, we study seven loss functions. Our experiments show that indeed in
+almost all cases, losses that are aligned with the Principle of Logit
+Separation obtain at least 20% relative accuracy improvement in the SLC task
+compared to losses that are not aligned with it, and sometimes considerably
+more. Furthermore, we show that fast SLC does not cause any drop in binary
+classification accuracy, compared to standard classification in which all
+logits are computed, and yields a speedup which grows with the number of
+classes. For instance, we demonstrate a 10x speedup when the number of classes
+is 400,000. Tensorflow code for optimizing the new batch losses is publicly
+available at this https URL Separation.
+"
+24872,Rigidity of powers and Kosniowski's conjecture,"  In this paper we state a problem on rigidity of powers, which has a strong
+topological background for the rigid Hirzebruch genera and Kosniowski's
+conjecture of unitary circle actions. However, our statement of this problem is
+elementary enough and does not require any knowledge of algebraic topology. We
+shall give a solution of this problem for some particular cases. As a
+consequence, we obtain that Kosniowski's conjecture holds in the case of
+dimension less than 11 or equal to 14.
+"
+24873,The effects of halogen elements on the opening of an icosahedral B12 framework,"  The fully halogenated or hydrogenated B12X12 (X = H, F, Cl, Br and I)
+clusters are confirmed to be icosahedral. On the other hand, the bare B12
+cluster is shown to have a planar structure. A previous study showed that a
+transformation from an icosahedron to a plane happens when 5 to 7 iodine atoms
+are substituted. Later, the transition was confirmed to be seven iodine
+substitutions based on an infrared spectroscopy study. In this study, we
+investigated the effects of different halogen atoms on the opening of the B12
+icosahedral cage by means of density functional theory calculations. We found
+that the halogen elements do not have significant effects on the geometries of
+the clusters. The computed IR spectra show similar representative peaks for all
+halogen substituted clusters. Interestingly, we found a blue-shift in the IR
+spectra with the increase in the mass of the halogen atoms. Further, we
+compared the Gibbs free energies at different temperatures for different
+halogen atoms. The results show that the Gibbs free energy differences between
+open and close structures of B12X7 become larger when heavier halogen atoms are
+present. This interesting finding was subsequently investigated by energy
+decomposition analysis.
+"
+24874,Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment,"  Actuarial risk assessments might be unduly perceived as a neutral way to
+counteract implicit bias and increase the fairness of decisions made at almost
+every juncture of the criminal justice system, from pretrial release to
+sentencing, parole and probation. In recent times these assessments have come
+under increased scrutiny, as critics claim that the statistical techniques
+underlying them might reproduce existing patterns of discrimination and
+historical biases that are reflected in the data. Much of this debate is
+centered around competing notions of fairness and predictive accuracy, resting
+on the contested use of variables that act as ""proxies"" for characteristics
+legally protected against discrimination, such as race and gender. We argue
+that a core ethical debate surrounding the use of regression in risk
+assessments is not simply one of bias or accuracy. Rather, it's one of purpose.
+If machine learning is operationalized merely in the service of predicting
+individual future crime, then it becomes difficult to break cycles of
+criminalization that are driven by the iatrogenic effects of the criminal
+justice system itself. We posit that machine learning should not be used for
+prediction, but rather to surface covariates that are fed into a causal model
+for understanding the social, structural and psychological drivers of crime. We
+propose an alternative application of machine learning and causal inference
+away from predicting risk scores to risk mitigation.
+"
+24875,The octet rule in chemical space: Generating virtual molecules,"  We present a generator of virtual molecules that selects valid chemistry on
+the basis of the octet rule. Also, we introduce a mesomer group key that allows
+a fast detection of duplicates in the generated structures.
+Compared to existing approaches, our model is simpler and faster, generates
+new chemistry and avoids invalid chemistry. Its versatility is illustrated by
+the correct generation of molecules containing third-row elements and a
+surprisingly adept handling of complex boron chemistry.
+Without any empirical parameters, our model is designed to be valid also in
+unexplored regions of chemical space. One first unexpected finding is the high
+prevalence of dipolar structures among generated molecules.
+"
+24876,Community detection and stochastic block models: recent developments,"  The stochastic block model (SBM) is a random graph model with planted
+clusters. It is widely employed as a canonical model to study clustering and
+community detection, and provides generally a fertile ground to study the
+statistical and computational tradeoffs that arise in network and data
+sciences.
+This note surveys the recent developments that establish the fundamental
+limits for community detection in the SBM, both with respect to
+information-theoretic and computational thresholds, and for various recovery
+requirements such as exact, partial and weak recovery (a.k.a., detection). The
+main results discussed are the phase transitions for exact recovery at the
+Chernoff-Hellinger threshold, the phase transition for weak recovery at the
+Kesten-Stigum threshold, the optimal distortion-SNR tradeoff for partial
+recovery, the learning of the SBM parameters and the gap between
+information-theoretic and computational thresholds.
+The note also covers some of the algorithms developed in the quest of
+achieving the limits, in particular two-round algorithms via graph-splitting,
+semi-definite programming, linearized belief propagation, classical and
+nonbacktracking spectral methods. A few open problems are also discussed.
+"
+24877,Estimation and svm classification of glucose-insulin model parameters from OGTT data. An aid for diabetes diagnostics,"  In the Oral Glucose Tolerance Test (OGTT), a patient, after an overnight fast
+ingests a load of glucose. Then measurements of glucose concentration are taken
+every 30 minutes during two hours. The test is used to aid diagnosis of
+diabetes, namely, type 2 diabetes mellitus and glucose intolerance. Several
+mathematical models have been introduced to describe the glucose-insulin system
+during an OGTT. Models consist on systems of differential equations where most
+parameters are unknown. Estimation of these parameters is an aim of this work.
+In a minimal model, two of such parameters are proposed for classification by
+means of a SVM technique. Consequently, a case is made for this classification
+as an aid for diagnosis.
+"
+24878,Branching rules for the Weyl Groups of the Unitary and Orthogonal Lie Groups,"  This note presents a procedure to determine the reduction of the irreducible
+and the induced characters of the symmetric group in terms of the irreducible
+and induced characters of the hyperoctahedral group
+Key Words: Symmetric Group, Hyperoctahedral group, Representations,
+Characters, Re- duction.
+"
+24879,Nuclear recoil effect on $g$ factor of heavy ions: prospects for tests of quantum electrodynamics in a new region,"  The nuclear recoil effect on the $g$ factor of H- and Li-like heavy ions is
+evaluated to all orders in $\alpha Z$. The calculations include an approximate
+treatment of the nuclear size and the electron-electron interaction corrections
+to the recoil effect. As the result, the second largest contribution to the
+theoretical uncertainty of the $g$-factor values of $^{208}$Pb$^{79+}$ and
+$^{238}$U$^{89+}$ is strongly reduced. Special attention is paid to tests of
+the QED recoil effect on the $g$ factor in experiments with heavy ions. It is
+found that, while the QED recoil effect on the $g$-factor value is masked by
+the uncertainties of the nuclear size and nuclear polarization contributions,
+it can be probed on a few-percent level in the specific difference of the $g$
+factors of H- and Li-like heavy ions. This provides a unique opportunity to
+test QED in a new region --- strong-coupling regime beyond the Furry picture.
+"
+24880,Retrieving Neptune's aerosol properties from Keck OSIRIS observations. I. Dark regions,"  We present and analyze three-dimensional data cubes of Neptune from the
+OSIRIS integral-field spectrograph on the 10-m Keck telescope, from July 2009.
+These data have a spatial resolution of 0.035""/pixel and spectral resolution of
+R~3800 in the H and K broad bands. We focus our analysis on regions of
+Neptune's atmosphere that are near-infrared dark- that is, free of discrete
+bright cloud features. We use a forward model coupled to a Markov chain Monte
+Carlo algorithm to retrieve properties of Neptune's aerosol structure and
+methane profile above ~4 bar in these near-infrared dark regions.
+Using a set of high signal-to-noise spectra in a cloud-free band from 2-12N,
+we find that Neptune's cloud opacity is dominated by a compact, optically thick
+cloud layer with a base near 3 bar and composed of low albedo, forward
+scattering particles, with an assumed characteristic size of ~1$\mu$m. Above
+this cloud, we require a vertically extended haze of smaller (~0.1 $\mu$m)
+particles, which reaches from the upper troposphere (~0.6 bar) into the
+stratosphere. The particles in this haze are brighter and more isotropically
+scattering than those in the deep cloud. When we extend our analysis to 18
+cloud-free locations from 20N to 87S, we observe that the optical depth in
+aerosols above 0.5 bar decreases by a factor of 2-3 or more at mid- and
+high-southern latitudes relative to low latitudes.
+We also consider Neptune's methane (CH$_4$) profile, and find that our
+retrievals indicate a strong preference for a low methane relative humidity at
+pressures where methane is expected to condense. Our preferred solution at most
+locations is for a methane relative humidity below 10% near the tropopause in
+addition to methane depletion down to 2.0-2.5 bar. We tentatively identify a
+trend of lower CH$_4$ columns above 2.5 bar at mid- and high-southern latitudes
+over low latitudes.
+"
+24881,Accurate expansion of cylindrical paraxial waves for its straightforward implementation in electromagnetic scattering,"  The evaluation of vector wave fields can be accurately performed by means of
+diffraction integrals, differential equations and also series expansions. In
+this paper, a Bessel series expansion which basis relies on the exact solution
+of the Helmholtz equation in cylindrical coordinates is theoretically developed
+for the straightforward yet accurate description of low-numerical-aperture
+focal waves. The validity of this approach is confirmed by explicit application
+to Gaussian beams and apertured focused fields in the paraxial regime. Finally
+we discuss how our procedure can be favorably implemented in scattering
+problems.
+"
+24882,Hedging with transient price impact for non-covered and covered options,"  We solve the superhedging problem for European options in a market with
+finite liquidity where trading has transient impact on prices, and possibly a
+permanent one in addition. Impact is multiplicative to ensure positive asset
+prices. Hedges and option prices depend on the physical and cash delivery
+specifications of the option settlement. For non-covered options, where impact
+at the inception and maturity dates matters, we characterize the superhedging
+price as a viscosity solution of a degenerate semilinear pde that can have
+gradient constraints. The non-linearity of the pde is governed by the transient
+nature of impact through a resilience function. For covered options, the
+pricing pde involves gamma constraints but is not affected by transience of
+impact. We use stochastic target techniques and geometric dynamic programming
+in reduced coordinates.
+"
+24883,The role of the Hilbert metric in a class of singular elliptic boundary value problem in convex domains,"  In a recent paper [7], we were led to consider a distance over a bounded open
+convex domain. It turns out to be the so-called Thompson metric, which is
+equivalent to the Hilbert metric. It plays a key role in the analysis of
+existence and uniqueness of solutions to a class of elliptic boundary-value
+problems that are singular at the boundary.
+"
+24884,Robust Continuous Co-Clustering,"  Clustering consists of grouping together samples giving their similar
+properties. The problem of modeling simultaneously groups of samples and
+features is known as Co-Clustering. This paper introduces ROCCO - a Robust
+Continuous Co-Clustering algorithm. ROCCO is a scalable, hyperparameter-free,
+easy and ready to use algorithm to address Co-Clustering problems in practice
+over massive cross-domain datasets. It operates by learning a graph-based
+two-sided representation of the input matrix. The underlying proposed
+optimization problem is non-convex, which assures a flexible pool of solutions.
+Moreover, we prove that it can be solved with a near linear time complexity on
+the input size. An exhaustive large-scale experimental testbed conducted with
+both synthetic and real-world datasets demonstrates ROCCO's properties in
+practice: (i) State-of-the-art performance in cross-domain real-world problems
+including Biomedicine and Text Mining; (ii) very low sensitivity to
+hyperparameter settings; (iii) robustness to noise and (iv) a linear empirical
+scalability in practice. These results highlight ROCCO as a powerful
+general-purpose co-clustering algorithm for cross-domain practitioners,
+regardless of their technical background.
+"
+24885,Probabilistic Impact Assessment of Network Tariffs in Low Voltage Distribution Networks,"  In this paper, we present a probabilistic framework to assess the impacts of
+different network tariffs on the consumption pattern of electricity consumers
+with distributed energy resources such as thermostatically controlled loads and
+battery storage; and the resultant effects on the distribution network. A mixed
+integer linear programming-based home energy management system with implicit
+modeling of peak demand charge is used to schedule the controllable devices of
+residential customers connected to a low voltage network in order to analyze
+the impacts of \textit{energy-} and \textit{demand-based tariffs} on network
+performance. The simulation results show that flat tariffs with a peak demand
+component perform best in terms of electricity cost reduction for the customer,
+as well as in mitigating the level of binding network constraints. This is
+beneficial for distribution network service providers where there is high
+PV-battery penetration.
+"
+24886,User Manual for the Apple CoreCapture Framework,"  CoreCapture is Apple's primary logging and tracing framework for IEEE 802.11
+on iOS and macOS. It allows users and developers to create comprehensive debug
+output for analysis by Apple. In this manual, we provide an overview into the
+concepts, show in detail how CoreCapture logs can be created on iOS and macOS,
+and introduce the first CoreCapture dissector for Wireshark.
+"
+24887,"On Whitehead's first free-group algorithm, cutvertices, and free-product factorizations","  Let $F$ be any nontrivial, finite-rank, free group, and $R$ be any finite set
+that consists of elements of $F$ and conjugacy classes of $F$. We show that the
+output of Whitehead's (fast) cutvertex algorithm is a longest possible sequence
+$H_1, H_2, \ldots, H_n$ of nontrivial subgroups of $F$ such that one has $H_1
+\ast H_2 \ast \cdots \ast H_n=F$ and each element of $R$ equals-or-contains an
+element of an $H_i$. Richard Stong showed this in the case where $R$ consists
+of elements of $F$ or conjugacy classes of $F$, thereby unifying and
+generalizing results of John Berge, Mladen Bestvina, Herbert Lyon, Abe
+Shenitzer, John Stallings, Edith Starr, and J.H.C. Whitehead. Our proof is
+graph-theoretic, set in a tricoloured Cayley tree.
+"
+24888,Estimation and adaptive-to-model testing for regressions with diverging number of predictors,"  The research described in this paper is motivated by model checking for
+parametric single-index models with diverging number of predictors. To
+construct a test statistic, we first study the asymptotic property of the
+estimators of involved parameters of interest under the null and alternative
+hypothesis when the dimension is divergent to infinity as the sample size goes
+to infinity. For the testing problem, we study an adaptive-to-model
+residual-marked empirical process as the basis for constructing a test
+statistic. By modifying the approach in the literature to suit the diverging
+dimension settings, we construct a martingale transformation. Under the null,
+local and global alternative hypothesis, the weak limits of the empirical
+process are derived and then the asymptotic properties of the test statistic
+are investigated. Simulation studies are carried out to examine the performance
+of the test.
+"
+24889,Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods,"  In this work, we consider two kinds of model reduction techniques to simulate
+blood flow through the largest systemic arteries, where a stenosis is located
+in a peripheral artery i.e. in an artery that is located far away from the
+heart. For our simulations we place the stenosis in one of the tibial arteries
+belonging to the right lower leg (right post tibial artery). The model
+reduction techniques that are used are on the one hand dimensionally reduced
+models (1-D and 0-D models, the so-called mixed-dimension model) and on the
+other hand surrogate models produced by kernel methods. Both methods are
+combined in such a way that the mixed-dimension models yield training data for
+the surrogate model, where the surrogate model is parametrised by the degree of
+narrowing of the peripheral stenosis. By means of a well-trained surrogate
+model, we show that simulation data can be reproduced with a satisfactory
+accuracy and that parameter optimisation or state estimation problems can be
+solved in a very efficient way. Furthermore it is demonstrated that a surrogate
+model enables us to present after a very short simulation time the impact of a
+varying degree of stenosis on blood flow, obtaining a speedup of several orders
+over the full model.
+"
+24890,Hypergeometric First Integrals of the Duffing and van der Pol Oscillators,"  The autonomous Duffing oscillator, and its van der Pol modification, are
+known to admit time-dependent first integrals for specific values of
+parameters. This corresponds to the existence of Darboux polynomials, and in
+fact more can be shown: that there exist Liouvillian first integrals which do
+not depend on time. They can be expressed in terms of the Gauss and Kummer
+hypergeometric functions, and are neither analytic, algebraic nor meromorphic.
+A criterion for this to happen in a general dynamical system is formulated as
+well.
+"
+24891,Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts,"  The diversification (generating slightly varying separating discriminators)
+of Support Vector Machines (SVMs) for boosting has proven to be a challenge due
+to the strong learning nature of SVMs. Based on the insight that perturbing the
+SVM kernel may help in diversifying SVMs, we propose two kernel perturbation
+based boosting schemes where the kernel is modified in each round so as to
+increase the resolution of the kernel-induced Reimannian metric in the vicinity
+of the datapoints misclassified in the previous round. We propose a method for
+identifying the disjuncts in a dataset, dispelling the dependence on rule-based
+learning methods for identifying the disjuncts. We also present a new
+performance measure called Geometric Small Disjunct Index (GSDI) to quantify
+the performance on small disjuncts for balanced as well as class imbalanced
+datasets. Experimental comparison with a variety of state-of-the-art algorithms
+is carried out using the best classifiers of each type selected by a new
+approach inspired by multi-criteria decision making. The proposed method is
+found to outperform the contending state-of-the-art methods on different
+datasets (ranging from mildly imbalanced to highly imbalanced and characterized
+by varying number of disjuncts) in terms of three different performance indices
+(including the proposed GSDI).
+"
+24892,Multiple component decomposition from millimeter single-channel data,"  We present an implementation of a blind source separation algorithm to remove
+foregrounds off millimeter surveys made by single-channel instruments. In order
+to make possible such a decomposition over single-wavelength data: we generate
+levels of artificial redundancy, then perform a blind decomposition, calibrate
+the resulting maps, and lastly measure physical information. We simulate the
+reduction pipeline using mock data: atmospheric fluctuations, extended
+astrophysical foregrounds, and point-like sources, but we apply the same
+methodology to the AzTEC/ASTE survey of the Great Observatories Origins Deep
+Survey-South (GOODS-S). In both applications, our technique robustly decomposes
+redundant maps into their underlying components, reducing flux bias, improving
+signal-to-noise, and minimizing information loss. In particular, the GOODS-S
+survey is decomposed into four independent physical components, one of them is
+the already known map of point sources, two are atmospheric and systematic
+foregrounds, and the fourth component is an extended emission that can be
+interpreted as the confusion background of faint sources.
+"
+24893,Systems of conservation laws with third-order Hamiltonian structures,"  We investigate $n$-component systems of conservation laws that possess
+third-order Hamiltonian structures of differential-geometric type. The
+classification of such systems is reduced to the projective classification of
+linear congruences of lines in $\mathbb{P}^{n+2}$ satisfying additional
+geometric constraints. Algebraically, the problem can be reformulated as
+follows: for a vector space $W$ of dimension $n+2$, classify $n$-tuples of
+skew-symmetric 2-forms $A^{\alpha} \in \Lambda^2(W)$ such that \[ \phi_{\beta
+\gamma}A^{\beta}\wedge A^{\gamma}=0, \] for some non-degenerate symmetric
+$\phi$.
+"
+24894,A Statistical Method for Corrupt Agents Detection,"  The statistical method is used to identify the hidden leaders of the
+corruption structure. The method is based on principal component analysis
+(PCA), linear regression, and Shannon information. It is applied to study the
+time series data of corrupt financial activity.
+Shannon's quantity of information is specified as a function of two
+arguments: a vector of hidden corruption factors and a subset of corrupt
+agents. Several optimization problems are solved to determine the contribution
+of corresponding corrupt agents to the total illegal behavior. An illustrative
+example is given. A convenient algorithm for computing the covariance matrix
+with missing data is proposed.
+"
+24895,Semiparametric Efficient Empirical Higher Order Influence Function Estimators,"  Robins et al. (2008, 2016) applied the theory of higher order influence
+functions (HOIFs) to derive an estimator of the mean of an outcome Y in a
+missing data model with Y missing at random conditional on a vector X of
+continuous covariates; their estimator, in contrast to previous estimators, is
+semiparametric efficient under minimal conditions. However, the Robins et al.
+(2008, 2016) estimator depends on a non-parametric estimate of the density of
+X. In this paper, we introduce a new HOIF estimator that has the same
+asymptotic properties as their estimator but does not require nonparametric
+estimation of a multivariate density, which is important because accurate
+estimation of a high dimensional density is not feasible at the moderate sample
+sizes often encountered in applications. We also show that our estimator can be
+generalized to the entire class of functionals considered by Robins et al.
+(2008) which include the average effect of a treatment on a response Y when a
+vector X suffices to control confounding and the expected conditional variance
+of a response Y given a vector X.
+"
+24896,Lasso Guarantees for $ β$-Mixing Heavy Tailed Time Series,"  Most existing theoretical results for the lasso require the samples to be
+iid. Recent work has provided guarantees for the lasso assuming that the time
+series is generated by a sparse Vector Auto-Regressive (VAR) model with
+Gaussian innovations. Proofs of these results rely critically on the fact that
+the true data generating mechanism (DGM) is a finite-order Gaussian VAR. This
+assumption is quite brittle: linear transformations, including selecting a
+subset of variables, can lead to the violation of this assumption. In order to
+break free from such assumptions, we derive non-asymptotic inequalities for
+estimation error and prediction error of the lasso estimate of the best linear
+predictor without assuming any special parametric form of the DGM. Instead, we
+rely only on (strict) stationarity and geometrically decaying $\beta$-mixing
+coefficients to establish error bounds for the lasso for subweibull random
+vectors. The class of subweibull random variables that we introduce includes
+subgaussian and subexponential random variables but also includes random
+variables with tails heavier than an exponential. We also show that, for
+Gaussian processes, the $\beta$-mixing condition can be relaxed to summability
+of the $\alpha$-mixing coefficients. Our work provides an alternative proof of
+the consistency of the lasso for sparse Gaussian VAR models. But the
+applicability of our results extends to non-Gaussian and non-linear times
+series models as the examples we provide demonstrate.
+"
+24897,Spontaneous Symmetry Breaking in Neural Networks,"  We propose a framework to understand the unprecedented performance and
+robustness of deep neural networks using field theory. Correlations between the
+weights within the same layer can be described by symmetries in that layer, and
+networks generalize better if such symmetries are broken to reduce the
+redundancies of the weights. Using a two parameter field theory, we find that
+the network can break such symmetries itself towards the end of training in a
+process commonly known in physics as spontaneous symmetry breaking. This
+corresponds to a network generalizing itself without any user input layers to
+break the symmetry, but by communication with adjacent layers. In the layer
+decoupling limit applicable to residual networks (He et al., 2015), we show
+that the remnant symmetries that survive the non-linear layers are
+spontaneously broken. The Lagrangian for the non-linear and weight layers
+together has striking similarities with the one in quantum field theory of a
+scalar. Using results from quantum field theory we show that our framework is
+able to explain many experimentally observed phenomena,such as training on
+random labels with zero error (Zhang et al., 2017), the information bottleneck,
+the phase transition out of it and gradient variance explosion (Shwartz-Ziv &
+Tishby, 2017), shattered gradients (Balduzzi et al., 2017), and many more.
+"
+24898,On indecomposability of $βX$,"  The following is an open problem in topology: Determine whether the
+Stone-Čech compactification of a widely-connected space is necessarily an
+indecomposable continuum. Herein we describe properties of $X$ that are
+necessary and sufficient in order for $\beta X$ to be indecomposable. We show
+that indecomposability and irreducibility are equivalent properties in
+compactifications of indecomposable separable metric spaces, leading to some
+equivalent formulations of the open problem. We also construct a
+widely-connected subset of Euclidean $3$-space which is contained in a
+composant of each of its compactifications. The example answers a question of
+Jerzy Mioduszewski.
+"
+24899,Triad interactions and the bidirectional turbulent cascade of magnetic helicity,"  Using direct numerical simulations we demonstrate that magnetic helicity
+exhibits a bidirectional turbulent cascade at high but finite magnetic Reynolds
+numbers. Despite the injection of positive magnetic helicity in the flow, we
+observe that magnetic helicity of opposite signs is generated between large and
+small scales. We explain these observations by carrying out an analysis of the
+magnetohydrodynamic equations reduced to triad interactions using the Fourier
+helical decomposition. Within this framework, the direct cascade of positive
+magnetic helicity arises through triad interactions that are associated with
+small scale dynamo action, while the occurrence of negative magnetic helicity
+at large scales is explained through triad interactions that are related to
+stretch-twist-fold dynamics and small scale dynamo action, which compete with
+the inverse cascade of positive magnetic helicity. Our analytical and numerical
+results suggest that the direct cascade of magnetic helicity is a finite
+magnetic Reynolds number $Rm$ effect that will vanish in the limit $Rm \to
+\infty$.
+"
+24900,Strictly Proper Kernel Scoring Rules and Divergences with an Application to Kernel Two-Sample Hypothesis Testing,"  We study strictly proper scoring rules in the Reproducing Kernel Hilbert
+Space. We propose a general Kernel Scoring rule and associated Kernel
+Divergence. We consider conditions under which the Kernel Score is strictly
+proper. We then demonstrate that the Kernel Score includes the Maximum Mean
+Discrepancy as a special case. We also consider the connections between the
+Kernel Score and the minimum risk of a proper loss function. We show that the
+Kernel Score incorporates more information pertaining to the projected embedded
+distributions compared to the Maximum Mean Discrepancy. Finally, we show how to
+integrate the information provided from different Kernel Divergences, such as
+the proposed Bhattacharyya Kernel Divergence, using a one-class classifier for
+improved two-sample hypothesis testing results.
+"
+24901,Comparative study on supervised learning methods for identifying phytoplankton species,"  Phytoplankton plays an important role in marine ecosystem. It is defined as a
+biological factor to assess marine quality. The identification of phytoplankton
+species has a high potential for monitoring environmental, climate changes and
+for evaluating water quality. However, phytoplankton species identification is
+not an easy task owing to their variability and ambiguity due to thousands of
+micro and pico-plankton species. Therefore, the aim of this paper is to build a
+framework for identifying phytoplankton species and to perform a comparison on
+different features types and classifiers. We propose a new features type
+extracted from raw signals of phytoplankton species. We then analyze the
+performance of various classifiers on the proposed features type as well as two
+other features types for finding the robust one. Through experiments, it is
+found that Random Forest using the proposed features gives the best
+classification results with average accuracy up to 98.24%.
+"
+24902,Entanglement-enhanced phase estimation without prior phase information,"  We study the generation of planar quantum squeezed (PQS) states by quantum
+non-demolition (QND) measurement of a cold ensemble of $^{87}$Rb atoms. Precise
+calibration of the QND measurement allows us to infer the conditional
+covariance matrix describing the $F_y$ and $F_z$ components of the PQS,
+revealing the dual squeezing characteristic of PQS. PQS states have been
+proposed for single-shot phase estimation without prior knowledge of the likely
+values of the phase. We show that for an arbitrary phase, the generated PQS
+gives a metrological advantage of at least 3.1 dB relative to classical states.
+The PQS also beats traditional squeezed states generated with the same QND
+resources, except for a narrow range of phase values. Using spin squeezing
+inequalities, we show that spin-spin entanglement is responsible for the
+metrological advantage.
+"
+24903,Inertial movements of the iris as the origin of post-saccadic oscillations,"  The use of eye-tracking techniques is becoming rapidly extended because of
+its relevance for acquiring information about cognition and behavior. Recent
+studies indicate that a correct characterization of the motion of the pupil
+inside the eyeball is needed, because this motion influences the eye-tracking
+results. In this work we face this problem and we develop a model that
+reproduces several findings of recent experiments on saccadic movements. In
+particular, the dependence of the amplitude and period of the post saccadic
+oscillations on the saccade size, as well as that of the peak velocity. Our
+results suggest that post saccadic oscillations could obey mainly to inertial
+phenomena experimented by the inner part of the iris when the eyeball rotates.
+"
+24904,Confluence laws and Hopf-Borel type theorem for operads,"  In 2008, Loday shed light on the existence of Hopf-Boreltheorems for operads.
+Using the vocabulary of category theory, Livernet,Mesablishvili and Wisbauer
+extended such theorems to monads. In bothcases, the reasoning was to start from
+a mixed distributive law andthen to prove that it induces an isomorphism of
+species to finally geta rigidity theorem. Our reasoning goes here backward: we
+prove thatfrom an isomorphism of species one can get what we called a
+confluencelaw, which generalises mixed distributive laws, and that it is enough
+toobtain a rigidity theorem. This enables us to show that for any operadsP and
+Q having the same underlying S-module, there exists a confluencelaw $\alpha$
+such that any conilpotent P coQ-bialgebra satisfying $\alpha$ is free andcofree
+over its primitive elements. Our reasoning permits us to generatemany new
+examples, while recovering the known ones by consideringdual relations.
+"
+24905,SEARNN: Training RNNs with Global-Local Losses,"  We propose SEARNN, a novel training algorithm for recurrent neural networks
+(RNNs) inspired by the ""learning to search"" (L2S) approach to structured
+prediction. RNNs have been widely successful in structured prediction
+applications such as machine translation or parsing, and are commonly trained
+using maximum likelihood estimation (MLE). Unfortunately, this training loss is
+not always an appropriate surrogate for the test error: by only maximizing the
+ground truth probability, it fails to exploit the wealth of information offered
+by structured losses. Further, it introduces discrepancies between training and
+predicting (such as exposure bias) that may hurt test performance. Instead,
+SEARNN leverages test-alike search space exploration to introduce global-local
+losses that are closer to the test error. We first demonstrate improved
+performance over MLE on two different tasks: OCR and spelling correction. Then,
+we propose a subsampling strategy to enable SEARNN to scale to large vocabulary
+sizes. This allows us to validate the benefits of our approach on a machine
+translation task.
+"
+24906,Strong dopant dependence of electric transport in ion-gated MoS2,"  We report modifications of the temperature-dependent transport properties of
+$\mathrm{MoS_2}$ thin flakes via field-driven ion intercalation in an electric
+double layer transistor. We find that intercalation with $\mathrm{Li^+}$ ions
+induces the onset of an inhomogeneous superconducting state. Intercalation with
+$\mathrm{K^+}$ leads instead to a disorder-induced incipient metal-to-insulator
+transition. These findings suggest that similar ionic species can provide
+access to different electronic phases in the same material.
+"
+24907,Magnetization induced dynamics of a Josephson junction coupled to a nanomagnet,"  We study the superconducting current of a Josephson junction (JJ) coupled to
+an external nanomagnet driven by a time dependent magnetic field both without
+and in the presence of an external AC drive. We provide an analytic, albeit
+perturbative, solution for the Landau-Lifshitz (LL) equations governing the
+coupled JJ-nanomagnet system in the presence of a magnetic field with arbitrary
+time-dependence oriented along the easy axis of the nanomagnet's magnetization
+and in the limit of weak dimensionless coupling $\epsilon_0$ between the JJ and
+the nanomagnet. We show the existence of Shapiro-like steps in the I-V
+characteristics of the JJ subjected to a voltage bias for a constant or
+periodically varying magnetic field and explore the effect of rotation of the
+magnetic field and the presence of an external AC drive on these steps. We
+support our analytic results with exact numerical solution of the LL equations.
+We also extend our results to dissipative nanomagnets by providing a
+perturbative solution to the Landau-Lifshitz-Gilbert (LLG) equations for weak
+dissipation. We study the fate of magnetization-induced Shapiro steps in the
+presence of dissipation both from our analytical results and via numerical
+solution of the coupled LLG equations. We discuss experiments which can test
+our theory.
+"
+24908,Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller,"  Implantable, closed-loop devices for automated early detection and
+stimulation of epileptic seizures are promising treatment options for patients
+with severe epilepsy that cannot be treated with traditional means. Most
+approaches for early seizure detection in the literature are, however, not
+optimized for implementation on ultra-low power microcontrollers required for
+long-term implantation. In this paper we present a convolutional neural network
+for the early detection of seizures from intracranial EEG signals, designed
+specifically for this purpose. In addition, we investigate approximations to
+comply with hardware limits while preserving accuracy. We compare our approach
+to three previously proposed convolutional neural networks and a feature-based
+SVM classifier with respect to detection accuracy, latency and computational
+needs. Evaluation is based on a comprehensive database with long-term EEG
+recordings. The proposed method outperforms the other detectors with a median
+sensitivity of 0.96, false detection rate of 10.1 per hour and median detection
+delay of 3.7 seconds, while being the only approach suited to be realized on a
+low power microcontroller due to its parsimonious use of computational and
+memory resources.
+"
+24909,Quenches from gaussian bosons to Tonks-Girardeau gas: stationary states and effects of a confining potential,"  We consider the general problem of quenching an interacting Bose gas from the
+noninteracting regime to the strong repulsive case described by the
+Tonks-Girardeau limit, with the initial state being a gaussian ensemble for the
+bosons. A generic multi-point correlation function in the steady state can be
+fully described in terms of a Fredholm-like determinant suitable both for a
+numerical study and for an analytical study in certain limiting cases. Finally,
+we extend the study to the presence of a smooth confining potential showing
+that, in the thermodynamic limit, the time evolution of the two-point function
+can be mapped to a classical problem in a properly defined phase-space.
+"
+24910,The Calderón problem and normal forms,"  We outline an approach to the inverse problem of Calderón that highlights
+the role of microlocal normal forms and propagation of singularities and
+extends a number of earlier results also in the anisotropic case. The main
+result states that from the boundary measurements it is possible to recover
+integrals of the unknown coefficient over certain two-dimensional manifolds
+called good bicharacteristic leaves. This reduces the Calderón problem into
+solving a linear integral geometry problem (inversion of a bicharacteristic
+leaf transform).
+"
+24911,On the Unique Determination of Modal Multiconductor Transmission-Line Properties,"  Some modal (or decoupled) transmission-line properties such as
+per-unit-length impedance, admittance, or characteristic impedance have long
+been held to be, in general, non-unique. This ambiguity arises from the nature
+of the similarity transformations used to relate the terminal and modal
+domains, for which the voltage transformation matrix has been shown to be only
+loosely related to the corresponding current transformation matrix. Modern
+methods have attempted to relate the two, but these relations typically rely on
+arbitrary normalizations, leading to strictly incorrect and/or non-unique
+results. This work introduces relations between the two transformations,
+derived from the physical equivalence of total power and currents between the
+two domains, by which the transformation matrices can be unambiguously related
+to each other, and the modal properties uniquely solved. This technique allows
+for the correct extraction of the modal transmission-line properties for any
+arbitrary system of conductors. Multiple examples are studied to validate the
+proposed solution process.
+"
+24912,Concurrency-induced transitions in epidemic dynamics on temporal networks,"  Social contact networks underlying epidemic processes in humans and animals
+are highly dynamic. The spreading of infections on such temporal networks can
+differ dramatically from spreading on static networks. We theoretically
+investigate the effects of concurrency, the number of neighbors that a node has
+at a given time point, on the epidemic threshold in the stochastic
+susceptible-infected-susceptible dynamics on temporal network models. We show
+that network dynamics can suppress epidemics (i.e., yield a higher epidemic
+threshold) when the nodes' concurrency is low, but can also enhance epidemics
+when the concurrency is high. We analytically determine different phases of
+this concurrency-induced transition, and confirm our results with numerical
+simulations.
+"
+24913,Coexistence of attractors in a coupled nonlinear delayed system modelling El Niño Southern Oscillations,"  We study the dynamics of the sea surface temperature (SST) anomaly using a
+model of the temporal patterns of two sub-regions, mimicking behaviour similar
+to El Niño Southern Oscillations (ENSO). Specifically, we present the
+existence, stability, and basins of attraction of the solutions arising in the
+model system in the space of these parameters: self delay, delay and
+inter-region coupling strengths. The emergence or suppression of oscillations
+in our models is a dynamical feature of utmost relevance, as it signals the
+presence or absence of ENSO-like oscillations. In contrast to the well-known
+low order model of ENSO, where the influence of the neighbouring regions on the
+region of interest is modelled as external noise, we consider neighbouring
+regions as a coupled deterministic dynamical systems. Different parameters
+yield a rich variety of dynamical patterns in our model, ranging from steady
+states and homogeneous oscillations to irregular oscillations and coexistence
+of oscillatory attractors, without explicit inclusion of noise. Interestingly,
+if we take the self-delay coupling strengths of the two sub-regions to be such
+that the temperature of one region goes to a fixed point regime when uncoupled,
+while the other system is in the oscillatory regime, then on coupling both
+systems show oscillations. We explicitly obtain the basins of attraction for
+the different steady states and oscillatory states in the model. Our results
+might be helpful for forecasting of El Niño (or La Niña) progress, as it
+indicates the combination of initial SST anomalies in the sub-regions that can
+result in a El Niño/La Niña episodes. In particular, the result suggests
+using an interval as a criterion to estimate the El-Niño or La-Niña
+progress instead of the currently used the single value criterion.
+"
+24914,On a Class of Polynomials Generated by F (xt -- R(t)),"  We investigate polynomial sets {P n } n$\ge$0 with generating power series of
+the form F (xt -- R(t)) and satisfying, for n $\ge$ 0, the (d + 1)-order
+recursion xP\_ n (x) = P\_{ n+1 }(x) +\sum\_{ l=0}^{d} \gamma^{l}\_{n} P\_{
+n--l} (x), where \ {\gamma ^{l}\_{ n}\ } is a complex sequence for 0 $\le$ l
+$\le$ d, P \_0 (x) = 1 and P \_n (x) = 0 for all negative integer n. We show
+that the formal power series R(t) is a polynomial of degree at most d + 1 if
+certain coefficients of R(t) are null or if F (t) is a generalized
+hypergeometric series. Moreover, for the d-symmetric case we demonstrate that
+R(t) is the monomial of degree d + 1 and F (t) is expressed by hypergeometric
+series.
+"
+24915,100 years of the Cosmological Constant: what's next?,"  The Cosmological Constant $\Lambda$, in different incarnations, has been with
+us for 100 years. Many surveys of dark energy are underway, indicating so far
+that the data are consistent with a dark energy equation of state of $w=-1$,
+i.e. a $\Lambda$ term in Einstein's equation, although time variation of $w$ is
+not yet ruled out. The ball is now back in the theoreticians' court, to explain
+the physical meaning of $\Lambda$. We discuss sociological aspects of this
+field, in particular to what extent the agreement on the cold dark matter +
+$\Lambda$ concordance model is a result of the globalization of research and
+over-communication.
+"
+24916,My chemistry with Francesco,"  Contributed paper to the Conference ""Francesco's Legacy: Star Formation in
+Space and Time"", in memory of Francesco Palla (1954-2016), held in Florence,
+June 5-9, 2017
+"
+24917,Conformal partition functions of critical percolation from $D_3$ Thermodynamic Bethe Ansatz equations,"  Using the planar Temperley-Lieb algebra, critical bond percolation on the
+square lattice is incorporated as ${\cal LM}(2,3)$ in the family of Yang-Baxter
+integrable logarithmic minimal models ${\cal LM}(p,p')$. We consider this model
+in the presence of boundaries and with periodic boundary conditions. Inspired
+by Kuniba, Sakai and Suzuki, we rewrite the recently obtained infinite
+$Y$-system of functional equations. We obtain nonlinear integral equations in
+the form of a closed finite set of TBA equations described by a $D_3$ Dynkin
+diagram. Following the methods of Klümper and Pearce, we solve the TBA
+equations for the conformal finite-size corrections. For the ground states of
+the standard modules on the strip, these agree with the known central charge
+$c=0$ and conformal weights $\Delta_{1,s}$ for $s\in {\Bbb Z_{\ge 1}}$ with
+$\Delta_{r,s}= \big((3r-2s)^2-1\big)/24$. For the periodic case, the
+finite-size corrections agree with the conformal weights $\Delta_{0,s}$,
+$\Delta_{1,s}$ with $s\in\frac12\Bbb Z_{\ge0}$. These are obtained analytically
+using Rogers dilogarithm identities. We incorporate all finite excitations by
+formulating empirical selection rules for the patterns of zeros of all the
+eigenvalues of the standard modules. We thus obtain the conformal partition
+functions on the cylinder and the modular invariant partition function (MIPF)
+on the torus. By applying $q$-binomial identities, it is shown that our
+finitized characters on the strip agree with those of Pearce, Rasmussen and
+Zuber. On the torus, the MIPF is a non-diagonal sesquilinear form in affine
+$u(1)$ characters given by the $u(1)$ partition function
+$Z_{2,3}(q)=Z_{2,3}^{\rm{Circ}}(q)$. This is compatible with the general
+conjecture of Pearce and Rasmussen, namely
+$Z_{p,p'}(q)=Z^{\rm{Proj}}_{p,p'}(q)+n_{p,p'}Z^{\rm{Min}}_{p,p'}(q)$ with
+$n_{p,p'}\in {\Bbb Z}$, and the lattice derivation fixes $n_{2,3}=-1$.
+"
+24918,Space-Optimal Majority in Population Protocols,"  Population protocols are a model of distributed computing, in which $n$
+agents with limited local state interact randomly, and cooperate to
+collectively compute global predicates. An extensive series of papers, across
+different communities, has examined the computability and complexity
+characteristics of this model. Majority, or consensus, is a central task, in
+which agents need to collectively reach a decision as to which one of two
+states $A$ or $B$ had a higher initial count. Two complexity metrics are
+important: the time that a protocol requires to stabilize to an output
+decision, and the state space size that each agent requires.
+It is known that majority requires $\Omega(\log \log n)$ states per agent to
+allow for poly-logarithmic time stabilization, and that $O(\log^2 n)$ states
+are sufficient. Thus, there is an exponential gap between the upper and lower
+bounds.
+We address this question. We provide a new lower bound of $\Omega(\log n)$
+states for any protocol which stabilizes in $O( n^{1-c} )$ time, for any $c >
+0$ constant. This result is conditional on basic monotonicity and output
+assumptions, satisfied by all known protocols. Technically, it represents a
+significant departure from previous lower bounds. Instead of relying on dense
+configurations, we introduce a new surgery technique to construct executions
+which contradict the correctness of algorithms that stabilize too fast.
+Subsequently, our lower bound applies to general initial configurations.
+We give an algorithm for majority which uses $O(\log n)$ states, and
+stabilizes in $O(\log^2 n)$ time. Central to the algorithm is a new leaderless
+phase clock, which allows nodes to synchronize in phases of $\Theta(n \log{n})$
+consecutive interactions using $O(\log n)$ states per node. We also employ our
+phase clock to build a leader election algorithm with $O(\log n )$ states,
+which stabilizes in $O(\log^2 n)$ time.
+"
+24919,Microwave photon generation in a doubly tunable superconducting resonator,"  We have developed and tested a doubly tunable resonator, with the intention
+to simulate fast motion of the resonator boundaries in real space. Our device
+is a superconducting coplanar-waveguide half-wavelength microwave resonator,
+with fundamental resonant frequency ~5 GHz. Both of its ends are terminated by
+dc-SQUIDs, which serve as magnetic-flux-controlled inductances. Applying a flux
+to either SQUID allows tuning of the resonant frequency by approximately 700
+MHz. By using two separate on-chip magnetic-flux lines, we modulate the SQUIDs
+with two tones of equal frequency, close to twice that of the resonator's
+fundamental mode. We observe photon generation, at the fundamental frequency,
+above a certain pump amplitude threshold. By varying the relative phase of the
+two pumps we are able to control the photon generation threshold, in good
+agreement with a theoretical model for the modulation of the boundary
+conditions. At the same time, some of our observations deviate from the
+theoretical predictions, which we attribute to parasitic couplings, resulting
+in current driving of the SQUIDs.
+"
+24920,Learning-based Formal Synthesis of Cooperative Multi-agent Systems,"  We propose a formal design framework for synthesizing coordination and
+control policies for cooperative multi-agent systems to accomplish a global
+mission. The global performance requirements are specified as regular languages
+while dynamics of each agent as well as the shared environment are
+characterized by finite automata, upon on which a formal design approach is
+carried out via divide-and-conquer. Specifically, the global mission is
+decomposed into local tasks; and local mission supervisors are designed to
+accomplish these local tasks while maintaining the multi-agent performance by
+integrating supervisor synthesis with compositional verification techniques;
+finally, motion plans are automatically synthesized based on the obtained
+mission plans. We present three modifications of the L* learning algorithm such
+that they are adapted for the synthesis of the local mission supervisors, the
+compositional verification and the synthesis of local motion plans, to
+guarantee that the collective behavior of the agents will ensure the
+satisfaction of the global specification. Furthermore, the effectiveness of the
+proposed framework is demonstrated by a detailed experimental study based on
+the implementation of a multi-robot coordination scenario. The proposed
+hardware-software architecture, with each robot's communication and
+localization capabilities, is exploited to examine the automatic supervisor
+synthesis with inter-robot communication.
+"
+24921,Effects of local periodic driving on transport and generation of bound states,"  We periodically kick a local region in a one-dimensional lattice and
+demonstrate, by studying wave packet dynamics, that the strength and the time
+period of the kicking can be used as tuning parameters to control the
+transmission probability across the region. Interestingly, we can tune the
+transmission to zero which is otherwise impossible to do in a time-independent
+system. We adapt the non-equilibrium Green's function method to take into
+account the effects of periodic driving; the results obtained by this method
+agree with those found by wave packet dynamics if the time period is small. We
+discover that Floquet bound states can exist in certain ranges of parameters;
+when the driving frequency is decreased, these states get delocalized and turn
+into resonances by mixing with the Floquet bulk states. We extend these results
+to incorporate the effects of local interactions at the driven site, and we
+find some interesting features in the transmission and the bound states.
+"
+24922,Employing Antenna Selection to Improve Energy-Efficiency in Massive MIMO Systems,"  Massive MIMO systems promise high data rates by employing large number of
+antennas, which also increases the power usage of the system as a consequence.
+This creates an optimization problem which specifies how many antennas the
+system should employ in order to operate with maximal energy efficiency. Our
+main goal is to consider a base station with a fixed number of antennas, such
+that the system can operate with a smaller subset of antennas according to the
+number of active user terminals, which may vary over time. Thus, in this paper
+we propose an antenna selection algorithm which selects the best antennas
+according to the better channel conditions with respect to the users, aiming at
+improving the overall energy efficiency. Then, due to the complexity of the
+mathematical formulation, a tight approximation for the consumed power is
+presented, using the Wishart theorem, and it is used to find a deterministic
+formulation for the energy efficiency. Simulation results show that the
+approximation is quite tight and that there is significant improvement in terms
+of energy efficiency when antenna selection is employed.
+"
+24923,Axisymmetric spreading of surfactant from a point source,"  Guided by computation, we theoretically calculate the steady flow driven by
+Marangoni stress due to surfactant introduced on a fluid interface at a
+constant rate. Two separate extreme cases, where the surfactant dynamics is
+dominated by the adsorbed phase or the dissolved phase are considered. We focus
+on the case where the size of the surfactant source is much smaller than the
+size of the fluid domain, and the resulting Marangoni stress overwhelms viscous
+forces so that the flow is strongest in a boundary layer close to the
+interface. We derive the resulting flow in a region much larger than the
+surfactant source but smaller than the domain size is described by
+approximating it with a self-similar profile. The radially outward component of
+fluid velocity decays with the radial distance $r$ as $r^{-3/5}$ when the
+surfactant spreads in an adsorbed phase, and as $r^{-1}$ when it spreads in a
+dissolved phase. Universal flow profiles that are independent of the system
+parameters emerge in both the cases. Three hydrodynamic signatures are
+identified to distinguish between the two cases and verify the applicability of
+our analysis with successively stringent tests.
+"
+24924,ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes,"  A key requirement for leveraging supervised deep learning methods is the
+availability of large, labeled datasets. Unfortunately, in the context of RGB-D
+scene understanding, very little data is available -- current datasets cover a
+small range of scene views and have limited semantic annotations. To address
+this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views
+in 1513 scenes annotated with 3D camera poses, surface reconstructions, and
+semantic segmentations. To collect this data, we designed an easy-to-use and
+scalable RGB-D capture system that includes automated surface reconstruction
+and crowdsourced semantic annotation. We show that using this data helps
+achieve state-of-the-art performance on several 3D scene understanding tasks,
+including 3D object classification, semantic voxel labeling, and CAD model
+retrieval. The dataset is freely available at this http URL.
+"
+24925,Stellar Intensity Interferometric Capabilities of IACT Arrays,"  Sub-milliarcsecond imaging of nearby main sequence stars and binary systems
+can provide critical information on stellar phenomena such as rotational
+deformation, accretion effects, and the universality of starspot (sunspot)
+cycles. Achieving this level of resolution in optical wavelength bands (U/V)
+requires use of a sparse array of interferometric telescopes with kilometer
+scale baseline separations. Current ground based VHE gamma-ray observatories,
+such as VERITAS, HESS, and MAGIC, employ arrays of > 10 m diameter optical
+Imaging Atmospheric Cherenkov Telescopes (IACTs) with >80 m telescope
+separations, and are therefore well suited for sub-milliarcsecond astronomical
+imaging in the U/V bands using Hanbury Brown and Twiss (HBT) interferometry
+[1,2]. We describe the development of instrumentation for the augmentation of
+IACT arrays to perform Stellar Intensity Interferometric (SII) imaging.
+Laboratory tests are performed using pseudo-random and thermal (blackbody)
+light to demonstrate the ability of high speed (250 MHz) digitizing electronics
+to continuously record photon intensity over long periods (minutes to hours)
+and validate the use of offline software correlation to calculate the squared
+degree of coherence . We then use as the interferometric observable to populate
+the Fourier reciporical image plane, and apply standard inversion techniques to
+recover the original 2-D source image. The commercial availability of
+inexpensive fiber-optic based sub-nanosecond multi-crate (White Rabbit[3])
+synchronization timing enables the extension of SII to baselines greater than
+10 km, theoretically allowing U/V band imaging with resolution <100 $\mu$
+arc-seconds. This article provides a description of typical designs of
+practical SII instrumentation for the VERITAS IACT observatory array (Amado,
+Arizona) and the future CTA IACT Observatory (Canary Islands, Spain and
+Paranal, Chile).
+"
+24926,Singular Stochastic Allen-Cahn equations with dynamic boundary conditions,"  We prove a well-posedness result for stochastic Allen-Cahn type equations in
+a bounded domain coupled with generic boundary conditions. The (nonlinear) flux
+at the boundary aims at describing the interactions with the hard walls and is
+motivated by some recent literature in physics. The singular character of the
+drift part allows for a large class of maximal monotone operators, generalizing
+the usual double-well potentials. One of the main novelties of the paper is the
+absence of any growth condition on the drift term of the evolution, neither on
+the domain nor on the boundary. A well-posedness result for variational
+solutions of the system is presented using a priori estimates as well as
+monotonicity and compactness techniques. A vanishing viscosity argument for the
+dynamic on the boundary is also presented.
+"
+24927,Braiding with Borromean Rings in (3+1)-Dimensional Spacetime,"  While winding a particle-like excitation around a loop-like excitation yields
+the celebrated Aharonov-Bohm phase, we find a distinctive braiding phase in the
+absence of such mutual winding. In this work, we propose an exotic
+particle-loop-loop braiding process, dubbed the \emph{Borromean-Rings
+braiding}. In the process, a particle moves around two unlinked loops, such
+that its trajectory and the two loops form the Borromean-Rings or more general
+Brunnian links. As the particle trajectory does not wind with any of the loops,
+the resulting braiding phase is fundamentally different from the Aharonov-Bohm
+phase. We derive an explicit expression for the braiding phase in terms of the
+underlying Milnor's triple linking number. We also propose Topological Quantum
+Field Theories consisting of an $AAB$-type topological term which realize the
+braiding statistics.
+"
+24928,"On the Tanner Graph Cycle Distribution of Random LDPC, Random Protograph-Based LDPC, and Random Quasi-Cyclic LDPC Code Ensembles","  In this paper, we study the cycle distribution of random low-density
+parity-check (LDPC) codes, randomly constructed protograph-based LDPC codes,
+and random quasi-cyclic (QC) LDPC codes. We prove that for a random bipartite
+graph, with a given (irregular) degree distribution, the distributions of
+cycles of different length tend to independent Poisson distributions, as the
+size of the graph tends to infinity. We derive asymptotic upper and lower
+bounds on the expected values of the Poisson distributions that are independent
+of the size of the graph, and only depend on the degree distribution and the
+cycle length. For a random lift of a bi-regular protograph, we prove that the
+asymptotic cycle distributions are essentially the same as those of random
+bipartite graphs as long as the degree distributions are identical. For random
+QC-LDPC codes, however, we show that the cycle distribution can be quite
+different from the other two categories. In particular, depending on the
+protograph and the value of $c$, the expected number of cycles of length $c$,
+in this case, can be either $\Theta(N)$ or $\Theta(1)$, where $N$ is the
+lifting degree (code length). We also provide numerical results that match our
+theoretical derivations. Our results provide a theoretical foundation for
+emperical results that were reported in the literature but were not
+well-justified. They can also be used for the analysis and design of LDPC codes
+and associated algorithms that are based on cycles.
+"
+24929,Localization landscape theory of disorder in semiconductors. III. Application to carrier transport and recombination in light emitting diodes,"  This paper introduces a novel method to account for quantum disorder effects
+into the classical drift-diffusion model of semiconductor transport through the
+localization landscape theory. Quantum confinement and quantum tunneling in the
+disordered system change dramatically the energy barriers acting on the
+perpendicular transport of heterostructures. In addition they lead to
+percolative transport through paths of minimal energy in the 2D landscape of
+disordered energies of multiple 2D quantum wells. This model solves the carrier
+dynamics with quantum effects self-consistently and provides a computationally
+much faster solver when compared with the Schrödinger equation resolution.
+The theory also provides a good approximation to the density of states for the
+disordered system over the full range of energies required to account for
+transport at room-temperature. The current-voltage characteristics modeled by
+3-D simulation of a full nitride-based light-emitting diode (LED) structure
+with compositional material fluctuations closely match the experimental
+behavior of high quality blue LEDs. The model allows also a fine analysis of
+the quantum effects involved in carrier transport through such complex
+heterostructures. Finally, details of carrier population and recombination in
+the different quantum wells are given.
+"
+24930,Determination of measurement uncertainty by simulation,"  Modern coordinate measurement machines (CMM) are universal tools to measure
+geometric features of complex three-dimensional workpieces. To use them as
+reliable means of quality control, the suitability of the device for the
+specific measurement task has to be proven. Therefore, the ISO 14253 standard
+requires, knowledge of the measurement uncertainty and, that it is in
+reasonable relation with the specified tolerances. Hence, the determination of
+the measurement uncertainty, which is a complex and also costly task, is of
+utmost importance. The measurement uncertainty is usually influenced by several
+contributions of various sources. Among those of the machine itself, e.g.,
+guideway errors and the influence of the probe and styli play an important
+role. Furthermore, several properties of the workpiece, such as its form
+deviations and surface roughness, have to be considered. Also the environmental
+conditions, i.e., temperature and its gradients, pressure, relative humidity
+and others contribute to the overall measurement uncertainty. Currently, there
+are different approaches to determine task-specific measurement uncertainties.
+This work reports on recent advancements extending the well-established method
+of PTB's Virtual Coordinate Measuring Machine (VCMM) to suit present-day needs
+in industrial applications. The VCMM utilizes numerical simulations to
+determine the task-specific measurement uncertainty incorporating broad
+knowledge about the contributions of, e.g., the used CMM, the environment and
+the workpiece.
+"
+24931,Compact modes in quasi one dimensional coupled magnetic oscillators,"  In this work we study analytically and numerically the spectrum and
+localization properties of three quasi-one-dimensional (ribbons) split-ring
+resonator arrays which possess magnetic flatbands, namely, the stub, Lieb and
+kagome lattices, and how their spectra is affected by the presence of
+perturbations that break the delicate geometrical interference needed for a
+magnetic flatband to exist. We find that the Stub and Lieb ribbons are stable
+against the three types of perturbations considered here, while the kagome
+ribbon is, in general, unstable. When losses are incorporated, all flatbands
+remain dispersionless but become complex, with the kagome ribbon exhibiting the
+highest loss rate.
+"
+24932,Interpretations of Presburger Arithmetic in Itself,"  Presburger arithmetic PrA is the true theory of natural numbers with
+addition. We study interpretations of PrA in itself. We prove that all
+one-dimensional self-interpretations are definably isomorphic to the identity
+self-interpretation. In order to prove the results we show that all linear
+orders that are interpretable in (N,+) are scattered orders with the finite
+Hausdorff rank and that the ranks are bounded in terms of the dimension of the
+respective interpretations. From our result about self-interpretations of PrA
+it follows that PrA isn't one-dimensionally interpretable in any of its finite
+subtheories. We note that the latter was conjectured by A. Visser.
+"
+24933,Relay-Assisted Mixed FSO/RF Systems over Málaga-$\mathcal{M}$ and $κ$-$μ$ Shadowed Fading Channels,"  This letter presents a unified analytical framework for relay-assisted mixed
+FSO/RF transmission. In addition to accounting for different FSO detection
+techniques, the mathematical model offers a twofold unification of mixed FSO/RF
+systems by considering mixed Málaga-$\mathcal{M}$/$\kappa$-$\mu$ shadowed
+fading, which includes as special cases nearly all linear turbulence/fading
+models adopted in the open literature.
+"
+24934,Ego-motion Sensor for Unmanned Aerial Vehicles Based on a Single-Board Computer,"  This paper describes the design and implementation of a ground-related
+odometry sensor suitable for micro aerial vehicles. The sensor is based on a
+ground-facing camera and a single-board Linux-based embedded computer with a
+multimedia System on a Chip (SoC). The SoC features a hardware video encoder
+which is used to estimate the optical flow online. The optical flow is then
+used in combination with a distance sensor to estimate the vehicle's velocity.
+The proposed sensor is compared to a similar existing solution and evaluated in
+both indoor and outdoor environments.
+"
+24935,Fault Detection of Broken Rotor Bar in LS-PMSM Using Random Forests,"  This paper proposes a new approach to diagnose broken rotor bar failure in a
+line start-permanent magnet synchronous motor (LS-PMSM) using random forests.
+The transient current signal during the motor startup was acquired from a
+healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13
+statistical time domain features from the startup transient current signal, and
+used these features to train and test a random forest to determine whether the
+motor was operating under normal or faulty conditions. For feature selection,
+we used the feature importances from the random forest to reduce the number of
+features to two features. The results showed that the random forest classifies
+the motor condition as healthy or faulty with an accuracy of 98.8% using all
+features and with an accuracy of 98.4% by using only the mean-index and
+impulsion features. The performance of the random forest was compared with a
+decision tree, Naïve Bayes classifier, logistic regression, linear ridge, and
+a support vector machine, with the random forest consistently having a higher
+accuracy than the other algorithms. The proposed approach can be used in
+industry for online monitoring and fault diagnostic of LS-PMSM motors and the
+results can be helpful for the establishment of preventive maintenance plans in
+factories.
+"
+24936,Bi-Level Online Control without Regret,"  This paper considers a bi-level discrete-time control framework with
+real-time constraints, consisting of several local controllers and a central
+controller. The objective is to bridge the gap between the online convex
+optimization and real-time control literature by proposing an online control
+algorithm with small dynamic regret, which is a natural performance criterion
+in nonstationary environments related to real-time control problems. We
+illustrate how the proposed algorithm can be applied to real-time control of
+power setpoints in an electrical grid.
+"
+24937,Enriched closure spaces as a novel framework for domain theory,"  We propose a generalization of continuous lattices and domains through the
+concept of enriched closure space, defined as a closure space equipped with a
+preclosure operator satisfying some compatibility conditions. In this framework
+we are able to define a notion of way-below relation; an appropriate definition
+of continuity then naturally follows. Characterizations of continuity of the
+enriched closure space and necessary and sufficient conditions for the
+interpolation property are proved. We also draw a link between continuity and
+the possibility for the subsets that are open with respect to the preclosure
+operator to form a topology.
+"
+24938,Integrating Latent Classes in the Bayesian Shared Parameter Joint Model of Longitudinal and Survival Outcomes,"  Cystic fibrosis is a chronic lung disease which requires frequent patient
+monitoring to maintain lung function over time and minimize onset of acute
+respiratory events known as pulmonary exacerbations. From the clinical point of
+view it is important to characterize the association between key biomarkers
+such as $FEV_1$ and time-to first exacerbation. Progression of the disease is
+heterogeneous, yielding different sub-groups in the population exhibiting
+distinct longitudinal profiles. It is desirable to categorize these unobserved
+sub-groups (latent classes) according to their distinctive trajectories.
+Accounting for these latent classes, in other words heterogeneity, will lead to
+improved estimates of association arising from the joint longitudinal-survival
+model.
+The joint model of longitudinal and survival data constitutes a popular
+framework to analyze such data arising from heterogeneous cohorts. In
+particular, two paradigms within this framework are the shared parameter joint
+models and the joint latent class models. The former paradigm allows one to
+quantify the strength of the association between the longitudinal and survival
+outcomes but does not allow for latent sub-populations. The latter paradigm
+explicitly postulates the existence of sub-populations but does not directly
+quantify the strength of the association.
+We propose to integrate latent classes in the shared parameter joint model in
+a fully Bayesian approach, which allows us to investigate the association
+between $FEV_1$ and time-to first exacerbation within each latent class. We,
+furthermore, focus on the selection of the optimal number of latent classes.
+"
+24939,On the Identifiability of Diagnostic Classification Models,"  This paper establishes fundamental results for statistical inference of
+diagnostic classification models (DCM). The results are developed at a high
+level of generality, applicable to essentially all diagnostic classification
+models. In particular, we establish identifiability results of various modeling
+parameters, notably item response probabilities, attribute distribution, and
+Q-matrix-induced partial information structure. Consistent estimators are
+constructed. Simulation results show that these estimators perform well under
+various modeling settings. We also use a real example to illustrate the new
+method. The results are stated under the setting of general latent class
+models. For DCM with a specific parameterization, the conditions may be adapted
+accordingly.
+"
+24940,Studying tidal effects in planetary systems with Posidonius. A N-body simulator written in Rust,"  Planetary systems with several planets in compact orbital configurations such
+as TRAPPIST-1 are surely affected by tidal effects. Its study provides us with
+important insight about its evolution. We developed a second generation of a
+N-body code based on the tidal model used in Mercury-T, re-implementing and
+improving its functionalities using Rust as programming language (including a
+Python interface for easy use) and the WHFAST integrator. The new open source
+code ensures memory safety, reproducibility of numerical N-body experiments, it
+improves the spin integration compared to Mercury-T and allows to take into
+account a new prescription for the dissipation of tidal inertial waves in the
+convective envelope of stars. Posidonius is also suitable for binary system
+simulations with evolving stars.
+"
+24941,Coalition formation for Multi-agent Pursuit based on Neural Network and AGRMF Model,"  An approach for coalition formation of multi-agent pursuit based on neural
+network and AGRMF model is proposed.This paper constructs a novel neural work
+called AGRMF-ANN which consists of feature extraction part and group generation
+part. On one hand,The convolutional layers of feature extraction part can
+abstract the features of agent group role membership function(AGRMF) for all of
+the groups,on the other hand,those features will be fed to the group generation
+part based on self-organizing map(SOM) layer which is used to group the
+pursuers with similar features in the same group. Besides, we also come up the
+group attractiveness function(GAF) to evaluate the quality of groups and the
+pursuers contribution in order to adjust the main ability indicators of AGRMF
+and other weight of all neural network. The simulation experiment showed that
+this proposal can improve the effectiveness of coalition formation for
+multi-agent pursuit and ability to adopt pursuit-evasion problem with the scale
+of pursuer team growing.
+"
+24942,Disguised and new Quasi-Newton methods for nonlinear eigenvalue problems,"  In this paper we take a quasi-Newton approach to nonlinear eigenvalue
+problems (NEPs) of the type $M(\lambda)v=0$, where
+$M:\mathbb{C}\rightarrow\mathbb{C}^{n\times n}$ is a holomorphic function. We
+investigate which types of approximations of the Jacobian matrix lead to
+competitive algorithms, and provide convergence theory. The convergence
+analysis is based on theory for quasi-Newton methods and Keldysh's theorem for
+NEPs. We derive new algorithms and also show that several well-established
+methods for NEPs can be interpreted as quasi-Newton methods, and thereby
+provide insight to their convergence behavior. In particular, we establish
+quasi-Newton interpretations of Neumaier's residual inverse iteration and
+Ruhe's method of successive linear problems.
+"
+24943,A Longitudinal Study of Identifying and Paying Down Architectural Debt,"  Architectural debt is a form of technical debt that derives from the gap
+between the architectural design of the system as it ""should be"" compared to
+""as it is"". We measured architecture debt in two ways: 1) in terms of
+system-wide coupling measures, and 2) in terms of the number and severity of
+architectural flaws. In recent work it was shown that the amount of
+architectural debt has a huge impact on software maintainability and evolution.
+Consequently, detecting and reducing the debt is expected to make software more
+amenable to change. This paper reports on a longitudinal study of a healthcare
+communications product created by Brightsquid Secure Communications Corp. This
+start-up company is facing the typical trade-off problem of desiring
+responsiveness to change requests, but wanting to avoid the ever-increasing
+effort that the accumulation of quick-and-dirty changes eventually incurs. In
+the first stage of the study, we analyzed the status of the ""before"" system,
+which indicated the impacts of change requests. This initial study motivated a
+more in-depth analysis of architectural debt. The results of this analysis were
+used to motivate a comprehensive refactoring of the software system. The third
+phase of the study was a follow-on architectural debt analysis which quantified
+the improvements made. Using this quantitative evidence, augmented by
+qualitative evidence gathered from in-depth interviews with Brightsquid's
+architects, we present lessons learned about the costs and benefits of paying
+down architecture debt in practice.
+"
+24944,Sentiment Classification using Images and Label Embeddings,"  In this project we analysed how much semantic information images carry, and
+how much value image data can add to sentiment analysis of the text associated
+with the images. To better understand the contribution from images, we compared
+models which only made use of image data, models which only made use of text
+data, and models which combined both data types. We also analysed if this
+approach could help sentiment classifiers generalize to unknown sentiments.
+"
+24945,Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy,"  A key problem in location-based modeling and forecasting lies in identifying
+suitable spatial and temporal resolutions. In particular, judicious spatial
+partitioning can play a significant role in enhancing the performance of
+location-based forecasting models. In this work, we investigate two widely used
+tessellation strategies for partitioning city space, in the context of
+real-time taxi demand forecasting. Our study compares (i) Geohash tessellation,
+and (ii) Voronoi tessellation, using two distinct taxi demand datasets, over
+multiple time scales. For the purpose of comparison, we employ classical
+time-series tools to model the spatio-temporal demand. Our study finds that the
+performance of each tessellation strategy is highly dependent on the city
+geography, spatial distribution of the data, and the time of the day, and that
+neither strategy is found to perform optimally across the forecast horizon. We
+propose a hybrid tessellation algorithm that picks the best tessellation
+strategy at each instant, based on their performance in the recent past. Our
+hybrid algorithm is a non-stationary variant of the well-known HEDGE algorithm
+for choosing the best advice from multiple experts. We show that the hybrid
+tessellation strategy performs consistently better than either of the two
+strategies across the data sets considered, at multiple time scales, and with
+different performance metrics. We achieve an average accuracy of above 80% per
+km^2 for both data sets considered at 60 minute aggregation levels.
+"
+24946,Bounding singular surfaces via Chern numbers,"  We prove the existence of a bound on the number of steps of the minimal model
+program for singular surfaces in terms of discrepancies and top Chern numbers.
+As an application, we prove that given $R\in\mathbb{R}$ and $\epsilon\in
+(0,1)$, the class $\mathcal{F}(R,\epsilon)$ of $2$-dimensional pairs $(X,D)$ of
+general type with $\epsilon$-klt singularities, $D$ with standard coefficients,
+and $4c_2(X,D)-c_1^2(X,D)\leq R$, forms a bounded family.
+"
+24947,Higher Kac-Moody algebras and moduli spaces of G-bundles,"  We provide a generalization to the higher dimensional case of the
+construction of the current algebra g((z)), of its Kac-Moody extension and of
+the classical results relating them to the theory of G-bundles over a curve.
+For a reductive algebraic group G with Lie algebra g, we define a dg-Lie
+algebra g_n of n-dimensional currents in g. We show that any symmetric
+G-invariant polynomial P on g of degree n+1 determines a central extension of
+g_n by the base field k that we call higher Kac-Moody algebra g_{n,P}
+associated to P. Further, for a smooth, projective variety X of dimension n>1,
+we show that g_n acts infinitesimally on the derived moduli space RBun_G(X,x)
+of G-bundles over X trivialized at the formal neighborhood of a point x of X.
+Finally, for a representation \phi: G-->GL_r, we construct an associated
+determinantal line bundle on RBun_G(X,x) and prove that the action of g_n
+extends to an action of g_{n,P_\phi} on such bundle for P_\phi the (n+1)-st
+Chern character of \phi.
+"
+24948,von Mises Tapering: A Circular Data Windowing,"  Continuous standard windowing is revisited and a new taper shape is
+introduced, which is based on the normal circular distribution by von Mises.
+Continuous-time windows are considered and their spectra obtained. A brief
+comparison with classical window families is performed in terms of their
+spectral properties. These windows can be used as an alternative in spectral
+analysis.
+"
+24949,Wideband Distributed Spectrum Sharing with Multichannel Immediate Multiple Access,"  This paper describes a radio architecture for distributed spectrum sharing of
+multiple channels among secondary users (SUs) in a wide band of frequencies and
+a localized area. A novel Multichannel Immediate Multiple Access (MIMA)
+physical layer is developed such that each SU can monitor all the channels
+simultaneously for incoming signals and achieve fast rendezvous within the
+multiple channels. The spectrum utilized by an SU pair can be changed
+dynamically based upon spectrum sensing at the transmitter and tracking
+synchronization and control messages at the receiver. Although information
+about the number of active SUs can be used to improve the spectrum sharing
+efficiency, the improvement is small relative to the cost of obtaining such
+information. Therefore, the architecture adopts Multichannel Carrier Sense
+Multiple Access (CSMA) for medium access control regardless of the number of
+active SUs. A prototype implementation of the architecture has been developed
+using an advanced software defined radio (SDR) platform. System tests
+demonstrate that the spectrum sharing efficiency of the prototype is close to
+an upper bound if the signal-to-noise ratio (SNR) is sufficiently high. Among
+other practical issues, imaged interference caused by hardware IQ imbalance
+limits system performance. In the prototype, the MIMA is based on an LTE
+waveform. Therefore, the spectrum sharing radio can be potentially applied to
+the 3.5 GHz radar band for Citizens Broadband Radio Service (CBRS).
+"
+24950,Software Engineering Challenges of Deep Learning,"  Surprisingly promising results have been achieved by deep learning (DL)
+systems in recent years. Many of these achievements have been reached in
+academic settings, or by large technology companies with highly skilled
+research groups and advanced supporting infrastructure. For companies without
+large research groups or advanced infrastructure, building high-quality
+production-ready systems with DL components has proven challenging. There is a
+clear lack of well-functioning tools and best practices for building DL
+systems. It is the goal of this research to identify what the main challenges
+are, by applying an interpretive research approach in close collaboration with
+companies of varying size and type.
+A set of seven projects have been selected to describe the potential with
+this new technology and to identify associated main challenges. A set of 12
+main challenges has been identified and categorized into the three areas of
+development, production, and organizational challenges. Furthermore, a mapping
+between the challenges and the projects is defined, together with selected
+motivating descriptions of how and why the challenges apply to specific
+projects.
+Compared to other areas such as software engineering or database
+technologies, it is clear that DL is still rather immature and in need of
+further work to facilitate development of high-quality systems. The challenges
+identified in this paper can be used to guide future research by the software
+engineering and DL communities. Together, we could enable a large number of
+companies to start taking advantage of the high potential of the DL technology.
+"
+24951,Impact of Resonance on Thermal Targets for Invisible Dark Photon Searches,"  Dark photons in the MeV to GeV mass range are important targets for
+experimental searches. We consider the case where dark photons $A'$ decay
+invisibly to hidden dark matter $X$ through $A' \to XX$. For generic masses,
+proposed accelerator searches are projected to probe the thermal target region
+of parameter space, where the $X$ particles annihilate through $XX \to A' \to
+\text{SM}$ in the early universe and freeze out with the correct relic density.
+However, if $m_{A'} \approx 2m_X$, dark matter annihilation is resonantly
+enhanced, shifting the thermal target region to weaker couplings. For $\sim
+10\%$ degeneracies, we find that the annihilation cross section is generically
+enhanced by four (two) orders of magnitude for scalar (pseudo-Dirac) dark
+matter. For such moderate degeneracies, the thermal target region drops to weak
+couplings beyond the reach of all proposed accelerator experiments in the
+scalar case and becomes extremely challenging in the pseudo-Dirac case.
+Proposed direct detection experiments can probe moderate degeneracies in the
+scalar case. For greater degeneracies, the effect of the resonance can be even
+more significant, and both scalar and pseudo-Dirac cases are beyond the reach
+of all proposed accelerator and direct detection experiments. For scalar dark
+matter, we find an absolute minimum that sets the ultimate experimental
+sensitivity required to probe the entire thermal target parameter space, but
+for pseudo-Dirac fermions, we find no such thermal target floor.
+"
+24952,Quenched asymptotics for interacting diffusions on inhomogeneous random graphs,"  The aim of the paper is to address the behavior in large population of
+diffusions interacting on a random, possibly diluted and inhomogeneous graph.
+This is the natural continuation of a previous work, where the homogeneous
+Erd\H os-Rényi case was considered. The class of graphs we consider includes
+disordered $W$-random graphs, with possibly unbounded graphons. The main result
+concerns a quenched convergence (that is true for almost every realization of
+the random graph) of the empirical measure of the system towards the solution
+of a nonlinear Fokker-Planck PDE with spatial extension, also appearing in
+different contexts, especially in neuroscience. The convergence of the spatial
+profile associated to the diffusions is also considered, and one proves that
+the limit is described in terms of a nonlinear integro-differential equation
+which matches the neural field equation in certain particular cases.
+"
+24953,Pathwise moderate deviations for option pricing,"  We provide a unifying treatment of pathwise moderate deviations for models
+commonly used in financial applications, and for related integrated
+functionals. Suitable scaling allows us to transfer these results into
+small-time, large-time and tail asymptotics for diffusions, as well as for
+option prices and realised variances. In passing, we highlight some intuitive
+relationships between moderate deviations rate functions and their large
+deviations counterparts; these turn out to be useful for numerical purposes, as
+large deviations rate functions are often difficult to compute.
+"
+24954,A shock wave instability induced on a periodically disturbed interface,"  The shock wave instability induced when interacting with a small waviness on
+an interface was investigated analytically and numerically. The perturbation to
+the shock was phenomenologically treated assuming this as the consequence of
+the shock refraction. The instability develops in the form of wave-like
+stretchings into the lower density medium followed with the loss of stability
+in the flow behind it, and eventually evolving into an intense vortex
+structure. The instability mode is aperiodical and unconditional, and either a
+transition to another stable state or continuous development as a secondary
+flow is possible. Among other interesting features are: a similarity law in the
+spatial and temporal evolution of the perturbations with respect to the
+interface curvature; the instability locus independence of the gas density
+distribution thus identifying the interface conditions as the sole triggering
+factor; the role of the density gradient in the instability evolution
+discriminating between qualitatively different outcomes; and the possibility of
+decay via non-viscous dumping mechanisms. The phenomenological connection
+between the shock and the interface stability is discussed.
+"
+24955,The Crowdfunding Game,"  The recent success of crowdfunding for supporting new and innovative products
+has been overwhelming with over 34 Billion Dollars raised in 2015. In many
+crowdfunding platforms, firms set a campaign threshold and contributions are
+collected only if this threshold is reached. During the campaign, consumers are
+uncertain as to the ex-post value of the product, the business model viability,
+and the seller's reliability. Consumer who commit to a contribution therefore
+gambles. This gamble is effected by the campaign's threshold. Contributions to
+campaigns with higher thresholds are collected only if a greater number of
+agents find the offering acceptable. Therefore, high threshold serves as a
+social insurance and thus in high-threshold campaigns, potential contributors
+feel more at ease with contributing. We introduce the crowdfunding game and
+explore the contributor's dilemma in the context of experience goods. We
+discuss equilibrium existence and related social welfare, information
+aggregation and revenue implications.
+"
+24956,Convergence of the MAC scheme for the incompressible Navier-Stokes equations,"  We prove in this paper the convergence of the Marker and cell (MAC) scheme
+for the dis-cretization of the steady-state and unsteady-state incompressible
+Navier-Stokes equations in primitive variables on non-uniform Cartesian grids,
+without any regularity assumption on the solution. A priori estimates on
+solutions to the scheme are proven ; they yield the existence of discrete
+solutions and the compactness of sequences of solutions obtained with family of
+meshes the space step of which tends to zero. We then establish that the limit
+is a weak solution to the continuous problem.
+"
+24957,Dialog Context Language Modeling with Recurrent Neural Networks,"  In this work, we propose contextual language models that incorporate dialog
+level discourse information into language modeling. Previous works on
+contextual language model treat preceding utterances as a sequence of inputs,
+without considering dialog interactions. We design recurrent neural network
+(RNN) based contextual language models that specially track the interactions
+between speakers in a dialog. Experiment results on Switchboard Dialog Act
+Corpus show that the proposed model outperforms conventional single turn based
+RNN language model by 3.3% on perplexity. The proposed models also demonstrate
+advantageous performance over other competitive contextual language models.
+"
+24958,Nonparametric estimation of galaxy cluster's emissivity and point source detection in astrophysics with two lasso penalties,"  Astrophysicists are interested in recovering the 3D gas emissivity of a
+galaxy cluster from a 2D image taken by a telescope. A blurring phenomenon and
+presence of point sources make this inverse problem even harder to solve. The
+current state-of-the-art technique is two step: first identify the location of
+potential point sources, then mask these locations and deproject the data.
+We instead model the data as a Poisson generalized linear model (involving
+blurring, Abel and wavelets operators) regularized by two lasso penalties to
+induce sparse wavelet representation and sparse point sources. The amount of
+sparsity is controlled by two quantile universal thresholds. As a result, our
+method outperforms the existing one.
+"
+24959,Online User Assessment for Minimal Intervention During Task-Based Robotic Assistance,"  We propose a novel criterion for evaluating user input for human-robot
+interfaces for known tasks. We use the mode insertion gradient (MIG)---a tool
+from hybrid control theory---as a filtering criterion that instantaneously
+assesses the impact of user actions on a dynamic system over a time window into
+the future. As a result, the filter is permissive to many chosen strategies,
+minimally engaging, and skill-sensitive---qualities desired when evaluating
+human actions. Through a human study with 28 healthy volunteers, we show that
+the criterion exhibits a low, but significant, negative correlation between
+skill level, as estimated from task-specific measures in unassisted trials, and
+the rate of controller intervention during assistance. Moreover, a MIG-based
+filter can be utilized to create a shared control scheme for training or
+assistance. In the human study, we observe a substantial training effect when
+using a MIG-based filter to perform cart-pendulum inversion, particularly when
+comparing improvement via the RMS error measure. Using simulation of a
+controlled spring-loaded inverted pendulum (SLIP) as a test case, we observe
+that the MIG criterion could be used for assistance to guarantee either task
+completion or safety of a joint human-robot system, while maintaining the
+system's flexibility with respect to user-chosen strategies.
+"
+24960,Statistically induced topological phase transitions in a one-dimensional superlattice anyon-Hubbard model,"  We theoretically investigate topological properties of the one-dimensional
+superlattice anyon-Hubbard model, which can be mapped to a superlattice
+bose-Hubbard model with an occupation-dependent phase factor by fractional
+Jordan-Wigner transformation. The topological anyon-Mott insulator is
+identified by topological invariant and edge modes using exact diagonalization
+and density-matrix renormalization-group algorithm. When only the statistical
+angle is varied and all other parameters are fixed, a statistically induced
+topological phase transition can be realized, which provides new insights into
+the topological phase transitions. What's more, we give an explanation of the
+statistically induced topological phase transition. The topological anyon-Mott
+phases can also appear in a variety of superlattice anyon-Hubbard models.
+"
+24961,Safe Adaptive Switching among Dynamical Movement Primitives: Application to 3D Limit-Cycle Walkers,"  Complex motions for robots are frequently generated by switching among a
+collection of individual movement primitives. We use this approach to formulate
+robot motion plans as sequences of primitives to be executed one after the
+other. When dealing with dynamical movement primitives, besides accomplishing
+the high-level objective, planners must also reason about the effect of the
+plan's execution on the safety of the platform. This task becomes more daunting
+in the presence of disturbances, such as external forces. To alleviate this
+issue, we present a framework that builds on rigorous control-theoretic tools
+to generate safely-executable motion plans for externally excited robotic
+systems. Our framework is illustrated on a 3D limit-cycle gait bipedal robot
+that adapts its walking pattern to persistent external forcing.
+"
+24962,Electronic and magnetic properties of low dimensional system Co2TeO3Cl2,"  The electronic and magnetic properties of transition metal oxyhalide compound
+Co2TeO3Cl2 is investigated using first principle calculations within the
+framework of density functional theory. In order to find underlying spin
+lattice of this compound, various hopping integrals and exchange interactions
+are calculated. The calculations reveal that the dominant inter-chain and
+intra-chain interactions are in ab plane. The exchange path is visualised by
+Wannier function plotting. The nearest neighbour and next nearest neighbour
+exchange interactions are antiferromagnetic, making the system frustrated in
+low dimension. The importance of spin orbit coupling in this compound is also
+investigated. The spin quantization axis is favoured along the crystallographic
+b direction.
+"
+24963,Observing Fermion Pair Instability of the Sachdev-Ye-Kitaev Model on a Quantum Spin Simulator,"  The Sachdev-Ye-Kitaev (SYK) model has become increasingly of great interest
+in studying exotic non-fermi liquid states without quasiparticle excitations,
+holography duality to Einstein gravity, and quantum chaos. However, the
+unnatural form of its Hamiltonian, including its strong randomness and fully
+nonlocal fermion interaction, makes its experimental investigation an
+intractable challenge. A promising solution to overcome this challenge is
+quantum simulation, whose role will be more pronounced particularly in the
+future when more qubits can be handled. We have enough control to demonstrate a
+first step towards quantum simulation of this system. We observed the fermion
+paring instability of the non-Fermi liquid state and the chaotic-nonchaotic
+transition at simulated temperatures, as was predicted by previous theory.
+These results demonstrate the feasibility of experimentally simulating the SYK
+model. It opens a new experimental avenue towards investigating the key
+features of non-Fermi liquid states, as well as the quantum chaotic systems and
+the AdS/CFT duality, thanks to the rich physics of the SYK model.
+"
+24964,Sharing LRU Cache Resources among Content Providers: A Utility-Based Approach,"  In this paper, we consider the problem of allocating cache resources among
+multiple content providers. The cache can be partitioned into slices and each
+partition can be dedicated to a particular content provider, or shared among a
+number of them. It is assumed that each partition employs the LRU policy for
+managing content. We propose utility-driven partitioning, where we associate
+with each content provider a utility that is a function of the hit rate
+observed by the content provider. We consider two scenarios: i)~content
+providers serve disjoint sets of files, ii)~there is some overlap in the
+content served by multiple content providers. In the first case, we prove that
+cache partitioning outperforms cache sharing as cache size and numbers of
+contents served by providers go to infinity. In the second case, It can be
+beneficial to have separate partitions for overlapped content. In the case of
+two providers, it is usually always beneficial to allocate a cache partition to
+serve all overlapped content and separate partitions to serve the
+non-overlapped contents of both providers. We establish conditions when this is
+true asymptotically but also present an example where it is not true
+asymptotically. We develop online algorithms that dynamically adjust partition
+sizes in order to maximize the overall utility and prove that they converge to
+optimal solutions, and through numerical evaluations, we show they are
+effective.
+"
+24965,Perspective: Energy Landscapes for Machine Learning,"  Machine learning techniques are being increasingly used as flexible
+non-linear fitting and prediction tools in the physical sciences. Fitting
+functions that exhibit multiple solutions as local minima can be analysed in
+terms of the corresponding machine learning landscape. Methods to explore and
+visualise molecular potential energy landscapes can be applied to these machine
+learning landscapes to gain new insight into the solution space involved in
+training and the nature of the corresponding predictions. In particular, we can
+define quantities analogous to molecular structure, thermodynamics, and
+kinetics, and relate these emergent properties to the structure of the
+underlying landscape. This Perspective aims to describe these analogies with
+examples from recent applications, and suggest avenues for new
+interdisciplinary research.
+"
+24966,"9.5 Hypotheses on the Informational Structure of Life which are Multi-scale, Human-readable, Provocative, Coherent, Plausible, Falsifiable, and Actionable","  This document has an unorthodox structure to accomplish an unorthodox goal:
+presenting the scaffolding for a zero-parameter Unified Theory of Life inside
+several dozen pages. Ideally, that length is short enough to be understood in a
+few hours, yet complete and principled enough to unfold into solutions to
+civilization's most urgent problem: the accelerating de-calibration of fluid
+human brains by compelling digitized signals. The stakes of understanding
+humans as informational beings are huge. Without drastic changes, crucial human
+functionality will vanish in a generation. Fortunately, the cures are cheap and
+easy if promoted properly. These ten hypotheses re-present in more abstract,
+encapsulated form a Framework published a few months ago in a reputable
+peer-reviewed journal, which concluded that human sensory systems must be
+accorded data of the same quality as the data which already trains algorithmic
+intelligences. While the lack of experiment-grade detail might make these
+hypotheses seem ""unscientific,"" in compensation the breadth of this Framework
+ought to provide the virtues of theory: clarity, simplicity, coherence, and
+self-evidence. Five of the hypotheses span humanity's current problem-space,
+and five a possible solution space. They are: H1.0 Stably evolving
+distributions must balance themselves between narrowing and broadening H2.0
+Stabilization and homeostasis are fragile in multiple ways H3.0 Representing
+spacetime requires micro-timing and mega-assumptions H4.0 When in doubt, ping!
+H5.0 Mediated communication becomes infected with pinging H6.0 If scaling and
+incentives are the problems, then entropy and affection are the solutions H7.0
+""Paleo everything but violence"" provides sensorimotor nutrition H8.0 Humans
+evolved to resonate ecstatically H9.0 Symmetric spinal health syndrome H9.5
+Helping the irresistible force beat the immovable object
+"
+24967,A Stein variational Newton method,"  Stein variational gradient descent (SVGD) was recently proposed as a general
+purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]:
+it minimizes the Kullback-Leibler divergence between the target distribution
+and its approximation by implementing a form of functional gradient descent on
+a reproducing kernel Hilbert space. In this paper, we accelerate and generalize
+the SVGD algorithm by including second-order information, thereby approximating
+a Newton-like iteration in function space. We also show how second-order
+information can lead to more effective choices of kernel. We observe
+significant computational gains over the original SVGD algorithm in multiple
+test cases.
+"
+24968,Towards Automated Network Mitigation Analysis (extended),"  Penetration testing is a well-established practical concept for the
+identification of potentially exploitable security weaknesses and an important
+component of a security audit. Providing a holistic security assessment for
+networks consisting of several hundreds hosts is hardly feasible though without
+some sort of mechanization. Mitigation, prioritizing counter-measures subject
+to a given budget, currently lacks a solid theoretical understanding and is
+hence more art than science. In this work, we propose the first approach for
+conducting comprehensive what-if analyses in order to reason about mitigation
+in a conceptually well-founded manner. To evaluate and compare mitigation
+strategies, we use simulated penetration testing, i.e., automated
+attack-finding, based on a network model to which a subset of a given set of
+mitigation actions, e.g., changes to the network topology, system updates,
+configuration changes etc. is applied. Using Stackelberg planning, we determine
+optimal combinations that minimize the maximal attacker success (similar to a
+Stackelberg game), and thus provide a well-founded basis for a holistic
+mitigation strategy. We show that these Stackelberg planning models can largely
+be derived from network scan, public vulnerability databases and manual
+inspection with various degrees of automation and detail, and we simulate
+mitigation analysis on networks of different size and vulnerability.
+"
+24969,Improved Coresets for Kernel Density Estimates,"  We study the construction of coresets for kernel density estimates. That is
+we show how to approximate the kernel density estimate described by a large
+point set with another kernel density estimate with a much smaller point set.
+For characteristic kernels (including Gaussian and Laplace kernels), our
+approximation preserves the $L_\infty$ error between kernel density estimates
+within error $\epsilon$, with coreset size $2/\epsilon^2$, but no other aspects
+of the data, including the dimension, the diameter of the point set, or the
+bandwidth of the kernel common to other approximations. When the dimension is
+unrestricted, we show this bound is tight for these kernels as well as a much
+broader set.
+This work provides a careful analysis of the iterative Frank-Wolfe algorithm
+adapted to this context, an algorithm called \emph{kernel herding}. This
+analysis unites a broad line of work that spans statistics, machine learning,
+and geometry.
+When the dimension $d$ is constant, we demonstrate much tighter bounds on the
+size of the coreset specifically for Gaussian kernels, showing that it is
+bounded by the size of the coreset for axis-aligned rectangles. Currently the
+best known constructive bound is $O(\frac{1}{\epsilon} \log^d
+\frac{1}{\epsilon})$, and non-constructively, this can be improved by
+$\sqrt{\log \frac{1}{\epsilon}}$. This improves the best constant dimension
+bounds polynomially for $d \geq 3$.
+"
+24970,Improved Linear Embeddings via Lagrange Duality,"  Near isometric orthogonal embeddings to lower dimensions are a fundamental
+tool in data science and machine learning. In this paper, we present the
+construction of such embeddings that minimizes the maximum distortion for a
+given set of points. We formulate the problem as a non convex constrained
+optimization problem. We first construct a primal relaxation and then use the
+theory of Lagrange duality to create dual relaxation. We also suggest a
+polynomial time algorithm based on the theory of convex optimization to solve
+the dual relaxation provably. We provide a theoretical upper bound on the
+approximation guarantees for our algorithm, which depends only on the spectral
+properties of the dataset. We experimentally demonstrate the superiority of our
+algorithm compared to baselines in terms of the scalability and the ability to
+achieve lower distortion.
+"
+24971,Algorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets,"  To select the best algorithm for a new problem is an expensive and difficult
+task. However, there are automatic solutions to address this problem: using
+Metalearning, which takes advantage of problem characteristics (i.e.
+metafeatures), one is able to predict the relative performance of algorithms.
+In the Collaborative Filtering scope, recent works have proposed diverse
+metafeatures describing several dimensions of this problem. Despite interesting
+and effective findings, it is still unknown whether these are the most
+effective metafeatures. Hence, this work proposes a new set of graph
+metafeatures, which approach the Collaborative Filtering problem from a Graph
+Theory perspective. Furthermore, in order to understand whether metafeatures
+from multiple dimensions are a better fit, we investigate the effects of
+comprehensive metafeatures. These metafeatures are a selection of the best
+metafeatures from all existing Collaborative Filtering metafeatures. The impact
+of the most representative metafeatures is investigated in a controlled
+experimental setup. Another contribution we present is the use of a
+Pareto-Efficient ranking procedure to create multicriteria metatargets. These
+new rankings of algorithms, which take into account multiple evaluation
+measures, allow to explore the algorithm selection problem in a fairer and more
+detailed way. According to the experimental results, the graph metafeatures are
+a good alternative to related work metafeatures. However, the results have
+shown that the feature selection procedure used to create the comprehensive
+metafeatures is is not effective, since there is no gain in predictive
+performance. Finally, an extensive metaknowledge analysis was conducted to
+identify the most influential metafeatures.
+"
+24972,One-Body Density Matrix and Momentum Distribution of Strongly Interacting One-Dimensional Spinor Quantum Gases,"  The one-body density matrix (OBDM) of a strongly interacting spinor quantum
+gas in one dimension can be written as a summation of products of spatial and
+spin parts. We find that there is a remarkable connection between the spatial
+part and the OBDM of a spinless hard-core anyon gas. This connection allows us
+to efficiently calculate the OBDM of the spinor system with particle numbers
+much larger than what was previously possible. Given the OBDM, we can easily
+calculate the momentum distribution of the spinor system, which again is
+related to the momentum distribution of the hard-core ayone gas.
+"
+24973,Perfectoid multiplier/test ideals in regular rings and bounds on symbolic powers,"  Using perfectoid algebras, we introduce a mixed characteristic analog of the
+multiplier ideal, respectively test ideal, from characteristic zero,
+respectively $p > 0$, in the case of a regular ambient ring. We prove several
+properties about this ideal such as subadditivity. We then use these techniques
+to derive a uniform bound on the growth of symbolic powers of radical ideals in
+all excellent regular rings. The analogous result was shown in equal
+characteristic by Ein-Lazarsfeld-Smith and Hochster-Huneke.
+"
+24974,Correction of magnetic optics and beam trajectory using LOCO based algorithm with expanded experimental data sets,"  Precise beam based measurement and correction of magnetic optics is essential
+for the successful operation of accelerators. The LOCO algorithm is a proven
+and reliable tool, which in some situations can be improved by using a broader
+class of experimental data. The standard data sets for LOCO include the closed
+orbit responses to dipole corrector variation, dispersion, and betatron tunes.
+This paper discusses the benefits from augmenting the data with four additional
+classes of experimental data: the beam shape measured with beam profile
+monitors; responses of closed orbit bumps to focusing field variations;
+betatron tune responses to focusing field variations; BPM-to-BPM betatron phase
+advances and beta functions in BPMs from turn-by-turn coordinates of kicked
+beam. All of the described features were implemented in the Sixdsimulation
+software that was used to correct the optics of the VEPP-2000 collider, the
+VEPP-5 injector booster ring, and the FAST linac.
+"
+24975,Mobile bound states of Rydberg excitations in a lattice,"  Spin lattice models play central role in the studies of quantum magnetism and
+non-equilibrium dynamics of spin excitations -- magnons. We show that a spin
+lattice with strong nearest-neighbor interactions and tunable long-range
+hopping of excitations can be realized by a regular array of laser driven
+atoms, with an excited Rydberg state representing the spin-up state and a
+Rydberg-dressed ground state corresponding to the spin-down state. We find
+exotic interaction-bound states of magnons that propagate in the lattice via
+the combination of resonant two-site hopping and non-resonant second-order
+hopping processes. Arrays of trapped Rydberg-dressed atoms can thus serve as a
+flexible platform to simulate and study fundamental few-body dynamics in spin
+lattices.
+"
+24976,Brownian motion near an elastic cell membrane: A theoretical study,"  Elastic confinements are an important component of many biological systems
+and dictate the transport properties of suspended particles under flow. In this
+chapter, we review the Brownian motion of a particle moving in the vicinity of
+a living cell whose membrane is endowed with a resistance towards shear and
+bending. The analytical calculations proceed through the computation of the
+frequency-dependent mobility functions and the application of the
+fluctuation-dissipation theorem. Elastic interfaces endow the system with
+memory effects that lead to a long-lived anomalous subdiffusive regime of
+nearby particles. In the steady limit, the diffusional behavior approaches that
+near a no-slip hard wall. The analytical predictions are validated and
+supplemented with boundary-integral simulations.
+"
+24977,Using Deep Learning and Satellite Imagery to Quantify the Impact of the Built Environment on Neighborhood Crime Rates,"  The built environment has been postulated to have an impact on neighborhood
+crime rates, however, measures of the built environment can be subjective and
+differ across studies leading to varying observations on its association with
+crime rates. Here, we illustrate an accurate and straightforward approach to
+quantify the impact of the built environment on neighborhood crime rates from
+high-resolution satellite imagery. Using geo-referenced crime reports and
+satellite images for three United States cities, we demonstrate how image
+features consistently identified using a convolutional neural network can
+explain up to 82% of the variation in neighborhood crime rates. Our results
+suggest the built environment is a strong predictor of crime rates, and this
+can lead to structural interventions shown to reduce crime incidence in urban
+settings.
+"
+24978,Tunable Half-metallic Magnetism in Atom-thin Holey Two-dimensional C$_2$N Monolayer,"  Exploring two-dimensional (2D) materials with magnetic ordering is a focus of
+current research. It remains a challenge to achieve tunable magnetism in a
+material of one-atom-thickness without introducing extrinsic magnetic atoms or
+defects. Here, based on first-principles calculations, we propose that tunable
+ferromagnetism can be realized in the recently synthesized holey 2D C$_2$N
+($h$2D-C$_2$N) monolayer via purely electron doping that can be readily
+achieved by gating. We show that owing to the prominent van Hove singularity in
+the band structure, the material exhibits ferromagnetism even at a small doping
+level. Remarkably, over a wide doping range of 4$\times$10$^{13}$/cm$^2$ to
+8$\times$10$^{13}$/cm$^2$, the system becomes half-metallic, with carriers
+fully spin-polarized. The estimated Curie temperature can be up to 320 K.
+Besides gating, we find that the magnetism can also be effectively tuned by
+lattice strain. Our result identifies $h$2D-C$_2$N as the first material with
+single-atom-thickness that can host gate-tunable room-temperature half-metallic
+magnetism, suggesting it as a promising platform to explore nanoscale magnetism
+and flexible spintronic devices.
+"
+24979,HySIA: Tool for Simulating and Monitoring Hybrid Automata Based on Interval Analysis,"  We present HySIA: a reliable runtime verification tool for nonlinear hybrid
+automata (HA) and signal temporal logic (STL) properties. HySIA simulates an HA
+with interval analysis techniques so that a trajectory is enclosed sharply
+within a set of intervals. Then, HySIA computes whether the simulated
+trajectory satisfies a given STL property; the computation is performed again
+with interval analysis to achieve reliability. Simulation and verification
+using HySIA are demonstrated through several example HA and STL formulas.
+"
+24980,Tight Inefficiency Bounds for Perception-Parameterized Affine Congestion Games,"  Congestion games constitute an important class of non-cooperative games which
+was introduced by Rosenthal in 1973. In recent years, several extensions of
+these games were proposed to incorporate aspects that are not captured by the
+standard model. Examples of such extensions include the incorporation of risk
+sensitive players, the modeling of altruistic player behavior and the
+imposition of taxes on the resources. These extensions were studied intensively
+with the goal to obtain a precise understanding of the inefficiency of
+equilibria of these games. In this paper, we introduce a new model of
+congestion games that captures these extensions (and additional ones) in a
+unifying way. The key idea here is to parameterize both the perceived cost of
+each player and the social cost function of the system designer. Intuitively,
+each player perceives the load induced by the other players by an extent of
+{\rho}, while the system designer estimates that each player perceives the load
+of all others by an extent of {\sigma}. The above mentioned extensions reduce
+to special cases of our model by choosing the parameters {\rho} and {\sigma}
+accordingly. As in most related works, we concentrate on congestion games with
+affine latency functions here. Despite the fact that we deal with a more
+general class of congestion games, we manage to derive tight bounds on the
+price of anarchy and the price of stability for a large range of pa- rameters.
+Our bounds provide a complete picture of the inefficiency of equilibria for
+these perception-parameterized congestion games. As a result, we obtain tight
+bounds on the price of anarchy and the price of stability for the above
+mentioned extensions. Our results also reveal how one should ""design"" the cost
+functions of the players in order to reduce the price of anar- chy.
+"
+24981,How does the shape of the stellar spectrum affect the Raman scattering features in the albedo of exoplanets?,"  Diagnostic potential of the spectral signatures of Raman scattering imprinted
+in planetary albedo spectra at short optical wavelengths has been demonstrated
+in research on Solar System planets and has recently been proposed as a probe
+of exoplanet atmospheres, complementary to albedo studies at longer
+wavelengths. Spectral features caused by Raman scattering offer insight into
+the properties of planetary atmospheres, such as the atmospheric depth,
+composition, and temperature, as well as the possibility of detecting and
+spectroscopically identifying spectrally inactive species, such as H$_2$ and
+N$_2$, in the visible wavelength range. Raman albedo features, however, depend
+on both the properties of the atmosphere and the shape of the incident stellar
+spectrum. Identical planetary atmospheres can produce very different albedo
+spectra depending on the spectral properties of the host star. Here we present
+a set of geometric albedo spectra calculated for atmospheres with H$_2$/He,
+N$_2$, and CO$_2$ composition, irradiated by different stellar types ranging
+from late A to late K stars. Prominent albedo features caused by Raman
+scattering appear at different wavelengths for different types of host stars.
+We investigate how absorption due to alkali elements, sodium and potassium, may
+affect the intensity of Raman features, and discuss the preferred observing
+strategies for detecting Raman features in future observations.
+"
+24982,Gauge-Transformation Properties of Cosmological Observables and its Application to the Light-Cone Average,"  Theoretical descriptions of observable quantities in cosmological
+perturbation theory should be independent of coordinate systems. This statement
+is often referred to as gauge-invariance of observable quantities, and the
+sanity of their theoretical description is verified by checking its
+gauge-invariance. We argue that cosmological observables are invariant scalars
+under diffeomorphisms and as a consequence their theoretical description is
+gauge-invariant, only at linear order in perturbations. Beyond linear order,
+they are usually not gauge-invariant, and we provide the general law for the
+gauge-transformation that the perturbation part of an observable does obey. We
+apply this finding to derive the second-order expression for the observational
+light-cone average in cosmology and demonstrate that our expression is indeed
+invariant under diffeomorphisms.
+"
+24983,Nonstandard Convergence Gives Bounds on Jumps,"  If we know that some kind of sequence always converges, we can ask how
+quickly and how uniformly it converges. Many convergent sequences converge
+non-uniformly and, relatedly, have no computable rate of convergence. However
+proof-theoretic ideas often guarantee the existence of a uniform ""meta-stable""
+rate of convergence.
+We show that obtaining a stronger bound---a uniform bound on the number of
+jumps the sequence makes---is equivalent to being able to strengthen
+convergence to occur in the nonstandard numbers. We use this to obtain bounds
+on the number of jumps in nonconventional ergodic averages.
+"
+24984,Emittance growth in the DARHT Axis-II Downstream Transport,"  Using a particle-in-cell (PIC) code, we investigated the possibilities for
+emittance growth through the quadrupole magnets of the system used to transport
+the high-current electron beam from an induction accelerator to the
+bremsstrahlung converter target used for flash radiography. We found that even
+highly mismatched beams exhibited little emittance growth (< 6%), which we
+attribute to softening of their initial hard edge current distributions. We
+also used this PIC code to evaluate the accuracy of emittance measurements
+using a solenoid focal scan following the quadrupole magnets. If the beam is
+round after the solenoids, the simulations indicate that the measurement is
+highly accurate, but it is substantially inaccurate for elliptical beams.
+"
+24985,Towards operational natural language,"  The multiplicity of software projects' stakeholders and activities leads to
+the multiplicity of software specification views and thus creates the need to
+establish mutual consistency between them. The process of establishing such
+consistency is error-prone and requires adequate tool support. The present
+article introduces specogramming - an approach that treats a modern
+object-oriented integrated development environment as a word processor. The
+approach turns the process of documenting initial specifications into a
+simplified form of programming and turns structured-natural-language
+specifications into runnable programs that yield multiple
+consistent-by-construction views, one of which is structured natural language.
+"
+24986,BitNet: Bit-Regularized Deep Neural Networks,"  We present a novel optimization strategy for training neural networks which
+we call ""BitNet"". The parameters of neural networks are usually unconstrained
+and have a dynamic range dispersed over all real values. Our key idea is to
+limit the expressive power of the network by dynamically controlling the range
+and set of values that the parameters can take. We formulate this idea using a
+novel end-to-end approach that circumvents the discrete parameter space by
+optimizing a relaxed continuous and differentiable upper bound of the typical
+classification loss function. The approach can be interpreted as a
+regularization inspired by the Minimum Description Length (MDL) principle. For
+each layer of the network, our approach optimizes real-valued translation and
+scaling factors and arbitrary precision integer-valued parameters (weights). We
+empirically compare BitNet to an equivalent unregularized model on the MNIST
+and CIFAR-10 datasets. We show that BitNet converges faster to a superior
+quality solution. Additionally, the resulting model has significant savings in
+memory due to the use of integer-valued parameters.
+"
+24987,"CycleGAN, a Master of Steganography","  CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a
+transformation between two image distributions. In a series of experiments, we
+demonstrate an intriguing property of the model: CycleGAN learns to ""hide""
+information about a source image into the images it generates in a nearly
+imperceptible, high-frequency signal. This trick ensures that the generator can
+recover the original sample and thus satisfy the cyclic consistency
+requirement, while the generated image remains realistic. We connect this
+phenomenon with adversarial attacks by viewing CycleGAN's training procedure as
+training a generator of adversarial examples and demonstrate that the cyclic
+consistency loss causes CycleGAN to be especially vulnerable to adversarial
+attacks.
+"
+24988,Bayesian Hierarchical Models with Conjugate Full-Conditional Distributions for Dependent Data from the Natural Exponential Family,"  We introduce a Bayesian approach for analyzing (possibly) high-dimensional
+dependent data that are distributed according to a member from the natural
+exponential family of distributions. This problem requires extensive
+methodological advancements, as jointly modeling high-dimensional dependent
+data leads to the so-called ""big n problem."" The computational complexity of
+the ""big n problem"" is further exacerbated when allowing for non-Gaussian data
+models, as is the case here. Thus, we develop new computationally efficient
+distribution theory for this setting. In particular, we introduce something we
+call the ""conjugate multivariate distribution,"" which is motivated by the
+univariate distribution introduced in Diaconis and Ylvisaker (1979).
+Furthermore, we provide substantial theoretical and methodological development
+including: results regarding conditional distributions, an asymptotic
+relationship with the multivariate normal distribution, conjugate prior
+distributions, and full-conditional distributions for a Gibbs sampler. The
+results in this manuscript are extremely general, and can be adapted to many
+different settings. We demonstrate the proposed methodology through simulated
+examples and three applications based on an epidemiology dataset, a federal
+statistics dataset, and an environmental dataset, respectively.
+"
+24989,Fractal Uncertainty Principle with Explicit Exponent,"  We prove an explicit formula for the dependence of the exponent in the
+fractal uncertainty principle of Bourgain-Dyatlov on the dimension and on the
+regularity constant for the regular set. In particular, this implies an
+explicit essential spectral gap for convex co-compact hyperbolic surfaces when
+the Hausdorff dimension of the limit set is close to 1.
+"
+24990,An Alon-Boppana Type Bound for Weighted Graphs and Lowerbounds for Spectral Sparsification,"  We prove the following Alon-Boppana type theorem for general (not necessarily
+regular) weighted graphs: if $G$ is an $n$-node weighted undirected graph of
+average combinatorial degree $d$ (that is, $G$ has $dn/2$ edges) and girth $g>
+2d^{1/8}+1$, and if $\lambda_1 \leq \lambda_2 \leq \cdots \lambda_n$ are the
+eigenvalues of the (non-normalized) Laplacian of $G$, then \[ \frac
+{\lambda_n}{\lambda_2} \geq 1 + \frac 4{\sqrt d} - O \left( \frac 1{d^{\frac
+58} }\right) \] (The Alon-Boppana theorem implies that if $G$ is unweighted and
+$d$-regular, then $\frac {\lambda_n}{\lambda_2} \geq 1 + \frac 4{\sqrt d} -
+O\left( \frac 1 d \right)$ if the diameter is at least $d^{1.5}$.)
+Our result implies a lower bound for spectral sparsifiers. A graph $H$ is a
+spectral $\epsilon$-sparsifier of a graph $G$ if \[ L(G) \preceq L(H) \preceq
+(1+\epsilon) L(G) \] where $L(G)$ is the Laplacian matrix of $G$ and $L(H)$ is
+the Laplacian matrix of $H$. Batson, Spielman and Srivastava proved that for
+every $G$ there is an $\epsilon$-sparsifier $H$ of average degree $d$ where
+$\epsilon \approx \frac {4\sqrt 2}{\sqrt d}$ and the edges of $H$ are a
+(weighted) subset of the edges of $G$. Batson, Spielman and Srivastava also
+show that the bound on $\epsilon$ cannot be reduced below $\approx \frac
+2{\sqrt d}$ when $G$ is a clique; our Alon-Boppana-type result implies that
+$\epsilon$ cannot be reduced below $\approx \frac 4{\sqrt d}$ when $G$ comes
+from a family of expanders of super-constant degree and super-constant girth.
+The method of Batson, Spielman and Srivastava proves a more general result,
+about sparsifying sums of rank-one matrices, and their method applies to an
+""online"" setting. We show that for the online matrix setting the $4\sqrt 2 /
+\sqrt d$ bound is tight, up to lower order terms.
+"
+24991,Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video,"  We present an automatic method to describe clinically useful information
+about scanning, and to guide image interpretation in ultrasound (US) videos of
+the fetal heart. Our method is able to jointly predict the visibility, viewing
+plane, location and orientation of the fetal heart at the frame level. The
+contributions of the paper are three-fold: (i) a convolutional neural network
+architecture is developed for a multi-task prediction, which is computed by
+sliding a 3x3 window spatially through convolutional maps. (ii) an anchor
+mechanism and Intersection over Union (IoU) loss are applied for improving
+localization accuracy. (iii) a recurrent architecture is designed to
+recursively compute regional convolutional features temporally over sequential
+frames, allowing each prediction to be conditioned on the whole video. This
+results in a spatial-temporal model that precisely describes detailed heart
+parameters in challenging US videos. We report results on a real-world clinical
+dataset, where our method achieves performance on par with expert annotations.
+"
+24992,Anomaly Detection in Wireless Sensor Networks,"  Wireless sensor networks usually comprise a large number of sensors
+monitoring changes in variables. These changes in variables represent changes
+in physical quantities. The changes can occur for various reasons; these
+reasons are highlighted in this work. Outliers are unusual measurements.
+Outliers are important; they are information-bearing occurrences. This work
+seeks to identify them based on an approach presented in [1]. A critical review
+of most previous works in this area has been presented in [2], and few more are
+considered here just to set the stage. The main work can be described as this;
+given a set of measurements from sensors that represent a normal situation, [1]
+proceeds by first estimating the probability density function (pdf) of the set
+using a data-split approach, then estimate the entropy of the set using the
+arithmetic mean as an approximation for the expectation. The increase in
+entropy that occurs when strange data is recorded is used to detect unusual
+measurements in the test set depending on the desired confidence interval or
+false alarm rate. The results presented in [1] have been confirmed for
+different test signals such as the Gaussian, Beta, in one dimension and beta in
+two dimensions, and a beta and uniform mixture distribution in two dimensions.
+Finally, the method was confirmed on real data and the results are presented.
+The major drawbacks of [1] were identified, and a method that seeks to mitigate
+this using the Bhattacharyya distance is presented. This method detects more
+subtle anomalies, especially the type that would pass as normal in [1].
+Finally, recommendations for future research are presented: the subject of
+interpretability, especially for subtle measurements, being the most elusive as
+of today.
+"
+24993,Point cloud discretization of Fokker-Planck operators for committor functions,"  The committor functions provide useful information to the understanding of
+transitions of a stochastic system between disjoint regions in phase space. In
+this work, we develop a point cloud discretization for Fokker-Planck operators
+to numerically calculate the committor function, with the assumption that the
+transition occurs on an intrinsically low-dimensional manifold in the ambient
+potentially high dimensional configurational space of the stochastic system.
+Numerical examples on model systems validate the effectiveness of the proposed
+method.
+"
+24994,Improved Space-efficient Linear Time Algorithms for Some Classical Graph Problems,"  This short note provides space-efficient linear time algorithms for computing
+bridges, topological sorting, and strongly connected components improving on
+several recent results of Elmasry et al. [STACS'15], Banerjee et al.
+[COCOON'16] and Chakraborty et al. [ISAAC'16]. En route, we also provide
+another DFS implementation with weaker input graph representation assumption
+without compromising on the time and space bounds of the earlier results of
+Banerjee et al. [COCOON'16] and Kammer et al. [MFCS'16].
+"
+24995,The Search for Higher $T_c$ in Houston,"  It is a great pleasure to be invited to join the chorus on this auspicious
+occasion to celebrate Professor K. Alex Mueller's 90th birthday by Professors
+Annette Bussman-Holder, Hugo Keller, and Antonio Bianconi. As a student in high
+temperature superconductivity, I am forever grateful to Professor Alex Mueller
+and Dr. Georg Bednorz ""for their important breakthrough in the discovery of
+superconductivity in the ceramic materials"" in 1986 as described in the
+citation of their 1987 Nobel Prize in Physics. It is this breakthrough
+discovery that has ushered in the explosion of research activities in high
+temperature superconductivity (HTS) and has provided immense excitement in HTS
+science and technology in the ensuing decades till now. Alex has not been
+resting on his laurels and has continued to search for the origin of the
+unusual high temperature superconductivity in cuprates.
+"
+24996,InterpNET: Neural Introspection for Interpretable Deep Learning,"  Humans are able to explain their reasoning. On the contrary, deep neural
+networks are not. This paper attempts to bridge this gap by introducing a new
+way to design interpretable neural networks for classification, inspired by
+physiological evidence of the human visual system's inner-workings. This paper
+proposes a neural network design paradigm, termed InterpNET, which can be
+combined with any existing classification architecture to generate natural
+language explanations of the classifications. The success of the module relies
+on the assumption that the network's computation and reasoning is represented
+in its internal layer activations. While in principle InterpNET could be
+applied to any existing classification architecture, it is evaluated via an
+image classification and explanation task. Experiments on a CUB bird
+classification and explanation dataset show qualitatively and quantitatively
+that the model is able to generate high-quality explanations. While the current
+state-of-the-art METEOR score on this dataset is 29.2, InterpNET achieves a
+much higher METEOR score of 37.9.
+"
+24997,Wall's finiteness obstruction,"  The purpose of this note is to give a self contained description of Walls
+finiteness obstruction.
+"
+24998,Physical aspects of self-organization processes in composites. 1. Simulation of percolation clusters of phases and of inner boundaries,"  A range of percolation models of cluster systems of composites is discussed.
+In the models the parameters of the clusters of a substance and inner
+boundaries were obtained by the Monte Carlo method, and the possibility of
+affecting the properties of composite materials at the early stages of
+structure formation was studied.
+"
+24999,Chern-Ricci flows on noncompact complex manifolds,"  In this work, we obtain existence criteria for Chern-Ricci flows on
+noncompact manifolds. We generalize a result by Tossati-Wienkove on Chern-Ricci
+flows to noncompact manifolds and at the same time generalize a result for
+Kahler-Ricci flows by Lott-Zhang to Chern-Ricci flows. Using the existence
+results, we prove that any complete noncollapsed Kahler metric with nonnegative
+bisectional curvature on a noncompact complex manifold can be deformed to a
+complete Kahler metric with nonnegative and bounded bisectional curvature which
+will have maximal volume growth if the initial metric has maximal volume.
+Combining this result with the result of Chau-Tam, we give another proof that a
+complete noncompact Kahler manifold with nonnegative bisectional curvature (not
+necessarily bounded) and maximal volume growth is biholomorphic to the complex
+Euclidean space. This last result has already been proved by Gang Liu recently
+using other methods. This last result is partial confirmation of a
+uniformization conjecture of Yau.
+"
+25000,Disorder chaos in some diluted spin glass models,"  We prove disorder chaos at zero temperature for three types of diluted models
+with large connectivity parameter: $K$-spin antiferromagnetic Ising model for
+even $K\geq 2$, $K$-spin spin glass model for even $K\geq 2$, and random
+$K$-sat model for all $K\geq 2$. We show that modifying even a small proportion
+of clauses results in near maximizers of the original and modified Hamiltonians
+being nearly orthogonal to each other with high probability. We use a standard
+technique of approximating diluted models by appropriate fully connected models
+and then apply disorder chaos results in this setting, which include both
+previously known results as well as new examples motivated by the random
+$K$-sat model.
+"
+25001,The second law of thermodynamics at the microscopic scale,"  In quantum statistical mechanics, equilibrium states have been shown to be
+the typical states for a system that is entangled with its environment,
+suggesting a possible identification between thermodynamic and von Neumann
+entropies. In this paper, we investigate how the relaxation toward equilibrium
+is made possible through interactions that do not lead to significant exchange
+of energy, and argue for the validity of the second law of thermodynamics at
+the microscopic scale.
+"
+25002,Discovery of 16 New z ~ 5.5 Quasars : Filling in the Redshift Gap of Quasar Color Selection,"  We present initial results from the first systematic survey of luminous
+$z\sim 5.5$ quasars. Quasars at $z \sim$ 5.5, the post-reionization epoch, are
+crucial tools to explore the evolution of intergalactic medium, quasar
+evolution and the early super-massive black hole growth. However, it has been
+very challenging to select quasars at redshifts 5.3 $\le z \le$ 5.7 using
+conventional color selections, due to their similar optical colors to late-type
+stars, especially M dwarfs, resulting in a glaring redshift gap in quasar
+redshift distributions. We develop a new selection technique for $z \sim$ 5.5
+quasars based on optical, near-IR and mid-IR photometric data from Sloan
+Digital Sky Survey (SDSS), UKIRT InfraRed Deep Sky Surveys - Large Area Survey
+(ULAS), VISTA Hemisphere Survey (VHS) and Wide field Infrared Survey Explorer
+(WISE). From our pilot observations in SDSS-ULAS/VHS area, we have discovered
+15 new quasars at 5.3 $\le z \le$ 5.7 and 6 new lower redshift quasars, with
+SDSS z band magnitude brighter than 20.5. Including other two $z \sim$ 5.5
+quasars already published in our previous work, we now construct an uniform
+quasar sample at 5.3 $\le z \le$ 5.7 with 17 quasars in a $\sim$ 4800 square
+degree survey area. For further application in a larger survey area, we apply
+our selection pipeline to do a test selection by using the new wide field J
+band photometric data from a preliminary version of the UKIRT Hemisphere Survey
+(UHS). We successfully discover the first UHS selected $z \sim$ 5.5 quasar.
+"
+25003,Least Squares Estimation in a Single Index Model with Convex Lipschitz link,"  We consider estimation and inference in a single index regression model with
+an unknown convex link function. We propose a Lipschitz constrained least
+squares estimator (LLSE) for both the parametric and the nonparametric
+components given independent and identically distributed observations. We prove
+the consistency and find the rates of convergence of the LLSE when the errors
+are assumed to have only $q \ge 2$ moments and are allowed to depend on the
+covariates. In fact, we prove a general theorem which can be used to find the
+rates of convergence of LSEs in a variety of nonparametric/semiparametric
+regression problems under the same assumptions on the errors. Moreover when
+$q\ge 5$, we establish $n^{-1/2}$-rate of convergence and asymptotic normality
+of the estimator of the parametric component. Moreover the LLSE is proved to be
+semiparametrically efficient if the errors happen to be homoscedastic.
+Furthermore, we develop the R package \texttt{simest} to compute the proposed
+estimator.
+"
+25004,Conditional Network Embeddings,"  Network Embeddings (NEs) map the nodes of a given network into
+$d$-dimensional Euclidean space $\mathbb{R}^d$. Ideally, this mapping is such
+that `similar' nodes are mapped onto nearby points, such that the NE can be
+used for purposes such as link prediction (if `similar' means being `more
+likely to be connected') or classification (if `similar' means `being more
+likely to have the same label'). In recent years various methods for NE have
+been introduced, all following a similar strategy: defining a notion of
+similarity between nodes (typically some distance measure within the network),
+a distance measure in the embedding space, and a loss function that penalizes
+large distances for similar nodes and small distances for dissimilar nodes.
+A difficulty faced by existing methods is that certain networks are
+fundamentally hard to embed due to their structural properties: (approximate)
+multipartiteness, certain degree distributions, assortativity, etc. To overcome
+this, we introduce a conceptual innovation to the NE literature and propose to
+create \emph{Conditional Network Embeddings} (CNEs); embeddings that maximally
+add information with respect to given structural properties (e.g. node degrees,
+block densities, etc.). We use a simple Bayesian approach to achieve this, and
+propose a block stochastic gradient descent algorithm for fitting it
+efficiently. We demonstrate that CNEs are superior for link prediction and
+multi-label classification when compared to state-of-the-art methods, and this
+without adding significant mathematical or computational complexity. Finally,
+we illustrate the potential of CNE for network visualization.
+"
+25005,F-GAMMA: Variability Doppler factors of blazars from multiwavelength monitoring,"  Recent population studies have shown that the variability Doppler factors can
+adequately describe blazars as a population. We use the flux density variations
+found within the extensive radio multi-wavelength datasets of the F-GAMMA
+program, a total of 10 frequencies from 2.64 up to 142.33 GHz, in order to
+estimate the variability Doppler factors for 58 $\gamma$-ray bright sources,
+for 20 of which no variability Doppler factor has been estimated before. We
+employ specifically designed algorithms in order to obtain a model for each
+flare at each frequency. We then identify each event and track its evolution
+through all the available frequencies for each source. This approach allows us
+to distinguish significant events producing flares from stochastic variability
+in blazar jets. It also allows us to effectively constrain the variability
+brightness temperature and hence the variability Doppler factor as well as
+provide error estimates. Our method can produce the most accurate (16\% error
+on average) estimates in the literature to date.
+"
+25006,Design Automation for Obfuscated Circuits with Multiple Viable Functions,"  Gate camouflaging is a technique for obfuscating the function of a circuit
+against reverse engineering attacks. However, if an adversary has pre-existing
+knowledge about the set of functions that are viable for an application, random
+camouflaging of gates will not obfuscate the function well. In this case, the
+adversary can target their search, and only needs to decide whether each of the
+viable functions could be implemented by the circuit.
+In this work, we propose a method for using camouflaged cells to obfuscate a
+design that has a known set of viable functions. The circuit produced by this
+method ensures that an adversary will not be able to rule out any viable
+functions unless she is able to uncover the gate functions of the camouflaged
+cells. Our method comprises iterated synthesis within an overall optimization
+loop to combine the viable functions, followed by technology mapping to deploy
+camouflaged cells while maintaining the plausibility of all viable functions.
+We evaluate our technique on cryptographic S-box functions and show that,
+relative to a baseline approach, it achieves up to 38\% area reduction in
+PRESENT-style S-Boxes and 48\% in DES S-boxes.
+"
+25007,Spinning and tumbling of micron-sized triangles in a micro-channel shear flow,"  We report on measurements of the angular dynamics of micron-sized
+equilaterally triangular platelets suspended in a micro-channel shear flow. Our
+measurements confirm that such particles spin and tumble like a spheroid in a
+simple shear. Since the triangle has corners we can observe the spinning
+directly. In general the spinning frequency is different from the tumbling
+frequency, and the spinning is affected by tumbling. This gives rise to
+doubly-periodic angular dynamics.
+"
+25008,Theoretical restrictions on longest implicit timescales in Markov state models of biomolecular dynamics,"  Markov state models (MSMs) have been widely used to analyze computer
+simulations of various biomolecular systems. They can capture conformational
+transitions much slower than an average or maximal length of a single molecular
+dynamics (MD) trajectory from the set of trajectories used to build the MSM. A
+rule of thumb claiming that the slowest implicit timescale captured by an MSM
+should be comparable by the order of magnitude to the aggregate duration of all
+MD trajectories used to build this MSM has been known in the field. However,
+this rule have never been formally proved. In this work, we present analytical
+results for the slowest timescale in several types of MSMs, supporting the
+above rule. We conclude that the slowest implicit timescale equals the product
+of the aggregate sampling and four factors that quantify: (1) how much
+statistics on the conformational transitions corresponding to the longest
+implicit timescale is available, (2) how good the sampling of the destination
+Markov state is, (3) the gain in statistics from using a sliding window for
+counting transitions between Markov states, and (4) a bias in the estimate of
+the implicit timescale arising from finite sampling of the conformational
+transitions. We demonstrate that in many practically important cases all these
+four factors are on the order of unity, and we analyze possible scenarios that
+could lead to their significant deviation from unity. Overall, we provide for
+the first time analytical results on the slowest timescales captured by MSMs.
+These results can guide further practical applications of MSMs to biomolecular
+dynamics and allow for higher computational efficiency of simulations.
+"
+25009,Design and development of a software system for swarm intelligence based research studies,"  This paper introduce a software system including widely-used Swarm
+Intelligence algorithms or approaches to be used for the related scientific
+research studies associated with the subject area. The programmatic
+infrastructure of the system allows working on a fast, easy-to-use, interactive
+platform to perform Swarm Intelligence based studies in a more effective,
+efficient and accurate way. In this sense, the system employs all of the
+necessary controls for the algorithms and it ensures an interactive platform on
+which computer users can perform studies on a wide spectrum of solution
+approaches associated with simple and also more advanced problems.
+"
+25010,Existence of solutions to fractional differential equations with three-point boundary conditions at resonance with general conditions,"  This paper presents a new technique to investigate the existence of solutions
+to fractional three-point boundary value problems at resonance in a Hilbert
+space. Based on the proposed method, the restricted conditions
+$A^2\xi^{2\alpha-2}=A\xi^{\alpha-1}$ and $A^2\xi^{2\alpha-2}=I$ on the operator
+$A$, which have been used in \cite{Zhou2}, are removed. It is shown that the
+system under consideration admits at least one solution by applying coincidence
+degree theory. Finally, an illustrative example is presented.
+"
+25011,Analytical Validation Formulas for Best Estimate Calculation in Traditional Life Insurance,"  Within the context of traditional life insurance, a model-independent
+relationship about how the market value of assets is attributed to the best
+estimate, the value of in-force business and tax is established. This
+relationship holds true for any portfolio under run-off assumptions and can be
+used for the validation of models set up for Solvency~II best estimate
+calculation. Furthermore, we derive a lower bound for the value of future
+discretionary benefits. Again, this bound can be used in practice for
+validation purposes.
+"
+25012,In (Stochastic) Search of a Fairer Alife,"  Economies and societal structures in general are complex stochastic systems
+which may not lend themselves well to algebraic analysis. An addition of
+subjective value criteria to the mechanics of interacting agents will further
+complicate analysis. The purpose of this short study is to demonstrate
+capabilities of agent-based computational economics to be a platform for
+fairness or equity analysis in both a broad and practical sense.
+"
+25013,"Bott-Chern-Aeppli, Dolbeault and Frolicher on Compact Complex 3-folds","  We give the complete Bott-Chern-Aeppli cohomology for compact complex 3-folds
+in terms of Dolbeault, Frolicher, a bi-degree DeRham-like type of cohomology,
+$K^{p,q}$, defined as $$ K^{p,q}=\frac{ker( \partial ) \cap ker(
+{\bar{\partial}}) }{im( \partial )\cap ker( {\bar{\partial}} )+im(
+{\bar{\partial}})\cap ker( \partial )}$$ and ${\check{H}}^1({\mathcal{PH}})$.
+(Here $\mathcal{PH}$ is the sheaf of phuri-harmonic functions.) We then work
+out the complete Bott-Chern-Aeppli cohomology in some examples. We give the
+Bott-Chern-Aeppli cohomology for a hypothetical complex structure on $S^6$ in
+terms of Dolbeault and Frolicher. We also give the Bott-Chern-Aeppli cohomology
+on a Calabi-Eckman 3-fold concurring with the calculations of Angella and
+Tomassini\cite{AngellaAndTomassini}. Finally, we show agreement of our results
+with the calculation by Angella\cite{Angella} of the Bott-Chern-Aeppli
+cohomology for small Kuranishi deformations of the Iwasawa manifold.
+"
+25014,"Topological Crystalline Materials - General Formulation, Module Structure, and Wallpaper Groups -","  We formulate topological crystalline materials on the basis of the twisted
+equivariant $K$-theory. Basic ideas of the twisted equivariant $K$-theory is
+explained with application to topological phases protected by crystalline
+symmetries in mind, and systematic methods of topological classification for
+crystalline materials are presented. Our formulation is applicable to bulk
+gapful topological crystalline insulators/superconductors and their gapless
+boundary and defect states, as well as bulk gapless topological materials such
+as Weyl and Dirac semimetals, and nodal superconductors. As an application of
+our formulation, we present a complete classification of topological
+crystalline surface states, in the absence of time-reversal invariance. The
+classification works for gapless surface states of three-dimensional
+insulators, as well as full gapped two-dimensional insulators. Such surface
+states and two-dimensional insulators are classified in a unified way by 17
+wallpaper groups, together with the presence or the absence of (sublattice)
+chiral symmetry. We identify the topological numbers and their representations
+under the wallpaper group operation. We also exemplify the usefulness of our
+formulation in the classification of bulk gapless phases. We present a new
+class of Weyl semimetals and Weyl superconductors that are topologically
+protected by inversion symmetry.
+"
+25015,High-dimensional dynamics of generalization error in neural networks,"  We perform an average case analysis of the generalization dynamics of large
+neural networks trained using gradient descent. We study the
+practically-relevant ""high-dimensional"" regime where the number of free
+parameters in the network is on the order of or even larger than the number of
+examples in the dataset. Using random matrix theory and exact solutions in
+linear models, we derive the generalization error and training error dynamics
+of learning and analyze how they depend on the dimensionality of data and
+signal to noise ratio of the learning problem. We find that the dynamics of
+gradient descent learning naturally protect against overtraining and
+overfitting in large networks. Overtraining is worst at intermediate network
+sizes, when the effective number of free parameters equals the number of
+samples, and thus can be reduced by making a network smaller or larger.
+Additionally, in the high-dimensional regime, low generalization error requires
+starting with small initial weights. We then turn to non-linear neural
+networks, and show that making networks very large does not harm their
+generalization performance. On the contrary, it can in fact reduce
+overtraining, even without early stopping or regularization of any sort. We
+identify two novel phenomena underlying this behavior in overcomplete models:
+first, there is a frozen subspace of the weights in which no learning occurs
+under gradient descent; and second, the statistical properties of the
+high-dimensional regime yield better-conditioned input correlations which
+protect against overtraining. We demonstrate that naive application of
+worst-case theories such as Rademacher complexity are inaccurate in predicting
+the generalization performance of deep neural networks, and derive an
+alternative bound which incorporates the frozen subspace and conditioning
+effects and qualitatively matches the behavior observed in simulation.
+"
+25016,"Multi-polaron solutions, nonlocal effects and internal modes in a nonlinear chain","  Multipolaron solutions were studied in the framework of the Holstein
+one-dimensional molecular crystal model. The study was performed in the
+continuous limit where the crystal model maps into the nonlinear Schrödinger
+equation for which a new periodic dnoidal solution was found for the
+multipolaron system. In addition, the stability of the multi-polaron solutions
+was examined, and it was found that cnoidal and dnoidal solutions stabilize in
+different ranges of the parameter space. Moreover, the model was studied under
+the influence of nonlocal effects and the polaronic dynamics was described in
+terms of internal solitonic modes.
+"
+25017,"What We Read, What We Search: Media Attention and Public Attention Among 193 Countries","  We investigate the alignment of international attention of news media
+organizations within 193 countries with the expressed international interests
+of the public within those same countries from March 7, 2016 to April 14, 2017.
+We collect fourteen months of longitudinal data of online news from Unfiltered
+News and web search volume data from Google Trends and build a multiplex
+network of media attention and public attention in order to study its
+structural and dynamic properties. Structurally, the media attention and the
+public attention are both similar and different depending on the resolution of
+the analysis. For example, we find that 63.2% of the country-specific media and
+the public pay attention to different countries, but local attention flow
+patterns, which are measured by network motifs, are very similar. We also show
+that there are strong regional similarities with both media and public
+attention that is only disrupted by significantly major worldwide incidents
+(e.g., Brexit). Using Granger causality, we show that there are a substantial
+number of countries where media attention and public attention are dissimilar
+by topical interest. Our findings show that the media and public attention
+toward specific countries are often at odds, indicating that the public within
+these countries may be ignoring their country-specific news outlets and seeking
+other online sources to address their media needs and desires.
+"
+25018,Predicting Session Length in Media Streaming,"  Session length is a very important aspect in determining a user's
+satisfaction with a media streaming service. Being able to predict how long a
+session will last can be of great use for various downstream tasks, such as
+recommendations and ad scheduling. Most of the related literature on user
+interaction duration has focused on dwell time for websites, usually in the
+context of approximating post-click satisfaction either in search results, or
+display ads. In this work we present the first analysis of session length in a
+mobile-focused online service, using a real world data-set from a major music
+streaming service. We use survival analysis techniques to show that the
+characteristics of the length distributions can differ significantly between
+users, and use gradient boosted trees with appropriate objectives to predict
+the length of a session using only information available at its beginning. Our
+evaluation on real world data illustrates that our proposed technique
+outperforms the considered baseline.
+"
+25019,A memory-induced diffusive-superdiffusive transition: ensemble and time-averaged observables,"  The ensemble properties and time-averaged observables of a memory-induced
+diffusive-superdiffusive transition are studied. The model consists in a random
+walker whose transitions in a given direction depend on a weighted linear
+combination of the number of both right and left previous transitions. The
+diffusion process is nonstationary and its probability develops the phenomenon
+of aging. Depending on the characteristic memory parameters, the ensemble
+behavior may be normal, superdiffusive, or ballistic. In contrast, the
+time-averaged mean squared displacement is equal to that of a normal undriven
+random walk, which renders the process non-ergodic. In addition, and similarly
+to Levy walks [Godec and Metzler, Phys. Rev. Lett. 110, 020603 (2013)], for
+trajectories of finite duration the time-averaged displacement apparently
+become random with properties that depend on the measurement time and also on
+the memory properties. These features are related to the non-stationary
+power-law decay of the transition probabilities to their stationary values.
+Time-averaged response to a bias is also calculated. In contrast with Levy
+walks [Froemberg and Barkai, Phys. Rev. E 87, 030104(R) (2013)], the response
+always vanishes asymptotically.
+"
+25020,Recovering Functions from the Spherical Mean Transform with Data on an Ellipse Using Eigenfunction Expansion in Elliptical Coordinates,"  The aim of this paper is to introduce a new inversion procedure for re-
+covering functions, defined on $\Bbb R^{2}$, from the spherical mean transform,
+which integrates functions on a prescribed family $\Lambda$ of circles, where
+$\Lambda$ consists of circles whose centers belong to a given ellipse E on the
+plane. The method presented here follows the same procedure which was used by
+S. J. Norton in [22] for recovering functions in case where $\Lambda$ consists
+of circles with centers on a circle. However, at some point we will have to
+modify the method in [22] by using expansion in elliptical coordinates, rather
+than spherical coordinates, in order to solve the more generalized elliptical
+case. We will rely on a recent result obtained by H.S. Cohl and H.Volkmer in
+[8] for the eigenfunction expansion of the Bessel function in elliptical
+coordinates.
+"
+25021,No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis,"  In this paper we develop a new framework that captures the common landscape
+underlying the common non-convex low-rank matrix problems including matrix
+sensing, matrix completion and robust PCA. In particular, we show for all above
+problems (including asymmetric cases): 1) all local minima are also globally
+optimal; 2) no high-order saddle points exists. These results explain why
+simple algorithms such as stochastic gradient descent have global converge, and
+efficiently optimize these non-convex objective functions in practice. Our
+framework connects and simplifies the existing analyses on optimization
+landscapes for matrix sensing and symmetric matrix completion. The framework
+naturally leads to new results for asymmetric matrix completion and robust PCA.
+"
+25022,"Theoretical analysis and propositions for ""ontology citation""","  Ontology citation, the practice of referring the ontology in a similar
+fashion the scientific community routinely follows in providing the
+bibliographic references to other scholarly works, has not received enough
+attention it supposed to. Interestingly, so far none of the existing standard
+citation styles (e.g., APA, CMOS, and IEEE) have included ontology as a citable
+information source in the list of citable information sources such as journal
+article, book, website, etc. Also, not much work can be found in the literature
+on this topic though there are various issues and aspects of it that demand a
+thorough study. For instance, what to cite? Is it the publication that
+describes the ontology, or the ontology itself? The citation format, style,
+illustration of motivations of ontology citation, the citation principles,
+ontology impact factor, citation analysis, and so forth. In this work, we
+primarily analyse the current ontology citation practices and the related
+issues. We illustrate the various motivations and the basic principles of
+ontology citation. We also propose a template for referring the source of
+ontologies.
+"
+25023,Multi-Dimensional Auction Mechanisms for Crowdsourced Mobile Video Streaming,"  Crowdsourced mobile video streaming enables nearby mobile video users to
+aggregate network resources to improve their video streaming performances.
+However, users are often selfish and may not be willing to cooperate without
+proper incentives. Designing an incentive mechanism for such a scenario is
+challenging due to the users' asynchronous downloading behaviors and their
+private valuations for multi-bitrate coded videos. In this work, we propose
+both single-object and multi-object multi-dimensional auction mechanisms,
+through which users sell the opportunities for downloading single and multiple
+video segments with multiple bitrates, respectively. Both auction mechanisms
+can achieves truthfulness (i.e, truthful private information revelation) and
+efficiency (i.e., social welfare maximization). Simulations with real traces
+show that crowdsourced mobile streaming facilitated by the auction mechanisms
+outperforms noncooperative stream ing by 48.6% (on average) in terms of social
+welfare. To evaluate the real-world performance, we also construct a demo
+system for crowdsourced mobile streaming and implement our proposed auction
+mechanism. Experiments over the demo system further show that those users who
+provide resources to others and those users who receive helps can increase
+their welfares by 15.5% and 35.4% (on average) via cooperation, respectively.
+"
+25024,Exploring the position of cities in global corporate research and development: a bibliometric analysis by two different geographical approaches,"  Global cities are defined, on the one hand, as the major command and control
+centres of the world economy and, on the other hand, as the most significant
+sites of the production of innovation. As command and control centres, they are
+home to the headquarters of the most powerful MNCs of the global economy, while
+as sites for the production of innovation they are supposed to be the most
+important sites of corporate research and development (R&D) activities. In this
+paper, we conduct a bibliometric analysis of the data located in the Scopus and
+Forbes 2000 databases to reveal the correlation between the characteristics of
+the above global city definitions. We explore which cities are the major
+control points of the global corporate R&D (home city approach), and which
+cities are the most important sites of corporate R&D activities (host city
+approach). According to the home city approach we assign articles produced by
+companies to cities where the decision-making headquarters are located (i.e. to
+cities that control the companies' R&D activities), while according to the host
+city approach we assign articles to cities where the R&D activities are
+actually conducted. Given Sassen's global city concept, we expect global cities
+to be both the leading home cities and host cities.
+"
+25025,A Conservation Law Method in Optimization,"  We propose some algorithms to find local minima in nonconvex optimization and
+to obtain global minima in some degree from the Newton Second Law without
+friction. With the key observation of the velocity observable and controllable
+in the motion, the algorithms simulate the Newton Second Law without friction
+based on symplectic Euler scheme. From the intuitive analysis of analytical
+solution, we give a theoretical analysis for the high-speed convergence in the
+algorithm proposed. Finally, we propose the experiments for strongly convex
+function, non-strongly convex function and nonconvex function in
+high-dimension.
+"
+25026,Ultraslow diffusion in language: Dynamics of appearance of already popular adjectives on Japanese blogs,"  What dynamics govern a time series representing the appearance of words in
+social media data? In this paper, we investigate an elementary dynamics, from
+which word-dependent special effects are segregated, such as breaking news,
+increasing (or decreasing) concerns, or seasonality. To elucidate this problem,
+we investigated approximately three billion Japanese blog articles over a
+period of six years, and analysed some corresponding solvable mathematical
+models. From the analysis, we found that a word appearance can be explained by
+the random diffusion model based on the power-law forgetting process, which is
+a type of long memory point process related to ARFIMA(0,0.5,0). In particular,
+we confirmed that ultraslow diffusion (where the mean squared displacement
+grows logarithmically), which the model predicts in an approximate manner,
+reproduces the actual data. In addition, we also show that the model can
+reproduce other statistical properties of a time series: (i) the fluctuation
+scaling, (ii) spectrum density, and (iii) shapes of the probability density
+functions.
+"
+25027,Stochastic Graphlet Embedding,"  Graph-based methods are known to be successful in many machine learning and
+pattern classification tasks. These methods consider semi-structured data as
+graphs where nodes correspond to primitives (parts, interest points, segments,
+etc.) and edges characterize the relationships between these primitives.
+However, these non-vectorial graph data cannot be straightforwardly plugged
+into off-the-shelf machine learning algorithms without a preliminary step of --
+explicit/implicit -- graph vectorization and embedding. This embedding process
+should be resilient to intra-class graph variations while being highly
+discriminant. In this paper, we propose a novel high-order stochastic graphlet
+embedding (SGE) that maps graphs into vector spaces. Our main contribution
+includes a new stochastic search procedure that efficiently parses a given
+graph and extracts/samples unlimitedly high-order graphlets. We consider these
+graphlets, with increasing orders, to model local primitives as well as their
+increasingly complex interactions. In order to build our graph representation,
+we measure the distribution of these graphlets into a given graph, using
+particular hash functions that efficiently assign sampled graphlets into
+isomorphic sets with a very low probability of collision. When combined with
+maximum margin classifiers, these graphlet-based representations have positive
+impact on the performance of pattern comparison and recognition as corroborated
+through extensive experiments using standard benchmark databases.
+"
+25028,Diagnosing a two-body state of ultracold atoms with light,"  An absorption of a weak pulse by two identical atoms moving in a trap is
+investigated. Based on atom-light interactions we present a microscopic model
+of a two-body wave function diagnosis. We study the influence of pulse
+properties on the results. We show that a pulse duration impact a resulting
+one-photon and two-photons absorption probabilities significantly.
+"
+25029,A Markov model for inferring flows in directed contact networks,"  Directed contact networks (DCNs) are a particularly flexible and convenient
+class of temporal networks, useful for modeling and analyzing the transfer of
+discrete quantities in communications, transportation, epidemiology, etc.
+Transfers modeled by contacts typically underlie flows that associate multiple
+contacts based on their spatiotemporal relationships. To infer these flows, we
+introduce a simple inhomogeneous Markov model associated to a DCN and show how
+it can be effectively used for data reduction and anomaly detection through an
+example of kernel-level information transfers within a computer.
+"
+25030,On radiating solitary waves in bi-layers with delamination and coupled Ostrovsky equations,"  We study the scattering of a long longitudinal radiating bulk strain solitary
+wave in the delaminated area of a two-layered elastic structure with soft
+(`imperfect') bonding between the layers within the scope of the coupled
+Boussinesq equations. The direct numerical modelling of this and similar
+problems is challenging and has natural limitations. We develop a
+semi-analytical approach, based on the use of several matched asymptotic
+multiple-scale expansions and averaging with respect to the fast space
+variable, leading to the coupled Ostrovsky equations in bonded regions and
+uncoupled Korteweg-de Vries equations in the delaminated region. We show that
+the semi-analytical approach agrees well with direct numerical simulations and
+use it to study the nonlinear dynamics and scattering of the radiating solitary
+wave in a wide range of bi-layers with delamination. The results indicate that
+radiating solitary waves could help us to control the integrity of layered
+structures with imperfect interfaces.
+"
+25031,A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics,"  We propose a new method of estimation in topic models, that is not a
+variation on the existing simplex finding algorithms, and that estimates the
+number of topics K from the observed data. We derive new finite sample minimax
+lower bounds for the estimation of A, as well as new upper bounds for our
+proposed estimator. We describe the scenarios where our estimator is minimax
+adaptive. Our finite sample analysis is valid for any number of documents (n),
+individual document length (N_i), dictionary size (p) and number of topics (K),
+and both p and K are allowed to increase with n, a situation not handled well
+by previous analyses. We complement our theoretical results with a detailed
+simulation study. We illustrate that the new algorithm is faster and more
+accurate than the current ones, although we start out with a computational and
+theoretical disadvantage of not knowing the correct number of topics K, while
+we provide the competing methods with the correct value in our simulations.
+"
+25032,Angular distribution of single photon superradiance in a dilute and cold atomic ensemble,"  On the basis of a quantum microscopic approach we study the dynamics of the
+afterglow of a dilute Gaussian atomic ensemble excited by pulsed radiation.
+Taking into account the vector nature of the electromagnetic field we analyze
+in detail the angular and polarization distribution of single-photon
+superradiance of such an ensemble. The dependence of the angular distribution
+of superradiance on the length of the pulse and its carrier frequency as well
+as on the size and the shape of the atomic clouds is studied. We show that
+there is substantial dependence of the superradiant emission on the
+polarization and the direction of fluorescence. We observe essential
+peculiarities of superradiance in the region of the forward diffraction zone
+and in the area of the coherent backscattering cone. We demonstrate that there
+are directions for which the rate of fluorescence is several times more than
+the decay rate of the timed-Dicke state. We show also that single-photon
+superradiance can be excited by incoherent excitation when atomic polarization
+in the ensemble is absent. Besides a quantum microscopic approach, we analyze
+single-photon superradiance on the basis of the theory of incoherent multiple
+scattering in optically thick media (random walk theory). In the case of very
+short resonant and long nonresonant pulses we derive simple analytical
+expressions for the decay rate of single-photon superradiance for incoherent
+fluorescence in an arbitrary direction.
+"
+25033,"On Fairness, Diversity and Randomness in Algorithmic Decision Making","  Consider a binary decision making process where a single machine learning
+classifier replaces a multitude of humans. We raise questions about the
+resulting loss of diversity in the decision making process. We study the
+potential benefits of using random classifier ensembles instead of a single
+classifier in the context of fairness-aware learning and demonstrate various
+attractive properties: (i) an ensemble of fair classifiers is guaranteed to be
+fair, for several different measures of fairness, (ii) an ensemble of unfair
+classifiers can still achieve fair outcomes, and (iii) an ensemble of
+classifiers can achieve better accuracy-fairness trade-offs than a single
+classifier. Finally, we introduce notions of distributional fairness to
+characterize further potential benefits of random classifier ensembles.
+"
+25034,Multifractal cross-correlations between the World Oil and other Financial Markets in 2012-2017,"  Statistical and multiscaling characteristics of WTI Crude Oil prices
+expressed in US dollar in relation to the most traded currencies as well as to
+gold futures and to the E-mini S$\&$P500 futures prices on 5 min intra-day
+recordings in the period January 2012 - December 2017 are studied. It is shown
+that in most of the cases the tails of return distributions of the considered
+financial instruments follow the inverse cubic power law. The only exception is
+the Russian ruble for which the distribution tail is heavier and scales with
+the exponent close to 2. From the perspective of multiscaling the analysed time
+series reveal the multifractal organization with the left-sided asymmetry of
+the corresponding singularity spectra. Even more, all the considered financial
+instruments appear to be multifractally cross-correlated with oil, especially
+on the level of medium-size fluctuations, as the multifractal cross-correlation
+analysis carried out by means of the multifractal cross-correlation analysis
+(MFCCA) and detrended cross-correlation coefficient $\rho_q$ show. The degree
+of such cross-correlations is however varying among the financial instruments.
+The strongest ties to the oil characterize currencies of the oil extracting
+countries. Strength of this multifractal coupling appears to depend also on the
+oil market trend. In the analysed time period the level of cross-correlations
+systematically increases during the bear phase on the oil market and it
+saturates after the trend reversal in 1st half of 2016. The same methodology is
+also applied to identify possible causal relations between considered
+observables. Searching for some related asymmetry in the information flow
+mediating cross-correlations indicates that it was the oil price that led the
+Russian ruble over the time period here considered rather than vice versa.
+"
+25035,One-step dispatching policy improvement in multiple-server queueing systems with Poisson arrivals,"  Policy iteration techniques for multiple-server dispatching rely on the
+computation of value functions. In this context, we consider the M/G/1-FCFS
+queue endowed with an arbitrarily-designed cost function for the waiting times
+of the incoming jobs, and we study an undiscounted value function integrating
+the total cost surplus expected from each state relative to the steady-state
+costs. When coupled with random initial policies, this value function takes
+closed-form expressions for polynomial and exponential costs, or for piecewise
+compositions of the latter, thus hinting in the most general case at the
+derivation of interval bounds for the value function in the form of power
+series or trigonometric sums. The value function approximations induced by
+Taylor polynomial expansions of the cost function prove however to converge
+only for entire cost functions with low growth orders, and to diverge
+otherwise. A more suitable approach for assessing convergent interval bounds is
+found in the uniform approximation framework. Bernstein polynomials constitute
+straightforward, yet slowly convergent, cost function approximators over
+intervals. The best convergence rate in the sense of D. Jackson's theorem is
+achieved by more sophisticated polynomial solutions derived from trigonometric
+sums. This study is organized as a guide to implementing multiple-server
+dispatching policies, from the specification of cost functions towards the
+computation of interval bounds for the value functions and the implementation
+of the policy improvement step.
+"
+25036,Chiral Hodge cohomology and Mathieu moonshin,"  We construct a filtration of chiral Hodge cohomolgy of a K3 surface $X$, such
+that its associated graded object is a unitary representation of the N=4 vertex
+algebra with central charge $6$ and its subspace of primitive vectors has the
+property: its equivariant character for a symplectic automorphism $g$ of $X$
+agrees with the McKay-Thompson series for $g$ in Mathieu moonshine.
+"
+25037,Gauge origin of the Dirac field and singular solutions to the Dirac equation,"  We present and enhance our previous statements (arXiv:0907.4736) on the
+non-canonical interrelations between the solutions to the free Dirac equation
+(DE) and the Klein-Gordon equation (KGE). We demonstrate that all the solutions
+to the DE (singular ones among them) can be obtained via differentiation of a
+pair of the KGE solutions for a doublet of scalar fields. In this way we obtain
+a ""spinor analogue"" of the mesonic Yukawa potential and previously unknown
+chains of solutions to the DE and KGE. The pair of scalar ""potentials"" is
+defined up to a gauge transformation under which the corresponding solution of
+the DE is invariant. Under transformations of the Lorentz group, canonical
+spinor transformations form only a subclass of more general transformations of
+the DE solutions, under these the generating scalar potentials undergo
+transformations of internal symmetry intermixing their components. In
+particular, under continuous rotation through one full revolution the
+transforming solutions, as a rule, take their original values (""spinor
+two-valuedness"" is absent). With an arbitrary solution of the DE one can
+associate, apart from the standard one, a non-canonical set of conserved
+quantities, a positive definite ""energy"" density among others, and with any KGE
+solution -- a positive definite ""probability density"", etc. Finally, we discuss
+a generalization of the proposed procedure to the case when an external
+electromagnetic field is present.
+"
+25038,Hybrid nanodiamond-YIG systems for efficient quantum information processing and nanoscale sensing,"  The nitrogen-vacancy (NV) center in diamond has been extensively studied in
+recent years for its remarkable quantum coherence properties that make it an
+ideal candidate for room temperature quantum computing and quantum sensing
+schemes. However, these schemes rely on spin-spin dipolar interactions, which
+require the NV centers to be within a few nanometers from each other while
+still separately addressable, or to be in close proximity of the diamond
+surface, where their coherence properties significantly degrade. Here we
+demonstrate a method for overcoming these limitations using a hybrid yttrium
+iron garnet (YIG)-nanodiamond quantum system constructed with the help of
+directed assembly and transfer printing techniques. We show that YIG spin-waves
+can amplify the oscillating field of a microwave source by more than two orders
+of magnitude and efficiently mediate its coherent interactions with an NV
+center ensemble. These results demonstrate that spin-waves in ferromagnets can
+be used as quantum buses for enhanced, long-range qubit interactions, paving
+the way to ultra-efficient manipulation and coupling of solid state defects in
+hybrid quantum networks and sensing devices.
+"
+25039,An Infinite Dimensional Model for a Many Server Priority Queue,"  We consider a Markovian many server queueing system in which customers are
+preemptively scheduled according to exogenously assigned priority levels. The
+priority levels are randomly assigned from a continuous probability measure
+rather than a discrete one and hence, the queue is modeled by an infinite
+dimensional stochastic process. We analyze the equilibrium behavior of the
+system and provide several results. We derive the Radon-Nikodym derivative
+(with respect to Lebesgue measure) of the measure that describes the average
+distribution of customer priority levels in the system; we provide a formula
+for the expected sojourn time of a customer as a function of his priority
+level; and we provide a formula for the expected waiting time of a customer as
+a function of his priority level. We verify our theoretical analysis with
+discrete-event simulations. We discuss how each of our results generalizes
+previous work on infinite dimensional models for single server priority queues.
+"
+25040,On spectral analysis in varieties containing the solutions of inhomogeneous linear functional equations,"  The aim of the paper is to investigate the solutions of special inhomogeneous
+linear functional equations by using spectral analysis in a translation
+invariant closed linear subspace of additive/multiadditive functions containing
+the restrictions of the solutions to finitely generated fields. The application
+of spectral analysis in some related varieties is a new and important trend in
+the theory of functional equations; especially they have successful
+applications in case of homogeneous linear functional equations. The foundation
+of the theory can be found in M. Laczkovich and G. Kiss \cite{KL}, see also G.
+Kiss and A. Varga \cite{KV}. We are going to adopt the main theoretical tools
+to solve some inhomogeneous problems due to T. Szostok \cite{KKSZ08}, see also
+\cite{KKSZ} and \cite{KKSZW}. They are motivated by quadrature rules of
+approximate integration.
+"
+25041,Sound Transpilation from Binary to Machine-Independent Code,"  In order to handle the complexity and heterogeneity of mod- ern instruction
+set architectures, analysis platforms share a common design, the adoption of
+hardware-independent intermediate representa- tions. The usage of these
+platforms to verify systems down to binary-level is appealing due to the high
+degree of automation they provide. How- ever, it introduces the need for
+trusting the correctness of the translation from binary code to intermediate
+language. Achieving a high degree of trust is challenging since this
+transpilation must handle (i) all the side effects of the instructions, (ii)
+multiple instruction encoding (e.g. ARM Thumb), and (iii) variable instruction
+length (e.g. Intel). We overcome these problems by formally modeling one of
+such intermediate languages in the interactive theorem prover HOL4 and by
+implementing a proof- producing transpiler. This tool translates ARMv8 programs
+to the in- termediate language and generates a HOL4 proof that demonstrates the
+correctness of the translation in the form of a simulation theorem. We also
+show how the transpiler theorems can be used to transfer properties verified on
+the intermediate language to the binary code.
+"
+25042,Multifunctionality in embodied agents: Three levels of neural reuse,"  The brain in conjunction with the body is able to adapt to new environments
+and perform multiple behaviors through reuse of neural resources and transfer
+of existing behavioral traits. Although mechanisms that underlie this ability
+are not well understood, they are largely attributed to neuromodulation. In
+this work, we demonstrate that an agent can be multifunctional using the same
+sensory and motor systems across behaviors, in the absence of modulatory
+mechanisms. Further, we lay out the different levels at which neural reuse can
+occur through a dynamical filtering of the brain-body-environment system's
+operation: structural network, autonomous dynamics, and transient dynamics.
+Notably, transient dynamics reuse could only be explained by studying the
+brain-body-environment system as a whole and not just the brain. The
+multifunctional agent we present here demonstrates neural reuse at all three
+levels.
+"
+25043,A Hybrid Profile-Gradient Approach for the Estimation of Surface Fluxes,"  The Monin--Obukhov similarity theory-based wind speed and potential
+temperature profiles are inherently coupled to each other. We have developed
+hybrid approaches to disentangle them, and as a direct consequence, the
+estimation of Obukhov length (and associated turbulent fluxes) from either
+wind-speed or temperature measurements becomes an effortless task.
+Additionally, our approaches give rise to two easily measurable indices of
+atmospheric stability. We compare these approaches with the traditional
+gradient and profile methods that require both wind-speed and temperature
+profile data. Using Monte-Carlo-type numerical experiments we demonstrate that,
+if the input profiles are free of any random errors, the performance of the
+proposed hybrid approaches is almost equivalent to the profile method and
+better than the gradient method. However, the proposed hybrid approaches are
+less competitive in comparison to their traditional counterparts in the
+presence of random errors.
+"
+25044,New conformal mapping for adaptive resolving of the complex singularities of Stokes wave,"  A new highly efficient method is developed for computation of traveling
+periodic waves (Stokes waves) on the free surface of deep water. A convergence
+of numerical approximation is determined by the complex singularites above the
+free surface for the analytical continuation of the travelling wave into the
+complex plane. An auxiliary conformal mapping is introduced which moves
+singularities away from the free surface thus dramatically speeding up
+numerical convergence by adapting the numerical grid for resolving
+singularities while being consistent with the fluid dynamics. The efficiency of
+that conformal mapping is demonstrated for Stokes wave approaching the limiting
+Stokes wave (the wave of the greatest height) which significantly expands the
+family of numerically accessible solutions. It allows to provide a detailed
+study of the oscillatory approach of these solutions to the limiting wave.
+Generalizations of the conformal mapping to resolve multiple singularities are
+also introduced.
+"
+25045,The AJ-conjecture for the Teichmüller TQFT,"  We formulate the AJ-conjecture for the Teichmüller TQFT and we prove it in
+the case of the figure-eight knot complement and the $5_2$-knot complement.
+This states that the level-$N$ Andersen-Kashaev invariant,
+$J^{(\mathrm{b},N)}_{M,K}$, is annihilated by the non-homogeneous
+$\widehat{A}$-polynomial, evaluated at appropriate $q$-commutative operators.
+These are obtained via geometric quantisation on the moduli space of flat
+$\operatorname{SL}(2,\mathbb{C})$-connections on a genus-$1$ surface. The
+construction depends on a parameter $\sigma$ in the Teichmüller space in a
+way measured by the Hitchin-Witten connection, and results in Hitchin-Witten
+covariantly constant quantum operators for the holonomy functions $m$ and
+$\ell$ along the meridian and longitude. Their action on
+$J^{(\mathrm{b},N)}_{M,K}$ is then defined via a trivialisation of the
+Hitchin-Witten connection and the Weil-Gel'Fand-Zak transform.
+"
+25046,"Black holes, hidden symmetries, and complete integrability","  The study of higher-dimensional black holes is a subject which has recently
+attracted a vast interest. Perhaps one of the most surprising discoveries is a
+realization that the properties of higher-dimensional black holes with the
+spherical horizon topology and described by the Kerr-NUT-(A)dS metrics are very
+similar to the properties of the well known four-dimensional Kerr metric. This
+remarkable result stems from the existence of a single object called the
+principal tensor. In our review we discuss explicit and hidden symmetries of
+higher-dimensional black holes. We start with the overview of the Liouville
+theory of completely integrable systems and introduce Killing and Killing-Yano
+objects representing explicit and hidden symmetries. We demonstrate that the
+principal tensor can be used as a `seed object' which generates all these
+symmetries. It determines the form of the black hole geometry, as well as
+guarantees its remarkable properties, such as special algebraic type of the
+spacetime, complete integrability of geodesic motion, and separability of the
+Hamilton-Jacobi, Klein-Gordon, and Dirac equations. The review also contains a
+discussion of different applications of the developed formalism and its
+possible generalizations.
+"
+25047,Stability and Generalization of Learning Algorithms that Converge to Global Optima,"  We establish novel generalization bounds for learning algorithms that
+converge to global minima. We do so by deriving black-box stability results
+that only depend on the convergence of a learning algorithm and the geometry
+around the minimizers of the loss function. The results are shown for nonconvex
+loss functions satisfying the Polyak-{\L}ojasiewicz (PL) and the quadratic
+growth (QG) conditions. We further show that these conditions arise for some
+neural networks with linear activations. We use our black-box results to
+establish the stability of optimization algorithms such as stochastic gradient
+descent (SGD), gradient descent (GD), randomized coordinate descent (RCD), and
+the stochastic variance reduced gradient method (SVRG), in both the PL and the
+strongly convex setting. Our results match or improve state-of-the-art
+generalization bounds and can easily be extended to similar optimization
+algorithms. Finally, we show that although our results imply comparable
+stability for SGD and GD in the PL setting, there exist simple neural networks
+with multiple local minima where SGD is stable but GD is not.
+"
+25048,On the Recovery of Core and Crustal Components of Geomagnetic Potential Fields,"  In Geomagnetism it is of interest to separate the Earth's core magnetic field
+from the crustal magnetic field. However, measurements by satellites can only
+sense the sum of the two contributions. In practice, the measured magnetic
+field is expanded in spherical harmonics and separation into crust and core
+contribution is achieved empirically, by a sharp cutoff in the spectral domain.
+In this paper, we derive a mathematical setup in which the two contributions
+are modeled by harmonic potentials $\Phi_0$ and $\Phi_1$ generated on two
+different spheres $\mathbb{S}_{R_0}$ (crust) and $\mathbb{S}_{R_1}$ (core) with
+radii $R_1<R_0$. Although it is not possible in general to recover $\Phi_0$ and
+$\Phi_1$ knowing their superposition $\Phi_0+\Phi_1$ on a sphere
+$\mathbb{S}_{R_2}$ with radius $R_2>R_0$, we show that it becomes possible if
+the magnetization $\mathbf{m}$ generating $\Phi_0$ is localized in a strict
+subregion of $\mathbb{S}_{R_0}$. Beyond unique recoverability, we show in this
+case how to numerically reconstruct characteristic features of $\Phi_0$ (e.g.,
+spherical harmonic Fourier coefficients). An alternative way of phrasing the
+results is that knowledge of $\mathbf{m}$ on a nonempty open subset of
+$\mathbb{S}_{R_0}$ allows one to perform separation.
+"
+25049,Double Reweighted Estimators for the Parameters of the Multivariate t Distribution,"  The t-distribution has many useful applications in robust statistical
+analysis. The parameter estimation of the t-distribution is carried out using
+ML estimation method, and the ML estimates are obtained via the EM algorithm.
+In this study, we consider an alternative estimation method for all the
+parameters of the multivariate-t distribution using the MLq estimation method.
+We adapt the EM algorithm to obtain the MLq estimates for all the parameters.
+We provide a small simulation study to illustrate the performance of the MLq
+estimators over the ML estimators and observe that the MLq estimators have
+considerable superiority over the ML estimators.
+"
+25050,Residual Convolutional CTC Networks for Automatic Speech Recognition,"  Deep learning approaches have been widely used in Automatic Speech
+Recognition (ASR) and they have achieved a significant accuracy improvement.
+Especially, Convolutional Neural Networks (CNNs) have been revisited in ASR
+recently. However, most CNNs used in existing work have less than 10 layers
+which may not be deep enough to capture all human speech signal information. In
+this paper, we propose a novel deep and wide CNN architecture denoted as
+RCNN-CTC, which has residual connections and Connectionist Temporal
+Classification (CTC) loss function. RCNN-CTC is an end-to-end system which can
+exploit temporal and spectral structures of speech signals simultaneously.
+Furthermore, we introduce a CTC-based system combination, which is different
+from the conventional frame-wise senone-based one. The basic subsystems adopted
+in the combination are different types and thus mutually complementary to each
+other. Experimental results show that our proposed single system RCNN-CTC can
+achieve the lowest word error rate (WER) on WSJ and Tencent Chat data sets,
+compared to several widely used neural network systems in ASR. In addition, the
+proposed system combination can offer a further error reduction on these two
+data sets, resulting in relative WER reductions of $14.91\%$ and $6.52\%$ on
+WSJ dev93 and Tencent Chat data sets respectively.
+"
+25051,Stochastic reconstruction of an oolitic limestone by generative adversarial networks,"  Stochastic image reconstruction is a key part of modern digital rock physics
+and materials analysis that aims to create numerous representative samples of
+material micro-structures for upscaling, numerical computation of effective
+properties and uncertainty quantification. We present a method of
+three-dimensional stochastic image reconstruction based on generative
+adversarial neural networks (GANs). GANs represent a framework of unsupervised
+learning methods that require no a priori inference of the probability
+distribution associated with the training data. Using a fully convolutional
+neural network allows fast sampling of large volumetric images.We apply a GAN
+based workflow of network training and image generation to an oolitic Ketton
+limestone micro-CT dataset. Minkowski functionals, effective permeability as
+well as velocity distributions of simulated flow within the acquired images are
+compared with the synthetic reconstructions generated by the deep neural
+network. While our results show that GANs allow a fast and accurate
+reconstruction of the evaluated image dataset, we address a number of open
+questions and challenges involved in the evaluation of generative network-based
+methods.
+"
+25052,Radio emission of air showers with extremely high energy measured by the Yakutsk radio array,"  The Yakutsk Array is designed to study cosmic rays at energy
+10$^{15}$-10$^{20}$ eV. It consists several independent arrays that register
+charged particles, muons with energy E$\geq$1 GeV, Cherenkov light and radio
+emission. The paper presents a technical description of the Yakutsk Radio Array
+and some preliminary results obtained from measurements of radio emission at
+30-35 MHz frequency induced by air shower particles with energy $\varepsilon$
+$\geq$ 1$\cdot$10$^{17}$ eV. The data obtained at the Yakutsk array in
+1986-1989 (first set of measurements) and 2009-2014 (new set of measurements).
+Based on the obtained results we determined:
+Lateral distribution function (LDF) of air showers radio emission with energy
+$\geq$ 10$^{17}$ eV. Radio emission amplitude empirical connection with air
+shower energy. Determination of depth of maximum by the ratio of amplitude at
+different distances from the shower axis. For the first time, at the Yakutsk
+array, radio emission from the air shower with energy $>$ 10$^{19}$ eV was
+registered including the shower with the highest energy ever registered at the
+Yakutsk array with energy $\sim$2 $\cdot$ 10$^{20}$ eV.
+"
+25053,Mechanical Design of a Cartesian Manipulator for Warehouse Pick and Place,"  Robotic manipulation and grasping in cluttered and unstructured environments
+is a current challenge for robotics. Enabling robots to operate in these
+challenging environments have direct applications from automating warehouses to
+harvesting fruit in agriculture. One of the main challenges associated with
+these difficult robotic manipulation tasks is the motion planning and control
+problem for multi-DoF (Degree of Freedom) manipulators. This paper presents the
+design and performance evaluation of a low-cost Cartesian manipulator, Cartman
+who took first place in the Amazon Robotics Challenge 2017. It can perform pick
+and place tasks of household items in a cluttered environment. The robot is
+capable of linear speeds of 1 m/s and angular speeds of 1.5 rad/s, capable of
+sub-millimetre static accuracy and safe payload capacity of 2kg. Cartman can be
+produced for under 10 000 AUD. The complete design is open sourced and can be
+found at this http URL.
+"
+25054,"Citation histories of papers: sometimes the rich get richer, sometimes they don't","  We describe a simple model of how a publication's citations change over time,
+based on pure-birth stochastic processes with a linear cumulative advantage
+effect. The model is applied to citation data from the Physical Review corpus
+provided by APS. Our model reveals that papers fall into three different
+clusters: papers that have rapid initial citations and ultimately high impact
+(fast-hi), fast to rise but quick to plateau (fast-flat), or late bloomers
+(slow-late), which may either never achieve many citations, or do so many years
+after publication. In ""fast-hi"" and ""slow-late"", there is a rich-get-richer
+effect: papers that have many citations accumulate additional citations more
+rapidly while the ""fast-flat"" papers do not display this effect. We conclude by
+showing that only a few years of post-publication statistics are needed to
+identify high impact (""fast-hi"") papers.
+"
+25055,Performance Bounds for Graphical Record Linkage,"  Record linkage involves merging records in large, noisy databases to remove
+duplicate entities. It has become an important area because of its widespread
+occurrence in bibliometrics, public health, official statistics production,
+political science, and beyond. Traditional linkage methods directly linking
+records to one another are computationally infeasible as the number of records
+grows. As a result, it is increasingly common for researchers to treat record
+linkage as a clustering task, in which each latent entity is associated with
+one or more noisy database records. We critically assess performance bounds
+using the Kullback-Leibler (KL) divergence under a Bayesian record linkage
+framework, making connections to Kolchin partition models. We provide an upper
+bound using the KL divergence and a lower bound on the minimum probability of
+misclassifying a latent entity. We give insights for when our bounds hold using
+simulated data and provide practical user guidance.
+"
+25056,Counting Higgs bundles and type A quiver bundles,"  We prove a closed formula counting semistable twisted (or meromorphic) Higgs
+bundles of fixed rank and degree over a smooth projective curve of genus g,
+defined over a finite field, when the degree of the twisting line bundle is at
+least 2g-2; this includes the case of usual Higgs bundles. We obtain at the
+same time a closed expression for the Donaldson-Thomas invariants of the moduli
+spaces of twisted Higgs bundles. We similarly deal with twisted quiver bundles
+of type A (affine or finite), obtaining in particular a Harder-Narasimhan-type
+formula counting semistable U(p,q)-bundles over any smooth projective curve
+over a finite field.
+"
+25057,Directed networks with a noisy bi-degree sequence,"  Although a lot of approaches are developed to release network data with a
+differentially privacy guarantee, inference using noisy data in many network
+models is still unknown or not properly explored.In this paper, we release the
+bi-degree sequences of directed networks under a general additive noisy
+mechanism with the Laplace mechanism as a special case and use the $p_0$ model
+for inferring the degree parameters. We show that the estimator of the
+parameter without the denoised process is asymptotically consistent and
+normally distributed. This is contrast sharply with known results that valid
+inference such as the existence and consistency of the estimator needs the
+denoised process. Along the way, a new phenomenon is revealed in which an
+additional variance factor appears in the asymptotic variance of the estimator
+when the noise becomes large. Further, we propose an efficient algorithm for
+finding the closet point lying in the set of all graphical bi-degree sequences
+under the global $L_1$ optimization problem. The algorithm simultaneously
+produces a synthetic directed graph. Numerical studies demonstrate our
+theoretical findings.
+"
+25058,A Labelling Framework for Probabilistic Argumentation,"  The combination of argumentation and probability paves the way to new
+accounts of qualitative and quantitative uncertainty, thereby offering new
+theoretical and applicative opportunities. Due to a variety of interests,
+probabilistic argumentation is approached in the literature with different
+frameworks, pertaining to structured and abstract argumentation, and with
+respect to diverse types of uncertainty, in particular the uncertainty on the
+credibility of the premises, the uncertainty about which arguments to consider,
+and the uncertainty on the acceptance status of arguments or statements.
+Towards a general framework for probabilistic argumentation, we investigate a
+labelling-oriented framework encompassing a basic setting for rule-based
+argumentation and its (semi-) abstract account, along with diverse types of
+uncertainty. Our framework provides a systematic treatment of various kinds of
+uncertainty and of their relationships and allows us to back or question
+assertions from the literature.
+"
+25059,Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning,"  One question central to Reinforcement Learning is how to learn a feature
+representation that supports algorithm scaling and re-use of learned
+information from different tasks. Successor Features approach this problem by
+learning a feature representation that satisfies a temporal constraint. We
+present an implementation of an approach that decouples the feature
+representation from the reward function, making it suitable for transferring
+knowledge between domains. We then assess the advantages and limitations of
+using Successor Features for transfer.
+"
+25060,Conformally Covariant Bi-Differential Operators on a Simple Real Jordan Algebra,"  For a simple real Jordan algebra $V,$ a family of bi-differential operators
+from $\mathcal{C}^\infty(V\times V)$ to $\mathcal{C}^\infty(V)$ is constructed.
+These operators are covariant under the rational action of the conformal group
+of $V.$ They generalize the classical {\em Rankin-Cohen} brackets (case
+$V=\mathbb{R}$).
+"
+25061,Self-Attentive Residual Decoder for Neural Machine Translation,"  Neural sequence-to-sequence networks with attention have achieved remarkable
+performance for machine translation. One of the reasons for their effectiveness
+is their ability to capture relevant source-side contextual information at each
+time-step prediction through an attention mechanism. However, the target-side
+context is solely based on the sequence model which, in practice, is prone to a
+recency bias and lacks the ability to capture effectively non-sequential
+dependencies among words. To address this limitation, we propose a
+target-side-attentive residual recurrent network for decoding, where attention
+over previous words contributes directly to the prediction of the next word.
+The residual learning facilitates the flow of information from the distant past
+and is able to emphasize any of the previously translated words, hence it gains
+access to a wider context. The proposed model outperforms a neural MT baseline
+as well as a memory and self-attention network on three language pairs. The
+analysis of the attention learned by the decoder confirms that it emphasizes a
+wider context, and that it captures syntactic-like structures.
+"
+25062,A Remark on the Piatetski-Shapiro-Hua Theorem,"  In this paper, we prove that for any fixed $205/243<\gamma\leqslant1$, every
+sufficiently large $N$ satisfying $N\equiv 5 \pmod {24}$ can be represented as
+five squares of primes with one prime in $\mathcal{P}_\gamma$, which improves
+the previous result of Zhang and Zhai.
+"
+25063,Greedy Approaches to Symmetric Orthogonal Tensor Decomposition,"  Finding the symmetric and orthogonal decomposition (SOD) of a tensor is a
+recurring problem in signal processing, machine learning and statistics. In
+this paper, we review, establish and compare the perturbation bounds for two
+natural types of incremental rank-one approximation approaches. Numerical
+experiments and open questions are also presented and discussed.
+"
+25064,Phylogenetic Tools in Astrophysics,"  Multivariate clustering in astrophysics is a recent development justified by
+the bigger and bigger surveys of the sky. The phylogenetic approach is probably
+the most unexpected technique that has appeared for the unsupervised
+classification of galaxies, stellar populations or globular clusters. On one
+side, this is a somewhat natural way of classifying astrophysical entities
+which are all evolving objects. On the other side, several conceptual and
+practical difficulties arize, such as the hierarchical representation of the
+astrophysical diversity, the continuous nature of the parameters, and the
+adequation of the result to the usual practice for the physical interpretation.
+Most of these have now been solved through the studies of limited samples of
+stellar clusters and galaxies. Up to now, only the Maximum Parsimony
+(cladistics) has been used since it is the simplest and most general
+phylogenetic technique. Probabilistic and network approaches are obvious
+extensions that should be explored in the future.
+"
+25065,Long Hitting time for translation flows and L-shaped billiards,"  We consider the flow in direction $\theta$ on a translation surface and we
+study the asymptotic behavior for $r\to 0$ of the time needed by orbits to hit
+the $r$-neighborhood of a prescribed point, or more precisely the exponent of
+the corresponding power law, which is known as hitting time. For flat tori the
+limsup of hitting time is equal to the diophantine type of the direction
+$\theta$. In higher genus, we consider a generalized geometric notion of
+diophantine type of a direction $\theta$ and we seek for relations with hitting
+time. For genus two surfaces with just one conical singularity we prove that
+the limsup of hitting time is always less or equal to the square of the
+diophantine type. For any square-tiled surface with the same topology the
+diophantine type itself is a lower bound, and any value between the two bounds
+can be realized, moreover this holds also for a larger class of origamis
+satisfying a specific topological assumption. Finally, for the so-called
+Eierlegende Wollmilchsau origami, the equality between limsup of hitting time
+and diophantine type subsists. Our results apply to L-shaped billiards.
+"
+25066,SEVEN: Deep Semi-supervised Verification Networks,"  Verification determines whether two samples belong to the same class or not,
+and has important applications such as face and fingerprint verification, where
+thousands or millions of categories are present but each category has scarce
+labeled examples, presenting two major challenges for existing deep learning
+models. We propose a deep semi-supervised model named SEmi-supervised
+VErification Network (SEVEN) to address these challenges. The model consists of
+two complementary components. The generative component addresses the lack of
+supervision within each category by learning general salient structures from a
+large amount of data across categories. The discriminative component exploits
+the learned general features to mitigate the lack of supervision within
+categories, and also directs the generative component to find more informative
+structures of the whole data manifold. The two components are tied together in
+SEVEN to allow an end-to-end training of the two components. Extensive
+experiments on four verification tasks demonstrate that SEVEN significantly
+outperforms other state-of-the-art deep semi-supervised techniques when labeled
+data are in short supply. Furthermore, SEVEN is competitive with fully
+supervised baselines trained with a larger amount of labeled data. It indicates
+the importance of the generative component in SEVEN.
+"
+25067,Multivariate Density Modeling for Retirement Finance,"  Prior to the financial crisis mortgage securitization models increased in
+sophistication as did products built to insure against losses. Layers of
+complexity formed upon a foundation that could not support it and as the
+foundation crumbled the housing market followed. That foundation was the
+Gaussian copula which failed to correctly model failure-time correlations of
+derivative securities in duress. In retirement, surveys suggest the greatest
+fear is running out of money and as retirement decumulation models become
+increasingly sophisticated, large financial firms and robo-advisors may
+guarantee their success. Similar to an investment bank failure the event of
+retirement ruin is driven by outliers and correlations in times of stress. It
+would be desirable to have a foundation able to support the increased
+complexity before it forms however the industry currently relies upon similar
+Gaussian (or lognormal) dependence structures. We propose a multivariate
+density model having fixed marginals that is tractable and fits data which are
+skewed, heavy-tailed, multimodal, i.e., of arbitrary complexity allowing for a
+rich correlation structure. It is also ideal for stress-testing a retirement
+plan by fitting historical data seeded with black swan events. A preliminary
+section reviews all concepts before they are used and fully documented C/C++
+source code is attached making the research self-contained. Lastly, we take the
+opportunity to challenge existing retirement finance dogma and also review some
+recent criticisms of retirement ruin probabilities and their suggested
+replacement metrics.
+"
+25068,Stability and $\ell^2$-gain Analysis of Adaptive Control Systems with Event-triggered Try-once-discard Protocols,"  This paper addresses the stability and $\ell^2$-gain analysis of adaptive
+control systems with event-triggered try-once-discard protocols. At every
+sampling time, an event trigger evaluates an error between the current value
+and the last released value of each measurement and determines whether to
+transmit the measurements and which measurements to transmit, based on the
+try-once-discard protocol and given lower and upper thresholds. For
+gain-scheduling controllers and switching controllers that are adaptive to the
+maximum error of the measurements, we obtain sufficient conditions for the
+practical stability and upper bounds on the $\ell^2$-gain of the closed-loop
+system.
+"
+25069,Detection of Generalized Synchronization using Echo State Networks,"  Generalized synchronization between coupled dynamical systems is a phenomenon
+of relevance in applications that range from secure communications to
+physiological modelling. Here we test the capabilities of reservoir computing
+and, in particular, echo state networks for the detection of generalized
+synchronization. A nonlinear dynamical system consisting of two coupled
+Rössler chaotic attractors is used to generate temporal series consisting of
+time-locked generalized synchronized sequences interleaved by unsynchronized
+ones. Correctly tuned, echo state networks are able to efficiently discriminate
+between unsynchronized and synchronized sequences. Compared to other
+state-of-the-art techniques of synchronization detection, the online
+capabilities of the proposed ESN based methodology make it a promising choice
+for real-time applications aiming to monitor dynamical synchronization changes
+in continuous signals.
+"
+25070,Equation level matching: An extension of the method of matched asymptotic expansion for problems of wave propagation,"  We introduce an alternative to the method of matched asymptotic expansions.
+In the ""traditional"" implementation, approximate solutions, valid in different
+(but overlapping) regions are matched by using ""intermediate"" variables. Here
+we propose to match at the level of the equations involved, via a ""uniform
+expansion"" whose equations enfold those of the approximations to be matched.
+This has the advantage that one does not need to explicitly solve the
+asymptotic equations to do the matching, which can be quite impossible for some
+problems. In addition, it allows matching to proceed in certain wave situations
+where the traditional approach fails because the time behaviors differ (e.g.,
+one of the expansions does not include dissipation). On the other hand, this
+approach does not provide the fairly explicit approximations resulting from
+standard matching. In fact, this is not even its aim, which to produce the
+""simplest"" set of equations that capture the behavior.
+"
+25071,Two-component instantaneous wall shear rate measurements in reversing flows,"  Validation experiments of the two-dimensional inverse algorithm are performed
+in a pulsed Poiseuille flow exposing shear reversal phases. The method is
+applied to the three-segment electrodiffusion (ED) probe for which a specific
+nondimensionalization process is suggested, allowing to better link
+measurements from a real ED probe to the modeled one in the inverse problem.
+This approach provided a two-component wall shear rate in good agreement with
+the one obtained from laser Doppler anemometry (LDA) measurements, thus
+validating the ability of ED probes to deal with high-amplitude unsteady flows.
+The classic linear velocity approximation ($u=sy$) in the probe vicinity is
+also investigated in such a flow.
+"
+25072,Absolute Ages and Distances of 22 GCs using Monte Carlo Main-Sequence Fitting,"  The recent Gaia Data Release 1 of stellar parallaxes provides ample
+opportunity to find metal-poor main-sequence stars with precise parallaxes. We
+select 21 such stars with parallax uncertainties better than
+$\sigma_\pi/\pi\leq0.10$ and accurate abundance determinations suitable for
+testing metal-poor stellar evolution models and determining the distance to
+Galactic globular clusters. A Monte Carlo analysis was used, taking into
+account uncertainties in the model construction parameters, to generate stellar
+models and isochrones to fit to the calibration stars. The isochrones which fit
+the calibration stars best were then used to determine the distances and ages
+of 22 globular clusters with metallicities ranging from -2.4 dex to -0.7 dex.
+We find distances with an average uncertainty of 0.15 mag and absolute ages
+ranging from 10.8 - 13.6 Gyr with an average uncertainty of 1.6 Gyr. Using
+literature proper motion data we calculate orbits for the clusters finding six
+that reside within the Galactic disk/bulge while the rest are considered halo
+clusters. We find no strong evidence for a relationship between age and
+Galactocentric distance, but we do find a decreasing age-[Fe/H] relation.
+"
+25073,Rapid Training of Very Large Ensembles of Diverse Neural Networks,"  Ensembles of deep neural networks with diverse architectures significantly
+improve generalization accuracy. However, training such ensembles requires a
+large amount of computational resources and time as every network in the
+ensemble has to be separately trained. In practice, this restricts the number
+of different deep neural network architectures that can be included within an
+ensemble. We propose a new approach to address this problem. Our approach
+captures the structural similarity between members of a neural network ensemble
+and train it only once. Subsequently, this knowledge is transferred to all
+members of the ensemble using function-preserving transformations. Then, these
+ensemble networks converge significantly faster as compared to training from
+scratch. We show through experiments on CIFAR-10, CIFAR-100, and SVHN data sets
+that our approach can train large and diverse ensembles of deep neural networks
+achieving comparable accuracy to existing approaches in a fraction of their
+training time. In particular, our approach trains an ensemble of $100$ variants
+of deep neural networks with diverse architectures up to $6 \times$ faster as
+compared to existing approaches. This improvement in training cost grows
+linearly with the size of the ensemble.
+"
+25074,Fine-grained reductions from approximate counting to decision,"  In this paper, we introduce a general framework for fine-grained reductions
+of approximate counting problems to their decision versions. (Thus we use an
+oracle that decides whether any witness exists to multiplicatively approximate
+the number of witnesses with minimal overhead.) This mirrors a foundational
+result of Sipser (STOC 1983) and Stockmeyer (SICOMP 1985) in the
+polynomial-time setting, and a similar result of Müller (IWPEC 2006) in the
+FPT setting. Using our framework, we obtain such reductions for some of the
+most important problems in fine-grained complexity: the Orthogonal Vectors
+problem, 3SUM, and the Negative-Weight Triangle problem (which is closely
+related to All-Pairs Shortest Path).
+We also provide a fine-grained reduction from approximate #SAT to SAT.
+Suppose the Strong Exponential Time Hypothesis (SETH) is false, so that for
+some $1<c<2$ and all $k$ there is an $O(c^n)$-time algorithm for k-SAT. Then we
+prove that for all $k$, there is an $O((c+o(1))^n)$-time algorithm for
+approximate #$k$-SAT. In particular, our result implies that the Exponential
+Time Hypothesis (ETH) is equivalent to the seemingly-weaker statement that
+there is no algorithm to approximate #3-SAT to within a factor of $1+\epsilon$
+in time $2^{o(n)}/\epsilon^2$ (taking $\epsilon > 0$ as part of the input).
+"
+25075,Evolutionary value of collective sensing,"  We propose a mathematical model for collective sensing in a population
+growing in a stochastically varying environment. In the population, individuals
+use an information channel for sensing the environment, and two channels for
+signal production and comprehension to communicate among themselves. We show
+that existence of such system has a positive effect on population growth, hence
+can have a positive evolutionary effect. We show that the gain in growth due to
+the collective sensing is related to information theoretic entities, which can
+be considered as the information content of this system from the environment.
+We further show that heterogeneity in communication resulted from network or
+spatial structure increases growth. We compute the growth rate of a population
+residing on a lattice and show that growth rate near the maximum noise level in
+observation or communication, increases exponentially as noise decreases. This
+exponential effect makes the emergence of collective observation an easy
+outcome in an evolutionary process. Furthermore, we are able to quantify
+interesting effects such as accelerated growth, and simplification of decision
+making due to information amplification by communication. Finally, we show that
+an amount of noise in representation formation has more disadvantageous effect
+compared to the same noise in signal production.
+"
+25076,Towards better understanding of gradient-based attribution methods for Deep Neural Networks,"  Understanding the flow of information in Deep Neural Networks (DNNs) is a
+challenging problem that has gain increasing attention over the last few years.
+While several methods have been proposed to explain network predictions, there
+have been only a few attempts to compare them from a theoretical perspective.
+What is more, no exhaustive empirical comparison has been performed in the
+past. In this work, we analyze four gradient-based attribution methods and
+formally prove conditions of equivalence and approximation between them. By
+reformulating two of these methods, we construct a unified framework which
+enables a direct comparison, as well as an easier implementation. Finally, we
+propose a novel evaluation metric, called Sensitivity-n and test the
+gradient-based attribution methods alongside with a simple perturbation-based
+attribution method on several datasets in the domains of image and text
+classification, using various network architectures.
+"
+25077,Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples,"  In robotics, it is essential to be able to plan efficiently in
+high-dimensional continuous state-action spaces for long horizons. For such
+complex planning problems, unguided uniform sampling of actions until a path to
+a goal is found is hopelessly inefficient, and gradient-based approaches often
+fall short when the optimization manifold of a given problem is not smooth. In
+this paper we present an approach that guides the search of a state-space
+planner, such as A*, by learning an action-sampling distribution that can
+generalize across different instances of a planning problem. The motivation is
+that, unlike typical learning approaches for planning for continuous action
+space that estimate a policy, an estimated action sampler is more robust to
+error since it has a planner to fall back on. We use a Generative Adversarial
+Network (GAN), and address an important issue: search experience consists of a
+relatively large number of actions that are not on a solution path and a
+relatively small number of actions that actually are on a solution path. We
+introduce a new technique, based on an importance-ratio estimation method, for
+using samples from a non-target distribution to make GAN learning more
+data-efficient. We provide theoretical guarantees and empirical evaluation in
+three challenging continuous robot planning problems to illustrate the
+effectiveness of our algorithm.
+"
+25078,Stochastic Coupon Probing in Social Networks,"  In this paper, we study stochastic coupon probing problem in social networks.
+Assume there is a social network and a set of coupons. We can offer coupons to
+some users adaptively and those users who accept the offer will act as seeds
+and influence their friends in the social network. There are two constraints
+which are called the inner and outer constraints, respectively. The set of
+coupons redeemed by users must satisfy inner constraints, and the set of all
+probed users must satisfy outer constraints. One seeks to develop a coupon
+probing policy that achieves the maximum influence while satisfying both inner
+and outer constraints. Our main result is a constant approximation policy for
+the stochastic coupon probing problem for any monotone submodular utility
+function.
+"
+25079,Wasserstein Distance Guided Representation Learning for Domain Adaptation,"  Domain adaptation aims at generalizing a high-performance learner on a target
+domain via utilizing the knowledge distilled from a source domain which has a
+different but related data distribution. One solution to domain adaptation is
+to learn domain invariant feature representations while the learned
+representations should also be discriminative in prediction. To learn such
+representations, domain adaptation frameworks usually include a domain
+invariant representation learning approach to measure and reduce the domain
+discrepancy, as well as a discriminator for classification. Inspired by
+Wasserstein GAN, in this paper we propose a novel approach to learn domain
+invariant feature representations, namely Wasserstein Distance Guided
+Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by
+the domain critic, to estimate empirical Wasserstein distance between the
+source and target samples and optimizes the feature extractor network to
+minimize the estimated Wasserstein distance in an adversarial manner. The
+theoretical advantages of Wasserstein distance for domain adaptation lie in its
+gradient property and promising generalization bound. Empirical studies on
+common sentiment and image classification adaptation datasets demonstrate that
+our proposed WDGRL outperforms the state-of-the-art domain invariant
+representation learning approaches.
+"
+25080,Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner,"  For homeland and transportation security applications, 2D X-ray explosive
+detection system (EDS) have been widely used, but they have limitations in
+recognizing 3D shape of the hidden objects. Among various types of 3D computed
+tomography (CT) systems to address this issue, this paper is interested in a
+stationary CT using fixed X-ray sources and detectors. However, due to the
+limited number of projection views, analytic reconstruction algorithms produce
+severe streaking artifacts. Inspired by recent success of deep learning
+approach for sparse view CT reconstruction, here we propose a novel image and
+sinogram domain deep learning architecture for 3D reconstruction from very
+sparse view measurement. The algorithm has been tested with the real data from
+a prototype 9-view dual energy stationary CT EDS carry-on baggage scanner
+developed by GEMSS Medical Systems, Korea, which confirms the superior
+reconstruction performance over the existing approaches.
+"
+25081,Semi-Supervised Model Training for Unbounded Conversational Speech Recognition,"  For conversational large-vocabulary continuous speech recognition (LVCSR)
+tasks, up to about two thousand hours of audio is commonly used to train state
+of the art models. Collection of labeled conversational audio however, is
+prohibitively expensive, laborious and error-prone. Furthermore, academic
+corpora like Fisher English (2004) or Switchboard (1992) are inadequate to
+train models with sufficient accuracy in the unbounded space of conversational
+speech. These corpora are also timeworn due to dated acoustic telephony
+features and the rapid advancement of colloquial vocabulary and idiomatic
+speech over the last decades. Utilizing the colossal scale of our unlabeled
+telephony dataset, we propose a technique to construct a modern, high quality
+conversational speech training corpus on the order of hundreds of millions of
+utterances (or tens of thousands of hours) for both acoustic and language model
+training. We describe the data collection, selection and training, evaluating
+the results of our updated speech recognition system on a test corpus of 7K
+manually transcribed utterances. We show relative word error rate (WER)
+reductions of {35%, 19%} on {agent, caller} utterances over our seed model and
+5% absolute WER improvements over IBM Watson STT on this conversational speech
+task.
+"
+25082,Language and Noise Transfer in Speech Enhancement Generative Adversarial Network,"  Speech enhancement deep learning systems usually require large amounts of
+training data to operate in broad conditions or real applications. This makes
+the adaptability of those systems into new, low resource environments an
+important topic. In this work, we present the results of adapting a speech
+enhancement generative adversarial network by finetuning the generator with
+small amounts of data. We investigate the minimum requirements to obtain a
+stable behavior in terms of several objective metrics in two very different
+languages: Catalan and Korean. We also study the variability of test
+performance to unseen noise as a function of the amount of different types of
+noise available for training. Results show that adapting a pre-trained English
+model with 10 min of data already achieves a comparable performance to having
+two orders of magnitude more data. They also demonstrate the relative stability
+in test performance with respect to the number of training noise types.
+"
+25083,Modular finite $W$-algebras,"  Let $k$ be an algebraically closed field of characteristic $p > 0$ and let
+$G$ be a connected reductive algebraic group over $k$. Under some standard
+hypothesis on $G$, we give a direct approach to the finite $W$-algebra
+$U(\mathfrak g,e)$ associated to a nilpotent element $e \in \mathfrak g =
+\operatorname{Lie} G$. We prove a PBW theorem and deduce a number of
+consequences, then move on to define and study the $p$-centre of $U(\mathfrak
+g,e)$, which allows us to define reduced finite $W$-algebras $U_\eta(\mathfrak
+g,e)$ and we verify that they coincide with those previously appearing in the
+work of Premet. Finally, we prove a modular version of Skryabin's equivalence
+of categories, generalizing recent work of the second author.
+"
+25084,Construction of quasi-potentials for stochastic dynamical systems: an optimization approach,"  The construction of effective and informative landscapes for stochastic
+dynamical systems has proven a long-standing and complex problem. In many
+situations, the dynamics may be described by a Langevin equation while
+constructing a landscape comes down to obtaining the quasi-potential, a scalar
+function that quantifies the likelihood of reaching each point in the
+state-space. In this work we provide a novel method for constructing such
+landscapes by extending a tool from control theory: the Sum-of-Squares method
+for generating Lyapunov functions. Applicable to any system described by
+polynomials, this method provides an analytical polynomial expression for the
+potential landscape, in which the coefficients of the polynomial are obtained
+via a convex optimization problem. The resulting landscapes are based upon a
+decomposition of the deterministic dynamics of the original system, formed in
+terms of the gradient of the potential and a remaining ""curl"" component. By
+satisfying the condition that the inner product of the gradient of the
+potential and the remaining dynamics is everywhere negative, our derived
+landscapes provide both upper and lower bounds on the true quasi-potential;
+these bounds becoming tight if the decomposition is orthogonal. The method is
+demonstrated to correctly compute the quasi-potential for high-dimensional
+linear systems and also for a number of nonlinear examples.
+"
+25085,Two-stage Algorithm for Fairness-aware Machine Learning,"  Algorithmic decision making process now affects many aspects of our lives.
+Standard tools for machine learning, such as classification and regression, are
+subject to the bias in data, and thus direct application of such off-the-shelf
+tools could lead to a specific group being unfairly discriminated. Removing
+sensitive attributes of data does not solve this problem because a
+\textit{disparate impact} can arise when non-sensitive attributes and sensitive
+attributes are correlated. Here, we study a fair machine learning algorithm
+that avoids such a disparate impact when making a decision. Inspired by the
+two-stage least squares method that is widely used in the field of economics,
+we propose a two-stage algorithm that removes bias in the training data. The
+proposed algorithm is conceptually simple. Unlike most of existing fair
+algorithms that are designed for classification tasks, the proposed method is
+able to (i) deal with regression tasks, (ii) combine explanatory attributes to
+remove reverse discrimination, and (iii) deal with numerical sensitive
+attributes. The performance and fairness of the proposed algorithm are
+evaluated in simulations with synthetic and real-world datasets.
+"
+25086,An optimal regularity result on the quasi-invariant Gaussian measures for the cubic fourth order nonlinear Schrödinger equation,"  We study the transport properties of the Gaussian measures on Sobolev spaces
+under the dynamics of the cubic fourth order nonlinear Schrödinger equation
+on the circle. In particular, we establish an optimal regularity result for
+quasi-invariance of the mean-zero Gaussian measures on Sobolev spaces. The main
+new ingredient is an improved energy estimate established by performing an
+infinite iteration of normal form reductions on the energy functional.
+Furthermore, we show that the dispersion is essential for such a
+quasi-invariance result by proving non quasi-invariance of the Gaussian
+measures under the dynamics of the dispersionless model.
+"
+25087,A Social Recommender System based on Bhattacharyya Coefficient,"  Recommender systems play a significant role in providing the appropriate data
+for each user among a huge amount of information. One of the important roles of
+a recommender system is to predict the preference of each user to some specific
+data. Some of these systems concentrate on user-item networks that each user
+rates some items. The main step for item recommendation is to predict the rate
+of unrated items. Each recommender system utilizes different criteria such as
+the similarity between users or social relations in the process of rate
+prediction. As social connections of each user affect his behaviors, it can be
+a valuable source to use in rate prediction. In this paper, we will provide a
+new social recommender system which uses Bhattacharyya coefficient in
+similarity computing to be able to evaluate similarity in sparse data and
+between users without co-rated items as well as integrating social ties into
+the rating prediction process.
+"
+25088,Cosmology with the Large Synoptic Survey Telescope: an Overview,"  The Large Synoptic Survey Telescope (LSST) is a high etendue imaging facility
+that is being constructed atop Cerro Pachon in Northern Chile. It is scheduled
+to begin science operations in 2022. With an 8.4m (6.5m effective) aperture, a
+three-mirror design achieving a seeing-limited 9.6deg^2 field of view, and a
+3.2 Gigapixel camera, the LSST has the deep-wide-fast imaging capability to
+carry out an 18,000deg^2 survey in six passbands (ugrizy) to a coadded depth of
+r~27.5 over 10 years using 90% of its observational time. The remaining 10%
+will be devoted to deeper and faster time-domain observations and smaller
+surveys. In total, each patch of the sky in the main survey will receive 800
+visits allocated across the six passbands with 30s exposure visits.
+The huge volume of high-quality LSST data will provide a wide range of
+science opportunities and open a new era of precision cosmology with
+unprecedented statistical power and tight control of systematic errors. In this
+review, we give a brief account of the LSST cosmology program with an emphasis
+on dark energy investigations. The LSST will address dark energy physics and
+cosmology in general by exploiting diverse precision probes including
+large-scale structure, weak lensing, type Ia supernovae, galaxy clusters, and
+strong lensing. Combined with the cosmic microwave background data, these
+probes form interlocking tests on the cosmological model and the nature of dark
+energy.
+The LSST data products will be made available to the U.S. and Chilean
+scientific communities and to international partners with no proprietary
+period. Close collaborations with coeval surveys observing at a variety of
+wavelengths, resolutions, depths, and timescales will be a vital part of the
+LSST science program, which will not only enhance specific studies but also
+allow a more complete understanding of the universe through different windows.
+"
+25089,K-method of calculating the mutual influence of nodes in a directed weight complex networks,"  A new characteristic of paired nodes in a directed weight complex network is
+considered. A method (named as K-method) of the characteristics calculation for
+complex networks is proposed. The method is based on transforming the initial
+network with the subsequent application of the Kirchhoff rules. The scope of
+the method for sparse complex networks is proposed. The nodes of these complex
+networks are concepts of the real world, and the connections have a
+cause-effect character of the so-called ""cognitive maps"". Two new
+characteristics of concept nodes having a semantic interpretation are proposed,
+namely ""pressure"" and ""influence"" taking into account the influence of all
+nodes on each other.
+"
+25090,SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization,"  We propose and analyze a new stochastic gradient method, which we call
+Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum
+optimization problems. SUCAG constitutes an unbiased total gradient tracking
+technique that uses Hessian information to accelerate con- vergence. We analyze
+our method under the general asynchronous model of computation, in which each
+function is selected infinitely often with possibly unbounded (but sublinear)
+delay. For strongly convex problems, we establish linear convergence for the
+SUCAG method. When the initialization point is sufficiently close to the
+optimal solution, the established convergence rate is only dependent on the
+condition number of the problem, making it strictly faster than the known rate
+for the SAGA method. Furthermore, we describe a Markov-driven approach of
+implementing the SUCAG method in a distributed asynchronous multi-agent
+setting, via gossiping along a random walk on an undirected communication
+graph. We show that our analysis applies as long as the graph is connected and,
+notably, establishes an asymptotic linear convergence rate that is robust to
+the graph topology. Numerical results demonstrate the merits of our algorithm
+over existing methods.
+"
+25091,The shortness of human life constitutes its limit,"  In this paper, we affirm our earlier findings of evidence for a limit to
+human lifespan. In particular, we assess the analyses in extreme value theory
+(EVT) performed by Rootzén and Zholud. We find that their criticisms of our
+work are unfounded and that their analyses are contradicted by several other
+papers using EVT. Furthermore, we find that even if we completely accept the
+conclusions about late-life human mortality reached by Rootzén and Zholud,
+their results do not actually contradict the findings presented in our original
+paper: whether unbounded or not, human lifespan is unlikely to greatly exceed
+120 years, and the improbability of longer survival---whether it is exactly
+zero or merely astronomically small---acts as a de facto limit. In order to
+eliminate the confusion surrounding the issue, we propose the adoption of the
+term ""limit"" to denote the age at which the chance of survival is exactly zero
+and the term ""effective limit"" to denote the age at which the change of
+survival falls below a given threshold. Once this distinction is made, it can
+be demonstrated that the final result of Rootzén and Zholud is essentially a
+recapitulation of the main conclusion of our paper. Ultimately, much of the
+controversy surrounding the issue of a limit to human lifespan can be avoided
+by carefully reading the literature and applying statistics to practical human
+scales.
+"
+25092,Generalized Degrees-of-Freedom of the 2-User Case MISO Broadcast Channel with Distributed CSIT,"  This work analyses the Generalized Degrees-of-Freedom (GDoF) of the 2-User
+Multiple-Input Single-Output (MISO) Broadcast Channel (BC) in the so-called
+Distributed CSIT regime, with application to decentralized wireless networks.
+This regime differs from the classical limited CSIT one in that the CSIT is not
+just noisy but also imperfectly shared across the transmitters (TXs). Hence,
+each TX precodes data on the basis of local CSIT and statistical quality
+information at other TXs. We derive the GDoF result and obtain the surprising
+outcome that by specific accounting of the pathloss information, it becomes
+possible for the decentralized precoded network to reach the same performance
+as a genie-aided centralized network where the central node has obtained the
+estimates of both TXs. The key ingredient in the scheme is the so-called
+Active-Passive Zero-Forcing (AP-ZF) precoding, which lets the precoder design
+adapt optimally with respect to different local CSIT qualities available at
+different TXs.
+"
+25093,Towards ontology based BPMN Implementation,"  Natural language is understandable by human and not machine. None technical
+persons can only use natural language to specify their business requirements.
+However, the current version of Business process management and notation (BPMN)
+tools do not allow business analysts to implement their business processes
+without having technical skills. BPMN tool is a tool that allows users to
+design and implement the business processes by connecting different business
+tasks and rules together. The tools do not provide automatic implementation of
+business tasks from users' specifications in natural language (NL). Therefore,
+this research aims to propose a framework to automatically implement the
+business processes that are expressed in NL requirements. Ontology is used as a
+mechanism to solve this problem by comparing between users' requirements and
+web services' descriptions. Web service is a software module that performs a
+specific task and ontology is a concept that defines the relationships between
+different terms.
+"
+25094,Three dimensional photonic Dirac points in metamaterials,"  Topological semimetals, representing a new topological phase that lacks a
+full bandgap in bulk states and exhibiting nontrivial topological orders,
+recently have been extended to photonic systems, predominantly in photonic
+crystals and to a lesser extent, metamaterials. Photonic crystal realizations
+of Dirac degeneracies are protected by various space symmetries, where Bloch
+modes span the spin and orbital subspaces. Here, we theoretically show that
+Dirac points can also be realized in effective media through the intrinsic
+degrees of freedom in electromagnetism under electromagnetic duality. A pair of
+spin polarized Fermi arc like surface states is observed at the interface
+between air and the Dirac metamaterials. These surface states show linear
+k-space dispersion relation, resulting in nearly diffraction-less propagation.
+Furthermore, eigen reflection fields show the decomposition from a Dirac point
+to two Weyl points. We also find the topological correlation between a Dirac
+point and vortex/vector beams in classic photonics. The theoretical proposal of
+photonic Dirac point lays foundation for unveiling the connection between
+intrinsic physics and global topology in electromagnetism.
+"
+25095,A new estimate for the constants of an inequality due to Hardy and Littlewood,"  In this paper we provide a family of inequalities, extending a recent result
+due to Albuquerque et al.
+"
+25096,Quantum efficiency modeling for a thick back-illuminated astronomical CCD,"  The quantum efficiency and reflectivity of thick, back-illuminated CCD's
+being fabricated at LBNL for astronomical applications are modeled and compared
+with experiment. The treatment differs from standard thin-film optics in that
+(a) absorption is permitted in any film, (b) the 200--500~$\mu$m thick silicon
+substrate is considered as a thin film in order to observe the fringing
+behavior at long wavelengths, and (c) by using approximate boundary conditions,
+absorption in the surface films is separated from absorption in the substrate.
+For the quantum efficiency measurements the CCD's are normally operated as
+CCD's, usually at $T = -140^\circ$C, and at higher temperatures as photodiodes.
+They are mounted on mechanical substrates. Reflectivity is measured on
+air-backed wafer samples at room temperature. The agreement between model
+expectation and quantum efficiency measurement is in general satisfactory.
+"
+25097,Minimum--Entropy Couplings and their Applications,"  Given two discrete random variables $X$ and $Y,$ with probability
+distributions ${\bf p}=(p_1, \ldots , p_n)$ and ${\bf q}=(q_1, \ldots , q_m)$,
+respectively, denote by ${\cal C}({\bf p}, {\bf q})$ the set of all couplings
+of ${\bf p}$ and ${\bf q}$, that is, the set of all bivariate probability
+distributions that have ${\bf p}$ and ${\bf q}$ as marginals. In this paper, we
+study the problem of finding a joint probability distribution in ${\cal C}({\bf
+p}, {\bf q})$ of \emph{minimum entropy} (equivalently, a coupling that
+\emph{maximizes} the mutual information between $X$ and $Y$), and we discuss
+several situations where the need for this kind of optimization naturally
+arises. Since the optimization problem is known to be NP-hard, we give an
+efficient algorithm to find a joint probability distribution in ${\cal C}({\bf
+p}, {\bf q})$ with entropy exceeding the minimum possible at most by {1 bit},
+thus providing an approximation algorithm with an additive gap of at most 1
+bit. Leveraging on this algorithm, we extend our result to the problem of
+finding a minimum--entropy joint distribution of arbitrary $k\geq 2$ discrete
+random variables $X_1, \ldots , X_k$, consistent with the known $k$ marginal
+distributions of the individual random variables $X_1, \ldots , X_k$. In this
+case, our algorithm has an { additive gap of at most $\log k$ from optimum.}
+We also discuss several related applications of our findings and {extensions
+of our results to entropies different from the Shannon entropy.}
+"
+25098,AGN luminosity and stellar age -- two missing ingredients for AGN unification as seen with iPTF supernovae,"  Active Galactic Nuclei (AGN) are extremely powerful cosmic objects, driven by
+accretion of hot gas upon super-massive black holes. The zoo of AGN classes are
+divided into two major groups, with Type-1 AGN displaying broad Balmer emission
+lines and Type-2 narrow ones. For a long time it was believed that a Type-2 AGN
+is a Type-1 AGN viewed through a dusty kiloparsec-size torus, but an emerging
+body of observations suggests more than just the viewing angle matters. Here we
+report significant differences in supernova counts and classes in the first
+study to date of supernovae near Type-1 and Type-2 AGN host galaxies, using
+data from the intermediate Palomar Transient Factory, the Sloan Digital Sky
+Survey Data Release 7 and Galaxy Zoo. We detect many more supernovae in Type-2
+AGN hosts (size of effect $\sim$ 5.1$\sigma$) compared to Type-1 hosts, which
+shows that the two classes of AGN are located inside host galaxies with
+different properties. In addition, Type-1 and Type-2 AGN that are dominated by
+star formation according to WISE colours $m_{W1} - m_{W2} < 0.5$ and are
+matched in 22 $\mu$m absolute magnitude differ by a factor of ten in
+$L$[OIII]$\lambda$5007 luminosity, suggesting that when residing in similar
+type of host galaxies Type-1 AGN are much more luminous. Our results
+demonstrate two more factors that play an important role in completing the
+current picture: the age of stellar populations and the AGN luminosity. This
+has immediate consequences for understanding the many AGN classes and galaxy
+evolution.
+"
+25099,Denoising random forests,"  This paper proposes a novel type of random forests called a denoising random
+forests that are robust against noises contained in test samples. Such
+noise-corrupted samples cause serious damage to the estimation performances of
+random forests, since unexpected child nodes are often selected and the leaf
+nodes that the input sample reaches are sometimes far from those for a clean
+sample. Our main idea for tackling this problem originates from a binary
+indicator vector that encodes a traversal path of a sample in the forest. Our
+proposed method effectively employs this vector by introducing denoising
+autoencoders into random forests. A denoising autoencoder can be trained with
+indicator vectors produced from clean and noisy input samples, and non-leaf
+nodes where incorrect decisions are made can be identified by comparing the
+input and output of the trained denoising autoencoder. Multiple traversal paths
+with respect to the nodes with incorrect decisions caused by the noises can
+then be considered for the estimation.
+"
+25100,Performance Analysis of Non-Orthogonal Multiple Access in Free Space Optical Communication System,"  In this paper, a multipoint-to-point system consisting K users and a central
+node with non-orthogonal multiple access (NOMA) technique over free space
+optical (FSO) channel is characterized for two cases. In the case of guaranteed
+user's quality of service (QoS), the outage probability is derived. Another
+case is that user's QoS is determined by their own channel conditions. The
+developed analytical results of ergodic sum data rate show that NOMA
+outperforms orthogonal multiple access (OMA). Besides, the power control scheme
+is proposed, and results show that the power control scheme has great influence
+to the user's QoS. Monto Carlo simulation has been done, and it matches quite
+well with the theoretical analysis.
+"
+25101,Pseudo-Hermitian Reduction of a Generalized Heisenberg Ferromagnet Equation. I. Auxiliary System and Fundamental Properties,"  We consider an auxiliary spectral problem originally introduced by Gerdjikov,
+Mikhailov and Valchev (GMV system) and its modification called pseudo-Hermitian
+reduction which is extensively studied here for the first time. We describe the
+integrable hierarchies of both systems in a parallel way and construct
+recursion operators. Using the concept of gauge equivalence, we construct
+expansions over the eigenfunctions of recursion operators. This permits us to
+obtain the expansions for both GMV systems with arbitrary constant asymptotic
+values of the potential functions in the auxiliary linear problems.
+"
+25102,Stochastic control in high-dimensional statistical arbitrage under an Ornstein-Uhlenbeck process,"  The present paper aims to provide the first systematic study of
+high-dimensional statistical arbitrage using both factor models and tools from
+stochastic control theory, obtaining closed-form optimal strategies in most
+cases and extending in multiple new directions the existing literature. Our
+setup is based on a general statistically-constructed factor model with
+mean-reverting residuals, in which we show how to construct analytically
+market-neutral portfolios and we analyze the problem of investing both in
+continuous time and along a discrete sequence of optimally chosen deterministic
+times, with different utilities and finite horizon. We also discuss how to
+introduce various constraints on the investor's portfolio and temporary
+quadratic transaction costs in both discrete and continuous time, provide Monte
+Carlo simulations of the previous strategies, and describe further extensions
+of our work.
+"
+25103,Transparent perovskite barium stannate with high electron mobility and thermal stability,"  Transparent conducting oxides (TCOs) and transparent oxide semiconductors
+(TOSs) have become necessary materials for a variety of applications in the
+information and energy technologies, ranging from transparent electrodes to
+active electronics components. Perovskite barium stannate (BaSnO3), a new TCO
+or TOS system, is a potential platform for realizing optoelectronic devices and
+observing novel electronic quantum states due to its high electron mobility,
+excellent thermal stability, high transparency, structural versatility, and
+flexible doping controllability at room temperature. This article reviews
+recent progress in the doped BaSnO3 system, discussing the wide physical
+properties, electron-scattering mechanism, and demonstration of key
+semiconducting devices such as pn diodes and field-effect transistors.
+Moreover, we discuss the pathways to achieving two-dimensional electron gases
+at the interface between BaSnO3 and other perovskite oxides and describe
+remaining challenges for observing novel quantum phenomena at the
+heterointerface.
+"
+25104,"Network Traffic Forensics on Firefox Mobile OS: Facebook, Twitter and Telegram as Case Studies","  Development of mobile web-centric OS such as Firefox OS has created new
+challenges, and opportunities for digital investigators. Network traffic
+forensic plays an important role in cybercrime investigation to detect
+subject(s) and object(s) of the crime. In this chapter, we detect and analyze
+residual network traffic artefacts of Firefox OS in relation to two popular
+social networking applications (Facebook and Twitter) and one instant messaging
+application (Telegram). We utilized a Firefox OS simulator to generate relevant
+traffic while all communication data were captured using network monitoring
+tools. Captured network packets were examined and remnants with forensic value
+were reported. This paper as the first focused study on mobile Firefox OS
+network traffic analysis should pave the way for the future research in this
+direction.
+"
+25105,Internal control of systems of semilinear coupled 1-D wave equations,"  We prove the internal controllability of some systems of two coupled wave
+equations in one space dimension, with one control, under certain conditions on
+the coupling. To do this we apply the ""fictitious control method"" in two cases:
+general systems with a ""non-degenerate"" coupling, and a particular case where
+the coupling is ""degenerate"", namely a cubic coupling.
+In the latter case, our proof requires to find nontrivial trajectories of the
+control system that go from $0$ to $0$. We build these trajectories by adapting
+(in $1$ space dimension) a construction developed by Jean-Michel Coron, Sergio
+Guerrero and Lionel Rosier for the study of coupled parabolic systems.
+"
+25106,An Analysis of Dropout for Matrix Factorization,"  Dropout is a simple yet effective algorithm for regularizing neural networks
+by randomly dropping out units through Bernoulli multiplicative noise, and for
+some restricted problem classes, such as linear or logistic regression, several
+theoretical studies have demonstrated the equivalence between dropout and a
+fully deterministic optimization problem with data-dependent Tikhonov
+regularization. This work presents a theoretical analysis of dropout for matrix
+factorization, where Bernoulli random variables are used to drop a factor,
+thereby attempting to control the size of the factorization. While recent work
+has demonstrated the empirical effectiveness of dropout for matrix
+factorization, a theoretical understanding of the regularization properties of
+dropout in this context remains elusive. This work demonstrates the equivalence
+between dropout and a fully deterministic model for matrix factorization in
+which the factors are regularized by the sum of the product of the norms of the
+columns. While the resulting regularizer is closely related to a variational
+form of the nuclear norm, suggesting that dropout may limit the size of the
+factorization, we show that it is possible to trivially lower the objective
+value by doubling the size of the factorization. We show that this problem is
+caused by the use of a fixed dropout rate, which motivates the use of a rate
+that increases with the size of the factorization. Synthetic experiments
+validate our theoretical findings.
+"
+25107,On qualitative properties of solutions for elliptic problems with the $p$-Laplacian through domain perturbations,"  We study the dependence of least nontrivial critical levels of the energy
+functional corresponding to the zero Dirichlet problem $-\Delta_p u = f(u)$ in
+a bounded domain $\Omega \subset \mathbb{R}^N$ upon domain perturbations. The
+nonlinearity $f$ is assumed to be superlinear and subcritical. We show that
+among all (generally eccentric) spherical annuli $\Omega$ least nontrivial
+critical levels attain maximums if and only if $\Omega$ is concentric. As a
+consequence of this fact we prove the nonradiality of least energy nodal
+solutions whenever $\Omega$ is a ball or concentric annulus.
+"
+25108,Model Based Diagnosis of Multiple Observations with Implicit Hitting Sets,"  Model based diagnosis finds a growing range of practical applications, and
+significant performance-wise improvements have been achieved in recent years.
+Some of these improvements result from formulating the problem with maximum
+satisfiability (MaxSAT). Whereas recent work focuses on analyzing failing
+observations separately, it is also the case that in practical settings there
+may exist many failing observations. This paper first investigates the
+drawbacks of analyzing failing observations separately. It then shows that
+existing solutions do not scale for large systems. Finally, the paper proposes
+a novel approach for diagnosing systems with many failing observations. The
+proposed approach is based on implicit hitting sets and so is tightly related
+with the original seminal work on model based diagnosis. The experimental
+results demonstrate not only the importance of analyzing multiple observations
+simultaneously, but also the significance of the implicit hitting set approach.
+"
+25109,Geometric formulation of the Cauchy invariants for incompressible Euler flow in flat and curved spaces,"  Cauchy invariants are now viewed as a powerful tool for investigating the
+Lagrangian structure of three-dimensional (3D) ideal flow (Frisch &
+Zheligovsky, Commun. Math. Phys., vol. 326, 2014, pp. 499-505, Podvigina et
+al., J. Comput. Phys., vol. 306, 2016, pp. 320-342). Looking at such invariants
+with the modern tools of differential geometry and of geodesic flow on the
+space SDiff of volume-preserving transformations (Arnold, Ann. Inst. Fourier,
+vol. 16, 1966, pp. 319-361), all manners of generalisations are here derived.
+The Cauchy invariants equation and the Cauchy formula, relating the vorticity
+and the Jacobian of the Lagrangian map, are shown to be two expressions of this
+Lie-advection invariance, which are duals of each other (specifically, Hodge
+dual). Actually, this is shown to be an instance of a general result, which
+holds for flow both in flat (Euclidean) space and in a curved Riemannian space:
+any Lie-advection invariant p-form which is exact (i.e. is a differential of a
+(p-1)-form) has an associated Cauchy invariants equation and a Cauchy formula.
+This constitutes a new fundamental result in linear transport theory, providing
+a Lagrangian formulation of Lie advection for some classes of differential
+forms. The result has a broad applicability: examples include the
+magnetohydrodynamics (MHD) equations and various extensions thereof, discussed
+by Lingam et al. (Phys. Lett. A, vol. 380, 2016, pp. 2400-2406), and include
+also the equations of Tao (2016, arXiv:1606.08481 [math.AP]), Euler equations
+with modified Biot-Savart law, displaying finite-time blow-up. Our main result
+is also used for new derivations, and several new results, concerning local
+helicity-type invariants for fluids and MHD flow in flat or curved spaces of
+arbitrary dimension.
+"
+25110,Intersections of Class Fields,"  Using class field theory, we prove a restriction on the intersection of the
+maximal abelian extensions associated with different number fields. This
+restriction is then used to improve a result of Rosen and Silverman about the
+linear independence of Heegner points. In addition, it yields effective
+restrictions for the special points lying on an algebraic subvariety in a
+product of modular curves. The latter application is related to the
+André-Oort conjecture.
+"
+25111,An Interface-Tracking Technique for Multiphase Flow with Soluble Surfactant,"  We adapt and extend a formulation for soluble surfactant transport in
+multiphase flows recently presented by Muradoglu & Tryggvason (JCP 274 (2014)
+737-757) to the context of the Level Contour Reconstruction Method (Shin et al.
+IJNMF 60 (2009) 753-778) which is a hybrid method that combines the advantages
+of the Front-tracking and Level Set methods. Particularly close attention is
+paid to the formulation and numerical implementation of the surface gradients
+of surfactant concentration and surface tension. Various benchmark tests are
+performed to demonstrate the accuracy of different elements of the algorithm.
+To verify surfactant mass conservation, values for surfactant diffusion along
+the interface are compared with the exact solution for the problem of uniform
+expansion of a sphere. The numerical implementation of the discontinuous
+boundary condition for the source term in the bulk concentration is compared
+with the approximate solution. Surface tension forces are tested for Marangoni
+drop translation. Our numerical results for drop deformation in simple shear
+are compared with experiments and results from previous simulations. All
+benchmarking tests compare well with existing data thus providing confidence
+that our adapted LCRM formulation for surfactant advection and diffusion is
+accurate and effective in three-dimensional multiphase flows. We also
+demonstrate that this approach applies easily to massively parallel
+simulations.
+"
+25112,Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network,"  This paper proposes to use low-level spatial features extracted from
+multichannel audio for sound event detection. We extend the convolutional
+recurrent neural network to handle more than one type of these multichannel
+features by learning from each of them separately in the initial stages. We
+show that instead of concatenating the features of each channel into a single
+feature vector the network learns sound events in multichannel audio better
+when they are presented as separate layers of a volume. Using the proposed
+spatial features over monaural features on the same network gives an absolute
+F-score improvement of 6.1% on the publicly available TUT-SED 2016 dataset and
+2.7% on the TUT-SED 2009 dataset that is fifteen times larger.
+"
+25113,Quantization Design and Channel Estimation for Massive MIMO Systems with One-Bit ADCs,"  We consider the problem of channel estimation for uplink multiuser massive
+MIMO systems, where, in order to significantly reduce the hardware cost and
+power consumption, one-bit analog-to-digital converters (ADCs) are used at the
+base station (BS) to quantize the received signal. Channel estimation for
+one-bit massive MIMO systems is challenging due to the severe distortion caused
+by the coarse quantization. It was shown in previous studies that an extremely
+long training sequence is required to attain an acceptable performance. In this
+paper, we study the problem of optimal one-bit quantization design for channel
+estimation in one-bit massive MIMO systems. Our analysis reveals that, if the
+quantization thresholds are optimally devised, using one-bit ADCs can achieve
+an estimation error close to (with an increase by a factor of $\pi/2$) that of
+an ideal estimator which has access to the unquantized data. The optimal
+quantization thresholds, however, are dependent on the unknown channel
+parameters. To cope with this difficulty, we propose an adaptive quantization
+(AQ) approach in which the thresholds are adaptively adjusted in a way such
+that the thresholds converge to the optimal thresholds, and a random
+quantization (RQ) scheme which randomly generate a set of nonidentical
+thresholds based on some statistical prior knowledge of the channel. Simulation
+results show that, our proposed AQ and RQ schemes, owing to their wisely
+devised thresholds, present a significant performance improvement over the
+conventional fixed quantization scheme that uses a fixed (typically zero)
+threshold, and meanwhile achieve a substantial training overhead reduction for
+channel estimation. In particular, even with a moderate number of pilot symbols
+(about 5 times the number of users), the AQ scheme can provide an achievable
+rate close to that of the perfect channel state information (CSI) case.
+"
+25114,Equivariant Euler characteristics of subspace posets,"  We compute the (primary) equivariant Euler characteristics of the building
+for the general linear group over a finite field.
+"
+25115,Ultrafast photonic reinforcement learning based on laser chaos,"  Reinforcement learning involves decision making in dynamic and uncertain
+environments, and constitutes one important element of artificial intelligence
+(AI). In this paper, we experimentally demonstrate that the ultrafast chaotic
+oscillatory dynamics of lasers efficiently solve the multi-armed bandit problem
+(MAB), which requires decision making concerning a class of difficult
+trade-offs called the exploration-exploitation dilemma. To solve the MAB, a
+certain degree of randomness is required for exploration purposes. However,
+pseudo-random numbers generated using conventional electronic circuitry
+encounter severe limitations in terms of their data rate and the quality of
+randomness due to their algorithmic foundations. We generate laser chaos
+signals using a semiconductor laser sampled at a maximum rate of 100 GSample/s,
+and combine it with a simple decision-making principle called tug-of-war with a
+variable threshold, to ensure ultrafast, adaptive and accurate decision making
+at a maximum adaptation speed of 1 GHz. We found that decision-making
+performance was maximized with an optimal sampling interval, and we highlight
+the exact coincidence between the negative autocorrelation inherent in laser
+chaos and decision-making performance. This study paves the way for a new realm
+of ultrafast photonics in the age of AI, where the ultrahigh bandwidth of
+photons can provide new value.
+"
+25116,QPTAS and Subexponential Algorithm for Maximum Clique on Disk Graphs,"  A (unit) disk graph is the intersection graph of closed (unit) disks in the
+plane. Almost three decades ago, an elegant polynomial-time algorithm was found
+for \textsc{Maximum Clique} on unit disk graphs [Clark, Colbourn, Johnson;
+Discrete Mathematics '90]. Since then, it has been an intriguing open question
+whether or not tractability can be extended to general disk graphs. We show the
+rather surprising structural result that a disjoint union of cycles is the
+complement of a disk graph if and only if at most one of those cycles is of odd
+length. From that, we derive the first QPTAS and subexponential algorithm
+running in time $2^{\tilde{O}(n^{2/3})}$ for \textsc{Maximum Clique} on disk
+graphs. In stark contrast, \textsc{Maximum Clique} on intersection graphs of
+filled ellipses or filled triangles is unlikely to have such algorithms, even
+when the ellipses are close to unit disks. Indeed, we show that there is a
+constant approximation which is not attainable even in time
+$2^{n^{1-\varepsilon}}$, unless the Exponential Time Hypothesis fails.
+"
+25117,Acoustic Scene Classification: A Competition Review,"  In this paper we study the problem of acoustic scene classification, i.e.,
+categorization of audio sequences into mutually exclusive classes based on
+their spectral content. We describe the methods and results discovered during a
+competition organized in the context of a graduate machine learning course;
+both by the students and external participants. We identify the most suitable
+methods and study the impact of each by performing an ablation study of the
+mixture of approaches. We also compare the results with a neural network
+baseline, and show the improvement over that. Finally, we discuss the impact of
+using a competition as a part of a university course, and justify its
+importance in the curriculum based on student feedback.
+"
+25118,Inverse Design of Near Unity Efficiency Perfectly Vertical Grating Couplers,"  Efficient coupling between integrated optical waveguides and optical fibers
+is essential to the success of integrated photonics. While many solutions
+exist, perfectly vertical grating couplers which scatter light out of a
+waveguide in the direction normal to the waveguide's top surface are an ideal
+candidate due to their potential to reduce packaging complexity. Designing such
+couplers with high efficiency, however, has proven difficult. In this paper, we
+use electromagnetic inverse design techniques to optimize a high efficiency
+two-layer perfectly vertical silicon grating coupler. Our base design achieves
+a chip-to-fiber coupling efficiency of over 99% (-0.04 dB) at 1550 nm. Using
+this base design, we apply subsequent constrained optimizations to achieve
+vertical couplers with over 96% efficiency which are fabricable using a 65 nm
+process.
+"
+25119,Self-Similar Tilings of Fractal Blow-Ups,"  New tilings of certain subsets of $\mathbb{R}^{M}$ are studied, tilings
+associated with fractal blow-ups of certain similitude iterated function
+systems (IFS). For each such IFS with attractor satisfying the open set
+condition, our construction produces a usually infinite family of tilings that
+satisfy the following properties: (1) the prototile set is finite; (2) the
+tilings are repetitive (quasiperiodic); (3) each family contains
+self-similartilings, usually infinitely many; and (4) when the IFS is rigid in
+an appropriate sense, the tiling has no non-trivial symmetry; in particular the
+tiling is non-periodic.
+"
+25120,Gauging Variational Inference,"  Computing partition function is the most important statistical inference task
+arising in applications of Graphical Models (GM). Since it is computationally
+intractable, approximate methods have been used to resolve the issue in
+practice, where mean-field (MF) and belief propagation (BP) are arguably the
+most popular and successful approaches of a variational type. In this paper, we
+propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP
+(G-BP), improving MF and BP, respectively. Both provide lower bounds for the
+partition function by utilizing the so-called gauge transformation which
+modifies factors of GM while keeping the partition function invariant.
+Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop
+of a special structure, even though the bare MF and BP perform badly in this
+case. Our extensive experiments, on complete GMs of relatively small size and
+on large GM (up-to 300 variables) confirm that the newly proposed algorithms
+outperform and generalize MF and BP.
+"
+25121,An introduction to presentations of monoid acts: quotients and subacts,"  The purpose of this paper is to introduce the theory of presentations of
+monoids acts. We aim to construct `nice' general presentations for various act
+constructions pertaining to subacts and Rees quotients. More precisely, given
+an $M$-act $A$ and a subact $B$ of $A$, on the one hand we construct
+presentations for $B$ and the Rees quotient $A/B$ using a presentation for $A$,
+and on the other hand we derive a presentation for $A$ from presentations for
+$B$ and $A/B$. We also construct a general presentation for the union of two
+subacts. From our general presentations, we deduce a number of finite
+presentability results. Finally, we consider the case where a subact $B$ has
+finite complement in an $M$-act $A$. We show that if $M$ is a finitely
+generated monoid and $B$ is finitely presented, then $A$ is finitely presented.
+We also show that if $M$ belongs to a wide class of monoids, including all
+finitely presented monoids, then the converse also holds.
+"
+25122,Ascertaining Uncertainty for Efficient Exact Cache Analysis,"  Static cache analysis characterizes a program's cache behavior by determining
+in a sound but approximate manner which memory accesses result in cache hits
+and which result in cache misses. Such information is valuable in optimizing
+compilers, worst-case execution time analysis, and side-channel attack
+quantification and mitigation.Cache analysis is usually performed as a
+combination of `must' and `may' abstract interpretations, classifying
+instructions as either `always hit', `always miss', or `unknown'. Instructions
+classified as `unknown' might result in a hit or a miss depending on program
+inputs or the initial cache state. It is equally possible that they do in fact
+always hit or always miss, but the cache analysis is too coarse to see it.Our
+approach to eliminate this uncertainty consists in (i) a novel abstract
+interpretation able to ascertain that a particular instruction may definitely
+cause a hit and a miss on different paths, and (ii) an exact analysis, removing
+all remaining uncertainty, based on model checking, using
+abstract-interpretation results to prune down the model for scalability.We
+evaluated our approach on a variety of examples; it notably improves precision
+upon classical abstract interpretation at reasonable cost.
+"
+25123,Tight closure of parameter ideals in local rings $F$-rational on the punctured spectrum,"  Let $(R, \mathfrak{m}, k)$ be an excellent equidimensional local ring of
+characteristic $p>0$. The aim of this paper is to show that
+$\ell_R(\mathfrak{q}^*/\mathfrak{q})$ does not depend on the choice of
+parameter ideal $\mathfrak{q}$ provided $R$ is an $F$-injective local ring that
+is $F$-rational on the punctured spectrum.
+"
+25124,On the geometric fixed points of real topological Hochschild homology,"  We compute the component group of the derived $G$-geometric fixed points of
+the real topological Hochschild homology of a ring with anti-involution, where
+$G$ denotes the Galois group Gal($\mathbb{C}$/$\mathbb{R}$) of order 2.
+"
+25125,Clogging in constricted suspension flows,"  The flow of a charged-stabilized suspension through a single constricted
+channel is studied experimentally by tracking the particles individually.
+Surprisingly, the behavior is found to be qualitatively similar to that of
+inertial dry granular systems: For small values of the neck-to-particle size
+ratio (D/d<3), clogs form randomly as arches of particle span the constriction.
+The statistics of the clogging events are Poissonian as reported for granular
+systems and agree, for moderate particle volume fraction ($\phi\approx20\%$),
+with a simple stochastic model for the number of particles at the neck. For
+larger neck sizes (D/d>3), even at the largest $\phi$ ($\approx60\%$)
+achievable in the experiments, an uninterrupted particle flow is observed,
+which resembles that of an hourglass. This particularly small value of D/d at
+the transition to a practically uninterrupted flow is attributed to the low
+effective friction between the particles, achieved by the particle's
+functionalization and lubrication.
+"
+25126,Truth and Regret in Online Scheduling,"  We consider a scheduling problem where a cloud service provider has multiple
+units of a resource available over time. Selfish clients submit jobs, each with
+an arrival time, deadline, length, and value. The service provider's goal is to
+implement a truthful online mechanism for scheduling jobs so as to maximize the
+social welfare of the schedule. Recent work shows that under a stochastic
+assumption on job arrivals, there is a single-parameter family of mechanisms
+that achieves near-optimal social welfare. We show that given any such family
+of near-optimal online mechanisms, there exists an online mechanism that in the
+worst case performs nearly as well as the best of the given mechanisms. Our
+mechanism is truthful whenever the mechanisms in the given family are truthful
+and prompt, and achieves optimal (within constant factors) regret.
+We model the problem of competing against a family of online scheduling
+mechanisms as one of learning from expert advice. A primary challenge is that
+any scheduling decisions we make affect not only the payoff at the current
+step, but also the resource availability and payoffs in future steps.
+Furthermore, switching from one algorithm (a.k.a. expert) to another in an
+online fashion is challenging both because it requires synchronization with the
+state of the latter algorithm as well as because it affects the incentive
+structure of the algorithms. We further show how to adapt our algorithm to a
+non-clairvoyant setting where job lengths are unknown until jobs are run to
+completion. Once again, in this setting, we obtain truthfulness along with
+asymptotically optimal regret (within poly-logarithmic factors).
+"
+25127,Classification Using Proximity Catch Digraphs (Technical Report),"  We employ random geometric digraphs to construct semi-parametric classifiers.
+These data-random digraphs are from parametrized random digraph families called
+proximity catch digraphs (PCDs). A related geometric digraph family, class
+cover catch digraph (CCCD), has been used to solve the class cover problem by
+using its approximate minimum dominating set. CCCDs showed relatively good
+performance in the classification of imbalanced data sets, and although CCCDs
+have a convenient construction in $\mathbb{R}^d$, finding minimum dominating
+sets is NP-hard and its probabilistic behaviour is not mathematically tractable
+except for $d=1$. On the other hand, a particular family of PCDs, called
+\emph{proportional-edge} PCDs (PE-PCDs), has mathematical tractable minimum
+dominating sets in $\mathbb{R}^d$; however their construction in higher
+dimensions may be computationally demanding. More specifically, we show that
+the classifiers based on PE-PCDs are prototype-based classifiers such that the
+exact minimum number of prototypes (equivalent to minimum dominating sets) are
+found in polynomial time on the number of observations. We construct two types
+of classifiers based on PE-PCDs. One is a family of hybrid classifiers depend
+on the location of the points of the training data set, and another type is a
+family of classifiers solely based on class covers. We assess the
+classification performance of our PE-PCD based classifiers by extensive Monte
+Carlo simulations, and compare them with that of other commonly used
+classifiers. We also show that, similar to CCCD classifiers, our classifiers
+are relatively better in classification in the presence of class imbalance.
+"
+25128,A Multilier Theorem on Anisotropic Hardy Spaces,"  We present a multiplier theorem on anisotropic Hardy spaces. When $m$
+satisfies the anisotropic, pointwise Mihlin condition, we obtain boundedness of
+the multiplier operator $T_m : H_A^p (\mathbb{R}^n) \rightarrow H_A^p
+(\mathbb{R}^n)$, for the range of $p$ that depends on the eccentricities of the
+dilation $A$ and the level of regularity of a multiplier symbol $m$. This
+extends the classical multiplier theorem of Taibleson and Weiss.
+"
+25129,Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements,"  The recent proposed Tensor Nuclear Norm (TNN) [Lu et al., 2016; 2018a] is an
+interesting convex penalty induced by the tensor SVD [Kilmer and Martin, 2011].
+It plays a similar role as the matrix nuclear norm which is the convex
+surrogate of the matrix rank. Considering that the TNN based Tensor Robust PCA
+[Lu et al., 2018a] is an elegant extension of Robust PCA with a similar tight
+recovery bound, it is natural to solve other low rank tensor recovery problems
+extended from the matrix cases. However, the extensions and proofs are
+generally tedious. The general atomic norm provides a unified view of
+low-complexity structures induced norms, e.g., the $\ell_1$-norm and nuclear
+norm. The sharp estimates of the required number of generic measurements for
+exact recovery based on the atomic norm are known in the literature. In this
+work, with a careful choice of the atomic set, we prove that TNN is a special
+atomic norm. Then by computing the Gaussian width of certain cone which is
+necessary for the sharp estimate, we achieve a simple bound for guaranteed low
+tubal rank tensor recovery from Gaussian measurements. Specifically, we show
+that by solving a TNN minimization problem, the underlying tensor of size
+$n_1\times n_2\times n_3$ with tubal rank $r$ can be exactly recovered when the
+given number of Gaussian measurements is $O(r(n_1+n_2-r)n_3)$. It is order
+optimal when comparing with the degrees of freedom $r(n_1+n_2-r)n_3$. Beyond
+the Gaussian mapping, we also give the recovery guarantee of tensor completion
+based on the uniform random mapping by TNN minimization. Numerical experiments
+verify our theoretical results.
+"
+25130,Poincaré-Einstein metrics and Yamabe invariants,"  In this note we prove the existence of infinitely many positive conformal
+classes on $S^7$ which cannot be the conformal infinity of a
+Poincaré-Einstein metric on the ball $B^8$. We also prove a sharp inequality
+between the Yamabe invariant of the conformal infinity and the Yamabe invariant
+of the interior (after a suitable compactification).
+"
+25131,Applied Koopman Operator Theory for Power Systems Technology,"  Koopman operator is a composition operator defined for a dynamical system
+described by nonlinear differential or difference equation. Although the
+original system is nonlinear and evolves on a finite-dimensional state space,
+the Koopman operator itself is linear but infinite-dimensional (evolves on a
+function space). This linear operator captures the full information of the
+dynamics described by the original nonlinear system. In particular, spectral
+properties of the Koopman operator play a crucial role in analyzing the
+original system. In the first part of this paper, we review the so-called
+Koopman operator theory for nonlinear dynamical systems, with emphasis on modal
+decomposition and computation that are direct to wide applications. Then, in
+the second part, we present a series of applications of the Koopman operator
+theory to power systems technology. The applications are established as
+data-centric methods, namely, how to use massive quantities of data obtained
+numerically and experimentally, through spectral analysis of the Koopman
+operator: coherency identification of swings in coupled synchronous generators,
+precursor diagnostic of instabilities in the coupled swing dynamics, and
+stability assessment of power systems without any use of mathematical models.
+Future problems of this research direction are identified in the last
+concluding part of this paper.
+"
+25132,A Tutorial on Statistically Sound Pattern Discovery,"  Statistically sound pattern discovery harnesses the rigour of statistical
+hypothesis testing to overcome many of the issues that have hampered standard
+data mining approaches to pattern discovery. Most importantly, application of
+appropriate statistical tests allows precise control over the risk of false
+discoveries -- patterns that are found in the sample data but do not hold in
+the wider population from which the sample was drawn. Statistical tests can
+also be applied to filter out patterns that are unlikely to be useful, removing
+uninformative variations of the key patterns in the data. This tutorial
+introduces the key statistical and data mining theory and techniques that
+underpin this fast developing field.
+We concentrate on two general classes of patterns: dependency rules that
+express statistical dependencies between condition and consequent parts and
+dependency sets that express mutual dependence between set elements. We clarify
+alternative interpretations of statistical dependence and introduce appropriate
+tests for evaluating statistical significance of patterns in different
+situations. We also introduce special techniques for controlling the likelihood
+of spurious discoveries when multitudes of patterns are evaluated.
+The paper is aimed at a wide variety of audiences. It provides the necessary
+statistical background and summary of the state-of-the-art for any data mining
+researcher or practitioner wishing to enter or understand statistically sound
+pattern discovery research or practice. It can serve as a general introduction
+to the field of statistically sound pattern discovery for any reader with a
+general background in data sciences.
+"
+25133,Radio occultations of the Io plasma torus by Juno are feasible,"  The flow of material from Io's volcanoes into the Io plasma torus, out into
+the magnetosphere, and along field lines into Jupiter's upper atmosphere is not
+adequately understood. The lack of observations of spatial and temporal
+variations in the Io plasma torus impedes attempts to understand the system as
+a whole. Here we propose that radio occultations of the Io plasma torus by the
+Juno spacecraft can measure plasma densities in the Io plasma torus. We find
+that the line-of-sight column density of plasma in each of the three regions of
+the Io plasma torus (cold torus, ribbon, and warm torus) can be measured with
+uncertainties of 10%. We also find that scale heights describing the spatial
+variation in plasma density in each of these three regions can be measured with
+similar uncertainties. Such observations will be sufficiently accurate to
+support system-scale studies of the flow of plasma through the magnetosphere of
+Jupiter.
+"
+25134,Superradiance in Inverted Multi-level Atomic Clouds,"  This work examines superradiance in initially inverted clouds of
+\textit{multi-level} atoms. We develop a set of equations that can
+approximately calculate the temporal evolution of $N$ coupled atoms. This
+allows us to simulate clouds containing hundreds of multi-level atoms while
+eschewing the assumption and/or approximation of symmetric dipole-dipole
+interactions. This treatment is used to explore the effects that dephasing
+caused by elastic dipole-dipole interactions, and competition between multiple
+transitions have on superradiance. Both of these mechanisms place strong
+parametrical restrictions on a given transition's ability to superradiate.
+These results are likely important to recent experiments that probe
+superradiance in Rydberg atoms.
+"
+25135,Dependency Grammar Induction with Neural Lexicalization and Big Training Data,"  We study the impact of big models (in terms of the degree of lexicalization)
+and big data (in terms of the training corpus size) on dependency grammar
+induction. We experimented with L-DMV, a lexicalized version of Dependency
+Model with Valence and L-NDMV, our lexicalized extension of the Neural
+Dependency Model with Valence. We find that L-DMV only benefits from very small
+degrees of lexicalization and moderate sizes of training corpora. L-NDMV can
+benefit from big training data and lexicalization of greater degrees,
+especially when enhanced with good model initialization, and it achieves a
+result that is competitive with the current state-of-the-art.
+"
+25136,Generative Adversarial Imitation from Observation,"  Imitation from observation (IfO) is the problem of learning directly from
+state-only demonstrations without having access to the demonstrator's actions.
+The lack of action information both distinguishes IfO from most of the
+literature in imitation learning, and also sets it apart as a method that may
+enable agents to learn from a large set of previously inapplicable resources
+such as internet videos. In this paper, we propose both a general framework for
+IfO approaches and also a new IfO approach based on generative adversarial
+networks called generative adversarial imitation from observation (GAIfO). We
+conduct experiments in two different settings: (1) when demonstrations consist
+of low-dimensional, manually-defined state features, and (2) when
+demonstrations consist of high-dimensional, raw visual data. We demonstrate
+that our approach performs comparably to classical imitation learning
+approaches (which have access to the demonstrator's actions) and significantly
+outperforms existing imitation from observation methods in high-dimensional
+simulation environments.
+"
+25137,Dynamic Linear Discriminant Analysis in High Dimensional Space,"  High-dimensional data that evolve dynamically feature predominantly in the
+modern data era. As a partial response to this, recent years have seen
+increasing emphasis to address the dimensionality challenge. However, the
+non-static nature of these datasets is largely ignored. This paper addresses
+both challenges by proposing a novel yet simple dynamic linear programming
+discriminant (DLPD) rule for binary classification. Different from the usual
+static linear discriminant analysis, the new method is able to capture the
+changing distributions of the underlying populations by modeling their means
+and covariances as smooth functions of covariates of interest. Under an
+approximate sparse condition, we show that the conditional misclassification
+rate of the DLPD rule converges to the Bayes risk in probability uniformly over
+the range of the variables used for modeling the dynamics, when the
+dimensionality is allowed to grow exponentially with the sample size. The
+minimax lower bound of the estimation of the Bayes risk is also established,
+implying that the misclassification rate of our proposed rule is minimax-rate
+optimal. The promising performance of the DLPD rule is illustrated via
+extensive simulation studies and the analysis of a breast cancer dataset.
+"
+25138,Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening,"  We consider the problem of statistical inference for ranking data,
+specifically rank aggregation, under the assumption that samples are incomplete
+in the sense of not comprising all choice alternatives. In contrast to most
+existing methods, we explicitly model the process of turning a full ranking
+into an incomplete one, which we call the coarsening process. To this end, we
+propose the concept of rank-dependent coarsening, which assumes that incomplete
+rankings are produced by projecting a full ranking to a random subset of ranks.
+For a concrete instantiation of our model, in which full rankings are drawn
+from a Plackett-Luce distribution and observations take the form of pairwise
+preferences, we study the performance of various rank aggregation methods. In
+addition to predictive accuracy in the finite sample setting, we address the
+theoretical question of consistency, by which we mean the ability to recover a
+target ranking when the sample size goes to infinity, despite a potential bias
+in the observations caused by the (unknown) coarsening.
+"
+25139,Structural Embedding of Syntactic Trees for Machine Comprehension,"  Deep neural networks for machine comprehension typically utilizes only word
+or character embeddings without explicitly taking advantage of structured
+linguistic information such as constituency trees and dependency trees. In this
+paper, we propose structural embedding of syntactic trees (SEST), an algorithm
+framework to utilize structured information and encode them into vector
+representations that can boost the performance of algorithms for the machine
+comprehension. We evaluate our approach using a state-of-the-art neural
+attention model on the SQuAD dataset. Experimental results demonstrate that our
+model can accurately identify the syntactic boundaries of the sentences and
+extract answers that are syntactically coherent over the baseline methods.
+"
+25140,Evidence of an exponential speed-up in the solution of hard optimization problems,"  Optimization problems pervade essentially every scientific discipline and
+industry. Many such problems require finding a solution that maximizes the
+number of constraints satisfied. Often, these problems are particularly
+difficult to solve because they belong to the NP-hard class, namely algorithms
+that always find a solution in polynomial time are not known. Over the past
+decades, research has focused on developing heuristic approaches that attempt
+to find an approximation to the solution. However, despite numerous research
+efforts, in many cases even approximations to the optimal solution are hard to
+find, as the computational time for further refining a candidate solution grows
+exponentially with input size. Here, we show a non-combinatorial approach to
+hard optimization problems that achieves an exponential speed-up and finds
+better approximations than the current state-of-the-art. First, we map the
+optimization problem into a boolean circuit made of specially designed,
+self-organizing logic gates, which can be built with (non-quantum) electronic
+components; the equilibrium points of the circuit represent the approximation
+to the problem at hand. Then, we solve its associated non-linear ordinary
+differential equations numerically, towards the equilibrium points. We
+demonstrate this exponential gain by comparing a sequential MatLab
+implementation of our solver with the winners of the 2016 Max-SAT competition
+on a variety of hard optimization instances. We show empirical evidence that
+our solver scales linearly with the size of the problem, both in time and
+memory, and argue that this property derives from the collective behavior of
+the simulated physical circuit. Our approach can be applied to other types of
+optimization problems and the results presented here have far-reaching
+consequences in many fields.
+"
+25141,Exploiting the Natural Dynamics of Series Elastic Robots by Actuator-Centered Sequential Linear Programming,"  Series elastic robots are best able to follow trajectories which obey the
+limitations of their actuators, since they cannot instantly change their joint
+forces. In fact, the performance of series elastic actuators can surpass that
+of ideal force source actuators by storing and releasing energy. In this paper,
+we formulate the trajectory optimization problem for series elastic robots in a
+novel way based on sequential linear programming. Our framework is unique in
+the separation of the actuator dynamics from the rest of the dynamics, and in
+the use of a tunable pseudo-mass parameter that improves the discretization
+accuracy of our approach. The actuator dynamics are truly linear, which allows
+them to be excluded from trust-region mechanics. This causes our algorithm to
+have similar run times with and without the actuator dynamics. We demonstrate
+our optimization algorithm by tuning high performance behaviors for a
+single-leg robot in simulation and on hardware for a single degree-of-freedom
+actuator testbed. The results show that compliance allows for faster motions
+and takes a similar amount of computation time.
+"
+25142,Learning Discrete Representations via Information Maximizing Self-Augmented Training,"  Learning discrete representations of data is a central machine learning task
+because of the compactness of the representations and ease of interpretation.
+The task includes clustering and hash learning as special cases. Deep neural
+networks are promising to be used because they can model the non-linearity of
+data and scale to large datasets. However, their model complexity is huge, and
+therefore, we need to carefully regularize the networks in order to learn
+useful representations that exhibit intended invariance for applications of
+interest. To this end, we propose a method called Information Maximizing
+Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose
+the invariance on discrete representations. More specifically, we encourage the
+predicted representations of augmented data points to be close to those of the
+original data points in an end-to-end fashion. At the same time, we maximize
+the information-theoretic dependency between data and their predicted discrete
+representations. Extensive experiments on benchmark datasets show that IMSAT
+produces state-of-the-art results for both clustering and unsupervised hash
+learning.
+"
+25143,"Integrability, Quantization and Moduli Spaces of Curves","  This paper has the purpose of presenting in an organic way a new approach to
+integrable (1+1)-dimensional field systems and their systematic quantization
+emerging from intersection theory of the moduli space of stable algebraic
+curves and, in particular, cohomological field theories, Hodge classes and
+double ramification cycles. This methods are alternative to the traditional
+Witten-Kontsevich framework and its generalizations by Dubrovin and Zhang and,
+among other advantages, have the merit of encompassing quantum integrable
+systems. Most of this material originates from an ongoing collaboration with A.
+Buryak, B. Dubrovin and J. Guéré.
+"
+25144,Formation of single-phase disordered CsxFe2-ySe2 at high pressure,"  A single-phase high pressure (HP) modification of CsxFe2-ySe2 was synthesized
+at 11.8 GPa at ambient temperature. Structurally this polymorph is similar to
+the minor low pressure (LP) superconducting phase, namely they both crystallize
+in a ThCr2Si2-type structure without ordering of the Fe vacancies within the
+Fe-deficient FeSe4 layers. The HP CsxFe2-ySe2 polymorph is found to be less
+crystalline and nearly twice as soft compared to the parent major and minor
+phases of CsxFe2-ySe2. It can be quenched to low pressures and is stable at
+least on the scale of weeks. At ambient pressure the HP polymorph of
+CsxFe2-ySe2 is expected to exhibit different superconducting properties
+compared to its LP minor phase (Tc = 27 K).
+"
+25145,Parallel Stochastic Gradient Descent with Sound Combiners,"  Stochastic gradient descent (SGD) is a well known method for regression and
+classification tasks. However, it is an inherently sequential algorithm at each
+step, the processing of the current example depends on the parameters learned
+from the previous examples. Prior approaches to parallelizing linear learners
+using SGD, such as HOGWILD! and ALLREDUCE, do not honor these dependencies
+across threads and thus can potentially suffer poor convergence rates and/or
+poor scalability. This paper proposes SYMSGD, a parallel SGD algorithm that, to
+a first-order approximation, retains the sequential semantics of SGD. Each
+thread learns a local model in addition to a model combiner, which allows local
+models to be combined to produce the same result as what a sequential SGD would
+have produced. This paper evaluates SYMSGD's accuracy and performance on 6
+datasets on a shared-memory machine shows upto 11x speedup over our heavily
+optimized sequential baseline on 16 cores and 2.2x, on average, faster than
+HOGWILD!.
+"
+25146,An Optimal Dimensionality Multi-shell Sampling Scheme with Accurate and Efficient Transforms for Diffusion MRI,"  This paper proposes a multi-shell sampling scheme and corresponding
+transforms for the accurate reconstruction of the diffusion signal in diffusion
+MRI by expansion in the spherical polar Fourier (SPF) basis. The sampling
+scheme uses an optimal number of samples, equal to the degrees of freedom of
+the band-limited diffusion signal in the SPF domain, and allows for
+computationally efficient reconstruction. We use synthetic data sets to
+demonstrate that the proposed scheme allows for greater reconstruction accuracy
+of the diffusion signal than the multi-shell sampling schemes obtained using
+the generalised electrostatic energy minimisation (gEEM) method used in the
+Human Connectome Project. We also demonstrate that the proposed sampling scheme
+allows for increased angular discrimination and improved rotational invariance
+of reconstruction accuracy than the gEEM schemes.
+"
+25147,Optimal Control of Constrained Stochastic Linear-Quadratic Model with Applications,"  This paper studies a class of continuous-time scalar-state stochastic
+Linear-Quadratic (LQ) optimal control problem with the linear control
+constraints. Applying the state separation theorem induced from its special
+structure, we develop the explicit solution for this class of problem. The
+revealed optimal control policy is a piece-wise affine function of system
+state. This control policy can be computed efficiently by solving two Riccati
+equations off-line. Under some mild conditions, the stationary optimal control
+policy can be also derived for this class of problem with infinite horizon.
+This result can be used to solve the constrained dynamic mean-variance
+portfolio selection problem. Examples shed light on the solution procedure of
+implementing our method.
+"
+25148,Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition,"  In this paper, a novel architecture for a deep recurrent neural network,
+residual LSTM is introduced. A plain LSTM has an internal memory cell that can
+learn long term dependencies of sequential data. It also provides a temporal
+shortcut path to avoid vanishing or exploding gradients in the temporal domain.
+The residual LSTM provides an additional spatial shortcut path from lower
+layers for efficient training of deep networks with multiple LSTM layers.
+Compared with the previous work, highway LSTM, residual LSTM separates a
+spatial shortcut path with temporal one by using output layers, which can help
+to avoid a conflict between spatial and temporal-domain gradient flows.
+Furthermore, residual LSTM reuses the output projection matrix and the output
+gate of LSTM to control the spatial information flow instead of additional gate
+networks, which effectively reduces more than 10% of network parameters. An
+experiment for distant speech recognition on the AMI SDM corpus shows that
+10-layer plain and highway LSTM networks presented 13.7% and 6.2% increase in
+WER over 3-layer aselines, respectively. On the contrary, 10-layer residual
+LSTM networks provided the lowest WER 41.0%, which corresponds to 3.3% and 2.8%
+WER reduction over plain and highway LSTM networks, respectively.
+"
+25149,Quasiclassical Theory on Third-Harmonic Generation in Conventional Superconductors with Paramagnetic Impurities,"  We investigate the third-harmonic generation (THG) of s-wave superconductors
+under microwave pulse irradiation. We consider the effect of paramagnetic
+impurities on the THG intensity of dirty superconductors. The nonlinear
+response function is calculated using the method of the quasiclassical Green
+function. It is shown that the amplitude mode is included as the vertex
+correction and makes a predominant contribution to the THG intensity. When the
+effect of paramagnetic impurities is weak, the THG intensity shows a peak at
+the temperature at which the superconducting gap is about the same as the
+frequency of the incident pulse, similarly to in experiments. As the effect of
+paramagnetic impurities is strengthened, the peak of the THG intensity
+disappears. This indicates that time-reversal symmetry breaking due to
+paramagnetic impurities eliminates the well-defined amplitude mode. The result
+of our calculation shows that the existence of the amplitude mode can be
+confirmed through the THG intensity. The result of a semiquantitative
+calculation is in good agreement with the experimental result, and it also
+shows that the diamagnetic term is negligible.
+"
+25150,On the Analysis of the DeGroot-Friedkin Model with Dynamic Relative Interaction Matrices,"  This paper analyses the DeGroot-Friedkin model for evolution of the
+individuals' social powers in a social network when the network topology varies
+dynamically (described by dynamic relative interaction matrices). The
+DeGroot-Friedkin model describes how individual social power (self-appraisal,
+self-weight) evolves as a network of individuals discuss a sequence of issues.
+We seek to study dynamically changing relative interactions because
+interactions may change depending on the issue being discussed. In order to
+explore the problem in detail, two different cases of issue-dependent network
+topologies are studied. First, if the topology varies between issues in a
+periodic manner, it is shown that the individuals' self-appraisals admit a
+periodic solution. Second, if the topology changes arbitrarily, under the
+assumption that each relative interaction matrix is doubly stochastic and
+irreducible, the individuals' self-appraisals asymptotically converge to a
+unique non-trivial equilibrium.
+"
+25151,Controlled Supercontinua via Spatial Beam Shaping,"  Recently, optimization techniques have had a significant impact in a variety
+of fields, leading to a higher signal-to-noise and more streamlined techniques.
+We consider the possibility for using programmable phase-only spatial
+optimization of the pump beam to influence the supercontinuum generation
+process. Preliminary results show that significant broadening and rough control
+of the supercontinuum spectrum are possible without loss of input energy. This
+serves as a proof-of-concept demonstration that spatial effects can
+controllably influence the supercontinuum spectrum, leading to possibilities
+for utilizing supercontinuum power more efficiently and achieving arbitrary
+spectral control.
+"
+25152,The impact of clustering and angular resolution on far-infrared and millimeter continuum observations,"  Follow-up observations at high-angular resolution of submillimeter galaxies
+showed that the single-dish sources are comprised of a blend of several
+galaxies. Consequently, number counts derived from low and high angular
+resolution observations are in disagreement. This demonstrates the importance
+of resolution effects and the need to have realistic simulations to explore
+them. We built a new 2deg^2 simulation of the extragalactic sky from the
+far-infrared to the submillimeter. It is based on an updated version of the two
+star-formation mode galaxy evolution model. Using global galaxy properties, we
+use the abundance matching technique to populate a dark-matter lightcone and
+thus simulate the clustering. We produce maps and extract the sources, and show
+that the limited angular resolution of single-dish instruments have a strong
+impact on (sub)millimeter continuum observations. Taking into account these
+resolution effects, we are reproducing a large set of observables, including
+number counts, redshift distributions, and cosmic infrared background power
+spectra. Our simulation describes consistently the number counts from
+single-dish telescopes and interferometers. In particular, at 350 and 500 um,
+we find that number counts measured by Herschel between 5 and 50 mJy are biased
+towards high values by a factor 2, and that redshift distributions are biased
+towards low z. We also show that the clustering has an important impact on the
+Herschel pixel histogram used to derive number counts from P(D) analysis.
+Finally, we demonstrate that the large number density of red Herschel sources
+found in observations but not in models could be an observational artifact
+caused by the combination of noise, resolution effects, and the steepness of
+color and flux density distributions. Our simulation (SIDES) is available at
+this http URL
+"
+25153,Proof Diagrams for Multiplicative Linear Logic: Syntax and Semantics,"  Proof nets are a syntax for linear logic proofs which gives a coarser notion
+of proof equivalence with respect to syntactic equality together with an
+intuitive geometrical representation of proofs.
+In this paper we give an alternative $2$-dimensional syntax for
+multiplicative linear logic derivations. The syntax of string diagrams
+authorizes the definition of a framework where the sequentializability of a
+term, i.e. deciding whether the term corresponds to a correct derivation, can
+be verified in linear time.
+Furthermore, we can use this syntax to define a denotational semantics for
+multiplicative linear logic with units by means of equivalence classes of proof
+diagrams modulo a terminating rewriting.
+"
+25154,Review of Data Structures for Computationally Efficient Nearest-Neighbour Entropy Estimators for Large Systems with Periodic Boundary Conditions,"  Information theoretic quantities are extremely useful in discovering
+relationships between two or more data sets. One popular method---particularly
+for continuous systems---for estimating these quantities is the nearest
+neighbour estimators. When system sizes are very large or the systems have
+periodic boundary conditions issues with performance and correctness surface,
+however solutions are known for each problem. Here we show that these solutions
+are inappropriate in systems that simultaneously contain both features and
+discuss a lesser known alternative solution involving Vantage Point trees that
+is capable of addressing both issues.
+"
+25155,Crime Topic Modeling,"  The classification of crime into discrete categories entails a massive loss
+of information. Crimes emerge out of a complex mix of behaviors and situations,
+yet most of these details cannot be captured by singular crime type labels.
+This information loss impacts our ability to not only understand the causes of
+crime, but also how to develop optimal crime prevention strategies. We apply
+machine learning methods to short narrative text descriptions accompanying
+crime records with the goal of discovering ecologically more meaningful latent
+crime classes. We term these latent classes ""crime topics"" in reference to
+text-based topic modeling methods that produce them. We use topic distributions
+to measure clustering among formally recognized crime types. Crime topics
+replicate broad distinctions between violent and property crime, but also
+reveal nuances linked to target characteristics, situational conditions and the
+tools and methods of attack. Formal crime types are not discrete in topic
+space. Rather, crime types are distributed across a range of crime topics.
+Similarly, individual crime topics are distributed across a range of formal
+crime types. Key ecological groups include identity theft, shoplifting,
+burglary and theft, car crimes and vandalism, criminal threats and confidence
+crimes, and violent crimes. Though not a replacement for formal legal crime
+classifications, crime topics provide a unique window into the heterogeneous
+causal processes underlying crime.
+"
+25156,A Sliceness Criterion for Odd Free Knots,"  The main goal of this paper is to prove that for odd free knots - that is
+free knots with all odd crossings - the problem of sliceness (the existence of
+a spanning disc) has an explicit answer based on the pairing of the knot
+diagram chords.
+"
+25157,Vector Space Model as Cognitive Space for Text Classification,"  In this era of digitization, knowing the user's sociolect aspects have become
+essential features to build the user specific recommendation systems. These
+sociolect aspects could be found by mining the user's language sharing in the
+form of text in social media and reviews. This paper describes about the
+experiment that was performed in PAN Author Profiling 2017 shared task. The
+objective of the task is to find the sociolect aspects of the users from their
+tweets. The sociolect aspects considered in this experiment are user's gender
+and native language information. Here user's tweets written in a different
+language from their native language are represented as Document - Term Matrix
+with document frequency as the constraint. Further classification is done using
+the Support Vector Machine by taking gender and native language as target
+classes. This experiment attains the average accuracy of 73.42% in gender
+prediction and 76.26% in the native language identification task.
+"
+25158,On $P_5$-free Chordal bipartite graphs,"  A bipartite graph is chordal bipartite if every cycle of length at least 6
+has a chord in it. In this paper, we investigate the structure of $P_5$-free
+chordal bipartite graphs and show that these graphs have a Nested Neighborhood
+Ordering, a special ordering among its vertices. Further, using this ordering,
+we present polynomial-time algorithms for classical problems such as
+Hamiltonian cycle (path) and longest path. Two variants of Hamiltonian path
+include Steiner path and minimum leaf spanning tree, and we obtain
+polynomial-time algorithms for these problems as well restricted to $P_5$-free
+chordal bipartite graphs.
+"
+25159,In-flight energy calibration of the space-borne Compton polarimeter POLAR,"  POLAR is a compact wide-field space-borne detector for precise measurements
+of the linear polarisation of hard X-rays emitted by transient sources in the
+energy range from 50 keV to 500 keV. It consists of a 40$\times$40 array of
+plastic scintillator bars used as a detection material. The bars are grouped in
+25 detector modules. The energy range sensitivity of POLAR is optimized to
+match with the prompt emission photons from the gamma-ray bursts (GRBs).
+Polarization measurements of the prompt emission would probe source geometries,
+emission mechanisms and magnetic structures in GRB jets. The instrument can
+also detect hard X-rays from solar flares and be used for precise measurement
+of their polarisation. POLAR was launched into a low Earth orbit on-board the
+Chinese space-lab TG-2 on September 15th, 2016. To achieve high accuracies in
+polarisation measurements it is essential to assure both before and during the
+flight a precise energy calibration. Such calibrations are performed with four
+low activity $^{22}$Na radioactive sources placed inside the instrument. Energy
+conversion factors are related to Compton edge positions from the collinear
+annihilation photons from the sources. This paper presents main principles of
+the in-flight calibration, describes studies of the method based on Monte Carlo
+simulations and its laboratory verification and finally provides some
+observation results based on the in-flight data analysis.
+"
+25160,Recent advances in the determination of some Galactic constants in the Milky Way,"  Here we statistically evaluate recent advances in determining the
+Sun-Galactic Center distance (Rsun) as well as recent measures of the orbital
+velocity around the Galactic Center (Vlsr), and the angular rotation parameters
+of various objects. Recent statistical results point to Rsun = 8.0 +- 0.2 kpc,
+Vlsr= 230 +- 3 km/s, and angular rotation at the Sun ({\omega}) near 29 +- 1
+km/s/kpc for the gas and stars at the Local Standard of Rest, and near 23 +- 2
+km/s/kpc for the spiral pattern itself.
+This angular difference is similar to what had been predicted by density wave
+models, along with the observation that the galactic longitude of each spiral
+arm tracer (dust, cold CO) for each spiral arm becomes reversed across the
+Galactic Meridian (Vallee 2016b).
+"
+25161,Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics,"  The most data-efficient algorithms for reinforcement learning in robotics are
+model-based policy search algorithms, which alternate between learning a
+dynamical model of the robot and optimizing a policy to maximize the expected
+return given the model and its uncertainties. Among the few proposed
+approaches, the recently introduced Black-DROPS algorithm exploits a black-box
+optimization algorithm to achieve both high data-efficiency and good
+computation times when several cores are used; nevertheless, like all
+model-based policy search approaches, Black-DROPS does not scale to high
+dimensional state/action spaces. In this paper, we introduce a new model
+learning procedure in Black-DROPS that leverages parameterized black-box priors
+to (1) scale up to high-dimensional systems, and (2) be robust to large
+inaccuracies of the prior information. We demonstrate the effectiveness of our
+approach with the ""pendubot"" swing-up task in simulation and with a physical
+hexapod robot (48D state space, 18D action space) that has to walk forward as
+fast as possible. The results show that our new algorithm is more
+data-efficient than previous model-based policy search algorithms (with and
+without priors) and that it can allow a physical 6-legged robot to learn new
+gaits in only 16 to 30 seconds of interaction time.
+"
+25162,"High-sensitivity, spin-based electrometry with an ensemble of nitrogen-vacancy centers in diamond","  We demonstrate a spin-based, all-dielectric electrometer based on an ensemble
+of nitrogen-vacancy (NV$^-$) defects in diamond. An applied electric field
+causes energy level shifts symmetrically away from the NV$^-$'s degenerate
+triplet states via the Stark effect; this symmetry provides immunity to
+temperature fluctuations allowing for shot-noise-limited detection. Using an
+ensemble of NV$^-$s, we demonstrate shot-noise limited sensitivities
+approaching 1 V/cm/$\sqrt{\text{Hz}}$ under ambient conditions, at low
+frequencies ($<$10 Hz), and over a large dynamic range (20 dB). A theoretical
+model for the ensemble of NV$^-$s fits well with measurements of the
+ground-state electric susceptibility parameter, $\langle k_\perp\rangle$.
+Implications of spin-based, dielectric sensors for micron-scale electric-field
+sensing are discussed.
+"
+25163,Invariant Hopf $2$-cocycles for affine algebraic groups,"  We generalize the theory of the second invariant cohomology group $H^2_{\rm
+inv}(G)$ for finite groups $G$, developed in [Da2,Da3,GK], to the case of
+affine algebraic groups $G$, using the methods of [EG1,EG2,G]. In particular,
+we show that for connected affine algebraic groups $G$ over an algebraically
+closed field of characteristic $0$, the map $\Theta$ from [GK] is bijective
+(unlike for some finite groups, as shown in [GK]). This allows us to compute
+$H^2_{\rm inv}(G)$ in this case, and in particular show that this group is
+commutative (while for finite groups it can be noncommutative, as shown in
+[GK]).
+"
+25164,Prediction of Alzheimer's disease-associated genes by integration of GWAS summary data and expression data,"  Alzheimer's disease is the most common cause of dementia. It is the
+fifth-leading cause of death among elderly people. With high genetic
+heritability (79%), finding disease causal genes is a crucial step in find
+treatment for AD. Following the International Genomics of Alzheimer's Project
+(IGAP), many disease-associated genes have been identified; however, we don't
+have enough knowledge about how those disease-associated genes affect gene
+expression and disease-related pathways. We integrated GWAS summary data from
+IGAP and five different expression level data by using TWAS method and
+identified 15 disease causal genes under strict multiple testing (alpha<0.05),
+4 genes are newly identified; identified additional 29 potential disease causal
+genes under false discovery rate(alpha < 0.05), 21 of them are newly
+identified. Many genes we identified are also associated with some autoimmune
+disorder.
+"
+25165,Differential transcendence of solutions of difference Riccati equations and Tietze's treatment,"  There is the paper by H. Tietze published in 1905 on differential
+transcendence of solutions of difference Riccati equations. In this paper, we
+clarify the essence of Tietze's treatment and make it purely algebraic. As an
+application, the $q$-Airy equation is studied.
+"
+25166,"Three-Dimensional Disk-Satellite Interaction: Torques, Migration, and Observational Signatures","  The interaction of a satellite with a gaseous disk results in the excitation
+of spiral density waves which remove angular momentum from the orbit. In
+addition, if the orbit is not coplanar with the disk, three-dimensional effects
+will excite bending and eccentricity waves. We perform three-dimensional
+hydrodynamic simulations to study nonlinear disk-satellite interaction in
+inviscid protoplanetary disks for a variety of orbital inclinations from
+$0^\circ$ to $180^\circ$. It is well known that three-dimensional effects are
+important even for zero inclination. In this work we (1) show that for planets
+with small inclinations (as in the Solar system), effects such as the total
+torque and migration rate strongly depend on the inclination and are
+significantly different (about 2.5 times smaller) from the two-dimensional
+case, (2) give formulae for the migration rate, inclination damping, and
+precession rate of planets with different inclination angles in disk with
+different scale heights, and (3) present the observational signatures of a
+planet on an inclined orbit with respect to the protoplanetary disk. For
+misaligned planets we find good agreement with linear theory in the limit of
+small inclinations, and with dynamical friction estimates for intermediate
+inclinations. We find that in the latter case, the dynamical friction force is
+not parallel to the relative planetary velocity. Overall, the derived formulae
+will be important for studying exoplanets with obliquity.
+"
+25167,Scale effects on spatially embedded contact networks,"  Spatial phenomena are subject to scale effects, but there are rarely studies
+addressing such effects on spatially embedded contact networks. There are two
+types of structure in these networks, network structure and spatial structure.
+The network structure has been actively studied. The spatial structure of these
+networks has received attention only in recent years. Certainly little is known
+whether the two structures respond to each other. This study examines the scale
+effects, in terms of spatial extent, on the network structure and the spatial
+structure of spatially embedded contact networks. Two issues are explored, how
+the two types of structures change in response to scale changes, and the range
+of the scale effects. Two sets of areal units, regular grids with 24 different
+levels of spatial extent and census units of three levels of spatial extent,
+are used to divide one observed and two reference random networks into multiple
+scales. Six metrics are used to represent the two structures. Results show
+different scale effects. In terms of the network structure, the properties of
+the observed network are sensitive to scale changes at fine scales. In
+comparison, the clustered spatial structure of the network is scale
+independent. The behaviors of the network structure are affected by the spatial
+structure. This information helps identify vulnerable households and
+communities to health risks and helps deploy intervention strategies to
+spatially targeted areas.
+"
+25168,Solving Distributed Constraint Optimization Problems Using Logic Programming,"  This paper explores the use of Answer Set Programming (ASP) in solving
+Distributed Constraint Optimization Problems (DCOPs). The paper provides the
+following novel contributions: (1) It shows how one can formulate DCOPs as
+logic programs; (2) It introduces ASP-DPOP, the first DCOP algorithm that is
+based on logic programming; (3) It experimentally shows that ASP-DPOP can be up
+to two orders of magnitude faster than DPOP (its imperative programming
+counterpart) as well as solve some problems that DPOP fails to solve, due to
+memory limitations; and (4) It demonstrates the applicability of ASP in a wide
+array of multi-agent problems currently modeled as DCOPs. Under consideration
+in Theory and Practice of Logic Programming (TPLP).
+"
+25169,Revisit Kerker's conditions by means of the phase diagram,"  For passive electromagnetic scatterers, we explore a variety of extreme
+limits on directional scattering patterns in phase diagram, regardless of
+details on the geometric configurations and material properties. By
+demonstrating the extinction cross-sections with the power conservation
+intrinsically embedded in phase diagram, we give an alternative interpretation
+for Kerker first and second conditions, associated with zero backward
+scattering (ZBS) and nearly zero forward scattering (NZFS). The physical
+boundary and limitation for these directional radiations are illustrated, along
+with a generalized Kerker condition with implicit parameters. By taking the
+dispersion relations of gold-silicon core-shell nanoparticles into account,
+based on the of phase diagram, we reveal the realistic parameters to
+experimentally implement ZBS and NZFS at optical frequencies.
+"
+25170,On unitarity of some representations of classical p-adic groups II,"  C. Jantzen has defined a correspondence which attaches to an irreducible
+representation of a classical $p$-adic group, a finite set of irreducible
+representations of classical $p$-adic groups supported in a single or in two
+cuspidal lines (the case of the single cuspidal lines is interesting for the
+unitarizability). It would be important to know if this correspondence
+preserves the unitarizability (in both directions). The main aim of this paper
+is to complete the proof started in the previous paper of the fact that if we
+have an irreducible unitarizable representation $\pi$ of a classical $p$-adic
+group whose one attached representation $X_\rho(\pi)$ supported by a cuspidal
+line, has the same infinitesimal character as the generalized Steinberg
+representation supported in that line, then $X_\rho(\pi)$ is unitarizable.
+"
+25171,Aggregate Fluctuations in Networks with Drift-Diffusion Models Driven by Stable Non-Gaussian Disturbances,"  The focus of this paper is to quantify measures of aggregate fluctuations for
+a class of consensus-seeking multiagent networks subject to exogenous noise
+with alpha-stable distributions. This type of noise is generated by a class of
+random measures with heavy-tailed probability distributions. We define a
+cumulative scale parameter using scale parameters of probability distributions
+of the output variables, as a measure of aggregate fluctuation. Although this
+class of measures can be characterized implicitly in closed-form in
+steady-state, finding their explicit forms in terms of network parameters is,
+in general, almost impossible. We obtain several tractable upper bounds in
+terms of Laplacian spectrum and statistics of the input noise. Our results
+suggest that relying on Gaussian-based optimal design algorithms will result in
+non-optimal solutions for networks that are driven by non-Gaussian noise inputs
+with alpha-stable distributions.
+The manuscript has been submitted for publication to IEEE Transactions on
+Control of Network Systems. It is the extended version of preliminary paper
+included in the proceedings of the 2018 American Control Conference.
+"
+25172,Higher order Cheeger inequalities for Steklov eigenvalues,"  We prove a lower bound for the $k$-th Steklov eigenvalues in terms of an
+isoperimetric constant called the $k$-th Cheeger-Steklov constant in three
+different situations: finite spaces, measurable spaces, and Riemannian
+manifolds. These lower bounds can be considered as higher order Cheeger type
+inequalities for the Steklov eigenvalues. In particular it extends the Cheeger
+type inequality for the first nonzero Steklov eigenvalue previously studied by
+Escobar in 1997 and by Jammes in 2015 to higher order Steklov eigenvalues. The
+technique we develop to get this lower bound is based on considering a family
+of accelerated Markov operators in the finite and mesurable situations and of
+mass concentration deformations of the Laplace-Beltrami operator in the
+manifold setting which converges uniformly to the Steklov operator. As an
+intermediary step in the proof of the higher order Cheeger type inequality, we
+define the Dirichlet-Steklov connectivity spectrum and show that the Dirichlet
+connectivity spectra of this family of operators converges to (or bounded by)
+the Dirichlet-Steklov spectrum uniformly. Moreover, we obtain bounds for the
+Steklov eigenvalues in terms of its Dirichlet-Steklov connectivity spectrum
+which is interesting in its own right and is more robust than the higher order
+Cheeger type inequalities. The Dirichlet-Steklov spectrum is closely related to
+the Cheeger-Steklov constants.
+"
+25173,Robust estimation in single index models when the errors have a unimodal density with unknown nuisance parameter,"  In this paper, we propose a robust profile estimation method for the
+parametric and nonparametric components of a single index model when the errors
+have a strongly unimodal density with unknown nuisance parameter. Under
+regularity conditions, we derive consistency results for the link function
+estimators as well as consistency and asymptotic distribution results for the
+single index parameter estimators. Under a log--Gamma model, the sensitivity to
+anomalous observations is studied by means of the empirical influence curve. We
+also discuss a robust $K-$fold procedure to select the smoothing parameters
+involved. A numerical study is conducted to evaluate the small sample
+performance of the robust proposal with that of their classical relatives, both
+for errors following a log--Gamma model and for contaminated schemes. The
+numerical experiment shows the good robustness properties of the proposed
+estimators and the advantages of considering a robust approach instead of the
+classical one.
+"
+25174,From non-symmetric particle systems to non-linear PDEs on fractals,"  We present new results and challenges in obtaining hydrodynamic limits for
+non-symmetric (weakly asymmetric) particle systems (exclusion processes on
+pre-fractal graphs) converging to a non-linear heat equation. We discuss a
+joint density-current law of large numbers and a corresponding large deviations
+principle.
+"
+25175,Magnetism and superconductivity in the layered hexagonal transition metal pnictides,"  We investigate the electronic and magnetic structures of the 122
+(AM$_2$B$_2$) hexagonal transition-metal pnictides with A=(Sr, Ca), M=(Cr, Mn,
+Fe, Co, Ni) and B=(As, P, Sb). It is found that the family of materials share
+critical similarities with those of tetragonal structures that include the
+famous iron-based high temperature superconductors. In both families, the next
+nearest neighbor(NNN) effective antiferromagnetic(AFM) exchange couplings reach
+the maximum value in the iron-based materials. While the NNN couplings in the
+latter are known to be responsible for the C-type AFM state and to result in
+the extended s-wave superconducting state upon doping, they cause the former to
+be extremely frustrated magnetic systems and can lead to an time reversal
+symmetry broken $d+id$ superconducting state upon doping. The iron-based
+compounds with the hexagonal structure, thus if synthesized, can help us to
+determine the origin of high temperature superconductivity.
+"
+25176,Direct Segmented Sonification of Characteristic Features of the Data Domain,"  Sonification and audification create auditory displays of datasets.
+Audification translates data points into digital audio samples and the auditory
+display's duration is determined by the playback rate. Like audification,
+auditory graphs maintain the temporal relationships of data while using
+parameter mappings (typically data-to-frequency) to represent the ordinate
+values. Such direct approaches have the advantage of presenting the data stream
+`as is' without the imposed interpretations or accentuation of particular
+features found in indirect approaches. However, datasets can often be
+subdivided into short non-overlapping variable length segments that each
+encapsulate a discrete unit of domain-specific significant information and
+current direct approaches cannot represent these. We present Direct Segmented
+Sonification (DSSon) for highlighting the segments' data distributions as
+individual sonic events. Using domain knowledge to segment data, DSSon presents
+segments as discrete auditory gestalts while retaining the overall temporal
+regime and relationships of the dataset. The method's structural decoupling
+from the sound stream's formation means playback speed is independent of the
+individual sonic event durations, thereby offering highly flexible time
+compression/stretching to allow zooming into or out of the data. Demonstrated
+by three models applied to biomechanical data, DSSon displays high directness,
+letting the data `speak' for themselves.
+"
+25177,Dehydrogenation through the pressure-induced polymerization processes of phosphine,"  PH3 is studied to understand the superconducting transition and responsible
+stoichiometry under high pressure by means of Raman, IR, and x-ray diffraction
+(XRD) measurements, and theoretical calculations. It is found PH3 is stable up
+to about 8 GPa and then starts to dehydrogenate through two dimerization
+processes at room temperature as pressure up to 25 GPa. Two resulting
+phosphorus hydrides, P2H4 and P4H6, are verified experimentally and can be
+recovered to ambient pressure. On further compression above 35 GPa, P4H6
+directly decomposes into elemental phosphorus. The superconductivity transition
+temperatures of P4H6 at 100 and 200 GPa have been predicted to be 13 and 67 K
+in agreement with reported results, suggesting it might responsible for the
+superconductivity at higher pressures. Our results clearly show that P2H4 and
+P4H6 are only stable P-H compounds between PH3 and elemental phosphorus,
+shedding light on the superconducting mechanism.
+"
+25178,Reduced Theoretical Error for QED Tests with 4He+ Spectroscopy,"  We apply point-particle effective field theory (PPEFT) to electronic and
+muonic 4He+ ions, and use it to identify linear combinations of spectroscopic
+measurements for which the theoretical uncertainties are much smaller than for
+any particular energy levels. The error is reduced because these combinations
+are independent of all short-range physics effects up to a given order in the
+expansion in the small parameters R/a_B and(Z alpha) (where R and a_B are the
+ion's nuclear and Bohr radii). In particular, the theory error is not limited
+by the precision with which nuclear matrix elements can be computed, or
+compromised by the existence of any novel short-range interactions, should
+these exist. These combinations of 4He+ measurements therefore provide
+particularly precise tests of QED. The restriction to 4He+ arises because our
+analysis assumes a spherically symmetric nucleus, but the argument used is more
+general and extendable to both nuclei with spin, and to higher orders in R/a_B
+and (Z alpha).
+"
+25179,Super-Resolution via Deep Learning,"  The recent phenomenal interest in convolutional neural networks (CNNs) must
+have made it inevitable for the super-resolution (SR) community to explore its
+potential. The response has been immense and in the last three years, since the
+advent of the pioneering work, there appeared too many works not to warrant a
+comprehensive survey. This paper surveys the SR literature in the context of
+deep learning. We focus on the three important aspects of multimedia - namely
+image, video and multi-dimensions, especially depth maps. In each case, first
+relevant benchmarks are introduced in the form of datasets and state of the art
+SR methods, excluding deep learning. Next is a detailed analysis of the
+individual works, each including a short description of the method and a
+critique of the results with special reference to the benchmarking done. This
+is followed by minimum overall benchmarking in the form of comparison on some
+common dataset, while relying on the results reported in various works.
+"
+25180,Weak convergence of quantile and expectile processes under general assumptions,"  We show weak convergence of quantile and expectile processes to Gaussian
+limit processes in the space of bounded functions endowed with an appropriate
+semimetric which is based on the concepts of epi- and hypo convergence as
+introduced in \citet{buecher2014}. We impose assumptions for which it is known
+that weak convergence with respect to the supremum norm or the Skorodhod metric
+generally fails to hold. For expectiles, we only require a distribution with
+finite second moment but no further smoothness properties of distribution
+function, for quantiles, the distribution is assumed to be absolutely
+continuous with a version of its Lebesgue density which is strictly positive
+and has left- and right-sided limits. We also show consistency of the bootstrap
+for this mode of convergence.
+"
+25181,Machine learning modeling of superconducting critical temperature,"  Superconductivity has been the focus of enormous research effort since its
+discovery more than a century ago. Yet, some features of this unique phenomenon
+remain poorly understood; prime among these is the connection between
+superconductivity and chemical/structural properties of materials. To bridge
+the gap, several machine learning schemes are developed herein to model the
+critical temperatures ($T_{\mathrm{c}}$) of the 12,000+ known superconductors
+available via the SuperCon database. Materials are first divided into two
+classes based on their $T_{\mathrm{c}}$ values, above and below 10 K, and a
+classification model predicting this label is trained. The model uses
+coarse-grained features based only on the chemical compositions. It shows
+strong predictive power, with out-of-sample accuracy of about 92%. Separate
+regression models are developed to predict the values of $T_{\mathrm{c}}$ for
+cuprate, iron-based, and ""low-$T_{\mathrm{c}}$"" compounds. These models also
+demonstrate good performance, with learned predictors offering potential
+insights into the mechanisms behind superconductivity in different families of
+materials. To improve the accuracy and interpretability of these models, new
+features are incorporated using materials data from the AFLOW Online
+Repositories. Finally, the classification and regression models are combined
+into a single integrated pipeline and employed to search the entire Inorganic
+Crystallographic Structure Database (ICSD) for potential new superconductors.
+We identify more than 30 non-cuprate and non-iron-based oxides as candidate
+materials.
+"
+25182,Automatic picking of P and S arrivals using a minimal uncertainty wavelet approach,"  The so-called ""minimum uncertainty"" or mu-wavelet approach has been shown to
+be very effective. Prior work has shown that using special algorithms based on
+a mu-wavelet decomposition of a seismic signal allows for arrival detection of
+various waves on a single-component seismogram with good precision. However,
+the question of interpretation of the type of wave arriving has not been
+addressed. The problem lies in the fact that the algorithm detects more than
+two arrivals even on seismograms with little noise, the peaks corresponding to
+P and S wave arrivals on the indicator function are not always the strongest,
+and the peak corresponding to the P wave arrival is not always the first peak
+detected. We propose to use a signal/noise relationship function in a running
+window as a weight coefficient of the indicator function obtained using the
+mu-wavelet transformation, which allows to isolate specifically those arrivals
+which correspond to significant changes in the signal amplitude. To interpret
+wave arrivals on linear registration systems (well observation systems for
+monitoring hydrolic fracturing, ultrasound investigations on models and samples
+of geological material), an algorithm was also developed to isolate arrivals
+that best correspond to arrivals on all registration points that have
+observation systems. As a result, we propose an algorithm that allows to
+automatically isolate arrivals of S and P waves on ""frac"" event recordings of a
+well observation system, and provides agreement with manual detection that
+doesn't exceed four discretes.
+"
+25183,"The Impact of Collisionality, FLR and Parallel Closure Effects on Instabilities in the Tomakak Pedestal: Numerical Studies with the NIMROD code","  The extended-MHD NIMROD code [C.R. Sovinec and J.R. King, J. Comput. Phys.
+229, 5803 (2010)] is verified against the ideal-MHD ELITE code [H.R. Wilson et
+al. Phys. Plasmas 9, 1277 (2002)] on a diverted tokamak discharge. When the
+NIMROD model complexity is increased incrementally, resistive and first-order
+finite-Larmour radius effects are destabilizing and stabilizing, respectively.
+The full result is compared to local analytic calculations which are found to
+overpredict both the resistive destabilization and drift stabilization in
+comparison to the NIMROD computations.
+"
+25184,Atiyah-Segal theorem for Deligne-Mumford stacks and applications,"  We prove an Atiyah-Segal correspondence for the algebraic K-theory of
+quotient Deligne-Mumford stacks over the field of complex numbers. As
+applications, we give an explicit isomorphism between the algebraic $K$-theory
+and higher Chow groups of such stacks with complex coefficients and show that
+this isomorphism defines a natural isomorphism between two covariant functors.
+As a byproduct, we get a version of K{ö}ck's conjecture for proper morphisms
+between (possibly singular) Deligne-Mumford stacks.
+"
+25185,"Autocompletion interfaces make crowd workers slower, but their use promotes response diversity","  Creative tasks such as ideation or question proposal are powerful
+applications of crowdsourcing, yet the quantity of workers available for
+addressing practical problems is often insufficient. To enable scalable
+crowdsourcing thus requires gaining all possible efficiency and information
+from available workers. One option for text-focused tasks is to allow assistive
+technology, such as an autocompletion user interface (AUI), to help workers
+input text responses. But support for the efficacy of AUIs is mixed. Here we
+designed and conducted a randomized experiment where workers were asked to
+provide short text responses to given questions. Our experimental goal was to
+determine if an AUI helps workers respond more quickly and with improved
+consistency by mitigating typos and misspellings. Surprisingly, we found that
+neither occurred: workers assigned to the AUI treatment were slower than those
+assigned to the non-AUI control and their responses were more diverse, not
+less, than those of the control. Both the lexical and semantic diversities of
+responses were higher, with the latter measured using word2vec. A crowdsourcer
+interested in worker speed may want to avoid using an AUI, but using an AUI to
+boost response diversity may be valuable to crowdsourcers interested in
+receiving as much novel information from workers as possible.
+"
+25186,Assessing Code Authorship: The Case of the Linux Kernel,"  Code authorship is a key information in large-scale open source systems.
+Among others, it allows maintainers to assess division of work and identify key
+collaborators. Interestingly, open-source communities lack guidelines on how to
+manage authorship. This could be mitigated by setting to build an empirical
+body of knowledge on how authorship-related measures evolve in successful
+open-source communities. Towards that direction, we perform a case study on the
+Linux kernel. Our results show that: (a) only a small portion of developers (26
+%) makes significant contributions to the code base; (b) the distribution of
+the number of files per author is highly skewed --- a small group of top
+authors (3 %) is responsible for hundreds of files, while most authors (75 %)
+are responsible for at most 11 files; (c) most authors (62 %) have a specialist
+profile; (d) authors with a high number of co-authorship connections tend to
+collaborate with others with less connections.
+"
+25187,Canonical Correlation Analysis of Datasets with a Common Source Graph,"  Canonical correlation analysis (CCA) is a powerful technique for discovering
+whether or not hidden sources are commonly present in two (or more) datasets.
+Its well-appreciated merits include dimensionality reduction, clustering,
+classification, feature selection, and data fusion. The standard CCA however,
+does not exploit the geometry of the common sources, which may be available
+from the given data or can be deduced from (cross-) correlations. In this
+paper, this extra information provided by the common sources generating the
+data is encoded in a graph, and is invoked as a graph regularizer. This leads
+to a novel graph-regularized CCA approach, that is termed graph (g) CCA. The
+novel gCCA accounts for the graph-induced knowledge of common sources, while
+minimizing the distance between the wanted canonical variables. Tailored for
+diverse practical settings where the number of data is smaller than the data
+vector dimensions, the dual formulation of gCCA is also developed. One such
+setting includes kernels that are incorporated to account for nonlinear data
+dependencies. The resultant graph-kernel (gk) CCA is also obtained in closed
+form. Finally, corroborating image classification tests over several real
+datasets are presented to showcase the merits of the novel linear, dual, and
+kernel approaches relative to competing alternatives.
+"
+25188,ArrayBridge: Interweaving declarative array processing with high-performance computing,"  Scientists are increasingly turning to datacenter-scale computers to produce
+and analyze massive arrays. Despite decades of database research that extols
+the virtues of declarative query processing, scientists still write, debug and
+parallelize imperative HPC kernels even for the most mundane queries. This
+impedance mismatch has been partly attributed to the cumbersome data loading
+process; in response, the database community has proposed in situ mechanisms to
+access data in scientific file formats. Scientists, however, desire more than a
+passive access method that reads arrays from files.
+This paper describes ArrayBridge, a bi-directional array view mechanism for
+scientific file formats, that aims to make declarative array manipulations
+interoperable with imperative file-centric analyses. Our prototype
+implementation of ArrayBridge uses HDF5 as the underlying array storage library
+and seamlessly integrates into the SciDB open-source array database system. In
+addition to fast querying over external array objects, ArrayBridge produces
+arrays in the HDF5 file format just as easily as it can read from it.
+ArrayBridge also supports time travel queries from imperative kernels through
+the unmodified HDF5 API, and automatically deduplicates between array versions
+for space efficiency. Our extensive performance evaluation in NERSC, a
+large-scale scientific computing facility, shows that ArrayBridge exhibits
+statistically indistinguishable performance and I/O scalability to the native
+SciDB storage engine.
+"
+25189,A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters,"  In this work, we consider the solution of boundary integral equations by
+means of a scalable hierarchical matrix approach on clusters equipped with
+graphics hardware, i.e. graphics processing units (GPUs). To this end, we
+extend our existing single-GPU hierarchical matrix library hmglib such that it
+is able to scale on many GPUs and such that it can be coupled to arbitrary
+application codes. Using a model GPU implementation of a boundary element
+method (BEM) solver, we are able to achieve more than 67 percent relative
+parallel speed-up going from 128 to 1024 GPUs for a model geometry test case
+with 1.5 million unknowns and a real-world geometry test case with almost 1.2
+million unknowns. On 1024 GPUs of the cluster Titan, it takes less than 6
+minutes to solve the 1.5 million unknowns problem, with 5.7 minutes for the
+setup phase and 20 seconds for the iterative solver. To the best of the
+authors' knowledge, we here discuss the first fully GPU-based
+distributed-memory parallel hierarchical matrix Open Source library using the
+traditional H-matrix format and adaptive cross approximation with an
+application to BEM problems.
+"
+25190,Analogical-based Bayesian Optimization,"  Some real-world problems revolve to solve the optimization problem
+\max_{x\in\mathcal{X}}f\left(x\right) where f\left(.\right) is a black-box
+function and X might be the set of non-vectorial objects (e.g., distributions)
+where we can only define a symmetric and non-negative similarity score on it.
+This setting requires a novel view for the standard framework of Bayesian
+Optimization that generalizes the core insightful spirit of this framework.
+With this spirit, in this paper, we propose Analogical-based Bayesian
+Optimization that can maximize black-box function over a domain where only a
+similarity score can be defined. Our pathway is as follows: we first base on
+the geometric view of Gaussian Processes (GP) to define the concept of
+influence level that allows us to analytically represent predictive means and
+variances of GP posteriors and base on that view to enable replacing kernel
+similarity by a more genetic similarity score. Furthermore, we also propose two
+strategies to find a batch of query points that can efficiently handle high
+dimensional data.
+"
+25191,A partition function for Schwarzschild-AdS and Kerr-AdS black holes and for quantized globally hyperbolic spacetimes with a negative cosmological constant,"  We apply quantum statistics to our quantized versions of Schwarzschild-AdS
+and Kerr-AdS black holes and also to the quantized globally hyperbolic
+spacetimes having an asymptotically Euclidean Cauchy hypersurface by first
+proving, for the temporal Hamiltonian $H_0$, that $e^{-\beta H_0}$, $\beta>0$,
+is of trace class and then, that this result is also valid for the spatial
+Hamiltonian $H_1$, which has the same eigenvalues but with larger
+multiplicities. Since the lowest eigenvalue is strictly positive the extension
+of $e^{-\beta H_1}$ to the corresponding symmetric Fock space is also of trace
+class and we are thus able to define a partition function $Z$, the operator
+density $\rho$, the entropy $S$, and the average energy $E$. We prove that $S$
+and $E$ tend to infinity if the cosmological constant $\Lambda$ tends to $0$
+and vanish if $|\Lambda|$ tends to infinity. We also conjecture that $E$ is the
+source of the dark matter and that the dark energy density is a multiple of the
+eigenvalue of $\rho$ with respect to the vacuum vector which is $Z^{-1}$.
+"
+25192,Probabilistic aspects of ZM-groups,"  In this paper we study probabilistic aspects such as (cyclic) subgroup
+commutativity degree and (cyclic) factorization number of ZM-groups. We show
+that these quantities can be computed using the sizes of the conjugacy classes
+of these groups. In the end, we point out a class of groups whose (cyclic)
+subgroup commutativity degree vanishes asymptotically.
+"
+25193,Does robustness imply tractability? A lower bound for planted clique in the semi-random model,"  We consider a robust analog of the planted clique problem. In this analog, a
+set $S$ of vertices is chosen and all edges in $S$ are included; then, edges
+between $S$ and the rest of the graph are included with probability
+$\frac{1}{2}$, while edges not touching $S$ are allowed to vary arbitrarily.
+For this semi-random model, we show that the information-theoretic threshold
+for recovery is $\tilde{\Theta}(\sqrt{n})$, in sharp contrast to the classical
+information-theoretic threshold of $\Theta(\log(n))$. This matches the
+conjectured computational threshold for the classical planted clique problem,
+and thus raises the intriguing possibility that, once we require robustness,
+there is no computational-statistical gap for planted clique. Our lower bound
+involves establishing a result regarding the KL divergence of a family of
+perturbed Bernoulli distributions, which may be of independent interest.
+"
+25194,FUSE Spectroscopy of the Accreting Hot Components in Symbiotic Variables,"  We have conducted a spectroscopic analysis of the far ultraviolet archival
+spectra of four symbiotic variables, EG And, AE Ara, CQ Dra and RW Hya. RW Hya
+and EG And have never had a recorded outburst while CQ Dra and AE Ara have
+outburst histories. We analyze these systems while they are in quiescence in
+order to help reveal the physical properties of their hot components via
+comparisons of the observations with optically thick accretion disk models and
+NLTE model white dwarf photospheres. We have extended the wavelength coverage
+down to the Lyman Limit with FUSE spectra. We find that the hot component in RW
+Hya is a low mass white dwarf with a surface temperature of 160,000K. We
+re-examine whether or not the symbiotic system CQ Dra is a triple system with a
+red giant transferring matter to a hot component made up of a cataclysmic
+variable in which the white dwarf has a surface temperature as low as
+$\sim$20,000K. The very small size of the hot component contributing to the
+shortest wavelengths of the FUSE spectrum of CQ Dra agrees with an optically
+thick and geometrically thin ($\sim$4\% of the WD surface) hot ($\sim
+120,000$K) boundary layer. Our analysis of EG And reveals that its hot
+component is a hot, bare, low mass white dwarf with a surface temperature of
+80-95,000K, with a surface gravity $\log(g)= 7.5$. For AE Ara, we also find
+that a low gravity ($\log(g) \sim 6$) hot ($T \sim 130,000$K) WD accounts for
+the hot component.
+"
+25195,Estimating the Spot Covariation of Asset Prices - Statistical Theory and Empirical Evidence,"  We propose a new estimator for the spot covariance matrix of a
+multi-dimensional continuous semi-martingale log asset price process which is
+subject to noise and non-synchronous observations. The estimator is constructed
+based on a local average of block-wise parametric spectral covariance
+estimates. The latter originate from a local method of moments (LMM) which
+recently has been introduced. We prove consistency and a point-wise stable
+central limit theorem for the proposed spot covariance estimator in a very
+general setup with stochastic volatility, leverage effects and general noise
+distributions. Moreover, we extend the LMM estimator to be robust against
+autocorrelated noise and propose a method to adaptively infer the
+autocorrelations from the data. Based on simulations we provide empirical
+guidance on the effective implementation of the estimator and apply it to
+high-frequency data of a cross-section of Nasdaq blue chip stocks. Employing
+the estimator to estimate spot covariances, correlations and volatilities in
+normal but also unusual periods yields novel insights into intraday covariance
+and correlation dynamics. We show that intraday (co-)variations (i) follow
+underlying periodicity patterns, (ii) reveal substantial intraday variability
+associated with (co-)variation risk, and (iii) can increase strongly and nearly
+instantaneously if new information arrives.
+"
+25196,Milliarcsecond compact structure in radio quasars and the geometry of the universe,"  In this paper, by using the recently compiled set of 120
+intermediate-luminosity quasars (ILQSO) observed in a single-frequency VLBI
+survey, we propose an improved model-independent method to probe cosmic
+curvature parameter $\Omega_k$ and make the first measurement of the cosmic
+curvature referring to a distant past, with redshifts up to $z\sim 3.0$.
+Compared with other methods, the proposed one involving the quasar data
+achieves constraints with higher precision in this redshift range. More
+importantly, our results indicate that the measured $\Omega_k$ is in good
+agreement with zero cosmic curvature, implying that there is no significant
+deviation from a flat Universe. Finally, we investigate the possibility of
+testing $\Omega_k$ with a much higher accuracy using quasars observed in the
+future VLBI surveys. It is shown that our method could provide a reliable and
+tight constraint on the prior $\Omega_k$ and one can expect the zero cosmic
+curvature to be established at the precision of $\Delta\Omega_k\sim 10^{-2}$
+with 250 well-observed radio quasars.
+"
+25197,Deep Speaker Feature Learning for Text-independent Speaker Verification,"  Recently deep neural networks (DNNs) have been used to learn speaker
+features. However, the quality of the learned features is not sufficiently
+good, so a complex back-end model, either neural or probabilistic, has to be
+used to address the residual uncertainty when applied to speaker verification,
+just as with raw features. This paper presents a convolutional time-delay deep
+neural network structure (CT-DNN) for speaker feature learning. Our
+experimental results on the Fisher database demonstrated that this CT-DNN can
+produce high-quality speaker features: even with a single feature (0.3 seconds
+including the context), the EER can be as low as 7.68%. This effectively
+confirmed that the speaker trait is largely a deterministic short-time property
+rather than a long-time distributional pattern, and therefore can be extracted
+from just dozens of frames.
+"
+25198,On Kuratowski partitions in the Marczewski structure and Ellentuck topology,"  We show that large sets in Ellentuck topology (i.e. sets which are not
+nowhere Ramsey) do not admit Kuratowski's partition. The similar result is true
+for the Sacks real forcing.
+"
+25199,Light Scattering From an Atomic Array Trapped Near a One-Dimensional Nanoscale Waveguide: a Microscopic Approach,"  The coupling of atomic arrays and one-dimensional subwavelength waveguides
+gives rise to in- teresting photon transport properties, such as recent
+experimental demonstrations of large Bragg reflection and paves the way for a
+variety of potential applications in the field of quantum non-linear optics.
+Here, we present a theoretical analysis for the process of single-photon
+scattering in this configuration using a full microscopic approach. Based on
+this formalism, we analyze the spectral dependencies for different scattering
+channels from either ordered or disordered arrays. The de- veloped approach is
+entirely applicable for a single-photon scattering from a quasi-one-dimensional
+array of multilevel atoms with degenerate ground state energy structure. Our
+approach provides an important framework for including not only Rayleigh but
+also Raman channels in the microscopic description of the cooperative
+scattering process.
+"
+25200,"BAT AGN Spectroscopic Survey - IV: Near-Infrared Coronal Lines, Hidden Broad Lines, and Correlation with Hard X-ray Emission","  We provide a comprehensive census of the near-Infrared (NIR, 0.8-2.4 $\mu$m)
+spectroscopic properties of 102 nearby (z < 0.075) active galactic nuclei
+(AGN), selected in the hard X-ray band (14-195 keV) from the Swift-Burst Alert
+Telescope (BAT) survey. With the launch of the James Webb Space Telescope this
+regime is of increasing importance for dusty and obscured AGN surveys. We
+measure black hole masses in 68% (69/102) of the sample using broad emission
+lines (34/102) and/or the velocity dispersion of the Ca II triplet or the CO
+band-heads (46/102). We find that emission line diagnostics in the NIR are
+ineffective at identifying bright, nearby AGN galaxies because ([Fe II]
+1.257$\mu$m/Pa$\beta$ and H$_2$ 2.12$\mu$m/Br$\gamma$) identify only 25%
+(25/102) as AGN with significant overlap with star forming galaxies and only
+20% of Seyfert 2 have detected coronal lines (6/30). We measure the coronal
+line emission in Seyfert 2 to be weaker than in Seyfert 1 of the same
+bolometric luminosity suggesting obscuration by the nuclear torus. We find that
+the correlation between the hard X-ray and the [Si VI] coronal line luminosity
+is significantly better than with the [O III] luminosity. Finally, we find 3/29
+galaxies (10%) that are optically classified as Seyfert 2 show broad emission
+lines in the NIR. These AGN have the lowest levels of obscuration among the
+Seyfert 2s in our sample ($\log N_{\rm H} < 22.43$ cm$^{-2}$), and all show
+signs of galaxy-scale interactions or mergers suggesting that the optical broad
+emission lines are obscured by host galaxy dust.
+"
+25201,Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events,"  Long-running, high-impact events such as the Boston Marathon bombing often
+develop through many stages and involve a large number of entities in their
+unfolding. Timeline summarization of an event by key sentences eases story
+digestion, but does not distinguish between what a user remembers and what she
+might want to re-check. In this work, we present a novel approach for timeline
+summarization of high-impact events, which uses entities instead of sentences
+for summarizing the event at each individual point in time. Such entity
+summaries can serve as both (1) important memory cues in a retrospective event
+consideration and (2) pointers for personalized event exploration. In order to
+automatically create such summaries, it is crucial to identify the ""right""
+entities for inclusion. We propose to learn a ranking function for entities,
+with a dynamically adapted trade-off between the in-document salience of
+entities and the informativeness of entities across documents, i.e., the level
+of new information associated with an entity for a time point under
+consideration. Furthermore, for capturing collective attention for an entity we
+use an innovative soft labeling approach based on Wikipedia. Our experiments on
+a real large news datasets confirm the effectiveness of the proposed methods.
+"
+25202,SDSS IV MaNGA - Metallicity and nitrogen abundance gradients in local galaxies,"  We study the gas phase metallicity (O/H) and nitrogen abundance gradients
+traced by star forming regions in a representative sample of 550 nearby
+galaxies in the stellar mass range $\rm 10^9-10^{11.5} M_\odot$ with resolved
+spectroscopic data from the SDSS-IV MaNGA survey. Using strong-line ratio
+diagnostics (R23 and O3N2 for metallicity and N2O2 for N/O) and referencing to
+the effective (half-light) radius ($\rm R_e$), we find that the metallicity
+gradient steepens with stellar mass, lying roughly flat among galaxies with
+$\rm log(M_\star/M_\odot) = 9.0$ but exhibiting slopes as steep as -0.14 dex
+$\rm R_e^{-1}$ at $\rm log(M_\star/M_\odot) = 10.5$ (using R23, but equivalent
+results are obtained using O3N2). At higher masses, these slopes remain typical
+in the outer regions of our sample ($\rm R > 1.5 ~R_e$), but a flattening is
+observed in the central regions ($\rm R < 1~ R_e$). In the outer regions ($\rm
+R > 2.0 ~R_e$) we detect a mild flattening of the metallicity gradient in
+stacked profiles, although with low significance. The N/O ratio gradient
+provides complementary constraints on the average chemical enrichment history.
+Unlike the oxygen abundance, the average N/O profiles do not flatten out in the
+central regions of massive galaxies. The metallicity and N/O profiles both
+depart significantly from an exponential form, suggesting a disconnect between
+chemical enrichment and stellar mass surface density on local scales. In the
+context of inside-out growth of discs, our findings suggest that central
+regions of massive galaxies today have evolved to an equilibrium metallicity,
+while the nitrogen abundance continues to increase as a consequence of delayed
+secondary nucleosynthetic production.
+"
+25203,Selling to a No-Regret Buyer,"  We consider the problem of a single seller repeatedly selling a single item
+to a single buyer (specifically, the buyer has a value drawn fresh from known
+distribution $D$ in every round). Prior work assumes that the buyer is fully
+rational and will perfectly reason about how their bids today affect the
+seller's decisions tomorrow. In this work we initiate a different direction:
+the buyer simply runs a no-regret learning algorithm over possible bids. We
+provide a fairly complete characterization of optimal auctions for the seller
+in this domain. Specifically:
+- If the buyer bids according to EXP3 (or any ""mean-based"" learning
+algorithm), then the seller can extract expected revenue arbitrarily close to
+the expected welfare. This auction is independent of the buyer's valuation $D$,
+but somewhat unnatural as it is sometimes in the buyer's interest to overbid. -
+There exists a learning algorithm $\mathcal{A}$ such that if the buyer bids
+according to $\mathcal{A}$ then the optimal strategy for the seller is simply
+to post the Myerson reserve for $D$ every round. - If the buyer bids according
+to EXP3 (or any ""mean-based"" learning algorithm), but the seller is restricted
+to ""natural"" auction formats where overbidding is dominated (e.g. Generalized
+First-Price or Generalized Second-Price), then the optimal strategy for the
+seller is a pay-your-bid format with decreasing reserves over time. Moreover,
+the seller's optimal achievable revenue is characterized by a linear program,
+and can be unboundedly better than the best truthful auction yet simultaneously
+unboundedly worse than the expected welfare.
+"
+25204,Collective Transport of Lennard-Jones Particles through One-Dimensional Periodic Potentials,"  Transport surrounding is full of all kinds of fields, like particle
+potential, external potential. Under these conditions, how elements work and
+how position and momentum redistribute in the diffusion? For enriching the Fick
+law in ordinary, nonequilibrium statistics physics need to be used to
+disintegrate the complex process. This study attempts to discuss the particle
+transport in the one-dimensional channel under external potential fields. Two
+type of potentials, potential well and barrier do not change the potential in
+total, are built during the diffusion process. There are quite distinct
+phenomena because of different one-dimensional periodic potentials. We
+meticulously explore reasons about why external potential field impacts
+transport by the subsection and statistics method. Besides, one of the
+evidences of Maxwell velocity distribution is confirmed in assumption of local
+equilibrium. In addition, we have also investigated the influence of
+temperature and concentration to the collective diffusion coefficient, by a
+variety of external force, attaching thermodynamics analyze to these opposite
+phenomena. So simply is this model that the most valuable point may be an idea,
+which relating flux to sectional statistics of position and momentum could be
+referenced in similar transport problems.
+"
+25205,Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations,"  In this paper, we propose a successive convex approximation framework for
+sparse optimization where the nonsmooth regularization function in the
+objective function is nonconvex and it can be written as the difference of two
+convex functions. The proposed framework is based on a nontrivial combination
+of the majorization-minimization framework and the successive convex
+approximation framework proposed in literature for a convex regularization
+function. The proposed framework has several attractive features, namely, i)
+flexibility, as different choices of the approximate function lead to different
+type of algorithms; ii) fast convergence, as the problem structure can be
+better exploited by a proper choice of the approximate function and the
+stepsize is calculated by the line search; iii) low complexity, as the
+approximate function is convex and the line search scheme is carried out over a
+differentiable function; iv) guaranteed convergence to a stationary point. We
+demonstrate these features by two example applications in subspace learning,
+namely, the network anomaly detection problem and the sparse subspace
+clustering problem. Customizing the proposed framework by adopting the
+best-response type approximation, we obtain soft-thresholding with exact line
+search algorithms for which all elements of the unknown parameter are updated
+in parallel according to closed-form expressions. The attractive features of
+the proposed algorithms are illustrated numerically.
+"
+25206,Theory of Heat Equations for Sigma Functions,"  We consider the heat equations satisfied by the sigma function associated
+with a planar curve, extending and developing earlier pioneering work of
+Buchstaber and Leykin. These heat equations lead to useful {\em linear}
+recursive relations for the coefficients of power series expansion of the sigma
+function. In particular we exhibit explicit results for curves of genus 3, and
+give a new constructive proof of an explicit expression for the main matrix in
+the theory for {\em any} hyperelliptic curve. We also state and prove a new
+explicit formula for the eigenvalues of the linear operators associated with
+this matrix, as well as other practical formulae.
+"
+25207,The SkyMapper Transient Survey,"  The SkyMapper 1.3 m telescope at Siding Spring Observatory has now begun
+regular operations. Alongside the Southern Sky Survey, a comprehensive digital
+survey of the entire southern sky, SkyMapper will carry out a search for
+supernovae and other transients. The search strategy, covering a total
+footprint area of ~2000 deg2 with a cadence of $\leq 5$ days, is optimised for
+discovery and follow-up of low-redshift type Ia supernovae to constrain cosmic
+expansion and peculiar velocities. We describe the search operations and
+infrastructure, including a parallelised software pipeline to discover variable
+objects in difference imaging; simulations of the performance of the survey
+over its lifetime; public access to discovered transients; and some first
+results from the Science Verification data.
+"
+25208,AirDraw: Leveraging Smart Watch Motion Sensors for Mobile Human Computer Interactions,"  Wearable computing is one of the fastest growing technologies today. Smart
+watches are poised to take over at least of half the wearable devices market in
+the near future. Smart watch screen size, however, is a limiting factor for
+growth, as it restricts practical text input. On the other hand, wearable
+devices have some features, such as consistent user interaction and hands-free,
+heads-up operations, which pave the way for gesture recognition methods of text
+entry. This paper proposes a new text input method for smart watches, which
+utilizes motion sensor data and machine learning approaches to detect letters
+written in the air by a user. This method is less computationally intensive and
+less expensive when compared to computer vision approaches. It is also not
+affected by lighting factors, which limit computer vision solutions. The
+AirDraw system prototype developed to test this approach is presented.
+Additionally, experimental results close to 71% accuracy are presented.
+"
+25209,On magnetic and vortical susceptibilities of the Cooper condensate,"  We discuss the susceptibility of the Cooper condensate in the s-wave $2+1$
+superconductor in the external magnetic field and in the rotating frame. The
+extended holographic model involving the charged rank-two field is considered
+and it is argued that the susceptibility does not vanish. We interpret
+non-vanishing susceptibilities as the admixture of the p-wave triplet component
+in the Cooper condensate in the external field.
+"
+25210,Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal,"  Model-free reinforcement learning has recently been shown to be effective at
+learning navigation policies from complex image input. However, these
+algorithms tend to require large amounts of interaction with the environment,
+which can be prohibitively costly to obtain on robots in the real world. We
+present an approach for efficiently learning goal-directed navigation policies
+on a mobile robot, from only a single coverage traversal of recorded data. The
+navigation agent learns an effective policy over a diverse action space in a
+large heterogeneous environment consisting of more than 2km of travel, through
+buildings and outdoor regions that collectively exhibit large variations in
+visual appearance, self-similarity, and connectivity. We compare pretrained
+visual encoders that enable precomputation of visual embeddings to achieve a
+throughput of tens of thousands of transitions per second at training time on a
+commodity desktop computer, allowing agents to learn from millions of
+trajectories of experience in a matter of hours. We propose multiple forms of
+computationally efficient stochastic augmentation to enable the learned policy
+to generalise beyond these precomputed embeddings, and demonstrate successful
+deployment of the learned policy on the real robot without fine tuning, despite
+environmental appearance differences at test time. The dataset and code
+required to reproduce these results and apply the technique to other datasets
+and robots is made publicly available at rl-navigation.github.io/deployable.
+"
+25211,Joint Distribution Optimal Transportation for Domain Adaptation,"  This paper deals with the unsupervised domain adaptation problem, where one
+wants to estimate a prediction function $f$ in a given target domain without
+any labeled sample by exploiting the knowledge available from a source domain
+where labels are known. Our work makes the following assumption: there exists a
+non-linear transformation between the joint feature/label space distributions
+of the two domain $\mathcal{P}_s$ and $\mathcal{P}_t$. We propose a solution of
+this problem with optimal transport, that allows to recover an estimated target
+$\mathcal{P}^f_t=(X,f(X))$ by optimizing simultaneously the optimal coupling
+and $f$. We show that our method corresponds to the minimization of a bound on
+the target error, and provide an efficient algorithmic solution, for which
+convergence is proved. The versatility of our approach, both in terms of class
+of hypothesis or loss functions is demonstrated with real world classification
+and regression problems, for which we reach or surpass state-of-the-art
+results.
+"
+25212,Martin boundary covers Floyd boundary,"  For finitely supported random walks on finitely generated groups $G$ we prove
+that the identity map on $G$ extends to a continuous equivariant surjection
+from the Martin boundary to the Floyd boundary, with preimages of conical
+points being singletons. This yields new results for relatively hyperbolic
+groups. Our key estimate relates the Green and Floyd metrics, generalizing
+results of Ancona for random walks on hyperbolic groups and of Karlsson for
+quasigeodesics.
+We then apply these techniques to obtain some results concerning the harmonic
+measure on the limit sets of geometrically finite isometry groups of Gromov
+hyperbolic spaces. .
+"
+25213,Majorana Zero-Modes in a Two-Dimensional $p$-Wave Superconductor,"  We analyze the formation of Majorana zero-modes at the edge of a
+two-dimensional topological superconductor. In particular, we study a
+time-reversal-invariant triplet phase that is likely to exist in doped
+Bi$_2$Se$_3$. Upon the introduction of an in-plane magnetic field to the
+superconductor, a gap is opened in the surface modes, which induces localized
+Majorana modes. The position of these modes can be simply manipulated by
+changing the orientation of the applied field, yielding novel methods for
+braiding these states with possible applications to topological quantum
+computation.
+"
+25214,Mean Estimation from Adaptive One-bit Measurements,"  We consider the problem of estimating the mean of a normal distribution under
+the following constraint: the estimator can access only a single bit from each
+sample from this distribution. We study the squared error risk in this
+estimation as a function of the number of samples and one-bit measurements $n$.
+We consider an adaptive estimation setting where the single-bit sent at step
+$n$ is a function of both the new sample and the previous $n-1$ acquired bits.
+For this setting, we show that no estimator can attain asymptotic mean squared
+error smaller than $\pi/(2n)+O(n^{-2})$ times the variance. In other words,
+one-bit restriction increases the number of samples required for a prescribed
+accuracy of estimation by a factor of at least $\pi/2$ compared to the
+unrestricted case. In addition, we provide an explicit estimator that attains
+this asymptotic error, showing that, rather surprisingly, only $\pi/2$ times
+more samples are required in order to attain estimation performance equivalent
+to the unrestricted case.
+"
+25215,Leveling the playing field for Visually Impaired using Transport Assistant,"  Visually impaired people face numerous challenges when it comes to
+transportation. Not only must they circumvent obstacles while navigating, but
+they also need access to essential information related to available public
+transport, up-to-date weather forecast, and convenient method for booking
+private taxis. In this paper we introduce Transport Assistant - a voice based
+assistive technology prototype, built with a goal of leveling the playing field
+for the visually impaired to solve these problems that they face in their day
+to day life. Being voice enabled makes it seamlessly integrate into the
+environment, and can be invoked by saying a hotword - hello assistant. The
+paper explores this research question, followed by investigating existing
+technologies, explains the methodology and design, then concludes by presenting
+the prototype and results.
+"
+25216,"Randomized Similar Triangles Method: A Unifying Framework for Accelerated Randomized Optimization Methods (Coordinate Descent, Directional Search, Derivative-Free Method)","  In this paper, we consider smooth convex optimization problems with simple
+constraints and inexactness in the oracle information such as value, partial or
+directional derivatives of the objective function. We introduce a unifying
+framework, which allows to construct different types of accelerated randomized
+methods for such problems and to prove convergence rate theorems for them. We
+focus on accelerated random block-coordinate descent, accelerated random
+directional search, accelerated random derivative-free method and, using our
+framework, provide their versions for problems with inexact oracle information.
+Our contribution also includes accelerated random block-coordinate descent with
+inexact oracle and entropy proximal setup as well as derivative-free version of
+this method.
+"
+25217,Active Learning for Convolutional Neural Networks: A Core-Set Approach,"  Convolutional neural networks (CNNs) have been successfully applied to many
+recognition and learning tasks using a universal recipe; training a deep model
+on a very large dataset of supervised examples. However, this approach is
+rather restrictive in practice since collecting a large set of labeled images
+is very expensive. One way to ease this problem is coming up with smart ways
+for choosing images to be labelled from a very large collection (ie. active
+learning).
+Our empirical study suggests that many of the active learning heuristics in
+the literature are not effective when applied to CNNs in batch setting.
+Inspired by these limitations, we define the problem of active learning as
+core-set selection, ie. choosing set of points such that a model learned over
+the selected subset is competitive for the remaining data points. We further
+present a theoretical result characterizing the performance of any selected
+subset using the geometry of the datapoints. As an active learning algorithm,
+we choose the subset which is expected to yield best result according to our
+characterization. Our experiments show that the proposed method significantly
+outperforms existing approaches in image classification experiments by a large
+margin.
+"
+25218,Average Stack Cost of Buechi Pushdown Automata,"  We study the average stack cost of Buechi pushdown automata (Buechi PDA). We
+associate a non-negative price with each stack symbol and define the cost of a
+stack as the sum of costs of all its elements. We introduce and study the
+average stack cost problem (ASC), which asks whether there exists an accepting
+run of a given Buechi PDA such that the long-run average of stack costs is
+below some given threshold. The ASC problem generalizes mean-payoff objective
+and can be used to express quantitative properties of pushdown systems. In
+particular, we can compute the average response time using the ASC problem. We
+show that the ASC problem can be solved in polynomial time.
+"
+25219,Counterexample to global convergence of DSOS and SDSOS hierarchies,"  We exhibit a convex polynomial optimization problem for which the
+diagonally-dominant sum-of-squares (DSOS) and the scaled diagonally-dominant
+sum-of-squares (SDSOS) hierarchies, based on linear programming and
+second-order conic programming respectively, do not converge to the global
+infimum. The same goes for the r-DSOS and r-SDSOS hierarchies. This refutes the
+claim in the literature according to which the DSOS and SDSOS hierarchies can
+solve any polynomial optimization problem to arbitrary accuracy. In contrast,
+the Lasserre hierarchy based on semidefinite programming yields the global
+infimum and the global minimizer with the first order relaxation. We further
+observe that the dual to the SDSOS hierarchy is the moment hierarchy where
+every positive semidefinite constraint is relaxed to all necessary second-order
+conic constraints. As a result, the number of second-order conic constraints
+grows quadratically in function of the size of the positive semidefinite
+constraints in the Lasserre hierarchy. Together with the counterexample, this
+suggests that DSOS and SDSOS are not necessarily more tractable alternatives to
+sum-of-squares.
+"
+25220,Rare-Event Simulation for Distribution Networks,"  We model equilibrium allocations in a distribution network as the solution of
+a linear program (LP) which minimizes the cost of unserved demands across nodes
+in the network. The constraints in the LP dictate that once a given node's
+supply is exhausted, its unserved demand is distributed among neighboring
+nodes. All nodes do the same and the resulting solution is the equilibrium
+allocation. Assuming that the demands are random (following a jointly Gaussian
+law), our goal is to study the probability that the optimal cost (i.e. the cost
+of unserved demands in equilibrium) exceeds a large threshold, which is a rare
+event. Our contribution is the development of importance sampling and
+conditional Monte Carlo algorithms for estimating this probability. We
+establish the asymptotic efficiency of our algorithms and also present
+numerical results which illustrate strong performance of our procedures.
+"
+25221,Sheath-Accumulating Propagation of Interplanetary Coronal Mass Ejection,"  Fast interplanetary coronal mass ejections (interplanetary CMEs, or ICMEs)
+are the drivers of strongest space weather storms such as solar energetic
+particle events and geomagnetic storms. The connection between space weather
+impacting solar wind disturbances associated with fast ICMEs at Earth and the
+characteristics of causative energetic CMEs observed near the Sun is a key
+question in the study of space weather storms as well as in the development of
+practical space weather prediction. Such shock-driving fast ICMEs usually
+expand at supersonic speed during the propagation, resulting in the continuous
+accumulation of shocked sheath plasma ahead. In this paper, we propose the
+""sheath-accumulating propagation"" (SAP) model that describe the coevolution of
+the interplanetary sheath and decelerating ICME ejecta by taking into account
+the process of upstream solar wind plasma accumulation within the sheath
+region. Based on the SAP model, we discussed (1) ICME deceleration
+characteristics, (2) the fundamental condition for fast ICME at Earth, (3)
+thickness of interplanetary sheath, (4) arrival time prediction and (5) the
+super-intense geomagnetic storms associated with huge solar flares. We
+quantitatively show that not only speed but also mass of the CME are crucial in
+discussing the above five points. The similarities and differences among the
+SAP model, the drag-based model and the`snow-plough' model proposed by
+\citet{tappin2006} are also discussed.
+"
+25222,Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders,"  Gravitational wave astronomy is a rapidly growing field of modern
+astrophysics, with observations being made frequently by the LIGO detectors.
+Gravitational wave signals are often extremely weak and the data from the
+detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary
+noise, often containing transient disturbances which can obscure real signals.
+Traditional denoising methods, such as principal component analysis and
+dictionary learning, are not optimal for dealing with this non-Gaussian noise,
+especially for low signal-to-noise ratio gravitational wave signals.
+Furthermore, these methods are computationally expensive on large datasets. To
+overcome these issues, we apply state-of-the-art signal processing techniques,
+based on recent groundbreaking advancements in deep learning, to denoise
+gravitational wave signals embedded either in Gaussian noise or in real LIGO
+noise. We introduce SMTDAE, a Staired Multi-Timestep Denoising Autoencoder,
+based on sequence-to-sequence bi-directional Long-Short-Term-Memory recurrent
+neural networks. We demonstrate the advantages of using our unsupervised deep
+learning approach and show that, after training only using simulated Gaussian
+noise, SMTDAE achieves superior recovery performance for gravitational wave
+signals embedded in real non-Gaussian LIGO noise.
+"
+25223,How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View,"  We study in this paper the rate of convergence for learning densities under
+the Generative Adversarial Networks (GAN) framework, borrowing insights from
+nonparametric statistics. We introduce an improved GAN estimator that achieves
+a faster rate, through simultaneously leveraging the level of smoothness in the
+target density and the evaluation metric, which in theory remedies the mode
+collapse problem reported in the literature. A minimax lower bound is
+constructed to show that when the dimension is large, the exponent in the rate
+for the new GAN estimator is near optimal. One can view our results as
+answering in a quantitative way how well GAN learns a wide range of densities
+with different smoothness properties, under a hierarchy of evaluation metrics.
+As a byproduct, we also obtain improved generalization bounds for GAN with
+deeper ReLU discriminator network.
+"
+25224,Cotorsion pairs in cluster categories of type $A_{\infty}^{\infty}$,"  In this paper, we give a complete classification of cotorsion pairs in a
+cluster category $\mathscr{C}$ of type $A^\infty_\infty$ via certain
+configurations of arcs, called $\tau$-compact Ptolemy diagrams, in an infinite
+strip with marked points. As applications, we classify $t$-structures and
+functorially finite rigid subcategories in $\mathscr{C}$, respectively. We also
+deduce Liu-Paquette's classification of cluster tilting categories of
+$\mathscr{C}$ and Ng's classification of torsion pairs in the cluster category
+of type $A_\infty$.
+"
+25225,Birationally rigid complete intersections with a singular point of high multiplicity,"  We prove the birational rigidity of Fano complete intersections of index 1
+with a singular point of high multiplicity, which can be close to the degree of
+the variety. In particular, the groups of birational and biregular
+automorphisms of these varieties are equal, and they are non-rational. The
+proof is based on the techniques of the method of maximal singularities, the
+generalized $4n^2$-inequality for complete intersection singularities and the
+technique of hypertangent divisors.
+"
+25226,iBoW-LCD: An Appearance-based Loop Closure Detection Approach using Incremental Bags of Binary Words,"  In this paper, we introduce iBoW-LCD, a novel appearance-based loop closure
+detection method. The presented approach makes use of an incremental
+Bag-of-Words (BoW) scheme based on binary descriptors to retrieve previously
+seen similar images, avoiding any vocabulary training stage usually required by
+classic BoW models. In addition, to detect loop closures, iBoW-LCD builds on
+the concept of dynamic islands, a simple but effective mechanism to group
+similar images close in time, which reduces the computational times typically
+associated to Bayesian frameworks. Our approach is validated using several
+indoor and outdoor public datasets, taken under different environmental
+conditions, achieving a high accuracy and outperforming other state-of-the-art
+solutions.
+"
+25227,Minimizers for the fractional Sobolev inequality on domains,"  We consider a version of the fractional Sobolev inequality in domains and
+study whether the best constant in this inequality is attained. For the
+half-space and a large class of bounded domains we show that a minimizer
+exists, which is in contrast to the classical Sobolev inequalities in domains.
+"
+25228,Bandgap widening by disorder in rainbow metamaterials,"  Stubbed plates, i.e., thin elastic sheets endowed with pillar-like
+scatterers, display subwavelength, locally-resonant bandgaps that are
+controlled by both the intrinsic resonance properties of the pillars and by the
+relative stiffness of the pillars to the baseplate. In this work, we focus on
+the response of a thin and compliant plate featuring heterogeneous families of
+pillars. We demonstrate experimentally that both the spatial arrangement and
+the resonant frequencies of the pillars greatly influence the filtering
+characteristics of the system. We highlight that both spatially graded as well
+as random (disordered) arrangements of pillars result in macroscopic bandgap
+widening. We further report that the spectral range over which wave attenuation
+achieved with random arrangements is on average wider than the one observed
+while working with graded configurations. We explore the robustness of these
+findings against small changes in the structural and material properties of the
+plate and pillars.
+"
+25229,Linear feedback stabilization of point vortex equilibria near a Kasper Wing,"  This paper concerns feedback stabilization of point vortex equilibria above
+an inclined thin plate and a three-plate configuration known as the Kasper Wing
+in the presence of an oncoming uniform flow. The flow is assumed to be
+potential and is modeled by the 2D incompressible Euler equations. Actuation
+has the form of blowing and suction localized on the main plate and is
+represented in terms of a sink-source singularity, whereas measurement of
+pressure across the plate serves as system output. We focus on point-vortex
+equilibria forming a one-parameter family with locus approaching the trailing
+edge of the main plate and show that these equilibria are either unstable or
+neutrally stable. Using methods of linear control theory we find that the
+system dynamics linearised around these equilibria are both controllable and
+observable for almost all actuator and sensor locations. The design of the
+feedback control is based on the Linear-Quadratic-Gaussian (LQG) compensator.
+Computational results demonstrate the effectiveness of this control and the key
+finding is that Kasper Wing configurations are in general more controllable
+than their single plate counterparts and also exhibit larger basins of
+attraction under LQG feedback control. The feedback control is then applied to
+systems with additional perturbations added to the flow in the form of random
+fluctuations of the angle of attack and a vorticity shedding mechanism. Another
+important observation is that, in the presence of these additional
+perturbations, the control remains robust, provided the system does not deviate
+too far from its original state. Furthermore, introducing a vorticity shedding
+mechanism tends to enhance the effectiveness of the control. Physical
+interpretation is provided for the results of the controllability and
+observability analysis as well as the response of the feedback control to
+different perturbations.
+"
+25230,Stochastic Coupled Cluster Theory: Efficient Sampling of the Coupled Cluster Expansion,"  We consider the sampling of the coupled cluster expansion within stochastic
+coupled cluster theory. Observing the limitations of previous approaches due to
+the inherently non-linear behaviour of a coupled cluster wavefunction
+representation we propose new approaches based upon an intuitive, well-defined
+condition for sampling weights and on sampling the expansion in cluster
+operators of different excitation levels. We term these modifications even and
+truncated selection respectively. Utilising both approaches demonstrates
+dramatically improved calculation stability as well as reduced computational
+and memory costs. These modifications are particularly effective at higher
+truncation levels owing to the large number of terms within the cluster
+expansion that can be neglected, as demonstrated by the reduction of the number
+of terms to be sampled at the level of CCSDT by 77% and at CCSDTQ56 by 98%.
+"
+25231,On the perturbation algebra,"  We introduce a certain differential graded bialgebra, neither commutative nor
+cocommutative, that governs perturbations of a differential on complexes
+supplied with an abstract Hodge decomposition. This leads to a conceptual
+treatment of the Homological Perturbation Lemma and its multiplicative version.
+As an application we give an explicit form of the decomposition theorem for
+$A_\infty$ algebras and $A_\infty$ modules and, more generally, for twisted
+objects in differential graded categories.
+"
+25232,Maximum likelihood estimation in Gaussian models under total positivity,"  We analyze the problem of maximum likelihood estimation for Gaussian
+distributions that are multivariate totally positive of order two (MTP2). By
+exploiting connections to phylogenetics and single-linkage clustering, we give
+a simple proof that the maximum likelihood estimator (MLE) for such
+distributions exists based on at least 2 observations, irrespective of the
+underlying dimension. Slawski and Hein, who first proved this result, also
+provided empirical evidence showing that the MTP2 constraint serves as an
+implicit regularizer and leads to sparsity in the estimated inverse covariance
+matrix, determining what we name the ML graph. We show that we can find an
+upper bound for the ML graph by adding edges corresponding to correlations in
+excess of those explained by the maximum weight spanning forest of the
+correlation matrix. Moreover, we provide globally convergent coordinate descent
+algorithms for calculating the MLE under the MTP2 constraint which are
+structurally similar to iterative proportional scaling. We conclude the paper
+with a discussion of signed MTP2 distributions.
+"
+25233,Thermal decay of the Coulomb blockade oscillations,"  We study transport properties and the charge quantization phenomenon in a
+small metallic island connected to the leads through two quantum point contacts
+(QPCs). The linear conductance is calculated perturbatively with respect to
+weak tunneling and weak backscattering at QPCs as a function of the temperature
+$T$ and gate voltage. The conductance shows Coulomb blockade (CB) oscillations
+as a function of the gate voltage that decay with the temperature as a result
+of thermally activated fluctuations of the charge in the island. The regimes of
+quantum, $T \ll E_C$, and thermal, $T \gg E_C$, fluctuations are considered,
+where $E_C$ is the charging energy of an isolated island. Our predictions for
+CB oscillations in the quantum regime coincide with previous findings in [A.
+Furusaki and K. A. Matveev, Phys. Rev. B {\bf 52}, 16676 (1995)]. In the
+thermal regime the visibility of Coulomb blockade oscillations decays with the
+temperature as $\sqrt{T/E_C}\exp(-\pi^2T/E_C)$, where the exponential
+dependence originates from the thermal averaging over the instant charge
+fluctuations, while the prefactor has a quantum origin. This dependence does
+not depend on the strength of couplings to the leads. The differential
+capacitance, calculated in the case of a single tunnel junction, shows the same
+exponential decay, however the prefactor is linear in the temperature. This
+difference can be attributed to the non-locality of the quantum effects. Our
+results agree with the recent experiment [S. Jezouin {\em et al}., Nature {\bf
+536}, 58 (2016)] in the whole range of the parameter $T/E_C$.
+"
+25234,Hom-Tensor Categories and the Hom-Yang-Baxter Equation,"  We introduce a new type of categorical object called a \emph{hom-tensor
+category} and show that it provides the appropriate setting for modules over an
+arbitrary hom-bialgebra. Next we introduce the notion of \emph{hom-braided
+category} and show that this is the right setting for modules over
+quasitriangular hom-bialgebras. We also show how the hom-Yang-Baxter equation
+fits into this framework and how the category of Yetter-Drinfeld modules over a
+hom-bialgebra with bijective structure map can be organized as a hom-braided
+category. Finally we prove that, under certain conditions, one can obtain a
+tensor category (respectively a braided tensor category) from a hom-tensor
+category (respectively a hom-braided category).
+"
+25235,Inverting normative city theories and computational urban models: Towards a coexistence with urban data streams,"  Two unavoidable processes punctuate our century: The unprecedented
+urbanisation of our planet (United Nations, 2014) and the spread of ubiquitous
+computing (Weiser, 1991) and urban data streams. This process of urbanisation
+corresponds with the process of digitalisation of urban life: while
+urbanisation acts on a physical infrastructural level, the digital develops as
+a kind of metastructure above the infrastructure. This metastructural level
+offers a flexible framework through which information is continuously and
+operatively being symbolized. Today, Information technology and the
+availability of abundant urban data streams could be considered as forerunners
+of our time, having unprecedented impacts comparable to the ones brought by the
+steam engine at the dawn of industrialisation and the electrification of
+cities. It is therefore no longer conceivable to think of the physical
+structure of the city without including its digital counterpart. Against this
+background, we will explore the role of computational power and information
+technologies as dominant factors in the formation of computational urban models
+and normative city theories. We will show how these models and theories,
+emerging mainly during the 19th and 20th centuries, present leaping
+correspondences with more ancient conceptions of the city, when observed from a
+meta-level or episteme (Foucault, 2002) approach. First, and for the sake of
+clarity, we will deal with some methodological elucidations around the concepts
+of theory, model and episteme, and how we will refer conceptually to these
+terms throughout this paper. Secondly, we will review these evolving
+technological and computational levels of abstraction and their influence on
+the different conceptions of the city. Thirdly, we will develop the hypothesis
+of a conceptual gap, between our current technological capacity -- grounded on
+the abundance and availability of urban data streams -- and the state of the
+art in urban modelling and city theory. Lastly, building upon Foucault's
+concept of episteme (Foucault, 1970) and genealogy (Foucault, 1977b), we will
+explore this gap by speculating around the possibility of an inversion in
+computational urban modelling and city theory. And above all, we will question
+the terms in which we can think of the city, in an age where the world can be
+virtually conceived as fully urban, and the continuity and abundance of urban
+data streams giving account of it can be taken for granted. How are we
+articulating the phenomena we call city on top of this generic common ground?
+"
+25236,Learning Seasonal Phytoplankton Communities with Topic Models,"  In this work we develop and demonstrate a probabilistic generative model for
+phytoplankton communities. The proposed model takes counts of a set of
+phytoplankton taxa in a timeseries as its training data, and models communities
+by learning sparse co-occurrence structure between the taxa. Our model is
+probabilistic, where communities are represented by probability distributions
+over the species, and each time-step is represented by a probability
+distribution over the communities. The proposed approach uses a non-parametric,
+spatiotemporal topic model to encourage the communities to form an
+interpretable representation of the data, without making strong assumptions
+about the communities. We demonstrate the quality and interpretability of our
+method by its ability to improve performance of a simplistic regression model.
+We show that simple linear regression is sufficient to predict the community
+distribution learned by our method, and therefore the taxon distributions, from
+a set of naively chosen environment variables. In contrast, a similar
+regression model is insufficient to predict the taxon distributions directly or
+through PCA with the same level of accuracy.
+"
+25237,Mid-Infrared Spectroscopy and Microscopy of Subcellular Structures in Eukaryotic Cells with Atomic Force Microscopy - Infrared Spectroscopy,"  Atomic Force Microscopy - Infrared (AFM-IR) spectroscopy allows spectroscopic
+studies in the mid-infrared spectral region with a spatial resolution better
+than 50 nm. We show that the high spatial resolution can be used to perform
+spectroscopic and imaging studies at the subcellular level in fixed eukaryotic
+cells. We collect AFM-IR images of subcellular structures that include lipid
+droplets, vesicles and cytoskeletal filaments, by relying on the intrinsic
+contrast from IR light absorption. We also obtain AFM-IR absorption spectra of
+individual structures. Most spectra show features that are recognizable in the
+IR absorption spectra of cells and tissue obtained with FTIR technology,
+including absorption bands characteristic of phospholipids and polypeptides. We
+also observe sharp spectral features that we attribute to the nonlinear
+photothermal response of the system and we propose that they can be used to
+perform sensitive spectroscopy on the nanoscale.
+"
+25238,SVM via Saddle Point Optimization: New Bounds and Distributed Algorithms,"  We study two important SVM variants: hard-margin SVM (for linearly separable
+cases) and $\nu$-SVM (for linearly non-separable cases). We propose new
+algorithms from the perspective of saddle point optimization. Our algorithms
+achieve $(1-\epsilon)$-approximations with running time $\tilde{O}(nd+n\sqrt{d
+/ \epsilon})$ for both variants, where $n$ is the number of points and $d$ is
+the dimensionality. To the best of our knowledge, the current best algorithm
+for $\nu$-SVM is based on quadratic programming approach which requires
+$\Omega(n^2 d)$ time in worst case~\cite{joachims1998making,platt199912}. In
+the paper, we provide the first nearly linear time algorithm for $\nu$-SVM. The
+current best algorithm for hard margin SVM achieved by Gilbert
+algorithm~\cite{gartner2009coresets} requires $O(nd / \epsilon )$ time. Our
+algorithm improves the running time by a factor of $\sqrt{d}/\sqrt{\epsilon}$.
+Moreover, our algorithms can be implemented in the distributed settings
+naturally. We prove that our algorithms require $\tilde{O}(k(d
++\sqrt{d/\epsilon}))$ communication cost, where $k$ is the number of clients,
+which almost matches the theoretical lower bound. Numerical experiments support
+our theory and show that our algorithms converge faster on high dimensional,
+large and dense data sets, as compared to previous methods.
+"
+25239,Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints,"  Language is increasingly being used to define rich visual recognition
+problems with supporting image collections sourced from the web. Structured
+prediction models are used in these tasks to take advantage of correlations
+between co-occurring labels and visual input but risk inadvertently encoding
+social biases found in web corpora. In this work, we study data and models
+associated with multilabel object classification and visual semantic role
+labeling. We find that (a) datasets for these tasks contain significant gender
+bias and (b) models trained on these datasets further amplify existing bias.
+For example, the activity cooking is over 33% more likely to involve females
+than males in a training set, and a trained model further amplifies the
+disparity to 68% at test time. We propose to inject corpus-level constraints
+for calibrating existing structured prediction models and design an algorithm
+based on Lagrangian relaxation for collective inference. Our method results in
+almost no performance loss for the underlying recognition task but decreases
+the magnitude of bias amplification by 47.5% and 40.5% for multilabel
+classification and visual semantic role labeling, respectively.
+"
+25240,Totally Reflexive Modules and Poincaré Series,"  We study Cohen-Macaulay non-Gorenstein local rings $(R,\mathfrak{m},k)$
+admitting certain totally reflexive modules. More precisely, we give a
+description of the Poincaré series of $k$ by using the Poincaré series
+of a non-zero totally reflexive module with minimal multiplicity. Our results
+generalize a result of Yoshino to higher-dimensional Cohen-Macaulay local
+rings. Moreover, from a quasi-Gorenstein ideal satisfying some conditions, we
+construct a family of non-isomorphic indecomposable totally reflexive modules
+having an arbitrarily large minimal number of generators.
+"
+25241,A New Approach to the $r$-Whitney Numbers by Using Combinatorial Differential Calculus,"  In the present article we introduce two new combinatorial interpretations of
+the $r$-Whitney numbers of the second kind obtained from the combinatorics of
+the differential operators associated to the grammar $G:=\{ y\rightarrow
+yx^{m}, x\rightarrow x\}$. By specializing $m=1$ we obtain also a new
+combinatorial interpretation of the $r$-Stirling numbers of the second kind.
+Again, by specializing to the case $r=0$ we introduce a new generalization of
+the Stirling number of the second kind and through them a binomial type family
+of polynomials that generalizes Touchard's. Moreover, we show several
+well-known identities involving the $r$-Dowling polynomials and the $r$-Whitney
+numbers using the combinatorial differential calculus. Finally we prove that
+the $r$-Dowling polynomials are a Sheffer family relative to the generalized
+Touchard binomial family, study their umbral inverses, and introduce
+$[m]$-Stirling numbers of the first kind. From the relation between umbral
+calculus and the Riordan matrices we give several new combinatorial identities
+involving the $r$-Whitney number of both kinds, Bernoulli and Euler
+polynomials.
+"
+25242,Towards Moral Autonomous Systems,"  Both the ethics of autonomous systems and the problems of their technical
+implementation have by now been studied in some detail. Less attention has been
+given to the areas in which these two separate concerns meet. This paper,
+written by both philosophers and engineers of autonomous systems, addresses a
+number of issues in machine ethics that are located at precisely the
+intersection between ethics and engineering. We first discuss the main
+challenges which, in our view, machine ethics posses to moral philosophy. We
+them consider different approaches towards the conceptual design of autonomous
+systems and their implications on the ethics implementation in such systems.
+Then we examine problematic areas regarding the specification and verification
+of ethical behavior in autonomous systems, particularly with a view towards the
+requirements of future legislation. We discuss transparency and accountability
+issues that will be crucial for any future wide deployment of autonomous
+systems in society. Finally we consider the, often overlooked, possibility of
+intentional misuse of AI systems and the possible dangers arising out of
+deliberately unethical design, implementation, and use of autonomous robots.
+"
+25243,Visual Semantic Planning using Deep Successor Representations,"  A crucial capability of real-world intelligent agents is their ability to
+plan a sequence of actions to achieve their goals in the visual world. In this
+work, we address the problem of visual semantic planning: the task of
+predicting a sequence of actions from visual observations that transform a
+dynamic environment from an initial state to a goal state. Doing so entails
+knowledge about objects and their affordances, as well as actions and their
+preconditions and effects. We propose learning these through interacting with a
+visual and dynamic environment. Our proposed solution involves bootstrapping
+reinforcement learning with imitation learning. To ensure cross task
+generalization, we develop a deep predictive model based on successor
+representations. Our experimental results show near optimal results across a
+wide range of tasks in the challenging THOR environment.
+"
+25244,Asymmetric percolation drives a double transition in sexual contact networks,"  Zika virus (ZIKV) exhibits unique transmission dynamics in that it is
+concurrently spread by a mosquito vector and through sexual contact. We show
+that this sexual component of ZIKV transmission induces novel processes on
+networks through the highly asymmetric durations of infectiousness between
+males and females -- it is estimated that males are infectious for periods up
+to ten times longer than females -- leading to an asymmetric percolation
+process on the network of sexual contacts. We exactly solve the properties of
+this asymmetric percolation on random sexual contact networks and show that
+this process exhibits two epidemic transitions corresponding to a
+core-periphery structure. This structure is not present in the underlying
+contact networks, which are not distinguishable from random networks, and
+emerges because of the asymmetric percolation. We provide an exact analytical
+description of this double transition and discuss the implications of our
+results in the context of ZIKV epidemics. Most importantly, our study suggests
+a bias in our current ZIKV surveillance as the community most at risk is also
+one of the least likely to get tested.
+"
+25245,The GAPS Experiment to Search for Dark Matter using Low-energy Antimatter,"  The GAPS experiment is designed to carry out a sensitive dark matter search
+by measuring low-energy cosmic ray antideuterons and antiprotons. GAPS will
+provide a new avenue to access a wide range of dark matter models and masses
+that is complementary to direct detection techniques, collider experiments and
+other indirect detection techniques. Well-motivated theories beyond the
+Standard Model contain viable dark matter candidates which could lead to a
+detectable signal of antideuterons resulting from the annihilation or decay of
+dark matter particles. The dark matter contribution to the antideuteron flux is
+believed to be especially large at low energies (E < 1 GeV), where the
+predicted flux from conventional astrophysical sources (i.e. from secondary
+interactions of cosmic rays) is very low. The GAPS low-energy antiproton search
+will provide stringent constraints on less than 10 GeV dark matter, will
+provide the best limits on primordial black hole evaporation on Galactic length
+scales, and will explore new discovery space in cosmic ray physics.
+Unlike other antimatter search experiments such as BESS and AMS that use
+magnetic spectrometers, GAPS detects antideuterons and antiprotons using an
+exotic atom technique. This technique, and its unique event topology, will give
+GAPS a nearly background-free detection capability that is critical in a
+rare-event search. GAPS is designed to carry out its science program using
+long-duration balloon flights in Antarctica. A prototype instrument was
+successfully flown from Taiki, Japan in 2012. GAPS has now been approved by
+NASA to proceed towards the full science instrument, with the possibility of a
+first long-duration balloon flight in late 2020. Here we motivate low-energy
+cosmic ray antimatter searches and discuss the current status of the GAPS
+experiment and the design of the payload.
+"
+25246,Decomposition of cohomology of vector bundles on homogeneous ind-spaces,"  Let $G$ be a locally semisimple ind-group, $P$ be a parabolic subgroup, and
+$E$ be a finite-dimensional $P$-module. We show that, under a certain condition
+on $E$, the nonzero cohomologies of the homogeneous vector bundle
+$\mathcal{O}_{G/P}(E^*)$ on $G/P$ induced by the dual $P$-module $E^*$
+decompose as direct sums of cohomologies of bundles of the form
+$\mathcal{O}_{G/P}(R)$ for (some) simple constituents $R$ of $E^*$. In the
+finite-dimensional case, this result is a consequence of the Bott-Borel-Weil
+theorem and Weyl's semisimplicity theorem. In the infinite-dimensional setting
+we consider, there is no relevant semisimplicity theorem. Instead, our results
+are based on the injectivity of the cohomologies of the bundles
+$\mathcal{O}_{G/P}(R)$.
+"
+25247,Secret sharing schemes based on additive codes over $GF(4)$,"  A secret sharing scheme (SSS) was introduced by Shamir in 1979 using
+polynomial interpolation. Later it turned out that it is equivalent to an SSS
+based on a Reed-Solomon code. SSSs based on linear codes have been studied by
+many researchers. However there is little research on SSSs based on additive
+codes. In this paper, we study SSSs based on additive codes over $GF(4)$ and
+show that they require at least two steps of calculations to reveal the secret.
+We also define minimal access structures of SSSs from additive codes over
+$GF(4)$ and describe SSSs using some interesting additive codes over $GF(4)$
+which contain generalized 2-designs.
+"
+25248,Metal-Insulator Transition in Ga doped ZnO via Controlled Thickness,"  We report thickness dependent metal insulator transition in Ga doped ZnO
+(Ga:ZnO) thin films grown by pulsed laser deposition technique. From the
+electrical transport measurements, we find that while the thinnest film
+exhibits a resistivity of 0.05 $\Omega$-cm, lying in the insulating regime, the
+thickest has resistivity of $6.6\times10^{-4}\Omega$-cm which shows metallic
+type of conduction. Our analysis reveals that the Mott's variable range hopping
+(VRH) model governs the insulating behavior in the thinner Ga:ZnO whereas the
+2D weak localization phenomena is appropriate to explain the electron transport
+in the thicker Ga:ZnO. Magnetoresistance study further confirms the presence of
+strong localization in 6 nm film while weak localization is observed in 20 nm
+and above thicker films. From the density functional calculations, it is found
+that due to surface reconstruction and Ga doping, strong crystalline disorder
+sets in very thin films to introduce localized states and thereby, restricts
+the donor electron mobility.
+"
+25249,Deep spectroscopy of nearby galaxy clusters: II. The Hercules cluster,"  We carried out the deep spectroscopic observations of the nearby cluster
+A2151 with AF2/WYFFOS@WHT. The caustic technique enables us to identify 360
+members brighter than $M_r = -16$ and within 1.3$R_{200}$. We separated the
+members into subsamples according to photometrical and dynamical properties
+such as colour, local environment and infall time. The completeness of the
+catalogue and our large sample allow us to analyse the velocity dispersion and
+the luminosity functions of the identified populations. We found evidence of a
+cluster still in its collapsing phase. The LF of the red population of A2151
+shows a deficit of dwarf red galaxies. Moreover, the normalized LFs of the red
+and blue populations of A2151 are comparable to the red and blue LFs of the
+field, even if the blue galaxies start dominating one magnitude fainter and the
+red LF is well represented by a single Schechter function rather than a double
+Schechter function. We discuss how the evolution of cluster galaxies depends on
+their mass: bright and intermediate galaxies are mainly affected by dynamical
+friction and internal/mass quenching, while the evolution of dwarfs is driven
+by environmental processes which need time and a hostile cluster environment to
+remove the gas reservoirs and halt the star formation.
+"
+25250,The $m$-functions of discrete Schrödinger operators are sparse compared to those for Jacobi operators,"  We explore the sparsity of Weyl-Titchmarsh $m$-functions of discrete
+Schrödinger operators. Due to this, the set of their $m$-functions cannot be
+dense on the set of those for Jacobi operators. All this reveals why an inverse
+spectral theory for discrete Schrödinger operators via their spectral
+measures should be difficult. To obtain the result, de Branges theory of
+canonical systems is applied to work on them, instead of Weyl-Titchmarsh
+$m$-functions.
+"
+25251,The Classical Moment Problem and Generalized Indefinite Strings,"  We show that the classical Hamburger moment problem can be included in the
+spectral theory of generalized indefinite strings. Namely, we introduce the
+class of Krein-Langer strings and show that there is a bijective correspondence
+between moment sequences and this class of generalized indefinite strings. This
+result can be viewed as a complement to the classical results of M. G. Krein on
+the connection between the Stieltjes moment problem and Krein-Stieltjes strings
+and I. S. Kac on the connection between the Hamburger moment problem and 2x2
+canonical systems with Hamburger Hamiltonians.
+"
+25252,SKIRT: hybrid parallelization of radiative transfer simulations,"  We describe the design, implementation and performance of the new hybrid
+parallelization scheme in our Monte Carlo radiative transfer code SKIRT, which
+has been used extensively for modeling the continuum radiation of dusty
+astrophysical systems including late-type galaxies and dusty tori. The hybrid
+scheme combines distributed memory parallelization, using the standard Message
+Passing Interface (MPI) to communicate between processes, and shared memory
+parallelization, providing multiple execution threads within each process to
+avoid duplication of data structures. The synchronization between multiple
+threads is accomplished through atomic operations without high-level locking
+(also called lock-free programming). This improves the scaling behavior of the
+code and substantially simplifies the implementation of the hybrid scheme. The
+result is an extremely flexible solution that adjusts to the number of
+available nodes, processors and memory, and consequently performs well on a
+wide variety of computing architectures.
+"
+25253,Eco-evolutionary Model of Rapid Phenotypic Diversification in Species-Rich Communities,"  Evolutionary and ecosystem dynamics are often treated as different processes
+--operating at separate timescales-- even if evidence reveals that rapid
+evolutionary changes can feed back into ecological interactions. A recent
+long-term field experiment has explicitly shown that communities of competing
+plant species can experience very fast phenotypic diversification, and that
+this gives rise to enhanced complementarity in resource exploitation and to
+enlarged ecosystem-level productivity. Here, we build on progress made in
+recent years in the integration of eco-evolutionary dynamics, and present a
+computational approach aimed at describing these empirical findings in detail.
+In particular we model a community of organisms of different but similar
+species evolving in time through mechanisms of birth, competition, sexual
+reproduction, descent with modification, and death. Based on simple rules, this
+model provides a rationalization for the emergence of rapid phenotypic
+diversification in species-rich communities. Furthermore, it also leads to
+non-trivial predictions about long-term phenotypic change and ecological
+interactions. Our results illustrate that the presence of highly specialized,
+non-competing species leads to very stable communities and reveals that
+phenotypically equivalent species occupying the same niche may emerge and
+coexist for very long times. Thus, the framework presented here provides a
+simple approach --complementing existing theories, but specifically devised to
+account for the specificities of the recent empirical findings for plant
+communities-- to explain the collective emergence of diversification at a
+community level, and paves the way to further scrutinize the intimate
+entanglement of ecological and evolutionary processes, especially in
+species-rich communities.
+"
+25254,The Microsoft 2017 Conversational Speech Recognition System,"  We describe the 2017 version of Microsoft's conversational speech recognition
+system, in which we update our 2016 system with recent developments in
+neural-network-based acoustic and language modeling to further advance the
+state of the art on the Switchboard speech recognition task. The system adds a
+CNN-BLSTM acoustic model to the set of model architectures we combined
+previously, and includes character-based and dialog session aware LSTM language
+models in rescoring. For system combination we adopt a two-stage approach,
+whereby subsets of acoustic models are first combined at the senone/frame
+level, followed by a word-level voting via confusion networks. We also added a
+confusion network rescoring step after system combination. The resulting system
+yields a 5.1\% word error rate on the 2000 Switchboard evaluation set.
+"
+25255,Recursive Neural Networks in Quark/Gluon Tagging,"  Since the machine learning techniques are improving rapidly, it has been
+shown that the image recognition techniques in deep neural networks can be used
+to detect jet substructure. And it turns out that deep neural networks can
+match or outperform traditional approach of expert features. However, there are
+disadvantages such as sparseness of jet images. Based on the natural tree-like
+structure of jet sequential clustering, the recursive neural networks (RecNNs),
+which embed jet clustering history recursively as in natural language
+processing, have a better behavior when confronted with these problems. We thus
+try to explore the performance of RecNNs in quark/gluon discrimination. The
+results show that RecNNs work better than the baseline boosted decision tree
+(BDT) by a few percent in gluon rejection rate. However, extra implementation
+of particle flow identification only increases the performance slightly. We
+also experimented on some relevant aspects which might influence the
+performance of the networks. It shows that even taking only particle flow
+identification as input feature without any extra information on momentum or
+angular position is already giving a fairly good result, which indicates that
+the most of the information for quark/gluon discrimination is already included
+in the tree-structure itself. As a bonus, a rough up/down quark jets
+discrimination is also explored.
+"
+25256,Determining Exoplanetary Oblateness Using Transit Depth Variations,"  The measurement of an exoplanet's oblateness and obliquity provides insights
+into the planet's internal structure and formation history. Previous work using
+small differences in the shape of the transit light curve has been moderately
+successful, but was hampered by the small signal and extreme photometric
+precision required. The measurement of changes in transit depth, caused by the
+spin precession of an oblate planet, was proposed as an alternative method.
+Here, we present the first attempt to measure these changes. Using Kepler
+photometry, we examined the brown dwarf Kepler-39b and the warm Saturn
+Kepler-427b. We could not reliably constrain the oblateness of Kepler-39b. We
+find transit depth variations for Kepler-427b at $90.1\%$ significance
+($1.65\sigma$) consistent with a precession period of $P_\mathrm{prec} =
+5.45^{+0.46}_{-0.37}~\mathrm{years}$ and an oblateness,
+$f~=~0.19^{+0.32}_{-0.16}$. This oblateness is comparable to Solar System gas
+giants, and would raise questions about the dynamics and tidal synchronization
+of Kepler-427b.
+"
+25257,Multi-task Deep Reinforcement Learning with PopArt,"  The reinforcement learning community has made great strides in designing
+algorithms capable of exceeding human performance on specific tasks. These
+algorithms are mostly trained one task at the time, each new task requiring to
+train a brand new agent instance. This means the learning algorithm is general,
+but each solution is not; each agent can only solve the one task it was trained
+on. In this work, we study the problem of learning to master not one but
+multiple sequential-decision tasks at once. A general issue in multi-task
+learning is that a balance must be found between the needs of multiple tasks
+competing for the limited resources of a single learning system. Many learning
+algorithms can get distracted by certain tasks in the set of tasks to solve.
+Such tasks appear more salient to the learning process, for instance because of
+the density or magnitude of the in-task rewards. This causes the algorithm to
+focus on those salient tasks at the expense of generality. We propose to
+automatically adapt the contribution of each task to the agent's updates, so
+that all tasks have a similar impact on the learning dynamics. This resulted in
+state of the art performance on learning to play all games in a set of 57
+diverse Atari games. Excitingly, our method learned a single trained policy -
+with a single set of weights - that exceeds median human performance. To our
+knowledge, this was the first time a single agent surpassed human-level
+performance on this multi-task domain. The same approach also demonstrated
+state of the art performance on a set of 30 tasks in the 3D reinforcement
+learning platform DeepMind Lab.
+"
+25258,Distributed Multi-Speaker Voice Activity Detection for Wireless Acoustic Sensor Networks,"  A distributed multi-speaker voice activity detection (DM-VAD) method for
+wireless acoustic sensor networks (WASNs) is proposed. DM-VAD is required in
+many signal processing applications, e.g. distributed speech enhancement based
+on multi-channel Wiener filtering, but is non-existent up to date. The proposed
+method neither requires a fusion center nor prior knowledge about the node
+positions, microphone array orientations or the number of observed sources. It
+consists of two steps: (i) distributed source-specific energy signal unmixing
+(ii) energy signal based voice activity detection. Existing computationally
+efficient methods to extract source-specific energy signals from the mixed
+observations, e.g., multiplicative non-negative independent component analysis
+(MNICA) quickly loose performance with an increasing number of sources, and
+require a fusion center. To overcome these limitations, we introduce a
+distributed energy signal unmixing method based on a source-specific node
+clustering method to locate the nodes around each source. To determine the
+number of sources that are observed in the WASN, a source enumeration method
+that uses a Lasso penalized Poisson generalized linear model is developed. Each
+identified cluster estimates the energy signal of a single (dominant) source by
+applying a two-component MNICA. The VAD problem is transformed into a
+clustering task, by extracting features from the energy signals and applying
+K-means type clustering algorithms. All steps of the proposed method are
+evaluated using numerical experiments. A VAD accuracy of $> 85 \%$ is achieved
+for a challenging scenario where 20 nodes observe 7 sources in a simulated
+reverberant rectangular room.
+"
+25259,Analysis of the design of a passive resonant miniature optical gyroscope based on integrated optics technologies,"  We present a simple analysis of the design of a passive miniature resonant
+optical gyroscope. By combining the requirements on the angular random walk and
+the bias stability, we end up with simple expressions of the minimum diameter
+of the ring waveguide cavity and the maximum power that should be used to probe
+it. Using state-of-the-art performances of photonic integrated circuit and
+whispering gallery mode technologies in terms of propagation losses and mode
+size, we show that tactical grade gyroscope performances can be achieved with a
+diameter of a few cm provided the detrimental influence of Kerr effect is
+mitigated, using for instance an active control of the unbalance in the
+intensities. We further extend the analysis to medium performance gyroscope and
+give some hints on the efforts to be made to potentially demonstrate a
+miniature resonant optical gyroscope with this level of performance.
+"
+25260,Federated Multi-Task Learning,"  Federated learning poses new statistical and systems challenges in training
+machine learning models over distributed networks of devices. In this work, we
+show that multi-task learning is naturally suited to handle the statistical
+challenges of this setting, and propose a novel systems-aware optimization
+method, MOCHA, that is robust to practical systems issues. Our method and
+theory for the first time consider issues of high communication cost,
+stragglers, and fault tolerance for distributed multi-task learning. The
+resulting method achieves significant speedups compared to alternatives in the
+federated setting, as we demonstrate through simulations on real-world
+federated datasets.
+"
+25261,Decomposition analysis to identify intervention targets for reducing disparities,"  There has been considerable interest in using decomposition methods in
+epidemiology (mediation analysis) and economics (Oaxaca-Blinder decomposition)
+to understand how health disparities arise and how they might change upon
+intervention. It has not been clear when estimates from the Oaxaca-Blinder
+decomposition can be interpreted causally because its implementation does not
+explicitly address potential confounding of target variables. While mediation
+analysis does explicitly adjust for confounders of target variables, it does so
+in a way that entails equalizing confounders across racial groups, which may
+not reflect the intended intervention. Revisiting prior analyses in the
+National Longitudinal Survey of Youth on disparities in wages, unemployment,
+incarceration, and overall health with test scores, taken as a proxy for
+educational attainment, as a target intervention, we propose and demonstrate a
+novel decomposition that controls for confounders of test scores (measures of
+childhood SES) while leaving their association with race intact. We compare
+this decomposition with others that use standardization (to equalize childhood
+SES alone), mediation analysis (to equalize test scores within levels of
+childhood SES), and one that equalizes both childhood SES and test scores. We
+also show how these decompositions, including our novel proposals, are
+equivalent to causal implementations of the Oaxaca-Blinder decomposition.
+"
+25262,Network Lens: Node Classification in Topologically Heterogeneous Networks,"  We study the problem of identifying different behaviors occurring in
+different parts of a large heterogenous network. We zoom in to the network
+using lenses of different sizes to capture the local structure of the network.
+These network signatures are then weighted to provide a set of predicted labels
+for every node. We achieve a peak accuracy of $\sim42\%$ (random=$11\%$) on two
+networks with $\sim100,000$ and $\sim1,000,000$ nodes each. Further, we perform
+better than random even when the given node is connected to up to 5 different
+types of networks. Finally, we perform this analysis on homogeneous networks
+and show that highly structured networks have high homogeneity.
+"
+25263,Unsupervised Construction of Human Body Models Using Principles of Organic Computing,"  Unsupervised learning of a generalizable model of the visual appearance of
+humans from video data is of major importance for computing systems interacting
+naturally with their users and others. We propose a step towards automatic
+behavior understanding by integrating principles of Organic Computing into the
+posture estimation cycle, thereby relegating the need for human intervention
+while simultaneously raising the level of system autonomy. The system extracts
+coherent motion from moving upper bodies and autonomously decides about limbs
+and their possible spatial relationships. The models from many videos are
+integrated into meta-models, which show good generalization to different
+individuals, backgrounds, and attire. These models allow robust interpretation
+of single video frames without temporal continuity and posture mimicking by an
+android robot.
+"
+25264,Further Results on Hilbert's Tenth Problem,"  Hilbert's Tenth Problem (HTP) asked for an algorithm to test whether an
+arbitrary polynomial Diophantine equation with integer coefficients has
+solutions over the ring $\mathbb Z$ of the integers. This was finally solved by
+Matiyasevich negatively in 1970. In this paper we obtain some further results
+on HTP over $\mathbb Z$. We show that there is no algorithm to determine for
+any $P(z_1,\ldots,z_9)\in\mathbb Z[z_1,\ldots,z_9]$ whether the equation
+$P(z_1,\ldots,z_9)=0$ has integral solutions with $z_9\ge0$. Consequently,
+there is no algorithm to test whether an arbitrary polynomial Diophantine
+equation $P(z_1,\ldots,z_{11})=0$ (with integer coefficients) in 11 unknowns
+has integral solutions, which provides the best record on the original HTP over
+$\mathbb Z$. We also show that there is no algorithm to test for any
+$P(z_1,\ldots,z_{17})\in\mathbb Z[z_1,\ldots,z_{17}]$ whether
+$P(z_1^2,\ldots,z_{17}^2)=0$ has integral solutions, and that there is a
+polynomial $Q(z_1,\ldots,z_{20})\in\mathbb Z[z_1,\ldots,z_{20}]$ such that
+$$\{Q(z_1^2,\ldots,z_{20}^2):\ z_1,\ldots,z_{20}\in\mathbb
+Z\}\cap\{0,1,2,\ldots\}$$ coincides with the set of all primes.
+"
+25265,Classification vs. Regression in Supervised Learning for Single Channel Speaker Count Estimation,"  The task of estimating the maximum number of concurrent speakers from single
+channel mixtures is important for various audio-based applications, such as
+blind source separation, speaker diarisation, audio surveillance or auditory
+scene classification. Building upon powerful machine learning methodology, we
+develop a Deep Neural Network (DNN) that estimates a speaker count. While DNNs
+efficiently map input representations to output targets, it remains unclear how
+to best handle the network output to infer integer source count estimates, as a
+discrete count estimate can either be tackled as a regression or a
+classification problem. In this paper, we investigate this important design
+decision and also address complementary parameter choices such as the input
+representation. We evaluate a state-of-the-art DNN audio model based on a
+Bi-directional Long Short-Term Memory network architecture for speaker count
+estimations. Through experimental evaluations aimed at identifying the best
+overall strategy for the task and show results for five seconds speech segments
+in mixtures of up to ten speakers.
+"
+25266,Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff,"  Lossy compression algorithms are typically designed and analyzed through the
+lens of Shannon's rate-distortion theory, where the goal is to achieve the
+lowest possible distortion (e.g., low MSE or high SSIM) at any given bit rate.
+However, in recent years, it has become increasingly accepted that ""low
+distortion"" is not a synonym for ""high perceptual quality"", and in fact
+optimization of one often comes at the expense of the other. In light of this
+understanding, it is natural to seek for a generalization of rate-distortion
+theory which takes perceptual quality into account. In this paper, we adopt the
+mathematical definition of perceptual quality recently proposed by Blau &
+Michaeli (2018), and use it to study the three-way tradeoff between rate,
+distortion, and perception. We show that restricting the perceptual quality to
+be high, generally leads to an elevation of the rate-distortion curve, thus
+necessitating a sacrifice in either rate or distortion. We prove several
+fundamental properties of this triple-tradeoff, calculate it in closed form for
+a Bernoulli source, and illustrate it visually on a toy MNIST example.
+"
+25267,Dynamical properties of the herding voter model with and without noise,"  Collective leadership and herding may arise in standard models of opinion
+dynamics as an interplay of a strong separation of time scales within the
+population and its hierarchical organization. Using the voter model as a simple
+opinion formation model, we show that, in the herding phase, a group of agents
+become effectively the leaders of the dynamics while the rest of the population
+follow blindly their opinion. Interestingly, in some cases such herding
+dynamics accelerates the time to consensus, which then become size independent
+or, on the contrary, makes the consensus nearly impossible. These new behaviors
+have important consequences when an external noise is added to the system that
+makes consensus (absorbing) states to disappear. We analyze this new model
+which shows an interesting phase diagram, with a purely diffusive phase, a
+herding (or two-states) phase, and mixed phases where both behaviors are
+possible.
+"
+25268,Conformative Filtering for Implicit Feedback Data,"  Implicit feedback is the simplest form of user feedback that can be used for
+item recommendation. It is easy to collect and domain independent. However,
+there is a lack of negative examples. Existing works circumvent this problem by
+making various assumptions regarding the unconsumed items, which fail to hold
+when the user did not consume an item because she was unaware of it. In this
+paper we propose Conformative Filtering (CoF) as a novel method for addressing
+the lack of negative examples in implicit feedback. The motivation is that if
+there is a large group of users who share the same taste and none of them
+consumed an item, then it is highly likely that the item is irrelevant to this
+taste. We use Hierarchical Latent Tree Analysis (HLTA) to identify taste-based
+user groups, and make recommendations for a user based on her memberships in
+the groups. Experiments on real-world datasets from different domains show that
+CoF has superior performance compared to other baselines and more than 10%
+improvement in Recall@5 and Recall@10 is observed.
+"
+25269,Malliavin-based Multilevel Monte Carlo Estimators for Densities of Max-stable Processes,"  We introduce a class of unbiased Monte Carlo estimators for the multivariate
+density of max-stable fields generated by Gaussian processes. Our estimators
+take advantage of recent results on exact simulation of max-stable fields
+combined with identities studied in the Malliavin calculus literature and ideas
+developed in the multilevel Monte Carlo literature. Our approach allows
+estimating multivariate densities of max-stable fields with precision
+$\varepsilon $ at a computational cost of order $O\left( \varepsilon ^{-2}\log
+\log \log \left( 1/\varepsilon \right) \right) $.
+"
+25270,Standard Model - Axion - Seesaw - H portal inflation,"  Extending the Standard Model with a new complex singlet scalar, right-handed
+neutrinos and a vector-like quark allows to simultaneously tackle several
+problems in particle physics and cosmology within a constrained framework that
+can be falsified by future probes of the cosmic microwave background, as well
+as by upcoming axion experiments. This Standard Model - Axion - Seesaw - H
+portal inflation theory (SMASH) provides predictive inflation and $H$ boson
+stabilization, and can explain baryogenesis, light neutrino masses, dark matter
+and the strong CP problem. The model contains a unique new mass scale which
+coincides with the axion decay constant, and also sets the scale for
+perturbative lepton-number violation processes. Testable predictions include a
+minimum value of the tensor-to-scalar ratio of $r\gtrsim 0.004$, a running of
+the spectral index $\alpha\gtrsim-8\times10^{-4}$, a change $\delta N_{\rm
+eff}\sim 0.03$ in the number of effective relativistic neutrinos, and an axion
+mass in the range $50\mu eV\leq m_A \leq 200 \mu eV$.
+"
+25271,Diagnosing missing always at random in multivariate data,"  Models for analyzing multivariate data sets with missing values require
+strong, often unassessable, assumptions. The most common of these is that the
+mechanism that created the missing data is ignorable - a twofold assumption
+dependent on the mode of inference. The first part, which is the focus here,
+under the Bayesian and direct-likelihood paradigms, requires that the missing
+data are missing at random; in contrast, the frequentist-likelihood paradigm
+demands that the missing data mechanism always produces missing at random data,
+a condition known as missing always at random. Under certain regularity
+conditions, assuming missing always at random leads to an assumption that can
+be tested using the observed data alone namely, the missing data indicators
+only depend on fully observed variables. Here, we propose three different
+diagnostic tests that not only indicate when this assumption is incorrect but
+also suggest which variables are the most likely culprits. Although missing
+always at random is not a necessary condition to ensure validity under the
+Bayesian and direct-likelihood paradigms, it is sufficient, and evidence for
+its violation should encourage the careful statistician to conduct targeted
+sensitivity analyses.
+"
+25272,On 3+1 Lorentzian Einstein Manifolds with One Rotational Isometry,"  We consider 3+1 rotationally symmetric Lorentzian Einstein spacetime
+manifolds with $\Lambda >0$ and reduce the equations to 2+1 Einstein equations
+coupled to `shifted' wave maps. Subsequently, we prove various (explicit)
+positive mass-energy theorems. No smallness is assumed.
+"
+25273,Adaptive Classification for Prediction Under a Budget,"  We propose a novel adaptive approximation approach for test-time
+resource-constrained prediction. Given an input instance at test-time, a gating
+function identifies a prediction model for the input among a collection of
+models. Our objective is to minimize overall average cost without sacrificing
+accuracy. We learn gating and prediction models on fully labeled training data
+by means of a bottom-up strategy. Our novel bottom-up method first trains a
+high-accuracy complex model. Then a low-complexity gating and prediction model
+are subsequently learned to adaptively approximate the high-accuracy model in
+regions where low-cost models are capable of making highly accurate
+predictions. We pose an empirical loss minimization problem with cost
+constraints to jointly train gating and prediction models. On a number of
+benchmark datasets our method outperforms state-of-the-art achieving higher
+accuracy for the same cost.
+"
+25274,A Survey on Multi-View Clustering,"  With advances in information acquisition technologies, multi-view data become
+ubiquitous. Multi-view learning has thus become more and more popular in
+machine learning and data mining fields. Multi-view unsupervised or
+semi-supervised learning, such as co-training, co-regularization has gained
+considerable attention. Although recently, multi-view clustering (MVC) methods
+have been developed rapidly, there has not been a survey to summarize and
+analyze the current progress. Therefore, this paper reviews the common
+strategies for combining multiple views of data and based on this summary we
+propose a novel taxonomy of the MVC approaches. We further discuss the
+relationships between MVC and multi-view representation, ensemble clustering,
+multi-task clustering, multi-view supervised and semi-supervised learning.
+Several representative real-world applications are elaborated. To promote
+future development of MVC, we envision several open problems that may require
+further investigation and thorough examination.
+"
+25275,Group invariance principles for causal generative models,"  The postulate of independence of cause and mechanism (ICM) has recently led
+to several new causal discovery algorithms. The interpretation of independence
+and the way it is utilized, however, varies across these methods. Our aim in
+this paper is to propose a group theoretic framework for ICM to unify and
+generalize these approaches. In our setting, the cause-mechanism relationship
+is assessed by comparing it against a null hypothesis through the application
+of random generic group transformations. We show that the group theoretic view
+provides a very general tool to study the structure of data generating
+mechanisms with direct applications to machine learning.
+"
+25276,Cointegrated Density-Valued Linear Processes,"  In data rich environments we may sometimes deal with time series that are
+probability density-function valued, such as observations of cross-sectional
+income distributions over time. To apply the methods of functional time series
+analysis to such observations, we should first embed them in a linear space in
+which the essential properties of densities are preserved under addition and
+scalar multiplication. Bayes Hilbert spaces provide one way to achieve this
+embedding. In this paper we investigate the use of Bayes Hilbert spaces to
+model cointegrated density-valued linear processes. We develop an I(1)
+representation theory for cointegrated linear processes in a Bayes Hilbert
+space, and adapt existing statistical procedures for estimating the
+corresponding attractor space to a Bayes Hilbert space setting. We revisit
+empirical applications involving earnings and wage densities to illustrate the
+utility of our approach.
+"
+25277,Private Information Retrieval Schemes for Coded Data with Arbitrary Collusion Patterns,"  In Private Information Retrieval (PIR), one wants to download a file from a
+database without revealing to the database which file is being downloaded. Much
+attention has been paid to the case of the database being encoded across
+several servers, subsets of which can collude to attempt to deduce the
+requested file. With the goal of studying the achievable PIR rates in realistic
+scenarios, we generalize results for coded data from the case of all subsets of
+servers of size $t$ colluding, to arbitrary subsets of the servers. We
+investigate the effectiveness of previous strategies in this new scenario, and
+present new results in the case where the servers are partitioned into disjoint
+colluding groups.
+"
+25278,Tracing interstellar magnetic field using velocity gradient technique: Application to Atomic Hydrogen data,"  The advancement of our understanding of MHD turbulence opens ways to develop
+new techniques to probe magnetic fields. In MHD turbulence, the velocity
+gradients are expected to be perpendicular to magnetic fields and this fact was
+used by Gonsalvez-Casanova & Lazarian to introduce a new technique to trace
+magnetic fields using velocity centroid gradients. The latter can be obtained
+from spectroscopic observations. We apply the technique to GALFA HI survey data
+and compare the directions of magnetic fields obtained with our technique with
+the direction of magnetic fields obtained using PLANCK polarization. We find
+excellent correspondence between the two ways of magnetic field tracing, which
+is obvious via visual comparison and through measuring of the statistics of
+magnetic field fluctuations obtained with the polarization data and our
+technique. This suggests that the velocity centroid gradients can provide a
+reliable way of measuring of the foreground magnetic field fluctuations and
+thus provide a new way of separating foreground and CMB polarization signals.
+"
+25279,Microscopic Origin of Interfacial Dzyaloshinskii-Moriya Interaction,"  Chiral spin textures at the interface between ferromagnetic and heavy
+nonmagnetic metals, such as Neel-type domain walls and skyrmions, have been
+studied intensively because of their great potential for future nanomagnetic
+devices. The Dyzaloshinskii-Moriya interaction (DMI) is an essential phenomenon
+for the formation of such chiral spin textures. In spite of recent theoretical
+progress aiming at understanding the microscopic origin of the DMI, an
+experimental investigation unravelling the physics at stake is still required.
+Here, we experimentally demonstrate the close correlation of the DMI with the
+anisotropy of the orbital magnetic moment and with the magnetic dipole moment
+of the ferromagnetic metal. The density functional theory and the tight-binding
+model calculations reveal that asymmetric electron occupation in orbitals gives
+rise to this correlation.
+"
+25280,"We are not alone ! (at least, most of us). Homonymy in large scale social groups","  This article brings forward an estimation of the proportion of homonyms in
+large scale groups based on the distribution of first names and last names in a
+subset of these groups. The estimation is based on the generalization of the
+""birthday paradox problem"". The main results is that, in societies such as
+France or the United States, identity collisions (based on first + last names)
+are frequent. The large majority of the population has at least one homonym.
+But in smaller settings, it is much less frequent : even if small groups of a
+few thousand people have at least one couple of homonyms, only a few
+individuals have an homonym.
+"
+25281,An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits,"  We present a new strategy for gap estimation in randomized algorithms for
+multiarmed bandits and combine it with the EXP3++ algorithm of Seldin and
+Slivkins (2014). In the stochastic regime the strategy reduces dependence of
+regret on a time horizon from $(\ln t)^3$ to $(\ln t)^2$ and eliminates an
+additive factor of order $\Delta e^{1/\Delta^2}$, where $\Delta$ is the minimal
+gap of a problem instance. In the adversarial regime regret guarantee remains
+unchanged.
+"
+25282,Calibration of the ART-XC mirror modules at MSFC,"  The Astronomical Roentgen Telescope X-ray Concentrator (ART-XC) is a hard
+X-ray telescope with energy response up to 30 keV, to be launched on board the
+Spectrum Roentgen Gamma (SRG) spacecraft in 2018. ART-XC consists of seven
+identical co-aligned mirror modules. Each mirror assembly is coupled with a
+CdTe double-sided strip (DSS) focal-plane detector. Eight X-ray mirror modules
+(seven flight and one spare units) for ART-XC were developed and fabricated at
+the Marshall Space Flight Center (MSFC), NASA, USA. We present results of
+testing procedures performed with an X-ray beam facility at MSFC to calibrate
+the point spread function (PSF) of the mirror modules. The shape of the PSF was
+measured with a high-resolution CCD camera installed in the focal plane with
+defocusing of 7 mm, as required by the ART-XC design. For each module, we
+performed a parametrization of the PSF at various angular distances Theta. We
+used a King function to approximate the radial profile of the near on-axis PSF
+(Theta<9 arcmin) and an ellipse fitting procedure to describe the morphology of
+the far off-axis angular response (9<Theta<24 arcmin). We found a good
+agreement between the seven ART-XC flight mirror modules at the level of 10%.
+The on-axis angular resolution of the ART-XC optics varies between 27 and 33
+arcsec (half-power diameter), except for the spare module.
+"
+25283,"""Haldane"" phases with ultracold fermionic atoms in double-well optical lattices","  We propose to realize one-dimensional topological phases protected by SU($N$)
+symmetry using alkali or alkaline-earth atoms loaded into a bichromatic optical
+lattice. We derive a realistic model for this system and investigate it
+theoretically. Depending on the parity of $N$, two different classes of
+symmetry-protected topological (SPT) phases are stabilized at half-filling for
+physical parameters of the model. For even $N$, the celebrated spin-1 Haldane
+phase and its generalization to SU($N$) are obtained with no local symmetry
+breaking. In stark contrast, at least for $N=3$, a new class of SPT phases,
+dubbed chiral Haldane phases, that spontaneously break inversion symmetry,
+emerge with a two-fold ground-state degeneracy. The latter ground states with
+open-boundary conditions are characterized by different left and right boundary
+spins which are related by conjugation. Our results show that topological
+phases are within close reach of the latest experiments on cold fermions in
+optical lattices.
+"
+25284,Fourier-Based Testing for Families of Distributions,"  We study the general problem of testing whether an unknown distribution
+belongs to a specified family of distributions. More specifically, given a
+distribution family $\mathcal{P}$ and sample access to an unknown discrete
+distribution $\mathbf{P}$, we want to distinguish (with high probability)
+between the case that $\mathbf{P} \in \mathcal{P}$ and the case that
+$\mathbf{P}$ is $\epsilon$-far, in total variation distance, from every
+distribution in $\mathcal{P}$. This is the prototypical hypothesis testing
+problem that has received significant attention in statistics and, more
+recently, in theoretical computer science.
+The sample complexity of this general inference task depends on the
+underlying family $\mathcal{P}$. The gold standard in distribution property
+testing is to design sample-optimal and computationally efficient algorithms
+for this task. The main contribution of this work is a simple and general
+testing technique that is applicable to all distribution families whose Fourier
+spectrum satisfies a certain approximate sparsity property. To the best of our
+knowledge, ours is the first use of the Fourier transform in the context of
+distribution testing.
+We apply our Fourier-based framework to obtain near sample-optimal and
+computationally efficient testers for the following fundamental distribution
+families: Sums of Independent Integer Random Variables (SIIRVs), Poisson
+Multinomial Distributions (PMDs), and Discrete Log-Concave Distributions. For
+the first two, ours are the first non-trivial testers in the literature, vastly
+generalizing previous work on testing Poisson Binomial Distributions. For the
+third, our tester improves on prior work in both sample and time complexity.
+"
+25285,Arithmetic degrees and dynamical degrees of endomorphisms on surfaces,"  For a dominant rational self-map on a smooth projective variety defined over
+a number field, Kawaguchi and Silverman conjectured that the (first) dynamical
+degree is equal to the arithmetic degree at a rational point whose forward
+orbit is well-defined and Zariski dense. We prove this conjecture for
+surjective endomorphisms on smooth projective surfaces. For surjective
+endomorphisms on any smooth projective varieties, we show the existence of
+rational points whose arithmetic degrees are equal to the dynamical degree.
+Moreover, we prove that there exists a Zariski dense set of rational points
+having disjoint orbits if the endomorphism is an automorphism.
+"
+25286,Invariance Feedback Entropy of Uncertain Control Systems,"  We introduce a novel notion of invariance feedback entropy to quantify the
+state information that is required by any controller that enforces a given
+subset of the state space to be invariant. We establish a number of elementary
+properties, e.g. we provide conditions that ensure that the invariance feedback
+entropy is finite and show for the deterministic case that we recover the
+well-known notion of entropy for deterministic control systems. We prove the
+data rate theorem, which shows that the invariance entropy is a tight lower
+bound of the data rate of any coder-controller that achieves invariance in the
+closed loop. We analyze uncertain linear control systems and derive a universal
+lower bound of the invariance feedback entropy. The lower bound depends on the
+absolute value of the determinant of the system matrix and a ratio involving
+the volume of the invariant set and the set of uncertainties. Furthermore, we
+derive a lower bound of the data rate of any static, memoryless
+coder-controller. Both lower bounds are intimately related and for certain
+cases it is possible to bound the performance loss due to the restriction to
+static coder-controllers by 1 bit/time unit. We provide various examples
+throughout the paper to illustrate and discuss different definitions and
+results.
+"
+25287,Stacked Average Far-Infrared Spectrum of Dusty Star-Forming Galaxies from the Herschel/SPIRE Fourier Transform Spectrometer,"  We present stacked average far-infrared spectra of a sample of 197 dusty,
+star-forming galaxies (DSFGs) at $0.005 < z < 4$ using close to 90% of the
+SPIRE Fourier Transform Spectrometer (FTS) extragalactic data archive from the
+Herschel Space Observatory based on 3.5 years of science operations. These
+spectra explore an observed-frame $\rm 447\,GHz-1568\,GHz$ ($\rm 191\,\mu
+m-671\,\mu m$) frequency (wavelength) range allowing us to observe the main
+atomic and molecular lines emitted by gas in the interstellar medium. The
+sample is sub-divided into five redshift bins at $0.005 < z < 0.05$, $0.05 < z
+< 0.2$, $0.2 < z < 0.5$, $0.8 < z <2$, and $2 < z < 4$. To study the dependence
+of observed spectral lines on total infrared luminosity, the sources in a
+subset of the redshift bins are stacked in luminosity bins. These stacked
+spectra are used to determine the average properties of the interstellar medium
+and dense molecular gas properties of DSFGs, in particular, the fine-structure
+line ([CII] 158 $\mu$m and [OI] 63 $\mu$m) luminosity ratios, and the line to
+far-IR luminosity ratios are used to model the gas density and radiation field
+strength in the photodissociation regions (PDRs). For the low-redshift sample,
+we additionally present the average spectral line energy distributions (SLED)
+of CO and $\rm{H_2O}$ rotational transitions and also consider PDR conditions
+based on a combination of [CI] 370 $\mu$m and 609 $\mu$m and $\rm CO (7-6)$
+lines. For the high-z ($0.8 < z < 4$) sample PDR models suggest a molecular gas
+distribution in the presence of a radiation field that is at least a factor of
+10$^3$ larger than the Milky-Way and with a neutral gas density of roughly
+10$^3$ to 10$^5$ cm$^{-3}$. The corresponding PDR models for the low-z sample
+suggest a UV radiation field and gas density comparable to those at high-z.
+"
+25288,On the regularity of the free boundary in the $p$-Laplacian obstacle problem,"  We study the regularity of the free boundary in the obstacle for the
+$p$-Laplacian, $\min\bigl\{-\Delta_p u,\,u-\varphi\bigr\}=0$ in
+$\Omega\subset\mathbb R^n$. Here, $\Delta_p u=\textrm{div}\bigl(|\nabla
+u|^{p-2}\nabla u\bigr)$, and $p\in(1,2)\cup(2,\infty)$.
+Near those free boundary points where $\nabla \varphi\neq0$, the operator
+$\Delta_p$ is uniformly elliptic and smooth, and hence the free boundary is
+well understood. However, when $\nabla \varphi=0$ then $\Delta_p$ is singular
+or degenerate, and nothing was known about the regularity of the free boundary
+at those points.
+Here we study the regularity of the free boundary where $\nabla \varphi=0$.
+On the one hand, for every $p\neq2$ we construct explicit global
+$2$-homogeneous solutions to the $p$-Laplacian obstacle problem whose free
+boundaries have a corner at the origin. In particular, we show that the free
+boundary is in general not $C^1$ at points where $\nabla \varphi=0$. On the
+other hand, under the ""concavity"" assumption $|\nabla \varphi|^{2-p}\Delta_p
+\varphi<0$, we show the free boundary is countably $(n-1)$-rectifiable and we
+prove a nondegeneracy property for $u$ at all free boundary points.
+"
+25289,Multiscale modelling of the morphology and spatial distribution of θ' precipitates in Al-Cu alloys,"  A multiscale approach based on the phase-field model is developed to simulate
+homogeneous and heterogeneous formation of {\theta}' precipitates during high
+temperature ageing in Al-Cu alloys. The model parameters that determine the
+different energy contributions (chemical free energy, interfacial energy,
+lattice parameters, elastic constants) were obtained from either computational
+thermodynamics databases or from first-principles density functional theory and
+molecular statics simulations. From the information, the evolution and
+equilibrium morphology of the {\theta}' precipitates is simulated in 3D using
+the phase-field model. The model was able to reproduce the evolution of the
+different orientation variants of plate-like shaped {\theta}' precipitates with
+orientation relationship (001){\theta}'//(001){\alpha} and
+[100]{\theta}'//[100]{\alpha} during homogeneous nucleation as well as the
+heterogeneous nucleation on dislocations, leading to the formation of
+precipitate arrays. Heterogeneous nucleation on pre-existing dislocation(s) was
+triggered by the interaction energy between the dislocation stress field and
+the stress-free transformation strain associated to the nucleation of the
+{\theta}' precipitates. Moreover, the mechanisms controlling the evolution of
+the morphology and the equilibrium aspect ratio of the precipitates were
+ascertained. All the predictions of the multiscale model were in good agreement
+with experimental data.
+"
+25290,On Extending Neural Networks with Loss Ensembles for Text Classification,"  Ensemble techniques are powerful approaches that combine several weak
+learners to build a stronger one. As a meta learning framework, ensemble
+techniques can easily be applied to many machine learning techniques. In this
+paper we propose a neural network extended with an ensemble loss function for
+text classification. The weight of each weak loss function is tuned within the
+training phase through the gradient propagation optimization method of the
+neural network. The approach is evaluated on several text classification
+datasets. We also evaluate its performance in various environments with several
+degrees of label noise. Experimental results indicate an improvement of the
+results and strong resilience against label noise in comparison with other
+methods.
+"
+25291,Opinion Conflicts: An Effective Route to Detect Incivility in Twitter,"  In Twitter, there is a rising trend in abusive behavior which often leads to
+incivility. This trend is affecting users mentally and as a result they tend to
+leave Twitter and other such social networking sites thus depleting the active
+user base. In this paper, we study factors associated with incivility. We
+observe that the act of incivility is highly correlated with the opinion
+differences between the account holder (i.e., the user writing the incivil
+tweet) and the target (i.e., the user for whom the incivil tweet is meant for
+or targeted), toward a named entity. We introduce a character level CNN model
+and incorporate the entity-specific sentiment information for efficient
+incivility detection which significantly outperforms multiple baseline methods
+achieving an impressive accuracy of 93.3% (4.9% improvement over the best
+baseline). In a post-hoc analysis, we also study the behavioral aspects of the
+targets and account holders and try to understand the reasons behind the
+incivility incidents. Interestingly, we observe that there are strong signals
+of repetitions in incivil behavior. In particular, we find that there are a
+significant fraction of account holders who act as repeat offenders - attacking
+the targets even more than 10 times. Similarly, there are also targets who get
+targeted multiple times. In general, the targets are found to have higher
+reputation scores than the account holders.
+"
+25292,Planck confronts large scale structure: methods to quantify discordance,"  Discordance in the $\Lambda$CDM cosmological model can be seen by comparing
+parameters constrained by CMB measurements to those inferred by probes of large
+scale structure. Recent improvements in observations, including final data
+releases from both Planck and SDSS-III BOSS, as well as improved astrophysical
+uncertainty analysis of CFHTLenS, allows for an update in the quantification of
+any tension between large and small scales. This paper is intended, primarily,
+as a discussion on the quantifications of discordance when comparing the
+parameter constraints of a model when given two different data sets. We
+consider KL-divergence, comparison of Bayesian evidences and other statistics
+which are sensitive to the mean, variance and shape of the distributions.
+However, as a by-product, we present an update to the similar analysis in
+(Battye, Charnock and Moss; 2015) where we find that, considering new data and
+treatment of priors, the constraints from the CMB and from a combination of LSS
+probes are in greater agreement and any tension only persists to a minor
+degree. In particular, we find the parameter constraints from the combination
+of LSS probes which are most discrepant with the Planck2015+Pol+BAO parameter
+distributions can be quantified at a 2.55$\sigma$ tension using the method
+introduced in (Battye, Charnock and Moss; 2015). If instead we use the
+distributions constrained by the combination of LSS probes which are in
+greatest agreement with those from Planck2015+Pol+BAO this tension is only
+0.76$\sigma$.
+"
+25293,Regression-aware decompositions,"  Linear least-squares regression with a ""design"" matrix A approximates a given
+matrix B via minimization of the spectral- or Frobenius-norm discrepancy
+||AX-B|| over every conformingly sized matrix X. Another popular approximation
+is low-rank approximation via principal component analysis (PCA) -- which is
+essentially singular value decomposition (SVD) -- or interpolative
+decomposition (ID). Classically, PCA/SVD and ID operate solely with the matrix
+B being approximated, not supervised by any auxiliary matrix A. However, linear
+least-squares regression models can inform the ID, yielding regression-aware
+ID. As a bonus, this provides an interpretation as regression-aware PCA for a
+kind of canonical correlation analysis between A and B. The regression-aware
+decompositions effectively enable supervision to inform classical
+dimensionality reduction, which classically has been totally unsupervised. The
+regression-aware decompositions reveal the structure inherent in B that is
+relevant to regression against A.
+"
+25294,An analogue of Dubrovin's conjecture,"  We propose an analogue of Dubrovin's conjecture for the case where Fano
+manifolds have quantum connections of exponential type. It includes the case
+where the quantum cohomology rings are not necessarily semisimple. The
+conjecture is described as an isomorphism of two linear algebraic structures,
+which we call ""mutation systems"". Given such a Fano manifold $X$, one of the
+structures is given by the Stokes structure of the quantum connection of $X$,
+and the other is given by a semiorthogonal decomposition of the derived
+category of coherent sheaves on $X$. We also prove the conjecture for a class
+of smooth Fano complete intersections in a projective space.
+"
+25295,A comparative study of WASP-67b and HAT-P-38b from WFC3 data,"  Atmospheric temperature and planetary gravity are thought to be the main
+parameters affecting cloud formation in giant exoplanet atmospheres. Recent
+attempts to understand cloud formation have explored wide regions of the
+equilibrium temperature-gravity parameter space. In this study, we instead
+compare the case of two giant planets with nearly identical equilibrium
+temperature ($T_\mathrm{eq}$ $\sim 1050 \, \mathrm{K}$) and gravity ($g \sim 10
+\, \mathrm{m \, s}^{-1})$. During $HST$ Cycle 23, we collected WFC3/G141
+observations of the two planets, WASP-67 b and HAT-P-38 b. HAT-P-38 b, with
+mass 0.42 M$_\mathrm{J}$ and radius 1.4 $R_\mathrm{J}$, exhibits a relatively
+clear atmosphere with a clear detection of water. We refine the orbital period
+of this planet with new observations, obtaining $P = 4.6403294 \pm 0.0000055 \,
+\mathrm{d}$. WASP-67 b, with mass 0.27 M$_\mathrm{J}$ and radius 0.83
+$R_\mathrm{J}$, shows a more muted water absorption feature than that of
+HAT-P-38 b, indicating either a higher cloud deck in the atmosphere or a more
+metal-rich composition. The difference in the spectra supports the hypothesis
+that giant exoplanet atmospheres carry traces of their formation history.
+Future observations in the visible and mid-infrared are needed to probe the
+aerosol properties and constrain the evolutionary scenario of these planets.
+"
+25296,Characterizing Curtailed and Uneconomic Renewable Power in the Mid-continent Independent System Operator,"  As power grids incorporate increased renewable generation such as wind and
+solar, their variability creates growing challenges for grid stability and
+efficiency. We study two facets: power the grid is unable to accept
+(curtailment), and power that is assigned zero economic value by the grid
+(negative or zero price). Collectively we term these ""stranded power"" or SP.
+We study stranded power in the Midcontinent Independent System Operator
+(MISO), characterizing quantity and temporal structure. First, stranded power
+is available in the MISO grid 99% of the time, and often in intervals >100
+hours, with characteristic seasonal and time-of-day patterns. Average stranded
+power often exceeds 1 GW, with duty factors as high as 30%. About 30% of all
+wind generation in MISO is stranded. Examination of the top 10 individual sites
+shows stranded power can be as high as 70% duty factor and 250MW. Trends over
+the past 3.5 years suggest stranded power is a persistent phenomenon. The study
+characterizes opportunities to exploit stranded power.
+We consider using energy storage to increase the utility of stranded power.
+For a range of power levels and uniformly-distributed storage, adding 5 hours
+of storage doubles duty factor to 30% at 4MW, but another 95 hours is required
+for the next 15% increase. At 4MW with 50 hours of storage, only 3 of 200 sites
+reach 100% duty factor, and with 100 hours required for the next 10 sites.
+Higher power levels require 100's of hours. Storage at the top 10 sites is more
+productive, 5 hours increases duty factor to 70% at 4MW, but further storage
+has diminishing benefits. Studies of the amount of power served by storage show
+that distribution to the best sites provides 2 to 3.7-fold advantages over
+uniform distribution.
+"
+25297,Coordinating Filters for Faster Deep Neural Networks,"  Very large-scale Deep Neural Networks (DNNs) have achieved remarkable
+successes in a large variety of computer vision tasks. However, the high
+computation intensity of DNNs makes it challenging to deploy these models on
+resource-limited systems. Some studies used low-rank approaches that
+approximate the filters by low-rank basis to accelerate the testing. Those
+works directly decomposed the pre-trained DNNs by Low-Rank Approximations
+(LRA). How to train DNNs toward lower-rank space for more efficient DNNs,
+however, remains as an open area. To solve the issue, in this work, we propose
+Force Regularization, which uses attractive forces to enforce filters so as to
+coordinate more weight information into lower-rank space. We mathematically and
+empirically verify that after applying our technique, standard LRA methods can
+reconstruct filters using much lower basis and thus result in faster DNNs. The
+effectiveness of our approach is comprehensively evaluated in ResNets, AlexNet,
+and GoogLeNet. In AlexNet, for example, Force Regularization gains 2x speedup
+on modern GPU without accuracy loss and 4.05x speedup on CPU by paying small
+accuracy degradation. Moreover, Force Regularization better initializes the
+low-rank DNNs such that the fine-tuning can converge faster toward higher
+accuracy. The obtained lower-rank DNNs can be further sparsified, proving that
+Force Regularization can be integrated with state-of-the-art sparsity-based
+acceleration methods. Source code is available in
+this https URL
+"
+25298,Vortices in Bose-Einstein condensates with $\mathcal{PT}$-symmetric gain and loss,"  We investigate vortex excitations in dilute Bose-Einstein condensates in the
+presence of complex $\mathcal{PT}$-symmetric potentials. These complex
+potentials are used to describe a balanced gain and loss of particles and allow
+for an easier calculation of stationary states in open systems than in a full
+dynamical calculation including the whole environment. We examine the
+conditions under which stationary vortex states can exist and consider
+transitions from vortex to non-vortex states. In addition, we study the
+influences of $\mathcal{PT}$ symmetry on the dynamics of non-stationary vortex
+states placed at off-center positions.
+"
+25299,Distance to the Low-velocity Cloud in the Direction of the High-velocity Compact Cloud CO--0.40--0.22,"  CO--0.40--0.22 is a peculiar molecular cloud that is compact and has an
+extraordinary broad velocity width. It is found in the central molecular zone
+of our Galaxy. In this direction, there is another cloud with an H$_2$O maser
+spot at a lower velocity. Collision with this low-velocity cloud could be
+responsible for the broad velocity width of CO--0.40--0.22. We performed
+phase-referencing VLBI astrometry with VERA and detected the annual parallax of
+the H$_2$O maser spot in the low-velocity cloud to be $0.33 \pm 0.14$ mas,
+which corresponds to a distance of $3.07^{+2.22}_{-0.91}$ kpc from the Sun.
+This implies that the low-velocity cloud is located in the Galactic disk on the
+near side of the central molecular zone.
+"
+25300,Ultrasensitive and highly accurate long-range surface plasmon resonance biosensors based on two-dimensional transition metal dichalcogenides,"  Two-dimensional transition metal dichalcogenides (TMDCs), as promising
+alternative plasmonics supporting materials to graphene, exhibit potential
+applications in sensing. Here, we propose an ultrasensitive, accurate
+long-range surface plasmon resonance (LRSPR) imaging biosensor with
+two-dimensional TMDC layers, which shows higher detection accuracy than that of
+conventional SPR biosensor. It is found that the imaging sensitivity of the
+proposed LRSPR biosensor can be enhanced by the integration of TMDC layers,
+which is different from the previous graphene-based LRSPR or SPR imaging
+sensor, whose imaging sensitivity usually decreases with the number of graphene
+layers. The sensitivity enhancement or degradation effect for the proposed
+chalcogenide-cytop-gold-TMDCs based biosensor depends on the thickness of gold
+thin film and cytop layer. Imaging sensitivity of more than 4000
+$\text{RIU}^{-1}$ can be obtained with a high detection accuracy of more than
+120 $\text{deg}^{-1}$. We expect that the proposed TMDCs mediated LRSPR imaging
+sensor could provide potential applications in chemical sensing and biosensing
+for a highly sensitive and accurate simultaneous detection of multiple
+biomolecular interactions.
+"
+25301,A Knowledge-Based Approach to Word Sense Disambiguation by distributional selection and semantic features,"  Word sense disambiguation improves many Natural Language Processing (NLP)
+applications such as Information Retrieval, Information Extraction, Machine
+Translation, or Lexical Simplification. Roughly speaking, the aim is to choose
+for each word in a text its best sense. One of the most popular method
+estimates local semantic similarity relatedness between two word senses and
+then extends it to all words from text. The most direct method computes a rough
+score for every pair of word senses and chooses the lexical chain that has the
+best score (we can imagine the exponential complexity that returns this
+comprehensive approach). In this paper, we propose to use a combinatorial
+optimization metaheuristic for choosing the nearest neighbors obtained by
+distributional selection around the word to disambiguate. The test and the
+evaluation of our method concern a corpus written in French by means of the
+semantic network BabelNet. The obtained accuracy rate is 78 % on all names and
+verbs chosen for the evaluation.
+"
+25302,Altered Modularity and Disproportional Integration in Functional Networks are Markers of Abnormal Brain Organization in Schizophrenia,"  Modularity plays an important role in brain networks' architecture and
+influences its dynamics and the ability to integrate and segregate different
+modules of cerebral regions. Alterations in community structure are associated
+with several clinical disorders, specially schizophrenia, although its time
+evolution is not clear yet. In the present work, we analyze fMRI functional
+networks of $65$ healthy subjects (HC) and $44$ patients of schizophrenia (SZ),
+$28$ of them in a chronic state (CR) of illness, and $16$ at early stage (ES).
+We find clear differences in edges' weights distribution, networks density,
+community structure consistency and robustness against edge removal. In
+comparison to healthy subjects, we found that networks from SZ patients
+exhibits wider weight distribution, larger overall connectivity, and are more
+consistent in the community structure across subjects. We also showed that the
+networks of SZ patients tend to be more robust to edge removal than healthy
+subjects, while having lower network density. In the case of early stages
+patients, we found that their networks exhibit topological features
+consistently in between the ones obtained from the other two groups, resulting
+in a tendency towards the chronic group state.
+"
+25303,Asymptotic Optimality of Rapidly Exploring Random Tree,"  In this paper we investigate the asymptotic optimality property of a
+randomized sampling based motion planner, namely RRT. We prove that a RRT
+planner is not an asymptotically optimal motion planner. Our result, while
+being consistent with similar results which exist in the literature, however,
+brings out an important characteristics of a RRT planner. We show that the
+degree distribution of the tree vertices follows a power law in an asymptotic
+sense. A simulation result is presented to support the theoretical claim. Based
+on these results we also try to establish a simple necessary condition for
+sampling based motion planners to be asymptotically optimal.
+"
+25304,"Adjoining Roots and Rational Powers of Generators in PSL(2,\RR) and Discreteness","  Let $G$ be a finitely generated group of isometries of $\HH^m$, hyperbolic
+$m$-space, for some positive integer $m$. %or equivalently elements of
+$PSL(2,\CC)$.
+The discreteness problem is to determine whether or not $G$ is discrete. Even
+in the case of a two generator non-elementary subgroup of $\HH^2$ (equivalently
+$PSL(2,\mathbb{R})$) the problem requires an algorithm \cite{GM,JGtwo}. If $G$
+is discrete, one can ask when adjoining an $n$th root of a generator results in
+a discrete group.
+In this paper we address the issue for pairs of hyperbolic generators in
+$PSL(2, \RR)$ with disjoint axes and obtain necessary and sufficient conditions
+for adjoining roots for the case when the two hyperbolics have a hyperbolic
+product and are what as known as {\sl stopping generators} for the
+Gilman-Maskit algorithm \cite{GM}. We give an algorithmic solution in other
+cases. It applies to all other types of pair of generators that arise in what
+is known as the {\sl intertwining case}. The results are geometrically
+motivated and stated as such, but also can be given computationally using the
+corresponding matrices.
+"
+25305,Learning Exact Topology of a Loopy Power Grid from Ambient Dynamics,"  Estimation of the operational topology of the power grid is necessary for
+optimal market settlement and reliable dynamic operation of the grid. This
+paper presents a novel framework for topology estimation for general power
+grids (loopy or radial) using time-series measurements of nodal voltage phase
+angles that arise from the swing dynamics. Our learning framework utilizes
+multivariate Wiener filtering to unravel the interaction between fluctuations
+in voltage angles at different nodes and identifies operational edges by
+considering the phase response of the elements of the multivariate Wiener
+filter. The performance of our learning framework is demonstrated through
+simulations on standard IEEE test cases.
+"
+25306,"Confirmation that T cell receptor activation is digital, not analog. Hence individual T cell cytolytic capacity is independent of initial stimulation strength","  Whether a sub-optimum lymphocyte stimulus is achieved by lowering ligand
+concentration, or decreasing its affinity, initial TCR activation responses are
+likely to be digital. We now know for cultured peripheral blood mononuclear
+cells (PBMCs), when activated by plant lectins such as concanavalin-A (Con-A),
+that the responding cells are T cells, and that early transcriptional and
+metabolic changes closely resemble those found with T cells responding to
+specific peptides complexed to MHC proteins (pMHC). Robbins long ago showed
+that lectin-activated PBMC responses were digital. This has now been confirmed
+for pMHC-activated T cells. Hence, in general, responses of individual T cells
+are independent of initial signal strength.
+"
+25307,An Optimal Online Method of Selecting Source Policies for Reinforcement Learning,"  Transfer learning significantly accelerates the reinforcement learning
+process by exploiting relevant knowledge from previous experiences. The problem
+of optimally selecting source policies during the learning process is of great
+importance yet challenging. There has been little theoretical analysis of this
+problem. In this paper, we develop an optimal online method to select source
+policies for reinforcement learning. This method formulates online source
+policy selection as a multi-armed bandit problem and augments Q-learning with
+policy reuse. We provide theoretical guarantees of the optimal selection
+process and convergence to the optimal policy. In addition, we conduct
+experiments on a grid-based robot navigation domain to demonstrate its
+efficiency and robustness by comparing to the state-of-the-art transfer
+learning method.
+"
+25308,Hard-core bosons in flat band systems above the critical density,"  We investigate the behaviour of hard-core bosons in one- and two-dimensional
+flat band systems, the chequerboard and the kagomé lattice and
+one-dimensional analogues thereof. The one dimensional systems have an exact
+local reflection symmetry which allows for exact results. We show that above
+the critical density an additional particle forms a pair with one of the other
+bosons and that the pair is localised. In the two-dimensional systems exact
+results are not available but variational results indicate a similar physical
+behaviour.
+"
+25309,Directional dimensions of ergodic currents on $\mathbb C \mathbb P (2)$,"  LLet $f$ be a holomorphic endomorphism of $\mathbb P^ 2$ of degree $d \geq
+2$. We estimate the local directional dimensions of closed positive currents
+$S$ with respect to ergodic dilating measures $\nu$. We infer several
+applications. The first one shows that the currents $S$ containing a measure of
+entropy $h\_\nu > \log d$ have a directional dimension $>2$, which answers a
+question by de Thélin-Vigny. The second application asserts that the
+Dujardin's semi-extremal endomorphisms are close to suspensions of
+one-dimensional Lattès maps. Finally, we obtain an upper bound for the
+dimension of the equilibrium measure, towards the formula conjectured by
+Binder-DeMarco.
+"
+25310,Longitudinal modes of spin fluctuations in iron-based superconductors,"  Iron-based superconductors can exhibit different magnetic ground states and
+are in a critical magnetic region where frustrated magnetic interactions
+strongly compete with each other. Here we investigate the longitudinal modes of
+spin fluctuations in an unified effective magnetic model for iron-based
+superconductors. We focus on the collinear antiferromagnetic phase (CAF) and
+calculate the behavior of the longitudinal modes when different phase
+boundaries are approached. The results can help to determine the nature of the
+magnetic fluctuations in iron-based superconductors.
+"
+25311,Impact of cyber-invasive species on a large ecological network,"  As impacts of introduced species cascade through trophic levels, they can
+cause indirect and counter-intuitive effects. To investigate the impact of
+invasive species at the network scale, we use a generalized food web model,
+capable of propagating changes through networks with a series of ecologically
+realistic criteria. Using data from a small British offshore island, we
+quantify the impacts of four virtual invasive species (an insectivore, a
+herbivore, a carnivore and an omnivore whose diet is based on a rat) and
+explore which clusters of species react in similar ways. We find that the
+predictions for the impacts of invasive species are ecologically plausible,
+even for large networks robust predictions for the impacts of invasive species
+can be obtained. Species in the same taxonomic group are similarly impacted by
+a virtual invasive species. However, interesting differences within a given
+taxonomic group can occur. The results suggest that some native species may be
+at risk from a wider range of invasives than previously believed. The
+implications of these results for ecologists and land managers are discussed.
+"
+25312,The Likelihood Ratio Test in High-Dimensional Logistic Regression Is Asymptotically a Rescaled Chi-Square,"  Logistic regression is used thousands of times a day to fit data, predict
+future outcomes, and assess the statistical significance of explanatory
+variables. When used for the purpose of statistical inference, logistic models
+produce p-values for the regression coefficients by using an approximation to
+the distribution of the likelihood-ratio test. Indeed, Wilks' theorem asserts
+that whenever we have a fixed number $p$ of variables, twice the log-likelihood
+ratio (LLR) $2\Lambda$ is distributed as a $\chi^2_k$ variable in the limit of
+large sample sizes $n$; here, $k$ is the number of variables being tested. In
+this paper, we prove that when $p$ is not negligible compared to $n$, Wilks'
+theorem does not hold and that the chi-square approximation is grossly
+incorrect; in fact, this approximation produces p-values that are far too small
+(under the null hypothesis). Assume that $n$ and $p$ grow large in such a way
+that $p/n\rightarrow\kappa$ for some constant $\kappa < 1/2$. We prove that for
+a class of logistic models, the LLR converges to a rescaled chi-square, namely,
+$2\Lambda~\stackrel{\mathrm{d}}{\rightarrow}~\alpha(\kappa)\chi_k^2$, where the
+scaling factor $\alpha(\kappa)$ is greater than one as soon as the
+dimensionality ratio $\kappa$ is positive. Hence, the LLR is larger than
+classically assumed. For instance, when $\kappa=0.3$,
+$\alpha(\kappa)\approx1.5$. In general, we show how to compute the scaling
+factor by solving a nonlinear system of two equations with two unknowns. Our
+mathematical arguments are involved and use techniques from approximate message
+passing theory, non-asymptotic random matrix theory and convex geometry. We
+also complement our mathematical study by showing that the new limiting
+distribution is accurate for finite sample sizes. Finally, all the results from
+this paper extend to some other regression models such as the probit regression
+model.
+"
+25313,Delving Deeper into MOOC Student Dropout Prediction,"  In order to obtain reliable accuracy estimates for automatic MOOC dropout
+predictors, it is important to train and test them in a manner consistent with
+how they will be used in practice. Yet most prior research on MOOC dropout
+prediction has measured test accuracy on the same course used for training the
+classifier, which can lead to overly optimistic accuracy estimates. In order to
+understand better how accuracy is affected by the training+testing regime, we
+compared the accuracy of a standard dropout prediction architecture
+(clickstream features + logistic regression) across 4 different training
+paradigms. Results suggest that (1) training and testing on the same course
+(""post-hoc"") can overestimate accuracy by several percentage points; (2)
+dropout classifiers trained on proxy labels based on students' persistence are
+surprisingly competitive with post-hoc training (87.33% versus 90.20% AUC
+averaged over 8 weeks of 40 HarvardX MOOCs); and (3) classifier performance
+does not vary significantly with the academic discipline. Finally, we also
+research new dropout prediction architectures based on deep, fully-connected,
+feed-forward neural networks and find that (4) networks with as many as 5
+hidden layers can statistically significantly increase test accuracy over that
+of logistic regression.
+"
+25314,Lightning Chemistry on Earth-like Exoplanets,"  We present a model for lightning shock induced chemistry that can be applied
+to atmospheres of arbitrary H/C/N/O chemistry, hence for extrasolar planets and
+brown dwarfs. The model couples hydrodynamics and the STAND2015 kinetic
+gas-phase chemistry. For an exoplanet analogue to the contemporary Earth, our
+model predicts NO and NO2 yields in agreement with observation. We predict
+height-dependent mixing ratios during a storm soon after a lightning shock of
+NO ~ 1e-3 at 40 km and NO2 ~ 1e-4 below 40 km, with O3 reduced to trace
+quantities (<< 1e-10). For an Earth-like exoplanet with a CO2/N2 dominated
+atmosphere and with an extremely intense lightning storm over its entire
+surface, we predict significant changes in the amount of NO, NO2, O3, H2O, H2,
+and predict significant abundance of C2N. We find that, for the Early Earth, O2
+is formed in large quantities by lightning but is rapidly processed by the
+photochemistry, consistent with previous work on lightning. The effect of
+persistent global lightning storms are predicted to be significant, primarily
+due to NO2, with the largest spectral features present at ~3.4 {\mu}m and ~6.2
+{\mu}m. The features within the transmission spectrum are on the order of 1 ppm
+and therefore are not likely detectable with JWST. Depending on its spectral
+properties, C2N could be a key tracer for lightning on Earth-like exoplanets
+with a N2/CO2 bulk atmosphere, unless destroyed by yet unknown chemical
+reactions.
+"
+25315,Recent progress in log-concave density estimation,"  In recent years, log-concave density estimation via maximum likelihood
+estimation has emerged as a fascinating alternative to traditional
+nonparametric smoothing techniques, such as kernel density estimation, which
+require the choice of one or more bandwidths. The purpose of this article is to
+describe some of the properties of the class of log-concave densities on
+$\mathbb{R}^d$ which make it so attractive from a statistical perspective, and
+to outline the latest methodological, theoretical and computational advances in
+the area.
+"
+25316,Grasp Pose Detection in Point Clouds,"  Recently, a number of grasp detection methods have been proposed that can be
+used to localize robotic grasp configurations directly from sensor data without
+estimating object pose. The underlying idea is to treat grasp perception
+analogously to object detection in computer vision. These methods take as input
+a noisy and partially occluded RGBD image or point cloud and produce as output
+pose estimates of viable grasps, without assuming a known CAD model of the
+object. Although these methods generalize grasp knowledge to new objects well,
+they have not yet been demonstrated to be reliable enough for wide use. Many
+grasp detection methods achieve grasp success rates (grasp successes as a
+fraction of the total number of grasp attempts) between 75% and 95% for novel
+objects presented in isolation or in light clutter. Not only are these success
+rates too low for practical grasping applications, but the light clutter
+scenarios that are evaluated often do not reflect the realities of real world
+grasping. This paper proposes a number of innovations that together result in a
+significant improvement in grasp detection performance. The specific
+improvement in performance due to each of our contributions is quantitatively
+measured either in simulation or on robotic hardware. Ultimately, we report a
+series of robotic experiments that average a 93% end-to-end grasp success rate
+for novel objects presented in dense clutter.
+"
+25317,FairJudge: Trustworthy User Prediction in Rating Platforms,"  Rating platforms enable large-scale collection of user opinion about items
+(products, other users, etc.). However, many untrustworthy users give
+fraudulent ratings for excessive monetary gains. In the paper, we present
+FairJudge, a system to identify such fraudulent users. We propose three
+metrics: (i) the fairness of a user that quantifies how trustworthy the user is
+in rating the products, (ii) the reliability of a rating that measures how
+reliable the rating is, and (iii) the goodness of a product that measures the
+quality of the product. Intuitively, a user is fair if it provides reliable
+ratings that are close to the goodness of the product. We formulate a mutually
+recursive definition of these metrics, and further address cold start problems
+and incorporate behavioral properties of users and products in the formulation.
+We propose an iterative algorithm, FairJudge, to predict the values of the
+three metrics. We prove that FairJudge is guaranteed to converge in a bounded
+number of iterations, with linear time complexity. By conducting five different
+experiments on five rating platforms, we show that FairJudge significantly
+outperforms nine existing algorithms in predicting fair and unfair users. We
+reported the 100 most unfair users in the Flipkart network to their review
+fraud investigators, and 80 users were correctly identified (80% accuracy). The
+FairJudge algorithm is already being deployed at Flipkart.
+"
+25318,Quasiparticles and phonon satellites in spectral functions of semiconductors and insulators: Cumulants applied to full first principles theory and Fröhlich polaron,"  The electron-phonon interaction causes thermal and zero-point motion shifts
+of electron quasiparticle (QP) energies $\epsilon_k(T)$. Other consequences of
+interactions, visible in angle-resolved photoemission spectroscopy (ARPES)
+experiments, are broadening of QP peaks and appearance of sidebands, contained
+in the electron spectral function
+$A(k,\omega)=-{\Im m}G_R(k,\omega) /\pi$, where $G_R$ is the retarded Green's
+function. Electronic structure codes (e.g. using density-functional theory) are
+now available that compute the shifts and start to address broadening and
+sidebands. Here we consider MgO and LiF, and determine their nonadiabatic
+Migdal self energy. The spectral function obtained from the Dyson equation
+makes errors in the weight and energy of the QP peak and the position and
+weight of the phonon-induced sidebands. Only one phonon satellite appears, with
+an unphysically large energy difference (larger than the highest phonon energy)
+with respect to the QP peak. By contrast, the spectral function from a cumulant
+treatment of the same self energy is physically better, giving a quite accurate
+QP energy and several satellites approximately spaced by the LO phonon energy.
+In particular, the positions of the QP peak and first satellite agree closely
+with those found for the Fröhlich Hamiltonian by Mishchenko $\textit{et al.}$
+(2000) using diagrammatic Monte Carlo. We provide a detailed comparison between
+the first-principles MgO and LiF results and those of the Fröhlich
+Hamiltonian. Such an analysis applies widely to materials with infra-red active
+phonons. We also compare the retarded and time-ordered cumulant treatments:
+they are equivalent for the Fröhlich Hamiltonian, and only slightly differ in
+first-principles electron-phonon results for wide-band gap materials.
+"
+25319,Approximate $l_0$-penalized estimation of piecewise-constant signals on graphs,"  We study recovery of piecewise-constant signals on graphs by the estimator
+minimizing an $l_0$-edge-penalized objective. Although exact minimization of
+this objective may be computationally intractable, we show that the same
+statistical risk guarantees are achieved by the $\alpha$-expansion algorithm
+which computes an approximate minimizer in polynomial time. We establish that
+for graphs with small average vertex degree, these guarantees are minimax
+rate-optimal over classes of edge-sparse signals. For spatially inhomogeneous
+graphs, we propose minimization of an edge-weighted objective where each edge
+is weighted by its effective resistance or another measure of its contribution
+to the graph's connectivity. We establish minimax optimality of the resulting
+estimators over corresponding edge-weighted sparsity classes. We show
+theoretically that these risk guarantees are not always achieved by the
+estimator minimizing the $l_1$/total-variation relaxation, and empirically that
+the $l_0$-based estimates are more accurate in high signal-to-noise settings.
+"
+25320,Dimension Splitting and a Long Time-Step Multi-Dimensional Scheme for Atmospheric Transport,"  Dimensionally split advection schemes are attractive for atmospheric
+modelling due to their efficiency and accuracy in each spatial dimension.
+Accurate long time-steps can be achieved without significant cost using the
+flux-form semi-Lagrangian technique. The dimensionally split scheme used here
+is constructed from the one-dimensional Piecewise Parabolic Method and extended
+to two dimensions using COSMIC splitting. The dimensionally split scheme is
+compared with a genuinely multi-dimensional, method of lines scheme with
+implicit time-stepping which is stable for large Courant numbers.
+Two-dimensional advection test cases on Cartesian planes are proposed that
+avoid the complexities of a spherical domain or multi-panel meshes. These are
+solid body rotation, horizontal advection over orography and deformational
+flow. The test cases use distorted meshes either to represent sloping terrain
+or to mimic the distortions of a cubed sphere.
+Such mesh distortions are expected to accentuate the errors associated with
+dimension splitting, however, the dimensionally split scheme is very accurate
+on orthogonal meshes and accuracy decreases only a little in the presence of
+large mesh distortions. The dimensionally split scheme also loses some accuracy
+when long time-steps are used. The multi-dimensional scheme is almost entirely
+insensitive to mesh distortions and asymptotes to second-order accuracy at high
+resolution. As is expected for implicit time-stepping, phase errors occur when
+using long time-steps but the spatially well resolved features are advected at
+the correct speed and the multi-dimensional scheme is always stable.
+An estimate of computational cost reveals that the implicit scheme is the
+most expensive, particularly for large Courant numbers. If the
+multi-dimensional scheme is used instead with explicit time-stepping, the cost
+becomes similar to the dimensionally split scheme.
+"
+25321,Casoratian Identities for the Discrete Orthogonal Polynomials in Discrete Quantum Mechanics with Real Shifts,"  In our previous papers, the Wronskian identities for the Hermite, Laguerre
+and Jacobi polynomials and the Casoratian identities for the Askey-Wilson
+polynomial and its reduced form polynomials were presented. These identities
+are naturally derived through quantum mechanical formulation of the classical
+orthogonal polynomials; ordinary quantum mechanics for the former and discrete
+quantum mechanics with pure imaginary shifts for the latter. In this paper we
+present the corresponding identities for the discrete quantum mechanics with
+real shifts. Infinitely many Casoratian identities for the $q$-Racah polynomial
+and its reduced form polynomials are obtained.
+"
+25322,NAPS: Natural Program Synthesis Dataset,"  We present a program synthesis-oriented dataset consisting of human written
+problem statements and solutions for these problems. The problem statements
+were collected via crowdsourcing and the program solutions were extracted from
+human-written solutions in programming competitions, accompanied by
+input/output examples. We propose using this dataset for the program synthesis
+tasks aimed for working with real user-generated data. As a baseline we present
+few models, with the best model achieving 8.8% accuracy, showcasing both the
+complexity of the dataset and large room for future research.
+"
+25323,Classification of parameter spaces for reaction-diffusion systems on stationary domains,"  This paper explores the classification of parameter spaces for
+reaction-diffusion systems of two chemical species on stationary domains. The
+dynamics of the system are explored both in the absence and presence of
+diffusion. The parameter space is fully classified in terms of the types and
+stability of the uniform steady state. In the absence of diffusion the results
+on the classification of parameter space are supported by simulations of the
+corresponding vector-field and some trajectories around the uniform steady
+state. In the presence of diffusion, the main findings are the quantitative
+analysis relating the domain-size with the reaction and diffusion rates and
+their corresponding influence on the dynamics of the reaction-diffusion system
+when perturbed in the neighbourhood of the uniform steady state. Theoretical
+predictions are supported by numerical simulations both in the presence as well
+as in the absence of diffusion. Conditions on the domain size with respect to
+the diffusion and reaction rates are related to the types of diffusion-driven
+instabilities namely Turing, Hopf and Transcritical types of bifurcations. The
+first condition is an upper bound on the area of a rectangular domain in terms
+of the diffusion and reaction rates, which forbids the existence of Hopf and
+Transcritical types of bifurcations, yet allowing Turing type instability to
+occur. The second condition (necessary) is a lower bound on the domain size in
+terms of the reaction and diffusion rates to give rise to Hopf and
+Transcritical types of bifurcations as well as Turing instability.
+"
+25324,Dynamic anticipation by Cdk2/Cyclin A-bound p27 mediates signal integration in cell cycle regulation,"  p27$^{Kip1}$ (p27) is an intrinsically disordered protein (IDP) that folds
+upon binding to cyclin-dependent kinase (Cdk)$/$cyclin complexes (e.g.,
+Cdk2$/$cyclin A), inhibiting their catalytic activity and causing cell cycle
+arrest. However, cell division progresses when stably Cdk2$/$cyclin A-bound p27
+is phosphorylated on one or two structurally occluded tyrosine residues
+$[$tyrosines 88 (Y88) and 74 (Y74)$]$ and a distal threonine residue
+$[$threonine 187 (T187)$]$. These events trigger ubiquitination and degradation
+of p27, fully activating Cdk2$/$cyclin A to drive cell division. Using an
+integrated approach comprising structural, biochemical, biophysical and
+single-molecule fluorescence methods, we show that Cdk2$/$cyclin A-bound p27
+samples lowly-populated conformations that dynamically anticipate the
+sequential steps of this signaling cascade. ""Dynamic anticipation"" provides
+access to the non-receptor tyrosine kinases, BCR-ABL and Src, which
+sequentially phosphorylate Y88 and Y74 and promote intra-assembly
+phosphorylation (of p27) on distal T187. Tyrosine phosphorylation also
+allosterically relieves p27-dependent inhibition of substrate binding to
+Cdk2$/$cyclin A, a phenomenon we term ""cross-complex allostery"". Even when
+tightly bound to Cdk2$/$cyclin A, intrinsic flexibility enables p27 to
+integrate and process signaling inputs, and generate outputs including altered
+Cdk2 activity, p27 stability, and, ultimately, cell cycle progression.
+Intrinsic dynamics within multi-component assemblies may be a general mechanism
+of signaling by regulatory IDPs, which can be subverted in human disease, as
+exemplified by hyper-active BCR-ABL and Src in certain cancers.
+"
+25325,Scalable Peaceman-Rachford Splitting Method with Proximal Terms,"  Along with developing of Peaceman-Rachford Splittling Method (PRSM), many
+batch algorithms based on it have been studied very deeply. But almost no
+algorithm focused on the performance of stochastic version of PRSM. In this
+paper, we propose a new stochastic algorithm based on PRSM, prove its
+convergence rate in ergodic sense, and test its performance on both artificial
+and real data. We show that our proposed algorithm, Stochastic Scalable PRSM
+(SS-PRSM), enjoys the $O(1/K)$ convergence rate, which is the same as those
+newest stochastic algorithms that based on ADMM but faster than general
+Stochastic ADMM (which is $O(1/\sqrt{K})$). Our algorithm also owns wide
+flexibility, outperforms many state-of-the-art stochastic algorithms coming
+from ADMM, and has low memory cost in large-scale splitting optimization
+problems.
+"
+25326,Stochastic Cubic Regularization for Fast Nonconvex Optimization,"  This paper proposes a stochastic variant of a classic algorithm---the
+cubic-regularized Newton method [Nesterov and Polyak 2006]. The proposed
+algorithm efficiently escapes saddle points and finds approximate local minima
+for general smooth, nonconvex functions in only
+$\mathcal{\tilde{O}}(\epsilon^{-3.5})$ stochastic gradient and stochastic
+Hessian-vector product evaluations. The latter can be computed as efficiently
+as stochastic gradients. This improves upon the
+$\mathcal{\tilde{O}}(\epsilon^{-4})$ rate of stochastic gradient descent. Our
+rate matches the best-known result for finding local minima without requiring
+any delicate acceleration or variance-reduction techniques.
+"
+25327,Smart Inverter Impacts on California Distribution Feeders with Increasing PV Penetration: A Case Study,"  The impacts of high PV penetration on distribution feeders have been well
+documented within the last decade. To mitigate these impacts, interconnection
+standards have been amended to allow PV inverters to regulate voltage locally.
+However, there is a deficiency of literature discussing how these inverters
+will behave on real feeders under increasing PV penetration. In this paper, we
+simulate several deployment scenarios of these inverters on a real California
+distribution feeder. We show that minimum and maximum voltage, tap operations,
+and voltage variability are improved due to the inverters. Line losses were
+shown to increase at high PV penetrations as a side effect. Furthermore, we
+find inverter sizing was shown to be important as PV penetration increased.
+Finally we show that increasing the number of inverters and removing the
+deadband from the Volt/VAr control curve improves the effectiveness.
+"
+25328,Holographic Entanglement of Purification,"  We study properties of the minimal cross section of entanglement wedge which
+connects two disconnected subsystems in holography. In particular we focus on
+various inequalities which are satisfied by this quantity. They suggest that it
+is a holographic counterpart of the quantity called entanglement of
+purification, which measures a bipartite correlation in a given mixed state. We
+give a heuristic argument which supports this identification based on a tensor
+network interpretation of holography. This implies that the entanglement of
+purification satisfies the strong superadditivity for holographic conformal
+field theories.
+"
+25329,Automated Classification of Text Sentiment,"  The ability to identify sentiment in text, referred to as sentiment analysis,
+is one which is natural to adult humans. This task is, however, not one which a
+computer can perform by default. Identifying sentiments in an automated,
+algorithmic manner will be a useful capability for business and research in
+their search to understand what consumers think about their products or
+services and to understand human sociology. Here we propose two new Genetic
+Algorithms (GAs) for the task of automated text sentiment analysis. The GAs
+learn whether words occurring in a text corpus are either sentiment or
+amplifier words, and their corresponding magnitude. Sentiment words, such as
+'horrible', add linearly to the final sentiment. Amplifier words in contrast,
+which are typically adjectives/adverbs like 'very', multiply the sentiment of
+the following word. This increases, decreases or negates the sentiment of the
+following word. The sentiment of the full text is then the sum of these terms.
+This approach grows both a sentiment and amplifier dictionary which can be
+reused for other purposes and fed into other machine learning algorithms. We
+report the results of multiple experiments conducted on large Amazon data sets.
+The results reveal that our proposed approach was able to outperform several
+public and/or commercial sentiment analysis algorithms.
+"
+25330,Triple point semimetal and topological phase transitions in NaCu$_{3}$Te$_{2}$,"  Quasiparticle excitations of free electrons in condensed-matter physics,
+characterized by the dimensionality of the band crossing, can find their
+elementary-particle analogs in high-energy physics, such as Majorana, Weyl, and
+Dirac fermions. While crystalline symmetry allows more quasiparticle
+excitations and exotic fermions to emerge. Using symmetry analysis and {\it
+ab-initio} calculations, we propose that the 3D honeycomb crystal
+NaCu$_3$Te$_2$ hosts triply degenerate nodal points (TDNPs) which are perfectly
+separated from the bulk states. We find a tunable phase transition between
+TDNPs and a weak TI triggered by a symmetry-allowed perturbation, and we
+further reveal the crucial role played by the spin-orbital coupling (SOC) for
+the emergence of the TDNPs in this system. Such topological non-trivial ternary
+compound not only serves as a perfect candidate for studying three-component
+fermions, but also provides a beautiful playground for understanding the
+topological phase transitions between TDNPs, TIs and trivial insulators, which
+distinguishes this system from other TDNP candidates.
+"
+25331,Scattering of flexural waves from an $N$-beam resonator in a thin plate,"  The impedance matrix method is applied to study the scattering of flexural
+waves propagating in an infinite thin plate containing an $N$-beam resonator.
+The resonator consists of a circular hole containing a smaller plate connected
+to the background plate by a number $N$ of rectangular beams. After
+representing the boundary conditions in a modal multipole expansion form, a
+compact expression is obtained for the T-matrix, which relates the incident and
+the scattered transverse (out-of-plane) waves. The analysis of the scattering
+cross-section reveals interesting scattering features, like resonances and
+anisotropy, associated with this type of resonators. Numerical experiments
+performed within the framework of the finite element method support the
+accuracy of the model here developed.
+"
+25332,An experimental study of low-velocity impacts into granular material in reduced gravity,"  In order to improve our understanding of landing on small bodies and of
+asteroid evolution, we use our novel drop tower facility to perform
+low-velocity (2-40 cm s^-1), shallow impact experiments of a 10 cm diameter
+aluminum sphere into quartz sand in low effective gravities (~0.2-1 m s^-2).
+Using in situ accelerometers, we measure the acceleration profile during the
+impacts and determine the peak accelerations, collision durations and maximum
+penetration depth. We find that the penetration depth scales linearly with the
+collision velocity but is independent of the effective gravity for the
+experimental range tested, and that the collision duration is independent of
+both the effective gravity and the collision velocity. No rebounds are observed
+in any of the experiments. Our low-gravity experimental results indicate that
+the transition from the quasi-static regime to the inertial regime occurs for
+impact energies two orders of magnitude smaller than in similar impact
+experiments under terrestrial gravity. The lower energy regime change may be
+due to the increased hydrodynamic drag of the surface material in our
+experiments, but may also support the notion that the quasi-static regime
+reduces as the effective gravity becomes lower.
+"
+25333,Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning,"  We formulate tracking as an online decision-making process, where a tracking
+agent must follow an object despite ambiguous image frames and a limited
+computational budget. Crucially, the agent must decide where to look in the
+upcoming frames, when to reinitialize because it believes the target has been
+lost, and when to update its appearance model for the tracked object. Such
+decisions are typically made heuristically. Instead, we propose to learn an
+optimal decision-making policy by formulating tracking as a partially
+observable decision-making process (POMDP). We learn policies with deep
+reinforcement learning algorithms that need supervision (a reward signal) only
+when the track has gone awry. We demonstrate that sparse rewards allow us to
+quickly train on massive datasets, several orders of magnitude more than past
+work. Interestingly, by treating the data source of Internet videos as
+unlimited streams, we both learn and evaluate our trackers in a single, unified
+computational stream.
+"
+25334,Direct numerical simulation of particulate flows with an overset grid method,"  We evaluate an efficient overset grid method for two-dimensional and
+three-dimensional particulate flows for small numbers of particles at finite
+Reynolds number. The rigid particles are discretised using moving overset grids
+overlaid on a Cartesian background grid. This allows for strongly-enforced
+boundary conditions and local grid refinement at particle surfaces, thereby
+accurately capturing the viscous boundary layer at modest computational cost.
+The incompressible Navier--Stokes equations are solved with a fractional-step
+scheme which is second-order-accurate in space and time, while the fluid--solid
+coupling is achieved with a partitioned approach including multiple
+sub-iterations to increase stability for light, rigid bodies. Through a series
+of benchmark studies we demonstrate the accuracy and efficiency of this
+approach compared to other boundary conformal and static grid methods in the
+literature. In particular, we find that fully resolving boundary layers at
+particle surfaces is crucial to obtain accurate solutions to many common test
+cases. With our approach we are able to compute accurate solutions using as
+little as one third the number of grid points as uniform grid computations in
+the literature. A detailed convergence study shows a 13-fold decrease in CPU
+time over a uniform grid test case whilst maintaining comparable solution
+accuracy.
+"
+25335,Nil Hecke algebras and Whittaker D-modules,"  Given a reductive group G, Kostant and Kumar defined a nil Hecke algebra that
+may be viewed as a degenerate version of the double affine nil Hecke algebra
+introduced by Cherednik. In this paper, we construct an isomorphism of the
+spherical subalgebra of the nil Hecke algebra with a Whittaker type quantum
+Hamiltonian reduction of the algebra of differential operators on G. This
+result has an interpretation in terms of the geometric Satake and the Langlands
+dual group. Specifically, the isomorphism provides a bridge between very
+differently looking descriptions of equivariant Borel-Moore homology of the
+affine flag variety (due to Kostant and Kumar) and of the affine Grassmannian
+(due to Bezrukavnikov and Finkelberg), respectively.
+It follows from our result that the category of Whittaker D-modules on G
+considered by Drinfeld is equivalent to the category of holonomic modules over
+the nil Hecke algebra, and it is also equivalent to a certain subcategory of
+the category of Weyl group equivariant holonomic D-modules on the maximal
+torus.
+"
+25336,On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data,"  Empirical risk minimization (ERM), with proper loss function and
+regularization, is the common practice of supervised classification. In this
+paper, we study training arbitrary (from linear to deep) binary classifier from
+only unlabeled (U) data by ERM. We prove that it is impossible to estimate the
+risk of an arbitrary binary classifier in an unbiased manner given a single set
+of U data, but it becomes possible given two sets of U data with different
+class priors. These two facts answer a fundamental question---what the minimal
+supervision is for training any binary classifier from only U data. Following
+these findings, we propose an ERM-based learning method from two sets of U
+data, and then prove it is consistent. Experiments demonstrate the proposed
+method could train deep models and outperform state-of-the-art methods for
+learning from two sets of U data.
+"
+25337,Constellation Queries over Big Data,"  A geometrical pattern is a set of points with all pairwise distances (or,
+more generally, relative distances) specified. Finding matches to such patterns
+has applications to spatial data in seismic, astronomical, and transportation
+contexts. For example, a particularly interesting geometric pattern in
+astronomy is the Einstein cross, which is an astronomical phenomenon in which a
+single quasar is observed as four distinct sky objects (due to gravitational
+lensing) when captured by earth telescopes. Finding such crosses, as well as
+other geometric patterns, is a challenging problem as the potential number of
+sets of elements that compose shapes is exponentially large in the size of the
+dataset and the pattern. In this paper, we denote geometric patterns as
+constellation queries and propose algorithms to find them in large data
+applications. Our methods combine quadtrees, matrix multiplication, and
+unindexed join processing to discover sets of points that match a geometric
+pattern within some additive factor on the pairwise distances. Our distributed
+experiments show that the choice of composition algorithm (matrix
+multiplication or nested loops) depends on the freedom introduced in the query
+geometry through the distance additive factor. Three clearly identified blocks
+of threshold values guide the choice of the best composition algorithm.
+Finally, solving the problem for relative distances requires a novel
+continuous-to-discrete transformation. To the best of our knowledge this paper
+is the first to investigate constellation queries at scale.
+"
+25338,Machine learning vortices at the Kosterlitz-Thouless transition,"  Efficient and automated classification of phases from minimally processed
+data is one goal of machine learning in condensed matter and statistical
+physics. Supervised algorithms trained on raw samples of microstates can
+successfully detect conventional phase transitions via learning a bulk feature
+such as an order parameter. In this paper, we investigate whether neural
+networks can learn to classify phases based on topological defects. We address
+this question on the two-dimensional classical XY model which exhibits a
+Kosterlitz-Thouless transition. We find significant feature engineering of the
+raw spin states is required to convincingly claim that features of the vortex
+configurations are responsible for learning the transition temperature. We
+further show a single-layer network does not correctly classify the phases of
+the XY model, while a convolutional network easily performs classification by
+learning the global magnetization. Finally, we design a deep network capable of
+learning vortices without feature engineering. We demonstrate the detection of
+vortices does not necessarily result in the best classification accuracy,
+especially for lattices of less than approximately 1000 spins. For larger
+systems, it remains a difficult task to learn vortices.
+"
+25339,Diophantine equations in separated variables and lacunary polynomials,"  We study Diophantine equations of type $f(x)=g(y)$, where $f$ and $g$ are
+lacunary polynomials. According to a well known finiteness criterion, for a
+number field $K$ and nonconstant $f, g\in K[x]$, the equation $f(x)=g(y)$ has
+infinitely many solutions in $S$-integers $x, y$ only if $f$ and $g$ are
+representable as a functional composition of lower degree polynomials in a
+certain prescribed way. The behaviour of lacunary polynomials with respect to
+functional composition is a topic of independent interest, and has been studied
+by several authors. In this paper we utilize known results and develop some new
+results on the latter topic.
+"
+25340,Testing Conditional Independence of Discrete Distributions,"  We study the problem of testing \emph{conditional independence} for discrete
+distributions. Specifically, given samples from a discrete random variable $(X,
+Y, Z)$ on domain $[\ell_1]\times[\ell_2] \times [n]$, we want to distinguish,
+with probability at least $2/3$, between the case that $X$ and $Y$ are
+conditionally independent given $Z$ from the case that $(X, Y, Z)$ is
+$\epsilon$-far, in $\ell_1$-distance, from every distribution that has this
+property. Conditional independence is a concept of central importance in
+probability and statistics with a range of applications in various scientific
+domains. As such, the statistical task of testing conditional independence has
+been extensively studied in various forms within the statistics and
+econometrics communities for nearly a century. Perhaps surprisingly, this
+problem has not been previously considered in the framework of distribution
+property testing and in particular no tester with sublinear sample complexity
+is known, even for the important special case that the domains of $X$ and $Y$
+are binary.
+The main algorithmic result of this work is the first conditional
+independence tester with {\em sublinear} sample complexity for discrete
+distributions over $[\ell_1]\times[\ell_2] \times [n]$. To complement our upper
+bounds, we prove information-theoretic lower bounds establishing that the
+sample complexity of our algorithm is optimal, up to constant factors, for a
+number of settings. Specifically, for the prototypical setting when $\ell_1,
+\ell_2 = O(1)$, we show that the sample complexity of testing conditional
+independence (upper bound and matching lower bound) is
+\[
+\Theta\left({\max\left(n^{1/2}/\epsilon^2,\min\left(n^{7/8}/\epsilon,n^{6/7}/\epsilon^{8/7}\right)\right)}\right)\,.
+\]
+"
+25341,Wavelet analysis of Wave motion,"  In this paper high resolution wave probe records are examined using wavelet
+techniques with a view to determining the sources and relative contributions of
+capillary wave energy along representative wind wave forms. Wavelets enable
+computations of conditional spectra and turn out to be powerful tools for the
+study of the development and propagation of capillary waves. They also enable
+the detailed analyses of the relative contributions to the spectrum of the wave
+peaks and troughs.
+"
+25342,A Unified Method for Accurate Inference in Random-effects Meta-analysis,"  Random-effects meta-analyses have been widely applied in evidence synthesis
+for various types of medical studies to adequately address between-studies
+heterogeneity. However, standard inference methods for average treatment
+effects (e.g., restricted maximum likelihood estimation) usually underestimate
+statistical errors and possibly provide highly overconfident results under
+realistic situations; for instance, coverage probabilities of confidence
+intervals can be substantially below the nominal level. The main reason is that
+these inference methods rely on large sample approximations even though the
+number of synthesized studies is usually small or moderate in practice. In this
+article we solve this problem using a unified inference method based on the
+Monte Carlo conditioning method for broad application to random-effects
+meta-analysis. The developed method provides accurate confidence intervals with
+coverage probabilities that are almost the same as the nominal level. As
+specific applications, we provide accurate inference procedures for three types
+of meta-analysis: conventional univariate meta-analysis for pairwise treatment
+comparisons, meta-analysis of diagnostic test accuracy, and multiple treatment
+comparisons via network meta-analysis. We also illustrate the practical
+effectiveness of these methods via real data applications.
+"
+25343,Smoothing causal functions,"  We describe, in the general setting of closed cone fields, the set of causal
+functions which can be approximated by smooth Lyapunov. We derive several
+consequences on causality theory.
+Dans le contexte général des champs de cones fermés, on décrit
+l'ensemble des fonctions causales qui peuvent être approchées par des
+fonctions de Lyapunov lisses. On en déduit quelques conséquence en
+théorie de la causalité.
+"
+25344,Deconvolving RNA Base Pairing Signals,"  The structure of an RNA sequence encodes information about its biological
+function. A sequence is typically predicted to fold to a single minimum free
+energy conformation. But, an increasing number of RNA molecules are now known
+to fold into multiple stable structures. Discrete optimization methods are
+commonly used to predict foldings, and adding experimental data as auxiliary
+information improves prediction accuracy when there is a single dominant
+conformation. In this paper, we analyze the outputs of existing structural
+prediction models when they receive auxiliary data derived from a mixture of
+structures. Under a binary model of auxiliary data, we find that current
+structural prediction methods typically favor distributions with one dominant
+structure, and hence cannot guarantee accurate reconstruction of multimodal
+distributions. Additionally, we analyze empirical distributions of auxiliary
+data used in current prediction models. We show that even when the structures
+in a distribution are known in advance, it is difficult to determine the
+weightings of the structures using auxiliary data.
+"
+25345,Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter,"  This paper describes the Amobee sentiment analysis system, adapted to compete
+in SemEval 2017 task 4. The system consists of two parts: a supervised training
+of RNN models based on a Twitter sentiment treebank, and the use of feedforward
+NN, Naive Bayes and logistic regression classifiers to produce predictions for
+the different sub-tasks. The algorithm reached the 3rd place on the 5-label
+classification task (sub-task C).
+"
+25346,One Shot Joint Colocalization and Cosegmentation,"  This paper presents a novel framework in which image cosegmentation and
+colocalization are cast into a single optimization problem that integrates
+information from low level appearance cues with that of high level localization
+cues in a very weakly supervised manner. In contrast to multi-task learning
+paradigm that learns similar tasks using a shared representation, the proposed
+framework leverages two representations at different levels and simultaneously
+discriminates between foreground and background at the bounding box and
+superpixel level using discriminative clustering. We show empirically that
+constraining the two problems at different scales enables the transfer of
+semantic localization cues to improve cosegmentation output whereas local
+appearance based segmentation cues help colocalization. The unified framework
+outperforms strong baseline approaches, of learning the two problems
+separately, by a large margin on four benchmark datasets. Furthermore, it
+obtains competitive results compared to the state of the art for cosegmentation
+on two benchmark datasets and second best result for colocalization on Pascal
+VOC 2007.
+"
+25347,Interpreting Convolutional Neural Networks Through Compression,"  Convolutional neural networks (CNNs) achieve state-of-the-art performance in
+a wide variety of tasks in computer vision. However, interpreting CNNs still
+remains a challenge. This is mainly due to the large number of parameters in
+these networks. Here, we investigate the role of compression and particularly
+pruning filters in the interpretation of CNNs. We exploit our recently-proposed
+greedy structural compression scheme that prunes filters in a trained CNN. In
+our compression, the filter importance index is defined as the classification
+accuracy reduction (CAR) of the network after pruning that filter. The filters
+are then iteratively pruned based on the CAR index. We demonstrate the
+interpretability of CAR-compressed CNNs by showing that our algorithm prunes
+filters with visually redundant pattern selectivity. Specifically, we show the
+importance of shape-selective filters for object recognition, as opposed to
+color-selective filters. Out of top 20 CAR-pruned filters in AlexNet, 17 of
+them in the first layer and 14 of them in the second layer are color-selective
+filters. Finally, we introduce a variant of our CAR importance index that
+quantifies the importance of each image class to each CNN filter. We show that
+the most and the least important class labels present a meaningful
+interpretation of each filter that is consistent with the visualized pattern
+selectivity of that filter.
+"
+25348,Tunable artificial vortex ice in nanostructured superconductors with frustrated kagome lattice of paired antidots,"  Theoretical proposals for spin ice analogs based on nanostructured
+superconductors have suggested larger flexibility for probing the effects of
+fluctuations and disorder than in the magnetic systems. In this work, we unveil
+the particularities of a vortex ice system by direct observation of the vortex
+distribution in a kagome lattice of paired antidots using scanning Hall probe
+microscopy. The theoretically suggested vortex ice distribution, lacking long
+range order, is observed at half matching field (H_{1}/2). Moreover, the vortex
+ice state formed by the pinned vortices is still preserved at 2H_{1}/3. This
+unexpected result is attributed to the introduction of interstitial vortices at
+these magnetic field values. Although the interstitial vortices increase the
+number of possible vortex configurations, it is clearly shown that the vortex
+ice state observed at 2H_{1}/3 is less prone to defects than at $H_{1}/2$. In
+addition, the non-monotonic variations of the vortex ice quality on the lattice
+spacing indicates that a highly ordered vortex ice state cannot be attained by
+simply reducing the lattice spacing. The optimal design to observe defect free
+vortex ice is discussed based on the experimental statistics. The direct
+observations of a tunable vortex ice state provides new opportunities to
+explore the order-disorder transition in artificial ice systems.
+"
+25349,Learning with Biased Complementary Labels,"  In this paper, we study the classification problem in which we have access to
+easily obtainable surrogate for true labels, namely complementary labels, which
+specify classes that observations do \textbf{not} belong to. Let $Y$ and
+$\bar{Y}$ be the true and complementary labels, respectively. We first model
+the annotation of complementary labels via transition probabilities
+$P(\bar{Y}=i|Y=j), i\neq j\in\{1,\cdots,c\}$, where $c$ is the number of
+classes. Previous methods implicitly assume that $P(\bar{Y}=i|Y=j), \forall
+i\neq j$, are identical, which is not true in practice because humans are
+biased toward their own experience. For example, as shown in Figure 1, if an
+annotator is more familiar with monkeys than prairie dogs when providing
+complementary labels for meerkats, she is more likely to employ ""monkey"" as a
+complementary label. We therefore reason that the transition probabilities will
+be different. In this paper, we propose a framework that contributes three main
+innovations to learning with \textbf{biased} complementary labels: (1) It
+estimates transition probabilities with no bias. (2) It provides a general
+method to modify traditional loss functions and extends standard deep neural
+network classifiers to learn with biased complementary labels. (3) It
+theoretically ensures that the classifier learned with complementary labels
+converges to the optimal one learned with true labels. Comprehensive
+experiments on several benchmark datasets validate the superiority of our
+method to current state-of-the-art methods.
+"
+25350,"Data Assimilation in the Geosciences - An overview on methods, issues and perspectives","  We commonly refer to state-estimation theory in geosciences as data
+assimilation. This term encompasses the entire sequence of operations that,
+starting from the observations of a system, and from additional statistical and
+dynamical information (such as a dynamical evolution model), provides an
+estimate of its state. Data assimilation is standard practice in numerical
+weather prediction, but its application is becoming widespread in many other
+areas of climate, atmosphere, ocean and environment modeling; in all
+circumstances where one intends to estimate the state of a large dynamical
+system based on limited information. While the complexity of data assimilation,
+and of the methods thereof, stands on its interdisciplinary nature across
+statistics, dynamical systems and numerical optimization, when applied to
+geosciences an additional difficulty arises by the continually increasing
+sophistication of the environmental models. Thus, in spite of data assimilation
+being nowadays ubiquitous in geosciences, it has so far remained a topic mostly
+reserved to experts. We aim this overview article at geoscientists with a
+background in mathematical and physical modeling, who are interested in the
+rapid development of data assimilation and its growing domains of application
+in environmental science, but so far have not delved into its conceptual and
+methodological complexities.
+"
+25351,Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks,"  By lifting the ReLU function into a higher dimensional space, we develop a
+smooth multi-convex formulation for training feed-forward deep neural networks
+(DNNs). This allows us to develop a block coordinate descent (BCD) training
+algorithm consisting of a sequence of numerically well-behaved convex
+optimizations. Using ideas from proximal point methods in convex analysis, we
+prove that this BCD algorithm will converge globally to a stationary point with
+R-linear convergence rate of order one. In experiments with the MNIST database,
+DNNs trained with this BCD algorithm consistently yielded better test-set error
+rates than identical DNN architectures trained via all the stochastic gradient
+descent (SGD) variants in the Caffe toolbox.
+"
+25352,Deep brain fluorescence imaging with minimally invasive ultra-thin optical fibers,"  A major open challenge in neuroscience is the ability to measure and perturb
+neural activity in vivo from well-defined neural sub-populations at cellular
+resolution anywhere in the brain. However, limitations posed by scattering and
+absorption prohibit non-invasive (surface) multiphoton approaches for deep
+(>2mm) structures, while Gradient Refreactive Index (GRIN) endoscopes are thick
+and cause significant damage upon insertion. Here, we demonstrate a novel
+microendoscope to image neural activity at arbitrary depths via an ultrathin
+multimode optical fiber (MMF) probe that is 5-10X thinner than commercially
+available microendoscopes. We demonstrate micron-scale resolution,
+multispectral and volumetric imaging. In contrast to previous approaches, we
+show that this method has an improved acquisition speed that is sufficient to
+capture rapid neuronal dynamics in-vivo in rodents expressing a genetically
+encoded calcium indicator. Our results emphasize the potential of this
+technology in neuroscience applications and open up possibilities for cellular
+resolution imaging in previously unreachable brain regions.
+"
+25353,Spin Hall effect emerging from a chiral magnetic lattice without spin-orbit coupling,"  The spin Hall effect (SHE), which converts a charge current into a transverse
+spin current, has long been believed to be a phenomenon induced by the
+spin--orbit coupling. Here, we propose an alternative mechanism to realize the
+intrinsic SHE through a chiral magnetic structure that breaks the spin rotation
+symmetry. No spin--orbit coupling is needed even when the scalar spin chirality
+vanishes, different from the case of the topological Hall effect. In known
+chiral antiferromagnetic compounds Mn$_3X$ ($X=$ Ga, Ge, and Sn), for example,
+we indeed obtain large spin Hall conductivities based on \textit{ab initio}
+calculations. Apart further developing the conceptual understanding of the SHE,
+our work suggests an alternative strategy to design spin Hall materials without
+involving heavy elements, which may be advantageous for technological
+applications.
+"
+25354,The Stochastic complexity of spin models: Are pairwise models really simple?,"  Models can be simple for different reasons: because they yield a simple and
+computationally efficient interpretation of a generic dataset (e.g. in terms of
+pairwise dependences) - as in statistical learning - or because they capture
+the essential ingredients of a specific phenomenon - as e.g. in physics -
+leading to non-trivial falsifiable predictions. In information theory and
+Bayesian inference, the simplicity of a model is precisely quantified in the
+stochastic complexity, which measures the number of bits needed to encode its
+parameters. In order to understand how simple models look like, we study the
+stochastic complexity of spin models with interactions of arbitrary order. We
+highlight the existence of invariances with respect to bijections within the
+space of operators, which allow us to partition the space of all models into
+equivalence classes, in which models share the same complexity. We thus found
+that the complexity (or simplicity) of a model is not determined by the order
+of the interactions, but rather by their mutual arrangements. Models where
+statistical dependencies are localized on non-overlapping groups of few
+variables (and that afford predictions on independencies that are easy to
+falsify) are simple. On the contrary, fully connected pairwise models, which
+are often used in statistical learning, appear to be highly complex, because of
+their extended set of interactions.
+"
+25355,On the use of the autonomous Birkhoff equations in Lie series perturbation theory,"  In this article, we present the Lie transformation algorithm for autonomous
+Birkhoff systems. Here, we are referring to Hamiltonian systems that obey a
+symplectic structure of the general form. Two examples of normalization in the
+restricted three-body problem are given to illustrate the application of the
+algorithm in perturbation theory. The efficiency of this algorithm for problems
+of asymptotic integration in dynamics is discussed for the case where there is
+a need to use non-canonical variables in the phase space.
+"
+25356,A dressing method for soliton solutions of the Camassa-Holm equation,"  The soliton solutions of the Camassa-Holm equation are derived by the
+implementation of the dressing method. The form of the one and two soliton
+solutions coincides with the form obtained by other methods.
+"
+25357,Une $q$-déformation de la transformation de Bargmann polyanalytique,"  We introduce a $q$-analog of the polyanalytic Bargmann transform on
+$\mathbb{C}$.
+"
+25358,Arc-swift: A Novel Transition System for Dependency Parsing,"  Transition-based dependency parsers often need sequences of local shift and
+reduce operations to produce certain attachments. Correct individual decisions
+hence require global information about the sentence context and mistakes cause
+error propagation. This paper proposes a novel transition system, arc-swift,
+that enables direct attachments between tokens farther apart with a single
+transition. This allows the parser to leverage lexical information more
+directly in transition decisions. Hence, arc-swift can achieve significantly
+better performance with a very small beam size. Our parsers reduce error by
+3.7--7.6% relative to those using existing transition systems on the Penn
+Treebank dependency parsing task and English Universal Dependencies.
+"
+25359,Parameter Estimation of absolute continuous four parameter Geometric Marshall-Olkin bivariate Pareto Distribution,"  In this paper we formulate a four parameter absolute continuous Geometric
+Marshall-Olkin bivariate Pareto distribution and study its parameter estimation
+through EM algorithm and also explore the bayesian analysis through slice cum
+Gibbs sampler approach. Numerical results are shown to verify the performance
+of the algorithms. We illustrate the procedures through a real life data
+analysis.
+"
+25360,Conjunctive Queries on Probabilistic Graphs: Combined Complexity,"  Query evaluation over probabilistic databases is known to be intractable in
+many cases, even in data complexity, i.e., when the query is fixed. Although
+some restrictions of the queries [19] and instances [4] have been proposed to
+lower the complexity, these known tractable cases usually do not apply to
+combined complexity, i.e., when the query is not fixed. This leaves open the
+question of which query and instance languages ensure the tractability of
+probabilistic query evaluation in combined complexity.
+This paper proposes the first general study of the combined complexity of
+conjunctive query evaluation on probabilistic instances over binary signatures,
+which we can alternatively phrase as a probabilistic version of the graph
+homomorphism problem, or of a constraint satisfaction problem (CSP) variant. We
+study the complexity of this problem depending on whether instances and queries
+can use features such as edge labels, disconnectedness, branching, and edges in
+both directions. We show that the complexity landscape is surprisingly rich,
+using a variety of technical tools: automata-based compilation to d-DNNF
+lineages as in [4], \b{eta}-acyclic lineages using [10], the X-property for
+tractable CSP from [24], graded DAGs [27] and various coding techniques for
+hardness proofs.
+"
+25361,"The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions","  Whereas it is believed that techniques such as Adam, batch normalization and,
+more recently, SeLU nonlinearities ""solve"" the exploding gradient problem, we
+show that this is not the case in general and that in a range of popular MLP
+architectures, exploding gradients exist and that they limit the depth to which
+networks can be effectively trained, both in theory and in practice. We explain
+why exploding gradients occur and highlight the *collapsing domain problem*,
+which can arise in architectures that avoid exploding gradients.
+ResNets have significantly lower gradients and thus can circumvent the
+exploding gradient problem, enabling the effective training of much deeper
+networks. We show this is a direct consequence of the Pythagorean equation. By
+noticing that *any neural network is a residual network*, we devise the
+*residual trick*, which reveals that introducing skip connections simplifies
+the network mathematically, and that this simplicity may be the major cause for
+their success.
+"
+25362,Counterexamples to conjectures about Subset Takeaway and counting linear extensions of a Boolean lattice,"  We develop an algorithm for efficiently computing recursively defined
+functions on posets. We illustrate this algorithm by disproving conjectures
+about the game Subset Takeaway (Chomp on a hypercube) and computing the number
+of linear extensions of the lattice of a 7-cube and related lattices.
+"
+25363,Deep Lidar CNN to Understand the Dynamics of Moving Vehicles,"  Perception technologies in Autonomous Driving are experiencing their golden
+age due to the advances in Deep Learning. Yet, most of these systems rely on
+the semantically rich information of RGB images. Deep Learning solutions
+applied to the data of other sensors typically mounted on autonomous cars (e.g.
+lidars or radars) are not explored much. In this paper we propose a novel
+solution to understand the dynamics of moving vehicles of the scene from only
+lidar information. The main challenge of this problem stems from the fact that
+we need to disambiguate the proprio-motion of the 'observer' vehicle from that
+of the external 'observed' vehicles. For this purpose, we devise a CNN
+architecture which at testing time is fed with pairs of consecutive lidar
+scans. However, in order to properly learn the parameters of this network,
+during training we introduce a series of so-called pretext tasks which also
+leverage on image data. These tasks include semantic information about
+vehicleness and a novel lidar-flow feature which combines standard image-based
+optical flow with lidar scans. We obtain very promising results and show that
+including distilled image information only during training, allows improving
+the inference results of the network at test time, even when image data is no
+longer used.
+"
+25364,Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise,"  We study the robustness of classifiers to various kinds of random noise
+models. In particular, we consider noise drawn uniformly from the $\ell\_p$
+ball for $p \in [1, \infty]$ and Gaussian noise with an arbitrary covariance
+matrix. We characterize this robustness to random noise in terms of the
+distance to the decision boundary of the classifier. This analysis applies to
+linear classifiers as well as classifiers with locally approximately flat
+decision boundaries, a condition which is satisfied by state-of-the-art deep
+neural networks. The predicted robustness is verified experimentally.
+"
+25365,Spatially periodic instantons: Nahm transform and moduli,"  This paper establishes that the Nahm transform sending spatially periodic
+instantons (instantons on the product of the real line and a three-torus) to
+singular monopoles on the dual three-torus is indeed a bijection as suggested
+by the heuristic. In the process, we show how the Nahm transform intertwines to
+a Fourier-Mukai transform via Kobayashi-Hitchin correspondences. We also prove
+existence and non-existence results.
+"
+25366,Primary gamma ray selection in a hybrid timing/imaging Cherenkov array,"  This work is a methodical study on hybrid reconstruction techniques for
+hybrid imaging/timing Cherenkov observations. This type of hybrid array is to
+be realized at the gamma-observatory TAIGA intended for very high energy
+gamma-ray astronomy (>30 TeV). It aims at combining the cost-effective
+timing-array technique with imaging telescopes. Hybrid operation of both of
+these techniques can lead to a relatively cheap way of development of a large
+area array. The joint approach of gamma event selection was investigated on
+both types of simulated data: the image parameters from the telescopes, and the
+shower parameters reconstructed from the timing array. The optimal set of
+imaging parameters and shower parameters to be combined is revealed. The cosmic
+ray background suppression factor depending on distance and energy is
+calculated. The optimal selection technique leads to cosmic ray background
+suppression of about 2 orders of magnitude on distances up to 450 m for
+energies greater than 50 TeV.
+"
+25367,Tilt Assembly: Algorithms for Micro-Factories That Build Objects with Uniform External Forces,"  We present algorithmic results for the parallel assembly of many micro-scale
+objects in two and three dimensions from tiny particles, which has been
+proposed in the context of programmable matter and self-assembly for building
+high-yield micro-factories. The underlying model has particles moving under the
+influence of uniform external forces until they hit an obstacle; particles can
+bond when being forced together with another appropriate particle. Due to the
+physical and geometric constraints, not all shapes can be built in this manner;
+this gives rise to the Tilt Assembly Problem (TAP) of deciding
+constructibility. For simply-connected polyominoes $P$ in 2D consisting of $N$
+unit-squares (""tiles""), we prove that TAP can be decided in $O(N\log N)$ time.
+For the optimization variant MaxTAP (in which the objective is to construct a
+subshape of maximum possible size), we show polyAPX-hardness: unless P=NP,
+MaxTAP cannot be approximated within a factor of $\Omega(N^{\frac{1}{3}})$; for
+tree-shaped structures, we give an $O(N^{\frac{1}{2}})$-approximation
+algorithm. For the efficiency of the assembly process itself, we show that any
+constructible shape allows pipelined assembly, which produces copies of $P$ in
+$O(1)$ amortized time, i.e., $N$ copies of $P$ in $O(N)$ time steps. These
+considerations can be extended to three-dimensional objects: For the class of
+polycubes $P$ we prove that it is NP-hard to decide whether it is possible to
+construct a path between two points of $P$; it is also NP-hard to decide
+constructibility of a polycube $P$. Moreover, it is expAPX-hard to maximize a
+path from a given start point.
+"
+25368,Detection of Spatiotemporally Coherent Rainfall Anomalies Using Markov Random Fields,"  Precipitation is a large-scale, spatio-temporally heterogeneous phenomenon,
+with frequent anomalies exhibiting unusually high or low values. We use Markov
+Random Fields (MRFs) to detect spatio-temporally coherent anomalies in gridded
+annual rainfall data across India from 1901-2005. MRFs are undirected graphical
+models where each node is associated with a \{location,year\} pair, with edges
+connecting nodes representing adjacent locations or years. Some nodes represent
+observations of precipitation, while the rest represent unobserved
+(\emph{latent}) states that can take one of three values: high/low/normal. The
+MRF represents a probability distribution over the variables, using \emph{node
+potential} and \emph{edge potential} functions defined on nodes and edges of
+the graph. Optimal values of latent state variables are estimated by maximizing
+the posterior probability of the observations, using Gibbs sampling. Edge
+potentials enforce spatial and temporal coherence, and node potentials
+influence threshold for anomalies by affecting the prior probabilities of the
+states. The model can be tuned to recover anomalies detected by threshold-based
+methods. The competing influences of spatial and temporal coherence can be
+adjusted through edge potentials.
+We study spatio-temporal properties of rainfall anomalies discovered by this
+method, using suitable measures. We identify nonstationarities in occurrence of
+positive and negative anomalies between the first and second halves of the 20th
+century. We find that between these periods, there has been decrease in
+rainfall during June-September (JJAS) and an increase during other months.
+These effects are highlighted prominently in the statistics of anomalies.
+Properties of anomalies learnt from this approach could present tests of
+regional-scale rainfall simulations by climate models and statistical
+simulators.
+"
+25369,"The discrete yet ubiquitous theorems of Carathéodory, Helly, Sperner, Tucker, and Tverberg","  We discuss five discrete results: the lemmas of Sperner and Tucker from
+combinatorial topology and the theorems of Carathéodory, Helly, and Tverberg
+from combinatorial geometry. We explore their connections and emphasize their
+broad impact in application areas such as game theory, graph theory,
+mathematical optimization, computational geometry, etc.
+"
+25370,A Generalized Multinomial Distribution from Dependent Categorical Random Variables,"  Categorical random variables are a common staple in machine learning methods
+and other applications across disciplines. Many times, correlation within
+categorical predictors exists, and has been noted to have an effect on various
+algorithm effectiveness, such as feature ranking and random forests. We present
+a mathematical construction of a sequence of identically distributed but
+dependent categorical random variables, and give a generalized multinomial
+distribution to model the probability of counts of such variables.
+"
+25371,Symbolic vs. Bounded Synthesis for Petri Games,"  Petri games are a multiplayer game model for the automatic synthesis of
+distributed systems. We compare two fundamentally different approaches for
+solving Petri games. The symbolic approach decides the existence of a winning
+strategy via a reduction to a two-player game over a finite graph, which in
+turn is solved by a fixed point iteration based on binary decision diagrams
+(BDDs). The bounded synthesis approach encodes the existence of a winning
+strategy, up to a given bound on the size of the strategy, as a quantified
+Boolean formula (QBF). In this paper, we report on initial experience with a
+prototype implementation of the bounded synthesis approach. We compare bounded
+synthesis to the existing implementation of the symbolic approach in the
+synthesis tool ADAM. We present experimental results on a collection of
+benchmarks, including one new benchmark family, modeling manufacturing and
+workflow scenarios with multiple concurrent processes.
+"
+25372,Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive Account,"  The Availability bias, manifested in the over-representation of extreme
+eventualities in decision-making, is a well-known cognitive bias, and is
+generally taken as evidence of human irrationality. In this work, we present
+the first rational, metacognitive account of the Availability bias, formally
+articulated at Marr's algorithmic level of analysis. Concretely, we present a
+normative, metacognitive model of how a cognitive system should over-represent
+extreme eventualities, depending on the amount of time available at its
+disposal for decision-making. Our model also accounts for two well-known
+framing effects in human decision-making under risk---the fourfold pattern of
+risk preferences in outcome probability (Tversky & Kahneman, 1992) and in
+outcome magnitude (Markovitz, 1952)---thereby providing the first
+metacognitively-rational basis for those effects. Empirical evidence,
+furthermore, confirms an important prediction of our model. Surprisingly, our
+model is unimaginably robust with respect to its focal parameter. We discuss
+the implications of our work for studies on human decision-making, and conclude
+by presenting a counterintuitive prediction of our model, which, if confirmed,
+would have intriguing implications for human decision-making under risk. To our
+knowledge, our model is the first metacognitive, resource-rational process
+model of cognitive biases in decision-making.
+"
+25373,Stratified Splitting for Efficient Monte Carlo Integration,"  The efficient evaluation of high-dimensional integrals is of importance in
+both theoretical and practical fields of science, such as data science,
+statistical physics, and machine learning. However, exact computation methods
+suffer from the curse of dimensionality. However, due to the curse of
+dimensionality, deterministic numerical methods are inefficient in
+high-dimensional settings. Consequentially, for many practical problems, one
+must resort to Monte Carlo estimation. In this paper, we introduce a novel
+Sequential Monte Carlo technique called Stratified Splitting. The method
+provides unbiased estimates and can handle various integrand types including
+indicator functions, which are used in rare-event probability estimation
+problems. Moreover, we demonstrate that a variant of the algorithm can achieve
+polynomial complexity. The results of our numerical experiments suggest that
+the Stratified Splitting method is capable of delivering accurate results for a
+variety of integration problems.
+"
+25374,Data Poisoning Attacks in Contextual Bandits,"  We study offline data poisoning attacks in contextual bandits, a class of
+reinforcement learning problems with important applications in online
+recommendation and adaptive medical treatment, among others. We provide a
+general attack framework based on convex optimization and show that by slightly
+manipulating rewards in the data, an attacker can force the bandit algorithm to
+pull a target arm for a target contextual vector. The target arm and target
+contextual vector are both chosen by the attacker. That is, the attacker can
+hijack the behavior of a contextual bandit. We also investigate the feasibility
+and the side effects of such attacks, and identify future directions for
+defense. Experiments on both synthetic and real-world data demonstrate the
+efficiency of the attack algorithm.
+"
+25375,Interference between two resonant transitions with distinct initial and final states connected by radiative decay,"  The resonant line shape from driving a transition between two states,
+$|\rm{a}\rangle$ and $|\rm{b}\rangle$, can be distorted due to a
+quantum-mechanical interference effect involving a resonance between two
+different states, $|\rm{c}\rangle$ and $|\rm{d}\rangle$, if $|\rm{c}\rangle$
+has a decay path to $|\rm{a}\rangle$ and $|\rm{d}\rangle$ has a decay path to
+$|\rm{b}\rangle$. This interference can cause a shift of the measured
+resonance, despite the fact that the two resonances do not have a common
+initial or final state. As an example, we demonstrate that such a shift affects
+measurements of the atomic hydrogen 2S$_{1/2}$-to-2P$_{1/2}$ Lamb-shift
+transition due to 3S-to-3P transitions if the 3S$_{1/2}$ state has some initial
+population.
+"
+25376,Rational Right Triangles of a Given Area,"  Starting from any given rational-sided, right triangle, for example the
+$(3,4,5)$-triangle with area $6$, we use Euclidean geometry to show that there
+are infinitely many other rational-sided, right triangles of the same area. We
+show further that the set of all such triangles of a given area is finitely
+generated under our geometric construction. Such areas are known as ""congruent
+numbers"" and have a rich history in which all the results in this article have
+been proved and far more. Yet, as far as we can tell, this seems to be the
+first exploration using this kind of geometric technique.
+"
+25377,DARLA: Improving Zero-Shot Transfer in Reinforcement Learning,"  Domain adaptation is an important open problem in deep reinforcement learning
+(RL). In many scenarios of interest data is hard to obtain, so agents may learn
+a source policy in a setting where data is readily available, with the hope
+that it generalises well to the target domain. We propose a new multi-stage RL
+agent, DARLA (DisentAngled Representation Learning Agent), which learns to see
+before learning to act. DARLA's vision is based on learning a disentangled
+representation of the observed environment. Once DARLA can see, it is able to
+acquire source policies that are robust to many domain shifts - even with no
+access to the target domain. DARLA significantly outperforms conventional
+baselines in zero-shot domain adaptation scenarios, an effect that holds across
+a variety of RL environments (Jaco arm, DeepMind Lab) and base RL algorithms
+(DQN, A3C and EC).
+"
+25378,Synthetic Data for Social Good,"  Data for good implies unfettered access to data. But data owners must be
+conservative about how, when, and why they share data or risk violating the
+trust of the people they aim to help, losing their funding, or breaking the
+law. Data sharing agreements can help prevent privacy violations, but require a
+level of specificity that is premature during preliminary discussions, and can
+take over a year to establish.
+We consider the generation and use of synthetic data to facilitate ad hoc
+collaborations involving sensitive data. A good synthetic dataset has two
+properties: it is representative of the original data, and it provides strong
+guarantees about privacy.
+In this paper, we discuss important use cases for synthetic data that
+challenge the state of the art in privacy-preserving data generation, and
+describe DataSynthesizer, a dataset generation tool that takes a sensitive
+dataset as input and generates a structurally and statistically similar
+synthetic dataset, with strong privacy guarantees, as output. The data owners
+need not release their data, while potential collaborators can begin developing
+models and methods with some confidence that their results will work similarly
+on the real dataset. The distinguishing feature of DataSynthesizer is its
+usability - in most cases, the data owner need not specify any parameters to
+start generating and sharing data safely and effectively.
+The code implementing DataSynthesizer is publicly available on GitHub at
+this https URL. The work on DataSynthesizer is part of the
+Data, Responsibly project, where the goal is to operationalize responsibility
+in data sharing, integration, analysis and use.
+"
+25379,Radio Exploration of Planetary Habitability: Conference Summary,"  Radio Exploration of Planetary Habitability was the fifth in the series of
+American Astronomical Society's Topical Conference Series. Notable aspects of
+the conference included the interdisciplinary nature of both the topics and the
+intellectual breadth of the participants, the diversity of approaches to
+studying this topic presented by recent discoveries and of the participants
+themselves, the expanding meaning of the topic of ""star-planet interactions,""
+and the expectation of an increasingly statistical approach to the topic.
+Potential areas of future research include the actual extent to which planetary
+magnetic fields shield planetary atmospheres; the planetary dynamo process
+itself, particularly once multiple extrasolar planetary magnetic fields are
+confirmed; and ""planet-star interactions."" A major major topic of the
+conference concerned observational opportunities, highlighted by a number of
+new or upcoming, specialized observatories to observe exoplanets especially at
+radio wavelengths. This article summarizes these main points of the conference
+and expands briefly upon these potential avenues for future investigation. A
+future meeting on this topic, given the variety of data sets being generated
+over the next few years, is warranted.
+"
+25380,Homogeneous rank one perturbations and inverse square potentials,"  Following [D,BDG,DR], I describe several exactly solvable families of closed
+operators. Some of these families are defined by the theory of singular rank
+one perturbations. The remaining families are Schrodinger operators with
+inverse square potentials and various boundary conditions. I describe a close
+relationship between these families. In all of them one can observe interesting
+renormalization group flows (action of the group of dilations).
+"
+25381,TVAE: Triplet-Based Variational Autoencoder using Metric Learning,"  Deep metric learning has been demonstrated to be highly effective in learning
+semantic representation and encoding information that can be used to measure
+data similarity, by relying on the embedding learned from metric learning. At
+the same time, variational autoencoder (VAE) has widely been used to
+approximate inference and proved to have a good performance for directed
+probabilistic models. However, for traditional VAE, the data label or feature
+information are intractable. Similarly, traditional representation learning
+approaches fail to represent many salient aspects of the data. In this project,
+we propose a novel integrated framework to learn latent embedding in VAE by
+incorporating deep metric learning. The features are learned by optimizing a
+triplet loss on the mean vectors of VAE in conjunction with standard evidence
+lower bound (ELBO) of VAE. This approach, which we call Triplet based
+Variational Autoencoder (TVAE), allows us to capture more fine-grained
+information in the latent embedding. Our model is tested on MNIST data set and
+achieves a high triplet accuracy of 95.60% while the traditional VAE (Kingma &
+Welling, 2013) achieves triplet accuracy of 75.08%.
+"
+25382,Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics,"  We introduce a scheme for molecular simulations, the Deep Potential Molecular
+Dynamics (DeePMD) method, based on a many-body potential and interatomic forces
+generated by a carefully crafted deep neural network trained with ab initio
+data. The neural network model preserves all the natural symmetries in the
+problem. It is ""first principle-based"" in the sense that there are no ad hoc
+components aside from the network model. We show that the proposed scheme
+provides an efficient and accurate protocol in a variety of systems, including
+bulk materials and molecules. In all these cases, DeePMD gives results that are
+essentially indistinguishable from the original data, at a cost that scales
+linearly with system size.
+"
+25383,A Complete Year of User Retrieval Sessions in a Social Sciences Academic Search Engine,"  In this paper, we present an open data set extracted from the transaction log
+of the social sciences academic search engine sowiport. The data set includes a
+filtered set of 484,449 retrieval sessions which have been carried out by
+sowiport users in the period from April 2014 to April 2015. We propose a
+description of interactions performed by the academic search engine users that
+can be used in different applications such as result ranking improvement, user
+modeling, query reformulation analysis, search pattern recognition.
+"
+25384,On the Contribution of Discourse Structure on Text Complexity Assessment,"  This paper investigates the influence of discourse features on text
+complexity assessment. To do so, we created two data sets based on the Penn
+Discourse Treebank and the Simple English Wikipedia corpora and compared the
+influence of coherence, cohesion, surface, lexical and syntactic features to
+assess text complexity.
+Results show that with both data sets coherence features are more correlated
+to text complexity than the other types of features. In addition, feature
+selection revealed that with both data sets the top most discriminating feature
+is a coherence feature.
+"
+25385,Development of Multi-Chroic MKIDs for Next-Generation CMB Polarization Studies,"  We report on the status of an ongoing effort to develop arrays of
+horn-coupled, polarization-sensitive microwave kinetic inductance detectors
+(MKIDs) that are each sensitive to two spectral bands between 125 and 280 GHz.
+These multi-chroic MKID arrays are tailored for next-generation,
+large-detector-count experiments that are being designed to simultaneously
+characterize the polarization properties of both the cosmic microwave
+background (CMB) and Galactic dust emission. We present our device design and
+describe laboratory-based measurement results from two 23-element prototype
+arrays. From dark measurements of our first engineering array we demonstrated a
+multiplexing factor of 92, showed the resonators respond to bath temperature
+changes as expected, and found that the fabrication yield was 100%. From our
+first optically loaded array we found the MKIDs respond to millimeter-wave
+pulses, additional optical characterization measurements are ongoing. We end by
+discussing our plans for scaling up this technology to kilo-pixel arrays over
+the next two years.
+"
+25386,Study and Analysis of MAC/IPAD Lab Configuration,"  This paper is about three virtualization modes: VMware, Parallels, and Boot
+Camping. The trade off of their testing is the hardware requirements. The main
+question is, among the three, which is the most suitable? The answer actually
+varies from user to user. It depends on the user needs. Moreover, it is
+necessary to consider its performance, graphics, efficiency and reliability,
+and interoperability, and that is our major scope. In order to take the final
+decision in choosing one of the modes it is important to run some tests, which
+costs a lot in terms of money, complexity, and time consumption. Therefore, in
+order to overcome this trade off, most of the research has been done through
+online benchmarking and my own anticipation. The final solution was extracted
+after comparing all previously mentioned above and after rigorous testing made
+which will be introduced later in this document.
+"
+25387,"Critique of ""Asynchronous Logic Implementation Based on Factorized DIMS""","  This paper comments on ""Asynchronous Logic Implementation Based on Factorized
+DIMS"" [Journal of Circuits, Systems, and Computers, vol. 26, no. 5, 1750087:
+1-9, May 2017] with respect to two main problematic issues: i) the gate orphan
+problem implicit in the factorized DIMS approach discussed in the referenced
+article which affects its strong-indication, and ii) how the enumeration of
+product terms to represent the synthesis cost is skewed in the referenced
+article because the logic expression contains sum of products and also product
+of sums. It is observed that the referenced article has not provided a general
+logic synthesis algorithm excepting only an example illustration involving a
+3-input AND logic function. The absence of a general logic synthesis algorithm
+would make it difficult to reproduce the research described in the referenced
+article. Moreover, the example illustration in the referenced article describes
+an unsafe logic decomposition which is not suitable for the multi-level
+synthesis of strong-indication asynchronous circuits. Further, a logic
+synthesis method which safely decomposes the DIMS solution to synthesize
+multi-level strong-indication asynchronous circuits is available in the
+existing literature, which was neither cited nor taken up for comparison in the
+referenced article, which is another drawback. Subsequently, it is concluded
+that the referenced article has not advanced existing knowledge in the field
+but on the contrary, has caused confusions. Hence, in the interest of readers,
+this paper additionally highlights some important and relevant literature which
+provide valuable information about robust asynchronous circuit synthesis
+techniques which employ delay-insensitive codes for data representation and
+processing and the 4-phase return-to-zero handshake protocol for data
+communication.
+"
+25388,The statistical physics of active matter: from self-catalytic colloids to living cells,"  These lecture notes are designed to provide a brief introduction into the
+phenomenology of active matter and to present some of the analytical tools used
+to rationalize the emergent behavior of active systems. Such systems are made
+of interacting agents able to extract energy stored in the environment to
+produce sustained directed motion. The local conversion of energy into
+mechanical work drives the system far from equilibrium, yielding new dynamics
+and phases. The emerging phenomena can be classified depending on the symmetry
+of the active particles and on the type of microscopic interactions. We focus
+here on steric and aligning interactions, as well as interactions driven by
+shape changes. The models that we present are all inspired by experimental
+realizations of either synthetic, biomimetic or living systems. Based on
+minimal ingredients, they are meant to bring a simple and synthetic
+understanding of the complex phenomenology of active matter.
+"
+25389,News-based trading strategies,"  The marvel of markets lies in the fact that dispersed information is
+instantaneously processed and used to adjust the price of goods, services and
+assets. Financial markets are particularly efficient when it comes to
+processing information; such information is typically embedded in textual news
+that is then interpreted by investors. Quite recently, researchers have started
+to automatically determine news sentiment in order to explain stock price
+movements. Interestingly, this so-called news sentiment works fairly well in
+explaining stock returns. In this paper, we design trading strategies that
+utilize textual news in order to obtain profits on the basis of novel
+information entering the market. We thus propose approaches for automated
+decision-making based on supervised and reinforcement learning. Altogether, we
+demonstrate how news-based data can be incorporated into an investment system.
+"
+25390,A Very Fast And Angular Momentum Conserving Tree Code,"  There are many methods used to compute the classical gravitational field in
+astrophysical simulation codes. With the exception of the typically impractical
+method of direct computation, none ensure conservation of angular momentum to
+machine precision. Under uniform time-stepping, the Cartesian fast multipole
+method of Dehnen (also known as the very fast tree code) conserves linear
+momentum to machine precision. We show it is possible to modify this method in
+a way that conserves both angular and linear momenta.
+"
+25391,Towards A Rigorous Science of Interpretable Machine Learning,"  As machine learning systems become ubiquitous, there has been a surge of
+interest in interpretable machine learning: systems that provide explanation
+for their outputs. These explanations are often used to qualitatively assess
+other criteria such as safety or non-discrimination. However, despite the
+interest in interpretability, there is very little consensus on what
+interpretable machine learning is and how it should be measured. In this
+position paper, we first define interpretability and describe when
+interpretability is needed (and when it is not). Next, we suggest a taxonomy
+for rigorous evaluation and expose open questions towards a more rigorous
+science of interpretable machine learning.
+"
+25392,"Time, Frequency & Time-Varying Causality Measures in Neuroscience","  This article proposes a systematic methodological review and objective
+criticism of existing methods enabling the derivation of time-varying
+Granger-causality statistics in neuroscience. The increasing interest and the
+huge number of publications related to this topic calls for this systematic
+review which describes the very complex methodological aspects. The capacity to
+describe the causal links between signals recorded at different brain locations
+during a neuroscience experiment is of primary interest for neuroscientists,
+who often have very precise prior hypotheses about the relationships between
+recorded brain signals that arise at a specific time and in a specific
+frequency band. The ability to compute a time-varying frequency-specific
+causality statistic is therefore essential. Two steps are necessary to achieve
+this: the first consists of finding a statistic that can be interpreted and
+that directly answers the question of interest. The second concerns the model
+that underlies the causality statistic and that has this time-frequency
+specific causality interpretation. In this article, we will review
+Granger-causality statistics with their spectral and time-varying extensions.
+"
+25393,The path algebra as a left adjoint functor,"  We consider an intermediate category between the category of finite quivers
+and a certain category of pseudocompact associative algebras whose objects
+include all pointed finite dimensional algebras. We define the completed path
+algebra and the Gabriel quiver as functors. We give an explicit quotient of the
+category of algebras on which these functors form an adjoint pair. We show that
+these functors respect ideals, obtaining in this way an equivalence between
+related categories.
+"
+25394,Fair prediction with disparate impact: A study of bias in recidivism prediction instruments,"  Recidivism prediction instruments (RPI's) provide decision makers with an
+assessment of the likelihood that a criminal defendant will reoffend at a
+future point in time. While such instruments are gaining increasing popularity
+across the country, their use is attracting tremendous controversy. Much of the
+controversy concerns potential discriminatory bias in the risk assessments that
+are produced. This paper discusses several fairness criteria that have recently
+been applied to assess the fairness of recidivism prediction instruments. We
+demonstrate that the criteria cannot all be simultaneously satisfied when
+recidivism prevalence differs across groups. We then show how disparate impact
+can arise when a recidivism prediction instrument fails to satisfy the
+criterion of error rate balance.
+"
+25395,Proceedings 2nd International Workshop on Causal Reasoning for Embedded and safety-critical Systems Technologies,"  The second international CREST workshop continued the focus of the first
+CREST workshop: addressing approaches to causal reasoning in engineering
+complex embedded and safety-critical systems. Relevant approaches to causal
+reasoning have been (usually independently) proposed by a variety of
+communities: AI, concurrency, model-based diagnosis, software engineering,
+security engineering, and formal methods. The goal of CREST is to bring
+together researchers and practitioners from these communities to exchange
+ideas, especially between communities, in order to advance the science of
+determining root cause(s) for failures of critical systems. The growing
+complexity of failures such as power grid blackouts, airplane crashes, security
+and privacy violations, and malfunctioning medical devices or automotive
+systems makes the goals of CREST more relevant than ever before.
+"
+25396,A Generative Model for Volume Rendering,"  We present a technique to synthesize and analyze volume-rendered images using
+generative models. We use the Generative Adversarial Network (GAN) framework to
+compute a model from a large collection of volume renderings, conditioned on
+(1) viewpoint and (2) transfer functions for opacity and color. Our approach
+facilitates tasks for volume analysis that are challenging to achieve using
+existing rendering techniques such as ray casting or texture-based methods. We
+show how to guide the user in transfer function editing by quantifying expected
+change in the output image. Additionally, the generative model transforms
+transfer functions into a view-invariant latent space specifically designed to
+synthesize volume-rendered images. We use this space directly for rendering,
+enabling the user to explore the space of volume-rendered images. As our model
+is independent of the choice of volume rendering process, we show how to
+analyze volume-rendered images produced by direct and global illumination
+lighting, for a variety of volume datasets.
+"
+25397,Understanding Community Structure in Layered Neural Networks,"  A layered neural network is now one of the most common choices for the
+prediction of high-dimensional practical data sets, where the relationship
+between input and output data is complex and cannot be represented well by
+simple conventional models. Its effectiveness is shown in various tasks,
+however, the lack of interpretability of the trained result by a layered neural
+network has limited its application area.
+In our previous studies, we proposed methods for extracting a simplified
+global structure of a trained layered neural network by classifying the units
+into communities according to their connection patterns with adjacent layers.
+These methods provided us with knowledge about the strength of the relationship
+between communities from the existence of bundled connections, which are
+determined by threshold processing of the connection ratio between pairs of
+communities.
+However, it has been difficult to understand the role of each community
+quantitatively by observing the modular structure. We could only know to which
+sets of the input and output dimensions each community was mainly connected, by
+tracing the bundled connections from the community to the input and output
+layers. Another problem is that the finally obtained modular structure is
+changed greatly depending on the setting of the threshold hyperparameter used
+for determining bundled connections.
+In this paper, we propose a new method for interpreting quantitatively the
+role of each community in inference, by defining the effect of each input
+dimension on a community, and the effect of a community on each output
+dimension. We show experimentally that our proposed method can reveal the role
+of each part of a layered neural network by applying the neural networks to
+three types of data sets, extracting communities from the trained network, and
+applying the proposed method to the community structure.
+"
+25398,Spontaneous Beliaev-Landau scattering out of equilibrium,"  We investigate Beliaev-Landau scattering in a gas of interacting photons in a
+coherently driven array of nonlinear dissipative resonators, as described by
+the 1D driven-dissipative Bose-Hubbard model. Due to the absence of detailed
+balance in such an out-of-equilibrium setup, steady-state properties can be
+much more sensitive to the underlying microscopic dynamics. As the popular
+truncated Wigner approximation dramatically fails in capturing this physics, we
+present an alternative approach, based on a systematic expansion beyond the
+Bogoliubov approximation, which includes the third-order correlation functions
+in the dynamics. As experimentally accessible signatures of Beliaev-Landau
+processes, we report a small but nonnegligible correction to the Bogoliubov
+prediction for the steady-state momentum distribution showing a characteristic
+series of peaks and dips, as well as non-Gaussian features in the statistics of
+the cavity output field.
+"
+25399,Decay Rates of global weak solutions for the MHD equations in $\dot{\mbox{\boldmath{$H$}}}^{s}(\mathbb{R}^n)$,"  We show that $t^{s/2} \Vert (\mbox{\boldmath $u$},\mbox{\boldmath
+$b$})(.,t)\Vert_{\dot{H}(\mathbb{R}^{n})} \rightarrow 0,$ as $t\rightarrow
+\infty$ for Leray solutions $(\mbox{\boldmath $u$}, \mbox{\boldmath $b$})(.,t)$
+of the incompressible MHD equations, where $2 \leq n \leq 4$ and $s \geq 0.$ As
+a corollary of main result described previously we have also that
+$\lim_{t\rightarrow\infty} t^{\frac{n}{2} - \frac{n}{2q}} \Vert(\mbox{\boldmath
+$u$},\mbox{\boldmath $b$})(.,t)\Vert_{L^{q}(\mathbb{R}^{n})} = 0, 2\leq q\leq
+\infty.$
+"
+25400,Interbank Credit and the Money Manufacturing Process. A Systemic Perspective on Financial Stability,"  Interbank lending and borrowing occur when financial institutions seek to
+settle and refinance their mutual positions over time and circumstances. This
+interactive process involves money creation at the aggregate level.
+Coordination mismatch on interbank credit may trigger systemic crises. This
+happened when, since summer 2007, interbank credit coordination did not longer
+work smoothly across financial institutions, eventually requiring exceptional
+monetary policies by central banks, and guarantee and bailout interventions by
+governments. Our article develops an interacting heterogeneous agents-based
+model of interbank credit coordination under minimal institutions. First, we
+explore the link between interbank credit coordination and the money generation
+process. Contrary to received understanding, interbank credit has the capacity
+to make the monetary system unbound. Second, we develop simulation analysis on
+imperfect interbank credit coordination, studying impact of interbank dynamics
+on financial stability and resilience at individual and aggregate levels.
+Systemically destabilizing forces prove to be related to the working of the
+banking system over time, especially interbank coordination conditions and
+circumstances.
+"
+25401,A sharp oracle inequality for Graph-Slope,"  Following recent success on the analysis of the Slope estimator, we provide a
+sharp oracle inequality in term of prediction error for Graph-Slope, a
+generalization of Slope to signals observed over a graph. In addition to
+improving upon best results obtained so far for the Total Variation denoiser
+(also referred to as Graph-Lasso or Generalized Lasso), we propose an efficient
+algorithm to compute Graph-Slope. The proposed algorithm is obtained by
+applying the forward-backward method to the dual formulation of the Graph-Slope
+optimization problem. We also provide experiments showing the interest of the
+method.
+"
+25402,full-FORCE: A Target-Based Method for Training Recurrent Networks,"  Trained recurrent networks are powerful tools for modeling dynamic neural
+computations. We present a target-based method for modifying the full
+connectivity matrix of a recurrent network to train it to perform tasks
+involving temporally complex input/output transformations. The method
+introduces a second network during training to provide suitable ""target""
+dynamics useful for performing the task. Because it exploits the full recurrent
+connectivity, the method produces networks that perform tasks with fewer
+neurons and greater noise robustness than traditional least-squares (FORCE)
+approaches. In addition, we show how introducing additional input signals into
+the target-generating network, which act as task hints, greatly extends the
+range of tasks that can be learned and provides control over the complexity and
+nature of the dynamics of the trained, task-performing network.
+"
+25403,Duality of Floating and Illumination Bodies,"  We investigate a duality relation between floating and illumination bodies.
+The definitions of these two bodies suggest that the polar of the floating body
+should be similar to the illumination body of the polar. We consider this
+question for the class of centrally symmetric convex bodies. We provide precise
+estimates for $(B_p^n)$ and for centrally symmetric convex bodies with
+everywhere positive Gauss curvature. Our estimates show that equality of the
+polar of the floating body and the illumination body of the polar can only be
+achieved in the case of ellipsoids.
+"
+25404,The Top-Down Complexity,"  The rising complexity of our terrestrial surrounding is an empirical fact.
+Details of this process evaded description in terms of physics for long time
+attracting attention and creating myriad of ideas including non-scientific
+ones. In this essay we explain the phenomenon of the growth of complexity by
+combining our up to date understanding of cosmology, non-equilibrium physics
+and thermodynamics. We argue that the observed increase of complexity is causal
+in nature, stands in agreement with the second law of thermodynamics and has
+it's origin in the cosmological expansion. Moreover, we highlight the
+connection between the leader of complexity growth in localized areas of space
+with free energy rate density, starting from the largest scales towards the
+smaller ones. Finally, in the light of recent advances in non-equilibrium
+statistical mechanics, our belief in the causal structure of modern scientific
+theories is transferred to biological systems. On relevant scales, adaptation
+and complexity growth follows a similar pattern, in which free energy rate
+density is provided by an external electromagnetic radiation. The presented,
+holistic approach, arms us with predicting power about variety of attributes of
+complex systems and leads to a chain of successful explanations on all scales
+of the Universe.
+"
+25405,Dissociative ionization of the H${_2}$O molecule induced by medium energy singly charged projectiles,"  We report on the fragmentation of the water molecule by $1$ MeV H$^{+}$,
+He$^{+}$ and 650 keV N$^{+}$ ion impact. The fragment-ion energy spectra were
+measured by an electrostatic spectrometer at different observation angles. The
+obtained double-differential fragmentation cross sections for N$^{+}$ is found
+to be more than an order of magnitude higher, than that for H$^{+}$. The
+relative ratios of the fragmentation channels are also different for the three
+projectiles. Additional fragmentation channels were observed in the spectra for
+He$^{+}$ and for N$^{+}$ impact, which are missing in the case of H$^{+}$. From
+the analysis of the kinetic energy of the fragments, the maximum observed
+degree of ionization was found to be $q\rm{_{max}}=3$, $4$, and $5$ for
+H${^+}$, He${^+}$ and N${^+}$ impact, respectively. Absolute multiple
+ionization cross sections have been determined. They are compared with the
+predictions of the classical trajectory Monte Carlo and
+continuum-distorted-wave eikonal-initial-state theories. At lower degrees of
+ionization, theories provide reasonable agreement with experiment. The
+systematic overestimation of the cross section by the theories towards higher
+degrees of ionization indicates the failure of the independent particle model.
+"
+25406,Zero-sum stochastic differential game in finite horizon involving impulse controls,"  This paper considers the problem of two-player zero-sum stochastic
+differential game with both players adopting impulse controls in finite horizon
+under rather weak assumptions on the cost functions ($c$ and $\chi$ not
+decreasing in time). We use the dynamic programming principle and viscosity
+solutions approach to show existence and uniqueness of a solution for the
+Hamilton-Jacobi-Bellman-Isaacs (HJBI) partial differential equation (PDE) of
+the game. We prove that the upper and lower value functions coincide.
+"
+25407,Fishnet Statistics for Strength Scaling of Nacreous Imbricated Lamellar Materials,"  Similar to nacre or brick-and-mortar structures, imbricated lamellar
+structures are widely found in natural and man-made materials and are of
+interest for biomimetics. These structures are known to be rather insensitive
+to defects and to have a very high fracture toughness. Their deterministic
+behavior has been intensely studied, but statistical studies have been rare and
+is so far there is no undisputed theoretical basis for the probability
+distribution of strength of nacre. This paper presents a numerical and
+theoretical study of the PDF of strength and of the corresponding statistical
+size effect. After a reasonable simplifications of the shear bonds, an axially
+loaded lamellar shell is statistically modelled as a square fishnet pulled
+diagonally. A finite element (FE) model is developed and used in Monte Carlo
+simulations of strength. An analytical model for failure probability of the
+fishnet is developed and matched to the computed statistical histograms of
+strength for various sizes. It appears that, with increasing size, the pdf of
+strength slowly transits from Gaussian to Weilbull distribution but the
+transition is different from that previously obtained at Northwestern for
+quasibrittle materials of random heterogeneous mesostructure.
+"
+25408,A Generic Axiomatic Characterization of Centrality Measures in Social Network,"  Centrality is an important notion in complex networks; it could be used to
+characterize how influential a node or an edge is in the network. It plays an
+important role in several other network analysis tools including community
+detection. Even though there are a small number of axiomatic frameworks
+associated with this notion, the existing formalizations are not generic in
+nature. In this paper we propose a generic axiomatic framework to capture all
+the intrinsic properties of a centrality measure (a.k.a. centrality index). We
+analyze popular centrality measures along with other novel measures of
+centrality using this framework. We observed that none of the centrality
+measures considered satisfies all the axioms.
+"
+25409,"Making Terrestrial Planets: High Temperatures, FU Orionis Outbursts, Earth, and Planetary System Architectures","  Current protoplanetary dust coagulation theory does not predict dry silicate
+planetesimals, in tension with the Earth. While remedies to this predicament
+have been proposed, they have generally failed numerical studies, or are in
+tension with the Earth's (low, volatility dependent) volatile and moderately
+volatile elemental abundances. Expanding on the work of Boley et al. (2014), we
+examine the implications of molten grain collisions and find that they may
+provide a solution to the dry silicate planetesimal problem. Further, the
+source of the heating, be it the hot inner disk or an FU Orionis scale
+accretion event, would dictate the location of the resulting planetesimals,
+potentially controlling subsequent planetary system architectures. We
+hypothesize that systems which did undergo FU Orionis scale accretion events
+host planetary systems similar to our own, while ones that did not instead host
+very close in, tightly packed planets such as seen by Kepler.
+"
+25410,Iris: A Conversational Agent for Complex Tasks,"  Today's conversational agents are restricted to simple standalone commands.
+In this paper, we present Iris, an agent that draws on human conversational
+strategies to combine commands, allowing it to perform more complex tasks that
+it has not been explicitly designed to support: for example, composing one
+command to ""plot a histogram"" with another to first ""log-transform the data"".
+To enable this complexity, we introduce a domain specific language that
+transforms commands into automata that Iris can compose, sequence, and execute
+dynamically by interacting with a user through natural language, as well as a
+conversational type system that manages what kinds of commands can be combined.
+We have designed Iris to help users with data science tasks, a domain that
+requires support for command combination. In evaluation, we find that data
+scientists complete a predictive modeling task significantly faster (2.6 times
+speedup) with Iris than a modern non-conversational programming environment.
+Iris supports the same kinds of commands as today's agents, but empowers users
+to weave together these commands to accomplish complex goals.
+"
+25411,Infinite loop spaces and positive scalar curvature in the presence of a fundamental group,"  This is a continuation of our previous work with Botvinnik on the
+nontriviality of the secondary index invariant on spaces of metrics of positive
+scalar curvature, in which we take the fundamental group of the manifolds into
+account. We show that the secondary index invariant associated to the vanishing
+of the Rosenberg index can be highly nontrivial, for positive scalar curvature
+Spin manifolds with torsionfree fundamental groups which satisfy the
+Baum--Connes conjecture. For example, we produce a compact Spin 6-manifold such
+that its space of positive scalar curvature metrics has each rational homotopy
+group infinite dimensional.
+At a more technical level, we introduce the notion of ""stable metrics"" and
+prove a basic existence theorem for them, which generalises the Gromov--Lawson
+surgery technique, and we also give a method for rounding corners of manifold
+with positive scalar curvature metrics.
+"
+25412,Sequential Composition in the Presence of Intermediate Termination (Extended Abstract),"  The standard operational semantics of the sequential composition operator
+gives rise to unbounded branching and forgetfulness when transparent process
+expressions are put in sequence. Due to transparency, the correspondence
+between context-free and pushdown processes fails modulo bisimilarity, and it
+is not clear how to specify an always terminating half counter. We propose a
+revised operational semantics for the sequential composition operator in the
+context of intermediate termination. With the revised operational semantics, we
+eliminate transparency, allowing us to establish a close correspondence between
+context-free processes and pushdown processes. Moreover, we prove the reactive
+Turing powerfulness of TCP with iteration and nesting with the revised
+operational semantics for sequential composition.
+"
+25413,Spectral approach to homogenization of hyperbolic equations with periodic coefficients,"  In $L_2(\mathbb{R}^d;\mathbb{C}^n)$, we consider selfadjoint strongly
+elliptic second order differential operators ${\mathcal A}_\varepsilon$ with
+periodic coefficients depending on ${\mathbf x}/ \varepsilon$, $\varepsilon>0$.
+We study the behavior of the operators $\cos( {\mathcal A}^{1/2}_\varepsilon
+\tau)$ and ${\mathcal A}^{-1/2}_\varepsilon \sin( {\mathcal
+A}^{1/2}_\varepsilon \tau)$, $\tau \in \mathbb{R}$, for small $\varepsilon$.
+Approximations for these operators in the $(H^s\to L_2)$-operator norm with a
+suitable $s$ are obtained. The results are used to study the behavior of the
+solution ${\mathbf v}_\varepsilon$ of the Cauchy problem for the hyperbolic
+equation $\partial^2_\tau {\mathbf v}_\varepsilon = - \mathcal{A}_\varepsilon
+{\mathbf v}_\varepsilon +\mathbf{F}$. General results are applied to the
+acoustics equation and the system of elasticity theory.
+"
+25414,Dating and localizing an invasion from post-introduction data and a coupled reaction-diffusion-absorption model,"  Invasion of new territories by alien organisms is of primary concern for
+environmental and health agencies and has been a core topic in mathematical
+modeling, in particular in the intents of reconstructing the past dynamics of
+the alien organisms and predicting their future spatial extents. Partial
+differential equations offer a rich and flexible modeling framework that has
+been applied to a large number of invasions. In this article, we are
+specifically interested in dating and localizing the introduction that led to
+an invasion using mathematical modeling, post-introduction data and an adequate
+statistical inference procedure. We adopt a mechanistic-statistical approach
+grounded on a coupled reaction-diffusion-absorption model representing the
+dynamics of an organism in an heterogeneous domain with respect to growth.
+Initial conditions (including the date and site of the introduction) and model
+parameters related to diffusion, reproduction and mortality are jointly
+estimated in the Bayesian framework by using an adaptive importance sampling
+algorithm. This framework is applied to the invasion of \textit{Xylella
+fastidiosa}, a phytopathogenic bacterium detected in South Corsica in 2015,
+France.
+"
+25415,Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning,"  Rapid identification of bacteria is essential to prevent the spread of
+infectious disease, help combat antimicrobial resistance, and improve patient
+outcomes. Raman optical spectroscopy promises to combine bacterial detection,
+identification, and antibiotic susceptibility testing in a single step.
+However, achieving clinically relevant speeds and accuracies remains
+challenging due to the weak Raman signal from bacterial cells and the large
+number of bacterial species and phenotypes. By amassing the largest known
+dataset of bacterial Raman spectra, we are able to apply state-of-the-art deep
+learning approaches to identify 30 of the most common bacterial pathogens from
+noisy Raman spectra, achieving antibiotic treatment identification accuracies
+of 99.0$\pm$0.1%. This novel approach distinguishes between
+methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA
+and MSSA) as well as a pair of isogenic MRSA and MSSA that are genetically
+identical apart from deletion of the mecA resistance gene, indicating the
+potential for culture-free detection of antibiotic resistance. Results from
+initial clinical validation are promising: using just 10 bacterial spectra from
+each of 25 isolates, we achieve 99.0$\pm$1.9% species identification accuracy.
+Our combined Raman-deep learning system represents an important
+proof-of-concept for rapid, culture-free identification of bacterial isolates
+and antibiotic resistance and could be readily extended for diagnostics on
+blood, urine, and sputum.
+"
+25416,"Conservation, Inertia, and Spacetime Geometry","  As Harvey Brown emphasizes in his book Physical Relativity, inertial motion
+in general relativity is best understood as a theorem, and not a postulate.
+Here I discuss the status of the ""conservation condition"", which states that
+the energy-momentum tensor associated with non-interacting matter is
+covariantly divergence-free, in connection with such theorems. I argue that the
+conservation condition is best understood as a consequence of the differential
+equations governing the evolution of matter in general relativity and many
+other theories. I conclude by discussing what it means to posit a certain
+spacetime geometry and the relationship between that geometry and the dynamical
+properties of matter.
+"
+25417,Ultrametrics in the genetic code and the genome,"  Ultrametric approach to the genetic code and the genome is considered and
+developed. $p$-Adic degeneracy of the genetic code is pointed out. Ultrametric
+tree of the codon space is presented. It is shown that codons and amino acids
+can be treated as $p$-adic ultrametric networks. Ultrametric modification of
+the Hamming distance is defined and noted how it can be useful. Ultrametric
+approach with $p$-adic distance is an attractive and promising trend towards
+investigation of bioinformation.
+"
+25418,Anomalous connection between antiferromagnetic and superconducting phases in pressurized non-centrosymmetric heavy fermion compound CeRhGe3,"  Unconventional superconductivity frequently emerges as the transition
+temperature of a magnetic phase, typically antiferromagnetic, is suppressed
+continuously toward zero temperature. Here, we report contrary behavior in
+pressurized CeRhGe3, a non-centrosymmetric heavy fermion compound. We find that
+its pressure-tuned antiferromagnetic transition temperature (TN) appears to
+avoid a continuous decrease to zero temperature by terminating abruptly above a
+dome of pressure-induced superconductivity. Near 21.5 GPa, evidence for TN
+suddenly vanishes, the electrical resistance becomes linear in temperature and
+the superconducting transition reaches a maximum. In light of X-ray absorption
+spectroscopy measurements, these characteristics appear to be related to a
+pressured-induced Ce valence instability, which reveals as a sharp increase in
+the rate of change of Ce valence with applied pressure.
+"
+25419,Bayesian Models of Data Streams with Hierarchical Power Priors,"  Making inferences from data streams is a pervasive problem in many modern
+data analysis applications. But it requires to address the problem of
+continuous model updating and adapt to changes or drifts in the underlying data
+generating distribution. In this paper, we approach these problems from a
+Bayesian perspective covering general conjugate exponential models. Our
+proposal makes use of non-conjugate hierarchical priors to explicitly model
+temporal changes of the model parameters. We also derive a novel variational
+inference scheme which overcomes the use of non-conjugate priors while
+maintaining the computational efficiency of variational methods over conjugate
+models. The approach is validated on three real data sets over three latent
+variable models.
+"
+25420,Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control,"  Trial-and-error based reinforcement learning (RL) has seen rapid advancements
+in recent times, especially with the advent of deep neural networks. However,
+the majority of autonomous RL algorithms require a large number of interactions
+with the environment. A large number of interactions may be impractical in many
+real-world applications, such as robotics, and many practical systems have to
+obey limitations in the form of state space or control constraints. To reduce
+the number of system interactions while simultaneously handling constraints, we
+propose a model-based RL framework based on probabilistic Model Predictive
+Control (MPC). In particular, we propose to learn a probabilistic transition
+model using Gaussian Processes (GPs) to incorporate model uncertainty into
+long-term predictions, thereby, reducing the impact of model errors. We then
+use MPC to find a control sequence that minimises the expected long-term cost.
+We provide theoretical guarantees for first-order optimality in the GP-based
+transition models with deterministic approximate inference for long-term
+planning. We demonstrate that our approach does not only achieve
+state-of-the-art data efficiency, but also is a principled way for RL in
+constrained environments.
+"
+25421,Spaces With Complexity One,"  An $A$-cellular space is a space built from $A$ and its suspensions,
+analogously to the way that $CW$-complexes are built from $S^0$ and its
+suspensions. The $A$-cellular approximation of a space $X$ is an $A$-cellular
+space $CW_{A}X$ which is closest to $X$ among all $A$-cellular spaces. The
+$A$-complexity of a space $X$ is an ordinal number that quantifies how
+difficult it is to build an $A$-cellular approximation of $X$. In this paper,
+we study spaces with low complexity. In particular we show that if $A$ is a
+sphere localized at a set of primes then the $A$-complexity of each space $X$
+is at most 1.
+"
+25422,Magmatic focusing to mid-ocean ridges: the role of grain size variability and non-Newtonian viscosity,"  Melting beneath mid-ocean ridges occurs over a region that is much broader
+than the zone of magmatic emplacement to form the oceanic crust. Magma is
+focused into this zone by lateral transport. This focusing has typically been
+explained by dynamic pressure gradients associated with corner flow, or by a
+sub-lithospheric channel sloping upward toward the ridge axis. Here we discuss
+a novel mechanism for magmatic focusing: lateral transport driven by gradients
+in compaction pressure within the asthenosphere. These gradients arise from the
+co-variation of melting rate and compaction viscosity. The compaction
+viscosity, in previous models, was given as a function of melt fraction and
+temperature. In contrast, we show that the viscosity variations relevant to
+melt focusing arise from grain-size variability and non-Newtonian creep. The
+asthenospheric distribution of melt fraction predicted by our models provides
+an improved ex- planation of the electrical resistivity structure beneath one
+location on the East Pacific Rise. More generally, although grain size and
+non-Newtonian viscosity are properties of the solid phase, we find that in the
+context of mid-ocean ridges, their effect on melt transport is more profound
+than their effect on the mantle corner-flow.
+"
+25423,UVSat: a concept of an ultraviolet/optical photometric satellite,"  Time-series photometry from space in the ultraviolet can be presently done
+with only a few platforms, none of which is able to provide wide-field
+long-term high-cadence photometry. We present a concept of UVSat, a twin space
+telescope which will be capable to perform this kind of photometry, filling an
+observational niche. The satellite will host two telescopes, one for
+observations in the ultraviolet, the other for observations in the optical
+band. We also briefly show what science can be done with UVSat.
+"
+25424,Encoding Motion Primitives for Autonomous Vehicles using Virtual Velocity Constraints and Neural Network Scheduling,"  Within the context of trajectory planning for autonomous vehicles this paper
+proposes methods for efficient encoding of motion primitives in neural networks
+on top of model-based and gradient-free reinforcement learning. It is
+distinguished between 5 core aspects: system model, network architecture,
+training algorithm, training tasks selection and hardware/software
+implementation. For the system model, a kinematic (3-states-2-controls) and a
+dynamic (16-states-2-controls) vehicle model are compared. For the network
+architecture, 3 feedforward structures are compared including weighted skip
+connections. For the training algorithm, virtual velocity constraints and
+network scheduling are proposed. For the training tasks, different feature
+vector selections are discussed. For the implementation, aspects of
+gradient-free learning using 1 GPU and the handling of perturbation noise
+therefore are discussed. The effects of proposed methods are illustrated in
+experiments encoding up to 14625 motion primitives. The capabilities of tiny
+neural networks with as few as 10 scalar parameters when scheduled on vehicle
+velocity are emphasized.
+"
+25425,A Linear Time Algorithm for Solving #2SAT on Cactus Formulas,"  An O(n+m)-time algorithm is presented for counting the number of models of a
+two Conjunctive Normal Form Formula F that represents a Cactus graph, where n
+is the number of variables and m is the number of clauses of F. Although, it
+was already known that this class of formulas could be computed in polynomial
+time, we compare our proposal algorithm with two state of the art
+implementations for the same problem, sharpSAT and countAntom. The results of
+the comparison show that our algorithm outperforms both implementations, and it
+can be considered as a base case for general counting of two Conjunctive Normal
+Form Formulas.
+"
+25426,The Inuence of Misspecified Covariance on False Discovery Control when Using Posterior Probabilities,"  This paper focuses on the influence of a misspecified covariance structure on
+false discovery rate for the large scale multiple testing problem.
+Specifically, we evaluate the influence on the marginal distribution of local
+fdr statistics, which are used in many multiple testing procedures and related
+to Bayesian posterior probabilities. Explicit forms of the marginal
+distributions under both correctly specified and incorrectly specified models
+are derived. The Kullback-Leibler divergence is used to quantify the influence
+caused by a misspecification. Several numerical examples are provided to
+illustrate the influence. A real spatio-temporal data on soil humidity is
+discussed.
+"
+25427,Quarter-pinched Einstein metrics interpolating between real and complex hyperbolic metrics,"  We show that the one-loop quantum deformation of the universal hypermultiplet
+provides a family of complete $1/4$-pinched negatively curved quaternionic
+Kähler (i.e. half conformally flat Einstein) metrics $g^c$, $c\ge 0$, on
+$\mathbb R^4$. The metric $g^0$ is the complex hyperbolic metric whereas the
+family $(g^c)_{c>0}$ is equivalent to a family of metrics $(h^b)_{b>0}$
+depending on $b=1/c$ and smoothly extending to $b=0$ for which $h^0$ is the
+real hyperbolic metric. In this sense the one-loop deformation interpolates
+between the real and the complex hyperbolic metrics. We also determine the
+(singular) conformal structure at infinity for the above families.
+"
+25428,Performance Characterization Using AoI in a Single-loop Networked Control System,"  The main motivation behind extending the notion of Age-of-Information (AoI)
+to Networked Control Systems (NCS) stems from recent results suggesting that
+AoI can be used to reformulate some of the traditional NCS problems with a new
+perspective. In fact, lower AoI in a NCS results in lower estimation/control
+cost. This is intuitive as to improve estimation/control performance it is
+often desirable to have access to fresher state information, which is
+conceptualized as AoI metric. However, AoI is rather a new concept and hence
+less understood compared to well-established notions, e.g. delay. Therefore, it
+has very recently attracted some attention to characterize the relationship
+between the AoI and performance metrics in networked systems. Despite some
+progress, there are many challenges still to address in order to develop the
+fundamental relationship between AoI and the conventional estimation/control
+performance metrics. Towards this end, we study an infinite horizon joint
+optimization problem of sampling and scheduling in a single-loop NCS with the
+objective of minimizing the mean square estimation error. Under some mild
+assumptions on information structures we show that the optimal controller can
+be computed separably while the remaining cost function is characterized as a
+non-decreasing function of AoI. Further, we identify a narrow class of LTI
+control systems for which minimizing the mean square estimation error coincides
+exactly with minimizing the expected AoI. Finally, noting that minimizing the
+non-decreasing function of AoI in our problem setting is a stochastic
+combinatorial optimization problem and is hard to solve, we study heuristic
+algorithms by extending existing algorithms in the AoI literature.
+"
+25429,Higher-order clustering in networks,"  A fundamental property of complex networks is the tendency for edges to
+cluster. The extent of the clustering is typically quantified by the clustering
+coefficient, which is the probability that a length-2 path is closed, i.e.,
+induces a triangle in the network. However, higher-order cliques beyond
+triangles are crucial to understanding complex networks, and the clustering
+behavior with respect to such higher-order network structures is not well
+understood. Here we introduce higher-order clustering coefficients that measure
+the closure probability of higher-order network cliques and provide a more
+comprehensive view of how the edges of complex networks cluster. Our
+higher-order clustering coefficients are a natural generalization of the
+traditional clustering coefficient. We derive several properties about
+higher-order clustering coefficients and analyze them under common random graph
+models. Finally, we use higher-order clustering coefficients to gain new
+insights into the structure of real-world networks from several domains.
+"
+25430,Closed-Loop Policies for Operational Tests of Safety-Critical Systems,"  Manufacturers of safety-critical systems must make the case that their
+product is sufficiently safe for public deployment. Much of this case often
+relies upon critical event outcomes from real-world testing, requiring
+manufacturers to be strategic about how they allocate testing resources in
+order to maximize their chances of demonstrating system safety. This work
+frames the partially observable and belief-dependent problem of test scheduling
+as a Markov decision process, which can be solved efficiently to yield
+closed-loop manufacturer testing policies. By solving for policies over a wide
+range of problem formulations, we are able to provide high-level guidance for
+manufacturers and regulators on issues relating to the testing of
+safety-critical systems. This guidance spans an array of topics, including
+circumstances under which manufacturers should continue testing despite
+observed incidents, when manufacturers should test aggressively, and when
+regulators should increase or reduce the real-world testing requirements for an
+autonomous vehicle.
+"
+25431,Fluency-Guided Cross-Lingual Image Captioning,"  Image captioning has so far been explored mostly in English, as most
+available datasets are in this language. However, the application of image
+captioning should not be restricted by language. Only few studies have been
+conducted for image captioning in a cross-lingual setting. Different from these
+works that manually build a dataset for a target language, we aim to learn a
+cross-lingual captioning model fully from machine-translated sentences. To
+conquer the lack of fluency in the translated sentences, we propose in this
+paper a fluency-guided learning framework. The framework comprises a module to
+automatically estimate the fluency of the sentences and another module to
+utilize the estimated fluency scores to effectively train an image captioning
+model for the target language. As experiments on two bilingual
+(English-Chinese) datasets show, our approach improves both fluency and
+relevance of the generated captions in Chinese, but without using any manually
+written sentences from the target language.
+"
+25432,"Partial Ferrimagnetism in S=1/2 Heisenberg Ladders with a Ferromagnetic Leg, an Antiferromagnetic Leg, and Antiferromagnetic Rungs","  Ground-state and finite-temperature properties of $S=1/2$ Heisenberg ladders
+with a ferromagnetic leg, an antiferromagnetic leg, and antiferromagnetic rungs
+are studied. It is shown that a partial ferrimagnetic phase extends over a wide
+parameter range in the ground state. The numerical results are supported by an
+analytical calculation based on a mapping onto the nonlinear $\sigma$ model and
+a perturbation calculation from the strong-rung limit. It is shown that the
+partial ferrimagnetic state is a spontaneously magnetized Tomonaga--Luttinger
+liquid with incommensurate magnetic correlation, which is confirmed by a DMRG
+calculation. The finite-temperature magnetic susceptibility is calculated using
+the thermal pure quantum state method. It is suggested that the susceptibility
+diverges as $T^{-2}$ in the ferrimagnetic phases as in the case of
+ferromagnetic Heisenberg chains.
+"
+25433,A Tight Approximation for Fully Dynamic Bin Packing without Bundling,"  We consider a variant of the classical Bin Packing Problem, called Fully
+Dynamic Bin Packing. In this variant, items of a size in $(0,1]$ must be packed
+in bins of unit size. In each time step, an item either arrives or departs from
+the packing. An algorithm for this problem must maintain a feasible packing
+while only repacking a bounded number of items in each time step.
+We develop an algorithm which repacks only a constant number of items per
+time step and, unlike previous work, does not rely on bundling of small items
+which allowed those solutions to move an unbounded number of small items as
+one. Our algorithm has an asymptotic approximation ratio of roughly $1.3871$
+which is complemented by a lower bound of Balogh et al., resulting in a tight
+approximation ratio for this problem. As a direct corollary, we also close the
+gap to the lower bound of the Relaxed Online Bin Packing Problem in which only
+insertions of items occur.
+"
+25434,Robust mixture of experts modeling using the $t$ distribution,"  Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in
+data for regression, classification, and clustering. For regression and cluster
+analyses of continuous data, MoE usually use normal experts following the
+Gaussian distribution. However, for a set of data containing a group or groups
+of observations with heavy tails or atypical observations, the use of normal
+experts is unsuitable and can unduly affect the fit of the MoE model. We
+introduce a robust MoE modeling using the $t$ distribution. The proposed $t$
+MoE (TMoE) deals with these issues regarding heavy-tailed and noisy data. We
+develop a dedicated expectation-maximization (EM) algorithm to estimate the
+parameters of the proposed model by monotonically maximizing the observed data
+log-likelihood. We describe how the presented model can be used in prediction
+and in model-based clustering of regression data. The proposed model is
+validated on numerical experiments carried out on simulated data, which show
+the effectiveness and the robustness of the proposed model in terms of modeling
+non-linear regression functions as well as in model-based clustering. Then, it
+is applied to the real-world data of tone perception for musical data analysis,
+and the one of temperature anomalies for the analysis of climate change data.
+The obtained results show the usefulness of the TMoE model for practical
+applications.
+"
+25435,Aprendendo Programacao Orientada a Objetos com uma Abordagem Ludica Baseada em Greenfoot e Robocode,"  One the major challenges in undergraduate computing programs is the learning
+of object-oriented programming (OOP). This paradigm has a variety of concepts
+with an abstraction level usually high for most beginners, even the ones who
+already code in an imperative language. Furthermore, transitioning from
+imperative programming to OOP is a complex issue, with various inappropriate
+side effects. A significant effort has been pursued in the search of motivating
+and attractive solutions for such issues. One of those is the use of playful
+environments that merge games with learning. In this work, we report our
+experience with OOP learning workshops by means of games, challenges and
+competitions, supported by Greenfoot and Robocode learning environments. A
+workshop with sophomore students in a Computer Engineering program is presented
+here. Lessons learning to motive students include: design of motivating
+examples, use of competitive challenges, and an appropriate ratio between
+tutors and students. Results suggest that the workshop was a practical and
+effective way to introduce OOP and motivate students to learn it.
+"
+25436,New bi-Hamiltonian systems on the plane,"  We discuss several new bi-Hamiltonian integrable systems on the plane with
+integrals of motion of third, fourth and sixth order in momenta. The
+corresponding variables of separation, separated relations, compatible Poisson
+brackets and recursion operators are also presented in the framework of the
+Jacobi method.
+"
+25437,The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization,"  This paper focuses on an examination of an applicability of Recurrent Neural
+Network models for detecting anomalous behavior of the CERN superconducting
+magnets. In order to conduct the experiments, the authors designed and
+implemented an adaptive signal quantization algorithm and a custom GRU-based
+detector and developed a method for the detector parameters selection. Three
+different datasets were used for testing the detector. Two artificially
+generated datasets were used to assess the raw performance of the system
+whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets
+was intended for real-life experiments and model training. Several different
+setups of the developed anomaly detection system were evaluated and compared
+with state-of-the-art OC-SVM reference model operating on the same data. The
+OC-SVM model was equipped with a rich set of feature extractors accounting for
+a range of the input signal properties. It was determined in the course of the
+experiments that the detector, along with its supporting design methodology,
+reaches F1 equal or very close to 1 for almost all test sets. Due to the
+profile of the data, the best_length setup of the detector turned out to
+perform the best among all five tested configuration schemes of the detection
+system. The quantization parameters have the biggest impact on the overall
+performance of the detector with the best values of input/output grid equal to
+16 and 8, respectively. The proposed solution of the detection significantly
+outperformed OC-SVM-based detector in most of the cases, with much more stable
+performance across all the datasets.
+"
+25438,Scalable Log Determinants for Gaussian Process Kernel Learning,"  For applications as varied as Bayesian neural networks, determinantal point
+processes, elliptical graphical models, and kernel learning for Gaussian
+processes (GPs), one must compute a log determinant of an $n \times n$ positive
+definite matrix, and its derivatives - leading to prohibitive
+$\mathcal{O}(n^3)$ computations. We propose novel $\mathcal{O}(n)$ approaches
+to estimating these quantities from only fast matrix vector multiplications
+(MVMs). These stochastic approximations are based on Chebyshev, Lanczos, and
+surrogate models, and converge quickly even for kernel matrices that have
+challenging spectra. We leverage these approximations to develop a scalable
+Gaussian process approach to kernel learning. We find that Lanczos is generally
+superior to Chebyshev for kernel learning, and that a surrogate approach can be
+highly efficient and accurate with popular kernels.
+"
+25439,Phase transitions in integer linear problems,"  The resolution of linear system with positive integer variables is a basic
+yet difficult computational problem with many applications. We consider sparse
+uncorrelated random systems parametrised by the density $c$ and the ratio
+$\alpha=N/M$ between number of variables $N$ and number of constraints $M$. By
+means of ensemble calculations we show that the space of feasible solutions
+endows a Van-Der-Waals phase diagram in the plane ($c$, $\alpha$). We give
+numerical evidence that the associated computational problems become more
+difficult across the critical point and in particular in the coexistence
+region.
+"
+25440,Distributional Lattices on Riemannian symmetric spaces,"  A Riemannian symmetric space is a Riemannian manifold in which it is possible
+to reflect all geodesics through a point by an isometry of the space. On such
+spaces, we introduce the notion of a distributional lattice, generalizing the
+notion of lattice. Distributional lattices exist in any Riemannian symmetric
+space: the Voronoi tessellation of a stationary Poisson point process is an
+example. We show that for an appropriate notion of amenability, the amenability
+of a distributional lattice is equivalent to the amenability of the ambient
+space. Using this equivalence, we show that the simple random walk on any
+distributional lattice in a nonamenable space has positive embedded speed. For
+nonpositively curved, simply connected spaces, we show that the simple random
+walk on a Poisson--Voronoi tessellation has positive graph speed by developing
+some additional structure for Poisson--Voronoi tessellations.
+"
+25441,A synchronous game for binary constraint systems,"  Recently, W. Slofstra proved that the set of quantum correlations is not
+closed. We prove that the set of synchronous quantum correlations is not
+closed, which implies his result, by giving an example of a synchronous game
+that has a perfect quantum approximate strategy but no perfect quantum
+strategy. We also exhibit a graph for which the quantum independence number and
+the quantum approximate independence number are different. We prove new
+characterisations of synchronous quantum approximate correlations and
+synchronous quantum spatial correlations. We solve the synchronous
+approximation problem of Dykema and the second author, which yields a new
+equivalence of Connes' embedding problem in terms of synchronous correlations.
+"
+25442,Geant4 Assisted Monte Carlo Prediction of Space Proton-Induced Soft Error Rate Based on Heavy Ion Testing Data,"  A procedure of space proton-induced Soft Error Rate calculation based on
+heavy ion testing data is proposed. The approach relies on Geant4-assisted
+Monte Carlo simulation of the secondary particle LET spectra produced by proton
+interactions.
+"
+25443,Human Interaction with Recommendation Systems,"  Many recommendation algorithms rely on user data to generate recommendations.
+However, these recommendations also affect the data obtained from future users.
+This work aims to understand the effects of this dynamic interaction. We
+propose a simple model where users with heterogeneous preferences arrive over
+time. Based on this model, we prove that naive estimators, i.e. those which
+ignore this feedback loop, are not consistent. We show that consistent
+estimators are efficient in the presence of myopic agents. Our results are
+validated using extensive simulations.
+"
+25444,Self-Representation on Twitter Using Emoji Skin Color Modifiers,"  Since 2015, it has been possible to modify certain emoji with a skin tone.
+The five different skin tones were introduced with the aim of representing more
+human diversity, but some commentators feared they might be used as a way to
+negatively represent other users/groups. This paper presents a quantitative
+analysis of the use of skin tone modifiers on emoji on Twitter, showing that
+users with darker-skinned profile photos employ them more often than users with
+lighter-skinned profile photos, and the vast majority of skin tone usage
+matches the color of a user's profile photo - i.e., tones represent the self,
+rather than the other. In the few cases where users do use opposite-toned
+emoji, we find no evidence of negative racial sentiment. Thus, the introduction
+of skin tones seems to have met the goal of better representing human
+diversity.
+"
+25445,Sequential Optimal Placement of Distributed Photovoltaics using Downstream Power Index,"  The optimization of the size and location of Photovoltaic (PV) Distributed
+Generation (DG) is a method for reducing distribution networks loss, cutting
+costs for utilities, and integrating renewable energy into the power grid.
+However, this optimization problem is a difficult mixed continuous discrete
+problem that is difficult to solve efficiently. Herein, we propose a novel
+Sequential PVDG Placement algorithm which utilizes a Downstream Power Index
+(DPI) for PV allocation. We compare Sequential PVDG Placement results with
+those of Shuffled Frog Leaping Algorithm (SFLA), a memetic heuristic algorithm.
+Our algorithm demonstrates similar accuracy and drastically less computation
+time compared to SFLA. Given its high accuracy and low computation time,
+sequential PVDG allocation algorithm may be useful for rapid online power
+dispatch, long-term planning, and microgrid operations under high penetration
+of renewable sources.
+"
+25446,Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural Network,"  Deep Learning can significantly benefit cancer proteomics and genomics. In
+this study, we attempt to determine a set of critical proteins that are
+associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia
+patients. A Deep Learning network consisting of autoencoders forming a
+hierarchical model from which high-level features are extracted without labeled
+training data. Dimensional reduction reduced the number of critical proteins
+from 231 to 20. Deep Learning found an excellent correlation between FLT3-ITD
+mutation with the levels of these 20 critical proteins (accuracy 97%,
+sensitivity 90%, specificity 100%). Our Deep Learning network could hone in on
+20 proteins with the strongest association with FLT3-ITD. The results of this
+study allow a novel approach to determine critical protein pathways in the
+FLT3-ITD mutation, and provide proof-of-concept for an accurate approach to
+model big data in cancer proteomics and genomics.
+"
+25447,Powerful H2 Line Cooling in Stephan's Quintet II. Group-wide Gas and Shock Modeling of the Warm H2 and a Comparison with [CII]157.7um Emission and Kinematics,"  We map for the first time the two-dimensional H_2 excitation of warm
+intergalactic gas in Stephan's Quintet on group-wide (50 x 35 kpc^2) scales to
+quantify the temperature, mass and warm-H_2 mass fraction as a function of
+position using Spitzer. Molecular gas temperatures are seen to rise (to T >
+700K) and the slope of the power--law density-temperature relation flattens
+along the main ridge of the filament, defining the region of maximum heating.
+We also performed MHD modeling of the excitation properties of the warm gas, to
+map the velocity structure and energy deposition rate of slow and fast
+molecular shocks. Slow magnetic shocks were required to explain the power
+radiated from the lowest--lying rotational states of H_2, and strongly support
+the idea that energy cascades down to small scales and low velocities from the
+fast collision of NGC 7318b with group-wide gas. The highest levels of heating
+of the warm H_2 is strongly correlated with the large-scale stirring of the
+medium as measured by [CII] spectroscopy with Herschel. H_2 is also seen
+associated with a separate bridge which extends towards the Seyfert nucleus in
+NGC 7319, both from Spitzer and CARMA CO observations. This opens up the
+possibility that both galaxy collisions and AGN outflows can turbulently heat
+gas on large-scales in compact groups. The observations provide a laboratory
+for studying the effects of turbulent energy dissipation on group-wide scales
+that may provide clues about the heating and cooling of gas at high-z in early
+galaxy and protogalaxy formation.
+"
+25448,NELIOTA: First temperature measurement of lunar impact flashes,"  We report the first scientific results from the NELIOTA (NEO Lunar Impacts
+and Optical TrAnsients) project, which has recently begun lunar monitoring
+observations with the 1.2-m Kryoneri telescope. NELIOTA aims to detect faint
+impact flashes produced by near-Earth meteoroids and asteroids and thereby help
+constrain the size-frequency distribution of near-Earth objects in the
+decimeter to meter range. The NELIOTA setup, consisting of two fast-frame
+cameras observing simultaneously in the $R$ and $I-$bands, enables - for the
+first time - direct analytical calculation of the flash temperatures. We
+present the first 10 flashes detected, for which we find temperatures in the
+range ~1,600-3,100 K, in agreement with theoretical values. Two of these
+flashes were detected on multiple frames in both filters and therefore yield
+the first measurements of the temperature drop for lunar flashes. In addition,
+we compute the impactor masses, which range between ~100 g and ~50 kg.
+"
+25449,On ranks and cranks of partitions modulo $4$ and $8$,"  Denote by $p(n)$ the number of partitions of $n$ and by $N(a,M;n)$ the number
+of partitions of $n$ with rank congruent to $a$ modulo $M$. By considering the
+deviation \begin{equation*} D(a,M) := \sum_{n= 0}^{\infty}\left(N(a,M;n) -
+\frac{p(n)}{M}\right) q^n, \end{equation*} we give new proofs of recent results
+of Andrews, Berndt, Chan, Kim and Malik on mock theta functions and ranks of
+partitions. By considering deviations of cranks, we give new proofs of Lewis
+and Santa-Gadea's rank-crank identities. We revisit ranks and cranks modulus
+$M=5$ and $7$, with our results on cranks appearing to be new. We also
+demonstrate how considering deviations of ranks and cranks gives first proofs
+of Lewis's conjectured identities and inequalities for rank-crank differences
+of modulus $M=8$.
+"
+25450,Resuspension threshold of a granular bed by localized heating,"  The resuspension and dispersion of particles occur in industrial fluid
+dynamic processes as well as environmental and geophysical situations. In this
+paper, we experimentally investigate the ability to fluidize a granular bed
+with a vertical gradient of temperature. Using laboratory experiments with a
+localized heat source, we observe a large entrainment of particles into the
+fluid volume beyond a threshold temperature. The buoyancy-driven fluidized bed
+then leads to the transport of solid particles through the generation of
+particle-laden plumes. We show that the destabilization process is driven by
+the thermal conductivity inside the granular bed and demonstrate that the
+threshold temperature depends on the thickness of the granular bed and the
+buoyancy number, i.e., the ratio of the stabilizing density contrast to the
+destabilizing thermal density contrast.
+"
+25451,Marginal Likelihoods from Monte Carlo Markov Chains,"  In this paper, we present a method for computing the marginal likelihood,
+also known as the model likelihood or Bayesian evidence, from Markov Chain
+Monte Carlo (MCMC), or other sampled posterior distributions. In order to do
+this, one needs to be able to estimate the density of points in parameter
+space, and this can be challenging in high numbers of dimensions. Here we
+present a Bayesian analysis, where we obtain the posterior for the marginal
+likelihood, using $k$th nearest-neighbour distances in parameter space, using
+the Mahalanobis distance metric, under the assumption that the points in the
+chain (thinned if required) are independent. We generalise the algorithm to
+apply to importance-sampled chains, where each point is assigned a weight. We
+illustrate this with an idealised posterior of known form with an analytic
+marginal likelihood, and show that for chains of length $\sim 10^5$ points, the
+technique is effective for parameter spaces with up to $\sim 20$ dimensions. We
+also argue that $k=1$ is the optimal choice, and discuss failure modes for the
+algorithm. In a companion paper (Heavens et al. 2017) we apply the technique to
+the main MCMC chains from the 2015 Planck analysis of cosmic background
+radiation data, to infer that quantitatively the simplest 6-parameter flat
+$\Lambda$CDM standard model of cosmology is preferred over all extensions
+considered.
+"
+25452,Incomplete thermalization from trap-induced integrability breaking: lessons from classical hard rods,"  We study a one-dimensional gas of hard rods trapped in a harmonic potential,
+which breaks integrability of the hard-rod interaction in a non-uniform way. We
+explore the consequences of such broken integrability for the dynamics of a
+large number of particles and find three distinct regimes: initial, chaotic,
+and stationary. The initial regime is captured by an evolution equation for the
+phase-space distribution function. For any finite number of particles, this
+hydrodynamics breaks down and the dynamics become chaotic after a
+characteristic time scale determined by the inter-particle distance and
+scattering length. The system fails to thermalize over the time-scale studied
+($10^4$ natural units), but the time-averaged ensemble is a stationary state of
+the hydrodynamic evolution. We close by discussing logical extensions of the
+results to similar systems of quantum particles.
+"
+25453,Gradient Aware - Shrinking Domain based Control Design for Reactive Planning Frameworks used in Autonomous Vehicles,"  In this paper, we present a novel control law for longitudinal speed control
+of autonomous vehicles. The key contributions of the proposed work include the
+design of a control law that reactively integrates the longitudinal surface
+gradient of road into its operation. In contrast to the existing works, we
+found that integrating the path gradient into the control framework improves
+the speed tracking efficacy. Since the control law is implemented over a
+shrinking domain scheme, it minimizes the integrated error by recomputing the
+control inputs at every discretized step and consequently provides less
+reaction time. This makes our control law suitable for motion planning
+frameworks that are operating at high frequencies. Furthermore, our work is
+implemented using a generalized vehicle model and can be easily extended to
+other classes of vehicles. The performance of gradient aware-shrinking domain
+based controller is implemented and tested on a stock electric vehicle on which
+a number of sensors are mounted. Results from the tests show the robustness of
+our control law for speed tracking on a terrain with varying gradient while
+also considering stringent time constraints imposed by the planning framework.
+"
+25454,Dynamics of interacting fermions under spin-orbit coupling in an optical lattice clock,"  Quantum statistics and symmetrization dictate that identical fermions do not
+interact via s-wave collisions. However, in the presence of spin-orbit coupling
+(SOC), fermions prepared in identical internal states with distinct momenta
+become distinguishable. The resulting strongly interacting system can exhibit
+exotic topological and pairing behaviors, many of which are yet to be observed
+in condensed matter systems. Ultracold atomic gases offer a promising pathway
+for simulating these rich phenomena. Two recent experiments reported the
+observation of single atom SOC in optical lattice clocks (OLCs) based on
+alkaline-earth atoms. In these works encoding the effective spin degree of
+freedom in the long-lived electronic clock states significantly reduced the
+detrimental effects of spontaneous emission and heating that have thus far
+hindered the study of interacting SOC with alkali atoms. Beyond first studies
+of interacting SOC with alkali atoms in a bulk gas and with two particles in a
+lattice, here we enter a new regime of many-body interacting SOC in an OLC.
+Using clock spectroscopy, we observe the precession of the collective
+magnetization and the emergence of spin locking effects arising from an
+interplay between p-wave and SOC-induced exchange interactions. The many-body
+dynamics are well captured by a collective XXZ spin model, which describes a
+broad class of condensed matter systems ranging from superconductors to quantum
+magnets. Furthermore, our work will aid in the design of next-generation OLCs
+by offering a route for avoiding the observed large density shifts caused by
+SOC-induced exchange interactions.
+"
+25455,Coded Caching with Linear Subpacketization is Possible using Ruzsa-Szeméredi Graphs,"  Coded caching is a problem where encoded broadcasts are used to satisfy users
+requesting popular files and having caching capabilities. Recent work by
+Maddah-Ali and Niesen showed that it is possible to satisfy a scaling number of
+users with only a constant number of broadcast transmissions by exploiting
+coding and caching. Unfortunately, all previous schemes required the splitting
+of files into an exponential number of packets before the significant coding
+gains of caching appeared. The question of what can be achieved with polynomial
+subpacketization (in the number of users) has been a central open problem in
+this area. We resolve this problem and present the first coded caching scheme
+with polynomial (in fact, linear) subpacketization. We obtain a number of
+transmissions that is not constant, but can be any polynomial in the number of
+users with an exponent arbitrarily close to zero. Our central technical tool is
+a novel connection between Ruzsa-Szeméredi graphs and coded caching.
+"
+25456,Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation,"  Sybil detection in social networks is a basic security research problem.
+Structure-based methods have been shown to be promising at detecting Sybils.
+Existing structure-based methods can be classified into Random Walk (RW)-based
+methods and Loop Belief Propagation (LBP)-based methods. RW-based methods
+cannot leverage labeled Sybils and labeled benign users simultaneously, which
+limits their detection accuracy, and/or they are not robust to noisy labels.
+LBP-based methods are not scalable and cannot guarantee convergence. In this
+work, we propose SybilSCAR, a novel structure-based method to detect Sybils in
+social networks. SybilSCAR is Scalable, Convergent, Accurate, and Robust to
+label noise. We first propose a framework to unify RW-based and LBP-based
+methods. Under our framework, these methods can be viewed as iteratively
+applying a (different) local rule to every user, which propagates label
+information among a social graph. Second, we design a new local rule, which
+SybilSCAR iteratively applies to every user to detect Sybils. We compare
+SybilSCAR with state-of-the-art RW-based and LBP-based methods theoretically
+and empirically. Theoretically, we show that, with proper parameter settings,
+SybilSCAR has a tighter asymptotical bound on the number of Sybils that are
+falsely accepted into a social network than existing structure-based methods.
+Empirically, we perform evaluation using both social networks with synthesized
+Sybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with real
+Sybils. Our results show that 1) SybilSCAR is substantially more accurate and
+more robust to label noise than state-of-the-art RW-based methods; 2) SybilSCAR
+is more accurate and one order of magnitude more scalable than state-of-the-art
+LBP-based methods.
+"
+25457,Coarse-grained modelling of the structural properties of DNA origami,"  We use the oxDNA coarse-grained model to provide a detailed characterization
+of the fundamental structural properties of DNA origamis, focussing on
+archetypal 2D and 3D origamis. The model reproduces well the characteristic
+pattern of helix bending in a 2D origami, showing that it stems from the
+intrinsic tendency of anti-parallel four-way junctions to splay apart, a
+tendency that is enhanced both by less screened electrostatic interactions and
+by increased thermal motion. We also compare to the structure of a 3D origami
+whose structure has been determined by cryo-electron microscopy. The oxDNA
+average structure has a root-mean-square deviation from the experimental
+structure of 8.4 Angstrom, which is of the order of the experimental
+resolution. These results illustrate that the oxDNA model is capable of
+providing detailed and accurate insights into the structure of DNA origamis,
+and has the potential to be used to routinely pre-screen putative origami
+designs.
+"
+25458,ALMA mapping of rapid gas and dust variations in comet C/2012 S1 (ISON): new insights into the origin of cometary HNC,"  Observations of the sungrazing comet C/2012 S1 (ISON) were carried out using
+the Atacama Large Millimeter/submillimeter Array (ALMA) at a heliocentric
+distance of 0.58-0.54 AU (pre-perihelion) on 2013 November 16-17. Temporally
+resolved measurements of the coma distributions of HNC, CH$_3$OH, H$_2$CO and
+dust were obtained over the course of about an hour on each day. During the
+period UT 10:10-11:00 on Nov. 16, the comet displayed a remarkable drop in
+activity, manifested as a $>42$% decline in the molecular line and continuum
+fluxes. The H$_2$CO observations are consistent with an abrupt, $\approx50$%
+reduction in the cometary gas production rate soon after the start of our
+observations. On Nov. 17, the total observed fluxes remained relatively
+constant during a similar period, but strong variations in the morphology of
+the HNC distribution were detected as a function of time, indicative of a
+clumpy, intermittent outflow for this species. Our observations suggest that at
+least part of the detected HNC originated from degradation of nitrogen-rich
+organic refractory material, released intermittently from confined regions of
+the nucleus. By contrast, the distributions of CH$_3$OH and H$_2$CO during the
+Nov. 17 observations were relatively uniform, consistent with isotropic outflow
+and stable activity levels for these species. These results highlight a large
+degree of variability in the production of gas and dust from comet ISON during
+its pre-perihelion outburst, consistent with repeated disruption of the nucleus
+interspersed with periods of relative quiescence.
+"
+25459,Shape-preserving wavelet-based multivariate density estimation,"  Wavelet estimators for a probability density f enjoy many good properties,
+however they are not ""shape-preserving"" in the sense that the final estimate
+may not be non-negative or integrate to unity. A solution to negativity issues
+may be to estimate first the square-root of f and then square this estimate up.
+This paper proposes and investigates such an estimation scheme, generalising to
+higher dimensions some previous constructions which are valid only in one
+dimension. The estimation is mainly based on nearest-neighbour-balls. The
+theoretical properties of the proposed estimator are obtained, and it is shown
+to reach the optimal rate of convergence uniformly over large classes of
+densities under mild conditions. Simulations show that the new estimator
+performs as well in general as the classical wavelet estimator, while
+automatically producing estimates which are bona fide densities.
+"
+25460,Cograph Editing: Merging Modules is equivalent to Editing P4's,"  The modular decomposition of a graph $G=(V,E)$ does not contain prime modules
+if and only if $G$ is a cograph, that is, if no quadruple of vertices induces a
+simple connected path $P_4$. The cograph editing problem consists in inserting
+into and deleting from $G$ a set $F$ of edges so that $H=(V,E\Delta F)$ is a
+cograph and $|F|$ is minimum. This NP-hard combinatorial optimization problem
+has recently found applications, e.g., in the context of phylogenetics.
+Efficient heuristics are hence of practical importance. The simple
+characterization of cographs in terms of their modular decomposition suggests
+that instead of editing $G$ one could operate directly on the modular
+decomposition. We show here that editing the induced $P_4$s is equivalent to
+resolving prime modules by means of a suitable defined merge operation on the
+submodules. Moreover, we characterize so-called module-preserving edit sets and
+demonstrate that optimal pairwise sequences of module-preserving edit sets
+exist for every non-cograph. This eventually leads to an exact algorithm for
+the cograph editing problem. In addition, we provide two heuristics with time
+complexity $O(|V|^3)$, resp., $O(|V|^2)$.
+"
+25461,Contactless and absolute linear displacement detection based upon 3D printed magnets combined with passive radio-frequency identification,"  Within this work a passive and wireless magnetic sensor, to monitor linear
+displacements is proposed. We exploit recent advances in 3D printing and
+fabricate a polymer bonded magnet with a spatially linear magnetic field
+component corresponding to the length of the magnet. Regulating the magnetic
+compound fraction during printing allows specific shaping of the magnetic field
+distribution. A giant magnetoresistance magnetic field sensor is combined with
+a radio-frequency identification tag in order to passively monitor the exerted
+magnetic field of the printed magnet. Due to the tailored magnetic field, a
+displacement of the magnet with respect to the sensor can be detected within
+the sub-mm regime. The sensor design provides good flexibility by controlling
+the 3D printing process according to application needs. Absolute displacement
+detection using low cost components and providing passive operation, long term
+stability and longevity renders the proposed sensor system ideal for structural
+health monitoring applications.
+"
+25462,AppSwitch: Resolving the Application Identity Crisis,"  Networked applications traditionally derive their identity from the identity
+of the host on which they run. The default application identity acquired from
+the host results in subtle and substantial problems related to application
+deployment, discovery and access, especially for modern distributed
+applications. A number of mechanisms and workarounds, often quite elaborate,
+are used to address those problems but they only address them indirectly and
+incompletely.
+This paper presents AppSwitch, a novel transport layer network element that
+decouples applications from underlying network at the system call layer and
+enables them to be identified independently of the network. Without requiring
+changes to existing applications or infrastructure, it removes the cost and
+complexity associated with operating distributed applications while offering a
+number of benefits including an efficient implementation of common network
+functions such as application firewall and load balancer. Experiments with our
+implementation show that AppSwitch model also effectively removes the
+performance penalty associated with unnecessary data path processing that is
+typical in those application environments.
+"
+25463,"Blockchain: Data Malls, Coin Economies and Keyless Payments","  We discuss several uses of blockchain (and, more generally, distributed
+ledger) technologies outside of cryptocurrencies with a pragmatic view. We
+mostly focus on three areas: the role of coin economies for what we refer to as
+data malls (specialized data marketplaces); data provenance (a historical
+record of data and its origins); and what we term keyless payments (made
+without having to know other users' cryptographic keys). We also discuss voting
+and other areas, and give a sizable list of academic and nonacademic
+references.
+"
+25464,Secure Lossy Source Coding for Some Classes of Helper and Gray-Wyner Models,"  In this work, we investigate two source coding models, a \emph{Helper}
+problem and a \emph{Gray-Wyner} problem, under equivocation constraints.
+Specifically, in the Helper problem, an encoder communicates with a legitimate
+receiver through noise-free rate-limited public and private links; and an
+external passive eavesdropper intercepts every information that is sent on the
+public link. We study two classes of this model: i) when a pair of arbitrarily
+correlated discrete memoryless sources is to be encoded such that one component
+has to be recovered lossily at the legitimate receiver while the equivocation
+about both components at the eavesdropper must be maintained no smaller than
+some prescribed level; and ii) when the legitimate receiver reproduces both
+components, one of which, that is recovered losslessly, has to be concealed
+from the eavesdropper to some equivocation level. For both classes problems, we
+establish single-letter characterizations of optimal
+rate-distortion-equivocation tradeoffs in the discrete memoryless case. Next,
+we extend our results to the case of two legitimate receivers, i.e., Gray-Wyner
+network with equivocation constraints. Here, two legitimate receivers are
+connected to the encoder each through a dedicated error-free private link as
+well as a common error-free public link; and an external passive eavesdropper
+overhears on the public link. We study two classes of this model that are
+extensions of the aforementioned instances of Helper problems to the case of
+two receivers. For each of the two classes, we establish a single-letter
+characterization of the optimal rate-distortion-equivocation region. Throughout
+the paper, the analysis sheds light on the role of the private links, and we
+illustrate the results by computing them for some binary examples. Also, we
+make some meaningful connections, e.g., with problems of secret-sharing and
+encryption.
+"
+25465,Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs,"  A number of problems can be formulated as prediction on graph-structured
+data. In this work, we generalize the convolution operator from regular grids
+to arbitrary graphs while avoiding the spectral domain, which allows us to
+handle graphs of varying size and connectivity. To move beyond a simple
+diffusion, filter weights are conditioned on the specific edge labels in the
+neighborhood of a vertex. Together with the proper choice of graph coarsening,
+we explore constructing deep neural networks for graph classification. In
+particular, we demonstrate the generality of our formulation in point cloud
+classification, where we set the new state of the art, and on a graph
+classification dataset, where we outperform other deep learning approaches. The
+source code is available at this https URL
+"
+25466,Model Misspecification in ABC: Consequences and Diagnostics,"  We analyze the behavior of approximate Bayesian computation (ABC) when the
+model generating the simulated data differs from the actual data generating
+process; i.e., when the data simulator in ABC is misspecified. We demonstrate
+both theoretically and in simple, but practically relevant, examples that when
+the model is misspecified different versions of ABC can lead to substantially
+different results. Our theoretical results demonstrate that under regularity
+conditions a version of the accept/reject ABC approach concentrates posterior
+mass on an appropriately defined pseudo-true parameter value. However, under
+model misspecification the ABC posterior does not yield credible sets with
+valid frequentist coverage and has non-standard asymptotic behavior. We also
+examine the theoretical behavior of the popular linear regression adjustment to
+ABC under model misspecification and demonstrate that this approach
+concentrates posterior mass on a completely different pseudo-true value than
+that obtained by the accept/reject approach to ABC. Using our theoretical
+results, we suggest two approaches to diagnose model misspecification in ABC.
+All theoretical results and diagnostics are illustrated in a simple running
+example.
+"
+25467,New Hybrid Neuro-Evolutionary Algorithms for Renewable Energy and Facilities Management Problems,"  This Ph.D. thesis deals with the optimization of several renewable energy
+resources development as well as the improvement of facilities management in
+oceanic engineering and airports, using computational hybrid methods belonging
+to AI to this end. Energy is essential to our society in order to ensure a good
+quality of life. This means that predictions over the characteristics on which
+renewable energies depend are necessary, in order to know the amount of energy
+that will be obtained at any time. The second topic tackled in this thesis is
+related to the basic parameters that influence in different marine activities
+and airports, whose knowledge is necessary to develop a proper facilities
+management in these environments. Within this work, a study of the
+state-of-the-art Machine Learning have been performed to solve the problems
+associated with the topics above-mentioned, and several contributions have been
+proposed: One of the pillars of this work is focused on the estimation of the
+most important parameters in the exploitation of renewable resources. The
+second contribution of this thesis is related to feature selection problems.
+The proposed methodologies are applied to multiple problems: the prediction of
+$H_s$, relevant for marine energy applications and marine activities, the
+estimation of WPREs, undesirable variations in the electric power produced by a
+wind farm, the prediction of global solar radiation in areas from Spain and
+Australia, really important in terms of solar energy, and the prediction of
+low-visibility events at airports. All of these practical issues are developed
+with the consequent previous data analysis, normally, in terms of
+meteorological variables.
+"
+25468,Photoionization cross section in a spherical quantum dot: Effects of some parabolic confining electric potentials,"  A theoretical investigation of the effects of spatial variation of confining
+electric potential on photoionization cross section (PCS) in a spherical
+quantum dot is presented. The potential profiles considered here are the
+shifted parabolic potential and the inverse lateral shifted parabolic potential
+compared with the well-studied parabolic potential. The primary findings are
+that parabolic potential and the inverse lateral shifted parabolic potential
+blue shift the peaks of the PCS while the shifted parabolic potential causes a
+red shift.
+"
+25469,An estimation for the lengths of reduction sequences of the $λμρθ$-calculus,"  Since it was realized that the Curry-Howard isomorphism can be extended to
+the case of classical logic as well, several calculi have appeared as
+candidates for the encodings of proofs in classical logic. One of the most
+extensively studied among them is the $\lambda\mu$-calculus of Parigot. In this
+paper, based on the result of Xi presented for the $\lambda$-calculus Xi, we
+give an upper bound for the lengths of the reduction sequences in the
+$\lambda\mu$-calculus extended with the $\rho$- and $\theta$-rules.
+Surprisingly, our results show that the new terms and the new rules do not add
+to the computational complexity of the calculus despite the fact that
+$\mu$-abstraction is able to consume an unbounded number of arguments by virtue
+of the $\mu$-rule.
+"
+25470,A Universal Simulation Platform for Flexible Systems,"  This article proposes a universal simulation platform for simulating systems
+undergoing duress. In other words, this paper introduces a total simulation
+package which includes a number of methods of simulating the flexibility of a
+given system. This platform includes detailed procedures for simulating a
+flexible link by a numerical method called finite difference method. In order
+to verify the effectiveness of the proposed process, two examples are covered
+in different situations to discuss the importance of boundary control and mesh
+selection in the way of ensuring the stability of the system. In addition, a
+graphical user interface (GUI) application called the SimuFlex is designed
+having a selection of methods that the user can choose along with the
+parameters of the controllers that can be easily manipulated from the GUI.
+"
+25471,Eigenvalues of Sturm-Liouville Operators with Distributional Potentials,"  We introduce a novel approach for dealing with eigenvalue problems of
+Sturm-Liouville operators generated by the differential expression
+\begin{equation*} Ly=\frac{1}{r}\left( -(p\left[ y^{\prime }+sy\right]
+)^{\prime }+sp\left[ y^{\prime }+sy\right] +qy\right) \end{equation*} which is
+based on norm resolvent convergence of classical Sturm-Liouville operators.
+This enables us to describe the continuous dependence of the $n$-th eigenvalue
+on the space of self-adjoint boundary conditions and the coefficients of the
+differential equation after giving the inequalities among the eigenvalues.
+Moreover, oscillation properties of the eigenfunctions are also characterized.
+In particular, our main results can be applied to solve a class of
+Sturm-Liouville problems with transmission conditions.
+"
+25472,Estimating Mixed Memberships with Sharp Eigenvector Deviations,"  We consider the problem of estimating overlapping community memberships.
+Existing provable algorithms for this problem either make strong assumptions
+about the population (Zhang et al., 2014; Jin et al., 2017), or are too
+computationally expensive (Anandkumar et al., 2014; Hopkins et al., 2017). We
+work under the popular Mixed Membership Stochastic Blockmodel (MMSB) (Airoldi
+et al., 2008). Using the inherent geometry of this model, we link the inference
+of overlapping communities to the problem of finding corners in a noisy rotated
+and scaled simplex, for which consistent algorithms exist (Gillis et al.,
+2008). We use this as a building block for our algorithm to infer the community
+memberships of each node. Furthermore, we prove that each node's soft
+membership vector converges to its population counterpart. To our knowledge,
+this is the first work to obtain rate of convergence for community membership
+vectors of each node, in contrast to previous work which obtain convergence
+results for memberships of all nodes as a whole. As a byproduct of our
+analysis, we derive sharp row-wise eigenvector deviation bounds, and provide a
+cleaning step that improves the performance significantly for sparse networks.
+We also propose both necessary and sufficient conditions for identifiability of
+the model, while existing methods typically present sufficient conditions. The
+empirical performance of our method is shown using simulated and real datasets
+scaling up to 100,000 nodes.
+"
+25473,A Constant Step Stochastic Douglas-Rachford Algorithm with Application to Non Separable Regularizations,"  The Douglas Rachford algorithm is an algorithm that converges to a minimizer
+of a sum of two convex functions. The algorithm consists in fixed point
+iterations involving computations of the proximity operators of the two
+functions separately. The paper investigates a stochastic version of the
+algorithm where both functions are random and the step size is constant. We
+establish that the iterates of the algorithm stay close to the set of solution
+with high probability when the step size is small enough. Application to
+structured regularization is considered.
+"
+25474,Towards a foundation for holistic power system validation and testing,"  Renewable energy sources and further electrificationof energy consumption are
+key enablers for decreasing green-house gas emissions, but also introduce
+increased complexitywithin the electric power system. The increased
+availability ofautomation, information and communication technology,
+andintelligent solutions for system operation have transformed thepower system
+into a smart grid. In order to support thedevelopment process of smart grid
+solutions on the system level,testing has to be done in a holistic manner,
+covering the multi-domain aspect of such complex systems. This paper
+introducesthe concept of holistic power system testing and discuss first
+stepstowards a corresponding methodology that is being developed inthe European
+ERIGrid research infrastructure project.
+"
+25475,Four self-dual integral forms of the moonshine module,"  We construct four self-dual integral forms of the moonshine vertex operator
+algebra, each of which has symmetries given by the Fischer-Griess monster
+simple group. The existence of these forms resolves the last remaining open
+assumption in the proof of the Modular Moonshine conjecture by Borcherds and
+Ryba. We conjecture that these forms are isomorphic.
+"
+25476,Semidefinite perturbations in the subspace perturbation problem,"  The variation of spectral subspaces for linear self-adjoint operators under
+an additive bounded semidefinite perturbation is considered. A variant of the
+Davis-Kahan $ \sin2\Theta $ theorem from [SIAM J. Numer. Anal. 7 (1970), 1--46]
+adapted to this situation is proved. Under a certain additional geometric
+assumption on the separation of the spectrum of the unperturbed operator, this
+leads to a sharp estimate on the norm of the difference of the spectral
+projections associated with isolated components of the spectrum of the
+perturbed and perturbed operators, respectively. Without this additional
+geometric assumption on the isolated components of the spectrum of the
+unperturbed operator, a corresponding estimate is obtained by transferring the
+optimization approach for general perturbations in [J. Anal. Math., to appear;
+arXiv:1310.4360 (2013)] to the present situation.
+"
+25477,Learning task structure via sparsity grouped multitask learning,"  Sparse mapping has been a key methodology in many high-dimensional scientific
+problems. When multiple tasks share the set of relevant features, learning them
+jointly in a group drastically improves the quality of relevant feature
+selection. However, in practice this technique is used limitedly since such
+grouping information is usually hidden. In this paper, our goal is to recover
+the group structure on the sparsity patterns and leverage that information in
+the sparse learning. Toward this, we formulate a joint optimization problem in
+the task parameter and the group membership, by constructing an appropriate
+regularizer to encourage sparse learning as well as correct recovery of task
+groups. We further demonstrate that our proposed method recovers groups and the
+sparsity patterns in the task parameters accurately by extensive experiments.
+"
+25478,Extremes of $2d$ Coulomb gas: universal intermediate deviation regime,"  In this paper, we study the extreme statistics in the complex Ginibre
+ensemble of $N \times N$ random matrices with complex Gaussian entries, but
+with no other symmetries. All the $N$ eigenvalues are complex random variables
+and their joint distribution can be interpreted as a $2d$ Coulomb gas with a
+logarithmic repulsion between any pair of particles and in presence of a
+confining harmonic potential $v(r) \propto r^2$. We study the statistics of the
+eigenvalue with the largest modulus $r_{\max}$ in the complex plane. The
+typical and large fluctuations of $r_{\max}$ around its mean had been studied
+before, and they match smoothly to the right of the mean. However, it remained
+a puzzle to understand why the large and typical fluctuations to the left of
+the mean did not match. In this paper, we show that there is indeed an
+intermediate fluctuation regime that interpolates smoothly between the large
+and the typical fluctuations to the left of the mean. Moreover, we compute
+explicitly this ""intermediate deviation function"" (IDF) and show that it is
+universal, i.e. independent of the confining potential $v(r)$ as long as it is
+spherically symmetric and increases faster than $\ln r^2$ for large $r$ with an
+unbounded support. If the confining potential $v(r)$ has a finite support, i.e.
+becomes infinite beyond a finite radius, we show via explicit computation that
+the corresponding IDF is different. Interestingly, in the borderline case where
+the confining potential grows very slowly as $v(r) \sim \ln r^2$ for $r \gg 1$
+with an unbounded support, the intermediate regime disappears and there is a
+smooth matching between the central part and the left large deviation regime.
+"
+25479,Knowledge Graph Completion via Complex Tensor Factorization,"  In statistical relational learning, knowledge graph completion deals with
+automatically understanding the structure of large knowledge graphs---labeled
+directed graphs---and predicting missing relationships---labeled edges.
+State-of-the-art embedding models propose different trade-offs between modeling
+expressiveness, and time and space complexity. We reconcile both expressiveness
+and complexity through the use of complex-valued embeddings and explore the
+link between such complex-valued embeddings and unitary diagonalization. We
+corroborate our approach theoretically and show that all real square
+matrices---thus all possible relation/adjacency matrices---are the real part of
+some unitarily diagonalizable matrix. This results opens the door to a lot of
+other applications of square matrices factorization. Our approach based on
+complex embeddings is arguably simple, as it only involves a Hermitian dot
+product, the complex counterpart of the standard dot product between real
+vectors, whereas other methods resort to more and more complicated composition
+functions to increase their expressiveness. The proposed complex embeddings are
+scalable to large data sets as it remains linear in both space and time, while
+consistently outperforming alternative approaches on standard link prediction
+benchmarks.
+"
+25480,Using clustering of rankings to explain brand preferences with personality and socio-demographic variables,"  The primary aim of market segmentation is to identify relevant groups of
+consumers that can be addressed efficiently by marketing or advertising
+campaigns. This paper addresses the issue whether consumer groups can be
+identified from background variables that are not brand-related and how much
+personality vs. socio-demographic variables contribute to the identification of
+consumer clusters. This is done by clustering aggregated preferences for 25
+brands across 5 different product categories, and by relating socio-demographic
+and personality variables to the clusters using logistic regression and random
+forests over a range of different numbers of clusters. Results indicate that
+some personality variables contribute significantly to the identification of
+consumer groups in one sample. However, these results were not replicated on a
+second sample that was more heterogeneous in terms of socio-demographic
+characteristics and not representative of the brands' target audience.
+"
+25481,A Finite-Volume discretization of viscoelastic Saint-Venant equations for FENE-P fluids,"  Saint-Venant equations can be generalized to account for a viscoelastic
+rheology in shallow flows. A Finite-Volume discretization for the 1D
+Saint-Venant system generalized to Upper-Convected Maxwell (UCM) fluids was
+proposed in [Bouchut \& Boyaval, 2013], which preserved a physically-natural
+stability property (i.e. free-energy dissipation) of the full system. It
+invoked a relaxation scheme of Suliciu type for the numerical computation of
+approximate solution to Riemann problems. Here, the approach is extended to the
+1D Saint-Venant system generalized to the finitely-extensible nonlinear elastic
+fluids of Peterlin (FENE-P). We are currently not able to ensure all stability
+conditions a priori, but numerical simulations went smoothly in a practically
+useful range of parameters.
+"
+25482,The HiCal 2 Instrument: Calibration and Antarctic Surface Reflectivity Measurement for the ANITA Experiment,"  The NASA supported High-Altitude Calibration (HiCal)-2 instrument flew as a
+companion balloon to the ANITA-4 experiment in December 2016. Based on a HV
+discharge pulser producing radio-frequency (RF) calibration pulses, HiCal-2
+comprised two payloads, which flew for a combined 18 days, covering 1.5
+revolutions of the Antarctic continent. ANITA-4 captured over 10,000 pulses
+from HiCal, both direct and reflected from the surface, at distances varying
+from 100-800 km, providing a large dataset for surface reflectivity
+measurements. Herein we present details on the design, construction and
+performance of HiCal-2.
+"
+25483,Understanding the non-linear clustering of high redshift galaxies,"  We incorporate the non-linear clustering of dark matter halos, as modelled by
+Jose et al. (2016) into the halo model to better understand the clustering of
+Lyman break galaxies (LBGs) in the redshift range $z=3-5$. We find that, with
+this change, the predicted LBG clustering increases significantly on
+quasi-linear scales ($0.1 \leq r\,/\,h^{-1} \,{\rm Mpc} \leq 10$) compared to
+that in the linear halo bias model. This in turn results in an increase in the
+clustering of LBGs by an order of magnitude on angular scales $5"" \leq \theta
+\leq 100""$. Remarkably, the predictions of our new model on the whole remove
+the systematic discrepancy between the linear halo bias predictions and the
+observations. The correlation length and large scale galaxy bias of LBGs are
+found to be significantly higher in the non-linear halo bias model than in the
+linear halo bias model. The resulting two-point correlation function retains an
+approximate power-law form in contrast with that computed using the linear halo
+bias theory. We also find that the non-linear clustering of LBGs increases with
+increasing luminosity and redshift. Our work emphasizes the importance of using
+non-linear halo bias in order to model the clustering of high-z galaxies to
+probe the physics of galaxy formation and extract cosmological parameters
+reliably.
+"
+25484,Parallelization of XPath Queries using Modern XQuery Processors,"  A practical and promising approach to parallelizing XPath queries was
+proposed by Bordawekar et al. in 2009, which enables parallelization on top of
+existing XML database engines. Although they experimentally demonstrated the
+speedup by their approach, their practice has already been out of date because
+the software environment has largely changed with the capability of XQuery
+processing. In this work, we implement their approach in two ways on top of a
+state-of-the-art XML database engine and experimentally demonstrate that our
+implementations can bring significant speedup on a commodity server.
+"
+25485,Polarization effects on intermolecular vibrational energy transfer analyzed by 2DIR spectroscopy,"  In the present work, we analyze the influence of the polarization effects
+taking place during the course of a 2DIR spectroscopy experiment performed on a
+molecular system undergoing an intermolecular vibrational energy transfer
+process. When both donor and acceptor molecules participating in the
+vibrational energy transfer are embedded in a host solvent, they face
+rotational diffusion that strongly distorts the resulting 2DIR spectra. It
+could be expected that the difference between rotational diffusion constants
+will be of particular interest. For this purpose, the polarization effects are
+discussed according to the different orderings of the laser-molecule
+interactions. Next, we study the distortions of the spectra as a function of
+the rotational diffusion constants of the individual molecules. The knowledge
+of these polarization effects are relevant to the interpretation of the
+spectra. Finally, the conclusions reached in this work for a vibrational energy
+transfer are valid for any other type of third-order optical process performed
+on the same molecular system.
+"
+25486,Salient Object Detection with Semantic Priors,"  Salient object detection has increasingly become a popular topic in cognitive
+and computational sciences, including computer vision and artificial
+intelligence research. In this paper, we propose integrating \textit{semantic
+priors} into the salient object detection process. Our algorithm consists of
+three basic steps. Firstly, the explicit saliency map is obtained based on the
+semantic segmentation refined by the explicit saliency priors learned from the
+data. Next, the implicit saliency map is computed based on a trained model
+which maps the implicit saliency priors embedded into regional features with
+the saliency values. Finally, the explicit semantic map and the implicit map
+are adaptively fused to form a pixel-accurate saliency map which uniformly
+covers the objects of interest. We further evaluate the proposed framework on
+two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental
+results demonstrate that our method outperforms other state-of-the-art methods.
+"
+25487,Adding successor: A transfer theorem for separation and covering,"  Given a class C of word languages, the C-separation problem asks for an
+algorithm that, given as input two regular languages, decides whether there
+exists a third language in C containing the first language, while being
+disjoint from the second. Separation is usually investigated as a means to
+obtain a deep understanding of the class C.
+In the paper, we are mainly interested in classes defined by logical
+formalisms. Such classes are often built on top of each other: given some
+logic, one builds a stronger one by adding new predicates to its signature. A
+natural construction is to enrich a logic with the successor relation. In this
+paper, we present a transfer result applying to this construction: we show that
+for suitable logically defined classes, separation for the logic enriched with
+the successor relation reduces to separation for the original logic. Our
+theorem also applies to a problem that is stronger than separation: covering.
+Moreover, we actually present two reductions: one for languages of finite words
+and the other for languages of infinite words.
+"
+25488,"Tetrathiafulvalene radical cation (TTF+.) Charge Transfer aggregates included in PMMA matrix, a Resonance Raman Study","  TTFClO4 salts were included in a PMMA matrix via solvent casting technique
+under vacuum, up to 72 hours of pumping. The synthesis of the salt, present in
+the literature was enriched with a further purification procedure, wich
+resulted important to obtain TTF+. radical cation aggregates. The thin films
+were characterized by low temperature UV-VIS-NIR absorption, and a
+multicomponent broad charge transfer band (CT) in the near infrared was found.
+Raman spectroscopy (RRS) in resonance with the CT transition allowed to study
+the origin of the CT absorption: the species present in the films were
+recognized as dimers and aggregates of TTF+. radical cation.
+"
+25489,Implicit Theories and Self-efficacy in an Introductory Programming Course,"  Contribution: This study examined student effort and performance in an
+introductory programming course with respect to student-held implicit theories
+and self-efficacy. Background: Implicit theories and self-efficacy shed a light
+into understanding academic success, which must be considered when developing
+effective learning strategies for programming. Research Questions: Are implicit
+theories of intelligence and programming, and programming-efficacy related to
+each other and student success in programming? Is it possible to predict
+student course performance using a subset of these constructs? Methodology: Two
+consecutive surveys (N=100 and N=81) were administered to non-CS engineering
+students in Iş{\i}k University. Findings: Implicit theories and
+self-beliefs are interrelated and correlated with effort, performance, and
+previous failures in the course and students explain failure in programming
+course with ""programming-aptitude is fixed"" theory, and also that programming
+is a difficult task for themselves.
+"
+25490,Resource-monotonicity and Population-monotonicity in Connected Cake-cutting,"  In the classic cake-cutting problem (Steinhaus, 1948), a heterogeneous
+resource has to be divided among n agents with different valuations in a
+proportional way --- giving each agent a piece with a value of at least 1/n of
+the total. In many applications, such as dividing a land-estate or a
+time-interval, it is also important that the pieces are connected. We propose
+two additional requirements: resource-monotonicity (RM) and
+population-monotonicity (PM). When either the cake or the set of agents changes
+and the cake is re-divided using the same rule, the utility of all remaining
+agents must change in the same direction.
+Classic cake-cutting protocols are neither RM nor PM. Moreover, we prove that
+no Pareto-optimal proportional division rule can be either RM or PM. Motivated
+by this negative result, we search for division rules that are
+weakly-Pareto-optimal --- no other division is strictly better for all agents.
+We present two such rules. The relative-equitable rule, which assigns the
+maximum possible relative value equal for all agents, is proportional and PM.
+The so-called rightmost-mark rule, which is an improved version of the Cut and
+Choose protocol, is proportional and RM for two agents.
+"
+25491,A Distributed Nash Equilibrium Seeking in Networked Graphical Games,"  This paper considers a distributed gossip approach for finding a Nash
+equilibrium in networked games on graphs. In such games a player's cost
+function may be affected by the actions of any subset of players. An
+interference graph is employed to illustrate the partially-coupled cost
+functions and the asymmetric information requirements. For a given interference
+graph, network communication between players is considered to be limited. A
+generalized communication graph is designed so that players exchange only their
+required information. An algorithm is designed whereby players, with possibly
+partially-coupled cost functions, make decisions based on the estimates of
+other players' actions obtained from local neighbors. It is shown that this
+choice of communication graph guarantees that all players' information is
+exchanged after sufficiently many iterations. Using a set of standard
+assumptions on the cost functions, the interference and the communication
+graphs, almost sure convergence to a Nash equilibrium is proved for diminishing
+step sizes. Moreover, the case when the cost functions are not known by the
+players is investigated and a convergence proof is presented for diminishing
+step sizes. The effect of the second largest eigenvalue of the expected
+communication matrix on the convergence rate is quantified. The trade-off
+between parameters associated with the communication graph and the ones
+associated with the interference graph is illustrated. Numerical results are
+presented for a large-scale networked game.
+"
+25492,Discretized Keiper/Li approach to the Riemann Hypothesis,"  The Keiper--Li sequence $\{ \lambda _n \}$ is most sensitive to the Riemann
+Hypothesis asymptotically ($n \to \infty$), but highly elusive both
+analytically and numerically. We deform it to fully explicit sequences, simpler
+to analyze and to compute (up to $n=5 \cdot 10^5$ by G. Misguich). We extend
+that to the Davenport--Heilbronn counterexamples, then demonstrate explicit
+tests that selectively react to zeros that are off the critical line.
+"
+25493,Extremal Determinants of Laplace-Beltrami Operators for Rectangular Tori,"  In this work we study the determinant of the Laplace-Beltrami operator on
+rectangular tori of unit area. We will see that the square torus gives the
+extremal determinant within this class of tori. The result is established by
+studying properties of the Dedekind eta function for special arguments and
+refined logarithmic convexity and concavity results of the classical Jacobi
+theta functions of one real variable are deeply involved.
+"
+25494,Interplay of interaction and disorder in the steady state of an open quantum system,"  Many types of dissipative processes can be found in nature or be engineered,
+and their interplay with a system can give rise to interesting phases of
+matter. Here we study the interplay among interaction, tunneling, and disorder
+in the steady state of a spin chain coupled to a tailored bath. We consider a
+dissipation which, in contrast to disorder, tends to generate a homogeneously
+polarized steady state. We find that the steady state can be highly sensitive
+even to weak disorder. We also establish that, in the presence of such
+dissipation, even in the absence of interaction, a finite amount of disorder is
+needed for localization. Last, we show that for strong disorder the system
+reveals signatures of localization both in the weakly and strongly interacting
+regimes.
+"
+25495,Thick distribution tails in models of cancer secondary tumors,"  Recent progress in microdissection and in DNA sequencing has enabled
+subsampling of multi-focal cancers in organs such as the liver in several
+hundred spots, helping to determine the pattern of mutations in each of these
+spots. This has led to the construction of genealogies of the primary,
+secondary, tertiary and so forth, foci of the tumor. These studies have led to
+diverse conclusions concerning the Darwinian (selective) or neutral evolution
+in cancer. Mathematical models of development of multifocal tumors have been
+developed to support these claims. We report a model of development of a
+multifocal tumor, which is a mathematically rigorous refinement of a model of
+Ling et al. (2015). Guided by numerical studies and simulations, we show that
+the rigorous model, in the form of an infinite-type branching process, displays
+distributions of tumors size which have heavy tails and moments that become
+infinite in finite time. To demonstrate these points, we obtain bounds on the
+tails of the distributions of the process and infinite-series expression for
+the first moments. In addition to its inherent mathematical interest, the model
+is corroborated by recent reports of apparent super-exponential growth in
+cancer metastases.
+"
+25496,Lα line of dark positronium as nongravitational detection of dark matter,"  An attempt to predict the new atomic dark matter lines is done on the example
+of a dark lepton atom - positronium. Its Layman-alpha line with the energy near
+3 GeV may be observable if the appropriate conditions realize. For this we have
+studied a {\gamma}-ray excess in the Center of our Galaxy. In principle, this
+excess may be produced by the L{\alpha} line of a dark positronium in the
+medium with Compton scattering. The possibility of observations of an
+annihilation line (E~300 TeV) of dark positronium is also predicted. Other
+proposals to observe the atomic dark matter are shortly described. Besides,
+H{\alpha} line (1.3{\mu}) of usual positronum must be observable in the
+direction on the Center of our Galaxy.
+"
+25497,Simulating outcomes of interventions using a multipurpose simulation program based on the Evolutionary Causal Matrices and Markov Chain,"  Predicting long-term outcomes of interventions is necessary for educational
+and social policy-making processes that might widely influence our society for
+the long-term. However, performing such predictions based on data from
+large-scale experiments might be challenging due to the lack of time and
+resources. In order to address this issue, computer simulations based on
+Evolutionary Causal Matrices and Markov Chain can be used to predict long-term
+outcomes with relatively small-scale lab data. In this paper, we introduce
+Python classes implementing a computer simulation model and presented some
+pilots implementations demonstrating how the model can be utilized for
+predicting outcomes of diverse interventions. We also introduce the
+class-structured simulation module both with real experimental data and with
+hypothetical data formulated based on social psychological theories. Classes
+developed and tested in the present study provide researchers and practitioners
+with a feasible and practical method to simulate intervention outcomes
+prospectively.
+"
+25498,Exploring Cosmic Origins with CORE: Cluster Science,"  We examine the cosmological constraints that can be achieved with a galaxy
+cluster survey with the future CORE space mission. Using realistic simulations
+of the millimeter sky, produced with the latest version of the Planck Sky
+Model, we characterize the CORE cluster catalogues as a function of the main
+mission performance parameters. We pay particular attention to telescope size,
+key to improved angular resolution, and discuss the comparison and the
+complementarity of CORE with ambitious future ground-based CMB experiments that
+could be deployed in the next decade. A possible CORE mission concept with a
+150 cm diameter primary mirror can detect of the order of 50,000 clusters
+through the thermal Sunyaev-Zeldovich effect (SZE). The total yield increases
+(decreases) by 25% when increasing (decreasing) the mirror diameter by 30 cm.
+The 150 cm telescope configuration will detect the most massive clusters
+($>10^{14}\, M_\odot$) at redshift $z>1.5$ over the whole sky, although the
+exact number above this redshift is tied to the uncertain evolution of the
+cluster SZE flux-mass relation; assuming self-similar evolution, CORE will
+detect $\sim 500$ clusters at redshift $z>1.5$. This changes to 800 (200) when
+increasing (decreasing) the mirror size by 30 cm. CORE will be able to measure
+individual cluster halo masses through lensing of the cosmic microwave
+background anisotropies with a 1-$\sigma$ sensitivity of $4\times10^{14}
+M_\odot$, for a 120 cm aperture telescope, and $10^{14} M_\odot$ for a 180 cm
+one. [abridged]
+"
+25499,Fast non-coplanar beam orientation optimization based on group sparsity,"  The selection of beam orientations, which is a key step in radiation
+treatment planning, is particularly challenging for non-coplanar radiotherapy
+systems due to the large number of candidate beams. In this paper, we report
+progress on the group sparsity approach to beam orientation optimization,
+wherein beam angles are selected by solving a large scale fluence map
+optimization problem with an additional group sparsity penalty term that
+encourages most candidate beams to be inactive. The optimization problem is
+solved using an accelerated proximal gradient method, the Fast Iterative
+Shrinkage-Thresholding Algorithm (FISTA). We derive a closed-form expression
+for a relevant proximal operator which enables the application of FISTA. The
+proposed algorithm is used to create non-coplanar treatment plans for four
+cases (including head and neck, lung, and prostate cases), and the resulting
+plans are compared with clinical plans. The dosimetric quality of the group
+sparsity treatment plans is superior to that of the clinical plans. Moreover,
+the runtime for the group sparsity approach is typically about 5 minutes.
+Problems of this size could not be handled using the previous group sparsity
+method for beam orientation optimization, which was slow to solve much smaller
+coplanar cases. This work demonstrates for the first time that the group
+sparsity approach, when combined with an accelerated proximal gradient method
+such as FISTA, works effectively for non-coplanar cases with 500-800 candidate
+beams.
+"
+25500,On h-Fibrations,"  In this paper, we study h-fibrations, a weak homotopical version of
+fibrations which have weak covering homotopy property. We present some
+homotopical analogue of the notions related to fibrations and characterize
+h-fibrations using them. Then we construct some new categories by h-fibrations
+and deduce some results in these categories such as the existence of products
+and coproducts.
+"
+25501,Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps,"  Concept maps can be used to concisely represent important information and
+bring structure into large document collections. Therefore, we study a variant
+of multi-document summarization that produces summaries in the form of concept
+maps. However, suitable evaluation datasets for this task are currently
+missing. To close this gap, we present a newly created corpus of concept maps
+that summarize heterogeneous collections of web documents on educational
+topics. It was created using a novel crowdsourcing approach that allows us to
+efficiently determine important elements in large document collections. We
+release the corpus along with a baseline system and proposed evaluation
+protocol to enable further research on this variant of summarization.
+"
+25502,"Measuring the happiness of large-scale written expression: Songs, Blogs, and Presidents","  The importance of quantifying the nature and intensity of emotional states at
+the level of populations is evident: we would like to know how, when, and why
+individuals feel as they do if we wish, for example, to better construct public
+policy, build more successful organizations, and, from a scientific
+perspective, more fully understand economic and social phenomena. Here, by
+incorporating direct human assessment of words, we quantify happiness levels on
+a continuous scale for a diverse set of large-scale texts: song titles and
+lyrics, weblogs, and State of the Union addresses. Our method is transparent,
+improvable, capable of rapidly processing Web-scale texts, and moves beyond
+approaches based on coarse categorization. Among a number of observations, we
+find that the happiness of song lyrics trends downward from the 1960's to the
+mid 1990's while remaining stable within genres, and that the happiness of
+blogs has steadily increased from 2005 to 2009, exhibiting a striking rise and
+fall with blogger age and distance from the equator.
+"
+25503,Fermi-edge exciton-polaritons in doped semiconductor microcavities with finite hole mass,"  The coupling between a 2D semiconductor quantum well and an optical cavity
+gives rise to combined light-matter excitations, the exciton-polaritons. These
+were usually measured when the conduction band is empty, making the single
+polariton physics a simple single-body problem. The situation is dramatically
+different in the presence of a finite conduction band population, where the
+creation or annihilation of a single exciton involves a many-body shakeup of
+the Fermi sea. Recent experiments in this regime revealed a strong modification
+of the exciton-polariton spectrum. Previous theoretical studies concerned with
+nonzero Fermi energy mostly relied on the approximation of an immobile valence
+band hole with infinite mass, which is appropriate for low-mobility samples
+only; for high-mobility samples, one needs to consider a mobile hole with large
+but finite mass. To bridge this gap we present an analytical diagrammatic
+approach and tackle a model with short-ranged (screened) electron-hole
+interaction, studying it in two complementary regimes. We find that the finite
+hole mass has opposite effects on the exciton-polariton spectra in the two
+regimes: In the first, where the Fermi energy is much smaller than the exciton
+binding energy, excitonic features are enhanced by the finite mass. In the
+second regime, where the Fermi energy is much larger than the exciton binding
+energy, finite mass effects cut off the excitonic features in the polariton
+spectra, in qualitative agreement with recent experiments.
+"
+25504,Isobe-Kakinuma model for water waves as a higher order shallow water approximation,"  We justify rigorously an Isobe-Kakinuma model for water waves as a higher
+order shallow water approximation in the case of a flat bottom. It is known
+that the full water wave equations are approximated by the shallow water
+equations with an error of order $O(\delta^2)$, where $\delta$ is a small
+nondimensional parameter defined as the ratio of the mean depth to the typical
+wavelength. The Green-Naghdi equations are known as higher order approximate
+equations to the water wave equations with an error of order $O(\delta^4)$. In
+this paper we show that the Isobe-Kakinuma model is a much higher order
+approximation to the water wave equations with an error of order $O(\delta^6)$.
+"
+25505,Transformation of a Metal-organic Framework for Tuned Catalytic Activity,"  Metal-organic frameworks (MOFs) are an attractive substrate for catalytic
+reactions due to the high area density of reaction sites and the ability to
+tailor an array of material attributes. This study focuses on a thermally
+stable crystalline UiO-66(Zr) MOF structure and the modulation of the
+electronic structure using two strategies to improve the catalytic conversion
+and selectivity of benzene alcohol to benzedehyate. Those two strategies
+include the functionalization of the organic struts with branched ligands and
+manually creating structural defects with unsaturated organic linkers. A
+combination of computational and experimental results provide evidence of
+improved catalytic activity of MOFs via these two approaches. Functional groups
+attached to the main organic strut modify the electronic environment of the
+photoactive aromatic carbon and thereby decrease the optical band gap by 1eV.
+Whereas the introduction of structural defects due to the organic linker
+desaturation provided a shift in the HUMO as a result of the decrease in strut
+coordination with the inorganic knots.
+"
+25506,Mirror symmetry for honeycombs,"  We prove a homological mirror symmetry equivalence between an $A$-brane
+category for the pair of pants, computed as a wrapped microlocal sheaf
+category, and a $B$-brane category for a mirror LG model, understood as a
+category of matrix factorizations. The equivalence improves upon prior results
+in two ways: it intertwines evident affine Weyl group symmetries on both sides,
+and it exhibits the relation of wrapped microlocal sheaves along different
+types of Lagrangian skeleta for the same hypersurface. The equivalence proceeds
+through the construction of a combinatorial realization of the $A$-model via
+arboreal singularities. The constructions here represent the start of a program
+to generalize to higher dimensions many of the structures which have appeared
+in topological approaches to Fukaya categories of surfaces.
+"
+25507,On the optimality of sliced inverse regression in high dimensions,"  The central subspace of a pair of random variables $(y,x) \in
+\mathbb{R}^{p+1}$ is the minimal subspace $\mathcal{S}$ such that $y \perp
+\hspace{-2mm} \perp x\mid P_{\mathcal{S}}x$. In this paper, we consider the
+minimax rate of estimating the central space of the multiple index models
+$y=f(\beta_{1}^{\tau}x,\beta_{2}^{\tau}x,...,\beta_{d}^{\tau}x,\epsilon)$ with
+at most $s$ active predictors where $x \sim N(0,I_{p})$. We first introduce a
+large class of models depending on the smallest non-zero eigenvalue $\lambda$
+of $var(\mathbb{E}[x|y])$, over which we show that an aggregated estimator
+based on the SIR procedure converges at rate
+$d\wedge((sd+s\log(ep/s))/(n\lambda))$. We then show that this rate is optimal
+in two scenarios: the single index models; and the multiple index models with
+fixed central dimension $d$ and fixed $\lambda$. By assuming a technical
+conjecture, we can show that this rate is also optimal for multiple index
+models with bounded dimension of the central space. We believe that these
+(conditional) optimal rate results bring us meaningful insights of general SDR
+problems in high dimensions.
+"
+25508,DALiuGE: A Graph Execution Framework for Harnessing the Astronomical Data Deluge,"  The Data Activated Liu Graph Engine - DALiuGE - is an execution framework for
+processing large astronomical datasets at a scale required by the Square
+Kilometre Array Phase 1 (SKA1). It includes an interface for expressing complex
+data reduction pipelines consisting of both data sets and algorithmic
+components and an implementation run-time to execute such pipelines on
+distributed resources. By mapping the logical view of a pipeline to its
+physical realisation, DALiuGE separates the concerns of multiple stakeholders,
+allowing them to collectively optimise large-scale data processing solutions in
+a coherent manner. The execution in DALiuGE is data-activated, where each
+individual data item autonomously triggers the processing on itself. Such
+decentralisation also makes the execution framework very scalable and flexible,
+supporting pipeline sizes ranging from less than ten tasks running on a laptop
+to tens of millions of concurrent tasks on the second fastest supercomputer in
+the world. DALiuGE has been used in production for reducing interferometry data
+sets from the Karl E. Jansky Very Large Array and the Mingantu Ultrawide
+Spectral Radioheliograph; and is being developed as the execution framework
+prototype for the Science Data Processor (SDP) consortium of the Square
+Kilometre Array (SKA) telescope. This paper presents a technical overview of
+DALiuGE and discusses case studies from the CHILES and MUSER projects that use
+DALiuGE to execute production pipelines. In a companion paper, we provide
+in-depth analysis of DALiuGE's scalability to very large numbers of tasks on
+two supercomputing facilities.
+"
+25509,Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields,"  Reliable obstacle detection and classification in rough and unstructured
+terrain such as agricultural fields or orchards remains a challenging problem.
+These environments involve large variations in both geometry and appearance,
+challenging perception systems that rely on only a single sensor modality.
+Geometrically, tall grass, fallen leaves, or terrain roughness can mistakenly
+be perceived as non-traversable or might even obscure actual obstacles.
+Likewise, traversable grass or dirt roads and obstacles such as trees and
+bushes might be visually ambiguous. In this paper, we combine appearance- and
+geometry-based detection methods by probabilistically fusing lidar and camera
+sensing using a conditional random field. We apply a state-of-the-art
+multi-modal fusion algorithm from the scene analysis domain and adjust it for
+obstacle detection in agriculture with moving ground vehicles. This involves
+explicitly handling sparse point cloud data and exploiting both spatial,
+temporal, and multi-modal links between corresponding 2D and 3D regions. The
+proposed method is evaluated on a diverse dataset, comprising a dairy paddock
+and a number of different orchards gathered with a perception research robot in
+Australia. Results show that for a two-class classification problem (ground and
+non-ground), only the camera leverages from information provided by the other
+modality. However, as more classes are introduced (ground, sky, vegetation, and
+object), both modalities complement each other and improve the mean
+classification score. Further improvement is achieved by introducing recursive
+inference with temporal links between successive frames.
+"
+25510,Firing Cell: An Artificial Neuron with a Simulation of Long-Term-Potentiation-Related Memory,"  We propose a computational model of neuron, called firing cell (FC),
+properties of which cover such phenomena as attenuation of receptors for
+external stimuli, delay and decay of postsynaptic potentials, modification of
+internal weights due to propagation of postsynaptic potentials through the
+dendrite, modification of properties of the analog memory for each input due to
+a pattern of short-time synaptic potentiation or long-time synaptic
+potentiation (LTP), output-spike generation when the sum of all inputs exceeds
+a threshold, and refraction. The cell may take one of the three forms:
+excitatory, inhibitory, and receptory. The computer simulations showed that,
+depending on the phase of input signals, the artificial neuron's output
+frequency may demonstrate various chaotic behaviors.
+"
+25511,Quantum Symmetry of Graph C*-algebras associated with connected Graphs,"  We define a notion of quantum automorphism group of Graph C*-algebras for
+finite, connected graphs. Under the assumption that the underlying graph does
+not have any multiple edge or loop, the quantum automorphism group of
+underlying directed graph in the sense of T. banica is shown to be a quantum
+subgroup of quantum automorphism group in our sense. Quantum symmetries for
+some concrete graph C*-algebras have been computed.
+"
+25512,A Decision Theoretic Approach to A/B Testing,"  A/B testing is ubiquitous within the machine learning and data science
+operations of internet companies. Generically, the idea is to perform a
+statistical test of the hypothesis that a new feature is better than the
+existing platform---for example, it results in higher revenue. If the p value
+for the test is below some pre-defined threshold---often, 0.05---the new
+feature is implemented. The difficulty of choosing an appropriate threshold has
+been noted before, particularly because dependent tests are often done
+sequentially, leading some to propose control of the false discovery rate (FDR)
+rather than use of a single, universal threshold. However, it is still
+necessary to make an arbitrary choice of the level at which to control FDR.
+Here we suggest a decision-theoretic approach to determining whether to adopt a
+new feature, which enables automated selection of an appropriate threshold. Our
+method has the basic ingredients of any decision-theory problem: a loss
+function, action space, and a notion of optimality, for which we choose Bayes
+risk. However, the loss function and the action space differ from the typical
+choices made in the literature, which has focused on the theory of point
+estimation. We give some basic results for Bayes-optimal thresholding rules for
+the feature adoption decision, and give some examples using eBay data. The
+results suggest that the 0.05 p-value threshold may be too conservative in some
+settings, but that its widespread use may reflect an ad-hoc means of
+controlling multiplicity in the common case of repeatedly testing variants of
+an experiment when the threshold is not reached.
+"
+25513,Truth Validation with Evidence,"  In the modern era, abundant information is easily accessible from various
+sources, however only a few of these sources are reliable as they mostly
+contain unverified contents. We develop a system to validate the truthfulness
+of a given statement together with underlying evidence. The proposed system
+provides supporting evidence when the statement is tagged as false. Our work
+relies on an inference method on a knowledge graph (KG) to identify the
+truthfulness of statements. In order to extract the evidence of falseness, the
+proposed algorithm takes into account combined knowledge from KG and
+ontologies. The system shows very good results as it provides valid and concise
+evidence. The quality of KG plays a role in the performance of the inference
+method which explicitly affects the performance of our evidence-extracting
+algorithm.
+"
+25514,Magnetotransport properties of FeSe in fields up to 50T,"  Magnetotransport properties of the high-quality FeSe crystal, measured in a
+wide temperature range and in magnetic fields up to 50 T, show the symmetry of
+the main holelike and electronlike bands in this compound. In addition to the
+main two bands, there is also a tiny, highly mobile, electronlike band which is
+responsible for the non-linear behavior of $\rho_{xy}$(B) at low temperatures
+and some other peculiarities of FeSe. We observe the inversion of the
+$\rho_{xx}$ temperature coeficient at a magnetic field higher than about 20 T
+which is an implicit conformation of the electron-hole symmetry in the main
+bands.
+"
+25515,Variable Version Lovász Local Lemma: Beyond Shearer's Bound,"  A tight criterion under which the abstract version Lovász Local Lemma
+(abstract-LLL) holds was given by Shearer decades ago. However, little is known
+about that of the variable version LLL (variable-LLL) where events are
+generated by independent random variables, though this model of events is
+applicable to almost all applications of LLL. We introduce a necessary and
+sufficient criterion for variable-LLL, in terms of the probabilities of the
+events and the event-variable graph specifying the dependency among the events.
+Based on this new criterion, we obtain boundaries for two families of
+event-variable graphs, namely, cyclic and treelike bigraphs. These are the
+first two non-trivial cases where the variable-LLL boundary is fully
+determined. As a byproduct, we also provide a universal constructive method to
+find a set of events whose union has the maximum probability, given the
+probability vector and the event-variable graph. Though it is #P-hard in
+general to determine variable-LLL boundaries, we can to some extent decide
+whether a gap exists between a variable-LLL boundary and the corresponding
+abstract-LLL boundary. In particular, we show that the gap existence can be
+decided without solving Shearer's conditions or checking our variable-LLL
+criterion. Equipped with this powerful theorem, we show that there is no gap if
+the base graph of the event-variable graph is a tree, while gap appears if the
+base graph has an induced cycle of length at least 4. The problem is almost
+completely solved except when the base graph has only 3-cliques, in which case
+we also get partial solutions. A set of reduction rules are established that
+facilitate to infer gap existence of an event-variable graph from known ones.
+As an application, various event-variable graphs, in particular combinatorial
+ones, are shown to be gapful/gapless.
+"
+25516,Generating derivative superstructures for systems with high configurational freedom,"  Modeling potential alloys requires the exploration of all possible
+configurations of atoms. Additionally, modeling the thermal properties of
+materials requires knowledge of the possible ways of displacing the atoms. One
+solution to finding all symmetrically unique configurations and displacements
+is to generate the complete list of possible configurations and remove those
+that are symmetrically equivalent. This approach, however, suffers from the
+combinatorial explosion that happens when the supercell size is large, when
+there are more than two atom types, or when there are multiple displaced atoms.
+This problem persists even when there are only a relatively small number of
+unique arrangements that survive the elimination process. Here, we extend an
+existing algorithm\cite{enum1,enum2,enum3} to include the extra configurational
+degree of freedom from the inclusion of displacement directions. The algorithm
+uses group theory to eliminate large classes of configurations, avoiding the
+combinatoric explosion. With this approach we can now enumerate previously
+inaccessible systems, including atomic displacements.
+"
+25517,On the Thermodynamic Formalism for the Farey Map,"  The chaotic phenomenon of intermittency is modeled by a simple map of the
+unit interval, the Farey map. The long term dynamical behaviour of a point
+under iteration of the map is translated into a spin system via symbolic
+dynamics. Methods from dynamical systems theory and statistical mechanics may
+then be used to analyse the map, respectively the zeta function and the
+transfer operator. Intermittency is seen to be problematic to analyze due to
+the presence of an `indifferent fixed point'. Points under iteration of the map
+move away from this point extremely slowly creating pathological convergence
+times for calculations. This difficulty is removed by going to an appropriate
+induced subsystem, which also leads to an induced zeta function and an induced
+transfer operator. Results obtained there can be transferred back to the
+original system. The main work is then divided into two sections. The first
+demonstrates a connection between the induced versions of the zeta function and
+the transfer operator providing useful results regarding the analyticity of the
+zeta function. The second section contains a detailed analysis of the pressure
+function for the induced system and hence the original by considering bounds on
+the radius of convergence of the induced zeta function. In particular, the
+asymptotic behaviour of the pressure function in the limit $\beta$, the inverse
+of `temperature', tends to negative infinity is determined and the existence
+and nature of a phase transition at $\beta=1$ is also discussed.
+"
+25518,Learning the distribution with largest mean: two bandit frameworks,"  Over the past few years, the multi-armed bandit model has become increasingly
+popular in the machine learning community, partly because of applications
+including online content optimization. This paper reviews two different
+sequential learning tasks that have been considered in the bandit literature ;
+they can be formulated as (sequentially) learning which distribution has the
+highest mean among a set of distributions, with some constraints on the
+learning process. For both of them (regret minimization and best arm
+identification) we present recent, asymptotically optimal algorithms. We
+compare the behaviors of the sampling rule of each algorithm as well as the
+complexity terms associated to each problem.
+"
+25519,On third Hankel determinants for subclasses of analytic functions and close-to-convex harmonic mappings,"  In this paper, we obtain the upper bounds to the third Hankel determinants
+for starlike functions of order $\alpha$, convex functions of order $\alpha$
+and bounded turning functions of order $\alpha$. Furthermore, several relevant
+results on a new subclass of close-to-convex harmonic mappings are obtained.
+Connections of the results presented here to those that can be found in the
+literature are also discussed.
+"
+25520,Clustering For Point Pattern Data,"  Clustering is one of the most common unsupervised learning tasks in machine
+learning and data mining. Clustering algorithms have been used in a plethora of
+applications across several scientific fields. However, there has been limited
+research in the clustering of point patterns - sets or multi-sets of unordered
+elements - that are found in numerous applications and data sources. In this
+paper, we propose two approaches for clustering point patterns. The first is a
+non-parametric method based on novel distances for sets. The second is a
+model-based approach, formulated via random finite set theory, and solved by
+the Expectation-Maximization algorithm. Numerical experiments show that the
+proposed methods perform well on both simulated and real data.
+"
+25521,Tunneling measurements of the electron-phonon interaction in $\rm Ba_{1-x}K_xBiO_3$,"  The conductance curves of point-contact tunnel junctions between Ag and $\rm
+Ba_{1-x}K_xBiO_3$ ($x\simeq 0.4$) reveal a BCS behavior with low leakage
+current at zero voltage and some broadening of the superconducting-gap
+structure. In the energy range above the superconducting energy gap, the
+structure in the voltage dependence of the second derivative $d^2V/dI^2$ of the
+voltage with respect to the current of the tunnel junction has been
+investigated in detail in magnetic fields up to $10\ T$. While part of this
+structure is rapidly changing in a magnetic field, three reproducible peaks in
+$d^2V/dI^2(V)$ remain stable up to the transition temperature from the
+superconducting to the normal state with only additional broadening in the
+applied magnetic field. An analysis of this structure in terms of
+strong-coupling effects yields the spectral function $\alpha^2F$ for the
+electron-phonon interaction. The obtained spectral weight in the energy region
+20-70~$meV$ points to the importance of the oxygen optical modes in the
+electron-phonon coupling for the superconductivity of $\rm Ba_{1-x}K_xBiO_3$.
+"
+25522,"A simple device for microinjections, manipulations and measurements using an electromorphological chip under microinterferometric control of the interface and membrane processes at the thickness range of 5-1000 nm at different angles","  Micromanipulations, perfusions and measurements performed using glass
+microelectrodes filled with an electrolyte is a conventional technique for
+experimental morphological and membrane electrophysiological studies at a
+single cell and membrane surface level. The typical (effective) diameter of the
+end of the glass microelectrode is from 500 up to less than 100 nm, which
+prevents one from observing it using a standard optical microscope in
+accordance with the optical resolution criteria, since the diameter less than
+500 nm is indistinguishable within the interference zone. Microprocessor
+programming of the puller (microforge) that provides pulling and tearing allows
+to obtain in certain regimes the adjusted diameter and shape of the
+micropipette tip, although this result is not fully controlled due to the above
+limitations. In this connection it is necessary to design the control devices
+for the micropipette tips both at the preparation and operation stages
+(intracellular or extracellular insertion). This method also should provide
+visualization of the processes occurring upon interaction of the microelectrode
+tip with the cell in real time, depending on the electrode type and state,
+which allows to level the artifacts arising with the systematic error frequency
+from the uncontrolled operation of the micropipette tip after different ways of
+the microelectrode filling with the electrolyte. We propose an installation
+scheme that solves the above problems by means of introducing an
+interferometric device for microscopic control of the microelectrode and
+micromanipulator or microperfusor, for the first time for a given type of
+optical instruments combined with the interferometric optical scheme.
+"
+25523,In-ear EEG biometrics for feasible and readily collectable real-world person authentication,"  The use of EEG as a biometrics modality has been investigated for about a
+decade, however its feasibility in real-world applications is not yet
+conclusively established, mainly due to the issues with collectability and
+reproducibility. To this end, we propose a readily deployable EEG biometrics
+system based on a `one-fits-all' viscoelastic generic in-ear EEG sensor
+(collectability), which does not require skilled assistance or cumbersome
+preparation. Unlike most existing studies, we consider data recorded over
+multiple recording days and for multiple subjects (reproducibility) while, for
+rigour, the training and test segments are not taken from the same recording
+days. A robust approach is considered based on the resting state with eyes
+closed paradigm, the use of both parametric (autoregressive model) and
+non-parametric (spectral) features, and supported by simple and fast cosine
+distance, linear discriminant analysis and support vector machine classifiers.
+Both the verification and identification forensics scenarios are considered and
+the achieved results are on par with the studies based on impractical on-scalp
+recordings. Comprehensive analysis over a number of subjects, setups, and
+analysis features demonstrates the feasibility of the proposed ear-EEG
+biometrics, and its potential in resolving the critical collectability,
+robustness, and reproducibility issues associated with current EEG biometrics.
+"
+25524,Factorization of second-order strictly hyperbolic operators with logarithmic slow scale coefficients and generalized microlocal approximations,"  We give a factorization procedure for a strictly hyperbolic partial
+differential operator of second order with logarithmic slow scale coefficients.
+From this we can microlocally diagonalize the full wave operator which results
+in a coupled system of two first-order pseudodifferential equations in a
+microlocal sense. Under the assumption that the full wave equation is
+microlocal regular in a fixed domain of the phase space, we can approximate the
+problem by two one-way wave equations where a dissipative term is added to
+suppress singularities outside the given domain. We obtain well-posedness of
+the corresponding Cauchy problem for the approximated one-way wave equation
+with a dissipative term.
+"
+25525,A Flexible Modeling Approach for Robust Multi-Lane Road Estimation,"  A robust estimation of road course and traffic lanes is an essential part of
+environment perception for next generations of Advanced Driver Assistance
+Systems and development of self-driving vehicles. In this paper, a flexible
+method for modeling multiple lanes in a vehicle in real time is presented.
+Information about traffic lanes, derived by cameras and other environmental
+sensors, that is represented as features, serves as input for an iterative
+expectation-maximization method to estimate a lane model. The generic and
+modular concept of the approach allows to freely choose the mathematical
+functions for the geometrical description of lanes. In addition to the current
+measurement data, the previously estimated result as well as additional
+constraints to reflect parallelism and continuity of traffic lanes, are
+considered in the optimization process. As evaluation of the lane estimation
+method, its performance is showcased using cubic splines for the geometric
+representation of lanes in simulated scenarios and measurements recorded using
+a development vehicle. In a comparison to ground truth data, robustness and
+precision of the lanes estimated up to a distance of 120 m are demonstrated. As
+a part of the environmental modeling, the presented method can be utilized for
+longitudinal and lateral control of autonomous vehicles.
+"
+25526,Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction,"  This paper introduces a novel neural network-based reinforcement learning
+approach for robot gaze control. Our approach enables a robot to learn and to
+adapt its gaze control strategy for human-robot interaction neither with the
+use of external sensors nor with human supervision. The robot learns to focus
+its attention onto groups of people from its own audio-visual experiences,
+independently of the number of people, of their positions and of their physical
+appearances. In particular, we use a recurrent neural network architecture in
+combination with Q-learning to find an optimal action-selection policy; we
+pre-train the network using a simulated environment that mimics realistic
+scenarios that involve speaking/silent participants, thus avoiding the need of
+tedious sessions of a robot interacting with people. Our experimental
+evaluation suggests that the proposed method is robust against parameter
+estimation, i.e. the parameter values yielded by the method do not have a
+decisive impact on the performance. The best results are obtained when both
+audio and visual information is jointly used. Experiments with the Nao robot
+indicate that our framework is a step forward towards the autonomous learning
+of socially acceptable gaze behavior.
+"
+25527,Massey products in cohomology of moment-angle manifolds corresponding to Pogorelov polytopes,"  In this work we construct nontrivial Massey products in the cohomology of
+moment-angle manifolds corresponding to polytopes from the Pogorelov class.
+This class includes the dodecahedron and all fullerenes, i. e. simple
+3-polytopes with only 5-gonal and 6-gonal facets. The existence of a nontrivial
+Massey product implies the nonformality of the space in the sense of rational
+homotopy theory.
+"
+25528,Mondrian Processes for Flow Cytometry Analysis,"  Analysis of flow cytometry data is an essential tool for clinical diagnosis
+of hematological and immunological conditions. Current clinical workflows rely
+on a manual process called gating to classify cells into their canonical types.
+This dependence on human annotation limits the rate, reproducibility, and
+complexity of flow cytometry analysis. In this paper, we propose using Mondrian
+processes to perform automated gating by incorporating prior information of the
+kind used by gating technicians. The method segments cells into types via
+Bayesian nonparametric trees. Examining the posterior over trees allows for
+interpretable visualizations and uncertainty quantification - two vital
+qualities for implementation in clinical practice.
+"
+25529,Eigencurves for linear elliptic equations,"  This paper provides results for eigencurves associated with self-adjoint
+linear elliptic boundary value problems. The elliptic problems are treated as a
+general two-parameter eigenproblem for a triple (a, b, m) of continuous
+symmetric bilinear forms on a real separable Hilbert space. Variational
+characterizations of the eigencurves associated with (a, b, m) are given and
+various orthogonality results for corresponding eigenspaces are found.
+Continuity and differentiability, as well as asymptotic results, for these
+eigencurves are proved. These results are then used to provide a geometric
+description of the eigencurves.
+"
+25530,Named Entity Evolution Analysis on Wikipedia,"  Accessing Web archives raises a number of issues caused by their temporal
+characteristics. Additional knowledge is needed to find and understand older
+texts. Especially entities mentioned in texts are subject to change. Most
+severe in terms of information retrieval are name changes. In order to find
+entities that have changed their name over time, search engines need to be
+aware of this evolution. We tackle this problem by analyzing Wikipedia in terms
+of entity evolutions mentioned in articles. We present statistical data on
+excerpts covering name changes, which will be used to discover similar text
+passages and extract evolution knowledge in future work.
+"
+25531,Stripe quadrupole order in the nematic phase of FeSe$_{1-x}$S$_x$,"  We use polarization-resolved electronic Raman spectroscopy to study charge
+dynamics in non-magnetic FeSe$_{1-x}$S$_x$ superconductor. We observe two
+features of the $XY$ quadrupole symmetry: a low-energy quasi-elastic peak (QEP)
+and an electronic continuum. The QEP exhibits critical enhancement upon cooling
+towards the structural transition at $T_S(x)$. Below $T_S(x)$, the QEP
+diminishes gradually, and a gap with temperature evolution reminiscent of a
+mean-field order parameter opens in the continuum. The intensity of the QEP
+develops with increasing sulfur doping $x$ and maximizes at $x\approx$ 0.15,
+while the gap magnitude decreases with the suppression of $T_S(x)$. We
+interpret the development of the gap in the quadrupole scattering channel as
+the formation of a stripe quadrupole order: a wave of quadrupole moment without
+charge or spin modulation.
+"
+25532,Oscillations on width and intensity of slender Ca II H fibrils from Sunrise/SuFI,"  We report the detection of oscillations in slender Ca II H fibrils (SCFs)
+from high-resolution observations acquired with the Sunrise balloon-borne solar
+observatory. The SCFs show obvious oscillations in their intensity, but also
+their width. The oscillatory behaviors are investigated at several positions
+along the axes of the SCFs. A large majority of fibrils show signs of
+oscillations in intensity. Their periods and phase speeds are analyzed using a
+wavelet analysis. The width and intensity perturbations have overlapping
+distributions of the wave period.
+The obtained distributions have median values of the period of $32\pm17$s and
+$36\pm25$s, respectively. We find that the fluctuations of both parameters
+propagate in the SCFs with speeds of ${11}^{+49}_{-11}$ km/s and
+${15}^{+34}_{-15}$ km/s, respectively. Furthermore, the width and intensity
+oscillations have a strong tendency to be either in anti-phase, or, to a
+smaller extent, in phase. This suggests that the oscillations of both
+parameters are caused by the same wave mode and that the waves are likely
+propagating. Taking all the evidence together, the most likely wave mode to
+explain all measurements and criteria is the fast sausage mode.
+"
+25533,Generating and Estimating Nonverbal Alphabets for Situated and Multimodal Communications,"  In this paper, we discuss the formalized approach for generating and
+estimating symbols (and alphabets), which can be communicated by the wide range
+of non-verbal means based on specific user requirements (medium, priorities,
+type of information that needs to be conveyed). The short characterization of
+basic terms and parameters of such symbols (and alphabets) with approaches to
+generate them are given. Then the framework, experimental setup, and some
+machine learning methods to estimate usefulness and effectiveness of the
+nonverbal alphabets and systems are presented. The previous results demonstrate
+that usage of multimodal data sources (like wearable accelerometer, heart
+monitor, muscle movements sensors, braincomputer interface) along with machine
+learning approaches can provide the deeper understanding of the usefulness and
+effectiveness of such alphabets and systems for nonverbal and situated
+communication. The symbols (and alphabets) generated and estimated by such
+methods may be useful in various applications: from synthetic languages and
+constructed scripts to multimodal nonverbal and situated interaction between
+people and artificial intelligence systems through Human-Computer Interfaces,
+such as mouse gestures, touchpads, body gestures, eyetracking cameras,
+wearables, and brain-computing interfaces, especially in applications for
+elderly care and people with disabilities.
+"
+25534,A note on counting flows in signed graphs,"  Tutte initiated the study of nowhere-zero flows and proved the following
+fundamental theorem: For every graph $G$ there is a polynomial $f$ so that for
+every abelian group $\Gamma$ of order $n$, the number of nowhere-zero
+$\Gamma$-flows in $G$ is $f(n)$. For signed graphs (which have bidirected
+orientations), the situation is more subtle. For a finite group $\Gamma$, let
+$\epsilon_2(\Gamma)$ be the largest integer $d$ so that $\Gamma$ has a subgroup
+isomorphic to $\mathbb{Z}_2^d$. We prove that for every signed graph $G$ and $d
+\ge 0$ there is a polynomial $f_d$ so that $f_d(n)$ is the number of
+nowhere-zero $\Gamma$-flows in $G$ for every abelian group $\Gamma$ with
+$\epsilon_2(\Gamma) = d$ and $|\Gamma| = 2^d n$. Beck and Zaslavsky had
+previously established the special case of this result when $d=0$ (i.e., when
+$\Gamma$ has odd order).
+"
+25535,Unique Entity Estimation with Application to the Syrian Conflict,"  Entity resolution identifies and removes duplicate entities in large, noisy
+databases and has grown in both usage and new developments as a result of
+increased data availability. Nevertheless, entity resolution has tradeoffs
+regarding assumptions of the data generation process, error rates, and
+computational scalability that make it a difficult task for real applications.
+In this paper, we focus on a related problem of unique entity estimation, which
+is the task of estimating the unique number of entities and associated standard
+errors in a data set with duplicate entities. Unique entity estimation shares
+many fundamental challenges of entity resolution, namely, that the
+computational cost of all-to-all entity comparisons is intractable for large
+databases. To circumvent this computational barrier, we propose an efficient
+(near-linear time) estimation algorithm based on locality sensitive hashing.
+Our estimator, under realistic assumptions, is unbiased and has provably low
+variance compared to existing random sampling based approaches. In addition, we
+empirically show its superiority over the state-of-the-art estimators on three
+real applications. The motivation for our work is to derive an accurate
+estimate of the documented, identifiable deaths in the ongoing Syrian conflict.
+Our methodology, when applied to the Syrian data set, provides an estimate of
+$191,874 \pm 1772$ documented, identifiable deaths, which is very close to the
+Human Rights Data Analysis Group (HRDAG) estimate of 191,369. Our work provides
+an example of challenges and efforts involved in solving a real, noisy
+challenging problem where modeling assumptions may not hold.
+"
+25536,Safe Policy Search with Gaussian Process Models,"  We propose a method to optimise the parameters of a policy which will be used
+to safely perform a given task in a data-efficient manner. We train a Gaussian
+process model to capture the system dynamics, based on the PILCO framework. Our
+model has useful analytic properties, which allow closed form computation of
+error gradients and estimating the probability of violating given state space
+constraints. During training, as well as operation, only policies that are
+deemed safe are implemented on the real system, minimising the risk of failure.
+"
+25537,Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs,"  In this paper, we propose an novel implementation of a simultaneous
+localization and mapping (SLAM) system based on a monocular camera from an
+unmanned aerial vehicle (UAV) using Depth prediction performed with Capsule
+Networks (CapsNet), which possess improvements over the drawbacks of the more
+widely-used Convolutional Neural Networks (CNN). An Extended Kalman Filter will
+assist in estimating the position of the UAV so that we are able to update the
+belief for the environment. Results will be evaluated on a benchmark dataset to
+portray the accuracy of our intended approach.
+"
+25538,Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets,"  In this paper, we revisit the Kalman filter theory. After giving the
+intuition on a simplified financial markets example, we revisit the maths
+underlying it. We then show that Kalman filter can be presented in a very
+different fashion using graphical models. This enables us to establish the
+connection between Kalman filter and Hidden Markov Models. We then look at
+their application in financial markets and provide various intuitions in terms
+of their applicability for complex systems such as financial markets. Although
+this paper has been written more like a self contained work connecting Kalman
+filter to Hidden Markov Models and hence revisiting well known and establish
+results, it contains new results and brings additional contributions to the
+field. First, leveraging on the link between Kalman filter and HMM, it gives
+new algorithms for inference for extended Kalman filters. Second, it presents
+an alternative to the traditional estimation of parameters using EM algorithm
+thanks to the usage of CMA-ES optimization. Third, it examines the application
+of Kalman filter and its Hidden Markov models version to financial markets,
+providing various dynamics assumptions and tests. We conclude by connecting
+Kalman filter approach to trend following technical analysis system and showing
+their superior performances for trend following detection.
+"
+25539,Candidates vs. Noises Estimation for Large Multi-Class Classification Problem,"  This paper proposes a method for multi-class classification problems, where
+the number of classes K is large. The method, referred to as Candidates vs.
+Noises Estimation (CANE), selects a small subset of candidate classes and
+samples the remaining classes. We show that CANE is always consistent and
+computationally efficient. Moreover, the resulting estimator has low
+statistical variance approaching that of the maximum likelihood estimator, when
+the observed label belongs to the selected candidates with high probability. In
+practice, we use a tree structure with leaves as classes to promote fast beam
+search for candidate selection. We further apply the CANE method to estimate
+word probabilities in learning large neural language models. Extensive
+experimental results show that CANE achieves better prediction accuracy over
+the Noise-Contrastive Estimation (NCE), its variants and a number of the
+state-of-the-art tree classifiers, while it gains significant speedup compared
+to standard O(K) methods.
+"
+25540,The Importance of Antipersistence for Traffic Jams,"  Universal characteristics of road networks and traffic patterns can help to
+forecast and control traffic congestion. The antipersistence of traffic flow
+time series has been found for many data sets, but its relevance for congestion
+has been overseen. Based on empirical data from motorways in Germany, we study
+how antipersistence of traffic flow time-series impacts the duration of traffic
+congestion on a wide range of time scales. We find a large number of short
+lasting traffic jams, which implies a large risk for rear-end collisions.
+"
+25541,Initial-boundary value problem for an integrable spin-1 Gross-Pitaevskii system with a 4x4 Lax pair on a finite interval,"  In this paper, we explore the initial-boundary value (IBV) problem for an
+integrable spin-1 Gross-Pitaevskii system with a 4x4 Lax pair on the finite
+interval by extending the Fokas unified transform approach. The solution of
+this system can be expressed in terms of the solution of a 4x4 matrix
+Riemann-Hilbert (RH) problem formulated in the complex k-plane. Furthermore,
+the relevant jump matrices with explicit (x, t)-dependence of the matrix RH
+problem can be explicitly found via three spectral functions {s(k), S(k),
+S_L(k)} arising from the initial data and the Dirichlet-Neumann boundary
+conditions at x=0 and x=L, respectively. The global relation is also found to
+deduce two distinct but equivalent types of representations (i.e., one via the
+large $k$ of asymptotics of the eigenfunctions and another one in terms of the
+Gel'fand-Levitan-Marchenko (GLM) approach) for the Dirichlet and Neumann
+boundary value problems. In particular, the formulae for IBV problems on the
+finite interval can reduce to ones on a half-line as the length $L$ of the
+interval approaches to infinity. Moreover, we also present the linearizable
+boundary conditions for the GLM representations.
+"
+25542,Tunneling induced electron transfer between separated protons,"  We study electron transfer between two separated nuclei using local control
+theory. By conditioning the algorithm in a symmetric system formed by two
+protons, one can favored slow transfer processes, where tunneling is the main
+mechanism, achieving transfer efficiencies close to unity assuming fixed
+nuclei. The solution can be parametrized using sequences of pump and dump pi
+pulses, where the pump pulse is used to excite the electron to a highly excited
+state where the time for tunneling to the target nuclei is on the order of
+femtoseconds. The time delay must be chosen to allow for full population
+transfer via tunneling, and the dump pulse is chosen to remove energy from the
+state to avoid tunneling back to the original proton. Finally, we study the
+effect of the nuclear kinetic energy on the transfer efficiency. Even in the
+absence of relative motion between the protons, the spreading of the nuclear
+wave function is enough to reduce the yield of electronic transfer to less than
+one half.
+"
+25543,A Finite State and Rule-based Akshara to Prosodeme (A2P) Converter in Hindi,"  This article describes a software module called Akshara to Prosodeme (A2P)
+converter in Hindi. It converts an input grapheme into prosedeme (sequence of
+phonemes with the specification of syllable boundaries and prosodic labels).
+The software is based on two proposed finite state machines\textemdash one for
+the syllabification and another for the syllable labeling. In addition to that,
+it also uses a set of nonlinear phonological rules proposed for foot formation
+in Hindi, which encompass solutions to schwa-deletion in simple, compound,
+derived and inflected words. The nonlinear phonological rules are based on
+metrical phonology with the provision of recursive foot structure. A software
+module is implemented in Python. The testing of the software for
+syllabification, syllable labeling, schwa deletion and prosodic labeling yield
+an accuracy of more than 99% on a lexicon of size 28664 words.
+"
+25544,On the Expansion Coefficients of KP Tau Function,"  We study the expansion coefficients of the tau function of the KP hierarchy.
+If the tau function does not vanish at the origin, it is known that the
+coefficients are given by Giambelli formula and that it characterizes solutions
+of the KP hierarchy. In this paper, we find a generalization of Giambelli
+formula to the case when the tau function vanishes at the origin. Again it
+characterizes solutions of the KP hierarchy.
+"
+25545,Symmetries and choreographies in families bifurcating from the polygonal relative equilibrium of the $n$-body problem,"  We use numerical continuation and bifurcation techniques in a boundary value
+setting to follow Lyapunov families of periodic orbits. These arise from the
+polygonal system of $n$ bodies in a rotating frame of reference. When the
+frequency of a Lyapunov orbit and the frequency of the rotating frame have a
+rational relationship then the orbit is also periodic in the inertial frame. We
+prove that a dense set of Lyapunov orbits, with frequencies satisfying a
+diophantine equation, correspond to choreographies. We present a sample of the
+many choreographies that we have determined numerically along the Lyapunov
+families and along bifurcating families, namely for the cases $n=4,~6,~7,~8$,
+and $9$. We also present numerical results for the case where there is a
+central body that affects the choreography, but that does not participate in
+it. Animations of the families and the choreographies can be seen at the link:
+this http URL
+"
+25546,Molecular clouds in the extreme outer Galaxy between $l=34.75^{\circ}$ to $45.25^{\circ}$,"  We present the result of an unbiased CO survey in Galactic range of
+34.75$^{\circ}\leq$l$\leq$ 45.25$^{\circ}$ and -5.25$^{\circ}\leq$b$\leq$
+5.25$^{\circ}$, and velocity range beyond the Outer arm. A total of 168
+molecular clouds (MCs) are identified within the Extreme Outer Galaxy~(EOG)
+region, and 31 of these MCs are associated with 13CO emission. However, none of
+them show significant C18O emission under current detection limit. The typical
+size and mass of these MCs are 5~pc and 3$\times$10$^{3}M_{\sun}$, implying the
+lack of large and massive MCs in the EOG region. Similar to MCs in the outer
+Galaxy, the velocity dispersions of EOG clouds are also correlated with their
+sizes, however, are well displaced below the scaling relationship defined by
+the inner Galaxy MCs. These MCs with a median Galactocentric radius of 12.6
+kpc, show very different distributions from those of the MCs in the Outer arm
+published in our previous paper, while roughly follow the Outer
+Scutum-Centaurus arm defined by Dame & Thaddeus 2011. This result may provide a
+robust evidence for the existence of the Outer Scutum-Centaurus arm. The lower
+limit of the total mass of this segment is about 2.7$\times$10$^5$ $M_{\sun}$,
+which is about one magnitude lower than that of the Outer arm. The mean
+thickness of gaseous disk is about 1.45$^{\circ}$ or 450~pc, and the scale
+height is about 1.27$^{\circ}$ or 400~pc above the $b=0^{\circ}$ plane. The
+warp traced by CO emission is very obvious in the EOG region and its amplitude
+is consistent with the predictions by other warp models using different
+tracers, such as dust, HI and stellar components of our Galaxy.
+"
+25547,An Abstract Method Linearization for Detecting Source Code Plagiarism in Object-Oriented Environment,"  Despite the fact that plagiarizing source code is a trivial task for most CS
+students, detecting such unethical behavior requires a considerable amount of
+effort. Thus, several plagiarism detection systems were developed to handle
+such issue. This paper extends Karnalim's work, a low-level approach for
+detecting Java source code plagiarism, by incorporating abstract method
+linearization. Such extension is incorporated to enhance the accuracy of
+low-level approach in term of detecting plagiarism in object-oriented
+environment. According to our evaluation, which was conducted based on 23
+design-pattern source code pairs, our extended low-level approach is more
+effective than state-of-the-art and Karnalim's approach. On the one hand, when
+compared to state-of-the-art approach, our approach can generate less
+coincidental similarities and provide more accurate result. On the other hand,
+when compared to Karnalim's approach, our approach, at some extent, can
+generate higher similarity when simple abstract method invocation is
+incorporated.
+"
+25548,Opinion formation and distribution in a bounded confidence model on various networks,"  In the social, behavioral, and economic sciences, it is an important problem
+to predict which individual opinions will eventually dominate in a large
+population, if there will be a consensus, and how long it takes a consensus to
+form. This idea has been studied heavily both in physics and in other
+disciplines, and the answer depends strongly on both the model for opinions and
+for the network structure on which the opinions evolve. One model that was
+created to study consensus formation quantitatively is the Deffuant model, in
+which the opinion distribution of a population evolves via sequential random
+pairwise encounters. To consider the heterogeneity of interactions in a
+population due to social influence, we study the Deffuant model on various
+network structures (deterministic synthetic networks, random synthetic
+networks, and social networks constructed from Facebook data) using several
+interaction mechanisms. We numerically simulate the Deffuant model and conduct
+regression analyses to investigate the dependence of the convergence time to
+equilibrium on parameters, including a confidence bound for opinion updates,
+the number of participating entities, and their willingness to compromise. We
+find that network structure and parameter values both have an effect on the
+convergence time, and for some network topologies, the convergence time
+undergoes a transition at a critical value of the confidence bound. We discuss
+the number of opinion groups that form at equilibrium in terms of a
+confidence-bound threshold for a transition from consensus to multiple-opinion
+equilibria.
+"
+25549,Disentangling Dynamics and Content for Control and Planning,"  In this paper, We study the problem of learning a controllable representation
+for high-dimensional observations of dynamical systems. Specifically, we
+consider a situation where there are multiple sets of observations of dynamical
+systems with identical underlying dynamics. Only one of these sets has
+information about the effect of actions on the observation and the rest are
+just some random observations of the system. Our goal is to utilize the
+information in that one set and find a representation for the other sets that
+can be used for planning and ling-term prediction.
+"
+25550,Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks,"  We derive a novel sensitivity analysis of input variables for predictive
+epistemic and aleatoric uncertainty. We use Bayesian neural networks with
+latent variables as a model class and illustrate the usefulness of our
+sensitivity analysis on real-world datasets. Our method increases the
+interpretability of complex black-box probabilistic models.
+"
+25551,Double resonance of Raman transitions in a degenerate Fermi gas,"  We measure momentum-resolved Raman spectra of a spin-polarized degenerate
+Fermi gas of $^{173}$Yb atoms for a wide range of magnetic fields, where the
+atoms are irradiated by a pair of counterpropagating Raman laser beams as in
+the conventional spin-orbit coupling scheme. Double resonance of first- and
+second-order Raman transitions occurs at a certain magnetic field and the
+spectrum exhibits a doublet splitting for high laser intensities. The measured
+spectral splitting is quantitatively accounted for by the Autler-Townes effect.
+We show that our measurement results are consistent with the spinful band
+structure of a Fermi gas in the spatially oscillating effective magnetic field
+generated by the Raman laser fields.
+"
+25552,Data-Driven Debugging for Functional Side Channels,"  Functional side channels arise when an attacker knows that the secret value
+of a server stays fixed for a certain time, and can observe the server executes
+on a sequence of different public inputs, each paired with the same secret
+input. Thus for each secret, the attackers observe a (partial) function from
+public values to (for instance) running time, and they can compare these
+functions for different secrets. First, we define a notion of noninterference
+for functional side channels. We focus on the case of noisy observations, where
+we demonstrate on examples that there is a practical functional side channel in
+programs that would be deemed information-leak-free using the standard
+definition. Second, we develop a framework and techniques for debugging
+programs for functional side channels. We adapt existing results and algorithms
+in functional data analysis (such as functional clustering) to discover the
+existence of side channels. We use a functional extension of standard decision
+tree learning to pinpoint the code fragments causing a side channel if there is
+one. Finally, we empirically evaluate the performance of our tool Fuschia on a
+series of micro-benchmarks, as well as on realistic Java programs with
+thousands of methods. Fuschia is able to discover (and locate in the code)
+functional side channels, including one that was since fixed by the original
+developers.
+"
+25553,Rapid parametric density estimation,"  Parametric density estimation, for example as Gaussian distribution, is the
+base of the field of statistics. Machine learning requires inexpensive
+estimation of much more complex densities, and the basic approach is relatively
+costly maximum likelihood estimation (MLE). There will be discussed inexpensive
+density estimation, for example literally fitting a polynomial (or Fourier
+series) to the sample, which coefficients are calculated by just averaging
+monomials (or sine/cosine) over the sample. Another discussed basic application
+is fitting distortion to some standard distribution like Gaussian - analogously
+to ICA, but additionally allowing to reconstruct the disturbed density.
+Finally, by using weighted average, it can be also applied for estimation of
+non-probabilistic densities, like modelling mass distribution, or for various
+clustering problems by using negative (or complex) weights: fitting a function
+which sign (or argument) determines clusters. The estimated parameters are
+approaching the optimal values with error dropping like $1/\sqrt{n}$, where $n$
+is the sample size.
+"
+25554,Continuous-wave room-temperature diamond maser,"  The maser, older sibling of the laser, has been confined to relative
+obscurity due to its reliance on cryogenic refrigeration and high-vacuum
+systems. Despite this it has found application in deep-space communications and
+radio astronomy due to its unparalleled performance as a low-noise amplifier
+and oscillator. The recent demonstration of a room-temperature solid- state
+maser exploiting photo-excited triplet states in organic pentacene molecules
+paves the way for a new class of maser that could find applications in
+medicine, security and sensing, taking advantage of its sensitivity and low
+noise. However, to date, only pulsed operation has been observed in this
+system. Furthermore, organic maser molecules have poor thermal and mechanical
+properties, and their triplet sub-level decay rates make continuous emission
+challenging: alternative materials are therefore required. Therefore, inorganic
+materials containing spin-defects such as diamond and silicon carbide have been
+proposed. Here we report a continuous-wave (CW) room-temperature maser
+oscillator using optically pumped charged nitrogen-vacancy (NV) defect centres
+in diamond. This demonstration unlocks the potential of room-temperature
+solid-state masers for use in a new generation of microwave devices.
+"
+25555,Curiosity Driven Exploration of Learned Disentangled Goal Spaces,"  Intrinsically motivated goal exploration processes enable agents to
+autonomously sample goals to explore efficiently complex environments with
+high-dimensional continuous actions. They have been applied successfully to
+real world robots to discover repertoires of policies producing a wide
+diversity of effects. Often these algorithms relied on engineered goal spaces
+but it was recently shown that one can use deep representation learning
+algorithms to learn an adequate goal space in simple environments. However, in
+the case of more complex environments containing multiple objects or
+distractors, an efficient exploration requires that the structure of the goal
+space reflects the one of the environment. In this paper we show that using a
+disentangled goal space leads to better exploration performances than an
+entangled goal space. We further show that when the representation is
+disentangled, one can leverage it by sampling goals that maximize learning
+progress in a modular manner. Finally, we show that the measure of learning
+progress, used to drive curiosity-driven exploration, can be used
+simultaneously to discover abstract independently controllable features of the
+environment.
+"
+25556,Weak convergence of the weighted empirical beta copula process,"  The empirical copula has proved to be useful in the construction and
+understanding of many statistical procedures related to dependence within
+random vectors. The empirical beta copula is a smoothed version of the
+empirical copula that enjoys better finite-sample properties. At the core lie
+fundamental results on the weak convergence of the empirical copula and
+empirical beta copula processes. Their scope of application can be increased by
+considering weighted versions of these processes. In this paper we show weak
+convergence for the weighted empirical beta copula process. The weak
+convergence result for the weighted empirical beta copula process is stronger
+than the one for the empirical copula and its use is more straightforward. The
+simplicity of its application is illustrated for weighted Cramér--von Mises
+tests for independence and for the estimation of the Pickands dependence
+function of an extreme-value copula.
+"
+25557,Almost periodic solution in distribution for stochastic differential equations with Stepanov almost periodic coefficients,"  This paper deals with the existence and uniqueness of ($\mu$-pseudo) almost
+periodic mild solution to some evolution equations with Stepanov ($\mu$-pseudo)
+almost periodic coefficients, in both determinist and stochastic cases. After
+revisiting some known concepts and properties of Stepanov ($\mu$-pseudo) almost
+periodicity in complete metric space, we consider a semilinear stochastic
+evolution equation on a Hilbert separable space with Stepanov ($\mu$-pseudo)
+almost periodic coefficients. We show existence and uniqueness of the mild
+solution which is ($\mu$-pseudo) almost periodic in 2-distribution. We also
+generalize a result by Andres and Pennequin, according to which there is no
+purely Stepanov almost periodic solutions to differential equations with
+Stepanov almost periodic coefficients.
+"
+25558,Suitable Spaces for Shape Optimization,"  The differential-geometric structure of certain shape spaces is investigated
+and applied to the theory of shape optimization problems constrained by partial
+differential equations and variational inequalities. Furthermore, we define a
+diffeological structure on a new space of so-called $H^{1/2}$-shapes. This can
+be seen as a first step towards the formulation of optimization techniques on
+diffeological spaces. The $H^{1/2}$-shapes are a generalization of smooth
+shapes and arise naturally in shape optimization problems.
+"
+25559,Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces,"  The nearest neighbor method together with the dynamic time warping (DTW)
+distance is one of the most popular approaches in time series classification.
+This method suffers from high storage and computation requirements for large
+training sets. As a solution to both drawbacks, this article extends learning
+vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ
+scheme uses asymmetric weighted averaging as update rule. Empirical results
+exhibited superior performance of asymmetric generalized LVQ (GLVQ) over other
+state-of-the-art prototype generation methods for nearest neighbor
+classification.
+"
+25560,Natural convection above circular disks of evaporating liquids,"  We investigate theoretically and experimentally the evaporation of liquid
+disks in the presence of natural convection due to a density difference between
+the vapor and the surrounding gas. From the analogy between thermal convection
+above a heated disk and our system, we derive scaling laws to describe the
+evaporation rate. The local evaporation rate depends on the presence of a
+boundary layer in the gas phase such that the total evaporation rate is given
+by a combination of different scaling contributions, which reflect the
+structure of the boundary layer. We compare our theoretical predictions to
+experiments performed with water in an environment controlled in humidity,
+which validate our approach.
+"
+25561,Electrostatic and whistler instabilities excited by an electron beam,"  The electron beam-plasma system is ubiquitous in the space plasma
+environment. Here, using a Darwin particle-in-cell method, the excitation of
+electrostatic and whistler instabilities by a gyrating electron beam is studied
+in support of recent laboratory experiments. It is assumed that the total
+plasma frequency $\omega_{pe}$ is larger than the electron cyclotron frequency
+$\Omega_e$. The fast-growing electrostatic beam-mode waves saturate in a few
+plasma oscillations by slowing down and relaxing the electron beam parallel to
+the background magnetic field. Upon their saturation, the finite amplitude
+electrostatic beam-mode waves can resonate with the tail of the background
+thermal electrons and accelerate them to the beam parallel velocity. The
+slower-growing whistler waves are excited in primarily two resonance modes: (a)
+through Landau resonance due to the inverted slope of the beam electrons in the
+parallel velocity; (b) through cyclotron resonance by scattering electrons to
+both lower pitch angles and smaller energies. It is demonstrated that, for a
+field-aligned beam, the whistler instability can be suppressed by the
+electrostatic instability due to a faster energy transfer rate between beam
+electrons and the electrostatic waves. Such a competition of growth between
+whistler and electrostatic waves depends on the ratio of
+$\omega_{pe}/\Omega_e$. In terms of wave propagation, beam-generated
+electrostatic waves are confined to the beam region whereas beam-generated
+whistler waves transport energy away from the beam.
+"
+25562,IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments,"  While several datasets for autonomous navigation have become available in
+recent years, they tend to focus on structured driving environments. This
+usually corresponds to well-delineated infrastructure such as lanes, a small
+number of well-defined categories for traffic participants, low variation in
+object or background appearance and strict adherence to traffic rules. We
+propose IDD, a novel dataset for road scene understanding in unstructured
+environments where the above assumptions are largely not satisfied. It consists
+of 10,004 images, finely annotated with 34 classes collected from 182 drive
+sequences on Indian roads. The label set is expanded in comparison to popular
+benchmarks such as Cityscapes, to account for new classes. It also reflects
+label distributions of road scenes significantly different from existing
+datasets, with most classes displaying greater within-class diversity.
+Consistent with real driving behaviours, it also identifies new classes such as
+drivable areas besides the road. We propose a new four-level label hierarchy,
+which allows varying degrees of complexity and opens up possibilities for new
+training methods. Our empirical study provides an in-depth analysis of the
+label characteristics. State-of-the-art methods for semantic segmentation
+achieve much lower accuracies on our dataset, demonstrating its distinction
+compared to Cityscapes. Finally, we propose that our dataset is an ideal
+opportunity for new problems such as domain adaptation, few-shot learning and
+behaviour prediction in road scenes.
+"
+25563,Laboratory simulations of the Vis-NIR spectra of comet 67P using sub-μm sized cosmochemical analogues,"  Laboratory spectral measurements of relevant analogue materials were
+performed in the framework of the Rosetta mission in order to explain the
+surface spectral properties of comet 67P. Fine powders of coal, iron sulphides,
+silicates and their mixtures were prepared and their spectra measured in the
+Vis-IR range. These spectra are compared to a reference spectrum of 67P nucleus
+obtained with the VIRTIS/Rosetta instrument up to 2.7 {\mu}m, excluding the
+organics band centred at 3.2 {\mu}m. The species used are known to be chemical
+analogues for cometary materials which could be present at the surface of 67P.
+Grain sizes of the powders range from tens of nanometres to hundreds of
+micrometres. Some of the mixtures studied here actually reach the very low
+reflectance level observed by VIRTIS on 67P. The best match is provided by a
+mixture of sub-micron coal, pyrrhotite, and silicates. Grain sizes are in
+agreement with the sizes of the dust particles detected by the GIADA, MIDAS and
+COSIMA instruments on board Rosetta. The coal used in the experiment is
+responsible for the spectral slope in the visible and infrared ranges.
+Pyrrhotite, which is strongly absorbing, is responsible for the low albedo
+observed in the NIR. The darkest components dominate the spectra, especially
+within intimate mixtures. Depending on sample preparation, pyrrhotite can coat
+the coal and silicate aggregates. Such coating effects can affect the spectra
+as much as particle size. In contrast, silicates seem to play a minor role.
+"
+25564,Generalised least squares estimation of regularly varying space-time processes based on flexible observation schemes,"  Regularly varying stochastic processes model extreme dependence between
+process values at different locations and/or time points. For such processes we
+propose a two-step parameter estimation of the extremogram, when some part of
+the domain of interest is fixed and another increasing. We provide conditions
+for consistency and asymptotic normality of the empirical extremogram centred
+by a pre-asymptotic version for such observation schemes. For max-stable
+processes with Fr{é}chet margins we provide conditions, such that the
+empirical extremogram (or a bias-corrected version) centred by its true version
+is asymptotically normal. In a second step, for a parametric extremogram model,
+we fit the parameters by generalised least squares estimation and prove
+consistency and asymptotic normality of the estimates. We propose subsampling
+procedures to obtain asymptotically correct confidence intervals. Finally, we
+apply our results to a variety of Brown-Resnick processes. A simulation study
+shows that the procedure works well also for moderate sample sizes.
+"
+25565,Soft Contract Verification for Higher-Order Stateful Programs,"  Software contracts allow programmers to state rich program properties using
+the full expressive power of an object language. However, since they are
+enforced at runtime, monitoring contracts imposes significant overhead and
+delays error discovery. So contract verification aims to guarantee all or most
+of these properties ahead of time, enabling valuable optimizations and yielding
+a more general assurance of correctness. Existing methods for static contract
+verification satisfy the needs of more restricted target languages, but fail to
+address the challenges unique to those conjoining untyped, dynamic programming,
+higher-order functions, modularity, and statefulness. Our approach tackles all
+these features at once, in the context of the full Racket system---a mature
+environment for stateful, higher-order, multi-paradigm programming with or
+without types. Evaluating our method using a set of both pure and stateful
+benchmarks, we are able to verify 99.94% of checks statically (all but 28 of
+49, 861).
+Stateful, higher-order functions pose significant challenges for static
+contract verification in particular. In the presence of these features, a
+modular analysis must permit code from the current module to escape permanently
+to an opaque context (unspecified code from outside the current module) that
+may be stateful and therefore store a reference to the escaped closure. Also,
+contracts themselves, being predicates wri en in unrestricted Racket, may
+exhibit stateful behavior; a sound approach must be robust to contracts which
+are arbitrarily expressive and interwoven with the code they monitor. In this
+paper, we present and evaluate our solution based on higher-order symbolic
+execution, explain the techniques we used to address such thorny issues,
+formalize a notion of behavioral approximation, and use it to provide a
+mechanized proof of soundness.
+"
+25566,Interacting lattice systems with quantum dissipation: a quantum Monte Carlo study,"  Quantum dissipation arises when a large system can be split in a quantum
+system and an environment where the energy of the former flows to.
+Understanding the effect of dissipation on quantum many-body systems is of
+particular importance due to its potential relations with quantum information
+processing. We propose a conceptually simple approach to introduce the
+dissipation into interacting quantum systems in a thermodynamical context, in
+which every site of a 1d lattice is coupled off-diagonally to its own bath. The
+interplay between quantum dissipation and interactions gives rise to
+counterintuitive interpretations such as a compressible zero-temperature state
+with spontaneous discrete symmetry breaking and a thermal phase transition in a
+one-dimensional dissipative quantum many-body system as revealed by Quantum
+Monte Carlo path integral simulations.
+"
+25567,Varieties of Cubical Sets,"  We define a variety of notions of cubical sets, based on sites organized
+using substructural algebraic theories presenting PRO(P)s or Lawvere theories.
+We prove that all our sites are test categories in the sense of Grothendieck,
+meaning that the corresponding presheaf categories of cubical sets model
+classical homotopy theory. We delineate exactly which ones are even strict test
+categories, meaning that products of cubical sets correspond to products of
+homotopy types.
+"
+25568,The KMOS Deep Survey (KDS) I: dynamical measurements of typical star-forming galaxies at z $\simeq$ 3.5,"  We present dynamical measurements from the KMOS (K-band Multi-Object
+Spectrograph) Deep Survey (KDS), which is comprised of 77 typical star-forming
+galaxies at z $\simeq$ 3.5 in the mass range 9.0 < log(M$_{\star}$/M$_{\odot}$)
+< 10.5. These measurements constrain the internal dynamics, the intrinsic
+velocity dispersions ({\sigma}$_{int}$) and rotation velocities (V$_{C}$) of
+galaxies in the high redshift Universe. The mean velocity dispersion of the
+galaxies in our sample is {\sigma}$_{int}$ = $70.8^{+3.3}_{-3.1}$ km s$^{-1}$,
+revealing that the increasing average {\sigma}$_{int}$ with increasing
+redshift, reported for z $\lesssim2$, continues out to z $\simeq$ 3.5. Only 34
+$\pm$ 8% of our galaxies are rotation-dominated (V$_{C}$/{\sigma}$_{int}$ > 1),
+with the sample average V$_{C}$/{\sigma}$_{int}$ value much smaller than at
+lower redshift. After carefully selecting comparable star-forming samples at
+multiple epochs, we find that the rotation-dominated fraction evolves with
+redshift with a z$^{-0.2}$ dependence. The rotation-dominated KDS galaxies show
+no clear offset from the local rotation velocity-stellar mass (i.e.
+V$_{C}$-M$_{\star}$) relation, although a smaller fraction of the galaxies are
+on the relation due to the increase in the dispersion-dominated fraction. These
+observations are consistent with a simple equilibrium model picture, in which
+random motions are boosted in high redshift galaxies by a combination of the
+increasing gas fractions, accretion efficiency, specific star-formation rate
+and stellar feedback and which may provide significant pressure support against
+gravity on the galactic disk scale.
+"
+25569,Conformational Nonequilibrium Enzyme Kinetics: Generalized Michaelis-Menten Equation,"  In a conformational nonequilibrium steady state (cNESS), enzyme turnover is
+modulated by the underlying conformational dynamics. Based on a discrete
+kinetic network model, we use the integrated probability flux balance method to
+derive the cNESS turnover rate for a conformation-modulated enzymatic reaction.
+The traditional Michaelis-Menten (MM) rate equation is extended to a
+generalized form, which includes non-MM corrections induced by conformational
+population currents within combined cyclic kinetic loops. When conformational
+detailed balance is satisfied, the turnover rate reduces to the MM functional
+form, explaining its validity for many enzymatic systems. For the first time, a
+one-to-one correspondence is established between non-MM terms and combined
+cyclic loops with unbalanced conformational currents. Cooperativity resulting
+from nonequilibrium conformational dynamics has been observed in enzymatic
+reactions, and we provide a novel, rigorous means of predicting and
+characterizing such behavior. Our generalized MM equation affords a systematic
+approach for exploring cNESS enzyme kinetics.
+"
+25570,A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models,"  This large-scale study, consisting of 24.5 million hand hygiene opportunities
+spanning 19 distinct facilities in 10 different states, uses linear predictive
+models to expose factors that may affect hand hygiene compliance. We examine
+the use of features such as temperature, relative humidity, influenza severity,
+day/night shift, federal holidays and the presence of new residents in
+predicting daily hand hygiene compliance. The results suggest that colder
+temperatures and federal holidays have an adverse effect on hand hygiene
+compliance rates, and that individual cultures and attitudes regarding hand
+hygiene seem to exist among facilities.
+"
+25571,Truncation Error Estimation in the p-Anisotropic Discontinuous Galerkin Spectral Element Method,"  In the context of Discontinuous Galerkin Spectral Element Methods (DGSEM),
+$\tau$-estimation has been successfully used for p-adaptation algorithms. This
+method estimates the truncation error of representations with different
+polynomial orders using the solution on a reference mesh of relatively high
+order.
+In this paper, we present a novel anisotropic truncation error estimator
+derived from the $\tau$-estimation procedure for DGSEM. We exploit the tensor
+product basis properties of the numerical solution to design a method where the
+total truncation error is calculated as a sum of its directional components. We
+show that the new error estimator is cheaper to evaluate than previous
+implementations of the $\tau$-estimation procedure and that it obtains more
+accurate extrapolations of the truncation error for representations of a higher
+order than the reference mesh. The robustness of the method allows performing
+the p-adaptation strategy with coarser reference solutions, thus further
+reducing the computational cost. The proposed estimator is validated using the
+method of manufactured solutions in a test case for the compressible
+Navier-Stokes equations.
+"
+25572,Environmentally controlled curvature of single collagen proteins,"  The predominant structural protein in vertebrates is collagen, which plays a
+key role in extracellular matrix and connective tissue mechanics. Despite its
+prevalence and physical importance in biology, the mechanical properties of
+molecular collagen are far from established. The flexibility of its triple
+helix is unresolved, with descriptions from different experimental techniques
+ranging from flexible to semirigid. Furthermore, it is unknown how collagen
+type (homo- vs. heterotrimeric) and source (tissue-derived vs. recombinant)
+influence flexibility. Using SmarTrace, a chain tracing algorithm we devised,
+we performed statistical analysis of collagen conformations collected with
+atomic force microscopy (AFM) to determine the protein's mechanical properties.
+Our results show that types I, II and III collagens - the key fibrillar
+varieties - exhibit molecular flexibilities that are very similar. However,
+collagen conformations are strongly modulated by salt, transitioning from
+compact to extended as KCl concentration increases, in both neutral and acidic
+pH. While analysis with a standard worm-like chain model suggests that the
+persistence length of collagen can attain almost any value within the
+literature range, closer inspection reveals that this modulation of collagen's
+conformational behavior is not due to changes in flexibility, but rather arises
+from the induction of curvature (either intrinsic or induced by interactions
+with the mica surface). By modifying standard polymer theory to include innate
+curvature, we show that collagen behaves as an equilibrated curved worm-like
+chain (cWLC) in two dimensions. Analysis within the cWLC model shows that
+collagen's curvature depends strongly on pH and salt, while its persistence
+length does not. Thus, we find that triple-helical collagen is well described
+as semiflexible, irrespective of source, type, pH and salt environment.
+"
+25573,Prediction of laminar vortex shedding over a cylinder using deep learning,"  Unsteady laminar vortex shedding over a circular cylinder is predicted using
+a deep learning technique, a generative adversarial network (GAN), with a
+particular emphasis on elucidating the potential of learning the solution of
+the Navier-Stokes equations. Numerical simulations at two different Reynolds
+numbers with different time-step sizes are conducted to produce training
+datasets of flow field variables. Unsteady flow fields in the future at a
+Reynolds number which is not in the training datasets are predicted using a
+GAN. Predicted flow fields are found to qualitatively and quantitatively agree
+well with flow fields calculated by numerical simulations. The present study
+suggests that a deep learning technique can be utilized for prediction of
+laminar wake flow in lieu of solving the Navier-Stokes equations.
+"
+25574,Flat Electronic Bands in Long Sequences of Rhombohedral-stacked Multilayer Graphene,"  The crystallographic stacking order in multilayer graphene plays an important
+role in determining its electronic properties. It has been predicted that a
+rhombohedral (ABC) stacking displays a conducting surface state with flat
+electronic dispersion. In such a flat band, the role of electron-electron
+correlation is enhanced possibly resulting in high Tc superconductivity, charge
+density wave or magnetic orders. Clean experimental band structure measurements
+of ABC stacked specimens are missing because the samples are usually too small
+in size. Here, we directly image the band structure of large multilayer
+graphene flake containing approximately 14 consecutive ABC layers.
+Angle-resolved photoemission spectroscopy experiments reveal the flat
+electronic bands near the K point extends by 0.13 {\AA}-1 at the Fermi level at
+liquid nitrogen temperature. First-principle calculations identify the
+electronic ground state as an antiferromagnetic state with a band gap of about
+40 meV.
+"
+25575,Laser Cooling of 85Rb Atoms to the Recoil Temperature Limit,"  We demonstrate the laser cooling of 85Rb atoms in a two-dimensional optical
+lattice. We follow the two-step degenerate Raman sideband cooling scheme
+[Kerman et al., Phys. Rev. Lett. 84, 439 (2000)], where a fast cooling of atoms
+to an auxiliary state is followed by a slow cooling to a dark state. This
+method has the advantage of independent control of the heating rate and cooling
+rate from the optical pumping beam. We operate the lattice at a Lamb-Dicke
+parameter eta=0.45 and show the cooling of spin-polarized 85Rb atoms to the
+recoil temperature in both dimension within 2.4 ms with the aid of adiabatic
+cooling.
+"
+25576,Preserving Privacy of Finite Impulse Response Systems,"  Adding input and output noises for increasing model identification error of
+finite impulse response (FIR) systems is considered. This is motivated by the
+desire to protect the model of the system as a trade secret by rendering model
+identification techniques ineffective. Optimal filters for constructing
+additive noises that maximizes the identification error subject to maintaining
+the closed-loop performance degradation below a limit are constructed.
+Furthermore, differential privacy is used for designing output noises that
+preserve the privacy of the model.
+"
+25577,Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations,"  Climate projections continue to be marred by large uncertainties, which
+originate in processes that need to be parameterized, such as clouds,
+convection, and ecosystems. But rapid progress is now within reach. New
+computational tools and methods from data assimilation and machine learning
+make it possible to integrate global observations and local high-resolution
+simulations in an Earth system model (ESM) that systematically learns from
+both. Here we propose a blueprint for such an ESM. We outline how
+parameterization schemes can learn from global observations and targeted
+high-resolution simulations, for example, of clouds and convection, through
+matching low-order statistics between ESMs, observations, and high-resolution
+simulations. We illustrate learning algorithms for ESMs with a simple dynamical
+system that shares characteristics of the climate system; and we discuss the
+opportunities the proposed framework presents and the challenges that remain to
+realize it.
+"
+25578,Time-domain thermoreflectance (TDTR) measurements of anisotropic thermal conductivity using a variable spot size approach,"  It is challenging to characterize thermal conductivity of materials with
+strong anisotropy. In this work, we extend the time-domain thermoreflectance
+(TDTR) method with a variable spot size approach to simultaneously measure the
+in-plane (Kr) and the through-plane (Kz) thermal conductivity of materials with
+strong anisotropy. We first determine Kz from the measurement using a larger
+spot size, when the heat flow is mainly one-dimensional along the through-plane
+direction, and the measured signals are sensitive to only Kz. We then extract
+the in-plane thermal conductivity Kr from a second measurement using the same
+modulation frequency but with a smaller spot size, when the heat flow becomes
+three-dimensional, and the signal is sensitive to both Kr and Kz. By choosing
+the same modulation frequency for the two sets of measurements, we can avoid
+potential artifacts introduced by the frequency-dependent Kz, which we have
+found to be non-negligible, especially for some two-dimensional layered
+materials like MoS2. After careful evaluation of the sensitivity of a series of
+hypothetical samples, we provided a guideline on choosing the most appropriate
+laser spot size and modulation frequency that yield the smallest uncertainty,
+and established a criterion for the range of thermal conductivities that can be
+measured reliably using our proposed variable spot size TDTR approach. We have
+demonstrated this variable spot size TDTR approach on samples with a wide range
+of in-plane thermal conductivity, including fused silica, rutile titania (TiO2
+[001]), zinc oxide (ZnO [0001]), molybdenum disulfide (MoS2), hexagonal boron
+nitride (h-BN), and highly ordered pyrolytic graphite (HOPG).
+"
+25579,Stochastic Nonconvex Optimization with Large Minibatches,"  We study stochastic optimization of nonconvex loss functions, which are
+typical objectives for training neural networks. We propose stochastic
+approximation algorithms which optimize a series of regularized, nonlinearized
+losses on large minibatches of samples, using only first-order gradient
+information. Our algorithms provably converge to an approximate critical point
+of the expected objective with faster rates than minibatch stochastic gradient
+descent, and facilitate better parallelization by allowing larger minibatches.
+"
+25580,The Inverse Gamma-Gamma Prior for Optimal Posterior Contraction and Multiple Hypothesis Testing,"  We study the well-known problem of estimating a sparse $n$-dimensional
+unknown mean vector $\theta = (\theta_1, ..., \theta_n)$ with entries corrupted
+by Gaussian white noise. In the Bayesian framework, continuous shrinkage priors
+which can be expressed as scale-mixture normal densities are popular for
+obtaining sparse estimates of $\theta$. In this article, we introduce a new
+fully Bayesian scale-mixture prior known as the inverse gamma-gamma (IGG)
+prior. We prove that the posterior distribution contracts around the true
+$\theta$ at (near) minimax rate under very mild conditions. In the process, we
+prove that the sufficient conditions for minimax posterior contraction given by
+Van der Pas et al. (2016) are not necessary for optimal posterior contraction.
+We further show that the IGG posterior density concentrates at a rate faster
+than those of the horseshoe or the horseshoe+ in the Kullback-Leibler (K-L)
+sense. To classify true signals ($\theta_i \neq 0$), we also propose a
+hypothesis test based on thresholding the posterior mean. Taking the loss
+function to be the expected number of misclassified tests, we show that our
+test procedure asymptotically attains the optimal Bayes risk exactly. We
+illustrate through simulations and data analysis that the IGG has excellent
+finite sample performance for both estimation and classification.
+"
+25581,Breathing multichimera states in nonlocally coupled phase oscillators,"  Chimera states for the one-dimensional array of nonlocally coupled phase
+oscillators in the continuum limit are assumed to be stationary states in most
+studies, but a few studies report the existence of breathing chimera states. We
+focus on multichimera states with two coherent and incoherent regions, and
+numerically demonstrate that breathing multichimera states, whose global order
+parameter oscillates temporally, can appear. Moreover, we show that the system
+exhibits a Hopf bifurcation from a stationary multichimera to a breathing one
+by the linear stability analysis for the stationary multichimera.
+"
+25582,A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction,"  The Nonlinear autoregressive exogenous (NARX) model, which predicts the
+current value of a time series based upon its previous values as well as the
+current and past values of multiple driving (exogenous) series, has been
+studied for decades. Despite the fact that various NARX models have been
+developed, few of them can capture the long-term temporal dependencies
+appropriately and select the relevant driving series to make predictions. In
+this paper, we propose a dual-stage attention-based recurrent neural network
+(DA-RNN) to address these two issues. In the first stage, we introduce an input
+attention mechanism to adaptively extract relevant driving series (a.k.a.,
+input features) at each time step by referring to the previous encoder hidden
+state. In the second stage, we use a temporal attention mechanism to select
+relevant encoder hidden states across all time steps. With this dual-stage
+attention scheme, our model can not only make predictions effectively, but can
+also be easily interpreted. Thorough empirical studies based upon the SML 2010
+dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can
+outperform state-of-the-art methods for time series prediction.
+"
+25583,Algorithms for Runtime Generation of Homogeneous Classes of Objects,"  This paper contains analysis of main modern approaches to dynamic code
+generation, in particular generation of new classes of objects during program
+execution. The main attention was paid to universal exploiters of homogeneous
+classes of objects, which were proposed as a part of such knowledge
+representation model as object-oriented dynamic networks, as the tools for
+generation of new classes of objects in program runtime. As the result,
+algorithms for implementation of such universal exploiters of classes of
+objects as union, intersection, difference and symmetric difference were
+developed. These algorithms can be used knowledge-based intelligent systems,
+which are based on object-oriented dynamic networks, and they can be adapted
+for some object-oriented programming languages with powerful metaprogramming
+opportunities.
+"
+25584,From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information,"  Dropout is used to avoid overfitting by randomly dropping units from the
+neural networks during training. Inspired by dropout, this paper presents
+GI-Dropout, a novel dropout method integrating with global information to
+improve neural networks for text classification. Unlike the traditional dropout
+method in which the units are dropped randomly according to the same
+probability, we aim to use explicit instructions based on global information of
+the dataset to guide the training process. With GI-Dropout, the model is
+supposed to pay more attention to inapparent features or patterns. Experiments
+demonstrate the effectiveness of the dropout with global information on seven
+text classification tasks, including sentiment analysis and topic
+classification.
+"
+25585,On The Transport Theory of Classical Gases in Rindler Space,"  We have obtained the Vlasov equation and Boltzmann kinetic equation using
+Poisson bracket (classical Hamilton equation) and Rindler Hamiltonian. Further,
+we treat the whole Universe as a statistical system with galaxies as the point
+particle constituents in large scale structure. Since the collisions of
+galaxies are very rare phenomena, we assume that the gas with the constituents
+as point galaxies satisfy Vlasov equation. Considering the astrophysical
+catastrophic event, e.g., the creation of gravity waves by the collisions of
+black holes, and further assuming that when such a wave passes through the gas
+causes a kind of polarization of mass distribution. This polarization of mass
+distribution will further gives rise to gravitational permittivity or
+dielectric constant. We have shown that the low frequency gravity waves will be
+absorbed, whereas the high frequency part will pass through the gas of point
+galaxies. It is further noticed that the region in space with extremely high
+gravitational field is transparent to gravity waves. In the other part of this
+work, using the Boltzmann equation and replacing the collision term by the
+relaxation time approximation and further assuming a small deviation from the
+equilibrium configuration of the stellar / galactic plasma in Rindler space, we
+have obtained the kinetic coefficients. For the first time we have derived an
+expression for the coefficient of gravitational flow. It has further been shown
+that in presence of strong gravitational field all the kinetic coefficients
+become vanishingly small.
+"
+25586,Coupled Atomic Wires in a Synthetic Magnetic Field,"  We propose and study systems of coupled atomic wires in a perpendicular
+synthetic magnetic field as a platform to realize exotic phases of quantum
+matter. This includes (fractional) quantum Hall states in arrays of many wires
+inspired by the pioneering work [Kane et al. PRL {\bf{88}}, 036401 (2002)], as
+well as Meissner phases and Vortex phases in double-wires. With one continuous
+and one discrete spatial dimension, the proposed setup naturally complements
+recently realized discrete counterparts, i.e. the Harper-Hofstadter model and
+the two leg flux ladder, respectively. We present both an in-depth theoretical
+study and a detailed experimental proposal to make the unique properties of the
+semi-continuous Harper-Hofstadter model accessible with cold atom experiments.
+For the minimal setup of a double-wire, we explore how a sub-wavelength spacing
+of the wires can be implemented. This construction increases the relevant
+energy scales by at least an order of magnitude compared to ordinary optical
+lattices, thus rendering subtle many-body phenomena such as Lifshitz
+transitions in Fermi gases observable in an experimentally realistic parameter
+regime. For arrays of many wires, we discuss the emergence of Chern bands with
+readily tunable flatness of the dispersion and show how fractional quantum Hall
+states can be stabilized in such systems. Using for the creation of optical
+potentials Laguerre-Gauss beams that carry orbital angular momentum, we detail
+how the coupled atomic wire setups can be realized in non-planar geometries
+such as cylinders, discs, and tori.
+"
+25587,Extraction of the electron mass from $g$ factor measurements on light hydrogenlike ions,"  The determination of the electron mass from Penning-trap measurements with
+$^{12}$C$^{5+}$ ions and from theoretical results for the bound-electron $g$
+factor is described in detail. Some recently calculated contributions slightly
+shift the extracted mass value. Prospects of a further improvement of the
+electron mass are discussed both from the experimental and from the theoretical
+point of view. Measurements with $^4$He$^+$ ions will enable a consistency
+check of the electron mass value, and in future an improvement of the $^4$He
+nuclear mass and a determination of the fine-structure constant.
+"
+25588,Atrous Convolutional Neural Network (ACNN) for Biomedical Semantic Segmentation with Dimensionally Lossless Feature Maps,"  Deep Convolutional Neural Networks (DCNNs) are showing impressive
+performances in biomedical semantic segmentation. However, current DCNNs
+usually use down-sampling layers to achieve significant receptive field
+increasing and to gain abstract semantic information. These down-sampling
+layers decrease the spatial dimension of feature maps as well, which is harmful
+for semantic segmentation. Atrous convolution is an alternative for the
+down-sampling layer. It could increase the receptive field significantly but
+also maintain the spatial dimension of feature maps. In this paper, firstly, an
+atrous rate setting is proposed to achieve the largest and fully-covered
+receptive field with a minimum number of layers. Secondly, six atrous blocks,
+three shortcut connections and four normalization methods are explored to
+select the optimal atrous block, shortcut connection and normalization method.
+Finally, a new and dimensionally lossless DCNN - Atrous Convolutional Neural
+Network (ACNN) is proposed with using cascaded atrous II-blocks, residual
+learning and Fine Group Normalization (FGN). The Right Ventricle (RV), Left
+Ventricle (LV) and aorta data are used for the validation. The results show
+that the proposed ACNN achieves comparable segmentation Dice Similarity
+Coefficients (DSCs) with U-Net, optimized U-Net and the hybrid network, but
+uses much less parameters. This advantage is considered to benefit from the
+dimensionally lossless feature maps.
+"
+25589,Emergent oscillations in dense adaptive cell populations,"  Dynamical quorum sensing is one of the simplest group behaviours in cell
+populations, where collective oscillations emerge via mutual signaling beyond a
+critical cell density. Although many examples are documented, no unifying
+principle has yet been proposed. Here, by considering the response of cells to
+the extracellular signal and vice versa, we develop a quantitative theory for
+the phenomenon, and present a necessary condition for collective oscillations
+in a communicating population. We further show that a sufficient condition for
+oscillations is fulfilled by adaptive cells which reset their signal secretion
+rate upon prolonged stimulation. These general results were elucidated from
+non-equilibrium thermodynamic principles, where stimulated energy release from
+active cells drives oscillations in the medium. The unexpected link between
+adaptation and oscillation is shown to underlie several known examples of
+dynamical quorum sensing, and as such may also be a source of inadvertent group
+behaviour in large populations of living organisms.
+"
+25590,Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting,"  We study the problem of sampling from a distribution where the negative
+logarithm of the target density is $L$-smooth everywhere and $m$-strongly
+convex outside a ball of radius $R$, but potentially non-convex inside this
+ball. We study both overdamped and underdamped Langevin MCMC and prove upper
+bounds on the time required to obtain a sample from a distribution that is
+within $\epsilon$ of the target distribution in $1$-Wasserstein distance. For
+the first-order method (overdamped Langevin MCMC), the time complexity is
+$\tilde{\mathcal{O}}\left(e^{cLR^2}\frac{d}{\epsilon^2}\right)$, where $d$ is
+the dimension of the underlying space. For the second-order method (underdamped
+Langevin MCMC), the time complexity is
+$\tilde{\mathcal{O}}\left(e^{cLR^2}\frac{\sqrt{d}}{\epsilon}\right)$ for some
+explicit positive constant $c$. Surprisingly, the convergence rate is only
+polynomial in the dimension $d$ and the target accuracy $\epsilon$. It is
+however exponential in the problem parameter $LR^2$, which is a measure of
+non-logconcavity of the target distribution.
+"
+25591,Spin mediated magneto-electro-thermal transport behavior in Ni80Fe20/MgO/p-Si thin films,"  In Si, spin-phonon interaction is the primary spin relaxation mechanism. At
+low temperatures, the absence of spin-phonon relaxation will lead to enhanced
+spin accumulation. Spin accumulation may change the electro-thermal transport
+within the material, and thus may serve as an investigative tool for
+characterizing spin-mediated behavior. Here we present the first experimental
+proof of spin accumulation induced electro-thermal transport behavior in a Pd
+(1 nm)/Ni80Fe20 (25 nm)/MgO (1 nm)/p-Si (2 um) specimen. The spin accumulation
+originates from the spin-Hall effect. The spin accumulation changes the
+phononic thermal transport in p-Si causing the observed magneto-electro-thermal
+transport behavior. We also observe the inverted switching behavior in
+magnetoresistance measurement at low temperatures in contrast to magnetic
+characterization, which is attributed to the canted spin states in p-Si due to
+spin accumulation. The spin accumulation is elucidated by current dependent
+anomalous Hall resistance measurement, which shows a decrease as the electric
+current is increased. This result may open a new paradigm in the field of
+spin-mediated transport behavior in semiconductor and semiconductor
+spintronics.
+"
+25592,Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice,"  We introduce a generic scheme for accelerating gradient-based optimization
+methods in the sense of Nesterov. The approach, called Catalyst, builds upon
+the inexact accelerated proximal point algorithm for minimizing a convex
+objective function, and consists of approximately solving a sequence of
+well-chosen auxiliary problems, leading to faster convergence. One of the keys
+to achieve acceleration in theory and in practice is to solve these
+sub-problems with appropriate accuracy by using the right stopping criterion
+and the right warm-start strategy. We give practical guidelines to use Catalyst
+and present a comprehensive analysis of its global complexity. We show that
+Catalyst applies to a large class of algorithms, including gradient descent,
+block coordinate descent, incremental algorithms such as SAG, SAGA, SDCA, SVRG,
+MISO/Finito, and their proximal variants. For all of these methods, we
+establish faster rates using the Catalyst acceleration, for strongly convex and
+non-strongly convex objectives. We conclude with extensive experiments showing
+that acceleration is useful in practice, especially for ill-conditioned
+problems.
+"
+25593,Droplet Bouncing and Breakup during Impact on Microgrooved Surface,"  We experimentally investigate impact dynamics of a microliter water droplet
+on a hydrophobic microgrooved surface. The surface is fabricated using
+photolithography and high-speed visualization is employed to record the
+time-varying droplet shapes in transverse as well as longitudinal direction.
+The effect of the pitch of the grooved surface and Weber number on droplet
+dynamics and impact outcome are studied. At low pitch and Weber number, the
+maximum droplet spreading is found to be greater in the longitudinal direction
+than the transverse direction to the grooves. The preferential spreading
+inversely scales with the pitch at a given Weber number. In this case, the
+outcome is no bouncing (NB); however, this changes at larger pitch or Weber
+number. Under these conditions, the following outcomes are obtained as function
+of the pitch and Weber number - droplet completely bounces off the surface
+(CB), bouncing occurs with droplet breakup (BDB) or no bouncing due to Cassie
+to Wenzel wetting transition (NBW). In BDB and NBW, the liquid penetrates the
+grooves partially or completely beneath the droplet due to the wetting
+transition. The former results in droplet breakup alongside bouncing while the
+latter suppresses the bouncing. These outcomes are demarcated on Weber number -
+dimensionless pitch plane and the proposed regime map suggests the existence of
+a critical Weber number or pitch for the transition from one regime to the
+other. CB and BDB are quantified by plotting coefficient of restitution of the
+bouncing droplet and volume of daughter droplet left on the surface,
+respectively. The critical Weber number needed for the transition from CB to
+BDB is estimated using an existing mathematical model and is compared with the
+measurements. The comparison is good and provides insights in mechanism of the
+liquid penetration into the grooves.
+"
+25594,An Inverse Problem for Infinitely Divisible Moving Average Random Fields,"  Given a low frequency sample of an infinitely divisible moving average random
+field $\{\int_{\mathbb{R}^d} f(x-t)\Lambda(dx); \ t \in \mathbb{R}^d \}$ with a
+known simple function $f$, we study the problem of nonparametric estimation of
+the Lévy characteristics of the independently scattered random measure
+$\Lambda$. We provide three methods, a simple plug-in approach, a method based
+on Fourier transforms and an approach involving decompositions with respect to
+$L^2$-orthonormal bases, which allow to estimate the Lévy density of
+$\Lambda$. For these methods, the bounds for the $L^2$-error are given. Their
+numerical performance is compared in a simulation study.
+"
+25595,Refined large N duality for knots,"  We formulate large $N$ duality of $\mathrm{U}(N)$ refined Chern-Simons theory
+with a torus knot/link in $S^3$. By studying refined BPS states in M-theory, we
+provide the explicit form of low-energy effective actions of Type IIA string
+theory with D4-branes on the $\Omega$-background. This form enables us to
+relate refined Chern-Simons invariants of a torus knot/link in $S^3$ to refined
+BPS invariants in the resolved conifold. Assuming that the extra
+$\mathrm{U}(1)$ global symmetry acts on BPS states trivially, the duality
+predicts graded dimensions of cohomology groups of moduli spaces of M2-M5 bound
+states associated to a torus knot/link in the resolved conifold. Thus, this
+formulation can be also interpreted as a positivity conjecture of refined
+Chern-Simons invariants of torus knots/links. We also discuss about an
+extension to non-torus knots.
+"
+25596,Strictly proper kernel scores and characteristic kernels on compact spaces,"  Strictly proper kernel scores are well-known tool in probabilistic
+forecasting, while characteristic kernels have been extensively investigated in
+the machine learning literature. We first show that both notions coincide, so
+that insights from one part of the literature can be used in the other. We then
+show that the metric induced by a characteristic kernel cannot reliably
+distinguish between distributions that are far apart in the total variation
+norm as soon as the underlying space of measures is infinite dimensional. In
+addition, we provide a characterization of characteristic kernels in terms of
+eigenvalues and -functions and apply this characterization to the case of
+continuous kernels on (locally) compact spaces. In the compact case we further
+show that characteristic kernels exist if and only if the space is metrizable.
+As special cases of our general theory we investigate translation-invariant
+kernels on compact Abelian groups and isotropic kernels on spheres. The latter
+are of particular interest for forecast evaluation of probabilistic predictions
+on spherical domains as frequently encountered in meteorology and climatology.
+"
+25597,"Clustering with feature selection using alternating minimization, Application to computational biology","  This paper deals with unsupervised clustering with feature selection. The
+problem is to estimate both labels and a sparse projection matrix of weights.
+To address this combinatorial non-convex problem maintaining a strict control
+on the sparsity of the matrix of weights, we propose an alternating
+minimization of the Frobenius norm criterion. We provide a new efficient
+algorithm named K-sparse which alternates k-means with projection-gradient
+minimization. The projection-gradient step is a method of splitting type, with
+exact projection on the $\ell^1$ ball to promote sparsity. The convergence of
+the gradient-projection step is addressed, and a preliminary analysis of the
+alternating minimization is made. The Frobenius norm criterion converges as the
+number of iterates in Algorithm K-sparse goes to infinity. Experiments on
+Single Cell RNA sequencing datasets show that our method significantly improves
+the results of PCA k-means, spectral clustering, SIMLR, and Sparcl methods, and
+achieves a relevant selection of genes. The complexity of K-sparse is linear in
+the number of samples (cells), so that the method scales up to large datasets.
+"
+25598,Limited individual attention and online virality of low-quality information,"  Social media are massive marketplaces where ideas and news compete for our
+attention. Previous studies have shown that quality is not a necessary
+condition for online virality and that knowledge about peer choices can distort
+the relationship between quality and popularity. However, these results do not
+explain the viral spread of low-quality information, such as the digital
+misinformation that threatens our democracy. We investigate quality
+discrimination in a stylized model of online social network, where individual
+agents prefer quality information, but have behavioral limitations in managing
+a heavy flow of information. We measure the relationship between the quality of
+an idea and its likelihood to become prevalent at the system level. We find
+that both information overload and limited attention contribute to a
+degradation in the market's discriminative power. A good tradeoff between
+discriminative power and diversity of information is possible according to the
+model. However, calibration with empirical data characterizing information load
+and finite attention in real social media reveals a weak correlation between
+quality and popularity of information. In these realistic conditions, the model
+predicts that high-quality information has little advantage over low-quality
+information.
+"
+25599,Computer simulations for the blow-up of complex solutions of the 3-d Navier-Stokes equations,"  We present a study by computer simulations of a class of complex-valued
+solutions of the three-dimensional Navier-Stokes equations in the whole space,
+which, according to Li and Sinai, present a blow-up (singularity) at a finite
+time. The computer results allow a detailed study of the blow-up mechanism, and
+show interesting features of the behavior of the solutions near the blow-up
+time, such as the concentration of energy and enstrophy in a small region
+around a few points of physical space, while outside this region the ""fluid""
+remains ""quiet"".
+"
+25600,A conjectural Peterson isomorphism in K-theory,"  We state a precise conjectural isomorphism between localizations of the
+equivariant quantum K-theory ring of a flag variety and the equivariant
+K-homology ring of the affine Grassmannian, in particular relating their
+Schubert bases and structure constants. This generalizes Peterson's isomorphism
+in (co)homology. We prove a formula for the Pontryagin structure constants in
+the K-homology ring, and we use it to check our conjecture in few situations.
+"
+25601,Flows and Shocks: Some Recent Developments in Symbiotic Star and Nova Research,"  There have been several surprising developments in our understanding of
+symbiotic binary stars and nova eruptions over the last decade or so based on
+multiwavelength data. For example, symbiotic stars without shell burning on
+their white dwarfs have been revealed through their X-ray emission, UV excess,
+and UV variability. These purely accretion powered symbiotic stars have much
+weaker optical emission lines than those with shell burning, and are therefore
+harder to discover. Yet they may be as numerous as symbiotic stars with shell
+burning. Interestingly, both types of symbiotic stars are capable of driving
+strong outflows, leading to colliding wind X-ray emission and spatially
+resolved jets. For nova eruptions, the most surprising discovery has been that
+they are capable of particle acceleration as evidenced by Fermi detection of
+novae as transient GeV gamma-ray sources. For nova eruptions in cataclysmic
+variables, this implicates internal shocks, between a slow, dense outflow and a
+fast outflow or wind. Other signatures of shocks include thermal X-rays and
+non-thermal radio emissions, and a substantial fraction of optical emission may
+be shock-powered in the early phase of novae. Radio (V959 Mon) and HST (V959
+Mon and T Pyx) images of nova shells within a few years of their respective
+eruptions suggest that the ejecta from a nova may commonly consist of an
+equatorial ring and a bipolar outflow.
+"
+25602,Quantum Plasmonic Nanoantennas,"  We study plasmonic excitations in the limit of few electrons, in one-atom
+thick sodium chains, and characterize them based on collectivity. We also
+compare the excitations to classical localised plasmon modes and find for the
+longitudinal mode a quantum-classical transition around 10 atoms. The
+transverse mode appears at much higher energies than predicted classically for
+all chain lengths. The electric field enhancement is also considered which is
+made possible by considering the effects of electron-phonon coupling on the
+broadening of the electronic spectra. Large field enhancements are possible on
+the molecular level allowing us to consider the validity of using molecules as
+the ultimate small size limit of plasmonic antennas. Additionally, we consider
+the case of a dimer system of two sodium chains, where the gap can be
+considered as a picocavity, and we analyse the charge-transfer states and their
+dependence on the gap size as well as chain size. Our results and methods are
+useful for understanding and developing ultra-small, tunable and novel
+plasmonic devices that utilise quantum effects that could have applications in
+quantum optics, quantum metamaterials, cavity-quantum electrodynamics and
+controlling chemical reactions, as well as deepening our understanding of
+localised plasmons in low dimensional molecular systems.
+"
+25603,Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning,"  A primary goal of computational phenotype research is to conduct medical
+diagnosis. In hospital, physicians rely on massive clinical data to make
+diagnosis decisions, among which laboratory tests are one of the most important
+resources. However, the longitudinal and incomplete nature of laboratory test
+data casts a significant challenge on its interpretation and usage, which may
+result in harmful decisions by both human physicians and automatic diagnosis
+systems. In this work, we take advantage of deep generative models to deal with
+the complex laboratory tests. Specifically, we propose an end-to-end
+architecture that involves a deep generative variational recurrent neural
+networks (VRNN) to learn robust and generalizable features, and a
+discriminative neural network (NN) model to learn diagnosis decision making,
+and the two models are trained jointly. Our experiments are conducted on a
+dataset involving 46,252 patients, and the 50 most frequent tests are used to
+predict the 50 most common diagnoses. The results show that our model, VRNN+NN,
+significantly (p<0.001) outperforms other baseline models. Moreover, we
+demonstrate that the representations learned by the joint training are more
+informative than those learned by pure generative models. Finally, we find that
+our model offers a surprisingly good imputation for missing values.
+"
+25604,Probabilistic properties of the elliptic motion,"  In this paper we consider the plane elliptic motion which occurs if the
+moving centrode is a circle of radius $r$ and the fixed centrode a circle of
+radius $2r$. Every point of the moving plane generates an ellipse in the fixed
+plane. Let a disk of radius $R$, $0 \le R < \infty$, concentric to the moving
+centrode be attached to the moving plane. If a point $P$ is chosen at random
+from this disk, then the area and the perimeter of the ellipse generated by $P$
+are random variables. We determine the moments and the distributions of these
+random variables for the case that $P$ is uniformly distributed over the area
+of the disk.
+"
+25605,Non-reciprocal geometric phase in nonlinear frequency conversion,"  We describe analytically and numerically the geometric phase arising from
+nonlinear frequency conversion and show that such a phase can be made
+non-reciprocal by momentum-dependent photonic transition. Such non-reciprocity
+is immune to the shortcomings imposed by dynamic reciprocity in Kerr and
+Kerr-like devices. We propose a simple and practical implementation, requiring
+only a single waveguide and one pump, while the geometric phase is controllable
+by the pump and promises robustness against fabrication errors.
+"
+25606,Dynamics of subarcsecond bright dots in the transition region above sunspot and their relation to penumbral micro-jets,"  Recent high-resolution observations reveal that subarcsecond bright dots
+(BDs) with sub-minute lifetimes appears ubiquitously in the transition region
+(TR) above sunspot penumbra. The presence of penumbral micro-jets (PMJs) in the
+chromosphere have also been reported earlier. It was proposed that both the
+PMJs and BDs are formed due to magnetic reconnection process and may play an
+important role in heating of the penumbra. Using simultaneous observation of
+the chromosphere from the Solar Optical Telescope (SOT) aboard Hinode and the
+TR from the Interface Region Imaging Spectrograph (IRIS), we study the dynamics
+of BDs and their relation with PMJs. We find two types of BDs, one which is
+related to PMJs and the others which do not show any visible dynamics in the
+SOT Ca II H images. From a statistical analysis we show that these two types
+have different properties. The BDs which are related to PMJs always appear at
+the top of the PMJs, the vast majority of which show inward motion and
+originate before the generation of the PMJs. These results may indicate that
+the reconnection occurs at the lower coronal/TR height and initiates PMJs at
+the chromosphere. This formation mechanism is in contrast with the currently
+believed formation of PMJs by reconnection in the (upper) photosphere between
+differently inclined fields.
+"
+25607,Contour location via entropy reduction leveraging multiple information sources,"  We introduce an algorithm to locate contours of functions that are expensive
+to evaluate. The problem of locating contours arises in many applications,
+including classification, constrained optimization, and performance analysis of
+mechanical and dynamical systems (reliability, probability of failure,
+stability, etc.). Our algorithm locates contours using information from
+multiple sources, which are available in the form of relatively inexpensive,
+biased, and possibly noisy approximations to the original function. Considering
+multiple information sources can lead to significant cost savings. We also
+introduce the concept of contour entropy, a formal measure of uncertainty about
+the location of the zero contour of a function approximated by a statistical
+surrogate model. Our algorithm locates contours efficiently by maximizing the
+reduction of contour entropy per unit cost.
+"
+25608,Implementation of the KDamper concept using disc springs,"  The KDamper is a novel passive vibration isolation and damping concept, based
+essentially on the optimal combination of appropriate stiffness elements, which
+include a negative stiffness el-ement. Among others, KDamper can provide
+comparative advantages in the very low fre-quency range. It should be
+mentioned, that the KDamping concept does not simply refer to a discrete
+vibration absorption device, but it consists a general vibration ab-sorption
+concept, applicable also for the design of advanced materials or complex
+structures. Such a concept thus presents the potential for numerous
+implementations in a large variety of technological applications, while further
+potential may emerge in a multi- physics environment. The paper proceeds to a
+review of the optimal design and selection of the parameters of the KDamper,
+which follows exactly the classical approach used for the design of the TMD.
+The negative stiffness elements have been replaced with a set of Belleville
+(disc) springs. It should be mentioned that two different cases for an initial
+displacement and an initial velocity have been considered. It is thus
+theoretically proven that the KDamper can inherently offer far better isolation
+and damping properties than the TMD. Finally, an application concerning the
+implementation of the KDamper for the design of a low frequency vertical
+vibration isolator is presented.
+"
+25609,Homotopies for Free!,"  We show ""free theorems"" in the style of Wadler for polymorphic functions in
+homotopy type theory as consequences of the abstraction theorem. As an
+application, it follows that every space defined as a higher inductive type has
+the same homotopy groups as some type of polymorphic functions defined without
+univalence or higher inductive types.
+"
+25610,A global feature extraction model for the effective computer aided diagnosis of mild cognitive impairment using structural MRI images,"  Multiple modalities of biomarkers have been proved to be very sensitive in
+assessing the progression of Alzheimer's disease (AD), and using these
+modalities and machine learning algorithms, several approaches have been
+proposed to assist in the early diagnosis of AD. Among the recent investigated
+state-of-the-art approaches, Gaussian discriminant analysis (GDA)-based
+approaches have been demonstrated to be more effective and accurate in the
+classification of AD, especially for delineating its prodromal stage of mild
+cognitive impairment (MCI). Moreover, among those binary classification
+investigations, the local feature extraction methods were mostly used, which
+made them hardly be applied to a practical computer aided diagnosis system.
+Therefore, this study presents a novel global feature extraction model taking
+advantage of the recent proposed GDA-based dual high-dimensional decision
+spaces, which can significantly improve the early diagnosis performance
+comparing to those local feature extraction methods. In the true test using 20%
+held-out data, for discriminating the most challenging MCI group from the
+cognitively normal control (CN) group, an F1 score of 91.06%, an accuracy of
+88.78%, a sensitivity of 91.80%, and a specificity of 83.78% were achieved that
+can be considered as the best performance obtained so far.
+"
+25611,Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models,"  Many machine learning algorithms are vulnerable to almost imperceptible
+perturbations of their inputs. So far it was unclear how much risk adversarial
+perturbations carry for the safety of real-world machine learning applications
+because most methods used to generate such perturbations rely either on
+detailed model information (gradient-based attacks) or on confidence scores
+such as class probabilities (score-based attacks), neither of which are
+available in most real-world scenarios. In many such cases one currently needs
+to retreat to transfer-based attacks which rely on cumbersome substitute
+models, need access to the training data and can be defended against. Here we
+emphasise the importance of attacks which solely rely on the final model
+decision. Such decision-based attacks are (1) applicable to real-world
+black-box models such as autonomous cars, (2) need less knowledge and are
+easier to apply than transfer-based attacks and (3) are more robust to simple
+defences than gradient- or score-based attacks. Previous attacks in this
+category were limited to simple models or simple datasets. Here we introduce
+the Boundary Attack, a decision-based attack that starts from a large
+adversarial perturbation and then seeks to reduce the perturbation while
+staying adversarial. The attack is conceptually simple, requires close to no
+hyperparameter tuning, does not rely on substitute models and is competitive
+with the best gradient-based attacks in standard computer vision tasks like
+ImageNet. We apply the attack on two black-box algorithms from Clarifai.com.
+The Boundary Attack in particular and the class of decision-based attacks in
+general open new avenues to study the robustness of machine learning models and
+raise new questions regarding the safety of deployed machine learning systems.
+An implementation of the attack is available as part of Foolbox at
+this https URL .
+"
+25612,ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases,"  ConvNets and Imagenet have driven the recent success of deep learning for
+image classification. However, the marked slowdown in performance improvement
+combined with the lack of robustness of neural networks to adversarial examples
+and their tendency to exhibit undesirable biases question the reliability of
+these methods. This work investigates these questions from the perspective of
+the end-user by using human subject studies and explanations. The contribution
+of this study is threefold. We first experimentally demonstrate that the
+accuracy and robustness of ConvNets measured on Imagenet are vastly
+underestimated. Next, we show that explanations can mitigate the impact of
+misclassified adversarial examples from the perspective of the end-user. We
+finally introduce a novel tool for uncovering the undesirable biases learned by
+a model. These contributions also show that explanations are a valuable tool
+both for improving our understanding of ConvNets' predictions and for designing
+more reliable models.
+"
+25613,Graph Classification via Deep Learning with Virtual Nodes,"  Learning representation for graph classification turns a variable-size graph
+into a fixed-size vector (or matrix). Such a representation works nicely with
+algebraic manipulations. Here we introduce a simple method to augment an
+attributed graph with a virtual node that is bidirectionally connected to all
+existing nodes. The virtual node represents the latent aspects of the graph,
+which are not immediately available from the attributes and local connectivity
+structures. The expanded graph is then put through any node representation
+method. The representation of the virtual node is then the representation of
+the entire graph. In this paper, we use the recently introduced Column Network
+for the expanded graph, resulting in a new end-to-end graph classification
+model dubbed Virtual Column Network (VCN). The model is validated on two tasks:
+(i) predicting bio-activity of chemical compounds, and (ii) finding software
+vulnerability from source code. Results demonstrate that VCN is competitive
+against well-established rivals.
+"
+25614,Deformable mirror interferometric analysis for the direct imagery of exoplanets,"  Direct imaging of exoplanet systems requires the use of coronagraphs to reach
+high contrast levels (10^-8 to 10^-11) at small angular separations (0.1"").
+However, the performance of these devices is drastically limited by aberrations
+(in phase or in amplitude, introduced either by atmosphere or by the optics).
+Coronagraphs must therefore be combined with extreme adaptive optic systems,
+composed of a focal plane wavefront sensor and of a high order deformable
+mirror. These adaptive optic systems must reach a residual error in the
+corrected wavefront of less than 0.1 nm (RMS) with a rate of 1 kHz. In
+addition, the surface defects of the deformable mirror, inherent from the
+fabrication process, must be limited in order to avoid the introduction of
+amplitude aberrations. An experimental high contrast bench has been developed
+at the Paris Observatory (LESIA). This bench includes a Boston Micromachine
+deformable mirror composed of 1024 actuators. For a precise analysis of its
+surface and performance, we characterized this mirror on the interferometric
+bench developed since 2004 at the Marseille Observatory (LAM). In this paper,
+we present this interferometric bench as well as the results of the analysis.
+This will include a precise surface characterization and a description of the
+behavior of the actuators, on a 10 by 10 actuator range (behavior of a single
+actuator, study of the cross-talk between neighbor actuators, influence of a
+stuck actuator) and on full mirror scale (general surface shape).
+"
+25615,Quasi-PTAS for Scheduling with Precedences using LP Hierarchies,"  A central problem in scheduling is to schedule $n$ unit size jobs with
+precedence constraints on $m$ identical machines so as to minimize the
+makespan. For $m=3$, it is not even known if the problem is NP-hard and this is
+one of the last open problems from the book of Garey and Johnson.
+We show that for fixed $m$ and $\epsilon$, $(\log n)^{O(1)}$ rounds of
+Sherali-Adams hierarchy applied to a natural LP of the problem provides a
+$(1+\epsilon)$-approximation algorithm running in quasi-polynomial time. This
+improves over the recent result of Levey and Rothvoss, who used $r=(\log
+n)^{O(\log \log n)}$ rounds of Sherali-Adams in order to get a
+$(1+\epsilon)$-approximation algorithm with a running time of $n^{O(r)}$.
+"
+25616,Population Extinction under Bursty Reproduction in a Time Modulated Environment,"  In recent years non-demographic variability has been shown to greatly affect
+dynamics of stochastic populations. For example, non-demographic noise in the
+form of a bursty reproduction process with an a-priori unknown burst size, or
+environmental variability in the form of time-varying reaction rates, have been
+separately found to dramatically impact the extinction risk of isolated
+populations. In this work we investigate the extinction risk of an isolated
+population under the combined influence of these two types of non-demographic
+variation. Using the so-called momentum-space WKB approach we arrive at a set
+of time-dependent Hamilton equations. In order to account for the explicit time
+dependence, we find the instanton of the time-perturbed Hamiltonian
+numerically, where analytical expressions are presented in particular limits
+using various perturbation techniques. We focus on two classes of time-varying
+environments: periodically-varying rates corresponding to seasonal effects, and
+a sudden decrease in the birth rate corresponding to a catastrophe. All our
+theoretical results are tested against numerical Monte Carlo simulations with
+time-dependent rates and also against a numerical solution of the corresponding
+time-dependent Hamilton equations.
+"
+25617,Unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis,"  Computer-assisted surgery (CAS) aims to provide the surgeon with the right
+type of assistance at the right moment. Such assistance systems are especially
+relevant in laparoscopic surgery, where CAS can alleviate some of the drawbacks
+that surgeons incur. For many assistance functions, e.g. displaying the
+location of a tumor at the appropriate time or suggesting what instruments to
+prepare next, analyzing the surgical workflow is a prerequisite. Since
+laparoscopic interventions are performed via endoscope, the video signal is an
+obvious sensor modality to rely on for workflow analysis.
+Image-based workflow analysis tasks in laparoscopy, such as phase
+recognition, skill assessment, video indexing or automatic annotation, require
+a temporal distinction between video frames. Generally computer vision based
+methods that generalize from previously seen data are used. For training such
+methods, large amounts of annotated data are necessary. Annotating surgical
+data requires expert knowledge, therefore collecting a sufficient amount of
+data is difficult, time-consuming and not always feasible.
+In this paper, we address this problem by presenting an unsupervised method
+for training a convolutional neural network (CNN) to differentiate between
+laparoscopic video frames on a temporal basis. We extract video frames at
+regular intervals from 324 unlabeled laparoscopic interventions, resulting in a
+dataset of approximately 2.2 million images. From this dataset, we extract
+image pairs from the same video and train a CNN to determine their temporal
+order. To solve this problem, the CNN has to extract features that are relevant
+for comprehending laparoscopic workflow.
+Furthermore, we demonstrate that such a CNN can be adapted for surgical
+workflow segmentation. We performed image-based workflow segmentation on a
+publicly available dataset of 7 cholecystectomies and 9 colorectal
+interventions.
+"
+25618,Multi-output Polynomial Networks and Factorization Machines,"  Factorization machines and polynomial networks are supervised polynomial
+models based on an efficient low-rank decomposition. We extend these models to
+the multi-output setting, i.e., for learning vector-valued functions, with
+application to multi-class or multi-task problems. We cast this as the problem
+of learning a 3-way tensor whose slices share a common basis and propose a
+convex formulation of that problem. We then develop an efficient conditional
+gradient algorithm and prove its global convergence, despite the fact that it
+involves a non-convex basis selection step. On classification tasks, we show
+that our algorithm achieves excellent accuracy with much sparser models than
+existing methods. On recommendation system tasks, we show how to combine our
+algorithm with a reduction from ordinal regression to multi-output
+classification and show that the resulting algorithm outperforms simple
+baselines in terms of ranking accuracy.
+"
+25619,Inference in Probabilistic Graphical Models by Graph Neural Networks,"  A fundamental computation for statistical inference and accurate
+decision-making is to compute the marginal probabilities or most probable
+states of task-relevant variables. Probabilistic graphical models can
+efficiently represent the structure of such complex data, but performing these
+inferences is generally difficult. Message-passing algorithms, such as belief
+propagation, are a natural way to disseminate evidence amongst correlated
+variables while exploiting the graph structure, but these algorithms can
+struggle when the conditional dependency graphs contain loops. Here we use
+Graph Neural Networks (GNNs) to learn a message-passing algorithm that solves
+these inference tasks. We first show that the architecture of GNNs is
+well-matched to inference tasks. We then demonstrate the efficacy of this
+inference approach by training GNNs on a collection of graphical models and
+showing that they substantially outperform belief propagation on loopy graphs.
+Our message-passing algorithms generalize out of the training set to larger
+graphs and graphs with different structure.
+"
+25620,A lower bound for the BCS functional with boundary conditions at infinity,"  We consider a many-body system of fermionic atoms interacting via a local
+pair potential and subject to an external potential within the framework of BCS
+theory. We measure the free energy of the whole sample with respect to the free
+energy of a reference state which allows us to define a BCS functional with
+boundary conditions at infinity. Our main result is a lower bound for this
+energy functional in terms of expressions that typically appear in
+Ginzburg-Landau functionals.
+"
+25621,VLA observations of the disk around the young brown dwarf 2MASS J044427+2512,"  We present multi-wavelength radio observations obtained with the VLA of the
+protoplanetary disk surrounding the young brown dwarf 2MASS J04442713+2512164
+(2M0444) in the Taurus star forming region. 2M0444 is the brightest known brown
+dwarf disk at millimeter wavelengths, making this an ideal target to probe
+radio emission from a young brown dwarf. Thermal emission from dust in the disk
+is detected at 6.8 and 9.1 mm, whereas the 1.36 cm measured flux is dominated
+by ionized gas emission. We combine these data with previous observations at
+shorter sub-mm and mm wavelengths to test the predictions of dust evolution
+models in gas-rich disks after adapting their parameters to the case of 2M0444.
+These models show that the radial drift mechanism affecting solids in a gaseous
+environment has to be either completely made inefficient, or significantly
+slowed down by very strong gas pressure bumps in order to explain the presence
+of mm/cm-sized grains in the outer regions of the 2M0444 disk. We also discuss
+the possible mechanisms for the origin of the ionized gas emission detected at
+1.36 cm. The inferred radio luminosity for this emission is in line with the
+relation between radio and bolometric luminosity valid for for more massive and
+luminous young stellar objects, and extrapolated down to the very low
+luminosity of the 2M0444 brown dwarf.
+"
+25622,On the connectedness of the set of Riemann surfaces with real moduli,"  The moduli space ${\mathcal{M}}_{g}$, of genus $g\geq2$ closed Riemann
+surfaces, is a complex orbifold of dimension $3(g-1)$ which carries a natural
+real structure i.e. it admits an anti-holomorphic involution $\sigma$. The
+involution $\sigma$ maps each point corresponding to a Riemann surface $S$ to
+its complex conjugate $\overline{S}$. The fixed point set of $\sigma$ consists
+of the isomorphism classes of closed Riemann surfaces admitting an
+anticonformal automorphism. Inside $\mathrm{Fix}(\sigma)$ is the locus
+${\mathcal{M}}_{g}(\mathbb{R})$, the set of real Riemann surfaces, which is
+known to be connected by results due to P. Buser, M. Seppälä and R.
+Silhol. The complement $\mathrm{Fix}(\sigma)-{\mathcal{M}}_{g}(\mathbb{R})$
+consists of the so called pseudo-real Riemann surfaces, which is known to be
+non-connected. In this short note we provide a simple argument to observe that
+$\mathrm{Fix}(\sigma)$ is connected.
+"
+25623,A scalable approach for tree segmentation within small-footprint airborne LiDAR data,"  This paper presents a distributed approach that scales up to segment tree
+crowns within a LiDAR point cloud representing an arbitrarily large forested
+area. The approach uses a single-processor tree segmentation algorithm as a
+building block in order to process the data delivered in the shape of tiles in
+parallel. The distributed processing is performed in a master-slave manner, in
+which the master maintains the global map of the tiles and coordinates the
+slaves that segment tree crowns within and across the boundaries of the tiles.
+A minimal bias was introduced to the number of detected trees because of trees
+lying across the tile boundaries, which was quantified and adjusted for.
+Theoretical and experimental analyses of the runtime of the approach revealed a
+near linear speedup. The estimated number of trees categorized by crown class
+and the associated error margins as well as the height distribution of the
+detected trees aligned well with field estimations, verifying that the
+distributed approach works correctly. The approach enables providing
+information of individual tree locations and point cloud segments for a
+forest-level area in a timely manner, which can be used to create detailed
+remotely sensed forest inventories. Although the approach was presented for
+tree segmentation within LiDAR point clouds, the idea can also be generalized
+to scale up processing other big spatial datasets.
+Highlights: - A scalable distributed approach for tree segmentation was
+developed and theoretically analyzed. - ~2 million trees in a 7440 ha forest
+was segmented in 2.5 hours using 192 cores. - 2% false positive trees were
+identified as a result of the distributed run. - The approach can be used to
+scale up processing other big spatial data
+"
+25624,"Polling bias and undecided voter allocations: US Presidential elections, 2004 - 2016","  Accounting for undecided and uncertain voters is a challenging issue for
+predicting election results from public opinion polls. Undecided voters typify
+the uncertainty of swing voters in polls but are often ignored or allocated to
+each candidate in a simple, deterministic manner. Historically this may have
+been adequate because the undecided were comparatively small enough to assume
+that they do not affect the relative proportions of the decided voters.
+However, in the presence of high numbers of undecided voters, these static
+rules may in fact bias election predictions from election poll authors and
+meta-poll analysts. In this paper, we examine the effect of undecided voters in
+the 2016 US presidential election to the previous three presidential elections.
+We show there were a relatively high number of undecided voters over the
+campaign and on election day, and that the allocation of undecided voters in
+this election was not consistent with two-party proportional (or even)
+allocations. We find evidence that static allocation regimes are inadequate for
+election prediction models and that probabilistic allocations may be superior.
+We also estimate the bias attributable to polling agencies, often referred to
+as ""house effects"".
+"
+25625,A Skew-Normal Copula-Driven GLMM,"  This paper presents a method for fitting a copula-driven generalized linear
+mixed models. For added flexibility, the skew-normal copula is adopted for
+fitting. The correlation matrix of the skew-normal copula is used to capture
+the dependence structure within units, while the fixed and random effects
+coefficients are estimated through the mean of the copula. For estimation, a
+Monte Carlo expectation-maximization algorithm is developed. Simulations are
+shown alongside a real data example from the Framingham Heart Study.
+"
+25626,Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection,"  Sound event detection is a challenging task, especially for scenes with
+multiple simultaneous events. While event classification methods tend to be
+fairly accurate, event localization presents additional challenges, especially
+when large amounts of labeled data are not available. Task4 of the 2018 DCASE
+challenge presents an event detection task that requires accuracy in both
+segmentation and recognition of events while providing only weakly labeled
+training data. Supervised methods can produce accurate event labels but are
+limited in event segmentation when training data lacks event timestamps. On the
+other hand, unsupervised methods that model the acoustic properties of the
+audio can produce accurate event boundaries but are not guided by the
+characteristics of event classes and sound categories. We present a hybrid
+approach that combines an acoustic-driven event boundary detection and a
+supervised label inference using a deep neural network. This framework
+leverages benefits of both unsupervised and supervised methodologies and takes
+advantage of large amounts of unlabeled data, making it ideal for large-scale
+weakly labeled event detection. Compared to a baseline system, the proposed
+approach delivers a 15% absolute improvement in F-score, demonstrating the
+benefits of the hybrid bottom-up, top-down approach.
+"
+25627,A Theory of Output-Side Unsupervised Domain Adaptation,"  When learning a mapping from an input space to an output space, the
+assumption that the sample distribution of the training data is the same as
+that of the test data is often violated. Unsupervised domain shift methods
+adapt the learned function in order to correct for this shift. Previous work
+has focused on utilizing unlabeled samples from the target distribution. We
+consider the complementary problem in which the unlabeled samples are given
+post mapping, i.e., we are given the outputs of the mapping of unknown samples
+from the shifted domain. Two other variants are also studied: the two sided
+version, in which unlabeled samples are give from both the input and the output
+spaces, and the Domain Transfer problem, which was recently formalized. In all
+cases, we derive generalization bounds that employ discrepancy terms.
+"
+25628,Minimax rates for statistical inverse problems under general source conditions,"  We describe the minimax reconstruction rates in linear ill-posed equations in
+Hilbert space when smoothness is given in terms of general source sets. The
+underlying fundamental result, the minimax rate on ellipsoids, is proved
+similarly to the seminal study by D. L. Donoho, R.~C. Liu, and B. MacGibbon,
+{\it Minimax risk over hyperrectangles, and implications}, Ann.~ Statist. 18,
+1990. These authors highlighted the special role of the truncated series
+estimator, and for such estimators the risk can explicitly be given. We provide
+several examples, indicating results for statistical estimation in ill-posed
+problems in Hilbert space.
+"
+25629,Increasing the Flow of Rumors in Social Networks by Spreading Groups,"  The paper addresses a method for spreading messages in social networks
+through an initial acceleration by Spreading Groups. These groups start the
+spread which eventually reaches a larger portion of the network. The use of
+spreading groups creates a final flow which resembles the spread through the
+nodes with the highest level of influence (opinion leaders). While harnessing
+opinion leaders to spread messages is generally costly, the formation of
+spreading groups is merely a technical issue, and can be done by computerized
+bots. The paper presents an information flow model and inspects the model
+through a dataset of Nasdaq-related tweets.
+"
+25630,"Borcherds Products on Unitary Group $U(2,1)$","  In this note, we construct canonical bases for the spaces of weakly
+holomorphic modular forms with poles supported at the cusp $\infty$ for
+$\Gamma_{0}(4)$ of integral weight $k$ for $k\leq-1$, and we make use of the
+basis elements for the case $k=-1$ to construct explicit Borcherds products on
+unitary group $U(2,1)$.
+"
+25631,"Prototype Tasks: Improving Crowdsourcing Results through Rapid, Iterative Task Design","  Low-quality results have been a long-standing problem on microtask
+crowdsourcing platforms, driving away requesters and justifying low wages for
+workers. To date, workers have been blamed for low-quality results: they are
+said to make as little effort as possible, do not pay attention to detail, and
+lack expertise. In this paper, we hypothesize that requesters may also be
+responsible for low-quality work: they launch unclear task designs that confuse
+even earnest workers, under-specify edge cases, and neglect to include
+examples. We introduce prototype tasks, a crowdsourcing strategy requiring all
+new task designs to launch a small number of sample tasks. Workers attempt
+these tasks and leave feedback, enabling the re- quester to iterate on the
+design before publishing it. We report a field experiment in which tasks that
+underwent prototype task iteration produced higher-quality work results than
+the original task designs. With this research, we suggest that a simple and
+rapid iteration cycle can improve crowd work, and we provide empirical evidence
+that requester ""quality"" directly impacts result quality.
+"
+25632,Linear Time Approximation Schemes for Geometric Maximum Coverage,"  We study approximation algorithms for the following geometric version of the
+maximum coverage problem: Let $\mathcal{P}$ be a set of $n$ weighted points in
+the plane. Let $D$ represent a planar object, such as a rectangle, or a disk.
+We want to place $m$ copies of $D$ such that the sum of the weights of the
+points in $\mathcal{P}$ covered by these copies is maximized. For any fixed
+$\varepsilon>0$, we present efficient approximation schemes that can find a
+$(1-\varepsilon)$-approximation to the optimal solution. In particular, for
+$m=1$ and for the special case where $D$ is a rectangle, our algorithm runs in
+time $O(n\log (\frac{1}{\varepsilon}))$, improving on the previous result. For
+$m>1$ and the rectangular case, our algorithm runs in
+$O(\frac{n}{\varepsilon}\log (\frac{1}{\varepsilon})+\frac{m}{\varepsilon}\log
+m +m(\frac{1}{\varepsilon})^{O(\min(\sqrt{m},\frac{1}{\varepsilon}))})$ time.
+For a more general class of shapes (including disks, polygons with $O(1)$
+edges), our algorithm runs in
+$O(n(\frac{1}{\varepsilon})^{O(1)}+\frac{m}{\epsilon}\log m +
+m(\frac{1}{\varepsilon})^{O(\min(m,\frac{1}{\varepsilon^2}))})$ time.
+"
+25633,Non-local field-like spin-orbit torques in Rashba systems: an ab-initio study of Ag$_{2}$Bi/Ag/Fe film,"  We investigate from first principles the field-like spin-orbit torques (SOTs)
+in a Ag$_{2}$Bi-terminated Ag(111) film grown on ferromagnetic Fe(110). We find
+that a large part of the SOT arises from the spin-orbit interaction (SOI) in
+the Ag$_{2}$Bi layer far away from the Fe layers. These results clearly hint at
+a long range spin transfer in the direction perpendicular to the film that does
+not originate in the spin Hall effect. In order to bring evidence of the
+non-local character of the computed SOT, we show that the torque acting on the
+Fe layers can be engineered by the introduction of Bi vacancies in the
+Ag$_{2}$Bi layer. Overall, we find a drastic dependence of the SOT on the
+disorder type, which we explain by a complex interplay of different
+contributions to the SOT in the Brillouin zone.
+"
+25634,Co-projection-plane based 3-D padding for polyhedron projection for 360-degree video,"  The polyhedron projection for 360-degree video is becoming more and more
+popular since it can lead to much less geometry distortion compared with the
+equirectangular projection. However, in the polyhedron projection, we can
+observe very obvious texture discontinuity in the area near the face boundary.
+Such a texture discontinuity may lead to serious quality degradation when
+motion compensation crosses the discontinuous face boundary. To solve this
+problem, in this paper, we first propose to fill the corresponding neighboring
+faces in the suitable positions as the extension of the current face to keep
+approximated texture continuity. Then a co-projection-plane based 3-D padding
+method is proposed to project the reference pixels in the neighboring face to
+the current face to guarantee exact texture continuity. Under the proposed
+scheme, the reference pixel is always projected to the same plane with the
+current pixel when performing motion compensation so that the texture
+discontinuity problem can be solved. The proposed scheme is implemented in the
+reference software of High Efficiency Video Coding. Compared with the existing
+method, the proposed algorithm can significantly improve the rate-distortion
+performance. The experimental results obviously demonstrate that the texture
+discontinuity in the face boundary can be well handled by the proposed
+algorithm.
+"
+25635,Predicting Short-Term Uber Demand Using Spatio-Temporal Modeling: A New York City Case Study,"  The demand for e-hailing services is growing rapidly, especially in large
+cities. Uber is the first and popular e-hailing company in the United Stated
+and New York City. A comparison of the demand for yellow-cabs and Uber in NYC
+in 2014 and 2015 shows that the demand for Uber has increased. However, this
+demand may not be distributed uniformly either spatially or temporally. Using
+spatio-temporal time series models can help us to better understand the demand
+for e-hailing services and to predict it more accurately. This paper analyzes
+the prediction performance of one temporal model (vector autoregressive (VAR))
+and two spatio-temporal models (Spatial-temporal autoregressive (STAR); least
+absolute shrinkage and selection operator applied on STAR (LASSO-STAR)) and for
+different scenarios (based on the number of time and space lags), and applied
+to both rush hours and non-rush hours periods. The results show the need of
+considering spatial models for taxi demand.
+"
+25636,On the Reliability Function of the Common-Message Broadcast Channel with Variable-Length Feedback,"  We derive upper and lower bounds on the reliability function for the
+common-message discrete memoryless broadcast channel with variable-length
+feedback. We show that the bounds are tight when the broadcast channel is
+stochastically degraded. For the achievability part, we adapt Yamamoto and
+Itoh's coding scheme by controlling the expectation of the maximum of a set of
+stopping times. For the converse part, we adapt Burnashev's proof techniques
+for establishing the reliability functions for (point-to-point) discrete
+memoryless channels with variable-length feedback and sequential hypothesis
+testing.
+"
+25637,Faster and more accurate computation of the $\mathcal{H}_\infty$ norm via optimization,"  In this paper, we propose an improved method for computing the
+$\mathcal{H}_\infty$ norm of linear dynamical systems that results in a code
+that is often several times faster than existing methods. By using standard
+optimization tools to rebalance the work load of the standard algorithm due to
+Boyd, Balakrishnan, Bruinsma, and Steinbuch, we aim to minimize the number of
+expensive eigenvalue computations that must be performed. Unlike the standard
+algorithm, our modified approach can also calculate the $\mathcal{H}_\infty$
+norm to full precision with little extra work, and also offers more opportunity
+to further accelerate its performance via parallelization. Finally, we
+demonstrate that the local optimization we have employed to speed up the
+standard globally-convergent algorithm can also be an effective strategy on its
+own for approximating the $\mathcal{H}_\infty$ norm of large-scale systems.
+"
+25638,PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction,"  We propose a novel 3D spatial representation for data fusion and scene
+reconstruction. Probabilistic Signed Distance Function (Probabilistic SDF,
+PSDF) is proposed to depict uncertainties in the 3D space. It is modeled by a
+joint distribution describing SDF value and its inlier probability, reflecting
+input data quality and surface geometry. A hybrid data structure involving
+voxel, surfel, and mesh is designed to fully exploit the advantages of various
+prevalent 3D representations. Connected by PSDF, these components reasonably
+cooperate in a consistent frame- work. Given sequential depth measurements,
+PSDF can be incrementally refined with less ad hoc parametric Bayesian
+updating. Supported by PSDF and the efficient 3D data representation,
+high-quality surfaces can be extracted on-the-fly, and in return contribute to
+reliable data fu- sion using the geometry information. Experiments demonstrate
+that our system reconstructs scenes with higher model quality and lower
+redundancy, and runs faster than existing online mesh generation systems.
+"
+25639,Highly crystalline 2D superconductors,"  Recent technological advances in controlling materials have developed methods
+to produce idealized two-dimensional (2D) electron systems such as
+heterogeneous interfaces, molecular-beam-epitaxy (MBE) grown atomic layers,
+exfoliated thin flakes and field-effect devices. These 2D electron systems are
+highly-crystalline with less disorder in common, some of which indeed show
+sheet resistance more than one order of magnitude lower even in atomic layers
+or single layers than that of conventional amorphous/granular thin films. Here,
+we present a review on the recent developments of highly-crystalline 2D
+superconductors and a series of unprecedented physical properties discovered in
+these systems. In particular, we highlight the quantum metallic state (or
+possible metallic ground state), the quantum Griffiths phase in out-of-plane
+magnetic fields, and the superconducting state maintained in anomalously large
+in-plane magnetic fields, which were observed in exfoliated 2D materials,
+MBE-grown atomic-layer thin films and electric-double-layer (ion-gated)
+interfaces. These phenomena are discussed on the basis of weakened disorder
+and/or broken spatial inversion symmetry. These novel aspects suggest that
+highly-crystalline 2D systems are promising platforms for exploring new quantum
+physics and superconductors.
+"
+25640,Weightless: Lossy Weight Encoding For Deep Neural Network Compression,"  The large memory requirements of deep neural networks limit their deployment
+and adoption on many devices. Model compression methods effectively reduce the
+memory requirements of these models, usually through applying transformations
+such as weight pruning or quantization. In this paper, we present a novel
+scheme for lossy weight encoding which complements conventional compression
+techniques. The encoding is based on the Bloomier filter, a probabilistic data
+structure that can save space at the cost of introducing random errors.
+Leveraging the ability of neural networks to tolerate these imperfections and
+by re-training around the errors, the proposed technique, Weightless, can
+compress DNN weights by up to 496x with the same model accuracy. This results
+in up to a 1.51x improvement over the state-of-the-art.
+"
+25641,Weak Coverage of a Rectangular Barrier,"  Assume n wireless mobile sensors are initially dispersed in an ad hoc manner
+in a rectangular region. They are required to move to final locations so that
+they can detect any intruder crossing the region in a direction parallel to the
+sides of the rectangle, and thus provide weak barrier coverage of the region.
+We study three optimization problems related to the movement of sensors to
+achieve weak barrier coverage: minimizing the number of sensors moved (MinNum),
+minimizing the average distance moved by the sensors (MinSum), and minimizing
+the maximum distance moved by the sensors (MinMax). We give an O(n^{3/2}) time
+algorithm for the MinNum problem for sensors of diameter 1 that are initially
+placed at integer positions; in contrast we show that the problem is NP-hard
+even for sensors of diameter 2 that are initially placed at integer positions.
+We show that the MinSum problem is solvable in O(n log n) time for homogeneous
+range sensors in arbitrary initial positions, while it is NP-hard for
+heterogeneous sensor ranges. Finally, we prove that even very restricted
+homogeneous versions of the MinMax problem are NP-hard.
+"
+25642,Isogeometric Analysis Simulation of TESLA Cavities Under Uncertainty,"  In the design of electromagnetic devices the accurate representation of the
+geometry plays a crucial role in determining the device performance. For
+accelerator cavities, in particular, controlling the frequencies of the
+eigenmodes is important in order to guarantee the synchronization between the
+electromagnetic field and the accelerated particles. The main interest of this
+work is in the evaluation of eigenmode sensitivities with respect to
+geometrical changes using Monte Carlo simulations and stochastic collocation.
+The choice of an IGA approach for the spatial discretization allows for an
+exact handling of the domains and their deformations, guaranteeing, at the same
+time, accurate and highly regular solutions.
+"
+25643,Prediction based on the Kennedy-O'Hagan calibration model: asymptotic consistency and other properties,"  Kennedy and O'Hagan (2001) propose a model for calibrating some unknown
+parameters in a computer model and estimating the discrepancy between the
+computer output and physical response. This model is known to have certain
+identifiability issues. Tuo and Wu (2016) show that there are examples for
+which the Kennedy-O'Hagan method renders unreasonable results in calibration.
+In spite of its unstable performance in calibration, the Kennedy-O'Hagan
+approach has a more robust behavior in predicting the physical response. In
+this work, we present some theoretical analysis to show the consistency of
+predictor based on their calibration model in the context of radial basis
+functions.
+"
+25644,Metric Perturbations of Extremal Surfaces,"  Motivated by the HRRT-formula for holographic entanglement entropy, we
+consider the following question: what are the position and the surface area of
+extremal surfaces in a perturbed geometry, given their anchor on the asymptotic
+boundary? We derive explicit expressions for the change in position and surface
+area, thereby providing a closed form expression for the canonical energy. We
+find that a perturbation governed by some small parameter $\lambda$ yields an
+expansion of the surface area in terms of a highly non-local expression
+involving multiple integrals of geometric quantities over the original extremal
+surface.
+"
+25645,Structural transition in social networks: The role of homophily,"  We introduce a model for the formation of social networks, which takes into
+account the homophily or the tendency of individuals to associate and bond with
+similar others, and the mechanisms of global and local attachment as well as
+tie reinforcement due to social interactions between people. We generalize the
+weighted social network model such that the nodes or individuals have $F$
+features and each feature can have $q$ different values. Here the tendency for
+the tie formation between two individuals due to the overlap in their features
+represents homophily. We find a phase transition as a function of $F$ or $q$,
+resulting in a phase diagram. For fixed $q$ and as a function of $F$ the system
+shows two phases separated at $F_c$. For $F{<}F_c$ large, homogeneous, and well
+separated communities can be identified within which the features match almost
+perfectly (segregated phase). When $F$ becomes larger than $F_c$, the nodes
+start to belong to several communities and within a community the features
+match only partially (overlapping phase). Several quantities reflect this
+transition, including the average degree, clustering coefficient, feature
+overlap, and the number of communities per node. We also make an attempt to
+interpret these results in terms of observations on social behavior of humans.
+"
+25646,BEGAN: Boundary Equilibrium Generative Adversarial Networks,"  We propose a new equilibrium enforcing method paired with a loss derived from
+the Wasserstein distance for training auto-encoder based Generative Adversarial
+Networks. This method balances the generator and discriminator during training.
+Additionally, it provides a new approximate convergence measure, fast and
+stable training and high visual quality. We also derive a way of controlling
+the trade-off between image diversity and visual quality. We focus on the image
+generation task, setting a new milestone in visual quality, even at higher
+resolutions. This is achieved while using a relatively simple model
+architecture and a standard training procedure.
+"
+25647,Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series,"  Deep Learning is a consolidated, state-of-the-art Machine Learning tool to
+fit a function when provided with large data sets of examples. However, in
+regression tasks, the straightforward application of Deep Learning models
+provides a point estimate of the target. In addition, the model does not take
+into account the uncertainty of a prediction. This represents a great
+limitation for tasks where communicating an erroneous prediction carries a
+risk. In this paper we tackle a real-world problem of forecasting impending
+financial expenses and incomings of customers, while displaying predictable
+monetary amounts on a mobile app. In this context, we investigate if we would
+obtain an advantage by applying Deep Learning models with a Heteroscedastic
+model of the variance of a network's output. Experimentally, we achieve a
+higher accuracy than non-trivial baselines. More importantly, we introduce a
+mechanism to discard low-confidence predictions, which means that they will not
+be visible to users. This should help enhance the user experience of our
+product.
+"
+25648,Langevin Markov Chain Monte Carlo with stochastic gradients,"  Monte Carlo sampling techniques have broad applications in machine learning,
+Bayesian posterior inference, and parameter estimation. Typically the target
+distribution takes the form of a product distribution over a dataset with a
+large number of entries. For sampling schemes utilizing gradient information it
+is cheaper for the derivative to be approximated using a random small subset of
+the data, introducing extra noise into the system. We present a new
+discretization scheme for underdamped Langevin dynamics when utilizing a
+stochastic (noisy) gradient. This scheme is shown to bias computed averages to
+second order in the stepsize while giving exact results in the special case of
+sampling a Gaussian distribution with a normally distributed stochastic
+gradient
+"
+25649,Predicting Rising Follower Counts on Twitter Using Profile Information,"  When evaluating the cause of one's popularity on Twitter, one thing is
+considered to be the main driver: Many tweets. There is debate about the kind
+of tweet one should publish, but little beyond tweets. Of particular interest
+is the information provided by each Twitter user's profile page. One of the
+features are the given names on those profiles. Studies on psychology and
+economics identified correlations of the first name to, e.g., one's school
+marks or chances of getting a job interview in the US. Therefore, we are
+interested in the influence of those profile information on the follower count.
+We addressed this question by analyzing the profiles of about 6 Million Twitter
+users. All profiles are separated into three groups: Users that have a first
+name, English words, or neither of both in their name field. The assumption is
+that names and words influence the discoverability of a user and subsequently
+his/her follower count. We propose a classifier that labels users who will
+increase their follower count within a month by applying different models based
+on the user's group. The classifiers are evaluated with the area under the
+receiver operator curve score and achieves a score above 0.800.
+"
+25650,On Moebius orthogonality for subshifts of finite type with positive topological entropy,"  In this note we prove that Moebius orthogonality does not hold for subshifts
+of finite type with positive topological entropy. This, in particular, shows
+that all $C^{1+\alpha}$ surface diffeomorphisms with positive entropy correlate
+with the Moebius function.
+"
+25651,The role of spin-orbit coupling in the electronic structure of IrO$_2$,"  The delicate interplay of electronic charge, spin, and orbital degrees of
+freedom is in the heart of many novel phenomena across the transition metal
+oxide family. Here, by combining high- resolution angle resolved photoemission
+spectroscopy and first principles calculations (with and without spin-orbit
+coupling), the electronic structure of the rutile binary iridate, IrO$_2$ is
+investigated. The detailed study of electronic bands measured on a high-quality
+single crystalline sample, and use of a wide range of photon energy provide a
+huge improvement over the previous studies. The excellent agreement between
+theory and experimental results shows that the single-particle DFT description
+of IrO$_2$ band structure is adequate, without the need of invoking any
+treatment of correlation effects. Although many observed features point to a 3D
+nature of the electronic structure, clear surface effects are revealed. The
+discussion of the orbital character of the relevant bands crossing the Fermi
+level sheds light on spin orbit coupling-driven phenomena in this material,
+unveiling a spin-orbit induced avoided crossing, a property likely to play key
+role in its large spin Hall effect.
+"
+25652,Loop Quasi-Invariant Chunk Motion by peeling with statement composition,"  Several techniques for analysis and transformations are used in compilers.
+Among them, the peeling of loops for hoisting quasi-invariants can be used to
+optimize generated code, or simply ease developers' lives. In this paper, we
+introduce a new concept of dependency analysis borrowed from the field of
+Implicit Computational Complexity (ICC), allowing to work with composed
+statements called Chunks to detect more quasi-invariants. Based on an
+optimization idea given on a WHILE language, we provide a transformation method
+- reusing ICC concepts and techniques - to compilers. This new analysis
+computes an invariance degree for each statement or chunks of statements by
+building a new kind of dependency graph, finds the maximum or worst dependency
+graph for loops, and recognizes if an entire block is Quasi-Invariant or not.
+This block could be an inner loop, and in that case the computational
+complexity of the overall program can be decreased. We already implemented a
+proof of concept on a toy C parser 1 analysing and transforming the AST
+representation. In this paper, we introduce the theory around this concept and
+present a prototype analysis pass implemented on LLVM. In a very near future,
+we will implement the corresponding transformation and provide benchmarks
+comparisons.
+"
+25653,Classification of COPD with Multiple Instance Learning,"  Chronic obstructive pulmonary disease (COPD) is a lung disease where early
+detection benefits the survival rate. COPD can be quantified by classifying
+patches of computed tomography images, and combining patch labels into an
+overall diagnosis for the image. As labeled patches are often not available,
+image labels are propagated to the patches, incorrectly labeling healthy
+patches in COPD patients as being affected by the disease. We approach
+quantification of COPD from lung images as a multiple instance learning (MIL)
+problem, which is more suitable for such weakly labeled data. We investigate
+various MIL assumptions in the context of COPD and show that although a concept
+region with COPD-related disease patterns is present, considering the whole
+distribution of lung tissue patches improves the performance. The best method
+is based on averaging instances and obtains an AUC of 0.742, which is higher
+than the previously reported best of 0.713 on the same dataset. Using the full
+training set further increases performance to 0.776, which is significantly
+higher (DeLong test) than previous results.
+"
+25654,A review of the W51 Cloud,"  The W51 cloud complex is one of the best laboratories in our Galaxy to study
+high-mass star formation. At a distance of about 5 kpc, it is the closest
+region containing a high-mass protocluster, and it has two. The cloud includes
+a long infrared-dark cloud, is interacting with a supernova remnant, and
+contains a variety of unique massive protostellar sources. This article is an
+observational review of the region.
+"
+25655,Deep Interest Evolution Network for Click-Through Rate Prediction,"  Click-through rate~(CTR) prediction, whose goal is to estimate the
+probability of the user clicks, has become one of the core tasks in advertising
+systems. For CTR prediction model, it is necessary to capture the latent user
+interest behind the user behavior data. Besides, considering the changing of
+the external environment and the internal cognition, user interest evolves over
+time dynamically. There are several CTR prediction methods for interest
+modeling, while most of them regard the representation of behavior as the
+interest directly, and lack specially modeling for latent interest behind the
+concrete behavior. Moreover, few work consider the changing trend of interest.
+In this paper, we propose a novel model, named Deep Interest Evolution
+Network~(DIEN), for CTR prediction. Specifically, we design interest extractor
+layer to capture temporal interests from history behavior sequence. At this
+layer, we introduce an auxiliary loss to supervise interest extracting at each
+step. As user interests are diverse, especially in the e-commerce system, we
+propose interest evolving layer to capture interest evolving process that is
+relative to the target item. At interest evolving layer, attention mechanism is
+embedded into the sequential structure novelly, and the effects of relative
+interests are strengthened during interest evolution. In the experiments on
+both public and industrial datasets, DIEN significantly outperforms the
+state-of-the-art solutions. Notably, DIEN has been deployed in the display
+advertisement system of Taobao, and obtained 20.7\% improvement on CTR.
+"
+25656,A Simple Algorithm for Computing a Cycle Separator,"  We present a linear time algorithm for computing a cycle separator in a
+planar graph that is (arguably) simpler than previously known algorithms. Our
+algorithm builds on, and is somewhat similar to, previous algorithms for
+computing separators. The main new ingredient is a specific layered
+decomposition of the planar graph constructed differently from previous
+BFS-based layerings.
+"
+25657,Adapting general-purpose speech recognition engine output for domain-specific natural language question answering,"  Speech-based natural language question-answering interfaces to enterprise
+systems are gaining a lot of attention. General-purpose speech engines can be
+integrated with NLP systems to provide such interfaces. Usually,
+general-purpose speech engines are trained on large `general' corpus. However,
+when such engines are used for specific domains, they may not recognize
+domain-specific words well, and may produce erroneous output. Further, the
+accent and the environmental conditions in which the speaker speaks a sentence
+may induce the speech engine to inaccurately recognize certain words. The
+subsequent natural language question-answering does not produce the requisite
+results as the question does not accurately represent what the speaker
+intended. Thus, the speech engine's output may need to be adapted for a domain
+before further natural language processing is carried out. We present two
+mechanisms for such an adaptation, one based on evolutionary development and
+the other based on machine learning, and show how we can repair the
+speech-output to make the subsequent natural language question-answering
+better.
+"
+25658,Collective modes in simple melts: Transition from soft spheres to the hard sphere limit,"  We study collective modes in a classical system of particles with repulsive
+inverse-power-law (IPL) interactions in the fluid phase, near the fluid-solid
+coexistence (IPL melts). The IPL exponent is varied from $n=10$ to $n=100$ to
+mimic the transition from moderately soft to hard-sphere-like interactions. We
+compare the longitudinal dispersion relations obtained using molecular dynamic
+(MD) simulations with those calculated using the quasi-crystalline
+approximation (QCA) and find that this simple theoretical approach becomes
+grossly inaccurate for $n\gtrsim 20$. Similarly, conventional expressions for
+high-frequency (instantaneous) elastic moduli, predicting their divergence as
+$n$ increases, are meaningless in this regime. Relations of the longitudinal
+and transverse elastic velocities of the QCA model to the adiabatic sound
+velocity, measured in MD simulations, are discussed for the regime where QCA is
+applicable. Two potentially useful freezing indicators for classical particle
+systems with steep repulsive interactions are discussed.
+"
+25659,Time Matters: Multi-scale Temporalization of Social Media Popularity,"  The evolution of social media popularity exhibits rich temporality, i.e.,
+popularities change over time at various levels of temporal granularity. This
+is influenced by temporal variations of public attentions or user activities.
+For example, popularity patterns of street snap on Flickr are observed to
+depict distinctive fashion styles at specific time scales, such as season-based
+periodic fluctuations for Trench Coat or one-off peak in days for Evening
+Dress. However, this fact is often overlooked by existing research of
+popularity modeling. We present the first study to incorporate multiple
+time-scale dynamics into predicting online popularity. We propose a novel
+computational framework in the paper, named Multi-scale Temporalization, for
+estimating popularity based on multi-scale decomposition and structural
+reconstruction in a tensor space of user, post, and time by joint low-rank
+constraints. By considering the noise caused by context inconsistency, we
+design a data rearrangement step based on context aggregation as preprocessing
+to enhance contextual relevance of neighboring data in the tensor space. As a
+result, our approach can leverage multiple levels of temporal characteristics
+and reduce the noise of data decomposition to improve modeling effectiveness.
+We evaluate our approach on two large-scale Flickr image datasets with over 1.8
+million photos in total, for the task of popularity prediction. The results
+show that our approach significantly outperforms state-of-the-art popularity
+prediction techniques, with a relative improvement of 10.9%-47.5% in terms of
+prediction accuracy.
+"
+25660,Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data,"  This paper presents the first deep reinforcement learning (DRL) framework to
+estimate the optimal Dynamic Treatment Regimes from observational medical data.
+This framework is more flexible and adaptive for high dimensional action and
+state spaces than existing reinforcement learning methods to model real-life
+complexity in heterogeneous disease progression and treatment choices, with the
+goal of providing doctor and patients the data-driven personalized decision
+recommendations. The proposed DRL framework comprises (i) a supervised learning
+step to predict the most possible expert actions, and (ii) a deep reinforcement
+learning step to estimate the long-term value function of Dynamic Treatment
+Regimes. Both steps depend on deep neural networks.
+As a key motivational example, we have implemented the proposed framework on
+a data set from the Center for International Bone Marrow Transplant Research
+(CIBMTR) registry database, focusing on the sequence of prevention and
+treatments for acute and chronic graft versus host disease after
+transplantation. In the experimental results, we have demonstrated promising
+accuracy in predicting human experts' decisions, as well as the high expected
+reward function in the DRL-based dynamic treatment regimes.
+"
+25661,Adversarial Examples Target Topological Holes in Deep Networks,"  It is currently unclear why adversarial examples are easy to construct for
+deep networks that are otherwise successful with respect to their training
+domain. However, it is suspected that these adversarial examples lie within
+some small perturbation from the network's decision boundaries or exist in
+low-density regions with respect to the training distribution. Using persistent
+homology, we find that deep networks effectively have ``holes'' in their
+activation graphs, making them blind to regions of the input space that can be
+exploited by adversarial examples. These holes are effectively dense in the
+input space, making it easy to find a perturbed image that can be
+misclassified. By studying the topology of network activation, we find global
+patterns in the form of activation subgraphs which can both reliably determine
+whether an example is adversarial and can recover the true category of the
+example well above chance, implying that semantic information about the input
+is embedded globally via the activation pattern in deep networks.
+"
+25662,A class of non-parametric deformed exponential statistical models,"  We study the class on non-parametric deformed statistical models where the
+deformed exponential has linear growth at infinity and is sub-exponential at
+zero. This class generalizes the class introduced by N.J.~Newton. We discuss
+the convexity and regularity of the normalization operator, the form of the
+deformed statistical divergences and their convex duality, the properties of
+the escort densities, and the affine manifold structure of the statistical
+bundle.
+"
+25663,Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference,"  Semi-supervised learning methods using Generative Adversarial Networks (GANs)
+have shown promising empirical success recently. Most of these methods use a
+shared discriminator/classifier which discriminates real examples from fake
+while also predicting the class label. Motivated by the ability of the GANs
+generator to capture the data manifold well, we propose to estimate the tangent
+space to the data manifold using GANs and employ it to inject invariances into
+the classifier. In the process, we propose enhancements over existing methods
+for learning the inverse mapping (i.e., the encoder) which greatly improves in
+terms of semantic similarity of the reconstructed sample with the input sample.
+We observe considerable empirical gains in semi-supervised learning over
+baselines, particularly in the cases when the number of labeled examples is
+low. We also provide insights into how fake examples influence the
+semi-supervised learning procedure.
+"
+25664,A note on minimum linear arrangement for BC graphs,"  A linear arrangement is a labeling or a numbering or a linear ordering of the
+vertices of a graph. In this paper we solve the minimum linear arrangement
+problem for bijective connection graphs (for short BC graphs) which include
+hypercubes, Möbius cubes, crossed cubes, twisted cubes, locally twisted
+cube, spined cube, $Z$-cubes, etc. as the subfamilies.
+"
+25665,Asymptotic multivariate expectiles,"  In [16], a new family of vector-valued risk measures called multivariate
+expectiles is introduced. In this paper, we focus on the asymptotic behavior of
+these measures in a multivariate regular variations context. For models with
+equivalent tails, we propose an estimator of these multivariate asymptotic
+expectiles, in the Fr{é}chet attraction domain case, with asymptotic
+independence, or in the comonotonic case.
+"
+25666,Dynamic Planar Embeddings of Dynamic Graphs,"  We present an algorithm to support the dynamic embedding in the plane of a
+dynamic graph. An edge can be inserted across a face between two vertices on
+the face boundary (we call such a vertex pair linkable), and edges can be
+deleted. The planar embedding can also be changed locally by flipping
+components that are connected to the rest of the graph by at most two vertices.
+Given vertices $u,v$, linkable$(u,v)$ decides whether $u$ and $v$ are
+linkable in the current embedding, and if so, returns a list of suggestions for
+the placement of $(u,v)$ in the embedding. For non-linkable vertices $u,v$, we
+define a new query, one-flip-linkable$(u,v)$ providing a suggestion for a flip
+that will make them linkable if one exists. We support all updates and queries
+in O(log$^2 n$) time. Our time bounds match those of Italiano et al. for a
+static (flipless) embedding of a dynamic graph.
+Our new algorithm is simpler, exploiting that the complement of a spanning
+tree of a connected plane graph is a spanning tree of the dual graph. The
+primal and dual trees are interpreted as having the same Euler tour, and a main
+idea of the new algorithm is an elegant interaction between top trees over the
+two trees via their common Euler tour.
+"
+25667,Optimizing Kernel Machines using Deep Learning,"  Building highly non-linear and non-parametric models is central to several
+state-of-the-art machine learning systems. Kernel methods form an important
+class of techniques that induce a reproducing kernel Hilbert space (RKHS) for
+inferring non-linear models through the construction of similarity functions
+from data. These methods are particularly preferred in cases where the training
+data sizes are limited and when prior knowledge of the data similarities is
+available. Despite their usefulness, they are limited by the computational
+complexity and their inability to support end-to-end learning with a
+task-specific objective. On the other hand, deep neural networks have become
+the de facto solution for end-to-end inference in several learning paradigms.
+In this article, we explore the idea of using deep architectures to perform
+kernel machine optimization, for both computational efficiency and end-to-end
+inferencing. To this end, we develop the DKMO (Deep Kernel Machine
+Optimization) framework, that creates an ensemble of dense embeddings using
+Nystrom kernel approximations and utilizes deep learning to generate
+task-specific representations through the fusion of the embeddings.
+Intuitively, the filters of the network are trained to fuse information from an
+ensemble of linear subspaces in the RKHS. Furthermore, we introduce the kernel
+dropout regularization to enable improved training convergence. Finally, we
+extend this framework to the multiple kernel case, by coupling a global fusion
+layer with pre-trained deep kernel machines for each of the constituent
+kernels. Using case studies with limited training data, and lack of explicit
+feature sources, we demonstrate the effectiveness of our framework over
+conventional model inferencing techniques.
+"
+25668,Nesting Probabilistic Programs,"  We formalize the notion of nesting probabilistic programming queries and
+investigate the resulting statistical implications. We demonstrate that while
+query nesting allows the definition of models which could not otherwise be
+expressed, such as those involving agents reasoning about other agents,
+existing systems take approaches which lead to inconsistent estimates. We show
+how to correct this by delineating possible ways one might want to nest queries
+and asserting the respective conditions required for convergence. We further
+introduce a new online nested Monte Carlo estimator that makes it substantially
+easier to ensure these conditions are met, thereby providing a simple framework
+for designing statistically correct inference engines. We prove the correctness
+of this online estimator and show that, when using the recommended setup, its
+asymptotic variance is always better than that of the equivalent fixed
+estimator, while its bias is always within a factor of two.
+"
+25669,Copper Tellurium Oxides - A Playground for Magnetism,"  A variety of copper tellurium oxide minerals are known, and many of them
+exhibit either unusual forms of magnetism, or potentially novel spin liquid
+behavior. Here, I review a number of the more interesting materials with a
+focus on their crystalline symmetry and, if known, the nature of their
+magnetism. Many of these exist (so far) in mineral form only, and most have yet
+to have their magnetic properties studied. This means a largely unexplored
+space of materials awaits our exploration.
+"
+25670,Affine and topological structural entropies in granular statistical mechanics: explicit calculations and equation of state,"  We identify two orthogonal sources of structural entropy in rattler-free
+granular systems - affine, involving structural changes that only deform the
+contact network, and topological, corresponding to different topologies of the
+contact network. We show that a recently developed connectivity-based granular
+statistical mechanics separates the two naturally by identifying the structural
+degrees of freedom with spanning trees on the graph of the contact network. We
+extend the connectivity-based formalism to include constraints on, and
+correlations between, degrees of freedom as interactions between branches of
+the spanning tree. We then use the statistical mechanics formalism to calculate
+the partition function generally and the different entropies in the
+high-angoricity limit. We also calculate the degeneracy of the affine entropy
+and a number of expectation values. From the latter, we derive an equipartition
+principle and an equation of state relating the macroscopic volume and boundary
+stress to the analogue of the temperature, the contactivity.
+"
+25671,Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear Loss,"  Deep learning has become the method of choice in many application domains of
+machine learning in recent years, especially for multi-class classification
+tasks. The most common loss function used in this context is the cross-entropy
+loss, which reduces to the log loss in the typical case when there is a single
+correct response label. While this loss is insensitive to the identity of the
+assigned class in the case of misclassification, in practice it is often the
+case that some errors may be more detrimental than others. Here we present the
+bilinear-loss (and related log-bilinear-loss) which differentially penalizes
+the different wrong assignments of the model. We thoroughly test this method
+using standard models and benchmark image datasets. As one application, we show
+the ability of this method to better contain error within the correct
+super-class, in the hierarchically labeled CIFAR100 dataset, without affecting
+the overall performance of the classifier.
+"
+25672,Kondo physics in double quantum dot based Cooper pair splitters,"  The Andreev transport properties of double quantum dot based Cooper pair
+splitters with one superconducting and two normal leads are studied
+theoretically in the Kondo regime. The influence of the superconducting pairing
+correlations on the local density of states, Andreev transmission coefficient
+and Cooper pair splitting efficiency is thoroughly analyzed. It is shown that
+finite superconducting pairing potential quickly suppresses the SU(2) Kondo
+effect, which can however reemerge for relatively large values of coupling to
+superconductor. In the SU(4) Kondo regime, a crossover from the SU(4) to the
+SU(2) Kondo state is found as the coupling to superconductor is enhanced. The
+analysis is performed by means of the density-matrix numerical renormalization
+group method.
+"
+25673,A new shift strategy for the implicitly restarted generalized second-order Arnoldi method,"  In this paper, a new shift strategy for the implicitly restarted generalized
+second-order Arnoldi (GSOAR) method is proposed. In implicitly restarted
+processes, we can get a $k$-step GSOAR decomposition from a $m$-step GSOAR
+decomposition by performing $p = m-k$ implicit shifted QR iterations. The
+problem of the implicitly restarted GSOAR is the mismatch between the number of
+shifts and the dimension of the subspace. There are $2p$ shifts for $p$ QR
+iterations. We use the shifts to filter out the unwanted information in the
+current subspace; when more shifts are used, one obtains a better updated
+subspace. But, if we use more than $p$ shifts, the structure of the GSOAR
+decomposition will be destroyed. We propose a novel method which can use all
+$2p$ candidates and preserve the special structure. The new method vastly
+enhances the overall efficiency of the algorithm. Numerical experiments
+illustrate the efficiency of every restart process.
+"
+25674,The MEGII detector,"  We present a report of the MEG II experiment, the upgrade of MEG, whose goal
+is to search for the forbidden decay \megc\ with increased precision. After
+having briefly reviewed the motivation for such a search and the current limit
+due to MEG, we present the conceptual design of the detector detailing for each
+subdetector the motivations and the extent of the upgrade and the expected
+resolution improvements. Novel subdetectors and calibration hardware are
+introduced. We conclude with the expected sensitivity of the MEGII experiment.
+"
+25675,Budgeted Batch Bayesian Optimization With Unknown Batch Sizes,"  Parameter settings profoundly impact the performance of machine learning
+algorithms and laboratory experiments. The classical grid search or trial-error
+methods are exponentially expensive in large parameter spaces, and Bayesian
+optimization (BO) offers an elegant alternative for global optimization of
+black box functions. In situations where the black box function can be
+evaluated at multiple points simultaneously, batch Bayesian optimization is
+used. Current batch BO approaches are restrictive in that they fix the number
+of evaluations per batch, and this can be wasteful when the number of specified
+evaluations is larger than the number of real maxima in the underlying
+acquisition function. We present the Budgeted Batch Bayesian Optimization (B3O)
+for hyper-parameter tuning and experimental design - we identify the
+appropriate batch size for each iteration in an elegant way. To set the batch
+size flexible, we use the infinite Gaussian mixture model (IGMM) for
+automatically identifying the number of peaks in the underlying acquisition
+functions. We solve the intractability of estimating the IGMM directly from the
+acquisition function by formulating the batch generalized slice sampling to
+efficiently draw samples from the acquisition function. We perform extensive
+experiments for both synthetic functions and two real world applications -
+machine learning hyper-parameter tuning and experimental design for alloy
+hardening. We show empirically that the proposed B3O outperforms the existing
+fixed batch BO approaches in finding the optimum whilst requiring a fewer
+number of evaluations, thus saving cost and time.
+"
+25676,"Systolic ratio, index of closed orbits and convexity for tight contact forms on the three-sphere","  We construct a dynamically convex contact form on the three-sphere whose
+systolic ratio is arbitrarily close to 2. This example is related to a
+conjecture of Viterbo, whose validity would imply that the systolic ratio of a
+convex contact form does not exceed 1. We also construct a sequence of tight
+contact forms $\alpha_n$, $n\geq 2$, with systolic ratio arbitrarily close to
+$n$ and suitable bounds on the mean rotation number of all the closed orbits of
+the induced Reeb flow.
+"
+25677,On the Power of Tree-Depth for Fully Polynomial FPT Algorithms,"  There are many classical problems in P whose time complexities have not been
+improved over the past decades. Recent studies of ""Hardness in P"" have revealed
+that, for several of such problems, the current fastest algorithm is the best
+possible under some complexity assumptions. To bypass this difficulty, Fomin et
+al. (SODA 2017) introduced the concept of fully polynomial FPT algorithms. For
+a problem with the current best time complexity $O(n^c)$, the goal is to design
+an algorithm running in $k^{O(1)}n^{c'}$ time for a parameter $k$ and a
+constant $c'<c$. In this paper, we investigate the complexity of graph problems
+in P parameterized by tree-depth, a graph parameter related to tree-width. We
+show that a simple divide-and-conquer method can solve many graph problems,
+including Weighted Matching, Negative Cycle Detection, Minimum Weight Cycle,
+Replacement Paths, and 2-hop Cover, in $O(\mathrm{td}\cdot m)$ time or
+$O(\mathrm{td}\cdot (m+n\log n))$ time, where $\mathrm{td}$ is the tree-depth
+of the input graph. Because any graph of tree-width $\mathrm{tw}$ has
+tree-depth at most $(\mathrm{tw}+1)\log_2 n$, our algorithms also run in
+$O(\mathrm{tw}\cdot m\log n)$ time or $O(\mathrm{tw}\cdot (m+n\log n)\log n)$
+time. These results match or improve the previous best algorithms parameterized
+by tree-width. Especially, we solve an open problem of fully polynomial FPT
+algorithm for Weighted Matching parameterized by tree-width posed by Fomin et
+al.
+"
+25678,Operads of genus zero curves and the Grothendieck-Teichmüller group,"  We show that the group of homotopy automorphisms of the profinite completion
+of the genus zero surface operad is isomorphic to the (profinite)
+Grothendieck-Teichmüller group. Using a result of Drummond-Cole, we deduce
+that the Grothendieck-Teichmüller group acts nontrivially on
+$\overline{\mathcal{M}}_{0,\bullet+1}$, the operad of stable curves of genus
+zero. As a second application, we give an alternative proof that the framed
+little 2-disks operad is formal.
+"
+25679,Visual Reinforcement Learning with Imagined Goals,"  For an autonomous agent to fulfill a wide range of user-specified goals at
+test time, it must be able to learn broadly applicable and general-purpose
+skill repertoires. Furthermore, to provide the requisite level of generality,
+these skills must handle raw sensory input such as images. In this paper, we
+propose an algorithm that acquires such general-purpose skills by combining
+unsupervised representation learning and reinforcement learning of
+goal-conditioned policies. Since the particular goals that might be required at
+test-time are not known in advance, the agent performs a self-supervised
+""practice"" phase where it imagines goals and attempts to achieve them. We learn
+a visual representation with three distinct purposes: sampling goals for
+self-supervised practice, providing a structured transformation of raw sensory
+inputs, and computing a reward signal for goal reaching. We also propose a
+retroactive goal relabeling scheme to further improve the sample-efficiency of
+our method. Our off-policy algorithm is efficient enough to learn policies that
+operate on raw image observations and goals for a real-world robotic system,
+and substantially outperforms prior techniques.
+"
+25680,Sequential Recurrent Neural Networks for Language Modeling,"  Feedforward Neural Network (FNN)-based language models estimate the
+probability of the next word based on the history of the last N words, whereas
+Recurrent Neural Networks (RNN) perform the same task based only on the last
+word and some context information that cycles in the network. This paper
+presents a novel approach, which bridges the gap between these two categories
+of networks. In particular, we propose an architecture which takes advantage of
+the explicit, sequential enumeration of the word history in FNN structure while
+enhancing each word representation at the projection layer through recurrent
+context information that evolves in the network. The context integration is
+performed using an additional word-dependent weight matrix that is also learned
+during the training. Extensive experiments conducted on the Penn Treebank (PTB)
+and the Large Text Compression Benchmark (LTCB) corpus showed a significant
+reduction of the perplexity when compared to state-of-the-art feedforward as
+well as recurrent neural network architectures.
+"
+25681,Factorization of Dirac operators on almost-regular fibrations of spin$^c$ manifolds,"  We establish the factorization of the Dirac operator on an almost-regular
+fibration of spin$^c$ manifolds in unbounded KK-theory. As a first intermediate
+result we establish that any vertically elliptic and symmetric first-order
+differential operator on a proper submersion defines an unbounded Kasparov
+module, and thus represents a class in KK-theory. Then, we generalize our
+previous results on factorizations of Dirac operators to proper Riemannian
+submersions of spin$^c$ manifolds. This allows us to show that the Dirac
+operator on the total space of an almost-regular fibration can be written as
+the tensor sum of a vertically elliptic family of Dirac operators with the
+horizontal Dirac operator, up to an explicit `obstructing' curvature term. We
+conclude by showing that the tensor sum factorization represents the interior
+Kasparov product in bivariant K-theory.
+"
+25682,Approximate Convex Hulls: sketching the convex hull using curvature,"  Convex hulls are fundamental objects in computational geometry. In moderate
+dimensions or for large numbers of vertices, computing the convex hull can be
+impractical due to the computational complexity of convex hull algorithms. In
+this article we approximate the convex hull in using a scalable algorithm which
+finds high curvature vertices with high probability. The algorithm is
+particularly effective for approximating convex hulls which have a relatively
+small number of extreme points.
+"
+25683,Supervised Quantile Normalisation,"  Quantile normalisation is a popular normalisation method for data subject to
+unwanted variations such as images, speech, or genomic data. It applies a
+monotonic transformation to the feature values of each sample to ensure that
+after normalisation, they follow the same target distribution for each sample.
+Choosing a ""good"" target distribution remains however largely empirical and
+heuristic, and is usually done independently of the subsequent analysis of
+normalised data. We propose instead to couple the quantile normalisation step
+with the subsequent analysis, and to optimise the target distribution jointly
+with the other parameters in the analysis. We illustrate this principle on the
+problem of estimating a linear model over normalised data, and show that it
+leads to a particular low-rank matrix regression problem that can be solved
+efficiently. We illustrate the potential of our method, which we term SUQUAN,
+on simulated data, images and genomic data, where it outperforms standard
+quantile normalisation.
+"
+25684,T-duality in an H-flux: exchange of momentum and winding,"  Using our earlier proposal for Ramond-Ramond fields in an H-flux on loop
+space, we extend the Hori isomorphism of Bouwknegt-Evslin-Mathai from invariant
+differential forms, to invariant exotic differential forms such that the
+momentum and winding numbers are exchanged, filling in a gap in the literature.
+We also extend the compatibility of the action of invariant exact Courant
+algebroids on the T-duality isomorphism of Cavalcanti-Gualtieri, to the
+T-duality isomorphism on exotic invariant differential forms.
+"
+25685,APOLLO clock performance and normal point corrections,"  The Apache Point Observatory Lunar Laser-ranging Operation (APOLLO) has
+produced a large volume of high-quality lunar laser ranging (LLR) data since it
+began operating in 2006. For most of this period, APOLLO has relied on a
+GPS-disciplined, high-stability quartz oscillator as its frequency and time
+standard. The recent addition of a cesium clock as part of a timing calibration
+system initiated a comparison campaign between the two clocks. This has allowed
+correction of APOLLO range measurements--called normal points--during the
+overlap period, but also revealed a mechanism to correct for systematic range
+offsets due to clock errors in historical APOLLO data. Drift of the GPS clock
+on ~1000 s timescales contributed typically 2.5 mm of range error to APOLLO
+measurements, and we find that this may be reduced to ~1.6 mm on average. We
+present here a characterization of APOLLO clock errors, the method by which we
+correct historical data, and the resulting statistics.
+"
+25686,"Truth, Beauty and Supergravity","  I use Supergravity as a test case to study the role and uses of
+elegance/simplicity in formulating and evaluating physical models, whose sole
+criterion is of course truth: an observationally verified description of Nature
+within a certain range of scales.
+"
+25687,Analysis of Student Behaviour in Habitable Worlds Using Continuous Representation Visualization,"  We introduce a novel approach to visualizing temporal clickstream behaviour
+in the context of a degree-satisfying online course, Habitable Worlds, offered
+through Arizona State University. The current practice for visualizing
+behaviour within a digital learning environment has been to generate plots
+based on hand engineered or coded features using domain knowledge. While this
+approach has been effective in relating behaviour to known phenomena, features
+crafted from domain knowledge are not likely well suited to make unfamiliar
+phenomena salient and thus can preclude discovery. We introduce a methodology
+for organically surfacing behavioural regularities from clickstream data,
+conducting an expert in-the-loop hyperparameter search, and identifying
+anticipated as well as newly discovered patterns of behaviour. While these
+visualization techniques have been used before in the broader machine learning
+community to better understand neural networks and relationships between word
+vectors, we apply them to online behavioural learner data and go a step
+further; exploring the impact of the parameters of the model on producing
+tangible, non-trivial observations of behaviour that are suggestive of
+pedagogical improvement to the course designers and instructors. The
+methodology introduced in this paper led to an improved understanding of
+passing and non-passing student behaviour in the course and is widely
+applicable to other datasets of clickstream activity where investigators and
+stakeholders wish to organically surface principal patterns of behaviour.
+"
+25688,Estimation and Optimization of Composite Outcomes,"  There is tremendous interest in precision medicine as a means to improve
+patient outcomes by tailoring treatment to individual characteristics. An
+individualized treatment rule formalizes precision medicine as a map from
+patient information to a recommended treatment. A treatment rule is defined to
+be optimal if it maximizes the mean of a scalar outcome in a population of
+interest, e.g., symptom reduction. However, clinical and intervention
+scientists often must balance multiple and possibly competing outcomes, e.g.,
+symptom reduction and the risk of an adverse event. One approach to precision
+medicine in this setting is to elicit a composite outcome which balances all
+competing outcomes; unfortunately, eliciting a composite outcome directly from
+patients is difficult without a high-quality instrument, and an expert-derived
+composite outcome may not account for heterogeneity in patient preferences. We
+propose a new paradigm for the study of precision medicine using observational
+data that relies solely on the assumption that clinicians are approximately
+(i.e., imperfectly) making decisions to maximize individual patient utility.
+Estimated composite outcomes are subsequently used to construct an estimator of
+an individualized treatment rule which maximizes the mean of patient-specific
+composite outcomes. The estimated composite outcomes and estimated optimal
+individualized treatment rule provide new insights into patient preference
+heterogeneity, clinician behavior, and the value of precision medicine in a
+given domain. We derive inference procedures for the proposed estimators under
+mild conditions and demonstrate their finite sample performance through a suite
+of simulation experiments and an illustrative application to data from a study
+of bipolar depression.
+"
+25689,Computing geometric Lorenz attractors with arbitrary precision,"  The Lorenz attractor was introduced in 1963 by E. N. Lorenz as one of the
+first examples of \emph{strange attractors}. However Lorenz' research was
+mainly based on (non-rigourous) numerical simulations and, until recently, the
+proof of the existence of the Lorenz attractor remained elusive. To address
+that problem some authors introduced geometric Lorenz models and proved that
+geometric Lorenz models have a strange attractor. In 2002 it was shown that the
+original Lorenz model behaves like a geometric Lorenz model and thus has a
+strange attractor. In this paper we show that geometric Lorenz attractors are
+computable, as well as their physical measures.
+"
+25690,Robust machine learning by median-of-means : theory and practice,"  We introduce new estimators for robust machine learning based on
+median-of-means (MOM) estimators of the mean of real valued random variables.
+These estimators achieve optimal rates of convergence under minimal assumptions
+on the dataset. The dataset may also have been corrupted by outliers on which
+no assumption is granted. We also analyze these new estimators with standard
+tools from robust statistics. In particular, we revisit the concept of
+breakdown point. We modify the original definition by studying the number of
+outliers that a dataset can contain without deteriorating the estimation
+properties of a given estimator. This new notion of breakdown number, that
+takes into account the statistical performances of the estimators, is
+non-asymptotic in nature and adapted for machine learning purposes. We proved
+that the breakdown number of our estimator is of the order of (number of
+observations)*(rate of convergence). For instance, the breakdown number of our
+estimators for the problem of estimation of a d-dimensional vector with a noise
+variance sigma^2 is sigma^2d and it becomes sigma^2 s log(d/s) when this vector
+has only s non-zero component. Beyond this breakdown point, we proved that the
+rate of convergence achieved by our estimator is (number of outliers) divided
+by (number of observation).
+Besides these theoretical guarantees, the major improvement brought by these
+new estimators is that they are easily computable in practice. In fact,
+basically any algorithm used to approximate the standard Empirical Risk
+Minimizer (or its regularized versions) has a robust version approximating our
+estimators. As a proof of concept, we study many algorithms for the classical
+LASSO estimator. A byproduct of the MOM algorithms is a measure of depth of
+data that can be used to detect outliers.
+"
+25691,Bianchi type-I Dust Filled Accelerating Brans- Dicke Cosmology,"  In this paper, spatially homogeneous and anisotropic Bianchi type-I
+cosmological models of Brans-Dicke theory of gravitation are investigated. The
+model represents accelerating universe at present and is considered to be
+dominated by dark energy. Cosmological constant $\Lambda$ is considered as a
+candidate for the dark energy that has negative pressure and is responsible for
+the present acceleration. The derived model agrees at par with the recent SN Ia
+observations. We have set BD-coupling constant $\omega$ to be ~$40000$, ~seeing
+the solar system tests and evidences. We have discussed the various physical
+and geometrical properties of the models and have compared them with the
+corresponding relativistic models.
+"
+25692,The Effect of Time Delay on the Average Data Rate and Performance in Networked Control Systems,"  This paper studies the performance of a feedback control loop closed via an
+error-free digital communication channel with transmission delay. The system
+comprises a discrete-time noisy linear time-invariant (LTI) plant whose single
+measurement output is mapped into its single control input by a causal, but
+otherwise arbitrary, coding and control scheme. We consider a single-input
+multiple-output (SIMO) channel between the encoder-controller and the
+decoder-controller which is lossless and imposes random time delay. We derive a
+lower bound on the minimum average feedback data rate that guarantees achieving
+a certain level of average quadratic performance over all possible realizations
+of the random delay. For the special case of a constant channel delay, we
+obtain an upper bound by proposing linear source-coding schemes that attain
+desired performance levels with rates that are at most 1.254 bits per sample
+greater than the lower bound. We give a numerical example demonstrating that
+bounds and operational rates are increasing functions of the constant delay. In
+other words, to achieve a specific performance level, greater channel delay
+necessitates spending higher data rate.
+"
+25693,Data-Driven Approach to Simulating Realistic Human Joint Constraints,"  Modeling realistic human joint limits is important for applications involving
+physical human-robot interaction. However, setting appropriate human joint
+limits is challenging because it is pose-dependent: the range of joint motion
+varies depending on the positions of other bones. The paper introduces a new
+technique to accurately simulate human joint limits in physics simulation. We
+propose to learn an implicit equation to represent the boundary of valid human
+joint configurations from real human data. The function in the implicit
+equation is represented by a fully connected neural network whose gradients can
+be efficiently computed via back-propagation. Using gradients, we can
+efficiently enforce realistic human joint limits through constraint forces in a
+physics engine or as constraints in an optimization problem.
+"
+25694,Exoplanet Biosignatures: Understanding Oxygen as a Biosignature in the Context of Its Environment,"  Here we review how environmental context can be used to interpret whether O2
+is a biosignature in extrasolar planetary observations. This paper builds on
+the overview of current biosignature research discussed in Schwieterman et al.
+(2017), and provides an in-depth, interdisciplinary example of biosignature
+identification and observation that serves as a basis for the development of
+the general framework for biosignature assessment described in Catling et al.,
+(2017). O2 is a potentially strong biosignature that was originally thought to
+be an unambiguous indicator for life at high-abundance. We describe the
+coevolution of life with the early Earth's environment, and how the interplay
+of sources and sinks in the planetary environment may have resulted in
+suppression of O2 release into the atmosphere for several billion years, a
+false negative for biologically generated O2. False positives may also be
+possible, with recent research showing potential mechanisms in exoplanet
+environments that may generate relatively high abundances of atmospheric O2
+without a biosphere being present. These studies suggest that planetary
+characteristics that may enhance false negatives should be considered when
+selecting targets for biosignature searches. Similarly our ability to interpret
+O2 observed in an exoplanetary atmosphere is also crucially dependent on
+environmental context to rule out false positive mechanisms. We describe future
+photometric, spectroscopic and time-dependent observations of O2 and the
+planetary environment that could increase our confidence that any observed O2
+is a biosignature, and help discriminate it from potential false positives. By
+observing and understanding O2 in its planetary context we can increase our
+confidence in the remote detection of life, and provide a model for
+biosignature development for other proposed biosignatures.
+"
+25695,Weak nodes detection in urban transport systems: Planning for resilience in Singapore,"  The availability of massive data-sets describing human mobility offers the
+possibility to design simulation tools to monitor and improve the resilience of
+transport systems in response to traumatic events such as natural and man-made
+disasters (e.g. floods terroristic attacks, etc...). In this perspective, we
+propose ACHILLES, an application to model people's movements in a given
+transport system mode through a multiplex network representation based on
+mobility data. ACHILLES is a web-based application which provides an
+easy-to-use interface to explore the mobility fluxes and the connectivity of
+every urban zone in a city, as well as to visualize changes in the transport
+system resulting from the addition or removal of transport modes, urban zones,
+and single stops. Notably, our application allows the user to assess the
+overall resilience of the transport network by identifying its weakest node,
+i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To
+demonstrate the impact of ACHILLES for humanitarian aid we consider its
+application to a real-world scenario by exploring human mobility in Singapore
+in response to flood prevention.
+"
+25696,From Depth Data to Head Pose Estimation: a Siamese approach,"  The correct estimation of the head pose is a problem of the great importance
+for many applications. For instance, it is an enabling technology in automotive
+for driver attention monitoring. In this paper, we tackle the pose estimation
+problem through a deep learning network working in regression manner.
+Traditional methods usually rely on visual facial features, such as facial
+landmarks or nose tip position. In contrast, we exploit a Convolutional Neural
+Network (CNN) to perform head pose estimation directly from depth data. We
+exploit a Siamese architecture and we propose a novel loss function to improve
+the learning of the regression network layer. The system has been tested on two
+public datasets, Biwi Kinect Head Pose and ICT-3DHP database. The reported
+results demonstrate the improvement in accuracy with respect to current
+state-of-the-art approaches and the real time capabilities of the overall
+framework.
+"
+25697,Astrometry and exoplanets in the Gaia era: a Bayesian approach to detection and parameter recovery,"  (abridged) We develop Bayesian methods and detection criteria for orbital
+fitting, and revise the detectability of exoplanets in light of the in-flight
+properties of Gaia. Limiting ourselves to one-planet systems as a first step of
+the development, we simulate Gaia data for exoplanet systems over a grid of
+S/N, orbital period, and eccentricity. The simulations are then fit using
+Markov chain Monte Carlo methods. We investigate the detection rate according
+to three information criteria and the delta chi^2. For the delta chi^2, the
+effective number of degrees of freedom depends on the mission length. We find
+that the choice of the Markov chain starting point can affect the quality of
+the results; we therefore consider two limit possibilities: an ideal case, and
+a very simple method that finds the starting point assuming circular orbits.
+Using Jeffreys' scale of evidence, the fraction of false positives passing a
+strong evidence criterion is < ~0.2% (0.6%) when considering a 5 yr (10 yr)
+mission and using the Akaike information criterion or the Watanabe-Akaike
+information criterion, and <0.02% (<0.06%) when using the Bayesian information
+criterion. We find that there is a 50% chance of detecting a planet with a
+minimum S/N=2.3 (1.7). This sets the maximum distance to which a planet is
+detectable to ~70 pc and ~3.5 pc for a Jupiter-mass and Neptune-mass planet,
+respectively, assuming a 10 yr mission, a 4 au semi-major axis, and a 1 M_sun
+star. The period is the orbital parameter that can be determined with the best
+accuracy, with a median relative difference between input and output periods of
+4.2% (2.9%) assuming a 5 yr (10 yr) mission. The median accuracy of the
+semi-major axis of the orbit can be recovered with a median relative error of
+7% (6%). The eccentricity can also be recovered with a median absolute accuracy
+of 0.07 (0.06).
+"
+25698,Single Image Super-Resolution based on Wiener Filter in Similarity Domain,"  Single image super resolution (SISR) is an ill-posed problem aiming at
+estimating a plausible high resolution (HR) image from a single low resolution
+(LR) image. Current state-of-the-art SISR methods are patch-based. They use
+either external data or internal self-similarity to learn a prior for a HR
+image. External data based methods utilize large number of patches from the
+training data, while self-similarity based approaches leverage one or more
+similar patches from the input image. In this paper we propose a
+self-similarity based approach that is able to use large groups of similar
+patches extracted from the input image to solve the SISR problem. We introduce
+a novel prior leading to collaborative filtering of patch groups in 1D
+similarity domain and couple it with an iterative back-projection framework.
+The performance of the proposed algorithm is evaluated on a number of SISR
+benchmark datasets. Without using any external data, the proposed approach
+outperforms the current non-CNN based methods on the tested datasets for
+various scaling factors. On certain datasets, the gain is over 1 dB, when
+compared to the recent method A+. For high sampling rate (x4) the proposed
+method performs similarly to very recent state-of-the-art deep convolutional
+network based approaches.
+"
+25699,Plethysm and fast matrix multiplication,"  Motivated by the symmetric version of matrix multiplication we study the
+plethysm $S^k(\mathfrak{sl}_n)$ of the adjoint representation $\mathfrak{sl}_n$
+of the Lie group $SL_n$. In particular, we describe the decomposition of this
+representation into irreducible components for $k=3$, and find highest weight
+vectors for all irreducible components. Relations to fast matrix
+multiplication, in particular the Coppersmith-Winograd tensor are presented.
+"
+25700,Floquet time crystal in the Lipkin-Meshkov-Glick model,"  In this work we discuss the existence of time-translation symmetry breaking
+in a kicked infinite-range-interacting clean spin system described by the
+Lipkin-Meshkov-Glick model. This Floquet time crystal is robust under
+perturbations of the kicking protocol, its existence being intimately linked to
+the underlying $\mathbb{Z}_2$ symmetry breaking of the time-independent model.
+We show that the model being infinite-range and having an extensive amount of
+symmetry breaking eigenstates is essential for having the time-crystal
+behaviour. In particular we discuss the properties of the Floquet spectrum, and
+show the existence of doublets of Floquet states which are respectively even
+and odd superposition of symmetry broken states and have quasi-energies
+differing of half the driving frequencies, a key essence of Floquet time
+crystals. Remarkably, the stability of the time-crystal phase can be directly
+analysed in the limit of infinite size, discussing the properties of the
+corresponding classical phase space. Through a detailed analysis of the
+robustness of the time crystal to various perturbations we are able to map the
+corresponding phase diagram. We finally discuss the possibility of an
+experimental implementation by means of trapped ions.
+"
+25701,Manipulability Maximization Using Continuous-Time Gaussian Processes,"  A significant challenge in motion planning is to avoid being in or near
+\emph{singular configurations} (\textit{singularities}), that is, joint
+configurations that result in the loss of the ability to move in certain
+directions in task space. A robotic system's capacity for motion is reduced
+even in regions that are in close proximity to (i.e., neighbouring) a
+singularity. In this work we examine singularity avoidance in a motion planning
+context, finding trajectories which minimize proximity to singular regions,
+subject to constraints. We define a manipulability-based likelihood associated
+with singularity avoidance over a continuous trajectory representation, which
+we then maximize using a \textit{maximum a posteriori} (MAP) estimator. Viewing
+the MAP problem as inference on a factor graph, we use gradient information
+from interpolated states to maximize the trajectory's overall manipulability.
+Both qualitative and quantitative analyses of experimental data show increases
+in manipulability that result in smooth trajectories with visibly more
+dexterous arm configurations.
+"
+25702,HANDY: A Hybrid Association Rules Mining Approach for Network Layer Discovery of Services for Mobile Ad hoc Network,"  Mobile Ad hoc Network (MANET) is an infrastructure-less network formed
+between a set of mobile nodes. The discovery of services in MANET is a
+challenging job due to the unique properties of network. In this paper, a novel
+service discovery framework called Hybrid Association Rules Based Network Layer
+Discovery of Services for Ad hoc Networks (HANDY) has been proposed. HANDY
+provides three major research contributions. At first, it adopts a cross-layer
+optimized design for discovery of services that is based on simultaneous
+discovery of services and corresponding routes. Secondly, it provides a
+multi-level ontology-based approach to describe the services. This resolves the
+issue of semantic interoperability among the service consumers in a scalable
+fashion. Finally, to further optimize the performance of the discovery process,
+HANDY recommends exploiting the inherent associations present among the
+services. These associations are used in two ways. First, periodic service
+advertisements are performed based on these associations. In addition, when a
+response of a service discovery request is generated, correlated services are
+also attached with the response. The proposed service discovery scheme has been
+implemented in JIST/SWANS simulator. The results demonstrate that the proposed
+modifications give rise to improvement in hit ratio of the service consumers
+and latency of discovery process.
+"
+25703,Vibronic Ground-state Degeneracies and the Berry Phase: A Continuous Symmetry Perspective,"  We develop a geometric construction to prove the inevitability of the
+electronic ground-state (adiabatic) Berry phase for a class of Jahn-Teller
+models with maximal continuous symmetries and N > 2 intersecting electronic
+states. Given that vibronic ground-state degeneracy in JT models may be seen as
+a consequence of the electronic Berry phase, and that any JT problem may be
+obtained from the subset we investigate in this letter by symmetry-breaking,
+our arguments reveal the fundamental origin of the vibronic ground-state
+degeneracy of JT models.
+"
+25704,"Spread, then Target, and Advertise in Waves: Optimal Budget Allocation Across Advertising Channels","  We analyze optimal strategies for the allocation of a finite budget that can
+be invested in different advertising channels over time with the objective of
+influencing social opinions in a network of individuals. In our analysis, we
+consider both exogenous influence mechanisms, such as advertising campaigns, as
+well as endogenous mechanisms of social influence, such as word-of-mouth and
+peer-pressure, which are modeled using diffusion dynamics.
+We show that for a broad family of objective functions, the optimal influence
+strategy at every time uses all channels at either their maximum rate or not at
+all, i.e., a bang-bang strategy. Furthermore, we prove that the number of
+switches between these extremes is bounded above by a term that is typically
+much smaller than the number of agents. This means that the optimal influence
+strategy is to exert maximum effort in waves for every channel, and then cease
+effort and let the effects propagate. We also show that, at the beginning of
+the campaign, the total cost-adjusted reach of an exogenous advertising channel
+determines its relative value. In contrast, as we approach our investment
+horizon (e.g., election day), the optimal strategy is to invest in channels
+able to target individuals instead of broad-reaching channels. We demonstrate
+that the optimal influence strategies are easily computable in several
+practical cases, and explicitly characterize the optimal controls for the case
+of linear objective functions in closed form. Finally, we see that, in the
+canonical example of designing an election campaign, identifying late-deciders
+is a critical component in the optimal design.
+"
+25705,Adversarial Feature Matching for Text Generation,"  The Generative Adversarial Network (GAN) has achieved great success in
+generating realistic (real-valued) synthetic data. However, convergence issues
+and difficulties dealing with discrete data hinder the applicability of GAN to
+text. We propose a framework for generating realistic text via adversarial
+training. We employ a long short-term memory network as generator, and a
+convolutional network as discriminator. Instead of using the standard objective
+of GAN, we propose matching the high-dimensional latent feature distributions
+of real and synthetic sentences, via a kernelized discrepancy metric. This
+eases adversarial training by alleviating the mode-collapsing problem. Our
+experiments show superior performance in quantitative evaluation, and
+demonstrate that our model can generate realistic-looking sentences.
+"
+25706,Constraints on the structure of hot exozodiacal dust belts,"  Recent interferometric surveys of nearby main-sequence stars show a faint but
+significant near-infrared excess in roughly two dozen systems, i.$\,$e. around
+$10\,\%$ to $30\,\%$ of stars surveyed. This excess is attributed to dust
+located in the immediate vicinity of the star, the origin of which is highly
+debated. We used previously published interferometric observations to constrain
+the properties and distribution of this hot dust. Considering both scattered
+radiation and thermal reemission, we modelled the observed excess in nine of
+these systems. We find that grains have to be sufficiently absorbing to be
+consistent with the observed excess, while dielectric grains with pure silicate
+compositions fail to reproduce the observations. The dust should be located
+within $\sim\,0.01-1\,au$ from the star depending on its luminosity.
+Furthermore, we find a significant trend for the disc radius to increase with
+the stellar luminosity. The dust grains are determined to be below
+$0.2-0.5\,\mu\textrm{m}$, but above $0.02-0.15\,\mu\textrm{m}$ in radius. The
+dust masses amount to $(0.2-3.5)\times10^{-9}\,M_\oplus$. The near-infrared
+excess is probably dominated by thermal reemission, though a contribution of
+scattered light up to $35\,\%$ cannot be completely excluded. The polarisation
+degree predicted by our models is always below $5\,\%$, and for grains smaller
+than $\sim0.2\,\mu\textrm{m}$ even below $1\,\%$. We also modelled the observed
+near-infrared excess of another ten systems with poorer data in the
+mid-infrared. The basic results for these systems appear qualitatively similar,
+yet the constraints on the dust location and the grain sizes are weaker.
+"
+25707,Long-Short Range Context Neural Networks for Language Modeling,"  The goal of language modeling techniques is to capture the statistical and
+structural properties of natural languages from training corpora. This task
+typically involves the learning of short range dependencies, which generally
+model the syntactic properties of a language and/or long range dependencies,
+which are semantic in nature. We propose in this paper a new multi-span
+architecture, which separately models the short and long context information
+while it dynamically merges them to perform the language modeling task. This is
+done through a novel recurrent Long-Short Range Context (LSRC) network, which
+explicitly models the local (short) and global (long) context using two
+separate hidden states that evolve in time. This new architecture is an
+adaptation of the Long-Short Term Memory network (LSTM) to take into account
+the linguistic properties. Extensive experiments conducted on the Penn Treebank
+(PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
+significant reduction of the perplexity when compared to state-of-the-art
+language modeling techniques.
+"
+25708,Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling,"  Advances in the field of inverse reinforcement learning (IRL) have led to
+sophisticated inference frameworks that relax the original modeling assumption
+of observing an agent behavior that reflects only a single intention. Instead
+of learning a global behavioral model, recent IRL methods divide the
+demonstration data into parts, to account for the fact that different
+trajectories may correspond to different intentions, e.g., because they were
+generated by different domain experts. In this work, we go one step further:
+using the intuitive concept of subgoals, we build upon the premise that even a
+single trajectory can be explained more efficiently locally within a certain
+context than globally, enabling a more compact representation of the observed
+behavior. Based on this assumption, we build an implicit intentional model of
+the agent's goals to forecast its behavior in unobserved situations. The result
+is an integrated Bayesian prediction framework that significantly outperforms
+existing IRL solutions and provides smooth policy estimates consistent with the
+expert's plan. Most notably, our framework naturally handles situations where
+the intentions of the agent change over time and classical IRL algorithms fail.
+In addition, due to its probabilistic nature, the model can be
+straightforwardly applied in active learning scenarios to guide the
+demonstration process of the expert.
+"
+25709,Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning,"  Humans are capable of attributing latent mental contents such as beliefs, or
+intentions to others. The social skill is critical in everyday life to reason
+about the potential consequences of their behaviors so as to plan ahead. It is
+known that humans use this reasoning ability recursively, i.e. considering what
+others believe about their own beliefs. In this paper, we start from level-$1$
+recursion and introduce a probabilistic recursive reasoning (PR2) framework for
+multi-agent reinforcement learning. Our hypothesis is that it is beneficial for
+each agent to account for how the opponents would react to its future
+behaviors. Under the PR2 framework, we adopt variational Bayes methods to
+approximate the opponents' conditional policy, to which each agent finds the
+best response and then improve their own policy. We develop
+decentralized-training-decentralized-execution algorithms, PR2-Q and
+PR2-Actor-Critic, that are proved to converge in the self-play scenario when
+there is one Nash equilibrium. Our methods are tested on both the matrix game
+and the differential game, which have a non-trivial equilibrium where common
+gradient-based methods fail to converge. Our experiments show that it is
+critical to reason about how the opponents believe about what the agent
+believes. We expect our work to contribute a new idea of modeling the opponents
+to the multi-agent reinforcement learning community.
+"
+25710,A Differential Evaluation Markov Chain Monte Carlo algorithm for Bayesian Model Updating,"  The use of the Bayesian tools in system identification and model updating
+paradigms has been increased in the last ten years. Usually, the Bayesian
+techniques can be implemented to incorporate the uncertainties associated with
+measurements as well as the prediction made by the finite element model (FEM)
+into the FEM updating procedure. In this case, the posterior distribution
+function describes the uncertainty in the FE model prediction and the
+experimental data. Due to the complexity of the modeled systems, the analytical
+solution for the posterior distribution function may not exist. This leads to
+the use of numerical methods, such as Markov Chain Monte Carlo techniques, to
+obtain approximate solutions for the posterior distribution function. In this
+paper, a Differential Evaluation Markov Chain Monte Carlo (DE-MC) method is
+used to approximate the posterior function and update FEMs. The main idea of
+the DE-MC approach is to combine the Differential Evolution, which is an
+effective global optimization algorithm over real parameter space, with Markov
+Chain Monte Carlo (MCMC) techniques to generate samples from the posterior
+distribution function. In this paper, the DE-MC method is discussed in detail
+while the performance and the accuracy of this algorithm are investigated by
+updating two structural examples.
+"
+25711,The Isostatic Conjecture,"  We show that a jammed packing of disks with generic radii, in a generic
+container, is such that the minimal number of contacts occurs and there is only
+one dimension of equilibrium stresses. We also point out some connections to
+packings with different radii and results in the theory of circle packings
+whose graph forms a triangulation of a given topological surface. We also point
+out a counterexample, due to F. Nazarov, to a previous conjecture that that
+triangulated packings with fixed numbers of disks with fixed numbers of disks
+for each radius claiming that such packings were the most dense.
+"
+25712,Relevance of the speed and direction of pulling in simple modular proteins,"  A theoretical analysis of the unfolding pathway of simple modular proteins in
+length- controlled pulling experiments is put forward. Within this framework,
+we predict the first module to unfold in a chain of identical units,
+emphasizing the ranges of pulling speeds in which we expect our theory to hold.
+These theoretical predictions are checked by means of steered molecular
+dynamics of a simple construct, specifically a chain composed of two
+coiled-coils motives, where anisotropic features are revealed. These
+simulations also allow us to give an estimate for the range of pulling
+velocities in which our theoretical approach is valid.
+"
+25713,Hydrodynamic simulations of moonlet induced propellers in Saturn's rings: Application to Bleriot,"  One of the biggest successes of the Cassini mission is the detection of small
+moons (moonlets) embedded in Saturn's rings which cause S-shaped density
+structures in their close vicinity, called propellers (Spahn and Sremcevic
+2000; Tiscareno et al. 2006; Sremcevic et al. 2007). Here, we present
+isothermal hydrodynamic simulations of moonlet-induced propellers in Saturn's A
+ring which denote a further development of the original model (Spahn and
+Sremcevic 2000). We find excellent agreement between these new hydrodynamic and
+corresponding N-body simulations. Furthermore, the hydrodynamic simulations
+confirm the predicted scaling laws (Spahn and Sremcevic 2000) and the
+analytical solution for the density in the propeller gaps (Sremcevic et al.
+2002). Finally, this mean field approach allows us to simulate the pattern of
+the giant propeller Bleriot, which is too large to be modeled by direct N-body
+simulations. Our results are compared to two stellar occultation observations
+by the Cassini Ultraviolet Imaging Spectrometer (UVIS), that intersect the
+propeller Bleriot. Best fits to the UVIS optical depth profiles are achieved
+for a Hill radius of 590 m, which implies a moonlet diameter of about 860 m.
+Furthermore, the model favours a kinematic shear viscosity of the surrounding
+ring material of $\nu_0 = 340$ cm^2/s, a dispersion velocity in the range of
+0.3 cm/s $< c_0 <$ 1.5 cm/s, and a fairly high bulk viscosity $7 < \xi_0/\nu_0
+< 17$. These large transport values might be overestimated by our isothermal
+ring model and should be reviewed by an extended model including thermal
+fluctuations.
+"
+25714,Bounds on 2-torsion in class groups of number fields and integral points on elliptic curves,"  We prove the first known nontrivial bounds on the sizes of the 2-torsion
+subgroups of the class groups of cubic and higher degree number fields $K$ (the
+trivial bound being $O_{\epsilon}(|{\rm Disc}(K)|^{1/2+\epsilon})$ by
+Brauer--Siegel). This yields corresponding improvements to: 1) bounds of Brumer
+and Kramer on the sizes of 2-Selmer groups and ranks of elliptic curves; 2)
+bounds of Helfgott and Venkatesh on the number of integral points on elliptic
+curves; 3) bounds on the sizes of 2-Selmer groups and ranks of Jacobians of
+hyperelliptic curves; and 4) bounds of Baily and Wong on the number of
+$A_4$-quartic fields of bounded discriminant.
+"
+25715,Thermodynamical Effects and High Resolution Methods for Compressible Fluid Flows,"  One of the fundamental differences of compressible fluid flows from
+incompressible fluid flows is the involvement of thermodynamics. This
+difference should be manifested in the design of numerical methods and seems
+often be neglected in addition that the entropy inequality, as a conceptual
+derivative, is taken into account to reflect irreversible processes and
+verified for some first order schemes. In this paper, we refine the GRP solver
+to illustrate how the thermodynamical variation is integrated into the design
+of high resolution methods for compressible fluid flows and demonstrate
+numerically the importance of thermodynamic effect in the resolution of strong
+waves. As a by-product, we show that the GRP solver works for generic equations
+of state, and is independent of technical arguments.
+"
+25716,Highly anisotropic quasiparticle interference patterns in the spin-density wave state of the iron pnictides,"  We investigate the impurity scattering induced quasiparticle interference in
+the ($\pi, 0$) spin-density wave phase of the iron pnictides. We use a five
+orbital tight binding model and our mean field theory in the clean limit
+captures key features of the Fermi surface observed in angle-resolved
+photoemission. We use a t-matrix formalism to incorporate the effect of doping
+induced impurities on this state. The impurities lead to a spatial modulation
+of the local density of states about the impurity site, with a periodicity of
+$\sim 8a_{{\rm Fe}-{\rm Fe}}$ along the antiferromagnetic direction. The
+associated momentum space quasiparticle interference pattern is anisotropic,
+with major peaks located at $\sim (\pm \pi/4,0)$, consistent with spectroscopic
+imaging scanning tunneling microscopy. We trace the origin of this pattern to
+an elliptical contour of constant energy around momentum (0,0), with major axis
+oriented along the (0,1) direction, in the mean field electronic structure.
+"
+25717,A Joint Resource Allocation Scheme for Multi-User Full-Duplex OFDMA Systems,"  Full-duplex is a promising technology and great system performance
+enhancement can be achieved especially when we apply it to base stations. In
+this paper, we consider a Multi-User OFDMA network consisting of one
+Full-duplex base station, several uplink users, and a number of downlink users.
+A joint subcarrier, downlink user and uplink user mapping and power allocation
+optimization problem is investigated. In detail, we first decompose the
+3-dimensional mapping problem into three 2-dimensional sub-problems and solve
+them by iteratively using classical Hungarian Method. Then, based on the dual
+method, we sequentially solve the power allocation and 3-dimensional mapping
+problem at each dual point. And the optimal solution to the dual problem is
+derived by using sub-gradient method. Unlike existing methods that only solve
+one sub-problem but with a high computation complexity, we tackle both of them
+and successfully reducing computation complexity from exponential to polynomial
+order. Numerical simulations are conducted to verify the proposed system.
+"
+25718,Conditioning Deep Generative Raw Audio Models for Structured Automatic Music,"  Existing automatic music generation approaches that feature deep learning can
+be broadly classified into two types: raw audio models and symbolic models.
+Symbolic models, which train and generate at the note level, are currently the
+more prevalent approach; these models can capture long-range dependencies of
+melodic structure, but fail to grasp the nuances and richness of raw audio
+generations. Raw audio models, such as DeepMind's WaveNet, train directly on
+sampled audio waveforms, allowing them to produce realistic-sounding, albeit
+unstructured music. In this paper, we propose an automatic music generation
+methodology combining both of these approaches to create structured,
+realistic-sounding compositions. We consider a Long Short Term Memory network
+to learn the melodic structure of different styles of music, and then use the
+unique symbolic generations from this model as a conditioning input to a
+WaveNet-based raw audio generator, creating a model for automatic, novel music.
+We then evaluate this approach by showcasing results of this work.
+"
+25719,Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity,"  We analyze the necessary number of samples for sparse vector recovery in a
+noisy linear prediction setup. This model includes problems such as linear
+regression and classification. We focus on structured graph models. In
+particular, we prove that sufficient number of samples for the weighted graph
+model proposed by Hegde and others is also necessary. We use the Fano's
+inequality on well constructed ensembles as our main tool in establishing
+information theoretic lower bounds.
+"
+25720,Stability of single and multiple matter-wave dark solitons in collisionally inhomogeneous Bose-Einstein condensates,"  We examine the spectral properties of single and multiple matter-wave dark
+solitons in Bose-Einstein condensates confined in parabolic traps, where the
+scattering length is periodically modulated. In addition to the large-density
+limit picture previously established for homogeneous nonlinearities, we explore
+a perturbative analysis in the vicinity of the linear limit, which provides
+good agreement with the observed spectral modes. Between these two analytically
+tractable limits, we use numerical computations to fill in the relevant
+intermediate regime. We find that the scattering length modulation can cause a
+variety of features absent for homogeneous nonlinearities. Among them, we note
+the potential oscillatory instability even of the single dark soliton, the
+potential absence of instabilities in the immediate vicinity of the linear
+limit for two dark solitons, and the existence of an exponential instability
+associated with the in-phase motion of three dark solitons.
+"
+25721,Linear Hashing is Awesome,"  We consider the hash function $h(x) = ((ax+b) \bmod p) \bmod n$ where $a,b$
+are chosen uniformly at random from $\{0,1,\ldots,p-1\}$. We prove that when we
+use $h(x)$ in hashing with chaining to insert $n$ elements into a table of size
+$n$ the expected length of the longest chain is
+$\tilde{O}\!\left(n^{1/3}\right)$. The proof also generalises to give the same
+bound when we use the multiply-shift hash function by Dietzfelbinger et al.
+[Journal of Algorithms 1997].
+"
+25722,The SUrvey for Pulsars and Extragalactic Radio Bursts I: Survey Description and Overview,"  We describe the Survey for Pulsars and Extragalactic Radio Bursts (SUPERB),
+an ongoing pulsar and fast transient survey using the Parkes radio telescope.
+SUPERB involves real-time acceleration searches for pulsars and single-pulse
+searches for pulsars and fast radio bursts. We report on the observational
+setup, data analysis, multi-wavelength/messenger connections, survey
+sensitivities to pulsars and fast radio bursts and the impact of radio
+frequency interference. We further report on the first 10 pulsars discovered in
+the project. Among these is PSR~J1306$-$40, a millisecond pulsar in a binary
+system where it appears to be eclipsed for a large fraction of the orbit.
+PSR~J1421$-$4407 is another binary millisecond pulsar; its orbital period is
+$30.7$ days. This orbital period is in a range where only highly eccentric
+binaries are known, and expected by theory; despite this its orbit has an
+eccentricity of $10^{-5}$.
+"
+25723,Security Game with Non-additive Utilities and Multiple Attacker Resources,"  There has been significant interest in studying security games for modeling
+the interplay of attacks and defenses on various systems involving critical
+infrastructure, financial system security, political campaigns, and civil
+safeguarding. However, existing security game models typically either assume
+additive utility functions, or that the attacker can attack only one target.
+Such assumptions lead to tractable analysis, but miss key inherent dependencies
+that exist among different targets in current complex networks. In this paper,
+we generalize the classical security game models to allow for non-additive
+utility functions. We also allow attackers to be able to attack multiple
+targets. We examine such a general security game from a theoretical perspective
+and provide a unified view. In particular, we show that each security game is
+equivalent to a combinatorial optimization problem over a set system
+$\varepsilon$, which consists of defender's pure strategy space. The key
+technique we use is based on the transformation, projection of a polytope, and
+the elipsoid method. This work settles several open questions in security game
+domain and significantly extends the state of-the-art of both the polynomial
+solvable and NP-hard class of the security game.
+"
+25724,Effect of boson on-site repulsion on the superfluidity in the boson-fermion-Hubbard model,"  We analyze the finite-temperature phase diagram of the boson-fermion-Hubbard
+model with Feshbach converting interaction, using the coherent-state
+path-integral method. We show that depending on the position of the bosonic
+band, this type of interaction, even if weak, can drive the system into the
+resonant superfluid phase in the strong bosonic interaction limit. It turns out
+that this phase can exist for an arbitrary number of fermions (i.e., fermionic
+concentration between 0 and 2) but with the bosonic particle number very close
+to an integer value. We point out that the standard time-of-flight method in
+optical lattice experiments can be an adequate technique to confirm the
+existence of this resonant phase. Moreover, in the non-resonant regime, the
+enhancement of the critical temperature of the superfluid phase due to Feshbach
+interaction is also observed. We account for this interesting phenomena for a
+hole- or particlelike pairing mechanism depending on the system density and
+mutual location of the fermionic and bosonic bands.
+"
+25725,"Topological Phase Transition in Layered XIn$_2$P$_2$ (X = Ca, Sr)","  Based on fully relativistic first-principles calculations, we studied the
+topological properties of layered XIn$_2$P$_2$ (X = Ca, Sr). Band inversion can
+be induced by strain without SOC, forming one nodal ring in the k$_z$ = 0
+plane, which is protected by the coexistence of time-reversal and
+mirror-reflection symmetry. Including SOC, a substantial band gap is opened
+along the nodal line and the line-node semimetal would evolve into a
+topological insulator. These results reveal a category of materials showing
+quantum phase transition from trivial semiconductor and topologically
+nontrivial insulator by the tuneable elastic strain engineering. Our
+investigations provide a new perspective about the formation of topological
+line-node semimetal under stain.
+"
+25726,Towards Low-Latency and Ultra-Reliable Vehicle-to-Vehicle Communication,"  Recently vehicle-to-vehicle (V2V) communication emerged as a key enabling
+technology to ensure traffic safety and other mission-critical applications. In
+this paper, a novel proximity and quality-of-service (QoS)-aware resource
+allocation for V2V communication is proposed. The proposed approach exploits
+the spatial-temporal aspects of vehicles in terms of their physical proximity
+and traffic demands, to minimize the total transmission power while considering
+queuing latency and reliability. Due to the overhead caused by frequent
+information exchange between vehicles and the roadside unit (RSU), the
+centralized problem is decoupled into two interrelated subproblems. First, a
+novel RSU-assisted virtual clustering mechanism is proposed to group vehicles
+in zones based on their physical proximity. Given the vehicles' traffic demands
+and their QoS requirements, resource blocks are assigned to each zone. Second,
+leveraging techniques from Lyapunov stochastic optimization, a power
+minimization solution is proposed for each V2V pair within each zone.
+Simulation results for a Manhattan model have shown that the proposed scheme
+outperforms the baseline in terms of average queuing latency reduction up to
+97% and significant improvement in reliability.
+"
+25727,Differential fields and Geodesic flows II : Geodesic flows of pseudo-Riemannian algebraic varieties,"  We define the notion of a smooth pseudo-Riemannian algebraic variety $(X,g)$
+over a field $k$ of characteristic $0$, which is an algebraic analogue of the
+notion of Riemannian manifold and we study, from a model-theoretic perspective,
+the algebraic differential equation describing the geodesics on $(X,g)$.
+When $k$ is the field of real numbers, we prove that if the real points of
+$X$ are Zariski-dense in $X$ and if the real analytification of $(X,g)$ is a
+compact Riemannian manifold with negative curvature, then the algebraic
+differential equation describing the geodesics on $(X,g)$ is absolutely
+irreducible and its generic type is orthogonal to the constants.
+"
+25728,The Representation Theorem of Persistent Homology Revisited and Generalized,"  The Representation Theorem by Zomorodian and Carlsson has been the starting
+point of the study of persistent homology under the lens of algebraic
+representation theory. In this work, we give a more accurate statement of the
+original theorem and provide a complete and self-contained proof. Furthermore,
+we generalize the statement from the case of linear sequences of $R$-modules to
+$R$-modules indexed over more general monoids. This generalization subsumes the
+Representation Theorem of multidimensional persistence as a special case.
+"
+25729,Proceedings of the First International Workshop on Deep Learning and Music,"  Proceedings of the First International Workshop on Deep Learning and Music,
+joint with IJCNN, Anchorage, US, May 17-18, 2017
+"
+25730,Cascade Adversarial Machine Learning Regularized with a Unified Embedding,"  Injecting adversarial examples during training, known as adversarial
+training, can improve robustness against one-step attacks, but not for unknown
+iterative attacks. To address this challenge, we first show iteratively
+generated adversarial images easily transfer between networks trained with the
+same strategy. Inspired by this observation, we propose cascade adversarial
+training, which transfers the knowledge of the end results of adversarial
+training. We train a network from scratch by injecting iteratively generated
+adversarial images crafted from already defended networks in addition to
+one-step adversarial images from the network being trained. We also propose to
+utilize embedding space for both classification and low-level (pixel-level)
+similarity learning to ignore unknown pixel level perturbation. During
+training, we inject adversarial images without replacing their corresponding
+clean images and penalize the distance between the two embeddings (clean and
+adversarial). Experimental results show that cascade adversarial training
+together with our proposed low-level similarity learning efficiently enhances
+the robustness against iterative attacks, but at the expense of decreased
+robustness against one-step attacks. We show that combining those two
+techniques can also improve robustness under the worst case black box attack
+scenario.
+"
+25731,Absence of Ergodicity without Quenched Disorder: from Quantum Disentangled Liquids to Many-Body Localization,"  We study the time evolution after a quantum quench in a family of models
+whose degrees of freedom are fermions coupled to spins, where quenched disorder
+appears neither in the Hamiltonian parameters nor in the initial state.
+Focussing on the behaviour of entanglement, both spatial and between
+subsystems, we show that the model supports a state exhibiting combined
+area/volume law entanglement, being characteristic of the quantum disentangled
+liquid. This behaviour appears for one set of variables, which is related via a
+duality mapping to another set, where this structure is absent. Upon adding
+density interactions between the fermions, we identify an exact mapping to an
+XXZ spin-chain in a random binary magnetic field, thereby establishing the
+existence of many-body localization with its logarithmic entanglement growth in
+a fully disorder-free system.
+"
+25732,Symbol Grounding via Chaining of Morphisms,"  A new model of symbol grounding is presented, in which the structures of
+natural language, logical semantics, perception and action are represented
+categorically, and symbol grounding is modeled via the composition of morphisms
+between the relevant categories. This model gives conceptual insight into the
+fundamentally systematic nature of symbol grounding, and also connects
+naturally to practical real-world AI systems in current research and commercial
+use. Specifically, it is argued that the structure of linguistic syntax can be
+modeled as a certain asymmetric monoidal category, as e.g. implicit in the link
+grammar formalism; the structure of spatiotemporal relationships and action
+plans can be modeled similarly using ""image grammars"" and ""action grammars"";
+and common-sense logical semantic structure can be modeled using
+dependently-typed lambda calculus with uncertain truth values. Given these
+formalisms, the grounding of linguistic descriptions in spatiotemporal
+perceptions and coordinated actions consists of following morphisms from
+language to logic through to spacetime and body (for comprehension), and vice
+versa (for generation). The mapping is indicated between the spatial
+relationships in the Region Connection Calculus and Allen Interval Algebra and
+corresponding entries in the link grammar syntax parsing dictionary. Further,
+the abstractions introduced here are shown to naturally model the structures
+and systems currently being deployed in the context of using the OpenCog
+cognitive architecture to control Hanson Robotics humanoid robots.
+"
+25733,"Micro Fourier Transform Profilometry ($μ$FTP): 3D shape measurement at 10,000 frames per second","  Recent advances in imaging sensors and digital light projection technology
+have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces
+of complex-shaped objects to be captured with improved resolution and accuracy.
+However, due to the large number of projection patterns required for phase
+recovery and disambiguation, the maximum fame rates of current 3D shape
+measurement techniques are still limited to the range of hundreds of frames per
+second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro
+Fourier Transform Profilometry ($\mu$FTP), which can capture 3D surfaces of
+transient events at up to 10,000 fps based on our newly developed high-speed
+fringe projection system. Compared with existing techniques, $\mu$FTP has the
+prominent advantage of recovering an accurate, unambiguous, and dense 3D point
+cloud with only two projected patterns. Furthermore, the phase information is
+encoded within a single high-frequency fringe image, thereby allowing
+motion-artifact-free reconstruction of transient events with temporal
+resolution of 50 microseconds. To show $\mu$FTP's broad utility, we use it to
+reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating
+fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a
+flying dart, which were previously difficult or even unable to be captured with
+conventional approaches.
+"
+25734,WaveCycleGAN: Synthetic-to-natural speech waveform conversion using cycle-consistent adversarial networks,"  We propose a learning-based filter that allows us to directly modify a
+synthetic speech waveform into a natural speech waveform. Speech-processing
+systems using a vocoder framework such as statistical parametric speech
+synthesis and voice conversion are convenient especially for a limited number
+of data because it is possible to represent and process interpretable acoustic
+features over a compact space, such as the fundamental frequency (F0) and
+mel-cepstrum. However, a well-known problem that leads to the quality
+degradation of generated speech is an over-smoothing effect that eliminates
+some detailed structure of generated/converted acoustic features. To address
+this issue, we propose a synthetic-to-natural speech waveform conversion
+technique that uses cycle-consistent adversarial networks and which does not
+require any explicit assumption about speech waveform in adversarial learning.
+In contrast to current techniques, since our modification is performed at the
+waveform level, we expect that the proposed method will also make it possible
+to generate `vocoder-less' sounding speech even if the input speech is
+synthesized using a vocoder framework. The experimental results demonstrate
+that our proposed method can 1) alleviate the over-smoothing effect of the
+acoustic features despite the direct modification method used for the waveform
+and 2) greatly improve the naturalness of the generated speech sounds.
+"
+25735,Differential Dynamic Programming for time-delayed systems,"  Trajectory optimization considers the problem of deciding how to control a
+dynamical system to move along a trajectory which minimizes some cost function.
+Differential Dynamic Programming (DDP) is an optimal control method which
+utilizes a second-order approximation of the problem to find the control. It is
+fast enough to allow real-time control and has been shown to work well for
+trajectory optimization in robotic systems. Here we extend classic DDP to
+systems with multiple time-delays in the state. Being able to find optimal
+trajectories for time-delayed systems with DDP opens up the possibility to use
+richer models for system identification and control, including recurrent neural
+networks with multiple timesteps in the state. We demonstrate the algorithm on
+a two-tank continuous stirred tank reactor. We also demonstrate the algorithm
+on a recurrent neural network trained to model an inverted pendulum with
+position information only.
+"
+25736,Automatic sleep monitoring using ear-EEG,"  The monitoring of sleep patterns without patient's inconvenience or
+involvement of a medical specialist is a clinical question of significant
+importance. To this end, we propose an automatic sleep stage monitoring system
+based on an affordable, unobtrusive, discreet, and long-term wearable in-ear
+sensor for recording the Electroencephalogram (ear-EEG). The selected features
+for sleep pattern classification from a single ear-EEG channel include the
+spectral edge frequency (SEF) and multi- scale fuzzy entropy (MSFE), a
+structural complexity feature. In this preliminary study, the manually scored
+hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects
+are used as labels for two analysis scenarios: 1) classification of ear-EEG
+hypnogram labels from ear-EEG recordings and 2) prediction of scalp-EEG
+hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class
+sleep scoring, with the achieved accuracies ranging from 78.5 % to 95.2 % for
+ear-EEG labels predicted from ear-EEG, and 76.8 % to 91.8 % for scalp-EEG
+labels predicted from ear-EEG. The corresponding kappa coefficients, which
+range from 0.64 to 0.83 for Scenario 1 and from 0.65 to 0.80 for Scenario 2,
+indicate a Substantial to Almost Perfect agreement, thus proving the
+feasibility of in-ear sensing for sleep monitoring in the community.
+"
+25737,A Dirichlet approximation theorem for group actions,"  If $G$ is a compact group acting continuously on a compact metric space $(X,
+m)$, we prove two results that generalize Dirichlet's classical theorem on
+Diophantine approximation. If $G$ is a noncommutative compact group of
+isometries, we obtain a noncommutative form of Dirichlet's theorem. We apply
+our general result to the special case of the unitary group $U(N)$ acting on
+the complex unit sphere, and obtain a noncommutative result in this setting.
+"
+25738,An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior,"  The presence of noisy instances in mobile phone data is a fundamental issue
+for classifying user phone call behavior (i.e., accept, reject, missed and
+outgoing), with many potential negative consequences. The classification
+accuracy may decrease and the complexity of the classifiers may increase due to
+the number of redundant training samples. To detect such noisy instances from a
+training dataset, researchers use naive Bayes classifier (NBC) as it identifies
+misclassified instances by taking into account independence assumption and
+conditional probabilities of the attributes. However, some of these
+misclassified instances might indicate usages behavioral patterns of individual
+mobile phone users. Existing naive Bayes classifier based noise detection
+techniques have not considered this issue and, thus, are lacking in
+classification accuracy. In this paper, we propose an improved noise detection
+technique based on naive Bayes classifier for effectively classifying users'
+phone call behaviors. In order to improve the classification accuracy, we
+effectively identify noisy instances from the training dataset by analyzing the
+behavioral patterns of individuals. We dynamically determine a noise threshold
+according to individual's unique behavioral patterns by using both the naive
+Bayes classifier and Laplace estimator. We use this noise threshold to identify
+noisy instances. To measure the effectiveness of our technique in classifying
+user phone call behavior, we employ the most popular classification algorithm
+(e.g., decision tree). Experimental results on the real phone call log dataset
+show that our proposed technique more accurately identifies the noisy instances
+from the training datasets that leads to better classification accuracy.
+"
+25739,Graph Structure Learning from Unlabeled Data for Event Detection,"  Processes such as disease propagation and information diffusion often spread
+over some latent network structure which must be learned from observation.
+Given a set of unlabeled training examples representing occurrences of an event
+type of interest (e.g., a disease outbreak), our goal is to learn a graph
+structure that can be used to accurately detect future events of that type.
+Motivated by new theoretical results on the consistency of constrained and
+unconstrained subset scans, we propose a novel framework for learning graph
+structure from unlabeled data by comparing the most anomalous subsets detected
+with and without the graph constraints. Our framework uses the mean normalized
+log-likelihood ratio score to measure the quality of a graph structure, and
+efficiently searches for the highest-scoring graph structure. Using simulated
+disease outbreaks injected into real-world Emergency Department data from
+Allegheny County, we show that our method learns a structure similar to the
+true underlying graph, but enables faster and more accurate detection.
+"
+25740,Extending the photon energy coverage of an x-ray self-seeding FEL via the reverse taper enhanced harmonic generation technique,"  In this paper, a simple method is proposed to extend the photon energy range
+of a soft x-ray self-seeding free-electron laser (FEL). A normal monochromator
+is first applied to purify the FEL spectrum and provide a coherent seeding
+signal. This coherent signal then interacts with the electron beam in the
+following reverse tapered undulator section to generate strong coherent
+microbunchings while maintain the good quality of the electron beam. After
+that, the pre-bunched electron beam is sent into the third undulator section
+which resonates at a target high harmonic of the seed to amplify the coherent
+radiation at shorter wavelength. Three dimensional simulations have been
+performed and the results demonstrate that the photon energy gap between 1.5
+keV and 4.5 keV of the self-seeding scheme can be fully covered and 100
+GW-level peak power can be achieved by using the proposed technique.
+"
+25741,Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform,"  Applying Machine Learning (ML) to business applications for automation
+usually faces difficulties when integrating diverse ML dependencies and
+services, mainly because of the lack of a common ML framework. In most cases,
+the ML models are developed for applications which are targeted for specific
+business domain use cases, leading to duplicated effort, and making reuse
+impossible. This paper presents Acumos, an open platform capable of packaging
+ML models into portable containerized microservices which can be easily shared
+via the platform's catalog, and can be integrated into various business
+applications. We present a case study of packaging sentiment analysis and
+classification ML models via the Acumos platform, permitting easy sharing with
+others. We demonstrate that the Acumos platform reduces the technical burden on
+application developers when applying machine learning models to their business
+applications. Furthermore, the platform allows the reuse of readily available
+ML microservices in various business domains.
+"
+25742,Sparse reconstruction of electric fields from radial magnetic data,"  Accurate estimates of the horizontal electric field on the Sun's visible
+surface are important not only for estimating the Poynting flux of magnetic
+energy into the corona but also for driving time-dependent magnetohydrodynamic
+models of the corona. In this paper, a method is developed for estimating the
+horizontal electric field from a sequence of radial-component magnetic field
+maps. This problem of inverting Faraday's law has no unique solution.
+Unfortunately, the simplest solution (a divergence-free electric field) is not
+realistically localized in regions of non-zero magnetic field, as would be
+expected from Ohm's law. Our new method generates instead a localized solution,
+using a basis pursuit algorithm to find a sparse solution for the electric
+field. The method is shown to perform well on test cases where the input
+magnetic maps are flux balanced, in both Cartesian and spherical geometries.
+However, we show that if the input maps have a significant imbalance of flux -
+usually arising from data assimilation - then it is not possible to find a
+localized, realistic, electric field solution. This is the main obstacle to
+driving coronal models from time sequences of solar surface magnetic maps.
+"
+25743,Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces,"  This paper presents the first ever approach for solving
+\emph{continuous-observation} Decentralized Partially Observable Markov
+Decision Processes (Dec-POMDPs) and their semi-Markovian counterparts,
+Dec-POSMDPs. This contribution is especially important in robotics, where a
+vast number of sensors provide continuous observation data. A
+continuous-observation policy representation is introduced using Stochastic
+Kernel-based Finite State Automata (SK-FSAs). An SK-FSA search algorithm titled
+Entropy-based Policy Search using Continuous Kernel Observations (EPSCKO) is
+introduced and applied to the first ever continuous-observation
+Dec-POMDP/Dec-POSMDP domain, where it significantly outperforms
+state-of-the-art discrete approaches. This methodology is equally applicable to
+Dec-POMDPs and Dec-POSMDPs, though the empirical analysis presented focuses on
+Dec-POSMDPs due to their higher scalability. To improve convergence, an entropy
+injection policy search acceleration approach for both continuous and discrete
+observation cases is also developed and shown to improve convergence rates
+without degrading policy quality.
+"
+25744,A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go,"  This paper applies a genetic algorithm and fuzzy markup language to construct
+a human and smart machine cooperative learning system on game of Go. The
+genetic fuzzy markup language (GFML)-based Robot Agent can work on various
+kinds of robots, including Palro, Pepper, and TMUs robots. We use the
+parameters of FAIR open source Darkforest and OpenGo AI bots to construct the
+knowledge base of Open Go Darkforest (OGD) cloud platform for student learning
+on the Internet. In addition, we adopt the data from AlphaGo Master sixty
+online games as the training data to construct the knowledge base and rule base
+of the co-learning system. First, the Darkforest predicts the win rate based on
+various simulation numbers and matching rates for each game on OGD platform,
+then the win rate of OpenGo is as the final desired output. The experimental
+results show that the proposed approach can improve knowledge base and rule
+base of the prediction ability based on Darkforest and OpenGo AI bot with
+various simulation numbers.
+"
+25745,Generalized Fit for Asymptotic Predictions of Heavy-Tail Experimental Transients,"  Transient responses in disordered systems typically show a heavy-tail
+relaxation behavior: the decay time constant increases as time increases,
+revealing a spectral distribution of time constants. The asymptotic value of
+such transients is notoriously difficult to experimentally measure due to the
+increasing decay time-scale. However, if the heavy-tail transient is plotted
+versus log-time, a reduced set of data around the inflection point of such a
+plot is sufficient for an accurate fit. From a derivative plot in log-time, the
+peak height, position, line width, and, most importantly, skewness are all that
+is needed to accurately predict the asymptotic value of various heavy-tail
+decay models to within less than a percent. This curve fitting strategy reduces
+by orders of magnitude the amount of experimental data required, and clearly
+identifies a threshold below which the amount of data is insufficient to
+distinguish various models. The skew normal spectral fit and dispersive
+diffusion transient fit are proposed as four-parameter fits, with the latter
+including the stretched exponential as a limiting case. The line fit and
+asymptotic prediction are demonstrated using experimental transient responses
+in previously published amorphous silicon and amorphous InGaZnO data.
+"
+25746,"Tarski's Theorem on Intuitionistic logic, for polyhedra","  In 1938, Tarski proved that a formula is not intuitionistically valid if, and
+only if, it has a counter-model in the Heyting algebra of open sets of some
+topological space. In fact, Tarski showed that any Euclidean space R^n with n
+>= 1 suffices, as does e.g. the Cantor space. In particular, intuitionistic
+logic cannot detect topological dimension in the frame of all open sets of a
+Euclidean space. By contrast, we consider the lattice of open subpolyhedra of a
+given compact polyhedron P \subseteq R^n, prove that it is a locally finite
+Heyting subalgebra of the (non-locally-finite) algebra of all open sets of R^n,
+and show that intuitionistic logic is able to capture the topological dimension
+of P through the bounded-depth axiom schemata. Further, we show that
+intuitionistic logic is precisely the logic of formul{\ae} valid in all Heyting
+algebras arising from polyhedra in this manner. Thus, our main theorem
+reconciles through polyhedral geometry two classical results: topological
+completeness in the style of Tarski, and Jaskowski's theorem that
+intuitionistic logic enjoys the finite model property. Several questions of
+interest remain open. E.g., what is the intermediate logic of all closed
+triangulable manifolds?
+"
+25747,Edge Attention-based Multi-Relational Graph Convolutional Networks,"  Graph convolutional network (GCN) is generalization of convolutional neural
+network (CNN) to work with arbitrarily structured graphs. A binary adjacency
+matrix is commonly used in training a GCN. Recently, the attention mechanism
+allows the network to learn a dynamic and adaptive aggregation of the
+neighborhood. We propose a new GCN model on the graphs where edges are
+characterized in multiple views or precisely in terms of multiple
+relationships. For instance, in chemical graph theory, compound structures are
+often represented by the hydrogen-depleted molecular graph where nodes
+correspond to atoms and edges correspond to chemical bonds. Multiple attributes
+can be important to characterize chemical bonds, such as atom pair (the types
+of atoms that a bond connects), aromaticity, and whether a bond is in a ring.
+The different attributes lead to different graph representations for the same
+molecule. There is growing interests in both chemistry and machine learning
+fields to directly learn molecular properties of compounds from the molecular
+graph, instead of from fingerprints predefined by chemists. The proposed GCN
+model, which we call edge attention-based multi-relational GCN (EAGCN), jointly
+learns attention weights and node features in graph convolution. For each bond
+attribute, a real-valued attention matrix is used to replace the binary
+adjacency matrix. By designing a dictionary for the edge attention, and forming
+the attention matrix of each molecule by looking up the dictionary, the EAGCN
+exploits correspondence between bonds in different molecules. The prediction of
+compound properties is based on the aggregated node features, which is
+independent of the varying molecule (graph) size. We demonstrate the efficacy
+of the EAGCN on multiple chemical datasets: Tox21, HIV, Freesolv, and
+Lipophilicity, and interpret the resultant attention weights.
+"
+25748,Direct Search Methods on Reductive Homogeneous Spaces,"  Direct search methods are mainly designed for use in problems with no
+equality constraints. However, there are many instances where the feasible set
+is of measure zero in the ambient space and no mesh point lies within it. There
+are methods for working with feasible sets that are (Riemannian) manifolds, but
+not all manifolds are created equal. In particular, reductive homogeneous
+spaces seem to be the most general space that can be conveniently optimized
+over. The reason is that a `law of motion' over the feasible region is also
+given. Examples include \Rn{n} and it's linear subspaces, Lie groups and coset
+manifolds such as Grassmannians and Stiefel manifolds. These are important
+arenas for optimization, for example, in the areas of image processing and data
+mining. We demonstrate optimization procedures over general reductive
+homogeneous spaces utilizing maps from the tangent space to the manifold. A
+concrete implementation of the probabilistic descent direct search method is
+shown. This is then extended to a procedure that works solely with the manifold
+elements, eliminating the need for the use of the tangent space.
+"
+25749,Fully reliable error control for evolutionary problems,"  This work is focused on the application of functional-type a posteriori error
+estimates and corresponding indicators to a class of time-dependent problems.
+We consider the algorithmic part of their derivation and implementation and
+also discuss the numerical properties of these bounds that comply with obtained
+numerical results. This paper examines two different methods of approximate
+solution reconstruction for evolutionary models, i.e., a time-marching
+technique and a space-time approach. The first part of the study presents an
+algorithm for global minimization of the majorant on each of discretization
+time-cylinders (time-slabs), the effectiveness of this algorithm is confirmed
+by extensive numerical tests. In the second part of the publication, the
+application of functional error estimates is discussed with respect to a
+space-time approach. It is followed by a set of extensive numerical tests that
+demonstrate the efficiency of proposed error control method.
+"
+25750,Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low,"  We study the behavior of a fundamental tool in sparse statistical modeling
+--the best-subset selection procedure (aka ""best-subsets""). Assuming that the
+underlying linear model is sparse, it is well known, both in theory and in
+practice, that the best-subsets procedure works extremely well in terms of
+several statistical metrics (prediction, estimation and variable selection)
+when the signal to noise ratio (SNR) is high. However, its performance degrades
+substantially when the SNR is low -- it is outperformed in predictive accuracy
+by continuous shrinkage methods, such as ridge regression and the Lasso. We
+explain why this behavior should not come as a surprise, and contend that the
+original version of the classical best-subsets procedure was, perhaps, not
+designed to be used in the low SNR regimes. We propose a close cousin of
+best-subsets, namely, its $\ell_{q}$-regularized version, for $q \in\{1, 2\}$,
+which (a) mitigates, to a large extent, the poor predictive performance of
+best-subsets in the low SNR regimes; (b) performs favorably and generally
+delivers a substantially sparser model when compared to the best predictive
+models available via ridge regression and the Lasso. Our estimator can be
+expressed as a solution to a mixed integer second order conic optimization
+problem and, hence, is amenable to modern computational tools from mathematical
+optimization. We explore the theoretical properties of the predictive
+capabilities of the proposed estimator and complement our findings via several
+numerical experiments.
+"
+25751,HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition,"  Nowadays, machine learning based Automatic Speech Recognition (ASR) technique
+has widely spread in smartphones, home devices, and public facilities. As
+convenient as this technology can be, a considerable security issue also raises
+-- the users' speech content might be exposed to malicious ASR monitoring and
+cause severe privacy leakage. In this work, we propose HASP -- a
+high-performance security enhancement approach to solve this security issue on
+mobile devices. Leveraging ASR systems' vulnerability to the adversarial
+examples, HASP is designed to cast human imperceptible adversarial noises to
+real-time speech and effectively perturb malicious ASR monitoring by increasing
+the Word Error Rate (WER). To enhance the practical performance on mobile
+devices, HASP is also optimized for effective adaptation to the human speech
+characteristics, environmental noises, and mobile computation scenarios. The
+experiments show that HASP can achieve optimal real-time security enhancement:
+it can lead an average WER of 84.55% for perturbing the malicious ASR
+monitoring, and the data processing speed is 15x to 40x faster compared to the
+state-of-the-art methods. Moreover, HASP can effectively perturb various ASR
+systems, demonstrating a strong transferability.
+"
+25752,Computation of gcd chain over the power of an irreducible polynomial,"  A notion of gcd chain has been introduced by the author at ISSAC 2017 for two
+univariate monic polynomials with coefficients in a ring R = k[x_1, ..., x_n
+]/(T) where T is a primary triangular set of dimension zero. A complete
+algorithm to compute such a gcd chain remains challenging. This work treats
+completely the case of a triangular set T = (T_1 (x)) in one variable, namely a
+power of an irreducible polynomial. This seemingly ""easy"" case reveals the main
+steps necessary for treating the general case, and it allows to isolate the
+particular one step that does not directly extend and requires more care.
+"
+25753,Weak Solutions to Complex Monge-Ampère Equations on Compact Kähler Manifold,"  We show a general existence theorem to the complex Monge-Ampère type
+equation on compact Kähler manifolds.
+"
+25754,Reducing state updates via Gaussian-gated LSTMs,"  Recurrent neural networks can be difficult to train on long sequence data due
+to the well-known vanishing gradient problem. Some architectures incorporate
+methods to reduce RNN state updates, therefore allowing the network to preserve
+memory over long temporal intervals. To address these problems of convergence,
+this paper proposes a timing-gated LSTM RNN model, called the Gaussian-gated
+LSTM (g-LSTM). The time gate controls when a neuron can be updated during
+training, enabling longer memory persistence and better error-gradient flow.
+This model captures long-temporal dependencies better than an LSTM and the time
+gate parameters can be learned even from non-optimal initialization values.
+Because the time gate limits the updates of the neuron state, the number of
+computes needed for the network update is also reduced. By adding a
+computational budget term to the training loss, we can obtain a network which
+further reduces the number of computes by at least 10x. Finally, by employing a
+temporal curriculum learning schedule for the g-LSTM, we can reduce the
+convergence time of the equivalent LSTM network on long sequences.
+"
+25755,Fast multiplication for skew polynomials,"  We describe an algorithm for fast multiplication of skew polynomials. It is
+based on fast modular multiplication of such skew polynomials, for which we
+give an algorithm relying on evaluation and interpolation on normal bases. Our
+algorithms improve the best known complexity for these problems, and reach the
+optimal asymptotic complexity bound for large degree. We also give an
+adaptation of our algorithm for polynomials of small degree. Finally, we use
+our methods to improve on the best known complexities for various arithmetics
+problems.
+"
+25756,Recent advances in iron-based superconductors toward applications,"  Iron with a large magnetic moment was widely believed to be harmful to the
+emergence of superconductivity because of the competition between the static
+ordering of electron spins and the dynamic formation of electron pairs (Cooper
+pairs). Thus, the discovery of a high critical temperature (Tc) iron-based
+superconductor (IBSC) in 2008 was accepted with surprise in the condensed
+matter community and rekindled extensive study globally. IBSCs have since grown
+to become a new class of high-Tc superconductors next to the high-Tc cuprates
+discovered in 1986. The rapid research progress in the science and technology
+of IBSCs over the past decade has resulted in the accumulation of a vast amount
+of knowledge on IBSC materials, mechanisms, properties, and applications with
+the publication of more than several tens of thousands of papers. This article
+reviews recent progress in the technical applications (bulk magnets, thin
+films, and wires) of IBSCs in addition to their fundamental material
+characteristics. Highlights of their applications include high-field bulk
+magnets workable at 15-25 K, thin films with high critical current density (Jc)
+> 1 MA/cm2 at ~10 T and 4 K, and an average Jc of 1.3*104 A/cm2 at 10 T and 4 K
+achieved for a 100-m-class-length wire. These achievements are based on the
+intrinsically advantageous properties of IBSCs such as the higher
+crystallographic symmetry of the superconducting phase, higher critical
+magnetic field, and larger critical grain boundary angle to maintain high Jc.
+These properties also make IBSCs promising for applications using high magnetic
+fields.
+"
+25757,Thinging for Software Engineers,"  The aim of this paper is to promote the terms thing and thinging (which
+refers to the act of defining a boundary around some portion of reality and
+labeling it with a name) as valued notions that play an important role in
+software engineering modeling. Additionally, we attempt to furnish operational
+definitions for terms thing, object, process, and thinging. The substantive
+discussion is based on the conception of an (abstract) machine, named the
+Thinging Machine (TM), used in several research works. The TM creates,
+processes, receives, releases, and transfers things. Accordingly, a
+diagrammatic representation of the TM is used to model reality. In the
+discussion section, this paper clarifies interesting issues related to
+conceptual modeling in software engineering. The substance of this paper and
+its conclusion suggest that thinging should be more meaningfully emphasized as
+a valuable research and teaching topic, at least in the requirement analysis
+phase of the software development cycle.
+"
+25758,Power domination in maximal planar graphs,"  Power domination in graphs emerged from the problem of monitoring an
+electrical system by placing as few measurement devices in the system as
+possible. It corresponds to a variant of domination that includes the
+possibility of propagation. For measurement devices placed on a set S of
+vertices of a graph G, the set of monitored vertices is initially the set S
+together with all its neighbors. Then iteratively, whenever some monitored
+vertex v has a single neighbor u not yet monitored, u gets monitored. A set S
+is said to be a power dominating set of the graph G if all vertices of G
+eventually are monitored. The power domination number of a graph is the minimum
+size of a power dominating set. In this paper, we prove that any maximal planar
+graph of order n $\ge$ 6 admits a power dominating set of size at most (n--2)/4 .
+"
+25759,On the arithmetic of graphs,"  The Zykov ring of signed finite simple graphs with topological join as
+addition and compatible multiplication is an integral domain but not a unique
+factorization domain. We know that because by taking graph complements, it
+becomes isomorphic to the strong Sabidussi ring with disjoint union as
+addition. We prove that the Euler characteristic is a ring homomorphism from
+the strong ring to the integers by demonstrating that the strong ring is
+homotopic to a Stanley-Reisner Cartesian ring. More generally, the Kuenneth
+formula holds on the strong ring so that the Poincare polynomial is compatible
+with the ring structure. The Zykov ring has the clique number as a ring
+homomorphism. Furthermore, the Cartesian ring has the property that the functor
+which attaches to a graph the spectrum of its connection Laplacian is
+multiplicative. The reason is that the connection Laplacians do tensor under
+multiplication, similarly to what the adjacency matrix does for the weak ring.
+The strong ring product of two graphs contains both the weak and direct product
+graphs as subgraphs. The Zykov, Sabidussi or Stanley-Reisner rings are so
+manifestations of a network arithmetic which has remarkable cohomological
+properties, dimension and spectral compatibility but where arithmetic questions
+like the complexity of detecting primes or factoring are not yet studied well.
+We illustrate the Zykov arithmetic with examples, especially from the subring
+generated by point graphs which contains spheres, stars or complete bipartite
+graphs. While things are formulated in the language of graph theory, all
+constructions generalize to the larger category of finite abstract simplicial
+complexes.
+"
+25760,GREENER: A Tool for Improving Energy Efficiency of Register Files,"  Graphics Processing Units (GPUs) maintain a large register file to increase
+the thread level parallelism (TLP). To increase the TLP further, recent GPUs
+have increased the number of on-chip registers in every generation. However,
+with the increase in the register file size, the leakage power increases. Also,
+with the technology advances, the leakage power component has increased and has
+become an important consideration for the manufacturing process. The leakage
+power of a register file can be reduced by turning infrequently used registers
+into low power (drowsy or off) state after accessing them. A major challenge in
+doing so is the lack of runtime register access information.
+This paper proposes GREENER (GPU REgister file ENErgy Reducer): a system to
+minimize leakage energy of the register file of GPUs. GREENER employs a
+compile-time analysis to estimate the run-time register access information. The
+result of the analysis is used to determine the power state of the registers
+(ON, SLEEP, or OFF) after each instruction. We propose a power optimized
+assembly instruction set that allows GREENER to encode the power state of the
+registers in the executable itself. The modified assembly, along with a
+run-time optimization to update the power state of a register during execution,
+results in significant power reduction.
+We implemented GREENER in GPGPU-Sim simulator, and used GPUWattch framework
+to measure the register file's leakage power. Evaluation of GREENER on 21
+kernels from CUDASDK, GPGPU-SIM, Parboil, and Rodinia benchmarks suites shows
+an average reduction of register leakage energy by 69.04% and maximum reduction
+of 87.95% with a negligible number of simulation cycles overhead (0.53% on
+average).
+"
+25761,Pseudo-Orbit Tracing and Algebraic actions of countable amenable groups,"  Consider a countable amenable group acting by homeomorphisms on a compact
+metrizable space. Chung and Li asked if expansiveness and positive entropy of
+the action imply existence of an off-diagonal asymptotic pair. For algebraic
+actions of polycyclic-by-finite groups, Chung and Li proved it does. We provide
+examples showing that Chung and Li's result is near-optimal in the sense that
+the conclusion fails for some non-algebraic action generated by a single
+homeomorphism, and for some algebraic actions of non-finitely generated abelian
+groups. On the other hand, we prove that every expansive action of an amenable
+group with positive entropy that has the pseudo-orbit tracing property must
+admit off-diagonal asymptotic pairs.
+Using Chung and Li's algebraic characterization of expansiveness, we prove
+the pseudo-orbit tracing property for a class of expansive algebraic actions.
+This class includes every expansive principal algebraic action of an arbitrary
+countable group.
+"
+25762,Secure multi-party linear regression at plaintext speed,"  We detail a scheme for scalable, distributed, secure multiparty linear
+regression at essentially the same speed as plaintext regression. While the
+core ideas are simple, the recognition of their broad utility when combined is
+novel. By leveraging a recent advance in secure multiparty principal component
+analysis, our scheme opens the door to efficient and secure genome-wide
+association studies across multiple biobanks.
+"
+25763,Unsupervised Learning Layers for Video Analysis,"  This paper presents two unsupervised learning layers (UL layers) for
+label-free video analysis: one for fully connected layers, and the other for
+convolutional ones. The proposed UL layers can play two roles: they can be the
+cost function layer for providing global training signal; meanwhile they can be
+added to any regular neural network layers for providing local training signals
+and combined with the training signals backpropagated from upper layers for
+extracting both slow and fast changing features at layers of different depths.
+Therefore, the UL layers can be used in either pure unsupervised or
+semi-supervised settings. Both a closed-form solution and an online learning
+algorithm for two UL layers are provided. Experiments with unlabeled synthetic
+and real-world videos demonstrated that the neural networks equipped with UL
+layers and trained with the proposed online learning algorithm can extract
+shape and motion information from video sequences of moving objects. The
+experiments demonstrated the potential applications of UL layers and online
+learning algorithm to head orientation estimation and moving object
+localization.
+"
+25764,Some variations on the standard theoretical models for the h-index: A comparative analysis,"  There are various mathematical models proposed in the recent literature for
+estimating the h-index through bibliometric measures, such as number of
+articles (P) and citations received (C). These models have been previously
+empirically tested assuming a mathematical model and predetermining the models
+parameter values at some fixed constant. Here, by adopting a statistical
+modelling view I investigate alternative distributions commonly used for this
+type of point data. I also show that the typical assumptions for the parameters
+of the h-index mathematical models in such representations are not always
+realistic, with more suitable specifications being favorable. Prediction of the
+hindex is also demonstrated.
+"
+25765,NC Algorithms for Weighted Planar Perfect Matching and Related Problems,"  Consider a planar graph $G=(V,E)$ with polynomially bounded edge weight
+function $w:E\to [0, poly(n)]$. The main results of this paper are NC
+algorithms for the following problems:
+- minimum weight perfect matching in $G$,
+- maximum cardinality and maximum weight matching in $G$ when $G$ is
+bipartite,
+- maximum multiple-source multiple-sink flow in $G$ where $c:E\to [1,
+poly(n)]$ is a polynomially bounded edge capacity function,
+- minimum weight $f$-factor in $G$ where $f:V\to [1, poly(n)]$,
+- min-cost flow in $G$ where $c:E\to [1, poly(n)]$ is a polynomially bounded
+edge capacity function and $b:V\to [1, poly(n)]$ is a polynomially bounded
+vertex demand function.
+There have been no known NC algorithms for any of these problems previously
+(Before this and independent paper by Anari and Vazirani). In order to solve
+these problems we develop a new relatively simple but versatile framework that
+is combinatorial in spirit. It handles the combinatorial structure of matchings
+directly and needs to only know weights of appropriately defined matchings from
+algebraic subroutines.
+"
+25766,Foundations for a Probabilistic Event Calculus,"  We present PEC, an Event Calculus (EC) style action language for reasoning
+about probabilistic causal and narrative information. It has an action language
+style syntax similar to that of the EC variant Modular-E. Its semantics is
+given in terms of possible worlds which constitute possible evolutions of the
+domain, and builds on that of EFEC, an epistemic extension of EC. We also
+describe an ASP implementation of PEC and show the sense in which this is sound
+and complete.
+"
+25767,Pressure-dependent Semiconductor to Semimetal and Lifshitz transitions in 2H-MoTe$_2$: Raman and First-principles studies,"  High pressure Raman spectroscopy of bulk 2H-MoTe$_2$ upto $\sim$ 29 GPa is
+shown to reveal two phase transitions (at $\sim$ 6 and 16.5 GPa), which are
+analyzed using first-principles density functional theoretical calculations.
+The transition at 6 GPa is marked by changes in the pressure coefficients of
+A$_{1g}$ and E$^{1}_{2g}$ Raman mode frequencies as well as in their relative
+intensity. Our calculations show that this is an isostructural semiconductor to
+a semimetal transition. The transition at $\sim$ 16.5 GPa is identified with
+the changes in linewidths of the Raman modes as well as in the pressure
+coefficients of their frequencies. Our theoretical analysis clearly shows that
+the structure remains the same upto 30 GPa. However, the topology of the
+Fermi-surface evolves as a function of pressure, and abrupt appearance of
+electron and hole pockets at P $\sim$ 20 GPa marks a Lifshitz transition.
+"
+25768,Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models,"  This paper presents a novel variational inference framework for deriving a
+family of Bayesian sparse Gaussian process regression (SGPR) models whose
+approximations are variationally optimal with respect to the full-rank GPR
+model enriched with various corresponding correlation structures of the
+observation noises.
+Our variational Bayesian SGPR (VBSGPR) models jointly treat both the
+distributions of the inducing variables and hyperparameters as variational
+parameters, which enables the decomposability of the variational lower bound
+that in turn can be exploited for stochastic optimization.
+Such a stochastic optimization involves iteratively following the stochastic
+gradient of the variational lower bound to improve its estimates of the optimal
+variational distributions of the inducing variables and hyperparameters (and
+hence the predictive distribution) of our VBSGPR models and is guaranteed to
+achieve asymptotic convergence to them.
+We show that the stochastic gradient is an unbiased estimator of the exact
+gradient and can be computed in constant time per iteration, hence achieving
+scalability to big data.
+We empirically evaluate the performance of our proposed framework on two
+real-world, massive datasets.
+"
+25769,A general construction of $n$-angulated categories using periodic injective resolutions,"  Let $\mathcal{C}$ be an additive category equipped with an automorphism
+$\Sigma$. We show how to obtain $n$-angulations of $(\mathcal{C},\Sigma)$ using
+some particular periodic injective resolutions. We give necessary and
+sufficient conditions on $(\mathcal{C},\Sigma)$ admitting an $n$-angulation.
+Then we apply these characterizations to explain the standard construction of
+$n$-angulated categories and the $n$-angulated categories arising from some
+local rings. Moreover, we obtain a class of new examples of $n$-angulated
+categories from quasi-periodic selfinjective algebras.
+"
+25770,"Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis","  In this paper we study different approaches for time series modeling. The
+forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine
+learning algorithm are described. Results of different model combinations are
+shown. For probabilistic modeling the approaches using copulas and Bayesian
+inference are considered.
+"
+25771,Zeros of Loschmidt echo in the presence of Anderson localization,"  We study the Loschmidt echo and the dynamical free energy of the Anderson
+model after a quench of the disorder strength. If the initial state is extended
+and the eigenstates of the post-quench Hamiltonian are strongly localized, we
+argue that the Loschmidt echo exhibits zeros periodically with the period $2\pi
+/D$ where $D$ is the width of spectra. At these zeros, the dynamical free
+energy diverges in a logarithmic way. We present numerical evidence of our
+argument in one- and three-dimensional Anderson models. Our findings connect
+the dynamical quantum phase transitions to the localization-delocalization
+phase transitions.
+"
+25772,Risk-Neutral Pricing and Hedging of In-Play Football Bets,"  A risk-neutral valuation framework is developed for pricing and hedging
+in-play football bets based on modelling scores by independent Poisson
+processes with constant intensities. The Fundamental Theorems of Asset Pricing
+are applied to this set-up which enables us to derive novel arbitrage-free
+valuation formul\ae\ for contracts currently traded in the market. We also
+describe how to calibrate the model to the market and how trades can be
+replicated and hedged.
+"
+25773,A Predictive Momentum-Based Whole-Body Torque Controller: Theory and Simulations for the iCub Stepping,"  When balancing, a humanoid robot can be easily subjected to unexpected
+disturbances like external pushes. In these circumstances, reactive movements
+as steps become a necessary requirement in order to avoid potentially harmful
+falling states. In this paper we conceive a Model Predictive Controller which
+determines a desired set of contact wrenches by predicting the future evolution
+of the robot, while taking into account constraints switching in case of steps.
+The control inputs computed by this strategy, namely the desired contact
+wrenches, are directly obtained on the robot through a modification of the
+momentum-based whole-body torque controller currently implemented on iCub. The
+proposed approach is validated through simulations in a stepping scenario,
+revealing high robustness and reliability when executing a recovery strategy.
+"
+25774,Typical Approximation Performance for Maximum Coverage Problem,"  This study investigated typical performance of approximation algorithms known
+as belief propagation, greedy algorithm, and linear-programming relaxation for
+maximum coverage problems on sparse biregular random graphs. After using the
+cavity method for a corresponding hard-core lattice--gas model, results show
+that two distinct thresholds of replica-symmetry and its breaking exist in the
+typical performance threshold of belief propagation. In the low-density region,
+the superiority of three algorithms in terms of a typical performance threshold
+is obtained by some theoretical analyses. Although the greedy algorithm and
+linear-programming relaxation have the same approximation ratio in worst-case
+performance, their typical performance thresholds are mutually different,
+indicating the importance of typical performance. Results of numerical
+simulations validate the theoretical analyses and imply further mutual
+relations of approximation algorithms.
+"
+25775,Time-frequency analysis of locally stationary Hawkes processes,"  Locally stationary Hawkes processes have been introduced in order to
+generalise classical Hawkes processes away from stationarity by allowing for a
+time-varying second-order structure. This class of self-exciting point
+processes has recently attracted a lot of interest in applications in the life
+sciences (seismology, genomics, neuro-science,...), but also in the modelling
+of high-frequency financial data. In this contribution we provide a fully
+developed nonparametric estimation theory of both local mean density and local
+Bartlett spectra of a locally stationary Hawkes process. In particular we apply
+our kernel estimation of the spectrum localised both in time and frequency to
+two data sets of transaction times revealing pertinent features in the data
+that had not been made visible by classical non-localised approaches based on
+models with constant fertility functions over time.
+"
+25776,From-Below Boolean Matrix Factorization Algorithm Based on MDL,"  During the past few years Boolean matrix factorization (BMF) has become an
+important direction in data analysis. The minimum description length principle
+(MDL) was successfully adapted in BMF for the model order selection.
+Nevertheless, a BMF algorithm performing good results from the standpoint of
+standard measures in BMF is missing. In this paper, we propose a novel
+from-below Boolean matrix factorization algorithm based on formal concept
+analysis. The algorithm utilizes the MDL principle as a criterion for the
+factor selection. On various experiments we show that the proposed algorithm
+outperforms---from different standpoints---existing state-of-the-art BMF
+algorithms.
+"
+25777,Light-Cone Observables and Gauge-Invariance in the Geodesic Light-Cone Formalism,"  The remarkable properties of the geodesic light-cone (GLC) coordinates allow
+analytic expressions for the light-cone observables, providing a new
+non-perturbative way for calculating the effects of inhomogeneities in our
+Universe. However, the gauge-invariance of these expressions in the GLC
+formalism has not been shown explicitly. Here we provide this missing part of
+the GLC formalism by proving the gauge-invariance of the GLC expressions for
+the light-cone observables, such as the observed redshift, the luminosity
+distance, and the physical area and volume of the observed sources. Our study
+provides a new insight on the properties of the GLC coordinates and it
+complements the previous work by the GLC collaboration, leading to a
+comprehensive description of light propagation in the GLC representation.
+"
+25778,Variable-Length Lossy Compression Allowing Positive Overflow and Excess Distortion Probabilities,"  This paper investigates the problem of variable-length lossy source coding
+allowing a positive excess distortion probability and an overflow probability
+of codeword lengths. Novel one-shot achievability and converse bounds of the
+optimal rate are established by a new quantity based on the smooth max entropy
+(the smooth Rényi entropy of order zero). To derive the achievability bounds,
+we give an explicit code construction based on a distortion ball instead of
+using the random coding argument. The basic idea of the code construction is
+similar to the optimal code construction in the variable-length lossless source
+coding. Our achievability bounds are slightly different, depending on whether
+the encoder is stochastic or deterministic. One-shot results yield a general
+formula of the optimal rate for blocklength $n$. In addition, our general
+formula is applied to asymptotic analysis for a stationary memoryless source.
+As a result, we derive a single-letter characterization of the optimal rate by
+using the rate-distortion and rate-dispersion functions.
+"
+25779,Program package for the analysis of high resolution high signal-to-noise stellar spectra,"  The program package SME (Spectroscopy Made Easy), designed to perform an
+analysis of stellar spectra using spectral fitting techniques, was updated due
+to adding new functions (isotopic and hyperfine splittins) in VALD and
+including grids of NLTE calculations for energy levels of few chemical
+elements. SME allows to derive automatically stellar atmospheric parameters:
+effective temperature, surface gravity, chemical abundances, radial and
+rotational velocities, turbulent velocities, taking into account all the
+effects defining spectral line formation. SME package uses the best grids of
+stellar atmospheres that allows us to perform spectral analysis with the
+similar accuracy in wide range of stellar parameters and metallicities - from
+dwarfs to giants of BAFGK spectral classes.
+"
+25780,Robust Non-Rigid Registration With Reweighted Dual Sparsities,"  Non-rigid registration is challenging because it is ill-posed with high
+degrees of freedom and is thus sensitive to noise and outliers. We propose a
+robust non-rigid registration method using reweighted sparsities on position
+and transformation to estimate the deformations between 3-D shapes. We
+formulate the energy function with dual sparsities on both the data term and
+the smoothness term, and define the smoothness constraint using local rigidity.
+The dual-sparsity based non-rigid registration model is enhanced with a
+reweighting scheme, and solved by transferring the model into some alternating
+optimized subproblems which have exact solutions and guaranteed convergence.
+Experimental results on both public datasets and real scanned datasets show
+that our method outperforms the state-of-the-art methods and is more robust to
+noise and outliers than conventional non-rigid registration methods.
+"
+25781,Interpretability via Model Extraction,"  The ability to interpret machine learning models has become increasingly
+important now that machine learning is used to inform consequential decisions.
+We propose an approach called model extraction for interpreting complex,
+blackbox models. Our approach approximates the complex model using a much more
+interpretable model; as long as the approximation quality is good, then
+statistical properties of the complex model are reflected in the interpretable
+model. We show how model extraction can be used to understand and debug random
+forests and neural nets trained on several datasets from the UCI Machine
+Learning Repository, as well as control policies learned for several classical
+reinforcement learning problems.
+"
+25782,Dust cloud evolution in sub-stellar atmospheres via plasma deposition and plasma sputtering,"  In contemporary sub-stellar model atmospheres, dust growth occurs through
+neutral gas-phase surface chemistry. Recently, there has been a growing body of
+theoretical and observational evidence suggesting that ionisation processes can
+also occur. As a result, atmospheres are populated by regions composed of
+plasma, gas and dust, and the consequent influence of plasma processes on dust
+evolution is enhanced. This paper aims to introduce a new model of dust growth
+and destruction in sub-stellar atmospheres via plasma deposition and plasma
+sputtering. Using example sub-stellar atmospheres from Drift-Phoenix, we have
+compared plasma deposition and sputtering timescales to those from neutral
+gas-phase surface chemistry to ascertain their regimes of influence. We
+calculated the plasma sputtering yield and discuss the circumstances where
+plasma sputtering dominates over deposition. Within the highest dust density
+cloud regions, plasma deposition and sputtering dominates over neutral
+gas-phase surface chemistry if the degree of ionisation is $\gtrsim10^{-4}$.
+Loosely bound grains with surface binding energies of the order of $0.1-1$ eV
+are susceptible to destruction through plasma sputtering for feasible degrees
+of ionisation and electron temperatures; whereas, strong crystalline grains
+with binding energies of the order $10$ eV are resistant to sputtering. The
+mathematical framework outlined sets the foundation for the inclusion of plasma
+deposition and plasma sputtering in global dust cloud formation models of
+sub-stellar atmospheres.
+"
+25783,Remarks on the thin obstacle problem and constrained Ginibre ensembles,"  We consider the problem of constrained Ginibre ensemble with prescribed
+portion of eigenvalues on a given curve $\Gamma\subset \mathbb R^2$ and relate
+it to a thin obstacle problem. The key step in the proof is the $H^1$ estimate
+for the logarithmic potential of the equilibrium measure. The coincidence set
+has two components: one in $\Gamma$ and another one in $\mathbb R^2\setminus
+\Gamma$ which are well separated. Our main result here asserts that this
+obstacle problem is well posed in $H^1(\mathbb R^2)$ which improves previous
+results in $H^1_{loc}(\mathbb R^2)$.
+"
+25784,Deep Learning with Topological Signatures,"  Inferring topological and geometrical information from data can offer an
+alternative perspective on machine learning problems. Methods from topological
+data analysis, e.g., persistent homology, enable us to obtain such information,
+typically in the form of summary representations of topological features.
+However, such topological signatures often come with an unusual structure
+(e.g., multisets of intervals) that is highly impractical for most machine
+learning techniques. While many strategies have been proposed to map these
+topological signatures into machine learning compatible representations, they
+suffer from being agnostic to the target learning task. In contrast, we propose
+a technique that enables us to input topological signatures to deep neural
+networks and learn a task-optimal representation during training. Our approach
+is realized as a novel input layer with favorable theoretical properties.
+Classification experiments on 2D object shapes and social network graphs
+demonstrate the versatility of the approach and, in case of the latter, we even
+outperform the state-of-the-art by a large margin.
+"
+25785,Reinforcement learning account of network reciprocity,"  Evolutionary game theory predicts that cooperation in social dilemma games is
+promoted when agents are connected as a network. However, when networks are
+fixed over time, humans do not necessarily show enhanced mutual cooperation.
+Here we show that reinforcement learning (specifically, the so-called
+Bush-Mosteller model) approximately explains the experimentally observed
+network reciprocity and the lack thereof in a parameter region spanned by the
+benefit-to-cost ratio and the node's degree. Thus, we significantly extend
+previously obtained numerical results.
+"
+25786,Approximation Algorithms for $\ell_0$-Low Rank Approximation,"  We study the $\ell_0$-Low Rank Approximation Problem, where the goal is,
+given an $m \times n$ matrix $A$, to output a rank-$k$ matrix $A'$ for which
+$\|A'-A\|_0$ is minimized. Here, for a matrix $B$, $\|B\|_0$ denotes the number
+of its non-zero entries. This NP-hard variant of low rank approximation is
+natural for problems with no underlying metric, and its goal is to minimize the
+number of disagreeing data positions. We provide approximation algorithms which
+significantly improve the running time and approximation factor of previous
+work. For $k > 1$, we show how to find, in poly$(mn)$ time for every $k$, a
+rank $O(k \log(n/k))$ matrix $A'$ for which $\|A'-A\|_0 \leq O(k^2 \log(n/k))
+\mathrm{OPT}$. To the best of our knowledge, this is the first algorithm with
+provable guarantees for the $\ell_0$-Low Rank Approximation Problem for $k >
+1$, even for bicriteria algorithms. For the well-studied case when $k = 1$, we
+give a $(2+\epsilon)$-approximation in {\it sublinear time}, which is
+impossible for other variants of low rank approximation such as for the
+Frobenius norm. We strengthen this for the well-studied case of binary matrices
+to obtain a $(1+O(\psi))$-approximation in sublinear time, where $\psi =
+\mathrm{OPT}/\lVert A\rVert_0$. For small $\psi$, our approximation factor is
+$1+o(1)$.
+"
+25787,The role of band filling in tuning the high field phases of URu2Si2,"  We present a detailed study of the low temperature and high magnetic field
+phases in the chemical substitution series URu$_2$Si$_{2-x}$P$_x$ using
+electrical transport and magnetization in pulsed magnetic fields up to 65T.
+Within the hidden order region (0 $\ x$$\ $ 0.035) the high field ordering is
+robust even as the hidden order temperature is suppressed. Earlier work shows
+that for 0.035 $\ x$ $\ $ 0.26 there is a Kondo lattice with a no-ordered state
+that is replaced by antiferromagnetism for 0.26 $\ x$ 0.5. We observe a
+simplified continuation of the high field ordering in the no-order $x$-region
+and an enhancement of the high field state upon the destruction of the
+antiferromagnetism with magnetic field. These results closely resemble what is
+seen for URu$_{2-x}$Rh$_x$Si$_2$\footnote{The concentration in this paper is
+defined as URu$_{2-x}$Rh$_x$Si$_2$ while the chemical formula in the literature
+is given as U(Ru$_{1-x}$Rh$_x$)$_2$Si$_2$ [24-26]}, from which we infer that
+charge tuning uniformly controls the ground state of URu$_2$Si$_2$, regardless
+of whether s/p or d-electrons are replaced. This provides guidance for
+determining the specific factors that lead to hidden order versus magnetism in
+this family of materials.
+"
+25788,Sampled-Data State Observation over Lossy Networks under Round-Robin Scheduling,"  In this paper, we study the problem of continuous-time state observation over
+lossy communication networks. We consider the situation in which the samplers
+for measuring the output of the plant are spatially distributed and their
+communication with the observer is scheduled according to a round-robin
+scheduling protocol. We allow the observer gains to dynamically change in
+synchronization with the scheduling of communications. In this context, we
+propose a linear matrix inequality (LMI) framework to design the observer gains
+that ensure the asymptotic stability of the error dynamics in continuous time.
+We illustrate the effectiveness of the proposed methods by several numerical
+simulations.
+"
+25789,Inverse design and demonstration of a compact on-chip narrowband three-channel wavelength demultiplexer,"  In wavelength division multiplexing (WDM) schemes, splitters must be used to
+combine and separate different wavelengths. Conventional splitters are fairly
+large with footprints in hundreds to thousands of square microns, and
+experimentally-demonstrated MMI-based and inverse-designed ultra-compact
+splitters operate with only two channels and large channel spacing ($>$100 nm).
+Here we inverse design and experimentally demonstrate a three-channel
+wavelength demultiplexer with 40 nm spacing (1500 nm, 1540 nm, and 1580 nm)
+with a footprint of 24.75 $\mu\mathrm{m}^2$. The splitter has a simulated peak
+insertion loss of -1.55 dB with under -15 dB crosstalk and a measured peak
+insertion loss of -2.29 dB with under -10.7 dB crosstalk.
+"
+25790,Motion of a Vortex Filament on a Slanted Plane,"  We consider a nonlinear model equation, known as the Localized Induction
+Equation, describing the motion of a vortex filament immersed in an
+incompressible and inviscid fluid. We prove the unique solvability of an
+initial-boundary value problem describing the motion of a vortex filament on a
+slanted plane.
+"
+25791,A Time-shared Photonic Reservoir Computer for Big Data Analytics,"  Information processing has reached the era of big data. Big data challenges
+are difficult to address with traditional Von Neumann or Turing approach. Hence
+implementation of new computational techniques is highly essential.
+Nanophotonics with its remarkable speed and multiplexing capability is a
+promising candidate for such implementations. This paper proposes a novel
+photonic computing system made-up of Mach-Zehnder interferometer and an optical
+fiber spool to emulate a powerful machine learning technique called reservoir
+computing. The proposed system is also integrated with a
+time-division-multiplexing circuit to facilitate parallel computation of
+multiple tasks which is first of its kind. The proposed design performs
+large-scale tasks like spoken digit recognition, channel equalization, and
+time-series prediction. Experimental results with standard photonic simulator
+demonstrate significant performance in terms of speed and accuracy compared to
+state of the art digital and software implementations.
+"
+25792,Analysis of Large Scale Web Experiments Using Sequences of Estimators,"  Experimental testing is vital in the optimization of web applications, and as
+such A/B testing has been widely adopted as a methodology for determining
+optimal content for many web applications. While some testing platforms provide
+sequentially valid inferences, a large proportion of online tests still utilize
+traditional statistical tests that do not allow for interim ""peeking"" at the
+data or extending the test past its proposed sample size.
+In this paper we develop results useful for the sequential analysis of large
+scale experiments. In particular, the properties of sequences of maximum
+likelihood and generalized method of moments estimators are examined. This
+leads to new tests of odds ratios and relative risks for binary outcomes. For
+continuous and ordinal outcome we develop a test of mean difference and a
+non-parametric test of Area Under the Curve (AUC). Additionally, multivariate
+versions of these tests are proposed.
+"
+25793,Detecting Location Fraud in Indoor Mobile Crowdsensing,"  Mobile crowdsensing allows a large number of mobile devices to measure
+phenomena of common interests and form a body of knowledge about natural and
+social environments. In order to get location annotations for indoor mobile
+crowdsensing, reference tags are usually deployed which are susceptible to
+tampering and compromises by attackers. In this work, we consider three types
+of location-related attacks including tag forgery, tag misplacement, and tag
+removal. Different detection algorithms are proposed to deal with these
+attacks. First, we introduce location-dependent fingerprints as supplementary
+information for better location identification. A truth discovery algorithm is
+then proposed to detect falsified data. Moreover, visiting patterns are
+utilized for the detection of tag misplacement and removal. Experiments on both
+crowdsensed and emulated dataset show that the proposed algorithms can detect
+all three types of attacks with high accuracy.
+"
+25794,Moduli Spaces of Affine Homogeneous Spaces,"  Apart from global topological problems an affine homogeneous space is locally
+described by its curvature, its torsion and a slightly less tangible object
+called its connection in a given base point. Using this description of the
+local geometry of an affine homogeneous space we construct an algebraic variety
+$\mathfrak{M}(\mathfrak{gl}\,V)$, which serves as a coarse moduli space for the
+local isometry classes of affine homogeneous spaces of dimension dim V.
+Moreover we associate a $\mathrm{Sym}V^*$-comodule to a point in
+$\mathfrak{M}(\mathfrak{gl}\,V\,)$ and use its Spencer cohomology in order to
+describes the infinitesimal deformations of this point in the true moduli space
+$\mathfrak{M}_\infty(\mathfrak{gl}\,V\,)$.
+"
+25795,Subspace-Aware Index Codes,"  In this paper, we generalize the well-known index coding problem to exploit
+the structure in the source-data to improve system throughput. In many
+applications, the data to be transmitted may lie (or can be well approximated)
+in a low-dimensional subspace. We exploit this low-dimensional structure of the
+data using an algebraic framework to solve the index coding problem (referred
+to as subspace-aware index coding) as opposed to the traditional index coding
+problem which is subspace-unaware. Also, we propose an efficient algorithm
+based on the alternating minimization approach to obtain near optimal index
+codes for both subspace-aware and -unaware cases. Our simulations indicate that
+under certain conditions, a significant throughput gain (about 90%) can be
+achieved by subspace-aware index codes over conventional subspace-unaware index
+codes.
+"
+25796,Stochastic Restricted Biased Estimators in misspecified regression model with incomplete prior information,"  In this article, the analysis of misspecification was extended to the
+recently introduced stochastic restricted biased estimators when
+multicollinearity exists among the explanatory variables. The Stochastic
+Restricted Ridge Estimator (SRRE), Stochastic Restricted Almost Unbiased Ridge
+Estimator (SRAURE), Stochastic Restricted Liu Estimator (SRLE), Stochastic
+Restricted Almost Unbiased Liu Estimator (SRAULE), Stochastic Restricted
+Principal Component Regression Estimator (SRPCR), Stochastic Restricted r-k
+class estimator (SRrk) and Stochastic Restricted r-d class estimator (SRrd)
+were examined in the misspecified regression model due to missing relevant
+explanatory variables when incomplete prior information of the regression
+coefficients is available. Further, the superiority conditions between
+estimators and their respective predictors were obtained in the mean square
+error matrix (MSEM) sense. Finally, a numerical example and a Monte Carlo
+simulation study were used to illustrate the theoretical findings.
+"
+25797,On the Recognizing Power of the Lambek Calculus with Brackets,"  Every language recognized by the Lambek calculus with brackets is
+context-free. This is shown by combining an observation by Jäger with an
+entirely straightforward adaptation of the method Pentus used for the original
+Lambek calculus. The case of the variant of the calculus allowing sequents with
+empty antecedents is slightly more complicated, requiring a restricted use of
+the multiplicative unit.
+"
+25798,Cosmological stochastic Higgs stabilization,"  We show that the stochastic evolution of an interacting system of the Higgs
+and a spectator scalar field naturally gives rise to an enhanced probability of
+settling down at the electroweak vacuum at the end of inflation. Subsequent
+destabilization due to parametric resonance between the Higgs and the spectator
+field can be avoided in a wide parameter range. We further argue that the
+spectator field can play the role of dark matter.
+"
+25799,Enter the Matrix: Safely Interruptible Autonomous Systems via Virtualization,"  Autonomous systems that operate around humans will likely always rely on kill
+switches that stop their execution and allow them to be remote-controlled for
+the safety of humans or to prevent damage to the system. It is theoretically
+possible for an autonomous system with sufficient sensor and effector
+capability that learn online using reinforcement learning to discover that the
+kill switch deprives it of long-term reward and thus learn to disable the
+switch or otherwise prevent a human operator from using the switch. This is
+referred to as the big red button problem. We present a technique that prevents
+a reinforcement learning agent from learning to disable the kill switch. We
+introduce an interruption process in which the agent's sensors and effectors
+are redirected to a virtual simulation where it continues to believe it is
+receiving reward. We illustrate our technique in a simple grid world
+environment.
+"
+25800,Indifference pricing of pure endowments via BSDEs under partial information,"  In this paper we investigate the pricing problem of a pure endowment contract
+when the insurer has a limited information on the mortality intensity of the
+policyholder. The payoff of this kind of policies depends on the residual life
+time of the insured as well as the trend of a portfolio traded in the financial
+market, where investments in a riskless asset, a risky asset and a longevity
+bond are allowed. We propose a modeling framework that takes into account
+mutual dependence between the financial and the insurance markets via an
+observable stochastic process, which affects the risky asset and the mortality
+index dynamics. Since the market is incomplete due to the presence of basis
+risk, in alternative to arbitrage pricing we use expected utility maximization
+under exponential preferences as evaluation approach, which leads to the
+so-called indifference price. Under partial information this methodology
+requires filtering techniques that can reduce the original control problem to
+an equivalent problem in complete information. Using stochastic dynamics
+techniques, we characterize the indifference price of the insurance derivative
+via the solutions of suitable backward stochastic differential equations.
+"
+25801,Optimizing Market Making using Multi-Agent Reinforcement Learning,"  In this paper, reinforcement learning is applied to the problem of optimizing
+market making. A multi-agent reinforcement learning framework is used to
+optimally place limit orders that lead to successful trades. The framework
+consists of two agents. The macro-agent optimizes on making the decision to
+buy, sell, or hold an asset. The micro-agent optimizes on placing limit orders
+within the limit order book. For the context of this paper, the proposed
+framework is applied and studied on the Bitcoin cryptocurrency market. The goal
+of this paper is to show that reinforcement learning is a viable strategy that
+can be applied to complex problems (with complex environments) such as market
+making.
+"
+25802,"Sufficient conditions for $C^{1,α}$ parametrization and rectifiability","  We say a measure is $C^{1,\alpha}$ $d$-rectifiable if there is a countable
+union of $C^{1,\alpha}$ $d$-surfaces whose complement has measure zero. We
+provide sufficient conditions for a Radon measure in $\mathbb{R}^n$ to be
+$C^{1,\alpha}$ $d$-rectifiable, with $\alpha \in (0,1]$. We assume a priori the
+measure to have positive and finite upper density. The conditions involve a
+Bishop-Jones type square function and all statements are quantitative in that
+the $C^{1,\alpha}$ constants depend on such a function. Along the way we also
+give sufficient conditions for $C^{1,\alpha}$ parametrizations for one-sided
+Reifenberg flat sets in terms of the same square function. Key tools for the
+proof come from Guy David and Tatiana Toro's Reifenberg parametrizations of
+sets with holes in the Hölder and Lipschitz categories.
+"
+25803,Graph-based regularization for regression problems with highly-correlated designs,"  Sparse models for high-dimensional linear regression and machine learning
+have received substantial attention over the past two decades. Model selection,
+or determining which features or covariates are the best explanatory variables,
+is critical to the interpretability of a learned model. Much of the current
+literature assumes that covariates are only mildly correlated. However, in
+modern applications ranging from functional MRI to genome-wide association
+studies, covariates are highly correlated and do not exhibit key properties
+(such as the restricted eigenvalue condition, RIP, or other related
+assumptions). This paper considers a high-dimensional regression setting in
+which a graph governs both correlations among the covariates and the similarity
+among regression coefficients. Using side information about the strength of
+correlations among features, we form a graph with edge weights corresponding to
+pairwise covariances. This graph is used to define a graph total variation
+regularizer that promotes similar weights for highly correlated features. The
+graph structure encapsulated by this regularizer helps precondition correlated
+features to yield provably accurate estimates. Using graph-based regularizers
+to develop theoretical guarantees for highly-correlated covariates has not been
+previously examined. This paper shows how our proposed graph-based
+regularization yields mean-squared error guarantees for a broad range of
+covariance graph structures and correlation strengths which in many cases are
+optimal by imposing additional structure on $\beta^{\star}$ which encourages
+\emph{alignment} with the covariance graph. Our proposed approach outperforms
+other state-of-the-art methods for highly-correlated design in a variety of
+experiments on simulated and real fMRI data.
+"
+25804,Deep Semi-Random Features for Nonlinear Function Approximation,"  We propose semi-random features for nonlinear function approximation. The
+flexibility of semi-random feature lies between the fully adjustable units in
+deep learning and the random features used in kernel methods. For one hidden
+layer models with semi-random features, we prove with no unrealistic
+assumptions that the model classes contain an arbitrarily good function as the
+width increases (universality), and despite non-convexity, we can find such a
+good function (optimization theory) that generalizes to unseen new data
+(generalization bound). For deep models, with no unrealistic assumptions, we
+prove universal approximation ability, a lower bound on approximation error, a
+partial optimization guarantee, and a generalization bound. Depending on the
+problems, the generalization bound of deep semi-random features can be
+exponentially better than the known bounds of deep ReLU nets; our
+generalization error bound can be independent of the depth, the number of
+trainable weights as well as the input dimensionality. In experiments, we show
+that semi-random features can match the performance of neural networks by using
+slightly more units, and it outperforms random features by using significantly
+fewer units. Moreover, we introduce a new implicit ensemble method by using
+semi-random features.
+"
+25805,On the arithmetic Kakeya conjecture of Katz and Tao,"  The arithmetic Kakeya conjecture, formulated by Katz and Tao in 2002, is a
+statement about addition of finite sets. It is known to imply a form of the
+Kakeya conjecture, namely that the upper Minkowski dimension of a Besicovitch
+set in $\mathbb{R}^n$ is $n$. In this note we discuss this conjecture, giving a
+number of equivalent forms of it. We show that a natural finite field variant
+of it does hold. We also give some lower bounds.
+"
+25806,Diagonal RNNs in Symbolic Music Modeling,"  In this paper, we propose a new Recurrent Neural Network (RNN) architecture.
+The novelty is simple: We use diagonal recurrent matrices instead of full. This
+results in better test likelihood and faster convergence compared to regular
+full RNNs in most of our experiments. We show the benefits of using diagonal
+recurrent matrices with popularly used LSTM and GRU architectures as well as
+with the vanilla RNN architecture, on four standard symbolic music datasets.
+"
+25807,Q-curvature type problem on bounded domains of R^n,"  In this paper, we establish compactness and existence results to a
+Branson-Paneitz type problem on a bounded domain of R^n with Navier boundary
+condition.
+"
+25808,A heat kernel proof of the index theorem for deformation quantization,"  We give a heat kernel proof of the algebraic index theorem for deformation
+quantization with separation of variables on a pseudo-Kahler manifold. We use
+normalizations of the canonical trace density of a star product and of the
+characteristic classes involved in the index formula for which this formula
+contains no extra constant factors.
+"
+25809,Learning pronunciation from a foreign language in speech synthesis networks,"  Although there are more than 65,000 languages in the world, the
+pronunciations of many phonemes sound similar across the languages. When people
+learn a foreign language, their pronunciation often reflect their native
+language's characteristics. That motivates us to investigate how the speech
+synthesis network learns the pronunciation when multi-lingual dataset is given.
+In this study, we train the speech synthesis network bilingually in English and
+Korean, and analyze how the network learns the relations of phoneme
+pronunciation between the languages. Our experimental result shows that the
+learned phoneme embedding vectors are located closer if their pronunciations
+are similar across the languages. Based on the result, we also show that it is
+possible to train networks that synthesize English speaker's Korean speech and
+vice versa. In another experiment, we train the network with limited amount of
+English dataset and large Korean dataset, and analyze the required amount of
+dataset to train a resource-poor language with the help of resource-rich
+languages.
+"
+25810,The Reversible Spin Switch by External Control of Interval Distance of CuPc and C59N with the investigation of DFT,"  In this paper, we introduce a new kind of spin switch based on a joint system
+of copper phthalocyanine (CuPc) and C59N. Using density functional theory, we
+investigate the total magnetic moment of this system when gradually changing
+the interval distance between two molecules. The spin hopping happens during
+the critical distance with very low energy. This phenomenon shows a possible
+reality of reversible spin switch by external control of the interval distance.
+With orbital analysis and electron transfer consideration, the form of C59N+-
+CuPc- ion pair support this spin hopping phenomenon.
+"
+25811,Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning,"  Active learning (AL) aims to enable training high performance classifiers
+with low annotation cost by predicting which subset of unlabelled instances
+would be most beneficial to label. The importance of AL has motivated extensive
+research, proposing a wide variety of manually designed AL algorithms with
+diverse theoretical and intuitive motivations. In contrast to this body of
+research, we propose to treat active learning algorithm design as a
+meta-learning problem and learn the best criterion from data. We model an
+active learning algorithm as a deep neural network that inputs the base learner
+state and the unlabelled point set and predicts the best point to annotate
+next. Training this active query policy network with reinforcement learning,
+produces the best non-myopic policy for a given dataset. The key challenge in
+achieving a general solution to AL then becomes that of learner generalisation,
+particularly across heterogeneous datasets. We propose a multi-task
+dataset-embedding approach that allows dataset-agnostic active learners to be
+trained. Our evaluation shows that AL algorithms trained in this way can
+directly generalise across diverse problems.
+"
+25812,A Forward-Backward Approach for Visualizing Information Flow in Deep Networks,"  We introduce a new, systematic framework for visualizing information flow in
+deep networks. Specifically, given any trained deep convolutional network model
+and a given test image, our method produces a compact support in the image
+domain that corresponds to a (high-resolution) feature that contributes to the
+given explanation. Our method is both computationally efficient as well as
+numerically robust. We present several preliminary numerical results that
+support the benefits of our framework over existing methods.
+"
+25813,Newtonian Limits of Isolated Cosmological Systems on Long Time Scales,"  We establish the existence of $1$-parameter families of $\epsilon$-dependent
+solutions to the Einstein-Euler equations with a positive cosmological constant
+$\Lambda >0$ and a linear equation of state $p=\epsilon^2 K \rho$, $0<K\leq
+1/3$, for the parameter values $0<\epsilon < \epsilon_0$. These solutions exist
+globally to the future, converge as $\epsilon \searrow 0$ to solutions of the
+cosmological Poison-Euler equations of Newtonian gravity, and are inhomogeneous
+non-linear perturbations of FLRW fluid solutions.
+"
+25814,Measuring the tendency of CNNs to Learn Surface Statistical Regularities,"  Deep CNNs are known to exhibit the following peculiarity: on the one hand
+they generalize extremely well to a test set, while on the other hand they are
+extremely sensitive to so-called adversarial perturbations. The extreme
+sensitivity of high performance CNNs to adversarial examples casts serious
+doubt that these networks are learning high level abstractions in the dataset.
+We are concerned with the following question: How can a deep CNN that does not
+learn any high level semantics of the dataset manage to generalize so well? The
+goal of this article is to measure the tendency of CNNs to learn surface
+statistical regularities of the dataset. To this end, we use Fourier filtering
+to construct datasets which share the exact same high level abstractions but
+exhibit qualitatively different surface statistical regularities. For the SVHN
+and CIFAR-10 datasets, we present two Fourier filtered variants: a low
+frequency variant and a randomly filtered variant. Each of the Fourier
+filtering schemes is tuned to preserve the recognizability of the objects. Our
+main finding is that CNNs exhibit a tendency to latch onto the Fourier image
+statistics of the training dataset, sometimes exhibiting up to a 28%
+generalization gap across the various test sets. Moreover, we observe that
+significantly increasing the depth of a network has a very marginal impact on
+closing the aforementioned generalization gap. Thus we provide quantitative
+evidence supporting the hypothesis that deep CNNs tend to learn surface
+statistical regularities in the dataset rather than higher-level abstract
+concepts.
+"
+25815,Relationship of Two Formulations for Shortest Bibranchings,"  The shortest bibranching problem is a common generalization of the
+minimum-weight edge cover problem in bipartite graphs and the minimum-weight
+arborescence problem in directed graphs. For the shortest bibranching problem,
+an efficient primal-dual algorithm is given by Keijsper and Pendavingh (1998),
+and the tractability of the problem is ascribed to total dual integrality in a
+linear programming formulation by Schrijver (1982). Another view on the
+tractability of this problem is afforded by a valuated matroid intersection
+formulation by Takazawa (2012). In the present paper, we discuss the
+relationship between these two formulations for the shortest bibranching
+problem. We first demonstrate that the valuated matroid intersection
+formulation can be derived from the linear programming formulation through the
+Benders decomposition, where integrality is preserved in the decomposition
+process and the resulting convex programming is endowed with discrete
+convexity. We then show how a pair of primal and dual optimal solutions of one
+formulation is constructed from that of the other formulation, thereby
+providing a connection between polyhedral combinatorics and discrete convex
+analysis.
+"
+25816,Perfect hedging under endogenous permanent market impacts,"  We model a nonlinear price curve quoted in a market as the utility
+indifference curve of a representative liquidity supplier. As the utility
+function we adopt a g-expectation. In contrast to the standard framework of
+financial engineering, a trader is no more price taker as any trade has a
+permanent market impact via an effect to the supplier's inventory. The P&L of a
+trading strategy is written as a nonlinear stochastic integral. Under this
+market impact model, we introduce a completeness condition under which any
+derivative can be perfectly replicated by a dynamic trading strategy. In the
+special case of a Markovian setting the corresponding pricing and hedging can
+be done by solving a semi-linear PDE.
+"
+25817,Voltage-Controllable Colossal Magnetocrystalline Anisotropy in Single Layer Transition Metal Dichalcogenides,"  Materials with large magnetocrystalline anisotropy and strong electric field
+effects are highly needed to develop new types of memory devices based on
+electric field control of spin orientations. Instead of using modified
+transition metal films, we propose that certain monolayer transition metal
+dichalcogenides are the ideal candidate materials for this purpose. Using
+density functional calculations, we show that they exhibit not only a large
+magnetocrystalline anisotropy (MCA), but also colossal voltage modulation under
+external field. Notably, in some materials like CrSe_2 and FeSe_2, where spins
+show a strong preference for in-plane orientation, they can be switched to
+out-of-plane direction. This effect is attributed to the large band character
+alteration that the transition metal d-states undergo around the Fermi energy
+due to the electric field. We further demonstrate that strain can also greatly
+change MCA, and can help to improve the modulation efficiency while combined
+with an electric field.
+"
+25818,"Extended Poisson INAR(1) processes with equidispersion, underdispersion and overdispersion","  Real count data time series often show the phenomenon of the underdispersion
+and overdispersion. In this paper, we develop two extensions of the first-order
+integer-valued autoregressive process with Poisson innovations, based on
+binomial thinning, for modeling integer-valued time series with equidispersion,
+underdispersion and overdispersion. The main properties of the models are
+derived. The methods of conditional maximum likelihood, Yule-Walker and
+conditional least squares are used for estimating the parameters, and their
+asymptotic properties are established. We also use a test based on our
+processes for checking if the count time series considered is overdispersed or
+underdispersed. The proposed models are fitted to time series of number of
+weekly sales and of cases of family violence illustrating its capabilities in
+challenging cases of overdispersed and underdispersed count data.
+"
+25819,Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation,"  We introduce and study a new data sketch for processing massive datasets. It
+addresses two common problems: 1) computing a sum given arbitrary filter
+conditions and 2) identifying the frequent items or heavy hitters in a data
+set. For the former, the sketch provides unbiased estimates with state of the
+art accuracy. It handles the challenging scenario when the data is
+disaggregated so that computing the per unit metric of interest requires an
+expensive aggregation. For example, the metric of interest may be total clicks
+per user while the raw data is a click stream with multiple rows per user. Thus
+the sketch is suitable for use in a wide range of applications including
+computing historical click through rates for ad prediction, reporting user
+metrics from event streams, and measuring network traffic for IP flows.
+We prove and empirically show the sketch has good properties for both the
+disaggregated subset sum estimation and frequent item problems. On i.i.d. data,
+it not only picks out the frequent items but gives strongly consistent
+estimates for the proportion of each frequent item. The resulting sketch
+asymptotically draws a probability proportional to size sample that is optimal
+for estimating sums over the data. For non i.i.d. data, we show that it
+typically does much better than random sampling for the frequent item problem
+and never does worse. For subset sum estimation, we show that even for
+pathological sequences, the variance is close to that of an optimal sampling
+design. Empirically, despite the disadvantage of operating on disaggregated
+data, our method matches or bests priority sampling, a state of the art method
+for pre-aggregated data and performs orders of magnitude better on skewed data
+compared to uniform sampling. We propose extensions to the sketch that allow it
+to be used in combining multiple data sets, in distributed systems, and for
+time decayed aggregation.
+"
+25820,On the Optimal Control of a Rolling Ball Robot Actuated by Internal Point Masses,"  The controlled motion of a rolling ball actuated by internal point masses
+that move along arbitrarily-shaped rails fixed within the ball is considered.
+Application of the variational Pontryagin's minimum principle yields the ball's
+controlled equations of motion, a solution of which obeys the ball's
+uncontrolled equations of motion, satisfies prescribed initial and final
+conditions, and minimizes a prescribed performance index. The controlled
+equations of motion are solved numerically using a predictor-corrector
+continuation method, starting from an initial solution obtained via a direct
+method, to realize trajectory tracking and obstacle avoidance maneuvers.
+"
+25821,Convergence rates for Kaczmarz-type algorithms,"  In this paper we make a theoretical analysis of the convergence rates of
+Kaczmarz and Extended Kaczmarz projection algorithms for some of the most
+practically used control sequences. We first prove an at least linear
+convergence rate for the Kaczmarz-Tanabe and its Extended version methods (the
+one in which a complete set of projections using row/column index is performed
+in each iteration). Then we apply the main ideas of this analysis in
+establishing an at least sublinear, respectively linear convergence rate for
+the Kaczmarz algorithm with almost cyclic and the remotest set control
+strategies, and their extended versions, respectively. These results complete
+the existing ones related to the random selection procedures.
+"
+25822,280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification,"  We propose a simple, yet effective, approach towards inducing multilingual
+taxonomies from Wikipedia. Given an English taxonomy, our approach leverages
+the interlanguage links of Wikipedia followed by character-level classifiers to
+induce high-precision, high-coverage taxonomies in other languages. Through
+experiments, we demonstrate that our approach significantly outperforms the
+state-of-the-art, heuristics-heavy approaches for six languages. As a
+consequence of our work, we release presumably the largest and the most
+accurate multilingual taxonomic resource spanning over 280 languages.
+"
+25823,D2D Communications in LoRaWAN Low Power Wide Area Network: From Idea to Empirical Validation,"  In this paper we advocate the use of device-to-device (D2D) communications in
+a LoRaWAN Low Power Wide Area Network (LPWAN). After overviewing the critical
+features of the LoRaWAN technology, we discuss the pros and cons of enabling
+the D2D communications for it. Subsequently we propose a network-assisted D2D
+communications protocol and show its feasibility by implementing it on top of a
+LoRaWAN-certified commercial transceiver. The conducted experiments show the
+performance of the proposed D2D communications protocol and enable us to assess
+its performance. More precisely, we show that the D2D communications can reduce
+the time and energy for data transfer by 6 to 20 times compared to conventional
+LoRaWAN data transfer mechanisms. In addition, the use of D2D communications
+may have a positive effect on the network by enabling spatial re-use of the
+frequency resources. The proposed LoRaWAN D2D communications can be used for a
+wide variety of applications requiring high coverage, e.g. use cases in
+distributed smart grid deployments for management and trading.
+"
+25824,Invariances and Data Augmentation for Supervised Music Transcription,"  This paper explores a variety of models for frame-based music transcription,
+with an emphasis on the methods needed to reach state-of-the-art on human
+recordings. The translation-invariant network discussed in this paper, which
+combines a traditional filterbank with a convolutional neural network, was the
+top-performing model in the 2017 MIREX Multiple Fundamental Frequency
+Estimation evaluation. This class of models shares parameters in the
+log-frequency domain, which exploits the frequency invariance of music to
+reduce the number of model parameters and avoid overfitting to the training
+data. All models in this paper were trained with supervision by labeled data
+from the MusicNet dataset, augmented by random label-preserving pitch-shift
+transformations.
+"
+25825,Expressivity in Natural and Artificial Systems,"  Roboticists are trying to replicate animal behavior in artificial systems.
+Yet, quantitative bounds on capacity of a moving platform (natural or
+artificial) to express information in the environment are not known. This paper
+presents a measure for the capacity of motion complexity -- the expressivity --
+of articulated platforms (both natural and artificial) and shows that this
+measure is stagnant and unexpectedly limited in extant robotic systems. This
+analysis indicates trends in increasing capacity in both internal and external
+complexity for natural systems while artificial, robotic systems have increased
+significantly in the capacity of computational (internal) states but remained
+more or less constant in mechanical (external) state capacity. This work
+presents a way to analyze trends in animal behavior and shows that robots are
+not capable of the same multi-faceted behavior in rich, dynamic environments as
+natural systems.
+"
+25826,Visualization of Wearable Data and Biometrics for Analysis and Recommendations in Childhood Obesity,"  Obesity is one of the major health risk factors be- hind the rise of
+non-communicable conditions. Understanding the factors influencing obesity is
+very complex since there are many variables that can affect the health
+behaviors leading to it. Nowadays, multiple data sources can be used to study
+health behaviors, such as wearable sensors for physical activity and sleep,
+social media, mobile and health data. In this paper we describe the design of a
+dashboard for the visualization of actigraphy and biometric data from a
+childhood obesity camp in Qatar. This dashboard allows quantitative discoveries
+that can be used to guide patient behavior and orient qualitative research.
+"
+25827,Thompson Sampling for Dynamic Pricing,"  In this paper we apply active learning algorithms for dynamic pricing in a
+prominent e-commerce website. Dynamic pricing involves changing the price of
+items on a regular basis, and uses the feedback from the pricing decisions to
+update prices of the items. Most popular approaches to dynamic pricing use a
+passive learning approach, where the algorithm uses historical data to learn
+various parameters of the pricing problem, and uses the updated parameters to
+generate a new set of prices. We show that one can use active learning
+algorithms such as Thompson sampling to more efficiently learn the underlying
+parameters in a pricing problem. We apply our algorithms to a real e-commerce
+system and show that the algorithms indeed improve revenue compared to pricing
+algorithms that use passive learning.
+"
+25828,Selective Harvesting over Networks,"  Active search (AS) on graphs focuses on collecting certain labeled nodes
+(targets) given global knowledge of the network topology and its edge weights
+under a query budget. However, in most networks, nodes, topology and edge
+weights are all initially unknown. We introduce selective harvesting, a variant
+of AS where the next node to be queried must be chosen among the neighbors of
+the current queried node set; the available training data for deciding which
+node to query is restricted to the subgraph induced by the queried set (and
+their node attributes) and their neighbors (without any node or edge
+attributes). Therefore, selective harvesting is a sequential decision problem,
+where we must decide which node to query at each step. A classifier trained in
+this scenario suffers from a tunnel vision effect: without recourse to
+independent sampling, the urge to query promising nodes forces classifiers to
+gather increasingly biased training data, which we show significantly hurts the
+performance of AS methods and standard classifiers. We find that it is possible
+to collect a much larger set of targets by using multiple classifiers, not by
+combining their predictions as an ensemble, but switching between classifiers
+used at each step, as a way to ease the tunnel vision effect. We discover that
+switching classifiers collects more targets by (a) diversifying the training
+data and (b) broadening the choices of nodes that can be queried next. This
+highlights an exploration, exploitation, and diversification trade-off in our
+problem that goes beyond the exploration and exploitation duality found in
+classic sequential decision problems. From these observations we propose D3TS,
+a method based on multi-armed bandits for non-stationary stochastic processes
+that enforces classifier diversity, matching or exceeding the performance of
+competing methods on seven real network datasets in our evaluation.
+"
+25829,From molecules to Young Stellar Clusters: the star formation cycle across the M33 disk,"  To shed light on the time evolution of local star formation episodes in M33,
+we study the association between 566 Giant Molecular Clouds (GMCs), identified
+through the CO (J=2-1) IRAM-all-disk survey, and 630 Young Stellar Cluster
+Candidates (YSCCs), selected via Spitzer-24~$\mu$m emission. The spatial
+correlation between YSCCs and GMCs is extremely strong, with a typical
+separation of 17~pc, less than half the CO(2--1) beamsize, illustrating the
+remarkable physical link between the two populations. GMCs and YSCCs follow the
+HI filaments, except in the outermost regions where the survey finds fewer GMCs
+than YSCCs, likely due to undetected, low CO-luminosity clouds. The GMCs have
+masses between 2$\times 10^4$ and 2$\times 10^6$ M$_\odot$ and are classified
+according to different cloud evolutionary stages: inactive clouds are 32$\%$ of
+the total, classified clouds with embedded and exposed star formation are
+16$\%$ and 52$\%$ of the total respectively. Across the regular southern spiral
+arm, inactive clouds are preferentially located in the inner part of the arm,
+possibly suggesting a triggering of star formation as the cloud crosses the
+arm. Some YSCCs are embedded star-forming sites while the majority have
+GALEX-UV and H$\alpha$ counterparts with estimated cluster masses and ages. The
+distribution of the non-embedded YSCC ages peaks around 5~Myrs with only a few
+being as old as 8--10~Myrs. These age estimates together with the number of
+GMCs in the various evolutionary stages lead us to conclude that 14~Myrs is a
+typical lifetime of a GMC in M33, prior to cloud dispersal. The inactive and
+embedded phases are short, lasting about 4 and 2~Myrs respectively. This
+underlines that embedded YSCCs rapidly break out from the clouds and become
+partially visible in H$\alpha$ or UV long before cloud dispersal.
+"
+25830,An Agent Based Modeling of Spatially Inhomogeneous Host-Vector Disease Transmission,"  In this article we consider a microscopic model for host-vector disease
+transmission based on configuration space analysis. Using Vlasov scaling we
+obtain the corresponding mesoscopic (kinetic) equations, describing the density
+of susceptible and infected compartments in space. The resulting system of
+equations can be seen as a generalization to a spatial SISUV model.
+"
+25831,Study of non-linear viscoelastic behavior of the human red blood cell,"  The non-linear behavior of human erythrocytes subjected to shear stress was
+analyzed using data series from the Erythrocyte Rheometer and a theoretical
+model was developed. Linear behavior was eliminated by means of a slot filter
+and a sixth order Savisky-Golay filter was applied to the resulting time series
+that allows the elimination of any possible white noise in the data. A fast
+Fourier transform was performed on the processed data, which resulted in a
+series of frequency dominant peaks. Results suggest the presence of a
+non-linear quadratic term in the Kelvin-Voigt phenomenological model. The
+correlation dimension studied through recurrence quantification analysis gave
+C2=2.58. Results suggest that the underlying dynamics is the same in each RBC
+sample corresponding to healthy donors.
+"
+25832,CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles,"  Connected and autonomous vehicles (CAVs) have recently attracted a
+significant amount of attention both from researchers and industry. Numerous
+studies targeting algorithms, software frameworks, and applications on the CAVs
+scenario have emerged. Meanwhile, several pioneer efforts have focused on the
+edge computing system and architecture design for the CAVs scenario and
+provided various heterogeneous platform prototypes for CAVs. However, a
+standard and comprehensive application benchmark for CAVs is missing, hindering
+the study of these emerging computing systems. To address this challenging
+problem, we present CAVBench, the first benchmark suite for the edge computing
+system in the CAVs scenario. CAVBench is comprised of six typical applications
+covering four dominate CAVs scenarios and takes four datasets as standard
+input. CAVBench provides quantitative evaluation results via application and
+system perspective output metrics. We perform a series of experiments and
+acquire three systemic characteristics of the applications in CAVBench. First,
+the operation intensity of the applications is polarized, which explains why
+heterogeneous hardware is important for a CAVs computing system. Second, all
+applications in CAVBench consume high memory bandwidth, so the system should be
+equipped with high bandwidth memory or leverage good memory bandwidth
+management to avoid the performance degradation caused by memory bandwidth
+competition. Third, some applications have worse data/instruction locality
+based on the cache miss observation, so the computing system targeting these
+applications should optimize the cache architecture. Last, we use the CAVBench
+to evaluate a typical edge computing platform and present the quantitative and
+qualitative analysis of the benchmarking results.
+"
+25833,Probabilistic Model Counting with Short XORs,"  The idea of counting the number of satisfying truth assignments (models) of a
+formula by adding random parity constraints can be traced back to the seminal
+work of Valiant and Vazirani, showing that NP is as easy as detecting unique
+solutions. While theoretically sound, the random parity constraints in that
+construction have the following drawback: each constraint, on average, involves
+half of all variables. As a result, the branching factor associated with
+searching for models that also satisfy the parity constraints quickly gets out
+of hand. In this work we prove that one can work with much shorter parity
+constraints and still get rigorous mathematical guarantees, especially when the
+number of models is large so that many constraints need to be added. Our work
+is based on the realization that the essential feature for random systems of
+parity constraints to be useful in probabilistic model counting is that the
+geometry of their set of solutions resembles an error-correcting code.
+"
+25834,Estimation of Risk Contributions with MCMC,"  Determining risk contributions of unit exposures to portfolio-wide economic
+capital is an important task in financial risk management. Computing risk
+contributions involves difficulties caused by rare-event simulations. In this
+study, we address the problem of estimating risk contributions when the total
+risk is measured by value-at-risk (VaR). Our proposed estimator of VaR
+contributions is based on the Metropolis-Hasting (MH) algorithm, which is one
+of the most prevalent Markov chain Monte Carlo (MCMC) methods. Unlike existing
+estimators, our MH-based estimator consists of samples from conditional loss
+distribution given a rare event of interest. This feature enhances sample
+efficiency compared with the crude Monte Carlo method. Moreover, our method has
+the consistency and asymptotic normality, and is widely applicable to various
+risk models having joint loss density. Our numerical experiments based on
+simulation and real-world data demonstrate that in various risk models, even
+those having high-dimensional (approximately 500) inhomogeneous margins, our MH
+estimator has smaller bias and mean squared error compared with existing
+estimators.
+"
+25835,Über-Gravity and the Cosmological Constant Problem,"  Recently, the idea of taking ensemble average over gravity models has been
+introduced. Based on this idea, we study the ensemble average over
+(effectively) all the gravity models (constructed from Ricci scalar) dubbing
+the name über-gravity which is a {\it{fixed point}} in the model space. The
+über-gravity has interesting universal properties, independent from the
+choice of basis: $i)$ it mimics Einstein-Hilbert gravity for high-curvature
+regime, $ii)$ it predicts stronger gravitational force for an
+intermediate-curvature regime, $iii)$ surprisingly, for low-curvature regime,
+i.e. $R<R_0$ where $R$ is Ricci scalar and $R_0$ is a given scale, the
+Lagrangian vanishes automatically and $iiii)$ there is a sharp transition
+between low- and intermediate-curvature regimes at $R=R_0$. We show that the
+über-gravity response is robust to all values of vacuum energy, $\rho_{vac}$
+when there is no other matter. So as a toy model, über-gravity, gives a way
+to think about the hierarchy problems e.g. the cosmological constant problem.
+Due to the transition at $R=R_0$ there is a chance for über-gravity to bypass
+Weinberg's no-go theorem. The cosmology of this model is also promising because
+of its non-trivial predictions for small curvature scales in comparison to
+$\Lambda$CDM model.
+"
+25836,Duality for finite Gelfand pairs,"  Let $\mathrm{G}$ be a split reductive group, $K$ be a non-Archimedean local
+field, and $O$ be its ring of integers. Satake isomorphism identifies the
+algebra of compactly supported invariants
+$\mathbb{C}_c[\mathrm{G}(K)/\mathrm{G}(O))]^{\mathrm{G}(O)}$ with a
+complexification of the algebra of characters of finite-dimensional
+representations
+$\mathcal{O}(\mathrm{G}^L(\mathbb{C}))^{\mathrm{G}^L(\mathbb{C})}$ of the
+Langlands dual group. In this note we report on the results of the study of
+analogues of such an isomorphism for finite groups. In our setup we replaced
+Gelfand pair $\mathrm{G}(O)\subset \mathrm{G}(K)$ by a finite pair $H\subset
+G$. It is convenient to rewrite the character side of the isomorphism as
+$\mathcal{O}(\mathrm{G}^L(\mathbb{C}))^{\mathrm{G}^L(\mathbb{C})}=\mathcal{O}((\mathrm{G}^L(\mathbb{C})\times
+\mathrm{G}^L(\mathbb{C}))/\mathrm{G}^L(\mathbb{C}))^{\mathrm{G}^L(\mathbb{C})}$.
+We replace diagonal Gelfand pair $\mathrm{G}^L(\mathbb{C})\subset
+\mathrm{G}^L(\mathbb{C})\times \mathrm{G}^L(\mathbb{C})$ by a dual finite pair
+$\check{H}\subset \check{G}$ and use Satake isomorphism as a defining property
+of the duality. In this text we make a preliminary study of such duality and
+compute a number of nontrivial examples of dual pairs $(H,G)$ and $(\check{H},
+\check{G})$. We discuss a possible relation of our constructions to String
+Topology.
+"
+25837,Seating Assignment Using Constrained Signed Spectral Clustering,"  In this paper, we present a novel method for constrained cluster size signed
+spectral clustering which allows us to subdivide large groups of people based
+on their relationships. In general, signed clustering only requires K hard
+clusters and does not constrain the cluster sizes. We extend signed clustering
+to include cluster size constraints. Using an example of seating assignment, we
+efficiently find groups of people with high social affinity while mitigating
+awkward social interaction between people who dislike each other.
+"
+25838,In-plane uniaxial strain in black phosphorus enables the identification of crystalline orientation,"  The identification of the crystalline axis of anisotropic black phosphorus
+(BP) is key for the study of its physical properties and for its optical and
+electronic applications. Herein, we show that by applying an in-plane uniaxial
+strain and measuring the changes of Raman shifts, the crystalline axis of BP
+can be reliably determined. The strain effects on Raman shifts are
+angle-dependent, which can be expressed as a combination of Raman responses
+under zigzag and armchair strains. Different from previous polarized optical
+spectroscopic means, the proposed method does not rely on the laser
+polarization, the excitation wavelength, the sample thickness and the material
+of the stretchable substrate. Besides, the effective strain applied on BP from
+the stretched substrate is estimated, showing that only 20% to 40% of the
+strain can be effectively transferred to BP flakes from polyethylene
+terephthalate substrates. Our method provides not only an effective and
+universal approach to identify the crystalline orientation of layered BP, but
+also a model to extract additional information in strain-related studies, and
+can be extended to other 2D anisotropic materials.
+"
+25839,Deep Learning Techniques for Music Generation - A Survey,"  This paper is a survey and an analysis of different ways of using deep
+learning (deep artificial neural networks) to generate musical content.
+We propose a methodology based on five dimensions for our analysis: -
+Objective - What musical content is to be generated? E.g., melody, polyphony,
+accompaniment and counterpoint - For what destination and for what use? To be
+performed by a human(s) or by a machine. - Representation - What are the
+concepts to be manipulated? E.g., waveform, spectrogram, note, chord, meter,
+and beat - What format is to be used? E.g., MIDI, piano roll and text - How
+will the representation be encoded? E.g., scalar, one-hot, and many-hot. -
+Architecture - What type of deep neural network is to be used? E.g.,
+feedforward network, recurrent network, autoencoder, and generative adversarial
+networks. - Challenges - What are the limitations\index and open challenges?
+E.g., variability, interactivity and creativity. - Strategy - How do we model
+and control the process of generation? E.g., single-step feedforward, decoder
+feedforward, sampling and input manipulation.
+For each dimension, we conduct a comparative analysis of various models and
+techniques and we propose some tentative multidimensional typology. This
+typology is bottom-up, based on the analysis of many existing deep-learning
+based systems for music generation selected from the relevant literature. These
+systems are described in this survey/analysis and are used to exemplify the
+various choices of objective, representation, architecture, challenges and
+strategies. The final part of the paper includes some discussion and some
+prospects.
+This paper is a simplified (weak DRM) version of the following book:
+Jean-Pierre Briot, Gaëtan Hadjeres and François Pachet, Deep Learning
+Techniques for Music Generation, Computational Synthesis and Creative Systems,
+Springer Nature, 2019.
+"
+25840,Clinical and Non-clinical Effects on Surgery Duration: Statistical Modeling and Analysis,"  Surgery duration is usually used as an input to the operation room (OR)
+allocation and surgery scheduling problems. A good estimation of surgery
+duration benefits the operation planning in ORs. In contrast, we would like to
+investigate whether the allocation decisions in turn influence surgery
+duration. Using almost two years of data from a large hospital in China, we
+find evidence in support of our conjecture. Surgery duration decreases with the
+number of surgeries a surgeon performs in a day. Numerically, surgery duration
+will decrease by 10 minutes on average if a surgeon performs one more surgery.
+Furthermore, we find a non-linear relationship between surgery duration and the
+number of surgeries allocated to an OR. Also, a surgery's duration is affected
+by its position in a sequence of surgeries performed by one surgeon. In
+addition, surgeons exhibit different patterns on the effects of surgery type
+and position. Since the findings are obtained from a particular data set, We do
+not claim the generalizability. Instead, the analysis in this paper provides
+insights into surgery duration study in ORs.
+"
+25841,Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles,"  With the rapidly growing interest in autonomous navigation, the body of
+research on motion planning and collision avoidance techniques has enjoyed an
+accelerating rate of novel proposals and developments. However, the complexity
+of new techniques and their safety requirements render the bulk of current
+benchmarking frameworks inadequate, thus leaving the need for efficient
+comparison techniques unanswered. This work proposes a novel framework based on
+deep reinforcement learning for benchmarking the behavior of collision
+avoidance mechanisms under the worst-case scenario of dealing with an optimal
+adversarial agent, trained to drive the system into unsafe states. We describe
+the architecture and flow of this framework as a benchmarking solution, and
+demonstrate its efficacy via a practical case study of comparing the
+reliability of two collision avoidance mechanisms in response to intentional
+collision attempts.
+"
+25842,Homology of Finite Topological Spaces,"  A new method is given for computing generators of the homology groups with
+integer coefficients for any finite $T_0$-space. An important role in this
+method is played by irreducible cycles which are defined here and give rise to
+continuous injective maps between spaces called immersions. The so-called orb
+complex which is much smaller than the order complex induces a surjective map
+of its homology to the simplicial homology of the space. An analysis of the
+kernel of this map allows to define an effective algorithm for computing the
+homology groups, whose time complexity is polynomial in the size of the space.
+"
+25843,Dirac's method for time-dependent Hamiltonian systems in the extended phase space,"  The Dirac's method for constrained systems is applied to the analysis of
+time-dependent Hamiltonians in the extended phase space. Our analysis provides
+a conceptually complete description and offers a different point of view of
+earlier works. We show that the Lewis invariant is a Dirac's observable and in
+consequence, it is invariant under time-reparametrizations. We compute the
+Feynman propagator using the extended phase space description and show that the
+quantum phase of the Feynman propagator is given by the boundary term of the
+canonical transformation of the extended phase space. We also give a new light
+to the physical character of this phase.
+"
+25844,Conservative Extensions in Guarded and Two-Variable Fragments,"  We investigate the decidability and computational complexity of (deductive)
+conservative extensions in fragments of first-order logic (FO), with a focus on
+the two-variable fragment FO$^2$ and the guarded fragment GF. We prove that
+conservative extensions are undecidable in any FO fragment that contains FO$^2$
+or GF (even the three-variable fragment thereof), and that they are decidable
+and 2\ExpTime-complete in the intersection GF$^2$ of FO$^2$ and GF.
+"
+25845,Sequences of purchases in credit card data reveal life styles in urban populations,"  Zipf-like distributions characterize a wide set of phenomena in physics,
+biology, economics and social sciences. In human activities, Zipf-laws describe
+for example the frequency of words appearance in a text or the purchases types
+in shopping patterns. In the latter, the uneven distribution of transaction
+types is bound with the temporal sequences of purchases of individual choices.
+In this work, we define a framework using a text compression technique on the
+sequences of credit card purchases to detect ubiquitous patterns of collective
+behavior. Clustering the consumers by their similarity in purchases sequences,
+we detect five consumer groups. Remarkably, post checking, individuals in each
+group are also similar in their age, total expenditure, gender, and the
+diversity of their social and mobility networks extracted by their mobile phone
+records. By properly deconstructing transaction data with Zipf-like
+distributions, this method uncovers sets of significant sequences that reveal
+insights on collective human behavior.
+"
+25846,Verifying Properties of Binarized Deep Neural Networks,"  Understanding properties of deep neural networks is an important challenge in
+deep learning. In this paper, we take a step in this direction by proposing a
+rigorous way of verifying properties of a popular class of neural networks,
+Binarized Neural Networks, using the well-developed means of Boolean
+satisfiability. Our main contribution is a construction that creates a
+representation of a binarized neural network as a Boolean formula. Our encoding
+is the first exact Boolean representation of a deep neural network. Using this
+encoding, we leverage the power of modern SAT solvers along with a proposed
+counterexample-guided search procedure to verify various properties of these
+networks. A particular focus will be on the critical property of robustness to
+adversarial perturbations. For this property, our experimental results
+demonstrate that our approach scales to medium-size deep neural networks used
+in image classification tasks. To the best of our knowledge, this is the first
+work on verifying properties of deep neural networks using an exact Boolean
+encoding of the network.
+"
+25847,A New form of Krönecker product-Lax pair and coupled systems,"  A new method is proposed to generate nonlinear integrable systems by starting
+with existing Lax pair and a new form of Krönecker product. It is observed
+that such equation can be generated with the help of a Hamiltonian structure.
+Actually they are all bi-Hamiltonian. A few explicit form of such equations are
+given also
+"
+25848,A Unified PTAS for Prize Collecting TSP and Steiner Tree Problem in Doubling Metrics,"  We present a unified polynomial-time approximation scheme (PTAS) for the
+prize collecting traveling salesman problem (PCTSP) and the prize collecting
+Steiner tree problem (PCSTP) in doubling metrics. Given a metric space and a
+penalty function on a subset of points known as terminals, a solution is a
+subgraph on points in the metric space, whose cost is the weight of its edges
+plus the penalty due to terminals not covered by the subgraph. Under our
+unified framework, the solution subgraph needs to be Eulerian for PCTSP, while
+it needs to be connected for PCSTP. Before our work, even a QPTAS for the
+problems in doubling metrics is not known.
+Our unified PTAS is based on the previous dynamic programming frameworks
+proposed in [Talwar STOC 2004] and [Bartal, Gottlieb, Krauthgamer STOC 2012].
+However, since it is unknown which part of the optimal cost is due to edge
+lengths and which part is due to penalties of uncovered terminals, we need to
+develop new techniques to apply previous divide-and-conquer strategies and
+sparse instance decompositions.
+"
+25849,Post-processing of the HST STIS coronagraphic observations,"  In the past 20 years, the Hubble Space Telescope (HST) STIS coronagraphic
+instrument has observed more than 100 stars, obtaining more than 4,000 readouts
+since its installment on HST in 1997 and the numbers are still increasing. We
+reduce the whole STIS coronagraphic archive at the most commonly observed
+positions (Wedge A0.6 and A1.0) with new post-processing methods, and present
+our results here. We are able to recover all of the 32 previously reported
+circumstellar disks, and obtain better contrast close to the star. For some of
+the disks, our results are limited by the over subtraction of the methods, and
+therefore the major regions of the disks can be recovered except the faintest
+regions. We also explain our efforts in the calibration of its new BAR5
+occulting position, enabling STIS to explore inner regions as close as 0.2"".
+"
+25850,Sums of quadratic functions with two discriminants,"  Zagier in [4] discusses a construction of a function $F_{k,D}(x)$ defined for
+an even integer $k \geq 2$, and a positive discriminant $D$. This construction
+is intimately related to half-integral weight modular forms. In particular, the
+average value of this function is a constant multiple of the $D$-th Fourier
+coefficient of weight $k+1/2$ Eisenstein series constructed by H. Cohen in
+\cite{Cohen}.
+In this note we consider a construction which works both for even and odd
+positive integers $k$. Our function $F_{k,D,d}(x)$ depends on two discriminants
+$d$ and $D$ with signs sign$(d)=$ sign$(D)=(-1)^k$, degenerates to Zagier's
+function when $d=1$, namely, \[ F_{k,D,1}(x)=F_{k,D}(x), \] and has very
+similar properties. In particular, we prove that the average value of
+$F_{k,D,d}(x)$ is again a Fourier coefficient of H. Cohen's Eisenstein series
+of weight $k+1/2$, while now the integer $k \geq 2$ is allowed to be both even
+and odd.
+"
+25851,More exact solutions of the constant astigmatism equation,"  By using Bäcklund transformation for the sine-Gordon equation, new periodic
+exact solutions of the constant astigmatism equation $ z_{yy} + ({1}/{z})_{xx}
++ 2 = 0 $ are generated from a seed which corresponds to Lipschitz surfaces of
+constant astigmatism.
+"
+25852,Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging,"  In this paper we show that reporting a single performance score is
+insufficient to compare non-deterministic approaches. We demonstrate for common
+sequence tagging tasks that the seed value for the random number generator can
+result in statistically significant (p < 10^-4) differences for
+state-of-the-art systems. For two recent systems for NER, we observe an
+absolute difference of one percentage point F1-score depending on the selected
+seed value, making these systems perceived either as state-of-the-art or
+mediocre. Instead of publishing and reporting single performance scores, we
+propose to compare score distributions based on multiple executions. Based on
+the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we
+present network architectures that produce both superior performance as well as
+are more stable with respect to the remaining hyperparameters.
+"
+25853,Posterior Sampling for Large Scale Reinforcement Learning,"  We propose a practical non-episodic PSRL algorithm that unlike recent
+state-of-the-art PSRL algorithms uses a deterministic, model-independent
+episode switching schedule. Our algorithm termed deterministic schedule PSRL
+(DS-PSRL) is efficient in terms of time, sample, and space complexity. We prove
+a Bayesian regret bound under mild assumptions. Our result is more generally
+applicable to multiple parameters and continuous state action problems. We
+compare our algorithm with state-of-the-art PSRL algorithms on standard
+discrete and continuous problems from the literature. Finally, we show how the
+assumptions of our algorithm satisfy a sensible parametrization for a large
+class of problems in sequential recommendations.
+"
+25854,Fuzzy Approach Topic Discovery in Health and Medical Corpora,"  The majority of medical documents and electronic health records (EHRs) are in
+text format that poses a challenge for data processing and finding relevant
+documents. Looking for ways to automatically retrieve the enormous amount of
+health and medical knowledge has always been an intriguing topic. Powerful
+methods have been developed in recent years to make the text processing
+automatic. One of the popular approaches to retrieve information based on
+discovering the themes in health & medical corpora is topic modeling, however,
+this approach still needs new perspectives. In this research we describe fuzzy
+latent semantic analysis (FLSA), a novel approach in topic modeling using fuzzy
+perspective. FLSA can handle health & medical corpora redundancy issue and
+provides a new method to estimate the number of topics. The quantitative
+evaluations show that FLSA produces superior performance and features to latent
+Dirichlet allocation (LDA), the most popular topic model.
+"
+25855,Symbolic inductive bias for visually grounded learning of spoken language,"  A widespread approach to processing spoken language is to first automatically
+transcribe it into text. An alternative is to use an end-to-end approach:
+recent works have proposed to learn semantic embeddings of spoken language from
+images with spoken captions, without an intermediate transcription step. We
+propose to use multitask learning to exploit existing transcribed speech within
+the end-to-end setting. We describe a three-task architecture which combines
+the objectives of matching spoken captions with corresponding images, speech
+with text, and text with images. We show that the addition of the speech/text
+task leads to substantial performance improvements when compared to training
+the speech/image task in isolation. We conjecture that this is due to a strong
+inductive bias transcribed speech provides to the model, and offer supporting
+evidence for this.
+"
+25856,The Function Transformation Omics - Funomics,"  There are no two identical leaves in the world, so how to find effective
+markers or features to distinguish them is an important issue. Function
+transformation, such as f(x,y) and f(x,y,z), can transform two, three, or
+multiple input/observation variables (in biology, it generally refers to the
+observed/measured value of biomarkers, biological characteristics, or other
+indicators) into a new output variable (new characteristics or indicators).
+This provided us a chance to re-cognize objective things or relationships
+beyond the original measurements. For example, Body Mass Index, which transform
+weight and high into a new indicator BMI=x/y^2 (where x is weight and y is
+high), is commonly used in to gauge obesity. Here, we proposed a new system,
+Funomics (Function Transformation Omics), for understanding the world in a
+different perspective. Funome can be understood as a set of math functions
+consist of basic elementary functions (such as power functions and exponential
+functions) and basic mathematical operations (such as addition, subtraction).
+By scanning the whole Funome, researchers can identify some special functions
+(called handsome functions) which can generate the novel important output
+variable (characteristics or indicators). We also start ""the Funome project"" to
+develop novel methods, function library and analysis software for Funome
+studies. The Funome project will accelerate the discovery of new useful
+indicators or characteristics, will improve the utilization efficiency of
+directly measured data, and will enhance our ability to understand the world.
+The analysis tools and data resources about the Funome project can be found
+gradually at this http URL.
+"
+25857,There may be no minimal non $σ$-scattered linear orders,"  In this paper we demonstrate that it is consistent, relative to the existence
+of a supercompact cardinal, that there is no linear order which is minimal with
+respect to being non $\sigma$-scattered. This shows that a theorem of Laver,
+which asserts that the class of $\sigma$-scattered linear orders is well
+quasi-ordered, is sharp. We also prove that PFA${}^+$ implies that every non
+$\sigma$-scattered linear order either contains a real type, an Aronszajn type,
+or a ladder system indexed by a stationary subset of $\omega_1$, equipped with
+either the lexicographic or reverse lexicographic order. Our work immediately
+implies that CH is consistent with ""no Aronszajn tree has a base of cardinality
+$\aleph_1$."" This gives an affirmative answer to a problem due to Baumgartner.
+"
+25858,Compact manifolds with fixed boundary and large Steklov eigenvalues,"  Let M be a compact Riemannian manifold with boundary. Let b>0 be the number
+of connected components of its boundary. For manifolds of dimension at least 3,
+we prove that it is possible to obtain an arbitrarily large (b+1)-th Steklov
+eigenvalue using a smooth conformal perturbation which is supported in a thin
+neighbourhood of the boundary, identically equal to 1 on the boundary. For
+j<b+1, it is also possible to obtain arbitrarily large j-th eigenvalue, but
+this require the conformal factor to spread throughout the interior of the
+manifold M. This is in stark contrast with the situation for the eigenvalues of
+the Laplace operator on a closed manifold, where a conformal factor that is
+large enough for the volume to become unbounded results in the spectrum
+collapsing to 0. We also prove that it is possible to obtain large Steklov
+eigenvalues while keeping different boundary components arbitrarily close to
+each other, by constructing a convenient Riemannian submersion.
+"
+25859,On Nested Diversities and Novel Correlation-Based Entropies,"  Essential defining characteristics of diversity include the number of
+categories involved, evenness of the element distribution among categories, and
+dissimilarity between categories. In addition, there are two intrinsic factors
+that also influence the quantification of diversity. These are how much weight
+to place on the category prevalence, and how much weight to place on the
+between-category dissimilarities. In Part I of this paper, we provide a unified
+framework for measuring, comparing and partitioning diversity that incorporates
+all of these aspects. A new principle called the Nesting Principle is
+established, stating that diversity remains invariant under arbitrary nesting
+or partitioning of the constituent categories or individual elements. An
+important consequence of this principle is that the weight on the category
+prevalence must be set such that the diversity function depends quadratically
+on the relative abundances of categories, while the other weight on the
+between-category dissimilarity can be adjusted independently and continuously.
+The resulting diversity index has a unique interpretation in terms of a certain
+correlation sum defined within the assemblage whose diversity is to be
+measured. The formula for the effective dissimilarity between two arbitrary
+assemblages is also provided, which takes the form of a cross-correlation sum,
+and is compatible with the Nesting Principle. Classic entropy measures of Renyi
+and Tsallis are also generalised to incorporate the dependence on correlations
+among the constituents. In Part II, we discuss applications of the
+correlation-based, nesting-invariant entropies to specific research contexts.
+Specifically, the extended Renyi entropy is used to analyse dynamical and
+fractal properties of a chaotic attractor system. Further, implications of the
+extended Tsallis entropy on non-extensive statistical physics are explored.
+"
+25860,Is Simple Better? Revisiting Non-linear Matrix Factorization for Learning Incomplete Ratings,"  Matrix factorization techniques have been widely used as a method for
+collaborative filtering for recommender systems. In recent times, different
+variants of deep learning algorithms have been explored in this setting to
+improve the task of making a personalized recommendation with user-item
+interaction data. The idea that the mapping between the latent user or item
+factors and the original features is highly nonlinear suggest that classical
+matrix factorization techniques are no longer sufficient. In this paper, we
+propose a multilayer nonlinear semi-nonnegative matrix factorization method,
+with the motivation that user-item interactions can be modeled more accurately
+using a linear combination of non-linear item features. Firstly, we learn
+latent factors for representations of users and items from the designed
+multilayer nonlinear Semi-NMF approach using explicit ratings. Secondly, the
+architecture built is compared with deep-learning algorithms like Restricted
+Boltzmann Machine and state-of-the-art Deep Matrix factorization techniques. By
+using both supervised rate prediction task and unsupervised clustering in
+latent item space, we demonstrate that our proposed approach achieves better
+generalization ability in prediction as well as comparable representation
+ability as deep matrix factorization in the clustering task.
+"
+25861,Remarks on the Extended Gross-Keating data and the Siegel series of a quadratic form,"  The Gross-Keating invariant is a key ingredient to compare the arithmetic
+intersection number on orthogonal Shimura varieties with the central derivative
+of the Fourier coefficients of the Siegel-Eisenstein series in the context of
+Kudla's program. In this paper, we give a formula for obtaining the
+Gross-Keating invariant and the extended GK (=Gross-Keating) datum defined over
+any finite extension of $\mathbb{Z}_p$ (for $p>2$) and over any finite
+unramified extension of $\mathbb{Z}_2$.
+"
+25862,Catalyst Acceleration for Gradient-Based Non-Convex Optimization,"  We introduce a generic scheme to solve nonconvex optimization problems using
+gradient-based algorithms originally designed for minimizing convex functions.
+Even though these methods may originally require convexity to operate, the
+proposed approach allows one to use them on weakly convex objectives, which
+covers a large class of non-convex functions typically appearing in machine
+learning and signal processing. In general, the scheme is guaranteed to produce
+a stationary point with a worst-case efficiency typical of first-order methods,
+and when the objective turns out to be convex, it automatically accelerates in
+the sense of Nesterov and achieves near-optimal convergence rate in function
+values. These properties are achieved without assuming any knowledge about the
+convexity of the objective, by automatically adapting to the unknown weak
+convexity constant. We conclude the paper by showing promising experimental
+results obtained by applying our approach to incremental algorithms such as
+SVRG and SAGA for sparse matrix factorization and for learning neural networks.
+"
+25863,Notes on optimal approximations for importance sampling,"  In this manuscript, we derive optimal conditions for building function
+approximations that minimize variance when used as importance sampling
+estimators for Monte Carlo integration problems. Particularly, we study the
+problem of finding the optimal projection $g$ of an integrand $f$ onto certain
+classes of piecewise constant functions, in order to minimize the variance of
+the unbiased importance sampling estimator $E_g[f/g]$, as well as the related
+problem of finding optimal mixture weights to approximate and importance sample
+a target mixture distribution $f = \sum_i \alpha_i f_i$ with components $f_i$
+in a family $\mathcal{F}$, through a corresponding mixture of importance
+sampling densities $g_i$ that are only approximately proportional to $f_i$. We
+further show that in both cases the optimal projection is different from the
+commonly used $\ell_1$ projection, and provide an intuitive explanation for the
+difference.
+"
+25864,Vafa-Witten invariants for projective surfaces I: stable case,"  On a polarised surface, solutions of the Vafa-Witten equations correspond to
+certain polystable Higgs pairs. When stability and semistability coincide, the
+moduli space admits a symmetric obstruction theory and a $\mathbb C^*$ action
+with compact fixed locus. Applying virtual localisation we define invariants
+constant under deformations.
+When the vanishing theorem of Vafa-Witten holds, the result is the (signed)
+Euler characteristic of the moduli space of instantons. In general there are
+other, rational, contributions. Calculations of these on surfaces with positive
+canonical bundle recover the first terms of modular forms predicted by Vafa and
+Witten.
+"
+25865,Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates,"  We consider the goodness-of-fit testing problem of distinguishing whether the
+data are drawn from a specified distribution, versus a composite alternative
+separated from the null in the total variation metric. In the discrete case, we
+consider goodness-of-fit testing when the null distribution has a possibly
+growing or unbounded number of categories. In the continuous case, we consider
+testing a Lipschitz density, with possibly unbounded support, in the
+low-smoothness regime where the Lipschitz parameter is not assumed to be
+constant. In contrast to existing results, we show that the minimax rate and
+critical testing radius in these settings depend strongly, and in a precise
+way, on the null distribution being tested and this motivates the study of the
+(local) minimax rate as a function of the null distribution. For multinomials
+the local minimax rate was recently studied in the work of Valiant and Valiant.
+We re-visit and extend their results and develop two modifications to the
+chi-squared test whose performance we characterize. For testing Lipschitz
+densities, we show that the usual binning tests are inadequate in the
+low-smoothness regime and we design a spatially adaptive partitioning scheme
+that forms the basis for our locally minimax optimal tests. Furthermore, we
+provide the first local minimax lower bounds for this problem which yield a
+sharp characterization of the dependence of the critical radius on the null
+hypothesis being tested. In the low-smoothness regime we also provide adaptive
+tests, that adapt to the unknown smoothness parameter. We illustrate our
+results with a variety of simulations that demonstrate the practical utility of
+our proposed tests.
+"
+25866,The statistics of radio frequency interference propagating from long distances to the Murchison Radio-astronomy Observatory,"  BIGHORNS is a total power radiometer developed to identify the signal from
+the Epoch of Reionisation in the all-sky averaged radio spectrum at low
+frequencies (70-300 MHz). In October 2014, the system with a conical log spiral
+antenna was deployed at the Murchison Radio-astronomy Observatory (MRO) and has
+been collecting data since then. The system has been monitoring the radio
+frequency interference (RFI) environment at the future site of the
+low-frequency component of the Square Kilometre Array. We have analyzed almost
+two years of data (October 2014 - August 2016 inclusive) in search for events
+of long distance propagation of the RFI in FM and digital TV bands due to
+tropospheric ducting and reflections (of the meteor trails or aircraft). We
+present statistics of tropospheric ducting events observed in the digital TV
+band over nearly two years, which shows seasonal changes. We also present a
+system using upper atmosphere data (temperature, humidity and pressure as a
+function of altitude) from all stations in Western Australia to calculate the
+modified refractive index and make predictions of tropospheric ducting events.
+Preliminary tests indicate that the system can be very useful in predicting
+tropospheric ducting events (even with limited amount of available upper
+atmosphere data).
+"
+25867,"Modular Learning Component Attacks: Today's Reality, Tomorrow's Challenge","  Many of today's machine learning (ML) systems are not built from scratch, but
+are compositions of an array of {\em modular learning components} (MLCs). The
+increasing use of MLCs significantly simplifies the ML system development
+cycles. However, as most MLCs are contributed and maintained by third parties,
+their lack of standardization and regulation entails profound security
+implications.
+In this paper, for the first time, we demonstrate that potentially harmful
+MLCs pose immense threats to the security of ML systems. We present a broad
+class of {\em logic-bomb} attacks in which maliciously crafted MLCs trigger
+host systems to malfunction in a predictable manner. By empirically studying
+two state-of-the-art ML systems in the healthcare domain, we explore the
+feasibility of such attacks. For example, we show that, without prior knowledge
+about the host ML system, by modifying only 3.3{\textperthousand} of the MLC's
+parameters, each with distortion below $10^{-3}$, the adversary is able to
+force the misdiagnosis of target victims' skin cancers with 100\% success rate.
+We provide analytical justification for the success of such attacks, which
+points to the fundamental characteristics of today's ML models: high
+dimensionality, non-linearity, and non-convexity. The issue thus seems
+fundamental to many ML systems. We further discuss potential countermeasures to
+mitigate MLC-based attacks and their potential technical challenges.
+"
+25868,Towards Generalization and Simplicity in Continuous Control,"  This work shows that policies with simple linear and RBF parameterizations
+can be trained to solve a variety of continuous control tasks, including the
+OpenAI gym benchmarks. The performance of these trained policies are
+competitive with state of the art results, obtained with more elaborate
+parameterizations such as fully connected neural networks. Furthermore,
+existing training and testing scenarios are shown to be very limited and prone
+to over-fitting, thus giving rise to only trajectory-centric policies. Training
+with a diverse initial state distribution is shown to produce more global
+policies with better generalization. This allows for interactive control
+scenarios where the system recovers from large on-line perturbations; as shown
+in the supplementary video.
+"
+25869,Extroverts Tweet Differently from Introverts in Weibo,"  Being dominant factors driving the human actions, personalities can be
+excellent indicators in predicting the offline and online behavior of different
+individuals. However, because of the great expense and inevitable subjectivity
+in questionnaires and surveys, it is challenging for conventional studies to
+explore the connection between personality and behavior and gain insights in
+the context of large amount individuals. Considering the more and more
+important role of the online social media in daily communications, we argue
+that the footprint of massive individuals, like tweets in Weibo, can be the
+inspiring proxy to infer the personality and further understand its functions
+in shaping the online human behavior. In this study, a map from self-reports of
+personalities to online profiles of 293 active users in Weibo is established to
+train a competent machine learning model, which then successfully identifies
+over 7,000 users as extroverts or introverts. Systematical comparisons from
+perspectives of tempo-spatial patterns, online activities, emotion expressions
+and attitudes to virtual honor surprisingly disclose that the extrovert indeed
+behaves differently from the introvert in Weibo. Our findings provide solid
+evidence to justify the methodology of employing machine learning to
+objectively study personalities of massive individuals and shed lights on
+applications of probing personalities and corresponding behaviors solely
+through online profiles.
+"
+25870,"Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods","  In this paper we study several classes of stochastic optimization algorithms
+enriched with heavy ball momentum. Among the methods studied are: stochastic
+gradient descent, stochastic Newton, stochastic proximal point and stochastic
+dual subspace ascent. This is the first time momentum variants of several of
+these methods are studied. We choose to perform our analysis in a setting in
+which all of the above methods are equivalent. We prove global nonassymptotic
+linear convergence rates for all methods and various measures of success,
+including primal function values, primal iterates (in L2 sense), and dual
+function values. We also show that the primal iterates converge at an
+accelerated linear rate in the L1 sense. This is the first time a linear rate
+is shown for the stochastic heavy ball method (i.e., stochastic gradient
+descent method with momentum). Under somewhat weaker conditions, we establish a
+sublinear convergence rate for Cesaro averages of primal iterates. Moreover, we
+propose a novel concept, which we call stochastic momentum, aimed at decreasing
+the cost of performing the momentum step. We prove linear convergence of
+several stochastic methods with stochastic momentum, and show that in some
+sparse data regimes and for sufficiently small momentum parameters, these
+methods enjoy better overall complexity than methods with deterministic
+momentum. Finally, we perform extensive numerical testing on artificial and
+real datasets, including data coming from average consensus problems.
+"
+25871,Rule- and context-based dynamic business process modelling and simulation,"  The traditional approach used to implement a business process (BP) in today's
+information systems (IS) no longer covers the actual needs of the dynamically
+changing business. Therefore, a necessity for a new approach of dynamic
+business process (DBP) modelling and simulation has arisen. To date, existing
+approaches to DBP modelling and simulation have been incomplete, i.e. they lack
+theory or a case study or both. Furthermore, there is no commonly accepted
+definition of BDP. Current BP modelling tools are suitable almost solely for
+the modelling and simulation of a static BP that strictly prescribes which
+activities, and in which sequence, to execute. Usually, a DBP is not defined
+strictly at the beginning of its execution, and it changes under new conditions
+at runtime. In our paper, we propose six requirements of DBP and an approach
+for rule- and context-based DBP modelling and simulation. The approach is based
+on changing BP rules, BP actions and their sequences at process instance
+runtime, according to the new business system context. Based on the proposed
+approach, a reference architecture and prototype of a DBP simulation tool were
+developed. Modelling and simulation were carried out using this prototype, and
+the case study shows correspondence to the needs of dynamically changing
+business, as well as possibilities for modelling and simulating DBP.
+"
+25872,As you like it: Localization via paired comparisons,"  Suppose that we wish to estimate a vector $\mathbf{x}$ from a set of binary
+paired comparisons of the form ""$\mathbf{x}$ is closer to $\mathbf{p}$ than to
+$\mathbf{q}$"" for various choices of vectors $\mathbf{p}$ and $\mathbf{q}$. The
+problem of estimating $\mathbf{x}$ from this type of observation arises in a
+variety of contexts, including nonmetric multidimensional scaling, ""unfolding,""
+and ranking problems, often because it provides a powerful and flexible model
+of preference. We describe theoretical bounds for how well we can expect to
+estimate $\mathbf{x}$ under a randomized model for $\mathbf{p}$ and
+$\mathbf{q}$. We also present results for the case where the comparisons are
+noisy and subject to some degree of error. Additionally, we show that under a
+randomized model for $\mathbf{p}$ and $\mathbf{q}$, a suitable number of binary
+paired comparisons yield a stable embedding of the space of target vectors.
+Finally, we also that we can achieve significant gains by adaptively changing
+the distribution for choosing $\mathbf{p}$ and $\mathbf{q}$.
+"
+25873,Evolution of clustered supernovae,"  We study the merging and evolution of isolated supernovae (SNe) remnants in a
+stellar cluster into a collective superbubble, with the help of 3-D
+hydrodynamic simulations. We particularly focus on the transition stage when
+the isolated SNe remnants gradually combine to form a superbubble. We find that
+when the SN rate is high ($\nu_{\rm sn}\sim 10^{-9}$ pc$^{-3}$ yr$^{-1}$), the
+merging phase lasts for $\sim 10^4$ yr, for $n=1\hbox{--}10$ cm$^{-3}$, and the
+merging phase lasts for a longer time ($\sim 0.1$ Myr or more) for lower SN
+rates ($\nu_{\rm sn}\le 10^{-10}$ pc$^{-3}$ yr$^{-1}$). During this transition
+phase, the growing superbubble is filled with dense and cool fragments of
+shells and most of the energy is radiated away during this merging process.
+After passing through the intermediate phase, the superbubble eventually
+settles on to a new power-law wind asymptote that is smaller than estimated in
+a continuous wind model. This results in a significant (more than {\it several
+times}) underestimation of the mechanical luminosity needed to feed the bubble.
+We determine the X-ray and H$\alpha$ surface brightnesses as functions of time
+for such merging SNe in a stellar cluster and find that clusters with high SN
+rate shine predominantly in soft X-rays and H$\alpha$. In particular, a low
+value of the volume averaged H$\alpha$ to H$\beta$ ratio and its large spread
+can be a good indicator of the transition phase of merging SNe.
+"
+25874,Solving Multivariate Polynomial Systems and an Invariant from Commutative Algebra,"  The security of several post-quantum cryptosystems is based on the assumption
+that solving a system of multivariate (quadratic) polynomial equations
+$p_1=\dots=p_r=0$ over a finite field is hard. Such a system can be solved by
+computing a lexicographic Gröbner basis of the ideal $(p_1,\dots,p_r)$. The
+most efficient algorithms for computing Gröbner bases transform the problem
+into several instances of Gaussian elimination. The computational complexity of
+these algorithms is not completely understood, especially when the polynomials
+$p_1,\dots,p_r$ are not homogeneous. In this paper, we prove that this
+complexity is controlled by the Castelnuovo-Mumford regularity of the ideal
+$(p_1^h,\dots,p_r^h)$ obtained by homogenizing the input polynomials. This
+allows us to bound the complexity of solving a system of polynomial equations
+when the associated ideal is zero-dimensional, a common situation in
+cryptography. In combination with some theorems in commutative algebra, our
+results also allow us to bound the complexity of the ABC and cubic simple
+matrix schemes, as well as some instances of the MinRank Problem.
+"
+25875,Visual Affordance and Function Understanding: A Survey,"  Nowadays, robots are dominating the manufacturing, entertainment and
+healthcare industries. Robot vision aims to equip robots with the ability to
+discover information, understand it and interact with the environment. These
+capabilities require an agent to effectively understand object affordances and
+functionalities in complex visual domains. In this literature survey, we first
+focus on Visual affordances and summarize the state of the art as well as open
+problems and research gaps. Specifically, we discuss sub-problems such as
+affordance detection, categorization, segmentation and high-level reasoning.
+Furthermore, we cover functional scene understanding and the prevalent
+functional descriptors used in the literature. The survey also provides
+necessary background to the problem, sheds light on its significance and
+highlights the existing challenges for affordance and functionality learning.
+"
+25876,Intent-Aware Contextual Recommendation System,"  Recommender systems take inputs from user history, use an internal ranking
+algorithm to generate results and possibly optimize this ranking based on
+feedback. However, often the recommender system is unaware of the actual intent
+of the user and simply provides recommendations dynamically without properly
+understanding the thought process of the user. An intelligent recommender
+system is not only useful for the user but also for businesses which want to
+learn the tendencies of their users. Finding out tendencies or intents of a
+user is a difficult problem to solve.
+Keeping this in mind, we sought out to create an intelligent system which
+will keep track of the user's activity on a web-application as well as
+determine the intent of the user in each session. We devised a way to encode
+the user's activity through the sessions. Then, we have represented the
+information seen by the user in a high dimensional format which is reduced to
+lower dimensions using tensor factorization techniques. The aspect of intent
+awareness (or scoring) is dealt with at this stage. Finally, combining the user
+activity data with the contextual information gives the recommendation score.
+The final recommendations are then ranked using filtering and collaborative
+recommendation techniques to show the top-k recommendations to the user. A
+provision for feedback is also envisioned in the current system which informs
+the model to update the various weights in the recommender system. Our overall
+model aims to combine both frequency-based and context-based recommendation
+systems and quantify the intent of a user to provide better recommendations.
+We ran experiments on real-world timestamped user activity data, in the
+setting of recommending reports to the users of a business analytics tool and
+the results are better than the baselines. We also tuned certain aspects of our
+model to arrive at optimized results.
+"
+25877,Explainable Entity-based Recommendations with Knowledge Graphs,"  Explainable recommendation is an important task. Many methods have been
+proposed which generate explanations from the content and reviews written for
+items. When review text is unavailable, generating explanations is still a hard
+problem. In this paper, we illustrate how explanations can be generated in such
+a scenario by leveraging external knowledge in the form of knowledge graphs.
+Our method jointly ranks items and knowledge graph entities using a
+Personalized PageRank procedure to produce recommendations together with their
+explanations.
+"
+25878,Notes on Pointed Gromov-Hausdorff Convergence,"  The present article addresses to everyone who starts working with (pointed)
+Gromov-Hausdorff convergence. In the major part, both Gromov-Hausdorff
+convergence of compact and of pointed metric spaces are introduced and
+investigated. Moreover, the relation of sublimits occurring with pointed
+Gromov-Hausdorff convergence and ultralimits is discussed.
+"
+25879,Was Proxima captured by alpha Centauri A and B?,"  The nearest stellar system consists of the stars Proxima, alpha Centauri A
+and B and at least one planet Proxima b. The habitability of Proxima b and any
+other planets are likely to be significantly influenced by the orbital
+evolution of the system. To study the dynamical evolution of the system, we
+simulate the motions of Proxima and alpha Centauri A and B due to the
+perturbations from the Galactic tide and stellar encounters in a Monte Carlo
+fashion. From 100 clones, we find that 74% orbits of Proxima Centauri are bound
+to alpha Centauri A and B while 17% and 9% orbits become unbound in the
+simulations over the past and future 5 Gyr. If the system migrated outward in
+the Milky Way to its current location, more than 50% of clones could become
+unstable in backward simulations. The ratio of unstable clones increases with
+the simulation time scale and encounter rate. This provides some evidence for a
+capture scenario for the formation of the current triple system. Despite large
+uncertainties, the metallicity difference between Proxima and alpha Centauri A
+and B is also suggestive of their different origin. Nonetheless, further
+improvements in the available data and models will be necessary for a reliable
+assessment of the history of the Proxima-alpha Centauri system and its impact
+on the habitability of Proxima b.
+"
+25880,An implementation of the maximum-caliber principle by replica-averaged time-resolved restrained simulations,"  Inferential methods can be used to integrate experimental informations and
+molecular simulations. The maximum entropy principle provides a framework for
+using equilibrium experimental data and it has been shown that replica-averaged
+simulations, restrained using a static potential, are a practical and powerful
+implementation of such principle. Here we show that replica-averaged
+simulations restrained using a time-dependent potential are equivalent to the
+principle of maximum caliber, the dynamic version of the principle of maximum
+entropy, and thus may allow to integrate time-resolved data in molecular
+dynamics simulations. We provide an analytical proof of the equivalence as well
+as a computational validation making use of simple models and synthetic data.
+Some limitations and possible solutions are also discussed.
+"
+25881,Contextual Equivalence for a Probabilistic Language with Continuous Random Variables and Recursion,"  We present a complete reasoning principle for contextual equivalence in an
+untyped probabilistic language. The language includes continuous (real-valued)
+random variables, conditionals, and scoring. It also includes recursion, since
+the standard call-by-value fixpoint combinator is expressible. We demonstrate
+the usability of our characterization by proving several equivalence schemas,
+including familiar facts from lambda calculus as well as results specific to
+probabilistic programming. In particular, we use it to prove that reordering
+the random draws in a probabilistic program preserves contextual equivalence.
+This allows us to show, for example, that (let x = $e_1$ in let y = $e_2$ in
+$e_0$) is equivalent to (let y = $e_2$ in let x = $e_1$ in $e_0$) (provided $x$
+does not occur free in $e_2$ and $y$ does not occur free in $e_1$) despite the
+fact that $e_1$ and $e_2$ may have sampling and scoring effects.
+"
+25882,Optimal design of three-planetary-gear power-split hybrid powertrains,"  Many of today's power-split hybrid electric vehicles (HEVs) utilize planetary
+gears (PGs) to connect the powertrain elements together. Recent power-split
+HEVs tend to use two PGs and some of them have multiple modes to achieve better
+fuel economy and driving performance. Looking to the future, hybrid powertrain
+technologies must be enhanced to design hybrid light trucks. For light trucks,
+the need for multi-mode and more PGs is stronger, to achieve the required
+performance. To systematically explore all the possible designs of multi-mode
+HEVs with three PGs, an efficient searching and optimization methodology is
+proposed. All possible clutch topology and modes for one existing configuration
+that uses three PGs were exhaustively searched. The launching performance is
+first used to screen out designs that fail to satisfy the required launching
+performance. A near-optimal and computationally efficient energy management
+strategy was then employed to identify designs that achieve good fuel economy.
+The proposed design process successfully identify 8 designs that achieve better
+launching performance and better fuel economy, while using fewer number of
+clutches than the benchmark and a patented design.
+"
+25883,"Computational investigation of multivalent binding of a ligand coated particle: Role of shape, size and ligand heterogeneity from a free energy landscape perspective","  We utilize a multiscale modeling framework to study the effect of shape, size
+and ligand composition on the efficacy of binding of a ligand-coated-particle
+to a substrate functionalized with the target receptors. First, we show how
+molecular dynamics (MD) along with Steered MD calculations can be used to
+accurately parameterize the molecular binding free energy and the effective
+spring constant for a receptor-ligand pair. We demonstrate this for two ligands
+that bind to the $\alpha_5\beta_1$-domain of integrin. Next, we show how these
+effective potentials can be used to build computational models at the meso- and
+continuum- scales. These models incorporate the molecular nature of the
+receptor-ligand interactions and yet provide an inexpensive route to study the
+multivalent interaction of receptors and ligands through the construction of
+Bell potentials customized to the molecular identities. We quantify the binding
+efficacy of the ligand-coated-particle in terms of its multivalency, binding
+free energy landscape and the losses in the configurational entropies. We show
+that (i) the binding avidity for particle sizes less than $350$ nm is set by
+the competition between the enthalpic and entropic contributions while that for
+sizes above $350$ nm is dominated by the enthalpy of binding, (ii) anisotropic
+particles display higher multivalent binding compared to spherical particles
+and (iii) variations in ligand composition can alter binding avidity without
+altering the average multivalency. The methods and results presented here have
+wide applications in the rational design of functionalized carriers and also in
+understanding cell adhesion.
+"
+25884,Information-theoretic analysis of generalization capability of learning algorithms,"  We derive upper bounds on the generalization error of a learning algorithm in
+terms of the mutual information between its input and output. The bounds
+provide an information-theoretic understanding of generalization in learning
+problems, and give theoretical guidelines for striking the right balance
+between data fit and generalization by controlling the input-output mutual
+information. We propose a number of methods for this purpose, among which are
+algorithms that regularize the ERM algorithm with relative entropy or with
+random noise. Our work extends and leads to nontrivial improvements on the
+recent results of Russo and Zou.
+"
+25885,Classification regions of deep neural networks,"  The goal of this paper is to analyze the geometric properties of deep neural
+network classifiers in the input space. We specifically study the topology of
+classification regions created by deep networks, as well as their associated
+decision boundary. Through a systematic empirical investigation, we show that
+state-of-the-art deep nets learn connected classification regions, and that the
+decision boundary in the vicinity of datapoints is flat along most directions.
+We further draw an essential connection between two seemingly unrelated
+properties of deep networks: their sensitivity to additive perturbations in the
+inputs, and the curvature of their decision boundary. The directions where the
+decision boundary is curved in fact remarkably characterize the directions to
+which the classifier is the most vulnerable. We finally leverage a fundamental
+asymmetry in the curvature of the decision boundary of deep nets, and propose a
+method to discriminate between original images, and images perturbed with small
+adversarial examples. We show the effectiveness of this purely geometric
+approach for detecting small adversarial perturbations in images, and for
+recovering the labels of perturbed images.
+"
+25886,Hubble Space Telescope Observations of BALQSO Ton 34 Reveal a Connection between the Broad Line Region and the BAL Outflow,"  Ton 34 recently transitioned from non-absorbing quasar into a BALQSO.Here, we
+report new HST-STIS observations of this quasar. Along with CIV absorption, we
+also detect absorption by NV+Ly alpha and possibly OVI+Ly beta. We follow the
+evolution of the CIV BAL, and find that, for the slower outflowing material,
+the absorption trough varies little (if at all) on a rest-frame timescale of 2
+yr. However, we detect a strong deepening of the absorption in the gas moving
+at larger velocities (-20,000 - -23,000 km s-1). The data is consistent with a
+multistreaming flow crossing our line of sight to the source. The transverse
+velocity of the flow should be few thousand km s-1, similar to the rotation
+velocity of the BLR gas (2,600 km s-1). By simply assuming Keplerian motion,
+these two components must have similar locations, pointing to a common outflow
+forming the BLR and the BAL. We speculate that BALs, mini-BALs, and NALs, are
+part of a common, ubiquitous, accretion-disk outflow in AGN, but become
+observable depending on the viewing angle towards the flow. The absorption
+troughs suggest a wind covering only 20% of the emitting source, implying a
+maximum size of 10^-3 pc for the clouds forming the BAL/BLR medium. This is
+consistent with constraints of the BLR clouds from X-ray occultations. Finally,
+we suggest that the low excitation broad emission lines detected in the spectra
+of this source lie beyond the wind, and this gas is probably excited by the
+shock of the BAL wind with the surrounding medium.
+"
+25887,Ultrametricity increases the predictability of cultural dynamics,"  A quantitative understanding of societies requires useful combinations of
+empirical data and mathematical models. Models of cultural dynamics aim at
+explaining the emergence of culturally homogeneous groups through social
+influence. Traditionally, the initial cultural traits of individuals are chosen
+uniformly at random, the emphasis being on characterizing the model outcomes
+that are independent of these (`annealed') initial conditions. Here, motivated
+by an increasing interest in forecasting social behavior in the real world, we
+reverse the point of view and focus on the effect of specific (`quenched')
+initial conditions, including those obtained from real data, on the final
+cultural state. We study the predictability, rigorously defined in an
+information-theoretic sense, of the \emph{social content} of the final cultural
+groups (i.e. who ends up in which group) from the knowledge of the initial
+cultural traits. We find that, as compared to random and shuffled initial
+conditions, the hierarchical ultrametric-like organization of empirical
+cultural states significantly increases the predictability of the final social
+content by largely confining cultural convergence within the lower levels of
+the hierarchy. Moreover, predictability correlates with the compatibility of
+short-term social coordination and long-term cultural diversity, a property
+that has been recently found to be strong and robust in empirical data. We also
+introduce a null model generating initial conditions that retain the
+ultrametric representation of real data. Using this ultrametric model,
+predictability is highly enhanced with respect to the random and shuffled
+cases, confirming the usefulness of the empirical hierarchical organization of
+culture for forecasting the outcome of social influence models.
+"
+25888,Generalised Bloch's conjecture on surfaces of general type with involution,"  In this short note we prove that an involution on certain examples of
+surfaces of general type with $p_g=0$, acts as identity on the Chow group of
+zero cycles of the relevant surface.
+"
+25889,Asymptotic Theory and Statistical Decomposability gap Estimation for Takayama's Index,"  In the spirit of recent asymptotic works on the General Poverty Index (GPI)
+in the field of Welfare Analysis, the asymptotic representation of the
+non-decomposable Takayama's index, which has failed to be incorporated in the
+unified GPI approach, is addressed and established here. This representation
+allows also to extend to it, recent results of statistical decomposability gaps
+estimations. The theoretical results are applied to real databases. The
+conclusions of the undertaken applications recommend to use Takayama's index as
+a practically decomposable one, in virtue of the low decomposability gaps with
+respect to the large values of the index.
+"
+25890,A simulation chain for the LAGO Space Weather Program,"  We simulate the expected variations of background flux at two particular
+sites of the Latin American Giant Observatory (LAGO) and found that these
+fluxes are sensible to the latitude and that neutrons and muons components, of
+cosmic rays, are affected due to the variation of the geomagnetic field (GF).
+"
+25891,Asynchronous Networks: Modularization of Dynamics Theorem,"  Building on the first part of this paper, we develop the theory of functional
+asynchronous networks. We show that a large class of functional asynchronous
+networks can be (uniquely) represented as feedforward networks connecting
+events or dynamical modules. For these networks we can give a complete
+description of the network function in terms of the function of the events
+comprising the network: the Modularization of Dynamics Theorem. We give
+examples to illustrate the main results.
+"
+25892,Multimodal Deep Domain Adaptation,"  Typically a classifier trained on a given dataset (source domain) does not
+performs well if it is tested on data acquired in a different setting (target
+domain). This is the problem that domain adaptation (DA) tries to overcome and,
+while it is a well explored topic in computer vision, it is largely ignored in
+robotic vision where usually visual classification methods are trained and
+tested in the same domain. Robots should be able to deal with unknown
+environments, recognize objects and use them in the correct way, so it is
+important to explore the domain adaptation scenario also in this context. The
+goal of the project is to define a benchmark and a protocol for multi-modal
+domain adaptation that is valuable for the robot vision community. With this
+purpose some of the state-of-the-art DA methods are selected: Deep Adaptation
+Network (DAN), Domain Adversarial Training of Neural Network (DANN), Automatic
+Domain Alignment Layers (AutoDIAL) and Adversarial Discriminative Domain
+Adaptation (ADDA). Evaluations have been done using different data types: RGB
+only, depth only and RGB-D over the following datasets, designed for the
+robotic community: RGB-D Object Dataset (ROD), Web Object Dataset (WOD),
+Autonomous Robot Indoor Dataset (ARID), Big Berkeley Instance Recognition
+Dataset (BigBIRD) and Active Vision Dataset. Although progresses have been made
+on the formulation of effective adaptation algorithms and more realistic object
+datasets are available, the results obtained show that, training a sufficiently
+good object classifier, especially in the domain adaptation scenario, is still
+an unsolved problem. Also the best way to combine depth with RGB informations
+to improve the performance is a point that needs to be investigated more.
+"
+25893,Identification of partially resolved binaries in Pan-STARRS1 data,"  Using shape measurement techniques developed for weak lensing surveys we have
+identified three new ultracool binaries in the Pan-STARRS1 survey. Binary
+companions which are not completely resolved can still alter the shapes of
+stellar images. These shape distortions can be measured if PSF anisotropy
+caused by the telescope is properly accounted for. We show using both a sample
+of known binary stars and simulated binaries that we can reliably recover
+binaries wider than around 0.3"" and with flux ratios greater than around 0.1.
+We then applied our method to a sample of ultracool dwarfs within 30pc with 293
+objects having sufficient Pan-STARRS1 data for our method. In total we
+recovered all but one of the 11 binaries wider than 0.3"" in this sample. Our
+one failure was a true binary detected with a significant but erroneously high
+ellipticity which led it to be rejected in our analysis. We identify three new
+binaries, one a simultaneous discovery, with primary spectral types M6.5, L1
+and T0.5. These latter two were confirmed with Keck/NIRC2 follow-up imaging.
+This technique will be useful for identifying large numbers of stellar and
+substellar binaries in the upcoming LSST and DES sky surveys.
+"
+25894,On the Parameterized Complexity of Approximating Dominating Set,"  We study the parameterized complexity of approximating the $k$-Dominating Set
+(DomSet) problem where an integer $k$ and a graph $G$ on $n$ vertices are given
+as input, and the goal is to find a dominating set of size at most $F(k) \cdot
+k$ whenever the graph $G$ has a dominating set of size $k$. When such an
+algorithm runs in time $T(k) \cdot poly(n)$ (i.e., FPT-time) for some
+computable function $T$, it is said to be an $F(k)$-FPT-approximation algorithm
+for $k$-DomSet. We prove the following for every computable functions $T, F$
+and every constant $\varepsilon > 0$:
+$\bullet$ Assuming $W[1]\neq FPT$, there is no $F(k)$-FPT-approximation
+algorithm for $k$-DomSet.
+$\bullet$ Assuming the Exponential Time Hypothesis (ETH), there is no
+$F(k)$-approximation algorithm for $k$-DomSet that runs in $T(k) \cdot
+n^{o(k)}$ time.
+$\bullet$ Assuming the Strong Exponential Time Hypothesis (SETH), for every
+integer $k \geq 2$, there is no $F(k)$-approximation algorithm for $k$-DomSet
+that runs in $T(k) \cdot n^{k - \varepsilon}$ time.
+$\bullet$ Assuming the $k$-Sum Hypothesis, for every integer $k \geq 3$,
+there is no $F(k)$-approximation algorithm for $k$-DomSet that runs in $T(k)
+\cdot n^{\lceil k/2 \rceil - \varepsilon}$ time.
+Our results are obtained by establishing a connection between communication
+complexity and hardness of approximation, generalizing the ideas from a recent
+breakthrough work of Abboud et al. [FOCS 2017]. Specifically, we show that to
+prove hardness of approximation of a certain parameterized variant of the label
+cover problem, it suffices to devise a specific protocol for a communication
+problem that depends on which hypothesis we rely on. Each of these
+communication problems turns out to be either a well studied problem or a
+variant of one; this allows us to easily apply known techniques to solve them.
+"
+25895,On the Effect of Suboptimal Estimation of Mutual Information in Feature Selection and Classification,"  This paper introduces a new property of estimators of the strength of
+statistical association, which helps characterize how well an estimator will
+perform in scenarios where dependencies between continuous and discrete random
+variables need to be rank ordered. The new property, termed the estimator
+response curve, is easily computable and provides a marginal distribution
+agnostic way to assess an estimator's performance. It overcomes notable
+drawbacks of current metrics of assessment, including statistical power, bias,
+and consistency. We utilize the estimator response curve to test various
+measures of the strength of association that satisfy the data processing
+inequality (DPI), and show that the CIM estimator's performance compares
+favorably to kNN, vME, AP, and H_{MI} estimators of mutual information. The
+estimators which were identified to be suboptimal, according to the estimator
+response curve, perform worse than the more optimal estimators when tested with
+real-world data from four different areas of science, all with varying
+dimensionalities and sizes.
+"
+25896,Scaling laws for mixing and dissipation in unforced rotating stratified turbulence,"  We present a model for the scaling of mixing in weakly rotating stratified
+flows characterized by their Rossby, Froude and Reynolds numbers Ro, Fr, Re. It
+is based on quasi-equipartition between kinetic and potential modes,
+sub-dominant vertical velocity and lessening of the energy transfer to small
+scales as measured by the ratio rE of kinetic energy dissipation to its
+dimensional expression. We determine their domains of validity for a numerical
+study of the unforced Boussinesq equations mostly on grids of 10243 points,
+with Ro/Fr> 2.5 and with 1600< Re<1.9x104; the Prandtl number is one, initial
+conditions are either isotropic and at large scale for the velocity, and zero
+for the temperature {\theta}, or in geostrophic balance. Three regimes in Fr
+are observed: dominant waves, eddy-wave interactions and strong turbulence. A
+wave-turbulence balance for the transfer time leads to rE growing linearly with
+Fr in the intermediate regime, with a saturation at ~0.3 or more, depending on
+initial conditions for larger Froude numbers. The Ellison scale is also found
+to scale linearly with Fr, and the flux Richardson number Rf transitions for
+roughly the same parameter values as well. Putting together the 3 relationships
+of the model allows for the prediction of mixing efficiency scaling as
+Fr-2~RB-1 in the low and intermediate regimes, whereas for higher Fr, it scales
+as RB-1/2, as already observed: as turbulence strengthens, rE~1, the velocity
+is isotropic and smaller buoyancy fluxes altogether correspond to a decoupling
+of velocity and temperature fluctuations, the latter becoming passive.
+"
+25897,Stability conditions and cluster varieties from quivers of type $A$,"  We describe the relationship between two spaces associated to a quiver with
+potential. The first is a complex manifold parametrizing Bridgeland stability
+conditions on a triangulated category, and the second is a cluster variety with
+a natural Poisson structure. For quivers of type $A$, we construct a local
+biholomorphism from the space of stability conditions to the cluster variety.
+The existence of this map follows from results of Sibuya in the classical
+theory of ordinary differential equations.
+"
+25898,Spatial-temporal wind field prediction by Artificial Neural Networks,"  The prediction of near surface wind speed is becoming increasingly vital for
+the operation of electrical energy grids as the capacity of installed wind
+power grows. The majority of predictive wind speed modeling has focused on
+point-based time-series forecasting. Effectively balancing demand and supply in
+the presence of distributed wind turbine electricity generation, however,
+requires the prediction of wind fields in space and time. Additionally,
+predictions of full wind fields are particularly useful for future power
+planning such as the optimization of electricity power supply systems. In this
+paper, we propose a composite artificial neural network (ANN) model to predict
+the 6-hour and 24-hour ahead average wind speed over a large area (~3.15*106
+km2). The ANN model consists of a convolutional input layer, a Long Short-Term
+Memory (LSTM) hidden layer, and a transposed convolutional layer as the output
+layer. We compare the ANN model with two non-parametric models, a null
+persistence model and a mean value model, and find that the ANN model has
+substantially smaller error than each of these models. Additionally, the ANN
+model also generally performs better than integrated autoregressive moving
+average models, which are trained for optimal performance in specific
+locations.
+"
+25899,Solution of a minimal model for many-body quantum chaos,"  We solve a minimal model for quantum chaos in a spatially extended many-body
+system. It consists of a chain of sites with nearest-neighbour coupling under
+Floquet time evolution. Quantum states at each site span a $q$-dimensional
+Hilbert space and time evolution for a pair of sites is generated by a
+$q^2\times q^2$ random unitary matrix. The Floquet operator is specified by a
+quantum circuit of depth two, in which each site is coupled to its neighbour on
+one side during the first half of the evolution period, and to its neighbour on
+the other side during the second half of the period. We show how dynamical
+behaviour averaged over realisations of the random matrices can be evaluated
+using diagrammatic techniques, and how this approach leads to exact expressions
+in the large-$q$ limit. We give results for the spectral form factor,
+relaxation of local observables, bipartite entanglement growth and operator
+spreading.
+"
+25900,Predicting influenza H3N2 vaccine efficacy from evolution of the dominant epitope,"  We predict vaccine efficacy with a measure of antigenic distance between
+influenza A(H3N2) and candidate vaccine viruses based on amino acid
+substitutions in the dominant epitopes. In 2016-2017, our model predicts 19%
+efficacy compared to 20% observed. This tool assists candidate vaccine
+selection by predicting human protection against circulating strains.
+"
+25901,Guided Signal Reconstruction Theory,"  An axiomatic approach to signal reconstruction is formulated, involving a
+sample consistent set and a guiding set, describing desired reconstructions.
+New frame-less reconstruction methods are proposed, based on a novel concept of
+a reconstruction set, defined as a shortest pathway between the sample
+consistent set and the guiding set. Existence and uniqueness of the
+reconstruction set are investigated in a Hilbert space, where the guiding set
+is a closed subspace and the sample consistent set is a closed plane, formed by
+a sampling subspace. Connections to earlier known consistent, generalized, and
+regularized reconstructions are clarified. New stability and reconstruction
+error bounds are derived, using the largest nontrivial angle between the
+sampling and guiding subspaces. Conjugate gradient iterative reconstruction
+algorithms are proposed and illustrated numerically for image magnification.
+"
+25902,Matrix Completion and Related Problems via Strong Duality,"  This work studies the strong duality of non-convex matrix factorization
+problems: we show that under certain dual conditions, these problems and its
+dual have the same optimum. This has been well understood for convex
+optimization, but little was known for non-convex problems. We propose a novel
+analytical framework and show that under certain dual conditions, the optimal
+solution of the matrix factorization program is the same as its bi-dual and
+thus the global optimality of the non-convex program can be achieved by solving
+its bi-dual which is convex. These dual conditions are satisfied by a wide
+class of matrix factorization problems, although matrix factorization problems
+are hard to solve in full generality. This analytical framework may be of
+independent interest to non-convex optimization more broadly.
+We apply our framework to two prototypical matrix factorization problems:
+matrix completion and robust Principal Component Analysis (PCA). These are
+examples of efficiently recovering a hidden matrix given limited reliable
+observations of it. Our framework shows that exact recoverability and strong
+duality hold with nearly-optimal sample complexity guarantees for matrix
+completion and robust PCA.
+"
+25903,Prediction Rule Reshaping,"  Two methods are proposed for high-dimensional shape-constrained regression
+and classification. These methods reshape pre-trained prediction rules to
+satisfy shape constraints like monotonicity and convexity. The first method can
+be applied to any pre-trained prediction rule, while the second method deals
+specifically with random forests. In both cases, efficient algorithms are
+developed for computing the estimators, and experiments are performed to
+demonstrate their performance on four datasets. We find that reshaping methods
+enforce shape constraints without compromising predictive accuracy.
+"
+25904,Geometrical effects on nonlinear electrodiffusion in cell physiology,"  We report here new electrical laws, derived from nonlinear electro-diffusion
+theory, about the effect of the local geometrical structure, such as curvature,
+on the electrical properties of a cell. We adopt the Poisson-Nernst-Planck
+(PNP) equations for charge concentration and electric potential as a model of
+electro-diffusion. In the case at hand, the entire boundary is impermeable to
+ions and the electric field satisfies the compatibility condition of Poisson's
+equation. We construct an asymptotic approximation for certain singular limits
+to the steady-state solution in a ball with an attached cusp-shaped funnel on
+its surface. As the number of charge increases, they concentrate at the end of
+cusp-shaped funnel. These results can be used in the design of nano-pipettes
+and help to understand the local voltage changes inside dendrites and axons
+with heterogenous local geometry.
+"
+25905,Citizen Astronomy in China: Present and Future,"  Citizen science refers to scientific research conducted or participated by
+non-professional scientists (such as hobbyists or members from the general
+public). Citizen astronomy is a classic example of citizen science. Citizen
+astronomers has benefited from technological advancements in the recent decades
+as they fill the scientific gaps left by professional astronomers, in the areas
+such as time domain observations, visual classification and data mining.
+Chinese citizen astronomers have made a visible contribution in the discoveries
+of new objects; however, comparing to their counterparts in the western world,
+they appear to be less interested in researches that do not involve making new
+discovery, such as visual classification, long-term monitoring of objects, and
+data mining. From a questionnaire survey that aimed to investigate the
+motivation of Chinese citizen astronomers, we find that this population is
+predominantly male ($92\%$) who mostly reside in economically developed
+provinces. A large fraction ($69\%$) of the respondents are students and young
+professionals younger than the age of 25, which differs significantly from the
+occupation and age distribution of typical Chinese Internet users as well as
+the user distribution of large international citizen science projects such as
+the Galaxy Zoo. This suggests that youth generation in China is more willing to
+participate citizen astronomy research than average generation. Additionally,
+we find that interests in astronomy, desire to learn new knowledges, have a fun
+experience and meet new friends in the community are all important driving
+factors for Chinese citizen astronomers to participate research. This also
+differs from their counterparts in western countries. (full abstract in PDF)
+"
+25906,Twisted Reed-Solomon Codes,"  We present a new general construction of MDS codes over a finite field
+$\mathbb{F}_q$. We describe two explicit subclasses which contain new MDS codes
+of length at least $q/2$ for all values of $q \ge 11$. Moreover, we show that
+most of the new codes are not equivalent to a Reed-Solomon code.
+"
+25907,Finite sizes and smooth cutoffs in superconducting circuits,"  We investigate the validity of two common assumptions in the modelling of
+superconducting circuits: first, that the superconducting qubits are pointlike,
+and second, that the UV behaviour of the transmission line is not relevant to
+the qubit dynamics. We show that in the experimentally accessible ultra-strong
+coupling regime and for short (but attainable) times, the use of an inaccurate
+cutoff model (such as sharp, or none at all) could introduce very significant
+inaccuracies in the model's predictions.
+"
+25908,PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space,"  Few prior works study deep learning on point sets. PointNet by Qi et al. is a
+pioneer in this direction. However, by design PointNet does not capture local
+structures induced by the metric space points live in, limiting its ability to
+recognize fine-grained patterns and generalizability to complex scenes. In this
+work, we introduce a hierarchical neural network that applies PointNet
+recursively on a nested partitioning of the input point set. By exploiting
+metric space distances, our network is able to learn local features with
+increasing contextual scales. With further observation that point sets are
+usually sampled with varying densities, which results in greatly decreased
+performance for networks trained on uniform densities, we propose novel set
+learning layers to adaptively combine features from multiple scales.
+Experiments show that our network called PointNet++ is able to learn deep point
+set features efficiently and robustly. In particular, results significantly
+better than state-of-the-art have been obtained on challenging benchmarks of 3D
+point clouds.
+"
+25909,PrettyCat: Adaptive guarantee-controlled software partitioning of security protocols,"  One single error can result in a total compromise of all security in today's
+large, monolithic software. Partitioning of software can help simplify
+code-review and verification, whereas isolated execution of software-components
+limits the impact of incorrect implementations. However, existing application
+partitioning techniques are too expensive, too imprecise, or involve unsafe
+manual steps. An automatic, yet safe, approach to dissect security protocols
+into component-based systems is not available. We present a method and toolset
+to automatically segregate security related software into an indefinite number
+of partitions, based on the security guarantees required by the deployed
+cryptographic building blocks. As partitioning imposes communication overhead,
+we offer a range of sound performance optimizations. Furthermore, by applying
+our approach to the secure messaging protocol OTR, we demonstrate its
+applicability and achieve a significant reduction of the trusted computing
+base. Compared to a monolithic implementation, only 29% of the partitioned
+protocol requires confidentiality guarantees with a process overhead comparable
+to common sandboxing techniques.
+"
+25910,PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications,"  PixelCNNs are a recently proposed class of powerful generative models with
+tractable likelihood. Here we discuss our implementation of PixelCNNs which we
+make available at this https URL. Our implementation
+contains a number of modifications to the original model that both simplify its
+structure and improve its performance. 1) We use a discretized logistic mixture
+likelihood on the pixels, rather than a 256-way softmax, which we find to speed
+up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels,
+simplifying the model structure. 3) We use downsampling to efficiently capture
+structure at multiple resolutions. 4) We introduce additional short-cut
+connections to further speed up optimization. 5) We regularize the model using
+dropout. Finally, we present state-of-the-art log likelihood results on
+CIFAR-10 to demonstrate the usefulness of these modifications.
+"
+25911,New algorithms for the Minimum Coloring Cut Problem,"  The Minimum Coloring Cut Problem is defined as follows: given a connected
+graph G with colored edges, find an edge cut E' of G (a minimal set of edges
+whose removal renders the graph disconnected) such that the number of colors
+used by the edges in E' is minimum. In this work, we present two approaches
+based on Variable Neighborhood Search to solve this problem. Our algorithms are
+able to find all the optimum solutions described in the literature.
+"
+25912,Mining Precision Interfaces From Query Logs,"  Interactive tools make data analysis both more efficient and more accessible
+to a broad population. Simple interfaces such as Google Finance as well as
+complex visual exploration interfaces such as Tableau are effective because
+they are tailored to the desired user tasks. Yet, designing interactive
+interfaces requires technical expertise and domain knowledge. Experts are
+scarce and expensive, and therefore it is currently infeasible to provide
+tailored (or precise) interfaces for every user and every task.
+We envision a data-driven approach to generate tailored interactive
+interfaces. We observe that interactive interfaces are designed to express sets
+of programs; thus, samples of programs-increasingly collected by data
+systems-may help us build interactive interfaces. Based on this idea, Precision
+Interfaces is a language-agnostic system that examines an input query log,
+identifies how the queries structurally change, and generates interactive web
+interfaces to express these changes. The focus of this paper is on applying
+this idea towards logs of structured queries. Our experiments show that
+Precision Interfaces can support multiple query languages (SQL and SPARQL),
+derive Tableau's salient interaction components from OLAP queries, analyze <75k
+queries in <12 minutes, and generate interaction designs that improve upon
+existing interfaces and are comparable to human-crafted interfaces.
+"
+25913,DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess,"  We present an end-to-end learning method for chess, relying on deep neural
+networks. Without any a priori knowledge, in particular without any knowledge
+regarding the rules of chess, a deep neural network is trained using a
+combination of unsupervised pretraining and supervised training. The
+unsupervised training extracts high level features from a given position, and
+the supervised training learns to compare two chess positions and select the
+more favorable one. The training relies entirely on datasets of several million
+chess games, and no further domain specific knowledge is incorporated.
+The experiments show that the resulting neural network (referred to as
+DeepChess) is on a par with state-of-the-art chess playing programs, which have
+been developed through many years of manual feature selection and tuning.
+DeepChess is the first end-to-end machine learning-based method that results in
+a grandmaster-level chess playing performance.
+"
+25914,Diluted banded random matrices: Scaling behavior of eigenfunction and spectral properties,"  We demonstrate that the normalised localization length $\beta$ of the
+eigenfunctions of diluted (sparse) banded random matrices follows the scaling
+law $\beta=x^*/(1+x^*)$. The scaling parameter of the model is defined as
+$x^*\propto(b_{eff}^2/N)^\delta$, where $b_{eff}$ is the average number of
+non-zero elements per matrix row, $N$ is the matrix size, and $\delta\sim 1$.
+Additionally, we show that $x^*$ also scales the spectral properties of the
+model (up to certain sparsity) characterized by the spacing distribution of
+eigenvalues.
+"
+25915,Hydrogenating VO2 with protons in acid solution,"  Hydrogenation is an effective way to tune material property1-5. Traditional
+techniques for doping hydrogen atoms into solid materials are very costly due
+to the need for noble metal catalysis and high-temperature/pressure annealing
+treatment or even high energy proton implantation in vacuum condition5-8. Acid
+solution contains plenty of freely-wandering protons, but it is difficult to
+act as a proton source for doping, since the protons always cause corrosions by
+destroying solid lattices before residing into them. Here we achieve a facile
+way to hydrogenate monoclinic vanadium dioxide (VO2) with protons in acid
+solution by attaching suitable metal to it. Considering the Schottky contact at
+the metal/VO2 interface, electrons flow from metal to VO2 due to workfunction
+difference and simultaneously attract free protons in acid solution to
+penetrate, forming the hydrogens dopants inside VO2 lattice. This metal-acid
+treatment constitutes an electron-proton co-doping strategy, which not only
+protects the VO2 lattice from corrosion, but also causes pronounced
+insulator-to-metal transitions. In addition, the metal-acid induced hydrogen
+doping behavior shows a ripple effect, and it can spread contagiously up to
+wafer-size area (>2 inch) even triggered by a tiny metal particle attachment
+(~1.0mm). This will stimulate a new way of simple and cost-effective atomic
+doping technique for some other oxide materials.
+"
+25916,The effects of the small-scale behaviour of dark matter power spectrum on CMB spectral distortion,"  After numerous astronomical and experimental searches, the precise particle
+nature of dark matter is still unknown. The standard Weakly Interacting Massive
+Particle(WIMP) dark matter, despite successfully explaining the large-scale
+features of the universe, has long-standing small-scale issues. The spectral
+distortion in the Cosmic Microwave Background(CMB) caused by Silk damping in
+the pre-recombination era allows one to access information on a range of small
+scales $0.3 \, {\rm Mpc} < k < 10^4 \, \rm Mpc^{-1}$, whose dynamics can be
+precisely described using linear theory. In this paper, we investigate the
+possibility of using the Silk damping induced CMB spectral distortion as a
+probe of the small-scale power. We consider four suggested alternative dark
+matter candidates---Warm Dark Matter (WDM), Late Forming Dark Matter, Ultra
+Light Axion dark matter and Charged Decaying Dark Matter; the matter power in
+all these models deviate significantly from the $\Lambda$CDM model at small
+scales. We compute the spectral distortion of CMB for these alternative models
+and compare our results with the $\Lambda$CDM model. We show that the main
+impact of alternative models is to alter the sub-horizon evolution of the
+Newtonian potential which affects the late-time behaviour of spectral
+distortion of CMB. The $y$-parameter diminishes by a few percent as compared to
+the $\Lambda$CDM model for a range of parameters of these models: LFDM for
+formation redshift $z_f = 10^5$ (7\%); WDM for mass $m_{\rm wdm} = 1 \, \rm
+keV$ (2\%); CHDM for decay redshift $z_{\rm decay} = 10^5$ (5\%); ULA for mass
+$m_a = 10^{-24} \, \rm eV$ (3\%). We also briefly discuss the detectability of
+this deviation in light of the upcoming CMB experiment PIXIE, which might have
+the sensitivity to detect this signal from the pre-recombination phase.
+"
+25917,Preliminary testing derivatives of a linear unified estimator in the logistic regression model,"  Recently, the well known Liu estimator (Liu, 1993) is attracted researcher's
+attention in regression parameter estimation for an ill conditioned linear
+model. It is also argued that imposing sub-space hypothesis restriction on
+parameters improves estimation by shrinking toward non-sample information.
+Chang (2015) proposed the almost unbiased Liu estimator (AULE) in the binary
+logistic regression. In this article, some improved unbiased Liu type
+estimators, namely, restricted AULE, preliminary test AULE, Stein-type
+shrinkage AULE and its positive part for estimating the regression parameters
+in the binary logistic regression model are proposed based on the work Chang
+(2015). The performances of the newly defined estimators are analysed through
+some numerical results. A real data example is also provided to support the
+findings.
+"
+25918,Construction of a Japanese Word Similarity Dataset,"  An evaluation of distributed word representation is generally conducted using
+a word similarity task and/or a word analogy task. There are many datasets
+readily available for these tasks in English. However, evaluating distributed
+representation in languages that do not have such resources (e.g., Japanese) is
+difficult. Therefore, as a first step toward evaluating distributed
+representations in Japanese, we constructed a Japanese word similarity dataset.
+To the best of our knowledge, our dataset is the first resource that can be
+used to evaluate distributed representations in Japanese. Moreover, our dataset
+contains various parts of speech and includes rare words in addition to common
+words.
+"
+25919,A method and tool for combining differential or inclusive measurements obtained with simultaneously constrained uncertainties,"  A method is discussed that allows combining sets of differential or inclusive
+measurements. It is assumed that at least one measurement was obtained with
+simultaneously fitting a set of nuisance parameters, representing sources of
+systematic uncertainties. As a result of beneficial constraints from the data
+all such fitted parameters are correlated among each other. The best approach
+for a combination of these measurements would be the maximisation of a combined
+likelihood, for which the full fit model of each measurement and the original
+data are required. However, only in rare cases this information is publicly
+available. In absence of this information most commonly used combination
+methods are not able to account for these correlations between uncertainties,
+which can lead to severe biases as shown in this article. The method discussed
+here provides a solution for this problem. It relies on the public result and
+its covariance or Hessian, only, and is validated against the
+combined-likelihood approach. A dedicated software package implementing this
+method is also presented. It provides a text-based user interface alongside a
+C++ interface. The latter also interfaces to ROOT classes for simple
+combination of binned measurements such as differential cross sections.
+"
+25920,Cohomology jump loci of quasi-compact Kähler manifolds,"  We give two applications of the exponential Ax-Lindemann Theorem to local
+systems. One application is to show that for a connected topological space, the
+existence of a finite model of the real homotopy type implies linearity of the
+cohomology jump loci around the trivial local system. Another application is
+the linearity of the cohomology jump loci of rank one local systems on
+quasi-compact Kähler manifolds.
+"
+25921,A Survey on Data Processing Methods and Cloud Computation,"  As new technologies move to the fore, our understanding of the world may seem
+to have shrunk in comparison, for despite new developments in research, much of
+it is reduced or rather, abstracted for marketability. Thus, the purpose of
+this work is to provide a brief study of current techniques used in data
+analysis that will yield some clarity and encourage new research. Several
+applications of which are discussed in relation to the high data volume fields
+e.g. health care and economics. Particular attention will be given to recent
+developments in health care.
+"
+25922,eQTL Mapping via Effective SNP Ranking and Screening,"  Genome-wide eQTL mapping explores the relationship between gene expression
+values and DNA variants to understand genetic causes of human disease. Due to
+the large number of genes and DNA variants that need to be assessed
+simultaneously, current methods for eQTL mapping often suffer from low
+detection power, especially for identifying trans-eQTLs. In this paper, we
+propose a new method that utilizes advanced techniques in large-scale signal
+detection to pursue the structure of eQTL data and improve the power for eQTL
+mapping. The new method greatly reduces the burden of joint modeling by
+developing a new ranking and screening strategy based on the higher criticism
+statistic. Numerical results in simulation studies demonstrate the superior
+performance of our method in detecting true eQTLs with reduced computational
+expense. The proposed method is also evaluated in HapMap eQTL data analysis and
+the results are compared to a database of known eQTLs.
+"
+25923,"Integrable systems in 4D associated with sixfolds in Gr(4,6)","  Let $Gr(d,n)$ be the Grassmannian of $d$-dimensional linear subspaces of an
+$n$-dimensional vector space $V$. A submanifold $X\subset Gr(d, n)$ gives rise
+to a differential system $\Sigma(X)$ that governs $d$-dimensional submanifolds
+of $V$ whose Gaussian image is contained in $X$. We investigate a special case
+of this construction where $X$ is a sixfold in $Gr(4, 6)$. The corresponding
+system $\Sigma(X)$ reduces to a pair of first-order PDEs for 2 functions of 4
+independent variables. Equations of this type arise in self-dual Ricci-flat
+geometry. Our main result is a complete description of integrable systems
+$\Sigma(X)$. These naturally fall into two subclasses.
+(1) Systems of Monge-Ampère type. The corresponding sixfolds $X$ are
+codimension 2 linear sections of the Plücker embedding
+$Gr(4,6)\subset\mathbb{P}^{14}$.
+(2) General linearly degenerate systems. The corresponding sixfolds $X$ are
+the images of quadratic maps $\mathbb{P}^6- Gr(4, 6)$ given by a version of the
+classical construction of Chasles.
+We prove that integrability is equivalent to the requirement that the
+characteristic variety of system $\Sigma(X)$ gives rise to a conformal
+structure which is self-dual on every solution. In fact, all solutions carry
+hyper-Hermitian geometry.
+"
+25924,Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation,"  In this paper, we study the trade-offs of different inference approaches for
+Bayesian matrix factorisation methods, which are commonly used for predicting
+missing values, and for finding patterns in the data. In particular, we
+consider Bayesian nonnegative variants of matrix factorisation and
+tri-factorisation, and compare non-probabilistic inference, Gibbs sampling,
+variational Bayesian inference, and a maximum-a-posteriori approach. The
+variational approach is new for the Bayesian nonnegative models. We compare
+their convergence, and robustness to noise and sparsity of the data, on both
+synthetic and real-world datasets. Furthermore, we extend the models with the
+Bayesian automatic relevance determination prior, allowing the models to
+perform automatic model selection, and demonstrate its efficiency.
+"
+25925,Local Cauchy theory for the nonlinear Schrödinger equation in spaces of infinite mass,"  We consider the Cauchy problem for the nonlinear Schrödinger equation on
+$\mathbb{R}^d$, where the initial data is in $\dot{H}^1(\mathbb{R}^d)\cap
+L^p(\mathbb{R}^d)$. We prove local well-posedness for large ranges of $p$ and
+discuss some global well-posedness results.
+"
+25926,On symplectic stabilisations and mapping classes,"  We are interested in comparing properties of symplectic mapping class groups
+of symplectic manifolds of dimension four or higher with properties of
+classical mapping class groups of surfaces. For $n \geq 2$, consider a
+configuration of Lagrangian $S^n$s in a Weinstein domain $M^{2n}$. If it is
+analogous, in some sense that we make precise, to a configuration of exact
+Lagrangian $S^1$s on a surface $\Sigma$, we show that any relation between Dehn
+twists in the $S^n$s must also hold between the $S^1$s. Such analogous pairs of
+configurations include plumbings of $T^\ast S^1$s and $T^\ast S^n$s with the
+same plumbing graph, and vanishing cycles for a two-variable singularity and
+for its stabilisation. We give a number of corollaries for subgroups of
+symplectic mapping class groups.
+"
+25927,Crystal Symmetry Lowering in Chiral Multiferroic Ba$_3$TaFe$_3$Si$_2$O$_{14}$ observed by X-Ray Magnetic Scattering,"  Chiral multiferroic langasites have attracted attention due to their
+doubly-chiral magnetic ground state within an enantiomorphic crystal. We report
+on a detailed resonant soft X-ray diffraction study of the multiferroic
+Ba$_3$TaFe$_3$Si$_2$O$_{14}$ at the Fe $L_{2,3}$ and oxygen $K$ edges. Below
+$T_N$ ($\approx27K$) we observe the satellite reflections $(0,0,\tau)$,
+$(0,0,2\tau)$, $(0,0,3\tau)$ and $(0,0,1-3\tau)$ where $\tau \approx 0.140 \pm
+0.001$. The dependence of the scattering intensity on X-ray polarization and
+azimuthal angle indicate that the odd harmonics are dominated by the
+out-of-plane ($\mathbf{\hat{c}}$-axis) magnetic dipole while the $(0,0,2\tau)$
+originates from the electron density distortions accompanying magnetic order.
+We observe dissimilar energy dependences of the diffraction intensity of the
+purely magnetic odd-harmonic satellites at the Fe $L_3$ edge. Utilizing
+first-principles calculations, we show that this is a consequence of the loss
+of threefold crystal symmetry in the multiferroic phase.
+"
+25928,360-degree videos: a new visualization technique for astrophysical simulations,"  360-degree videos are a new type of movie that renders over all 4$\pi$
+steradian. Video sharing sites such as YouTube now allow this unique content to
+be shared via virtual reality (VR) goggles, hand-held smartphones/tablets, and
+computers. Creating 360$^\circ$ videos from astrophysical simulations is not
+only a new way to view these simulations as you are immersed in them, but is
+also a way to create engaging content for outreach to the public. We present
+what we believe is the first 360$^\circ$ video of an astrophysical simulation:
+a hydrodynamics calculation of the central parsec of the Galactic centre. We
+also describe how to create such movies, and briefly comment on what new
+science can be extracted from astrophysical simulations using 360$^\circ$
+videos.
+"
+25929,SCAN: Learning Hierarchical Compositional Visual Concepts,"  The seemingly infinite diversity of the natural world arises from a
+relatively small set of coherent rules, such as the laws of physics or
+chemistry. We conjecture that these rules give rise to regularities that can be
+discovered through primarily unsupervised experiences and represented as
+abstract concepts. If such representations are compositional and hierarchical,
+they can be recombined into an exponentially large set of new concepts. This
+paper describes SCAN (Symbol-Concept Association Network), a new framework for
+learning such abstractions in the visual domain. SCAN learns concepts through
+fast symbol association, grounding them in disentangled visual primitives that
+are discovered in an unsupervised manner. Unlike state of the art multimodal
+generative model baselines, our approach requires very few pairings between
+symbols and images and makes no assumptions about the form of symbol
+representations. Once trained, SCAN is capable of multimodal bi-directional
+inference, generating a diverse set of image samples from symbolic descriptions
+and vice versa. It also allows for traversal and manipulation of the implicit
+hierarchy of visual concepts through symbolic instructions and learnt logical
+recombination operations. Such manipulations enable SCAN to break away from its
+training data distribution and imagine novel visual concepts through
+symbolically instructed recombination of previously learnt concepts.
+"
+25930,The Impact of Road Configuration on V2V-based Cooperative Localization,"  Cooperative localization with map matching has been shown to reduce Global
+Navigation Satellite System (GNSS) localization error from several meters to
+sub-meter level by fusing the GNSS measurements of four vehicles in our
+previous work. While further error reduction is expected to be achievable by
+increasing the number of vehicles, the quantitative relationship between the
+estimation error and the number of connected vehicles has neither been
+systematically investigated nor analytically proved. In this work, a
+theoretical study is presented that analytically proves the correlation between
+the localization error and the number of connected vehicles in two cases of
+practical interest. More specifically, it is shown that, under the assumption
+of small non-common error, the expected square error of the GNSS common error
+correction is inversely proportional to the number of vehicles, if the road
+directions obey a uniform distribution, or inversely proportional to logarithm
+of the number of vehicles, if the road directions obey a Bernoulli
+distribution. Numerical simulations are conducted to justify these analytic
+results. Moreover, the simulation results show that the aforementioned error
+decrement rates hold even when the assumption of small non-common error is
+violated.
+"
+25931,Safe and Nested Subgame Solving for Imperfect-Information Games,"  In imperfect-information games, the optimal strategy in a subgame may depend
+on the strategy in other, unreached subgames. Thus a subgame cannot be solved
+in isolation and must instead consider the strategy for the entire game as a
+whole, unlike perfect-information games. Nevertheless, it is possible to first
+approximate a solution for the whole game and then improve it by solving
+individual subgames. This is referred to as subgame solving. We introduce
+subgame-solving techniques that outperform prior methods both in theory and
+practice. We also show how to adapt them, and past subgame-solving techniques,
+to respond to opponent actions that are outside the original action
+abstraction; this significantly outperforms the prior state-of-the-art
+approach, action translation. Finally, we show that subgame solving can be
+repeated as the game progresses down the game tree, leading to far lower
+exploitability. These techniques were a key component of Libratus, the first AI
+to defeat top humans in heads-up no-limit Texas hold'em poker.
+"
+25932,Efficient construction of threshold networks of stock markets,"  Although the threshold network is one of the most used tools to characterize
+the underlying structure of a stock market, the identification of the optimal
+threshold to construct a reliable stock network remains challenging. In this
+paper, the concept of dynamic consistence between the threshold network and the
+stock market is proposed. The optimal threshold is estimated by maximizing the
+consistence function. The application of this procedure to stocks belonging to
+Standard \& Pool's 500 Index from January 2006 to December 2011 yields the
+threshold value 0.28. In analyzing topological characteristics of the generated
+network, three globally financial crises can be distinguished well from the
+evolutionary perspective.
+"
+25933,Trace-free characters and abelian knot contact homology I,"  In this paper, we investigate abelian knot contact homology by using the
+trace-free slice of a knot, which is the cross-section of the character variety
+of a knot cut by the hyperplanes defined by meridionally trace-free (traceless)
+conditions. We first give a set of equations whose common solutions coincide
+with the trace-free slice. This gives us a strong tool to describe exactly the
+relationship between the trace-free slice, degree 0 abelian knot contact
+homology and the character variety of the 2-fold branched cover of the 3-sphere
+branched along the knot. Using this relationship, we introduce a notion called
+ghost characters of a knot to check the followings: (1) when the conjecture
+given by L. Ng concerned with the above relationship does not hold, (2) when
+the map from the trace-free slice to the character variety of the 2-fold
+branched cover, constructed by Y. Yamaguchi and the author, is not surjective.
+Note that in the recent paper of Christopher R. Cornwell, he has translated the
+results on the above relationship into the language of ""reflective
+augmentation"".
+"
+25934,Huddle test measurement of a near Johnson noise limited geophone,"  In this paper the sensor noise of two geophone configurations (L-22D and L-4C
+geophones from Sercel with custom built amplifiers) was measured by performing
+two huddle tests. It is shown that the accuracy of the results can be
+significantly improved by performing the huddle test in a seismically quiet
+environment and by using a large number of reference sensors to remove the
+seismic foreground signal from the data. Using these two techniques, the
+measured sensor noise of the two geophone configurations matched calculated
+predictions remarkably well in the bandwidth of interest (0.01 Hz to 100 Hz).
+Low noise operational amplifiers OPA188 were utilized to amplify the L-4C
+geophone to give a sensor that was characterized to be near Johnson noise
+limited in the bandwidth of interest with a noise value of $10^{-11}
+\text{m}/\sqrt{\text{Hz}}$ at 1 Hz.
+"
+25935,Charge and energy fractionalization mechanism in one-dimensional channels,"  We study the problem of injecting single electrons into interacting
+one-dimensional quantum systems, a fundamental building block for electron
+quantum optics. It is well known that such injection leads to charge and energy
+fractionalization. We elucidate this concept by calculating the nonequilibrium
+electron distribution function in the momentum and energy domains after the
+injection of an energy-resolved electron. Our results shed light on how
+fractionalization occurs via the creation of particle-hole pairs by the
+injected electron. In particular, we focus on systems with a pair of
+counterpropagating channels, and we fully analyze the properties of each chiral
+fractional excitation which is created by the injection. We suggest possible
+routes to access their energy and momentum distribution functions in
+topological quantum Hall or quantum spin-Hall edge states.
+"
+25936,Mapping substructure in the HST Frontier Fields cluster lenses and in cosmological simulations,"  We map the lensing-inferred substructure in the first three clusters observed
+by the Hubble Space Telescope Frontier Fields Initiative (HSTFF): Abell 2744 (z
+= 0.308), MACSJ0416, (z = 0.396) and MACSJ1149 (z = 0.543). Statistically
+resolving dark-matter subhaloes down to ~10^{9.5} solar masses, we compare the
+derived subhalo mass functions (SHMFs) to theoretical predictions from
+analytical models and with numerical simulations in a Lambda Cold Dark Matter
+(LCDM) cosmology. Mimicking our observational cluster member selection criteria
+in the HSTFF, we report excellent agreement in both amplitude and shape of the
+SHMF over four decades in subhalo mass (10^{9-13} solar masses). Projection
+effects do not appear to introduce significant errors in the determination of
+SHMFs from simulations. We do not find evidence for a substructure crisis,
+analogous to the missing satellite problem in the Local Group, on cluster
+scales, but rather excellent agreement of the count-matched HSTFF SHMF down to
+M_{sub halo}/M_{halo} ~ 10^{-5}. However, we do find discrepancies in the
+radial distribution of sub haloes inferred from HSTFF cluster lenses compared
+to determinations from simulated clusters. This suggests that although the
+selected simulated clusters match the HSTFF sample in mass, they do not
+adequately capture the dynamical properties and complex merging morphologies of
+these observed cluster lenses. Therefore, HSTFF clusters are likely observed in
+a transient evolutionary stage that is presently insufficiently sampled in
+cosmological simulations. The abundance and mass function of dark matter
+substructure in cluster lenses continues to offer an important test of the LCDM
+paradigm, and at present we find no tension between model predictions and
+observations.
+"
+25937,Quantitative unique continuation for the heat equation with Coulomb potentials,"  In this paper, we establish a Hölder-type quantitative estimate of unique
+continuation for solutions to the heat equation with Coulomb potentials in
+either a bounded convex domain or a $C^2$-smooth bounded domain. The approach
+is based on the frequency function method, as well as some parabolic-type Hardy
+inequalities.
+"
+25938,A Polynomial Method Approach to Zero-Sum Subsets in $\mathbb{F}_{p}^{2}$,"  In this paper we prove that every subset of $\mathbb{F}_p^2$ meeting all
+$p+1$ lines passing through the origin has a zero-sum subset. This is motivated
+by a result of Gao, Ruzsa and Thangadurai which states that
+$OL(\mathbb{F}_{p}^{2})=p+OL(\mathbb{F}_{p})-1$, for sufficiently large primes
+$p$. Here $OL(G)$ denotes the so-called Olson constant of the additive group
+$G$ and represents the smallest integer such that no subset of cardinality
+$OL(G)$ is zero-sum-free. Our proof is in the spirit of the Combinatorial
+Nullstellensatz.
+"
+25939,Reachability Analysis of Large Linear Systems with Uncertain Inputs in the Krylov Subspace,"  One often wishes for the ability to formally analyze large-scale
+systems---typically, however, one can either formally analyze a rather small
+system or informally analyze a large-scale system. This work tries to further
+close this performance gap for reachability analysis of linear systems.
+Reachability analysis can capture the whole set of possible solutions of a
+dynamic system and is thus used to prove that unsafe states are never reached;
+this requires full consideration of arbitrarily varying uncertain inputs, since
+sensor noise or disturbances usually do not follow any patterns. We use Krylov
+methods in this work to compute reachable sets for large-scale linear systems.
+While Krylov methods have been used before in reachability analysis, we
+overcome the previous limitation that inputs must be (piecewise) constant. As a
+result, we can compute reachable sets of systems with several thousand state
+variables for bounded, but arbitrarily varying inputs, as demonstrated using a
+bridge model subject to disturbances.
+"
+25940,Functional renormalization group approach to SU(N) Heisenberg models: Momentum-space RG for the large-N limit,"  In frustrated magnetism, making a stringent connection between microscopic
+spin models and macroscopic properties of spin liquids remains an important
+challenge. A recent step towards this goal has been the development of the
+pseudofermion functional renormalization group approach (pf-FRG) which,
+building on a fermionic parton construction, enables the numerical detection of
+the onset of spin liquid states as temperature is lowered. In this work,
+focusing on the SU(N) Heisenberg model at large N, we extend this approach in a
+way that allows us to directly enter the low-temperature spin liquid phase, and
+to probe its character. Our approach proceeds in momentum space, making it
+possible to keep the truncation minimalistic, while also avoiding the bias
+introduced by an explicit decoupling of the fermionic parton interactions into
+a given channel. We benchmark our findings against exact mean-field results in
+the large-N limit, and show that even without prior knowledge the pf-FRG
+approach identifies the correct mean-field decoupling channel. On a technical
+level, we introduce an alternative finite temperature regularization scheme
+that is necessitated to access the spin liquid ordered phase. In a companion
+paper arXiv:1711.02182 we present a different set of modifications of the
+pf-FRG scheme that allow us to study SU(N) Heisenberg models (using a
+real-space RG approach) for arbitrary values of N, albeit only up to the phase
+transition towards spin liquid physics.
+"
+25941,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,"  Despite the remarkable progress in face recognition related technologies,
+reliably recognizing faces across ages still remains a big challenge. The
+appearance of a human face changes substantially over time, resulting in
+significant intra-class variations. As opposed to current techniques for
+age-invariant face recognition, which either directly extract age-invariant
+features for recognition, or first synthesize a face that matches target age
+before feature extraction, we argue that it is more desirable to perform both
+tasks jointly so that they can leverage each other. To this end, we propose a
+deep Age-Invariant Model (AIM) for face recognition in the wild with three
+distinct novelties. First, AIM presents a novel unified deep architecture
+jointly performing cross-age face synthesis and recognition in a mutual
+boosting way. Second, AIM achieves continuous face rejuvenation/aging with
+remarkable photorealistic and identity-preserving properties, avoiding the
+requirement of paired data and the true age of testing samples. Third, we
+develop effective and novel training strategies for end-to-end learning the
+whole deep architecture, which generates powerful age-invariant face
+representations explicitly disentangled from the age variation. Moreover, we
+propose a new large-scale Cross-Age Face Recognition (CAFR) benchmark dataset
+to facilitate existing efforts and push the frontiers of age-invariant face
+recognition research. Extensive experiments on both our CAFR and several other
+cross-age datasets (MORPH, CACD and FG-NET) demonstrate the superiority of the
+proposed AIM model over the state-of-the-arts. Benchmarking our model on one of
+the most popular unconstrained face recognition datasets IJB-C additionally
+verifies the promising generalizability of AIM in recognizing faces in the
+wild.
+"
+25942,Learnable Explicit Density for Continuous Latent Space and Variational Inference,"  In this paper, we study two aspects of the variational autoencoder (VAE): the
+prior distribution over the latent variables and its corresponding posterior.
+First, we decompose the learning of VAEs into layerwise density estimation, and
+argue that having a flexible prior is beneficial to both sample generation and
+inference. Second, we analyze the family of inverse autoregressive flows
+(inverse AF) and show that with further improvement, inverse AF could be used
+as universal approximation to any complicated posterior. Our analysis results
+in a unified approach to parameterizing a VAE, without the need to restrict
+ourselves to use factorial Gaussians in the latent real space.
+"
+25943,"Nonunitary representations of the groups of $U(p,q)$-currents for $q\geq p>1$","  The purpose of this paper is to give a construction of representations of the
+group of currents for semisimple groups of rank greater than one. Such groups
+have no unitary representations in the Fock space, since the semisimple groups
+of this form have no nontrivial cohomology in faithful irreducible
+representations. Thus we first construct cohomology of the semisimple groups in
+nonunitary representations. The principal method is to reduce all constructions
+to Iwasawa subgroups (solvable subgroups of the semisimple groups), with
+subsequent extension to the original group. The resulting representation is
+realized in the so-called quasi-Poisson Hilbert space associated with natural
+measures on infinite-dimensional spaces.
+Key words: Iwasawa subgroup, cohomology, group of currents, nonunitary
+representations.
+"
+25944,Cost-sensitive detection with variational autoencoders for environmental acoustic sensing,"  Environmental acoustic sensing involves the retrieval and processing of audio
+signals to better understand our surroundings. While large-scale acoustic data
+make manual analysis infeasible, they provide a suitable playground for machine
+learning approaches. Most existing machine learning techniques developed for
+environmental acoustic sensing do not provide flexible control of the trade-off
+between the false positive rate and the false negative rate. This paper
+presents a cost-sensitive classification paradigm, in which the
+hyper-parameters of classifiers and the structure of variational autoencoders
+are selected in a principled Neyman-Pearson framework. We examine the
+performance of the proposed approach using a dataset from the HumBug project
+which aims to detect the presence of mosquitoes using sound collected by simple
+embedded devices.
+"
+25945,A homopolar disc dynamo experiment with liquid metal contacts,"  We present experimental results of a homopolar disc dynamo constructed at
+CICATA-Querétaro in Mexico. The device consists of a flat, multi-arm spiral
+coil which is placed above a fast-spinning metal disc and connected to the
+latter by sliding liquid-metal electrical contacts. Theoretically,
+self-excitation of the magnetic field is expected at the critical magnetic
+Reynolds number Rm~45, which corresponds to a critical rotation rate of about
+10 Hz. We measured the magnetic field above the disc and the voltage drop on
+the coil for the rotation rate up to 14 Hz, at which the liquid metal started
+to leak from the outer sliding contact. Instead of the steady magnetic field
+predicted by the theory we detected a strongly fluctuating magnetic field with
+a strength comparable to that of Earth's magnetic field which was accompanied
+by similar voltage fluctuations in the coil. These fluctuations seem to be
+caused by the intermittent electrical contact through the liquid metal. The
+experimental results suggest that the dynamo with the actual electrical
+resistance of liquid metal contacts could be excited at the rotation rate of
+around 21 Hz provided that the leakage of liquid metal is prevented.
+"
+25946,Optical Flow augmented Semantic Segmentation networks for Automated Driving,"  Motion is a dominant cue in automated driving systems. Optical flow is
+typically computed to detect moving objects and to estimate depth using
+triangulation. In this paper, our motivation is to leverage the existing dense
+optical flow to improve the performance of semantic segmentation. To provide a
+systematic study, we construct four different architectures which use RGB only,
+flow only, RGBF concatenated and two-stream RGB + flow. We evaluate these
+networks on two automotive datasets namely Virtual KITTI and Cityscapes using
+the state-of-the-art flow estimator FlowNet v2. We also make use of the ground
+truth optical flow in Virtual KITTI to serve as an ideal estimator and a
+standard Farneback optical flow algorithm to study the effect of noise. Using
+the flow ground truth in Virtual KITTI, two-stream architecture achieves the
+best results with an improvement of 4% IoU. As expected, there is a large
+improvement for moving objects like trucks, vans and cars with 38%, 28% and 6%
+increase in IoU. FlowNet produces an improvement of 2.4% in average IoU with
+larger improvement in the moving objects corresponding to 26%, 11% and 5% in
+trucks, vans and cars. In Cityscapes, flow augmentation provided an improvement
+for moving objects like motorcycle and train with an increase of 17% and 7% in
+IoU.
+"
+25947,A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities,"  The hidden metric space behind complex network topologies is a fervid topic
+in current network science and the hyperbolic space is one of the most studied,
+because it seems associated to the structural organization of many real complex
+systems. The Popularity-Similarity-Optimization (PSO) model simulates how
+random geometric graphs grow in the hyperbolic space, reproducing strong
+clustering and scale-free degree distribution, however it misses to reproduce
+an important feature of real complex networks, which is the community
+organization. The Geometrical-Preferential-Attachment (GPA) model was recently
+developed to confer to the PSO also a community structure, which is obtained by
+forcing different angular regions of the hyperbolic disk to have variable level
+of attractiveness. However, the number and size of the communities cannot be
+explicitly controlled in the GPA, which is a clear limitation for real
+applications. Here, we introduce the nonuniform PSO (nPSO) model that,
+differently from GPA, forces heterogeneous angular node attractiveness by
+sampling the angular coordinates from a tailored nonuniform probability
+distribution, for instance a mixture of Gaussians. The nPSO differs from GPA in
+other three aspects: it allows to explicitly fix the number and size of
+communities; it allows to tune their mixing property through the network
+temperature; it is efficient to generate networks with high clustering. After
+several tests we propose the nPSO as a valid and efficient model to generate
+networks with communities in the hyperbolic space, which can be adopted as a
+realistic benchmark for different tasks such as community detection and link
+prediction.
+"
+25948,Line and surface defects in Reshetikhin-Turaev TQFT,"  A modular tensor category $\mathcal{C}$ gives rise to a Reshetikhin-Turaev
+type topological quantum field theory which is defined on 3-dimensional
+bordisms with embedded $\mathcal{C}$-coloured ribbon graphs. We extend this
+construction to include bordisms with surface defects which in turn can meet
+along line defects. The surface defects are labelled by $\Delta$-separable
+symmetric Frobenius algebras and the line defects by ""multi-modules"" which are
+equivariant with respect to a cyclic group action. Our invariant cannot
+distinguish non-isotopic embeddings of 2-spheres, but we give an example where
+it distinguishes non-isotopic embeddings of 2-tori.
+"
+25949,BreathRNNet: Breathing Based Authentication on Resource-Constrained IoT Devices using RNNs,"  Recurrent neural networks (RNNs) have shown promising results in audio and
+speech processing applications due to their strong capabilities in modelling
+sequential data. In many applications, RNNs tend to outperform conventional
+models based on GMM/UBMs and i-vectors. Increasing popularity of IoT devices
+makes a strong case for implementing RNN based inferences for applications such
+as acoustics based authentication, voice commands, and edge analytics for smart
+homes. Nonetheless, the feasibility and performance of RNN based inferences on
+resources-constrained IoT devices remain largely unexplored. In this paper, we
+investigate the feasibility of using RNNs for an end-to-end authentication
+system based on breathing acoustics. We evaluate the performance of RNN models
+on three types of devices; smartphone, smartwatch, and Raspberry Pi and show
+that unlike CNN models, RNN models can be easily ported onto
+resource-constrained devices without a significant loss in accuracy.
+"
+25950,PruneTrain: Gradual Structured Pruning from Scratch for Faster Neural Network Training,"  Model pruning is a popular mechanism to make a network more efficient for
+inference. In this paper, we explore the use of pruning to also make the
+training of such neural networks more efficient. Unlike all prior model pruning
+methods that sparsify a pre-trained model and then prune it, we train the
+network from scratch, while gradually and structurally pruning parameters
+during the training. We build on our key observations: 1) once parameters are
+sparsified via regularization, they rarely re-appear in later steps, and 2)
+setting the appropriate regularization penalty at the beginning of training
+effectively converges the loss. We train ResNet and VGG networks on CIFAR10/100
+and ImageNet datasets from scratch, and achieve 30-50% improvement in training
+FLOPs and 20-30% improvement in measured training time on modern GPUs.
+"
+25951,HADES RV Programme with HARPS-N at TNG: V. A super-Earth on the inner edge of the habitable zone of the nearby M-dwarf GJ 625,"  We report the discovery of a super-Earth orbiting at the inner edge of the
+habitable zone of the star GJ 625 based on the analysis of the radial-velocity
+(RV) time series from the HARPS-N spectrograph, consisting in 151 HARPS-N
+measurements taken over 3.5 yr. GJ 625 b is a planet with a minimum mass M sin
+$i$ of 2.82 $\pm$ 0.51 M$_{\oplus}$ with an orbital period of 14.628 $\pm$
+0.013 days at a distance of 0.078 AU of its parent star. The host star is the
+quiet M2 V star GJ 625, located at 6.5 pc from the Sun. We find the presence of
+a second radial velocity signal in the range 74-85 days that we relate to
+stellar rotation after analysing the time series of Ca II H\&K and H${\alpha}$
+spectroscopic indicators, the variations of the FWHM of the CCF and and the
+APT2 photometric light curves. We find no evidence linking the short period
+radial velocity signal to any activity proxy.
+"
+25952,An Evaluation of Bayesian Methods for Bathymetry-based Localization of Autonomous Underwater Robots,"  This paper presents novel probabilistic algorithms for localization of
+autonomous underwater vehicles (AUVs) using bathymetry data. The algorithms,
+based on the principles of the Bayes filter, work by fusing bathymetry
+information with depth and altitude data from an AUV. Four different Bayes
+filter-based algorithms are used to design the localization algorithms: the
+Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter
+(PF), and Marginalized Particle Filter (MPF). The goal is to have these four
+filters available for localization under different bathymetric conditions and
+available computational resources. The localization algorithms overcome unique
+challenges of the underwater domain, such as visual distortion and RF signal
+attenuation, which make landmark-based localization infeasible. We evaluate the
+accuracy and computational cost of the algorithms in a range of simulations
+that use real-world bathymetry data under different operating conditions.
+"
+25953,Learning Representations from Road Network for End-to-End Urban Growth Simulation,"  From our experiences in the past, we have seen that the growth of cities is
+very much dependent on the transportation networks. In mega cities,
+transportation networks determine to a significant extent as to where the
+people will move and houses will be built. Hence, transportation network data
+is crucial to an urban growth prediction system. Existing works have used
+manually derived distance based features based on the road networks to build
+models on urban growth. But due to the non-generic and laborious nature of the
+manual feature engineering process, we can shift to End-to-End systems which do
+not rely on manual feature engineering. In this paper, we propose a method to
+integrate road network data to an existing Rule based End-to-End framework
+without manual feature engineering. Our method employs recurrent neural
+networks to represent road networks in a structured way such that it can be
+plugged into the previously proposed End-to-End framework. The proposed
+approach enhances the performance in terms of Figure of Merit, Producer's
+accuracy, User's accuracy and Overall accuracy of the existing Rule based
+End-to-End framework.
+"
+25954,Markerless visual servoing on unknown objects for humanoid robot platforms,"  To precisely reach for an object with a humanoid robot, it is of central
+importance to have good knowledge of both end-effector, object pose and shape.
+In this work we propose a framework for markerless visual servoing on unknown
+objects, which is divided in four main parts: I) a least-squares minimization
+problem is formulated to find the volume of the object graspable by the robot's
+hand using its stereo vision; II) a recursive Bayesian filtering technique,
+based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose
+(position and orientation) of the robot's end-effector without the use of
+markers; III) a nonlinear constrained optimization problem is formulated to
+compute the desired graspable pose about the object; IV) an image-based visual
+servo control commands the robot's end-effector toward the desired pose. We
+demonstrate effectiveness and robustness of our approach with extensive
+experiments on the iCub humanoid robot platform, achieving real-time
+computation, smooth trajectories and sub-pixel precisions.
+"
+25955,Learning to Learn from Weak Supervision by Full Supervision,"  In this paper, we propose a method for training neural networks when we have
+a large set of data with weak labels and a small amount of data with true
+labels. In our proposed model, we train two neural networks: a target network,
+the learner and a confidence network, the meta-learner. The target network is
+optimized to perform a given task and is trained using a large set of unlabeled
+data that are weakly annotated. We propose to control the magnitude of the
+gradient updates to the target network using the scores provided by the second
+confidence network, which is trained on a small amount of supervised data. Thus
+we avoid that the weight updates computed from noisy labels harm the quality of
+the target network model.
+"
+25956,"Probing the Broad Line Region and the Accretion Disk in the Lensed Quasars HE0435-1223, WFI2033-4723, and HE2149-2745 using Gravitational Microlensing","  We use single-epoch spectroscopy of three gravitationally lensed quasars,
+HE0435-1223, WFI2033-4723, and HE2149-2745, to study their inner structure (BLR
+and continuum source). We detect microlensing-induced magnification in the
+wings of the broad emission lines of two of the systems (HE0435-1223 and
+WFI2033-4723). In the case of WFI2033-4723, microlensing affects two ""bumps"" in
+the spectra which are almost symmetrically arranged on the blue (coincident
+with an AlIII emission line) and red wings of CIII]. These match the typical
+double-peaked profile that follows from disk kinematics. The presence of
+microlensing in the wings of the emission lines indicates the existence of two
+different regions in the BLR: a relatively small one with kinematics possibly
+related to an accretion disk, and another one that is substantially more
+extended and insensitive to microlensing. There is good agreement between the
+estimated size of the region affected by microlensing in the emission lines,
+$r_s=10^{+15}_{-7} \sqrt{M/M_{\odot}}$ light-days (red wing of CIV in
+HE0435-1223) and $r_s=11^{+28}_{-7} \sqrt{M/M_{\odot}}$ light-days (CIII] bumps
+in WFI2033-4723) with the sizes inferred from the continuum emission,
+$r_s=13^{+5}_{-4} \sqrt{M/M_{\odot}}$ light-days (HE0435-1223) and
+$r_s=10^{+3}_{-2} \sqrt{M/M_{\odot}}$ light-days (WFI2033-4723). For
+HE2149-2745 we measure an accretion disk size $r_s=8^{+11}_{-5}
+\sqrt{M/M_{\odot}}$ light-days. The estimates of $p$, the exponent of the size
+vs. wavelength ($r_s\propto\lambda^p$), are $1.2\pm0.6$, $0.8\pm0.2$, and
+$0.4\pm0.3$ for HE0435-1223, WFI2033-4723, and HE2149-2745, respectively. In
+conclusion, the continuum microlensing amplitude in the three quasars and
+chromaticity in WFI2033-4723 and HE2149-2745 are below expectations for the
+thin disk model. The disks are larger and their temperature gradients are
+flatter than predicted by this model.
+"
+25957,Entanglement entropy from SU(2) Chern-Simons theory and symmetric webs,"  A path integral on a link complement of a three-sphere fixes a vector (the
+""link state"") in Chern-Simons theory. The link state can be written in a
+certain basis with the colored link invariants as its coefficients. We use
+symmetric webs to systematically compute the colored link invariants, by which
+we can write down the multi-partite entangled state of any given link. It is
+still unknown if a product state necessarily implies that the corresponding
+components are unlinked, and we leave it as a conjecture.
+"
+25958,Impact of marital status on health,"  The Farr-Bertillon law states that the mortality rate of single and widowed
+persons is about three times the rate of married people of same age. This
+excess mortality can be measured with good accuracy for all ages except for
+young widowers. The reason is that, at least nowadays, very few people become
+widowed under the age of 30. Here we show that disability data from census
+records can also be used as a reliable substitute for mortality rates. In fact
+excess-disability and excess-mortality go hand in hand. Moreover, as there are
+about ten times more cases of disability than deaths, the disability variable
+is able to offer more accurate measurements in all cases where the number of
+deaths is small. This allows a more accurate investigation of the young widower
+effect; it confirms that, as already suspected from death rate data, there is a
+huge spike between the ages of 20 and 30. By using disability rates we can also
+study additional features not accessible using death rate data. For example we
+can examine the health impact of a change in living place. The observed
+temporary inflated disability rate confirms what could be expected by invoking
+the ""Transient Shock"" conjecture formulated by the authors in a previous paper.
+"
+25959,Constructing Category-Specific Models for Monocular Object-SLAM,"  We present a new paradigm for real-time object-oriented SLAM with a monocular
+camera. Contrary to previous approaches, that rely on object-level models, we
+construct category-level models from CAD collections which are now widely
+available. To alleviate the need for huge amounts of labeled data, we develop a
+rendering pipeline that enables synthesis of large datasets from a limited
+amount of manually labeled data. Using data thus synthesized, we learn
+category-level models for object deformations in 3D, as well as discriminative
+object features in 2D. These category models are instance-independent and aid
+in the design of object landmark observations that can be incorporated into a
+generic monocular SLAM framework. Where typical object-SLAM approaches usually
+solve only for object and camera poses, we also estimate object shape
+on-the-fly, allowing for a wide range of objects from the category to be
+present in the scene. Moreover, since our 2D object features are learned
+discriminatively, the proposed object-SLAM system succeeds in several scenarios
+where sparse feature-based monocular SLAM fails due to insufficient features or
+parallax. Also, the proposed category-models help in object instance retrieval,
+useful for Augmented Reality (AR) applications. We evaluate the proposed
+framework on multiple challenging real-world scenes and show --- to the best of
+our knowledge --- first results of an instance-independent monocular
+object-SLAM system and the benefits it enjoys over feature-based SLAM methods.
+"
+25960,Evaluating navigational RDF queries over the Web,"  Semantic Web, and its underlying data format RDF, lend themselves naturally
+to navigational querying due to their graph-like structure. This is
+particularly evident when considering RDF data on the Web, where various
+separately published datasets reference each other and form a giant graph known
+as the Web of Linked Data. And while navigational queries over singular RDF
+datasets are supported through SPARQL property paths, not much is known about
+evaluating them over Linked Data. In this paper we propose a method for
+evaluating property path queries over the Web based on the classical AI search
+algorithm A*, show its optimality in the open world setting of the Web, and
+test it using real world queries which access a variety of RDF datasets
+available online.
+"
+25961,Measurement of the absolute Crab flux with NuSTAR,"  We present results from a NuSTAR observation of the Crab made at a large
+off-axis angle of 1.5\degree. At these angles X-rays do not pass through the
+optics, but rather illuminate the detectors directly due to incomplete
+baffling. Due to the simplicity of the instrument response in this
+configuration and the good absolute calibration of the detectors, we are able
+to measure the absolute intrinsic flux of the Crab to better than 4%. We find
+the spectral parameters of the powerlaw to be Gamma=2.106 +/- 0.006, N=9.71
++/-0.16, in agreement with the values measured 42 years ago by Toor & Seward
+(1974). This suggests that the observed variability of the Crab is not part of
+a long term trend, but instead results from fluctuations around a steady mean.
+The NuSTAR observation also enabled improved measurement of the detector
+absorption parameters without the added complications of the mirror response.
+"
+25962,Isotropization of two-dimensional hydrodynamic turbulence in the direct cascade,"  We present results of numerical simulation of the direct cascade in
+two-dimensional hydrodynamic turbulence (with spatial resolution up to $16384
+\times 16384$). If at the earlier stage (at the time of order of the inverse
+pumping growth rate $\tau\sim\Gamma_{max}^{-1}$), the turbulence develops
+according to the same scenario as in the case of a freely decaying turbulence
+\cite{KNNR-07, KKS}: quasi-singular distributions of di-vorticity are formed,
+which in $k$-space correspond to jets, leading to a strong turbulence
+anisotropy, then for times of the order of $10\tau$ turbulence becomes almost
+isotropic. In particular, at these times any significant anisotropy in the
+angular fluctuations for the energy spectrum (for a fixed $k$) is not visible,
+while the probability distribution function of vorticity for large arguments
+has the exponential tail with the exponent linearly dependent on vorticity, in
+the agreement with the theoretical prediction \cite{FalkovichLebedev2011}.
+"
+25963,"Almost-automorphisms of trees, cloning systems and finiteness properties","  We prove that the group of almost-automorphisms of the infinite rooted
+regular $d$-ary tree $\mathcal{T}_d$ arises naturally as the Thompson-like
+group of a so called $d$-ary cloning system. A similar phenomenon occurs for
+any Nekrashevych group $V_d(G)$, for $G\le Aut(\mathcal{T}_d)$ a self-similar
+group. We use this framework to expand on work of Belk and Matucci, who proved
+that the Röver group, using the Grigorchuk group for $G$, is of type
+$F_\infty$. Namely, we find some natural conditions on subgroups of $G$ to
+ensure that $V_d(G)$ is of type $F_\infty$, and in particular we prove this for
+all $G$ in the infinite family of Šunić groups. We also prove that if $G$
+is itself of type $F_\infty$ then so is $V_d(G)$, and that every finitely
+generated virtually free group is self-similar, so in particular every finitely
+generated virtually free group $G$ yields a type $F_\infty$ Nekrashevych group
+$V_d(G)$.
+"
+25964,Relative Effect of Inclinations for Moonlets in the Triple Asteroidal Systems,"  We present the analysis and computational results for the inclination
+relative effect of moonlets of triple asteroidal systems. Perturbations on
+moonlets due to the primarys non-sphericity gravity, the solar gravity, and
+moonlets relative gravity are discussed. The inclination vector for each
+moonlet follows a periodic elliptical motion; the motion period depends on the
+moonlets semi-major axis and the primarys J2 perturbations. Perturbation on
+moonlets from the Solar gravity and moonlets relative gravity makes the motion
+of the x component of the inclination vector of moonlet 1 and the y component
+of the inclination vector of moonlet 2 to be periodic.The mean motion of x
+component and the y component of the inclination vector of each moonlet forms
+an ellipse. However, the instantaneous motion of x component and the y
+component of the inclination vector may be an elliptical disc due to the
+coupling effect of perturbation forces. Furthermore, the x component of the
+inclination vector of moonlet 1 and the y component of the inclination vector
+of moonlet 2 form a quasi-periodic motion. Numerical calculation of dynamical
+configurations of two triple asteroidal systems (216) Kleopatra and (153591)
+2001 SN263 validates the conclusion.
+"
+25965,Learning Optimal Policies from Observational Data,"  Choosing optimal (or at least better) policies is an important problem in
+domains from medicine to education to finance and many others. One approach to
+this problem is through controlled experiments/trials - but controlled
+experiments are expensive. Hence it is important to choose the best policies on
+the basis of observational data. This presents two difficult challenges: (i)
+missing counterfactuals, and (ii) selection bias. This paper presents
+theoretical bounds on estimation errors of counterfactuals from observational
+data by making connections to domain adaptation theory. It also presents a
+principled way of choosing optimal policies using domain adversarial neural
+networks. We illustrate the effectiveness of domain adversarial training
+together with various features of our algorithm on a semi-synthetic breast
+cancer dataset and a supervised UCI dataset (Statlog).
+"
+25966,"Solving the ""false positives"" problem in fraud prediction","  In this paper, we present an automated feature engineering based approach to
+dramatically reduce false positives in fraud prediction. False positives plague
+the fraud prediction industry. It is estimated that only 1 in 5 declared as
+fraud are actually fraud and roughly 1 in every 6 customers have had a valid
+transaction declined in the past year. To address this problem, we use the Deep
+Feature Synthesis algorithm to automatically derive behavioral features based
+on the historical data of the card associated with a transaction. We generate
+237 features (>100 behavioral patterns) for each transaction, and use a random
+forest to learn a classifier. We tested our machine learning model on data from
+a large multinational bank and compared it to their existing solution. On an
+unseen data of 1.852 million transactions, we were able to reduce the false
+positives by 54% and provide a savings of 190K euros. We also assess how to
+deploy this solution, and whether it necessitates streaming computation for
+real time scoring. We found that our solution can maintain similar benefits
+even when historical features are computed once every 7 days.
+"
+25967,Energy-Efficient Transceiver Design for Hybrid Sub-Array Architecture MIMO Systems,"  Millimeter-wave (mmWave) communication operated in frequency bands between 30
+and 300 GHz has attracted extensive attention due to the potential ability of
+offering orders of magnitude greater bandwidths combined with further gains via
+beamforming and spatial multiplexing from multi-element antenna arrays. MmWave
+system may exploit the hybrid analog and digital precoding to achieve
+simultaneously the diversity, array and multiplexing gain with a lower cost of
+implementation. Motivated by this, in this paper, we investigate the design of
+hybrid precoder and combiner with sub-connected architecture, where each radio
+frequency chain is connected to only a subset of base station (BS) antennas
+from the perspective of energy efficient transmission. The problem of interest
+is a non-convex and NP-hard problem that is difficult to solve directly. In
+order to address it, we resort to design a two-layer optimization method to
+solve the problem of interest by exploiting jointly the interference alignment
+and fractional programming. First, the analog precoder and combiner are
+optimized via the alternating-direction optimization method (ADOM) where the
+phase shifter can be easily adjusted with an analytical structure. Then, we
+optimize the digital precoder and combiner based on an effective multiple-input
+multiple-output (MIMO) channel coefficient. The convergence of the proposed
+algorithms is proved using the monotonic boundary theorem and fractional
+programming theory. Extensive simulation results are given to validate the
+effectiveness of the presented method and to evaluate the energy efficiency
+performance under various system configurations.
+"
+25968,Associated Legendre Functions and Spherical Harmonics of Fractional Degree and Order,"  Trigonometric formulas are derived for certain families of associated
+Legendre functions of fractional degree and order, for use in approximation
+theory. These functions are algebraic, and when viewed as Gauss hypergeometric
+functions, belong to types classified by Schwarz, with dihedral, tetrahedral,
+or octahedral monodromy. The dihedral Legendre functions are expressed in terms
+of Jacobi polynomials. For the last two monodromy types, an underlying
+`octahedral' polynomial, indexed by the degree and order and having a
+non-classical kind of orthogonality, is identified, and recurrences for it are
+worked out. It is a (generalized) Heun polynomial, not a hypergeometric one.
+For each of these families of algebraic associated Legendre functions, a
+representation of the rank-2 Lie algebra so(5,C) is generated by the ladder
+operators that shift the degree and order of the corresponding solid harmonics.
+All such representations of so(5,C) are shown to have a common value for each
+of its two Casimir invariants. The Dirac singleton representations of so(3,2)
+are included.
+"
+25969,Recent Advances of Isogeometric Analysis in Computational Electromagnetics,"  In this communication the advantages and drawbacks of the isogeometric
+analysis (IGA) are reviewed in the context of electromagnetic simulations. IGA
+extends the set of polynomial basis functions, commonly employed by the
+classical Finite Element Method (FEM). While identical to FEM with
+Nédélec's basis functions in the lowest order case, it is based on B-spline
+and Non-Uniform Rational B-spline basis functions. The main benefit of this is
+the exact representation of the geometry in the language of computer aided
+design (CAD) tools. This simplifies the meshing as the computational mesh is
+implicitly created by the engineer using the CAD tool. The curl- and
+div-conforming spline function spaces are recapitulated and the available
+software is discussed. Finally, several non-academic benchmark examples in two
+and three dimensions are shown which are used in optimization and uncertainty
+quantification workflows.
+"
+25970,The highly active Anhur-Bes regions in the 67P/Churyumov - Gerasimenko comet: results from OSIRIS/ROSETTA observations,"  The Southern hemisphere of the 67P/Churyumov-Gerasimenko comet has become
+visible from Rosetta only since March 2015. It was illuminated during the
+perihelion passage and therefore it contains the regions that experienced the
+strongest heating and erosion rate, thus exposing the subsurface most pristine
+material. In this work we investigate, thanks to the OSIRIS images, the
+geomorphology, the spectrophotometry and some transient events of two Southern
+hemisphere regions: Anhur and part of Bes. Bes is dominated by outcropping
+consolidated terrain covered with fine particle deposits, while Anhur appears
+strongly eroded with elongated canyon-like structures, scarp retreats,
+different kinds of deposits, and degraded sequences of strata indicating a
+pervasive layering. We discovered a new 140 m long and 10 m high scarp formed
+in the Anhur/Bes boundary during/after the perihelion passage, close to the
+area where exposed CO$_2$ and H$_2$O ices were previously detected. Several
+jets have been observed originating from these regions, including the strong
+perihelion outburst, an active pit, and a faint optically thick dust plume. We
+identify several areas with a relatively bluer slope (i.e. a lower spectral
+slope value) than their surroundings, indicating a surface composition enriched
+with some water ice. These spectrally bluer areas are observed especially in
+talus and gravitational accumulation deposits where freshly exposed material
+had fallen from nearby scarps and cliffs. The investigated regions become
+spectrally redder beyond 2 au outbound when the dust mantle became thicker,
+masking the underlying ice-rich layers.
+"
+25971,Anomalous Hall effect in two-dimensional non-collinear antiferromagnetic semiconductor Cr0.68Se,"  Cr0.68Se single crystals with two-dimensional (2D) character have been grown,
+and the detailed magnetization M(T), electrical transport properties (including
+longitudinal resistivity and Hall resistivity and thermal transport ones
+(including heat capacity Cp(T) and thermoelectric power (TEP) S(T)) have been
+measured. There are some interesting phenomena: (i) Cr0.68Se presents a
+non-collinear antiferromagnetic (AFM) semiconducting behavior with the Neel
+temperature TN = 42 K and the activated energy Eg=3.9 meV; (ii) It exhibits the
+anomalous Hall effect (AHE) below TN and large negative magnetoresistance (MR)
+about 83.7% (2 K, 8.5 T). The AHE coefficient RS is 0.385 cm-3/C at T=2 K and
+the AHE conductivity {\sigma}H is about 1 ohm-1cm-1 at T=40 K, respectively;
+(iii) The scaling behavior between the anomalous Hall resistivity and the
+longitudinal resistivity is linear and further analysis implies that the origin
+of the AHE in Cr0.68Se is dominated by the skew-scattering mechanism. Our
+results may be helpful for exploring the potential application of these kind of
+2D AFM semiconductors.
+"
+25972,Magnetometry using fluorescence of sodium vapor,"  Magnetic resonance of sodium fluorescence is studied with varying laser
+intensity, duty cycle, and field strength. A magnetometer based on sodium vapor
+cell filled with He buffer gas is demonstrated, which uses a single
+amplitude-modulated laser beam. With a 589 nm laser tuned at D1 or D2 line, the
+magnetic field is inferred from the variation of fluorescence. A magnetic field
+sensitivity of 150 pT per Hz square root is achieved at D1 line. The work is a
+step towards sensitive remote magnetometry with mesospheric sodium.
+"
+25973,A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising,"  Image denoising techniques are essential to reducing noise levels and
+enhancing diagnosis reliability in low-dose computed tomography (CT). Machine
+learning based denoising methods have shown great potential in removing the
+complex and spatial-variant noises in CT images. However, some residue
+artifacts would appear in the denoised image due to complexity of noises. A
+cascaded training network was proposed in this work, where the trained CNN was
+applied on the training dataset to initiate new trainings and remove artifacts
+induced by denoising. A cascades of convolutional neural networks (CNN) were
+built iteratively to achieve better performance with simple CNN structures.
+Experiments were carried out on 2016 Low-dose CT Grand Challenge datasets to
+evaluate the method's performance.
+"
+25974,Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation,"  In the past few years, deep reinforcement learning has been proven to solve
+problems which have complex states like video games or board games. The next
+step of intelligent agents would be able to generalize between tasks, and using
+prior experience to pick up new skills more quickly. However, most
+reinforcement learning algorithms for now are often suffering from catastrophic
+forgetting even when facing a very similar target task. Our approach enables
+the agents to generalize knowledge from a single source task, and boost the
+learning progress with a semisupervised learning method when facing a new task.
+We evaluate this approach on Atari games, which is a popular reinforcement
+learning benchmark, and show that it outperforms common baselines based on
+pre-training and fine-tuning.
+"
+25975,"RPRG: Toward Real-time Robotic Perception, Reasoning and Grasping with One Multi-task Convolutional Neural Network","  Autonomous robotic grasp plays an important role in intelligent robotics.
+However, it is challenging due to: (1) robotic grasp is a comprehensive task
+involving perception, planning and control; (2) autonomous robotic grasp in
+complex scenarios requires reasoning ability. In this paper, we propose a
+multi-task convolutional neural network for Robotic Perception, Reasoning and
+Grasping (RPRG), which can help robot find the target, make the plan for
+grasping and finally grasp the target step by step in object stacking scenes.
+We integrate vision-based robotic grasp detection and visual manipulation
+relationship reasoning in one single deep network and build the autonomous
+robotic grasp system. The proposed network has state-of-the-art performance in
+both tasks. Experiments demonstrate that with our model, Baxter robot can
+autonomously grasp the target with a success rate of 94.2%, 77.1% and 62.5% in
+object cluttered scenes, familiar stacking scenes and complex stacking scenes
+respectively at a speed of 6.5 FPS for each detection.
+"
+25976,Computational applications of the Many Interacting Worlds interpretation of quantum mechanics,"  While historically many quantum mechanical simulations of molecular dynamics
+have relied on the Born-Oppenheimer approximation to separate electronic and
+nuclear behavior, recently a lot of interest has arisen towards quantum effects
+in nuclear dynamics as well. Due to the computational difficulty of solving the
+Schroedinger equation in full, these effects are often treated with approximate
+quasi-classical methods.
+In this paper we present a new algorithm to tackle these problems, using an
+extension to the Many Interacting Worlds approach to quantum mechanics. This
+technique uses a kernel function to rebuild the probability density and
+therefore, at a difference with the approximation presented in the original
+paper, can be naturally extended to n-dimensional systems. This opens up the
+possibility of performing quantum ground state searches with steepest descent
+methods as well as real time quantum molecular dynamics simulations.
+The behavior of the algorithm is studied in different potentials and numbers
+of dimensions and compared both to the original approach and to exact
+Schroedinger equation solutions whenever possible.
+"
+25977,Statistical Analysis and Parameter Selection for Mapper,"  In this article, we study the question of the statistical convergence of the
+1-dimensional Mapper to its continuous analogue, the Reeb graph. We show that
+the Mapper is an optimal estimator of the Reeb graph, which gives, as a
+byproduct, a method to automatically tune its parameters and compute confidence
+regions on its topological features, such as its loops and flares. This allows
+to circumvent the issue of testing a large grid of parameters and keeping the
+most stable ones in the brute-force setting, which is widely used in
+visualization, clustering and feature selection with the Mapper.
+"
+25978,Relativistic elliptic matrix tops and finite Fourier transformations,"  We consider a family of classical elliptic integrable systems including
+(relativistic) tops and their matrix extensions of different types. These
+models can be obtained from the ""off-shell"" Lax pairs, which do not satisfy the
+Lax equations in general case but become true Lax pairs under various
+conditions (reductions). At the level of the off-shell Lax matrix there is a
+natural symmetry between the spectral parameter $z$ and relativistic parameter
+$\eta$. It is generated by the finite Fourier transformation, which we describe
+in detail. The symmetry allows to consider $z$ and $\eta$ on an equal footing.
+Depending on the type of integrable reduction any of the parameters can be
+chosen to be the spectral one. Then another one is the relativistic deformation
+parameter. As a by-product we describe the model of $N^2$ interacting $GL(M)$
+matrix tops and/or $M^2$ interacting $GL(N)$ matrix tops depending on a choice
+of the spectral parameter.
+"
+25979,Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach,"  Visual exploration of high-dimensional real-valued datasets is a fundamental
+task in exploratory data analysis (EDA). Existing methods use predefined
+criteria to choose the representation of data. There is a lack of methods that
+(i) elicit from the user what she has learned from the data and (ii) show
+patterns that she does not know yet. We construct a theoretical model where
+identified patterns can be input as knowledge to the system. The knowledge
+syntax here is intuitive, such as ""this set of points forms a cluster"", and
+requires no knowledge of maths. This background knowledge is used to find a
+Maximum Entropy distribution of the data, after which the system provides the
+user data projections in which the data and the Maximum Entropy distribution
+differ the most, hence showing the user aspects of the data that are maximally
+informative given the user's current knowledge. We provide an open source EDA
+system with tailored interactive visualizations to demonstrate these concepts.
+We study the performance of the system and present use cases on both synthetic
+and real data. We find that the model and the prototype system allow the user
+to learn information efficiently from various data sources and the system works
+sufficiently fast in practice. We conclude that the information theoretic
+approach to exploratory data analysis where patterns observed by a user are
+formalized as constraints provides a principled, intuitive, and efficient basis
+for constructing an EDA system.
+"
+25980,Scaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to Regularized Regression and PCA,"  We present a framework for analyzing the exact dynamics of a class of online
+learning algorithms in the high-dimensional scaling limit. Our results are
+applied to two concrete examples: online regularized linear regression and
+principal component analysis. As the ambient dimension tends to infinity, and
+with proper time scaling, we show that the time-varying joint empirical
+measures of the target feature vector and its estimates provided by the
+algorithms will converge weakly to a deterministic measured-valued process that
+can be characterized as the unique solution of a nonlinear PDE. Numerical
+solutions of this PDE can be efficiently obtained. These solutions lead to
+precise predictions of the performance of the algorithms, as many practical
+performance metrics are linear functionals of the joint empirical measures. In
+addition to characterizing the dynamic performance of online learning
+algorithms, our asymptotic analysis also provides useful insights. In
+particular, in the high-dimensional limit, and due to exchangeability, the
+original coupled dynamics associated with the algorithms will be asymptotically
+""decoupled"", with each coordinate independently solving a 1-D effective
+minimization problem via stochastic gradient descent. Exploiting this insight
+for nonconvex optimization problems may prove an interesting line of future
+research.
+"
+25981,Detection of a possible superluminous supernova in the epoch of reionization,"  An interesting transient has been detected in one of our three Dark Energy
+Camera deep fields. Observations of these deep fields take advantage of the
+high red sensitivity of DECam on the Cerro Tololo Interamerican Observatory
+Blanco telescope. The survey includes the Y band with rest wavelength 1430{\AA}
+at z = 6. Survey fields (the Prime field 0555-6130, the 16hr field 1600-75 and
+the SUDSS New Southern Field) are deeper in Y than other infrared surveys. They
+are circumpolar, allowing all night to be used efficiently, exploiting the moon
+tolerance of 1 micron observations to minimize conflict with the Dark Energy
+Survey. As an i-band dropout (meaning that the flux decrement shortward of
+Lyman alpha is in the i bandpass), the transient we report here is a supernova
+candidate with z ~ 6, with a luminosity comparable to the brightest known
+current epoch superluminous supernova (i.e., ~ 2 x 10^11 solar luminosities).
+"
+25982,Feasibility of Corneal Imaging for Handheld Augmented Reality,"  Smartphones are a popular device class for mobile Augmented Reality but
+suffer from a limited input space. Around-device interaction techniques aim at
+extending this input space using various sensing modalities. In this paper we
+present our work towards extending the input area of mobile devices using
+front-facing device-centered cameras that capture reflections in the cornea. As
+current generation mobile devices lack high resolution front-facing cameras, we
+study the feasibility of around-device interaction using corneal reflective
+imaging based on a high resolution camera. We present a workflow, a technical
+prototype and a feasibility evaluation.
+"
+25983,Deep Cross-Modal Audio-Visual Generation,"  Cross-modal audio-visual perception has been a long-lasting topic in
+psychology and neurology, and various studies have discovered strong
+correlations in human perception of auditory and visual stimuli. Despite works
+in computational multimodal modeling, the problem of cross-modal audio-visual
+generation has not been systematically studied in the literature. In this
+paper, we make the first attempt to solve this cross-modal generation problem
+leveraging the power of deep generative adversarial training. Specifically, we
+use conditional generative adversarial networks to achieve cross-modal
+audio-visual generation of musical performances. We explore different encoding
+methods for audio and visual signals, and work on two scenarios:
+instrument-oriented generation and pose-oriented generation. Being the first to
+explore this new problem, we compose two new datasets with pairs of images and
+sounds of musical performances of different instruments. Our experiments using
+both classification and human evaluations demonstrate that our model has the
+ability to generate one modality, i.e., audio/visual, from the other modality,
+i.e., visual/audio, to a good extent. Our experiments on various design choices
+along with the datasets will facilitate future research in this new problem
+space.
+"
+25984,Conservation laws by symmetries and adjoint symmetries,"  Conservation laws are formulated for systems of differential equations by
+using symmetries and adjoint symmetries, and an application to systems of
+evolution equations is made, together with illustrative examples. The
+formulation does not require the existence of a Lagrangian for a given system,
+and the presented examples include computations of conserved densities for the
+heat equation, Burgers' equation and the Korteweg-de Vries equation.
+"
+25985,Accessibility and Delay in Random Temporal Networks,"  In a wide range of complex networks, the links between the nodes are temporal
+and may sporadically appear and disappear. This temporality is fundamental to
+analyze the formation of paths within such networks. Moreover, the presence of
+the links between the nodes is a random process induced by nature in many
+real-world networks. In this paper, we study random temporal networks at a
+microscopic level and formulate the \emph{probability of accessibility} from a
+node \emph{i} to a node \emph{j} after a certain number of discrete time units
+$T$. While solving the original problem is computationally intractable, we
+provide an upper and two lower bounds on this probability for a very general
+case with arbitrary time-varying probabilities of links' existence. Moreover,
+for a special case where the links have identical probabilities across the
+network at each time slot, we obtain the exact probability of accessibility
+between any two nodes. Finally, we discuss scenarios where the information
+regarding the presence and absence of links is initially available in the form
+of time duration (of presence or absence intervals) continuous probability
+distributions rather than discrete probabilities over time slots. We provide a
+method for transforming such distributions to discrete probabilities which
+enables us to apply the given bounds in this paper to a broader range of
+problem settings.
+"
+25986,1.5 $μ$m lasers with sub 10 mHz linewidth,"  We report on two ultrastable lasers each stabilized to independent silicon
+Fabry-Pérot cavities operated at 124 K. The fractional frequency instability
+of each laser is completely determined by the fundamental thermal Brownian
+noise of the mirror coatings with a flicker noise floor of $4 \times 10^{-17}$
+for integration times between 0.8 s and a few tens of seconds. We rigorously
+treat the notorious divergencies encountered with the associated flicker
+frequency noise and derive methods to relate this noise to observable and
+practically relevant linewidths and coherence times. The individual laser
+linewidth obtained from the phase noise spectrum or the direct beat note
+between the two lasers can be as small as 5 mHz at 194 THz. From the measured
+phase evolution between the two laser fields we derive usable phase coherence
+times for different applications of 11 s and 60 s.
+"
+25987,Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models,"  Generalized linear models (GLMs) arise in high-dimensional machine learning,
+statistics, communications and signal processing. In this paper we analyze GLMs
+when the data matrix is random, as relevant in problems such as compressed
+sensing, error-correcting codes or benchmark models in neural networks. We
+evaluate the mutual information (or ""free entropy"") from which we deduce the
+Bayes-optimal estimation and generalization errors. Our analysis applies to the
+high-dimensional limit where both the number of samples and the dimension are
+large and their ratio is fixed. Non-rigorous predictions for the optimal errors
+existed for special cases of GLMs, e.g. for the perceptron, in the field of
+statistical physics based on the so-called replica method. Our present paper
+rigorously establishes those decades old conjectures and brings forward their
+algorithmic interpretation in terms of performance of the generalized
+approximate message-passing algorithm. Furthermore, we tightly characterize,
+for many learning problems, regions of parameters for which this algorithm
+achieves the optimal performance, and locate the associated sharp phase
+transitions separating learnable and non-learnable regions. We believe that
+this random version of GLMs can serve as a challenging benchmark for
+multi-purpose algorithms. This paper is divided in two parts that can be read
+independently: The first part (main part) presents the model and main results,
+discusses some applications and sketches the main ideas of the proof. The
+second part (supplementary informations) is much more detailed and provides
+more examples as well as all the proofs.
+"
+25988,"Overlapping Clustering Models, and One (class) SVM to Bind Them All","  People belong to multiple communities, words belong to multiple topics, and
+books cover multiple genres; overlapping clusters are commonplace. Many
+existing overlapping clustering methods model each person (or word, or book) as
+a non-negative weighted combination of ""exemplars"" who belong solely to one
+community, with some small noise. Geometrically, each person is a point on a
+cone whose corners are these exemplars. This basic form encompasses the widely
+used Mixed Membership Stochastic Blockmodel of networks (Airoldi et al., 2008)
+and its degree-corrected variants (Jin et al., 2017), as well as topic models
+such as LDA (Blei et al., 2003). We show that a simple one-class SVM yields
+provably consistent parameter inference for all such models, and scales to
+large datasets. Experimental results on several simulated and real datasets
+show our algorithm (called SVM-cone) is both accurate and scalable.
+"
+25989,Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections,"  We propose a method to generate multiple diverse and valid human pose
+hypotheses in 3D all consistent with the 2D detection of joints in a monocular
+RGB image. We use a novel generative model uniform (unbiased) in the space of
+anatomically plausible 3D poses. Our model is compositional (produces a pose by
+combining parts) and since it is restricted only by anatomical constraints it
+can generalize to every plausible human 3D pose. Removing the model bias
+intrinsically helps to generate more diverse 3D pose hypotheses. We argue that
+generating multiple pose hypotheses is more reasonable than generating only a
+single 3D pose based on the 2D joint detection given the depth ambiguity and
+the uncertainty due to occlusion and imperfect 2D joint detection. We hope that
+the idea of generating multiple consistent pose hypotheses can give rise to a
+new line of future work that has not received much attention in the literature.
+We used the Human3.6M dataset for empirical evaluation.
+"
+25990,CollabLoc: Privacy-Preserving Multi-Modal Localization via Collaborative Information Fusion,"  Mobile phones provide an excellent opportunity for building context-aware
+applications. In particular, location-based services are important
+context-aware services that are more and more used for enforcing security
+policies, for supporting indoor room navigation, and for providing personalized
+assistance. However, a major problem still remains unaddressed---the lack of
+solutions that work across buildings while not using additional infrastructure
+and also accounting for privacy and reliability needs. In this paper, a
+privacy-preserving, multi-modal, cross-building, collaborative localization
+platform is proposed based on Wi-Fi RSSI (existing infrastructure), Cellular
+RSSI, sound and light levels, that enables room-level localization as main
+application (though sub room level granularity is possible). The privacy is
+inherently built into the solution based on onion routing, and
+perturbation/randomization techniques, and exploits the idea of weighted
+collaboration to increase the reliability as well as to limit the effect of
+noisy devices (due to sensor noise/privacy). The proposed solution has been
+analyzed in terms of privacy, accuracy, optimum parameters, and other overheads
+on location data collected at multiple indoor and outdoor locations using an
+Android app.
+"
+25991,Berezinskii-Kosteriltz-Thouless transition in disordered multi-channel Luttinger liquids,"  We study the phase transition between conducting and insulating states taking
+place in disordered multi-channel Luttinger liquids with inter-channel
+interactions. We derive renormalisation group equations which are perturbative
+in disorder but nonperturbative in interaction. In the vicinity of the
+simultaneous phase transition in all channels, these equations become a set of
+coupled Berezinskii--Kosterlitz--Thouless equations, which we analyze within
+two models: an array of identical wires and a two-channel model with distinct
+channels. We show that a competition between disorder and interaction results
+in a variety of phases, expected to be observable at intermediate temperatures
+where the interaction and disorder are relevant but weak hybridization and the
+charge-density wave interaction may be ignored.
+"
+25992,Gamma and Factorial in the Monthly,"  The Monthly has published roughly fifty papers on the $\Gamma$ function or
+Stirling's formula. We survey those papers (discussing only our favourites in
+any detail) and place them in the context of the larger mathematical literature
+on $\Gamma$.
+"
+25993,Empirical optimal transport on countable metric spaces: Distributional limits and statistical applications,"  We derive distributional limits for empirical transport distances between
+probability measures supported on countable sets. Our approach is based on
+sensitivity analysis of optimal values of infinite dimensional mathematical
+programs and a delta method for non-linear derivatives. A careful calibration
+of the norm on the space of probability measures is needed in order to combine
+differentiability and weak convergence of the underlying empirical process.
+Based on this we provide a sufficient and necessary condition for the
+underlying distribution on the countable metric space for such a distributional
+limit to hold. We give an explicit form of the limiting distribution for
+ultra-metric spaces. Finally, we apply our findings to optimal transport based
+inference in large scale problems. An application to nanoscale microscopy is
+given.
+"
+25994,State-by-state Minimax Adaptive Estimation for Nonparametric Hidden Markov Models,"  In this paper, we introduce a new estimator for the emission densities of a
+nonparametric hidden Markov model. It is adaptive and minimax with respect to
+each state's regularity--as opposed to globally minimax estimators, which adapt
+to the worst regularity among the emission densities. Our method is based on
+Goldenshluger and Lepski's methodology. It is computationally efficient and
+only requires a family of preliminary estimators, without any restriction on
+the type of estimators considered. We present two such estimators that allow to
+reach minimax rates up to a logarithmic term: a spectral estimator and a least
+squares estimator. We show how to calibrate it in practice and assess its
+performance on simulations and on real data.
+"
+25995,Role Playing Learning for Socially Concomitant Mobile Robot Navigation,"  In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
+robot to navigate socially with its human companion in populated environments.
+Neural networks (NN) are constructed to parameterize a stochastic policy that
+directly maps sensory data collected by the robot to its velocity outputs,
+while respecting a set of social norms. An efficient simulative learning
+environment is built with maps and pedestrians trajectories collected from a
+number of real-world crowd data sets. In each learning iteration, a robot
+equipped with the NN policy is created virtually in the learning environment to
+play itself as a companied pedestrian and navigate towards a goal in a socially
+concomitant manner. Thus, we call this process Role Playing Learning, which is
+formulated under a reinforcement learning (RL) framework. The NN policy is
+optimized end-to-end using Trust Region Policy Optimization (TRPO), with
+consideration of the imperfectness of robot's sensor measurements. Simulative
+and experimental results are provided to demonstrate the efficacy and
+superiority of our method.
+"
+25996,Galaxy Formation with BECDM: I. Turbulence and relaxation of idealised haloes,"  We present a theoretical analysis of some unexplored aspects of relaxed
+Bose-Einstein condensate dark matter (BECDM) haloes. This type of ultralight
+bosonic scalar field dark matter is a viable alternative to the standard cold
+dark matter (CDM) paradigm, as it makes the same large-scale predictions as CDM
+and potentially overcomes CDM's small-scale problems via a galaxy-scale de
+Broglie wavelength. We simulate BECDM halo formation through mergers, evolved
+under the Schrödinger-Poisson equations. The formed haloes consist of a
+soliton core supported against gravitational collapse by the quantum pressure
+tensor and an asymptotic $r^{-3}$ NFW-like profile. We find a fundamental
+relation of the core=to-halo mass with the dimensionless invariant $\Xi \equiv
+\lvert E \rvert/M^3/(Gm/\hbar)^2$ or $M_{\rm c}/M \simeq 2.6 \Xi^{1/3}$,
+linking the soliton to global halo properties. For $r \geq 3.5 \,r_{\rm c}$
+core radii, we find equipartition between potential, classical kinetic, and
+quantum gradient energies. The haloes also exhibit a conspicuous turbulent
+behavior driven by the continuous reconnection of vortex lines due to wave
+interference. We analyse the turbulence 1D velocity power spectrum and find a
+$k^{-1.1}$ power-law. This suggests the vorticity in BECDM haloes is
+homogeneous, similar to thermally-driven counterflow BEC systems from condensed
+matter physics, in contrast to a $k^{-5/3}$ Kolmogorov power-law seen in
+mechanically-driven quantum systems. The mode where the power spectrum peaks is
+approximately the soliton width, implying the soliton-sized granules carry most
+of the turbulent energy in BECDM haloes.
+"
+25997,Fronthaul-Aware Software-Defined Joint Resource Allocation and User Scheduling for 5G Networks,"  Software-defined networking (SDN) is the concept of decoupling the control
+and data planes to create a flexible and agile network, assisted by a central
+controller. However, the performance of SDN highly depends on the limitations
+in the fronthaul which are inadequately discussed in the existing literature.
+In this paper, a fronthaul-aware software-defined resource allocation mechanism
+is proposed for 5G wireless networks with in-band wireless fronthaul
+constraints. Considering the fronthaul capacity, the controller maximizes the
+time-averaged network throughput by enforcing a coarse correlated equilibrium
+(CCE) and incentivizing base stations (BSs) to locally optimize their decisions
+to ensure mobile users' (MUs) quality-of-service (QoS) requirements. By
+marrying tools from Lyapunov stochastic optimization and game theory, we
+propose a two-timescale approach where the controller gives recommendations,
+i.e., sub-carriers with low interference, in a long-timescale whereas BSs
+schedule their own MUs and allocate the available resources in every time slot.
+Numerical results show considerable throughput enhancements and delay
+reductions over a non-SDN network baseline.
+"
+25998,Higher-dimensional Willmore energies via minimal submanifold asymptotics,"  A conformally invariant generalization of the Willmore energy for compact
+immersed submanifolds of even dimension in a Riemannian manifold is derived and
+studied. The energy arises as the coefficient of the log term in the
+renormalized area expansion of a minimal submanifold in a Poincare-Einstein
+space with prescribed boundary at infinity. Its first variation is identified
+as the obstruction to smoothness of the minimal submanifold. The energy is
+explicitly identified for the case of submanifolds of dimension four.
+Variational properties of this four-dimensional energy are studied in detail
+when the background is a Euclidean space or a sphere, including identifications
+of critical embeddings, questions of boundedness above and below for various
+topologies, and second variation.
+"
+25999,Nil clean graph of rings,"  In this article, we have defined nil clean graph of a ring $R$. The vertex
+set is the ring $R$, two ring elements $a$ and $b$ are adjacent if and only if
+$a + b$ is nil clean in $R$. Graph theoretic properties like girth, dominating
+set, diameter etc. of nil clean graph have been studied for finite commutative
+rings.
+"
+26000,Solitonic Excitations in Collisions Of Superfluid Nuclei,"  We investigate the role of the pairing field dynamics in low-energy heavy ion
+reactions within the nuclear time-dependent density functional theory extended
+to superfluid systems. Recently, we have reported on unexpectedly large effects
+associated with the relative phase of the pairing field of colliding nuclei on
+the reaction outcomes, such as the total kinetic energy and the fusion cross
+section [P. Magierski, K. Sekizawa, and G. Wlaz{\l}owski, arXiv:1611.10261
+[nucl-th]]. We have elucidated that the effects are due to creation of a
+""domain wall"" or a ""solitonic structure"" of the pairing field in the neck
+region, which hinders energy dissipation as well as the neck formation, leading
+to significant changes of the reaction dynamics. The situation nicely mimics
+the one extensively studied experimentally with ultracold atomic gases, where
+two clouds of superfluid atoms possessing different phases of the pairing field
+are forced to merge, creating various topological excitations, quantum vortices
+and solitons, as well as Josephson currents. In this paper, we present
+unpublished results for a lighter system, namely, $^{44}$Ca+$^{44}$Ca. It is
+shown that the pairing effects on the fusion hindrance are rather small in
+lighter systems, due to a strong tendency towards fusion, which is consistent
+with an earlier study.
+"
+26001,Encoder Based Lifelong Learning,"  This paper introduces a new lifelong learning solution where a single model
+is trained for a sequence of tasks. The main challenge that vision systems face
+in this context is catastrophic forgetting: as they tend to adapt to the most
+recently seen task, they lose performance on the tasks that were learned
+previously. Our method aims at preserving the knowledge of the previous tasks
+while learning a new one by using autoencoders. For each task, an
+under-complete autoencoder is learned, capturing the features that are crucial
+for its achievement. When a new task is presented to the system, we prevent the
+reconstructions of the features with these autoencoders from changing, which
+has the effect of preserving the information on which the previous tasks are
+mainly relying. At the same time, the features are given space to adjust to the
+most recent environment as only their projection into a low dimension
+submanifold is controlled. The proposed system is evaluated on image
+classification tasks and shows a reduction of forgetting over the
+state-of-the-art
+"
+26002,Optimal stopping of one-dimensional diffusions with integral criteria,"  This paper provides a full characterization of the value function and
+solution(s) of an optimal stopping problem for a one-dimensional diffusion with
+an integral criterion. The results hold under very weak assumptions, namely,
+the diffusion is assumed to be a weak solution of stochastic differential
+equation satisfying the Engelbert-Schmidt conditions, while the (stochastic)
+discount rate and the integrand are required to satisfy only general
+integrability conditions.
+"
+26003,Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches,"  Risk stratification (characterization) of tumors from radiology images can be
+more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor
+characterization through such tools can also enable non-invasive cancer
+staging, prognosis, and foster personalized treatment planning as a part of
+precision medicine. In this study, we propose both supervised and unsupervised
+machine learning strategies to improve tumor characterization. Our first
+approach is based on supervised learning for which we demonstrate significant
+gains with deep learning algorithms, particularly by utilizing a 3D
+Convolutional Neural Network and Transfer Learning. Motivated by the
+radiologists' interpretations of the scans, we then show how to incorporate
+task dependent feature representations into a CAD system via a
+graph-regularized sparse Multi-Task Learning (MTL) framework. In the second
+approach, we explore an unsupervised learning algorithm to address the limited
+availability of labeled training data, a common problem in medical imaging
+applications. Inspired by learning from label proportion (LLP) approaches in
+computer vision, we propose to use proportion-SVM for characterizing tumors. We
+also seek the answer to the fundamental question about the goodness of ""deep
+features"" for unsupervised tumor classification. We evaluate our proposed
+supervised and unsupervised learning algorithms on two different tumor
+diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans,
+respectively, and obtain the state-of-the-art sensitivity and specificity
+results in both problems.
+"
+26004,Novel Algorithms for Sampling Abstract Simplicial Complexes,"  We provide dual algorithms for sampling the space of abstract simplicial
+complexes on a fixed number of vertices. We develop a generative and
+descriptive sampler designed with heuristics to help balance the combinatorial
+multiplicities of the states and more widely sample across the space of
+nonisomorphic complexes. We provide a formula for the exact probabilities with
+which this algorithm will produce a requested labeled state, and compare with
+an existing benchmark. We also design a highly conductive local ergodic random
+walk with known transition probabilities. We characterize the autocorrelation
+of the walk, and numerically test it against our sampler to illustrate its
+efficacy.
+"
+26005,GPGPU Performance Estimation with Core and Memory Frequency Scaling,"  Graphics Processing Units (GPUs) support dynamic voltage and frequency
+scaling (DVFS) in order to balance computational performance and energy
+consumption. However, there still lacks simple and accurate performance
+estimation of a given GPU kernel under different frequency settings on real
+hardware, which is important to decide best frequency configuration for energy
+saving. This paper reveals a fine-grained model to estimate the execution time
+of GPU kernels with both core and memory frequency scaling. Over a 2.5x range
+of both core and memory frequencies among 12 GPU kernels, our model achieves
+accurate results (within 3.5\%) on real hardware. Compared with the cycle-level
+simulators, our model only needs some simple micro-benchmark to extract a set
+of hardware parameters and performance counters of the kernels to produce this
+high accuracy.
+"
+26006,Cosmic abundances of SIMP dark matter,"  Thermal production of light dark matter with sub-GeV scale mass can be
+attributed to $3\rightarrow 2$ self-annihilation processes. We consider the
+thermal average for annihilation cross sections of dark matter at $3\rightarrow
+2$ and general higher-order interactions. A correct thermal average for initial
+dark matter particles is important, in particular, for annihilation cross
+sections with overall velocity dependence and/or resonance poles. We apply our
+general results to benchmark models for SIMP dark matter and discuss the
+effects of the resonance pole in determining the relic density.
+"
+26007,Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms,"  This paper proposes and implements an intuitive and pervasive solution for
+neonatal EEG monitoring assisted by sonification and deep learning AI that
+provides information about neonatal brain health to all neonatal healthcare
+professionals, particularly those without EEG interpretation expertise. The
+system aims to increase the demographic of clinicians capable of diagnosing
+abnormalities in neonatal EEG. The proposed system uses a low-cost and
+low-power EEG acquisition system. An Android app provides single-channel EEG
+visualization, traffic-light indication of the presence of neonatal seizures
+provided by a trained, deep convolutional neural network and an algorithm for
+EEG sonification, designed to facilitate the perception of changes in EEG
+morphology specific to neonatal seizures. The multifaceted EEG interpretation
+framework is presented and the implemented mobile platform architecture is
+analyzed with respect to its power consumption and accuracy.
+"
+26008,More on the restricted almost unbiased Liu-estimator in Logistic regression,"  To address the problem of multicollinearity in the logistic regression model,
+in this paper we propose a new estimator called Stochastic restricted almost
+unbiased logistic Liu-estimator (SRAULLE) when the prior information is
+available in the form of stochastic linear restrictions. A Monte Carlo
+simulation study was carried out to compare the performance of the proposed
+estimator with some existing estimators in the scalar mean squared error (SMSE)
+sense. Finally, a real data example was given to appraise the performance of
+the estimators.
+"
+26009,Data-driven Random Fourier Features using Stein Effect,"  Large-scale kernel approximation is an important problem in machine learning
+research. Approaches using random Fourier features have become increasingly
+popular [Rahimi and Recht, 2007], where kernel approximation is treated as
+empirical mean estimation via Monte Carlo (MC) or Quasi-Monte Carlo (QMC)
+integration [Yang et al., 2014]. A limitation of the current approaches is that
+all the features receive an equal weight summing to 1. In this paper, we
+propose a novel shrinkage estimator from ""Stein effect"", which provides a
+data-driven weighting strategy for random features and enjoys theoretical
+justifications in terms of lowering the empirical risk. We further present an
+efficient randomized algorithm for large-scale applications of the proposed
+method. Our empirical results on six benchmark data sets demonstrate the
+advantageous performance of this approach over representative baselines in both
+kernel approximation and supervised learning tasks.
+"
+26010,Learning to Represent Mechanics via Long-term Extrapolation and Interpolation,"  While the basic laws of Newtonian mechanics are well understood, explaining a
+physical scenario still requires manually modeling the problem with suitable
+equations and associated parameters. In order to adopt such models for
+artificial intelligence, researchers have handcrafted the relevant states, and
+then used neural networks to learn the state transitions using simulation runs
+as training data. Unfortunately, such approaches can be unsuitable for modeling
+complex real-world scenarios, where manually authoring relevant state spaces
+tend to be challenging. In this work, we investigate if neural networks can
+implicitly learn physical states of real-world mechanical processes only based
+on visual data, and thus enable long-term physical extrapolation. We develop a
+recurrent neural network architecture for this task and also characterize
+resultant uncertainties in the form of evolving variance estimates. We evaluate
+our setup to extrapolate motion of a rolling ball on bowl of varying shape and
+orientation using only images as input, and report competitive results with
+approaches that assume access to internal physics models and parameters.
+"
+26011,On the equivalence between multiclass processor sharing and random order scheduling policies,"  Consider a single server system serving a multiclass population. Some popular
+scheduling policies for such system are the discriminatory processor sharing
+(DPS), discriminatory random order service (DROS), generalized processor
+sharing (GPS) and weighted fair queueing (WFQ). In this paper, we propose two
+classes of policies, namely MPS (multiclass processor sharing) and MROS
+(multiclass random order service), that generalize the four policies mentioned
+above. For the special case when the multiclass population arrive according to
+Poisson processes and have independent and exponential service requirement with
+parameter $\mu$, we show that the tail of the sojourn time distribution for a
+class $i$ customer in a system with the MPS policy is a constant multiple of
+the tail of the waiting time distribution of a class $i$ customer in a system
+with the MROS policy. This result implies that for a class $i$ customer, the
+tail of the sojourn time distribution in a system with the DPS (GPS) scheduling
+policy is a constant multiple of the tail of the waiting time distribution in a
+system with the DROS (respectively WFQ) policy.
+"
+26012,Local dynamics of non-invertible maps near normal surface singularities,"  We study the problem of finding algebraically stable models for
+non-invertible holomorphic fixed point germs $f\colon (X,x_0)\to (X,x_0)$,
+where $X$ is a complex surface having $x_0$ as a normal singularity. We prove
+that as long as $x_0$ is not a cusp singularity of $X$, then it is possible to
+find arbitrarily high modifications $\pi\colon X_\pi\to (X,x_0)$ such that the
+dynamics of $f$ (or more precisely of $f^N$ for $N$ big enough) on $X_\pi$ is
+algebraically stable. This result is proved by understanding the dynamics
+induced by $f$ on a space of valuations associated to $X$; in fact, we are able
+to give a strong classification of all the possible dynamical behaviors of $f$
+on this valuation space. We also deduce a precise description of the behavior
+of the sequence of attraction rates for the iterates of $f$. Finally, we prove
+that in this setting the first dynamical degree is always a quadratic integer.
+"
+26013,Weakly holomorphic modular forms in prime power levels of genus zero,"  Let $M_k^\sharp(N)$ be the space of weight $k$, level $N$ weakly holomorphic
+modular forms with poles only at the cusp at $\infty$. We explicitly construct
+a canonical basis for $M_k^\sharp(N)$ for $N\in\{8,9,16,25\}$, and show that
+many of the Fourier coefficients of the basis elements in $M_0^\sharp(N)$ are
+divisible by high powers of the prime dividing the level $N$. Additionally, we
+show that these basis elements satisfy a Zagier duality property, and extend
+Griffin's results on congruences in level 1 to levels 2, 3, 4, 5, 7, 8, 9, 16,
+and 25.
+"
+26014,On the cardinality of the manifold set,"  We study the cardinality of the set of manifolds homotopy equivalent to a
+given manifold M and compare it to the cardinality of the structure set of M.
+"
+26015,Probabilistic treatment of the uncertainty from the finite size of weighted Monte Carlo data,"  Parameter estimation in HEP experiments often involves Monte-Carlo simulation
+to model the experimental response function. A typical application are
+forward-folding likelihood analyses with re-weighting, or time-consuming
+minimization schemes with a new simulation set for each parameter value.
+Problematically, the finite size of such Monte Carlo samples carries intrinsic
+uncertainty that can lead to a substantial bias in parameter estimation if it
+is neglected and the sample size is small. We introduce a probabilistic
+treatment of this problem by replacing the usual likelihood functions with
+novel generalized probability distributions that incorporate the finite
+statistics via suitable marginalization. These new PDFs are analytic, and can
+be used to replace the Poisson, multinomial, and sample-based unbinned
+likelihoods, which covers many use cases in high-energy physics. In the limit
+of infinite statistics, they reduce to the respective standard probability
+distributions. In the general case of arbitrary Monte Carlo weights, the
+expressions involve the fourth Lauricella function $F_D$, for which we find a
+new finite-sum representation in a certain parameter setting. The result also
+represents an exact form for Carlson's Dirichlet average $R_n$ with $n>0$, and
+thereby an efficient way to calculate the probability generating function of
+the Dirichlet-multinomial distribution, the extended divided difference of a
+monomial, or arbitrary moments of univariate B-splines. We demonstrate the bias
+reduction of our approach with a typical toy Monte Carlo problem, estimating
+the normalization of a peak in a falling energy spectrum, and compare the
+results with previously published methods from the literature.
+"
+26016,K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection,"  Dynamic Ensemble Selection (DES) techniques aim to select locally competent
+classifiers for the classification of each new test sample. Most DES techniques
+estimate the competence of classifiers using a given criterion over the region
+of competence of the test sample (its the nearest neighbors in the validation
+set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers
+that correctly classify all samples in the region of competence of the test
+sample, if such classifier exists, otherwise, it removes from the region of
+competence the sample that is furthest from the test sample, and the process
+repeats. When the region of competence has samples of different classes,
+KNORA-E can reduce the region of competence in such a way that only samples of
+a single class remain in the region of competence, leading to the selection of
+locally incompetent classifiers that classify all samples in the region of
+competence as being from the same class. In this paper, we propose two DES
+techniques: K-Nearest Oracles Borderline (KNORA-B) and K-Nearest Oracles
+Borderline Imbalanced (KNORA-BI). KNORA-B is a DES technique based on KNORA-E
+that reduces the region of competence but maintains at least one sample from
+each class that is in the original region of competence. KNORA-BI is a
+variation of KNORA-B for imbalance datasets that reduces the region of
+competence but maintains at least one minority class sample if there is any in
+the original region of competence. Experiments are conducted comparing the
+proposed techniques with 19 DES techniques from the literature using 40
+datasets. The results show that the proposed techniques achieved interesting
+results, with KNORA-BI outperforming state-of-art techniques.
+"
+26017,Low-complexity implementation of convex optimization-based phase retrieval,"  Phase retrieval has important applications in optical imaging, communications
+and sensing. Lifting the dimensionality of the problem allows phase retrieval
+to be approximated as a convex optimization problem in a higher-dimensional
+space. Convex optimization-based phase retrieval has been shown to yield high
+accuracy, yet its low-complexity implementation has not been explored. In this
+paper, we study three fundamental approaches for its low-complexity
+implementation: the projected gradient method, the Nesterov accelerated
+gradient method, and the alternating direction method of multipliers (ADMM)
+method. We derive the corresponding estimation algorithms and evaluate their
+complexities. We compare their performance in the application area of
+direct-detection mode-division multiplexing. We demonstrate that they yield
+negligible estimation penalties (less than 0.2 dB for transmitter processing
+and less than 0.6 dB for receiver equalization) while yielding low
+computational cost, as their implementation complexities all scale
+quadratically in the number of unknown parameters. Among the three methods,
+ADMM achieves convergence after the smallest number of iterations.
+"
+26018,Reconstructing the velocity dispersion profiles from the line-of-sight kinematic data in disc galaxies,"  We present a modification of the method for reconstructing the stellar
+velocity ellipsoid (SVE) in disc galaxies. Our version does not need any
+parametrization of the velocity dispersion profiles and uses only one
+assumption that the ratio $\sigma_z/\sigma_R$ remains constant along the
+profile or along several pieces of the profile. The method was tested on two
+galaxies from the sample of other authors and for the first time was applied to
+three lenticular galaxies NGC~1167, NGC~3245 and NGC~4150 as well as to one Sab
+galaxy NGC~338. We found that for galaxies with a high inclination ($i
+>55-60^\circ$) it is difficult or rather impossible to extract the information
+about SVE while for galaxies at an intermediate inclination the procedure of
+extracting is successful. For NGC~1167 we managed to reconstruct SVE, provided
+that the value of $\sigma_z/\sigma_R$ is piecewise constant. We found
+$\sigma_z/\sigma_R=0.7$ for the inner parts of the disc and
+$\sigma_z/\sigma_R=0.3$ for the outskirts. We also obtained a rigid constrain
+on the value of the radial velocity dispersion $\sigma_R$ for highly inclined
+galaxies and tested the result using the asymmetric drift equation, provided
+that the gas rotation curve is available.
+"
+26019,Unusual ferroelectric and magnetic phases in multiferroic 2H-BaMnO$_3$ ceramics,"  The structural phase transition in hexagonal BaMnO$_3$ occurring at $T_c$=130
+K was studied in ceramic samples using electron and X-ray diffraction, second
+harmonic generation as well as by dielectric and lattice dynamic
+spectroscopies. The low-temperature phase (space group $P6_{3}cm$) is
+ferroelectric with a triplicated unit cell. The phase transition is driven by
+an optical soft mode from the Brillouin-zone boundary [$q =
+(\frac{1}{3},\frac{1}{3},0)$]; this mode activates in infrared and Raman
+spectra below $T_c$ and it hardens according to the Cochran law. Upon cooling
+below $T_c$, the permittivity exhibits an unusual linear increase with
+temperature; below 60 K, in turn, a frequency-dependent decrease is observed,
+which can be explained by slowing-down of ferroelectric domain wall motions.
+Based on our data we could not distinguish whether the high-temperature phase
+is paraelectric or polar (space groups $P6_{3}/mmc$ or $P6_{3}mc$,
+respectively). Both variants of the phase transition to the ferroelectric phase
+are discussed based on the Landau theory. Electron paramagnetic resonance and
+magnetic susceptibility measurements reveal an onset of one-dimensional
+antiferromagnetic ordering below $\approx220\,\rm K$ which develops fully near
+140 K and, below $T_{n} \approx 59\,\rm K$, it transforms into a
+three-dimensional antiferromagnetic order.
+"
+26020,Kobayashi-Hitchin correspondence for analytically stable bundles,"  We prove the existence of a Hermitian-Einstein metric on holomorphic vector
+bundles with a Hermitian metric satisfying the analytic stability condition,
+under some assumption for the underlying Kähler manifolds. We also study the
+curvature decay of the Hermitian-Einstein metrics. It is useful for the study
+of the classification of instantons and monopoles on the quotient of
+$4$-dimensional Euclidean space by some types of closed subgroups. We also
+explain examples of doubly periodic monopoles corresponding to some algebraic
+data.
+"
+26021,A three-dimensional statistical model for imaged microstructures of porous polymer films,"  A thresholded Gaussian random field model is developed for the microstructure
+of porous materials. Defining the random field as a solution to stochastic
+partial differential equation allows for flexible modelling of
+non-stationarities in the material and facilitates computationally efficient
+methods for simulation and model fitting. A Markov Chain Monte Carlo algorithm
+is developed and used to fit the model to three-dimensional confocal laser
+scanning microscopy images. The methods are applied to study a porous
+ethylcellulose/hydroxypropylcellulose polymer blend that is used as a coating
+to control drug release from pharmaceutical tablets. The aim is to investigate
+how mass transport through the material depends on the microstructure. We
+derive a number of goodness-of-fit measures based on numerically calculated
+diffusion through the material. These are used in combination with measures
+that characterize the geometry of the pore structure to assess model fit. The
+model is found to fit stationary parts of the material well.
+"
+26022,Polyhedral Predictive Regions For Power System Applications,"  Despite substantial improvement in the development of forecasting approaches,
+conditional and dynamic uncertainty estimates ought to be accommodated in
+decision-making in power system operation and market, in order to yield either
+cost-optimal decisions in expectation, or decision with probabilistic
+guarantees. The representation of uncertainty serves as an interface between
+forecasting and decision-making problems, with different approaches handling
+various objects and their parameterization as input. Following substantial
+developments based on scenario-based stochastic methods, robust and
+chance-constrained optimization approaches have gained increasing attention.
+These often rely on polyhedra as a representation of the convex envelope of
+uncertainty. In the work, we aim to bridge the gap between the probabilistic
+forecasting literature and such optimization approaches by generating forecasts
+in the form of polyhedra with probabilistic guarantees. For that, we see
+polyhedra as parameterized objects under alternative definitions (under $L_1$
+and $L_\infty$ norms), the parameters of which may be modelled and predicted.
+We additionally discuss assessing the predictive skill of such multivariate
+probabilistic forecasts. An application and related empirical investigation
+results allow us to verify probabilistic calibration and predictive skills of
+our polyhedra.
+"
+26023,Structured Neural Network Dynamics for Model-based Control,"  We present a structured neural network architecture that is inspired by
+linear time-varying dynamical systems. The network is designed to mimic the
+properties of linear dynamical systems which makes analysis and control simple.
+The architecture facilitates the integration of learned system models with
+gradient-based model predictive control algorithms, and removes the requirement
+of computing potentially costly derivatives online. We demonstrate the efficacy
+of this modeling technique in computing autonomous control policies through
+evaluation in a variety of standard continuous control domains.
+"
+26024,A chemical signature from fast-rotating low-metallicity massive stars: ROA 276 in omega Centauri,"  We present a chemical abundance analysis of a metal-poor star, ROA 276, in
+the stellar system omega Centauri. We confirm that this star has an unusually
+high [Sr/Ba] abundance ratio. Additionally, ROA 276 exhibits remarkably high
+abundance ratios, [X/Fe], for all elements from Cu to Mo along with normal
+abundance ratios for the elements from Ba to Pb. The chemical abundance pattern
+of ROA 276, relative to a primordial omega Cen star ROA 46, is best fit by a
+fast-rotating low-metallicity massive stellar model of 20 Msun, [Fe/H] = -1.8,
+and an initial rotation 0.4 times the critical value; no other nucleosynthetic
+source can match the neutron-capture element distribution. ROA 276 arguably
+offers the most definitive proof to date that fast-rotating massive stars
+contributed to the production of heavy elements in the early Universe.
+"
+26025,Dynamic Quantile Function Models,"  We offer a novel way of thinking about the modelling of the time-varying
+distributions of financial asset returns. Borrowing ideas from symbolic data
+analysis, we consider data representations beyond scalars and vectors.
+Specifically, we consider a quantile function as an observation, and develop a
+new class of dynamic models for quantile-function-valued (QF-valued) time
+series. In order to make statistical inferences and account for parameter
+uncertainty, we propose a method whereby a likelihood function can be
+constructed for QF-valued data, and develop an adaptive MCMC sampling algorithm
+for simulating from the posterior distribution. Compared to modelling realised
+measures, modelling the entire quantile functions of intra-daily returns allows
+one to gain more insight into the dynamic structure of price movements. Via
+simulations, we show that the proposed MCMC algorithm is effective in
+recovering the posterior distribution, and that the posterior means are
+reasonable point estimates of the model parameters. For empirical studies, the
+new model is applied to analysing one-minute returns of major international
+stock indices. Through quantile scaling, we further demonstrate the usefulness
+of our method by forecasting one-step-ahead the Value-at-Risk of daily returns.
+"
+26026,Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models,"  Assuming stationarity is unrealistic in many time series applications. A more
+realistic alternative is to allow for piecewise stationarity, where the model
+is allowed to change at given time points. We propose a three-stage procedure
+for consistent estimation of both structural change points and parameters of
+high-dimensional piecewise vector autoregressive (VAR) models. In the first
+step, we reformulate the change point detection problem as a high-dimensional
+variable selection one, and propose a penalized least square estimator using a
+total variation penalty. We show that the proposed penalized estimation method
+over-estimates the number of change points. We then propose a backward
+selection criterion in conjunction with a penalized least square estimator to
+tackle this issue. In the last step of our procedure, we estimate the VAR
+parameters in each of the segments. We prove that the proposed procedure
+consistently detects the number of change points and their locations. We also
+show that the procedure consistently estimates the VAR parameters. The
+performance of the method is illustrated through several simulation scenarios
+and real data examples.
+"
+26027,Characteristic-Sorted Portfolios: Estimation and Inference,"  Portfolio sorting is ubiquitous in the empirical finance literature, where it
+has been widely used to identify pricing anomalies. Despite its popularity,
+little attention has been paid to the statistical properties of the procedure.
+We develop a general framework for portfolio sorting by casting it as a
+nonparametric estimator. We present valid asymptotic inference methods, and a
+valid mean square error expansion of the estimator leading to an optimal choice
+for the number of portfolios. In practical settings, the optimal choice may be
+much larger than standard choices of 5 or 10. To illustrate the relevance of
+our results, we revisit the size and momentum anomalies.
+"
+26028,Folding approach to topological orders enriched by mirror symmetry,"  We develop a folding approach to study two-dimensional symmetry-enriched
+topological (SET) phases with the mirror reflection symmetry. Our folding
+approach significantly transforms the mirror SETs, such that their properties
+can be conveniently studied through previously known tools: (i) it maps the
+nonlocal mirror symmetry to an onsite $\mathbb{Z}_2$ layer-exchange symmetry
+after folding the SET along the mirror axis, so that we can gauge the symmetry;
+(ii) it maps all mirror SET information into the boundary properties of the
+folded system, so that they can be studied by the anyon condensation theory---a
+general theory for studying gapped boundaries of topological orders; (iii) it
+makes the mirror anomalies explicitly exposed in the boundary properties, i.e.,
+strictly 2D SETs and those that can only live on the surface of a 3D system can
+be easily distinguished through the folding approach. With the folding
+approach, we derive a set of physical constraints on data that describes mirror
+SET, namely mirror permutation and mirror symmetry fractionalization on the
+anyon excitations in the topological order. We conjecture that these
+constraints may be complete, in the sense that all solutions are realizable in
+physical systems. Several examples are discussed to justify this. Previously
+known general results on the classification and anomalies are also reproduced
+through our approach.
+"
+26029,Automatic Selection of t-SNE Perplexity,"  t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely
+used dimensionality reduction methods for data visualization, but it has a
+perplexity hyperparameter that requires manual selection. In practice, proper
+tuning of t-SNE perplexity requires users to understand the inner working of
+the method as well as to have hands-on experience. We propose a model selection
+objective for t-SNE perplexity that requires negligible extra computation
+beyond that of the t-SNE itself. We empirically validate that the perplexity
+settings found by our approach are consistent with preferences elicited from
+human experts across a number of datasets. The similarities of our approach to
+Bayesian information criteria (BIC) and minimum description length (MDL) are
+also analyzed.
+"
+26030,Status of Dark Matter in the Universe,"  Over the past few decades, a consensus picture has emerged in which roughly a
+quarter of the universe consists of dark matter. I begin with a review of the
+observational evidence for the existence of dark matter: rotation curves of
+galaxies, gravitational lensing measurements, hot gas in clusters, galaxy
+formation, primordial nucleosynthesis and cosmic microwave background
+observations. Then I discuss a number of anomalous signals in a variety of data
+sets that may point to discovery, though all of them are controversial. The
+annual modulation in the DAMA detector and/or the gamma-ray excess seen in the
+Fermi Gamma Ray Space Telescope from the Galactic Center could be due to WIMPs;
+a 3.5 keV X-ray line from multiple sources could be due to sterile neutrinos;
+or the 511 keV line in INTEGRAL data could be due to MeV dark matter. All of
+these would require further confirmation in other experiments or data sets to
+be proven correct. In addition, a new line of research on dark stars is
+presented, which suggests that the first stars to exist in the universe were
+powered by dark matter heating rather than by fusion: the observational
+possibility of discovering dark matter in this way is discussed.
+"
+26031,Non-weight representations of Cartan type S Lie algebras,"  For the two Cartan type S subalgebras of the Witt algebra $\W_n$, called Lie
+algebras of divergence-zero vector fields, we determine all module structures
+on the universal enveloping algebra of their Cartan subalgebra $\h_n$. We also
+give all submodules of these modules.
+"
+26032,Solutions of fractional logistic equations by Euler's numbers,"  In this paper, we solve in the convergence set, the fractional logistic
+equation making use of Euler's numbers. To our knowledge, the answer is still
+an open question. The key point is that the coefficients can be connected with
+Euler's numbers, and then they can be explicitly given. The constrained of our
+approach is that the formula is not valid outside the convergence set,
+The idea of the proof consists to explore some analogies with logistic
+function and Euler's numbers, and then to generalize them in the fractional
+case.
+"
+26033,ParseIT: A Question-Answer based Tool to Learn Parsing Techniques,"  Parsing (also called syntax analysis) techniques cover a substantial portion
+of any undergraduate Compiler Design course. We present ParseIT, a tool to help
+students understand the parsing techniques through question-answering. ParseIT
+automates the generation of tutorial questions based on the Context Free
+Grammar provided by the student and generates feedback for the student
+solutions. The tool generates multiple-choice questions (MCQs) and fill in the
+blank type questions, and evaluates students' attempts. It provides hints for
+incorrect attempts, again in terms of MCQs. The hints questions are generated
+for any correct choice that is missed or any incorrect choice that is selected.
+Another interesting form of hint generated is an input string that helps the
+students identify incorrectly filled cells of a parsing table. We also present
+results of a user study conducted to measure the effectiveness of ParseIT.
+"
+26034,Graph Convolutional Matrix Completion,"  We consider matrix completion for recommender systems from the point of view
+of link prediction on graphs. Interaction data such as movie ratings can be
+represented by a bipartite user-item graph with labeled edges denoting observed
+ratings. Building on recent progress in deep learning on graph-structured data,
+we propose a graph auto-encoder framework based on differentiable message
+passing on the bipartite interaction graph. Our model shows competitive
+performance on standard collaborative filtering benchmarks. In settings where
+complimentary feature information or structured data such as a social network
+is available, our framework outperforms recent state-of-the-art methods.
+"
+26035,Traffic Profiling for Mobile Video Streaming,"  This paper describes a novel system that provides key parameters of HTTP
+Adaptive Streaming (HAS) sessions to the lower layers of the protocol stack. A
+non-intrusive traffic profiling solution is proposed that observes packet flows
+at the transmit queue of base stations, edge-routers, or gateways. By analyzing
+IP flows in real time, the presented scheme identifies different phases of an
+HAS session and estimates important application-layer parameters, such as
+play-back buffer state and video encoding rate. The introduced estimators only
+use IP-layer information, do not require standardization and work even with
+traffic that is encrypted via Transport Layer Security (TLS). Experimental
+results for a popular video streaming service clearly verify the high accuracy
+of the proposed solution. Traffic profiling, thus, provides a valuable
+alternative to cross-layer signaling and Deep Packet Inspection (DPI) in order
+to perform efficient network optimization for video streaming.
+"
+26036,Kolmogorov turbulence defeated by Anderson localization for a Bose-Einstein condensate in a Sinai-oscillator trap,"  We study the dynamics of a Bose-Einstein condensate in a Sinai-oscillator
+trap under a monochromatic driving force. Such a trap is formed by a harmonic
+potential and a repulsive disk located in the center vicinity corresponding to
+the first experiments of condensate formation by Ketterle group in 1995. We
+argue that the external driving allows to model the regime of weak wave
+turbulence with the Kolmogorov energy flow from low to high energies. We show
+that in a certain regime of weak driving and weak nonlinearity such a turbulent
+energy flow is defeated by the Anderson localization that leads to localization
+of energy on low energy modes. A critical threshold is determined above which
+the turbulent flow to high energies becomes possible. We argue that this
+phenomenon can be studied with ultra cold atoms in magneto-optical traps.
+"
+26037,"Effectful Applicative Bisimilarity: Monads, Relators, and Howe's Method (Long Version)","  We study Abramsky's applicative bisimilarity abstractly, in the context of
+call-by-value $\lambda$-calculi with algebraic effects. We first of all endow a
+computational $\lambda$-calculus with a monadic operational semantics. We then
+show how the theory of relators provides precisely what is needed to generalise
+applicative bisimilarity to such a calculus, and to single out those monads and
+relators for which applicative bisimilarity is a congruence, thus a sound
+methodology for program equivalence. This is done by studying Howe's method in
+the abstract.
+"
+26038,Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications,"  With the recent exponential growth of applications using artificial
+intelligence (AI), the development of efficient and ultrafast brain-like
+(neuromorphic) systems is crucial for future information and communication
+technologies. While the implementation of AI systems using computer algorithms
+of neural networks is emerging rapidly, scientists are just taking the very
+first steps in the development of the hardware elements of an artificial brain,
+specifically neuromorphic microchips. In this review article, we present the
+current state of neuromorphic photonic circuits based on solid-state
+optoelectronic oscillators formed by nanoscale double barrier quantum well
+resonant tunneling diodes. We address, both experimentally and theoretically,
+the key dynamic properties of recently developed artificial solid-state neuron
+microchips with delayed perturbations and describe their role in the study of
+neural activity and regenerative memory. This review covers our recent research
+work on excitable and delay dynamic characteristics of both single and autaptic
+(delayed) artificial neurons including all-or-none response, spike-based data
+encoding, storage, signal regeneration and signal healing. Furthermore, the
+neural responses of these neuromorphic microchips display all the signatures of
+extended spatio-temporal localized structures (LSs) of light, which are
+reviewed here in detail. By taking advantage of the dissipative nature of LSs,
+we demonstrate potential applications in optical data reconfiguration and clock
+and timing at high-speeds and with short transients. The results reviewed in
+this article are a key enabler for the development of high-performance
+optoelectronic devices in future high-speed brain-inspired optical memories and
+neuromorphic computing.
+"
+26039,DimReader: Axis lines that explain non-linear projections,"  Non-linear dimensionality reduction (NDR) methods such as LLE and t-SNE are
+popular with visualization researchers and experienced data analysts, but
+present serious problems of interpretation. In this paper, we present
+DimReader, a technique that recovers readable axes from such techniques.
+DimReader is based on analyzing infinitesimal perturbations of the dataset with
+respect to variables of interest. The perturbations define exactly how we want
+to change each point in the original dataset and we measure the effect that
+these changes have on the projection. The recovered axes are in direct analogy
+with the axis lines (grid lines) of traditional scatterplots. We also present
+methods for discovering perturbations on the input data that change the
+projection the most. The calculation of the perturbations is efficient and
+easily integrated into programs written in modern programming languages. We
+present results of DimReader on a variety of NDR methods and datasets both
+synthetic and real-life, and show how it can be used to compare different NDR
+methods. Finally, we discuss limitations of our proposal and situations where
+further research is needed.
+"
+26040,Stable Wireless Network Control Under Service Constraints,"  We consider the design of wireless queueing network control policies with
+particular focus on combining stability with additional application-dependent
+requirements. Thereby, we consequently pursue a cost function based approach
+that provides the flexibility to incorporate constraints and requirements of
+particular services or applications. As typical examples of such requirements,
+we consider the reduction of buffer underflows in case of streaming traffic,
+and energy efficiency in networks of battery powered nodes. Compared to the
+classical throughput optimal control problem, such requirements significantly
+complicate the control problem. We provide easily verifyable theoretical
+conditions for stability, and, additionally, compare various candidate cost
+functions applied to wireless networks with streaming media traffic. Moreover,
+we demonstrate how the framework can be applied to the problem of energy
+efficient routing, and we demonstrate the aplication of our framework in
+cross-layer control problems for wireless multihop networks, using an advanced
+power control scheme for interference mitigation, based on successive convex
+approximation. In all scenarios, the performance of our control framework is
+evaluated using extensive numerical simulations.
+"
+26041,"Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking","  For crowded scenes, the accuracy of object-based computer vision methods
+declines when the images are low-resolution and objects have severe occlusions.
+Taking counting methods for example, almost all the recent state-of-the-art
+counting methods bypass explicit detection and adopt regression-based methods
+to directly count the objects of interest. Among regression-based methods,
+density map estimation, where the number of objects inside a subregion is the
+integral of the density map over that subregion, is especially promising
+because it preserves spatial information, which makes it useful for both
+counting and localization (detection and tracking). With the power of deep
+convolutional neural networks (CNNs) the counting performance has improved
+steadily. The goal of this paper is to evaluate density maps generated by
+density estimation methods on a variety of crowd analysis tasks, including
+counting, detection, and tracking. Most existing CNN methods produce density
+maps with resolution that is smaller than the original images, due to the
+downsample strides in the convolution/pooling operations. To produce an
+original-resolution density map, we also evaluate a classical CNN that uses a
+sliding window regressor to predict the density for every pixel in the image.
+We also consider a fully convolutional (FCNN) adaptation, with skip connections
+from lower convolutional layers to compensate for loss in spatial information
+during upsampling. In our experiments, we found that the lower-resolution
+density maps sometimes have better counting performance. In contrast, the
+original-resolution density maps improved localization tasks, such as detection
+and tracking, compared to bilinear upsampling the lower-resolution density
+maps. Finally, we also propose several metrics for measuring the quality of a
+density map, and relate them to experiment results on counting and
+localization.
+"
+26042,"A Compact, Wide Field-of-View Gradient-index Lens Antenna for Millimeter-wave MIMO on Mobile Devices","  Lens-based beam-forming antennas offer a low-power, low-cost alternative to
+hybrid beamforming antenna arrays. They are ideally suited to millimeter-wave
+massive MIMO systems due to their native beam-space operation and angular
+selectivity and minimal dependence of high-speed data converters. We discuss
+the design of a compact and low-cost lens-based beam-forming antenna for small
+form-factor platforms such as small-cells and mobile devices in 5G wireless
+networks. We discuss a gradient-index design method and low-cost fabrication
+method based on perforated dielectrics. We discuss the need for high-contrast
+permittivity ranges to achieve wide scan angles which are essential for
+leveraging the full capability of massive MIMO systems (e.g., full stream
+capacity). Finally, we show that by using an appropriately designed perforated
+medium, gradient-index lenses with low minimum permittivity of 1.25 can achieve
+a maximum beam-steering angle of 44 degrees. We suggest that such an approach
+can enable practical low-loss, low-cost, and compact beam-steering lens
+antennas for millimeter-wave MIMO with wide beam-steering angles.
+"
+26043,Odd-symplectic forms via surgery and minimality in symplectic dynamics,"  We construct an infinite family of odd-symplectic forms (also known as
+Hamiltonian structures) on the 3-sphere that do not admit a symplectic
+cobordism to the standard contact structure on the 3-sphere. This answers in
+the negative a question raised by Joel Fish motivated by the search for minimal
+characteristic flows.
+"
+26044,Object-Oriented Sokoban Solver: A Serious Game Project for OOAD and AI Education,"  Serious games are beneficial for education in various computer science areas.
+Numerous works have reported the experiences of using games (not only playing
+but also development) in teaching and learning. Considering it could be
+difficult for teachers/students to prepare/develop a game from scratch during
+one semester, assistant educational materials would be crucial in the
+corresponding courses. Unfortunately, the literature shows that not many
+materials from educational game projects are shared. To help different
+educators identify suitable courseware and help students implement game
+development, it is worth further investigating and accumulating the educational
+resources from individual game projects. Following such an idea, this paper
+proposes a game development project of an object-oriented Sokoban solver, and
+exposes relevant educational materials. The documented system design can be
+viewed as a ready-to-use resource for education in object-oriented analysis and
+design (OOAD), while the Sokoban solver itself may be used as an assignment
+platform for teaching artificial intelligence (AI). Further documentation,
+platform, and APIs will be realized and shared in the future to facilitate
+others' educational activities. Overall, this work is supposed to inspire and
+encourage other researchers and educators to post available materials of more
+game projects for the purpose of sharing and reuse.
+"
+26045,The irreducible representations of 3-dimensional Sklyanin algebras,"  In this article a complete description is given of the simple representations
+of a 3-dimensional Sklyanin algebra associated to a torsion point. In order to
+determine these irreducible representations, a review is given of classical
+results regarding representation theory of graded rings with excellent
+homological and algebraic properties.
+"
+26046,A Multi-model Combination Approach for Probabilistic Wind Power Forecasting,"  Short-term probabilistic wind power forecasting can provide critical
+quantified uncertainty information of wind generation for power system
+operation and control. As the complicated characteristics of wind power
+prediction error, it would be difficult to develop a universal forecasting
+model dominating over other alternative models. Therefore, a novel multi-model
+combination (MMC) approach for short-term probabilistic wind generation
+forecasting is proposed in this paper to exploit the advantages of different
+forecasting models. The proposed approach can combine different forecasting
+models those provide different kinds of probability density functions to
+improve the probabilistic forecast accuracy. Three probabilistic forecasting
+models based on the sparse Bayesian learning, kernel density estimation and
+beta distribution fitting are used to form the combined model. The parameters
+of the MMC model are solved based on Bayesian framework. Numerical tests
+illustrate the effectiveness of the proposed MMC approach.
+"
+26047,Extended localized structures and the onset of turbulence in channel flow,"  In this letter, it is shown numerically that in plane Poiseuille flow and
+before the threshold of equilibrium turbulence defined by the
+directed-percolation universality class, a sparse turbulent state in form of
+localized turbulent band can sustain either by continuous increase of the
+turbulence fraction due to band extension when the flow domain is large enough,
+or by a dynamic balance between the band extension and the band breaking and
+decay caused by the band interaction in a finite domain. The width and tilt
+angle of the band keep statistically invariant during its oblique extension, a
+process which is not sensitive to random disturbances.
+"
+26048,Sign-change of the Fourier coefficients of a Hauptmodul for $Γ_{0}(2)$,"  In this short note, we aim to prove that the Fourier coefficients of the
+modular function $ \frac{\eta^{24}(\tau)}{\eta^{24}(2\tau)} $ possess a
+sign-change property.
+"
+26049,Test statistics and p-values,"  We point out that the traditional notion of test statistic is too narrow, and
+we propose a natural generalization that is arguably maximal. The study is
+restricted to simple statistical hypotheses.
+"
+26050,On the Resistance of Neural Nets to Label Noise,"  We investigate the behavior of convolutional neural networks (CNN) in the
+presence of label noise. We show empirically that CNN prediction for a given
+test sample depends on the labels of the training samples in its local
+neighborhood. This is similar to the way that the K-nearest neighbors (K-NN)
+classifier works. With this understanding, we derive an analytical expression
+for the expected accuracy of a K-NN, and hence a CNN, classifier for any level
+of noise. In particular, we show that K-NN, and CNN, are resistant to label
+noise that is randomly spread across the training set, but are very sensitive
+to label noise that is concentrated. Experiments on real datasets validate our
+analytical expression by showing that they match the empirical results for
+varying degrees of label noise.
+"
+26051,Unlabeled Data for Morphological Generation With Character-Based Sequence-to-Sequence Models,"  We present a semi-supervised way of training a character-based
+encoder-decoder recurrent neural network for morphological reinflection, the
+task of generating one inflected word form from another. This is achieved by
+using unlabeled tokens or random strings as training data for an autoencoding
+task, adapting a network for morphological reinflection, and performing
+multi-task training. We thus use limited labeled data more effectively,
+obtaining up to 9.9% improvement over state-of-the-art baselines for 8
+different languages.
+"
+26052,The Pointing Self Calibration algorithm for aperture synthesis radio telescopes,"  This paper is concerned with algorithms for calibration of direction
+dependent effects (DDE) in aperture synthesis radio telescopes (ASRT). After
+correction of Direction Independent Effects (DIE) using self-calibration,
+imaging performance can be limited by the imprecise knowledge of the forward
+gain of the elements in the array. In general, the forward gain pattern is
+directionally dependent and varies with time due to a number of reasons. Some
+factors, such as rotation of the primary beam with Parallactic Angle for
+Azimuth-Elevation mount antennas are known a priori. Some, such as antenna
+pointing errors and structural deformation/projection effects for
+aperture-array elements cannot be measured {\em a priori}. Thus, in addition to
+algorithms to correct for DD effects known a priori, algorithms to solve for DD
+gains are required for high dynamic range imaging. Here, we discuss a
+mathematical framework for antenna-based DDE calibration algorithms and show
+that this framework leads to computationally efficient optimal algorithms which
+scale well in a parallel computing environment. As an example of an
+antenna-based DD calibration algorithm, we demonstrate the Pointing SelfCal
+algorithm to solve for the antenna pointing errors. Our analysis show that the
+sensitivity of modern ASRT is sufficient to solve for antenna pointing errors
+and other DD effects. We also discuss the use of the Pointing SelfCal algorithm
+in real-time calibration systems and extensions for antenna Shape SelfCal
+algorithm for real-time tracking and corrections for pointing offsets and
+changes in antenna shape.
+"
+26053,Interacting Attention-gated Recurrent Networks for Recommendation,"  Capturing the temporal dynamics of user preferences over items is important
+for recommendation. Existing methods mainly assume that all time steps in
+user-item interaction history are equally relevant to recommendation, which
+however does not apply in real-world scenarios where user-item interactions can
+often happen accidentally. More importantly, they learn user and item dynamics
+separately, thus failing to capture their joint effects on user-item
+interactions. To better model user and item dynamics, we present the
+Interacting Attention-gated Recurrent Network (IARN) which adopts the attention
+model to measure the relevance of each time step. In particular, we propose a
+novel attention scheme to learn the attention scores of user and item history
+in an interacting way, thus to account for the dependencies between user and
+item dynamics in shaping user-item interactions. By doing so, IARN can
+selectively memorize different time steps of a user's history when predicting
+her preferences over different items. Our model can therefore provide
+meaningful interpretations for recommendation results, which could be further
+enhanced by auxiliary features. Extensive validation on real-world datasets
+shows that IARN consistently outperforms state-of-the-art methods.
+"
+26054,Unconventional pairing induced anomalous transverse shift in Andreev reflection,"  Superconductors with unconventional pairings have been a fascinating subject
+of research, for which a central issue is to explore effects that can be used
+to characterize the pairing. The process of Andreev reflection--the reflection
+of an electron as a hole at a normal-mental-superconductor interface by
+transferring a Cooper pair into the superconductor--offers a basic mechanism to
+probe the pairing through transport. Here we predict that in Andreev reflection
+from unconventional superconductors, the reflected hole acquires an anomalous
+spatial shift normal to the plane of incidence, arising from the unconventional
+pairing. The transverse shift is sensitive to the superconducting gap
+structure, exhibiting characteristic features for each pairing type, and can be
+detected as voltage signals. Our work not only unveils a fundamentally new
+effect but also suggests a powerful new technique capable of probing the
+structure of unconventional pairings.
+"
+26055,A Deep Learning Perspective on the Origin of Facial Expressions,"  Facial expressions play a significant role in human communication and
+behavior. Psychologists have long studied the relationship between facial
+expressions and emotions. Paul Ekman et al., devised the Facial Action Coding
+System (FACS) to taxonomize human facial expressions and model their behavior.
+The ability to recognize facial expressions automatically, enables novel
+applications in fields like human-computer interaction, social gaming, and
+psychological research. There has been a tremendously active research in this
+field, with several recent papers utilizing convolutional neural networks (CNN)
+for feature extraction and inference. In this paper, we employ CNN
+understanding methods to study the relation between the features these
+computational networks are using, the FACS and Action Units (AU). We verify our
+findings on the Extended Cohn-Kanade (CK+), NovaEmotions and FER2013 datasets.
+We apply these models to various tasks and tests using transfer learning,
+including cross-dataset validation and cross-task performance. Finally, we
+exploit the nature of the FER based CNN models for the detection of
+micro-expressions and achieve state-of-the-art accuracy using a simple
+long-short-term-memory (LSTM) recurrent neural network (RNN).
+"
+26056,Optimal Dividend of Compound Poisson Process under a Stochastic Interest Rate,"  In this paper we assume the insurance wealth process is driven by the
+compound Poisson process. The discounting factor is modelled as a geometric
+Brownian motion at first and then as an exponential function of an integrated
+Ornstein-Uhlenbeck process. The objective is to maximize the cumulated value of
+expected discounted dividends up to the time of ruin. We give an explicit
+expression of the value function and the optimal strategy in the case of
+interest rate following a geometric Brownian motion. For the case of the
+Vasicek model, we explore some properties of the value function. Since we can
+not find an explicit expression for the value function in the second case, we
+prove that the value function is the viscosity solution of the corresponding
+HJB equation.
+"
+26057,Improved Community Detection using Deep Embeddings from Multilayer Graphs,"  Community detection is a challenging, yet crucial, problem while mining
+large-scale graph structured data. Most existing approaches solve this problem
+by mapping nodes into a vector space and performing unsupervised learning with
+the resulting embeddings. In cases where multiple types of connectivity
+patterns exist for the set of nodes, commonly modeled as multilayer graphs, new
+strategies are required to model the inter-layer dependencies in order to
+perform effective inferencing. In this paper, we focus on learning embeddings
+for each node of a multilayer graph through neural modeling techniques, such
+that the complex dependencies can be concisely encoded into low-dimensional
+representations. Referred to as multilayer graph embeddings, these
+representations can be utilized for discovering community structure in a
+scalable fashion, even with a large number of layers. Furthermore, in order to
+ensure that the semantics that persist over a longer range in the network are
+well modeled, we propose to refine the multilayer embeddings via a proxy
+clustering loss and a graph modularity measure. Using real-world datasets, we
+demonstrate that this algorithm generates scalable and robust representations,
+and outperforms existing multilayer community detection approaches.
+"
+26058,"Design, Modeling and Dynamic Compensation PID Control of a Fully-Actuated Aerial Manipulation System","  This paper addresses design, modeling and dynamic-compensation PID (dc-PID)
+control of a novel type of fully-actuated aerial manipulation (AM) system.
+Firstly, design of novel mechanical structure of the AM is presented. Secondly,
+kinematics and dynamics of AM are modeled using Craig parameters and recursion
+Newton-Euler equations respectively, which give rise to a more accurate dynamic
+relationship between aerial platform and manipulator. Then, the
+dynamic-compensation PID control is proposed to solve the problem of
+fully-actuated control of AM. Finally, uniform coupled matrix equations between
+driving forces/moments and rotor speeds are derived, which can support design
+and analysis of parameters and decoupling theoretically. It is taken into
+account practical problems including noise and perturbation, parameter
+uncertainty, and power limitation in simulations, and results from simulations
+shows that the AM system presented can be fully-actued controlled with advanced
+control performances, which can not achieved theoretically in traditional AM.
+And with compared to backstepping control dc-PID has better control accuracy
+and capability to disturbance rejection in two simulations of aerial operation
+tasks with motion of joint. The experiment of dc-pid proves the availability
+and effectiveness of the method proposed.
+"
+26059,Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders,"  Traditional image clustering methods take a two-step approach, feature
+learning and clustering, sequentially. However, recent research results
+demonstrated that combining the separated phases in a unified framework and
+training them jointly can achieve a better performance. In this paper, we first
+introduce fully convolutional auto-encoders for image feature learning and then
+propose a unified clustering framework to learn image representations and
+cluster centers jointly based on a fully convolutional auto-encoder and soft
+$k$-means scores. At initial stages of the learning procedure, the
+representations extracted from the auto-encoder may not be very discriminative
+for latter clustering. We address this issue by adopting a boosted
+discriminative distribution, where high score assignments are highlighted and
+low score ones are de-emphasized. With the gradually boosted discrimination,
+clustering assignment scores are discriminated and cluster purities are
+enlarged. Experiments on several vision benchmark datasets show that our
+methods can achieve a state-of-the-art performance.
+"
+26060,Quantum Monte Carlo calculation of the bridge reaction barrier to hydrogen dissociation on Pt(111),"  The yardstick of new first-principles approaches to key points on reaction
+paths at metal surfaces is chemical accuracy compared to reliable experiment.
+By this we mean that such values as the activation barrier are required to
+within 1 kcal/mol. Quantum Monte Carlo (QMC) is a promising (albeit lengthy)
+first-principles method for this and we are now beyond the dawn of QMC
+benchmarks for these systems, since hydrogen dissociation on Cu(111) has been
+studied with quite adequate accuracy in two improving QMC studies Hoggan ArXiv
+2015, K. Doblhoff-Deir et al JCTC (2017).
+Pt and Cu require the use of pseudo-potentials in these calculations and we
+show that those of Pt are less problematic than those for Cu, particularly for
+QMC work.
+In this letter, we determine the activation barrier to hydrogen dissociation
+on Pt(111) using the bridging geometry. This is found to agree to better than
+chemical accuracy with recent Specific Reaction Parameter (SRP-DFT) work, which
+is itself within chemical accuracy of measurement (Kroes et al Cplett 2017).
+The results suggest the dissociation barrier for hydrogen on Pt(111) is close
+to 5.4 (QMC) and 6.2 (SRP-DFT) kcal/mol with a QMC standard error of 1.08
+kcal/mol.
+This is encouraging for establishing less well-known benchmark values of
+industrial reaction barriers on Pt(111).
+"
+26061,Economics of disagreement -- financial intuition for the Rényi divergence,"  Disagreement is an essential element of science and life in general.
+Currently, the amount of disagreement between two probability distributions is
+quantified by highly abstract entropic measures such as the Rényi divergence.
+Despite their widespread use in science and engineering, such quantities lack
+numerical intuition and their axiomatic definitions contain no practical
+insight as to how the disagreement can be resolved. An economic approach
+addresses both of these problems by transforming disagreements into tangible
+investment opportunities. The Rényi divergence appears connected to the
+optimized performance of such investments. Optimization around individual
+opinions provides a social mechanism by which funds flow naturally to support a
+more accurate view. Such social mechanisms can help to resolve difficult
+disagreements (e.g., financial arguments concerning the global climate).
+"
+26062,Numerical Study of Nonlinear Dynamics of a Population System with Time Delay,"  Mathematical models of interacting populations are often constructed as
+systems of differential equations, which describe how populations change with
+time. Below we study one such model connected to the nonlinear dynamics of a
+system of populations in presence of time delay. The consequence of the
+presence of the time delay is that the nonlinear dynamics of the studied system
+become more rich, e.g., new orbits in the phase space of the system arise which
+are dependent on the time-delay parameters. In more detail we introduce a time
+delay and generalize the model system of differential equations for the
+interaction of three populations based on generalized Volterra equations in
+which the growth rates and competition coefficients of populations depend on
+the number of members of all populations
+\cite{Dimitrova2001a},\cite{Dimitrova2001b} and then numerically solve the
+system with and without time delay. We use a modification of the method of
+Adams for the numerical solution of the system of model equations with time
+delay. By appropriate selection of the parameters and initial conditions we
+show the impact of the delay time on the dynamics of the studied population
+system.
+"
+26063,Numerical Synthesis of Pontryagin Optimal Control Minimizers Using Sampling-Based Methods,"  Optimal control remains as one of the most versatile frameworks in systems
+theory, enabling applications ranging from classical robust control to
+real-time safe operation of fleets of vehicles. While some optimal control
+problems can be efficiently solved using algebraic or convex methods, most
+general forms of optimal control must be solved using memory- expensive
+numerical methods. In this paper we present a theoretical formulation and a
+corresponding numerical algorithm that can find Pontryagin- optimal inputs for
+general dynamical systems by using a direct method. Pontryagin-optimal inputs,
+those satisfying the Minimum Principle, can be found for many classes of
+problems using indirect methods. But convergent numerical methods to solve
+indirect problems are hard to find and often converge slowly. On the other
+hand, convergent direct optimal control methods are fast and founded on solid
+theory, but their limit points are usually Banach-optimal inputs, which are a
+weaker form of optimality condition. Our result, founded on the theory of
+relaxed inputs as defined by J. Warga, establishes an equivalence between
+Pontryagin- optimal inputs and optimal relaxed inputs. Then, we formu- late a
+sampling-based numerical method to approximate the Pontryagin-optimal relaxed
+inputs using an iterative method. Finally, using a provably-convergent
+numerical method, we synthesize approximations of the Pontryagin-optimal inputs
+from the sampled relaxed inputs.
+"
+26064,Generalized thermoelastic band structures of Rayleigh wave in one-dimensional phononic crystals,"  We investigate generalized band structures of Rayleigh wave in 1-D phononic
+crystals in the context of Green-Nagdhi thermoelastic (TE) theory. Solutions of
+the coupled equations included thermal field are obtained firstly. Then
+according to boundary conditions, phase velocity is derived. The transfer
+matrix methodology is adopted in order to formulate the band structures of
+generalized TE Rayleigh wave. It is demonstrated that the band structure of TE
+Rayleigh wave is comprised of elastic and thermal bands. For Aluminum-Epoxy
+phononic crystal, the thermoelasticity can influence the transmission ability
+of band gaps as well as narrow down the band gap width.
+"
+26065,Central limit theorem for Fourier transform and periodogram of random fields,"  In this paper we show that the limiting distribution of the real and the
+imaginary part of the double Fourier transform of a stationary random field is
+almost surely an independent vector with Gaussian marginal distributions, whose
+variance is, up to a constant, the field's spectral density. The dependence
+structure of the random field is general and we do not impose any restrictions
+on the speed of convergence to zero of the covariances, or smoothness of the
+spectral density. The only condition required is that the variables are adapted
+to a commuting filtration and are regular in some sense. The results go beyond
+the Bernoulli fields and apply to both short range and long range dependence.
+They can be easily applied to derive the asymptotic behavior of the periodogram
+associated to the random field. The method of proof is based on new
+probabilistic methods involving martingale approximations and also on borrowed
+and new tools from harmonic analysis.
+"
+26066,Vacuum Stability in the Early Universe and the Backreaction of Classical Gravity,"  In the case of a metastable electroweak vacuum the quantum corrected
+effective potential plays a crucial role in the potential instability of the
+Standard Model. In the Early Universe, in particular during inflation and
+reheating, this instability can be triggered leading to catastrophic vacuum
+decay. We discuss how the large spacetime curvature of the Early Universe can
+be incorporated in the calculation and in many cases significantly modify the
+flat space prediction. The two key new elements are the unavoidable generation
+of the non-minimal coupling between the Higgs field and the scalar curvature of
+gravity and a curvature induced contribution to the running of the constants.
+For the minimal set up of the Standard Model and a decoupled inflation sector
+we show how a metastable vacuum can lead to very tight bounds for the
+non-minimal coupling. We also discuss a novel and very much related dark matter
+generation mechanism.
+"
+26067,CUBE: An Information-optimized parallel Cosmological $N$-body Algorithm,"  Cosmological large scale structure $N$-body simulations are
+computation-light, memory-heavy problems in supercomputing. The considerable
+amount of memory is usually dominated by an inefficient way of storing more
+than sufficient phase space information of particles. We present a new
+parallel, information-optimized, particle-mesh-based $N$-body code CUBE, in
+which information-efficiency and memory-efficiency are increased by nearly an
+order of magnitude. This is accomplished by storing particle's relative phase
+space coordinates instead of global values, and in the format of fixed point as
+light as 1 byte. The remaining information is given by complementary density
+and velocity fields (negligible in memory space) and proper ordering of
+particles (no extra memory). Our numerical experiments show that this
+information-optimized $N$-body algorithm provides accurate results within the
+error of the particle-mesh algorithm. This significant lowering of the
+memory-to-computation ratio breaks the bottleneck of scaling up and speeding up
+large cosmological $N$-body simulations on multi-core and heterogeneous
+computing systems.
+"
+26068,Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification,"  We develop three efficient approaches for generating visual explanations from
+3D convolutional neural networks (3D-CNNs) for Alzheimer's disease
+classification. One approach conducts sensitivity analysis on hierarchical 3D
+image segmentation, and the other two visualize network activations on a
+spatial map. Visual checks and a quantitative localization benchmark indicate
+that all approaches identify important brain parts for Alzheimer's disease
+diagnosis. Comparative analysis show that the sensitivity analysis based
+approach has difficulty handling loosely distributed cerebral cortex, and
+approaches based on visualization of activations are constrained by the
+resolution of the convolutional layer. The complementarity of these methods
+improves the understanding of 3D-CNNs in Alzheimer's disease classification
+from different perspectives.
+"
+26069,Structural Conditions for Projection-Cost Preservation via Randomized Matrix Multiplication,"  Projection-cost preservation is a low-rank approximation guarantee which
+ensures that the cost of any rank-$k$ projection can be preserved using a
+smaller sketch of the original data matrix. We present a general structural
+result outlining four sufficient conditions to achieve projection-cost
+preservation. These conditions can be satisfied using tools from the Randomized
+Linear Algebra literature.
+"
+26070,Kähler structures on spaces of framed curves,"  We consider the space $\mathcal{M}$ of Euclidean similarity classes of framed
+loops in $\mathbb{R}^3$. Framed loop space is shown to be an
+infinite-dimensional Kähler manifold by identifying it with a complex
+Grassmannian. We show that the space of isometrically immersed loops studied by
+Millson and Zombro is realized as the symplectic reduction of $\mathcal{M}$ by
+the action of the based loop group of the circle, giving a smooth version of a
+result of Hausmann and Knutson on polygon space. The identification with a
+Grassmannian allows us to describe the geodesics of $\mathcal{M}$ explicitly.
+Using this description, we show that $\mathcal{M}$ and its quotient by the
+reparameterization group are nonnegatively curved. We also show that the planar
+loop space studied by Younes, Michor, Shah and Mumford in the context of
+computer vision embeds in $\mathcal{M}$ as a totally geodesic, Lagrangian
+submanifold. The action of the reparameterization group on $\mathcal{M}$ is
+shown to be Hamiltonian and this is used to characterize the critical points of
+the weighted total twist functional.
+"
+26071,Knots having the same Seifert form and primary decomposition of knot concordance,"  We show that for each Seifert form of an algebraically slice knot with
+nontrivial Alexander polynomial, there exists an infinite family of knots
+having the Seifert form such that the knots are linearly independent in the
+knot concordance group and not concordant to any knot with coprime Alexander
+polynomial. Key ingredients for the proof are Cheeger-Gromov-von Neumann
+rho-invariants for amenable groups developed by Cha and Orr and polynomial
+splittings of metabelian rho-invariants.
+"
+26072,MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks,"  We present MorphNet, an approach to automate the design of neural network
+structures. MorphNet iteratively shrinks and expands a network, shrinking via a
+resource-weighted sparsifying regularizer on activations and expanding via a
+uniform multiplicative factor on all layers. In contrast to previous
+approaches, our method is scalable to large networks, adaptable to specific
+resource constraints (e.g. the number of floating-point operations per
+inference), and capable of increasing the network's performance. When applied
+to standard network architectures on a wide variety of datasets, our approach
+discovers novel structures in each domain, obtaining higher performance while
+respecting the resource constraint.
+"
+26073,Mimicking Dark Matter,"  I show that a very simple model in the context of Newtonian physics promoted
+to a first approximation of general relativity can mimic Dark matter and
+explain most of its intriguing properties. Namely: i) Dark matter is a halo
+associated to ordinary matter; ii) Dark matter does not interact with ordinary
+matter nor with itself; iii) Its influence grows with the size of the aggregate
+of ordinary matter that is considered, and iv) Dark matter influences the
+propagation of light.
+"
+26074,Fixed points of coisotropic subgroups of $Γ_{k}$ on decomposition spaces,"  We study the equivariant homotopy type of the poset of orthogonal
+decompositions of a finite-dimensional complex vector space. Suppose that n is
+a power of a prime p, and that D is an elementary abelian p-subgroup of U(n)
+acting on complex n-space by the regular representation. We prove that the
+fixed point space of D acting on the decomposition poset of complex n-space
+contains as a retract the unreduced suspension of the Tits building for GL(k),
+which a wedge of (k-1)-dimensional spheres. Let Gamma be the projective
+elementary abelian subgroup of U(n) that contains the center of U(n) and acts
+irreducibly on complex n-space. We prove that the fixed point space of Gamma
+acting on the space of proper orthogonal decompositions of complex n-space is
+homeomorphic to a symplectic Tits building, which is also a wedge of
+(k-1)-dimensional spheres. As a consequence of these results, we find that the
+fixed point space of any coisotropic subgroup of Gamma contains, as a retract,
+a wedge of (k-1)-dimensional spheres. We make a conjecture about the full
+homotopy type of the fixed point space of D, based on a more general branching
+conjecture, and we show that the conjecture is consistent with our results.
+"
+26075,Electromagnetic Wave Propagation in GLHUA Invisible Sphere by GL No Scattering Full Wave Modeling and Inversion,"  Using GL no scattering full wave modeling and inversion, we create a GLHUA
+pre cloak electromagnetic (EM) material in the virtual sphere that makes the
+sphere is invisible. The invisible sphere is called GLHUA sphere. In GLHUA
+sphere, the Pre cloak relative parameter is not less than 1; the parameters and
+their derivative are continuous across the boundary r=R2 and the parameters are
+going to infinity at origin r=0. The phase velocity of EM wave in the sphere is
+less than light speed and going to zero at origin. The EM wave field excited in
+the outside of the sphere can not be disturbed by GLHUA sphere. By GL full wave
+method, we rigorously proved the incident EM wave field excited in outside of
+GLHUA sphere and propagation through the sphere without any scattering by the
+sphere, the total EM field in outside of the sphere equal to the incident wave
+field. Moreover, we prove that in GLHUA sphere with the pre cloak material,
+when r is going to origin, EM wave field propagation in GLHUA sphere is going
+to zero. All copyright and patent of the GLHUA EM cloaks,GLHUA sphere and GL
+modeling and inversion methods are reserved by authors in GL Geophysical
+Laboratory.
+"
+26076,A Network Perspective on Attitude Strength: Testing the Connectivity Hypothesis,"  Attitude strength is a key characteristic of attitudes. Strong attitudes are
+durable and impactful, while weak attitudes are fluctuating and
+inconsequential. Recently, the Causal Attitude Network (CAN) model was proposed
+as a comprehensive measurement model of attitudes, which conceptualizes
+attitudes as networks of causally connected evaluative reactions (i.e.,
+beliefs, feelings, and behavior toward an attitude object). Here, we test the
+central postulate of the CAN model that highly connected attitude networks
+correspond to strong attitudes. We use data from the American National Election
+Studies 1980-2012 on attitudes toward presidential candidates (total n =
+18,795). We first show that political interest predicts connectivity of
+attitude networks toward presidential candidates. Second, we show that
+connectivity is strongly related to two defining features of strong attitudes -
+stability of the attitude and the attitude's impact on behavior. We conclude
+that network theory provides a promising framework to advance the understanding
+of attitude strength.
+"
+26077,The conception of reality in Quantum Mechanics,"  Disputes on the foundations of Quantum Mechanics often involve the conception
+of reality, without a clear definition on which aspect of this broad concept of
+reality one refers. We provide an overview of conceptions of reality in
+classical physics, in philosophy of science and in Quantum Mechanics and its
+interpretations and analyse their differences and subtleties. Structural
+realism as conception in philosophy of science will turn out to be a promising
+candidate to settle old problems regarding the conception of reality in Quantum
+Mechanics. We amend the analysis of three main interpretations and their
+relation to structural realism by three well-known informational approaches in
+the interpretations of Quantum Mechanics. Last we propose an additional class
+of structural realism supported by QBism.
+"
+26078,Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys,"  Current synoptic sky surveys monitor large areas of the sky to find variable
+and transient astronomical sources. As the number of detections per night at a
+single telescope easily exceeds several thousand, current detection pipelines
+make intensive use of machine learning algorithms to classify the detected
+objects and to filter out the most interesting candidates. A number of upcoming
+surveys will produce up to three orders of magnitude more data, which renders
+high-precision classification systems essential to reduce the manual and,
+hence, expensive vetting by human experts. We present an approach based on
+convolutional neural networks to discriminate between true astrophysical
+sources and artefacts in reference-subtracted optical images. We show that
+relatively simple networks are already competitive with state-of-the-art
+systems and that their quality can further be improved via slightly deeper
+networks and additional preprocessing steps -- eventually yielding models
+outperforming state-of-the-art systems. In particular, our best model correctly
+classifies about 97.3% of all 'real' and 99.7% of all 'bogus' instances on a
+test set containing 1,942 'bogus' and 227 'real' instances in total.
+Furthermore, the networks considered in this work can also successfully
+classify these objects at hand without relying on difference images, which
+might pave the way for future detection pipelines not containing image
+subtraction steps at all.
+"
+26079,Algorithmic Complexity of Weakly Semiregular Partitioning and the Representation Number,"  A graph $G$ is {\it weakly semiregular} if there are two numbers $a,b$, such
+that the degree of every vertex is $a$ or $b$. The {\it weakly semiregular
+number} of a graph $G$, denoted by $wr(G)$, is the minimum number of subsets
+into which the edge set of $G$ can be partitioned so that the subgraph induced
+by each subset is a weakly semiregular graph. We present a polynomial time
+algorithm to determine whether the weakly semiregular number of a given tree is
+two. On the other hand, we show that determining whether $ wr(G) = 2 $ for a
+given bipartite graph $ G $ with at most three numbers in its degree set is
+{\bf NP}-complete. Among other results, for every tree $T$, we show that
+$wr(T)\leq 2\log_2 \Delta(T) + \mathcal{O}(1)$, where $\Delta(T)$ denotes the
+maximum degree of $T$. In the second part of the work, we consider the
+representation number. A graph $G$ has a {\it representation modulo $r$} if
+there exists an injective map $\ell: V (G) \rightarrow \mathbb{Z}_r$ such that
+vertices $v$ and $u$ are adjacent if and only if $|\ell(u) -\ell(v)|$ is
+relatively prime to $r$. The {\it representation number}, denoted by $rep(G)$,
+is the smallest $r$ such that $G$ has a representation modulo $r$. Narayan and
+Urick conjectured that the determination of $rep (G)$ for an arbitrary graph
+$G$ is a difficult problem \cite{narayan2007representations}. In this work, we
+confirm this conjecture and show that if $\mathbf{NP\neq P}$, then for any
+$\epsilon >0$, there is no polynomial time
+$(1-\epsilon)\frac{n}{2}$-approximation algorithm for the computation of
+representation number of regular graphs with $n$ vertices.
+"
+26080,Summary of the 12th IACHEC Meeting,"  We summarize the outcome of the 12th meeting of the International
+Astronomical Consortium for High Energy Calibration (IACHEC), held at the UCLA
+conference center in Lake Arrowhead (California) in March 2017. 56 scientists
+directly involved in the calibration of operational and future high-energy
+missions gathered during 3.5 days to discuss the status of the X-ray payload
+inter-calibration, as well as possible ways to improve it. The ""Thermal
+Supernovas Remnant"" (SNR) Working Group presented a recently published paper on
+1E0102.2-7219 as a calibration standard in the 0.5-1.0 keV band. A new method
+to measure the high-energy spectrum of the Crab Nebula and pulsar with NuSTAR
+without using its optics may yield a new absolute flux standard in the 3-7 keV
+band. A new ACIS contamination model - released with CALDB version 4.7.3 -
+leads to a significant improvement in modeling the spectral, spatial, and
+temporal properties of the contaminant. The first calibration results of the
+scientific payload on board Hitomi confirm the excellent performance of the
+instruments before the spacecraft operation problems leading to its loss.
+Finally, the meeting discussed extensively a novel statistic approach to
+formally identify in which direction the effective areas of different
+instruments would need to be changed to bring them into concordance. This
+method could inform future further calibration efforts.
+"
+26081,Unitary representations with Dirac cohomology: finiteness in the real case,"  Let $G$ be a complex connected simple algebraic group with a fixed real form
+$\sigma$. Let $G(\mathbb{R})=G^\sigma$ be the corresponding group of real
+points. This paper reports a finiteness theorem for the classification of
+irreducible unitary Harish-Chandra modules of $G(\mathbb{R})$ (up to
+equivalence) having non-vanishing Dirac cohomology. Moreover, we study the
+distribution of the spin norm along Vogan pencils for certain $G(\mathbb{R})$,
+with particular attention paid to the unitarily small convex hull introduced by
+Salamanca-Riba and Vogan.
+"
+26082,Calculus of Variations with Differential Forms,"  We study integrals of the form $\int_{\Omega}f\left( d\omega\right)$, where
+$1\leq k\leq n$, $f:\Lambda^{k}\rightarrow\mathbb{R}$ is continuous and
+$\omega$ is a $\left(k-1\right)$-form. We introduce the appropriate notions of
+convexity, namely ext. one convexity, ext. quasiconvexity and ext.
+polyconvexity. We study their relations, give several examples and
+counterexamples. We finally conclude with an application to a minimization
+problem.
+"
+26083,"Canonical fibrations of contact metric $(κ,μ)$-spaces","  We present a classification of the complete, simply connected, contact metric
+$(\kappa,\mu)$-spaces as homogeneous contact metric manifolds, by studying the
+base space of their canonical fibration. According to the value of the Boeckx
+invariant, it turns out that the base is a complexification or a
+para-complexification of a sphere or of a hyperbolic space. In particular, we
+obtain a new homogeneous representation of the contact metric
+$(\kappa,\mu)$-spaces with Boeckx invariant less than $-1$.
+"
+26084,LRC: Dependency-Aware Cache Management for Data Analytics Clusters,"  Memory caches are being aggressively used in today's data-parallel systems
+such as Spark, Tez, and Piccolo. However, prevalent systems employ rather
+simple cache management policies--notably the Least Recently Used (LRU)
+policy--that are oblivious to the application semantics of data dependency,
+expressed as a directed acyclic graph (DAG). Without this knowledge, memory
+caching can at best be performed by ""guessing"" the future data access patterns
+based on historical information (e.g., the access recency and/or frequency),
+which frequently results in inefficient, erroneous caching with low hit ratio
+and a long response time. In this paper, we propose a novel cache replacement
+policy, Least Reference Count (LRC), which exploits the application-specific
+DAG information to optimize the cache management. LRC evicts the cached data
+blocks whose reference count is the smallest. The reference count is defined,
+for each data block, as the number of dependent child blocks that have not been
+computed yet. We demonstrate the efficacy of LRC through both empirical
+analysis and cluster deployments against popular benchmarking workloads. Our
+Spark implementation shows that, compared with LRU, LRC speeds up typical
+applications by 60%.
+"
+26085,On the Annihilator Ideal of an Inverse Form,"  Let $K$ be a field. We simplify and extend work of Althaler \& Dür on
+finite sequences over $K$ by regarding $K[x^{-1},z^{-1}]$ as a $K[x,z]$ module,
+and studying forms in $K[x^{-1},z^{-1}]$ from first principles. Then we apply
+our results to finite sequences.
+First we define the annihilator ideal $I_F$ of a non-zero form $F\in
+K[x^{-1},z^{-1}]$, a homogeneous ideal. We inductively construct an ordered
+pair ($f_1$\,,\,$f_2$) of forms which generate $I_F$\,; our generators are
+special in that $z$ does not divide the leading grlex monomial of $f_1$ but $z$
+divides $f_2$\,, and the sum of their total degrees is always $2-|F|$, where
+$|F|$ is the total degree of $F$. We show that $f_1,f_2$ is a maximal regular
+sequence for $I_F$, so that the height of $I_F$ is 2. The corresponding
+algorithm is $\sim |F|^2/2$.
+The row vector obtained by accumulating intermediate forms of the
+construction gives a minimal grlex Gröbner basis for $I_F$ for no extra
+computational cost other than storage and apply this to determining $\dim_K
+(K[x,z] /I_F)$\,. We show that either the form vector is reduced or a monomial
+of $f_1$ can be reduced by $f_2$\,. This enables us to efficiently construct
+the unique reduced Gröbner basis for $I_F$ from the vector extension of our
+algorithm.
+Then we specialise to the inverse form of a finite sequence, obtaining
+generator forms for its annihilator ideal and a corresponding algorithm which
+does not use the last 'length change' of Massey. We compute the intersection of
+two annihilator ideals using syzygies in $K[x,z]^5$. This improves a result of
+Althaler \& Dür. Finally, dehomogenisation induces a one-to-one
+correspondence ($f_1$\,,$f_2$) $\mapsto$ (minimal polynomial, auxiliary
+polynomial), the output of the author's variant of the Berlekamp-Massey
+algorithm. So we can also solve the LFSR synthesis problem via the
+corresponding algorithm for sequences.
+"
+26086,Superconductivity in the Ferromagnet URhGe under uniaxial pressure,"  Uniaxial pressure applied in the b crystallographic direction perpendicular
+to spontaneous magnetization in heavy fermion ferromagnet URhGe strongly
+stimulates superconductivity in this compound. The phenomenological approach
+allows point out two mechanisms of superconducting temperature raising. They
+originates from stimulation by the uniaxial stress both intraband and interband
+amplitudes of triplet Cooper pairing. The phenomenon of reentrant
+superconductivity under magnetic field along b-axis is also strongly sensitive
+to the uniaxial stress in the same direction. The uniaxial stress accelerates
+suppression the Curie temperature by the transversal magnetic field. The
+emergence of the first order transition to the paramagnetic state occurs at
+much lower field than in the absence of uniaxial stress.
+"
+26087,"Centralized ""big science"" communities more likely generate non-replicable results","  Growing concern that most published results, including those widely agreed
+upon, may be false are rarely examined against rapidly expanding research
+production. Replications have only occurred on small scales due to prohibitive
+expense and limited professional incentive. We introduce a novel,
+high-throughput replication strategy aligning 51,292 published claims about
+drug-gene interactions with high-throughput experiments performed through the
+NIH LINCS L1000 program. We show (1) that unique claims replicate 19% more
+frequently than at random, while those widely agreed upon replicate 45% more
+frequently, manifesting collective correction mechanisms in science; but (2)
+centralized scientific communities perpetuate claims that are less likely to
+replicate even if widely agreed upon, demonstrating how centralized,
+overlapping collaborations weaken collective understanding. Decentralized
+research communities involve more independent teams and use more diverse
+methodologies, generating the most robust, replicable results. Our findings
+highlight the importance of science policies that foster decentralized
+collaboration to promote robust scientific advance.
+"
+26088,Cosmological signatures of ultralight dark matter with an axionlike potential,"  Nonlinearities in a realistic axion field potential may play an important
+role in the cosmological dynamics. In this paper we use the Boltzmann code
+CLASS to solve the background and linear perturbations evolution of an axion
+field and contrast our results with those of CDM and the free axion case. We
+conclude that there is a slight delay in the onset of the axion field
+oscillations when nonlinearities in the axion potential are taken into account.
+Besides, we identify a tachyonic instability of linear modes resulting in the
+presence of a bump in the power spectrum at small scales. Some comments are in
+turn about the true source of the tachyonic instability, how the parameters of
+the axionlike potential can be constrained by Ly-$\alpha$ observations, and the
+consequences in the stability of self-gravitating objects made of axions.
+"
+26089,Another proof of the local curvature estimate for the Ricci flow,"  By using the De Giorgi iteration method we will give a new simple proof of
+the recent result of B.Kotschwar, O.Munteanu, J.Wang [KMW] and N.Sesum [S] on
+the local boundedness of the Riemmanian curvature tensor of solutions of Ricci
+flow in terms of its inital value on a given ball and a local uniform bound on
+the Ricci curvature.
+"
+26090,Dynamical local connector approximation for electron addition and removal spectra,"  Realistic calculations of electron addition and removal spectra rely most
+often on Green's functions and complex, non-local self-energies. We introduce a
+shortcut to obtain the spectral function directly from a local and
+frequency-dependent, yet real, potential. We calculate this potential in the
+homogeneous electron gas (HEG), and we design a connector which prescribes the
+use of the HEG results to calculate spectral functions of real materials.
+Benchmark results for several solids demonstrate the potential of our approach.
+"
+26091,Bayesian Pseudo Posterior Synthesis for Data Privacy Protection,"  Statistical agencies utilize models to synthesize respondent-level data for
+release to the general public as an alternative to the actual data records. A
+Bayesian model synthesizer encodes privacy protection by employing a
+hierarchical prior construction that induces smoothing of the real data
+distribution. Synthetic respondent-level data records are often preferred to
+summary data tables due to the many possible uses by researchers and data
+analysts. Agencies balance a trade-off between utility of the synthetic data
+versus disclosure risks and hold a specific target threshold for disclosure
+risk before releasing synthetic datasets. We introduce a pseudo posterior
+likelihood that exponentiates each contribution by an observation
+record-indexed weight in (0, 1), defined to be inversely proportional to the
+disclosure risk for that record in the synthetic data. Our use of a vector of
+weights allows more precise downweighting of high risk records in a fashion
+that better preserves utility as compared with using a scalar weight. We
+illustrate our method with a simulation study and an application to the
+Consumer Expenditure Survey of the U.S. Bureau of Labor Statistics. We
+demonstrate how the frequentist consistency and uncertainty quantification are
+affected by the inverse risk-weighting.
+"
+26092,Nontrivial dynamics beyond the logarithmic shift in two-dimensional Fisher-KPP equations,"  We study the asymptotic behaviour, as time goes to infinity, of the
+Fisher-KPP equation $\partial_t u=\Delta u +u-u^2$ in spatial dimension $2$,
+when the initial condition looks like a Heaviside function. Thus the solution
+is, asymptotically in time, trapped between two planar critical waves whose
+positions are corrected by the Bramson logarithmic shift. The issue is whether,
+in this reference frame, the solutions will converge to a travelling wave, or
+will exhibit more complex behaviours. We prove here that both convergence and
+nonconvergence may happen: the solution may converge towards one translate of
+the planar wave, or oscillate between two of its translates. This relies on the
+behaviour of the initial condition at infinity in the transverse direction.
+"
+26093,Hotspot-aware DSA Grouping and Mask Assignment,"  In Directed Self Assembly (DSA), poor printing of guiding templates can cause
+misassembly resulting in high defect probability. Therefore, hotspots should be
+avoided in the choice of the DSA groups. Accordingly, Directed Self-Assembly
+(DSA) technologies which use Multiple Patterning (MP) to print the guiding
+templates need to be aware of hotspots during the DSA grouping and MP
+Decomposition. In this paper, we present a hotspot-aware heuristic for DSA
+grouping and MP decomposition. Results show that that the proposed heuristic
+eliminates 78% of the hotspots and conflicts that result from using a
+hotspot-unaware grouping and decomposition algorithm. In comparison to the
+optimal solution using Integer Linear Programming, the proposed heuristic
+results in ~24% more violations.
+"
+26094,Vanadium Transitions in the Spectrum of Arcturus,"  We derive a new abundance for vanadium in the bright, mildly metal-poor red
+giant Arcturus. This star has an excellent high-resolution spectral atlas and
+well-understood atmospheric parameters, and it displays a rich set of neutral
+vanadium lines that are available for abundance extraction. We employ a newly
+recorded set of laboratory FTS spectra to investigate any potential
+discrepancies in previously reported V I log(gf) values near 900 nm. These new
+spectra support our earlier laboratory transition data and the calibration
+method utilized in that study. We then perform a synthetic spectrum analysis of
+weak V I features in Arcturus, deriving log {\epsilon}(V) = 3.54 $\pm$ 0.01
+({\sigma} = 0.04) from 55 lines. There are no significant abundance trends with
+wavelength, line strength, or lower excitation energy.
+"
+26095,Can GAN Learn Topological Features of a Graph?,"  This paper is first-line research expanding GANs into graph topology
+analysis. By leveraging the hierarchical connectivity structure of a graph, we
+have demonstrated that generative adversarial networks (GANs) can successfully
+capture topological features of any arbitrary graph, and rank edge sets by
+different stages according to their contribution to topology reconstruction.
+Moreover, in addition to acting as an indicator of graph reconstruction, we
+find that these stages can also preserve important topological features in a
+graph.
+"
+26096,Electrical Control of Structural and Physical Properties via Strong Spin-Orbit Interactions in Sr2IrO4,"  Electrical control of structural and physical properties is a long-sought,
+but elusive goal of contemporary science and technology. We demonstrate that a
+combination of strong spin-orbit interactions (SOI) and a canted
+antiferromagnetic (AFM) Mott state is sufficient to attain that goal. The AFM
+insulator Sr2IrO4 provides a model system in which strong SOI lock canted Ir
+magnetic moments to IrO6-octahedra, causing them to rigidly rotate together. A
+novel coupling between an applied electrical current and the canting angle
+reduces the Néel temperature and drives a large, non-linear lattice expansion
+that closely tracks the magnetization, increases the electron mobility, and
+precipitates a unique resistive switching effect. Our observations open new
+avenues for understanding fundamental physics driven by strong SOI in condensed
+matter, and provide a new paradigm for functional materials and devices.
+"
+26097,TripletGAN: Training Generative Model with Triplet Loss,"  As an effective way of metric learning, triplet loss has been widely used in
+many deep learning tasks, including face recognition and person-ReID, leading
+to many states of the arts. The main innovation of triplet loss is using
+feature map to replace softmax in the classification task. Inspired by this
+concept, we propose here a new adversarial modeling method by substituting the
+classification loss of discriminator with triplet loss. Theoretical proof based
+on IPM (Integral probability metric) demonstrates that such setting will help
+the generator converge to the given distribution theoretically under some
+conditions. Moreover, since triplet loss requires the generator to maximize
+distance within a class, we justify tripletGAN is also helpful to prevent mode
+collapse through both theory and experiment.
+"
+26098,"Halfspace depths for scatter, concentration and shape matrices","  We propose halfspace depth concepts for scatter, concentration and shape
+matrices. For scatter matrices, our concept is similar to those from Chen, Gao
+and Ren (2017) and Zhang (2002). Rather than focusing, as in these earlier
+works, on deepest scatter matrices, we thoroughly investigate the properties of
+the proposed depth and of the corresponding depth regions. We do so under
+minimal assumptions and, in particular, we do not restrict to elliptical
+distributions nor to absolutely continuous distributions. Interestingly, fully
+understanding scatter halfspace depth requires considering different
+geometries/topologies on the space of scatter matrices. We also discuss, in the
+spirit of Zuo and Serfling (2000), the structural properties a scatter depth
+should satisfy, and investigate whether or not these are met by scatter
+halfspace depth. Companion concepts of depth for concentration matrices and
+shape matrices are also proposed and studied. We show the practical relevance
+of the depth concepts considered in a real-data example from finance.
+"
+26099,Importance sampling for metastable and multiscale dynamical systems,"  In this article, we address the issues that come up in the design of
+importance sampling schemes for rare events associated to stochastic dynamical
+systems. We focus on the issue of metastability and on the effect of multiple
+scales. We discuss why seemingly reasonable schemes that follow large
+deviations optimal paths may perform poorly in practice, even though they are
+asymptotically optimal. Pre-asymptotic optimality is important when one deals
+with metastable dynamics and we discuss possible ways as to how to address this
+issue. Moreover, we discuss how the effect of the multiple scales (either in
+periodic or random environments) on the efficient design of importance sampling
+should be addressed. We discuss the mathematical and practical issues that come
+up, how to overcome some of the issues and discuss future challenges.
+"
+26100,Programmable Neural Network Trojan for Pre-Trained Feature Extractor,"  Neural network (NN) trojaning attack is an emerging and important attack
+model that can broadly damage the system deployed with NN models. Existing
+studies have explored the outsourced training attack scenario and transfer
+learning attack scenario in some small datasets for specific domains, with
+limited numbers of fixed target classes. In this paper, we propose a more
+powerful trojaning attack method for both outsourced training attack and
+transfer learning attack, which outperforms existing studies in the capability,
+generality, and stealthiness. First, The attack is programmable that the
+malicious misclassification target is not fixed and can be generated on demand
+even after the victim's deployment. Second, our trojan attack is not limited in
+a small domain; one trojaned model on a large-scale dataset can affect
+applications of different domains that reuse its general features. Thirdly, our
+trojan design is hard to be detected or eliminated even if the victims
+fine-tune the whole model.
+"
+26101,Distributed Subgradient Projection Algorithm over Directed Graphs: Alternate Proof,"  We propose Directed-Distributed Projected Subgradient (D-DPS) to solve a
+constrained optimization problem over a multi-agent network, where the goal of
+agents is to collectively minimize the sum of locally known convex functions.
+Each agent in the network owns only its local objective function, constrained
+to a commonly known convex set. We focus on the circumstance when
+communications between agents are described by a \emph{directed} network. The
+D-DPS combines surplus consensus to overcome the asymmetry caused by the
+directed communication network. The analysis shows the convergence rate to be
+$O(\frac{\ln k}{\sqrt{k}})$.
+"
+26102,A geometric view of Biodiversity: scaling to metagenomics,"  We have designed a new efficient dimensionality reduction algorithm in order
+to investigate new ways of accurately characterizing the biodiversity, namely
+from a geometric point of view, scaling with large environmental sets produced
+by NGS ($\sim 10^5$ sequences). The approach is based on Multidimensional
+Scaling (MDS) that allows for mapping items on a set of $n$ points into a low
+dimensional euclidean space given the set of pairwise distances. We compute all
+pairwise distances between reads in a given sample, run MDS on the distance
+matrix, and analyze the projection on first axis, by visualization tools. We
+have circumvented the quadratic complexity of computing pairwise distances by
+implementing it on a hyperparallel computer (Turing, a Blue Gene Q), and the
+cubic complexity of the spectral decomposition by implementing a dense random
+projection based algorithm. We have applied this data analysis scheme on a set
+of $10^5$ reads, which are amplicons of a diatom environmental sample from Lake
+Geneva. Analyzing the shape of the point cloud paves the way for a geometric
+analysis of biodiversity, and for accurately building OTUs (Operational
+Taxonomic Units), when the data set is too large for implementing unsupervised,
+hierarchical, high-dimensional clustering.
+"
+26103,On extractable shared information,"  We consider the problem of quantifying the information shared by a pair of
+random variables $X_{1},X_{2}$ about another variable $S$. We propose a new
+measure of shared information, called extractable shared information, that is
+left monotonic; that is, the information shared about $S$ is bounded from below
+by the information shared about $f(S)$ for any function $f$. We show that our
+measure leads to a new nonnegative decomposition of the mutual information
+$I(S;X_1X_2)$ into shared, complementary and unique components. We study
+properties of this decomposition and show that a left monotonic shared
+information is not compatible with a Blackwell interpretation of unique
+information. We also discuss whether it is possible to have a decomposition in
+which both shared and unique information are left monotonic.
+"
+26104,Pseudo-Poisson Nijenhuis manifolds,"  We introduce the notion of pseudo-Poisson Nijenhuis manifolds. These
+manifolds are generalizations of Poisson Nijenhuis manifolds by Magri and
+Morosi \cite{MM}. We show that any pseudo-Poisson Nijenhuis manifold has an
+associated quasi-Lie bialgebroid as in the case of Poisson quasi-Nijenhuis
+manifolds by Sti$\acute{\mathrm{e}}$non and Xu \cite{SX}. Hence, since a
+quasi-Lie bialgebroid has an associated Courant algebroid, we have new
+materials to construct Courant algebroids. In the ""nondegenerate"" case, we show
+that the conditions of pseudo-Poisson Nijenhuis structures can be reduced.
+Therefore we can provide lots of non-trivial examples of pseudo-Poisson
+Nijenhuis manifolds.
+"
+26105,"Multiferroic rust: magnetoelectric effect in göthite, $α$-FeOOH","  By means of symmetry analysis, density functional theory calculations, and
+Monte Carlo simulations we show that goethite, $\alpha$-FeOOH, is a linear
+magnetoelectric below its Néel temperature $T_{\rm N}=400$ K. The
+experimentally observed magnetic field induced spin-flop phase transition
+results in either change of direction of electric polarization or its
+suppression. Calculated value of magnetoelectric coefficient is 0.24 $\mu$C
+m$^{-2}$ T$^{-1}$. The abundance of goethite in nature makes it arguably the
+most widespread magnetoelectric material.
+"
+26106,Convolutional Recurrent Neural Networks for Bird Audio Detection,"  Bird sounds possess distinctive spectral structure which may exhibit small
+shifts in spectrum depending on the bird species and environmental conditions.
+In this paper, we propose using convolutional recurrent neural networks on the
+task of automated bird audio detection in real-life environments. In the
+proposed method, convolutional layers extract high dimensional, local frequency
+shift invariant features, while recurrent layers capture longer term
+dependencies between the features extracted from short time frames. This method
+achieves 88.5% Area Under ROC Curve (AUC) score on the unseen evaluation data
+and obtains the second place in the Bird Audio Detection challenge.
+"
+26107,Why Boltzmann Brains Are Bad,"  Some modern cosmological models predict the appearance of Boltzmann Brains:
+observers who randomly fluctuate out of a thermal bath rather than naturally
+evolving from a low-entropy Big Bang. A theory in which most observers are of
+the Boltzmann Brain type is generally thought to be unacceptable, although
+opinions differ. I argue that such theories are indeed unacceptable: the real
+problem is with fluctuations into observers who are locally identical to
+ordinary observers, and their existence cannot be swept under the rug by a
+choice of probability distributions over observers. The issue is not that the
+existence of such observers is ruled out by data, but that the theories that
+predict them are cognitively unstable: they cannot simultaneously be true and
+justifiably believed.
+"
+26108,Peterson-Gorenstein-Zierler algorithm for skew RS codes,"  We design a non-commutative version of the Peterson-Gorenstein-Zierler
+decoding algorithm for a class of codes that we call skew RS codes. These codes
+are left ideals of a quotient of a skew polynomial ring, which endow them of a
+sort of non-commutative cyclic structure. Since we work over an arbitrary
+field, our techniques may be applied both to linear block codes and
+convolutional codes. In particular, our decoding algorithm applies for block
+codes beyond the classical cyclic case.
+"
+26109,A Statistical Comparison of Objective Functions for the Vehicle Routing Problem with Route Balancing,"  The Vehicle Routing Problem with Route Balancing (VRPRB) is a biobjective
+version of the original Vehicle Routing Problem (VRP) in which, besides
+minimizing the total distance traveled by the vehicles involved, the balance
+among route loads is also pursued. Different objective functions (OFs) to
+achieve balanced route configurations have been proposed in the literature,
+however to the best of the authors knowledge there is still no consensus on
+which OF is the most suitable one for addressing, through metaheuristics, this
+challenging multiobjective optimization problem. This paper inquires into the
+effectiveness of seven different OFs for the VRPRB. Their influence on the
+performance of a basic single solution based evolutionary algorithm is analyzed
+by comparing the quality of the Pareto approximations produced for a set of
+well known benchmark instances. The obtained results indicate that studying
+alternative evaluation schemes for the VRPRB represents a highly valuable
+direction for future research which merits more attention.
+"
+26110,Unconstrained Fashion Landmark Detection via Hierarchical Recurrent Transformer Networks,"  Fashion landmarks are functional key points defined on clothes, such as
+corners of neckline, hemline, and cuff. They have been recently introduced as
+an effective visual representation for fashion image understanding. However,
+detecting fashion landmarks are challenging due to background clutters, human
+poses, and scales. To remove the above variations, previous works usually
+assumed bounding boxes of clothes are provided in training and test as
+additional annotations, which are expensive to obtain and inapplicable in
+practice. This work addresses unconstrained fashion landmark detection, where
+clothing bounding boxes are not provided in both training and test. To this
+end, we present a novel Deep LAndmark Network (DLAN), where bounding boxes and
+landmarks are jointly estimated and trained iteratively in an end-to-end
+manner. DLAN contains two dedicated modules, including a Selective Dilated
+Convolution for handling scale discrepancies, and a Hierarchical Recurrent
+Spatial Transformer for handling background clutters. To evaluate DLAN, we
+present a large-scale fashion landmark dataset, namely Unconstrained Landmark
+Database (ULD), consisting of 30K images. Statistics show that ULD is more
+challenging than existing datasets in terms of image scales, background
+clutters, and human poses. Extensive experiments demonstrate the effectiveness
+of DLAN over the state-of-the-art methods. DLAN also exhibits excellent
+generalization across different clothing categories and modalities, making it
+extremely suitable for real-world fashion analysis.
+"
+26111,Globally smooth approximations for shock pressure decay in impacts,"  New forms of empirical formulae that provide an approximate description of
+the decay of shock pressure with distance in hypervelocity impacts are
+proposed. These forms, which are intended for use in applications such as
+large-scale mantle convection models, are continuous and smooth from the point
+of impact to arbitrarily large distances, thereby avoiding the need to divide
+the domain into different decay regimes and yielding the maximum pressure in a
+self-consistent way without resorting to the impedance-match solution.
+Individual fits for different impact velocities as well as a tentative general
+fitting formula are given, especially for the case of dunite-on-dunite impacts.
+The temperature effects resulting from the shock are estimated for different
+decay models, and the differences between them are found to be substantial in
+some cases, potentially leading to over- or underestimates of impact heating
+and melt production in modeling contexts like mantle convection, where such
+parameterizations are commonly used to represent giant impacts.
+"
+26112,The effect of close-in giant planets' evolution on tidal-induced migration of exomoons,"  Hypothetical exomoons around close-in giant planets may migrate inwards
+and/or outwards in virtue of the interplay of the star, planet and moon tidal
+interactions. These processes could be responsible for the disruption of lunar
+systems, the collision of moons with planets or could provide a mechanism for
+the formation of exorings. Several models have been developed to determine the
+fate of exomoons when subject to the tidal effects of their host planet. None
+of them have taken into account the key role that planetary evolution could
+play in this process. In this paper we put together numerical models of exomoon
+tidal-induced orbital evolution, results of planetary evolution and interior
+structure models, to study the final fate of exomoons around evolving close-in
+gas giants. We have found that planetary evolution significantly affects not
+only the time-scale of exomoon migration but also its final fate. Thus, if any
+change in planetary radius, internal mass distribution and rotation occurs in
+time-scales lower or comparable to orbital evolution, exomoon may only migrate
+outwards and prevent tidal disruption or a collision with the planet. If
+exomoons are discovered in the future around close-in giant planets, our
+results may contribute to constraint planetary evolution and internal structure
+models.
+"
+26113,Unravelling intermittent features in single particle trajectories by a local convex hull method,"  We propose a new model-free method to detect change points between distinct
+phases in a single random trajectory of an intermittent stochastic process. The
+local convex hull (LCH) is constructed for each trajectory point, while its
+geometric properties (e.g., the diameter or the volume) are used as
+discriminators between phases. The efficiency of the LCH method is validated
+for six models of intermittent motion, including Brownian motion with different
+diffusivities or drifts, fractional Brownian motion with different Hurst
+exponents, and surface-mediated diffusion. We discuss potential applications of
+the method for detection of active and passive phases in the intracellular
+transport, temporal trapping or binding of diffusing molecules, alternating
+bulk and surface diffusion, run and tumble (or search) phases in the motion of
+bacteria and foraging animals, and instantaneous firing rates in neurons.
+"
+26114,"Technical Problems With ""Programmable self-assembly in a thousand-robot swarm""","  Rubenstein et al. present an interesting system of programmable
+self-assembled structure formation using 1000 Kilobot robots. The paper claims
+to advance work in artificial swarms similar to capabilities of natural systems
+besides being highly robust. However, the system lacks in terms of matching
+motility and complex shapes with holes, thereby limiting practical similarity
+to self-assembly in living systems.
+"
+26115,HADES RV Programme with HARPS-N at TNG VI. GJ 3942 b behind dominant activity signals,"  Short- to mid-term magnetic phenomena on the stellar surface of M-type stars
+cannot only resemble the effects of planets in radial velocity data, but also
+may hide them. We analyze 145 spectroscopic HARPS-N observations of GJ 3942
+taken over the past five years and additional photometry to disentangle stellar
+activity effects from genuine Doppler signals as a result of the orbital motion
+of the star around the common barycenter with its planet. To achieve this, we
+use the common methods of pre-whitening, and treat the correlated red noise by
+a first-order moving average term and by Gaussian-process regression following
+an MCMC analysis. We identify the rotational period of the star at 16.3 days
+and discover a new super-Earth, GJ 3942 b, with an orbital period of 6.9 days
+and a minimum mass of 7.1 Me. An additional signal in the periodogram of the
+residuals is present but we cannot claim it to be related to a second planet
+with sufficient significance at this point. If confirmed, such planet candidate
+would have a minimum mass of 6.3 Me and a period of 10.4 days, which might
+indicate a 3:2 mean-motion resonance with the inner planet.
+"
+26116,Linear and nonlinear spectroscopy from quantum master equations,"  We investigate the accuracy of the second-order time-convolutionless (TCL2)
+quantum master equation for the calculation of linear and nonlinear
+spectroscopies of multichromophore systems. We show that, even for systems with
+non-adiabatic coupling, the TCL2 master equation predicts linear absorption
+spectra that are accurate over an extremely broad range of parameters and well
+beyond what would be expected based on the perturbative nature of the approach;
+non-equilibrium population dynamics calculated with TCL2 for identical
+parameters are significantly less accurate. For third-order (two-dimensional)
+spectroscopy, the importance of population dynamics and the violation of the
+so-called quantum regression theorem degrade the accuracy of TCL2 dynamics. To
+correct these failures, we combine the TCL2 approach with a classical ensemble
+sampling of slow microscopic bath degrees of freedom, leading to an efficient
+hybrid quantum-classical scheme that displays excellent accuracy over a wide
+range of parameters. In the spectroscopic setting, the success of such a hybrid
+scheme can be understood through its separate treatment of homogeneous and
+inhomogeneous broadening. Importantly, the presented approach has the
+computational scaling of TCL2, with the modest addition of an embarrassingly
+parallel prefactor associated with ensemble sampling. The presented approach
+can be understood as a generalized inhomogeneous cumulant expansion technique,
+capable of treating multilevel systems with non-adiabatic dynamics.
+"
+26117,Multi-scale Online Learning and its Applications to Online Auctions,"  We consider revenue maximization in online auction/pricing problems. A seller
+sells an identical item in each period to a new buyer, or a new set of buyers.
+For the online posted pricing problem, we show regret bounds that scale with
+the best fixed price, rather than the range of the values. We also show regret
+bounds that are almost scale free, and match the offline sample complexity,
+when comparing to a benchmark that requires a lower bound on the market share.
+These results are obtained by generalizing the classical learning from experts
+and multi-armed bandit problems to their multi-scale versions. In this version,
+the reward of each action is in a different range, and the regret w.r.t. a
+given action scales with its own range, rather than the maximum range.
+"
+26118,Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks,"  Learning-based representations have become the de facto means to address
+computer vision tasks. Despite their massive adoption, the amount of work
+aiming at understanding the internal representations learned by these models is
+rather limited. Existing methods for model interpretation either require
+exhaustive manual inspection of visualizations, or link internal network
+activations with external ""possibly useful"" annotated concepts. In this paper,
+we propose an intermediate scheme in which, given a pretrained model, we
+automatically identify internal features relevant for the set of classes
+considered by the model, without requiring additional annotations. We interpret
+the model through average visualizations of these features. Then, at test time,
+we explain the network prediction by accompanying the predicted class label
+with supporting heatmap visualizations derived from the identified relevant
+features. In addition, we propose a method to address the artifacts introduced
+by strided operations in deconvnet-based visualizations. Moreover, we introduce
+an8Flower, a dataset specifically designed for the qualitative and quantitative
+evaluation of methods for model explanation. Our evaluation on the MNIST,
+ILSVRC12, Fashion144k and an8Flower datasets shows that the proposed method is
+able to identify relevant internal features for the classes of interest while
+improving the quality of the produced visualizations.
+"
+26119,Towards a fast and precise forward model for air shower radio simulation,"  The radio detection method for cosmic rays relies on coherent emission from
+electrons and positrons which is beamed in a narrow cone along the shower axis.
+Currently the only mod- els to reproduce this emission with sufficient accuracy
+are Monte Carlo based simulations of the particle and radio emission physics,
+which require large investments of computation time. The work presented here
+focuses on condensing the simulation results into a semi-analytical model. This
+relies on building a framework based on theoretical predictions of radio
+emission, but instead of calculating the radio signal directly these models are
+used to map template simu- lations to the specifications of a given radio
+event. Our current approach slices the radio signal based on atmospheric depth
+of origin and weights these slices based on a shower parameter such as electron
+number or an effective dipole moment. One significant gain over the existing
+Monte Carlo codes lies in the fact this makes the depth of the shower maximum a
+direct input to the simulation where currently one has to pre-select showers
+based on their random number seed. Such a model has great potential for heavily
+simulation-based analysis methods, for example the LOFAR air shower
+reconstruction. These techniques are severely limited by the available
+computation time but have the lowest errors in real measurement applications.
+"
+26120,Classical Widely Linear Estimation of Real Valued Parameter Vectors in Complex Valued Environments,"  This work investigates the task of estimating a real valued parameter vector
+based on complex valued measurements in a classical set-up. The application of
+standard estimators in general results in complex valued estimates of the real
+valued parameter vector. To avoid this systematic error, widely linear
+classical estimators that produce real valued estimates are investigated. One
+of these estimators is the widely linear least squares (WLLS) estimator
+proposed in this work, which does not utilize any noise statistics. Further, we
+introduce the best widely linear unbiased estimator (BWLUE) for real valued
+parameter vectors. The proposed estimators in general outperform their standard
+counterparts LS estimator and BWLUE, respectively, and they only require half
+as many complex valued measurements. We compare the novel approaches to
+standard classical estimators in two application scenarios. One of these
+applications considers the estimation of a real valued impulse response based
+on noisy measurements of the system's magnitude and phase response. For this
+problem, we propose a novel two-step approach based on the introduced widely
+linear concepts that outperforms standard estimators.
+"
+26121,Symplectic Geometry of Constrained Optimization,"  These are the notes of rather informal lectures given by the first co-author
+in UPMC, Paris, in January 2017. Practical goal is to explain how to compute or
+estimate the Morse index of the second variation. Symplectic geometry allows to
+effectively do it even for very degenerate problems with complicated
+constraints. Main geometric and analytic tool is the appropriately rearranged
+Maslov index.
+In these lectures, we try to emphasize geometric structure and omit analytic
+routine. Proofs are often substituted by informal explanations but a
+well-trained mathematician will easily re-write them in a conventional way.
+"
+26122,GAIN: Missing Data Imputation using Generative Adversarial Nets,"  We propose a novel method for imputing missing data by adapting the
+well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call
+our method Generative Adversarial Imputation Nets (GAIN). The generator (G)
+observes some components of a real data vector, imputes the missing components
+conditioned on what is actually observed, and outputs a completed vector. The
+discriminator (D) then takes a completed vector and attempts to determine which
+components were actually observed and which were imputed. To ensure that D
+forces G to learn the desired distribution, we provide D with some additional
+information in the form of a hint vector. The hint reveals to D partial
+information about the missingness of the original sample, which is used by D to
+focus its attention on the imputation quality of particular components. This
+hint ensures that G does in fact learn to generate according to the true data
+distribution. We tested our method on various datasets and found that GAIN
+significantly outperforms state-of-the-art imputation methods.
+"
+26123,Clarifying Cognitive Control and the Controllable Connectome,"  Cognitive control researchers aim to describe the processes that support
+adaptive cognition to achieve specific goals. Control theorists consider how to
+influence the state of systems to reach certain user-defined goals. In brain
+networks, some conceptual and lexical similarities between cognitive control
+and control theory offer appealing avenues for scientific discovery. However,
+these opportunities also come with the risk of conceptual confusion. Here, I
+suggest that each field of inquiry continues to produce novel and distinct
+insights. Then, I describe opportunities for synergistic research at the
+intersection of these subdisciplines with a critical stance that reduces the
+risk of conceptual confusion. Through this exercise, we can observe that both
+cognitive neuroscience and systems engineering have much to contribute to
+cognitive control research in human brain networks.
+"
+26124,Interaction and propagation characteristics of two counter and co-propagating Mach cones in a dusty plasma,"  We theoretically investigate the interaction and propagation characteristics
+of two co/counter propagating Mach cones triggered by two projectile particles
+moving with supersonic velocities in the same/opposite directions through a
+dusty plasma medium. The Mach cone solutions are obtained by solving a model
+set of fluid equations for a heavily charged dust fluid that includes the
+contributions of the projectile particles in the Poisson equation. The density
+profiles and velocity vector maps of the Mach wings show interesting structural
+changes when they interact with each other and form patterns similar to
+interference fringes. Compared to the co-propagating Mach cones, the wings of
+counter propagating Mach cones produce a larger number of maxima and minima in
+the pattern resulting from their mutual interaction. In addition the time
+duration of the formation of two maxima or minima at a particular point
+decreases due to the interactions of Mach cones. Another notable feature is
+that the spacing between adjacent maxima increases while the fringe angle
+decreases with the increase of relative velocity of the counter propagating
+projectile particles.
+"
+26125,A Complete Quantitative Deduction System for the Bisimilarity Distance on Markov Chains,"  In this paper we propose a complete axiomatization of the bisimilarity
+distance of Desharnais et al. for the class of finite labelled Markov chains.
+Our axiomatization is given in the style of a quantitative extension of
+equational logic recently proposed by Mardare, Panangaden, and Plotkin (LICS
+2016) that uses equality relations $t \equiv_\varepsilon s$ indexed by
+rationals, expressing that `$t$ is approximately equal to $s$ up to an error
+$\varepsilon$'. Notably, our quantitative deduction system extends in a natural
+way the equational system for probabilistic bisimilarity given by Stark and
+Smolka by introducing an axiom for dealing with the Kantorovich distance
+between probability distributions. The axiomatization is then used to propose a
+metric extension of a Kleene's style representation theorem for finite labelled
+Markov chains, that was proposed (in a more general coalgebraic fashion) by
+Silva et al. (Inf. Comput. 2011).
+"
+26126,Regime-Specific Multi-Objective Variational Principle of Compromise in Competition at Mesoscales,"  The energy-minimization multiscale (EMMS) principle of compromise in
+competition is believed to be generally applicable for all mesoscale problems
+at different levels in the real world, spanning from elementary particles to
+the universe. This stimulated a fundamental proposition of the concept of
+mesoscience. This article discusses a potential universality of the
+regime-specific multi-objective variational feature of this underlying
+principle through case studies in chemical engineering. It is also elucidated
+why the currently available variational principles are not applicable to the
+mesoscale problems. The paper concludes with prospects on future study.
+"
+26127,Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks,"  Minimizing non-convex and high-dimensional objective functions is
+challenging, especially when training modern deep neural networks. In this
+paper, a novel approach is proposed which divides the training process into two
+consecutive phases to obtain better generalization performance: Bayesian
+sampling and stochastic optimization. The first phase is to explore the energy
+landscape and to capture the ""fat"" modes; and the second one is to fine-tune
+the parameter learned from the first phase. In the Bayesian learning phase, we
+apply continuous tempering and stochastic approximation into the Langevin
+dynamics to create an efficient and effective sampler, in which the temperature
+is adjusted automatically according to the designed ""temperature dynamics"".
+These strategies can overcome the challenge of early trapping into bad local
+minima and have achieved remarkable improvements in various types of neural
+networks as shown in our theoretical analysis and empirical experiments.
+"
+26128,An $L_p$-theory for diffusion equations related to stochastic processes with non-stationary independent increment,"  Let $X=(X_t)_{t \ge 0}$ be a stochastic process which has an (not necessarily
+stationary) independent increment on a probability space $(\Omega,
+\mathbb{P})$. In this paper, we study the following Cauchy problem related to
+the stochastic process $X$:
+$\label{main eqn} \frac{\partial u}{\partial t}(t,x) = \cA(t)u(t,x) +f(t,x),
+\quad u(0,\cdot)=0, \quad (t,x) \in (0,T) \times \mathbf{R}^d, \end{align}
+where $f \in L_p( (0,T) ; L_p(\mathbf{R}^d))=L_p( (0,T) ; L_p)$ and
+\begin{align*} \cA(t)u(t,x) = \lim_{h \downarrow
+0}\frac{\mathbb{E}\left[u(t,x+X_{t+h}-X_t)-u(t,x)\right]}{h}$. We provide a
+sufficient condition on $X$ to guarantee the unique solvability of equation
+(\ref{ab main}) in $L_p\left( [0,T] ; H^\phi_{p}\right)$, where $H^\phi_{p}$ is
+a $\phi$-potential space on $\mathbf{R}^d$ . Furthemore we show that for this
+solution, \| u\|_{L_p\left( [0,T] ; H^\phi_{p}\right)} \leq N \|f\|_{L_p\left(
+[0,T] ; L_p\right)}, where $N$ is independent of $u$ and $f$.
+"
+26129,Parallel Complexity Analysis with Temporal Session Types,"  We study the problem of parametric parallel complexity analysis of
+concurrent, message-passing programs. To make the analysis local and
+compositional, it is based on a conservative extension of binary session types,
+which structure the type and direction of communication between processes and
+stand in a Curry-Howard correspondence with intuitionistic linear logic. The
+main innovation is to enrich session types with the temporal modalities next
+($\bigcirc A$), always ($\Box A$), and eventually ($\Diamond A$), to
+additionally prescribe the timing of the exchanged messages in a way that is
+precise yet flexible. The resulting temporal session types uniformly express
+properties such as the message rate of a stream, the latency of a pipeline, the
+response time of a concurrent queue, or the span of a fork/join parallel
+program. The analysis is parametric in the cost model and the presentation
+focuses on communication cost as a concrete example. The soundness of the
+analysis is established by proofs of progress and type preservation using a
+timed multiset rewriting semantics. Representative examples illustrate the
+scope and usability of the approach.
+"
+26130,A Note on the Convergence of the Gaussian Mean Shift Algorithm,"  Mean shift (MS) algorithms are popular methods for mode finding in pattern
+analysis. Each MS algorithm can be phrased as a fixed-point iteration scheme,
+which operates on a kernel density estimate (KDE) based on some data. The
+ability of an MS algorithm to obtain the modes of its KDE depends on whether or
+not the fixed-point scheme converges. The convergence of MS algorithms have
+recently been proved under some general conditions via first principle
+arguments. We complement the recent proofs by demonstrating that the MS
+algorithm operating on a Gaussian KDE can be viewed as an MM
+(minorization-maximization) algorithm, and thus permits the application of
+convergence techniques for such constructions. For the Gaussian case, we extend
+upon the previously results by showing that the fixed-points of the MS
+algorithm are all stationary points of the KDE in cases where the stationary
+points may not necessarily be isolated.
+"
+26131,Site-specific Gordian distances of spatial graphs,"  A site-specific Gordian distance between two spatial embeddings of an
+abstract graph is the minimal number of crossing changes from one to another
+where each crossing change is performed between two previously specified
+abstract edges of the graph. It is infinite in some cases. We determine the
+site-specific Gordian distance between two spatial embeddings of an abstract
+graph in certain cases. It has an application to puzzle ring problem. The
+site-specific Gordian distances between Milnor links and trivial links are
+determined. We use covering space theory for the proofs.
+"
+26132,The Next SRF Technologies,"  In this talk, I introduce the proposed next superconducting radio-frequency
+(SRF) technologies that will make it possible to achieve much higher
+accelerating electric field than the present SRF technologies. Audiences are
+assumed to be non-experts. We start from a brief review of basics of SRF,
+history of the high gradient technologies and the layered structure behind it.
+The multiple benefit of the layered structure is introduced. We then move to
+the next SRF technologies: superconductor-superconductor (SS) structure and
+superconductor-insulator-superconductor (SIS) structure. We discuss the SS
+structure in detail. Experimental results are also introduced and compared with
+theoretical considerations.
+"
+26133,Multivariate predictions of local reduced-order-model errors and dimensions,"  This paper introduces multivariate input-output models to predict the errors
+and bases dimensions of local parametric Proper Orthogonal Decomposition
+reduced-order models. We refer to these multivariate mappings as the MP-LROM
+models. We employ Gaussian Processes and Artificial Neural Networks to
+construct approximations of these multivariate mappings. Numerical results with
+a viscous Burgers model illustrate the performance and potential of the machine
+learning based regression MP-LROM models to approximate the characteristics of
+parametric local reduced-order models. The predicted reduced-order models
+errors are compared against the multi-fidelity correction and reduced order
+model error surrogates methods predictions, whereas the predicted reduced-order
+dimensions are tested against the standard method based on the spectrum of
+snapshots matrix. Since the MP-LROM models incorporate more features and
+elements to construct the probabilistic mappings they achieve more accurate
+results. However, for high-dimensional parametric spaces, the MP-LROM models
+might suffer from the curse of dimensionality. Scalability challenges of
+MP-LROM models and the feasible ways of addressing them are also discussed in
+this study.
+"
+26134,Rainbow vertex connection of digraphs,"  An edge-coloured path is \emph{rainbow} if its edges have distinct colours.
+An edge-coloured connected graph is said to be \emph{rainbow connected} if any
+two vertices are connected by a rainbow path, and \emph{strongly rainbow
+connected} if any two vertices are connected by a rainbow geodesic. The
+(\emph{strong}) \emph{rainbow connection number} of a connected graph is the
+minimum number of colours needed to make the graph (strongly) rainbow
+connected. These two graph parameters were introduced by Chartrand, Johns,
+McKeon and Zhang in 2008. As an extension, Krivelevich and Yuster proposed the
+concept of \emph{rainbow vertex-connection}. The topic of rainbow connection in
+graphs drew much attention and various similar parameters were introduced,
+mostly dealing with undirected graphs.
+Dorbec, Schiermeyer, Sidorowicz and Sopena extended the concept of the
+rainbow connection to digraphs. In this paper, we consider the (strong) rainbow
+vertex-connection number of digraphs. Results on the (strong) rainbow
+vertex-connection number of biorientations of graphs, cycle digraphs, circulant
+digraphs and tournaments are presented.
+"
+26135,Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations,"  We evaluate the character-level translation method for neural semantic
+parsing on a large corpus of sentences annotated with Abstract Meaning
+Representations (AMRs). Using a sequence-to-sequence model, and some trivial
+preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1
+(F-score on AMR-triples). We examine five different approaches to improve this
+baseline result: (i) reordering AMR branches to match the word order of the
+input sentence increases performance to 58.3; (ii) adding part-of-speech tags
+(automatically produced) to the input shows improvement as well (57.2); (iii)
+So does the introduction of super characters (conflating frequent sequences of
+characters to a single character), reaching 57.4; (iv) optimizing the training
+process by using pre-training and averaging a set of models increases
+performance to 58.7; (v) adding silver-standard training data obtained by an
+off-the-shelf parser yields the biggest improvement, resulting in an F-score of
+64.0. Combining all five techniques leads to an F-score of 71.0 on holdout
+data, which is state-of-the-art in AMR parsing. This is remarkable because of
+the relative simplicity of the approach.
+"
+26136,A Probabilistic Embedding Clustering Method for Urban Structure Detection,"  Urban structure detection is a basic task in urban geography. Clustering is a
+core technology to detect the patterns of urban spatial structure, urban
+functional region, and so on. In big data era, diverse urban sensing datasets
+recording information like human behaviour and human social activity, suffer
+from complexity in high dimension and high noise. And unfortunately, the
+state-of-the-art clustering methods does not handle the problem with high
+dimension and high noise issues concurrently. In this paper, a probabilistic
+embedding clustering method is proposed. Firstly, we come up with a
+Probabilistic Embedding Model (PEM) to find latent features from high
+dimensional urban sensing data by learning via probabilistic model. By latent
+features, we could catch essential features hidden in high dimensional data
+known as patterns; with the probabilistic model, we can also reduce uncertainty
+caused by high noise. Secondly, through tuning the parameters, our model could
+discover two kinds of urban structure, the homophily and structural
+equivalence, which means communities with intensive interaction or in the same
+roles in urban structure. We evaluated the performance of our model by
+conducting experiments on real-world data and experiments with real data in
+Shanghai (China) proved that our method could discover two kinds of urban
+structure, the homophily and structural equivalence, which means clustering
+community with intensive interaction or under the same roles in urban space.
+"
+26137,Semi-supervised classification for dynamic Android malware detection,"  A growing number of threats to Android phones creates challenges for malware
+detection. Manually labeling the samples into benign or different malicious
+families requires tremendous human efforts, while it is comparably easy and
+cheap to obtain a large amount of unlabeled APKs from various sources.
+Moreover, the fast-paced evolution of Android malware continuously generates
+derivative malware families. These families often contain new signatures, which
+can escape detection when using static analysis. These practical challenges can
+also cause traditional supervised machine learning algorithms to degrade in
+performance.
+In this paper, we propose a framework that uses model-based semi-supervised
+(MBSS) classification scheme on the dynamic Android API call logs. The
+semi-supervised approach efficiently uses the labeled and unlabeled APKs to
+estimate a finite mixture model of Gaussian distributions via conditional
+expectation-maximization and efficiently detects malwares during out-of-sample
+testing. We compare MBSS with the popular malware detection classifiers such as
+support vector machine (SVM), $k$-nearest neighbor (kNN) and linear
+discriminant analysis (LDA). Under the ideal classification setting, MBSS has
+competitive performance with 98\% accuracy and very low false positive rate for
+in-sample classification. For out-of-sample testing, the out-of-sample test
+data exhibit similar behavior of retrieving phone information and sending to
+the network, compared with in-sample training set. When this similarity is
+strong, MBSS and SVM with linear kernel maintain 90\% detection rate while
+$k$NN and LDA suffer great performance degradation. When this similarity is
+slightly weaker, all classifiers degrade in performance, but MBSS still
+performs significantly better than other classifiers.
+"
+26138,Deep Reinforcement Learning using Capsules in Advanced Game Environments,"  Reinforcement Learning (RL) is a research area that has blossomed
+tremendously in recent years and has shown remarkable potential for artificial
+intelligence based opponents in computer games. This success is primarily due
+to vast capabilities of Convolutional Neural Networks (ConvNet), enabling
+algorithms to extract useful information from noisy environments. Capsule
+Network (CapsNet) is a recent introduction to the Deep Learning algorithm group
+and has only barely begun to be explored. The network is an architecture for
+image classification, with superior performance for classification of the MNIST
+dataset. CapsNets have not been explored beyond image classification.
+This thesis introduces the use of CapsNet for Q-Learning based game
+algorithms. To successfully apply CapsNet in advanced game play, three main
+contributions follow. First, the introduction of four new game environments as
+frameworks for RL research with increasing complexity, namely Flash RL, Deep
+Line Wars, Deep RTS, and Deep Maze. These environments fill the gap between
+relatively simple and more complex game environments available for RL research
+and are in the thesis used to test and explore the CapsNet behavior.
+Second, the thesis introduces a generative modeling approach to produce
+artificial training data for use in Deep Learning models including CapsNets. We
+empirically show that conditional generative modeling can successfully generate
+game data of sufficient quality to train a Deep Q-Network well.
+Third, we show that CapsNet is a reliable architecture for Deep Q-Learning
+based algorithms for game AI. A capsule is a group of neurons that determine
+the presence of objects in the data and is in the literature shown to increase
+the robustness of training and predictions while lowering the amount training
+data needed. It should, therefore, be ideally suited for game plays.
+"
+26139,The cohomology of $C_2$-equivariant $A(1)$ and the homotopy of $ko_{C_2}$,"  We compute the cohomology of the subalgebra $A^{C_2}(1)$ of the
+$C_2$-equivariant Steenrod algebra $A^{C_2}$. This serves as the input to the
+$C_2$-equivariant Adams spectral sequence converging to the $RO(C_2)$-graded
+homotopy groups of an equivariant spectrum $ko_{C_2}$. Our approach is to use
+simpler $\mathbb{C}$-motivic and $\mathbb{R}$-motivic calculations as stepping
+stones.
+"
+26140,Muscle Excitation Estimation in Biomechanical Simulation Using NAF Reinforcement Learning,"  Motor control is a set of time-varying muscle excitations which generate
+desired motions for a biomechanical system. Muscle excitations cannot be
+directly measured from live subjects. An alternative approach is to estimate
+muscle activations using inverse motion-driven simulation. In this article, we
+propose a deep reinforcement learning method to estimate the muscle excitations
+in simulated biomechanical systems. Here, we introduce a custom-made reward
+function which incentivizes faster point-to-point tracking of target motion.
+Moreover, we deploy two new techniques, namely, episode-based hard update and
+dual buffer experience replay, to avoid feedback training loops. The proposed
+method is tested in four simulated 2D and 3D environments with 6 to 24 axial
+muscles. The results show that the models were able to learn muscle excitations
+for given motions after nearly 100,000 simulated steps. Moreover, the root mean
+square error in point-to-point reaching of the target across experiments was
+less than 1% of the length of the domain of motion. Our reinforcement learning
+method is far from the conventional dynamic approaches as the muscle control is
+derived functionally by a set of distributed neurons. This can open paths for
+neural activity interpretation of this phenomenon.
+"
+26141,Evaluating Ternary Adders using a hybrid Memristor / CMOS approach,"  This paper investigates the potentials of using a hybrid memristor CMOS
+technology, called MeMOS, for the realisation of ternary adders. Ternary adders
+exploit the qualitative advantage of multi-value storage capability of
+memristors compared to conventional CMOS flip-flops storing only binary values
+in one cell. Furthermore they carry out an addition in $O(1)$ and are therefore
+considered. The MeMOS approach is compared to a CMOS solution for the ternary
+adders using multi value memristors as registers concerning the achievable
+latency and the energy consumption. It is shown that using the TEAM, VTEAM
+model and a model considering commercially available memristors from Known the
+approach of using CMOS ternary adders using memristors as multi-value register
+memory is to prefer. MeMOS circuits have advantages for a static operation
+mode, i.e. if they are operated after a reset.
+"
+26142,Pattern Dependence Detection using n-TARP Clustering,"  Consider an experiment involving a potentially small number of subjects. Some
+random variables are observed on each subject: a high-dimensional one called
+the ""observed"" random variable, and a one-dimensional one called the ""outcome""
+random variable. We are interested in the dependencies between the observed
+random variable and the outcome random variable. We propose a method to
+quantify and validate the dependencies of the outcome random variable on the
+various patterns contained in the observed random variable. Different degrees
+of relationship are explored (linear, quadratic, cubic, ...). This work is
+motivated by the need to analyze educational data, which often involves
+high-dimensional data representing a small number of students. Thus our
+implementation is designed for a small number of subjects; however, it can be
+easily modified to handle a very large dataset. As an illustration, the
+proposed method is used to study the influence of certain skills on the course
+grade of students in a signal processing class. A valid dependency of the grade
+on the different skill patterns is observed in the data.
+"
+26143,The Capacity of Cache Aided Private Information Retrieval,"  The problem of cache enabled private information retrieval (PIR) is
+considered in which a user wishes to privately retrieve one out of $K$
+messages, each of size $L$ bits from $N$ distributed databases. The user has a
+local cache of storage $SL$ bits which can be used to store any function of the
+$K$ messages. The main contribution of this work is the exact characterization
+of the capacity of cache aided PIR as a function of the storage parameter $S$.
+In particular, for a given cache storage parameter $S$, the
+information-theoretically optimal download cost $D^{*}(S)/L$ (or the inverse of
+capacity) is shown to be equal to $(1- \frac{S}{K})\left(1+ \frac{1}{N}+ \ldots
++ \frac{1}{N^{K-1}}\right)$. Special cases of this result correspond to the
+settings when $S=0$, for which the optimal download cost was shown by Sun and
+Jafar to be $\left(1+ \frac{1}{N}+ \ldots + \frac{1}{N^{K-1}}\right)$, and the
+case when $S=K$, i.e., cache size is large enough to store all messages
+locally, for which the optimal download cost is $0$. The intermediate points
+$S\in (0, K)$ can be readily achieved through a simple memory-sharing based PIR
+scheme. The key technical contribution of this work is the converse, i.e., a
+lower bound on the download cost as a function of storage $S$ which shows that
+memory sharing is information-theoretically optimal.
+"
+26144,Grounded Recurrent Neural Networks,"  In this work, we present the Grounded Recurrent Neural Network (GRNN), a
+recurrent neural network architecture for multi-label prediction which
+explicitly ties labels to specific dimensions of the recurrent hidden state (we
+call this process ""grounding""). The approach is particularly well-suited for
+extracting large numbers of concepts from text. We apply the new model to
+address an important problem in healthcare of understanding what medical
+concepts are discussed in clinical text. Using a publicly available dataset
+derived from Intensive Care Units, we learn to label a patient's diagnoses and
+procedures from their discharge summary. Our evaluation shows a clear advantage
+to using our proposed architecture over a variety of strong baselines.
+"
+26145,Non-standard hierarchies of the runnings of the spectral index in inflation,"  Recent analyses of cosmic microwave background surveys have revealed hints
+that there may be a non-trivial running of the running of the spectral index.
+If future experiments were to confirm these hints, it would prove a powerful
+discriminator of inflationary models, ruling out simple single field models. We
+discuss how isocurvature perturbations in multi-field models can be invoked to
+generate large runnings in a non-standard hierarchy, and find that a minimal
+model capable of practically realising this would be a two-field model with a
+non-canonical kinetic structure. We also consider alternative scenarios such as
+variable speed of light models and canonical quantum gravity effects and their
+implications for runnings of the spectral index.
+"
+26146,On curvatures of homogeneous sub-Riemannian manifolds,"  The author discusses in some detail the old definitions of the curvature
+tensors for rigged metrized distributions on manifolds given by Schouten,
+Wagner, and Solov'ev. To calculate the Solov'ev sectional and Ricci curvatures
+for homogeneous sub-Riemannian manifolds, the author suggests to use in some
+cases special riggings of invariant completely non- holonomic distributions on
+manifolds. As a justification, we find a foliation on the cotangent bundle over
+a Lie group G whose leaves are tangent to invariant Hamiltonian vector fields
+for the Pontryagin-Hamilton func- tion. This function was applied in the
+Pontryagin maximum principle for the time-optimal problem. The foliation is
+entirely described by the co-adjoint representation of the Lie group G. Also we
+use the canonical symplectic form and its values for the above mentioned
+invari- ant Hamiltonian vector fields. In particular, the above rigging method
+is applicable to contact sub-Riemannian manifolds, sub-Riemannian Carnot
+groups, and homogeneous sub-Riemannian manifolds possessing a subme- try onto a
+Riemannian manifold. At the end, some examples are presented.
+"
+26147,Binary Hypothesis Testing with Byzantine Sensors: Fundamental Trade-off Between Security and Efficiency,"  This paper studies binary hypothesis testing based on measurements from a set
+of sensors, a subset of which can be compromised by an attacker. The
+measurements from a compromised sensor can be manipulated arbitrarily by the
+adversary. The asymptotic exponential rate, with which the probability of error
+goes to zero, is adopted to indicate the detection performance of a detector.
+In practice, we expect the attack on sensors to be sporadic, and therefore the
+system may operate with all the sensors being benign for extended period of
+time. This motivates us to consider the trade-off between the detection
+performance of a detector, i.e., the probability of error, when the attacker is
+absent (defined as efficiency) and the worst-case detection performance when
+the attacker is present (defined as security). We first provide the fundamental
+limits of this trade-off, and then propose a detection strategy that achieves
+these limits. We then consider a special case, where there is no trade-off
+between security and efficiency. In other words, our detection strategy can
+achieve the maximal efficiency and the maximal security simultaneously. Two
+extensions of the secure hypothesis testing problem are also studied and
+fundamental limits and achievability results are provided: 1) a subset of
+sensors, namely ""secure"" sensors, are assumed to be equipped with better
+security countermeasures and hence are guaranteed to be benign, 2) detection
+performance with unknown number of compromised sensors. Numerical examples are
+given to illustrate the main results.
+"
+26148,Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging,"  Word segmentation plays a pivotal role in improving any Arabic NLP
+application. Therefore, a lot of research has been spent in improving its
+accuracy. Off-the-shelf tools, however, are: i) complicated to use and ii)
+domain/dialect dependent. We explore three language-independent alternatives to
+morphological segmentation using: i) data-driven sub-word units, ii) characters
+as a unit of learning, and iii) word embeddings learned using a character CNN
+(Convolution Neural Network). On the tasks of Machine Translation and POS
+tagging, we found these methods to achieve close to, and occasionally surpass
+state-of-the-art performance. In our analysis, we show that a neural machine
+translation system is sensitive to the ratio of source and target tokens, and a
+ratio close to 1 or greater, gives optimal performance.
+"
+26149,Emergence of Chiral Spin Liquids via Quantum Melting of Non-Coplanar Magnetic Orders,"  Quantum spin liquids (QSLs) are long-range entangled states of quantum
+magnets which lie beyond the Landau paradigm of classifying phases of matter
+via broken symmetries. A physical route to arriving at QSLs is via
+frustration-induced quantum melting of ordered states such as valence bond
+crystals or magnetic orders. Here, we show, using extensive exact
+diagonalization (ED) and density-matrix renormalization group (DMRG) studies of
+concrete $SU(2)$ invariant spin models on honeycomb, triangular and square
+lattices, that chiral spin liquids (CSLs) emerge as descendants of triple-$Q$
+spin crystals with tetrahedral magnetic order and a large scalar spin
+chirality. Such ordered-to-CSL melting transitions may yield lattice
+realizations of effective Chern-Simons-Higgs field theories. Our work provides
+a distinct unifying perspective on the emergence of CSLs, and suggests that
+materials with magnetic skyrmion crystal order might provide a good starting
+point to search for CSLs.
+"
+26150,Generative Interest Estimation for Document Recommendations,"  Learning distributed representations of documents has pushed the
+state-of-the-art in several natural language processing tasks and was
+successfully applied to the field of recommender systems recently. In this
+paper, we propose a novel content-based recommender system based on learned
+representations and a generative model of user interest. Our method works as
+follows: First, we learn representations on a corpus of text documents. Then,
+we capture a user's interest as a generative model in the space of the document
+representations. In particular, we model the distribution of interest for each
+user as a Gaussian mixture model (GMM). Recommendations can be obtained
+directly by sampling from a user's generative model. Using Latent semantic
+analysis (LSA) as comparison, we compute and explore document representations
+on the Delicious bookmarks dataset, a standard benchmark for recommender
+systems. We then perform density estimation in both spaces and show that
+learned representations outperform LSA in terms of predictive performance.
+"
+26151,A novel second order finite difference discrete scheme for fractal mobile/immobile transport model based on equivalent transformative Caputo formulation,"  In this article, we present a new second order finite difference discrete
+scheme for fractal mobile/immobile transport model based on equivalent
+transformative Caputo formulation. The new transformative formulation takes the
+singular kernel away to make the integral calculation more efficient.
+Furthermore, this definition is also effective where $\alpha$ is a positive
+integer. Besides, the T-Caputo derivative also helps to increase the
+convergence rate of the discretization of $\alpha$-order($0<\alpha<1$) Caputo
+derivative from $O(\tau^{2-\alpha})$ to $O(\tau^{3-\alpha})$, where $\tau$ is
+the time step. For numerical analysis, a Crank-Nicholson finite difference
+scheme to solve fractal mobile/immobile transport model is introduced and
+analyzed. The unconditional stability and a priori estimates of the scheme are
+given rigorously. Moreover, the applicability and accuracy of the scheme are
+demonstrated by numerical experiments to support our theoretical analysis.
+"
+26152,Photoinduced Nonequilibrium Dynamics in Charge Ordered Materials,"  We study the nonequilibrium dynamics of photoinduced phase transitions in
+charge ordered (CO) systems with a strong electron-lattice interaction and
+analyze the interplay between electrons, periodic lattice distortions, and a
+phonon thermal reservoir. Simulations based on a tight-binding Hamiltonian and
+Boltzmann equations reveal partially decoupled oscillations of the electronic
+order parameter and the periodic lattice distortion during CO melting, which
+becomes more energy efficient with lower photon energy. The cooling rate of the
+electron system correlates with the CO gap dynamics, responsible for an order
+of magnitude decrease in the cooling rate upon the gap reopening. We also find
+that the time-dependent frequency of coherent oscillation reflects the dynamics
+of the energy landscape, such as transition between single-well and
+double-well, which sensitively depends on the photon energy and the pump
+fluence. The results demonstrate the intricate nonequilibrium dynamics in CO
+materials.
+"
+26153,Multivariate Constrained Robust M-Regression for Shaping Forward Curves in Electricity Markets,"  In this paper, a multivariate constrained robust M-regression (MCRM) method
+is developed to estimate shaping coefficients for electricity forward prices.
+An important benefit of the new method is that model arbitrage can be ruled out
+at an elementary level, as all shaping coefficients are treated simultaneously.
+Moreover, the new method is robust to outliers, such that the provided results
+are stable and not sensitive to isolated sparks or dips in the market. An
+efficient algorithm is presented to estimate all shaping coefficients at a low
+computational cost. To illustrate its good performance, the method is applied
+to German electricity prices.
+"
+26154,Variable Instruction Fetch Rate to Reduce Control Dependent Penalties,"  In order to overcome the branch execution penalties of hard-to-predict
+instruction branches, two new instruction fetch micro-architectural methods are
+proposed in this paper. In addition, to compare performance of the two proposed
+methods, different instruction fetch policy schemes of existing multi-branch
+path architectures are evaluated. An improvement in Instructions Per Cycle
+(IPC) of 29.4% on average over single-thread execution with gshare branch
+predictor on SPEC 2000/2006 benchmark is shown. In this paper, wide pipeline
+machines are simulated for evaluation purposes. The methods discussed in this
+paper can be extended to High Performance Scientific Computing needs, if the
+demands of IPC improvement are far more critical than $cost.
+"
+26155,Optical absorption and emission mechanisms of single defects in hexagonal boron nitride,"  We investigate the polarization selection rules of sharp zero-phonon lines
+(ZPLs) from isolated defects in hexagonal boron nitride (h-BN) and compare our
+findings with the predictions of a configuration coordinate model involving two
+electronic states. Our survey, which spans the spectral range ~550-740 nm,
+reveals that, in disagreement with a two-level model, the absorption and
+emission dipoles are often misaligned. We relate the dipole misalignment angle
+(${\Delta}{\theta}$) to the ZPL Stokes shift (${\Delta}E$) and find that
+${\Delta}{\theta}\sim 0°$ when ${\Delta}E$ corresponds to an allowed h-BN
+phonon frequency and that $0°\leq{\Delta}{\theta}\leq 90°$ when
+${\Delta}E$ exceeds the maximum allowed h-BN phonon frequency. Consequently, a
+two-level configuration coordinate model succeeds at describing excitations
+mediated by the creation of one optical phonon but fails at describing
+excitations that require the creation of multiple phonons. We propose that
+direct excitations requiring the creation of multiple phonons are inefficient
+due to the low Huang-Rhys factors in h-BN and that these ZPLs are instead
+excited indirectly via an intermediate electronic state. This hypothesis is
+corroborated by polarization measurements of an individual ZPL excited with two
+distinct wavelengths that indicate a single ZPL may be excited by multiple
+mechanisms. These findings provide new insight on the nature of the optical
+cycle of novel defect-based single photon sources in h-BN.
+"
+26156,Developing an ontology for the access to the contents of an archival fonds: the case of the Catasto Gregoriano,"  The research was proposed to exploit and extend the relational and contextual
+nature of the information assets of the Catasto Gregoriano, kept at the
+Archivio di Stato in Rome. Developed within the MODEUS project (Making Open
+Data Effectively Usable), this study originates from the following key ideas of
+MODEUS: to require Open Data to be expressed in terms of an ontology, and to
+include such an ontology as a documentation of the data themselves. Thus, Open
+Data are naturally linked by means of the ontology, which meets the
+requirements of the Linked Open Data vision.
+"
+26157,A dynamical stability limit for the charge density wave in K0.3MoO3,"  We study the response of the one-dimensional charge density wave in K0.3MoO3
+to different types of excitation with femtosecond optical pulses. We compare
+the response to direct excitation of the lattice at mid-infrared frequencies
+with that to the injection of quasi-particles across the low-energy charge
+density wave gap and to charge transfer excitations in the near infrared. For
+all three cases, we observe a fluence threshold above which the amplitude-mode
+oscillation frequency is softened and the mode becomes increasingly damped. We
+show that all the data can be collapsed onto a universal curve in which the
+melting of the charge density wave occurs abruptly at a critical lattice
+excursion. These data highlight the existence of a universal stability limit
+for a charge density wave, reminiscent of the empirical Lindemann criterion for
+the stability of a crystal lattice.
+"
+26158,An experimental and theoretical study of Ni impurity centers in Ba$_{0.8}$Sr$_{0.2}$TiO$_3$,"  The local environment and the charge state of a nickel impurity in cubic
+Ba$_{0.8}$Sr$_{0.2}$TiO$_3$ are studied by XAFS spectroscopy. According to the
+XANES data, the mean Ni charge state is $\sim$2.5+. An analysis of the EXAFS
+spectra and their comparison with the results of first-principle calculations
+of the defect geometry suggest that Ni$^{2+}$ ions are in a high-spin state at
+the $B$ sites of the perovskite structure and the difference of the Ni$^{2+}$
+and Ti$^{4+}$ charges is mainly compensated by distant oxygen vacancies. In
+addition, a considerable amount of nickel in the sample is in a second phase
+BaNiO$_{3-\delta}$. The measurements of the lattice parameter show a decrease
+in the unit cell volume upon doping, which can indicate the existence of a
+small amount of Ni$^{4+}$ ions at the $B$ sites.
+"
+26159,Maximum Likelihood Threshold and Generic Completion Rank of Graphs,"  The minimum number of observations such that the maximum likelihood estimator
+in a Gaussian graphical model exists with probability one is called the maximum
+likelihood threshold of the underlying graph G. The natural algebraic
+relaxation is the generic completion rank introduced by Uhler. We show that the
+maximum likelihood threshold and the generic completion rank behave in the same
+way under clique sums, which gives us large families of graphs on which these
+invariants coincide. On the other hand we determine both invariants for
+complete bipartite graphs $K_{m,n}$ and show that for some choices of m and n
+the two parameters may be quite far apart. In particular, this gives the first
+examples of graphs on which the maximum likelihood threshold and the generic
+completion rank do not agree.
+"
+26160,Dimension-Dependent Upper Bounds for Grobner Bases,"  We improve certain degree bounds for Grobner bases of polynomial ideals in
+generic position. We work exclusively in deterministically verifiable and
+achievable generic positions of a combinatorial nature, namely either strongly
+stable position or quasi stable position. Furthermore, we exhibit new
+dimension- (and depth-)dependent upper bounds for the Castelnuovo-Mumford
+regularity and the degrees of the elements of the reduced Grobner basis (w.r.t.
+the degree reverse lexicographical ordering) of a homogeneous ideal in these
+positions.
+"
+26161,Avoiding Discrimination through Causal Reasoning,"  Recent work on fairness in machine learning has focused on various
+statistical discrimination criteria and how they trade off. Most of these
+criteria are observational: They depend only on the joint distribution of
+predictor, protected attribute, features, and outcome. While convenient to work
+with, observational criteria have severe inherent limitations that prevent them
+from resolving matters of fairness conclusively.
+Going beyond observational criteria, we frame the problem of discrimination
+based on protected attributes in the language of causal reasoning. This
+viewpoint shifts attention from ""What is the right fairness criterion?"" to
+""What do we want to assume about the causal data generating process?"" Through
+the lens of causality, we make several contributions. First, we crisply
+articulate why and when observational criteria fail, thus formalizing what was
+before a matter of opinion. Second, our approach exposes previously ignored
+subtleties and why they are fundamental to the problem. Finally, we put forward
+natural causal non-discrimination criteria and develop algorithms that satisfy
+them.
+"
+26162,The Lie-Trotter integrator in the dynamics of the symmetric free rigid body,"  The numerical integration plays a fundamental role in understanding the
+behaviour of many mechanical systems. In this paper some important aspects of
+the mechanical integrators on the dynamics of a mechanical system are studied.
+More specific, we have shown that if that the Lie-Trotter integrator is
+obtained, in case of Euler equations for the dynamics of symmetric free rigid
+body, then it is a Poisson integrator. At the end of the paper some important
+remarks are presented.
+"
+26163,Spectra of supernovae in the nebular phase,"  When supernovae enter the nebular phase after a few months, they reveal
+spectral fingerprints of their deep interiors, glowing by radioactivity
+produced in the explosion. We are given a unique opportunity to see what an
+exploded star looks like inside. The line profiles and luminosities encode
+information about physical conditions, explosive and hydrostatic
+nucleosynthesis, and ejecta morphology, which link to the progenitor properties
+and the explosion mechanism. Here, the fundamental properties of spectral
+formation of supernovae in the nebular phase are reviewed. The formalism
+between ejecta morphology and line profile shapes is derived, including effects
+of scattering and absorption. Line luminosity expressions are derived in
+various physical limits, with examples of applications from the literature. The
+physical processes at work in the supernova ejecta, including gamma-ray
+deposition, non-thermal electron degradation, ionization and excitation, and
+radiative transfer are described and linked to the computation and application
+of advanced spectral models. Some of the results derived so far from
+nebular-phase supernova analysis are discussed.
+"
+26164,Bayesian Quadrature for Multiple Related Integrals,"  Bayesian probabilistic numerical methods are a set of tools providing
+posterior distributions on the output of numerical methods. The use of these
+methods is usually motivated by the fact that they can represent our
+uncertainty due to incomplete/finite information about the continuous
+mathematical problem being approximated. In this paper, we demonstrate that
+this paradigm can provide additional advantages, such as the possibility of
+transferring information between several numerical methods. This allows users
+to represent uncertainty in a more faithful manner and, as a by-product,
+provide increased numerical efficiency. We propose the first such numerical
+method by extending the well-known Bayesian quadrature algorithm to the case
+where we are interested in computing the integral of several related functions.
+We then prove convergence rates for the method in the well-specified and
+misspecified cases, and demonstrate its efficiency in the context of
+multi-fidelity models for complex engineering systems and a problem of global
+illumination in computer graphics.
+"
+26165,A characterization of the Macaulay dual generators for quadratic complete intersections,"  Let $F$ be a homogeneous polynomial in $n$ variables of degree $d$ over a
+field $K$. Let $A(F)$ be the associated Artinian graded $K$-algebra. If $B
+\subset A(F)$ is a subalgebra of $A(F)$ which is Gorenstein with the same socle
+degree as $A(F)$, we describe the Macaulay dual generator for $B$ in terms of
+$F$. Furthermore when $n=d$, we give necessary and sufficient conditions on the
+polynomial $F$ for $A(F)$ to be a complete intersection.
+"
+26166,(Biased) Majority Rule Cellular Automata,"  Consider a graph $G=(V,E)$ and a random initial vertex-coloring, where each
+vertex is blue independently with probability $p_{b}$, and red with probability
+$p_r=1-p_b$. In each step, all vertices change their current color
+synchronously to the most frequent color in their neighborhood and in case of a
+tie, a vertex conserves its current color; this model is called majority model.
+If in case of a tie a vertex always chooses blue color, it is called biased
+majority model. We are interested in the behavior of these deterministic
+processes, especially in a two-dimensional torus (i.e., cellular automaton with
+(biased) majority rule). In the present paper, as a main result we prove both
+majority and biased majority cellular automata exhibit a threshold behavior
+with two phase transitions. More precisely, it is shown that for a
+two-dimensional torus $T_{n,n}$, there are two thresholds $0\leq p_1, p_2\leq
+1$ such that $p_b \ll p_1$, $p_1 \ll p_b \ll p_2$, and $p_2 \ll p_b$ result in
+monochromatic configuration by red, stable coexistence of both colors, and
+monochromatic configuration by blue, respectively in $\mathcal{O}(n^2)$ number
+of steps
+"
+26167,"Helen of Troy, and the birth of Fuzzy Logic","  The poem Helen of the Nobel laureate George Seferis was inspired by the anti
+war play Helen of Euripides. In his poem, Seferis empathizes with the hero of
+the tragedy, Teucer, who opposed the involvement of The Gods in the lives of
+the humans, posing unanswered and contradictory questions. With the verse What
+is god; What is not a god; what is there in between them, the ancient poet
+Euripides sets foundations to the kind of logic that one can consider as a
+predecessor of Fuzzy Logic. It is worth noting that when Seferis received the
+Nobel Prize in Stockholm in 1963, he stated: Right now I feel I am a
+contradiction myself, a sentence charged with the new language of mathematical
+logic of the 20th century. Interplaying with the famous words of Karl
+Weierstrass It is true that a mathematician who is not somewhat of a poet, will
+never be a perfect mathematician in this article we discuss, through two poems,
+of how poetry and mathematical logic might have influenced each other.
+"
+26168,Life stages of wall-bounded decay of Taylor-Couette turbulence,"  The decay of Taylor-Couette turbulence, i.e~the flow between two coaxial and
+independently rotating cylinders, is numerically studied by instantaneously
+stopping the forcing from an initially statistically stationary flow field at a
+Reynolds number of $Re=3.5\times 10^4$. The effect of wall-friction is analysed
+by comparing three separate cases, in which the cylinders are either suddenly
+made no-slip or stress-free. Different life stages are observed during the
+decay. In the first stage, the decay is dominated by large-scale rolls.
+Counterintuitively, when these rolls fade away, if the flow inertia is small a
+redistribution of energy occurs, the energy of the azimuthal velocity behaves
+non-monotonically: first decreasing by almost two orders of magnitude, and then
+increasing during the redistribution. The second stage is dominated by
+non-normal transient growth of perturbations in the axial (spanwise) direction.
+Once this mechanism is exhausted, the flow enters the final life stage, viscous
+decay, which is dominated by wall-friction. We show that this stage can be
+modeled by a one-dimensional heat equation, and that self-similar velocity
+profiles collapse onto the theoretical solution.
+"
+26169,Collision vs non-Collision Distributed Time Synchronization for Dense IoT Deployments,"  Massive co-located devices require new paradigms to allow proper network
+connectivity. Internet of things (IoT) is the paradigm that offers a solution
+for the inter-connectivity of devices, but in dense IoT networks time
+synchronization is a critical aspect. Further, the scalability is another
+crucial aspect. This paper focuses on synchronization for uncoordinated dense
+networks without any external timing reference. Two synchronization methods are
+proposed and compared: i) conventional synchronization that copes with the high
+density of nodes by frame collision-avoidance methods (e.g., CSMA/CA) to avoid
+the superimposition (or collision) of synchronization signals; and ii)
+distributed synchronization that exploits the frames' collision to drive the
+network to a global synchronization. The distributed synchronization algorithm
+allows the network to reach a timing synchronization status based on a common
+beacon with the same signature broadcasted by every device. The superimposition
+of beacons from all the other devices enables the network synchronization,
+rather than preventing it. Numerical analysis evaluates the synchronization
+performance based on the convergence time and synchronization dispersion, both
+on collision and non-collision scenario, by investigating the scalability of
+the network. Results prove that in dense network the ensemble of signatures
+provides remarkable improvements of synchronization performance compared to
+conventional master-slave reference.
+"
+26170,Existence of harmonic maps into CAT(1) spaces,"  Let $\varphi\in C^0 \cap W^{1,2}(\Sigma, X)$ where $\Sigma$ is a compact
+Riemann surface, $X$ is a compact locally CAT(1) space, and $W^{1,2}(\Sigma,X)$
+is defined as in Korevaar-Schoen. We use the technique of harmonic replacement
+to prove that either there exists a harmonic map $u:\Sigma \to X$ homotopic to
+$\varphi$ or there exists a conformal harmonic map $v:\mathbb S^2 \to X$. To
+complete the argument, we prove compactness for energy minimizers and a
+removable singularity theorem for conformal harmonic maps.
+"
+26171,General hyperplane sections of threefolds in positive characteristic,"  In this paper, we study the singularities of a general hyperplane section $H$
+of a three-dimensional quasi-projective variety $X$ over an algebraically
+closed field of characteristic $p>0$. We prove that if $X$ has only canonical
+singularities, then $H$ has only rational double points. We also prove, under
+the assumption that $p>5$, that if $X$ has only klt singularities, then so does
+$H$.
+"
+26172,A Deep Compositional Framework for Human-like Language Acquisition in Virtual Environment,"  We tackle a task where an agent learns to navigate in a 2D maze-like
+environment called XWORLD. In each session, the agent perceives a sequence of
+raw-pixel frames, a natural language command issued by a teacher, and a set of
+rewards. The agent learns the teacher's language from scratch in a grounded and
+compositional manner, such that after training it is able to correctly execute
+zero-shot commands: 1) the combination of words in the command never appeared
+before, and/or 2) the command contains new object concepts that are learned
+from another task but never learned from navigation. Our deep framework for the
+agent is trained end to end: it learns simultaneously the visual
+representations of the environment, the syntax and semantics of the language,
+and the action module that outputs actions. The zero-shot learning capability
+of our framework results from its compositionality and modularity with
+parameter tying. We visualize the intermediate outputs of the framework,
+demonstrating that the agent truly understands how to solve the problem. We
+believe that our results provide some preliminary insights on how to train an
+agent with similar abilities in a 3D environment.
+"
+26173,Three-dimensional maps and subgroup growth,"  In this paper we derive a generating series for the number of cellular
+complexes known as pavings or three-dimensional maps, on $n$ darts, thus
+solving an analogue of Tutte's problem in dimension three. The generating
+series we derive also counts free subgroups of index $n$ in $\Delta^+ =
+\mathbb{Z}_2*\mathbb{Z}_2*\mathbb{Z}_2$ via a simple bijection between pavings
+and finite index subgroups which can be deduced from the action of $\Delta^+$
+on the cosets of a given subgroup. We then show that this generating series is
+non-holonomic. Furthermore, we provide and study the generating series for
+isomorphism classes of pavings, which correspond to conjugacy classes of free
+subgroups of finite index in $\Delta^+$. Asymptotic formulas are provided for
+the numbers of pavings and their isomorphism classes or, equivalently, for the
+number of free subgroups of given finite index in $\Delta^+$, and conjugacy
+classes of such subgroups.
+"
+26174,Generalized Bregman and Jensen divergences which include some f-divergences,"  In this paper, we introduce new classes of divergences by extending the
+definitions of the Bregman divergence and the skew Jensen divergence. These new
+divergence classes (g-Bregman divergence and skew g-Jensen divergence) satisfy
+some properties similar to the Bregman or skew Jensen divergence. We show these
+g-divergences include divergences which belong to a class of f-divergence (the
+Hellinger distance, the chi-square divergence and the alpha-divergence in
+addition to the Kullback-Leibler divergence). Moreover, we derive an inequality
+between the g-Bregman divergence and the skew g-Jensen divergence and show this
+inequality is a generalization of Lin's inequality.
+"
+26175,Segmentation-free Vehicle License Plate Recognition using ConvNet-RNN,"  While vehicle license plate recognition (VLPR) is usually done with a sliding
+window approach, it can have limited performance on datasets with characters
+that are of variable width. This can be solved by hand-crafting algorithms to
+prescale the characters. While this approach can work fairly well, the
+recognizer is only aware of the pixels within each detector window, and fails
+to account for other contextual information that might be present in other
+parts of the image. A sliding window approach also requires training data in
+the form of presegmented characters, which can be more difficult to obtain. In
+this paper, we propose a unified ConvNet-RNN model to recognize real-world
+captured license plate photographs. By using a Convolutional Neural Network
+(ConvNet) to perform feature extraction and using a Recurrent Neural Network
+(RNN) for sequencing, we address the problem of sliding window approaches being
+unable to access the context of the entire image by feeding the entire image as
+input to the ConvNet. This has the added benefit of being able to perform
+end-to-end training of the entire model on labelled, full license plate images.
+Experimental results comparing the ConvNet-RNN architecture to a sliding
+window-based approach shows that the ConvNet-RNN architecture performs
+significantly better.
+"
+26176,Emergence and seismological implications of phase transition and universality in a system with interaction between thermal pressurization and dilatancy,"  A dynamic earthquake source process is modeled by assuming interaction among
+frictional heat, fluid pressure, and inelastic porosity. In particular, fluid
+pressure increase due to frictional heating (thermal pressurization effect) and
+fluid pressure decrease due to inelastic porosity increase (dilatancy effect)
+play important roles in this process. Two nullclines become exactly the same in
+the system of governing equations, which generates non-isolated fixed points in
+the phase space. These lead to a type of phase transition, which produces a
+universality described by the power law between the initial value of one
+variable and the final value of the other variable. The universal critical
+exponent is found to be 1/2, which is independent of the details of the
+porosity evolution law. We can regard the dynamic earthquake slip process as a
+phase transition by considering the final porosity or slip as the order
+parameter. Physical prediction of phase emergence is difficult because the
+porosity evolution law has uncertainties, and the final slip amount is
+difficult to predict because of the universality. Finally, nonlinear
+mathematical application of the result is also discussed.
+"
+26177,STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery,"  In recent years, a class of dictionaries have been proposed for
+multidimensional (tensor) data representation that exploit the structure of
+tensor data by imposing a Kronecker structure on the dictionary underlying the
+data. In this work, a novel algorithm called ""STARK"" is provided to learn
+Kronecker structured dictionaries that can represent tensors of any order. By
+establishing that the Kronecker product of any number of matrices can be
+rearranged to form a rank-1 tensor, we show that Kronecker structure can be
+enforced on the dictionary by solving a rank-1 tensor recovery problem. Because
+rank-1 tensor recovery is a challenging nonconvex problem, we resort to solving
+a convex relaxation of this problem. Empirical experiments on synthetic and
+real data show promising results for our proposed algorithm.
+"
+26178,Sparse High-Dimensional Linear Regression. Algorithmic Barriers and a Local Search Algorithm,"  We consider a sparse high dimensional regression model where the goal is to
+recover a k-sparse unknown vector \beta^* from n noisy linear observations of
+the form Y=X\beta^*+W \in R^n where X \in R^{n \times p} has iid N(0,1) entries
+and W \in R^n has iid N(0,\sigma^2) entries. Under certain assumptions on the
+parameters, an intriguing assymptotic gap appears between the minimum value of
+n, call it n^*, for which the recovery is information theoretically possible,
+and the minimum value of n, call it n_{alg}, for which an efficient algorithm
+is known to provably recover \beta^*. In a recent paper it was conjectured that
+the gap is not artificial, in the sense that for sample sizes n \in
+[n^*,n_{alg}] the problem is algorithmically hard.
+We support this conjecture in two ways. Firstly, we show that a well known
+recovery mechanism called Basis Pursuit Denoising Scheme provably fails to
+\ell_2-stably recover the vector when n \in [n^*,c n_{alg}], for some
+sufficiently small constant c>0. Secondly, we establish that n_{alg}, up to a
+multiplicative constant factor, is a phase transition point for the appearance
+of a certain Overlap Gap Property (OGP) over the space of k-sparse vectors. The
+presence of such an Overlap Gap Property phase transition, which originates in
+statistical physics, is known to provide evidence of an algorithmic hardness.
+Finally we show that if n>C n_{alg} for some large enough constant C>0, a very
+simple algorithm based on a local search improvement is able to infer correctly
+the support of the unknown vector \beta^*, adding it to the list of provably
+successful algorithms for the high dimensional linear regression problem.
+"
+26179,QoS-aware Mesh based Multicast Routing Protocols in Ad-Hoc Networks: Concepts and Challenges,"  Multicast communication plays a crucial role in Mobile Adhoc Networks
+(MANETs). MANETs provide low cost, self configuring devices for multimedia data
+communication in military battlefield scenarios, disaster and public safety
+networks (PSN). Multicast communication improves the network performance in
+terms of bandwidth consumption, battery power and routing overhead as compared
+to unicast for same volume of data communication. In recent past, a number of
+multicast routing protocols (MRPs) have been proposed that tried to resolve
+issues and challenges in MRP. Multicast based group communication demands
+dynamic construction of efficient and reliable route for multimedia data
+communication during high node mobility, contention, routing and channel
+overhead. This paper gives an insight into the merits and demerits of the
+currently known research techniques and provides a better environment to make
+reliable MRP. It presents a ample study of various Quality of Service (QoS)
+techniques and existing enhancement in mesh based MRPs. Mesh topology based
+MRPs are classified according to their enhancement in routing mechanism and QoS
+modification on On-Demand Multicast Routing Protocol (ODMRP) protocol to
+improve performance metrics. This paper covers the most recent, robust and
+reliable QoS and Mesh based MRPs, classified based on their operational
+features, with their advantages and limitations, and provides comparison of
+their performance parameters.
+"
+26180,Continuity of Channel Parameters and Operations under Various DMC Topologies,"  We study the continuity of many channel parameters and operations under
+various topologies on the space of equivalent discrete memoryless channels
+(DMC). We show that mutual information, channel capacity, Bhattacharyya
+parameter, probability of error of a fixed code, and optimal probability of
+error for a given code rate and blocklength, are continuous under various DMC
+topologies. We also show that channel operations such as sums, products,
+interpolations, and Ar{\i}kan-style transformations are continuous.
+"
+26181,Variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations,"  Hidden Markov models provide a natural statistical framework for the
+detection of the copy number variations (CNV) in genomics. In this paper, we
+consider a Hidden Markov Model involving several correlated hidden processes at
+the same time. When dealing with a large number of series, maximum likelihood
+inference (performed classically using the EM algorithm) becomes intractable.
+We thus propose an approximate inference algorithm based on a variational
+approach (VEM). A simulation study is performed to assess the performance of
+the proposed method and an application to the detection of structural
+variations in plant genomes is presented.
+"
+26182,A note on the affine vertex algebra associated to $\frak{gl}(1 \vert 1)$ at the critical level and its generalizations,"  In this note we present an explicit realization of the affine vertex algebra
+$V^{cri}(\frak{gl}(1 \vert 1)) $ inside of the tensor product $F\otimes M$
+where $F$ is a fermionic verex algebra and $M$ is a commutative vertex algebra.
+This immediately gives an alternative description of the center of
+$V^{cri}(\frak{gl}(1 \vert 1) ) )$ as a subalgebra $M _ 0$ of $M$. We
+reconstruct the Molev-Mukhin formula for the Hilbert-Poincare series of the
+center of $V^ {cri}(\frak{gl}(1 \vert 1) )$. Moreover, we construct a family of
+irreducible $V^{cri}(\frak{gl}(1 \vert 1))$ -modules realized on $F$ and
+parameterized by $\chi^+, \chi ^- \in {\Bbb C}((z)). $ We propose a
+generalization of $V^ {cri}(\frak{gl}(1 \vert 1))$ as a critical level version
+of the super $\mathcal W_{1+\infty}$ vertex algebra.
+"
+26183,Re-ranking Person Re-identification with k-reciprocal Encoding,"  When considering person re-identification (re-ID) as a retrieval process,
+re-ranking is a critical step to improve its accuracy. Yet in the re-ID
+community, limited effort has been devoted to re-ranking, especially those
+fully automatic, unsupervised solutions. In this paper, we propose a
+k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is
+that if a gallery image is similar to the probe in the k-reciprocal nearest
+neighbors, it is more likely to be a true match. Specifically, given an image,
+a k-reciprocal feature is calculated by encoding its k-reciprocal nearest
+neighbors into a single vector, which is used for re-ranking under the Jaccard
+distance. The final distance is computed as the combination of the original
+distance and the Jaccard distance. Our re-ranking method does not require any
+human interaction or any labeled data, so it is applicable to large-scale
+datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW
+datasets confirm the effectiveness of our method.
+"
+26184,Size consistent excited states via algorithmic transformations between variational principles,"  We demonstrate that a broad class of excited state variational principles is
+not size consistent. In light of this difficulty, we develop and test an
+approach to excited state optimization that transforms between variational
+principles in order to achieve state selectivity, size consistency, and
+compatibility with quantum Monte Carlo. To complement our formal analysis, we
+provide numerical examples that confirm these properties and demonstrate how
+they contribute to a more black box approach to excited states in quantum Monte
+Carlo.
+"
+26185,The lure of ice-templating: recent trends and opportunities for porous materials,"  Ice-templating is a simple materials processing and shaping route where the
+growth of crystals from a solvent template the porosity. Although the
+principles of ice-templating were established a long time ago, it has lured
+considerable attention over the past 5 years to become a well-established
+processing route for porous materials of all kinds. In this viewpoint, I
+summarize the current status and recent trends of ice-templating studies. I
+then review the unique assets of ice-templating and propose a roadmap of the
+most promising or pressing questions and opportunities in this domain.
+"
+26186,Duality-free Methods for Stochastic Composition Optimization,"  We consider the composition optimization with two expected-value functions in
+the form of $\frac{1}{n}\sum\nolimits_{i = 1}^n F_i(\frac{1}{m}\sum\nolimits_{j
+= 1}^m G_j(x))+R(x)$, { which formulates many important problems in statistical
+learning and machine learning such as solving Bellman equations in
+reinforcement learning and nonlinear embedding}. Full Gradient or classical
+stochastic gradient descent based optimization algorithms are unsuitable or
+computationally expensive to solve this problem due to the inner expectation
+$\frac{1}{m}\sum\nolimits_{j = 1}^m G_j(x)$. We propose a duality-free based
+stochastic composition method that combines variance reduction methods to
+address the stochastic composition problem. We apply SVRG and SAGA based
+methods to estimate the inner function, and duality-free method to estimate the
+outer function. We prove the linear convergence rate not only for the convex
+composition problem, but also for the case that the individual outer functions
+are non-convex while the objective function is strongly-convex. We also provide
+the results of experiments that show the effectiveness of our proposed methods.
+"
+26187,Low-energy spin dynamics and critical hole concentrations in La$_{2-x}$Sr$_x$CuO$_4$ ($0.07\leq x \leq 0.2$) revealed by $^{139}$La and $^{63}$Cu nuclear magnetic resonance,"  We report a comprehensive $^{139}$La and $^{63}$Cu nuclear magnetic resonance
+study on La$_{2-x}$Sr$_x$CuO$_4$ ($0.07\leq x \leq 0.2$) single crystals. The
+$^{139}$La spin-lattice relaxation rate $^{139}T_1^{-1}$ is drastically
+influenced by Sr doping $x$ at low temperatures. A detailed field dependence of
+$^{139}T_1^{-1}$ at $x=1/8$ suggests that charge ordering induces the critical
+slowing down of spin fluctuations toward glassy spin order and competes with
+superconductivity. On the other hand, the $^{63}$Cu relaxation rate
+$^{63}T_1^{-1}$ is well described by a Curie-Weiss law at high temperatures,
+yielding the Curie-Weiss temperature $\Theta$ as a function of doping. $\Theta$
+changes sharply through a critical hole concentration $x_c\sim 0.09$. $x_c$
+appears to correspond to the delocalization limit of doped holes, above which
+the bulk nature of superconductivity is established.
+"
+26188,Generative Models of Visually Grounded Imagination,"  It is easy for people to imagine what a man with pink hair looks like, even
+if they have never seen such a person before. We call the ability to create
+images of novel semantic concepts visually grounded imagination. In this paper,
+we show how we can modify variational auto-encoders to perform this task. Our
+method uses a novel training objective, and a novel product-of-experts
+inference network, which can handle partially specified (abstract) concepts in
+a principled and efficient way. We also propose a set of easy-to-compute
+evaluation metrics that capture our intuitive notions of what it means to have
+good visual imagination, namely correctness, coverage, and compositionality
+(the 3 C's). Finally, we perform a detailed comparison of our method with two
+existing joint image-attribute VAE methods (the JMVAE method of Suzuki et.al.
+and the BiVCCA method of Wang et.al.) by applying them to two datasets: the
+MNIST-with-attributes dataset (which we introduce here), and the CelebA
+dataset.
+"
+26189,A class of non-linear fractional-order system stabilisation via fixed-order dynamic output feedback controller,"  This paper investigates the robust stabilisation of a class of
+fractional-order non-linear system with positive real uncertainty via
+fixed-order dynamic output feedback controller in terms of linear matrix
+inequalities (LMIs). the systematic stabilisation algorithm design for
+low-order controller based on direct Lyapunov approach is proposed for
+uncertain fractional-order systems. In the presented algorithm the conditions
+containing the bilinear variables are decoupled into separate conditions
+without imposing equality constraints or considering an iterative search of the
+controller parameters. There is no any limiting constraint on state space
+matrices and also we assumed the most complete output feedback controller.
+Simulations results are given to approve the effectiveness and the
+straightforwardness of the proposed design.
+"
+26190,Modes of rapid polygenic adaptation,"  Many experimental and field studies have shown that adaptation can occur very
+rapidly. Two qualitatively different modes of fast adaptation have been
+proposed: selective sweeps wherein large shifts in the allele frequencies occur
+at a few loci and evolution via small changes in the allele frequencies at many
+loci. While the first process has been thoroughly investigated within the
+framework of population genetics, the latter is based on quantitative genetics
+and is much less understood. Here we summarize results from our recent
+theoretical studies of a quantitative genetic model of polygenic adaptation
+that makes explicit reference to population genetics to bridge the gap between
+the two frameworks. Our key results are that polygenic adaptation may be a
+rapid process and can proceed via subtle or dramatic changes in the allele
+frequency depending on the sizes of the phenotypic effects relative to a
+threshold value. We also discuss how the signals of polygenic selection may be
+detected in the genome. While powerful methods are available to identify
+signatures of selective sweeps at loci controling quantitative traits, the
+development of statistical tests for detecting small shifts of allele
+frequencies at quantitative trait loci is still in its infancy.
+"
+26191,The Sharkovsky Theorem,"  The original proof of the Sharkovsky theorem is presented in full detail. The
+proof should be accessible to readers with basic Real Analysis background.
+Although nowadays there are several alternative proofs of this classical
+result, we believe the original proof has independent interest. On the one
+hand, it shows the path followed by the author to derive his result; on the
+other hand, many auxiliary results in this proof have independent interest.
+Moreover, the proof itself is not widely available, is very terse, and contains
+several typographical errors.
+"
+26192,An App Performance Optimization Advisor for Mobile Device App Marketplaces,"  On mobile phones, users and developers use apps official marketplaces serving
+as repositories of apps. The Google Play Store and Apple Store are the official
+marketplaces of Android and Apple products which offer more than a million
+apps. Although both repositories offer description of apps, information
+concerning performance is not available. Due to the constrained hardware of
+mobile devices, users and developers have to meticulously manage the resources
+available and they should be given access to performance information about
+apps. Even if this information was available, the selection of apps would still
+depend on user preferences and it would require a huge cognitive effort to make
+optimal decisions. Considering this fact we propose APOA, a recommendation
+system which can be implemented in any marketplace for helping users and
+developers to compare apps in terms of performance.
+APOA uses as input metric values of apps and a set of metrics to optimize. It
+solves an optimization problem and it generates optimal sets of apps for
+different user's context. We show how APOA works over an Android case study.
+Out of 140 apps, we define typical usage scenarios and we collect measurements
+of power, CPU, memory, and network usages to demonstrate the benefit of using
+APOA.
+"
+26193,A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices,"  Discrimination via algorithmic decision making has received considerable
+attention. Prior work largely focuses on defining conditions for fairness, but
+does not define satisfactory measures of algorithmic unfairness. In this paper,
+we focus on the following question: Given two unfair algorithms, how should we
+determine which of the two is more unfair? Our core idea is to use existing
+inequality indices from economics to measure how unequally the outcomes of an
+algorithm benefit different individuals or groups in a population. Our work
+offers a justified and general framework to compare and contrast the
+(un)fairness of algorithmic predictors. This unifying approach enables us to
+quantify unfairness both at the individual and the group level. Further, our
+work reveals overlooked tradeoffs between different fairness notions: using our
+proposed measures, the overall individual-level unfairness of an algorithm can
+be decomposed into a between-group and a within-group component. Earlier
+methods are typically designed to tackle only between-group unfairness, which
+may be justified for legal or other reasons. However, we demonstrate that
+minimizing exclusively the between-group component may, in fact, increase the
+within-group, and hence the overall unfairness. We characterize and illustrate
+the tradeoffs between our measures of (un)fairness and the prediction accuracy.
+"
+26194,Convergent Star Products for Projective Limits of Hilbert Spaces,"  Given a locally convex vector space with a topology induced by Hilbert
+seminorms and a continuous bilinear form on it we construct a topology on its
+symmetric algebra such that the usual star product of exponential type becomes
+continuous. Many properties of the resulting locally convex algebra are
+explained. We compare this approach to various other discussions of convergent
+star products in finite and infinite dimensions. We pay special attention to
+the case of a Hilbert space and to nuclear spaces.
+"
+26195,Large photocathode 20-inch PMT testing methods for the JUNO experiment,"  The 20kt Liquid Scintillator (LS) JUNO detector is being constructed by the
+International Collaboration in China, with the primary goal of addressing the
+question of neutrino mass ordering (hierarchy). The main challenge for JUNO is
+to achieve a record energy resolution, ~3% at 1MeV of energy released in the
+LS, which is required to perform the neutrino mass hierarchy determination.
+About 20 000 large 20-inch PMTs with high Photon Detection Efficiency (PDE) and
+good photocathode uniformity will ensure an approximately 80% surface coverage
+of the JUNO detector. The JUNO collaboration is preparing equipment for the
+mass tests of all PMTs using 4 dedicated containers. This approach allows us to
+test 144 PMTs in parallel. The primary measurement in the container will be the
+PMT response to illumination of its photocathode by a low-intensity uniform
+light. Each of the 20 000 PMTs will undergo the container test. Additionally, a
+dedicated scanning system was constructed for sampled tests of PMTs that allows
+us to study the variation of the PDE over the entire PMT photocathode surface.
+The core of the scanning station is a rotating frame with 7 LED sources of
+calibrated short light flashes that are placed along the photocathode surface
+covering zenith angles from the top of a PMT to its equator. The collection
+efficiency of a large PMT is known to be very sensitive to the Earth Magnetic
+Field (EMF), therefore, understanding the necessary level of EMF suppression is
+crucial for the JUNO Experiment. A dark room with Helmholtz coils compensating
+the EMF components is available for these tests at a JUNO facility. The
+Hamamatsu R12860 20-inch PMT is a candidate for the JUNO experiment. In this
+article the container design and mass-testing method, the scanning setup and
+scanning method are briefly described and preliminary results for performance
+test of this PMT are reported.
+"
+26196,Phase Locking Value revisited: teaching new tricks to an old dog,"  Despite the increase in calculation power in the last decades, the estimation
+of brain connectivity is still a tedious task. The high computational cost of
+the algorithms escalates with the square of the number of signals evaluated,
+usually in the range of thousands. In this work we propose a re-formulation of
+a widely used algorithm that allows the estimation of whole brain connectivity
+in much smaller times. We start from the original implementation of Phase
+Locking Value (PLV) and re-formulated it in a highly computational efficient
+way. Besides, this formulation stresses its strong similarity with coherence,
+which we used to introduce two new metrics insensitive to zero lag
+synchronization, the imaginary part of PLV (iPLV) and its corrected counterpart
+(ciPLV). The new implementation of PLV avoids some highly CPU-expensive
+operations, and achieved a 100-fold speedup over the original algorithm. The
+new derived metrics were highly robust in the presence of volume conduction.
+ciPLV, in particular, proved capable of ignoring zero-lag connectivity, while
+correctly estimating nonzero-lag connectivity. Our implementation of PLV makes
+it possible to calculate whole-brain connectivity in much shorter times. The
+results of the simulations using ciPLV suggest that this metric is ideal to
+measure synchronization in the presence of volume conduction or source leakage
+effects.
+"
+26197,On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics,"  In cheminformatics, compound-target binding profiles has been a main source
+of data for research. For data repositories that only provide positive
+profiles, a popular assumption is that unreported profiles are all negative. In
+this paper, we caution audience not to take this assumption for granted, and
+present empirical evidence of its ineffectiveness from a machine learning
+perspective. Our examination is based on a setting where binding profiles are
+used as features to train predictive models; we show (1) prediction performance
+degrades when the assumption fails and (2) explicit recovery of unreported
+profiles improves prediction performance. In particular, we propose a framework
+that jointly recovers profiles and learns predictive model, and show it
+achieves further performance improvement. The presented study not only suggests
+applying matrix recovery methods to recover unreported profiles, but also
+initiates a new missing feature problem which we called Learning with Positive
+and Unknown Features.
+"
+26198,Supervised Classification: Quite a Brief Overview,"  The original problem of supervised classification considers the task of
+automatically assigning objects to their respective classes on the basis of
+numerical measurements derived from these objects. Classifiers are the tools
+that implement the actual functional mapping from these measurements---also
+called features or inputs---to the so-called class label---or output. The
+fields of pattern recognition and machine learning study ways of constructing
+such classifiers. The main idea behind supervised methods is that of learning
+from examples: given a number of example input-output relations, to what extent
+can the general mapping be learned that takes any new and unseen feature vector
+to its correct class? This chapter provides a basic introduction to the
+underlying ideas of how to come to a supervised classification problem. In
+addition, it provides an overview of some specific classification techniques,
+delves into the issues of object representation and classifier evaluation, and
+(very) briefly covers some variations on the basic supervised classification
+task that may also be of interest to the practitioner.
+"
+26199,Improving Deep Learning using Generic Data Augmentation,"  Deep artificial neural networks require a large corpus of training data in
+order to effectively learn, where collection of such training data is often
+expensive and laborious. Data augmentation overcomes this issue by artificially
+inflating the training set with label preserving transformations. Recently
+there has been extensive use of generic data augmentation to improve
+Convolutional Neural Network (CNN) task performance. This study benchmarks
+various popular data augmentation schemes to allow researchers to make informed
+decisions as to which training methods are most appropriate for their data
+sets. Various geometric and photometric schemes are evaluated on a
+coarse-grained data set using a relatively simple CNN. Experimental results,
+run using 4-fold cross-validation and reported in terms of Top-1 and Top-5
+accuracy, indicate that cropping in geometric augmentation significantly
+increases CNN task performance.
+"
+26200,Topological Hochschild homology of X(n),"  We show that Ravenel's spectrum $X(2)$ is the versal $E_1$-$S$-algebra of
+characteristic $\eta$. This implies that every $E_1$-$S$-algebra $R$ of
+characteristic $\eta$ admits an $E_1$-ring map $X(2)\to R$, i.e. an
+$\mathbb{A}_\infty$ complex orientation of degree 2. This implies that
+$R^\ast(\mathbb{C}P^2)\cong R_\ast[x]/x^3$. Additionally, if $R$ is an
+$\mathbb{E}_2$-ring Thom spectrum admitting a map (of homotopy ring spectra)
+from $X(2)$, e.g. $X(n)$, its topological Hochschild homology has a simple
+description.
+"
+26201,Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments,"  Implicit semantic role labeling (iSRL) is the task of predicting the semantic
+roles of a predicate that do not appear as explicit arguments, but rather
+regard common sense knowledge or are mentioned earlier in the discourse. We
+introduce an approach to iSRL based on a predictive recurrent neural semantic
+frame model (PRNSFM) that uses a large unannotated corpus to learn the
+probability of a sequence of semantic arguments given a predicate. We leverage
+the sequence probabilities predicted by the PRNSFM to estimate selectional
+preferences for predicates and their arguments. On the NomBank iSRL test set,
+our approach improves state-of-the-art performance on implicit semantic role
+labeling with less reliance than prior work on manually constructed language
+resources.
+"
+26202,Residue-type indices and holomorphic foliations,"  We investigate residue-type indices for germs of holomorphic foliations in
+the plane and characterize second type foliations - those not containing
+tangent saddle-nodes in the reduction of singularities - by an expression
+involving the Baum-Bott, variation and polar excess indices. These local
+results are applied in the study of logarithmic foliations on compact complex
+surfaces.
+"
+26203,Together We Know How to Achieve: An Epistemic Logic of Know-How,"  The existence of a coalition strategy to achieve a goal does not necessarily
+mean that the coalition has enough information to know how to follow the
+strategy. Neither does it mean that the coalition knows that such a strategy
+exists. The article studies an interplay between the distributed knowledge,
+coalition strategies, and coalition ""know-how"" strategies. The main technical
+result is a sound and complete trimodal logical system that describes the
+properties of this interplay.
+"
+26204,Houdini for Astrophysical Visualization,"  The rapid growth in scale and complexity of both computational and
+observational astrophysics over the past decade necessitates efficient and
+intuitive methods for examining and visualizing large datasets. Here we discuss
+some newly developed tools to import and manipulate astrophysical data into the
+three dimensional visual effects software, {\it Houdini}. This software is
+widely used by visual effects artists, but a recently implemented Python API
+now allows astronomers to more easily use Houdini as a visualization tool. This
+paper includes a description of features, work flow, and various example
+visualizations. The project website, www.ytini.com, contains Houdini tutorials
+and links to the Python script Bitbucket repository aimed at a scientific
+audience to simplify the process of importing and rendering astrophysical data.
+"
+26205,Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions,"  In online communities, antisocial behavior such as trolling disrupts
+constructive discussion. While prior work suggests that trolling behavior is
+confined to a vocal and antisocial minority, we demonstrate that ordinary
+people can engage in such behavior as well. We propose two primary trigger
+mechanisms: the individual's mood, and the surrounding context of a discussion
+(e.g., exposure to prior trolling behavior). Through an experiment simulating
+an online discussion, we find that both negative mood and seeing troll posts by
+others significantly increases the probability of a user trolling, and together
+double this probability. To support and extend these results, we study how
+these same mechanisms play out in the wild via a data-driven, longitudinal
+analysis of a large online news discussion community. This analysis reveals
+temporal mood effects, and explores long range patterns of repeated exposure to
+trolling. A predictive model of trolling behavior shows that mood and
+discussion context together can explain trolling behavior better than an
+individual's history of trolling. These results combine to suggest that
+ordinary people can, under the right circumstances, behave like trolls.
+"
+26206,"CoHSI II; The average length of proteins, evolutionary pressure and eukaryotic fine structure","  The CoHSI (Conservation of Hartley-Shannon Information) distribution is at
+the heart of a wide-class of discrete systems, defining (amongst other
+properties) the length distribution of their components. Discrete systems such
+as the known proteome, computer software and texts are all known to fit this
+distribution accurately. In a previous paper, we explored the properties of
+this distribution in detail. Here we will use these properties to show why the
+average length of components in general and proteins in particular is highly
+conserved, howsoever measured, demonstrating this on various aggregations of
+proteins taken from the UniProt database. We will go on to define departures
+from this equilibrium state, identifying fine structure in the average length
+of eukaryotic proteins that result from evolutionary processes.
+"
+26207,Preliminary Exploration of Formula Embedding for Mathematical Information Retrieval: can mathematical formulae be embedded like a natural language?,"  While neural network approaches are achieving breakthrough performance in the
+natural language related fields, there have been few similar attempts at
+mathematical language related tasks. In this study, we explore the potential of
+applying neural representation techniques to Mathematical Information Retrieval
+(MIR) tasks. In more detail, we first briefly analyze the characteristic
+differences between natural language and mathematical language. Then we design
+a ""symbol2vec"" method to learn the vector representations of formula symbols
+(numbers, variables, operators, functions, etc.) Finally, we propose a
+""formula2vec"" based MIR approach and evaluate its performance. Preliminary
+experiment results show that there is a promising potential for applying
+formula embedding models to mathematical language representation and MIR tasks.
+"
+26208,On the solution to the separated equation in the 3-particle Calogero-Moser problem,"  We propose the exact solution of the equation in separated variable which
+appears in the process of constructing solutions to the quantum Calogero-Moser
+three-particle problem with elliptic two-particle potential $g(g-1)\wp(q)$.
+This solution is found for special values of coupling constants $g\in {\mathbb
+Z}, \, g>1$. It can be used for solving three-paricle CM problem under
+appropriate boundary conditions.
+"
+26209,Parity bifurcations in trapped multistable phase locked exciton-polariton condensates,"  We present a theoretical scheme for multistability in planar microcavity
+exciton-polariton condensates under nonresonant driving. Using an excitation
+profile resulting in a spatially patterned condensate, we observe organized
+phase locking which can abruptly reorganize as a result of pump induced
+instability made possible by nonlinear interactions. For $\pi/2$ symmetric
+systems this reorganization can be regarded as a parity transition and is found
+to be a fingerprint of multistable regimes existing over a finite range of
+excitation strengths. The natural degeneracy of the planar equations of motion
+gives rise to parity bifurcation points where the condensate, as a function of
+excitation intensity, bifurcates into one of two anisotropic degenerate
+solutions. Deterministic transitions between multistable states are made
+possible using controlled nonresonant pulses, perturbing the solution from one
+attractor to another.
+"
+26210,A Framework for Wasserstein-1-Type Metrics,"  We propose a unifying framework for generalising the Wasserstein-1 metric to
+a discrepancy measure between nonnegative measures of different mass. This
+generalization inherits the convexity and computational efficiency from the
+Wasserstein-1 metric, and it includes several previous approaches from the
+literature as special cases. For various specific instances of the generalized
+Wasserstein-1 metric we furthermore demonstrate their usefulness in
+applications by numerical experiments.
+"
+26211,Topological entropy for locally linearly compact vector spaces,"  In analogy to the topological entropy for continuous endomorphisms of totally
+disconnected locally compact groups, we introduce a notion of topological
+entropy for continuous endomorphisms of locally linearly compact vector spaces.
+We study the fundamental properties of this entropy and we prove the Addition
+Theorem, showing that the topological entropy is additive with respect to short
+exact sequences. By means of Lefschetz Duality, we connect the topological
+entropy to the algebraic entropy in a Bridge Theorem.
+"
+26212,LittleDarwin: a Feature-Rich and Extensible Mutation Testing Framework for Large and Complex Java Systems,"  Mutation testing is a well-studied method for increasing the quality of a
+test suite. We designed LittleDarwin as a mutation testing framework able to
+cope with large and complex Java software systems, while still being easily
+extensible with new experimental components. LittleDarwin addresses two
+existing problems in the domain of mutation testing: having a tool able to work
+within an industrial setting, and yet, be open to extension for cutting edge
+techniques provided by academia. LittleDarwin already offers higher-order
+mutation, null type mutants, mutant sampling, manual mutation, and mutant
+subsumption analysis. There is no tool today available with all these features
+that is able to work with typical industrial software systems.
+"
+26213,Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study,"  Any-gram kernels are a flexible and efficient way to employ bag-of-n-gram
+features when learning from textual data. They are also compatible with the use
+of word embeddings so that word similarities can be accounted for. While the
+original any-gram kernels are implemented on top of tree kernels, we propose a
+new approach which is independent of tree kernels and is more efficient. We
+also propose a more effective way to make use of word embeddings than the
+original any-gram formulation. When applied to the task of sentiment
+classification, our new formulation achieves significantly better performance.
+"
+26214,"Halo Histories vs. Galaxy Properties at z=0, III: The Properties of Star-Forming Galaxies","  We measure how the properties of star-forming central galaxies correlate with
+large-scale environment, $\delta$, measured on $10$Mpc/h scales. We use group
+catalogs to isolate a robust sample of central galaxies with high purity and
+completeness. The properties we investigate are star formation rate (SFR),
+exponential disk scale length $R_{\rm exp}$, and Sersic index of the light
+profile, $n$. We find that, at all stellar masses, there is an inverse
+correlation between SFR and $\delta$, meaning that above-average star forming
+centrals live in underdense regions. For $n$ and $R_{\rm exp}$, there is no
+correlation with $\delta$ at $M_{\rm star}\lesssim 10^{10.5}$ $M_\odot$, but at
+higher masses there are positive correlations; a weak correlation with $R_{\rm
+exp}$ and a strong correlation with $n$. These data are evidence of assembly
+bias within the star-forming population. The results for SFR are consistent
+with a model in which SFR correlates with present-day halo accretion rate,
+$\dot{M}_h$. In this model, galaxies are assigned to halos using the abundance
+matching ansatz, which maps galaxy stellar mass onto halo mass. At fixed halo
+mass, SFR is assigned to galaxies using the same approach,but $\dot{M}_h$ is
+used to map onto SFR. The best-fit model requires some scatter in the
+$\dot{M}_h$-SFR relation. The $R_{\rm exp}$ and $n$ measurements are consistent
+with a model in which these quantities are correlated with the spin parameter
+of the halo, $\lambda$. Halo spin does not correlate with $\delta$ at low halo
+masses, but for higher mass halos, high-spin halos live in higher density
+environments at fixed $M_h$. Put together with the earlier installments of this
+series, these data demonstrate that quenching processes have limited
+correlation with halo formation history, but the growth of active galaxies, as
+well as other detailed properties, are influenced by the details of halo
+assembly.
+"
+26215,"On moduli space of symmetric orthogonal matrices and exclusive Racah matrix $\bar S$ for representation $R=[3,1]$ with multiplicities","  Racah matrices and higher $j$-symbols are used in description of braiding
+properties of conformal blocks and in construction of knot polynomials.
+However, in complicated cases the logic is actually inverted: they are much
+better deduced from these applications than from the basic representation
+theory. Following the recent proposal of arXiv:1612.00422, we obtain the
+exclusive Racah matrix $\bar S$ for the currently-front-line case of
+representation $R=[3,1]$ with non-trivial multiplicities, where it is actually
+operator valued, i.e. depends on the choice of basises in the intertwiner
+spaces. Effective field theory for arborescent knots in this case possesses
+gauge invariance, which is not yet properly described and understood. Because
+of this lack of knowledge a big part (about a half) of $\bar S$ needs to be
+reconstructed from orthogonality conditions. Therefore we discuss the abundance
+of symmetric orthogonal matrices, to which $\bar S$ belong, and explain that
+dimension of their moduli space is also about a half of that for the ordinary
+orthogonal matrices. Thus the knowledge approximately matches the freedom and
+this explains why the method can work -- with some limited addition of educated
+guesses. A similar calculation for $R=[r,1]$ for $r>3$ should also be doable.
+"
+26216,Design and Analysis of a Task-based Parallelization over a Runtime System of an Explicit Finite-Volume CFD Code with Adaptive Time Stepping,"  FLUSEPA (Registered trademark in France No. 134009261) is an advanced
+simulation tool which performs a large panel of aerodynamic studies. It is the
+unstructured finite-volume solver developed by Airbus Safran Launchers company
+to calculate compressible, multidimensional, unsteady, viscous and reactive
+flows around bodies in relative motion. The time integration in FLUSEPA is done
+using an explicit temporal adaptive method. The current production version of
+the code is based on MPI and OpenMP. This implementation leads to important
+synchronizations that must be reduced. To tackle this problem, we present the
+study of a task-based parallelization of the aerodynamic solver of FLUSEPA
+using the runtime system StarPU and combining up to three levels of
+parallelism. We validate our solution by the simulation (using a finite-volume
+mesh with 80 million cells) of a take-off blast wave propagation for Ariane 5
+launcher.
+"
+26217,Massive Open and Online Courses and Open Education Resources in Singapore,"  This paper looks at the increasing popularity of massive open and online
+courses (MOOCs) and open educational resources (OERs) offered in Singapore.
+Despite being a relatively new phenomenon, the Singapore government has
+collaborated with different organizations to improve the quality and
+accessibility of MOOCs, and many institutions of higher learning (IHLs) are
+spearheading efforts to improve OERs to facilitate greater public access to
+educational resources. It will also explore the benefits and potential problems
+that MOOCs and OERs face. For example, both MOOCs and OERs are able to lower
+the costs of university-level education and increase public access to such
+courses. They also provide skills and job training for members of the public as
+well as encourage lifelong learning. However, both MOOCs and OERs may not be
+sustainable in the long run, as the financial gains of both may not be able to
+cover the costs of mounting them. Each system also has its own set of problems.
+For example, formal structures to guarantee the quality of MOOCs offered remain
+lacking. MOOCs also tend to have low completion rates and there have been
+issues regarding plagiarism with the use of MOOCs as learning platforms. OERs
+pose challenges to traditional copyright policies while lack of sustainable
+funding prevents them from being adopted more widely. Even though both systems
+may potentially transform the traditional education system, a deeper
+understanding of MOOCs and OERs as well as their implications on learning will
+be useful.
+"
+26218,Investigating context features hidden in End-to-End TTS,"  Recent studies have introduced end-to-end TTS, which integrates the
+production of context and acoustic features in statistical parametric speech
+synthesis. As a result, a single neural network replaced laborious feature
+engineering with automated feature learning. However, little is known about
+what types of context information end-to-end TTS extracts from text input
+before synthesizing speech, and the previous knowledge about context features
+is barely utilized. In this work, we first point out the model similarity
+between end-to-end TTS and parametric TTS. Based on the similarity, we evaluate
+the quality of encoder outputs from an end-to-end TTS system against eight
+criteria that are derived from a standard set of context information used in
+parametric TTS. We conduct experiments using an evaluation procedure that has
+been newly developed in the machine learning literature for quantitative
+analysis of neural representations, while adapting it to the TTS domain.
+Experimental results show that the encoder outputs reflect both linguistic and
+phonetic contexts, such as vowel reduction at phoneme level, lexical stress at
+syllable level, and part-of-speech at word level, possibly due to the joint
+optimization of context and acoustic features.
+"
+26219,Inspiring Computer Vision System Solutions,"  The ""digital Michelangelo project"" was a seminal computer vision project in
+the early 2000's that pushed the capabilities of acquisition systems and
+involved multiple people from diverse fields, many of whom are now leaders in
+industry and academia. Reviewing this project with modern eyes provides us with
+the opportunity to reflect on several issues, relevant now as then to the field
+of computer vision and research in general, that go beyond the technical
+aspects of the work.
+This article was written in the context of a reading group competition at the
+week-long International Computer Vision Summer School 2017 (ICVSS) on Sicily,
+Italy. To deepen the participants understanding of computer vision and to
+foster a sense of community, various reading groups were tasked to highlight
+important lessons which may be learned from provided literature, going beyond
+the contents of the paper. This report is the winning entry of this guided
+discourse (Fig. 1). The authors closely examined the origins, fruits and most
+importantly lessons about research in general which may be distilled from the
+""digital Michelangelo project"". Discussions leading to this report were held
+within the group as well as with Hao Li, the group mentor.
+"
+26220,SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements,"  We develop SHOPPER, a sequential probabilistic model of shopping data.
+SHOPPER uses interpretable components to model the forces that drive how a
+customer chooses products; in particular, we designed SHOPPER to capture how
+items interact with other items. We develop an efficient posterior inference
+algorithm to estimate these forces from large-scale data, and we analyze a
+large dataset from a major chain grocery store. We are interested in answering
+counterfactual queries about changes in prices. We found that SHOPPER provides
+accurate predictions even under price interventions, and that it helps identify
+complementary and substitutable pairs of products.
+"
+26221,Windowed Green Function method for the Helmholtz equation in presence of multiply layered media,"  This paper presents a new methodology for the solution of problems of two-
+and three-dimensional acoustic scattering (and, in particular, two-dimensional
+electromagnetic scattering) by obstacles and defects in presence an arbitrary
+number of penetrable layers. Relying on use of certain slow-rise windowing
+functions, the proposed Windowed Green Function approach (WGF) efficiently
+evaluates oscillatory integrals over unbounded domains, with high accuracy,
+without recourse to the highly expensive Sommerfeld integrals that have
+typically been used to account for the effect of underlying planar multi-layer
+structures. The proposed methodology, whose theoretical basis was presented in
+the recent contribution (SIAM J. Appl. Math. 76(5), p. 1871, 2016), is fast,
+accurate, flexible, and easy to implement. Our numerical experiments
+demonstrate that the numerical errors resulting from the proposed approach
+decrease faster than any negative power of the window size. In a number of
+examples considered in this paper the proposed method is up to thousands of
+times faster, for a given accuracy, than corresponding methods based on use of
+Sommerfeld integrals.
+"
+26222,Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge,"  Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is
+demonstrated to efficiently solve eigenvalue problems for graph Laplacians that
+appear in spectral clustering. For static graph partitioning, 10-20 iterations
+of LOBPCG without preconditioning result in ~10x error reduction, enough to
+achieve 100% correctness for all Challenge datasets with known truth
+partitions, e.g., for graphs with 5K/.1M (50K/1M) Vertices/Edges in 2 (7)
+seconds, compared to over 5,000 (30,000) seconds needed by the baseline Python
+code. Our Python code 100% correctly determines 98 (160) clusters from the
+Challenge static graphs with 0.5M (2M) vertices in 270 (1,700) seconds using
+10GB (50GB) of memory. Our single-precision MATLAB code calculates the same
+clusters at half time and memory. For streaming graph partitioning, LOBPCG is
+initiated with approximate eigenvectors of the graph Laplacian already computed
+for the previous graph, in many cases reducing 2-3 times the number of required
+LOBPCG iterations, compared to the static case. Our spectral clustering is
+generic, i.e. assuming nothing specific of the block model or streaming, used
+to generate the graphs for the Challenge, in contrast to the base code.
+Nevertheless, in 10-stage streaming comparison with the base code for the 5K
+graph, the quality of our clusters is similar or better starting at stage 4 (7)
+for emerging edging (snowballing) streaming, while the computations are over
+100-1000 faster.
+"
+26223,A Temporal Slow Growth Formulation for Direct Numerical Simulation of Compressible Wall--Bounded Flows,"  A new slow growth formulation for DNS of wall-bounded turbulent flow is
+developed and demonstrated to enable extension of slow growth modeling concepts
+to complex boundary layer flows. As in previous slow growth approaches, the
+formulation assumes scale separation between the fast scales of turbulence and
+the slow evolution of statistics such as the mean flow. This separation enables
+the development of approaches where the fast scales of turbulence are directly
+simulated while the forcing provided by the slow evolution is modeled. The
+resulting model admits periodic boundary conditions in the streamwise
+direction, which avoids the need for extremely long domains and complex inflow
+conditions that typically accompany spatially developing simulations. Further,
+it enables the use of efficient Fourier numerics. Unlike previous approaches,
+the present approach is based on a temporally evolving boundary layer and is
+specifically tailored to give results for calibration and validation of RANS
+turbulence models. The use of a temporal homogenization simplifies the
+modeling, enabling straightforward extension to flows with complicating
+features, including cold and blowing walls. To generate data useful for
+calibration and validation of RANS models, special care is taken to ensure that
+the mean slow growth forcing is closed in terms of the mean and other
+quantities that appear in standard RANS models, ensuring that there is no
+confounding between typical RANS closures and additional closures required for
+the slow growth problem. The performance of the method is demonstrated on two
+problems: an essentially incompressible, zero-pressure-gradient boundary layer
+and a transonic boundary layer over a cooled wall with wall transpiration.
+"
+26224,Computing a Score of Navigability in Large Graphs,"  The groundbreaking experiment of Travers and Milgram demonstrated the
+so-called ""six degrees of separation"" phenomenon, by which any individual in
+the world is able to contact an arbitrary, hitherto-unknown, individual by
+means of a short chain of social ties. Despite the large number of empirical
+and theoretical studies to explain the Travers-Milgram experiment, some
+fundamental questions are still open: why some individuals are more likely than
+others to discover short friend-of-a-friend communication chains? Can we rank
+individuals on the basis of their ability to discover short chains? To answer
+these questions, we extend the concept of potential gain, originally defined in
+the context of Web analysis, to social networks and we define a novel index,
+called ""the navigability score,"" that ranks nodes in a network on the basis of
+how their position facilitates the discover of short chains that connect to
+arbitrary target nodes in the network. We define two variants of potential
+gain, called the geometric and the exponential potential gain, and present fast
+algorithms to compute them. Our theoretical and experimental analysis proves
+that the computation of the geometric and exponential gain are affordable even
+on large real-life graphs.
+"
+26225,Shift-Invariant Kernel Additive Modelling for Audio Source Separation,"  A major goal in blind source separation to identify and separate sources is
+to model their inherent characteristics. While most state-of-the-art approaches
+are supervised methods trained on large datasets, interest in non-data-driven
+approaches such as Kernel Additive Modelling (KAM) remains high due to their
+interpretability and adaptability. KAM performs the separation of a given
+source applying robust statistics on the time-frequency bins selected by a
+source-specific kernel function, commonly the K-NN function. This choice
+assumes that the source of interest repeats in both time and frequency. In
+practice, this assumption does not always hold. Therefore, we introduce a
+shift-invariant kernel function capable of identifying similar spectral content
+even under frequency shifts. This way, we can considerably increase the amount
+of suitable sound material available to the robust statistics. While this leads
+to an increase in separation performance, a basic formulation, however, is
+computationally expensive. Therefore, we additionally present acceleration
+techniques that lower the overall computational complexity.
+"
+26226,Relaxing the Irrevocability Requirement for Online Graph Algorithms,"  Online graph problems are considered in models where the irrevocability
+requirement is relaxed. Motivated by practical examples where, for example,
+there is a cost associated with building a facility and no extra cost
+associated with doing it later, we consider the Late Accept model, where a
+request can be accepted at a later point, but any acceptance is irrevocable.
+Similarly, we also consider a Late Reject model, where an accepted request can
+later be rejected, but any rejection is irrevocable (this is sometimes called
+preemption). Finally, we consider the Late Accept/Reject model, where late
+accepts and rejects are both allowed, but any late reject is irrevocable. For
+Independent Set, the Late Accept/Reject model is necessary to obtain a constant
+competitive ratio, but for Vertex Cover the Late Accept model is sufficient and
+for Minimum Spanning Forest the Late Reject model is sufficient. The Matching
+problem has a competitive ratio of 2, but in the Late Accept/Reject model, its
+competitive ratio is 3/2.
+"
+26227,Cytometry inference through adaptive atomic deconvolution,"  In this paper we consider a statistical estimation problem known as atomic
+deconvolution. Introduced in reliability, this model has a direct application
+when considering biological data produced by flow cytometers. In these
+experiments, biologists measure the fluorescence emission of treated cells and
+compare them with their natural emission to study the presence of specific
+molecules on the cells' surface. They observe a signal which is composed of a
+noise (the natural fluorescence) plus some additional signal related to the
+quantity of molecule present on the surface if any. From a statistical point of
+view, we aim at inferring the percentage of cells expressing the selected
+molecule and the probability distribution function associated with its
+fluorescence emission. We propose here an adap-tive estimation procedure based
+on a previous deconvolution procedure introduced by [vEGS08, GvES11]. For both
+estimating the mixing parameter and the mixing density automatically, we use
+the Lepskii method based on the optimal choice of a bandwidth using a
+bias-variance decomposition. We then derive some concentration inequalities for
+our estimators and obtain the convergence rates, that are shown to be minimax
+optimal (up to some log terms) in Sobolev classes. Finally, we apply our
+algorithm on simulated and real biological data.
+"
+26228,Coalgebras in symmetric monoidal categories of spectra,"  We show that all coalgebras over the sphere spectrum are cocommutative in the
+category of symmetric spectra, orthogonal spectra, $\Gamma$-spaces,
+$\mathcal{W}$-spaces and EKMM $\mathbb{S}$-modules. Our result only applies to
+these strict monoidal categories of spectra and does not apply to the
+$\infty$-category setting.
+"
+26229,Contact real hypersurfaces in the complex hyperbolic quadric,"  We give a new proof of the classification of contact real hypersurfaces with
+constant mean curvature in the complex hyperbolic quadric ${Q^m}^* =
+SO_{m,2}^o/SO_mSO_2$, where $m\geq 3$. We show that a contact real hypersurface
+$M$ in ${Q^m}^*$ for $m\geq 3$ is locally congruent to a tube of radius
+$r{\in}{\mathbb R}^+$ around the complex hyperbolic quadric ${Q^{m-1}}^*$, or
+to a tube of radius $r\in\mathbb{R}^+$ around the $\mathfrak A$-principal
+$m$-dimensional real hyperbolic space ${\mathbb R}H^m$ in ${Q^m}^* =
+SO_{m,2}^o/SO_mSO_2$, or to a horosphere in ${Q^{m-1}}^*$ induced by a class of
+$\mathfrak A$-principal geodesics in ${Q^m}^*$.
+"
+26230,A Unified Batch Online Learning Framework for Click Prediction,"  We present a unified framework for Batch Online Learning (OL) for Click
+Prediction in Search Advertisement. Machine Learning models once deployed, show
+non-trivial accuracy and calibration degradation over time due to model
+staleness. It is therefore necessary to regularly update models, and do so
+automatically. This paper presents two paradigms of Batch Online Learning, one
+which incrementally updates the model parameters via an early stopping
+mechanism, and another which does so through a proximal regularization. We
+argue how both these schemes naturally trade-off between old and new data. We
+then theoretically and empirically show that these two seemingly different
+schemes are closely related. Through extensive experiments, we demonstrate the
+utility of of our OL framework; how the two OL schemes relate to each other and
+how they trade-off between the new and historical data. We then compare batch
+OL to full model retrains, and show how online learning is more robust to data
+issues. We also demonstrate the long term impact of Online Learning, the role
+of the initial Models in OL, the impact of delays in the update, and finally
+conclude with some implementation details and challenges in deploying a real
+world online learning system in production. While this paper mostly focuses on
+application of click prediction for search advertisement, we hope that the
+lessons learned here can be carried over to other problem domains.
+"
+26231,Alternative (Oriented) Singular Cochains and the Modified Cup Product,"  A special subcomplex of the singular chain complex for a topological space,
+historically called oriented singular chain complex is used here with the new
+name ""alternative"" singular chain complex. It was already known that this
+subcomplex and so its dual complex are chain homotopy equivalent to singular
+chains and cochains respectively and thus have the same homology and
+cohomology. Here, in addition to revisiting some aspects of this subcomplex, it
+is shown that alternative singular cochains (dual of alternative singular
+chains) with coefficients in rational or real numbers are indeed summands of
+singular cochains through a natural splitting. It is shown that this natural
+splitting also hold for cohomologies: At any order, the singular cohomology
+splits into the alternative cohomology and another summand which is zero if the
+considered topological space is compact. Also in this case similar to the wedge
+product for differential forms, a modified cup product can be defined with the
+same algebraic properties as in the wedge product in differential forms. This
+provides an idea to investigate some topological and structure-free aspects of
+nonlinear global differential equations on manifolds..
+"
+26232,Temporal Variations of Telluric Water Vapor Absorption at Apache Point Observatory,"  Time-variable absorption by water vapor in Earth's atmosphere presents an
+important source of systematic error for a wide range of ground-based
+astronomical measurements, particularly at near-infrared wavelengths. We
+present results from the first study on the temporal and spatial variability of
+water vapor absorption at Apache Point Observatory (APO). We analyze
+$\sim$400,000 high-resolution, near-infrared ($H$-band) spectra of hot stars
+collected as calibration data for the APO Galactic Evolution Explorer (APOGEE)
+survey. We fit for the optical depths of telluric water vapor absorption
+features in APOGEE spectra and convert these optical depths to Precipitable
+Water Vapor (PWV) using contemporaneous data from a GPS-based PWV monitoring
+station at APO. Based on simultaneous measurements obtained over a 3$^{\circ}$
+field of view, we estimate that our PWV measurement precision is $\pm0.11$ mm.
+We explore the statistics of PWV variations over a range of timescales from
+less than an hour to days. We find that the amplitude of PWV variations within
+an hour is less than 1 mm for most (96.5%) APOGEE field visits. By considering
+APOGEE observations that are close in time but separated by large distances on
+the sky, we find that PWV is homogeneous across the sky at a given epoch, with
+90% of measurements taken up to 70$^{\circ}$ apart within 1.5 hr having
+$\Delta\,\rm{PWV}<1.0$ mm. Our results can be used to help simulate the impact
+of water vapor absorption on upcoming surveys at continental observing sites
+like APO, and also to help plan for simultaneous water vapor metrology that may
+be carried out in support of upcoming photometric and spectroscopic surveys.
+"
+26233,Universal Spike Classifier,"  In electrophysiology, microelectrodes are the primary source for recording
+neural data of single neurons (single unit activity). These microelectrodes can
+be implanted individually, or in the form of microelectrodes arrays, consisting
+of hundreds of electrodes. During recordings, some channels capture the
+activity of neurons, which is usually contaminated with external artifacts and
+noise. Another considerable fraction of channels does not record any neural
+data, but external artifacts and noise. Therefore, an automatic identification
+and tracking of channels containing neural data is of great significance and
+can accelerate the process of analysis, e.g. automatic selection of meaningful
+channels during offline and online spike sorting. Another important aspect is
+the selection of meaningful channels during online decoding in brain-computer
+interface applications, where threshold crossing events are usually for feature
+extraction, even though they do not necessarily correspond to neural events.
+Here, we propose a novel algorithm based on the newly introduced way of feature
+vector extraction and a supervised deep learning method: a universal spike
+classifier (USC). The USC enables us to address both above-raised issues. The
+USC uses the standard architecture of convolutional neural networks (Conv net).
+It takes the batch of the waveforms, instead of a single waveform as an input,
+propagates it through the multilayered structure, and finally classifies it as
+a channel containing neural spike data or artifacts. We have trained the model
+of USC on data recorded from single tetraplegic patient with Utah arrays
+implanted in different brain areas. This trained model was then evaluated
+without retraining on the data collected from six epileptic patients implanted
+with depth electrodes and two tetraplegic patients implanted with two Utah
+arrays, individually.
+"
+26234,Superluminal X-waves in a polariton quantum fluid,"  In this work we experimentally demonstrate for the first time spontaneous
+generation of two-dimensional exciton-polariton X-waves. X-waves belong to the
+family of localized packets, which are capable of sustaining their shape with
+no spreading even in the linear regime. This allows to keep the packet shape
+and size for very low densities and very long times compared, for instance, to
+soliton waves, which always necessitate a nonlinearity to compensate the
+diffusion. Here we exploit the polariton nonlinearity and unique structured
+dispersion, comprising both positive- and negative-mass curvatures, to trigger
+an asymmetric four wave mixing in the momentum space. This ultimately enables
+self-formation of a spatial X-wave front. By means of ultrafast imaging
+experiments we observe the early reshaping of the initial Gaussian packet into
+the X-pulse and its propagation even for vanishing small densities. This allows
+us to outline the crucial effects and parameters driving the phenomena and to
+tune the degree of peak superluminal propagation, which we found to be in a
+good agreement with numerical simulations.
+"
+26235,Ab initio calculations of spectroscopic constants and properties of BeLi +,"  We have calculated the ground state spectroscopic constants and the molecular
+properties, of a molecular ion BeLi + , such as dipole moment, quadrupole
+moment and dipole polarizability at different levels of correlation: many-body
+perturbation theory (MP2), coupled cluster method with single and double
+excitations (CCSD) and CCSD with perturbative triples (CCSD(T)). The
+correlation consistent polarized valence cc-pVXZ (X=D, T, Q) basis sets and
+also their augmented counterparts are used together with the non-relativistic
+and relativistic Hamiltonians. The results are extrapolated to the complete
+basis set limit (CBS) using exponential-Gaussian function. Thus, accurate and
+reliable results for BeLi + with the most conservative error estimates on them
+are reported.
+"
+26236,The Finite Difference Time Domain (FDTD) Method to Determine Energies and Wave Functions of Two-Electron Quantum Dot,"  The finite difference time domain (FDTD) method has been successfully applied
+to obtain energies and wave functions for two electrons in a quantum dot
+modeled by a three dimensional harmonic potential. The FDTD method uses the
+time-dependent Schrödinger equation (TDSE) in imaginary time. The TDSE is
+numerically solved with an initial random wave function and after enough
+simulation time, the wave function converges to the ground state wave function.
+The excited states are determined by using the same procedure for the ground
+state with additional constraints that the wave function must be orthogonal
+with all lower energy wave functions. The numerical results for energies and
+wave functions for different parameters of confinement potentials are given and
+compared with published results using other numerical methods. It is shown that
+the FDTD method gives accurate energies and wave functions.
+"
+26237,Mining Social Media to Inform Peatland Fire and Haze Disaster Management,"  Peatland fires and haze events are disasters with national, regional and
+international implications. The phenomena lead to direct damage to local
+assets, as well as broader economic and environmental losses. Satellite imagery
+is still the main and often the only available source of information for
+disaster management. In this article, we test the potential of social media to
+assist disaster management. To this end, we compare insights from two datasets:
+fire hotspots detected via NASA satellite imagery and almost all GPS-stamped
+tweets from Sumatra Island, Indonesia, posted during 2014. Sumatra Island is
+chosen as it regularly experiences a significant number of haze events, which
+affect citizens in Indonesia as well as in nearby countries including Malaysia
+and Singapore. We analyse temporal correlations between the datasets and their
+geo-spatial interdependence. Furthermore, we show how Twitter data reveals
+changes in users' behavior during severe haze events. Overall, we demonstrate
+that social media is a valuable source of complementary and supplementary
+information for haze disaster management. Based on our methodology and
+findings, an analytics tool to improve peatland fire and haze disaster
+management by the Indonesian authorities is under development.
+"
+26238,Minimizing Energy Consumption and Peak Power of Series Elastic Actuators: a Convex Optimization Framework for Elastic Element Design,"  Compared to rigid actuators, Series Elastic Actuators (SEAs) offer a
+potential reduction of motor energy consumption and peak power, though these
+benefits are highly dependent on the design of the torque-elongation profile of
+the elastic element. In the case of linear springs, natural dynamics is a
+traditional method for this design, but it has two major limitations: arbitrary
+load trajectories are difficult or impossible to analyze and it does not
+consider actuator constraints. Parametric optimization is also a popular design
+method that addresses these limitations, but solutions are only optimal within
+the space of the parameters. To overcome these limitations, we propose a
+non-parametric convex optimization program for the design of the nonlinear
+elastic element that minimizes energy consumption and peak power for an
+arbitrary periodic reference trajectory. To obtain convexity, we introduce a
+convex approximation to the expression of peak power; energy consumption is
+shown to be convex without approximation. The combination of peak power and
+energy consumption in the cost function leads to a multiobjective convex
+optimization framework that comprises the main contribution of this paper. As a
+case study, we recover the elongation-torque profile of a cubic spring, given
+its natural oscillation as the reference load. We then design nonlinear SEAs
+for an ankle prosthesis that minimize energy consumption and peak power for
+different trajectories and extend the range of achievable tasks when subject to
+actuator constraints.
+"
+26239,PeX 1. Multi-spectral expansion of residual speckles for planet detection,"  The detection of exoplanets in coronographic images is severely limited by
+residual starlight speckles. Dedicated post-processing can drastically reduce
+this ""stellar leakage"" and thereby increase the faintness of detectable
+exoplanets. Based on a multi-spectral series expansion of the diffraction
+pattern, we derive a multi-mode model of the residuals which can be exploited
+to estimate and thus remove the residual speckles in multi-spectral
+coronographic images. Compared to other multi-spectral processing methods, our
+model is physically grounded and is suitable for use in an (optimal) inverse
+approach. We demonstrate the ability of our model to correctly estimate the
+speckles in simulated data and demonstrate that very high contrasts can be
+achieved. We further apply our method to removing speckles from a real data
+cube obtained with the SPHERE IFS instrument.
+"
+26240,Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction,"  This paper presents a method for constructing human-robot interaction
+policies in settings where multimodality, i.e., the possibility of multiple
+highly distinct futures, plays a critical role in decision making. We are
+motivated in this work by the example of traffic weaving, e.g., at highway
+on-ramps/off-ramps, where entering and exiting cars must swap lanes in a short
+distance---a challenging negotiation even for experienced drivers due to the
+inherent multimodal uncertainty of who will pass whom. Our approach is to learn
+multimodal probability distributions over future human actions from a dataset
+of human-human exemplars and perform real-time robot policy construction in the
+resulting environment model through massively parallel sampling of human
+responses to candidate robot action sequences. Direct learning of these
+distributions is made possible by recent advances in the theory of conditional
+variational autoencoders (CVAEs), whereby we learn action distributions
+simultaneously conditioned on the present interaction history, as well as
+candidate future robot actions in order to take into account response dynamics.
+We demonstrate the efficacy of this approach with a human-in-the-loop
+simulation of a traffic weaving scenario.
+"
+26241,A radio-to-mm census of star-forming galaxies in protocluster 4C23.56 at z=2.5 : Gas mass and its fraction revealed with ALMA,"  We investigate gas contents of star-forming galaxies associated with
+protocluster 4C23.56 at z = 2.49 by using the redshifted CO(3-2) and 1.1 mm
+dust continuum with the Atacama Large Millimeter/submillimeter Array. The
+observations unveil seven CO detections out of 22 targeted H$\alpha$ emitters
+(HAEs) and four out of 19 in 1.1 mm dust continuum. They have high stellar mass
+($M_{\star}>4\times 10^{10}$ $M_{\odot}$) and exhibit a specific star-formation
+rate typical of main-sequence star forming galaxies at $z\sim2.5$. Different
+gas mass estimators from CO(3-2) and 1.1 mm yield consistent values for
+simultaneous detections. The gas mass ($M_{\rm gas}$) and gas fraction ($f_{\rm
+gas}$) are comparable to those of field galaxies, with $M_{\rm gas}=[0.3,
+1.8]\times10^{11} \times (\alpha_{\rm CO}/(4.36\times A(Z)$)) M$_{\odot}$,
+where $\alpha_{\rm CO}$ is the CO-to-H$_2$ conversion factor and $A(Z)$ the
+additional correction factor for the metallicity dependence of $\alpha_{\rm
+CO}$, and $\langle f_{\rm gas}\rangle = 0.53 \pm 0.07$ from CO(3-2). Our
+measurements place a constraint on the cosmic gas density of high-$z$
+protoclusters, indicating the protocluster is characterized by a gas density
+higher than that of the general fields by an order of magnitude. We found $\rho
+(H_2)\sim 5 \times 10^9 \,M_{\odot}\,{\rm Mpc^{-3}}$ with the CO(3-2)
+detections. The five ALMA CO detections occur in the region of highest galaxy
+surface density, where the density positively correlates with global
+star-forming efficiency (SFE) and stellar mass. Such correlations imply a
+potentially critical role of environment on early galaxy evolution at high-z
+protoclusters, although future observations are necessary for confirmation.
+"
+26242,A multilayer network dataset of interaction and influence spreading in a virtual world,"  Presented data contains the record of five spreading campaigns that occurred
+in a virtual world platform. Users distributed avatars between each other
+during the campaigns. The processes varied in time and range and were either
+incentivized or not incentivized. Campaign data is accompanied by events. The
+data can be used to build a multilayer network to place the campaigns in a
+wider context. To the best of the authors knowledge, the study is the first
+publicly available dataset containing a complete real multilayer social network
+together, along with five complete spreading processes in it.
+"
+26243,Texture and composition of Titan's equatorial region inferred from Cassini SAR inversion: Implications for aeolian transport at Saturn's largest moon,"  Sand seas on Titan may reflect the present and past climatic conditions.
+Understanding the morphodynamics and physico-chemical properties of Titan's
+dunes is therefore essential for a better comprehension of the climatic and
+geological history of the largest Saturn's moon. We derived quantitatively
+surface properties (texture, composition) from the modelling of microwave
+backscattered signal and Monte-Carlo inversion of despeckled Cassini/SAR data
+over sand sea. We show that dunes and interdunes have significantly different
+physical properties. Dunes are globally more microwave absorbent than the
+interdunes. The inter-dunes present multi-scale roughness with a higher
+dielectric constant than the dunes. Considering the composition, the interdunes
+are in between the dunes and the radar bright inselbergs, suggesting the
+presence of a shallow layer of non-mobilized sediment in between the dunes.
+Additionally potential secondary bedforms, such as ripples and avalanches, may
+have been detected. Our findings strongly suggest that sand seas evolve under
+current multi-directional wind regimes. Consequently sediment inventory and
+climatic conditions are being re-evaluated.
+"
+26244,Energy Harvesting Networks with General Utility Functions: Near Optimal Online Policies,"  We consider online scheduling policies for single-user energy harvesting
+communication systems, where the goal is to characterize online policies that
+maximize the long term average utility, for some general concave and
+monotonically increasing utility function. In our setting, the transmitter
+relies on energy harvested from nature to send its messages to the receiver,
+and is equipped with a finite-sized battery to store its energy. Energy packets
+are independent and identically distributed (i.i.d.) over time slots, and are
+revealed causally to the transmitter. Only the average arrival rate is known a
+priori. We first characterize the optimal solution for the case of Bernoulli
+arrivals. Then, for general i.i.d. arrivals, we first show that fixed fraction
+policies [Shaviv-Ozgur] are within a constant multiplicative gap from the
+optimal solution for all energy arrivals and battery sizes. We then derive a
+set of sufficient conditions on the utility function to guarantee that fixed
+fraction policies are within a constant additive gap as well from the optimal
+solution.
+"
+26245,Assortment Optimization under the Sequential Multinomial Logit Model,"  We study the assortment optimization problem under the Sequential Multinomial
+Logit (SML), a discrete choice model that generalizes the multinomial logit
+(MNL). Under the SML model, products are partitioned into two levels, to
+capture differences in attractiveness, brand awareness and, or visibility of
+the products in the market. When a consumer is presented with an assortment of
+products, she first considers products in the first level and, if none of them
+is purchased, products in the second level are considered. This model is a
+special case of the Perception-Adjusted Luce Model (PALM) recently proposed by
+Echenique et al (2018). It can explain many behavioural phenomena such as the
+attraction, compromise, similarity effects and choice overload which cannot be
+explained by the MNL model or any discrete choice model based on random
+utility. In particular, the SML model allows violations to regularity which
+states that the probability of choosing a product cannot increase if the offer
+set is enlarged.
+This paper shows that the seminal concept of revenue-ordered assortment sets,
+which contain an optimal assortment under the MNL model, can be generalized to
+the SML model. More precisely, the paper proves that all optimal assortments
+under the SML are revenue-ordered by level, a natural generalization of
+revenue-ordered assortments that contains, at most, a quadratic number of
+assortments. As a corollary, assortment optimization under the SML is
+polynomial-time solvable. This result is particularly interesting given that
+the SML model does not satisfy the regularity condition and, therefore, it can
+explain choice behaviours that cannot be explained by any choice model based on
+random utility.
+"
+26246,Renormalized solutions for the fractional p(x)-Laplacian equation with L^1 data,"  In this paper, we prove the existence and uniqueness of nonnegative
+renormalized solutions for the fractional p(x)-Laplacian problem with L1 data.
+Our results are new even in the constant exponent fractional p-Laplacian
+equation case.
+"
+26247,Reconciling the Bayes Factor and Likelihood Ratio for Two Non-Nested Model Selection Problems,"  In statistics, there are a variety of methods for performing model selection
+that all stem from slightly different paradigms of statistical inference. The
+reasons for choosing one particular method over another seem to be based
+entirely on philosophical preferences. In the case of non-nested model
+selection, two of the prevailing techniques are the Bayes Factor and the
+likelihood ratio. This article focuses on reconciling the likelihood ratio and
+the Bayes Factor for comparing a pair of non-nested models under two different
+problem frameworks typical in forensic science, the common-source and the
+specific-source problem. We show that the Bayes Factor can be expressed as the
+expected value of the corresponding likelihood ratio function with respect to
+the posterior distribution for the parameters given the entire set of data
+where the set(s) of unknown-source observations has been generated according to
+the second model. This expression leads to a number of useful theoretic and
+practical results relating the two statistical approaches. This relationship is
+quite meaningful in many scientific applications where there is a general
+confusion between the various statistical methods, and particularly in the case
+of forensic science.
+"
+26248,Thin front limit of an integro--differential Fisher--KPP equation with fat--tailed kernels,"  We study the asymptotic behavior of solutions to a monostable
+integro-differential Fisher-KPP equation , that is where the standard Laplacian
+is replaced by a convolution term, when the dispersal kernel is fat-tailed. We
+focus on two different regimes. Firstly, we study the long time/long range
+scaling limit by introducing a relevant rescaling in space and time and prove a
+sharp bound on the (super-linear) spreading rate in the Hamilton-Jacobi sense
+by means of sub-and super-solutions. Secondly, we investigate a long time/small
+mutation regime for which, after identifying a relevant rescaling for the size
+of mutations, we derive a Hamilton-Jacobi limit.
+"
+26249,Thermodynamics of the Fermi gas in a nanotube,"  For the ideal Fermi gas that fills the space inside a cylindrical tube, there
+are calculated the thermodynamic characteristics in general form for arbitrary
+temperatures, namely: the thermodynamic potential, energy, entropy, equations
+of state, heat capacities and compressibilities. All these quantities are
+expressed through the introduced standard functions and their derivatives. The
+radius of the tube is considered as an additional thermodynamic variable. It is
+shown that at low temperatures in the quasi-one-dimensional case the
+temperature dependencies of the entropy and heat capacities remain linear. The
+dependencies of the entropy and heat capacities on the chemical potential have
+sharp maximums at the points where the filling of a new discrete level begins.
+The character of dependencies of thermodynamic quantities on the tube radius
+proves to be qualitatively different in the cases of fixed linear and fixed
+total density. At the fixed linear density these dependencies are monotonous
+and at the fixed total density they have an oscillating character. Key words:
+Fermi particle, nanotube, thermodynamic functions, low-dimensional systems,
+equation of state, heat capacity, compressibility
+"
+26250,Multiple solutions for resonant problems of the Robin $p$-Laplacian plus an indefinite potential,"  We study a nonlinear boundary value problem driven by the $p$-Laplacian plus
+an indefinite potential with Robin boundary condition. The reaction term is a
+Carathéodory function which is asymptotically resonant at $\pm\infty$ with
+respect to a nonprincipal Ljusternik-Schnirelmann eigenvalue. Using variational
+methods, together with Morse theory and truncation-perturbation techniques, we
+show that the problem has at least three nontrivial smooth solutions, two of
+which have a fixed sign.
+"
+26251,Measurement of the ionization yield of nuclear recoils in liquid argon using a two-phase detector with electroluminescence gap,"  A measurement of ionization yields in noble-gas liquids is relevant to the
+energy calibration of nuclear recoil detectors for dark matter search and
+coherent neutrino-nucleus scattering experiments. In this work we further study
+the ionization yield of nuclear recoils in liquid Ar, using a two-phase
+detector with an electroluminescence gap and DD neutron generator. The
+ionization yields of nuclear recoils in liquid Ar were measured at 233 keV and
+electric fields of 0.56 and 0.62 kV/cm; their values amounted to 5.9 $\pm$ 0.8
+and 7.4 $\pm$ 1 e$^-$/keV, respectively. The characteristic dependences of the
+ionization yield on energy and electric field were determined, while comparing
+the results obtained to those at lower energies and higher fields.
+"
+26252,The impact of the carrier envelope phase -- dependence on system and laser parameters,"  We investigate, from a theoretical perspective, photo-emission of electrons
+induced by ultra-short infrared pulses covering only a few photon cycles. In
+particular, we investigate the impact of the Carrier-Envelope Phase (CEP) of
+the laser pulse which plays an increasingly large role for decreasing pulse
+length. As key observable we look at the asymmetry of the angular distribution
+as function of kinetic energy of the emitted electrons. The focus of the
+present study lies on the system dependence of the reaction. To this end, we
+study two very different systems in comparison, an Ar atom and the Na9+
+cluster. The study employs a fully quantum-mechanical description of electron
+dynamics at the level of Time-Dependent Density Functional Theory (TDDFT). We
+find a sensitive dependence on the system which can be related to the different
+spectral response properties. Results can be understood from an interplay of
+the ponderomotive motion driven by the external photon field and dynamical
+polarization of the system.
+"
+26253,Sums of finitely many distinct reciprocals,"  ${\cal F}$ denotes the family of all finite nonempty $S\subseteq{\mathbb
+N}:=\{1,2,\ldots\}$, and ${\cal F}(X):={\cal F}\cap\{S:S\subseteq X\}$ when
+$X\subseteq{\mathbb N}$. This paper treats the function $\sigma:{\cal
+F}\rightarrow{\mathbb Q}^+$ given by $\sigma:S\mapsto\sigma S :=\sum\{1/x:x\in
+S\}$, and the function $\delta:{\cal F}\rightarrow{\mathbb N}$ defined by
+$\sigma S = \nu S/\delta S$ where the integers $\nu S$ and $\delta S$ are
+coprime.
+{\bf Theorem 1.1.} \ For each $r\in{\mathbb Q}^+$, there exists an infinite
+pairwise disjoint subfamily ${\cal H}_r\subseteq{\cal F}$ such that $r=\sigma
+S$ for all $S\in{\cal H}_r$.
+{\bf Theorem 1.2.} \ Let $X$ be a pairwise coprime set of positive integers.
+Then $\sigma$ restricted to ${\cal F}(X)$ and $\delta$ restricted to ${\cal
+F}(X)$ are injective. Also, $\sigma C\in{\mathbb N}$ for $C\in{\cal F}(X)$ only
+if $C=\{1\}$.
+"
+26254,Uncovering the Edge of the Polar Vortex,"  The polar vortices play a crucial role in the formation of the ozone hole and
+can cause severe weather anomalies. Their boundaries, known as the vortex
+`edges', are typically identified via methods that are either frame-dependent
+or return non-material structures, and hence are unsuitable for assessing
+material transport barriers. Using two-dimensional velocity data on isentropic
+surfaces in the northern hemisphere, we show that elliptic Lagrangian Coherent
+Structures (LCSs) identify the correct outermost material surface dividing the
+coherent vortex core from the surrounding incoherent surf zone. Despite the
+purely kinematic construction of LCSs, we find a remarkable contrast in
+temperature and ozone concentration across the identified vortex boundary. We
+also show that potential vorticity-based methods, despite their simplicity,
+misidentify the correct extent of the vortex edge. Finally, exploiting the
+shrinkage of the vortex at various isentropic levels, we observe a trend in the
+magnitude of vertical motion inside the vortex which is consistent with
+previous results.
+"
+26255,"Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings","  Cardinal scores (numeric ratings) collected from people are well known to
+suffer from miscalibrations. A popular approach to address this issue is to
+assume simplistic models of miscalibration (such as linear biases) to de-bias
+the scores. This approach, however, often fares poorly because people's
+miscalibrations are typically far more complex and not well understood. In the
+absence of simplifying assumptions on the miscalibration, it is widely believed
+by the crowdsourcing community that the only useful information in the cardinal
+scores is the induced ranking. In this paper, inspired by the framework of
+Stein's shrinkage, empirical Bayes, and the classic two-envelope problem, we
+contest this widespread belief. Specifically, we consider cardinal scores with
+arbitrary (or even adversarially chosen) miscalibrations which are only
+required to be consistent with the induced ranking. We design estimators which
+despite making no assumptions on the miscalibration, strictly and uniformly
+outperform all possible estimators that rely on only the ranking. Our
+estimators are flexible in that they can be used as a plug-in for a variety of
+applications, and we provide a proof-of-concept for A/B testing and ranking.
+Our results thus provide novel insights in the eternal debate between cardinal
+and ordinal data.
+"
+26256,Nuclear-size effects and a numerical approach to the Dirac equation,"  Due to some current interest in this subject we have produced this note.
+There is no claim to anything new, except possibly to show that a direct
+numerical approach is quite simple and instructive. For comparison purposes we
+include a section on the Coulomb Klein-Gordon equation.
+"
+26257,The applications of deep neural networks to sdBV classification,"  With several new large-scale surveys on the horizon, including LSST, TESS,
+ZTF, and Evryscope, faster and more accurate analysis methods will be required
+to adequately process the enormous amount of data produced. Deep learning, used
+in industry for years now, allows for advanced feature detection in minimally
+prepared datasets at very high speeds; however, despite the advantages of this
+method, its application to astrophysics has not yet been extensively explored.
+This dearth may be due to a lack of training data available to researchers.
+Here we generate synthetic data loosely mimicking the properties of acoustic
+mode pulsating stars and we show that two separate paradigms of deep learning -
+the Artificial Neural Network And the Convolutional Neural Network - can both
+be used to classify this synthetic data effectively. And that additionally this
+classification can be performed at relatively high levels of accuracy with
+minimal time spent adjusting network hyperparameters.
+"
+26258,Asymptotic performance of PCA for high-dimensional heteroscedastic data,"  Principal Component Analysis (PCA) is a classical method for reducing the
+dimensionality of data by projecting them onto a subspace that captures most of
+their variation. Effective use of PCA in modern applications requires
+understanding its performance for data that are both high-dimensional and
+heteroscedastic. This paper analyzes the statistical performance of PCA in this
+setting, i.e., for high-dimensional data drawn from a low-dimensional subspace
+and degraded by heteroscedastic noise. We provide simplified expressions for
+the asymptotic PCA recovery of the underlying subspace, subspace amplitudes and
+subspace coefficients; the expressions enable both easy and efficient
+calculation and reasoning about the performance of PCA. We exploit the
+structure of these expressions to show that, for a fixed average noise
+variance, the asymptotic recovery of PCA for heteroscedastic data is always
+worse than that for homoscedastic data (i.e., for noise variances that are
+equal across samples). Hence, while average noise variance is often a
+practically convenient measure for the overall quality of data, it gives an
+overly optimistic estimate of the performance of PCA for heteroscedastic data.
+"
+26259,Competition of striped magnetic order and partial Kondo screened state in the Kondo lattice model,"  We analyze the magnetically ordered phases in the Kondo lattice model on a
+square lattice around quarter filling using the dynamical mean field theory. We
+find that close to quarter filling besides the paramagnetic phase, at least
+three magnetic phases compete. These phases are a ferromagnetic state, a
+partial Kondo screened state, which is a combination of charge order and
+magnetic order, and a striped magnetic phase, which is ferromagnetically
+ordered in one direction and antiferromagnetically in the other direction. We
+analyze the spectral properties of these states and show that the PKS state is
+insulating with a flat band above the Fermi energy, while the other states are
+metallic. Furthermore, we demonstrate that the energy gain by the Kondo effect
+is larger in the PKS state, while the striped magnetic state can gain more
+energy from the RKKY interaction. Thus, while the striped magnetic state is
+stable at weak coupling, the PKS state becomes stable at intermediate coupling.
+"
+26260,Characterization of photomultiplier tubes with a realistic model through GPU-boosted simulation,"  The accurate characterization of a photomultiplier tube (PMT) is crucial in a
+wide-variety of applications. However, current methods do not give fully
+accurate representations of the response of a PMT, especially at very low light
+levels. In this work, we present a new and more realistic model of the response
+of a PMT, called the cascade model, and use it to characterize two different
+PMTs at various voltages and light levels. The cascade model is shown to
+outperform the more common Gaussian model in almost all circumstances and to
+agree well with a newly introduced model independent approach. The technical
+and computational challenges of this model are also presented along with the
+employed solution of developing a robust GPU-based analysis framework for this
+and other non-analytical models.
+"
+26261,A Rural Lens on a Research Agenda for Intelligent Infrastructure,"  A National Agenda for Intelligent Infrastructure is not complete without
+explicit consideration of the needs of rural communities. While the American
+population has urbanized, the United States depends on rural communities for
+agriculture, fishing, forestry, manufacturing and mining. Approximately 20% of
+the US population lives in rural areas with a skew towards aging adults.
+Further, nearly 25% of Veterans live in rural America. And yet, when
+intelligent infrastructure is imagined, it is often done so with implicit or
+explicit bias towards cities. In this brief we describe the unique
+opportunities for rural communities and offer an inclusive vision of
+intelligent infrastructure research. In this paper, we argue for a set of
+coordinated actions to ensure that rural Americans are not left behind in this
+digital revolution. These technological platforms and applications, supported
+by appropriate policy, will address key issues in transportation, energy,
+agriculture, public safety and health. We believe that rather than being a set
+of needs, the rural United States presents a set of exciting possibilities for
+novel innovation benefiting not just those living there, but the American
+economy more broadly
+"
+26262,Neural System Identification with Spike-triggered Non-negative Matrix Factorization,"  Neuronal circuits formed in the brain are complex with intricate connection
+patterns. Such a complexity is also observed in the retina as a relatively
+simple neuronal circuit. A retinal ganglion cell receives excitatory inputs
+from neurons in previous layers as driving forces to fire spikes. Analytical
+methods are required that can decipher these components in a systematic manner.
+Recently a method termed spike-triggered non-negative matrix factorization
+(STNMF) has been proposed for this purpose. In this study, we extend the scope
+of the STNMF method. By using the retinal ganglion cell as a model system, we
+show that STNMF can detect various biophysical properties of upstream bipolar
+cells, including spatial receptive fields, temporal filters, and transfer
+nonlinearity. In addition, we recover synaptic connection strengths from the
+weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of
+a ganglion cell into a few subsets of spikes where each subset is contributed
+by one presynaptic bipolar cell. Taken together,these results corroborate that
+STNMF is a useful method for deciphering the structure of neuronal circuits.
+"
+26263,The impact of hysteresis on the electrocaloric effect at first-order phase transitions,"  We study the impact of thermal hysteresis at the first-order
+structural/ferroelectric phase transitions on the electrocaloric response in
+bulk BaTiO$_3$ by performing molecular dynamics simulations for a
+first-principles-based effective Hamiltonian. We demonstrate that the
+electrocaloric response can conceptually be separated in two contributions: a
+transitional part, stemming from the discontinuous jump in entropy at the first
+order phase transition, and a configurational part, due to the continuous
+change of polarization and entropy within each phase. This latter part
+increases with the strength of the applied field, but for small fields it is
+very small. In contrast, we find a large temperature change of $\sim 1$ K
+resulting from the transition entropy, which is essentially independent of the
+field strength. However, due to the coexistence region close to the first order
+phase transition, this large electrocaloric response depends on the thermal
+history of the sample and is generally not reversible. We show that this
+irreversibility can be overcome by using larger fields.
+"
+26264,Why concave rather than convex inflaton potential?,"  The Planck data on cosmic microwave background indicates that the
+Starobinsky-type model with concave inflation potential is favored over the
+convex-type chaotic inflation. Is there any reason for that? Here we argue that
+if our universe began with a Euclidean wormhole, then the Starobinsky-type
+inflation is probabilistically favored. It is known that for a more generic
+choice of parameters than that originally assumed by Hartle and Hawking, the
+Hartle-Hawking wave function is dominated by Euclidean wormholes, which can be
+interpreted as the creation of two classical universes from nothing. We show
+that only one end of the wormhole can be classicalized for a convex potential,
+while both ends can be classicalized for a concave potential. The latter is
+therefore more probable.
+"
+26265,"On the ""persistency"" of scientific publications: introducing an h-index for journals","  What do we really mean by a ""good"" scientific journal? Do we care more about
+the short-time impact of our papers, or about the chance that they will still
+be read and cited on the long run? Here I show that, by regarding a journal as
+a ""virtual scientist"" that can be attributed a time-dependent Hirsch h-index,
+we can introduce a parameter that, arguably, better captures the ""persistency""
+of a scientific publication. Curiously, however, this parameter seems to depend
+above all on the ""thickness"" of a journal.
+"
+26266,Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials.gov,"  Objectives: Trial registries can be used to measure reporting biases and
+support systematic reviews but 45% of registrations do not provide a link to
+the article reporting on the trial. We evaluated the use of document similarity
+methods to identify unreported links between ClinicalTrials.gov and PubMed.
+Study Design and Setting: We extracted terms and concepts from a dataset of
+72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles, and tested
+methods for ranking articles across 16,005 reported links and 90
+manually-identified unreported links. Performance was measured by the median
+rank of matching articles, and the proportion of unreported links that could be
+found by screening ranked candidate articles in order. Results: The best
+performing concept-based representation produced a median rank of 3 (IQR 1-21)
+for reported links and 3 (IQR 1-19) for the manually-identified unreported
+links, and term-based representations produced a median rank of 2 (1-20) for
+reported links and 2 (IQR 1-12) in unreported links. The matching article was
+ranked first for 40% of registrations, and screening 50 candidate articles per
+registration identified 86% of the unreported links. Conclusions: Leveraging
+the growth in the corpus of reported links between ClinicalTrials.gov and
+PubMed, we found that document similarity methods can assist in the
+identification of unreported links between trial registrations and
+corresponding articles.
+"
+26267,DCASE 2018 Challenge Surrey Cross-Task convolutional neural network baseline,"  The Detection and Classification of Acoustic Scenes and Events (DCASE)
+consists of five audio classification and sound event detection tasks: 1)
+Acoustic scene classification, 2) General-purpose audio tagging of Freesound,
+3) Bird audio detection, 4) Weakly-labeled semi-supervised sound event
+detection and 5) Multi-channel audio classification. In this paper, we create a
+cross-task baseline system for all five tasks based on a convlutional neural
+network (CNN): a ""CNN Baseline"" system. We implemented CNNs with 4 layers and 8
+layers originating from AlexNet and VGG from computer vision. We investigated
+how the performance varies from task to task with the same configuration of
+neural networks. Experiments show that deeper CNN with 8 layers performs better
+than CNN with 4 layers on all tasks except Task 1. Using CNN with 8 layers, we
+achieve an accuracy of 0.680 on Task 1, an accuracy of 0.895 and a mean average
+precision (MAP) of 0.928 on Task 2, an accuracy of 0.751 and an area under the
+curve (AUC) of 0.854 on Task 3, a sound event detection F1 score of 20.8% on
+Task 4, and an F1 score of 87.75% on Task 5. We released the Python source code
+of the baseline systems under the MIT license for further research.
+"
+26268,GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly,"  The computational problem of inferring the full haplotype of a cell starting
+from read sequencing data is known as haplotype assembly, and consists in
+assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly
+one of the two chromosomes. Indeed, the knowledge of complete haplotypes is
+generally more informative than analyzing single SNPs and plays a fundamental
+role in many medical applications. To reconstruct the two haplotypes, we
+addressed the weighted Minimum Error Correction (wMEC) problem, which is a
+successful approach for haplotype assembly. This NP-hard problem consists in
+computing the two haplotypes that partition the sequencing reads into two
+disjoint sub-sets, with the least number of corrections to the SNP values. To
+this aim, we propose here GenHap, a novel computational method for haplotype
+assembly based on Genetic Algorithms, yielding optimal solutions by means of a
+global search process. In order to evaluate the effectiveness of our approach,
+we run GenHap on two synthetic (yet realistic) datasets, based on the Roche/454
+and PacBio RS II sequencing technologies. We compared the performance of GenHap
+against HapCol, an efficient state-of-the-art algorithm for haplotype phasing.
+Our results show that GenHap always obtains high accuracy solutions (in terms
+of haplotype error rate), and is up to 4x faster than HapCol in the case of
+Roche/454 instances and up to 20x faster when compared on the PacBio RS II
+dataset. Finally, we assessed the performance of GenHap on two different real
+datasets. Future-generation sequencing technologies, producing longer reads
+with higher coverage, can highly benefit from GenHap, thanks to its capability
+of efficiently solving large instances of the haplotype assembly problem.
+"
+26269,Effect of the Early Reionization on the Cosmic Microwave Background and Cosmological Parameter Estimates,"  The early reionization (ERE) is supposed to be a physical process which
+happens after recombination, but before the instantaneous reionization caused
+by the first generation of stars. We investigate the effect of the ERE on the
+temperature and polarization power spectra of cosmic microwave background
+(CMB), and adopt principal components analysis (PCA) to model-independently
+reconstruct the ionization history during the ERE. In addition, we also discuss
+how the ERE affects the cosmological parameter estimates, and find that the ERE
+does not impose any significant influences on the tensor-to-scalar ratio $r$
+and the neutrino mass at the sensitivities of current experiments. The better
+CMB polarization data can be used to give a tighter constraint on the ERE and
+might be important for more precisely constraining cosmological parameters in
+the future.
+"
+26270,A Stronger Foundation for Computer Science and P=NP,"  This article describes a Turing machine which can solve for $\beta^{'}$ which
+is RE-complete. RE-complete problems are proven to be undecidable by Turing's
+accepted proof on the Entscheidungsproblem. Thus, constructing a machine which
+decides over $\beta^{'}$ implies inconsistency in ZFC. We then discover that
+unrestricted use of the axiom of substitution can lead to hidden assumptions in
+a certain class of proofs by contradiction. These hidden assumptions create an
+implied axiom of incompleteness for ZFC. Later, we offer a restriction on the
+axiom of substitution by introducing a new axiom which prevents impredicative
+tautologies from producing theorems. Our discovery in regards to these
+foundational arguments, disproves the SPACE hierarchy theorem which allows us
+to solve the P vs NP problem using a TIME-SPACE equivalence oracle.
+"
+26271,Memetic search for identifying critical nodes in sparse graphs,"  Critical node problems involve identifying a subset of critical nodes from an
+undirected graph whose removal results in optimizing a pre-defined measure over
+the residual graph. As useful models for a variety of practical applications,
+these problems are computational challenging. In this paper, we study the
+classic critical node problem (CNP) and introduce an effective memetic
+algorithm for solving CNP. The proposed algorithm combines a double
+backbone-based crossover operator (to generate promising offspring solutions),
+a component-based neighborhood search procedure (to find high-quality local
+optima) and a rank-based pool updating strategy (to guarantee a healthy
+population). Specially, the component-based neighborhood search integrates two
+key techniques, i.e., two-phase node exchange strategy and node weighting
+scheme. The double backbone-based crossover extends the idea of general
+backbone-based crossovers. Extensive evaluations on 42 synthetic and real-world
+benchmark instances show that the proposed algorithm discovers 21 new upper
+bounds and matches 18 previous best-known upper bounds. We also demonstrate the
+relevance of our algorithm for effectively solving a variant of the classic
+CNP, called the cardinality-constrained critical node problem. Finally, we
+investigate the usefulness of each key algorithmic component.
+"
+26272,Mixed $L^p(L^2)$ norms of the lattice point discrepancy,"  We estimate some mixed $L^{p}\left( L^{2}\right) $ norms of the discrepancy
+between the volume and the number of integer points in $r\Omega-x$, a dilated
+by a factor $r$ and translated by a vector $x$ of a convex body $\Omega$ in
+$\mathbb{R}^{d}$, $ \left\{ {\int_{\mathbb{T}^{d}}}\left( \frac{1}{H}
+{\int_{R}^{R+H}}\left\vert \sum_{k\in\mathbb{Z}^{d}}\chi
+_{r\Omega-x}(k)-r^{d}\left\vert \Omega\right\vert
+\right\vert^{2}dr\right)^{p/2}dx\right\} ^{1/p}. $ We obtain estimates for
+fixed values of $H$ and $R\to\infty$, and also asymptotic estimates when
+$H\to\infty$.
+"
+26273,Some deterministic structured population models which are limit of stochastic individual based models,"  The aim of this paper is to tackle part of the program set by Diekmann et al.
+in their seminal paper Diekmann et al. (2001). We quote ""It remains to
+investigate whether, and in what sense, the nonlinear determin-istic model
+formulation is the limit of a stochastic model for initial population size
+tending to infinity"" We set a precise and general framework for a stochastic
+individual based model : it is a piecewise deterministic Markov process defined
+on the set of finite measures. We then establish a law of large numbers under
+conditions easy to verify. Finally we show how this applies to old and new
+examples.
+"
+26274,CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,"  We develop an algorithm that can detect pneumonia from chest X-rays at a
+level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer
+convolutional neural network trained on ChestX-ray14, currently the largest
+publicly available chest X-ray dataset, containing over 100,000 frontal-view
+X-ray images with 14 diseases. Four practicing academic radiologists annotate a
+test set, on which we compare the performance of CheXNet to that of
+radiologists. We find that CheXNet exceeds average radiologist performance on
+the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and
+achieve state of the art results on all 14 diseases.
+"
+26275,Nonequilibrium Green's function theory for nonadiabatic effects in quantum electron transport,"  We develop nonequilibribrium Green's function based transport theory, which
+includes effects of nonadiabatic nuclear motion in the calculation of the
+electric current in molecular junctions. Our approach is based on the
+separation of slow and fast timescales in the equations of motion for the
+Green's functions by means of the Wigner representation. Time derivatives with
+respect to central time serves as a small parameter in the perturbative
+expansion enabling the computation of nonadiabatic corrections to molecular
+Green's functions. Consequently, we produce series of analytic expressions for
+non-adiabatic electronic Green's functions (up to the second order in the
+central time derivatives); which depend not solely on instantaneous molecular
+geometry but likewise on nuclear velocities and accelerations. Extended formula
+for electric current is derived which accounts for the non-adiabatic
+corrections. This theory is concisely illustrated by the calculations on a
+model molecular junction.
+"
+26276,Analysis of Strategy and Spread of Russia-sponsored Content in the US in 2017,"  The Russia-based Internet Research Agency (IRA) carried out a broad
+information campaign in the U.S. before and after the 2016 presidential
+election. The organization created an expansive set of internet properties: web
+domains, Facebook pages, and Twitter bots, which received traffic via purchased
+Facebook ads, tweets, and search engines indexing their domains. We investigate
+the scope of IRA activities in 2017, joining data from Facebook and Twitter
+with logs from the Internet Explorer 11 and Edge browsers and the Bing.com
+search engine. The studies demonstrate both the ease with which malicious
+actors can harness social media and search engines for propaganda campaigns,
+and the ability to track and understand such activities by fusing content and
+activity resources from multiple internet services. We show how cross-platform
+analyses can provide an unprecedented lens on attempts to manipulate opinions
+and elections in democracies.
+"
+26277,"Eccentricity in planetary systems and the role of binarity: sample definition, initial results, and the system of HD 211847","  We explore the multiplicity of exoplanet host stars with high-resolution
+images obtained with VLT/SPHERE. Two different samples of systems were
+observed: one containing low-eccentricity outer planets, and the other
+containing high-eccentricity outer planets. We find that 10 out of 34 stars in
+the high-eccentricity systems are members of a binary, while the proportion is
+3 out of 27 for circular systems. Eccentric-exoplanet hosts are, therefore,
+significantly more likely to have a stellar companion than circular-exoplanet
+hosts. The median magnitude contrast over the 68 data sets is 11.26 and 9.25,
+in H and K, respectively, at 0.30 arcsec. The derived detection limits reveal
+that binaries with separations of less than 50au are rarer for exoplanet hosts
+than for field stars. Our results also imply that the majority of
+high-eccentricity planets are not embedded in multiple stellar systems (24 out
+of 34), since our detection limits exclude the presence of a stellar companion.
+We detect the low-mass stellar companions of HD 7449 and HD 211847, both
+members of our high-eccentricity sample. HD 7449B was already detected by
+Rodigas et al (2016) and our independent observation is in agreement with this
+earlier work. HD 211847's substellar companion, previously detected by the
+radial velocity method, is actually a low-mass star seen face-on. The role of
+stellar multiplicity in shaping planetary systems is confirmed by this work,
+although it does not appear as the only source of dynamical excitation.
+"
+26278,Double Link Failure Protection using a Single P-cycle,"  In this letter, we investigate survivability in optical networks for
+protection from two simultaneous link failures. Failure probability of two
+links with overlapping protection can be high if these links are geographically
+close. In a network with deterministic single link protection, simultaneous
+failure of two links may lead to partial or full loss of traffic on the failed
+links. Two link failure protection will make the network more resilient by
+protecting double failures having overlapping protection. A method for
+achieving double fault tolerance is double cycle method (DB); it uses two
+pre-configured cycles (p-cycles) to protect a link. Single p-cycle (SG) method,
+which uses one p-cycle to protect a link from two simultaneous link failure is
+introduced in this letter. Integer linear programs (ILP) are formulated for the
+SG method as well as DB method. It has been observed that the SG method
+provides a solution to bigger networks with lesser computational resources as
+compared to the DB method.
+"
+26279,Finite size analysis of the detectability limit of the stochastic block model,"  It has been shown in recent years that the stochastic block model (SBM) is
+sometimes undetectable in the sparse limit, i.e., that no algorithm can
+identify a partition correlated with the partition used to generate an
+instance, if the instance is sparse enough and infinitely large. In this
+contribution, we treat the finite case explicitly, using arguments drawn from
+information theory and statistics. We give a necessary condition for
+finite-size detectability in the general SBM. We then distinguish the concept
+of average detectability from the concept of instance-by-instance detectability
+and give explicit formulas for both definitions. Using these formulas, we prove
+that there exist large equivalence classes of parameters, where widely
+different network ensembles are equally detectable with respect to our
+definitions of detectability. In an extensive case study, we investigate the
+finite-size detectability of a simplified variant of the SBM, which encompasses
+a number of important models as special cases. These models include the
+symmetric SBM, the planted coloring model, and more exotic SBMs not previously
+studied. We conclude with three appendices, where we study the interplay of
+noise and detectability, establish a connection between our
+information-theoretic approach and random matrix theory, and provide proofs of
+some of the more technical results.
+"
+26280,The PICARD Payload Data Centre,"  PICARD is a scientific space mission dedicated to the study of the solar
+variability origin. A French micro-satellite will carry an imaging telescope
+for measuring the solar diameter, limb shape and solar oscillations, and two
+radiometers for measuring the total solar irradiance and the irradiance in five
+spectral domains, from ultraviolet to infrared. The mission is planed to be
+launched in 2009 for a 3-year duration. This article presents the PICARD
+Payload Data Centre, which role is to collect, process and distribute the
+PICARD data. The Payload Data Centre is a joint project between laboratories,
+space agency and industries. The Belgian scientific policy office funds the
+industrial development and future operations under the European Space Agency
+program. The development is achieved by the SPACEBEL Company. The Belgian
+operation centre is in charge of operating the PICARD Payload Data Centre. The
+French space agency leads the development in partnership with the French
+scientific research centre, which is responsible for providing all the
+scientific algorithms. The architecture of the PICARD Payload Data Centre
+(software and hardware) is presented. The software system is based on a Service
+Oriented Architecture. The host structure is made up of the basic functions
+such as data management, task scheduling and system supervision including a
+graphical interface used by the operator to interact with the system. The other
+functions are mission-specific: data exchange (acquisition, distribution), data
+processing (scientific and non-scientific processing) and managing the payload
+(programming, monitoring). The PICARD Payload Data Centre is planned to be
+operated for 5 years. All the data will be stored into a specific data centre
+after this period.
+"
+26281,Distributed Estimation Via a Roaming Token,"  We present an algorithm for the problem of linear distributed estimation of a
+parameter in a network where a set of agents are successively taking
+measurements. The approach considers a roaming token in a network that carries
+the estimate, and jumps from one agent to another in its vicinity according to
+the probabilities of a Markov chain. When the token is at an agent it records
+the agent's local information. We analyze the proposed algorithm and show that
+it is consistent and asymptotically optimal, in the sense that its
+mean-square-error (MSE) rate of decay approaches the centralized one as the
+number of iterations increases. We show these results for a scenario where the
+network changes over time, and we consider two different set of assumptions on
+the network instantiations: they are i.i.d. and connected on the average, or
+they are deterministic and strongly connected for every finite time window of a
+fixed size. Simulations show our algorithm is competitive with
+consensus+innovations type algorithms, achieving a smaller MSE at each
+iteration in all considered scenarios.
+"
+26282,Generating virtual scenarios of multivariate financial data for quantitative trading applications,"  In this paper, we present a novel approach to the generation of virtual
+scenarios of multivariate financial data of arbitrary length and composition of
+assets. With this approach, decades of realistic time-synchronized data can be
+simulated for a large number of assets, producing diverse scenarios to test and
+improve quantitative investment strategies. Our approach is based on the
+analysis and synthesis of the time-dependent individual and joint
+characteristics of real financial time series, using stochastic sequences of
+market trends to draw multivariate returns from time-dependent probability
+functions preserving both distributional properties of asset returns and
+time-dependent correlation among time series. Moreover, new time-synchronized
+assets can be arbitrarily generated through a PCA-based procedure to obtain any
+number of assets in the final virtual scenario. For the validation of such
+simulated data, they are tested with an extensive set of measurements showing a
+significant degree of agreement with the reference performance of real
+financial series, better than that obtained with other classical and
+state-of-the-art approaches.
+"
+26283,PMV: Pre-partitioned Generalized Matrix-Vector Multiplication for Scalable Graph Mining,"  How can we analyze enormous networks including the Web and social networks
+which have hundreds of billions of nodes and edges? Network analyses have been
+conducted by various graph mining methods including shortest path computation,
+PageRank, connected component computation, random walk with restart, etc. These
+graph mining methods can be expressed as generalized matrix-vector
+multiplication which consists of few operations inspired by typical
+matrix-vector multiplication. Recently, several graph processing systems based
+on matrix-vector multiplication or their own primitives have been proposed to
+deal with large graphs; however, they all have failed on Web-scale graphs due
+to insufficient memory space or the lack of consideration for I/O costs. In
+this paper, we propose PMV (Pre-partitioned generalized Matrix-Vector
+multiplication), a scalable distributed graph mining method based on
+generalized matrix-vector multiplication on distributed systems. PMV
+significantly decreases the communication cost, which is the main bottleneck of
+distributed systems, by partitioning the input graph in advance and judiciously
+applying execution strategies based on the density of the pre-partitioned
+sub-matrices. Experiments show that PMV succeeds in processing up to 16x larger
+graphs than existing distributed memory-based graph mining methods, and
+requires 9x less time than previous disk-based graph mining methods by reducing
+I/O costs significantly.
+"
+26284,Tunable Moiré Bands and Strong Correlations in Small-Twist-Angle Bilayer Graphene,"  According to electronic structure theory, bilayer graphene is expected to
+have anomalous electronic properties when it has long-period moiré patterns
+produced by small misalignments between its individual layer honeycomb
+lattices. We have realized bilayer graphene moiré crystals with accurately
+controlled twist angles smaller than 1 degree and studied their properties
+using scanning probe microscopy and electron transport. We observe conductivity
+minima at charge neutrality, satellite gaps that appear at anomalous carrier
+densities for twist angles smaller than 1 degree, and tunneling
+densities-of-states that are strongly dependent on carrier density. These
+features are robust up to large transverse electric fields. In perpendicular
+magnetic fields, we observe the emergence of a Hofstadter butterfly in the
+energy spectrum, with four-fold degenerate Landau levels, and broken symmetry
+quantum Hall states at filling factors 1, 2, 3. These observations demonstrate
+that at small twist angles, the electronic properties of bilayer graphene
+moiré crystals are strongly altered by electron-electron interactions.
+"
+26285,Dark matter statistics for large galaxy catalogs: power spectra and covariance matrices,"  Upcoming and existing large-scale surveys of galaxies require accurate
+theoretical predictions of the dark matter clustering statistics for thousands
+of mock galaxy catalogs. We demonstrate that this goal can be achieve with our
+new Parallel Particle-Mesh (PM) Nbody code (PPM-GLAM) at a very low
+computational cost. We run about 15,000 simulations with ~2 billion particles
+that provide ~1% accuracy of the dark matter power spectra P(k) for
+wave-numbers up to k~ 1h/Mpc. Using this large data-set we study the power
+spectrum covariance matrix, the stepping stone for producing mock catalogs. In
+contrast to many previous analytical and numerical results, we find that the
+covariance matrix normalised to the power spectrum C(k,k')/P(k)P(k') has a
+complex structure of non-diagonal components. It has an upturn at small k,
+followed by a minimum at k=0.1-0.2h/Mpc. It also has a maximum at
+k=0.5-0.6h/Mpc. The normalised covariance matrix strongly evolves with
+redshift: C(k,k')~delta(t)^alpha P(k)P(k'), where delta is the linear growth
+factor and alpha ~ 1-1.25, which indicates that the covariance matrix depends
+on cosmological parameters. We also show that waves longer than 1Gpc have very
+little impact on the power spectrum and covariance matrix. This significantly
+reduces the computational costs and complexity of theoretical predictions:
+relatively small volume ~ (1Gpc)^3 simulations capture the necessary properties
+of dark matter clustering statistics. All the power spectra obtained from many
+thousands of our simulations are publicly available.
+"
+26286,Network assembly of scientific communities of varying size and specificity,"  How does the collaboration network of researchers coalesce around a
+scientific topic? What sort of social restructuring occurs as a new field
+develops? Previous empirical explorations of these questions have examined the
+evolution of co-authorship networks associated with several fields of science,
+each noting a characteristic shift in network structure as fields develop.
+Historically, however, such studies have tended to rely on manually annotated
+datasets and therefore only consider a handful of disciplines, calling into
+question the universality of the observed structural signature.To overcome this
+limitation and test the robustness of this phenomenon, we use a comprehensive
+dataset of over 189,000 scientific articles and develop a framework for
+partitioning articles and their authors into coherent, semantically-related
+groups representing scientific fields of varying size and specificity. We then
+use the resulting population of fields to study the structure of evolving
+co-authorship networks. Consistent with earlier findings, we observe a global
+topological transition as the co-authorship networks coalesce from a disjointed
+aggregate into a dense giant connected component that dominates the network. We
+validate these results using a separate, complimentary corpus of scientific
+articles, and, overall, we find that the previously reported characteristic
+structural evolution of a scientific field's associated co-authorship network
+is robust across a large number of scientific fields of varying size, scope,
+and specificity. Additionally, the framework developed in this study may be
+used in other scientometric contexts in order to extend studies to compare
+across a larger range of scientific disciplines.
+"
+26287,Protofold II: Enhanced Model and Implementation for Kinetostatic Protein Folding,"  A reliable prediction of 3D protein structures from sequence data remains a
+big challenge due to both theoretical and computational difficulties. We have
+previously shown that our kinetostatic compliance method (KCM) implemented into
+the Protofold package can overcome some of the key difficulties faced by other
+de novo structure prediction methods, such as the very small time steps
+required by the molecular dynamics (MD) approaches or the very large number of
+samples needed by the Monte Carlo (MC) sampling techniques. In this article, we
+improve the free energy formulation used in Protofold by including the
+typically underrated entropic effects, imparted due to differences in
+hydrophobicity of the chemical groups, which dominate the folding of most
+water-soluble proteins. In addition to the model enhancement, we revisit the
+numerical implementation by redesigning the algorithms and introducing
+efficient data structures that reduce the expected complexity from quadratic to
+linear. Moreover, we develop and optimize parallel implementations of the
+algorithms on both central and graphics processing units (CPU/GPU) achieving
+speed-ups up to two orders of magnitude on the GPU. Our simulations are
+consistent with the general behavior observed in the folding process in aqueous
+solvent, confirming the effectiveness of model improvements. We report on the
+folding process at multiple levels; namely, the formation of secondary
+structural elements and tertiary interactions between secondary elements or
+across larger domains. We also observe significant enhancements in running
+times that make the folding simulation tractable for large molecules.
+"
+26288,"Subspace arrangements, graph rigidity and derandomization through submodular optimization","  This paper presents a deterministic, strongly polynomial time algorithm for
+computing the matrix rank for a class of symbolic matrices (whose entries are
+polynomials over a field). This class was introduced, in a different language,
+by Lovász [Lov] in his study of flats in matroids, and proved a duality
+theorem putting this problem in $NP \cap coNP$. As such, our result is another
+demonstration where ``good characterization'' in the sense of Edmonds leads to
+an efficient algorithm. In a different paper Lovász [Lov79] proved that all
+such symbolic rank problems have efficient probabilistic algorithms, namely are
+in $BPP$. As such, our algorithm may be interpreted as a derandomization
+result, in the long sequence special cases of the PIT (Polynomial Identity
+Testing) problem. Finally, Lovász and Yemini [LoYe] showed how the same
+problem generalizes the graph rigidity problem in two dimensions. As such, our
+algorithm may be seen as a generalization of the well-known deterministic
+algorithm for the latter problem.
+There are two somewhat unusual technical features in this paper. The first is
+the translation of Lovász' flats problem into a symbolic rank one. The second
+is the use of submodular optimization for derandomization. We hope that the
+tools developed for both will be useful for related problems, in particular for
+better understanding of graph rigidity in higher dimensions.
+"
+26289,Lifts of Hilbert modular forms and application to modularity of abelian varieties,"  In this paper, we prove the existence of certain lifts of Hilbert cusp forms
+to general odd spin groups. We then use those lifts to provide evidence for a
+conjecture of Gross on the modularity of abelian varieties not of ${\rm
+GL}_2$-type.
+"
+26290,Using Mineral Oil to Improve the Performance of Multi-Crystal Detector for Dark Matter Searching,"  The inorganic crystals have been widely used for dark matter direct searching
+for many decades. However, limited by the crystal growth technique, a lot of
+small crystals have to be used together for large target mass, which results in
+a degradation of light collection efficiency. An experiment was built up to
+study the degradation, and the method of soaking crystals into mineral oil to
+improve the efficiency as well as reduce the interface effect were proposed and
+validated. Good data and MC agreements were achieved in the experiment.
+"
+26291,Implementing an Edge-Fog-Cloud architecture for stream data management,"  The Internet of Moving Things (IoMT) requires support for a data life cycle
+process ranging from sorting, cleaning and monitoring data streams to more
+complex tasks such as querying, aggregation, and analytics. Current solutions
+for stream data management in IoMT have been focused on partial aspects of a
+data life cycle process, with special emphasis on sensor networks. This paper
+aims to address this problem by developing streaming data life cycle process
+that incorporates an edge/fog/cloud architecture that is needed for handling
+heterogeneous, streaming and geographically-dispersed IoMT devices. We propose
+a 3-tier architecture to support an instant intra-layer communication that
+establishes a stream data flow in real-time to respond to immediate data life
+cycle tasks in the system. Communication and process are thus the defining
+factors in the design of our stream data management solution for IoMT. We
+describe and evaluate our prototype implementation using real-time transit data
+feeds. Preliminary results are showing the advantages of running data life
+cycle tasks for reducing the volume of data streams that are redundant and
+should not be transported to the cloud.
+"
+26292,Are Comprehensive Quality Models Necessary for Evaluating Software Quality?,"  The concept of software quality is very complex and has many facets.
+Reflecting all these facets and at the same time measuring everything related
+to these facets results in comprehensive but large quality models and extensive
+measurements. In contrast, there are also many smaller, focused quality models
+claiming to evaluate quality with few measures.
+We investigate if and to what extent it is possible to build a focused
+quality model with similar evaluation results as a comprehensive quality model
+but with far less measures needed to be collected and, hence, reduced effort.
+We make quality evaluations with the comprehensive Quamoco base quality model
+and build focused quality models based on the same set of measures and data
+from over 2,000 open source systems. We analyse the ability of the focused
+model to predict the results of the Quamoco model by comparing them with a
+random predictor as a baseline. We calculate the standardised accuracy measure
+SA and effect sizes.
+We found that for the Quamoco model and its 378 automatically collected
+measures, we can build a focused model with only 10 measures but an accuracy of
+61% and a medium to high effect size. We conclude that we can build focused
+quality models to get an impression of a system's quality similar to
+comprehensive models. However, when including manually collected measures, the
+accuracy of the models stayed below 50%. Hence, manual measures seem to have a
+high impact and should therefore not be ignored in a focused model.
+"
+26293,Breaking the integrability of the Heisenberg model through periodic driving,"  We study the fate of interacting quantum systems which are periodically
+driven by switching back and forth between two integrable Hamiltonians. This
+provides an unconventional and tunable way of breaking integrability, in the
+sense that the stroboscopic time evolution will generally be described by a
+Floquet Hamiltonian which progressively becomes less integrable as the driving
+frequency is reduced. Here, we exemplify this idea in spin chains subjected to
+periodic switching between two integrable anisotropic Heisenberg Hamiltonians.
+We distinguish the integrability-breaking effects of resonant interactions and
+perturbative (local) interactions, and illustrate these by contrasting
+different measures of energy in Floquet states and through a study of level
+spacing statistics. This scenario is argued to be representative for general
+driven interacting integrable systems.
+"
+26294,A dynamic intranet-based online-portal support for Computer Science teaching,"  The paper is a suggested experiment in effectively teaching subjects in
+Computer Science. The paper addresses effective content-delivery with the help
+of a university intranet. The proposal described herein is for teaching a
+subject like Combinatorics and Graph Theory - the main idea is to supplement
+lectures with a teacher-moderated online forum against an associated intranet
+portal.
+Keywords and phrases -computer-assisted learning; learning portal; active
+learning; OEIS; intranet portal; undergraduate teaching; Combinatorics and
+Graph theory
+"
+26295,Reinforcement Learning of Markov Decision Processes with Peak Constraints,"  In this paper, we consider reinforcement learning of Markov Decision
+Processes (MDP) with peak constraints, where an agent chooses a policy to
+optimize an objective and at the same time satisfy additional constraints. The
+agent has to take actions based on the observed states, reward outputs, and
+constraint-outputs, without any knowledge about the dynamics, reward functions,
+and/or the knowledge of the constraint-functions. We introduce a game theoretic
+approach to construct reinforcement learning algorithms where the agent
+maximizes an unconstrained objective that depends on the simulated action of
+the minimizing opponent which acts on a finite set of actions and the output
+data of the constraint functions (rewards). We show that the policies obtained
+from maximin Q-learning converge to the optimal policies. To the best of our
+knowledge, this is the first time learning algorithms guarantee convergence to
+optimal stationary policies for the MDP problem with peak constraints for both
+discounted and expected average rewards.
+"
+26296,Superconducting fluctuations in FeSe$_{0.5}$Te$_{0.5}$ thin films probed via microwave spectroscopy,"  We investigated the microwave conductivity spectrum of FeSe$_{0.5}$Te$_{0.5}$
+epitaxial films on CaF$_2$ in the vicinity of the superconducting transition.
+We observed the critical behavior of the superconducting fluctuations in these
+films with a dimensional crossover from two-dimensional to three-dimensional as
+the film thickness increased. From the temperature dependence of the scaling
+parameters we conclude that the universality class of the superconducting
+transition in FeSe$_{0.5}$Te$_{0.5}$ is that of the 3D-XY model. The lower
+limit of the onset temperature of the superconducting fluctuations, Tonset,
+determined by our measurements was 1.1 Tc, suggesting that the superconducting
+fluctuations of FeSe$_{0.5}$Te$_{0.5}$ are at least as large as those of
+optimally- and over-doped cuprates.
+"
+26297,A short proof of the large time energy growth for the Boussinesq system,"  We give a direct proof of the fact that the $L^{p)$-norms of global solutions
+of the Boussinesq system in $R^{3}$ grow large as $ t \rightarrow + \infty $
+for $ 1 < p < 3 $ and decay to zero for $ 3 < p \leq \infty $, providing exact
+estimates from below and above using a suitable decomposition of the space-time
+space $ R^{+} \times R^{3} $. In particular, the kinetic energy blows up as $
+\| u(t) \|_{2}^{2} \sim c t^{1/2} $ for large time. This contrasts with the
+case of the Navier-Stokes equations.
+"
+26298,ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules,"  One of the grand challenges in modern theoretical chemistry is designing and
+implementing approximations that expedite ab initio methods without loss of
+accuracy. Machine learning (ML), in particular neural networks, are emerging as
+a powerful approach to constructing various forms of transferable atomistic
+potentials. They have been successfully applied in a variety of applications in
+chemistry, biology, catalysis, and solid-state physics. However, these models
+are heavily dependent on the quality and quantity of data used in their
+fitting. Fitting highly flexible ML potentials comes at a cost: a vast amount
+of reference data is required to properly train these models. We address this
+need by providing access to a large computational DFT database, which consists
+of 20M conformations for 57,454 small organic molecules. We believe it will
+become a new standard benchmark for comparison of current and future methods in
+the ML potential community.
+"
+26299,Learning ReLUs via Gradient Descent,"  In this paper we study the problem of learning Rectified Linear Units (ReLUs)
+which are functions of the form $max(0,<w,x>)$ with $w$ denoting the weight
+vector. We study this problem in the high-dimensional regime where the number
+of observations are fewer than the dimension of the weight vector. We assume
+that the weight vector belongs to some closed set (convex or nonconvex) which
+captures known side-information about its structure. We focus on the realizable
+model where the inputs are chosen i.i.d.~from a Gaussian distribution and the
+labels are generated according to a planted weight vector. We show that
+projected gradient descent, when initialization at 0, converges at a linear
+rate to the planted model with a number of samples that is optimal up to
+numerical constants. Our results on the dynamics of convergence of these very
+shallow neural nets may provide some insights towards understanding the
+dynamics of deeper architectures.
+"
+26300,Fitting A Mixture Distribution to Data: Tutorial,"  This paper is a step-by-step tutorial for fitting a mixture distribution to
+data. It merely assumes the reader has the background of calculus and linear
+algebra. Other required background is briefly reviewed before explaining the
+main algorithm. In explaining the main algorithm, first, fitting a mixture of
+two distributions is detailed and examples of fitting two Gaussians and
+Poissons, respectively for continuous and discrete cases, are introduced.
+Thereafter, fitting several distributions in general case is explained and
+examples of several Gaussians (Gaussian Mixture Model) and Poissons are again
+provided. Model-based clustering, as one of the applications of mixture
+distributions, is also introduced. Numerical simulations are also provided for
+both Gaussian and Poisson examples for the sake of better clarification.
+"
+26301,Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution,"  Efficiency of single-objective optimization can be improved by introducing
+some auxiliary objectives. Ideally, auxiliary objectives should be helpful.
+However, in practice, objectives may be efficient on some optimization stages
+but obstructive on others. In this paper we propose a modification of the EA+RL
+method which dynamically selects optimized objectives using reinforcement
+learning. The proposed modification prevents from losing the best found
+solution. We analysed the proposed modification and compared it with the EA+RL
+method and Random Local Search on XdivK, Generalized OneMax and LeadingOnes
+problems. The proposed modification outperforms the EA+RL method on all problem
+instances. It also outperforms the single objective approach on the most
+problem instances. We also provide detailed analysis of how different
+components of the considered algorithms influence efficiency of optimization.
+In addition, we present theoretical analysis of the proposed modification on
+the XdivK problem.
+"
+26302,Rational invariants of even ternary forms under the orthogonal group,"  In this article we determine a generating set of rational invariants of
+minimal cardinality for the action of the orthogonal group $\mathrm{O}_3$ on
+the space $\mathbb{R}[x,y,z]_{2d}$ of ternary forms of even degree $2d$. The
+construction relies on two key ingredients: On one hand, the Slice Lemma allows
+us to reduce the problem to dermining the invariants for the action on a
+subspace of the finite subgroup $\mathrm{B}_3$ of signed permutations. On the
+other hand, our construction relies in a fundamental way on specific bases of
+harmonic polynomials. These bases provide maps with prescribed
+$\mathrm{B}_3$-equivariance properties. Our explicit construction of these
+bases should be relevant well beyond the scope of this paper. The expression of
+the $\mathrm{B}_3$-invariants can then be given in a compact form as the
+composition of two equivariant maps. Instead of providing (cumbersome) explicit
+expressions for the $\mathrm{O}_3$-invariants, we provide efficient algorithms
+for their evaluation and rewriting. We also use the constructed
+$\mathrm{B}_3$-invariants to determine the $\mathrm{O}_3$-orbit locus and
+provide an algorithm for the inverse problem of finding an element in
+$\mathbb{R}[x,y,z]_{2d}$ with prescribed values for its invariants. These are
+the computational issues relevant in brain imaging.
+"
+26303,Fast online low-rank tensor subspace tracking by CP decomposition using recursive least squares from incomplete observations,"  We consider the problem of online subspace tracking of a partially observed
+high-dimensional data stream corrupted by noise, where we assume that the data
+lie in a low-dimensional linear subspace. This problem is cast as an online
+low-rank tensor completion problem. We propose a novel online tensor subspace
+tracking algorithm based on the CANDECOMP/PARAFAC (CP) decomposition, dubbed
+OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). The
+proposed algorithm especially addresses the case in which the subspace of
+interest is dynamically time-varying. To this end, we build up our proposed
+algorithm exploiting the recursive least squares (RLS), which is the
+second-order gradient algorithm. Numerical evaluations on synthetic datasets
+and real-world datasets such as communication network traffic, environmental
+data, and surveillance videos, show that the proposed OLSTEC algorithm
+outperforms state-of-the-art online algorithms in terms of the convergence rate
+per iteration.
+"
+26304,Temperature evolution of spin dynamics in two- and three-dimensional Kitaev models: Influence of fluctuating gauge fluxes,"  The long-sought quantum spin liquid is a quantum-entangled magnetic state
+leading to the fractionalization of spin degrees of freedom. Quasiparticles
+emergent from the fractionalization affect not only the ground state properties
+but also thermodynamic behavior in a peculiar manner. We here investigate how
+the spin dynamics evolves from the high-temperature paramagnet to the quantum
+spin liquid ground state, for the Kitaev spin model describing the
+fractionalization into itinerant matter fermions and localized $Z_2$ gauge
+fluxes. Beyond the previous study [J. Yoshitake, J. Nasu, and Y. Motome, Phys.
+Rev. Lett. $\textbf{117}$, 157203 (2016)], in which the mean-field nature of
+the cluster dynamical mean-field theory prevented us from studying
+low-temperature properties, we develop a numerical technique by applying the
+continuous-time quantum Monte Carlo (CTQMC) method to statistical samples
+generated by the quantum Monte Carlo (QMC) method in a Majorana fermion
+representation. This QMC+CTQMC method is fully unbiased and enables us to
+investigate the low-temperature spin dynamics dominated by thermally excited
+gauge fluxes, including the unconventional phase transition caused by gauge
+flux loops in three dimensions, which was unreachable by the previous methods.
+We apply this technique to the Kitaev model in both two and three dimensions.
+Our results clearly distinguish two cases: while the dynamics changes smoothly
+through the crossover in the two-dimensional honeycomb case, it exhibits
+singular behaviors at the phase transition in the three-dimensional
+hyperhoneycomb case. We show that the low-temperature spin dynamics is a
+sensitive probe for thermally fluctuating gauge fluxes that behave very
+differently between two and three dimensions.
+"
+26305,Cycle-centrality in complex networks,"  Networks are versatile representations of the interactions between entities
+in complex systems. Cycles on such networks represent feedback processes which
+play a central role in system dynamics. In this work, we introduce a measure of
+the importance of any individual cycle, as the fraction of the total
+information flow of the network passing through the cycle. This measure is
+computationally cheap, numerically well-conditioned, induces a centrality
+measure on arbitrary subgraphs and reduces to the eigenvector centrality on
+vertices. We demonstrate that this measure accurately reflects the impact of
+events on strategic ensembles of economic sectors, notably in the US economy.
+As a second example, we show that in the protein-interaction network of the
+plant Arabidopsis thaliana, a model based on cycle-centrality better accounts
+for pathogen activity than the state-of-art one. This translates into
+pathogen-targeted-proteins being concentrated in a small number of triads with
+high cycle-centrality. Algorithms for computing the centrality of cycles and
+subgraphs are available for download.
+"
+26306,Coding with transient trajectories in recurrent neural networks,"  Following a stimulus, the neural response typically strongly varies in time
+and across neurons before settling to a steady-state. While classical
+population coding theory disregards the temporal dimension, recent works have
+argued that trajectories of transient activity can be particularly informative
+about stimulus identity and may form the basis of computations through
+dynamics. Yet an understanding of the dynamical mechanisms needed to generate a
+population code based on transient trajectories is still missing. Here we show
+that a broad class of high-dimensional linear networks of recurrently connected
+units exhibits the properties needed for transient coding. We first identify a
+general criterion that distinguishes two classes of networks depending on
+properties of the connectivity matrix: networks in which all inputs lead to
+weak, decaying transients, and networks in which specific inputs elicit
+strongly amplified transient responses and are mapped onto orthogonal output
+states during the dynamics. For the second class of networks, we provide a
+procedure to identify transiently amplified inputs and the corresponding
+readouts for arbitrary connectivity matrices. We then exploit our approach to
+build the minimal connectivity which robustly implements trajectories that map
+a specific input onto a specific output state, and we demonstrate that the
+capacity of the obtained networks increases proportionally with their size.
+"
+26307,On the unified zero-inflated cure-rate survival models,"  In this paper, we propose a unified version for survival models that includes
+zero-inflation and cure rate proportions, and allows different distributions
+for the unknown competitive causes. Our model has as particular cases several
+usual cure rate survival models.
+"
+26308,The Chazy XII Equation and Schwarz Triangle Functions,"  Dubrovin [Lecture Notes in Math., Vol. 1620, Springer, Berlin, 1996, 120-348]
+showed that the Chazy XII equation $y'''- 2yy''+3y'^2 = K(6y'-y^2)^2$, $K \in
+\mathbb{C}$, is equivalent to a projective-invariant equation for an affine
+connection on a one-dimensional complex manifold with projective structure. By
+exploiting this geometric connection it is shown that the Chazy XII solution,
+for certain values of $K$, can be expressed as $y=a_1w_1+a_2w_2+a_3w_3$ where
+$w_i$ solve the generalized Darboux-Halphen system. This relationship holds
+only for certain values of the coefficients $(a_1,a_2,a_3)$ and the
+Darboux-Halphen parameters $(\alpha, \beta, \gamma)$, which are enumerated in
+Table 2. Consequently, the Chazy XII solution $y(z)$ is parametrized by a
+particular class of Schwarz triangle functions $S(\alpha, \beta, \gamma; z)$
+which are used to represent the solutions $w_i$ of the Darboux-Halphen system.
+The paper only considers the case where $\alpha+\beta+\gamma<1$. The associated
+triangle functions are related among themselves via rational maps that are
+derived from the classical algebraic transformations of hypergeometric
+functions. The Chazy XII equation is also shown to be equivalent to a
+Ramanujan-type differential system for a triple $(\hat{P}, \hat{Q},\hat{R})$.
+"
+26309,Reactive Trajectory Generation in an Unknown Environment,"  Autonomous trajectory generation for unmanned aerial vehicles (UAVs) in
+unknown environments continues to be an important research area as UAVs become
+more prolific. We define a trajectory generation algorithm for a vehicle in an
+unknown environment with wind disturbances, that relies only on the vehicle's
+on-board distance sensors and communication with other vehicles within a finite
+region to generate a smooth, collision-free trajectory up to the fourth
+derivative. The proposed trajectory generation algorithm can be used in
+conjunction with high-level planners and low-level motion controllers. The
+algorithm provides guarantees that the trajectory does not violate the
+vehicle's thrust limitation, sensor constraints, or a user-defined clearance
+radius around other vehicles and obstacles. Simulation results of a quadrotor
+moving through an unknown environment with a moving obstacle demonstrates the
+trajectory generation performance.
+"
+26310,Characterization of the Split Closure via Geometric Lifting,"  We analyze split cuts from the perspective of cut generating functions via
+geometric lifting. We show that $\alpha$-cuts, a natural higher-dimensional
+generalization of the $k$-cuts of Cornuéjols et al., gives all the split
+cuts for the mixed-integer corner relaxation. As an immediate consequence we
+obtain that the $k$-cuts are equivalent to split cuts for the 1-row
+mixed-integer relaxation. Further, we show that split cuts for
+finite-dimensional corner relaxations are restrictions of split cuts for the
+infinite-dimensional relaxation. In a final application of this equivalence, we
+exhibit a family of pure-integer programs whose split closures have arbitrarily
+bad integrality gap. This complements the mixed-integer example provided by
+Basu et al [On the relative strength of split, triangle and quadrilateral cuts,
+Math. Program. 126(2):281--314, 2011].
+"
+26311,Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise,"  The problem of multi-speaker localization is formulated as a multi-class
+multi-label classification problem, which is solved using a convolutional
+neural network (CNN) based source localization method. Utilizing the common
+assumption of disjoint speaker activities, we propose a novel method to train
+the CNN using synthesized noise signals. The proposed localization method is
+evaluated for two speakers and compared to a well-known steered response power
+method.
+"
+26312,3D angle-of-arrival positioning using von Mises-Fisher distribution,"  We propose modeling an angle-of-arrival (AOA) positioning measurement as a
+von Mises-Fisher (VMF) distributed unit vector instead of the conventional
+normally distributed azimuth and elevation measurements. Describing the
+2-dimensional AOA measurement with three numbers removes discontinuities and
+reduces nonlinearity at the poles of the azimuth-elevation coordinate system.
+Our computer simulations show that the proposed VMF measurement noise model
+based filters outperform the normal distribution based algorithms in accuracy
+in a scenario where close-to-pole measurements occur frequently.
+"
+26313,Opinion Dynamics with Varying Susceptibility to Persuasion,"  A long line of work in social psychology has studied variations in people's
+susceptibility to persuasion -- the extent to which they are willing to modify
+their opinions on a topic. This body of literature suggests an interesting
+perspective on theoretical models of opinion formation by interacting parties
+in a network: in addition to considering interventions that directly modify
+people's intrinsic opinions, it is also natural to consider interventions that
+modify people's susceptibility to persuasion. In this work, we adopt a popular
+model for social opinion dynamics, and we formalize the opinion maximization
+and minimization problems where interventions happen at the level of
+susceptibility.
+We show that modeling interventions at the level of susceptibility lead to an
+interesting family of new questions in network opinion dynamics. We find that
+the questions are quite different depending on whether there is an overall
+budget constraining the number of agents we can target or not. We give a
+polynomial-time algorithm for finding the optimal target-set to optimize the
+sum of opinions when there are no budget constraints on the size of the
+target-set. We show that this problem is NP-hard when there is a budget, and
+that the objective function is neither submodular nor supermodular. Finally, we
+propose a heuristic for the budgeted opinion optimization and show its efficacy
+at finding target-sets that optimize the sum of opinions compared on real world
+networks, including a Twitter network with real opinion estimates.
+"
+26314,From effective Hamiltonian to anomaly inflow in topological orders with boundaries,"  Whether two boundary conditions of a two-dimensional topological order can be
+continuously connected without a phase transition in between remains a
+challenging question. We tackle this challenge by constructing an effective
+Hamiltonian, describing anyon interaction, that realizes such a continuous
+deformation. At any point along the deformation, the model remains a fixed
+point model describing a gapped topological order with gapped boundaries. That
+the deformation retains the gap is due to the anomaly cancelation between the
+boundary and bulk. Such anomaly inflow is quantitatively studied using our
+effective Hamiltonian. We apply our method of effective Hamiltonian to the
+extended twisted quantum double model with boundaries (constructed by two of us
+in Ref.[1]). We show that for a given gauge group $G$ and a three-cocycle in
+$H^3[G,U(1)]$ in the bulk, any two gapped boundaries for a fixed subgroup
+$K\subseteq G$ on the boundary can be continuously connected via an effective
+Hamiltonian. Our results can be straightforwardly generalized to the extended
+Levin-Wen model with boundaries (constructed by two of us in Ref.[2].
+"
+26315,Towards a property graph generator for benchmarking,"  The use of synthetic graph generators is a common practice among
+graph-oriented benchmark designers, as it allows obtaining graphs with the
+required scale and characteristics. However, finding a graph generator that
+accurately fits the needs of a given benchmark is very difficult, thus
+practitioners end up creating ad-hoc ones. Such a task is usually
+time-consuming, and often leads to reinventing the wheel. In this paper, we
+introduce the conceptual design of DataSynth, a framework for property graphs
+generation with customizable schemas and characteristics. The goal of DataSynth
+is to assist benchmark designers in generating graphs efficiently and at scale,
+saving from implementing their own generators. Additionally, DataSynth
+introduces novel features barely explored so far, such as modeling the
+correlation between properties and the structure of the graph. This is achieved
+by a novel property-to-node matching algorithm for which we present preliminary
+promising results.
+"
+26316,Faster Reinforcement Learning Using Active Simulators,"  In this work, we propose several online methods to build a \emph{learning
+curriculum} from a given set of target-task-specific training tasks in order to
+speed up reinforcement learning (RL). These methods can decrease the total
+training time needed by an RL agent compared to training on the target task
+from scratch. Unlike traditional transfer learning, we consider creating a
+sequence from several training tasks in order to provide the most benefit in
+terms of reducing the total time to train.
+Our methods utilize the learning trajectory of the agent on the curriculum
+tasks seen so far to decide which tasks to train on next. An attractive feature
+of our methods is that they are weakly coupled to the choice of the RL
+algorithm as well as the transfer learning method. Further, when there is
+domain information available, our methods can incorporate such knowledge to
+further speed up the learning. We experimentally show that these methods can be
+used to obtain suitable learning curricula that speed up the overall training
+time on two different domains.
+"
+26317,Deep Learning for Energy Markets,"  Deep Learning (DL) provides a methodology to predict extreme loads observed
+in energy grids. Forecasting energy loads and prices is challenging due to
+sharp peaks and troughs that arise from intraday system constraints due to
+supply and demand fluctuations. We propose deep spatio-temporal models and
+extreme value theory (DL-EVT) to capture the tail behavior of load spikes. Deep
+architectures, such as ReLU and LSTM can model generation trends and temporal
+dependencies while EVT captures highly volatile load spikes. To illustrate our
+methodology, we use hourly price and demand data from the PJM interconnection
+for 4719 nodes and we develop a deep predictor. DL-EVT outperforms traditional
+Fourier and time series methods, both in-and out-of-sample, by capturing the
+nonlinearities in prices. Finally, we conclude with directions for future
+research.
+"
+26318,Truncated Toeplitz operators and complex symmetries,"  We show that truncated Toeplitz operators are characterized by a collection
+of complex symmetries. This was conjectured by Klis-Garlicka, Lanucha, and
+Ptak, and proved by them in some special cases.
+"
+26319,Primes of the form n^2+n+p have density 1,"  We consider the representation of primes as a sum of a prime and twice a
+triangular number. We prove that a subset of the primes having density 1 is
+expressible in this form. We conjecture that every odd prime number is
+expressible as a sum of a twin prime and twice a triangular number. We show
+that this conjecture implies the existence of infinitely many twin primes.
+"
+26320,Finite-rank approximations of spectral zeta residues,"  We use the asymptotic expansion of the heat trace to express all residues of
+spectral zeta functions as regularized sums over the spectrum. The method
+extends to those spectral zeta functions that are localized by a bounded
+operator.
+"
+26321,Some estimates for the higher eigenvalues of sets close to the ball,"  In this paper we investigate the behavior of the eigenvalues of the Dirichlet
+Laplacian on sets in $\mathbb{R}^N$ whose first eigenvalue is close to the one
+of the ball with the same volume. In particular in our main Theorem we prove
+that, for all $k\in\mathbb{N}$, there is a positive constant $C=C(k,N)$ such
+that for every open set $\Omega\subseteq \mathbb{R}^N$ with unit measure and
+with $\lambda_1(\Omega)$ not excessively large one has \begin{align*}
+|\lambda_k(\Omega)-\lambda_k(B)|\leq C
+(\lambda_1(\Omega)-\lambda_1(B))^\beta\,, && \lambda_k(B)-\lambda_k(\Omega)\leq
+Cd(\Omega)^{\beta'}\,, \end{align*} where $d(\Omega)$ is the Fraenkel asymmetry
+of $\Omega$, and where $\beta$ and $\beta'$ are explicit exponents, not
+depending on $k$ nor on $N$; for the special case $N=2$, a better estimate
+holds.
+"
+26322,Dissolution instability and roughening transition,"  We theoretically investigate the pattern formation observed when a fluid
+flows over a solid substrate that can dissolve or melt. We use a turbulent
+mixing description that includes the effect of the bed roughness. We show that
+the dissolution instability at the origin of the pattern is associated with an
+anomaly at the transition from a laminar to a turbulent hydrodynamic response
+with respect to the bed elevation. This anomaly, and therefore the instability,
+disappears when the bed becomes hydrodynamically rough, above a threshold
+roughness-based Reynolds number. This suggests a possible mechanism for the
+selection of the pattern amplitude. The most unstable wavelength scales as
+observed in nature on the thickness of the viscous sublayer, with a
+multiplicative factor that depends on the dimensionless parameters of the
+problem.
+"
+26323,Compositional study of asteroids in the Erigone collisional family using visible spectroscopy at the 10.4 m GTC,"  Two primitive near Earth asteroids, (101955) Bennu and (162173) Ryugu, will
+be visited by a spacecraft with the aim of returning samples back to Earth.
+Since these objects are believed to originate in the inner main belt primitive
+collisional families (Erigone, Polana, Clarissa, and Sulamitis) or in the
+background of asteroids outside these families, the characterization of these
+primitive populations will enhance the scientific return of the missions. The
+main goal of this work is to shed light on the composition of the Erigone
+collisional family by means of visible spectroscopy. Asteroid (163) Erigone has
+been classified as a primitive object, and we expect the members of this family
+to be consistent with the spectral type of the parent body. We have obtained
+visible spectra (0.5 to 0.9 microns) for 101 members of the Erigone family,
+using the OSIRIS instrument at the 10.4 m Gran Telescopio Canarias. We found
+that 87 percent of the objects have typically primitive visible spectra
+consistent with that of (163) Erigone. In addition, we found that a significant
+fraction of these objects (approximately 50 percent) present evidence of
+aqueous alteration.
+"
+26324,Adult content in Social Live Streaming Services: Characterizing deviant users and relationships,"  Social Live Stream Services (SLSS) exploit a new level of social interaction.
+One of the main challenges in these services is how to detect and prevent
+deviant behaviors that violate community guidelines. In this work, we focus on
+adult content production and consumption in two widely used SLSS, namely
+Live.me and Loops Live, which have millions of users producing massive amounts
+of video content on a daily basis. We use a pre-trained deep learning model to
+identify broadcasters of adult content. Our results indicate that moderation
+systems in place are highly ineffective in suspending the accounts of such
+users. We create two large datasets by crawling the social graphs of these
+platforms, which we analyze to identify characterizing traits of adult content
+producers and consumers, and discover interesting patterns of relationships
+among them, evident in both networks.
+"
+26325,Low Rank plus Sparse Decomposition of ODFs for Improved Detection of Group-level Differences and Variable Correlations in White Matter,"  A novel approach is presented for group statistical analysis of diffusion
+weighted MRI datasets through voxelwise Orientation Distribution Functions
+(ODF). Recent advances in MRI acquisition make it possible to use high quality
+diffusion weighted protocols (multi-shell, large number of gradient directions)
+for routine in vivo study of white matter architecture. The dimensionality of
+these data sets is however often reduced to simplify statistical analysis.
+While these approaches may detect large group differences, they do not fully
+capitalize on all acquired image volumes. Incorporation of all available
+diffusion information in the analysis however risks biasing the outcome by
+outliers. Here we propose a statistical analysis method operating on the ODF,
+either the diffusion ODF or fiber ODF. To avoid outlier bias and reliably
+detect voxelwise group differences and correlations with demographic or
+behavioral variables, we apply the Low-Rank plus Sparse (L + S) matrix
+decomposition on the voxelwise ODFs which separates the sparse individual
+variability in the sparse matrix S whilst recovering the essential ODF features
+in the low-rank matrix L. We demonstrate the performance of this ODF L + S
+approach by replicating the established negative association between global
+white matter integrity and physical obesity in the Human Connectome dataset.
+The volume of positive findings agrees with and expands on the volume found by
+TBSS, Connectivity based fixel enhancement and Connectometry. In the same
+dataset we further localize the correlations of brain structure with
+neurocognitive measures such as fluid intelligence and episodic memory. The
+presented ODF L + S approach will aid in the full utilization of all acquired
+diffusion weightings leading to the detection of smaller group differences in
+clinically relevant settings as well as in neuroscience applications.
+"
+26326,Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems,"  Stochastic gradient descent algorithm has been successfully applied on
+support vector machines (called PEGASOS) for many classification problems. In
+this paper, stochastic gradient descent algorithm is investigated to twin
+support vector machines for classification. Compared with PEGASOS, the proposed
+stochastic gradient twin support vector machines (SGTSVM) is insensitive on
+stochastic sampling for stochastic gradient descent algorithm. In theory, we
+prove the convergence of SGTSVM instead of almost sure convergence of PEGASOS.
+For uniformly sampling, the approximation between SGTSVM and twin support
+vector machines is also given, while PEGASOS only has an opportunity to obtain
+an approximation of support vector machines. In addition, the nonlinear SGTSVM
+is derived directly from its linear case. Experimental results on both
+artificial datasets and large scale problems show the stable performance of
+SGTSVM with a fast learning speed.
+"
+26327,Using red clump stars to correct the Gaia DR1 parallaxes,"  Recent results have suggested that there is tension between the Gaia DR1 TGAS
+distances and the distances obtained using luminosities determined by eclipsing
+binaries or asteroseismology on red giant stars. We use the $K_s$-band
+luminosities of red clump stars, identified and characterized by
+asteroseismology, to make independent distance estimates. Our results suggest
+that Gaia TGAS distances contain a systematic error that decreases with
+increasing distance. We propose a correction to mitigate this offset as a
+function of parallax that is valid for the Kepler field and values of parallax
+that are less than ${\sim} 1.6 \rm \, mas$. For parallaxes greater than this we
+find agreement with previously published values. We note that the TGAS
+distances to the red clump stars of the open cluster M67 show a high level of
+disagreement that is difficult to correct for.
+"
+26328,Multi-UAV Routing for Persistent Intelligence Surveillance & Reconnaissance Missions,"  We consider a Persistent Intelligence, Surveillance and Reconnaissance (PISR)
+routing problem, which includes collecting data from a set of specified task
+locations and delivering that data to a control station. Each task is assigned
+a refresh rate based on its priority, where higher priority tasks require
+higher refresh rates. The UAV team's objective is to minimize the maximum of
+the delivery times of all the tasks' data to the control station, while
+simultaneously, satisfying each task's revisit period constraint. The
+centralized path planning problem for this PISR routing problem is formulated
+using mixed integer linear programming and solved using a branch-and-cut
+algorithm. Heuristics are presented to find sub-optimal feasible solutions that
+require much less computation time. The algorithms are tested on several
+instances and their performance is compared with respect to the optimal cost
+and computation time.
+"
+26329,Zeta-functions of root systems and Poincaré polynomials of Weyl groups,"  We consider a certain linear combination $S(\mathbf{s},\mathbf{y};I;\Delta)$
+of zeta-functions of root systems, where $\Delta$ is a root system of rank $r$
+and $I\subset\{1,2,\ldots,r\}$. Showing two different expressions of
+$S(\mathbf{s},\mathbf{y};I;\Delta)$, we find that a certain signed sum of
+zeta-functions of root systems is equal to a sum involving Bernoulli functions
+of root systems. This identity gives a non-trivial functional relation among
+zeta-functions of root systems, if the signed sum does not identically vanish.
+This is a genralization of the authors' previous result proved in
+\cite{KMTLondon}, in the case when $I=\emptyset$. We present several explicit
+examples of such functional relations. A criterion of the non-vanishing of the
+signed sum, in terms of Poincar{é} polynomials of associated Weyl groups, is
+given. Moreover we prove a certain converse theorem, which implies that the
+generating function for the case $I=\emptyset$ essentially knows all
+information on generating functions for general $I$.
+"
+26330,Inference of Personal Attributes from Tweets Using Machine Learning,"  Using machine learning algorithms, including deep learning, we studied the
+prediction of personal attributes from the text of tweets, such as gender,
+occupation, and age groups. We applied word2vec to construct word vectors,
+which were then used to vectorize tweet blocks. The resulting tweet vectors
+were used as inputs for training models, and the prediction accuracy of those
+models was examined as a function of the dimension of the tweet vectors and the
+size of the tweet blacks. The results showed that the machine learning
+algorithms could predict the three personal attributes of interest with 60-70%
+accuracy.
+"
+26331,A stabilized cut discontinuous Galerkin framework: II. Hyperbolic problems,"  We present the second part of a stabilized cut discontinuous Galerkin (cutDG)
+framework initiated in [1] and develop a novel cutDG method for scalar
+hyperbolic problems. The domain of interest is embedded into a structured,
+unfitted background mesh in $\mathbb{R}^d$ where the domain boundary can cut
+through the mesh in an arbitrary fashion. To cope with robustness problems
+caused by small cut elements, we employ and extend ghost penalty techniques
+from recently developed, continuous cut finite element methods instead of the
+cell merging technique commonly used in unfitted discontinuous Galerkin
+methods, thus allowing for a minimal extension of existing fitted discontinuous
+Galerkin software to handle unfitted geometries. Identifying a few abstract
+assumptions on the ghost penalty, we derive geometrically robust a priori error
+and condition number estimates for the scalar hyperbolic problem which hold
+irrespective of the particular cut configuration. Possible realizations of
+suitable ghost penalties are discussed. The theoretical results are
+corroborated by a number of computational studies for various approximation
+orders and for two and three-dimensional test problems.
+"
+26332,A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit,"  Determining the appropriate batch size for mini-batch gradient descent is
+always time consuming as it often relies on grid search. This paper considers a
+resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed
+bandit for achieving best performance in grid search by selecting an
+appropriate batch size at each epoch with a probability defined as a function
+of its previous success/failure. This probability encourages exploration of
+different batch size and then later exploitation of batch size with history of
+success. At each epoch, the RMGD samples a batch size from its probability
+distribution, then uses the selected batch size for mini-batch gradient
+descent. After obtaining the validation loss at each epoch, the probability
+distribution is updated to incorporate the effectiveness of the sampled batch
+size. The RMGD essentially assists the learning process to explore the possible
+domain of the batch size and exploit successful batch size. Experimental
+results show that the RMGD achieves performance better than the best performing
+single batch size. Furthermore, it, obviously, attains this performance in a
+shorter amount of time than grid search. It is surprising that the RMGD
+achieves better performance than grid search.
+"
+26333,Cosmological Dynamics of D-BIonic and DBI Scalar Field and Coincidence Problem of Dark Energy,"  We study the cosmological dynamics of D-BIonic and DBI scalar field, which is
+coupled to matter fluid. For the exponential potential and the exponential
+couplings, we find a new analytic scaling solution yielding the accelerated
+expansion of the Universe. Since it is shown to be an attractor for some range
+of the coupling parameters, the density parameter of matter fluid can be the
+observed value, as in the coupled quintessence with a canonical scalar field.
+Contrary to the usual coupled quintessence, where the value of matter couple
+giving observed density parameter is too large to satisfy observational
+constraint from CMB, we show that the D-BIonic theory can give similar solution
+with much smaller value of matter coupling. As a result, together with the fact
+that the D-BIonic theory has a screening mechanism, the D-BIonic theory can
+solve the so-called coincidence problem as well as the dark energy problem.
+"
+26334,Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics,"  In recent years, analyzing task-based fMRI (tfMRI) data has become an
+essential tool for understanding brain function and networks. However, due to
+the sheer size of tfMRI data, its intrinsic complex structure, and lack of
+ground truth of underlying neural activities, modeling tfMRI data is hard and
+challenging. Previously proposed data-modeling methods including Independent
+Component Analysis (ICA) and Sparse Dictionary Learning only provided a weakly
+established model based on blind source separation under the strong assumption
+that original fMRI signals could be linearly decomposed into time series
+components with corresponding spatial maps. Meanwhile, analyzing and learning a
+large amount of tfMRI data from a variety of subjects has been shown to be very
+demanding but yet challenging even with technological advances in computational
+hardware. Given the Convolutional Neural Network (CNN), a robust method for
+learning high-level abstractions from low-level data such as tfMRI time series,
+in this work we propose a fast and scalable novel framework for distributed
+deep Convolutional Autoencoder model. This model aims to both learn the complex
+hierarchical structure of the tfMRI data and to leverage the processing power
+of multiple GPUs in a distributed fashion. To implement such a model, we have
+created an enhanced processing pipeline on the top of Apache Spark and
+Tensorflow library, leveraging from a very large cluster of GPU machines.
+Experimental data from applying the model on the Human Connectome Project (HCP)
+show that the proposed model is efficient and scalable toward tfMRI big data
+analytics, thus enabling data-driven extraction of hierarchical neuroscientific
+information from massive fMRI big data in the future.
+"
+26335,Distinguishing $\Bbbk$-configurations,"  A $\Bbbk$-configuration is a set of points $\mathbb{X}$ in $\mathbb{P}^2$
+that satisfies a number of geometric conditions. Associated to a
+$\Bbbk$-configuration is a sequence $(d_1,\ldots,d_s)$ of positive integers,
+called its type, which encodes many of its homological invariants. We
+distinguish $\Bbbk$-configurations by counting the number of lines that contain
+$d_s$ points of $\mathbb{X}$. In particular, we show that for all integers $m
+\gg 0$, the number of such lines is precisely the value of $\Delta
+\mathbf{H}_{m\mathbb{X}}(m d_s -1)$. Here, $\Delta \mathbf{H}_{m\mathbb{X}}(-)$
+is the first difference of the Hilbert function of the fat points of
+multiplicity $m$ supported on $\mathbb{X}$.
+"
+26336,The $SU(4)-SU(2)$ crossover and spin filter properties of a double quantum dot nanosystem,"  The $SU(4)-SU(2)$ crossover, driven by an external magnetic field $h$, is
+analyzed in a capacitively-coupled double-quantum-dot device connected to
+independent leads. As one continuously charges the dots from empty to
+quarter-filled, by varying the gate potential $V_g$, the crossover starts when
+the magnitude of the spin polarization of the double quantum dot, as measured
+by $\langle n_{\uparrow}\rangle -\langle n_{\downarrow}\rangle$, becomes
+finite. Although the external magnetic field breaks the $SU(4)$ symmetry of the
+Hamiltonian, the ground state preserves it in a region of $V_g$, where $\langle
+n_{\uparrow}\rangle -\langle n_{\downarrow}\rangle =0$. Once the spin
+polarization becomes finite, it initially increases slowly until a sudden
+change occurs, in which $\langle n_{\downarrow}\rangle$ (polarization direction
+opposite to the magnetic field) reaches a maximum and then decreases to
+negligible values abruptly, at which point an orbital $SU(2)$ ground state is
+fully established. This crossover from one Kondo state, with emergent $SU(4)$
+symmetry, where spin and orbital degrees of freedom all play a role, to
+another, with $SU(2)$ symmetry, where only orbital degrees of freedom
+participate, is triggered by a competition between $g\mu_Bh$, the energy gain
+by the Zeeman-split polarized state and the Kondo temperature $T_K^{SU(4)}$,
+the gain provided by the $SU(4)$ unpolarized Kondo-singlet state.
+"
+26337,Data Augmentation Generative Adversarial Networks,"  Effective training of neural networks requires much data. In the low-data
+regime, parameters are underdetermined, and learnt networks generalise poorly.
+Data Augmentation alleviates this by using existing data more effectively.
+However standard data augmentation produces only limited plausible alternative
+data. Given there is potential to generate a much broader set of augmentations,
+we design and train a generative model to do data augmentation. The model,
+based on image conditional Generative Adversarial Networks, takes data from a
+source domain and learns to take any data item and generalise it to generate
+other within-class data items. As this generative process does not depend on
+the classes themselves, it can be applied to novel unseen classes of data. We
+show that a Data Augmentation Generative Adversarial Network (DAGAN) augments
+standard vanilla classifiers well. We also show a DAGAN can enhance few-shot
+learning systems such as Matching Networks. We demonstrate these approaches on
+Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. In
+our experiments we can see over 13% increase in accuracy in the low-data regime
+experiments in Omniglot (from 69% to 82%), EMNIST (73.9% to 76%) and VGG-Face
+(4.5% to 12%); in Matching Networks for Omniglot we observe an increase of 0.5%
+(from 96.9% to 97.4%) and an increase of 1.8% in EMNIST (from 59.5% to 61.3%).
+"
+26338,An Economic Bubble Model and Its First Passage Time,"  We introduce a new diffusion process Xt to describe asset prices within an
+economic bubble cycle. The main feature of the process, which differs from
+existing models, is the drift term where a mean-reversion is taken based on an
+exponential decay of the scaled price. Our study shows the scaling factor on Xt
+is crucial for modelling economic bubbles as it mitigates the dependence
+structure between the price and parameters in the model. We prove both the
+process and its first passage time are well-defined. An efficient calibration
+scheme, together with the probability density function for the process are
+given. Moreover, by employing the perturbation technique, we deduce the
+closed-form density for the downward first passage time, which therefore can be
+used in estimating the burst time of an economic bubble. The object of this
+study is to understand the asset price dynamics when a financial bubble is
+believed to form, and correspondingly provide estimates to the bubble crash
+time. Calibration examples on the US dot-com bubble and the 2007 Chinese stock
+market crash verify the effectiveness of the model itself. The example on
+BitCoin prediction confirms that we can provide meaningful estimate on the
+downward probability for asset prices.
+"
+26339,Information-gain computation,"  Despite large incentives, ecorrectness in software remains an elusive goal.
+Declarative programming techniques, where algorithms are derived from a
+specification of the desired behavior, offer hope to address this problem,
+since there is a combinatorial reduction in complexity in programming in terms
+of specifications instead of algorithms, and arbitrary desired properties can
+be expressed and enforced in specifications directly. However, limitations on
+performance have prevented programming with declarative specifications from
+becoming a mainstream technique for general-purpose programming. To address the
+performance bottleneck in deriving an algorithm from a specification, I propose
+information-gain computation, a framework where an adaptive evaluation strategy
+is used to efficiently perform a search which derives algorithms that provide
+information about a query most directly. Within this framework, opportunities
+to compress the search space present themselves, which suggest that
+information-theoretic bounds on the performance of such a system might be
+articulated and a system designed to achieve them. In a preliminary empirical
+study of adaptive evaluation for a simple test program, the evaluation strategy
+adapts successfully to evaluate a query efficiently.
+"
+26340,Identifying Best Interventions through Online Importance Sampling,"  Motivated by applications in computational advertising and systems biology,
+we consider the problem of identifying the best out of several possible soft
+interventions at a source node $V$ in an acyclic causal directed graph, to
+maximize the expected value of a target node $Y$ (located downstream of $V$).
+Our setting imposes a fixed total budget for sampling under various
+interventions, along with cost constraints on different types of interventions.
+We pose this as a best arm identification bandit problem with $K$ arms where
+each arm is a soft intervention at $V,$ and leverage the information leakage
+among the arms to provide the first gap dependent error and simple regret
+bounds for this problem. Our results are a significant improvement over the
+traditional best arm identification results. We empirically show that our
+algorithms outperform the state of the art in the Flow Cytometry data-set, and
+also apply our algorithm for model interpretation of the Inception-v3 deep net
+that classifies images.
+"
+26341,Stable Boundary Spike Clusters for the Two-Dimensional Gierer-Meinhardt System,"  We consider the Gierer-Meinhardt system with small inhibitor diffusivity and
+very small activator diffusivity in a bounded and smooth two-dimensional
+domain. For any given positive integer $k$ we construct a spike cluster
+consisting of $k$ boundary spikes which all approach the same nondegenerate
+local maximum point of the boundary curvature. We show that this spike cluster
+is linearly stable.
+The main idea underpinning these stable spike clusters is the following: due
+to the small inhibitor diffusivity the interaction between spikes is repulsive
+and the spikes are attracted towards a nondegenerate local maximum point of the
+boundary curvature. Combining these two effects can lead to an equilibrium of
+spike positions within the cluster such that the cluster is linearly stable.
+"
+26342,All-Optical Nanopositioning of High-Q Silica Microspheres,"  A tunable, all-optical, coupling method has been realized for a
+high-\textit{Q} silica microsphere and an optical waveguide. By means of a
+novel optical nanopositioning method, induced thermal expansion of an
+asymmetric microsphere stem for laser powers up to 171~mW has been observed and
+used to fine tune the microsphere-waveguide coupling. Microcavity displacements
+ranging from (0.612~$\pm$~0.13) -- (1.5 $\pm$ 0.13) $\mu$m and nanometer scale
+sensitivities varying from (2.81 $\pm$ 0.08) -- (7.39 $\pm$ 0.17) nm/mW, with
+an apparent linear dependency of coupling distance on stem laser heating, were
+obtained. Using this method, the coupling was altered such that different
+coupling regimes could be explored for particular samples. This tunable
+coupling method, in principle, could be incorporated into lab-on-a-chip
+microresonator systems, photonic molecule systems, and other nanopositioning
+frameworks.
+"
+26343,Social Emotion Mining Techniques for Facebook Posts Reaction Prediction,"  As of February 2016 Facebook allows users to express their experienced
+emotions about a post by using five so-called `reactions'. This research paper
+proposes and evaluates alternative methods for predicting these reactions to
+user posts on public pages of firms/companies (like supermarket chains). For
+this purpose, we collected posts (and their reactions) from Facebook pages of
+large supermarket chains and constructed a dataset which is available for other
+researches. In order to predict the distribution of reactions of a new post,
+neural network architectures (convolutional and recurrent neural networks) were
+tested using pretrained word embeddings. Results of the neural networks were
+improved by introducing a bootstrapping approach for sentiment and emotion
+mining on the comments for each post. The final model (a combination of neural
+network and a baseline emotion miner) is able to predict the reaction
+distribution on Facebook posts with a mean squared error (or misclassification
+rate) of 0.135.
+"
+26344,Tuning electronic properties of FeSe$_{0.5}$Te$_{0.5}$ thin flakes by a novel field effect transistor,"  Using a field-effect transistor (FET) configuration with solid Li-ion
+conductor (SIC) as gate dielectric, we have successfully tuned carrier density
+in FeSe$_{0.5}$Te$_{0.5}$ thin flakes, and the electronic phase diagram has
+been mapped out. It is found that electron doping controlled by SIC-FET leads
+to a suppression of the superconducting phase, and eventually gives rise to an
+insulating state in FeSe$_{0.5}$Te$_{0.5}$. During the gating process, the
+(001) peak in XRD patterns stays at the same position and no new diffraction
+peak emerges, indicating no evident Li$^+$ ions intercalation into the
+FeSe$_{0.5}$Te$_{0.5}$. It indicates that a systematic change of electronic
+properties in FeSe$_{0.5}$Te$_{0.5}$ arises from the electrostatic doping
+induced by the accumulation of Li$^+$ ions at the interface between
+FeSe$_{0.5}$Te$_{0.5}$ and solid ion conductor in the devices. It is striking
+that these findings are drastically different from the observation in FeSe thin
+flakes using the same SIC-FET, in which $T_c$ is enhanced from 8 K to larger
+than 40 K, then the system goes into an insulating phase accompanied by
+structural transitions.
+"
+26345,Sequential Matrix Completion,"  We propose a novel algorithm for sequential matrix completion in a
+recommender system setting, where the $(i,j)$th entry of the matrix corresponds
+to a user $i$'s rating of product $j$. The objective of the algorithm is to
+provide a sequential policy for user-product pair recommendation which will
+yield the highest possible ratings after a finite time horizon. The algorithm
+uses a Gamma process factor model with two posterior-focused bandit policies,
+Thompson Sampling and Information-Directed Sampling. While Thompson Sampling
+shows competitive performance in simulations, state-of-the-art performance is
+obtained from Information-Directed Sampling, which makes its recommendations
+based off a ratio between the expected reward and a measure of information
+gain. To our knowledge, this is the first implementation of Information
+Directed Sampling on large real datasets.
+This approach contributes to a recent line of research on bandit approaches
+to collaborative filtering including Kawale et al. (2015), Li et al. (2010),
+Bresler et al. (2014), Li et al. (2016), Deshpande & Montanari (2012), and Zhao
+et al. (2013). The setting of this paper, as has been noted in Kawale et al.
+(2015) and Zhao et al. (2013), presents significant challenges to bounding
+regret after finite horizons. We discuss these challenges in relation to
+simpler models for bandits with side information, such as linear or gaussian
+process bandits, and hope the experiments presented here motivate further
+research toward theoretical guarantees.
+"
+26346,Operationalization of Topology of Sustainable Management to Estimate Qualitatively Different Regions in State Space,"  To apply the framework of Topology of Sustainable Management (TSM) by Heitzig
+et al. (2016) to dynamical models, we connect it to viability theory (VT) via a
+variant definition of the former. This enables us to use the Saint-Pierre
+algorithm to estimate the main partition of TSM, improving the
+operationalization of TSM. Furthermore, we present an extension of the
+algorithm to compute implicitly defined capture basins, a notion from VT that
+is more elaborated in the article, as these come up in TSM. We use a low-
+complexity model coupling environmental and socio-economic dynamics to
+demonstrate the applicability of this approach. Two common problems of
+estimations in VT are critical for this example: (i) an unbounded state space
+and (ii) highly varying time scales. We solve both by introducing appropriate
+coordinate transformations. These solutions are applicable for general systems,
+too.
+"
+26347,Energy Considerations for Lifting the Greenland Ice-Melt from the Earth's Gravitational Well,"  Climatologists have calculated that a complete melting of the Greenland Ice
+Sheet would cause global sea levels to rise by some 7.2 meters (23.6 feet).
+This article investigates the possibility of physically removing this excess
+water from the surface of the Earth by lifting the water into space. The
+theoretical minimum amount of work for this task is calculated, and is found to
+be equal to the amount of solar energy intercepted by the Earth in just 32
+years.
+"
+26348,Spin Dynamics in a Stripe-ordered Buckled Honeycomb Lattice Antiferromagnet Ba$_{2}$NiTeO$_{6}$,"  We carried out inelastic neutron scattering experiments on a buckled
+honeycomb lattice antiferromagnet Ba$_{2}$NiTeO$_{6}$ exhibiting a stripe
+structure at a low temperature. Magnetic excitations are observed in the energy
+range of $\hbar \omega \lesssim 10$ meV having an anisotropy gap of 2 meV at 2
+K. We perform spin-wave calculations to identify the spin model. The obtained
+microscopic parameters are consistent with the location of the stripe structure
+in the classical phase diagram. Furthermore, the Weiss temperature
+independently estimated from a bulk magnetic susceptibility is consistent with
+the microscopic parameters. The results reveal that a competition between the
+NN and NNN interactions that together with a relatively large single ion
+magnetic anisotropy stabilize the stripe magnetic structure.
+"
+26349,Cut to the chase: Revisiting the relevance of software engineering research,"  Software engineering (SE) research should be relevant to industrial practice.
+There has been a debate on this issue in the community since 1980's by pioneers
+such as Robert Glass and Colin Potts. As we pass the milestone of ""50 Years of
+Software Engineering"", some recent positive efforts have been made in this
+direction, e.g., establishing ""industrial"" tracks in several SE conferences.
+However, we, as a community, are still struggling with research relevance and
+utility. The goal of this paper is to act as another ""wake-up call"" for the
+community to reflect and act on the relevance of SE research. The contributions
+of this paper are as follows: (1) a review of the debate on research relevance
+in other fields; (2) a Multi-vocal Literature Review (MLR) of the debate in SE
+(46 sources) and the suggestions discussed in the community for improving the
+situation; (3) a summary of the experience of the authors in conducting SE
+research with varying degrees of relevance; and (4) a review of recent
+activities being done in the SE community to improve relevance. There has been
+no systematic literature review on the topic of research relevance in SE yet.
+Some of our MLR findings are that: the top-3 root causes of low relevance, as
+discussed in the community are: (1) Simplistic view (wrong assumptions) about
+SE in practice; (2) Wrong identification of research problems (needs); and (3)
+Issues with research mindset. The top-3 suggestions for improving research
+relevance are: (1) Using appropriate research approaches such as
+action-research, that would increase chances of research relevance; (2)
+Choosing relevant (practical) problems; and (3) Collaborating with industry. By
+synthesizing all the discussions on this debate so far, this paper hopes to
+encourage further discussions and actions in the community to increase our
+collective efforts to improve the research relevance in our discipline.
+"
+26350,Torsional Rigidity and Bergman Analytic Content of Simply Connected Regions,"  We exploit the equality of Bergman analytic content and torsional rigidity of
+a simply connected domain to develop a new method for calculating these
+quantities. This method is particularly suitable for the case when the region
+in question is a polygon. A large number of examples are computed in explicit
+detail to demonstrate the utility of our ideas.
+"
+26351,Sorting by Reversals and the Theory of 4-Regular Graphs,"  We show that the theory of sorting by reversals fits into the
+well-established theory of circuit partitions of 4-regular multigraphs (which
+also involves the combinatorial structures of circle graphs and
+delta-matroids). In this way, we expose strong connections between the two
+theories that have not been fully appreciated before. We also discuss a
+generalization of sorting by reversals involving the double-cut-and-join (DCJ)
+operation. Finally, we also show that the theory of sorting by reversals is
+closely related to that of gene assembly in ciliates.
+"
+26352,Targeted Random Projection for Prediction from High-Dimensional Features,"  We consider the problem of computationally-efficient prediction from high
+dimensional and highly correlated predictors in challenging settings where
+accurate variable selection is effectively impossible. Direct application of
+penalization or Bayesian methods implemented with Markov chain Monte Carlo can
+be computationally daunting and unstable. Hence, some type of dimensionality
+reduction prior to statistical analysis is in order. Common solutions include
+application of screening algorithms to reduce the regressors, or dimension
+reduction using projections of the design matrix. The former approach can be
+highly sensitive to threshold choice in finite samples, while the later can
+have poor performance in very high-dimensional settings. We propose a TArgeted
+Random Projection (TARP) approach that combines positive aspects of both
+strategies to boost performance. In particular, we propose to use information
+from independent screening to order the inclusion probabilities of the features
+in the projection matrix used for dimension reduction, leading to data-informed
+sparsity. We provide theoretical support for a Bayesian predictive algorithm
+based on TARP, including both statistical and computational complexity
+guarantees. Examples for simulated and real data applications illustrate gains
+relative to a variety of competitors.
+"
+26353,From Kondo to local singlet state in graphene nanoribbons with magnetic impurities,"  A detailed analysis of the Kondo effect of a magnetic impurity in a zigzag
+graphene nanoribbon is addressed. An adatom is coupled to the graphene
+nanoribbon via a hybridization amplitude $\Gamma_{imp}$ in a hollow or top site
+configuration. In addition, the adatom is also weakly coupled to a metallic STM
+tip by a hybridization function $\Gamma_{tip}$ that provides a Kondo screening
+of its magnetic moment. The entire system is described by an Anderson-like
+Hamiltonian whose low-temperature physics is accessed by employing the
+numerical renormalization group approach, which allows us to obtain the
+thermodynamic properties used to compute the Kondo temperature of the system.
+We find two screening regimes when the adatom is close to the edge of the
+zigazag graphene nanoribbon: (1) a weak coupling regime ($\Gamma_{imp}\ll
+\Gamma_{tip}$), in which the edge states produce an enhancement of the Kondo
+temperature $T_K$ and (2) a strong coupling regime ($\Gamma_{imp}\gg
+\Gamma_{tip}$), in which a local singlet is formed, in detriment to the Kondo
+screening by the STM tip. These two regimes can be clearly distinguished by the
+dependence of their characteristic temperature $T^*$ on the coupling between
+the adatom and the carbon sites of the graphene nanoribon ($V_{imp}$). We
+observe that in the weak coupling regime $T^*$ increases exponentially with
+$V_{imp}^2$. Differently, in the strong coupling regime, $T^*$ increases
+linearly with $V_{imp}^2$.
+"
+26354,On the isotope effect in compressed superconducting H$_\textrm{3}$S and D$_\textrm{3}$S,"  A maximum superconductive transition temperature $T_\textrm{C}$ = 203.5 K has
+recently been reported for a sample of the binary compound tri-hydrogen sulfide
+(H$_\textrm{3}$S) prepared at high pressure and with room temperature
+annealing. Measurements of $T_\textrm{C}$ for H$_\textrm{3}$S and its deuterium
+counterpart D$_\textrm{3}$S have suggested a mass isotope effect exponent
+${\alpha}$ with anomalous enhancements for reduced applied pressures. While
+widely cited for evidence of phonon-based superconductivity, the measured
+$T_\textrm{C}$ is shown to exhibit important dependences on the quality and
+character of the H$_\textrm{3}$S and D$_\textrm{3}$S materials under study;
+examination of resistance versus temperature data shows that variations in
+$T_\textrm{C}$ and apparent ${\alpha}$ are strongly correlated with residual
+resistance ratio, indicative of sensitivity to metallic order. Correlations
+also extend to the fractional widths of the superconducting transitions. Using
+resistance data to quantify and compensate for the evident materials
+differences between H$_\textrm{3}$S and D$_\textrm{3}$S samples, a value of
+${\alpha}$ = 0.043 $\pm$ 0.140 is obtained. Thus, when corrected for the
+varying levels of disorder, the experimental upper limit ($\leq$0.183) lies
+well below ${\alpha}$ derived in phonon-based theories.
+"
+26355,Computational Complexity of Atomic Chemical Reaction Networks,"  Informally, a chemical reaction network is ""atomic"" if each reaction may be
+interpreted as the rearrangement of indivisible units of matter. There are
+several reasonable definitions formalizing this idea. We investigate the
+computational complexity of deciding whether a given network is atomic
+according to each of these definitions.
+Our first definition, primitive atomic, which requires each reaction to
+preserve the total number of atoms, is to shown to be equivalent to mass
+conservation. Since it is known that it can be decided in polynomial time
+whether a given chemical reaction network is mass-conserving, the equivalence
+gives an efficient algorithm to decide primitive atomicity.
+Another definition, subset atomic, further requires that all atoms are
+species. We show that deciding whether a given network is subset atomic is in
+$\textsf{NP}$, and the problem ""is a network subset atomic with respect to a
+given atom set"" is strongly $\textsf{NP}$-$\textsf{Complete}$.
+A third definition, reachably atomic, studied by Adleman, Gopalkrishnan et
+al., further requires that each species has a sequence of reactions splitting
+it into its constituent atoms. We show that there is a $\textbf{polynomial-time
+algorithm}$ to decide whether a given network is reachably atomic, improving
+upon the result of Adleman et al. that the problem is $\textbf{decidable}$. We
+show that the reachability problem for reachably atomic networks is
+$\textsf{Pspace}$-$\textsf{Complete}$.
+Finally, we demonstrate equivalence relationships between our definitions and
+some special cases of another existing definition of atomicity due to Gnacadja.
+"
+26356,Generalized Pauli constraints in small atoms,"  The natural occupation numbers of fermionic systems are subject to
+non-trivial constraints, which include and extend the original Pauli principle.
+A recent mathematical breakthrough has clarified their mathematical structure
+and has opened up the possibility of a systematic analysis. Early
+investigations have found evidence that these constraints are exactly saturated
+in several physically relevant systems, e.g., in a certain electronic state of
+the Beryllium atom. It has been suggested that in such cases, the constraints,
+rather than the details of the Hamiltonian, dictate the system's qualitative
+behaviour. Here, we revisit this question with state-of-the-art numerical
+methods for small atoms. We find that the constraints are, in fact, not exactly
+saturated, but that they lie much closer to the surface defined by the
+constraints than the geometry of the problem would suggest. While the results
+seem incompatible with the statement that the generalized Pauli constraints
+drive the behaviour of these systems, they suggest that the qualitatively
+correct wave-function expansions can in some systems already be obtained on the
+basis of a limited number of Slater determinants, which is in line with
+numerical evidence from quantum chemistry.
+"
+26357,Optimal Approximation with Sparsely Connected Deep Neural Networks,"  We derive fundamental lower bounds on the connectivity and the memory
+requirements of deep neural networks guaranteeing uniform approximation rates
+for arbitrary function classes in $L^2(\mathbb R^d)$. In other words, we
+establish a connection between the complexity of a function class and the
+complexity of deep neural networks approximating functions from this class to
+within a prescribed accuracy. Additionally, we prove that our lower bounds are
+achievable for a broad family of function classes. Specifically, all function
+classes that are optimally approximated by a general class of representation
+systems---so-called \emph{affine systems}---can be approximated by deep neural
+networks with minimal connectivity and memory requirements. Affine systems
+encompass a wealth of representation systems from applied harmonic analysis
+such as wavelets, ridgelets, curvelets, shearlets, $\alpha$-shearlets, and more
+generally $\alpha$-molecules. Our central result elucidates a remarkable
+universality property of neural networks and shows that they achieve the
+optimum approximation properties of all affine systems combined. As a specific
+example, we consider the class of $\alpha^{-1}$-cartoon-like functions, which
+is approximated optimally by $\alpha$-shearlets. We also explain how our
+results can be extended to the case of functions on low-dimensional immersed
+manifolds. Finally, we present numerical experiments demonstrating that the
+standard stochastic gradient descent algorithm generates deep neural networks
+providing close-to-optimal approximation rates. Moreover, these results
+indicate that stochastic gradient descent can actually learn approximations
+that are sparse in the representation systems optimally sparsifying the
+function class the network is trained on.
+"
+26358,The eccentric Kozai-Lidov effect as a resonance phenomenon,"  Exploring weakly perturbed Keplerian motion within the restricted three-body
+problem, Lidov (1962) and, independently, Kozai (1962) discovered coupled
+oscillations of eccentricity and inclination (the KL-cycles). Their classical
+studies were based on an integrable model of the secular evolution, obtained by
+double averaging of the disturbing function approximated with its first
+non-trivial term. This was the quadrupole term in the series expansion with
+respect to the ratio of the semimajor axis of the disturbed body to that of the
+disturbing body. If the next (octupole) term is kept in the expression for the
+disturbing function, long-term modulation of the KL-cycles can established
+(Ford et al., 2000, Naoz et al., 2011, Katz et al., 2011). Specifically, flips
+between the prograde and retrograde orbits become possible. Since such flips
+are observed only when the perturber has a non-zero eccentricity, the term
+""Eccentric Kozai-Lidov Effect"" (or EKL-effect) was proposed by Lithwick and
+Naoz (2011) to specify such behaviour. We demonstrate that the EKL-effect can
+be interpreted as a resonance phenomenon. To this end, we write down the
+equations of motion in terms of ""action-angle"" variables emerging in the
+integrable Kozai-Lidov model. It turns out that for some initial values the
+resonance is degenerate and the usual ""pendulum"" approximation is insufficient
+to describe the evolution of the resonance phase. Analysis of the related
+bifurcations allows us to estimate the typical time between the successive
+flips for different parts of the phase space.
+"
+26359,Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test,"  This paper proposes a new procedure to construct test statistics for
+hypothesis testing by computer vision and metrics learning. The application
+highlighted in this paper is applying computer vision on Q-Q plot to construct
+a new test statistic for normality test. Traditionally, there are two families
+of approaches for verifying the probability distribution of a random variable.
+Researchers either subjectively assess the Q-Q plot or objectively use a
+mathematical formula, such as Kolmogorov-Smirnov test, to formally conduct a
+normality test. Graphical assessment by human beings is not rigorous whereas
+normality test statistics may not be accurate enough when the uniformly most
+powerful test does not exist. It may take tens of years for statistician to
+develop a new and more powerful test statistic. The first step of the proposed
+method is to apply computer vision techniques, such as pre-trained ResNet, to
+convert a Q-Q plot into a numerical vector. Next step is to apply metric
+learning to find an appropriate distance function between a Q-Q plot and the
+centroid of all Q-Q plots under the null hypothesis, which assumes the target
+variable is normally distributed. This distance metric is the new test
+statistic for normality test. Our experimentation results show that the
+machine-learning-based test statistics can outperform traditional normality
+tests in all cases, particularly when the sample size is small. This study
+provides convincing evidence that the proposed method could objectively create
+a powerful test statistic based on Q-Q plots and this method could be modified
+to construct many more powerful test statistics for other applications in the
+future.
+"
+26360,Probabilistic Completeness of Randomized Possibility Graphs Applied to Bipedal Walking in Semi-unstructured Environments,"  We present a theoretical analysis of a recent whole body motion planning
+method, the Randomized Possibility Graph, which uses a high-level decomposition
+of the feasibility constraint manifold in order to rapidly find routes that may
+lead to a solution. These routes are then examined by lower-level planners to
+determine feasibility. In this paper, we show that this approach is
+probabilistically complete for bipedal robots performing quasi-static walking
+in ""semi-unstructured"" environments. Furthermore, we show that the
+decomposition into higher and lower level planners allows for a considerably
+higher rate of convergence in the probability of finding a solution when one
+exists. We illustrate this improved convergence with a series of simulated
+scenarios.
+"
+26361,Microservices: a Language-based Approach,"  Microservices is an emerging development paradigm where software is obtained
+by composing autonomous entities, called (micro)services. However, microservice
+systems are currently developed using general-purpose programming languages
+that do not provide dedicated abstractions for service composition. Instead,
+current practice is focused on the deployment aspects of microservices, in
+particular by using containerization. In this chapter, we make the case for a
+language-based approach to the engineering of microservice architectures, which
+we believe is complementary to current practice. We discuss the approach in
+general, and then we instantiate it in terms of the Jolie programming language.
+"
+26362,Foothill: A Quasiconvex Regularization Function,"  Deep neural networks (DNNs) have demonstrated success for many supervised
+learning tasks, ranging from voice recognition, object detection, to image
+classification. However, their increasing complexity yields poor generalization
+error. Adding noise to the input data or using a concrete regularization
+function helps to improve generalization. Here we introduce foothill function,
+an infinitely differentiable quasiconvex function. This regularizer is flexible
+enough to deform towards $L_1$ and $L_2$ penalties. Foothill can be used as a
+loss, as a regularizer, or as a binary quantizer.
+"
+26363,Building an Effective Data Warehousing for Financial Sector,"  This article presents the implementation process of a Data Warehouse and a
+multidimensional analysis of business data for a holding company in the
+financial sector. The goal is to create a business intelligence system that, in
+a simple, quick but also versatile way, allows the access to updated,
+aggregated, real and/or projected information, regarding bank account balances.
+The established system extracts and processes the operational database
+information which supports cash management information by using Integration
+Services and Analysis Services tools from Microsoft SQL Server. The end-user
+interface is a pivot table, properly arranged to explore the information
+available by the produced cube. The results have shown that the adoption of
+online analytical processing cubes offers better performance and provides a
+more automated and robust process to analyze current and provisional aggregated
+financial data balances compared to the current process based on static reports
+built from transactional databases.
+"
+26364,Computing minimum-time trajectories for quadrotors via transverse coordinates,"  In this paper we present a novel strategy to compute minimum-time
+trajectories for quadrotors. In particular, we consider the motion in
+constrained environments, taking into account the physical limitations of the
+vehicle. Instead of approaching the optimization problem in its standard
+time-parameterized formulation, the proposed strategy is based on an appealing
+re-formulation. Transverse coordinates, expressing the distance from a
+""reference"" path, are used to parameterize the vehicle position and a spatial
+parameter is used as independent variable. This re-formulation allows us to (i)
+obtain a fixed horizon problem and (ii) easily formulate (even complex)
+position constraints. The effectiveness of the proposed strategy is proven by
+numerical computations on two different illustrative scenarios.
+"
+26365,Images of Galois representations in mod $p$ Hecke algebras,"  Let $(\mathbb{T}_f,\mathfrak{m}_f)$ denote the mod $p$ local Hecke algebra
+attached to a normalised Hecke eigenform $f$, which is a commutative algebra
+over some finite field $\mathbb{F}_q$ of characteristic $p$ and with residue
+field $\mathbb{F}_q$. By a result of Carayol we know that, if the residual
+Galois representation
+$\overline{\rho}_f:G_\mathbb{Q}\rightarrow\mathrm{GL}_2(\mathbb{F}_q)$ is
+absolutely irreducible, then one can attach to this algebra a Galois
+representation $\rho_f:G_\mathbb{Q}\rightarrow\mathrm{GL}_2(\mathbb{T}_f)$ that
+is a lift of $\overline{\rho}_f$. We will show how one can determine the image
+of $\rho_f$ under the assumptions that $(i)$ the image of the residual
+representation contains $\mathrm{SL}_2(\mathbb{F}_q)$, $(ii)$ that
+$\mathfrak{m}_f^2=0$ and $(iii)$ that the coefficient ring is generated by the
+traces. As an application we will see that the methods that we use allow us to
+deduce the existence of certain $p$-elementary abelian extensions of big
+non-solvable number fields.
+"
+26366,Online Machine Learning in Big Data Streams,"  The area of online machine learning in big data streams covers algorithms
+that are (1) distributed and (2) work from data streams with only a limited
+possibility to store past data. The first requirement mostly concerns software
+architectures and efficient algorithms. The second one also imposes nontrivial
+theoretical restrictions on the modeling methods: In the data stream model,
+older data is no longer available to revise earlier suboptimal modeling
+decisions as the fresh data arrives.
+In this article, we provide an overview of distributed software architectures
+and libraries as well as machine learning models for online learning. We
+highlight the most important ideas for classification, regression,
+recommendation, and unsupervised modeling from streaming data, and we show how
+they are implemented in various distributed data stream processing systems.
+This article is a reference material and not a survey. We do not attempt to
+be comprehensive in describing all existing methods and solutions; rather, we
+give pointers to the most important resources in the field. All related
+sub-fields, online algorithms, online learning, and distributed data processing
+are hugely dominant in current research and development with conceptually new
+research results and software components emerging at the time of writing. In
+this article, we refer to several survey results, both for distributed data
+processing and for online machine learning. Compared to past surveys, our
+article is different because we discuss recommender systems in extended detail.
+"
+26367,Multiparameter spectral analysis for aeroelastic instability problems,"  This paper presents a novel application of multiparameter spectral theory to
+the study of structural stability, with particular emphasis on aeroelastic
+flutter. Methods of multiparameter analysis allow the development of new
+solution algorithms for aeroelastic flutter problems; most significantly, a
+direct solver for polynomial problems of arbitrary order and size, something
+which has not before been achieved. Two major variants of this direct solver
+are presented, and their computational characteristics are compared. Both are
+effective for smaller problems arising in reduced-order modelling and
+preliminary design optimization. Extensions and improvements to this new
+conceptual framework and solution method are then discussed.
+"
+26368,"Modelling ultraviolet-line diagnostics of stars, the ionized and the neutral interstellar medium in star-forming galaxies","  We combine state-of-the-art models for the production of stellar radiation
+and its transfer through the interstellar medium (ISM) to investigate
+ultraviolet-line diagnostics of stars, the ionized and the neutral ISM in
+star-forming galaxies. We start by assessing the reliability of our stellar
+population synthesis modelling by fitting absorption-line indices in the
+ISM-free ultraviolet spectra of 10 Large-Magellanic-Cloud clusters. In doing
+so, we find that neglecting stochastic sampling of the stellar initial mass
+function in these young ($\sim10$-100 Myr), low-mass clusters affects
+negligibly ultraviolet-based age and metallicity estimates but can lead to
+significant overestimates of stellar mass. Then, we proceed and develop a
+simple approach, based on an idealized description of the main features of the
+ISM, to compute in a physically consistent way the combined influence of
+nebular emission and interstellar absorption on ultraviolet spectra of
+star-forming galaxies. Our model accounts for the transfer of radiation through
+the ionized interiors and outer neutral envelopes of short-lived stellar birth
+clouds, as well as for radiative transfer through a diffuse intercloud medium.
+We use this approach to explore the entangled signatures of stars, the ionized
+and the neutral ISM in ultraviolet spectra of star-forming galaxies. We find
+that, aside from a few notable exceptions, most standard ultraviolet indices
+defined in the spectra of ISM-free stellar populations are prone to significant
+contamination by the ISM, which increases with metallicity. We also identify
+several nebular-emission and interstellar-absorption features, which stand out
+as particularly clean tracers of the different phases of the ISM.
+"
+26369,The Robust Manifold Defense: Adversarial Training using Generative Models,"  Deep neural networks are demonstrating excellent performance on several
+classical vision problems. However, these networks are vulnerable to
+adversarial examples, minutely modified images that induce arbitrary
+attacker-chosen output from the network. We propose a mechanism to protect
+against these adversarial inputs based on a generative model of the data. We
+introduce a pre-processing step that projects on the range of a generative
+model using gradient descent before feeding an input into a classifier. We show
+that this step provides the classifier with robustness against first-order,
+substitute model, and combined adversarial attacks. Using a min-max
+formulation, we show that there may exist adversarial examples even in the
+range of the generator, natural-looking images extremely close to the decision
+boundary for which the classifier has unjustifiedly high confidence. We show
+that adversarial training on the generative manifold can be used to make a
+classifier that is robust to these attacks.
+Finally, we show how our method can be applied even without a pre-trained
+generative model using a recent method called the deep image prior. We evaluate
+our method on MNIST, CelebA and Imagenet and show robustness against the
+current state of the art attacks.
+"
+26370,MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data?,"  We conduct an empirical study of machine learning functionalities provided by
+major cloud service providers, which we call machine learning clouds. Machine
+learning clouds hold the promise of hiding all the sophistication of running
+large-scale machine learning: Instead of specifying how to run a machine
+learning task, users only specify what machine learning task to run and the
+cloud figures out the rest. Raising the level of abstraction, however, rarely
+comes free - a performance penalty is possible. How good, then, are current
+machine learning clouds on real-world machine learning workloads?
+We study this question with a focus on binary classication problems. We
+present mlbench, a novel benchmark constructed by harvesting datasets from
+Kaggle competitions. We then compare the performance of the top winning code
+available from Kaggle with that of running machine learning clouds from both
+Azure and Amazon on mlbench. Our comparative study reveals the strength and
+weakness of existing machine learning clouds and points out potential future
+directions for improvement.
+"
+26371,Frequent Query Matching in Dynamic Data Warehousing,"  With the need for flexible and on-demand decision support, Dynamic Data
+Warehouses (DDW) provide benefits over traditional data warehouses due to their
+dynamic characteristics in structuring and access mechanism. A DDW is a data
+framework that accommodates data source changes easily to allow seamless
+querying to users. Materialized Views (MV) are proven to be an effective
+methodology to enhance the process of retrieving data from a DDW as results are
+pre-computed and stored in it. However, due to the static nature of
+materialized views, the level of dynamicity that can be provided at the MV
+access layer is restricted. As a result, the collection of materialized views
+is not compatible with ever-changing reporting requirements. It is important
+that the MV collection is consistent with current and upcoming queries. The
+solution to the above problem must consider the following aspects: (a) MV must
+be matched against an OLAP query in order to recognize whether the MV can
+answer the query, (b) enable scalability in the MV collection, an intuitive
+mechanism to prune it and retrieve closely matching MVs must be incorporated,
+(c) MV collection must be able to evolve in correspondence to the regularly
+changing user query patterns. Therefore, the primary objective of this paper is
+to explore these aspects and provide a well-rounded solution for the MV access
+layer to remove the mismatch between the MV collection and reporting
+requirements. Our contribution to solve the problem includes a Query Matching
+Technique, a Domain Matching Technique and Maintenance of the MV collection. We
+developed an experimental platform using real data-sets to evaluate the
+effectiveness in terms of performance and precision of the proposed techniques.
+"
+26372,Minimal Sum of Powered Distances from the Sides of a Triangle,"  In this paper we apply the KKT conditions to find the minimal sum of powered
+distances from the sides of an arbitrary triangle.
+"
+26373,Fast subsurface fingerprint imaging with full-field optical coherence tomography system equipped with a silicon camera,"  Images recorded below the surface of a finger can have more details and be of
+higher quality than the conventional surface fingerprint images. This is
+particularly true when the quality of the surface fingerprints is compromised
+by, for example, moisture or surface damage. However, there is an unmet need
+for an inexpensive fingerprint sensor that is able to acquire high-quality
+images deep below the surface in short time. To this end, we report on a
+cost-effective full-field optical coherent tomography (FF-OCT) system comprised
+of a silicon camera and a powerful near-infrared LED light source. The system,
+for example, is able to record 1.7 cm x 1.7 cm en face images in 0.12 s with
+the spatial sampling rate of 2116 dots per inch and the sensitivity of 93 dB.
+We show that the system can be used to image internal fingerprints and sweat
+ducts with a good contrast. Finally, to demonstrate its biometric performance,
+we acquired subsurface fingerprint images from 240 individual fingers and
+estimated the equal-error-rate to be ~0.8 %. The developed instrument could
+also be used in other en face deep-tissue imaging applications because of its
+high sensitivity, such as in vivo skin imaging.
+"
+26374,"WU-NEAT: A clinically validated, open- source MATLAB toolbox for limited-channel neonatal EEG analysis","  Goal: Limited-channel EEG research in neonates is hindered by lack of open,
+accessible analytic tools. To overcome this limitation, we have created the
+Washington University- Neonatal EEG Analysis Toolbox (WU-NEAT), containing two
+of the most commonly used tools, provided in an open-source,
+clinically-validated package running within MATLAB. Methods: The first
+algorithm is the amplitude-integrated EEG (aEEG), which is generated by
+filtering, rectifying and time-compressing the original EEG recording, with
+subsequent semi-logarithmic display. The second algorithm is the spectral edge
+frequency (SEF), calculated as the critical frequency below which a user-
+defined proportion of the EEG spectral power is located. The aEEG algorithm was
+validated by three experienced reviewers. Reviewers evaluated aEEG recordings
+of fourteen preterm/term infants, displayed twice in random order, once using a
+reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard
+methodology, reviewers assigned a background pattern classification.
+Inter/intra-rater reliability was assessed. For the SEF, calculations were made
+using the same fourteen recordings, first with the reference and then with the
+WU-NEAT algorithm. Results were compared using Pearson's correlation
+coefficient. Results: For the aEEG algorithm, intra- and inter-rater
+reliability was 100% and 98%, respectively. For the SEF, the mean (SD) Pearson
+correlation coefficient between algorithms was 0.96 (0.04). Conclusion: We have
+demonstrated a clinically-validated toolbox for generating the aEEG as well as
+calculating the SEF from EEG data. Open-source access will enable widespread
+use of common analytic algorithms which are device-independent and not subject
+to obsolescence, thereby facilitating future collaborative research in neonatal
+EEG.
+"
+26375,Deformable Deep Convolutional Generative Adversarial Network in Microwave Based Hand Gesture Recognition System,"  Traditional vision-based hand gesture recognition systems is limited under
+dark circumstances. In this paper, we build a hand gesture recognition system
+based on microwave transceiver and deep learning algorithm. A Doppler radar
+sensor with dual receiving channels at 5.8GHz is used to acquire a big database
+of hand gestures signals. The received hand gesture signals are then processed
+with time-frequency analysis. Based on these big databases of hand gesture, we
+propose a new machine learning architecture called deformable deep
+convolutional generative adversarial network. Experimental results show the new
+architecture can upgrade the recognition rate by 10% and the deformable kernel
+can reduce the testing time cost by 30%.
+"
+26376,Rademacher Generalization Bounds for Classifier Chains,"  In this paper, we propose a new framework to study the generalization
+property of classifier chains trained over observations associated with
+multiple and interdependent class labels. The results are based on large
+deviation inequalities for Lipschitz functions of weakly dependent sequences
+proposed by Rio in 2000. We believe that the resulting generalization error
+bound brings many advantages and could be adapted to other frameworks that
+consider interdependent outputs. First, it explicitly exhibits the dependencies
+between class labels. Secondly, it provides insights of the effect of the order
+of the chain on the algorithm generalization performances. Finally, the two
+dependency coefficients that appear in the bound could also be used to design
+new strategies to decide the order of the chain.
+"
+26377,Deep driven fMRI decoding of visual categories,"  Deep neural networks have been developed drawing inspiration from the brain
+visual pathway, implementing an end-to-end approach: from image data to video
+object classes. However building an fMRI decoder with the typical structure of
+Convolutional Neural Network (CNN), i.e. learning multiple level of
+representations, seems impractical due to lack of brain data. As a possible
+solution, this work presents the first hybrid fMRI and deep features decoding
+approach: collected fMRI and deep learnt representations of video object
+classes are linked together by means of Kernel Canonical Correlation Analysis.
+In decoding, this allows exploiting the discriminatory power of CNN by relating
+the fMRI representation to the last layer of CNN (fc7). We show the
+effectiveness of embedding fMRI data onto a subspace related to deep features
+in distinguishing semantic visual categories based solely on brain imaging
+data.
+"
+26378,Response of microchannel plates in ionization mode to single particles and electromagnetic showers,"  Hundreds of concurrent collisions per bunch crossing are expected at future
+hadron colliders. Precision timing calorimetry has been advocated as a way to
+mitigate the pileup effects and, thanks to their excellent time resolution,
+microchannel plates (MCPs) are good candidate detectors for this goal. We
+report on the response of MCPs, used as secondary emission detectors, to single
+relativistic particles and to electromagnetic showers. Several prototypes, with
+different geometries and characteristics, were exposed to particle beams at the
+INFN-LNF Beam Test Facility and at CERN. Their time resolution and efficiency
+are measured for single particles and as a function of the multiplicity of
+particles. Efficiencies between 50% and 90% to single relativistic particles
+are reached, and up to 100% in presence of a large number of particles. Time
+resolutions between 20ps and 30ps are obtained.
+"
+26379,The Engineering Development Array: A low frequency radio telescope utilising SKA precursor technology,"  We describe the design and performance of the Engineering Development Array
+(EDA), which is a low frequency radio telescope comprising 256
+dual-polarisation dipole antennas working as a phased-array. The EDA was
+conceived of, developed, and deployed in just 18 months via re-use of Square
+Kilometre Array (SKA) precursor technology and expertise, specifically from the
+Murchison Widefield Array (MWA) radio telescope. Using drift scans and a model
+for the sky brightness temperature at low frequencies, we have derived the
+EDA's receiver temperature as a function of frequency. The EDA is shown to be
+sky-noise limited over most of the frequency range measured between 60 and 240
+MHz. By using the EDA in interferometric mode with the MWA, we used calibrated
+visibilities to measure the absolute sensitivity of the array. The measured
+array sensitivity matches very well with a model based on the array layout and
+measured receiver temperature. The results demonstrate the practicality and
+feasibility of using MWA-style precursor technology for SKA-scale stations. The
+modular architecture of the EDA allows upgrades to the array to be rolled out
+in a staged approach. Future improvements to the EDA include replacing the
+second stage beamformer with a fully digital system, and to transition to using
+RF-over-fibre for the signal output from first stage beamformers.
+"
+26380,Realization of room-temperature ferromagnetic semiconducting state in graphene monolayer,"  Room-temperature ferromagnetic semiconductor is vital in nonvolatile digital
+circuits and it can provide an idea system where we can make use of both charge
+and spin of electrons. However, seeking room-temperature ferromagnetic
+semiconductors is still just an appealing idea that has never been realized in
+practice up to now. Here we demonstrate that graphene monolayer, hybridized
+with underlying Ni(111) substrate, is the room-temperature ferromagnetic
+semiconductor that has been continuously searched for decades. Our
+spin-polarized scanning tunnelling microscopy (STM) experiments, complemented
+by first-principles calculations, demonstrate explicitly that the interaction
+between graphene and the Ni substrate generates a large gap in graphene and
+simultaneously leads to a relatively shift between majority- and minority-spin
+bands. Consequently, the graphene sheet on the Ni substrate exhibits a
+spin-polarized gap with energy of several tens meV even at room-temperature.
+This result makes the science and applications of room-temperature
+ferromagnetic semiconductors achievable and raises hopes of graphene-based
+novel information technologies.
+"
+26381,Outlier Detection for Text Data : An Extended Version,"  The problem of outlier detection is extremely challenging in many domains
+such as text, in which the attribute values are typically non-negative, and
+most values are zero. In such cases, it often becomes difficult to separate the
+outliers from the natural variations in the patterns in the underlying data. In
+this paper, we present a matrix factorization method, which is naturally able
+to distinguish the anomalies with the use of low rank approximations of the
+underlying data. Our iterative algorithm TONMF is based on block coordinate
+descent (BCD) framework. We define blocks over the term-document matrix such
+that the function becomes solvable. Given most recently updated values of other
+matrix blocks, we always update one block at a time to its optimal. Our
+approach has significant advantages over traditional methods for text outlier
+detection. Finally, we present experimental results illustrating the
+effectiveness of our method over competing methods.
+"
+26382,SOC: hunting the underground inside story of the ethereum Social-network Opinion and Comment,"  The cryptocurrency is attracting more and more attention because of the
+blockchain technology. Ethereum is gaining a significant popularity in
+blockchain community, mainly due to the fact that it is designed in a way that
+enables developers to write smart contracts and decentralized applications
+(Dapps). There are many kinds of cryptocurrency information on the social
+network. The risks and fraud problems behind it have pushed many countries
+including the United States, South Korea, and China to make warnings and set up
+corresponding regulations. However, the security of Ethereum smart contracts
+has not gained much attention. Through the Deep Learning approach, we propose a
+method of sentiment analysis for Ethereum's community comments. In this
+research, we first collected the users' cryptocurrency comments from the social
+network and then fed to our LSTM + CNN model for training. Then we made
+prediction through sentiment analysis. With our research result, we have
+demonstrated that both the precision and the recall of sentiment analysis can
+achieve 0.80+. More importantly, we deploy our sentiment analysis1 on
+RatingToken and Coin Master (mobile application of Cheetah Mobile Blockchain
+Security Center23). We can effectively provide detail information to resolve
+the risks of being fake and fraud problems.
+"
+26383,Phase Space Analysis of the Accelerating Multi-fluid Universe,"  We study in detail the phase space of a Friedmann-Robertson-Walker Universe
+filled with various cosmological fluids which may or may not interact. We use
+various expressions for the equation of state, and we analyze the physical
+significance of the resulting fixed points. In addition we discuss the effects
+of the stability or an instability of some fixed points. Moreover we study an
+interesting phenomenological scenario for which there is an oscillating
+interaction between the dark energy and dark matter fluid. As we demonstrate,
+in the context of the model we use, at early times the interaction is
+negligible and it starts to grow as the cosmic time approaches the late-time
+era. Also the cosmological dynamical system is split into two distinct
+dynamical systems which have two distinct de Sitter fixed points, with the
+early-time de Sitter point being unstable. This framework gives an explicit
+example of the unification of the early-time with late-time acceleration.
+Finally, we discuss in some detail the physical interpretation of the various
+models we present in this work.
+"
+26384,Basics of the magnetocaloric effect,"  This chapter reviews the basic physics and thermodynamics that govern
+magnetocaloric materials. The thermodynamics of magnetic materials is discussed
+by introducing relevant free energy terms together with their microscopic
+origin leading to a discussion of the sources of entropy that can change in an
+applied magnetic field. Such entropies account for measurable magnetocaloric
+effects, especially in the vicinity of magnetic phase transitions. Particular
+attention is devoted to first order magnetic transitions that involve the
+coupling of spin, lattice, electronic and anisotropic magneto-crystalline
+degrees of freedom. The problem of irreversibility and hysteresis, present in
+magnetocaloric materials with first order phase transitions is discussed in the
+context of out-of-equilibrium thermodynamics and hysteresis modeling.
+"
+26385,Inflated nodes and surface states in superconducting half-Heusler compounds,"  Two topics of high current interest in the field of unconventional
+superconductivity are non-centrosymmetric superconductors and multiband
+superconductivity. Half-Heusler superconductors such as YPtBi exemplify both.
+In this paper, we study bulk and surface states in nodal superconducting phases
+of the half-Heusler compounds, belonging to the $A_1$ ($s+p$-like) and $T_2$
+($k_zk_x+ik_zk_y$-like) irreducible representations of the point group. These
+two phases preserve and break time-reversal symmetry, respectively. For the
+$A_1$ case, we find that flat surface bands persist in the multiband system. In
+addition, the system has dispersive surface bands with zero-energy crossings
+forming Fermi arcs, which are protected by mirror symmetries. For the $T_2$
+case, there is an interesting coexistence of point and line nodes, known from
+the single-band case, with Bogoliubov Fermi surfaces (two-dimensional nodes).
+There are no flat-band surface states, as expected, but dispersive surface
+bands with Fermi arcs exist. If these arcs do not lie in high-symmetry planes,
+they are split by the antisymmetric spin-orbit coupling so that their number is
+doubled compared to the inversion-symmetric case.
+"
+26386,"Comparatives, Quantifiers, Proportions: A Multi-Task Model for the Learning of Quantities from Vision","  The present work investigates whether different quantification mechanisms
+(set comparison, vague quantification, and proportional estimation) can be
+jointly learned from visual scenes by a multi-task computational model. The
+motivation is that, in humans, these processes underlie the same cognitive,
+non-symbolic ability, which allows an automatic estimation and comparison of
+set magnitudes. We show that when information about lower-complexity tasks is
+available, the higher-level proportional task becomes more accurate than when
+performed in isolation. Moreover, the multi-task model is able to generalize to
+unseen combinations of target/non-target objects. Consistently with behavioral
+evidence showing the interference of absolute number in the proportional task,
+the multi-task model no longer works when asked to provide the number of target
+objects in the scene.
+"
+26387,Smoothed Combined Field Integral Equations for Exterior Helmholtz Problems,"  This paper presents smoothed combined field integral equations for the
+solution of Dirichlet and Neumann exterior Helmholtz problems. The integral
+equations introduced in this paper are smooth in the sense that they only
+involve continuously differentiable integrands in both Dirichlet and Neumann
+cases. These integral equations coincide with the well-known combined field
+equations and are therefore uniquely solvable for all frequencies. In
+particular, a novel regularization of the hypersingular operator is obtained,
+which, unlike regularizations based on Maue's integration-by-parts formula,
+does not give rise to involved Cauchy principal value integrals. The smoothed
+integral operators and layer potentials, on the other hand, can be numerically
+evaluated at target points that are arbitrarily close to the boundary without
+severely compromising their accuracy. A variety of numerical examples in two
+spatial dimensions that consider three different Nyström discretizations for
+smooth domains and domains with corners---one of which is based on direct
+application of the trapezoidal rule---demonstrates the effectiveness of the
+proposed integral approach. In certain aspects, this work extends to the
+uniquely solvable Dirichlet and Neumann combined field integral equations, the
+ideas presented in the recent contribution R. Soc. Open Sci. 2(140520), 2015.
+"
+26388,Non-parametric estimation of time varying AR(1)--processes with local stationarity and periodicity,"  Extending the ideas of [7], this paper aims at providing a kernel based
+non-parametric estimation of a new class of time varying AR(1) processes (Xt),
+with local stationarity and periodic features (with a known period T), inducing
+the definition Xt = at(t/nT)X t--1 + $\xi$t for t $\in$ N and with a t+T
+$\not\equiv$ at. Central limit theorems are established for kernel estima-tors
+as(u) reaching classical minimax rates and only requiring low order moment
+conditions of the white noise ($\xi$t)t up to the second order.
+"
+26389,Stabilization of the coupled pendula chain under parametric PT-symmetric driving force,"  We consider a chain of coupled pendula pairs, where each pendulum is
+connected to the nearest neighbors in the longitudinal and transverse
+directions. The common strings in each pair are modulated periodically by an
+external force. In the limit of small coupling and near the 1:2 parametric
+resonance, we derive a novel system of coupled PT-symmetric discrete nonlinear
+Schrodinger equation, which has Hamiltonian symmetry but has no gauge symmetry.
+By using the conserved energy, we find the parameter range for the linear and
+nonlinear stability of the zero equilibrium. Numerical experiments illustrate
+how destabilization of the zero equilibrium takes place when the stability
+constraints are not satisfied. The central pendulum excites nearest pendula and
+this process continues until a dynamical equilibrium is reached where each
+pendulum in the chain oscillates at a finite amplitude.
+"
+26390,"Topological tensor product of bimodules, complete Hopf Algebroids and convolution algebras","  Given a finitely generated and projective Lie-Rinehart algebra, we show that
+there is a continuous homomorphism of complete commutative Hopf algebroids
+between the completion of the finite dual of its universal enveloping Hopf
+algebroid and the associated convolution algebra. The topological Hopf
+algebroid structure of this convolution algebra is here clarified, by providing
+an explicit description of its topological antipode as well as of its other
+structure maps. Conditions under which that homomorphism becomes an
+homeomorphism are also discussed. These results, in particular, apply to the
+smooth global sections of any Lie algebroid over a smooth (connected) manifold
+and they lead a new formal groupoid scheme to enter into the picture. In the
+Appendix we develop the necessary machinery behind complete Hopf algebroid
+constructions, which involves also the topological tensor product of filtered
+bimodules over filtered rings.
+"
+26391,Covert Communication in Wireless Relay Networks,"  Covert communication aims to shield the very existence of wireless
+transmissions in order to guarantee a strong security in wireless networks. In
+this work, for the first time we examine the possibility and achievable
+performance of covert communication in one-way relay networks. Specifically,
+the relay opportunistically transmits its own information to the destination
+covertly on top of forwarding the source's message, while the source tries to
+detect this covert transmission to discover the illegitimate usage of the
+recourse (e.g., power, spectrum) allocated only for the purpose of forwarding
+source's information. The necessary condition that the relay can transmit
+covertly without being detected is identified and the source's detection limit
+is derived in terms of the false alarm and miss detection rates. Our analysis
+indicates that boosting the forwarding ability of the relay (e.g., increasing
+its maximum transmit power) also increases its capacity to perform the covert
+communication in terms of achieving a higher effective covert rate subject to
+some specific requirement on the source's detection performance.
+"
+26392,An almost complex Chern-Ricci flow,"  We consider the evolution of an almost Hermitian metric by the $(1,1)$ part
+of its Chern-Ricci form on almost complex manifolds. This is an evolution
+equation first studied by Chu and coincides with the Chern-Ricci flow if the
+complex structure is integrable and with the Kähler-Ricci flow if moreover
+the initial metric is Kähler. We find the maximal existence time for the flow
+in term of the initial data and also give some convergence results. As an
+example, we study this flow on the (locally) homogeneous manifolds in more
+detail.
+"
+26393,Is a Data-Driven Approach still Better than Random Choice with Naive Bayes classifiers?,"  We study the performance of data-driven, a priori and random approaches to
+label space partitioning for multi-label classification with a Gaussian Naive
+Bayes classifier. Experiments were performed on 12 benchmark data sets and
+evaluated on 5 established measures of classification quality: micro and macro
+averaged F1 score, Subset Accuracy and Hamming loss. Data-driven methods are
+significantly better than an average run of the random baseline. In case of F1
+scores and Subset Accuracy - data driven approaches were more likely to perform
+better than random approaches than otherwise in the worst case. There always
+exists a method that performs better than a priori methods in the worst case.
+The advantage of data-driven methods against a priori methods with a weak
+classifier is lesser than when tree classifiers are used.
+"
+26394,Inflight Calibration of the Hitomi Soft X-ray Spectrometer (2) Point Spread Function,"  We present results of inflight calibration of the point spread function (PSF)
+of the Soft X-ray Telescope (SXT-S) that focuses X-ray onto the pixel array of
+the Soft X-ray Spectrometer system (SXS). We make a full array image of a
+point-like source by extracting a pulsed component of the Crab nebula emission.
+Within the limited statistics afforded by an exposure time of only 6.9~ksec and
+the limited knowledge of the systematic uncetainties, we find that the
+raytracing model of 1'.2 half-power-diameter (HPD) is consistent with an image
+of the observed event distributions across pixels. The ratio between the Crab
+pulsar image and the raytracing shows scatter from pixel to pixel that is 40%
+or less in all except one pixel. The pixel-to-pixel ratio has a spread of 20%,
+on average, for the 15 edge pixels, with an averaged statistical error of 17%
+(1 sigma). In the central 16 pixels, the corresponding ratio is 15% with an
+error of 6%.
+"
+26395,Observational Artifacts of NuSTAR: Ghost Rays and Stray Light,"  The Nuclear Spectroscopic Telescope Array (NuSTAR), launched in June 2012,
+flies two conical approximation Wolter-I mirrors at the end of a 10.15m mast.
+The optics are coated with multilayers of Pt/C and W/Si that operate from 3--80
+keV. Since the optical path is not shrouded, aperture stops are used to limit
+the field of view from background and sources outside the field of view.
+However, there is still a sliver of sky (~1.0--4.0 degrees) where photons may
+bypass the optics altogether and fall directly on the detector array. We term
+these photons Stray-light. Additionally, there are also photons that do not
+undergo the focused double reflections in the optics and we term these Ghost
+Rays. We present detailed analysis and characterization of these two components
+and discuss how they impact observations. Finally, we discuss how they could
+have been prevented and should be in future observatories.
+"
+26396,Improved endpoint bounds for the lacunary spherical maximal operator,"  We prove new endpoint bounds for the lacunary spherical maximal operator and
+as a consequence obtain almost everywhere pointwise convergence of lacunary
+spherical means for functions locally in $L\log\log\log L(\log\log\log\log
+L)^{1+\epsilon}$ for any $\epsilon>0$.
+"
+26397,Long-term area coverage and radio relay positioning using swarms of UAVs,"  Unmanned Aerial Vehicles (UAVs) are becoming increasingly useful for tasks
+which require the acquisition of data over large areas. The coverage problem,
+i.e., the problem of periodically visiting all subregions of an area at a
+desired frequency, is especially interesting because of its practical
+applications, both in industry and long-term monitoring of areas hit by a
+natural disaster. We focus here on the latter scenario, and take into
+consideration its peculiar characteristic, i.e. the a coverage system should be
+resilient to a changing environment and not be dependent on pre-existing
+infrastructures for communication. To this purpose, we designed a novel
+algorithm for online area coverage and simultaneous signal relay that allows a
+UAV to cover an area freely, while a variable number of other UAVs provide a
+stable communication with the base and support in the coverage process at the
+same time. Finally, a test architecture based on the algorithm has been
+developed and tests have been performed. By comparison with a simple relay
+chain system, our approach employs up to 64% less time to reach a certain goal
+of coverage iterations over the map with only 17% worse average communication
+cost and no impact on the worst case communication cost.
+"
+26398,Micromagnetic simulations of magnetoelastic spin wave excitation in scaled magnetic waveguides,"  We study the excitation of spin waves in scaled magnetic waveguides using the
+magnetoelastic effect. In uniformly magnetized systems, normal strains parallel
+or perpendicular to the magnetization direction do not lead to spin wave
+excitation since the magnetoelastic torque is zero. Using micromagnetic
+simulations, we show that the nonuniformity of the magnetization in submicron
+waveguides due to the effect of the demagnetizing field leads to the excitation
+of spin waves for oscillating normal strains both parallel and perpendicular to
+the magnetization. The excitation by biaxial normal in-plane strain was found
+to be much more efficient than by uniaxial normal out-of-plane strain. For
+narrow waveguides with widths of 200\,nm, the excitation efficiency of biaxial
+normal in-plane strain was comparable to that of shear strain.
+"
+26399,Statistics with Set-Valued Functions: Applications to Inverse Approximate Optimization,"  Much of statistics relies upon four key elements: a law of large numbers, a
+calculus to operationalize stochastic convergence, a central limit theorem, and
+a framework for constructing local approximations. These elements are
+well-understood for objects in a vector space (e.g., points or functions);
+however, much statistical theory does not directly translate to sets because
+they do not form a vector space. Building on probability theory for random
+sets, this paper uses variational analysis to develop operational tools for
+statistics with set-valued functions. These tools are first applied to
+nonparametric estimation (kernel regression of set-valued functions). The
+second application is to the problem of inverse approximate optimization, in
+which approximate solutions (corrupted by noise) to an optimization problem are
+observed and then used to estimate the amount of suboptimality of the solutions
+and the parameters of the optimization problem that generated the solutions. We
+show that previous approaches to this problem are statistically inconsistent
+when the data is corrupted by noise, whereas our approach is consistent under
+mild conditions.
+"
+26400,The Lie Representation of the Complex Unit Sphere,"  We present the derivation of the 6-dimensional Eulerian Lie group of the form
+SO(3,C). We describe our derivation process, which involves the creation of a
+finite group by using permutation matrices, and the exponentiation of the
+adjoint form of the subset representing the generators of the finite group. We
+take clues from the 2-dimensional complex rotation matrix to present, what we
+believe, is a true representation of the Lie group for the six-dimensional
+complex unit sphere and proceed to study its dynamics. With this approach, we
+can proceed to present this SO(3,C) group and derive its unitary counterpart
+that is U(3). The following findings can prove useful in mathematical physics,
+complex analysis and applications in deriving higher dimensional forms of
+similar division algebras.
+"
+26401,"Cauchy Tetrahedron Argument and the Proofs of the Existence of Stress Tensor, a Comprehensive Review, Challenges, and Improvements","  In 1822, Cauchy presented the idea of traction vector that contains both the
+normal and tangential components of the internal surface forces per unit area
+and gave the tetrahedron argument to prove the existence of stress tensor.
+These great achievements form the main part of the foundation of continuum
+mechanics. For about two centuries, some versions of tetrahedron argument and a
+few other proofs of the existence of stress tensor are presented in every text
+on continuum mechanics, fluid mechanics, and the relevant subjects. In this
+article, we show the birth, importance, and location of these Cauchy's
+achievements, then by presenting the formal tetrahedron argument in detail, for
+the first time, we extract some fundamental challenges. These conceptual
+challenges are related to the result of applying the conservation of linear
+momentum to any mass element, the order of magnitude of the surface and volume
+terms, the definition of traction vectors on the surfaces that pass through the
+same point, the approximate processes in the derivation of stress tensor, and
+some others. In a comprehensive review, we present the different tetrahedron
+arguments and the proofs of the existence of stress tensor, discuss the
+challenges in each one, and classify them in two general approaches. In the
+first approach that is followed in most texts, the traction vectors do not
+exactly define on the surfaces that pass through the same point, so most of the
+challenges hold. But in the second approach, the traction vectors are defined
+on the surfaces that pass exactly through the same point, therefore some of the
+relevant challenges are removed. We also study the improved works of Hamel and
+Backus, and indicate that the original work of Backus removes most of the
+challenges. This article shows that the foundation of continuum mechanics is
+not a finished subject and there are still some fundamental challenges.
+"
+26402,Clustering Small Samples with Quality Guarantees: Adaptivity with One2all pps,"  Clustering of data points is a fundamental tool in data analysis. We consider
+points $X$ in a relaxed metric space, where the triangle inequality holds
+within a constant factor. The {\em cost} of clustering $X$ by $Q$ is
+$V(Q)=\sum_{x\in X} d_{xQ}$. Two basic tasks, parametrized by $k \geq 1$, are
+{\em cost estimation}, which returns (approximate) $V(Q)$ for queries $Q$ such
+that $|Q|=k$ and {\em clustering}, which returns an (approximate) minimizer of
+$V(Q)$ of size $|Q|=k$. With very large data sets $X$, we seek efficient
+constructions of small samples that act as surrogates to the full data for
+performing these tasks. Existing constructions that provide quality guarantees
+are either worst-case, and unable to benefit from structure of real data sets,
+or make explicit strong assumptions on the structure. We show here how to avoid
+both these pitfalls using adaptive designs.
+At the core of our design is the {\em one2all} construction of
+multi-objective probability-proportional-to-size (pps) samples: Given a set $M$
+of centroids and $\alpha \geq 1$, one2all efficiently assigns probabilities to
+points so that the clustering cost of {\em each} $Q$ with cost $V(Q) \geq
+V(M)/\alpha$ can be estimated well from a sample of size $O(\alpha
+|M|\epsilon^{-2})$. For cost queries, we can obtain worst-case sample size
+$O(k\epsilon^{-2})$ by applying one2all to a bicriteria approximation $M$, but
+we adaptively balance $|M|$ and $\alpha$ to further reduce sample size. For
+clustering, we design an adaptive wrapper that applies a base clustering
+algorithm to a sample $S$. Our wrapper uses the smallest sample that provides
+statistical guarantees that the quality of the clustering on the sample carries
+over to the full data set. We demonstrate experimentally the huge gains of
+using our adaptive instead of worst-case methods.
+"
+26403,Explicit Belyi maps over Q having almost simple primitive monodromy groups,"  We present all Belyi maps P^1(C) -> P^1(C) having almost simple primitive
+monodromy groups (not isomorphic to A_n or S_n) containing rigid and rational
+generating triples of degree between 50 and 250. This also leads to new
+polynomials having almost simple Galois groups over Q(t).
+"
+26404,"Nonlocal symmetry, Darboux transformation and soliton-cnoidal wave interaction solution for the shallow water wave equation","  In classical shallow water wave (SWW) theory, there exist two integrable
+one-dimensional SWW equation [Hirota-Satsuma (HS) type and
+Ablowitz-Kaup-Newell-Segur (AKNS) type] in the Boussinesq approximation. In
+this paper, we mainly focus on the integrable SWW equation of AKNS type. The
+nonlocal symmetry in form of square spectral function is derived starting from
+its Lax pair. Infinitely many nonlocal symmetries are presented by introducing
+the arbitrary spectrum parameter. These nonlocal symmetries can be localized
+and the SWW equation is extended to enlarged system with auxiliary dependent
+variables. Then Darboux transformation for the prolonged system is found by
+solving the initial value problem. Similarity reductions related to the
+nonlocal symmetry and explicit group invariant solutions are obtained. It is
+shown that the soliton-cnoidal wave interaction solution, which represents
+soliton lying on a cnoidal periodic wave background, can be obtained
+analytically. Interesting characteristics of the interaction solution between
+soliton and cnoidal periodic wave are displayed graphically.
+"
+26405,On time scales and quasi-stationary distributions for multitype birth-and-death processes,"  We consider a class of birth-and-death processes describing a population made
+of $d$ sub-populations of different types which interact with one another. The
+state space is $\mathbb{Z}_+^d$ (unbounded). We assume that the population goes
+almost surely to extinction, so that the unique stationary distribution is the
+Dirac measure at the origin. These processes are parametrized by a scaling
+parameter $K$ which can be thought as the order of magnitude of the total size
+of the population at time $0$. For any fixed finite time span, it is well-known
+that such processes, when renormalized by $K$, are close, in the limit
+$K\to+\infty$, to the solutions of a certain differential equation in
+$\mathbb{R}_+^d$ whose vector field is determined by the birth and death rates.
+We consider the case where there is a unique attractive fixed point (off the
+boundary of the positive orthant) for the vector field (while the origin is
+repulsive). What is expected is that, for $K$ large, the process will stay in
+the vicinity of the fixed point for a very long time before being absorbed at
+the origin. To precisely describe this behavior, we prove the existence of a
+quasi-stationary distribution (qsd). In fact, we establish a bound for the
+total variation distance between the process conditioned to non-extinction
+before time $t$ and the qsd. This bound is exponentially small in $t$, for
+$t\gg \log K$. As a by-product, we obtain an estimate for the mean time to
+extinction in the qsd. We also quantify how close is the law of the process
+(not conditioned to non-extinction) either to the Dirac measure at the origin
+or to the qsd, for times much larger than $\log K$ and much smaller than the
+mean time to extinction, which is exponentially large as a function of $K$. Let
+us stress that we are interested in what happens for finite $K$. We obtain
+results much beyond what large deviation techniques could provide.
+"
+26406,Exploring the Evolution of Node Neighborhoods in Dynamic Networks,"  Dynamic Networks are a popular way of modeling and studying the behavior of
+evolving systems. However, their analysis constitutes a relatively recent
+subfield of Network Science, and the number of available tools is consequently
+much smaller than for static networks. In this work, we propose a method
+specifically designed to take advantage of the longitudinal nature of dynamic
+networks. It characterizes each individual node by studying the evolution of
+its direct neighborhood, based on the assumption that the way this neighborhood
+changes reflects the role and position of the node in the whole network. For
+this purpose, we define the concept of \textit{neighborhood event}, which
+corresponds to the various transformations such groups of nodes can undergo,
+and describe an algorithm for detecting such events. We demonstrate the
+interest of our method on three real-world networks: DBLP, LastFM and Enron. We
+apply frequent pattern mining to extract meaningful information from temporal
+sequences of neighborhood events. This results in the identification of
+behavioral trends emerging in the whole network, as well as the individual
+characterization of specific nodes. We also perform a cluster analysis, which
+reveals that, in all three networks, one can distinguish two types of nodes
+exhibiting different behaviors: a very small group of active nodes, whose
+neighborhood undergo diverse and frequent events, and a very large group of
+stable nodes.
+"
+26407,Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models,"  Supervisory signals have the potential to make low-dimensional data
+representations, like those learned by mixture and topic models, more
+interpretable and useful. We propose a framework for training latent variable
+models that explicitly balances two goals: recovery of faithful generative
+explanations of high-dimensional data, and accurate prediction of associated
+semantic labels. Existing approaches fail to achieve these goals due to an
+incomplete treatment of a fundamental asymmetry: the intended application is
+always predicting labels from data, not data from labels. Our
+prediction-constrained objective for training generative models coherently
+integrates loss-based supervisory signals while enabling effective
+semi-supervised learning from partially labeled data. We derive learning
+algorithms for semi-supervised mixture and topic models using stochastic
+gradient descent with automatic differentiation. We demonstrate improved
+prediction quality compared to several previous supervised topic models,
+achieving predictions competitive with high-dimensional logistic regression on
+text sentiment analysis and electronic health records tasks while
+simultaneously learning interpretable topics.
+"
+26408,The pragmatics of clone detection and elimination,"  The occurrence of similar code, or `code clones', can make program code
+difficult to read, modify and maintain. This paper describes industrial case
+studies of clone detection and elimination using a refactoring and clone
+detection tool. We discuss how the studies have informed the design of the
+tool; more importantly, we use the studies to illustrate the complex set of
+decisions that have to be taken when performing clone elimination in practice.
+The case studies were performed in collaboration with engineers from Ericsson
+AB, and used the refactoring tool Wrangler for Erlang. However, the conclusions
+we draw are largely language-independent, and set out the pragmatics of clone
+detection and elimination in real-world projects as well as design principles
+for clone detection decision-support tools.
+"
+26409,Understanding Information Transmission in Complex Networks,"  Information Theory concepts and methodologies conform the background of how
+communication systems are studied and understood. They are mainly focused on
+the source-channel-receiver problem and on the asymptotic limits of accuracy
+and communication rates, which are the classical problems studied by Shannon.
+However, the impact of Information Theory on networks (acting as the channel)
+is just starting. Here, we present an approach to understand how information
+flows in any connected complex network. Our approach is based on defining
+linear conservative flows that travel through the network from source to
+receiver. This framework allows us to have an analytical description of the
+problem and also linking the topological invariants of the network, such as the
+node degree, with the information flow. In particular, our approach is able to
+deal with information transmission in modular networks (networks containing
+community structures) or multiplex networks (networks with multiple layers),
+which are nowadays of paramount importance.
+"
+26410,The Dispersion Bias,"  Estimation error has plagued quantitative finance since Harry Markowitz
+launched modern portfolio theory in 1952. Using random matrix theory, we
+characterize a source of bias in the sample eigenvectors of financial
+covariance matrices. Unchecked, the bias distorts weights of minimum variance
+portfolios and leads to risk forecasts that are severely biased downward. To
+address these issues, we develop an eigenvector bias correction. Our approach
+is distinct from the regularization and eigenvalue shrinkage methods found in
+the literature. We provide theoretical guarantees on the improvement our
+correction provides as well as estimation methods for computing the optimal
+correction from data.
+"
+26411,Enabling Smart Data: Noise filtering in Big Data classification,"  In any knowledge discovery process the value of extracted knowledge is
+directly related to the quality of the data used. Big Data problems, generated
+by massive growth in the scale of data observed in recent years, also follow
+the same dictate. A common problem affecting data quality is the presence of
+noise, particularly in classification problems, where label noise refers to the
+incorrect labeling of training instances, and is known to be a very disruptive
+feature of data. However, in this Big Data era, the massive growth in the scale
+of the data poses a challenge to traditional proposals created to tackle noise,
+as they have difficulties coping with such a large amount of data. New
+algorithms need to be proposed to treat the noise in Big Data problems,
+providing high quality and clean data, also known as Smart Data. In this paper,
+two Big Data preprocessing approaches to remove noisy examples are proposed: an
+homogeneous ensemble and an heterogeneous ensemble filter, with special
+emphasis in their scalability and performance traits. The obtained results show
+that these proposals enable the practitioner to efficiently obtain a Smart
+Dataset from any Big Data classification problem.
+"
+26412,"Maximum vanishing subspace problem, CAT(0)-space relaxation, and block-triangularization of partitioned matrix","  In this paper, we address the following algebraic generalization of the
+bipartite stable set problem. We are given a block-structured matrix
+(partitioned matrix) $A = (A_{\alpha \beta})$, where $A_{\alpha \beta}$ is an
+$m_{\alpha}$ by $n_{\beta}$ matrix over field ${\bf F}$ for
+$\alpha=1,2,\ldots,\mu$ and $\beta = 1,2,\ldots,\nu$. The maximum vanishing
+subspace problem (MVSP) is to maximize $\sum_{\alpha} \dim X_{\alpha} +
+\sum_{\beta} \dim Y_{\beta}$ over vector subspaces $X_{\alpha} \subseteq {\bf
+F}^{m_{\alpha}}$ for $\alpha=1,2,\ldots,\mu$ and $Y_{\beta} \subseteq {\bf
+F}^{n_{\beta}}$ for $\beta = 1,2,\ldots,\nu$ such that each $A_{\alpha \beta}$
+vanishes on $X_{\alpha} \times Y_{\beta}$ when $A_{\alpha \beta}$ is viewed as
+a bilinear form ${\bf F}^{m_{\alpha}} \times {\bf F}^{n_{\beta}} \to {\bf F}$.
+This problem arises from a study of a canonical block-triangular form of $A$ by
+Ito, Iwata, and Murota~(1994), and is closely related to the noncommutative
+rank of a matrix with indeterminates. We prove that a weighted version (WMVP)
+of MVSP can be solved in psuedo polynomial time, provided arithmetic operations
+on ${\bf F}$ can be done in constant time. Our proof is a novel combination of
+submodular optimization on modular lattice and convex optimization on
+CAT(0)-space. We present implications of this result on block-triangularization
+of partitioned matrix.
+"
+26413,SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation,"  Lifelong learning aims to develop machine learning systems that can learn new
+tasks while preserving the performance on previous learned tasks. In this paper
+we present a method to overcome catastrophic forgetting on convolutional neural
+networks, that learns new tasks and preserves the performance on old tasks
+without accessing the data of the original model, by selective network
+augmentation. The experiment results showed that SeNA-CNN, in some scenarios,
+outperforms the state-of-art Learning without Forgetting algorithm. Results
+also showed that in some situations it is better to use SeNA-CNN instead of
+training a neural network using isolated learning.
+"
+26414,Increasing Peer Pressure on any Connected Graph Leads to Consensus,"  In this paper, we study a model of opinion dynamics in a social network in
+the presence increasing interpersonal influence, i.e., increasing peer
+pressure. Each agent in the social network has a distinct social stress
+function given by a weighted sum of internal and external behavioral pressures.
+We assume a weighted average update rule and prove conditions under which a
+connected group of agents converge to a fixed opinion distribution, and under
+which conditions the group reaches consensus. We show that the update rule is a
+gradient descent and explain its transient and asymptotic convergence
+properties. Through simulation, we study the rate of convergence on a
+scale-free network and then validate the assumption of increasing peer pressure
+in a simple empirical model.
+"
+26415,Efficient generation of series expansions for $\pm J$ Ising spin-glasses in a classical or a quantum (transverse) field,"  We discuss generation of series expansions for Ising spin-glasses with a
+symmetric $\pm J$ (i.e. bimodal) distribution on d-dimensional hypercubic
+lattices using linked-cluster methods. Simplifications for the bimodal
+distribution allow us to go to higher order than for a general distribution. We
+discuss two types of problem, one classical and one quantum. The classical
+problem is that of the Ising spin glass in a longitudinal magnetic field, $h$,
+for which we obtain high temperature series expansions in variables
+$\tanh(J/T)$ and $\tanh(h/T)$. The quantum problem is a $T=0$ study of the
+Ising spin glass in a transverse magnetic field $h_T$ for which we obtain a
+perturbation theory in powers of $J/h_T$. These methods require (i) enumeration
+and counting of \textit{all} connected clusters that can be embedded in the
+lattice up to some order $n$, and (ii) an evaluation of the contribution of
+each cluster for the quantity being calculated, known as the weight. We discuss
+a general method that takes the much smaller list (and count) of all no
+free-end (NFE) clusters on a lattice up to some order $n$, and automatically
+generates all other clusters and their counts up to the same order. The weights
+for finite clusters in both cases have a simple graphical interpretation that
+allows us to proceed efficiently for a general configuration of the $\pm J$
+bonds, and at the end perform suitable disorder averaging. The order of our
+computations is limited by the weight calculations for the high-temperature
+expansions of the classical model, while they are limited by graph counting for
+the $T=0$ quantum system. Details of the calculational methods are presented.
+"
+26416,LogitBoost autoregressive networks,"  Multivariate binary distributions can be decomposed into products of
+univariate conditional distributions. Recently popular approaches have modeled
+these conditionals through neural networks with sophisticated weight-sharing
+structures. It is shown that state-of-the-art performance on several standard
+benchmark datasets can actually be achieved by training separate probability
+estimators for each dimension. In that case, model training can be trivially
+parallelized over data dimensions. On the other hand, complexity control has to
+be performed for each learned conditional distribution. Three possible methods
+are considered and experimentally compared. The estimator that is employed for
+each conditional is LogitBoost. Similarities and differences between the
+proposed approach and autoregressive models based on neural networks are
+discussed in detail.
+"
+26417,Ranking Functions for Vector Addition Systems,"  Vector addition systems are an important model in theoretical computer
+science and have been used for the analysis of systems in a variety of areas.
+Termination is a crucial property of vector addition systems and has received
+considerable interest in the literature. In this paper we give a complete
+method for the construction of ranking functions for vector addition systems
+with states. The interest in ranking functions is motivated by the fact that
+ranking functions provide valuable additional information in case of
+termination: They provide an explanation for the progress of the vector
+addition system, which can be reported to the user of a verification tool, and
+can be used as certificates for termination. Moreover, we show how ranking
+functions can be used for the computational complexity analysis of vector
+addition systems (here complexity refers to the number of steps the vector
+addition system under analysis can take in terms of the given initial vector).
+"
+26418,On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow,"  Abundant data is the key to successful machine learning. However, supervised
+learning requires annotated data that are often hard to obtain. In a
+classification task with limited resources, Active Learning (AL) promises to
+guide annotators to examples that bring the most value for a classifier. AL can
+be successfully combined with self-training, i.e., extending a training set
+with the unlabelled examples for which a classifier is the most certain. We
+report our experiences on using AL in a systematic manner to train an SVM
+classifier for Stack Overflow posts discussing performance of software
+components. We show that the training examples deemed as the most valuable to
+the classifier are also the most difficult for humans to annotate. Despite
+carefully evolved annotation criteria, we report low inter-rater agreement, but
+we also propose mitigation strategies. Finally, based on one annotator's work,
+we show that self-training can improve the classification accuracy. We conclude
+the paper by discussing implication for future text miners aspiring to use AL
+and self-training.
+"
+26419,Interior C2 regularity of convex solutions to prescribing scalar curvature equations,"  We establish interior $C^2$ estimates for convex solutions of scalar
+curvature equation and $\sigma_2$-Hessian equation. We also prove interior
+curvature estimate for isometrically immersed hypersurfaces $(M^n,g)\subset
+\mathbb R^{n+1}$ with positive scalar curvature. These estimates are
+consequences of an interior estimate for these equations obtained under a
+weakened condition.
+"
+26420,On the characterization of vortex configurations in the steady rotating Bose--Einstein condensates,"  Motivated by experiments in atomic Bose-Einstein condensates (BECs), we
+compare predictions of a system of ordinary differential equations (ODE) for
+dynamics of one and two individual vortices in the rotating BECs with those of
+the partial differential equation (PDE). In particular, we characterize
+orbitally stable vortex configurations in a symmetric harmonic trap due to a
+cubic repulsive interaction and the steady rotation. The ODE system is analyzed
+in details and the PDE model is approximated numerically. Good agreement
+between the two models is established in the semi-classical (Thomas-Fermi)
+limit that corresponds to the BECs at the large chemical potentials.
+"
+26421,No evidence for the evolution of mass density power-law index $γ$ from strong gravitational lensing observation,"  In this paper, we consider the singular isothermal sphere lensing model that
+has a spherically symmetric power-law mass distribution $\rho_{tot}(r)\sim
+r^{-\gamma}$. We investigate whether the mass density power-law index $\gamma$
+is cosmologically evolutionary by using the strong gravitational lensing (SGL)
+observation, in combination with other cosmological observations. We also check
+whether the constraint result of $\gamma$ is affected by the cosmological
+model, by considering several simple dynamical dark energy models. We find that
+the constraint on $\gamma$ is mainly decided by the SGL observation and
+independent of the cosmological model, and we find no evidence for the
+evolution of $\gamma$ from the SGL observation.
+"
+26422,Diversity-Promoting Bayesian Learning of Latent Variable Models,"  To address three important issues involved in latent variable models (LVMs),
+including capturing infrequent patterns, achieving small-sized but expressive
+models and alleviating overfitting, several studies have been devoted to
+""diversifying"" LVMs, which aim at encouraging the components in LVMs to be
+diverse. Most existing studies fall into a frequentist-style regularization
+framework, where the components are learned via point estimation. In this
+paper, we investigate how to ""diversify"" LVMs in the paradigm of Bayesian
+learning. We propose two approaches that have complementary advantages. One is
+to define a diversity-promoting mutual angular prior which assigns larger
+density to components with larger mutual angles and use this prior to affect
+the posterior via Bayes' rule. We develop two efficient approximate posterior
+inference algorithms based on variational inference and MCMC sampling. The
+other approach is to impose diversity-promoting regularization directly over
+the post-data distribution of components. We also extend our approach to
+""diversify"" Bayesian nonparametric models where the number of components is
+infinite. A sampling algorithm based on slice sampling and Hamiltonian Monte
+Carlo is developed. We apply these methods to ""diversify"" Bayesian mixture of
+experts model and infinite latent feature model. Experiments on various
+datasets demonstrate the effectiveness and efficiency of our methods.
+"
+26423,Optimizing Human Learning,"  Spaced repetition is a technique for efficient memorization which uses
+repeated, spaced review of content to improve long-term retention. Can we find
+the optimal reviewing schedule to maximize the benefits of spaced repetition?
+In this paper, we introduce a novel, flexible representation of spaced
+repetition using the framework of marked temporal point processes and then
+address the above question as an optimal control problem for stochastic
+differential equations with jumps. For two well-known human memory models, we
+show that the optimal reviewing schedule is given by the recall probability of
+the content to be learned. As a result, we can then develop a simple, scalable
+online algorithm, Memorize, to sample the optimal reviewing times. Experiments
+on both synthetic and real data gathered from Duolingo, a popular
+language-learning online platform, show that our algorithm may be able to help
+learners memorize more effectively than alternatives.
+"
+26424,Equivalences between three presentations of orthogonal and symplectic Yangians,"  We prove the equivalence of two presentations of the Yangian
+$Y(\mathfrak{g})$ of a simple Lie algebra $\mathfrak{g}$ and we also show the
+equivalence with a third presentation when $\mathfrak{g}$ is either an
+orthogonal or a symplectic Lie algebra. As an application, we obtain an
+explicit correspondence between two versions of the classification theorem of
+finite-dimensional irreducible modules for orthogonal and symplectic Yangians.
+"
+26425,Fluorescence spectrum and thermalization in a driven coupled cavity array,"  We calculate the fluorescence spectra of a driven lattice of coupled
+cavities. To do this, we extend methods of evaluating two-time correlations in
+infinite lattices to open quantum systems; this allows access to momentum
+resolved fluorescence spectrum. We illustrate this for a driven-dissipative
+transverse field anisotropic XY model. By studying the fluctuation dissipation
+theorem, we find the emergence of a quasi-thermalized steady state with a
+temperature dependent on system parameters; for blue detuned driving, we show
+this effective temperature is negative. In the low excitation density limit, we
+compare these numerical results to analytical spin-wave theory, providing an
+understanding of the form of the distribution function and the origin of
+quasi-thermalization.
+"
+26426,Plant species accumulation curves are determined by evenness and spatial aggregation in drylands worldwide,"  Species accumulation curves (SAC), i.e. the relationship between species
+richness and the number of sampling units in a given community, can be used to
+describe diversity patterns while accounting for the well-known
+scale-dependence of species richness. Despite their value, the functional form
+and the parameters of SAC, as well as their determinants, have barely been
+investigated in plant communities, particularly in drylands. We characterized
+the SAC of perennial plant communities from 233 dryland ecosystems from six
+continents by comparing the fit of major functions (power-law, logarithmic and
+Michaelis-Menten). We tested the theoretical prediction that the effects of
+aridity and soil pH on SAC are mediated by vegetation attributes such as
+evenness, cover, and spatial aggregation. We found that the logarithmic
+relationship was the most common functional form, followed by Michaelis-Menten
+and power-law. Functional form was mainly determined by evenness while the SAC
+parameters (intercept and slope) were largely determined by spatial
+aggregation. In addition, aridity decreased small scale richness (intercept of
+SAC) but did not affect accumulation rate (slope of the SAC). Our results
+highlight the role that attributes such as spatial aggregation and evenness
+play as main mediators of the SAC of vegetation in drylands, the Earth's
+largest biome.
+"
+26427,Complementary results for the spectral analysis of matrices in Galerkin methods with GB-splines,"  We collect some new results relative to the study of the spectral analysis of
+matrices in Galerkin methods based on generalized B-splines with high
+smoothness.
+"
+26428,Empirical analysis and agent-based modeling of Lithuanian parliamentary elections,"  In this contribution we analyze a parties' vote share distribution across the
+polling stations during the Lithuanian parliamentary elections of 1992, 2008
+and 2012. We find that the distribution is rather well fitted by the Beta
+distribution. To reproduce this empirical observation we propose a simple
+multi-state agent-based model of the voting behavior. In the proposed model
+agents change the party they vote for either idiosyncratically or due to a
+linear recruitment mechanism. We use the model to reproduce the vote share
+distribution observed during the election of 1992. We discuss model extensions
+needed to reproduce the vote share distribution observed during the other
+elections.
+"
+26429,Weihrauch Complexity in Computable Analysis,"  We provide a self-contained introduction into Weihrauch complexity and its
+applications to computable analysis. This includes a survey on some
+classification results and a discussion of the relation to other approaches.
+"
+26430,Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images,"  Images convey a broad spectrum of personal information. If such images are
+shared on social media platforms, this personal information is leaked which
+conflicts with the privacy of depicted persons. Therefore, we aim for automated
+approaches to redact such private information and thereby protect privacy of
+the individual. By conducting a user study we find that obfuscating the image
+regions related to the private information leads to privacy while retaining
+utility of the images. Moreover, by varying the size of the regions different
+privacy-utility trade-offs can be achieved. Our findings argue for a ""redaction
+by segmentation"" paradigm. Hence, we propose the first sizable dataset of
+private images ""in the wild"" annotated with pixel and instance level labels
+across a broad range of privacy classes. We present the first model for
+automatic redaction of diverse private information.
+"
+26431,Mid-infrared frequency comb generation with silicon nitride nano-photonic waveguides,"  Mid-infrared optical frequency combs are of significant interest for
+molecular spectroscopy due to the large absorption of molecular vibrational
+modes on one hand, and the ability to implement superior comb-based
+spectroscopic modalities with increased speed, sensitivity and precision on the
+other hand. Substantial advances in mid-infrared frequency comb generation have
+been made in recent years based on nonlinear frequency conversion,
+microresonator Kerr frequency combs, quantum cascade lasers and mode locking
+regimes. Here we demonstrate a simple, yet effective method for the direct
+generation of mid-infrared optical frequency combs in the region from
+${2.5-4~\mu{\rm m}}$, i.e. ${2500-4000~{\rm cm}^{-1}}$ covering a large
+fraction of the functional group region, directly from a conventional and
+compact erbium-fiber-based femtosecond laser in the telecommunication band
+(i.e. ${1.55~\mu{\rm m}}$). The wavelength conversion is based on dispersive
+wave generation within the supercontinuum process in large-cross-section and
+dispersion-engineered silicon nitride (${\rm Si_3N_4}$) waveguides. The
+long-wavelength dispersive wave, with its position lithographically determined,
+performs as a mid-infrared frequency comb, whose coherence is demonstrated via
+optical heterodyne measurements. Such a simple and versatile approach to
+mid-infrared frequency comb generation is suitable for spectroscopic
+applications in the first mid-infrared atmospheric window. Moreover, the
+compactness and simplicity of the approach have the potential to realize
+compact dual-comb spectrometers. The generated combs have a fine teeth-spacing,
+making them also suitable for gas phase analysis.
+"
+26432,Non-existence of Higgs fields on Calabi-Yau Manifolds,"  In this article, we study the Higgs $G$-bundles $(E,\theta)$ on a compact
+Calabi-Yau manifolds $X$. Our main result is that there is non-existence Higgs
+fields $\theta$ on a semistable Higgs $G$-bundle over a compact connected
+Calabi-Yau surface. The vanish theorem can extends to higher dimensional
+Calabi-Yau compact $n$-folds, but we should addition the principal $E$ with
+vanishing Chern-classes. In particular, the $G$-bundle $E$ must be a semistable
+bundle in both cases.
+"
+26433,Opening Scholarly Communication in Social Sciences by Connecting Collaborative Authoring to Peer Review,"  The objective of the OSCOSS research project on ""Opening Scholarly
+Communication in the Social Sciences"" is to build a coherent collaboration
+environment that facilitates scholarly communication workflows of social
+scientists in the roles of authors, reviewers, editors and readers. This paper
+presents the implementation of the core of this environment: the integration of
+the Fidus Writer academic word processor with the Open Journal Systems (OJS)
+submission and review management system.
+"
+26434,The control structure of the nematode Caenorhabditis elegans: neuro-sensory integration and propioceptive feedback,"  We develop a biophysically realistic model of the nematode C. elegans that
+includes: (i) its muscle structure and activation, (ii) key connectomic
+activation circuitry, and (iii) a weighted and time-dynamic proprioception. In
+combination, we show that these model components can reproduce the complex
+waveforms exhibited in C. elegans locomotive behaviors, chiefly omega turns.
+This is achieved via weighted, time-dependent suppression of the proprioceptive
+signal. Though speculative, such dynamics are biologically plausible due to the
+presence of neuromodulators which have recently been experimentally implicated
+in the escape response, which includes an omega turn. This is the first
+integrated neuromechanical model to reveal a mechanism capable of generating
+the complex waveforms observed in the behavior of C. elegans, thus contributing
+to a mathematical framework for understanding how control decisions can be
+executed at the connectome level in order to produce the full repertoire of
+observed behaviors.
+"
+26435,Theory of the Josephson Junction Laser,"  We develop an analytic theory for the recently demonstrated Josephson
+Junction laser (Science 355, p. 939, 2017). By working in the time-domain
+representation (rather than the frequency-domain) a single non-linear equation
+is obtained for the dynamics of the device, which is fully solvable in some
+regimes of operation. The nonlinear drive is seen to lead to mode-locked
+output, with a period set by the round-trip time of the resonant cavity.
+"
+26436,Learning Deep Matrix Representations,"  We present a new distributed representation in deep neural nets wherein the
+information is represented in native form as a matrix. This differs from
+current neural architectures that rely on vector representations. We consider
+matrices as central to the architecture and they compose the input, hidden and
+output layers. The model representation is more compact and elegant -- the
+number of parameters grows only with the largest dimension of the incoming
+layer rather than the number of hidden units. We derive several new deep
+networks: (i) feed-forward nets that map an input matrix into an output matrix,
+(ii) recurrent nets which map a sequence of input matrices into a sequence of
+output matrices. We also reinterpret existing models for (iii) memory-augmented
+networks and (iv) graphs using matrix notations. For graphs we demonstrate how
+the new notations lead to simple but effective extensions with multiple
+attentions. Extensive experiments on handwritten digits recognition, face
+reconstruction, sequence to sequence learning, EEG classification, and
+graph-based node classification demonstrate the efficacy and compactness of the
+matrix architectures.
+"
+26437,Data protection by means of fragmentation in various different distributed storage systems - a survey,"  This paper analyzes various distributed storage systems that use data
+fragmentation and dispersal as a way of protection.Existing solutions have been
+organized into two categories: bitwise and structurewise. Systems from the
+bitwise category are operating on unstructured data and in a uniform
+environment. Those having structured input data with predefined confidentiality
+level and disposing of a heterogeneous environment in terms of machine
+trustworthiness were classified as structurewise. Furthermore, we outline
+high-level requirements and desirable architecture traits of an eficient data
+fragmentation system, which will address performance (including latency),
+availability, resilience and scalability.
+"
+26438,On uniformly continuous maps between function spaces,"  In this paper we develop a technique of constructing uni- formly continuous
+maps between function spaces Cp(X) endowed with the pointwise topology. We
+prove that if a space X is compact metrizable and strongly
+countable-dimensional, then there exists a uniformly contin- uous surjection
+from Cp([0,1]) onto Cp(X). We provide a partial result concerning the reverse
+implication. We also show that, for every infinite Polish zero-dimensional
+space X, the spaces Cp(X) and Cp(X) x Cp(X) are uniformly homeomorphic. This
+partially answers two questions posed by Krupski and Marciszewski.
+"
+26439,On the conversion of multivalued to Boolean dynamics,"  Results and tools on discrete interaction networks are often concerned with
+Boolean variables, whereas considering more than two levels is sometimes
+useful. Multivalued networks can be converted to partial Boolean maps, in a way
+that preserves the asynchronous dynamics. We investigate the problem of
+extending these maps to non-admissible states, i.e. states that do not have a
+multivalued counterpart. We observe that attractors are preserved if a stepwise
+version of the original function is considered for conversion. Different
+extensions of the Boolean conversion affect the structure of the interaction
+graphs in different ways. A particular technique for extending the partial
+Boolean conversion is identified, that ensures that feedback cycles are
+preserved. This property, combined with the conservation of the asymptotic
+behaviour, can prove useful for the application of results and analyses defined
+in the Boolean setting to multivalued networks, and vice versa. As a first
+application, by considering the conversion of a known example for the discrete
+multivalued case, we create a Boolean map showing that the existence of a
+cyclic attractor and the absence of fixed points are compatible with the
+absence of local negative cycles. We then state a multivalued version of a
+result connecting mirror states and local feedback cycles.
+"
+26440,Permutation Games for the Weakly Aconjunctive $μ$-Calculus,"  We introduce a natural notion of limit-deterministic parity automata and
+present a method that uses such automata to construct satisfiability games for
+the weakly aconjunctive fragment of the $\mu$-calculus. To this end we devise a
+method that determinizes limit-deterministic parity automata of size $n$ with
+$k$ priorities through limit-deterministic Büchi automata to deterministic
+parity automata of size $\mathcal{O}((nk)!)$ and with $\mathcal{O}(nk)$
+priorities. The construction relies on limit-determinism to avoid the full
+complexity of the Safra/Piterman-construction by using partial permutations of
+states in place of Safra-Trees. By showing that limit-deterministic parity
+automata can be used to recognize unsuccessful branches in pre-tableaux for the
+weakly aconjunctive $\mu$-calculus, we obtain satisfiability games of size
+$\mathcal{O}((nk)!)$ with $\mathcal{O}(nk)$ priorities for weakly aconjunctive
+input formulas of size $n$ and alternation-depth $k$. A prototypical
+implementation that employs a tableau-based global caching algorithm to solve
+these games on-the-fly shows promising initial results.
+"
+26441,Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants,"  In this paper we propose a scalable version of a state-of-the-art
+deterministic time-invariant feature extraction approach based on consecutive
+changes of basis and nonlinearities, namely, the scattering network. The first
+focus of the paper is to extend the scattering network to allow the use of
+higher order nonlinearities as well as extracting nonlinear and Fourier based
+statistics leading to the required invariants of any inherently structured
+input. In order to reach fast convolutions and to leverage the intrinsic
+structure of wavelets, we derive our complete model in the Fourier domain. In
+addition of providing fast computations, we are now able to exploit sparse
+matrices due to extremely high sparsity well localized in the Fourier domain.
+As a result, we are able to reach a true linear time complexity with inputs in
+the Fourier domain allowing fast and energy efficient solutions to machine
+learning tasks. Validation of the features and computational results will be
+presented through the use of these invariant coefficients to perform
+classification on audio recordings of bird songs captured in multiple different
+soundscapes. In the end, the applicability of the presented solutions to deep
+artificial neural networks is discussed.
+"
+26442,Kantorovich Continuity of Probabilistic Programs,"  The Kantorovich metric is a canonical lifting of a distance from sets to
+distributions over this set. The metric also arises naturally when proving
+continuity properties of probabilistic programs. For instance, algorithmic
+stability of machine learning algorithms is upper bounded by the maximal
+Kantorovich distance between program executions, for a suitable notion of
+metric on the underlying space. Motivated by these applications, we develop a
+sound method to approximate the Kantorovich distance between two executions of
+a probabilistic program. Our method takes the form of a relational
+pre-expectation calculus. We illustrate our methods for proving stability of
+machine learning algorithms and convergence of probabilistic processes.
+"
+26443,DSRIM: A Deep Neural Information Retrieval Model Enhanced by a Knowledge Resource Driven Representation of Documents,"  The state-of-the-art solutions to the vocabulary mismatch in information
+retrieval (IR) mainly aim at leveraging either the relational semantics
+provided by external resources or the distributional semantics, recently
+investigated by deep neural approaches. Guided by the intuition that the
+relational semantics might improve the effectiveness of deep neural approaches,
+we propose the Deep Semantic Resource Inference Model (DSRIM) that relies on:
+1) a representation of raw-data that models the relational semantics of text by
+jointly considering objects and relations expressed in a knowledge resource,
+and 2) an end-to-end neural architecture that learns the query-document
+relevance by leveraging the distributional and relational semantics of
+documents and queries. The experimental evaluation carried out on two TREC
+datasets from TREC Terabyte and TREC CDS tracks relying respectively on WordNet
+and MeSH resources, indicates that our model outperforms state-of-the-art
+semantic and deep neural IR models.
+"
+26444,Endo-reversible heat engines coupled to finite thermal reservoirs: A rigorous treatment,"  We consider two specific thermodynamic cycles of engine operating in a finite
+time coupled to two thermal reservoirs with a finite heat capacity: The
+Carnot-type cycle and the Lorenz-type cycle. By means of the endo-reversible
+thermodynamics, we then discuss the power output of engine and its
+optimization. In doing so, we treat the temporal duration of a single cycle
+rigorously, i.e., without neglecting the duration of its adiabatic parts. Then
+we find that the maximally obtainable power output P_m and the engine
+efficiency \eta_m at the point of P_m explicitly depend on the heat conductance
+and the compression ratio. From this, it is immediate to observe that the
+well-known results available in many references, in particular the
+(compression-ratio-independent) Curzon-Ahlborn-Novikov expressions such as
+\eta_m --> \eta_{CAN} = 1 - (T_L/T_H)^(1/2) with the temperatures (T_H, T_L) of
+hot and cold reservoirs only, can be recovered, but, significantly enough, in
+the limit of a vanishingly small heat conductance and an infinitely large
+compression ratio only. Our result implies that the endo-reversible model of a
+thermal machine operating in a finite time and so producing a finite power
+output with the Curzon-Ahlborn-Novikov results should be limited in its own
+validity regime.
+"
+26445,Mining Frequent Learning Pathways from a Large Educational Dataset,"  In this paper, we describe data mining techniques used to extract frequent
+learning pathways from a large educational dataset. These pathways were
+extracted as a directed graph that encoded student learning processes. Our
+dataset contains more than 800 million interactions of over 3 million
+anonymized students in an online learning platform. Performing process mining
+on large and complex datasets regularly yields incomprehensible process models.
+Although, if we cluster data and obtain groups following similar processes, we
+can greatly improve process mining results. To this end, we developed a
+sequence clustering algorithm that let us group students who followed similar
+learning pathways. To extract frequent learning pathways from these clusters of
+data, we developed a graph-based process discovery algorithm that revealed to
+us the sequences of learning activities that many students followed. These
+sequences represented highways of student learning.
+"
+26446,Minimum energy for linear systems with finite horizon: a non-standard Riccati equation,"  This paper deals with a non-standard infinite dimensional linear-quadratic
+control problem arising in the physics of non-stationary states (see e.g. [6]):
+finding the minimum energy to drive a fixed stationary state x = 0 into an
+arbitrary non-stationary state x. The Riccati Equation (RE) associated to this
+problem is not standard since the sign of the linear part is opposite to the
+usual one, thus preventing the use of the known theory. Here we consider the
+finite horizon case. We prove that the linear selfadjoint operator P(t),
+associated to the value function, solves the above mentioned RE (Theorem 4.12).
+Uniqueness does not hold in general but we are able to prove a partial
+uniqueness result in the class of invertible operators (Theorem 4.13). In the
+special case where the involved operators commute, a more detailed analysis of
+the set of solutions is given (Theorems 4.14, 4.15 and 4.16). Examples of
+applications are given.
+"
+26447,Rejection Sampling for Tempered Levy Processes,"  We extend the idea of tempering stable Levy processes to tempering more
+general classes of Levy processes. We show that the original process can be
+decomposed into the sum of the tempered process and an independent point
+process of large jumps. We then use this to set up a rejection sampling
+algorithm for sampling from the tempered process. A small scale simulation
+study is given to help understand the performance of this algorithm.
+"
+26448,A global fit of the MSSM with GAMBIT,"  We study the seven-dimensional Minimal Supersymmetric Standard Model (MSSM7)
+with the new GAMBIT software framework, with all parameters defined at the weak
+scale. Our analysis significantly extends previous weak-scale, phenomenological
+MSSM fits, by adding more and newer experimental analyses, improving the
+accuracy and detail of theoretical predictions, including dominant
+uncertainties from the Standard Model, the Galactic dark matter halo and the
+quark content of the nucleon, and employing novel and highly-efficient
+statistical sampling methods to scan the parameter space. We find regions of
+the MSSM7 that exhibit co-annihilation of neutralinos with charginos, stops and
+sbottoms, as well as models that undergo resonant annihilation via both light
+and heavy Higgs funnels. We find high-likelihood models with light charginos,
+stops and sbottoms that have the potential to be within the future reach of the
+LHC. Large parts of our preferred parameter regions will also be accessible to
+the next generation of direct and indirect dark matter searches, making
+prospects for discovery in the near future rather good.
+"
+26449,Accelerate RNN-based Training with Importance Sampling,"  Importance sampling (IS) as an elegant and efficient variance reduction (VR)
+technique for the acceleration of stochastic optimization problems has
+attracted many researches recently. Unlike commonly adopted stochastic uniform
+sampling in stochastic optimizations, IS-integrated algorithms sample training
+data at each iteration with respect to a weighted sampling probability
+distribution $P$, which is constructed according to the precomputed importance
+factors. Previous experimental results show that IS has achieved remarkable
+progresses in the acceleration of training convergence. Unfortunately, the
+calculation of the sampling probability distribution $P$ causes a major
+limitation of IS: it requires the input data to be well-structured, i.e., the
+feature vector is properly defined. Consequently, recurrent neural networks
+(RNN) as a popular learning algorithm is not able to enjoy the benefits of IS
+due to the fact that its raw input data, i.e., the training sequences, are
+often unstructured which makes calculation of $P$ impossible. In considering of
+the the popularity of RNN-based learning applications and their relative long
+training time, we are interested in accelerating them through IS. This paper
+propose a novel Fast-Importance-Mining algorithm to calculate the importance
+factor for unstructured data which makes the application of IS in RNN-based
+applications possible. Our experimental evaluation on popular open-source
+RNN-based learning applications validate the effectiveness of IS in improving
+the convergence rate of RNNs.
+"
+26450,A semiparametric modeling approach using Bayesian Additive Regression Trees with an application to evaluate heterogeneous treatment effects,"  Bayesian Additive Regression Trees (BART) is a flexible machine learning
+algorithm capable of capturing nonlinearities between an outcome and covariates
+and interaction among covariates. We extend BART to a semiparametric regression
+framework in which the conditional expectation of an outcome is a function of
+treatment, its effect modifiers, and confounders. The confounders, not of
+scientific interest, are allowed to have unspecified functional form, while
+treatment and other covariates that do have scientific importance are given the
+usual linear form from parametric regression. The result is a Bayesian
+semiparametric linear regression model where the posterior distribution of the
+parameters of the linear part can be interpreted as in parametric Bayesian
+regression. This is useful in situations where a subset of the variables are of
+substantive interest and the others are nuisance variables that we would like
+to control for. An example of this occurs in causal modeling with the
+structural mean model (SMM). Under certain causal assumptions, our method can
+be used as a Bayesian SMM. Our methods are demonstrated with simulation studies
+and an application to dataset involving adults with HIV/Hepatitis C coinfection
+who newly initiate antiretroviral therapy. The methods are available in an R
+package semibart.
+"
+26451,A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events,"  We present a sequential model for temporal relation classification between
+intra-sentence events. The key observation is that the overall syntactic
+structure and compositional meanings of the multi-word context between events
+are important for distinguishing among fine-grained temporal relations.
+Specifically, our approach first extracts a sequence of context words that
+indicates the temporal relation between two events, which well align with the
+dependency path between two event mentions. The context word sequence, together
+with a parts-of-speech tag sequence and a dependency relation sequence that are
+generated corresponding to the word sequence, are then provided as input to
+bidirectional recurrent neural network (LSTM) models. The neural nets learn
+compositional syntactic and semantic representations of contexts surrounding
+the two events and predict the temporal relation between them. Evaluation of
+the proposed approach on TimeBank corpus shows that sequential modeling is
+capable of accurately recognizing temporal relations between events, which
+outperforms a neural net model using various discrete features as input that
+imitates previous feature based models.
+"
+26452,Uniform Consistency of the Highly Adaptive Lasso Estimator of Infinite Dimensional Parameters,"  Consider the case that we observe $n$ independent and identically distributed
+copies of a random variable with a probability distribution known to be an
+element of a specified statistical model. We are interested in estimating an
+infinite dimensional target parameter that minimizes the expectation of a
+specified loss function. In \cite{generally_efficient_TMLE} we defined an
+estimator that minimizes the empirical risk over all multivariate real valued
+cadlag functions with variation norm bounded by some constant $M$ in the
+parameter space, and selects $M$ with cross-validation. We referred to this
+estimator as the Highly-Adaptive-Lasso estimator due to the fact that the
+constrained can be formulated as a bound $M$ on the sum of the coefficients a
+linear combination of a very large number of basis functions. Specifically, in
+the case that the target parameter is a conditional mean, then it can be
+implemented with the standard LASSO regression estimator. In
+\cite{generally_efficient_TMLE} we proved that the HAL-estimator is consistent
+w.r.t. the (quadratic) loss-based dissimilarity at a rate faster than
+$n^{-1/2}$ (i.e., faster than $n^{-1/4}$ w.r.t. a norm), even when the
+parameter space is completely nonparametric. The only assumption required for
+this rate is that the true parameter function has a finite variation norm. The
+loss-based dissimilarity is often equivalent with the square of an
+$L^2(P_0)$-type norm. In this article, we establish that under some weak
+continuity condition, the HAL-estimator is also uniformly consistent.
+"
+26453,Fredholmness of Toeplitz operators on the Fock space,"  The Fredholm property of Toeplitz operators on the $p$-Fock spaces
+$F_\alpha^p$ on $\mathbb{C}^n$ is studied. A general Fredholm criterion for
+arbitrary operators from the Toeplitz algebra $\mathcal{T}_{p,\alpha}$ on
+$F_\alpha^p$ in terms of the invertibility of limit operators is derived. This
+paper is based on previous work, which establishes corresponding results on the
+unit balls $\mathbb{B}^n$.
+"
+26454,The entropic regularization of the Monge problem on the real line,"  We study the entropic regularization of the optimal transport problem in
+dimension 1 when the cost function is the distance c(x, y) = |y -- x|. The
+selected plan at the limit is, among those which are optimal for the
+non-penalized problem, the most 'diffuse' one on the zones where it may have a
+density.
+"
+26455,Reinforcement Learning Ship Autopilot: Sample-efficient and Model Predictive Control-based Approach,"  In this research, we focus on developing a reinforcement learning system for
+a challenging task: real autonomous ship control, with difficulties arising
+from uncertainties due to the complex ocean environment and the high cost of
+random exploration and sampling with real ships. To this end, we explore a
+novel Gaussian Processes (GP) based reinforcement learning approach that
+combines 1) sample efficient model-based reinforcement learning and 2) model
+predictive control (MPC). Our approach, Gaussian process model predictive
+control-based reinforcement learning (GP-MPC-RL), iteratively learns a Gaussian
+Process dynamics model and uses it to efficiently update control signals in
+real-time within the MPC closed control loop. A reinforcement learning system
+using GP-MPC-RL is then built to learn autopilot task with full size ships.
+After investigating its performance in an autonomous ship simulation built with
+real ship driving data, the proposed system successfully learns to complete an
+autopilot task on a real ship equipped with a single engine, GPS, and sensors
+for detecting speed, direction, and wind.
+"
+26456,Optimized Field/Circuit Coupling for the Simulation of Quenches in Superconducting Magnets,"  In this paper, we propose an optimized field/circuit coupling approach for
+the simulation of magnetothermal transients in superconducting magnets. The
+approach improves the convergence of the iterative coupling scheme between a
+magnetothermal partial differential model and an electrical lumped-element
+circuit. Such a multi-physics, multi-rate and multi-scale problem requires a
+consistent formulation and a dedicated framework to tackle the challenging
+transient effects occurring at both circuit and magnet level during normal
+operation and in case of faults. We derive an equivalent magnet model at the
+circuit side for the linear and the non-linear settings and discuss the
+convergence of the overall scheme in the framework of optimized Schwarz
+methods. The efficiency of the developed approach is illustrated by a numerical
+example of an accelerator dipole magnet with accompanying protection system.
+"
+26457,The inverse problem of the calculus of variations for discrete systems,"  We develop a geometric version of the inverse problem of the calculus of
+variations for discrete mechanics and constrained discrete mechanics. The
+geometric approach consists of using suitable Lagrangian and isotropic
+submanifolds. We also provide a transition between the discrete and the
+continuous problems and propose variationality as an interesting geometric
+property to take into account in the design and computer simulation of
+numerical integrators.
+"
+26458,Output Average Consensus Over Heterogeneous Multi-Agent Systems via Two-Level Approach,"  In this paper, a novel two-level framework was proposed and applied to solve
+the output average consensus problem over heterogeneous multi-agent systems.
+This approach is mainly based on the recent technique of system abstraction.
+For given multi-agent systems, we first constructed their abstractions as the
+upper level and solved their average consensus problem by leveraging well-known
+results for single integrators. Then the control protocols for physical agents
+in the lower level were synthesized in a hierarchical way by embedding the
+designed law for abstractions into an interface between two levels. In this
+way, the complexity coming from heterogeneous dynamics of agents is totally
+decoupled from that of the coordination task and the communication topologies.
+An example was given to show its effectiveness.
+"
+26459,Hopf potentials for the Schrödinger operator,"  We establish the Hopf boundary point lemma for the Schrödinger operator
+$-\Delta + V$ involving potentials $V$ that merely belong to the space
+$L^{1}_{loc}(\Omega)$. More precisely, we prove that among all supersolutions
+$u$ of $-\Delta + V$ which vanish on the boundary $\partial\Omega$ and are such
+that $V u \in L^{1}(\Omega)$, if there exists one supersolution which satisfies
+$\partial u/\partial n < 0$ almost everywhere on $\partial\Omega$ with respect
+to the outward unit vector $n$, then such a property holds for every nontrivial
+supersolution in the same class. We rely on the existence of nontrivial
+solutions of the nonhomogeneous Dirichlet problem with boundary datum in
+$L^{\infty}(\partial\Omega)$.
+"
+26460,Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning,"  We identify two issues with the family of algorithms based on the Adversarial
+Imitation Learning framework. The first problem is implicit bias present in the
+reward functions used in these algorithms. While these biases might work well
+for some environments, they can also lead to sub-optimal behavior in others.
+Secondly, even though these algorithms can learn from few expert
+demonstrations, they require a prohibitively large number of interactions with
+the environment in order to imitate the expert for many real-world
+applications. In order to address these issues, we propose a new algorithm
+called Discriminator-Actor-Critic that uses off-policy Reinforcement Learning
+to reduce policy-environment interaction sample complexity by an average factor
+of 10. Furthermore, since our reward function is designed to be unbiased, we
+can apply our algorithm to many problems without making any task-specific
+adjustments.
+"
+26461,Super learning in the SAS system,"  Background and objective: Stacking is an ensemble machine learning method
+that averages predictions from multiple other algorithms, such as generalized
+linear models and regression trees. A recent iteration of stacking, called
+super learning, has been developed as a general approach to black box
+supervised learning and has seen frequent usage, in part due to the
+availability of an R package. I develop super learning in the SAS software
+system using a new macro, and demonstrate its performance relative to the R
+package.
+Methods: I follow closely previous work using the R SuperLearner package and
+assess the performance of super learning in a number of domains. I compare the
+R package with the new SAS macro in a small set of simulations assessing curve
+fitting in a prediction model, a set of 14 publicly available datasets to
+assess cross-validated, expected loss, and data from a randomized trial of job
+seekers' training to assess the utility of super learning in causal inference
+using inverse probability weighting.
+Results: Across the simulated data and the publicly available data, the macro
+performed similarly to the R package, even with a different set of potential
+algorithms available natively in R and SAS. The example with inverse
+probability weighting demonstrated the ability of the SAS macro to include
+algorithms developed in R.
+Conclusions: The super learner macro performs as well as the R package at a
+number of tasks. Further, by extending the macro to include the use of R
+packages, the macro can leverage both the robust, enterprise oriented
+procedures in SAS and the nimble, cutting edge packages in R. In the spirit of
+ensemble learning, this macro extends the potential library of algorithms
+beyond a single software system and provides a simple avenue into machine
+learning in SAS.
+"
+26462,Successive Quadratic Upper-Bounding for Discrete Mean-Risk Minimization and Network Interdiction,"  The advances in conic optimization have led to its increased utilization for
+modeling data uncertainty. In particular, conic mean-risk optimization gained
+prominence in probabilistic and robust optimization. Whereas the corresponding
+conic models are solved efficiently over convex sets, their discrete
+counterparts are intractable. In this paper, we give a highly effective
+successive quadratic upper-bounding procedure for discrete mean-risk
+minimization problems. The procedure is based on a reformulation of the
+mean-risk problem through the perspective of its convex quadratic term.
+Computational experiments conducted on the network interdiction problem with
+stochastic capacities show that the proposed approach yields solutions within
+1-2% of optimality in a small fraction of the time required by exact search
+algorithms. We demonstrate the value of the proposed approach for constructing
+efficient frontiers of flow-at-risk vs. interdiction cost for varying
+confidence levels.
+"
+26463,Evolving imputation strategies for missing data in classification problems with TPOT,"  Missing data has a ubiquitous presence in real-life applications of machine
+learning techniques. Imputation methods are algorithms conceived for restoring
+missing values in the data, based on other entries in the database. The choice
+of the imputation method has an influence on the performance of the machine
+learning technique, e.g., it influences the accuracy of the classification
+algorithm applied to the data. Therefore, selecting and applying the right
+imputation method is important and usually requires a substantial amount of
+human intervention. In this paper we propose the use of genetic programming
+techniques to search for the right combination of imputation and classification
+algorithms. We build our work on the recently introduced Python-based TPOT
+library, and incorporate a heterogeneous set of imputation algorithms as part
+of the machine learning pipeline search. We show that genetic programming can
+automatically find increasingly better pipelines that include the most
+effective combinations of imputation methods, feature pre-processing, and
+classifiers for a variety of classification problems with missing data.
+"
+26464,How to Become Instagram Famous: Post Popularity Prediction with Dual-Attention,"  With a growing number of social apps, people have become increasingly willing
+to share their everyday photos and events on social media platforms, such as
+Facebook, Instagram, and WeChat. In social media data mining, post popularity
+prediction has received much attention from both data scientists and
+psychologists. Existing research focuses more on exploring the post popularity
+on a population of users and including comprehensive factors such as temporal
+information, user connections, number of comments, and so on. However, these
+frameworks are not suitable for guiding a specific user to make a popular post
+because the attributes of this user are fixed. Therefore, previous frameworks
+can only answer the question ""whether a post is popular"" rather than ""how to
+become famous by popular posts"". In this paper, we aim at predicting the
+popularity of a post for a specific user and mining the patterns behind the
+popularity. To this end, we first collect data from Instagram. We then design a
+method to figure out the user environment, representing the content that a
+specific user is very likely to post. Based on the relevant data, we devise a
+novel dual-attention model to incorporate image, caption, and user environment.
+The dual-attention model basically consists of two parts, explicit attention
+for image-caption pairs and implicit attention for user environment. A
+hierarchical structure is devised to concatenate the explicit attention part
+and implicit attention part. We conduct a series of experiments to validate the
+effectiveness of our model and investigate the factors that can influence the
+popularity. The classification results show that our model outperforms the
+baselines, and a statistical analysis identifies what kind of pictures or
+captions can help the user achieve a relatively high ""likes"" number.
+"
+26465,CCL: Cross-modal Correlation Learning with Multi-grained Fusion by Hierarchical Network,"  Cross-modal retrieval has become a highlighted research topic for retrieval
+across multimedia data such as image and text. A two-stage learning framework
+is widely adopted by most existing methods based on Deep Neural Network (DNN):
+The first learning stage is to generate separate representation for each
+modality, and the second learning stage is to get the cross-modal common
+representation. However, the existing methods have three limitations: (1) In
+the first learning stage, they only model intra-modality correlation, but
+ignore inter-modality correlation with rich complementary context. (2) In the
+second learning stage, they only adopt shallow networks with single-loss
+regularization, but ignore the intrinsic relevance of intra-modality and
+inter-modality correlation. (3) Only original instances are considered while
+the complementary fine-grained clues provided by their patches are ignored. For
+addressing the above problems, this paper proposes a cross-modal correlation
+learning (CCL) approach with multi-grained fusion by hierarchical network, and
+the contributions are as follows: (1) In the first learning stage, CCL exploits
+multi-level association with joint optimization to preserve the complementary
+context from intra-modality and inter-modality correlation simultaneously. (2)
+In the second learning stage, a multi-task learning strategy is designed to
+adaptively balance the intra-modality semantic category constraints and
+inter-modality pairwise similarity constraints. (3) CCL adopts multi-grained
+modeling, which fuses the coarse-grained instances and fine-grained patches to
+make cross-modal correlation more precise. Comparing with 13 state-of-the-art
+methods on 6 widely-used cross-modal datasets, the experimental results show
+our CCL approach achieves the best performance.
+"
+26466,Radio Weak BL Lac objects in the Fermi era,"  The existence of ""radio weak BL Lac objects"" (RWBLs) has been an open
+question, still unsolved, since the discovery that quasars could be radio-quiet
+or radio-loud. Recently several groups identified RWBL candidates, mostly found
+while searching for low energy counterparts of the unidentified/unassociated
+gamma-ray sources listed in the Fermi catalogs. Confirming RWBLs is a
+challenging task since they could be confused with white dwarfs (WDs) or weak
+emission line quasars (WELQs) when there are not sufficient data to precisely
+draw their broad band spectral energy distribution and their classification is
+mainly based on a featureless optical spectra. Motivated by the recent
+discovery that Fermi BL Lacs appear to have very peculiar mid-IR emission, we
+show that it is possible to distinguish between WDs, WELQs and BL Lacs using
+the [3.4]-[4.6]-[12]$\mu$m color-color plot built using the WISE magnitudes
+when the optical spectrum is available. On the basis of this analysis, we
+identify WISE J064459.38+603131 and WISE J141046.00+740511.2 as the first two
+genuine RWBLs, both potentially associated with Fermi sources. Finally, to
+strengthen our identification of these objects as true RWBLs, we present
+multifrequency observations for these two candidates to show that their
+spectral behavior is indeed consistent with those of the BL Lac population.
+"
+26467,Stochastic Comparisons of Lifetimes of Two Series and Parallel Systems with Location-Scale Family Distributed Components having Archimedean Copulas,"  In this paper, we compare the lifetimes of two series and two parallel
+systems stochastically where the lifetime of each component follows
+location-scale (LS) family of distributions. The comparison is carried out
+under two scenarios: one, that the components of the systems have a dependent
+structure sharing Archimedean copula and two, that the components are
+independently distributed. It is shown that the systems with components in
+series or parallel sharing Archimedean copula with more dispersion in the
+location or scale parameters results in better performance in the sense of the
+usual stochastic order. It is also shown that if the components are
+independently distributed, it is possible to obtain more generalized results as
+compared to the dependent set-up. The results in this paper generalizes similar
+results in both independent and dependent set up for exponential and Weibull
+distributed components.
+"
+26468,Nanosatellite aerobrake maneuvering device,"  In this paper, we present the project of the heliogyro solar sail unit for
+deployment of CubeSat constellation and satellite deorbiting. The ballistic
+calculations show that constellation deployment period can vary from 0.18 years
+for 450km initial orbit and 2 CubeSats up to 1.4 years for 650km initial orbit
+and 8 CubeSats. We also describe the structural and electrical design of the
+unit and consider aspects of its integration into a standard CubeSat frame.
+"
+26469,Quenching Global Star Formation: Dominance of Gravitational Shock Heating at z<2,"  We systematically study, in the context of the standard cold dark matter
+model, star-formation suppression effects of two important known physical
+processes---photoheating due to reionization of the intergalactic medium and
+gravitational shock heating due to formation of massive halos and large-scale
+structure---on the global evolution of star formation rate (SFR) density and
+the so-called cosmic downsizing phenomenon in the redshift range z=0-6. We show
+that the steep decline of cosmic SFR density from z~2 to z=0 can be primarily
+explained by gravitational shock heating in two forms: massive halo
+self-quenching and hot environment. Simultaneously, we show a decreasing trend
+in the average SFR of star-forming galaxies from z=2 to z=0, reproducing the
+observed cosmic downsizing at z<2. Nevertheless, the average halo mass of
+star-forming galaxies is found to continue upsizing from z=2 to z=0. In stark
+contrast to z<2, both photoheating and gravitational shock heating effects are
+found to play a minor role in suppressing star formation. Additional negative
+feedback effects are required to reconcile our model with observations at z>2.
+Internal feedback from stellar evolution and supermassive black hole growth are
+the natural candidates for this role, as galaxies at z>2 are more moderate in
+mass but stronger in star formation and are thus more vulnerable. Our physical
+model can be used to treat star formation in cosmological N-body simulations.
+"
+26470,Learning to Bid Without Knowing your Value,"  We address online learning in complex auction settings, such as sponsored
+search auctions, where the value of the bidder is unknown to her, evolving in
+an arbitrary manner and observed only if the bidder wins an allocation. We
+leverage the structure of the utility of the bidder and the partial feedback
+that bidders typically receive in auctions, in order to provide algorithms with
+regret rates against the best fixed bid in hindsight, that are exponentially
+faster in convergence in terms of dependence on the action space, than what
+would have been derived by applying a generic bandit algorithm and almost
+equivalent to what would have been achieved in the full information setting.
+Our results are enabled by analyzing a new online learning setting with
+outcome-based feedback, which generalizes learning with feedback graphs. We
+provide an online learning algorithm for this setting, of independent interest,
+with regret that grows only logarithmically with the number of actions and
+linearly only in the number of potential outcomes (the latter being very small
+in most auction settings). Last but not least, we show that our algorithm
+outperforms the bandit approach experimentally and that this performance is
+robust to dropping some of our theoretical assumptions or introducing noise in
+the feedback that the bidder receives.
+"
+26471,On line colorings of finite projective spaces,"  In this paper, we prove lower and upper bounds on the achromatic and the
+pseudoachromatic indices of the $n$-dimensional finite projective space of
+order $q$.
+"
+26472,On the difference in values of the Euler totient function near prime arguments,"  We prove unconditionally that for each $\ell \geq 1$, the difference
+$\phi(p-\ell) - \phi(p+\ell)$ is positive for $50\%$ of odd primes $p$ and
+negative for $50\%$.
+"
+26473,Dataset Quality Assessment: An extension for analogy based effort estimation,"  Estimation by Analogy (EBA) is an increasingly active research method in the
+area of software engineering. The fundamental assumption of this method is that
+the similar projects in terms of attribute values will also be similar in terms
+of effort values. It is well recognized that the quality of software datasets
+has a considerable impact on the reliability and accuracy of such method.
+Therefore, if the software dataset does not satisfy the aforementioned
+assumption then it is not rather useful for EBA method. This paper presents a
+new method based on Kendall's row-wise rank correlation that enables data
+quality evaluation and providing a data preprocessing stage for EBA. The
+proposed method provides sound statistical basis and justification for the
+process of data quality evaluation. Unlike Analogy-X, our method has the
+ability to deal with categorical attributes individually without the need for
+partitioning the dataset. Experimental results showed that the proposed method
+could form a useful extension for EBA as it enables: dataset quality
+evaluation, attribute selection and identifying abnormal observations.
+"
+26474,Characterization of Water Dissociation on $α$-Al$_{2}$O$_{3}$$(1\bar{1}02)$: Theory and Experiment,"  The interaction of water with $\alpha$-alumina (i.e. $\alpha$-Al$_{2}$O$_{3}$
+surfaces is important in a variety of applications and a useful model for the
+interaction of water with environmentally abundant aluminosilicate phases.
+Despite its significance, studies of water interaction with
+$\alpha$-Al$_{2}$O$_{3}$ surfaces other than the $(0001)$ are extremely
+limited. Here we characterize the interaction of water (D$_{2}$O) with a well
+defined $\alpha$-Al$_{2}$O$_{3}$$(1\bar{1}02)$ surface in UHV both
+experimentally, using temperature programmed desorption and surface-specific
+vibrational spectroscopy, and theoretically, using periodic-slab density
+functional theory calculations. This combined approach makes it possible to
+demonstrate that water adsorption occurs only at a single well defined surface
+site (the so-called 1-4 configuration) and that at this site the barrier
+between the molecularly and dissociatively adsorbed forms is very low: 0.06 eV.
+A subset of OD stretch vibrations are parallel to this dissociation coordinate,
+and thus would be expected to be shifted to low frequencies relative to an
+uncoupled harmonic oscillator. To quantify this effect we solve the vibrational
+Schrödinger equation along the dissociation coordinate and find fundamental
+frequencies red-shifted by more than 1,500 cm$^{\text{-1}}$. Within the context
+of this model, at moderate temperatures, we further find that some fraction of
+surface deuterons are likely delocalized: dissociatively and molecularly
+absorbed states are no longer distinguishable.
+"
+26475,Approximate Shifted Combinatorial Optimization,"  Shifted combinatorial optimization is a new nonlinear optimization framework,
+which is a broad extension of standard combinatorial optimization, involving
+the choice of several feasible solutions at a time. It captures well studied
+and diverse problems ranging from congestive to partitioning problems. In
+particular, every standard combinatorial optimization problem has its shifted
+counterpart, which is typically much harder. Here we initiate a study of
+approximation algorithms for this broad optimization framework.
+"
+26476,"On Landsberg general $(α,β)$-metrics with a conformal 1-form","  In this paper, we study almost regular Landsberg general
+$(\alpha,\beta)$-metrics in Finsler geometry. The corresponding equivalent
+equations are given. By solving the equations, we give the classification of
+Landsberg general $(\alpha,\beta)$-metrics under the conditon that $\beta$ is
+closed and conformal to $\alpha$. Under this condition, we prove that regular
+Landsberg general $(\alpha,\beta)$-metrics must be Berwaldian when the
+dimension is greater than two. For the almost regular case, the classification
+also is given and some new non-Berwaldian Landsberg metrics are found.
+"
+26477,Structured Evolution with Compact Architectures for Scalable Policy Optimization,"  We present a new method of blackbox optimization via gradient approximation
+with the use of structured random orthogonal matrices, providing more accurate
+estimators than baselines and with provable theoretical guarantees. We show
+that this algorithm can be successfully applied to learn better quality compact
+policies than those using standard gradient estimation techniques. The compact
+policies we learn have several advantages over unstructured ones, including
+faster training algorithms and faster inference. These benefits are important
+when the policy is deployed on real hardware with limited resources. Further,
+compact policies provide more scalable architectures for derivative-free
+optimization (DFO) in high-dimensional spaces. We show that most robotics tasks
+from the OpenAI Gym can be solved using neural networks with less than 300
+parameters, with almost linear time complexity of the inference phase, with up
+to 13x fewer parameters relative to the Evolution Strategies (ES) algorithm
+introduced by Salimans et al. (2017). We do not need heuristics such as fitness
+shaping to learn good quality policies, resulting in a simple and theoretically
+motivated training mechanism.
+"
+26478,Equilibration of energy in slow-fast systems,"  Ergodicity is a fundamental requirement for a dynamical system to reach a
+state of statistical equilibrium. On the other hand, it is known that in
+slow-fast systems ergodicity of the fast sub- system impedes the equilibration
+of the whole system due to the presence of adiabatic invariants. Here, we show
+that the violation of ergodicity in the fast dynamics effectively drives the
+whole system to equilibrium. To demonstrate this principle we investigate
+dynamics of the so-called springy billiards. These consist of a point particle
+of a small mass which bounces elastically in a billiard where one of the walls
+can move - the wall is of a finite mass and is attached to a spring. We propose
+a random process model for the slow wall dynamics and perform numerical
+experiments with the springy billiards themselves and the model. The
+experiments show that for such systems equilibration is always achieved; yet,
+in the adiabatic limit, the system equilibrates with a positive exponential
+rate only when the fast particle dynamics has more than one ergodic component
+for certain wall positions.
+"
+26479,Sequence Modelling For Analysing Student Interaction with Educational Systems,"  The analysis of log data generated by online educational systems is an
+important task for improving the systems, and furthering our knowledge of how
+students learn. This paper uses previously unseen log data from Edulab, the
+largest provider of digital learning for mathematics in Denmark, to analyse the
+sessions of its users, where 1.08 million student sessions are extracted from a
+subset of their data. We propose to model students as a distribution of
+different underlying student behaviours, where the sequence of actions from
+each session belongs to an underlying student behaviour. We model student
+behaviour as Markov chains, such that a student is modelled as a distribution
+of Markov chains, which are estimated using a modified k-means clustering
+algorithm. The resulting Markov chains are readily interpretable, and in a
+qualitative analysis around 125,000 student sessions are identified as
+exhibiting unproductive student behaviour. Based on our results this student
+representation is promising, especially for educational systems offering many
+different learning usages, and offers an alternative to common approaches like
+modelling student behaviour as a single Markov chain often done in the
+literature.
+"
+26480,Optimal modularity and memory capacity of neural networks,"  The neural network is a powerful computing framework that has been exploited
+by biological evolution and by humans for solving diverse problems. Although
+the computational capabilities of neural networks are determined by their
+structure, the current understanding of the relationships between a neural
+network's architecture and function is still primitive. Here we reveal that
+neural network's modular architecture plays a vital role in determining the
+neural dynamics and memory performance of the network. In particular, we
+demonstrate that there exists an optimal modularity for memory performance,
+where a balance between local cohesion and global connectivity is established,
+allowing optimally modular networks to remember longer. Our results suggest
+that insights from dynamical analysis of neural networks and information
+spreading processes can be leveraged to better design neural networks and may
+shed light on the brain's modular organization.
+"
+26481,Random forward models and log-likelihoods in Bayesian inverse problems,"  We consider the use of randomised forward models and log-likelihoods within
+the Bayesian approach to inverse problems. Such random approximations to the
+exact forward model or log-likelihood arise naturally when a computationally
+expensive model is approximated using a cheaper stochastic surrogate, as in
+Gaussian process emulation (kriging), or in the field of probabilistic
+numerical methods. We show that the Hellinger distance between the exact and
+approximate Bayesian posteriors is bounded by moments of the difference between
+the true and approximate log-likelihoods. Example applications of these
+stability results are given for randomised misfit models in large data
+applications and the probabilistic solution of ordinary differential equations.
+"
+26482,An Invariant-EKF VINS Algorithm for Improving Consistency,"  The main contribution of this paper is an invariant extended Kalman filter
+(EKF) for visual inertial navigation systems (VINS). It is demonstrated that
+the conventional EKF based VINS is not invariant under the stochastic
+unobservable transformation, associated with translations and a rotation about
+the gravitational direction. This can lead to inconsistent state estimates as
+the estimator does not obey a fundamental property of the physical system. To
+address this issue, we use a novel uncertainty representation to derive a Right
+Invariant error extended Kalman filter (RIEKF-VINS) that preserves this
+invariance property. RIEKF-VINS is then adapted to the multistate constraint
+Kalman filter framework to obtain a consistent state estimator. Both Monte
+Carlo simulations and real-world experiments are used to validate the proposed
+method.
+"
+26483,Deep Learning in the Wild,"  Deep learning with neural networks is applied by an increasing number of
+people outside of classic research environments, due to the vast success of the
+methodology on a wide range of machine perception tasks. While this interest is
+fueled by beautiful success stories, practical work in deep learning on novel
+tasks without existing baselines remains challenging. This paper explores the
+specific challenges arising in the realm of real world tasks, based on case
+studies from research \& development in conjunction with industry, and extracts
+lessons learned from them. It thus fills a gap between the publication of
+latest algorithmic and methodical developments, and the usually omitted
+nitty-gritty of how to make them work. Specifically, we give insight into deep
+learning projects on face matching, print media monitoring, industrial quality
+control, music scanning, strategy game playing, and automated machine learning,
+thereby providing best practices for deep learning in practice.
+"
+26484,Uplink Interference Reduction in Large Scale Antenna Systems,"  A massive MIMO system entails a large number (tens or hundreds) of base
+station antennas serving a much smaller number of terminals. These systems
+demonstrate large gains in spectral and energy efficiency compared with
+conventional MIMO technology. As the number of antennas grows, the performance
+of a massive MIMO system gets limited by the interference caused by pilot
+contamination. Earlier A. Ashikhmin and T. Marzetta proposed (under the name of
+Pilot Contamination Precoding) Large Scale Fading Precoding (LSFP) and Decoding
+(LSFD) based on limited cooperation between base stations. They showed that
+Zero-Forcing LSFP and LSFD eliminate pilot contamination entirely and lead to
+an infinite throughput as the number of antennas grows.
+In this paper, we focus on the uplink and show that even in the case of a
+finite number of base station antennas, LSFD yields a very large performance
+gain. In particular, one of our algorithms gives a more than 140 fold increase
+in the 5% outage data transmission rate! We show that the performance can be
+improved further by optimizing the transmission powers of the users. Finally,
+we present decentralized LSFD that requires limited cooperation only between
+neighboring cells.
+"
+26485,Tuning Over-Relaxed ADMM,"  The framework of Integral Quadratic Constraints (IQC) reduces the computation
+of upper bounds on the convergence rate of several optimization algorithms to a
+semi-definite program (SDP). In the case of over-relaxed Alternating Direction
+Method of Multipliers (ADMM), an explicit and closed form solution to this SDP
+was derived in our recent work [1]. The purpose of this paper is twofold.
+First, we summarize these results. Second, we explore one of its consequences
+which allows us to obtain general and simple formulas for optimal parameter
+selection. These results are valid for arbitrary strongly convex objective
+functions.
+"
+26486,Learning Task Priorities from Demonstrations,"  Bimanual operations in humanoids offer the possibility to carry out more than
+one manipulation task at the same time, which in turn introduces the problem of
+task prioritization. We address this problem from a learning from demonstration
+perspective, by extending the Task-Parameterized Gaussian Mixture Model
+(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
+on bimanual skills but can be applied in any scenario where the prioritization
+between potentially conflicting tasks needs to be learned. We evaluate the
+proposed framework in: two different tasks with humanoids requiring the
+learning of priorities and a loco-manipulation scenario, showing that the
+approach can be exploited to learn the prioritization of multiple tasks in
+parallel.
+"
+26487,Control of an Unmanned Surface Vehicle with Uncertain Displacement and Drag,"  Experimental testing of an unmanned surface vehicle (USV) has been performed
+to evaluate the performance of two low-level controllers when displacement and
+drag properties are time-varying and uncertain. The USV is a 4.3 meter long,
+150 kilogram wave adaptive modular vessel (WAM-V) with an inflatable twin hull
+configuration and waterjet propulsion. Open loop maneuvering tests were
+conducted to characterize the dynamics of the vehicle. The hydrodynamic
+coefficients of the vehicle were determined through system identification of
+the maneuvering data and were used for simulations during control system
+development. The resulting controllers were experimentally field tested
+on-water. Variable mass and drag tests show that the vehicle is best controlled
+by a model reference adaptive backstepping speed and heading controller. The
+backstepping controller developed by Liao et. al (2010) is modified to account
+for an overprediction of necessary control action and motor saturation. It is
+shown that when an adaptive algorithm is implemented for the surge control
+subsystem of the modified backstepping controller, the effects of variable mass
+and drag are mitigated.
+"
+26488,Speckle correction in polychromatic light with the self-coherent camera for the direct detection of exoplanets,"  Direct detection is a very promising field in exoplanet science. It allows
+the detection of companions with large separation and allows their spectral
+analysis. A few planets have already been detected and are under spectral
+analysis. But the full spectral characterization of smaller and colder planets
+requires higher contrast levels over large spectral bandwidths. Coronagraphs
+can be used to reach these contrasts, but their efficiency is limited by
+wavefront aberrations. These deformations induce speckles, star lights leaks,
+in the focal plane after the coronagraph. The wavefront aberrations should be
+estimated directly in the science image to avoid usual limitations by
+differential aberrations in classical adaptive optics. In this context, we
+introduce the Self- Coherent Camera (SCC). The SCC uses the coherence of the
+star light to produce a spatial modulation of the speckles in the focal plane
+and estimate the associated electric complex field. Controlling the wavefront
+with a deformable mirror, high contrasts have already been reached in
+monochromatic light with this technique. The performance of the current version
+of the SCC is limited when widening the spectral bandwidth. We will present a
+theoretical analysis of these issues and their possible solution. Finally, we
+will present test bench performance in polychromatic light.
+"
+26489,Representation Learning for Grounded Spatial Reasoning,"  The interpretation of spatial references is highly contextual, requiring
+joint inference over both language and the environment. We consider the task of
+spatial reasoning in a simulated environment, where an agent can act and
+receive rewards. The proposed model learns a representation of the world
+steered by instruction text. This design allows for precise alignment of local
+neighborhoods with corresponding verbalizations, while also handling global
+references in the instructions. We train our model with reinforcement learning
+using a variant of generalized value iteration. The model outperforms
+state-of-the-art approaches on several metrics, yielding a 45% reduction in
+goal localization error.
+"
+26490,Dispersive shallow water wave modelling. Part I: Model derivation on a globally flat space,"  In this paper, we review the history and current state-of-the-art in the
+modelling of long nonlinear dispersive waves. For the sake of conciseness of
+this review, we omit the unidirectional models and focus especially on some
+classical and improved Boussinesq-type and Serre-Green-Naghdi equations.
+Finally, we propose also a unified modelling framework which incorporates
+several well-known and some less known dispersive wave models. The present
+manuscript is the first part of a series of two papers. The second part will be
+devoted to the numerical discretization of a practically important model on
+moving adaptive grids.
+"
+26491,Periodic balanced binary triangles,"  A binary triangle of size $n$ is a triangle of zeroes and ones, with $n$
+rows, built with the same local rule as the standard Pascal triangle modulo
+$2$. A binary triangle is said to be balanced if the absolute difference
+between the numbers of zeroes and ones that constitute this triangle is at most
+$1$. In this paper, the existence of balanced binary triangles of size $n$, for
+all positive integers $n$, is shown. This is achieved by considering periodic
+balanced binary triangles, that are balanced binary triangles where each row,
+column or diagonal is a periodic sequence.
+"
+26492,Photosensor Oculography: Survey and Parametric Analysis of Designs using Model-Based Simulation,"  This paper presents a renewed overview of photosensor oculography (PSOG), an
+eye-tracking technique based on the principle of using simple photosensors to
+measure the amount of reflected (usually infrared) light when the eye rotates.
+Photosensor oculography can provide measurements with high precision, low
+latency and reduced power consumption, and thus it appears as an attractive
+option for performing eye-tracking in the emerging head-mounted interaction
+devices, e.g. augmented and virtual reality (AR/VR) headsets. In our current
+work we employ an adjustable simulation framework as a common basis for
+performing an exploratory study of the eye-tracking behavior of different
+photosensor oculography designs. With the performed experiments we explore the
+effects from the variation of some basic parameters of the designs on the
+resulting accuracy and cross-talk, which are crucial characteristics for the
+seamless operation of human-computer interaction applications based on
+eye-tracking. Our experimental results reveal the design trade-offs that need
+to be adopted to tackle the competing conditions that lead to optimum
+performance of different eye-tracking characteristics. We also present the
+transformations that arise in the eye-tracking output when sensor shifts occur,
+and assess the resulting degradation in accuracy for different combinations of
+eye movements and sensor shifts.
+"
+26493,A Graph Theoretic Analysis of Leverage Centrality,"  In 2010, Joyce et. al defined the leverage centrality of vertices in a graph
+as a means to analyze functional connections within the human brain. In this
+metric a degree of a vertex is compared to the degrees of all it neighbors. We
+investigate this property from a mathematical perspective. We first outline
+some of the basic properties and then compute leverage centralities of vertices
+in different families of graphs. In particular, we show there is a surprising
+connection between the number of distinct leverage centralities in the
+Cartesian product of paths and the triangle numbers.
+"
+26494,On controlling the bound states in quantum-dot hybrid-nanowire,"  Recent experiments using the quantum dot coupled to the topological
+superconducting nanowire [M.T. Deng et al., Science 354, 1557 (2016)}] revealed
+that zero-energy bound state coalesces from the Andreev bound states. Such
+quasiparticle states, present in the quantum dot, can be controlled by the
+magnetic and electrostatic means. We use microscopic model of the
+quantum-dot--nanowire structure to reproduce the experimental results, applying
+the Bogoliubov--de~Gennes technique. This is done by studying the gate voltage
+dependence of the various types of bound states and mutual influence between
+them. We show that the zero energy bound states can emerge from the Andreev
+bound states in topologically trivial phase and can be controlled using various
+means. In non-trivial topological phase we show the possible resonance between
+this zero energy levels with Majorana bound states. We discuss and explain this
+phenomena as a result of dominant spin character of discussed bound states.
+Presented results can be applied in experimental studies by using the proposed
+nanodevice.
+"
+26495,A possible new cosmological redshift effect due to $Λ$ on traveling gravitational waves in Friedmann universes,"  In this paper we continue the investigation concerning the propagation of
+gravitational waves in a cosmological background using Laplace transform
+\cite{A}. We analyze the possible physical consequences of the result present
+in \cite{A} where it is argued that a non-vanishing positive abscissa of
+convergence caused by the de Sitter expansion factor $a(t)=e^{Ht}$ implies a
+shift in the frequencies domain of a traveling gravitational waves as measured
+by a comoving observer. In particular, we show that in a generic asymptotically
+de Sitter cosmological universe this redshift effect does also arise.
+Conversely, in a universe expanding with, for example, a power law expansion,
+this phenomenon does not happen. This physically possible new redshift effect,
+although negligible for the actual very low value of $\Lambda$, can have
+interesting physical consequences concerning for example its relation with
+Bose-Einstein condensation or more speculatively with the nature of the
+cosmological constant in terms of gravitons, as recently suggested in \cite{B}
+near a Bose-Einstein condensation phase.
+"
+26496,Pattern Division Multiple Access with Large-scale Antenna Array,"  In this paper, pattern division multiple access with large-scale antenna
+array (LSA-PDMA) is proposed as a novel non-orthogonal multiple access (NOMA)
+scheme. In the proposed scheme, pattern is designed in both beam domain and
+power domain in a joint manner. At the transmitter, pattern mapping utilizes
+power allocation to improve the system sum rate and beam allocation to enhance
+the access connectivity and realize the integration of LSA into multiple access
+spontaneously. At the receiver, hybrid detection of spatial filter (SF) and
+successive interference cancellation (SIC) is employed to separate the
+superposed multiple-domain signals. Furthermore, we formulate the sum rate
+maximization problem to obtain the optimal pattern mapping policy, and the
+optimization problem is proved to be convex through proper mathematical
+manipulations. Simulation results show that the proposed LSA-PDMA scheme
+achieves significant performance gain on system sum rate compared to both the
+orthogonal multiple access scheme and the power-domain NOMA scheme.
+"
+26497,Automated Hate Speech Detection and the Problem of Offensive Language,"  A key challenge for automatic hate-speech detection on social media is the
+separation of hate speech from other instances of offensive language. Lexical
+detection methods tend to have low precision because they classify all messages
+containing particular terms as hate speech and previous work using supervised
+learning has failed to distinguish between the two categories. We used a
+crowd-sourced hate speech lexicon to collect tweets containing hate speech
+keywords. We use crowd-sourcing to label a sample of these tweets into three
+categories: those containing hate speech, only offensive language, and those
+with neither. We train a multi-class classifier to distinguish between these
+different categories. Close analysis of the predictions and the errors shows
+when we can reliably separate hate speech from other offensive language and
+when this differentiation is more difficult. We find that racist and homophobic
+tweets are more likely to be classified as hate speech but that sexist tweets
+are generally classified as offensive. Tweets without explicit hate keywords
+are also more difficult to classify.
+"
+26498,Near-linear Time Algorithms for Approximate Minimum Degree Spanning Trees,"  Given a graph $G = (V, E)$, $n=|V|, m=|E|$, we wish to compute a spanning
+tree whose maximum vertex degree is as small as possible. Computing the exact
+optimal solution is known to be NP-hard, since it generalizes the Hamiltonian
+path problem. For the approximation version of this problem, a $\tilde{O}(mn)$
+time algorithm that computes a spanning tree of degree at most $\Delta^* +1$ is
+previously known [Fürer, Raghavachari 1994]; here $\Delta^*$ denotes the
+optimal tree degree. In this paper we give the first near-linear time algorithm
+for this problem. Specifically speaking, we first propose a simple
+$\tilde{O}(m)$ time algorithm that achieves an $O(\Delta^*\log n)$
+approximation; then we further improve this algorithm to obtain a
+$(1+\delta)\Delta^* + O(\frac{1}{\delta^2}\log n)$ approximation in
+$\tilde{O}(\frac{1}{\delta^6}m)$ time.
+"
+26499,Printed Arabic Text Recognition using Linear and Nonlinear Regression,"  Arabic language is one of the most popular languages in the world. Hundreds
+of millions of people in many countries around the world speak Arabic as their
+native speaking. However, due to complexity of Arabic language, recognition of
+printed and handwritten Arabic text remained untouched for a very long time
+compared with English and Chinese. Although, in the last few years, significant
+number of researches has been done in recognizing printed and handwritten
+Arabic text, it stills an open research field due to cursive nature of Arabic
+script. This paper proposes automatic printed Arabic text recognition technique
+based on linear and ellipse regression techniques. After collecting all
+possible forms of each character, unique code is generated to represent each
+character form. Each code contains a sequence of lines and ellipses. To
+recognize fonts, a unique list of codes is identified to be used as a
+fingerprint of font. The proposed technique has been evaluated using over 14000
+different Arabic words with different fonts and experimental results show that
+average recognition rate of the proposed technique is 86%.
+"
+26500,Edgeworth-type expansion in the entropic free CLT,"  We prove an expansion for densities in the free CLT and apply this result to
+an expansion in the entropic free central limit theorem assuming a moment
+condition of order four for the free summands.
+"
+26501,Cheating by Duplication: Equilibrium Requires Global Knowledge,"  The question of what global information must distributed rational agents
+a-priori know about the network in order for equilibrium to be possible is
+researched here. Until now, distributed algorithms with rational agents have
+assumed that $n$, the size of the network, is a-priori known to the
+participants. We investigate the above question, considering different
+distributed computing problems and showing how much each agent must a-priori
+know about $n$ in order for distributed algorithms to be equilibria. The main
+tool considered throughout the paper is the advantage an agent may gain by
+duplication- pretending to be more than one agent.
+We start by proving that when no bound on $n$ is given equilibrium for
+Coloring and Knowledge Sharing is impossible. %We prove that when agents have
+no a-priori knowledge on $n$, or even a known bound, equilibrium for both
+Knowledge Sharing and Coloring is impossible. We provide new algorithms for
+both problems when $n$ \emph{is} a-priori known to all agents, thus showing
+that there are algorithms in which the only way for an agent to gain an
+advantage is duplication. We further show that for each distributed problem
+there is an a-priori known range, an upper and a lower bound on $n$, such that
+if the actual $n$ is guaranteed to lay in that range, equilibrium is possible.
+By providing equilibria for a specific range, and impossibility results for any
+larger range, we prove the tight range necessary for equilibrium in: Leader
+Election, Knowledge Sharing, Coloring, Partition and Orientation.
+"
+26502,Recurrence in the high-order nonlinear Schrödinger equation: a low dimensional analysis,"  We study a three-wave truncation of the high-order nonlinear Schrödinger
+equation for deepwater waves (HONLS, also named Dysthe equation). We validate
+our approach by comparing it to numerical simulation, distinguish the impact of
+the different fourth-order terms and classify the solutions according to their
+topology. This allows us to properly define the temporary spectral upshift
+occurring in the nonlinear stage of Benjamin-Feir instability and provides a
+tool for studying further generalizations of this model.
+"
+26503,Relatively Hyperbolic Groups have Semistabile Fundamental Group at Infinity,"  Suppose $G$ is a 1-ended finitely generated group that is hyperbolic relative
+to P a finite collection of 1-ended finitely generated subgroups. Our main
+theorem states that if the boundary $\partial (G, P)$ has no cut point, then
+$G$ has semistable fundamental group at $\infty$. Under mild conditions on $G$
+and the members of P the 1-ended hypotheses and the no cut point condition can
+be eliminated to obtain the same semistability conclusion. We give an example
+that shows our main result is somewhat optimal. Finally, we improve a ""double
+dagge"" result of F. Dahmani and D. Groves.
+"
+26504,Experimental demonstration of a fifth force due to chameleon field via cold atoms,"  We tested a fifth force using cold atom experiments. The accelerated
+expansion of the universe implies the possibility of the presence of a scalar
+field throughout the universe driving the acceleration. This field would result
+in a detectable force between normal-matter objects. Theory of the chameleon
+field states that the force should be strong in a thin shell near the surface
+of a source object but greatly suppressed inside and outside of the source
+object. We used two atom clouds: one as the source and the other as the test
+mass; so the test mass can pass through the thin-shell region of the source
+mass. We detected the chameleon force and obtained the couple constant of about
+4.5E11 between matter and the field. The chameleon force is considerably larger
+than Newtonian gravity at short distance; the interaction range is short enough
+to satisfy all experimental bounds on deviations from general relativity.
+"
+26505,Numerical stochastic homogenization by quasilocal effective diffusion tensors,"  This paper proposes a numerical upscaling procedure for elliptic boundary
+value problems with diffusion tensors that vary randomly on small scales. The
+resulting effective deterministic model is given through a quasilocal discrete
+integral operator, which can be further compressed to an effective partial
+differential operator. Error estimates consisting of a priori and a posteriori
+terms are provided that allow one to quantify the impact of uncertainty in the
+diffusion coefficient on the expected effective response of the process.
+"
+26506,Enhancing Engagement in Token-Curated Registries via an Inflationary Mechanism,"  Token Curated Registries (TCR) are decentralized recommendation systems that
+can be implemented using Blockchain smart contracts. They allow participants to
+vote for or against adding items to a list through a process that involves
+staking tokens intrinsic to the registry, with winners receiving the staked
+tokens for each vote. A TCR aims to provide incentives to create a well-curated
+list. In this work, we consider a challenge for these systems - incentivizing
+token-holders to actually engage and participate in the voting process. We
+propose a novel token-inflation mechanism for enhancing engagement, whereby
+only voting participants see their token supply increased by a pre-defined
+multiple after each round of voting. To evaluate this proposal, we propose a
+simple 4-class model of voters that captures all possible combinations of two
+key dimensions: whether they are engaged (likely to vote at all for a given
+item) or disengaged, and whether they are informed (likely to vote in a way
+that increases the quality of the list) or uninformed, and a simple metric to
+evaluate the quality of the list as a function of the vote outcomes. We conduct
+simulations using this model of voters and show that implementing
+token-inflation results in greater wealth accumulation for engaged voters. In
+particular, when the number of informed voters is sufficiently high, our
+simulations show that voters that are both informed and engaged see the
+greatest benefits from participating in the registry when our proposed
+token-inflation mechanism is employed. We further validate this finding using a
+simplified mathematical analysis.
+"
+26507,Controlling IL-7 injections in HIV-infected patients,"  Immune interventions consisting in repeated injection are broadly used as
+they are thought to improve the quantity and the quality of the immune
+response. However, they also raised several questions that remains unanswered,
+in particular the number of injections to make or the delay to respect between
+different injections to acheive this goal. Practical and financial
+considerations add constraints to these questions, especially in the framework
+of human studies. We specifically focus here on the use of interleukine-7
+(IL-7) injections in HIV-infected patients under antiretroviral treatment, but
+still unable to restore normal levels of CD4+ T lymphocytes. Clinical trials
+have already shown that repeated cycles of injections of IL-7 could help
+maintaining CD4+ T lymphocytes levels over the limit of 500 cells per microL,
+by affecting proliferation and survival of CD4+ T cells. We then aim at
+answering the question : how to maintain a patient's level of CD4+ T
+lymphocytes by using a minimum number of injections (ie optimizing the strategy
+of injections) ? Based on mechanistic models that were previously developed for
+the dynamics of CD4+ T lymphocytes in this context, we model the process by a
+piecewise deterministic Markov model. We then address the question by using
+some recently established theory on impulse control problem in order to develop
+a numerical tool determining the optimal strategy. Results are obtained on a
+reduced model, as a proof of concept : the method allows to defined an optimal
+strategy for a given patient. This method could applied to optimize injections
+schedules in clinical trials.
+"
+26508,Transport coefficients from QCD Kondo effect,"  We study the transport coefficients from the QCD Kondo effect in quark matter
+which contains heavy quarks as impurity particles. We estimate the coupling
+constant of the interaction between a light quark and a heavy quark at finite
+density and temperature by using the renormalization group equation up to
+two-loop order. We also estimate the coupling constant at zero temperature by
+using the mean-field approximation as non-perturbative treatment. To calculate
+the transport coefficients, we use the relativistic Boltzmann equation and
+apply the relaxation time approximation. We calculate the electric resistivity
+from the relativistic kinetic theory, and the viscosities from the relativistic
+hydrodynamics. We find that the electric resistivity is enhanced and the shear
+viscosity is suppressed due to the QCD Kondo effect at low temperature.
+"
+26509,Conceptual Modeling of a Procurement Process: Case study of RFP for Public Key Infrastructure,"  Procurement refers to a process resulting in delivery of goods or services
+within a set time period. The process includes aspects of purchasing,
+specifications to be met, and solicitation notifications as in the case of
+Request For Proposals (RFPs). Typically such an RFP is described in a verbal ad
+hoc fashion, in English, with tables and graphs, resulting in imprecise
+specifications of requirements. It has been proposed that BPMN diagrams be used
+to specify requirements to be included in RFP. This paper is a merger of three
+topics: (i) Procurement development with a focus on operational specification
+of RFP, (ii) Public key infrastructure (PKI) as an RFP subject, and (iii)
+Conceptual modeling that produces a diagram as a supplement to an RFP to
+clarify requirements more precisely than traditional tools such as natural
+language, tables, and ad hoc graphs.
+"
+26510,"Why ""Redefining Statistical Significance"" Will Not Improve Reproducibility and Could Make the Replication Crisis Worse","  A recent proposal to ""redefine statistical significance"" (Benjamin, et al.
+Nature Human Behaviour, 2017) claims that false positive rates ""would
+immediately improve"" by factors greater than two and replication rates would
+double simply by changing the conventional cutoff for 'statistical
+significance' from P<0.05 to P<0.005. I analyze the veracity of these claims,
+focusing especially on how Benjamin, et al neglect the effects of P-hacking in
+assessing the impact of their proposal. My analysis shows that once P-hacking
+is accounted for the perceived benefits of the lower threshold all but
+disappear, prompting two main conclusions: (i) The claimed improvements to
+false positive rate and replication rate in Benjamin, et al (2017) are
+exaggerated and misleading. (ii) There are plausible scenarios under which the
+lower cutoff will make the replication crisis worse.
+"
+26511,Separating the signal from the noise: Evidence for deceleration in old-age death rates,"  Widespread population aging has made it critical to understand death rates at
+old ages. However, studying mortality at old ages is challenging because the
+data are sparse: numbers of survivors and deaths get smaller and smaller with
+age. We show how to address this challenge by using principled model selection
+techniques to empirically evaluate theoretical mortality models. We test nine
+different theoretical models of old-age death rates by fitting them to 360
+high-quality datasets on cohort mortality above age 80. Models that allow for
+the possibility of decelerating death rates tend to fit better than models that
+assume exponentially increasing death rates. No single model is capable of
+universally explaining observed old-age mortality patterns, but the
+Log-Quadratic model most consistently predicts well. Patterns of model fit
+differ by country and sex; we discuss possible mechanisms, including sample
+size, period effects, and regional or cultural factors that may be important
+keys to understanding patterns of old-age mortality. We introduce a freely
+available R package that enables researchers to extend our analysis to other
+models, age ranges, and data sources.
+"
+26512,Intelligent Vehicle-Trust Point: Reward based Intelligent Vehicle Communication using Blockchain,"  The Intelligent vehicle (IV) is experiencing revolutionary growth in research
+and industry, but it still suffers from many security vulnerabilities.
+Traditional security methods are incapable to provide secure IV communication.
+The major issues in IV communication, are trust, data accuracy and reliability
+of communication data in the communication channel. Blockchain technology works
+for the crypto currency, Bit-coin, which is recently used to build trust and
+reliability in peer-to-peer networks having similar topologies as IV
+Communication. In this paper, we are proposing, Intelligent Vehicle-Trust Point
+(IV-TP) mechanism for IV communication among IVs using Blockchain technology.
+The IVs communicated data provides security and reliability using our proposed
+IV-TP. Our IV-TP mechanism provides trustworthiness for vehicles behavior, and
+vehicles legal and illegal action. Our proposal presents a reward based system,
+an exchange of some IV-TP among IVs, during successful communication. For the
+data management of the IV-TP, we are using blockchain technology in the
+intelligent transportation system (ITS), which stores all IV-TP details of
+every vehicle and is accessed ubiquitously by IVs. In this paper, we evaluate
+our proposal with the help of intersection use case scenario for intelligent
+vehicles communication.
+"
+26513,Comparative Performance Analysis of the Cumulative Sum Chart and the Shiryaev-Roberts Procedure for Detecting Changes in Autocorrelated Data,"  We consider the problem of quickest change-point detection where the
+observations form a first-order autoregressive (AR) process driven by
+temporally independent standard Gaussian noise. Subject to possible change are
+both the drift of the AR(1) process ($\mu$) as well as its correlation
+coefficient ($\lambda$), both known. The change is abrupt and persistent, and
+is of known magnitude, with $\vert\lambda\vert<1$ throughout. For this
+scenario, we carry out a comparative performance analysis of the popular
+Cumulative Sum (CUSUM) chart and its less well-known but worthy competitor --
+the Shiryaev-Roberts (SR) procedure. Specifically, the performance is measured
+through Pollak's Supremum (conditional) Average Delay to Detection (SADD)
+constrained to a pre-specified level of the Average Run Length (ARL) to false
+alarm. Particular attention is drawn to the sensitivity of each procedure's
+SADD and ARL with respect to the value of $\lambda$ before and after the
+change. The performance is studied through the solution of the respective
+integral renewal equations obtained via Monte Carlo simulations. The
+simulations are designed to estimate the sought performance metrics in an
+unbiased and asymptotically strongly consistent manner, and to within a
+prescribed proportional closeness (also asymptotically). Our extensive
+numerical studies suggest that both the CUSUM chart and the SR procedure are
+asymptotically second-order optimal, even though the CUSUM chart is found to be
+slightly better than the SR procedure, irrespective of the model parameters.
+Moreover, the existence of a worst-case post-change correlation parameter
+corresponding to the poorest detectability of the change for a given ARL to
+false alarm is established as well. To the best of our knowledge, this is the
+first time the performance of the SR procedure is studied for autocorrelated
+data.
+"
+26514,Data-driven online monitoring of wind turbines,"  Condition based maintenance is a modern approach to maintenance which has
+been successfully used in several industrial sectors. In this paper we present
+a concrete statistical approach to condition based maintenance for wind turbine
+by applying ideas from statistical process control. A specific problem in wind
+turbine maintenance is that failures of a certain part may have causes that
+originate in other parts a long time ago. This calls for methods that can
+produce timely warnings by combining sensor data from different sources. Our
+method improves on existing methods used in wind turbine maintenance by using
+adaptive alarm thresholds for the monitored parameters that correct for values
+of other relevant parameters. We illustrate our method with a case study that
+shows that our method is able to predict upcoming failures much earlier than
+currently used methods.
+"
+26515,Nonparametric independence testing via mutual information,"  We propose a test of independence of two multivariate random vectors, given a
+sample from the underlying population. Our approach, which we call MINT, is
+based on the estimation of mutual information, whose decomposition into joint
+and marginal entropies facilitates the use of recently-developed efficient
+entropy estimators derived from nearest neighbour distances. The proposed
+critical values, which may be obtained from simulation (in the case where one
+marginal is known) or resampling, guarantee that the test has nominal size, and
+we provide local power analyses, uniformly over classes of densities whose
+mutual information satisfies a lower bound. Our ideas may be extended to
+provide a new goodness-of-fit tests of normal linear models based on assessing
+the independence of our vector of covariates and an appropriately-defined
+notion of an error vector. The theory is supported by numerical studies on both
+simulated and real data.
+"
+26516,Efficient Private ERM for Smooth Objectives,"  In this paper, we consider efficient differentially private empirical risk
+minimization from the viewpoint of optimization algorithms. For strongly convex
+and smooth objectives, we prove that gradient descent with output perturbation
+not only achieves nearly optimal utility, but also significantly improves the
+running time of previous state-of-the-art private optimization algorithms, for
+both $\epsilon$-DP and $(\epsilon, \delta)$-DP. For non-convex but smooth
+objectives, we propose an RRPSGD (Random Round Private Stochastic Gradient
+Descent) algorithm, which provably converges to a stationary point with privacy
+guarantee. Besides the expected utility bounds, we also provide guarantees in
+high probability form. Experiments demonstrate that our algorithm consistently
+outperforms existing method in both utility and running time.
+"
+26517,On the incomplete recurrence of modulationally unstable deep-water surface gravity waves,"  The issue of a recurrence of the modulationally unstable water wave trains
+within the framework of the fully nonlinear potential Euler equations is
+addressed. It is examined, in particular, if a modulation which appears from
+nowhere (i.e., is infinitesimal initially) and generates a rogue wave which
+then disappears with no trace. If so, this wave solution would be a breather
+solution of the primitive hydrodynamic equations. It is shown with the help of
+the fully nonlinear numerical simulation that when a rogue wave occurs from a
+uniform Stokes wave train, it excites other waves which have different lengths,
+what prevents the complete recurrence and, eventually, results in a
+quasi-periodic breathing of the wave envelope. Meanwhile the discovered effects
+are rather small in magnitude, and the period of the modulation breathing may
+be thousands of the dominant wave periods. Thus, the obtained solution may be
+called a quasi-breather of the Euler equations.
+"
+26518,Anisotropic functional Fourier deconvolution with long-memory dependent errors: a minimax study,"  We investigate minimax results for the anisotropic functional deconvolution
+model when observations are affected by the presence of long-memory. Under
+specific conditions about the covariance matrices of the errors, we follow a
+standard procedure to construct an adaptive wavelet-based estimator that
+attains asymptotically near-optimal convergence rates. These rates depend on
+the parameter associated with the weakest long-range dependence, and
+deteriorate as the intensity of long-memory increases. This behavior suggests
+that the estimator adjusts to the best case scenario and that the weakest LM
+dominates.
+"
+26519,Graph Equivalence Classes for Spectral Projector-Based Graph Fourier Transforms,"  We define and discuss the utility of two equivalence graph classes over which
+a spectral projector-based graph Fourier transform is equivalent: isomorphic
+equivalence classes and Jordan equivalence classes. Isomorphic equivalence
+classes show that the transform is equivalent up to a permutation on the node
+labels. Jordan equivalence classes permit identical transforms over graphs of
+nonidentical topologies and allow a basis-invariant characterization of total
+variation orderings of the spectral components. Methods to exploit these
+classes to reduce computation time of the transform as well as limitations are
+discussed.
+"
+26520,Optimal designs for regression with spherical data,"  In this paper optimal designs for regression problems with spherical
+predictors of arbitrary dimension are considered. Our work is motivated by
+applications in material sciences, where crystallographic textures such as the
+missorientation distribution or the grain boundary distribution (depending on a
+four dimensional spherical predictor) are represented by series of
+hyperspherical harmonics, which are estimated from experimental or simulated
+data. For this type of estimation problems we explicitly determine optimal
+designs with respect to Kiefers $\Phi_p$-criteria and a class of orthogonally
+invariant information criteria recently introduced in the literature. In
+particular, we show that the uniform distribution on the $m$-dimensional sphere
+is optimal and construct discrete and implementable designs with the same
+information matrices as the continuous optimal designs. Finally, we illustrate
+the advantages of the new designs for series estimation by hyperspherical
+harmonics, which are symmetric with respect to the first and second
+crystallographic point group.
+"
+26521,Real-time policy generation and its application to robot grasping,"  Real time applications such as robotic require real time actions based on the
+immediate available data. Machine learning and artificial intelligence rely on
+high volume of training informative data set to propose a comprehensive and
+useful model for later real time action. Our goal in this paper is to provide a
+solution for robot grasping as a real time application without the time and
+memory consuming pertaining phase. Grasping as one of the most important
+ability of human being is defined as a suitable configuration which depends on
+the perceived information from the object. For human being, the best results
+obtain when one incorporates the vision data such as the extracted edges and
+shape from the object into grasping task. Nevertheless, in robotics, vision
+will not suite for every situation. Another possibility to grasping is using
+the object shape information from its vicinity. Based on these Haptic
+information, similar to human being, one can propose different approaches to
+grasping which are called grasping policies. In this work, we are trying to
+introduce a real time policy which aims at keeping contact with the object
+during movement and alignment on it. First we state problem by system dynamic
+equation incorporated by the object constraint surface into dynamic equation.
+In next step, the suggested policy to accomplish the task in real time based on
+the available sensor information will be presented. The effectiveness of
+proposed approach will be evaluated by demonstration results.
+"
+26522,Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition,"  Named Entity Recognition (NER) is one of the most common tasks of the natural
+language processing. The purpose of NER is to find and classify tokens in text
+documents into predefined categories called tags, such as person names,
+quantity expressions, percentage expressions, names of locations,
+organizations, as well as expression of time, currency and others. Although
+there is a number of approaches have been proposed for this task in Russian
+language, it still has a substantial potential for the better solutions. In
+this work, we studied several deep neural network models starting from vanilla
+Bi-directional Long Short-Term Memory (Bi-LSTM) then supplementing it with
+Conditional Random Fields (CRF) as well as highway networks and finally adding
+external word embeddings. All models were evaluated across three datasets:
+Gareev's dataset, Person-1000, FactRuEval-2016. We found that extension of
+Bi-LSTM model with CRF significantly increased the quality of predictions.
+Encoding input tokens with external word embeddings reduced training time and
+allowed to achieve state of the art for the Russian NER task.
+"
+26523,Measurement of complete and continuous Wigner functions for discrete atomic systems,"  We measure complete and continuous Wigner functions of a two-level cesium
+atom in both a nearly pure state and highly mixed states. We apply the method
+[T. Tilma et al., Phys. Rev. Lett. 117, 180401 (2016)] of strictly constructing
+continuous Wigner functions for qubit or spin systems. We find that the Wigner
+function of all pure states of a qubit has negative regions and the negativity
+completely vanishes when the purity of an arbitrary mixed state is less than
+$\frac{2}{3}$. We experimentally demonstrate these findings using a single
+cesium atom confined in an optical dipole trap, which undergoes a nearly pure
+dephasing process. Our method can be applied straightforwardly to multi-atom
+systems for measuring the Wigner function of their collective spin state.
+"
+26524,Gaussian mixture models with Wasserstein distance,"  Generative models with both discrete and continuous latent variables are
+highly motivated by the structure of many real-world data sets. They present,
+however, subtleties in training often manifesting in the discrete latent being
+under leveraged. In this paper, we show that such models are more amenable to
+training when using the Optimal Transport framework of Wasserstein
+Autoencoders. We find our discrete latent variable to be fully leveraged by the
+model when trained, without any modifications to the objective function or
+significant fine tuning. Our model generates comparable samples to other
+approaches while using relatively simple neural networks, since the discrete
+latent variable carries much of the descriptive burden. Furthermore, the
+discrete latent provides significant control over generation.
+"
+26525,Modeling observations of solar coronal mass ejections with heliospheric imagers verified with the Heliophysics System Observatory,"  We present an advance towards accurately predicting the arrivals of coronal
+mass ejections (CMEs) at the terrestrial planets, including Earth. For the
+first time, we are able to assess a CME prediction model using data over 2/3 of
+a solar cycle of observations with the Heliophysics System Observatory. We
+validate modeling results of 1337 CMEs observed with the Solar Terrestrial
+Relations Observatory (STEREO) heliospheric imagers (HI) (science data) from 8
+years of observations by 5 in situ observing spacecraft. We use the
+self-similar expansion model for CME fronts assuming 60 degree longitudinal
+width, constant speed and constant propagation direction. With these
+assumptions we find that 23%-35% of all CMEs that were predicted to hit a
+certain spacecraft lead to clear in situ signatures, so that for 1 correct
+prediction, 2 to 3 false alarms would have been issued. In addition, we find
+that the prediction accuracy does not degrade with the HI longitudinal
+separation from Earth. Predicted arrival times are on average within 2.6 +/-
+16.6 hours difference of the in situ arrival time, similar to analytical and
+numerical modeling, and a true skill statistic of 0.21. We also discuss various
+factors that may improve the accuracy of space weather forecasting using
+wide-angle heliospheric imager observations. These results form a first order
+approximated baseline of the prediction accuracy that is possible with HI and
+other methods used for data by an operational space weather mission at the
+Sun-Earth L5 point.
+"
+26526,Deep context: end-to-end contextual speech recognition,"  In automatic speech recognition (ASR) what a user says depends on the
+particular context she is in. Typically, this context is represented as a set
+of word n-grams. In this work, we present a novel, all-neural, end-to-end (E2E)
+ASR sys- tem that utilizes such context. Our approach, which we re- fer to as
+Contextual Listen, Attend and Spell (CLAS) jointly- optimizes the ASR
+components along with embeddings of the context n-grams. During inference, the
+CLAS system can be presented with context phrases which might contain out-of-
+vocabulary (OOV) terms not seen during training. We com- pare our proposed
+system to a more traditional contextualiza- tion approach, which performs
+shallow-fusion between inde- pendently trained LAS and contextual n-gram models
+during beam search. Across a number of tasks, we find that the pro- posed CLAS
+system outperforms the baseline method by as much as 68% relative WER,
+indicating the advantage of joint optimization over individually trained
+components. Index Terms: speech recognition, sequence-to-sequence models,
+listen attend and spell, LAS, attention, embedded speech recognition.
+"
+26527,Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network,"  The success of deep convolutional neural network (CNN) in computer vision
+especially image classification problems requests a new information theory for
+function of image, instead of image itself. In this article, after establishing
+a deep mathematical connection between image classification problem and quantum
+spin model, we propose to use entanglement entropy, a generalization of
+classical Boltzmann-Shannon entropy, as a powerful tool to characterize the
+information needed for representation of general function of image. We prove
+that there is a sub-volume-law bound for entanglement entropy of target
+functions of reasonable image classification problems. Therefore target
+functions of image classification only occupy a small subspace of the whole
+Hilbert space. As a result, a neural network with polynomial number of
+parameters is efficient for representation of such target functions of image.
+The concept of entanglement entropy can also be useful to characterize the
+expressive power of different neural networks. For example, we show that to
+maintain the same expressive power, number of channels $D$ in a convolutional
+neural network should scale with the number of convolution layers $n_c$ as
+$D\sim D_0^{\frac{1}{n_c}}$. Therefore, deeper CNN with large $n_c$ is more
+efficient than shallow ones.
+"
+26528,Low-energy modes of spin-imbalanced Fermi gases in BCS phase,"  The low-energy modes of a spin-imbalanced superfluid Fermi gas in the
+Bardeen-Cooper-Schrieffer (BCS) side are studied. The gas is assumed to be
+sufficiently dilute so that the pairing of atoms can be considered effective
+only in s-wave between fermions of different internal state. The order
+parameter at equilibrium is determined by the mean-field approximation, while
+the properties of the collective modes are calculated within a Gaussian
+approximation for the fluctuations of the order parameter. In particular we
+investigate the effects of asymmetry between the populations of the two
+different components and of temperature on the frequency and damping of
+collective modes. It is found that the temperature does not much affect the
+frequency and the damping of the modes, whereas an increase of the imbalance
+shifts the frequency toward lower values and enhances the damping sensitively.
+Besides the Bogoliubov-Anderson phonons, we observe modes at zero frequency for
+finite values of the wave-number. These modes indicate that an instability
+develops driving the system toward two separate phases, normal and superfluid.
+"
+26529,Murphy Diagrams: Forecast Evaluation of Expected Shortfall,"  Motivated by the Basel 3 regulations, recent studies have considered joint
+forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring
+functions can be used to evaluate forecast performance in this context.
+However, little intuitive or empirical guidance is currently available, which
+renders the choice of scoring function awkward in practice. We therefore
+develop graphical checks (Murphy diagrams) of whether one forecast method
+dominates another under a relevant class of scoring functions, and propose an
+associated hypothesis test. We illustrate these tools with simulation examples
+and an empirical analysis of S&P 500 and DAX returns.
+"
+26530,Generalization and Equilibrium in Generative Adversarial Nets (GANs),"  We show that training of generative adversarial network (GAN) may not have
+good generalization properties; e.g., training may appear successful but the
+trained distribution may be far from target distribution in standard metrics.
+However, generalization does occur for a weaker metric called neural net
+distance. It is also shown that an approximate pure equilibrium exists in the
+discriminator/generator game for a special class of generators with natural
+training objectives when generator capacity and training set sizes are
+moderate.
+This existence of equilibrium inspires MIX+GAN protocol, which can be
+combined with any existing GAN training, and empirically shown to improve some
+of them.
+"
+26531,Monte Carlo on manifolds: sampling densities and integrating functions,"  We describe and analyze some Monte Carlo methods for manifolds in Euclidean
+space defined by equality and inequality constraints. First, we give an MCMC
+sampler for probability distributions defined by un-normalized densities on
+such manifolds. The sampler uses a specific orthogonal projection to the
+surface that requires only information about the tangent space to the manifold,
+obtainable from first derivatives of the constraint functions, hence avoiding
+the need for curvature information or second derivatives. Second, we use the
+sampler to develop a multi-stage algorithm to compute integrals over such
+manifolds. We provide single-run error estimates that avoid the need for
+multiple independent runs. Computational experiments on various test problems
+show that the algorithms and error estimates work in practice. The method is
+applied to compute the entropies of different sticky hard sphere systems. These
+predict the temperature or interaction energy at which loops of hard sticky
+spheres become preferable to chains.
+"
+26532,Dark states and delocalization: competing effects of quantum coherence on the efficiency of light harvesting systems,"  Natural light harvesting systems exploit electronic coupling of identical
+chromophores to generate efficient and robust excitation transfer and
+conversion. Dark states created by strong coupling between chromophores in the
+antenna structure can significantly reduce radiative recombination and enhance
+energy conversion efficiency. Increasing the number of the chromophores
+increases the number of dark states and the associated enhanced energy
+conversion efficiency, yet also delocalizes excitations away from the trapping
+center and reduces the energy conversion rate. Therefore, a competition between
+dark state protection and delocalization must be considered when designing the
+optimal size of a light harvesting system. In this study, we explore the two
+competing mechanisms in a chain-structured antenna and show that dark state
+protection is the dominant mechanism, with an intriguing dependence on the
+parity of the number of chromophores. This dependence is linked to the exciton
+distribution among eigenstates which is strongly affected by the coupling
+strength between chromophores and the temperature. Combining these findings, we
+propose that increasing the coupling strength between the chromophores can
+significantly increase the power output of the light harvesting system.
+"
+26533,"Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning","  Long Short-Term Memory (LSTM) is a popular approach to boosting the ability
+of Recurrent Neural Networks to store longer term temporal information. The
+capacity of an LSTM network can be increased by widening and adding layers.
+However, usually the former introduces additional parameters, while the latter
+increases the runtime. As an alternative we propose the Tensorized LSTM in
+which the hidden states are represented by tensors and updated via a
+cross-layer convolution. By increasing the tensor size, the network can be
+widened efficiently without additional parameters since the parameters are
+shared across different locations in the tensor; by delaying the output, the
+network can be deepened implicitly with little additional runtime since deep
+computations for each timestep are merged into temporal computations of the
+sequence. Experiments conducted on five challenging sequence learning tasks
+show the potential of the proposed model.
+"
+26534,Continuous Global Optimization in Surface Reconstruction from an Oriented Point Cloud,"  We introduce a continuous global optimization method to the field of surface
+reconstruction from discrete noisy cloud of points with weak information on
+orientation. The proposed method uses an energy functional combining flux-based
+data-fit measures and a regularization term. A continuous convex relaxation
+scheme assures the global minima of the geometric surface functional. The
+reconstructed surface is implicitly represented by the binary segmentation of
+vertices of a 3D uniform grid and a triangulated surface can be obtained by
+extracting an appropriate isosurface. Unlike the discrete graph-cut solution,
+the continuous global optimization entails advantages like memory requirements,
+reduction of metrication errors for geometric quantities, allowing globally
+optimal surface reconstruction at higher grid resolutions. We demonstrate the
+performance of the proposed method on several oriented point clouds captured by
+laser scanners. Experimental results confirm that our approach is robust to
+noise, large holes and non-uniform sampling density under the condition of very
+coarse orientation information.
+"
+26535,Multielectron effects in strong field ionization of CO$_2$: impact on differential photoelectron spectra,"  We report fully differential photoelectron spectra from an {\it ab-inito}
+coupled channels treatment of CO$_2$. Photoionization by laser pulses centered
+at 400 nm and 800 nm wavelength are considered, with arbitrary molecular
+alignment and polarization (linear and elliptic). Calculations reveal
+significant excited state channel contributions that are, at certain molecular
+orientations, an order of magnitude larger than the ground state channel in the
+rescattering plateau. Partial wash out of the multiphoton structure in the ATI
+spectra and of the nodal features in angle resolved spectra is observed due to
+ionization to excited state channels. The qualitative nature of the spectra is
+determined by ionization thresholds, orbital symmetries and interchannel
+coupling in the order of precedence.
+"
+26536,Multimodal Interactive Learning of Primitive Actions,"  We describe an ongoing project in learning to perform primitive actions from
+demonstrations using an interactive interface. In our previous work, we have
+used demonstrations captured from humans performing actions as training samples
+for a neural network-based trajectory model of actions to be performed by a
+computational agent in novel setups. We found that our original framework had
+some limitations that we hope to overcome by incorporating communication
+between the human and the computational agent, using the interaction between
+them to fine-tune the model learned by the machine. We propose a framework that
+uses multimodal human-computer interaction to teach action concepts to
+machines, making use of both live demonstration and communication through
+natural language, as two distinct teaching modalities, while requiring few
+training samples.
+"
+26537,Simultaneous merging multiple grid maps using the robust motion averaging,"  Mapping in the GPS-denied environment is an important and challenging task in
+the field of robotics. In the large environment, mapping can be significantly
+accelerated by multiple robots exploring different parts of the environment.
+Accordingly, a key problem is how to integrate these local maps built by
+different robots into a single global map. In this paper, we propose an
+approach for simultaneous merging of multiple grid maps by the robust motion
+averaging. The main idea of this approach is to recover all global motions for
+map merging from a set of relative motions. Therefore, it firstly adopts the
+pair-wise map merging method to estimate relative motions for grid map pairs.
+To obtain as many reliable relative motions as possible, a graph-based sampling
+scheme is utilized to efficiently remove unreliable relative motions obtained
+from the pair-wise map merging. Subsequently, the accurate global motions can
+be recovered from the set of reliable relative motions by the motion averaging.
+Experimental results carried on real robot data sets demonstrate that proposed
+approach can achieve simultaneous merging of multiple grid maps with good
+performances.
+"
+26538,ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks,"  A fundamental feature of learning in animals is the ""ability to forget"" that
+allows an organism to perceive, model and make decisions from disparate streams
+of information and adapt to changing environments. Against this backdrop, we
+present a novel unsupervised learning mechanism ASP (Adaptive Synaptic
+Plasticity) for improved recognition with Spiking Neural Networks (SNNs) for
+real time on-line learning in a dynamic environment. We incorporate an adaptive
+weight decay mechanism with the traditional Spike Timing Dependent Plasticity
+(STDP) learning to model adaptivity in SNNs. The leak rate of the synaptic
+weights is modulated based on the temporal correlation between the spiking
+patterns of the pre- and post-synaptic neurons. This mechanism helps in gradual
+forgetting of insignificant data while retaining significant, yet old,
+information. ASP, thus, maintains a balance between forgetting and immediate
+learning to construct a stable-plastic self-adaptive SNN for continuously
+changing inputs. We demonstrate that the proposed learning methodology
+addresses catastrophic forgetting while yielding significantly improved
+accuracy over the conventional STDP learning method for digit recognition
+applications. Additionally, we observe that the proposed learning model
+automatically encodes selective attention towards relevant features in the
+input data while eliminating the influence of background noise (or denoising)
+further improving the robustness of the ASP learning.
+"
+26539,Control of a Bucket-Wheel for Surface Mining of Asteroids and Small-Bodies,"  Near Earth Asteroids (NEAs) are thought to contain a wealth of resources,
+including water, iron, titanium, nickel, platinum and silicates. Future space
+missions that can exploit these resources by performing In-Situ Resource
+Utilization (ISRU) gain substantial benefit in terms of range, payload capacity
+and mission flexibility. Compared to the Moon or Mars, the milligravity on some
+asteroids demands a fraction of the energy for digging and accessing hydrated
+regolith just below the surface. However, asteroids and small-bodies, because
+of their low gravity present a major challenge in landing, surface excavation
+and resource capture. These challenges have resulted in adoption of a ""touch
+and go techniques"", like the upcoming Osiris-rex sample-return mission.
+Previous asteroid excavation efforts have focused on discrete capture events
+(an extension of sampling technology) or whole-asteroid capture and processing.
+This paper analyzes the control of a bucket-wheel design for asteroid or
+small-body excavation. Our study focuses on system design of two counter
+rotating bucket-wheels that are attached to a hovering spacecraft. Regolith is
+excavated and heated to 1000 C to extract water. The water in turn is
+electrolyzed to produce hydrogen and oxygen for rocket fuel. We analyze control
+techniques to maximize traction of the bucket-wheels on the asteroid surface
+and minimize lift-off the surface, together with methods to dig deeper into the
+asteroid surface. Our studies combine analytical models, with simulation and
+hardware testing. For initial evaluation of material-spacecraft dynamics and
+mechanics, we assume lunar-like regolith for bulk density, particle size and
+cohesion. Our early studies point towards a promising pathway towards
+refinement of this technology for demonstration aboard a future space mission.
+"
+26540,Constraining holographic cosmology using Planck data,"  Holographic cosmology offers a novel framework for describing the very early
+Universe in which cosmological predictions are expressed in terms of the
+observables of a three dimensional quantum field theory (QFT). This framework
+includes conventional slow-roll inflation, which is described in terms of a
+strongly coupled QFT, but it also allows for qualitatively new models for the
+very early Universe, where the dual QFT may be weakly coupled. The new models
+describe a universe which is non-geometric at early times. While standard
+slow-roll inflation leads to a (near-)power-law primordial power spectrum,
+perturbative superrenormalizable QFT's yield a new holographic spectral shape.
+Here, we compare the two predictions against cosmological observations. We use
+CosmoMC to determine the best fit parameters, and MultiNest for Bayesian
+Evidence, comparing the likelihoods. We find that the dual QFT should be
+non-perturbative at the very low multipoles ($l \lesssim 30$), while for higher
+multipoles ($l \gtrsim 30$) the new holographic model, based on perturbative
+QFT, fits the data just as well as the standard power-law spectrum assumed in
+$\Lambda$CDM cosmology. This finding opens the door to applications of
+non-perturbative QFT techniques, such as lattice simulations, to observational
+cosmology on gigaparsec scales and beyond.
+"
+26541,Light Absorption in Arctic Sea Ice: Chlorophyll and Black Carbon,"  Arctic sea ice extent has declined continuously for the past decade, owing
+partially to light absorption by black carbon (BC) and other impurities
+deposited on snow and the underlying pack. We present simulations for the
+contemporary period showing that the optical depth contributed by Arctic ice
+algal chlorophyll may be comparable during Boreal Spring to the corresponding
+values attributable to BC. The largest chlorophyll attenuation is obtained in
+the bottom layer, which supports pigment concentrations of about 300 to 1000
+mg/m3 in the Bering Sea and Sea of Okhotsk. However, chlorophyll concentrations
+for the ice interior in regions north of 75° N and across the Canadian
+Archipelago are less than 0.1 micro g/m3. Freeboard and infiltration
+communities lead to intermediate levels of light removal. Since BC works its
+way downward from the atmospheric interface, there will be regions where it
+appropriates photosynthetic capability. Where ice thicknesses permit
+significant penetration through the pack column the ice algae may be crucial
+absorbers. We propose a continuous increase in relative chlorophyll activity
+and attenuation in the future, as biological activity becomes stronger in thin
+ice toward the center of the Arctic basin. A shift in relative importance of
+the two absorber types could occur as total BC mixing ratios are reduced
+because of environmental advocacy.
+"
+26542,Asymptotic estimates of entire functions of bounded $\mathbf{L}$-index in joint variables,"  In this paper, there are obtained growth estimates of entire in
+$\mathbb{C}^n$ function of bounded $\mathbf{L}$-index in joint variables. They
+describe the behaviour of maximum modulus of entire function on a skeleton in a
+polydisc by behaviour of the function $\mathbf{L}(z)=(l_1(z),\ldots,l_n(z)),$
+where for every $j\in\{1,\ldots, n\}$ \ $l_j:\mathbb{C}^n\to \mathbb{R}_+$ is a
+continuous function. We generalised known results of W. K. Hayman, M. M.
+Sheremeta, A. D. Kuzyk, M. T. Borduyak, T. O. Banakh and V. O. Kushnir for a
+wider class of functions $\mathbf{L}.$ One of our estimates is sharper even for
+entire in $\mathbb{C}$ functions of bounded $l$-index than Sheremeta's
+estimate.
+"
+26543,μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching,"  NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used
+in deep learning. Specifically, cuDNN implements several equivalent convolution
+algorithms, whose performance and memory footprint may vary considerably,
+depending on the layer dimensions. When an algorithm is automatically selected
+by cuDNN, the decision is performed on a per-layer basis, and thus it often
+resorts to slower algorithms that fit the workspace size constraints. We
+present {\mu}-cuDNN, a transparent wrapper library for cuDNN, which divides
+layers' mini-batch computation into several micro-batches. Based on Dynamic
+Programming and Integer Linear Programming, {\mu}-cuDNN enables faster
+algorithms by decreasing the workspace requirements. At the same time,
+{\mu}-cuDNN keeps the computational semantics unchanged, so that it decouples
+statistical efficiency from the hardware efficiency safely. We demonstrate the
+effectiveness of {\mu}-cuDNN over two frameworks, Caffe and TensorFlow,
+achieving speedups of 1.63x for AlexNet and 1.21x for ResNet-18 on P100-SXM2
+GPU. These results indicate that using micro-batches can seamlessly increase
+the performance of deep learning, while maintaining the same memory footprint.
+"
+26544,A New Technique for Reachability of States in Concatenation Automata,"  We present a new technique for demonstrating the reachability of states in
+deterministic finite automata representing the concatenation of two languages.
+Such demonstrations are a necessary step in establishing the state complexity
+of the concatenation of two languages, and thus in establishing the state
+complexity of concatenation as an operation. Typically, ad-hoc induction
+arguments are used to show particular states are reachable in concatenation
+automata. We prove some results that seem to capture the essence of many of
+these induction arguments. Using these results, reachability proofs in
+concatenation automata can often be done more simply and without using
+induction directly.
+"
+26545,Mitigating Read-disturbance Errors in STT-RAM Caches by Using Data Compression,"  Due to its high density and close-to-SRAM read latency, spin transfer torque
+RAM (STT-RAM) is considered one of the most-promising emerging memory
+technologies for designing large last level caches (LLCs). However, in deep
+sub-micron region, STT-RAM shows read-disturbance error (RDE) whereby a read
+operation may modify the stored data value and this presents a severe threat to
+performance and reliability of STT-RAM caches. In this paper, we present a
+technique, named SHIELD, to mitigate RDE in STT-RAM LLCs. SHIELD uses data
+compression to reduce number of read operations from STT-RAM blocks to avoid
+RDE and also to reduce the number of bits written to cache during both write
+and restore operations. Experimental results have shown that SHIELD provides
+significant improvement in performance and energy efficiency. SHIELD consumes
+smaller energy than two previous RDE-mitigation techniques, namely high-current
+restore required read (HCRR, also called restore-after-read) and low-current
+long latency read (LCLL) and even an ideal RDE-free STT-RAM cache.
+"
+26546,Constrained Receiver Scheduling in Flexible Time and Wavelength Division Multiplexed Optical,"  An increasing bandwidth demand has mandated a shift to the time and
+wavelength division multiplexing (TWDM) techniques in optical access networks
+(OAN). Typical TWDM scheduling schemes consider scheduling of the optical line
+terminal receiver only. In this paper we have identified an additional
+collision domain that is present in TWDM schemes that offer security, in
+addition to bandwidth flexibility. Scheduling of the identified collision
+domain is termed as group scheduling. We illustrate that consideration of
+receiver scheduling only (as done in typical TWDM schemes) severely affects
+their throughput when implemented on flexible and secure TWDM architectures. A
+novel media access control protocol has been proposed in this paper that
+considers the multiple collision domains. Through simulations, we are able to
+illustrate that the proposed scheme achieves a high throughput. A theoretical
+upper bound of throughput has also been derived to explain the simulation
+results. Complexity reduction of the proposed scheme has been illustrated,
+thereby making it an attractive proposal.
+"
+26547,Galactic Winds with MUSE: A Direct Detection of FeII* Emission from a z = 1.29 Galaxy,"  Emission signatures from galactic winds provide an opportunity to directly
+map the outflowing gas, but this is traditionally challenging because of the
+low surface brightness. Using deep observations (27 hours) of the Hubble Deep
+Field South from the Multi Unit Spectroscopic Explorer (MUSE) instrument, we
+identify signatures of an outflow in both emission and absorption from a
+spatially resolved galaxy at z = 1.29 with a stellar mass M* = 8 x 10^9 Msun,
+star formation rate SFR = 77 Msun/yr, and star formation rate surface
+brightness 1.6 Msun/kpc^2 within the [OII] half-light radius R_1/2,[OII] = 2.76
++- 0.17 kpc. From a component of the strong resonant MgII and FeII absorptions
+at -350 km/s, we infer a mass outflow rate that is comparable to the star
+formation rate. We detect non-resonant FeII* emission, at lambda 2626, 2612,
+2396, and 2365, at 1.2-2.4-1.5-2.7 x 10^-18 egs s-1 cm-2 respectively. These
+flux ratios are consistent with the expectations for optically thick gas. By
+combining the four non-resonant FeII* emission lines, we spatially map the
+FeII* emission from an individual galaxy for the first time. The FeII* emission
+has an elliptical morphology that is roughly aligned with the galaxy minor
+kinematic axis, and its integrated half-light radius R_1/2,FeII* = 4.1 +- 0.4
+kpc is 50% larger than the stellar continuum (R_1/2,* = 2.34 +- 0.17 kpc) or
+the [OII] nebular line. Moreover, the FeII* emission shows a blue wing
+extending up to -400 km/s, which is more pronounced along the galaxy minor
+kinematic axis and reveals a C-shaped pattern in a p-v diagram along that axis.
+These features are consistent with a bi-conical outflow.
+"
+26548,Geometric Loss Functions for Camera Pose Regression with Deep Learning,"  Deep learning has shown to be effective for robust and real-time monocular
+image relocalisation. In particular, PoseNet is a deep convolutional neural
+network which learns to regress the 6-DOF camera pose from a single image. It
+learns to localize using high level features and is robust to difficult
+lighting, motion blur and unknown camera intrinsics, where point based SIFT
+registration fails. However, it was trained using a naive loss function, with
+hyper-parameters which require expensive tuning. In this paper, we give the
+problem a more fundamental theoretical treatment. We explore a number of novel
+loss functions for learning camera pose which are based on geometry and scene
+reprojection error. Additionally we show how to automatically learn an optimal
+weighting to simultaneously regress position and orientation. By leveraging
+geometry, we demonstrate that our technique significantly improves PoseNet's
+performance across datasets ranging from indoor rooms to a small city.
+"
+26549,Deep imaging search for planets forming in the TW Hya protoplanetary disk with the Keck/NIRC2 vortex coronagraph,"  Distinct gap features in the nearest protoplanetary disk, TW Hya (distance of
+59.5$\pm$0.9 pc), may be signposts of ongoing planet formation. We performed
+long-exposure thermal infrared coronagraphic imaging observations to search for
+accreting planets especially within dust gaps previously detected in scattered
+light and submm-wave thermal emission. Three nights of observations with the
+Keck/NIRC2 vortex coronagraph in $L^\prime$ (3.4-4.1$\mu$m) did not reveal any
+statistically significant point sources. We thereby set strict upper limits on
+the masses of non-accreting planets. In the four most prominent disk gaps at
+24, 41, 47, and 88 au, we obtain upper mass limits of 1.6-2.3, 1.1-1.6,
+1.1-1.5, and 1.0-1.2 Jupiter masses ($M_J$) assuming an age range of 7-10 Myr
+for TW Hya. These limits correspond to the contrast at 95\% completeness (true
+positive fraction of 0.95) with a 1\% chance of a false positive within
+$1^{\prime\prime}$ of the star. We also approximate an upper limit on the
+product of planet mass and planetary accretion rate of
+$M_p\dot{M}\lesssim10^{-8} M_J^2/yr$ implying that any putative $\sim0.1 M_J$
+planet, which could be responsible for opening the 24 au gap, is presently
+accreting at rates insufficient to build up a Jupiter mass within TW Hya's
+pre-main sequence lifetime.
+"
+26550,Online Convex Optimization with Time-Varying Constraints,"  This paper considers online convex optimization with time-varying constraint
+functions. Specifically, we have a sequence of convex objective functions
+$\{f_t(x)\}_{t=0}^{\infty}$ and convex constraint functions
+$\{g_{t,i}(x)\}_{t=0}^{\infty}$ for $i \in \{1, ..., k\}$. The functions are
+gradually revealed over time. For a given $\epsilon>0$, the goal is to choose
+points $x_t$ every step $t$, without knowing the $f_t$ and $g_{t,i}$ functions
+on that step, to achieve a time average at most $\epsilon$ worse than the best
+fixed-decision that could be chosen with hindsight, subject to the time average
+of the constraint functions being nonpositive. It is known that this goal is
+generally impossible. This paper develops an online algorithm that solves the
+problem with $O(1/\epsilon^2)$ convergence time in the special case when all
+constraint functions are nonpositive over a common subset of $\mathbb{R}^n$.
+Similar performance is shown in an expected sense when the common subset
+assumption is removed but the constraint functions are assumed to vary
+according to a random process that is independent and identically distributed
+(i.i.d.) over time slots $t \in \{0, 1, 2, \ldots\}$. Finally, in the special
+case when both the constraint and objective functions are i.i.d. over time
+slots $t$, the algorithm is shown to come within $\epsilon$ of optimality with
+respect to the best (possibly time-varying) causal policy that knows the full
+probability distribution.
+"
+26551,Non-Abelian topological phases in three spatial dimensions from coupled wires,"  Starting from an array of interacting fermionic quantum wires, we construct a
+family of non-Abelian topologically ordered states of matter in three spatial
+dimensions (3D). These states of matter inherit their non-Abelian topological
+properties from the $su(2)^{\,}_{k}$ conformal field theories that characterize
+the constituent interacting quantum wires in the decoupled limit. Thus, the
+resulting topological phases can be viewed as 3D generalizations of the
+(bosonic) $su(2)^{\,}_{k}$ Read-Rezayi sequence of fractional quantum Hall
+states. Focusing in detail on the $su(2)^{\,}_{2}$ case, we first review how to
+determine the nature of the non-Abelian topological order (in particular, the
+topological degeneracy on the torus) in the two-dimensional (2D) case, before
+generalizing this approach to the 3D case. We also investigate the 2D boundary
+of the 3D phases, and show for the $su(2)^{\,}_{2}$ case that there are
+anomalous gapless surface states protected by an analog of time-reversal
+symmetry, similar to the massless Dirac surface states of the noninteracting 3D
+topological insulator.
+"
+26552,Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception,"  This paper presents novel Gaussian process decentralized data fusion
+algorithms exploiting the notion of agent-centric support sets for distributed
+cooperative perception of large-scale environmental phenomena. To overcome the
+limitations of scale in existing works, our proposed algorithms allow every
+mobile sensing agent to choose a different support set and dynamically switch
+to another during execution for encapsulating its own data into a local summary
+that, perhaps surprisingly, can still be assimilated with the other agents'
+local summaries (i.e., based on their current choices of support sets) into a
+globally consistent summary to be used for predicting the phenomenon. To
+achieve this, we propose a novel transfer learning mechanism for a team of
+agents capable of sharing and transferring information encapsulated in a
+summary based on a support set to that utilizing a different support set with
+some loss that can be theoretically bounded and analyzed. To alleviate the
+issue of information loss accumulating over multiple instances of transfer
+learning, we propose a new information sharing mechanism to be incorporated
+into our algorithms in order to achieve memory-efficient lazy transfer
+learning. Empirical evaluation on real-world datasets show that our algorithms
+outperform the state-of-the-art methods.
+"
+26553,Mapping Instructions and Visual Observations to Actions with Reinforcement Learning,"  We propose to directly map raw visual observations and text input to actions
+for instruction execution. While existing approaches assume access to
+structured environment representations or use a pipeline of separately trained
+models, we learn a single model to jointly reason about linguistic and visual
+input. We use reinforcement learning in a contextual bandit setting to train a
+neural network agent. To guide the agent's exploration, we use reward shaping
+with different forms of supervision. Our approach does not require intermediate
+representations, planning procedures, or training different models. We evaluate
+in a simulated environment, and show significant improvements over supervised
+learning and common reinforcement learning variants.
+"
+26554,Localized Higgs modes of superfluid Bose gases in optical lattices: A Guzwiller mean-field study,"  We study effects of a potential barrier on collective modes of superfluid
+Bose gases in optical lattices. We assume that the barrier is created by local
+suppression of the hopping amplitude. When the system is in a close vicinity of
+the Mott transition at commensurate fillings, where an approximate
+particle-hole symmetry emerges, there exist bound states of Higgs amplitude
+mode that are localized around the barrier. By applying the Gutzwiller
+mean-field approximation to the Bose-Hubbard model, we analyze properties of
+normal modes of the system with a special focus on the Higgs bound states. We
+show that when the system becomes away from the Mott transition point, the
+Higgs bound states turn into quasi-bound states due to inevitable breaking of
+the particle-hole symmetry. We use a stabilization method to compute the
+resonance energy and line width of the quasi-bound states. We compare the
+results obtained by the Gutzwiller approach with those by the Ginzburg-Landau
+theory. We find that the Higgs bound states survive even in a parameter region
+far from the Mott transition, where the Ginzburg-Landau theory fails.
+"
+26555,To Infinity and Beyond,"  We prove that if a group generated by a bireversible Mealy automaton contains
+an element of infinite order, its growth blows up and is necessarily
+exponential. As a direct consequence, no infinite virtually nilpotent group can
+be generated by a bireversible Mealy automaton.
+"
+26556,Barcodes of Towers and a Streaming Algorithm for Persistent Homology,"  A tower is a sequence of simplicial complexes connected by simplicial maps.
+We show how to compute a filtration, a sequence of nested simplicial complexes,
+with the same persistent barcode as the tower. Our approach is based on the
+coning strategy by Dey et al. (SoCG 2014). We show that a variant of this
+approach yields a filtration that is asymptotically only marginally larger than
+the tower and can be efficiently computed by a streaming algorithm, both in
+theory and in practice. Furthermore, we show that our approach can be combined
+with a streaming algorithm to compute the barcode of the tower via matrix
+reduction. The space complexity of the algorithm does not depend on the length
+of the tower, but the maximal size of any subcomplex within the tower.
+"
+26557,Punny Captions: Witty Wordplay in Image Descriptions,"  Wit is a form of rich interaction that is often grounded in a specific
+situation (e.g., a comment in response to an event). In this work, we attempt
+to build computational models that can produce witty descriptions for a given
+image. Inspired by a cognitive account of humor appreciation, we employ
+linguistic wordplay, specifically puns, in image descriptions. We develop two
+approaches which involve retrieving witty descriptions for a given image from a
+large corpus of sentences, or generating them via an encoder-decoder neural
+network architecture. We compare our approach against meaningful baseline
+approaches via human studies and show substantial improvements. We find that
+when a human is subject to similar constraints as the model regarding word
+usage and style, people vote the image descriptions generated by our model to
+be slightly wittier than human-written witty descriptions. Unsurprisingly,
+humans are almost always wittier than the model when they are free to choose
+the vocabulary, style, etc.
+"
+26558,"Parameter Estimation in Gaussian Mixture Models with Malicious Noise, without Balanced Mixing Coefficients","  We consider the problem of estimating means of two Gaussians in a 2-Gaussian
+mixture, which is not balanced and is corrupted by noise of an arbitrary
+distribution. We present a robust algorithm to estimate the parameters,
+together with upper bounds on the numbers of samples required for the estimate
+to be correct, where the bounds are parametrised by the dimension, ratio of the
+mixing coefficients, a measure of the separation of the two Gaussians, related
+to Mahalanobis distance, and a condition number of the covariance matrix. In
+theory, this is the first sample-complexity result for imbalanced mixtures
+corrupted by adversarial noise. In practice, our algorithm outperforms the
+vanilla Expectation-Maximisation (EM) algorithm in terms of estimation error.
+"
+26559,Consistent Multitask Learning with Nonlinear Output Relations,"  Key to multitask learning is exploiting relationships between different tasks
+to improve prediction performance. If the relations are linear, regularization
+approaches can be used successfully. However, in practice assuming the tasks to
+be linearly related might be restrictive, and allowing for nonlinear structures
+is a challenge. In this paper, we tackle this issue by casting the problem
+within the framework of structured prediction. Our main contribution is a novel
+algorithm for learning multiple tasks which are related by a system of
+nonlinear equations that their joint outputs need to satisfy. We show that the
+algorithm is consistent and can be efficiently implemented. Experimental
+results show the potential of the proposed method.
+"
+26560,Exploratory and Confirmatory Factor Analyses of Religiosity. A Four-Factor Conceptual Model,"  We describe an exploratory and confirmatory factor analysis of the
+International Social Survey Programme Religion Cumulation (1991-1998-2008) data
+set, to identify the factors of individual religiosity and their interrelations
+in quantitative terms. The exploratory factor analysis was performed using data
+from the first two waves (1991 and 1998), and led to the identification of four
+strongly correlated and reliable factors which we labeled Religious formation,
+Supernatural beliefs, Belief in God, and Religious practice. The confirmatory
+factor analysis was run using data from 2008, and led to the confirmation of
+this four-factor structure with very good fit measures. We also ran a set of
+structural equation models in an attempt to determine the causality links
+between these four factors. It was found that for the models which provide the
+best fit Belief in God does not cause Supernatural beliefs, Religious practice
+can cause Belief in God and that there are multiple paths leading to Belief in
+God, most of which include Religious formation as a source. The exploratory
+factor analysis also led to the identification of other factors related to
+traditional values, confidence in institutions and influence of religious
+leaders on politics, but these were found to have lower reliability or
+insufficient number of items to meet the acceptance criteria, and thus were not
+included in the confirmatory factor analysis and the investigation of causal
+links. The results obtained in this work have important material implications
+for the conceptualization of ""religiosity,"" and important methodological
+implications for the scientific study of religion.
+"
+26561,Two stage pressure-induced Yb valence change in the Hexagonal Laves Phase YbAg$_2$: Investigation by time differential perturbed angular $γ$-$γ$ correlation spectroscopy method and density functional calculations,"  We have studied the C14 hexagonal Laves phase of YbAg$_2$ at normal
+conditions and under external pressure up to 19 GPa by the time-differential
+perturbed angular $\gamma-\gamma$ correlation spectroscopy (TDPAC) using
+$^{111}$Cd probe nuclei. Under pressure the valence of Yb undergoes a two stage
+transition from 2.8 to 3. The two stage scenario is characterized by two
+distinct quadrupole frequencies of $^{111}$Cd probes in silver sublattice,
+monotonically increasing with pressure and saturating at 8 and 16 GPa. Our
+experimental data are compared with the density functional studies of the
+electron band structure of YbAg$_2$, whose results are used for discussion and
+interpretation of these experiments. We have found that there are two different
+electric field gradients at inequivalent silver sites and that $4d$-states of
+silver participate in metal bonding, allowing for the formation of the
+hexagonal Laves phase.
+"
+26562,Can Topological Transitions be Exploited to Engineer Intrinsically Quench-resistant Wires?,"  We investigate whether by synthesising superconductors that are tuned to a
+topological, node-reconstruction transition point we could create
+superconducting wires that are intrinsically resilient to quenches. Recent work
+shows that the exponent characterising the temperature dependence of the
+specific heat of a nodal superconductor is lowered over a region of the phase
+diagram near topological transitions where nodal lines form or reconnect. Our
+idea is that the resulting enhancement of the low-temperature specific heat
+could have potential application in the prevention of superconductor quenches.
+We perform numerical simulations of a simplified superconductor quench model.
+Results show that decreasing the specific heat exponent can prevent a quench
+from occurring and improve quench resilience, though in our simple model the
+effects are small. Further work will be necessary to establish the practical
+feasibility of this approach.
+"
+26563,$t$-aspect subconvexity for $GL(2)$ $L$-functions,"  Let $f$ be a holomorphic cusp form for $SL_2(\mathbb{Z})$ of weight $k>1$. In
+these notes, we follow Munshi to prove the Burgess bound $$
+L(1/2+it,f)\ll_{f,\varepsilon} (1+|t|)^{1/2-1/8+\varepsilon}. $$
+"
+26564,Mathematical Modeling of Biofilm Development,"  We perform mathematical anaysis of the biofilm development process. A model
+describing biomass growth is proposed: It arises from coupling three parabolic
+nonlinear equations: a biomass equation with degenerate and singular diffusion,
+a nutrient tranport equation with a biomass-density dependent diffusion, and an
+equation of the Navier-Stokes type, describing the fluid flow in which the
+biofilm develops. This flow is subject to a biomass--density dependent
+obstacle. The model is treated as a system of three inclusions, or variational
+inequalities; the third one causes major difficulties for the system's
+solvability. Our approach is based on the recent development of the theory on
+Navier-Stokes variational inequalities.
+"
+26565,Spread of the dust temperature distribution in circumstellar disks,"  Accurate temperature calculations for circumstellar disks are particularly
+important for their chemical evolution. Their temperature distribution is
+determined by the optical properties of the dust grains, which, among other
+parameters, depend on their radius. However, in most disk studies, only average
+optical properties and thus an average temperature is assumed to account for an
+ensemble of grains with different radii. We investigate the impact of
+subdividing the grain radius distribution into multiple sub-intervals on the
+resulting dust temperature distribution and spectral energy distribution (SED).
+These quantities were computed for two different scenarios: (1) Radius
+distribution represented by 16 logarithmically distributed radius intervals,
+and (2) radius distribution represented by a single grain species with averaged
+optical properties (reference). Within the considered parameter range, i.e., of
+grain radii between 5 nm and 1 mm and an optically thin and thick disk with a
+parameterized density distribution, we obtain the following results: In
+optically thin disk regions, the temperature spread can be as large as ~63% and
+the relative grain surface below a certain temperature is lower than in the
+reference disk. With increasing optical depth, the difference in the midplane
+temperature and the relative grain surface below a certain temperature
+decreases. Furthermore, below ~20K, this fraction is higher for the reference
+disk than for the case of multiple grain radii, while it shows the opposite
+behavior for temperatures above this threshold. The thermal emission in the
+case of multiple grain radii at short wavelengths is stronger than for the
+reference disk. The freeze-out radius is a function of grain radius, spanning a
+radial range between the coldest and warmest grain species of ~30AU.
+"
+26566,Upsilon invariants of L-space cable knots,"  We compute the Upsilon invariant of L-space cable knots $K_{p,q}$ in terms of
+$p,\Upsilon_K$ and $\Upsilon_{T_{p,q}}$. The integral value of the Upsilon
+invariant gives a ${\mathbb Q}$-valued knot concordance invariant. We also
+compute the integral values of the Upsilon of L-space cable knots.
+"
+26567,Koopman analysis of Burgers equation,"  The emergence of Dynamic Mode Decomposition (DMD) as a practical way to
+attempt a Koopman mode decomposition of a nonlinear PDE presents exciting
+prospects for identifying invariant sets and slowly decaying transient
+structures buried in the PDE dynamics. However, there are many subtleties in
+connecting DMD to Koopman analysis and it remains unclear how realistic Koopman
+analysis is for complex systems such as the Navier-Stokes equations. With this
+as motivation, we present here a full Koopman decomposition for the velocity
+field in Burgers equation by deriving explicit expressions for the Koopman
+modes and eigenfunctions - the first time this has been done for a nonlinear
+PDE. The decomposition highlights the fact that different observables can
+require different subsets of Koopman eigenfunctions to express them and
+presents a nice example where: (i) the Koopman modes are linearly dependent and
+so cannot be fit a posteriori to snapshots of the flow without knowledge of the
+Koopman eigenfunctions; and (ii) the Koopman eigenvalues are highly degenerate
+which means that computed Koopman modes become initial-condition dependent. As
+way of illustration, we discuss the form of the Koopman expansion with various
+initial conditions and assess the capability of DMD to extract the decaying
+nonlinear coherent structures in run-down simulations.
+"
+26568,Galaxy and Mass Assembly (GAMA): Probing the merger histories of massive galaxies via stellar populations,"  The merging history of galaxies can be traced with studies of dynamically
+close pairs. These consist of a massive primary galaxy and a less massive
+secondary (or satellite) galaxy. The study of the stellar populations of
+secondary (lower mass) galaxies in close pairs provides a way to understand
+galaxy growth by mergers. Here we focus on systems involving at least one
+massive galaxy - with stellar mass above $10^{11}M_\odot$ in the highly
+complete GAMA survey. Our working sample comprises 2,692 satellite galaxy
+spectra (0.1<z<0.3). These spectra are combined into high S/N stacks, and
+binned according to both an ""internal"" parameter, the stellar mass of the
+satellite galaxy (i.e. the secondary), and an ""external"" parameter, selecting
+either the mass of the primary in the pair, or the mass of the corresponding
+dark matter halo. We find significant variations in the age of the populations
+with respect to environment. At fixed mass, satellites around the most massive
+galaxies are older and possibly more metal rich, with age differences ~1-2Gyr
+within the subset of lower mass satellites ($\sim 10^{10}M_\odot$). These
+variations are similar when stacking with respect to the halo mass of the group
+where the pair is embedded. The population trends in the lower-mass satellites
+are consistent with the old stellar ages found in the outer regions of massive
+galaxies.
+"
+26569,Visualizing Topographic Independent Component Analysis with Movies,"  Independent component analysis (ICA) has often been used as a tool to model
+natural image statistics by separating multivariate signals in the image into
+components that are assumed to be independent. However, these estimated
+components oftentimes have higher order dependencies, such as co-activation of
+components, that are not accounted for in the model. Topographic independent
+component analysis(TICA), a modification of ICA, takes into account higher
+order dependencies and orders components topographically as a function of
+dependence. Here, we aim to visualize the time course of TICA basis activations
+to movie stimuli. We find that the activity of TICA bases are often clustered
+and move continuously, potentially resembling activity of topographically
+organized cells in the visual cortex.
+"
+26570,Isolating the chiral contribution in optical two-dimensional chiral spectroscopy using linearly polarized light,"  The full development of mono- or multi-dimensional time-resolved spectroscopy
+techniques incorporating optical activity signals has been strongly hampered by
+the challenge of identifying the small chiral signals over the large achiral
+background. Here we propose a new methodology to isolate chiral signals
+removing the achiral background from two commonly used configurations for
+performing two dimensional optical spectroscopy, known as BOXCARS and GRadient
+Assisted Photon Echo Spectroscopy (GRAPES). It is found that in both cases an
+achiral signal from an isotropic system can be completely eliminated by small
+manipulations of the relative angles between the linear polarizations of the
+four input laser pulses. Starting from the formulation of a perturbative
+expansion of the signal in the angle between the beams and the propagation
+axis, we derive analytic expressions that can be used to estimate how to change
+the polarization angles of the four pulses to minimize achiral contributions in
+the studied configurations. The generalization to any other possible
+experimental configurations has also been discussed. %We derive analytic
+expressions to changes required to the polarizations in terms of a perturbative
+expansion in the angle between the beams and the colinear axis. We also
+numerically estimate higher order coefficients which cover arbitrarily large
+angles and thus any experimental configuration.
+"
+26571,Stochastic Particle Gradient Descent for Infinite Ensembles,"  The superior performance of ensemble methods with infinite models are well
+known. Most of these methods are based on optimization problems in
+infinite-dimensional spaces with some regularization, for instance, boosting
+methods and convex neural networks use $L^1$-regularization with the
+non-negative constraint. However, due to the difficulty of handling
+$L^1$-regularization, these problems require early stopping or a rough
+approximation to solve it inexactly. In this paper, we propose a new ensemble
+learning method that performs in a space of probability measures, that is, our
+method can handle the $L^1$-constraint and the non-negative constraint in a
+rigorous way. Such an optimization is realized by proposing a general purpose
+stochastic optimization method for learning probability measures via
+parameterization using transport maps on base models. As a result of running
+the method, a transport map to output an infinite ensemble is obtained, which
+forms a residual-type network. From the perspective of functional gradient
+methods, we give a convergence rate as fast as that of a stochastic
+optimization method for finite dimensional nonconvex problems. Moreover, we
+show an interior optimality property of a local optimality condition used in
+our analysis.
+"
+26572,Simple model of fractal networks formed by self-organized critical dynamics,"  In this paper, a simple dynamical model in which fractal networks are formed
+by self-organized critical (SOC) dynamics is proposed; the proposed model
+consists of growth and collapse processes. It has been shown that SOC dynamics
+are realized by the combined processes in the model. Thus, the distributions of
+the cluster size and collapse size follow a power-law function in the
+stationary state. Moreover, through SOC dynamics, the networks become fractal
+in nature. The criticality of SOC dynamics is the same as the universality
+class of mean-field theory. The model explains the possibility that the fractal
+nature in complex networks emerges by SOC dynamics in a manner similar to the
+case with fractal objects embedded in a Euclidean space.
+"
+26573,Classification and Representation via Separable Subspaces: Performance Limits and Algorithms,"  We study the classification performance of Kronecker-structured models in two
+asymptotic regimes and developed an algorithm for separable, fast and compact
+K-S dictionary learning for better classification and representation of
+multidimensional signals by exploiting the structure in the signal. First, we
+study the classification performance in terms of diversity order and pairwise
+geometry of the subspaces. We derive an exact expression for the diversity
+order as a function of the signal and subspace dimensions of a K-S model. Next,
+we study the classification capacity, the maximum rate at which the number of
+classes can grow as the signal dimension goes to infinity. Then we describe a
+fast algorithm for Kronecker-Structured Learning of Discriminative Dictionaries
+(K-SLD2). Finally, we evaluate the empirical classification performance of K-S
+models for the synthetic data, showing that they agree with the diversity order
+analysis. We also evaluate the performance of K-SLD2 on synthetic and
+real-world datasets showing that the K-SLD2 balances compact signal
+representation and good classification performance.
+"
+26574,"Interactive, Intelligent Tutoring for Auxiliary Constructions in Geometry Proofs","  Geometry theorem proving forms a major and challenging component in the K-12
+mathematics curriculum. A particular difficult task is to add auxiliary
+constructions (i.e, additional lines or points) to aid proof discovery.
+Although there exist many intelligent tutoring systems proposed for geometry
+proofs, few teach students how to find auxiliary constructions. And the few
+exceptions are all limited by their underlying reasoning processes for
+supporting auxiliary constructions. This paper tackles these weaknesses of
+prior systems by introducing an interactive geometry tutor, the Advanced
+Geometry Proof Tutor (AGPT). It leverages a recent automated geometry prover to
+provide combined benefits that any geometry theorem prover or intelligent
+tutoring system alone cannot accomplish. In particular, AGPT not only can
+automatically process images of geometry problems directly, but also can
+interactively train and guide students toward discovering auxiliary
+constructions on their own. We have evaluated AGPT via a pilot study with 78
+high school students. The study results show that, on training students how to
+find auxiliary constructions, there is no significant perceived difference
+between AGPT and human tutors, and AGPT is significantly more effective than
+the state-of-the-art geometry solver that produces human-readable proofs.
+"
+26575,Differentially Private Identity and Closeness Testing of Discrete Distributions,"  We investigate the problems of identity and closeness testing over a discrete
+population from random samples. Our goal is to develop efficient testers while
+guaranteeing Differential Privacy to the individuals of the population. We
+describe an approach that yields sample-efficient differentially private
+testers for these problems. Our theoretical results show that there exist
+private identity and closeness testers that are nearly as sample-efficient as
+their non-private counterparts. We perform an experimental evaluation of our
+algorithms on synthetic data. Our experiments illustrate that our private
+testers achieve small type I and type II errors with sample size sublinear in
+the domain size of the underlying distributions.
+"
+26576,The coupled Fokas-Lenells equations by a Riemann-Hilbert approach,"  In this paper, we use the unified transform method to consider the
+initial-boundary value problem for the coupled Fokas-Lenells equations on the
+half-line, assuming that the solution $\{q(x,t),r(x,t)\}$ of the coupled
+Fokas-Lenells equations exists, we show that $\{q_x(x,t),r_x(x,t)\}$ can be
+expressed in terms of the unique solution of a matrix Riemann-Hilbert problem
+formulated in the plane of the complex spectral parameter $\lambda$. Thus, the
+solution $\{q(x,t),r(x,t)\}$ can be obtained by integration with respect to
+$x$.
+"
+26577,A Gronwall inequality and the Cauchy-type problem by means of $ψ$-Hilfer operator,"  In this paper, we propose a generalized Gronwall inequality through the
+fractional integral with respect to another function. The Cauchy-type problem
+for a nonlinear differential equation involving the $\psi$-Hilfer fractional
+derivative and the existence and uniqueness of solutions are discussed.
+Finally, through generalized Gronwall inequality, we prove the continuous
+dependence of data on the Cauchy-type problem.
+"
+26578,Dielectric haloscopes: sensitivity to the axion dark matter velocity,"  We study the effect of the axion dark matter velocity in the recently
+proposed dielectric haloscopes, a promising avenue to search for well-motivated
+high mass ($40-400~\mu$eV) axions. We describe non-zero velocity effects for
+axion-photon mixing in a magnetic field and for the phenomenon of photon
+emission from interfaces between different dielectric media. As velocity
+effects are only important when the haloscope is larger than about 20% of the
+axion de Broglie wavelength, for the planned MADMAX experiment with 80
+dielectric disks the velocity dependence can safely be neglected. However, an
+augmented MADMAX or a second generation experiment would be directionally
+sensitive to the axion velocity, and thus a sensitive measure of axion
+astrophysics.
+"
+26579,Norm-Resolvent Convergence in Perforated Domains,"  For several different boundary conditions (Dirichlet, Neumann, Robin), we
+prove norm-resolvent convergence for the operator $-\Delta$ in the perforated
+domain $\Omega\setminus \bigcup_{ i\in 2\varepsilon\mathbb Z^d
+}B_{a_\varepsilon}(i),$ $a_\varepsilon\ll\varepsilon,$ to the limit operator
+$-\Delta+\mu_{\iota}$ on $L^2(\Omega)$, where $\mu_\iota\in\mathbb C$ is a
+constant depending on the choice of boundary conditions.
+This is an improvement of previous results [Cioranescu & Murat. A Strange
+Term Coming From Nowhere, Progress in Nonlinear Differential Equations and
+Their Applications, 31, (1997)], [S. Kaizu. The Robin Problems on Domains with
+Many Tiny Holes. Pro c. Japan Acad., 61, Ser. A (1985)], which show strong
+resolvent convergence. In particular, our result implies Hausdorff convergence
+of the spectrum of the resolvent for the perforated domain problem.
+"
+26580,Attention based visual analysis for fast grasp planning with multi-fingered robotic hand,"  We present an attention based visual analysis framework to compute
+grasp-relevant information in order to guide grasp planning using a
+multi-fingered robotic hand. Our approach uses a computational visual attention
+model to locate regions of interest in a scene, and uses a deep convolutional
+neural network to detect grasp type and point for a sub-region of the object
+presented in a region of interest. We demonstrate the proposed framework in
+object grasping tasks, in which the information generated from the proposed
+framework is used as prior information to guide the grasp planning. Results
+show that the proposed framework can not only speed up grasp planning with more
+stable configurations, but also is able to handle unknown objects. Furthermore,
+our framework can handle cluttered scenarios. A new Grasp Type Dataset (GTD)
+that considers 6 commonly used grasp types and covers 12 household objects is
+also presented.
+"
+26581,Learning More Universal Representations for Transfer-Learning,"  A representation is supposed universal if it encodes any element of the
+visual world (e.g., objects, scenes) in any configuration (e.g., scale,
+context). While not expecting pure universal representations, the goal in the
+literature is to improve the universality level, starting from a representation
+with a certain level. To do so, the state-of-the-art consists in learning
+CNN-based representations on a diversified training problem (e.g., ImageNet
+modified by adding annotated data). While it effectively increases
+universality, such approach still requires a large amount of efforts to satisfy
+the needs in annotated data. In this work, we propose two methods to improve
+universality, but pay special attention to limit the need of annotated data. We
+also propose a unified framework of the methods based on the diversifying of
+the training problem. Finally, to better match Atkinson's cognitive study about
+universal human representations, we proposed to rely on the transfer-learning
+scheme as well as a new metric to evaluate universality. This latter, aims us
+to demonstrates the interest of our methods on 10 target-problems, relating to
+the classification task and a variety of visual domains.
+"
+26582,Cartan Ribbonization of Surfaces and a Topological Inspection,"  We develop the concept of Cartan ribbons and a method by which they can be
+used to ribbonize any given surface in space by intrinsically flat ribbons. The
+geodesic curvature along the center curve on the surface agrees with the
+geodesic curvature of the corresponding Cartan development curve, and this
+makes a rolling strategy successful. Essentially, it follows from the
+orientational alignment of the two co-moving Darboux frames during the rolling.
+Using closed center curves we obtain closed approximating Cartan ribbons that
+contribute zero to the total curvature integral of the ribbonization. This
+paves the way for a particular simple topological inspection -- it is reduced
+to the question of how the ribbons organize their edges relative to each other.
+The Gauss-Bonnet theorem leads to this topological inspection of the vertices.
+Finally, we display two examples of ribbonizations of surfaces, namely of a
+torus using two ribbons, and of an ellipsoid using its closed curvature lines
+as center curves for the ribbons. The topological inspection of the torus
+ribbonization is particularly simple as it has no vertex points, giving
+directly the Euler characteristic $0$. The ellipsoid has $4$ vertices --
+corresponding to the $4$ umbilical points -- each of degree one and each
+therefore contributing one-half to the Euler characteristic.
+"
+26583,Probing the role of carbon solubility in transition metal catalyzing Single-Walled Carbon Nanotubes growth,"  Metal catalysts supporting the growth of Single Wall Carbon Nanotubes display
+different carbon solubilities and chemical reactivities. In order to
+specifically assess the role of carbon solubility, we take advantage of the
+physical transparency of a tight binding model established for Ni-C alloys, to
+develop metal carbon models where all properties, except carbon solubility, are
+similar. These models are used to analyze carbon incorporation mechanisms,
+modifications of metal / carbon wall interfacial properties induced thereby,
+and the associated nanotube growth mechanisms. Fine tuning carbon solubility is
+shown to be essential to support sustainable growth, preventing growth
+termination by either nanoparticle encapsulation or detachment.
+"
+26584,Markovian Statistics on Evolving Systems,"  A novel framework for the analysis of observation statistics on time discrete
+linear evolutions in Banach space is presented. The model differs from
+traditional models for stochastic processes and, in particular, clearly
+distinguishes between the deterministic evolution of a system and the
+stochastic nature of observations on the evolving system. General Markov chains
+are defined in this context and it is shown how typical traditional models of
+classical or quantum random walks and Markov processes fit into the framework
+and how a theory of quantum statistics ({\it sensu} Barndorff-Nielsen, Gill and
+Jupp) may be developed from it. The framework permits a general theory of joint
+observability of two or more observation variables which may be viewed as an
+extension of the Heisenberg uncertainty principle and, in particular, offers a
+novel mathematical perspective on the violation of Bell's inequalities in
+quantum models. Main results include a general sampling theorem relative to
+Riesz evolution operators in the spirit of von Neumann's mean ergodic theorem
+for normal operators in Hilbert space.
+"
+26585,Temporally Efficient Deep Learning with Spikes,"  The vast majority of natural sensory data is temporally redundant. Video
+frames or audio samples which are sampled at nearby points in time tend to have
+similar values. Typically, deep learning algorithms take no advantage of this
+redundancy to reduce computation. This can be an obscene waste of energy. We
+present a variant on backpropagation for neural networks in which computation
+scales with the rate of change of the data - not the rate at which we process
+the data. We do this by having neurons communicate a combination of their
+state, and their temporal change in state. Intriguingly, this simple
+communication rule give rise to units that resemble biologically-inspired leaky
+integrate-and-fire neurons, and to a weight-update rule that is equivalent to a
+form of Spike-Timing Dependent Plasticity (STDP), a synaptic learning rule
+observed in the brain. We demonstrate that on MNIST and a temporal variant of
+MNIST, our algorithm performs about as well as a Multilayer Perceptron trained
+with backpropagation, despite only communicating discrete values between
+layers.
+"
+26586,Caffarelli-Kohn-Nirenberg inequalities on Lie groups of polynomial growth,"  In the setting of a Lie group of polynomial volume growth, we derive
+inequalities of Caffarelli-Kohn-Nirenberg type, where the weights involved are
+powers of the Carnot-Caratheodory distance associated with a fixed system of
+vector fields which satisfy the Hörmander condition.
+The use of weak $L^p$ spaces is crucial in our proofs and we formulate these
+inequalities within the framework of $L^{p,q}$ Lorentz spaces (a scale of
+(quasi)-Banach spaces which extend the more classical $L^p$ Lebesgue spaces)
+thereby obtaining a refinement of, for instance, Sobolev and Hardy-Sobolev
+inequalities.
+"
+26587,Provably efficient neural network representation for image classification,"  The state-of-the-art approaches for image classification are based on neural
+networks. Mathematically, the task of classifying images is equivalent to
+finding the function that maps an image to the label it is associated with. To
+rigorously establish the success of neural network methods, we should first
+prove that the function has an efficient neural network representation, and
+then design provably efficient training algorithms to find such a
+representation. Here, we achieve the first goal based on a set of assumptions
+about the patterns in the images. The validity of these assumptions is very
+intuitive in many image classification problems, including but not limited to,
+recognizing handwritten digits.
+"
+26588,The nonlinear Schrödinger Equation driven by jump processes,"  The main result of the paper is the existence of a solution of the nonlinear
+Schrödinger equation with a \levy noise with infinite activity. To be more
+precise, let $A=\Delta$ be the Laplace operator with $D(A)=\{ u\in L ^2
+(\mathbb{R} ^d): \Delta u \in L ^2 (\mathbb{R} ^d)\}$. Let $Z\hookrightarrow L
+^2(\mathbb{R} ^d)$ be a function space and $\eta$ be a Poisson random measure
+on $Z$, let $g:\mathbb{R}\to\mathbb{C}$ and $h:\mathbb{R}\to\mathbb{C}$ be some
+given functions, satisfying certain conditions specified later. Let $\alpha\ge
+1$ and $\lambda\ge 0$. We are interested in the solution of the following
+equation % $$ i \, d u(t,x) - \Delta u(t,x)\,dt +\lambda |u(t,x)|^{\alpha-1}
+u(t,x) \, dt $$ $$= \int_Z u(t,x)\, g(z(x))\,\tilde \eta (dz,dt)+\int_Z
+u(t,x)\, h (z(x))\, \gamma (dz, dt), $$ $$ u(0)= u_0. $$ First we consider the
+case, where the \levy process is a compound Poisson process. With the help of
+this result we can tackle the general case, and show that the equation above
+has a solution.
+"
+26589,Positive feedback in coordination games: stochastic evolutionary dynamics and the logit choice rule,"  We show that under the logit dynamics, positive feedback among agents (also
+called bandwagon property) induces evolutionary paths along which agents repeat
+the same actions consecutively so as to minimize the payoff loss incurred by
+the feedback effects. In particular, for paths escaping the domain of
+attraction of a given equilibrium-called a convention-positive feedback implies
+that along the minimum cost escaping paths, agents always switch first from the
+status quo convention strategy before switching from other strategies. In
+addition, the relative strengths of positive feedback effects imply that the
+same transitions occur repeatedly in the cost minimizing escape paths. By
+combining these two effects, we show that in an escaping transition from one
+convention to another, the least unlikely escape paths from the status quo
+convention consist of only the repeated identical mistakes of agents. Using our
+results on the exit problem, we then characterize the stochastically stable
+states under the logit choice rule for a class of non-potential games with an
+arbitrary number of strategies.
+"
+26590,Analysis of association football playing styles: an innovative method to cluster networks,"  In this work we develop an innovative hierarchical clustering method to
+divide a sample of undirected weighted networks into groups. The methodology
+consists of two phases: the first phase is aimed at putting the single networks
+in a broader framework by including the characteristics of the population in
+the data, while the second phase creates a subdivision of the sample on the
+basis of the similarity between the community structures of the processed
+networks. Starting from the representation of the team's playing style as a
+network, we apply the method to group the Italian Serie A teams' performances
+and consequently detect the main 15 tactics shown during the 2015-2016 season.
+The information obtained is used to verify the effect of the styles of play on
+the number of goals scored, and we prove the key role of one of them by
+implementing an extension of the Dixon and Coles model (Dixon and Coles, 1997).
+"
+26591,Transient wave resistance upon a real shear current,"  We study the waves and wave-making forces acting on ships travelling on
+currents which vary as a function of depth. Our concern is realism; we consider
+a real current profile from the Columbia River, and model ships with dimensions
+and Froude numbers typical of three classes of vessels operating in these
+waters. To this end we employ the most general theory of waves from
+free-surface sources on shear current to date, which we derive and present
+here. Expressions are derived for ship waves which satisfy an arbitrary
+dispersion relation and are generated by a wave source acting on the free
+surface, with the source's shape and time-dependence is also being arbitrary.
+Practical calculation procedures for numerically calculating dispersion on a
+shear current which may vary arbitrarily with depth both in direction and
+magnitude, are indicated.
+For ships travelling at oblique angle to a shear-current, the ship wave
+pattern is asymmetrical, and wave-making radiation forces have a lateral
+component in addition to the conventional wave resistance, the sternward
+component. No corresponding lateral force exists in the absence of shear. We
+consider the dependence of wave resistance and lateral force for upstream,
+downstream and cross-stream motion on the Columbia River current, both in
+steady motion and during two different maneouvres: a ship suddenly set in
+motion, and a ship turning through 360 deg. We find that for smaller ships
+(tugboats, fishing-boats) the wave resistance can differ drastically from that
+in quiescent water, and depends strongly on Froude number and direction of
+motion. For Froude numbers typical of such boats, wave resistance can vary by a
+factor 3 between upstream and downstream motion, and the strong Froude number
+dependence is made more complicated by interference effects. The lateral
+radiation force ... [abstract truncated due to ArXiV's space restrictions]
+"
+26592,Nudged elastic band calculations accelerated with Gaussian process regression,"  Minimum energy paths for transitions such as atomic and/or spin
+rearrangements in thermalized systems are the transition paths of largest
+statistical weight. Such paths are frequently calculated using the nudged
+elastic band method, where an initial path is iteratively shifted to the
+nearest minimum energy path. The computational effort can be large, especially
+when ab initio or electron density functional calculations are used to evaluate
+the energy and atomic forces. Here, we show how the number of such evaluations
+can be reduced by an order of magnitude using a Gaussian process regression
+approach where an approximate energy surface is generated and refined in each
+iteration. When the goal is to evaluate the transition rate within harmonic
+transition state theory, the evaluation of the Hessian matrix at the initial
+and final state minima can be carried out beforehand and used as input in the
+minimum energy path calculation, thereby improving stability and reducing the
+number of iterations needed for convergence. A Gaussian process model also
+provides an uncertainty estimate for the approximate energy surface, and this
+can be used to focus the calculations on the lesser-known part of the path,
+thereby reducing the number of needed energy and force evaluations to a half in
+the present calculations. The methodology is illustrated using the
+two-dimensional Müller-Brown potential surface and performance assessed on an
+established benchmark involving 13 rearrangement transitions of a heptamer
+island on a solid surface.
+"
+26593,Transition Watchpoints: Teaching Old Debuggers New Tricks,"  Reversible debuggers and process replay have been developed at least since
+1970. This vision enables one to execute backwards in time under a debugger.
+Two important problems in practice are that, first, current reversible
+debuggers are slow when reversing over long time periods, and, second, after
+building one reversible debugger, it is difficult to transfer that achievement
+to a new programming environment. The user observes a bug when arriving at an
+error. Searching backwards for the corresponding fault may require many reverse
+steps. Ultimately, the user prefers to write an expression that will transition
+to false upon arriving at the fault. The solution is an expression-transition
+watchpoint facility based on top of snapshots and record/replay.
+Expression-transition watch- points are implemented as binary search through
+the timeline of a program execution, while using the snapshots as landmarks
+within that timeline. This allows for debugging of subtle bugs that appear only
+after minutes or more of program execution. When a bug occurs within seconds of
+program startup, repeated debugging sessions suffice. Reversible debugging is
+preferred for bugs seen only after minutes. This architecture allows for an
+efficient and easy-to-write snapshot-based reversibe debugger on top of a
+conventional debugger. The validity of this approach was tested by developing
+four personalities (for GDB, MATLAB, Perl, and Python), with each personality
+typically requiring just 100 lines of code.
+"
+26594,"Parametric Instability, Inverse Cascade, and the $1/f$ Range of Solar-Wind Turbulence","  In this paper, weak turbulence theory is used to investigate the nonlinear
+evolution of the parametric instability in 3D low-$\beta$ plasmas at
+wavelengths much greater than the ion inertial length under the assumption that
+slow magnetosonic waves are strongly damped. It is shown analytically that the
+parametric instability leads to an inverse cascade of Alfvén wave quanta, and
+several exact solutions to the wave kinetic equations are presented. The main
+results of the paper concern the parametric decay of Alfvén waves that
+initially satisfy $e^+ \gg e^-$, where $e^+$ and $e^-$ are the frequency
+($\,f$) spectra of Alfvén waves propagating in opposite directions along the
+magnetic field lines. If $e^+$ initially has a peak frequency $f_0$ (at which
+$f e^+$ is maximized) and an ""infrared"" scaling $f^p$ at smaller $f$ with $-1 <
+p < 1$, then $e^+$ acquires an $f^{-1}$ scaling throughout a range of
+frequencies that spreads out in both directions from $f_0$. At the same time,
+$e^-$ acquires an $f^{-2}$ scaling within this same frequency range. If the
+plasma parameters and infrared $e^+$ spectrum are chosen to match conditions in
+the fast solar wind at a heliocentric distance of 0.3 astronomical units (AU),
+then the nonlinear evolution of the parametric instability leads to an $e^+$
+spectrum that matches fast-wind measurements from the Helios spacecraft at 0.3
+AU, including the observed $f^{-1}$ scaling at $f \gtrsim 3 \times 10^{-4}
+\mbox{ Hz}$. The results of this paper suggest that the $f^{-1}$ spectrum seen
+by Helios in the fast solar wind at $f \gtrsim 3\times 10^{-4} \mbox{ Hz}$ is
+produced in situ by parametric decay and that the $f^{-1}$ range of $e^+$
+extends over an increasingly narrow range of frequencies as $r$ decreases below
+0.3 AU. This prediction will be tested by measurements from the Parker Solar
+Probe.
+"
+26595,Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk,"  We examine the theoretical properties of enforcing priors provided by
+generative deep neural networks via empirical risk minimization. In particular
+we consider two models, one in which the task is to invert a generative neural
+network given access to its last layer and another in which the task is to
+invert a generative neural network given only compressive linear observations
+of its last layer. We establish that in both cases, in suitable regimes of
+network layer sizes and a randomness assumption on the network weights, that
+the non-convex objective function given by empirical risk minimization does not
+have any spurious stationary points. That is, we establish that with high
+probability, at any point away from small neighborhoods around two scalar
+multiples of the desired solution, there is a descent direction. Hence, there
+are no local minima, saddle points, or other stationary points outside these
+neighborhoods. These results constitute the first theoretical guarantees which
+establish the favorable global geometry of these non-convex optimization
+problems, and they bridge the gap between the empirical success of enforcing
+deep generative priors and a rigorous understanding of non-linear inverse
+problems.
+"
+26596,Integration through transients for inelastic hard sphere fluids,"  The Integration Through Transients (ITT) formalism provides a kinetic theory
+to go beyond the linear response regime of low density and shear rate. Its main
+advantage is to express observables under shear as expectation values in the
+quiescent system. Here we show that the quiescent fluid does not have to be in
+thermal equilibrium. Considering a fluidized granular medium under shear, we
+introduce a granular ITT formalism (gITT) providing quantitative predictions
+for the flow curves that reflect the rich phenomenology of granular fluids.
+"
+26597,Preconditioning immersed isogeometric finite element methods with application to flow problems,"  Immersed finite element methods generally suffer from conditioning problems
+when cut elements intersect the physical domain only on a small fraction of
+their volume. De Prenter et al. [Computer Methods in Applied Mechanics and
+Engineering, 316 (2017) pp. 297-327] present an analysis for symmetric positive
+definite (SPD) immersed problems, and for this class of problems an algebraic
+preconditioner is developed. In this contribution the conditioning analysis is
+extended to immersed finite element methods for systems that are not SPD and
+the preconditioning technique is generalized to a connectivity-based
+preconditioner inspired by Additive-Schwarz preconditioning. This
+Connectivity-based Additive-Schwarz (CbAS) preconditioner is applicable to
+problems that are not SPD and to mixed problems, such as the Stokes and
+Navier-Stokes equations. A detailed numerical investigation of the effectivity
+of the CbAS preconditioner to a range of flow problems is presented.
+"
+26598,Parameterized Algorithms for Recognizing Monopolar and 2-Subcolorable Graphs,"  A graph $G$ is a $(\Pi_A,\Pi_B)$-graph if $V(G)$ can be bipartitioned into
+$A$ and $B$ such that $G[A]$ satisfies property $\Pi_A$ and $G[B]$ satisfies
+property $\Pi_B$. The $(\Pi_{A},\Pi_{B})$-Recognition problem is to recognize
+whether a given graph is a $(\Pi_A,\Pi_B)$-graph. There are many
+$(\Pi_{A},\Pi_{B})$-Recognition problems, including the recognition problems
+for bipartite, split, and unipolar graphs. We present efficient algorithms for
+many cases of $(\Pi_A,\Pi_B)$-Recognition based on a technique which we dub
+inductive recognition. In particular, we give fixed-parameter algorithms for
+two NP-hard $(\Pi_{A},\Pi_{B})$-Recognition problems, Monopolar Recognition and
+2-Subcoloring. We complement our algorithmic results with several hardness
+results for $(\Pi_{A},\Pi_{B})$-Recognition.
+"
+26599,A Latent Trait Model for Multivariate Longitudinal Data With Two Sources of Measurement Error,"  Personality traits are latent variables, and as such, are impossible to
+measure without the use of an assessment. Responses on the assessments can be
+influenced by both transient (state-related) error and measurement error,
+obscuring the true trait levels. Typically, these assessments utilize Likert
+scales, which yield only discrete data. The loss of information due to the
+discrete nature of the data represents an additional challenge in assessing the
+ability of these instruments to measure the latent trait of interest.
+This paper is concerned with parameter estimation in a model relating a
+latent variable, as well transient error and measurement error components when
+data are longitudinal and measured using a Likert scale. Two methods for
+parameter estimation are detailed: correlation reconstruction, a method that
+uses polychoric correlations, and maximum likelihood implemented using a
+Stochastic EM algorithm. These methods are applied to a motivating dataset of
+440 college students taking the Big Five inventory twice in a two month period.
+"
+26600,Automatic Taxonomy Generation - A Use-Case in the Legal Domain,"  A key challenge in the legal domain is the adaptation and representation of
+the legal knowledge expressed through texts, in order for legal practitioners
+and researchers to access this information easier and faster to help with
+compliance related issues. One way to approach this goal is in the form of a
+taxonomy of legal concepts. While this task usually requires a manual
+construction of terms and their relations by domain experts, this paper
+describes a methodology to automatically generate a taxonomy of legal noun
+concepts. We apply and compare two approaches on a corpus consisting of
+statutory instruments for UK, Wales, Scotland and Northern Ireland laws.
+"
+26601,Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion,"  Online review communities are dynamic as users join and leave, adopt new
+vocabulary, and adapt to evolving trends. Recent work has shown that
+recommender systems benefit from explicit consideration of user experience.
+However, prior work assumes a fixed number of discrete experience levels,
+whereas in reality users gain experience and mature continuously over time.
+This paper presents a new model that captures the continuous evolution of user
+experience, and the resulting language model in reviews and other posts. Our
+model is unsupervised and combines principles of Geometric Brownian Motion,
+Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal
+progression of user experience and language model respectively. We develop
+practical algorithms for estimating the model parameters from data and for
+inference with our model (e.g., to recommend items). Extensive experiments with
+five real-world datasets show that our model not only fits data better than
+discrete-model baselines, but also outperforms state-of-the-art methods for
+predicting item ratings.
+"
+26602,Deep Learning for Video Game Playing,"  In this article, we review recent Deep Learning advances in the context of
+how they have been applied to play different types of video games such as
+first-person shooters, arcade games, and real-time strategy games. We analyze
+the unique requirements that different game genres pose to a deep learning
+system and highlight important open challenges in the context of applying these
+machine learning methods to video games, such as general game playing, dealing
+with extremely large decision spaces and sparse rewards.
+"
+26603,Applications of some special numbers obtained from a difference equation of degree three,"  In this paper we present applications of some special numbers obtained from a
+difference equation of degree three, especially in the Coding Theory. As a
+particular case, we obtain the generalized Pell-Fibonacci-Lucas numbers, which
+were extended to the generalized quaternions.
+"
+26604,When does the Weyl-von Neumann Theorem hold?,"  A famous theorem due to Weyl and von Neumann asserts that two bounded
+self-adjoint operators are unitarily equivalent modulo the compacts, if and
+only if their essential spectrum agree. The above theorem does not hold for
+unbounded operators. Nevertheless, there exist closed subsets $M$ of
+$\mathbb{R}$ on which the Weyl--von Neumann Theorem hold: all (not necessarily
+bounded) self-adjoint operators with essential spectrum $M$ are unitarily
+equivalent modulo the compacts. In this paper, we determine exactly which $M$
+satisfies this property.
+"
+26605,Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples,"  The problem of detecting whether a test sample is from in-distribution (i.e.,
+training distribution by a classifier) or out-of-distribution sufficiently
+different from it arises in many real-world machine learning applications.
+However, the state-of-art deep neural networks are known to be highly
+overconfident in their predictions, i.e., do not distinguish in- and
+out-of-distributions. Recently, to handle this issue, several threshold-based
+detectors have been proposed given pre-trained neural classifiers. However, the
+performance of prior works highly depends on how to train the classifiers since
+they only focus on improving inference procedures. In this paper, we develop a
+novel training method for classifiers so that such inference algorithms can
+work better. In particular, we suggest two additional terms added to the
+original loss (e.g., cross entropy). The first one forces samples from
+out-of-distribution less confident by the classifier and the second one is for
+(implicitly) generating most effective training samples for the first one. In
+essence, our method jointly trains both classification and generative neural
+networks for out-of-distribution. We demonstrate its effectiveness using deep
+convolutional neural networks on various popular image datasets.
+"
+26606,Correct Composition of Dephased Behavioural Models,"  Scenarios of execution are commonly used to specify partial behaviour and
+interactions between different objects and components in a system. To avoid
+overall inconsistency in specifications, various automated methods have emerged
+in the literature to compose (behavioural) models. In recent work, we have
+shown how the theorem prover Isabelle can be combined with the constraint
+solver Z3 to efficiently detect inconsistencies in two or more behavioural
+models and, in their absence, generate the composition. Here, we extend our
+approach further and show how to generate the correct composition (as a set of
+valid traces) of dephased models. This work has been inspired by a problem from
+a medical domain where different care pathways (for chronic conditions) may be
+applied to the same patient with different starting points.
+"
+26607,Phase Slips in Superconducting Weak Links,"  Superconducting vortices and phase slips are primary mechanisms of
+dissipation in superconducting, superfluid, and cold atom systems. While the
+dynamics of vortices is fairly well described, phase slips occurring in
+quasi-one dimensional superconducting wires still elude understanding. The main
+reason is that phase slips are strongly non-linear time-dependent phenomena
+that cannot be cast in terms of small perturbations of the superconducting
+state. Here we study phase slips occurring in superconducting weak links.
+Thanks to partial suppression of superconductivity in weak links, we employ a
+weakly nonlinear approximation for dynamic phase slips. This approximation is
+not valid for homogeneous superconducting wires and slabs. Using the numerical
+solution of the time-dependent Ginzburg-Landau equation and bifurcation
+analysis of stationary solutions, we show that the onset of phase slips occurs
+via an infinite period bifurcation, which is manifested in a specific
+voltage-current dependence. Our analytical results are in good agreement with
+simulations.
+"
+26608,Connection formulae for new q-deformed Laguerre-Gould-Hopper polynomials,"  We introduce new families of q-deformed 2D Laguerre-Gould-Hopper polynomials.
+For these polynomials we establish connection formulae which extend some known
+ones.
+"
+26609,Variations on the sum-product problem II,"  This is a sequel to the paper arXiv:1312.6438 by the same authors. In this
+sequel, we quantitatively improve several of the main results of
+arXiv:1312.6438, and build on the methods therein.
+The main new results is that, for any finite set $A \subset \mathbb R$, there
+exists $a \in A$ such that $|A(A+a)| \gtrsim |A|^{\frac{3}{2}+\frac{1}{186}}$.
+We give improved bounds for the cardinalities of $A(A+A)$ and $A(A-A)$. Also,
+we prove that $|\{(a_1+a_2+a_3+a_4)^2+\log a_5 : a_i \in A \}| \gg
+\frac{|A|^2}{\log |A|}$. The latter result is optimal up to the logarithmic
+factor.
+"
+26610,"Corrigendum : An inverse problem in corrosion detection:stability estimates, J. Inv. Ill-posed Problems 12 (4) (2004), 349-367","  Corrigendum : An inverse problem in corrosion detection:stability estimates,
+J. Inv. Ill-posed Problems 12 (4) (2004), 349-367.
+"
+26611,Numerical solution of stochastic master equations using stochastic interacting wave functions,"  We develop a new approach for solving stochastic quantum master equations
+with mixed initial states. First, we obtain that the solution of the
+jump-diffusion stochastic master equation is represented by a mixture of pure
+states satisfying a system of stochastic differential equations of
+Schrödinger type. Then, we design three exponential schemes for these coupled
+stochastic Schrödinger equations, which are driven by Brownian motions and
+jump processes. Hence, we have constructed efficient numerical methods for the
+stochastic master equations based on quantum trajectories. The good performance
+of the new numerical integrators is illustrated by simulations of two quantum
+measurement processes.
+"
+26612,Alternating Minimization for Hybrid Precoding in Multiuser OFDM mmWave Systems,"  Hybrid precoding is a cost-effective approach to support directional
+transmissions for millimeter wave (mmWave) communications. While existing works
+on hybrid precoding mainly focus on single-user single-carrier transmission, in
+practice multicarrier transmission is needed to combat the much increased
+bandwidth, and multiuser MIMO can provide additional spatial multiplexing
+gains. In this paper, we propose a new hybrid precoding structure for multiuser
+OFDM mmWave systems, which greatly simplifies the hybrid precoder design and is
+able to approach the performance of the fully digital precoder. In particular,
+two groups of phase shifters are combined to map the signals from radio
+frequency (RF) chains to antennas. Then an effective hybrid precoding algorithm
+based on alternating minimization (AltMin) is proposed, which will alternately
+optimize the digital and analog precoders. A major algorithmic innovation is a
+LASSO formulation for the analog precoder, which yields computationally
+efficient algorithms. Simulation results will show the performance gain of the
+proposed algorithm. Moreover, it will reveal that canceling the interuser
+interference is critical in multiuser OFDM hybrid precoding systems.
+"
+26613,A remark on the Bismut-Ricci form on 2-step nilmanifolds,"  In this note we observe that on a 2-step nilpotent Lie group equipped with a
+left-invariant SKT structure the (1,1)-part of the Bismut-Ricci form is
+seminegative definite. As application we give a simplified proof of the
+non-existence of invariant SKT static metrics on 2-step nilmanifolds and of the
+existence of a long time solution to the pluriclosed flow in 2-step
+nilmanifolds.
+"
+26614,Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees,"  Developing classification algorithms that are fair with respect to sensitive
+attributes of the data has become an important problem due to the growing
+deployment of classification algorithms in various social contexts. Several
+recent works have focused on fairness with respect to a specific metric,
+modeled the corresponding fair classification problem as a constrained
+optimization problem, and developed tailored algorithms to solve them. Despite
+this, there still remain important metrics for which we do not have fair
+classifiers and many of the aforementioned algorithms do not come with
+theoretical guarantees; perhaps because the resulting optimization problem is
+non-convex. The main contribution of this paper is a new meta-algorithm for
+classification that takes as input a large class of fairness constraints, with
+respect to multiple non-disjoint sensitive attributes, and which comes with
+provable guarantees. This is achieved by first developing a meta-algorithm for
+a large family of classification problems with convex constraints, and then
+showing that classification problems with general types of fairness constraints
+can be reduced to those in this family. We present empirical results that show
+that our algorithm can achieve near-perfect fairness with respect to various
+fairness metrics, and that the loss in accuracy due to the imposed fairness
+constraints is often small. Overall, this work unifies several prior works on
+fair classification, presents a practical algorithm with theoretical
+guarantees, and can handle fairness metrics that were previously not possible.
+"
+26615,An Investigation into the Pedagogical Features of Documents,"  Characterizing the content of a technical document in terms of its learning
+utility can be useful for applications related to education, such as generating
+reading lists from large collections of documents. We refer to this learning
+utility as the ""pedagogical value"" of the document to the learner. While
+pedagogical value is an important concept that has been studied extensively
+within the education domain, there has been little work exploring it from a
+computational, i.e., natural language processing (NLP), perspective. To allow a
+computational exploration of this concept, we introduce the notion of
+""pedagogical roles"" of documents (e.g., Tutorial and Survey) as an intermediary
+component for the study of pedagogical value. Given the lack of available
+corpora for our exploration, we create the first annotated corpus of
+pedagogical roles and use it to test baseline techniques for automatic
+prediction of such roles.
+"
+26616,A Markovian genomic concatenation model guided by persymmetric matrices,"  Sobottka and Hart (2011) made use of a Markovian concatenation model to
+observe novel statistical symmetries in the mononucleotide and dinucleotide
+distributions of a collection of bacterial chromosomes. The model roughly
+approximates the first-order stochastic structure of DNA sequences by means of
+a Markov chain whose one-step transition matrix is obtained from a
+concatenation process guided by a positive persymmetric matrix $\mathfrak{L}$
+of probabilities together with a positive parameter $m$. In this article we
+carry out a detailed analysis of such stochastic matrices, here called
+$\aleph$-generated matrices, and draw a number of conclusions. Necessary and
+sufficient conditions for a stochastic matrix to be $\aleph$-generated are
+given, as well as a way to determine if two $\aleph$-generated matrices can be
+generated using the same persymmetric matrix with different values of $m$. The
+results obtained in this research can be used to design new algorithms for
+statistical analyses of real bacterial genomes.
+"
+26617,Inferring transportation modes from GPS trajectories using a convolutional neural network,"  Identifying the distribution of users' transportation modes is an essential
+part of travel demand analysis and transportation planning. With the advent of
+ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach
+for inferring commuters' mobility mode(s) is to leverage their GPS
+trajectories. A majority of studies have proposed mode inference models based
+on hand-crafted features and traditional machine learning algorithms. However,
+manual features engender some major drawbacks including vulnerability to
+traffic and environmental conditions as well as possessing human's bias in
+creating efficient features. One way to overcome these issues is by utilizing
+Convolutional Neural Network (CNN) schemes that are capable of automatically
+driving high-level features from the raw input. Accordingly, in this paper, we
+take advantage of CNN architectures so as to predict travel modes based on only
+raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
+and train. Our key contribution is designing the layout of the CNN's input
+layer in such a way that not only is adaptable with the CNN schemes but
+represents fundamental motion characteristics of a moving object including
+speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
+quality of GPS logs through several data preprocessing steps. Using the clean
+input layer, a variety of CNN configurations are evaluated to achieve the best
+CNN architecture. The highest accuracy of 84.8% has been achieved through the
+ensemble of the best CNN configuration. In this research, we contrast our
+methodology with traditional machine learning algorithms as well as the seminal
+and most related studies to demonstrate the superiority of our framework.
+"
+26618,Submodular Goal Value of Boolean Functions,"  Recently, Deshpande et al. introduced a new measure of the complexity of a
+Boolean function. We call this measure the ""goal value"" of the function. The
+goal value of $f$ is defined in terms of a monotone, submodular utility
+function associated with $f$. As shown by Deshpande et al., proving that a
+Boolean function $f$ has small goal value can lead to a good approximation
+algorithm for the Stochastic Boolean Function Evaluation problem for $f$. Also,
+if $f$ has small goal value, it indicates a close relationship between two
+other measures of the complexity of $f$, its average-case decision tree
+complexity and its average-case certificate complexity. In this paper, we
+explore the goal value measure in detail. We present bounds on the goal values
+of arbitrary and specific Boolean functions, and present results on properties
+of the measure. We compare the goal value measure to other, previously studied,
+measures of the complexity of Boolean functions. Finally, we discuss a number
+of open questions provoked by our work.
+"
+26619,Cascade Ranking for Operational E-commerce Search,"  In the 'Big Data' era, many real-world applications like search involve the
+ranking problem for a large number of items. It is important to obtain
+effective ranking results and at the same time obtain the results efficiently
+in a timely manner for providing good user experience and saving computational
+costs. Valuable prior research has been conducted for learning to efficiently
+rank like the cascade ranking (learning) model, which uses a sequence of
+ranking functions to progressively filter some items and rank the remaining
+items. However, most existing research of learning to efficiently rank in
+search is studied in a relatively small computing environments with simulated
+user queries.
+This paper presents novel research and thorough study of designing and
+deploying a Cascade model in a Large-scale Operational E-commerce Search
+application (CLOES), which deals with hundreds of millions of user queries per
+day with hundreds of servers. The challenge of the real-world application
+provides new insights for research: 1). Real-world search applications often
+involve multiple factors of preferences or constraints with respect to user
+experience and computational costs such as search accuracy, search latency,
+size of search results and total CPU cost, while most existing search solutions
+only address one or two factors; 2). Effectiveness of e-commerce search
+involves multiple types of user behaviors such as click and purchase, while
+most existing cascade ranking in search only models the click behavior. Based
+on these observations, a novel cascade ranking model is designed and deployed
+in an operational e-commerce search application. An extensive set of
+experiments demonstrate the advantage of the proposed work to address multiple
+factors of effectiveness, efficiency and user experience in the real-world
+application.
+"
+26620,Observational calibration of the projection factor of Cepheids - III. The long-period Galactic Cepheid RS Puppis,"  The projection factor (p-factor) is an essential component of the classical
+Baade-Wesselink (BW) technique, that is commonly used to determine the
+distances to pulsating stars. It is a multiplicative parameter used to convert
+radial velocities into pulsational velocities. As the BW distances are linearly
+proportional to the p-factor, its accurate calibration for Cepheids is of
+critical importance for the reliability of their distance scale. We focus on
+the observational determination of the p-factor of the long-period Cepheid RS
+Pup (P = 41.5 days). This star is particularly important as this is one of the
+brightest Cepheids in the Galaxy and an analog of the Cepheids used to
+determine extragalactic distances. An accurate distance of 1910 +/- 80 pc (+/-
+4.2%) has recently been determined for RS Pup using the light echoes
+propagating in its circumstellar nebula. We combine this distance with new
+VLTI/PIONIER interferometric angular diameters, photometry and radial
+velocities to derive the p-factor of RS Pup using the code
+Spectro-Photo-Interferometry of Pulsating Stars (SPIPS). We obtain p = 1.250
++/- 0.064 (+/-5.1%), defined for cross-correlation radial velocities. Together
+with measurements from the literature, the p-factor of RS Pup confirms the good
+agreement of a constant p = 1.293 +/- 0.039 (+/-3.0%) model with the
+observations. We conclude that the p-factor of Cepheids is constant or mildly
+variable over a broad range of periods (3.7 to 41.5 days).
+"
+26621,Bijections for Dyck paths with all peak heights of the same parity,"  We show bijectively that Dyck paths with all peaks at odd height are counted
+by the Motzkin numbers and Dyck paths with all peaks at even height are counted
+by the Riordan numbers.
+"
+26622,From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor,"  Deep learning has the potential to have the impact on robot touch that it has
+had on robot vision. Optical tactile sensors act as a bridge between the
+subjects by allowing techniques from vision to be applied to touch. In this
+paper, we apply deep learning to an optical biomimetic tactile sensor, the
+TacTip, which images an array of papillae (pins) inside its sensing surface
+analogous to structures within human skin. Our main result is that the
+application of a deep CNN can give reliable edge perception and thus a robust
+policy for planning contact points to move around object contours. Robustness
+is demonstrated over several irregular and compliant objects with both tapping
+and continuous sliding, using a model trained only by tapping onto a disk.
+These results relied on using techniques to encourage generalization to tasks
+beyond which the model was trained. We expect this is a generic problem in
+practical applications of tactile sensing that deep learning will solve. A
+video demonstrating the approach can be found at
+this https URL
+"
+26623,Hardy-Hodge Decomposition of Vector Fields in $Rn$,"  We prove that a $IR n+1$-valued vector field on IR n is the sum of the traces
+of two harmonic gradients, one in each component of $IR n+1 \ IR n$ , and of a
+$IR n$-valued divergence free vector field. We apply this to the description of
+vanishing potentials in divergence form. The results are stated in terms of
+Clifford Hardy spaces, the structure of which is important for our study.
+"
+26624,Sharp frequency bounds for eigenfunctions of the Ornstein-Uhlenbeck operator,"  We prove sharp bounds for the growth rate of eigenfunctions of the
+Ornstein-Uhlenbeck operator and its natural generalizations. The bounds are
+sharp even up to lower order terms and have important applications to geometric
+flows.
+"
+26625,Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates,"  In large-scale distributed learning, security issues have become increasingly
+important. Particularly in a decentralized environment, some computing units
+may behave abnormally, or even exhibit Byzantine failures---arbitrary and
+potentially adversarial behavior. In this paper, we develop distributed
+learning algorithms that are provably robust against such failures, with a
+focus on achieving optimal statistical performance. A main result of this work
+is a sharp analysis of two robust distributed gradient descent algorithms based
+on median and trimmed mean operations, respectively. We prove statistical error
+rates for three kinds of population loss functions: strongly convex,
+non-strongly convex, and smooth non-convex. In particular, these algorithms are
+shown to achieve order-optimal statistical error rates for strongly convex
+losses. To achieve better communication efficiency, we further propose a
+median-based distributed algorithm that is provably robust, and uses only one
+communication round. For strongly convex quadratic loss, we show that this
+algorithm achieves the same optimal error rate as the robust distributed
+gradient descent algorithms.
+"
+26626,A note on Euclidean cyclic cubic fields,"  Let $K$ be a cyclic cubic field and $\mathcal{O}_K$ be its ring of integers.
+In this note we prove that all cyclic cubic number fields with conductors in
+the interval $ [73, 11971]$ and with class number one are Euclidean.
+"
+26627,"Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks","  Stochastic gradient descent (SGD) is widely believed to perform implicit
+regularization when used to train deep neural networks, but the precise manner
+in which this occurs has thus far been elusive. We prove that SGD minimizes an
+average potential over the posterior distribution of weights along with an
+entropic regularization term. This potential is however not the original loss
+function in general. So SGD does perform variational inference, but for a
+different loss than the one used to compute the gradients. Even more
+surprisingly, SGD does not even converge in the classical sense: we show that
+the most likely trajectories of SGD for deep networks do not behave like
+Brownian motion around critical points. Instead, they resemble closed loops
+with deterministic components. We prove that such ""out-of-equilibrium"" behavior
+is a consequence of highly non-isotropic gradient noise in SGD; the covariance
+matrix of mini-batch gradients for deep networks has a rank as small as 1% of
+its dimension. We provide extensive empirical validation of these claims,
+proven in the appendix.
+"
+26628,SneakPeek: Interest Mining of Images based on User Interaction,"  Nowadays, eye tracking is the most used technology to detect areas of
+interest. This kind of technology requires specialized equipment recording
+user's eyes. In this paper, we propose SneakPeek, a different approach to
+detect areas of interest on images displayed in web pages based on the zooming
+and panning actions of the users through the image. We have validated our
+proposed solution with a group of test subjects that have performed a test in
+our on-line prototype. Being this the first iteration of the algorithm, we have
+found both good and bad results, depending on the type of image. In specific,
+SneakPeek works best with medium/big objects in medium/big sized images. The
+reason behind it is the limitation on detection when smartphone screens keep
+getting bigger and bigger. SneakPeek can be adapted to any website by simply
+adapting the controller interface for the specific case.
+"
+26629,Discrete Hall resistivity contribution from Néel skyrmions in multilayer nanodiscs,"  Magnetic skyrmions are knot-like quasiparticles. They are candidates for
+non-volatile data storage in which information is moved between fixed read and
+write terminals. Read-out operation of skyrmion-based spintronic devices will
+rely upon electrical detection of a single magnetic skyrmion within a
+nanostructure. Here, we present Pt/Co/Ir nanodiscs which support skyrmions at
+room temperature. We measured the Hall resistivity whilst simultaneously
+imaging the spin texture using magnetic scanning transmission x-ray microscopy
+(STXM). The Hall resistivity is correlated to both the presence and size of the
+skyrmion. The size-dependent part matches the expected anomalous Hall signal
+when averaging the magnetisation over the entire disc. We observed a
+resistivity contribution which only depends on the number and sign of
+skyrmion-like objects present in the disc. Each skyrmion gives rise to 22$\pm$2
+n{\Omega} cm irrespective of its size. This contribution needs to be considered
+in all-electrical detection schemes applied to skyrmion-based devices.
+"
+26630,Fast Scene Understanding for Autonomous Driving,"  Most approaches for instance-aware semantic labeling traditionally focus on
+accuracy. Other aspects like runtime and memory footprint are arguably as
+important for real-time applications such as autonomous driving. Motivated by
+this observation and inspired by recent works that tackle multiple tasks with a
+single integrated architecture, in this paper we present a real-time efficient
+implementation based on ENet that solves three autonomous driving related tasks
+at once: semantic scene segmentation, instance segmentation and monocular depth
+estimation. Our approach builds upon a branched ENet architecture with a shared
+encoder but different decoder branches for each of the three tasks. The
+presented method can run at 21 fps at a resolution of 1024x512 on the
+Cityscapes dataset without sacrificing accuracy compared to running each task
+separately.
+"
+26631,A proof of the Dold$-$Thom theorem via factorization homology,"  The Dold$-$Thom theorem states that for a sufficiently nice topological
+space, M, there is an isomorphism between the homotopy groups of the infinite
+symmetric product of M and the homology groups of M itself. The crux of most
+known proofs of this is to check that a certain map is a quasi-fibration. It is
+our goal to present a more direct proof of the Dold$-$Thom theorem which does
+appeal to any such fact. The heart of our proof lies in identification of the
+infinite symmetric product as an instance of factorization homology.
+"
+26632,Gerrymandering and the net number of US House seats won due to vote-distribution asymmetries,"  Using the recently introduced declination function, we estimate the net
+number of seats won in the US House of Representatives due to asymmetries in
+vote distributions. Such asymmetries can arise from combinations of partisan
+gerrymandering and inherent geographic advantage. Our estimates show
+significant biases in favor of the Democrats prior to the mid 1990s and
+significant biases in favor of Republicans since then. We find net differences
+of 28, 20 and 25 seats in favor of the Republicans in the years 2012, 2014 and
+2016, respectively. The validity of our results is supported by the technique
+of simulated packing and cracking. We also use this technique to show that the
+presidential-vote logistic regression model is insensitive to the packing and
+cracking by which partisan gerrymanders are achieved.
+"
+26633,Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH,"  The total complexity (measured as the total number of gradient computations)
+of a stochastic first-order optimization algorithm that finds a first-order
+stationary point of a finite-sum smooth nonconvex objective function
+$F(w)=\frac{1}{n} \sum_{i=1}^n f_i(w)$ has been proven to be at least
+$\Omega(\sqrt{n}/\epsilon)$ where $\epsilon$ denotes the attained accuracy
+$\mathbb{E}[ \|\nabla F(\tilde{w})\|^2] \leq \epsilon$ for the outputted
+approximation $\tilde{w}$ (Fang et al.,2018). This paper is the first to show
+that this lower bound is tight for the class of variance reduction methods
+which only assume the Lipschitz continuous gradient assumption. We prove this
+complexity result for a slightly modified version of the SARAH algorithm in
+(Nguyen et al.,2017a;b) - showing that SARAH is optimal and dominates all
+existing results. For convex optimization, we propose SARAH++ with sublinear
+convergence for general convex and linear convergence for strongly convex
+problems; and we provide a practical version for which numerical experiments on
+various datasets show an improved performance.
+"
+26634,Rethinking Convolutional Semantic Segmentation Learning,"  Deep convolutional semantic segmentation (DCSS) learning doesn't converge to
+an optimal local minimum with random parameters initializations; a pre-trained
+model on the same domain becomes necessary to achieve convergence.In this work,
+we propose a joint cooperative end-to-end learning method for DCSS. It
+addresses many drawbacks with existing deep semantic segmentation learning; the
+proposed approach simultaneously learn both segmentation and classification;
+taking away the essential need of the pre-trained model for learning
+convergence. We present an improved inception based architecture with partial
+attention gating (PAG) over encoder information. The PAG also adds to achieve
+faster convergence and better accuracy for segmentation task. We will show the
+effectiveness of this learning on a diabetic retinopathy classification and
+segmentation dataset.
+"
+26635,Comments to the Dispersion Properties of the Closed-Ring Accelerating Structure,"  Closed Ring accelerating Structure (CRS) externally has a set of very
+attractive properties, both in RF parameters and in technological aspects. The
+structure was proposed for application in compact linear accelerators of
+electrons. Procedure of tuning and structure manufacturing was developed. The
+structure is now proposed for protons acceleration in medium energy range.\\
+For this structure very unusual dispersion properties were declared. It
+simulates an additional consideration, to understand problem, and additional
+analysis was performed.\\ Results of analysis indicate particularities of the
+structure, generating strong hesitations for CRS.
+"
+26636,Dualing GANs,"  Generative adversarial nets (GANs) are a promising technique for modeling a
+distribution from samples. It is however well known that GAN training suffers
+from instability due to the nature of its maximin formulation. In this paper,
+we explore ways to tackle the instability problem by dualizing the
+discriminator. We start from linear discriminators in which case conjugate
+duality provides a mechanism to reformulate the saddle point objective into a
+maximization problem, such that both the generator and the discriminator of
+this 'dualing GAN' act in concert. We then demonstrate how to extend this
+intuition to non-linear formulations. For GANs with linear discriminators our
+approach is able to remove the instability in training, while for GANs with
+nonlinear discriminators our approach provides an alternative to the commonly
+used GAN training algorithm.
+"
+26637,A Minimum Mass Nebula for M Dwarfs,"  Recently revealed differences in planets around M dwarf vs. solar-type stars
+could arise from differences in their primordial disks, and surveys of T Tauri
+stars find a correlation between stellar mass and disk mass. ""Minimum"" disks
+have been reconstructed for the Solar System and solar-type stars and here this
+exercise is performed for M dwarfs using Kepler-detected planets. Distribution
+of planet mass between current orbits produces a disk with total mass of
+~0.009Msun and a power-law profile with index 2.2. Disk reconstruction from the
+output of a forward model of planet formation indicates that the effect of
+detection bias on disk profile is slight and that the observed scatter in
+planet masses and semi-major axes is consistent with a universal disk profile.
+This nominal M dwarf disk is more centrally concentrated than those inferred
+around the solar-type stars observed by Kepler, and the mass surface density
+beyond 0.02 AU is sufficient for in situ accretion of planets as single
+embryos. The mass of refractory solids within 0.5 AU is 5.6Mearth compared to
+4Mearth for solar-type stars, in contrast with the trend with total disk mass.
+The total solids beyond 0.5 AU is sufficient for the core of at least one giant
+planet.
+"
+26638,"Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect","  Monocular visual odometry (VO) and simultaneous localization and mapping
+(SLAM) have seen tremendous improvements in accuracy, robustness and
+efficiency, and have gained increasing popularity over recent years.
+Nevertheless, not so many discussions have been carried out to reveal the
+influences of three very influential yet easily overlooked aspects: photometric
+calibration, motion bias and rolling shutter effect. In this work, we evaluate
+these three aspects quantitatively on the state of the art of direct,
+feature-based and semi-direct methods, providing the community with useful
+practical knowledge both for better applying existing methods and developing
+new algorithms of VO and SLAM. Conclusions (some of which are
+counter-intuitive) are drawn with both technical and empirical analyses to all
+of our experiments. Possible improvements on existing methods are directed or
+proposed, such as a sub-pixel accuracy refinement of ORB-SLAM which boosts its
+performance.
+"
+26639,Adversarial Deep Reinforcement Learning in Portfolio Management,"  In this paper, we implement three state-of-art continuous reinforcement
+learning algorithms, Deep Deterministic Policy Gradient (DDPG), Proximal Policy
+Optimization (PPO) and Policy Gradient (PG)in portfolio management. All of them
+are widely-used in game playing and robot control. What's more, PPO has
+appealing theoretical propeties which is hopefully potential in portfolio
+management. We present the performances of them under different settings,
+including different learning rates, objective functions, feature combinations,
+in order to provide insights for parameters tuning, features selection and data
+preparation. We also conduct intensive experiments in China Stock market and
+show that PG is more desirable in financial market than DDPG and PPO, although
+both of them are more advanced. What's more, we propose a so called Adversarial
+Training method and show that it can greatly improve the training efficiency
+and significantly promote average daily return and sharpe ratio in back test.
+Based on this new modification, our experiments results show that our agent
+based on Policy Gradient can outperform UCRP.
+"
+26640,Anomaly Detection on Graph Time Series,"  In this paper, we use variational recurrent neural network to investigate the
+anomaly detection problem on graph time series. The temporal correlation is
+modeled by the combination of recurrent neural network (RNN) and variational
+inference (VI), while the spatial information is captured by the graph
+convolutional network. In order to incorporate external factors, we use feature
+extractor to augment the transition of latent variables, which can learn the
+influence of external factors. With the target function as accumulative ELBO,
+it is easy to extend this model to on-line method. The experimental study on
+traffic flow data shows the detection capability of the proposed method.
+"
+26641,Deep Clustering with a Dynamic Autoencoder,"  In unsupervised learning, there is no obvious straightforward loss function
+which can capture the major factors of variations and similarities. Since
+natural systems have smooth dynamics, an opportunity is lost if an unsupervised
+loss function remains static during the training process. The absence of
+concrete supervision suggests that smooth complex dynamics should be integrated
+as a substitute to the classical static loss functions to better make use of
+the gradual and uncertain knowledge acquired through self-supervision. In this
+paper, we propose Dynamic Autoencoder (DynAE), a new model for deep clustering
+that allows to solve a clustering-reconstruction trade-off by gradually and
+smoothly eliminating the reconstruction objective in favor of a construction
+one while preserving the space topology. Experimental evaluations on benchmark
+datasets show that our approach achieves state-of-the-art results compared to
+all the other autoencoder-based clustering methods.
+"
+26642,On a conjecture of Sokal concerning roots of the independence polynomial,"  A conjecture of Sokal (2001) regarding the domain of non-vanishing for
+independence polynomials of graphs, states that given any natural number
+$\Delta \ge 3$, there exists a neighborhood in $\mathbb C$ of the interval $[0,
+\frac{(\Delta-1)^{\Delta-1}}{(\Delta-2)^{\Delta}})$ on which the independence
+polynomial of any graph with maximum degree at most $\Delta$ does not vanish.
+We show here that Sokal's Conjecture holds, as well as a multivariate version,
+and prove optimality for the domain of non-vanishing. An important step is to
+translate the setting to the language of complex dynamical systems.
+"
+26643,On Leighton's Comparison Theorem,"  We give a simple proof of a fairly flexible comparison theorem for equations
+of the type $-(p(u'+su))'+rp(u'+su)+qu=0$ on a finite interval where $1/p$,
+$r$, $s$, and $q$ are real and integrable. Flexibility is provided by two
+functions which may be chosen freely (within limits) according to the situation
+at hand. We illustrate this by presenting some examples and special cases which
+include Schrödinger equations with distributional potentials as well as
+Jacobi difference equations.
+"
+26644,Distillation Strategies for Proximal Policy Optimization,"  Vision-based deep reinforcement learning (RL), similar to deep learning,
+typically obtains a performance benefit by using high capacity and relatively
+large convolutional neural networks (CNN). However, a large network leads to
+higher inference costs (power, latency, silicon area, MAC count). Many
+inference optimization have been developed for CNNs. Some optimization
+techniques offer theoretical efficiency, but designing actual hardware to
+support them is difficult. On the other hand, ""distillation"" is a simple
+general-purpose optimization technique which is broadly applicable for
+transferring knowledge from a trained, high capacity, teacher network to an
+untrained, low capacity, student network. ""DQN distillation"" extended the
+original distillation idea to transfer information stored in a high
+performance, high capacity teacher Q-function trained via the Deep Q-Learning
+(DQN) algorithm. Our work adapts the DQN distillation work to the actor-critic
+Proximal Policy Optimization algorithm. PPO is simple to implement and has much
+higher performance than the seminal DQN algorithm. We show that a distilled PPO
+student can attain far higher performance compared to a DQN teacher. We also
+show that a low capacity distilled student is generally able to outperform a
+low capacity agent that directly trains in the environment. Finally, we show
+that distillation, followed by ""fine-tuning"" in the environment, enables the
+distilled PPO student to achieve parity with teacher performance. In general,
+the lessons learned in this work should transfer to other actor-critic RL
+algorithms.
+"
+26645,Learning Visual Reasoning Without Strong Priors,"  Achieving artificial visual reasoning - the ability to answer image-related
+questions which require a multi-step, high-level process - is an important step
+towards artificial general intelligence. This multi-modal task requires
+learning a question-dependent, structured reasoning process over images from
+language. Standard deep learning approaches tend to exploit biases in the data
+rather than learn this underlying structure, while leading methods learn to
+visually reason successfully but are hand-crafted for reasoning. We show that a
+general-purpose, Conditional Batch Normalization approach achieves
+state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4%
+error rate. We outperform the next best end-to-end method (4.5%) and even
+methods that use extra supervision (3.1%). We probe our model to shed light on
+how it reasons, showing it has learned a question-dependent, multi-step
+process. Previous work has operated under the assumption that visual reasoning
+calls for a specialized architecture, but we show that a general architecture
+with proper conditioning can learn to visually reason effectively.
+"
+26646,Eye In-Painting with Exemplar Generative Adversarial Networks,"  This paper introduces a novel approach to in-painting where the identity of
+the object to remove or change is preserved and accounted for at inference
+time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize
+exemplar information to produce high-quality, personalized in painting results.
+We propose using exemplar information in the form of a reference image of the
+region to in-paint, or a perceptual code describing that object. Unlike
+previous conditional GAN formulations, this extra information can be inserted
+at multiple points within the adversarial network, thus increasing its
+descriptive power. We show that ExGANs can produce photo-realistic personalized
+in-painting results that are both perceptually and semantically plausible by
+applying them to the task of closed to-open eye in-painting in natural
+pictures. A new benchmark dataset is also introduced for the task of eye
+in-painting for future comparisons.
+"
+26647,Modified Regularized Dual Averaging Method for Training Sparse Convolutional Neural Networks,"  We proposed a modified regularized dual averaging method for training sparse
+deep convolutional neural networks. The regularized dual averaging method has
+been proven to be effective in obtaining sparse solutions in convex
+optimization problems, but not applied to deep learning fields before. We
+analyzed the new version in convex conditions and prove the convergence of it.
+The modified method can obtain more sparse solutions than traditional sparse
+optimization methods such as proximal-SGD, while keeping almost the same
+accuracy as stochastic gradient method with momentum on certain datasets.
+"
+26648,Embodying probabilistic inference in biochemical circuits,"  Probabilistic inference provides a language for describing how organisms may
+learn from and adapt to their environment. The computations needed to implement
+probabilistic inference often require specific representations, akin to having
+the suitable data structures for implementing certain algorithms in computer
+programming. Yet it is unclear how such representations can be instantiated in
+the stochastic, parallel-running biochemical machinery found in cells (such as
+single-celled organisms). Here, we show how representations for supporting
+inference in Markov models can be embodied in cellular circuits, by combining a
+concentration-dependent scheme for encoding probabilities with a mechanism for
+directional counting. We show how the logic of protein production and
+degradation constrains the computation we set out to implement. We argue that
+this process by which an abstract computation is shaped by its biochemical
+realization strikes a compromise between ""rationalistic"" information-processing
+perspectives and alternative approaches that emphasize embodiment.
+"
+26649,A Random Attention Model,"  We introduce a Random Attention Model (RAM) allowing for a large class of
+stochastic consideration maps in the context of an otherwise canonical limited
+attention model for decision theory. The model relies on a new restriction on
+the unobserved, possibly stochastic consideration map, termed \textit{Monotonic
+Attention}, which is intuitive and nests many recent contributions in the
+literature on limited attention. We develop revealed preference theory within
+RAM and obtain precise testable implications for observable choice
+probabilities. Using these results, we show that a set (possibly a singleton)
+of strict preference orderings compatible with RAM is identifiable from the
+decision maker's choice probabilities, and establish a representation of this
+identified set of unobserved preferences as a collection of inequality
+constrains on her choice probabilities. Given this nonparametric identification
+result, we develop uniformly valid inference methods for the (partially)
+identifiable preferences. We showcase the performance of our proposed
+econometric methods using simulations, and provide general-purpose software
+implementation of our estimation and inference results in the \texttt{R}
+software package \texttt{ramchoice}. Our proposed econometric methods are
+computationally very fast to implement.
+"
+26650,Least energy nodal solutions of Hamiltonian elliptic systems with Neumann boundary conditions,"  We study existence, regularity, and qualitative properties of solutions to
+the system \[
+-\Delta u = |v|^{q-1} v\quad \text{ in }\Omega,\qquad -\Delta v = |u|^{p-1}
+u\quad \text{ in }\Omega,\qquad \partial_\nu u=\partial_\nu v=0\quad \text{ on
+}\partial\Omega, \] with $\Omega\subset \mathbb R^N$ bounded; in this setting,
+all nontrivial solutions are sign changing. Our proofs use a variational
+formulation in dual spaces, considering sublinear $pq< 1$ and superlinear
+$pq>1$ problems in the subcritical regime. In balls and annuli we show that
+least energy solutions (l.e.s.) are foliated Schwarz symmetric and, due to a
+symmetry-breaking phenomenon, l.e.s. are not radial functions; a key element in
+the proof is a new $L^t$-norm-preserving transformation, which combines a
+suitable flipping with a decreasing rearrangement. This combination allows us
+to treat annular domains, sign-changing functions, and Neumann problems, which
+are non-standard settings to use rearrangements and symmetrizations. In
+particular, we show that our transformation diminishes the (dual) energy and,
+as a consequence, radial l.e.s. are strictly monotone. We also study unique
+continuation properties and simplicity of zeros. Our theorems also apply to the
+scalar associated model, where our approach provides new results as well as
+alternative proofs of known facts.
+"
+26651,Black-box Testing of First-Order Logic Ontologies Using WordNet,"  Artificial Intelligence aims to provide computer programs with commonsense
+knowledge to reason about our world. This paper offers a new practical approach
+towards automated commonsense reasoning with first-order logic (FOL)
+ontologies. We propose a new black-box testing methodology of FOL SUMO-based
+ontologies by exploiting WordNet and its mapping into SUMO. Our proposal
+includes a method for the (semi-)automatic creation of a very large benchmark
+of competency questions and a procedure for its automated evaluation by using
+automated theorem provers (ATPs). Applying different quality criteria, our
+testing proposal enables a successful evaluation of a) the competency of
+several translations of SUMO into FOL and b) the performance of various
+automated ATPs. Finally, we also provide a fine-grained and complete analysis
+of the commonsense reasoning competency of current FOL SUMO-based ontologies.
+"
+26652,Consistencies and inconsistencies between model selection and link prediction in networks,"  A principled approach to understand network structures is to formulate
+generative models. Given a collection of models, however, an outstanding key
+task is to determine which one provides a more accurate description of the
+network at hand, discounting statistical fluctuations. This problem can be
+approached using two principled criteria that at first may seem equivalent:
+selecting the most plausible model in terms of its posterior probability; or
+selecting the model with the highest predictive performance in terms of
+identifying missing links. Here we show that while these two approaches yield
+consistent results in most of cases, there are also notable instances where
+they do not, that is, where the most plausible model is not the most
+predictive. We show that in the latter case the improvement of predictive
+performance can in fact lead to overfitting both in artificial and empirical
+settings. Furthermore, we show that, in general, the predictive performance is
+higher when we average over collections of models that are individually less
+plausible, than when we consider only the single most plausible model.
+"
+26653,Sampling molecular conformers in solution with quantum mechanical accuracy at a nearly molecular mechanics cost,"  We introduce a method to evaluate the relative populations of different
+conformers of molecular species in solution, aiming at quantum mechanical
+accuracy, while keeping the computational cost at a nearly molecular-mechanics
+level. This goal is achieved by combining long classical molecular-dynamics
+simulations to sample the free-energy landscape of the system, advanced
+clustering techniques to identify the most relevant conformers, and
+thermodynamic perturbation theory to correct the resulting populations, using
+quantum-mechanical energies from density-functional theory. A quantitative
+criterion for assessing the accuracy thus achieved is proposed. The resulting
+methodology is demonstrated in the specific case of cyanin
+(cyanidin-3-glucoside) in water solution.
+"
+26654,ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections,"  Deep neural networks have become ubiquitous for applications related to
+visual recognition and language understanding tasks. However, it is often
+prohibitive to use typical neural networks on devices like mobile phones or
+smart watches since the model sizes are huge and cannot fit in the limited
+memory available on such devices. While these devices could make use of machine
+learning models running on high-performance data centers with CPUs or GPUs,
+this is not feasible for many applications because data can be privacy
+sensitive and inference needs to be performed directly ""on"" device.
+We introduce a new architecture for training compact neural networks using a
+joint optimization framework. At its core lies a novel objective that jointly
+trains using two different types of networks--a full trainer neural network
+(using existing architectures like Feed-forward NNs or LSTM RNNs) combined with
+a simpler ""projection"" network that leverages random projections to transform
+inputs or intermediate representations into bits. The simpler network encodes
+lightweight and efficient-to-compute operations in bit space with a low memory
+footprint. The two networks are trained jointly using backpropagation, where
+the projection network learns from the full network similar to apprenticeship
+learning. Once trained, the smaller network can be used directly for inference
+at low memory and computation cost. We demonstrate the effectiveness of the new
+approach at significantly shrinking the memory requirements of different types
+of neural networks while preserving good accuracy on visual recognition and
+text classification tasks. We also study the question ""how many neural bits are
+required to solve a given task?"" using the new framework and show empirical
+results contrasting model predictive capacity (in bits) versus accuracy on
+several datasets.
+"
+26655,"Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events","  This paper investigates bias in coverage between Western and Arab media on
+Twitter after the November 2015 Beirut and Paris terror attacks. Using two
+Twitter datasets covering each attack, we investigate how Western and Arab
+media differed in coverage bias, sympathy bias, and resulting information
+propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets
+across four languages (English, Arabic, French, German), built a regression
+model to characterize sympathy, and thereafter trained a deep convolutional
+neural network to predict sympathy. Key findings show: (a) both events were
+disproportionately covered (b) Western media exhibited less sympathy, where
+each media coverage was more sympathetic towards the country affected in their
+respective region (c) Sympathy predictions supported ground truth analysis that
+Western media was less sympathetic than Arab media (d) Sympathetic tweets do
+not spread any further. We discuss our results in light of global news flow,
+Twitter affordances, and public perception impact.
+"
+26656,Mechanisms of spin-dependent heat generation in spin valves,"  The extra heat generation in spin transport is usually interpreted in terms
+of the spin relaxation. By reformulating the heat generation rate, we found
+alternative current-force pairs without cross effects, which enable us to
+interpret the product of each pair as a distinct mechanism of heat generation.
+The results show that the spin-dependent part of the heat generation includes
+two terms. One of them is proportional to the square of the spin accumulation
+and arises from the spin relaxation. However, the other is proportional to the
+square of the spin-accumulation gradient and should be attributed to another
+mechanism, the spin diffusion. We illustrated the characteristics of the two
+mechanisms in a typical spin valve with a finite nonmagnetic spacer layer.
+"
+26657,Toward the analysis of JWST exoplanet spectra: Identifying troublesome model parameters,"  Given the forthcoming launch of the James Webb Space Telescope (JWST) which
+will allow observ- ing exoplanet atmospheres with unprecedented
+signal-over-noise ratio, spectral coverage and spatial resolution, the
+uncertainties in the atmosphere modelling used to interpret the data need to be
+as- sessed. As the first step, we compare three independent 1D
+radiative-convective models: ATMO, Exo-REM and petitCODE. We identify
+differences in physical and chemical processes taken into ac- count thanks to a
+benchmark protocol we developed. We study the impact of these differences on
+the analysis of observable spectra. We show the importance of selecting
+carefully relevant molecular linelists to compute the atmospheric opacity.
+Indeed, differences between spectra calculated with Hitran and ExoMol exceed
+the expected uncertainties of future JWST observations. We also show the
+limitation in the precision of the models due to uncertainties on alkali and
+molecule lineshape, which induce spectral effects also larger than the expected
+JWST uncertainties. We compare two chemical models, Exo-REM and Venot Chemical
+Code, which do not lead to significant differences in the emission or
+transmission spectra. We discuss the observational consequences of using
+equilibrium or out-of- equilibrium chemistry and the major impact of phosphine,
+detectable with the JWST.Each of the models has benefited from the benchmarking
+activity and has been updated. The protocol developed in this paper and the
+online results can constitute a test case for other models.
+"
+26658,Convergence Analysis of Gradient Descent Algorithms with Proportional Updates,"  The rise of deep learning in recent years has brought with it increasingly
+clever optimization methods to deal with complex, non-linear loss functions.
+These methods are often designed with convex optimization in mind, but have
+been shown to work well in practice even for the highly non-convex optimization
+associated with neural networks. However, one significant drawback of these
+methods when they are applied to deep learning is that the magnitude of the
+update step is sometimes disproportionate to the magnitude of the weights (much
+smaller or larger), leading to training instabilities such as vanishing and
+exploding gradients. An idea to combat this issue is gradient descent with
+proportional updates. Gradient descent with proportional updates was introduced
+in 2017. It was independently developed by You et al (Layer-wise Adaptive Rate
+Scaling (LARS) algorithm) and by Abu-El-Haija (PercentDelta algorithm). The
+basic idea of both of these algorithms is to make each step of the gradient
+descent proportional to the current weight norm and independent of the gradient
+magnitude. It is common in the context of new optimization methods to prove
+convergence or derive regret bounds under the assumption of Lipschitz
+continuity and convexity. However, even though LARS and PercentDelta were shown
+to work well in practice, there is no theoretical analysis of the convergence
+properties of these algorithms. Thus it is not clear if the idea of gradient
+descent with proportional updates is used in the optimal way, or if it could be
+improved by using a different norm or specific learning rate schedule, for
+example. Moreover, it is not clear if these algorithms can be extended to other
+problems, besides neural networks. We attempt to answer these questions by
+establishing the theoretical analysis of gradient descent with proportional
+updates, and verifying this analysis with empirical examples.
+"
+26659,Toward an Optimal Sampling of Peculiar Velocity Surveys For Wiener Filter Reconstructions,"  The Wiener Filter (WF) technique enables the reconstruction of density and
+velocity fields from observed radial peculiar velocities. This paper aims at
+identifying the optimal design of peculiar velocity surveys within the WF
+framework. The prime goal is to test the dependence of the quality of the
+reconstruction on the distribution and nature of data points. Mock datasets,
+extending to 250 Mpc/h, are drawn from a constrained simulation that mimics the
+local Universe to produce realistic mock catalogs. Reconstructed fields
+obtained with these mocks are compared to the reference simulation.
+Comparisons, including residual distributions, cell-to-cell and bulk
+velocities, imply that the presence of field data points is essential to
+properly measure the flows. The fields reconstructed from mocks that consist
+only of galaxy cluster data points exhibit poor quality bulk velocities. In
+addition, the quality of the reconstruction depends strongly on the grouping of
+individual data points into single points to suppress virial motions in high
+density regions. Conversely, the presence of a Zone of Avoidance hardly affects
+the reconstruction. For a given number of data points, a uniform sample does
+not score any better than a sample with decreasing number of data points with
+the distance. The best reconstructions are obtained with a grouped survey
+containing field galaxies: Assuming no error, they differ from the simulated
+field by less than 100 km/s up to the extreme edge of the catalogs or up to a
+distance of three times the mean distance of data points for non-uniform
+catalogs. The overall conclusions hold when errors are added.
+"
+26660,A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks,"  Recent decades have witnessed the tremendous development of network science,
+which indeed brings a new and insightful language to model real systems of
+different domains. Betweenness, a widely employed centrality in network
+science, is a decent proxy in investigating network loads and rankings.
+However, the extremely high computational cost greatly prevents its applying on
+large networks. Though several parallel algorithms have been presented to
+reduce its calculation cost on unweighted networks, a fast solution for
+weighted networks, which are in fact more ubiquitous than unweighted ones in
+reality, is still missing. In this study, we develop an efficient parallel
+GPU-based approach to boost the calculation of betweenness centrality on very
+large and weighted networks. Comprehensive and systematic evaluations on both
+synthetic and real-world networks demonstrate that our solution can arrive the
+performance of 30x to 150x speedup over the CPU implementation by integrating
+the work-efficient and warp-centric strategies. Our algorithm is completely
+open-sourced and free to the community and it is public available through
+this https URL. Considering the pervasive
+deployment and declining price of GPU on personal computers and servers, our
+solution will indeed offer unprecedented opportunities for exploring the
+betweenness related problems in network science.
+"
+26661,Generalized Hofstadter model on a cubic optical lattice: From nodal bands to the three-dimensional quantum Hall effect,"  We propose that a tunable generalized three-dimensional Hofstadter
+Hamiltonian can be realized by engineering the Raman-assisted hopping of
+ultracold atoms in a cubic optical lattice. The Hamiltonian describes a
+periodic lattice system under artificial magnetic fluxes in three dimensions.
+For certain hopping configurations, the bulk bands can have Weyl points and
+nodal loops, respectively, allowing the study of both the two nodal semimetal
+states within this system. Furthermore, we illustrate that with proper rational
+fluxes and hopping parameters, the system can exhibit the three-dimensional
+quantum Hall effect when the Fermi level lies in the band gaps, which is
+topologically characterized by one or two nonzero Chern numbers. Our proposed
+optical-lattice system provides a promising platform for exploring various
+exotic topological phases in three dimensions.
+"
+26662,One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks,"  We consider the problem of learning multi-stage vision-based tasks on a real
+robot from a single video of a human performing the task, while leveraging
+demonstration data of subtasks with other objects. This problem presents a
+number of major challenges. Video demonstrations without teleoperation are easy
+for humans to provide, but do not provide any direct supervision. Learning
+policies from raw pixels enables full generality but calls for large function
+approximators with many parameters to be learned. Finally, compound tasks can
+require impractical amounts of demonstration data, when treated as a monolithic
+skill. To address these challenges, we propose a method that learns both how to
+learn primitive behaviors from video demonstrations and how to dynamically
+compose these behaviors to perform multi-stage tasks by ""watching"" a human
+demonstrator. Our results on a simulated Sawyer robot and real PR2 robot
+illustrate our method for learning a variety of order fulfillment and kitchen
+serving tasks with novel objects and raw pixel inputs.
+"
+26663,A Nonparametric Motion Flow Model for Human Robot Cooperation,"  In this paper, we present a novel nonparametric motion flow model that
+effectively describes a motion trajectory of a human and its application to
+human robot cooperation. To this end, motion flow similarity measure which
+considers both spatial and temporal properties of a trajectory is proposed by
+utilizing the mean and variance functions of a Gaussian process. We also
+present a human robot cooperation method using the proposed motion flow model.
+Given a set of interacting trajectories of two workers, the underlying reward
+function of cooperating behaviors is optimized by using the learned motion
+description as an input to the reward function where a stochastic trajectory
+optimization method is used to control a robot. The presented human robot
+cooperation method is compared with the state-of-the-art algorithm, which
+utilizes a mixture of interaction primitives (MIP), in terms of the RMS error
+between generated and target trajectories. While the proposed method shows
+comparable performance with the MIP when the full observation of human
+demonstrations is given, it shows superior performance with respect to given
+partial trajectory information.
+"
+26664,A deep generative model for gene expression profiles from single-cell RNA sequencing,"  We propose a probabilistic model for interpreting gene expression levels that
+are observed through single-cell RNA sequencing. In the model, each cell has a
+low-dimensional latent representation. Additional latent variables account for
+technical effects that may erroneously set some observations of gene expression
+levels to zero. Conditional distributions are specified by neural networks,
+giving the proposed model enough flexibility to fit the data well. We use
+variational inference and stochastic optimization to approximate the posterior
+distribution. The inference procedure scales to over one million cells, whereas
+competing algorithms do not. Even for smaller datasets, for several tasks, the
+proposed procedure outperforms state-of-the-art methods like ZIFA and
+ZINB-WaVE. We also extend our framework to account for batch effects and other
+confounding factors, and propose a Bayesian hypothesis test for differential
+expression that outperforms DESeq2.
+"
+26665,An improved quality control chart to monitor the mean based on ranked sets,"  In this study, we considered the design and performance of control charts
+using neoteric ranked set sampling (NRSS) in monitoring normal distributed
+processes. NRSS is a recently proposed sampling design, based on the
+traditional ranked set sampling (RSS). We evaluated NRSS control charts by
+average run length (ARL), based on Monte Carlo simulation results. NRSS control
+charts performed the best, compared to RSS and some of its extensions, in most
+simulated scenarios. The impact of imperfect ranking was also evaluated. An
+application on strength concrete data serves as an illustration of the proposed
+method.
+"
+26666,Zariski-like Topologies for Lattices with Applications to Modules over Commutative Rings,"  We study Zariski-like topologies on a proper class $X\varsubsetneqq L$ of a
+complete lattice $\mathcal{L}=(L,\wedge ,\vee ,0,1)$. We consider $X$ with the
+so called classical Zariski topology $(X,\tau ^{cl})$ and study its topological
+properties (e.g. the separation axioms, the connectedness, the compactness) and
+provide sufficient conditions for it to be $\textit{spectral}$. We say that
+$\mathcal{L}$ is $X$\emph{-top} iff% \begin{equation*} \tau :=\{X\backslash
+V(a)\mid a\in L\},\text{ where }V(a)=\{x\in L\mid a\leq x\} \end{equation*}% is
+a topology. We study the interplay between the \textit{algebraic properties} of
+an $X$-top complete lattice $\mathcal{L}$ and the $\textit{% topological
+properties}$ of $(X,\tau ^{cl})=(X,\tau ).$ Our results are applied to several
+spectra which are proper classes of $\mathcal{L}% :=LAT(_{R}M)$ where $M$ is a
+left module over an arbitrary associative ring $% R$ (e.g. the spectra of
+prime, coprime, fully prime submodules) of $M$ as well as to several spectra of
+the dual complete lattice $\mathcal{L}^{0}$ (e.g. the spectra of first, second
+and fully coprime submodules of $M$).
+"
+26667,Asymptotic optimality of adaptive importance sampling,"  Adaptive importance sampling (AIS) uses past samples to update the
+\textit{sampling policy} $q_t$ at each stage $t$. Each stage $t$ is formed with
+two steps : (i) to explore the space with $n_t$ points according to $q_t$ and
+(ii) to exploit the current amount of information to update the sampling
+policy. The very fundamental question raised in this paper concerns the
+behavior of empirical sums based on AIS. Without making any assumption on the
+allocation policy $n_t$, the theory developed involves no restriction on the
+split of computational resources between the explore (i) and the exploit (ii)
+step. It is shown that AIS is asymptotically optimal : the asymptotic behavior
+of AIS is the same as some ""oracle"" strategy that knows the targeted sampling
+policy from the beginning. From a practical perspective, weighted AIS is
+introduced, a new method that allows to forget poor samples from early stages.
+"
+26668,AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods,"  We study a new aggregation operator for gradients coming from a mini-batch
+for stochastic gradient (SG) methods that allows a significant speed-up in the
+case of sparse optimization problems. We call this method AdaBatch and it only
+requires a few lines of code change compared to regular mini-batch SGD
+algorithms. We provide a theoretical insight to understand how this new class
+of algorithms is performing and show that it is equivalent to an implicit
+per-coordinate rescaling of the gradients, similarly to what Adagrad methods
+can do. In theory and in practice, this new aggregation allows to keep the same
+sample efficiency of SG methods while increasing the batch size.
+Experimentally, we also show that in the case of smooth convex optimization,
+our procedure can even obtain a better loss when increasing the batch size for
+a fixed number of samples. We then apply this new algorithm to obtain a
+parallelizable stochastic gradient method that is synchronous but allows
+speed-up on par with Hogwild! methods as convergence does not deteriorate with
+the increase of the batch size. The same approach can be used to make
+mini-batch provably efficient for variance-reduced SG methods such as SVRG.
+"
+26669,House Price Prediction Using LSTM,"  In this paper, we use the house price data ranging from January 2004 to
+October 2016 to predict the average house price of November and December in
+2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply
+Autoregressive Integrated Moving Average model to generate the baseline while
+LSTM networks to build prediction model. These algorithms are compared in terms
+of Mean Squared Error. The result shows that the LSTM model has excellent
+properties with respect to predict time series. Also, stateful LSTM networks
+and stack LSTM networks are employed to further study the improvement of
+accuracy of the house prediction model.
+"
+26670,Universal Slope Sets for 1-Bend Planar Drawings,"  We describe a set of $\Delta -1$ slopes that are universal for 1-bend planar
+drawings of planar graphs of maximum degree $\Delta \geq 4$; this establishes a
+new upper bound of $\Delta-1$ on the 1-bend planar slope number. By universal
+we mean that every planar graph of degree $\Delta$ has a planar drawing with at
+most one bend per edge and such that the slopes of the segments forming the
+edges belong to the given set of slopes. This improves over previous results in
+two ways: Firstly, the best previously known upper bound for the 1-bend planar
+slope number was $\frac{3}{2} (\Delta -1)$ (the known lower bound being
+$\frac{3}{4} (\Delta -1)$); secondly, all the known algorithms to construct
+1-bend planar drawings with $O(\Delta)$ slopes use a different set of slopes
+for each graph and can have bad angular resolution, while our algorithm uses a
+universal set of slopes, which also guarantees that the minimum angle between
+any two edges incident to a vertex is $\frac{\pi}{(\Delta-1)}$.
+"
+26671,"Radial length, radial John disks and $K$-quasiconformal harmonic mappings","  In this article, we continue our investigations of the boundary behavior of
+harmonic mappings. We first discuss the classical problem on the growth of
+radial length and obtain a sharp growth theorem of the radial length of
+$K$-quasiconformal harmonic mappings. Then we present an alternate
+characterization of radial John disks. In addition, we investigate the linear
+measure distortion and the Lipschitz continuity on $K$-quasiconformal harmonic
+mappings of the unit disk onto a radial John disk. Finally, using Pommerenke
+interior domains, we characterize certain differential properties of
+$K$-quasiconformal harmonic mappings.
+"
+26672,Pushing the envelope in deep visual recognition for mobile platforms,"  Image classification is the task of assigning to an input image a label from
+a fixed set of categories. One of its most important applicative fields is that
+of robotics, in particular the needing of a robot to be aware of what's around
+and the consequent exploitation of that information as a benefit for its tasks.
+In this work we consider the problem of a robot that enters a new environment
+and wants to understand visual data coming from its camera, so to extract
+knowledge from them. As main novelty we want to overcome the needing of a
+physical robot, as it could be expensive and unhandy, so to hopefully enhance,
+speed up and ease the research in this field. That's why we propose to develop
+an application for a mobile platform that wraps several deep visual recognition
+tasks. First we deal with a simple Image classification, testing a model
+obtained from an AlexNet trained on the ILSVRC 2012 dataset. Several photo
+settings are considered to better understand which factors affect most the
+quality of classification. For the same purpose we are interested to integrate
+the classification task with an extra module dealing with segmentation of the
+object inside the image. In particular we propose a technique for extracting
+the object shape and moving out all the background, so to focus the
+classification only on the region occupied by the object. Another significant
+task that is included is that of object discovery. Its purpose is to simulate
+the situation in which the robot needs a certain object to complete one of its
+activities. It starts searching for what it needs by looking around and trying
+to understand the location of the object by scanning the surrounding
+environment. Finally we provide a tool for dealing with the creation of
+customized task-specific databases, meant to better suit to one's needing in a
+particular vision task.
+"
+26673,Learning models for visual 3D localization with implicit mapping,"  We consider learning based methods for visual localization that do not
+require the construction of explicit maps in the form of point clouds or
+voxels. The goal is to learn an implicit representation of the environment at a
+higher, more abstract level. We propose to use a generative approach based on
+Generative Query Networks (GQNs, Eslami et al. 2018), asking the following
+questions: 1) Can GQN capture more complex scenes than those it was originally
+demonstrated on? 2) Can GQN be used for localization in those scenes? To study
+this approach we consider procedurally generated Minecraft worlds, for which we
+can generate images of complex 3D scenes along with camera pose coordinates. We
+first show that GQNs, enhanced with a novel attention mechanism can capture the
+structure of 3D scenes in Minecraft, as evidenced by their samples. We then
+apply the models to the localization problem, comparing the results to a
+discriminative baseline, and comparing the ways each approach captures the task
+uncertainty.
+"
+26674,"Is ""Better Data"" Better than ""Better Data Miners""? (On the Benefits of Tuning SMOTE for Defect Prediction)","  We report and fix an important systematic error in prior studies that ranked
+classifiers for software analytics. Those studies did not (a) assess
+classifiers on multiple criteria and they did not (b) study how variations in
+the data affect the results. Hence, this paper applies (a) multi-criteria tests
+while (b) fixing the weaker regions of the training data (using SMOTUNED, which
+is a self-tuning version of SMOTE). This approach leads to dramatically large
+increases in software defect predictions. When applied in a 5*5
+cross-validation study for 3,681 JAVA classes (containing over a million lines
+of code) from open source systems, SMOTUNED increased AUC and recall by 60% and
+20% respectively. These improvements are independent of the classifier used to
+predict for quality. Same kind of pattern (improvement) was observed when a
+comparative analysis of SMOTE and SMOTUNED was done against the most recent
+class imbalance technique. In conclusion, for software analytic tasks like
+defect prediction, (1) data pre-processing can be more important than
+classifier choice, (2) ranking studies are incomplete without such
+pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.
+"
+26675,Localization for $K$-Contact Manifolds,"  We prove an analogue of the Atiyah-Bott-Berline-Vergne localization formula
+in the setting of equivariant basic cohomology of $K$-contact manifolds. As a
+consequence, we deduce analogues of Witten's nonabelian localization and the
+Jeffrey-Kirwan residue formula, which relate equivariant basic integrals on a
+contact manifold $M$ to basic integrals on the contact quotient $M_0 :=
+\mu^{-1}(0)/G$, where $\mu$ denotes the contact moment map for the action of a
+torus $G$. In the special case that $M \to N$ is an equivariant Boothby-Wang
+fibration, our formulae reduce to the usual ones for the symplectic manifold
+$N$.
+"
+26676,Solving ill-posed inverse problems using iterative deep neural networks,"  We propose a partially learned approach for the solution of ill posed inverse
+problems with not necessarily linear forward operators. The method builds on
+ideas from classical regularization theory and recent advances in deep learning
+to perform learning while making use of prior information about the inverse
+problem encoded in the forward operator, noise model and a regularizing
+functional. The method results in a gradient-like iterative scheme, where the
+""gradient"" component is learned using a convolutional network that includes the
+gradients of the data discrepancy and regularizer as input in each iteration.
+We present results of such a partially learned gradient scheme on a non-linear
+tomographic inversion problem with simulated data from both the Sheep-Logan
+phantom as well as a head CT. The outcome is compared against FBP and TV
+reconstruction and the proposed method provides a 5.4 dB PSNR improvement over
+the TV reconstruction while being significantly faster, giving reconstructions
+of 512 x 512 volumes in about 0.4 seconds using a single GPU.
+"
+26677,On Convergence of Epanechnikov Mean Shift,"  Epanechnikov Mean Shift is a simple yet empirically very effective algorithm
+for clustering. It localizes the centroids of data clusters via estimating
+modes of the probability distribution that generates the data points, using the
+`optimal' Epanechnikov kernel density estimator. However, since the procedure
+involves non-smooth kernel density functions, the convergence behavior of
+Epanechnikov mean shift lacks theoretical support as of this writing---most of
+the existing analyses are based on smooth functions and thus cannot be applied
+to Epanechnikov Mean Shift. In this work, we first show that the original
+Epanechnikov Mean Shift may indeed terminate at a non-critical point, due to
+the non-smoothness nature. Based on our analysis, we propose a simple remedy to
+fix it. The modified Epanechnikov Mean Shift is guaranteed to terminate at a
+local maximum of the estimated density, which corresponds to a cluster
+centroid, within a finite number of iterations. We also propose a way to avoid
+running the Mean Shift iterates from every data point, while maintaining good
+clustering accuracies under non-overlapping spherical Gaussian mixture models.
+This further pushes Epanechnikov Mean Shift to handle very large and
+high-dimensional data sets. Experiments show surprisingly good performance
+compared to the Lloyd's K-means algorithm and the EM algorithm.
+"
+26678,Three Factors Influencing Minima in SGD,"  We investigate the dynamical and convergent properties of stochastic gradient
+descent (SGD) applied to Deep Neural Networks (DNNs). Characterizing the
+relation between learning rate, batch size and the properties of the final
+minima, such as width or generalization, remains an open question. In order to
+tackle this problem we investigate the previously proposed approximation of SGD
+by a stochastic differential equation (SDE). We theoretically argue that three
+factors - learning rate, batch size and gradient covariance - influence the
+minima found by SGD. In particular we find that the ratio of learning rate to
+batch size is a key determinant of SGD dynamics and of the width of the final
+minima, and that higher values of the ratio lead to wider minima and often
+better generalization. We confirm these findings experimentally. Further, we
+include experiments which show that learning rate schedules can be replaced
+with batch size schedules and that the ratio of learning rate to batch size is
+an important factor influencing the memorization process.
+"
+26679,Degeneration of Bethe subalgebras in the Yangian of $\mathfrak{gl}_n$,"  We study degenerations of Bethe subalgebras $B(C)$ in the Yangian
+$Y(\mathfrak{gl}_n)$, where $C$ is a regular diagonal matrix. We show that
+closure of the parameter space of the family of Bethe subalgebras, which
+parametrizes all possible degenerations, is the Deligne-Mumford moduli space of
+stable rational curves $\overline{M_{0,n+2}}$. All subalgebras corresponding to
+the points of $\overline{M_{0,n+2}}$ are free and maximal commutative. We
+describe explicitly the ""simplest"" degenerations and show that every
+degeneration is the composition of the simplest ones. The Deligne-Mumford space
+$\overline{M_{0,n+2}}$ generalizes to other root systems as some De
+Concini-Procesi resolution of some toric variety. We state a conjecture
+generalizing our results to Bethe subalgebras in the Yangian of arbitrary
+simple Lie algebra in terms of this De Concini-Procesi resolution.
+"
+26680,Nitrile versus isonitrile adsorption at interstellar grain surfaces II. Carbonaceous aromatic surfaces,"  Almost 20 % of the ~ 200 different species detected in the interstellar and
+circumstellar media present a carbon atom linked to nitrogen by a triple bond.
+Of these 37 molecules, 30 are nitrile R-CN compounds, the remaining 7 belonging
+to the isonitrile R-NC family. How these species behave in their interactions
+with the grain surfaces is still an open question. In a previous work, we have
+investigated whether the difference between nitrile and isonitrile functional
+groups may induce differences in the adsorption energies of the related isomers
+at the surfaces of interstellar grains of various nature and morphologies. This
+study is a follow up of this work, where we focus on the adsorption on
+carbonaceous aromatic surfaces. The question is addressed by means of a
+concerted experimental and theoretical approach of the adsorption energies of
+CH$_3$CN and CH$_3$NC on the surface of graphite (with and without surface
+defects). The experimental determination of the molecule and surface
+interaction energies is carried out using temperature-programmed desorption
+(TPD) in an ultra-high vacuum (UHV) between 70 and 160 K. Theoretically, the
+question is addressed using first-principle periodic density functional theory
+(DFT) to represent the organised solid support. The adsorption energy of each
+compound is found to be very sensitive to the structural defects of the
+aromatic carbonaceous surface: these defects, expected to be present in a large
+numbers and great diversity on a realistic surface, significantly increase the
+average adsorption energies to more than 50\% as compared to adsorption on
+perfect graphene planes. The most stable isomer (CH$_3$CN) interacts more
+efficiently with the carbonaceous solid support than the higher energy isomer
+(CH$_3$NC), however.
+"
+26681,Morphology-based Entity and Relational Entity Extraction Framework for Arabic,"  Rule-based techniques to extract relational entities from documents allow
+users to specify desired entities with natural language questions, finite state
+automata, regular expressions and structured query language. They require
+linguistic and programming expertise and lack support for Arabic morphological
+analysis. We present a morphology-based entity and relational entity extraction
+framework for Arabic (MERF). MERF requires basic knowledge of linguistic
+features and regular expressions, and provides the ability to interactively
+specify Arabic morphological and synonymity features, tag types associated with
+regular expressions, and relations and code actions defined over matches of
+subexpressions. MERF constructs entities and relational entities from matches
+of the specifications. We evaluated MERF with several case studies. The results
+show that MERF requires shorter development time and effort compared to
+existing application specific techniques and produces reasonably accurate
+results within a reasonable overhead in run time.
+"
+26682,"Nesting-induced Local Electronic Patterns around a Single As (Te, Se) Vacancy in Iron-based Superconductors","  The local electronic states around a single As (Te, Se) vacancy are
+investigated in order to shed light on the role of ligands in a series of
+iron-based superconductors. Such a vacancy can produce a local hopping
+correction ranging from $-0.22$ eV to 0.12 eV and always induce two in-gap
+resonance peaks in the local density of states (LDOS) at the fixed symmetrical
+bias voltages, which are rather robust and irrelevant to the phase of
+superconducting order parameter. The LDOS images near the defect predominantly
+possess $0^o$ and $45^o$ stripes. These energy-dependent charge modulations
+created by quasiparticle interference are originated in the nesting effect
+between the inner (outer) hole Fermi surface around $\Gamma$ point and the
+inner (outer) electron Fermi surface around $M$ point.
+"
+26683,The Design of Order-of-Addition Experiments,"  We introduce systematic methods to create optimal designs for
+order-of-addition (OofA) experiments, those that study the order in which $m$
+components are applied---for example, the order in which chemicals are added to
+a reaction or layers are added to a film. Full designs require $m!$ runs, so we
+investigate design fractions. Balance criteria for creating such designs employ
+an extension of orthogonal arrays (OA's) to OofA-OA's. A connection is made
+between $D$-efficient and OofA-OA designs. Necessary conditions are found for
+the number of runs needed to create OofA-OA's of strengths 2 and 3. We create a
+number of new, optimal, designs: 12-run OofA-OA's in 4 and 5 components, 24-run
+OofA-OA's in 5 and 6 components, and near OofA-OA's in 7 components. We extend
+these designs to include (a) process factors, and (b) the common case in which
+component orderings are restricted. We also suggest how such designs may be
+analyzed.
+"
+26684,Towards a holographic approach to spherical aberration correction in scanning transmission electron microscopy,"  Recent progress in phase modulation using nanofabricated electron holograms
+has demonstrated how the phase of an electron beam can be controlled. In this
+paper, we apply this concept to the correction of spherical aberration in a
+scanning transmission electron microscope and demonstrate an improvement in
+spatial resolution. Such a holographic approach to spherical aberration
+correction is advantageous for its simplicity and cost-effectiveness.
+"
+26685,Magnetic Domain-Wall Tilting due to Domain-Wall Speed Asymmetry,"  Chiral magnetic materials provide a number of challenging issues such as the
+highly efficient domain wall (DW) and skyrmion motions driven by electric
+current, as of the operation principles of emerging spintronic devices. The DWs
+in the chiral materials exhibit asymmetric DW speed variation under application
+of in plane magnetic field. Here, we show that such DW speed asymmetry causes
+the DW tilting during the motion along wire structure. It has been known that
+the DW tilting can be induced by the direct Zeeman interaction of the DW
+magnetization under application of in plane magnetic field. However, our
+experimental observations manifests that there exists another dominant process
+with the DW speed asymmetry caused by either the Dzyaloshinskii Moriya
+interaction (DMI) or the chirality dependent DW speed variation. A theoretical
+model based on the DW geometry reveals that the DW tilting is initiated by the
+DW pinning at wire edges and then, the direction of the DW tilting is
+determined by the DW speed asymmetry, as confirmed by a numerical simulation.
+The present observation reveals the decisive role of the DW pinning with the DW
+speed asymmetry, which determines the DW geometry and consequently, the
+dynamics.
+"
+26686,Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy,"  Objective: Surgical data science is evolving into a research field that aims
+to observe everything occurring within and around the treatment process to
+provide situation-aware data-driven assistance. In the context of endoscopic
+video analysis, the accurate classification of organs in the field of view of
+the camera proffers a technical challenge. Herein, we propose a new approach to
+anatomical structure classification and image tagging that features an
+intrinsic measure of confidence to estimate its own performance with high
+reliability and which can be applied to both RGB and multispectral imaging (MI)
+data. Methods: Organ recognition is performed using a superpixel classification
+strategy based on textural and reflectance information. Classification
+confidence is estimated by analyzing the dispersion of class probabilities.
+Assessment of the proposed technology is performed through a comprehensive in
+vivo study with seven pigs. Results: When applied to image tagging, mean
+accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB)
+and 96% (MI) with the confidence measure. Conclusion: Results showed that the
+confidence measure had a significant influence on the classification accuracy,
+and MI data are better suited for anatomical structure labeling than RGB data.
+Significance: This work significantly enhances the state of art in automatic
+labeling of endoscopic videos by introducing the use of the confidence metric,
+and by being the first study to use MI data for in vivo laparoscopic tissue
+classification. The data of our experiments will be released as the first in
+vivo MI dataset upon publication of this paper.
+"
+26687,A generalized action-angle representation of wave interaction in stratified shear flows,"  In this paper we express the linearized dynamics of interacting interfacial
+waves in stratified shear flows in the compact form of action-angle Hamilton
+equations. The pseudo-energy serves as the Hamiltonian of the system, the
+action coordinates are the contribution of the interfacial waves to the
+wave-action, and the angles are their phases. The term ""generalized
+action-angle"" aims to emphasize that the action of each wave is generally time
+dependent and this allows instability. An attempt is made to relate this
+formalism to the action at a distance resonance instability mechanism between
+counter-propagating vorticity waves via the global conservations of
+pseudo-energy and pseudo-momentum.
+"
+26688,Quantitative estimates of sampling constants in model spaces,"  We establish quantitative estimates for sampling (dominating) sets in model
+spaces associated with meromorphic inner functions, i.e. those corresponding to
+de Branges spaces. Our results encompass the Logvinenko-Sereda-Panejah (LSP)
+Theorem including Kovrijkine's optimal sampling constants for Paley-Wiener
+spaces. It also extends Dyakonov's LSP theoremfor model spaces associated with
+bounded derivative inner functions. Considering meromorphic inner functions
+allows us tointroduce a new geometric density condition, in terms of which the
+sampling sets are completely characterized. This, incomparison to Volberg's
+characterization of sampling measures in terms of harmonic measure, enables us
+to obtain explicitestimates on the sampling constants. The methods combine
+Baranov-Bernstein inequalities, reverse Carleson measures andRemez inequalities .
+"
+26689,Recognizing Linked Domain in Polynomial Time,"  The celebrated Gibbard-Satterthwaite Theorem states that any surjective
+social choice function which is defined over the universal domain of
+preferences and is strategy-proof must be dictatorial. Aswal, Chatterji and Sen
+generalize the Gibbard-Satterthwaite theorem by showing that the
+Gibbard-Satterthwaite theorem still holds if the universal domain is replaced
+with the linked domain. In this note, we show that determining whether an
+election is linked can be done in polynomial time.
+"
+26690,On Heilbronn's Triangle-Problem,"  Let n points be placed on a closed convex domain on the plane, no three
+points on a straight line.
+"
+26691,Study of Light Backgrounds from Relativistic Electrons in Air Light-Guides,"  The MOLLER experiment proposed at the Thomas Jefferson National Accelerator
+Facility plans a precision low energy determination of the weak mixing angle
+via the measurement of the parity-violating asymmetry in the scattering of high
+energy longitudinally polarized electrons from electrons bound in a liquid
+hydrogen target (M{\o}ller scattering). A relative measure of the scattering
+rate is planned to be obtained by intercepting the M{\o}ller scattered
+electrons with a circular array of thin fused silica tiles attached to air
+light guides, which facilitate the transport of Cherenkov photons generated
+within the tiles to photomultiplier tubes (PMTs). The scattered flux will also
+pass through the light guides of downstream tiles, generating additional
+Cherenkov as well as scintillation light and is a potential background. In
+order to estimate the rate of these backgrounds, a gas-filled tube detector was
+designed and deployed in an electron beam at the MAMI facility at Johannes
+Gutenberg University, Mainz, Germany. Described in this paper is the design of
+a detector to measure separately the scintillation and Cherenkov responses of
+gas mixtures from relativistic electrons, the results of studies of several gas
+mixtures with comparisons to simulations, and conclusions about the
+implications for the design of the MOLLER detector apparatus.
+"
+26692,A New Class of Generalized Burr III Distribution for Lifetime Data,"  For the first time, the Generalized Gamma Burr III (GGBIII) is introduced as
+an important model for problems in several areas such as actuarial sciences,
+meteorology, economics, finance, environmental studies, reliability, and
+censored data in survival analysis. A review of some existing gamma families
+have been presented. It was found that the distributions cannot exhibit
+complicated shapes such as unimodal and modified unimodal shapes which are very
+common in medical field. The Generalized Gamma Burr III (GGBIII) distribution
+which includes the family of Zografos and Balakrishnan as special cases is
+proposed and studied. It is expressed as the linear combination of Burr III
+distribution and it has a tractable properties. Some mathematical properties of
+the new distribution including hazard, survival, reverse hazard rate function,
+moments, moments generating function, mean and median deviations, distribution
+of the order statistics are presented. Maximum likelihood estimation technique
+is used to estimate the model parameters and applications to real datasets in
+order to illustrate the usefulness of the model are presented. Examples and
+applications as well as comparisons of the GGBIII to the existing Gamma-G
+families are given.
+"
+26693,Stochastic Recursive Gradient Algorithm for Nonconvex Optimization,"  In this paper, we study and analyze the mini-batch version of StochAstic
+Recursive grAdient algoritHm (SARAH), a method employing the stochastic
+recursive gradient, for solving empirical loss minimization for the case of
+nonconvex losses. We provide a sublinear convergence rate (to stationary
+points) for general nonconvex functions and a linear convergence rate for
+gradient dominated functions, both of which have some advantages compared to
+other modern stochastic gradient algorithms for nonconvex losses.
+"
+26694,Reaction-diffusion kinetics on lattice at the microscopic scale,"  Lattice-based stochastic simulators are commonly used to study biological
+reaction-diffusion processes. Some of these schemes that are based on the
+reaction-diffusion master equation (RDME), can simulate for extended spatial
+and temporal scales but cannot directly account for the microscopic effects in
+the cell such as volume exclusion and diffusion-influenced reactions.
+Nonetheless, schemes based on the high-resolution microscopic lattice method
+(MLM) can directly simulate these effects by representing each finite-sized
+molecule explicitly as a random walker on fine lattice voxels. The theory and
+consistency of MLM in simulating diffusion-influenced reactions have not been
+clarified in detail. Here, we examine MLM in solving diffusion-influenced
+reactions in 3D space by employing the Spatiocyte simulation scheme. Applying
+the random walk theory, we construct the general theoretical framework
+underlying the method and obtain analytical expressions for the total rebinding
+probability and the effective reaction rate. By matching Collins-Kimball and
+lattice-based rate constants, we obtained the exact expressions to determine
+the reaction acceptance probability and voxel size. We found that the size of
+voxel should be about 2% larger than the molecule. MLM is validated by
+numerical simulations, showing good agreement with the off-lattice
+particle-based method, eGFRD. MLM run time is more than an order of magnitude
+faster than eGFRD when diffusing macromolecules with typical concentrations in
+the cell. MLM also showed good agreements with eGFRD and mean-field models in
+case studies of two basic motifs of intracellular signaling, the protein
+production-degradation process and the dual phosphorylation cycle. Moreover,
+when a reaction compartment is populated with volume-excluding obstacles, MLM
+captures the non-classical reaction kinetics caused by anomalous diffusion of
+reacting molecules.
+"
+26695,Andersen-Weeks-Chandler perturbation theory and one-component sticky-hard-spheres,"  We apply second order Andersen-Weeks-Chandler perturbation theory to the
+one-component sticky-hard-spheres fluid. We compare the results with the mean
+spherical approximation, the Percus-Yevick approximation, two generalized
+Percus-Yevick approximations, and the Monte Carlo simulations.
+"
+26696,Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field,"  In this paper, we consider Bayesian image denoising based on a Gaussian
+Markov random field (GMRF) model, for which we propose an new algorithm. Our
+method can solve Bayesian image denoising problems, including hyperparameter
+estimation, in $O(n)$-time, where $n$ is the number of pixels in a given image.
+From the perspective of the order of the computational time, this is a
+state-of-the-art algorithm for the present problem setting. Moreover, the
+results of our numerical experiments we show our method is in fact effective in
+practice.
+"
+26697,Efficient Parallel Strategy Improvement for Parity Games,"  We study strategy improvement algorithms for solving parity games. While
+these algorithms are known to solve parity games using a very small number of
+iterations, experimental studies have found that a high step complexity causes
+them to perform poorly in practice. In this paper we seek to address this
+situation. Every iteration of the algorithm must compute a best response, and
+while the standard way of doing this uses the Bellman-Ford algorithm, we give
+experimental results that show that one-player strategy improvement
+significantly outperforms this technique in practice. We then study the best
+way to implement one-player strategy improvement, and we develop an efficient
+parallel algorithm for carrying out this task, by reducing the problem to
+computing prefix sums on a linked list. We report experimental results for
+these algorithms, and we find that a GPU implementation of this algorithm shows
+a significant speedup over single-core and multi-core CPU implementations.
+"
+26698,LRMM: Learning to Recommend with Missing Modalities,"  Multimodal learning has shown promising performance in content-based
+recommendation due to the auxiliary user and item information of multiple
+modalities such as text and images. However, the problem of incomplete and
+missing modality is rarely explored and most existing methods fail in learning
+a recommendation model with missing or corrupted modalities. In this paper, we
+propose LRMM, a novel framework that mitigates not only the problem of missing
+modalities but also more generally the cold-start problem of recommender
+systems. We propose modality dropout (m-drop) and a multimodal sequential
+autoencoder (m-auto) to learn multimodal representations for complementing and
+imputing missing modalities. Extensive experiments on real-world Amazon data
+show that LRMM achieves state-of-the-art performance on rating prediction
+tasks. More importantly, LRMM is more robust to previous methods in alleviating
+data-sparsity and the cold-start problem.
+"
+26699,Versatility of nodal affiliation to communities,"  Graph theoretical analysis of the community structure of networks attempts to
+identify the communities (or modules) to which each node affiliates. However,
+this is in most cases an ill-posed problem, as the affiliation of a node to a
+single community is often ambiguous. Previous solutions have attempted to
+identify all of the communities to which each node affiliates. Instead of
+taking this approach, we introduce versatility, $V$, as a novel metric of nodal
+affiliation: $V \sim 0$ means that a node is consistently assigned to a
+specific community; $V \gg 0$ means it is inconsistently assigned to different
+communities. Versatility works in conjunction with existing community detection
+algorithms, and it satisfies many theoretically desirable properties in
+idealised networks designed to maximise ambiguity of modular decomposition. The
+local minima of global mean versatility identified the resolution parameters of
+a hierarchical community detection algorithm that least ambiguously decomposed
+the community structure of a social (karate club) network and the mouse brain
+connectome. Our results suggest that nodal versatility is useful in quantifying
+the inherent ambiguity of modular decomposition.
+"
+26700,Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent,"  We consider the problem of distributed statistical machine learning in
+adversarial settings, where some unknown and time-varying subset of working
+machines may be compromised and behave arbitrarily to prevent an accurate model
+from being learned. This setting captures the potential adversarial attacks
+faced by Federated Learning -- a modern machine learning paradigm that is
+proposed by Google researchers and has been intensively studied for ensuring
+user privacy. Formally, we focus on a distributed system consisting of a
+parameter server and $m$ working machines. Each working machine keeps $N/m$
+data samples, where $N$ is the total number of samples. The goal is to
+collectively learn the underlying true model parameter of dimension $d$.
+In classical batch gradient descent methods, the gradients reported to the
+server by the working machines are aggregated via simple averaging, which is
+vulnerable to a single Byzantine failure. In this paper, we propose a Byzantine
+gradient descent method based on the geometric median of means of the
+gradients. We show that our method can tolerate $q \le (m-1)/2$ Byzantine
+failures, and the parameter estimate converges in $O(\log N)$ rounds with an
+estimation error of $\sqrt{d(2q+1)/N}$, hence approaching the optimal error
+rate $\sqrt{d/N}$ in the centralized and failure-free setting. The total
+computational complexity of our algorithm is of $O((Nd/m) \log N)$ at each
+working machine and $O(md + kd \log^3 N)$ at the central server, and the total
+communication cost is of $O(m d \log N)$. We further provide an application of
+our general results to the linear regression problem.
+A key challenge arises in the above problem is that Byzantine failures create
+arbitrary and unspecified dependency among the iterations and the aggregated
+gradients. We prove that the aggregated gradient converges uniformly to the
+true gradient function.
+"
+26701,Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification,"  In this paper, we propose a contact-implicit trajectory optimization (CITO)
+method based on a variable smooth contact model (VSCM) and successive
+convexification (SCvx). The VSCM facilitates the convergence of gradient-based
+optimization without compromising physical fidelity. On the other hand, SCvx
+combines the advantages of direct and shooting methods for CITO. For
+evaluations, non-prehensile manipulation tasks are considered. We compare the
+proposed method to a version based on iterative linear quadratic regulator
+(iLQR) on a planar example. The proposed SCvx-based method is also tested on a
+standard robot platform. The results demonstrate that both methods can find
+physically-consistent motions that achieve the tasks without a meaningful
+initial guess owing to the VSCM. The proposed SCvx-based method outperforms the
+iLQR-based method in terms of convergence, computation time, and the quality of
+motions found. Finally, the proposed approach is shown to perform efficiently
+for real-world applications.
+"
+26702,Adaptive Supremum Norm Posterior Contraction: Wavelet Spike-and-Slab and Anisotropic Besov Spaces,"  Supremum norm loss is intuitively more meaningful to quantify function
+estimation error in statistics. In the context of multivariate nonparametric
+regression with unknown error, we propose a Bayesian procedure based on
+spike-and-slab prior and wavelet projections to estimate the regression
+function and all its mixed partial derivatives. We show that their posterior
+distributions contract to the truth optimally and adaptively under
+supremum-norm loss. The master theorem through tests with exponential errors
+used in Bayesian nonparametrics was not adequate to deal with this problem, and
+we developed a new idea such that posterior under the regression model is
+systematically reduced to a posterior arising from some quasi-white noise
+model, where the latter model greatly simplifies our rate calculations. Hence,
+this paper takes the first step in showing explicitly how one can translate
+results from white noise to regression model in a Bayesian setting.
+"
+26703,The Single-Phase ProtoDUNE Technical Design Report,"  ProtoDUNE-SP is the single-phase DUNE Far Detector prototype that is under
+construction and will be operated at the CERN Neutrino Platform (NP) starting
+in 2018. ProtoDUNE-SP, a crucial part of the DUNE effort towards the
+construction of the first DUNE 10-kt fiducial mass far detector module (17 kt
+total LAr mass), is a significant experiment in its own right. With a total
+liquid argon (LAr) mass of 0.77 kt, it represents the largest monolithic
+single-phase LArTPC detector to be built to date. It's technical design is
+given in this report.
+"
+26704,Location reference identification from tweets during emergencies: A deep learning approach,"  Twitter is recently being used during crises to communicate with officials
+and provide rescue and relief operation in real time. The geographical location
+information of the event, as well as users, are vitally important in such
+scenarios. The identification of geographic location is one of the challenging
+tasks as the location information fields, such as user location and place name
+of tweets are not reliable. The extraction of location information from tweet
+text is difficult as it contains a lot of non-standard English, grammatical
+errors, spelling mistakes, non-standard abbreviations, and so on. This research
+aims to extract location words used in the tweet using a Convolutional Neural
+Network (CNN) based model. We achieved the exact matching score of 0.929,
+Hamming loss of 0.002, and $F_1$-score of 0.96 for the tweets related to the
+earthquake. Our model was able to extract even three- to four-word long
+location references which is also evident from the exact matching score of over
+92\%. The findings of this paper can help in early event localization,
+emergency situations, real-time road traffic management, localized
+advertisement, and in various location-based services.
+"
+26705,Near-Balanced Incomplete Block Designs with An Application to Poster Competitions,"  Judging scholarly posters creates a challenge to assign the judges
+efficiently. If there are many posters and few reviews per judge, the commonly
+used Balanced Incomplete Block Design is not a feasible option. An additional
+challenge is an unknown number of judges before the event. We propose two
+connected near-balanced incomplete block designs that both satisfy the
+requirements of our setting: one that generates a connected assignment and
+balances the treatments and another one that further balances pairs of
+treatments. We describe both fixed and random effects models to estimate the
+population marginal means of the poster scores and rationalize the use of the
+random effects model. We evaluate the estimation accuracy and efficiency,
+especially the winning chance of the truly best posters, of the two designs in
+comparison with a random assignment via simulation studies. The two proposed
+designs both demonstrate accuracy and efficiency gains over the random
+assignment.
+"
+26706,Belief Control Strategies for Interactions over Weakly-Connected Graphs,"  In diffusion social learning over weakly-connected graphs, it has been shown
+recently that influential agents shape the beliefs of non-influential agents.
+This paper analyzes this mechanism more closely and addresses two main
+questions. First, the article examines how much freedom influential agents have
+in controlling the beliefs of the receiving agents, namely, whether receiving
+agents can be driven to arbitrary beliefs and whether the network structure
+limits the scope of control by the influential agents. Second, even if there is
+a limit to what influential agents can accomplish, this article develops
+mechanisms by which they can lead receiving agents to adopt certain beliefs.
+These questions raise interesting possibilities about belief control over
+networked agents. Once addressed, one ends up with design procedures that allow
+influential agents to drive other agents to endorse particular beliefs
+regardless of their local observations or convictions. The theoretical findings
+are illustrated by means of examples.
+"
+26707,Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks,"  We present a machine-learning photometric redshift analysis of the
+Kilo-Degree Survey Data Release 3, using two neural-network based techniques:
+ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets,
+these ML codes provide photo-zs of quality comparable to, if not better than,
+those from the BPZ code, at least up to zphot<0.9 and r<23.5. At the bright end
+of r<20, where very complete spectroscopic data overlapping with KiDS are
+available, the performance of the ML photo-zs clearly surpasses that of BPZ,
+currently the primary photo-z method for KiDS.
+Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as
+calibration, we furthermore study how photo-zs improve for bright sources when
+photometric parameters additional to magnitudes are included in the photo-z
+derivation, as well as when VIKING and WISE infrared bands are added. While the
+fiducial four-band ugri setup gives a photo-z bias $\delta z=-2e-4$ and scatter
+$\sigma_z<0.022$ at mean z = 0.23, combining magnitudes, colours, and galaxy
+sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once
+the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12
+$\mu$, the scatter decreases by more than 10% over the fiducial case. Finally,
+using the 12 bands together with optical colours and linear sizes gives $\delta
+z<4e-5$ and $\sigma_z<0.019$.
+This paper also serves as a reference for two public photo-z catalogues
+accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of
+general purpose, includes all the 39 million KiDS sources with four-band ugri
+measurements in DR3. The second dataset, optimized for low-redshift studies
+such as galaxy-galaxy lensing, is limited to r<20, and provides photo-zs of
+much better quality than in the full-depth case thanks to incorporating optical
+magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.
+"
+26708,Tracking Detector Performance and Data Quality in the NOvA Experiment,"  NOvA is a long-baseline neutrino oscillation experiment. It uses the NuMI
+beam from Fermilab and two sampling calorimeter detectors located off-axis from
+the beam. The NOvA experiment measures the rate of electron-neutrino appearance
+in the almost pure muon-neutrino NuMI beam, with the data measured at the Near
+Detector being used to accurately determine the expected rate at the Far
+Detector. It is very important to have automated and accurate monitoring of the
+data recorded by the detectors so any hardware, DAQ or beam issues arising in
+the 344k (20k) channels of the Far (Near) detector which could affect the
+quality of the data taking are determined. This paper will cover the techniques
+and detector monitoring systems in various stages of data taking.
+"
+26709,The Bousfield-Kuhn functor and Topological Andre-Quillen cohomology,"  We construct a natural transformation from the Bousfield-Kuhn functor
+evaluated on a space to the Topological Andre-Quillen cohomology of the
+K(n)-local Spanier-Whitehead dual of the space, and show that the map is an
+equivalence in the case where the space is a sphere. This results in a method
+for computing unstable v_n-periodic homotopy groups of spheres from their
+Morava E-cohomology (as modules over the Dyer-Lashof algebra of Morava
+E-theory). We relate the resulting algebraic computations to the algebraic
+geometry of isogenies between Lubin-Tate formal groups.
+"
+26710,Periodically stationary multivariate autoregressive models,"  A class of multivariate periodic autoregressive models is proposed where
+coupling between time series is achieved through linear mean functions. Various
+response distributions with quadratic mean-variance relationships fit into the
+framework, including the negative binomial, gamma and Gaussian distributions.
+We develop an iterative algorithm to obtain unconditional means, variances and
+auto-/cross-covariances for models with higher order lags. Analytical solutions
+are given for the univariate model with lag one and multivariate models with
+linear mean-variance relationship. A special case of the model class is an
+established framework for modelling multivariate time series of counts from
+routine surveillance of infectious diseases. We extend this model class to
+allow for distributed lags and apply it to a dataset on norovirus
+gastroenteritis in two German states. The availability of unconditional moments
+and auto/cross-correlations enhances model assessment and interpretation.
+"
+26711,Anticanonical modules of Segre products,"  We compute the anticanonical module of the Segre product of standard graded
+$K$-algebras and determine its depth.
+"
+26712,Modified multi-dimensional limiting process with enhanced shock stability on unstructured grids,"  The basic concept of multi-dimensional limiting process (MLP) on unstructured
+grids is inherited and modified for improving shock stabilities and reducing
+numerical dissipation on smooth regions. A relaxed version of MLP condition,
+simply named as weak-MLP, is proposed for reducing dissipation. Moreover, a
+stricter condition, that is the strict-MLP condition, is proposed to enhance
+the numerical stability. The maximum/minimum principle is fulfilled by both the
+strict- and weak-MLP condition. A differentiable pressure weight function is
+applied for the combination of two novel conditions, and thus the modified
+limiter is named as MLP-pw(pressure-weighted). A series of numerical test cases
+show that MLP-pw limiter has improved stability and convergence, especially in
+hypersonic simulations. Furthermore, the limiter also shows lower dissipation
+in regions without significant pressure transition. Therefore, MLP-pw limiter
+can capture contact discontinuity and expansion accurately.
+"
+26713,On varying coefficients of spatial inhomogeneous nonlinear Schrödinger equation,"  A nonlinear evolution equation for wave packet surface gravity waves with
+variation in topography is revisited in this article. The equation is modeled
+by a spatial inhomogeneous nonlinear Schrödinger (NLS) equation with varying
+coefficients, derived by Djordjević and Redekopp (1978) and the nonlinear
+coefficient is later corrected by Dingemans and Otta (2001). We show
+analytically and qualitatively that the nonlinear coefficient and the
+corresponding averaging value, stated but not derived, by Benilov, Flanagan and
+Howlin (2005) and Benilov and Howlin (2006) are inaccurate. For a particular
+choice of topography and wave characteristics, the NLS equation alternates
+between focusing and defocusing case and hence, it does not admit the formation
+of a classical soliton, neither bright nor dark one.
+"
+26714,Forman-Ricci Curvature for Hypergraphs,"  In contrast to graph-based models for complex networks, hypergraphs are more
+general structures going beyond binary relations of graphs. For graphs,
+statistics gauging different aspects of their structures have been devised and
+there is undergoing research for devising them for hypergraphs. Forman-Ricci
+curvature is a statistics for graphs, which is based on Riemannian geometry,
+and that stresses the relational character of vertices in a network through the
+analysis of edges rather than vertices. In spite of the different applications
+of this curvature, it has not yet been formulated for hypergraphs. Here we
+devise the Forman-Ricci curvature for directed and undirected hypergraphs,
+where the curvature for graphs is a particular case. We report its upper and
+lower bounds and the respective bounds for the graph case. The curvature
+quantifies the trade-off between hyperedge(arc) size and the degree of
+participation of hyperedge(arc) vertices in other hyperedges(arcs). We
+calculated the curvature for two large networks: Wikipedia vote network and
+\emph{Escherichia coli} metabolic network. In the first case the curvature is
+ruled by hyperedge size, while in the second by hyperedge degree. We found that
+the number of users involved in Wikipedia elections goes hand-in-hand with the
+participation of experienced users. The curvature values of the metabolic
+network allowed detecting redundant and bottle neck reactions. It is found that
+ADP phosphorilation is the metabolic bottle neck reaction but that the reverse
+reaction is not that central for the metabolism.
+"
+26715,A Machine-Learning Framework for Design for Manufacturability,"  this is a duplicate submission(original is arXiv:1612.02141). Hence want to
+withdraw it
+"
+26716,Capsule Network Performance on Complex Data,"  In recent years, convolutional neural networks (CNN) have played an important
+role in the field of deep learning. Variants of CNN's have proven to be very
+successful in classification tasks across different domains. However, there are
+two big drawbacks to CNN's: their failure to take into account of important
+spatial hierarchies between features, and their lack of rotational invariance.
+As long as certain key features of an object are present in the test data,
+CNN's classify the test data as the object, disregarding features' relative
+spatial orientation to each other. This causes false positives. The lack of
+rotational invariance in CNN's would cause the network to incorrectly assign
+the object another label, causing false negatives. To address this concern,
+Hinton et al. propose a novel type of neural network using the concept of
+capsules in a recent paper. With the use of dynamic routing and reconstruction
+regularization, the capsule network model would be both rotation invariant and
+spatially aware. The capsule network has shown its potential by achieving a
+state-of-the-art result of 0.25% test error on MNIST without data augmentation
+such as rotation and scaling, better than the previous baseline of 0.39%. To
+further test out the application of capsule networks on data with higher
+dimensionality, we attempt to find the best set of configurations that yield
+the optimal test error on CIFAR10 dataset.
+"
+26717,Comparing global input-output behavior of frozen-equivalent LPV state-space models,"  It is known that in general, \emph{frozen equivalent} (Linear
+Parameter-Varying) LPV models, \emph{i.e.}, LPV models which have the same
+input-output behavior for each constant scheduling signal, might exhibit
+different input-output behavior for non-constant scheduling signals. In this
+paper, we provide an analytic error bound on the difference between the
+input-output behaviors of two LPV models which are frozen equivalent. This
+error bound turns out to be a function of both the speed of the change of the
+scheduling signal and the discrepancy between the coherent bases of the two LPV
+models. In particular, the difference between the outputs of the two models can
+be made arbitrarily small by choosing a scheduling signal which changes slowly
+enough. An illustrative example is presented to show that the choice of the
+scheduling signal can reduce the difference between the input-output behaviors
+of frozen-equivalent LPV models.
+"
+26718,Definable groups in topological differential fields,"  For certain theories of existentially closed topological differential fields,
+we show that there is a strong relationship between $\mathcal
+L\cup\{D\}$-definable sets and their $\mathcal L$-reducts, where $\mathcal L$
+is a relational expansion of the field language and $D$ a symbol for a
+derivation. This enables us to associate with an $\mathcal
+L\cup\{D\}$-definable group in models of such theories, a local $\mathcal
+L$-definable group. As a byproduct, we show that in closed ordered differential
+fields, one has the descending chain condition on centralisers.
+"
+26719,Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network,"  Brain is an organ that controls activities of all the parts of the body.
+Recognition of automated brain tumor in Magnetic resonance imaging (MRI) is a
+difficult task due to complexity of size and location variability. This
+automatic method detects all the type of cancer present in the body. Previous
+methods for tumor are time consuming and less accurate. In the present work,
+statistical analysis morphological and thresholding techniques are used to
+process the images obtained by MRI. Feed-forward back-prop neural network is
+used to classify the performance of tumors part of the image. This method
+results high accuracy and less iterations detection which further reduces the
+consumption time.
+"
+26720,"Urban morphology meets deep learning: Exploring urban forms in one million cities, town and villages across the planet","  Study of urban form is an important area of research in urban planning/design
+that contributes to our understanding of how cities function and evolve.
+However, classical approaches are based on very limited observations and
+inconsistent methods. As an alternative, availability of massive urban data
+collections such as Open Street Map from the one hand and the recent
+advancements in machine learning methods such as deep learning techniques on
+the other have opened up new possibilities to automatically investigate urban
+forms at the global scale. In this work for the first time, by collecting a
+large data set of street networks in more than one million cities, towns and
+villages all over the world, we trained a deep convolutional auto-encoder, that
+automatically learns the hierarchical structures of urban forms and represents
+them via dense and comparable vectors. We showed how the learned urban vectors
+could be used for different investigations. Using the learned urban vectors,
+one is able to easily find and compare similar urban forms all over the world,
+considering their overall spatial structure and other factors such as
+orientation, graphical structure, and density and partial deformations. Further
+cluster analysis reveals the distribution of the main patterns of urban forms
+all over the planet.
+"
+26721,Near-IR trigonometric parallaxes of nearby stars in the Galactic plane using the VVV,"  We use the multi-epoch K$\rm _S$ band observations, covering a $\sim$5 years
+baseline to obtain milli and sub-milli arcsec precision astrometry for a sample
+of eighteen previously known high proper motion sources, including precise
+parallaxes for these sources for the first time. In this study we show the
+capability of the VVV project to measure high precision trigonometric
+parallaxes for very low mass stars (VLMS) up to distances of $\sim$250\,pc
+reaching farther than most other ground based surveys or space missions for
+these types of stars. Additionally, we used spectral energy distribution to
+search for evidence of unresolved binary systems and cool sub-dwarfs. We
+detected five systems that are most likely VLMS belonging to the Galactic halo
+based on their tangential velocities, and four objects within 60 pc that are
+likely members of the thick disk. A more comprehensive study of high proper
+motion sources and parallaxes of VLMS and brown dwarfs with the VVV is ongoing
+, including thousands of newly discovered objects.
+"
+26722,Using Social Media to Predict the Future: A Systematic Literature Review,"  Social media (SM) data provides a vast record of humanity's everyday
+thoughts, feelings, and actions at a resolution previously unimaginable.
+Because user behavior on SM is a reflection of events in the real world,
+researchers have realized they can use SM in order to forecast, making
+predictions about the future. The advantage of SM data is its relative ease of
+acquisition, large quantity, and ability to capture socially relevant
+information, which may be difficult to gather from other data sources.
+Promising results exist across a wide variety of domains, but one will find
+little consensus regarding best practices in either methodology or evaluation.
+In this systematic review, we examine relevant literature over the past decade,
+tabulate mixed results across a number of scientific disciplines, and identify
+common pitfalls and best practices. We find that SM forecasting is limited by
+data biases, noisy data, lack of generalizable results, a lack of
+domain-specific theory, and underlying complexity in many prediction tasks. But
+despite these shortcomings, recurring findings and promising results continue
+to galvanize researchers and demand continued investigation. Based on the
+existing literature, we identify research practices which lead to success,
+citing specific examples in each case and making recommendations for best
+practices. These recommendations will help researchers take advantage of the
+exciting possibilities offered by SM platforms.
+"
+26723,"Wavefunctions, integrability, and open strings","  It has been recently conjectured that the exact eigenfunctions of quantum
+mirror curves can be obtained by combining their WKB expansion with the open
+topological string wavefunction. In this paper we give further evidence for
+this conjecture. We present closed expressions for the wavefunctions in the
+so-called maximally supersymmetric case, in various geometries. In the higher
+genus case, our conjecture provides a solution to the quantum Baxter equation
+of the corresponding cluster integrable system, and we argue that the
+quantization conditions of the integrable system follow from imposing
+appropriate asymptotic conditions on the wavefunction. We also present checks
+of the conjecture for general values of the Planck constant.
+"
+26724,Quantitative Perspectives on Fifty Years of the Journal of the History of Biology,"  Journal of the History of Biology provides a fifty-year long record for
+examining the evolution of the history of biology as a scholarly discipline. In
+this paper, we present a new dataset and preliminary quantitative analysis of
+the thematic content of JHB from the perspectives of geography, organisms, and
+thematic fields. The geographic diversity of authors whose work appears in JHB
+has increased steadily since 1968, but the geographic coverage of the content
+of JHB articles remains strongly lopsided toward the United States, United
+Kingdom, and western Europe and has diversified much less dramatically over
+time. The taxonomic diversity of organisms discussed in JHB increased steadily
+between 1968 and the late 1990s but declined in later years, mirroring broader
+patterns of diversification previously reported in the biomedical research
+literature. Finally, we used a combination of topic modeling and nonlinear
+dimensionality reduction techniques to develop a model of multi-article fields
+within JHB. We found evidence for directional changes in the representation of
+fields on multiple scales. The diversity of JHB with regard to the
+representation of thematic fields has increased overall, with most of that
+diversification occurring in recent years. Drawing on the dataset generated in
+the course of this analysis, as well as web services in the emerging digital
+history and philosophy of science ecosystem, we have developed an interactive
+web platform for exploring the content of JHB, and we provide a brief overview
+of the platform in this article. As a whole, the data and analyses presented
+here provide a starting-place for further critical reflection on the evolution
+of the history of biology over the past half-century.
+"
+26725,Bayesian Compressive Sensing Using Normal Product Priors,"  In this paper, we introduce a new sparsity-promoting prior, namely, the
+""normal product"" prior, and develop an efficient algorithm for sparse signal
+recovery under the Bayesian framework. The normal product distribution is the
+distribution of a product of two normally distributed variables with zero means
+and possibly different variances. Like other sparsity-encouraging distributions
+such as the Student's $t$-distribution, the normal product distribution has a
+sharp peak at origin, which makes it a suitable prior to encourage sparse
+solutions. A two-stage normal product-based hierarchical model is proposed. We
+resort to the variational Bayesian (VB) method to perform the inference.
+Simulations are conducted to illustrate the effectiveness of our proposed
+algorithm as compared with other state-of-the-art compressed sensing
+algorithms.
+"
+26726,Pengaruh Perangkat Server Terhadap Kualitas Pengontrolan Jarak Jauh Melalui Internet,"  Internet greatly assist people in improving their quality of life. Almost all
+areas of human life can be accessed using the internet. Human aided by the
+internet that provides all sorts of information that they need. Along with the
+development of the Internet network infrastructure remotely control began to
+change using the internet. In this study using notebooks and servers Raspberry
+Pi to find out the quality control of each device server used. In this study we
+investigate the possibility of improving the quality of web-based remote
+control to implement Raspberry Pi as a web server and how much improvement the
+quality of web-based remote control obtained in this research.
+"
+26727,An implicit boundary integral method for computing electric potential of macromolecules in solvent,"  A numerical method using implicit surface representations is proposed to
+solve the linearized Poisson-Boltzmann equations that arise in mathematical
+models for the electrostatics of molecules in solvent. The proposed method used
+an implicit boundary integral formulation to derived a linear system defined on
+Cartesian nodes in a narrowband surrounding the closed surface that separate
+the molecule and the solvent. The needed implicit surfaces is constructed from
+the given atomic description of the molecules, by a sequence of standard level
+set algorithms. A fast multipole method is applied to accelerate the solution
+of the linear system. A few numerical studies involving some standard test
+cases are presented and compared to other existing results.
+"
+26728,"Sustainability, collapse and oscillations of global climate, population and economy in a simple World-Earth model","  The Anthropocene is characterized by close interdependencies between the
+natural Earth system and the human society, posing novel challenges to model
+development. Here we present a conceptual model describing the long-term
+coevolution of natural and socioeconomic subsystems of Earth. While the climate
+is represented via a global carbon cycle, we use economic concepts to model
+socio-metabolic flows of biomass and fossil fuels between nature and society. A
+wellbeing-dependent parametrization of fertility and mortality governs human
+population dynamics. Our analysis focuses on assessing possible asymptotic
+states of the Earth system for a qualitative understanding of its complex
+dynamics rather than quantitative predictions. Low dimension and simple
+equations enable a parameter-space analysis allowing us to identify
+preconditions of several asymptotic states and hence fates of humanity and
+planet. These include a sustainable co-evolution of nature and society, a
+global collapse and everlasting oscillations. We consider scenarios
+corresponding to different socio-cultural stages of human history. The
+necessity of accounting for the ""human factor"" in Earth system models is
+highlighted by the fact that carbon stocks during the past centuries evolved
+opposing to what would ""naturally"" be expected on a planet without humans. The
+intensity of biomass use and the contribution of ecosystem services to human
+well-being are found to be crucial determinants of the asymptotic state in a
+biomass-only scenario. The capitalistic, fossil-based scenario reveals that
+trajectories with fundamentally different asymptotic states might still be
+almost indistinguishable during a very long transient phase. Given current
+population levels, our study also supports the claim that besides reducing the
+global demand for energy, only the extensive use of renewable energies may pave
+the way into a sustainable future.
+"
+26729,Statistical Topology and the Random Interstellar Medium,"  Current astrophysical models of the interstellar medium assume that small
+scale variation and noise can be modelled as Gaussian random fields or simple
+transformations thereof, such as lognormal. We use topological methods to
+investigate this assumption for three regions of the southern sky. We consider
+Gaussian random fields on two-dimensional lattices and investigate the expected
+distribution of topological structures quantified through Betti numbers. We
+demonstrate that there are circumstances where differences in topology can
+identify differences in distributions when conventional marginal or correlation
+analyses may not. We propose a non-parametric method for comparing two fields
+based on the counts of topological features and the geometry of the associated
+persistence diagrams. When we apply the methods to the astrophysical data, we
+find strong evidence against a Gaussian random field model for each of the
+three regions of the interstellar medium that we consider. Further, we show
+that there are topological differences at a local scale between these different
+regions.
+"
+26730,Minimax theorem and Nash equilibrium of symmetric multi-players zero-sum game with two strategic variables,"  We consider a symmetric multi-players zero-sum game with two strategic
+variables. There are $n$ players, $n\geq 3$. Each player is denoted by $i$. Two
+strategic variables are $t_i$ and $s_i$, $i\in \{1, \dots, n\}$. They are
+related by invertible functions. Using the minimax theorem by \cite{sion} we
+will show that Nash equilibria in the following states are equivalent.
+1. All players choose $t_i,\ i\in \{1, \dots, n\}$, (as their strategic
+variables). 2. Some players choose $t_i$'s and the other players choose
+$s_i$'s. 3. All players choose $s_i,\ i\in \{1, \dots, n\}$.
+"
+26731,Predictive inference for locally stationary time series with an application to climate data,"  The Model-free Prediction Principle of Politis (2015) has been successfully
+applied to general regression problems, as well as problems involving
+stationary time series. However, with long time series, e.g. annual temperature
+measurements spanning over 100 years or daily financial returns spanning
+several years, it may be unrealistic to assume stationarity throughout the span
+of the dataset. In the paper at hand, we show how Model-free Prediction can be
+applied to handle time series that are only locally stationary, i.e., they can
+be assumed to be as stationary only over short time-windows. Surprisingly there
+is little literature on point prediction for general locally stationary time
+series even in model-based setups and there is no literature on the
+construction of prediction intervals of locally stationary time series. We
+attempt to fill this gap here as well. Both one-step-ahead point predictors and
+prediction intervals are constructed, and the performance of model-free is
+compared to model-based prediction using models that incorporate a trend and/or
+heteroscedasticity. Both aspects of the paper, model-free and model-based, are
+novel in the context of time-series that are locally (but not globally)
+stationary. We also demonstrate the application of our Model-based and
+Model-free prediction methods to speleothem climate data which exhibits local
+stationarity and show that our best model-free point prediction results
+outperform that obtained with the RAMPFIT algorithm previously used for
+analysis of this data.
+"
+26732,Emergent stochastic oscillations and signal detection in tree networks of excitable elements,"  We study the stochastic dynamics of strongly-coupled excitable elements on a
+tree network. The peripheral nodes receive independent random inputs which may
+induce large spiking events propagating through the branches of the tree and
+leading to global coherent oscillations in the network. This scenario may be
+relevant to action potential generation in certain sensory neurons, which
+possess myelinated distal dendritic tree-like arbors with excitable nodes of
+Ranvier at peripheral and branching nodes and exhibit noisy periodic sequences
+of action potentials.
+We focus on the spiking statistics of the central node, which fires in
+response to a noisy input at peripheral nodes. We show that, in the strong
+coupling regime, relevant to myelinated dendritic trees, the spike train
+statistics can be predicted from an isolated excitable element with rescaled
+parameters according to the network topology. Furthermore, we show that by
+varying the network topology the spike train statistics of the central node can
+be tuned to have a certain firing rate and variability, or to allow for an
+optimal discrimination of inputs applied at the peripheral nodes.
+"
+26733,Maximising the Utility of Enterprise Millimetre-Wave Networks,"  Millimetre-wave (mmWave) technology is a promising candidate for meeting the
+intensifying demand for ultra fast wireless connectivity, especially in
+high-end enterprise networks. Very narrow beam forming is mandatory to mitigate
+the severe attenuation specific to the extremely high frequency (EHF) bands
+exploited. Simultaneously, this greatly reduces interference, but generates
+problematic communication blockages. As a consequence, client association
+control and scheduling in scenarios with densely deployed mmWave access points
+become particularly challenging, while policies designed for traditional
+wireless networks remain inappropriate. In this paper we formulate and solve
+these tasks as utility maximisation problems under different traffic regimes,
+for the first time in the mmWave context. We specify a set of low-complexity
+algorithms that capture distinctive terminal deafness and user demand
+constraints, while providing near-optimal client associations and airtime
+allocations, despite the problems' inherent NP-completeness. To evaluate our
+solutions, we develop an NS-3 implementation of the IEEE 802.11ad protocol,
+which we construct upon preliminary 60GHz channel measurements. Simulation
+results demonstrate that our schemes provide up to 60% higher throughput as
+compared to the commonly used signal strength based association policy for
+mmWave networks, and outperform recently proposed load-balancing oriented
+solutions, as we accommodate the demand of 33% more clients in both static and
+mobile scenarios.
+"
+26734,Fundamental bounds on learning performance in neural circuits,"  How does the size of a neural circuit influence its learning performance?
+Intuitively, we expect the learning capacity of a neural circuit to grow with
+the number of neurons and synapses. Larger brains tend to be found in species
+with higher cognitive function and learning ability. Similarly, adding
+connections and units to artificial neural networks can allow them to solve
+more complex tasks. However, we show that in a biologically relevant setting
+where synapses introduce an unavoidable amount of noise, there is an optimal
+size of network for a given task. Beneath this optimal size, our analysis shows
+how adding apparently redundant neurons and connections can make tasks more
+learnable. Therefore large neural circuits can either devote connectivity to
+generating complex behaviors, or exploit this connectivity to achieve faster
+and more precise learning of simpler behaviors. Above the optimal network size,
+the addition of neurons and synaptic connections starts to impede learning
+performance. This suggests that overall brain size may be constrained by the
+need to learn efficiently with unreliable synapses, and may explain why some
+neurological learning deficits are associated with hyperconnectivity. Our
+analysis is independent of specific learning rules and uncovers fundamental
+relationships between learning rate, task performance, network size and
+intrinsic noise in neural circuits.
+"
+26735,Periodic Steiner networks minimizing length,"  We study a problem of geometric graph theory: We determine the triply
+periodic graph in Euclidean 3-space which minimizes length among all graphs
+spanning a fundamental domain of 3-space with the same volume. The minimizer is
+the so-called srs network with quotient the complete graph on four vertices
+$K_4$. The network spans the body centred cubic lattice and is related to the
+gyroid triply periodic surface.
+"
+26736,On the $ψ$-Hilfer fractional derivative,"  In this paper we introduce a new fractional derivative with respect to
+another function the so-called $\psi$-Hilfer fractional derivative. We discuss
+some properties and important results of the fractional calculus. In this
+sense, we present some uniformly convergent sequence of function results and
+examples involving the Mittag-Leffler function with one parameter. Finally, we
+present a wide class of integrals and fractional derivatives, by means of the
+fractional integral with respect to another function and the $\psi$-Hilfer
+fractional derivative.
+"
+26737,A class of non-holomorphic modular forms II : equivariant iterated Eisenstein integrals,"  We define and study a family of real analytic modular forms on the upper half
+plane constructed out of real and imaginary parts of iterated integrals of
+Eisenstein series. They form an algebra of functions, which contains the real
+analytic Eisenstein series, satisfying many algebraic and differential
+properties. For example, they admit expansions in $q, \overline{q}$ and $\log
+|q|$ involving only rational numbers and certain combinations of multiple zeta
+values. This class is arguably the simplest family of `mixed' versions of
+modular forms for $\mathrm{SL}_2(\mathbb{Z})$ of level one.
+"
+26738,Learning Robotic Assembly from CAD,"  In this work, motivated by recent manufacturing trends, we investigate
+autonomous robotic assembly. Industrial assembly tasks require contact-rich
+manipulation skills, which are challenging to acquire using classical control
+and motion planning approaches. Consequently, robot controllers for assembly
+domains are presently engineered to solve a particular task, and cannot easily
+handle variations in the product or environment. Reinforcement learning (RL) is
+a promising approach for autonomously acquiring robot skills that involve
+contact-rich dynamics. However, RL relies on random exploration for learning a
+control policy, which requires many robot executions, and often gets trapped in
+locally suboptimal solutions. Instead, we posit that prior knowledge, when
+available, can improve RL performance. We exploit the fact that in modern
+assembly domains, geometric information about the task is readily available via
+the CAD design files. We propose to leverage this prior knowledge by guiding RL
+along a geometric motion plan, calculated using the CAD data. We show that our
+approach effectively improves over traditional control approaches for tracking
+the motion plan, and can solve assembly tasks that require high precision, even
+without accurate state estimation. In addition, we propose a neural network
+architecture that can learn to track the motion plan, and generalize the
+assembly controller to changes in the object positions.
+"
+26739,On Hidden Sums Compatible with A Given Block Cipher Diffusion Layer,"  Sometimes it is possible to embed an algebraic trapdoor into a block cipher.
+Building on previous research, in this paper we investigate an especially
+dangerous algebraic structure, which is called a hidden sum and which is
+related to some regular subgroups of the affine group. Mixing group theory
+arguments and cryptographic tools, we pass from characterizing our hidden sums
+to designing an efficient algorithm to perform the necessary preprocessing for
+the exploitation of the trapdoor.
+"
+26740,Magnetocrystalline anisotropy in YCo5 and LaCo5: A choice of correlation parameters and the relativistic effects,"  The dependence of the magnetocrystalline anisotropy energy (MAE) of MCo5 (M =
+Y, La) on the Coulomb correlations and strength of spin orbit (SO) interaction
+within the GGA + U scheme is investigated. A range of parameters suitable for
+the satisfactory description of key magnetic properties is determined. The
+origin of MAE in these materials is mostly related to the large orbital moment
+anisotropy of Co atoms on the 2c crystallographic site. Dependence of
+relativistic effects on Coulomb correlations, applicability of the second order
+perturbation theory for the description of MAE and effective screening of the
+SO interaction in these systems are discussed using a generalized virial
+theorem.
+"
+26741,Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps,"  Recent developments in the field of robot grasping have shown great
+improvements in the grasp success rates when dealing with unknown objects. In
+this work we improve on one of the most promising approaches, the Grasp Quality
+Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We
+propose a new architecture for the GQ-CNN and describe practical improvements
+that increase the model validation accuracy from 92.2% to 95.8% and from 85.9%
+to 88.0% on respectively image-wise and object-wise training and validation
+splits.
+"
+26742,The role of contact resistance in graphene field-effect devices,"  The extremely high carrier mobility and the unique band structure, make
+graphene very useful for field-effect transistor applications. According to
+several works, the primary limitation to graphene based transistor performance
+is not related to the material quality, but to extrinsic factors that affect
+the electronic transport properties. One of the most important parasitic
+element is the contact resistance appearing between graphene and the metal
+electrodes functioning as the source and the drain. Ohmic contacts to graphene,
+with low contact resistances, are necessary for injection and extraction of
+majority charge carriers to prevent transistor parameter fluctuations caused by
+variations of the contact resistance. The International Technology Roadmap for
+Semiconductors, toward integration and down-scaling of graphene electronic
+devices, identifies as a challenge the development of a CMOS compatible process
+that enables reproducible formation of low contact resistance. However, the
+contact resistance is still not well understood despite it is a crucial barrier
+towards further improvements. In this paper, we review the experimental and
+theoretical activity that in the last decade has been focusing on the reduction
+of the contact resistance in graphene transistors. We will summarize the
+specific properties of graphene-metal contacts with particular attention to the
+nature of metals, impact of fabrication process, Fermi level pinning, interface
+modifications induced through surface processes, charge transport mechanism,
+and edge contact formation.
+"
+26743,Learning How to Listen: A Temporal-Frequential Attention Model for Sound Event Detection,"  In this paper, we propose a temporal-frequential attention model for sound
+event detection (SED). Our network learns how to listen with two attention
+models: a temporal attention model and a frequential attention model. Proposed
+system learns when to listen using the temporal attention model while it learns
+where to listen on the frequency axis using the frequential attention model.
+With these two models, we attempt to make our system pay more attention to
+important frames or segments and important frequency components for sound event
+detection. Our proposed method is demonstrated on the task 2 of Detection and
+Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge and
+achieves competitive performance.
+"
+26744,Demonstrating research subcommunities in mathematical networks,"  We propose a method for demonstrating sub community structure in scientific
+networks of relatively small size from analyzing databases of publications.
+Research relationships between the network members can be visualized as a graph
+with vertices corresponding to authors and with edges indicating joint
+authorship. Using a fast clustering algorithm combined with a graph layout
+algorithm, we demonstrate how to display these clustering results in an
+attractive and informative way. The small size of the graph allows us to
+develop tools that keep track of how these research sub communities evolve in
+time, as well as to present the research articles that create the links between
+the network members. These tools are included in a web app, where the visitor
+can easily identify the various sub communities, providing also valuable
+information for administrational purposes. Our method was developed for the
+GEAR mathematical network and it can be applied to other networks.
+"
+26745,Robust Sparse Coding via Self-Paced Learning,"  Sparse coding (SC) is attracting more and more attention due to its
+comprehensive theoretical studies and its excellent performance in many signal
+processing applications. However, most existing sparse coding algorithms are
+nonconvex and are thus prone to becoming stuck into bad local minima,
+especially when there are outliers and noisy data. To enhance the learning
+robustness, in this paper, we propose a unified framework named Self-Paced
+Sparse Coding (SPSC), which gradually include matrix elements into SC learning
+from easy to complex. We also generalize the self-paced learning schema into
+different levels of dynamic selection on samples, features and elements
+respectively. Experimental results on real-world data demonstrate the efficacy
+of the proposed algorithms.
+"
+26746,Random walks on Ramanujan complexes and digraphs,"  The cutoff phenomenon was recently confirmed for random walks on Ramanujan
+graphs by the first author and Peres. In this work, we obtain analogs in higher
+dimensions, for random walk operators on any Ramanujan complex associated with
+a simple group $G$ over a local field $F$. We show that if $T$ is any
+$k$-regular $G$-equivariant operator on the Bruhat-Tits building with a simple
+combinatorial property (collision-free), the associated random walk on the
+$n$-vertex Ramanujan complex has cutoff at time $\log_k n$. The high
+dimensional case, unlike that of graphs, requires tools from non-commutative
+harmonic analysis and the infinite-dimensional representation theory of $G$.
+Via these, we show that operators $T$ as above on Ramanujan complexes give rise
+to Ramanujan digraphs with a special property ($r$-normal), implying cutoff.
+Applications include geodesic flow operators, geometric implications, and a
+confirmation of the Riemann Hypothesis for the associated zeta functions over
+every group $G$, previously known for groups of type $\widetilde A_n$ and
+$\widetilde C_2$.
+"
+26747,Random Hermitian Matrices and Gaussian Multiplicative Chaos,"  We prove that when suitably normalized, small enough powers of the absolute
+value of the characteristic polynomial of random Hermitian matrices, drawn from
+one-cut regular unitary invariant ensembles, converge in law to Gaussian
+multiplicative chaos measures. We prove this in the so-called $L^2$-phase of
+multiplicative chaos. Our main tools are asymptotics of Hankel determinants
+with Fisher-Hartwig singularities. Using Riemann-Hilbert methods, we prove a
+rather general Fisher-Hartwig formula for one-cut regular unitary invariant
+ensembles.
+"
+26748,On Von Schelling Formula for the Generalized Coupon Collector Problem,"  This paper gives an algebraic proof of the correctness of Von Schelling
+formula for the probability of the coupon collector problem waiting time for
+non-uniform distributions and partial collections. It introduces a theorem on
+sums of powers of subset probabilities which to our knowledge is new. A set of
+binomial coefficients is used as a basis for decomposition of these sums of
+powers.
+"
+26749,Test for Incremental Value of New Biomarkers Based on OR Rules,"  In early detection of disease, a single biomarker often has inadequate
+classification performance, making it important to identify new biomarkers to
+combine with the existing marker for improved performance. A biologically
+natural method to combine biomarkers is to use logic rules, e.g. the OR/AND
+rules. In our motivating example of early detection of pancreatic cancer, the
+established biomarker CA19-9 is only present in a subclass of cancer; it is of
+interest to identify new biomarkers present in the other subclasses and declare
+disease when either marker is positive. While there has been research on
+developing biomarker combinations using the OR/AND rules, the inference
+regarding the incremental value of the new marker within this framework is
+lacking and challenging due to a statistical non-regularity. In this paper, we
+aim to answer the inferential question of whether combining the new biomarker
+achieves better classification performance than using the existing biomarker
+alone, based on a nonparametrically estimated OR rule that maximizes the
+weighted average of sensitivity and specificity. We propose and compare various
+procedures for testing the incremental value of the new biomarker and
+constructing its confidence interval, using bootstrap, cross-validation, and a
+novel fuzzy p-value-based technique. We compare the performance of different
+methods via extensive simulation studies and apply them to the pancreatic
+cancer example.
+"
+26750,Graphene Klein tunnel transistors for high speed analog RF applications,"  We propose Graphene Klein tunnel transistors (GKTFET) as a way to enforce
+current saturation while maintaining large mobility for high speed radio
+frequency (RF) applications. The GKTFET consists of a sequence of angled
+graphene p-n junctions (GPNJs). Klein tunneling creates a collimation of
+electrons across each GPNJ, so that the lack of substantial overlap between
+transmission lobes across successive junctions creates a gate-tunable transport
+gap without significantly compromising the on-current. Electron scattering at
+the device edge tends to bleed parasitic states into the gap, but the resulting
+pseudogap is still sufficient to create a saturated output ($I_D-V_D$)
+characteristic and a high output resistance. The modulated density of states
+generates a higher transconductance ($g_m$) and unity current gain cut-off
+frequency ($f_T$) than GFETs. More significantly the high output resistance
+makes the unity power gain cut-off frequency ($f_{max}$) of GKTFETs
+considerably larger than GFETs, making analog GKTFET potentially useful for RF
+electronics. Our estimation shows the $f_T$/$f_{max}$ of a GKTFET with 1 $\mu$m
+channel reaches 33 GHz/17 GHz, and scale up to 350 GHz/53 GHz for 100 nm
+channel (assuming a single, scalable trapezoidal gate). The $f_{max}$ of a
+GKTFET is 10 times higher than a GFET with the same channel length.
+"
+26751,Allocating dissipation across a molecular machine cycle to maximize flux,"  Biomolecular machines consume free energy to break symmetry and make directed
+progress. Nonequilibrium ATP concentrations are the typical free energy source,
+with one cycle of a molecular machine consuming a certain number of ATP,
+providing a fixed free energy budget. Since evolution is expected to favor
+rapid-turnover machines that operate efficiently, we investigate how this free
+energy budget can be allocated to maximize flux. Unconstrained optimization
+eliminates intermediate metastable states, indicating that flux is enhanced in
+molecular machines with fewer states. When maintaining a set number of states,
+we show that---in contrast to previous findings---the flux-maximizing
+allocation of dissipation is not even. This result is consistent with the
+coexistence of both `irreversible' and reversible transitions in molecular
+machine models that successfully describe experimental data, which suggests
+that in evolved machines different transitions differ significantly in their
+dissipation.
+"
+26752,Belief Propagation in Conditional RBMs for Structured Prediction,"  Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular
+models for a wide range of applications. In previous work, learning on such
+models has been dominated by contrastive divergence~(CD) and its variants.
+Belief propagation~(BP) algorithms are believed to be slow for structured
+prediction on conditional RBMs~(e.g., Mnih et al. [2011]), and not as good as
+CD when applied in learning~(e.g., Larochelle et al. [2012]). In this work, we
+present a matrix-based implementation of belief propagation algorithms on
+CRBMs, which is easily scalable to tens of thousands of visible and hidden
+units. We demonstrate that, in both maximum likelihood and max-margin learning,
+training conditional RBMs with BP as the inference routine can provide
+significantly better results than current state-of-the-art CD methods on
+structured prediction problems. We also include practical guidelines on
+training CRBMs with BP, and some insights on the interaction of learning and
+inference algorithms for CRBMs.
+"
+26753,PaToPa: A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Grids,"  The increasing integration of distributed energy resources (DERs) calls for
+new planning and operational tools. However, such tools depend on system
+topology and line parameters, which may be missing or inaccurate in
+distribution grids. With abundant data, one idea is to use linear regression to
+find line parameters, based on which topology can be identified. Unfortunately,
+the linear regression method is accurate only if there is no noise in both the
+input measurements (e.g., voltage magnitude and phase angle) and output
+measurements (e.g., active and reactive power). For topology estimation, even
+with a small error in measurements, the regression-based method is incapable of
+finding the topology using non-zero line parameters with a proper metric. To
+model input and output measurement errors simultaneously, we propose the
+error-in-variables (EIV) model in a maximum likelihood estimation (MLE)
+framework for joint line parameter and topology estimation. While directly
+solving the problem is NP-hard, we successfully adapt the problem into a
+generalized low-rank approximation problem via variable transformation and
+noise decorrelation. For accurate topology estimation, we let it interact with
+parameter estimation in a fashion that is similar to expectation-maximization
+fashion in machine learning. The proposed PaToPa approach does not require a
+radial network setting and works for mesh networks. We demonstrate the superior
+performance in accuracy for our method on IEEE test cases with actual feeder
+data from South California Edison.
+"
+26754,Plasmonics of Topological Insulators at Optical Frequencies,"  The development of nanoplasmonic devices, such as plasmonic circuits and
+metamaterial superlenses in the visible to ultraviolet frequency range, is
+hampered by the lack of low-loss plasmonic media. Recently, strong plasmonic
+response was reported in a certain class of topological insulators. Here, we
+present a first-principles density functional theory analysis of the dielectric
+functions of topologically insulating quaternary (Bi,Sb)2(Te,Se)3
+trichalcogenide compounds. Bulk plasmonic properties, dominated by interband
+transitions, are observed from 2-3 eV and extend to higher frequencies.
+Moreover, trichalcogenide compounds are better plasmonic media than gold and
+silver at blue and UV wavelengths. By analysing thin slabs, we also show that
+these materials exhibit topologically protected surface states, which are
+capable of supporting propagating plasmon polariton modes over an extremely
+broad spectral range, from the visible to the mid-infrared and beyond, owing to
+a combination of inter- and intra-surface band transitions.
+"
+26755,Efficient coordinate-wise leading eigenvector computation,"  We develop and analyze efficient ""coordinate-wise"" methods for finding the
+leading eigenvector, where each step involves only a vector-vector product. We
+establish global convergence with overall runtime guarantees that are at least
+as good as Lanczos's method and dominate it for slowly decaying spectrum. Our
+methods are based on combining a shift-and-invert approach with coordinate-wise
+algorithms for linear regression.
+"
+26756,Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning,"  We present an end-to-end imitation learning system for agile, off-road
+autonomous driving using only low-cost on-board sensors. By imitating a model
+predictive controller equipped with advanced sensors, we train a deep neural
+network control policy to map raw, high-dimensional observations to continuous
+steering and throttle commands. Compared with recent approaches to similar
+tasks, our method requires neither state estimation nor on-the-fly planning to
+navigate the vehicle. Our approach relies on, and experimentally validates,
+recent imitation learning theory. Empirically, we show that policies trained
+with online imitation learning overcome well-known challenges related to
+covariate shift and generalize better than policies trained with batch
+imitation learning. Built on these insights, our autonomous driving system
+demonstrates successful high-speed off-road driving, matching the
+state-of-the-art performance.
+"
+26757,General Supervision via Probabilistic Transformations,"  Different types of training data have led to numerous schemes for supervised
+classification. Current learning techniques are tailored to one specific scheme
+and cannot handle general ensembles of training data. This paper presents a
+unifying framework for supervised classification with general ensembles of
+training data, and proposes the learning methodology of generalized robust risk
+minimization (GRRM). The paper shows how current and novel supervision schemes
+can be addressed under the proposed framework by representing the relationship
+between examples at test and training via probabilistic transformations. The
+results show that GRRM can handle different types of training data in a unified
+manner, and enable new supervision schemes that aggregate general ensembles of
+training data.
+"
+26758,The Power of Color: A Study on the Effective Use of Colored Light in Human-Robot Interaction,"  In times of more and more complex interaction techniques, we point out the
+powerfulness of colored light as a simple and cheap feedback mechanism. Since
+it is visible over a distance and does not interfere with other modalities, it
+is especially interesting for mobile robots. In an online survey, we asked 56
+participants to choose the most appropriate colors for scenarios that were
+presented in the form of videos. In these scenarios a mobile robot accomplished
+tasks, in some with success, in others it failed because the task is not
+feasible, in others it stopped because it waited for help. We analyze in what
+way the color preferences differ between these three categories. The results
+show a connection between colors and meanings and that it depends on the
+participants' technical affinity, experience with robots and gender how clear
+the color preference is for a certain category. Finally, we found out that the
+participants' favorite color is not related to color preferences.
+"
+26759,On a relational theory of biological systems: a natural model for complex biological behavior,"  In this paper, we develop a natural (empirical) relational theory for
+describing and modeling complex biological phenomena. We have as stepping stone
+the assertion: function implies structure. The theory is built upon a graph's
+theory structure in which a diffusion model of information takes place, and
+where dynamics can be investigated in order to generate steady quantifiers. In
+this context, we improve a seminal work by adding a free context biological
+importance measure given by the Shannon's Entropy. We also introduce the
+concept of biological loci. Such concept stands for closely related biological
+agents which plays a role as an agent by itself. Our results allow us to
+synthesize a natural model for complex biological behavior that takes into
+account: system's update, irreducibility, and exploit of the dynamical behavior
+mounted over a diffusion model. The model deals in final terms to its natural
+capacity to model plasticity and environmental changes, which has an intrinsic
+relationship with Shannon's Entropy and the sort of dynamics that biological
+systems can display.
+"
+26760,Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities,"  We propose Cognitive Databases, an approach for transparently enabling
+Artificial Intelligence (AI) capabilities in relational databases. A novel
+aspect of our design is to first view the structured data source as meaningful
+unstructured text, and then use the text to build an unsupervised neural
+network model using a Natural Language Processing (NLP) technique called word
+embedding. This model captures the hidden inter-/intra-column relationships
+between database tokens of different types. For each database token, the model
+includes a vector that encodes contextual semantic relationships. We seamlessly
+integrate the word embedding model into existing SQL query infrastructure and
+use it to enable a new class of SQL-based analytics queries called cognitive
+intelligence (CI) queries. CI queries use the model vectors to enable complex
+queries such as semantic matching, inductive reasoning queries such as
+analogies, predictive queries using entities not present in a database, and,
+more generally, using knowledge from external sources. We demonstrate unique
+capabilities of Cognitive Databases using an Apache Spark based prototype to
+execute inductive reasoning CI queries over a multi-modal database containing
+text and images. We believe our first-of-a-kind system exemplifies using AI
+functionality to endow relational databases with capabilities that were
+previously very hard to realize in practice.
+"
+26761,The MICROSCOPE mission: first results of a space test of the Equivalence Principle,"  According to the Weak Equivalence Principle, all bodies should fall at the
+same rate in a gravitational field. The MICROSCOPE satellite, launched in April
+2016, aims to test its validity at the $10^{-15}$ precision level, by measuring
+the force required to maintain two test masses (of titanium and platinum
+alloys) exactly in the same orbit. A non-vanishing result would correspond to a
+violation of the Equivalence Principle, or to the discovery of a new long-range
+force. Analysis of the first data gives $\delta\rm{(Ti,Pt)}= [-1 \pm 9
+(\mathrm{stat}) \pm 9 (\mathrm{syst})] \times 10^{-15}$ (1$\sigma$ statistical
+uncertainty) for the titanium-platinum Eötvös parameter characterizing the
+relative difference in their free-fall accelerations.
+"
+26762,Signal Yields of keV Electronic Recoils and Their Discrimination from Nuclear Recoils in Liquid Xenon,"  We report on the response of liquid xenon to low energy electronic recoils
+below 15 keV from beta decays of tritium at drift fields of 92 V/cm, 154 V/cm
+and 366 V/cm using the XENON100 detector. A data-to-simulation fitting method
+based on Markov Chain Monte Carlo is used to extract the photon yields and
+recombination fluctuations from the experimental data. The photon yields
+measured at the two lower fields are in agreement with those from literature;
+additional measurements at a higher field of 366 V/cm are presented. The
+electronic and nuclear recoil discrimination as well as its dependence on the
+drift field and photon detection efficiency are investigated at these low
+energies. The results provide new measurements in the energy region of interest
+for dark matter searches using liquid xenon.
+"
+26763,Completing a joint PMF from projections: a low-rank coupled tensor factorization approach,"  There has recently been considerable interest in completing a low-rank matrix
+or tensor given only a small fraction (or few linear combinations) of its
+entries. Related approaches have found considerable success in the area of
+recommender systems, under machine learning. From a statistical estimation
+point of view, the gold standard is to have access to the joint probability
+distribution of all pertinent random variables, from which any desired optimal
+estimator can be readily derived. In practice high-dimensional joint
+distributions are very hard to estimate, and only estimates of low-dimensional
+projections may be available. We show that it is possible to identify
+higher-order joint PMFs from lower-order marginalized PMFs using coupled
+low-rank tensor factorization. Our approach features guaranteed identifiability
+when the full joint PMF is of low-enough rank, and effective approximation
+otherwise. We provide an algorithmic approach to compute the sought factors,
+and illustrate the merits of our approach using rating prediction as an
+example.
+"
+26764,Inertial effects in systems with magnetic charge,"  This short article sets out some of the basic considerations that go into
+detecting the mass of quasiparticles with effective magnetic charge in solids.
+Effective magnetic charges may be appear as defects in particular magnetic
+textures. A magnetic monopole is a defect in this texture and as such these are
+not monopoles in the actual magnetic field B, but instead in the auxiliary
+field H. They may have particular properties expected for such quasiparticles
+such as magnetic charge and mass. This effective mass may -- in principle -- be
+detected in the same fashion that the mass is detected of other particles
+classically e.g. through their inertial response to time-dependent
+electromagnetic fields. I discuss this physics in the context of the ""simple""
+case of the quantum spin ices, but aspects are broadly applicable. Based on
+extensions to Ryzkhin's model for classical spin ice, a hydrodynamic
+formulation can be given that takes into account inertial and entropic forces.
+Ultimately, a form for the susceptibility is obtained that is equivalent to the
+Rocard equation, which is a classic form used to account for inertial effects
+in the context of Debye-like relaxation.
+"
+26765,Error identities for variational problems with obstacles,"  The paper is concerned with a class of nonlinear free boundary problems,
+which are usually solved by variational methods based on primal (or
+primal-dual) variational settings. We deduce and investigate special relations
+(error identities). They show that a certain nonlinear measure of the distance
+to the exact solution (specific for each problem) is equivalent to the
+respective duality gap, which minimization is a keystone of all variational
+numerical methods. Therefore, the identity defines the measure that contains
+maximal quantitative information on the quality of a numerical solution
+available through these methods. The measure has quadratic terms generated by
+the linear part of the differential operator and nonlinear terms associated
+with free boundaries. We obtain fully computable two sided bounds of this
+measure and show that they provide efficient estimates of the distance between
+the minimizer and any function from the corresponding energy space. Several
+examples show that for different minimization sequence the balance between
+different components of the overall error measure may be different and
+domination of nonlinear terms may indicate that coincidence sets are
+approximated incorrectly.
+"
+26766,A Maximal Heterogeneity Based Clustering Approach for Obtaining Samples,"  Medical and social sciences demand sampling techniques which are robust,
+reliable, replicable and have the least dissimilarity between the samples
+obtained. Majority of the applications of sampling use randomized sampling,
+albeit with stratification where applicable. The randomized technique is not
+consistent, and may provide different samples each time, and the different
+samples themselves may not be similar to each other. In this paper, we
+introduce a novel non-statistical no-replacement sampling technique called
+Wobbly Center Algorithm, which relies on building clusters iteratively based on
+maximizing the heterogeneity inside each cluster. The algorithm works on the
+principle of stepwise building of clusters by finding the points with the
+maximal distance from the cluster center. The obtained results are validated
+statistically using Analysis of Variance tests by comparing the samples
+obtained to check if they are representative of each other. The obtained
+results generated from running the Wobbly Center algorithm on benchmark
+datasets when compared against other sampling algorithms indicate the
+superiority of the Wobbly Center Algorithm.
+"
+26767,Global Optimization based on Growth Transform Dynamical Model,"  Conservation principles like conservation of charge or energy provide a
+natural way to couple and constrain different physical variables. In this
+letter, we propose a dynamical system model that exploits these constraints for
+solving non-convex global optimization problems. Unlike the traditional
+simulated annealing or quantum annealing based global optimization techniques,
+the proposed method optimizes a target objective function by continuously
+evolving a driver functional over a conservation manifold using a generalized
+variant of growth transformations. As a result, the driver functional converges
+to a Dirac-delta function that is centered at the global optimum of the target
+objective function. We provide an outline of the proof of convergence for the
+dynamical system model and we demonstrate the application of the model for
+implementing linear-time and constant-time decentralized sorting algorithms.
+"
+26768,WIMP dark matter candidates and searches - current status and future prospects,"  We review several current aspects of dark matter theory and experiment. We
+overview the present experimental status, which includes current bounds and
+recent claims and hints of a possible signal in a wide range of experiments:
+direct detection in underground laboratories, gamma-ray, cosmic ray, X-ray,
+neutrino telescopes, and the LHC. We briefly review several possible particle
+candidates for a Weakly Interactive Massive Particle (WIMP) and dark matter
+that have recently been considered in the literature. We pay particular
+attention to the lightest neutralino of supersymmetry as it remains the best
+motivated candidate for dark matter and also shows excellent detection
+prospects. Finally we briefly review some alternative scenarios that can
+considerably alter properties and prospects for the detection of dark matter
+obtained within the standard thermal WIMP paradigm.
+"
+26769,Electron tunneling times,"  Tunneling is one of the most bizarre phenomena in quantum mechanics. An
+attempt to understand it led to the next natural question of how long does a
+particle need to tunnel a barrier. The latter gave rise to several definitions
+such as the phase, dwell, Larmor and traversal times among others. A short
+review of the evolution of these time concepts, followed by an account of
+experiments involving field-induced tunnel ionization and electron tunneling in
+a solid state junction is presented here. Whereas the former experiments use
+sophisticated techniques involving femtosecond laser pulses and determine the
+tunneling time by mapping the angle of rotation of the field vector to time,
+like the hands of a watch, the latter provides a simpler method through the
+measurement of current-voltage characteristics of the junction.
+"
+26770,Spectrally approximating large graphs with smaller graphs,"  How does coarsening affect the spectrum of a general graph? We provide
+conditions such that the principal eigenvalues and eigenspaces of a coarsened
+and original graph Laplacian matrices are close. The achieved approximation is
+shown to depend on standard graph-theoretic properties, such as the degree and
+eigenvalue distributions, as well as on the ratio between the coarsened and
+actual graph sizes. Our results carry implications for learning methods that
+utilize coarsening. For the particular case of spectral clustering, they imply
+that coarse eigenvectors can be used to derive good quality assignments even
+without refinement---this phenomenon was previously observed, but lacked formal
+justification.
+"
+26771,The Stochastic Shortest Path Problem : A polyhedral combinatorics perspective,"  In this paper, we give a new framework for the stochastic shortest path
+problem in finite state and action spaces. Our framework generalizes both the
+frameworks proposed by Bertsekas and Tsitsikli and by Bertsekas and Yu. We
+prove that the problem is well-defined and (weakly) polynomial when (i) there
+is a way to reach the target state from any initial state and (ii) there is no
+transition cycle of negative costs (a generalization of negative cost cycles).
+These assumptions generalize the standard assumptions for the deterministic
+shortest path problem and our framework encapsulates the latter problem (in
+contrast with prior works). In this new setting, we can show that (a) one can
+restrict to deterministic and stationary policies, (b) the problem is still
+(weakly) polynomial through linear programming, (c) Value Iteration and Policy
+Iteration converge, and (d) we can extend Dijkstra's algorithm.
+"
+26772,Artificial Neural Network in Cosmic Landscape,"  In this paper we propose that artificial neural network, the basis of machine
+learning, is useful to generate the inflationary landscape from a cosmological
+point of view. Traditional numerical simulations of a global cosmic landscape
+typically need an exponential complexity when the number of fields is large.
+However, a basic application of artificial neural network could solve the
+problem based on the universal approximation theorem of the multilayer
+perceptron. A toy model in inflation with multiple light fields is investigated
+numerically as an example of such an application.
+"
+26773,$A^{4}NT$: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation,"  Text-based analysis methods allow to reveal privacy relevant author
+attributes such as gender, age and identify of the text's author. Such methods
+can compromise the privacy of an anonymous author even when the author tries to
+remove privacy sensitive content. In this paper, we propose an automatic
+method, called Adversarial Author Attribute Anonymity Neural Translation
+($A^4NT$), to combat such text-based adversaries. We combine
+sequence-to-sequence language models used in machine translation and generative
+adversarial networks to obfuscate author attributes. Unlike machine translation
+techniques which need paired data, our method can be trained on unpaired
+corpora of text containing different authors. Importantly, we propose and
+evaluate techniques to impose constraints on our $A^4NT$ to preserve the
+semantics of the input text. $A^4NT$ learns to make minimal changes to the
+input text to successfully fool author attribute classifiers, while aiming to
+maintain the meaning of the input. We show through experiments on two different
+datasets and three settings that our proposed method is effective in fooling
+the author attribute classifiers and thereby improving the anonymity of
+authors.
+"
+26774,Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks,"  The Dice score is widely used for binary segmentation due to its robustness
+to class imbalance. Soft generalisations of the Dice score allow it to be used
+as a loss function for training convolutional neural networks (CNN). Although
+CNNs trained using mean-class Dice score achieve state-of-the-art results on
+multi-class segmentation, this loss function does neither take advantage of
+inter-class relationships nor multi-scale information. We argue that an
+improved loss function should balance misclassifications to favour predictions
+that are semantically meaningful. This paper investigates these issues in the
+context of multi-class brain tumour segmentation. Our contribution is
+threefold. 1) We propose a semantically-informed generalisation of the Dice
+score for multi-class segmentation based on the Wasserstein distance on the
+probabilistic label space. 2) We propose a holistic CNN that embeds spatial
+information at multiple scales with deep supervision. 3) We show that the joint
+use of holistic CNNs and generalised Wasserstein Dice scores achieves
+segmentations that are more semantically meaningful for brain tumour
+segmentation.
+"
+26775,An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework,"  In this paper, a neural network-based stock price prediction and trading
+system using technical analysis indicators is presented. The model developed
+first converts the financial time series data into a series of buy-sell-hold
+trigger signals using the most commonly preferred technical analysis
+indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN)
+model is trained in the learning stage on the daily stock prices between 1997
+and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used
+in the training stage. The trained model is then tested with data from 2007 to
+2017. The results indicate that by choosing the most appropriate technical
+indicators, the neural network model can achieve comparable results against the
+Buy and Hold strategy in most of the cases. Furthermore, fine tuning the
+technical indicators and/or optimization strategy can enhance the overall
+trading performance.
+"
+26776,Soil Property and Class Maps of the Conterminous US at 100 meter Spatial Resolution based on a Compilation of National Soil Point Observations and Machine Learning,"  With growing concern for the depletion of soil resources, conventional soil
+data must be updated to support spatially explicit human-landscape models.
+Three US soil point datasetswere combined with a stack of over 200
+environmental datasets to generate complete coverage gridded predictions at 100
+m spatial resolution of soil properties (percent organic C, total N, bulk
+density, pH, and percent sand and clay) and US soil taxonomic classes (291
+great groups and 78 modified particle size classes) for the conterminous US.
+Models were built using parallelized random forest and gradient boosting
+algorithms. Soil property predictions were generated at seven standard soil
+depths (0, 5, 15, 30, 60, 100 and 200 cm). Prediction probability maps for US
+soil taxonomic classifications were also generated. Model validation results
+indicate an out-of-bag classification accuracy of 60 percent for great groups,
+and 66 percent for modified particle size classes; for soil properties
+cross-validated R-square ranged from 62 percent for total N to 87 percent for
+pH. Nine independent validation datasets were used to assess prediction
+accuracies for soil class models and results ranged between 24-58 percent and
+24-93 percent for great group and modified particle size class prediction
+accuracies, respectively. The hybrid ""SoilGrids+"" modeling system that
+incorporates remote sensing data, local predictions of soil properties,
+conventional soil polygon maps, and machine learning opens the possibility for
+updating conventional soil survey data with machine learning technology to make
+soil information easier to integrate with spatially explicit models, compared
+to multi-component map units.
+"
+26777,Some Extensions to Touchard's Theorem on Odd Perfect Numbers,"  The multiplicative structure of an odd perfect number $n$, if any, is
+$n=\pi^\alpha M^2$, where $\pi$ is prime, $\gcd(\pi,M)=1$ and $\pi\equiv
+\alpha\equiv1\pmod{4}$. An additive structure of $n$, established by Touchard,
+is that ""$\bigl(n\equiv 9\pmod{36}\bigr )$ OR $\bigl (n\equiv1\pmod{12}\bigr
+)$"". A first extension of Touchard's result is that the proposition
+""$\bigl(n\equiv x^2\pmod{4 x^2}\bigr )$ OR $\bigl (n\equiv \pi\equiv1\pmod{4
+x}\bigr )$"" holds for $x=3$ (the extension is due to the fact that the second
+congruence contains also $\pi$). We further extend the proof to $x=\alpha+2$,
+$\alpha+2$ prime, with the restriction that the congruence modulo $4 x$ does
+not include $n$. Besides, we note that the first extension of Touchard's result
+holds also with an exclusive disjunction, so that $\pi\equiv 1\pmod{12}$ is a
+sufficient condition because $3\nmid n$.
+"
+26778,Bifurcations of the conjugate locus,"  The conjugate locus of a point $p$ in a surface $\mathcal{S}$ will have a
+certain number of cusps. As the point $p$ is moved in the surface the conjugate
+locus may spontaneously gain or lose cusps. In this paper we explain this
+`bifurcation' in terms of the vanishing of higher derivatives of the
+exponential map; we derive simple equations for these higher derivatives in
+terms of scalar invariants; we classify the bifurcations of cusps in terms of
+the local structure of the conjugate locus; and we describe an intuitive
+picture of the bifurcation as the intersection between certain contours in the
+tangent plane.
+"
+26779,On the spectrum of the multiplicative Hilbert matrix,"  We study the multiplicative Hilbert matrix, i.e. the infinite matrix with
+entries $(\sqrt{mn}\log(mn))^{-1}$ for $m,n\geq2$. This matrix was recently
+introduced within the context of the theory of Dirichlet series, and it was
+shown that the multiplicative Hilbert matrix has no eigenvalues and that its
+continuous spectrum coincides with $[0,\pi]$. Here we prove that the
+multiplicative Hilbert matrix has no singular continuous spectrum and that its
+absolutely continuous spectrum has multiplicity one. Our argument relies on the
+tools of spectral perturbation theory and scattering theory. Finding an
+explicit diagonalisation of the multiplicative Hilbert matrix remains an
+interesting open problem.
+"
+26780,Black hole spin of Cyg X-1 determined from the softest state ever observed,"  We show the softest ever spectrum from Cyg~X-1, detected in 2013 with Suzaku.
+This has the weakest high energy Compton tail ever seen from this object, so
+should give the cleanest view of the underlying disk spectrum, and hence the
+best determination of black hole spin from disk continuum fitting. Using the
+standard model of a disk with simple non-thermal Comptonisation to produce the
+weak high energy tail gives a high spin black hole. However, we get a
+significantly better fit by including an additional, low temperature thermal
+Comptonisation component, which allows a much lower black hole spin.
+Corroboration of the existence of an additional Compton component comes from
+the frequency dependent hard lags seen in the rapid variability in archival
+high/soft state data. These can not be explained if the continuum is a single
+non-thermal Comptonisation component, but are instead consistent with a
+radially stratified, multi zone Comptonisation spectrum, where the spectrum is
+softer further from the black hole. A complex multi-zone Comptonisation
+continuum is required to explain both spectra and timing together, and this has
+an impact on the derived black hole spin.
+"
+26781,Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning,"  Management of chronic diseases such as heart failure (HF) is a major public
+health problem. A standard approach to managing chronic diseases by medical
+community is to have a committee of experts develop guidelines that all
+physicians should follow. Due to their complexity, these guidelines are
+difficult to implement and are adopted slowly by the medical community at
+large. We have developed a physician advisory system that codes the entire set
+of clinical practice guidelines for managing HF using answer set
+programming(ASP). In this paper we show how abductive reasoning can be deployed
+to find missing symptoms and conditions that the patient must exhibit in order
+for a treatment prescribed by a physician to work effectively. Thus, if a
+physician does not make an appropriate recommendation or makes a non-adherent
+recommendation, our system will advise the physician about symptoms and
+conditions that must be in effect for that recommendation to apply. It is under
+consideration for acceptance in TPLP.
+"
+26782,On Polynomial Time Methods for Exact Low Rank Tensor Completion,"  In this paper, we investigate the sample size requirement for exact recovery
+of a high order tensor of low rank from a subset of its entries. We show that a
+gradient descent algorithm with initial value obtained from a spectral method
+can, in particular, reconstruct a ${d\times d\times d}$ tensor of multilinear
+ranks $(r,r,r)$ with high probability from as few as
+$O(r^{7/2}d^{3/2}\log^{7/2}d+r^7d\log^6d)$ entries. In the case when the ranks
+$r=O(1)$, our sample size requirement matches those for nuclear norm
+minimization (Yuan and Zhang, 2016a), or alternating least squares assuming
+orthogonal decomposability (Jain and Oh, 2014). Unlike these earlier
+approaches, however, our method is efficient to compute, easy to implement, and
+does not impose extra structures on the tensor. Numerical results are presented
+to further demonstrate the merits of the proposed approach.
+"
+26783,Searching for effective and efficient way of knowledge transfer within an organization,"  In this paper three models of knowledge transfer in organization are
+considered. In the first model (A) the transfer of chunks of knowledge among
+agents is possible only when the sender has exactly one more chunks of
+knowledge than recipient. This is not dissimilar with bounded confidence model
+of opinion dynamics. In the second model (B) the knowledge transfer take place
+when sender is ""smarter"" than recipient. Finally, in the third scenario (model
+C) we allow for knowledge transfer also when sender posses the same or greater
+number of chunks of knowledge as recipient. The simulation bases on cellular
+automata technique. The organization members occupy nodes of square lattice and
+they interact only with their nearest neighbors. With computer simulations we
+show, that the efficiency and the effectiveness of knowledge transfer i) for
+model C is better than for model B ii) and it is worse for model A than for
+model B.
+"
+26784,Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations,"  We would like to learn a representation of the data which decomposes an
+observation into factors of variation which we can independently control.
+Specifically, we want to use minimal supervision to learn a latent
+representation that reflects the semantics behind a specific grouping of the
+data, where within a group the samples share a common factor of variation. For
+example, consider a collection of face images grouped by identity. We wish to
+anchor the semantics of the grouping into a relevant and disentangled
+representation that we can easily exploit. However, existing deep probabilistic
+models often assume that the observations are independent and identically
+distributed. We present the Multi-Level Variational Autoencoder (ML-VAE), a new
+deep probabilistic model for learning a disentangled representation of a set of
+grouped observations. The ML-VAE separates the latent representation into
+semantically meaningful parts by working both at the group level and the
+observation level, while retaining efficient test-time inference. Quantitative
+and qualitative evaluations show that the ML-VAE model (i) learns a
+semantically meaningful disentanglement of grouped data, (ii) enables
+manipulation of the latent representation, and (iii) generalises to unseen
+groups.
+"
+26785,Derivative and divergence formulae for diffusion semigroups,"  For a semigroup $P_t$ generated by an elliptic operator on a smooth manifold
+$M$, we use straightforward martingale arguments to derive probabilistic
+formulae for $P_t(V(f))$, not involving derivatives of $f$, where $V$ is a
+vector field on $M$. For non-symmetric generators, such formulae correspond to
+the derivative of the heat kernel in the forward variable. As an application,
+these formulae can be used to derive various shift-Harnack inequalities.
+"
+26786,TLib: A Flexible C++ Tensor Framework for Numerical Tensor Calculus,"  Numerical tensor calculus comprise basic tensor operations such as the
+entrywise addition and contraction of higher-order tensors. We present, TLib,
+flexible tensor framework with generic tensor functions and tensor classes that
+assists users to implement generic and flexible tensor algorithms in C++. The
+number of dimensions, the extents of the dimensions of the tensors and the
+contraction modes of the tensor operations can be runtime variable. Our
+framework provides tensor classes that simplify the management of
+multidimensional data and utilization of tensor operations using
+object-oriented and generic programming techniques. Additional stream classes
+help the user to verify and compare of numerical results with MATLAB. Tensor
+operations are implemented with generic tensor functions and in terms of
+multidimensional iterator types only, decoupling data storage representation
+and computation. The user can combine tensor functions with different tensor
+types and extend the framework without further modification of the classes or
+functions. We discuss the design and implementation of the framework and
+demonstrate its usage with examples that have been discussed in the literature.
+"
+26787,Finding counterparts for All-sky X-ray surveys with Nway: a Bayesian algorithm for cross-matching multiple catalogues,"  We release the AllWISE counterparts and Gaia matches to 106,573 and 17,665
+X-ray sources detected in the ROSAT 2RXS and XMMSL2 surveys with |b|>15. These
+are the brightest X-ray sources in the sky, but their position uncertainties
+and the sparse multi-wavelength coverage until now rendered the identification
+of their counterparts a demanding task with uncertain results. New all-sky
+multi-wavelength surveys of sufficient depth, like AllWISE and Gaia, and a new
+Bayesian statistics based algorithm, NWAY, allow us, for the first time, to
+provide reliable counterpart associations. NWAY extends previous distance and
+sky density based association methods and, using one or more priors (e.g.,
+colors, magnitudes), weights the probability that sources from two or more
+catalogues are simultaneously associated on the basis of their observable
+characteristics. Here, counterparts have been determined using a WISE
+color-magnitude prior. A reference sample of 4524 XMM/Chandra and Swift X-ray
+sources demonstrates a reliability of ~ 94.7% (2RXS) and 97.4% (XMMSL2).
+Combining our results with Chandra-COSMOS data, we propose a new separation
+between stars and AGN in the X-ray/WISE flux-magnitude plane, valid over six
+orders of magnitude. We also release the NWAY code and its user manual. NWAY
+was extensively tested with XMM-COSMOS data. Using two different sets of
+priors, we find an agreement of 96% and 99% with published Likelihood Ratio
+methods. Our results were achieved faster and without any follow-up visual
+inspection. With the advent of deep and wide area surveys in X-rays (e.g.
+SRG/eROSITA, Athena/WFI) and radio (ASKAP/EMU, LOFAR, APERTIF, etc.) NWAY will
+provide a powerful and reliable counterpart identification tool.
+"
+26788,Decoding vagal contributions to fetal heart rate variability,"  Heart rate variability (HRV) is an important indicator of health and disease,
+yet its physiological origins are poorly understood. The exact neural
+contributions to HRV are not well understood. In near-term fetal sheep, we
+aimed to identify fetal HRV measures reflecting the vagus nerve contributions
+to HRV. Fetal HRV was recorded during surgery and 72 hours later without (n=24)
+and with intra-surgical bilateral cervical vagotomy (n=15). We report the most
+encompassing set of fetal HRV measures known to-date and identify those HRV
+measures that are specific to the vagal modulation of HRV and show recovery
+dynamic due to fetal surgery. Our findings have clinical significance for in
+utero monitoring of fetal health during surgery. We provide a systematic
+delineation of vagal contributions to HRV across signal-analytical domains
+which should be relevant for the emerging field of bioelectronic medicine and
+the deciphering of the vagus code.
+"
+26789,Extreme Value Estimation for Discretely Sampled Continuous Processes,"  In environmental applications of extreme value statistics, the underlying
+stochastic process is often modeled either as a max-stable process in
+continuous time/space or as a process in the domain of attraction of such a
+max-stable process. In practice, however, the processes are typically only
+observed at discrete points and one has to resort to interpolation to fill in
+the gaps. We discuss the influence of such an interpolation on estimators of
+marginal parameters as well as estimators of the exponent measure. In
+particular, natural conditions on the fineness of the observational scheme are
+developed which ensure that asymptotically the interpolated estimators behave
+in the same way as the estimators which use fully observed continuous
+processes.
+"
+26790,Argon Cluster-Ion Sputter Yield: Molecular Dynamics Simulations on Silicon and an Equation for Estimating Total Sputter Yield,"  Argon Gas Cluster-Ion Beam sources have become widely-used on x-ray
+photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS)
+instruments in recent years, but there is little reference data on sputter
+yields in the literature as yet. Total sputter yield reference data is needed
+in order to plan, and later calibrate the depth scale, of XPS or SIMS depth
+profiles. We previously published a semi-empirical Threshold equation for
+estimating cluster total sputter yield from the energy-per-atom of the cluster
+and the effective monatomic sputter threshold of the material. This has already
+been shown to agree extremely well with sputter yield measurements on a range
+of organic and inorganic materials for clusters of around a thousand atoms.
+Here we use the molecular dynamics (MD) approach to explore a wider range of
+energy and cluster size than is easy to do experimentally to high precision. We
+have performed MD simulations using the Large-scale Atomic/Molecular Massively
+Parallel Simulator (LAMMPS) parallel MD code on high-performance computer (HPC)
+systems at Cincinnati and Newcastle. We performed 1,150 simulations of
+individual collisions with a silicon (100) surface as an archetypal inorganic
+substrate, for cluster sizes between 30 and 3,000 argon atoms and energies in
+the range 5 to 40eV per atom. This corresponds to the most important regime for
+experimental cluster depth-profiling in SIMS and XPS. Our MD results show a
+dependence on cluster size as well as energy-per-atom. Using the exponent
+previously suggested by Paruch et al, we modified the Threshold model equation
+published previously to take this into account. The modified Threshold equation
+fits all our MD results extremely well, building on its success in fitting
+experimental sputter yield measurements.
+"
+26791,On the Simultaneous Minimum Spanning Trees Problem,"  Simultaneous Embedding with Fixed Edges (SEFE) is a problem where given $k$
+planar graphs we ask whether they can be simultaneously embedded so that the
+embedding of each graph is planar and common edges are drawn the same. Problems
+of SEFE type have inspired questions of Simultaneous Geometrical
+Representations and further derivations. Based on this motivation we
+investigate the generalization of the simultaneous paradigm on the classical
+combinatorial problem of minimum spanning trees. Given $k$ graphs with weighted
+edges, such that they have a common intersection, are there minimum spanning
+trees of the respective graphs such that they agree on the intersection? We
+show that the unweighted case is polynomial-time solvable while the weighted
+case is only polynomial-time solvable for $k=2$ and it is NP-complete for
+$k\geq 3$.
+"
+26792,Linear divisibility sequences and Salem numbers,"  We study linear divisibility sequences of order 4, providing a
+characterization by means of their characteristic polynomials and finding their
+factorization as a product of linear divisibility sequences of order 2.
+Moreover, we show a new interesting connection between linear divisibility
+sequences and Salem numbers. Specifically, we generate linear divisibility
+sequences of order 4 by means of Salem numbers modulo 1.
+"
+26793,Contextualization of topics: Browsing through the universe of bibliographic information,"  This paper describes how semantic indexing can help to generate a contextual
+overview of topics and visually compare clusters of articles. The method was
+originally developed for an innovative information exploration tool, called
+Ariadne, which operates on bibliographic databases with tens of millions of
+records. In this paper, the method behind Ariadne is further developed and
+applied to the research question of the special issue ""Same data, different
+results"" - the better understanding of topic (re-)construction by different
+bibliometric approaches. For the case of the Astro dataset of 111,616 articles
+in astronomy and astrophysics, a new instantiation of the interactive exploring
+tool, LittleAriadne, has been created. This paper contributes to the overall
+challenge to delineate and define topics in two different ways. First, we
+produce two clustering solutions based on vector representations of articles in
+a lexical space. These vectors are built on semantic indexing of entities
+associated with those articles. Second, we discuss how LittleAriadne can be
+used to browse through the network of topical terms, authors, journals,
+citations and various cluster solutions of the Astro dataset. More
+specifically, we treat the assignment of an article to the different clustering
+solutions as an additional element of its bibliographic record. Keeping the
+principle of semantic indexing on the level of such an extended list of
+entities of the bibliographic record, LittleAriadne in turn provides a
+visualization of the context of a specific clustering solution. It also conveys
+the similarity of article clusters produced by different algorithms, hence
+representing a complementary approach to other possible means of comparison.
+"
+26794,"Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection","  General human action recognition requires understanding of various visual
+cues. In this paper, we propose a network architecture that computes and
+integrates the most important visual cues for action recognition: pose, motion,
+and the raw images. For the integration, we introduce a Markov chain model
+which adds cues successively. The resulting approach is efficient and
+applicable to action classification as well as to spatial and temporal action
+localization. The two contributions clearly improve the performance over
+respective baselines. The overall approach achieves state-of-the-art action
+classification performance on HMDB51, J-HMDB and NTU RGB+D datasets. Moreover,
+it yields state-of-the-art spatio-temporal action localization results on
+UCF101 and J-HMDB.
+"
+26795,Evaluative Assessment of an X-band Microstrip Patch Antenna for Wireless Systems,"  Microstrip patch antennas (MPAs) are rapidly gaining more attention due to
+the proliferation of communication devices and systems with frequencies
+becoming more suitable for the size and performance of this type of antenna.
+Due to recent advancements in semiconductor technology, high dielectric
+constant materials are used to achieve additional size reduction which has made
+MPAs very useful and popular in the design of mobile devices and wireless
+systems. However, MPAs suffer from problems associated with narrow bandwidth
+and low gain. Techniques employed for improving the performance of MPA hinge on
+tweaking features such as the patch size, substrate height, ground plane size
+and feeding method. In view of this, this research designs and analyzes the
+performance of an X-band MPA for wireless systems using CST Microwave Studio.
+Including the ground plane, the proposed design has a low profile structure of
+17 mm x 17 mm x 1.6 mm which is suitable for wireless systems. The proposed
+design also resonates at a frequency of 10 GHz with an omnidirectional
+radiation pattern exhibiting a gain of 7.2 dBi. Return Loss, VSWR, Gain and
+Radiation Pattern are the performance indicators employed in this research. The
+proposed MPA design demonstrates marked performance improvement when
+benchmarked with a similar MPA designed for 5G applications.
+"
+26796,Probing new spin-independent interactions through precision spectroscopy in atoms with few electrons,"  The very high precision of current measurements and theory predictions of
+spectral lines in few-electron atoms allows to efficiently probe the existence
+of exotic forces between electrons, neutrons and protons. We investigate the
+sensitivity to new spin-independent interactions in transition frequencies (and
+their isotopic shifts) of hydrogen, helium and some helium-like ions. We find
+that present data probe new regions of the force-carrier couplings to electrons
+and neutrons around the MeV mass range. We also find that, below few keV, the
+sensitivity to the electron coupling in precision spectroscopy of helium and
+positronium is comparable to that of the anomalous magnetic moment of the
+electron. Finally, we interpret our results in the dark-photon model where a
+new gauge boson is kinetically mixed with the photon. There, we show that
+helium transitions, combined with the anomalous magnetic moment of the
+electron, provide the strongest indirect bound from laboratory experiments
+above 100keV.
+"
+26797,Piecewise Diffusion Synthetic Acceleration Scheme for Neutron Transport Simulations in Diffusive Media,"  The method of discrete ordinates ($S_N$) is a popular choice for the solution
+of the neutron transport equation. It is however well known that it suffers
+from slow convergence of the scattering source in optically thick and diffusive
+media, such as pressurized water nuclear reactors (PWR). In reactor physics
+applications, the $S_N$ method is thus often accompanied by an acceleration
+algorithm, such as the Diffusion Synthetic Acceleration (DSA). With the recent
+increase in computational power, whole core transport calculations have become
+a reasonable objective. It however requires using large computers and
+parallelizing the transport solver. Due to the elliptic nature of the DSA
+operator, its parallelization is not straightforward. In this paper, we present
+an acceleration operator derived from the DSA, but defined in a piecewise way
+such that its parallel implementation is straightforward. We show that, for
+optically thick enough media, this Piecewise Diffusion Synthetic Acceleration
+(PDSA) preserves the good properties of the DSA. This conclusion is supported
+by numerical experiments.
+"
+26798,Fastron: An Online Learning-Based Model and Active Learning Strategy for Proxy Collision Detection,"  We introduce the Fastron, a configuration space (C-space) model to be used as
+a proxy to kinematic-based collision detection. The Fastron allows iterative
+updates to account for a changing environment through a combination of a novel
+formulation of the kernel perceptron learning algorithm and an active learning
+strategy. Our simulations on a 7 degree-of-freedom arm indicate that proxy
+collision checks may be performed at least 2 times faster than an efficient
+polyhedral collision checker and at least 8 times faster than an efficient
+high-precision collision checker. The Fastron model provides conservative
+collision status predictions by padding C-space obstacles, and proxy collision
+checking time does not scale poorly as the number of workspace obstacles
+increases. All results were achieved without GPU acceleration or parallel
+computing.
+"
+26799,The Core-Collapse Supernova Explosion Mechanism,"  The explosion mechanism of core-collapse supernovae is a long-standing
+problem in stellar astrophysics. We briefly outline the main contenders for a
+solution and review recent efforts to model core-collapse supernova explosions
+by means of multi-dimensional simulations. We discuss several suggestions for
+solving the problem of missing or delayed neutrino-driven explosions in
+three-dimensional supernova models, including -- among others -- variations in
+the microphysics and large seed perturbations in convective burning shells.
+Focusing on the neutrino-driven mechanism, we summarise currents efforts to
+predict supernova explosion and remnant properties based on first-principle
+models and on more phenomenological approaches.
+"
+26800,Search for domain wall dark matter with atomic clocks on board global positioning system satellites,"  Cosmological observations indicate that 85% of all matter in the Universe is
+dark matter (DM), yet its microscopic composition remains a mystery. One
+hypothesis is that DM arises from ultralight quantum fields that form
+macroscopic objects such as topological defects. Here we use GPS as a ~ 50,000
+km aperture DM detector to search for such defects in the form of domain walls.
+GPS navigation relies on precision timing signals furnished by atomic clocks
+hosted on board GPS satellites. As the Earth moves through the galactic DM
+halo, interactions with topological defects could cause atomic clock glitches
+that propagate through the GPS satellite constellation at galactic velocities ~
+300 km/s. Mining 16 years of archival GPS data, we find no evidence for DM in
+the form of domain walls at our current sensitivity level. This allows us to
+improve the limits on certain quadratic scalar couplings of domain wall DM to
+standard model particles by several orders of magnitude.
+"
+26801,Learning Graphical Models from a Distributed Stream,"  A current challenge for data management systems is to support the
+construction and maintenance of machine learning models over data that is
+large, multi-dimensional, and evolving. While systems that could support these
+tasks are emerging, the need to scale to distributed, streaming data requires
+new models and algorithms. In this setting, as well as computational
+scalability and model accuracy, we also need to minimize the amount of
+communication between distributed processors, which is the chief component of
+latency. We study Bayesian networks, the workhorse of graphical models, and
+present a communication-efficient method for continuously learning and
+maintaining a Bayesian network model over data that is arriving as a
+distributed stream partitioned across multiple processors. We show a strategy
+for maintaining model parameters that leads to an exponential reduction in
+communication when compared with baseline approaches to maintain the exact MLE
+(maximum likelihood estimation). Meanwhile, our strategy provides similar
+prediction errors for the target distribution and for classification tasks.
+"
+26802,Big Data for Social Sciences: Measuring patterns of human behavior through large-scale mobile phone data,"  Through seven publications this dissertation shows how anonymized mobile
+phone data can contribute to the social good and provide insights into human
+behaviour on a large scale. The size of the datasets analysed ranges from 500
+million to 300 billion phone records, covering millions of people. The key
+contributions are two-fold:
+1. Big Data for Social Good: Through prediction algorithms the results show
+how mobile phone data can be useful to predict important socio-economic
+indicators, such as income, illiteracy and poverty in developing countries.
+Such knowledge can be used to identify where vulnerable groups in society are,
+reduce economic shocks and is a critical component for monitoring poverty rates
+over time. Further, the dissertation demonstrates how mobile phone data can be
+used to better understand human behaviour during large shocks in society,
+exemplified by an analysis of data from the terror attack in Norway and a
+natural disaster on the south-coast in Bangladesh. This work leads to an
+increased understanding of how information spreads, and how millions of people
+move around. The intention is to identify displaced people faster, cheaper and
+more accurately than existing survey-based methods.
+2. Big Data for efficient marketing: Finally, the dissertation offers an
+insight into how anonymised mobile phone data can be used to map out large
+social networks, covering millions of people, to understand how products spread
+inside these networks. Results show that by including social patterns and
+machine learning techniques in a large-scale marketing experiment in Asia, the
+adoption rate is increased by 13 times compared to the approach used by
+experienced marketers. A data-driven and scientific approach to marketing,
+through more tailored campaigns, contributes to less irrelevant offers for the
+customers, and better cost efficiency for the companies.
+"
+26803,On edge intersection graphs of paths with 2 bends,"  An EPG-representation of a graph $G$ is a collection of paths in a plane
+square grid, each corresponding to a single vertex of $G$, so that two vertices
+are adjacent if and only if their corresponding paths share infinitely many
+points. In this paper we focus on graphs admitting EPG-representations by paths
+with at most 2 bends. We show hardness of the recognition problem for this
+class of graphs, along with some subclasses.
+We also initiate the study of graphs representable by unaligned polylines,
+and by polylines, whose every segment is parallel to one of prescribed slopes.
+We show hardness of recognition and explore the trade-off between the number of
+bends and the number of slopes.
+"
+26804,First eigenvalue of the $p$-Laplacian under integral curvature condition,"  We give various estimates of the first eigenvalue of the $p$-Laplace operator
+on closed Riemannian manifold with integral curvature conditions.
+"
+26805,A High-Resolution Finite Volume Seismic Model to Generate Seafloor Deformation for Tsunami Modeling,"  A high-resolution finite volume method approach to incorporating
+time-dependent slip across rectangular subfaults when modeling general fault
+geometry is presented. The fault slip is induced by a modification of the
+Riemann problem to the linear elasticity equations across cell interfaces
+aligned with the subfaults. This is illustrated in the context of the
+high-resolution wave-propagation algorithms that are implemented in the open
+source Clawpack software (www.clawpack.org), but this approach could be easily
+incorporated into other Riemann solver based numerical methods. Surface
+deformation results are obtained in both two and three dimensions and compared
+to those given by the steady-state, homogeneous half-space Okada solution.
+"
+26806,Probing low-mass vector bosons with parity nonconservation and nuclear anapole moment measurements in atoms and molecules,"  In the presence of P-violating interactions, the exchange of vector bosons
+between electrons and nucleons induces parity-nonconserving (PNC) effects in
+atoms and molecules, while the exchange of vector bosons between nucleons
+induces anapole moments of nuclei. We perform calculations of such
+vector-mediated PNC effects in Cs, Ba$^+$, Yb, Tl, Fr and Ra$^+$ using the same
+relativistic many-body approaches as in earlier calculations of standard-model
+PNC effects, but with the long-range operator of the weak interaction. We
+calculate nuclear anapole moments due to vector boson exchange using a simple
+nuclear model. From measured and predicted (within the standard model) values
+for the PNC amplitudes in Cs, Yb and Tl, as well as the nuclear anapole moment
+of $^{133}$Cs, we constrain the P-violating vector-pseudovector
+nucleon-electron and nucleon-proton interactions mediated by a generic vector
+boson of arbitrary mass. Our limits improve on existing bounds from other
+experiments by many orders of magnitude over a very large range of vector-boson
+masses.
+"
+26807,A rank inequality for the annular Khovanov homology of 2-periodic links,"  For a 2-periodic link $\tilde L$ in the thickened annulus and its quotient
+link $L$, we exhibit a spectral sequence with $E^1 \cong AKh(\tilde L)
+\otimes_{\mathbb{F}_2} \mathbb{F}_2[\theta, \theta^{-1}] \rightrightarrows
+E^\infty \cong AKh(L) \otimes_{\mathbb{F}_2} \mathbb{F}_2[\theta,
+\theta^{-1}].$ This spectral sequence splits along quantum and $sl_2$ weight
+space gradings, proving a rank inequality $rank\ AKh^{j,k}(L) \leq rank\
+AKh^{2j-k,k} (\tilde L)$ for every pair of quantum and $sl_2$ weight space
+gradings $(j,k)$. We also present a few decategorified consequences and discuss
+partial results toward a similar statement for the Khovanov homology of
+2-periodic links.
+"
+26808,Linarite - a quasi-critical J$_1$-J$_2$ chain,"  The mineral linarite, PbCuSO$_4$(OH)$_2$, is a spin 1/2 chain with
+frustrating nearest neighbor ferromagnetic and next-nearest neighbor
+antiferromagnetic exchange interactions. Our inelastic neutron scattering
+experiments performed above the saturation field establish that the ratio
+between these exchanges is such that linarite is extremely close to the quantum
+critical point between spin-multipolar phases and the ferromagnetic state.
+However, the measured complex magnetic phase diagram depends strongly on the
+magnetic field direction. The field-dependent phase sequence is explained by
+our classical simulations of a nearly critical model with tiny orthorhombic
+exchange anisotropy. The simulations also capture qualitatively the measured
+variations of the wave vector as well as the staggered and the uniform
+magnetizations in an applied field.
+"
+26809,When Waiting is not an Option : Learning Options with a Deliberation Cost,"  Recent work has shown that temporally extended actions (options) can be
+learned fully end-to-end as opposed to being specified in advance. While the
+problem of ""how"" to learn options is increasingly well understood, the question
+of ""what"" good options should be has remained elusive. We formulate our answer
+to what ""good"" options should be in the bounded rationality framework (Simon,
+1957) through the notion of deliberation cost. We then derive practical
+gradient-based learning algorithms to implement this objective. Our results in
+the Arcade Learning Environment (ALE) show increased performance and
+interpretability.
+"
+26810,Statistics of ambiguous rotations,"  The orientation of a rigid object can be described by a rotation that
+transforms it into a standard position. For a symmetrical object the rotation
+is known only up to multiplication by an element of the symmetry group. Such
+ambiguous rotations arise in biomechanics, crystallography and seismology. We
+develop methods for analyzing data of this form. A test of uniformity is given.
+Parametric models for ambiguous rotations are presented, tests of location are
+considered, and a regression model is proposed. A brief illustrative example
+involving orientations of diopside crystals is given.
+"
+26811,Sparse Compositional Metric Learning,"  We propose a new approach for metric learning by framing it as learning a
+sparse combination of locally discriminative metrics that are inexpensive to
+generate from the training data. This flexible framework allows us to naturally
+derive formulations for global, multi-task and local metric learning. The
+resulting algorithms have several advantages over existing methods in the
+literature: a much smaller number of parameters to be estimated and a
+principled way to generalize learned metrics to new testing data points. To
+analyze the approach theoretically, we derive a generalization bound that
+justifies the sparse combination. Empirically, we evaluate our algorithms on
+several datasets against state-of-the-art metric learning methods. The results
+are consistent with our theoretical findings and demonstrate the superiority of
+our approach in terms of classification performance and scalability.
+"
+26812,Optimal Switchable Load Sizing and Scheduling for Standalone Renewable Energy Systems,"  The variability of solar energy in off-grid systems dictates the sizing of
+energy storage systems along with the sizing and scheduling of loads present in
+the off-grid system. Unfortunately, energy storage may be costly, while
+frequent switching of loads in the absence of an energy storage system causes
+wear and tear and should be avoided. Yet, the amount of solar energy utilized
+should be maximized and the problem of finding the optimal static load size of
+a finite number of discrete electric loads on the basis of a load response
+optimization is considered in this paper. The objective of the optimization is
+to maximize solar energy utilization without the need for costly energy storage
+systems in an off-grid system. Conceptual and real data for solar photovoltaic
+power production is provided the input to the off-grid system. Given the number
+of units, the following analytical solutions and computational algorithms are
+proposed to compute the optimal load size of each unit: mixed-integer linear
+programming and constrained least squares. Based on the available solar power
+profile, the algorithms select the optimal on/off switch times and maximize
+solar energy utilization by computing the optimal static load sizes. The
+effectiveness of the algorithms is compared using one year of solar power data
+from San Diego, California and Thuwal, Saudi Arabia. It is shown that the
+annual system solar energy utilization is optimized to 73% when using two loads
+and can be boosted up to 98% using a six load configuration.
+"
+26813,Role of hydrodynamic flows in chemically driven droplet division,"  We study the hydrodynamics and shape changes of chemically active droplets.
+In non-spherical droplets, surface tension generates hydrodynamic flows that
+drive liquid droplets into a spherical shape. Here we show that spherical
+droplets that are maintained away from thermodynamic equilibrium by chemical
+reactions may not remain spherical but can undergo a shape instability which
+can lead to spontaneous droplet division. In this case chemical activity acts
+against surface tension and tension-induced hydrodynamic flows. By combining
+low Reynolds-number hydrodynamics with phase separation dynamics and chemical
+reaction kinetics we determine stability diagrams of spherical droplets as a
+function of dimensionless viscosity and reaction parameters. We determine
+concentration and flow fields inside and outside the droplets during shape
+changes and division. Our work shows that hydrodynamic flows tends to stabilize
+spherical shapes but that droplet division occurs for sufficiently strong
+chemical driving, sufficiently large droplet viscosity or sufficiently small
+surface tension. Active droplets could provide simple models for prebiotic
+protocells that are able to proliferate. Our work captures the key
+hydrodynamics of droplet division that could be observable in chemically active
+colloidal droplets.
+"
+26814,Nehari method for locally Lipschitz functionals with examples in problems in the space of bounded variation functions,"  In this work we prove some abstract results about the existence of a
+minimizer for locally Lipschitz functionals, without any assumption of
+homogeneity, over a set which has its definition inspired in the Nehari
+manifold. As applications we present a result of existence of ground state
+bounded variation solutions of problems involving the 1-Laplacian and the
+mean-curvature operator, where the nonlinearity satisfies mild assumptions.
+"
+26815,Semi-parametric Bayesian change-point model based on the Dirichlet process,"  In this work we introduce a semi-parametric Bayesian change-point model,
+defining its time dynamic as a latent Markov process based on the Dirichlet
+process. We treat the number of change point as a random variable and we
+estimate it during model fitting. Posterior inference is carried out using a
+Markov chain Monte Carlo algorithm based on a marginalized version of the
+proposed model. The model is illustrated using simulated examples and two real
+datasets, namely the coal- mining disasters, that is a widely used dataset for
+illustrative purpose, and a dataset of indoor radon recordings. With the
+simulated examples we show that the model is able to recover the parameters and
+number of change points, and we compare our results with the ones of the-state-
+of-the-art models, showing a clear improvement in terms of change points
+identification. The results obtained on the coal-mining disasters and radon
+data are coherent with previous literature.
+"
+26816,Cyclicity in $\ell^p$ spaces and zero sets of the Fourier transforms,"  We study the cyclicity of vectors $u$ in $\ell^p(\mathbb{Z})$. It is known
+that a vector $u$ is cyclic in $\ell^2(\mathbb{Z})$ if and only if the zero
+set, $\mathcal{Z}(\widehat{u})$, of its Fourier transform, $\widehat{u}$, has
+Lebesgue measure zero and $\log |\widehat{u}| \not \in L^1(\mathbb{T})$, where
+$\mathbb{T}$ is the unit circle. Here we show that, unlike
+$\ell^2(\mathbb{Z})$, there is no characterization of the cyclicity of $u$ in
+$\ell^p(\mathbb{Z})$, $1<p<2$, in terms of $\mathcal{Z}(\widehat{u})$ and the
+divergence of the integral $\int\_\mathbb{T} \log |\widehat{u}| $. Moreover we
+give both necessary conditions and sufficient conditions for $u$ to be cyclic
+in $\ell^p(\mathbb{Z})$, $1<p<2$.
+"
+26817,Continuity of Hausdorff Dimension Across Generic Dynamical Lagrange and Markov Spectra II,"  Let $g_0$ be a smooth pinched negatively curved Riemannian metric on a
+complete surface $N$, and let $\Lambda_0$ be a basic hyperbolic set of the
+geodesic flow of $g_0$ with Hausdorff dimension strictly smaller than two.
+Given a small smooth perturbation $g$ of $g_0$ and a smooth real-valued
+function $f$ on the unit tangent bundle to $N$ with respect to $g$, let
+$L_{g,\Lambda,f}$, resp. $M_{g,\Lambda,f}$ be the Lagrange, resp. Markov
+spectrum of asymptotic highest, resp. highest values of $f$ along the geodesics
+in the hyperbolic continuation $\Lambda$ of $\Lambda_0$.
+We prove that, for generic choices of $g$ and $f$, the Hausdorff dimension of
+the sets $L_{g,\Lambda, f}\cap (-\infty, t)$ vary continuously with
+$t\in\mathbb{R}$ and, moreover, $M_{g,\Lambda, f}\cap (-\infty, t)$ has the
+same Hausdorff dimension of $L_{g,\Lambda, f}\cap (-\infty, t)$ for all
+$t\in\mathbb{R}$.
+"
+26818,Fano's inequality for random variables,"  We extend Fano's inequality, which controls the average probability of events
+in terms of the average of some f--divergences, to work with arbitrary events
+(not necessarily forming a partition) and even with arbitrary [0,1]--valued
+random variables, possibly in continuously infinite number. We provide two
+applications of these extensions, in which the consideration of random
+variables is particularly handy: we offer new and elegant proofs for existing
+lower bounds, on Bayesian posterior concentration (minimax or
+distribution-dependent) rates and on the regret in non-stochastic sequential
+learning.
+"
+26819,Predicting acoustic relaxation absorption in gas mixtures for extraction of composition relaxation contributions,"  The existing molecular relaxation models based on both parallel relaxation
+theory and series relaxation theory cannot extract the contributions of gas
+compositions to acoustic relaxation absorption in mixtures. In this paper, we
+propose an analytical model to predict acoustic relaxation absorption and
+clarify composition relaxation contributions based on the rate-determining
+energy transfer processes in molecular relaxation in excitable gases. By
+combining parallel and series relaxation theory, the proposed model suggests
+that the vibration-translation process of the lowest vibrational mode in each
+composition provides the primary deexcitation path of the relaxation energy,
+and the rate-determining vibration-vibration processes between the lowest mode
+and others dominate the coupling energy transfer between different modes. Thus,
+each gas composition contributes directly one single relaxation process to the
+molecular relaxation in mixture, which can be illustrated by the decomposed
+acoustic relaxation absorption spectrum of the single relaxation process. The
+proposed model is validated by simulation results in good agreement with
+experimental data such as $\mathrm{N_2}$, $\mathrm{O_2}$, $\mathrm{CO_2}$,
+$\mathrm{CH_4}$ and their mixtures.
+"
+26820,"Thresholds, valuations, and K-stability","  Let X be a normal complex projective variety with at worst klt singularities,
+and L a big line bundle on X. We use valuations to study the log canonical
+threshold of L, as well as another invariant, the stability threshold. The
+latter generalizes a notion by Fujita and Odaka, and can be used to
+characterize when a Q-Fano variety is K-semistable or uniformly K-stable. It
+can also be used to generalize volume bounds due to Fujita and Liu. The two
+thresholds can be written as infima of certain functionals on the space of
+valuations on X. When L is ample, we prove that these infima are attained. In
+the toric case, toric valuations acheive these infima, and we obtain simple
+expressions for the two thresholds in terms of the moment polytope of L.
+"
+26821,A Kalman filtering induced heuristic optimization based partitional data clustering,"  Clustering algorithms have regained momentum with recent popularity of data
+mining and knowledge discovery approaches. To obtain good clustering in
+reasonable amount of time, various meta-heuristic approaches and their
+hybridization, sometimes with K-Means technique, have been employed. A Kalman
+Filtering based heuristic approach called Heuristic Kalman Algorithm (HKA) has
+been proposed a few years ago, which may be used for optimizing an objective
+function in data/feature space. In this paper at first HKA is employed in
+partitional data clustering. Then an improved approach named HKA-K is proposed,
+which combines the benefits of global exploration of HKA and the fast
+convergence of K-Means method. Implemented and tested on several datasets from
+UCI machine learning repository, the results obtained by HKA-K were compared
+with other hybrid meta-heuristic clustering approaches. It is shown that HKA-K
+is atleast as good as and often better than the other compared algorithms.
+"
+26822,Optimal estimation of a signal perturbed by a fractional Brownian noise,"  We consider the problem of optimal estimation of the value of a vector
+parameter $\thetavector=(\theta_0,\ldots,\theta_n)^{\top}$ of the drift term in
+a fractional Brownian motion represented by the finite sum
+$\sum_{i=0}^{n}\theta_{i}\varphi_{i}(t)$ over known functions $\varphi_i(t)$,
+$\alli$. For the value of parameter $\thetavector$, we obtain a maximum
+likelihood estimate as well as Bayesian estimates for normal and uniform a
+priori distributions.
+"
+26823,Effective bilinear-biquadratic model for noncoplanar ordering in itinerant magnets,"  Noncollinear and noncoplanar magnetic textures including skyrmions and
+vortices act as emergent electromagnetic fields and give rise to novel
+electronic and transport properties. We here report a unified understanding of
+noncoplanar magnetic orderings emergent from the spin-charge coupling in
+itinerant magnets. The mechanism has its roots in effective multiple spin
+interactions beyond the conventional Ruderman-Kittel-Kasuya-Yosida (RKKY)
+mechanism, which are ubiquitously generated in itinerant electron systems with
+local magnetic moments. Carefully examining the higher-order perturbations in
+terms of the spin-charge coupling, we construct a minimal effective spin model
+composed of the bilinear and biquadratic interactions with particular wave
+numbers dictated by the Fermi surface. We find that our effective model
+captures the underlying physics of the instability toward noncoplanar
+multiple-$Q$ states discovered recently in itinerant magnets: a single-$Q$
+helical state expected from the RKKY theory is replaced by a double-$Q$ vortex
+crystal with chirality density waves even for an infinitely small spin-charge
+coupling on generic lattices [R. Ozawa $\mathit{et \ al.}$, J. Phys. Soc. Jpn.
+$\mathbf{85}$, 103703 (2016)], and a triple-$Q$ skyrmion crystal with a high
+topological number of two appears with increasing the spin-charge coupling on a
+triangular lattice [R. Ozawa, S. Hayami, and Y. Motome, to appear in Phys. Rev.
+Lett. (arXiv: 1703.03227)]. We find that, by introducing an external magnetic
+field, our effective model exhibits a plethora of multiple-$Q$ states. Our
+findings will serve as a guide for exploring further exotic magnetic orderings
+in itinerant magnets.
+"
+26824,A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards,"  We present new methods for apple detection and counting based on recent deep
+learning approaches and compare them with state-of-the-art results based on
+classical methods. Our goal is to quantify performance improvements by neural
+network-based methods compared to methods based on classical approaches.
+Additionally, we introduce a complete system for counting apples in an entire
+row. This task is challenging as it requires tracking fruits in images from
+both sides of the row. We evaluate the performances of three fruit detection
+methods and two fruit counting methods on six datasets. Results indicate that
+the classical detection approach still outperforms the deep learning based
+methods in the majority of the datasets. For fruit counting though, the deep
+learning based approach performs better for all of the datasets. Combining the
+classical detection method together with the neural network based counting
+approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.
+"
+26825,Dynamic Analysis of Regression Problems in Industrial Systems: Challenges and Solutions,"  This paper presents the result of our experience with the ap- plication of
+runtime verification, testing and static analysis techniques to several
+industrial projects. We discuss the eight most relevant challenges that we
+experienced, and the strategies that we elaborated to face them.
+"
+26826,On classification of Lie algebra realizations,"  We study realizations of Lie algebras by vector fields. A correspondence
+between classification of transitive local realizations and classification of
+subalgebras is generalized to the case of regular local realizations. A
+reasonable classification problem for general realizations is rigorously
+formulated and an algorithm for construction of such classification is
+presented.
+"
+26827,Testing Lipschitz non normally embedded complex spaces,"  We introduce a sectional criterion for testing if complex analytic germs
+$(X,0) \subset (\bC^n, 0)$ are Lipschitz non normally embedded.
+"
+26828,Topological Automorphic Forms via Curves,"  We produce first examples of p-local height three TAF homology theories. The
+corresponding one-dimensional formal groups arise as split summands of the
+formal groups of certain abelian three-folds, the Shimura variety of which can
+be reinterpreted as moduli of a family of Picard curves. This allows an
+explicit description of an automorphic form valued genus in terms of the
+coefficients of these curves. Moreover, our construction is such that the
+theories naturally come with restriction maps to TAF theories of lower height.
+"
+26829,Stochastic IMT (insulator-metal-transition) neurons: An interplay of thermal and threshold noise at bifurcation,"  Artificial neural networks can harness stochasticity in multiple ways to
+enable a vast class of computationally powerful models. Electronic
+implementation of such stochastic networks is currently limited to addition of
+algorithmic noise to digital machines which is inherently inefficient; albeit
+recent efforts to harness physical noise in devices for stochasticity have
+shown promise. To succeed in fabricating electronic neuromorphic networks we
+need experimental evidence of devices with measurable and controllable
+stochasticity which is complemented with the development of reliable
+statistical models of such observed stochasticity. Current research literature
+has sparse evidence of the former and a complete lack of the latter. This
+motivates the current article where we demonstrate a stochastic neuron using an
+insulator-metal-transition (IMT) device, based on electrically induced
+phase-transition, in series with a tunable resistance. We show that an IMT
+neuron has dynamics similar to a piecewise linear FitzHugh-Nagumo (FHN) neuron
+and incorporates all characteristics of a spiking neuron in the device
+phenomena. We experimentally demonstrate spontaneous stochastic spiking along
+with electrically controllable firing probabilities using Vanadium Dioxide
+(VO$_2$) based IMT neurons which show a sigmoid-like transfer function. The
+stochastic spiking is explained by two noise sources - thermal noise and
+threshold fluctuations, which act as precursors of bifurcation. As such, the
+IMT neuron is modeled as an Ornstein-Uhlenbeck (OU) process with a fluctuating
+boundary resulting in transfer curves that closely match experiments. As one of
+the first comprehensive studies of a stochastic neuron hardware and its
+statistical properties, this article would enable efficient implementation of a
+large class of neuro-mimetic networks and algorithms.
+"
+26830,Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation,"  We tackle the problem of object detection and pose estimation in a shared
+space downtown environment. For perception multiple laser scanners with
+360° coverage were fused in a dynamic occupancy grid map (DOGMa). A
+single-stage deep convolutional neural network is trained to provide object
+hypotheses comprising of shape, position, orientation and an existence score
+from a single input DOGMa. Furthermore, an algorithm for offline object
+extraction was developed to automatically label several hours of training data.
+The algorithm is based on a two-pass trajectory extraction, forward and
+backward in time. Typical for engineered algorithms, the automatic label
+generation suffers from misdetections, which makes hard negative mining
+impractical. Therefore, we propose a loss function counteracting the high
+imbalance between mostly static background and extremely rare dynamic grid
+cells. Experiments indicate, that the trained network has good generalization
+capabilities since it detects objects occasionally lost by the label algorithm.
+Evaluation reaches an average precision (AP) of 75.9%
+"
+26831,Keep it Fair: Equivalences,"  For models of concurrent and distributed systems, it is important and also
+challenging to establish correctness in terms of safety and/or liveness
+properties. Theories of distributed systems consider equivalences fundamental,
+since they (1) preserve desirable correctness characteristics and (2) often
+allow for component substitution making compositional reasoning feasible.
+Modeling distributed systems often requires abstraction utilizing
+nondeterminism which induces unintended behaviors in terms of infinite
+executions with one nondeterministic choice being recurrently resolved, each
+time neglecting a single alternative. These situations are considered
+unrealistic or highly improbable. Fairness assumptions are commonly used to
+filter system behaviors, thereby distinguishing between realistic and
+unrealistic executions. This allows for key arguments in correctness proofs of
+distributed systems, which would not be possible otherwise. Our contribution is
+an equivalence spectrum in which fairness assumptions are preserved. The
+identified equivalences allow for (compositional) reasoning about correctness
+incorporating fairness assumptions.
+"
+26832,An improved Ant Colony System for the Sequential Ordering Problem,"  It is not rare that the performance of one metaheuristic algorithm can be
+improved by incorporating ideas taken from another. In this article we present
+how Simulated Annealing (SA) can be used to improve the efficiency of the Ant
+Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering
+Problem (SOP). Moreover, we show how the very same ideas can be applied to
+improve the convergence of a dedicated local search, i.e. the SOP-3-exchange
+algorithm. A statistical analysis of the proposed algorithms both in terms of
+finding suitable parameter values and the quality of the generated solutions is
+presented based on a series of computational experiments conducted on SOP
+instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed
+ACS-SA and EACS-SA algorithms often generate solutions of better quality than
+the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with
+the proposed SOP-3-exchange-SA local search was able to find 10 new best
+solutions for the SOP instances from the SOPLIB2006 repository, thus improving
+the state-of-the-art results as known from the literature. Overall, the best
+known or improved solutions were found in 41 out of 48 cases.
+"
+26833,Kernel Pre-Training in Feature Space via m-Kernels,"  This paper presents a novel approach to kernel tuning. The method presented
+borrows techniques from reproducing kernel Banach space (RKBS) theory and
+tensor kernels and leverages them to convert (re-weight in feature space)
+existing kernel functions into new, problem-specific kernels using auxiliary
+data. The proposed method is applied to accelerating Bayesian optimisation via
+covariance (kernel) function pre-tuning for short-polymer fibre manufacture and
+alloy design.
+"
+26834,Citation Analysis of Innovative ICT and Advances of Governance (2008-2017),"  This paper opens by introducing the Internet Plus Government (IPG), a new
+government initiative emerging in the last decade. To understand benefits and
+challenges associated with this initiative worldwide, we conducted analyses on
+research articles published in the e-governance area between 2008 and 2017.
+Content analysis and citation analysis were performed on 2105 articles to
+address three questions: (1) What types of new ICT have been adopted in the IPG
+initiative in the past decade? (2) How did scholars investigate interactions
+between the new ICTs and governance core to IPG? (3) How did the new ICTs
+interact and shape while also being shaped by the evolution of governance in
+the past decade? Our analysis suggests that IPG initiative has enriched the
+government information infrastructure. It presented opportunities to accumulate
+and use huge volume of data for better decision making and proactive
+government-citizen interaction. At the same time, the advance of open data, the
+widespread use of social media and the potential of data analytics also
+generated great pressure to address challenging questions and issues in the
+domain of e-democracy.
+"
+26835,A phenomenological position and energy resolving Lindblad approach to quantum kinetics,"  A general theoretical approach to study the quantum kinetics in a system
+coupled to a bath is proposed. Starting with the microscopic interaction, a
+Lindblad master equation is established, which goes beyond the common secular
+approximation. This allows for the treatment of systems, where coherences are
+generated by the bath couplings while avoiding the negative occupations
+occurring in the Bloch-Wangsness-Redfield kinetic equations. The versatility
+and accuracy of the approach is verified by its application to three entirely
+different physical systems: (i) electric transport through a double-dot system
+coupled to electronic reservoirs, (ii) exciton kinetics in coupled chromophores
+in the presence of a heat bath, and (iii) the simulation of quantum cascade
+lasers, where the coherent electron transport is established by scattering with
+phonons and impurities.
+"
+26836,"Wide-field LOFAR-LBA power-spectra analyses: Impact of calibration, polarization leakage and ionosphere","  Contamination due to foregrounds (Galactic and Extra-galactic), calibration
+errors and ionospheric effects pose major challenges in detection of the cosmic
+21 cm signal in various Epoch of Reionization (EoR) experiments. We present the
+results of a pilot study of a field centered on 3C196 using LOFAR Low Band
+(56-70 MHz) observations, where we quantify various wide field and calibration
+effects such as gain errors, polarized foregrounds, and ionospheric effects. We
+observe a `pitchfork' structure in the 2D power spectrum of the polarized
+intensity in delay-baseline space, which leaks into the modes beyond the
+instrumental horizon (EoR/CD window). We show that this structure largely
+arises due to strong instrumental polarization leakage ($\sim30\%$) towards
+{Cas\,A} ($\sim21$ kJy at 81 MHz, brightest source in northern sky), which is
+far away from primary field of view. We measure an extremely small ionospheric
+diffractive scale ($r_{\text{diff}} \approx 430$ m at 60 MHz) towards {Cas\,A}
+resembling pure Kolmogorov turbulence compared to $r_{\text{diff}} \sim3 - 20$
+km towards zenith at 150 MHz for typical ionospheric conditions. This is one of
+the smallest diffractive scales ever measured at these frequencies. Our work
+provides insights in understanding the nature of aforementioned effects and
+mitigating them in future Cosmic Dawn observations (e.g. with SKA-low and HERA)
+in the same frequency window.
+"
+26837,Towards Understanding the Evolution of Vocabulary Terms in Knowledge Graphs,"  Vocabularies are used for modeling data in Knowledge Graphs (KG) like the
+Linked Open Data Cloud and Wikidata. During their lifetime, the vocabularies of
+the KGs are subject to changes. New terms are coined, while existing terms are
+modified or declared as deprecated. We first quantify the amount and frequency
+of changes in vocabularies. Subsequently, we investigate to which extend and
+when the changes are adopted in the evolution of the KGs. We conduct our
+experiments on three large-scale KGs for which time-stamped snapshots are
+available, namely the Billion Triples Challenge datasets, Dynamic Linked Data
+Observatory dataset, and Wikidata. Our results show that the change frequency
+of terms is rather low, but can have high impact when adopted on a large amount
+of distributed graph data on the web. Furthermore, not all coined terms are
+used and most of the deprecated terms are still used by data publishers. There
+are variations in the adoption time of terms coming from different vocabularies
+ranging from very fast (few days) to very slow (few years). Surprisingly, there
+are also adoptions we could observe even before the vocabulary changes are
+published. Understanding this adoption is important, since otherwise it may
+lead to wrong assumptions about the modeling status of data published on the
+web and may result in difficulties when querying the data from distributed
+sources.
+"
+26838,Local Type I Metrics with Holonomy in ${\rm G}_{2}^*$,"  By [arXiv:1604.00528], a list of possible holonomy algebras for
+pseudo-Riemannian manifolds with an indecomposable torsion free ${\rm
+G}_{2}^*$-structure is known. Here indecomposability means that the standard
+representation of the algebra on ${\mathbb R}^{4,3}$ does not leave invariant
+any proper non-degenerate subspace. The dimension of the socle of this
+representation is called the type of the Lie algebra. It is equal to one, two
+or three. In the present paper, we use Cartan's theory of exterior differential
+systems to show that all Lie algebras of Type I from the list in
+[arXiv:1604.00528] can indeed be realised as the holonomy of a local metric.
+All these Lie algebras are contained in the maximal parabolic subalgebra
+$\mathfrak p_1$ that stabilises one isotropic line of ${\mathbb R}^{4,3}$. In
+particular, we realise $\mathfrak p_1$ by a local metric.
+"
+26839,Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction,"  We study the problem of recovering an underlying 3D shape from a set of
+images. Existing learning based approaches usually resort to recurrent neural
+nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean pooling, to
+fuse multiple deep features encoded from input images. However, GRU based
+approaches are unable to consistently estimate 3D shapes given the same set of
+input images as the recurrent unit is permutation variant. It is also unlikely
+to refine the 3D shape given more images due to the long-term memory loss of
+GRU. The widely used pooling approaches are limited to capturing only the first
+order/moment information, ignoring other valuable features. In this paper, we
+present a new feed-forward neural module, named AttSets, together with a
+dedicated training algorithm, named JTSO, to attentionally aggregate an
+arbitrary sized deep feature set for multi-view 3D reconstruction. AttSets is
+permutation invariant, computationally efficient, flexible and robust to
+multiple input images. We thoroughly evaluate various properties of AttSets on
+large public datasets. Extensive experiments show AttSets together with JTSO
+algorithm significantly outperforms existing aggregation approaches.
+"
+26840,Pooling Facial Segments to Face: The Shallow and Deep Ends,"  Generic face detection algorithms do not perform very well in the mobile
+domain due to significant presence of occluded and partially visible faces. One
+promising technique to handle the challenge of partial faces is to design face
+detectors based on facial segments. In this paper two such face detectors
+namely, SegFace and DeepSegFace, are proposed that detect the presence of a
+face given arbitrary combinations of certain face segments. Both methods use
+proposals from facial segments as input that are found using weak boosted
+classifiers. SegFace is a shallow and fast algorithm using traditional
+features, tailored for situations where real time constraints must be
+satisfied. On the other hand, DeepSegFace is a more powerful algorithm based on
+a deep convolutional neutral network (DCNN) architecture. DeepSegFace offers
+certain advantages over other DCNN-based face detectors as it requires
+relatively little amount of data to train by utilizing a novel data
+augmentation scheme and is very robust to occlusion by design. Extensive
+experiments show the superiority of the proposed methods, specially
+DeepSegFace, over other state-of-the-art face detectors in terms of
+precision-recall and ROC curve on two mobile face datasets.
+"
+26841,A scale space based algorithm for automated segmentation of single shot tagged MRI of shearing deformation,"  Object This study proposes a scale space based algorithm for automated
+segmentation of single-shot tagged images of modest SNR. Furthermore the
+algorithm was designed for analysis of discontinuous or shearing types of
+motion, i.e. segmentation of broken tag patterns.
+Materials and methods The proposed algorithm utilizes non-linear scale space
+for automatic segmentation of single-shot tagged images. The algorithm's
+ability to automatically segment tagged shearing motion was evaluated in a
+numerical simulation and in vivo. A typical shearing deformation was simulated
+in a Shepp-Logan phantom allowing for quantitative evaluation of the
+algorithm's success rate as a function of both SNR and the amount of
+deformation. For a qualitative in vivo evaluation tagged images showing
+deformations in the calf muscles and eye movement in a healthy volunteer were
+acquired.
+Results Both the numerical simulation and the in vivo tagged data
+demonstrated the algorithm's ability for automated segmentation of single-shot
+tagged MR provided that SNR of the images is above 10 and the amount of
+deformation does not exceed the tag spacing. The latter constraint can be met
+by adjusting the tag delay or the tag spacing.
+Conclusion The scale space based algorithm for automatic segmentation of
+single-shot tagged MR enables the application of tagged MR to complex
+(shearing) deformation and the processing of datasets with relatively low SNR.
+"
+26842,Bifurcation of nonlinear bound states in the periodic Gross-Pitaevskii equation with PT-symmetry,"  The stationary Gross-Pitaevskii equation in one dimension is considered with
+a complex periodic potential satisfying the conditions of the PT (parity-time
+reversal) symmetry. Under rather general assumptions on the potentials we prove
+bifurcations of PT-symmetric nonlinear bound states from the end points of a
+real interval in the spectrum of the non-selfadjoint linear Schrodinger
+operator with a complex PT-symmetric periodic potential. The nonlinear bound
+states are approximated by the effective amplitude equation, which bears the
+form of the cubic nonlinear Schrodinger equation. In addition we provide
+sufficient conditions for the appearance of complex spectral bands when the
+complex $\PT$-symmetric potential has an asymptotically small imaginary part.
+"
+26843,Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks,"  Recent works have cast some light on the mystery of why deep nets fit any
+data and generalize despite being very overparametrized. This paper analyzes
+training and generalization for a simple 2-layer ReLU net with random
+initialization, and provides the following improvements over recent works:
+(i) Using a tighter characterization of training speed than recent papers, an
+explanation for why training a neural net with random labels leads to slower
+training, as originally observed in [Zhang et al. ICLR'17].
+(ii) Generalization bound independent of network size, using a data-dependent
+complexity measure. Our measure distinguishes clearly between random labels and
+true labels on MNIST and CIFAR, as shown by experiments. Moreover, recent
+papers require sample complexity to increase (slowly) with the size, while our
+sample complexity is completely independent of the network size.
+(iii) Learnability of a broad class of smooth functions by 2-layer ReLU nets
+trained via gradient descent.
+The key idea is to track dynamics of training and generalization via
+properties of a related kernel.
+"
+26844,Higher physical fitness levels are associated with less language decline in healthy ageing,"  Healthy ageing is associated with decline in cognitive abilities such as
+language. Aerobic fitness has been shown to ameliorate decline in some
+cognitive domains, but the potential benefits for language have not been
+examined. In a cross-sectional sample, we investigated the relationship between
+aerobic fitness and tip-of-the-tongue states. These are among the most frequent
+cognitive failures in healthy older adults and occur when a speaker knows a
+word but is unable to produce it. We found that healthy older adults indeed
+experience more tip-of-the-tongue states than young adults. Importantly, higher
+aerobic fitness levels decrease the probability of experiencing
+tip-of-the-tongue states in healthy older adults. Fitness-related differences
+in word finding abilities are observed over and above effects of age. This is
+the first demonstration of a link between aerobic fitness and language
+functioning in healthy older adults.
+"
+26845,Data-adaptive harmonic spectra and multilayer Stuart-Landau models,"  Harmonic decompositions of multivariate time series are considered for which
+we adopt an integral operator approach with periodic semigroup kernels.
+Spectral decomposition theorems are derived that cover the important cases of
+two-time statistics drawn from a mixing invariant measure.
+The corresponding eigenvalues can be grouped per Fourier frequency, and are
+actually given, at each frequency, as the singular values of a cross-spectral
+matrix depending on the data. These eigenvalues obey furthermore a variational
+principle that allows us to define naturally a multidimensional power spectrum.
+The eigenmodes, as far as they are concerned, exhibit a data-adaptive character
+manifested in their phase which allows us in turn to define a multidimensional
+phase spectrum.
+The resulting data-adaptive harmonic (DAH) modes allow for reducing the
+data-driven modeling effort to elemental models stacked per frequency, only
+coupled at different frequencies by the same noise realization. In particular,
+the DAH decomposition extracts time-dependent coefficients stacked by Fourier
+frequency which can be efficiently modeled---provided the decay of temporal
+correlations is sufficiently well-resolved---within a class of multilayer
+stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators.
+Applications to the Lorenz 96 model and to a stochastic heat equation driven
+by a space-time white noise, are considered. In both cases, the DAH
+decomposition allows for an extraction of spatio-temporal modes revealing key
+features of the dynamics in the embedded phase space. The multilayer
+Stuart-Landau models (MSLMs) are shown to successfully model the typical
+patterns of the corresponding time-evolving fields, as well as their statistics
+of occurrence.
+"
+26846,Canonical decomposition of a difference of convex sets,"  Let $N$ be a lattice of rank $n$ and let $M = N^{\vee}$ be its dual lattice.
+In this note we show that given two compact, bounded, full-dimensional convex
+sets $K_1 \subseteq K_2 \subseteq M_{\R} \coloneqq M \otimes_{\Z} \R$, there is
+a canonical convex decomposition of the difference $K_2 \setminus K_1$ and we
+interpret the volume of the pieces geometrically in terms of intersection
+numbers of toric $b$-divisors.
+"
+26847,Intertwined electron-nuclear motion in frustrated double ionization in driven heteronuclear molecules,"  We study frustrated double ionization in a strongly-driven heteronuclear
+molecule HeH$^{+}$ and compare with H$_2$. We compute the probability
+distribution of the sum of the final kinetic energies of the nuclei for
+strongly-driven HeH$^{+}$. We find that this distribution has more than one
+peak for strongly-driven HeH$^{+}$, a feature we do not find to be present for
+strongly-driven H$_{2}$. Moreover, we compute the probability distribution of
+the n quantum number of frustrated double ionization. We find that this
+distribution has several peaks for strongly-driven HeH$^{+}$, while the
+respective distribution has one main peak and a ""shoulder"" at lower n quantum
+numbers for strongly-driven H$_{2}$. Surprisingly, we find this feature to be a
+clear signature of the intertwined electron-nuclear motion.
+"
+26848,Task-Driven Estimation and Control via Information Bottlenecks,"  Our goal is to develop a principled and general algorithmic framework for
+task-driven estimation and control for robotic systems. State-of-the-art
+approaches for controlling robotic systems typically rely heavily on accurately
+estimating the full state of the robot (e.g., a running robot might estimate
+joint angles and velocities, torso state, and position relative to a goal).
+However, full state representations are often excessively rich for the specific
+task at hand and can lead to significant computational inefficiency and
+brittleness to errors in state estimation. In contrast, we present an approach
+that eschews such rich representations and seeks to create task-driven
+representations. The key technical insight is to leverage the theory of
+information bottlenecks}to formalize the notion of a ""task-driven
+representation"" in terms of information theoretic quantities that measure the
+minimality of a representation. We propose novel iterative algorithms for
+automatically synthesizing (offline) a task-driven representation (given in
+terms of a set of task-relevant variables (TRVs)) and a performant control
+policy that is a function of the TRVs. We present online algorithms for
+estimating the TRVs in order to apply the control policy. We demonstrate that
+our approach results in significant robustness to unmodeled measurement
+uncertainty both theoretically and via thorough simulation experiments
+including a spring-loaded inverted pendulum running to a goal location.
+"
+26849,Theory of antiferromagnetic Heisenberg spins on breathing pyrochlore lattice,"  Spin-singlet orders are studied for the antiferromagnetic Heisenberg model
+with spin $S$>1/2 on a breathing pyrochlore lattice, where tetrahedron units
+are weakly coupled and exchange constants have two values $0<J' \ll J$. The
+ground state has a thermodynamic degeneracy at $J'$=0, and I have studied
+lattice symmetry breaking associated to lifting this degeneracy. Third-order
+perturbation in $J'$ for general spin $S$ shows that the effective Hamiltonian
+has a form of three-tetrahedron interactions of pseudospins $\tau$, which is
+identical to that previously derived for $S$=1/2, and I have calculated their
+matrix elements for general $S$. For this effective Hamiltonian, I have
+obtained its mean-field ground state and investigated the possibility of
+lattice symmetry breaking for the cases of $S$=3/2 and $1$. In contrast to the
+$S$=1/2 case,$\tau$'s response to conjugate field has a $Z_3$ anisotropy in its
+internal space, and this stabilizes the mean-field ground state. The mean-field
+ground state has a characteristic spatial pattern of spin correlations related
+to the lattice symmetry breaking. Spin structure factor $S(q)$ is calculated
+and found to have symmetry broken parts with amplitudes of the same order as
+the isotropic part.
+"
+26850,On the multiplicites of zeros of $ζ(s)$ and its values over short intervals,"  We investigate bounds for the multiplicities $m(\beta+i\gamma)$, where
+$\beta+i\gamma\,$ ($\beta\ge \1/2, \gamma>0)$ denotes complex zeros of
+$\zeta(s)$. It is seen that the problem can be reduced to the estimation of the
+integrals of the zeta-function over ""very short"" intervals. A new, explicit
+bound for $m(\beta+i\gamma)$ is also derived, which is relevant when $\beta$ is
+close to unity. The related Karatsuba conjectures are also discussed.
+"
+26851,On degenerations of algebras over an arbitrary field,"  For each $n\ge2$ we classify all $n$-dimensional algebras over an arbitrary
+infinite field which have the property that the $n$-dimensional abelian Lie
+algebra is their only proper degeneration.
+"
+26852,On M-metric spaces and fixed point theorems,"  In this paper we make some observations concerning m-metric spaces and point
+out some discrepancies in the proofs found in the literature. To remedy this,
+we propose a new topological construction and prove that it is in fact a
+generalization of a partial metric space. Then, using this construction, we
+present our main theorem having as its corollaries the fixed point theorems
+found in previous publications.
+"
+26853,Subcritical convection in an internally heated layer,"  Thermal convection in a horizontal layer with uniform internal heating and
+stress-free constant-temperature boundaries is analyzed numerically. The work
+is motivated by the questions arising in development of liquid metal batteries,
+in which convection is induced by the Joule heating of electrolyte. It is
+demonstrated that three-dimensional convection cells exist at subcritical
+Rayleigh numbers.
+"
+26854,Quiver varieties and elliptic quantum groups,"  We define a sheafified elliptic quantum group for any symmetric Kac-Moody Lie
+algebra. This definition is naturally obtained from the elliptic cohomological
+Hall algebra of a preprojective algebra. The sheafified elliptic quantum group
+is an algebra object in a certain monoidal category of coherent sheaves on the
+colored Hilbert scheme of an elliptic curve.
+We show that the elliptic quantum group acts on the equivariant elliptic
+cohomology of Nakajima quiver varieties. This action is compatible with the
+action induced by Hecke correspondence, a construction similar to that of
+Nakajima. The elliptic Drinfeld currents are obtained as generating series of
+certain rational sections of the sheafified elliptic quantum group. We show
+that the Drinfeld currents satisfy the commutation relations of the dynamical
+elliptic quantum group studied by Felder and Gautam-Toledano Laredo.
+"
+26855,Generalization in Deep Learning,"  Throughout this chapter, we provide theoretical insights into why and how
+deep learning can generalize well, despite its large capacity, complexity,
+possible algorithmic instability, nonrobustness, and sharp minima, responding
+to an open question in the literature. We also propose new open problems and
+discuss the limitations of our results.
+"
+26856,Robust Particle Swarm Optimizer based on Chemomimicry,"  A particle swarm optimizer (PSO) loosely based on the phenomena of
+crystallization and a chaos factor which follows the complimentary error
+function is described. The method features three phases: diffusion, directed
+motion, and nucleation. During the diffusion phase random walk is the only
+contributor to particle motion. As the algorithm progresses the contribution
+from chaos decreases and movement toward global best locations is pursued until
+convergence has occurred. The algorithm was found to be more robust to local
+minima in multimodal test functions than a standard PSO algorithm and is
+designed for problems which feature experimental precision.
+"
+26857,Example Setups of Navier-Stokes Equations with Control and Observation: Spatial Discretization and Representation via Linear-quadratic Matrix Coefficients,"  We provide spatial discretizations of nonlinear incompressible Navier-Stokes
+equations with inputs and outputs in the form of matrices ready to use in any
+numerical linear algebra package. We discuss the assembling of the system
+operators and the realization of boundary conditions and inputs and outputs. We
+describe the two benchmark problems - the driven cavity and the cylinder wake -
+and provide the corresponding data. The use of the data is illustrated by
+numerous example setups. The test cases are provided as plain PYTHON or
+OCTAVE/MATLAB script files for immediate replication.
+"
+26858,"Light Axinos from Freeze-in: production processes, phase space distributions, and Ly-$α$ constraints","  We consider the freeze-in production of 7 keV axino dark matter (DM) in the
+supersymmetric Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) model in light of the
+3.5 keV line excess. The warmness of such 7 keV DM produced from the thermal
+bath, in general, appears in tension with Ly-$\alpha$ forest data, although a
+direct comparison is not straightforward. This is because the Ly-$\alpha$
+forest constraints are usually reported on the mass of the conventional warm
+dark matter (WDM), where large entropy production is implicitly assumed to
+occur in the thermal bath after WDM particles decouple. The phase space
+distribution of freeze-in axino DM varies depending on production processes and
+axino DM may alleviate the tension with the tight Ly-$\alpha$ forest
+constraint. By solving the Boltzmann equation, we first obtain the resultant
+phase space distribution of axinos produced by 2-body decay, 3-body decay, and
+2-to-2 scattering respectively. The reduced collision term and resultant phase
+space distribution are useful for studying other freeze-in scenarios as well.
+We then calculate the resultant linear matter power spectra for such axino DM
+and directly compare them with the linear matter power spectra for the
+conventional WDM. In order to demonstrate realistic axino DM production, we
+consider benchmark points with Higgsino next-to-light supersymmetric particle
+(NLSP) and wino NLSP. In the case of Higgsino NLSP, the phase space
+distribution of axinos is colder than that in the conventional WDM case, so the
+most stringent Ly-$\alpha$ forest constraint can be evaded with mild entropy
+production from saxion decay inherent in the supersymmetric DFSZ axion model.
+"
+26859,Cauchy-Stieltjes families with polynomial variance functions and generalized orthogonality,"  This paper studies variance functions of Cauchy-Stieltjes Kernel families
+generated by compactly supported centered probability measures. We describe
+several operations that allow us to construct additional variance functions
+from known ones. We construct a class of examples which exhausts all cubic
+variance functions, and provide examples of polynomial variance functions of
+arbitrary degree. We also relate Cauchy-Stieltjes Kernel families with
+polynomial variance functions to generalized orthogonality.
+Our main results are stated solely in terms of classical probability; some
+proofs rely on analytic machinery of free probability.
+"
+26860,A general explanation on the correlation of dark matter halo spin with the large scale environment,"  Both simulations and observations have found that the spin of halo/galaxy is
+correlated with the large scale environment, and particularly the spin of halo
+flips in filament. A consistent picture of halo spin evolution in different
+environments is still lacked. Using N-body simulation we find that halo spin
+with its environment evolves continuously from sheet to cluster, and the flip
+of halo spin happens both in filament and nodes. For the flip in filament can
+be explained by halo formation time and the migrating time when its environment
+changes from sheet to filament. For low-mass haloes, they form first in sheets
+and migrate into filaments later, so their mass and spin growth inside filament
+are lower, and the original spin is still parallel to filament. For massive
+haloes, they migrate into filaments first, and most of their mass and spin
+growth are obtained in filaments, so the resulted spin is perpendicular to
+filament. Our results well explain the overall evolution of cosmic web in the
+cold dark matter model and can be tested using high-redshift data. The scenario
+can also be tested against alternative models of dark matter, such as warm/hot
+dark matter, where the structure formation will proceed in a different way.
+"
+26861,Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks,"  In this article, we investigate the cost-effective 5G-enabled vehicular
+networks to support emerging vehicular applications, such as autonomous
+driving, in-car infotainment and location-based road services. To this end,
+self-sustaining caching stations (SCSs) are introduced to liberate on-road base
+stations from the constraints of power lines and wired backhauls. Specifically,
+the cache-enabled SCSs are powered by renewable energy and connected to core
+networks through wireless backhauls, which can realize ""drop-and-play""
+deployment, green operation, and low-latency services. With SCSs integrated, a
+5G-enabled heterogeneous vehicular networking architecture is further proposed,
+where SCSs are deployed along roadside for traffic offloading while
+conventional macro base stations (MBSs) provide ubiquitous coverage to
+vehicles. In addition, a hierarchical network management framework is designed
+to deal with high dynamics in vehicular traffic and renewable energy, where
+content caching, energy management and traffic steering are jointly
+investigated to optimize the service capability of SCSs with balanced power
+demand and supply in different time scales. Case studies are provided to
+illustrate SCS deployment and operation designs, and some open research issues
+are also discussed.
+"
+26862,Effects of shear flows on the evolution of fluctuations in interchange turbulence,"  We report a non-perturbative study of the effect of different type of shear
+flows on the evolution of vorticity and particle density fluctuations in
+interchange turbulence. For the same shear strength, the transport of density
+is less reduced by streamers than by zonal flows, zonal flows leading to
+oscillation death. In the inviscid limit, vorticity (density) grows (decays) as
+a power law due to streamer or zonal flow, and exponentially due to the
+combined effect of zonal flow and streamer with the same sign of shear. Zonal
+flow and streamer with the opposite sign of shear lead to oscillation at
+multiple frequencies.
+"
+26863,An Optimal Distributed $(Δ+1)$-Coloring Algorithm?,"  Vertex coloring is one of the classic symmetry breaking problems studied in
+distributed computing. In this paper we present a new algorithm for
+$(\Delta+1)$-list coloring in the randomized ${\sf LOCAL}$ model running in
+$O(\log^\ast \Delta) + O(\mathsf{Det}_{\scriptscriptstyle d}(\text{poly} \log
+n)) = O(\mathsf{Det}_{\scriptscriptstyle d}(\text{poly} \log n))$ time, where
+$\mathsf{Det}_{\scriptscriptstyle d}(n')$ is the deterministic complexity of
+$(\text{deg}+1)$-list coloring on $n'$-vertex graphs. (In this problem, each
+$v$ has a palette of size $\text{deg}(v)+1$.) This improves upon a previous
+randomized algorithm of Harris, Schneider, and Su [STOC'16, JACM'18] with
+complexity $O(\sqrt{\log \Delta} + \log\log n +
+\mathsf{Det}_{\scriptscriptstyle d}(\text{poly} \log n))$, and, for some range
+of $\Delta$, is much faster than the best known deterministic algorithm of
+Fraigniaud, Heinrich, and Kosowski [FOCS'16] and Barenboim, Elkin, and
+Goldenberg [PODC'18], with complexity $O(\sqrt{\Delta\log \Delta}\log^\ast
+\Delta + \log^* n)$.
+Our algorithm ""appears to be"" optimal, in view of the
+$\Omega(\mathsf{Det}(\text{poly} \log n))$ randomized lower bound due to Chang,
+Kopelowitz, and Pettie [FOCS'16], where $\mathsf{Det}$ is the deterministic
+complexity of $(\Delta+1)$-list coloring. At present, the best upper bounds on
+$\mathsf{Det}_{\scriptscriptstyle d}(n')$ and $\mathsf{Det}(n')$ are both
+$2^{O(\sqrt{\log n'})}$ and use a black box application of network
+decompositions (Panconesi and Srinivasan [Journal of Algorithms'96]). It is
+quite possible that the true complexities of both problems are the same,
+asymptotically, which would imply the randomized optimality of our
+$(\Delta+1)$-list coloring algorithm.
+"
+26864,Equilibrium measure for one-dimensional Lorenz-like expanding Maps,"  Let $L:[0,1]\setminus\{d\}\rightarrow [0,1]$ be a one-dimensional Lorenz like
+expanding map ($d$ is the point of discontinuity), $\mathcal{P}=\{ (0,d),(d,1)
+\}$ be a partition of $[0,1]$ and $C^{\alpha}([0,1],\mathcal{P})$ the set of
+piecewise Hölder-continuous potential of [0,1] with the usual $\mathcal{C}^0$
+topology. In this context, we prove, improving a result of \cite{BS03}, that
+piecewise Hölder-continuous potential $\phi$ satisfying \linebreak
+$\max\left\{ \limsup_{n \rightarrow \infty}\frac{1}{n}(S_{n}\phi)(0),\limsup_{n
+\rightarrow \infty}\frac{1}{n}(S_{n}\phi)(1)\right\}<P_{\text{top}}(\phi,T)$
+support an unique equilibrium state. Indeed, we prove there exists an open and
+dense subset $\mathcal{H}$ of $C^{\alpha}([0,1],\mathcal{P})$ such that, if
+$\phi \in \mathcal{H}$, then $\phi$ admits one equilibrium measure.
+"
+26865,Topological Tutte Polynomial,"  This is a survey recent works on topological extensions of the Tutte
+polynomial.
+"
+26866,Normal bundles of lines on hypersurfaces,"  Let $X \subset \mathbb{P}^n$ be a smooth hypersurface. Given a sequence of
+integers $\vec{a} = (a_1, \ldots, a_{n-2})$ with $a_1 \leq \cdots \leq
+a_{n-2}$, let $F_{\vec{a}}(X)$ be the parameter space of lines $L$ on $X$ such
+that $N_{L/X} \cong \mathcal{O}(a_1) \oplus \cdots \oplus
+\mathcal{O}(a_{n-2})$. The loci $F_{\vec{a}}(X)$ form a stratification of the
+Fano scheme of lines on $X$. We show that for general hypersurfaces, the
+$F_{\vec{a}}(X)$ have the expected dimension and, in this case, compute the
+class of $\overline{F_{\vec{a}}(X)}$ in the Chow ring of the Grassmannian of
+lines in $\mathbb{P}^n$. For certain splitting types $\vec{a}$, we also provide
+non-trivial upper bounds on the dimension of $F_{\vec{a}}(X)$ that hold for all
+smooth $X$.
+"
+26867,Efficient polarization analysis for focusing neutron instruments,"  Polarized neutrons are a powerful probe to investigate magnetism in condensed
+matter on length scales from single atomic distances to micrometers. With the
+ongoing advancement of neutron optics, that allow to transport beams with
+increased divergence, the demands on neutron polarizes and analyzers have grown
+as well. The situation becomes especially challenging for new instruments at
+pulsed sources, where a large wavelength band needs to be polarized to make
+efficient use of the time structure of the beam.
+Here we present a polarization analysis concept for highly focused neutron
+beams that is based on transmission supermirrors that are bend in the shape of
+equiangular spirals. The method allows polarizations above 95\% and good
+transmission, without negative impact on other beam characteristics. An example
+of a compact polarizing device already tested on the AMOR reflectometer is
+presented as well as the concept for the next generation implementation of the
+technique that will be installed on the Estia instrument being build for the
+European Spallation Source.
+"
+26868,Red clump stars and Gaia: Calibration of the standard candle using a hierarchical probabilistic model,"  Distances to individual stars in our own Galaxy are critical in order to
+piece together the nature of its velocity and spatial structure. Core helium
+burning red clump (RC) stars have similar luminosities, are abundant throughout
+the Galaxy, and thus constitute good standard candles. We build a hierarchical
+probabilistic model to quantify the quality of RC stars as standard candles
+using parallax measurements from the first Gaia data release. A unique aspect
+of our methodology is to fully account for (and marginalize over) parallax,
+photometry, and dust corrections uncertainties, which leads to more robust
+results than standard approaches. We determine the absolute magnitude and
+intrinsic dispersion of the RC in 2MASS bands J, H, Ks, Gaia G band, and WISE
+bands W1, W2, W3, and W4. We find that the absolute magnitude of the RC is
+$-1.61 \pm$ 0.01 (in Ks), $+0.44 \pm$ 0.01 (in G) , $-0.93 \pm$ 0.01 (in J),
+$-1.46 \pm$ 0.01 (in H), $-1.68 \pm$ 0.02 (in W1), $-1.69\pm$ 0.02 (in W2),
+$-1.67 \pm$ 0.02 (in W3), $1.76 \pm$ 0.01 mag (in W4). The mean intrinsic
+dispersion is $\sim 0.17 \pm$ 0.03 mag across all bands (yielding a typical
+distance precision of $\sim$ 8%). Thus RC stars are reliable and precise
+standard candles. In addition, we have also re-calibrated the zero point of the
+absolute magnitude of the RC in each band, which provide a benchmark for future
+studies to estimate distances to RC stars. Finally, the parallax error
+shrinkage in the hierarchical model outlined in this work can be used to obtain
+more precise parallaxes than Gaia for the most distant RC stars across the
+Galaxy.
+"
+26869,Interactions between species introduce spurious associations in microbiome studies,"  Microbiota contribute to many dimensions of host phenotype, including
+disease. To link specific microbes to specific phenotypes, microbiome-wide
+association studies compare microbial abundances between two groups of samples.
+Abundance differences, however, reflect not only direct associations with the
+phenotype, but also indirect effects due to microbial interactions. We found
+that microbial interactions could easily generate a large number of spurious
+associations that provide no mechanistic insight. Using techniques from
+statistical physics, we developed a method to remove indirect associations and
+applied it to the largest dataset on pediatric inflammatory bowel disease. Our
+method corrected the inflation of p-values in standard association tests and
+showed that only a small subset of associations is directly linked to the
+disease. Direct associations had a much higher accuracy in separating cases
+from controls and pointed to immunomodulation, butyrate production, and the
+brain-gut axis as important factors in the inflammatory bowel disease.
+"
+26870,Small Superposition Dimension and Active Set Construction for Multivariate Integration Under Modest Error Demand,"  Constructing active sets is a key part of the Multivariate Decomposition
+Method. An algorithm for constructing optimal or quasi-optimal active sets is
+proposed in the paper. By numerical experiments, it is shown that the new
+method can provide sets that are significantly smaller than the sets
+constructed by the already existing method. The experiments also show that the
+superposition dimension could surprisingly be very small, at most 3, when the
+error demand is not smaller than $10^{-3}$ and the weights decay sufficiently
+fast.
+"
+26871,Fast Sketch-based Recovery of Correlation Outliers,"  Many data sources can be interpreted as time-series, and a key problem is to
+identify which pairs out of a large collection of signals are highly
+correlated. We expect that there will be few, large, interesting correlations,
+while most signal pairs do not have any strong correlation. We abstract this as
+the problem of identifying the highly correlated pairs in a collection of n
+mostly pairwise uncorrelated random variables, where observations of the
+variables arrives as a stream. Dimensionality reduction can remove dependence
+on the number of observations, but further techniques are required to tame the
+quadratic (in n) cost of a search through all possible pairs.
+We develop a new algorithm for rapidly finding large correlations based on
+sketch techniques with an added twist: we quickly generate sketches of random
+combinations of signals, and use these in concert with ideas from coding theory
+to decode the identity of correlated pairs. We prove correctness and compare
+performance and effectiveness with the best LSH (locality sensitive hashing)
+based approach.
+"
+26872,A Deformable Interface for Human Touch Recognition using Stretchable Carbon Nanotube Dielectric Elastomer Sensors and Deep Neural Networks,"  User interfaces provide an interactive window between physical and virtual
+environments. A new concept in the field of human-computer interaction is a
+soft user interface; a compliant surface that facilitates touch interaction
+through deformation. Despite the potential of these interfaces, they currently
+lack a signal processing framework that can efficiently extract information
+from their deformation. Here we present OrbTouch, a device that uses
+statistical learning algorithms, based on convolutional neural networks, to map
+deformations from human touch to categorical labels (i.e., gestures) and touch
+location using stretchable capacitor signals as inputs. We demonstrate this
+approach by using the device to control the popular game Tetris. OrbTouch
+provides a modular, robust framework to interpret deformation in soft media,
+laying a foundation for new modes of human computer interaction through shape
+changing solids.
+"
+26873,Integer-Forcing Linear Receivers: A Design Criterion for Full-Diversity STBCs,"  In multiple-input multiple-output (MIMO) fading channels, the design
+criterion for full-diversity space-time block codes (STBCs) is primarily
+determined by the decoding method at the receiver. Although constructions of
+STBCs have predominantly matched the maximum-likelihood (ML) decoder, design
+criteria and constructions of full-diversity STBCs have also been reported for
+low-complexity linear receivers. A new receiver architecture called
+Integer-Forcing (IF) linear receiver has been proposed to MIMO channels by Zhan
+et al. which showed promising results for the high-rate V-BLAST encoding
+scheme. In this work we address the design of full-diversity STBCs for IF
+linear receivers. We derive an upper bound on the probability of decoding
+error, and show that STBCs that satisfy the non-vanishing singular value (NVS)
+property provide full-diversity for the IF receiver. We also present simulation
+results to demonstrate that linear designs with NVS property provide full
+diversity for IF receiver. As a special case of our analysis on STBCs, we
+present an upper bound on the error probability for the V-BLAST architecture
+presented by Zhan \emph{et al.}, and demonstrate that the IF linear receivers
+provide full receive diversity. Our results supplement the existing outage
+probability based results for the IF receiver.
+"
+26874,Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics,"  For many important network types (e.g., sensor networks in complex harsh
+environments and social networks) physical coordinate systems (e.g.,
+Cartesian), and physical distances (e.g., Euclidean), are either difficult to
+discern or inapplicable. Accordingly, coordinate systems and characterizations
+based on hop-distance measurements, such as Topology Preserving Maps (TPMs) and
+Virtual-Coordinate (VC) systems are attractive alternatives to Cartesian
+coordinates for many network algorithms. Herein, we present an approach to
+recover geometric and topological properties of a network with a small set of
+distance measurements. In particular, our approach is a combination of shortest
+path (often called geodesic) recovery concepts and low-rank matrix completion,
+generalized to the case of hop-distances in graphs. Results for sensor networks
+embedded in 2-D and 3-D spaces, as well as a social networks, indicates that
+the method can accurately capture the network connectivity with a small set of
+measurements. TPM generation can now also be based on various context
+appropriate measurements or VC systems, as long as they characterize different
+nodes by distances to small sets of random nodes (instead of a set of global
+anchors). The proposed method is a significant generalization that allows the
+topology to be extracted from a random set of graph shortest paths, making it
+applicable in contexts such as social networks where VC generation may not be
+possible.
+"
+26875,A Summary of the 4th International Workshop on Recovering 6D Object Pose,"  This document summarizes the 4th International Workshop on Recovering 6D
+Object Pose which was organized in conjunction with ECCV 2018 in Munich. The
+workshop featured four invited talks, oral and poster presentations of accepted
+workshop papers, and an introduction of the BOP benchmark for 6D object pose
+estimation. The workshop was attended by 100+ people working on relevant topics
+in both academia and industry who shared up-to-date advances and discussed open
+problems.
+"
+26876,Stochastic Channel Modeling for Diffusive Mobile Molecular Communication Systems,"  In this paper, we develop a mathematical framework for modeling the
+time-variant stochastic channels of diffusive mobile MC systems. In particular,
+we consider a diffusive mobile MC system consisting of a pair of transmitter
+and receiver nano-machines suspended in a fluid medium with a uniform bulk
+flow, where we assume that either the transmitter, or the receiver, or both are
+mobile and we model the mobility by Brownian motion. The transmitter and
+receiver nano-machines exchange information via diffusive signaling molecules.
+Due to the random movements of the transmitter and receiver nano-machines, the
+statistics of the channel impulse response (CIR) change over time. We derive
+closed-form expressions for the mean, the autocorrelation function (ACF), the
+cumulative distribution function (CDF), and the probability density function
+(PDF) of the time-variant CIR. Exploiting the ACF, we define the coherence time
+of the time-variant MC channel as a metric for characterization of the
+variations of the CIR. The derived CDF is employed for calculation of the
+outage probability of the system. We also show that under certain conditions,
+the PDF of the CIR can be accurately approximated by a Log-normal distribution.
+Based on this approximation, we derive a simple model for outdated channel
+state information (CSI). Moreover, we derive an analytical expression for
+evaluation of the expected error probability of a simple detector for the
+considered MC system. In order to investigate the impact of CIR decorrelation
+over time, we compare the performances of a detector with perfect CSI knowledge
+and a detector with outdated CSI knowledge. The accuracy of the proposed
+analytical expressions is verified via particle-based simulation of the
+Brownian motion.
+"
+26877,Multi-objective integer programming: Synergistic parallel approaches,"  Exactly solving multi-objective integer programming (MOIP) problems is often
+a very time consuming process, especially for large and complex problems.
+Parallel computing has the potential to significantly reduce the time taken to
+solve such problems, but only if suitable algorithms are used. The first of our
+new algorithms follows a simple technique that demonstrates impressive
+performance for its design. We then go on to introduce new theory for
+developing more efficient parallel algorithms. The theory utilises elements of
+the symmetric group to apply a permutation to the objective functions to assign
+different workloads, and applies to algorithms that order the objective
+functions lexicographically. As a result, information and updated bounds can be
+shared in real time, creating a synergy between threads. We design and
+implement two algorithms that take advantage of such theory. To properly
+analyse the running time of our three algorithms, we compare them against two
+existing algorithms from the literature, and against using multiple threads
+within our chosen IP solver, CPLEX. This survey of six different parallel
+algorithms, the first of its kind, demonstrates the advantages of parallel
+computing. Across all problem types tested, our new algorithms are on par with
+existing algorithms on smaller cases and massively outperform the competition
+on larger cases. These new algorithms, and freely available implementations,
+allows the investigation of complex MOIP problems with four or more objectives.
+"
+26878,Ejecta cloud from the AIDA space project kinetic impact on the secondary of a binary asteroid: II. fates and evolutionary dependencies,"  This paper presents a quantitative study of the evolution of the ejecta cloud
+released from a hypervelocity impact on a binary asteroid. We performed
+numerical simulations of the post-impact dynamics of the ejecta cloud in the
+framework of the current mission scenario of AIDA mission project. A grid
+search of launching sites of ejecta was defined over the globe of Didymoon, and
+considering a wide range of possible ejection speeds, we determined the
+dependency of ejecta fate on launching sites (projectile impact sites) and
+speeds. This range allows us to track all the complex cases that include
+different types of dynamical fates. Two major mechanisms are found to be
+working broadly during the post-ejection evolution of the ejecta cloud: 1)
+ejecta on mean motion resonance orbits with Didymoon produce long-term
+quasi-periodic showers onto Didymoon over at least a couple of weeks after the
+projectile impact, 2) ejecta on non-resonant orbits produce a rapid and high
+re-accretion flux. This rapid and high flux occurs just once because ejecta on
+such orbits leave the system unless they experience a collision during their
+first encounter. For the second part of this study, we performed full-scale
+simulations of the ejecta cloud released from 6 hypothetical impact sites. We
+considered two kinds of material composing Didymoon's subsurface and then
+combined a power-law size distribution of the ejecta with an ejection speed
+distribution. We find that the ejecta cloud evolution can be divided in two
+periods. It starts with a first violent period (<10 hr) with fast re-accretion
+or ejection from the system. A second period is found to be more sensitive to
+the launching site than the first one. During this second period, ejecta will
+either re-accrete or being ejected from the system, depending both on their
+sizes and on their average survival time in close proximity of the binary
+components.
+"
+26879,Distribution of zeros of the S-matrix of chaotic cavities with localized losses and Coherent Perfect Absorption: non-perturbative results,"  We employ the Random Matrix Theory framework to calculate the density of
+zeroes of an $M$-channel scattering matrix describing a chaotic cavity with a
+single localized absorber embedded in it. Our approach extends beyond the
+weak-coupling limit of the cavity with the channels and applies for any
+absorption strength. Importantly it provides an insight for the optimal amount
+of loss needed to realize a chaotic coherent perfect absorbing (CPA) trap. Our
+predictions are tested against simulations for two types of traps: a complex
+network of resonators and quantum graphs.
+"
+26880,Relations Between the Sizes of Galaxies and their Dark Matter Halos at Redshifts $0 < z < 3$,"  We derive relations between the effective radii $R_{\rm{eff}}$ of galaxies
+and the virial radii $R_{200c}$ of their dark matter halos over the redshift
+range $0 < z < 3$. For galaxies, we use the measured sizes from deep images
+taken with \emph{Hubble Space Telescope} for the Cosmic Assembly Near-infrared
+Deep Extragalactic Legacy Survey; for halos, we use the inferred sizes from
+abundance matching to cosmological dark matter simulations via a stellar
+mass--halo mass (SMHM) relation. For this purpose, we derive a new SMHM
+relation based on the same selection criteria and other assumptions as for our
+sample of galaxies with size measurements. As a check on the robustness of our
+results, we also derive $R_{\rm{eff}}$--$R_{200c}$ relations for three
+independent SMHM relations from the literature. We find that galaxy
+$R_{\rm{eff}}$ is proportional on average to halo $R_{200c}$, confirming and
+extending to high redshifts the $z=0$ results of Kravtsov. Late-type galaxies
+(with low Sérsic index and high specific star formation rate [sSFR]) follow a
+linear $R_{\rm{eff}}$--$R_{200c}$ relation, with effective radii at $0.5 < z <
+3$ close to those predicted by simple models of disk formation; at $z < 0.5$,
+the sizes of late-type galaxies appear to be slightly below this prediction.
+Early-type galaxies (with high Sérsic index and low sSFR) follow a roughly
+parallel $R_{\rm{eff}}$--$R_{200c}$ relation, $\sim$ 0.2--0.3 dex below the one
+for late-type galaxies. Our observational results, reinforced by recent
+hydrodynamical simulations, indicate that galaxies grow quasi-homologously with
+their dark matter halos.
+"
+26881,Explicit Solution and Fine Asymptotics for a Critical Growth-Fragmentation Equation,"  We give here an explicit formula for the following critical case of the
+growth-fragmentation equation $$\frac{\partial}{\partial t} u(t, x) +
+\frac{\partial}{\partial x} (gxu(t, x)) + bu(t, x) = b\alpha^2 u(t, \alpha x),
+\qquad u(0, x) = u\_0 (x),$$ for some constants $g > 0$, $b > 0$ and $\alpha >
+1$ - the case $\alpha = 2$ being the emblematic binary fission case. We discuss
+the links between this formula and the asymptotic ones previously obtained in
+(Doumic, Escobedo, Kin. Rel. Mod., 2016), and use them to clarify how
+periodicity may appear asymptotically.
+"
+26882,Scaled Rate Optimization for Beta-Binomial Models,"  Rates of binomial processes are modeled using beta-binomial distributions
+(for example, from Beta Regression). We treat the offline optimization scenario
+and then the online one, where we optimize the exploration-exploitation
+problem. The rates given by two processes are compared through their
+distributions, but we would like to optimize the net payout (given a constant
+value per successful event, unique for each of the processes). The result is an
+analytically-closed, probabilistic, hypergeometric expression for comparing the
+payout distributions of two processes. To conclude, we contrast this Bayesian
+result with an alternative frequentist approach and find 4.5 orders of
+magnitude improvement in performance, for a numerical accuracy level of 0.01%.
+"
+26883,More on the density zero ideal,"  The main result of this paper is an improvement of the upper bound on the
+cardinal invariant ${\mathord{\mathrm{cov}}}^{\ast}({\mathcal{Z}}_{0})$ that
+was discovered by Raghavan and Shelah in an earlier paper. Here
+${\mathcal{Z}}_{0}$ is the ideal of subsets of the set of natural numbers that
+have asymptotic density zero. This improved upper bound is also dualized to get
+a better lower bound on the cardinal
+${\mathord{\mathrm{non}}}^{\ast}({\mathcal{Z}}_{0})$. En route some variations
+on the splitting number are introduced and several relationships between these
+variants are proved.
+"
+26884,Monitoring and manipulating Higgs and Goldstone modes in a supersolid quantum gas,"  Access to collective excitations lies at the heart of our understanding of
+quantum many-body systems. We study the Higgs and Goldstone modes in a
+supersolid quantum gas that is created by coupling a Bose-Einstein condensate
+symmetrically to two optical cavities. The cavity fields form a U(1)-symmetric
+order parameter that can be modulated and monitored along both quadratures in
+real time. This enables us to measure the excitation energies across the
+superfluid-supersolid phase transition, establish their amplitude and phase
+nature, as well as characterize their dynamics from an impulse response.
+Furthermore, we can give a tunable mass to the Goldstone mode at the crossover
+between continuous and discrete symmetry by changing the coupling of the
+quantum gas with either cavity.
+"
+26885,A Faster Method to Estimate Closeness Centrality Ranking,"  Closeness centrality is one way of measuring how central a node is in the
+given network. The closeness centrality measure assigns a centrality value to
+each node based on its accessibility to the whole network. In real life
+applications, we are mainly interested in ranking nodes based on their
+centrality values. The classical method to compute the rank of a node first
+computes the closeness centrality of all nodes and then compares them to get
+its rank. Its time complexity is $O(n \cdot m + n)$, where $n$ represents total
+number of nodes, and $m$ represents total number of edges in the network. In
+the present work, we propose a heuristic method to fast estimate the closeness
+rank of a node in $O(\alpha \cdot m)$ time complexity, where $\alpha = 3$. We
+also propose an extended improved method using uniform sampling technique. This
+method better estimates the rank and it has the time complexity $O(\alpha \cdot
+m)$, where $\alpha \approx 10-100$. This is an excellent improvement over the
+classical centrality ranking method. The efficiency of the proposed methods is
+verified on real world scale-free social networks using absolute and weighted
+error functions.
+"
+26886,Iterative Closest Labeled Point for Tactile Object Shape Recognition,"  Tactile data and kinesthetic cues are two important sensing sources in robot
+object recognition and are complementary to each other. In this paper, we
+propose a novel algorithm named Iterative Closest Labeled Point (iCLAP) to
+recognize objects using both tactile and kinesthetic information.The iCLAP
+first assigns different local tactile features with distinct label numbers. The
+label numbers of the tactile features together with their associated 3D
+positions form a 4D point cloud of the object. In this manner, the two sensing
+modalities are merged to form a synthesized perception of the touched object.
+To recognize an object, the partial 4D point cloud obtained from a number of
+touches iteratively matches with all the reference cloud models to identify the
+best fit. An extensive evaluation study with 20 real objects shows that our
+proposed iCLAP approach outperforms those using either of the separate sensing
+modalities, with a substantial recognition rate improvement of up to 18%.
+"
+26887,Irreducible $φ$-Verma modules for hyperelliptic Heisenberg algebras,"  We defined the hyperelliptic Heisenberg algebra as the Heisenberg subalgebra
+of a hyperlliptic Krichever-Novikov algebra. Then we gave a explicit
+irreducibility criteria for $\varphi$-Verma modules for these algebras.
+"
+26888,A note on 0-bipolar knots of concordance order two,"  Let $\mathcal{T}$ be the group of smooth concordance classes of topologically
+slice knots, and $\{0\}\subset\cdots\subset
+\mathcal{T}_{n+1}\subset\mathcal{T}_{n}\subset \cdots\subset
+\mathcal{T}_{0}\subset \mathcal{T}$ be the bipolar filtration. In this paper,
+we show that a proper collection of the knots employed by Hedden, Kim, and
+Livingston to prove $\mathbb{Z}_2^{\infty} < \mathcal{T}$ can be used to see
+$\mathbb{Z}_2^{\infty} < \mathcal{T}_0/\mathcal{T}_1$.
+"
+26889,A Formal Characterization of the Local Search Topology of the Gap Heuristic,"  The pancake puzzle is a classic optimization problem that has become a
+standard benchmark for heuristic search algorithms. In this paper, we provide
+full proofs regarding the local search topology of the gap heuristic for the
+pancake puzzle. First, we show that in any non-goal state in which there is no
+move that will decrease the number of gaps, there is a move that will keep the
+number of gaps constant. We then classify any state in which the number of gaps
+cannot be decreased in a single action into two groups: those requiring 2
+actions to decrease the number of gaps, and those which require 3 actions to
+decrease the number of gaps.
+"
+26890,What can spin glass theory and analogies tell us about ferroic glasses?,"  As well as several different kinds of periodically ordered ferroic phases,
+there are now recognized several different examples of ferroic glassiness,
+although not always described as such and in material fields of study that have
+mostly been developed separately. In this chapter an attempt is made to
+indicate common conceptual origins and features, observed or anticipated.
+Throughout, this aim is pursued through the use of simple models, in an attempt
+to determine probable fundamental origins within a larger picture of greater
+complication, and analogies between systems in different areas, both
+experimental and theoretical, in the light of significant progress in spin
+glass understanding.
+"
+26891,The Relationship Between Emotion Models and Artificial Intelligence,"  Emotions play a central role in most forms of natural human interaction so we
+may expect that computational methods for the processing and expression of
+emotions will play a growing role in human-computer interaction. The OCC model
+has established itself as the standard model for emotion synthesis. A large
+number of studies employed the OCC model to generate emotions for their
+embodied characters. Many developers of such characters believe that the OCC
+model will be all they ever need to equip their character with emotions. This
+study reflects on the limitations of the OCC model specifically, and on the
+emotion models in general due to their dependency on artificial intelligence.
+"
+26892,Quantized Memory-Augmented Neural Networks,"  Memory-augmented neural networks (MANNs) refer to a class of neural network
+models equipped with external memory (such as neural Turing machines and memory
+networks). These neural networks outperform conventional recurrent neural
+networks (RNNs) in terms of learning long-term dependency, allowing them to
+solve intriguing AI tasks that would otherwise be hard to address. This paper
+concerns the problem of quantizing MANNs. Quantization is known to be effective
+when we deploy deep models on embedded systems with limited resources.
+Furthermore, quantization can substantially reduce the energy consumption of
+the inference procedure. These benefits justify recent developments of
+quantized multi layer perceptrons, convolutional networks, and RNNs. However,
+no prior work has reported the successful quantization of MANNs. The in-depth
+analysis presented here reveals various challenges that do not appear in the
+quantization of the other networks. Without addressing them properly, quantized
+MANNs would normally suffer from excessive quantization error which leads to
+degraded performance. In this paper, we identify memory addressing
+(specifically, content-based addressing) as the main reason for the performance
+degradation and propose a robust quantization method for MANNs to address the
+challenge. In our experiments, we achieved a computation-energy gain of 22x
+with 8-bit fixed-point and binary quantization compared to the floating-point
+implementation. Measured on the bAbI dataset, the resulting model, named the
+quantized MANN (Q-MANN), improved the error rate by 46% and 30% with 8-bit
+fixed-point and binary quantization, respectively, compared to the MANN
+quantized using conventional techniques.
+"
+26893,GEM Foil Quality Assurance For The ALICE TPC Upgrade,"  The ALICE (A Large Ion Collider Experiment) experiment at the Large Hadron
+Collider (LHC) at CERN is dedicated to heavy ion physics to explore the
+structure of strongly interacting matter. The Time Projection Chamber (TPC) of
+ALICE is a tracking detector located in the central region of the experiment.
+It offers excellent tracking capabilities as well as particle identification.
+After the second long shutdown (LS2) the LHC will run at substantially higher
+luminosities. To be able to increase the data acquisition rate by a factor of
+100, the ALICE TPC experiment has to replace the Multi-Wire Proportional
+Chamber (MWPC) -based readout chambers. The MWPC are operated with gating grid
+that limits the rate to O(kHz). The new ReadOut Chamber (ROC) design is based
+on Gas Electron Multiplier (GEM) technology operating in continuous mode. The
+current GEM productions scheme foresees the production of more than 800 GEM
+foils of different types. To fulfill the requirements on the performance of the
+GEM TPC readout, necessitates thorough Quality Assurance (QA) measures. The QA
+scheme, developed by the ALICE collaboration, will be presented in detail.
+"
+26894,Vietoris topology on hyperspaces associated to a noncommutative compact space,"  We study some topological spaces that can be considered as hyperspaces
+associated to noncommutative spaces. More precisely, for a NC compact space
+associated to a unital C*-algebra, we consider the set of closed projections of
+the second dual of the C*-algebra as the hyperspace of closed subsets of the NC
+space. We endow this hyperspace with an analog of Vietoris topology. In the
+case that the NC space has a quantum metric space structure in the sense of
+Rieffel we study the analogs of Hausdorff and infimum distances on the
+hyperspace. We also formulate some interesting problems about distances between
+sub-circles of a quantum torus.
+"
+26895,The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement,"  This paper describes a new evolutionary algorithm that is especially well
+suited to AI-Assisted Game Design. The approach adopted in this paper is to use
+observations of AI agents playing the game to estimate the game's quality. Some
+of best agents for this purpose are General Video Game AI agents, since they
+can be deployed directly on a new game without game-specific tuning; these
+agents tend to be based on stochastic algorithms which give robust but noisy
+results and tend to be expensive to run. This motivates the main contribution
+of the paper: the development of the novel N-Tuple Bandit Evolutionary
+Algorithm, where a model is used to estimate the fitness of unsampled points
+and a bandit approach is used to balance exploration and exploitation of the
+search space. Initial results on optimising a Space Battle game variant suggest
+that the algorithm offers far more robust results than the Random Mutation Hill
+Climber and a Biased Mutation variant, which are themselves known to offer
+competitive performance across a range of problems. Subjective observations are
+also given by human players on the nature of the evolved games, which indicate
+a preference towards games generated by the N-Tuple algorithm.
+"
+26896,Driving Policy Transfer via Modularity and Abstraction,"  End-to-end approaches to autonomous driving have high sample complexity and
+are difficult to scale to realistic urban driving. Simulation can help
+end-to-end driving systems by providing a cheap, safe, and diverse training
+environment. Yet training driving policies in simulation brings up the problem
+of transferring such policies to the real world. We present an approach to
+transferring driving policies from simulation to reality via modularity and
+abstraction. Our approach is inspired by classic driving systems and aims to
+combine the benefits of modular architectures and end-to-end deep learning
+approaches. The key idea is to encapsulate the driving policy such that it is
+not directly exposed to raw perceptual input or low-level vehicle dynamics. We
+evaluate the presented approach in simulated urban environments and in the real
+world. In particular, we transfer a driving policy trained in simulation to a
+1/5-scale robotic truck that is deployed in a variety of conditions, with no
+finetuning, on two continents. The supplementary video can be viewed at
+this https URL
+"
+26897,Electromagnetic Scattering Laws in Weyl Systems,"  Wavelength determines the length scale of the cross section when
+electromagnetic waves are scattered by an electrically small object. The cross
+section diverges for resonant scattering, and diminishes for non-resonant
+scattering, when wavelength approaches infinity. This scattering law explains
+the color of the sky as well as the strength of a mobile phone signal. We show
+that such wavelength scaling comes from free space's conical dispersion at zero
+frequency. Emerging Weyl systems, offering similar dispersion at non-zero
+frequencies, lead to new laws of electromagnetic scattering that allow cross
+sections to be decoupled from the wavelength limit. Diverging and diminishing
+cross sections can be realized at any target wavelength in a Weyl system,
+providing unprecedented ability to tailor the strength of wave-matter
+interactions for radio-frequency and optical applications.
+"
+26898,Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction,"  Small unmanned aircraft can help firefighters combat wildfires by providing
+real-time surveillance of the growing fires. However, guiding the aircraft
+autonomously given only wildfire images is a challenging problem. This work
+models noisy images obtained from on-board cameras and proposes two approaches
+to filtering the wildfire images. The first approach uses a simple Kalman
+filter to reduce noise and update a belief map in observed areas. The second
+approach uses a particle filter to predict wildfire growth and uses
+observations to estimate uncertainties relating to wildfire expansion. The
+belief maps are used to train a deep reinforcement learning controller, which
+learns a policy to navigate the aircraft to survey the wildfire while avoiding
+flight directly over the fire. Simulation results show that the proposed
+controllers precisely guide the aircraft and accurately estimate wildfire
+growth, and a study of observation noise demonstrates the robustness of the
+particle filter approach.
+"
+26899,Local compactness in right bounded asymmetric normed spaces,"  We characterize the finite dimensional asymmetric normed spaces which are
+right bounded and the relation of this property with the natural compactness
+properties of the unit ball, as compactness and strong compactness. In contrast
+with some results found in the existing literature, we show that not all right
+bounded asymmetric norms have compact closed balls. We also prove that there
+are finite dimensional asymmetric normed spaces that satisfy that the closed
+unit ball is compact, but not strongly compact, closing in this way an open
+question on the topology of finite dimensional asymmetric normed spaces. In the
+positive direction, we will prove that a finite dimensional asymmetric normed
+space is strongly locally compact if and only if it is right bounded.
+"
+26900,Affine spheres with prescribed Blaschke metric,"  It is proved that the equality $\Delta\ln|\kappa-\lambda|=6\kappa$, where
+$\kappa$ is the Gaussian curvature of a metric tensor g on a 2-dimensional
+manifold is a sufficient and necessary condition for local realizability of the
+metric as the Blaschke metric of some affine sphere.
+"
+26901,A Herschel/PACS Far Infrared Line Emission Survey of Local Luminous Infrared Galaxies,"  We present an analysis of [OI]63, [OIII]88, [NII]122 and [CII]158
+far-infrared (FIR) fine-structure line observations obtained with
+Herschel/PACS, for ~240 local luminous infrared galaxies (LIRGs) in the Great
+Observatories All-sky LIRG Survey (GOALS). We find pronounced declines
+-deficits- of line-to-FIR-continuum emission for [NII]122, [OI]63 and [CII]158
+as a function of FIR color and infrared luminosity surface density,
+$\Sigma_{\rm IR}$. The median electron density of the ionized gas in LIRGs,
+based on the [NII]122/[NII]205 ratio, is $n_{\rm e}$ = 41 cm$^{-3}$. We find
+that the dispersion in the [CII]158 deficit of LIRGs is attributed to a varying
+fractional contribution of photo-dissociation-regions (PDRs) to the observed
+[CII]158 emission, f([CII]PDR) = [CII]PDR/[CII], which increases from ~60% to
+~95% in the warmest LIRGs. The [OI]63/[CII]158PDR ratio is tightly correlated
+with the PDR gas kinetic temperature in sources where [OI]63 is not
+optically-thick or self-absorbed. For each galaxy, we derive the average PDR
+hydrogen density, $n_{\rm H}$, and intensity of the interstellar radiation
+field, in units of G$_0$, and find G$_0$/$n_{\rm H}$ ratios ~0.1-50 cm$^3$,
+with ULIRGs populating the upper end of the distribution. There is a relation
+between G$_0$/$n_{\rm H}$ and $\Sigma_{\rm IR}$, showing a critical break at
+$\Sigma_{\rm IR}^{\star}$ ~ 5 x 10$^{10}$ Lsun/kpc$^2$. Below $\Sigma_{\rm
+IR}^{\star}$, G$_0$/$n_{\rm H}$ remains constant, ~0.32 cm$^3$, and variations
+in $\Sigma_{\rm IR}$ are driven by the number density of star-forming regions
+within a galaxy, with no change in their PDR properties. Above $\Sigma_{\rm
+IR}^{\star}$, G$_0$/$n_{\rm H}$ increases rapidly with $\Sigma_{\rm IR}$,
+signaling a departure from the typical PDR conditions found in normal
+star-forming galaxies towards more intense/harder radiation fields and compact
+geometries typical of starbursting sources.
+"
+26902,Resonance oscillation of a damped driven simple pendulum,"  The resonance characteristics of a driven damped harmonic oscillator are well
+known. Unlike harmonic oscillators which are guided by parabolic potentials, a
+simple pendulum oscillates under sinusoidal potentials. The problem of an
+undamped pendulum has been investigated to a great extent. However, the
+resonance characteristics of a driven damped pendulum have not been re- ported
+so far due to the difficulty in solving the problem analytically. In the
+present work we report the resonance characteristics of a driven damped
+pendulum calculated numerically. The results are compared with the resonance
+characteristics of a damped driven harmonic oscillator. The work can be of
+pedagogic interest too as it reveals the richness of driven damped motion of a
+simple pendulum in comparison to and how strikingly it differs from the motion
+of a driven damped harmonic oscillator. We confine our work only to the
+nonchaotic regime of pendulum motion.
+"
+26903,Asymptotic Bias of Stochastic Gradient Search,"  The asymptotic behavior of the stochastic gradient algorithm with a biased
+gradient estimator is analyzed. Relying on arguments based on the dynamic
+system theory (chain-recurrence) and the differential geometry (Yomdin theorem
+and Lojasiewicz inequality), tight bounds on the asymptotic bias of the
+iterates generated by such an algorithm are derived. The obtained results hold
+under mild conditions and cover a broad class of high-dimensional nonlinear
+algorithms. Using these results, the asymptotic properties of the
+policy-gradient (reinforcement) learning and adaptive population Monte Carlo
+sampling are studied. Relying on the same results, the asymptotic behavior of
+the recursive maximum split-likelihood estimation in hidden Markov models is
+analyzed, too.
+"
+26904,"Type, cotype and twisted sums induced by complex interpolation","  This paper deals with extensions or twisted sums of Banach spaces that come
+induced by complex interpolation and the relation between the type and cotype
+of the spaces in the interpolation scale and the nontriviality and singularity
+of the induced extension. The results are presented in the context of
+interpolation of families of Banach spaces, and are applied to the study of
+submodules of Schatten classes. We also obtain nontrivial extensions of spaces
+without the CAP which also fail the CAP.
+"
+26905,struc2vec: Learning Node Representations from Structural Identity,"  Structural identity is a concept of symmetry in which network nodes are
+identified according to the network structure and their relationship to other
+nodes. Structural identity has been studied in theory and practice over the
+past decades, but only recently has it been addressed with representational
+learning techniques. This work presents struc2vec, a novel and flexible
+framework for learning latent representations for the structural identity of
+nodes. struc2vec uses a hierarchy to measure node similarity at different
+scales, and constructs a multilayer graph to encode structural similarities and
+generate structural context for nodes. Numerical experiments indicate that
+state-of-the-art techniques for learning node representations fail in capturing
+stronger notions of structural identity, while struc2vec exhibits much superior
+performance in this task, as it overcomes limitations of prior approaches. As a
+consequence, numerical experiments indicate that struc2vec improves performance
+on classification tasks that depend more on structural identity.
+"
+26906,Cocycle deformations and Galois objects of semisimple Hopf algebras of dimension $16$,"  In this paper, we determine the cocycle deformations and Galois objects for
+non-commutative and non-cocommutative Hopf algebras of dimension $16$. We show
+that these Hopf algebras are pairwise twist inequivalent by calculating the
+higher Frobenius-Schur indicators, and that except three Hopf algebras which
+are cocycle deformations of dual group algebras, none of these Hopf algebras
+admit non-trivial cocycle deformations, though all of them have more than one
+Galois objects.
+"
+26907,Optical Feshbach resonances and ground state molecule production in the RbHg system,"  We present the prospects for photoassociation, optical control of
+interspecies scattering lengths and finally, the production of ultracold
+absolute ground state molecules in the Rb+Hg system. We use the ""gold standard""
+ab initio methods for the calculations of ground (CCSD(T)) and excited state
+(EOM-CCSD) potential curves. The RbHg system, thanks to the wide range of
+stable Hg bosonic isotopes, offers possibilities for mass-tuning of ground
+state interactions. The optical lengths describing the strengths of optical
+Feshbach resonances near the Rb transitions are favorable even at large laser
+detunings. Ground state RbHg molecules can be produced with efficiencies
+ranging from about 20% for deeply bound to at least 50% for weakly bound states
+close to the dissociation limit. Finally, electronic transitions with favorable
+Franck-Condon factors can be found for the purposes of a STIRAP transfer of the
+weakly bound RbHg molecules to the absolute ground state using commercially
+available lasers.
+"
+26908,The Short Term Stability of a Simulated Differential Astrometric Reference Frame in the Gaia era,"  We use methods of differential astrometry to construct a small field inertial
+reference frame stable at the micro-arcsecond level. Such a high level of
+astrometric precision can be expected with the end-of-mission standard errors
+to be achieved with the Gaia space satellite using global astrometry. We
+harness Gaia measurements of field angles and look at the influence of the
+number of reference stars and the star's magnitude as well as astrometric
+systematics on the total error budget with the help of Gaia-like simulations
+around the Ecliptic Pole in a differential astrometric scenario. We find that
+the systematic errors are modeled and reliably estimated to the $\mu$as level
+even in fields with a modest number of 37 stars with G $<$13 mag over a 0.24
+sq.degs. field of view for short time scales of the order of a day with
+high-cadence observations such as those around the North Ecliptic Pole during
+the EPSL scanning mode of Gaia for a perfect instrument. The inclusion of the
+geometric instrument model over such short time scales accounting for
+large-scale calibrations requires fainter stars down to G = 14 mag without
+diminishing the accuracy of the reference frame. We discuss several future
+perspectives of utilizing this methodology over different and longer
+timescales.
+"
+26909,Asymptotically Locally Optimal Weight Vector Design for a Tighter Correlation Lower Bound of Quasi-Complementary Sequence Sets,"  A quasi-complementary sequence set (QCSS) refers to a set of two-dimensional
+matrices with low non-trivial aperiodic auto- and cross- correlation sums. For
+multicarrier code-division multiple-access applications, the availability of
+large QCSSs with low correlation sums is desirable. The generalized Levenshtein
+bound (GLB) is a lower bound on the maximum aperiodic correlation sum of QCSSs.
+The bounding expression of GLB is a fractional quadratic function of a weight
+vector $\mathbf{w}$ and is expressed in terms of three additional parameters
+associated with QCSS: the set size $K$, the number of channels $M$, and the
+sequence length $N$. It is known that a tighter GLB (compared to the Welch
+bound) is possible only if the condition $M\geq2$ and $K\geq \overline{K}+1$,
+where $\overline{K}$ is a certain function of $M$ and $N$, is satisfied. A
+challenging research problem is to determine if there exists a weight vector
+which gives rise to a tighter GLB for \textit{all} (not just \textit{some})
+$K\geq \overline{K}+1$ and $M\geq2$, especially for large $N$, i.e., the
+condition is {asymptotically} both necessary and sufficient. To achieve this,
+we \textit{analytically} optimize the GLB which is (in general) non-convex as
+the numerator term is an indefinite quadratic function of the weight vector.
+Our key idea is to apply the frequency domain decomposition of the circulant
+matrix (in the numerator term) to convert the non-convex problem into a convex
+one. Following this optimization approach, we derive a new weight vector
+meeting the aforementioned objective and prove that it is a local minimizer of
+the GLB under certain conditions.
+"
+26910,Dynamical compensation in biological systems as a particular case of structural non-identifiability,"  Dynamical compensation (DC) has been recently defined as the ability of a
+biological system to keep its output dynamics unchanged in the face of varying
+parameters. This concept is purported to describe a design principle that
+provides robustness to physiological circuits. Here we note the similitude
+between DC and Structural Identifiability (SI), and we argue that the former
+can be explained in terms of (lack of) the latter. We propose to exploit this
+fact by using currently existing tools for SI analysis to perform DC analysis.
+We demonstrate the feasibility of this approach with four physiological
+circuits, for which we confirm the correspondence between DC and lack of SI. We
+also warn that care should we taken when using an unidentifiable model to
+extract biological insight, since lack of SI can be the result of an
+inappropriate choice of model structure and therefore not necessarily a sign of
+biological robustness.
+"
+26911,Intelligent Fault Analysis in Electrical Power Grids,"  Power grids are one of the most important components of infrastructure in
+today's world. Every nation is dependent on the security and stability of its
+own power grid to provide electricity to the households and industries. A
+malfunction of even a small part of a power grid can cause loss of
+productivity, revenue and in some cases even life. Thus, it is imperative to
+design a system which can detect the health of the power grid and take
+protective measures accordingly even before a serious anomaly takes place. To
+achieve this objective, we have set out to create an artificially intelligent
+system which can analyze the grid information at any given time and determine
+the health of the grid through the usage of sophisticated formal models and
+novel machine learning techniques like recurrent neural networks. Our system
+simulates grid conditions including stimuli like faults, generator output
+fluctuations, load fluctuations using Siemens PSS/E software and this data is
+trained using various classifiers like SVM, LSTM and subsequently tested. The
+results are excellent with our methods giving very high accuracy for the data.
+This model can easily be scaled to handle larger and more complex grid
+architectures.
+"
+26912,KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis,"  In 2014, more than 10 million people in the US were affected by an ambulatory
+disability. Thus, gait rehabilitation is a crucial part of health care systems.
+The quantification of human locomotion enables clinicians to describe and
+analyze a patient's gait performance in detail and allows them to base clinical
+decisions on objective data. These assessments generate a vast amount of
+complex data which need to be interpreted in a short time period. We conducted
+a design study in cooperation with gait analysis experts to develop a novel
+Knowledge-Assisted Visual Analytics solution for clinical Gait analysis
+(KAVAGait). KAVAGait allows the clinician to store and inspect complex data
+derived during clinical gait analysis. The system incorporates innovative and
+interactive visual interface concepts, which were developed based on the needs
+of clinicians. Additionally, an explicit knowledge store (EKS) allows
+externalization and storage of implicit knowledge from clinicians. It makes
+this information available for others, supporting the process of data
+inspection and clinical decision making. We validated our system by conducting
+expert reviews, a user study, and a case study. Results suggest that KAVAGait
+is able to support a clinician during clinical practice by visualizing complex
+gait data and providing knowledge of other clinicians.
+"
+26913,NestedNet: Learning Nested Sparse Structures in Deep Neural Networks,"  Recently, there have been increasing demands to construct compact deep
+architectures to remove unnecessary redundancy and to improve the inference
+speed. While many recent works focus on reducing the redundancy by eliminating
+unneeded weight parameters, it is not possible to apply a single deep
+architecture for multiple devices with different resources. When a new device
+or circumstantial condition requires a new deep architecture, it is necessary
+to construct and train a new network from scratch. In this work, we propose a
+novel deep learning framework, called a nested sparse network, which exploits
+an n-in-1-type nested structure in a neural network. A nested sparse network
+consists of multiple levels of networks with a different sparsity ratio
+associated with each level, and higher level networks share parameters with
+lower level networks to enable stable nested learning. The proposed framework
+realizes a resource-aware versatile architecture as the same network can meet
+diverse resource requirements. Moreover, the proposed nested network can learn
+different forms of knowledge in its internal networks at different levels,
+enabling multiple tasks using a single network, such as coarse-to-fine
+hierarchical classification. In order to train the proposed nested sparse
+network, we propose efficient weight connection learning and channel and layer
+scheduling strategies. We evaluate our network in multiple tasks, including
+adaptive deep compression, knowledge distillation, and learning class
+hierarchy, and demonstrate that nested sparse networks perform competitively,
+but more efficiently, compared to existing methods.
+"
+26914,Embedding arithmetic hyperbolic manifolds,"  We prove that any arithmetic hyperbolic $n$-manifold of simplest type can
+either be geodesically embedded into an arithmetic hyperbolic $(n+1)$-manifold
+or its universal $\mathrm{mod}~2$ Abelian cover can.
+"
+26915,Quantifying Age and Model Uncertainties in Paleoclimate Data and Dynamical Climate Models with a Joint Inferential Analysis,"  A major goal in paleoclimate science is to reconstruct historical climates
+using proxies for climate variables such as those observed in sediment cores,
+and in the process learn about climate dynamics. This is hampered by
+uncertainties in how sediment core depths relate to ages, how proxy quantities
+relate to climate variables, how climate models are specified, and the values
+of parameters in climate models. Quantifying these uncertainties is key in
+drawing well founded conclusions. Analyses are often performed in separate
+stages with, for example, a sediment core's depth-age relation being estimated
+as stage one, then fed as an input to calibrate climate models as stage two.
+Here, we show that such ""multi-stage"" approaches can lead to misleading
+conclusions. We develop a joint inferential approach for climate
+reconstruction, model calibration, and age model estimation. We focus on the
+glacial-interglacial cycle over the past 780 kyr, analysing two sediment cores
+that span this range. Our age estimates are largely in agreement with previous
+studies, but provides the full joint specification of all uncertainties,
+estimation of model parameters, and the model evidence. By sampling plausible
+chronologies from the posterior distribution, we demonstrate that downstream
+scientific conclusions can differ greatly both between different sampled
+chronologies, and in comparison with conclusions obtained in the complete joint
+inferential analysis. We conclude that multi-stage analyses are insufficient
+when dealing with uncertainty, and that to draw sound conclusions the full
+joint inferential analysis must be performed.
+"
+26916,A New Outlook on Cofiniteness,"  Let $\mathfrak{a}$ be an ideal of a commutative noetherian (not necessarily
+local) ring $R$. In the case $\cd(\mathfrak{a},R)\leq 1$, we show that the
+subcategory of $\mathfrak{a}$-cofinite $R$-modules is abelian. Using this and
+the technique of way-out functors, we show that if $\cd(\mathfrak{a},R)\leq 1$,
+or $\dim(R/\mathfrak{a}) \leq 1$, or $\dim(R) \leq 2$, then the local
+cohomology module $H^{i}_{\mathfrak{a}}(X)$ is $\mathfrak{a}$-cofinite for
+every $R$-complex $X$ with finitely generated homology modules and every $i \in
+\mathbb{Z}$. We further answer Question 1.3 in the three aforementioned cases,
+and reveal a correlation between Questions 1.1, 1.2, and 1.3.
+"
+26917,Wishart exponential families on cones related to An graphs,"  Let G = An be the graph corresponding to the graphical model of nearest
+neighbour interaction in a Gaussian character. We study Natural Exponential
+Families( NEF) ofWishart distributions on convex cones QG and PG, where PG is
+the cone of positive definite real symmetric matrices with obligatory zeros
+prescribed by G, and QG is the dual cone of PG. The Wishart NEF that we
+construct include Wishart distributions considered earlier by Lauritzen (1996)
+and Letac and Massam (2007) for models based on decomposable graphs. Our
+approach is however different and allows us to study the basic objects
+ofWishart NEF on the cones QG and PG.We determine Riesz measures generating
+Wishart exponential families on QG and PG, and we give the quadratic
+construction of these Riesz measures and exponential families. The mean,
+inverse-mean, covariance and variance functions, as well as moments of higher
+order are studied and their explicit formulas are given.
+"
+26918,A New Method for Performance Analysis in Nonlinear Dimensionality Reduction,"  In this paper, we develop a local rank correlation measure which quantifies
+the performance of dimension reduction methods. The local rank correlation is
+easily interpretable, and robust against the extreme skewness of nearest
+neighbor distributions in high dimensions. Some benchmark datasets are studied.
+We find that the local rank correlation closely corresponds to our visual
+interpretation of the quality of the output. In addition, we demonstrate that
+the local rank correlation is useful in estimating the intrinsic dimensionality
+of the original data, and in selecting a suitable value of tuning parameters
+used in some algorithms.
+"
+26919,Genetic Algorithms for Evolving Deep Neural Networks,"  In recent years, deep learning methods applying unsupervised learning to
+train deep layers of neural networks have achieved remarkable results in
+numerous fields. In the past, many genetic algorithms based methods have been
+successfully applied to training neural networks. In this paper, we extend
+previous work and propose a GA-assisted method for deep learning. Our
+experimental results indicate that this GA-assisted approach improves the
+performance of a deep autoencoder, producing a sparser neural network.
+"
+26920,Unconventional Superconductivity,"  Conventional superconductivity, as used in this review, refers to
+electron-phonon coupled superconducting electron-pairs described by BCS theory.
+Unconventional superconductivity refers to superconductors where the Cooper
+pairs are not bound together by phonon exchange but instead by exchange of some
+other kind, e. g. spin fluctuations in a superconductor with magnetic order
+either coexistent or nearby in the phase diagram. Such unconventional
+superconductivity has been known experimentally since heavy fermion CeCu2Si2,
+with its strongly correlated 4f electrons, was discovered to superconduct below
+0.6 K in 1979. Since the discovery of unconventional superconductivity in the
+layered cuprates in 1986, the study of these materials saw Tc jump to 164 K by
+1994. Further progress in high temperature superconductivity would be aided by
+understanding the cause of such unconventional pairing. This review compares
+the fundamental properties of 9 unconventional superconducting classes of
+materials - from 4f-electron heavy fermions to organic superconductors to
+classes where only three known members exist to the cuprates with over 200
+examples, with the hope that common features will emerge to help theory explain
+(and predict!) these phenomena. In addition, three new emerging classes of
+superconductors (topological, interfacial [e. g. FeSe on SrTiO3], and H2S under
+high pressure) are briefly covered, even though their conventionality is not
+yet fully determined.
+"
+26921,A Machine Learning Approach for Evaluating Creative Artifacts,"  Much work has been done in understanding human creativity and defining
+measures to evaluate creativity. This is necessary mainly for the reason of
+having an objective and automatic way of quantifying creative artifacts. In
+this work, we propose a regression-based learning framework which takes into
+account quantitatively the essential criteria for creativity like novelty,
+influence, value and unexpectedness. As it is often the case with most creative
+domains, there is no clear ground truth available for creativity. Our proposed
+learning framework is applicable to all creative domains; yet we evaluate it on
+a dataset of movies created from IMDb and Rotten Tomatoes due to availability
+of audience and critic scores, which can be used as proxy ground truth labels
+for creativity. We report promising results and observations from our
+experiments in the following ways : 1) Correlation of creative criteria with
+critic scores, 2) Improvement in movie rating prediction with inclusion of
+various creative criteria, and 3) Identification of creative movies.
+"
+26922,Moduli spaces of vector bundles with fixed determinant over a real curve,"  Let $(\Sigma,\tau)$ denote a Riemann surface of genus $g \geq 2$ equipped
+with an anti-holomorphic involution $\tau$. In this paper we study the topology
+of the moduli space $M(r,\xi)^\tau$ of stable Real vector bundles over
+$(\Sigma,\tau)$ of rank $r$ and fixed determinant $\xi$ of degree coprime to
+$r$.
+We prove that $M(r,\xi)^{\tau}$ is an orientable and monotone Lagrangian
+submanifold of the complex moduli space $M(r,\xi)$ so it determines an object
+in the appropriate Fukaya category. We derive recursive formulas for the mod
+$2$ Betti numbers of $M(r,\xi)^\tau$ and compute mod $p$ Betti numbers for odd
+$p$ through a range of degrees. We deduce that if $r$ is even and $ g >>0$,
+then $M(r,\xi)^{\tau}$ and $M(r,\xi')^{\tau}$ have non-isomorphic cohomology
+groups unless $\xi$ and $\xi'$ have equivalent Stieffel-Whitney classes modulo
+automorphisms of $(\Sigma,\tau)$. If $r$ is even, and $g>>0$ is even, we prove
+that the Betti numbers of $M(r,\xi)^{\tau}$ distinguish topological types of
+$(\Sigma, \tau; \xi)$. If $r=2$ and $g$ is odd, we compute all mod $p$ Betti
+numbers of $M(2,\xi)^\tau$.
+MR 32L05, 14P25.
+"
+26923,On Horizontal Recurrent Finsler Connections,"  In this paper we adopt the pullback approach to global Finsler geometry. We
+investigate horizontally recurrent Finsler connections. We prove that for each
+scalar ($\pi$)1-form $A$, there exists a unique horizontally recurrent Finsler
+connection whose $h$-recurrence form is $A$. This result generalizes the
+existence and uniqueness theorem of Cartan connection. We then study some
+properties of a special kind of horizontally recurrent Finsler connection,
+which we call special HRF-connection.
+"
+26924,Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation,"  The goal of counterfactual learning for statistical machine translation (SMT)
+is to optimize a target SMT system from logged data that consist of user
+feedback to translations that were predicted by another, historic SMT system. A
+challenge arises by the fact that risk-averse commercial SMT systems
+deterministically log the most probable translation. The lack of sufficient
+exploration of the SMT output space seemingly contradicts the theoretical
+requirements for counterfactual learning. We show that counterfactual learning
+from deterministic bandit logs is possible nevertheless by smoothing out
+deterministic components in learning. This can be achieved by additive and
+multiplicative control variates that avoid degenerate behavior in empirical
+risk minimization. Our simulation experiments show improvements of up to 2 BLEU
+points by counterfactual learning from deterministic bandit feedback.
+"
+26925,Large Deviations and the Lukic Conjecture,"  We use the large deviation approach to sum rules pioneered by Gamboa, Nagel
+and Rouault to prove higher order sum rules for orthogonal polynomials on the
+unit circle. In particular, we prove one half of a conjectured sum rule of
+Lukic in the case of two singular points, one simple and one double. This is
+important because it is known that the conjecture of Simon fails in exactly
+this case, so this paper provides support for the idea that Lukic's replacement
+for Simon's conjecture might be true.
+"
+26926,Effect of texture randomization on the slip and interfacial robustness in turbulent flows over superhydrophobic surfaces,"  Superhydrophobic surfaces demonstrate promising potential for skin friction
+reduction in naval and hydrodynamic applications. Recent developments of
+superhydrophobic surfaces aiming for scalable applications use random
+distribution of roughness, such as spray coating and etched process. However,
+most of previous analyses of the interaction between flows and superhydrophobic
+surfaces studied periodic geometries that are economically feasible only in
+lab-scale experiments. We conduct direct numerical simulations of turbulent
+flows over randomly patterned interfaces considering a range of texture widths
+$w^+\approx 4-26$, and solid fractions $\phi_s=11\%$ to $25\%$. Slip and
+no-slip boundary conditions are implemented in a pattern, modeling the presence
+of gas-liquid interfaces and solid elements. Our results indicate that slip of
+randomly distributed textures under turbulent flows are about $30\%$ less than
+those of surfaces with aligned features of the same size. In the small texture
+size limit $w^+\approx 4$, the slip length of the randomly distributed textures
+in turbulent flows is well described by a previously introduced Stokes flow
+solution of randomly distributed shear-free holes. By comparing DNS results for
+patterned slip and no-slip boundary against the corresponding homogenized slip
+length boundary conditions, we show that turbulent flows over randomly
+distributed posts can be represented by an isotropic slip length in streamwise
+and spanwise direction. The average pressure fluctuation on gas pocket is
+similar to that of the aligned features with the same texture size and gas
+fraction, but the maximum interface deformation at the leading edge of the
+roughness element is about twice larger when the textures are randomly
+distributed.
+"
+26927,Spatial Coherence of Oriented White Matter Microstructure: Applications to White Matter Regions Associated with Genetic Similarity,"  We present a method to discover differences between populations with respect
+to the spatial coherence of their oriented white matter microstructure in
+arbitrarily shaped white matter regions. This method is applied to diffusion
+MRI scans of a subset of the Human Connectome Project dataset: 57 pairs of
+monozygotic and 52 pairs of dizygotic twins. After controlling for
+morphological similarity between twins, we identify 3.7% of all white matter as
+being associated with genetic similarity (35.1k voxels, $p < 10^{-4}$, false
+discovery rate 1.5%), 75% of which spatially clusters into twenty-two
+contiguous white matter regions. Furthermore, we show that the orientation
+similarity within these regions generalizes to a subset of 47 pairs of non-twin
+siblings, and show that these siblings are on average as similar as dizygotic
+twins. The regions are located in deep white matter including the superior
+longitudinal fasciculus, the optic radiations, the middle cerebellar peduncle,
+the corticospinal tract, and within the anterior temporal lobe, as well as the
+cerebellum, brain stem, and amygdalae.
+These results extend previous work using undirected fractional anisotrophy
+for measuring putative heritable influences in white matter. Our
+multidirectional extension better accounts for crossing fiber connections
+within voxels. This bottom up approach has at its basis a novel measurement of
+coherence within neighboring voxel dyads between subjects, and avoids some of
+the fundamental ambiguities encountered with tractographic approaches to white
+matter analysis that estimate global connectivity.
+"
+26928,Perelman's $λ$-functional on manifolds with conical singularities,"  In this paper, we prove that on a compact manifold with isolated conical
+singularity the spectrum of the Schrödinger operator $-4\Delta+R$ consists of
+discrete eigenvalues with finite multiplicities, if the scalar curvature $R$
+satisfies a certain condition near the singularity. Moreover, we obtain an
+asymptotic behavior for eigenfunctions near the singularity. As a consequence
+of these spectral properties, we extend the theory of the Perelman's
+$\lambda$-functional on smooth compact manifolds to compact manifolds with
+isolated conical singularities.
+"
+26929,Updated 34-Band Photometry for the SINGS/KINGFISH Samples of Nearby Galaxies,"  We present an update to the ultraviolet-to-radio database of global broadband
+photometry for the 79 nearby galaxies that comprise the union of the KINGFISH
+(Key Insights on Nearby Galaxies: A Far-Infrared Survey with Herschel) and
+SINGS (Spitzer Infrared Nearby Galaxies Survey) samples. The 34-band dataset
+presented here includes contributions from observational work carried out with
+a variety of facilities including GALEX, SDSS, PS, NOAO, 2MASS, WISE, Spitzer,
+Herschel, Planck, JCMT, and the VLA. Improvements of note include
+recalibrations of previously-published SINGS BVRcIc and KINGFISH
+far-infrared/submillimeter photometry. Similar to previous results in the
+literature, an excess of submillimeter emission above model predictions is seen
+primarily for low-metallicity dwarf/irregular galaxies. This 34-band
+photometric dataset for the combined KINGFISH$+$SINGS sample serves as an
+important multi-wavelength reference for the variety of galaxies observed at
+low redshift. A thorough analysis of the observed spectral energy distributions
+is carried out in a companion paper.
+"
+26930,Equilibria configurations for epitaxial crystal growth with adatoms,"  The behavior of a surface energy $\mathcal F(E,u)$, where $E$ is a set of
+finite perimeter and $u\in L^1(\partial^* E, \mathbb R_+)$ is studied. These
+energies have been recently considered in the context of materials science to
+derive a new model in crystal growth that takes into account the effect of
+atoms freely diffusing on the surface (called adatoms), which are responsible
+for morphological evolution through an attachment and detachment process.
+Regular critical points, existence and uniqueness of minimizers are discussed
+and the relaxation of $\mathcal F$ in a general setting under the $L^1$
+convergence of sets and the vague convergence of measures is characterized.
+This is part of an ongoing project aimed at an analytical study of diffuse
+interface approximations of the associated evolution equations.
+"
+26931,Complex geodesics in convex domains and $\mathbb C$-convexity of semitube domains,"  In the paper the complex geodesics of a convex domain in $\mathbb C^n$ are
+studied. One of the main results of the paper provides certain necessary
+condition for a holomorphic map to be a complex geodesic for a convex domain in
+$\mathbb C^n$. The established condition is of geometric nature and it allows
+to find a formula for every complex geodesic. The $\mathbb C$-convexity of
+semitube domains is also discussed.
+"
+26932,Thermometry of a deeply degenerate Fermi gas with a Bose-Einstein condensate,"  We measure the temperature of a deeply degenerate Fermi gas, by using a
+weakly interacting sample of heavier bosonic atoms as a probe. This thermometry
+method relies on the thermalization between the two species and on the
+determination of the condensate fraction of the bosons. In our experimental
+implementation, a small sample of 41K atoms serves as the thermometer for a 6Li
+Fermi sea. We investigate the evaporative cooling of a 6Li spin mixture in a
+single-beam optical dipole trap and observe how the condensate fraction of the
+thermometry atoms depends on the final trap depth. From the condensate
+fraction, the temperature can be readily extracted. We show that the lowest
+temperature of 6.3(5)% of the Fermi temperature is obtained, when the
+decreasing trap depth closely approaches the Fermi energy. To understand the
+systematic effects that may in uence the results, we carefully investigate the
+role of the number of bosons and the thermalization dynamics between the two
+species. Our thermometry approach provides a conceptually simple, accurate, and
+general way to measure the temperature of deeply degenerate Fermi gases. Since
+the method is independent of the specific interaction conditions within the
+Fermi gas, it applies to both weakly and strongly interacting Fermi gases.
+"
+26933,Discovering Political Topics in Facebook Discussion threads with Graph Contextualization,"  We propose a graph contextualization method, pairGraphText, to study
+political engagement on Facebook during the 2012 French presidential election.
+It is a spectral algorithm that contextualizes graph data with text data for
+online discussion thread. In particular, we examine the Facebook posts of the
+eight leading candidates and the comments beneath these posts. We find evidence
+of both (i) candidate-centered structure, where citizens primarily comment on
+the wall of one candidate and (ii) issue-centered structure (i.e. on political
+topics), where citizens' attention and expression is primarily directed towards
+a specific set of issues (e.g. economics, immigration, etc). To identify
+issue-centered structure, we develop pairGraphText, to analyze a network with
+high-dimensional features on the interactions (i.e. text). This technique
+scales to hundreds of thousands of nodes and thousands of unique words. In the
+Facebook data, spectral clustering without the contextualizing text information
+finds a mixture of (i) candidate and (ii) issue clusters. The contextualized
+information with text data helps to separate these two structures. We conclude
+by showing that the novel methodology is consistent under a statistical model.
+"
+26934,Global Convergence of the (1+1) Evolution Strategy,"  We establish global convergence of the (1+1) evolution strategy, i.e.,
+convergence to a critical point independent of the initial state. More
+precisely, we show the existence of a critical limit point, using a suitable
+extension of the notion of a critical point to measurable functions. At its
+core, the analysis is based on a novel progress guarantee for elitist,
+rank-based evolutionary algorithms. By applying it to the (1+1) evolution
+strategy we are able to provide an accurate characterization of whether global
+convergence is guaranteed with full probability, or whether premature
+convergence is possible. We illustrate our results on a number of example
+applications ranging from smooth (non-convex) cases over different types of
+saddle points and ridge functions to discontinuous and extremely rugged
+problems.
+"
+26935,On Summation of $p$-Adic Series,"  Summation of the $p$-adic functional series $\sum \varepsilon^n \, n! \,
+P_k^\varepsilon (n; x)\, x^n ,$ where $P_k^\varepsilon (n; x)$ is a polynomial
+in $x$ and $n$ with rational coefficients, and $\varepsilon = \pm 1$, is
+considered. The series is convergent in the domain $|x|_p \leq 1$ for all
+primes $p$. It is found the general form of polynomials $P_k^\varepsilon (n;
+x)$ which provide rational sums when $x \in \mathbb{Z}$. A class of generating
+polynomials $A_k^\varepsilon (n; x)$ plays a central role in the summation
+procedure. These generating polynomials are related to many sequences of
+integers. This is a brief review with some new results.
+"
+26936,Training Neural Networks with Local Error Signals,"  Supervised training of neural networks for classification is typically
+performed with a global loss function. The loss function provides a gradient
+for the output layer, and this gradient is back-propagated to hidden layers to
+dictate an update direction for the weights. An alternative approach is to
+train the network with layer-wise loss functions. In this paper we demonstrate,
+for the first time, that layer-wise training can approach the state-of-the-art
+on a variety of image datasets. We use single-layer sub-networks and two
+different supervised loss functions to generate local error signals for the
+hidden layers, and we show that the combination of these losses help with
+optimization in the context of local learning. Using local errors could be a
+step towards more biologically plausible deep learning because the global error
+does not have to be transported back to hidden layers. A completely backprop
+free variant outperforms previously reported results among methods aiming for
+higher biological plausibility. Code is available
+this https URL
+"
+26937,MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms,"  MCBooster is a header-only, C++11-compliant library that provides routines to
+generate and perform calculations on large samples of phase space Monte Carlo
+events. To achieve superior performance, MCBooster is capable to perform most
+of its calculations in parallel using CUDA- and OpenMP-enabled devices.
+MCBooster is built on top of the Thrust library and runs on Linux systems. This
+contribution summarizes the main features of MCBooster. A basic description of
+the user interface and some examples of applications are provided, along with
+measurements of performance in a variety of environments
+"
+26938,Deep Reinforcement Learning framework for Autonomous Driving,"  Reinforcement learning is considered to be a strong AI paradigm which can be
+used to teach machines through interaction with the environment and learning
+from their mistakes. Despite its perceived utility, it has not yet been
+successfully applied in automotive applications. Motivated by the successful
+demonstrations of learning of Atari games and Go by Google DeepMind, we propose
+a framework for autonomous driving using deep reinforcement learning. This is
+of particular relevance as it is difficult to pose autonomous driving as a
+supervised learning problem due to strong interactions with the environment
+including other vehicles, pedestrians and roadworks. As it is a relatively new
+area of research for autonomous driving, we provide a short overview of deep
+reinforcement learning and then describe our proposed framework. It
+incorporates Recurrent Neural Networks for information integration, enabling
+the car to handle partially observable scenarios. It also integrates the recent
+work on attention models to focus on relevant information, thereby reducing the
+computational complexity for deployment on embedded hardware. The framework was
+tested in an open source 3D car racing simulator called TORCS. Our simulation
+results demonstrate learning of autonomous maneuvering in a scenario of complex
+road curvatures and simple interaction of other vehicles.
+"
+26939,On the Generalized Burnside Theorem,"  The Generalized Burnside Theorem, due to Laudal, generalizes the classical
+Burnside Theorem and is obtained using noncommutative deformations of the
+family of simple right $A$-modules when $A$ is a finite dimensional associative
+algebra over an algebraically closed field. In this paper, we prove a form of
+the Generalized Burnside Theorem that is more general, where we do not assume
+that $k$ is algebraically closed. The main purpose of the paper is to clarify
+and generalize the proof. As an application of the theorem, we introduce a
+standard form for finite dimensional algebras.
+"
+26940,Multistationarity in Structured Reaction Networks,"  Many dynamical systems arising in biology and other areas exhibit
+multistationarity (two or more positive steady states with the same conserved
+quantities). Although deciding multistationarity for a polynomial dynamical
+system is an effective question in real algebraic geometry, it is in general
+difficult to determine whether a given network can give rise to a
+multistationary system, and if so, to identify witnesses to multistationarity,
+that is, specific parameter values for which the system exhibits multiple
+steady states. Here we investigate both problems. First, we build on work of
+Conradi, Feliu, Mincheva, and Wiuf, who showed that for certain reaction
+networks whose steady states admit a positive parametrization,
+multistationarity is characterized by whether a certain ""critical function""
+changes sign. Here, we allow for more general parametrizations, which make it
+much easier to determine the existence of a sign change. This is particularly
+simple when the steady-state equations are linearly equivalent to binomials; we
+give necessary conditions for this to happen, which hold for many networks
+studied in the literature. We also give a sufficient condition for
+multistationarity of networks whose steady-state equations can be replaced by
+equivalent triangular-form equations. Finally, we present methods for finding
+witnesses to multistationarity, which we show work well for certain structured
+reaction networks, including those common to biological signaling pathways. Our
+work relies on results from degree theory, on the existence of explicit
+rational parametrizations of the steady states, and on the specialization of
+Groebner bases.
+"
+26941,A Robust Variable Step Size Fractional Least Mean Square (RVSS-FLMS) Algorithm,"  In this paper, we propose an adaptive framework for the variable step size of
+the fractional least mean square (FLMS) algorithm. The proposed algorithm named
+the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step
+size of the FLMS to achieve high convergence rate with low steady state error.
+For the evaluation purpose, the problem of system identification is considered.
+The experiments clearly show that the proposed approach achieves better
+convergence rate compared to the FLMS and adaptive step-size modified FLMS
+(AMFLMS).
+"
+26942,"Fertilizer usage and cadmium in soils, crops and food","  Phosphate fertilizers were first implicated by Schroeder and Balassa in 1963
+for increasing the Cd concentration in cultivated soils and crops. This
+suggestion has become a part of the accepted paradigm on soil toxicity.
+Consequently, stringent fertilizer control programs to monitor Cd have been
+launched. Attempts to link Cd toxicity and fertilizers to chronic diseases are
+common. A re-assessment of this ""accepted"" paradigm is timely, given the larger
+body of data available today. The data show that both the input and output of
+Cd per hectare from fertilizers are negligibly small compared to the total
+amount of Cd/hectare usually present in the soil itself. Calculations based on
+current agricultural practices are used to show that it will take about 18
+centuries to double the ambient soil-cadmium level, and about 8 centuries to
+double the soil-fluoride level, even after neglecting leaching and other
+removal effects. Hence the concern of long-term agriculture should be the
+depletion of available phosphate fertilizers, rather than the contamination of
+the soil by trace metals or fluoride. This conclusion is confirmed by showing
+that the claimed correlations between fertilizer input and cadmium accumulation
+in crops are not robust. Alternative scenarios that explain the data are
+examined. Thus soil acidulation on fertilizer loading, and the effect of
+magnesium, zinc, and fluoride ions contained in fertilizers are considered
+using recent Cd$^{2+}$, Mg$^{2+}$ and F$^-$ ion-association theories. The
+protective role of ions like Zn, Se, Fe, etc., is emphasized, and the question
+of cadmium toxicity in the presence of other ions is considered. These help to
+clarify and rectify difficulties found in the standard point of view. This
+analysis does not modify the accepted views on Cd contamination by airborne
+delivery, smoking, and industrial activity, or P-contamination causing algal
+blooms.
+"
+26943,"General Upper Bounds for Gate Complexity and Depth of Reversible Circuits Consisting of NOT, CNOT and 2-CNOT Gates","  The paper discusses the gate complexity and the depth of reversible circuits
+consisting of NOT, CNOT and 2-CNOT gates in the case, when the number of
+additional inputs is limited. We study Shannon's gate complexity function $L(n,
+q)$ and depth function $D(n, q)$ for a reversible circuit implementing a
+Boolean transformation $f\colon \mathbb Z_2^n \to \mathbb Z_2^n$ with $8n < q
+\lesssim n2^{n-o(n)}$ additional inputs. The general upper bounds $L(n,q)
+\lesssim 2^n + 8n2^n \mathop / (\log_2 (q-4n) - \log_2 n - 2)$ and $D(n,q)
+\lesssim 2^{n+1}(2,5 + \log_2 n - \log_2 (\log_2 (q - 4n) - \log_2 n - 2))$ are
+proved for this case.
+"
+26944,First-principles study on various point defects formed by hydrogen and helium atoms in tungsten,"  The different point defects formed by two hydrogen atoms or two helium atoms
+in tungsten were investigated through first-principles calculation. The
+energetically favorable site for a hydrogen atom is tetrahedral interstitial
+site while substitutional site is the most preferred site for a helium atom.
+The formation energies of two hydrogen or helium atoms are determined by their
+positions, and there are not a multiple of a single atom's. After relaxation,
+two adjacent hydrogen atoms are away from each other while helium atoms are
+close to. The reasons for the interaction between two hydrogen or helium atoms
+are also discussed.
+"
+26945,Improved bounds for Square-Root Lasso and Square-Root Slope,"  Extending the results of Bellec, Lecué and Tsybakov to the setting of
+sparse high-dimensional linear regression with unknown variance, we show that
+two estimators, the Square-Root Lasso and the Square-Root Slope can achieve the
+optimal minimax prediction rate, which is $(s/n) \log (p/s)$, up to some
+constant, under some mild conditions on the design matrix. Here, $n$ is the
+sample size, $p$ is the dimension and $s$ is the sparsity parameter. We also
+prove optimality for the estimation error in the $l_q$-norm, with $q \in [1,2]$
+for the Square-Root Lasso, and in the $l_2$ and sorted $l_1$ norms for the
+Square-Root Slope. Both estimators are adaptive to the unknown variance of the
+noise. The Square-Root Slope is also adaptive to the sparsity $s$ of the true
+parameter. Next, we prove that any estimator depending on $s$ which attains the
+minimax rate admits an adaptive to $s$ version still attaining the same rate.
+We apply this result to the Square-root Lasso. Moreover, for both estimators,
+we obtain valid rates for a wide range of confidence levels, and improved
+concentration properties as in [Bellec, Lecué and Tsybakov, 2017] where the
+case of known variance is treated. Our results are non-asymptotic.
+"
+26946,Optimal diffusion in ecological dynamics with Allee effect in a metapopulation,"  How diffusion impacts on ecological dynamics under the Allee effect and
+spatial constraints? That is the question we address. Employing a microscopic
+minimal model in a metapopulation (without imposing nonlinear birth and death
+rates) we evince --- both numerically and analitically --- the emergence of an
+optimal diffusion that maximises the survival probability. Even though, at
+first such result seems counter-intuitive, it has empirical support from recent
+experiments with engineered bacteria. Moreover, we show that this optimal
+diffusion disappears for loose spatial constraints.
+"
+26947,Parametric Markov Chains: PCTL Complexity and Fraction-free Gaussian Elimination,"  Parametric Markov chains have been introduced as a model for families of
+stochastic systems that rely on the same graph structure, but differ in the
+concrete transition probabilities. The latter are specified by polynomial
+constraints for the parameters. Among the tasks typically addressed in the
+analysis of parametric Markov chains are (1) the computation of closed-form
+solutions for reachabilty probabilities and other quantitative measures and (2)
+finding symbolic representations of the set of parameter valuations for which a
+given temporal logical formula holds as well as (3) the decision variant of (2)
+that asks whether there exists a parameter valuation where a temporal logical
+formula holds. Our contribution to (1) is to show that existing implementations
+for computing rational functions for reachability probabilities or expected
+costs in parametric Markov chains can be improved by using fraction-free
+Gaussian elimination, a long-known technique for linear equation systems with
+parametric coefficients. Our contribution to (2) and (3) is a
+complexity-theoretic discussion of the model checking problem for parametric
+Markov chains and probabilistic computation tree logic (PCTL) formulas. We
+present an exponential-time algorithm for (2) and a PSPACE upper bound for (3).
+Moreover, we identify fragments of PCTL and subclasses of parametric Markov
+chains where (1) and (3) are solvable in polynomial time and establish
+NP-hardness for other PCTL fragments.
+"
+26948,Swimming against the tide: Gender bias in the physics classroom,"  This study examines physics students' evaluations of identical,
+video-recorded lectures performed by female and male actors playing the role of
+professors. The results indicate that evaluations by male students show
+statistically significant overall biases with male professors rated more
+positively than female professors. Female students tended to be egalitarian,
+except in two areas. Female students evaluated female professors'
+interpersonal/communicative skills more positively than male professors'. They
+evaluated female professors' scientific knowledge and skills less positively
+than that of male professors just as male students did. These findings are
+relevant to two areas of research on bias in evaluation: rater-ratee similarity
+bias and stereotype confirmation bias. Results from this study have important
+implications for efforts focused on educating students and mentoring faculty
+members in order to increase the representation of women in the physical
+sciences.
+"
+26949,Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning,"  The problem of sparse rewards is one of the hardest challenges in
+contemporary reinforcement learning. Hierarchical reinforcement learning (HRL)
+tackles this problem by using a set of temporally-extended actions, or options,
+each of which has its own subgoal. These subgoals are normally handcrafted for
+specific tasks. Here, though, we introduce a generic class of subgoals with
+broad applicability in the visual domain. Underlying our approach (in common
+with work using ""auxiliary tasks"") is the hypothesis that the ability to
+control aspects of the environment is an inherently useful skill to have. We
+incorporate such subgoals in an end-to-end hierarchical reinforcement learning
+system and test two variants of our algorithm on a number of games from the
+Atari suite. We highlight the advantage of our approach in one of the hardest
+games -- Montezuma's revenge -- for which the ability to handle sparse rewards
+is key. Our agent learns several times faster than the current state-of-the-art
+HRL agent in this game, reaching a similar level of performance. UPDATE
+22/11/17: We found that a standard A3C agent with a simple shaped reward, i.e.
+extrinsic reward + feature control intrinsic reward, has comparable performance
+to our agent in Montezuma Revenge. In light of the new experiments performed,
+the advantage of our HRL approach can be attributed more to its ability to
+learn useful features from intrinsic rewards rather than its ability to explore
+and reuse abstracted skills with hierarchical components. This has led us to a
+new conclusion about the result.
+"
+26950,Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuant,"  Deep learning is finding its way into the embedded world with applications
+such as autonomous driving, smart sensors and aug- mented reality. However, the
+computation of deep neural networks is demanding in energy, compute power and
+memory. Various approaches have been investigated to reduce the necessary
+resources, one of which is to leverage the sparsity occurring in deep neural
+networks due to the high levels of redundancy in the network parameters. It has
+been shown that sparsity can be promoted specifically and the achieved sparsity
+can be very high. But in many cases the methods are evaluated on rather small
+topologies. It is not clear if the results transfer onto deeper topologies. In
+this paper, the TensorQuant toolbox has been extended to offer a platform to
+investigate sparsity, especially in deeper models. Several practical relevant
+topologies for varying classification problem sizes are investigated to show
+the differences in sparsity for activations, weights and gradients.
+"
+26951,Optimal location of resources for biased movement of species: the 1D case,"  In this paper, we investigate an optimal design problem motivated by some
+issues arising in population dynamics. In a nutshell, we aim at determining the
+optimal shape of a region occupied by resources for maximizing the survival
+ability of a species in a given box and we consider the general case of Robin
+boundary conditions on its boundary. Mathematically, this issue can be modeled
+with the help of an extremal indefinite weight linear eigenvalue problem. The
+optimal spatial arrangement is obtained by minimizing the positive principal
+eigenvalue with respect to the weight, under a L 1 constraint standing for
+limitation of the total amount of resources. The specificity of such a problem
+rests upon the presence of nonlinear functions of the weight both in the
+numerator and denominator of the Rayleigh quotient. By using adapted
+symmetrization procedures, a well-chosen change of variable, as well as
+necessary optimality conditions, we completely solve this optimization problem
+in the unidimensional case by showing first that every minimizer is unimodal
+and bang-bang. This leads to investigate a finite dimensional optimization
+problem. This allows to show in particular that every minimizer is (up to
+additive constants) the characteristic function of three possible domains: an
+interval that sticks on the boundary of the box, an interval that is
+symmetrically located at the middle of the box, or, for a precise value of the
+Robin coefficient, all intervals of a given fixed length.
+"
+26952,Triplet Fermions and Dirac Fermions in Borophene,"  Borophene is a monolayer materials made of boron. A perfect planar boropehene
+called $\beta_{12}$ borophene has Dirac cones and they are well reproduced by a
+tight-binding model according to recent experimental and first-principles
+calculation results. We explicitly derive a Dirac theory for them. Dirac cones
+are gapless when the inversion symmetry exists, while they are gapped when it
+is broken. In addition, three-band touching points emerge together with
+pseudospin triplet fermions when all transfer energy is equal and all on-site
+energy is equal. The three-band touching is slightly resolved otherwise. We
+construct effective three-band theories for triplet fermions. We also study the
+edge states of borophene nanoribbons, which show various behaviors depending on
+the way of edge terminations.
+"
+26953,Generative Adversarial Positive-Unlabelled Learning,"  In this work, we consider the task of classifying binary positive-unlabeled
+(PU) data. The existing discriminative learning based PU models attempt to seek
+an optimal reweighting strategy for U data, so that a decent decision boundary
+can be found. However, given limited P data, the conventional PU models tend to
+suffer from overfitting when adapted to very flexible deep neural networks. In
+contrast, we are the first to innovate a totally new paradigm to attack the
+binary PU task, from perspective of generative learning by leveraging the
+powerful generative adversarial networks (GAN). Our generative
+positive-unlabeled (GenPU) framework incorporates an array of discriminators
+and generators that are endowed with different roles in simultaneously
+producing positive and negative realistic samples. We provide theoretical
+analysis to justify that, at equilibrium, GenPU is capable of recovering both
+positive and negative data distributions. Moreover, we show GenPU is
+generalizable and closely related to the semi-supervised classification. Given
+rather limited P data, experiments on both synthetic and real-world dataset
+demonstrate the effectiveness of our proposed framework. With infinite
+realistic and diverse sample streams generated from GenPU, a very flexible
+classifier can then be trained using deep neural networks.
+"
+26954,Evaluating (and Improving) Estimates of the Solar Radial Magnetic Field Component from Line-of-Sight Magnetograms,"  Although for many solar physics problems the desirable or meaningful boundary
+is the radial component of the magnetic field $B_{\rm r}$, the most readily
+available measurement is the component of the magnetic field along the
+line-of-sight to the observer, $B_{\rm los}$. As this component is only equal
+to the radial component where the viewing angle is exactly zero, some
+approximation is required to estimate $B_{\rm r}$ at all other observed
+locations. In this study, a common approximation known as the
+""$\mu$-correction"", which assumes all photospheric field to be radial, is
+compared to a method which invokes computing a potential field that matches the
+observed $B_{\rm los}$, from which the potential field radial component,
+$B_{\rm r}^{\rm pot}$ is recovered. We demonstrate that in regions that are
+truly dominated by radially-oriented field at the resolution of the data
+employed, the $\mu$-correction performs acceptably if not better than the
+potential-field approach. However, it is also shown that for any solar
+structure which includes horizontal fields, i.e. active regions, the
+potential-field method better recovers both the strength of the radial field
+and the location of magnetic neutral line.
+"
+26955,Comparison between continuous and discrete doses using Escalation With Overdose Control,"  Although there is an extensive statistical literature showing the
+disadvantages of discretizing continuous variables, categorization is a common
+practice in clinical research which results in substantial loss of information.
+A large collection of methods in cancer phase I clinical trial design
+establishes dose of a new agent as a discrete variable. A noteworthy exception
+is the Escalation With Overdose Control (EWOC) design, where doses can be
+defined either as continuous or as a grid of discrete doses. A Monte Carlo
+simulation study was performed to compare the operating characteristics of
+continuous and discrete dose EWOC designs. Four equally spaced grids with
+different interval lengths were considered. The loss of information was
+measured by several operating characteristics easier for clinicians to
+interpret, in addition to the usual statistical measures of bias and mean
+squared error. Based on the simulations, if there is not enough knowledge about
+the true MTD value as commonly happens in phase I clinical trials, continuous
+dose scheme arises as an attractive option.
+"
+26956,The analytical solution of the problem on plasma oscillations in half-space with diffusion boundary conditions,"  The boundary problem about behaviour (oscillations) of the electronic plasmas
+with arbitrary degree of degeneration of electronic gas in half-space with
+diffusion boundary conditions is analytically solved. The kinetic equation of
+Vlasov - Boltzmann with integral of collisions of type BGK (Bhatnagar, Gross,
+Krook) and Maxwell equation for electric field are applied. Distribution
+function for electrons and electric field in plasma in the form of expansion
+under eigen solutions of the initial system of equations are received.
+Coefficients of these expansions are found by means of the boundary conditions.
+"
+26957,Scale-Robust Localization Using General Object Landmarks,"  Visual localization under large changes in scale is an important capability
+in many robotic mapping applications, such as localizing at low altitudes in
+maps built at high altitudes, or performing loop closure over long distances.
+Existing approaches, however, are robust only up to about a 3x difference in
+scale between map and query images.
+We propose a novel combination of deep-learning-based object features and
+state-of-the-art SIFT point-features that yields improved robustness to scale
+change. This technique is training-free and class-agnostic, and in principle
+can be deployed in any environment out-of-the-box. We evaluate the proposed
+technique on the KITTI Odometry benchmark and on a novel dataset of outdoor
+images exhibiting changes in visual scale of $7\times$ and greater, which we
+have released to the public. Our technique consistently outperforms
+localization using either SIFT features or the proposed object features alone,
+achieving both greater accuracy and much lower failure rates under large
+changes in scale.
+"
+26958,Optimal control of linear systems with limited control actions: threshold-based event-triggered control,"  We consider a finite-horizon linear-quadratic optimal control problem where
+only a limited number of control messages are allowed for sending from the
+controller to the actuator. To restrict the number of control actions computed
+and transmitted by the controller, we employ a threshold-based event-triggering
+mechanism that decides whether or not a control message needs to be calculated
+and delivered. Due to the nature of threshold-based event-triggering
+algorithms, finding the optimal control sequence requires minimizing a
+quadratic cost function over a non-convex domain. In this paper, we firstly
+provide an exact solution to the non-convex problem mentioned above by solving
+an exponential number of quadratic programs. To reduce computational
+complexity, we, then, propose two efficient heuristic algorithms based on
+greedy search and the Alternating Direction Method of Multipliers (ADMM)
+method. Later, we consider a receding horizon control strategy for linear
+systems controlled by event-triggered controllers, and we also provide a
+complete stability analysis of receding horizon control that uses finite
+horizon optimization in the proposed class. Numerical examples testify to the
+viability of the presented design technique.
+"
+26959,Supervising Unsupervised Learning,"  We introduce a framework to leverage knowledge acquired from a repository of
+(heterogeneous) supervised datasets to new unsupervised datasets. Our
+perspective avoids the subjectivity inherent in unsupervised learning by
+reducing it to supervised learning, and provides a principled way to evaluate
+unsupervised algorithms. We demonstrate the versatility of our framework via
+simple agnostic bounds on unsupervised problems. In the context of clustering,
+our approach helps choose the number of clusters and the clustering algorithm,
+remove the outliers, and provably circumvent the Kleinberg's impossibility
+result. Experimental results across hundreds of problems demonstrate improved
+performance on unsupervised data with simple algorithms, despite the fact that
+our problems come from heterogeneous domains. Additionally, our framework lets
+us leverage deep networks to learn common features from many such small
+datasets, and perform zero shot learning.
+"
+26960,Some computations with the $\mathscr{F}$-homotopy limit spectral sequence,"  The uniform $\mathcal{F}_p$-isomorphism theorem of Quillen gives a comparison
+map between the Borel equivariant $\mathbf{Z}/p$-cohomology of a space and a
+limit involving only the Borel equivariant cohomology groups of the same space
+with the action restricted to the elementary abelian $p$-subgroups. The theorem
+states that the elements in the kernel are nilpotent, and that every element in
+the codomain has a power that is in the image. By work of Mathew-Naumann-Noel,
+for a fixed group and an arbitrary space, there is a uniform bound on the
+nilpotence degree of the elements in the kernel, and on the power necessary to
+raise an element in the codomain by to get an element in the image. Using their
+methods, we give for $p=2$ explicit upper bounds for finite groups with 2-Sylow
+of order less than or equal to 16. In particular, we show that the elements in
+the kernel have in that case nilpotence degree less than or equal to 4, and
+every element in the codomain raised to the power 8 is in the image. We do this
+by bounding from above the $\mathscr{E}_{(2)}$-exponent, as defined by
+Mathew-Naumann-Noel, of Borel equivariant singular $\mathbf{Z}/2$-cohomology
+for the 2-groups of order less than or equal to 16. We also bound the exponent
+from below by evaluating a homotopy limit spectral sequence for all these
+groups.
+"
+26961,On classification and invariants of second order non-parabolic linear partial differential equations in two variables,"  The paper deals with second order abstract linear partial differential
+equations (LPDE) over a partial differential field with two commuting
+differential operators. In terms of usual differential equations the main
+content can be presented as follows. The classification and invariants problems
+for second order LPDEs with respect to transformations \[x=x(\xi,\eta),\
+y=y(\xi,\eta),\ u=h(x,y)v(\xi,\eta),\] where $x,y$ are independent and $u$ is
+the dependent variable of the LPDE, are considered. Solutions to these problems
+are given for different subclasses of non-parabolic LPDE which appear naturally
+in the equivalence problem of LPDE. A criterion for reducibility of such LPDE
+to LPDE with constant coefficients is offered as well.
+"
+26962,"Universal representations:The missing link between faces, text, planktons, and cat breeds","  With the advent of large labelled datasets and high-capacity models, the
+performance of machine vision systems has been improving rapidly. However, the
+technology has still major limitations, starting from the fact that different
+vision problems are still solved by different models, trained from scratch or
+fine-tuned on the target data. The human visual system, in stark contrast,
+learns a universal representation for vision in the early life of an
+individual. This representation works well for an enormous variety of vision
+problems, with little or no change, with the major advantage of requiring
+little training data to solve any of them.
+"
+26963,Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning,"  Flatland is a simple, lightweight environment for fast prototyping and
+testing of reinforcement learning agents. It is of lower complexity compared to
+similar 3D platforms (e.g. DeepMind Lab or VizDoom), but emulates physical
+properties of the real world, such as continuity, multi-modal
+partially-observable states with first-person view and coherent physics. We
+propose to use it as an intermediary benchmark for problems related to Lifelong
+Learning. Flatland is highly customizable and offers a wide range of task
+difficulty to extensively evaluate the properties of artificial agents. We
+experiment with three reinforcement learning baseline agents and show that they
+can rapidly solve a navigation task in Flatland. A video of an agent acting in
+Flatland is available here: this https URL.
+"
+26964,Online Fall Detection using Recurrent Neural Networks,"  Unintentional falls can cause severe injuries and even death, especially if
+no immediate assistance is given. The aim of Fall Detection Systems (FDSs) is
+to detect an occurring fall. This information can be used to trigger the
+necessary assistance in case of injury. This can be done by using either
+ambient-based sensors, e.g. cameras, or wearable devices. The aim of this work
+is to study the technical aspects of FDSs based on wearable devices and
+artificial intelligence techniques, in particular Deep Learning (DL), to
+implement an effective algorithm for on-line fall detection. The proposed
+classifier is based on a Recurrent Neural Network (RNN) model with underlying
+Long Short-Term Memory (LSTM) blocks. The method is tested on the publicly
+available SisFall dataset, with extended annotation, and compared with the
+results obtained by the SisFall authors.
+"
+26965,Embedding Logical Queries on Knowledge Graphs,"  Learning low-dimensional embeddings of knowledge graphs is a powerful
+approach used to predict unobserved or missing edges between entities. However,
+an open challenge in this area is developing techniques that can go beyond
+simple edge prediction and handle more complex logical queries, which might
+involve multiple unobserved edges, entities, and variables. For instance, given
+an incomplete biological knowledge graph, we might want to predict ""em what
+drugs are likely to target proteins involved with both diseases X and Y?"" -- a
+query that requires reasoning about all possible proteins that {\em might}
+interact with diseases X and Y. Here we introduce a framework to efficiently
+make predictions about conjunctive logical queries -- a flexible but tractable
+subset of first-order logic -- on incomplete knowledge graphs. In our approach,
+we embed graph nodes in a low-dimensional space and represent logical operators
+as learned geometric operations (e.g., translation, rotation) in this embedding
+space. By performing logical operations within a low-dimensional embedding
+space, our approach achieves a time complexity that is linear in the number of
+query variables, compared to the exponential complexity required by a naive
+enumeration-based approach. We demonstrate the utility of this framework in two
+application studies on real-world datasets with millions of relations:
+predicting logical relationships in a network of drug-gene-disease interactions
+and in a graph-based representation of social interactions derived from a
+popular web forum.
+"
+26966,The Role of Criticality of Gene Regulatory Networks in Morphogenesis,"  Gene regulatory network (GRN)-based morphogenetic models have recently gained
+an increasing attention. However, the relationship between microscopic
+properties of intracellular GRNs and macroscopic properties of morphogenetic
+systems has not been fully understood yet. Here we propose a theoretical
+morphogenetic model representing an aggregation of cells, and reveal the
+relationship between criticality of GRNs and morphogenetic pattern formation.
+In our model, the positions of the cells are determined by spring-mass-damper
+kinetics. Each cell has an identical Kauffman's $NK$ random Boolean network
+(RBN) as its GRN. We varied the properties of GRNs from ordered, through
+critical, to chaotic by adjusting node in-degree $K$. We randomly assigned four
+cell fates to the attractors of RBNs for cellular behaviors. By comparing
+diverse morphologies generated in our morphogenetic systems, we investigated
+what the role of the criticality of GRNs is in forming morphologies. We found
+that nontrivial spatial patterns were generated most frequently when GRNs were
+at criticality. Our finding indicates that the criticality of GRNs facilitates
+the formation of nontrivial morphologies in GRN-based morphogenetic systems.
+"
+26967,Heat Kernel Smoothing in Irregular Image Domains,"  We present the discrete version of heat kernel smoothing on graph data
+structure. The method is used to smooth data in an irregularly shaped domains
+in 3D images.
+New statistical properties are derived. As an application, we show how to
+filter out data in the lung blood vessel trees obtained from computed
+tomography. The method can be further used in representing the complex vessel
+trees parametrically and extracting the skeleton representation of the trees.
+"
+26968,Line Profile Based Segmentation Algorithm for Touching Corn Kernels,"  Image segmentation of touching objects plays a key role in providing accurate
+classification for computer vision technologies. A new line profile based
+imaging segmentation algorithm has been developed to provide a robust and
+accurate segmentation of a group of touching corns. The performance of the line
+profile based algorithm has been compared to a watershed based imaging
+segmentation algorithm. Both algorithms are tested on three different patterns
+of images, which are isolated corns, single-lines, and random distributed
+formations. The experimental results show that the algorithm can segment a
+large number of touching corn kernels efficiently and accurately.
+"
+26969,Polarization-multiplexing ghost imaging,"  A novel technique for polarization-multiplexing ghost imaging is proposed to
+simultaneously obtain multiple polarimetric information by a single detector.
+Here, polarization-division multiplexing speckles are employed for object
+illumination. The light reflected from the objects is detected by a
+single-pixel detector. An iterative reconstruction method is used to restore
+the fused image containing the different polarimetric information by summing
+the multiplexed speckles and detected intensities. Next, clear images of the
+different polarimetric information are recovered by demultiplexing the fused
+image. The application of this method to the detection of two different
+polarized objects is presented, and the results clearly demonstrate that the
+proposed method is effective. An encryption experiment for polarimetric
+information is also performed by setting the multiplexed speckles' encoding as
+the keys.
+"
+26970,Boosting algorithms for uplift modeling,"  Uplift modeling is an area of machine learning which aims at predicting the
+causal effect of some action on a given individual. The action may be a medical
+procedure, marketing campaign, or any other circumstance controlled by the
+experimenter. Building an uplift model requires two training sets: the
+treatment group, where individuals have been subject to the action, and the
+control group, where no action has been performed. An uplift model allows then
+to assess the gain resulting from taking the action on a given individual, such
+as the increase in probability of patient recovery or of a product being
+purchased. This paper describes an adaptation of the well-known boosting
+techniques to the uplift modeling case. We formulate three desirable properties
+which an uplift boosting algorithm should have. Since all three properties
+cannot be satisfied simultaneously, we propose three uplift boosting
+algorithms, each satisfying two of them. Experiments demonstrate the usefulness
+of the proposed methods, which often dramatically improve performance of the
+base models and are thus new and powerful tools for uplift modeling.
+"
+26971,Learning and Inference on Generative Adversarial Quantum Circuits,"  Quantum mechanics is inherently probabilistic in light of Born's rule. Using
+quantum circuits as probabilistic generative models for classical data exploits
+their superior expressibility and efficient direct sampling ability. However,
+training of quantum circuits can be more challenging compared to classical
+neural networks due to lack of efficient differentiable learning algorithm. We
+devise an adversarial quantum-classical hybrid training scheme via coupling a
+quantum circuit generator and a classical neural network discriminator
+together. After training, the quantum circuit generative model can infer
+missing data with quadratic speed up via amplitude amplification. We
+numerically simulate the learning and inference of generative adversarial
+quantum circuit using the prototypical Bars-and-Stripes dataset. Generative
+adversarial quantum circuits is a fresh approach to machine learning which may
+enjoy the practically useful quantum advantage on near-term quantum devices.
+"
+26972,Dynamic Connectivity Game for Adversarial Internet of Battlefield Things Systems,"  In this paper, the problem of network connectivity is studied for an
+adversarial Internet of Battlefield Things (IoBT) system in which an attacker
+aims at disrupting the connectivity of the network by choosing to compromise
+one of the IoBT nodes at each time epoch. To counter such attacks, an IoBT
+defender attempts to reestablish the IoBT connectivity by either deploying new
+IoBT nodes or by changing the roles of existing nodes. This problem is
+formulated as a dynamic multistage Stackelberg connectivity game that extends
+classical connectivity games and that explicitly takes into account the
+characteristics and requirements of the IoBT network. In particular, the
+defender's payoff captures the IoBT latency as well as the sum of weights of
+disconnected nodes at each stage of the game. Due to the dependence of the
+attacker's and defender's actions at each stage of the game on the network
+state, the feedback Stackelberg solution (FSE) is used to solve the IoBT
+connectivity game. Then, sufficient conditions under which the IoBT system will
+remain connected, when the FSE solution is used, are determined analytically.
+Numerical results show that the expected number of disconnected sensors, when
+the FSE solution is used, decreases up to 62% compared to a baseline scenario
+in which a Stackelberg game with no feedback is used, and up to 57% compared to
+a baseline equal probability policy.
+"
+26973,Impacts of Community and Distributed Energy Storage Systems on Unbalanced Low Voltage Networks,"  Energy storage systems (EES) are expected to be an indispensable resource for
+mitigating the effects on networks of high penetrations of distributed
+generation in the near future. This paper analyzes the benefits of EES in
+unbalanced low voltage (LV) networks regarding three aspects, namely, power
+losses, the hosting capacity and network unbalance. For doing so, a mixed
+integer quadratic programmming model (MIQP) is developed to minimize annual
+energy losses and determine the sizing and placement of ESS, while satisfying
+voltage constraints. A real unbalanced LV UK grid is adopted to examine the
+effects of ESS under two scenarios: the installation of one community ESS
+(CESS) and multiple distributed ESSs (DESSs). The results illustrate that both
+scenarios present high performance in accomplishing the above tasks, while
+DESSs, with the same aggregated size, are slightly better. This margin is
+expected to be amplified as the aggregated size of DESSs increases.
+"
+26974,End-to-End Simulation of 5G mmWave Networks,"  Due to its potential for multi-gigabit and low latency wireless links,
+millimeter wave (mmWave) technology is expected to play a central role in 5th
+generation cellular systems. While there has been considerable progress in
+understanding the mmWave physical layer, innovations will be required at all
+layers of the protocol stack, in both the access and the core network.
+Discrete-event network simulation is essential for end-to-end, cross-layer
+research and development. This paper provides a tutorial on a recently
+developed full-stack mmWave module integrated into the widely used open-source
+ns--3 simulator. The module includes a number of detailed statistical channel
+models as well as the ability to incorporate real measurements or ray-tracing
+data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and
+highly customizable, making it easy to integrate algorithms or compare
+Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example.
+The module is interfaced with the core network of the ns--3 Long Term Evolution
+(LTE) module for full-stack simulations of end-to-end connectivity, and
+advanced architectural features, such as dual-connectivity, are also available.
+To facilitate the understanding of the module, and verify its correct
+functioning, we provide several examples that show the performance of the
+custom mmWave stack as well as custom congestion control algorithms designed
+specifically for efficient utilization of the mmWave channel.
+"
+26975,Generalized Network Dismantling,"  Finding the set of nodes, which removed or (de)activated can stop the spread
+of (dis)information, contain an epidemic or disrupt the functioning of a
+corrupt/criminal organization is still one of the key challenges in network
+science. In this paper, we introduce the generalized network dismantling
+problem, which aims to find the set of nodes that, when removed from a network,
+results in a network fragmentation into subcritical network components at
+minimum cost. For unit costs, our formulation becomes equivalent to the
+standard network dismantling problem. Our non-unit cost generalization allows
+for the inclusion of topological cost functions related to node centrality and
+non-topological features such as the price, protection level or even social
+value of a node. In order to solve this optimization problem, we propose a
+method, which is based on the spectral properties of a novel node-weighted
+Laplacian operator. The proposed method is applicable to large-scale networks
+with millions of nodes. It outperforms current state-of-the-art methods and
+opens new directions in understanding the vulnerability and robustness of
+complex systems.
+"
+26976,Transforming Bell's Inequalities into State Classifiers with Machine Learning,"  Quantum information science has profoundly changed the ways we understand,
+store, and process information. A major challenge in this field is to look for
+an efficient means for classifying quantum state. For instance, one may want to
+determine if a given quantum state is entangled or not. However, the process of
+a complete characterization of quantum states, known as quantum state
+tomography, is a resource-consuming operation in general. An attractive
+proposal would be the use of Bell's inequalities as an entanglement witness,
+where only partial information of the quantum state is needed. The problem is
+that entanglement is necessary but not sufficient for violating Bell's
+inequalities, making it an unreliable state classifier. Here we aim at solving
+this problem by the methods of machine learning. More precisely, given a family
+of quantum states, we randomly picked a subset of it to construct a
+quantum-state classifier, accepting only partial information of each quantum
+state. Our results indicated that these transformed Bell-type inequalities can
+perform significantly better than the original Bell's inequalities in
+classifying entangled states. We further extended our analysis to three-qubit
+and four-qubit systems, performing classification of quantum states into
+multiple species. These results demonstrate how the tools in machine learning
+can be applied to solving problems in quantum information science.
+"
+26977,"On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks","  Transferability captures the ability of an attack against a machine-learning
+model to be effective against a different, potentially unknown, model. Studying
+transferability of attacks has gained interest in the last years due to the
+deployment of cyber-attack detection services based on machine learning. For
+these applications of machine learning, service providers avoid disclosing
+information about their machine-learning algorithms. As a result, attackers
+trying to bypass detection are forced to craft their attacks against a
+surrogate model instead of the actual target model used by the service. While
+previous work has shown that finding test-time transferable attack samples is
+possible, it is not well understood how an attacker may construct adversarial
+examples that are likely to transfer against different models, in particular in
+the case of training-time poisoning attacks. In this paper, we present the
+first empirical analysis aimed to investigate the transferability of both
+test-time evasion and training-time poisoning attacks. We provide a unifying,
+formal definition of transferability of such attacks and show how it relates to
+the input gradients of the surrogate and of the target classification models.
+We assess to which extent some of the most well-known machine-learning systems
+are vulnerable to transfer attacks, and explain why such attacks succeed (or
+not) across different models. To this end, we leverage some interesting
+connections highlighted in this work among the adversarial vulnerability of
+machine-learning models, their regularization hyperparameters and input
+gradients.
+"
+26978,Simple and Near-Optimal Distributed Coloring for Sparse Graphs,"  Graph coloring is one of the central problems in distributed graph
+algorithms. Much of the research on this topic has focused on coloring with
+$\Delta+1$ colors, where $\Delta$ denotes the maximum degree. Using $\Delta+1$
+colors may be unsatisfactory in sparse graphs, where not all nodes have such a
+high degree; it would be more desirable to use a number of colors that improves
+with sparsity. A standard measure that captures sparsity is arboricity, which
+is the smallest number of forests into which the edges of the graph can be
+partitioned.
+We present simple randomized distributed algorithms that, with high
+probability, color any $n$-node $\alpha$-arboricity graph:
+- using $(2+\varepsilon)\cdot \alpha$ colors, for constant $\varepsilon>0$,
+in $O(\log n)$ rounds, if $\alpha=\tilde{\Omega}(\log n)$, or
+- using $O(\alpha \log \alpha )$ colors, in $O(\log n)$ rounds, or
+- using $O(\alpha)$ colors, in $O(\log n \cdot \min\{\log\log n,\; \log
+\alpha\})$ rounds.
+These algorithms are nearly-optimal, as it is known by results of Linial
+[FOCS'87] and Barenboim and Elkin [PODC'08] that coloring with $\Theta(\alpha)$
+colors, or even poly$(\alpha)$ colors, requires $\Omega(\log_{\alpha} n)$
+rounds. The previously best-known $O(\log n)$-time result was a deterministic
+algorithm due to Barenboim and Elkin [PODC'08], which uses $\Theta(\alpha ^2)$
+colors. Barenboim and Elkin stated improving this number of colors as an open
+problem in their Distributed Graph Coloring Book.
+"
+26979,"Use Generalized Representations, But Do Not Forget Surface Features","  Only a year ago, all state-of-the-art coreference resolvers were using an
+extensive amount of surface features. Recently, there was a paradigm shift
+towards using word embeddings and deep neural networks, where the use of
+surface features is very limited. In this paper, we show that a simple SVM
+model with surface features outperforms more complex neural models for
+detecting anaphoric mentions. Our analysis suggests that using generalized
+representations and surface features have different strength that should be
+both taken into account for improving coreference resolution.
+"
+26980,Quantum Kählerian Lie groups from multiplicative unitaries,"  We show that the deformation theory of Fréchet algebras for actions of
+Kählerian Lie groups developed by two of us, leads in a natural way to
+examples of non-compact locally compact quantum groups. This is achieved by
+constructing a manageable multiplicative unitary out of the Fréchet
+deformation of $C_0(G)$ for the action $\lambda\otimes \rho$ of $G\times G$ and
+the undeformed coproduct. We also prove that these quantum groups are
+isomorphic to those constructed out of the unitary dual $2$-cocycle discovered
+by Neshveyev and Tuset and associated with Bieliavsky's covariant
+$\star$-product, via the De Commer's results.
+"
+26981,Information Structure Design in Team Decision Problems,"  We consider a problem of information structure design in team decision
+problems and team games. We propose simple, scalable greedy algorithms for
+adding a set of extra information links to optimize team performance and
+resilience to non-cooperative and adversarial agents. We show via a simple
+counterexample that the set function mapping additional information links to
+team performance is in general not supermodular. Although this implies that the
+greedy algorithm is not accompanied by worst-case performance guarantees, we
+illustrate through numerical experiments that it can produce effective and
+often optimal or near optimal information structure modifications.
+"
+26982,MAGAN: Margin Adaptation for Generative Adversarial Networks,"  We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs)
+algorithm, a novel training procedure for GANs to improve stability and
+performance by using an adaptive hinge loss function. We estimate the
+appropriate hinge loss margin with the expected energy of the target
+distribution, and derive principled criteria for when to update the margin. We
+prove that our method converges to its global optimum under certain
+assumptions. Evaluated on the task of unsupervised image generation, the
+proposed training procedure is simple yet robust on a diverse set of data, and
+achieves qualitative and quantitative improvements compared to the
+state-of-the-art.
+"
+26983,Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior,"  In sensing applications, sensors cannot always measure the latent quantity of
+interest at the required resolution, sometimes they can only acquire a blurred
+version of it due the sensor's transfer function. To recover latent signals
+when only noisy mixed measurements of the signal are available, we propose the
+Gaussian process mixture of measurements (GPMM), which models the latent signal
+as a Gaussian process (GP) and allows us to perform Bayesian inference on such
+signal conditional to a set of noisy mixture of measurements. We describe how
+to train GPMM, that is, to find the hyperparameters of the GP and the mixing
+weights, and how to perform inference on the latent signal under GPMM;
+additionally, we identify the solution to the underdetermined linear system
+resulting from a sensing application as a particular case of GPMM. The proposed
+model is validated in the recovery of three signals: a smooth synthetic signal,
+a real-world heart-rate time series and a step function, where GPMM
+outperformed the standard GP in terms of estimation error, uncertainty
+representation and recovery of the spectral content of the latent signal.
+"
+26984,A dynamical system for prioritizing and coordinating motivations,"  We develop a dynamical systems approach to prioritizing and selecting
+multiple recurring tasks with the aim of conferring a degree of deliberative
+goal selection to a mobile robot confronted with competing objectives. We take
+navigation as our prototypical task, and use reactive (i.e., vector field)
+planners derived from navigation functions to encode control policies that
+achieve each individual task. We associate a scalar ""value"" with each task
+representing its current urgency and let that quantity evolve in time as the
+robot evaluates the importance of its assigned task relative to competing
+tasks. The robot's motion control input is generated as a convex combination of
+the individual task vector fields. Their weights, in turn, evolve dynamically
+according to a decision model adapted from the literature on bioinspired swarm
+decision making, driven by the values. In this paper we study a simple case
+with two recurring, competing navigation tasks and derive conditions under
+which it can be guaranteed that the robot will repeatedly serve each in turn.
+Specifically, we provide conditions sufficient for the emergence of a stable
+limit cycle along which the robot repeatedly and alternately navigates to the
+two goal locations. Numerical study suggests that the basin of attraction is
+quite large so that significant perturbations are recovered with a reliable
+return to the desired task coordination pattern.
+"
+26985,Enhancing Predicate Pairing with Abstraction for Relational Verification,"  Relational verification is a technique that aims at proving properties that
+relate two different program fragments, or two different program runs. It has
+been shown that constrained Horn clauses (CHCs) can effectively be used for
+relational verification by applying a CHC transformation, called predicate
+pairing, which allows the CHC solver to infer relations among arguments of
+different predicates. In this paper we study how the effects of the predicate
+pairing transformation can be enhanced by using various abstract domains based
+on linear arithmetic (i.e., the domain of convex polyhedra and some of its
+subdomains) during the transformation. After presenting an algorithm for
+predicate pairing with abstraction, we report on the experiments we have
+performed on over a hundred relational verification problems by using various
+abstract domains. The experiments have been performed by using the VeriMAP
+transformation and verification system, together with the Parma Polyhedra
+Library (PPL) and the Z3 solver for CHCs.
+"
+26986,A comparative study of estimation methods in quantum tomography,"  As quantum tomography is becoming a key component of the quantum engineering
+toolbox, there is a need for a deeper understanding of the multitude of
+estimation methods available. Here we investigate and compare several such
+methods: maximum likelihood, least squares, generalised least squares, positive
+least squares, thresholded least squares and projected least squares. The
+common thread of the analysis is that each estimator projects the measurement
+data onto a parameter space with respect to a specific metric, thus allowing us
+to study the relationships between different estimators.
+The asymptotic behaviour of the least squares and the projected least squares
+estimators is studied in detail for the case of the covariant measurement and a
+family of states of varying ranks. This gives insight into the rank-dependent
+risk reduction for the projected estimator, and uncovers an interesting
+non-monotonic behaviour of the Bures risk. These asymptotic results complement
+recent non-asymptotic concentration bounds of \cite{GutaKahnKungTropp} which
+point to strong optimality properties, and high computational efficiency of the
+projected linear estimators.
+To illustrate the theoretical methods we present results of an extensive
+simulation study. An app running the different estimators has been made
+available online.
+"
+26987,Resolvability in c.c.c. generic extensions,"  Every crowded space $X$ is ${\omega}$-resolvable in the c.c.c generic
+extension $V^{Fn(|X|,2})$ of the ground model. We investigate what we can say
+about ${\lambda}$-resolvability in c.c.c-generic extensions for
+${\lambda}>{\omega}$? A topological space is ""monotonically
+$\omega_1$-resolvable"" if there is a function $f:X\to {\omega_1}$ such that
+$$\{x\in X: f(x)\ge {\alpha} \}\subset^{dense}X $$ for each
+${\alpha}<{\omega_1}$. We show that given a $T_1$ space $X$ the following
+statements are equivalent:
+(1) $X$ is ${\omega}_1$-resolvable in some c.c.c-generic extension,
+(2) $X$ is monotonically $\omega_1$-resolvable.
+(3) $X$ is ${\omega}_1$-resolvable in the Cohen-generic extension
+$V^{Fn({\omega_1},2)}$.
+We investigate which spaces are monotonically $\omega_1$-resolvable. We show
+that if a topological space $X$ is c.c.c, and ${\omega}_1\le \Delta(X)\le
+|X|<{\omega}_{\omega}$, then $X$ is monotonically $\omega_1$-resolvable. On the
+other hand, it is also consistent, modulo the existence of a measurable
+cardinal, that there is a space $Y$ with $|Y|=\Delta(Y)=\aleph_\omega$ which is
+not monotonically $\omega_1$-resolvable. The characterization of
+${\omega_1}$-resolvability in c.c.c generic extension raises the following
+question: is it true that crowded spaces from the ground model are
+${\omega}$-resolvable in $V^{Fn({\omega},2)}$? We show that (i) if $V=L$ then
+every crowded c.c.c. space $X$ is ${\omega}$-resolvable in
+$V^{Fn({\omega},2)}$, (ii) if there is no weakly inaccesssible cardinals, then
+every crowded space $X$ is ${\omega}$-resolvable in $V^{Fn({\omega}_1,2)}$. On
+the other hand, it is also consistent that there is a crowded space $X$ with
+$|X|=\Delta(X)={\omega_1}$ such that $X$ remains irresolvable after adding a
+single Cohen real.
+"
+26988,The splitting of double-component active asteroid P/2016 J1 (PANSTARRS),"  We present deep imaging observations, orbital dynamics, and dust tail model
+analyses of the double-component asteroid P/2016 J1 (J1-A and J1-B). The
+observations were acquired at the Gran Telescopio Canarias (GTC) and the
+Canada-France-Hawaii Telescope (CFHT) from mid March to late July, 2016. A
+statistical analysis of backward-in-time integrations of the orbits of a large
+sample of clone objects of P/2016 J1-A and J1-B shows that the minimum
+separation between them occurred most likely $\sim$2300 days prior to the
+current perihelion passage, i.e., during the previous orbit near perihelion.
+This closest approach was probably linked to a fragmentation event of their
+parent body. Monte Carlo dust tail models show that those two components became
+active simultaneously $\sim$250 days before the current perihelion, with
+comparable maximum loss rates of $\sim$0.7 kg s$^{-1}$ and $\sim$0.5 kg
+s$^{-1}$, and total ejected masses of 8$\times$10$^{6}$ kg and
+6$\times$10$^{6}$ kg for fragments J1-A and J1-B, respectively. In consequence,
+the fragmentation event and the present dust activity are unrelated. The
+simultaneous activation times of the two components and the fact that the
+activity lasted 6 to 9 months or longer, strongly indicate ice sublimation as
+the most likely mechanism involved in the dust emission process.
+"
+26989,On entropy of spherical twists,"  In this paper, we compute categorical entropy of spherical twists. In
+particular, we prove that Gromov-Yomdin type conjecture holds for spherical
+twists. Moreover, we construct counterexamples of Gromov-Yomdin type conjecture
+for K3 surfaces modifying Fan's construction for even higher dimensional
+Calabi-Yau manifolds. The appendix, by Arend Bayer, shows non-emptyness of
+complements of a number of spherical objects in the derived categories of K3
+surfaces.
+"
+26990,The Commutator as Least Fixed Point of a Closure Operator,"  We present a description of the (non-modular) commutator, inspired by that of
+Kearnes in~\cite[p.~930]{MR1358491}, that provides a simple recipe for
+computing the commutator.
+"
+26991,Decomposing Weighted Graphs,"  We solve the following problem: Can an undirected weighted graph G be parti-
+tioned into two non-empty induced subgraphs satisfying minimum constraints for
+the sum of edge weights at vertices of each subgraph? We show that this is
+possible for all constraints a(x), b(x) satisfying d_G(x) >= a(x) + b(x) +
+2W_G(x), for every vertex x, where d_G(x), W_G(x) are, respectively, the sum
+and maximum of incident edge weights.
+"
+26992,The metal-insulator transition in the organic conductor $β^{\prime\prime}$-(BEDT-TTF)$_{2}$Hg(SCN)$_{2}$Cl,"  We explore the nature of the metal-insulator transition in
+$\beta^{\prime\prime}$-(BEDT-TTF)$_{2}$Hg(SCN)$_{2}$Cl by x-ray, electrical
+transport, ESR, Raman and infrared investigations. Magnetic and vibrational
+spectroscopy concurrently reveal a gradual dimerization along the stacking
+direction $a$, setting in already at the crossover temperature of 150K from the
+metallic to the insulating state. A spin gap $\Delta_\sigma = 47$meV is
+extracted. From the activated resistivity behavior below 55K a charge gap
+$\Delta_\rho=60$meV is derived. At $T_{\rm CO} = 72$K the C=C vibrational modes
+reveal the development of a charge-ordered state with a charge
+disproportionation of $2\delta_{\rho}=0.34e$
+"
+26993,Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions,"  We study the online learning problem of a bidder who participates in repeated
+auctions. With the goal of maximizing his T-period payoff, the bidder
+determines the optimal allocation of his budget among his bids for $K$ goods at
+each period. As a bidding strategy, we propose a polynomial-time algorithm,
+inspired by the dynamic programming approach to the knapsack problem. The
+proposed algorithm, referred to as dynamic programming on discrete set (DPDS),
+achieves a regret order of $O(\sqrt{T\log{T}})$. By showing that the regret is
+lower bounded by $\Omega(\sqrt{T})$ for any strategy, we conclude that DPDS is
+order optimal up to a $\sqrt{\log{T}}$ term. We evaluate the performance of
+DPDS empirically in the context of virtual trading in wholesale electricity
+markets by using historical data from the New York market. Empirical results
+show that DPDS consistently outperforms benchmark heuristic methods that are
+derived from machine learning and online learning approaches.
+"
+26994,Displacement Convexity in Spatially Coupled Scalar Recursions,"  We introduce a technique for the analysis of general spatially coupled
+systems that are governed by scalar recursions. Such systems can be expressed
+in variational form in terms of a potential functional. We show, under mild
+conditions, that the potential functional is \emph{displacement convex} and
+that the minimizers are given by the fixed points of the recursions.
+Furthermore, we give the conditions on the system such that the minimizing
+fixed point is unique up to translation along the spatial direction. The
+condition matches those in \cite{KRU12} for the existence of spatial fixed
+points. \emph{Displacement convexity} applies to a wide range of spatially
+coupled recursions appearing in coding theory, compressive sensing, random
+constraint satisfaction problems, as well as statistical mechanical models. We
+illustrate it with applications to Low-Density Parity-Check and generalized
+LDPC codes used for transmission on the binary erasure channel, or general
+binary memoryless symmetric channels within the Gaussian reciprocal channel
+approximation, as well as compressive sensing.
+"
+26995,Probabilistic Matching: Causal Inference under Measurement Errors,"  The abundance of data produced daily from large variety of sources has
+boosted the need of novel approaches on causal inference analysis from
+observational data. Observational data often contain noisy or missing entries.
+Moreover, causal inference studies may require unobserved high-level
+information which needs to be inferred from other observed attributes. In such
+cases, inaccuracies of the applied inference methods will result in noisy
+outputs. In this study, we propose a novel approach for causal inference when
+one or more key variables are noisy. Our method utilizes the knowledge about
+the uncertainty of the real values of key variables in order to reduce the bias
+induced by noisy measurements. We evaluate our approach in comparison with
+existing methods both on simulated and real scenarios and we demonstrate that
+our method reduces the bias and avoids false causal inference conclusions in
+most cases.
+"
+26996,Online and Linear-Time Attention by Enforcing Monotonic Alignments,"  Recurrent neural network models with an attention mechanism have proven to be
+extremely effective on a wide variety of sequence-to-sequence problems.
+However, the fact that soft attention mechanisms perform a pass over the entire
+input sequence when producing each element in the output sequence precludes
+their use in online settings and results in a quadratic time complexity. Based
+on the insight that the alignment between input and output sequence elements is
+monotonic in many problems of interest, we propose an end-to-end differentiable
+method for learning monotonic alignments which, at test time, enables computing
+attention online and in linear time. We validate our approach on sentence
+summarization, machine translation, and online speech recognition problems and
+achieve results competitive with existing sequence-to-sequence models.
+"
+26997,The road map toward room temperature superconductivity: manipulating different pairing channels in systems composed of multiple electronic components,"  While it is known that the amplification of the superconducting critical
+temperature Tc is possible in a system of multiple electronic components in
+comparison with a single component system, many different road maps for room
+temperature superconductivity have been proposed for a variety of
+multicomponent scenarios. Here we focus on the scenario where the first
+electronic component is assumed to have a vanishing Fermi velocity
+corresponding to a case of the intermediate polaronic regime, and the second
+electronic component is in the weak coupling regime with standard high Fermi
+velocity using a mean field theory for multiband superconductivity. This
+roadmap is motivated by compelling experimental evidence for one component in
+the proximity of a Lifshitz transition in cuprates, diborides and iron based
+superconductors. By keeping a constant and small exchange interaction between
+the two electron fluids, we search for the optimum coupling strength in the
+electronic polaronic component which gives the largest amplification of the
+superconducting critical temperature in comparison with the case of a single
+electronic component.
+"
+26998,Automatic SDF-based Code Generation from Simulink Models for Embedded Software Development,"  Matlab/Simulink is a wide-spread tool for model-based design of embedded
+systems. Supporting hierarchy, domain specific building blocks, functional
+simulation and automatic code-generation, makes it well-suited for the design
+of control and signal processing systems. In this work, we propose an automated
+translation methodology for a subset of Simulink models to Synchronous dataflow
+Graphs (SDFGs) including the automatic code-generation of SDF-compatible
+embedded code. A translation of Simulink models to SDFGs, is very suitable due
+to Simulink actor-oriented modeling nature, allowing the application of several
+optimization techniques from the SDFG domain. Because of their well-defined
+semantics, SDFGs can be analyzed at compiling phase to obtain deadlock-free and
+memory-efficient schedules. In addition, several real-time analysis methods
+exist which allow throughput-optimal mappings of SDFGs to Multiprocessor on
+Chip (MPSoC) while guaranteeing upper-bounded latencies. The correctness of our
+translation is justified by integrating the SDF generated code as a
+software-in-the-loop (SIL) and comparing its results with the results of the
+model-in-the-loop (MIL) simulation of reference Simulink models. The
+translation is demonstrated with the help of two case studies: a Transmission
+Controller Unit (TCU) and an Automatic Climate Control.
+"
+26999,Subsonic Flow over a Thin Airfoil in Ground Effect,"  In this paper, the problem of compressible flow over a thin airfoil located
+near the ground is studied. A singular integral equation, also known as Possio
+equation, that relates the pressure jump along the airfoil to its downwash is
+derived. The derivation of the equation utilizes Laplace transform, Fourier
+transform, method of images, and theory of Mikhlin multipliers. The existence
+and uniqueness of solution to the Possio equation is verified for the steady
+state case and an approximate solution is obtained. The aerodynamic loads are
+then calculated based on the approximate solution. Moreover, the divergence
+speed of a continuum wing structure located near the ground is obtained based
+on the derived expressions for the aerodynamic loads.
+"
+27000,A new class of tests for multinormality with i.i.d. and Garch data based on the empirical moment generating function,"  We generalize a recent class of tests for univariate normality that are based
+on the empirical moment generating function to the multivariate setting, thus
+obtaining a class of affine invariant, consistent and easy-to-use
+goodness-of-fit tests for multinormality. The test statistics are suitably
+weighted $L^2$-statistics, and we provide their asymptotic behavior both for
+i.i.d. observations as well as in the context of testing that the innovation
+distribution of a multivariate GARCH model is Gaussian. We study the
+finite-sample behavior of the new tests, compare the criteria with alternative
+existing procedures, and apply the new procedure to a data set of monthly log
+returns.
+"
+27001,The 3d Stress-Tensor Bootstrap,"  We study the conformal bootstrap for 4-point functions of stress tensors in
+parity-preserving 3d CFTs. To set up the bootstrap equations, we analyze the
+constraints of conformal symmetry, permutation symmetry, and conservation on
+the stress-tensor 4-point function and identify a non-redundant set of crossing
+equations. Studying these equations numerically using semidefinite
+optimization, we compute bounds on the central charge as a function of the
+independent coefficient in the stress-tensor 3-point function. With no
+additional assumptions, these bounds numerically reproduce the conformal
+collider bounds and give a general lower bound on the central charge. We also
+study the effect of gaps in the scalar, spin-2, and spin-4 spectra on the
+central charge bound. We find general upper bounds on these gaps as well as
+tighter restrictions on the stress-tensor 3-point function coefficients for
+theories with moderate gaps. When the gap for the leading scalar or spin-2
+operator is sufficiently large to exclude large N theories, we also obtain
+upper bounds on the central charge, thus finding compact allowed regions.
+Finally, assuming the known low-lying spectrum and central charge of the
+critical 3d Ising model, we determine its stress-tensor 3-point function and
+derive a bound on its leading parity-odd scalar.
+"
+27002,Three-dimensionality of the bulk electronic structure in WTe2,"  We use temperature- and field-dependent resistivity measurements
+[Shubnikov--de Haas (SdH) quantum oscillations] and ultrahigh resolution,
+tunable, vacuum ultraviolet (VUV) laser-based angle-resolved photoemission
+spectroscopy (ARPES) to study the three-dimensionality (3D) of the bulk
+electronic structure in WTe2, a type-II Weyl semimetal. The bulk Fermi surface
+(FS) consists of two pairs of electron pockets and two pairs of hole pockets
+along the X-Gamma-X direction as detected by using an incident photon energy of
+6.7 eV, which is consistent with the previously reported data. However, if
+using an incident photon energy of 6.36 eV, another pair of tiny electron
+pockets is detected on both sides of the Gamma point, which is in agreement
+with the small quantum oscillation frequency peak observed in the
+magnetoresistance. Therefore, the bulk, 3D FS consists of three pairs of
+electron pockets and two pairs of hole pockets in total. With the ability of
+fine tuning the incident photon energy, we demonstrate the strong
+three-dimensionality of the bulk electronic structure in WTe2. The combination
+of resistivity and ARPES measurements reveal the complete, and consistent,
+picture of the bulk electronic structure of this material.
+"
+27003,Stabilizing switching signals: a transition from point-wise to asymptotic conditions,"  Characterization of classes of switching signals that ensure stability of
+switched systems occupies a significant portion of the switched systems
+literature. This article collects a multitude of stabilizing switching signals
+under an umbrella framework. We achieve this in two steps: Firstly, given a
+family of systems, possibly containing unstable dynamics, we propose a new and
+general class of stabilizing switching signals. Secondly, we demonstrate that
+prior results based on both point-wise and asymptotic characterizations follow
+our result. This is the first attempt in the switched systems literature where
+these switching signals are unified under one banner.
+"
+27004,Spectral theory for non-unitary twists,"  Let $G$ be a Lie-group and $\Ga\subset G$ a cocompact lattice. For a
+finite-dimensional, not necessarily unitary representation $\om$ of $\Ga$ we
+show that the $G$-representation on $L^2(\Ga\bs G,\om)$ admits a complete
+filtration with irreducible quotients. As a consequence, we show the trace
+formula for non-unitary twists and arbitrary locally compact groups.
+"
+27005,Early Stage Malware Prediction Using Recurrent Neural Networks,"  Static malware analysis is well-suited to endpoint anti-virus systems as it
+can be conducted quickly by examining the features of an executable piece of
+code and matching it to previously observed malicious code. However, static
+code analysis can be vulnerable to code obfuscation techniques. Behavioural
+data collected during file execution is more difficult to obfuscate, but takes
+a relatively long time to capture - typically up to 5 minutes, meaning the
+malicious payload has likely already been delivered by the time it is detected.
+In this paper we investigate the possibility of predicting whether or not an
+executable is malicious based on a short snapshot of behavioural data. We find
+that an ensemble of recurrent neural networks are able to predict whether an
+executable is malicious or benign within the first 5 seconds of execution with
+94% accuracy. This is the first time general types of malicious file have been
+predicted to be malicious during execution rather than using a complete
+activity log file post-execution, and enables cyber security endpoint
+protection to be advanced to use behavioural data for blocking malicious
+payloads rather than detecting them post-execution and having to repair the
+damage.
+"
+27006,Large Fermi Arcs in Unconventional Weyl Semimetal RhSi,"  The theoretical proposal of chiral fermions in topological semimetals has led
+to a significant effort towards their experimental realization. In particular,
+the Fermi surfaces of chiral semimetals carry quantized Chern numbers, making
+them an attractive platform for the observation of exotic transport and optical
+phenomena. While the simplest example of a chiral fermion in condensed matter
+is a conventional $|C|=1$ Weyl fermion, recent theoretical works have proposed
+a number of unconventional chiral fermions beyond the Standard Model which are
+protected by unique combinations of topology and crystalline symmetries.
+However, materials candidates for experimentally probing the transport and
+response signatures of these unconventional fermions have thus far remained
+elusive. In this paper, we propose the RhSi family in space group (SG) $\#$198
+as the ideal platform for the experimental examination of unconventional chiral
+fermions. We find that RhSi is a filling-enforced semimetal that features near
+its Fermi surface a chiral double six-fold-degenerate spin-1 Weyl node at $R$
+and a previously uncharacterized four-fold-degenerate chiral fermion at
+$\Gamma$. Each unconventional fermion displays Chern number $\pm4$ at the Fermi
+level. We also show that RhSi displays the largest possible momentum separation
+of compensative chiral fermions, the largest proposed topologically nontrivial
+energy window, and the longest possible Fermi arcs on its surface. We conclude
+by proposing signatures of an exotic bulk photogalvanic response in RhSi.
+"
+27007,An Enhanced Framework with Advanced Study to Incorporate the Searching of E-Commerce Products Using Modernization of Database Queries,"  This study aims to inspect and evaluate the integration of database queries
+and their use in e-commerce product searches. It has been observed that
+e-commerce is one of the most prominent trends, which have been emerged in the
+business world, for the past decade. E-commerce has gained tremendous
+popularity, as it offers higher flexibility, cost efficiency, effectiveness,
+and convenience, to both, consumers and businesses. Large number of retailing
+companies has adopted this technology, in order to expand their operations,
+across of the globe; hence they needs to have highly responsive and integrated
+databases. In this regard, the approach of database queries is found to be the
+most appropriate and adequate techniques, as it simplifies the searches of
+e-commerce products.
+"
+27008,Brightest galaxies as halo centre tracers in SDSS DR7,"  Determining the positions of halo centres in large-scale structure surveys is
+crucial for many cosmological studies. A common assumption is that halo centres
+correspond to the location of their brightest member galaxies. In this paper,
+we study the dynamics of brightest galaxies with respect to other halo members
+in the Sloan Digital Sky Survey DR7. Specifically, we look at the line-of-sight
+velocity and spatial offsets between brightest galaxies and their neighbours.
+We compare those to detailed mock catalogues, constructed from high-resolution,
+dark-matter-only $N$-body simulations, in which it is assumed that satellite
+galaxies trace dark matter subhaloes. This allows us to place constraints on
+the fraction $f_{\rm BNC}$ of haloes in which the brightest galaxy is not the
+central. Compared to previous studies we explicitly take into account the
+unrelaxed state of the host haloes, velocity offsets of halo cores and
+correlations between $f_{\rm BNC}$ and the satellite occupation. We find that
+$f_{\rm BNC}$ strongly decreases with the luminosity of the brightest galaxy
+and increases with the mass of the host halo. Overall, in the halo mass range
+$10^{13} - 10^{14.5} h^{-1} M_\odot$ we find $f_{\rm BNC} \sim 30\%$, in good
+agreement with a previous study by Skibba et al. We discuss the implications of
+these findings for studies inferring the galaxy--halo connection from satellite
+kinematics, models of the conditional luminosity function and galaxy formation
+in general.
+"
+27009,Adaptive Network Flow with $k$-Arc Destruction,"  When a flow is not allowed to be reoriented the Maximum Residual Flow Problem
+with $k$-Arc Destruction is known to be $NP$-hard for $k=2$. We show that when
+a flow is allowed to be adaptive the problem becomes polynomial for every fixed
+$k$.
+"
+27010,Cross-identification of stellar catalogs with multiple stars: Complexity and Resolution,"  In this work, I present an optimization problem which consists of assigning
+entries of a stellar catalog to multiple entries of another stellar catalog
+such that the probability of such assignment is maximum. I show a way of
+modeling it as a Maximum Weighted Stable Set Problem which is further used to
+solve a real astronomical instance and I partially characterize the forbidden
+subgraphs of the resulting family of graphs given by that reduction. Finally, I
+prove that the problem is NP-Hard.
+"
+27011,QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures,"  We present QuickNet, a fast and accurate network architecture that is both
+faster and significantly more accurate than other fast deep architectures like
+SqueezeNet. Furthermore, it uses less parameters than previous networks, making
+it more memory efficient. We do this by making two major modifications to the
+reference Darknet model (Redmon et al, 2015): 1) The use of depthwise separable
+convolutions and 2) The use of parametric rectified linear units. We make the
+observation that parametric rectified linear units are computationally
+equivalent to leaky rectified linear units at test time and the observation
+that separable convolutions can be interpreted as a compressed Inception
+network (Chollet, 2016). Using these observations, we derive a network
+architecture, which we call QuickNet, that is both faster and more accurate
+than previous models. Our architecture provides at least four major advantages:
+(1) A smaller model size, which is more tenable on memory constrained systems;
+(2) A significantly faster network which is more tenable on computationally
+constrained systems; (3) A high accuracy of 95.7 percent on the CIFAR-10
+Dataset which outperforms all but one result published so far, although we note
+that our works are orthogonal approaches and can be combined (4) Orthogonality
+to previous model compression approaches allowing for further speed gains to be
+realized.
+"
+27012,On estimation of the noise variance in high-dimensional linear models,"  We consider the problem of recovering the unknown noise variance in the
+linear regression model. To estimate the nuisance (a vector of regression
+coefficients) we use a family of spectral regularisers of the maximum
+likelihood estimator. The noise estimation is based on the adaptive
+normalisation of the squared error. We derive the upper bound for the
+concentration of the proposed method around the ideal estimator (the case of
+zero nuisance).
+"
+27013,Optimal Localization of Diffusion Sources in Complex Networks,"  Locating sources of diffusion and spreading from minimum data is a
+significant problem in network science with great applied values to the
+society. However, a general theoretical framework dealing with optimal source
+localization is lacking. Combining the controllability theory for complex
+networks and compressive sensing, we develop a framework with high efficiency
+and robustness for optimal source localization in arbitrary weighted networks
+with arbitrary distribution of sources. We offer a minimum output analysis to
+quantify the source locatability through a minimal number of messenger nodes
+that produce sufficient measurement for fully locating the sources. When the
+minimum messenger nodes are discerned, the problem of optimal source
+localization becomes one of sparse signal reconstruction, which can be solved
+using compressive sensing. Application of our framework to model and empirical
+networks demonstrates that sources in homogeneous and denser networks are more
+readily to be located. A surprising finding is that, for a connected undirected
+network with random link weights and weak noise, a single messenger node is
+sufficient for locating any number of sources. The framework deepens our
+understanding of the network source localization problem and offers efficient
+tools with broad applications.
+"
+27014,Mathematical modelling of calcium signalling taking into account mechanical effects,"  Most of the calcium in the body is stored in bone. The rest is stored
+elsewhere, and calcium signalling is one of the most important mechanisms of
+information propagation in the body. Yet, many questions remain open. In this
+work, we initially consider the mathematical model proposed in Atri et al.
+\cite{ atri1993single}. Omitting diffusion, the model is a system of two
+nonlinear ordinary differential equations (ODEs) for the calcium concentration,
+and the fraction of $IP_3$ receptors that have not been inactivated by the
+calcium. We analyse in detail the system as the $IP_3$ concentration, the
+\textit{bifurcation parameter}, increases presenting some new insights. We
+analyse asymptotically the relaxation oscillations of the model by exploiting a
+separation of timescales. Furthermore, motivated by experimental evidence that
+cells release calcium when mechanically stimulated and that, in turn, calcium
+release affects the mechanical behaviour of cells, we propose an extension of
+the Atri model to a 3D nonlinear ODE mechanochemical model, where the
+additional equation, derived consistently from a full viscoelastic
+\emph{ansatz}, models the evolution of cell/tissue dilatation. Furthermore, in
+the calcium dynamics equation we introduce a new ""stretch-activation"" source
+term that induces calcium release and which involves a new bifurcation
+parameter, the ""strength"" of the source. Varying the two bifurcation
+parameters, we analyse in detail the interplay of the mechanical and the
+chemical effects, and we find that as the strength of the mechanical stimulus
+is increased, the $IP_3$ parameter range for which oscillations emerge
+decreases, until oscillations eventually vanish at a critical value. Finally,
+we analyse the model when the calcium dynamics are assumed faster than the
+dynamics of the other two variables.
+"
+27015,Learning Algorithms for Active Learning,"  We introduce a model that learns active learning algorithms via metalearning.
+For a distribution of related tasks, our model jointly learns: a data
+representation, an item selection heuristic, and a method for constructing
+prediction functions from labeled training sets. Our model uses the item
+selection heuristic to gather labeled training sets from which to construct
+prediction functions. Using the Omniglot and MovieLens datasets, we test our
+model in synthetic and practical settings.
+"
+27016,State-Regularized Recurrent Neural Networks,"  Recurrent neural networks are a widely used class of neural architectures.
+They have, however, two shortcomings. First, it is difficult to understand what
+exactly they learn. Second, they tend to work poorly on sequences requiring
+long-term memorization, despite having this capacity in principle. We aim to
+address both shortcomings with a class of recurrent networks that use a
+stochastic state transition mechanism between cell applications. This
+mechanism, which we term state-regularization, makes RNNs transition between a
+finite set of learnable states. We evaluate state-regularized RNNs on (1)
+regular languages for the purpose of automata extraction; (2) nonregular
+languages such as balanced parentheses, palindromes, and the copy task where
+external memory is required; and (3) real-word sequence learning tasks for
+sentiment analysis, visual object recognition, and language modeling. We show
+that state-regularization (a) simplifies the extraction of finite state
+automata modeling an RNN's state transition dynamics; (b) forces RNNs to
+operate more like automata with external memory and less like finite state
+machines; (c) makes RNNs have better interpretability and explainability.
+"
+27017,Circuit Bounds on Stochastic Transport in the Lorenz Equations,"  In turbulent Rayleigh-Bénard convection one seeks the relationship between
+the heat transport, captured by the Nusselt number, and the temperature drop
+across the convecting layer, captured by Rayleigh number. In experiments, one
+measures the Nusselt number for a given Rayleigh number, and the question of
+how close that value is to the maximal transport is a key prediction of
+variational fluid mechanics in the form of an upper bound. The Lorenz equations
+have traditionally been studied as a simplified model of turbulent
+Rayleigh-Bénard convection, and hence it is natural to investigate their
+upper bounds, which has previously been done numerically and analytically, but
+they are not as easily accessible in an experimental context. Here we describe
+a specially built circuit that is the experimental analogue of the Lorenz
+equations and compare its output to the recently determined upper bounds of the
+stochastic Lorenz equations \cite{AWSUB:2016}. The circuit is substantially
+more efficient than computational solutions, and hence we can more easily
+examine the system. Because of offsets that appear naturally in the circuit, we
+are motivated to study unique bifurcation phenomena that arise as a result.
+Namely, for a given Rayleigh number, we find a reentrant behavior of the
+transport on noise amplitude and this varies with Rayleigh number passing from
+the homoclinic to the Hopf bifurcation.
+"
+27018,A Broad Learning Approach for Context-Aware Mobile Application Recommendation,"  With the rapid development of mobile apps, the availability of a large number
+of mobile apps in application stores brings challenge to locate appropriate
+apps for users. Providing accurate mobile app recommendation for users becomes
+an imperative task. Conventional approaches mainly focus on learning users'
+preferences and app features to predict the user-app ratings. However, most of
+them did not consider the interactions among the context information of apps.
+To address this issue, we propose a broad learning approach for
+\textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor
+\textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to
+effectively integrate user's preference, app category information and
+multi-view features to facilitate the performance of app rating prediction. The
+multidimensional structure is employed to capture the hidden relationships
+between multiple app categories with multi-view features. We develop an
+efficient factorization method which applies Tucker decomposition to learn the
+full-order interactions within multiple categories and features. Furthermore,
+we employ a group $\ell_{1}-$norm regularization to learn the group-wise
+feature importance of each view with respect to each app category. Experiments
+on two real-world mobile app datasets demonstrate the effectiveness of the
+proposed method.
+"
+27019,Knot theory of Morse-Bott critical loci,"  We give an alternative proof of that a critical knot of a Morse-Bott function
+$f: S^3 \rightarrow \mathbb{R}$ is a graph knot where the critical set of $f$
+is a link in $S^3$. Our proof inducts on the number of index-1 critical knots
+of $f$.
+"
+27020,Closure Operators and Spam Resistance for PageRank,"  We study the spammablility of ranking functions on the web. Although
+graph-theoretic ranking functions, such as Hubs and Authorities and PageRank
+exist, there is no graph theoretic notion of how spammable such functions are.
+We introduce a very general cost model that only depends on the observation
+that changing the links of a page that you own is free, whereas changing the
+links on pages owned by others requires effort or money. We define spammability
+to be the ratio between the amount of benefit one receives for one's spamming
+efforts and the amount of effort/money one must spend to spam. The more
+effort/money it takes to get highly ranked, the less spammable the function.
+Our model helps explain why both hubs and authorities and standard PageRank
+are very easy to spam. Although standard PageRank is easy to spam, we show that
+there exist spam-resistant PageRanks. Specifically, we propose a ranking
+method, Min-k-PPR, that is the component-wise min of a set of personalized
+PageRanks centered on k trusted sites. Our main results are that Min-k-PPR is,
+itself, a type of PageRank and that it is expensive to spam.
+We elucidate a surprisingly elegant algebra for PageRank. We define the space
+of all possible PageRanks and show that this space is closed under some
+operations. Most notably, we show that PageRanks are closed under (normalized)
+component-wise min, which establishes that (normalized) Min-k-PPRis a PageRank.
+This algebraic structure is also key to demonstrating the spam resistance of
+Min-k-PPR.
+"
+27021,Study of tau-neutrino production at the CERN SPS,"  The DsTau project proposes to study tau-neutrino production in high-energy
+proton interactions. The outcome of this experiment are prerequisite for
+measuring the $\nu_\tau$ charged-current cross section that has never been well
+measured. Precisely measuring the cross section would enable testing of lepton
+universality in $\nu_\tau$ scattering and it also has practical implications
+for neutrino oscillation experiments and high-energy astrophysical $\nu_\tau$
+observations. $D_s$ mesons, the source of tau neutrinos, following high-energy
+proton interactions will be studied by a novel approach to detect the
+double-kink topology of the decays $D_s \rightarrow \tau\nu_\tau$ and
+$\tau\rightarrow\nu_\tau X$. Directly measuring $D_s\rightarrow \tau$ decays
+will provide an inclusive measurement of the $D_s$ production rate and decay
+branching ratio to $\tau$. The momentum reconstruction of $D_s$ will be
+performed by combining topological variables. This project aims to detect 1,000
+$D_s \rightarrow \tau$ decays in $2.3 \times 10^8$ proton interactions in
+tungsten target to study the differential production cross section of $D_s$
+mesons. To achieve this, state-of-the-art emulsion detectors with a
+nanometric-precision readout will be used. The data generated by this project
+will enable the $\nu_\tau$ cross section from DONUT to be re-evaluated, and
+this should significantly reduce the total systematic uncertainty. Furthermore,
+these results will provide essential data for future $\nu_\tau$ experiments
+such as the $\nu_\tau$ program in the SHiP project at CERN. In addition, the
+analysis of $2.3 \times 10^8$ proton interactions, combined with the expected
+high yield of $10^5$ charmed decays as by-products, will enable the extraction
+of additional physical quantities.
+"
+27022,Time Limits in Reinforcement Learning,"  In reinforcement learning, it is common to let an agent interact for a fixed
+amount of time with its environment before resetting it and repeating the
+process in a series of episodes. The task that the agent has to learn can
+either be to maximize its performance over (i) that fixed period, or (ii) an
+indefinite period where time limits are only used during training to diversify
+experience. In this paper, we provide a formal account for how time limits
+could effectively be handled in each of the two cases and explain why not doing
+so can cause state-aliasing and invalidation of experience replay, leading to
+suboptimal policies and training instability. In case (i), we argue that the
+terminations due to time limits are in fact part of the environment, and thus a
+notion of the remaining time should be included as part of the agent's input to
+avoid violation of the Markov property. In case (ii), the time limits are not
+part of the environment and are only used to facilitate learning. We argue that
+this insight should be incorporated by bootstrapping from the value of the
+state at the end of each partial episode. For both cases, we illustrate
+empirically the significance of our considerations in improving the performance
+and stability of existing reinforcement learning algorithms, showing
+state-of-the-art results on several control tasks.
+"
+27023,Theoretical analysis of effective electric fields in mercury monohalides,"  Mercury monohalides are promising candidates for electron electric dipole
+moment searches. This is due to their extremely large values of effective
+electric fields, besides other attractive experimental features. We have
+elucidated the theoretical reasons of our previous work. We have also presented
+a detailed analysis of our calculations, by including the most important of the
+correlation effects' contributions. We have also analyzed the major
+contributions to the effective electric field, at the Dirac- Fock level, and
+identified those atomic orbitals' mixings that contribute significantly to it.
+"
+27024,Solar plasma radio emission in the presence of imbalanced turbulence of kinetic-scale Alfvén waves,"  We study the influence of kinetic-scale Alfvénic turbulence on the
+generation of plasma radio emission in the solar coronal regions where the
+plasma/magnetic pressure ratio $\beta $ is smaller than the electron/ion mass
+ratio $m_{e}/m_{i}$. The present study is motivated by the phenomenon of solar
+type I radio storms associated with the strong magnetic field of active
+regions. The measured brightness temperature of the type I storms can be up to
+$10^{10}$ K for continuum emission, and can exceed $10^{11}$ K for type I
+bursts. At present, there is no generally accepted theory explaining such high
+brightness temperatures and some other properties of the type I storms. We
+propose the model with the imbalanced turbulence of kinetic-scale Alfvén
+waves producing an asymmetric quasilinear plateau on the upward half of the
+electron velocity distribution. The Landau damping of resonant Langmuir waves
+is suppressed and their amplitudes grow spontaneously above the thermal level.
+The estimated saturation level of Langmuir waves is high enough to generate
+observed type I radio emission at the fundamental plasma frequency. Harmonic
+emission does not appear in our model because the backward-propagating Langmuir
+waves undergo a strong Landau damping. Our model predicts $100\%$ polarization
+in the sense of the ordinary (o-) mode of type I emission.
+"
+27025,Position Reconstruction in LUX,"  The $(x, y)$ position reconstruction method used in the analysis of the
+complete exposure of the Large Underground Xenon (LUX) experiment is presented.
+The algorithm is based on a statistical test that makes use of an iterative
+method to recover the photomultiplier tube (PMT) light response directly from
+the calibration data. The light response functions make use of a two
+dimensional functional form to account for the photons reflected on the inner
+walls of the detector. To increase the resolution for small pulses, a photon
+counting technique was employed to describe the response of the PMTs. The
+reconstruction was assessed with calibration data including
+${}^{\mathrm{83m}}$Kr (releasing a total energy of 41.5 keV) and ${}^{3}$H
+($\beta^-$ with Q = 18.6 keV) decays, and a deuterium-deuterium (D-D) neutron
+beam (2.45 MeV). In the horizontal plane, the reconstruction has achieved an
+$(x, y)$ position uncertainty of $\sigma$= 0.82 cm for events of only 200
+electroluminescence photons and $\sigma$ = 0.17 cm for 4,000
+electroluminescence photons. Such signals are associated with electron recoils
+of energies $\sim$0.25 keV and $\sim$10 keV, respectively. The reconstructed
+position of the smallest events with a single electron emitted from the liquid
+surface has a horizontal $(x, y)$ uncertainty of 2.13 cm.
+"
+27026,A Mixture Model for Learning Multi-Sense Word Embeddings,"  Word embeddings are now a standard technique for inducing meaning
+representations for words. For getting good representations, it is important to
+take into account different senses of a word. In this paper, we propose a
+mixture model for learning multi-sense word embeddings. Our model generalizes
+the previous works in that it allows to induce different weights of different
+senses of a word. The experimental results show that our model outperforms
+previous models on standard evaluation tasks.
+"
+27027,Path Planning Algorithms for a Car-Like Robot visiting a set of Waypoints with Field of View Constraints,"  This article considers two variants of a shortest path problem for a car-like
+robot visiting a set of waypoints. The sequence of waypoints to be visited is
+specified in the first variant while the robot is allowed to visit the
+waypoints in any sequence in the second variant. Field of view constraints are
+also placed when the robot arrives at a waypoint, i.e., the orientation of the
+robot at any waypoint is restricted to belong to a given interval of angles at
+the waypoint. The shortest path problem is first solved for two waypoints with
+the field of view constraints using Pontryagin's minimum principle. Using the
+results for the two point problem, tight lower and upper bounds on the length
+of the shortest path are developed for visiting n points by relaxing the
+requirement that the arrival angle must be equal to the departure angle of the
+robot at each waypoint. Theoretical bounds are also provided on the length of
+the feasible solutions obtained by the proposed algorithm. Simulation results
+verify the performance of the bounds for instances with 20 waypoints.
+"
+27028,Changing the prior: absolute neutrino mass constraints in nonlocal gravity,"  Prior change is discussed in observational constraints studies of nonlocally
+modified gravity. In the latter, a model characterized by a modification of the
+form $\sim m^2 R\Box^{-2}R$ to the Einstein-Hilbert action was compared against
+the base $\Lambda$CDM one in a Bayesian way. It was found that the competing
+modified gravity model is significantly disfavored (at $22 \,$:$\, 1$ in terms
+of betting-odds) against $\Lambda$CDM given CMB+SNIa+BAO data, because of a
+dominant tension appearing in the $H_0 \,$-$\, \Omega_M$ plan. We identify the
+underlying mechanism generating such a tension and show that it is mostly
+caused by the late-time, quite smooth, phantom nature of the effective dark
+energy described by the nonlocal model. We find possible solutions for it to be
+resolved and explore a given one that consists in extending the initial
+baseline from one massive neutrino eigenstate to three degenerate ones, whose
+absolute mass $\sum m_\nu \, / \, 3$ is allowed to take values within a
+reasonable prior interval. As a net effect, the absolute neutrino mass is
+inferred to be non-vanishing at $2 \sigma$ level, best-fitting at $\sum m_\nu
+\approx 0.21 {\, \rm eV}$, and the Bayesian tension disappears rendering the
+nonlocal gravity model statistically equivalent to $\Lambda$CDM, given recent
+CMB+SNIa+BAO data. We also discuss constraints from growth rate measurements $f
+\sigma_8$ whose fit is found to be improved by a larger massive neutrino
+fraction as well. The $\nu$-extended nonlocal model also prefers a higher value
+of $H_0$ than $\Lambda$CDM, therefore in better agreement with local
+measurements.
+"
+27029,Exploring Sparsity in Recurrent Neural Networks,"  Recurrent Neural Networks (RNN) are widely used to solve a variety of
+problems and as the quantity of data and the amount of available compute have
+increased, so have model sizes. The number of parameters in recent
+state-of-the-art networks makes them hard to deploy, especially on mobile
+phones and embedded devices. The challenge is due to both the size of the model
+and the time it takes to evaluate it. In order to deploy these RNNs
+efficiently, we propose a technique to reduce the parameters of a network by
+pruning weights during the initial training of the network. At the end of
+training, the parameters of the network are sparse while accuracy is still
+close to the original dense neural network. The network size is reduced by 8x
+and the time required to train the model remains constant. Additionally, we can
+prune a larger dense network to achieve better than baseline performance while
+still reducing the total number of parameters significantly. Pruning RNNs
+reduces the size of the model and can also help achieve significant inference
+time speed-up using sparse matrix multiply. Benchmarks show that using our
+technique model size can be reduced by 90% and speed-up is around 2x to 7x.
+"
+27030,Spin Liquid versus Spin Orbit Coupling on the Triangular Lattice,"  In this paper, we explore the relationship between strong spin-orbit coupling
+and spin liquid physics. We study a very general model on the triangular
+lattice where spin-orbit coupling leads to the presence of highly anisotropic
+interactions. We use variational Monte Carlo to study both $U(1)$ quantum spin
+liquid states and ordered ones, via the Gutzwiller projected fermion
+construction. We thereby obtain the ground state phase diagram in this phase
+space. We furthermore consider effects beyond the Gutzwiller wavefunctions for
+the spinon Fermi surface quantum spin liquid, which are of particular
+importance when spin-orbit coupling is present.
+"
+27031,f-GANs in an Information Geometric Nutshell,"  Nowozin \textit{et al} showed last year how to extend the GAN
+\textit{principle} to all $f$-divergences. The approach is elegant but falls
+short of a full description of the supervised game, and says little about the
+key player, the generator: for example, what does the generator actually
+converge to if solving the GAN game means convergence in some space of
+parameters? How does that provide hints on the generator's design and compare
+to the flourishing but almost exclusively experimental literature on the
+subject?
+In this paper, we unveil a broad class of distributions for which such
+convergence happens --- namely, deformed exponential families, a wide superset
+of exponential families --- and show tight connections with the three other key
+GAN parameters: loss, game and architecture. In particular, we show that
+current deep architectures are able to factorize a very large number of such
+densities using an especially compact design, hence displaying the power of
+deep architectures and their concinnity in the $f$-GAN game. This result holds
+given a sufficient condition on \textit{activation functions} --- which turns
+out to be satisfied by popular choices. The key to our results is a variational
+generalization of an old theorem that relates the KL divergence between regular
+exponential families and divergences between their natural parameters. We
+complete this picture with additional results and experimental insights on how
+these results may be used to ground further improvements of GAN architectures,
+via (i) a principled design of the activation functions in the generator and
+(ii) an explicit integration of proper composite losses' link function in the
+discriminator.
+"
+27032,Robust inference for threshold regression models,"  This paper is concerned with inference in threshold regression models when
+the practitioners do not know whether at the threshold point the true
+specification has a kink or a jump. We nest previous works that assume either
+continuity or discontinuity at the threshold point and develop robust inference
+methods on the parameters of the model, which are valid under both
+specifications. In particular, we found that the parameter values under the
+kink restriction are irregular points of the Hessian matrix of the expected
+Gaussian quasi-likelihood. This irregularity destroys the asymptotic normality
+and induces the non-standard cube root convergence rate for the threshold
+estimate. However, it also enables us to obtain the same asymptotic
+distribution as in Hansen (2000) for the quasi-likelihood ratio statistic for
+the unknown threshold up to an unknown scale parameter. We show that this scale
+parameter can be consistently estimated by a kernel method as long as no higher
+order kernel is used. Furthermore, we propose to construct confidence intervals
+for the unknown threshold by bootstrap test inversion, also known as grid
+bootstrap. Finite sample performances of the grid bootstrap confidence
+intervals are examined through Monte Carlo simulations. We also implement our
+procedure to an economic empirical application.
+"
+27033,The wrapping hull and a unified framework for volume estimation,"  This paper develops a unified framework for estimating the volume of a set in
+$\mathbb{R}^d$ based on observations of points uniformly distributed over the
+set. The framework applies to all classes of sets satisfying one simple axiom:
+a class is assumed to be intersection stable. No further hypotheses on the
+boundary of the set are imposed; in particular, the convex sets and the
+so-called weakly-convex sets are covered by the framework. The approach rests
+upon a homogeneous Poisson point process model. We introduce the so-called
+wrapping hull, a generalization of the convex hull, and prove that it is a
+sufficient and complete statistic. The proposed estimator of the volume is
+simply the volume of the wrapping hull scaled with an appropriate factor. It is
+shown to be consistent for all classes of sets satisfying the axiom and mimics
+an unbiased estimator with uniformly minimal variance. The construction and
+proofs hinge upon an interplay between probabilistic and geometric arguments.
+The tractability of the framework is numerically confirmed in a variety of
+examples.
+"
+27034,Line-soliton and rational solutions to (2+1)-dimensional Boussinesq equation by Dbar-problem,"  We present a generalized (2+1)-dimensional Boussinesq equation, including two
+cases which are called the plus Boussinesq equation and the minus one. To
+investigate these equations, we apply the $\bar{\partial}$ approach to a
+coupled (2+1)-dimensional nonlinear equation, which reduces to the Boussinesq
+equation. For the plus equation, we give the line-solitons and rational
+solutions, for the minus one, we give some freak solutions.
+"
+27035,Scattering theory for non-selfadjoint extensions of symmetric operators,"  This work deals with the functional model for extensions of symmetric
+operators and its applications to the theory of wave scattering. In terms of
+Boris Pavlov's spectral form of this model, we find explicit formulae for the
+action of the unitary group of exponentials corresponding to almost solvable
+extensions of a given closed symmetric operator with equal deficiency indices.
+On the basis of these formulae, we are able to construct wave operators and
+derive a new representation for the scattering matrix for pairs of such
+extensions in both self-adjoint and non-self-adjoint situations.
+"
+27036,On odd dimensional complex analytic Kleinian groups,"  We shall explain here an idea to generalize classical complex analytic
+Kleinian group theory to any odd dimensional cases. For a certain class of
+discrete subgroups of $\PGL_{2n+1}(\C)$ acting on $¶^{2n+1}$, we can define
+their domains of discontinuity in a canonical manner, regarding an
+$n$-dimensional projective linear subspace in $¶^{2n+1}$ as a point, like a
+point in the classical $1$-dimensional case. Many interesting (compact)
+non-Kähler manifolds appear systematically as the canonical quotients of the
+domains. In the last section, we shall give some examples.
+"
+27037,Implicit Sensor-based Authentication of Smartphone Users with Smartwatch,"  Smartphones are now frequently used by end-users as the portals to
+cloud-based services, and smartphones are easily stolen or co-opted by an
+attacker. Beyond the initial log-in mechanism, it is highly desirable to
+re-authenticate end-users who are continuing to access security-critical
+services and data, whether in the cloud or in the smartphone. But attackers who
+have gained access to a logged-in smartphone have no incentive to
+re-authenticate, so this must be done in an automatic, non-bypassable way.
+Hence, this paper proposes a novel authentication system, iAuth, for implicit,
+continuous authentication of the end-user based on his or her behavioral
+characteristics, by leveraging the sensors already ubiquitously built into
+smartphones. We design a system that gives accurate authentication using
+machine learning and sensor data from multiple mobile devices. Our system can
+achieve 92.1% authentication accuracy with negligible system overhead and less
+than 2% battery consumption.
+"
+27038,Sodium layer chiral distribution and spin structure of Na$_2$Ni$_2$TeO$_6$ with a honeycomb network,"  The nature of Na ion distribution, diffusion path, and the spin structure of
+$P2$-type Na$_2$Ni$_2$TeO$_6$ with a Ni honeycomb network has been explored.
+The nuclear density distribution of Na ions reveals a 2D chiral pattern within
+Na layers without breaking the original 3D crystal symmetry, which has been
+achieved uniquely via an inverse Fourier transform (iFT)-assisted neutron
+diffraction technique. The Na diffusion pathway described by the calculated
+iso-surface of Na ion bond valence sum (BVS) map is found consistent to a
+chiral diffusion mechanism. The Na site occupancy and Ni$^{2+}$ spin ordering
+were examined in detail with the electron density mapping, neutron diffraction,
+magnetic susceptibility, specific heat, thermal conductivity and transport
+measurements. Signatures of both strong incommensurate (ICM) and weak
+commensurate (CM) antiferromagnetic (AFM) spin ordering were identified in the
+polycrystalline sample studied, and the CM-AFM spin ordering was confirmed by
+using a single crystal sample through the $k$-scan in the momentum space
+corresponding to the AFM peak of ($\frac{1}{2}$, 0, 1).
+"
+27039,Mixingales on Riesz spaces,"  A mixingale is a stochastic process which combines properties of martingales
+and mixing sequences. McLeish introduced the term mixingale at the $4^{th}$
+Conference on Stochastic Processes and Application, at York University,
+Toronto, 1974, in the context of $L^2$. In this paper we generalize the concept
+of a mixingale to the measure-free Riesz space setting (this generalizes all of
+the $L^p, 1\le p\le \infty$ variants) and prove that a weak law of large
+numbers holds for Riesz space mixingales. In the process we also generalize the
+concept of uniform integrability to the Riesz space setting.
+"
+27040,A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion,"  In this work we present a randomized gossip algorithm for solving the average
+consensus problem while at the same time protecting the information about the
+initial private values stored at the nodes. We give iteration complexity bounds
+for the method and perform extensive numerical experiments.
+"
+27041,Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator,"  For node level graph encoding, a recent important state-of-art method is the
+graph convolutional networks (GCN), which nicely integrate local vertex
+features and graph topology in the spectral domain. However, current studies
+suffer from several drawbacks: (1) graph CNNs relies on Chebyshev polynomial
+approximation which results in oscillatory approximation at jump
+discontinuities; (2) Increasing the order of Chebyshev polynomial can reduce
+the oscillations issue, but also incurs unaffordable computational cost; (3)
+Chebyshev polynomials require degree $\Omega$(poly(1/$\epsilon$)) to
+approximate a jump signal such as $|x|$, while rational function only needs
+$\mathcal{O}$(poly log(1/$\epsilon$))\cite{liang2016deep,telgarsky2017neural}.
+However, it's non-trivial to apply rational approximation without increasing
+computational complexity due to the denominator. In this paper, the superiority
+of rational approximation is exploited for graph signal recovering. RatioanlNet
+is proposed to integrate rational function and neural networks. We show that
+rational function of eigenvalues can be rewritten as a function of graph
+Laplacian, which can avoid multiplication by the eigenvector matrix. Focusing
+on the analysis of approximation on graph convolution operation, a graph signal
+regression task is formulated. Under graph signal regression task, its time
+complexity can be significantly reduced by graph Fourier transform. To overcome
+the local minimum problem of neural networks model, a relaxed Remez algorithm
+is utilized to initialize the weight parameters. Convergence rate of
+RatioanlNet and polynomial based methods on jump signal is analyzed for a
+theoretical guarantee. The extensive experimental results demonstrated that our
+approach could effectively characterize the jump discontinuities, outperforming
+competing methods by a substantial margin on both synthetic and real-world
+graphs.
+"
+27042,Stability of the lepton bag model based on the Kerr-Newman solution,"  We show that the considered in previous paper \cite{BurGrBag} lepton bag
+model, generating the external gravitational and electromagnetic fields of the
+Kerr-Newman (KN) solution is supersymmetric and represents a BPS-saturated
+soliton, interpolating between internal vacuum state and external KN solution.
+We obtain Bogomolnyi equations for this phase transition, and show that
+Bogomolnyi bound determines all important features of this bag model, including
+its stable shape.
+In particular, for stationary KN solution the BPS-bound provides stability of
+the ellipsoidal form of the bag and formation of the ring-string structure at
+its border, while for the periodic electromagnetic excitations of the KN
+solution, the BPS-bound controls deformation of the surface of the bag,
+reproducing the known flexibility of bag models.
+"
+27043,Robust Tracking via Weighted Online Extreme Learning Machine,"  The tracking method based on the extreme learning machine (ELM) is efficient
+and effective. ELM randomly generates input weights and biases in the hidden
+layer, and then calculates and computes the output weights by reducing the
+iterative solution to the problem of linear equations. Therefore, ELM offers
+the satisfying classification performance and fast training time than other
+discriminative models in tracking. However, the original ELM method often
+suffers from the problem of the imbalanced classification distribution, which
+is caused by few target objects, leading to under-fitting and more background
+samples leading to over-fitting. Worse still, it reduces the robustness of
+tracking under special conditions including occlusion, illumination, etc. To
+address above problems, in this paper, we present a robust tracking algorithm.
+First, we introduce the local weight matrix that is the dynamic creation from
+the data distribution at the current frame in the original ELM so as to balance
+between the empirical and structure risk, and fully learn the target object to
+enhance the classification performance. Second, we improve it to the
+incremental learning method ensuring tracking real-time and efficient. Finally,
+the forgetting factor is used to strengthen the robustness for changing of the
+classification distribution with time. Meanwhile, we propose a novel optimized
+method to obtain the optimal sample as the target object, which avoids tracking
+drift resulting from noisy samples. Therefore, our tracking method can fully
+learn both of the target object and background information to enhance the
+tracking performance, and it is evaluated in 20 challenge image sequences with
+different attributes including illumination, occlusion, deformation, etc.,
+which achieves better performance than several state-of-the-art methods in
+terms of effectiveness and robustness.
+"
+27044,A Gelfand-Levitan trace formula for generic quantum graphs,"  We formulate and prove a Gelfand-Levitan trace formula for general quantum
+graphs with arbitrary edge lengths and coupling conditions which cover all
+self-adjoint operators on quantum graphs, except for a set of measure zero. The
+formula is reminiscent of the original Gelfand-Levitan result on the segment
+with Neumann boundary conditions.
+"
+27045,A Note on the Quasi-Stationary Distribution of the Shiryaev Martingale on the Positive Half-Line,"  We obtain a closed-form formula for the quasi-stationary distribution of the
+classical Shiryaev martingale diffusion considered on the positive half-line
+$[A,+\infty)$ with $A>0$ fixed; the state space's left endpoint is assumed to
+be the killing boundary. The formula is obtained analytically as the solution
+of the appropriate singular Sturm-Liouville problem; the latter was first
+considered in Section 7.8.2 of Collet et al. (2013), but has heretofore
+remained unsolved.
+"
+27046,Quantifying Local Randomness in Human DNA and RNA Sequences Using Erdos Motifs,"  In 1932, Paul Erdos asked whether a random walk constructed from a binary
+sequence can achieve the lowest possible deviation (lowest discrepancy), for
+the sequence itself and for all its subsequences formed by homogeneous
+arithmetic progressions. Although avoiding low discrepancy is impossible for
+infinite sequences, as recently proven by Terence Tao, attempts were made to
+construct such sequences with finite lengths. We recognize that such
+constructed sequences (we call these ""Erdos sequences"") exhibit certain
+hallmarks of randomness at the local level: they show roughly equal frequencies
+of subsequences, and at the same time exclude the trivial periodic patterns.
+For the human DNA we examine the frequency of a set of Erdos motifs of
+length-10 using three nucleotides-to-binary mappings. The particular length-10
+Erdos sequence is derived by the length-11 Mathias sequence and is identical
+with the first 10 digits of the Thue-Morse sequence, underscoring the fact that
+both are deficient in periodicities. Our calculations indicate that: (1) the
+purine (A and G)/pyridimine (C and T) based Erdos motifs are greatly
+underrepresented in the human genome, (2) the strong(G and C)/weak(A and T)
+based Erdos motifs are slightly overrepresented, (3) the densities of the two
+are negatively correlated, (4) the Erdos motifs based on all three mappings
+being combined are slightly underrepresented, and (5) the strong/weak based
+Erdos motifs are greatly overrepresented in the human messenger RNA sequences.
+"
+27047,"Vector-valued operators, optimal weighted estimates and the $C_p$ condition","  Sharp weighted estimates are obtained for vector-valued extensions of the
+Hardy-Littlewood maximal operator, Calderón-Zygmund operators and
+Coifman-Rochberg-Weiss commutator. Those estimates will rely upon suitable
+pointwise estimates in terms of sparse operators. We also prove some new
+results for the $C_p$ classes introduced by Muckenhoupt and later extended by
+Sawyer, in particular we extend the result to the full expected range $p > 0$,
+to the weak norm, to other operators and to the their vector-valued extensions.
+"
+27048,Restricted sums of four integral squares,"  We give a simple quaternionic proof of a recent result of Goldmakher and
+Pollack on restricted sums of four integral squares.
+"
+27049,Population changes in residential clusters in Japan,"  Population dynamics in urban and rural areas are different. Understanding
+factors that contribute to local population changes has various socioeconomic
+and political implications. In the present study, we use population census data
+in Japan to examine contributors to the population growth of residential
+clusters between years 2005 and 2010. The data set covers the entirety of Japan
+and has a high spatial resolution of 500$\times$500$\textrm{m}^2$, enabling us
+to examine population dynamics in various parts of the country (urban and
+rural) using statistical analysis. We found that, in addition to the area,
+population density, and age, the shape of the cluster and the spatial
+distribution of inhabitants within the cluster are significantly related to the
+population growth rate of a residential cluster. Specifically, the population
+tends to grow if the cluster is ""round"" shaped (given the area) and the
+population is concentrate near the center rather than periphery of the cluster.
+"
+27050,Effect of active case finding on dengue control: Implications from a mathematical model,"  Dengue control in India is a challenging task due to complex healthcare
+settings. In yesteryears, an amplification of dengue infections in India posed
+the need for introspection of existing dengue control policies. Prior
+understanding of the impacts of control interventions is necessary for their
+future implementation. In this paper, we propose and analyze a compartmental
+model of dengue to assess the impact of active case finding (ACF) on dengue
+disease transmission. Currently, primary prevention of dengue is possible only
+with vector control and personal protection from the bites of infected
+mosquitoes. Although a few experimental studies are performed to assess ACF in
+dengue disease, but this is the first attempt to represent and study the
+dynamics of disease using ACF as a control strategy. Local and global dynamics
+of the system are studied. We use sensitivity analysis to see the effects of
+controllable parameters of the model on the basic reproduction number and total
+number of infective population. We find that decrease in the biting rate of
+mosquitoes, and increase in the rate of hospitalization and/or notification,
+death rate of mosquitoes and ACF for asymptomatic and symptomatic individuals
+play crucial role for the reduction of disease prevalence. We calibrate our
+model to the yearly dengue cases in eight dengue endemic states of India. The
+results of our study show that ACF of symptomatic individuals will have
+significant effect on dengue case reduction but ACF of asymptomatic individuals
+cannot be ignored. Our findings indicate that the healthcare organizations must
+focus on ACF of symptomatic as well as asymptomatic individuals along with
+personal protection and mosquitoes control to achieve rapid reduction of dengue
+cases in India.
+"
+27051,Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multiorder Statistical Features,"  Hashing techniques have been applied broadly in large-scale retrieval tasks
+due to their low storage requirements and high speed of processing. Many
+hashing methods have shown promising performance but as they fail to exploit
+all structural information in learning the hashing function, they leave a scope
+for improvement. The paper proposes a novel discrete hashing learning framework
+which jointly performs classifier learning and subspace learning for
+cross-modal retrieval. Concretely, the framework proposed in the paper includes
+two stages, namely a kernelization process and a quantization process. The aim
+of kernelization is to learn a common subspace where heterogeneous data can be
+fused. The quantization process is designed to learn discriminative unified
+hashing codes. Extensive experiments on three publicly available datasets
+clearly indicate the superiority of our method compared with the
+state-of-the-art methods.
+"
+27052,Reinforcement Learning for Relation Classification from Noisy Data,"  Existing relation classification methods that rely on distant supervision
+assume that a bag of sentences mentioning an entity pair are all describing a
+relation for the entity pair. Such methods, performing classification at the
+bag level, cannot identify the mapping between a relation and a sentence, and
+largely suffers from the noisy labeling problem. In this paper, we propose a
+novel model for relation classification at the sentence level from noisy data.
+The model has two modules: an instance selector and a relation classifier. The
+instance selector chooses high-quality sentences with reinforcement learning
+and feeds the selected sentences into the relation classifier, and the relation
+classifier makes sentence level prediction and provides rewards to the instance
+selector. The two modules are trained jointly to optimize the instance
+selection and relation classification processes. Experiment results show that
+our model can deal with the noise of data effectively and obtains better
+performance for relation classification at the sentence level.
+"
+27053,Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark,"  This paper presents a new 3D point cloud classification benchmark data set
+with over four billion manually labelled points, meant as input for data-hungry
+(deep) learning methods. We also discuss first submissions to the benchmark
+that use deep convolutional neural networks (CNNs) as a work horse, which
+already show remarkable performance improvements over state-of-the-art. CNNs
+have become the de-facto standard for many tasks in computer vision and machine
+learning like semantic segmentation or object detection in images, but have no
+yet led to a true breakthrough for 3D point cloud labelling tasks due to lack
+of training data. With the massive data set presented in this paper, we aim at
+closing this data gap to help unleash the full potential of deep learning
+methods for 3D labelling tasks. Our semantic3D.net data set consists of dense
+point clouds acquired with static terrestrial laser scanners. It contains 8
+semantic classes and covers a wide range of urban outdoor scenes: churches,
+streets, railroad tracks, squares, villages, soccer fields and castles. We
+describe our labelling interface and show that our data set provides more dense
+and complete point clouds with much higher overall number of labelled points
+compared to those already available to the research community. We further
+provide baseline method descriptions and comparison between methods submitted
+to our online system. We hope semantic3D.net will pave the way for deep
+learning methods in 3D point cloud labelling to learn richer, more general 3D
+representations, and first submissions after only a few months indicate that
+this might indeed be the case.
+"
+27054,On the Global-Local Dichotomy in Sparsity Modeling,"  The traditional sparse modeling approach, when applied to inverse problems
+with large data such as images, essentially assumes a sparse model for small
+overlapping data patches. While producing state-of-the-art results, this
+methodology is suboptimal, as it does not attempt to model the entire global
+signal in any meaningful way - a nontrivial task by itself. In this paper we
+propose a way to bridge this theoretical gap by constructing a global model
+from the bottom up. Given local sparsity assumptions in a dictionary, we show
+that the global signal representation must satisfy a constrained
+underdetermined system of linear equations, which can be solved efficiently by
+modern optimization methods such as Alternating Direction Method of Multipliers
+(ADMM). We investigate conditions for unique and stable recovery, and provide
+numerical evidence corroborating the theory.
+"
+27055,Upgrade of the ATLAS hadronic Tile calorimeter for the High luminosity LHC,"  The Tile Calorimeter (TileCal) is the hadronic calorimeter covering the
+central region of the ATLAS detector at the LHC. It is a sampling calorimeter
+consisting of alternating thin steel plates and scintillating tiles. Wavelength
+shifting fibers coupled to the tiles collect the produced light and are read
+out by photomultiplier tubes. Currently, an analog sum of the processed signal
+of several photomultipliers serves as input to the first level of trigger.
+Photomultiplier signals are then digitized and stored on detector and are only
+transferred off detector once the first trigger acceptance has been confirmed.
+The Large Hadron Collider (LHC) has envisaged a series of upgrades towards a
+High Luminosity LHC (HL-LHC) delivering five times the LHC nominal
+instantaneous luminosity. The ATLAS Phase II upgrade, in 2024, will accommodate
+the detector and data acquisition system for the HL-LHC. In particular, TileCal
+will undergo a major replacement of its on- and off-detector electronics. All
+signals will be digitized and then transferred directly to the off-detector
+electronics, where the signals will be reconstructed, stored, and sent to the
+first level of trigger at a rate of 40 MHz. This will provide better precision
+of the calorimeter signals used by the trigger system and will allow the
+development of more complex trigger algorithms. Changes to the electronics will
+also contribute to the reliability and redundancy of the system. Three
+different front-end options are presently being investigated for the upgrade.
+Results of extensive laboratory tests and with beams of the three options will
+be presented, as well as the latest results on the development of the power
+distribution and the off-detector electronics.
+"
+27056,RoboCup 2016 Humanoid TeenSize Winner NimbRo: Robust Visual Perception and Soccer Behaviors,"  The trend in the RoboCup Humanoid League rules over the past few years has
+been towards a more realistic and challenging game environment. Elementary
+skills such as visual perception and walking, which had become mature enough
+for exciting gameplay, are now once again core challenges. The field goals are
+both white, and the walking surface is artificial grass, which constitutes a
+much more irregular surface than the carpet used before. In this paper, team
+NimbRo TeenSize, the winner of the TeenSize class of the RoboCup 2016 Humanoid
+League, presents its robotic platforms, the adaptations that had to be made to
+them, and the newest developments in visual perception and soccer behaviour.
+"
+27057,A Comparison of Testing Methods in Scalar-on-Function Regression,"  A scalar-response functional model describes the association between a scalar
+response and a set of functional covariates. An important problem in the
+functional data literature is to test the nullity or linearity of the effect of
+the functional covariate in the context of scalar-on-function regression. This
+article provides an overview of the existing methods for testing both the null
+hypotheses that there is no relationship and that there is a linear
+relationship between the functional covariate and scalar response, and a
+comprehensive numerical comparison of their performance. The methods are
+compared for a variety of realistic scenarios: when the functional covariate is
+observed at dense or sparse grids and measurements include noise or not.
+Finally, the methods are illustrated on the Tecator data set.
+"
+27058,Low-Resource Neural Headline Generation,"  Recent neural headline generation models have shown great results, but are
+generally trained on very large datasets. We focus our efforts on improving
+headline quality on smaller datasets by the means of pretraining. We propose
+new methods that enable pre-training all the parameters of the model and
+utilize all available text, resulting in improvements by up to 32.4% relative
+in perplexity and 2.84 points in ROUGE.
+"
+27059,A new belief Markov chain model and its application in inventory prediction,"  Markov chain model is widely applied in many fields, especially the field of
+prediction. The classical Discrete-time Markov chain(DTMC) is a widely used
+method for prediction. However, the classical DTMC model has some limitation
+when the system is complex with uncertain information or state space is not
+discrete. To address it, a new belief Markov chain model is proposed by
+combining Dempster-Shafer evidence theory with Markov chain. In our model, the
+uncertain data is allowed to be handle in the form of interval number and the
+basic probability assignment(BPA) is generated based on the distance between
+interval numbers. The new belief Markov chain model overcomes the shortcomings
+of classical Markov chain and has an efficient ability in dealing with
+uncertain information. Moreover, an example of inventory prediction and the
+comparison between our model and classical DTMC model can show the
+effectiveness and rationality of our proposed model.
+"
+27060,Upsilon-like concordance invariants from sl(n) knot cohomology,"  We construct smooth concordance invariants of knots which take the form of
+piecewise linear maps from [0,1] to R, one for each n greater than or equal to
+2. These invariants arise from sl(n) knot cohomology. We verify some properties
+which are analogous to those of the invariant Upsilon (which arises from knot
+Floer homology), and some which differ. We make some explicit computations and
+give some topological applications. Further to this, we define a concordance
+invariant from equivariant sl(n) knot cohomology which subsumes many known
+concordance invariants arising from quantum knot cohomologies.
+"
+27061,The Interactive Sum Choice Number of Graphs,"  We introduce a variant of the well-studied sum choice number of graphs, which
+we call the interactive sum choice number. In this variant, we request colours
+to be added to the vertices' colour-lists one at a time, and so we are able to
+make use of information about the colours assigned so far to determine our
+future choices. The interactive sum choice number cannot exceed the sum choice
+number and we conjecture that, except in the case of complete graphs, the
+interactive sum choice number is always strictly smaller than the sum choice
+number. In this paper we provide evidence in support of this conjecture,
+demonstrating that it holds for a number of graph classes, and indeed that in
+many cases the difference between the two quantities grows as a linear function
+of the number of vertices.
+"
+27062,Dissipation in Relativistic Pair-plasma Reconnection: Revisited,"  Basic properties of relativistic magnetic reconnection in electron-positron
+pair plasmas are investigated by using a particle-in-cell (PIC) simulation. We
+first revisit a problem by Hesse & Zenitani (2007), who examined the kinetic
+Ohm's law across the X line. We formulate a relativistic Ohm's law by
+decomposing the stress-energy tensor. Then, the role of the new term, called
+the heat-flow inertial term, is examined in the PIC simulation data. We further
+evaluate the energy balance in the reconnection system. These analyses
+demonstrate physically transparent ways to diagnose relativistic kinetic data.
+"
+27063,SkiMap: An Efficient Mapping Framework for Robot Navigation,"  We present a novel mapping framework for robot navigation which features a
+multi-level querying system capable to obtain rapidly representations as
+diverse as a 3D voxel grid, a 2.5D height map and a 2D occupancy grid. These
+are inherently embedded into a memory and time efficient core data structure
+organized as a Tree of SkipLists. Compared to the well-known Octree
+representation, our approach exhibits a better time efficiency, thanks to its
+simple and highly parallelizable computational structure, and a similar memory
+footprint when mapping large workspaces. Peculiarly within the realm of mapping
+for robot navigation, our framework supports realtime erosion and
+re-integration of measurements upon reception of optimized poses from the
+sensor tracker, so as to improve continuously the accuracy of the map.
+"
+27064,Do GANs actually learn the distribution? An empirical study,"  Do GANS (Generative Adversarial Nets) actually learn the target distribution?
+The foundational paper of (Goodfellow et al 2014) suggested they do, if they
+were given sufficiently large deep nets, sample size, and computation time. A
+recent theoretical analysis in Arora et al (to appear at ICML 2017) raised
+doubts whether the same holds when discriminator has finite size. It showed
+that the training objective can approach its optimum value even if the
+generated distribution has very low support ---in other words, the training
+objective is unable to prevent mode collapse. The current note reports
+experiments suggesting that such problems are not merely theoretical. It
+presents empirical evidence that well-known GANs approaches do learn
+distributions of fairly low support, and thus presumably are not learning the
+target distribution. The main technical contribution is a new proposed test,
+based upon the famous birthday paradox, for estimating the support size of the
+generated distribution.
+"
+27065,On the Tradeoff Region of Secure Exact-Repair Regenerating Codes,"  We consider the $(n,k,d,\ell)$ secure exact-repair regenerating code problem,
+which generalizes the $(n,k,d)$ exact-repair regenerating code problem with the
+additional constraint that the stored file needs to be kept
+information-theoretically secure against an eavesdropper, who can access the
+data transmitted to regenerate a total of $\ell$ different failed nodes. For
+all known results on this problem, the achievable tradeoff regions between the
+normalized storage capacity and repair bandwidth have a single corner point,
+achieved by a scheme proposed by Shah, Rashmi and Kumar (the SRK point). Since
+the achievable tradeoff regions of the exact-repair regenerating code problem
+without any secrecy constraints are known to have multiple corner points in
+general, these existing results suggest a phase-change-like behavior, i.e.,
+enforcing a secrecy constraint ($\ell\geq 1$) immediately reduces the tradeoff
+region to one with a single corner point. In this work, we first show that when
+the secrecy parameter $\ell$ is sufficiently large, the SRK point is indeed the
+only corner point of the tradeoff region. However, when $\ell$ is small, we
+show that the tradeoff region can in fact have multiple corner points. In
+particular, we establish a precise characterization of the tradeoff region for
+the $(7,6,6,1)$ problem, which has exactly two corner points. Thus, a smooth
+transition, instead of a phase-change-type of transition, should be expected as
+the secrecy constraint is gradually strengthened.
+"
+27066,Fractal dimension of interfaces in Edwards-Anderson spin glasses for up to six space dimensions,"  The fractal dimension of domain walls produced by changing the boundary
+conditions from periodic to anti-periodic in one spatial direction is studied
+using both the strong-disorder renormalization group and the greedy algorithm
+for the Edwards-Anderson Ising spin-glass model for up to six space dimensions.
+We find that for five or less space dimensions, the fractal dimension is less
+than the space dimension. This means that interfaces are not space filling,
+thus implying replica symmetry breaking is absent in space dimensions fewer
+than six. However, the fractal dimension approaches the space dimension in six
+dimensions, indicating that replica symmetry breaking occurs above six
+dimensions. In two space dimensions, the strong-disorder renormalization group
+results for the fractal dimension are in good agreement with essentially exact
+numerical results, but the small difference is significant. We discuss the
+origin of this close agreement. For the greedy algorithm there is analytical
+expectation that the fractal dimension is equal to the space dimension in six
+dimensions and our numerical results are consistent with this expectation.
+"
+27067,Local properties and global structure of McVittie spacetimes with non-flat FLRW backgrounds,"  McVittie spacetimes embed the vacuum Schwarzschild(-(anti) de Sitter)
+spacetime in an isotropic FLRW background universe. We study the global
+structure of McVittie spacetimes with spatially non-flat FLRW backgrounds. This
+requires the extension of the definition of such spacetimes, previously given
+only for the flat and open cases, to the closed case. We revisit this
+definition and show how it gives rise to a unique spacetime (given the FLRW
+background, the mass parameter $M$ and the cosmological constant $\Lambda$) in
+the open and flat cases. In the closed case, an additional free function of the
+cosmic time arises. We derive some basic results on the metric, curvature and
+matter content of McVittie spacetimes and derive a representation of the line
+element that makes the study of their global properties possible. In the closed
+case (independently of the free function mentioned above), the spacetime is
+confined (at each instant of time) to a region bounded by a minimum and a
+maximum area radius, and is bounded either to the future or to the past by a
+scalar curvature singularity. This allowed region only exists when the
+background scale factor is above a certain minimum. In the open case, radial
+null geodesics originate in finite affine time in the past at a boundary formed
+by the union of the Big Bang singularity of the FLRW background and a
+non-singular hypersurface of varying causal character. Furthermore, in the case
+of eternally expanding open universes, we show that black holes are ubiquitous:
+ingoing radial null geodesics extend in finite affine time to a hypersurface
+that forms the boundary of the region from which photons can escape to future
+null infinity. We revisit the black hole interpretation of McVittie spacetimes
+in the spatially flat case, and show that this interpretation holds also in the
+case of a vanishing cosmological constant, contrary to a previous claim of
+ours.
+"
+27068,The Processing of Enriched Germanium for the MAJORANA DEMONSTRATOR and R&D for a Possible Future Ton-Scale Ge-76 Double-Beta Decay Experiment,"  The MAJORANA DEMONSTRATOR is an array of point-contact Ge detectors
+fabricated from Ge isotopically enriched to 88% in Ge-76 to search for
+neutrinoless double beta decay. The processing of Ge for germanium detectors is
+a well-known technology. However, because of the high cost of Ge enriched in
+Ge-76, special procedures were required to maximize the yield of detector mass
+and to minimize exposure to cosmic rays. These procedures include careful
+accounting for the material; shielding it to reduce cosmogenic generation of
+radioactive isotopes; and development of special reprocessing techniques for
+contaminated solid germanium, shavings, grindings, acid etchant and cutting
+fluids from detector fabrication. Processing procedures were developed that
+resulted in a total yield in detector mass of 70%. However, none of the
+acid-etch solution and only 50% of the cutting fluids from detector fabrication
+were reprocessed. Had they been processed, the projections for the recovery
+yield would be between 80 -- 85%. Maximizing yield is critical to justify a
+possible future ton-scale experiment. A process for recovery of germanium from
+the acid-etch solution was developed with yield of about 90%. All material was
+shielded or stored underground whenever possible to minimize the formation of
+Ge-68 by cosmic rays, which contributes background in the double-beta decay
+region of interest and cannot be removed by zone refinement and crystal growth.
+Formation of Ge-68 was reduced by a significant factor over that in natural
+abundance detectors not protected from cosmic rays.
+"
+27069,Thermal and electrical transport in metals and superconductors across antiferromagnetic and topological quantum transitions,"  We study thermal and electrical transport in metals and superconductors near
+a quantum phase transition where antiferromagnetic order disappears. The same
+theory can also be applied to quantum phase transitions involving the loss of
+certain classes of intrinsic topological order. For a clean superconductor, we
+recover and extend the well-known universal results. The heat conductivity for
+commensurate and incommensurate antiferromagnetism coexisting with
+superconductivity shows a markedly different doping dependence near the quantum
+critical point, thus allowing to distinguish the states. In the dirty limit,
+the results for the conductivities are qualitatively similar for the metal and
+the superconductor. In this regime, the geometric properties of the Fermi
+surface allow for a very good phenomenological understanding of the numerical
+results on the conductivities. In the simplest model, we find that the
+conductivities do not track the doping evolution of the Hall coefficient, in
+contrast to recent experimental findings. We propose a doping dependent
+scattering rate, possibly due to quenched short-range charge fluctuations below
+optimal doping, to consistently describe both the Hall data and the
+longitudinal conductivities.
+"
+27070,N=2 S-duality Revisited,"  Using the chiral algebra bootstrap, we revisit the simplest Argyres-Douglas
+(AD) generalization of Argyres-Seiberg S-duality. We argue that the exotic AD
+superconformal field theory (SCFT), $T_{3,{3\over2}}$, emerging in this duality
+splits into a free piece and an interacting piece, T_X, even though this
+factorization seems invisible in the Seiberg-Witten (SW) curve derived from the
+corresponding M5-brane construction. Without a Lagrangian, an associated
+topological field theory, a BPS spectrum, or even an SW curve, we nonetheless
+obtain exact information about T_X by bootstrapping its chiral algebra,
+chi(T_X), and finding the corresponding vacuum character in terms of Affine
+Kac-Moody characters. By a standard 4D/2D correspondence, this result gives us
+the Schur index for T_X and, by studying this quantity in the limit of small
+S^1, we make contact with a proposed S^1 reduction. Along the way, we discuss
+various properties of T_X: as an N=1 theory, it has flavor symmetry
+SU(3)XSU(2)XU(1), the central charge of chi(T_X) matches the central charge of
+the bc ghosts in bosonic string theory, and its global SU(2) symmetry has a
+Witten anomaly. This anomaly does not prevent us from building conformal
+manifolds out of arbitrary numbers of T_X theories (giving us a surprisingly
+close AD relative of Gaiotto's T_N theories), but it does lead to some open
+questions in the context of the chiral algebra / 4D N=2 SCFT correspondence.
+"
+27071,In Search of Effectful Dependent Types,"  Real world programming languages crucially depend on the availability of
+computational effects to achieve programming convenience and expressive power
+as well as program efficiency. Logical frameworks rely on predicates, or
+dependent types, to express detailed logical properties about entities.
+According to the Curry-Howard correspondence, programming languages and logical
+frameworks should be very closely related. However, a language that has both
+good support for real programming and serious proving is still missing from the
+programming languages zoo. We believe this is due to a fundamental lack of
+understanding of how dependent types should interact with computational
+effects. In this thesis, we make a contribution towards such an understanding,
+with a focus on semantic methods.
+"
+27072,Off-the-grid Two-Dimensional Line Spectral Estimation With Prior Information,"  In this paper, we provide a method to recover off-the-grid frequencies of a
+signal in two-dimensional (2-D) line spectral estimation. Most of the
+literature in this field focuses on the case in which the only information is
+spectral sparsity in a continuous domain and does not consider prior
+information. However, in many applications such as radar and sonar, one has
+extra information about the spectrum of the signal of interest. The common way
+of accommodating prior information is to use weighted atomic norm minimization.
+We present a new semidefinite program using the theory of positive
+trigonometric polynomials that incorporate this prior information into 2-D line
+spectral estimation. Specifically, we assume prior knowledge of 2-D frequency
+subbands in which signal frequency components are located. Our approach
+improves the recovery performance compared with the previous work that does not
+consider prior information. Through numerical experiments, we find out that the
+amount of this improvement depends on prior information we have about the
+locations of the true frequencies.
+"
+27073,Optimal Unateness Testers for Real-Valued Functions: Adaptivity Helps,"  We study the problem of testing unateness of functions $f:\{0,1\}^d \to
+\mathbb{R}.$ We give a $O(\frac{d}{\epsilon} \cdot
+\log\frac{d}{\epsilon})$-query nonadaptive tester and a
+$O(\frac{d}{\epsilon})$-query adaptive tester and show that both testers are
+optimal for a fixed distance parameter $\epsilon$. Previously known unateness
+testers worked only for Boolean functions, and their query complexity had worse
+dependence on the dimension both for the adaptive and the nonadaptive case.
+Moreover, no lower bounds for testing unateness were known. We also generalize
+our results to obtain optimal unateness testers for functions $f:[n]^d \to
+\mathbb{R}$.
+Our results establish that adaptivity helps with testing unateness of
+real-valued functions on domains of the form $\{0,1\}^d$ and, more generally,
+$[n]^d$. This stands in contrast to the situation for monotonicity testing
+where there is no adaptivity gap for functions $f:[n]^d \to \mathbb{R}$.
+"
+27074,Atomic physics and modern solar spectro-polarimetry,"  Observational solar physics is entering a new era with the advent of new 1.5
+m class telescopes with adaptive optics, as well as the Daniel K. Inouye 4 m
+telescope which will become operational in 2019. Major outstanding problems in
+solar physics all relate to the solar magnetic field. Spectropolarimetry offers
+the best, and sometimes only, method for accurate measurements of the magnetic
+field. In this paper we highlight how certain atomic transitions can help us
+provide both calibration data, as well as diagnostic information on solar
+magnetic fields, in the presence of residual image distortions through the
+atmosphere close to, but not at the diffraction limits of large and polarizing
+telescopes. Particularly useful are spectral lines of neutrals and singly
+charged ions of iron and other complex atoms. As a proof-of-concept, we explore
+atomic transitions that might be used to study magnetic fields without the need
+for an explicit calibration sequence, offering practical solutions to the
+difficult challenges of calibrating the next generation of solar
+spectropolarimetric telescopes. Suggestions for additional work on atomic
+theory and measurements, particularly at infrared wavelengths, are given. There
+is some promise for continued symbiotic advances between solar physics and
+atomic physics.
+"
+27075,Learning Bundled Care Opportunities from Electronic Medical Records,"  Objectives: The fee-for-service approach to healthcare leads to the
+management of a patient's conditions in an independent manner, inducing various
+negative consequences. It is recognized that a bundled care approach to
+healthcare-one that manages a collection of health conditions together-may
+enable greater efficacy and cost savings. However, it is not always evident
+which sets of conditions should be managed in a bundled program. Study Design:
+Retrospective inference of clusters of health conditions from an electronic
+medical record (EMR) system. A survey of healthcare experts to ascertain the
+plausibility of the clusters for bundled care programs. Methods: We designed a
+data-driven framework to infer clusters of health conditions via their shared
+clinical workflows according to EMR utilization by healthcare employees. We
+evaluated the framework with approximately 16,500 inpatient stays from a large
+medical center. The plausibility of the clusters for bundled care was assessed
+through a survey of a panel of healthcare experts using an analysis of variance
+(ANOVA) under a 95% confidence interval. Results: The framework inferred four
+condition clusters: 1) fetal abnormalities, 2) late pregnancies, 3) prostate
+problems, and 4) chronic diseases (with congestive heart failure featuring
+prominently). Each cluster was deemed plausible by the experts for bundled
+care. Conclusions: The findings suggest that data from EMRs may provide a basis
+for discovering new directions in bundled care. Still, translating such
+findings into actual care management will require further refinement,
+implementation, and evaluation.
+"
+27076,Drowning by numbers: topology and physics in fluid dynamics,"  Since its very beginnings, topology has forged strong links with physics and
+the last Nobel prize in physics, awarded in 2016 to Thouless, Haldane and
+Kosterlitz "" for theoretical discoveries of topological phase transitions and
+topological phases of matter"", confirmed that these connections have been
+maintained up to contemporary physics. To give some (very) selected
+illustrations of what is, and still will be, a cross fertilization between
+topology and physics, hydrodynamics provides a natural domain through the
+common theme offered by the notion of vortex, relevant both in classical (§2)
+and in quantum fluids (§3). Before getting into the details, I will sketch in
+§1 a general perspective from which this intertwining between topology and
+physics can be appreciated: the old dichotomy between discreteness and
+continuity, first dealing with antithetic thesis, eventually appears to be made
+of two complementary sides of a single coin.
+"
+27077,Adversarial Examples for Semantic Segmentation and Object Detection,"  It has been well demonstrated that adversarial examples, i.e., natural images
+with visually imperceptible perturbations added, generally exist for deep
+networks to fail on image classification. In this paper, we extend adversarial
+examples to semantic segmentation and object detection which are much more
+difficult. Our observation is that both segmentation and detection are based on
+classifying multiple targets on an image (e.g., the basic target is a pixel or
+a receptive field in segmentation, and an object proposal in detection), which
+inspires us to optimize a loss function over a set of pixels/proposals for
+generating adversarial perturbations. Based on this idea, we propose a novel
+algorithm named Dense Adversary Generation (DAG), which generates a large
+family of adversarial examples, and applies to a wide range of state-of-the-art
+deep networks for segmentation and detection. We also find that the adversarial
+perturbations can be transferred across networks with different training data,
+based on different architectures, and even for different recognition tasks. In
+particular, the transferability across networks with the same architecture is
+more significant than in other cases. Besides, summing up heterogeneous
+perturbations often leads to better transfer performance, which provides an
+effective method of black-box adversarial attack.
+"
+27078,Ricci Flow of Compact Locally Homogeneous Geometries on 5-Manifolds,"  This project serves to analyze the behavior of Ricci Flow in five dimensional
+manifolds. Ricci Flow was introduced by Richard Hamilton in 1982 and was an
+essential tool in proving the Geometrization and Poincare Conjectures. In
+general, Ricci Flow is a nonlinear PDE whose solutions are rather difficult to
+calculate; however, in a homogeneous manifold, the Ricci Flow reduces to an
+ODE. The behavior of Ricci Flow in two, three, and four dimensional homogenous
+manifolds has been calculated and is well understood. The work presented here
+will describe efforts to better understand the behavior of Ricci Flow in a
+certain class of five dimensional homogeneous manifolds.
+"
+27079,Complex Multiplication and Shimura Stacks,"  We prove a variant of the reciprocity laws for CM abelian varieties, CM K3
+surfaces, and CM points on Shimura varieties. Given a CM object over the
+complex numbers, our variation describes the set of all models over a given
+number field $F$ in terms of associated representations of the absolute Galois
+group of $F$. An essential feature is that we work with stacky Shimura
+varieties to deal with objects that have non-trivial automorphisms.
+To prove the result on K3 surfaces, we show that the stack of polarized K3
+surfaces of given degree is an open substack of a certain Shimura stack. The
+precise statement of this folklore fact seems to be missing from the
+literature.
+"
+27080,Context Attentive Bandits: Contextual Bandit with Restricted Context,"  We consider a novel formulation of the multi-armed bandit model, which we
+call the contextual bandit with restricted context, where only a limited number
+of features can be accessed by the learner at every iteration. This novel
+formulation is motivated by different online problems arising in clinical
+trials, recommender systems and attention modeling. Herein, we adapt the
+standard multi-armed bandit algorithm known as Thompson Sampling to take
+advantage of our restricted context setting, and propose two novel algorithms,
+called the Thompson Sampling with Restricted Context(TSRC) and the Windows
+Thompson Sampling with Restricted Context(WTSRC), for handling stationary and
+nonstationary environments, respectively. Our empirical results demonstrate
+advantages of the proposed approaches on several real-life datasets
+"
+27081,Can Dips of Boyajian's Star be Explained by Circumsolar Rings?,"  Could the dips of ""Boyajian's Star"" (KIC 8462852) have been caused by matter
+in our Solar System? The interval between periods of deep dips is nearly twice
+the orbital period of the Kepler satellite. I consider heliocentric obscuring
+rings in the outer Solar System that graze the line of sight to the star once
+per orbit of Kepler. The hypothesis predicts that future dips may be observed
+from Earth during windows separated by a year, although their detailed
+structure depends on the distribution of particles along the ring. Dips
+observed at terrestrial sites separated by > 1000 km in a direction
+perpendicular to the Earth's orbital motion may be uncorrelated.
+"
+27082,A mechanical model of bacteriophage DNA ejection,"  Single molecule experiments on bacteriophages show an exponential scaling for
+the dependence of mobility on the length of DNA within the capsid. It has been
+suggested that this could be due to the ""capstan mechanism"" -- the exponential
+amplification of friction forces that result when a rope is wound around a
+cylinder as in a ship's capstan. Here we describe a desktop experiment that
+illustrates the effect. Though our model phage is a million times larger, it
+exhibits the same scaling observed in single molecule experiments.
+"
+27083,Direct transfer of pump amplitude to parametric down-converted photons,"  We numerically and experimentally show that all photons generated by
+spontaneous parametric down-conversion (SPDC) follow a transverse amplitude
+similar to that of the pump. This amplitude transfer from pump to SPDC is
+revealed in the Fourier image plane of the down-converted photons restricted by
+an aperture. We also observe a considerable shift of the image plane from the
+actual Fourier plane, when size of the aperture is gradually increased. The
+shift of the Fourier image of down-converted photons affects the quality of
+spatial mode-based projection in various quantum correlation experiments with
+parametric down-converted photon pairs. The results may be useful in
+applications of down-converted photons for quantum imaging and quantum
+communication.
+"
+27084,Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes,"  Automating statistical modelling is a challenging problem in artificial
+intelligence. The Automatic Statistician takes a first step in this direction,
+by employing a kernel search algorithm with Gaussian Processes (GP) to provide
+interpretable statistical models for regression problems. However this does not
+scale due to its $O(N^3)$ running time for the model selection. We propose
+Scalable Kernel Composition (SKC), a scalable kernel search algorithm that
+extends the Automatic Statistician to bigger data sets. In doing so, we derive
+a cheap upper bound on the GP marginal likelihood that sandwiches the marginal
+likelihood with the variational lower bound . We show that the upper bound is
+significantly tighter than the lower bound and thus useful for model selection.
+"
+27085,Some consequences of categorification,"  Several conjectures on acyclic skew-symmetrizable cluster algebras are proven
+as direct consequences of their categorification via valued quivers. These
+include conjectures of Fomin-Zelevinsky, Reading-Speyer, and Reading-Stella
+related to d-vectors, g-vectors, and F-polynomials.
+"
+27086,Elevating commodity storage with the SALSA host translation layer,"  To satisfy increasing storage demands in both capacity and performance,
+industry has turned to multiple storage technologies, including Flash SSDs and
+SMR disks. These devices employ a translation layer that conceals the
+idiosyncrasies of their mediums and enables random access. Device translation
+layers are, however, inherently constrained: resources on the drive are scarce,
+they cannot be adapted to application requirements, and lack visibility across
+multiple devices. As a result, performance and durability of many storage
+devices is severely degraded.
+In this paper, we present SALSA: a translation layer that executes on the
+host and allows unmodified applications to better utilize commodity storage.
+SALSA supports a wide range of single- and multi-device optimizations and,
+because is implemented in software, can adapt to specific workloads. We
+describe SALSA's design, and demonstrate its significant benefits using
+microbenchmarks and case studies based on three applications: MySQL, the Swift
+object store, and a video server.
+"
+27087,Cosmogenic activation of materials,"  Experiments looking for rare events like the direct detection of dark matter
+particles, neutrino interactions or the nuclear double beta decay are operated
+deep underground to suppress the effect of cosmic rays. But the production of
+radioactive isotopes in materials due to previous exposure to cosmic rays is an
+hazard when ultra-low background conditions are required. In this context, the
+generation of long-lived products by cosmic nucleons has been studied for many
+detector media and for other materials commonly used. Here, the main results
+obtained on the quantification of activation yields on the Earth's surface will
+be summarized, considering both measurements and calculations following
+different approaches. The isotope production cross sections and the cosmic ray
+spectrum are the two main ingredients when calculating this cosmogenic
+activation; the different alternatives for implementing them will be discussed.
+Activation that can take place deep underground mainly due to cosmic muons will
+be briefly commented too. Presently, the experimental results for the
+cosmogenic production of radioisotopes are scarce and discrepancies between
+different calculations are important in many cases, but the increasing interest
+on this background source which is becoming more and more relevant can help to
+change this situation.
+"
+27088,Multimodal Social Media Analysis for Gang Violence Prevention,"  Gang violence is a severe issue in major cities across the U.S. and recent
+studies [Patton et al. 2017] have found evidence of social media communications
+that can be linked to such violence in communities with high rates of exposure
+to gang activity. In this paper we partnered computer scientists with social
+work researchers, who have domain expertise in gang violence, to analyze how
+public tweets with images posted by youth who mention gang associations on
+Twitter can be leveraged to automatically detect psychosocial factors and
+conditions that could potentially assist social workers and violence outreach
+workers in prevention and early intervention programs. To this end, we
+developed a rigorous methodology for collecting and annotating tweets. We
+gathered 1,851 tweets and accompanying annotations related to visual concepts
+and the psychosocial codes: aggression, loss, and substance use. These codes
+are relevant to social work interventions, as they represent possible pathways
+to violence on social media. We compare various methods for classifying tweets
+into these three classes, using only the text of the tweet, only the image of
+the tweet, or both modalities as input to the classifier. In particular, we
+analyze the usefulness of mid-level visual concepts and the role of different
+modalities for this tweet classification task. Our experiments show that
+individually, text information dominates classification performance of the loss
+class, while image information dominates the aggression and substance use
+classes. Our multimodal approach provides a very promising improvement (18%
+relative in mean average precision) over the best single modality approach.
+Finally, we also illustrate the complexity of understanding social media data
+and elaborate on open challenges.
+"
+27089,Understanding State level Variations in U.S. Infant Mortality: 2000 to 2015,"  Objective: To exploit state variations in infant mortality, identify
+diagnoses that contributed to reduction of the infant mortality rate (IMR), and
+examine factors associated with preterm related mortality rate (PMR). Methods:
+Using linked birth-infant deaths files, we examined patterns in the leading
+causes of IMR. We compared these rates at both national and state levels to
+find reduction trends. Creating a cross-sectional time series of states' PMR
+and some explanatory variables, we implemented a fixed-effect regression model.
+Results: We found substantial state-level variations in changes of the IMR
+(range= -2.87 to 2.08) and PMR (-1.77 to 0.67). Twenty-one states in which the
+IMR declined more than the national average of 0.99 (6.89 to 5.90) were labeled
+as successful. In the successful states, we found a reduction in the PMR
+accounted for the largest decline in the IMR (0.90 fewer deaths). Changes in
+the other subgroups of leading causes did not differ significantly in
+successful and unsuccessful states. Policy Implications: Although its impact is
+not large, reducing the percentage of pregnant women with inadequate care is
+one of the mechanisms through which the PMR might decrease.
+"
+27090,Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees,"  Sampling efficiency in a highly constrained environment has long been a major
+challenge for sampling-based planners. In this work, we propose
+Rapidly-exploring Random disjointed-Trees* (RRdT*), an incremental optimal
+multi-query planner. RRdT* uses multiple disjointed-trees to exploit
+local-connectivity of spaces via Markov Chain random sampling, which utilises
+neighbourhood information derived from previous successful and failed samples.
+To balance local exploitation, RRdT* actively explore unseen global spaces when
+local-connectivity exploitation is unsuccessful. The active trade-off between
+local exploitation and global exploration is formulated as a multi-armed bandit
+problem. We argue that the active balancing of global exploration and local
+exploitation is the key to improving sample efficient in sampling-based motion
+planners. We provide rigorous proofs of completeness and optimal convergence
+for this novel approach. Furthermore, we demonstrate experimentally the
+effectiveness of RRdT*'s locally exploring trees in granting improved
+visibility for planning. Consequently, RRdT* outperforms existing
+state-of-the-art incremental planners, especially in highly constrained
+environments.
+"
+27091,The Capacity of Private Information Retrieval with Private Side Information,"  The problem of private information retrieval with private side information
+(PIR-PSI) is recently introduced by Kadhe et al. In this problem, $N$
+replicated databases each store $K$ messages, of which $M$ are known to the
+user as side-information. The identity of these $M$ messages is unknown to the
+databases. The user wishes to retrieve, as efficiently as possible, a new
+desired message without revealing any information about the joint indices of
+the desired and side-information messages. We show that the capacity of PIR-PSI
+is $\left(1+\frac{1}{N}+\frac{1}{N^2}+\cdots+\frac{1}{N^{K-M-1}}\right)^{-1}$.
+Thus, the capacity of PIR-PSI with $K$ messages, $N$ databases and
+side-information of $M$ messages is identical to the capacity of PIR with $K-M$
+messages, $N$ databases and no side-information, which was found previously by
+Sun and Jafar. The achievability and converse proofs are essentially inherited
+from the original work of Sun and Jafar.
+"
+27092,Parameter redundancy in Type III functional response models with consumer interference,"  The consumption rate is a process critically important for the stability of
+consumer-resource systems and the persistence, sustainability and biodiversity
+of complex food webs. Its mathematical description in the form of functional
+response equations is a key problem for describing all trophic interactions.
+Because some of the functional response models used in this study presented
+redundancy between its parameters two methods were used to check for parameter
+redundancy: the Hessian matrix calculation using automatic differentiation (AD)
+which calculates derivatives numerically, but does not use finite differences,
+and the symbolic method that calculates a derivative matrix and its rank. In
+this work, we found that the models that better describe the functional
+response of a rotifer is consumer dependant even at low consumer densities, but
+their parameters can not be estimated simultaneously because parameter
+redundancy. This means that fewer parameters or parameter combination can be
+estimated than the original number of parameters in the models. Here, the model
+parameters that incorporate intraspecific competition by interference in the
+consumer-resource interaction are not identifiable, suggesting that this
+problem may be more widespread than is generally appreciated in the literature
+of food webs. Including knowledge on competitive interactions in current model
+predictions will be a necessity in the next years for ecology as ecological
+models are getting more complex and more real. Identifiability of biological
+parameters in nonlinear ecological models will be an issue to consider.
+"
+27093,On an integral formula for Fredholm determinants related to pairs of spectral projections,"  We consider Fredholm determinants of the form identity minus product of
+spectral projections corresponding to isolated parts of the spectrum of a pair
+of self-adjoint operators. We show an identity relating such determinants to an
+integral over the spectral shift function in the case of a rank-one
+perturbation. More precisely, we prove $$ -\ln \left(\det \big(\mathbf{1}
+-\mathbf{1} _{I}(A) \mathbf{1}_{\mathbb R\backslash I}(B)\mathbf{1}_{I}(A)\big)
+\right) = \int_I \text{d} x \int_{\mathbb R\backslash I} \text{d} y\,
+\frac{\xi(x)\xi(y)}{(y-x)^2}, $$ where $\mathbf{1}_J (\cdot)$ denotes the
+spectral projection of a self-adjoint operator on a set $J\in
+\text{Borel}(\mathbb R)$. The operators $A$ and $B$ are self-adjoint, bounded
+from below and differ by a rank-one perturbation and $\xi$ denotes the
+corresponding spectral shift function. The set $I$ is a union of intervals on
+the real line such that its boundary lies in the resolvent set of $A$ and $B$
+and such that the spectral shift function vanishes there i.e. $I$ contains
+isolated parts of the spectrum of $A$ and $B$. We apply this formula to the
+subspace perturbation problem.
+"
+27094,A note on dual demodulator continuous transmission frequency modulation technique,"  The range resolution in conventional continuous time frequency modulation
+(CTFM) is inversely proportional to the signal bandwidth. The dual-demodulator
+continuous time frequency modulation (DD-CTFM) processing technique was
+proposed by Gough et al [1] as a method to increase the range resolution by
+making the output of DD-CTFM truly continuous. However, it has been found that
+in practice the range resolution is still limited by the signal bandwidth. The
+limitation of DD-CTFM has been explained using simulations and mathematically
+in this paper.
+"
+27095,Variational Policy for Guiding Point Processes,"  Temporal point processes have been widely applied to model event sequence
+data generated by online users. In this paper, we consider the problem of how
+to design the optimal control policy for point processes, such that the
+stochastic system driven by the point process is steered to a target state. In
+particular, we exploit the key insight to view the stochastic optimal control
+problem from the perspective of optimal measure and variational inference. We
+further propose a convex optimization framework and an efficient algorithm to
+update the policy adaptively to the current system state. Experiments on
+synthetic and real-world data show that our algorithm can steer the user
+activities much more accurately and efficiently than other stochastic control
+methods.
+"
+27096,Practical Hash Functions for Similarity Estimation and Dimensionality Reduction,"  Hashing is a basic tool for dimensionality reduction employed in several
+aspects of machine learning. However, the perfomance analysis is often carried
+out under the abstract assumption that a truly random unit cost hash function
+is used, without concern for which concrete hash function is employed. The
+concrete hash function may work fine on sufficiently random input. The question
+is if it can be trusted in the real world when faced with more structured
+input.
+In this paper we focus on two prominent applications of hashing, namely
+similarity estimation with the one permutation hashing (OPH) scheme of Li et
+al. [NIPS'12] and feature hashing (FH) of Weinberger et al. [ICML'09], both of
+which have found numerous applications, i.e. in approximate near-neighbour
+search with LSH and large-scale classification with SVM.
+We consider mixed tabulation hashing of Dahlgaard et al.[FOCS'15] which was
+proved to perform like a truly random hash function in many applications,
+including OPH. Here we first show improved concentration bounds for FH with
+truly random hashing and then argue that mixed tabulation performs similar for
+sparse input. Our main contribution, however, is an experimental comparison of
+different hashing schemes when used inside FH, OPH, and LSH.
+We find that mixed tabulation hashing is almost as fast as the
+multiply-mod-prime scheme ax+b mod p. Mutiply-mod-prime is guaranteed to work
+well on sufficiently random data, but we demonstrate that in the above
+applications, it can lead to bias and poor concentration on both real-world and
+synthetic data. We also compare with the popular MurmurHash3, which has no
+proven guarantees. Mixed tabulation and MurmurHash3 both perform similar to
+truly random hashing in our experiments. However, mixed tabulation is 40%
+faster than MurmurHash3, and it has the proven guarantee of good performance on
+all possible input.
+"
+27097,Kernel-based Outlier Detection using the Inverse Christoffel Function,"  Outlier detection methods have become increasingly relevant in recent years
+due to increased security concerns and because of its vast application to
+different fields. Recently, Pauwels and Lasserre (2016) noticed that the
+sublevel sets of the inverse Christoffel function accurately depict the shape
+of a cloud of data using a sum-of-squares polynomial and can be used to perform
+outlier detection. In this work, we propose a kernelized variant of the inverse
+Christoffel function that makes it computationally tractable for data sets with
+a large number of features. We compare our approach to current methods on 15
+different data sets and achieve the best average area under the precision
+recall curve (AUPRC) score, the best average rank and the lowest root mean
+square deviation.
+"
+27098,Self-referential basis of undecidable dynamics: from The Liar Paradox and The Halting Problem to The Edge of Chaos,"  In this paper we explore several fundamental relations between formal
+systems, algorithms, and dynamical systems, focussing on the roles of
+undecidability, universality, diagonalization, and self-reference in each of
+these computational frameworks. Some of these interconnections are well-known,
+while some are clarified in this study as a result of a fine-grained comparison
+between recursive formal systems, Turing machines, and Cellular Automata (CAs).
+In particular, we elaborate on the diagonalization argument applied to
+distributed computation carried out by CAs, illustrating the key elements of
+Gödel's proof for CAs. The comparative analysis emphasizes three factors
+which underlie the capacity to generate undecidable dynamics within the
+examined computational frameworks: (i) the program-data duality; (ii) the
+potential to access an infinite computational medium; and (iii) the ability to
+implement negation. The considered adaptations of Gödel's proof distinguish
+between computational universality and undecidability, and show how the
+diagonalization argument exploits, on several levels, the self-referential
+basis of undecidability.
+"
+27099,Characterizing the local vectorial electric field near an atom chip using Rydberg state spectroscopy,"  We use the sensitive response to electric fields of Rydberg atoms to
+characterize all three vector components of the local electric field close to
+an atom-chip surface. We measured Stark-Zeeman maps of $S$ and $D$ Rydberg
+states using an elongated cloud of ultracold Rubidium atoms ($T\sim2.5$ $\mu$K)
+trapped magnetically $100$ $\mu$m from the chip surface. The spectroscopy of
+$S$ states yields a calibration for the generated local electric field at the
+position of the atoms. The values for different components of the field are
+extracted from the more complex response of $D$ states to the combined electric
+and magnetic fields. From the analysis we find residual fields in the two
+uncompensated directions of $0.0\pm0.2$ V/cm and $1.98\pm0.09$ V/cm
+respectively. This method also allows us to extract a value for the relevant
+field gradient along the long axis of the cloud. The manipulation of electric
+fields and the magnetic trapping are both done using on-chip wires, making this
+setup a promising candidate to observe Rydberg-mediated interactions on a chip.
+"
+27100,Biaxial compression of centimeter scale graphene on strictly 2D substrate,"  Biaxial compression of centimetre-scale graphene, freely standing on the
+surface of water is studied. Within this platform, we report full stress-strain
+compression of graphene identifying elastic and plastic deformations. The
+Young's modulus follows a scaling law and falls two orders of magnitude below
+the commonly reported values with microscale graphene samples. Such results
+strongly confirm that graphene - in its very natural form - lacks any intrinsic
+elastic parameters. Different functionalization/manipulation of graphene
+lattice affects the mechanics of graphene differently; particularly the effect
+of the sp3 hybridization and crystalline voids on the yield strength of
+graphene is explored. Crumpling of graphene is accompanied by gradual
+generation and transformation of wrinkles which brings about viscoelasticity in
+graphene, observed for the first time in this paper. Additionally, we report a
+peculiar correlation between the morphology and the distribution of the strain
+in graphene lattice.
+"
+27101,Quantum Variational Principle and quantum multiform structure: the case of quadratic Lagrangians,"  A modern notion of integrability is that of multidimensional consistency
+(MDC), which classically implies the coexistence of (commuting) dynamical flows
+in several independent variables for one and the same dependent variable. This
+property holds for both continuous dynamical systems as well as for discrete
+ones defined in discrete space-time. Possibly the simplest example in the
+discrete case is that of a linear quadrilateral lattice equation, which can be
+viewed as a linearised version of the well-known lattice potential Korteweg-de
+Vries (KdV) equation. In spite of the linearity, the MDC property is
+non-trivial in terms of the parameters of the system. The Lagrangian aspects of
+such equations, and their nonlinear analogues, has led to the notion of
+Lagrangian multiform structures, where the Lagrangians are no longer scalar
+functions (or volume forms) but genuine forms in a multidimensional space of
+independent variables. The variational principle involves variations not only
+with respect to the field variables, but also with respect to the geometry in
+the space of independent variables. In this paper we consider a quantum
+analogue of this new variational principle by means of quantum propagators (or
+equivalently Feynman path integrals). In the case of quadratic Lagrangians
+these can be evaluated in terms of Gaussian integrals. We study also periodic
+reductions of the lattice leading to discrete multi-time dynamical commuting
+mappings, the simplest example of which is the discrete harmonic oscillator,
+which surprisingly reveals a rich integrable structure behind it. On the basis
+of this study we propose a new quantum variational principle in terms of
+multiform path integrals.
+"
+27102,Topological and geometrical quantities in active cellular structures,"  Topological and geometrical properties and the associated topological defects
+find a rapidly growing interest in studying the interplay between mechanics and
+the collective behavior of cells on the tissue level. We here test if well
+studied equilibrium laws for polydisperse passive systems such as the Lewis's
+and the Aboav-Weaire's law are applicable also for active cellular structures.
+Large scale simulations, which are based on a multi phase field active polar
+gel model, indicate that these active cellular structures follow these laws. If
+the system is in a state of collective motion also quantitative agreement with
+typical values for passive systems is observed. If this state has not developed
+quantitative differences can be found. We further compare the model with
+discrete modeling approaches for cellular structures and show that essential
+properties, such as T1 transitions and rosettes are naturally fulfilled.
+"
+27103,Integrable U(1)-invariant peakon equations from the NLS hierarchy,"  Two integrable $U(1)$-invariant peakon equations are derived from the NLS
+hierarchy through the tri-Hamiltonian splitting method. A Lax pair, a recursion
+operator, a bi-Hamiltonian formulation, and a hierarchy of symmetries and
+conservation laws are obtained for both peakon equations. These equations are
+also shown to arise as potential flows in the NLS hierarchy by applying the NLS
+recursion operator to flows generated by space translations and $U(1)$-phase
+rotations on a potential variable. Solutions for both equations are derived
+using a peakon ansatz combined with an oscillatory temporal phase. This yields
+the first known example of a peakon breather. Spatially periodic counterparts
+of these solutions are also obtained.
+"
+27104,In Praise of Artifice Reloaded: Caution with subjective image quality databases,"  Subjective image quality databases are a major source of raw data on how the
+visual system works in naturalistic environments. These databases describe the
+sensitivity of many observers to a wide range of distortions (of different
+nature and with different suprathreshold intensities) seen on top of a variety
+of natural images. They seem like a dream for the vision scientist to check the
+models in realistic scenarios.
+However, while these natural databases are great benchmarks for models
+developed in some other way (e.g. by using the well-controlled artificial
+stimuli of traditional psychophysics), they should be carefully used when
+trying to fit vision models. Given the high dimensionality of the image space,
+it is very likely that some basic phenomenon (e.g. sensitivity to distortions
+in certain environments) are under-represented in the database. Therefore, a
+model fitted on these large-scale natural databases will not reproduce these
+under-represented basic phenomena that could otherwise be easily illustrated
+with well selected artificial stimuli.
+In this work we study a specific example of the above statement. A
+wavelet+divisive normalization layer of a sensible cascade of linear+nonlinear
+layers fitted to maximize the correlation with subjective opinion of observers
+on a large image quality database fails to reproduce basic crossmasking. Here
+we outline a solution for this problem using artificial stimuli. Then, we show
+that the resulting model is also a competitive solution for the large-scale
+database. In line with Rust and Movshon (2005), our report (misrepresentation
+of basic visual phenomena in subjectively-rated natural image databases) is an
+additional argument in praise of artifice.
+"
+27105,"Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning","  The use of ensembles of neural networks (NNs) for the quantification of
+predictive uncertainty is widespread. However, the current justification is
+intuitive rather than analytical. This work proposes one minor modification to
+the normal ensembling methodology, which we prove allows the ensemble to
+perform Bayesian inference, hence converging to the corresponding Gaussian
+Process as both the total number of NNs, and the size of each, tend to
+infinity. This working paper provides early-stage results in a reinforcement
+learning setting, analysing the practicality of the technique for an ensemble
+of small, finite number. Using the uncertainty estimates produced by anchored
+ensembles to govern the exploration-exploitation process results in steadier,
+more stable learning.
+"
+27106,Stable Tensor Neural Networks for Rapid Deep Learning,"  We propose a tensor neural network ($t$-NN) framework that offers an exciting
+new paradigm for designing neural networks with multidimensional (tensor) data.
+Our network architecture is based on the $t$-product (Kilmer and Martin, 2011),
+an algebraic formulation to multiply tensors via circulant convolution. In this
+$t$-product algebra, we interpret tensors as $t$-linear operators analogous to
+matrices as linear operators, and hence our framework inherits mimetic matrix
+properties. To exemplify the elegant, matrix-mimetic algebraic structure of our
+$t$-NNs, we expand on recent work (Haber and Ruthotto, 2017) which interprets
+deep neural networks as discretizations of non-linear differential equations
+and introduces stable neural networks which promote superior generalization.
+Motivated by this dynamic framework, we introduce a stable $t$-NN which
+facilitates more rapid learning because of its reduced, more powerful
+parameterization. Through our high-dimensional design, we create a more compact
+parameter space and extract multidimensional correlations otherwise latent in
+traditional algorithms. We further generalize our $t$-NN framework to a family
+of tensor-tensor products (Kernfeld, Kilmer, and Aeron, 2015) which still
+induce a matrix-mimetic algebraic structure. Through numerical experiments on
+the MNIST and CIFAR-10 datasets, we demonstrate the more powerful
+parameterizations and improved generalizability of stable $t$-NNs.
+"
+27107,Mitigating Congestion in Complex Transportation Networks via Maximum Entropy,"  In this paper, we reveal the relationship between entropy rate and the
+congestion in complex network and solve it analytically for special cases.
+Finding maximizing entropy rate will lead to an improvement of traffic
+efficiency, we propose a method to mitigate congestion by allocating limited
+traffic capacity to the nodes in network rationally. Different from former
+strategies, our method only requires local and observable information of
+network, and is low-cost and widely applicable in practice. In the simulation
+of the phase transition for various network models, our method performs well in
+mitigating congestion both locally and globally. By comparison, we also uncover
+the deficiency of former degree-biased approaches. Owing to the rapid
+development of transportation networks, our method may be helpful for modern
+society.
+"
+27108,Human-Guided Data Exploration,"  The outcome of the explorative data analysis (EDA) phase is vital for
+successful data analysis. EDA is more effective when the user interacts with
+the system used to carry out the exploration. In the recently proposed paradigm
+of iterative data mining the user controls the exploration by inputting
+knowledge in the form of patterns observed during the process. The system then
+shows the user views of the data that are maximally informative given the
+user's current knowledge. Although this scheme is good at showing surprising
+views of the data to the user, there is a clear shortcoming: the user cannot
+steer the process. In many real cases we want to focus on investigating
+specific questions concerning the data. This paper presents the Human Guided
+Data Exploration framework, generalising previous research. This framework
+allows the user to incorporate existing knowledge into the exploration process,
+focus on exploring a subset of the data, and compare different complex
+hypotheses concerning relations in the data. The framework utilises a
+computationally efficient constrained randomisation scheme. To showcase the
+framework, we developed a free open-source tool, using which the empirical
+evaluation on real-world datasets was carried out. Our evaluation shows that
+the ability to focus on particular subsets and being able to compare hypotheses
+are important additions to the interactive iterative data mining process.
+"
+27109,Summarization of ICU Patient Motion from Multimodal Multiview Videos,"  Clinical observations indicate that during critical care at the hospitals,
+patients sleep positioning and motion affect recovery. Unfortunately, there is
+no formal medical protocol to record, quantify, and analyze patient motion.
+There is a small number of clinical studies, which use manual analysis of sleep
+poses and motion recordings to support medical benefits of patient positioning
+and motion monitoring. Manual processes are not scalable, are prone to human
+errors, and strain an already taxed healthcare workforce. This study introduces
+DECU (Deep Eye-CU): an autonomous mulitmodal multiview system, which addresses
+these issues by autonomously monitoring healthcare environments and enabling
+the recording and analysis of patient sleep poses and motion. DECU uses three
+RGB-D cameras to monitor patient motion in a medical Intensive Care Unit (ICU).
+The algorithms in DECU estimate pose direction at different temporal
+resolutions and use keyframes to efficiently represent pose transition
+dynamics. DECU combines deep features computed from the data with a modified
+version of Hidden Markov Model to more flexibly model sleep pose duration,
+analyze pose patterns, and summarize patient motion. Extensive experimental
+results are presented. The performance of DECU is evaluated in ideal (BC:
+Bright and Clear/occlusion-free) and natural (DO: Dark and Occluded) scenarios
+at two motion resolutions in a mock-up and a real ICU. The results indicate
+that deep features allow DECU to match the classification performance of
+engineered features in BC scenes and increase the accuracy by up to 8% in DO
+scenes. In addition, the overall pose history summarization tracing accuracy
+shows an average detection rate of 85% in BC and of 76% in DO scenes. The
+proposed keyframe estimation algorithm allows DECU to reach an average 78%
+transition classification accuracy.
+"
+27110,Collaborative Filtering with Social Local Models,"  Matrix Factorization (MF) is a very popular method for recommendation
+systems. It assumes that the underneath rating matrix is low-rank. However,
+this assumption can be too restrictive to capture complex relationships and
+interactions among users and items. Recently, Local LOw-Rank Matrix
+Approximation (LLORMA) has been shown to be very successful in addressing this
+issue. It just assumes the rating matrix is composed of a number of low-rank
+submatrices constructed from subsets of similar users and items. Although
+LLORMA outperforms MF, how to construct such submatrices remains a big problem.
+Motivated by the availability of rich social connections in today's
+recommendation systems, we propose a novel framework, i.e., Social LOcal
+low-rank Matrix Approximation (SLOMA), to address this problem. To the best of
+our knowledge, SLOMA is the first work to incorporate social connections into
+the local low-rank framework. Furthermore, we enhance SLOMA by applying social
+regularization to submatrices factorization, denoted as SLOMA++. Therefore, the
+proposed model can benefit from both social recommendation and the local
+low-rank assumption. Experimental results from two real-world datasets, Yelp
+and Douban, demonstrate the superiority of the proposed models over LLORMA and
+MF.
+"
+27111,Asymptotics of fast rotating density-dependent incompressible fluids in two space dimensions,"  In the present paper we study the fast rotation limit for viscous
+incompressible fluids with variable density, whose motion is influenced by the
+Coriolis force. We restrict our analysis to two dimensional flows. In the case
+when the initial density is a small perturbation of a constant state, we
+recover in the limit the convergence to the homogeneous incompressible
+Navier-Stokes equations (up to an additional term, due to density
+fluctuations). For general non-homogeneous fluids, the limit equations are
+instead linear, and the limit dynamics is described in terms of the vorticity
+and the density oscillation function: we lack enough regularity on the latter
+to prove convergence on the momentum equation itself. The proof of both results
+relies on a compensated compactness argument, which enables one to treat also
+the possible presence of vacuum.
+"
+27112,The secret chambers in the Chephren Pyramid,"  Pyramids are the greatest architectural achievement of ancient civilization,
+so people all over the world are curious as to the purpose of such huge
+constructions. No other structure has been studied as thoroughly, nor have so
+many books and articles been written about it. We created a computer model of
+the pyramid. To validate the model, we compare our calculations with the
+experimental data of Luis W. Alvarez. The fact that the Egyptians set up 2.5
+million stone blocks without any purpose seems to be unimaginable. Therefore,
+we attempt to examine the internal structure of the pyramid using muon
+tomography. With our measurements we performed and verified a calibration of
+the attenuation of muons for primary beam momenta of 2.5 GeV/c and 3 GeV/c at
+the T9 experimental area of CERN.
+"
+27113,A Proof of the Explicit Formula for Product Moments of Multivariate Gaussian Random Variables,"  A detailed proof of a recent result on explicit formulae for the product
+moments $E \left \{ X_1^{a_1} X_2^{a_2} \cdots X_n^{a_n}\right \}$ of
+multivariate Gaussian random variables is provided in this note.
+"
+27114,"SemTK: An Ontology-first, Open Source Semantic Toolkit for Managing and Querying Knowledge Graphs","  The relatively recent adoption of Knowledge Graphs as an enabling technology
+in multiple high-profile artificial intelligence and cognitive applications has
+led to growing interest in the Semantic Web technology stack. Many
+semantics-related tools, however, are focused on serving experts with a deep
+understanding of semantic technologies. For example, triplification of
+relational data is available but there is no open source tool that allows a
+user unfamiliar with OWL/RDF to import data into a semantic triple store in an
+intuitive manner. Further, many tools require users to have a working
+understanding of SPARQL to query data. Casual users interested in benefiting
+from the power of Knowledge Graphs have few tools available for exploring,
+querying, and managing semantic data. We present SemTK, the Semantics Toolkit,
+a user-friendly suite of tools that allow both expert and non-expert semantics
+users convenient ingestion of relational data, simplified query generation, and
+more. The exploration of ontologies and instance data is performed through
+SPARQLgraph, an intuitive web-based user interface in SemTK understandable and
+navigable by a lay user. The open source version of SemTK is available at
+this http URL
+"
+27115,Piecewise-Deterministic Markov Chain Monte Carlo,"  A novel class of non-reversible Markov chain Monte Carlo schemes relying on
+continuous-time piecewise-deterministic Markov Processes has recently emerged.
+In these algorithms, the state of the Markov process evolves according to a
+deterministic dynamics which is modified using a Markov transition kernel at
+random event times. These methods enjoy remarkable features including the
+ability to update only a subset of the state components while other components
+implicitly keep evolving and the ability to use an unbiased estimate of the
+gradient of the log-target while preserving the target as invariant
+distribution. However, they also suffer from important limitations. The
+deterministic dynamics used so far do not exploit the structure of the target.
+Moreover, exact simulation of the event times is feasible for an important yet
+restricted class of problems and, even when it is, it is application specific.
+This limits the applicability of these techniques and prevents the development
+of a generic software implementation of them. We introduce novel MCMC methods
+addressing these shortcomings. In particular, we introduce novel
+continuous-time algorithms relying on exact Hamiltonian flows and novel
+non-reversible discrete-time algorithms which can exploit complex dynamics such
+as approximate Hamiltonian dynamics arising from symplectic integrators while
+preserving the attractive features of continuous-time algorithms. We
+demonstrate the performance of these schemes on a variety of applications.
+"
+27116,Depth Distribution in High Dimensions,"  Motivated by the analysis of range queries in databases, we introduce the
+computation of the Depth Distribution of a set $\mathcal{B}$ of axis aligned
+boxes, whose computation generalizes that of the Klee's Measure and of the
+Maximum Depth. In the worst case over instances of fixed input size $n$, we
+describe an algorithm of complexity within $O({n^\frac{d+1}{2}\log n})$, using
+space within $O({n\log n})$, mixing two techniques previously used to compute
+the Klee's Measure. We refine this result and previous results on the Klee's
+Measure and the Maximum Depth for various measures of difficulty of the input,
+such as the profile of the input and the degeneracy of the intersection graph
+formed by the boxes.
+"
+27117,Detection of Block-Exchangeable Structure in Large-Scale Correlation Matrices,"  Correlation matrices are omnipresent in multivariate data analysis. When the
+number d of variables is large, the sample estimates of correlation matrices
+are typically noisy and conceal underlying dependence patterns. We consider the
+case when the variables can be grouped into K clusters with exchangeable
+dependence; this assumption is often made in applications, e.g., in finance and
+econometrics. Under this partial exchangeability condition, the corresponding
+correlation matrix has a block structure and the number of unknown parameters
+is reduced from d(d-1)/2 to at most K(K+1)/2. We propose a robust algorithm
+based on Kendall's rank correlation to identify the clusters without assuming
+the knowledge of K a priori or anything about the margins except continuity.
+The corresponding block-structured estimator performs considerably better than
+the sample Kendall rank correlation matrix when K < d. The new estimator can
+also be much more efficient in finite samples even in the unstructured case K =
+d, although there is no gain asymptotically. When the distribution of the data
+is elliptical, the results extend to linear correlation matrices and their
+inverses. The procedure is illustrated on financial stock returns.
+"
+27118,False Data Injection Cyber-Attack Detection,"  State estimation estimates the system condition in real-time and provides a
+base case for other energy management system (EMS) applications including
+real-time contingency analysis and security-constrained economic dispatch.
+Recent work in the literature shows malicious cyber-attack can inject false
+measurements that bypass traditional bad data detection and cause actual
+overloads. Thus, it is very important to detect such cyber-attacks. In this
+paper, multiple metrics are proposed to monitor abnormal load deviations and
+suspicious branch flow changes. A systematic two-stage approach is proposed to
+detect false data injection (FDI) cyber-attack. The first stage determines
+whether the system is under attack while the second stage identifies the target
+branch. Numerical simulations verify that FDI can cause severe system
+violations and demonstrate the effectiveness of the proposed two-stage FDI
+detection (FDID) method. It is concluded that the proposed FDID approach can
+efficiently detect FDI cyber-attacks and identify the target branch;
+furthermore, the associated false alarm rate and false dismissal rate are very
+low.
+"
+27119,Hook length property of $d$-complete posets via $q$-integrals,"  The hook length formula for $d$-complete posets states that the $P$-partition
+generating function for them is given by a product in terms of hook lengths. We
+give a new proof of the hook length formula using $q$-integrals. The proof is
+done by a case-by-case analysis consisting of two steps. First, we express the
+$P$-partition generating function for each case as a $q$-integral and then we
+evaluate the $q$-integrals. Several $q$-integrals are evaluated using partial
+fraction expansion identities and others are verified by computer.
+"
+27120,Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks,"  Speech enhancement (SE) aims to reduce noise in speech signals. Most SE
+techniques focus only on addressing audio information. In this work, inspired
+by multimodal learning, which utilizes data from different modalities, and the
+recent success of convolutional neural networks (CNNs) in SE, we propose an
+audio-visual deep CNNs (AVDCNN) SE model, which incorporates audio and visual
+streams into a unified network model. We also propose a multi-task learning
+framework for reconstructing audio and visual signals at the output layer.
+Precisely speaking, the proposed AVDCNN model is structured as an audio-visual
+encoder-decoder network, in which audio and visual data are first processed
+using individual CNNs, and then fused into a joint network to generate enhanced
+speech (the primary task) and reconstructed images (the secondary task) at the
+output layer. The model is trained in an end-to-end manner, and parameters are
+jointly learned through back-propagation. We evaluate enhanced speech using
+five instrumental criteria. Results show that the AVDCNN model yields a notably
+superior performance compared with an audio-only CNN-based SE model and two
+conventional SE approaches, confirming the effectiveness of integrating visual
+information into the SE process. In addition, the AVDCNN model also outperforms
+an existing audio-visual SE model, confirming its capability of effectively
+combining audio and visual information in SE.
+"
+27121,Sampling-based speech parameter generation using moment-matching networks,"  This paper presents sampling-based speech parameter generation using
+moment-matching networks for Deep Neural Network (DNN)-based speech synthesis.
+Although people never produce exactly the same speech even if we try to express
+the same linguistic and para-linguistic information, typical statistical speech
+synthesis produces completely the same speech, i.e., there is no
+inter-utterance variation in synthetic speech. To give synthetic speech natural
+inter-utterance variation, this paper builds DNN acoustic models that make it
+possible to randomly sample speech parameters. The DNNs are trained so that
+they make the moments of generated speech parameters close to those of natural
+speech parameters. Since the variation of speech parameters is compressed into
+a low-dimensional simple prior noise vector, our algorithm has lower
+computation cost than direct sampling of speech parameters. As the first step
+towards generating synthetic speech that has natural inter-utterance variation,
+this paper investigates whether or not the proposed sampling-based generation
+deteriorates synthetic speech quality. In evaluation, we compare speech quality
+of conventional maximum likelihood-based generation and proposed sampling-based
+generation. The result demonstrates the proposed generation causes no
+degradation in speech quality.
+"
+27122,Anisotropic superfluidity of two-dimensional excitons in a periodic potential,"  We study anisotropies of helicity modulus, excitation spectrum, sound
+velocity and angle-resolved luminescence spectrum in a two-dimensional system
+of interacting excitons in a periodic potential. Analytical expressions for
+anisotropic corrections to the quantities characterizing superfluidity are
+obtained. We consider particularly the case of dipolar excitons in quantum
+wells. For GaAs/AlGaAs heterostructures as well as MoS$_2$/hBN/MoS$_2$ and
+MoSe$_2$/hBN/WSe$_2$ transition metal dichalcogenide bilayers estimates of the
+magnitude of the predicted effects are given. We also present a method to
+control superfluid motion and to determine the helicity modulus in generic
+dipolar systems.
+"
+27123,People on Drugs: Credibility of User Statements in Health Communities,"  Online health communities are a valuable source of information for patients
+and physicians. However, such user-generated resources are often plagued by
+inaccuracies and misinformation. In this work we propose a method for
+automatically establishing the credibility of user-generated medical statements
+and the trustworthiness of their authors by exploiting linguistic cues and
+distant supervision from expert sources. To this end we introduce a
+probabilistic graphical model that jointly learns user trustworthiness,
+statement credibility, and language objectivity. We apply this methodology to
+the task of extracting rare or unknown side-effects of medical drugs --- this
+being one of the problems where large scale non-expert data has the potential
+to complement expert medical knowledge. We show that our method can reliably
+extract side-effects and filter out false statements, while identifying
+trustworthy users that are likely to contribute valuable medical information.
+"
+27124,Patchwork Kriging for Large-scale Gaussian Process Regression,"  This paper presents a new approach for Gaussian process (GP) regression for
+large datasets. The approach involves partitioning the regression input domain
+into multiple local regions with a different local GP model fitted in each
+region. Unlike existing local partitioned GP approaches, we introduce a
+technique for patching together the local GP models nearly seamlessly to ensure
+that the local GP models for two neighboring regions produce nearly the same
+response prediction and prediction error variance on the boundary between the
+two regions. This largely mitigates the well-known discontinuity problem that
+degrades the boundary accuracy of existing local partitioned GP methods. Our
+main innovation is to represent the continuity conditions as additional
+pseudo-observations that the differences between neighboring GP responses are
+identically zero at an appropriately chosen set of boundary input locations. To
+predict the response at any input location, we simply augment the actual
+response observations with the pseudo-observations and apply standard GP
+prediction methods to the augmented data. In contrast to heuristic continuity
+adjustments, this has an advantage of working within a formal GP framework, so
+that the GP-based predictive uncertainty quantification remains valid. Our
+approach also inherits a sparse block-like structure for the sample covariance
+matrix, which results in computationally efficient closed-form expressions for
+the predictive mean and variance. In addition, we provide a new spatial
+partitioning scheme based on a recursive space partitioning along local
+principal component directions, which makes the proposed approach applicable
+for regression domains having more than two dimensions. Using three spatial
+datasets and three higher dimensional datasets, we investigate the numerical
+performance of the approach and compare it to several state-of-the-art
+approaches.
+"
+27125,Testing physical models for dipolar asymmetry with CMB polarization,"  The cosmic microwave background (CMB) temperature anisotropies exhibit a
+large-scale dipolar power asymmetry. To determine whether this is due to a
+real, physical modulation or is simply a large statistical fluctuation requires
+the measurement of new modes. Here we forecast how well CMB polarization data
+from \Planck\ and future experiments will be able to confirm or constrain
+physical models for modulation. Fitting several such models to the \Planck\
+temperature data allows us to provide predictions for polarization asymmetry.
+While for some models and parameters \Planck\ polarization will decrease error
+bars on the modulation amplitude by only a small percentage, we show,
+importantly, that cosmic-variance-limited (and in some cases even \Planck)
+polarization data can decrease the errors by considerably better than the
+expectation of $\sqrt 2$ based on simple $\ell$-space arguments. We project
+that if the primordial fluctuations are truly modulated (with parameters as
+indicated by \Planck\ temperature data) then \Planck\ will be able to make a
+2$\sigma$ detection of the modulation model with 20--75\% probability,
+increasing to 45--99\% when cosmic-variance-limited polarization is considered.
+We stress that these results are quite model dependent. Cosmic variance in
+temperature is important: combining statistically isotropic polarization with
+temperature data will spuriously increase the significance of the temperature
+signal with 30\% probability for \Planck.
+"
+27126,Lagrangian basis method for dimensionality reduction of convection dominated nonlinear flows,"  Foundations of a new projection-based model reduction approach for convection
+dominated nonlinear fluid flows are summarized. In this method the evolution of
+the flow is approximated in the Lagrangian frame of reference. Global basis
+functions are used to approximate both the state and the position of the
+Lagrangian computational domain. It is demonstrated that in this framework,
+certain wave-like solutions exhibit low-rank structure and thus, can be
+efficiently compressed using relatively few global basis. The proposed approach
+is successfully demonstrated for the reduction of several simple but
+representative problems.
+"
+27127,Proper Bayes and Minimax Predictive Densities for a Matrix-variate Normal Distribution,"  This paper deals with the problem of estimating predictive densities of a
+matrix-variate normal distribution with known covariance matrix. Our main aim
+is to establish some Bayesian predictive densities related to matricial
+shrinkage estimators of the normal mean matrix. The Kullback-Leibler loss is
+used for evaluating decision-theoretical optimality of predictive densities. It
+is shown that a proper hierarchical prior yields an admissible and minimax
+predictive density. Also, superharmonicity of prior densities is paid attention
+to for finding out a minimax predictive density with good numerical
+performance.
+"
+27128,Control of effective free energy landscape in a frustrated magnet by a field pulse,"  Thermal fluctuations can lift the degeneracy of a ground state manifold,
+producing a free energy landscape without accidentally degenerate minima. In a
+process known as order by disorder, a subset of states incorporating
+symmetry-breaking may be selected. Here, we show that such a free energy
+landscape can be controlled in a non-equilibrium setting as the slow motion
+within the ground state manifold is governed by the fast modes out of it. For
+the paradigmatic case of the classical pyrochlore XY antiferromagnet, we show
+that a uniform magnetic field pulse can excite these fast modes to generate a
+tunable effective free energy landscape with minima at thermodynamically
+unstable portions of the ground state manifold.
+"
+27129,End-to-end Multimodal Emotion and Gender Recognition with Dynamic Joint Loss Weights,"  Multi-task learning is a method for improving the generalizability of
+multiple tasks. In order to perform multiple classification tasks with one
+neural network model, the losses of each task should be combined. Previous
+studies have mostly focused on multiple prediction tasks using joint loss with
+static weights for training models, choosing the weights between tasks without
+making sufficient considerations by setting them uniformly or empirically. In
+this study, we propose a method to calculate joint loss using dynamic weights
+to improve the total performance, instead of the individual performance, of
+tasks. We apply this method to design an end-to-end multimodal emotion and
+gender recognition model using audio and video data. This approach provides
+proper weights for the loss of each task when the training process ends. In our
+experiments, emotion and gender recognition with the proposed method yielded a
+lower joint loss, which is computed as the negative log-likelihood, than using
+static weights for joint loss. Moreover, our proposed model has better
+generalizability than other models. To the best of our knowledge, this research
+is the first to demonstrate the strength of using dynamic weights for joint
+loss for maximizing overall performance in emotion and gender recognition
+tasks.
+"
+27130,Regression Analysis of Proportion Outcomes with Random Effects,"  A regression method for proportional, or fractional, data with mixed effects
+is outlined, designed for analysis of datasets in which the outcomes have
+substantial weight at the bounds. In such cases a normal approximation is
+particularly unsuitable as it can result in incorrect inference. To resolve
+this problem, we employ a logistic regression model and then apply a bootstrap
+method to correct conservative confidence intervals. This paper outlines the
+theory of the method, and demonstrates its utility using simulated data.
+Working code for the R platform is provided through the package glmmboot,
+available on CRAN.
+"
+27131,Throughput and Robustness Guaranteed Beam Tracking for mmWave Wireless Networks,"  With the increasing demand of ultra-high-speed wireless communications and
+the existing low frequency band (e.g., sub-6GHz) becomes more and more crowded,
+millimeter-wave (mmWave) with large spectra available is considered as the most
+promising frequency band for future wireless communications. Since the mmWave
+suffers a serious path-loss, beamforming techniques shall be adopted to
+concentrate the transmit power and receive region on a narrow beam for
+achieving long distance communications. However, the mobility of users will
+bring frequent beam handoff, which will decrease the quality of experience
+(QoE). Therefore, efficient beam tracking mechanism should be carefully
+researched. However, the existing beam tracking mechanisms concentrate on
+system throughput maximization without considering beam handoff and link
+robustness. This paper proposes a throughput and robustness guaranteed beam
+tracking mechanism for mobile mmWave communication systems which takes account
+of both system throughput and handoff probability. Simulation results show that
+the proposed throughput and robustness guaranteed beam tracking mechanism can
+provide better performance than the other beam tracking mechanisms.
+"
+27132,Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding,"  We consider the problem of describing excursion sets of a real-valued
+function $f$, i.e. the set of inputs where $f$ is above a fixed threshold. Such
+regions are hard to visualize if the input space dimension, $d$, is higher than
+2. For a given projection matrix from the input space to a lower dimensional
+(usually $1,2$) subspace, we introduce profile sup (inf) functions that
+associate to each point in the projection's image the sup (inf) of the function
+constrained over the pre-image of this point by the considered projection.
+Plots of profile extrema functions convey a simple, although intrinsically
+partial, visualization of the set. We consider expensive to evaluate functions
+where only a very limited number of evaluations, $n$, is available, e.g.
+$n<100d$, and we surrogate $f$ with a posterior quantity of a Gaussian process
+(GP) model. We first compute profile extrema functions for the posterior mean
+given $n$ evaluations of $f$. We quantify the uncertainty on such estimates by
+studying the distribution of GP profile extrema with posterior
+quasi-realizations obtained from an approximating process. We control such
+approximation with a bound inherited from the Borell-TIS inequality. The
+technique is applied to analytical functions ($d=2,3$) and to a $5$-dimensional
+coastal flooding test case for a site located on the Atlantic French coast.
+Here $f$ is a numerical model returning the area of flooded surface in the
+coastal region given some offshore conditions. Profile extrema functions
+allowed us to better understand which offshore conditions impact large flooding
+events.
+"
+27133,Deep Kernelized Autoencoders,"  In this paper we introduce the deep kernelized autoencoder, a neural network
+model that allows an explicit approximation of (i) the mapping from an input
+space to an arbitrary, user-specified kernel space and (ii) the back-projection
+from such a kernel space to input space. The proposed method is based on
+traditional autoencoders and is trained through a new unsupervised loss
+function. During training, we optimize both the reconstruction accuracy of
+input samples and the alignment between a kernel matrix given as prior and the
+inner products of the hidden representations computed by the autoencoder.
+Kernel alignment provides control over the hidden representation learned by the
+autoencoder. Experiments have been performed to evaluate both reconstruction
+and kernel alignment performance. Additionally, we applied our method to
+emulate kPCA on a denoising task obtaining promising results.
+"
+27134,A Spiking Neural Dynamical Drift-Diffusion Model on Collective Decision Making with Self-Organized Criticality,"  This article proposes a novel collective decision making scheme to solve the
+multi-agent drift-diffusion-model problem with the help of spiking neural
+networks. The exponential integrate-and-fire model is used here to capture the
+individual dynamics of each agent in the system, and we name this new model as
+Exponential Decision Making (EDM) model. We demonstrate analytically and
+experimentally that the gating variable for instantaneous activation follows
+Boltzmann probability distribution, and the collective system reaches
+meta-stable critical states under the Markov chain premises. With mean field
+analysis, we derive the global criticality from local dynamics and achieve a
+power law distribution. Critical behavior of EDM exhibits the convergence
+dynamics of Boltzmann distribution, and we conclude that the EDM model inherits
+the property of self-organized criticality, that the system will eventually
+evolve toward criticality.
+"
+27135,Generic Vehicle Tracking Framework Capable of Handling Occlusions Based on Modified Mixture Particle Filter,"  Accurate and robust tracking of surrounding road participants plays an
+important role in autonomous driving. However, there is usually no prior
+knowledge of the number of tracking targets due to object emergence, object
+disappearance and false alarms. To overcome this challenge, we propose a
+generic vehicle tracking framework based on modified mixture particle filter,
+which can make the number of tracking targets adaptive to real-time
+observations and track all the vehicles within sensor range simultaneously in a
+uniform architecture without explicit data association. Each object corresponds
+to a mixture component whose distribution is non-parametric and approximated by
+particle hypotheses. Most tracking approaches employ vehicle kinematic models
+as the prediction model. However, it is hard for these models to make proper
+predictions when sensor measurements are lost or become low quality due to
+partial or complete occlusions. Moreover, these models are incapable of
+forecasting sudden maneuvers. To address these problems, we propose to
+incorporate learning-based behavioral models instead of pure vehicle kinematic
+models to realize prediction in the prior update of recursive Bayesian state
+estimation. Two typical driving scenarios including lane keeping and lane
+change are demonstrated to verify the effectiveness and accuracy of the
+proposed framework as well as the advantages of employing learning-based
+models.
+"
+27136,Knowledge Graph Embedding with Iterative Guidance from Soft Rules,"  Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of
+current research. Combining such an embedding model with logic rules has
+recently attracted increasing attention. Most previous attempts made a one-time
+injection of logic rules, ignoring the interactive nature between embedding
+learning and logical inference. And they focused only on hard rules, which
+always hold with no exception and usually require extensive manual effort to
+create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a
+novel paradigm of KG embedding with iterative guidance from soft rules. RUGE
+enables an embedding model to learn simultaneously from 1) labeled triples that
+have been directly observed in a given KG, 2) unlabeled triples whose labels
+are going to be predicted iteratively, and 3) soft rules with various
+confidence levels extracted automatically from the KG. In the learning process,
+RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and
+integrates such newly labeled triples to update the embedding model. Through
+this iterative procedure, knowledge embodied in logic rules may be better
+transferred into the learned embeddings. We evaluate RUGE in link prediction on
+Freebase and YAGO. Experimental results show that: 1) with rule knowledge
+injected iteratively, RUGE achieves significant and consistent improvements
+over state-of-the-art baselines; and 2) despite their uncertainties,
+automatically extracted soft rules are highly beneficial to KG embedding, even
+those with moderate confidence levels. The code and data used for this paper
+can be obtained from this https URL.
+"
+27137,Aproximación métrica de grupos: una breve perspectiva,"  This is an expository paper (in Spanish) about the metric approximation of
+groups.
+"
+27138,Cascades and Dissipative Anomalies in Compressible Fluid Turbulence,"  We investigate dissipative anomalies in a turbulent fluid governed by the
+compressible Navier-Stokes equation. We follow an exact approach pioneered by
+Onsager, which we explain as a non-perturbative application of the principle of
+renormalization-group invariance. In the limit of high Reynolds and Péclet
+numbers, the flow realizations are found to be described as distributional or
+""coarse-grained"" solutions of the compressible Euler equations, with standard
+conservation laws broken by turbulent anomalies. The anomalous dissipation of
+kinetic energy is shown to be due not only to local cascade, but also to a
+distinct mechanism called pressure-work defect. Irreversible heating in
+stationary, planar shocks with an ideal-gas equation of state exemplifies the
+second mechanism. Entropy conservation anomalies are also found to occur by two
+mechanisms: an anomalous input of negative entropy (negentropy) by
+pressure-work and a cascade of negentropy to small scales. We derive
+""4/5th-law""-type expressions for the anomalies, which allow us to characterize
+the singularities (structure-function scaling exponents) required to sustain
+the cascades. We compare our approach with alternative theories and empirical
+evidence. It is argued that the ""Big Power-Law in the Sky"" observed in electron
+density scintillations in the interstellar medium is a manifestation of a
+forward negentropy cascade, or an inverse cascade of usual thermodynamic
+entropy.
+"
+27139,Two-channel Kondo Effect Emerging from Nd Ions,"  We discuss Kondo phenomena in a seven-orbital impurity Anderson model
+hybridized with $\Gamma_8$ conduction electrons by employing a numerical
+renormalization group method. In particular, we focus on the case with three
+local $f$ electrons, corresponding to a Nd$^{3+}$ ion. For realistic values of
+Coulomb interactions, spin-orbit coupling, cubic crystalline electric field
+potentials, and hybridization, we find a residual entropy of $0.5\log 2$, a
+characteristic of two-channel Kondo phenomena, for the wide range of parameters
+of the local $\Gamma_6$ ground state. This is considered to be the magnetic
+two-channel Kondo effect, consistent with the result from an extended $s$-$d$
+model constructed on the basis of the $j$-$j$ coupling scheme. Finally, we
+briefly discuss candidates of Nd compounds to observe the two-channel Kondo
+effect.
+"
+27140,Balanced Truncation Model Reduction of Nonstationary Systems Interconnected over Arbitrary Graphs,"  This paper deals with the balanced truncation model reduction of
+discrete-time, linear time-varying, heterogeneous subsystems interconnected
+over finite arbitrary directed graphs. The information transfer between the
+subsystems is subject to a communication latency of one time-step. The
+presented method guarantees the preservation of the interconnection structure
+and further allows for its simplification. In addition to truncating temporal
+states associated with the subsystems, the method allows for the order
+reduction of spatial states associated with the interconnections between the
+subsystems and even the removal of whole interconnections. Upper bounds on the
+l2-induced norm of the resulting error system are derived. The proposed method
+is illustrated through an example.
+"
+27141,Resonance capture at arbitrary inclination: II. Effect of the radial drift rate,"  The effect of radial drift rate on mean motion resonance capture is studied
+for prograde, polar and retrograde orbits. We employ the numerical framework of
+our earlier exploration of resonance capture at arbitrary inclination. Randomly
+constructed samples of massless particles are set to migrate radially from
+outside the orbit of a Jupiter-mass planet at different drift rates totalling
+more than $1.6\times 10^6$ numerical simulations. Slower drift rates reduce
+overall capture probability especially for prograde orbits and enhance capture
+at specific initial inclinations of high order resonances such as the outer
+1:5, 1:4, 1:3, 2:5, 3:7 and 5:7. Global capture is reduced with increasing
+eccentricity at all inclinations as high order resonances capture more
+particles that are subsequently lost by disruptive close encounters with the
+planet. The relative efficiency of retrograde resonances at long-lived capture
+with respect to prograde resonances is explained by the reduced effect of
+planet encounters as such events occur with a shorter duration and a higher
+relative velocity for retrograde motion. Capture in the coorbital 1:1 resonance
+is marginally affected by the radial drift rate except for nearly coplanar
+retrograde eccentric orbits whose capture likelihood is increased significantly
+with slower drift rates. An unexpected finding is the presence of a dynamical
+corridor for capture in high order inner prograde resonances with initial
+inclinations in the range [50$^\circ$,80$^\circ$] especially at the inner 5:2
+resonance whose capture likelihood peaks at 80% to 90% depending on initial
+eccentricity.
+"
+27142,Scholarly Twitter metrics,"  Twitter has arguably been the most popular among the data sources that form
+the basis of so-called altmetrics. Tweets to scholarly documents have been
+heralded as both early indicators of citations as well as measures of societal
+impact. This chapter provides an overview of Twitter activity as the basis for
+scholarly metrics from a critical point of view and equally describes the
+potential and limitations of scholarly Twitter metrics. By reviewing the
+literature on Twitter in scholarly communication and analyzing 24 million
+tweets linking to scholarly documents, it aims to provide a basic understanding
+of what tweets can and cannot measure in the context of research evaluation.
+Going beyond the limited explanatory power of low correlations between tweets
+and citations, this chapter considers what types of scholarly documents are
+popular on Twitter, and how, when and by whom they are diffused in order to
+understand what tweets to scholarly documents measure. Although this chapter is
+not able to solve the problems associated with the creation of meaningful
+metrics from social media, it highlights particular issues and aims to provide
+the basis for advanced scholarly Twitter metrics.
+"
+27143,GeneSIS-RT: Generating Synthetic Images for training Secondary Real-world Tasks,"  We propose a novel approach for generating high-quality, synthetic data for
+domain-specific learning tasks, for which training data may not be readily
+available. We leverage recent progress in image-to-image translation to bridge
+the gap between simulated and real images, allowing us to generate realistic
+training data for real-world tasks using only unlabeled real-world images and a
+simulation. GeneSIS-RT ameliorates the burden of having to collect labeled
+real-world images and is a promising candidate for generating high-quality,
+domain-specific, synthetic data.
+To show the effectiveness of using GeneSIS-RT to create training data, we
+study two tasks: semantic segmentation and reactive obstacle avoidance. We
+demonstrate that learning algorithms trained using data generated by GeneSIS-RT
+make high-accuracy predictions and outperform systems trained on raw simulated
+data alone, and as well or better than those trained on real data. Finally, we
+use our data to train a quadcopter to fly 60 meters at speeds up to 3.4 m/s
+through a cluttered environment, demonstrating that our GeneSIS-RT images can
+be used to learn to perform mission-critical tasks.
+"
+27144,Are homeostatic states stable? Dynamical stability in morphoelasticity,"  Biological growth is often driven by mechanical cues, such as changes in
+external pressure or tensile loading. Moreover, it is well known that many
+living tissues actively maintain a preferred level of mechanical internal
+stress, called the mechanical homeostasis. The tissue-level feedback mechanism
+by which changes of the local mechanical stresses affect growth is called a
+growth law within the theory of morphoelasticity, a theory for understanding
+the coupling between mechanics and geometry in growing and evolving biological
+materials. The goal of this article is to develop mathematical techniques to
+analyze growth laws and to explore issues of heterogeneity and growth
+stability. We discuss the growth dynamics of tubular structures, which are very
+common in biology (e.g. arteries, plant stems, airways) and model the
+homeostasis-driven growth dynamics of tubes which produces spatially
+inhomogeneous residual stress. We show that the stability of the homeostatic
+state depends nontrivially on the anisotropy of the growth response. The key
+role of anisotropy may provide a foundation for experimental testing of
+homeostasis-driven growth laws.
+"
+27145,The structure and composition statistics of 6A binary and ternary crystalline materials,"  The fundamental principles underlying the arrangement of elements into solid
+compounds with an enormous variety of crystal structures are still largely
+unknown. This study presents a general overview of the structure types
+appearing in an important subset of the solid compounds, i.e., binary and
+ternary compounds of the 6A column oxides, sulfides and selenides. It contains
+an analysis of these compounds, including the prevalence of various structure
+types, their symmetry properties, compositions, stoichiometries and unit cell
+sizes. It is found that these compound families include preferred
+stoichiometries and structure types that may reflect both their specific
+chemistry and research bias in the available empirical data. Identification of
+non-overlapping gaps and missing stoichiometries in these structure populations
+may be used as guidance in the search for new materials.
+"
+27146,Wave-like Decoding of Tail-biting Spatially Coupled LDPC Codes Through Iterative Demapping,"  For finite coupling lengths, terminated spatially coupled low-density
+parity-check (SC-LDPC) codes show a non-negligible rate-loss. In this paper, we
+investigate if this rate loss can be mitigated by tail-biting SC-LDPC codes in
+conjunction with iterative demapping of higher order modulation formats.
+Therefore, we examine the BP threshold of different coupled and uncoupled
+ensembles. A comparison between the decoding thresholds approximated by EXIT
+charts and the density evolution results of the coupled and uncoupled ensemble
+is given. We investigate the effect and potential of different labelings for
+such a set-up using per-bit EXIT curves, and exemplify the method for a 16-QAM
+system, e.g., using set partitioning labelings. A hybrid mapping is proposed,
+where different sub-blocks use different labelings in order to further optimize
+the decoding thresholds of tail-biting codes, while the computational
+complexity overhead through iterative demapping remains small.
+"
+27147,"Complete random matrix classification of SYK models with $\mathcal{N}=0$, $1$ and $2$ supersymmetry","  We present a complete symmetry classification of the Sachdev-Ye-Kitaev (SYK)
+model with $\mathcal{N}=0$, $1$ and $2$ supersymmetry (SUSY) on the basis of
+the Altland-Zirnbauer scheme in random matrix theory (RMT). For $\mathcal{N}=0$
+and $1$ we consider generic $q$-body interactions in the Hamiltonian and find
+RMT classes that were not present in earlier classifications of the same model
+with $q=4$. We numerically establish quantitative agreement between the
+distributions of the smallest energy levels in the $\mathcal{N}=1$ SYK model
+and RMT. Furthermore, we delineate the distinctive structure of the
+$\mathcal{N}=2$ SYK model and provide its complete symmetry classification
+based on RMT for all eigenspaces of the fermion number operator. We corroborate
+our classification by detailed numerical comparisons with RMT and thus
+establish the presence of quantum chaotic dynamics in the $\mathcal{N}=2$ SYK
+model. We also introduce a new SYK-like model without SUSY that exhibits hybrid
+properties of the $\mathcal{N}=1$ and $\mathcal{N}=2$ SYK models and uncover
+its rich structure both analytically and numerically.
+"
+27148,Remarks on NIP in a model,"  We define the notion $\phi(x,y)$ has $NIP$ in $A$, where $A$ is a subset of a
+model, and give some equivalences by translating results from [1]. Using
+additional material from [11] we discuss the number of coheirs when $A$ is not
+necessarily countable. We also revisit the notion ""$\phi(x,y)$ has $NOP$ in a
+model $M$"" from [8].
+"
+27149,The formation and coalescence sites of the first gravitational wave events,"  We present a novel theoretical model to characterize the formation and
+coalescence sites of compact binaries in a cosmological context. This is based
+on the coupling between the binary population synthesis code SeBa with a
+simulation following the formation of a Milky Way-like halo in a well resolved
+cosmic volume of 4 cMpc, performed with the GAMESH pipeline. We have applied
+this technique to investigate when and where systems with properties similar to
+the recently observed LIGO/VIRGO events are more likely to form and where they
+are more likely to reside when they coalesce. We find that more than 70% of
+GW151226 and LVT151012-like systems form in galaxies with stellar mass M* >
+10^8 Msun in the redshift range [0.06 - 3] and [0.14 - 11.3], respectively. All
+GW150914-like systems form in low-metallicity dwarfs with M* < 5 10^6 Msun at
+2.4 < z < 4.2. Despite these initial differences, by the time they reach
+coalescence the observed events are most likely hosted by star forming galaxies
+with M* > 10^{10} Msun. Due to tidal stripping and radiative feedback, a non
+negligible fraction of GW150914-like candidates end-up in galaxies with
+properties similar to dwarf spheroidals and ultra-faint satellites.
+"
+27150,Role of three-particle vertex within dual fermion calculations,"  We investigate the influence of self-energy diagrams beyond the two-particle
+vertex level within dual fermion theory. Specifically, we calculate the local
+three-particle vertex and construct from it selected dual fermion self-energy
+corrections to dynamical mean field theory. For the two-dimensional Hubbard
+model, the thus obtained self-energy corrections are small in the parameter
+space where dual fermion corrections based on the two-particle vertex only are
+small. However, in other parts of the parameter space, they are of a similar
+magnitude and qualitatively different from standard dual fermion theory. The
+high-frequency behaviour of the self-energy correction is - surprisingly - even
+dominated by corrections stemming from the three-particle vertex.
+"
+27151,Revisiting $^{63}$Cu NMR evidence for charge order in superconducting La$_{1.885}$Sr$_{0.115}$CuO$_4$,"  The presence of charge and spin stripe order in the La2CuO4-based family of
+superconductors continues to lead to new insight on the unusual ground state
+properties of high Tc cuprates. Soon after the discovery of charge stripe order
+at T(charge)=65K in Nd3+ co-doped LSCO ($T_{c}\simeq6$~K) [Tranquada et al.,
+Nature {\bf 375} (1995) 561], Hunt et al. demonstrated that
+La$_{1.48}$Nd$_{0.4}$Sr$_{0.12}$CuO$_4$ and superconducting LSCO with x~1/8 (Tc
+~ 30K) share nearly identical NMR anomalies near $T_{charge}$ of the former
+[Phys. Rev. Lett. {\bf 82} (1999) 4300]. Their inevitable conclusion that LSCO
+also undergoes charge order at a comparable temperature became controversial,
+because diffraction measurements at the time were unable to detect Bragg peaks
+associated with charge order. Recent advances in x-ray diffraction techniques
+finally led to definitive confirmations of the charge order Bragg peaks in LSCO
+with an onset at as high as T(charge)=80K. Meanwhile, improved instrumental
+technology has enabled routine NMR measurements that were not feasible two
+decades ago. Motivated by these new developments, we revisit the charge order
+transition of a LSCO single crystal based on 63Cu NMR techniques. We
+demonstrate that 63Cu NMR properties of the nuclear spin $I_{z}$ = -1/2 to +1/2
+central transition below T(charge) exhibit unprecedentedly strong dependence on
+the measurement time scale set by the NMR pulse separation time $\tau$; a new
+kind of anomalous, very broad wing-like 63Cu NMR signals gradually emerge below
+T(charge) only for extremely short $\tau \lesssim 4~\mu$s, while the spectral
+weight of the normal NMR signals is progressively wiped out. The NMR linewidth
+and relaxation rates depend strongly on $\tau$ below T(charge), and their
+enhancement in the charge ordered state indicates that charge order turns on
+strong but inhomogeneous growth of Cu spin-spin correlations.
+"
+27152,Modeling the correlated activity of neural populations: A review,"  The principles of neural encoding and computations are inherently collective
+and usually involve large populations of interacting neurons with highly
+correlated activities. While theories of neural function have long recognized
+the importance of collective effects in populations of neurons, only in the
+past two decades has it become possible to record from many cells
+simulatenously using advanced experimental techniques with single-spike
+resolution, and to relate these correlations to function and behaviour. This
+review focuses on the modeling and inference approaches that have been recently
+developed to describe the correlated spiking activity of populations of
+neurons. We cover a variety of models describing correlations between pairs of
+neurons as well as between larger groups, synchronous or delayed in time, with
+or without the explicit influence of the stimulus, and including or not latent
+variables. We discuss the advantages and drawbacks or each method, as well as
+the computational challenges related to their application to recordings of ever
+larger populations.
+"
+27153,Position and line-of-sight stabilization of spherical robot using feedforward proportional-derivative geometric controller,"  In this paper we present a geometric control law for position and
+line-of-sight stabilization of the nonholonomic spherical robot actuated by
+three independent actuators. A simple configuration error function with an
+appropriately defined transport map is proposed to extract feedforward and
+proportional-derivative control law. Simulations are provided to validate the
+controller performance.
+"
+27154,Training-image based geostatistical inversion using a spatial generative adversarial neural network,"  Probabilistic inversion within a multiple-point statistics framework is often
+computationally prohibitive for high-dimensional problems. To partly address
+this, we introduce and evaluate a new training-image based inversion approach
+for complex geologic media. Our approach relies on a deep neural network of the
+generative adversarial network (GAN) type. After training using a training
+image (TI), our proposed spatial GAN (SGAN) can quickly generate 2D and 3D
+unconditional realizations. A key characteristic of our SGAN is that it defines
+a (very) low-dimensional parameterization, thereby allowing for efficient
+probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC)
+methods. In addition, available direct conditioning data can be incorporated
+within the inversion. Several 2D and 3D categorical TIs are first used to
+analyze the performance of our SGAN for unconditional geostatistical
+simulation. Training our deep network can take several hours. After training,
+realizations containing a few millions of pixels/voxels can be produced in a
+matter of seconds. This makes it especially useful for simulating many
+thousands of realizations (e.g., for MCMC inversion) as the relative cost of
+the training per realization diminishes with the considered number of
+realizations. Synthetic inversion case studies involving 2D steady-state flow
+and 3D transient hydraulic tomography with and without direct conditioning data
+are used to illustrate the effectiveness of our proposed SGAN-based inversion.
+For the 2D case, the inversion rapidly explores the posterior model
+distribution. For the 3D case, the inversion recovers model realizations that
+fit the data close to the target level and visually resemble the true model
+well.
+"
+27155,2D and 3D topological phases in BiTe$X$ compounds,"  Recently, it was shown that quantum spin Hall insulator (QSHI) phase with a
+gap wide enough for practical applications can be realized in the ultra thin
+films constructed from two inversely stacked structural elements of trivial
+band insulator BiTeI. Here, we study the edge states in the free-standing
+Bi$_2$Te$_2$I$_2$ sextuple layer (SL) and the electronic structure of the
+Bi$_2$Te$_2$I$_2$ SL on the natural BiTeI substrate. We show that the
+topological properties of the Bi$_2$Te$_2$I$_2$ SL on this substrate keep
+$\mathbb Z_2$ invariant. We also demonstrate that ultra thin centrosymmetric
+films constructed in the similar manner but from related material BiTeBr are
+trivial band insulators up to five-SL film thickness. In contrast to
+Bi$_2$Te$_2$I$_2$ for which the stacking of nontrivial SLs in 3D limit gives a
+strong topological insulator (TI) phase, strong TI is realized in 3D
+Bi$_2$Te$_2$Br$_2$ in spite of the SL is trivial. For the last material of the
+BiTe$X$ ($X$=I,Br,Cl) series, BiTeCl, both 2D and 3D centrosymmetric phases are
+characterized by topologically trivial band structure.
+"
+27156,On the theory of Gordan-Noether on homogeneous forms with zero Hessian,"  We give a detailed proof for Gordan-Noether's results in ""Ueber die
+algebraischen Formen, deren Hesse'sche Determinante identisch verschwindet."" C.
+Lossen has written a paper in a similar direction as the present paper.
+However, our paper is independent of Lossen's paper and includes a considerable
+number of new observations
+"
+27157,Linear frequency conversion via sudden merging of resonances in time-variant metasurfaces,"  Energy conversion in a physical system requires time-translation invariance
+breaking according to Noether's theorem. Closely associated with this
+symmetry-conservation relation, the frequencies of electromagnetic waves are
+found to be converted as the waves propagate through a temporally varying
+medium. Thus, effective temporal control of the medium, be it artificial or
+natural, through which the waves are propagating, lies at the heart of linear
+optical frequency conversion. Here, we propose rapidly time-variant
+metasurfaces as a frequency-conversion platform and experimentally demonstrate
+their efficacy at THz frequencies. The proposed metasurface is designed for the
+sudden merging of two distinct resonances into a single resonance upon
+ultrafast optical excitation. From this spectrally-engineered temporal boundary
+onward, the merged-resonance frequency component is radiated. In addition,
+temporal coherence of the two original resonating modes with respect to the
+abrupt temporal boundary is found to be strongly related to the amount of
+frequency conversion as well as the phase of the converted wave. Due to their
+design flexibility, time-variant metasurfaces may become on-demand frequency
+synthesizers for various frequency ranges.
+"
+27158,Distributed Optimization Using the Primal-Dual Method of Multipliers,"  In this paper, we propose the primal-dual method of multipliers (PDMM) for
+distributed optimization over a graph. In particular, we optimize a sum of
+convex functions defined over a graph, where every edge in the graph carries a
+linear equality constraint. In designing the new algorithm, an augmented
+primal-dual Lagrangian function is constructed which smoothly captures the
+graph topology. It is shown that a saddle point of the constructed function
+provides an optimal solution of the original problem. Further under both the
+synchronous and asynchronous updating schemes, PDMM has the convergence rate of
+O(1/K) (where K denotes the iteration index) for general closed, proper and
+convex functions. Other properties of PDMM such as convergence speeds versus
+different parameter- settings and resilience to transmission failure are also
+investigated through the experiments of distributed averaging.
+"
+27159,Dispersion optimized quadratures for isogeometric analysis,"  We develop and analyze quadrature blending schemes that minimize the
+dispersion error of isogeometric analysis up to polynomial order seven with
+maximum continuity in the span ($C^{p-1}$). The schemes yield two extra orders
+of convergence (superconvergence) on the eigenvalue errors, while the
+eigenfunction errors are of optimal convergence order. Both dispersion and
+spectrum analysis are unified in the form of a Taylor expansion for eigenvalue
+errors. As a consequence, the schemes increase the accuracy and robustness of
+isogeometric analysis for wave propagation as well as the differential
+eigenvalue problems. We analyze the methods' robustness and efficacy and
+utilize numerical examples to verify our analysis of the performance of the
+proposed schemes.
+"
+27160,Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations,"  While there is currently a lot of enthusiasm about ""big data"", useful data is
+usually ""small"" and expensive to acquire. In this paper, we present a new
+paradigm of learning partial differential equations from {\em small} data. In
+particular, we introduce \emph{hidden physics models}, which are essentially
+data-efficient learning machines capable of leveraging the underlying laws of
+physics, expressed by time dependent and nonlinear partial differential
+equations, to extract patterns from high-dimensional data generated from
+experiments. The proposed methodology may be applied to the problem of
+learning, system identification, or data-driven discovery of partial
+differential equations. Our framework relies on Gaussian processes, a powerful
+tool for probabilistic inference over functions, that enables us to strike a
+balance between model complexity and data fitting. The effectiveness of the
+proposed approach is demonstrated through a variety of canonical problems,
+spanning a number of scientific domains, including the Navier-Stokes,
+Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional
+equations. The methodology provides a promising new direction for harnessing
+the long-standing developments of classical methods in applied mathematics and
+mathematical physics to design learning machines with the ability to operate in
+complex domains without requiring large quantities of data.
+"
+27161,Quantitative gradient estimates for harmonic maps into singular spaces,"  In this paper, we will show the Yau's gradient estimate for harmonic maps
+into a metric space $(X,d_X)$ with curvature bounded above by a constant
+$\kappa$, $\kappa\geq0$, in the sense of Alexandrov. As a direct application,
+it gives some Liouville theorems for such harmonic maps. This extends the works
+of S. Y. Cheng [4] and H. I. Choi [5] to harmonic maps into singular spaces.
+"
+27162,A shape optimal control problem and its probabilistic counterpart,"  In this paper we consider a shape optimization problem in which the data in
+the cost functional and in the state equation may change sign, and so no
+monotonicity assumption is satisfied. Nevertheless, we are able to prove that
+an optimal domain exists. We also deduce some necessary conditions of
+optimality for the optimal domain. The results are applied to show the
+existence of an optimal domain in the case where the cost functional is
+completely identified, while the right-hand side in the state equation is only
+known up to a probability P in the space $L^2$.
+"
+27163,Smooth polyhedral surfaces,"  Polyhedral surfaces are fundamental objects in architectural geometry and
+industrial design. Whereas closeness of a given mesh to a smooth reference
+surface and its suitability for numerical simulations were already studied
+extensively, the aim of our work is to find and to discuss suitable assessments
+of smoothness of polyhedral surfaces that only take the geometry of the
+polyhedral surface itself into account. Motivated by analogies to classical
+differential geometry, we propose a theory of smoothness of polyhedral surfaces
+including suitable notions of normal vectors, tangent planes, asymptotic
+directions, and parabolic curves that are invariant under projective
+transformations. It is remarkable that seemingly mild conditions significantly
+limit the shapes of faces of a smooth polyhedral surface. Besides being of
+theoretical interest, we believe that smoothness of polyhedral surfaces is of
+interest in the architectural context, where vertices and edges of polyhedral
+surfaces are highly visible.
+"
+27164,A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things,"  Convolutional neural network (CNN) offers significant accuracy in image
+detection. To implement image detection using CNN in the internet of things
+(IoT) devices, a streaming hardware accelerator is proposed. The proposed
+accelerator optimizes the energy efficiency by avoiding unnecessary data
+movement. With unique filter decomposition technique, the accelerator can
+support arbitrary convolution window size. In addition, max pooling function
+can be computed in parallel with convolution by using separate pooling unit,
+thus achieving throughput improvement. A prototype accelerator was implemented
+in TSMC 65nm technology with a core size of 5mm2. The accelerator can support
+major CNNs and achieve 152GOPS peak throughput and 434GOPS/W energy efficiency
+at 350mW, making it a promising hardware accelerator for intelligent IoT
+devices.
+"
+27165,Determinants of Representations of Coxeter Groups,"  In [APS], the authors characterize the partitions of $n$ whose corresponding
+representations of $S_n$ have nontrivial determinant. The present paper extends
+this work to all irreducible finite Coxeter groups $W$. Namely, given a
+nontrivial multiplicative character $\omega$ of $W$, we give a closed formula
+for the number of irreducible representations of $W$ with determinant $\omega$.
+For Coxeter groups of type $B_n$ and $D_n$, this is accomplished by
+characterizing the bipartitions associated to such representations.
+"
+27166,Selection bias in dynamically-measured super-massive black hole samples: dynamical masses and dependence on Sérsic index,"  We extend the comparison between the set of local galaxies having dynamically
+measured black holes with galaxies in the Sloan Digital Sky Survey (SDSS). We
+first show that the most up-to-date local black hole samples of early-type
+galaxies with measurements of effective radii, luminosities, and Sérsic
+indices of the bulges of their host galaxies, have dynamical mass and Sérsic
+index distributions consistent with those of SDSS early-type galaxies of
+similar bulge stellar mass. The host galaxies of local black hole samples thus
+do not appear structurally different from SDSS galaxies, sharing similar
+dynamical masses, light profiles and light distributions. Analysis of the
+residuals reveals that velocity dispersion is more fundamental than Sérsic
+index n in the scaling relations between black holes and galaxies. Indeed,
+residuals with Sérsic index could be ascribed to the (weak) correlation with
+bulge mass or even velocity dispersion. Finally, targetted Monte Carlo
+simulations that include the effects of the sphere of influence of the black
+hole, and tuned to reproduce the observed residuals and scaling relations in
+terms of velocity dispersion and stellar mass, show that, at least for galaxies
+with Mbulge > 1e10 Msun and n>5, the observed mean black hole mass at fixed
+Sérsic index is biased significantly higher than the intrinsic value.
+"
+27167,"Secular light curves of 25 members of the Themis family of asteroids, suspected of exhibiting low level cometary activity","  From 1996 to 2015 sixteen main belt asteroids were discovered exhibiting
+cometary activity (less than one per year), all of them during searches at the
+telescope. In this work we will explore another way to discover them. We
+reduced 192016 magnitude observations of 165 asteroids of the Themis family,
+using data from the astrometric-photometric database of the Minor Planet
+Center, MPCOBS, and measuring the absolute magnitudes from the phase plots. 25
+objects of 165 (15.2%), exhibited bumps or enhancements in brightness that
+might indicate low level cometary activity. Since activity repeats at the same
+place in different orbits and in many occasions is centered at perihelion,
+activity might be due to water ice sublimation. As of September 2016, there are
+717768 asteroids listed in the MPC files. If we assume that we do not have any
+false positives and the above percentage can be extrapolated to the whole Main
+Belt, the number of potentially active asteroid gets to the very large number
+of ~111.000. This number is much larger than the ones predicted in previous
+surveys and indicates one of three scenarios: A) there are many false positives
+in our detections and the real number of active asteroid is much smaller than
+we found, implying that the MPC astrometric-photometric database is only
+astrometric and not photometric. B) The location of active asteroids is
+restricted to the Themis family and an extrapolation to the whole belt is not
+possible. Or C) there are few false positives in our candidates and the main
+belt actually contains many low level active asteroids undetected by current
+surveys. Case C) would imply that the main belt is not a field of bare rocks
+but a graveyard of extinct comets, changing our current paradigm of the main
+belt. So it is of the outmost importance to verify observationally our
+candidates, and determine which of these scenarios is valid.
+"
+27168,Measurement and control of a Coulomb-blockaded parafermion box,"  Parafermionic zero modes are fractional topologically protected
+quasiparticles expected to arise in various platforms. We show that Coulomb
+charging effects define a parafermion box with unique access options via
+fractional edge states and/or quantum antidots. Basic protocols for the
+detection, manipulation, and control of parafermionic quantum states are
+formulated. With those tools, one may directly observe the dimension of the
+zero-mode Hilbert space, prove the degeneracy of this space, and perform
+on-demand digital operations satisfying a parafermionic algebra.
+"
+27169,On the Aubin property of a class of parameterized variational systems,"  The paper deals with a new sharp criterion ensuring the Aubin property of
+solution maps to a class of parameterized variational systems. This class
+includes parameter-dependent variational inequalities with non-polyhedral
+constraint sets and also parameterized generalized equations with conic
+constraints. The new criterion requires computation of directional limiting
+coderivatives of the normal-cone mapping for the so-called critical directions.
+The respective formulas have the form of a second-order chain rule and extend
+the available calculus of directional limiting objects. The suggested procedure
+is illustrated by means of examples.
+"
+27170,Change of Measure in Midcurve Pricing,"  We derive measure change formulae required to price midcurve swaptions in the
+forward swap annuity measure with stochastic annuities' ratios. We construct
+the corresponding linear and exponential terminal swap rate pricing models and
+show how they capture the midcurve swaption correlation skew.
+"
+27171,Calculating particle pair potentials from fluid-state pair correlations: Iterative Ornstein-Zernike Inversion,"  An iterative Monte Carlo inversion method for the calculation of particle
+pair potentials from given particle pair correlations is proposed in this
+paper. The new method, which is best referred to as Iterative Ornstein-Zernike
+Inversion, represents a generalization and an improvement of the established
+Iterative Boltzmann Inversion technique [Reith, Pütz \& Müller-Plathe,
+J. Comput. Chem. 24, 1624 (2003)]. Our modification of Iterative Boltzmann
+Inversion consists of replacing the potential of mean force as an approximant
+for the pair potential with another, generally more accurate approximant that
+is based on a trial bridge function in the Ornstein-Zernike integral equation
+formalism. As an input, the new method requires the particle pair correlations
+both in real space and in the Fourier conjugate wavenumber space. An
+accelerated iteration method is included in the discussion, by which the
+required number of iterations can be greatly reduced below that of the simple
+Picard iteration that underlies most common implementations of Iterative
+Boltzmann Inversion. Comprehensive tests with various pair potentials show that
+the new method generally surpasses the Iterative Boltzmann Inversion method in
+terms of reliability of the numerical solution for the particle pair potential.
+"
+27172,On the number of fuzzy subgroups of symmetric group $ S_5 $,"  This article computes the number of fuzzy subgroups of symmetric group $ S_{5
+}$ . First, an equivalence relation on the set of all fuzzy subgroups of a
+group G is defined.Without any equivalence relation on fuzzy subgroups of group
+G, the number of fuzzy subgroups is infinite, even for the trivial group.\\
+The\emph{ Inclusion-Exclusion principle} is used to determine the number of
+distinct fuzzy subgroups of \textbf{ symmetric group} $ S_{5} $ . Some
+inequalities satisfied by this number are also established for $ n \geq 5 $
+"
+27173,Quench dynamics in strongly coupled laser cavities,"  Strongly coupled dissipative optical cavities with nonlinear interactions
+give new opportunities to explore symmetry breaking phenomena and phase
+transitions, Josephson dynamics and quantum criticality. Among the different
+experimental realizations, photonic crystal coupled nanocavities operating in
+the laser regime are outstanding systems since nonlinearity, gain/dissipation
+and intercavity coupling can be judiciously tailored. Yet, although most common
+scenarios emerge from quasi-dynamical equilibrium where the gain nearly
+compensates for losses, little is known about far out-of-equilibrium dynamics
+resulting, for instance, from short pulsed pumping inducing a classical
+""quench"". Here we show that bimodal nanolasers generically display transient
+dynamics after quench which, when projected onto the nonlasing mode, exhibit
+superthermal light. Such a mechanism is akin to the fast cooling of a
+suspension of Brownian particles under gravity, with the intracavity intensity
+playing the role of the inverse temperature. We implement a simple experimental
+technique to access the probability density functions, that enabled quantifying
+the distance from thermal equilibrium --and hence the degree of residual
+order-- via the Gibbs entropy. This allowed us to further detect mixing of
+thermal states with coherent broken parity phases, thus paving the road for
+investigating far nonequilibrium thermodynamics with multimode optical
+oscillators.
+"
+27174,Structured Probabilistic Pruning for Convolutional Neural Network Acceleration,"  In this paper, we propose a novel progressive parameter pruning method for
+Convolutional Neural Network acceleration, named Structured Probabilistic
+Pruning (SPP), which effectively prunes weights of convolutional layers in a
+probabilistic manner. Unlike existing deterministic pruning approaches, where
+unimportant weights are permanently eliminated, SPP introduces a pruning
+probability for each weight, and pruning is guided by sampling from the pruning
+probabilities. A mechanism is designed to increase and decrease pruning
+probabilities based on importance criteria in the training process. Experiments
+show that, with 4x speedup, SPP can accelerate AlexNet with only 0.3% loss of
+top-5 accuracy and VGG-16 with 0.8% loss of top-5 accuracy in ImageNet
+classification. Moreover, SPP can be directly applied to accelerate
+multi-branch CNN networks, such as ResNet, without specific adaptations. Our 2x
+speedup ResNet-50 only suffers 0.8% loss of top-5 accuracy on ImageNet. We
+further show the effectiveness of SPP on transfer learning tasks.
+"
+27175,Motion Prediction Under Multimodality with Conditional Stochastic Networks,"  Given a visual history, multiple future outcomes for a video scene are
+equally probable, in other words, the distribution of future outcomes has
+multiple modes. Multimodality is notoriously hard to handle by standard
+regressors or classifiers: the former regress to the mean and the latter
+discretize a continuous high dimensional output space. In this work, we present
+stochastic neural network architectures that handle such multimodality through
+stochasticity: future trajectories of objects, body joints or frames are
+represented as deep, non-linear transformations of random (as opposed to
+deterministic) variables. Such random variables are sampled from simple
+Gaussian distributions whose means and variances are parametrized by the output
+of convolutional encoders over the visual history. We introduce novel
+convolutional architectures for predicting future body joint trajectories that
+outperform fully connected alternatives \cite{DBLP:journals/corr/WalkerDGH16}.
+We introduce stochastic spatial transformers through optical flow warping for
+predicting future frames, which outperform their deterministic equivalents
+\cite{DBLP:journals/corr/PatrauceanHC15}. Training stochastic networks involves
+an intractable marginalization over stochastic variables. We compare various
+training schemes that handle such marginalization through a) straightforward
+sampling from the prior, b) conditional variational autoencoders
+\cite{NIPS2015_5775,DBLP:journals/corr/WalkerDGH16}, and, c) a proposed
+K-best-sample loss that penalizes the best prediction under a fixed ""prediction
+budget"". We show experimental results on object trajectory prediction, human
+body joint trajectory prediction and video prediction under varying future
+uncertainty, validating quantitatively and qualitatively our architectural
+choices and training schemes.
+"
+27176,"Biased Embeddings from Wild Data: Measuring, Understanding and Removing","  Many modern Artificial Intelligence (AI) systems make use of data embeddings,
+particularly in the domain of Natural Language Processing (NLP). These
+embeddings are learnt from data that has been gathered ""from the wild"" and have
+been found to contain unwanted biases. In this paper we make three
+contributions towards measuring, understanding and removing this problem. We
+present a rigorous way to measure some of these biases, based on the use of
+word lists created for social psychology applications; we observe how gender
+bias in occupations reflects actual gender bias in the same occupations in the
+real world; and finally we demonstrate how a simple projection can
+significantly reduce the effects of embedding bias. All this is part of an
+ongoing effort to understand how trust can be built into AI systems.
+"
+27177,Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property,"  The Echo State Network (ESN) is a specific recurrent network, which has
+gained popularity during the last years. The model has a recurrent network
+named reservoir, that is fixed during the learning process. The reservoir is
+used for transforming the input space in a larger space. A fundamental property
+that provokes an impact on the model accuracy is the Echo State Property (ESP).
+There are two main theoretical results related to the ESP. First, a sufficient
+condition for the ESP existence that involves the singular values of the
+reservoir matrix. Second, a necessary condition for the ESP. The ESP can be
+violated according to the spectral radius value of the reservoir matrix. There
+is a theoretical gap between these necessary and sufficient conditions. This
+article presents an empirical analysis of the accuracy and the projections of
+reservoirs that satisfy this theoretical gap. It gives some insights about the
+generation of the reservoir matrix. From previous works, it is already known
+that the optimal accuracy is obtained near to the border of stability control
+of the dynamics. Then, according to our empirical results, we can see that this
+border seems to be closer to the sufficient conditions than to the necessary
+conditions of the ESP.
+"
+27178,MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification,"  Class imbalance problem has been a challenging research problem in the fields
+of machine learning and data mining as most real life datasets are imbalanced.
+Several existing machine learning algorithms try to maximize the accuracy
+classification by correctly identifying majority class samples while ignoring
+the minority class. However, the concept of the minority class instances
+usually represents a higher interest than the majority class. Recently, several
+cost sensitive methods, ensemble models and sampling techniques have been used
+in literature in order to classify imbalance datasets. In this paper, we
+propose MEBoost, a new boosting algorithm for imbalanced datasets. MEBoost
+mixes two different weak learners with boosting to improve the performance on
+imbalanced datasets. MEBoost is an alternative to the existing techniques such
+as SMOTEBoost, RUSBoost, Adaboost, etc. The performance of MEBoost has been
+evaluated on 12 benchmark imbalanced datasets with state of the art ensemble
+methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost.
+Experimental results show significant improvement over the other methods and it
+can be concluded that MEBoost is an effective and promising algorithm to deal
+with imbalance datasets. The python version of the code is available here:
+this https URL
+"
+27179,An improvement of the convergence proof of the ADAM-Optimizer,"  A common way to train neural networks is the Backpropagation. This algorithm
+includes a gradient descent method, which needs an adaptive step size. In the
+area of neural networks, the ADAM-Optimizer is one of the most popular adaptive
+step size methods. It was invented in \cite{Kingma.2015} by Kingma and Ba. The
+$5865$ citations in only three years shows additionally the importance of the
+given paper. We discovered that the given convergence proof of the optimizer
+contains some mistakes, so that the proof will be wrong. In this paper we give
+an improvement to the convergence proof of the ADAM-Optimizer.
+"
+27180,Towards Donovan's conjecture for abelian defect groups,"  We define a new invariant for a $p$-block, the strong Frobenius number, which
+we use to address the problem of reducing Donovan's conjecture to normal
+subgroups of index p. As an application we use the strong Frobenius number to
+complete the proof of Donovan's conjecture for 2-blocks with abelian defect
+groups of rank at most 4 and for 2-blocks with abelian defect groups of order
+at most 64.
+"
+27181,On the Diophantine equations $ \sum_{i=1}^n a_ix_{i} ^6+\sum_{i=1}^m b_iy_{i} ^3= \sum_{i=1}^na_iX_{i}^6\pm\sum_{i=1}^m b_iY_{i} ^3 $,"  In this paper, the elliptic curves theory is used for solving the Diophantine
+equations $\sum_{i=1}^n a_ix_{i} ^6+\sum_{i=1}^m b_iy_{i} ^3=
+\sum_{i=1}^na_iX_{i}^6\pm\sum_{i=1}^m b_iY_{i} ^3$, where $n$, $m$ $\geq 1$ and
+$a_i$, $b_i$, are fixed arbitrary nonzero integers. By our method, we may find
+infinitely many nontrivial positive solutions and also obtain infinitely many
+nontrivial parametric solutions for the Diophantine equations for every
+arbitrary integers $n$, $m$, $a_i$ and $b_i$.
+"
+27182,Simple interval observers for linear impulsive systems with applications to sampled-data and switched systems,"  Sufficient conditions for the design of a simple class of interval observers
+for linear impulsive systems subject to minimum and range dwell-time
+constraints are obtained and formulated in terms of infinite-dimensional linear
+programs. The proposed approach is fully constructive in the sense that
+suitable observer gains can be extracted from the solution of the optimization
+problems and is flexible enough to be extended to include performance
+constraints and parametric uncertainties. In order to be solvable, the
+infinite-dimensional linear programs are relaxed using a method based on sum of
+squares which is known to be asymptotically exact in the present case. Three
+examples are given for illustration: the first one pertains on the interval
+observation of an impulsive system under a minimum dwell-time constraint, the
+second one is about the interval observation of an aperiodic sampled-data
+system and the last one is about the interval observation of a linear switched
+system.
+"
+27183,Predicting Glass-to-Glass and Liquid-to-Liquid Phase Transitions in Water using Classical Nucleation Temperature,"  Glass-to-glass and liquid-to-liquid phase transitions are observed in bulk
+and confined water, with or without applied pressure. They result from the
+competition of two liquid phases separated by an enthalpy difference depending
+on temperature. The classical nucleation equation of these phases is completed
+by this quantity existing at all temperatures, a pressure contribution, and an
+enthalpy excess. This equation leads to two homogeneous nucleation temperatures
+in each liquid phase, the first one being the formation temperature of an
+ordered liquid phase below the melting temperature and the second one
+corresponding to the overheating temperature. Thermodynamic properties, double
+glass transition temperatures, sharp enthalpy and volume changes are predicted
+in agreement with experimental results. The first-order transition line between
+fragile and strong liquids joins two critical points. Glass phase above its
+transition temperature becomes ordered liquid phase disappearing at a
+first-order transition temperature at low pressure and at a temperature larger
+than the melting temperature at high pressure.
+"
+27184,Variance-based Robust Optimization of a Permanent Magnet Synchronous Machine,"  This paper focuses on the application of the variance-based global
+sensitivity analysis for a topology derivative method in order to solve a
+stochastic nonlinear time-dependent magnetoquasistatic interface problem. To
+illustrate the approach a permanent magnet synchronous machine has been
+considered. Our key objective is to provide a robust design of the rotor poles
+and of the tooth base in a stator for the reduction of the torque ripple and
+electromagnetic losses, while taking material uncertainties into account. Input
+variations of material parameters are modeled using the polynomial chaos
+expansion technique, which is incorporated into the stochastic collocation
+method in order to provide a response surface model. Additionally, we can
+benefit from the variance-based sensitivity analysis. This allows us to reduce
+the dimensionality of the stochastic optimization problems, described by the
+random-dependent cost functional. Finally, to validate our approach, we provide
+the two-dimensional simulations and analysis, which confirm the usefulness of
+the proposed method and yield a novel topology of a permanent magnet
+synchronous machine.
+"
+27185,Computing Maximum Entropy Distributions Everywhere,"  We study the problem of computing the maximum entropy distribution with a
+specified expectation over a large discrete domain. Maximum entropy
+distributions arise and have found numerous applications in economics, machine
+learning and various sub-disciplines of mathematics and computer science. The
+key computational questions related to maximum entropy distributions are
+whether they have succinct descriptions and whether they can be efficiently
+computed. Here we provide positive answers to both of these questions for very
+general domains and, importantly, with no restriction on the expectation. This
+completes the picture left open by the prior work on this problem which
+requires that the expectation vector is polynomially far in the interior of the
+convex hull of the domain. As a consequence we obtain a general algorithmic
+tool and show how it can be applied to derive several old and new results in a
+unified manner. In particular, our results imply that certain recent continuous
+optimization formulations, for instance, for discrete counting and optimization
+problems, the matrix scaling problem, and the worst case Brascamp-Lieb
+constants in the rank-1 regime, are efficiently computable. Attaining these
+implications requires reformulating the underlying problem as a version of
+maximum entropy computation where optimization also involves the expectation
+vector and, hence, cannot be assumed to be sufficiently deep in the interior.
+The key new technical ingredient in our work is a polynomial bound on the bit
+complexity of near-optimal dual solutions to the maximum entropy convex
+program. This result is obtained by a geometrical reasoning that involves
+convex analysis and polyhedral geometry, avoiding combinatorial arguments based
+on the specific structure of the domain. We also provide a lower bound on the
+bit complexity of near-optimal solutions showing the tightness of our results.
+"
+27186,Backhaul-aware Robust 3D Drone Placement in 5G+ Wireless Networks,"  Using drones as flying base stations is a promising approach to enhance the
+network coverage and area capacity by moving supply towards demand when
+required. However deployment of such base stations can face some restrictions
+that need to be considered. One of the limitations in drone base stations
+(drone-BSs) deployment is the availability of reliable wireless backhaul link.
+This paper investigates how different types of wireless backhaul offering
+various data rates would affect the number of served users. Two approaches,
+namely, network-centric and user-centric, are introduced and the optimal 3D
+backhaul-aware placement of a drone-BS is found for each approach. To this end,
+the total number of served users and sum-rates are maximized in the
+network-centric and user-centric frameworks, respectively. Moreover, as it is
+preferred to decrease drone-BS movements to save more on battery and increase
+flight time and to reduce the channel variations, the robustness of the network
+is examined as how sensitive it is with respect to the users displacements.
+"
+27187,Pathway for Multivariate Dependence Modeling in Long-Term Horizon of Electrical Power System,"  Future electricity consumption is fundamentally uncertain and dependent on
+many variables such as economic activity, weather, electricity rates and demand
+side management. The stochasticity of system load as well as power generation
+from renewable energy sources (RES) (i.e., wind and solar) poses special
+challenges to power system planners. Increasing penetration levels of wind and
+solar exacerbate the uncertainty and variability that must be addressed in
+coming years, and can be extremely relevant to power system planners. With this
+paper, pathways for multivariate dependence modeling using vine copula is
+proposed which includes both electrical load and power generation from RES.
+"
+27188,Sensitivity Analysis with Manifolds,"  The course of dimensionality is a common problem in statistics and data
+analysis. Variable sensitivity analysis methods are a well studied and
+established set of tools designed to overcome these sorts of problems. However,
+as this work shows, these methods fail to capture relevant features and
+patterns hidden within the geometry of the enveloping manifold projected into a
+variable. We propose a sensitivity index that captures and reflects the
+relevance of distinct variables within a model by focusing at the geometry of
+their projections.
+"
+27189,Quantifying Transient Spreading Dynamics on Networks,"  Spreading phenomena on networks are essential for the collective dynamics of
+various natural and technological systems, from information spreading in gene
+regulatory networks to neural circuits or from epidemics to supply networks
+experiencing perturbations. Still, how local disturbances spread across
+networks is not yet quantitatively understood. Here we analyze generic
+spreading dynamics in deterministic network dynamical systems close to a given
+operating point. Standard dynamical systems' theory does not explicitly provide
+measures for arrival times and amplitudes of a transient, spreading signal
+because it focuses on invariant sets, invariant measures and other quantities
+less relevant for transient behavior. We here change the perspective and
+introduce effective expectation values for deterministic dynamics to work out a
+theory explicitly quantifying when and how strongly a perturbation initiated at
+one unit of a network impacts any other. The theory provides explicit timing
+and amplitude information as a function of the relative position of initially
+perturbed and responding unit as well as on the entire network topology.
+"
+27190,On the greatest common divisor of $n$ and the $n$th Fibonacci number,"  Let $\mathcal{A}$ be the set of all integers of the form $\gcd(n, F_n)$,
+where $n$ is a positive integer and $F_n$ denotes the $n$th Fibonacci number.
+We prove that $\#\left(\mathcal{A} \cap [1, x]\right) \gg x / \log x$ for all
+$x \geq 2$, and that $\mathcal{A}$ has zero asymptotic density. Our proofs rely
+on a recent result of Cubre and Rouse which gives, for each positive integer
+$n$, an explicit formula for the density of primes $p$ such that $n$ divides
+the rank of appearance of $p$, that is, the smallest positive integer $k$ such
+that $p$ divides $F_k$.
+"
+27191,Dynamics and Coalitions in Sequential Games,"  We consider N-player non-zero sum games played on finite trees (i.e.,
+sequential games), in which the players have the right to repeatedly update
+their respective strategies (for instance, to improve the outcome wrt to the
+current strategy profile). This generates a dynamics in the game which may
+eventually stabilise to a Nash Equilibrium (as with Kukushkin's lazy
+improvement), and we argue that it is interesting to study the conditions that
+guarantee such a dynamics to terminate.
+We build on the works of Le Roux and Pauly who have studied extensively one
+such dynamics, namely the Lazy Improvement Dynamics. We extend these works by
+first defining a turn-based dynamics, proving that it terminates on subgame
+perfect equilibria, and showing that several variants do not terminate. Second,
+we define a variant of Kukushkin's lazy improvement where the players may now
+form coalitions to change strategies. We show how properties of the players'
+preferences on the outcomes affect the termination of this dynamics, and we
+thereby characterise classes of games where it always terminates (in particular
+two-player games).
+"
+27192,Stacked Kernel Network,"  Kernel methods are powerful tools to capture nonlinear patterns behind data.
+They implicitly learn high (even infinite) dimensional nonlinear features in
+the Reproducing Kernel Hilbert Space (RKHS) while making the computation
+tractable by leveraging the kernel trick. Classic kernel methods learn a single
+layer of nonlinear features, whose representational power may be limited.
+Motivated by recent success of deep neural networks (DNNs) that learn
+multi-layer hierarchical representations, we propose a Stacked Kernel Network
+(SKN) that learns a hierarchy of RKHS-based nonlinear features. SKN interleaves
+several layers of nonlinear transformations (from a linear space to a RKHS) and
+linear transformations (from a RKHS to a linear space). Similar to DNNs, a SKN
+is composed of multiple layers of hidden units, but each parameterized by a
+RKHS function rather than a finite-dimensional vector. We propose three ways to
+represent the RKHS functions in SKN: (1)nonparametric representation,
+(2)parametric representation and (3)random Fourier feature representation.
+Furthermore, we expand SKN into CNN architecture called Stacked Kernel
+Convolutional Network (SKCN). SKCN learning a hierarchy of RKHS-based nonlinear
+features by convolutional operation with each filter also parameterized by a
+RKHS function rather than a finite-dimensional matrix in CNN, which is suitable
+for image inputs. Experiments on various datasets demonstrate the effectiveness
+of SKN and SKCN, which outperform the competitive methods.
+"
+27193,Categorical diagonalization,"  This paper lays the groundwork for the theory of categorical diagonalization.
+Given a diagonalizable operator, tools in linear algebra (such as Lagrange
+interpolation) allow one to construct a collection of idempotents which project
+to each eigenspace. These idempotents are mutually orthogonal and sum to the
+identity. We categorify these tools. At the categorical level, one has not only
+eigenobjects and eigenvalues but also eigenmaps, which relate an endofunctor to
+its eigenvalues. Given an invertible endofunctor of a triangulated category
+with a sufficiently nice collection of eigenmaps, we construct idempotent
+functors which project to eigencategories. These idempotent functors are
+mutually orthogonal, and a convolution thereof is isomorphic to the identity
+functor.
+In several sequels to this paper, we will use this technology to study the
+categorical representation theory of Hecke algebras. In particular, for Hecke
+algebras of type A, we will construct categorified Young symmetrizers by
+simultaneously diagonalizing certain functors associated to the full twist
+braids.
+"
+27194,Cosmogenic production of tritium in dark matter detectors,"  The direct detection of dark matter particles requires ultra-low background
+conditions at energies below a few tens of keV. Radioactive isotopes are
+produced via cosmogenic activation in detectors and other materials and those
+isotopes constitute a background source which has to be under control. In
+particular, tritium is specially relevant due to its decay properties (very low
+endpoint energy and long half-life) when induced in the detector medium, and
+because it can be generated in any material as a spallation product.
+Quantification of cosmogenic production of tritium is not straightforward,
+neither experimentally nor by calculations. In this work, a method for the
+calculation of production rates at sea level has been developed and applied to
+some of the materials typically used as targets in dark matter detectors
+(germanium, sodium iodide, argon and neon); it is based on a selected
+description of tritium production cross sections over the entire energy range
+of cosmic nucleons. Results have been compared to available data in the
+literature, either based on other calculations or from measurements. The
+obtained tritium production rates, ranging from a few tens to a few hundreds of
+nuclei per kg and per day at sea level, point to a significant contribution to
+the background in dark matter experiments, requiring the application of
+specific protocols for target material purification, material storing
+underground and limiting the time the detector is on surface during the
+building process in order to minimize the exposure to the most dangerous cosmic
+ray components.
+"
+27195,A New Numerical Method for $\mathbb{Z}_2$ Topological Insulators with Strong Disorder,"  We propose a new method to numerically compute the $\mathbb{Z}_2$ indices for
+disordered topological insulators in Kitaev's periodic table. All of the
+$\mathbb{Z}_2$ indices are known to be derived from the index formulae which
+are expressed in terms of a pair of projections introduced by Avron, Seiler,
+and Simon. For a given pair of projections, the corresponding index is
+determined by the spectrum of the difference between the two projections. This
+difference exhibits remarkable and useful properties, as it is compact and has
+a supersymmetric structure in the spectrum. These properties make it possible
+to numerically determine the indices of disordered topological insulators
+highly efficiently. The method is demonstrated for the Bernevig-Hughes-Zhang
+and Wilson-Dirac models whose topological phases are characterized by a
+$\mathbb{Z}_2$ index in two and three dimensions, respectively.
+"
+27196,Node-centric community detection in multilayer networks with layer-coverage diversification bias,"  The problem of node-centric, or local, community detection in information
+networks refers to the identification of a community for a given input node,
+having limited information about the network topology. Existing methods for
+solving this problem, however, are not conceived to work on complex networks.
+In this paper, we propose a novel framework for local community detection based
+on the multilayer network model. Our approach relies on the maximization of the
+ratio between the community internal connection density and the external
+connection density, according to multilayer similarity-based community
+relations. We also define a biasing scheme that allows the discovery of local
+communities characterized by different degrees of layer-coverage
+diversification. Experimental evaluation conducted on real-world multilayer
+networks has shown the significance of our approach.
+"
+27197,Evolution of cooperation on an epithelium,"  Cooperation is prevalent in nature, not only in the context of social
+interactions within the animal kingdom, but also on the cellular level. In
+cancer for example, tumour cells can cooperate by producing growth factors. The
+evolution of cooperation has traditionally been studied for well-mixed
+populations under the framework of evolutionary game theory, and more recently
+for structured populations using evolutionary graph theory. The population
+structures arising due to cellular arrangement in tissues however are dynamic
+and thus cannot be accurately represented by either of these frameworks. In
+this work we compare the conditions for cooperative success in an epithelium
+modelled using evolutionary graph theory, to those in a mechanical model of an
+epithelium =- the Voronoi tessellation model. Crucially, in this latter model
+cells are able to move, and birth and death are not spatially coupled. We
+calculate fixation probabilities in the Voronoi tessellation model through
+simulation and an approximate analytic technique and show that this leads to
+stronger promotion of cooperation in comparison with the evolutionary graph
+theory model.
+"
+27198,A Preferential Attachment Paradox: How Preferential Attachment Combines with Growth to Produce Networks with Log-normal In-degree Distributions,"  Every network scientist knows that preferential attachment combines with
+growth to produce networks with power-law in-degree distributions. How, then,
+is it possible for the network of American Physical Society journal collection
+citations to enjoy a log-normal citation distribution when it was found to have
+grown in accordance with preferential attachment? This anomalous result, which
+we exalt as the preferential attachment paradox, has remained unexplained since
+the physicist Sidney Redner first made light of it over a decade ago. Here we
+propose a resolution. The chief source of the mischief, we contend, lies in
+Redner having relied on a measurement procedure bereft of the accuracy required
+to distinguish preferential attachment from another form of attachment that is
+consistent with a log-normal in-degree distribution. There was a high-accuracy
+measurement procedure in use at the time, but it would have have been difficult
+to use it to shed light on the paradox, due to the presence of a systematic
+error inducing design flaw. In recent years the design flaw had been recognised
+and corrected. We show that the bringing of the newly corrected measurement
+procedure to bear on the data leads to a resolution of the paradox.
+"
+27199,Voting patterns in 2016: Exploration using multilevel regression and poststratification (MRP) on pre-election polls,"  We analyzed 2012 and 2016 YouGov pre-election polls in order to understand
+how different population groups voted in the 2012 and 2016 elections. We broke
+the data down by demographics and state. We display our findings with a series
+of graphs and maps. The R code associated with this project is available at
+this https URL.
+"
+27200,"Hierarchical evolutive systems, fuzzy categories and the living single cell","  In this article, the theory of hierarchical evolutive systems of Ehresmann
+and Vandremeersch [Bull. Math. Bio. 49, 13-50 (1987)] is improved by
+considering the categories of the theory as fuzzy sets whose elements are the
+composite objects formed by the arrows and corresponding vertices of their
+embedded graphs. This way each category can be represented as a point in the
+states space [0.1]**N. The introduction of a diffeomorphism, that acts in this
+context as a functor between categories, allows to define a measure preserving
+dynamical system. In particular, we apply this formalism to describe a living
+single cell. We propose for its state at a given time a hirerchical category
+with three levels (molecular, coarse-grained and cellular levels) related by
+adequate colimits. Each level involves the main functional and structural
+modules in which the cell can be partitioned. The time evolution of the cell is
+drived by a transformation which is a N-dimensional generalization of the
+Ricker map whose parameters we propose to be determined by requiring that, as
+hallmark of its behavior, the living cell to evolve at the edge of chaos. From
+the dynamical point of view this property manifests in the fact that the
+largest Lyapunov exponent is equal to zero. Since in a rather complete model of
+the living cell the huge number of involved parameters can make of the
+calculations a hard task, we also propose a toy model, with fewer parameters to
+be determined, which emphasizes the cellular fission.
+"
+27201,"To Cluster, or Not to Cluster: An Analysis of Clusterability Methods","  Clustering is an essential data mining tool that aims to discover inherent
+cluster structure in data. For most applications, applying clustering is only
+appropriate when cluster structure is present. As such, the study of
+clusterability, which evaluates whether data possesses such structure, is an
+integral part of cluster analysis. However, methods for evaluating
+clusterability vary radically, making it challenging to select a suitable
+measure. In this paper, we perform an extensive comparison of measures of
+clusterability and provide guidelines that clustering users can reference to
+select suitable measures for their applications.
+"
+27202,Partial Identification of Expectations with Interval Data,"  A conditional expectation function (CEF) can at best be partially identified
+when the conditioning variable is interval censored. When the number of bins is
+small, existing methods often yield minimally informative bounds. We propose
+three innovations that make meaningful inference possible in interval data
+contexts. First, we prove novel nonparametric bounds for contexts where the
+distribution of the censored variable is known. Second, we show that a class of
+measures that describe the conditional mean across a fixed interval of the
+conditioning space can often be bounded tightly even when the CEF itself
+cannot. Third, we show that a constraint on CEF curvature can either tighten
+bounds or can substitute for the monotonicity assumption often made in interval
+data applications. We derive analytical bounds that use the first two
+innovations, and develop a numerical method to calculate bounds under the
+third. We show the performance of the method in simulations and then present
+two applications. First, we resolve a known problem in the estimation of
+mortality as a function of education: because individuals with high school or
+less are a smaller and thus more negatively selected group over time, estimates
+of their mortality change are likely to be biased. Our method makes it possible
+to hold education rank bins constant over time, revealing that current
+estimates of rising mortality for less educated women are biased upward in some
+cases by a factor of three. Second, we apply the method to the estimation of
+intergenerational mobility, where researchers frequently use coarsely measured
+education data in the many contexts where matched parent-child income data are
+unavailable. Conventional measures like the rank-rank correlation may be
+uninformative once interval censoring is taken into account; CEF interval-based
+measures of mobility are bounded tightly.
+"
+27203,Every locally finite Borel measure on $\mathbb{R}$ has conformal dimension zero,"  A result of P. Tukia from 1989 says that Lebesgue measure on $\mathbb{R}$ has
+conformal dimension zero: for every $\epsilon > 0$, there is a Borel set $G
+\subset \mathbb{R}$ of full Lebesgue measure, and a quasisymmetric
+homeomorphism $f \colon \mathbb{R} \to \mathbb{R}$ such that $\dim_{\mathrm{H}}
+f(G) < \epsilon$. In this short note, I show that the same is true for every
+locally finite Borel measure on $\mathbb{R}$.
+"
+27204,On Signatures of Clouds in Exoplanetary Transit Spectra,"  Transmission spectra of exoplanetary atmospheres have been used to infer the
+presence of clouds/hazes. Such inferences are typically based on spectral
+slopes in the optical deviant from gaseous Rayleigh scattering or low-amplitude
+spectral features in the infrared. We investigate three observable metrics that
+could allow constraints on cloud properties from transmission spectra, namely,
+the optical slope, the uniformity of this slope, and condensate features in the
+infrared. We derive these metrics using model transmission spectra considering
+Mie extinction from a wide range of condensate species, particle sizes, and
+scale heights. Firstly, we investigate possible degeneracies among the cloud
+properties for an observed slope. We find, for example, that spectra with very
+steep optical slopes suggest sulphide clouds (e.g. MnS, ZnS, Na$_2$S) in the
+atmospheres. Secondly, (non)uniformities in optical slopes provide additional
+constraints on cloud properties, e.g., MnS, ZnS, TiO$_2$, and Fe$_2$O$_3$ have
+significantly non-uniform slopes. Thirdly, infrared spectra provide an
+additional powerful probe into cloud properties, with SiO$_2$, Fe$_2$O$_3$,
+Mg$_2$SiO$_4$, and MgSiO$_3$ bearing strong infrared features observable with
+the James Webb Space Telescope. We investigate observed spectra of eight hot
+Jupiters and discuss their implications. In particular, no single or composite
+condensate species considered here conforms to the steep and non-uniform
+optical slope observed for HD 189733b. Our work highlights the importance of
+the three above metrics to investigate cloud properties in exoplanetary
+atmospheres using high-precision transmission spectra and detailed cloud
+models. We make our Mie scattering data for condensates publicly available to
+the community.
+"
+27205,Trisections and spun 4-manifolds,"  We study trisections of 4-manifolds obtained by spinning and twist-spinning
+3-manifolds, and we show that, given a (suitable) Heegaard diagram for the
+3-manifold, one can perform simple local modifications to obtain a trisection
+diagram for the 4-manifold. We also show that this local modification can be
+used to convert a (suitable) doubly-pointed Heegaard diagram for a
+3-manifold/knot pair into a doubly-pointed trisection diagram for the
+4-manifold/2-knot pair resulting from the twist-spinning operation.
+This technique offers a rich list of new manifolds that admit trisection
+diagrams that are amenable to study. We formulate a conjecture about
+4-manifolds with trisection genus three and provide some supporting evidence.
+"
+27206,Diving into the shallows: a computational perspective on large-scale shallow learning,"  In this paper we first identify a basic limitation in gradient descent-based
+optimization methods when used in conjunctions with smooth kernels. An analysis
+based on the spectral properties of the kernel demonstrates that only a
+vanishingly small portion of the function space is reachable after a polynomial
+number of gradient descent iterations. This lack of approximating power
+drastically limits gradient descent for a fixed computational budget leading to
+serious over-regularization/underfitting. The issue is purely algorithmic,
+persisting even in the limit of infinite data.
+To address this shortcoming in practice, we introduce EigenPro iteration,
+based on a preconditioning scheme using a small number of approximately
+computed eigenvectors. It can also be viewed as learning a new kernel optimized
+for gradient descent. It turns out that injecting this small (computationally
+inexpensive and SGD-compatible) amount of approximate second-order information
+leads to major improvements in convergence. For large data, this translates
+into significant performance boost over the standard kernel methods. In
+particular, we are able to consistently match or improve the state-of-the-art
+results recently reported in the literature with a small fraction of their
+computational budget.
+Finally, we feel that these results show a need for a broader computational
+perspective on modern large-scale learning to complement more traditional
+statistical and convergence analyses. In particular, many phenomena of
+large-scale high-dimensional inference are best understood in terms of
+optimization on infinite dimensional Hilbert spaces, where standard algorithms
+can sometimes have properties at odds with finite-dimensional intuition. A
+systematic analysis concentrating on the approximation power of such algorithms
+within a budget of computation may lead to progress both in theory and
+practice.
+"
+27207,Stochastic Lotka-Volterra food chains,"  We study the persistence and extinction of species in a simple food chain
+that is modelled by a Lotka-Volterra system with environmental stochasticity.
+There exist sharp results for deterministic Lotka-Volterra systems in the
+literature but few for their stochastic counterparts. The food chain we analyze
+consists of one prey and $n-1$ predators. The $j$th predator eats the $j-1$th
+species and is eaten by the $j+1$th predator; this way each species only
+interacts with at most two other species - the ones that are immediately above
+or below it in the trophic chain. We show that one can classify, based on an
+explicit quantity depending on the interaction coefficients of the system,
+which species go extinct and which converge to their unique invariant
+probability measure. Our work can be seen as a natural extension of the
+deterministic results of Gard and Hallam '79 to a stochastic setting.
+As one consequence we show that environmental stochasticity makes species
+more likely to go extinct. However, if the environmental fluctuations are
+small, persistence in the deterministic setting is preserved in the stochastic
+system. Our analysis also shows that the addition of a new apex predator makes,
+as expected, the different species more prone to extinction.
+Another novelty of our analysis is the fact that we can describe the behavior
+the system when the noise is degenerate. This is relevant because of the
+possibility of strong correlations between the effects of the environment on
+the different species.
+"
+27208,Task-based parallelization of an implicit kinetic scheme,"  In this paper we present and implement the Palindromic Discontinuous Galerkin
+(PDG) method in dimensions higher than one. The method has already been exposed
+and tested in [4] in the one-dimensional context. The PDG method is a general
+implicit high order method for approximating systems of conservation laws. It
+relies on a kinetic interpretation of the conservation laws containing stiff
+relaxation terms. The kinetic system is approximated with an
+asymptotic-preserving high order DG method. We describe the parallel
+implementation of the method, based on the StarPU runtime library. Then we
+apply it on preliminary test cases.
+"
+27209,"Infrared observation of OC-C2H2, OC-(C2H2)2 and their isotopologues","  The fundamental band for the OC-C2H2 dimer and two combination bands
+involving the intermolecular bending modes nu9 and nu8 in the carbon monoxide
+CO stretch region are re-examined. Spectra are obtained using a pulsed
+supersonic slit jet expansion probed with a mode-hop free tunable infrared
+quantum cascade laser. Analogous bands for OC-C2D2 and the fundamental for
+OC-DCCH as an impurity are also observed and analysed. A much weaker band in
+the same spectral region is assigned to a new mixed trimer, CO-(C2H2)2. The
+trimer band is composed uniquely of a-type transitions, establishing that the
+CO monomer is nearly aligned with the a-inertial axis. The observed rotational
+constants agree well with ab initio calculations and a small inertial defect
+value indicates that the trimer is planar. The structure is a compromise
+between the T-shaped structure of free acetylene dimer and the linear geometry
+of free OC-C2H2. A similar band for the fully deuterated isotopologue
+CO-(C2D2)2 confirms our assignment.
+"
+27210,On- and Off-Policy Monotonic Policy Improvement,"  Monotonic policy improvement and off-policy learning are two main desirable
+properties for reinforcement learning algorithms. In this paper, by lower
+bounding the performance difference of two policies, we show that the monotonic
+policy improvement is guaranteed from on- and off-policy mixture samples. An
+optimization procedure which applies the proposed bound can be regarded as an
+off-policy natural policy gradient method. In order to support the theoretical
+result, we provide a trust region policy optimization method using experience
+replay as a naive application of our bound, and evaluate its performance in two
+classical benchmark problems.
+"
+27211,Adversarial Scene Editing: Automatic Object Removal from Weak Supervision,"  While great progress has been made recently in automatic image manipulation,
+it has been limited to object centric images like faces or structured scene
+datasets. In this work, we take a step towards general scene-level image
+editing by developing an automatic interaction-free object removal model. Our
+model learns to find and remove objects from general scene images using
+image-level labels and unpaired data in a generative adversarial network (GAN)
+framework. We achieve this with two key contributions: a two-stage editor
+architecture consisting of a mask generator and image in-painter that
+co-operate to remove objects, and a novel GAN based prior for the mask
+generator that allows us to flexibly incorporate knowledge about object shapes.
+We experimentally show on two datasets that our method effectively removes a
+wide variety of objects using weak supervision only
+"
+27212,A Methodology for Oracle Selection of Monitors and Knobs for Configuring an HPC System running a Flood Management Application,"  This paper defines a methodology for the oracle selection of the monitors and
+knobs to use to configure an HPC system running a scientific application while
+satisfying the application's requirements and not violating any system
+constraints. This methodology relies on a heuristic correlation analysis
+between requirements, monitors and knobs to determine the minimum subset of
+monitors to observe and knobs to explore, to determine the optimal system
+configuration for the HPC application. At the end of this analysis, we reduce
+an 11-dimensional space to a 3-dimensional space for monitors and a
+6-dimensional space to a 3-dimensional space for knobs. This reduction shows
+the potential and highlights the need for a realistic methodology to help
+identify such minimum set of monitors and knobs.
+"
+27213,Sharp Bounds for Generalized Uniformity Testing,"  We study the problem of generalized uniformity testing \cite{BC17} of a
+discrete probability distribution: Given samples from a probability
+distribution $p$ over an {\em unknown} discrete domain $\mathbf{\Omega}$, we
+want to distinguish, with probability at least $2/3$, between the case that $p$
+is uniform on some {\em subset} of $\mathbf{\Omega}$ versus $\epsilon$-far, in
+total variation distance, from any such uniform distribution.
+We establish tight bounds on the sample complexity of generalized uniformity
+testing. In more detail, we present a computationally efficient tester whose
+sample complexity is optimal, up to constant factors, and a matching
+information-theoretic lower bound. Specifically, we show that the sample
+complexity of generalized uniformity testing is
+$\Theta\left(1/(\epsilon^{4/3}\|p\|_3) + 1/(\epsilon^{2} \|p\|_2) \right)$.
+"
+27214,On finite sample properties of nonparametric discrete asymmetric kernel estimators,"  The discrete kernel method was developed to estimate count data
+distributions, distinguishing discrete associated kernels based on their
+asymptotic behaviour. This study investigates the class of discrete asymmetric
+kernels and their resulting non-consistent estimators, but this theoretical
+drawback of the estimators is balanced by some interesting features in
+small/medium samples. The role of modal probability and variance of discrete
+asymmetric kernels is highlighted to help better understand the performance of
+these kernels, in particular how the binomial kernel outperforms other
+asymmetric kernels. The performance of discrete asymmetric kernel estimators of
+probability mass functions is illustrated using simulations, in addition to
+applications to real data sets.
+"
+27215,Operational Decision Support in the Presence of Uncertainties,"  This book addresses the scientific domains of operations research,
+information science and statistics with a focus on engineering applications.
+The purpose of this book is to report on the implications of the loop equations
+formulation of the state estimation procedure of the network systems, for the
+purpose of the implementation of Decision Support (DS) systems for the
+operational control of the network systems. In general an operational DS
+comprises a series of standalone applications from which the mathematical
+modeling and simulation of the distribution systems and the managing of the
+uncertainty in the decision-making process are essential in order to obtain
+efficient control and monitoring of the distribution systems. The mathematical
+modeling and simulation forms the basis for detailed optimization of the
+network operations and the second one uses uncertainty based reasoning in order
+to reduce the complexity of the network system and to increase the credibility
+of its model. This book reports on the integration of the two aspects of
+operational DS into a single computational framework of loop network equations.
+The proposed DS system will be validated using case studies taken from the
+water industry. The optimal control of water distribution systems is an
+important problem because the models are non-linear and large-scale and
+measurements are prone to errors and very often they are incomplete.
+"
+27216,Classifying Unordered Feature Sets with Convolutional Deep Averaging Networks,"  Unordered feature sets are a nonstandard data structure that traditional
+neural networks are incapable of addressing in a principled manner. Providing a
+concatenation of features in an arbitrary order may lead to the learning of
+spurious patterns or biases that do not actually exist. Another complication is
+introduced if the number of features varies between each set. We propose
+convolutional deep averaging networks (CDANs) for classifying and learning
+representations of datasets whose instances comprise variable-size, unordered
+feature sets. CDANs are efficient, permutation-invariant, and capable of
+accepting sets of arbitrary size. We emphasize the importance of nonlinear
+feature embeddings for obtaining effective CDAN classifiers and illustrate
+their advantages in experiments versus linear embeddings and alternative
+permutation-invariant and -equivariant architectures.
+"
+27217,Active vision for dexterous grasping of novel objects,"  How should a robot direct active vision so as to ensure reliable grasping? We
+answer this question for the case of dexterous grasping of unfamiliar objects.
+By dexterous grasping we simply mean grasping by any hand with more than two
+fingers, such that the robot has some choice about where to place each finger.
+Such grasps typically fail in one of two ways, either unmodeled objects in the
+scene cause collisions or object reconstruction is insufficient to ensure that
+the grasp points provide a stable force closure. These problems can be solved
+more easily if active sensing is guided by the anticipated actions. Our
+approach has three stages. First, we take a single view and generate candidate
+grasps from the resulting partial object reconstruction. Second, we drive the
+active vision approach to maximise surface reconstruction quality around the
+planned contact points. During this phase, the anticipated grasp is continually
+refined. Third, we direct gaze to improve the safety of the planned reach to
+grasp trajectory. We show, on a dexterous manipulator with a camera on the
+wrist, that our approach (80.4% success rate) outperforms a randomised
+algorithm (64.3% success rate).
+"
+27218,Multi-Color Simultaneous Photometry of the T-Tauri Star Having A Planetary Candidate CVSO 30,"  We present three-band simultaneous observations of a weak-line T-Tauri star
+CVSO~30 (PTFO~8-8695), which is one of the youngest objects having a candidate
+transiting planet. The data were obtained with the Multicolor Simultaneous
+Camera for studying Atmospheres of Transiting exoplanets (MuSCAT) on the 188 cm
+telescope at Okayama Astrophysical Observatory in Japan. We observed the fading
+event in the $g^{\prime}_2$-, $r^{\prime}_2$-, and $z_{\rm s,2}$-bands
+simultaneously. As a result, we find a significant wavelength dependence of
+fading depths of about 3.1\%, 1.7\%, 1.0\% for the $g^{\prime}_2$-,
+$r^{\prime}_2$-, and $z_{\rm s,2}$-bands, respectively. A cloudless H/He
+dominant atmosphere of a hot Jupiter cannot explain this large wavelength
+dependence. Additionally, we rule out a scenario by the occultation of the
+gravity-darkened host star. Thus our result is in favor of the fading origin as
+circumstellar dust clump or occultation of an accretion hotspot.
+"
+27219,Categorical structures for type theory in univalent foundations,"  In this paper, we analyze and compare three of the many algebraic structures
+that have been used for modeling dependent type theories: categories with
+families, split type-categories, and representable maps of presheaves. We study
+these in univalent type theory, where the comparisons between them can be given
+more elementarily than in set-theoretic foundations. Specifically, we construct
+maps between the various types of structures, and show that assuming the
+Univalence axiom, some of the comparisons are equivalences.
+We then analyze how these structures transfer along (weak and strong)
+equivalences of categories, and, in particular, show how they descend from a
+category (not assumed univalent/saturated) to its Rezk completion. To this end,
+we introduce relative universes, generalizing the preceding notions, and study
+the transfer of such relative universes along suitable structure.
+We work throughout in (intensional) dependent type theory; some results, but
+not all, assume the univalence axiom. All the material of this paper has been
+formalized in Coq, over the UniMath library.
+"
+27220,Neutron-induced strike: Study of multiple node charge collection in 14nm FinFETs,"  FinFETs have replaced the conventional bulk CMOS transistors in the sub-20nm
+technology. One of the key issues to consider is, the vulnerability of FinFET
+based circuits to multiple node charge collection due to neutron-induced
+strikes. In this paper, we perform a device simulation based characterization
+study on representative layouts of 14nm bulk FinFETs in order to study the
+extent to which multiple transistors are affected. We find that multiple
+transistors do get affected and the impact can last up to five transistors away
+(~200nm). We show that the potential of source/drain regions in the
+neighborhood of the strike is a significant contributing factor. In the case of
+multi-fin FinFETs, the charge collected per fin is seen to reduce as the number
+of fins increase. Thus, smaller FinFETs are susceptible to high amounts of
+charge collection.
+"
+27221,Lattice of closure endomorphisms of a Hilbert algebra,"  A closure endomorphism of a Hilbert algebra A is a mapping that is
+simultaneously an endomorphism of and a closure operator on A. It is known that
+the set CE of all closure endomorphisms of A is a distributive lattice where
+the meet of two elements is defined pointwise and their join is given by their
+composition. This lattice is shown in the paper to be isomorphic to the lattice
+of certain filters of A, anti-isomorphic to the lattice of certain closure
+retracts of A, and compactly generated. The set of compact elements of CE
+coincides with the adjoint semilattice of A, conditions under which two Hilbert
+algebras have isomorphic adjoint semilattices (equivalently, minimal Brouwerian
+extensions) are discussed. Several consequences are drawn also for implication
+algebras.
+"
+27222,Unsupervised Domain Adaptation with Copula Models,"  We study the task of unsupervised domain adaptation, where no labeled data
+from the target domain is provided during training time. To deal with the
+potential discrepancy between the source and target distributions, both in
+features and labels, we exploit a copula-based regression framework. The
+benefits of this approach are two-fold: (a) it allows us to model a broader
+range of conditional predictive densities beyond the common exponential family,
+(b) we show how to leverage Sklar's theorem, the essence of the copula
+formulation relating the joint density to the copula dependency functions, to
+find effective feature mappings that mitigate the domain mismatch. By
+transforming the data to a copula domain, we show on a number of benchmark
+datasets (including human emotion estimation), and using different regression
+models for prediction, that we can achieve a more robust and accurate
+estimation of target labels, compared to recently proposed feature
+transformation (adaptation) methods.
+"
+27223,Universality in the Hubbard model,"  A new viewpoint of universality is introduced in strongly correlated electron
+systems; Our description relies on the operator theoretic correlation
+inequalities. We explain the Marshall-Lieb-Mattis theorem and Lieb\rq{}s
+theorem from a viewpoint of universality; in addition, from the new
+perspective, we prove that Lieb\rq{}s theorem still holds true even if the
+electron-phonon and electron-photon interactions are taken into account. We
+also study Nagaoka-Thouless\rq{} theorem and its stabilities in terms of
+universality.
+"
+27224,Secure two-way communication via a wireless powered untrusted relay and friendly jammer,"  In this paper, we propose a self-dependent two-way secure communication where
+two sources exchange confidential messages via a wireless powered untrusted
+amplify-and-forward (AF) relay and friendly jammer (FJ). By adopting the time
+switching (TS) architecture at the relay, the data transmission is accomplished
+in three phases: Phase I) Energy harvesting by the untrusted relay and the FJ
+through non-information transmissions from the sources, Phase II) Information
+transmission by the sources and jamming transmissions from the FJ to reduce
+information leakage to the untrusted relay; and Phase III) Forwarding the
+scaled version of the received signal from the untrusted relay to the sources.
+For the proposed system, we derive a new closed-form lower bound expression for
+the ergodic secrecy sum rate (ESSR). Numerical examples are provided to
+demonstrate the impacts of different system parameters such as energy
+harvesting time, transmit signal-to-noise ratio (SNR) and the relay/FJ location
+on the secrecy performance. The numerical results illustrate that the proposed
+network with friendly jamming (WFJ) outperforms traditional one-way
+communication and the two-way without friendly jamming (WoFJ) policy.
+"
+27225,Solid State Thermionic Power Generators: an analytical analysis in the nonlinear regime,"  Solid-state thermionic power generators are an alternative to thermoelectric
+modules. In this manuscript, we develop an analytical model to investigate the
+performance of these generators in the non-linear regime. We identify
+dimensionless parameters determining their performance and provide measures to
+estimate acceptable range of thermal and electrical resistances of thermionic
+generators. We find the relation between the optimum load resistance and the
+internal resistance and suggest guide lines for the design of thermionic power
+generators. Finally, we show that in the nonlinear regime, thermionic power
+generators can have efficiency values higher than the state of the art
+thermoelectric modules.
+"
+27226,On learning the structure of Bayesian Networks and submodular function maximization,"  Learning the structure of dependencies among multiple random variables is a
+problem of considerable theoretical and practical interest. In practice, score
+optimisation with multiple restarts provides a practical and surprisingly
+successful solution, yet the conditions under which this may be a well founded
+strategy are poorly understood. In this paper, we prove that the problem of
+identifying the structure of a Bayesian Network via regularised score
+optimisation can be recast, in expectation, as a submodular optimisation
+problem, thus guaranteeing optimality with high probability. This result both
+explains the practical success of optimisation heuristics, and suggests a way
+to improve on such algorithms by artificially simulating multiple data sets via
+a bootstrap procedure. We show on several synthetic data sets that the
+resulting algorithm yields better recovery performance than the state of the
+art, and illustrate in a real cancer genomic study how such an approach can
+lead to valuable practical insights.
+"
+27227,Social Media Text Processing and Semantic Analysis for Smart Cities,"  With the rise of Social Media, people obtain and share information almost
+instantly on a 24/7 basis. Many research areas have tried to gain valuable
+insights from these large volumes of freely available user generated content.
+With the goal of extracting knowledge from social media streams that might be
+useful in the context of intelligent transportation systems and smart cities,
+we designed and developed a framework that provides functionalities for
+parallel collection of geo-located tweets from multiple pre-defined bounding
+boxes (cities or regions), including filtering of non-complying tweets, text
+pre-processing for Portuguese and English language, topic modeling, and
+transportation-specific text classifiers, as well as, aggregation and data
+visualization.
+We performed an exploratory data analysis of geo-located tweets in 5
+different cities: Rio de Janeiro, São Paulo, New York City, London and
+Melbourne, comprising a total of more than 43 million tweets in a period of 3
+months. Furthermore, we performed a large scale topic modelling comparison
+between Rio de Janeiro and São Paulo. Interestingly, most of the topics are
+shared between both cities which despite being in the same country are
+considered very different regarding population, economy and lifestyle.
+We take advantage of recent developments in word embeddings and train such
+representations from the collections of geo-located tweets. We then use a
+combination of bag-of-embeddings and traditional bag-of-words to train
+travel-related classifiers in both Portuguese and English to filter
+travel-related content from non-related. We created specific gold-standard data
+to perform empirical evaluation of the resulting classifiers. Results are in
+line with research work in other application areas by showing the robustness of
+using word embeddings to learn word similarities that bag-of-words is not able
+to capture.
+"
+27228,An efficient algorithm to decide periodicity of b-recognisable sets using LSDF convention,"  Let b be an integer strictly greater than 1. Each set of nonnegative integers
+is represented in base b by a language over {0,1,...,b-1}. The set is said to
+be b-recognisable if it is represented by a regular language. It is known that
+ultimately periodic sets are b-recognisable, for every base b, and Cobham's
+theorem implies the converse: no other set is b-recognisable in every base b.
+We consider the following decision problem: let S be a set of nonnegative
+integers that is b-recognisable, given as a finite automaton over { 0, 1, ...,
+b-1 }, is S periodic? Honkala showed in 1986 that this problem is decidable.
+Later on, Leroux used in 2005 the convention to write number representations
+with the least significant digit first (LSDF), and designed a quadratic
+algorithm to solve a more general problem. We use here LSDF convention as well
+and give a structural description of the minimal automata that accept periodic
+sets. Then, we show that it can be verified in linear time if a minimal
+automaton meets this description. In general, this yields a O(bnlog(n))
+procedure to decide whether an automaton with n states accepts an ultimately
+periodic set of nonnegative integers.
+"
+27229,How the investor's risk preferences influence the optimal allocation in a credibilistic portfolio problem,"  A classical portfolio theory deals with finding the optimal proportion in
+which an agent invests a wealth in a risk-free asset and a probabilistic risky
+asset. Formulating and solving the problem depend on how the risk is
+represented and how, combined with the utility function defines a notion of
+expected utility. In this paper the risk is a fuzzy variable and the notion of
+expected utility is defined in the setting of Liu's credibility theory. Thus
+the portfolio choice problem is formulated as an optimization problem in which
+the objective function is a credibilistic expected utility. Different
+approximation calculation formulas for the optimal allocation of the
+credibilistic risky asset are proved. These formulas contain two types of
+parameters: various credibilistic moments associated with fuzzy variables
+(expected value, variance, skewness and kurtosis) and the risk aversion,
+prudence and temperance indicators of the utility function.
+"
+27230,"On a new (p,q)-Mtahieu type power series and its applications","  Our aim in this paper, is to establish certain new integrals for the the
+(p,q)-Mathieu--power series. In particular, we investigate the Mellin-Barnes
+type integral representations for a particular case of thus special function.
+Moreover, we introduce the notion of the (p,q)-Mittag-Leffler functions and we
+present a relationships between thus two functions. Some other applications are
+proved, in particular two Turán type inequalities for the (p,q)-Mathieu
+series are proved.
+"
+27231,Probabilistically Safe Policy Transfer,"  Although learning-based methods have great potential for robotics, one
+concern is that a robot that updates its parameters might cause large amounts
+of damage before it learns the optimal policy. We formalize the idea of safe
+learning in a probabilistic sense by defining an optimization problem: we
+desire to maximize the expected return while keeping the expected damage below
+a given safety limit. We study this optimization for the case of a robot
+manipulator with safety-based torque limits. We would like to ensure that the
+damage constraint is maintained at every step of the optimization and not just
+at convergence. To achieve this aim, we introduce a novel method which predicts
+how modifying the torque limit, as well as how updating the policy parameters,
+might affect the robot's safety. We show through a number of experiments that
+our approach allows the robot to improve its performance while ensuring that
+the expected damage constraint is not violated during the learning process.
+"
+27232,A classification of star log symplectic structures on a compact oriented surface,"  Given a compact oriented surface, we classify log Poisson bi-vectors whose
+degeneracy loci are locally modeled by a finite set of lines in the plane
+intersecting at a point. Further, we compute the Poisson cohomology of such
+structures and discuss the relationship between our classification and the
+second Poisson cohomology.
+"
+27233,Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features,"  The use of covariance kernels is ubiquitous in the field of spatial
+statistics. Kernels allow data to be mapped into high-dimensional feature
+spaces and can thus extend simple linear additive methods to nonlinear methods
+with higher order interactions. However, until recently, there has been a
+strong reliance on a limited class of stationary kernels such as the Matern or
+squared exponential, limiting the expressiveness of these modelling approaches.
+Recent machine learning research has focused on spectral representations to
+model arbitrary stationary kernels and introduced more general representations
+that include classes of nonstationary kernels. In this paper, we exploit the
+connections between Fourier feature representations, Gaussian processes and
+neural networks to generalise previous approaches and develop a simple and
+efficient framework to learn arbitrarily complex nonstationary kernel functions
+directly from the data, while taking care to avoid overfitting using
+state-of-the-art methods from deep learning. We highlight the very broad array
+of kernel classes that could be created within this framework. We apply this to
+a time series dataset and a remote sensing problem involving land surface
+temperature in Eastern Africa. We show that without increasing the
+computational or storage complexity, nonstationary kernels can be used to
+improve generalisation performance and provide more interpretable results.
+"
+27234,"Graph Embedding Techniques, Applications, and Performance: A Survey","  Graphs, such as social networks, word co-occurrence networks, and
+communication networks, occur naturally in various real-world applications.
+Analyzing them yields insight into the structure of society, language, and
+different patterns of communication. Many approaches have been proposed to
+perform the analysis. Recently, methods which use the representation of graph
+nodes in vector space have gained traction from the research community. In this
+survey, we provide a comprehensive and structured analysis of various graph
+embedding techniques proposed in the literature. We first introduce the
+embedding task and its challenges such as scalability, choice of
+dimensionality, and features to be preserved, and their possible solutions. We
+then present three categories of approaches based on factorization methods,
+random walks, and deep learning, with examples of representative algorithms in
+each category and analysis of their performance on various tasks. We evaluate
+these state-of-the-art methods on a few common datasets and compare their
+performance against one another. Our analysis concludes by suggesting some
+potential applications and future directions. We finally present the
+open-source Python library we developed, named GEM (Graph Embedding Methods,
+available at this https URL), which provides all presented
+algorithms within a unified interface to foster and facilitate research on the
+topic.
+"
+27235,On the Estimation of Parameters from Time Traces originating from an Ornstein-Uhlenbeck Process,"  In this article, we develop a Bayesian approach to estimate parameters from
+time traces that originate from an overdamped Brownian particle in a harmonic
+oscillator. We show that least-square fitting the autocorrelation function,
+which is often the standard way of analyzing such data, is significantly
+overestimating the confidence in the fitted parameters. Here, we develop a
+rigorous maximum likelihood theory that properly captures the underlying
+statistics. This claim is further supported by simulating time series with
+subsequent application of least-square and maximum likelihood methods. Our
+result suggests that it is quite dangerous to apply least-squares to
+autocorrelation functions since an overestimation of confidence could easily
+lead to irreproducibility of results. To see whether our results apply to other
+methods where autocorrelation functions are fitted by least-squares, we
+explored the analysis of membrane fluctuations and fluorescence correlation
+spectroscopy. In both cases, least-square fits significantly overestimated the
+confidence in the fitted parameters. We conclude by emphasizing the need for
+the development of proper maximum likelihood approaches for these methods.
+"
+27236,Tangent-Space Regularization for Neural-Network Models of Dynamical Systems,"  This work introduces the concept of tangent space regularization for
+neural-network models of dynamical systems. The tangent space to the dynamics
+function of many physical systems of interest in control applications exhibits
+useful properties, e.g., smoothness, motivating regularization of the model
+Jacobian along system trajectories using assumptions on the tangent space of
+the dynamics. Without assumptions, large amounts of training data are required
+for a neural network to learn the full non-linear dynamics without overfitting.
+We compare different network architectures on one-step prediction and
+simulation performance and investigate the propensity of different
+architectures to learn models with correct input-output Jacobian. Furthermore,
+the influence of $L_2$ weight regularization on the learned Jacobian eigenvalue
+spectrum, and hence system stability, is investigated.
+"
+27237,Magnetic cooling for microkelvin nanoelectronics on a cryofree platform,"  We present a parallel network of 16 demagnetization refrigerators mounted on
+a cryofree dilution refrigerator aimed to cool nanoelectronic devices to
+sub-millikelvin temperatures. To measure the refrigerator temperature, the
+thermal motion of electrons in a Ag wire -- thermalized by a spot-weld to one
+of the Cu nuclear refrigerators -- is inductively picked-up by a
+superconducting gradiometer and amplified by a SQUID mounted at 4 K. The noise
+thermometer as well as other thermometers are used to characterize the
+performance of the system, finding magnetic field independent heat-leaks of a
+few nW/mol, cold times of several days below 1 mK, and a lowest temperature of
+150 microK of one of the nuclear stages in a final field of 80 mT, close to the
+intrinsic SQUID noise of about 100 microK. A simple thermal model of the system
+capturing the nuclear refrigerator, heat leaks, as well as thermal and Korringa
+links describes the main features very well, including rather high refrigerator
+efficiencies typically above 80%.
+"
+27238,Work distributions for random sudden quantum quenches,"  The statistics of work performed on a system by a sudden random quench is
+investigated. Considering systems with finite dimensional Hilbert spaces we
+model a sudden random quench by randomly choosing elements from a Gaussian
+unitary ensemble (GUE) consisting of hermitean matrices with identically,
+Gaussian distributed matrix elements. A probability density function (pdf) of
+work in terms of initial and final energy distributions is derived and
+evaluated for a two-level system. Explicit results are obtained for quenches
+with a sharply given initial Hamiltonian, while the work pdfs for quenches
+between Hamiltonians from two independent GUEs can only be determined in
+explicit form in the limits of zero and infinite temperature.
+"
+27239,Scalable prediction of global online media news virality,"  News reports shape the public perception of the critical social, political
+and economical events around the world. Yet, the way in which emergent
+phenomena are reported in the news makes the early prediction of such phenomena
+a challenging task. We propose a scalable community-based probabilistic
+framework to model the spreading of news about events in online media. Our
+approach exploits the latent community structure in the global news media and
+uses the affiliation of the early adopters with a variety of communities to
+identify the events widely reported in the news at the early stage of their
+spread. The time complexity of our approach is linear in the number of news
+reports. It is also amenable to efficient parallelization. To demonstrate these
+features, the inference algorithm is parallelized for message passing paradigm
+and tested on RPI Advanced Multiprocessing Optimized System (AMOS), one of the
+fastest Blue Gene/Q supercomputers in the world. Thanks to the community-level
+features of the early adopters, the model gains an improvement of 20% in the
+early detection of the most massively reported events compared to the
+feature-based machine learning algorithm. Its parallelization scheme achieves
+orders of magnitude speedup.
+"
+27240,Fast & Flexible IO : A Compositional Approach to Storage Construction for High-Performance Devices,"  Building storage systems has remained the domain of systems experts for many
+years. They are complex and difficult to implement. Extreme care is needed to
+ensure necessary guarantees of performance and operational correctness.
+Furthermore, because of restrictions imposed by kernel-based designs, many
+legacy implementations have traded software flexibility for performance. Their
+implementation is restricted to compiled languages such as C and assembler, and
+reuse tends to be difficult or constrained. Nevertheless, storage systems are
+implicitly well-suited to software reuse and compositional software
+construction. There are many logical functions, such as block allocation,
+caching, partitioning, metadata management and so forth, that are common across
+most variants of storage. In this paper, we present Comanche, an open-source
+project that considers, as first-class concerns, both compositional design and
+reuse, and the need for high-performance.
+"
+27241,Efficient Computation of Sparse Matrix Functions for Large-Scale Electronic Structure Calculations: The CheSS Library,"  We present CheSS, the ""Chebyshev Sparse Solvers"" library, which has been
+designed to solve typical problems arising in large-scale electronic structure
+calculations using localized basis sets. The library is based on a flexible and
+efficient expansion in terms of Chebyshev polynomials and presently features
+the calculation of the density matrix, the calculation of matrix powers for
+arbitrary powers, and the extraction of eigenvalues in a selected interval.
+CheSS is able to exploit the sparsity of the matrices and scales linearly with
+respect to the number of nonzero entries, making it well-suited for large-scale
+calculations. The approach is particularly adapted for setups leading to small
+spectral widths of the involved matrices and outperforms alternative methods in
+this regime. By coupling CheSS to the DFT code BigDFT, we show that such a
+favorable setup is indeed possible in practice. In addition, the approach based
+on Chebyshev polynomials can be massively parallelized, and CheSS exhibits
+excellent scaling up to thousands of cores even for relatively small matrix
+sizes.
+"
+27242,Hypoxia-inducible factor 1a protects peripheral sensory neurons from diabetic peripheral neuropathy by suppressing accumulation of reactive oxygen species,"  Diabetic peripheral neuropathy (DPN) is one of the most common diabetic
+complications. Mechanisms underlying nerve damage and sensory loss following
+metabolic dysfunction remain large unclear. Recently, hyperglycemia-induced
+mitochondrial dysfunction and the generation of ROS have gained attention as
+possible mechanisms of organ damage in diabetes. Hypoxia-inducible factor
+1(HIF1a) is a key transcription factor activated by hypoxia, hyperglycemia,
+nitric oxide as well as ROS, suggesting a fundamental role in DPN
+susceptibility. Genetically-modified mutant mice, which conditionally lack
+HIF1a in peripheral sensory neurons (SNS-HIF1a-/-), were analyzed
+longitudinally up to 6 months in the streptozotocin (STZ) model of type1
+diabetes. Behavioral measurements of sensitivity to thermal and mechanical
+stimuli, quantitative morphological analyses of intraepidermal nerve fiber
+density and measurements of reactive oxygen species (ROS) in sensory neurons in
+vivo were undertaken over several months post-STZ injections to delineate the
+role of HIF1a in DPN. Longitudinal behavioral and morphological analyses at 5,
+13 and 24 wks post-STZ treatment revealed that SNS-HIF1a-/- developed stronger
+hyperglycemia-evoked losses of peripheral nociceptive sensory axons associated
+with stronger losses of mechano- and heat sensation with a faster onset than
+HIF1afl/fl mice. Mechanistically, these histomorphologic and behavioral
+differences were associated with significantly higher level of STZ-induced
+production of ROS in sensory neurons of SNS-HIF1a-/- mice as compared with
+HIF1afl/fl. Our results indicate that HIF1a is as an upstream modulator of ROS
+in peripheral sensory neurons and exerts a protective function in suppressing
+hyperglycemia-induced nerve damage by limiting ROS levels. HIF1a stabilization
+may be thus a new strategy target for limiting sensory loss, a debilitating
+late complication of diabetes.
+"
+27243,Spin-orbit coupling and transport of strongly correlated two-dimensional systems,"  Measuring the magnetoresistance (MR) of ultraclean {\it GaAs} two-dimensional
+holes in a large $r_s$ range of 20-50, two striking behaviors in relation to
+the spin-orbit coupling (SOC) emerge in response to strong electron-electron
+interaction. First, in exact correspondence to the zero-field
+metal-to-insulator transition (MIT), the sign of the MR switches from being
+positive in the metallic regime to being negative in the insulating regime when
+the carrier density crosses the critical density $p_c$ of MIT ($r_s\sim 39$).
+Second, as the SOC-driven correction $\Delta\rho$ to the MR decreases with
+reducing carrier density (or the in-plane wave vector), it exhibits an upturn
+in the close proximity just above $p_c$ where $r_s$ is beyond 30, indicating a
+substantially enhanced SOC effect. This peculiar behavior echoes with a trend
+of delocalization long suspected for the SOC-interaction interplay. Meanwhile,
+for $p<p_c$ or $r_s>40$, in contrast to the common belief that a magnet field
+enhances Wigner crystallization, the negative MR is likely linked to enhanced
+interaction.
+"
+27244,Toward Inverse Control of Physics-Based Sound Synthesis,"  Long Short-Term Memory networks (LSTMs) can be trained to realize inverse
+control of physics-based sound synthesizers. Physics-based sound synthesizers
+simulate the laws of physics to produce output sound according to input gesture
+signals. When a user's gestures are measured in real time, she or he can use
+them to control physics-based sound synthesizers, thereby creating simulated
+virtual instruments. An intriguing question is how to program a computer to
+learn to play such physics-based models. This work demonstrates that LSTMs can
+be trained to accomplish this inverse control task with four physics-based
+sound synthesizers.
+"
+27245,Experimental Phase Estimation Enhanced By Machine Learning,"  Phase estimation protocols provide a fundamental benchmark for the field of
+quantum metrology. The latter represents one of the most relevant applications
+of quantum theory, potentially enabling the capability of measuring unknown
+physical parameters with improved precision over classical strategies. Within
+this context, most theoretical and experimental studies have focused on
+determining the fundamental bounds and how to achieve them in the asymptotic
+regime where a large number of resources is employed. However, in most
+applications it is necessary to achieve optimal precisions by performing only a
+limited number of measurements. To this end, machine learning techniques can be
+applied as a powerful optimization tool. Here, we implement experimentally
+single-photon adaptive phase estimation protocols enhanced by machine learning,
+showing the capability of reaching optimal precision after a small number of
+trials. In particular, we introduce a new approach for Bayesian estimation that
+exhibit best performances for very low number of photons N. Furthermore, we
+study the resilience to noise of the tested methods, showing that the optimized
+Bayesian approach is very robust in the presence of imperfections. Application
+of this methodology can be envisaged in the more general multiparameter case,
+that represents a paradigmatic scenario for several tasks including imaging or
+Hamiltonian learning.
+"
+27246,Spectral results for perturbed periodic Jacobi matrices using the discrete Levinson technique,"  For an arbitrary Hermitian period-$T$ Jacobi operator, we assume a
+perturbation by a Wigner-von Neumann type potential to devise subordinate
+solutions to the formal spectral equation for a (possibly infinite) real set,
+$S$, of the spectral parameter. We employ discrete Levinson type techniques to
+achieve this, which allow the analysis of the asymptotic behaviour of the
+solution. This enables us to construct infinitely many spectral singularities
+on the absolutely continuous spectrum of the periodic Jacobi operator, which
+are stable with respect to an $l^1$-perturbation. An analogue of the
+quantisation conditions from the continuous case appears, relating the
+frequency of the oscillation of the potential to the quasi-momentum associated
+with the purely periodic operator.
+"
+27247,A spatial scientometric analysis of the publication output of cities worldwide,"  In tandem with the rapid globalisation of science, spatial scientometrics has
+become an important research sub-field in scientometric studies. Recently,
+numerous spatial scientometric contributions have focused on the examination of
+cities' scientific output by using various scientometric indicators. In this
+paper, I analyse cities' scientific output worldwide in terms of the number of
+journal articles indexed by the Scopus database, in the period from 1986 to
+2015. Furthermore, I examine which countries are the most important
+collaborators of cities. Finally, I identify the most productive disciplines in
+each city. I use GPS Visualizer to illustrate the scientometric data of nearly
+2,200 cities on maps. Results show that cities with the highest scientific
+output are mostly located in developed countries and China. Between 1986 and
+2015, the greatest number of scientific articles were created in Beijing. The
+international hegemony of the United States in science has been described by
+many studies, and is also reinforced by the fact that the United States is the
+most important collaborator to more than 75 percent of all cities. Medicine is
+the most productive discipline in two-thirds of cities. Furthermore, cities
+having the highest scientific output in specific disciplines show well-defined
+geographical patterns.
+"
+27248,On Ramond Decorations,"  We impose constraints on the odd coordinates of super Teichmüller space in
+the uniformization picture for the monodromies around Ramond punctures, thus
+reducing the overall odd dimension to be compatible with that of the moduli
+spaces of super Riemann surfaces. Namely, the monodromy of a puncture must be a
+true parabolic element of the canonical subgroup $PSL(2,\mathbb{R})$.
+"
+27249,Analysis of the noise in back-projection light field acquisition and its optimization,"  Light field reconstruction from images captured by focal plane sweeping can
+achieve high lateral resolution comparable to the modern camera sensor. This is
+impossible for the conventional micro-lenslet based light field capture
+systems. However, the severe defocus noise and the low depth resolution limit
+its applications. In this paper, we analyze the defocus noise and the depth
+resolution in the focal plane sweeping based light field reconstruction
+technique, and propose a method to reduce the defocus noise and improve the
+depth resolution. Both numerical and experimental results verify the proposed
+method.
+"
+27250,Characteristic polynomials of p-adic matrices,"  We analyze the precision of the characteristic polynomial of an $n\times n$
+p-adic matrix A using differential precision methods developed previously. When
+A is integral with precision O(p^N), we give a criterion (checkable in time
+O~(n^omega)) for $\chi$(A) to have precision exactly O(p^N). We also give a
+O~(n^3) algorithm for determining the optimal precision when the criterion is
+not satisfied, and give examples when the precision is larger than O(p^N).
+"
+27251,Bayesian Computation for Log-Gaussian Cox Processes--A Comparative Analysis of Methods,"  The Log-Gaussian Cox Process is a commonly used model for the analysis of
+spatial point patterns. Fitting this model is difficult because of its
+doubly-stochastic property, i.e., it is an hierarchical combination of a
+Poisson process at the first level and a Gaussian Process at the second level.
+Different methods have been proposed to estimate such a process, including
+traditional likelihood-based approaches as well as Bayesian methods. We focus
+here on Bayesian methods and several approaches that have been considered for
+model fitting within this framework, including Hamiltonian Monte Carlo, the
+Integrated nested Laplace approximation, and Variational Bayes. We consider
+these approaches and make comparisons with respect to statistical and
+computational efficiency. These comparisons are made through several
+simulations studies as well as through applications examining both ecological
+data and neuroimaging data.
+"
+27252,Some variations on Ensembled Random Survival Forest with application to Cancer Research,"  In this paper we describe a novel implementation of adaboost for prediction
+of survival function. We take different variations of the algorithm and compare
+the algorithms based on system run time and root mean square error. Our
+construction includes right censoring data and competing risk data too. We take
+different data set to illustrate the performance of the algorithms.
+"
+27253,A combinatorial formula for graded multiplicities in excellent filtrations,"  A filtration of a representation whose successive quotients are isomorphic to
+Demazure modules is called an excellent filtration. In this paper we study
+graded multiplicities in excellent filtrations of fusion products for the
+current algebra $\mathfrak{sl}_2[t]$. We give a combinatorial formula for the
+polynomials encoding these multiplicities in terms of two dimensional lattice
+paths. Corollaries to our main theorem include a combinatorial interpretation
+of various objects such as the coeffficients of Ramanujan's fifth order mock
+theta functions $\phi_0, \phi_1, \psi_0, \psi_1$, Kostka polynomials for hook
+partitions and quotients of Chebyshev polynomials. We also get a combinatorial
+interpretation of the graded multiplicities in a level one flag of a local Weyl
+module associated to the simple Lie algebras of type $B_n \text{ and } G_2$.
+"
+27254,On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data,"  Ever growing volume and velocity of data coupled with decreasing attention
+span of end users underscore the critical need for real-time analytics. In this
+regard, anomaly detection plays a key role as an application as well as a means
+to verify data fidelity. Although the subject of anomaly detection has been
+researched for over 100 years in a multitude of disciplines such as, but not
+limited to, astronomy, statistics, manufacturing, econometrics, marketing, most
+of the existing techniques cannot be used as is on real-time data streams.
+Further, the lack of characterization of performance -- both with respect to
+real-timeliness and accuracy -- on production data sets makes model selection
+very challenging. To this end, we present an in-depth analysis, geared towards
+real-time streaming data, of anomaly detection techniques. Given the
+requirements with respect to real-timeliness and accuracy, the analysis
+presented in this paper should serve as a guide for selection of the ""best""
+anomaly detection technique. To the best of our knowledge, this is the first
+characterization of anomaly detection techniques proposed in very diverse set
+of fields, using production data sets corresponding to a wide set of
+application domains.
+"
+27255,Influence of Neighborhood SES on Functional Brain Network Development,"  Higher socioeconomic status (SES) in childhood is associated with increased
+cognitive abilities, higher academic achievement, and decreased incidence of
+mental illness later in development. Accumulating evidence suggests that these
+effects may be due to changes in brain development induced by environmental
+factors. While prior work has mapped the associations between neighborhood SES
+and brain structure, little is known about the relationship between SES and
+intrinsic neural dynamics. Here, we capitalize upon a large community-based
+sample (Philadelphia Neurodevelopmental Cohort, ages 8-22 years, n=1012) to
+examine developmental changes in functional brain network topology as estimated
+from resting state functional magnetic resonance imaging data. We
+quantitatively characterize this topology using a local measure of network
+segregation known as the clustering coefficient, and find that it accounts for
+a greater degree of SES-associated variance than meso-scale segregation
+captured by modularity. While whole-brain clustering increased with age,
+high-SES youth displayed faster increases in clustering than low-SES youth, and
+this effect was most pronounced for regions in the limbic, somatomotor, and
+ventral attention systems. The effect of SES on developmental increases in
+clustering was strongest for connections of intermediate physical length,
+consistent with faster decreases in local connectivity in these regions in
+low-SES youth, and tracked changes in BOLD signal complexity in the form of
+regional homogeneity. Our findings suggest that neighborhood SES may
+fundamentally alter intrinsic patterns of inter-regional interactions in the
+human brain in a manner that is consistent with greater segregation of
+information processing in late childhood and adolescence.
+"
+27256,Rapid and Robust Automated Macroscopic Wood Identification System using Smartphone with Macro-lens,"  Wood Identification has never been more important to serve the purpose of
+global forest species protection and timber regulation. Macroscopic level wood
+identification practiced by wood anatomists can identify wood up to genus
+level. This is sufficient to serve as a frontline identification to fight
+against illegal wood logging and timber trade for law enforcement authority.
+However, frontline enforcement official may lack of the accuracy and confidence
+of a well trained wood anatomist. Hence, computer assisted method such as
+machine vision methods are developed to do rapid field identification for law
+enforcement official. In this paper, we proposed a rapid and robust macroscopic
+wood identification system using machine vision method with off-the-shelf
+smartphone and retrofitted macro-lens. Our system is cost effective, easily
+accessible, fast and scalable at the same time provides human-level accuracy on
+identification. Camera-enabled smartphone with Internet connectivity coupled
+with a macro-lens provides a simple and effective digital acquisition of
+macroscopic wood images which are essential for macroscopic wood
+identification. The images are immediately streamed to a cloud server via
+Internet connection for identification which are done within seconds.
+"
+27257,Is Non-Neutrality Profitable for the Stakeholders of the Internet Market? - Part II,"  In this part of the paper, we obtain analytical results for the case that
+transport costs are not small (complement of Part I), and combine them with the
+results in Part I of the paper to provide general results for all values of
+transport costs. We show that, in general, if an SPNE exists, it would be one
+of the five possible strategies each of which we explicitly characterize. We
+also prove that when EUs have sufficiently high inertia for at least one of the
+ISPs, there exists a unique SPNE with a non-neutral outcome in which both of
+the ISPs are active, and the CP offers her content with free quality on the
+neutral ISP and with premium quality on the non-neutral ISP. Moreover, we show
+that an SPNE does not always exist. We also analyze a benchmark case in which
+both ISPs are neutral, and prove that there exists a unique SPNE in which the
+CP offers her content with free quality on both ISPs, and both ISPs are active.
+We also provide extensive numerical results and discussions for all ranges of
+transport costs. Simulation results suggest that if the SPNE exists, it would
+be unique. In addition, results reveal that the neutral ISP receives a lower
+payoff and the non-neutral ISP receives a higher payoff (most of the time) in a
+non-neutral scenario. However, we also identify scenarios in which the
+non-neutral ISP loses payoff by adopting non-neutrality. In addition, we show
+that a non-neutral regime yields a higher welfare for EUs than a neutral one if
+the market power of the non-neutral ISP is small, the sensitivity of EUs
+(respectively, the CP) to the quality is low (respectively, high), or a
+combinations of these factors.
+"
+27258,Characterizing the zenithal night sky brightness in large territories: How many samples per square kilometer are needed?,"  A recurring question arises when trying to characterize, by means of
+measurements or theoretical calculations, the zenithal night sky brightness
+throughout a large territory: how many samples per square kilometer are needed?
+The optimum sampling distance should allow reconstructing, with sufficient
+accuracy, the continuous zenithal brightness map across the whole region,
+whilst at the same time avoiding unnecessary and redundant oversampling. This
+paper attempts to provide some tentative answers to this issue, using two
+complementary tools: the luminance structure function and the Nyquist-Shannon
+spatial sampling theorem. The anaysis of several regions of the world, based on
+the data from the New world atlas of artificial night sky brightness (Falchi et
+al 2016, Sci. Adv. 2, e1600377) suggests that, as a rule of thumb, about one
+measurement per square kilometer could be sufficient for determining the
+zenithal night sky brightness of artificial origin at any point in a region to
+within 0.1 magV/arcsec2 (in the root-mean-square sense) of its true value in
+the Johnson-Cousins V band. The exact reconstruction of the zenithal night sky
+brightness maps from samples taken at the Nyquist rate seems to be considerably
+more demanding.
+"
+27259,A causation coefficient and taxonomy of correlation/causation relationships,"  This paper introduces a causation coefficient which is defined in terms of
+probabilistic causal models. This coefficient is suggested as the natural
+causal analogue of the Pearson correlation coefficient and permits comparing
+causation and correlation to each other in a simple, yet rigorous manner.
+Together, these coefficients provide a natural way to classify the possible
+correlation/causation relationships that can occur in practice and examples of
+each relationship are provided. In addition, the typical relationship between
+correlation and causation is analyzed to provide insight into why correlation
+and causation are often conflated. Finally, example calculations of the
+causation coefficient are shown on a real data set.
+"
+27260,Generalised contact geometry as reduced generalised complex geometry,"  Generalised contact structures are studied from the point of view of reduced
+generalised complex structures, naturally incorporating non-coorientable
+structures as non-trivial fibering. The infinitesimal symmetries are described
+in detail, with a geometric description given in terms of gerbes. As an
+application of the reduction procedure, generalised coKähler structures are
+defined in a way which extends the Kähler/coKähler correspondence.
+"
+27261,Reduction of a pair of skew-symmetric matrices to its canonical form under congruence,"  Let $(A,B)$ be a pair of skew-symmetric matrices over a field of
+characteristic not 2. Its regularization decomposition is a direct sum \[
+(\underline{\underline A},\underline{\underline B})\oplus
+(A_1,B_1)\oplus\dots\oplus(A_t,B_t) \] that is congruent to $(A,B)$, in which
+$(\underline{\underline A},\underline{\underline B})$ is a pair of nonsingular
+matrices and $(A_1,B_1),$ $\dots,$ $(A_t,B_t)$ are singular indecomposable
+canonical pairs of skew-symmetric matrices under congruence. We give an
+algorithm that constructs a regularization decomposition. We also give a
+constructive proof of the known canonical form of $(A,B)$ under congruence over
+an algebraically closed field of characteristic not 2.
+"
+27262,Finite time singularity formation for moving interface Euler equations,"  This paper proposes a new general methodology for finite-time singularity
+formation for moving interface problems involving the incompressible Euler
+equations in the plane.
+The first problem considered is the two-phase Euler vortex sheets problem
+with surface tension for which is proved the finite time singularity of the
+natural norm of the problem for suitable initial data. This is in striking
+contrast with the case of finite time splash and splat singularity formation
+for the one phase Euler equations introduced in [4] and studied in a more
+general context in [8], for which the natural norm (in the one phase fluid)
+stays finite all the way until contact.
+The second problem considered involves the presence of a heavier rigid body
+moving in the perfect fluid. We first establish that the rigid body will hit
+the bottom of the fluid domain in finite time (in a more general context and
+very different methodology than first done for this problem in [19]). We next
+establish that a surface energy blows up, and characterize the acceleration of
+the rigid body at contact, depending on the nature of the contact zone:
+Acceleration opposes the motion at contact, and is either positive finite if
+the contact zone contains a curve, or infinite otherwise.
+"
+27263,On the Pollicott-Ruelle resonances,"  The purpose of this survey is to present the recent advances about the
+Pollicott-Ruelle resonances.
+"
+27264,Ramification filtration of the Galois group of a local field via deformations,"  Let $\mathcal K$ be a field of formal Laurent series with coefficients in a
+finite field of characteristic $p$, $\mathcal G_{<p}$ --- the maximal quotient
+of $\operatorname{Gal} (\mathcal K_{sep}/\mathcal K)$ of period $p$ and
+nilpotent class $<p$ and $\{\mathcal G_{<p}^{(v)}\}_{v\geqslant 0}$ --- its
+filtration by ramification subgroups in the upper numbering. Let $\mathcal
+G_{<p}=G(\mathcal L)$ be the identification of nilpotent Artin-Schreier theory:
+here $G(\mathcal L)$ is the group obtained from a suitable profinite Lie
+$\mathbb{F} _p$-algebra $\mathcal L$ via the Campbell-Hausdorff composition
+law. We develop a new technique to characterize the ideals $\mathcal L^{(v)}$
+such that $G(\mathcal L^{(v)})=\mathcal G_{<p}^{(v)}$ and to find their
+generators. Given $v_0>0$ we construct epimorphism of Lie algebras $\bar\eta
+^{†}:\mathcal L\longrightarrow \bar{\mathcal L}^{†}$ and the action
+$\Omega _U$ of the formal group $\alpha _p=\operatorname{Spec}\,\mathbb{F}
+_p[U]$, $U^p=0$, of order $p$ on $\bar{\mathcal L}^{†}$. Suppose $d\Omega
+_U=B^{†}U$, where $B^{†}\in\operatorname{Diff}\bar{\mathcal L}^{†
+}$, and $\bar{\mathcal L}^{†}[v_0]$ is the ideal of $\bar{\mathcal L}^{†
+}$ generated by the elements of $B^{†}(\bar{\mathcal L}^{†})$. The main
+result of the paper states that $\mathcal L^{(v_0)}=(\bar\eta ^{†
+})^{-1}\bar{\mathcal L}^{†}[v_0]$. In the last sections we relate this
+result to the explicit construction of generators of $\mathcal L^{(v_0)}$
+obtained earlier by the author and develop its more efficient version.
+"
+27265,Universal Quantum Computation with Gapped Boundaries,"  This Letter discusses topological quantum computation with gapped boundaries
+of two-dimensional topological phases. Systematic methods are presented to
+encode quantum information topologically using gapped boundaries, and to
+perform topologically protected operations on this encoding. In particular, we
+introduce a new and general computational primitive of topological charge
+measurement and present a symmetry-protected implementation of this primitive.
+Throughout the Letter, a concrete physical example, the $\mathbb{Z}_3$ toric
+code ($\mathfrak{D}(\mathbb{Z}_3)$), is discussed. For this example, we have a
+qutrit encoding and an abstract universal gate set. Physically, gapped
+boundaries of $\mathfrak{D}(\mathbb{Z}_3)$ can be realized in bilayer
+fractional quantum Hall $1/3$ systems. If a practical implementation is found
+for the required topological charge measurement, these boundaries will give
+rise to a direct physical realization of a universal quantum computer based on
+a purely abelian topological phase.
+"
+27266,Resilient Distributed Estimation Through Adversary Detection,"  This paper studies resilient multi-agent distributed estimation of an unknown
+vector parameter when a subset of the agents is adversarial. We present and
+analyze a Flag Raising Distributed Estimator ($\mathcal{FRDE}$) that allows the
+agents under attack to perform accurate parameter estimation and detect the
+adversarial agents. The $\mathcal{FRDE}$ algorithm is a consensus+innovations
+estimator in which agents combine estimates of neighboring agents (consensus)
+with local sensing information (innovations). We establish that, under
+$\mathcal{FRDE}$, either the uncompromised agents' estimates are almost surely
+consistent or the uncompromised agents detect compromised agents if and only if
+the network of uncompromised agents is connected and globally observable.
+Numerical examples illustrate the performance of $\mathcal{FRDE}$.
+"
+27267,Memory Augmented Neural Networks with Wormhole Connections,"  Recent empirical results on long-term dependency tasks have shown that neural
+networks augmented with an external memory can learn the long-term dependency
+tasks more easily and achieve better generalization than vanilla recurrent
+neural networks (RNN). We suggest that memory augmented neural networks can
+reduce the effects of vanishing gradients by creating shortcut (or wormhole)
+connections. Based on this observation, we propose a novel memory augmented
+neural network model called TARDIS (Temporal Automatic Relation Discovery in
+Sequences). The controller of TARDIS can store a selective set of embeddings of
+its own previous hidden states into an external memory and revisit them as and
+when needed. For TARDIS, memory acts as a storage for wormhole connections to
+the past to propagate the gradients more effectively and it helps to learn the
+temporal dependencies. The memory structure of TARDIS has similarities to both
+Neural Turing Machines (NTM) and Dynamic Neural Turing Machines (D-NTM), but
+both read and write operations of TARDIS are simpler and more efficient. We use
+discrete addressing for read/write operations which helps to substantially to
+reduce the vanishing gradient problem with very long sequences. Read and write
+operations in TARDIS are tied with a heuristic once the memory becomes full,
+and this makes the learning problem simpler when compared to NTM or D-NTM type
+of architectures. We provide a detailed analysis on the gradient propagation in
+general for MANNs. We evaluate our models on different long-term dependency
+tasks and report competitive results in all of them.
+"
+27268,Depth Structure Preserving Scene Image Generation,"  Key to automatically generate natural scene images is to properly arrange
+among various spatial elements, especially in the depth direction. To this end,
+we introduce a novel depth structure preserving scene image generation network
+(DSP-GAN), which favors a hierarchical and heterogeneous architecture, for the
+purpose of depth structure preserving scene generation. The main trunk of the
+proposed infrastructure is built on a Hawkes point process that models the
+spatial dependency between different depth layers. Within each layer generative
+adversarial sub-networks are trained collaboratively to generate realistic
+scene components, conditioned on the layer information produced by the point
+process. We experiment our model on a sub-set of SUNdataset with annotated
+scene images and demonstrate that our models are capable of generating
+depth-realistic natural scene image.
+"
+27269,X-Ray morphological analysis of the Planck ESZ clusters,"  X-ray observations show that galaxy clusters have a very large range of
+morphologies. The most disturbed systems which are good to study how clusters
+form and grow and to test physical models, may potentially complicate
+cosmological studies because the cluster mass determination becomes more
+challenging. Thus, we need to understand the cluster properties of our samples
+to reduce possible biases. This is complicated by the fact that different
+experiments may detect different cluster populations. For example, SZ selected
+cluster samples have been found to include a greater fraction of disturbed
+systems than X-ray selected samples. In this paper we determined eight
+morphological parameters for the Planck Early Sunyaev-Zeldovich (ESZ) objects
+observed with XMM-Newton. We found that two parameters, concentration and
+centroid-shift, are the best to distinguish between relaxed and disturbed
+systems. For each parameter we provide the values that allow one to select the
+most relaxed or most disturbed objects from a sample. We found that there is no
+mass dependence on the cluster dynamical state. By comparing our results with
+what was obtained with REXCESS clusters, we also confirm that indeed the ESZ
+clusters tend to be more disturbed, as found by previous studies.
+"
+27270,Mixin Composition Synthesis based on Intersection Types,"  We present a method for synthesizing compositions of mixins using type
+inhabitation in intersection types. First, recursively defined classes and
+mixins, which are functions over classes, are expressed as terms in a lambda
+calculus with records. Intersection types with records and record-merge are
+used to assign meaningful types to these terms without resorting to recursive
+types. Second, typed terms are translated to a repository of typed combinators.
+We show a relation between record types with record-merge and intersection
+types with constructors. This relation is used to prove soundness and partial
+completeness of the translation with respect to mixin composition synthesis.
+Furthermore, we demonstrate how a translated repository and goal type can be
+used as input to an existing framework for composition synthesis in bounded
+combinatory logic via type inhabitation. The computed result is a class typed
+by the goal type and generated by a mixin composition applied to an existing
+class.
+"
+27271,A Novel Millimeter-Wave Channel Simulator and Applications for 5G Wireless Communications,"  This paper presents details and applications of a novel channel simulation
+software named NYUSIM, which can be used to generate realistic temporal and
+spatial channel responses to support realistic physical- and link-layer
+simulations and design for fifth-generation (5G) cellular communications.
+NYUSIM is built upon the statistical spatial channel model for broadband
+millimeter-wave (mmWave) wireless communication systems developed by
+researchers at New York University (NYU). The simulator is applicable for a
+wide range of carrier frequencies (500 MHz to 100 GHz), radio frequency (RF)
+bandwidths (0 to 800 MHz), antenna beamwidths (7 to 360 degrees for azimuth and
+7 to 45 degrees for elevation), and operating scenarios (urban microcell, urban
+macrocell, and rural macrocell), and also incorporates multiple-input
+multiple-output (MIMO) antenna arrays at the transmitter and receiver. This
+paper also provides examples to demonstrate how to use NYUSIM for analyzing
+MIMO channel conditions and spectral efficiencies, which show that NYUSIM is an
+alternative and more realistic channel model compared to the 3rd Generation
+Partnership Project (3GPP) and other channel models for mmWave bands.
+"
+27272,"Communications in Choreographies, Revisited","  Choreographic Programming is a paradigm for developing
+correct-by-construction concurrent programs, by writing high-level descriptions
+of the desired communications and then synthesising process implementations
+automatically. So far, choreographic programming has been explored in the
+monadic setting: interaction terms express point-to-point communications of a
+single value. However, real-world systems often rely on interactions of
+polyadic nature, where multiple values are communicated among two or more
+parties, like multicast, scatter-gather, and atomic exchanges. We introduce a
+new model for choreographic programming equipped with a primitive for grouped
+interactions that subsumes all the above scenarios. Intuitively, grouped
+interactions can be thought of as being carried out as one single interaction.
+In practice, they are implemented by processes that carry them out in a
+concurrent fashion. After formalising the intuitive semantics of grouped
+interactions, we prove that choreographic programs and their implementations
+are correct and deadlock-free by construction.
+"
+27273,Lensing substructure quantification in RXJ1131-1231: A 2 keV lower bound on dark matter thermal relic mass,"  We study the substructure content of the strong gravitational lens
+RXJ1131-1231 through a forward modelling approach that relies on generating an
+extensive suite of realistic simulations. We use a semi-analytic merger tree
+prescription that allows us to stochastically generate substructure populations
+whose properties depend on the dark matter particle mass. These synthetic halos
+are then used as lenses to produce realistic mock images that have the same
+features, e.g. luminous arcs, quasar positions, instrumental noise and PSF, as
+the data. We then analyse the data and the simulations in the same way with
+summary statistics that are sensitive to the signal being targeted and are able
+to constrain models of dark matter statistically using Approximate Bayesian
+Computing (ABC) techniques. In this work, we focus on the thermal relic mass
+estimate and fix the semi-analytic descriptions of the substructure evolution
+based on recent literature. We are able, based on the HST data for
+RXJ1131-1231, to rule out a warm dark matter thermal relic mass below 2 keV at
+the 2$\sigma$ confidence level.
+"
+27274,A Streaming Algorithm for Graph Clustering,"  We introduce a novel algorithm to perform graph clustering in the edge
+streaming setting. In this model, the graph is presented as a sequence of edges
+that can be processed strictly once. Our streaming algorithm has an extremely
+low memory footprint as it stores only three integers per node and does not
+keep any edge in memory. We provide a theoretical justification of the design
+of the algorithm based on the modularity function, which is a usual metric to
+evaluate the quality of a graph partition. We perform experiments on massive
+real-life graphs ranging from one million to more than one billion edges and we
+show that this new algorithm runs more than ten times faster than existing
+algorithms and leads to similar or better detection scores on the largest
+graphs.
+"
+27275,Raising Graphs From Randomness to Reveal Information Networks,"  We analyze the fine-grained connections between the average degree and the
+power-law degree distribution exponent in growing information networks. Our
+starting observation is a power-law degree distribution with a decreasing
+exponent and increasing average degree as a function of the network size. Our
+experiments are based on three Twitter at-mention networks and three more from
+the Koblenz Network Collection. We observe that popular network models cannot
+explain decreasing power-law degree distribution exponent and increasing
+average degree at the same time.
+We propose a model that is the combination of exponential growth, and a
+power-law developing network, in which new ""homophily"" edges are continuously
+added to nodes proportional to their current homophily degree. Parameters of
+the average degree growth and the power-law degree distribution exponent
+functions depend on the ratio of the network growth exponent parameters.
+Specifically, we connect the growth of the average degree to the decreasing
+exponent of the power-law degree distribution. Prior to our work, only one of
+the two cases were handled. Existing models and even their combinations can
+only reproduce some of our key new observations in growing information
+networks.
+"
+27276,Operating Gravitational Wave Detectors far from equilibrium,"  Tiny vibrations of mechanical structures are the main limiting cause in a
+number of high sensitivity measurement apparatus, chief among them the most
+sensitive displacement apparatus on earth: gravitational wave interferometers.
+Such devices are usually operated at equilibrium and small fluctuations are
+perceived as noise that sets a lower limit to the detection capabilities. An
+example is the so-called thermal noise, ubiquitous and unavoidable. In this
+letter we present an approach aimed at operating the interferometer out of
+equilibrium. We show that selective cooling of single modes of the mechanical
+structure is able to positively impact the measurement sensitivity, in selected
+frequency ranges. Experiments conducted on thin silica membranes show promising
+results for the implementation of such technique in next generation
+gravitational wave detectors.
+"
+27277,Binary sampling from discrete distributions,"  This paper considers direct sampling methods from discrete target
+distributions. The inverse transform sampling (ITS) method is one of the most
+popular direct sampling methods. The main purpose of this paper is to propose a
+direct sampling algorithm that supersedes the binary-search ITS method (which
+is an improvement of the ITS method with binary search). The proposed algorithm
+is based on binarizing the support set of the target distribution. Thus, the
+proposed algorithm is referred to as binary sampling (BS). The BS algorithm
+consists of two procedures: backward binary sampling (BBS) and forward binary
+sampling (FBS). The BBS procedure draws a single sample (the first sample) from
+the target distribution while constructing a one-way random walk on a binary
+tree for the FBS procedure. By running the random walk, the FBS procedure
+generates the second and subsequent samples. The BBS and FBS procedures have
+$O(N)$ and $O(\ln N)$ time complexities, respectively, and they also have
+$O(N)$ space complexity, where $N+1$ is the cardinality of the support set of
+the target distribution. Therefore, the time and space complexities of the BS
+algorithm are equivalent to those of the standard (possibly best) binary-search
+ITS algorithm. However, the BS algorithm has two advantages over the standard
+binary-search ITS algorithm. First, the BBS procedure is parallelizable and
+thus the total running time of the BS algorithm can be reduced. Second, the BS
+algorithm is more accurate in terms of relative rounding error that influences
+generated samples.
+"
+27278,Virtual Adversarial Ladder Networks For Semi-supervised Learning,"  Semi-supervised learning (SSL) partially circumvents the high cost of
+labeling data by augmenting a small labeled dataset with a large and relatively
+cheap unlabeled dataset drawn from the same distribution. This paper offers a
+novel interpretation of two deep learning-based SSL approaches, ladder networks
+and virtual adversarial training (VAT), as applying distributional smoothing to
+their respective latent spaces. We propose a class of models that fuse these
+approaches. We achieve near-supervised accuracy with high consistency on the
+MNIST dataset using just 5 labels per class: our best model, ladder with
+layer-wise virtual adversarial noise (LVAN-LW), achieves 1.42% +/- 0.12 average
+error rate on the MNIST test set, in comparison with 1.62% +/- 0.65 reported
+for the ladder network. On adversarial examples generated with L2-normalized
+fast gradient method, LVAN-LW trained with 5 examples per class achieves
+average error rate 2.4% +/- 0.3 compared to 68.6% +/- 6.5 for the ladder
+network and 9.9% +/- 7.5 for VAT.
+"
+27279,The Caffarelli-Kohn-Nirenberg Inequalities on Metric Measure Spaces,"  In this paper, we prove that if a metric measure space satisfies the volume
+doubling condition and the Caffarelli-Kohn-Nirenberg inequality with same
+exponent n(n>1), then it has exactly n-dimensional volume growth. As
+application, we obtain geometric and topological properties of Alexandrov
+space, Riemannian manifold and Finsler space which support a
+Caffarelli-Kohn-Nirenberg inequality.
+"
+27280,Resolving the effects of nanoscale membrane curvature on lipid mobility,"  The biophysical consequences of nanoscale curvature have been challenging to
+resolve due to size-dependent membrane behavior and the experimental resolution
+limits imposed by optical diffraction. Recent advances in nanoengineering and
+super-resolution techniques have enabled new capabilities for creating and
+observing curvature. In particular, draping supported lipid bilayers over
+lithographically patterned substrates provides a model system for endocytic
+pits. The experiments and simulations presented below describe the possible
+detection of membrane curvature through fluorescence recovery after
+photobleaching (FRAP), fluorescence correlation spectroscopy (FCS), single
+particle tracking (SPT), and polarized localization microscopy (PLM). FRAP and
+FCS depend on diffraction-limited illumination and detection. In particular, a
+simulation of FRAP shows no effects on lipids diffusion due to a 50 nm diameter
+membrane bud at any stage in the budding process. Simulated FCS demonstrated
+small effects due to a 50 nm radius membrane bud that was amplified with
+curvature-dependent lipid mobility changes. However, PLM and SPT achieve
+sub-diffraction-limited resolution of membrane budding and lipid mobility
+through the identification of the single-lipid positions with <15 nm spatial
+and <20 ms temporal resolution. By mapping the single-lipid step lengths to
+locations on the membrane, the effects of curvature on lipid behavior have been
+resolved.
+"
+27281,Large time behavior of solutions of the heat equation with inverse square potential,"  Let $L:=-\Delta+V$ be a nonnegative Schrödinger operator on $L^2({\bf
+R}^N)$, where $N\ge 2$ and $V$ is a radially symmetric inverse square
+potential. In this paper we assume either $L$ is subcritical or null-critical
+and we establish a method for obtaining the precise description of the large
+time behavior of $e^{-tL}\varphi$, where $\varphi\in L^2({\bf
+R}^N,e^{|x|^2/4}\,dx)$.
+"
+27282,Physics of cosmological cascades and observable properties,"  TeV photons from extragalactic sources are absorbed in the intergalactic
+medium and initiate electromagnetic cascades. These cascades offer a unique
+tool to probe the properties of the universe at cosmological scales. We present
+a new Monte Carlo code dedicated to the physics of such cascades. This code has
+been tested against both published results and analytical approximations, and
+is made publicly available. Using this numerical tool, we investigate the main
+cascade properties (spectrum, halo extension, time delays), and study in detail
+their dependence on the physical parameters (extra-galactic magnetic field,
+extra-galactic background light, source redshift, source spectrum and beaming
+emission). The limitations of analytical solutions are emphasised. In
+particular, analytical approximations account only for the first generation of
+photons and higher branches of the cascade tree are neglected.
+"
+27283,Finite schemes and secant varieties over arbitrary characteristic,"  We present scheme theoretic methods that apply to the study of secant
+varieties. This mainly concerns finite schemes and their smoothability. The
+theory generalises to the base fields of any characteristic, and even to
+non-algebraically closed fields. In particular, the smoothability of finite
+schemes does not depend on the embedding into a smooth variety or on base field
+extensions. Independent of the base field, secant varieties to high degree
+Veronese reembeddings behave well with respect to the intersection and they are
+defined by minors of catalecticants whenever a suitable smoothability condition
+for Gorenstein subschemes holds. The content of the article is largely
+expository, although many results are presented in a stronger form than in the
+literature.
+"
+27284,Structure and energetics of embedded Si patterns in graphene,"  Recent experiments have revealed the possibility of precise electron beam
+manipulation of silicon impurities in graphene. Motivated by these findings and
+studies on metal surface quantum corrals, the question arises what kind of
+embedded Si structures are possible within the hexagonal lattice, and how these
+are limited by the distortion caused by the preference of Si for $sp^{3}$
+hybridization. In this work, we study the geometry and stability of elementary
+Si patterns in graphene, including lines, hexagons, triangles, circles and
+squares. Due to the size of the required unit cells, to obtain the relaxed
+geometries we use an empirical bond-order potential as a starting point for
+density functional theory. Despite some interesting discrepancies, the
+classical geometries provide an effective route for the simulation of large
+structures.
+"
+27285,On Hermitian LCD codes from cyclic codes and their applications to orthogonal direct sum masking,"  Cyclic codes are an interesting type of linear codes and have wide
+applications in communication and storage systems due to their efficient
+encoding and decoding algorithms. It was proved that asymptotically good
+Hermitian LCD codes exist. The objective of this paper is to construct some
+cyclic Hermitian LCD codes over finite fields and analyse their parameters. The
+dimensions of these codes are settled and the lower bounds on their minimum
+distances are presented. Most Hermitian LCD codes presented in this paper are
+not BCH codes. In addition, we employ Hermitian LCD codes to propose a
+Hermitian orthogonal direct sum masking scheme that achieves protection against
+fault injection attacks. It is shown that the codes with great minimum
+distances are desired to improve the resistance.
+"
+27286,Open-Category Classification by Adversarial Sample Generation,"  In real-world classification tasks, it is difficult to collect training
+samples from all possible categories of the environment. Therefore, when an
+instance of an unseen class appears in the prediction stage, a robust
+classifier should be able to tell that it is from an unseen class, instead of
+classifying it to be any known category. In this paper, adopting the idea of
+adversarial learning, we propose the ASG framework for open-category
+classification. ASG generates positive and negative samples of seen categories
+in the unsupervised manner via an adversarial learning strategy. With the
+generated samples, ASG then learns to tell seen from unseen in the supervised
+manner. Experiments performed on several datasets show the effectiveness of
+ASG.
+"
+27287,Nonvolatile Solid-State Charged-Polymer Gating of Topological Insulators into the Topological Insulating Regime,"  We demonstrate the ability to reduce the carrier concentration of thin films
+of the topological insulator (TI) Bi2Se3 by utilizing a novel approach, namely
+non-volatile electrostatic gating via corona charging of electret polymers.
+Sufficient electric field can be imparted to a polymer-TI bilayer to result in
+significant electron density depletion, even without the continuous connection
+of a gate electrode or the chemical modification of the TI. We show that the
+Fermi level of Bi2Se3 is shifted towards the Dirac point with this method.
+Using THz spectroscopy, we find that the surface chemical potential is lowered
+into the bulk band gap (~ 50 meV above the Dirac point and 170 meV below the
+conduction band minimum) and it is stabilized in the intrinsic regime while
+enhancing electron mobility. This represents the first use of a charged polymer
+gate for modulating TI charge density. The mobility of surface state electrons
+is enhanced to a value as high as ~1600 cm^2/Vs at 5K.
+"
+27288,Tribopolymer Formation Mechanism on the RuO$_2$(110) Surface,"  Tribopolymer formed on the contacts of microelectromechanical and
+nanoelectromechanical system (MEMS/NEMS) devices is a major concern hampering
+their practical use in information technology. Conductive metal oxides, such as
+RuO$_2$ and ReO$_3$, have been regarded as promising candidate materials for
+MEMS/NEMS contacts due to their conductivity, hardness, and relatively
+chemically inert surfaces. However, recent experimental works demonstrate that
+trace amounts of polymer could still form on RuO$_2$ surfaces. In this study,
+we demonstrate the mechanism of this class of unexpected tribopolymer formation
+by conducting density functional theory based computational compression
+experiments with benzene as the contamination gas. First, mechanical force
+during compression changes the benzene molecules from slightly physisorbed to
+strongly chemisorbed. Further compression causes deformation and chemical
+linkage of the benzene molecules. Finally, the two contacts detach, with one
+having a complex organic molecule attached and the other with a more reactive
+surface. The complex organic molecule, which has an oxabicyclic segment, can be
+viewed as the rudiment of tribopolymer, and the more reactive surface can
+trigger the next adsorption--reaction--tribopolymer formation cycle. Based on
+these results, we also predict tribopolymer formation rates by using
+transition--state theory and the second--order rate law. This study deepens our
+understanding of tribopolymer formation (especially on metal oxides) and
+provides strategies for suppressing tribopolymerization.
+"
+27289,Learning Partially Structured Environmental Dynamics for Marine Robotic Navigation,"  We investigate the scenario that a robot needs to reach a designated goal
+after taking a sequence of appropriate actions in a non-static environment that
+is partially structured. One application example is to control a marine vehicle
+to move in the ocean. The ocean environment is dynamic and oftentimes the ocean
+waves result in strong disturbances that can disturb the vehicle's motion.
+Modeling such dynamic environment is non-trivial, and integrating such model in
+the robotic motion control is particularly difficult. Fortunately, the ocean
+currents usually form some local patterns (e.g. vortex) and thus the
+environment is partially structured. The historically observed data can be used
+to train the robot to learn to interact with the ocean tidal disturbances. In
+this paper we propose a method that applies the deep reinforcement learning
+framework to learn such partially structured complex disturbances. Our results
+show that, by training the robot under artificial and real ocean disturbances,
+the robot is able to successfully act in complex and spatiotemporal
+environments.
+"
+27290,Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix,"  Recent developments in the field of deep learning have motivated many
+researchers to apply these methods to problems in quantum information. Torlai
+and Melko first proposed a decoder for surface codes based on neural networks.
+Since then, many other researchers have applied neural networks to study a
+variety of problems in the context of decoding. An important development in
+this regard was due to Varsamopoulos et al. who proposed a two-step decoder
+using neural networks. Subsequent work of Maskara et al. used the same concept
+for decoding for various noise models. We propose a similar two-step neural
+decoder using inverse parity-check matrix for topological color codes. We show
+that it outperforms the state-of-the-art performance of non-neural decoders for
+independent Pauli errors noise model on a 2D hexagonal color code. Our final
+decoder is independent of the noise model and achieves a threshold of $10 \%$.
+Our result is comparable to the recent work on neural decoder for quantum error
+correction by Maskara et al.. It appears that our decoder has significant
+advantages with respect to training cost and complexity of the network for
+higher lengths when compared to that of Maskara et al.. Our proposed method can
+also be extended to arbitrary dimension and other stabilizer codes.
+"
+27291,Volatility swaps valuation under stochastic volatility with jumps and stochastic intensity,"  In this paper, a pricing formula for volatility swaps is delivered when the
+underlying asset follows the stochastic volatility model with jumps and
+stochastic intensity. By using Feynman-Kac theorem, a partial integral
+differential equation is obtained to derive the joint moment generating
+function of the previous model.
+Moreover, discrete and continuous sampled volatility swap pricing formulas
+are given by employing transform techniques and the relationship between two
+pricing formulas is discussed. Finally, some numerical simulations are reported
+to support the results presented in this paper.
+"
+27292,MREAK : Morphological Retina Keypoint Descriptor,"  A variety of computer vision applications depend on the efficiency of image
+matching algorithms used. Various descriptors are designed to detect and match
+features in images. Deployment of this algorithms in mobile applications
+creates a need for low computation time. Binary descriptors requires less
+computation time than float-point based descriptors because of the intensity
+comparison between pairs of sample points and comparing after creating a binary
+string. In order to decrease time complexity, quality of keypoints matched is
+often compromised. We propose a keypoint descriptor named Morphological Retina
+Keypoint Descriptor (MREAK) inspired by the function of human pupil which
+dilates and constricts responding to the amount of light. By using
+morphological operators of opening and closing and modifying the retinal
+sampling pattern accordingly, an increase in the number of accurately matched
+keypoints is observed. Our results show that matched keypoints are more
+efficient than FREAK descriptor and requires low computation time than various
+descriptors like SIFT, BRISK and SURF.
+"
+27293,Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning,"  Learning policies for complex tasks that require multiple different skills is
+a major challenge in reinforcement learning (RL). It is also a requirement for
+its deployment in real-world scenarios. This paper proposes a novel framework
+for efficient multi-task reinforcement learning. Our framework trains agents to
+employ hierarchical policies that decide when to use a previously learned
+policy and when to learn a new skill. This enables agents to continually
+acquire new skills during different stages of training. Each learned task
+corresponds to a human language description. Because agents can only access
+previously learned skills through these descriptions, the agent can always
+provide a human-interpretable description of its choices. In order to help the
+agent learn the complex temporal dependencies necessary for the hierarchical
+policy, we provide it with a stochastic temporal grammar that modulates when to
+rely on previously learned skills and when to execute new skills. We validate
+our approach on Minecraft games designed to explicitly test the ability to
+reuse previously learned skills while simultaneously learning new skills.
+"
+27294,Continuity of spectral radius and type I $C^*$-algebras,"  It is shown that the spectral radius is continuous on a $C^*$-algebra if and
+only if the $C^*$-algebra is type I. This answers a question of V. Shulman and
+Yu.~Turovskii [10]. It is shown also that the closure of nilpotents in a
+$C^*$-algebra contains an element with non-zero spectrum if and only if the
+$C^*$-algebra is not type I.
+"
+27295,Extending Defensive Distillation,"  Machine learning is vulnerable to adversarial examples: inputs carefully
+modified to force misclassification. Designing defenses against such inputs
+remains largely an open problem. In this work, we revisit defensive
+distillation---which is one of the mechanisms proposed to mitigate adversarial
+examples---to address its limitations. We view our results not only as an
+effective way of addressing some of the recently discovered attacks but also as
+reinforcing the importance of improved training techniques.
+"
+27296,Constructing Permutation Rational Functions From Isogenies,"  A permutation rational function $f\in \mathbb{F}_q(x)$ is a rational function
+that induces a bijection on $\mathbb{F}_q$, that is, for all $y\in\mathbb{F}_q$
+there exists exactly one $x\in\mathbb{F}_q$ such that $f(x)=y$. Permutation
+rational functions are intimately related to exceptional rational functions,
+and more generally exceptional covers of the projective line, of which they
+form the first important example.
+In this paper, we show how to efficiently generate many permutation rational
+functions over large finite fields using isogenies of elliptic curves, and
+discuss some cryptographic applications. Our algorithm is based on Fried's
+modular interpretation of certain dihedral exceptional covers of the projective
+line (Cont. Math., 1994).
+"
+27297,Nuclear Discrepancy for Active Learning,"  Active learning algorithms propose which unlabeled objects should be queried
+for their labels to improve a predictive model the most. We study active
+learners that minimize generalization bounds and uncover relationships between
+these bounds that lead to an improved approach to active learning. In
+particular we show the relation between the bound of the state-of-the-art
+Maximum Mean Discrepancy (MMD) active learner, the bound of the Discrepancy,
+and a new and looser bound that we refer to as the Nuclear Discrepancy bound.
+We motivate this bound by a probabilistic argument: we show it considers
+situations which are more likely to occur. Our experiments indicate that active
+learning using the tightest Discrepancy bound performs the worst in terms of
+the squared loss. Overall, our proposed loosest Nuclear Discrepancy
+generalization bound performs the best. We confirm our probabilistic argument
+empirically: the other bounds focus on more pessimistic scenarios that are
+rarer in practice. We conclude that tightness of bounds is not always of main
+importance and that active learning methods should concentrate on realistic
+scenarios in order to improve performance.
+"
+27298,Integrable crystals and restriction to Levi via generalized slices in the affine Grassmannian,"  Let $G$ be a connected reductive algebraic group over $\mathbb{C}$. Let
+$\Lambda^{+}_{G}$ be the monoid of dominant weights of $G$. We construct the
+integrable crystals $\mathbf{B}^{G}(\lambda),\ \lambda\in\Lambda^{+}_{G}$,
+using the geometry of generalized transversal slices in the affine Grassmannian
+of the Langlands dual group. We construct the tensor product maps
+$\mathbf{p}_{\lambda_{1},\lambda_{2}}\colon \mathbf{B}^{G}(\lambda_{1}) \otimes
+\mathbf{B}^{G}(\lambda_{2}) \rightarrow
+\mathbf{B}^{G}(\lambda_{1}+\lambda_{2})\cup\{0\}$ in terms of multiplication of
+generalized transversal slices. Let $L \subset G$ be a Levi subgroup of $G$. We
+describe the restriction to Levi
+$\operatorname{Res}^G_L\colon\operatorname{Rep}(G)\rightarrow\operatorname{Rep}(L)$
+in terms of the hyperbolic localization functors for the generalized
+transversal slices.
+"
+27299,Nonlinear Properties of Gated Graphene in a Strong Electromagnetic Field,"  We develop a microscopic theory of a strong electromagnetic field interaction
+with gated bilayer graphene. Quantum kinetic equations for density matrix are
+obtained using a tight binding approach within second quantized Hamiltonian in
+an intense laser field. We show that adiabatically changing the gate potentials
+with time may produce (at resonant photon energy) a full inversion of the
+electron population with high density between valence and conduction bands. In
+the linear regime, excitonic absorption of an electromagnetic radiation in a
+graphene monolayer with opened energy gap is also studied.
+"
+27300,Learning Language Representations for Typology Prediction,"  One central mystery of neural NLP is what neural models ""know"" about their
+subject matter. When a neural machine translation system learns to translate
+from one language to another, does it learn the syntax or semantics of the
+languages? Can this knowledge be extracted from the system to fill holes in
+human scientific knowledge? Existing typological databases contain relatively
+full feature specifications for only a few hundred languages. Exploiting the
+existence of parallel texts in more than a thousand languages, we build a
+massive many-to-one neural machine translation (NMT) system from 1017 languages
+into English, and use this to predict information missing from typological
+databases. Experiments show that the proposed method is able to infer not only
+syntactic, but also phonological and phonetic inventory features, and improves
+over a baseline that has access to information about the languages' geographic
+and phylogenetic neighbors.
+"
+27301,Faster Algorithms for Growing Prioritized Disks and Rectangles,"  Motivated by map labeling, we study the problem in which we are given a
+collection of $n$ disks $D_1, \dots, D_n$ in the plane that grow at possibly
+different speeds. Whenever two disks meet, the one with the lower index
+disappears. This problem was introduced by Funke, Krumpe, and Storandt [IWOCA
+2016]. We provide the first general subquadratic algorithm for computing the
+times and the order of disappearance. This algorithm also works for other
+shapes (such as rectangles) and in any fixed dimension.
+Using quadtrees, we provide an alternative algorithm that runs in near linear
+time, although this second algorithm has a logarithmic dependence on either the
+ratio of the fastest speed to the slowest speed of disks or the spread of disk
+centers (the ratio of the maximum to the minimum distance between them). Our
+result improves the running times of previous algorithms by Funke, Krumpe, and
+Storandt [IWOCA 2016], Bahrdt et al. [ALENEX 2017] and Funke and Storandt [EWCG
+2017]. Finally, we give an $\Omega(n\log n)$ lower bound on the problem,
+showing that our quadtree algorithms are almost tight.
+"
+27302,Resolving the debate about proposed expressions for the classical entropy,"  Despite well over a century of effort, the proper expression for the
+classical entropy in statistical mechanics remains a subject of debate. The
+Boltzmann entropy (calculated from a surface in phase space) has been
+criticized as not being an adiabatic invariant. It has been suggested that the
+Gibbs entropy (volume in phase space) is correct, which would forbid the
+concept of negative temperatures. An apparently innocuous assumption turns out
+to be responsible for much of the controversy, namely, that the energy $E$ and
+the number of particles $N$ are given exactly. The true distributions are known
+to be extremely narrow (of order $1/\sqrt{N}$), so that it is surprising that
+this is a problem. The canonical and grand canonical ensembles provide
+alternative expressions for the entropy that satisfy all requirements. The
+consequences are that negative temperatures are thermodynamically valid, the
+validity of the Gibbs entropy is limited to increasing densities of states, and
+the completely correct expression for the entropy is given by the grand
+canonical formulation. The Boltzmann entropy is shown to provide an excellent
+approximation in almost all cases.
+"
+27303,Fusing Multifaceted Transaction Data for User Modeling and Demographic Prediction,"  Inferring user characteristics such as demographic attributes is of the
+utmost importance in many user-centric applications. Demographic data is an
+enabler of personalization, identity security, and other applications. Despite
+that, this data is sensitive and often hard to obtain. Previous work has shown
+that purchase history can be used for multi-task prediction of many demographic
+fields such as gender and marital status. Here we present an embedding based
+method to integrate multifaceted sequences of transaction data, together with
+auxiliary relational tables, for better user modeling and demographic
+prediction.
+"
+27304,Generalising Random Forest Parameter Optimisation to Include Stability and Cost,"  Random forests are among the most popular classification and regression
+methods used in industrial applications. To be effective, the parameters of
+random forests must be carefully tuned. This is usually done by choosing values
+that minimize the prediction error on a held out dataset. We argue that error
+reduction is only one of several metrics that must be considered when
+optimizing random forest parameters for commercial applications. We propose a
+novel metric that captures the stability of random forests predictions, which
+we argue is key for scenarios that require successive predictions. We motivate
+the need for multi-criteria optimization by showing that in practical
+applications, simply choosing the parameters that lead to the lowest error can
+introduce unnecessary costs and produce predictions that are not stable across
+independent runs. To optimize this multi-criteria trade-off, we present a new
+framework that efficiently finds a principled balance between these three
+considerations using Bayesian optimisation. The pitfalls of optimising forest
+parameters purely for error reduction are demonstrated using two publicly
+available real world datasets. We show that our framework leads to parameter
+settings that are markedly different from the values discovered by error
+reduction metrics.
+"
+27305,Échantillonnage de signaux sur graphes via des processus déterminantaux,"  We consider the problem of sampling k-bandlimited graph signals, ie, linear
+combinations of the first k graph Fourier modes. We know that a set of k nodes
+embedding all k-bandlimited signals always exists, thereby enabling their
+perfect reconstruction after sampling. Unfortunately, to exhibit such a set,
+one needs to partially diagonalize the graph Laplacian, which becomes
+prohibitive at large scale. We propose a novel strategy based on determinantal
+point processes that side-steps partial diagonalisation and enables
+reconstruction with only O(k) samples. While doing so, we exhibit a new general
+algorithm to sample determinantal process, faster than the state-of-the-art
+algorithm by an order k.
+"
+27306,Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement,"  We propose an algorithm to automate fault management in an outdoor cellular
+network using deep reinforcement learning (RL) against wireless impairments.
+This algorithm enables the cellular network cluster to self-heal by allowing RL
+to learn how to improve the downlink signal to interference plus noise ratio
+through exploration and exploitation of various alarm corrective actions. The
+main contributions of this paper are to 1) introduce a deep RL-based fault
+handling algorithm which self-organizing networks can implement in a polynomial
+runtime and 2) show that this fault management method can improve the radio
+link performance in a realistic network setup. Simulation results show that our
+proposed algorithm learns an action sequence to clear alarms and improve the
+performance in the cellular cluster better than existing algorithms, even
+against the randomness of the network fault occurrences and user movements.
+"
+27307,On the Application of Optimal Control Theory to Climate Engineering,"  The new method is considered for identifying the perfect (optimal) scenario
+of climate engineering operations based on the optimal control theory. The
+application of this approach is demonstrated using zero-dimensional
+energy-balance climate model. The global average surface temperature represents
+the state variable and the reflective power of aerosols injected into the
+stratosphere is designated as the control variable. Maximum principle is used
+to find the best possible control and the associated climate system trajectory
+for a specific objective function (performance measure). Discussed illustrative
+results were analytically and numerically calculated using the four greenhouse
+gas concentration scenarios based on the Representative Concentration Pathways.
+"
+27308,Steady Kähler-Ricci solitons on crepant resolutions of finite quotients of $\mathbb{C}^n$,"  We prove the existence of steady Kähler-Ricci solitons on equivariant
+crepant resolutions of $\mathbb{C}^n/G$, where $G$ is a finite subgroup of
+$SU(n)$.
+"
+27309,Special zeta values using tensor powers of Drinfeld modules,"  We study tensor powers of rank 1 sign-normalized Drinfeld A-modules, where A
+is the coordinate ring of an elliptic curve over a finite field. Using the
+theory of vector valued Anderson generating functions, we give formulas for the
+coefficients of the logarithm and exponential functions associated to these
+A-modules. We then show that there exists a vector whose bottom coordinate
+contains a Goss zeta value, whose evaluation under the exponential function is
+defined over the Hilbert class field. This allows us to prove the transcendence
+of Goss zeta values and periods of Drinfeld modules as well as the
+transcendence of certain ratios of those quantities.
+"
+27310,Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions,"  Deep neural networks are widely used for classification. These deep models
+often suffer from a lack of interpretability -- they are particularly difficult
+to understand because of their non-linear nature. As a result, neural networks
+are often treated as ""black box"" models, and in the past, have been trained
+purely to optimize the accuracy of predictions. In this work, we create a novel
+network architecture for deep learning that naturally explains its own
+reasoning for each prediction. This architecture contains an autoencoder and a
+special prototype layer, where each unit of that layer stores a weight vector
+that resembles an encoded training input. The encoder of the autoencoder allows
+us to do comparisons within the latent space, while the decoder allows us to
+visualize the learned prototypes. The training objective has four terms: an
+accuracy term, a term that encourages every prototype to be similar to at least
+one encoded input, a term that encourages every encoded input to be close to at
+least one prototype, and a term that encourages faithful reconstruction by the
+autoencoder. The distances computed in the prototype layer are used as part of
+the classification process. Since the prototypes are learned during training,
+the learned network naturally comes with explanations for each prediction, and
+the explanations are loyal to what the network actually computes.
+"
+27311,Constraining Delta-T from Babylonian Lunar Appulse and Occultation Observations,"  We examine in detail 15 Babylonian observations of lunar appulses and
+occultations made between 80 and 419 BC for the purpose of setting useful
+limits on Earth's clock error, as quantified by $\Delta$T, the difference
+between Terrestrial Time and Universal time. Our results are generally in
+agreement with reconstructions of $\Delta$T using untimed solar eclipse
+observations from the same period. We suggest a revised version of the simple
+quadratic fit to $\Delta$T in light of the new results.
+"
+27312,On the eccentricity of Proxima b,"  We apply Monte Carlo projection to the radial velocity data set that Anglada
+Escude et al. (2016) use for the discovery of Proxima b. They find an upper
+limit to the orbital eccentricity of 0.35. To investigate the eccentricity
+issue further, we calculate a suite of monovariate and bivariate densities o
+eccentricity. After discarding apparent artifacts at eccentricity = 0 and = 1,
+we find that eccentricity has a trimodal sampling distribution: three chimeras
+or types of orbit compatible with the RV data set. The three modes or peaks in
+the density of eccentricity are located at eccentricity = 0.25, 0.75, and 0.95,
+with relative weights 0.79, 0.10, and 0.11. Future RV observations will clarify
+which of the three chimeras represents the true eccentricity of Proxima b. The
+most-likely estimate is eccentricity = 0.25, and our lower limit is
+eccentricity = 0.025. Our strategic, long-term goal is to elevate the orbital
+analyses of exoplanets to meet the challenges of sometimes complex probability
+density distributions.
+"
+27313,DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification,"  This paper presents a novel deep learning based method for automatic malware
+signature generation and classification. The method uses a deep belief network
+(DBN), implemented with a deep stack of denoising autoencoders, generating an
+invariant compact representation of the malware behavior. While conventional
+signature and token based methods for malware detection do not detect a
+majority of new variants for existing malware, the results presented in this
+paper show that signatures generated by the DBN allow for an accurate
+classification of new malware variants. Using a dataset containing hundreds of
+variants for several major malware families, our method achieves 98.6%
+classification accuracy using the signatures generated by the DBN. The
+presented method is completely agnostic to the type of malware behavior that is
+logged (e.g., API calls and their parameters, registry entries, websites and
+ports accessed, etc.), and can use any raw input from a sandbox to successfully
+train the deep neural network which is used to generate malware signatures.
+"
+27314,A Lower Bound on the Probability of Error of Polar Codes over BMS Channels,"  Polar codes are a family of capacity-achieving codes that have explicit and
+low-complexity construction, encoding, and decoding algorithms. Decoding of
+polar codes is based on the successive-cancellation decoder, which decodes in a
+bit- wise manner. A decoding error occurs when at least one bit is erroneously
+decoded. The various codeword bits are correlated, yet performance analysis of
+polar codes ignores this dependence: the upper bound is based on the union
+bound, and the lower bound is based on the worst-performing bit. Improvement of
+the lower bound is afforded by considering error probabilities of two bits
+simultaneously. These are difficult to compute explicitly due to the large
+alphabet size inherent to polar codes. In this research we propose a method to
+lower-bound the error probabilities of bit pairs. We develop several
+transformations on pairs of synthetic channels that make the resultant
+synthetic channels amenable to alphabet reduction. Our method yields lower
+bounds that significantly improve upon currently known lower bounds for polar
+codes under successive-cancellation decoding.
+"
+27315,A two-parameter finite field Erdős-Falconer distance problem,"  We study the following two-parameter variant of the Erd\H os-Falconer
+distance problem. Given $E,F \subset {\Bbb F}_q^{k+l}$, $l \geq k \ge 2$, the
+$k+l$-dimensional vector space over the finite field with $q$ elements, let
+$B_{k,l}(E,F)$ be given by $$\{(\Vert x'-y'\Vert, \Vert x""-y"" \Vert): x=(x',x"")
+\in E, y=(y',y"") \in F; x',y' \in {\Bbb F}_q^k, x"",y"" \in {\Bbb F}_q^l \}.$$
+We prove that if $|E||F| \geq C q^{k+2l+1}$, then $B_{k,l}(E,F)={\Bbb F}_q
+\times {\Bbb F}_q$. Furthermore this result is sharp if $k$ is odd. For the
+case of $l=k=2$ and $q$ a prime with $q \equiv 3 \mod 4$ we get that for every
+positive $C$ there is $c$ such that $$ \text{if } |E||F|>C
+q^{6+\frac{2}{3}}\text{, then } |B_{2,2}(E,F)|> c q^{2}.$$
+"
+27316,Extragradient method with variance reduction for stochastic variational inequalities,"  We propose an extragradient method with stepsizes bounded away from zero for
+stochastic variational inequalities requiring only pseudo-monotonicity. We
+provide convergence and complexity analysis, allowing for an unbounded feasible
+set, unbounded operator, non-uniform variance of the oracle and, also, we do
+not require any regularization. Alongside the stochastic approximation
+procedure, we iteratively reduce the variance of the stochastic error. Our
+method attains the optimal oracle complexity $\mathcal{O}(1/\epsilon^2)$ (up to
+a logarithmic term) and a faster rate $\mathcal{O}(1/K)$ in terms of the mean
+(quadratic) natural residual and the D-gap function, where $K$ is the number of
+iterations required for a given tolerance $\epsilon>0$. Such convergence rate
+represents an acceleration with respect to the stochastic error. The generated
+sequence also enjoys a new feature: the sequence is bounded in $L^p$ if the
+stochastic error has finite $p$-moment. Explicit estimates for the convergence
+rate, the oracle complexity and the $p$-moments are given depending on problem
+parameters and distance of the initial iterate to the solution set. Moreover,
+sharper constants are possible if the variance is uniform over the solution set
+or the feasible set. Our results provide new classes of stochastic variational
+inequalities for which a convergence rate of $\mathcal{O}(1/K)$ holds in terms
+of the mean-squared distance to the solution set. Our analysis includes the
+distributed solution of pseudo-monotone Cartesian variational inequalities
+under partial coordination of parameters between users of a network.
+"
+27317,"Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video","  This work presents a new robust PCA method for foreground-background
+separation on freely moving camera video with possible dense and sparse
+corruptions. Our proposed method registers the frames of the corrupted video
+and then encodes the varying perspective arising from camera motion as missing
+data in a global model. This formulation allows our algorithm to produce a
+panoramic background component that automatically stitches together corrupted
+data from partially overlapping frames to reconstruct the full field of view.
+We model the registered video as the sum of a low-rank component that captures
+the background, a smooth component that captures the dynamic foreground of the
+scene, and a sparse component that isolates possible outliers and other sparse
+corruptions in the video. The low-rank portion of our model is based on a
+recent low-rank matrix estimator (OptShrink) that has been shown to yield
+superior low-rank subspace estimates in practice. To estimate the smooth
+foreground component of our model, we use a weighted total variation framework
+that enables our method to reliably decouple the true foreground of the video
+from sparse corruptions. We perform extensive numerical experiments on both
+static and moving camera video subject to a variety of dense and sparse
+corruptions. Our experiments demonstrate the state-of-the-art performance of
+our proposed method compared to existing methods both in terms of foreground
+and background estimation accuracy.
+"
+27318,Vortex-vortex control in exciton-polariton condensates,"  Vortices are widely studied in fields ranging from nonlinear optics to
+magnetic systems and superconductors. A vortex carries a binary information
+corresponding to its topological charge, `plus' or `minus', that can be used
+for information storage and processing. In spatially extended optical and
+condensed many-particle systems, achieving full control over vortex formation
+and its charge is particularly difficult and is not easily extended to systems
+of multiple vortices. Here we demonstrate the optical creation of multiplets of
+phase-locked vortices in polariton condensates using off-resonant excitation
+with ring-shaped pump beams. We find that the vorticity of one vortex can be
+controlled solely using the phase-locking with other nearby vortices. Using
+this mechanism, we demonstrate how an existing vortex with a specific
+topological charge can be inverted to the oppositely charged state, and how the
+charge state of one reference vortex can be copied to a neighboring vortex.
+This way we can optically encode any set of binary information onto a chain of
+vortices. We further show that this information can be modified later by using
+the possibility to address and manipulate each vortex in the chain
+individually.
+"
+27319,Principal bundle structure of matrix manifolds,"  In this paper, we introduce a new geometric description of the manifolds of
+matrices of fixed rank. The starting point is a geometric description of the
+Grassmann manifold $\mathbb{G}_r(\mathbb{R}^k)$ of linear subspaces of
+dimension $r<k$ in $\mathbb{R}^k$ which avoids the use of equivalence classes.
+The set $\mathbb{G}_r(\mathbb{R}^k)$ is equipped with an atlas which provides
+it with the structure of an analytic manifold modelled on
+$\mathbb{R}^{(k-r)\times r}$. Then we define an atlas for the set
+$\mathcal{M}_r(\mathbb{R}^{k \times r})$ of full rank matrices and prove that
+the resulting manifold is an analytic principal bundle with base
+$\mathbb{G}_r(\mathbb{R}^k)$ and typical fibre $\mathrm{GL}_r$, the general
+linear group of invertible matrices in $\mathbb{R}^{k\times k}$. Finally, we
+define an atlas for the set $\mathcal{M}_r(\mathbb{R}^{n \times m})$ of
+non-full rank matrices and prove that the resulting manifold is an analytic
+principal bundle with base $\mathbb{G}_r(\mathbb{R}^n) \times
+\mathbb{G}_r(\mathbb{R}^m)$ and typical fibre $\mathrm{GL}_r$. The atlas of
+$\mathcal{M}_r(\mathbb{R}^{n \times m})$ is indexed on the manifold itself,
+which allows a natural definition of a neighbourhood for a given matrix, this
+neighbourhood being proved to possess the structure of a Lie group. Moreover,
+the set $\mathcal{M}_r(\mathbb{R}^{n \times m})$ equipped with the topology
+induced by the atlas is proven to be an embedded submanifold of the matrix
+space $\mathbb{R}^{n \times m}$ equipped with the subspace topology. The
+proposed geometric description then results in a description of the matrix
+space $\mathbb{R}^{n \times m}$, seen as the union of manifolds
+$\mathcal{M}_r(\mathbb{R}^{n \times m})$, as an analytic manifold equipped with
+a topology for which the matrix rank is a continuous map.
+"
+27320,"Pole, Pericenter, and Nodes of the Interstellar Minor Body A/2017 U1","  Here, we explore some peculiar orbital features of the recently discovered
+asteroid A/2017 U1, which is a clear outlier when considering the average value
+of the eccentricity of known hyperbolic comets. As for the orientation of its
+orbit in space, the orbital plane of A/2017 U1 seems to be away from any
+obvious clusters present for this population. The orbital nodes of A/2017 U1
+are well away from the paths of the planets of the Solar System and the Sun.
+All these orbital properties appear to confirm A/2017 U1 as the first known
+interstellar asteroid.
+"
+27321,Learning Robust Bed Making using Deep Imitation Learning with DART,"  Bed-making is a universal home task that can be challenging for senior
+citizens due to reaching motions. Automating bed-making has multiple technical
+challenges such as perception in an unstructured environments, deformable
+object manipulation, obstacle avoidance and sequential decision making. We
+explore how DART, an LfD algorithm for learning robust policies, can be applied
+to automating bed making without fiducial markers with a Toyota Human Support
+Robot (HSR). By gathering human demonstrations for grasping the sheet and
+failure detection, we can learn deep neural network policies that leverage
+pre-trained YOLO features to automate the task. Experiments with a 1/2 scale
+twin bed and distractors placed on the bed, suggest policies learned on 50
+demonstrations with DART achieve 96% sheet coverage, which is over 200% better
+than a corner detector baseline using contour detection.
+"
+27322,Yang-Mills moduli spaces over an orientable closed surface via Fréchet reduction,"  Given a principal bundle on an orientable closed surface with compact
+connected structure group, we endow the space of based gauge equivalence
+classes of smooth connections relative to smooth based gauge transformations
+with the structure of a Fréchet manifold. Using Wilson loop holonomies and a
+certain characteristic class determined by the topology of the bundle, we then
+impose suitable constraints on that Fréchet manifold that single out the
+based gauge equivalence classes of central Yang-Mills connections but do not
+directly involve the Yang-Mills equation. We also explain how our theory yields
+the based and unbased gauge equivalence classes of all Yang-Mills connections
+and deduce the stratified symplectic structure on the space of unbased gauge
+equivalence classes of central Yang-Mills connections. The crucial new
+technical tool is a slice analysis in the Fréchet setting.
+"
+27323,Getting Virtualized Wireless Sensor Networks IaaS Ready for PaaS,"  With the recent advances in sensor hardware and software, architectures for
+virtualized Wireless Sensor Networks (vWSNs) are now emerging. Through node-
+and network-level virtualization, vWSNs can be offered as
+Infrastructure-as-a-Service (IaaS) which can aid in realizing the true
+potential of Internet-of-Things (IoT). Cloud computing offers elastic
+provisioning of large-scale infrastructures to multiple concurrent users where
+Platform-as-a-Service (PaaS) interacts with IaaS in order to efficiently host
+and execute applications over these infrastructures. Amalgamating IoT with
+cloud computing potentially allows rapid application and service provisioning
+in an efficient, scalable and robust manner. However, interactions between
+vWSNs and PaaS are largely an unexplored area. Indeed, existing vWSN IaaS are
+not yet ready for PaaS. This paper proposes a vWSN IaaS architecture which is
+ready for interactions with PaaS. The proposed architecture is based on our
+previous works and is rooted in the fundamental differences between traditional
+IaaS and vWSN IaaS. We built a prototype using Java Sunspot as the WSN tool kit
+and made early performance measurements.
+"
+27324,What Factors Control Shale Gas Production and Production Decline Trend in Fractured Systems: A Comprehensive Analysis and Investigation,"  A multi-physics model for shale gas production in fractured system, that
+couples real gas property, nano-flow mechanism and geomechanics
+"
+27325,On $r$-gaps between zeros of the Riemann zeta-function,"  Under the Riemann Hypothesis, we prove for any natural number $r$ there exist
+infinitely many large natural numbers $n$ such that
+$(\gamma_{n+r}-\gamma_n)/(2\pi /\log \gamma_n) > r + \Theta\sqrt{r}$ and
+$(\gamma_{n+r}-\gamma_n)/(2\pi /\log \gamma_n) < r - \vartheta\sqrt{r}$ for
+explicit absolute positive constants $\Theta$ and $\vartheta$, where $\gamma$
+denotes an ordinate of a zero of the Riemann zeta-function on the critical
+line. Selberg published announcements of this result several times but did not
+include a proof. We also suggest a general framework which might lead to
+stronger statements concerning the vertical distribution of nontrivial zeros of
+the Riemann zeta-function.
+"
+27326,Uniformly locally univalent harmonic mappings associated with the pre-Schwarzian norm,"  In this paper, we consider the class of uniformly locally univalent harmonic
+mappings in the unit disk and build a relationship between its pre-Schwarzian
+norm and uniformly hyperbolic radius. Also, we establish eight ways of
+characterizing uniformly locally univalent sense-preserving harmonic mappings.
+We also present some sharp distortions and growth estimates and investigate
+their connections with Hardy spaces. Finally, we study subordination principles
+of norm estimates.
+"
+27327,Theoretical analysis of optoporation efficiency and bubble formation during femtosecond pulse interaction with gold nanoshell,"  Our group has recently developed a finite element model of a
+nanoparticle-mediated optical breakdown phenomena. Previously, this model was
+used to analyze the role of the nanoparticle morphology and the wavelength
+dependence of a nanoparticle-mediated optical breakdown threshold during
+near-infrared ps and fs pulse exposures. In this study, we provide a
+theoretical insight into the optoporation efficiency of live cells and bubble
+formation threshold during nanoparticle-mediated optical breakdown. It was done
+by the calculation of maximum temperature and free electron density in the
+vicinity of a single gold nanoshell in water during 70 femtosecond single pulse
+exposure and comparison against published experimental data.
+"
+27328,Strange metal from local quantum chaos,"  How to make a model of a non-Fermi-liquid metal with efficient current
+dissipation is a long-standing problem. Results from holographic duality
+suggest a framework where local critical fermionic degrees of freedom provide
+both a source of decoherence for the Landau quasiparticle, and a sink for its
+momentum. This leads us to study a Kondo lattice type model with SYK models in
+place of the spin impurities. We find evidence for a stable phase at
+intermediate couplings.
+"
+27329,Classification and clustering for observations of event time data using non-homogeneous Poisson process models,"  Data of the form of event times arise in various applications. A simple model
+for such data is a non-homogeneous Poisson process (NHPP) which is specified by
+a rate function that depends on time. We consider the problem of having access
+to multiple independent observations of event time data, observed on a common
+interval, from which we wish to classify or cluster the observations according
+to their rate functions. Each rate function is unknown but assumed to belong to
+a finite number of rate functions each defining a distinct class. We model the
+rate functions using a spline basis expansion, the coefficients of which need
+to be estimated from data. The classification approach consists of using
+training data for which the class membership is known, to calculate maximum
+likelihood estimates of the coefficients for each group, then assigning test
+observations to a group by a maximum likelihood criterion. For clustering, by
+analogy to the Gaussian mixture model approach for Euclidean data, we consider
+mixtures of NHPP and use the expectation-maximisation algorithm to estimate the
+coefficients of the rate functions for the component models and group
+membership probabilities for each observation. The classification and
+clustering approaches perform well on both synthetic and real-world data sets.
+Code associated with this paper is available at
+this https URL .
+"
+27330,Filling the holes in the CaFe$_{4}$As$_{3}$ structure: synthesis and magnetism of CaCo$_{5}$As$_{3}$,"  Here we investigate single crystals of CaCo$_{5}$As$_{3}$ by means of single
+crystal X-ray diffraction, microprobe, magnetic susceptibility, heat capacity,
+and pressure-dependent transport measurements. CaCo$_{5}$As$_{3}$ shares the
+same structure of CaFe$_{4}$As$_{3}$ with an additional Co atom filling a
+lattice vacancy and undergoes a magnetic transition at $T_{M} = 16$ K
+associated with a frustrated magnetic order. CaCo$_{5}$As$_{3}$ displays
+metallic behavior and its Sommerfeld coefficient ($\gamma = 70$ mJ/mol.K$^{2}$)
+indicates a moderate enhancement of electron-electron correlations. Transport
+data under pressures to $2.5$ GPa reveal a suppression of $T_{M}$ at a rate of
+$-0.008$ K/GPa. First-principle electronic structure calculations show a
+complex 3D band structure and magnetic moments that depend on the local
+environment at each Co site. Our results are compared with previous data on
+CaFe$_{4}$As$_{3}$ and provide a scenario for a magnetically frustrated ground
+state in this family of compounds.
+"
+27331,X-ray Scattering from Random Rough Surfaces,"  This paper presents a new method to model X-ray scattering on random rough
+surfaces. It combines the approaches we presented in two previous papers --
+\zs\cite{zhao03} \& \pz\cite{zhao15}. An actual rough surface is (incompletely)
+described by its Power Spectral Density (PSD). For a given PSD, model surfaces
+with the same roughness as the actual surface are constructed by preserving the
+PSD amplitudes and assigning a random phase to each spectral component. Rays
+representing the incident wave are reflected from the model surface and
+projected onto a flat plane, which is the first order approximation of the
+model surface, as outgoing rays and corrected for phase delays. The projected
+outgoing rays are then corrected for wave densities and redistributed onto an
+uniform grid where the model surface is constructed. The scattering is then
+calculated using the Fourier Transform of the resulting distribution. This
+method provides the exact solutions for scattering in all directions, without
+small angle approximation. It is generally applicable to any wave scatterings
+on random rough surfaces and is not limited to small scattering angles.
+Examples are given for the Chandra X-ray Observatory optics. This method is
+also useful for the future generation X-ray astronomy missions.
+"
+27332,3D Reconstruction of the Magnetic Vector Potential using Model Based Iterative Reconstruction,"  Lorentz Transmission Electron Microscopy (TEM) observations of magnetic
+nanoparticles contain information on the magnetic and electrostatic potentials.
+Vector Field Electron Tomography (VFET) can be used to reconstruct
+electromagnetic potentials of the nanoparticles from their corresponding LTEM
+images. The VFET approach is based on the conventional filtered back projection
+approach to tomographic reconstructions and the availability of an incomplete
+set of measurements due to experimental limitations means that the
+reconstructed vector fields exhibit significant artifacts. In this paper, we
+outline a model-based iterative reconstruction (MBIR) algorithm to reconstruct
+the magnetic vector potential of magnetic nanoparticles. We combine a forward
+model for image formation in TEM experiments with a prior model to formulate
+the tomographic problem as a maximum a-posteriori probability estimation
+problem (MAP). The MAP cost function is minimized iteratively to determine the
+vector potential. A comparative reconstruction study of simulated as well as
+experimental data sets show that the MBIR approach yields quantifiably better
+reconstructions than the VFET approach.
+"
+27333,Spin glasses : experimental signatures and salient outcomes,"  Within the wide class of disordered materials, spin glasses occupy a special
+place because of their conceptually simple definition of randomly interacting
+spins. Their modelling has triggered spectacular developments of
+out-of-equilibrium statistical physics, as well analytically as numerically,
+opening the way to a new vision of glasses in general. ""Real"" spin glasses are
+disordered magnetic materials which can be very diverse from the chemist's
+point of view, but all share a number of common properties, laying down the
+definition of generic spin glass behaviour. This paper aims at giving to
+non-specialist readers an idea of what spin glasses are from an
+experimentalist's point of view, describing as simply as possible their main
+features as they can be observed in the laboratory, referring to numerous
+detailed publications for more substantial discussions and for all theoretical
+developments, which are hardly sketched here. We strived to provide the readers
+who are interested in other glassy materials with some clues about the
+potential of spin glasses for improving their understanding of disordered
+matter. At least, arousing their curiosity for this fascinating subject will be
+considered a success.
+"
+27334,Online Voltage Stability Assessment for Load Areas Based on the Holomorphic Embedding Method,"  This paper proposes an online steady-state voltage stability assessment
+scheme to evaluate the proximity to voltage collapse at each bus of a load
+area. Using a non-iterative holomorphic embedding method (HEM) with a proposed
+physical germ solution, an accurate loading limit at each load bus can be
+calculated based on online state estimation on the entire load area and a
+measurement-based equivalent for the external system. The HEM employs a power
+series to calculate an accurate Power-Voltage (P-V) curve at each load bus and
+accordingly evaluates the voltage stability margin considering load variations
+in the next period. An adaptive two-stage Pade approximants method is proposed
+to improve the convergence of the power series for accurate determination of
+the nose point on the P-V curve with moderate computational burden. The
+proposed method is illustrated in detail on a 4-bus test system and then
+demonstrated on a load area of the Northeast Power Coordinating Council (NPCC)
+48-geneartor, 140-bus power system.
+"
+27335,Ground state of the conformal flow on $\mathbb{S}^3$,"  We consider the conformal flow model derived by Bizoń, Craps, Evnin, Hunik,
+Luyten, and Maliborski [Commun. Math. Phys. 353 (2017) 1179-1199] as a normal
+form for the conformally invariant cubic wave equation on $\mathbb{S}^3$. We
+prove that the energy attains a global constrained maximum at a family of
+particular stationary solutions which we call the ground state family. Using
+this fact and spectral properties of the linearized flow (which are interesting
+on their own due to a supersymmetric structure) we prove nonlinear orbital
+stability of the ground state family. The main difficulty in the proof is due
+to the degeneracy of the ground state family as a constrained maximizer of the
+energy.
+"
+27336,Slip-Size Distribution and Self-Organized Criticality in Block-Spring Models with Quenched Randomness,"  We study slowly pulling block-spring models in random media. Second-order
+phase transitions exist in a model pulled by a constant force in the case of
+velocity-strengthening friction. If external forces are slowly increased,
+nearly critical states are self-organized. Slips of various sizes occur, and
+the probability distributions of slip size roughly obey power laws. The
+exponent is close to that in the quenched Edwards--Wilkinson model.
+Furthermore, the slip-size distributions are investigated in cases of Coulomb
+friction, velocity-weakening friction.
+"
+27337,"Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution","  We present a novel deep neural network architecture for representing robot
+experiences in an episodic-like memory which facilitates encoding, recalling,
+and predicting action experiences. Our proposed unsupervised deep episodic
+memory model 1) encodes observed actions in a latent vector space and, based on
+this latent encoding, 2) infers most similar episodes previously experienced,
+3) reconstructs original episodes, and 4) predicts future frames in an
+end-to-end fashion. Results show that conceptually similar actions are mapped
+into the same region of the latent vector space. Based on these results, we
+introduce an action matching and retrieval mechanism, benchmark its performance
+on two large-scale action datasets, 20BN-something-something and ActivityNet
+and evaluate its generalization capability in a real-world scenario on a
+humanoid robot.
+"
+27338,Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy,"  Patients with idiopathic generalised epilepsy (IGE) typically have normal
+conventional magnetic resonance imaging (MRI), hence MRI based diagnosis is
+challenging. Anatomical abnormalities underlying brain dysfunctions in IGE are
+unclear and their relation to the pathomechanisms of epileptogenesis is poorly
+understood. In this study, we applied connectometry, an advanced quantitative
+neuroimaging technique for investigating localised changes in white-matter
+tissue. Analysing white matter structures of 32 subjects we incorporated our
+findings in a computational model of seizure dynamics to suggest a plausible
+mechanism of epileptogenesis. Patients with IGE have significant bilateral
+alterations in major white-matter fascicles. In the cingulum, fornix, and
+superior longitudinal fasciculus, tract integrity is compromised, whereas in
+specific parts of tracts between thalamus and the precentral gyrus, tract
+integrity is enhanced in patients. Combining these alterations in a logistic
+regression model, we computed the decision boundary that discriminated patients
+and controls. The computational model, informed with the findings on the tract
+abnormalities, specifically highlighted the importance of enhanced
+cortico-reticular connections along with impaired cortico-cortical connections
+in inducing pathological seizure-like dynamics. We emphasise taking
+directionality of brain connectivity into consideration towards understanding
+the pathological mechanisms; this is possible by combining neuroimaging and
+computational modelling. Our imaging evidence of structural alterations suggest
+the loss of cortico-cortical and enhancement of cortico-thalamic fibre
+integrity in IGE. We further suggest that impaired connectivity from cortical
+regions to the thalamic reticular nucleus offers a therapeutic target for
+selectively modifying the brain circuit for reversing the mechanisms leading to
+epileptogenesis.
+"
+27339,GPU parallel simulation algorithm of Brownian particles with excluded volume using Delaunay triangulations,"  A novel parallel simulation algorithm on the GPU, implemented in CUDA and
+C++, is presented for the simulation of Brownian particles that display
+excluded volume repulsion and interact with long and short range forces. When
+an explicit Euler-Maruyama integration step is performed to take into account
+the pairwise forces and Brownian motion, particle overlaps can appear. The
+excluded volume property brings up the need for correcting these overlaps as
+they happen, since predicting them is not feasible due to the random
+displacement of Brownian particles. The proposed solution handles, at each time
+step, a Delaunay triangulation of the particle positions because it allows us
+to efficiently solve overlaps between particles by checking just their
+neighborhood. The algorithm starts by generating a Delaunay triangulation of
+the particle initial positions on CPU, but after that the triangulation is
+always kept on GPU memory. We used a parallel edge-flip implementation to keep
+the triangulation updated during each time step, checking previously that the
+triangulation was not rendered invalid due to the particle displacements. The
+algorithm is validated with two models of active colloidal particles. Upon
+testing the parallel implementation of a long range forces simulation, the
+results show a performance improvement of up to two orders of magnitude when
+compared to the previously existing sequential solution.
+"
+27340,Visual Analytics of Movement Pattern Based on Time-Spatial Data: A Neural Net Approach,"  Time-Spatial data plays a crucial role for different fields such as traffic
+management. These data can be collected via devices such as surveillance
+sensors or tracking systems. However, how to efficiently an- alyze and
+visualize these data to capture essential embedded pattern information is
+becoming a big challenge today. Classic visualization ap- proaches focus on
+revealing 2D and 3D spatial information and modeling statistical test. Those
+methods would easily fail when data become mas- sive. Recent attempts concern
+on how to simply cluster data and perform prediction with time-oriented
+information. However, those approaches could still be further enhanced as they
+also have limitations for han- dling massive clusters and labels. In this
+paper, we propose a visualiza- tion methodology for mobility data using
+artificial neural net techniques. This method aggregates three main parts that
+are Back-end Data Model, Neural Net Algorithm including clustering method
+Self-Organizing Map (SOM) and prediction approach Recurrent Neural Net (RNN)
+for ex- tracting the features and lastly a solid front-end that displays the
+results to users with an interactive system. SOM is able to cluster the
+visiting patterns and detect the abnormal pattern. RNN can perform the predic-
+tion for time series analysis using its dynamic architecture. Furthermore, an
+interactive system will enable user to interpret the result with graph- ics,
+animation and 3D model for a close-loop feedback. This method can be
+particularly applied in two tasks that Commercial-based Promotion and abnormal
+traffic patterns detection.
+"
+27341,Quasi-stationary states in nonlocal stochastic growth models with infinitely many absorbing states,"  We study a two parameter ($u,p$) extension of the conformally invariant raise
+and peel model. The model also represents a nonlocal and biased-asymmetric
+exclusion process with local and nonlocal jumps of excluded volume particles in
+the lattice. The model exhibits an unusual and interesting critical phase
+where, in the bulk limit, there are an infinite number of absorbing states. In
+spite of these absorbing states the system stays, during a time that increases
+exponentially with the lattice size, in a critical quasi-stationary state. In
+this critical phase the critical exponents depend only on one of the parameters
+defining the model ($u$). The endpoint of this critical phase belongs to a
+distinct universality class, where the system changes from an active to an
+inactive frozen state. This new behavior we believe to be due to the appearance
+of Jordan cells in the Hamiltonian describing the time evolution. The
+dimensions of these cells increases with the lattice size. In a special case
+($u=0$) where the model has no adsorptions we are able to calculate
+analytically the time evolution of some of the observables. A polynomial time
+dependence is obtained due to the Jordan cells structure of the Hamiltonian.
+"
+27342,Bayesian Alternatives to the Black-Litterman Model,"  The Black-Litterman model combines investors' personal views with historical
+data and gives optimal portfolio weights. In this paper we will introduce the
+original Black-Litterman model (section 1), we will modify the model such that
+it fits in a Bayesian framework by considering the investors' personal views to
+be a direct prior on the means of the returns and by adding a typical Inverse
+Wishart prior on the covariance matrix of the returns (section 2). Lastly, we
+will use Leonard and Hsu's (1992) idea of adding a prior on the logarithm of
+the covariance matrix (section 3). Sensitivity simulations for the level of
+confidence that the investor has in their own personal views were performed and
+performance of the models was assessed on a test data set consisting of returns
+over the month of January 2018.
+"
+27343,On the Generalizability of Linear and Non-Linear Region of Interest-Based Multivariate Regression Models for fMRI Data,"  In contrast to conventional, univariate analysis, various types of
+multivariate analysis have been applied to functional magnetic resonance
+imaging (fMRI) data. In this paper, we compare two contemporary approaches for
+multivariate regression on task-based fMRI data: linear regression with ridge
+regularization and non-linear symbolic regression using genetic programming.
+The data for this project is representative of a contemporary fMRI experimental
+design for visual stimuli. Linear and non-linear models were generated for 10
+subjects, with another 4 withheld for validation. Model quality is evaluated by
+comparing $R$ scores (Pearson product-moment correlation) in various contexts,
+including single run self-fit, within-subject generalization, and
+between-subject generalization. Propensity for modelling strategies to overfit
+is estimated using a separate resting state scan. Results suggest that neither
+method is objectively or inherently better than the other.
+"
+27344,Effects of Grain Boundaries and Defects on Anisotropic Magnon Transport in Textured Sr14Cu24O41,"  The strong spin-spin exchange interaction in some low-dimensional magnetic
+materials can give rise to a high group velocity and thermal conductivity
+contribution from magnons. One example is the incommensurate layered compounds
+(Sr,Ca,La)14Cu24O41. The effects of grain boundaries and defects on
+quasi-one-dimensional magnon transport in these compounds are not well
+understood. Here we report the microstructures and anisotropic thermal
+transport properties of textured Sr14Cu24O41, which are prepared by solid-state
+reaction followed by spark plasma sintering. Transmission electron microscopy
+clearly reveals nano-layered grains and the presence of dislocations and planar
+defects. The thermal conductivity contribution and mean free paths of magnons
+in the textured samples are evaluated with the use of a kinetic model for
+one-dimensional magnon transport, and found to be suppressed significantly as
+compared to single crystals at low temperatures. The experimental results can
+be explained by a one-dimensional magnon-defect scattering model, provided that
+the magnon-grain boundary scattering mean free path in the anisotropic magnetic
+structure is smaller than the average length of these nano-layers along the c
+axis. The finding suggests low transmission coefficients for magnons across
+grain boundaries.
+"
+27345,Optimal Warping Paths are unique for almost every Pair of Time Series,"  Update rules for learning in dynamic time warping spaces are based on optimal
+warping paths between parameter and input time series. In general, optimal
+warping paths are not unique resulting in adverse effects in theory and
+practice. Under the assumption of squared error local costs, we show that no
+two warping paths have identical costs almost everywhere in a measure-theoretic
+sense. Two direct consequences of this result are: (i) optimal warping paths
+are unique almost everywhere, and (ii) the set of all pairs of time series with
+multiple equal-cost warping paths coincides with the union of exponentially
+many zero sets of quadratic forms. One implication of the proposed results is
+that typical distance-based cost functions such as the k-means objective are
+differentiable almost everywhere and can be minimized by subgradient methods.
+"
+27346,A note on the size of query trees,"  We consider query trees of graphs with degree bounded by a constant, $d$. We
+give simple proofs that the size of a query tree is constant in expectation and
+$2^{O(d)}\log{n}$ w.h.p.
+"
+27347,The properties of the first galaxies in the BLUETIDES simulation,"  We employ the very large cosmological hydrodynamical simulation BLUETIDES to
+investigate the predicted properties of the galaxy population during the epoch
+of reionisation ($z>8$). BLUETIDES has a resolution and volume ($(400/h\approx
+577)^{3}\,{\rm cMpc^3}$) providing a population of galaxies which is well
+matched to depth and area of current observational surveys targeting the
+high-redshift Universe. At $z=8$ BLUETIDES includes almost 160,000 galaxies
+with stellar masses $>10^{8}\,{\rm M_{\odot}}$. The population of galaxies
+predicted by BLUETIDES closely matches observational constraints on both the
+galaxy stellar mass function and far-UV ($150\,{\rm nm}$) luminosity function.
+Galaxies in BLUETIDES are characterised by rapidly increasing star formation
+histories. Specific star formation rates decrease with redshift though remain
+largely insensitive to stellar mass. As a result of the enhanced surface
+density of metals more massive galaxies are predicted to have higher dust
+attenuation resulting in a significant steepening of the observed far-UV
+luminosity function at high luminosities. The contribution of active SMBHs to
+the UV luminosities of galaxies with stellar masses $10^{9-10}\,{\rm
+M_{\odot}}$ is around $3\%$ on average. Approximately $25\%$ of galaxies with
+$M_{*}\approx 10^{10}\,{\rm M_{\odot}}$ are predicted to have active SMBH which
+contribute $>10\%$ of the total UV luminosity.
+"
+27348,SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine,"  We publicly release a new large-scale dataset, called SearchQA, for machine
+comprehension, or question-answering. Unlike recently released datasets, such
+as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to
+reflect a full pipeline of general question-answering. That is, we start not
+from an existing article and generate a question-answer pair, but start from an
+existing question-answer pair, crawled from J! Archive, and augment it with
+text snippets retrieved by Google. Following this approach, we built SearchQA,
+which consists of more than 140k question-answer pairs with each pair having
+49.6 snippets on average. Each question-answer-context tuple of the SearchQA
+comes with additional meta-data such as the snippet's URL, which we believe
+will be valuable resources for future research. We conduct human evaluation as
+well as test two baseline methods, one simple word selection and the other deep
+learning based, on the SearchQA. We show that there is a meaningful gap between
+the human and machine performances. This suggests that the proposed dataset
+could well serve as a benchmark for question-answering.
+"
+27349,Linear Projections of the Vandermonde Polynomial,"  An n-variate Vandermonde polynomial is the determinant of the n x n matrix
+where the ith column is the vector (1, x_i, x_i^2, ...., x_i^{n-1})^T.
+Vandermonde polynomials play a crucial role in the theory of alternating
+polynomials and occur in Lagrangian polynomial interpolation as well as in the
+theory of error correcting codes. In this work we study structural and
+computational aspects of linear projections of Vandermonde polynomials.
+Firstly, we consider the problem of testing if a given polynomial is linearly
+equivalent to the Vandermonde polynomial. We obtain a deterministic polynomial
+time algorithm to test if the polynomial f is linearly equivalent to the
+Vandermonde polynomial when f is given as product of linear factors. In the
+case when the polynomial f is given as a black-box our algorithm runs in
+randomized polynomial time. Exploring the structure of projections of
+Vandermonde polynomials further, we describe the group of symmetries of a
+Vandermonde polynomial and show that the associated Lie algebra is simple.
+"
+27350,A conjecture on hyponormality for the Cesàro matrix of positive integer order,"  It is already known that the Cesàro matrices of orders one and two are
+coposinormal, hyponormal operators on $\ell^2$. Here it is shown that the
+Cesàro matrices of order three and four are also coposinormal, hyponormal;
+the proofs employ posinormality, achieved by means of a diagonal interrupter,
+and elementary computational techniques from calculus. A conjecture is then
+propounded for the Cesàro matrix of positive integer order greater than
+four.
+"
+27351,"Human Echolocation in Static Situations: Auditory Models of Detection Thresholds for Distance, Pitch, Loudness and Timbre","  We investigated, by using auditory models, how three perceptual parameters,
+loudness, pitch and sharpness, determine human echolocation. We used acoustic
+recordings from two previous studies, both from stationary situations, and
+their resulting perceptual data as input to our analysis. An initial analysis
+was on the room acoustics of the recordings. The parameters of interest were
+sound pressure level, autocorrelation and spectral centroid. The auditory
+models were used to analyze echolocation resulting from the perceptual
+variables, i.e. loudness, pitch and sharpness. Relevant auditory models were
+chosen to simulate each variable. Based on these results, we calculated
+psychophysical thresholds for detecting a reflecting object with constant
+physical size. A non-parametric method was used to determine thresholds for
+distance, loudness, pitch and sharpness. Difference thresholds were calculated
+for the psychophysical variables, since a 2-Alternative-Forced-Choice Paradigm
+had originally been used. We found that (1) blind persons could detect objects
+at lower loudness values, lower pitch strength, different sharpness values and
+at further distances than sighted persons, (2) detection thresholds based on
+repetition pitch, loudness and sharpness varied and depended on room acoustics
+and type of sound stimuli, (3) repetition pitch was useful for detection at
+shorter distances and was determined from the peaks in the temporal profile of
+the autocorrelation function, (4) loudness at shorter distances provides
+echolocation information, (5) at longer distances, timbre aspects, such as
+sharpness, might be used to detect objects. We also discuss binaural
+information, movements and the auditory model approach. Autocorrelation was
+assumed as a proper measure for pitch, but the question is raised whether a
+mechanism based on strobe integration is a viable possibility.
+"
+27352,Block Analysis for the Calculation of Dynamic and Static Length Scales in Glass-Forming Liquids,"  We present {\it block analysis}, an efficient method to perform finite-size
+scaling for obtaining the length scale of dynamic heterogeneity and the
+point-to-set length scale for generic glass-forming liquids. This method
+involves considering blocks of varying sizes embedded in a system of a fixed
+(large) size. The length scale associated with dynamic heterogeneity is
+obtained from a finite-size scaling analysis of the dependence of the
+four-point dynamic susceptibility on the block size. The block size dependence
+of the variance of the $\alpha$-relaxation time yields the static point-to-set
+length scale. The values of the obtained length scales agree quantitatively
+with those obtained from other conventional methods. This method provides an
+efficient experimental tool for studying the growth of length scales in systems
+such as colloidal glasses for which performing finite-size scaling by carrying
+out experiments for varying system sizes may not be feasible.
+"
+27353,A continuous analogue of lattice path enumeration,"  Following the work of Cano and Diaz, we consider a continuous analog of
+lattice path enumeration. This allows us to define a continuous version of any
+discrete object that counts certain types of lattice paths. We define
+continuous versions of binomials and multinomials, and describe some identities
+and partial differential equations they satisfy. Finally, we illustrate a
+general process to recover discrete combinatorial quantities from their
+continuous analogs.
+"
+27354,CloudScan - A configuration-free invoice analysis system using recurrent neural networks,"  We present CloudScan; an invoice analysis system that requires zero
+configuration or upfront annotation. In contrast to previous work, CloudScan
+does not rely on templates of invoice layout, instead it learns a single global
+model of invoices that naturally generalizes to unseen invoice layouts. The
+model is trained using data automatically extracted from end-user provided
+feedback. This automatic training data extraction removes the requirement for
+users to annotate the data precisely. We describe a recurrent neural network
+model that can capture long range context and compare it to a baseline logistic
+regression model corresponding to the current CloudScan production system. We
+train and evaluate the system on 8 important fields using a dataset of 326,471
+invoices. The recurrent neural network and baseline model achieve 0.891 and
+0.887 average F1 scores respectively on seen invoice layouts. For the harder
+task of unseen invoice layouts, the recurrent neural network model outperforms
+the baseline with 0.840 average F1 compared to 0.788.
+"
+27355,On the Rainbow Neighbourhood Number of Set-Graphs,"  In this paper, we present results for the rainbow neighbourhood numbers of
+set-graphs. It is also shown that set-graphs are perfect graphs. The intuitive
+colouring dilemma in respect of the rainbow neighbourhood convention is
+clarified as well. Finally, the new notion of the maximax independence, maximum
+proper colouring of a graph and a new graph parameter called the $i$-max number
+of $G$ are introduced as a new research direction.
+"
+27356,Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs,"  We present a simple neural rendering architecture that helps variational
+autoencoders (VAEs) learn disentangled representations. Instead of the
+deconvolutional network typically used in the decoder of VAEs, we tile
+(broadcast) the latent vector across space, concatenate fixed X- and
+Y-""coordinate"" channels, and apply a fully convolutional network with 1x1
+stride. This provides an architectural prior for dissociating positional from
+non-positional features in the latent distribution of VAEs, yet without
+providing any explicit supervision to this effect. We show that this
+architecture, which we term the Spatial Broadcast decoder, improves
+disentangling, reconstruction accuracy, and generalization to held-out regions
+in data space. It provides a particularly dramatic benefit when applied to
+datasets with small objects. We also emphasize a method for visualizing learned
+latent spaces that helped us diagnose our models and may prove useful for
+others aiming to assess data representations. Finally, we show the Spatial
+Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling
+techniques and when incorporated improves their performance.
+"
+27357,Neumann boundary conditions with null external quasi-momenta in finite-systems,"  The order parameter of a critical system defined in a layered parallel plate
+geometry subject to Neumann boundary conditions at the limiting surfaces is
+studied. We utilize a one-particle irreducible vertex parts framework in order
+to study the critical behavior of such a system. The renormalized vertex parts
+are defined at zero external quasi-momenta, which makes the analysis
+particularly simple. The distance between the boundary plates $L$
+characterizing the finite size system direction perpendicular to the
+hyperplanes plays a similar role here in comparison with our recent unified
+treatment for Neumann and Dirichlet boundary conditions. Critical exponents are
+computed using diagrammatic expansion at least up to two-loop order and are
+shown to be identical to those from the bulk theory (limit $L \rightarrow
+\infty$).
+"
+27358,A Logic for Global and Local Announcements,"  In this paper we introduce {\em global and local announcement logic} (GLAL),
+a dynamic epistemic logic with two distinct announcement operators --
+$[\phi]^+_A$ and $[\phi]^-_A$ indexed to a subset $A$ of the set $Ag$ of all
+agents -- for global and local announcements respectively. The boundary case
+$[\phi]^+_{Ag}$ corresponds to the public announcement of $\phi$, as known from
+the literature. Unlike standard public announcements, which are {\em model
+transformers}, the global and local announcements are {\em pointed model
+transformers}. In particular, the update induced by the announcement may be
+different in different states of the model. Therefore, the resulting
+computations are trees of models, rather than the typical sequences. A
+consequence of our semantics is that modally bisimilar states may be
+distinguished in our logic. Then, we provide a stronger notion of bisimilarity
+and we show that it preserves modal equivalence in GLAL. Additionally, we show
+that GLAL is strictly more expressive than public announcement logic with
+common knowledge. We prove a wide range of validities for GLAL involving the
+interaction between dynamics and knowledge, and show that the satisfiability
+problem for GLAL is decidable. We illustrate the formal machinery by means of
+detailed epistemic scenarios.
+"
+27359,X20CoCrWMo10-9//Co3O4: a Metal-Ceramic Composite with Unique Efficiency Values for Water-Splitting in Neutral Regime,"  Water splitting allows the storage of solar energy into chemical bonds
+(H2+O2) and will help to implement the urgently needed replacement of limited
+available fossil fuels. Particularly in neutral environment electrochemically
+initiated water splitting suffers from low efficiency due to high
+overpotentials caused by the anode. Electro-activation of X20CoCrWMo10-9, a
+Co-based tool steel resulted in a new composite material
+(X20CoCrWMo10-9//Co3O4) that catalyzes the anode half-cell reaction of water
+electrolysis with a so far unequalled effectiveness. The current density
+achieved with this new anode in pH 7 corrected 0.1 M phosphate buffer is over a
+wide range of overpotentials around 10 times higher compared to recently
+developed, up-to-date electrocatalysts and represents the benchmark performance
+advanced catalysts show in regimes that support water splitting significantly
+better than pH 7 medium. X20CoCrWMo10-9//Co3O4 exhibited electrocatalytic
+properties not only at pH 7, but also at pH 13, which is much superior to the
+ones of IrO2-RuO2, single-phase Co3O4- or Fe/Ni- based catalysts. Both XPS and
+FT-IR experiments unmasked Co3O4 as the dominating compound on the surface of
+the X20CoCrWMo10-9//Co3O4 composite. Upon a comprehensive dual beam FIB-SEM
+(focused ion beam-scanning electron microscopy) study we could show that the
+new composite does not exhibit a classical substrate-layer structure due to the
+intrinsic formation of the Co-enriched outer zone. This structural
+particularity is basically responsible for the outstanding electrocatalytic OER
+performance.
+"
+27360,Probing Laser-induced Microenvironment Changes in Room Temperature Ionic Liquids,"  Modulating heterogeneous microstructure in room temperature ionic liquids
+(RTILs) by external stimuli is an important approach for understanding and
+designing the external field induced chemical reactions in natural and
+applicable systems. Here, we report for the first time the redistribution of
+oxygen molecules in RTILs due to laser-induced microstructure changes probed by
+triplet excited state dynamics of porphyrin and rotational dynamics of coumarin
+153. A remarkably long-lived triplet excited state of porphyrin is observed in
+air-saturated ionic liquid with the changes of microstructure after
+irradiation, suggesting that more charge-shifted O2 induced by external laser
+field move into the polar domains of ionic liquid [C8mim][PF6] from nonpolar
+domains through electrostatic interactions. The results presented here suggest
+that the heterogeneous systems of ionic liquids upon external stimuli can be
+designed for those oxygen-related chemical reactions with extensive
+inspirations for potential applications in lithium-air batteries, gaseous
+sensing, photoelectrical catalysis and so on.
+"
+27361,Bordered Floer homology and incompressible surfaces,"  We show that bordered Heegaard Floer homology detects incompressible surfaces
+and bordered-sutured Floer homology detects partly boundary parallel tangles
+and bridges, in natural ways. For example, there is a bimodule Lambda so that
+the tensor product of CFD(Y) and Lambda is Hom-orthogonal to CFD(Y) if and only
+if the boundary of Y admits an essential compressing disk. In the process, we
+sharpen a nonvanishing result of Ni's. We also extend
+Lipshitz-Ozsváth-Thurston's ""factoring"" algorithm for computing HF-hat to
+compute bordered-sutured Floer homology, to make both results on detecting
+incompressibility practical. In particular, this makes Zarev's tangle invariant
+manifestly combinatorial.
+"
+27362,On the number of connected components of the set of fixed points of isometries of geometrically finite hyperbolic manifolds,"  Let $M$ be a geometrically finite $m$-dimensional hyperbolic manifold, where
+$m \geq 2$, and let $G$ be a finite group of its isometries. We provide a
+formula for the number of connected components of the locus of fixed points of
+any element of $G$ in terms of its action. This formula extends Macbetah's one
+for the case of compact Riemann surfaces of genus at least two, which was
+initially used to obtain the character of the representation associated to the
+induced action of $G$ on the first homology group, and later turned out to be
+extremely useful in many other contexts.
+"
+27363,Electrostatics of Patchy Surfaces,"  In the study of colloidal, biological and electrochemical systems, it is
+customary to treat surfaces, macromolecules and electrodes as homogeneously
+charged. This simplified approach is proven successful in most cases, but fails
+to describe a wide range of heterogeneously charged surfaces commonly used in
+experiments. For example, recent experiments have revealed a long-range
+attraction between overall neutral surfaces, locally charged in a mosaic-like
+structure of positively and negatively charged domains (""patches""). Here we
+review experimental and theoretical studies addressing the stability of
+heterogeneously charged surfaces, their ionic strength in solution, and the
+interaction between two such surfaces. We focus on electrostatics, and
+highlight the important new physical parameters appearing in the heterogeneous
+case, such as the largest patch size and inter-surface charge correlations.
+"
+27364,A Condensation-Coalescence Cloud Model for Exoplanetary Atmospheres: Formulation and Test Applications to Terrestrial and Jovian Clouds,"  A number of transiting exoplanets have featureless transmission spectra that
+might suggest the presence of clouds at high altitudes. A realistic cloud model
+is necessary to understand the atmospheric conditions under which such
+high-altitude clouds can form. In this study, we present a new cloud model that
+takes into account the microphysics of both condensation and coalescence. Our
+model provides the vertical profiles of the size and density of cloud and rain
+particles in an updraft for a given set of physical parameters, including the
+updraft velocity and the number density of cloud condensation nuclei (CCN). We
+test our model by comparing with observations of trade-wind cumuli on the Earth
+and ammonia ice clouds in Jupiter. For trade-wind cumuli, the model including
+both condensation and coalescence gives predictions that are consistent with
+observations, while the model including only condensation overestimates the
+mass density of cloud droplets by up to an order of magnitude. For Jovian
+ammonia clouds, the condensation-coalescence model simultaneously reproduces
+the effective particle radius, cloud optical thickness, and cloud geometric
+thickness inferred from Voyager observations if the updraft velocity and CCN
+number density are taken to be consistent with the results of moist convection
+simulations and Galileo probe measurements, respectively. These results suggest
+that the coalescence of condensate particles is important not only in
+terrestrial water clouds but also in Jovian ice clouds. Our model will be
+useful to understand how the dynamics, compositions, and nucleation processes
+in exoplanetary atmospheresaffects the vertical extent and optical thickness of
+exoplanetary clouds via cloud microphysics.
+"
+27365,Learning Sparse Visual Representations with Leaky Capped Norm Regularizers,"  Sparsity inducing regularization is an important part for learning
+over-complete visual representations. Despite the popularity of $\ell_1$
+regularization, in this paper, we investigate the usage of non-convex
+regularizations in this problem. Our contribution consists of three parts.
+First, we propose the leaky capped norm regularization (LCNR), which allows
+model weights below a certain threshold to be regularized more strongly as
+opposed to those above, therefore imposes strong sparsity and only introduces
+controllable estimation bias. We propose a majorization-minimization algorithm
+to optimize the joint objective function. Second, our study over monocular 3D
+shape recovery and neural networks with LCNR outperforms $\ell_1$ and other
+non-convex regularizations, achieving state-of-the-art performance and faster
+convergence. Third, we prove a theoretical global convergence speed on the 3D
+recovery problem. To the best of our knowledge, this is the first convergence
+analysis of the 3D recovery problem.
+"
+27366,Accurate gradient computations at interfaces using finite element methods,"  New finite element methods are proposed for elliptic interface problems in
+one and two dimensions. The main motivation is not only to get an accurate
+solution but also an accurate first order derivative at the interface (from
+each side). The key in 1D is to use the idea from \cite{wheeler1974galerkin}.
+For 2D interface problems, the idea is to introduce a small tube near the
+interface and introduce the gradient as part of unknowns, which is similar to a
+mixed finite element method, except only at the interface. Thus the
+computational cost is just slightly higher than the standard finite element
+method. We present rigorous one dimensional analysis, which show second order
+convergence order for both of the solution and the gradient in 1D. For two
+dimensional problems, we present numerical results and observe second order
+convergence for the solution, and super-convergence for the gradient at the
+interface.
+"
+27367,"Ultra-high Compton Frequency, Parity Independent, Mesoscopic Schrödinger Cat Atom Interferometer with Heisenberg Limited Sensitivity","  We present a protocol for an atomic interferometer that reaches the
+Heisenberg Limit (HL), within a factor of $\sim$ $\sqrt{2}$, via collective
+state detection and critical tuning of one-axis twist spin squeezing. It
+generates a Schrödinger cat (SC) state, as a superposition of two extremal
+collective states. When this SC interferometer is used as a gyroscope, the
+interference occurs at an ultrahigh Compton frequency, corresponding to a
+mesoscopic single object with a mass of $Nm$, where $N$ is the number of
+particles in the ensemble, and $m$ is the mass of each particle. For $^{87}$Rb
+atoms, with $N=10^{6}$, for example, the intereference would occur at a Compton
+frequency of $\sim$ $2 \times 10^{31}$ Hz. Under this scheme, the signal is
+found to depend critically on the parity of $N$. We present two variants of the
+protocol. Under Protocol A, the fringes are narrowed by a factor of $N$ for one
+parity, while for the other parity the signal is zero. Under Protocol B, the
+fringes are narrowed by a factor of $N$ for one parity, and by a factor of
+$\sqrt{N}$ for the other parity. Both protocols can be modified in a manner
+that reverses the behavior of the signals for the two parities. Over repeated
+measurements under which the probability of being even or odd is equal, the
+averaged sensitivity is smaller than the HL by a factor of $\sim$ $\sqrt{2}$
+for both versions of the protocol. We show that when the SC interferometer is
+configured as an accelerometer, the effective two-photon wave vector is
+enhanced by a factor of $N$, leading to the same degree of enhancement in
+sensitivity. We also show that such a mesoscopic single object can be used to
+increase the effective base frequency of an atomic clock by a factor of $N$,
+with a sensitivity that is equivalent to the HL, within a factor of $\sim$
+$\sqrt{2}$.
+"
+27368,Influence of the Galactic gravitational field on the positional accuracy of extragalactic sources,"  We investigate the influence of random variations of the Galactic
+gravitational field on the apparent celestial positions of extragalactic
+sources. The basic statistical characteristics of a stochastic process
+(first-order moments, an autocorrelation function and a power spectral density)
+are used to describe a light ray deflection in a gravitational field of
+randomly moving point masses as a function of the source coordinates. We map a
+2D distribution of the standard deviation of the angular shifts in positions of
+distant sources (including reference sources of the International Celestial
+Reference Frame) with respect to their true positions. For different Galactic
+matter distributions the standard deviation of the offset angle can reach
+several tens of $\mu as$ (microarcsecond) toward the Galactic center,
+decreasing down to 4-6 $\mu as$ at high galactic latitudes. The conditional
+standard deviation (`jitter') of 2.5 $\mu as$ is reached within 10 years at
+high galactic latitudes and within a few months toward the inner part of the
+Galaxy. The photometric microlensing events are not expected to be disturbed by
+astrometric random variations anywhere except the inner part of the Galaxy as
+the Einstein--Chvolson times are typically much shorter than the jittering
+timescale. While a jitter of a single reference source can be up to dozens of
+$\mu as$ over some reasonable observational time, using a sample of reference
+sources would reduce the error in relative astrometry. The obtained results can
+be used for estimating the physical upper limits on the time-dependent accuracy
+of astrometric measurements.
+"
+27369,GalRotpy: an educational tool to understand and parametrize the rotation curve and gravitational potential of disk-like galaxies,"  \textbf{GalRotpy} is an educational \verb+Python3+-based visual tool, which
+is useful to undestand how is the contribution of each mass component to the
+gravitational potential of disc-like galaxies by means of their rotation curve.
+Besides, \textbf{GalRotpy} allows the user to perform a parametric fit of a
+given rotation curve, which relies on a MCMC procedure implemented by using
+\verb+emcee+ package. Here the gravitational potential of disc-like galaxies is
+built from the contribution of a Miyamoto-Nagai potential model for the
+bulge/core and the thin/thick disc, an exponential disc, together with the NFW
+(Navarro-Frenk- White) potential or the Burkert (cored density profile)
+potential for the Dark Matter halo, where each contribution is implemented by
+using \verb+galpy+ package. We summarize the properties of each contribution to
+the rotation curve involved, and then describe how \textbf{GalRotpy} is
+implemented along with its capabilities. Finally we present the
+characterization of two galaxies, NGC6361 and M33, and show that the results
+for M33 provided by \textbf{GalRotpy} are consistent with those found in the
+literature.
+"
+27370,Shape-Color Differential Moment Invariants under Affine Transformations,"  We propose the general construction formula of shape-color primitives by
+using partial differentials of each color channel in this paper. By using all
+kinds of shape-color primitives, shape-color differential moment invariants can
+be constructed very easily, which are invariant to the shape affine and color
+affine transforms. 50 instances of SCDMIs are obtained finally. In experiments,
+several commonly used color descriptors and SCDMIs are used in image
+classification and retrieval of color images, respectively. By comparing the
+experimental results, we find that SCDMIs get better results.
+"
+27371,Synthesizing Filamentary Structured Images with GANs,"  This paper aims at synthesizing filamentary structured images such as retinal
+fundus images and neuronal images, as follows: Given a ground-truth, to
+generate multiple realistic looking phantoms. A ground-truth could be a binary
+segmentation map containing the filamentary structured morphology, while the
+synthesized output image is of the same size as the ground-truth and has
+similar visual appearance to what have been presented in the training set. Our
+approach is inspired by the recent progresses in generative adversarial nets
+(GANs) as well as image style transfer. In particular, it is dedicated to our
+problem context with the following properties: Rather than large-scale dataset,
+it works well in the presence of as few as 10 training examples, which is
+common in medical image analysis; It is capable of synthesizing diverse images
+from the same ground-truth; Last and importantly, the synthetic images produced
+by our approach are demonstrated to be useful in boosting image analysis
+performance. Empirical examination over various benchmarks of fundus and
+neuronal images demonstrate the advantages of the proposed approach.
+"
+27372,Dynamics of Mixed Convective--Stably-Stratified Fluids,"  We study the dynamical regimes of a density-stratified fluid confined between
+isothermal no-slip top and bottom boundaries (at temperatures $T_t$ and $T_b$)
+via direct numerical simulation. The thermal expansion coefficient of the fluid
+is temperature dependent and chosen such that the fluid density is maximum at
+the inversion temperature $T_b>T_i>T_t$. Thus, the lower layer of the fluid is
+convectively unstable while the upper layer is stably stratified. We show that
+the characteristics of the convection change significantly depending on the
+degree of stratification of the stable layer. For strong stable stratification,
+the convection zone coincides with the fraction of the fluid that is
+convectively unstable (i.e. where $T>T_i$), and convective motions consist of
+rising and sinking plumes of large density anomaly, as is the case in canonical
+Rayleigh-Benard convection; internal gravity waves are generated by turbulent
+fluctuations in the convective layer and propagate in the upper layer. For weak
+stable stratification, we demonstrate that a large fraction of the stable fluid
+(i.e. with temperature $T<T_i$) is instead destabilized and entrained by
+buoyant plumes emitted from the bottom boundary. The convection thus mixes cold
+patches of low density-anomaly fluid with hot upward plumes, and the end result
+is that the $T_i$ isotherm sinks within the bottom boundary layer and that the
+convection is entrainment-dominated. We provide a phenomenological description
+of the transition between the regimes of plume-dominated and
+entrainment-dominated convection through analysis of quantities for different
+stratification strengths. We find that the effect of the stable layer on the
+convection decreases only weakly with increasing stratification strength,
+meaning that the dynamics of the stable layer and convection should be studied
+self-consistently in a wide range of applications.
+"
+27373,Characterization of the Ito Integral,"  This paper provides an existence-and-uniqueness theorem characterizing the
+stochastic integral with respect to a Wiener process. The integral is
+represented as a mapping from the space of measurable and adapted pathwise
+locally integrable processes to the space of continuous adapted processes. It
+is characterized in terms of two properties: (1) how the stochastic integrals
+of simple processes are calculated and (2) how these integrals converge in
+probability when the time integrals of the squared integrands converge in
+probability.
+"
+27374,Twisted Molecular Excitons as Mediators for Changing the Angular Momentum of Light,"  Molecules with C_N or C_Nh symmetry can absorb quanta of optical angular
+momentum to generate twisted excitons with well-defined quasi-angular momenta
+of their own. Angular momentum is conserved in such interactions at the level
+of a paraxial approximation for the light beam. A sequence of absorption events
+can thus be used to create a range of excitonic angular momenta. Subsequent
+decay can produce radiation with a single angular momentum equal to that
+accumulated. Such molecules can thus be viewed as mediators for changing the
+angular momentum of light. This sidesteps the need to exploit nonlinear
+light-matter interactions based on higher-order susceptibilities. A
+tight-binding paradigm is used to verify angular momentum conservation and
+demonstrate how it can be exploited to change the angular momentum of light.
+The approach is then extended to a time-dependent density functional theory
+setting where the key results are shown to hold in a many-body, multi-level
+setting.
+"
+27375,$n$-cluster tilting subcategories of representation-directed algebras,"  We give a characterization of $n$-cluster tilting subcategories of
+representation-directed algebras based on the $n$-Auslander-Reiten
+translations. As an application we classify acyclic Nakayama algebras with
+homogeneous relations which admit an $n$-cluster tilting subcategory. Finally,
+we classify Nakayama algebras of global dimension $d<\infty$ which admit a
+$d$-cluster tilting subcategory.
+"
+27376,Two types of quasiperiodic partial synchrony in oscillator ensembles,"  We analyze quasiperiodic partially synchronous states in an ensemble of
+Stuart-Landau oscillators with global nonlinear coupling. We reveal two types
+of such dynamics: in the first case the time-averaged frequencies of
+oscillators and of the mean field differ, while in the second case they are
+equal, but the motion of oscillators is additionally modulated. We describe
+transitions from the synchronous state to both types of quasiperiodic dynamics,
+and a transition between two different quasiperiodic states. We present an
+example of a bifurcation diagram, where we show the borderlines for all these
+transitions, as well as domain of bistability.
+"
+27377,"Prior-preconditioned conjugate gradient method for accelerated Gibbs sampling in ""large n & large p"" sparse Bayesian regression","  In a modern observational study based on healthcare databases, the number of
+observations typically ranges in the order of 10^5 ~ 10^6 and that of the
+predictors in the order of 10^4 ~ 10^5. Despite the large sample size, data
+rarely provide sufficient information to reliably estimate such a large number
+of parameters. Sparse regression provides a potential solution. Bayesian
+approaches based on shrinkage priors possess many desirable theoretical
+properties and, under linear and logistic models, yield posterior distributions
+amenable to Gibbs sampling. A major computational bottleneck arises in the
+""large n & large p"" setting, however, from the need to sample from a
+high-dimensional Gaussian distribution at each iteration; despite the
+availability of a closed-form expression for the precision matrix $\Phi$,
+computing and factorizing such a large matrix is computationally expensive
+nonetheless. In this article, we present a novel algorithm to speed up this
+bottleneck based on the following observation: we can cheaply generate a random
+vector $b$ such that the solution to the linear system $\Phi \beta = b$ has the
+desired Gaussian distribution. We can then solve the linear system by the
+conjugate gradient (CG) algorithm through the matrix-vector multiplications by
+$\Phi$, without ever explicitly inverting $\Phi$. As practical performance of
+CG depends critically on appropriate preconditioning of the linear system, we
+develop a theory of prior-preconditioning to turn CG into a highly effective
+algorithm for sparse Bayesian regression. We apply our algorithm to a
+clinically relevant large-scale observational study with n = 72,489 and p =
+22,175, designed to assess the relative risk of intracranial hemorrhage from
+two alternative blood anti-coagulants. Our algorithm demonstrates an order of
+magnitude speed-up in the posterior computation.
+"
+27378,Pricing average price advertising options when underlying spot market prices are discontinuous,"  Advertising options have been recently studied as a special type of
+guaranteed contracts in online advertising, which are an alternative sales
+mechanism to real-time auctions. An advertising option is a contract which
+gives its buyer a right but not obligation to enter into transactions to
+purchase page views or link clicks at one or multiple pre-specified prices in a
+specific future period. Different from typical guaranteed contracts, the option
+buyer pays a lower upfront fee but can have greater flexibility and more
+control of advertising. Many studies on advertising options so far have been
+restricted to the situations where the option payoff is determined by the
+underlying spot market price at a specific time point and the price evolution
+over time is assumed to be continuous. The former leads to a biased calculation
+of option payoff and the latter is invalid empirically for many online
+advertising slots. This paper addresses these two limitations by proposing a
+new advertising option pricing framework. First, the option payoff is
+calculated based on an average price over a specific future period. Therefore,
+the option becomes path-dependent. The average price is measured by the power
+mean, which contains several existing option payoff functions as its special
+cases. Second, jump-diffusion stochastic models are used to describe the
+movement of the underlying spot market price, which incorporate several
+important statistical properties including jumps and spikes, non-normality, and
+absence of autocorrelations. A general option pricing algorithm is obtained
+based on Monte Carlo simulation. In addition, an explicit pricing formula is
+derived for the case when the option payoff is based on the geometric mean.
+This pricing formula is also a generalized version of several other option
+pricing models discussed in related studies.
+"
+27379,Identification of PAH Isomeric Structure in Cosmic Dust Analogues: the AROMA setup,"  We developed a new analytical experimental setup called AROMA (Astrochemistry
+Research of Organics with Molecular Analyzer) that combines laser
+desorption/ionization techniques with ion trap mass spectrometry. We report
+here on the ability of the apparatus to detect aromatic species in complex
+materials of astrophysical interests and characterize their structures. A limit
+of detection of 100 femto-grams has been achieved using pure polycyclic
+aromatic hydrocarbon (PAH) samples, which corresponds to 2x10^8 molecules in
+the case of coronene (C24H12). We detected the PAH distribution in the
+Murchison meteorite, which is made of a complex mixture of extraterrestrial
+organic compounds. In addition, collision induced dissociation experiments were
+performed on selected species detected in Murchison, which led to the first
+firm identification of pyrene and its methylated derivatives in this sample.
+"
+27380,Resonances for random highly oscillatory potentials,"  We study discrete spectral quantities associated to Schrödinger operators
+of the form $-\Delta_{\mathbb{R}^d}+V_N$, $d$ odd. The potential $V_N$ models a
+highly disordered crystal; it varies randomly at scale $N^{-1} \ll 1$. We use
+perturbation analysis to obtain almost sure convergence of the eigenvalues and
+scattering resonances of $-\Delta_{\mathbb{R}^d}+V_N$ as $N \rightarrow
+\infty$. We identify a stochastic and a deterministic regime for the speed of
+convergence. The type of regime depends whether the low frequencies effects due
+to large deviations overcome the (deterministic) constructive interference
+between highly oscillatory terms.
+"
+27381,Vanishing Pohozaev constant and removability of singularities,"  Conformal invariance of two-dimensional variational problems is a condition
+known to enable a blow-up analysis of solutions and to deduce the removability
+of singularities. In this paper, we identify another condition that is not only
+sufficient, but also necessary for such a removability of singularities. This
+is the validity of the Pohozaev identity. In situations where such an identity
+fails to hold, we introduce a new quantity, called the {\it Pohozaev constant},
+which on one hand measures the extent to which the Pohozaev identity fails and
+on the other hand provides a characterization of the singular behavior of a
+solution at an isolated singularity. We apply this to the blow-up analysis for
+super-Liouville type equations on Riemann surfaces with conical singularities,
+because in the presence of such singularities, conformal invariance no longer
+holds and a local singularity is in general non-removable unless the Pohozaev
+constant is vanishing.
+"
+27382,Gene Hunting with Knockoffs for Hidden Markov Models,"  Modern scientific studies often require the identification of a subset of
+relevant explanatory variables, in the attempt to understand an interesting
+phenomenon. Several statistical methods have been developed to automate this
+task, but only recently has the framework of model-free knockoffs proposed a
+general solution that can perform variable selection under rigorous type-I
+error control, without relying on strong modeling assumptions. In this paper,
+we extend the methodology of model-free knockoffs to a rich family of problems
+where the distribution of the covariates can be described by a hidden Markov
+model (HMM). We develop an exact and efficient algorithm to sample knockoff
+copies of an HMM. We then argue that combined with the knockoffs selective
+framework, they provide a natural and powerful tool for performing principled
+inference in genome-wide association studies with guaranteed FDR control.
+Finally, we apply our methodology to several datasets aimed at studying the
+Crohn's disease and several continuous phenotypes, e.g. levels of cholesterol.
+"
+27383,Variable selection via Group LASSO Approach : Application to the Cox Regression and frailty model,"  In the analysis of survival outcome supplemented with both clinical
+information and high-dimensional gene expression data, use of the traditional
+Cox proportional hazards model (1972) fails to meet some emerging needs in
+biomedical research. First, the number of covariates is generally much larger
+the sample size. Secondly, predicting an outcome based on individual gene
+expression is inadequate because multiple biological processes and functional
+pathways regulate the expression associated with a gene. Another challenge is
+that the Cox model assumes that populations are homogenous, implying that all
+individuals have the same risk of death, which is rarely true due to unmeasured
+risk factors among populations. In this paper we propose group LASSO with
+gamma-distributed frailty for variable selection in Cox regression by extending
+previous scholarship to account for heterogeneity among group structures
+related to exposure and susceptibility. The consistency property of the
+proposed method is established. This method is appropriate for addressing a
+wide variety of research questions from genetics to air pollution. Simulated
+analysis shows promising performance by group LASSO compared with other
+methods, including group SCAD and group MCP. Future directions include
+expanding the use of frailty with adaptive group LASSO and sparse group LASS.
+"
+27384,Fast Black-box Variational Inference through Stochastic Trust-Region Optimization,"  We introduce TrustVI, a fast second-order algorithm for black-box variational
+inference based on trust-region optimization and the reparameterization trick.
+At each iteration, TrustVI proposes and assesses a step based on minibatches of
+draws from the variational distribution. The algorithm provably converges to a
+stationary point. We implemented TrustVI in the Stan framework and compared it
+to two alternatives: Automatic Differentiation Variational Inference (ADVI) and
+Hessian-free Stochastic Gradient Variational Inference (HFSGVI). The former is
+based on stochastic first-order optimization. The latter uses second-order
+information, but lacks convergence guarantees. TrustVI typically converged at
+least one order of magnitude faster than ADVI, demonstrating the value of
+stochastic second-order information. TrustVI often found substantially better
+variational distributions than HFSGVI, demonstrating that our convergence
+theory can matter in practice.
+"
+27385,Toward the measurement of the hyperfine structure of muonic hydrogen in the FAMU experiment. Multi-pass cavity optimization for experiments with pulsed sources,"  We consider a simplified model of the optical multi-pass cavity that is being
+currently developed by the FAMU collaboration for the measurement of the
+hyperfine splitting in the ground state of muonic hydrogen and of the Zemach
+radius of the proton. The model is focused on the time distribution of the
+events of laser-stimulated hyperfine transitions in the muonuc atom and may be
+helpful in the preliminary design of the FAMU experimental set-up and, more
+generally, in the optimization of multi-pass optical cavities for experiments
+with pulsed lasers.
+"
+27386,Effective limit theorems for Markov chains with a spectral gap,"  Applying quantitative perturbation theory for linear operators, we prove
+non-asymptotic limit theorems for Markov chains whose transition kernel has a
+spectral gap in an arbitrary Banach algebra of functions X . The main results
+are concentration inequalities and Berry-Esseen bounds, obtained assuming
+neither reversibility nor `warm start' hypothesis: the law of the first term of
+the chain can be arbitrary. The spectral gap hypothesis is basically a uniform
+X-ergodicity hypothesis, and when X consist in regular functions this is weaker
+than uniform ergodicity. We show on a few examples how the flexibility in the
+choice of function space can be used. The constants are completely explicit and
+reasonable enough to make the results usable in practice, notably in MCMC
+methods.v2: Introduction rewritten, Section 3 applying the main results to
+examples improved (uniformly ergodic chains and Bernoulli convolutions have
+been notably added) . Main results and their proofs are unchanged.
+"
+27387,Faster Support Vector Machines,"  The time complexity of support vector machines (SVMs) prohibits training on
+huge data sets with millions of samples. Recently, multilevel approaches to
+train SVMs have been developed to allow for time efficient training on huge
+data sets. While regular SVMs perform the entire training in one - time
+consuming - optimization step, multilevel SVMs first build a hierarchy of
+problems decreasing in size that resemble the original problem and then train
+an SVM model for each hierarchy level benefiting from the solved models of
+previous levels. We present a faster multilevel support vector machine that
+uses a label propagation algorithm to construct the problem hierarchy.
+Extensive experiments show that our new algorithm achieves speed-ups up to two
+orders of magnitude while having similar or better classification quality over
+state-of-the-art algorithms.
+"
+27388,Regular pseudo-hyperovals and regular pseudo-ovals in even characteristic,"  S. Rottey and G. Van de Voorde characterized regular pseudo-ovals of PG(3n -
+1, q), q = 2^h, h >1 and n prime. Here an alternative proof is given and
+slightly stronger results are obtained.
+"
+27389,A Bloch-based procedure for dispersion analysis of lattices with periodic time-varying properties,"  We present a procedure for the systematic estimation of the dispersion
+properties of linear discrete systems with periodic time-varying coefficients.
+The approach relies on the analysis of a single unit cell, making use of Bloch
+theorem along with the application of a harmonic balance methodology over an
+imposed solution ansatz. The solution of the resulting eigenvalue problem is
+followed by a procedure that selects the eigen-solutions corresponding to the
+ansatz, which is a plane wave defined by a frequency-wavenumber pair. Examples
+on spring-mass superlattices demonstrate the effectiveness of the method at
+predicting the dispersion behavior of linear elastic media. The matrix
+formulation of the problem suggests the broad applicability of the proposed
+technique. Furthermore, it is shown how dispersion can inform about the dynamic
+behavior of time-modulated finite lattices. The technique can be extended to
+multiple areas of physics, such as acoustic, elastic and electromagnetic
+systems, where periodic time-varying material properties may be used to obtain
+non-reciprocal wave propagation.
+"
+27390,Alloying strategy for two-dimensional GaN optical emitters,"  The recent progress in formation of two-dimensional (2D) GaN by a
+migration-enhanced encapsulated technique opens up new possibilities for group
+III-V 2D semiconductors with a band gap within the visible energy spectrum.
+Using first-principles calculations we explored alloying of 2D-GaN to achieve
+an optically active material with a tuneable band gap. The effect of
+isoelectronic III-V substitutional elements on the band gaps, band offsets, and
+spatial electron localization is studied. In addition to optoelectronic
+properties, the formability of alloys is evaluated using impurity formation
+energies. A dilute highly-mismatched solid solution 2D-GaN$_{1-x}$P$_x$
+features an efficient band gap reduction in combination with a moderate energy
+penalty associated with incorporation of phosphorous in 2D-GaN, which is
+substantially lower than in the case of the bulk GaN. The group-V alloying
+elements also introduce significant disorder and localization at the valence
+band edge that facilitates direct band gap optical transitions thus implying
+the feasibility of using III-V alloys of 2D-GaN in light-emitting devices.
+"
+27391,Origraph: Interactive Network Wrangling,"  Data wrangling is widely acknowledged to be a critical part of the data
+analysis pipeline. Nevertheless, there are currently no techniques to
+efficiently wrangle network datasets. Here we introduce a set of interaction
+techniques that enable analysts to carry out complex network wrangling
+operations. These operations include deriving attributes across connected
+classes, converting nodes to edges and vice-versa, and faceting nodes and edges
+based on attributes. We implement these operations in a web-based, open-source
+system, Origraph, which provides interfaces to execute the operations and
+investigate the results. Designed for wrangling, rather than analysis, Origraph
+can be used to load data in many forms, wrangle and transform the network, and
+export it in formats compatible with common network visualization tools. We
+demonstrate Origraph's usefulness in a series of examples with different
+datasets from a variety of sources.
+"
+27392,A Tropical F5 algorithm,"  Let K be a field equipped with a valuation. Tropical varieties over K can be
+defined with a theory of Gr{ö}bner bases taking into account the valuation of
+K. While generalizing the classical theory of Gr{ö}bner bases, it is not
+clear how modern algorithms for computing Gr{ö}bner bases can be adapted to
+the tropical case. Among them, one of the most efficient is the celebrated F5
+Algorithm of Faug{è}re. In this article, we prove that, for homogeneous
+ideals, it can be adapted to the tropical case. We prove termination and
+correctness. Because of the use of the valuation, the theory of tropical
+Gr{ö}b-ner bases is promising for stable computations over polynomial rings
+over a p-adic field. We provide numerical examples to illustrate
+time-complexity and p-adic stability of this tropical F5 algorithm.
+"
+27393,Formal Analysis of V2X Revocation Protocols,"  Research on vehicular networking (V2X) security has produced a range of
+security mechanisms and protocols tailored for this domain, addressing both
+security and privacy. Typically, the security analysis of these proposals has
+largely been informal. However, formal analysis can be used to expose flaws and
+ultimately provide a higher level of assurance in the protocols.
+This paper focusses on the formal analysis of a particular element of
+security mechanisms for V2X found in many proposals: the revocation of
+malicious or misbehaving vehicles from the V2X system by invalidating their
+credentials. This revocation needs to be performed in an unlinkable way for
+vehicle privacy even in the context of vehicles regularly changing their
+pseudonyms. The REWIRE scheme by Forster et al. and its subschemes BASIC and
+RTOKEN aim to solve this challenge by means of cryptographic solutions and
+trusted hardware.
+Formal analysis using the TAMARIN prover identifies two flaws with some of
+the functional correctness and authentication properties in these schemes. We
+then propose Obscure Token (OTOKEN), an extension of REWIRE to enable
+revocation in a privacy preserving manner. Our approach addresses the
+functional and authentication properties by introducing an additional key-pair,
+which offers a stronger and verifiable guarantee of successful revocation of
+vehicles without resolving the long-term identity. Moreover OTOKEN is the first
+V2X revocation protocol to be co-designed with a formal model.
+"
+27394,Profinite semigroups,"  We present a survey of results on profinite semigroups and their link with
+symbolic dynamics. We develop a series of results, mostly due to Almeida and
+Costa and we also include some original results on the Schützenberger groups
+associated to a uniformly recurrent set.
+"
+27395,An alternative to the spin-coupled interface resistance for describing heat generation,"  Using a macroscopic approach, we studied theoretically the heat generation in
+a typical spin valve with nonmagnetic spacer layer of finite thickness. Our
+analysis shows that the spin-dependent heat generation cannot be interpreted as
+the Joule heating of the spin-coupled interface resistance except for some
+special segments. Moreover, the spin-coupled interface resistance can be
+negative in certain situation, and thus its ""Joule heating"" should be
+understood instead as the work done by the extra field in the ferromagnetic
+layers and at the spin-selective interfaces. Effective resistances are proposed
+as alternatives so that the spin-dependent heat generation can still be
+expressed in a form resembling Joule's law.
+"
+27396,Graph Based Analysis for Gene Segment Organization In a Scrambled Genome,"  DNA rearrangement processes recombine gene segments that are organized on the
+chromosome in a variety of ways. The segments can overlap, interleave or one
+may be a subsegment of another. We use directed graphs to represent segment
+organizations on a given locus where contigs containing rearranged segments
+represent vertices and the edges correspond to the segment relationships. Using
+graph properties we associate a point in a higher dimensional Euclidean space
+to each graph such that cluster formations and analysis can be performed with
+methods from topological data analysis. The method is applied to a recently
+sequenced model organism \textit{Oxytricha trifallax}, a species of ciliate
+with highly scrambled genome that undergoes massive rearrangement process after
+conjugation. The analysis shows some emerging star-like graph structures
+indicating that segments of a single gene can interleave, or even contain all
+of the segments from fifteen or more other genes in between its segments. We
+also observe that as many as six genes can have their segments mutually
+interleaving or overlapping.
+"
+27397,Navigation of brain networks,"  Understanding the mechanisms of neural communication in large-scale brain
+networks remains a major goal in neuroscience. We investigated whether
+navigation is a parsimonious routing model for connectomics. Navigating a
+network involves progressing to the next node that is closest in distance to a
+desired destination. We developed a measure to quantify navigation efficiency
+and found that connectomes in a range of mammalian species (human, mouse and
+macaque) can be successfully navigated with near-optimal efficiency (>80% of
+optimal efficiency for typical connection densities). Rewiring network topology
+or repositioning network nodes resulted in 45%-60% reductions in navigation
+performance. Specifically, we found that brain networks cannot be progressively
+rewired (randomized or clusterized) to result in topologies with significantly
+improved navigation performance. Navigation was also found to: i) promote a
+resource-efficient distribution of the information traffic load, potentially
+relieving communication bottlenecks; and, ii) explain significant variation in
+functional connectivity. Unlike prevalently studied communication strategies in
+connectomics, navigation does not mandate biologically unrealistic assumptions
+about global knowledge of network topology. We conclude that the wiring and
+spatial embedding of brain networks is conducive to effective decentralized
+communication. Graph-theoretic studies of the connectome should consider
+measures of network efficiency and centrality that are consistent with
+decentralized models of neural communication.
+"
+27398,Path Homotopy Invariants and their Application to Optimal Trajectory Planning,"  We consider the problem of optimal path planning in different homotopy
+classes in a given environment. Though important in robotics applications,
+path-planning with reasoning about homotopy classes of trajectories has
+typically focused on subsets of the Euclidean plane in the robotics literature.
+The problem of finding optimal trajectories in different homotopy classes in
+more general configuration spaces (or even characterizing the homotopy classes
+of such trajectories) can be difficult. In this paper we propose automated
+solutions to this problem in several general classes of configuration spaces by
+constructing presentations of fundamental groups and giving algorithms for
+solving the \emph{word problem} in such groups. We present explicit results
+that apply to knot and link complements in 3-space, discuss how to extend to
+cylindrically-deleted coordination spaces of arbitrary dimension, and also
+present results in the coordination space of robots navigating on an Euclidean
+plane.
+"
+27399,Towards reduction of autocorrelation in HMC by machine learning,"  In this paper we propose new algorithm to reduce autocorrelation in Markov
+chain Monte-Carlo algorithms for euclidean field theories on the lattice. Our
+proposing algorithm is the Hybrid Monte-Carlo algorithm (HMC) with restricted
+Boltzmann machine. We examine the validity of the algorithm by employing the
+phi-fourth theory in three dimension. We observe reduction of the
+autocorrelation both in symmetric and broken phase as well. Our proposing
+algorithm provides consistent central values of expectation values of the
+action density and one-point Green's function with ones from the original HMC
+in both the symmetric phase and broken phase within the statistical error. On
+the other hand, two-point Green's functions have slight difference between one
+calculated by the HMC and one by our proposing algorithm in the symmetric
+phase. Furthermore, near the criticality, the distribution of the one-point
+Green's function differs from the one from HMC. We discuss the origin of
+discrepancies and its improvement.
+"
+27400,Early stopping for kernel boosting algorithms: A general analysis with localized complexities,"  Early stopping of iterative algorithms is a widely-used form of
+regularization in statistics, commonly used in conjunction with boosting and
+related gradient-type algorithms. Although consistency results have been
+established in some settings, such estimators are less well-understood than
+their analogues based on penalized regularization. In this paper, for a
+relatively broad class of loss functions and boosting algorithms (including
+L2-boost, LogitBoost and AdaBoost, among others), we exhibit a direct
+connection between the performance of a stopped iterate and the localized
+Gaussian complexity of the associated function class. This connection allows us
+to show that local fixed point analysis of Gaussian or Rademacher complexities,
+now standard in the analysis of penalized estimators, can be used to derive
+optimal stopping rules. We derive such stopping rules in detail for various
+kernel classes, and illustrate the correspondence of our theory with practice
+for Sobolev kernel classes.
+"
+27401,Hydrodynamic ablation of protoplanetary disks via supernovae,"  We present three-dimensional simulations of a protoplanetary disk subject to
+the effect of a nearby (0.3pc distant) supernova, using a time-dependent flow
+from a one dimensional numerical model of the supernova remnant (SNR), in
+addition to constant peak ram pressure simulations. Simulations are performed
+for a variety of disk masses and inclination angles. We find disk mass-loss
+rates that are typically 1e-7 to 1e-6 Msol/yr (but peak near 1e-5 Msol/yr
+during the ""instantaneous"" stripping phase) and are sustained for around 200
+yr. Inclination angle has little effect on the mass loss unless the disk is
+close to edge-on. Inclined disks also strip asymmetrically with the trailing
+edge ablating more easily. Since the interaction lasts less than one outer
+rotation period, there is not enough time for the disk to restore its symmetry,
+leaving the disk asymmetrical after the flow has passed. Of the low-mass disks
+considered, only the edge-on disk is able to survive interaction with the SNR
+(with 50% of its initial mass remaining). At the end of the simulations, disks
+that survive contain fractional masses of SN material up to 5e-6. This is too
+low to explain the abundance of short-lived radionuclides in the early solar
+system, but a larger disk and the inclusion of radiative cooling might allow
+the disk to capture a higher fraction of SN material.
+"
+27402,$\ell$-distance-balanced graphs,"  Let $\ell$ denote a positive integer. A connected graph $\G$ of diameter at
+least $\ell$ is said to be $\ell${\it -distance-balanced} whenever for any pair
+of vertices $u,v$ of $\G$ such that $d(u,v)=\ell$, the number of vertices
+closer to $u$ than to $v$ is equal to the number of vertices closer to $v$ than
+to $u$. In this paper we present some basic properties of
+$\ell$-distance-balanced graphs and study in more detail
+$\ell$-distance-balanced graphs of diameter at most $3$. We also investigate
+the $\ell$-distance-balanced property of some well known families of graphs
+such as the generalized Petersen graphs.
+"
+27403,Linearizability and critical period bifurcations of a generalized Riccati system,"  In this paper we investigate the isochronicity and linearizability problem
+for a cubic polynomial differential system which can be considered as a
+generalization of the Riccati system. Conditions for isochronicity and
+linearizability are found. The global structure of systems of the family with
+an isochronous center is determined. Furthermore, we find the order of weak
+center and study the problem of local bifurcation of critical periods in a
+neighborhood of the center.
+"
+27404,Kirillov's orbit method: the case of discrete series representations,"  Let V be an Harish-Chandra discrete series representation of a real
+semi-simple Lie group G' and let G be a semi-simple subgroup of G'. In this
+paper, we give a geometric expression of the G-multiplicities in V when the
+representation V is supposed to be G-admissible.
+"
+27405,Fragility of Decentralized Load-Side Frequency Control in Stochastic Environment,"  In this paper, we demonstrate the fragility of decentralized load-side
+frequency algorithms proposed in [1] against stochastic parametric uncertainty
+in power network model. The stochastic parametric uncertainty is motivated
+through the presence of renewable energy resources in power system model. We
+show that relatively small variance value of the parametric uncertainty
+affecting the system bus voltages cause the decentralized load-side frequency
+regulation algorithm to become stochastically unstable. The critical variance
+value of the stochastic bus voltages above which the decentralized control
+algorithm become mean square unstable is computed using an analytical framework
+developed in [2], [3]. Furthermore, the critical variance value is shown to
+decrease with the increase in the cost of the controllable loads and with the
+increase in penetration of renewable energy resources. Finally, simulation
+results on IEEE 68 bus system are presented to verify the main findings of the
+paper.
+"
+27406,Intriguing Properties of Adversarial Examples,"  It is becoming increasingly clear that many machine learning classifiers are
+vulnerable to adversarial examples. In attempting to explain the origin of
+adversarial examples, previous studies have typically focused on the fact that
+neural networks operate on high dimensional data, they overfit, or they are too
+linear. Here we argue that the origin of adversarial examples is primarily due
+to an inherent uncertainty that neural networks have about their predictions.
+We show that the functional form of this uncertainty is independent of
+architecture, dataset, and training protocol; and depends only on the
+statistics of the logit differences of the network, which do not change
+significantly during training. This leads to adversarial error having a
+universal scaling, as a power-law, with respect to the size of the adversarial
+perturbation. We show that this universality holds for a broad range of
+datasets (MNIST, CIFAR10, ImageNet, and random data), models (including
+state-of-the-art deep networks, linear models, adversarially trained networks,
+and networks trained on randomly shuffled labels), and attacks (FGSM, step
+l.l., PGD). Motivated by these results, we study the effects of reducing
+prediction entropy on adversarial robustness. Finally, we study the effect of
+network architectures on adversarial sensitivity. To do this, we use neural
+architecture search with reinforcement learning to find adversarially robust
+architectures on CIFAR10. Our resulting architecture is more robust to white
+\emph{and} black box attacks compared to previous attempts.
+"
+27407,A polynomial-time randomized reduction from tournament isomorphism to tournament asymmetry,"  The paper develops a new technique to extract a characteristic subset from a
+random source that repeatedly samples from a set of elements. Here a
+characteristic subset is a set that when containing an element contains all
+elements that have the same probability. With this technique at hand the paper
+looks at the special case of the tournament isomorphism problem that stands in
+the way towards a polynomial-time algorithm for the graph isomorphism problem.
+Noting that there is a reduction from the automorphism (asymmetry) problem to
+the isomorphism problem, a reduction in the other direction is nevertheless not
+known and remains a thorny open problem. Applying the new technique, we develop
+a randomized polynomial-time Turing-reduction from the tournament isomorphism
+problem to the tournament automorphism problem. This is the first such
+reduction for any kind of combinatorial object not known to have a
+polynomial-time solvable isomorphism problem.
+"
+27408,A Multi-Class Dispatching and Charging Scheme for Autonomous Electric Mobility On-Demand,"  Despite the significant advances in vehicle automation and electrification,
+the next-decade aspirations for massive deployments of autonomous electric
+mobility on demand (AEMoD) services are still threatened by two major
+bottlenecks, namely the computational and charging delays. This paper proposes
+a solution for these two challenges by suggesting the use of fog computing for
+AEMoD systems, and developing an optimized multi-class charging and dispatching
+scheme for its vehicles. A queuing model representing the proposed multi-class
+charging and dispatching scheme is first introduced. The stability conditions
+of this model and the number of classes that fit the charging capabilities of
+any given city zone are then derived. Decisions on the proportions of each
+class vehicles to partially/fully charge, or directly serve customers are then
+optimized using a stochastic linear program that minimizes the maximum response
+time of the system. Results show the merits of our proposed model and optimized
+decision scheme compared to both the always-charge and the equal split schemes.
+"
+27409,Plasmoid Instability in Evolving Current Sheets and Onset of Fast Reconnection,"  The scaling of plasmoid instability maximum linear growth rate with respect
+to Lundquist number $S$ in a Sweet-Parker current sheet, $\gamma_{max}\sim
+S^{1/4}$, indicates that at high $S$, the current sheet will break apart before
+it approaches the Sweet-Parker width. Therefore, a proper description for the
+onset of the plasmoid instability must incorporate the evolving process of the
+current sheet. We carry out a series of two-dimensional simulations and develop
+diagnostics to separate fluctuations from an evolving background. It is found
+that the fluctuation amplitude starts to grow only when the linear growth rate
+is sufficiently large ($\gamma_{max}\tau_{A}>O(1)$) to overcome convective
+losses. The linear growth rate continues to rise until the sizes of plasmoids
+become comparable to the inner layer width of the tearing mode. At this point
+the current sheet is disrupted and the instability enters the early nonlinear
+regime. The growth rate suddenly decreases, but the fluctuation amplitude
+continues to grow until it reaches nonlinear saturation. We identify important
+time scales of the instability development, as well as scalings for the linear
+growth rate, current sheet width, and dominant wavenumber at current sheet
+disruption. These scalings depend on not only the Lundquist number, but also
+the initial noise amplitude. A phenomenological model that reproduces scalings
+from simulation results is proposed. The model incorporates the effect of
+reconnection outflow, which is crucial for yielding a critical Lundquist number
+$S_{c}$ below which disruption does not occur. The critical Lundquist number
+$S_{c}$ is not a constant value but has a weak dependence on the noise
+amplitude.
+"
+27410,Towards a unified theory for testing statistical hypothesis: Multinormal mean with nuisance covariance matrix,"  Under a multinormal distribution with arbitrary unknown covariance matrix,
+the main purpose of this paper is to propose a framework to achieve the goal of
+reconciliation of Bayesian, frequentist and Fisherian paradigms for the
+problems of testing mean against restricted alternatives (closed convex cones).
+Combining Fisher's fiducial inference and Wald's decision theory via
+d-admissibility into an unified approach, the goal can then be achieved. To
+proceed, the tests constructed via the union-intersection principle are
+studied.
+"
+27411,Causal Embeddings for Recommendation,"  Many current applications use recommendations in order to modify the natural
+user behavior, such as to increase the number of sales or the time spent on a
+website. This results in a gap between the final recommendation objective and
+the classical setup where recommendation candidates are evaluated by their
+coherence with past user behavior, by predicting either the missing entries in
+the user-item matrix, or the most likely next event. To bridge this gap, we
+optimize a recommendation policy for the task of increasing the desired outcome
+versus the organic user behavior. We show this is equivalent to learning to
+predict recommendation outcomes under a fully random recommendation policy. To
+this end, we propose a new domain adaptation algorithm that learns from logged
+data containing outcomes from a biased recommendation policy and predicts
+recommendation outcomes according to random exposure. We compare our method
+against state-of-the-art factorization methods, in addition to new approaches
+of causal recommendation and show significant improvements.
+"
+27412,Modelling visual-vestibular integration and behavioural adaptation in the driving simulator,"  It is well established that not only vision but also other sensory modalities
+affect drivers' control of their vehicles, and that drivers adapt over time to
+persistent changes in sensory cues (for example in driving simulators), but the
+mechanisms underlying these behavioural phenomena are poorly understood. Here,
+we consider the existing literature on how driver steering in slalom tasks is
+affected by the down-scaling of vestibular cues, and propose a driver model
+that can explain the empirically observed effects, namely: decreased task
+performance and increased steering effort during initial exposure, followed by
+a partial reversal of these effects as task exposure is prolonged.
+Unexpectedly, the model also reproduced another empirical finding: a local
+optimum for motion down-scaling, where path-tracking is better than when
+one-to-one motion cues are available. Overall, the results imply that: (1)
+drivers make direct use of vestibular information as part of determining
+appropriate steering, and (2) motion down-scaling causes a yaw rate
+underestimation phenomenon, where drivers behave as if the simulated vehicle is
+rotating more slowly than it is. However, (3) in the slalom task, a certain
+degree of such yaw rate underestimation is beneficial to path tracking
+performance. Furthermore, (4) behavioural adaptation, as empirically observed
+in slalom tasks, may occur due to (a) down-weighting of vestibular cues, and/or
+(b) increased sensitivity to control errors, in determining when to adjust
+steering and by how much, but (c) seemingly not in the form of a full
+compensatory rescaling of the received vestibular input. The analyses presented
+here provide new insights and hypotheses about simulator driving, and the
+developed models can be used to support research on multisensory integration
+and behavioural adaptation in both driving and other task domains.
+"
+27413,Hybrid Chebyshev function bases for sparse spectral methods in parity-mixed PDEs on an infinite domain,"  We present a numerical spectral method to solve systems of differential
+equations on an infinite interval $y\in (-\infty, \infty)$ in presence of
+linear differential operators of the form $Q(y)
+\left(\partial/\partial_y\right)^b$ (where $Q(y)$ is a rational fraction and
+$b$ a positive integer). Even when these operators are not parity-preserving,
+we demonstrate how a mixed expansion in interleaved Chebyshev rational
+functions $TB_n(y)$ and $SB_n(y)$ preserves the sparsity of their
+discretization. This paves the way for fast $O(N\ln N)$ and spectrally accurate
+mixed implicit-explicit time-marching of sets of linear and nonlinear equations
+in unbounded geometries.
+"
+27414,Weight One Jacobi Forms and Umbral Moonshine,"  We analyze holomorphic Jacobi forms of weight one with level. One such form
+plays an important role in umbral moonshine, leading to simplifications of the
+statements of the umbral moonshine conjectures. We prove that non-zero
+holomorphic Jacobi forms of weight one do not exist for many combinations of
+index and level, and use this to establish a characterization of the
+McKay--Thompson series of umbral moonshine in terms of Rademacher sums.
+"
+27415,A Comparative Study of Bug Algorithms for Robot Navigation,"  This paper presents a literature survey and a comparative study of Bug
+Algorithms, with the goal of investigating their potential for robotic
+navigation. At first sight, these methods seem to provide an efficient
+navigation paradigm, ideal for implementations on tiny robots with limited
+resources. Closer inspection, however, shows that many of these Bug Algorithms
+assume perfect global position estimate of the robot which in GPS-denied
+environments implies considerable expenses of computation and memory -- relying
+on accurate Simultaneous Localization And Mapping (SLAM) or Visual Odometry
+(VO) methods. We compare a selection of Bug Algorithms in a simulated robot and
+environment where they endure different types noise and failure-cases of their
+on-board sensors. From the simulation results, we conclude that the implemented
+Bug Algorithms' performances are sensitive to many types of sensor-noise, which
+was most noticeable for odometry-drift. This raises the question if Bug
+Algorithms are suitable for real-world, on-board, robotic navigation as is.
+Variations that use multiple sensors to keep track of their progress towards
+the goal, were more adept in completing their task in the presence of
+sensor-failures. This shows that Bug Algorithms must spread their risk, by
+relying on the readings of multiple sensors, to be suitable for real-world
+deployment.
+"
+27416,Universal Semantic Parsing,"  Universal Dependencies (UD) offer a uniform cross-lingual syntactic
+representation, with the aim of advancing multilingual applications. Recent
+work shows that semantic parsing can be accomplished by transforming syntactic
+dependencies to logical forms. However, this work is limited to English, and
+cannot process dependency graphs, which allow handling complex phenomena such
+as control. In this work, we introduce UDepLambda, a semantic interface for UD,
+which maps natural language to logical forms in an almost language-independent
+fashion and can process dependency graphs. We perform experiments on question
+answering against Freebase and provide German and Spanish translations of the
+WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation.
+Results show that UDepLambda outperforms strong baselines across languages and
+datasets. For English, it achieves a 4.9 F1 point improvement over the
+state-of-the-art on GraphQuestions. Our code and data can be downloaded at
+this https URL.
+"
+27417,Learning From Noisy Large-Scale Datasets With Minimal Supervision,"  We present an approach to effectively use millions of images with noisy
+annotations in conjunction with a small subset of cleanly-annotated images to
+learn powerful image representations. One common approach to combine clean and
+noisy data is to first pre-train a network using the large noisy dataset and
+then fine-tune with the clean dataset. We show this approach does not fully
+leverage the information contained in the clean set. Thus, we demonstrate how
+to use the clean annotations to reduce the noise in the large dataset before
+fine-tuning the network using both the clean set and the full set with reduced
+noise. The approach comprises a multi-task network that jointly learns to clean
+noisy annotations and to accurately classify images. We evaluate our approach
+on the recently released Open Images dataset, containing ~9 million images,
+multiple annotations per image and over 6000 unique classes. For the small
+clean set of annotations we use a quarter of the validation set with ~40k
+images. Our results demonstrate that the proposed approach clearly outperforms
+direct fine-tuning across all major categories of classes in the Open Image
+dataset. Further, our approach is particularly effective for a large number of
+classes with wide range of noise in annotations (20-80% false positive
+annotations).
+"
+27418,Electronic band gaps of confined linear carbon chains ranging from polyyne to carbyne,"  Ultra long linear carbon chains of more than 6000 carbon atoms have recently
+been synthesized within double-walled carbon nanotubes, and they show a
+promising new route to one--atom--wide semiconductors with a direct band gap.
+Theoretical studies predicted that this band gap can be tuned by the length of
+the chains, the end groups, and their interactions with the environment.
+However, different density functionals lead to very different values of the
+band gap of infinitely long carbyne. In this work, we applied resonant Raman
+excitation spectroscopy with more than 50 laser wavelengths to determine for
+the first time the band gap of long carbon chains encapsulated inside DWCNTs.
+The experimentally determined band gaps ranging from 2.253 to 1.848 eV follow a
+linear relation with Raman frequency. This lower bound is the smallest band gap
+of linear carbon chains observed so far. The comparison with experimental data
+obtained for short chains in gas phase or in solution demonstrates the effect
+of the DWCNT encapsulation, leading to an essential downshift of the band gap.
+This is explained by the interaction between the carbon chain and the host
+tube, which greatly modifies the chain's bond length alternation.
+"
+27419,Paranom: A Parallel Anomaly Dataset Generator,"  In this paper, we present Paranom, a parallel anomaly dataset generator. We
+discuss its design and provide brief experimental results demonstrating its
+usefulness in improving the classification correctness of LSTM-AD, a
+state-of-the-art anomaly detection model.
+"
+27420,A Space-Time Approach for the Time-Domain Simulation in a Rotating Reference Frame,"  We approach the discretisation of Maxwell's equations directly in space-time
+without making any non-relativistic assumptions with the particular focus on
+simulations in rotating reference frames. As a research example we study
+Sagnac's effect in a rotating ring resonator. After the discretisation, we
+express the numerical scheme in a form resembling 3D FIT with leapfrog. We
+compare the stability and convergence properties of two 4D approaches, namely
+FIT and FEM, both using Whitney interpolation.
+"
+27421,Direct Detection of MeV-Scale Dark Matter Utilizing Germanium Internal Amplification for the Charge Created by the Ionization of Impurities,"  Light, MeV-scale dark matter (DM) is an exciting DM candidate that is
+undetectable by current experiments. A germanium (Ge) detector utilizing
+internal charge amplification for the charge carriers created by the ionization
+of impurities is a promising new technology with experimental sensitivity for
+detecting MeV-scale DM. We analyze the physics mechanisms of the signal
+formation, charge creation, charge internal amplification, and the projected
+sensitivity for directly detecting MeV-scale DM particles. We present a design
+for a novel Ge detector at helium temperature ($\sim$4 K) enabling ionization
+of impurities from DM impacts. With large localized E-fields, the ionized
+excitations can be accelerated to kinetic energies larger than the Ge bandgap
+at which point they can create additional electron-hole pairs, producing
+intrinsic amplification to achieve an ultra-low energy threshold of $\sim$0.1
+eV for detecting low-mass DM particles in the MeV scale. Correspondingly, such
+a Ge detector with 1 kg-year exposure will have high sensitivity to a
+DM-nucleon cross section of $\sim$5$\times$10$^{-45}$ cm$^{2}$ at a DM mass of
+$\sim$10 MeV/c$^{2}$ and a DM-electron cross section of
+$\sim$5$\times$10$^{-46}$cm$^{2}$ at a DM mass of $\sim$1 MeV/c$^2$.
+"
+27422,Transferring Physical Motion Between Domains for Neural Inertial Tracking,"  Inertial information processing plays a pivotal role in ego-motion awareness
+for mobile agents, as inertial measurements are entirely egocentric and not
+environment dependent. However, they are affected greatly by changes in sensor
+placement/orientation or motion dynamics, and it is infeasible to collect
+labelled data from every domain. To overcome the challenges of domain
+adaptation on long sensory sequences, we propose a novel framework that
+extracts domain-invariant features of raw sequences from arbitrary domains, and
+transforms to new domains without any paired data. Through the experiments, we
+demonstrate that it is able to efficiently and effectively convert the raw
+sequence from a new unlabelled target domain into an accurate inertial
+trajectory, benefiting from the physical motion knowledge transferred from the
+labelled source domain. We also conduct real-world experiments to show our
+framework can reconstruct physically meaningful trajectories from raw IMU
+measurements obtained with a standard mobile phone in various attachments.
+"
+27423,"Trends in scientific research in Online Information Review. Part 1. Production, impact and research collaboration","  The study, based on the Web of Science, analyses 758 articles published from
+2000 to 2014. Our analysis includes the publications' output, authorship,
+institutional and country patterns of production, citations and collaboration.
+A Social Network Analysis was conducted to identify primary groups of
+researchers and institutions and the collaboration between countries. The study
+reveals that 1097 authors and 453 Institutions have contributed to the journal.
+The collaboration index has increased progressively, and the average degree of
+collaboration during the study period was 1.98. The majority of the papers were
+contributed by professionals affiliated with a university. Highly cited papers
+address online and digital environments, e-learning systems, mobile services,
+web 2.0 and citation analyses. This work is a bibliometric analysis of a
+leading journal in library and information science, Online Information Review.
+"
+27424,On Prediction and Tolerance Intervals for Dynamic Treatment Regimes,"  We develop and evaluate tolerance interval methods for dynamic treatment
+regimes (DTRs) that can provide more detailed prognostic information to
+patients who will follow an estimated optimal regime. Although the problem of
+constructing confidence intervals for DTRs has been extensively studied,
+prediction and tolerance intervals have received little attention. We begin by
+reviewing in detail different interval estimation and prediction methods and
+then adapting them to the DTR setting. We illustrate some of the challenges
+associated with tolerance interval estimation stemming from the fact that we do
+not typically have data that were generated from the estimated optimal regime.
+We give an extensive empirical evaluation of the methods and discussed several
+practical aspects of method choice, and we present an example application using
+data from a clinical trial. Finally, we discuss future directions within this
+important emerging area of DTR research.
+"
+27425,Smart Augmentation - Learning an Optimal Data Augmentation Strategy,"  A recurring problem faced when training neural networks is that there is
+typically not enough data to maximize the generalization capability of deep
+neural networks(DNN). There are many techniques to address this, including data
+augmentation, dropout, and transfer learning. In this paper, we introduce an
+additional method which we call Smart Augmentation and we show how to use it to
+increase the accuracy and reduce overfitting on a target network. Smart
+Augmentation works by creating a network that learns how to generate augmented
+data during the training process of a target network in a way that reduces that
+networks loss. This allows us to learn augmentations that minimize the error of
+that network.
+Smart Augmentation has shown the potential to increase accuracy by
+demonstrably significant measures on all datasets tested. In addition, it has
+shown potential to achieve similar or improved performance levels with
+significantly smaller network sizes in a number of tested cases.
+"
+27426,On Connected Sublevel Sets in Deep Learning,"  We study sublevel sets of the loss function in training deep neural networks.
+For linearly independent data, we prove that every sublevel set of the loss is
+connected and unbounded. We then apply this result to prove similar properties
+on the loss surface of deep over-parameterized neural nets with piecewise
+linear activation functions.
+"
+27427,Real-Time Control for Autonomous Racing Based on Viability Theory,"  In this paper we consider autonomous driving of miniature race cars. The
+viability kernel is used to efficiently generate finite look-ahead trajectories
+that maximize progress while remaining recursively feasible with respect to
+static obstacles (e.g., stay inside the track). Together with a low-level model
+predictive controller, this method makes real-time autonomous racing possible.
+The viability kernel computation is based on space discretization. To make the
+calculation robust against discretization errors, we propose a novel numerical
+scheme based on game theoretical methods, in particular the discriminating
+kernel. We show that the resulting algorithm provides an inner approximation of
+the viability kernel and guarantees that, for all states in the cell
+surrounding a viable grid point, there exists a control that keeps the system
+within the kernel. The performance of the proposed control method is studied in
+simulation where we determine the effects of various design choices and
+parameters and in experiments on an autonomous racing set-up maintained at the
+Automatic Control Laboratory of ETH Zurich. Both simulation and experimental
+results suggest that the more conservative approximation using the
+discriminating kernel results in safer driving style at the cost of a small
+increase in lap time.
+"
+27428,TraX: The visual Tracking eXchange Protocol and Library,"  In this paper we address the problem of developing on-line visual tracking
+algorithms. We present a specialized communication protocol that serves as a
+bridge between a tracker implementation and utilizing application. It decouples
+development of algorithms and application, encouraging re-usability. The
+primary use case is algorithm evaluation where the protocol facilitates more
+complex evaluation scenarios that are used nowadays thus pushing forward the
+field of visual tracking. We present a reference implementation of the protocol
+that makes it easy to use in several popular programming languages and discuss
+where the protocol is already used and some usage scenarios that we envision
+for the future.
+"
+27429,Methods of algebraic geometry applied to the study of measures over bounded lattices,"  In this paper we investigate measures over bounded lattices using algebraic
+geometry techniques. We prove that measures over a bounded lattice can be seen
+as a classical measure over a measurable space. Then, we define the
+measurability of a lattice and describe measures over finite lattices.
+"
+27430,Cosmological perturbations and stability of nonsingular cosmologies with limiting curvature,"  We revisit nonsingular cosmologies in which the limiting curvature hypothesis
+is realized. We study the cosmological perturbations of the theory and
+determine the general criteria for stability. For the simplest model, we find
+generic Ostrogradski instabilities unless the action contains the Weyl tensor
+squared with the appropriate coefficient. When considering two specific
+nonsingular cosmological scenarios(one inflationary and one genesis model), we
+find ghost and gradient instabilities throughout most of the cosmic evolution.
+Furthermore, we show that the theory is equivalent to a theory of gravity where
+the action is a general function of the Ricci and Gauss-Bonnet scalars, and
+this type of theory is known to suffer from instabilities in anisotropic
+backgrounds. This leads us to construct a new type of curvature invariant
+scalar function. We show that it does not have Ostrogradski instabilities, and
+it avoids ghost and gradient instabilities for most of the interesting
+background inflationary and genesis trajectories. We further show that it does
+not possess additional new degrees of freedom in an anisotropic spacetime. This
+opens the door for studying stable alternative nonsingular very early universe
+cosmologies.
+"
+27431,Continuous-Time Flows for Efficient Inference and Density Estimation,"  Two fundamental problems in unsupervised learning are efficient inference for
+latent-variable models and robust density estimation based on large amounts of
+unlabeled data. Algorithms for the two tasks, such as normalizing flows and
+generative adversarial networks (GANs), are often developed independently. In
+this paper, we propose the concept of {\em continuous-time flows} (CTFs), a
+family of diffusion-based methods that are able to asymptotically approach a
+target distribution. Distinct from normalizing flows and GANs, CTFs can be
+adopted to achieve the above two goals in one framework, with theoretical
+guarantees. Our framework includes distilling knowledge from a CTF for
+efficient inference, and learning an explicit energy-based distribution with
+CTFs for density estimation. Both tasks rely on a new technique for
+distribution matching within amortized learning. Experiments on various tasks
+demonstrate promising performance of the proposed CTF framework, compared to
+related techniques.
+"
+27432,EUTelescope Guide for ATLAS ITk Strip: Reconstruction and Analysis,"  EUTelescope is arguably the most used software to reconstruct tracks from
+telescope data. This guide explains how to install a version adapted for the
+reconstruction of ITk Strip test beam data. In this version, an example of such
+reconstruction with 2016 data is provided. In addition, this example will be
+explained step-by-step and a general overview of how to operate EUTelescope is
+given. It is also briefly explained how the post-reconstruction analysis has
+been performed. The aim of this guide is to be used a starting point for
+EUTelescope beginners in future test beam reconstruction and analysis works. It
+is focused on reconstruction for the ATLAS ITk Strip detector using a binary
+read-out.
+"
+27433,Asymptotic expansions of holonomy,"  Given a principal bundle with a connection, we look for an asymptotic
+expansion of the holonomy of a loop in terms of its length. This length is
+defined relative to some Riemannian or sub-Riemannian structure. We are able to
+give an asymptotic formula that is independent of choice of gauge.
+"
+27434,"Portrait growth in contracting, regular branch groups","  We address a question of Grigorchuk by providing both a system of recursive
+formulas and an asymptotic result for the portrait growth of the first
+Grigorchuk group. The results are obtained through analysis of some features of
+the branching subgroup structure of the group. More generally, we provide
+recursive formulas for the portrait growth of any finitely generated,
+contracting, regular branch group, based on the coset decomposition of the
+groups that are higher in the branching subgroup structure in terms of the
+lower subgroups. Using the same general approach we fully describe the portrait
+growth for all non-symmetric GGS-groups and for the Apollonian group.
+"
+27435,Reentrant Topological Phase Transition in a Bridging Model between Kitaev and Haldane Chains,"  We present a reentrant phase transition in a bridging model between two
+different topological models: Kitaev and Haldane chains. This model is
+activated by introducing a bond alternation into the Kitaev chain [A. Yu
+Kitaev, Phys.-Usp. {\bf 44} 131 (2001)]. Without the bond alternation, finite
+pairing potential induces a topological state defined by zero-energy Majorana
+edge mode, while finite bond alternation without the pairing potential makes a
+different topological state similar to the Haldane state, which is defined by
+local Berry phase in the bulk. The topologically-ordered state corresponds to
+the Su-Schrieffer-Heeger state, which is classified as the same symmetry class.
+We thus find a phase transition between the two topological phases with a
+reentrant phenomenon, and extend the phase diagram in the plane of the pairing
+potential and the bond alternation by using three techniques: recursive
+equation, fidelity, and Pfaffian. In addition, we find that the phase
+transition is characterized by both the change of the position of Majorana
+zero-energy modes from one edge to the other edge and the emergence of a string
+order in the bulk, and that the reentrance is based on a sublattice U(1)
+rotation. Consequently, our study and model do not only open a direct way to
+discuss the bulk and edge topologies, but demonstrate an example of the
+reentrant topologies.
+"
+27436,Truncated $\mathcal{V}$-fractional Taylor's formula with applications,"  In this paper, we present and prove a new truncated $\mathcal{V}$-fractional
+Taylor's formula using the truncated $\mathcal{V}$-fractional variation of
+constants formula. In this sense, we present the truncated
+$\mathcal{V}$-fractional Taylor's remainder by means of
+$\mathcal{V}$-fractional integral, essential for analyzing and comparing the
+error, when approaching functions by polynomials. From these new results, some
+applications were made involving some inequalities, specifically, we generalize
+the Cauchy-Schwartz inequality.
+"
+27437,Game-Theoretic Vaccination Against Networked SIS Epidemics and Impacts of Human Decision-Making,"  We study decentralized protection strategies against
+Susceptible-Infected-Susceptible (SIS) epidemics on networks. We consider a
+population game framework where nodes choose whether or not to vaccinate
+themselves, and the epidemic risk is defined as the infection probability at
+the endemic state of the epidemic under a degree-based mean-field
+approximation. Motivated by studies in behavioral economics showing that humans
+perceive probabilities and risks in a nonlinear fashion, we specifically
+examine the impacts of such misperceptions on the Nash equilibrium protection
+strategies. We first establish the existence and uniqueness of a threshold
+equilibrium where nodes with degrees larger than a certain threshold vaccinate.
+When the vaccination cost is sufficiently high, we show that behavioral biases
+cause fewer players to vaccinate, and vice versa. We quantify this effect for a
+class of networks with power-law degree distributions by proving tight bounds
+on the ratio of equilibrium thresholds under behavioral and true perceptions of
+probabilities. We further characterize the socially optimal vaccination policy
+and investigate the inefficiency of Nash equilibrium.
+"
+27438,The sum and the product of two quadratic matrices,"  Let $p$ and $q$ be polynomials with degree $2$ over an arbitrary field
+$\mathbb{F}$.
+In the first part of this article, we characterize the matrices that can be
+decomposed as $A+B$ for some pair $(A,B)$ of square matrices such that $p(A)=0$
+and $q(B)=0$. The case when both polynomials $p$ and $q$ are split was already
+known. In the first half of this article, we complete the study by tackling the
+case when at least one of the polynomials $p$ and $q$ is irreducible over
+$\mathbb{F}$.
+In the second half of the article, we use a similar method to characterize,
+under the assumption that $p(0)q(0) \neq 0$, the matrices that can be
+decomposed as $AB$ for some pair $(A,B)$ of square matrices such that $p(A)=0$
+and $q(B)=0$.
+"
+27439,The effect of ISM absorption on stellar activity measurements and its relevance for exoplanet studies,"  Past UV and optical observations of stars hosting hot Jupiters have shown
+that some of these stars present an anomalously low chromospheric activity,
+significantly below the basal level. For WASP-13, observations have shown that
+the apparent lack of activity is possibly caused by absorption from the
+intervening ISM. Inspired by this result, we study the effect of ISM absorption
+on activity measurements (S and logR'$_{\rm HK}$ indices) for main-sequence
+late-type stars. To this end, we employ synthetic stellar photospheric spectra
+combined with varying amounts of chromospheric emission and ISM absorption. We
+present the effect of ISM absorption on activity measurements by varying
+several instrumental, stellar, and ISM parameters. We find that for relative
+velocities between the stellar and ISM lines smaller than 30-40 km/s and for
+ISM CaII column densities logN$_{\rm CaII}$>12, the ISM absorption has a
+significant influence on activity measurements. Direct measurements and three
+dimensional maps of the Galactic ISM absorption indicate that an ISM CaII
+column density of logN$_{\rm CaII}$=12 is typically reached by a distance of
+about 100 pc along most sight lines. In particular, for a Sun-like star lying
+at a distance greater than 100 pc, we expect a depression (bias) in the
+logR'$_{\rm HK}$ value larger than 0.05-0.1 dex, about the same size as the
+typical measurement and calibration uncertainties on this parameter. This work
+shows that the bias introduced by ISM absorption must always be considered when
+measuring activity for stars lying beyond 100 pc. We also consider the effect
+of multiple ISM absorption components. We discuss the relevance of this result
+for exoplanet studies.
+"
+27440,Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees,"  Model-based reinforcement learning (RL) is considered to be a promising
+approach to reduce the sample complexity that hinders model-free RL. However,
+the theoretical understanding of such methods has been rather limited. This
+paper introduces a novel algorithmic framework for designing and analyzing
+model-based RL algorithms with theoretical guarantees. We design a
+meta-algorithm with a theoretical guarantee of monotone improvement to a local
+maximum of the expected reward. The meta-algorithm iteratively builds a lower
+bound of the expected reward based on the estimated dynamical model and sample
+trajectories, and then maximizes the lower bound jointly over the policy and
+the model. The framework extends the optimism-in-face-of-uncertainty principle
+to non-linear dynamical models in a way that requires \textit{no explicit}
+uncertainty quantification. Instantiating our framework with simplification
+gives a variant of model-based RL algorithms Stochastic Lower Bounds
+Optimization (SLBO). Experiments demonstrate that SLBO achieves
+state-of-the-art performance when only one million or fewer samples are
+permitted on a range of continuous control benchmark tasks.
+"
+27441,LIUM Machine Translation Systems for WMT17 News Translation Task,"  This paper describes LIUM submissions to WMT17 News Translation Task for
+English-German, English-Turkish, English-Czech and English-Latvian language
+pairs. We train BPE-based attentive Neural Machine Translation systems with and
+without factored outputs using the open source nmtpy framework. Competitive
+scores were obtained by ensembling various systems and exploiting the
+availability of target monolingual corpora for back-translation. The impact of
+back-translation quantity and quality is also analyzed for English-Turkish
+where our post-deadline submission surpassed the best entry by +1.6 BLEU.
+"
+27442,Mean motion resonances at high eccentricities: the 2:1 and the 3:2 interior resonances,"  Mean motion resonances [MMRs] play an important role in the formation and
+evolution of planetary systems and have significantly influenced the orbital
+properties and distribution of planets and minor planets in the solar system as
+well as exo-planetary systems. Most previous theoretical analyses have focused
+on the low-to-moderate eccentricity regime, but with new discoveries of high
+eccentricity resonant minor planets and even exoplanets, there is increasing
+motivation to examine MMRs in the high eccentricity regime. Here we report on a
+study of the high eccentricity regime of MMRs in the circular planar restricted
+three-body problem. Non-perturbative numerical analyses of the 2:1 and the 3:2
+interior resonances are carried out for a wide range of secondary-to-primary
+mass ratio, and for a wide range of eccentricity of the test particle. The
+surface-of-section technique is used to study the phase space structure near
+resonances. We identify transitions in phase space at certain critical
+eccentricities related to the geometry of resonant orbits; new stable libration
+zones appear at high eccentricity at libration centers shifted from those at
+low eccentricities. We present novel results on the mass and eccentricity
+dependence of the resonance libration centers and their widths in semi-major
+axis. Our results show that MMRs have sizable libration zones at high
+eccentricities, comparable to those at lower eccentricities.
+"
+27443,Noether's Theorem in Multisymplectic Geometry,"  We extend Noether's theorem to the setting of multisymplectic geometry by
+exhibiting a correspondence between conserved quantities and continuous
+symmetries on a multi-Hamiltonian system. We show that a homotopy co-momentum
+map interacts with this correspondence in a way analogous to the moment map in
+symplectic geometry.
+We apply our results to generalize the theory of the classical momentum and
+position functions from the phase space of a given physical system to the
+multisymplectic phase space. We also apply our results to manifolds with a
+torsion-free $G_2$ structure.
+"
+27444,Boundary Differential Equations and Their Applications to Scattering Problems,"  In this paper, new boundary differential equations for the two-dimensional
+exterior scattering problem have been derived. It has been shown that the
+Helmholtz equation can be reduced to an inhomogeneous Bessel's equation in a
+body-fitted coordinate system. By imposing the Sommerfeld radiation condition
+to the general solution of the Bessel's equation, an integro-differential
+equation, which is equivalent to the original Helmholtz equation, can be
+obtained. The boundary differential equation can then be established by use of
+the integration by parts to get rid of the integral in the integro-differential
+equation for high frequency problems. Numerical examples have been presented to
+demonstrate the validity of the new boundary differential equations.
+"
+27445,Interactive reconstructions of cranial 3D implants under MeVisLab as an alternative to commercial planning software,"  In this publication, the interactive planning and reconstruction of cranial
+3D Implants under the medical prototyping platform MeVisLab as alternative to
+commercial planning software is introduced. In doing so, a MeVisLab prototype
+consisting of a customized data-flow network and an own C++ module was set up.
+As a result, the Computer-Aided Design (CAD) software prototype guides a user
+through the whole workflow to generate an implant. Therefore, the workflow
+begins with loading and mirroring the patients head for an initial curvature of
+the implant. Then, the user can perform an additional Laplacian smoothing,
+followed by a Delaunay triangulation. The result is an aesthetic looking and
+well-fitting 3D implant, which can be stored in a CAD file format, e.g.
+STereoLithography (STL), for 3D printing. The 3D printed implant can finally be
+used for an in-depth pre-surgical evaluation or even as a real implant for the
+patient. In a nutshell, our research and development shows that a customized
+MeVisLab software prototype can be used as an alternative to complex commercial
+planning software, which may also not be available in every clinic. Finally,
+not to conform ourselves directly to available commercial software and look for
+other options that might improve the workflow.
+"
+27446,A New Signal Model for Axion Cavity Searches from N-Body Simulations,"  Signal estimates for direct axion dark matter searches have used the
+isothermal sphere halo model for the last several decades. While insightful,
+the isothermal model does not capture effects from a halo's infall history nor
+the influence of baryonic matter, which has been shown to significantly
+influence a halo's inner structure. The high resolution of cavity axion
+detectors can make use of modern cosmological structure-formation simulations,
+which begin from realistic initial conditions, incorporate a wide range of
+baryonic physics, and are capable of resolving detailed structure. This letter
+uses a state-of-the-art cosmological N-body+Smoothed-Particle Hydrodynamics
+simulation to develop an improved signal model for axion cavity searches.
+Signal shapes from a class of galaxies encompassing the Milky Way are found to
+depart significantly from the isothermal sphere. A new signal model for axion
+detectors is proposed and projected sensitivity bounds on the Axion Dark Matter
+eXperiment data are presented.
+"
+27447,A microlocal approach to the enhanced Fourier-Sato transform in dimension one,"  Let $\mathcal{M}$ be a holonomic algebraic $\mathcal{D}$-module on the affine
+line. Its exponential factors are Puiseux germs describing the growth of
+holomorphic solutions to $\mathcal{M}$ at irregular points. The stationary
+phase formula states that the exponential factors of the Fourier transform of
+$\mathcal{M}$ are obtained by Legendre transform from the exponential factors
+of $\mathcal{M}$. We give a microlocal proof of this fact, by translating it in
+terms of enhanced ind-sheaves through the Riemann-Hilbert correspondence.
+"
+27448,Inline Co-Evolution between Users and Information Presentation for Data Exploration,"  This paper presents an intelligent user interface model dedicated to the
+exploration of complex databases. This model is implemented on a 3D metaphor :
+a virtual museum. In this metaphor, the database elements are embodied as
+museum objects. The objects are grouped in rooms according to their semantic
+properties and relationships and the rooms organization forms the museum. Rooms
+organization is not predefi-ned but defined incrementally by taking into
+account not only the relationships between objects, but also the users centers
+of interest. The latter are evaluated in real-time through user interactions
+within the virtual museum. This interface allows for a personal reading and
+favors the discovery of unsuspec-ted links between data. In this paper, we
+present our model's formalization as well as its application to the context of
+cultural heritage.
+"
+27449,Operational thermal load forecasting in district heating networks using machine learning and expert advice,"  Forecasting thermal load is a key component for the majority of optimization
+solutions for controlling district heating and cooling systems. Recent studies
+have analysed the results of a number of data-driven methods applied to thermal
+load forecasting, this paper presents the results of combining a collection of
+these individual methods in an expert system. The expert system will combine
+multiple thermal load forecasts in a way that it always tracks the best expert
+in the system. This solution is tested and validated using a thermal load
+dataset of 27 months obtained from 10 residential buildings located in Rottne,
+Sweden together with outdoor temperature information received from a weather
+forecast service. The expert system is composed of the following data-driven
+methods: linear regression, extremely randomized trees regression, feed-forward
+neural network and support vector machine. The results of the proposed solution
+are compared with the results of the individual methods.
+"
+27450,Cartesian products of directed graphs with loops,"  We show that every nontrivial finite or infinite connected directed graph
+with loops and at least one vertex without a loop is uniquely representable as
+a Cartesian or weak Cartesian product of prime graphs. For finite graphs the
+factorization can be computed in linear time and space.
+"
+27451,Rejection-free Ensemble MCMC with applications to Factorial Hidden Markov Models,"  Bayesian inference for complex models is challenging due to the need to
+explore high-dimensional spaces and multimodality and standard Monte Carlo
+samplers can have difficulties effectively exploring the posterior. We
+introduce a general purpose rejection-free ensemble Markov Chain Monte Carlo
+(MCMC) technique to improve on existing poorly mixing samplers. This is
+achieved by combining parallel tempering and an auxiliary variable move to
+exchange information between the chains. We demonstrate this ensemble MCMC
+scheme on Bayesian inference in Factorial Hidden Markov Models. This
+high-dimensional inference problem is difficult due to the exponentially sized
+latent variable space. Existing sampling approaches mix slowly and can get
+trapped in local modes. We show that the performance of these samplers is
+improved by our rejection-free ensemble technique and that the method is
+attractive and ""easy-to-use"" since no parameter tuning is required.
+"
+27452,Some finiteness properties of generalized graded local cohomology modules,"  Let $R = \bigoplus_{n \in \mathbb{N}_0} R_n$ be a Noetherian homogeneous ring
+with local base ring $(R_0, \mathfrak{m}_0)$ and let $M$ and $N$ be finitely
+generated graded $R$-modules. Let $i,j\in\mathbb{N}_0$. In this paper we will
+study Artinianess of $\Gamma_{\mathfrak m_0R}(H_{R_+}^i(M,N)), H_{\mathfrak
+m_0R}^1(H_{R_+}^i(M,N)), H_{R_+}^i(M,N)/{\mathfrak m_0}H_{R_+}^i(M,N),
+H_{R_+}^j(M,H_{\mathfrak m_0R}^i(N)), H_{\mathfrak m_0R}^j(M,H_{R_+}^i(N))$,
+where $R_+$ denotes the irrelevant ideal of $R$.
+"
+27453,Simple Signal Extension Method for Discrete Wavelet Transform,"  Discrete wavelet transform of finite-length signals must necessarily handle
+the signal boundaries. The state-of-the-art approaches treat such boundaries in
+a complicated and inflexible way, using special prolog or epilog phases. This
+holds true in particular for images decomposed into a number of scales,
+exemplary in JPEG 2000 coding system. In this paper, the state-of-the-art
+approaches are extended to perform the treatment using a compact streaming
+core, possibly in multi-scale fashion. We present the core focused on CDF 5/3
+wavelet and the symmetric border extension method, both employed in the JPEG
+2000. As a result of our work, every input sample is visited only once, while
+the results are produced immediately, i.e. without buffering.
+"
+27454,Interactive 3D Modeling with a Generative Adversarial Network,"  This paper proposes the idea of using a generative adversarial network (GAN)
+to assist a novice user in designing real-world shapes with a simple interface.
+The user edits a voxel grid with a painting interface (like Minecraft). Yet, at
+any time, he/she can execute a SNAP command, which projects the current voxel
+grid onto a latent shape manifold with a learned projection operator and then
+generates a similar, but more realistic, shape using a learned generator
+network. Then the user can edit the resulting shape and snap again until he/she
+is satisfied with the result. The main advantage of this approach is that the
+projection and generation operators assist novice users to create 3D models
+characteristic of a background distribution of object shapes, but without
+having to specify all the details. The core new research idea is to use a GAN
+to support this application. 3D GANs have previously been used for shape
+generation, interpolation, and completion, but never for interactive modeling.
+The new challenge for this application is to learn a projection operator that
+takes an arbitrary 3D voxel model and produces a latent vector on the shape
+manifold from which a similar and realistic shape can be generated. We develop
+algorithms for this and other steps of the SNAP processing pipeline and
+integrate them into a simple modeling tool. Experiments with these algorithms
+and tool suggest that GANs provide a promising approach to computer-assisted
+interactive modeling.
+"
+27455,Development of theoretical descriptors for cytotoxicity evaluation of metallic nanoparticles,"  Motivated by the recent development of quantitative structure-activity
+relationship (QSAR) methods in the area of nanotoxicology, we proposed an
+approach to develop additional descriptors based on results of first principles
+calculations. For evaluation of the biochemical activity of metallic
+nanoparticles, we consider two processes: ion extraction from the surface of a
+specimen to aqueous media and water dissociation on the surface. We performed
+calculations for a set of metals (Al, Fe, Cu, Ag, Au, Pt). Taking into account
+the diversity of atomic structures of real metallic nanoparticles, we performed
+calculations for different models such as (001) and (111) surfaces, nanorods,
+and two different cubic nanoparticles of 0.6 and 0.3 nm size. Significant
+energy dependence of the processes from the selected model of nanoparticle
+suggests that for the correct description we should combine the calculations
+for the several representative models. In addition to the descriptors of
+chemical activity of the metallic nanoparticles for the two studied processes,
+we propose descriptors for taking into account the dependence of chemical
+activity from the size and shape of nanoparticles. Routes to minimization of
+computational costs for these calculations are also discussed.
+"
+27456,Unmanned Aerial Vehicle Path Planning for Traffic Estimation and Detection of Non-Recurrent Congestion,"  Unmanned aerial vehicles (drones) can be used in traffic and road monitoring
+applications. We investigate the benefit of using drones for simultaneous
+traffic state estimation and incident detection. Specifically, we propose a
+coupled planning and estimation framework where we adaptively navigate a drone
+to minimize the uncertainty on parameter and traffic state estimates. We show
+that the use of a drone provides significant improvement in incident detection
+under congested conditions. Without a drone, the estimation procedure in
+congested conditions is not able to distinguish between observations due to
+congestion under normal operating conditions and similar observations due to a
+reduction in capacity.
+"
+27457,"Clear Thinking, vague thinking and paradoxes","  Many undergraduate students of engineering and the exact sciences have
+difficulty with their mathematics courses due to insufficient proficiency in
+what we in this paper have termed clear thinking. We believe that this lack of
+proficiency is one of the primary causes underlying the common difficulties
+students face, leading to mistakes like the improper use of definitions and the
+improper phrasing of definitions, claimes and proofs. We further argue that
+clear thinking is not a skill that is acquired easily and naturally - it must
+be consciously learned and developed. The paper describes, using concrete
+examples, how the examination and analysis of classical paradoxes can be a fine
+tool for developing students' clear thinking. It also looks closely at the
+paradoxes themselves, and at the various solutions that have been proposed for
+them. We believe that the extensive literature on paradoxes has not always
+given clear thinking its due emphasis as an analytical tool. We therefore
+suggest that other discipkunes could also benefit from drawing upon the
+strategies employed by mathematicians to describe and examine the foundations
+of the problems they encounter.
+"
+27458,The rigidity of $\mathbb{S}^3\times\mathbb{R}$ under ancient Ricci flow,"  In this paper we generalize the neck-stability theorem of Kleiner-Lott to a
+special class of four-dimensional nonnegatively curved Type I
+$\kappa$-solutions, namely, those whose asymptotic shrinkers are the standard
+cylinder $\mathbb{S}^3\times\mathbb{R}$. We use this stability result to prove
+a rigidity theorem: if a four-dimensional Type I $\kappa$-solution with
+nonnegative curvature operator has the standard cylinder
+$\mathbb{S}^3\times\mathbb{R}$ as its asymptotic shrinker, then it is exactly
+the cylinder with its standard shrinking metric.
+"
+27459,3d Global Modeling of the Solar Dynamo,"  We are attempting to use 3D global MHD models to accurately reproduce the
+magnetic dynamo and other patterns of solar activity. Our work is focused on
+modeling the entire solar convection zone so that we can try to reproduce the
+interconnected effects that generate the solar cycle. Using data from standard
+reference models, such as the model S [4], we map the ambient state for a
+polytropic ideal gas and solve for its advection to model the propagation of
+turbulent convective transport and magnetic induction throughout the region. We
+use new approaches to these models to better understand the impact that
+different parameters can have on an evolving system. We model 3 different
+resolutions with the following grid points: (512 {\phi}, 256 {\theta}, 256 R),
+(256 {\phi}, 128 {\theta}, 128 R) and (128 {\phi}, 64 {\theta}, 64 R), as well
+as varying models of convection. The 2 higher resolution models express an
+implicit viscosity that is just too unrealistic to take away any significant
+results, however the changes in convection profiles in the low resolution
+models show how minor changes in convection can change the axisymmetric pattern
+of the dynamo cycle.
+"
+27460,"Fixed point theorems for $(\varepsilon,λ)$-uniformly locally contractive mapping defined on $\varepsilon$-chainable $G$-metric type spaces","  In this article, we discuss fixed point results for
+$(\varepsilon,\lambda)$-uniformly locally contractive self mapping defined on
+$\varepsilon$-chainable $G$-metric type spaces. In particular, we show that
+under some more general conditions, certain fixed point results already
+obtained in the literature remain true. Moreover, in the last sections of this
+paper, we make use of the newly introduced notion of $\lambda$-sequences to
+derive new results.
+"
+27461,Understanding Systematic Errors Through Modeling of ALMA Primary Beams,"  Many aspects of the Atacama Large Millimeter Array (ALMA) instrument are
+still unknown due to its young age. One such aspect is the true nature of the
+primary beam of each baseline, and how changes to the individual primary beams
+affect astronomical observations when said changes are ignored during imaging.
+This paper aims to create a more thorough understanding of the strengths and
+weaknesses of ALMA through realistic modeling of the primary beams and
+simulated observations, which in turn can inform the user of the necessity of
+implementing more computationally costly algorithms, such as A-Projection, and
+when simpler, quicker algorithms will suffice. We quantify our results by
+examining the dynamic range of each observation, along with the ability to
+reconstruct the Stokes I amplitude of the test sources. These tests conclude
+that for dynamic ranges of less than 1000, for point sources and sources much
+smaller than the main lobe of the primary beam, the accuracy of the primary
+beam model beyond the physical size of the aperture simply doesn't matter. In
+observations of large extended sources, deconvolution errors dominate the
+reconstructed images and the individual primary beam errors were
+indistinguishable from each other.
+"
+27462,Numerical Analysis of Automodel Solutions for Superdiffusive Transport,"  The distributed computing analysis of the accuracy of automodel solutions for
+the Green's function of a wide class of superdiffusive transport of
+perturbation on a uniform background is carried out. The approximate automodel
+solutions have been suggested for the 1D transport equation with a model
+long-tailed step-length probability distribution function (PDF) with various
+power-law exponents. These PDFs describe the transport dominated by the Lévy
+flights. Massive computing experiments were done to verify automodel solutions.
+The Everest distributed computing platform and the cluster at NRC Kurchatov
+Institute were used. The results verify the high accuracy of automodel
+solutions in a wide range of space-time variables and suggest extending the
+developed method of automodel solutions to a wider class of stochastic
+phenomena.
+"
+27463,Core-hole-induced dynamical effects in the x-ray emission spectrum of liquid methanol,"  We compute the x-ray emission spectrum (XES) of liquid methanol, with the
+dynamical effects that result from the creation of the core hole included in a
+semiclassical way. Our method closely reproduces a fully quantum mechanical
+description of the dynamical effects for relevant one-dimensional models of the
+hydrogen-bonded methanol molecules. For the liquid we find excellent agreement
+with the experimental spectrum, including the large isotope effect in the first
+split peak. The dynamical effects depend sensitively on the initial structure
+in terms of the local hydrogen-bonding (H-bonding) character; non-donor
+molecules contribute mainly to the high-energy peak while molecules with a
+strong donated H-bond contribute to the peak at lower energy. The spectrum thus
+reflects the initial structure mediated by the dynamical effects that are,
+however, seen to be crucial in order to reproduce the intensity distribution of
+the recently measured spectrum.
+"
+27464,Diffusion of Oxygen Isotopes in Thermally Evolving Planetesimals and Size Ranges of Presolar Silicate Grains,"  Presolar grains are small particles found in meteorites through their
+isotopic compositions which are considerably different from those of materials
+in the Solar System. If some isotopes in presolar grains diffused out beyond
+their grain sizes when they were embedded in parent bodies of meteorites, their
+isotopic compositions could be washed out, and hence the grains cannot be
+identified as presolar grains any more. We explore this possibility for the
+first time by self-consistently simulating the thermal evolution of
+planetesimals and the diffusion length of $^{18}$O in presolar silicate grains.
+Our results show that presolar silicate grains smaller than $\sim$ 0.03 $\mu m$
+cannot keep their original isotopic compositions even if the host planetesimals
+experienced maximum temperature as low as 600 $^{\circ}$C. Since this
+temperature corresponds to the one experienced by petrologic type 3 chondrites,
+the isotopic diffusion can constrain the size of presolar silicate grains
+discovered in such chondrites to be larger than $\sim$ 0.03 $\mu m$. We also
+find that the diffusion lengths of $^{18}$O reach $\sim$ 0.3-2 $\mu m$ in
+planetesimals that were heated up to 700-800 $^{\circ}$C. This indicates that,
+if the original size of presolar grains spans a range from $\sim$ 0.001 $\mu m$
+to $\sim$ 0.3 $\mu m$ like that in the interstellar medium, the isotopic
+records of the presolar grains may be almost completely lost in such highly
+thermalized parent bodies. We propose that isotopic diffusion could be a key
+process to control the size distribution and abundance of presolar grains in
+some types of chondrites.
+"
+27465,"On Smooth Orthogonal and Octilinear Drawings: Relations, Complexity and Kandinsky Drawings","  We study two variants of the well-known orthogonal drawing model: (i) the
+smooth orthogonal, and (ii) the octilinear. Both models form an extension of
+the orthogonal, by supporting one additional type of edge segments (circular
+arcs and diagonal segments, respectively).
+For planar graphs of max-degree 4, we analyze relationships between the graph
+classes that can be drawn bendless in the two models and we also prove
+NP-hardness for a restricted version of the bendless drawing problem for both
+models. For planar graphs of higher degree, we present an algorithm that
+produces bi-monotone smooth orthogonal drawings with at most two segments per
+edge, which also guarantees a linear number of edges with exactly one segment.
+"
+27466,"On relations between extreme value statistics, extreme random matrices and Peak-Over-Threshold method","  Using the \emph{thinning method}, we explain the link between classical
+Fisher-Tippett-Gnedenko classification of extreme events and their free
+analogue obtained by Ben Arous and Voiculescu in the context of free
+probability calculus. In particular, we present explicit examples of large
+random matrix ensembles, realizing free Weibull, free Fréchet and free
+Gumbel limiting laws, respectively. We also explain, why these free laws are
+identical to Balkema-de Haan-Pickands limiting distribution for exceedances,
+i.e. why they have the form of generalized Pareto distributions and derive a
+simple exponential relation between classical and free extreme laws.
+"
+27467,Data-driven Planning via Imitation Learning,"  Robot planning is the process of selecting a sequence of actions that
+optimize for a task specific objective. The optimal solutions to such tasks are
+heavily influenced by the implicit structure in the environment, i.e. the
+configuration of objects in the world. State-of-the-art planning approaches,
+however, do not exploit this structure, thereby expending valuable effort
+searching the action space instead of focusing on potentially good actions. In
+this paper, we address the problem of enabling planners to adapt their search
+strategies by inferring such good actions in an efficient manner using only the
+information uncovered by the search up until that time. We formulate this as a
+problem of sequential decision making under uncertainty where at a given
+iteration a planning policy must map the state of the search to a planning
+action. Unfortunately, the training process for such partial information based
+policies is slow to converge and susceptible to poor local minima. Our key
+insight is that if we could fully observe the underlying world map, we would
+easily be able to disambiguate between good and bad actions. We hence present a
+novel data-driven imitation learning framework to efficiently train planning
+policies by imitating a clairvoyant oracle - an oracle that at train time has
+full knowledge about the world map and can compute optimal decisions. We
+leverage the fact that for planning problems, such oracles can be efficiently
+computed and derive performance guarantees for the learnt policy. We examine
+two important domains that rely on partial information based policies -
+informative path planning and search based motion planning. We validate the
+approach on a spectrum of environments for both problem domains, including
+experiments on a real UAV, and show that the learnt policy consistently
+outperforms state-of-the-art algorithms.
+"
+27468,Monte Carlo Action Programming,"  This paper proposes Monte Carlo Action Programming, a programming language
+framework for autonomous systems that act in large probabilistic state spaces
+with high branching factors. It comprises formal syntax and semantics of a
+nondeterministic action programming language. The language is interpreted
+stochastically via Monte Carlo Tree Search. Effectiveness of the approach is
+shown empirically.
+"
+27469,Fast k-Nearest Neighbour Search via Prioritized DCI,"  Most exact methods for k-nearest neighbour search suffer from the curse of
+dimensionality; that is, their query times exhibit exponential dependence on
+either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing
+(DCI) offers a promising way of circumventing the curse and successfully
+reduces the dependence of query time on intrinsic dimensionality from
+exponential to sublinear. In this paper, we propose a variant of DCI, which we
+call Prioritized DCI, and show a remarkable improvement in the dependence of
+query time on intrinsic dimensionality. In particular, a linear increase in
+intrinsic dimensionality, or equivalently, an exponential increase in the
+number of points near a query, can be mostly counteracted with just a linear
+increase in space. We also demonstrate empirically that Prioritized DCI
+significantly outperforms prior methods. In particular, relative to
+Locality-Sensitive Hashing (LSH), Prioritized DCI reduces the number of
+distance evaluations by a factor of 14 to 116 and the memory consumption by a
+factor of 21.
+"
+27470,A Distributed Approach for Networked Flying Platform Association with Small Cells in 5G+ Networks,"  The densification of small-cell base stations in a 5G architecture is a
+promising approach to enhance the coverage area and facilitate the ever
+increasing capacity demand of end users. However, the bottleneck is an
+intelligent management of a backhaul/fronthaul network for these small-cell
+base stations. This involves efficient association and placement of the
+backhaul hubs that connects these small-cells with the core network.
+Terrestrial hubs suffer from an inefficient non line of sight link limitations
+and unavailability of a proper infrastructure in an urban area. Seeing the
+popularity of flying platforms, we employ here an idea of using networked
+flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned
+balloons flying at different altitudes, as aerial backhaul hubs. The
+association problem of these NFP-hubs and small-cell base stations is
+formulated considering backhaul link and NFP related limitations such as
+maximum number of supported links and bandwidth. Then, this paper presents an
+efficient and distributed solution of the designed problem, which performs a
+greedy search in order to maximize the sum rate of the overall network. A
+favorable performance is observed via a numerical comparison of our proposed
+method with optimal exhaustive search algorithm in terms of sum rate and
+run-time speed.
+"
+27471,Low synchronization GMRES algorithms,"  Communication-avoiding and pipelined variants of Krylov solvers are critical
+for the scalability of linear system solvers on future exascale architectures.
+We present low synchronization variants of iterated classical (CGS) and
+modified Gram-Schmidt (MGS) algorithms that require one and two global
+reduction communication steps. Derivations of low synchronization iterated CGS
+algorithms are based on previous work by Ruhe. Our main contribution is to
+introduce a backward normalization lag into the compact $WY$ form of MGS
+resulting in a ${\cal O}(\eps)\kappa(A)$ stable GMRES algorithm that requires
+only one global synchronization per iteration. The reduction operations are
+overlapped with computations and pipelined to optimize performance. Further
+improvements in performance are achieved by accelerating GMRES BLAS-2
+operations on GPUs.
+"
+27472,Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis,"  In order to decode the human brain, Multivariate Pattern (MVP) classification
+generates cognitive models by using functional Magnetic Resonance Imaging
+(fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in
+multi-subject fMRI dataset must be mapped to a shared space and then a
+classification model is generated by employing the mapped patterns. However,
+the MVP models may not provide stable performance on a new fMRI dataset because
+the standard pipeline uses disjoint steps for generating these models. Indeed,
+each step in the pipeline includes an objective function with independent
+optimization approach, where the best solution of each step may not be optimum
+for the next steps. For tackling the mentioned issue, this paper introduces the
+Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective
+function for MVP analysis rather than just using those disjoint steps. For
+solving the integrated problem, we proposed a customized multi-objective
+optimization approach, where all possible solutions are firstly generated, and
+then our method ranks and selects the robust solutions as the final results.
+Empirical studies confirm that the proposed method can generate superior
+performance in comparison with other techniques.
+"
+27473,Machine Learning with Clos Networks,"  We present a new methodology for improving the accuracy of small neural
+networks by applying the concept of a clos network to achieve maximum
+expression in a smaller network. We explore the design space to show that more
+layers is beneficial, given the same number of parameters. We also present
+findings on how the relu nonlinearity ffects accuracy in separable networks. We
+present results on early work with Cifar-10 dataset.
+"
+27474,Finite Inverse Categories as Signatures,"  We define a simple dependent type theory and prove that its well-formed types
+correspond exactly to finite inverse categories.
+"
+27475,A comparative study of fluid-particle coupling methods for fully resolved lattice Boltzmann simulations,"  The direct numerical simulation of particulate systems offers a unique
+approach to study the dynamics of fluid-solid suspensions by fully resolving
+the submerged particles and without introducing empirical models. For the
+lattice Boltzmann method, different variants exist to incorporate the
+fluid-particle interaction into the simulation. This paper provides a detailed
+and systematic comparison of two different methods, namely the momentum
+exchange method and the partially saturated cells method by Noble and
+Torczynski. Three subvariants of each method are used in the benchmark scenario
+of a single heavy sphere settling in ambient fluid to study their
+characteristics and accuracy for particle Reynolds numbers from 185 up to 365.
+The sphere must be resolved with at least 24 computational cells per diameter
+to achieve velocity errors below 5%. The momentum exchange method is found to
+be more accurate in predicting the streamwise velocity component whereas the
+partially saturated cells method is more accurate in the spanwise components.
+The study reveals that the resolution should be chosen with respect to the
+coupling dynamics, and not only based on the flow properties, to avoid large
+errors in the fluid-particle interaction.
+"
+27476,"Effects of P, As, and Sb heavy doping on band gap narrowing of germanium as light-emitting materials","  The n-type tensile-strained Ge can be used as high-efficient light-emitting
+materials. To reveal the influence of n-type doping on the electronic structure
+of Ge, we have computed the electronic structure of P, As and Sb doped Ge using
+first-principles calculation and band unfolding technique. We find that these
+n-type doping can induce both indirect and direct band gap narrowing, which
+well reproduce experimental observation that red-shifts occur in
+photoluminescence spectra of Ge with n-type doping. We reveal that the indirect
+band gap narrowing is mainly caused by impurity state, while the direct band
+gap narrowing is a result of lattice distortion induced by the dopant atom.
+Moreover, we find that it can use E_g^{\Gamma}-E_g^L to explain the voltage
+increase was needed to reach the same current densities of light emission
+through the different samples with increasing doping concentrations.
+"
+27477,Neural Component Analysis for Fault Detection,"  Principal component analysis (PCA) is largely adopted for chemical process
+monitoring and numerous PCA-based systems have been developed to solve various
+fault detection and diagnosis problems. Since PCA-based methods assume that the
+monitored process is linear, nonlinear PCA models, such as autoencoder models
+and kernel principal component analysis (KPCA), has been proposed and applied
+to nonlinear process monitoring. However, KPCA-based methods need to perform
+eigen-decomposition (ED) on the kernel Gram matrix whose dimensions depend on
+the number of training data. Moreover, prefixed kernel parameters cannot be
+most effective for different faults which may need different parameters to
+maximize their respective detection performances. Autoencoder models lack the
+consideration of orthogonal constraints which is crucial for PCA-based
+algorithms. To address these problems, this paper proposes a novel nonlinear
+method, called neural component analysis (NCA), which intends to train a
+feedforward neural work with orthogonal constraints such as those used in PCA.
+NCA can adaptively learn its parameters through backpropagation and the
+dimensionality of the nonlinear features has no relationship with the number of
+training samples. Extensive experimental results on the Tennessee Eastman (TE)
+benchmark process show the superiority of NCA in terms of missed detection rate
+(MDR) and false alarm rate (FAR). The source code of NCA can be found in
+this https URL.
+"
+27478,Distribution-Free One-Pass Learning,"  In many large-scale machine learning applications, data are accumulated with
+time, and thus, an appropriate model should be able to update in an online
+paradigm. Moreover, as the whole data volume is unknown when constructing the
+model, it is desired to scan each data item only once with a storage
+independent with the data volume. It is also noteworthy that the distribution
+underlying may change during the data accumulation procedure. To handle such
+tasks, in this paper we propose DFOP, a distribution-free one-pass learning
+approach. This approach works well when distribution change occurs during data
+accumulation, without requiring prior knowledge about the change. Every data
+item can be discarded once it has been scanned. Besides, theoretical guarantee
+shows that the estimate error, under a mild assumption, decreases until
+convergence with high probability. The performance of DFOP for both regression
+and classification are validated in experiments.
+"
+27479,Simple stably projectionless C*-algebras with generalized tracial rank one,"  We study a class of stably projectionless simple C*-algebras which may be
+viewed as having generalized tracial rank one in analogy with the unital case.
+Some structural question concerning these simple C*-algebras are studied. The
+paper also serves as a technical support for the classification of separable
+stably projectionless simple amenable Jiang-Su stable C*-algebras.
+"
+27480,Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks,"  The effectiveness of deep neural architectures has been widely supported in
+terms of both experimental and foundational principles. There is also clear
+evidence that the activation function (e.g. the rectifier and the LSTM units)
+plays a crucial role in the complexity of learning. Based on this remark, this
+paper discusses an optimal selection of the neuron non-linearity in a
+functional framework that is inspired from classic regularization arguments. It
+is shown that the best activation function is represented by a kernel expansion
+in the training set, that can be effectively approximated over an opportune set
+of points modeling 1-D clusters. The idea can be naturally extended to
+recurrent networks, where the expressiveness of kernel-based activation
+functions turns out to be a crucial ingredient to capture long-term
+dependencies. We give experimental evidence of this property by a set of
+challenging experiments, where we compare the results with neural architectures
+based on state of the art LSTM cells.
+"
+27481,Semi-hyperbolic rational maps and size of Fatou components,"  Recently Merenkov and Sabitova introduced the notion of a homogeneous planar
+set. Using this notion they proved a result for Sierpi${ń}$ski carpet Julia
+sets of hyperbolic rational maps that relates the diameters of the peripheral
+circles to the Hausdorff dimension of the Julia set. We extend this theorem to
+Julia sets (not necessarily Sierpi${ń}$ski carpets) of semi-hyperbolic
+rational maps, and prove a stronger version of the theorem that was conjectured
+by Merenkov and Sabitova.
+"
+27482,The shape of cubic fields,"  We use the method of Shintani, as developed by Taniguchi and Thorne, to prove
+the quantitative equidistribution of the shape of cubic fields when the fields
+are ordered by discriminant.
+"
+27483,word2vec Skip-Gram with Negative Sampling is a Weighted Logistic PCA,"  We show that the skip-gram formulation of word2vec trained with negative
+sampling is equivalent to a weighted logistic PCA. This connection allows us to
+better understand the objective, compare it to other word embedding methods,
+and extend it to higher dimensional models.
+"
+27484,"Using a ""Study of Studies"" to help statistics students assess research findings","  One learning goal of the introductory statistics course is to develop the
+ability to make sense of research findings in published papers. The Atlantic
+magazine regularly publishes a feature called ""Study of Studies"" that
+summarizes multiple articles published in a particular domain. We describe a
+classroom activity to develop this capacity using the ""Study of Studies."" In
+this activity, students read capsule summaries of twelve research papers
+related to restaurants and dining that was published in April 2015. The
+selected papers report on topics such as how seating arrangement, server
+posture, plate color and size, and the use of background music relate to
+revenue, ambiance, and perceived food quality. The students are assigned one of
+the twelve papers to read and critique as part of a small group. Their group
+critiques are shared with the class and the instructor.
+A pilot study was conducted during the 2015-2016 academic year at Amherst
+College. Students noted that key details were not included in the published
+summary. They were generally skeptical of the published conclusions. The
+students often provided additional summarization of information from the
+journal articles that better describe the results. By independently assessing
+and comparing the original study conclusions with the capsule summary in the
+""Study of Studies,"" students can practice developing judgment and assessing the
+validity of statistical results.
+"
+27485,Elementary equivalence vs commensurability for hyperbolic groups,"  We study to what extent torsion-free (Gromov)-hyperbolic groups are
+elementarily equivalent to their finite index subgroups. In particular, we
+prove that a hyperbolic limit group either is a free product of cyclic groups
+and surface groups, or admits infinitely many subgroups of finite index which
+are pairwise non elementarily equivalent.
+"
+27486,Line Segment Covering of Cells in Arrangements,"  Given a collection $L$ of line segments, we consider its arrangement and
+study the problem of covering all cells with line segments of $L$. That is, we
+want to find a minimum-size set $L'$ of line segments such that every cell in
+the arrangement has a line from $L'$ defining its boundary. We show that the
+problem is NP-hard, even when all segments are axis-aligned. In fact, the
+problem is still NP-hard when we only need to cover rectangular cells of the
+arrangement. For the latter problem we also show that it is fixed parameter
+tractable with respect to the size of the optimal solution. Finally we provide
+a linear time algorithm for the case where cells of the arrangement are created
+by recursively subdividing a rectangle using horizontal and vertical cutting
+segments.
+"
+27487,A Machine Learning Driven IoT Solution for Noise Classification in Smart Cities,"  We present a machine learning based method for noise classification using a
+low-power and inexpensive IoT unit. We use Mel-frequency cepstral coefficients
+for audio feature extraction and supervised classification algorithms (that is,
+support vector machine and k-nearest neighbors) for noise classification. We
+evaluate our approach experimentally with a dataset of about 3000 sound samples
+grouped in eight sound classes (such as, car horn, jackhammer, or street
+music). We explore the parameter space of support vector machine and k-nearest
+neighbors algorithms to estimate the optimal parameter values for
+classification of sound samples in the dataset under study. We achieve a noise
+classification accuracy in the range 85% -- 100%. Training and testing of our
+k-nearest neighbors (k = 1) implementation on Raspberry Pi Zero W is less than
+a second for a dataset with features of more than 3000 sound samples.
+"
+27488,Charge densities that change linearly with time are effectively electrostatic,"  We consider classical electromagnetic systems in which the current density
+${\bf J}$ does not change with time, but has nonzero divergence, so that
+electric charge does build up steadily over time. We show that for such a
+system, the \emph{exact} electromagnetic fields are given by the Coulomb and
+Biot-Savart laws with the sources evaluated at the \emph{present} (rather than
+retarded) time, even though the charge density is changing over time.
+"
+27489,Predicting crypto-currencies using sparse non-Gaussian state space models,"  In this paper we forecast daily returns of crypto-currencies using a wide
+variety of different econometric models. To capture salient features commonly
+observed in financial time series like rapid changes in the conditional
+variance, non-normality of the measurement errors and sharply increasing
+trends, we develop a time-varying parameter VAR with t-distributed measurement
+errors and stochastic volatility. To control for overparameterization, we rely
+on the Bayesian literature on shrinkage priors that enables us to shrink
+coefficients associated with irrelevant predictors and/or perform model
+specification in a flexible manner. Using around one year of daily data we
+perform a real-time forecasting exercise and investigate whether any of the
+proposed models is able to outperform the naive random walk benchmark. To
+assess the economic relevance of the forecasting gains produced by the proposed
+models we moreover run a simple trading exercise.
+"
+27490,Additive non-approximability of chromatic number in proper minor-closed classes,"  Robin Thomas asked whether for every proper minor-closed class C, there
+exists a polynomial-time algorithm approximating the chromatic number of graphs
+from C up to a constant additive error independent on the class C. We show this
+is not the case: unless P=NP, for every integer k>=1, there is no
+polynomial-time algorithm to color a K_{4k+1}-minor-free graph G using at most
+chi(G)+k-1 colors. More generally, for every k>=1 and 1<=\beta<=4/3, there is
+no polynomial-time algorithm to color a K_{4k+1}-minor-free graph G using less
+than beta.chi(G)+(4-3beta)k colors. As far as we know, this is the first
+non-trivial non-approximability result regarding the chromatic number in proper
+minor-closed classes.
+We also give somewhat weaker non-approximability bound for
+K_{4k+1}-minor-free graphs with no cliques of size 4. On the positive side, we
+present additive approximation algorithm whose error depends on the apex number
+of the forbidden minor, and an algorithm with additive error 6 under the
+additional assumption that the graph has no 4-cycles.
+"
+27491,Calculations of electronic properties and vibrational parameters of alkaline-earth lithides: MgLi^+ and CaLi^+,"  The ^1{\Sigma}^+ electronic ground states of MgLi^+ and CaLi^+ molecular ions
+are investigated for their spectroscopic constants and properties such as the
+dipole- and quadrupole moments, and static dipole polarizabilities. The
+quadrupole moments and the static dipole polarizabilities for these ions have
+been calculated and reported here, for the first time. The maximum possible
+error bars, arising due to the finite basis set and the exclusion of higher
+correlation effects beyond partial triples, are quoted for reliability.
+Further, the adiabatic effects such as diagonal Born-Oppenheimer corrections
+are also calculated for these molecules. The vibrational energies, the
+wavefunctions, and the relevant vibrational parameters are obtained by solving
+the vibrational Schrödinger equation using the potential energy curve and the
+permanent dipole moment curve of the molecular electronic ground state.
+Thereafter, spontaneous and black-body radiation induced transition rates are
+calculated to obtain the lifetimes of the vibrational states. The lifetime of
+rovibronic ground state for MgLi^+ , at room temperature, is found to be 2.81 s
+and for CaLi^+ it is 3.19 s. It has been observed that the lifetime of the
+highly excited vibrational state is several times larger than (comparable to)
+that of the vibrational ground state of MgLi^+ (CaLi^+ ). In addition, a few
+low-lying electronic excited states of {\Sigma} and {\Pi} symmetries have been
+investigated for their electronic and vibrational properties, using EOM-CCSD
+method together with the QZ basis sets.
+"
+27492,Accretion of rotating fluids onto stationary solutions,"  We consider a general stationary solution and derive the general laws for
+accretion of rotating perfect fluids. For non-degenerate and degenerate Fermi
+and Bose fluids we derive new effects that mimic the center-of-mass-energy
+effect of two colliding particle in the vicinity of horizons. Non-degenerate
+fluids see their chemical potential grow arbitrarily and ultra-relativistic
+Fermi fluids see their specific enthalpy and Fermi momentum grow arbitrarily
+too while the latter vanishes gradually for non-relativistic Fermi fluids. For
+degenerate Bose fluids two scenarios remain possible as the fluid approaches a
+horizon: a) The Bose-Einstein condensation ceases or b) the temperature drops
+gradually down to zero.
+"
+27493,Testing for Independence of Large Dimensional Vectors,"  In this paper new tests for the independence of two high-dimensional vectors
+are investigated. We consider the case where the dimension of the vectors
+increases with the sample size and propose multivariate analysis of
+variance-type statistics for the hypothesis of a block diagonal covariance
+matrix. The asymptotic properties of the new test statistics are investigated
+under the null hypothesis and the alternative hypothesis using random matrix
+theory. For this purpose we study the weak convergence of linear spectral
+statistics of central and (conditionally) non-central Fisher matrices. In
+particular, a central limit theorem for linear spectral statistics of large
+dimensional (conditionally) non-central Fisher matrices is derived which is
+then used to analyse the power of the tests under the alternative.
+The theoretical results are illustrated by means of a simulation study where
+we also compare the new tests with several alternative, in particular with the
+commonly used corrected likelihood ratio test. It is demonstrated that the
+latter test does not keep its nominal level, if the dimension of one sub-vector
+is relatively small compared to the dimension of the other sub-vector. On the
+other hand the tests proposed in this paper provide a reasonable approximation
+of the nominal level in such situations. Moreover, we observe that one of the
+proposed tests is most powerful under a variety of correlation scenarios.
+"
+27494,Bounds on the length of a game of Cops and Robbers,"  In the game of Cops and Robbers, a team of cops attempts to capture a robber
+on a graph $G$. All players occupy vertices of $G$. The game operates in
+rounds; in each round the cops move to neighboring vertices, after which the
+robber does the same. The minimum number of cops needed to guarantee capture of
+a robber on $G$ is the cop number of $G$, denoted $c(G)$, and the minimum
+number of rounds needed for them to do so is the capture time. It has long been
+known that the capture time of an $n$-vertex graph with cop number $k$ is
+$O(n^{k+1})$. More recently, Bonato, Golovach, Hahn, and Kratochvíl (2009)
+and Gavenčiak (2010) showed that for $k = 1$, this upper bound is not
+asymptotically tight: for graphs with cop number 1, the cop can always win
+within $n-4$ rounds. In this paper, we show that the upper bound is tight when
+$k \ge 2$: for fixed $k \ge 2$, we construct arbitrarily large graphs $G$
+having capture time at least $\left (\frac{\vert V(G) \vert}{40k^4}\right
+)^{k+1}$.
+In the process of proving our main result, we establish results that may be
+of independent interest. In particular, we show that the problem of deciding
+whether $k$ cops can capture a robber on a directed graph is polynomial-time
+equivalent to deciding whether $k$ cops can capture a robber on an undirected
+graph. As a corollary of this fact, we obtain a relatively short proof of a
+major conjecture of Goldstein and Reingold (1995), which was recently proved
+through other means by the author (2015). We also show that $n$-vertex
+strongly-connected directed graphs with cop number 1 can have capture time
+$\Omega(n^2)$, thereby showing that the result of Bonato et al. does not extend
+to the directed setting.
+"
+27495,Behavior Trees in Robotics and AI: An Introduction,"  A Behavior Tree (BT) is a way to structure the switching between different
+tasks in an autonomous agent, such as a robot or a virtual entity in a computer
+game. BTs are a very efficient way of creating complex systems that are both
+modular and reactive. These properties are crucial in many applications, which
+has led to the spread of BT from computer game programming to many branches of
+AI and Robotics. In this book, we will first give an introduction to BTs, then
+we describe how BTs relate to, and in many cases generalize, earlier switching
+structures. These ideas are then used as a foundation for a set of efficient
+and easy to use design principles. Properties such as safety, robustness, and
+efficiency are important for an autonomous system, and we describe a set of
+tools for formally analyzing these using a state space description of BTs. With
+the new analysis tools, we can formalize the descriptions of how BTs generalize
+earlier approaches. We also show the use of BTs in automated planning and
+machine learning. Finally, we describe an extended set of tools to capture the
+behavior of Stochastic BTs, where the outcomes of actions are described by
+probabilities. These tools enable the computation of both success probabilities
+and time to completion.
+"
+27496,Automatic Differentiation for Tensor Algebras,"  Kjolstad et. al. proposed a tensor algebra compiler. It takes expressions
+that define a tensor element-wise, such as $f_{ij}(a,b,c,d) =
+\exp\left[-\sum_{k=0}^4 \left((a_{ik}+b_{jk})^2\, c_{ii} + d_{i+k}^3 \right)
+\right]$, and generates the corresponding compute kernel code.
+For machine learning, especially deep learning, it is often necessary to
+compute the gradient of a loss function $l(a,b,c,d)=l(f(a,b,c,d))$ with respect
+to parameters $a,b,c,d$. If tensor compilers are to be applied in this field,
+it is necessary to derive expressions for the derivatives of element-wise
+defined tensors, i.e. expressions for $(da)_{ik}=\partial l/\partial a_{ik}$.
+When the mapping between function indices and argument indices is not 1:1,
+special attention is required. For the function $f_{ij} (x) = x_i^2$, the
+derivative of the loss is $(dx)_i=\partial l/\partial x_i=\sum_j
+(df)_{ij}2x_i$; the sum is necessary because index $j$ does not appear in the
+indices of $f$. Another example is $f_{i}(x)=x_{ii}^2$, where $x$ is a matrix;
+here we have $(dx)_{ij}=\delta_{ij}(df)_i2x_{ii}$; the Kronecker delta is
+necessary because the derivative is zero for off-diagonal elements. Another
+indexing scheme is used by $f_{ij}(x)=\exp x_{i+j}$; here the correct
+derivative is $(dx)_{k}=\sum_i (df)_{i,k-i} \exp x_{k}$, where the range of the
+sum must be chosen appropriately.
+In this publication we present an algorithm that can handle any case in which
+the indices of an argument are an arbitrary linear combination of the indices
+of the function, thus all the above examples can be handled. Sums (and their
+ranges) and Kronecker deltas are automatically inserted into the derivatives as
+necessary. Additionally, the indices are transformed, if required (as in the
+last example). The algorithm outputs a symbolic expression that can be
+subsequently fed into a tensor algebra compiler.
+Source code is provided.
+"
+27497,Coalitional game with opinion exchange,"  In coalitional games, traditional coalitional game theory does not apply if
+different participants hold different opinions about the payoff function that
+corresponds to each subset of the coalition. In this paper, we propose a
+framework in which players can exchange opinions about their views of payoff
+functions and then decide the distribution of the value of the grand coalition.
+When all players are truth-telling, the problem of opinion consensus is
+decoupled from the coalitional game, but interesting dynamics will arise when
+players are strategic in the consensus phase. Assuming that all players are
+rational, the model implies that, if influential players are risk-averse, an
+efficient fusion of the distributed data is achieved at pure strategy Nash
+equilibrium, meaning that the average opinion will not drift. Also, without the
+assumption that all players are rational, each player can use an algorithmic
+R-learning process, which gives the same result as the pure strategy Nash
+equilibrium with rational players.
+"
+27498,On the homotopy transfer of $A_\infty$ structures,"  The present article is devoted to the study of transfers for $A_\infty$
+structures, their maps and homotopies, as developed in \cite{Markl06}. In
+particular, we supply the proofs of claims formulated therein and provide their
+extension by comparing them with the former approach based on the homological
+perturbation lemma.
+"
+27499,Towards a universality picture for the relaxation to equilibrium of kinetically constrained models,"  Recent years have seen a great deal of progress in our understanding of
+bootstrap percolation models, a particular class of monotone cellular automata.
+In the two dimensional lattice there is now a quite satisfactory understanding
+of their evolution starting from a random initial condition, with a strikingly
+beautiful universality picture for their critical behaviour. Much less is known
+for their non-monotone stochastic counterpart, namely kinetically constrained
+models (KCM). In KCM each vertex is resampled (independently) at rate one by
+tossing a p-coin iff it can be infected in the next step by the bootstrap
+model. In particular infection can also heal, hence the non-monotonicity.
+Besides the connection with bootstrap percolation, KCM have an interest in
+their own as they feature some of the most striking features of the
+liquid/glass transition, a major and still largely open problem in condensed
+matter physics. In this paper we pave the way towards proving universality
+results for KCM similar to those for bootstrap percolation. Our novel and
+general approach establishes a close connection between the critical scaling of
+characteristic time scales for KCM and the scaling of the critical length in
+critical bootstrap models. Although the full proof of universality for KCM is
+deferred to a forthcoming paper, here we apply our general method to the
+Friedrickson-Andersen k-facilitated models, amongst the most studied KCM, and
+to the Gravner-Griffeath model. In both cases our results are close to optimal.
+"
+27500,Computationally Efficient Simulation of Queues: The R Package queuecomputer,"  Large networks of queueing systems model important real-world systems such as
+MapReduce clusters, web-servers, hospitals, call-centers and airport passenger
+terminals.To model such systems accurately we must infer queueing parameters
+from data. Unfortunately, for many queueing networks there is no clear way to
+proceed with parameter inference from data. Approximate Bayesian computation
+could offer a straight-forward way to infer parameters for such networks if we
+could simulate data quickly enough.
+We present a computationally efficient method for simulating from a very
+general set of queueing networks with the R package queuecomputer. Remarkable
+speedups of more than 2 orders of magnitude are observed relative to the
+popular DES packages simmer and simpy. We replicate output from these packages
+to validate the package.
+The package is modular and integrates well with the popular R package dplyr.
+Complex queueing networks with tandem, parallel and fork/join topologies can
+easily be built with these two packages together. We show how to use this
+package with two examples: a call-centre and an airport terminal.
+"
+27501,Polar Codes for Arbitrary Classical-Quantum Channels and Arbitrary cq-MACs,"  We prove polarization theorems for arbitrary classical-quantum (cq) channels.
+The input alphabet is endowed with an arbitrary Abelian group operation and an
+Ar{\i}kan-style transformation is applied using this operation. It is shown
+that as the number of polarization steps becomes large, the synthetic
+cq-channels polarize to deterministic homomorphism channels which project their
+input to a quotient group of the input alphabet. This result is used to
+construct polar codes for arbitrary cq-channels and arbitrary classical-quantum
+multiple access channels (cq-MAC). The encoder can be implemented in $O(N\log
+N)$ operations, where $N$ is the blocklength of the code. A quantum successive
+cancellation decoder for the constructed codes is proposed. It is shown that
+the probability of error of this decoder decays faster than $2^{-N^{\beta}}$
+for any $\beta<\frac{1}{2}$.
+"
+27502,Side-Channel Inference Attacks on Mobile Keypads using Smartwatches,"  Smartwatches enable many novel applications and are fast gaining popularity.
+However, the presence of a diverse set of on-board sensors provides an
+additional attack surface to malicious software and services on these devices.
+In this paper, we investigate the feasibility of key press inference attacks on
+handheld numeric touchpads by using smartwatch motion sensors as a
+side-channel. We consider different typing scenarios, and propose multiple
+attack approaches to exploit the characteristics of the observed wrist
+movements for inferring individual key presses. Experimental evaluation using
+commercial off-the-shelf smartwatches and smartphones show that key press
+inference using smartwatch motion sensors is not only fairly accurate, but also
+comparable with similar attacks using smartphone motion sensors. Additionally,
+hand movements captured by a combination of both smartwatch and smartphone
+motion sensors yields better inference accuracy than either device considered
+individually.
+"
+27503,Analytic Theory to Differential Privacy,"  The purpose of this paper is to develop a mathematical analysis theory to
+solve differential privacy problems. The heart of our approaches is to use
+analytic tools to characterize the correlations among the outputs of different
+datasets, which makes it feasible to represent a differentially private
+mechanism with minimal number of parameters. These results are then used to
+construct differentially private mechanisms analytically. Furthermore, our
+approaches are universal to almost all query functions. We believe that the
+approaches and results of this paper are indispensable complements to the
+current studies of differential privacy that are ruled by the ad hoc and
+algorithmic approaches.
+"
+27504,Network patterns in exponentially growing 2D biofilms,"  Anisotropic collective patterns occur frequently in the morphogenesis of 2D
+biofilms. These patterns are often attributed to growth regulation mechanisms
+and differentiation based on gradients of diffusing nutrients and signalling
+molecules. Here, we employ a model of bacterial growth dynamics to show that
+even in the absence of growth regulation or differentiation, confinement by an
+enclosing medium such as agar can itself lead to stable pattern formation over
+time scales that are employed in experiments. The underlying mechanism relies
+on path formation through physical deformation of the enclosing environment.
+"
+27505,Extension and approximation of $m$-subharmonic functions,"  Let $\Omega\subset \mathbb C^n$ be a bounded domain, and let $f$ be a
+real-valued function defined on the whole topological boundary $\partial
+\Omega$. The aim of this paper is to find a characterization of the functions
+$f$ which can be extended to the inside to a $m$-subharmonic function under
+suitable assumptions on $\Omega$. We shall do so by using a function algebraic
+approach with focus on $m$-subharmonic functions defined on compact sets. We
+end this note with some remarks on approximation of $m$-subharmonic functions.
+"
+27506,Cooperative epidemic spreading on a two-layered interconnected network,"  This study is concerned with the dynamical behaviors of epidemic spreading
+over a two-layered interconnected network. Three models in different levels are
+proposed to describe cooperative spreading processes over the interconnected
+network, wherein the disease in one network can spread to the other.
+Theoretical analysis is provided for each model to reveal that the global
+epidemic threshold in the interconnected network is not larger than the
+epidemic thresholds for the two isolated layered networks. In particular, in an
+interconnected homogenous network, detailed theoretical analysis is presented,
+which allows quick and accurate calculations of the global epidemic threshold.
+Moreover, in an interconnected heterogeneous network with inter-layer
+correlation between node degrees, it is found that the inter-layer correlation
+coefficient has little impact on the epidemic threshold, but has significant
+impact on the total prevalence. Simulations further verify the analytical
+results, showing that cooperative epidemic processes promote the spreading of
+diseases.
+"
+27507,From Euler class groups to Mennicke symbols and a monic inversion principle,"  Let $R$ be a regular domain of dimension $d\geq 2$ which is essentially of
+finite type over an infinite perfect field $k$. We compare the Euler class
+group $E^d(R)$ with the van der Kallen group $Um_{d+1}(R)/E_{d+1}(R)$. In the
+case $2R=R$, we define a map from $E^d(R)$ to $Um_{d+1}(R)/E_{d+1}(R)$ and
+study it in intricate details. As application, this map enables us to carry out
+some interesting computations on real varieties, using some very basic
+arguments. The formalism required to carry out the above investigation also
+provides us a requisite tool to show that the monic inversion principle holds
+for the Euler class groups.
+"
+27508,"Plan, Attend, Generate: Planning for Sequence-to-Sequence Models","  We investigate the integration of a planning mechanism into
+sequence-to-sequence models using attention. We develop a model which can plan
+ahead in the future when it computes its alignments between input and output
+sequences, constructing a matrix of proposed future alignments and a commitment
+vector that governs whether to follow or recompute the plan. This mechanism is
+inspired by the recently proposed strategic attentive reader and writer (STRAW)
+model for Reinforcement Learning. Our proposed model is end-to-end trainable
+using primarily differentiable operations. We show that it outperforms a strong
+baseline on character-level translation tasks from WMT'15, the algorithmic task
+of finding Eulerian circuits of graphs, and question generation from the text.
+Our analysis demonstrates that the model computes qualitatively intuitive
+alignments, converges faster than the baselines, and achieves superior
+performance with fewer parameters.
+"
+27509,Chow motives associated to certain algebraic Hecke characters,"  Shimura and Taniyama proved that if $A$ is a potentially CM abelian variety
+over a number field $F$ with CM by a field $K$ linearly disjoint from F, then
+there is an algebraic Hecke character $\lambda_A$ of $K$ such that
+$L(A/F,s)=L(\lambda_A,s)$. We consider a certain converse to their result.
+Namely, let $A$ be a potentially CM abelian variety appearing as a factor of
+the Jacobian of a curve of the form $y^e=\gamma x^f+\delta$. Fix positive
+integers $a$ and $n$ such that $n/2 < a \leq n$. Under mild conditions on $e,
+f, \gamma, \delta$, we construct a Chow motive $M$, defined over
+$F=\mathbb{Q}(\gamma,\delta)$, such that $L(M/F,s)$ and
+$L(\lambda_A^a\bar{\lambda}_A^{n-a},s)$ have the same Euler factors outside
+finitely many primes.
+"
+27510,Super Congruences Involving Multiple Harmonic Sums and Bernoulli Numbers,"  Let $m$, $r$ and $n$ be positive integers. We denote by ${\bf k}\vdash n$ any
+tuple of odd positive integers ${\bf k}=(k_1,\dots,k_t)$ such that
+$k_1+\dots+k_t=n$ and $k_j\ge 3$ for all $j$. In this paper we prove that for
+every sufficiently large prime $p$ $$ \sum_{\substack{l_1+l_2+\cdots+l_n=mp^r
+p\nmid l_1 l_2 \cdots l_n }} \frac1{l_1l_2\cdots l_n} \equiv p^{r-1} \sum_{{\bf
+k}\vdash n} C_{m,{\bf k}} B_{p-{\bf k}} \pmod{p^r} $$ where $B_{p-{\bf
+k}}=B_{p-k_1}B_{p-k_2}\cdots B_{p-k_t}$ are products of Bernoulli numbers and
+the coefficients $C_{m,{\bf k}}$ are polynomials of $m$ independent of $p$ and
+$r$. This generalizes previous results by many different authors and confirms a
+conjecture by the authors and their collaborators.
+"
+27511,On Time-frequency Scattering and Computer Music,"  Time-frequency scattering is a mathematical transformation of sound waves.
+Its core purpose is to mimick the way the human auditory system extracts
+information from its environment. In the context of improving the artificial
+intelligence of sounds, it has found succesful applications in automatic speech
+transcription as well as the recognition of urban sounds and musical sounds. In
+this article, we show that time-frequency scattering can also be useful for
+applications in contemporary music creations.
+To appear as the preface to: ""Florian Hecker: Halluzination, Perspektive,
+Synthese"". Nicolaus Schafhausen, Vanessa Joan Müller, editors. Sternberg
+Press, Berlin
+"
+27512,Analysis of the Extended Coupled-Cluster Method in Quantum Chemistry,"  The mathematical foundation of the so-called extended coupled-cluster method
+for the solution of the many-fermion Schrödinger equation is here developed.
+We prove an existence and uniqueness result, both in the full
+infinite-dimensional amplitude space as well as for discretized versions of it.
+The extended coupled-cluster method is formulated as a critical point of an
+energy function using a generalization of the Rayleigh-Ritz principle: the
+bivariational principle. This gives a quadratic bound for the energy error in
+the discretized case. The existence and uniqueness results are proved using a
+type of monotonicity property for the flipped gradient of the energy function.
+Comparisons to the analysis of the standard coupled-cluster method is made, and
+it is argued that the bivariational principle is a useful tool, both for
+studying coupled-cluster type methods, and for developing new computational
+schemes in general.
+"
+27513,Detecting independence of random vectors: generalized distance covariance and Gaussian covariance,"  Distance covariance is a quantity to measure the dependence of two random
+vectors. We show that the original concept introduced and developed by
+Székely, Rizzo and Bakirov can be embedded into a more general framework
+based on symmetric Lévy measures and the corresponding real-valued
+continuous negative definite functions. The Lévy measures replace the
+weight functions used in the original definition of distance covariance. All
+essential properties of distance covariance are preserved in this new
+framework. From a practical point of view this allows less restrictive moment
+conditions on the underlying random variables and one can use other distance
+functions than Euclidean distance, e.g. Minkowski distance. Most importantly,
+it serves as the basic building block for distance multivariance, a quantity to
+measure and estimate dependence of multiple random vectors, which is introduced
+in a follow-up paper [Distance Multivariance: New dependence measures for
+random vectors (submitted). Revised version of arXiv: 1711.07775v1] to the
+present article.
+"
+27514,On the Decay of Strong Magnetization in Global Disc Simulations with Toroidal Fields,"  Strong magnetization in accretion discs could resolve a number of outstanding
+issues related to stability and state transitions in low-mass X-ray binaries.
+However, it is unclear how real discs become strongly magnetized and, even if
+they do, whether they can remain in such a state. In this paper, we address the
+latter issue through a pair of global disc simulations. Here, we only consider
+cases of initially purely toroidal magnetic fields contained entirely within a
+compact torus. We find that, over only a few tens of orbital periods, the
+magnetization of an initially strongly magnetized disc,
+$P_\mathrm{mag}/P_\mathrm{gas} \ge 10$, drops to $\lesssim 0.1$, similar to the
+steady-state value reached in initially weakly magnetized discs. This is
+consistent with recent shearing box simulations with initially strong toroidal
+fields, the robust conclusion being that strongly magnetized toroidal fields
+can not be locally self-sustaining. These results appear to leave net poloidal
+flux or extended radial fields as the only avenues for establishing strongly
+magnetized discs, ruling out the thermal collapse scenario.
+"
+27515,Towards Cooperative Motion Planning for Automated Vehicles in Mixed Traffic,"  While motion planning techniques for automated vehicles in a reactive and
+anticipatory manner are already widely presented, approaches to cooperative
+motion planning are still remaining. In this paper, we present an approach to
+enhance common motion planning algorithms, that allows for cooperation with
+human-driven vehicles. Unlike previous approaches, we integrate the prediction
+of other traffic participants into the motion planning, such that the influence
+of the ego vehicle's behavior on the other traffic participants can be taken
+into account. For this purpose, a new cost functional is presented, containing
+the cost for all relevant traffic participants in the scene. Finally, we
+propose a path-velocity-decomposing sampling-based implementation of our
+approach for selected scenarios, which is evaluated in a simulation.
+"
+27516,End-to-end Learning for Short Text Expansion,"  Effectively making sense of short texts is a critical task for many real
+world applications such as search engines, social media services, and
+recommender systems. The task is particularly challenging as a short text
+contains very sparse information, often too sparse for a machine learning
+algorithm to pick up useful signals. A common practice for analyzing short text
+is to first expand it with external information, which is usually harvested
+from a large collection of longer texts. In literature, short text expansion
+has been done with all kinds of heuristics. We propose an end-to-end solution
+that automatically learns how to expand short text to optimize a given learning
+task. A novel deep memory network is proposed to automatically find relevant
+information from a collection of longer documents and reformulate the short
+text through a gating mechanism. Using short text classification as a
+demonstrating task, we show that the deep memory network significantly
+outperforms classical text expansion methods with comprehensive experiments on
+real world data sets.
+"
+27517,Continued fractions in function fields: polynomial analogues of McMullen's and Zaremba's conjectures,"  We examine the polynomial analogues of McMullen's and Zaremba's conjectures
+on continued fractions with bounded partial quotients. It has already been
+proved by Blackburn that if the base field is infinite, then the polynomial
+analogue of Zaremba's conjecture holds; we will prove this again with a
+different method and examine some known results for finite base fields.
+Translating to the polynomial setting a result of Mercat, we will prove that
+the polynomial analogue of McMullen's conjecture holds over infinite algebraic
+extensions of finite fields and that, over finite fields, it would be a
+consequence of the polynomial analogue of Zaremba's conjecture. We will then
+prove that the polynomial analogue of McMullen's conjecture holds over
+uncountable base fields, over $\overline{\mathbb Q}$ (thanks to the theory of
+reduction of a formal Laurent series modulo a prime) and over number fields.
+For this purpose, we will examine the connection between the continued
+fractions of polynomial multiples of $\sqrt D$ and pullbacks of generalized
+Jacobians of the hyperelliptic curve $U^2=D(T)$.
+"
+27518,A Large-Diameter Hollow-Shaft Cryogenic Motor Based on a Superconducting Magnetic Bearing for Millimeter-Wave Polarimetry,"  In this paper we present the design and measured performance of a novel
+cryogenic motor based on a superconducting magnetic bearing (SMB). The motor is
+tailored for use in millimeter-wave half-wave plate (HWP) polarimeters, where a
+HWP is rapidly rotated in front of a polarization analyzer or
+polarization-sensitive detector. This polarimetry technique is commonly used in
+cosmic microwave background (CMB) polarization studies. The SMB we use is
+composed of fourteen yttrium barium copper oxide (YBCO) disks and a contiguous
+neodymium iron boron (NdFeB) ring magnet. The motor is a hollow-shaft motor
+because the HWP is ultimately installed in the rotor. The motor presented here
+has a 100 mm diameter rotor aperture. However, the design can be scaled up to
+rotor aperture diameters of approximately 500 mm. Our motor system is composed
+of four primary subsystems: (i) the rotor assembly, which includes the NdFeB
+ring magnet, (ii) the stator assembly, which includes the YBCO disks, (iii) an
+incremental encoder, and (iv) the drive electronics. While the YBCO is cooling
+through its superconducting transition, the rotor is held above the stator by a
+novel hold and release mechanism (HRM). The encoder subsystem consists of a
+custom-built encoder disk read out by two fiber optic readout sensors. For the
+demonstration described in this paper, we ran the motor at 50 K and tested
+rotation frequencies up to approximately 10 Hz. The feedback system was able to
+stabilize the the rotation speed to approximately 0.4%, and the measured rotor
+orientation angle uncertainty is less than 0.15 deg. Lower temperature
+operation will require additional development activities, which we will
+discuss.
+"
+27519,Demonstration of a magnetic Prandtl number disc instability from first principles,"  Understanding what determines the strength of MHD turbulence in accretion
+discs is a question of fundamental theoretical and observational importance. In
+this work we investigate whether the dependence of the turbulent accretion disc
+stress ($\alpha$) on the magnetic Prandtl number (Pm) is sufficiently sensitive
+to induce thermal-viscous instability using 3D MHD simulations. We first
+investigate whether the $\alpha$-Pm dependence, found by many previous authors,
+has a physical or numerical origin by conducting a suite of local shearing-box
+simulations. We find that a definite $\alpha$-Pm dependence persists when
+simultaneously increasing numerical resolution and decreasing the absolute
+values of both the viscous and resistive dissipation coefficients. This points
+to a physical origin of the $\alpha$-Pm dependence. Using a further set of
+simulations which include realistic turbulent heating and radiative cooling,
+and by giving Pm a realistic physical dependence on the plasma temperature and
+density, we demonstrate that the $\alpha$-Pm dependence is sufficiently strong
+to lead to a local instability. We confirm that the instability manifests
+itself as an unstable limit cycle by mapping the local thermal-equilibrium
+curve of the disc. This is the first self-consistent MHD simulation
+demonstrating the Pm instability from first principles. This result is
+important because a physical Pm instability would lead to the global
+propagation of heating and cooling fronts and a transition between disc states
+on timescales compatible with the observed hard/soft state transitions in black
+hole binaries.
+"
+27520,Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms,"  Recent work has shown that the end-to-end approach using convolutional neural
+network (CNN) is effective in various types of machine learning tasks. For
+audio signals, the approach takes raw waveforms as input using an 1-D
+convolution layer. In this paper, we improve the 1-D CNN architecture for music
+auto-tagging by adopting building blocks from state-of-the-art image
+classification models, ResNets and SENets, and adding multi-level feature
+aggregation to it. We compare different combinations of the modules in building
+CNN architectures. The results show that they achieve significant improvements
+over previous state-of-the-art models on the MagnaTagATune dataset and
+comparable results on Million Song Dataset. Furthermore, we analyze and
+visualize our model to show how the 1-D CNN operates.
+"
+27521,Anisotropic features in the electronic structure of the two-dimensional transition metal trichalcogenide TiS$_3$: electron doping and plasmons,"  Analysis of the band structure of TiS$_3$ single-layers suggests the
+possibility of changing their physical behaviour by injecting electron
+carriers. The anisotropy of the valence and conduction bands is explained in
+terms of their complex orbital composition. The nature of the Fermi surface and
+Lindhard response function for different doping concentrations is studied by
+means of first-principles DFT calculations. It is suggested that for electron
+doping levels $x$ (number of electrons per unit cell) $\sim$ 0.18-0.30$e^-$ the
+system could exhibit incommensurate charge or spin modulations which, however,
+would keep the metallic state whereas systems doped with smaller $x$ would be
+2D metals without any electronic instability. The effect of spin-orbit coupling
+in the band dispersion is analysed. The DFT effective masses are used to study
+the plasmon spectrum from an effective low energy model. We find that this
+material supports highly anisotropic plasmons, with opposite anisotropy for the
+electron and hole bands.
+"
+27522,Diversity driven Attention Model for Query-based Abstractive Summarization,"  Abstractive summarization aims to generate a shorter version of the document
+covering all the salient points in a compact and coherent fashion. On the other
+hand, query-based summarization highlights those points that are relevant in
+the context of a given query. The encode-attend-decode paradigm has achieved
+notable success in machine translation, extractive summarization, dialog
+systems, etc. But it suffers from the drawback of generation of repeated
+phrases. In this work we propose a model for the query-based summarization task
+based on the encode-attend-decode paradigm with two key additions (i) a query
+attention model (in addition to document attention model) which learns to focus
+on different portions of the query at different time steps (instead of using a
+static representation for the query) and (ii) a new diversity based attention
+model which aims to alleviate the problem of repeating phrases in the summary.
+In order to enable the testing of this model we introduce a new query-based
+summarization dataset building on debatepedia. Our experiments show that with
+these two additions the proposed model clearly outperforms vanilla
+encode-attend-decode models with a gain of 28% (absolute) in ROUGE-L scores.
+"
+27523,The mixability of elliptical distributions with supermodular functions,"  The concept of $\phi$-complete mixability and $\phi$-joint mixability was
+first introduced in Bignozzi and Puccetti (2015), which is a direct extension
+of complete and joint mixability. Following Bignozzi and Puccetti (2015), we
+consider two cases of $\phi$ and investigate the $\phi$-joint mixability for
+elliptical distributions and logarithmic elliptical distributions. We obtain a
+necessary and sufficient condition for the $\phi$-joint mixability of some
+distributions and a sufficient condition for uniqueness of the center of
+$\phi$-joint mixability for some elliptical distributions.
+"
+27524,Solid-state-processing of d_PVDF,"  Poly(vinylidene fluoride) (PVDF) has long been regarded as an ideal
+piezoelectric plastic because it exhibits a large piezoelectric response and a
+high thermal stability. However, the realization of piezoelectric PVDF elements
+has proven to be problematic, amongst others, due to the lack of
+industrially-scalable methods to process PVDF into the appropriate polar
+crystalline forms. Here, we show that fully piezoelectric PVDF films can be
+produced via a single-step process that exploits the fact that PVDF can be
+molded at temperatures below its melting temperature, i.e. via
+solid-state-processing. We demonstrate that we thereby produce d_PVDF, the
+piezoelectric charge coefficient of which is comparable to that of biaxially
+stretched d_PVDF. We expect that the simplicity and scalability of solid-state
+processing combined with the excellent piezoelectric properties of our PVDF
+structures will provide new opportunities for this commodity polymer and will
+open a range of possibilities for future, large-scale, industrial production of
+plastic piezoelectric films
+"
+27525,The Populations of Carina. I. Decoding the Color--Magnitude Diagram,"  We investigate the color-magnitude diagram (CMD) of the Carina dwarf
+spheroidal galaxy using data of Stetson et al. (2011) and synthetic CMDs based
+on isochrones of Dotter et al. (2008), in terms of the parameters [Fe/H], age,
+and [alpha/Fe], for the cases when (i) [alpha/Fe] is held constant and (ii)
+[alpha/Fe] is varied. The data are well described by four basic epochs of star
+formation, having [Fe/H] = -1.85, -1.5, -1.2, and ~-1.15 and ages ~13, 7, ~3.5,
+and ~1.5 Gyr, respectively (for [alpha/Fe] = 0.1 (constant [alpha/Fe]) and
+[alpha/Fe] = 0.2, 0.1, -0.2, -0.2 (variable [alpha/Fe])), with small spreads in
+[Fe/H] and age of order 0.1 dex and 1 - 3 Gyr. Within an elliptical radius 13.1
+arcmin, the mass fractions of the populations, at their times of formation,
+were (in decreasing age order) 0.34, 0.39, 0.23, and 0.04. This formalism
+reproduces five observed CMD features (two distinct subgiant branches of old
+and intermediate-age populations, two younger, main-sequence components, and
+the small color dispersion on the red giant branch (RGB)). The parameters of
+the youngest population are less certain than those of the others, and given it
+is less centrally concentrated it may not be directly related to them.
+High-resolution spectroscopically analyzed RGB samples appear statistically
+incomplete compared with those selected using radial velocity, which contain
+bluer stars comprising ~5 - 10% of the samples. We conjecture these objects
+may, at least in part, be members of the youngest population. We use the CMD
+simulations to obtain insight into the population structure of Carina's upper
+RGB.
+"
+27526,Solving optimal control problems governed by random Navier-Stokes equations using low-rank methods,"  Many problems in computational science and engineering are simultaneously
+characterized by the following challenging issues: uncertainty, nonlinearity,
+nonstationarity and high dimensionality. Existing numerical techniques for such
+models would typically require considerable computational and storage
+resources. This is the case, for instance, for an optimization problem governed
+by time-dependent Navier-Stokes equations with uncertain inputs. In particular,
+the stochastic Galerkin finite element method often leads to a prohibitively
+high dimensional saddle-point system with tensor product structure. In this
+paper, we approximate the solution by the low-rank Tensor Train decomposition,
+and present a numerically efficient algorithm to solve the optimality equations
+directly in the low-rank representation. We show that the solution of the
+vorticity minimization problem with a distributed control admits a
+representation with ranks that depend modestly on model and discretization
+parameters even for high Reynolds numbers. For lower Reynolds numbers this is
+also the case for a boundary control. This opens the way for a reduced-order
+modeling of the stochastic optimal flow control with a moderate cost at all
+stages.
+"
+27527,Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data,"  Real-time optimization of traffic flow addresses important practical
+problems: reducing a driver's wasted time, improving city-wide efficiency,
+reducing gas emissions and improving air quality. Much of the current research
+in traffic-light optimization relies on extending the capabilities of traffic
+lights to either communicate with each other or communicate with vehicles.
+However, before such capabilities become ubiquitous, opportunities exist to
+improve traffic lights by being more responsive to current traffic situations
+within the current, already deployed, infrastructure. In this paper, we
+introduce a traffic light controller that employs bidding within micro-auctions
+to efficiently incorporate traffic sensor information; no other outside sources
+of information are assumed. We train and test traffic light controllers on
+large-scale data collected from opted-in Android cell-phone users over a period
+of several months in Mountain View, California and the River North neighborhood
+of Chicago, Illinois. The learned auction-based controllers surpass (in both
+the relevant metrics of road-capacity and mean travel time) the currently
+deployed lights, optimized static-program lights, and longer-term planning
+approaches, in both cities, measured using real user driving data.
+"
+27528,A $(2 + ε)$-approximation for precedence constrained single machine scheduling with release dates and total weighted completion time objective,"  We give a $(2 + \epsilon)$-approximation algorithm for minimizing total
+weighted completion time on a single machine under release time and precedence
+constraints. This settles a recent conjecture made in [18]
+"
+27529,Change Acceleration and Detection,"  A generalization of the Bayesian sequential change detection problem is
+proposed, where the change is a latent event that should be not only detected,
+but also accelerated. It is assumed that the sequentially collected
+observations are responses to treatments selected in real time. The assigned
+treatments not only determine the distribution of responses before and after
+the change, but also influence when the change happens. The problem is to find
+a treatment assignment rule and a stopping rule to minimize the average total
+number of observations subject to a bound on the false-detection probability.
+An intuitive solution is proposed, which is easy to implement and achieves for
+a large class of change-point models the optimal performance up to a
+first-order asymptotic approximation. A simulation study suggests the almost
+exact optimality of the proposed scheme under a Markovian change-point model.
+"
+27530,"Generalized perturbation (n, M)-fold Darboux transformations and multi-rogue-wave structures for the modified self-steepening nonlinear Schrodinger equation","  In this paper, a simple and constructive method is presented to find the
+generalized perturbation (n,M)-fold Darboux transformations (DTs) of the
+modified nonlinear Schrodinger (MNLS) equation in terms of fractional forms of
+determinants. In particular, we apply the generalized perturbation (1,N-1)-fold
+DTs to find its explicit multi-rogue-wave solutions. The wave structures of
+these rogue-wave solutions of the MNLS equation are discussed in detail for
+different parameters, which display abundant interesting wave structures,
+including the triangle and pentagon, etc. and may be useful to study the
+physical mechanism of multirogue waves in optics. The dynamical behaviors of
+these multi-rogue-wave solutions are illustrated using numerical simulations.
+The same Darboux matrix can also be used to investigate the Gerjikov-Ivanov
+equation such that its multi-rogue-wave solutions and their wave structures are
+also found. The method can also be extended to find multi-rogue-wave solutions
+of other nonlinear integrable equations.
+"
+27531,Interpretable Machine Learning for Privacy-Preserving Pervasive Systems,"  The presence of pervasive systems in our everyday lives and the interaction
+of users with connected devices such as smartphones or home appliances generate
+increasing amounts of traces that reflect users' behavior. A plethora of
+machine learning techniques enable service providers to process these traces to
+extract latent information about the users. While most of the existing projects
+have focused on the accuracy of these techniques, little work has been done on
+the interpretation of the inference and identification algorithms based on
+them. In this paper, we propose a machine learning interpretability framework
+for inference algorithms based on data collected through pervasive systems and
+we outline the open challenges in this research area. Our interpretability
+framework enable users to understand how the traces they generate could expose
+their privacy, while allowing for usable and personalized services at the same
+time.
+"
+27532,A Low-cost Environmental Control System for Precise Radial Velocity Spectrometers,"  We present an Environmental Control System (ECS) designed to achieve
+milliKelvin (mK) level temperature stability for small-scale astronomical
+instruments. This ECS is inexpensive and is primarily built from commercially
+available components. The primary application for our ECS is the high-precision
+Doppler spectrometer MINERVA-Red, where the thermal variations of the optical
+components within the instrument represent a major source of systematic error.
+We demonstrate $\pm 2$ mK temperature stability within a 0.5 m$^{3}$ Thermal
+Enclosure using resistive heaters in conjunction with a commercially available
+PID controller and off-the-shelf thermal sensors. The enclosure is maintained
+above ambient temperature, enabling rapid cooling through heat dissipation into
+the surrounding environment. We demonstrate peak-to-valley (PV) temperature
+stability of better than 5 mK within the MINERVA-Red vacuum chamber, which is
+located inside the Thermal Enclosure, despite large temperature swings in the
+ambient laboratory environment. During periods of stable laboratory conditions,
+the PV variations within the vacuum chamber are less than 3 mK. This
+temperature stability is comparable to the best stability demonstrated for
+Doppler spectrometers currently achieving 1 m s$^{-1}$ radial velocity
+precision. We discuss the challenges of using commercially available
+thermoelectrically cooled CCD cameras in a temperature-stabilized environment,
+and demonstrate that the effects of variable heat output from the CCD camera
+body can be mitigated using PID-controlled chilled water systems. The ECS
+presented here could potentially provide the stable operating environment
+required for future compact, ""astro-photonic"" precise radial velocity (PRV)
+spectrometers to achieve high Doppler measurement precision with a modest
+budget.
+"
+27533,"Estimates of the derivatives of the heat kernel on symmetric and locally symmetric spaces, and applications","  We obtain pointwise upper bounds on the derivatives of the heat kernel on
+symmetric and locally symmetric spaces of noncompact type. Applying these
+estimates we prove the $L^p$-boundedness of Littlewood-Paley-Stein operators,
+the Laplacian and the gradient of the heat operator, and the Riesz operator.
+"
+27534,Modularity-like objective function in annotated networks,"  We ascertain the modularity-like objective function whose optimization is
+equivalent to the maximum likelihood in annotated networks. We demonstrate that
+the modularity-like objective function is a linear combination of modularity
+and conditional entropy. In contrast with statistical inference methods, in our
+method, the influence of the metadata is adjustable; when its influence is
+strong enough, the metadata can be recovered. Conversely, when it is weak, the
+detection may correspond to another partition. Between the two, there is a
+transition. This paper provides a concept for expanding the scope of modularity
+methods.
+"
+27535,"Train longer, generalize better: closing the generalization gap in large batch training of neural networks","  Background: Deep learning models are typically trained using stochastic
+gradient descent or one of its variants. These methods update the weights using
+their gradient, estimated from a small fraction of the training data. It has
+been observed that when using large batch sizes there is a persistent
+degradation in generalization performance - known as the ""generalization gap""
+phenomena. Identifying the origin of this gap and closing it had remained an
+open problem.
+Contributions: We examine the initial high learning rate training phase. We
+find that the weight distance from its initialization grows logarithmically
+with the number of weight updates. We therefore propose a ""random walk on
+random landscape"" statistical model which is known to exhibit similar
+""ultra-slow"" diffusion behavior. Following this hypothesis we conducted
+experiments to show empirically that the ""generalization gap"" stems from the
+relatively small number of updates rather than the batch size, and can be
+completely eliminated by adapting the training regime used. We further
+investigate different techniques to train models in the large-batch regime and
+present a novel algorithm named ""Ghost Batch Normalization"" which enables
+significant decrease in the generalization gap without increasing the number of
+updates. To validate our findings we conduct several additional experiments on
+MNIST, CIFAR-10, CIFAR-100 and ImageNet. Finally, we reassess common practices
+and beliefs concerning training of deep models and suggest they may not be
+optimal to achieve good generalization.
+"
+27536,Domains of existence for finely holomorphic functions,"  We show that fine domains in $\mathbf{C}$ with the property that they are
+Euclidean $F_\sigma$ and $G_\delta$, are in fact fine domains of existence for
+finely holomorphic functions. Moreover \emph{regular} fine domains are also
+fine domains of existence. Next we show that fine domains such as
+$\mathbf{C}\setminus \mathbf{Q}$ or $\mathbf{C}\setminus (\mathbf{Q}\times
+i\mathbf{Q})$, more specifically fine domains $V$ with the properties that
+their complement contains a non-empty polar set $E$ that is of the first Baire
+category in its Euclidean closure $K$ and that $(K\setminus E)\subset V$, are
+NOT fine domains of existence.
+"
+27537,Developing Knowledge-enhanced Chronic Disease Risk Prediction Models from Regional EHR Repositories,"  Precision medicine requires the precision disease risk prediction models. In
+literature, there have been a lot well-established (inter-)national risk
+models, but when applying them into the local population, the prediction
+performance becomes unsatisfactory. To address the localization issue, this
+paper exploits the way to develop knowledge-enhanced localized risk models. On
+the one hand, we tune models by learning from regional Electronic Health Record
+(EHR) repositories, and on the other hand, we propose knowledge injection into
+the EHR data learning process. For experiments, we leverage the Pooled Cohort
+Equations (PCE, as recommended in ACC/AHA guidelines to estimate the risk of
+ASCVD) to develop a localized ASCVD risk prediction model in diabetes. The
+experimental results show that, if directly using the PCE algorithm on our
+cohort, the AUC is only 0.653, while our knowledge-enhanced localized risk
+model can achieve higher prediction performance with AUC of 0.723 (improved by
+10.7%).
+"
+27538,A hierarchical model of non-homogeneous Poisson processes for Twitter retweets,"  We present a hierarchical model of non-homogeneous Poisson processes (NHPP)
+for information diffusion on online social media, in particular Twitter
+retweets. The retweets of each original tweet are modelled by a NHPP, for which
+the intensity function is a product of time-decaying components and another
+component that depends on the follower count of the original tweet author. The
+latter allows us to explain or predict the ultimate retweet count by a network
+centrality-related covariate. The inference algorithm enables the Bayes factor
+to be computed, in order to facilitate model selection. Finally, the model is
+applied to the retweet data sets of two hashtags.
+"
+27539,Chiral edge mode in the coupled dynamics of magnetic solitons in a honeycomb lattice,"  Motivated by a recent experimental demonstration of a chiral edge mode in an
+array of spinning gyroscopes, we theoretically study the coupled gyration modes
+of topological magnetic solitons, vortices and magnetic bubbles, arranged as a
+honeycomb lattice. The soliton lattice under suitable conditions is shown to
+support a chiral edge mode like its mechanical analogue, the existence of which
+can be understood by mapping the system to the Haldane model for an electronic
+system. The direction of the chiral edge mode is associated with the
+topological charge of the constituent solitons, which can be manipulated by an
+external field or by an electric-current pulse. The direction can also be
+controlled by distorting the honeycomb lattice. Our results indicate that the
+lattices of magnetic solitons can serve as reprogrammable topological
+metamaterials.
+"
+27540,Multiple D3-instantons and mock modular forms II,"  We analyze the modular properties of D3-brane instanton corrections to the
+hypermultiplet moduli space in type IIB string theory compactified on a
+Calabi-Yau threefold. In Part I, we found a necessary condition for the
+existence of an isometric action of S-duality on this moduli space: the
+generating function of DT invariants in the large volume attractor chamber must
+be a vector-valued mock modular form with specified modular properties. In this
+work, we prove that this condition is also sufficient at two-instanton order.
+This is achieved by producing a holomorphic action of SL(2,Z) on the twistor
+space which preserves the holomorphic contact structure. The key step is to
+cancel the anomalous modular variation of the Darboux coordinates by a local
+holomorphic contact transformation, which is generated by a suitable indefinite
+theta series. For this purpose we introduce a new family of theta series of
+signature (2,n-2), find their modular completion, and conjecture sufficient
+conditions for their convergence, which may be of independent mathematical
+interest.
+"
+27541,Operads of decorated cliques,"  The vector space of all polygons with configurations of diagonals is endowed
+with an operad structure. This is the consequence of a functorial construction
+$\mathsf{C}$ introduced here, which takes unitary magmas $\mathcal{M}$ as input
+and produces operads. The obtained operads involve regular polygons with
+configurations of arcs labeled on $\mathcal{M}$, called $\mathcal{M}$-decorated
+cliques and generalizing usual polygons with configurations of diagonals. We
+provide here a complete study of the operads $\mathsf{C}\mathcal{M}$. By
+considering combinatorial subfamilies of $\mathcal{M}$-decorated cliques
+defined, for instance, by limiting the maximal number of crossing diagonals or
+the maximal degree of the vertices, we obtain suboperads and quotients of
+$\mathsf{C}\mathcal{M}$. This leads to a new hierarchy of operads containing,
+among others, operads on noncrossing configurations, Motzkin configurations,
+forests, dissections of polygons, and involutions. We show that the suboperad
+of noncrossing configurations is Koszul and exhibit its presentation by
+generators and relations. Besides, the construction $\mathsf{C}$ leads to
+alternative definitions of several operads, like the operad of bicolored
+noncrossing configurations and the operads of simple and double multi-tildes.
+"
+27542,Verisig: verifying safety properties of hybrid systems with neural network controllers,"  This paper presents Verisig, a hybrid system approach to verifying safety
+properties of closed-loop systems using neural networks as controllers.
+Although techniques exist for verifying input/output properties of the neural
+network itself, these methods cannot be used to verify properties of the
+closed-loop system (since they work with piecewise-linear constraints that do
+not capture non-linear plant dynamics). To overcome this challenge, we focus on
+sigmoid-based networks and exploit the fact that the sigmoid is the solution to
+a quadratic differential equation, which allows us to transform the neural
+network into an equivalent hybrid system. By composing the network's hybrid
+system with the plant's, we transform the problem into a hybrid system
+verification problem which can be solved using state-of-the-art reachability
+tools. We show that reachability is decidable for networks with one hidden
+layer and decidable for general networks if Schanuel's conjecture is true. We
+evaluate the applicability and scalability of Verisig in two case studies, one
+from reinforcement learning and one in which the neural network is used to
+approximate a model predictive controller.
+"
+27543,Feature Learning and Classification in Neuroimaging: Predicting Cognitive Impairment from Magnetic Resonance Imaging,"  Due to the rapid innovation of technology and the desire to find and employ
+biomarkers for neurodegenerative disease, high-dimensional data classification
+problems are routinely encountered in neuroimaging studies. To avoid
+over-fitting and to explore relationships between disease and potential
+biomarkers, feature learning and selection plays an important role in
+classifier construction and is an important area in machine learning. In this
+article, we review several important feature learning and selection techniques
+including lasso-based methods, PCA, the two-sample t-test, and stacked
+auto-encoders. We compare these approaches using a numerical study involving
+the prediction of Alzheimer's disease from Magnetic Resonance Imaging.
+"
+27544,Deep Supervised Discrete Hashing,"  With the rapid growth of image and video data on the web, hashing has been
+extensively studied for image or video search in recent years. Benefit from
+recent advances in deep learning, deep hashing methods have achieved promising
+results for image retrieval. However, there are some limitations of previous
+deep hashing methods (e.g., the semantic information is not fully exploited).
+In this paper, we develop a deep supervised discrete hashing algorithm based on
+the assumption that the learned binary codes should be ideal for
+classification. Both the pairwise label information and the classification
+information are used to learn the hash codes within one stream framework. We
+constrain the outputs of the last layer to be binary codes directly, which is
+rarely investigated in deep hashing algorithm. Because of the discrete nature
+of hash codes, an alternating minimization method is used to optimize the
+objective function. Experimental results have shown that our method outperforms
+current state-of-the-art methods on benchmark datasets.
+"
+27545,The angular momentum of cosmological coronae and the inside-out growth of spiral galaxies,"  Massive and diffuse haloes of hot gas (coronae) are important intermediaries
+between cosmology and galaxy evolution, storing mass and angular momentum
+acquired from the cosmic web until eventual accretion on to star-forming discs.
+We introduce a method to reconstruct the rotation of a galactic corona, based
+on its angular momentum distribution (AMD). This allows us to investigate in
+what conditions the angular momentum acquired from tidal torques can be
+transferred to star forming discs and explain observed galaxy-scale processes,
+such as inside-out growth and the build-up of abundance gradients. We find that
+a simple model of an isothermal corona with a temperature slightly smaller than
+virial and a cosmologically motivated AMD is in good agreement with galaxy
+evolution requirements, supporting hot-mode accretion as a viable driver for
+the evolution of spiral galaxies in a cosmological context. We predict
+moderately sub-centrifugal rotation close to the disc and slow rotation close
+to the virial radius. Motivated by the observation that the Milky Way has a
+relatively hot corona (T ~ 2 x 10^6 K), we also explore models with a
+temperature larger than virial. To be able to drive inside-out growth, these
+models must be significantly affected by feedback, either mechanical (ejection
+of low angular momentum material) or thermal (heating of the central regions).
+However, the agreement with galaxy evolution constraints becomes, in these
+cases, only marginal, suggesting that our first and simpler model may apply to
+a larger fraction of galaxy evolution history.
+"
+27546,Learn&Fuzz: Machine Learning for Input Fuzzing,"  Fuzzing consists of repeatedly testing an application with modified, or
+fuzzed, inputs with the goal of finding security vulnerabilities in
+input-parsing code. In this paper, we show how to automate the generation of an
+input grammar suitable for input fuzzing using sample inputs and
+neural-network-based statistical machine-learning techniques. We present a
+detailed case study with a complex input format, namely PDF, and a large
+complex security-critical parser for this format, namely, the PDF parser
+embedded in Microsoft's new Edge browser. We discuss (and measure) the tension
+between conflicting learning and fuzzing goals: learning wants to capture the
+structure of well-formed inputs, while fuzzing wants to break that structure in
+order to cover unexpected code paths and find bugs. We also present a new
+algorithm for this learn&fuzz challenge which uses a learnt input probability
+distribution to intelligently guide where to fuzz inputs.
+"
+27547,Artificial Intelligence and Data Science in the Automotive Industry,"  Data science and machine learning are the key technologies when it comes to
+the processes and products with automatic learning and optimization to be used
+in the automotive industry of the future. This article defines the terms ""data
+science"" (also referred to as ""data analytics"") and ""machine learning"" and how
+they are related. In addition, it defines the term ""optimizing analytics"" and
+illustrates the role of automatic optimization as a key technology in
+combination with data analytics. It also uses examples to explain the way that
+these technologies are currently being used in the automotive industry on the
+basis of the major subprocesses in the automotive value chain (development,
+procurement; logistics, production, marketing, sales and after-sales, connected
+customer). Since the industry is just starting to explore the broad range of
+potential uses for these technologies, visionary application examples are used
+to illustrate the revolutionary possibilities that they offer. Finally, the
+article demonstrates how these technologies can make the automotive industry
+more efficient and enhance its customer focus throughout all its operations and
+activities, extending from the product and its development process to the
+customers and their connection to the product.
+"
+27548,Machine Learning for RealisticBall Detection in RoboCup SPL,"  In this technical report, we describe the use of a machine learning approach
+for detecting the realistic black and white ball currently in use in the
+RoboCup Standard Platform League. Our aim is to provide a ready-to-use software
+module that can be useful for the RoboCup SPL community. To this end, the
+approach is integrated within the official B-Human code release 2016. The
+complete code for the approach presented in this work can be downloaded from
+the SPQR Team homepage at this http URL and from the SPQR Team
+GitHub repository at this https URL. The
+approach has been tested in multiple environments, both indoor and outdoor.
+Furthermore, the ball detector described in this technical report has been used
+by the SPQR Robot Soccer Team during the competitions of the Robocup German
+Open 2017. To facilitate the use of our code by other teams, we have prepared a
+step-by-step installation guide.
+"
+27549,Rotational random walk of the harmonic three body system,"  When Robert Brown first observed colloidal pollen grains in water he
+inaccurately concluded that their motion arose ""neither from currents in the
+fluid, nor from its gradual evaporation, but belonged to the particle itself"".
+In this work we study the dynamics of a classical molecule consisting of three
+masses and three harmonic springs in free space that does display a rotational
+random walk ""belonging to the particle itself"". The geometric nonlinearities
+arising from the non-zero rest lengths of the springs connecting the masses
+break the integrability of the harmonic system and lead to chaotic dynamics in
+many regimes of phase space. The non-trivial connection of the system's shape
+space allows it, much like falling cats, to rotate with zero angular momentum
+and manifest its chaotic dynamics as an orientational random walk. In the
+transition to chaos the system displays random orientation reversals and
+provides a simple realization of Lévy walks.
+"
+27550,A note on the almost one half holomorphic pinching,"  Motivated by a previous work of Zheng and the second named author, we study
+pinching constants of compact Kähler manifolds with positive holomorphic
+sectional curvature. In particular we prove a gap theorem following the work of
+Petersen and Tao on Riemannian manifolds with almost quarter-pinched sectional
+curvature.
+"
+27551,Performance of information criteria used for model selection of Hawkes process models of financial data,"  We test three common information criteria (IC) for selecting the order of a
+Hawkes process with an intensity kernel that can be expressed as a mixture of
+exponential terms. These processes find application in high-frequency financial
+data modelling. The information criteria are Akaike's information criterion
+(AIC), the Bayesian information criterion (BIC) and the Hannan-Quinn criterion
+(HQ). Since we work with simulated data, we are able to measure the performance
+of model selection by the success rate of the IC in selecting the model that
+was used to generate the data. In particular, we are interested in the relation
+between correct model selection and underlying sample size. The analysis
+includes realistic sample sizes and parameter sets from recent literature where
+parameters were estimated using empirical financial intra-day data. We compare
+our results to theoretical predictions and similar empirical findings on the
+asymptotic distribution of model selection for consistent and inconsistent IC.
+"
+27552,Ecological invasion in competition-diffusion systems when the exotic species is either very strong or very weak,"  Reaction-diffusion systems with a Lotka-Volterra-type reaction term, also
+known as competition-diffusion systems, have been used to investigate the
+dynamics of the competition among $m$ ecological species for a limited resource
+necessary to their survival and growth. Notwithstanding their rather simple
+mathematical structure, such systems may display quite interesting behaviours.
+In particular, while for $m=2$ no coexistence of the two species is usually
+possible, if $m \ge 3$ we may observe coexistence of all or a subset of the
+species, sensitively depending on the parameter values. Such coexistence can
+take the form of very complex spatio-temporal patterns and oscillations.
+Unfortunately, at the moment there are no known tools for a complete
+analytical study of such systems for $m \ge 3$. This means that establishing
+general criteria for the occurrence of coexistence appears to be very hard. In
+this paper we will instead give some criteria for the non-coexistence of
+species, motivated by the ecological problem of the invasion of an ecosystem by
+an exotic species. We will show that when the environment is very favourable to
+the invading species the invasion will always be successful and the native
+species will be driven to extinction. On the other hand, if the environment is
+not favourable enough, the invasion will always fail.
+"
+27553,An R-process enhanced star in the dwarf galaxy Tucana III,"  Chemically peculiar stars in dwarf galaxies provide a window for exploring
+the birth environment of stars with varying chemical enrichment. We present a
+chemical abundance analysis of the brightest star in the newly discovered
+ultra-faint dwarf galaxy candidate Tucana III. Because it is particularly
+bright for a star in an ultra-faint Milky Way satellite, we are able to measure
+the abundance of 28 elements, including 13 neutron-capture species. This star,
+DES J235532.66$-$593114.9 (DES J235532), shows a mild enhancement in
+neutron-capture elements associated with the $r$-process and can be classified
+as an $r$-I star. DES J235532 is the first $r$-I star to be discovered in an
+ultra-faint satellite, and Tuc III is the second extremely low-luminosity
+system found to contain $r$-process enriched material, after Reticulum II.
+Comparison of the abundance pattern of DES J235532 with $r$-I and $r$-II stars
+found in other dwarf galaxies and in the Milky Way halo suggests a common
+astrophysical origin for the neutron-capture elements seen in all $r$-process
+enhanced stars. We explore both internal and external scenarios for the
+$r$-process enrichment of Tuc III and show that with abundance patterns for
+additional stars it should be possible to distinguish between them.
+"
+27554,Estimating Tail Probabilities of the Ratio of the Largest Eigenvalue to the Trace of a Wishart Matrix,"  This paper develops an efficient Monte Carlo method to estimate the tail
+probabilities of the ratio of the largest eigenvalue to the trace of the
+Wishart matrix, which plays an important role in multivariate data analysis.
+The estimator is constructed based on a change-of-measure technique and it is
+proved to be asymptotically efficient for both the real and complex Wishart
+matrices. Simulation studies further show the outperformance of the proposed
+method over existing approaches based on asymptotic approximations, especially
+when estimating probabilities of rare events.
+"
+27555,Topological lattice using multi-frequency radiation,"  We describe a novel technique for creating an artificial magnetic field for
+ultra-cold atoms using a periodically pulsed pair of counter propagating Raman
+lasers that drive transitions between a pair of internal atomic spin states: a
+multi-frequency coupling term. In conjunction with a magnetic field gradient,
+this dynamically generates a rectangular lattice with a non-staggered magnetic
+flux. For a wide range of parameters, the resulting Bloch bands have
+non-trivial topology, reminiscent of Landau levels, as quantified by their
+Chern numbers.
+"
+27556,"Astrophysical Multimessenger Observatory Network (AMON): Science, Infrastructure, and Status","  The realization of multimessenger astrophysics will open new vistas upon the
+most energetic events in the universe. Messenger particles of all four of
+nature's fundamental forces, recorded by detectors on the ground and satellites
+in space, enable coincidence searches for multimessenger phenomena that will
+allow us to discover, observe, and explore these sources. The Astrophysical
+Multimessenger Observatory Network (AMON) links multiple high-energy neutrino,
+cosmic ray, and gamma-ray observatories as well as gravitational wave
+facilities into a single virtual system, enabling near real-time coincidence
+searches for multimessenger astrophysical transients and their electromagnetic
+counterparts, and providing alerts to follow-up observatories. The science
+case, design elements, partner observatories, and status of the AMON project
+are presented, followed by recent results from AMON real-time and archival
+analyses.
+"
+27557,A maximum entropy network reconstruction of macroeconomic models,"  In this article the problem of reconstructing the pattern of connection
+between agents from partial empirical data in a macro-economic model is
+addressed, given a set of behavioral equations. This systemic point of view
+puts the focus on distributional and network effects, rather than
+time-dependence. Using the theory of complex networks we compare several models
+to reconstruct both the topology and the flows of money of the different types
+of monetary transactions, while imposing a series of constraints related to
+national accounts, and to empirical network sparsity. Some properties of
+reconstructed networks are compared with their empirical counterpart.
+"
+27558,Two golden times in two-step contagion models,"  The two-step contagion model is a simple toy model for understanding pandemic
+outbreaks that occur in the real world. The model takes into account that a
+susceptible person either gets immediately infected or weakened when getting
+into contact with an infectious one. As the number of weakened people
+increases, they eventually can become infected in a short time period and a
+pandemic outbreak occurs. The time required to reach such a pandemic outbreak
+allows for intervention and is often called golden time. Understanding the
+size-dependence of the golden time is useful for controlling pandemic outbreak.
+Here we find that there exist two types of golden times in the two-step
+contagion model, which scale as $O(N^{1/3})$ and $O(N^{\zeta})$ with the system
+size $N$ on Erdős-Rényi networks, where the measured $\zeta$ is slightly
+larger than $1/4$. They are distinguished by the initial number of infected
+nodes, $o(N)$ and $O(N)$, respectively. While the exponent $1/3$ of the
+$N$-dependence of the golden time is universal even in other models showing
+discontinuous transitions induced by cascading dynamics, the measured $\zeta$
+exponents are all close to $1/4$ but show model-dependence. It remains open
+whether or not $\zeta$ reduces to $1/4$ in the asymptotically large-$N$ limit.
+"
+27559,"Dispenser Concept for Unmanned Aerial Vehicles (UAV, Drone, UAS)","  System, design and methodology to load and dispense different articles from
+an autonomous aircraft are disclosed. In one embodiment, the design of a unique
+detachable dispenser for delivery of articles is described along with an
+intelligent methodology of loading and delivering the articles to and from the
+dispenser. Design of the dispenser, interaction of the dispenser with the
+flight control unit and ground control or base-station, and interaction of the
+base station with the sender or recipient of the article, are also described.
+"
+27560,Stokes flow with kinematic and dynamic boundary conditions,"  We review the first and second boundary value problems for the Stokes system
+posed in a bounded Lipschitz domain in $\mathbb{R}^n.$ Particular attention is
+given to the mixed boundary condition: a Dirichlet condition is imposed for the
+velocity on one part of the boundary while a Neumann condition for the stress
+tensor is imposed on the remaining part. Some minor modifications to the
+standard theory are therefore required. The most noteworthy result is that both
+pressure and velocity are unique.
+"
+27561,Mechanisms of DNA Hybridization: Transition Path Analysis of a Simulation-Informed Markov Model,"  Complementary DNA strands in solution reliably hybridize to form stable
+duplexes. We study the kinetics of the hybridization process and the mechanisms
+by which two initially isolated strands come together to form a stable double
+helix. We adopt a multi-step computational approach. First, we perform a large
+number of Brownian dynamics simulations of the hybridization process using the
+coarse-grained oxDNA2 model. Second, we use these simulations to construct a
+Markov State Model of DNA dynamics that uses a state decomposition based on the
+inter-strand hydrogen bonding pattern. Third, we take advantage of Transition
+Path Theory to obtain quantitative information about the thermodynamic and
+dynamic properties of the hybridization process. We find that while there is a
+large ensemble of possible hybridization pathways there is a single dominant
+mechanism in which an initial base pair forms close to either end of the
+nascent double helix, and the remaining bases pair sequentially in a
+zipper-like fashion. We also show that the number of formed base pairs by
+itself is insufficient to describe the transition state of the hybridization
+process.
+"
+27562,"A Semiglobal, Practical, Strict Pseudogradient Property for Iterative Methods","  We consider a class of iterative numerical methods and introduce the notion
+of semiglobally, practically, strictly pseudogradient (SPSP) search directions.
+We demonstrate the relevance of the SPSP property in modelling a variety of
+optimization algorithms, including those subject to absolute and relative
+errors. We show that the attractors of iterative methods with SPSP search
+directions exhibit semiglobal, practical, asymptotic stability. Moreover, the
+SPSP property is robust in the sense that perturbations of SPSP search
+directions also have the SPSP property.
+"
+27563,The High-Dimensional Geometry of Binary Neural Networks,"  Recent research has shown that one can train a neural network with binary
+weights and activations at train time by augmenting the weights with a
+high-precision continuous latent variable that accumulates small changes from
+stochastic gradient descent. However, there is a dearth of theoretical analysis
+to explain why we can effectively capture the features in our data with binary
+weights and activations. Our main result is that the neural networks with
+binary weights and activations trained using the method of Courbariaux, Hubara
+et al. (2016) work because of the high-dimensional geometry of binary vectors.
+In particular, the ideal continuous vectors that extract out features in the
+intermediate representations of these BNNs are well-approximated by binary
+vectors in the sense that dot products are approximately preserved. Compared to
+previous research that demonstrated the viability of such BNNs, our work
+explains why these BNNs work in terms of the HD geometry. Our theory serves as
+a foundation for understanding not only BNNs but a variety of methods that seek
+to compress traditional neural networks. Furthermore, a better understanding of
+multilayer binary neural networks serves as a starting point for generalizing
+BNNs to other neural network architectures such as recurrent neural networks.
+"
+27564,"""Is this an example image?"" -- Predicting the Relative Abstractness Level of Image and Text","  Successful multimodal search and retrieval requires the automatic
+understanding of semantic cross-modal relations, which, however, is still an
+open research problem. Previous work has suggested the metrics cross-modal
+mutual information and semantic correlation to model and predict cross-modal
+semantic relations of image and text. In this paper, we present an approach to
+predict the (cross-modal) relative abstractness level of a given image-text
+pair, that is whether the image is an abstraction of the text or vice versa.
+For this purpose, we introduce a new metric that captures this specific
+relationship between image and text at the Abstractness Level (ABS). We present
+a deep learning approach to predict this metric, which relies on an autoencoder
+architecture that allows us to significantly reduce the required amount of
+labeled training data. A comprehensive set of publicly available scientific
+documents has been gathered. Experimental results on a challenging test set
+demonstrate the feasibility of the approach.
+"
+27565,Simultaneous Optical and Meteor Head Echo measurements using the Middle Atmosphere Alomar Radar System (MAARSY): Data collection and preliminary analysis,"  The initial results of a two year simultaneous optical-radar meteor campaign
+are described. Analysis of 105 double-station optical meteors having plane of
+sky intersection angles greater than 5 degrees and trail lengths in excess of 2
+km also detected by the Middle Atmosphere Alomar Radar System (MAARSY) as head
+echoes was performed. These events show a median deviation in radiants between
+radar and optical determinations of 1.5 degrees, with 1/3 of events having
+radiant agreement to less than one degree. MAARSY tends to record average
+speeds roughly 0.5 km/s and 1.3 km higher than optical records, in part due to
+the higher sensitivity of MAARSY as compared to the optical instruments. More
+than 98% of all head echoes are not detected with the optical system. Using
+this non-detection ratio and the known limiting sensitivity of the cameras, we
+estimate that the limiting meteoroid detection mass of MAARSY is in the 10-9 kg
+to 10-10 kg (astronomical limiting meteor magnitudes of +11 to +12) appropriate
+to speeds from 30-60 km/s. There is a clear trend of higher peak RCS for
+brighter meteors between 35 and -30 dBsm. For meteors with similar magnitudes,
+the MAARSY head echo radar cross-section is larger at higher speeds. Brighter
+meteors at fixed heights and similar speeds have consistently, on average,
+larger RCS values, in accordance with established scattering theory. However,
+our data show RCS ~ v/2, much weaker than the normally assumed RCS ~ v^3, a
+consequence of our requiring head echoes to also be detectable optically. Most
+events show a smooth variation of RCS with height broadly following the light
+production behavior. A significant minority of meteors show large variations in
+RCS relative to the optical light curve over common height intervals,
+reflecting fragmentation or possibly differential ablation.
+"
+27566,Cloaking the Clock: Emulating Clock Skew in Controller Area Networks,"  Automobiles are equipped with Electronic Control Units (ECU) that communicate
+via in-vehicle network protocol standards such as Controller Area Network
+(CAN). These protocols are designed under the assumption that separating
+in-vehicle communications from external networks is sufficient for protection
+against cyber attacks. This assumption, however, has been shown to be invalid
+by recent attacks in which adversaries were able to infiltrate the in-vehicle
+network. Motivated by these attacks, intrusion detection systems (IDSs) have
+been proposed for in-vehicle networks that attempt to detect attacks by making
+use of device fingerprinting using properties such as clock skew of an ECU. In
+this paper, we propose the cloaking attack, an intelligent masquerade attack in
+which an adversary modifies the timing of transmitted messages in order to
+match the clock skew of a targeted ECU. The attack leverages the fact that,
+while the clock skew is a physical property of each ECU that cannot be changed
+by the adversary, the estimation of the clock skew by other ECUs is based on
+network traffic, which, being a cyber component only, can be modified by an
+adversary. We implement the proposed cloaking attack and test it on two IDSs,
+namely, the current state-of-the-art IDS and a new IDS that we develop based on
+the widely-used Network Time Protocol (NTP). We implement the cloaking attack
+on two hardware testbeds, a prototype and a real connected vehicle, and show
+that it can always deceive both IDSs. We also introduce a new metric called the
+Maximum Slackness Index to quantify the effectiveness of the cloaking attack
+even when the adversary is unable to precisely match the clock skew of the
+targeted ECU.
+"
+27567,On generalization of Bailey's identity involving product of generalized hypergeometric series,"  The aim of this research paper is to obtain explicit expressions of
+(i) $ {}_1F_1 \left[\begin{array}{c} \alpha \\ 2\alpha + i \end{array} ; x
+\right]. {}_1F_1\left[ \begin{array}{c} \beta \\ 2\beta + j \end{array} ; x
+\right]$
+(ii) ${}_1F_1 \left[ \begin{array}{c} \alpha \\ 2\alpha - i \end{array} ; x
+\right] . {}_1F_1 \left[ \begin{array}{c} \beta \\ 2\beta - j \end{array} ; x
+\right]$
+(iii) ${}_1F_1 \left[ \begin{array}{c} \alpha \\ 2\alpha + i \end{array} ; x
+\right] . {}_1F_1 \left[\begin{array}{c} \beta \\ 2\beta - j \end{array} ; x
+\right]$
+in the most general form for any $i,j=0,1,2,\ldots$
+For $i=j=0$, we recover well known and useful identity due to Bailey. The
+results are derived with the help of a well known Bailey's formula involving
+products of generalized hypergeometric series and generalization of Kummer's
+second transformation formulas available in the literature. A few interesting
+new as well as known special cases have also been given.
+"
+27568,NEXT: A Neural Network Framework for Next POI Recommendation,"  The task of next POI recommendation has been studied extensively in recent
+years. However, developing an unified recommendation framework to incorporate
+multiple factors associated with both POIs and users remains challenging,
+because of the heterogeneity nature of these information. Further, effective
+mechanisms to handle cold-start and endow the system with interpretability are
+also difficult topics. Inspired by the recent success of neural networks in
+many areas, in this paper, we present a simple but effective neural network
+framework for next POI recommendation, named NEXT. NEXT is an unified framework
+to learn the hidden intent regarding user's next move, by incorporating
+different factors in an unified manner. Specifically, in NEXT, we incorporate
+meta-data information and two kinds of temporal contexts (i.e., time interval
+and visit time). To leverage sequential relations and geographical influence,
+we propose to adopt DeepWalk, a network representation learning technique, to
+encode such knowledge. We evaluate the effectiveness of NEXT against
+state-of-the-art alternatives and neural networks based solutions. Experimental
+results over three publicly available datasets demonstrate that NEXT
+significantly outperforms baselines in real-time next POI recommendation.
+Further experiments demonstrate the superiority of NEXT in handling cold-start.
+More importantly, we show that NEXT provides meaningful explanation of the
+dimensions in hidden intent space.
+"
+27569,Automatic Contraction of Unstructured Tensor Networks,"  The evaluation of partition functions is central to statistical physics. For
+lattice systems and other finite models the partition function may be expressed
+as the contraction of a tensor network. Unfortunately computing such
+contractions is difficult, with naive contraction methods requiring exponential
+time in system size. In specific cases tensor renormalization methods have made
+this tractable, but these are hand-crafted to the model of interest and
+typically require periodic or otherwise structured networks. As a result they
+cannot readily be applied to networks with disorder, aperiodic connectivity, or
+otherwise unstructured components. In this work I present a new algorithm for
+automatically contracting tensor networks. This method makes no assumptions
+about the structure of the network and performs well in both structured and
+unstructured cases so long as the correlation structure is local. This means
+that it can be applied as a general tool to study correlations in local lattice
+systems without placing significant constraints on either their connectivity or
+their interactions.
+"
+27570,Faster truncated integer multiplication,"  We present new algorithms for computing the low n bits or the high n bits of
+the product of two n-bit integers. We show that these problems may be solved in
+asymptotically 75% of the time required to compute the full 2n-bit product,
+assuming that the underlying integer multiplication algorithm relies on
+computing cyclic convolutions of real sequences.
+"
+27571,Ablation Studies in Artificial Neural Networks,"  Ablation studies have been widely used in the field of neuroscience to tackle
+complex biological systems such as the extensively studied Drosophila central
+nervous system, the vertebrate brain and more interestingly and most
+delicately, the human brain. In the past, these kinds of studies were utilized
+to uncover structure and organization in the brain, i.e. a mapping of features
+inherent to external stimuli onto different areas of the neocortex. considering
+the growth in size and complexity of state-of-the-art artificial neural
+networks (ANNs) and the corresponding growth in complexity of the tasks that
+are tackled by these networks, the question arises whether ablation studies may
+be used to investigate these networks for a similar organization of their inner
+representations. In this paper, we address this question and performed two
+ablation studies in two fundamentally different ANNs to investigate their inner
+representations of two well-known benchmark datasets from the computer vision
+domain. We found that features distinct to the local and global structure of
+the data are selectively represented in specific parts of the network.
+Furthermore, some of these representations are redundant, awarding the network
+a certain robustness to structural damages. We further determined the
+importance of specific parts of the network for the classification task solely
+based on the weight structure of single units. Finally, we examined the ability
+of damaged networks to recover from the consequences of ablations by means of
+recovery training. We argue that ablations studies are a feasible method to
+investigate knowledge representations in ANNs and are especially helpful to
+examine a networks robustness to structural damages, a feature of ANNs that
+will become increasingly important for future safety-critical applications.
+"
+27572,Hyperbolic Entailment Cones for Learning Hierarchical Embeddings,"  Learning graph representations via low-dimensional embeddings that preserve
+relevant network properties is an important class of problems in machine
+learning. We here present a novel method to embed directed acyclic graphs.
+Following prior work, we first advocate for using hyperbolic spaces which
+provably model tree-like structures better than Euclidean geometry. Second, we
+view hierarchical relations as partial orders defined using a family of nested
+geodesically convex cones. We prove that these entailment cones admit an
+optimal shape with a closed form expression both in the Euclidean and
+hyperbolic spaces, and they canonically define the embedding learning process.
+Experiments show significant improvements of our method over strong recent
+baselines both in terms of representational capacity and generalization.
+"
+27573,A probabilistic gridded product for daily precipitation extremes over the United States,"  Gridded data products, for example interpolated daily measurements of
+precipitation from weather stations, are commonly used as a convenient
+substitute for direct observations because these products provide a spatially
+and temporally continuous and complete source of data. However, when the goal
+is to characterize climatological features of extreme precipitation over a
+spatial domain (e.g., a map of return values) at the native spatial scales of
+these phenomena, then gridded products may lead to incorrect conclusions
+because daily precipitation is a fractal field and hence any smoothing
+technique will dampen local extremes. To address this issue, we create a new
+""probabilistic"" gridded product specifically designed to characterize the
+climatological properties of extreme precipitation by applying spatial
+statistical analyses to daily measurements of precipitation from the GHCN over
+CONUS. The essence of our method is to first estimate the climatology of
+extreme precipitation based on station data and then use a data-driven
+statistical approach to interpolate these estimates to a fine grid. We argue
+that our method yields an improved characterization of the climatology within a
+grid cell because the probabilistic behavior of extreme precipitation is much
+better behaved (i.e., smoother) than daily weather. Furthermore, the spatial
+smoothing innate to our approach significantly increases the signal-to-noise
+ratio in the estimated extreme statistics relative to an analysis without
+smoothing. Finally, by deriving a data-driven approach for translating extreme
+statistics to a spatially complete grid, the methodology outlined in this paper
+resolves the issue of how to properly compare station data with output from
+earth system models. We conclude the paper by comparing our probabilistic
+gridded product with a standard extreme value analysis of the Livneh gridded
+daily precipitation product.
+"
+27574,Effective Calibration Transfer via Möbius and Affine Transformations,"  A novel technique for calibration transfer called the Modified Four Point
+Interpolant (MFPI) method is introduced for near infrared spectra. The method
+is founded on physical intuition and utilizes a series of quasiconformal maps
+in the frequency domain to transfer spectra from a slave instrument to a master
+instrument's approximated space. Comparisons between direct standardization
+(DS), piecewise direct standardization (PDS), and MFPI for two publicly
+available datasets are detailed herein. The results suggest that MFPI can
+outperform DS and PDS with respect to root mean squared errors of transfer and
+prediction. Combinations of MFPI with DS/PDS are also shown to reduce
+predictive errors after transfer.
+"
+27575,Coverage threshold for laser-induced lithography,"  Recent experimental observations of laser-induced adsorption at the interface
+between an alkali vapor and a dielectric surface have demonstrated the
+possibility of growing metallic films of nanometric thickness on dielectric
+surfaces, with arbitrary shapes determined by the intensity profile of the
+light. The mechanisms directly responsible for the accumulation of atoms at the
+irradiated surface have been shown to involve photo-ionization of atoms very
+close to the surface. However, the existence of a vapor-pressure threshold for
+initiating the film growth still raises questions on the processes occurring at
+the surface. In this letter, we report on the observation that the
+vapor-pressure threshold corresponds to a minimum adatom coverage necessary for
+the surface to effectively neutralize the incoming ions and make possible the
+growth of a multilayer film. We discuss the hypothesis that the coverage
+threshold is a surface conductivity threshold.
+"
+27576,Spin wave dispersion just above the magnetic order-order transition in the metallic antiferromagnet Mn$_3$Pt,"  Spin wave dispersion in the metallic antiferromagnet Mn$_3$Pt was
+investigated just above the order-order transition temperature by using the
+inelastic neutron scattering technique. The spin wave dispersion at $T = 400$ K
+along [100], [110] and [111] directions was isotropic within the measurement
+accuracy. The dispersion was described by $({\hbar\omega})^2 = c^2q^2 +
+\Delta^2$ with $c = 190$ meV {\AA} and $\Delta = 3.3$ meV. Compared with the
+dispersion at $T = 419$ K previously reported, the result demonstrates a large
+reduction of the stiffness constant $c$ with increasing temperature. This is
+similar to that observed in the metallic antiferromagnet FePt$_3$, and is an
+indication of the itinerancy of the magnetic moments.
+"
+27577,The influence of orbital resonances on the water transport to objects in the circumprimary habitable zone of binary star systems,"  We investigate the role of secular and mean motion resonances on the water
+transport from a belt of icy asteroids onto planets or embryos orbiting inside
+the circumprimary habitable zone (HZ) of a binary star system. In addition, the
+host-star has an accompanying gas giant planet. For a comparison, we perform
+two case studies where a secular resonance (SR) is located either inside the HZ
+close to 1.0 au (causing eccentric motion of a planet or embryos therein) or in
+the asteroid belt, beyond the snow line. In the latter case, a higher flux of
+icy objects moving towards the HZ is expected. Collisions between asteroids and
+objects in the HZ are treated analytically. Our purely dynamical study shows
+that the SR in the HZ boosts the water transport; however, collisions can occur
+at very high impact speeds. In this paper, we treat for the first time,
+realistic collisions using a GPU 3D-SPH code to assess the water loss in the
+projectile. Including the water loss into the dynamical results, we get more
+realistic values for the water mass fraction of the asteroid during an impact.
+We highlight that collisions occurring at high velocities greatly reduce the
+water content of the projectile and thus the amount of water transported to
+planets or embryos orbiting inside the HZ. Moreover, we discuss other effects
+that could modify our results, namely the asteroid's surface rate recession due
+to ice sublimation and the atmospheric drag contribution on the asteroids' mass
+loss.
+"
+27578,Sparse Bayesian vector autoregressions in huge dimensions,"  We develop a Bayesian vector autoregressive (VAR) model that is capable of
+handling vast dimensional information sets. Three features are introduced to
+permit reliable estimation of the model. First, we assume that the reduced-form
+errors in the VAR feature a factor stochastic volatility structure, allowing
+for conditional equation-by-equation estimation. Second, we apply a
+Dirichlet-Laplace prior to the VAR coefficients to cure the curse of
+dimensionality. Finally, since simulation-based methods are needed to simulate
+from the joint posterior distribution, we utilize recent innovations to
+efficiently sample from high-dimensional multivariate Gaussian distributions
+that improve upon recent algorithms by large margins. In the empirical exercise
+we apply the model to US data and evaluate its forecasting capabilities.
+"
+27579,A simple data discretizer,"  Data discretization is an important step in the process of machine learning,
+since it is easier for classifiers to deal with discrete attributes rather than
+continuous attributes. Over the years, several methods of performing
+discretization such as Boolean Reasoning, Equal Frequency Binning, Entropy have
+been proposed, explored, and implemented. In this article, a simple supervised
+discretization approach is introduced. The prime goal of MIL is to maximize
+classification accuracy of classifier, minimizing loss of information while
+discretization of continuous attributes. The performance of the suggested
+approach is compared with the supervised discretization algorithm Minimum
+Information Loss (MIL), using the state-of-the-art rule inductive algorithms-
+J48 (Java implementation of C4.5 classifier). The presented approach is,
+indeed, the modified version of MIL. The empirical results show that the
+modified approach performs better in several cases in comparison to the
+original MIL algorithm and Minimum Description Length Principle (MDLP) .
+"
+27580,Randomized incomplete $U$-statistics in high dimensions,"  This paper studies inference for the mean vector of a high-dimensional
+$U$-statistic. In the era of Big Data, the dimension $d$ of the $U$-statistic
+and the sample size $n$ of the observations tend to be both large, and the
+computation of the $U$-statistic is prohibitively demanding. Data-dependent
+inferential procedures such as the empirical bootstrap for $U$-statistics is
+even more computationally expensive. To overcome such computational bottleneck,
+incomplete $U$-statistics obtained by sampling fewer terms of the $U$-statistic
+are attractive alternatives. In this paper, we introduce randomized incomplete
+$U$-statistics with sparse weights whose computational cost can be made
+independent of the order of the $U$-statistic. We derive non-asymptotic
+Gaussian approximation error bounds for the randomized incomplete
+$U$-statistics in high dimensions, namely in cases where the dimension $d$ is
+possibly much larger than the sample size $n$, for both non-degenerate and
+degenerate kernels. In addition, we propose generic bootstrap methods for the
+incomplete $U$-statistics that are computationally much less-demanding than
+existing bootstrap methods, and establish finite sample validity of the
+proposed bootstrap methods. Our methods are illustrated on the application to
+nonparametric testing for the pairwise independence of a high-dimensional
+random vector under weaker assumptions than those appearing in the literature.
+"
+27581,Interaction of molecular nitrogen with Free-Electron-Laser radiation,"  We compute molecular continuum orbitals in the single center expansion
+scheme. We then employ these orbitals to obtain molecular Auger rates and
+single-photon ionization cross sections to study the interaction of N2 with
+Free-Electron-Laser (FEL) pulses. The nuclei are kept fixed. We formulate rate
+equations for the energetically allowed molecular and atomic transitions and we
+account for dissociation through additional terms in the rate equations.
+Solving these equations for different parameters of the FEL pulse, allows us to
+identify the most efficient parameters of the FEL pulse for obtaining the
+highest contribution of double core hole states (DCH) in the final atomic ion
+fragments. Finally we identify the contribution of DCH states in the electron
+spectra and show that the DCH state contribution is more easily identified in
+the photo-ionization rather than the Auger transitions.
+"
+27582,Characterizing Allegheny County Opioid Overdoses with an Interactive Data Explorer and Synthetic Prediction Tool,"  The United States has an opioid epidemic, and Pennsylvania's Allegheny County
+is among the worst. This motivates a deeper exploration of what characterizes
+the epidemic, such as what are risk factors for people who ultimately overdose
+and die due to opioids. We show that some interesting trends and factors can be
+identified from openly available autopsy data, and demonstrate the power of
+building an interactive data exploration tool for policy makers. However, there
+is still a pressing need to incorporate further demographic factors. We show
+this by using synthetic Electronic Medical Record (EMR) data to simulate the
+predictive power of random forests and neural networks when given additional
+loosely correlated features. In addition, we give examples of useful feature
+extraction that enable model enhancement without sacrificing privacy.
+"
+27583,Non-invasive acquisition of fetal ECG from the maternal xyphoid process: a feasibility study in pregnant sheep and a call for open data sets,"  Objective: The utility of fetal heart rate (FHR) monitoring can only be
+achieved with an acquisition sampling rate that preserves the underlying
+physiological information on the millisecond time scale (1000 Hz rather than 4
+Hz). For such acquisition, fetal ECG (fECG) is required, rather than the
+ultrasound to derive FHR. We tested one recently developed algorithm, SAVER,
+and two widely applied algorithms to extract fECG from a single channel
+maternal ECG signal recorded over the xyphoid process rather than the routine
+abdominal signal. Approach: At 126dG, ECG was attached to near-term ewe and
+fetal shoulders, manubrium and xyphoid processes (n=12). FECG served as the
+ground-truth to which the fetal ECG signal extracted from the
+simultaneously-acquired maternal ECG was compared. All fetuses were in good
+health during surgery (pH 7.29+/-0.03, pO2 33.2+/-8.4, pCO2 56.0+/-7.8, O2Sat
+78.3+/-7.6, lactate 2.8+/-0.6, BE -0.3+/-2.4). Main result: In all animals,
+single lead fECG extraction algorithm could not extract fECG from the maternal
+ECG signal over the xyphoid process with the F1 less than 50%. Significance:
+The applied fECG extraction algorithms might be unsuitable for the maternal ECG
+signal over the xyphoid process, or the latter does not contain strong enough
+fECG signal, although the lead is near the mother's abdomen. Fetal sheep model
+is widely used to mimic various fetal conditions, yet ECG recordings in a
+public data set form are not available to test the predictive ability of fECG
+and FHR. We are making this data set openly available to other researchers to
+foster non-invasive fECG acquisition in this animal model.
+"
+27584,Evaluation of Parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology,"  Models of biological systems often have many unknown parameters that must be
+determined in order for model behavior to match experimental observations.
+Commonly-used methods for parameter estimation that return point estimates of
+the best-fit parameters are insufficient when models are high dimensional and
+under-constrained. As a result, Bayesian methods, which treat model parameters
+as random variables and attempt to estimate their probability distributions
+given data, have become popular in systems biology. Bayesian parameter
+estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample
+model parameter distributions, but the slow convergence of MCMC sampling can be
+a major bottleneck. One approach to improving performance is parallel tempering
+(PT), a physics-based method that uses swapping between multiple Markov chains
+run in parallel at different temperatures to accelerate sampling. The
+temperature of a Markov chain determines the probability of accepting an
+unfavorable move, so swapping with higher temperatures chains enables the
+sampling chain to escape from local minima. In this work we compared the MCMC
+performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on
+six biological models of varying complexity. We found that for simpler models
+PT accelerated convergence and sampling, and that for more complex models, PT
+often converged in cases MH became trapped in non-optimal local minima. We also
+developed a freely-available MATLAB package for Bayesian parameter estimation
+called PTempEst (this http URL), which is closely
+integrated with the popular BioNetGen software for rule-based modeling of
+biological systems.
+"
+27585,Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios,"  In the last decade, driven also by the availability of an unprecedented
+computational power and storage capabilities in cloud environments we assisted
+to the proliferation of new algorithms, methods, and approaches in two areas of
+artificial intelligence: knowledge representation and machine learning. On the
+one side, the generation of a high rate of structured data on the Web led to
+the creation and publication of the so-called knowledge graphs. On the other
+side, deep learning emerged as one of the most promising approaches in the
+generation and training of models that can be applied to a wide variety of
+application fields. More recently, autoencoders have proven their strength in
+various scenarios, playing a fundamental role in unsupervised learning. In this
+paper, we instigate how to exploit the semantic information encoded in a
+knowledge graph to build connections between units in a Neural Network, thus
+leading to a new method, SEM-AUTO, to extract and weigh semantic features that
+can eventually be used to build a recommender system. As adding content-based
+side information may mitigate the cold user problems, we tested how our
+approach behave in the presence of a few rating from a user on the Movielens 1M
+dataset and compare results with BPRSLIM.
+"
+27586,Continuous monitoring of $\ell_p$ norms in data streams,"  In insertion-only streaming, one sees a sequence of indices $a_1, a_2,
+\ldots, a_m\in [n]$. The stream defines a sequence of $m$ frequency vectors
+$x^{(1)},\ldots,x^{(m)}\in\mathbb{R}^n$ with $(x^{(t)})_i = |\{j : j\in[t], a_j
+= i\}|$. That is, $x^{(t)}$ is the frequency vector after seeing the first $t$
+items in the stream. Much work in the streaming literature focuses on
+estimating some function $f(x^{(m)})$. Many applications though require
+obtaining estimates at time $t$ of $f(x^{(t)})$, for every $t\in[m]$. Naively
+this guarantee is obtained by devising an algorithm with failure probability
+$\ll 1/m$, then performing a union bound over all stream updates to guarantee
+that all $m$ estimates are simultaneously accurate with good probability. When
+$f(x)$ is some $\ell_p$ norm of $x$, recent works have shown that this union
+bound is wasteful and better space complexity is possible for the continuous
+monitoring problem, with the strongest known results being for $p=2$ [HTY14,
+BCIW16, BCINWW17]. In this work, we improve the state of the art for all
+$0<p<2$, which we obtain via a novel analysis of Indyk's $p$-stable sketch
+[Indyk06].
+"
+27587,Deep Orthogonal Representations: Fundamental Properties and Applications,"  Several representation learning and, more broadly, dimensionality reduction
+techniques seek to produce representations of the data that are orthogonal
+(uncorrelated). Examples include PCA, CCA, Kernel/Deep CCA, the ACE algorithm
+and correspondence analysis (CA). For a fixed data distribution, all finite
+variance representations belong to the same function space regardless of how
+they are derived. In this work, we present a theoretical framework for
+analyzing this function space, and demonstrate how a basis for this space can
+be found using neural networks. We show that this framework (i) underlies
+recent multi-view representation learning methods, (ii) enables classical
+exploratory statistical techniques such as CA to be scaled via neural networks,
+and (iii) can be used to derive new methods for comparing black-box models. We
+illustrate these applications empirically through different datasets.
+"
+27588,SF-sketch: A Two-stage Sketch for Data Streams,"  A sketch is a probabilistic data structure used to record frequencies of
+items in a multi-set. Sketches are widely used in various fields, especially
+those that involve processing and storing data streams. In streaming
+applications with high data rates, a sketch ""fills up"" very quickly. Thus, its
+contents are periodically transferred to the remote collector, which is
+responsible for answering queries. In this paper, we propose a new sketch,
+called Slim-Fat (SF) sketch, which has a significantly higher accuracy compared
+to prior art, a much smaller memory footprint, and at the same time achieves
+the same speed as the best prior sketch. The key idea behind our proposed
+SF-sketch is to maintain two separate sketches: a small sketch called
+Slim-subsketch and a large sketch called Fat-subsketch. The Slim-subsketch is
+periodically transferred to the remote collector for answering queries quickly
+and accurately. The Fat-subsketch, however, is not transferred to the remote
+collector because it is used only to assist the Slim-subsketch during the
+insertions and deletions and is not used to answer queries. We implemented and
+extensively evaluated SF-sketch along with several prior sketches and compared
+them side by side. Our experimental results show that SF-sketch outperforms the
+most widely used CM-sketch by up to 33.1 times in terms of accuracy. We have
+released the source codes of our proposed sketch as well as existing sketches
+at Github. The short version of this paper will appear in ICDE 2017.
+"
+27589,Network Summarization with Preserved Spectral Properties,"  Large-scale networks are widely used to represent object relationships in
+many real world applications. The occurrence of large-scale networks presents
+significant computational challenges to process, analyze, and extract
+information from such networks. Network summarization techniques are commonly
+used to reduce the computational load while attempting to maintain the basic
+structural properties of the original network. Previous works have primarily
+focused on some type of network partitioning strategies with
+application-dependent regularizations, most often resulting in strongly
+connected clusters.
+In this paper, we introduce a novel perspective regarding the network
+summarization problem based on concepts from spectral graph theory. We propose
+a new distance measurement to characterize the spectral differences between the
+original and coarsened networks. We rigorously justify the spectral distance
+with the interlacing theorem as well the results from the stochastic block
+model. We provide an efficient algorithm to generate the coarsened networks
+that maximally preserves the spectral properties of the original network. Our
+proposed network summarization framework allows the flexibility to generate a
+set of coarsened networks with significantly different structures preserved
+from different aspects of the original network, which distinguishes our work
+from others. We conduct extensive experimental tests on a variety of
+large-scale networks, both from real-world applications and the random graph
+model. We show that our proposed algorithms consistently perform better results
+in terms of the spectral measurements and running time compared to previous
+network summarization algorithms.
+"
+27590,A Flexible Joint Longitudinal-Survival Model for Analysis of End-Stage Renal Disease Data,"  We propose a flexible joint longitudinal-survival framework to examine the
+association between longitudinally collected biomarkers and a time-to-event
+endpoint. More specifically, we use our method for analyzing the survival
+outcome of end-stage renal disease patients with time-varying serum albumin
+measurements. Our proposed method is robust to common parametric assumptions in
+that it avoids explicit distributional assumptions on longitudinal measures and
+allows for subject-specific baseline hazard in the survival component. Fully
+joint estimation is performed to account for the uncertainty in the estimated
+longitudinal biomarkers included in the survival model.
+"
+27591,Additive Partially Linear Models for Massive Heterogeneous Data,"  We consider an additive partially linear framework for modelling massive
+heterogeneous data. The major goal is to extract multiple common features
+simultaneously across all sub-populations while exploring heterogeneity of each
+sub-population. We propose an aggregation type of estimators for the
+commonality parameters that possess the asymptotic optimal bounds and the
+asymptotic distributions as if there were no heterogeneity. This oracle result
+holds when the number of sub-populations does not grow too fast and the tuning
+parameters are selected carefully. A plug-in estimator for the heterogeneity
+parameter is further constructed, and shown to possess the asymptotic
+distribution as if the commonality information were available. Furthermore, we
+develop a heterogeneity test for the linear components and a homogeneity test
+for the non-linear components accordingly. The performance of the proposed
+methods is evaluated via simulation studies and an application to the Medicare
+Provider Utilization and Payment data.
+"
+27592,Coordinated Online Learning With Applications to Learning User Preferences,"  We study an online multi-task learning setting, in which instances of related
+tasks arrive sequentially, and are handled by task-specific online learners. We
+consider an algorithmic framework to model the relationship of these tasks via
+a set of convex constraints. To exploit this relationship, we design a novel
+algorithm -- COOL -- for coordinating the individual online learners: Our key
+idea is to coordinate their parameters via weighted projections onto a convex
+set. By adjusting the rate and accuracy of the projection, the COOL algorithm
+allows for a trade-off between the benefit of coordination and the required
+computation/communication. We derive regret bounds for our approach and analyze
+how they are influenced by these trade-off factors. We apply our results on the
+application of learning users' preferences on the Airbnb marketplace with the
+goal of incentivizing users to explore under-reviewed apartments.
+"
+27593,DBFT: Efficient Byzantine Consensus with a Weak Coordinator and its Application to Consortium Blockchains,"  This paper introduces a deterministic Byzantine consensus algorithm that
+relies on a new weak coordinator. As opposed to previous algorithms that cannot
+terminate in the presence of a faulty or slow coordinator, our algorithm can
+terminate even when its coordinator is faulty, hence the name weak coordinator.
+The key idea is to allow processes to complete asynchronous rounds as soon as
+they receive a threshold of messages, instead of having to wait for a message
+from a coordinator that may be slow.
+The resulting algorithm assumes partial synchrony, is resilience optimal,
+time optimal and does not need signatures. Our presentation is didactic: we
+first present a simple safe binary Byzantine consensus algorithm, modify it to
+ensure termination, and finally present an optimized reduction from multivalue
+consensus to binary consensus that may terminate in 4 message delays. To
+evaluate our algorithm, we deployed it on 100 machines distributed in 5
+datacenters across different continents and compared its performance against
+the randomized solution from Mostefaoui, Moumem and Raynal [PODC14] that
+terminates in O(1) rounds in expectation. Our algorithm always outperforms the
+latter even in the presence of Byzantine behaviors. Our algorithm has a
+subsecond average latency in most of our geo-distributed experiments, even when
+attacked by a well-engineered coalition of Byzantine processes.
+"
+27594,Generic 3D Representation via Pose Estimation and Matching,"  Though a large body of computer vision research has investigated developing
+generic semantic representations, efforts towards developing a similar
+representation for 3D has been limited. In this paper, we learn a generic 3D
+representation through solving a set of foundational proxy 3D tasks:
+object-centric camera pose estimation and wide baseline feature matching. Our
+method is based upon the premise that by providing supervision over a set of
+carefully selected foundational tasks, generalization to novel tasks and
+abstraction capabilities can be achieved. We empirically show that the internal
+representation of a multi-task ConvNet trained to solve the above core problems
+generalizes to novel 3D tasks (e.g., scene layout estimation, object pose
+estimation, surface normal estimation) without the need for fine-tuning and
+shows traits of abstraction abilities (e.g., cross-modality pose estimation).
+In the context of the core supervised tasks, we demonstrate our representation
+achieves state-of-the-art wide baseline feature matching results without
+requiring apriori rectification (unlike SIFT and the majority of learned
+features). We also show 6DOF camera pose estimation given a pair local image
+patches. The accuracy of both supervised tasks come comparable to humans.
+Finally, we contribute a large-scale dataset composed of object-centric street
+view scenes along with point correspondences and camera pose information, and
+conclude with a discussion on the learned representation and open research
+questions.
+"
+27595,Magnetic reheating,"  We provide a new bound on the amplitude of primordial magnetic fields (PMFs)
+by using a novel mechanism, named {\it magnetic reheating}. Before the epoch of
+recombination, PMFs induce the fluid motions in a photon-baryon plasma through
+the Lorentz force. Due to the viscosity in the plasma, such induced fluid
+motions would be damped and this means the dissipation of PMFs. In the early
+Universe with $z \gtrsim 2 \times 10^6$, cosmic microwave background (CMB)
+photons are quickly thermalized with the dissipated energy and shift to a
+different Planck distribution with a new temperature. In other words, the
+energy injection due to the dissipation of PMFs changes the baryon-photon
+number ratio during this era and we name such a process {\it magnetic
+reheating}. By using the current results of the baryon-photon number ratio
+obtained from the Big Bang nucleosynthesis and CMB observations, we put a
+strongest constraint on the amplitude of PMFs on small scales which we can not
+access through CMB anisotropy and CMB distortions, $B_{0} \lesssim 1.0 \;
+\mu{\rm G}$ at the scales $10^{4} \; h{\rm Mpc}^{-1} < k < 10^{8} \; h{\rm
+Mpc}^{-1}$. Moreover, when the spectrum of PMFs is given by the power-law, the
+magnetic reheating puts a quite strong constraint in the case of the
+blue-tilted spectrum, for example, $B_0 \lesssim 10^{-17} \;{\rm nG}$,
+$10^{-23} \;{\rm nG}$, and $10^{-29} \;{\rm nG}$ at 1~comoving Mpc for
+$n_{B}=1.0$, $2.0$, and $3.0$, respectively. This constraint would give an
+impact on generation mechanisms of PMFs in the early Universe.
+"
+27596,Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology,"  Bayesian network modelling is a well adapted approach to study messy and
+highly correlated datasets which are very common in, e.g., systems
+epidemiology. A popular approach to learn a Bayesian network from an
+observational datasets is to identify the maximum a posteriori network in a
+search-and-score approach. Many scores have been proposed both Bayesian or
+frequentist based. In an applied perspective, a suitable approach would allow
+multiple distributions for the data and is robust enough to run autonomously. A
+promising framework to compute scores are generalized linear models. Indeed,
+there exists fast algorithms for estimation and many tailored solutions to
+common epidemiological issues. The purpose of this paper is to present an R
+package abn that has an implementation of multiple frequentist scores and some
+realistic simulations that show its usability and performance. It includes
+features to deal efficiently with data separation and adjustment which are very
+common in systems epidemiology.
+"
+27597,IVOA Recommendation: SSO - Single-Sign-On Profile: Authentication Mechanisms Version 2.0,"  Approved client-server authentication mechanisms are described for the IVOA
+single-sign-on profile: No Authentication; HTTP Basic Authentication; TLS with
+passwords; TLS with client certificates; Cookies; Open Authentication; Security
+Assertion Markup Language; OpenID. Normative rules are given for the
+implementation of these mechanisms, mainly by reference to pre-existing
+standards. The Authorization mechanisms are out of the scope of this document.
+"
+27598,Three-dimensional Bose-Einstein Condensation in the Spin-1/2 Ferromagnetic-leg Ladder 3-Br-4-F-V,"  The critical exponent of the phase boundary has been examined on the
+three-dimensional incommensurate ordering phase in the spin-1/2
+ferromagnetic-leg ladder 3-Br-4-F-V
+[=3-(3-bromo-4-fluorophenyl)-1,5-diphenylverdazyl]. Using the
+temperature-window fitting technique, we obtained the critical exponents which
+agreed with the three-dimensional (3D) Bose-Einstein condensation (BEC)
+universality class at both sides of the lower critical field and the saturation
+field. 3-Br-4-F-V thus becomes a new member of the quantum magnets which prove
+the universality of the 3D BEC exponent.
+"
+27599,Compatible orders and fermion-induced emergent symmetry in Dirac systems,"  We study the quantum multicritical point in a (2+1)-dimensional Dirac system
+between the semimetallic phase and two ordered phases that are characterized by
+anticommuting mass terms with $O(N_1)$ and $O(N_2)$ symmetry, respectively.
+Using $\epsilon$ expansion around the upper critical space-time dimension of
+four, we demonstrate the existence of a stable renormalization-group fixed
+point, enabling a direct and continuous transition between the two ordered
+phases directly at the multicritical point. This point is found to be
+characterized by an emergent $O(N_1+N_2)$ symmetry for arbitrary values of
+$N_1$ and $N_2$ and fermion flavor numbers $N_f$, as long as the corresponding
+representation of the Clifford algebra exists. Small $O(N)$-breaking
+perturbations near the chiral $O(N)$ fixed point are therefore irrelevant. This
+result can be traced back to the presence of gapless Dirac degrees of freedom
+at criticality, and it is in clear contrast to the purely bosonic $O(N)$ fixed
+point, which is stable only when $N < 3$. As a by-product, we obtain
+predictions for the critical behavior of the chiral $O(N)$ universality classes
+for arbitrary $N$ and fermion flavor number $N_f$. Implications for critical
+Weyl and Dirac systems in 3+1 dimensions are also briefly discussed.
+"
+27600,Proximal Nerve Complexes. A Computational Topology Approach,"  This article introduces a theory of proximal nerve complexes and nerve
+spokes, restricted to the triangulation of finite regions in the Euclidean
+plane. A nerve complex is a collection of filled triangles with a common
+vertex, covering a finite region of the plane. Structures called $k$-spokes,
+$k\geq 1$, are a natural extension of nerve complexes. A $k$-spoke is the union
+of a collection of filled triangles that pairwise either have a common edge or
+a common vertex. A consideration of the closeness of nerve complexes leads to a
+proximal view of simplicial complexes. A practical application of proximal
+nerve complexes is given, briefly, in terms of object shape geometry in digital
+images.
+"
+27601,Triton's Evolution with a Primordial Neptunian Satellite System,"  The Neptunian satellite system is unusual. The major satellites of Jupiter,
+Saturn, and Uranus are all in prograde, low-inclination orbits. Neptune on the
+other hand, has the fewest satellites, and most of the system's mass is within
+one irregular satellite, Triton. Triton was most likely captured by Neptune and
+destroyed the primordial regular satellite system. We investigate the
+interactions between a newly captured Triton and a prior Neptunian satellite
+system. We find that a prior satellite system with a mass ratio similar to the
+Uranian system or smaller has a substantial likelihood of reproducing the
+current Neptunian system, while a more massive system has a low probability of
+leading to the current configuration. Moreover, Triton's interaction with a
+prior satellite system may offer a mechanism to decrease its high initial
+semimajor axis fast enough to preserve small irregular satellites (Nereid-like)
+that might otherwise be lost during a prolonged Triton circularization via
+tides alone.
+"
+27602,Coarse-Grained Descriptions of Dynamics for Networks with both Intrinsic and Structural Heterogeneities,"  Finding accurate reduced descriptions for large, complex, dynamically
+evolving networks is a crucial enabler to their simulation, analysis, and,
+ultimately, design. Here we propose and illustrate a systematic and powerful
+approach to obtaining good collective coarse-grained observables-- variables
+successfully summarizing the detailed state of such networks. Finding such
+variables can naturally lead to successful reduced dynamic models for the
+networks. The main premise enabling our approach is the assumption that the
+behavior of a node in the network depends (after a short initial transient) on
+the node identity: a set of descriptors that quantify the node properties,
+whether intrinsic (e.g. parameters in the node evolution equations) or
+structural (imparted to the node by its connectivity in the particular network
+structure). The approach creates a natural link with modeling and
+""computational enabling technology"" developed in the context of Uncertainty
+Quantification. In our case, however, we will not focus on ensembles of
+different realizations of a problem, each with parameters randomly selected
+from a distribution. We will instead study many coupled heterogeneous units,
+each characterized by randomly assigned (heterogeneous) parameter value(s). One
+could then coin the term Heterogeneity Quantification for this approach, which
+we illustrate through a model dynamic network consisting of coupled oscillators
+with one intrinsic heterogeneity (oscillator individual frequency) and one
+structural heterogeneity (oscillator degree in the undirected network). The
+computational implementation of the approach, its shortcomings and possible
+extensions are also discussed.
+"
+27603,Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering,"  In this work we study the non-parametric reconstruction of spatio-temporal
+dynamical Gaussian processes (GPs) via GP regression from sparse and noisy
+data. GPs have been mainly applied to spatial regression where they represent
+one of the most powerful estimation approaches also thanks to their universal
+representing properties. Their extension to dynamical processes has been
+instead elusive so far since classical implementations lead to unscalable
+algorithms. We then propose a novel procedure to address this problem by
+coupling GP regression and Kalman filtering. In particular, assuming space/time
+separability of the covariance (kernel) of the process and rational time
+spectrum, we build a finite-dimensional discrete-time state-space process
+representation amenable of Kalman filtering. With sampling over a finite set of
+fixed spatial locations, our major finding is that the Kalman filter state at
+instant $t_k$ represents a sufficient statistic to compute the minimum variance
+estimate of the process at any $t \geq t_k$ over the entire spatial domain.
+This result can be interpreted as a novel Kalman representer theorem for
+dynamical GPs. We then extend the study to situations where the set of spatial
+input locations can vary over time. The proposed algorithms are finally tested
+on both synthetic and real field data, also providing comparisons with standard
+GP and truncated GP regression techniques.
+"
+27604,Analysis of Carrier Accumulation in Active Region by Energy Loss Mechanisms in InGaN Light-Emitting Diodes,"  Carrier recombination and transport processes play key roles in determining
+the optoelectronic performances such as the efficiency droop and forward
+voltage in InGaN/GaN multiple-quantum-well (MQW) light-emitting diodes (LEDs).
+In this work, we investigate the dominant carrier transport and recombination
+processes inside and outside the MQW region as a function of injection current
+from a new point of view by separately examining the carrier energy loss.
+Analysis of the measurement results reveals that the carrier accumulation and
+subsequent spill-over from the MQW active region to the clad is the most
+probable mechanism of explaining the efficiency and forward voltage variation
+with the injection current.
+"
+27605,Doping dependence of ordered phases and emergent quasiparticles in the doped Hubbard-Holstein model,"  We present determinant quantum Monte Carlo simulations of the hole-doped
+single-band Hubbard-Holstein model on a square lattice, to investigate how
+quasiparticles emerge when doping a Mott insulator (MI) or a Peierls insulator
+(PI). The MI regime at large Hubbard interaction $U$ and small relative
+electron-phonon coupling strength $\lambda$ is quickly suppressed upon doping,
+by drawing spectral weight from the upper Hubbard band and shifting the lower
+Hubbard band towards the Fermi level, leading to a metallic state with emergent
+quasiparticles at the Fermi level. On the other hand, the PI regime at large
+$\lambda$ and small $U$ persists out to relatively high doping levels. We study
+the evolution of the $d$-wave superconducting susceptibility with doping, and
+find that it increases with lowering temperature in a regime of intermediate
+values of $U$ and $\lambda$.
+"
+27606,Joint Geometrical and Statistical Alignment for Visual Domain Adaptation,"  This paper presents a novel unsupervised domain adaptation method for
+cross-domain visual recognition. We propose a unified framework that reduces
+the shift between domains both statistically and geometrically, referred to as
+Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two
+coupled projections that project the source domain and target domain data into
+low dimensional subspaces where the geometrical shift and distribution shift
+are reduced simultaneously. The objective function can be solved efficiently in
+a closed form. Extensive experiments have verified that the proposed method
+significantly outperforms several state-of-the-art domain adaptation methods on
+a synthetic dataset and three different real world cross-domain visual
+recognition tasks.
+"
+27607,Theory and optical design of x-ray echo spectrometers,"  X-ray echo spectroscopy, a space-domain counterpart of neutron spin echo, is
+a recently proposed inelastic x-ray scattering (IXS) technique. X-ray echo
+spectroscopy relies on imaging IXS spectra, and does not require x-ray
+monochromatization. Due to this, the echo-type IXS spectrometers are broadband,
+and thus have a potential to simultaneously provide dramatically increased
+signal strength, reduced measurement times, and higher resolution compared to
+the traditional narrow-band scanning-type IXS spectrometers. The theory of
+x-ray echo spectrometers presented in [1] is developed here further with a
+focus on questions of practical importance, which could facilitate optical
+design and assessment of the feasibility and performance of the echo
+spectrometers. Among others, the following questions are addressed: spectral
+resolution, refocusing condition, echo spectrometer tolerances, refocusing
+condition adjustment, effective beam size on the sample, spectral window of
+imaging and scanning range, impact of the secondary source size on the spectral
+resolution, angular dispersive optics, focusing and collimating optics, and
+detector's spatial resolution. Examples of optical designs and characteristics
+of echo spectrometers with 1-meV and 0.1-meV resolutions are presented.
+"
+27608,Bezier developable surfaces,"  In this paper we address the issue of designing developable surfaces with
+Bezier patches. We show that developable surfaces with a polynomial edge of
+regression are the set of developable surfaces which can be constructed with
+Aumann's algorithm. We also obtain the set of polynomial developable surfaces
+which can be constructed using general polynomial curves. The conclusions can
+be extended to spline surfaces as well.
+"
+27609,Heuristic for Maximizing Grouping Efficiency in the Cell Formation Problem,"  In our paper we consider the Cell Formation Problem in Group Technology with
+grouping efficiency as an objective function. We present a heuristic approach
+for obtaining high-quality solutions of the CFP. The suggested heuristic
+applies an improvement procedure to obtain solutions with high grouping
+efficiency. This procedure is repeated many times for randomly generated cell
+configurations. Our computational experiments are performed for popular
+benchmark instances taken from the literature with sizes from 10x20 to 50x150.
+Better solutions unknown before are found for 23 instances of the 24
+considered.
+"
+27610,Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms,"  We present an interactive version of an evidence-driven state-merging (EDSM)
+algorithm for learning variants of finite state automata. Learning these
+automata often amounts to recovering or reverse engineering the model
+generating the data despite noisy, incomplete, or imperfectly sampled data
+sources rather than optimizing a purely numeric target function. Domain
+expertise and human knowledge about the target domain can guide this process,
+and typically is captured in parameter settings. Often, domain expertise is
+subconscious and not expressed explicitly. Directly interacting with the
+learning algorithm makes it easier to utilize this knowledge effectively.
+"
+27611,Self-Organization of Self-Clearing Beating Patterns in an Array of Locally Interacting Ciliated Cells Formulated as an Adaptive Boolean Network,"  The observed spatio-temporal ciliary beat patterns on multiciliated epithelia
+are suspected to be the result of self-organizing processes on various levels.
+Here, we present an abstract epithelium model at the pluricellular level, which
+intends to make the self-organization of ciliary beating patterns as well as of
+the associated fluid transport across the airway epithelium plausible. Ciliated
+cells are modeled in terms of locally interacting oscillating two-state
+actuators. The local interactions among these boolean actuators are triggered
+by seeded mucus lumps. In the course of a simulation the actuators' state and
+the associated mucus velocity field self-organize in tandem. We suggest to
+consider the dynamics on multiciliated epithelia in the context of adaptive
+(boolean) networks. Within the framework of adaptive boolean networks ciliated
+cells represent the nodes and as the mucus establishes the local interactions
+among nodes, its distribution determines the topology of the network.
+Furthermore, we present the results and insights from comprehensive parameter
+studies. The results show evidence that so called deterministic update schemes,
+which are meant to represent intercellular signaling, lead to more realistic
+and robust dynamics and may therefore be favored by nature. Finally, we suppose
+that unciliated cells introduce a modular network topology on ciliated
+epithelia causing the self-organization taking place simultaneously in each
+""ciliated module"". This reasoning provides the first consistent explanation for
+the meaning of the observed patchy expression patterns of the mucus modulation
+wave fields. Modularity may therefore be seen as a modular construction plan of
+nature for ciliated epithelia whose number of cells range over several order of
+magnitudes.
+"
+27612,Extragalactic Peaked-Spectrum Radio Sources at Low Frequencies,"  We present a sample of 1,483 sources that display spectral peaks between 72
+MHz and 1.4 GHz, selected from the GaLactic and Extragalactic All-sky Murchison
+Widefield Array (GLEAM) survey. The GLEAM survey is the widest fractional
+bandwidth all-sky survey to date, ideal for identifying peaked-spectrum sources
+at low radio frequencies. Our peaked-spectrum sources are the low frequency
+analogues of gigahertz-peaked spectrum (GPS) and compact-steep spectrum (CSS)
+sources, which have been hypothesized to be the precursors to massive radio
+galaxies. Our sample more than doubles the number of known peaked-spectrum
+candidates, and 95% of our sample have a newly characterized spectral peak. We
+highlight that some GPS sources peaking above 5 GHz have had multiple epochs of
+nuclear activity, and demonstrate the possibility of identifying high redshift
+($z > 2$) galaxies via steep optically thin spectral indices and low observed
+peak frequencies. The distribution of the optically thick spectral indices of
+our sample is consistent with past GPS/CSS samples but with a large dispersion,
+suggesting that the spectral peak is a product of an inhomogeneous environment
+that is individualistic. We find no dependence of observed peak frequency with
+redshift, consistent with the peaked-spectrum sample comprising both local CSS
+sources and high-redshift GPS sources. The 5 GHz luminosity distribution lacks
+the brightest GPS and CSS sources of previous samples, implying that a
+convolution of source evolution and redshift influences the type of
+peaked-spectrum sources identified below 1 GHz. Finally, we discuss sources
+with optically thick spectral indices that exceed the synchrotron
+self-absorption limit.
+"
+27613,Feedback Control For Cassie With Deep Reinforcement Learning,"  Bipedal locomotion skills are challenging to develop. Control strategies
+often use local linearization of the dynamics in conjunction with reduced-order
+abstractions to yield tractable solutions. In these model-based control
+strategies, the controller is often not fully aware of many details, including
+torque limits, joint limits, and other non-linearities that are necessarily
+excluded from the control computations for simplicity. Deep reinforcement
+learning (DRL) offers a promising model-free approach for controlling bipedal
+locomotion which can more fully exploit the dynamics. However, current results
+in the machine learning literature are often based on ad-hoc simulation models
+that are not based on corresponding hardware. Thus it remains unclear how well
+DRL will succeed on realizable bipedal robots. In this paper, we demonstrate
+the effectiveness of DRL using a realistic model of Cassie, a bipedal robot. By
+formulating a feedback control problem as finding the optimal policy for a
+Markov Decision Process, we are able to learn robust walking controllers that
+imitate a reference motion with DRL. Controllers for different walking speeds
+are learned by imitating simple time-scaled versions of the original reference
+motion. Controller robustness is demonstrated through several challenging
+tests, including sensory delay, walking blindly on irregular terrain and
+unexpected pushes at the pelvis. We also show we can interpolate between
+individual policies and that robustness can be improved with an interpolated
+policy.
+"
+27614,"Mapping the local spatial charge in defective diamond by means of NV sensors - A ""self-diagnostic"" concept","  Electrically-active defects have a significant impact on the performance of
+electronic devices based on wide band-gap materials such as diamond. This issue
+is ubiquitous in diamond science and technology, since the presence of charge
+traps in the active regions of different classes of diamond-based devices
+(detectors, power diodes, transistors) can significantly affect their
+performances, due to the formation of space charge, memory effects and the
+degradation of the electronic response associated with radiation damage. Among
+the most common defects in diamond, the nitrogen-vacancy (NV) center possesses
+unique spin properties which enable high-sensitivity field sensing at the
+nanoscale. Here we demonstrate that NV ensembles can be successfully exploited
+to perform a direct local mapping of the internal electric field distribution
+of a graphite-diamond-graphite junction exhibiting electrical properties
+dominated by trap- and space-charge-related conduction mechanisms. By
+performing optically-detected magnetic resonance measurements, we performed
+both punctual readout and spatial mapping of the electric field in the active
+region at different bias voltages. In this novel ""self-diagnostic"" approach,
+defect complexes represent not only the source of detrimental space charge
+effects, but also a unique tool to directly investigate them, by providing
+experimental evidences on the conduction mechanisms that in previous studies
+could only be indirectly inferred on the basis of conventional electrical and
+optical characterization.
+"
+27615,Field patterns without blow up,"  Field patterns, first proposed by the authors in [Milton, Mattei. Proc R Soc
+A. 2017], are orderly patterns of characteristic lines that arise in specific
+space-time microstructures whose geometry in one spatial dimension plus time is
+somehow commensurate to the slope of the characteristic lines. In particular,
+in [Milton, Mattei. Proc R Soc A. 2017] the authors propose two examples of
+space-time geometries in which field patterns occur: they are two-phase
+microstructures in which rectangular space-time inclusions of one material are
+embedded in another material. After a sufficient large period of time, the
+field patterns in both microstructures have local periodicity both in time and
+space, due to the PT-symmetry of the chosen space-time geometries. This allows
+one to focus only on solving the problem on a discrete network and to define a
+suitable transfer matrix that, given the solution at a certain time, provides
+the solution after one time period. Many of the eigenvalues of this transfer
+matrix have unit norm and hence the corresponding eigenvectors correspond to
+propagating modes. However, there are also modes that blow up exponentially
+with time coupled with modes that decrease exponentially with time. The
+question as to whether there are space-time microstructures such that the
+transfer matrix only has eigenvalues on the unit circle finds answer in this
+paper where we see that certain space-time checkerboards have the property that
+all the modes are propagating modes, within a certain range of the material
+parameters. Interestingly, when there is no blow-up, the waves generated by an
+instantaneous disturbance at a point look like shocks with a wake of
+oscillatory waves. This sets an arrow of time in the sense that it would be
+extremely difficult to produce the initial conditions necessary to create the
+corresponding time-reversed waves.
+"
+27616,A sampling theorem for functions in Besov spaces on spaces of homogeneous type,"  In this work we establish a sampling theorem for functions in Besov spaces on
+spaces of homogeneous type as defined in [HY] in the spirit of their recent
+counterpart for R d established by Jaming-Malinnikova in [JM]. The main tool is
+the wavelet decomposition presented by Deng-Han in [DH].
+"
+27617,Spin excitation anisotropy in the paramagnetic tetragonal phase of BaFe2 As2,"  We use neutron polarization analysis to study temperature dependence of the
+spin excitation anisotropy in BaFe$_2$As$_2$, which has a
+tetragonal-to-orthorhombic structural distortion at $T_s$ and antiferromagnetic
+(AF) phase transition at $T_N$ with ordered moments along the orthorhombic
+$a$-axis below $T_s\approx T_N\approx 136$ K. In the paramagnetic tetragonal
+state at 160 K, spin excitations are isotropic in spin space with
+$M_a=M_b=M_c$, where $M_a$, $M_b$, and $M_c$ are spin excitations polarized
+along the $a$, $b$, and $c$-axis directions of the orthorhombic lattice,
+respectively. On cooling towards $T_N$, significant spin excitation anisotropy
+with $M_a>M_b\approx M_c$ develops below 3 meV with a diverging $M_a$ at $T_N$.
+The in-plane spin excitation anisotropy in the tetragonal phase of
+BaFe$_2$As$_2$ is similar to those seen in the tetragonal phase of its electron
+and hole-doped superconductors, suggesting that spin excitation anisotropy is a
+direct probe of doping dependence of spin-orbit coupling and its connection to
+superconductivity in iron pnictides.
+"
+27618,Multivariate Gaussian and Student$-t$ Process Regression for Multi-output Prediction,"  Gaussian process model for vector-valued function has been shown to be useful
+for multi-output prediction. The existing method for this model is to
+re-formulate the matrix-variate Gaussian distribution as a multivariate normal
+distribution. Although it is effective in many cases, re-formulation is not
+always workable and is difficult to apply to other distributions because not
+all matrix-variate distributions can be transformed to respective multivariate
+distributions, such as the case for matrix-variate Student$-t$ distribution. In
+this paper, we propose a unified framework which is used not only to introduce
+a novel multivariate Student$-t$ process regression model (MV-TPR) for
+multi-output prediction, but also to reformulate the multivariate Gaussian
+process regression (MV-GPR) that overcomes some limitations of the existing
+methods. Both MV-GPR and MV-TPR have closed-form expressions for the marginal
+likelihoods and predictive distributions under this unified framework and thus
+can adopt the same optimization approaches as used in the conventional GPR. The
+usefulness of the proposed methods is illustrated through several simulated and
+real data examples. In particular, we verify empirically that MV-TPR has
+superiority for the datasets considered, including air quality prediction and
+bike rent prediction. At last, the proposed methods are shown to produce
+profitable investment strategies in the stock markets.
+"
+27619,Safe end-to-end imitation learning for model predictive control,"  We propose the use of Bayesian networks, which provide both a mean value and
+an uncertainty estimate as output, to enhance the safety of learned control
+policies under circumstances in which a test-time input differs significantly
+from the training set. Our algorithm combines reinforcement learning and
+end-to-end imitation learning to simultaneously learn a control policy as well
+as a threshold over the predictive uncertainty of the learned model, with no
+hand-tuning required. Corrective action, such as a return of control to the
+model predictive controller or human expert, is taken when the uncertainty
+threshold is exceeded. We validate our method on fully-observable and
+vision-based partially-observable systems using cart-pole and autonomous
+driving simulations using deep convolutional Bayesian neural networks. We
+demonstrate that our method is robust to uncertainty resulting from varying
+system dynamics as well as from partial state observability.
+"
+27620,Birational characterization of abelian varieties and ordinary abelian varieties in characteristic p>0,"  Let $k$ be an algebraically closed field of characteristic $p>0$. We give a
+birational characterization of ordinary abelian varieties over $k$: a smooth
+projective variety $X$ is birational to an ordinary abelian variety if and only
+if $\kappa_S(X)=0$ and $b_1(X)=2 \dim X$. We also give a similar
+characterization of abelian varieties as well: a smooth projective variety $X$
+is birational to an abelian variety if and only if $\kappa(X)=0$, and the
+Albanese morphism $a: X \to A$ is generically finite. Along the way, we also
+show that if $\kappa _S (X)=0$ (or if $\kappa(X)=0$ and $a$ is generically
+finite) then the Albanese morphism $a:X\to A$ is surjective and in particular
+$\dim A\leq \dim X$.
+"
+27621,Theoretical results on the topological properties of the limited penetrable horizontal visibility graph family,"  The limited penetrable horizontal visibility graph algorithm was recently
+introduced to map time series in complex networks. We extend this visibility
+graph and create a directed limited penetrable horizontal visibility graph and
+an image limited penetrable horizontal visibility graph. We define the two
+algorithms and provide theoretical results on the topological properties of
+these graphs associated with different types of real-value series (or
+matrices). We perform several numerical simulations to further check the
+accuracy of our theoretical results. Finally we present an application of the
+directed limited penetrable horizontal visibility graph for measuring
+real-value time series irreversibility, and an application of the image limited
+penetrable horizontal visibility graph that discriminates noise from chaos. The
+empirical results show the effectiveness of our proposed algorithms.
+"
+27622,On the Sum Capacity of Many-to-one and One-to-many Gaussian Interference Channels,"  We obtain new sum capacity results for the Gaussian many-to-one and
+one-to-many interference channels in channel parameter regimes where the sum
+capacity was known only up to a constant gap. Simple Han-Kobayashi (HK)
+schemes, i.e., HK schemes with Gaussian signaling, no time-sharing, and no
+common-private power splitting, achieve sum capacity under the channel
+conditions for which the new results are obtained. To obtain sum capacity
+results, we show that genie-aided upper bounds match the achievable sum rate of
+simple HK schemes under certain channel conditions.
+"
+27623,A pointing solution for the medium size telescopes for the Cherenkov Telescope Array,"  The pointing capability of a telescope in the Cherenkov Telescope Array (CTA)
+is a crucial aspect in the calibration of the instrument. It describes how a
+position in the sky is transformed to the focal plane of the telescope and
+allows precise directional reconstructions of atmospheric particle showers. The
+favoured approach for pointing calibrations of the Medium Size Telescopes (MST)
+is the utilisation of an CCD-camera installed in the centre of the dish, which
+images the night sky and the focal plane simultaneously. The technical
+implementation of this solution and test results taken over a period of one
+year at the MST prototype in Berlin/Adlershof are presented. Investigations of
+pointing calibration precision with simulated data and real data taken during
+test runs of the prototype telescope will also be shown.
+"
+27624,Fuel Minimisation for a Vehicle Equipped with a Flywheel and Battery on a Three-Dimensional Track,"  An optimal control based methodology is proposed for minimising the
+combustible fuel consumption of a hybrid vehicle equipped with an internal
+combustion engine, a high-speed flywheel and a battery. The
+three-dimensionality of the road is recognised by the optimal control
+calculations. Fuel efficiency is achieved by optimally exploiting the primary
+and secondary energy sources and controlling the vehicle so that the fuel
+consumption is minimised for a given, but arbitrary three-dimensional route. A
+time-of-arrival constraint rather than a driving cycle is used. The benefits of
+using multiple auxiliary storage systems are demonstrated and a lower-bound
+estimate of the fuel consumption is presented.
+"
+27625,Applications of the Laurent-Stieltjes constants for Dirichlet $L$-series,"  The Laurent Stieltjes constants $\gamma_n(\chi)$ are, up to a trivial
+coefficient, the coefficients of the Laurent expansion of the usual Dirichlet
+$L$-series: when $\chi$ is non principal, $(-1)^n\gamma_n(\chi)$ is simply the
+value of the $n$-th derivative of $L(s,\chi)$ at $s=1$. In this paper, we give
+an approximation of the Dirichlet L-functions in the neighborhood of $s=1$ by a
+short Taylor polynomial. We also prove that the Riemann zeta function
+$\zeta(s)$ has no zeros in the region $|s-1|\leq 2.2093,$ with $0\leq
+\Re{(s)}\leq 1.$
+"
+27626,A new method of verification of security protocols,"  In the paper we introduce a process model of security protocols, where
+processes are graphs with edges labelled by actions, and present a new method
+of specification and verification of security protocols based on this model.
+"
+27627,On A New Convergence Class in Sup-sober Spaces,"  Recently, J. D. Lawson encouraged the domain theory community to consider the
+scientific program of developing domain theory in the wider context of
+$T_0$-spaces instead of restricting to posets. In this paper, we respond to
+this calling by proving a topological parallel of a 2005 result due to B. Zhao
+and D. Zhao, i.e., an order-theoretic characterisation of those posets for
+which the Scott-convergence is topological. We do this by adopting a recent
+approach due to D. Zhao and W. K. Ho by replacing directed subsets with
+irreducible sets. As a result, we formulate a new convergence class
+$\mathcal{I}$ in $T_0$-spaces called ${\operatorname{Irr}}$-convergence and
+establish that a sup-sober space $X$ is ${\operatorname{SI}}^{-}$-continuous if
+and only if it satisfies $*$-property and the convergence class $\mathcal{I}$
+in it is topological.
+"
+27628,Design paradigms of intelligent control systems on a chip,"  This paper focuses on the Field Programmable Gate Array (FPGA) design and
+implementation of intelligent control system applications on a chip,
+specifically fuzzy logic and genetic algorithm processing units. Initially, an
+overview of the FPGA technology is presented, followed by design methodologies,
+development tools and the use of hardware description languages (HDL). Two FPGA
+design examples with the use of Hardware Description Languages (HDLs) of
+parameterized fuzzy logic controller cores are discussed. Thereinafter, a
+System-on-a-Chip (SoC) designed by the authors in previous work and realized on
+FPGA featuring a Digital Fuzzy Logic Controller (DFLC) and a soft processor
+core for the path tracking problem of mobile robots is discussed. Finally a
+Genetic Algorithm implementation (previously published by the authors) in FPGA
+chip for the Traveling Salesman Problem (TSP) is also discussed.
+"
+27629,SelectiveNet: A Deep Neural Network with an Integrated Reject Option,"  We consider the problem of selective prediction (also known as reject option)
+in deep neural networks, and introduce SelectiveNet, a deep neural architecture
+with an integrated reject option. Existing rejection mechanisms are based
+mostly on a threshold over the prediction confidence of a pre-trained network.
+In contrast, SelectiveNet is trained to optimize both classification (or
+regression) and rejection simultaneously, end-to-end. The result is a deep
+neural network that is optimized over the covered domain. In our experiments,
+we show a consistently improved risk-coverage trade-off over several well-known
+classification and regression datasets, thus reaching new state-of-the-art
+results for deep selective classification.
+"
+27630,On Using Backpropagation for Speech Texture Generation and Voice Conversion,"  Inspired by recent work on neural network image generation which rely on
+backpropagation towards the network inputs, we present a proof-of-concept
+system for speech texture synthesis and voice conversion based on two
+mechanisms: approximate inversion of the representation learned by a speech
+recognition neural network, and on matching statistics of neuron activations
+between different source and target utterances. Similar to image texture
+synthesis and neural style transfer, the system works by optimizing a cost
+function with respect to the input waveform samples. To this end we use a
+differentiable mel-filterbank feature extraction pipeline and train a
+convolutional CTC speech recognition network. Our system is able to extract
+speaker characteristics from very limited amounts of target speaker data, as
+little as a few seconds, and can be used to generate realistic speech babble or
+reconstruct an utterance in a different voice.
+"
+27631,"Cost-Based Intuitionist Probabilities on Spaces of Graphs, Hypergraphs and Theorems","  A novel partial order is defined on the space of digraphs or hypergraphs,
+based on assessing the cost of producing a graph via a sequence of elementary
+transformations. Leveraging work by Knuth and Skilling on the foundations of
+inference, and the structure of Heyting algebras on graph space, this partial
+order is used to construct an intuitionistic probability measure that applies
+to either digraphs or hypergraphs. As logical inference steps can be
+represented as transformations on hypergraphs representing logical statements,
+this also yields an intuitionistic probability measure on spaces of theorems.
+The central result is also extended to yield intuitionistic probabilities based
+on more general weighted rule systems defined over bicartesian closed
+categories.
+"
+27632,An improved Moser-Trudinger inequality involving the first non-zero Neumann eigenvalue with mean value zero in $\mathbf R^2$,"  Let $\Omega$ be a smooth bounded domain in $\mathbf R^2$ and
+$\lambda^{\mathsf N} (\Omega)$ the first non-zero Neumann eigenvalue of the
+operator $-\Delta$ on $\Omega$. In this paper, for any $\gamma \in [0,
+\lambda^{\mathsf N} (\Omega) )$, we establish the following improved
+Moser-Trudinger inequality \[ \sup_{u} \int_{\Omega} e^{2\pi u^2} dx < +\infty
+\] for arbitrary functions $u$ in $H^1(\Omega)$ satisfying $\int_\Omega u dx
+=0$ and $\|\nabla u\|_2^2 -\alpha \|u\|_2^2 \leqslant 1$. Furthermore, this
+supremum is attained by some function $u^*\in H^1(\Omega)$. This strengthens
+the results of Chang and Yang (J. Differential Geom. 27 (1988) 259-296) and of
+Lu and Yang (Nonlinear Anal. 70 (2009) 2992-3001).
+"
+27633,Magnetization and the Concurrence of the Spin-1/2 Ising-Heisenberg Pyrochlore Ladder,"  We have established a quantum antiferromagnetic Heisenberg-Ising model on a
+spin-1/2 pyrochlore edge-shared ladder with Heisenberg intra-rung and Ising
+inter-rung interactions as a perspicuous candidate to exhibit magnetization mid
+and zero plateaus, characteristic peaks of magnetic susceptibility, and thermal
+entanglement mid plateau. The model is exactly solvable and thus, all the
+essential properties such as the thermal entanglement and the magnetic
+properties of the system can be exactly calculated. The calculations are done
+both through the transfer matrix technique and through the reduced density
+matrix. The magnetization plateaus are observed at zero and half the saturation
+value and the magnetic susceptibility exhibits a clear demonstration of the
+associated characteristic peaks. The model also displays the mid plateau of the
+thermal entanglement as a function of the external magnetic field at low
+temperatures.
+"
+27634,A development of superconducting differential double contour interferometer,"  We study operation of a new device, the superconducting differential double
+contour interferometer (DDCI), in application for the ultra sensitive detection
+of magnetic flux and for digital read out of the state of the superconducting
+flux qubit. DDCI consists of two superconducting contours weakly coupled by
+Josephson Junctions. In such a device a change of the critical current and the
+voltage happens in a step-like manner when the angular momentum quantum number
+changes in one of the two contours. The DDCI may outperform traditional
+Superconducting Quantum Interference Devices when the change of the quantum
+number occurs in a narrow magnetic field region near the half of the flux
+quantum due to thermal fluctuations, quantum fluctuations, or the switching a
+loop segment in the normal state for a while by short pulse of an external
+current.
+"
+27635,Genetic algorithms in Forth,"  A method for automatically finding a program (bytecode) realizing the given
+algorithm is developed. The algorithm is specified as a set of tests
+(input\_data) $ \rightarrow $ (output\_data). Genetic methods made it possible
+to find the implementation of relatively complex algorithms: sorting, decimal
+digits, GCD, LCM, factorial, prime divisors, binomial coefficients, and others.
+The algorithms are implemented on a highly simplified version of Forth
+language.
+"
+27636,Recent Advances in Zero-shot Recognition,"  With the recent renaissance of deep convolution neural networks, encouraging
+breakthroughs have been achieved on the supervised recognition tasks, where
+each class has sufficient training data and fully annotated training data.
+However, to scale the recognition to a large number of classes with few or now
+training samples for each class remains an unsolved problem. One approach to
+scaling up the recognition is to develop models capable of recognizing unseen
+categories without any training instances, or zero-shot recognition/ learning.
+This article provides a comprehensive review of existing zero-shot recognition
+techniques covering various aspects ranging from representations of models, and
+from datasets and evaluation settings. We also overview related recognition
+tasks including one-shot and open set recognition which can be used as natural
+extensions of zero-shot recognition when limited number of class samples become
+available or when zero-shot recognition is implemented in a real-world setting.
+Importantly, we highlight the limitations of existing approaches and point out
+future research directions in this existing new research area.
+"
+27637,"Weak Modular Product of Bipartite Graphs, Bicliques and Isomorphism","  A 1978 theorem of Kozen states that two graphs on $n$ vertices are isomorphic
+if and only if there is a clique of size $n$ in the weak modular product
+between the two graphs. Restricting to bipartite graphs and considering
+complete bipartite subgraphs (bicliques) therein, we study the combinatorics of
+the weak modular product. We identify cases where isomorphism is tractable
+using this approach, which we call Isomorphism via Biclique Enumeration (IvBE).
+We find that IvBE is polynomial for bipartite $2K_2$-free graphs and
+quasi-polynomial for families of bipartite graphs, where the largest induced
+matching and largest induced crown graph grows slowly in $n$, that is,
+$O(\mathrm{polylog }\, n)$. Furthermore, as expected a straightforward
+corollary of Kozen's theorem and Lovász's sandwich theorem is if the weak
+modular product between two graphs is perfect, then checking if the graphs are
+isomorphic is polynomial in $n$. However, we show that for balanced, bipartite
+graphs this is only true in a few trivial cases. In doing so we define a new
+graph product on bipartite graphs, the very weak modular product. The results
+pertaining to bicliques in bipartite graphs proved here may be of independent
+interest.
+"
+27638,Multiband Dual Fermion Approach to Quantum Criticality in the Hubbard Honeycomb Lattice,"  We study the Hubbard model on the honeycomb lattice in the vicinity of the
+quantum critical point by means of a multiband formulation of the Dual Fermion
+approach. Beyond the strong local correlations of the dynamical mean field,
+critical fluctuations on all length scales are included by means of a ladder
+diagram summation. Analysis of the susceptibility yields an estimate of the
+critical interaction strength of the quantum phase transition from a
+paramagnetic semimetal to an antiferromagnetic insulator, in good agreement to
+other numerical methods. We further estimate the crossover temperature to the
+renormalized classical regime. Our data imply that, at large interaction
+strengths, the Hubbard model on the honeycomb lattice behaves like a quantum
+nonlinear sigma model, while being in a non-Fermi liquid state.
+"
+27639,Decision-Making and Biases in Cybersecurity Capability Development: Evidence from a Simulation Game Experiment,"  We developed a simulation game to study the effectiveness of decision-makers
+in overcoming two complexities in building cybersecurity capabilities:
+potential delays in capability development; and uncertainties in predicting
+cyber incidents. Analyzing 1,479 simulation runs, we compared the performances
+of a group of experienced professionals with those of an inexperienced control
+group. Experienced subjects did not understand the mechanisms of delays any
+better than inexperienced subjects; however, experienced subjects were better
+able to learn the need for proactive decision-making through an iterative
+process. Both groups exhibited similar errors when dealing with the uncertainty
+of cyber incidents. Our findings highlight the importance of training for
+decision-makers with a focus on systems thinking skills, and lay the groundwork
+for future research on uncovering mental biases about the complexities of
+cybersecurity.
+"
+27640,Low-Energy Deep Belief Networks using Intrinsic Sigmoidal Spintronic-based Probabilistic Neurons,"  A low-energy hardware implementation of deep belief network (DBN)
+architecture is developed using near-zero energy barrier probabilistic spin
+logic devices (p-bits), which are modeled to realize an intrinsic sigmoidal
+activation function. A CMOS/spin based weighted array structure is designed to
+implement a restricted Boltzmann machine (RBM). Device-level simulations based
+on precise physics relations are used to validate the sigmoidal relation
+between the output probability of a p-bit and its input currents.
+Characteristics of the resistive networks and p-bits are modeled in SPICE to
+perform a circuit-level simulation investigating the performance, area, and
+power consumption tradeoffs of the weighted array. In the application-level
+simulation, a DBN is implemented in MATLAB for digit recognition using the
+extracted device and circuit behavioral models. The MNIST data set is used to
+assess the accuracy of the DBN using 5,000 training images for five distinct
+network topologies. The results indicate that a baseline error rate of 36.8%
+for a 784x10 DBN trained by 100 samples can be reduced to only 3.7% using a
+784x800x800x10 DBN trained by 5,000 input samples. Finally, Power dissipation
+and accuracy tradeoffs for probabilistic computing mechanisms using resistive
+devices are identified.
+"
+27641,Semantic Augmented Reality Environment with Material-Aware Physical Interactions,"  In Augmented Reality (AR) environment, realistic interactions between the
+virtual and real objects play a crucial role in user experience. Much of recent
+advances in AR has been largely focused on developing geometry-aware
+environment, but little has been done in dealing with interactions at the
+semantic level. High-level scene understanding and semantic descriptions in AR
+would allow effective design of complex applications and enhanced user
+experience. In this paper, we present a novel approach and a prototype system
+that enables the deeper understanding of semantic properties of the real world
+environment, so that realistic physical interactions between the real and the
+virtual objects can be generated. A material-aware AR environment has been
+created based on the deep material learning using a fully convolutional network
+(FCN). The state-of-the-art dense Simultaneous Localisation and Mapping (SLAM)
+has been used for the semantic mapping. Together with efficient accelerated 3D
+ray casting, natural and realistic physical interactions are generated for
+interactive AR games. Our approach has significant impact on the future
+development of advanced AR systems and applications.
+"
+27642,Neural-Symbolic Learning and Reasoning: A Survey and Interpretation,"  The study and understanding of human behaviour is relevant to computer
+science, artificial intelligence, neural computation, cognitive science,
+philosophy, psychology, and several other areas. Presupposing cognition as
+basis of behaviour, among the most prominent tools in the modelling of
+behaviour are computational-logic systems, connectionist models of cognition,
+and models of uncertainty. Recent studies in cognitive science, artificial
+intelligence, and psychology have produced a number of cognitive models of
+reasoning, learning, and language that are underpinned by computation. In
+addition, efforts in computer science research have led to the development of
+cognitive computational systems integrating machine learning and automated
+reasoning. Such systems have shown promise in a range of applications,
+including computational biology, fault diagnosis, training and assessment in
+simulators, and software verification. This joint survey reviews the personal
+ideas and views of several researchers on neural-symbolic learning and
+reasoning. The article is organised in three parts: Firstly, we frame the scope
+and goals of neural-symbolic computation and have a look at the theoretical
+foundations. We then proceed to describe the realisations of neural-symbolic
+computation, systems, and applications. Finally we present the challenges
+facing the area and avenues for further research.
+"
+27643,BranchHull: Convex bilinear inversion from the entrywise product of signals with known signs,"  We consider the bilinear inverse problem of recovering two vectors, $x$ and
+$w$, in $\mathbb{R}^L$ from their entrywise product. For the case where the
+vectors have known signs and belong to known subspaces, we introduce the convex
+program BranchHull, which is posed in the natural parameter space that does not
+require an approximate solution or initialization in order to be stated or
+solved. Under the structural assumptions that $x$ and $w$ are members of known
+$K$ and $N$ dimensional random subspaces, we present a recovery guarantee for
+the noiseless case and a noisy case. In the noiseless case, we prove that the
+BranchHull recovers $x$ and $w$ up to the inherent scaling ambiguity with high
+probability when $L\ \gg\ 2(K+N)$. The analysis provides a precise upper bound
+on the coefficient for the sample complexity. In a noisy case, we show that
+with high probability the BranchHull is robust to small dense noise when $L =
+\Omega(K+N)$. BranchHull is motivated by the sweep distortion removal task in
+dielectric imaging, where one of the signals is a nonnegative reflectivity, and
+the other signal lives in a known wavelet subspace. Additional potential
+applications are blind deconvolution and self-calibration.
+"
+27644,On a property of norming constants of Sturm-Liouville problem,"  A connection, which shows the dependence of norming constants on boundary
+conditions, was found using the Gelfand-Levitan method for the solution of
+inverse Sturm-Liouville problem.
+"
+27645,A conjecture for the regularized fourth moment of Eisenstein series,"  We formulate a version of the Random Wave Conjecture for the fourth moment of
+Eisenstein series which is based on Zagier's regularized inner product. We
+prove an asymptotic formula expressing the regularized fourth moment as a mean
+value of L-functions. This is an advantage over previous work in the
+literature, which has approached the fourth moment problem through truncated
+Eisenstein series and not yielded a suitable expression in terms of
+L-functions.
+"
+27646,Device Placement Optimization with Reinforcement Learning,"  The past few years have witnessed a growth in size and computational
+requirements for training and inference with neural networks. Currently, a
+common approach to address these requirements is to use a heterogeneous
+distributed environment with a mixture of hardware devices such as CPUs and
+GPUs. Importantly, the decision of placing parts of the neural models on
+devices is often made by human experts based on simple heuristics and
+intuitions. In this paper, we propose a method which learns to optimize device
+placement for TensorFlow computational graphs. Key to our method is the use of
+a sequence-to-sequence model to predict which subsets of operations in a
+TensorFlow graph should run on which of the available devices. The execution
+time of the predicted placements is then used as the reward signal to optimize
+the parameters of the sequence-to-sequence model. Our main result is that on
+Inception-V3 for ImageNet classification, and on RNN LSTM, for language
+modeling and neural machine translation, our model finds non-trivial device
+placements that outperform hand-crafted heuristics and traditional algorithmic
+methods.
+"
+27647,The low-energy positron scattering upon endohedrals,"  We investigate positron scattering upon endohedrals and compare it with
+electron-endohedral scattering. We show that the polarization of the fullerene
+shell considerably alters the polarization potential of an atom, stuffed inside
+a fullerene. This essentially affects both the positron and electron elastic
+scattering phases as well as corresponding cross-sections. Of great importance
+is also the interaction between the incoming positron and the target electrons
+that leads to formation the virtual positronium. We illustrate the general
+trend by concrete examples of positron and electron scattering upon endohedrals
+He@C60 and Ar@C60, and compare it to scattering upon fullerene C60. To obtain
+the presented results, we have employed new simplified approaches that permit
+to incorporate the effect of fullerenes polarizability into the He@C60 and
+Ar@C60 polarization potential and to take into account the virtual positronium
+formation. Using these approaches, we obtained numeric results that show strong
+variations in shape and magnitudes of scattering phases and cross-sections due
+to effect of endohedral polarization and virtual positronium formation.
+"
+27648,Torsion effects on Condensed Matter: like a magnetic field but not so much,"  In this work, we study the effects of torsion due to a uniform distribution
+of topological defects (screw dislocations) on free spin/carrier dynamics in
+elastic solids. When a particle moves in such a medium, the effect of the
+torsion associated to the defect distribution is analogous to that of an
+applied magnetic field but with subtle differences. Analogue Landau levels are
+present in this system but they cannot be confined to two dimensions. In the
+case of spinless carriers, zero modes, which do not appear in the magnetic
+Landau levels, show up for quantized values of the linear momentum projected on
+the defects axis. Particles with spin are subjected to a Zeeman-like coupling
+between spin and torsion, which is insensitive to charge. This suggests the
+possibility of spin resonance experiments without a magnetic field for charged
+carriers or quasiparticles without electrical charge, like triplet excitons,
+for instance.
+"
+27649,Distributive Laws for Monotone Specifications,"  Turi and Plotkin introduced an elegant approach to structural operational
+semantics based on universal coalgebra, parametric in the type of syntax and
+the type of behaviour. Their framework includes abstract GSOS, a categorical
+generalisation of the classical GSOS rule format, as well as its categorical
+dual, coGSOS. Both formats are well behaved, in the sense that each
+specification has a unique model on which behavioural equivalence is a
+congruence. Unfortunately, the combination of the two formats does not feature
+these desirable properties. We show that monotone specifications - that
+disallow negative premises - do induce a canonical distributive law of a monad
+over a comonad, and therefore a unique, compositional interpretation.
+"
+27650,Finite size scaling of density of states in photonic bandgap crystals,"  The famous vanishing of the density of states (DOS) in a band gap, be it
+photonic or electronic, pertains to the infinite-crystal limit. In contrast,
+all experiments and device applications refer to finite crystals, which raises
+the question: Upon increasing the linear size $L$ of a crystal, how fast does
+the DOS approach the infinite-crystal limit? We present a theory for finite
+crystals that includes Bloch-mode broadening due to the presence of crystal
+boundaries. Our results demonstrate that the DOS for frequencies inside a band
+gap has a $1/L$ scale dependence for crystals in one, two and three dimensions.
+"
+27651,Energy spectrum of localized quasiparticles renormalized by multi-phonon processes at finite temperature,"  The theory of renormalized energy spectrum of localized quasi-particle
+interacting with polarization phonons at finite temperature is developed within
+the Feynman-Pines diagram technique. The created computer program effectively
+takes into account multi-phonon processes, exactly defining all diagrams of
+mass operator together with their analytical expressions in arbitrary order
+over the coupling constant. Now it is possible to separate the pole and
+non-pole mass operator terms and perform a partial summing of their main terms.
+The renormalized spectrum of the system is obtained within the solution of
+dispersion equation in the vicinity of the main state where the high- and
+low-energy complexes of bound states are observed. The properties of the
+spectrum are analyzed depending on the coupling constant and the temperature.
+"
+27652,On the Model Shrinkage Effect of Gamma Process Edge Partition Models,"  The edge partition model (EPM) is a fundamental Bayesian nonparametric model
+for extracting an overlapping structure from binary matrix. The EPM adopts a
+gamma process ($\Gamma$P) prior to automatically shrink the number of active
+atoms. However, we empirically found that the model shrinkage of the EPM does
+not typically work appropriately and leads to an overfitted solution. An
+analysis of the expectation of the EPM's intensity function suggested that the
+gamma priors for the EPM hyperparameters disturb the model shrinkage effect of
+the internal $\Gamma$P. In order to ensure that the model shrinkage effect of
+the EPM works in an appropriate manner, we proposed two novel generative
+constructions of the EPM: CEPM incorporating constrained gamma priors, and DEPM
+incorporating Dirichlet priors instead of the gamma priors. Furthermore, all
+DEPM's model parameters including the infinite atoms of the $\Gamma$P prior
+could be marginalized out, and thus it was possible to derive a truly infinite
+DEPM (IDEPM) that can be efficiently inferred using a collapsed Gibbs sampler.
+We experimentally confirmed that the model shrinkage of the proposed models
+works well and that the IDEPM indicated state-of-the-art performance in
+generalization ability, link prediction accuracy, mixing efficiency, and
+convergence speed.
+"
+27653,The electromagnetic waves propagation in unmagnetized plasma media using parallelized finite-difference time-domain method,"  The finite-difference time-domain (FDTD) method has been commonly utilized to
+simulate the electromagnetic (EM) waves propagation in the plasma media.
+However, the FDTD method may bring about extra run-time on concerning
+computationally large and complicated EM problems. Fortunately, the FDTD method
+is easy to parallelize. Besides, GPU has been widely used for parallel
+computing due to its unique SPMD (Single Program Multiple Data) architecture.
+In this paper, we represent the parallel Runge-Kutta exponential time
+differencing scheme FDTD (RKETD) method for the unmagnetized plasma implemented
+on GPU. The detailed flowchart of parallel RKETD-FDTD method is described. The
+accuracy and acceleration performance of the proposed parallel RKETD-FDTD
+method implemented on GPU are substantiated by calculating the reflection and
+transmission coefficients for one-dimensional unmagnetized plasma slab. The
+results indicate that the numerical precision of the parallel RKETD-FDTD scheme
+is consistent with that of the code implemented on CPU. The computation
+efficiency is greatly improved compared with merely CPU-based serial RKETD-FDTD
+method. Moreover, the comparisons of the performance of CUDA-based GPU parallel
+program, OpenMP (Open Multi-Processing)-based CPU parallel program, and
+single-CPU serial program on the same host computer are done. Compared with the
+serial program, both parallel programs get good results, while GPU-based
+parallel program gains better result.
+"
+27654,Optimal Repair Schemes for Some Families of Full-Length Reed-Solomon Codes,"  Reed-Solomon codes have found many applications in practical storage systems,
+but were until recently considered unsuitable for distributed storage
+applications due to the widely-held belief that they have poor repair
+bandwidth. The work of Guruswami and Wootters (STOC'16) has shown that one can
+actually perform bandwidth-efficient linear repair with Reed-Solomon codes:
+When the codes are over the field $\mathbb{F}_{q^t}$ and the number of parities
+$r \geq q^s$, where $(t-s)$ divides $t$, there exists a linear scheme that
+achieves a repair bandwidth of $(n-1)(t-s)\log_2 q$ bits. We extend this result
+by showing the existence of such a linear repair scheme for every $1 \leq s <
+t$. Moreover, our new schemes are optimal among all linear repair schemes for
+Reed-Solomon codes when $n = q^t$ and $r = q^s$. Additionally, we improve the
+lower bound on the repair bandwidth for Reed-Solomon codes, also established in
+the work of Guruswami and Wootters.
+"
+27655,Some features of bending of a rod under a strong longitudinal compression,"  Considered typical processes of rod bending under strong longitudinal
+compression. The dynamic equation of bending correspponds to a perturbation of
+the two- dimensional Laplace equation. It is established that, for these
+processes, expanding of do- mains of rapid increasing of bending begins in
+small neighborhoods of singularity points of solutions of the limiting
+Laplace's equation. The initial stages of these increases are described using
+the Hardy integral.
+"
+27656,An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise,"  This paper addresses tracking of a moving target in a multi-agent network.
+The target follows a linear dynamics corrupted by an adversarial noise, i.e.,
+the noise is not generated from a statistical distribution. The location of the
+target at each time induces a global time-varying loss function, and the global
+loss is a sum of local losses, each of which is associated to one agent. Agents
+noisy observations could be nonlinear. We formulate this problem as a
+distributed online optimization where agents communicate with each other to
+track the minimizer of the global loss. We then propose a decentralized version
+of the Mirror Descent algorithm and provide the non-asymptotic analysis of the
+problem. Using the notion of dynamic regret, we measure the performance of our
+algorithm versus its offline counterpart in the centralized setting. We prove
+that the bound on dynamic regret scales inversely in the network spectral gap,
+and it represents the adversarial noise causing deviation with respect to the
+linear dynamics. Our result subsumes a number of results in the distributed
+optimization literature. Finally, in a numerical experiment, we verify that our
+algorithm can be simply implemented for multi-agent tracking with nonlinear
+observations.
+"
+27657,Recurrent Predictive State Policy Networks,"  We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent
+architecture that brings insights from predictive state representations to
+reinforcement learning in partially observable environments. Predictive state
+policy networks consist of a recursive filter, which keeps track of a belief
+about the state of the environment, and a reactive policy that directly maps
+beliefs to actions, to maximize the cumulative reward. The recursive filter
+leverages predictive state representations (PSRs) (Rosencrantz and Gordon,
+2004; Sun et al., 2016) by modeling predictive state-- a prediction of the
+distribution of future observations conditioned on history and future actions.
+This representation gives rise to a rich class of statistically consistent
+algorithms (Hefny et al., 2018) to initialize the recursive filter. Predictive
+state serves as an equivalent representation of a belief state. Therefore, the
+policy component of the RPSP-network can be purely reactive, simplifying
+training while still allowing optimal behaviour. Moreover, we use the PSR
+interpretation during training as well, by incorporating prediction error in
+the loss function. The entire network (recursive filter and reactive policy) is
+still differentiable and can be trained using gradient based methods. We
+optimize our policy using a combination of policy gradient based on rewards
+(Williams, 1992) and gradient descent based on prediction error. We show the
+efficacy of RPSP-networks under partial observability on a set of robotic
+control tasks from OpenAI Gym. We empirically show that RPSP-networks perform
+well compared with memory-preserving networks such as GRUs, as well as finite
+memory models, being the overall best performing method.
+"
+27658,Orthogonal Matching Pursuit for Text Classification,"  In text classification, the problem of overfitting arises due to the high
+dimensionality, making regularization essential. Although classic regularizers
+provide sparsity, they fail to return highly accurate models. On the contrary,
+state-of-the-art group-lasso regularizers provide better results at the expense
+of low sparsity. In this paper, we apply a greedy variable selection algorithm,
+called Orthogonal Matching Pursuit, for the text classification task. We also
+extend standard group OMP by introducing overlapping Group OMP to handle
+overlapping groups of features. Empirical analysis verifies that both OMP and
+overlapping GOMP constitute powerful regularizers, able to produce effective
+and very sparse models. Code and data are available online:
+this https URL .
+"
+27659,Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review,"  In this survey paper, we systematically summarize the current literature on
+studies that apply machine learning (ML) and data mining techniques to bearing
+fault diagnostics. Conventional ML methods, including artificial neural network
+(ANN), principal component analysis (PCA), support vector machines (SVM), etc.,
+have been successfully applied to detecting and categorizing bearing faults
+since the last decade, while the application of deep learning (DL) methods has
+sparked great interest in both the industry and academia in the last five
+years. In this paper, we will first review the conventional ML methods, before
+taking a deep dive into the latest developments in DL algorithms for bearing
+fault applications. Specifically, the superiority of the DL based methods over
+the conventional ML methods are analyzed in terms of metrics directly related
+to fault feature extraction and classifier performances; the new
+functionalities offered by DL techniques that cannot be accomplished before are
+also summarized. In addition, to obtain a more intuitive insight, a comparative
+study is performed on the classifier performance and accuracy for a number of
+papers utilizing the open source Case Western Reserve University (CWRU) bearing
+data set. Finally, based on the nature of the time-series 1-D data obtained
+from sensors monitoring the bearing conditions, recommendations and suggestions
+are provided to applying DL algorithms on bearing fault diagnostics based on
+specific applications, as well as future research directions to further improve
+its performance.
+"
+27660,Quick X-ray microtomography using a laser-driven betatron source,"  Laser-driven X-ray sources are an emerging alternative to conventional X-ray
+tubes and synchrotron sources. We present results on microtomographic X-ray
+imaging of a cancellous human bone sample using synchrotron-like betatron
+radiation. The source is driven by a 100-TW-class titanium-sapphire laser
+system and delivers over $10^8$ X-ray photons per second. Compared to earlier
+studies, the acquisition time for an entire tomographic dataset has been
+reduced by more than an order of magnitude. Additionally, the reconstruction
+quality benefits from the use of statistical iterative reconstruction
+techniques. Depending on the desired resolution, tomographies are thereby
+acquired within minutes, which is an important milestone towards real-life
+applications of laser-plasma X-ray sources.
+"
+27661,Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise,"  In many real-world optimization problems, the objective function evaluation
+is subject to noise, and we cannot obtain the exact objective value.
+Evolutionary algorithms (EAs), a type of general-purpose randomized
+optimization algorithm, have shown able to solve noisy optimization problems
+well. However, previous theoretical analyses of EAs mainly focused on
+noise-free optimization, which makes the theoretical understanding largely
+insufficient. Meanwhile, the few existing theoretical studies under noise often
+considered the one-bit noise model, which flips a randomly chosen bit of a
+solution before evaluation; while in many realistic applications, several bits
+of a solution can be changed simultaneously. In this paper, we study a natural
+extension of one-bit noise, the bit-wise noise model, which independently flips
+each bit of a solution with some probability. We analyze the running time of
+the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first
+time, and derive the ranges of the noise level for polynomial and
+super-polynomial running time bounds. The analysis on LeadingOnes under
+bit-wise noise can be easily transferred to one-bit noise, and improves the
+previously known results. Since our analysis discloses that the (1+1)-EA can be
+efficient only under low noise levels, we also study whether the sampling
+strategy can bring robustness to noise. We prove that using sampling can
+significantly increase the largest noise level allowing a polynomial running
+time, that is, sampling is robust to noise.
+"
+27662,Optimality condition and complexity analysis for linearly-constrained optimization without differentiability on the boundary,"  In this paper we consider the minimization of a continuous function that is
+potentially not differentiable or not twice differentiable on the boundary of
+the feasible region. By exploiting an interior point technique, we present
+first- and second-order optimality conditions for this problem that reduces to
+classical ones when the derivative on the boundary is available. For this type
+of problems, existing necessary conditions often rely on the notion of
+subdifferential or become non-trivially weaker than the KKT condition in the
+(twice-)differentiable counterpart problems. In contrast, this paper presents a
+new set of first- and second-order necessary conditions that are derived
+without the use of subdifferential and reduces to exactly the KKT condition
+when (twice-)differentiability holds. As a result, these conditions are
+stronger than some existing ones considered for the discussed minimization
+problem when only non-negativity constraints are present. To solve for these
+optimality conditions in the special but important case of linearly constrained
+problems, we present two novel interior trust-region point algorithms and show
+that their worst-case computational efficiency in achieving the potentially
+stronger optimality conditions match the best known complexity bounds. Since
+this work considers a more general problem than the literature, our results
+also indicate that best known complexity bounds hold for a wider class of
+nonlinear programming problems.
+"
+27663,A Decision Support System for Communication Network Analysis Based on Hybrid Petri Net Models,"  It is well known that the complex system operation requires the use of new
+scientific tools and computer simulation. This paper presents a modular
+approach for modeling and analysis of the complex systems (in communication or
+transport systems area) using Hybrid Petri nets. The performance evaluation of
+the hybrid model is made by a simulation methodology that allows building up
+various functioning scenarios. A Decision Support System based on the above
+mentioned methodologies of modeling and analysis will be designed for
+performance evaluation and time optimization of large scale communication
+and/or transport systems.
+"
+27664,The sample complexity of multi-reference alignment,"  The growing role of data-driven approaches to scientific discovery has
+unveiled a large class of models that involve latent transformations with a
+rigid algebraic constraint. Among them, multi-reference alignment (MRA) is a
+simple model that captures fundamental aspects of the statistical and
+algorithmic challenges arising from this new paradigm. In this model, an
+unknown signal is subject to two types of corruption: a latent cyclic shift and
+the more traditional additive white noise. The goal is to recover the signal at
+a certain precision from independent samples. While at high signal-to-noise
+ratio (SNR), the number of observations needed to recover a generic signal is
+proportional to 1/SNR, we show that it rises to 1/SNR^3 in the more realistic
+low SNR regime. We propose an algorithm that achieves this optimal dependence
+on the SNR. Furthermore, we extend our results to cover a heterogeneous MRA
+model where the samples come from a mixture of signals, as is often the case in
+applications such as Cryo-Electron Microscopy, where molecules may have
+different conformations. We provide the first known procedure that provably
+achieves signal recovery in the low SNR regime for heterogeneous MRA.
+"
+27665,Novel Baseband Equivalent Models of Quadrature Modulated All-Digital Transmitters,"  In this paper an exact baseband equivalent model of a quadrature modulated
+all-digital transmitter is derived. No restrictions on the number of levels of
+a digital switched-mode power amplifier (SMPA) driving input, nor the pulse
+encoding scheme employed, are made. This implies a high level of generality of
+the proposed model. We show that all-digital transmitter (ADT) can be
+represented as a series connection of the pulse encoder, discrete-time Volterra
+series model of fixed degree and memory depth, and a linear time-varying system
+with special properties. This result suggests a new analytically motivated
+structure of a digital predistortion (DPD) of SMPA nonlinearities in ADT.
+Numerical simulations in MATLAB are used to verify proposed baseband equivalent
+model.
+"
+27666,Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification,"  Topological data analysis (TDA) has emerged as one of the most promising
+techniques to reconstruct the unknown shapes of high-dimensional spaces from
+observed data samples. TDA, thus, yields key shape descriptors in the form of
+persistent topological features that can be used for any supervised or
+unsupervised learning task, including multi-way classification. Sparse
+sampling, on the other hand, provides a highly efficient technique to
+reconstruct signals in the spatial-temporal domain from just a few
+carefully-chosen samples. Here, we present a new method, referred to as the
+Sparse-TDA algorithm, that combines favorable aspects of the two techniques.
+This combination is realized by selecting an optimal set of sparse pixel
+samples from the persistent features generated by a vector-based TDA algorithm.
+These sparse samples are selected from a low-rank matrix representation of
+persistent features using QR pivoting. We show that the Sparse-TDA method
+demonstrates promising performance on three benchmark problems related to human
+posture recognition and image texture classification.
+"
+27667,Yoneda Lemma for Simplicial Spaces,"  We study and prove the Yoneda lemma for simplicial spaces. In order to do
+that we define left fibrations for simplicial spaces and show it comes with a
+model structure. We will then show how the Yoneda Lemma can be applied to
+understand the theory of left fibrations.
+"
+27668,"Existence, uniqueness and stability of $L^1$ solutions for multidimensional BSDEs with generators of one-sided Osgood type","  We establish a general existence and uniqueness result of $L^1$ solution for
+a multidimensional backward stochastic differential equation (BSDE for short)
+with generator $g$ satisfying a one-sided Osgood condition as well as a general
+growth condition in $y$, and a Lipschitz condition together with a sublinear
+growth condition in $z$, which improves some existing results. In particular,
+we put forward and prove a stability theorem of the $L^1$ solutions for the
+first time. A new type of $L^1$ solution is also investigated. Some delicate
+techniques involved in the relationship between convergence in $L^1$ and in
+probability and dividing appropriately the time interval play crucial roles in
+our proofs.
+"
+27669,Disintegration and Bayesian Inversion via String Diagrams,"  The notions of disintegration and Bayesian inversion are fundamental in
+conditional probability theory. They produce channels, as conditional
+probabilities, from a joint state, or from an already given channel (in
+opposite direction). These notions exist in the literature, in concrete
+situations, but are presented here in abstract graphical formulations. The
+resulting abstract descriptions are used for proving basic results in
+conditional probability theory. The existence of disintegration and Bayesian
+inversion is discussed for discrete probability, and also for measure-theoretic
+probability --- via standard Borel spaces and via likelihoods. Finally, the
+usefulness of disintegration and Bayesian inversion is illustrated in several
+examples.
+"
+27670,Entanglement dynamics in random media,"  We study how the entanglement dynamics between two-level atoms is impacted by
+random fluctuations of the light cone. In our model the two-atom system is
+envisaged as an open system coupled with an electromagnetic field in the vacuum
+state. We employ the quantum master equation in the Born-Markov approximation
+in order to describe the completely positive time evolution of the atomic
+system. We restrict our investigations to the situation in which the atoms are
+coupled individually to two spatially separated cavities, one of which
+displaying the emergence of light-cone fluctuations. In such a disordered
+cavity, we assume that the coefficients of the Klein-Gordon equation are random
+functions of the spatial coordinates. The disordered medium is modeled by a
+centered, stationary and Gaussian process. We demonstrate that disorder has the
+effect of slowing down the entanglement decay. We conjecture that in a strong
+disorder environment the mean life of entangled states can be enhanced in such
+a way as to almost completely suppress quantum nonlocal decoherence.
+"
+27671,Dynamics of the localized nonlinear waves in spin-1 Bose-Einstein condensates with time-space modulation,"  We investigate the dynamics of the localized nonlinear matter wave in spin-1
+Bose-Einstein condensates with trapping potentials and nonlinearities dependent
+on time and space. We solve the three coupled Gross-Pitaevskii equation by
+similarity transformation and obtain two families of exact matter wave
+solutions in terms of Jacobi elliptic functions and Mathieu equation. The
+localized states of the spinor matter wave describe the dynamics of vector
+breathing solitons, moving breathing solitons, quasibreathing solitons and
+resonant solitons. The results of stability show that one order vector
+breathing solitons, quasibreathing solitons, resonant solitons, and the moving
+breathing solitons \psi_{\pm1} are all stable but the moving breathing solitons
+\psi_0 is unstable. We also present the experimental parameters to realize
+these phenomena in the future experiments.
+"
+27672,Learning to Collaborate in Markov Decision Processes,"  We consider a two-agent MDP framework where agents repeatedly solve a task in
+a collaborative setting. We study the problem of designing a learning algorithm
+for the first agent (A1) that facilitates a successful collaboration even in
+cases when the second agent (A2) is adapting its policy in an unknown way. The
+key challenge in our setting is that the presence of the second agent leads to
+non-stationarity and non-obliviousness of rewards and transitions for the first
+agent.
+We design novel online learning algorithms for agent A1 whose regret decays
+as $O(T^{1-\frac{3}{7} \cdot \alpha})$ with $T$ learning episodes provided that
+the magnitude of agent A2's policy changes between any two consecutive episodes
+are upper bounded by $O(T^{-\alpha})$. Here, the parameter $\alpha$ is assumed
+to be strictly greater than $0$, and we show that this assumption is necessary
+provided that the {\em learning parity with noise} problem is computationally
+hard. We show that sub-linear regret of agent A1 further implies
+near-optimality of the agents' joint return for MDPs that manifest the
+properties of a {\em smooth} game.
+"
+27673,Accelerating Approximate Bayesian Computation with Quantile Regression: Application to Cosmological Redshift Distributions,"  Approximate Bayesian Computation (ABC) is a method to obtain a posterior
+distribution without a likelihood function, using simulations and a set of
+distance metrics. For that reason, it has recently been gaining popularity as
+an analysis tool in cosmology and astrophysics. Its drawback, however, is a
+slow convergence rate. We propose a novel method, which we call qABC, to
+accelerate ABC with Quantile Regression. In this method, we create a model of
+quantiles of distance measure as a function of input parameters. This model is
+trained on a small number of simulations and estimates which regions of the
+prior space are likely to be accepted into the posterior. Other regions are
+then immediately rejected. This procedure is then repeated as more simulations
+are available. We apply it to the practical problem of estimation of redshift
+distribution of cosmological samples, using forward modelling developed in
+previous work. The qABC method converges to nearly same posterior as the basic
+ABC. It uses, however, only 20\% of the number of simulations compared to basic
+ABC, achieving a fivefold gain in execution time for our problem. For other
+problems the acceleration rate may vary; it depends on how close the prior is
+to the final posterior. We discuss possible improvements and extensions to this
+method.
+"
+27674,"Modeling of the outburst on July 29th, 2015 observed with OSIRIS cameras in the southern hemisphere of comet 67P/Churyumov-Gerasimenko","  Images of the nucleus and the coma (gas and dust) of comet 67P/Churyumov-
+Gerasimenko have been acquired by the OSIRIS (Optical, Spectroscopic, and
+Infrared Remote Imaging System) cameras since March 2014 using both the Wide
+Angle Camera (WAC) and the Narrow Angle Camera (NAC). We use images from the
+NAC camera to study a bright outburst observed in the southern hemisphere on
+July 29, 2015. The high spatial resolution of the NAC is needed to localize the
+source point of the outburst on the surface of the nucleus. The heliocentric
+distance is 1.25 au and the spacecraft-comet distance is 186 km. Aiming to
+better understand the physics that led to the outgassing, we used the Direct
+Simulation Monte Carlo (DSMC) method to study the gas flow close to the nucleus
+and the dust trajectories. The goal is to understand the mechanisms producing
+the outburst. We reproduce the opening angle of the outburst in the model and
+constrain the outgassing ratio between the outburst source and the local
+region. The outburst is in fact a combination of both gas and dust, in which
+the active surface is approximately 10 times more active than the average rate
+found in the surrounding areas. We need a number of dust particles 7.83
+$\times$ 10$^{11}$ - 6.90 $\times$ 10$^{15}$ (radius 1.97 - 185 {\mu}m), which
+corresponds to a mass of dust 220 - 21 $\times$ 10$^{3}$kg.
+"
+27675,Lubricated Immersed Boundary Method in Two Dimensions,"  Many biological examples of fluid-structure interaction, including the
+transit of red blood cells through the narrow slits in the spleen and the
+intracellular trafficking of vesicles into dendritic spines, involve the
+near-contact of elastic structures separated by thin layers of fluid. Motivated
+by such problems, we introduce an immersed boundary method that uses elements
+of lubrication theory to resolve thin fluid layers between immersed boundaries.
+We demonstrate 2nd-order accurate convergence for simple two-dimensional flows
+with known exact solutions to showcase the increased accuracy of this method
+compared to the standard immersed boundary method. Motivated by the phenomenon
+of wall-induced migration, we apply the lubricated immersed boundary method to
+simulate an elastic vesicle near a wall in shear flow. We also simulate the
+dynamics of a vesicle traveling through a narrow channel and observe the
+ability of the lubricated method to capture the vesicle motion on relatively
+coarse fluid grids.
+"
+27676,Efficient time stepping for reactive turbulent simulations with stiff chemistry,"  A combination of a steady-state preserving operator splitting method and a
+semi-implicit integration scheme is proposed for efficient time stepping in
+simulations of unsteady reacting flows, such as turbulent flames, using
+detailed chemical kinetic mechanisms. The operator splitting is based on the
+Simpler balanced splitting method, which is constructed with improved stability
+properties and reduced computational cost. The method is shown to be capable of
+stable and accurate prediction of ignition and extinction for
+reaction-diffusion systems near critical conditions. The ROK4E scheme is
+designed for semi-implicit integration of spatially independent chemically
+reacting systems. Being a Rosenbrock-Krylov method, ROK4E utilizes the low-rank
+approximation of the Jacobian to reduce the cost for integrating the system of
+ODEs that have relative few stiff components. The efficiency of the scheme is
+further improved via the careful choice of coefficients to require three
+right-hand-side evaluations over four stages. Combing these two methods,
+efficient calculation is achieved for large-scale parallel simulations of
+turbulent flames.
+"
+27677,Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering,"  We address the problem of cross-language adaptation for question-question
+similarity reranking in community question answering, with the objective to
+port a system trained on one input language to another input language given
+labeled training data for the first language and only unlabeled data for the
+second language. In particular, we propose to use adversarial training of
+neural networks to learn high-level features that are discriminative for the
+main learning task, and at the same time are invariant across the input
+languages. The evaluation results show sizable improvements for our
+cross-language adversarial neural network (CLANN) model over a strong
+non-adversarial system.
+"
+27678,Continuous tensor categories from quantum groups I: algebraic aspects,"  We describe the algebraic ingredients of a proof of the conjecture of Frenkel
+and Ip that the category of positive representations $\mathcal{P}_\lambda$ of
+the quantum group $U_q(\mathfrak{sl}_{n+1})$ is closed under tensor products.
+Our results generalize those of Ponsot and Teschner in the rank 1 case of
+$U_q(\mathfrak{sl}_2)$. In higher rank, many nontrivial features appear, the
+most important of these being a surprising connection to the quantum
+integrability of the open Coxeter-Toda lattice. We show that the closure under
+tensor products follows from the orthogonality and completeness of the Toda
+eigenfunctions (i.e. the q-Whittaker functions), and obtain an explicit
+construction of the Clebsch-Gordan intertwiner giving the decomposition of
+$\mathcal{P}_\lambda \otimes \mathcal{P}_\mu$ into irreducibles.
+"
+27679,Investigating the Role of Socially Mediated Metacognition During Collaborative Troubleshooting of Electric Circuits,"  Developing students' ability to troubleshoot is an important learning outcome
+for many undergraduate physics lab courses, especially electronics courses. In
+other work, metacognition has been identified as an important feature of
+troubleshooting. However, that work has focused primarily on individual
+students' metacognitive processes or troubleshooting abilities. In contrast,
+electronics courses often require students to work in pairs, and hence
+students' in-class experiences likely have significant social dimensions that
+are not well understood. In this work, we use an existing framework for
+socially mediated metacognition to analyze audiovisual data from think-aloud
+activities in which eight pairs of students from two institutions attempted to
+diagnose and repair a malfunctioning electric circuit. In doing so, we provide
+insight into some of the social metacognitive dynamics that arise during
+collaborative troubleshooting. We find that students engaged in socially
+mediated metacognition at multiple key transitions during the troubleshooting
+process. Reciprocated metacognitive dialogue arose when students were
+collectively strategizing about which measurements to perform, or reaching a
+shared understanding of the circuit's behavior. In addition to elaborating upon
+these findings, we discuss implications for instruction, and we identify areas
+for potential future investigation.
+"
+27680,Counting Lyndon factors,"  In this paper, we determine the maximum number of distinct Lyndon factors
+that a word of length $n$ can contain. We also derive formulas for the expected
+total number of Lyndon factors in a word of length $n$ on an alphabet of size
+$\sigma$, as well as the expected number of distinct Lyndon factors in such a
+word. The minimum number of distinct Lyndon factors in a word of length $n$ is
+$1$ and the minimum total number is $n$, with both bounds being achieved by
+$x^n$ where $x$ is a letter. A more interesting question to ask is what is the
+minimum number of distinct Lyndon factors in a Lyndon word of length $n$? In
+this direction, it is known (Saari, 2014) that an optimal lower bound for the
+number of distinct Lyndon factors in a Lyndon word of length $n$ is
+$\lceil\log_{\phi}(n) + 1\rceil$, where $\phi$ denotes the golden ratio $(1 +
+\sqrt{5})/2$. Moreover, this lower bound is attained by the so-called finite
+""Fibonacci Lyndon words"", which are precisely the Lyndon factors of the
+well-known ""infinite Fibonacci word"" -- a special example of a ""infinite
+Sturmian word"". Saari (2014) conjectured that if $w$ is Lyndon word of length
+$n$, $n\ne 6$, containing the least number of distinct Lyndon factors over all
+Lyndon words of the same length, then $w$ is a Christoffel word (i.e., a Lyndon
+factor of an infinite Sturmian word). We give a counterexample to this
+conjecture. Furthermore, we generalise Saari's result on the number of distinct
+Lyndon factors of a Fibonacci Lyndon word by determining the number of distinct
+Lyndon factors of a given Christoffel word. We end with two open problems.
+"
+27681,A distributed algorithm for average aggregative games with coupling constraints,"  We consider the framework of average aggregative games, where the cost
+function of each agent depends on his own strategy and on the average
+population strategy. We focus on the case in which the agents are coupled not
+only via their cost functions, but also via constraints coupling their
+strategies. We propose a distributed algorithm that achieves an almost Nash
+equilibrium by requiring only local communications of the agents, as specified
+by a sparse communication network. The proof of convergence of the algorithm
+relies on the auxiliary class of network aggregative games and exploits a novel
+result of parametric convergence of variational inequalities, which is
+applicable beyond the context of games. We apply our theoretical findings to a
+multi-market Cournot game with transportation costs and maximum market
+capacity.
+"
+27682,Cosmological CP odd axion field as the coherent Berry's phase of the Universe,"  We consider a dark matter (DM) model offering a very natural explanation of
+the observed relation, $\Omega_{\rm dark} \sim \Omega_{\rm visible}$. This
+generic consequence of the model is a result of the common origin of both types
+of matter (DM and visible) which are formed during the same QCD transition. The
+masses of both types of matter in this framework are proportional to one and
+the same dimensional parameter of the system, $\Lambda_{\rm QCD}$. The focus of
+the present work is the detail study of the dynamics of the $\cal{CP}$-odd
+coherent axion field $a(x)$ just before the QCD transition. We argue that the
+baryon charge separation effect on the largest possible scales inevitably
+occurs as a result of merely existence of the coherent axion field in early
+Universe. It leads to preferential formation of one species of nuggets on the
+scales of the visible Universe where the axion field $a(x)$ is coherent. A
+natural outcome of this preferential evolution is that only one type of the
+visible baryons remains in the system after the nuggets complete their
+formation. This represents a specific mechanism on how the baryon charge
+separation mechanism (when the Universe is neutral, but visible part of matter
+consists the baryons only) replaces the conventional ""baryogenesis"" scenarios.
+"
+27683,Elementary Symmetric Polynomials for Optimal Experimental Design,"  We revisit the classical problem of optimal experimental design (OED) under a
+new mathematical model grounded in a geometric motivation. Specifically, we
+introduce models based on elementary symmetric polynomials; these polynomials
+capture ""partial volumes"" and offer a graded interpolation between the widely
+used A-optimal design and D-optimal design models, obtaining each of them as
+special cases. We analyze properties of our models, and derive both greedy and
+convex-relaxation algorithms for computing the associated designs. Our analysis
+establishes approximation guarantees on these algorithms, while our empirical
+results substantiate our claims and demonstrate a curious phenomenon concerning
+our greedy method. Finally, as a byproduct, we obtain new results on the theory
+of elementary symmetric polynomials that may be of independent interest.
+"
+27684,Revisiting the bulge-halo conspiracy I: Dependence on galaxy properties and halo mass,"  We carry out a systematic investigation of the total mass density profile of
+massive (Mstar>2e11 Msun) early-type galaxies and its dependence on galactic
+properties and host halo mass with the aid of a variety of lensing/dynamical
+data and large mock galaxy catalogs. The latter are produced via semi-empirical
+models that, by design, are based on just a few basic input assumptions.
+Galaxies, with measured stellar masses, effective radii and Sérsic indices,
+are assigned, via abundance matching relations, host dark matter halos
+characterized by a typical LCDM profile. Our main results are as follows: (i)
+In line with observational evidence, our semi-empirical models naturally
+predict that the total, mass-weighted density slope at the effective radius
+gamma' is not universal, steepening for more compact and/or massive galaxies,
+but flattening with increasing host halo mass. (ii) Models characterized by a
+Salpeter or variable initial mass function and uncontracted dark matter
+profiles are in good agreement with the data, while a Chabrier initial mass
+function and/or adiabatic contractions/expansions of the dark matter halos are
+highly disfavored. (iii) Currently available data on the mass density profiles
+of very massive galaxies (Mstar>1e12 Msun), with Mhalo>3e14 Msun, favor instead
+models with a stellar profile flatter than a Sérsic one in the very inner
+regions (r<3-5 kpc), and a cored NFW or Einasto dark matter profile with median
+halo concentration a factor of ~2 or <1.3, respectively, higher than those
+typically predicted by N-body numerical simulations.
+"
+27685,Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR,"  There has been much discussion of the right to explanation in the EU General
+Data Protection Regulation, and its existence, merits, and disadvantages.
+Implementing a right to explanation that opens the black box of algorithmic
+decision-making faces major legal and technical barriers. Explaining the
+functionality of complex algorithmic decision-making systems and their
+rationale in specific cases is a technically challenging problem. Some
+explanations may offer little meaningful information to data subjects, raising
+questions around their value. Explanations of automated decisions need not
+hinge on the general public understanding how algorithmic systems function.
+Even though such interpretability is of great importance and should be pursued,
+explanations can, in principle, be offered without opening the black box.
+Looking at explanations as a means to help a data subject act rather than
+merely understand, one could gauge the scope and content of explanations
+according to the specific goal or action they are intended to support. From the
+perspective of individuals affected by automated decision-making, we propose
+three aims for explanations: (1) to inform and help the individual understand
+why a particular decision was reached, (2) to provide grounds to contest the
+decision if the outcome is undesired, and (3) to understand what would need to
+change in order to receive a desired result in the future, based on the current
+decision-making model. We assess how each of these goals finds support in the
+GDPR. We suggest data controllers should offer a particular type of
+explanation, unconditional counterfactual explanations, to support these three
+aims. These counterfactual explanations describe the smallest change to the
+world that can be made to obtain a desirable outcome, or to arrive at the
+closest possible world, without needing to explain the internal logic of the
+system.
+"
+27686,Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization,"  Electroencephalography (EEG) headsets are the most commonly used sensing
+devices for Brain-Computer Interface. In real-world applications, there are
+advantages to extrapolating data from one user session to another. However,
+these advantages are limited if the data arise from different hardware systems,
+which often vary between application spaces. Currently, this creates a need to
+recalibrate classifiers, which negatively affects people's interest in using
+such systems. In this paper, we employ active weighted adaptation
+regularization (AwAR), which integrates weighted adaptation regularization
+(wAR) and active learning, to expedite the calibration process. wAR makes use
+of labeled data from the previous headset and handles class-imbalance, and
+active learning selects the most informative samples from the new headset to
+label. Experiments on single-trial event-related potential classification show
+that AwAR can significantly increase the classification accuracy, given the
+same number of labeled samples from the new headset. In other words, AwAR can
+effectively reduce the number of labeled samples required from the new headset,
+given a desired classification accuracy, suggesting value in collating data for
+use in wide scale transfer-learning applications.
+"
+27687,Excitonic coupling dominates the homogeneous photoluminescence excitation linewidth in semicrystalline polymeric semiconductors,"  We measure the homogeneous excitation linewidth of regioregular
+poly(3-hexylthiophene), a model semicrystalline polymeric semiconductor, by
+means of two-dimensional coherent photoluminescence excitation spectroscopy. At
+a temperature of 8\,K, we find a linewidth that is always $\gtrsim 110$\,meV
+full-width-at-half-maximum, which is a significant fraction of the total
+linewidth. It displays a spectral dependence and is minimum near the 0--0
+origin peak. We interpret this spectral dependence of the homogeneous
+excitation linewidth within the context of a weakly coupled aggregate model.
+"
+27688,Non-invasive Scanning Raman Spectroscopy and Tomography for Graphene Membrane Characterization,"  Graphene has extraordinary mechanical and electronic properties, making it a
+promising material for membrane based nanoelectromechanical systems (NEMS).
+Here, chemical-vapor-deposited graphene is transferred onto target substrates
+to suspend it over cavities and trenches for pressure-sensor applications. The
+development of such devices requires suitable metrology methods, i.e.,
+large-scale characterization techniques, to confirm and analyze successful
+graphene transfer with intact suspended graphene membranes. We propose fast and
+noninvasive Raman spectroscopy mapping to distinguish between freestanding and
+substrate-supported graphene, utilizing the different strain and doping levels.
+The technique is expanded to combine two-dimensional area scans with
+cross-sectional Raman spectroscopy, resulting in three-dimensional Raman
+tomography of membrane-based graphene NEMS. The potential of Raman tomography
+for in-line monitoring is further demonstrated with a methodology for automated
+data analysis to spatially resolve the material composition in micrometer-scale
+integrated devices, including free-standing and substrate-supported graphene.
+Raman tomography may be applied to devices composed of other two-dimensional
+materials as well as silicon micro- and nanoelectromechanical systems.
+"
+27689,"Parallelization of Path Planning Algorithms for AUVs Concepts, Opportunities, and Program-Technical Implementation","  Modern autonomous underwater vehicles (AUVs) have advanced sensing
+capabilities including sonar, cameras, acoustic communication, and diverse
+bio-sensors. Instead of just sensing its environment and storing the data for
+post-Mission inspection, an AUV could use the collected information to gain an
+understanding of its environment, and based on this understanding autonomously
+adapt its behavior to enhance the overall effectiveness of its mission. Many
+such tasks are highly computation intensive. This paper presents the results of
+a case study that illustrates the effectiveness of an energy-aware, many-core
+computing architecture to perform on-board path planning within a
+batteryoperated AUV. A previously published path planning algorithm was ported
+onto the SCC, an experimental 48 core single-chip system developed by Intel.
+The performance, power, and energy consumption of the application were measured
+for different numbers of cores and other system parameters. This case study
+shows that computation intensive tasks can be executed within an AUV that
+relies mainly on battery power. Future plans include the deployment and testing
+of an SCC system within a Teledyne Webb Research Slocum glider.
+"
+27690,Two phase helium cooling characteristics in Cable-in Conduit Conductors,"  Cable-in-Conduit Conductors (CICCs) are used in the fabrication of
+superconducting fusion grade magnets. It acts as a narrow cryostat to provide
+cryo-stability with direct contact of coolant fluid to conductor. The
+superconducting magnets are cooled using forced flow (FF), supercritical helium
+or two phase (TP) cooling through void space in the CICC. Thermo-hydraulics
+using supercritical helium single phase flow is well-known and established.
+Research topic of behavior of forced flow, two phase (TP) helium cooling in
+CICC involves perceived risks of the CICC running into flow chocking and
+possible thermo-acoustic oscillations leading to flow instabilities. This
+research work involves study of forced flow two phase helium cooling in CICC
+wound superconducting magnets. The TP flow provides cryo-stability by the
+latent heat of helium not by enthalpy as in case of CICC being cooled with
+supercritical helium. Study reveals some attractive regimes in the case of TP
+cooling, at a given mass flow rate of single phase helium at the inlet and a
+heat flux acting on the CICC. Analysis carried out predicts significant gains
+with TP cooling on a prototype CICC, which is circular in cross section and
+appropriate for fusion devices for high magnetic field applications. These
+general formalisms may be extended to specific magnets wound with CICC. This
+paper describes analysis of TP cooling of a CICC.
+"
+27691,Algorithmic interpretations of fractal dimension,"  We study algorithmic problems on subsets of Euclidean space of low fractal
+dimension. These spaces are the subject of intensive study in various branches
+of mathematics, including geometry, topology, and measure theory. There are
+several well-studied notions of fractal dimension for sets and measures in
+Euclidean space. We consider a definition of fractal dimension for finite
+metric spaces which agrees with standard notions used to empirically estimate
+the fractal dimension of various sets. We define the fractal dimension of some
+metric space to be the infimum $\delta>0$, such that for any $\epsilon > 0$,
+for any ball $B$ of radius $r\geq 2\epsilon$, and for any $\epsilon $-net $N$
+(that is, for any maximal $\epsilon $-packing), we have $|B\cap
+N|=O((r/\epsilon)^\delta)$.
+Using this definition we obtain faster algorithms for a plethora of classical
+problems on sets of low fractal dimension in Euclidean space. Our results apply
+to exact and fixed-parameter algorithms, approximation schemes, and spanner
+constructions. Interestingly, the dependence of the performance of these
+algorithms on the fractal dimension nearly matches the currently best-known
+dependence on the standard Euclidean dimension. Thus, when the fractal
+dimension is strictly smaller than the ambient dimension, our results yield
+improved solutions in all of these settings.
+"
+27692,Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data,"  This paper measures social media activity of 15 broad scientific disciplines
+indexed in Scopus database using Altmetric.com data. First, the presence of
+Altmetric.com data in Scopus database is investigated, overall and across
+disciplines. Second, the correlation between the bibliometric and altmetric
+indices is examined using Spearman correlation. Third, a zero-truncated
+negative binomial model is used to determine the association of various factors
+with increasing or decreasing citations. Lastly, the effectiveness of altmetric
+indices to identify publications with high citation impact is comprehensively
+evaluated by deploying Area Under the Curve (AUC) - an application of receiver
+operating characteristic. Results indicate a rapid increase in the presence of
+Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. A
+zero-truncated negative binomial model is implemented to measure the extent to
+which different bibliometric and altmetric factors contribute to citation
+counts. Blog count appears to be the most important factor increasing the
+number of citations by 38.6% in the field of Health Professions and Nursing,
+followed by Twitter count increasing the number of citations by 8% in the field
+of Physics and Astronomy. Interestingly, both Blog count and Twitter count
+always show positive increase in the number of citations across all fields.
+While there was a positive weak correlation between bibliometric and altmetric
+indices, the results show that altmetric indices can be a good indicator to
+discriminate highly cited publications, with an encouragingly AUC= 0.725
+between highly cited publications and total altmetric count. Overall, findings
+suggest that altmetrics could better distinguish highly cited publications.
+"
+27693,"A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds","  Recently, much work has been done on extending the scope of online learning
+and incremental stochastic optimization algorithms. In this paper we contribute
+to this effort in two ways: First, based on a new regret decomposition and a
+generalization of Bregman divergences, we provide a self-contained, modular
+analysis of the two workhorses of online learning: (general) adaptive versions
+of Mirror Descent (MD) and the Follow-the-Regularized-Leader (FTRL) algorithms.
+The analysis is done with extra care so as not to introduce assumptions not
+needed in the proofs and allows to combine, in a straightforward way, different
+algorithmic ideas (e.g., adaptivity, optimism, implicit updates) and learning
+settings (e.g., strongly convex or composite objectives). This way we are able
+to reprove, extend and refine a large body of the literature, while keeping the
+proofs concise. The second contribution is a byproduct of this careful
+analysis: We present algorithms with improved variational bounds for smooth,
+composite objectives, including a new family of optimistic MD algorithms with
+only one projection step per round. Furthermore, we provide a simple extension
+of adaptive regret bounds to practically relevant non-convex problem settings
+with essentially no extra effort.
+"
+27694,Approximating Continuous Functions by ReLU Nets of Minimal Width,"  This article concerns the expressive power of depth in deep feed-forward
+neural nets with ReLU activations. Specifically, we answer the following
+question: for a fixed $d_{in}\geq 1,$ what is the minimal width $w$ so that
+neural nets with ReLU activations, input dimension $d_{in}$, hidden layer
+widths at most $w,$ and arbitrary depth can approximate any continuous,
+real-valued function of $d_{in}$ variables arbitrarily well? It turns out that
+this minimal width is exactly equal to $d_{in}+1.$ That is, if all the hidden
+layer widths are bounded by $d_{in}$, then even in the infinite depth limit,
+ReLU nets can only express a very limited class of functions, and, on the other
+hand, any continuous function on the $d_{in}$-dimensional unit cube can be
+approximated to arbitrary precision by ReLU nets in which all hidden layers
+have width exactly $d_{in}+1.$ Our construction in fact shows that any
+continuous function $f:[0,1]^{d_{in}}\to\mathbb R^{d_{out}}$ can be
+approximated by a net of width $d_{in}+d_{out}$. We obtain quantitative depth
+estimates for such an approximation in terms of the modulus of continuity of
+$f$.
+"
+27695,3D Shape Segmentation via Shape Fully Convolutional Networks,"  We desgin a novel fully convolutional network architecture for shapes,
+denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented
+as graph structures in the SFCN architecture, based on novel graph convolution
+and pooling operations, which are similar to convolution and pooling operations
+used on images. Meanwhile, to build our SFCN architecture in the original image
+segmentation fully convolutional network (FCN) architecture, we also design and
+implement a generating operation} with bridging function. This ensures that the
+convolution and pooling operation we have designed can be successfully applied
+in the original FCN architecture. In this paper, we also present a new shape
+segmentation approach based on SFCN. Furthermore, we allow more general and
+challenging input, such as mixed datasets of different categories of shapes}
+which can prove the ability of our generalisation. In our approach, SFCNs are
+trained triangles-to-triangles by using three low-level geometric features as
+input. Finally, the feature voting-based multi-label graph cuts is adopted to
+optimise the segmentation results obtained by SFCN prediction. The experiment
+results show that our method can effectively learn and predict mixed shape
+datasets of either similar or different characteristics, and achieve excellent
+segmentation results.
+"
+27696,A Hubble Space Telescope survey for novae in M87. III. Are novae good standard candles 15 days after maximum brightness?,"  Ten weeks of daily imaging of the giant elliptical galaxy M87 with the Hubble
+Space Telescope (HST) has yielded 41 nova light curves of unprecedented quality
+for extragalactic cataclysmic variables. We have recently used these light
+curves to demonstrate that the observational scatter in the so-called
+Maximum-Magnitude Rate of Decline (MMRD) relation for classical novae is so
+large as to render the nova-MMRD useless as a standard candle. Here we
+demonstrate that a modified Buscombe - de Vaucouleurs hypothesis, namely that
+novae with decline times t2 > 10 days converge to nearly the same absolute
+magnitude about two weeks after maximum light in a giant elliptical galaxy, is
+supported by our M87 nova data. For 13 novae with daily-sampled light curves,
+well determined times of maximum light in both the F606W and F814W filters, and
+decline times $t2 > 10 days we find that M87 novae display M(606W,15) = -6.37
++/- 0.46 and M(814W,15) = -6.11 +/- 0.43. If very fast novae with decline times
+t2 < 10 days are excluded, the distances to novae in elliptical galaxies with
+stellar binary populations similar to those of M87 should be determinable with
+1 sigma accuracies of +/-20% with the above calibrations.
+"
+27697,Medical image reconstruction: a brief overview of past milestones and future directions,"  This paper briefly reviews past milestones in the field of medical image
+reconstruction and describes some future directions. It is part of an overview
+paper on ""open problems in signal processing"" that will appear in IEEE Signal
+Processing Magazine, but presented here with citations and equations.
+"
+27698,Sparse and weighted estimates for generalized Hörmander operators and commutators,"  In this paper we obtain a pointwise sparse domination for generalized
+Hörmander operators and also for iterated commutators with those operators.
+As a particular case of our result we obtain a extension of the sparse
+domination for commutators obtained by A. Lerner, S. Ombrosi and the second
+author to iterated commutators. Relying upon that sparse domination a number of
+quantitative estimates such as Coifman-Fefferman estimates, strong type
+estimates, and endpoint estimates that improve and complete results in papers
+by M. Lorente, J.M. Martell, C. Pérez, M.S. Riveros and A. de la Torre are
+obtained. We also provide a new local decay estimate and we also extend results
+in a paper due to J.M. Martell, C. Perez and R. Trujillo-Gonzalez to kernels
+satisfying generalized Hörmander conditions. Among other applications, as a
+particular case of our result for endpoint estimates, we extend the endpoint
+estimate obtained in a work by A. Lerner, S. Ombrosi and the second author to
+iterated commutators.
+"
+27699,Sanya lectures : geometry of spherical varieties,"  These are expanded notes from lectures on the geometry of spherical varieties
+given in Sanya. We review some aspects of the geometry of spherical varieties.
+We first describe the structure of $B$-orbits. Using the local structure
+theorems, we describe the Picard group and the group of Weyl divisors and give
+some necessary conditions for smoothness. We later on consider $B$-stable
+curves and describe in details the structure of the Chow group of curves as
+well as the pairing between curves and divisors. Building on these results we
+give an explicit $B$-stable canonical divisor on any spherical variety.
+"
+27700,A novel TOF-PET MRI detector for diagnosis and follow up of the prostate cancer,"  Prostate cancer is the most common disease in men and the second leading
+cause of death from cancer. Generic large imaging instruments used in cancer
+diagnosis have sensitivity, spatial resolution, and contrast inadequate for the
+task of imaging details of a small organ such as the prostate. In addition,
+multimodality imaging can play a significant role merging anatomical and
+functional details coming from simultaneous PET and MRI. Indeed,
+multi-parametric PET/MRI was demonstrated to improve diagnosis, but it suffers
+from too many false positives. In order to address the above limits of the
+current techniques, we have proposed, built and tested, thanks to the TOPEM
+project funded by Italian National Institute of Nuclear Phisics a prototype of
+an endorectal PET-TOF/MRI probe. In the applied magnification PET geometry,
+performance is dominated by a high-resolution detector placed closer to the
+source. The expected spatial resolution in the selected geometry is about 1.5
+mm FWHM and efficiency a factor of 2 with respect to what obtained with the
+conventional PET scanner. In our experimental studies, we have obtained timing
+resolution of ~ 320 ps FWHM and at the same time Depth of Interaction (DOI)
+resolution of under 1 mm. Tests also showed that mutual adverse PET-MR effects
+are minimal. In addition, the matching endorectal RF coil was designed, built
+and tested. In the next planned studies, we expect that benefiting from the
+further progress in scintillator crystal surface treatment, in SiPM technology
+and associated electronics would allow us to significantly improve TOF
+resolution
+"
+27701,Integration of QoS aspects in the Cloud Computing Research and Selection System,"  Cloud Computing is a business model revolution more than a technological one.
+It capitalized on various technologies that have proved themselves and reshaped
+the use of computers by replacing their local use by a centralized one where
+shared resources are stored and managed by a third-party in a way transparent
+to end-users. With this new use came new needs and one of them is the need to
+search through Cloud services and select the ones that meet certain
+requirements. To address this need, we have developed, in a previous work, the
+Cloud Service Research and Selection System (CSRSS) which aims to allow Cloud
+users to search through Cloud services in the database and find the ones that
+match their requirements. It is based on the Skyline and ELECTRE IS. In this
+paper, we improve the system by introducing 7 new dimensions related to QoS
+constraints. Our work's main contribution is conceiving an Agent that uses both
+the Skyline and an outranking method, called ELECTREIsSkyline, to determine
+which Cloud services meet better the users' requirements while respecting QoS
+properties. We programmed and tested this method for a total of 10 dimensions
+and for 50 000 cloud services. The first results are very promising and show
+the effectiveness of our approach.
+"
+27702,"Sur les paquets d'Arthur aux places réelles, translation","  This article is part of a project which aims to describe as explicitly as
+possible the Arthur packets of classical real groups and to prove a
+multiplicity one result for them. Let $G$ be a symplectic or special orthogonal
+real group, and $\psi: W_{\mathbb R}\times \mathbf{SL}_2(\mathbb C)\rightarrow
+{}^LG$ be an Arthur parameter for $G$. Let $A(\psi)$ the component group of the
+centralizer of $\psi$ in $\hat G$. Attached to $\psi$ is a finite length
+unitary representation $\pi^A(\psi)$ of $G\times A(\psi)$, which is
+characterized by the endoscopic identities (ordinary and twisted) it satisfies.
+In [arXiv:1703.07226] we gave a description of the irreducible components of
+$\pi^A(\psi)$ when the parameter $\psi$ is ""very regular, with good parity"". In
+the present paper, we use translation of infinitesimal character to describe
+$\pi^A(\psi)$ in the general good parity case from the representation
+$\pi^A(\psi_+)$ attached to a very regular, with good parity, parameter
+$\psi_+$ obtained from $\psi$ by a simple shift.
+"
+27703,"Enumeration, structural and dimensional synthesis of robotic hands: theory and implementation","  Designing robotic hands for specific tasks could help in the creation of
+optimized end-effectors for grasping and manipulation. However the systematic
+design of robotic hands for a simultaneous task of all fingertips presents many
+challenges. In this work the algorithms and implementation of an overall
+synthesis process is presented, which could be a first step towards a complete
+design tool for robotic end-effectors.
+Type synthesis for a given task and number of fingers, solvability and
+dimensional synthesis for arbitrary topologies are developed and implemented.
+The resulting solver is a powerful tool that can aid in the creation of
+innovative robotic hands with arbitrary number of fingers and palms. Several
+examples of type synthesis, solvability calculations and dimensional synthesis
+are presented.
+"
+27704,Preventing Poisoning Attacks on AI based Threat Intelligence Systems,"  As AI systems become more ubiquitous, securing them becomes an emerging
+challenge. Over the years, with the surge in online social media use and the
+data available for analysis, AI systems have been built to extract, represent
+and use this information. The credibility of this information extracted from
+open sources, however, can often be questionable. Malicious or incorrect
+information can cause a loss of money, reputation, and resources; and in
+certain situations, pose a threat to human life. In this paper, we use an
+ensembled semi-supervised approach to determine the credibility of Reddit posts
+by estimating their reputation score to ensure the validity of information
+ingested by AI systems. We demonstrate our approach in the cybersecurity
+domain, where security analysts utilize these systems to determine possible
+threats by analyzing the data scattered on social media websites, forums,
+blogs, etc.
+"
+27705,Absence of a metallicity effect for ultra-short-period planets,"  Ultra-short-period (USP) planets are a newly recognized class of planets with
+periods shorter than one day and radii smaller than about 2 Earth radii. It has
+been proposed that USP planets are the solid cores of hot Jupiters that lost
+their gaseous envelopes due to photo-evaporation or Roche lobe overflow. We
+test this hypothesis by asking whether USP planets are associated with
+metal-rich stars, as has long been observed for hot Jupiters. We find the
+metallicity distributions of USP-planet and hot-Jupiter hosts to be
+significantly different ($p = 3\times 10^{-4}$), based on Keck spectroscopy of
+Kepler stars. Evidently, the sample of USP planets is not dominated by the
+evaporated cores of hot Jupiters. The metallicity distribution of stars with
+USP planets is indistinguishable from that of stars with short-period planets
+with sizes between 2--4~$R_\oplus$. Thus it remains possible that the USP
+planets are the solid cores of formerly gaseous planets smaller than Neptune.
+"
+27706,"Multi-spectral Image Panchromatic Sharpening, Outcome and Process Quality Assessment Protocol","  Multispectral (MS) image panchromatic (PAN) sharpening algorithms proposed to
+the remote sensing community are ever increasing in number and variety. Their
+aim is to sharpen a coarse spatial resolution MS image with a fine spatial
+resolution PAN image acquired simultaneously by a spaceborne or airborne Earth
+observation (EO) optical imaging sensor pair. Unfortunately, to date, no
+standard evaluation procedure for MS image PAN sharpening outcome and process
+is community agreed upon, in contrast with the Quality Assurance Framework for
+Earth Observation (QA4EO) guidelines proposed by the intergovernmental Group on
+Earth Observations (GEO). In general, process is easier to measure, outcome is
+more important. The original contribution of the present study is fourfold.
+First, existing procedures for quantitative quality assessment (Q2A) of the
+(sole) PAN sharpened MS product are critically reviewed. Their conceptual and
+implementation drawbacks are highlighted to be overcome for quality
+improvement. Second, a novel (to the best of these authors' knowledge, the
+first) protocol for Q2A of MS image PAN sharpening product and process is
+designed, implemented and validated by independent means. Third, within this
+protocol, an innovative categorization of spectral and spatial image quality
+indicators and metrics is presented. Fourth, according to this new taxonomy, an
+original third order isotropic multi scale gray level co occurrence matrix
+(TIMS GLCM) calculator and a TIMS GLCM texture feature extractor are proposed
+to replace popular second order GLCMs.
+"
+27707,An algorithm for the reconstruction of neutrino-induced showers in the ANTARES neutrino telescope,"  Muons created by $\nu_\mu$ charged current (CC) interactions in the water
+surrounding the ANTARES neutrino telescope have been almost exclusively used so
+far in searches for cosmic neutrino sources. Due to their long range, highly
+energetic muons inducing Cherenkov radiation in the water are reconstructed
+with dedicated algorithms that allow the determination of the parent neutrino
+direction with a median angular resolution of about \unit{0.4}{\degree} for an
+$E^{-2}$ neutrino spectrum. In this paper, an algorithm optimised for accurate
+reconstruction of energy and direction of shower events in the ANTARES detector
+is presented. Hadronic showers of electrically charged particles are produced
+by the disintegration of the nucleus both in CC and neutral current (NC)
+interactions of neutrinos in water. In addition, electromagnetic showers result
+from the CC interactions of electron neutrinos while the decay of a tau lepton
+produced in $\nu_\tau$ CC interactions will in most cases lead to either a
+hadronic or an electromagnetic shower. A shower can be approximated as a point
+source of photons. With the presented method, the shower position is
+reconstructed with a precision of about \unit{1}{\metre}, the neutrino
+direction is reconstructed with a median angular resolution between
+\unit{2}{\degree} and \unit{3}{\degree} in the energy range of
+\SIrange{1}{1000}{TeV}. In this energy interval, the uncertainty on the
+reconstructed neutrino energy is about \SIrange{5}{10}{\%}. The increase in the
+detector sensitivity due to the use of additional information from shower
+events in the searches for a cosmic neutrino flux is also presented.
+"
+27708,"Beyond the ""faster is slower"" effect","  The ""faster is slower"" effect raises when crowded people push each other to
+escape through an exit during an emergency situation. As individuals push
+harder, a statistical slowing down in the evacuation time can be achieved. The
+slowing down is caused by the presence of small groups of pedestrians (say, a
+small human cluster) that temporary blocks the way out when trying to leave the
+room. The pressure on the pedestrians belonging to this blocking cluster raises
+for increasing anxiety levels and/or larger number of individuals trying to
+leave the room through the same door. Our investigation shows, however, that
+very high pressures alters the dynamics in the blocking cluster, and thus,
+changes the statistics of the time delays along the escaping process. It can be
+acknowledged a reduction in the long lasting delays, while the overall
+evacuation performance improves. We present results on this novel phenomenon
+taking place beyond the ""faster is slower"" regime.
+"
+27709,Modular Continual Learning in a Unified Visual Environment,"  A core aspect of human intelligence is the ability to learn new tasks quickly
+and switch between them flexibly. Here, we describe a modular continual
+reinforcement learning paradigm inspired by these abilities. We first introduce
+a visual interaction environment that allows many types of tasks to be unified
+in a single framework. We then describe a reward map prediction scheme that
+learns new tasks robustly in the very large state and action spaces required by
+such an environment. We investigate how properties of module architecture
+influence efficiency of task learning, showing that a module motif
+incorporating specific design principles (e.g. early bottlenecks, low-order
+polynomial nonlinearities, and symmetry) significantly outperforms more
+standard neural network motifs, needing fewer training examples and fewer
+neurons to achieve high levels of performance. Finally, we present a
+meta-controller architecture for task switching based on a dynamic neural
+voting scheme, which allows new modules to use information learned from
+previously-seen tasks to substantially improve their own learning efficiency.
+"
+27710,Chern-Simons theory and atypical Hall conductivity in the Varma phase,"  In this letter, we analyze the topological response of a fermionic model
+defined on the Lieb lattice in presence of an electromagnetic field. The
+tight-binding model is built in terms of three species of spinless fermions and
+supports a topological Varma phase due to the spontaneous breaking of
+time-reversal symmetry. In the low-energy regime, the emergent effective
+Hamiltonian coincides with the so-called Duffin-Kemmer-Petiau (DKP)
+Hamiltonian, which describes relativistic pseudospin-0 quasiparticles. By
+considering a minimal coupling between the DKP quasiparticles and an external
+Abelian gauge field, we calculate both the Landau-level spectrum and the
+emergent Chern-Simons theory. The corresponding Hall conductivity reveals an
+atypical quantum Hall effect, which can be simulated in an artificial Lieb
+lattice.
+"
+27711,Charged compact star model in Einstein-Maxwell-Gauss-Bonnet gravity,"  Present paper provides a model of static charged anisotropic fluid sphere in
+the EinsteinMaxwell- GaussBonnet (EMGB) theory of gravitation. We select KB
+anstz as the metric co-efficient along with electric field intensity. To
+develop our model we assume a linear EoS between the radial pressure and the
+matter density. The model obtained here is found to satisfy the elementary
+physical requirements like non-negativity nature of pressure and density, the
+energy conditions, continuity of the metric co-efficients, subliminal velocity
+of sound and also pressure free hypersurface etc. It may also noted that the
+solution is free from any kind of singularities.
+"
+27712,Efficient configurational-bias Monte-Carlo simulations of chain molecules with `swarms' of trial configurations,"  Proposed here is a dynamic Monte-Carlo algorithm that is efficient in
+simulating dense systems of long flexible chain molecules. It expands on the
+configurational-bias Monte-Carlo method through the simultaneous generation of
+a large set of trial configurations. This process is directed by attempting to
+terminate unfinished chains with a low statistical weight, and replacing these
+chains with clones (enrichments) of stronger chains. The efficiency of the
+resulting method is explored by simulating dense polymer brushes. A gain in
+efficiency of at least three orders of magnitude is observed with respect to
+the configurational-bias approach, and almost one order of magnitude with
+respect to recoil-growth Monte-Carlo. Furthermore, the inclusion of `waste
+recycling' is observed to be a powerful method for extracting meaningful
+statistics from the discarded configurations.
+"
+27713,Shifted Equivalent Sources and FFT acceleration for Periodic Scattering Problems including Wood Anomalies,"  This paper introduces a fast algorithm, applicable throughout the
+electromagnetic spectrum, for the numerical solution of problems of scattering
+by periodic surfaces in two-dimensional space. The proposed algorithm remains
+highly accurate and efficient for challenging configurations including randomly
+rough surfaces, deep corrugations, large periods, near grazing incidences, and,
+importantly, Wood-anomaly resonant frequencies. The proposed approach is based
+on use of certain `shifted equivalent sources' which enable FFT acceleration of
+a Wood-anomaly-capable quasi-periodic Green function introduced recently (Bruno
+and Delourme, Jour. Computat. Phys., 262--290, 2014). The Green-function
+strategy additionally incorporates an exponentially convergent shifted version
+of the classical spectral series for the Green function. While the
+computing-cost asymptotics depend on the asymptotic configuration assumed, the
+computing costs rise at most linearly with the size of the problem for a number
+of important rough-surface cases we consider. In practice, single-core runs in
+computing times ranging from a fraction of a second to a few seconds suffice
+for the proposed algorithm to produce highly-accurate solutions in some of the
+most challenging contexts arising in applications.
+"
+27714,I Would Not Plant Apple Trees If the World Will Be Wiped: Analyzing Hundreds of Millions of Behavioral Records of Players During an MMORPG Beta Test,"  In this work, we use player behavior during the closed beta test of the
+MMORPG ArcheAge as a proxy for an extreme situation: at the end of the closed
+beta test, all user data is deleted, and thus, the outcome (or penalty) of
+players' in-game behaviors in the last few days loses its meaning. We analyzed
+270 million records of player behavior in the 4th closed beta test of ArcheAge.
+Our findings show that there are no apparent pandemic behavior changes, but
+some outliers were more likely to exhibit anti-social behavior (e.g., player
+killing). We also found that contrary to the reassuring adage that ""Even if I
+knew the world would go to pieces tomorrow, I would still plant my apple tree,""
+players abandoned character progression, showing a drastic decrease in quest
+completion, leveling, and ability changes at the end of the beta test.
+"
+27715,Ergodic complex structures on hyperkahler manifolds: an erratum,"  Let $M$ be a hyperkahler manifold, $\Gamma$ its mapping class group, and
+$Teich$ the Teichmuller space of complex structures of hyperkahler type. After
+we glue together birationally equivalent points, we obtain the so-called
+birational Teichmuller space $Teich_b$. Every connected component of $Teich_b$
+is identified with its period space $P$ by global Torelli theorem. The mapping
+class group of $M$ acts on $P$ as a finite index subgroup of the group of
+isometries of the integer cohomology lattice, that is, satisfies assumptions of
+Ratner theorem. We prove that there are three classes of orbits, closed, dense
+and the intermediate class which corresponds to varieties with $Re(H^{2,0}(M))$
+containing a given rational vector. The closure of the later orbits is a fixed
+point set of an anticomplex involution of $P$. This fixes an error in the paper
+1306.1498, where this third class of orbits was overlooked. We explain how this
+affects the works based on 1306.1498.
+"
+27716,The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection,"  In this paper, we describe the outcomes of the challenge organized and run by
+Airbus and partners in 2018. The challenge consisted of two tasks applied to
+Air Traffic Control (ATC) speech in English: 1) automatic speech-to-text
+transcription, 2) call sign detection (CSD). The registered participants were
+provided with 40 hours of speech along with manual transcriptions. Twenty-two
+teams submitted predictions on a five hour evaluation set. ATC speech
+processing is challenging for several reasons: high speech rate,
+foreign-accented speech with a great diversity of accents, noisy communication
+channels. The best ranked team achieved a 7.62% Word Error Rate and a 82.41%
+CSD F1-score. Transcribing pilots' speech was found to be twice as harder as
+controllers' speech. Remaining issues towards solving ATC ASR are also
+discussed.
+"
+27717,Robotic Control for Cognitive UWB Radar,"  In the article, we describe a trajectory planning problem for a 6-DOF robotic
+manipulator arm that carries an ultra-wideband (UWB) radar sensor with
+synthetic aperture (SAR). The resolution depends on the trajectory and velocity
+profile of the sensor head. The constraints can be modelled as an optimization
+problem to obtain a feasible, collision-free target trajectory of the
+end-effector of the manipulator arm in Cartesian coordinates that minimizes
+observation time. For 3D-reconstruction, the target is observed in multiple
+height slices. For Through-the-Wall radar the sensor can be operated in sliding
+mode for scanning larger areas. For IED inspection the spot-light mode is
+preferred, constantly pointing the antennas towards the target to obtain
+maximum azimuth resolution.
+"
+27718,Cheap and near exact CASSCF with large active spaces,"  We use the recently-developed Heat-bath Configuration Interaction (HCI)
+algorithm as an efficient active-space solver to perform multi-configuration
+self-consistent field calculations (HCISCF) with large active spaces. We give a
+detailed derivation of the theory and show that difficulties associated with
+non-variationality of the HCI procedure can be overcome by making use of the
+Lagrangian formulation to calculate the HCI relaxed two body reduced density
+matrix. HCISCF is then used to study the electronic structure of butadiene,
+pentacene, and Fe-porphyrin. One of the most striking results of our work is
+that the converged active space orbitals obtained from HCISCF are relatively
+insensitive to the accuracy of the HCI calculation. This allows us to obtain
+nearly converged CASSCF energies with an estimated error of less than 1 mHa
+using the orbitals obtained from the HCISCF procedure in which the integral
+transformation is the dominant cost. For example, an HCISCF calculation on
+Fe-Porphyrin model complex with an active space of (44e, 44o) took only 412
+seconds per iteration on a single node containing 28 cores, out of which 185
+seconds were spent in the HCI calculation and the remaining 227 seconds were
+mainly used for integral transformation. Finally, we also show that
+active-space orbitals can be optimized using HCISCF to substantially speed up
+the convergence of the HCI energy to the Full CI limit because HCI is not
+invariant to unitary transformations within the active space.
+"
+27719,K2-137 b: an Earth-sized planet in a 4.3-hour orbit around an M-dwarf,"  We report the discovery from K2 of a transiting terrestrial planet in an
+ultra-short-period orbit around an M3-dwarf. K2-137 b completes an orbit in
+only 4.3 hours, the second-shortest orbital period of any known planet, just 4
+minutes longer than that of KOI 1843.03, which also orbits an M-dwarf. Using a
+combination of archival images, AO imaging, RV measurements, and light curve
+modelling, we show that no plausible eclipsing binary scenario can explain the
+K2 light curve, and thus confirm the planetary nature of the system. The
+planet, whose radius we determine to be 0.89 +/- 0.09 Earth radii, and which
+must have a iron mass fraction greater than 0.45, orbits a star of mass 0.463
++/- 0.052 Msol and radius 0.442 +/- 0.044 Rsol.
+"
+27720,Measurement of low energy ionization signals from Compton scattering in a CCD dark matter detector,"  An important source of background in direct searches for low-mass dark matter
+particles are the energy deposits by small-angle scattering of environmental
+$\gamma$ rays. We report detailed measurements of low-energy spectra from
+Compton scattering of $\gamma$ rays in the bulk silicon of a charge-coupled
+device (CCD). Electron recoils produced by $\gamma$ rays from $^{57}$Co and
+$^{241}$Am radioactive sources are measured between 60 eV and 4 keV. The
+observed spectra agree qualitatively with theoretical predictions, and
+characteristic spectral features associated with the atomic structure of the
+silicon target are accurately measured for the first time. A
+theoretically-motivated parametrization of the data that describes the Compton
+spectrum at low energies for any incident $\gamma$-ray flux is derived. The
+result is directly applicable to background estimations for low-mass dark
+matter direct-detection experiments based on silicon detectors, in particular
+for the DAMIC experiment down to its current energy threshold.
+"
+27721,Measuring neuronal avalanches in disordered systems with absorbing states,"  Power-law-shaped avalanche-size distributions are widely used to probe for
+critical behavior in many different systems, particularly in neural networks.
+The definition of avalanche is ambiguous. Usually, theoretical avalanches are
+defined as the activity between a stimulus and the relaxation to an inactive
+absorbing state. On the other hand, experimental neuronal avalanches are
+defined by the activity between consecutive silent states. We claim that the
+latter definition may be extended to some theoretical models to characterize
+their power-law avalanches and critical behavior. We study a system in which
+the separation of driving and relaxation time scales emerges from its
+structure. We apply both definitions of avalanche to our model. Both yield
+power-law-distributed avalanches that scale with system size in the critical
+point as expected. Nevertheless, we find restricted power-law-distributed
+avalanches outside of the critical region within the experimental procedure,
+which is not expected by the standard theoretical definition. We remark that
+these results are dependent on the model details.
+"
+27722,Weights in a Benson-Solomon block,"  To each pair consisting of a saturated fusion system over a $p$-group
+together with a compatible family of Külshammer-Puig cohomology classes, one
+can count weights in a hypothetical block algebra arising from these data. When
+the pair arises from a bonafide block of a finite group algebra in
+characteristic $p$, the number of conjugacy classes of weights is supposed to
+be the number of simple modules in the block. We show that there is unique such
+pair associated with each Benson-Solomon exotic fusion system, and that the
+number of weights in a hypothetical Benson-Solomon block is $12$, independently
+of the field of definition. This is carried out in part by listing explicitly
+up to conjugacy all centric radical subgroups and their outer automorphism
+groups in these systems.
+"
+27723,MoleculeNet: A Benchmark for Molecular Machine Learning,"  Molecular machine learning has been maturing rapidly over the last few years.
+Improved methods and the presence of larger datasets have enabled machine
+learning algorithms to make increasingly accurate predictions about molecular
+properties. However, algorithmic progress has been limited due to the lack of a
+standard benchmark to compare the efficacy of proposed methods; most new
+algorithms are benchmarked on different datasets making it challenging to gauge
+the quality of proposed methods. This work introduces MoleculeNet, a large
+scale benchmark for molecular machine learning. MoleculeNet curates multiple
+public datasets, establishes metrics for evaluation, and offers high quality
+open-source implementations of multiple previously proposed molecular
+featurization and learning algorithms (released as part of the DeepChem open
+source library). MoleculeNet benchmarks demonstrate that learnable
+representations are powerful tools for molecular machine learning and broadly
+offer the best performance. However, this result comes with caveats. Learnable
+representations still struggle to deal with complex tasks under data scarcity
+and highly imbalanced classification. For quantum mechanical and biophysical
+datasets, the use of physics-aware featurizations can be more important than
+choice of particular learning algorithm.
+"
+27724,Analysis of fluctuations around non linear effective dynamics,"  We consider the derivation of effective equations approximating the many-body
+quantum dynamics of a large system of $N$ bosons in three dimensions,
+interacting through a two-body potential $N^{3\beta-1}V(N^\beta x)$. For any $0
+\leq \beta \leq 1$ well known results establish the trace norm convergence of
+the k-particle reduced density matrices associated with the solution of the
+many-body Schrödinger equation towards products of solutions of a
+one-particle non linear Schrödinger equation, as $N \to \infty$. In
+collaboration with C. Boccato and B. Schlein we studied fluctuations around the
+approximate non linear Schrödinger dynamics, obtaining for all $0 < \beta <
+1$ a norm approximation of the evolution of an appropriate class of data on the
+Fock space.
+"
+27725,Federated Reinforcement Learning,"  In reinforcement learning, building policies of high-quality is challenging
+when the feature space of states is small and the training data is limited.
+Directly transferring data or knowledge from an agent to another agent will not
+work due to the privacy requirement of data and models. In this paper, we
+propose a novel reinforcement learning approach to considering the privacy
+requirement and building Q-network for each agent with the help of other
+agents, namely federated reinforcement learning (FRL). To protect the privacy
+of data and models, we exploit Gausian differentials on the information shared
+with each other when updating their local models. In the experiment, we
+evaluate our FRL framework in two diverse domains, Grid-world and Text2Action
+domains, by comparing to various baselines.
+"
+27726,Data-Driven Koopman Analysis of Tropical Climate Space-Time Variability,"  We study nonlinear dynamics of the Earth's tropical climate system. For that,
+we apply a recently developed technique for feature extraction and mode
+decomposition of spatiotemporal data generated by ergodic dynamical systems.
+The method relies on constructing low-dimensional representations (temporal
+patterns) of signals using eigenfunctions of Koopman operators governing the
+evolution of observables in ergodic dynamical systems. We apply this technique
+to a variety of tropical climate datasets and extract a multiscale hierarchy of
+spatiotemporal patterns on diurnal to interannual timescales. In particular, we
+detect without prefiltering the input data modes operating on intraseasonal and
+shorter timescales that correspond to propagation of organized convection. We
+discuss the salient properties of these propagating features and in particular
+we focus on how the activity of certain types of these traveling patterns is
+related to lower-frequency dynamics. As an extension of this work, we discuss
+their potential predictability based on a range of nonparametric techniques and
+potential advances related to understanding the deterministic and stochastic
+aspects of the variability of these modes.
+"
+27727,Magnetohydrodynamic shocks in a dissipative quantum plasma with exchange-correlation effects,"  We investigate the nonlinear propagation of multidimensional magnetosonic
+shock waves (MSWs) in a dissipative quantum magnetoplasma. A macroscopic
+quantum magnetohydrodynamic (QMHD) model is used to include the quantum force
+associated with the Bohm potential, the pressure-like spin force, the exchange
+and correlation force of electrons, as well as the dissipative force due to the
+kinematic viscosity of ions and the magnetic diffusivity. The effects of these
+forces on the properties of arbitrary amplitude MSWs are examined numerically.
+It is found that the contribution from the exchange-correlation force appears
+to be dominant over those from the pressure gradient and the other similar
+quantum forces, and it results into a transition from monotonic to oscillatory
+shocks in presence of either the ion kinematic viscosity or the magnetic
+diffusivity.
+"
+27728,Blind Multiclass Ensemble Classification,"  The rising interest in pattern recognition and data analytics has spurred the
+development of innovative machine learning algorithms and tools. However, as
+each algorithm has its strengths and limitations, one is motivated to
+judiciously fuse multiple algorithms in order to find the ""best"" performing
+one, for a given dataset. Ensemble learning aims at such high-performance
+meta-algorithm, by combining the outputs from multiple algorithms. The present
+work introduces a blind scheme for learning from ensembles of classifiers,
+using a moment matching method that leverages joint tensor and matrix
+factorization. Blind refers to the combiner who has no knowledge of the
+ground-truth labels that each classifier has been trained on. A rigorous
+performance analysis is derived and the proposed scheme is evaluated on
+synthetic and real datasets.
+"
+27729,19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology,"  The marginal likelihood of a model is a key quantity for assessing the
+evidence provided by the data in support of a model. The marginal likelihood is
+the normalizing constant for the posterior density, obtained by integrating the
+product of the likelihood and the prior with respect to model parameters. Thus,
+the computational burden of computing the marginal likelihood scales with the
+dimension of the parameter space. In phylogenetics, where we work with tree
+topologies that are high-dimensional models, standard approaches to computing
+marginal likelihoods are very slow. Here we study methods to quickly compute
+the marginal likelihood of a single fixed tree topology. We benchmark the speed
+and accuracy of 19 different methods to compute the marginal likelihood of
+phylogenetic topologies on a suite of real datasets. These methods include
+several new ones that we develop explicitly to solve this problem, as well as
+existing algorithms that we apply to phylogenetic models for the first time.
+Altogether, our results show that the accuracy of these methods varies widely,
+and that accuracy does not necessarily correlate with computational burden. Our
+newly developed methods are orders of magnitude faster than standard
+approaches, and in some cases, their accuracy rivals the best established
+estimators.
+"
+27730,$θ_0$ thermal Josephson junction,"  We predict the thermal counterpart of the anomalous Josephson effect in
+superconductor/ferromagnet/superconductor junctions with non-coplanar magnetic
+texture. The heat current through the junction is shown to have the
+phase-sensitive interference component proportional to $\cos(\theta -
+\theta_0)$, where $\theta$ is the Josephson phase difference and $\theta_0$ is
+the texture-dependent phase shift. In the generic tri-layer magnetic structure
+with the spin-filtering tunnel barrier $\theta_0$ is determined by the spin
+chirality of magnetic configuration and can be considered as the direct
+manifestation of the energy transport with participation of spin-triplet Cooper
+pairs. In case of the ideal spin filter the phase shift is shown to be robust
+against spin relaxation caused by the spin-orbital scattering. Possible
+applications of the coupling between heat flow and magnetic precession are
+discussed.
+"
+27731,The tilting rate of the Milky Way's disc,"  We present tilting rates for galaxies comparable to the Milky Way (MW) in a
+$\Lambda$ cold dark matter cosmological hydrodynamical simulation, and compare
+these with the predicted tilting rate detection limit of the {\it Gaia}
+satellite $0.28\degrees$Gyr$^{-1}$. We first identify galaxies with mass
+comparable to the MW ($9 \times 10^{11} \le M_{200} \le 1.2 \times 10^{12}
+\Msun $) and consider the tilting rates between $z=0.3$ and $0$. This sample
+yields a tilting rate of $7.6\degrees \pm 4.5\degrees$Gyr$^{-1}$. We constrain
+our sample further to exclude any galaxies that have high stellar accretion
+during the same time. We still find significant tilting, with an average rate
+of $6.3\degrees$Gyr$^{-1}$. Both subsamples tilt with rates significantly above
+{\it Gaia}'s predicted detection limit. We show that our sample of galaxies
+covers a wide range of environments, including some similar to the MW's. We
+find galaxies in denser regions tilt with higher rates then galaxies in less
+dense regions. We also find correlations between the angular misalignment of
+the hot gas corona, and the tilting rate. {\it Gaia} is likely to be able to
+directly measure tilting in the MW. Such a detection will provide an important
+constraint on the environment of the MW, including the rate of gas cooling onto
+the disc, the shape and orientation of its dark matter halo, and the mass of
+the Large Magellanic Cloud. Conversely, failure to detect tilting may suggest
+the MW is in a very quiet configuration.
+"
+27732,FFLO correlation and free fluids in the one-dimensional attractive Hubbard model,"  In this Rapid Communication we show that low energy macroscopic properties of
+the one-dimensional (1D) attractive Hubbard model exhibit two fluids of bound
+pairs and of unpaired fermions. Using the thermodynamic Bethe ansatz equations
+of the model, we first determine the low temperature phase diagram and
+analytically calculate the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) pairing
+correlation function for the partially-polarized phase. We then show that for
+such a FFLO-like state in the low density regime the effective chemical
+potentials of bound pairs and unpaired fermions behave like two free fluids.
+Consequently, the susceptibility, compressibility and specific heat obey simple
+additivity rules, indicating the `free' particle nature of interacting fermions
+on a 1D lattice. In contrast to the continuum Fermi gases, the correlation
+critical exponents and thermodynamics of the attractive Hubbard model
+essentially depend on two lattice interacting parameters. Finally, we study
+scaling functions, the Wilson ratio and susceptibility which provide universal
+macroscopic properties/dimensionless constants of interacting fermions at low
+energy.
+"
+27733,Genus one fibered knots in 3-manifolds with reducible genus two Heegaard splittings,"  We give a necessary and sufficient condition for a simple closed curve on the
+boundary of a genus two handlebody to decompose the handlebody into (torus with
+one boundary component times [0,1]. We use this condition to decide whether a
+simple closed curve on a genus two Heegaard surface is a GOF-knot (genus one
+fibered knot) which induces the Heegaard splitting. By using this, we determine
+the number and the positions with respect to the Heegaard splittings of
+GOF-knots in the 3-manifolds with reducible genus two Heegaard splittings. This
+is another proof of results of Morimoto and Baker.
+"
+27734,Bayesian Dyadic Trees and Histograms for Regression,"  Many machine learning tools for regression are based on recursive
+partitioning of the covariate space into smaller regions, where the regression
+function can be estimated locally. Among these, regression trees and their
+ensembles have demonstrated impressive empirical performance. In this work, we
+shed light on the machinery behind Bayesian variants of these methods. In
+particular, we study Bayesian regression histograms, such as Bayesian dyadic
+trees, in the simple regression case with just one predictor. We focus on the
+reconstruction of regression surfaces that are piecewise constant, where the
+number of jumps is unknown. We show that with suitably designed priors,
+posterior distributions concentrate around the true step regression function at
+a near-minimax rate. These results do not require the knowledge of the true
+number of steps, nor the width of the true partitioning cells. Thus, Bayesian
+dyadic regression trees are fully adaptive and can recover the true piecewise
+regression function nearly as well as if we knew the exact number and location
+of jumps. Our results constitute the first step towards understanding why
+Bayesian trees and their ensembles have worked so well in practice. As an
+aside, we discuss prior distributions on balanced interval partitions and how
+they relate to an old problem in geometric probability. Namely, we relate the
+probability of covering the circumference of a circle with random arcs whose
+endpoints are confined to a grid, a new variant of the original problem.
+"
+27735,"La ""Nihaya al-sul fi tashih al-usul"" d'Ibn al-Shatir: Édition, traduction et commentaire mathématique","  This book is a critical edition of a treatise of astronomy by the syrian
+scholar Ibn al-Shatir (1304-1375). The arabic text has been established on the
+basis of several manuscript copies, and it has been translated into french; a
+bilingual index is also provided. This treatise is one of the latest and one of
+the most elaborate medieval work on theoretical astronomy; it is famous today
+for having provided the first viable lunar model in terms of the distance
+Moon--Earth. Although demonstrations have been consigned to another manuscript
+which is now lost, we reconstruct the physical models in our mathematical
+commentary, and we attempt at a better historical understanding of the nascent
+concepts of space transformations, solid reference frames, trajectory, etc.
+Predictions of the models are to be compared to observations and to other
+contemporary models, as well as theoretical premises; in turn, when records of
+observations are missing, those have to be recalculated by modern means. Part
+of our commentary is devoted to the latitudinal movement of the five planets:
+Ibn al-Shatir's work on this problem belongs to a tradition initiated by Ibn
+al-Haytham three centuries before. Ibn al-Shatir addresses a clever criticism
+to the work of his predecessor Nasir al-Din al-Tusi on this problem, thus
+revealing a better understanding of affine rotations in space. Many other
+criticisms by Ibn al-Shatir, especially to the astronomers working at Maragha
+during the XIIIth century, also contribute to a better knowledge of their own
+work.
+"
+27736,Microlocal analysis and beyond,"  We shall explain how the idea of microlocal analysis of the seventies has
+been reformulated in the framework of sheaf theory in the eighties and then
+applied to various branches of mathematics, such as linear partial differential
+equations or symplectic topology.
+"
+27737,Link Prediction in Dynamic Graphs for Recommendation,"  Recent advances in employing neural networks on graph domains helped push the
+state of the art in link prediction tasks, particularly in recommendation
+services. However, the use of temporal contextual information, often modeled as
+dynamic graphs that encode the evolution of user-item relationships over time,
+has been overlooked in link prediction problems. In this paper, we consider the
+hypothesis that leveraging such information enables models to make better
+predictions, proposing a new neural network approach for this. Our experiments,
+performed on the widely used ML-100k and ML-1M datasets, show that our approach
+produces better predictions in scenarios where the pattern of user-item
+relationships change over time. In addition, they suggest that existing
+approaches are significantly impacted by those changes.
+"
+27738,An Empirical Evaluation of Visual Question Answering for Novel Objects,"  We study the problem of answering questions about images in the harder
+setting, where the test questions and corresponding images contain novel
+objects, which were not queried about in the training data. Such setting is
+inevitable in real world-owing to the heavy tailed distribution of the visual
+categories, there would be some objects which would not be annotated in the
+train set. We show that the performance of two popular existing methods drop
+significantly (up to 28%) when evaluated on novel objects cf. known objects. We
+propose methods which use large existing external corpora of (i) unlabeled
+text, i.e. books, and (ii) images tagged with classes, to achieve novel object
+based visual question answering. We do systematic empirical studies, for both
+an oracle case where the novel objects are known textually, as well as a fully
+automatic case without any explicit knowledge of the novel objects, but with
+the minimal assumption that the novel objects are semantically related to the
+existing objects in training. The proposed methods for novel object based
+visual question answering are modular and can potentially be used with many
+visual question answering architectures. We show consistent improvements with
+the two popular architectures and give qualitative analysis of the cases where
+the model does well and of those where it fails to bring improvements.
+"
+27739,Forbidden coherent transfer observed between two realizations of quasi-harmonic spin system,"  The multi-level system $^{55}$Mn$^{2+}$ is used to generate two
+pseudo-harmonic level systems, as representations of the same electronic
+sextuplet at different nuclear spin projections. The systems are coupled using
+a forbidden nuclear transition induced by the crystalline anisotropy. We
+demonstrate Rabi oscillations between the two representations in conditions
+similar to two coupled quasi-harmonic quantum oscillators. Rabi oscillations
+are performed at a detuned pumping frequency which matches energy difference
+between electro-nuclear states of different oscillators. We measure a coupling
+stronger than the decoherence rate, to indicate the possibility fast
+information exchange between the systems.
+"
+27740,Flat affine subvarieties in Oeljeklaus-Toma manifolds,"  The Oeljeklaus-Toma (OT-) manifolds are compact, complex, non-Kahler
+manifolds constructed by Oeljeklaus and Toma, and generalizing the Inoue
+surfaces. Their construction uses the number-theoretic data: a number field $K$
+and a torsion-free subgroup $U$ in the group of units of the ring of integers
+of $K$, with rank of $U$ equal to the number of real embeddings of $K$. We
+prove that any complex subvariety of smallest possible positive dimension in an
+OT-manifold is also flat affine. This is used to show that if all non-trivial
+elements in $U$ are primitive in $K$, then $X$ contains no proper complex
+subvarieties.
+"
+27741,Adaptive Threshold Sampling and Estimation,"  Sampling is a fundamental problem in both computer science and statistics. A
+number of issues arise when designing a method based on sampling. These include
+statistical considerations such as constructing a good sampling design and
+ensuring there are good, tractable estimators for the quantities of interest as
+well as computational considerations such as designing fast algorithms for
+streaming data and ensuring the sample fits within memory constraints.
+Unfortunately, existing sampling methods are only able to address all of these
+issues in limited scenarios.
+We develop a framework that can be used to address these issues in a broad
+range of scenarios. In particular, it addresses the problem of drawing and
+using samples under some memory budget constraint. This problem can be
+challenging since the memory budget forces samples to be drawn
+non-independently and consequently, makes computation of resulting estimators
+difficult.
+At the core of the framework is the notion of a data adaptive thresholding
+scheme where the threshold effectively allows one to treat the non-independent
+sample as if it were drawn independently. We provide sufficient conditions for
+a thresholding scheme to allow this and provide ways to build and compose such
+schemes.
+Furthermore, we provide fast algorithms to efficiently sample under these
+thresholding schemes.
+"
+27742,The European Far-Infrared Space Roadmap,"  The European Far-Infrared (FIR) Space Roadmap focuses on fundamental, yet
+still unresolved, astrophysical questions that can only be answered through a
+far-infrared space mission and gives an overview of the technology required to
+answer them. The document discusses topics ranging from Solar System and Planet
+Formation, Our Galaxy and nearby Galaxies and Distant Galaxies and Galaxy
+Evolution. The FIR Roadmap was open to comments from the wider astronomical
+community following a presentation during EWASS 2016.
+"
+27743,SLDR-DL: A Framework for SLD-Resolution with Deep Learning,"  This paper introduces an SLD-resolution technique based on deep learning.
+This technique enables neural networks to learn from old and successful
+resolution processes and to use learnt experiences to guide new resolution
+processes. An implementation of this technique is named SLDR-DL. It includes a
+Prolog library of deep feedforward neural networks and some essential functions
+of resolution. In the SLDR-DL framework, users can define logical rules in the
+form of definite clauses and teach neural networks to use the rules in
+reasoning processes.
+"
+27744,A note on estimation in a simple probit model under dependency,"  We consider a probit model without covariates, but the latent Gaussian
+variables having compound symmetry covariance structure with a single parameter
+characterizing the common correlation. We study the parameter estimation
+problem under such one-parameter probit models. As a surprise, we demonstrate
+that the likelihood function does not yield consistent estimates for the
+correlation. We then formally prove the parameter's nonestimability by deriving
+a non-vanishing minimax lower bound. This counter-intuitive phenomenon provides
+an interesting insight that one bit information of the latent Gaussian
+variables is not sufficient to consistently recover their correlation. On the
+other hand, we further show that trinary data generated from the Gaussian
+variables can consistently estimate the correlation with parametric convergence
+rate. Hence we reveal a phase transition phenomenon regarding the
+discretization of latent Gaussian variables while preserving the estimability
+of the correlation.
+"
+27745,Gaussian Multiple Access Channels with One-Bit Quantizer at the Receiver,"  The capacity region of a two-transmitter Gaussian multiple access channel
+(MAC) under average input power constraints is studied, when the receiver
+employs a zero-threshold one-bit analog-to-digital converter (ADC). It is
+proved that the input distributions of the two transmitters that achieve the
+boundary points of the capacity region are discrete. Based on the position of a
+boundary point, upper bounds on the number of the mass points of the
+corresponding distributions are derived.
+"
+27746,Goal-Oriented Mutation Testing with Focal Methods,"  Mutation testing is the state-of-the-art technique for assessing the
+fault-detection capacity of a test suite. Unfortunately, mutation testing
+consumes enormous computing resources because it runs the whole test suite for
+each and every injected mutant. In this paper we explore fine-grained
+traceability links at method level (named focal methods), to reduce the
+execution time of mutation testing and to verify the quality of the test cases
+for each individual method, instead of the usually verified overall test suite
+quality. Validation of our approach on the open source Apache Ant project shows
+a speed-up of 573.5x for the mutants located in focal methods with a quality
+score of 80%.
+"
+27747,Learning Deep Features via Congenerous Cosine Loss for Person Recognition,"  Person recognition aims at recognizing the same identity across time and
+space with complicated scenes and similar appearance. In this paper, we propose
+a novel method to address this task by training a network to obtain robust and
+representative features. The intuition is that we directly compare and optimize
+the cosine distance between two features - enlarging inter-class distinction as
+well as alleviating inner-class variance. We propose a congenerous cosine loss
+by minimizing the cosine distance between samples and their cluster centroid in
+a cooperative way. Such a design reduces the complexity and could be
+implemented via softmax with normalized inputs. Our method also differs from
+previous work in person recognition that we do not conduct a second training on
+the test subset. The identity of a person is determined by measuring the
+similarity from several body regions in the reference set. Experimental results
+show that the proposed approach achieves better classification accuracy against
+previous state-of-the-arts.
+"
+27748,Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces,"  It has been a long-standing problem to efficiently learn a halfspace using as
+few labels as possible in the presence of noise. In this work, we propose an
+efficient Perceptron-based algorithm for actively learning homogeneous
+halfspaces under the uniform distribution over the unit sphere. Under the
+bounded noise condition~\cite{MN06}, where each label is flipped with
+probability at most $\eta < \frac 1 2$, our algorithm achieves a near-optimal
+label complexity of
+$\tilde{O}\left(\frac{d}{(1-2\eta)^2}\ln\frac{1}{\epsilon}\right)$ in time
+$\tilde{O}\left(\frac{d^2}{\epsilon(1-2\eta)^3}\right)$. Under the adversarial
+noise condition~\cite{ABL14, KLS09, KKMS08}, where at most a $\tilde
+\Omega(\epsilon)$ fraction of labels can be flipped, our algorithm achieves a
+near-optimal label complexity of $\tilde{O}\left(d\ln\frac{1}{\epsilon}\right)$
+in time $\tilde{O}\left(\frac{d^2}{\epsilon}\right)$. Furthermore, we show that
+our active learning algorithm can be converted to an efficient passive learning
+algorithm that has near-optimal sample complexities with respect to $\epsilon$
+and $d$.
+"
+27749,Real-time eSports Match Result Prediction,"  In this paper, we try to predict the winning team of a match in the
+multiplayer eSports game Dota 2. To address the weaknesses of previous work, we
+consider more aspects of prior (pre-match) features from individual players'
+match history, as well as real-time (during-match) features at each minute as
+the match progresses. We use logistic regression, the proposed Attribute
+Sequence Model, and their combinations as the prediction models. In a dataset
+of 78362 matches where 20631 matches contain replay data, our experiments show
+that adding more aspects of prior features improves accuracy from 58.69% to
+71.49%, and introducing real-time features achieves up to 93.73% accuracy when
+predicting at the 40th minute.
+"
+27750,Similarity Learning for High-Dimensional Sparse Data,"  A good measure of similarity between data points is crucial to many tasks in
+machine learning. Similarity and metric learning methods learn such measures
+automatically from data, but they do not scale well respect to the
+dimensionality of the data. In this paper, we propose a method that can learn
+efficiently similarity measure from high-dimensional sparse data. The core idea
+is to parameterize the similarity measure as a convex combination of rank-one
+matrices with specific sparsity structures. The parameters are then optimized
+with an approximate Frank-Wolfe procedure to maximally satisfy relative
+similarity constraints on the training data. Our algorithm greedily
+incorporates one pair of features at a time into the similarity measure,
+providing an efficient way to control the number of active features and thus
+reduce overfitting. It enjoys very appealing convergence guarantees and its
+time and memory complexity depends on the sparsity of the data instead of the
+dimension of the feature space. Our experiments on real-world high-dimensional
+datasets demonstrate its potential for classification, dimensionality reduction
+and data exploration.
+"
+27751,Quantum mean field asymptotics and multiscale analysis,"  We study, via multiscale analysis, some defect of compactness phenomena which
+occur in bosonic and fermionic quantum mean-field problems. The approach relies
+on a combination of mean-field asymptotics and second microlocalized
+semiclassical measures. The phase space geometric description is illustrated by
+various examples.
+"
+27752,The HI content of isolated ultra-diffuse galaxies: A sign of multiple formation mechanisms?,"  We report on the results of radio observations in the 21 cm emission line of
+atomic hydrogen (HI) of four relatively isolated ultra-diffuse galaxies (UDGs):
+DGSAT I, R-127-1, M-161-1, and SECCO-dI-2. Our Effelsberg observations resulted
+in non-detections for the first three UDGs, and a clear detection for the last.
+DGSAT I, R-127-1, and M-161-1 are quiescent galaxies with gas fractions that
+are much lower than those of typical field galaxies of the same stellar mass.
+On the other hand, SECCO-dI-2 is a star forming gas-rich dwarf, similar to two
+other field UDGs that have literature HI data: SECCO-dI-1 and UGC 2162. This
+group of three gas-rich UDGs have stellar and gaseous properties that are
+compatible with a recently proposed theoretical mechanism for the formation of
+UDGs, based on feedback-driven outflows. In contrast, the physical
+characteristics of R-127-1 and M-161-1 are puzzling, given their isolated
+nature. We interpret this dichotomy in the gaseous properties of field UDGs as
+a sign of the existence of multiple mechanisms for their formation, with the
+formation of the quiescent gas-poor UDGs remaining a mystery.
+"
+27753,Superfluid Density of Neutrons in the Inner Crust of Neutron Stars: New Life for Pulsar Glitch Models,"  Calculations of the effects of band structure on the neutron superfluid
+density in the crust of neutron stars made under the assumption that the
+effects of pairing are small [N. Chamel, Phys. Rev. C 85, 035801 (2012)] lead
+to moments of inertia of superfluid neutrons so small that the crust alone is
+insufficient to account for the magnitude of neutron star glitches. Inspired by
+earlier work on ultracold atomic gases in an optical lattice, we investigate
+fermions with attractive interactions in a periodic lattice in the mean-field
+approximation. The effects of band structure are suppressed when the pairing
+gap is of order or greater than the strength of the lattice potential. By
+applying the results to the inner crust of neutron stars, we conclude that the
+reduction of the neutron superfluid density is considerably less than
+previously estimated and, consequently, it is premature to rule out models of
+glitches based on neutron superfluidity in the crust.
+"
+27754,Scaling Active Search using Linear Similarity Functions,"  Active Search has become an increasingly useful tool in information retrieval
+problems where the goal is to discover as many target elements as possible
+using only limited label queries. With the advent of big data, there is a
+growing emphasis on the scalability of such techniques to handle very large and
+very complex datasets.
+In this paper, we consider the problem of Active Search where we are given a
+similarity function between data points. We look at an algorithm introduced by
+Wang et al. [2013] for Active Search over graphs and propose crucial
+modifications which allow it to scale significantly. Their approach selects
+points by minimizing an energy function over the graph induced by the
+similarity function on the data. Our modifications require the similarity
+function to be a dot-product between feature vectors of data points, equivalent
+to having a linear kernel for the adjacency matrix. With this, we are able to
+scale tremendously: for $n$ data points, the original algorithm runs in
+$O(n^2)$ time per iteration while ours runs in only $O(nr + r^2)$ given
+$r$-dimensional features.
+We also describe a simple alternate approach using a weighted-neighbor
+predictor which also scales well. In our experiments, we show that our method
+is competitive with existing semi-supervised approaches. We also briefly
+discuss conditions under which our algorithm performs well.
+"
+27755,Detection of Rotational Spectral Variation on the M-type asteroid (16) Psyche,"  The asteroid (16) Psyche is of scientific interest because it contains ~ 1%
+of the total mass of the asteroid belt and is thought to be the remnant
+metallic core of a protoplanet. Radar observations have indicated the
+significant presence of metal on the surface with a small percentage of
+silicates. Prior ground-based observations showed rotational variations in the
+near-infrared (NIR) spectra and radar albedo of this asteroid. However, no
+comprehensive study that combines multi-wavelength data has been conducted so
+far. Here we present rotationally resolved NIR spectra (0.7-2.5 microns) of
+(16) Psyche obtained with the NASA Infrared Telescope Facility. These data have
+been combined with shape models of the asteroid for each rotation phase.
+Spectral band parameters extracted from the NIR spectra show that the pyroxene
+band center varies from ~ 0.92 to 0.94 microns. Band center values were used to
+calculate the pyroxene chemistry of the asteroid, whose average value was found
+to be Fs30En65Wo5. Variations in the band depth were also observed, with values
+ranging from 1.0 to 1.5%. Using a new laboratory spectral calibration we
+estimated an average orthopyroxene content of 6+/-1%. The mass-deficit region
+of Psyche, which exhibits the highest radar albedo, also shows the highest
+value for spectral slope and the minimum band depth. The spectral
+characteristics of Psyche suggest that its parent body did not have the typical
+structure expected for a differentiated body or that the sequence of events
+that led to its current state was more complex than previously thought.
+"
+27756,Biologically Inspired Feedforward Supervised Learning for Deep Self-Organizing Map Networks,"  In this study, we propose a novel deep neural network and its supervised
+learning method that uses a feedforward supervisory signal. The method is
+inspired by the human visual system and performs human-like association-based
+learning without any backward error propagation. The feedforward supervisory
+signal that produces the correct result is preceded by the target signal and
+associates its confirmed label with the classification result of the target
+signal. It effectively uses a large amount of information from the feedforward
+signal, and forms a continuous and rich learning representation. The method is
+validated using visual recognition tasks on the MNIST handwritten dataset.
+"
+27757,"A Model for Information Networks: Efficiency, Stability and Dynamics","  We introduce a simple network model that is inspired by social information
+networks such as twitter. Agents are nodes, connecting to another agent by
+building a directed edge has a cost, and reaching other agents via short
+directed paths has a benefit; in effect, an agent wants to reach others
+quickly, but without the cost of directly connecting each and every one. Even
+in its simplest form, edges in this framework are neither substitutes or
+complements in general; hence, standard techniques are required to study the
+model's properties and dynamics do not apply.
+We prove that an asynchronous edge dynamics always converge to a stable
+network; in fact, for this convergence is fast for a range of parameters.
+Moreover, the set of stable networks are nontrivial and can support the type of
+network structures that have been observed to appear in social information
+networks -- from community clusters to broadcast networks, depending on the
+parameters many natural formations can emerge. We further study the static
+game, and give classes of stable and efficient networks for nontrivial
+parameter ranges. We close several problems, and leave many interesting ones
+open.
+"
+27758,Relaxation dynamics of modulated magnetic phases in skyrmion host GaV4S8: an ac magnetic susceptibility study,"  We report on the slow magnetization dynamics observed upon the magnetic phase
+transitions of GaV4S8, a multiferroic compound featuring a long-ranged
+cycloidal magnetic order and a Néel-type skyrmion lattice in a relatively
+broad temperature range below its Curie temperature. The fundamental difference
+between GaV4S8 and the chiral helimagnets, wherein the skyrmion phase was first
+discovered, lies within the polar symmetry of GaV4S8, promoting a cycloidal
+instead of a helical magnetic order and rendering the magnetic phase diagram
+essentially different from that in the cubic helimagnets. Our ac magnetic
+susceptibility study reveals slow relaxation dynamics at the field-driven phase
+transitions between the cycloidal, skyrmion lattice and field-polarized states.
+At each phase boundary, the characteristic relaxation times were found to
+exhibit a strong temperature dependence, starting from the minute range at low
+temperatures, decreasing to the micro- to millisecond range at higher
+temperatures.
+"
+27759,Spectral Variability of Two Rapidly Rotating Brown Dwarfs: 2MASS J08354256-0819237 and 2MASS J18212815+1414010,"  L dwarfs exhibit low-level, rotationally-modulated photometric variability
+generally associated with heterogeneous, cloud-covered atmospheres. The
+spectral character of these variations yields insight into the particle sizes
+and vertical structure of the clouds. Here we present the results of a high
+precision, ground-based, near-infrared, spectral monitoring study of two
+mid-type L dwarfs that have variability reported in the literature, 2MASS
+J08354256-0819237 and 2MASS J18212815+1414010, using the SpeX instrument on the
+Infrared Telescope Facility. By simultaneously observing a nearby reference
+star, we achieve <0.15% per-band sensitivity in relative brightness changes
+across the 0.9--2.4um bandwidth. We find that 2MASS J0835-0819 exhibits
+marginal (< ~0.5% per band) variability with no clear spectral dependence,
+while 2MASS J1821+1414 varies by up to +/-1.5% at 0.9 um, with the variability
+amplitude declining toward longer wavelengths. The latter result extends the
+variability trend observed in prior HST/WFC3 spectral monitoring of 2MASS
+J1821+1414, and we show that the full 0.9-2.4 um variability amplitude spectrum
+can be reproduced by Mie extinction from dust particles with a log-normal
+particle size distribution with a median radius of 0.24 um. We do not detect
+statistically significant phase variations with wavelength. The different
+variability behavior of 2MASS J0835-0819 and 2MASS J1821+1414 suggests
+dependencies on viewing angle and/or overall cloud content, underlying factors
+that can be examined through a broader survey.
+"
+27760,DLL: A Blazing Fast Deep Neural Network Library,"  Deep Learning Library (DLL) is a new library for machine learning with deep
+neural networks that focuses on speed. It supports feed-forward neural networks
+such as fully-connected Artificial Neural Networks (ANNs) and Convolutional
+Neural Networks (CNNs). It also has very comprehensive support for Restricted
+Boltzmann Machines (RBMs) and Convolutional RBMs. Our main motivation for this
+work was to propose and evaluate novel software engineering strategies with
+potential to accelerate runtime for training and inference. Such strategies are
+mostly independent of the underlying deep learning algorithms. On three
+different datasets and for four different neural network models, we compared
+DLL to five popular deep learning frameworks. Experimentally, it is shown that
+the proposed framework is systematically and significantly faster on CPU and
+GPU. In terms of classification performance, similar accuracies as the other
+frameworks are reported.
+"
+27761,Ax-Schanuel for Shimura varieties,"  We prove the Ax-Schanuel theorem for a general (pure) Shimura variety.
+"
+27762,Fast initial conditions for Glauber dynamics,"  In the study of Markov chain mixing times, analysis has centered on the
+performance from a worst-case starting state. Here, in the context of Glauber
+dynamics for the one-dimensional Ising model, we show how new ideas from
+information percolation can be used to establish mixing times from other
+starting states. At high temperatures we show that the alternating initial
+condition is asymptotically the fastest one, and, surprisingly, its mixing time
+is faster than at infinite temperature, accelerating as the inverse-temperature
+$\beta$ ranges from 0 to $\beta_0=\frac12\mathrm{arctanh}(\frac13)$. Moreover,
+the dominant test function depends on the temperature: at $\beta<\beta_0$ it is
+autocorrelation, whereas at $\beta>\beta_0$ it is the Hamiltonian.
+"
+27763,New measurements on water ice photodesorption and product formation under ultraviolet irradiation,"  The photodesorption of icy grain mantles has been claimed to be responsible
+for the abundance of gas-phase molecules toward cold regions. Being water a
+ubiquitous molecule, it is crucial to understand its role in photochemistry and
+its behavior under an ultraviolet field. We report new measurements on the
+UV-photodesorption of water ice and its H$_2$, OH, and O$_2$ photoproducts
+using a calibrated quadrupole mass spectrometer. Solid water was deposited
+under ultra-high-vacuum conditions and then UV-irradiated at various
+temperatures starting from 8 K with a microwave discharged hydrogen lamp.
+Deuterated water was used for confirmation of the results. We found a
+photodesorption yield of 1.3 $\times$ 10$^{-3}$ molecules per incident photon
+for water, and 0.7 $\times$ 10$^{-3}$ molecules per incident photon for
+deuterated water at the lowest irradiation temperature, 8 K. The
+photodesorption yield per absorbed photon is given and comparison with
+astrophysical scenarios, where water ice photodesorption could account for the
+presence of gas-phase water toward cold regions in the absence of a thermal
+desorption process is addressed.
+"
+27764,Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Tehran metropolitan area in Iran,"  The SLEUTH model, based on the Cellular Automata (CA), can be applied to city
+development simulation in metropolitan areas. In this study the SLEUTH model
+was used to model the urban expansion and predict the future possible behavior
+of the urban growth in Tehran. The fundamental data were five Landsat TM and
+ETM images of 1988, 1992, 1998, 2001 and 2010. Three scenarios were designed to
+simulate the spatial pattern. The first scenario assumed historical
+urbanization mode would persist and the only limitations for development were
+height and slope. The second one was a compact scenario which makes the growth
+mostly internal and limited the expansion of suburban areas. The last scenario
+proposed a polycentric urban structure which let the little patches grow
+without any limitation and would not consider the areas beyond the specific
+buffer zone from the larger patches for development. Results showed that the
+urban growth rate was greater in the first scenario in comparison with the
+other two scenarios. Also it was shown that the third scenario was more
+suitable for Tehran since it could avoid undesirable effects such as congestion
+and pollution and was more in accordance with the conditions of Tehran city.
+"
+27765,Rayleigh scattering in dense fluid helium,"  Iglesias et al. (2002) showed that the Rayleigh scattering from helium atoms
+decreases by collective effects in the atmospheres of cool white dwarf stars.
+Their study is here extended to consider an accurate evaluation of the atomic
+polarizability and the density effects involved in the Rayleigh cross section
+over a wide density-temperature region. The dynamic dipole polarizability of
+helium atoms in the ground state is determinated with the oscillator-strength
+distribution approach. The spectral density of oscillator strength considered
+includes most significant single and doubly excited transitions to discrete and
+continuum energies. Static and dynamic polarizability results are confronted
+with experiments and other theoretical evaluations shown a very good agreement.
+In addition, the refractive index of helium is evaluated with the
+Lorentz-Lorenz equation and shows a satisfactory agreement with the most recent
+experiments. The effect of spatial correlation of atoms on the Rayleigh
+scattering is calculated with Monte Carlo simulations and effective energy
+potentials that represent the particle interactions, covering fluid densities
+between 0.005 and a few g/cm$^3$ and temperatures between $1000$ K and $15000$
+K. We provide analytical fits from which the Rayleigh cross section of fluid
+helium can be easily calculated at wavelength $\lambda>505.35$ \AA.
+Collision-induced light scattering was estimated to be the dominant scattering
+process at densities greater than 1-2 g/cm$^3$ depending on the temperature.
+"
+27766,Is Generator Conditioning Causally Related to GAN Performance?,"  Recent work (Pennington et al, 2017) suggests that controlling the entire
+distribution of Jacobian singular values is an important design consideration
+in deep learning. Motivated by this, we study the distribution of singular
+values of the Jacobian of the generator in Generative Adversarial Networks
+(GANs). We find that this Jacobian generally becomes ill-conditioned at the
+beginning of training. Moreover, we find that the average (with z from p(z))
+conditioning of the generator is highly predictive of two other ad-hoc metrics
+for measuring the 'quality' of trained GANs: the Inception Score and the
+Frechet Inception Distance (FID). We test the hypothesis that this relationship
+is causal by proposing a 'regularization' technique (called Jacobian Clamping)
+that softly penalizes the condition number of the generator Jacobian. Jacobian
+Clamping improves the mean Inception Score and the mean FID for GANs trained on
+several datasets. It also greatly reduces inter-run variance of the
+aforementioned scores, addressing (at least partially) one of the main
+criticisms of GANs.
+"
+27767,Improving Negative Sampling for Word Representation using Self-embedded Features,"  Although the word-popularity based negative sampler has shown superb
+performance in the skip-gram model, the theoretical motivation behind
+oversampling popular (non-observed) words as negative samples is still not well
+understood. In this paper, we start from an investigation of the gradient
+vanishing issue in the skipgram model without a proper negative sampler. By
+performing an insightful analysis from the stochastic gradient descent (SGD)
+learning perspective, we demonstrate that, both theoretically and intuitively,
+negative samples with larger inner product scores are more informative than
+those with lower scores for the SGD learner in terms of both convergence rate
+and accuracy. Understanding this, we propose an alternative sampling algorithm
+that dynamically selects informative negative samples during each SGD update.
+More importantly, the proposed sampler accounts for multi-dimensional
+self-embedded features during the sampling process, which essentially makes it
+more effective than the original popularity-based (one-dimensional) sampler.
+Empirical experiments further verify our observations, and show that our
+fine-grained samplers gain significant improvement over the existing ones
+without increasing computational complexity.
+"
+27768,Temporal Discounting in Technical Debt: How do Software Practitioners Discount the Future?,"  Technical Debt management decisions always imply a trade-off among outcomes
+at different points in time. In such intertemporal choices, distant outcomes
+are often valued lower than close ones, a phenomenon known as temporal
+discounting. Technical Debt research largely develops prescriptive approaches
+for how software engineers should make such decisions. Few have studied how
+they actually make them. This leaves open central questions about how software
+practitioners make decisions.
+This paper investigates how software practitioners discount uncertain future
+outcomes and whether they exhibit temporal discounting. We adopt experimental
+methods from intertemporal choice, an active area of research. We administered
+an online questionnaire to 33 developers from two companies in which we
+presented choices between developing a feature and making a longer-term
+investment in architecture. The results show wide-spread temporal discounting
+with notable differences in individual behavior. The results are consistent
+with similar studies in consumer behavior and raise a number of questions about
+the causal factors that influence temporal discounting in software engineering.
+As the first empirical study on intertemporal choice in SE, the paper
+establishes an empirical basis for understanding how software developers
+approach intertemporal choice and provides a blueprint for future studies.
+"
+27769,L483: Warm Carbon-Chain Chemistry Source Harboring Hot Corino Activity,"  The Class 0 protostar, L483, has been observed in various molecular lines in
+the 1.2 mm band at a sub-arcsecond resolution with ALMA. An infalling-rotating
+envelope is traced by the CS line, while a very compact component with a broad
+velocity width is observed for the CS, SO, HNCO, NH$_2$CHO, and HCOOCH$_3$
+lines. Although this source is regarded as the warm carbon-chain chemistry
+(WCCC) candidate source at a 1000 au scale, complex organic molecules
+characteristic of hot corinos such as NH$_2$CHO and HCOOCH$_3$ are detected in
+the vicinity of the protostar. Thus, both hot corino chemistry and WCCC are
+seen in L483. Although such a mixed chemical character source has been
+recognized as an intermediate source in previous single-dish observations, we
+here report the first spatially-resolved detection. A kinematic structure of
+the infalling-rotating envelope is roughly explained by a simple ballistic
+model with the protostellar mass of 0.1--0.2 $M_\odot$ and the radius of the
+centrifugal barrier (a half of the centrifugal radius) of 30--200 au, assuming
+the inclination angle of 80\degr\ (0\degr\ for a face-on). The broad line
+emission observed in the above molecules most likely comes from the disk
+component inside the centrifugal barrier. Thus, a drastic chemical change is
+seen around the centrifugal barrier.
+"
+27770,Finite-Length Bounds for Joint Source-Channel Coding with Markovian Source and Additive Channel Noise to Achieve Large and Moderate Deviation Bounds,"  We derive novel upper and lower finite-length bounds of the error probability
+in joint source-channel coding when the source obeys an ergodic Markov process
+and the channel is a Markovian additive channel or a Markovian conditional
+additive channel. These bounds are tight in the large and moderate deviation
+regimes.
+"
+27771,Effect of Cell-Selection on the Effective Fading Distribution in a Downlink K-tier HetNet,"  This paper characterizes the statistics of effective fading gain in
+multi-tier cellular networks with strongest base station (BS) cell association
+policy. First, we derive the probability of association with the $n$-th nearest
+BS in the $k$-th tier. Next, we use this result to derive the probability
+density function (PDF) of the channel fading gain (effective fading)
+experienced by the user when associating with the strongest BS. Interestingly,
+our results show that the effective channel gain distribution solely depends
+upon the original channel fading and the path-loss exponent. Moreover, we show
+that in the case of Nakagami-$m$ fading channels (Gamma distribution), the
+distribution of the effective fading is also Gamma but with a gain of
+$\frac{\alpha}{2}$ in the shape parameter, where $\alpha$ is the path-loss
+exponent.
+"
+27772,Audio signal encryption using chaotic Hénon map and lifting wavelet transforms,"  We propose a new audio signal encryption scheme based on the chaotic Hénon
+map. The scheme mainly comprises two phases: one is the preprocessing stage
+where the audio signal is transformed into a data by the lifting wavelet scheme
+and the other in which the transformed data is encrypted by chaotic data set
+and hyperbolic functions. Furthermore, we use dynamic keys and consider the key
+space size to be large enough to resist any kind of cryptographic attacks. A
+statistical investigation is also made to test the security and the efficiency
+of the proposed scheme.
+"
+27773,Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation,"  Deep reinforcement learning (RL) has shown impressive results in a variety of
+domains, learning directly from high-dimensional sensory streams. However, when
+neural networks are trained in a fixed environment, such as a single level in a
+video game, they will usually overfit and fail to generalize to new levels.
+When RL models overfit, even slight modifications to the environment can result
+in poor agent performance. This paper explores how procedurally generated
+levels during training can increase generality. We show that for some games
+procedural level generation enables generalization to new levels within the
+same distribution. Additionally, it is possible to achieve better performance
+with less data by manipulating the difficulty of the levels in response to the
+performance of the agent. The generality of the learned behaviors is also
+evaluated on a set of human-designed levels. The results suggest that the
+ability to generalize to human-designed levels highly depends on the design of
+the level generators. We apply dimensionality reduction and clustering
+techniques to visualize the generators' distributions of levels and analyze to
+what degree they can produce levels similar to those designed by a human.
+"
+27774,Classical analogue of the Unruh effect,"  In the Unruh effect an observer with constant acceleration perceives the
+quantum vacuum as thermal radiation. The Unruh effect has been believed to be a
+pure quantum phenomenon, but here we show theoretically how the effect arises
+from the classical correlation of noise. We demonstrate this idea with a simple
+experiment on water waves where we see the first indications of a Planck
+spectrum in the correlation energy.
+"
+27775,The value of a liability cash flow in discrete time subject to capital requirements,"  The aim of this paper is to define the market-consistent multi-period value
+of an insurance liability cash flow in discrete time subject to repeated
+capital requirements, and explore its properties. In line with current
+regulatory frameworks, the approach presented is based on a hypothetical
+transfer of the original liability and a replicating portfolio to an empty
+corporate entity whose owner must comply with repeated one-period capital
+requirements but has the option to terminate the ownership at any time. The
+value of the liability is defined as the no-arbitrage price of the cash flow to
+the policyholders, optimally stopped from the owner's perspective, taking
+capital requirements into account. The value is computed as the solution to a
+sequence of coupled optimal stopping problems or, equivalently, as the solution
+to a backward recursion.
+"
+27776,Asymptotic normality of element-wise weighted total least squares estimator in a multivariate errors-in-variables model,"  A multivariable measurement error model $AX \approx B$ is considered. Here
+$A$ and $B$ are input and output matrices of measurements and $X$ is a
+rectangular matrix of fixed size to be estimated. The errors in $[A,B]$ are
+row-wise independent, but within each row the errors may be correlated. Some of
+the columns are observed without errors and the error covariance matrices may
+differ from row to row. The total covariance structure of the errors is known
+up to a scalar factor. The fully weighted total least squares estimator of $X$
+is studied. We give conditions for asymptotic normality of the estimator, as
+the number of rows in $A$ is increasing. We provide that the covariance
+structure of the limiting Gaussian random matrix is nonsingular.
+"
+27777,QCD-Electroweak First-Order Phase Transition in a Supercooled Universe,"  If the electroweak sector of the standard model is described by classically
+conformal dynamics, the early Universe evolution can be substantially altered.
+It is already known that---contrarily to the standard model case---a first
+order electroweak phase transition may occur. Here we show that, depending on
+the model parameters, a dramatically different scenario may happen: A
+first-order, six massless quark QCD phase transition occurs first, which then
+triggers the electroweak symmetry breaking. We derive the necessary conditions
+for this dynamics to occur, using the specific example of the classically
+conformal B-L model. In particular, relatively light weakly coupled particles
+are predicted, with implications for collider searches. This scenario is also
+potentially rich in cosmological consequences, such as renewed possibilities
+for electroweak baryogenesis, altered dark matter production, and gravitational
+wave production, as we briefly comment upon.
+"
+27778,Symmetry Learning for Function Approximation in Reinforcement Learning,"  In this paper we explore methods to exploit symmetries for ensuring sample
+efficiency in reinforcement learning (RL), this problem deserves ever
+increasing attention with the recent advances in the use of deep networks for
+complex RL tasks which require large amount of training data. We introduce a
+novel method to detect symmetries using reward trails observed during episodic
+experience and prove its completeness. We also provide a framework to
+incorporate the discovered symmetries for functional approximation. Finally we
+show that the use of potential based reward shaping is especially effective for
+our symmetry exploitation mechanism. Experiments on various classical problems
+show that our method improves the learning performance significantly by
+utilizing symmetry information.
+"
+27779,Sparse Coding and Autoencoders,"  In ""Dictionary Learning"" one tries to recover incoherent matrices $A^* \in
+\mathbb{R}^{n \times h}$ (typically overcomplete and whose columns are assumed
+to be normalized) and sparse vectors $x^* \in \mathbb{R}^h$ with a small
+support of size $h^p$ for some $0 <p < 1$ while having access to observations
+$y \in \mathbb{R}^n$ where $y = A^*x^*$. In this work we undertake a rigorous
+analysis of whether gradient descent on the squared loss of an autoencoder can
+solve the dictionary learning problem. The ""Autoencoder"" architecture we
+consider is a $\mathbb{R}^n \rightarrow \mathbb{R}^n$ mapping with a single
+ReLU activation layer of size $h$.
+Under very mild distributional assumptions on $x^*$, we prove that the norm
+of the expected gradient of the standard squared loss function is
+asymptotically (in sparse code dimension) negligible for all points in a small
+neighborhood of $A^*$. This is supported with experimental evidence using
+synthetic data. We also conduct experiments to suggest that $A^*$ is a local
+minimum. Along the way we prove that a layer of ReLU gates can be set up to
+automatically recover the support of the sparse codes. This property holds
+independent of the loss function. We believe that it could be of independent
+interest.
+"
+27780,Tests and problems of the standard model in Cosmology,"  The main foundations of the standard $\Lambda $CDM model of cosmology are
+that: 1) The redshifts of the galaxies are due to the expansion of the Universe
+plus peculiar motions; 2) The cosmic microwave background radiation and its
+anisotropies derive from the high energy primordial Universe when matter and
+radiation became decoupled; 3) The abundance pattern of the light elements is
+explained in terms of primordial nucleosynthesis; and 4) The formation and
+evolution of galaxies can be explained only in terms of gravitation within a
+inflation+dark matter+dark energy scenario. Numerous tests have been carried
+out on these ideas and, although the standard model works pretty well in
+fitting many observations, there are also many data that present apparent
+caveats to be understood with it. In this paper, I offer a review of these
+tests and problems, as well as some examples of alternative models.
+"
+27781,Curiosity-driven Exploration by Self-supervised Prediction,"  In many real-world scenarios, rewards extrinsic to the agent are extremely
+sparse, or absent altogether. In such cases, curiosity can serve as an
+intrinsic reward signal to enable the agent to explore its environment and
+learn skills that might be useful later in its life. We formulate curiosity as
+the error in an agent's ability to predict the consequence of its own actions
+in a visual feature space learned by a self-supervised inverse dynamics model.
+Our formulation scales to high-dimensional continuous state spaces like images,
+bypasses the difficulties of directly predicting pixels, and, critically,
+ignores the aspects of the environment that cannot affect the agent. The
+proposed approach is evaluated in two environments: VizDoom and Super Mario
+Bros. Three broad settings are investigated: 1) sparse extrinsic reward, where
+curiosity allows for far fewer interactions with the environment to reach the
+goal; 2) exploration with no extrinsic reward, where curiosity pushes the agent
+to explore more efficiently; and 3) generalization to unseen scenarios (e.g.
+new levels of the same game) where the knowledge gained from earlier experience
+helps the agent explore new places much faster than starting from scratch. Demo
+video and code available at this https URL
+"
+27782,Magnus expansions and pseudospectra of Master Equations,"  New directions in research on master equations are showcased by example.
+Magnus expansions, time-varying rates, and pseudospectra are highlighted. Exact
+eigenvalues are found and contrasted with the large errors produced by standard
+numerical methods in some cases. Isomerisation provides a running example and
+an illustrative application to chemical kinetics. We also give a brief example
+of the totally asymmetric exclusion process.
+"
+27783,Geometric explanation of the rich-club phenomenon in complex networks,"  The rich club organization (the presence of highly connected hub core in a
+network) influences many structural and functional characteristics of networks
+including topology, the efficiency of paths and distribution of load. Despite
+its major role, the literature contains only a very limited set of models
+capable of generating networks with realistic rich club structure. One possible
+reason is that the rich club organization is a divisive property among complex
+networks which exhibit great diversity, in contrast to other metrics (e.g.
+diameter, clustering or degree distribution) which seem to behave very
+similarly across many networks. Here we propose a simple yet powerful
+geometry-based growing model which can generate realistic complex networks with
+high rich club diversity by controlling a single geometric parameter. The
+growing model is validated against the Internet, protein-protein interaction,
+airport and power grid networks.
+"
+27784,Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model,"  Large-scale collaborative analysis of brain imaging data, in psychiatry and
+neu-rology, offers a new source of statistical power to discover features that
+boost ac-curacy in disease classification, differential diagnosis, and outcome
+prediction. However, due to data privacy regulations or limited accessibility
+to large datasets across the world, it is challenging to efficiently integrate
+distributed information. Here we propose a novel classification framework
+through multi-site weighted LASSO: each site performs an iterative weighted
+LASSO for feature selection separately. Within each iteration, the
+classification result and the selected features are collected to update the
+weighting parameters for each feature. This new weight is used to guide the
+LASSO process at the next iteration. Only the fea-tures that help to improve
+the classification accuracy are preserved. In tests on da-ta from five sites
+(299 patients with major depressive disorder (MDD) and 258 normal controls),
+our method boosted classification accuracy for MDD by 4.9% on average. This
+result shows the potential of the proposed new strategy as an ef-fective and
+practical collaborative platform for machine learning on large scale
+distributed imaging and biobank data.
+"
+27785,Polarizability Expressions for Predicting Resonances in Plasmonic and Mie Scatterers,"  Polarizability expressions are commonly used in optics and photonics to model
+the light scattering by small particles. Models based on Taylor series of the
+scattering coefficients of the particles fail to predict the morphologic
+resonances hosted by dielectric particles. Here, we propose to use the
+factorization of the special functions appearing in the expression of the Mie
+scattering coefficients to derive point-like models. These models can be
+applied to reproduce both Mie resonances of dielectric particles and plasmonic
+resonances of metallic particles. They provide simple but robust tools to
+predict accurately the electric and magnetic Mie resonances in dielectric
+particles.
+"
+27786,Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis,"  Dementia is a syndrome characterised by the decline of different cognitive
+abilities. Alzheimer's Disease (AD) is the most common dementia affecting
+cognitive domains such as memory and learning, perceptual-motion or executive
+function. High rate of deaths and high cost for detection, treatments and
+patient's care count amongst its consequences. Early detection of AD is
+considered of high importance for improving the quality of life of patients and
+their families. The aim of this thesis is to introduce novel non-invasive early
+diagnosis methods in order to speed the diagnosis, reduce the associated costs
+and make them widely accessible. Novel AD's screening tests based on virtual
+environments using new immersive technologies combined with advanced Human
+Computer Interaction (HCI) systems are introduced. Four tests demonstrate the
+wide range of screening mechanisms based on cognitive domain impairments that
+can be designed using virtual environments. The use of emotion recognition to
+analyse AD symptoms has been also proposed. A novel multimodal dataset was
+specifically created to remark the autobiographical memory deficits of AD
+patients. Data from this dataset is used to introduce novel descriptors for
+Electroencephalogram (EEG) and facial images data. EEG features are based on
+quaternions in order to keep the correlation information between sensors,
+whereas, for facial expression recognition, a preprocessing method for motion
+magnification and descriptors based on origami crease pattern algorithm are
+proposed to enhance facial micro-expressions. These features have been proved
+on classifiers such as SVM and Adaboost for the classification of reactions to
+autobiographical stimuli such as long and short term memories.
+"
+27787,Field of Groves: An Energy-Efficient Random Forest,"  Machine Learning (ML) algorithms, like Convolutional Neural Networks (CNN),
+Support Vector Machines (SVM), etc. have become widespread and can achieve high
+statistical performance. However their accuracy decreases significantly in
+energy-constrained mobile and embedded systems space, where all computations
+need to be completed under a tight energy budget. In this work, we present a
+field of groves (FoG) implementation of random forests (RF) that achieves an
+accuracy comparable to CNNs and SVMs under tight energy budgets. Evaluation of
+the FoG shows that at comparable accuracy it consumes ~1.48x, ~24x, ~2.5x, and
+~34.7x lower energy per classification compared to conventional RF, SVM_RBF ,
+MLP, and CNN, respectively. FoG is ~6.5x less energy efficient than SVM_LR, but
+achieves 18% higher accuracy on average across all considered datasets.
+"
+27788,Generalized Twisted Quantum Doubles of a Finite Group and Rational Orbifolds,"  In previous work the authors introduced a new class of modular quasi-Hopf
+algebras $D^{\omega}(G, A)$ associated to a finite group $G$, a central
+subgroup $A$, and a $3$-cocycle $\omega\in Z^3(G, C^x)$. In the present paper
+we propose a description of the class of orbifold models of rational vertex
+operator algebras whose module category is tensor equivalent to $D^{\omega}(G,
+A)$-mod. The paper includes background on quasi-Hopf algebras and a discussion
+of some relevant orbifolds.
+"
+27789,"Superconducting, Insulating, and Anomalous Metallic Regimes in a Gated Two-Dimensional Semiconductor-Superconductor Array","  The superconductor-insulator transition in two dimensions has been widely
+investigated as a paradigmatic quantum phase transition. The topic remains
+controversial, however, because many experiments exhibit a metallic regime with
+saturating low-temperature resistance, at odds with conventional theory. Here,
+we explore this transition in a novel, highly controllable system, a
+semiconductor heterostructure with epitaxial Al, patterned to form a regular
+array of superconducting islands connected by a gateable quantum well. Spanning
+nine orders of magnitude in resistance, the system exhibits regimes of
+superconducting, metallic, and insulating behavior, along with signatures of
+flux commensurability and vortex penetration. An in-plane magnetic field
+eliminates the metallic regime, restoring the direct superconductor-insulator
+transition, and improves scaling, while strongly altering the scaling exponent.
+"
+27790,A Lambda Calculus for Transfinite Arrays: Unifying Arrays and Streams,"  Array programming languages allow for concise and generic formulations of
+numerical algorithms, thereby providing a huge potential for program
+optimisation such as fusion, parallelisation, etc. One of the restrictions that
+these languages typically have is that the number of elements in every array
+has to be finite. This means that implementing streaming algorithms in such
+languages requires new types of data structures, with operations that are not
+immediately compatible with existing array operations or compiler
+optimisations.
+In this paper, we propose a design for a functional language that natively
+supports infinite arrays. We use ordinal numbers to introduce the notion of
+infinity in shapes and indices. By doing so, we obtain a calculus that
+naturally extends existing array calculi and, at the same time, allows for
+recursive specifications as they are found in stream- and list-based settings.
+Furthermore, the main language construct that can be thought of as an $n$-fold
+cons operator gives rise to expressing transfinite recursion in data, something
+that lists or streams usually do not support. This makes it possible to treat
+the proposed calculus as a unifying theory of arrays, lists and streams. We
+give an operational semantics of the proposed language, discuss design choices
+that we have made, and demonstrate its expressibility with several examples. We
+also demonstrate that the proposed formalism preserves a number of well-known
+universal equalities from array/list/stream theories, and discuss
+implementation-related challenges.
+"
+27791,Homogeneous almost complex manifolds and their compact quotients,"  The paper investigates the (non)existence of compact quotients, by a discrete
+subgroup, of the homogeneous almost-complex strongly-pseudoconvex manifolds
+disconvered and classified by Gaussier-Sukhov and K.-H. Lee.
+"
+27792,A Comparison of Two Methods for Estimating Black Hole Spin in Active Galactic Nuclei,"  Angular momentum, or spin, is a fundamental property of black holes (BHs),
+yet it is much more difficult to estimate than mass or accretion rate (for
+actively accreting systems). In recent years, high-quality X-ray observations
+have allowed for detailed measurements of the Fe K$\alpha$ emission line, where
+relativistic line broadening allows constraints on the spin parameter (the
+X-ray reflection method). Another technique uses accretion disk models to fit
+the AGN continuum emission (the continuum-fitting, or CF, method). Although
+each technique has model-dependent uncertainties, these are the best empirical
+tools currently available and should be vetted in systems where both techniques
+can be applied. A detailed comparison of the two methods is also useful because
+neither method can be applied to all AGN. The X-ray reflection technique
+targets mostly local (z $\lesssim$ 0.1) systems, while the CF method can be
+applied at higher redshift, up to and beyond the peak of AGN activity and
+growth. Here, we apply the CF method to two AGN with X-ray reflection
+measurements. For both the high-mass AGN, H1821+643, and the Seyfert 1, NGC
+3783, we find a range in spin parameter consistent with the X-ray reflection
+measurements. However, the near-maximal spin favored by the reflection method
+for NGC 3783 is more probable if we add a disk wind to the model. Refinement of
+these techniques, together with improved X-ray measurements and tighter BH mass
+constraints, will permit this comparison in a larger sample of AGN and increase
+our confidence in these spin estimation techniques.
+"
+27793,Deep Private-Feature Extraction,"  We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model
+which is trained and evaluated based on information theoretic constraints.
+Using the selective exchange of information between a user's device and a
+service provider, DPFE enables the user to prevent certain sensitive
+information from being shared with a service provider, while allowing them to
+extract approved information using their model. We introduce and utilize the
+log-rank privacy, a novel measure to assess the effectiveness of DPFE in
+removing sensitive information and compare different models based on their
+accuracy-privacy tradeoff. We then implement and evaluate the performance of
+DPFE on smartphones to understand its complexity, resource demands, and
+efficiency tradeoffs. Our results on benchmark image datasets demonstrate that
+under moderate resource utilization, DPFE can achieve high accuracy for primary
+tasks while preserving the privacy of sensitive features.
+"
+27794,Set Matching: An Enhancement of the Hales-Jewett Pairing Strategy,"  When solving k-in-a-Row games, the Hales-Jewett pairing strategy [4] is a
+well-known strategy to prove that specific positions are (at most) a draw. It
+requires two empty squares per possible winning line (group) to be marked,
+i.e., with a coverage ratio of 2.0. In this paper we present a new strategy,
+called Set Matching. A matching set consists of a set of nodes (the markers), a
+set of possible winning lines (the groups), and a coverage set indicating how
+all groups are covered after every first initial move. This strategy needs less
+than two markers per group. As such it is able to prove positions in k-in-a-Row
+games to be draws, which cannot be proven using the Hales-Jewett pairing
+strategy. We show several efficient configurations with their matching sets.
+These include Cycle Configurations, BiCycle Configurations, and PolyCycle
+Configurations involving more than two cycles. Depending on configuration, the
+coverage ratio can be reduced to 1.14. Many examples in the domain of solving
+k-in-a-Row games are given, including the direct proof (without further
+investigation) that the empty 4 x 4 board is a draw for 4-in-a-Row.
+"
+27795,Supergravity at 40: Reflections and Perspectives,"  The fortieth anniversary of the original construction of Supergravity
+provides an opportunity to combine some reminiscences of its early days with an
+assessment of its impact on the quest for a quantum theory of gravity.
+"
+27796,From two-dimensional to three-dimensional turbulence through two-dimensional three-component flows,"  The relevance of two-dimensional three-components (2D3C) flows goes well
+beyond their occurrence in nature, and a deeper understanding of their dynamics
+might be also helpful in order to shed further light on the dynamics of pure
+two-dimenional (2D) or three-dimensional (3D) flows and vice versa. The purpose
+of the present paper is to make a step in this direction through a combination
+of numerical and analytical work. The analytical part is mainly concerned with
+the behavior of 2D3C flows in isolation and the connection between the geometry
+of the nonlinear interactions and the resulting energy transfer directions.
+Special emphasis is given to the role of helicity. We show that a generic 2D3C
+flow can be described by two stream functions corresponding to the two helical
+sectors of the velocity field. The projection onto one helical sector
+(homochiral flow) leads to a full 3D constraint and to the inviscid
+conservation of the total (three dimensional) enstrophy and hence to an inverse
+cascade of the kinetic energy of the third component also. The coupling between
+several 2D3C flows is studied through a set of suitably designed direct
+numerical simulations (DNS), where we also explore the transition between 2D
+and fully 3D turbulence. In particular, we find that the coupling of three 2D3C
+flows on mutually orthogonal planes subject to small-scale forcing leads to
+stationary 3D out-of-equilibrium dynamics at the energy containing scales. The
+transition between 2D and 3D turbulence is then explored through adding a
+percentage of fully 3D Fourier modes in the volume.
+"
+27797,O'Nan moonshine and arithmetic,"  Answering a question posed by Conway and Norton in their seminal 1979 paper
+on moonshine, we prove the existence of a graded infinite-dimensional module
+for the sporadic simple group of O'Nan, for which the McKay--Thompson series
+are weight $3/2$ modular forms. These series are linear combinations of
+generating functions for traces of singular moduli, class numbers, and central
+critical values of quadratic twists of weight 2 modular $L$-functions. As a
+consequence, for primes $p$ dividing the order of the O'Nan group we obtain
+congruences between O'Nan group character values and class numbers, $p$-parts
+of Selmer groups, and Tate--Shafarevich groups of certain elliptic curves. This
+work represents the first example of moonshine involving arithmetic invariants
+of this type, and provides evidence supporting the view that moonshine is a
+phenomenon in which infinite-dimensional graded modules organize the arithmetic
+of products of Jacobians of modular curves.
+"
+27798,Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets,"  Acoustic events often have a visual counterpart. Knowledge of visual
+information can aid the understanding of complex auditory scenes, even when
+only a stereo mixdown is available in the audio domain, \eg identifying which
+musicians are playing in large musical ensembles. In this paper, we consider a
+vision-based approach to note onset detection. As a case study we focus on
+challenging, real-world clarinetist videos and carry out preliminary
+experiments on a 3D convolutional neural network based on multiple streams and
+purposely avoiding temporal pooling. We release an audiovisual dataset with 4.5
+hours of clarinetist videos together with cleaned annotations which include
+about 36,000 onsets and the coordinates for a number of salient points and
+regions of interest. By performing several training trials on our dataset, we
+learned that the problem is challenging. We found that the CNN model is highly
+sensitive to the optimization algorithm and hyper-parameters, and that treating
+the problem as binary classification may prevent the joint optimization of
+precision and recall. To encourage further research, we publicly share our
+dataset, annotations and all models and detail which issues we came across
+during our preliminary experiments.
+"
+27799,Some characterizations of acyclic maps,"  We discuss two categorical characterizations of the class of acyclic maps
+between (path-connected) spaces. The first one is in terms of the higher
+categorical notion of an epimorphism. The second one employs the notion of a
+balanced map, that is, a map whose homotopy pullbacks define also homotopy
+pushouts. We also identify the modality in the homotopy theory of spaces that
+is defined by the class of acyclic maps, and discuss the content of the
+generalized Blakers-Massey theorem for this modality.
+"
+27800,Sparse Stochastic Bandits,"  In the classical multi-armed bandit problem, d arms are available to the
+decision maker who pulls them sequentially in order to maximize his cumulative
+reward. Guarantees can be obtained on a relative quantity called regret, which
+scales linearly with d (or with sqrt(d) in the minimax sense). We here consider
+the sparse case of this classical problem in the sense that only a small number
+of arms, namely s < d, have a positive expected reward. We are able to leverage
+this additional assumption to provide an algorithm whose regret scales with s
+instead of d. Moreover, we prove that this algorithm is optimal by providing a
+matching lower bound - at least for a wide and pertinent range of parameters
+that we determine - and by evaluating its performance on simulated data.
+"
+27801,PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method,"  As the number of seismic sensors grows, it is becoming increasingly difficult
+for analysts to pick seismic phases manually and comprehensively, yet such
+efforts are fundamental to earthquake monitoring. Despite years of improvements
+in automatic phase picking, it is difficult to match the performance of
+experienced analysts. A more subtle issue is that different seismic analysts
+may pick phases differently, which can introduce bias into earthquake
+locations. We present a deep-neural-network-based arrival-time picking method
+called ""PhaseNet"" that picks the arrival times of both P and S waves. Deep
+neural networks have recently made rapid progress in feature learning, and with
+sufficient training, have achieved super-human performance in many
+applications. PhaseNet uses three-component seismic waveforms as input and
+generates probability distributions of P arrivals, S arrivals, and noise as
+output. We engineer PhaseNet such that peaks in probability provide accurate
+arrival times for both P and S waves, and have the potential to increase the
+number of S-wave observations dramatically over what is currently available.
+This will enable both improved locations and improved shear wave velocity
+models. PhaseNet is trained on the prodigious available data set provided by
+analyst-labeled P and S arrival times from the Northern California Earthquake
+Data Center. The dataset we use contains more than seven million waveform
+samples extracted from over thirty years of earthquake recordings. We
+demonstrate that PhaseNet achieves much higher picking accuracy and recall rate
+than existing methods.
+"
+27802,On the Kesten-Stigum theorem in $L^2$ beyond $λ$-positivity,"  We study supercritical branching processes in which all particles evolve
+according to some general Markovian motion (which may possess absorbing states)
+and branch independently at a fixed constant rate. Under fairly natural
+assumptions on the asymptotic distribution of the underlying motion, we first
+show using only probablistic tools that there is convergence in $L^2$ of the
+empirical measure (normalized by the mean number of particles) if and only if
+an associated additive martingale is bounded in $L^2$. This is a significant
+improvement over previous results, which were mainly restricted to
+$\lambda$-positive motions. We then investigate under which conditions this
+limit is strictly positive on the event of non-extinction and show that this
+occurs whenever, on the latter event, particles do not accumulate on the
+boundary of the state space. In particular, this property also yields the
+convergence of the real empirical measure (normalized by the number of
+particles). Moreover, building on previous results we prove that if the motion
+is $\lambda$-positive then these limits hold also almost surely whenever the
+Doob's $h$-transform of this motion admits a suitable Lyapunov functional.
+Finally, we illustrate our results for a variety of different motions: ergodic
+motions without absorption, $\lambda$-positive systems either transient or with
+absorption, and also certain non $\lambda$-positive systems such as the
+Brownian motion with negative drift killed at $0$. A strong law of large
+numbers for the empirical measure in this last example was announced by Kesten
+in the late seventies, although a proof of it has remained undisclosed so far.
+Our results allow us to give a partial proof to Kesten's claim.
+"
+27803,Formal Semantics of Architectural Decision Models,"  A software architecture is the result of multiple decisions made by a
+software architect. These decisions are called architectural decisions, as they
+bring solutions to architectural problems. Relations between decisions can be
+captured in architectural decision models. Such models are then a form of
+reusable knowledge for software architects. Several models have been described
+in the literature, introducing necessary concepts and relations. These concepts
+and relations were usually explained using natural language. Not much work has
+been done so far on their formal definitions. Specifically, such a definition
+of an architectural decision model is still missing. The purpose of this paper
+is filling this gap by providing the formal definition of an architectural
+decision model at both syntax and semantics levels. At the syntax level,
+different concepts and relations that are elements of a model have been
+mathematically defined. At the semantics level, the meaning of a model has been
+defined in a form of denotational semantics. The formalization not only allows
+for better understanding of architectural decision models but opens the
+possibility to reason on such models, e.g., checking their consistency -
+something that is very limited for the models proposed so far. A practical
+example of the semantics of an architectural decision model is also presented.
+"
+27804,Leveraging Sparsity for Efficient Submodular Data Summarization,"  The facility location problem is widely used for summarizing large datasets
+and has additional applications in sensor placement, image retrieval, and
+clustering. One difficulty of this problem is that submodular optimization
+algorithms require the calculation of pairwise benefits for all items in the
+dataset. This is infeasible for large problems, so recent work proposed to only
+calculate nearest neighbor benefits. One limitation is that several strong
+assumptions were invoked to obtain provable approximation guarantees. In this
+paper we establish that these extra assumptions are not necessary---solving the
+sparsified problem will be almost optimal under the standard assumptions of the
+problem. We then analyze a different method of sparsification that is a better
+model for methods such as Locality Sensitive Hashing to accelerate the nearest
+neighbor computations and extend the use of the problem to a broader family of
+similarities. We validate our approach by demonstrating that it rapidly
+generates interpretable summaries.
+"
+27805,Variational Adaptive-Newton Method for Explorative Learning,"  We present the Variational Adaptive Newton (VAN) method which is a black-box
+optimization method especially suitable for explorative-learning tasks such as
+active learning and reinforcement learning. Similar to Bayesian methods, VAN
+estimates a distribution that can be used for exploration, but requires
+computations that are similar to continuous optimization methods. Our
+theoretical contribution reveals that VAN is a second-order method that unifies
+existing methods in distinct fields of continuous optimization, variational
+inference, and evolution strategies. Our experimental results show that VAN
+performs well on a wide-variety of learning tasks. This work presents a
+general-purpose explorative-learning method that has the potential to improve
+learning in areas such as active learning and reinforcement learning.
+"
+27806,A note on chromatic blending of colour clusters,"  For a colour cluster $\C =(\mathcal{C}_1,\mathcal{C}_2,
+\mathcal{C}_3,\dots,\mathcal{C}_\ell)$, $\mathcal{C}_i$ is a colour class, and
+$|\mathcal{C}_i|=r_i \geq 1$, we investigate a simple connected graph structure
+$G^{\C}$, which represents a graphical embodiment of the colour cluster such
+that the chromatic number $\chi(G^{\C})= \ell,$ and the number of edges is a
+maximum, denoted $\varepsilon^+(G^{\C})$. We also extend the study by inducing
+new colour clusters recursively by blending the colours of all pairs of
+adjacent vertices. Recursion repeats until a maximal homogeneous blend between
+all $\ell$ colours is obtained. This is called total chromatic blending. Total
+chromatic blending models for example, total genetic, chemical, cultural or
+social orderliness integration.
+"
+27807,SPARTan: Scalable PARAFAC2 for Large & Sparse Data,"  In exploratory tensor mining, a common problem is how to analyze a set of
+variables across a set of subjects whose observations do not align naturally.
+For example, when modeling medical features across a set of patients, the
+number and duration of treatments may vary widely in time, meaning there is no
+meaningful way to align their clinical records across time points for analysis
+purposes. To handle such data, the state-of-the-art tensor model is the
+so-called PARAFAC2, which yields interpretable and robust output and can
+naturally handle sparse data. However, its main limitation up to now has been
+the lack of efficient algorithms that can handle large-scale datasets.
+In this work, we fill this gap by developing a scalable method to compute the
+PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method
+exploits special structure within PARAFAC2, leading to a novel algorithmic
+reformulation that is both fast (in absolute time) and more memory-efficient
+than prior work. We evaluate SPARTan on both synthetic and real datasets,
+showing 22X performance gains over the best previous implementation and also
+handling larger problem instances for which the baseline fails. Furthermore, we
+are able to apply SPARTan to the mining of temporally-evolving phenotypes on
+data taken from real and medically complex pediatric patients. The clinical
+meaningfulness of the phenotypes identified in this process, as well as their
+temporal evolution over time for several patients, have been endorsed by
+clinical experts.
+"
+27808,Zeros of random orthogonal polynomials with complex Gaussian coefficients,"  Let $\{f_j\}_{j=0}^n$ be a sequence of orthonormal polynomials where the
+orthogonality relation is satisfied on either the real line or on the unit
+circle. We study zero distribution of random linear combinations of the form
+$$P_n(z)=\sum_{j=0}^n\eta_jf_j(z),$$ where $\eta_0,\dots,\eta_n$ are
+complex-valued i.i.d.~standard Gaussian random variables. Using the
+Christoffel-Darboux formula, the density function for the expected number of
+zeros of $P_n$ in these cases takes a very simple shape. From these
+expressions, under the mere assumption that the orthogonal polynomials are from
+the Nevai class, we give the limiting value of the density function away from
+their respective sets where the orthogonality holds. In the case when $\{f_j\}$
+are orthogonal polynomials on the unit circle, the density function shows that
+the expected number of zeros of $P_n$ are clustering near the unit circle. To
+quantify this phenomenon, we give a result that estimates the expected number
+of complex zeros of $P_n$ in shrinking neighborhoods of compact subsets of the
+unit circle.
+"
+27809,The BiPo-3 detector for the measurement of ultra low natural radioactivities of thin materials,"  The BiPo-3 detector, running in the Canfranc Underground Laboratory
+(Laboratorio Subterráneo de Canfranc, LSC, Spain) since 2013, is a
+low-radioactivity detector dedicated to measuring ultra low natural
+radionuclide contaminations of $^{208}$Tl ($^{232}$Th chain) and $^{214}$Bi
+($^{238}$U chain) in thin materials. The total sensitive surface area of the
+detector is 3.6 m$^2$. The detector has been developed to measure radiopurity
+of the selenium double $\beta$-decay source foils of the SuperNEMO experiment.
+In this paper the design and performance of the detector, and results of the
+background measurements in $^{208}$Tl and $^{214}$Bi, are presented, and
+validation of the BiPo-3 measurement with a calibrated aluminium foil is
+discussed. Results of the $^{208}$Tl and $^{214}$Bi activity measurements of
+the first enriched $^{82}$Se foils of the double $\beta$-decay SuperNEMO
+experiment are reported. The sensitivity of the BiPo-3 detector for the
+measurement of the SuperNEMO $^{82}$Se foils is $\mathcal{A}$($^{208}$Tl) $<2$
+$\mu$Bq/kg (90\% C.L.) and $\mathcal{A}$($^{214}$Bi) $<140$ $\mu$Bq/kg (90\%
+C.L.) after 6 months of measurement.
+"
+27810,Photometric Stereo by UV-Induced Fluorescence to Detect Protrusions on Georgia O'Keeffe's Paintings,"  A significant number of oil paintings produced by Georgia O'Keeffe
+(1887-1986) show surface protrusions of varying width, up to several hundreds
+of microns. These protrusions are similar to those described in the art
+conservation literature as metallic soaps. Since the presence of these
+protrusions raises questions about the state of conservation and long-term
+prospects for deterioration of these artworks, a 3D-imaging technique,
+photometric stereo using ultraviolet illumination, was developed for the
+long-term monitoring of the surface-shape of the protrusions and the
+surrounding paint. Because the UV fluorescence response of painting materials
+is isotropic, errors typically caused by non-Lambertian (anisotropic)
+specularities when using visible reflected light can be avoided providing a
+more accurate estimation of shape. As an added benefit, fluorescence provides
+additional contrast information contributing to materials characterization. The
+developed methodology aims to detect, characterize, and quantify the
+distribution of micro-protrusions and their development over the surface of
+entire artworks. Combined with a set of analytical in-situ techniques, and
+computational tools, this approach constitutes a novel methodology to
+investigate the selective distribution of protrusions in correlation with the
+composition of painting materials at the macro-scale. While focused on
+O'Keeffe's paintings as a case study, we expect the proposed approach to have
+broader significance by providing a non-invasive protocol to the conservation
+community to probe topological changes for any relatively flat painted surface
+of an artwork, and more specifically to monitor the dynamic formation of
+protrusions, in relation to paint composition and modifications of
+environmental conditions, loans, exhibitions and storage over the long-term.
+"
+27811,Dynamic Markers: UAV landing proof of concept,"  In this paper, we introduce a dynamic fiducial marker which can change its
+appearance according to the spatiotemporal requirements of the visual
+perception task of a mobile robot using a camera as the sensor. We present a
+control scheme to dynamically change the appearance of the marker in order to
+increase the range of detection and to assure a better accuracy on the close
+range. The marker control takes into account the camera to marker distance
+(which influences the scale of the marker in image coordinates) to select which
+fiducial markers to display. Hence, we realize a tight coupling between the
+visual pose control of the mobile robot and the appearance of the dynamic
+fiducial marker. Additionally, we discuss the practical implications of time
+delays due to processing time and communication delays between the robot and
+the marker. Finally, we propose a real-time dynamic marker visual servoing
+control scheme for quadcopter landing and evaluate the performance on a
+real-world example.
+"
+27812,The Hausdorff Mapping Is Nonexpanding,"  In the present paper we investigate the properties of the Hausdorff mapping
+$\mathcal{H}$, which takes each compact metric space to the space of its
+nonempty closed subspaces. It is shown that this mapping is nonexpanding
+(Lipschitz mapping with constant $1$). This paper gives several examples of
+classes of metric spaces, distances between which are preserved by the mapping
+$\mathcal{H}$. We also calculate distance between any connected metric space
+and any simplex with greater diameter than the former one. At the end of the
+paper we discuss some properties of the Hausdorff mapping which may help to
+prove that it is isometric
+"
+27813,An Efficient LQR Design for Discrete-Time Linear Periodic System Based on a Novel Lifting Method,"  This paper proposes a novel lifting method which converts the standard
+discrete-time linear periodic system to an augmented linear time-invariant
+system. The linear quadratic optimal control is then based on the solution of
+the discrete-time algebraic Riccati equation associated with the augmented
+linear time-invariant model. An efficient algorithm for solving the Riccati
+equation is derived by using the special structure of the augmented linear
+time-invariant system. It is shown that the proposed method is very efficient
+compared to the ones that use algorithms for discrete-time periodic algebraic
+Riccati equation. The efficiency and effectiveness of the proposed algorithm is
+demonstrated by the simulation test for the design problem of spacecraft
+attitude control using magnetic torques.
+"
+27814,Image of the Artin groups of classical types inside the finite Iwahori-Hecke algebras,"  We determine the image of the Artin groups of types B and D inside the
+Iwahori-Hecke algebras, when defined over finite fields, in the semisimple
+case. This generalizes earlier work on type A by Brunat, Magaard and Marin. In
+this multi-parameter case, this image depends heavily on the parameters.
+"
+27815,Effective Demagnetization Factors of Diamagnetic Samples of Various Shapes,"  Effective demagnetizing factors that connect the sample magnetic moment with
+the applied magnetic field are calculated numerically for perfectly diamagnetic
+samples of various non-ellipsoidal shapes. The procedure is based on
+calculating total magnetic moment by integrating the magnetic induction
+obtained from a full three dimensional solution of the Maxwell equations using
+adaptive mesh. The results are relevant for superconductors (and conductors in
+AC fields) when the London penetration depth (or the skin depth) is much
+smaller than the sample size. Simple but reasonably accurate approximate
+formulas are given for practical shapes including rectangular cuboids, finite
+cylinders in axial and transverse field as well as infinite rectangular and
+elliptical cross-section strips.
+"
+27816,Comparing Alternatives to Measure the Impact of DDoS Attack Announcements on Target Stock Prices,"  The attack intensity of distributed denial of service (DDoS) attacks is
+increasing every year. Botnets based on internet of things (IOT) devices are
+now being used to conduct DDoS attacks. The estimation of direct and indirect
+economic damages caused by these attacks is a complex problem. One of the
+indirect damage of a DDoS attack can be on the market value of the victim firm.
+In this article we analyze the impact of 45 different DDoS attack announcements
+on victim's stock prices. We find that previous studies have a mixed conclusion
+on the impact of DDoS attack announcements on the victim's stock price. Hence,
+in this article we evaluate this impact using three different approaches and
+compare the results. In the first approach, we use the assume the cumulative
+abnormal returns to be normally distributed and test the hypothesis that a DDoS
+attack announcement has no impact on the victim's stock price. In the latter
+two methods, we do not assume a distribution and use the empirical distribution
+of cumulative abnormal returns to test the hypothesis. We find that the
+assumption of cumulative abnormal returns being normally distributed leads to
+overestimation/underestimation of the impact. Finally, we analyze the impact of
+DDoS attack announcement on victim's stock price in each of the 45 cases and
+present our results.
+"
+27817,Toward Deeper Understanding of Nonconvex Stochastic Optimization with Momentum using Diffusion Approximations,"  Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely applied
+to many nonconvex optimization problems in machine learning. Popular examples
+include training deep neural networks, dimensionality reduction, and etc. Due
+to the lack of convexity and the extra momentum term, the optimization theory
+of MSGD is still largely unknown. In this paper, we study this fundamental
+optimization algorithm based on the so-called ""strict saddle problem."" By
+diffusion approximation type analysis, our study shows that the momentum helps
+escape from saddle points, but hurts the convergence within the neighborhood of
+optima (if without the step size annealing). Our theoretical discovery
+partially corroborates the empirical success of MSGD in training deep neural
+networks. Moreover, our analysis applies the martingale method and
+""Fixed-State-Chain"" method from the stochastic approximation literature, which
+are of independent interest.
+"
+27818,Classical fields in the one-dimensional Bose gas: applicability and determination of the optimal cutoff,"  To finalize information about the accuracy of the classical field approach
+for the 1d Bose gas, the lowest temperature quasicondensate was studied by
+comparing the extended Bogoliubov model of Mora and Castin, to its classical
+field analogue. The parameters for which the physics is well described by
+matter waves are now presented for all 1d regimes, and concurrently, the
+optimal cutoff that best matches all observables together is also provided.
+This cutoff rises strongly with density when the chemical potential is higher
+than the thermal energy to account for kinetic energy. As a consequence, clouds
+that reach this coldest quantum fluctuating regime are better described using a
+trap basis than plane waves. This contrasts with higher temperature clouds for
+which the basis choice is less important. In passing, estimates for chemical
+potential, density fluctuations, kinetic and interaction energy in the low
+temperature quasicondensate are obtained up to several leading terms.
+"
+27819,The Beta Flexible Weibull Distribution,"  We introduce in this paper a new generalization of the flexible Weibull
+distribution with four parameters. This model based on the Beta generalized
+(BG) distribution, Eugene et al. \cite{Eugeneetal2002}, they first using the BG
+distribution for generating new generalizations. This new model is called the
+beta flexible Weibull BFW distribution. Some statistical properties such as the
+mode, the $r$th moment, skewness and kurtosis are derived. The moment
+generating function and the order statistics are obtained. Moreover, the
+estimations of the parameters are given by maximum likelihood method and the
+Fisher's information matrix is derived. Finally, we study the advantage of the
+BFW distribution by an application using real data set.
+"
+27820,The Impact of Random Models on Clustering Similarity,"  Clustering is a central approach for unsupervised learning. After clustering
+is applied, the most fundamental analysis is to quantitatively compare
+clusterings. Such comparisons are crucial for the evaluation of clustering
+methods as well as other tasks such as consensus clustering. It is often argued
+that, in order to establish a baseline, clustering similarity should be
+assessed in the context of a random ensemble of clusterings. The prevailing
+assumption for the random clustering ensemble is the permutation model in which
+the number and sizes of clusters are fixed. However, this assumption does not
+necessarily hold in practice; for example, multiple runs of K-means clustering
+returns clusterings with a fixed number of clusters, while the cluster size
+distribution varies greatly. Here, we derive corrected variants of two
+clustering similarity measures (the Rand index and Mutual Information) in the
+context of two random clustering ensembles in which the number and sizes of
+clusters vary. In addition, we study the impact of one-sided comparisons in the
+scenario with a reference clustering. The consequences of different random
+models are illustrated using synthetic examples, handwriting recognition, and
+gene expression data. We demonstrate that the choice of random model can have a
+drastic impact on the ranking of similar clustering pairs, and the evaluation
+of a clustering method with respect to a random baseline; thus, the choice of
+random clustering model should be carefully justified.
+"
+27821,"Super-potentials, densities of currents and number of periodic points for holomorphic maps","  We prove that if a positive closed current is bounded by another one with
+bounded, continuous or Hoelder continuous super-potentials, then it inherits
+the same property. There are two different methods to define wedge-products of
+positive closed currents of arbitrary bi-degree on compact Kaehler manifolds
+using super-potentials and densities. When the first method applies, we show
+that the second method also applies and gives the same result. As an
+application, we obtain a sharp upper bound for the number of isolated periodic
+points of holomorphic maps on compact Kaehler manifolds whose actions on
+cohomology are simple. A similar result still holds for a large class of
+holomorphic correspondences.
+"
+27822,Some surprises in the neutrino cross sections associated with neutrino spin,"  It is generally assumed that neutrino masses can be neglected to a high
+degree of approximation in cross section calculations. This assumption seems
+very reasonable since the neutrino masses are extremely small and the neutrinos
+are ultrarelativistic fermions at the energy scales of current experiments.
+Consequently, in cross section calculations in the Quantum Field Theory, the
+Standard Model neutrinos are frequently assumed to be described by 100%
+negative helicity states. This assumption is true in a sense that in the
+Standard Model processes the positive helicity states can be safely neglected
+for ultrarelativistic neutrinos. On the other hand, the assumption tacitly
+assert that the neutrino fields are completely longitudinally polarized, i.e.,
+the contribution to the cross section coming from transverse polarization can
+be neglected. We show that this tacit assertion is not correct. Although the
+Standard Model cross section for a neutrino with positive helicity goes to zero
+as m-> 0, the cross section for a neutrino with transverse polarization remains
+finite in that limit. Thus the contribution coming from transverse polarization
+cannot be neglected even in the ultrarelativistic/zero-mass limit. We examine
+the consequences of this fact and deduce that it has some unexpected results in
+the neutrino cross sections.
+"
+27823,Distributed Clustering for Multiuser Networks through Coalition Formation,"  Clustering mechanisms are essential in certain multiuser networks for
+achieving efficient resource utilization. This lecture note presents the theory
+of coalition formation as a useful tool for distributed clustering problems. We
+reveal the generality of the theory, and study complexity aspects which must be
+considered in multiuser networks.
+"
+27824,"Synthesis, crystal structure and superconductivity in Rb$Ln_2$Fe$_4$As$_4$O$_2$ ($Ln$ = Sm, Tb, Dy and Ho)","  We have synthesized four iron-based oxyarsenide superconductors
+Rb$Ln_2$Fe$_4$As$_4$O$_2$ ($Ln$ = Sm, Tb, Dy and Ho) resulting from the
+intergrowth of RbFe$_2$As$_2$ and $Ln$FeAsO. It is found that the lattice match
+between RbFe$_2$As$_2$ and $Ln$FeAsO is crucial for the phase formation. The
+structural intergrowth leads to double asymmetric Fe$_2$As$_2$ layers that are
+separated by insulating $Ln_2$O$_2$ slabs. Consequently, the materials are
+intrinsically doped at a level of 0.25 holes/Fe-atom and, bulk
+superconductivity emerges at $T_\mathrm{c}$ = 35.8, 34.7, 34.3 and 33.8 K,
+respectively, for $Ln$ = Sm, Tb, Dy and Ho. Investigation on the correlation
+between crystal structure and $T_\mathrm{c}$ suggests that interlayer couplings
+may play an additional role for optimization of superconductivity.
+"
+27825,"Fermi Arcs in Tilted Weyl Semimetals: Classification, Evolution and Transport Properties","  The Weyl semimetal is a new quantum state of topological semimetal, of which
+topological surface states -- the Fermi arcs exist. In this paper, the Fermi
+arcs in Weyl semimetals are classified into two classes -- class-1 and class-2.
+Based on a tight-binding model, the evolution and transport properties of
+class-1/2 Fermi arcs are studied via the tilting strength of the bulk Weyl
+cones. The (residual) anomalous Hall conductivity of topological surface states
+is a physical consequence of class-1 Fermi arc and thus class-1 Fermi arc
+becomes a nontrivial topological property for hybrid or type-II Weyl semimetal.
+Therefore, this work provides an intuitive method to learn topological
+properties of Weyl semimetal.
+"
+27826,Induced subgraphs of graphs with large chromatic number. IX. Rainbow paths,"  We prove that for all nonnegative integers k,s there exists c with the
+following property. Let G be a graph with clique number at most k and chromatic
+number more than c. Then for every vertex-colouring (not necessarily optimal)
+of G, some induced subgraph of G is an s-vertex path, and all its vertices have
+different colours. This extends a recent result of Gyarfas and Sarkozy, who
+proved the same (when k=2) for graphs G with girth at least five.
+"
+27827,The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising,"  The higher order singular value decomposition (HOSVD) of tensors is a
+generalization of matrix SVD. The perturbation analysis of HOSVD under random
+noise is more delicate than its matrix counterpart. Recently, polynomial time
+algorithms have been proposed where statistically optimal estimates of the
+singular subspaces and the low rank tensors are attainable in the Euclidean
+norm. In this article, we analyze the sup-norm perturbation bounds of HOSVD and
+introduce estimators of the singular subspaces with sharp deviation bounds in
+the sup-norm. We also investigate a low rank tensor denoising estimator and
+demonstrate its fast convergence rate with respect to the entry-wise errors.
+The sup-norm perturbation bounds reveal unconventional phase transitions for
+statistical learning applications such as the exact clustering in high
+dimensional Gaussian mixture model and the exact support recovery in sub-tensor
+localizations. In addition, the bounds established for HOSVD also elaborate the
+one-sided sup-norm perturbation bounds for the singular subspaces of unbalanced
+(or fat) matrices.
+"
+27828,Provably Efficient Algorithms for Joint Placement and Allocation of Virtual Network Functions,"  Network Function Virtualization (NFV) has the potential to significantly
+reduce the capital and operating expenses, shorten product release cycle, and
+improve service agility. In this paper, we focus on minimizing the total number
+of Virtual Network Function (VNF) instances to provide a specific service
+(possibly at different locations) to all the flows in a network. Certain
+network security and analytics applications may allow fractional processing of
+a flow at different nodes (corresponding to datacenters), giving an opportunity
+for greater optimization of resources. Through a reduction from the set cover
+problem, we show that this problem is NP-hard and cannot even be approximated
+within a factor of (1 - o(1)) ln(m) (where m is the number of flows) unless
+P=NP. Then, we design two simple greedy algorithms and prove that they achieve
+an approximation ratio of (1 - o(1)) ln(m) + 2, which is asymptotically
+optimal. For special cases where each node hosts multiple VNF instances (which
+is typically true in practice), we also show that our greedy algorithms have a
+constant approximation ratio. Further, for tree topologies we develop an
+optimal greedy algorithm by exploiting the inherent topological structure.
+Finally, we conduct extensive numerical experiments to evaluate the performance
+of our proposed algorithms in various scenarios.
+"
+27829,DRACO: Byzantine-resilient Distributed Training via Redundant Gradients,"  Distributed model training is vulnerable to byzantine system failures and
+adversarial compute nodes, i.e., nodes that use malicious updates to corrupt
+the global model stored at a parameter server (PS). To guarantee some form of
+robustness, recent work suggests using variants of the geometric median as an
+aggregation rule, in place of gradient averaging. Unfortunately, median-based
+rules can incur a prohibitive computational overhead in large-scale settings,
+and their convergence guarantees often require strong assumptions. In this
+work, we present DRACO, a scalable framework for robust distributed training
+that uses ideas from coding theory. In DRACO, each compute node evaluates
+redundant gradients that are used by the parameter server to eliminate the
+effects of adversarial updates. DRACO comes with problem-independent robustness
+guarantees, and the model that it trains is identical to the one trained in the
+adversary-free setup. We provide extensive experiments on real datasets and
+distributed setups across a variety of large-scale models, where we show that
+DRACO is several times, to orders of magnitude faster than median-based
+approaches.
+"
+27830,Witnessing the Growth of the Nearest Galaxy Cluster: Thermodynamics of the Virgo Cluster Outskirts,"  We present results from Suzaku Key Project observations of the Virgo Cluster,
+the nearest galaxy cluster to us, mapping its X-ray properties along four long
+`arms' extending beyond the virial radius. The entropy profiles along all four
+azimuths increase with radius, then level out beyond $0.5r_{200}$, while the
+average pressure at large radii exceeds Planck Sunyaev-Zel'dovich measurements.
+These results can be explained by enhanced gas density fluctuations (clumping)
+in the cluster's outskirts. Using a standard Navarro, Frenk and White (1997)
+model, we estimate a virial mass, radius, and concentration parameter of
+$M_{200}=1.05\pm0.02\times10^{14}$ M$_\odot$, $r_{200}=974.1\pm5.7$ kpc, and $c
+= 8.8 \pm0.2$, respectively. The inferred cumulative baryon fraction exceeds
+the cosmic mean at $r\sim r_{200}$ along the major axis, suggesting enhanced
+gas clumping possibly sourced by a candidate large-scale structure filament
+along the north-south direction. The Suzaku data reveal a large-scale sloshing
+pattern, with two new cold fronts detected at radii of 233 kpc and 280 kpc
+along the western and southern arms, respectively. Two high-temperature regions
+are also identified 1 Mpc towards the south and 605 kpc towards the west of
+M87, likely representing shocks associated with the ongoing cluster growth.
+Although systematic uncertainties in measuring the metallicity for low
+temperature plasma remain, the data at large radii appear consistent with a
+uniform metal distribution on scales of $\sim 90\times180$ kpc and larger,
+providing additional support for the early chemical enrichment scenario driven
+by galactic winds at redshifts of 2-3.
+"
+27831,Optimum thresholding using mean and conditional mean square error,"  We consider a univariate semimartingale model for (the logarithm of) an asset
+price, containing jumps having possibly infinite activity (IA). The
+nonparametric threshold estimator of the integrated variance IV proposed in
+Mancini 2009 is constructed using observations on a discrete time grid, and
+precisely it sums up the squared increments of the process when they are below
+a threshold, a deterministic function of the observation step and possibly of
+the coefficients of X. All the threshold functions satisfying given conditions
+allow asymptotically consistent estimates of IV, however the finite sample
+properties of the truncated realized variation can depend on the specific
+choice of the threshold. We aim here at optimally selecting the threshold by
+minimizing either the estimation mean square error (MSE) or the conditional
+mean square error (cMSE). The last criterion allows to reach a threshold which
+is optimal not in mean but for the specific volatility (and jumps paths) at
+hand. A parsimonious characterization of the optimum is established, which
+turns out to be asymptotically proportional to the Lévy's modulus of
+continuity of the underlying Brownian motion. Moreover, minimizing the cMSE
+enables us to propose a novel implementation scheme for approximating the
+optimal threshold. Monte Carlo simulations illustrate the superior performance
+of the proposed method.
+"
+27832,Dynamic scaling in the 2D Ising spin glass with Gaussian couplings,"  We carry out simulated annealing and employ a generalized Kibble-Zurek
+scaling hypothesis to study the 2D Ising spin glass with normal-distributed
+couplings. The system has an equilibrium glass transition at temperature $T=0$.
+From a scaling analysis when $T\rightarrow 0$ at different annealing
+velocities, we extract the dynamic critical exponent $z$, i.e., the exponent
+relating the relaxation time $\tau$ to the system length $L$; $\tau\sim L^z$.
+We find $z=13.6 \pm 0.4$ for both the Edwards-Anderson spin-glass order
+parameter and the excess energy. This is different from a previous study of the
+system with bimodal couplings [S. J. Rubin, N. Xu, and A. W. Sandvik, Phys.
+Rev. E {\bf 95}, 052133 (2017)] where the dynamics is faster and the above two
+quantities relax with different exponents (and that of the energy is larger).
+We here argue that the different behaviors arise as a consequence of the
+different low-energy landscapes---for normal-distributed couplings the ground
+state is unique (up to a spin reflection) while the system with bimodal
+couplings is massively degenerate. Our results reinforce the conclusion of
+anomalous entropy-driven relaxation behavior in the bimodal Ising glass. In the
+case of a continuous coupling distribution, our results presented here indicate
+that, although Kibble-Zurek scaling holds, the perturbative behavior normally
+applying in the slow limit breaks down, likely due to quasi-degenerate states,
+and the scaling function takes a different form.
+"
+27833,Visualizing Convolutional Neural Network Protein-Ligand Scoring,"  Protein-ligand scoring is an important step in a structure-based drug design
+pipeline. Selecting a correct binding pose and predicting the binding affinity
+of a protein-ligand complex enables effective virtual screening. Machine
+learning techniques can make use of the increasing amounts of structural data
+that are becoming publicly available. Convolutional neural network (CNN)
+scoring functions in particular have shown promise in pose selection and
+affinity prediction for protein-ligand complexes. Neural networks are known for
+being difficult to interpret. Understanding the decisions of a particular
+network can help tune parameters and training data to maximize performance.
+Visualization of neural networks helps decompose complex scoring functions into
+pictures that are more easily parsed by humans. Here we present three methods
+for visualizing how individual protein-ligand complexes are interpreted by 3D
+convolutional neural networks. We also present a visualization of the
+convolutional filters and their weights. We describe how the intuition provided
+by these visualizations aids in network design.
+"
+27834,Recent Advances in Features Extraction and Description Algorithms: A Comprehensive Survey,"  Computer vision is one of the most active research fields in information
+technology today. Giving machines and robots the ability to see and comprehend
+the surrounding world at the speed of sight creates endless potential
+applications and opportunities. Feature detection and description algorithms
+can be indeed considered as the retina of the eyes of such machines and robots.
+However, these algorithms are typically computationally intensive, which
+prevents them from achieving the speed of sight real-time performance. In
+addition, they differ in their capabilities and some may favor and work better
+given a specific type of input compared to others. As such, it is essential to
+compactly report their pros and cons as well as their performances and recent
+advances. This paper is dedicated to provide a comprehensive overview on the
+state-of-the-art and recent advances in feature detection and description
+algorithms. Specifically, it starts by overviewing fundamental concepts. It
+then compares, reports and discusses their performance and capabilities. The
+Maximally Stable Extremal Regions algorithm and the Scale Invariant Feature
+Transform algorithms, being two of the best of their type, are selected to
+report their recent algorithmic derivatives.
+"
+27835,Controlling Lipschitz functions,"  Given any positive integers $m$ and $d$, we say the a sequence of points
+$(x_i)_{i\in I}$ in $\mathbb R^m$ is {\em Lipschitz-$d$-controlling} if one can
+select suitable values $y_i\; (i\in I)$ such that for every Lipschitz function
+$f:\mathbb R^m\rightarrow \mathbb R^d$ there exists $i$ with $|f(x_i)-y_i|<1$.
+We conjecture that for every $m\le d$, a sequence $(x_i)_{i\in I}\subset\mathbb
+R^m$ is $d$-controlling if and only if $$\sup_{n\in\mathbb N}\frac{|\{i\in I\,
+:\, |x_i|\le n\}|}{n^d}=\infty.$$ We prove that this condition is necessary and
+a slightly stronger one is already sufficient for the sequence to be
+$d$-controlling. We also prove the conjecture for $m=1$.
+"
+27836,Localizing Finite-Depth Kripke Models,"  We can look at a first-order (or propositional) intuitionistic Kripke model
+as an ordered set of classical models. In this paper, we show that for a
+finite-depth Kripke model in an arbitrary first-order language or propositional
+language, local (classical) truth of a formula is equivalent to non-classical
+truth (truth in the Kripke semantics) of a Friedman's translation of that
+formula, i.e. $ \alpha\Vdash A^\rho \Leftrightarrow \mathfrak{M}_\alpha\models
+A$. We introduce some applications of this fact. We extend the result of
+[Ardeshir and Hessam 2002] and show that semi-narrow Kripke models of Heyting
+Arithmetic $ {\sf HA} $ are locally $ {\sf PA} $.
+"
+27837,"Super Vertex Algebras, Meromorphic Jacobi Forms and Umbral Moonshine","  The vector-valued mock modular forms of umbral moonshine may be repackaged
+into meromorphic Jacobi forms of weight one. In this work we constructively
+solve two cases of the meromorphic module problem for umbral moonshine.
+Specifically, for the type A Niemeier root systems with Coxeter numbers seven
+and thirteen, we construct corresponding bigraded super vertex operator
+algebras, equip them with actions of the corresponding umbral groups, and
+verify that the resulting trace functions on canonically twisted modules
+recover the meromorphic Jacobi forms that are specified by umbral moonshine. We
+also obtain partial solutions to the meromorphic module problem for the type A
+Niemeier root systems with Coxeter numbers four and five, by constructing super
+vertex operator algebras that recover the meromorphic Jacobi forms attached to
+maximal subgroups of the corresponding umbral groups.
+"
+27838,On inverse optimal control via polynomial optimization,"  We consider the class of control systems where the differential equation,
+state and control system are described by polynomials. Given a set of
+trajectories and a class of Lagrangians, we are interested to find a Lagrangian
+in this class for which these trajectories are optimal. To model this inverse
+problem we use a relaxed version of Hamilton-Jacobi-Bellman optimality
+conditions, in the continuity of previous work in this vein. Then we provide a
+general numerical scheme based on polynomial optimization and positivity
+certificates, and illustrate the concepts on a few academic examples.
+"
+27839,Decorrelation of Neutral Vector Variables: Theory and Applications,"  In this paper, we propose novel strategies for neutral vector variable
+decorrelation. Two fundamental invertible transformations, namely serial
+nonlinear transformation and parallel nonlinear transformation, are proposed to
+carry out the decorrelation. For a neutral vector variable, which is not
+multivariate Gaussian distributed, the conventional principal component
+analysis (PCA) cannot yield mutually independent scalar variables. With the two
+proposed transformations, a highly negatively correlated neutral vector can be
+transformed to a set of mutually independent scalar variables with the same
+degrees of freedom. We also evaluate the decorrelation performances for the
+vectors generated from a single Dirichlet distribution and a mixture of
+Dirichlet distributions. The mutual independence is verified with the distance
+correlation measurement. The advantages of the proposed decorrelation
+strategies are intensively studied and demonstrated with synthesized data and
+practical application evaluations.
+"
+27840,Recognizing the real line,"  Let $(\Omega, \leq)$ be a totally ordered set. We prove that if
+Aut$(\Omega,\leq)$ is transitive and satisfies the same first-order sentences
+as the automorphism group of the real line (in the language of groups) then
+$\Omega$ and and the real line are isomorphic ordered sets. This improvement of
+a theorem of Gurevich and Holland is obtained as a consequence of a study of
+centralizers associated with certain transitive subgroups of
+Aut$(\Omega,\leq)$.
+"
+27841,Towards Runtime Monitoring of Node.js and Its Application to the Internet of Things,"  In the last years Node.js has emerged as a framework particularly suitable
+for implementing lightweight IoT applications, thanks to its underlying
+asynchronous event-driven, non blocking I/O model. However, verifying the
+correctness of programs with asynchronous nested callbacks is quite difficult,
+and, hence, runtime monitoring can be a valuable support to tackle such a
+complex task.
+Runtime monitoring is a useful software verification technique that
+complements static analysis and testing, but has not been yet fully explored in
+the context of Internet of Things (IoT) systems. Trace expressions have been
+successfully employed for runtime monitoring in widespread multiagent system
+platforms. Recently, their expressive power has been extended to allow
+parametric specifications on data that can be captured and monitored only at
+runtime. Furthermore, they can be language and system agnostic, through the
+notion of event domain and type. This paper investigates the use of parametric
+trace expressions as a first step towards runtime monitoring of programs
+developed in Node.js and Node-RED, a flow-based IoT programming tool built on
+top of Node.js. Runtime verification of such systems is a task that mostly
+seems to have been overlooked so far in the literature.
+A prototype implementing the proposed system for Node.js, in order to
+dynamically check with trace expressions the correct usage of API functions, is
+presented. The tool exploits the dynamic analysis framework Jalangi for
+monitoring Node.js programs and allows detection of errors that would be
+difficult to catch with other techniques. Furthermore, it offers a simple REST
+interface which can be exploited for runtime verification of Node-RED
+components, and, more generally, IoT devices.
+"
+27842,Analytical approximations for the higher energy structure in strong field ionization with inhomogeneous electric fields,"  Recently, the emergence of a higher energy structure (HES) due to a spatial
+inhomogeneity in the laser electric field, as is typically found close to a
+nano tip, was reported in Phys.~Rev. Letter {\bf 119}, 053204 (2017). For
+practical applications, such as the characterization of near-fields or the
+creation of localized sources of monoenergetic electron beams with tunable
+energies, further insight into the nature of this higher energy structure is
+needed. Here, we give a closed form analytical approximation to describe the
+movement of the electron in the inhomogeneous electric field. In particular, we
+derive a simple scaling law for the location of the HES peak and give a scheme
+to analytically tune the width of the peak, both of which will prove useful in
+optimizing the nanostructure size or geometry for creating the HES in
+experimental settings.
+"
+27843,Exact solutions for silicon photomultipliers models and application to measurements,"  Dark count rate and correlated noise rate are among the main parameters that
+characterize silicon photomultipliers (SiPM). Typically, these parameters are
+evaluated by applying approximate formulas, or by fitting specific models, to
+the measured SiPM noise spectra. Here a novel approach is presented, where
+exact formulas are derived from a statistical model of dark counts and
+correlated noise generation. The method allows one to measure the true value of
+such parameters from the areas of just the first peaks in the dark spectrum. A
+numerical analysis shows the accuracy of the method.
+"
+27844,The use of multilayer network analysis in animal behaviour,"  Network analysis has driven key developments in research on animal behaviour
+by providing quantitative methods to study the social structures of animal
+groups and populations. A recent formalism, known as \emph{multilayer network
+analysis}, has advanced the study of multifaceted networked systems in many
+disciplines. It offers novel ways to study and quantify animal behaviour as
+connected 'layers' of interactions. In this article, we review common questions
+in animal behaviour that can be studied using a multilayer approach, and we
+link these questions to specific analyses. We outline the types of behavioural
+data and questions that may be suitable to study using multilayer network
+analysis. We detail several multilayer methods, which can provide new insights
+into questions about animal sociality at individual, group, population, and
+evolutionary levels of organisation. We give examples for how to implement
+multilayer methods to demonstrate how taking a multilayer approach can alter
+inferences about social structure and the positions of individuals within such
+a structure. Finally, we discuss caveats to undertaking multilayer network
+analysis in the study of animal social networks, and we call attention to
+methodological challenges for the application of these approaches. Our aim is
+to instigate the study of new questions about animal sociality using the new
+toolbox of multilayer network analysis.
+"
+27845,Towards Understanding Regularization in Batch Normalization,"  Batch Normalization (BN) improves both convergence and generalization in
+training neural networks. This work understands these phenomena theoretically.
+We analyze BN by using a basic block of neural networks, consisting of a kernel
+layer, a BN layer, and a nonlinear activation function. This basic network
+helps us understand the impacts of BN in three aspects. First, by viewing BN as
+an implicit regularizer, BN can be decomposed into population normalization
+(PN) and gamma decay as an explicit regularization. Second, learning dynamics
+of BN and the regularization show that training converged with large maximum
+and effective learning rate. Third, generalization of BN is explored by using
+statistical mechanics. Experiments demonstrate that BN in convolutional neural
+networks share the same traits of regularization as the above analyses.
+"
+27846,Optimizing Google Shopping Campaigns Structures With Query-Level Matching,"  How to bid on a Google shopping account (set of shopping campaigns) with
+query-level matching like in Google Adwords.
+"
+27847,Learning Data-adaptive Nonparametric Kernels,"  Traditional kernels or their combinations are often not sufficiently flexible
+to fit the data in complicated practical tasks. In this paper, we present a
+Data-Adaptive Nonparametric Kernel (DANK) learning framework by imposing an
+adaptive matrix on the kernel/Gram matrix in an entry-wise strategy. Since we
+do not specify the formulation of the adaptive matrix, each entry in it can be
+directly and flexibly learned from the data. Therefore, the solution space of
+the learned kernel is largely expanded, which makes DANK flexible to adapt to
+the data. Specifically, the proposed kernel learning framework can be
+seamlessly embedded to support vector machines (SVM) and support vector
+regression (SVR), which has the capability of enlarging the margin between
+classes and reducing the model generalization error. Theoretically, we
+demonstrate that the objective function of our devised model is
+gradient-Lipschitz continuous. Thereby, the training process for kernel and
+parameter learning in SVM/SVR can be efficiently optimized in a unified
+framework. Further, to address the scalability issue in DANK, a
+decomposition-based scalable approach is developed, of which the effectiveness
+is demonstrated by both empirical studies and theoretical guarantees.
+Experimentally, our method outperforms other representative kernel learning
+based algorithms on various classification and regression benchmark datasets.
+"
+27848,Observations of a new stabilizing effect for polar water ice on Mars,"  Using the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM), we
+map the temporal variability of water ice absorption bands over the near-polar
+ice mound in Louth crater, Mars. The absorption band depth of water ice at 1.5
+microns can be used as a proxy for ice grain size and so sudden reductions can
+time any switches from ablation to condensation. A short period of deposition
+on the outer edge of the ice mound during late spring coincides with the
+disappearance of seasonal water frost from the surrounding regolith suggesting
+that this deposition is locally sourced. The outer unit at Louth ice mound
+differs from its central regions by being rough, finely layered, and lacking
+wind-blown sastrugi. This suggests we are observing a new stabilizing effect
+wherein the outer unit is being seasonally replenished with water ice from the
+surrounding regolith during spring. We observe the transport distance for water
+migration at Louth crater to be ~4km, and we use this new finding to address
+why no water ice mounds are observed in craters <9km in diameter.
+"
+27849,Asymptotics of the spectral radius for directed Chung-Lu random graphs with community structure,"  The spectral radius of the adjacency matrix can impact both algorithmic
+efficiency as well as the stability of solutions to an underlying dynamical
+process. Although much research has considered the distribution of the spectral
+radius for undirected random graph models, as symmetric adjacency matrices are
+amenable to spectral analysis, very little work has focused on directed graphs.
+Consequently, we provide novel concentration results for the spectral radius of
+the directed Chung-Lu random graph model. We emphasize that our concentration
+results are applicable both asymptotically and to networks of finite size.
+Subsequently, we extend our concentration results to a generalization of the
+directed Chung-Lu model that allows for community structure.
+"
+27850,A note on strong-consistency of componentwise ARH(1) predictors,"  This paper presents a new result on strong-consistency, in the trace norm, of
+a diagonal componentwise parameter estimator of the autocorrelation operator of
+an autoregressive process of order one (ARH(1) process), allowing
+strong-consistency of the associated plug-in predictor. These results are
+derived, when the eigenvectors of the autocovariance operator are unknown, and
+the autocorrelation operator does not admit a diagonal spectral representation
+with respect to the eigenvectors of the autocovariance operator.
+"
+27851,Towards Developing an Easy-To-Use Scripting Environment for Animating Virtual Characters,"  This paper presents the three scripting commands and main functionalities of
+a novel character animation environment called CHASE. CHASE was developed for
+enabling inexperienced programmers, animators, artists, and students to animate
+in meaningful ways virtual reality characters. This is achieved by scripting
+simple commands within CHASE. The commands identified, which are associated
+with simple parameters, are responsible for generating a number of predefined
+motions and actions of a character. Hence, the virtual character is able to
+animate within a virtual environment and to interact with tasks located within
+it. An additional functionality of CHASE is supplied. It provides the ability
+to generate multiple tasks of a character, such as providing the user the
+ability to generate scenario-related animated sequences. However, since
+multiple characters may require simultaneous animation, the ability to script
+actions of different characters at the same time is also provided.
+"
+27852,Verification of PCP-Related Computational Reductions in Coq,"  We formally verify several computational reductions concerning the Post
+correspondence problem (PCP) using the proof assistant Coq. Our verifications
+include a reduction of a string rewriting problem generalising the halting
+problem for Turing machines to PCP, and reductions of PCP to the intersection
+problem and the palindrome problem for context-free grammars. Interestingly,
+rigorous correctness proofs for some of the reductions are missing in the
+literature.
+"
+27853,An Approach for Spatial-temporal Traffic Modeling in Mobile Cellular Networks,"  The volume and types of traffic data in mobile cellular networks have been
+increasing continuously. Meanwhile, traffic data change dynamically in several
+dimensions such as time and space. Thus, traffic modeling is essential for
+theoretical analysis and energy efficient design of future ultra-dense cellular
+networks. In this paper, the authors try to build a tractable and accurate
+model to describe the traffic variation pattern for a single base station in
+real cellular networks. Firstly a sinusoid superposition model is proposed for
+describing the temporal traffic variation of multiple base stations based on
+real data in a current cellular network. It shows that the mean traffic volume
+of many base stations in an area changes periodically and has three main
+frequency components. Then, lognormal distribution is verified for spatial
+modeling of real traffic data. The spatial traffic distributions at both spare
+time and busy time are analyzed. Moreover, the parameters of the model are
+presented in three typical regions: park, campus and central business district.
+Finally, an approach for combined spatial-temporal traffic modeling of single
+base station is proposed based on the temporal and spatial traffic distribution
+of multiple base stations. All the three models are evaluated through
+comparison with real data in current cellular networks. The results show that
+these models can accurately describe the variation pattern of real traffic data
+in cellular networks.
+"
+27854,Effective Grothendieck-Witt motives of smooth varieties,"  The category of effective Grothendieck-Witt-motives
+$\mathbf{DM}^{GW}_{\mathrm{eff},-}(k)$ (and Witt-motives
+$\mathbf{DM}^W_{\mathrm{eff},-}(k)$) by Voevodsky-Suslin method starting with
+some category of GW-correspondences (and Witt-correspondences) over a perfect
+field $k$, $char\,k\neq 2$, is defined. The functor $M^{GW}_{eff}\colon Sm_k\to
+\mathbf{DM}^{GW}_{\mathrm{eff},-}(k)$ of Grothendieck-Witt-motives of smooth
+varieties is computed and it is proved that for any smooth scheme $X$ and
+homotopy invariant sheave with GW-transfers $F$ $$
+Hom_{\mathbf{DM}^{GW}_{\mathrm{eff},-}(k)}(M^{GW}_{eff}(X), F[i]) \simeq
+H^i_{nis}(X, F) $$ naturally in $X$ and $F$.
+"
+27855,Regularization by noise in one-dimensional continuity equation,"  A linear stochastic continuity equation with non-regular coefficients is
+considered. We prove existence and uniqueness of strong solution, in the
+probabilistic sense, to the Cauchy problem when the vector field has low
+regularity, in which the classical DiPerna-Lions-Ambrosio theory of uniqueness
+of distributional solutions does not apply. We solve partially the open problem
+that is the case when the vector-field has random dependence. In addition, we
+prove a stability result for the solutions.
+"
+27856,Large scale classification in deep neural network with Label Mapping,"  In recent years, deep neural network is widely used in machine learning. The
+multi-class classification problem is a class of important problem in machine
+learning. However, in order to solve those types of multi-class classification
+problems effectively, the required network size should have hyper-linear growth
+with respect to the number of classes. Therefore, it is infeasible to solve the
+multi-class classification problem using deep neural network when the number of
+classes are huge. This paper presents a method, so called Label Mapping (LM),
+to solve this problem by decomposing the original classification problem to
+several smaller sub-problems which are solvable theoretically. Our method is an
+ensemble method like error-correcting output codes (ECOC), but it allows base
+learners to be multi-class classifiers with different number of class labels.
+We propose two design principles for LM, one is to maximize the number of base
+classifier which can separate two different classes, and the other is to keep
+all base learners to be independent as possible in order to reduce the
+redundant information. Based on these principles, two different LM algorithms
+are derived using number theory and information theory. Since each base learner
+can be trained independently, it is easy to scale our method into a large scale
+training system. Experiments show that our proposed method outperforms the
+standard one-hot encoding and ECOC significantly in terms of accuracy and model
+complexity.
+"
+27857,Comparison of Lubrication Approximation and Navier-Stokes Solutions for Dam-Break Flows in Thin Films,"  The levelling of a thin liquid film has been analysed experimentally,
+theoretically, and numerically. The purpose of this contribution is to compare
+solutions from two different finite element models: one based on the 2D
+Navier-Stokes equations, and the other based on a simplification thereof, known
+as the lubrication approximation. The latter is significantly less
+computationally expensive, hence it is desirable to know in which parameter
+range this approximation is valid. As a benchmark problem, we consider the
+classical dam-break problem. This paper establishes the validity window of the
+lubrication approximation in terms of the Reynolds number, Froude number, and
+film aspect ratio. To validate the solutions produced by the solver,
+experimental data has been obtained for the levelling of silicone oil. The free
+surface velocity measured experimentally is compared to that produced by the
+two numerical models and found to have relatively good agreement. The
+lubrication approximation is then compared with the Navier-Stokes model for a
+range of Reynolds and Froude numbers, and for a range of film aspect ratios.
+The results show that the lubrication approximation is remarkably robust and
+particularly accurate for low Reynolds and Froude numbers, for which inertial
+effects are negligible. Using fixed Reynolds and Froude numbers of 100 and 0.01
+respectively, the lubrication approximation is accurate for aspect ratios up to
+1%.
+"
+27858,Local nearest neighbour classification with applications to semi-supervised learning,"  We derive a new asymptotic expansion for the global excess risk of a local
+$k$-nearest neighbour classifier, where the choice of $k$ may depend upon the
+test point. This expansion elucidates conditions under which the dominant
+contribution to the excess risk comes from the locus of points at which each
+class label is equally likely to occur, but we also show that if these
+conditions are not satisfied, the dominant contribution may arise from the
+tails of the marginal distribution of the features. Moreover, we prove that,
+provided the $d$-dimensional marginal distribution of the features has a finite
+$\rho$th moment for some $\rho > 4$ (as well as other regularity conditions), a
+local choice of $k$ can yield a rate of convergence of the excess risk of
+$O(n^{-4/(d+4)})$, where $n$ is the sample size, whereas for the standard
+$k$-nearest neighbour classifier, our theory would require $d \geq 5$ and $\rho
+> 4d/(d-4)$ finite moments to achieve this rate. Our results motivate a new
+$k$-nearest neighbour classifier for semi-supervised learning problems, where
+the unlabelled data are used to obtain an estimate of the marginal feature
+density, and fewer neighbours are used for classification when this density
+estimate is small. The potential improvements over the standard $k$-nearest
+neighbour classifier are illustrated both through our theory and via a
+simulation study.
+"
+27859,Dynamics and stability of surface waves with bulk-soluble surfactants,"  In this paper we study the dynamics of a layer of incompressible viscous
+fluid bounded below by a rigid boundary and above by a free boundary, in the
+presence of a uniform gravitational field. We assume that a mass of surfactant
+is present both at the free surface and in the bulk of fluid, and that
+conversion from one species to the other is possible. The surfactants couple to
+the fluid dynamics through the coefficient of surface tension, which depends on
+the the surface density of surfactants. Gradients in this concentration give
+rise to Marangoni stress on the free surface. In turn, the fluids advect the
+surfactants and distort their concentration through geometric distortions of
+the free surface. We model the surfactants in a way that allows absorption and
+desorption of surfactant between the surface and bulk. We prove that small
+perturbations of the equilibrium solutions give rise to global-in-time
+solutions that decay to equilibrium at an exponential rate. This establishes
+the asymptotic stability of the equilibrium solutions.
+"
+27860,Identifiability from some variables,"  Under mass-action kinetics, biochemical reaction networks give rise to
+polynomial autonomous dynamical systems whose parameters are often difficult to
+estimate. We deal in this paper with the problem of identifying the kinetic
+parameters of a class of biochemical networks which are abundant, such as
+multisite phosphorylation systems and phosphorylation cascades (for example,
+MAPK cascades). For any system of this class we explicitly exhibit a single
+species for each connected component of the associated digraph such that the
+successive total derivatives of its concentration allow us to identify all the
+parameters occurring in the component. The number of derivatives needed is
+bounded essentially by the length of the corresponding connected component of
+the digraph. Moreover, in the particular case of the cascades, we show that the
+parameters can be identified from a bounded number of successive derivatives of
+the last product of the last layer. This theoretical result induces also a
+heuristic interpolation-based identifiability procedure to recover the values
+of the rate constants from exact measurements.
+"
+27861,"Community detection, link prediction, and layer interdependence in multilayer networks","  Complex systems are often characterized by distinct types of interactions
+between the same entities. These can be described as a multilayer network where
+each layer represents one type of interaction. These layers may be
+interdependent in complicated ways, revealing different kinds of structure in
+the network. In this work we present a generative model, and an efficient
+expectation-maximization algorithm, which allows us to perform inference tasks
+such as community detection and link prediction in this setting. Our model
+assumes overlapping communities that are common between the layers, while
+allowing these communities to affect each layer in a different way, including
+arbitrary mixtures of assortative, disassortative, or directed structure. It
+also gives us a mathematically principled way to define the interdependence
+between layers, by measuring how much information about one layer helps us
+predict links in another layer. In particular, this allows us to bundle layers
+together to compress redundant information, and identify small groups of layers
+which suffice to predict the remaining layers accurately. We illustrate these
+findings by analyzing synthetic data and two real multilayer networks, one
+representing social support relationships among villagers in South India and
+the other representing shared genetic substrings material between genes of the
+malaria parasite.
+"
+27862,Bayesian Neural Networks,"  This paper describes and discusses Bayesian Neural Network (BNN). The paper
+showcases a few different applications of them for classification and
+regression problems. BNNs are comprised of a Probabilistic Model and a Neural
+Network. The intent of such a design is to combine the strengths of Neural
+Networks and Stochastic modeling. Neural Networks exhibit continuous function
+approximator capabilities. Stochastic models allow direct specification of a
+model with known interaction between parameters to generate data. During the
+prediction phase, stochastic models generate a complete posterior distribution
+and produce probabilistic guarantees on the predictions. Thus BNNs are a unique
+combination of neural network and stochastic models with the stochastic model
+forming the core of this integration. BNNs can then produce probabilistic
+guarantees on it's predictions and also generate the distribution of parameters
+that it has learnt from the observations. That means, in the parameter space,
+one can deduce the nature and shape of the neural network's learnt parameters.
+These two characteristics makes them highly attractive to theoreticians as well
+as practitioners. Recently there has been a lot of activity in this area, with
+the advent of numerous probabilistic programming libraries such as: PyMC3,
+Edward, Stan etc. Further this area is rapidly gaining ground as a standard
+machine learning approach for numerous problems
+"
+27863,Thermo-optical interactions in a dye-microcavity photon Bose-Einstein condensate,"  Superfluidity and Bose-Einstein condensation are usually considered as two
+closely related phenomena. Indeed, in most macroscopic quantum systems, like
+liquid helium, ultracold atomic Bose gases, and exciton-polaritons,
+condensation and superfluidity occur in parallel. In photon Bose-Einstein
+condensates realized in the dye microcavity system, thermalization does not
+occur by direct interaction of the condensate particles as in the above
+described systems, i.e. photon-photon interactions, but by absorption and
+re-emission processes on the dye molecules, which act as a heat reservoir.
+Currently, there is no experimental evidence for superfluidity in the dye
+microcavity system, though effective photon interactions have been observed
+from thermo-optic effects in the dye medium. In this work, we theoretically
+investigate the implications of effective thermo-optic photon interactions, a
+temporally delayed and spatially non-local effect, on the photon condensate,
+and derive the resulting Bogoliubov excitation spectrum. The calculations
+suggest a linear photon dispersion at low momenta, fulfilling the Landau's
+criterion of superfluidity . We envision that the temporally delayed and
+long-range nature of the thermo-optic photon interaction offer perspectives for
+novel quantum fluid phenomena.
+"
+27864,How Functional Complexity affects the Scalability-Energy Efficiency Trade-Off of HCC WSN Clustering,"  Even though clustering algorithms in Wireless Sensor Networks (WSN) are a
+well investigate subject, the increasing interest in the Internet of Things
+(IoT) and 5G technologies has precipitated the need of new ways to comprehend
+and overcome a new set of challenges. While studies mainly propose new
+algorithms and compare these algorithms based on a set of properties (e.g.
+energy efficiency, scalability), none of them focuses on the underlying
+mechanisms and organizational patterns that lead to these properties. We
+address this lack of understanding by applying a complex systems science
+approach to investigate the properties of WSNs arising from the communication
+patterns of the network nodes. We represent different implementations of
+clustering in WSNs with a functional topology graph. Moreover, we employ a
+complexity metric - functional complexity (CF) - to explain how local
+interactions give rise to the global behavior of the network. Our analysis
+shows that higher values of CF indicate higher scalability and lower energy
+efficiency.
+"
+27865,"Integrated Model, Batch and Domain Parallelism in Training Neural Networks","  We propose a new integrated method of exploiting model, batch and domain
+parallelism for the training of deep neural networks (DNNs) on large
+distributed-memory computers using minibatch stochastic gradient descent (SGD).
+Our goal is to find an efficient parallelization strategy for a fixed batch
+size using $P$ processes. Our method is inspired by the communication-avoiding
+algorithms in numerical linear algebra. We see $P$ processes as logically
+divided into a $P_r \times P_c$ grid where the $P_r$ dimension is implicitly
+responsible for model/domain parallelism and the $P_c$ dimension is implicitly
+responsible for batch parallelism. In practice, the integrated matrix-based
+parallel algorithm encapsulates these types of parallelism automatically. We
+analyze the communication complexity and analytically demonstrate that the
+lowest communication costs are often achieved neither with pure model nor with
+pure data parallelism. We also show how the domain parallel approach can help
+in extending the theoretical scaling limit of the typical batch parallel
+method.
+"
+27866,Reaction-diffusion problems on time-dependent Riemannian manifolds: stability of periodic solutions,"  We investigate the stability of time-periodic solutions of semilinear
+parabolic problems with Neumann boundary conditions. Such problems are posed on
+compact submanifolds evolving periodically in time. The discussion is based on
+the principal eigenvalue of periodic parabolic operators. The study is
+motivated by biological models on the effect of growth and curvature on
+patterns formation. The Ricci curvature plays an important role.
+"
+27867,Mechanics of epithelial tissue formation,"  A key process in the life of any multicellular organism is its development
+from a single egg into a full grown adult. The first step in this process often
+consists of forming a tissue layer out of randomly placed cells on the surface
+of the egg. We present a model for generating such a tissue, and find that the
+resulting cellular pattern corresponds to the Voronoi tessellation of the
+nuclei of the cells. Experimentally, we obtain the same result in both fruit
+flies and flour beetles, with a distribution of cell shapes that matches that
+of the model, without any adjustable parameters. Finally, we show that this
+pattern is broken when the cells do not all grow at the same rate.
+"
+27868,Wave propagation and shock formation in the most general scalar-tensor theories,"  This work studies wave propagation in the most general scalar-tensor
+theories, particularly focusing on the causal structure realized in these
+theories and also the shock formation process induced by nonlinear effects. For
+these studies we use the Horndeski theory and its generalization to the two
+scalar field case. We show that propagation speeds of gravitational wave and
+scalar field wave in these theories may differ from the light speed depending
+on background field configuration, and find that a Killing horizon becomes a
+boundary of causal domain if the scalar fields share the symmetry of the
+background spacetime. About the shock formation, we focus on transport of
+discontinuity in second derivatives of the metric and scalar field in the
+shift-symmetric Horndeski theory. We find that amplitude of the discontinuity
+generically diverges within finite time, which corresponds to shock formation.
+It turns out that the canonical scalar field and the scalar DBI model, among
+other theories described by the Horndeski theory, are free from such shock
+formation even when the background geometry and scalar field configuration are
+nontrivial. We also observe that gravitational wave is protected against shock
+formation when the background has some symmetries at least. This fact may
+indicate that the gravitational wave in this theory is more well-behaved
+compared to the scalar field, which typically suffers from shock formation.
+"
+27869,Quadratic Lagrange spectrum: I,"  In this paper we prove the existence of Hall's ray for the quadratic Lagrange
+spectrums of all real quadratic numbers. For a large class of real quadratic
+numbers, we compute the Hurwitz constants of their quadratic Lagrange spectrums
+"
+27870,Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex,"  Several recently proposed architectures of neural networks such as ResNeXt,
+Inception, Xception, SqueezeNet and Wide ResNet are based on the designing idea
+of having multiple branches and have demonstrated improved performance in many
+applications. We show that one cause for such success is due to the fact that
+the multi-branch architecture is less non-convex in terms of duality gap. The
+duality gap measures the degree of intrinsic non-convexity of an optimization
+problem: smaller gap in relative value implies lower degree of intrinsic
+non-convexity. The challenge is to quantitatively measure the duality gap of
+highly non-convex problems such as deep neural networks. In this work, we
+provide strong guarantees of this quantity for two classes of network
+architectures. For the neural networks with arbitrary activation functions,
+multi-branch architecture and a variant of hinge loss, we show that the duality
+gap of both population and empirical risks shrinks to zero as the number of
+branches increases. This result sheds light on better understanding the power
+of over-parametrization where increasing the network width tends to make the
+loss surface less non-convex. For the neural networks with linear activation
+function and $\ell_2$ loss, we show that the duality gap of empirical risk is
+zero. Our two results work for arbitrary depths and adversarial data, while the
+analytical techniques might be of independent interest to non-convex
+optimization more broadly. Experiments on both synthetic and real-world
+datasets validate our results.
+"
+27871,A derivation of the Black-Scholes option pricing model using a central limit theorem argument,"  The Black-Scholes model (sometimes known as the Black-Scholes-Merton model)
+gives a theoretical estimate for the price of European options. The price
+evolution under this model is described by the Black-Scholes formula, one of
+the most well-known formulas in mathematical finance. For their discovery,
+Merton and Scholes have been awarded the 1997 Nobel prize in Economics. The
+standard method of deriving the Black-Scholes European call option pricing
+formula involves stochastic differential equations. This approach is out of
+reach for most students learning the model for the first time. We provide an
+alternate derivation using the Lindeberg-Feller central limit theorem under
+suitable assumptions. Our approach is elementary and can be understood by
+undergraduates taking a standard undergraduate course in probability.
+"
+27872,graphTPP: A multivariate based method for interactive graph layout and analysis,"  Graph layout is the process of creating a visual representation of a graph
+through a node-link diagram. Node-attribute graphs have additional data stored
+on the nodes which describe certain properties of the nodes called attributes.
+Typical force-directed representations often produce hairball-like structures
+that neither aid in understanding the graph's topology nor the relationship to
+its attributes. The aim of this research was to investigate the use of
+node-attributes for graph layout in order to improve the analysis process and
+to give further insight into the graph over purely topological layouts. In this
+article we present graphTPP, a graph based extension to targeted projection
+pursuit (TPP) --- an interactive, linear, dimension reduction technique --- as
+a method for graph layout and subsequent further analysis. TPP allows users to
+control the projection and is optimised for clustering. Three case studies were
+conducted in the areas of influence graphs, network security, and citation
+networks. In each case graphTPP was shown to outperform standard force-directed
+techniques and even other dimension reduction methods in terms of clarity of
+clustered structure in the layout, the association between the structure and
+the attributes and the insights elicited in each domain area.
+"
+27873,Argument-based Belief in Topological Structures,"  This paper combines two studies: a topological semantics for epistemic
+notions and abstract argumentation theory. In our combined setting, we use a
+topological semantics to represent the structure of an agent's collection of
+evidence, and we use argumentation theory to single out the relevant sets of
+evidence through which a notion of beliefs grounded on arguments is defined. We
+discuss the formal properties of this newly defined notion, providing also a
+formal language with a matching modality together with a sound and complete
+axiom system for it. Despite the fact that our agent can combine her evidence
+in a 'rational' way (captured via the topological structure), argument-based
+beliefs are not closed under conjunction. This illustrates the difference
+between an agent's reasoning abilities (i.e. the way she is able to combine her
+available evidence) and the closure properties of her beliefs. We use this
+point to argue for why the failure of closure under conjunction of belief
+should not bear the burden of the failure of rationality.
+"
+27874,From Parity to Preference-based Notions of Fairness in Classification,"  The adoption of automated, data-driven decision making in an ever expanding
+range of applications has raised concerns about its potential unfairness
+towards certain social groups. In this context, a number of recent studies have
+focused on defining, detecting, and removing unfairness from data-driven
+decision systems. However, the existing notions of fairness, based on parity
+(equality) in treatment or outcomes for different social groups, tend to be
+quite stringent, limiting the overall decision making accuracy. In this paper,
+we draw inspiration from the fair-division and envy-freeness literature in
+economics and game theory and propose preference-based notions of fairness --
+given the choice between various sets of decision treatments or outcomes, any
+group of users would collectively prefer its treatment or outcomes, regardless
+of the (dis)parity as compared to the other groups. Then, we introduce
+tractable proxies to design margin-based classifiers that satisfy these
+preference-based notions of fairness. Finally, we experiment with a variety of
+synthetic and real-world datasets and show that preference-based fairness
+allows for greater decision accuracy than parity-based fairness.
+"
+27875,Single-lead f-wave extraction using diffusion geometry,"  A novel single-lead f-wave extraction algorithm based on the modern diffusion
+geometry data analysis framework is proposed. The algorithm is essentially an
+averaged beat subtraction algorithm, where the ventricular activity template is
+estimated by combining a newly designed metric, the ""diffusion distance,"" and
+the non-local Euclidean median based on the non-linear manifold setup. We
+coined the algorithm DD-NLEM. Two simulation schemes are considered, and the
+new algorithm DD-NLEM outperforms traditional algorithms, including the average
+beat subtraction, principal component analysis, and adaptive singular value
+cancellation, in different evaluation metrics with statistical significance.
+The clinical potential is shown in the real Holter signal, and we introduce a
+new score to evaluate the performance of the algorithm.
+"
+27876,Talking Condition Identification Using Second-Order Hidden Markov Models,"  This work focuses on enhancing the performance of text-dependent and
+speaker-dependent talking condition identification systems using second-order
+hidden Markov models (HMM2s). Our results show that the talking condition
+identification performance based on HMM2s has been improved significantly
+compared to first-order hidden Markov models (HMM1s). Our talking conditions in
+this work are neutral, shouted, loud, angry, happy, and fear.
+"
+27877,Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining,"  In many areas of data mining, data is collected from humans beings. In this
+contribution, we ask the question of how people actually respond to ordinal
+scales. The main problem observed is that users tend to be volatile in their
+choices, i.e. complex cognitions do not always lead to the same decisions, but
+to distributions of possible decision outputs. This human uncertainty may
+sometimes have quite an impact on common data mining approaches and thus, the
+question of effective modelling this so called human uncertainty emerges
+naturally.
+Our contribution introduces two different approaches for modelling the human
+uncertainty of user responses. In doing so, we develop techniques in order to
+measure this uncertainty at the level of user inputs as well as the level of
+user cognition. With support of comprehensive user experiments and large-scale
+simulations, we systematically compare both methodologies along with their
+implications for personalisation approaches. Our findings demonstrate that
+significant amounts of users do submit something completely different (action)
+than they really have in mind (cognition). Moreover, we demonstrate that
+statistically sound evidence with respect to algorithm assessment becomes quite
+hard to realise, especially when explicit rankings shall be built.
+"
+27878,Enumerating multiplex juggling patterns,"  Mathematics has been used in the exploration and enumeration of juggling
+patterns. In the case when we catch and throw one ball at a time the number of
+possible juggling patterns is well-known. When we are allowed to catch and
+throw any number of balls at a given time (known as multiplex juggling) the
+enumeration is more difficult and has only been established in a few special
+cases. We give a method of using cards related to ""embeddings"" of ordered
+partitions to enumerate the count of multiplex juggling sequences, determine
+these counts for small cases, and establish some combinatorial properties of
+the set of cards needed.
+"
+27879,Macdonald-positive specializations of the algebra of symmetric functions: Proof of the Kerov conjecture,"  We prove the classification of homomorphisms from the algebra of symmetric
+functions to $\mathbb{R}$ with non-negative values on Macdonald symmetric
+functions $P_{\lambda}$, that was conjectured by S.V. Kerov in 1992.
+"
+27880,Acceleration Profiles and Processing Methods for Parabolic Flight,"  Parabolic flights provide cost-effective, time-limited access to ""weightless""
+or reduced gravity conditions experienced in space or on planetary surfaces,
+e.g. the Moon or Mars. These flights facilitate fundamental research - from
+materials science to space biology - and testing/validation activities that
+support and complement infrequent and costly access to space. While parabolic
+flights have been conducted for decades, reference acceleration profiles and
+processing methods are not widely available - yet are critical for assessing
+the results of these activities. Here we present a method for collecting,
+analyzing, and classifying the altered gravity environments experienced during
+a parabolic flight. We validated this method using a commercially available
+accelerometer during a Boeing 727-200F flight with $20$ parabolas. All data and
+analysis code are freely available. Our solution can be easily integrated with
+a variety of experimental designs, does not depend upon accelerometer
+orientation, and allows for unsupervised and repeatable classification of all
+phases of flight, providing a consistent and open-source approach to
+quantifying gravito-intertial accelerations (GIA), or $g$ levels. As academic,
+governmental, and commercial use of space increases, data availability and
+validated processing methods will enable better planning, execution, and
+analysis of parabolic flight experiments, and thus, facilitate future space
+activities.
+"
+27881,Structural and magnetic properties of spin-$1/2$ dimer compound Cu$_2$(IPA)$_2$(DMF)(H$_2$O) with a large spin gap,"  We present the synthesis and a detailed investigation of structural and
+magnetic properties of metal-organic compound Cu$_2$(IPA)$_2$(DMF)(H$_2$O) by
+means of x-ray diffraction, magnetization, and heat capacity measurements.
+Single crystals of the title compound were synthesized by judicious selection
+of organic ligand and employing a selective hydrothermal reaction route. It
+crystallizes in an orthorhombic structure with space group $Cmca$. The
+structural analysis revealed that two Cu$^{2+}$ ions are held together by the
+organic component (-O-C-O-) in a square paddle-wheel to form spin dimers which
+are aligned perpendicular to each other and are further coupled through organic
+ligands (isophthalic acid) forming two-dimensional layers. Temperature
+dependent magnetic susceptibility $\chi(T)$ could be described well using
+spin-$1/2$ dimer model. The spin susceptibility $\chi_{\rm spin} (T)$ shows an
+exponential decrease in the low temperature region, below the broad maximum,
+confirming the singlet ground state with a large spin gap of $\Delta/k_{\rm B}
+\simeq 409$~K. The heat capacity $C_{\rm p}$ measured as a function of
+temperature also confirms the absence of magnetic long-range-order down to 2~K.
+"
+27882,Routes Obey Hierarchy in Complex Networks,"  Various hypotheses exist about the paths used for communication between the
+nodes of complex networks. Most studies simply suppose that communication goes
+via shortest paths, while others have more explicit assumptions about how
+routing (alternatively navigation or search) works or should work in real
+networks. However, these assumptions are rarely checked against real data. Here
+we directly analyze the structure of operational paths using real measurements.
+For this purpose we use existing and newly created datasets having both the
+topology of the network and a sufficient number of empirically-determined paths
+over it. Such datasets are processed for air transportation networks, the human
+brain, the Internet and the fit-fat-cat word ladder game. Our results suggest
+that from the great number of possible paths, nature seems to pick according to
+some simple rules, which we will refer to as routing policies. First we
+confirm, that the preference of short paths is an inevitable policy element,
+however the observed stretch of the paths suggests that there are other
+policies at work simultaneously. We identify two additional policies common in
+our networks: the ""conform hierarchy"", meaning that the paths should obey the
+structural hierarchy of the network, and the ""prefer downstream"" policy which
+promotes avoiding the network core if possible. Building upon these simple
+policies, we propose a synthetic routing policy which can recover the basic
+statistical properties of the operational paths in networks. Our results can be
+helpful in estimating the reaction of complex systems for stress coming from
+the outside more accurately than the shortest path assumption permits.
+"
+27883,Discretization of continuous-time quantum walks via the staggered model with Hamiltonians,"  We characterize a close connection between the continuous-time quantum-walk
+model and a discrete-time quantum-walk version, based on the staggered model
+with Hamiltonians in a class of Cayley graphs, which can be considered as a
+discretization of continuous-time quantum walks. This connection provides
+examples of perfect state transfer and instantaneous uniform mixing in the
+staggered model. On the other hand, we provide some more examples of perfect
+state transfer and instantaneous uniform mixing in the staggered model that
+cannot be reproduced by the continuous-time model.
+"
+27884,Titan organic aerosols: molecular composition and structure of laboratory analogues inferred from systematic pyrolysis gas chromatography mass spectrometry analysis,"  Numerous studies have been carried out to characterize the chemical
+composition of laboratory analogues of Titan aerosols (tholins), but their
+molecular composition as well as their structure are still poorly known. If
+pyrolysis gas chromatography mass spectrometry (pyr-GCMS) has been used for
+years to give clues about this composition, the highly disparate results
+obtained can be attributed to the analytical conditions used and/or to
+differences in the nature of the analogues studied. In order to have a better
+description of Titan tholins molecular composition, we led a systematic
+analysis of these materials using pyr-GCMS with two major objectives: (i)
+exploring the analytical parameters to estimate the biases this technique can
+induce and to find an optimum for analyses allowing the detection of a wide
+range of compounds and thus a characterization of the tholins composition as
+comprehensive as possible, and (ii) highlighting the role of the CH4 ratio in
+the gaseous reactive medium on the tholins molecular structure. With this aim,
+we used a radio-frequency plasma discharge to synthetize tholins with different
+concentrations of CH4 diluted in N2. The samples were systematically pyrolyzed
+from 200 to 600°C. The extracted gases were then analyzed by GCMS for
+their molecular identification.
+"
+27885,The Level I Multiverse is not the same as the Level III Multiverse,"  Anthony Aguirre and Max Tegmark have famously speculated that the Level I
+Multiverse is the same as the Level III Multiverse. By this, they mean that the
+parallel universes of the Level III Multiverse can be regarded as similar or
+identical copies of our own Hubble volume distributed throughout the whole of
+our (possibly infinite) bubble universe. However, we show that our bubble
+universe is in a single quantum eigenstate that extends to regions of space
+that are receding from each other at superluminal velocities because of general
+relativistic expansion. Such a bubble universe cannot accommodate Hubble
+volumes in the different orthogonal eigenstates required by the Level III
+Multiverse. Instead, quantum uncertainty arises from large numbers of
+alternative bubble universes in Hilbert space, isolated from each other. The
+conclusion of the paper is that the Level I Multiverse is not the same as the
+Level III Multiverse.
+"
+27886,A Stabilized Normal Form Algorithm for Generic Systems of Polynomial Equations,"  We propose a numerical linear algebra based method to find the multiplication
+operators of the quotient ring $\mathbb{C}[x]/I$ associated to a
+zero-dimensional ideal $I$ generated by $n$ $\mathbb{C}$-polynomials in $n$
+variables. We assume that the polynomials are generic in the sense that the
+number of solutions in $\mathbb{C}^n$ equals the Bézout number. The main
+contribution of this paper is an automated choice of basis for
+$\mathbb{C}[x]/I$, which is crucial for the feasibility of normal form methods
+in finite precision arithmetic. This choice is based on numerical linear
+algebra techniques and governed by the numerical properties of the given
+generators of $I$.
+"
+27887,Intrinsic ac anomalous Hall effect of nonsymmorphic chiral superconductors with an application to $\mathrm{UPt_3}$,"  We identify an intrinsic mechanism of the anomalous Hall effect for
+non-symmorphic chiral superconductors. This mechanism relies on both a
+nontrivial multi-band chiral superconducting order parameter, which is a
+mixture of pairings of even and odd angular momentum channels, and a complex
+normal state inter-sublattice hopping, both of which are consequences of the
+nonsymmorphic group symmetry of the underlying lattice. We apply this mechanism
+to the putative chiral superconducting phase of the heavy-fermion
+superconductor $\mathrm{UPt_3}$ and calculate the anomalous ac Hall
+conductivity in a simplified two-band model. From the ac Hall conductivity and
+optical data we estimate the polar Kerr rotation angle and compare it to the
+measured results for $\mathrm{UPt_3}$ [E. R. Schemm \textit{et al.}, Science
+\textbf{345},190(2014)].
+"
+27888,Phase transitions of polariton condensate in 2D Dirac materials,"  For the quantum well in an optical microcavity, the interplay of the Coulomb
+interaction and the electron-photon (e-ph) coupling can lead to the
+hybridizations of the exciton and the cavity photon known as polaritons, which
+can form the Bose-Einstein condensate above a threshold density. Additional
+physics due to the nontrivial Berry phase comes into play when the quantum well
+consists of the gapped two-dimensional (2D) Dirac material such as the
+transition metal dichalcogenide (TMDC) MoS$_2$ or WSe$_2$. Specifically, in
+forming the polariton, the e-ph coupling from the optical selection rule due to
+the Berry phase can compete against the Coulomb electron-electron (e-e)
+interaction. We find that this competition gives rise to a rich phase diagram
+for the polariton condensate involving both topological and symmetry breaking
+phase transitions, with the former giving rise to the quantum anomalous Hall
+and the quantum spin Hall phases.
+"
+27889,High-resolution transport of regional level sets for evolving complex interface networks,"  In this paper we describe a high-resolution transport formulation of the
+regional level-set approach for an improved prediction of the evolution of
+complex interface networks. The novelty of this method is twofold: (i)
+construction of local level sets and reconstruction of a global regional level
+sets, (ii) locally transporting the interface network by employing high-order
+spatial discretization schemes for improved representation of complex
+topologies. Various numerical test cases of multi-region flow problems,
+including triple-point advection, single vortex flow, mean curvature flow,
+normal driven flow and dry foam dynamics, show that the method is accurate and
+suitable for a wide range of complex interface-network evolutions. Its overall
+computational cost is comparable to the Semi-Lagrangian regional level-set
+method while the prediction accuracy is significantly improved. The approach
+thus offers a \textbf{viable} alternative to previous interface-network
+level-set method.
+"
+27890,First results of the cosmic ray NUCLEON experiment,"  The NUCLEON experiment was designed to study the chemical composition and
+energy spectra of galactic cosmic ray nuclei from protons to zinc at energies
+of $\sim10^{11}$--$10^{15}$\,eV per particle. The research was carried out with
+the NUCLEON scientific equipment installed on the Russian satellite ""Resurs-P""
+No.\,2 as an additional payload. This article presents the results for the
+measured nuclei spectra related to the first approximately 250 days of the
+scientific data collection during 2015 and 2016. The all-particle spectrum and
+the spectra of p, He, C, O, Ne, Mg, Si and Fe are presented. Some interesting
+ratios of the spectra are also presented and discussed. The experiment is now
+in its beginning stage and the data still have a preliminary character, but
+they already give numerous indications of the existence of various
+non-canonical phenomena in the physics of cosmic rays, which are expressed in
+the violation of a simple universal power law of the energy spectra. These
+features of the the data are briefly discussed.
+"
+27891,NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit,"  This paper demonstrates neural network-based toolkit namely NNVLP for
+essential Vietnamese language processing tasks including part-of-speech (POS)
+tagging, chunking, named entity recognition (NER). Our toolkit is a combination
+of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network
+(CNN), Conditional Random Field (CRF), using pre-trained word embeddings as
+input, which achieves state-of-the-art results on these three tasks. We provide
+both API and web demo for this toolkit.
+"
+27892,Additive uniqueness of $\mathtt{PRIMES}-1$ for multiplicative functions,"  Let $\mathtt{PRIMES}$ be the set of all primes. We show that a multiplicative
+function which satisfies \[ f(p+q-2) = f(p) + f(q) - f(2) \text{ for }p,q \in
+\mathtt{PRIMES} \] is one of the following: \begin{enumerate} \item $f$ is the
+identity function \item $f$ is the constant function with $f(n)=1$ \item
+$f(n)=0$ for $n \ge2$ unless $n$ is odd and squareful. \end{enumerate} As a
+consequence, a multiplicative function which satisfies \[ f(a+b) = f(a) + f(b)
+\text{ for }a,b \in \mathtt{PRIMES}-1 \] is the identity function.
+"
+27893,Excitonic Phase Diagram of the Three-Chain Hubbard Model for Semiconducting and Semimetallic Ta$_2$NiSe$_5$,"  Transition metal chalcogenide Ta$_2$NiSe$_5$, a promising material for the
+excitonic insulator, is investigated on the basis of the quasi-one-dimensional
+three-chain Hubbard model with two conduction ($c$) bands and one valence ($f$)
+band. In the semimetallic case where only one of two $c$ bands and the $f$ band
+cross the Fermi level, the transition from the $c$-$f$ compensated semimetal to
+the uniform excitonic insulator takes place at low temperature as the same as
+in the semiconducting case. On the other hand, when another $c$ band also
+crosses the Fermi level, the system shows three types of
+Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) excitonic orders characterized by the
+condensation of excitons with finite center-of-mass momentum $q$ corresponding
+to the three types of nesting vectors between the imbalanced two $c$ and one
+$f$ Fermi surfaces. The obtained FFLO states are metallic in contrast to the
+excitonic insulator and are expected to be observed in semimetallic
+Ta$_2$NiSe$_5$ under high pressure.
+"
+27894,Viscosity of a sheared correlated (near-critical) model fluid in confinement,"  Second-order phase transitions are characterized by a divergence of the
+spatial correlation length of the order parameter fluctuations. For confined
+systems, this is known to lead to remarkable equilibrium physical phenomena,
+including finite-size effects and critical Casimir forces. We explore here some
+non-equilibrium aspects of these effects in the stationary state resulting from
+the action of external forces: by analyzing a model of a correlated fluid under
+shear, spatially confined by two parallel plates, we study the resulting
+viscosity within the setting of (Gaussian) Landau-Ginzburg theory.
+Specifically, we introduce a model in which the hydrodynamic velocity field
+(obeying the Stokes equation) is coupled to an order parameter with dissipative
+dynamics. The well-known Green-Kubo relation for bulk systems is generalized
+for confined systems. This is shown to result in a non-local Stokes equation
+for the fluid flow, due to the correlated fluctuations. The resulting effective
+shear viscosity shows universal as well as non-universal contributions, which
+we study in detail. In particular, the deviation from the bulk behavior is
+universal, depending on the ratio of the correlation length and the film
+thickness $L$. In addition, at the critical point the viscosity is proportional
+to $\ell/L$, where $\ell$ is a dynamic length scale. These findings are
+expected to be experimentally observable, especially for systems where the bulk
+viscosity is affected by critical fluctuations.
+"
+27895,Cold denaturation of RNA secondary structures with loop entropy and quenched disorder,"  We study the folding of RNA secondary structures with quenched sequence
+randomness by means of the constrained annealing method. A thermodynamic phase
+transition is induced by including the conformational weight of loop
+structures. In addition to the expected melting at high temperature, a cold
+melting transition appears. Our results suggest that the cold denaturation of
+RNA found experimentally is, in fact, a continuous phase transition triggered
+by quenched sequence disorder. We calculate both hot and cold melting critical
+temperatures for the competing energy scenario between favorable and
+unfavorable base pairs and present a phase diagram as a function of the loop
+exponent and temperature.
+"
+27896,Quantum curves and $q$-deformed Painlevé equations,"  We propose that the grand canonical topological string partition functions
+satisfy finite-difference equations in the closed string moduli. In the case of
+genus one mirror curve these are conjectured to be the q-difference Painlevé
+equations as in Sakai's classification. More precisely, we propose that the
+tau-functions of q-Painlevé equations are related to the grand canonical
+topological string partition functions on the corresponding geometry. In the
+toric cases we use topological string/spectral theory duality to give a
+Fredholm determinant representation for the above tau-functions in terms of the
+underlying quantum mirror curve. As a consequence, the zeroes of the
+tau-functions compute the exact spectrum of the associated quantum integrable
+systems. We provide details of this construction for the local
+$\mathbb{P}^1\times \mathbb{P}^1$ case, which is related to q-difference
+Painlevé with affine $A_1$ symmetry, to $SU(2)$ Super Yang-Mills in five
+dimensions and to relativistic Toda system.
+"
+27897,The critical point of the transition to turbulence in pipe flow,"  Reynolds proposed that after sufficiently long times, the flow in a pipe
+should settle to a steady condition: below a critical Reynolds number, flows
+should (regardless of initial conditions) always return to laminar, while
+above, eddying motion should persist. As shown, even in pipes several thousand
+diameters long, the spatio-temporal intermittent flow patterns observed at the
+end of the pipe strongly depend on the initial conditions, with no indication
+of an approach to a (statistical) steady state. Exploiting the fact that
+turbulent puffs do not age, we continuously recreate the puff sequence exiting
+the pipe at the entrance, thus introducing periodic boundary conditions for the
+flow pattern. This procedure allows us to study the evolution of the flow
+patterns for arbitrary long times. We find that after times in excess of $10^7$
+advective time units, a statistical steady state is reached. Though the
+resulting flows remain spatio-temporally intermittent, puff splitting and decay
+rates eventually reach a balance so that the turbulent fraction fluctuates
+around a well defined level which only depends on $Re$. We find that at lower
+$Re$ (here 2020), flows eventually always laminarize, while for higher $Re$
+($>=2060$) turbulence persists. The critical point for pipe flow hence lies in
+the interval $2020<Re_c<2060$, which is in good agreement with the recently
+proposed value of $Re_c=2040$. The latter estimate was based on single puff
+statistics and entirely neglected puff interactions. Unlike typical contact
+processes where such interactions strongly affect the critical point, in pipe
+flow it is only marginally influenced. Interactions on the other hand, are
+responsible for the approach to the statistical steady state. As shown, they
+strongly affect the resulting flow patterns, where they cause `puff
+clustering', with the clusters traveling across the puff pattern in a wave like
+fashion.
+"
+27898,Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real,"  Accurate and robust recognition and prediction of traffic situation plays an
+important role in autonomous driving, which is a prerequisite for risk
+assessment and effective decision making. Although there exist a lot of works
+dealing with modeling driver behavior of a single object, it remains a
+challenge to make predictions for multiple highly interactive agents that react
+to each other simultaneously. In this work, we propose a generic probabilistic
+hierarchical recognition and prediction framework which employs a two-layer
+Hidden Markov Model (TLHMM) to obtain the distribution of potential situations
+and a learning-based dynamic scene evolution model to sample a group of future
+trajectories. Instead of predicting motions of a single entity, we propose to
+get the joint distribution by modeling multiple interactive agents as a whole
+system. Moreover, due to the decoupling property of the layered structure, our
+model is suitable for knowledge transfer from simulation to real world
+applications as well as among different traffic scenarios, which can reduce the
+computational efforts of training and the demand for a large data amount. A
+case study of highway ramp merging scenario is demonstrated to verify the
+effectiveness and accuracy of the proposed framework.
+"
+27899,High-Contrast Chirped-Pulse Amplification Enabled by In-Band Noise Filtering,"  Lasers that generate ultra-intense light pulses are under development for
+experiments in high-field and high-energy-density physics, as well as for
+applications such as particle acceleration. Extensions to even higher powers
+are being considered for future investigations that can only be imagined today,
+such as the quantum electrodynamics of plasmas and isolated attosecond-pulse
+generation with solid targets. For all of these areas, it is vital to produce
+high-contrast pulses, so that no pre-plasma is created in the target before the
+arrival of the main pulse. However, noise is unavoidable in high-gain
+amplification, and is manifested in the form of background light that
+accompanies pulses generated by chirped-pulse amplification (CPA). Here, we
+introduce a linear filtering technique based on spatio-spectral coupling, which
+allows in-band filtering of amplified pulses for the first time. Experiments
+demonstrate approximately 40 times contrast enhancement in optical parametric
+chirped-pulse amplification (OPCPA) and provide a foundation for scaling to
+much higher performance. The simplicity, efficiency, and direct compatibility
+with existing techniques for short-pulse generation will make spatio-spectral
+filtering attractive to a wide range of applications in ultrafast optics and
+time-resolved spectroscopy, and may open new directions in noise reduction.
+"
+27900,Transverse spin relaxation and magnetic correlation in $Pr_{1-x}Ca_{x}MnO_{3}$ : influence of particle size variation and chemical doping,"  The short ranged magnetic correlations and dynamics of hole doped
+$Pr_{1-x}Ca_{x}MnO_{3}$ (0.33 < x < 0.5) of different crystallite sizes have
+been investigated using electron spin resonance spectroscopy (ESR). The major
+contribution to the temperature dependence of paramagnetic line-width is
+attributed to the spin-lattice relaxation dominated by thermally activated
+hopping of small polarons with typical activation energy of 20-50 meV.
+Irrespective of the crystallite size and dopant concentration, the transverse
+spin relaxation time ($t_{2}$) follows a universal scaling behaviour of the
+type $t_{2}$~($T/T_{0})^n$ in the paramagnetic regime, where $T_0$ and n are
+scaling parameters. Using the temperature dependence of $t_{2}$, we construct a
+phase diagram which shows that near half-doping, the magnetic correlations
+associated with charge ordering not just survives even down to the crystallite
+size of 22 nm, but is actually enhanced. We conclude that the eventual
+suppression of charge ordering with reduction in particle size is possibly more
+to do with greater influence of chemical disorder than any intrinsic effect.
+"
+27901,DeepLink: A Novel Link Prediction Framework based on Deep Learning,"  Recently, link prediction has attracted more attentions from various
+disciplines such as computer science, bioinformatics and economics. In this
+problem, unknown links between nodes are discovered based on numerous
+information such as network topology, profile information and user generated
+contents. Most of the previous researchers have focused on the structural
+features of the networks. While the recent researches indicate that contextual
+information can change the network topology. Although, there are number of
+valuable researches which combine structural and content information, but they
+face with the scalability issue due to feature engineering. Because, majority
+of the extracted features are obtained by a supervised or semi supervised
+algorithm. Moreover, the existing features are not general enough to indicate
+good performance on different networks with heterogeneous structures. Besides,
+most of the previous researches are presented for undirected and unweighted
+networks. In this paper, a novel link prediction framework called ""DeepLink"" is
+presented based on deep learning techniques. In contrast to the previous
+researches which fail to automatically extract best features for the link
+prediction, deep learning reduces the manual feature engineering. In this
+framework, both the structural and content information of the nodes are
+employed. The framework can use different structural feature vectors, which are
+prepared by various link prediction methods. It considers all proximity orders
+that are presented in a network during the structural feature learning. We have
+evaluated the performance of DeepLink on two real social network datasets
+including Telegram and irBlogs. On both datasets, the proposed framework
+outperforms several structural and hybrid approaches for link prediction
+problem.
+"
+27902,Spontaneous dimerization and moment formation in the Hida model of spin-1 kagomé antiferromagnet,"  The Hida model, defined on honeycomb lattice, is a spin-1/2 Heisenberg model
+of aniferromagnetic hexagons (with nearest-neighbor interaction, $J_A>0$)
+coupled via ferromagnetic bonds (with exchange interaction, $J_F <0$). It
+applies to the spin-gapped organic materials, m-MPYNN.X (for $\mathrm{X}=$I,
+BF4, ClO4), and for $|J_F|\gg J_A$, it reduces to the spin-$1$ kagomé
+Heisenberg antiferromagnet (KHA). Motivated by the recent finding of trimerized
+singlet (TS) ground state for spin-1 KHA, we investigate the evolution of the
+ground state of Hida model from weak to strong $J_F/J_A$ using mean-field
+triplon analysis and Schwinger boson mean-field theory. Our triplon analysis of
+Hida model shows that its uniform hexagonal singlet (HS) ground state (for weak
+$J_F/J_A$) gives way to the dimerized hexagonal singlet (D-HS) ground state for
+all $J_F$'s beyond a moderate $J_F/J_A$. From the Schwinger boson calculations,
+we find that the evolution from the uniform HS phase for spin-1/2 Hida model to
+the TS phase for spin-1 KHA happens through two quantum phase transitions: 1)
+the spontaneous dimerization transition at $J_F/J_A \sim -0.28$ from the
+uniform HS to D-HS phase, and 2) the moment formation transition at $J_F/J_A
+\sim -1.46$, across which the pair of spin-1/2's on every FM bond begin to
+express as a bound moment that tends to spin-1 for large negative $J_F$'s. The
+TS ground state of spin-1 KHA is thus adiabatically connected to the D-HS
+ground state of the Hida model. Our calculations clearly imply that the
+\ce{m-MPYNN.X} salts realize the D-HS phase at low temperatures, which can be
+ascertained through neutron diffraction.
+"
+27903,Josephson Metamaterial with a widely tunable positive/negative Kerr constant,"  We report on the microwave characterization of a novel one-dimensional
+Josephson metamaterial composed of a chain of asymmetric superconducting
+quantum interference devices (SQUIDs) with nearest-neighbor coupling through
+common Josephson junctions. This metamaterial demonstrates a strong Kerr
+nonlinearity, with a Kerr constant tunable over a wide range, from positive to
+negative values, by a magnetic flux threading the SQUIDs. The experimental
+results are in good agreement with the theory of nonlinear effects in Josephson
+chains. The metamaterial is very promising as an active medium for Josephson
+traveling-wave parametric amplifiers; its use facilitates phase matching in a
+four-wave mixing process for efficient parametric gain.
+"
+27904,Connections between Representation-Finite and Köthe Rings,"  A ring $R$ is called left $k$-cyclic if every left $R$-module is a direct sum
+of indecomposable modules which are homomorphic image of $_{R}R^k$. In this
+paper, we give a characterization of left $k$-cyclic rings. As a consequence,
+we give a characterization of left Köthe rings, which is a generalization of
+Köthe-Cohen-Kaplansky theorem. We also characterize rings which are Morita
+equivalent to a basic left $k$-cyclic ring. As a corollary, we show that $R$ is
+Morita equivalent to a basic left Köthe ring if and only if $R$ is an
+artinian left multiplicity-free top ring.
+"
+27905,Generator matrix for two-dimensional cyclic codes of arbitrary length,"  Two-dimensional cyclic codes of length $n=\ell s$ over the finite field
+$\mathbb{F}$ are ideals of the polynomial ring $\frac{\mathbb{F}[x,y]}{<
+x^{s}-1,y^{\ell}-1 >}$. The aim of this paper, is to present a novel method to
+study the algebraic structure of two-dimensional cyclic codes of any length
+$n=\ell s$ over the finite field $\mathbb{F}$. By using this method, we find
+the generator polynomials for ideals of $\frac{\mathbb{F}[x,y]}{<
+x^{s}-1,y^{\ell}-1 >}$ corresponding to two dimensional cyclic codes. These
+polynomials will be applied to obtain the generator matrix for two- dimensional
+cyclic codes.
+"
+27906,Eliminating Inertia in a Stochastic Model of a Micro-Swimmer with Constant Speed,"  We are concerned with the dynamical description of the motion of a stochastic
+micro-swimmer with constant speed and fluctuating orientation in the long time
+limit by adiabatic elimination of the orientational variable. Starting with the
+corresponding full set of Langevin equations, we eliminate the memory in the
+stochastic orientation and obtain a stochastic equation for the position alone
+in the overdamped limit. An equivalent procedure based on the Fokker-Planck
+equation is presented as well.
+"
+27907,Observing the space- and time-dependent growth of correlations in dynamically tuned synthetic Ising antiferromagnets,"  We explore the dynamics of artificial one- and two-dimensional Ising-like
+quantum antiferromagnets with different lattice geometries by using a Rydberg
+quantum simulator of up to 36 spins in which we dynamically tune the parameters
+of the Hamiltonian. We observe a region in parameter space with
+antiferromagnetic (AF) ordering, albeit with only finite-range correlations. We
+study systematically the influence of the ramp speeds on the correlations and
+their growth in time. We observe a delay in their build-up associated to the
+finite speed of propagation of correlations in a system with short-range
+interactions. We obtain a good agreement between experimental data and
+numerical simulations taking into account experimental imperfections measured
+at the single particle level. Finally, we develop an analytical model, based on
+a short-time expansion of the evolution operator, which captures the observed
+spatial structure of the correlations, and their build-up in time.
+"
+27908,Requirements Engineering Practice and Problems in Agile Projects: Results from an International Survey,"  Requirements engineering (RE) is considerably different in agile development
+than in more traditional development processes. Yet, there is little empirical
+knowledge on the state of the practice and contemporary problems in agile RE.
+As part of a bigger survey initiative (Naming the Pain in Requirements
+Engineering), we build an empirical basis on such aspects of agile RE. Based on
+the responses of representatives from 92 different organisations, we found that
+agile RE concentrates on free-text documentation of requirements elicited with
+a variety of techniques. Often, traces between requirements and code are
+explicitly managed and also software testing and RE are aligned. Furthermore,
+continuous improvement of RE is performed due to intrinsic motivation.
+Important experienced problems include unclear requirements and communication
+flaws. Overall, we found that most organisations conduct RE in a way we would
+expect and that agile RE is in several aspects not so different from RE in
+other development processes.
+"
+27909,Additive Spanners and Distance Oracles in Quadratic Time,"  Let $G$ be an unweighted, undirected graph. An additive $k$-spanner of $G$ is
+a subgraph $H$ that approximates all distances between pairs of nodes up to an
+additive error of $+k$, that is, it satisfies $d_H(u,v) \le d_G(u,v)+k$ for all
+nodes $u,v$, where $d$ is the shortest path distance. We give a deterministic
+algorithm that constructs an additive $O\!\left(1\right)$-spanner with
+$O\!\left(n^{4/3}\right)$ edges in $O\!\left(n^2\right)$ time. This should be
+compared with the randomized Monte Carlo algorithm by Woodruff [ICALP 2010]
+giving an additive $6$-spanner with $O\!\left(n^{4/3}\log^3 n\right)$ edges in
+expected time $O\!\left(n^2\log^2 n\right)$.
+An $(\alpha,\beta)$-approximate distance oracle for $G$ is a data structure
+that supports the following distance queries between pairs of nodes in $G$.
+Given two nodes $u$, $v$ it can in constant time compute a distance estimate
+$\tilde{d}$ that satisfies $d \le \tilde{d} \le \alpha d + \beta$ where $d$ is
+the distance between $u$ and $v$ in $G$. Sommer [ICALP 2016] gave a randomized
+Monte Carlo $(2,1)$-distance oracle of size $O\!\left(n^{5/3}\text{poly} \log
+n\right)$ in expected time $O\!\left(n^2\text{poly} \log n\right)$. As an
+application of the additive $O(1)$-spanner we improve the construction by
+Sommer [ICALP 2016] and give a Las Vegas $(2,1)$-distance oracle of size
+$O\!\left(n^{5/3}\right)$ in time $O\!\left(n^2\right)$. This also implies an
+algorithm that in $O\!\left(n^2\right)$ gives approximate distance for all
+pairs of nodes in $G$ improving on the $O\!\left(n^2 \log n\right)$ algorithm
+by Baswana and Kavitha [SICOMP 2010].
+"
+27910,Deep residual learning in CT physics: scatter correction for spectral CT,"  Recently, spectral CT has been drawing a lot of attention in a variety of
+clinical applications primarily due to its capability of providing quantitative
+information about material properties. The quantitative integrity of the
+reconstructed data depends on the accuracy of the data corrections applied to
+the measurements. Scatter correction is a particularly sensitive correction in
+spectral CT as it depends on system effects as well as the object being imaged
+and any residual scatter is amplified during the non-linear material
+decomposition. An accurate way of removing scatter is subtracting the scatter
+estimated by Monte Carlo simulation. However, to get sufficiently good scatter
+estimates, extremely large numbers of photons is required, which may lead to
+unexpectedly high computational costs. Other approaches model scatter as a
+convolution operation using kernels derived using empirical methods. These
+techniques have been found to be insufficient in spectral CT due to their
+inability to sufficiently capture object dependence. In this work, we develop a
+deep residual learning framework to address both issues of computation
+simplicity and object dependency. A deep convolution neural network is trained
+to determine the scatter distribution from the projection content in training
+sets. In test cases of a digital anthropomorphic phantom and real water
+phantom, we demonstrate that with much lower computing costs, the proposed
+network provides sufficiently accurate scatter estimation.
+"
+27911,Statistical Inference based on Bridge Divergences,"  M-estimators offer simple robust alternatives to the maximum likelihood
+estimator. Much of the robustness literature, however, has focused on the
+problems of location, location-scale and regression estimation rather than on
+estimation of general parameters. The density power divergence (DPD) and the
+logarithmic density power divergence (LDPD) measures provide two classes of
+competitive M-estimators (obtained from divergences) in general parametric
+models which contain the MLE as a special case. In each of these families, the
+robustness of the estimator is achieved through a density power down-weighting
+of outlying observations. Both the families have proved to be very useful tools
+in the area of robust inference. However, the relation and hierarchy between
+the minimum distance estimators of the two families are yet to be
+comprehensively studied or fully established. Given a particular set of real
+data, how does one choose an optimal member from the union of these two classes
+of divergences? In this paper, we present a generalized family of divergences
+incorporating the above two classes; this family provides a smooth bridge
+between the DPD and the LDPD measures. This family helps to clarify and settle
+several longstanding issues in the relation between the important families of
+DPD and LDPD, apart from being an important tool in different areas of
+statistical inference in its own right.
+"
+27912,Loss compensation symmetry in dimers made of gain and lossy nanoparticles,"  The eigenoscillations in a two-dimensional dimer made of gain and lossy
+nanoparticles were investigated within exact analytical approach. It was shown
+that there are eigenoscillations for which all Joule losses are exactly
+compensated by the gain. Among such solutions, there are solutions with a new
+type of symmetry, which we refer to as Loss Compensation Symmetry (LCS) as well
+as well-known PT (parity-time) symmetric solutions. The modes with LCS unlike
+PT symmetric ones allow one to achieve full loss compensation with
+significantly less gain that in the case of PT symmetry. This effect paves way
+to the new loss compensation methods in optics.
+"
+27913,xGASS: Gas-rich central galaxies in small groups and their connections to cosmic web gas feeding,"  We use deep HI observations obtained as part of the extended GALEX Arecibo
+SDSS survey (xGASS) to study the cold gas properties of central galaxies across
+environments. We find that, below stellar masses of 10^10.2 Msun, central
+galaxies in groups have an average atomic hydrogen gas fraction ~0.3dex higher
+than those in isolation at the same stellar mass. At these stellar masses,
+group central galaxies are usually found in small groups of N=2 members. The
+higher HI content in these low mass group central galaxies is mirrored by their
+higher average star formation activity and molecular hydrogen content. At
+larger stellar masses, this difference disappears and central galaxies in
+groups have similar (or even smaller) gas reservoirs and star formation
+activity compared to those in isolation. We discuss possible scenarios able to
+explain our findings and suggest that the higher gas content in low mass group
+central galaxies is likely due to contributions from the cosmic web or HI-rich
+minor mergers, which also fuel their enhanced star formation activity.
+"
+27914,Deep Reinforcement Learning for Robotic Manipulation-The state of the art,"  The focus of this work is to enumerate the various approaches and algorithms
+that center around application of reinforcement learning in robotic ma-
+]]nipulation tasks. Earlier methods utilized specialized policy representations
+and human demonstrations to constrict the policy. Such methods worked well with
+continuous state and policy space of robots but failed to come up with
+generalized policies. Subsequently, high dimensional non-linear function
+approximators like neural networks have been used to learn policies from
+scratch. Several novel and recent approaches have also embedded control policy
+with efficient perceptual representation using deep learning. This has led to
+the emergence of a new branch of dynamic robot control system called deep r
+inforcement learning(DRL). This work embodies a survey of the most recent
+algorithms, architectures and their implementations in simulations and real
+world robotic platforms. The gamut of DRL architectures are partitioned into
+two different branches namely, discrete action space algorithms(DAS) and
+continuous action space algorithms(CAS). Further, the CAS algorithms are
+divided into stochastic continuous action space(SCAS) and deterministic
+continuous action space(DCAS) algorithms. Along with elucidating an organ-
+isation of the DRL algorithms this work also manifests some of the state of the
+art applications of these approaches in robotic manipulation tasks.
+"
+27915,Power-law liquid in cuprate superconductors from fermionic unparticles,"  Recent photoemission spectroscopy measurements [arXiv:1509.01611] on cuprate
+superconductors have inferred that over a wide range of doping, the imaginary
+part of the electron self-energy scales as
+$\Sigma^{\prime\prime}\sim(\omega^2+\pi^2T^2)^a$ with $a=1$ in the overdoped
+Fermi-liquid state and $a<0.5$ in the optimal to underdoped regime. We show
+that this non-Fermi-liquid scaling behavior can naturally be explained by the
+presence of a scale-invariant state of matter known as unparticles. We evaluate
+analytically the electron self-energy due to interactions with fermionic
+unparticles. We find that, in agreement with experiments, the imaginary part of
+the self-energy scales with respect to temperature and energy as
+$\Sigma^{\prime\prime}\sim T^{2+2\alpha}$ and $\omega^{2+2\alpha}$, where
+$\alpha$ is the anomalous dimension of the unparticle propagator. In addition,
+the calculated occupancy and susceptibility of fermionic unparticles, unlike
+those of normal fermions, have significant spectral weights even at high
+energies. This unconventional behavior is attributed to the branch cut in the
+unparticle propagator which broadens the unparticle spectral function over a
+wide energy range and non-trivially alters the scattering phase space by
+enhancing (suppressing) the intrinsic susceptibility at low energies for
+negative (positive) $\alpha$. Our work presents new evidence suggesting that
+unparticles might be important low-energy degrees of freedom in strongly
+coupled systems such as the cuprate superconductors.
+"
+27916,ReproServer: Making Reproducibility Easier and Less Intensive,"  Reproducibility in the computational sciences has been stymied because of the
+complex and rapidly changing computational environments in which modern
+research takes place. While many will espouse reproducibility as a value, the
+challenge of making it happen (both for themselves and testing the
+reproducibility of others' work) often outweigh the benefits. There have been a
+few reproducibility solutions designed and implemented by the community. In
+particular, the authors are contributors to ReproZip, a tool to enable
+computational reproducibility by tracing and bundling together research in the
+environment in which it takes place (e.g. one's computer or server). In this
+white paper, we introduce a tool for unpacking ReproZip bundles in the cloud,
+ReproServer. ReproServer takes an uploaded ReproZip bundle (.rpz file) or a
+link to a ReproZip bundle, and users can then unpack them in the cloud via
+their browser, allowing them to reproduce colleagues' work without having to
+install anything locally. This will help lower the barrier to reproducing
+others' work, which will aid reviewers in verifying the claims made in papers
+and reusing previously published research.
+"
+27917,A Laplacian Framework for Option Discovery in Reinforcement Learning,"  Representation learning and option discovery are two of the biggest
+challenges in reinforcement learning (RL). Proto-value functions (PVFs) are a
+well-known approach for representation learning in MDPs. In this paper we
+address the option discovery problem by showing how PVFs implicitly define
+options. We do it by introducing eigenpurposes, intrinsic reward functions
+derived from the learned representations. The options discovered from
+eigenpurposes traverse the principal directions of the state space. They are
+useful for multiple tasks because they are discovered without taking the
+environment's rewards into consideration. Moreover, different options act at
+different time scales, making them helpful for exploration. We demonstrate
+features of eigenpurposes in traditional tabular domains as well as in Atari
+2600 games.
+"
+27918,Verifying Controllers Against Adversarial Examples with Bayesian Optimization,"  Recent successes in reinforcement learning have lead to the development of
+complex controllers for real-world robots. As these robots are deployed in
+safety-critical applications and interact with humans, it becomes critical to
+ensure safety in order to avoid causing harm. A first step in this direction is
+to test the controllers in simulation. To be able to do this, we need to
+capture what we mean by safety and then efficiently search the space of all
+behaviors to see if they are safe. In this paper, we present an active-testing
+framework based on Bayesian Optimization. We specify safety constraints using
+logic and exploit structure in the problem in order to test the system for
+adversarial counter examples that violate the safety specifications. These
+specifications are defined as complex boolean combinations of smooth functions
+on the trajectories and, unlike reward functions in reinforcement learning, are
+expressive and impose hard constraints on the system. In our framework, we
+exploit regularity assumptions on individual functions in form of a Gaussian
+Process (GP) prior. We combine these into a coherent optimization framework
+using problem structure. The resulting algorithm is able to provably verify
+complex safety specifications or alternatively find counter examples.
+Experimental results show that the proposed method is able to find adversarial
+examples quickly.
+"
+27919,Learning Contracting Vector Fields For Stable Imitation Learning,"  We propose a new non-parametric framework for learning incrementally stable
+dynamical systems x' = f(x) from a set of sampled trajectories. We construct a
+rich family of smooth vector fields induced by certain classes of matrix-valued
+kernels, whose equilibria are placed exactly at a desired set of locations and
+whose local contraction and curvature properties at various points can be
+explicitly controlled using convex optimization. With curl-free kernels, our
+framework may also be viewed as a mechanism to learn potential fields and
+gradient flows. We develop large-scale techniques using randomized kernel
+approximations in this context. We demonstrate our approach, called contracting
+vector fields (CVF), on imitation learning tasks involving complex
+point-to-point human handwriting motions.
+"
+27920,Vortex Lattices in Binary Mixtures of Repulsive Superfluids,"  We present an extension of the framework introduced in [1] to treat
+multicomponent systems, showing that new degrees of freedom are necessary in
+order to obtain the desired boundary conditions. We then apply this extended
+framework to the coupled Gross-Pitaevskii equations to investigate the ground
+states of two-component systems with equal masses thereby extending previous
+work in the lowest Landau limit [2] to arbitrary interactions within
+Gross-Pitaevskii theory. We show that away from the lowest-Landau level limit,
+the predominant vortex lattice consists of two interlaced triangular lattices.
+Finally, we derive a linear relation which accurately describes the phase
+boundaries in the strong interacting regimes.
+"
+27921,"Finite-size Gap, Magnetization, and Entanglement of Deformed Fredkin Spin Chain","  We investigate ground- and excited-state properties of the deformed Fredkin
+spin chain proposed by Salberger, Zhang, Klich, Korepin, and the authors. This
+model is a one-parameter deformation of the Fredkin spin chain, whose
+Hamiltonian is $3$-local and translationally invariant in the bulk. The model
+is frustration-free and its unique ground state can be expressed as a weighted
+superposition of colored Dyck paths. We focus on the case where the deformation
+parameter $t>1$. By using a variational method, we prove that the finite-size
+gap decays at least exponentially with increasing the system size. We prove
+that the magnetization in the ground state is along the $z$-direction, namely
+$\langle s^x \rangle =\langle s^y \rangle=0$, and show that the $z$-component
+$\langle s^z \rangle$ exhibits a domain-wall structure. We then study the
+entanglement properties of the chain. In particular, we derive upper and lower
+bounds for the von Neumann and Rényi entropies, and entanglement spectrum for
+any bipartition of the chain.
+"
+27922,Beyond conventional photon-number detection with click detectors,"  Photon-number measurements are a fundamental technique for the discrimination
+and characterization of quantum states of light. Beyond the abilities of
+state-of-the-art devices, we present measurements with an array of 100
+avalanche photodiodes exposed to photon-numbers ranging from well below to
+significantly above one photon per diode. Despite each single diode only
+discriminating between zero and non-zero photon-numbers we are able to extract
+characteristic information about the quantum state. We demonstrate a vast
+enhancement of the applicable intensity range by two orders of magnitude
+relative to the standard application of such devices. It turns out that the
+probabilistic mapping of arbitrary photon-numbers on a finite number of
+registered clicks is not per se a disadvantage compared with true photon
+counters. Such detector arrays can bridge the gap between single-photon and
+linear detection, by directly using the recorded data, without the need of
+elaborate data reconstruction methods.
+"
+27923,The turbulent chiral-magnetic cascade in the early universe,"  The presence of asymmetry between fermions of opposite handedness in plasmas
+of relativistic particles can lead to exponential growth of a helical magnetic
+field via a small-scale chiral dynamo instability known as the chiral magnetic
+effect. Here, we show, using dimensional arguments and numerical simulations,
+that this process produces through the Lorentz force chiral magnetically driven
+turbulence. A k^{-2} magnetic energy spectrum emerges via inverse transfer over
+a certain range of wavenumbers k. The total chirality (magnetic helicity plus
+normalized chiral chemical potential) is conserved in this system. Therefore,
+as the helical magnetic field grows, most of the total chirality gets
+transferred into magnetic helicity until the chiral magnetic effect terminates.
+Quantitative results for height, slope, and extent of the spectrum are
+obtained. Consequences of this effect for cosmic magnetic fields are discussed.
+"
+27924,Unsupervised Feature Selection Based on Space Filling Concept,"  The paper deals with the adaptation of a new measure for the unsupervised
+feature selection problems. The proposed measure is based on space filling
+concept and is called the coverage measure. This measure was used for judging
+the quality of an experimental space filling design. In the present work, the
+coverage measure is adapted for selecting the smallest informative subset of
+variables by reducing redundancy in data. This paper proposes a simple analogy
+to apply this measure. It is implemented in a filter algorithm for unsupervised
+feature selection problems.
+The proposed filter algorithm is robust with high dimensional data and can be
+implemented without extra parameters. Further, it is tested with simulated data
+and real world case studies including environmental data and hyperspectral
+image. Finally, the results are evaluated by using random forest algorithm.
+"
+27925,From Concurrent Programs to Simulating Sequential Programs: Correctness of a Transformation,"  Frama-C is a software analysis framework that provides a common
+infrastructure and a common behavioral specification language to plugins that
+implement various static and dynamic analyses of C programs. Most plugins do
+not support concurrency. We have proposed Conc2Seq, a Frama-C plugin based on
+program transformation, capable to leverage the existing huge code base of
+plugins and to handle concurrent C programs.
+In this paper we formalize and sketch the proof of correctness of the program
+transformation principle behind Conc2Seq, and present an effort towards the
+full mechanization of both the formalization and proofs with the proof
+assistant Coq.
+"
+27926,Probing the Gravitational Dependence of the Fine-Structure Constant from Observations of White Dwarf Stars,"  Hot white dwarf stars are the ideal probe for a relationship between the
+fine-structure constant and strong gravitational fields, providing us with an
+opportunity for a direct observational test. We study a sample of hot white
+dwarf stars, combining far-UV spectroscopic observations, atomic physics,
+atmospheric modelling and fundamental physics, in the search for variation in
+the fine structure constant. This variation manifests as shifts in the observed
+wavelengths of absorption lines, such as quadruply ionized iron (FeV) and
+quadruply ionized nickel (NiV), when compared to laboratory wavelengths.
+Berengut et al. (Phys. Rev. Lett. 2013, 111, 010801) demonstrated the validity
+of such an analysis using high-resolution Hubble Space Telescope (HST)/Space
+Telescope Imaging Spectrograph (STIS) spectra of G191-B2B. We have made three
+important improvements by: (a) using three new independent sets of laboratory
+wavelengths, (b) analysing a sample of objects, and (c) improving the
+methodology by incorporating robust techniques from previous studies towards
+quasars (the Many Multiplet method). A successful detection would be the first
+direct measurement of a gravitational field effect on a bare constant of
+nature. Here we describe our approach and present preliminary results from nine
+objects using both FeV and NiV.
+"
+27927,SparseStep: Approximating the Counting Norm for Sparse Regularization,"  The SparseStep algorithm is presented for the estimation of a sparse
+parameter vector in the linear regression problem. The algorithm works by
+adding an approximation of the exact counting norm as a constraint on the model
+parameters and iteratively strengthening this approximation to arrive at a
+sparse solution. Theoretical analysis of the penalty function shows that the
+estimator yields unbiased estimates of the parameter vector. An iterative
+majorization algorithm is derived which has a straightforward implementation
+reminiscent of ridge regression. In addition, the SparseStep algorithm is
+compared with similar methods through a rigorous simulation study which shows
+it often outperforms existing methods in both model fit and prediction
+accuracy.
+"
+27928,The Kinetically Dominated Quasar 3C~418,"  The existence of quasars that are kinetically dominated, where the jet
+kinetic luminosity, $Q$, is larger than the total (IR to X-ray) thermal
+luminosity of the accretion flow, $L_{\rm{bol}}$, provides a strong constraint
+on the fundamental physics of relativistic jet formation. Since quasars have
+high values of $L_{\rm{bol}}$ by definition, only $\sim 10$ kinetically
+dominated quasars (with $\overline{Q}/L_{\rm{bol}}>1$) have been found, where
+$\overline{Q}$ is the long term time averaged jet power. We use low frequency
+(151 MHz$-$1.66 GHz) observations of the quasar 3C\,418 to determine
+$\overline{Q}\approx 5.5 \pm 1.3 \times 10^{46} \rm{ergs~s^{-1}}$. Analysis of
+the rest frame ultraviolet spectrum indicates that this equates to $0.57 \pm
+0.28$ times the Eddington luminosity of the central supermassive black hole and
+$\overline{Q}/L_{\rm{bol}} \approx 4.8 \pm 3.1$, making 3C\,418 one of the most
+kinetically dominated quasars found to date. It is shown that this maximal
+$\overline{Q}/L_{\rm{bol}}$ is consistent with models of magnetically arrested
+accretion of jet production in which the jet production reproduces the observed
+trend of a decrement in the extreme ultraviolet continuum as the jet power
+increases. This maximal condition corresponds to an almost complete saturation
+of the inner accretion flow with vertical large scale magnetic flux (maximum
+saturation).
+"
+27929,MicroBooNE and its Cross Section Measurement,"  MicroBooNE (the Micro Booster Neutrino Experiment) is a short-baseline
+neutrino experiment based on the technology of a liquid-argon time-projection
+chamber (LArTPC), and has recently completed its first year of data-taking in
+the Fermilab Booster Neutrino Beam. It aims to address the anomalous excess of
+events with an electromagnetic final state in MiniBooNE, to measure
+neutrino-argon interaction cross sections, and to provide relevant R\&D for the
+future LArTPC experiments, such as DUNE. In these proceedings, we present the
+first reconstructed energy spectrum of Michel electrons from cosmic muon
+decays, the first kinematic distributions of the candidate muon tracks from
+$\nu_{\mu}$-argon charged-current interactions, and a demonstration of an
+electromagnetic shower reconstruction from $\pi^0$s produced by
+$\nu_{\mu}$-argon charged-current interactions. The results demonstrate the
+first fully automated reconstruction and selection algorithms in a large LArTPC
+and serve as foundations for future measurements.
+"
+27930,Real-time 3D Human Tracking for Mobile Robots with Multisensors,"  Acquiring the accurate 3-D position of a target person around a robot
+provides fundamental and valuable information that is applicable to a wide
+range of robotic tasks, including home service, navigation and entertainment.
+This paper presents a real-time robotic 3-D human tracking system which
+combines a monocular camera with an ultrasonic sensor by the extended Kalman
+filter (EKF). The proposed system consists of three sub-modules: monocular
+camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor
+fusion. An improved visual tracking algorithm is presented to provide partial
+location estimation (2-D). The algorithm is designed to overcome severe
+occlusions, scale variation, target missing and achieve robust re-detection.
+The scale accuracy is further enhanced by the estimated 3-D information. An
+ultrasonic sensor array is employed to provide the range information from the
+target person to the robot and Gaussian Process Regression is used for partial
+location estimation (2-D). EKF is adopted to sequentially process multiple,
+heterogeneous measurements arriving in an asynchronous order from the vision
+sensor and the ultrasonic sensor separately. In the experiments, the proposed
+tracking system is tested in both simulation platform and actual mobile robot
+for various indoor and outdoor scenes. The experimental results show the
+superior performance of the 3-D tracking system in terms of both the accuracy
+and robustness.
+"
+27931,Unconventional quantum Hall effects in two-dimensional massive spin-1 fermion systems,"  Unconventional fermions with high degeneracies in three dimensions beyond
+Weyl and Dirac fermions have sparked tremendous interest in condensed matter
+physics. Here, we study quantum Hall effects (QHEs) in a two-dimensional (2D)
+unconventional fermion system with a pair of gapped spin-1 fermions. We find
+that the original unlimited number of zero energy Landau levels (LLs) in the
+gapless case develop into a series of bands, leading to a novel QHE phenomenon
+that the Hall conductance first decreases (or increases) to zero and then
+revives as an infinite ladder of fine staircase when the Fermi surface is moved
+toward zero energy, and it suddenly reverses with its sign being flipped due to
+a Van Hove singularity when the Fermi surface is moved across zero. We further
+investigate the peculiar QHEs in a dice model with a pair of spin-1 fermions,
+which agree well with the results of the continuous model.
+"
+27932,The Power of Perturbation Theory,"  We study quantum mechanical systems with a discrete spectrum. We show that
+the asymptotic series associated to certain paths of steepest-descent
+(Lefschetz thimbles) are Borel resummable to the full result. Using a
+geometrical approach based on the Picard-Lefschetz theory we characterize the
+conditions under which perturbative expansions lead to exact results. Even when
+such conditions are not met, we explain how to define a different perturbative
+expansion that reproduces the full answer without the need of transseries, i.e.
+non-perturbative effects, such as real (or complex) instantons. Applications to
+several quantum mechanical systems are presented.
+"
+27933,Functional ANOVA with Multiple Distributions: Implications for the Sensitivity Analysis of Computer Experiments,"  The functional ANOVA expansion of a multivariate mapping plays a fundamental
+role in statistics. The expansion is unique once a unique distribution is
+assigned to the covariates. Recent investigations in the environmental and
+climate sciences show that analysts may not be in a position to assign a unique
+distribution in realistic applications. We offer a systematic investigation of
+existence, uniqueness, orthogonality, monotonicity and ultramodularity of the
+functional ANOVA expansion of a multivariate mapping when a multiplicity of
+distributions is assigned to the covariates. In particular, we show that a
+multivariate mapping can be associated with a core of probability measures that
+guarantee uniqueness. We obtain new results for variance decomposition and
+dimension distribution under mixtures. Implications for the global sensitivity
+analysis of computer experiments are also discussed.
+"
+27934,Distributional Reinforcement Learning with Quantile Regression,"  In reinforcement learning an agent interacts with the environment by taking
+actions and observing the next state and reward. When sampled
+probabilistically, these state transitions, rewards, and actions can all induce
+randomness in the observed long-term return. Traditionally, reinforcement
+learning algorithms average over this randomness to estimate the value
+function. In this paper, we build on recent work advocating a distributional
+approach to reinforcement learning in which the distribution over returns is
+modeled explicitly instead of only estimating the mean. That is, we examine
+methods of learning the value distribution instead of the value function. We
+give results that close a number of gaps between the theoretical and
+algorithmic results given by Bellemare, Dabney, and Munos (2017). First, we
+extend existing results to the approximate distribution setting. Second, we
+present a novel distributional reinforcement learning algorithm consistent with
+our theoretical formulation. Finally, we evaluate this new algorithm on the
+Atari 2600 games, observing that it significantly outperforms many of the
+recent improvements on DQN, including the related distributional algorithm C51.
+"
+27935,Cutoff on Hyperbolic Surfaces,"  In this paper we study the common distance between points and the behavior of
+a constant length step discrete random walk on finite area hyperbolic surfaces.
+We show that if the second smallest eigenvalue of the Laplacian is at least
+1/4, then the distances on the surface are highly concentrated around the
+minimal possible value, and that the discrete random walk exhibits cutoff. This
+extends the results of Lubetzky and Peres ([20]) from the setting of Ramanujan
+graphs to the setting of hyperbolic surfaces. By utilizing density theorems of
+exceptional eigenvalues from [27], we are able to show that the results apply
+to congruence subgroups of $SL_{2}\left(\mathbb{Z}\right)$ and other arithmetic
+lattices, without relying on the well known conjecture of Selberg ([28]).
+Conceptually, we show the close relation between the cutoff phenomenon and
+temperedness of representations of algebraic groups over local fields, partly
+answering a question of Diaconis ([7]), who asked under what general phenomena
+cutoff exists.
+"
+27936,Proximal Backpropagation,"  We propose proximal backpropagation (ProxProp) as a novel algorithm that
+takes implicit instead of explicit gradient steps to update the network
+parameters during neural network training. Our algorithm is motivated by the
+step size limitation of explicit gradient descent, which poses an impediment
+for optimization. ProxProp is developed from a general point of view on the
+backpropagation algorithm, currently the most common technique to train neural
+networks via stochastic gradient descent and variants thereof. Specifically, we
+show that backpropagation of a prediction error is equivalent to sequential
+gradient descent steps on a quadratic penalty energy, which comprises the
+network activations as variables of the optimization. We further analyze
+theoretical properties of ProxProp and in particular prove that the algorithm
+yields a descent direction in parameter space and can therefore be combined
+with a wide variety of convergent algorithms. Finally, we devise an efficient
+numerical implementation that integrates well with popular deep learning
+frameworks. We conclude by demonstrating promising numerical results and show
+that ProxProp can be effectively combined with common first order optimizers
+such as Adam.
+"
+27937,Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks,"  Docking is an important tool in computational drug discovery that aims to
+predict the binding pose of a ligand to a target protein through a combination
+of pose scoring and optimization. A scoring function that is differentiable
+with respect to atom positions can be used for both scoring and gradient-based
+optimization of poses for docking. Using a differentiable grid-based atomic
+representation as input, we demonstrate that a scoring function learned by
+training a convolutional neural network (CNN) to identify binding poses can
+also be applied to pose optimization. We also show that an iteratively-trained
+CNN that includes poses optimized by the first CNN in its training set performs
+even better at optimizing randomly initialized poses than either the first CNN
+scoring function or AutoDock Vina.
+"
+27938,Scalable Relaxations of Sparse Packing Constraints: Optimal Biocontrol in Predator-Prey Network,"  Cascades represent rapid changes in networks. A cascading phenomenon of
+ecological and economic impact is the spread of invasive species in geographic
+landscapes. The most promising management strategy is often biocontrol, which
+entails introducing a natural predator able to control the invading population,
+a setting that can be treated as two interacting cascades of predator and prey
+populations. We formulate and study a nonlinear problem of optimal biocontrol:
+optimally seeding the predator cascade over time to minimize the harmful prey
+population. Recurring budgets, which typically face conservation organizations,
+naturally leads to sparse constraints which make the problem amenable to
+approximation algorithms. Available methods based on continuous relaxations
+scale poorly, to remedy this we develop a novel and scalable randomized
+algorithm based on a width relaxation, applicable to a broad class of
+combinatorial optimization problems. We evaluate our contributions in the
+context of biocontrol for the insect pest Hemlock Wolly Adelgid (HWA) in
+eastern North America. Our algorithm outperforms competing methods in terms of
+scalability and solution quality, and finds near optimal strategies for the
+control of the HWA for fine-grained networks -- an important problem in
+computational sustainability.
+"
+27939,Uncoordinated Frequency Shifts based Pilot Contamination Attack Detection,"  Pilot contamination attack is an important kind of active eavesdropping
+activity conducted by a malicious user during channel training phase. In this
+paper, motivated by the fact that frequency asynchronism could introduce
+divergence of the transmitted pilot signals between intended user and attacker,
+we propose a new uncoordinated frequency shift (UFS) scheme for detection of
+pilot contamination attack in multiple antenna system. An attack detection
+algorithm is further developed based on source enumeration method. Both the
+asymptotic detection performance analysis and numerical results are provided to
+verify the proposed studies. The results demonstrate that the proposed UFS
+scheme can achieve comparable detection performance as the existing
+superimposed random sequence based scheme, without sacrifice of legitimate
+channel estimation performance.
+"
+27940,Beyond delta: Tailoring marked statistics to reveal modified gravity,"  Models that seek to explain cosmic acceleration through modifications to
+General Relativity (GR) evade stringent Solar System constraints through a
+restoring, screening mechanism. Down-weighting the high density, screened
+regions in favor of the low density, unscreened ones offers the potential to
+enhance the amount of information carried in such modified gravity models.
+In this work, we assess the performance of a new ""marked"" transformation and
+perform a systematic comparison with the clipping and logarithmic
+transformations, in the context of $\Lambda$CDM and the symmetron and $f(R)$
+modified gravity models. Performance is measured in terms of the fractional
+boost in the Fisher information and the signal-to-noise ratio (SNR) for these
+models relative to the statistics derived from the standard density
+distribution. We find that all three statistics provide improved Fisher boosts
+over the basic density statistics. The model parameters for the ""marked"" and
+clipped transformation that best enhance signals and the Fisher boosts are
+determined. We also show that the mark is useful both as a Fourier and real
+space transformation; a marked correlation function also enhances the SNR
+relative to the standard correlation function, and can on mildly non-linear
+scales show a significant difference between the $\Lambda$CDM and the modified
+gravity models.
+Our results demonstrate how a series of simple analytical transformations
+could dramatically increase the predicted information extracted on deviations
+from GR, from large-scale surveys, and give the prospect for a potential
+detection much more feasible.
+"
+27941,Remarks on Artin approximation with constraints,"  We study various approximation results of solutions of equations $f(x,Y)=0$
+where $f(x,Y)\in\mathbb K[[x]][Y]^r$ and $x$ and $Y$ are two sets of variables,
+and where some components of the solutions $y(x)\in\mathbb K[[x]]^m$ do not
+depend on all the variables $x_j$. These problems have been highlighted by M.
+Artin.
+"
+27942,Learning Implicit Sampling Distributions for Motion Planning,"  Sampling-based motion planners have experienced much success due to their
+ability to efficiently and evenly explore the state space. However, for many
+tasks, it may be more efficient to not uniformly explore the state space,
+especially when there is prior information about its structure. Previous
+methods have attempted to modify the sampling distribution using hand selected
+heuristics that can work well for specific environments but not universally. In
+this paper, a policy- search based method is presented as an adaptive way to
+learn implicit sampling distributions for different environments. It utilizes
+information from past searches in similar environments to generate better
+distributions in novel environments, thus reducing overall computational cost.
+Our method can be incor- porated with a variety of sampling-based planners to
+improve performance. Our approach is validated on a number of tasks, including
+a 7DOF robot arm, showing marked improvement in number of collision checks as
+well as number of nodes expanded compared with baseline methods.
+"
+27943,Multi-Stage JavaScript,"  Multi-stage languages support generative metaprogramming via macros evaluated
+in a process preceding the actual interpretation or compilation of the program
+in which they are used. Macros update the source of their hosting program by
+emitting code that takes their place in the file, while their code may also be
+produced, fully or partially, by nested macros. All macros at the same nesting
+belong to the same stage, with the outer stage collecting the macros affect-ing
+only the main program. We extended JavaScript with staging annotations and
+implemented them in Spider Monkey, emitting pure JavaScript code as the final
+outcome of stage computation. We discuss how the original Spider Monkey system
+is minimally affected with extensions in the syntax, parser and internal AST
+structures, and the addition of an unparser, a staging loop, some library
+functions and a debugger backend component for AST inspection. Since stages
+have a generative metaprogramming role we do not foresee any interplay with the
+browser DOM, and thus there is no reason to repeat their evalua-tion on every
+page load. Hence, such JavaScript extensions are meant only for
+development-time, emitting pure JavaScript code that can be run in any browser.
+Finally, to enable debugging stages in any browser we implemented a pure
+JavaScript client, communicating with the extended Spider Monkey, and offering
+the necessary AST display and unparsing that a browser debugger does not
+provide.
+"
+27944,Smooth Transition HYGARCH Model: Stability and Forecasting,"  HYGARCH process is the commonly used long memory process in modeling the
+long-rang dependence in volatility.
+Financial time series are characterized by transition between phases of
+different volatility levels. The smooth transition HYGARCH (ST-HYGARCH) model
+is proposed to model time-varying structure with long memory property. The
+asymptotic behavior of the second moment is studied and an upper bound for it
+is derived. A score test is developed to check the smooth transition property.
+The asymptotic behavior of the proposed model and the score test is examined by
+simulation. The proposed model is applied to the \textit{S}\&\textit{P}500
+indices for some period which show evidence of smooth transition property and
+demonstrates out-performance of the ST-HYGARCH than HYGARCH in forecasting.
+"
+27945,A Survey of Interdependency Models for Critical Infrastructure Networks,"  The critical infrastructures of the nation such as the power grid and the
+communication network are highly interdependent. Also, it has been observed
+that there exists complex interdependent relationships between individual
+entities of the power grid and the communication network that further
+obfuscates the analysis, and mitigation of faults in such multi-layered
+networks. In recent years, the research community has made significant efforts
+towards gaining insight and understanding of the interdependency relations in
+such multi-layered networks, and accordingly, a number of models have been
+proposed and analyzed towards realizing this goal. In this chapter we study
+existing interdependency models proposed in the recent literature and discuss
+their approach, and inherent features, towards modeling interdependent
+multi-layer networks. We also provide a brief discussion into the drawbacks of
+each of these models and propose an alternate model that addresses these
+drawbacks by capturing the interdependency relationships using a combination of
+conjunctive and disjunctive relations.
+"
+27946,Temporal patterns behind the strength of persistent ties,"  Social networks are made out of strong and weak ties having very different
+structural and dynamical properties. But, what features of human interaction
+build a strong tie? Here we approach this question from an practical way by
+finding what are the properties of social interactions that make ties more
+persistent and thus stronger to maintain social interactions in the future.
+Using a large longitudinal mobile phone database we build a predictive model of
+tie persistence based on intensity, intimacy, structural and temporal patterns
+of social interaction. While our results confirm that structural (embeddedness)
+and intensity (number of calls) are correlated with tie persistence, we find
+that temporal features of communication events are better and more efficient
+predictors for tie persistence. Specifically, although communication within
+ties is always bursty we find that ties that are more bursty than the average
+are more likely to decay, signaling that tie strength is not only reflected in
+the intensity or topology of the network, but also on how individuals
+distribute time or attention across their relationships. We also found that
+stable relationships have and require a constant rhythm and if communication is
+halted for more than 8 times the previous communication frequency, most likely
+the tie will decay. Our results not only are important to understand the
+strength of social relationships but also to unveil the entanglement between
+the different temporal scales in networks, from microscopic tie burstiness and
+rhythm to macroscopic network evolution.
+"
+27947,Topological Insulators and the Kane-Mele Invariant: Obstruction and Localisation Theory,"  We present homotopy theoretic and geometric interpretations of the Kane-Mele
+invariant for gapped fermionic quantum systems in three dimensions with
+time-reversal symmetry. We show that the invariant is related to a certain
+4-equivalence which lends it an interpretation as an obstruction to a block
+decomposition of the sewing matrix up to nonequivariant homotopy. We prove a
+Mayer-Vietoris Theorem for manifolds with $\mathbb{Z}_2$-actions which
+intertwines Real and $\mathbb{Z}_2$-equivariant de Rham cohomology groups, and
+apply it to derive a new localisation formula for the Kane-Mele invariant. This
+provides a unified cohomological explanation for the equivalence between the
+discrete Pfaffian formula and the known local geometric computations of the
+index for periodic lattice systems. We build on the relation between the
+Kane-Mele invariant and the theory of bundle gerbes with $\mathbb{Z}_2$-actions
+to obtain geometric refinements of this obstruction and localisation technique.
+In the preliminary part we review the Freed-Moore theory of band insulators on
+Galilean spacetimes with emphasis on geometric constructions, and present a
+bottom-up approach to time-reversal symmetric topological phases.
+"
+27948,Two-Sample Test for Sparse High Dimensional Multinomial Distributions,"  In this paper we consider testing the equality of probability vectors of two
+independent multinomial distributions in high dimension. The classical
+chi-square test may have some drawbacks in this case since many of cell counts
+may be zero or may not be large enough. We propose a new test and show its
+asymptotic normality and the asymptotic power function. Based on the asymptotic
+power function, we present an application of our result to neighborhood type
+test which has been previously studied, especially for the case of fairly small
+$p$-values. To compare the proposed test with existing tests, we provide
+numerical studies including simulations and real data examples.
+"
+27949,Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets,"  The way how recurrently connected networks of spiking neurons in the brain
+acquire powerful information processing capabilities through learning has
+remained a mystery. This lack of understanding is linked to a lack of learning
+algorithms for recurrent networks of spiking neurons (RSNNs) that are both
+functionally powerful and can be implemented by known biological mechanisms.
+Since RSNNs are simultaneously a primary target for implementations of
+brain-inspired circuits in neuromorphic hardware, this lack of algorithmic
+insight also hinders technological progress in that area. The gold standard for
+learning in recurrent neural networks in machine learning is back-propagation
+through time (BPTT), which implements stochastic gradient descent with regard
+to a given loss function. But BPTT is unrealistic from a biological
+perspective, since it requires a transmission of error signals backwards in
+time and in space, i.e., from post- to presynaptic neurons. We show that an
+online merging of locally available information during a computation with
+suitable top-down learning signals in real-time provides highly capable
+approximations to BPTT. For tasks where information on errors arises only late
+during a network computation, we enrich locally available information through
+feedforward eligibility traces of synapses that can easily be computed in an
+online manner. The resulting new generation of learning algorithms for
+recurrent neural networks provides a new understanding of network learning in
+the brain that can be tested experimentally. In addition, these algorithms
+provide efficient methods for on-chip training of RSNNs in neuromorphic
+hardware.
+"
+27950,Guardian of the HAN: Thwarting Mobile Attacks on Smart-Home Devices Using OS-level Situation Awareness,"  A new development of smart-home systems is to use mobile apps to control IoT
+devices across a Home Area Network (HAN). Those systems tend to rely on the
+Wi-Fi router to authenticate other devices; as verified in our study, IoT
+vendors tend to trust all devices connected to the HAN. This treatment exposes
+them to the attack from malicious apps, particularly those running on
+authorized phones, which the router does not have information to control, as
+confirmed in our measurement study. Mitigating this threat cannot solely rely
+on IoT manufacturers, which may need to change the hardware on the devices to
+support encryption, increasing the cost of the device, or software developers
+who we need to trust to implement security correctly.
+In this work, we present a new technique to control the communication between
+the IoT devices and their apps in a unified, backward-compatible way. Our
+approach, called Hanguard, does not require any changes to the IoT devices
+themselves, the IoT apps or the OS of the participating phones. Hanguard
+achieves a fine-grained, per-app protection through bridging the OS-level
+situation awareness and the router-level per-flow control: each phone runs a
+non-system userspace Monitor app to identify the party that attempts to access
+the protected IoT device and inform the router through a control plane of its
+access decision; the router enforces the decision on the data plane after
+verifying whether the phone should be allowed to talk to the device. Hanguard
+uses a role-based access control (RBAC) schema which leverages type enforcement
+(TE) and multi-category security (MCS) primitives to define highly flexible
+access control rules. We implemented our design over both Android and iOS (>95%
+of mobile OS market share) and a popular router. Our study shows that Hanguard
+is both efficient and effective in practice.
+"
+27951,Comparing robustness properties of optimal designs under standard and compound criteria,"  Standard optimality criteria (e.g. A-, D-optimality criterion, etc.) have
+been commonly used for obtaining optimal designs. For a given statistical
+model, standard criteria assume the error variance is known at the design
+stage. However, in practice the error variance is estimated to make inference
+about the model parameters. Modified criteria are defined as a function of the
+standard criteria and the corresponding error degrees of freedom, which may
+lead to extreme optimal design. Compound criteria are defined as the function
+of different modified criteria and corresponding user specified weights.
+Standard, modified, and compound criteria based optimal designs are obtained
+for $3^3$ factorial design. Robustness properties of the optimal designs are
+also compared.
+"
+27952,Coproducts of Finite Groups,"  We show that for any pair of non-trivial finite groups, their coproduct in
+the category of finite groups is not representable.
+"
+27953,Deep Multi-view Models for Glitch Classification,"  Non-cosmic, non-Gaussian disturbances known as ""glitches"", show up in
+gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave
+Observatory, or aLIGO. In this paper, we propose a deep multi-view
+convolutional neural network to classify glitches automatically. The primary
+purpose of classifying glitches is to understand their characteristics and
+origin, which facilitates their removal from the data or from the detector
+entirely. We visualize glitches as spectrograms and leverage the
+state-of-the-art image classification techniques in our model. The suggested
+classifier is a multi-view deep neural network that exploits four different
+views for classification. The experimental results demonstrate that the
+proposed model improves the overall accuracy of the classification compared to
+traditional single view algorithms.
+"
+27954,Prodorshok I: A Bengali Isolated Speech Dataset for Voice-Based Assistive Technologies - A comparative analysis of the effects of data augmentation on HMM-GMM and DNN classifiers,"  Prodorshok I is a Bengali isolated word dataset tailored to help create
+speaker-independent, voice-command driven automated speech recognition (ASR)
+based assistive technologies to help improve human-computer interaction (HCI).
+This paper presents the results of an objective analysis that was undertaken
+using a subset of words from Prodorshok I to assess its reliability in ASR
+systems that utilize Hidden Markov Models (HMM) with Gaussian emissions and
+Deep Neural Networks (DNN). The results show that simple data augmentation
+involving a small pitch shift can make surprisingly tangible improvements to
+accuracy levels in speech recognition.
+"
+27955,Structured Deep Neural Network Pruning by Varying Regularization Parameters,"  Convolutional Neural Networks (CNN's) are restricted by their massive
+computation and high storage. Parameter pruning is a promising approach for CNN
+compression and acceleration, which aims at eliminating redundant model
+parameters with tolerable performance loss. Despite its effectiveness, existing
+regularization-based parameter pruning methods usually assign a fixed
+regularization parameter to all weights, which neglects the fact that different
+weights may have different importance to CNN. To solve this problem, we propose
+a theoretically sound regularization-based pruning method to incrementally
+assign different regularization parameters to different weights based on their
+importance to the network. On AlexNet and VGG-16, our method can achieve 4x
+theoretical speedup with similar accuracies compared with the baselines. For
+ResNet-50, the proposed method also achieves 2x acceleration and only suffers
+0.1% top-5 accuracy loss.
+"
+27956,Influence of Magnetic Scattering on Superfluidity of 3He in Nematic Aerogel,"  We report results of experiments with superfluid 3He confined in aerogels
+with parallel strands which lead to anisotropic scattering of 3He
+quasiparticles. We vary boundary conditions for the scattering by covering the
+strands by different numbers of atomic 4He layers and observe that the
+superfluid phase diagram and the nature of superfluid phases strongly depend on
+the coverage. We assume that the main reason of these phenomena is a magnetic
+channel of the scattering which becomes important at low coverages. Our results
+show that the magnetic channel also may be important in other Fermi systems
+with the triplet pairing.
+"
+27957,"Structural, Chemical and Optical Properties of Cerium Dioxide Film Prepared by Atomic Layer Deposition on TiN and Si Substrates","  Thin films of cerium dioxide (CeO2) were deposited by atomic layer deposition
+(ALD) at 250 °C on both Si and TiN substrates. The ALD growth produces
+CeO2 films with polycrystalline cubic phase on both substrates. However, the
+films show a preferential orientation along <200> crystallographic direction
+for CeO2/Si or <111> for CeO2/TiN, as revealed by X-ray diffraction.
+Additionally, CeO2 films differ in interface roughness depending on the
+substrate. Furthermore, the relative concentration of Ce3+ is 22.0% in CeO2/Si
+and around 18% in CeO2/TiN, as obtained by X-ray photoelectron spectroscopy
+(XPS). Such values indicate a ~10% off-stoichiometry and are indicative of the
+presence of oxygen vacancies in the films. Nonetheless, CeO2 bandgap energy and
+refractive index at 550 nm are 3.54+/-0.63 eV and 2.3 for CeO2/Si, and
+3.63+/-0.18 eV and 2.4 for CeO2/TiN, respectively. Our results extend the
+knowledge on the structural and chemical properties of ALD-deposited CeO2
+either on Si or TiN substrates, underlying films differences and similarities,
+thus contributing to boost the use of CeO2 through ALD deposition as foreseen
+in a wide number of applications.
+"
+27958,Energy Imbalance Management Using a Robust Pricing Scheme,"  This paper focuses on the problem of energy imbalance management in
+amicrogrid. The problem is investigated from the power market perspective.
+Unlike the traditional power grid, a microgrid can obtain extra energy froma
+renewable energy source (RES) such as a solar panel or a wind turbine. However,
+the stochastic input from the RES brings difficulty in balancing the energy
+supply and demand. In this study, a novel pricing scheme is proposed that
+provides robustness against such intermittent power input. The proposed scheme
+considers possible uncertainty in the marginal benefit and the marginal cost of
+the power market. It uses all available information on the power supply, power
+demand, and imbalanced energy. The parameters of the scheme are evaluated using
+an performance index. It is shown that the parameters can be obtained by
+solving a linear matrix inequality problem, which is efficiently solvable due
+to its convexity. Simulation examples are given to show the favorable
+performance of the proposed scheme in comparison with existing area control
+error pricing schemes.
+"
+27959,Moving obstacle potential in a spin-orbit-coupled Bose-Einstein condensate,"  We investigate the dynamics around an obstacle potential moving in the
+plane-wave state of a pseudospin-$1/2$ Bose-Einstein condensate with Rashba
+spin-orbit coupling. We numerically investigate the dynamics of the system and
+find that it depends not only on the velocity of the obstacle but also
+significantly on the direction of obstacle motion, which are verified by a
+Bogoliubov analysis. The excitation diagram with respect to the velocity and
+direction is obtained. The dependence of the critical velocity on the strength
+of the spin-orbit coupling and the size of the obstacle is also investigated.
+"
+27960,The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning,"  In this paper we aim to formally explain the phenomenon of fast convergence
+of SGD observed in modern machine learning. The key observation is that most
+modern learning architectures are over-parametrized and are trained to
+interpolate the data by driving the empirical loss (classification and
+regression) close to zero. While it is still unclear why these interpolated
+solutions perform well on test data, we show that these regimes allow for fast
+convergence of SGD, comparable in number of iterations to full gradient
+descent.
+For convex loss functions we obtain an exponential convergence bound for {\it
+mini-batch} SGD parallel to that for full gradient descent. We show that there
+is a critical batch size $m^*$ such that: (a) SGD iteration with mini-batch
+size $m\leq m^*$ is nearly equivalent to $m$ iterations of mini-batch size $1$
+(\emph{linear scaling regime}). (b) SGD iteration with mini-batch $m> m^*$ is
+nearly equivalent to a full gradient descent iteration (\emph{saturation
+regime}).
+Moreover, for the quadratic loss, we derive explicit expressions for the
+optimal mini-batch and step size and explicitly characterize the two regimes
+above. The critical mini-batch size can be viewed as the limit for effective
+mini-batch parallelization. It is also nearly independent of the data size,
+implying $O(n)$ acceleration over GD per unit of computation. We give
+experimental evidence on real data which closely follows our theoretical
+analyses.
+Finally, we show how our results fit in the recent developments in training
+deep neural networks and discuss connections to adaptive rates for SGD and
+variance reduction.
+"
+27961,FRiED: A novel three-dimensional model of coronal mass ejections,"  We present a novel three-dimensional (3D) model of coronal mass ejections
+(CMEs) that unifies all key evolutionary aspects of CMEs and encapsulates their
+3D magnetic field configuration. This fully analytic model is capable of
+reproducing the global geometrical shape of a CME with all major deformations
+taken into account, i.e., deflection, rotation, expansion, ""pancaking"", front
+flattening and rotational skew. Encapsulation of 3D magnetic structure allows
+the model to reproduce in-situ measurements of magnetic field for trajectories
+of spacecraft-CME encounters of any degree of complexity. As such, the model
+can be used single-handedly for consistent analysis of both remote and in-situ
+observations of CMEs at any heliocentric distance. We demonstrate the latter by
+successfully applying the model for analysis of two CMEs.
+"
+27962,A necessary and sufficient criterion for the existence of ratio limits of sequences generated by linear recurrences,"  We introduce a necessary and sufficient criterion for determining the
+existence and the values of ratio limits of complex sequences generated by
+arbitrary linear recurrences.
+"
+27963,Distributed Control of Hybrid AC/DC Microgrids: from Synchronous to Asynchronous Algorithms,"  Hybrid AC/DC microgrids (MGs) have become a promising manner for better
+interconnection of various kinds of distributed generators that are inherently
+AC or DC electric sources. This paper addresses the distributed asynchronous
+power control problem of hybrid microgrids, considering imperfect communication
+due to non-identical sampling rates and communication delays, etc.. To this
+end, we first formulate the optimal power control problem of MGs and devise a
+synchronous algorithm. Then, we analyze the impact of asynchrony on optimal
+power control and propose an asynchronous iteration algorithm based on the
+synchronous version. By introducing a random clock at each iteration, different
+types of asynchrony are fitted into a unified framework, where the asynchronous
+algorithm is converted into a fixed-point problem, leading to a convergence
+proof. We further provide an upper bound estimation of the time delay in
+communication. Finally, a benchmark MG is utilized to verify the effectiveness
+and advantages of the proposed algorithm.
+"
+27964,"SXP7.92: A Recently Rediscovered Be/X-ray Binary in the Small Magellanic Cloud, Viewed Edge On","  We present a detailed optical and X-ray study of the 2013 outburst of the
+Small Magellanic Cloud Be/X-ray binary SXP7.92, as well as an overview of the
+last 18 years of observations from OGLE, RXTE, Chandra and XMM-Newton. We
+revise the position of this source to RA(J2000)=00:57:58.4,
+Dec(J2000)=-72:22:29.5 with a $1\sigma$ uncertainty of 1.5 arcsec, correcting
+the previously reported position by Coe et al. (2009) by more than 20
+arcminutes. We identify and spectrally classify the correct counterpart as a
+B1Ve star. The optical spectrum is distinguished by an uncharacteristically
+deep narrow Balmer series, with the H$\alpha$ line in particular having a
+distinctive shell profile, i.e. a deep absorption core embedded in an emission
+line. We interpret this as evidence that we are viewing the system edge on and
+are seeing self obscuration of the circumstellar disc. We derive an optical
+period for the system of 40.0$\pm0.3$ days, which we interpret as the orbital
+period, and present several mechanisms to describe the X-ray/Optical behaviour
+in the recent outburst, in particular the ""flares"" and ""dips"" seen in the
+optical light curve, including a transient accretion disc and an elongated
+precessing disc.
+"
+27965,Effect of laser induced orbital momentum on magnetization switching,"  The observed magnetization switching by circularly polarized ultrafast laser
+pulses has been attributed to the inverse Faraday effect in which the induced
+non-equilibrium orbital momentum serves as an effective magnetic filed via
+spin-orbit coupling for magnetization rotation and switching. We critically
+examine this scenario by explicitly calculating the magnitude of the induced
+orbital momentum for generic itinerant band. We show that the calculated
+induced angular momentum is not large enough for reversing the magnetization in
+one laser pulse with the order of 100 femtosecond duration. Instead, we propose
+that each laser pulse is capable to expand a reverse domain a few nano-meters
+and it takes multiple pulses to complete the magnetization reversal process via
+domain wall motion.
+"
+27966,MoS2 Heterostructure with Tunable Phase Stability: Strain Induced Interlayer Covalent Bond Formation,"  Due to the distinguished properties offered by different structural phases of
+monolayer MoS2, phase engineering design are urgently required for achieving
+switchable structural phase. Strain engineering is widely accepted as a clean
+and flexible method, however, cannot be achieved in engineering monolayer MoS2
+phase transition because the critical biaxial strain required (~15%) is much
+larger than measured elastic limit (~11%). In this study, employing density
+functional theoretical calculations, it has been found out that with the
+forming of heterostructure between MoS2 with buckled 2D materials such as
+silicence, germanene and stanene, only a small strain can trigger the phase
+transition. As being suggested by the constructed phase stability diagram,
+biaxial deformation as low as 3% in MoS2/silicene and MoS2/stanene sandwich
+structure, would be sufficient to induce the structural phase transition in
+MoS2 lattice. This strain falls well within experimental elastic limit, thus
+would be feasible to realize in experiment. The origin of such behavior can be
+understood as strain induced interlayer covalent bond formation, which finally
+make MoS2 lattice more sensitive to external strain. This theoretical work
+provides one realistic route for achieving flexible phase stabilities in
+experimental design.
+"
+27967,The KMS Condition for the homoclinic equivalence relation and Gibbs probabilities,"  D. Ruelle considered a general setting where he is able to characterize
+equilibrium states for Hölder potentials based on properties of conjugating
+homeomorphism in the so called Smale spaces. On this setting he also shows a
+relation of KMS states of $C^*$-algebras and equilibrium probabilities of
+Thermodynamic Formalism. A later paper by N. Haydn and D. Ruelle presents a
+shorter proof of this equivalence.
+Here we consider similar problems but now on the symbolic space $\Omega =
+\{1,2,...,d\}^{\mathbb{Z} - \{ 0 \} }$ and the dynamics will be given by the
+shift $\tau$. In the case of potentials depending on a finite coordinates we
+will present a simplified proof of the equivalence mentioned above which is the
+main issue of the papers by D. Ruelle and N. Haydn. The class of conjugating
+homeomorphism is explicit and reduced to a minimal set of conditions.
+We also present with details (following D. Ruelle) the relation of these
+probabilities with the KMS dynamical $C^*$-state on the $C^*$-Algebra
+associated to the groupoid defined by the homoclinic equivalence relation.
+The topics presented here are not new but we believe the main ideas of the
+proof of the results by Ruelle and Haydn will be quite transparent in our
+exposition.
+"
+27968,"Comment on ""The renormalized superperturbation theory (rSPT) approach to the Anderson model in and out of equilibrium""","  I present briefly some facts about nonequilibrium renormalized perturbation
+theory, correcting recent misleading statements in [E. Muñoz, F. Zamani, L.
+Merker, T. A. Costi, and S. Kirchner, Journal of Physics: Conf. Series 807,
+092001 (2017)], and discuss some results of this work using rSPT at
+equilibrium.
+"
+27969,Inverse Classification for Comparison-based Interpretability in Machine Learning,"  In the context of post-hoc interpretability, this paper addresses the task of
+explaining the prediction of a classifier, considering the case where no
+information is available, neither on the classifier itself, nor on the
+processed data (neither the training nor the test data). It proposes an
+instance-based approach whose principle consists in determining the minimal
+changes needed to alter a prediction: given a data point whose classification
+must be explained, the proposed method consists in identifying a close
+neighbour classified differently, where the closeness definition integrates a
+sparsity constraint. This principle is implemented using observation generation
+in the Growing Spheres algorithm. Experimental results on two datasets
+illustrate the relevance of the proposed approach that can be used to gain
+knowledge about the classifier.
+"
+27970,MetaLDA: a Topic Model that Efficiently Incorporates Meta information,"  Besides the text content, documents and their associated words usually come
+with rich sets of meta informa- tion, such as categories of documents and
+semantic/syntactic features of words, like those encoded in word embeddings.
+Incorporating such meta information directly into the generative process of
+topic models can improve modelling accuracy and topic quality, especially in
+the case where the word-occurrence information in the training data is
+insufficient. In this paper, we present a topic model, called MetaLDA, which is
+able to leverage either document or word meta information, or both of them
+jointly. With two data argumentation techniques, we can derive an efficient
+Gibbs sampling algorithm, which benefits from the fully local conjugacy of the
+model. Moreover, the algorithm is favoured by the sparsity of the meta
+information. Extensive experiments on several real world datasets demonstrate
+that our model achieves comparable or improved performance in terms of both
+perplexity and topic quality, particularly in handling sparse texts. In
+addition, compared with other models using meta information, our model runs
+significantly faster.
+"
+27971,"Modeling and replicating statistical topology, and evidence for CMB non-homogeneity","  Under the banner of `Big Data', the detection and classification of structure
+in extremely large, high dimensional, data sets, is, one of the central
+statistical challenges of our times. Among the most intriguing approaches to
+this challenge is `TDA', or `Topological Data Analysis', one of the primary
+aims of which is providing non-metric, but topologically informative,
+pre-analyses of data sets which make later, more quantitative analyses
+feasible. While TDA rests on strong mathematical foundations from Topology, in
+applications it has faced challenges due to an inability to handle issues of
+statistical reliability and robustness and, most importantly, in an inability
+to make scientific claims with verifiable levels of statistical confidence. We
+propose a methodology for the parametric representation, estimation, and
+replication of persistence diagrams, the main diagnostic tool of TDA. The power
+of the methodology lies in the fact that even if only one persistence diagram
+is available for analysis -- the typical case for big data applications --
+replications can be generated to allow for conventional statistical hypothesis
+testing. The methodology is conceptually simple and computationally practical,
+and provides a broadly effective statistical procedure for persistence diagram
+TDA analysis. We demonstrate the basic ideas on a toy example, and the power of
+the approach in a novel and revealing analysis of CMB non-homogeneity.
+"
+27972,Functional correlation decay and multivariate normal approximation for non-uniformly expanding maps,"  In the setting of intermittent Pomeau-Manneville maps with time dependent
+parameters, we show a functional correlation bound widely useful for the
+analysis of the statistical properties of the model. We give two applications
+of this result, by showing that in a suitable range of parameters the bound
+implies the conditions of the normal approximation methods of Stein and Rio.
+For a single Pomeau-Manneville map belonging to this parameter range, both
+methods then yield a multivariate central limit theorem with a rate of
+convergence.
+"
+27973,A Second Order Cumulant Spectrum Based Test for Strict Stationarity,"  This article develops a statistical test for the null hypothesis of strict
+stationarity of a discrete time stochastic process. When the null hypothesis is
+true, the second order cumulant spectrum is zero at all the discrete Fourier
+frequency pairs present in the principal domain of the cumulant spectrum. The
+test uses a frame (window) averaged sample estimate of the second order
+cumulant spectrum to build a test statistic that has an asymptotic complex
+standard normal distribution. We derive the test statistic, study the size and
+power properties of the test, and demonstrate its implementation with intraday
+stock market return data. The test has conservative size properties and good
+power to detect varying variance and unit root in the presence of varying
+variance.
+"
+27974,Elastohydrodynamics of a pre-stretched finite elastic sheet lubricated by a thin viscous film with application to microfluidic soft actuators,"  The interaction of a thin viscous film with an elastic sheet results in
+coupling of pressure and deformation, which can be utilized as an actuation
+mechanism for surface deformations in a wide range of applications, including
+microfluidics, optics, and soft robotics. Implementation of such configurations
+inherently takes place over finite domains and often requires some
+pre-stretching of the sheet. Under the assumptions of strong pre-stretching and
+small deformations of the lubricated elastic sheet, we use the linearized
+Reynolds and Foppl-von Karman equations to derive closed-form analytical
+solutions describing the deformation in a finite domain due to external forces,
+accounting for both bending and tension effects. We provide a closed-form
+solution for the case of a square-shaped actuation region and present the
+effect of pre-stretching on the dynamics of the deformation. We further present
+the dependence of the deformation magnitude and timescale on the spatial
+wavenumber, as well as the transition between stretching- and bending-dominant
+regimes. We also demonstrate the effect of spatial discretization of the
+forcing (representing practical actuation elements) on the achievable
+resolution of the deformation. Extending the problem to an axisymmetric domain,
+we investigate the effects arising from nonlinearity of the Reynolds and
+Foppl-von Karman equations and present the deformation behavior as it becomes
+comparable to the initial film thickness and dependent on the induced tension.
+These results set the theoretical foundation for implementation of microfluidic
+soft actuators based on elastohydrodynanmics.
+"
+27975,"""When and Where?"": Behavior Dominant Location Forecasting with Micro-blog Streams","  The proliferation of smartphones and wearable devices has increased the
+availability of large amounts of geospatial streams to provide significant
+automated discovery of knowledge in pervasive environments, but most prominent
+information related to altering interests have not yet adequately capitalized.
+In this paper, we provide a novel algorithm to exploit the dynamic fluctuations
+in user's point-of-interest while forecasting the future place of visit with
+fine granularity. Our proposed algorithm is based on the dynamic formation of
+collective personality communities using different languages, opinions,
+geographical and temporal distributions for finding out optimized equivalent
+content. We performed extensive empirical experiments involving, real-time
+streams derived from 0.6 million stream tuples of micro-blog comprising 1945
+social person fusion with graph algorithm and feed-forward neural network model
+as a predictive classification model. Lastly, The framework achieves 62.10%
+mean average precision on 1,20,000 embeddings on unlabeled users and
+surprisingly 85.92% increment on the state-of-the-art approach.
+"
+27976,Information Maximizing Exploration with a Latent Dynamics Model,"  All reinforcement learning algorithms must handle the trade-off between
+exploration and exploitation. Many state-of-the-art deep reinforcement learning
+methods use noise in the action selection, such as Gaussian noise in policy
+gradient methods or $\epsilon$-greedy in Q-learning. While these methods are
+appealing due to their simplicity, they do not explore the state space in a
+methodical manner. We present an approach that uses a model to derive reward
+bonuses as a means of intrinsic motivation to improve model-free reinforcement
+learning. A key insight of our approach is that this dynamics model can be
+learned in the latent feature space of a value function, representing the
+dynamics of the agent and the environment. This method is both theoretically
+grounded and computationally advantageous, permitting the efficient use of
+Bayesian information-theoretic methods in high-dimensional state spaces. We
+evaluate our method on several continuous control tasks, focusing on improving
+exploration.
+"
+27977,Non-equilibrium phase diagram of a 1D quasiperiodic system with a single-particle mobility edge,"  We investigate and map out the non-equilibrium phase diagram of a
+generalization of the well known Aubry-André-Harper (AAH) model. This
+generalized AAH (GAAH) model is known to have a single-particle mobility edge
+which also has an additional self-dual property akin to that of the critical
+point of AAH model. By calculating the imbalance, we get hints of a rich phase
+diagram. We find a fascinating connection between single particle wavefunctions
+near the mobility edge of GAAH model and the wavefunctions of the critical AAH
+model. By placing this model far-from-equilibrium with the aid of two baths, we
+investigate the open system transport via system size scaling of
+non-equilibrium steady state (NESS) current. Current is calculated by fully
+exact non-equilibrium Green's function (NEGF) formalism. The critical point of
+the AAH model now generalizes to a 'critical' line separating regions of
+ballistic and localized transport. Like critical point of AAH model, current
+scales sub-diffusively with system size on the 'critical' line ($I\sim
+N^{-2\pm0.1}$). However, remarkably, the scaling exponent on this line is
+distinctly different from that obtained for the critical AAH model (where
+$I\sim N^{-1.4\pm0.05}$). All these results can be understood from the
+above-mentioned connection between states near mobility edge of GAAH model and
+those of critical AAH model. A very interesting high temperature
+non-equilibrium phase diagram of the GAAH model emerges from our calculations.
+"
+27978,From the Lab to the Street: Solving the Challenge of Accelerating Automated Vehicle Testing,"  As automated vehicles and their technology become more advanced and
+technically sophisticated, evaluation procedures that can measure the safety
+and reliability of these new driverless cars must develop far beyond existing
+safety tests. To get an accurate assessment in field tests, such cars would
+have to be driven millions or even billions of miles to arrive at an acceptable
+level of certainty - a time-consuming process that would cost tens of millions
+of dollars.
+Instead, researchers affiliated with the University of Michigan's Mcity
+connected and automated vehicle center have developed an accelerated evaluation
+process that eliminates the many miles of uneventful driving activity to filter
+out only the potentially dangerous driving situations where an automated
+vehicle needs to respond, creating a faster, less expensive testing program.
+This approach can reduce the amount of testing needed by a factor of 300 to
+100,000 so that an automated vehicle driven for 1,000 test miles can yield the
+equivalent of 300,000 to 100 million miles of real-world driving.
+While more research and development needs to be done to perfect this
+technique, the accelerated evaluation procedure offers a ground-breaking
+solution for safe and efficient testing that is crucial to deploying automated
+vehicles.
+"
+27979,Quantum transport senses community structure in networks,"  Quantum time evolution exhibits rich physics, attributable to the interplay
+between the density and phase of a wave function. However, unlike classical
+heat diffusion, the wave nature of quantum mechanics has not yet been
+extensively explored in modern data analysis. We propose that the Laplace
+transform of quantum transport (QT) can be used to construct an ensemble of
+maps from a given complex network to a circle $S^1$, such that closely-related
+nodes on the network are grouped into sharply concentrated clusters on $S^1$.
+The resulting QT clustering (QTC) algorithm is as powerful as the
+state-of-the-art spectral clustering in discerning complex geometric patterns
+and more robust when clusters show strong density variations or heterogeneity
+in size. The observed phenomenon of QTC can be interpreted as a collective
+behavior of the microscopic nodes that evolve as macroscopic cluster orbitals
+in an effective tight-binding model recapitulating the network. Python source
+code implementing the algorithm and examples are available at
+this https URL.
+"
+27980,In situ coherent diffractive imaging,"  Coherent diffractive imaging (CDI) has been widely applied in the physical
+and biological sciences using synchrotron radiation, XFELs, high harmonic
+generation, electrons and optical lasers. One of CDI's important applications
+is to probe dynamic phenomena with high spatio-temporal resolution. Here, we
+report the development of a general in situ CDI method for real-time imaging of
+dynamic processes in solution. By introducing a time-invariant overlapping
+region as a real-space constraint, we show that in situ CDI can simultaneously
+reconstruct a time series of the complex exit wave of dynamic processes with
+robust and fast convergence. We validate this method using numerical
+simulations with coherent X-rays and performing experiments on a materials
+science and a biological specimen in solution with an optical laser. Our
+numerical simulations further indicate that in situ CDI can potentially reduce
+the radiation dose by more than an order of magnitude relative to conventional
+CDI. As coherent X-rays are under rapid development worldwide, we expect in
+situ CDI could be applied to probe dynamic phenomena ranging from
+electrochemistry, structural phase transitions, charge transfer, transport,
+crystal nucleation, melting and fluid dynamics to biological imaging.
+"
+27981,On the information in spike timing: neural codes derived from polychronous groups,"  There is growing evidence regarding the importance of spike timing in neural
+information processing, with even a small number of spikes carrying
+information, but computational models lag significantly behind those for rate
+coding. Experimental evidence on neuronal behavior is consistent with the
+dynamical and state dependent behavior provided by recurrent connections. This
+motivates the minimalistic abstraction investigated in this paper, aimed at
+providing insight into information encoding in spike timing via recurrent
+connections. We employ information-theoretic techniques for a simple reservoir
+model which encodes input spatiotemporal patterns into a sparse neural code,
+translating the polychronous groups introduced by Izhikevich into codewords on
+which we can perform standard vector operations. We show that the distance
+properties of the code are similar to those for (optimal) random codes. In
+particular, the code meets benchmarks associated with both linear
+classification and capacity, with the latter scaling exponentially with
+reservoir size.
+"
+27982,Freier Induktionszerfall und Linienform im dreidimensionalen magnetischen Dipolfeld,"  In this work, the time evolution of the free induction decay caused by the
+local dipole field of a spherical magnetic perturber is analyzed. The
+complicated treatment of the diffusion process is considered by the
+strong-collision approximation that allows a determination of the free
+induction decay in dependence of the underlying microscopic tissue parameters
+such as diffusion coefficient, sphere radius and susceptibility difference. The
+interplay between susceptibility- and diffusion-mediated effects yields several
+dephasing regimes of which, so far, only the classical regimes of motional
+narrowing and static dephasing for dominant and negligible diffusion,
+respectively, were extensively examined. Due to the asymmetric form of the
+three-dimensional dipole field for spherical objects, the free induction decay
+exhibits a complex component in contradiction to the cylindrical case, where
+the symmetric local two-dimensional dipole field only causes a purely real
+induction decay. Knowledge of the shape of the corresponding frequency
+distribution is necessary for the evaluation of more sophisticated pulse
+sequences and a detailed understanding of the off-resonance distribution allows
+improved quantification of transverse relaxation.
+"
+27983,Trisections of 4-manifolds via Lefschetz fibrations,"  We develop a technique for gluing relative trisection diagrams of
+$4$-manifolds with nonempty connected boundary to obtain trisection diagrams
+for closed $4$-manifolds. As an application, we describe a trisection of any
+closed $4$-manifold which admits a Lefschetz fibration over $S^2$ equipped with
+a section of square $-1$, by an explicit diagram determined by the vanishing
+cycles of the Lefschetz fibration. In particular, we obtain a trisection
+diagram for some simply connected minimal complex surface of general type. As a
+consequence, we obtain explicit trisection diagrams for a pair of closed
+$4$-manifolds which are homeomorphic but not diffeomorphic. Moreover, we
+describe a trisection for any oriented $S^2$-bundle over any closed surface and
+in particular we draw the corresponding diagrams for $T^2 \times S^2$ and $T^2
+\tilde{\times} S^2$ using our gluing technique. Furthermore, we provide an
+alternate proof of a recent result of Gay and Kirby which says that every
+closed $4$-manifold admits a trisection. The key feature of our proof is that
+Cerf theory takes a back seat to contact geometry.
+"
+27984,Fully Automatic Segmentation and Objective Assessment of Atrial Scars for Longstanding Persistent Atrial Fibrillation Patients Using Late Gadolinium-Enhanced MRI,"  Purpose: Atrial fibrillation (AF) is the most common cardiac arrhythmia and
+is correlated with increased morbidity and mortality. It is associated with
+atrial fibrosis, which may be assessed non-invasively using late
+gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is
+visualised as a region of signal enhancement. In this study, we proposed a
+novel fully automatic pipeline to achieve an accurate and objective atrial
+scarring segmentation and assessment of LGE MRI scans for the AF patients.
+Methods: Our fully automatic pipeline uniquely combined: (1) a multi-atlas
+based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from
+an MRI Roadmap acquisition which is then mapped to LGE MRI, and (2) a
+super-pixel and supervised learning based approach to delineate the
+distribution and extent of atrial scarring in LGE MRI. Results: Both our MA-WHS
+and atrial scarring segmentation showed accurate delineations of cardiac
+anatomy (mean Dice = 89%) and atrial scarring (mean Dice =79%) respectively
+compared to the established ground truth from manual segmentation. Compared
+with previously studied methods with manual interventions, our innovative
+pipeline demonstrated comparable results, but was computed fully automatically.
+Conclusion: The proposed segmentation methods allow LGE MRI to be used as an
+objective assessment tool for localisation, visualisation and quantification of
+atrial scarring.
+"
+27985,Material Decomposed Cargo Imaging with Dual Energy Megavoltage Radiography,"  Cargo imaging with Megavoltage (MV) radiography has important applications
+for detecting illicit materials. It enables decomposing and quantifying
+materials with different atomic numbers by imaging cargo at two different x-ray
+energies, or dual energy (DE) radiography. The performance of both single
+energy and DE radiography depends on beam energy, beam filtration, radiation
+dose, object size, and object content. The purpose of this work was to perform
+comprehensive qualitative and quantitative investigations of the image quality
+in MV radiography depending on the above parameters. A digital phantom was
+designed including Fe background with thicknesses of 2cm, 6cm, and 18cm, and
+materials samples of Polyethylene, Fe, Pb, and U. The single energy images were
+generated at x-ray beam energies 3.5MV, 6MV, and 9MV. The DE material
+decomposed images were generated using interlaced low and high energy beams
+3.5/6MV and 6/9MV. The x-rays beams were filtered by low-Z (Polyethylene) and
+high-Z (Pb) filters with variable thicknesses. The radiation output of the
+accelerator was kept constant for all beam energies. The image quality metrics
+was signal-to-noise ratio (SNR) of the particular sample over a particular
+background. It was found that the SNR depends on the above parameters in a
+complex way, but can be optimized by selecting a particular set of parameters.
+For some imaging setups increased filter thicknesses, while strongly absorbed
+the beams, increased the SNR of material decomposed images. Beam hardening due
+to polyenergetic x-ray spectra resulted in material decomposition errors, but
+this could be addressed using region of interest decomposition. It was shown
+that it is not feasible to separate the materials with close atomic numbers
+using the DE method. Particularly, Pb and U were difficult to decompose, at
+least at the dose levels allowed by radiation source and safety requirements.
+"
+27986,Improving particle filter performance by smoothing observations,"  This article shows that increasing the observation variance at small scales
+can reduce the ensemble size required to avoid collapse in particle filtering
+of spatially-extended dynamics and improve the resulting uncertainty
+quantification at large scales. Particle filter weights depend on how well
+ensemble members agree with observations, and collapse occurs when a few
+ensemble members receive most of the weight. Collapse causes catastrophic
+variance underestimation. Increasing small-scale variance in the observation
+error model reduces the incidence of collapse by de-emphasizing small-scale
+differences between the ensemble members and the observations. Doing so smooths
+the posterior mean, though it does not smooth the individual ensemble members.
+Two options for implementing the proposed observation error model are
+described. Taking discretized elliptic differential operators as an observation
+error covariance matrix provides the desired property of a spectrum that grows
+in the approach to small scales. This choice also introduces structure
+exploitable by scalable computation techniques, including multigrid solvers and
+multiresolution approximations to the corresponding integral operator.
+Alternatively the observations can be smoothed and then assimilated under the
+assumption of independent errors, which is equivalent to assuming large errors
+at small scales. The method is demonstrated on a linear stochastic partial
+differential equation, where it significantly reduces the occurrence of
+particle filter collapse while maintaining accuracy. It also improves
+continuous ranked probability scores by as much as 25%, indicating that the
+weighted ensemble more accurately represents the true distribution. The method
+is compatible with other techniques for improving the performance of particle
+filters.
+"
+27987,Sum-set Inequalities from Aligned Image Sets: Instruments for Robust GDoF Bounds,"  We present sum-set inequalities specialized to the generalized degrees of
+freedom (GDoF) framework. These are information theoretic lower bounds on the
+entropy of bounded density linear combinations of discrete, power-limited
+dependent random variables in terms of the joint entropies of arbitrary linear
+combinations of new random variables that are obtained by power level
+partitioning of the original random variables. These bounds generalize the
+aligned image sets approach, and are useful instruments to obtain GDoF
+characterizations for wireless networks, especially with multiple antenna
+nodes, subject to arbitrary channel strength and channel uncertainty levels. To
+demonstrate the utility of these bounds, we consider a non-trivial instance of
+wireless networks - a two user interference channel with different number of
+antennas at each node, and different levels of partial channel knowledge
+available to the transmitters. We obtain tight GDoF characterization for
+specific instance of this channel with the aid of sum-set inequalities.
+"
+27988,TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network,"  In this work, we present the Text Conditioned Auxiliary Classifier Generative
+Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network
+(GAN) for synthesizing images from their text descriptions. Former approaches
+have tried to condition the generative process on the textual data; but allying
+it to the usage of class information, known to diversify the generated samples
+and improve their structural coherence, has not been explored. We trained the
+presented TAC-GAN model on the Oxford-102 dataset of flowers, and evaluated the
+discriminability of the generated images with Inception-Score, as well as their
+diversity using the Multi-Scale Structural Similarity Index (MS-SSIM). Our
+approach outperforms the state-of-the-art models, i.e., its inception score is
+3.45, corresponding to a relative increase of 7.8% compared to the recently
+introduced StackGan. A comparison of the mean MS-SSIM scores of the training
+and generated samples per class shows that our approach is able to generate
+highly diverse images with an average MS-SSIM of 0.14 over all generated
+classes.
+"
+27989,An Immersed Boundary Method with Divergence-Free Velocity Interpolation and Force Spreading,"  The Immersed Boundary (IB) method is a mathematical framework for
+constructing robust numerical methods to study fluid-structure interaction in
+problems involving an elastic structure immersed in a viscous fluid. The IB
+formulation uses an Eulerian representation of the fluid and a Lagrangian
+representation of the structure. The Lagrangian and Eulerian frames are coupled
+by integral transforms with delta function kernels. The discretized IB
+equations use approximations to these transforms with regularized delta
+function kernels to interpolate the fluid velocity to the structure, and to
+spread structural forces to the fluid. It is well-known that the conventional
+IB method can suffer from poor volume conservation since the interpolated
+Lagrangian velocity field is not generally divergence-free, and so this can
+cause spurious volume changes. In practice, the lack of volume conservation is
+especially pronounced for cases where there are large pressure differences
+across thin structural boundaries. The aim of this paper is to greatly reduce
+the volume error of the IB method by introducing velocity-interpolation and
+force-spreading schemes with the properties that the interpolated velocity
+field in which the structure moves is at least C1 and satisfies a continuous
+divergence-free condition, and that the force-spreading operator is the adjoint
+of the velocity-interpolation operator. We confirm through numerical
+experiments in two and three spatial dimensions that this new IB method is able
+to achieve substantial improvement in volume conservation compared to other
+existing IB methods, at the expense of a modest increase in the computational
+cost. Further, the new method provides smoother Lagrangian forces (tractions)
+than traditional IB methods. The method presented here is restricted to
+periodic computational domains. Its generalization to non-periodic domains is
+important future work.
+"
+27990,"Individuals, Institutions, and Innovation in the Debates of the French Revolution","  The French Revolution brought principles of ""liberty, equality, and
+brotherhood"" to bear on the day-to-day challenges of governing what was then
+the largest country in Europe. Its experiments provided a model for future
+revolutions and democracies across the globe, but this first modern revolution
+had no model to follow. Using reconstructed transcripts of debates held in the
+Revolution's first parliament, we present a quantitative analysis of how this
+system managed innovation. We use information theory to track the creation,
+transmission, and destruction of patterns of word-use across over 40,000
+speeches and more than one thousand speakers. The parliament as a whole was
+biased toward the adoption of new patterns, but speakers' individual qualities
+could break these overall trends. Speakers on the left innovated at higher
+rates while speakers on the right acted, often successfully, to preserve prior
+patterns. Key players such as Robespierre (on the left) and Abbé Maury (on
+the right) played information-processing roles emblematic of their politics.
+Newly-created organizational functions---such as the Assembly's President and
+committee chairs---had significant effects on debate outcomes, and a distinct
+transition appears mid-way through the parliament when committees, external to
+the debate process, gain new powers to ""propose and dispose"" to the body as a
+whole. Taken together, these quantitative results align with existing
+qualitative interpretations but also reveal crucial information-processing
+dynamics that have hitherto been overlooked. Great orators had the public's
+attention, but deputies (mostly on the political left) who mastered the
+committee system gained new powers to shape revolutionary legislation.
+"
+27991,Sub-GeV Dark Matter Detection with Electron Recoils in Carbon Nanotubes,"  Directional detection of Dark Matter particles (DM) in the MeV mass range
+could be accomplished by studying electron recoils in large arrays of parallel
+carbon nanotubes. In a scattering process with a lattice electron, a DM
+particle might transfer sufficient energy to eject it from the nanotube
+surface. An external electric field is added to drive the electron from the
+open ends of the array to the detection region. The anisotropic response of
+this detection scheme, as a function of the orientation of the target with
+respect to the DM wind, is calculated, and it is concluded that no direct
+measurement of the electron ejection angle is needed to explore significant
+regions of the light DM exclusion plot. A compact sensor, in which the cathode
+element is substituted with a dense array of parallel carbon nanotubes, could
+serve as the basic detection unit.
+"
+27992,The Time-Domain Spectroscopic Survey: Target Selection for Repeat Spectroscopy,"  As astronomers increasingly exploit the information available in the time
+domain, spectroscopic variability in particular opens broad new channels of
+investigation. Here we describe the selection algorithms for all targets
+intended for repeat spectroscopy in the Time Domain Spectroscopic Survey
+(TDSS), part of the extended Baryon Oscillation Spectroscopic Survey within the
+Sloan Digital Sky Survey-IV. Also discussed are the scientific rationale and
+technical constraints leading to these target selections. The TDSS includes a
+large ""Repeat Quasar Spectroscopy"" (RQS) program delivering ~13,000 repeat
+spectra of confirmed SDSS quasars, and several smaller ""Few-Epoch Spectroscopy""
+(FES) programs targeting specific classes of quasars as well as stars. The RQS
+program aims to provide a large and diverse quasar data set for studying
+variations in quasar spectra on timescales of years, a comparison sample for
+the FES quasar programs, and opportunity for discovering rare, serendipitous
+events. The FES programs cover a wide variety of phenomena in both quasars and
+stars. Quasar FES programs target broad absorption line quasars, high
+signal-to-noise ratio normal broad line quasars, quasars with double-peaked or
+very asymmetric broad emission line profiles, binary supermassive black hole
+candidates, and the most photometrically variable quasars. Strongly variable
+stars are also targeted for repeat spectroscopy, encompassing many types of
+eclipsing binary systems, and classical pulsators like RR Lyrae. Other stellar
+FES programs allow spectroscopic variability studies of active ultracool dwarf
+stars, dwarf carbon stars, and white dwarf/M dwarf spectroscopic binaries. We
+present example TDSS spectra and describe anticipated sample sizes and results.
+"
+27993,Dependent landmark drift: robust point set registration with a Gaussian mixture model and a statistical shape model,"  The goal of point set registration is to find point-by-point correspondences
+between point sets, each of which characterizes the shape of an object. Because
+local preservation of object geometry is assumed, prevalent algorithms in the
+area can often elegantly solve the problems without using geometric information
+specific to the objects. This means that registration performance can be
+further improved by using prior knowledge of object geometry. In this paper, we
+propose a novel point set registration method using the Gaussian mixture model
+with prior shape information encoded as a statistical shape model. Our
+transformation model is defined as a combination of the similar transformation,
+motion coherence, and the statistical shape model. Therefore, the proposed
+method works effectively if the target point set includes outliers and missing
+regions, or if it is rotated. The computational cost can be reduced to linear,
+and therefore the method is scalable to large point sets. The effectiveness of
+the method will be verified through comparisons with existing algorithms using
+datasets concerning human body shapes, hands, and faces.
+"
+27994,Atomic-Scale Magnetometry of Dynamic Magnetization,"  The spatial resolution of imaging magnetometers has benefited from scanning
+probe techniques. The requirement that the sample perturbs the scanning probe
+through a magnetic field external to its volume limits magnetometry to samples
+with pre-existing magnetization. We propose a magnetometer in which the
+perturbation is reversed: the probe's magnetic field generates a response of
+the sample, which acts back on the probe and changes its energy. For an NV$^-$
+spin center in diamond this perturbation changes the fine-structure splitting
+of the spin ground state. Sensitive measurement techniques using coherent
+detection schemes then permit detection of the magnetic response of
+paramagnetic and diamagnetic materials. This technique can measure the
+thickness of magnetically dead layers with better than $0.1$ \AA$~$accuracy.
+"
+27995,Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding,"  Background: We sought to determine if ripple oscillations (80-120Hz),
+detected in intracranial EEG (iEEG) recordings of epilepsy patients, correlate
+with an enhancement or disruption of verbal episodic memory encoding. Methods:
+We defined ripple and spike events in depth iEEG recordings during list
+learning in 107 patients with focal epilepsy. We used logistic regression
+models (LRMs) to investigate the relationship between the occurrence of ripple
+and spike events during word presentation and the odds of successful word
+recall following a distractor epoch, and included the seizure onset zone (SOZ)
+as a covariate in the LRMs. Results: We detected events during 58,312 word
+presentation trials from 7,630 unique electrode sites. The probability of
+ripple on spike (RonS) events was increased in the seizure onset zone (SOZ,
+p<0.04). In the left temporal neocortex RonS events during word presentation
+corresponded with a decrease in the odds ratio (OR) of successful recall,
+however this effect only met significance in the SOZ (OR of word recall 0.71,
+95% CI: 0.59-0.85, n=158 events, adaptive Hochberg p<0.01). Ripple on
+oscillation events (RonO) that occurred in the left temporal neocortex non-SOZ
+also correlated with decreased odds of successful recall (OR 0.52, 95% CI:
+0.34-0.80, n=140, adaptive Hochberg , p<0.01). Spikes and RonS that occurred
+during word presentation in the left middle temporal gyrus during word
+presentation correlated with the most significant decrease in the odds of
+successful recall, irrespective of the location of the SOZ (adaptive Hochberg,
+p<0.01). Conclusion: Ripples and spikes generated in left temporal neocortex
+are associated with impaired verbal episodic memory encoding.
+"
+27996,On a four-parameters generalization of some special sequences,"  We introduce a new four-parameters sequence that simultaneously generalizes
+some well-known integer sequences, including Fibonacci, Padovan, Jacobsthatl,
+Pell, and Lucas numbers. Combinatorial interpretations are discussed and many
+identities for this general sequence are derived. As a consequence, a number of
+identities for Fibonacci, Lucas, Pell, Jacobsthal, Padovan, and Narayana
+numbers as well as some of their generalizations are obtained. We also present
+the Cassini formula for the new sequence.
+"
+27997,Optimal Reachability in Divergent Weighted Timed Games,"  Weighted timed games are played by two players on a timed automaton equipped
+with weights: one player wants to minimise the accumulated weight while
+reaching a target, while the other has an opposite objective. Used in a
+reactive synthesis perspective, this quantitative extension of timed games
+allows one to measure the quality of controllers. Weighted timed games are
+notoriously difficult and quickly undecidable, even when restricted to
+non-negative weights. Decidability results exist for subclasses of one-clock
+games, and for a subclass with non-negative weights defined by a semantical
+restriction on the weights of cycles. In this work, we introduce the class of
+divergent weighted timed games as a generalisation of this semantical
+restriction to arbitrary weights. We show how to compute their optimal value,
+yielding the first decidable class of weighted timed games with negative
+weights and an arbitrary number of clocks. In addition, we prove that
+divergence can be decided in polynomial space. Last, we prove that for untimed
+games, this restriction yields a class of games for which the value can be
+computed in polynomial time.
+"
+27998,"Borophene as an anode material for Ca, Mg, Na or Li ion storage: A first-principle study","  Borophene, the boron atom analogue to graphene, being atomic thick have been
+just recently experimentally fabricated. In this work, we employ
+first-principles density functional theory calculations to investigate the
+interaction of Ca, Mg, Na or Li atoms with single-layer and free-standing
+borophene. We first identified the most stable binding sites and their
+corresponding binding energies as well and then we gradually increased the ions
+concentration. Our calculations predict strong binding energies of around 4.03
+eV, 2.09 eV, 2.92 eV and 3.28 eV between the borophene substrate and Ca, Mg, Na
+or Li ions, respectively. We found that the binding energy generally decreases
+by increasing the ions content. Using the Bader charge analysis, we evaluate
+the charge transfer between the adatoms and the borophene sheet. Our
+investigation proposes the borophene as a 2D material with a remarkably high
+capacity of around 800 mAh/g, 1960 mAh/g, 1380 mAh/g and 1720 mAh/g for Ca, Mg,
+Na or Li ions storage, respectively. This study can be useful for the possible
+application of borophene for the rechargeable ion batteries.
+"
+27999,The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts,"  Building classification models is an intrinsically practical exercise that
+requires many design decisions prior to deployment. We aim to provide some
+guidance in this decision making process. Specifically, given a classification
+problem with real valued attributes, we consider which classifier or family of
+classifiers should one use. Strong contenders are tree based homogeneous
+ensembles, support vector machines or deep neural networks. All three families
+of model could claim to be state-of-the-art, and yet it is not clear when one
+is preferable to the others. Our extensive experiments with over 200 data sets
+from two distinct archives demonstrate that, rather than choose a single family
+and expend computing resources on optimising that model, it is significantly
+better to build simpler versions of classifiers from each family and ensemble.
+We show that the Heterogeneous Ensembles of Standard Classification Algorithms
+(HESCA), which ensembles based on error estimates formed on the train data, is
+significantly better (in terms of error, balanced error, negative log
+likelihood and area under the ROC curve) than its individual components,
+picking the component that is best on train data, and a support vector machine
+tuned over 1089 different parameter configurations. We demonstrate HESCA+,
+which contains a deep neural network, a support vector machine and two decision
+tree forests, is significantly better than its components, picking the best
+component, and HESCA. We analyse the results further and find that HESCA and
+HESCA+ are of particular value when the train set size is relatively small and
+the problem has multiple classes. HESCA is a fast approach that is, on average,
+as good as state-of-the-art classifiers, whereas HESCA+ is significantly better
+than average and represents a strong benchmark for future research.
+"
+28000,Nonlinear response of biased bilayer graphene at terahertz frequencies,"  A density-matrix formalism within the length gauge is developed to calculate
+the nonlinear response of both doped and undoped biased bilayer graphene (BBLG)
+at terahertz frequencies. Employing a tight-binding model, we derive an
+effective two-band Hamiltonian with which we calculate the conduction and
+valence band dispersion, as well as their respective Bloch states. We then
+solve for the dynamic equations of the density matrix elements, allowing for
+the calculation of the intraband and interband current densities and the
+transmitted and reflected terahertz fields. We find that for undoped BBLG with
+a gap size of 4 meV, the reflected field exhibits a third harmonic amplitude
+that is 45% of the fundamental in the reflected field (0.07% of the incident
+field fundamental) for an incident 1 THz single-cycle pulse with a field
+amplitude of 2.0 kV/cm. We find for doped BBLG, although the dispersion becomes
+highly nonparabolic as a bias is applied, the third harmonic is a maximum of 8%
+of the fundamental in the reflected field (0.56% of the incident field
+fundamental) when there is no bias and diminishes with an increase in bias.
+"
+28001,Is the Multiset of $n$ Integers Uniquely Determined by the Multiset of Its $s$-sums?,"  In 1957 Leo Moser published a problem in American Mathematical Monthly asking
+whether knowing the set of all pairwise sums of five numbers one could
+determine the original numbers. Problem was quickly generalized as ""Is it
+always possible to restore a collection of $n$ numbers from the collection of
+its $s$-sums?""; it turned out to be a very interesting and nontrivial question
+in additive number theory and combinatorics. On its sixtieth anniversary we
+present here a survey of all the known research, results and techniques used in
+various attempts to solve this problem. Some new findings and open questions
+are presented as well.
+"
+28002,Proxy Fairness,"  We consider the problem of improving fairness when one lacks access to a
+dataset labeled with protected groups, making it difficult to take advantage of
+strategies that can improve fairness but require protected group labels, either
+at training or runtime. To address this, we investigate improving fairness
+metrics for proxy groups, and test whether doing so results in improved
+fairness for the true sensitive groups. Results on benchmark and real-world
+datasets demonstrate that such a proxy fairness strategy can work well in
+practice. However, we caution that the effectiveness likely depends on the
+choice of fairness metric, as well as how aligned the proxy groups are with the
+true protected groups in terms of the constrained model parameters.
+"
+28003,Model-based image analysis of a tethered Brownian fibre for shear stress sensing,"  The measurement of shear stress acting on a biologically relevant surface is
+a challenging problem, particularly in the complex environment of, for example,
+the vasculature. While an experimental method for the direct detection of wall
+shear stress via the imaging of a synthetic biology nanorod has recently been
+developed, the data interpretation so far has been limited to phenomenological
+random walk modelling, small angle approximation, and image analysis techniques
+which do not take into account the production of an image from a 3D subject. In
+this report we develop a mathematical and statistical framework to estimate
+shear stress from rapid imaging sequences based firstly on stochastic modelling
+of the dynamics of a tethered Brownian fibre in shear flow, and secondly on
+novel model-based image analysis, which reconstructs phage positions by solving
+the inverse problem of image formation. This framework is tested on
+experimental data, providing the first mechanistically rational analysis of the
+novel assay. What follows further develops the established theory for an
+untethered particle in a semi-dilute suspension, which is of relevance to, for
+example, the study of Brownian nanowires without flow, and presents new ideas
+in the field of multidisciplinary image analysis.
+"
+28004,2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose Estimation,"  This study considers the 3D human pose estimation problem in a single RGB
+image by proposing a conditional random field (CRF) model over 2D poses, in
+which the 3D pose is obtained as a byproduct of the inference process. The
+unary term of the proposed CRF model is defined based on a powerful heat-map
+regression network, which has been proposed for 2D human pose estimation. This
+study also presents a regression network for lifting the 2D pose to 3D pose and
+proposes the prior term based on the consistency between the estimated 3D pose
+and the 2D pose. To obtain the approximate solution of the proposed CRF model,
+the N-best strategy is adopted. The proposed inference algorithm can be viewed
+as sequential processes of bottom-up generation of 2D and 3D pose proposals
+from the input 2D image based on deep networks and top-down verification of
+such proposals by checking their consistencies. To evaluate the proposed
+method, we use two large-scale datasets: Human3.6M and HumanEva. Experimental
+results show that the proposed method achieves the state-of-the-art 3D human
+pose estimation performance.
+"
+28005,The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,"  The Jaccard index, also referred to as the intersection-over-union score, is
+commonly employed in the evaluation of image segmentation results given its
+perceptual qualities, scale invariance - which lends appropriate relevance to
+small objects, and appropriate counting of false negatives, in comparison to
+per-pixel losses. We present a method for direct optimization of the mean
+intersection-over-union loss in neural networks, in the context of semantic
+image segmentation, based on the convex Lovász extension of submodular
+losses. The loss is shown to perform better with respect to the Jaccard index
+measure than the traditionally used cross-entropy loss. We show quantitative
+and qualitative differences between optimizing the Jaccard index per image
+versus optimizing the Jaccard index taken over an entire dataset. We evaluate
+the impact of our method in a semantic segmentation pipeline and show
+substantially improved intersection-over-union segmentation scores on the
+Pascal VOC and Cityscapes datasets using state-of-the-art deep learning
+segmentation architectures.
+"
+28006,The Structure and Charge Density Waves in NbSe_2 by First-Principles Calculations,"  In this paper, we investigated structures and charge density waves, including
+its origin of CDW, by using first-principles calculations. Firstly, we
+performed structure searches of NbSe_2 under different pressures to explore the
+possible structures of the CDW phase in NbSe_2. The stability of the resulted
+structure is proved by extensive studies of energy calculations and phonon
+spectra. Based on the detailed analysis on electronic properties (both in real
+space and K space) and band structures during CDW transitions, we believe the
+structure distortion and the displacement of atoms are the origin of CDW, which
+also cause the change of the Fermi surfaces and electronic density of states
+(DOS). In the end, the results of band structure and DOS were used to explain
+the reason why CDW and superconducting orders compete with each other at low
+temperature.
+"
+28007,Classical Spin Nematic Transition in LiGa$_{0.95}$In$_{0.05}$Cr$_4$O$_8$,"  We present the results of a combined $^7$Li NMR and diffraction study on
+LiGa$_{0.95}$In$_{0.05}$Cr$_4$O$_8$, a member of
+LiGa$_{1-x}$In$_{x}$Cr$_4$O$_8$ ""breathing"" pyrochlore family. Via specific
+heat and NMR measurements, we find that the complex sequence of first-order
+transitions observed for $x=0$ is replaced by a single, apparently second-order
+transition at $T_f=11$ K. Neutron and X-ray diffraction rule out both
+structural symmetry lowering and magnetic long-range order as the origin of
+this transition. Instead, reverse Monte Carlo fitting of the magnetic diffuse
+scattering indicates that the low temperature phase may be described as a
+collinear spin nematic state, characterized by a quadrupolar order parameter.
+This state also shows signs of short range order between collinear spin
+arrangements on tetrahedra, revealed by mapping the reverse Monte Carlo spin
+configurations onto a three-state color model describing the manifold of
+nematic states.
+"
+28008,Data Reduction Pipeline for the CHARIS Integral-Field Spectrograph I: Detector Readout Calibration and Data Cube Extraction,"  We present the data reduction pipeline for CHARIS, a high-contrast
+integral-field spectrograph for the Subaru Telescope. The pipeline constructs a
+ramp from the raw reads using the measured nonlinear pixel response, and
+reconstructs the data cube using one of three extraction algorithms: aperture
+photometry, optimal extraction, or $\chi^2$ fitting. We measure and apply both
+a detector flatfield and a lenslet flatfield and reconstruct the wavelength-
+and position-dependent lenslet point-spread function (PSF) from images taken
+with a tunable laser. We use these measured PSFs to implement a $\chi^2$-based
+extraction of the data cube, with typical residuals of ~5% due to imperfect
+models of the undersampled lenslet PSFs. The full two-dimensional residual of
+the $\chi^2$ extraction allows us to model and remove correlated read noise,
+dramatically improving CHARIS' performance. The $\chi^2$ extraction produces a
+data cube that has been deconvolved with the line-spread function, and never
+performs any interpolations of either the data or the individual lenslet
+spectra. The extracted data cube also includes uncertainties for each spatial
+and spectral measurement. CHARIS' software is parallelized, written in Python
+and Cython, and freely available on github with a separate documentation page.
+Astrometric and spectrophotometric calibrations of the data cubes and PSF
+subtraction will be treated in a forthcoming paper.
+"
+28009,Eigenfunctions of Composition Operators on Bloch-type Spaces,"  Suppose $\varphi$ is a holomorphic self map of the unit disk and $C_\varphi$
+is a composition operator with symbol $\varphi$ that fixes the origin and
+$0<|\varphi'(0)|<1$. This work explores sufficient conditions that ensure all
+holomorphic solutions of Schröder equation for the composition operator
+$C_\varphi$ belong to a Bloch-type space $\mathcal{B}_\alpha$ for some
+$\alpha>0$. The results from composition operators have been extended to
+weighted composition operators in the second part of this work.
+"
+28010,Stochastic Training of Graph Convolutional Networks with Variance Reduction,"  Graph convolutional networks (GCNs) are powerful deep neural networks for
+graph-structured data. However, GCN computes the representation of a node
+recursively from its neighbors, making the receptive field size grow
+exponentially with the number of layers. Previous attempts on reducing the
+receptive field size by subsampling neighbors do not have a convergence
+guarantee, and their receptive field size per node is still in the order of
+hundreds. In this paper, we develop control variate based algorithms which
+allow sampling an arbitrarily small neighbor size. Furthermore, we prove new
+theoretical guarantee for our algorithms to converge to a local optimum of GCN.
+Empirical results show that our algorithms enjoy a similar convergence with the
+exact algorithm using only two neighbors per node. The runtime of our
+algorithms on a large Reddit dataset is only one seventh of previous neighbor
+sampling algorithms.
+"
+28011,Sheaves and Duality in the Two-Vertex Graph Riemann-Roch Theorem,"  For each graph on two vertices, and each divisor on the graph in the sense of
+Baker-Norine, we describe a sheaf of vector spaces on a finite category whose
+zeroth Betti number is the Baker-Norine ""Graph Riemann-Roch"" rank of the
+divisor plus one. We prove duality theorems that generalize the Baker-Norine
+""Graph Riemann-Roch"" Theorem.
+"
+28012,Optical Selection Rules in Spin-Orbit Coupled Systems on Honeycomb Lattice,"  We investigate the optical properties in a minimal model of spin-orbit
+coupled systems on honeycomb lattice at half-filling. The absorption of the
+circularly polarized light in the charge and antiferromagnetic ordered states
+is particularly studied, and the spin-valley selective excitation depending on
+the handedness of the light is found. It is shown that the optical selection
+rules plainly differ with the type of broken symmetries and topological
+properties of the electronic states. Consequently, the abrupt change of
+spin-valley selectivity in the optical absorption occurs at the topological
+transition caused by changing the magnitude of the order parameters.
+"
+28013,Learning from lions: inferring the utility of agents from their trajectories,"  We build a model using Gaussian processes to infer a spatio-temporal vector
+field from observed agent trajectories. Significant landmarks or influence
+points in agent surroundings are jointly derived through vector calculus
+operations that indicate presence of sources and sinks. We evaluate these
+influence points by using the Kullback-Leibler divergence between the posterior
+and prior Laplacian of the inferred spatio-temporal vector field. Through
+locating significant features that influence trajectories, our model aims to
+give greater insight into underlying causal utility functions that determine
+agent decision-making. A key feature of our model is that it infers a joint
+Gaussian process over the observed trajectories, the time-varying vector field
+of utility and canonical vector calculus operators. We apply our model to both
+synthetic data and lion GPS data collected at the Bubye Valley Conservancy in
+southern Zimbabwe.
+"
+28014,Count-Based Exploration with Neural Density Models,"  Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from
+a density model, to generalize count-based exploration to non-tabular
+reinforcement learning. This pseudo-count was used to generate an exploration
+bonus for a DQN agent and combined with a mixed Monte Carlo update was
+sufficient to achieve state of the art on the Atari 2600 game Montezuma's
+Revenge. We consider two questions left open by their work: First, how
+important is the quality of the density model for exploration? Second, what
+role does the Monte Carlo update play in exploration? We answer the first
+question by demonstrating the use of PixelCNN, an advanced neural density model
+for images, to supply a pseudo-count. In particular, we examine the intrinsic
+difficulties in adapting Bellemare et al.'s approach when assumptions about the
+model are violated. The result is a more practical and general algorithm
+requiring no special apparatus. We combine PixelCNN pseudo-counts with
+different agent architectures to dramatically improve the state of the art on
+several hard Atari games. One surprising finding is that the mixed Monte Carlo
+update is a powerful facilitator of exploration in the sparsest of settings,
+including Montezuma's Revenge.
+"
+28015,Constant Time Graph Neural Networks,"  Recent advancements in graph neural networks (GNN) have led to
+state-of-the-art performance in various applications including
+chemo-informatics, question answering systems, and recommendation systems, to
+name a few. However, making these methods scalable to huge graphs such as
+web-mining remains a challenge. In particular, the existing methods for
+accelerating GNN are either not theoretically guaranteed in terms of
+approximation error or require at least linear time computation cost. In this
+paper, we propose a constant time approximation algorithm for the inference and
+training of GNN that theoretically guarantees arbitrary precision with
+arbitrary probability. The key advantage of the proposed algorithm is that the
+complexity is completely independent of the number of nodes, edges, and
+neighbors of the input. To the best of our knowledge, this is the first
+constant time approximation algorithm for GNN with theoretical guarantee.
+Through experiments using synthetic and real-world datasets, we evaluate our
+proposed approximation algorithm and show that the algorithm can successfully
+approximate GNN in constant time.
+"
+28016,Remarks on BEC on graphs,"  We consider Bose--Einstein condensation (BEC) on graphs with transient
+adjacency matrix and obtain a quasi-free state exhibiting BEC is non-factor and
+decompose into generalized coherent states. We review necessary and sufficient
+conditions that a generalized coherent state is faithful, factor, and pure and
+generalized coherent states are quasi-equivalent as well.
+"
+28017,Cooper pairs without 'glue' in high-$T_c$ superconductors,"  We address the origin of the Cooper pairs in high-$T_c$ cuprates and the
+unique nature of the superconducting (SC) condensate. Itinerant holes in an
+antiferromagnetic background form pairs spontaneously, without any `glue',
+defining a new quantum object the `pairon'. In the incoherent pseudogap phase,
+above $T_c$ or within the vortex core, the pairon binding energies are
+distributed statistically, forming a `Cooper-pair glass'. Contrary to
+conventional SC, it is the mutual pair-pair interaction that is responsable for
+the condensation. We give a natural explanation for the {\it ergodic rigidity}
+of the excitation gap, being uniquely determined by the carrier concentration
+$p$ and $J$. The phase diagram can be understood, without spin fluctuations, in
+terms of a single energy scale $\sim J$, the exchange energy at the
+metal-insulator transition.
+"
+28018,A $(1.4 + ε)$-approximation algorithm for the $2$-Max-Duo problem,"  The maximum duo-preservation string mapping (Max-Duo) problem is the
+complement of the well studied minimum common string partition (MCSP) problem,
+both of which have applications in many fields including text compression and
+bioinformatics. $k$-Max-Duo is the restricted version of Max-Duo, where every
+letter of the alphabet occurs at most $k$ times in each of the strings, which
+is readily reduced into the well known maximum independent set (MIS) problem on
+a graph of maximum degree $\Delta \le 6(k-1)$. In particular, $2$-Max-Duo can
+then be approximated arbitrarily close to $1.8$ using the state-of-the-art
+approximation algorithm for the MIS problem. $2$-Max-Duo was proved APX-hard
+and very recently a $(1.6 + \epsilon)$-approximation was claimed, for any
+$\epsilon > 0$. In this paper, we present a vertex-degree reduction technique,
+based on which, we show that $2$-Max-Duo can be approximated arbitrarily close
+to $1.4$.
+"
+28019,Generic second-order macroscopic traffic node model for general multi-input multi-output road junctions via a dynamic system approach,"  This paper addresses an open problem in traffic modeling: the second-order
+macroscopic node problem. A second-order macroscopic traffic model, in contrast
+to a first-order model, allows for variation of driving behavior across
+subpopulations of vehicles in the flow. The second-order models are thus more
+descriptive (e.g., they have been used to model variable mixtures of
+behaviorally-different traffic, like car/truck traffic, autonomous/human-driven
+traffic, etc.), but are much more complex. The second-order node problem is a
+particularly complex problem, as it requires the resolution of discontinuities
+in traffic density and mixture characteristics, and solving of throughflows for
+arbitrary numbers of input and output roads to a node (in other words, this is
+an arbitrary-dimensional Riemann problem with two conserved quantities). We
+propose a solution to this problem by making use of a recently-introduced
+dynamic system characterization of the first-order node model problem, which
+gives insight and intuition as to the continuous-time dynamics implicit in
+first-order node models. We use this intuition to extend the dynamic system
+node model to the second-order setting. We also extend the well-known ""Generic
+Class of Node Model"" constraints to the second order and present a simple
+solution algorithm to the second-order node problem. This node model has
+immediate applications in allowing modeling of behaviorally-complex traffic
+flows of contemporary interest (like partially-autonomous-vehicle flows) in
+arbitrary road networks.
+"
+28020,Choreographies meet Communication Failures,"  Choreographies are global descriptions of communication structures, inspired
+by the ""Alice and Bob"" notation of security protocols. They have been
+successfully employed in the design and implementation of distributed systems.
+However, there is still limited evidence of the applicability of choreographies
+in the real-world setting of distributed programming, where communication
+actions may fail. In this work, we propose the first choreography model that
+allows for communication failures and the programming of user-defined code to
+deal with such failures. We validate our model by implementing common
+strategies for handling communication failures in a robust way, which in turn
+can be used as a library by choreographies that assume reliable communication.
+We equip our model with a typing discipline that can statically verify
+reliability properties, in particular at-most-once and exactly-once delivery.
+We demonstrate the applicability of our model by defining a
+semantics-preserving compilation procedure towards a process calculus equipped
+with unreliable I/O actions.
+"
+28021,Null-finite sets in metric groups and their applications,"  In the paper we introduce a new family of ""small"" sets which is tightly
+connected with two well known $\sigma$-ideals: of Haar-null sets and of
+Haar-meager sets. We define a subset $A$ of a topological group $X$ to be
+$\mathit{null}$-$\mathit{finite}$ if there exists an infinite compact subset
+$K\subset X$ such that for every $x\in X$ the intersection $K\cap (x+A)$ is
+finite. We prove that each null-finite Borel set in a complete metric Abelian
+group is Haar-null and Haar-meager. The Borel restriction in the above result
+is essential as each non-discrete metric Abelian group is the union of two
+null-finite sets. Applying null-finite sets to the theory of functional
+equations and inequalities, we prove that a mid-point convex function
+$f:G\to\mathbb R$ defined on an open convex subset $G$ of a metric linear space
+$X$ is continuous if it is upper bounded on a subset $B$ which is not
+null-finite and whose closure is contained in $G$. This gives an alternative
+short proof of a known generalization of Bernstein-Doetsch theorem (saying that
+a mid-point convex function $f:G\to\mathbb R$ defined on an open covex subset
+$G$ of a metric linear space $X$ is continuous if it is upper bounded on a
+non-empty open subset $B$ of $G$). Since Borel null-finite sets are Haar-meager
+and Haar-null, we conclude that a mid-point convex function $f:G\to\mathbb{R}$
+defined on an open convex subset $G$ of a complete linear metric space $X$ is
+continuous if it is upper bounded on a Borel subset $B\subset G$ which is not
+Haar-null or not Haar-meager in $X$. The last result resolves an old problem in
+the theory of functional equations and inequalities posed by Baron and Ger in
+1983.
+"
+28022,Remote optimization of an ultra-cold atoms experiment by experts and citizen scientists,"  We introduce a novel remote interface to control and optimize the
+experimental production of Bose-Einstein condensates (BECs) and find improved
+solutions using two distinct implementations. First, a team of theoreticians
+employed a Remote version of their dCRAB optimization algorithm (RedCRAB), and
+second a gamified interface allowed 600 citizen scientists from around the
+world to participate in real-time optimization. Quantitative studies of player
+search behavior demonstrated that they collectively engage in a combination of
+local and global search. This form of adaptive search prevents premature
+convergence by the explorative behavior of low-performing players while
+high-performing players locally refine their solutions. In addition, many
+successful citizen science games have relied on a problem representation that
+directly engaged the visual or experiential intuition of the players. Here we
+demonstrate that citizen scientists can also be successful in an entirely
+abstract problem visualization. This gives encouragement that a much wider
+range of challenges could potentially be open to gamification in the future.
+"
+28023,"P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher's meta-analysis method in omics applications","  Meta-analysis methods have been widely used to combine results from multiple
+clinical or genomic studies to increase statistical power and ensure robust and
+accurate conclusion. Adaptively weighted Fisher's method (AW-Fisher) is an
+effective approach to combine p-values from $K$ independent studies and to
+provide better biological interpretation by characterizing which studies
+contribute to meta-analysis. Currently, AW-Fisher suffers from lack of fast,
+accurate p-value computation and variability estimate of AW weights. When the
+number of studies $K$ is large, the $3^K - 1$ possible differential expression
+pattern categories can become intractable. In this paper, we apply an
+importance sampling technique with spline interpolation to increase accuracy
+and speed of p-value calculation. Using resampling techniques, we propose a
+variability index for the AW weight estimator and a co-membership matrix to
+characterize pattern similarities between genes. The co-membership matrix is
+further used to categorize differentially expressed genes based on their
+meta-patterns for further biological investigation. The superior performance of
+the proposed methods is shown in simulations. These methods are also applied to
+two real applications to demonstrate intriguing biological findings.
+"
+28024,Practical Attacks Against Graph-based Clustering,"  Graph modeling allows numerous security problems to be tackled in a general
+way, however, little work has been done to understand their ability to
+withstand adversarial attacks. We design and evaluate two novel graph attacks
+against a state-of-the-art network-level, graph-based detection system. Our
+work highlights areas in adversarial machine learning that have not yet been
+addressed, specifically: graph-based clustering techniques, and a global
+feature space where realistic attackers without perfect knowledge must be
+accounted for (by the defenders) in order to be practical. Even though less
+informed attackers can evade graph clustering with low cost, we show that some
+practical defenses are possible.
+"
+28025,Solving nonlinear diffusive problems in buildings by means of a Spectral Reduced-Order Model,"  This paper proposes the use of a Spectral method to simulate diffusive
+moisture transfer through porous materials as a Reduced-Order Model (ROM). The
+Spectral approach is an a priori method assuming a separated representation of
+the solution. The method is compared with both classical Euler implicit and
+Crank-Nicolson schemes, considered as large original models. Their performance
+- in terms of accuracy, complexity reduction and CPU time reduction - are
+discussed for linear and nonlinear cases of moisture diffusive transfer through
+single and multi-layered one-dimensional domains, considering highly
+moisture-dependent properties. Results show that the Spectral reduced-order
+model approach enables to simulate accurately the field of interest.
+Furthermore, numerical gains become particularly interesting for nonlinear
+cases since the proposed method can drastically reduce the computer run time,
+by a factor of 100, when compared to the traditional Crank-Nicolson scheme for
+one-dimensional applications.
+"
+28026,Multi-Objective Convolutional Neural Networks for Robot Localisation and 3D Position Estimation in 2D Camera Images,"  The field of collaborative robotics and human-robot interaction often focuses
+on the prediction of human behaviour, while assuming the information about the
+robot setup and configuration being known. This is often the case with fixed
+setups, which have all the sensors fixed and calibrated in relation to the rest
+of the system. However, it becomes a limiting factor when the system needs to
+be reconfigured or moved. We present a deep learning approach, which aims to
+solve this issue. Our method learns to identify and precisely localise the
+robot in 2D camera images, so having a fixed setup is no longer a requirement
+and a camera can be moved. In addition, our approach identifies the robot type
+and estimates the 3D position of the robot base in the camera image as well as
+3D positions of each of the robot joints. Learning is done by using a
+multi-objective convolutional neural network with four previously mentioned
+objectives simultaneously using a combined loss function. The multi-objective
+approach makes the system more flexible and efficient by reusing some of the
+same features and diversifying for each objective in lower layers. A fully
+trained system shows promising results in providing an accurate mask of where
+the robot is located and an estimate of its base and joint positions in 3D. We
+compare the results to our previous approach of using cascaded convolutional
+neural networks.
+"
+28027,Fifty Years of Candidate Pulsar Selection - What next?,"  For fifty years astronomers have been searching for pulsar signals in
+observational data. Throughout this time the process of choosing detections
+worthy of investigation, so called candidate selection, has been effective,
+yielding thousands of pulsar discoveries. Yet in recent years technological
+advances have permitted the proliferation of pulsar-like candidates, straining
+our candidate selection capabilities, and ultimately reducing selection
+accuracy. To overcome such problems, we now apply intelligent machine learning
+tools. Whilst these have achieved success, candidate volumes continue to
+increase, and our methods have to evolve to keep pace with the change. This
+talk considers how to meet this challenge as a community.
+"
+28028,Rich Semantic Models and Knowledgebases for Highly-Structured Scientific Communication,"  Rather than using text for scientific research reports, we have proposed
+developing highly-structured reports with rich semantic models. In this paper,
+we consider detailed structures for the components of research reports using a
+modeling framework based on a rigorous upper ontology. For instance, we
+consider the use of structured descriptions of Research Designs to support
+evaluation of internal and external validity. In addition, collections of
+highly-structured scientific research reports would be the key component of a
+set of evolving and interlocking highly-structured scientific knowledgebases.
+"
+28029,Constant Size Molecular Descriptors For Use With Machine Learning,"  A set of molecular descriptors whose length is independent of molecular size
+is developed for machine learning models that target thermodynamic and
+electronic properties of molecules. These features are evaluated by monitoring
+performance of kernel ridge regression models on well-studied data sets of
+small organic molecules. The features include connectivity counts, which
+require only the bonding pattern of the molecule, and encoded distances, which
+summarize distances between both bonded and non-bonded atoms and so require the
+full molecular geometry. In addition to having constant size, these features
+summarize information regarding the local environment of atoms and bonds, such
+that models can take advantage of similarities resulting from the presence of
+similar chemical fragments across molecules. Combining these two types of
+features leads to models whose performance is comparable to or better than the
+current state of the art. The features introduced here have the advantage of
+leading to models that may be trained on smaller molecules and then used
+successfully on larger molecules.
+"
+28030,Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging,"  Brain extraction or whole brain segmentation is an important first step in
+many of the neuroimage analysis pipelines. The accuracy and robustness of brain
+extraction, therefore, is crucial for the accuracy of the entire brain analysis
+process. With the aim of designing a learning-based, geometry-independent and
+registration-free brain extraction tool in this study, we present a technique
+based on an auto-context convolutional neural network (CNN), in which intrinsic
+local and global image features are learned through 2D patches of different
+window sizes. In this architecture three parallel 2D convolutional pathways for
+three different directions (axial, coronal, and sagittal) implicitly learn 3D
+image information without the need for computationally expensive 3D
+convolutions. Posterior probability maps generated by the network are used
+iteratively as context information along with the original image patches to
+learn the local shape and connectedness of the brain, to extract it from
+non-brain tissue.
+The brain extraction results we have obtained from our algorithm are superior
+to the recently reported results in the literature on two publicly available
+benchmark datasets, namely LPBA40 and OASIS, in which we obtained Dice overlap
+coefficients of 97.42% and 95.40%, respectively. Furthermore, we evaluated the
+performance of our algorithm in the challenging problem of extracting
+arbitrarily-oriented fetal brains in reconstructed fetal brain magnetic
+resonance imaging (MRI) datasets. In this application our algorithm performed
+much better than the other methods (Dice coefficient: 95.98%), where the other
+methods performed poorly due to the non-standard orientation and geometry of
+the fetal brain in MRI. Our CNN-based method can provide accurate,
+geometry-independent brain extraction in challenging applications.
+"
+28031,Regression Adjustments for Estimating the Global Treatment Effect in Experiments with Interference,"  Standard estimators of the global average treatment effect can be biased in
+the presence of interference. This paper proposes regression adjustment
+estimators for removing bias due to interference in Bernoulli randomized
+experiments. We use a fitted model to predict the counterfactual outcomes of
+global control and global treatment. Our work differs from standard regression
+adjustments in that the adjustment variables are constructed from functions of
+the treatment assignment vector, and that we allow the researcher to use a
+collection of any functions correlated with the response, turning the problem
+of detecting interference into a feature engineering problem. We characterize
+the distribution of the proposed estimator in a linear model setting and
+connect the results to the standard theory of regression adjustments under
+SUTVA. We then propose an estimator that allows for flexible machine learning
+estimators to be used for fitting a nonlinear interference functional form,
+borrowing ideas from the double machine learning literature. We propose
+conducting statistical inference via bootstrap and resampling methods, which
+allow us to sidestep the complicated dependences implied by interference and
+instead rely on empirical covariance structures. Such variance estimation
+relies on an exogeneity assumption akin to the standard unconfoundedness
+assumption invoked in observational studies. In simulation experiments, our
+methods are better at debiasing estimates than existing inverse propensity
+weighted estimators based on neighborhood exposure modeling. We use our method
+to reanalyze an experiment concerning weather insurance adoption conducted on a
+collection of villages in rural China.
+"
+28032,Morse functions to graphs and topological complexity for hyperbolic 3-manifolds,"  Scharlemann and Thompson define the width of a 3-manifold M as a notion of
+complexity based on the topology of M. Their original definition had the
+property that the adjacency relation on handles gave a linear order on handles,
+but here we consider a more general definition due to Saito, Scharlemann and
+Schultens, in which the adjacency relation on handles may give an arbitrary
+graph. We show that for compact hyperbolic 3-manifolds, this is linearly
+related to a notion of metric complexity, based on the areas of level sets of
+Morse functions to graphs, which we call Gromov area.
+"
+28033,Credit Default Mining Using Combined Machine Learning and Heuristic Approach,"  Predicting potential credit default accounts in advance is challenging.
+Traditional statistical techniques typically cannot handle large amounts of
+data and the dynamic nature of fraud and humans. To tackle this problem, recent
+research has focused on artificial and computational intelligence based
+approaches. In this work, we present and validate a heuristic approach to mine
+potential default accounts in advance where a risk probability is precomputed
+from all previous data and the risk probability for recent transactions are
+computed as soon they happen. Beside our heuristic approach, we also apply a
+recently proposed machine learning approach that has not been applied
+previously on our targeted dataset [15]. As a result, we find that these
+applied approaches outperform existing state-of-the-art approaches.
+"
+28034,Quantum oscillations in metallic Sb2Te2Se topological insulator,"  We have studied the magnetotransport properties of the metallic, p-type
+Sb2Te2Se which is a topological insulator. Magnetoresistance shows Shubnikov de
+Haas oscillations in fields above B=15 T. The maxima/minima positions of
+oscillations measured at different tilt angles with respect to the B direction
+align with the normal component of field Bcosine, implying the existence of a
+2D Fermi surface in Sb2Te2Se. The value of the Berry phase determined from a
+Landau level fan diagram is very close to 0.5, further suggesting that the
+oscillations result from topological surface states. From Lifshitz-Kosevich
+analyses, the position of the Fermi level is found to be EF =250 meV, above the
+Dirac point. This value of EF is almost 3 times as large as that in our
+previous study on the Bi2Se2:1Te0:9 topological insulator; however, it still
+touches the tip of the bulk valence band. This explains the metallic behavior
+and hole-like bulk charge carriers in the Sb2Te2Se compound.
+"
+28035,Learning The Sequential Temporal Information with Recurrent Neural Networks,"  Recurrent Networks are one of the most powerful and promising artificial
+neural network algorithms to processing the sequential data such as natural
+languages, sound, time series data. Unlike traditional feed-forward network,
+Recurrent Network has a inherent feed back loop that allows to store the
+temporal context information and pass the state of information to the entire
+sequences of the events. This helps to achieve the state of art performance in
+many important tasks such as language modeling, stock market prediction, image
+captioning, speech recognition, machine translation and object tracking etc.,
+However, training the fully connected RNN and managing the gradient flow are
+the complicated process. Many studies are carried out to address the mentioned
+limitation. This article is intent to provide the brief details about recurrent
+neurons, its variances and trips & tricks to train the fully recurrent neural
+network. This review work is carried out as a part of our IPO studio software
+module 'Multiple Object Tracking'.
+"
+28036,Fodis: software for protein unfolding analysis,"  The folding dynamics of proteins at the single molecule level has been
+studied with single-molecule force spectroscopy (SMFS) experiments for twenty
+years, but a common standardized method for the analysis of the collected data
+and for the sharing among the scientific community members is still not
+available. We have developed a new open source tool, Fodis, for the analysis of
+the Force-distance curves obtained in SMFS experiments, providing an almost
+automatic processing, analysis and classification of the obtained data. Our
+method provides also a classification of the possible unfolding pathways and
+structural heterogeneity, present during the unfolding of proteins.
+"
+28037,On Distance Preserving and Sequentially Distance Preserving Graphs,"  A graph $H$ is an \emph{isometric} subgraph of $G$ if $d_H(u,v)= d_G(u,v)$,
+for every pair~$u,v\in V(H)$. A graph is \emph{distance preserving} if it has
+an isometric subgraph of every possible order. A graph is \emph{sequentially
+distance preserving} if its vertices can be ordered such that deleting the
+first $i$ vertices results in an isometric subgraph, for all $i\ge1$. We give
+an equivalent condition to sequentially distance preserving based upon
+simplicial orderings. Using this condition, we prove that if a graph does not
+contain any induced cycles of length~$5$ or greater, then it is sequentially
+distance preserving and thus distance preserving. Next we consider the distance
+preserving property on graphs with a cut vertex. Finally, we define a family of
+non-distance preserving graphs constructed from cycles.
+"
+28038,A survey on Morse boundaries & stability,"  In this paper we survey many of the known results about Morse boundaries and
+stability.
+"
+28039,A growth-fragmentation model related to Ornstein-Uhlenbeck type processes,"  Growth-fragmentation processes describe the evolution of particles that grow
+and divide randomly as time proceeds. Unlike previous studies, which have
+focused mainly on the self-similar case, we introduce a new type of
+growth-fragmentation which is closely related to Lévy driven
+Ornstein-Uhlenbeck type processes. Our model can be viewed as a generalization
+of compensated fragmentation processes introduced by Bertoin (Ann. Prob. 2015),
+or the stochastic counterpart of a family of growth-fragmentation equations. We
+establish a convergence criterion for a sequence of such growth-fragmentations.
+We also prove that, under certain conditions, the average size of the particles
+converges to a stationary distribution as time tends to infinity.
+"
+28040,A New Model Variance Estimator for an Area Level Small Area Model to Solve Multiple Problems Simultaneously,"  The two-level normal hierarchical model (NHM) has played a critical role in
+the theory of small area estimation (SAE), one of the growing areas in
+statistics with numerous applications in different disciplines. In this paper,
+we address major well-known shortcomings associated with the empirical best
+linear unbiased prediction (EBLUP) of a small area mean and its mean squared
+error (MSE) estimation by considering an appropriate model variance estimator
+that satisfies multiple properties. The proposed model variance estimator
+simultaneously (i) improves on the estimation of the related shrinkage factors,
+(ii) protects EBLUP from the common overshrinkage problem, (iii) avoids complex
+bias correction in generating strictly positive second-order unbiased mean
+square error (MSE) estimator either by the Taylor series or single parametric
+bootstrap method. The idea of achieving multiple desirable properties in an
+EBLUP method through a suitably devised model variance estimator is the first
+of its kind and holds promise in providing good inferences for small area means
+under the classical linear mixed model prediction framework. The proposed
+methodology is also evaluated using a Monte Carlo simulation study and real
+data analysis.
+"
+28041,Generalized Knudsen Number for Unsteady Fluid Flow,"  We explore the scaling behavior of an unsteady flow that is generated by an
+oscillating body of finite size in a gas. If the gas is gradually rarefied, the
+Navier-Stokes equations begin to fail and a kinetic description of the flow
+becomes more appropriate. The failure of the Navier-Stokes equations can be
+thought to take place via two different physical mechanisms: either the
+continuum hypothesis breaks down as a result of a finite size effect; or local
+equilibrium is violated due to the high rate of strain. By independently tuning
+the relevant linear dimension and the frequency of the oscillating body, we can
+experimentally observe these two different physical mechanisms. All the
+experimental data, however, can be collapsed using a single dimensionless
+scaling parameter that combines the relevant linear dimension and the frequency
+of the body. This proposed Knudsen number for an unsteady flow is rooted in a
+fundamental symmetry principle, namely Galilean invariance.
+"
+28042,"On Principal Components Regression, Random Projections, and Column Subsampling","  Principal Components Regression (PCR) is a traditional tool for dimension
+reduction in linear regression that has been both criticized and defended. One
+concern about PCR is that obtaining the leading principal components tends to
+be computationally demanding for large data sets. While random projections do
+not possess the optimality properties of the leading principal subspace, they
+are computationally appealing and hence have become increasingly popular in
+recent years. In this paper, we present an analysis showing that for random
+projections satisfying a Johnson-Lindenstrauss embedding property, the
+prediction error in subsequent regression is close to that of PCR, at the
+expense of requiring a slightly large number of random projections than
+principal components. Column sub-sampling constitutes an even cheaper way of
+randomized dimension reduction outside the class of Johnson-Lindenstrauss
+transforms. We provide numerical results based on synthetic and real data as
+well as basic theory revealing differences and commonalities in terms of
+statistical performance.
+"
+28043,Signatures of monic polynomials,"  To a univariate monic polynomial is attached a special planar forest that is
+called the picture of the polynomial. Isotopy classes of pictures are called
+signatures. All combinatorially possible signatures are realized and spaces of
+polynomials realizing a given signature are contractible. A finite cell complex
+for the cohomology of the braid groups is obtained.
+"
+28044,A new simple and powerful normality test for progressively Type-II censored data,"  In this paper, a new goodness-of-fit test for a location-scale family based
+on progressively Type-II censored order statistics is proposed. Using Monte
+Carlo simulation studies, the present researchers have observed that the
+proposed test for normality is consistent and quite powerful in comparison with
+existing goodness-of-fit tests based on progressively Type-II censored data.
+Also, the new test statistic for a real data set is used and the results show
+that our new test statistic performs well.
+"
+28045,Admissibility in Concurrent Games,"  In this paper, we study the notion of admissibility for randomised strategies
+in concurrent games. Intuitively, an admissible strategy is one where the
+player plays `as well as possible', because there is no other strategy that
+dominates it, i.e., that wins (almost surely) against a super set of
+adversarial strategies. We prove that admissible strategies always exist in
+concurrent games, and we characterise them precisely. Then, when the objectives
+of the players are omega-regular, we show how to perform assume-admissible
+synthesis, i.e., how to compute admissible strategies that win (almost surely)
+under the hypothesis that the other players play admissible
+"
+28046,Preliminary Study on Bit-String Modelling of Opinion Formation in Complex Networks,"  Opinion formation has been gaining increasing research interests recently,
+and various models have been proposed. These models, however, have their
+limitations, among which noticeably include (i) it is generally assumed that
+adjacent nodes holding similar opinions will further reduce their difference in
+between, while adjacent nodes holding significantly different opinions would
+either do nothing, or cut the link in between them; (ii) opinion mutation,
+which describes ""opinion changes not due to neighborhood influences"" in real
+life, is typically random. While such models enjoy their simplicity and
+nevertheless help reveal lots of useful insights, they lack the capability of
+describing many complex behaviors which we may easily observe in real life. In
+this paper, we propose a new bit-string modeling approach. Preliminary study on
+the new model demonstrates its great potentials in revealing complex behaviors
+of social opinion evolution and formation.
+"
+28047,Tyler shape depth,"  In many problems from multivariate analysis, the parameter of interest is a
+shape matrix, that is, a normalized version of the corresponding scatter or
+dispersion matrix. In this paper, we propose a depth concept for shape matrices
+that involves data points only through their directions from the center of the
+distribution. We use the terminology Tyler shape depth since the resulting
+estimator of shape, namely the deepest shape matrix, is the median-based
+counterpart of the M-estimator of shape of Tyler (1987). Beyond estimation,
+shape depth, like its Tyler antecedent, also allows hypothesis testing on
+shape. Its main benefit, however, lies in the ranking of shape matrices it
+provides, whose practical relevance is illustrated in principal component
+analysis and in shape-based outlier detection. We study the invariance,
+quasi-concavity and continuity properties of Tyler shape depth, the topological
+and boundedness properties of the corresponding depth regions, existence of a
+deepest shape matrix and prove Fisher consistency in the elliptical case.
+Finally, we derive a Glivenko-Cantelli-type result and establish almost sure
+consistency of the deepest shape matrix estimator.
+"
+28048,Hyperparameter Optimization: A Spectral Approach,"  We give a simple, fast algorithm for hyperparameter optimization inspired by
+techniques from the analysis of Boolean functions. We focus on the
+high-dimensional regime where the canonical example is training a neural
+network with a large number of hyperparameters. The algorithm --- an iterative
+application of compressed sensing techniques for orthogonal polynomials ---
+requires only uniform sampling of the hyperparameters and is thus easily
+parallelizable.
+Experiments for training deep neural networks on Cifar-10 show that compared
+to state-of-the-art tools (e.g., Hyperband and Spearmint), our algorithm finds
+significantly improved solutions, in some cases better than what is attainable
+by hand-tuning. In terms of overall running time (i.e., time required to sample
+various settings of hyperparameters plus additional computation time), we are
+at least an order of magnitude faster than Hyperband and Bayesian Optimization.
+We also outperform Random Search 8x.
+Additionally, our method comes with provable guarantees and yields the first
+improvements on the sample complexity of learning decision trees in over two
+decades. In particular, we obtain the first quasi-polynomial time algorithm for
+learning noisy decision trees with polynomial sample complexity.
+"
+28049,Kriging-Based Robotic Exploration for Soil Moisture Mapping Using a Cosmic-Ray Sensor,"  Soil moisture monitoring is a fundamental process to enhance agricultural
+outcomes and to protect the environment. The traditional methods for measuring
+moisture content in soil are laborious and expensive, and therefore there is a
+growing interest in developing sensors and technologies which can reduce the
+effort and costs. In this work, we propose to use an autonomous mobile robot
+equipped with a state-of-the-art non-contact soil moisture sensor that builds
+moisture maps on the fly and automatically selects the most optimal sampling
+locations. The robot is guided by an autonomous exploration strategy driven by
+the quality of the soil moisture model which indicates areas of the field where
+the information is less precise. The sensor model follows the Poisson
+distribution and we demonstrate how to integrate such measurements into the
+kriging framework. We also investigate a range of different exploration
+strategies and assess their usefulness through a set of evaluation experiments
+based on real soil moisture data collected from two different fields. We
+demonstrate the benefits of using the adaptive measurement interval and
+adaptive sampling strategies for building better quality soil moisture models.
+The presented method is general and can be applied to other scenarios where the
+measured phenomena directly affects the acquisition time and needs to be
+spatially mapped.
+"
+28050,Private Social Network Data Sharing,"  The increasing popularity of online social network brings huge privacy threat
+for the end users. While existing work focus on inferring sensitive attributes
+from the social network such as age, location and gender, little has been done
+on how to protect the users' privacy by preventing the malicious inference. In
+this paper we investigated the privacy vulnerability of the existing social
+network and designed a privacy-preserving framework. We evaluated the
+framework's privacy and usefulness guarantees, demonstrated its effectiveness
+on classification and the defense against the privacy attack.
+"
+28051,Sums of Palindromes: an Approach via Automata,"  Recently, Cilleruelo, Luca, & Baxter proved, for all bases b >= 5, that every
+natural number is the sum of at most 3 natural numbers whose base-b
+representation is a palindrome. However, the cases b = 2, 3, 4 were left
+unresolved.
+We prove, using a decision procedure based on automata, that every natural
+number is the sum of at most 4 natural numbers whose base-2 representation is a
+palindrome. Here the constant 4 is optimal. We obtain similar results for bases
+3 and 4, thus completely resolving the problem.
+We consider some other variations on this problem, and prove similar results.
+We argue that heavily case-based proofs are a good signal that a decision
+procedure may help to automate the proof.
+"
+28052,Homotopy Type Theory in Lean,"  We discuss the homotopy type theory library in the Lean proof assistant. The
+library is especially geared toward synthetic homotopy theory. Of particular
+interest is the use of just a few primitive notions of higher inductive types,
+namely quotients and truncations, and the use of cubical methods.
+"
+28053,Sparse principal component analysis via random projections,"  We introduce a new method for sparse principal component analysis, based on
+the aggregation of eigenvector information from carefully-selected random
+projections of the sample covariance matrix. Unlike most alternative
+approaches, our algorithm is non-iterative, so is not vulnerable to a bad
+choice of initialisation. Our theory provides great detail on the statistical
+and computational trade-off in our procedure, revealing a subtle interplay
+between the effective sample size and the number of random projections that are
+required to achieve the minimax optimal rate. Numerical studies provide further
+insight into the procedure and confirm its highly competitive finite-sample
+performance.
+"
+28054,Efron's monotonicity property for measures on $\mathbb{R}^2$,"  First we prove some kernel representations for the covariance of two
+functions taken on the same random variable and deduce kernel representations
+for some functionals of a continuous one-dimensional measure. Then we apply
+these formulas to extend Efron's monotonicity property, given in Efron [1965]
+and valid for independent log-concave measures, to the case of general measures
+on $\mathbb{R}^2$. The new formulas are also used to derive some further
+quantitative estimates in Efron's monotonicity property.
+"
+28055,Physical Principles of Retroviral Integration in the Human Genome,"  Certain retroviruses, including HIV, insert their DNA in a non-random
+fraction of the host genome via poorly understood selection mechanisms. Here,
+we develop a biophysical model for retroviral integrations as stochastic and
+quasi-equilibrium topological reconnections between polymers. We discover that
+physical effects, such as DNA accessibility and elasticity, play important and
+universal roles in this process. Our simulations predict that integration is
+favoured within nucleosomal and flexible DNA, in line with experiments, and
+that these biases arise due to competing energy barriers associated with DNA
+deformations. By considering a long chromosomal region in human T-cells during
+interphase, we discover that at these larger scales integration sites are
+predominantly determined by chromatin accessibility. Finally, we propose and
+solve a reaction-diffusion problem that recapitulates the distribution of HIV
+hot-spots within T-cells. With few generic assumptions, our model can
+rationalise experimental observations and identifies previously unappreciated
+physical contributions to retroviral integration site selection.
+"
+28056,Homological stability of topological moduli spaces,"  Given a graded $E_1$-module over an $E_2$-algebra in spaces, we construct an
+augmented semi-simplicial space up to higher coherent homotopy over it, called
+its canonical resolution, whose graded connectivity yields homological
+stability for the graded pieces of the module with respect to constant and
+abelian coefficients. We furthermore introduce a notion of coefficient systems
+of finite degree in this context and show that, without further assumptions,
+the corresponding twisted homology groups stabilize as well. This generalizes a
+framework of Randal-Williams and Wahl for families of discrete groups.
+In many examples, the canonical resolution recovers geometric resolutions
+with known connectivity bounds. As a consequence, we derive new twisted
+homological stability results for e.g. moduli spaces of high-dimensional
+manifolds, unordered configuration spaces of manifolds with labels in a
+fibration, and moduli spaces of manifolds equipped with unordered embedded
+discs. This in turn implies representation stability for the ordered variants
+of the latter examples.
+"
+28057,Probing new light force-mediators by isotope shift spectroscopy,"  In this Letter we explore the potential of probing new light force-carriers,
+with spin-independent couplings to the electron and the neutron, using
+precision isotope shift spectroscopy. We develop a formalism to interpret
+linear King plots as bounds on new physics with minimal theory inputs. We focus
+only on bounding the new physics contributions that can be calculated
+independently of the Standard Model nuclear effects. We apply our method to
+existing Ca+ data and project its sensitivity to possibly existing new bosons
+using narrow transitions in other atoms and ions (specifically, Sr and Yb).
+Future measurements are expected to improve the relative precision by five
+orders of magnitude, and can potentially lead to an unprecedented sensitivity
+for bosons within the 10 keV to 10 MeV mass range.
+"
+28058,Numerical solution of time-dependent problems with fractional power elliptic operator,"  An unsteady problem is considered for a space-fractional equation in a
+bounded domain. A first-order evolutionary equation involves a fractional power
+of an elliptic operator of second order. Finite element approximation in space
+is employed. To construct approximation in time, standard two-level schemes are
+used. The approximate solution at a new time-level is obtained as a solution of
+a discrete problem with the fractional power of the elliptic operator. A
+Pade-type approximation is constructed on the basis of special quadrature
+formulas for an integral representation of the fractional power elliptic
+operator using explicit schemes. A similar approach is applied in the numerical
+implementation of implicit schemes. The results of numerical experiments are
+presented for a test two-dimensional problem.
+"
+28059,Ground state solution of fractional Schrödinger equations with a general nonlinearity,"  In this paper, we study the following fractional Schrödinger equation: \[
+\left\{\begin{gathered}
+{(- \Delta)^s}u + mu = f(u){\text{in}}{\mathbb{R}^N}, \hfill
+u \in {H^s}({\mathbb{R}^N}),{\text{}}u > 0{\text{on}}{\mathbb{R}^N}, \hfill
+\\ \end{gathered} \right. \] where $m>0$, $N>2s$, ${(- \Delta)^s}$, $s \in
+(0,1)$ is the fractional Laplacian. Using minimax arguments, we obtain a
+positive ground state solution under general conditions on $f$ which we believe
+to be almost optimal.
+"
+28060,Correlation between photoemissive and morphological properties of KBr thin film photocathodes,"  In the present work, the morphological properties of KBr photocathodes are
+correlated with their photoemissive behaviour using a combination of analyzing
+techniques including SEM, TEM and AFM. From morphological studies, it is
+observed that KBr films have granular characteristics with varied average grain
+size and grain density. Structure and orientation of individual grains have
+been investigated by selected area electron diffraction technique and found to
+be the crystalline in nature with a face centered cubic structure. It is
+evident from the AFM analysis that the root mean square roughness and maximum
+area peak height have been decreased with the deposition of more KBr layers.
+The photoemission studies reveal that the resultant photocurrent is enhanced
+with increasing film thickness and it is directly related to the surface area
+coverage and grain density.
+"
+28061,Broad absorption line disappearance and emergence using multiple-epoch spectroscopy from the Sloan Digital Sky Survey,"  We investigate broad absorption line (BAL) disappearance and emergence using
+a 470 BAL-quasar sample over < 0.10-5.25 rest-frame years with at least three
+spectroscopic epochs for each quasar from the Sloan Digital Sky Survey. We
+identify 14 disappearing BALs over < 1.73-4.62 rest-frame years and 18 emerging
+BALs over < 1.46-3.66 rest-frame years associated with the CIV 1548,1550 and/or
+SiIV 1393,1402 doublets, and report on their variability behavior. BAL quasars
+in our dataset exhibit disappearing/emerging CIV BALs at a rate of 2.3 and 3.0
+per cent, respectively, and the frequency for BAL to non-BAL quasar transitions
+is 1.7 per cent. We detect four re-emerging BALs over < 3.88 rest-frame years
+on average and three re-disappearing BALs over < 4.15 rest-frame years on
+average, the first reported cases of these types. We infer BAL lifetimes along
+the line of sight to be nominally < 100-1000 yr using disappearing CIV BALs in
+our sample. Interpretations of (re-)emerging and (re-)disappearing BALs reveal
+evidence that collectively supports both transverse-motion and
+ionization-change scenarios to explain BAL variations. We constrain a nominal
+CIV/SiIV BAL outflow location of < 100 pc from the central source and a radial
+size of > 1x10^-7 pc (0.02 au) using the ionization-change scenario, and
+constrain a nominal outflow location of < 0.5 pc and a transverse size of ~0.01
+pc using the transverse-motion scenario. Our findings are consistent with
+previous work, and provide evidence in support of BALs tracing compact flow
+geometries with small filling factors.
+"
+28062,Solvable models of an open well and a bottomless barrier in 1-D,"  We present one dimensional potentials $V(x)= V_0[e^{2|x|/a}-1]$ as solvable
+models of a well $(V_0>0)$ and a barrier ($V_0<0$). Apart from being new
+addition to solvable models, these models are instructive for finding bound and
+scattering states from the analytic solutions of Schr{ö}dinger equation. The
+exact analytic (semi-classical and quantal) forms for bound states of the well
+and reflection/transmission $(R/T)$ co-efficients for the barrier have been
+derived. Interestingly, the crossover energy $E_c$ where $R(E_c)=1/2=T(E_c)$
+may occur below/above or at the barrier-top. A connection between poles of
+these co-efficients and bound state eigenvalues of the well has also been
+demonstrated.
+"
+28063,Action Dimension of Lattices in Euclidean Buildings,"  The action dimension of a group G is the minimal dimension of a contractible
+manifold that G acts on properly discontinuously. We show that if G acts
+properly and cocompactly on a thick Euclidean building, then the action
+dimension is bounded below by twice the dimension of the building. We also
+compute the action dimension of S-arithmetic groups over number fields,
+partially answering a question of Bestvina, Kapovich, and Kleiner.
+"
+28064,Size invariance sector for an agent-based innovation diffusion model,"  It is shown that under certain conditions it is possible to model a complex
+system in a way that leads to results that do not depend on system size. As an
+example of complex system an innovation diffusion model is considered. In that
+model a set of individuals (the agents), which are interconnected, must decide
+if adopt or not an innovation. The agents are connected in a member of the
+networks family known as small worlds networks (SWN). It is found that for a
+subfamily of the SWN the saturation time and the form of the adoption curve are
+invariants respect to the change in the size of the system.
+"
+28065,Adversarial Examples that Fool both Computer Vision and Time-Limited Humans,"  Machine learning models are vulnerable to adversarial examples: small changes
+to images can cause computer vision models to make mistakes such as identifying
+a school bus as an ostrich. However, it is still an open question whether
+humans are prone to similar mistakes. Here, we address this question by
+leveraging recent techniques that transfer adversarial examples from computer
+vision models with known parameters and architecture to other models with
+unknown parameters and architecture, and by matching the initial processing of
+the human visual system. We find that adversarial examples that strongly
+transfer across computer vision models influence the classifications made by
+time-limited human observers.
+"
+28066,Robust entropy requires strong and balanced excitatory and inhibitory synapses,"  It is widely appreciated that well-balanced excitation and inhibition are
+necessary for proper function in neural networks. However, in principle, such
+balance could be achieved by many possible configurations of excitatory and
+inhibitory strengths, and relative numbers of excitatory and inhibitory
+neurons. For instance, a given level of excitation could be balanced by either
+numerous inhibitory neurons with weak synapses, or few inhibitory neurons with
+strong synapses. Among the continuum of different but balanced configurations,
+why should any particular configuration be favored? Here we address this
+question in the context of the entropy of network dynamics by studying an
+analytically tractable network of binary neurons. We find that entropy is
+highest at the boundary between excitation-dominant and inhibition-dominant
+regimes. Entropy also varies along this boundary with a trade-off between high
+and robust entropy: weak synapse strengths yield high network entropy which is
+fragile to parameter variations, while strong synapse strengths yield a lower,
+but more robust, network entropy. In the case where inhibitory and excitatory
+synapses are constrained to have similar strength, we find that a small, but
+non-zero fraction of inhibitory neurons, like that seen in mammalian cortex,
+results in robust and relatively high entropy.
+"
+28067,Deterministic Stretchy Regression,"  An extension of the regularized least-squares in which the estimation
+parameters are stretchable is introduced and studied in this paper. The
+solution of this ridge regression with stretchable parameters is given in
+primal and dual spaces and in closed-form. Essentially, the proposed solution
+stretches the covariance computation by a power term, thereby compressing or
+amplifying the estimation parameters. To maintain the computation of power root
+terms within the real space, an input transformation is proposed. The results
+of an empirical evaluation in both synthetic and real-world data illustrate
+that the proposed method is effective for compressive learning with
+high-dimensional data.
+"
+28068,On two chain models for the gravity operad,"  In this note we recall the construction of two chain level lifts of the
+gravity operad, one due to Getzler-Kapranov and one due to Westerland. We prove
+that these two operads are formal and that they indeed have isomorphic
+homology.
+"
+28069,Direct Acoustics-to-Word Models for English Conversational Speech Recognition,"  Recent work on end-to-end automatic speech recognition (ASR) has shown that
+the connectionist temporal classification (CTC) loss can be used to convert
+acoustics to phone or character sequences. Such systems are used with a
+dictionary and separately-trained Language Model (LM) to produce word
+sequences. However, they are not truly end-to-end in the sense of mapping
+acoustics directly to words without an intermediate phone representation. In
+this paper, we present the first results employing direct acoustics-to-word CTC
+models on two well-known public benchmark tasks: Switchboard and CallHome.
+These models do not require an LM or even a decoder at run-time and hence
+recognize speech with minimal complexity. However, due to the large number of
+word output units, CTC word models require orders of magnitude more data to
+train reliably compared to traditional systems. We present some techniques to
+mitigate this issue. Our CTC word model achieves a word error rate of
+13.0%/18.8% on the Hub5-2000 Switchboard/CallHome test sets without any LM or
+decoder compared with 9.6%/16.0% for phone-based CTC with a 4-gram LM. We also
+present rescoring results on CTC word model lattices to quantify the
+performance benefits of a LM, and contrast the performance of word and phone
+CTC models.
+"
+28070,Solar Radiation Pressure Resonances in Low Earth Orbits,"  The aim of this work is to highlight the crucial role that orbital resonances
+associated with solar radiation pressure can have in Low Earth Orbit. We review
+the corresponding literature, and provide an analytical tool to estimate the
+maximum eccentricity which can be achieved for well-defined initial conditions.
+We then compare the results obtained with the simplified model with the results
+obtained with a more comprehensive dynamical model. The analysis has important
+implications both from a theoretical point of view, because it shows that the
+role of some resonances was underestimated in the past, but also from a
+practical point of view in the perspective of passive deorbiting solutions for
+satellites at the end-of-life.
+"
+28071,Econophysics: Still fringe after 30 years?,"  Some personal reflections on the past and future of ""econophysics"", to appear
+in Europhysics News
+"
+28072,Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery,"  We adopt data structure in the form of cover trees and iteratively apply
+approximate nearest neighbour (ANN) searches for fast compressed sensing
+reconstruction of signals living on discrete smooth manifolds. Levering on the
+recent stability results for the inexact Iterative Projected Gradient (IPG)
+algorithm and by using the cover tree's ANN searches, we decrease the
+projection cost of the IPG algorithm to be logarithmically growing with data
+population for low dimensional smooth manifolds. We apply our results to
+quantitative MRI compressed sensing and in particular within the Magnetic
+Resonance Fingerprinting (MRF) framework. For a similar (or sometimes better)
+reconstruction accuracy, we report 2-3 orders of magnitude reduction in
+computations compared to the standard iterative method which uses brute-force
+searches.
+"
+28073,Broadband sensitivity enhancement of detuned dual-recycled Michelson interferometers with EPR entanglement,"  We demonstrate the applicability of the EPR entanglement squeezing scheme for
+enhancing the shot-noise-limited sensitivity of a detuned dual-recycled
+Michelson interferometers. In particular, this scheme is applied to the
+GEO\,600 interferometer. The effect of losses throughout the interferometer,
+arm length asymmetries, and imperfect separation of the signal and idler beams
+are considered.
+"
+28074,Identifying robust landmarks in feature-based maps,"  To operate in an urban environment, an automated vehicle must be capable of
+accurately estimating its position within a global map reference frame. This is
+necessary for optimal path planning and safe navigation. To accomplish this
+over an extended period of time, the global map requires long-term maintenance.
+This includes the addition of newly observable features and the removal of
+transient features belonging to dynamic objects. The latter is especially
+important for the long-term use of the map as matching against a map with
+features that no longer exist can result in incorrect data associations, and
+consequently erroneous localisation. This paper addresses the problem of
+removing features from the map that correspond to objects that are no longer
+observable/present in the environment. This is achieved by assigning a single
+score which depends on the geometric distribution and characteristics when the
+features are re-detected (or not) on different occasions. Our approach not only
+eliminates ephemeral features, but also can be used as a reduction algorithm
+for highly dense maps. We tested our approach using half a year of weekly
+drives over the same 500-metre section of road in an urban environment. The
+results presented demonstrate the validity of the long-term approach to map
+maintenance.
+"
+28075,Introducing an old calculating instrument in a new technologies environment: a praxeological analysis of students' tasks using different registers,"  The Chinese abacus is the resource presented in this paper, to teach and
+learn number sense and place-value system at primary level. The Chinese abacus
+can be material, virtual (software) or drawn on a worksheet. We present three
+tasks and analyse them in term of techniques and relative knowledge. We show
+how these tasks can be solved by students in different registers (material,
+software, paper-and-pencil, fingers, oral) which is important for both
+students' understanding and teachers' activity. KEYWORDS Material and virtual
+resources, praxeology, task, technique, technology, register, number sense,
+place-value system, Chinese abacus. R{É}SUM{É} Le boulier chinois est la
+ressource pr{é}sent{é}e dans cet article, pour enseigner et apprendre la
+construction du nombre et le syst{è}me de num{é}ration d{é}cimal {à}
+l'{é}cole. Le boulier chinois peut {ê}tre mat{é}riel, virtuel (logiciel)
+ou dessin{é} sur une feuille. Nous pr{é}sentons trois t{â}ches et les
+analysons en terme de techniques et connaissances sous-jacentes. Nous montrons
+comment ces t{â}ches peuvent {ê}tre r{é}solues par des {é}l{è}ves
+dans diff{é}rents registres (mat{é}riel, logiciel, papier-crayon, mains,
+oral) ce qui est important pour la compr{é}hension des {é}l{è}ves et
+{é}galement pour l'activit{é} des professeurs.
+"
+28076,First-principles study of superconducting ScRhP and ScIrP pnictides,"  For the first time, we have reported in this study an ab initio investigation
+on elastic properties, Debye temperature, Mulliken population, Vickers
+hardness, and charge density of superconducting ScRhP and ScIrP phosphides. The
+optimized cell parameters show fair agreement with experimental results. The
+elastic constants and moduli, Poisson's as well as Pugh's ratio and elastic
+anisotropy factors have also been calculated to understand the mechanical
+behavior of these ternary compounds. Their mechanical stability is confirmed
+via the calculated elastic constants. The calculated values for Poisson's and
+Pugh's ratio indicate the ductile nature of these compounds. ScIrP is expected
+to be elastically more anisotropic than ScRhP. The estimated value of Debye
+temperature predicts that ScRhP is thermally more conductive than ScIrP and the
+phonon frequency in ScRhP is higher than that in ScIrP. The hardness of ScRhP
+is lower due to the presence of anti-bonding Rh-Rh in ScRhP. The investigated
+electronic structure predict that the metallic conductivity of ScRhP reduces
+significantly when Rh is replaced with Ir. The main contribution to the total
+density of states (TDOS) at Fermi-level (EF) comes from the d-electrons of Sc
+and Rh/Ir in both compounds. These two ternary compounds are characterized
+mainly by metallic and covalent bonding with little ionic contribution. As far
+as superconductivity is concerned, the matrix elements of electron-phonon
+interaction are noticeably enhanced in ScIrP compared to that in ScRhP.
+"
+28077,The VACCINE Framework for Building DLP Systems,"  Conventional Data Leakage Prevention (DLP) systems suffer from the following
+major drawback: Privacy policies that define what constitutes data leakage
+cannot be seamlessly defined and enforced across heterogeneous forms of
+communication. Administrators have the dual burden of: (1) manually
+self-interpreting policies from handbooks to specify rules (which is
+error-prone); (2) extracting relevant information flows from heterogeneous
+communication protocols and enforcing policies to determine which flows should
+be admissible. To address these issues, we present the Verifiable and
+ACtionable Contextual Integrity Norms Engine (VACCINE), a framework for
+building adaptable and modular DLP systems. VACCINE relies on (1) the theory of
+contextual integrity to provide an abstraction layer suitable for specifying
+reusable protocol-agnostic leakage prevention rules and (2) programming
+language techniques to check these rules against correctness properties and to
+enforce them faithfully within a DLP system implementation. We applied VACCINE
+to the Family Educational Rights and Privacy Act and Enron Corporation privacy
+regulations. We show that by using contextual integrity in conjunction with
+verification techniques, we can effectively create reusable privacy rules with
+specific correctness guarantees, and check the integrity of information flows
+against these rules. Our experiments in emulated enterprise settings indicate
+that VACCINE improves over current DLP system design approaches and can be
+deployed in enterprises involving tens of thousands of actors.
+"
+28078,Planck/SDSS Cluster Mass and Gas Scaling Relations for a Volume-Complete redMaPPer Sample,"  Using Planck satellite data, we construct SZ gas pressure profiles for a
+large, volume-complete sample of optically selected clusters. We have defined a
+sample of over 8,000 redMaPPer clusters from the Sloan Digital Sky Survey
+(SDSS), within the volume-complete redshift region 0.100 < z < 0.325, for which
+we construct Sunyaev-Zel'dovich (SZ) effect maps by stacking Planck data over
+the full range of richness. Dividing the sample into richness bins we
+simultaneously solve for the mean cluster mass in each bin together with the
+corresponding radial pressure profile parameters, employing an MCMC analysis.
+These profiles are well detected over a much wider range of cluster mass and
+radius than previous work, showing a clear trend towards larger break radius
+with increasing cluster mass. Our SZ-based masses fall ~24% below the
+mass-richness relations from weak lensing, in a similar fashion as the
+""hydrostatic bias"" related with X-ray derived masses. We correct for this bias
+to derive an optimal mass-richness relation finding a slope 1.22 +/- 0.04 and a
+pivot mass log(M_500/M_0)= 14.432 +/- 0.041, evaluated at a richness lambda=60.
+Finally, we derive a tight Y_500-M_500 relation over a wide range of cluster
+mass, with a power law slope equal to 1.72 +/- 0.07, that agrees well with the
+independent slope obtained by the Planck team with an SZ-selected cluster
+sample, but extends to lower masses with higher precision.
+"
+28079,Vertical perimeter versus horizontal perimeter,"  The discrete Heisenberg group $\mathbb{H}_{\mathbb{Z}}^{2k+1}$ is the group
+generated by $a_1,b_1,\ldots,a_k,b_k,c$, subject to the relations
+$[a_1,b_1]=\ldots=[a_k,b_k]=c$ and
+$[a_i,a_j]=[b_i,b_j]=[a_i,b_j]=[a_i,c]=[b_i,c]=1$ for every distinct $i,j\in
+\{1,\ldots,k\}$. Denote $S=\{a_1^{\pm 1},b_1^{\pm 1},\ldots,a_k^{\pm
+1},b_k^{\pm 1}\}$. The horizontal boundary of $\Omega\subset
+\mathbb{H}_{\mathbb{Z}}^{2k+1}$, denoted $\partial_{h}\Omega$, is the set of
+all $(x,y)\in \Omega\times (\mathbb{H}_{\mathbb{Z}}^{2k+1}\setminus \Omega)$
+such that $x^{-1}y\in S$. The horizontal perimeter of $\Omega$ is
+$|\partial_{h}\Omega|$. For $t\in \mathbb{N}$, define $\partial^t_{v} \Omega$
+to be the set of all $(x,y)\in \Omega\times
+(\mathbb{H}_{\mathsf{Z}}^{2k+1}\setminus \Omega)$ such that $x^{-1}y\in
+\{c^t,c^{-t}\}$. The vertical perimeter of $\Omega$ is defined by
+$|\partial_{v}\Omega|= \sqrt{\sum_{t=1}^\infty |\partial^t_{v}\Omega|^2/t^2}$.
+It is shown here that if $k\ge 2$, then $|\partial_{v}\Omega|\lesssim
+\frac{1}{k} |\partial_{h}\Omega|$. The proof of this ""vertical versus
+horizontal isoperimetric inequality"" uses a new structural result that
+decomposes sets of finite perimeter in the Heisenberg group into pieces that
+admit an ""intrinsic corona decomposition."" This allows one to deduce an
+endpoint $W^{1,1}\to L_2(L_1)$ boundedness of a certain singular integral
+operator from a corresponding lower-dimensional $W^{1,2}\to L_2(L_2)$
+boundedness. The above inequality has several applications, including that any
+embedding into $L_1$ of a ball of radius $n$ in the word metric on
+$\mathbb{H}_{\mathbb{Z}}^{5}$ incurs bi-Lipschitz distortion that is at least a
+constant multiple of $\sqrt{\log n}$. It follows that the integrality gap of
+the Goemans--Linial semidefinite program for the Sparsest Cut Problem on inputs
+of size $n$ is at least a constant multiple of $\sqrt{\log n}$.
+"
+28080,Rota-Baxter operators on BiHom-associative algebras and related structures,"  The purpose of this paper is to study Rota-Baxter structures for
+BiHom-associative algebras. Moreover, we introduce and discuss the properties
+of the notions of BiHom-(tri)dendriform algebras and BiHom-quadri-algebras. We
+construct the free Rota-Baxter BiHom-associative algebra and present some
+observations about categories and functors related to Rota-Baxter structures.
+"
+28081,Stochastic Primal Dual Coordinate Method with Non-Uniform Sampling Based on Optimality Violations,"  We study primal-dual type stochastic optimization algorithms with non-uniform
+sampling. Our main theoretical contribution in this paper is to present a
+convergence analysis of Stochastic Primal Dual Coordinate (SPDC) Method with
+arbitrary sampling. Based on this theoretical framework, we propose Optimality
+Violation-based Sampling SPDC (ovsSPDC), a non-uniform sampling method based on
+Optimality Violation. We also propose two efficient heuristic variants of
+ovsSPDC called ovsSDPC+ and ovsSDPC++. Through intensive numerical experiments,
+we demonstrate that the proposed method and its variants are faster than other
+state-of-the-art primal-dual type stochastic optimization methods.
+"
+28082,Learning to Mix n-Step Returns: Generalizing lambda-Returns for Deep Reinforcement Learning,"  Reinforcement Learning (RL) can model complex behavior policies for
+goal-directed sequential decision making tasks. A hallmark of RL algorithms is
+Temporal Difference (TD) learning: value function for the current state is
+moved towards a bootstrapped target that is estimated using next state's value
+function. $\lambda$-returns generalize beyond 1-step returns and strike a
+balance between Monte Carlo and TD learning methods. While lambda-returns have
+been extensively studied in RL, they haven't been explored a lot in Deep RL.
+This paper's first contribution is an exhaustive benchmarking of
+lambda-returns. Although mathematically tractable, the use of exponentially
+decaying weighting of n-step returns based targets in lambda-returns is a
+rather ad-hoc design choice. Our second major contribution is that we propose a
+generalization of lambda-returns called Confidence-based Autodidactic Returns
+(CAR), wherein the RL agent learns the weighting of the n-step returns in an
+end-to-end manner. This allows the agent to learn to decide how much it wants
+to weigh the n-step returns based targets. In contrast, lambda-returns restrict
+RL agents to use an exponentially decaying weighting scheme. Autodidactic
+returns can be used for improving any RL algorithm which uses TD learning. We
+empirically demonstrate that using sophisticated weighted mixtures of
+multi-step returns (like CAR and lambda-returns) considerably outperforms the
+use of n-step returns. We perform our experiments on the Asynchronous Advantage
+Actor Critic (A3C) algorithm in the Atari 2600 domain.
+"
+28083,Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling,"  This paper introduces a novel framework for combining scientific knowledge of
+physics-based models with neural networks to advance scientific discovery. This
+framework, termed as physics-guided neural network (PGNN), leverages the output
+of physics-based model simulations along with observational features to
+generate predictions using a neural network architecture. Further, this paper
+presents a novel framework for using physics-based loss functions in the
+learning objective of neural networks, to ensure that the model predictions not
+only show lower errors on the training set but are also scientifically
+consistent with the known physics on the unlabeled set. We illustrate the
+effectiveness of PGNN for the problem of lake temperature modeling, where
+physical relationships between the temperature, density, and depth of water are
+used to design a physics-based loss function. By using scientific knowledge to
+guide the construction and learning of neural networks, we are able to show
+that the proposed framework ensures better generalizability as well as
+scientific consistency of results.
+"
+28084,Optical absorption in interacting and nonlinear Weyl semimetals,"  It has been recently predicted that the interplay between Coulomb
+interactions and Berry curvature can produce interesting optical phenomena in
+topologically nontrivial two-dimensional insulators. Here, we present a theory
+of the optical absorption for three-dimensional, hole-doped Weyl semimetals. We
+find that the Berry curvature, Coulomb interactions and the nonlinearity in the
+single-particle energy spectrum can together enable a light-induced valley
+polarization. We support and supplement our numerical results with an
+analytical toy model calculation, which unveils topologically nontrivial Mahan
+excitons with nonzero vorticity.
+"
+28085,Generalized bipyramids and hyperbolic volumes of alternating $k$-uniform tiling links,"  We present explicit geometric decompositions of the hyperbolic complements of
+alternating $k$-uniform tiling links, which are alternating links whose
+projection graphs are $k$-uniform tilings of $S^2$, $\mathbb{E}^2$, or
+$\mathbb{H}^2$. A consequence of this decomposition is that the volumes of
+spherical alternating $k$-uniform tiling links are precisely twice the maximal
+volumes of the ideal Archimedean solids of the same combinatorial description,
+and the hyperbolic structures for the hyperbolic alternating tiling links come
+from the equilateral realization of the $k$-uniform tiling on $\mathbb{H}^2$.
+In the case of hyperbolic tiling links, we are led to consider links embedded
+in thickened surfaces $S_g \times I$ with genus $g \ge 2$ and totally geodesic
+boundaries. We generalize the bipyramid construction of Adams to truncated
+bipyramids and use them to prove that the set of possible volume densities for
+all hyperbolic links in $S_g \times I$, ranging over all $g \ge 2$, is a dense
+subset of the interval $[0, 2v_\text{oct}]$, where $v_\text{oct} \approx
+3.66386$ is the volume of the ideal regular octahedron.
+"
+28086,Multilayer Perceptron Algebra,"  Artificial Neural Networks(ANN) has been phenomenally successful on various
+pattern recognition tasks. However, the design of neural networks rely heavily
+on the experience and intuitions of individual developers. In this article, the
+author introduces a mathematical structure called MLP algebra on the set of all
+Multilayer Perceptron Neural Networks(MLP), which can serve as a guiding
+principle to build MLPs accommodating to the particular data sets, and to build
+complex MLPs from simpler ones.
+"
+28087,Detectability of nonparametric signals: higher criticism versus likelihood ratio,"  We study the signal detection problem in high dimensional noise data
+(possibly) containing rare and weak signals. Log-likelihood ratio (LLR) tests
+depend on unknown parameters, but they are needed to judge the quality of
+detection tests since they determine the detection regions. The popular Tukey's
+higher criticism (HC) test was shown to achieve the same completely detectable
+region as the LLR test does for different (mainly) parametric models. We
+present a novel technique to prove this result for very general signal models,
+including even nonparametric $p$-value models. Moreover, we address the
+following questions which are still pending since the initial paper of Donoho
+and Jin: What happens on the border of the completely detectable region, the
+so-called detection boundary? Does HC keep its optimality there? In particular,
+we give a complete answer for the heteroscedastic normal mixture model. As a
+byproduct, we give some new insights about the LLR test's behavior on the
+detection boundary by discussing, among others, Pitmans's asymptotic efficiency
+as an application of Le Cam's theory.
+"
+28088,Latent Gaussian Process Regression,"  We introduce Latent Gaussian Process Regression which is a latent variable
+extension allowing modelling of non-stationary multi-modal processes using GPs.
+The approach is built on extending the input space of a regression problem with
+a latent variable that is used to modulate the covariance function over the
+training data. We show how our approach can be used to model multi-modal and
+non-stationary processes. We exemplify the approach on a set of synthetic data
+and provide results on real data from motion capture and geostatistics.
+"
+28089,A set of 12 numbers is not determined by its set of 4-sums,"  We present two sets of 12 integers that have the same sets of 4-sums. The
+proof of the fact that a set of 12 numbers is uniquely determined by the set of
+its 4-sums published 50 years ago is wrong, and we demonstrate an incorrect
+calculation in it.
+"
+28090,Knowledge transfer for surgical activity prediction,"  Lack of training data hinders automatic recognition and prediction of
+surgical activities necessary for situation-aware operating rooms. We propose
+using knowledge transfer to compensate for data deficit and improve prediction.
+We used two approaches to extract and transfer surgical process knowledge.
+First, we encoded semantic information about surgical terms using word
+embedding which boosted learning process. Secondly, we passed knowledge between
+different clinical datasets of neurosurgical procedures using transfer
+learning. Transfer learning was shown to be more effective than a simple
+combination of data, especially for less similar procedures. The combination of
+two methods provided 22% improvement of activity prediction. We also made
+several pertinent observations about surgical practices.
+"
+28091,Diagnosing Coronal Heating Processes with Spectrally Resolved Soft X-ray Measurements,"  Decades of astrophysical observations have convincingly shown that soft X-ray
+(SXR; ~0.1--10 keV) emission provides unique diagnostics for the high
+temperature plasmas observed in solar flares and active regions. SXR
+observations critical for constraining models of energy release in these
+phenomena can be provided using instruments that have already been flown on
+sounding rockets and CubeSats, including miniaturized high-resolution
+photon-counting spectrometers and a novel diffractive spectral imager. These
+instruments have relatively low cost and high TRL, and would complement a wide
+range of mission concepts. In this white paper, we detail the scientific
+background and open questions motivating these instruments, the measurements
+required, and the instruments themselves that will make groundbreaking progress
+in answering these questions.
+"
+28092,Inference in generative models using the Wasserstein distance,"  A growing range of generative statistical models are such the numerical
+evaluation of their likelihood functions is intractable. Approximate Bayesian
+computation and indirect inference have become popular approaches to overcome
+this issue, simulating synthetic data given parameters and comparing summaries
+of these simulations with the corresponding observed values. We propose to
+avoid these summaries and the ensuing loss of information through the use of
+Wasserstein distances between empirical distributions of observed and synthetic
+data. We describe how the approach can be used in the setting of dependent data
+such as time series, and how approximations of the Wasserstein distance allow
+the method to scale to large data sets. In particular, we propose a new
+approximation to the optimal assignment problem using the Hilbert space-filling
+curve. We provide an in-depth theoretical study, including consistency in the
+number of simulated data sets for a fixed number of observations and posterior
+concentration rates. The approach is illustrated with various examples,
+including a multivariate g-and-k distribution, a toggle switch model from
+systems biology, a queueing model, and a Lévy-driven stochastic volatility
+model.
+"
+28093,Efficient Intersection Control for Minimally Guided Vehicles: A Self-Organised and Decentralized Approach,"  An important question for the practical applicability of the highly efficient
+traffic intersection control is about the minimal level of intelligence the
+vehicles need to have so as to move beyond the traffic light control. We
+propose an efficient intersection traffic control scheme without the traffic
+lights, that only requires a majority of vehicles on the road to be equipped
+with a simple driver assistance system. The algorithm of our scheme is
+completely decentralized, and takes into full account the non-linear
+interaction between the vehicles at high density. For vehicles approaching the
+intersection in different directions, our algorithm imposes simple interactions
+between vehicles around the intersection, by defining specific conditions on
+the real-time basis, for which the involved vehicles are required to briefly
+adjust their dynamics. This leads to a self-organised traffic flow that is
+safe, robust, and efficient. We also take into account of the driver comfort
+level and study its effect on the control efficiency. The scheme has low
+technological barrier, minimal impact on the conventional driving behaviour,
+and can coexist with the traffic light control. It also has the advantages of
+being easily scalable, and fully compatible with both the conventional road
+systems as well as the futuristic scenario in which driverless vehicles
+dominate the road. The mathematical formulation of our scheme permits large
+scale realistic numerical simulations of busy intersections, allowing a more
+complete evaluation of the control performance, instead of just the collision
+avoidance at the intersection.
+"
+28094,Subjective Knowledge Acquisition and Enrichment Powered By Crowdsourcing,"  Knowledge bases (KBs) have attracted increasing attention due to its great
+success in various areas, such as Web and mobile search.Existing KBs are
+restricted to objective factual knowledge, such as city population or fruit
+shape, whereas,subjective knowledge, such as big city, which is commonly
+mentioned in Web and mobile queries, has been neglected. Subjective knowledge
+differs from objective knowledge in that it has no documented or observed
+ground truth. Instead, the truth relies on people's dominant opinion. Thus, we
+can use the crowdsourcing technique to get opinion from the crowd. In our work,
+we propose a system, called crowdsourced subjective knowledge acquisition
+(CoSKA),for subjective knowledge acquisition powered by crowdsourcing and
+existing KBs. The acquired knowledge can be used to enrich existing KBs in the
+subjective dimension which bridges the gap between existing objective knowledge
+and subjective queries.The main challenge of CoSKA is the conflict between
+large scale knowledge facts and limited crowdsourcing resource. To address this
+challenge, in this work, we define knowledge inference rules and then select
+the seed knowledge judiciously for crowdsourcing to maximize the inference
+power under the resource constraint. Our experimental results on real knowledge
+base and crowdsourcing platform verify the effectiveness of CoSKA system.
+"
+28095,TeV/m Nano-Accelerator: Current Status of CNT-Channeling Acceleration Experiment,"  Crystal channeling technology has offered various opportunities in the
+accelerator community with a viability of ultrahigh gradient (TV/m)
+acceleration for future HEP collider. The major challenge of channeling
+acceleration is that ultimate acceleration gradients might require a high power
+driver in the hard x-ray regime (~ 40 keV). This x-ray energy exceeds those for
+x-rays as of today, although x-ray lasers can efficiently excite solid plasma
+and accelerate particles inside a crystal channel. Moreover, only disposable
+crystal accelerators are possible at such high externally excited fields which
+would exceed the ionization thresholds destroying the atomic structure, so
+acceleration will take place only in a short time before full dissociation of
+the lattice. Carbon-based nanostructures have great potential with a wide range
+of flexibility and superior physical strength, which can be applied to
+channeling acceleration. This paper presents a beam-driven channeling
+acceleration concept with CNTs and discusses feasible experiments with the
+Advanced Superconducting Test Accelerator (ASTA) in Fermilab.
+"
+28096,Quasi-Monte Carlo Variational Inference,"  Many machine learning problems involve Monte Carlo gradient estimators. As a
+prominent example, we focus on Monte Carlo variational inference (MCVI) in this
+paper. The performance of MCVI crucially depends on the variance of its
+stochastic gradients. We propose variance reduction by means of Quasi-Monte
+Carlo (QMC) sampling. QMC replaces N i.i.d. samples from a uniform probability
+distribution by a deterministic sequence of samples of length N. This sequence
+covers the underlying random variable space more evenly than i.i.d. draws,
+reducing the variance of the gradient estimator. With our novel approach, both
+the score function and the reparameterization gradient estimators lead to much
+faster convergence. We also propose a new algorithm for Monte Carlo objectives,
+where we operate with a constant learning rate and increase the number of QMC
+samples per iteration. We prove that this way, our algorithm can converge
+asymptotically at a faster rate than SGD. We furthermore provide theoretical
+guarantees on QMC for Monte Carlo objectives that go beyond MCVI, and support
+our findings by several experiments on large-scale data sets from various
+domains.
+"
+28097,3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition,"  Audio-visual recognition (AVR) has been considered as a solution for speech
+recognition tasks when the audio is corrupted, as well as a visual recognition
+method used for speaker verification in multi-speaker scenarios. The approach
+of AVR systems is to leverage the extracted information from one modality to
+improve the recognition ability of the other modality by complementing the
+missing information. The essential problem is to find the correspondence
+between the audio and visual streams, which is the goal of this work. We
+propose the use of a coupled 3D Convolutional Neural Network (3D-CNN)
+architecture that can map both modalities into a representation space to
+evaluate the correspondence of audio-visual streams using the learned
+multimodal features. The proposed architecture will incorporate both spatial
+and temporal information jointly to effectively find the correlation between
+temporal information for different modalities. By using a relatively small
+network architecture and much smaller dataset for training, our proposed method
+surpasses the performance of the existing similar methods for audio-visual
+matching which use 3D CNNs for feature representation. We also demonstrate that
+an effective pair selection method can significantly increase the performance.
+The proposed method achieves relative improvements over 20% on the Equal Error
+Rate (EER) and over 7% on the Average Precision (AP) in comparison to the
+state-of-the-art method.
+"
+28098,Quantifying vulnerability of secret generation using hyper-distributions (extended version),"  Traditional approaches to Quantitative Information Flow (QIF) represent the
+adversary's prior knowledge of possible secret values as a single probability
+distribution. This representation may miss important structure. For instance,
+representing prior knowledge about passwords of a system's users in this way
+overlooks the fact that many users generate passwords using some strategy.
+Knowledge of such strategies can help the adversary in guessing a secret, so
+ignoring them may underestimate the secret's vulnerability. In this paper we
+explicitly model strategies as distributions on secrets, and generalize the
+representation of the adversary's prior knowledge from a distribution on
+secrets to an environment, which is a distribution on strategies (and, thus, a
+distribution on distributions on secrets, called a hyper-distribution). By
+applying information-theoretic techniques to environments we derive several
+meaningful generalizations of the traditional approach to QIF. In particular,
+we disentangle the vulnerability of a secret from the vulnerability of the
+strategies that generate secrets, and thereby distinguish security by
+aggregation--which relies on the uncertainty over strategies--from security by
+strategy--which relies on the intrinsic uncertainty within a strategy. We also
+demonstrate that, in a precise way, no further generalization of prior
+knowledge (e.g., by using distributions of even higher order) is needed to
+soundly quantify the vulnerability of the secret.
+"
+28099,A Study on the Extraction and Analysis of a Large Set of Eye Movement Features during Reading,"  This work presents a study on the extraction and analysis of a set of 101
+categories of eye movement features from three types of eye movement events:
+fixations, saccades, and post-saccadic oscillations. The eye movements were
+recorded during a reading task. For the categories of features with multiple
+instances in a recording we extract corresponding feature subtypes by
+calculating descriptive statistics on the distributions of these instances. A
+unified framework of detailed descriptions and mathematical formulas are
+provided for the extraction of the feature set. The analysis of feature values
+is performed using a large database of eye movement recordings from a normative
+population of 298 subjects. We demonstrate the central tendency and overall
+variability of feature values over the experimental population, and more
+importantly, we quantify the test-retest reliability (repeatability) of each
+separate feature. The described methods and analysis can provide valuable tools
+in fields exploring the eye movements, such as in behavioral studies, attention
+and cognition research, medical research, biometric recognition, and
+human-computer interaction.
+"
+28100,Higher Dimensional Möbius Bands and Their Boundaries,"  We give a characterisation of Bieberbach manifolds which are geodesic
+boundaries of a compact flat manifold, and discuss the low dimensional cases,
+up to dimension 4.
+"
+28101,"Magnetism in Mn Nanowires and Clusters as δ-doped Layers in Group IV Semiconductors (Si, Ge)","  Mn doping of group-IV semiconductors (Si/Ge) is achieved by embedding a thin
+Mn-film as a {\delta}-doped layer in group-IV matrix. The Mn-layer consists of
+a dense layer of monoatomic Mn-wires, which are oriented perpendicular to the
+Si(001)-(2x1) dimer rows, or Mn-clusters. The nanostructures are covered with
+an amorphous Si or Ge capping layer, which conserves the identity of the
+{\delta}-doped layer. The analysis of the bonding environment with STM is
+combined with the element-specific detection of the magnetic signature with
+X-ray magnetic circular dichroism. The largest moment (2.5 {\mu}B/Mn) is
+measured for Mn-wires, which have ionic bonding character, with an a-Ge
+overlayer cap, a-Si capping leads to a slightly reduced moment which has its
+origin in subtle variation of bonding geometry. Our results directly confirm
+theoretical predictions on magnetism for Mn-adatoms on Si(001). The moment is
+quenched to 0.5{\mu}B/Mn for {\delta}-doped layers, which are dominated by
+clusters, and thus develop an antiferromagnetic component from Mn-Mn bonding.
+"
+28102,A phase field approach to shape optimization in Navier--Stokes flow with integral state constraint,"  We consider the shape optimization of an object in Navier--Stokes flow by
+employing a combined phase field and porous medium approach, along with
+additional perimeter regularization. By considering integral control and state
+constraints, we extend the results of earlier works concerning the existence of
+optimal shapes and the derivation of first order optimality conditions. The
+control variable is a phase field function that prescribes the shape and
+topology of the object, while the state variables are the velocity and the
+pressure of the fluid. In our analysis, we cover a multitude of constraints
+which include constraints on the center of mass, the volume of the fluid
+region, and the drag of the object. Finally, we present numerical results of
+the optimization problem that is solved using the variable metric projection
+type (VMPT) method proposed by Blank and Rupprecht, where we consider one
+example of topology optimization without constraints and one example of
+maximizing the lift of the object with a state constraint, as well as a
+comparison with earlier results for the drag minimization.
+"
+28103,Demonstration of a passive photon-atom swap gate,"  Deterministic quantum interactions between single photons and single quantum
+emitters are a vital building block towards the distribution of quantum
+information between remote systems. Deterministic photon-atom state transfer
+has been demonstrated by using protocols that include active feedback or
+synchronized control pulses. Here we demonstrate a completely passive swap gate
+between the states of a single photon and a single atom. The underlying
+mechanism is single-photon Raman interaction (SPRINT) - an interference-based
+effect in which a photonic qubit deterministically controls the state of a
+material qubit encoded in the two ground states of a {\Lambda} system, and vice
+versa. Using a nanofiber-coupled microsphere resonator coupled to single Rb
+atoms we swap a photonic qubit into the atom and back, demonstrating
+nonclassical fidelities in both directions. Requiring no control fields or
+feedback protocol, the gate takes place automatically at the timescale of the
+atom's cavity- enhanced spontaneous emission time. Applicable to any
+waveguide-coupled {\Lambda} system, this scheme provides a versatile building
+block for the modular scaling up of quantum information processing systems.
+"
+28104,Study of high energy phenomena from near space using low-cost meteorological balloons,"  Indian Centre for Space Physics has taken a novel strategy to study low
+energy cosmic rays and astrophysical X-ray sources which involve very light
+weight payloads up to about five kilograms on board a single or multiple
+balloons which are usually used for meteorological purposes. The mission
+duration could be anywhere from 3-12 hours. Our strategy provides extreme
+flexibility in mission preparation and its operation using a very economical
+budget. There are several limitations but our innovative approach has been able
+to extract significant amount of scientific data out of these missions. So far,
+over one hundred missions have been completed by us to near space and a wealth
+of data has been collected. The payloads are recovered and are used again.
+Scientific data is stored on board computer and the atmospheric data or payload
+location is sent to ground in real time. Since each mission is different, we
+present here the general strategy for a typical payload and provide some
+results we obtained in some of these missions.
+"
+28105,GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction,"  In voxel-based neuroimage analysis, lesion features have been the main focus
+in disease prediction due to their interpretability with respect to the related
+diseases. However, we observe that there exists another type of features
+introduced during the preprocessing steps and we call them ""\textbf{Procedural
+Bias}"". Besides, such bias can be leveraged to improve classification accuracy.
+Nevertheless, most existing models suffer from either under-fit without
+considering procedural bias or poor interpretability without differentiating
+such bias from lesion ones. In this paper, a novel dual-task algorithm namely
+\emph{GSplit LBI} is proposed to resolve this problem. By introducing an
+augmented variable enforced to be structural sparsity with a variable splitting
+term, the estimators for prediction and selecting lesion features can be
+optimized separately and mutually monitored by each other following an
+iterative scheme. Empirical experiments have been evaluated on the Alzheimer's
+Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of
+proposed model is verified by improved stability of selected lesion features
+and better classification results.
+"
+28106,Constructive fractals in set theory. Tutorial. (In Russian),"  Classical geometric fractals - Cantor set and Sierpinski continua - are
+presented in the manual as set-theoretic objects.
+"
+28107,A preliminary study of agility in business and production - Cases of early-stage hardware startups,"  [Context]Advancement in technologies, popularity of small-batch manufacturing
+and the recent trend of investing in hardware startups are among the factors
+leading to the rise of hardware startups nowadays. It is essential for hardware
+startups to be not only agile to develop their business but also efficient to
+develop the right products. [Objective] We investigate how hardware startups
+achieve agility when developing their products in early stages. [Methods] A
+qualitative research is conducted with data from 20 hardware startups. [Result]
+Preliminary results show that agile development is known to hardware
+entrepreneurs, however it is adopted limitedly. We also found tactics in four
+domains (1) strategy, (2) personnel, (3) artifact and (4) resource that enable
+hardware startups agile in their early stage business and product development.
+[Conclusions] Agile methodologies should be adopted with the consideration of
+specific features of hardware development, such as up-front design and vendor
+dependencies.
+"
+28108,Linear stability of confined flow around a 180-degree sharp bend,"  This study seeks to characterise the breakdown of the steady 2D solution in
+the flow around a 180-degree sharp bend to infinitesimal 3D disturbances using
+a linear stability analysis. The stability analysis predicts that 3D transition
+is via a synchronous instability of the steady flows. A highly accurate global
+linear stability analysis of the flow was conducted with Reynolds number
+$Re<1150$ and bend opening ratio (ratio of bend width to inlet height)
+$0.2\leq\beta\leq5$. This range of $Re$ and $\beta$ captures both steady-state
+2D flow solutions as well as the inception of unsteady 2D flow. For
+$0.2\leq\beta\leq1$, the 2D base flow transitions from steady to unsteady at
+higher Reynolds number as $\beta$ increases. The stability analysis shows that
+at the onset of instability, the base flow becomes three-dimensionally unstable
+in two different modes, namely spanwise oscillating mode for $\beta=0.2$, and
+spanwise synchronous mode for $\beta \geq 0.3$. The critical Reynolds number
+and the spanwise wavelength of perturbations increase as $\beta$ increases. For
+$1<\beta\leq2$ both the critical Reynolds for onset of unsteadiness and the
+spanwise wavelength decrease as $\beta$ increases. Finally, for $2<\beta\leq5$,
+the critical Reynolds number and spanwise wavelength remain almost constant.
+The linear stability analysis also shows that the base flow becomes unstable to
+different 3D modes depending on the opening ratio. The modes are found to be
+localised near the reattachment point of the first recirculation bubble.
+"
+28109,Electrical and photoelectrical characteristic investigation of a new generation photodiode based on bromothymol blue dye,"  Bromothymol blue (BTB) with the molecular formula of C27H28Br2O5S was grown
+onto p-Si substrate to fabricate heterojunction by spin coating technique. The
+current voltage (I-V) measurements of diode were carried out in dark and under
+different illumination intensity at room temperature. The photoelectrical
+properties of heterojunction based on BTB were investigated using the
+illumination intensity dependent I-V data. The results showed that photo
+current of diode increases with the increase in light intensity. Also, the
+electrical parameters of device were determined via I-V, and capacitance-
+voltage (C-V), conductance-voltage (G-V) measurements at different frequencies.
+It is observed that the excess capacitance is created at low frequencies due to
+the contribution of interface states charge which can follow the alternative
+current signal to capacitance. It is stated that, both the electrical
+photoelectrical parameters of diode can be changed, and also the performance of
+the device could be affected by the organic thin film interlayer.
+"
+28110,A class of numbers for which the Even Goldbach Conjecture is true,"  We define a $G-class$: A set of numbers for which the even Goldbach
+conjecture is true. The elements of a $G-class$ are $G numbers$. Main result:
+We prove that every $G number$ can be expressed as a sum of two primes and
+there are infinitely many $G numbers$.
+"
+28111,DeepDeath: Learning to Predict the Underlying Cause of Death with Big Data,"  Multiple cause-of-death data provides a valuable source of information that
+can be used to enhance health standards by predicting health related
+trajectories in societies with large populations. These data are often
+available in large quantities across U.S. states and require Big Data
+techniques to uncover complex hidden patterns. We design two different classes
+of models suitable for large-scale analysis of mortality data, a Hadoop-based
+ensemble of random forests trained over N-grams, and the DeepDeath, a deep
+classifier based on the recurrent neural network (RNN). We apply both classes
+to the mortality data provided by the National Center for Health Statistics and
+show that while both perform significantly better than the random classifier,
+the deep model that utilizes long short-term memory networks (LSTMs), surpasses
+the N-gram based models and is capable of learning the temporal aspect of the
+data without a need for building ad-hoc, expert-driven features.
+"
+28112,Design of a multifunctional sample probe for transport measurements and technological device characterizations,"  We describe a multifunctional sample probe for transport measurements,
+equipped with a two-axis goniometer providing computer controlled 360${^\circ}$
+out of plane and manual 360${^\circ}$ in-plane rotation. The multifunctional
+sample probe has been successfully implemented to a host dewar and
+electromagnet system. The developed probe is capable of performing transport
+measurements and device characterizations with high flexibility of controlling
+magnetic and electrical fields, angle, temperature and current/voltage
+parameters in a wide range. The design of the multifunctional sample probe
+allows easy connection of other external devices and easy installation into
+other dewar and magnet systems. A software interface based on NI Labview visual
+programming language has been developed to control the system. The setup has
+been tested in the hysteresis loop measurements of perpendicularly magnetized
+Co/Pt/CoO ultra-thin films with anomalous Hall effect (AHE) method. We have
+also performed temperature dependent exchange bias measurements of the samples.
+The developed probe revealed much better resolution in the case of
+perpendicularly magnetized ultra-thin Co/Pt/CoO films compared to a standard
+vibrating sample magnetometry system.
+"
+28113,Online Dynamic Programming,"  We consider the problem of repeatedly solving a variant of the same dynamic
+programming problem in successive trials. An instance of the type of problems
+we consider is to find a good binary search tree in a changing environment.At
+the beginning of each trial, the learner probabilistically chooses a tree with
+the $n$ keys at the internal nodes and the $n+1$ gaps between keys at the
+leaves. The learner is then told the frequencies of the keys and gaps and is
+charged by the average search cost for the chosen tree. The problem is online
+because the frequencies can change between trials. The goal is to develop
+algorithms with the property that their total average search cost (loss) in all
+trials is close to the total loss of the best tree chosen in hindsight for all
+trials. The challenge, of course, is that the algorithm has to deal with
+exponential number of trees. We develop a general methodology for tackling such
+problems for a wide class of dynamic programming algorithms. Our framework
+allows us to extend online learning algorithms like Hedge and Component Hedge
+to a significantly wider class of combinatorial objects than was possible
+before.
+"
+28114,Overlap in Observational Studies with High-Dimensional Covariates,"  Causal inference in observational settings typically rests on a pair of
+identifying assumptions: first, unconfoundedness, and second, covariate
+overlap, also known as positivity or common support. Investigators often argue
+that unconfoundedness is more plausible when more covariates are included in
+the analysis. Less discussed is the fact that covariate overlap is more
+difficult to satisfy in this setting. In this paper, we explore the
+implications of overlap in high-dimensional observational studies, arguing that
+these assumptions are stronger than investigators likely realize. Our key
+innovation is to explore how strict overlap restricts global discrepancies
+between the covariate distributions in the treated and control populations. In
+our main result, we derive explicit bounds on the average imbalance in
+covariate means under strict overlap. Importantly, these bounds become more
+restrictive as the dimension grows large. We discuss how these implications
+interact with assumptions and procedures commonly deployed in observational
+causal inference, including sparsity and trimming.
+"
+28115,Remedies against the Vocabulary Gap in Information Retrieval,"  Search engines rely heavily on term-based approaches that represent queries
+and documents as bags of words. Text---a document or a query---is represented
+by a bag of its words that ignores grammar and word order, but retains word
+frequency counts. When presented with a search query, the engine then ranks
+documents according to their relevance scores by computing, among other things,
+the matching degrees between query and document terms. While term-based
+approaches are intuitive and effective in practice, they are based on the
+hypothesis that documents that exactly contain the query terms are highly
+relevant regardless of query semantics. Inversely, term-based approaches assume
+documents that do not contain query terms as irrelevant. However, it is known
+that a high matching degree at the term level does not necessarily mean high
+relevance and, vice versa, documents that match null query terms may still be
+relevant. Consequently, there exists a vocabulary gap between queries and
+documents that occurs when both use different words to describe the same
+concepts. It is the alleviation of the effect brought forward by this
+vocabulary gap that is the topic of this dissertation. More specifically, we
+propose (1) methods to formulate an effective query from complex textual
+structures and (2) latent vector space models that circumvent the vocabulary
+gap in information retrieval.
+"
+28116,Nonfillable Legendrian knots in the 3-sphere,"  If a Legendrian knot $\Lambda$ in the standard contact 3-sphere bounds an
+orientable exact Lagrangian surface $\Sigma$ in the standard symplectic 4-ball,
+then the genus of $\Sigma$ is equal to the slice genus of (the smooth knot
+underlying) $\Lambda$, the sum of the Thurston-Bennequin number of L and the
+Euler characteristic of $\Sigma$ is zero as well as the rotation number of
+$\Lambda$, and moreover, the linearized contact homology of $\Lambda$ with
+respect to the augmentation induced by $\Sigma$ is isomorphic to the (singular)
+homology of $\Sigma$. It was asked in arXiv:1212.1519 whether the converse of
+this statement is true. We give a negative answer to this question by providing
+a family of Legendrian knots with augmentations not induced by any exact
+Lagrangian filling although the associated linearized contact homology is
+isomorphic to the homology of the smooth surface of minimal genus in the 4-ball
+bounding the knot.
+"
+28117,Skyline Computation with Noisy Comparisons,"  Given a set of n points in a d-dimensional space, we seek to compute the
+skyline, i.e., those points that are not strictly dominated by any other point,
+using few comparisons between elements. We study the crowdsourcing-inspired
+setting ([FRPU94]) where comparisons fail with constant probability. In this
+model, Groz & Milo [GM15] show three bounds on the query complexity for the
+skyline problem. We provide two output-sensitive algorithms computing the
+skyline with query complexity O(nd log(dk)) and O(ndk log(k)), where k is the
+size of the skyline. These results improve significantly on the
+state-of-the-art and are tight for low dimensions.
+"
+28118,"An infinite family of Steiner systems $S(2, 4, 2^m)$ from cyclic codes","  Steiner systems are a fascinating topic of combinatorics. The most studied
+Steiner systems are $S(2, 3, v)$ (Steiner triple systems), $S(3, 4, v)$
+(Steiner quadruple systems), and $S(2, 4, v)$. There are a few infinite
+families of Steiner systems $S(2, 4, v)$ in the literature. The objective of
+this paper is to present an infinite family of Steiner systems $S(2, 4, 2^m)$
+for all $m \equiv 2 \pmod{4} \geq 6$ from cyclic codes. This may be the first
+coding-theoretic construction of an infinite family of Steiner systems $S(2, 4,
+v)$. As a by-product, many infinite families of $2$-designs are also reported
+in this paper.
+"
+28119,SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial Objects,"  With the proliferation of mobile devices and location-based services,
+continuous generation of massive volume of streaming spatial objects (i.e.,
+geo-tagged data) opens up new opportunities to address real-world problems by
+analyzing them. In this paper, we present a novel continuous bursty region
+detection problem that aims to continuously detect a bursty region of a given
+size in a specified geographical area from a stream of spatial objects.
+Specifically, a bursty region shows maximum spike in the number of spatial
+objects in a given time window. The problem is useful in addressing several
+real-world challenges such as surge pricing problem in online transportation
+and disease outbreak detection. To solve the problem, we propose an exact
+solution and two approximate solutions, and the approximation ratio is
+$\frac{1-\alpha}{4}$ in terms of the burst score, where $\alpha$ is a parameter
+to control the burst score. We further extend these solutions to support
+detection of top-$k$ bursty regions. Extensive experiments with real-world data
+are conducted to demonstrate the efficiency and effectiveness of our solutions.
+"
+28120,Diffusive hydrodynamics of out-of-time-ordered correlators with charge conservation,"  The scrambling of quantum information in closed many-body systems, as
+measured by out-of-time-ordered correlation functions (OTOCs), has lately
+received considerable attention. Recently, a hydrodynamical description of
+OTOCs has emerged from considering random local circuits, aspects of which are
+conjectured to be universal to ergodic many-body systems, even without
+randomness. Here we extend this approach to systems with locally conserved
+quantities (e.g., energy). We do this by considering local random unitary
+circuits with a conserved U$(1)$ charge and argue, with numerical and
+analytical evidence, that the presence of a conservation law slows relaxation
+in both time ordered {\textit{and}} out-of-time-ordered correlation functions,
+both can have a diffusively relaxing component or ""hydrodynamic tail"" at late
+times. We verify the presence of such tails also in a deterministic,
+peridocially driven system. We show that for OTOCs, the combination of
+diffusive and ballistic components leads to a wave front with a specific,
+asymmetric shape, decaying as a power law behind the front. These results also
+explain existing numerical investigations in non-noisy ergodic systems with
+energy conservation. Moreover, we consider OTOCs in Gibbs states, parametrized
+by a chemical potential $\mu$, and apply perturbative arguments to show that
+for $\mu\gg 1$ the ballistic front of information-spreading can only develop at
+times exponentially large in $\mu$ -- with the information traveling
+diffusively at earlier times. We also develop a new formalism for describing
+OTOCs and operator spreading, which allows us to interpret the saturation of
+OTOCs as a form of thermalization on the Hilbert space of operators.
+"
+28121,Finite Sample Guarantees for PCA in Non-Isotropic and Data-Dependent Noise,"  This work obtains novel finite sample guarantees for Principal Component
+Analysis (PCA). These hold even when the corrupting noise is non-isotropic, and
+a part (or all of it) is data-dependent. Because of the latter, in general, the
+noise and the true data are correlated. The results in this work are a
+significant improvement over those given in our earlier work where this
+""correlated-PCA"" problem was first studied. In fact, in certain regimes, our
+results imply that the sample complexity required to achieve subspace recovery
+error that is a constant fraction of the noise level is near-optimal. Useful
+corollaries of our result include guarantees for PCA in sparse data-dependent
+noise and for PCA with missing data. An important application of the former is
+in proving correctness of the subspace update step of a popular online
+algorithm for dynamic robust PCA.
+"
+28122,Solving the Navier-Lame Equation in Cylindrical Coordinates Using the Buchwald Representation: Some Parametric Solutions with Applications,"  Using a separable Buchwald representation in cylindrical coordinates, we show
+how under certain conditions the coupled equations of motion governing the
+Buchwald potentials can be decoupled and then solved using well-known
+techniques from the theory of PDEs. Under these conditions, we then construct
+three parametrized families of particular solutions to the Navier-Lame equation
+in cylindrical coordinates. In this paper, we specifically construct solutions
+having 2pi-periodic angular parts. These particular solutions can be directly
+applied to a fundamental set of linear elastic boundary value problems in
+cylindrical coordinates and are especially suited to problems involving one or
+more physical parameters. As an illustrative example, we consider the problem
+of determining the response of a solid elastic cylinder subjected to a
+time-harmonic surface pressure that varies sinusoidally along its axis, and we
+demonstrate how the obtained parametric solutions can be used to efficiently
+construct an exact solution to this problem. We also briefly consider
+applications to some related forced-relaxation type problems.
+"
+28123,"Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series","  This article proposes a Bayesian nonparametric method for forecasting,
+imputation, and clustering in sparsely observed, multivariate time series data.
+The method is appropriate for jointly modeling hundreds of time series with
+widely varying, non-stationary dynamics. Given a collection of $N$ time series,
+the Bayesian model first partitions them into independent clusters using a
+Chinese restaurant process prior. Within a cluster, all time series are modeled
+jointly using a novel ""temporally-reweighted"" extension of the Chinese
+restaurant process mixture. Markov chain Monte Carlo techniques are used to
+obtain samples from the posterior distribution, which are then used to form
+predictive inferences. We apply the technique to challenging forecasting and
+imputation tasks using seasonal flu data from the US Center for Disease Control
+and Prevention, demonstrating superior forecasting accuracy and competitive
+imputation accuracy as compared to multiple widely used baselines. We further
+show that the model discovers interpretable clusters in datasets with hundreds
+of time series, using macroeconomic data from the Gapminder Foundation.
+"
+28124,Multiple Equivalent Solutions for the Lasso,"  Feature selection is an important problem studied in data analytics seeking
+to identify a minimal-size feature subset that is optimally predictive for an
+outcome of interest. It is also a powerful tool in Knowledge Discovery as a
+means for gaining domain insight, e.g., identifying which medical quantities
+carry unique information for the disease status. It is arguably less recognized
+however, that the problem may have multiple, equivalent solutions. In that
+case, it is misleading to domain experts to report only one of them and ignore
+all other equivalent solutions. In this paper, we extend a well-established
+single, feature selection algorithm (i.e., reporting a single solution), namely
+the Lasso algorithm, to the multiple solution problem based on formalized
+notion of equivalence for both classification and regression tasks. Empirical
+results are obtained using a fully automated pipeline called Just Add Data Bio
+or JAD Bio training and selecting multiple, linear as well as nonlinear
+learners, optimizing hyper-parameter values, and correcting for the bias of
+multiple inductions (model selection). The results show that multiple solutions
+do exist in real datasets, as well as the ability of the algorithm to identify
+a subset of them. A comparison with the Statistical Equivalent Solutions (SES)
+algorithm shows that Lasso equivalent solutions have better prediction
+performance at the cost of selecting more features.
+"
+28125,On the Design and Invariants of a Ruled Surface,"  This paper deals with a kind of design of a ruled surface. It combines
+concepts from the fields of computer aided geometric design and kinematics. A
+dual unit spherical Bézier-like curve on the dual unit sphere (DUS) is
+obtained with respect the control points by a new method. So, with the aid of
+Study [1] transference principle, a dual unit spherical Bézier-like curve
+corresponds to a ruled surface. Furthermore, closed ruled surfaces are
+determined via control points and integral invariants of these surfaces are
+investigated. The results are illustrated by examples.
+"
+28126,Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality,"  In an era where accumulating data is easy and storing it inexpensive, feature
+selection plays a central role in helping to reduce the high-dimensionality of
+huge amounts of otherwise meaningless data. In this paper, we propose a
+graph-based method for feature selection that ranks features by identifying the
+most important ones into arbitrary set of cues. Mapping the problem on an
+affinity graph-where features are the nodes-the solution is given by assessing
+the importance of nodes through some indicators of centrality, in particular,
+the Eigen-vector Centrality (EC). The gist of EC is to estimate the importance
+of a feature as a function of the importance of its neighbors. Ranking central
+nodes individuates candidate features, which turn out to be effective from a
+classification point of view, as proved by a thoroughly experimental section.
+Our approach has been tested on 7 diverse datasets from recent literature
+(e.g., biological data and object recognition, among others), and compared
+against filter, embedded and wrappers methods. The results are remarkable in
+terms of accuracy, stability and low execution time.
+"
+28127,Diffeological vector spaces,"  We study the relationship between many natural conditions that one can put on
+a diffeological vector space, including being fine or projective, having enough
+smooth (or smooth linear) functions to separate points, having a diffeology
+determined by the smooth linear functionals, and more. Our main result is that
+the majority of the conditions fit into a total order. We also give many
+examples in order to show which implications do not hold.
+"
+28128,On a new formula for the Gorenstein dimension,"  Let $A$ be a finite dimensional algebra over a field $K$ with enveloping
+algebra $A^e=A^{op} \otimes_K A$. We call algebras $A$ that have the property
+that the subcategory of Gorenstein projective modules in $mod-A$ coincide with
+the subcategory $\{ X \in mod-A | Ext_A^i(X,A)=0 $ for all $i \geq 1 \}$ left
+nearly Gorenstein. The class of left nearly Gorenstein algebras is a large
+class that includes for example all Gorenstein algebras and all
+representation-finite algebras. We prove that the Gorenstein dimension of $A$
+coincides with the Gorenstein projective dimension of the regular module as
+$A^e$-module for left nearly Gorenstein algebras $A$. We give three application
+of this result. The first generalises a formula by Happel for the global
+dimension of algebras. The second application generalises a criterion of Shen
+for an algebra to be selfinjective. As a final application we prove a stronger
+version of the first Tachikawa conjecture for left nearly Gorenstein algebras.
+"
+28129,Global a priori estimates for the inhomogeneous Landau equation with moderately soft potentials,"  We establish a priori upper bounds for solutions to the spatially
+inhomogeneous Landau equation in the case of moderately soft potentials, with
+arbitrary initial data, under the assumption that mass, energy and entropy
+densities stay under control. Our pointwise estimates decay polynomially in the
+velocity variable. We also show that if the initial data satisfies a Gaussian
+upper bound, this bound is propagated for all positive times.
+"
+28130,MirBot: A collaborative object recognition system for smartphones using convolutional neural networks,"  MirBot is a collaborative application for smartphones that allows users to
+perform object recognition. This app can be used to take a photograph of an
+object, select the region of interest and obtain the most likely class (dog,
+chair, etc.) by means of similarity search using features extracted from a
+convolutional neural network (CNN). The answers provided by the system can be
+validated by the user so as to improve the results for future queries. All the
+images are stored together with a series of metadata, thus enabling a
+multimodal incremental dataset labeled with synset identifiers from the WordNet
+ontology. This dataset grows continuously thanks to the users' feedback, and is
+publicly available for research. This work details the MirBot object
+recognition system, analyzes the statistics gathered after more than four years
+of usage, describes the image classification methodology, and performs an
+exhaustive evaluation using handcrafted features, convolutional neural codes
+and different transfer learning techniques. After comparing various models and
+transformation methods, the results show that the CNN features maintain the
+accuracy of MirBot constant over time, despite the increasing number of new
+classes. The app is freely available at the Apple and Google Play stores.
+"
+28131,Relevance of Tidal Heating on Large TNOs,"  We examine the relevance of tidal heating for large Trans-Neptunian Objects,
+with a focus on its potential to melt and maintain layers of subsurface liquid
+water. Depending on their past orbital evolution, tidal heating may be an
+important part of the heat budget for a number of discovered and hypothetical
+TNO systems and may enable formation of, and increased access to, subsurface
+liquid water. Tidal heating induced by the process of despinning is found to be
+particularly able to compete with heating due to radionuclide decay in a number
+of different scenarios. In cases where radiogenic heating alone may establish
+subsurface conditions for liquid water, we focus on the extent by which tidal
+activity lifts the depth of such conditions closer to the surface. While it is
+common for strong tidal heating and long lived tides to be mutually exclusive,
+we find this is not always the case, and highlight when these two traits occur
+together.
+"
+28132,Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics,"  Algorithmic collusion is an emerging concept in current artificial
+intelligence age. Whether algorithmic collusion is a creditable threat remains
+as an argument. In this paper, we propose an algorithm which can extort its
+human rival to collude in a Cournot duopoly competing market. In experiments,
+we show that, the algorithm can successfully extorted its human rival and gets
+higher profit in long run, meanwhile the human rival will fully collude with
+the algorithm. As a result, the social welfare declines rapidly and stably.
+Both in theory and in experiment, our work confirms that, algorithmic collusion
+can be a creditable threat. In application, we hope, the frameworks, the
+algorithm design as well as the experiment environment illustrated in this
+work, can be an incubator or a test bed for researchers and policymakers to
+handle the emerging algorithmic collusion.
+"
+28133,Random Shuffling Beats SGD after Finite Epochs,"  A long-standing problem in the theory of stochastic gradient descent (SGD) is
+to prove that its without-replacement version RandomShuffle converges faster
+than the usual with-replacement version. We present the first (to our
+knowledge) non-asymptotic solution to this problem, which shows that after a
+""reasonable"" number of epochs RandomShuffle indeed converges faster than SGD.
+Specifically, we prove that under strong convexity and second-order smoothness,
+the sequence generated by RandomShuffle converges to the optimal solution at
+the rate O(1/T^2 + n^3/T^3), where n is the number of components in the
+objective, and T is the total number of iterations. This result shows that
+after a reasonable number of epochs RandomShuffle is strictly better than SGD
+(which converges as O(1/T)). The key step toward showing this better dependence
+on T is the introduction of n into the bound; and as our analysis will show, in
+general a dependence on n is unavoidable without further changes to the
+algorithm. We show that for sparse data RandomShuffle has the rate O(1/T^2),
+again strictly better than SGD. Furthermore, we discuss extensions to nonconvex
+gradient dominated functions, as well as non-strongly convex settings.
+"
+28134,An innovation diffusion model of a local electricity network that is influenced by internal and external factors,"  Haynes et al. (1977) derived a nonlinear differential equation to determine
+the spread of innovations within a social network across space and time. This
+model depends upon the imitators and the innovators within the social system,
+where the imitators respond to internal influences, whilst the innovators react
+to external factors. Here, this differential equation is applied to simulate
+the uptake of a low-carbon technology (LCT) within a local electricity network
+situated in the UK. This network comprises of many households that are assigned
+to certain feeders. Firstly, travelling wave solutions of Haynes' model are
+used to predict adoption times as a function of the imitation and innovation
+influences. Then, the grid that represents the electricity network is created
+so that the finite element method (FEM) can be implemented. Next, innovation
+diffusion is modelled with Haynes' equation and the FEM, where varying
+magnitudes of the internal and external pressures are imposed. Consequently,
+the impact of these model parameters is examined. Moreover, LCT adoption
+trajectories at fixed feeder locations are calculated, which give a macroscopic
+understanding of the uptake behaviour at specific network sites. Lastly, the
+adoption of LCTs at a household level is discussed.
+"
+28135,IoT Stream Processing and Analytics in The Fog,"  The emerging Fog paradigm has been attracting increasing interests from both
+academia and industry, due to the low-latency, resilient, and cost-effective
+services it can provide. Many Fog applications such as video mining and event
+monitoring, rely on data stream processing and analytics, which are very
+popular in the Cloud, but have not been comprehensively investigated in the
+context of Fog architecture. In this article, we present the general models and
+architecture of Fog data streaming, by analyzing the common properties of
+several typical applications. We also analyze the design space of Fog streaming
+with the consideration of four essential dimensions (system, data, human, and
+optimization), where both new design challenges and the issues arise from
+leveraging existing techniques are investigated, such as Cloud stream
+processing, computer networks, and mobile computing.
+"
+28136,A search for circularly polarised emission from young exoplanets,"  We report the results of a 154 MHz survey to search for emission from
+exoplanets located in the Upper Scorpius subgroup of the Sco Cen OB2
+Association, the closest substantial region of recent star formation. This
+survey is different from previous efforts in that it is the first to target
+exoplanets orbiting Myr-old stars. Young exoplanet systems are expected to be
+the best candidates for radio detections given the higher magnetic field
+strengths predicted for young planets as well as the stronger and more dense
+stellar wind expected for the host stars. The radio emission from exoplanets is
+expected to be highly circularly polarised therefore we restricted our search
+to the circular polarisation images rather than the total intensity images. We
+carried out two different search methods using this data. The first method was
+a targeted search for exoplanet emission using catalogues of known stars and
+Hot Jupiters within the Upper Scorpius field. The second search method was a
+blind search for highly circularly polarised sources in the field and for
+sources identified only in our polarisation images. Both the blind and targeted
+search resulted in non-detections with typical 3-sigma flux density limits of
+4--235 mJy over timescales of 1.87--1000 minutes. In particular, we place the
+first limits on low-frequency emission from the Hot Jupiter systems WASP-17 b
+and K2-33 b. These are the first results from a larger program to
+systematically search for low-frequency radio emission from planets orbiting
+young stars.
+"
+28137,Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling,"  Satellite imagery and remote sensing provide explanatory variables at
+relatively high resolutions for modeling geospatial phenomena, yet regional
+summaries are often desirable for analysis and actionable insight. In this
+paper, we propose a novel method of inducing spatial aggregations as a
+component of the machine learning process, yielding regional model features
+whose construction is driven by model prediction performance rather than prior
+assumptions. Our results demonstrate that Genetic Programming is particularly
+well suited to this type of feature construction because it can automatically
+synthesize appropriate aggregations, as well as better incorporate them into
+predictive models compared to other regression methods we tested. In our
+experiments we consider a specific problem instance and real-world dataset
+relevant to predicting snow properties in high-mountain Asia.
+"
+28138,Neural eliminators and classifiers,"  Classification may not be reliable for several reasons: noise in the data,
+insufficient input information, overlapping distributions and sharp definition
+of classes. Faced with several possibilities neural network may in such cases
+still be useful if instead of a classification elimination of improbable
+classes is done. Eliminators may be constructed using classifiers assigning new
+cases to a pool of several classes instead of just one winning class.
+Elimination may be done with the help of several classifiers using modified
+error functions. A real life medical application of neural network is presented
+illustrating the usefulness of elimination.
+"
+28139,Exploiting Structure for Fast Kernel Learning,"  We propose two methods for exact Gaussian process (GP) inference and learning
+on massive image, video, spatial-temporal, or multi-output datasets with
+missing values (or ""gaps"") in the observed responses. The first method ignores
+the gaps using sparse selection matrices and a highly effective low-rank
+preconditioner is introduced to accelerate computations. The second method
+introduces a novel approach to GP training whereby response values are inferred
+on the gaps before explicitly training the model. We find this second approach
+to be greatly advantageous for the class of problems considered. Both of these
+novel approaches make extensive use of Kronecker matrix algebra to design
+massively scalable algorithms which have low memory requirements. We
+demonstrate exact GP inference for a spatial-temporal climate modelling problem
+with 3.7 million training points as well as a video reconstruction problem with
+1 billion points.
+"
+28140,Sparse Reduced Rank Regression With Nonconvex Regularization,"  In this paper, the estimation problem for sparse reduced rank regression
+(SRRR) model is considered. The SRRR model is widely used for dimension
+reduction and variable selection with applications in signal processing,
+econometrics, etc. The problem is formulated to minimize the least squares loss
+with a sparsity-inducing penalty considering an orthogonality constraint.
+Convex sparsity-inducing functions have been used for SRRR in literature. In
+this work, a nonconvex function is proposed for better sparsity inducing. An
+efficient algorithm is developed based on the alternating minimization (or
+projection) method to solve the nonconvex optimization problem. Numerical
+simulations show that the proposed algorithm is much more efficient compared to
+the benchmark methods and the nonconvex function can result in a better
+estimation accuracy.
+"
+28141,Dual frequency parametric excitation of a nonlinear multi degree of freedom amplifier with a digitally modified topology,"  Mechanical or electromechanical amplifiers can exploit the high-Q and low
+noise features of mechanical resonance, in particular when parametric
+excitation is employed. Multi-frequency parametric excitation introduces
+tunability and is able to project weak input signals on a selected resonance.
+The present paper addresses multi degree of freedom mechanical amplifiers or
+resonators whose analysis and features require treatment of the spatial as well
+as temporal behavior. In some cases, virtual electronic coupling can alter the
+given topology of the resonator to better amplify specific inputs. An
+analytical development is followed by a numerical and experimental sensitivity
+and performance verifications, illustrating the advantages and disadvantages of
+such topologies.
+"
+28142,STEREO: Search for sterile neutrinos at the ILL,"  Recent studies have shown that there are discrepancies between observations
+and the theoretical predictions in some neutrino experiments at short
+distances. In the so-called ""Reactor Antineutrino Anomaly"" and in the ""Gallium
+Anomaly"", these differences from the expectations are at the $\sim$3 $\sigma$
+level in both cases. Oscillations into a light sterile neutrino state ($\Delta
+m^{2} \sim 1eV^{2}$) could account for the deficits in observed rates. The
+STEREO experiment has been conceived to confirm or reject the sterile neutrino
+hypothesis. It will search for an oscillation pattern at short baselines (9-11
+m) in the energy spectrum of the antineutrinos emitted by the research nuclear
+reactor of the Institut Laue Langevin. To this end, the detector is filled with
+two tons of Gd-loaded liquid scintillator read out by an array of PMTs and is
+segmented into 6 cells in the direction of the antineutrino's propagation.
+STEREO should be capable of excluding the best fit parameters region of a
+candidate sterile neutrino at 5 $\sigma$. Data taking will start at the end of
+2016 and the first physics results are expected by the first semester of 2017.
+"
+28143,Effective field theory of statistical anisotropies for primordial bispectrum and gravitational waves,"  We present the effective field theory studies of primordial statistical
+anisotropies in models of anisotropic inflation. The general action in unitary
+gauge is presented to calculate the leading interactions between the gauge
+field fluctuations, the curvature perturbations and the tensor perturbations.
+The anisotropies in scalar power spectrum and bispectrum are calculated and the
+dependence of these anisotropies to EFT couplings are presented. In addition,
+we calculate the statistical anisotropy in tensor power spectrum and the
+scalar-tensor cross correlation. Our EFT approach incorporates anisotropies
+generated in models with non-trivial speed for the gauge field fluctuations and
+sound speed for scalar perturbations such as in DBI inflation.
+"
+28144,Degree-regular triangulations of surfaces,"  A degree-regular triangulation is one in which each vertex has identical
+degree. Our main result is that any such triangulation of a (possibly
+non-compact) surface $S$ is geometric, that is, it is combinatorially
+equivalent to a geodesic triangulation with respect to a constant curvature
+metric on $S$, and we list the possibilities. A key ingredient of the proof is
+to show that any two $d$-regular triangulations of the plane for $d> 6 $ are
+combinatorially equivalent. The proof of this uniqueness result, which is of
+independent interest, is based on an inductive argument involving some
+combinatorial topology.
+"
+28145,Numerical study on thermal transpiration flows through a rectangular channel,"  Gaseous thermal transpiration flows through a rectangular micro-channel are
+simulated by the direct simulation BGK (DSBGK) method. These flows are
+rarefied, within the slip and transitional flow regimes, which are beyond many
+traditional computational fluid dynamic simulation schemes, such as those based
+on the continuum flow assumption. The flows are very slow and thus many
+traditional particle simulation methods suffer large statistical noises. The
+adopted method is a combination of particle and gas kinetic methods and it can
+simulate micro-flows properly. The simulation results of mass flow rates have
+excellent agreement with experimental measurements. Another finding from this
+study is that numerical simulations by including two reservoirs at the channel
+ends lead to appreciable differences in simulation results of velocity and
+pressure distributions within the micro-channel. This is due to the inhaling
+and exhaling effects of reservoirs at the channel ends. Even though excluding
+those reservoirs may accelerate the simulations significantly by using a single
+channel in simulations, special attentions are needed because this treatment
+may over-simplify the problem, and some procedures and results may be
+questionable. One example is to determine the surface momentum accommodation
+coefficient by using analytical solution of the mass flow rate obtained in a
+single-channel problem without the confinement effect of reservoirs at the two
+ends.
+"
+28146,Stability of a planar front in a multidimensional reaction-diffusion system,"  We study the planar front solution for a class of reaction diffusion
+equations in multidimensional space in the case when the essential spectrum of
+the linearization in the direction of the front touches the imaginary axis. At
+the linear level, the spectrum is stabilized by using an exponential weight.
+A-priori estimates for the nonlinear terms of the equation governing the
+evolution of the perturbations of the front are obtained when perturbations
+belong to the intersection of the exponentially weighted space with the
+original space without a weight. These estimates are then used to show that in
+the original norm, initially small perturbations to the front remain bounded,
+while in the exponentially weighted norm, they algebraically decay in time.
+"
+28147,Fractality in nonequilibrium steady states of quasiperiodic systems,"  We investigate the nonequilibrium response of quasiperiodic systems to
+boundary driving. In particular we focus on the Aubry-André-Harper model at
+its metal-insulator transition and the diagonal Fibonacci model. We find that
+opening the system at the boundaries provides a transparent and experimentally
+viable technique to probe its underlying fractality, which is reflected in the
+fractal spatial dependence of simple observables (such as magnetization) in the
+nonequilibrium steady state. We also find that the dynamics in the
+nonequilibrium steady state depends on the length of the chain chosen: generic
+length chains harbour qualitatively slower transport (different scaling
+exponent) than Fibonacci length chains, which is in turn slower than in the
+closed system. We conjecture that such fractal nonequilibrium steady states
+should arise in generic driven critical systems that have fractal properties.
+"
+28148,Electronic structure and magnetic properties of magnetically dead layers in epitaxial CoFe2O4/Al2O3/Si(111) films studied by X-ray magnetic circular dichroism,"  Epitaxial CoFe2O4/Al2O3 bilayers are expected to be highly efficient spin
+injectors into Si owing to the spin filter effect of CoFe2O4. To exploit the
+full potential of this system, understanding the microscopic origin of
+magnetically dead layers at the CoFe2O4/Al2O3 interface is necessary. In this
+paper, we study the crystallographic and electronic structures and the magnetic
+properties of CoFe2O4(111) layers with various thicknesses (thickness d = 1.4,
+2.3, 4, and 11 nm) in the epitaxial CoFe2O4(111)/Al2O3(111)/Si(111) structures
+using soft X-ray absorption spectroscopy (XAS) and X-ray magnetic circular
+dichroism (XMCD) combined with cluster-model calculation. The magnetization of
+CoFe2O4 measured by XMCD gradually decreases with decreasing thickness d and
+finally a magnetically dead layer is clearly detected at d = 1.4 nm. The
+magnetically dead layer has frustration of magnetic interactions which is
+revealed from comparison between the magnetizations at 300 and 6 K. From
+analysis using configuration-interaction cluster-model calculation, the
+decrease of d leads to a decrease in the inverse-to-normal spinel structure
+ratio and also a decrease in the average valence of Fe at the octahedral sites.
+These results strongly indicate that the magnetically dead layer at the
+CoFe2O4/Al2O3 interface originates from various complex networks of
+superexchange interactions through the change in the crystallographic and
+electronic structures. Furthermore, from comparison of the magnetic properties
+between d = 1.4 and 2.3 nm, it is found that ferrimagnetic order of the
+magnetically dead layer at d = 1.4 nm is restored by the additional growth of
+the 0.9-nm-thick CoFe2O4 layer on it.
+"
+28149,Generalization Properties of Doubly Stochastic Learning Algorithms,"  Doubly stochastic learning algorithms are scalable kernel methods that
+perform very well in practice. However, their generalization properties are not
+well understood and their analysis is challenging since the corresponding
+learning sequence may not be in the hypothesis space induced by the kernel. In
+this paper, we provide an in-depth theoretical analysis for different variants
+of doubly stochastic learning algorithms within the setting of nonparametric
+regression in a reproducing kernel Hilbert space and considering the square
+loss. Particularly, we derive convergence results on the generalization error
+for the studied algorithms either with or without an explicit penalty term. To
+the best of our knowledge, the derived results for the unregularized variants
+are the first of this kind, while the results for the regularized variants
+improve those in the literature. The novelties in our proof are a sample error
+bound that requires controlling the trace norm of a cumulative operator, and a
+refined analysis of bounding initial error.
+"
+28150,Termination of Nondeterministic Recursive Probabilistic Programs,"  We study the termination problem for nondeterministic recursive probabilistic
+programs. First, we show that a ranking-supermartingales-based approach is both
+sound and complete for bounded terminiation (i.e., bounded expected termination
+time over all schedulers). Our result also clarifies previous results which
+claimed that ranking supermartingales are not a complete approach even for
+nondeterministic probabilistic programs without recursion. Second, we show that
+conditionally difference-bounded ranking supermartingales provide a sound
+approach for lower bounds of expected termination time. Finally, we show that
+supermartingales with lower bounds on conditional absolute difference provide a
+sound approach for almost-sure termination, along with explicit bounds on tail
+probabilities of nontermination within a given number of steps. We also present
+several illuminating counterexamples that establish the necessity of certain
+prerequisites (such as conditionally difference-bounded condition).
+"
+28151,Fine-graind Image Classification via Combining Vision and Language,"  Fine-grained image classification is a challenging task due to the large
+intra-class variance and small inter-class variance, aiming at recognizing
+hundreds of sub-categories belonging to the same basic-level category. Most
+existing fine-grained image classification methods generally learn part
+detection models to obtain the semantic parts for better classification
+accuracy. Despite achieving promising results, these methods mainly have two
+limitations: (1) not all the parts which obtained through the part detection
+models are beneficial and indispensable for classification, and (2)
+fine-grained image classification requires more detailed visual descriptions
+which could not be provided by the part locations or attribute annotations. For
+addressing the above two limitations, this paper proposes the two-stream model
+combining vision and language (CVL) for learning latent semantic
+representations. The vision stream learns deep representations from the
+original visual information via deep convolutional neural network. The language
+stream utilizes the natural language descriptions which could point out the
+discriminative parts or characteristics for each image, and provides a flexible
+and compact way of encoding the salient visual aspects for distinguishing
+sub-categories. Since the two streams are complementary, combining the two
+streams can further achieves better classification accuracy. Comparing with 12
+state-of-the-art methods on the widely used CUB-200-2011 dataset for
+fine-grained image classification, the experimental results demonstrate our CVL
+approach achieves the best performance.
+"
+28152,Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity,"  Learning the directed acyclic graph (DAG) structure of a Bayesian network
+from observational data is a notoriously difficult problem for which many
+hardness results are known. In this paper we propose a provably polynomial-time
+algorithm for learning sparse Gaussian Bayesian networks with equal noise
+variance --- a class of Bayesian networks for which the DAG structure can be
+uniquely identified from observational data --- under high-dimensional
+settings. We show that $O(k^4 \log p)$ number of samples suffices for our
+method to recover the true DAG structure with high probability, where $p$ is
+the number of variables and $k$ is the maximum Markov blanket size. We obtain
+our theoretical guarantees under a condition called Restricted Strong Adjacency
+Faithfulness, which is strictly weaker than strong faithfulness --- a condition
+that other methods based on conditional independence testing need for their
+success. The sample complexity of our method matches the information-theoretic
+limits in terms of the dependence on $p$. We show that our method out-performs
+existing state-of-the-art methods for learning Gaussian Bayesian networks in
+terms of recovering the true DAG structure while being comparable in speed to
+heuristic methods.
+"
+28153,Scenario Aggregation using Binary Decision Diagrams for Stochastic Programs with Endogenous Uncertainty,"  Modeling decision-dependent scenario probabilities in stochastic programs is
+difficult and typically leads to large and highly non-linear MINLPs that are
+very difficult to solve. In this paper, we develop a new approach to obtain a
+compact representation of the recourse function using a set of binary decision
+diagrams (BDDs) that encode a nested cover of the scenario set. The resulting
+BDDs can then be used to efficiently characterize the decision-dependent
+scenario probabilities by a set of linear inequalities, which essentially
+factorizes the probability distribution and thus allows to reformulate the
+entire problem as a small mixed-integer linear program. The approach is
+applicable to a large class of stochastic programs with multivariate binary
+scenario sets, such as stochastic network design, network reliability, or
+stochastic network interdiction problems. Computational results show that the
+BDD-based scenario representation reduces the problem size, and hence the
+computation time, significant compared to previous approaches.
+"
+28154,Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue,"  We describe the adaptation and refinement of a graphical user interface
+designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot
+dialogue data. The data collected will be used to develop a dialogue system for
+robot navigation. Building on an interface previously used in the development
+of dialogue systems for virtual agents and video playback, we add templates
+with open parameters which allow the wizard to quickly produce a wide variety
+of utterances. Our research demonstrates that this approach to data collection
+is viable as an intermediate step in developing a dialogue system for physical
+robots in remote locations from their users - a domain in which the human and
+robot need to regularly verify and update a shared understanding of the
+physical environment. We show that our WoZ interface and the fixed set of
+utterances and templates therein provide for a natural pace of dialogue with
+good coverage of the navigation domain.
+"
+28155,Eliminating the non-Gaussian spectral response of X-ray absorbers for transition-edge sensors,"  Transition-edge sensors (TES) as microcalorimeters for high-energy-resolution
+X-ray spectroscopy are often fabricated with an absorber made of materials with
+high Z (for X-ray stopping power) and low heat capacity (for high resolving
+power). Bismuth represents one of the most compelling options. TESs with
+evaporated bismuth absorbers have shown spectra with undesirable and
+unexplained low-energy tails. We have developed TESs with electroplated bismuth
+absorbers over a gold layer that are not afflicted by this problem and that
+retain the other positive aspects of this material. To better understand these
+phenomena, we have studied a series of TESs with gold, gold/evaporated bismuth,
+and gold/electroplated bismuth absorbers, fabricated on the same die with
+identical thermal coupling. We show that bismuth morphology is linked to the
+spectral response of X-ray TES microcalorimeters.
+"
+28156,Hierarchical Salient Object Detection for Assisted Grasping,"  Visual scene decomposition into semantic entities is one of the major
+challenges when creating a reliable object grasping system. Recently, we
+introduced a bottom-up hierarchical clustering approach which is able to
+segment objects and parts in a scene. In this paper, we introduce a transform
+from such a segmentation into a corresponding, hierarchical saliency function.
+In comprehensive experiments we demonstrate its ability to detect salient
+objects in a scene. Furthermore, this hierarchical saliency defines a most
+salient corresponding region (scale) for every point in an image. Based on
+this, an easy-to-use pick and place manipulation system was developed and
+tested exemplarily.
+"
+28157,Castelnuovo-Mumford regularity of Koszul cycles and Koszul homologies,"  We extend to one dimensional quotients the result of A. Conca and S. Murai on
+the convexity of the regularity of Koszul cycles. By providing a relation
+between the regularity of Koszul cycles and Koszul homologies we prove a sharp
+regularity bound for the Koszul homologies of a homogeneous ideal in a
+polynomial ring under the same conditions.
+"
+28158,Roles of anisotropic and unequal gaps in the quasiparticle interference of superconducting iron pnictides,"  We investigate the role of gap characteristics such as anisotropy and
+inequality of the gaps in the quasiparticle interferences of iron pnictides
+using a five-orbital tight-binding model. We examine how the difference in the
+sensitivities exhibited by the sign-changing and -preserving $s$-wave
+superconductivity in an annular region around ($\pi, 0$), which can be used to
+determine the sign change of the superconducting gap, gets affected when the
+gaps are unequal on the electron and hole pocket. In addition, we also discuss
+how robust these differentiating features are on changing the quasiparticle
+energy or when the gap is anisotropic.
+"
+28159,A geographically distributed bio-hybrid neural network with memristive plasticity,"  Throughout evolution the brain has mastered the art of processing real-world
+inputs through networks of interlinked spiking neurons. Synapses have emerged
+as key elements that, owing to their plasticity, are merging neuron-to-neuron
+signalling with memory storage and computation. Electronics has made important
+steps in emulating neurons through neuromorphic circuits and synapses with
+nanoscale memristors, yet novel applications that interlink them in
+heterogeneous bio-inspired and bio-hybrid architectures are just beginning to
+materialise. The use of memristive technologies in brain-inspired architectures
+for computing or for sensing spiking activity of biological neurons8 are only
+recent examples, however interlinking brain and electronic neurons through
+plasticity-driven synaptic elements has remained so far in the realm of the
+imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where
+memristors work as ""synaptors"" between rat neural circuits and VLSI neurons.
+The two fundamental synaptors, from artificial-to-biological (ABsyn) and from
+biological-to- artificial (BAsyn), are interconnected over the Internet. The
+bNN extends across Europe, collapsing spatial boundaries existing in natural
+brain networks and laying the foundations of a new geographically distributed
+and evolving architecture: the Internet of Neuro-electronics (IoN).
+"
+28160,Reconstructing Generalized Staircase Polygons with Uniform Step Length,"  Visibility graph reconstruction, which asks us to construct a polygon that
+has a given visibility graph, is a fundamental problem with unknown complexity
+(although visibility graph recognition is known to be in PSPACE). We show that
+two classes of uniform step length polygons can be reconstructed efficiently by
+finding and removing rectangles formed between consecutive convex boundary
+vertices called tabs. In particular, we give an $O(n^2m)$-time reconstruction
+algorithm for orthogonally convex polygons, where $n$ and $m$ are the number of
+vertices and edges in the visibility graph, respectively. We further show that
+reconstructing a monotone chain of staircases (a histogram) is fixed-parameter
+tractable, when parameterized on the number of tabs, and polynomially solvable
+in time $O(n^2m)$ under reasonable alignment restrictions.
+"
+28161,Join irreducible semigroups,"  We begin a systematic study of those finite semigroups that generate join
+irreducible members of the lattice of pseudovarieties of finite semigroups,
+which are important for the spectral theory of this lattice. Finite semigroups
+$S$ that generate join irreducible pseudovarieties are characterized as
+follows: whenever $S$ divides a direct product $A \times B$ of finite
+semigroups, then $S$ divides either $A^n$ or $B^n$ for some $n \geq 1$. We
+present a new operator ${ \mathbf{V} \mapsto \mathbf{V}^\mathsf{bar} }$ that
+preserves the property of join irreducibility, as does the dual operator, and
+show that iteration of these operators on any nontrivial join irreducible
+pseudovariety leads to an infinite hierarchy of join irreducible
+pseudovarieties.
+We also describe all join irreducible pseudovarieties generated by a
+semigroup of order at most five. It turns out that there are $30$ such
+pseudovarieties, and there is a relatively easy way to remember them. In
+addition, we survey most results known about join irreducible pseudovarieties
+to date and generalize a number of results in Chapter 7.3 of [The $q$-Theory of
+Finite Semigroups, Springer Monographs in Mathematics, Springer, Berlin, 2009].
+"
+28162,The interpolation problem for a set of three fat points in $\mathbb{P}^1\times\mathbb{P}^1$,"  This is an appendix to the recent paper of Favacchio and Guardo. In these
+notes we describe explicitly a minimal bigraded free resolution and the
+bigraded Hilbert function of a set of 3 fat points whose support is an almost
+complete intersection (ACI) in $\mathbb{P}^1\times\mathbb{P}^1.$ This solve the
+interpolation problem for three points with an ACI support.
+"
+28163,A Measurement Method of Diffusion Coefficient of Liquid Using Radial Laser Rays Formed By Cylindrical Refractive System,"  The precision measurement of diffusion coefficient of solution is very
+important for the clear understanding of material transfer and interaction
+between the materials. But the high precision measurement of diffusion
+coefficient is very difficult compared to that of the other physical
+quantities. In this paper, we have proposed a new method to determine the
+diffusion coefficient by using the radial laser rays formed by cylindrical
+refractive system (cylindrical glass rod) and CCD camera. The cylindrical glass
+rod is placed in front of laser device and then, the laser rays spread
+radially. The slant curve with normal distribution profile is observed by
+illuminating these rays to the diffusion layer boundary. This curve is recorded
+on PC by CCD (charge coupled device) camera and the shape of this curve varies
+with time. The variation of maximum value with time in the curve determines
+diffusion coefficient. The measured diffusivity value is in good agreement with
+the previous experimental result in the tendency.
+"
+28164,Measurement of shower development and its Molière radius with a four-plane LumiCal test set-up,"  A prototype of a luminometer, designed for a future e+e- collider detector,
+and consisting at present of a four-plane module, was tested in the CERN PS
+accelerator T9 beam. The objective of this beam test was to demonstrate a
+multi-plane tungsten/silicon operation, to study the development of the
+electromagnetic shower and to compare it with MC simulations. The Molière
+radius has been determined to be 24.0 +/- 0.6 (stat.) +/- 1.5 (syst.) mm using
+a parametrization of the shower shape. Very good agreement was found between
+data and a detailed Geant4 simulation.
+"
+28165,A decomposition result for a singular elliptic equation on compact Riemannian manifolds,"  On compact Riemannian manifolds, we prove a decomposition theorem for
+arbitrarily bounded energy sequence of solutions of a singular elliptic
+equation.
+"
+28166,Recursive functions and existentially closed structures,"  The purpose of this paper is to clarify the relationship between various
+conditions implying essential undecidability: our main result is that there
+exists a theory $T$ in which all partially recursive functions are
+representable, yet $T$ does not interpret Robinson's theory $R$. To this end,
+we borrow tools from model theory---specifically, we investigate
+model-theoretic properties of the model completion of the empty theory in a
+language with function symbols. We obtain a certain characterization of
+$\exists\forall$ theories interpretable in existential theories in the process.
+"
+28167,Homological invariants relating the super Jordan plane to the Virasoro algebra,"  Nichols algebras are an important tool for the classification of Hopf
+algebras. Within those with finite GK dimension, we study homological
+invariants of the super Jordan plane, that is, the Nichols algebra
+$A=B(V(-1,2))$. These invariants are Hochschild homology, the Hochschild
+cohomology algebra, the Lie structure of the first cohomology space - which is
+a Lie subalgebra of the Virasoro algebra - and its representations $H^n(A,A)$
+and also the Yoneda algebra. We prove that the algebra $A$ is $K_2$ . Moreover,
+we prove that the Yoneda algebra of the bosonization of $A$ is also finitely
+generated, but not $K_2$ .
+"
+28168,No Time for Dead Time: Use the Fourier Amplitude Differences to Normalize Dead-time-affected Periodograms,"  Dead time affects many of the instruments used in X-ray astronomy, by
+producing a strong distortion in power density spectra. This can make it
+difficult to model the aperiodic variability of the source or look for
+quasi-periodic oscillations. Whereas in some instruments a simple a priori
+correction for dead-time-affected power spectra is possible, this is not the
+case for others such as NuSTAR, where the dead time is non-constant and long
+(~2.5 ms). Bachetti et al. 2015 suggested the cospectrum obtained from light
+curves of independent detectors within the same instrument as a possible way
+out, but this solution has always only been a partial one: the measured rms was
+still affected by dead time, because the width of the power distribution of the
+cospectrum was modulated by dead time in a frequency-dependent way.
+In this Letter we suggest a new, powerful method to normalize cospectra and,
+with some caveats, even power density spectra. Our approach uses the difference
+of the Fourier amplitudes from two independent detectors to characterize and
+filter out the effect of dead time. This method is crucially important for the
+accurate modelling of periodograms derived from instruments affected by dead
+time on board current missions like NuSTAR and ASTROSAT, but also future
+missions such as IXPE
+"
+28169,Train Feedfoward Neural Network with Layer-wise Adaptive Rate via Approximating Back-matching Propagation,"  Stochastic gradient descent (SGD) has achieved great success in training deep
+neural network, where the gradient is computed through back-propagation.
+However, the back-propagated values of different layers vary dramatically. This
+inconsistence of gradient magnitude across different layers renders
+optimization of deep neural network with a single learning rate problematic. We
+introduce the back-matching propagation which computes the backward values on
+the layer's parameter and the input by matching backward values on the layer's
+output. This leads to solving a bunch of least-squares problems, which requires
+high computational cost. We then reduce the back-matching propagation with
+approximations and propose an algorithm that turns to be the regular SGD with a
+layer-wise adaptive learning rate strategy. This allows an easy implementation
+of our algorithm in current machine learning frameworks equipped with
+auto-differentiation. We apply our algorithm in training modern deep neural
+networks and achieve favorable results over SGD.
+"
+28170,Nonparametric inference of gradual changes in the jump behaviour of time-continuous processes,"  In applications the properties of a stochastic feature often change gradually
+rather than abruptly, that is: after a constant phase for some time they slowly
+start to vary. In this paper we discuss statistical inference for the detection
+and the localisation of gradual changes in the jump characteristic of a
+discretely observed Ito semimartingale. We propose a new measure of time
+variation for the jump behaviour of the process. The statistical uncertainty of
+a corresponding estimate is analyzed by deriving new results on the weak
+convergence of a sequential empirical tail integral process and a corresponding
+multiplier bootstrap procedure.
+"
+28171,Stieltjes-Bethe equations in higher genus and branched coverings with even ramifications,"  We describe projective structures on a Riemann surface corresponding to
+monodromy groups which have trivial $SL(2)$ monodromies around singularities
+and trivial $PSL(2)$ monodromies along homologically non-trivial loops on a
+Riemann surface. We propose a natural higher genus analog of Stieltjes-Bethe
+equations. Links with branched projective structures and with Hurwitz spaces
+with ramifications of even order are established. We find a higher genus analog
+of the genus zero Yang-Yang function (the function generating accessory
+parameters) and describe its similarity and difference with Bergman
+tau-function on the Hurwitz spaces.
+"
+28172,On distinct consecutive $r$-differences,"  Suppose $A\subset \mathbb{R}$ of size $k$ has distinct consecutive
+$r$--differences, that is for $1 \leq i \leq k -r$, the $r$--tuples $$(a_{i+1}
+- a_i , \ldots , a_{i+r} - a_{i + r -1})$$ are distinct. Then for any finite
+$B \subset \mathbb{R}$, one has $$|A+B| \gg_r |A||B|^{1/(r+1)}.$$ Utilizing
+de Bruijn sequences, we show this inequality is sharp up to the constant.
+Moreover, for the sequence $\{n\alpha\}$, a sharp upper bound for the size of
+the distinct consecutive $r$--differences is obtained, which generalizes
+Steinhaus' three gap theorem. A dual problem on the consecutive
+$r$--differences of the returning times for some $\phi \in \mathbb{R}$ defined
+by $\{T : \{T\theta\}<\phi\}$ is also considered, which generalizes a result of
+Slater.
+"
+28173,Renormalization group study of quantum criticality in gapped Kondo model,"  Quantum impurity models are the prototypical examples of quantum many-body
+dynamics which manifests in their spectral and transport properties. Single
+channel Anderson(and Kondo model) leads to the Fermi liquid ground state in the
+strong coupling regime which corresponds to a stable infrared fixed point at
+which the quantum impurity gets completely screened by the conduction
+electrons. Quantum impurity models with non-trivial density of states exhibit
+quantum phase transition and this quantum criticality lies in the universality
+class of local quantum critical systems. In this paper, we report first study
+of the flow equation renormalization of gapped Kondo model which has gapped
+density of states, the gap being at the Fermi level. Flow equation
+renormalization group method has proved to be one of the very robust
+renormalization methods to study Kondo physics both in equilibrium as well as
+in non-equilibrium. Here we have shown that this method can also be employed to
+study local quantum criticality. We have calculated the flow equations for the
+Kondo coupling and solved them for various values of the gap parameter and we
+find that there is suppression of Kondo divergence as gap is increased which
+signifies that as gap is increased, renormalization flow goes away from the
+strong coupling fixed point. We have also calculated the spin susceptibility
+and we find that as gap is increased, susceptibility goes over to the Curie
+behaviour and hence confirming the renormalization flow towards the local
+moment fixed point.
+"
+28174,Partial regularity for doubly nonlinear parabolic systems of the first type,"  We study solutions ${\bf v}$ of the parabolic system of PDE $$
+\partial_t\left(D\psi({\bf v})\right)=\text{div}DF(D{\bf v}). $$ Here $\psi$
+and $F$ are convex functions, and this is a model equation for more general
+doubly nonlinear evolutions that arise in the study of phase transitions in
+materials. We show that if ${\bf v}$ is a weak solution, then $D{\bf v}$ is
+locally Hölder continuous except for possibly on a lower dimensional subset
+of the domain of ${\bf v}$. Our proof is based on compactness properties of
+solutions, two integral identities and a fractional time derivative estimate
+for $D{\bf v}$.
+"
+28175,On Guarding Orthogonal Polygons with Bounded Treewidth,"  There exist many variants of guarding an orthogonal polygon in an orthogonal
+fashion: sometimes a guard can see an entire rectangle, or along a staircase,
+or along an orthogonal path with at most $k$ bends. In this paper, we study all
+these guarding models in the special case of orthogonal polygons that have
+bounded treewidth in some sense. Exploiting algorithms for graphs of bounded
+treewidth, we show that the problem of finding the minimum number of guards in
+these models becomes linear-time solvable in orthogonal polygons of bounded
+treewidth.
+"
+28176,Testing isomorphism of central Cayley graphs over almost simple groups in polynomial time,"  A Cayley graph over a group G is said to be central if its connection set is
+a normal subset of G. It is proved that for any two central Cayley graphs over
+explicitly given almost simple groups of order n, the set of all isomorphisms
+from the first graph onto the second can be found in time poly(n).
+"
+28177,Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks,"  Linking human whole-body motion and natural language is of great interest for
+the generation of semantic representations of observed human behaviors as well
+as for the generation of robot behaviors based on natural language input. While
+there has been a large body of research in this area, most approaches that
+exist today require a symbolic representation of motions (e.g. in the form of
+motion primitives), which have to be defined a-priori or require complex
+segmentation algorithms. In contrast, recent advances in the field of neural
+networks and especially deep learning have demonstrated that sub-symbolic
+representations that can be learned end-to-end usually outperform more
+traditional approaches, for applications such as machine translation. In this
+paper we propose a generative model that learns a bidirectional mapping between
+human whole-body motion and natural language using deep recurrent neural
+networks (RNNs) and sequence-to-sequence learning. Our approach does not
+require any segmentation or manual feature engineering and learns a distributed
+representation, which is shared for all motions and descriptions. We evaluate
+our approach on 2,846 human whole-body motions and 6,187 natural language
+descriptions thereof from the KIT Motion-Language Dataset. Our results clearly
+demonstrate the effectiveness of the proposed model: We show that our model
+generates a wide variety of realistic motions only from descriptions thereof in
+form of a single sentence. Conversely, our model is also capable of generating
+correct and detailed natural language descriptions from human motions.
+"
+28178,Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction,"  Feature extraction is a very crucial task in image and pixel (voxel)
+classification and regression in biomedical image modeling. In this work we
+present a machine learning based feature extraction scheme based on inception
+models for pixel classification tasks. We extract features under multi-scale
+and multi-layer schemes through convolutional operators. Layers of Fully
+Convolutional Network are later stacked on this feature extraction layers and
+trained end-to-end for the purpose of classification. We test our model on the
+DRIVE and STARE public data sets for the purpose of segmentation and centerline
+detection and it out performs most existing hand crafted or deterministic
+feature schemes found in literature. We achieve an average maximum Dice of 0.85
+on the DRIVE data set which out performs the scores from the second human
+annotator of this data set. We also achieve an average maximum Dice of 0.85 and
+kappa of 0.84 on the STARE data set. Though these datasets are mainly 2-D we
+also propose ways of extending this feature extraction scheme to handle 3-D
+datasets.
+"
+28179,Extended Red Emission in IC59 and IC63,"  We analysed new wide-field, wide- and narrow-band optical images of IC 59 and
+IC 63, two nebulae which are externally illuminated by the early B-star
+{\gamma} Cas, with the objective of mapping the extended red emission (ERE), a
+dust-related photoluminescence process that is still poorly understood, in
+these two clouds. The spatial distribution of the ERE relative to the direction
+of the incident radiation and relative to other emission processes, whose
+carriers and excitation requirements are known, provides important constraints
+on the excitation of the ERE. In both nebulae, we find the ERE intensity to
+peak spatially well before the more extended distribution of mid-infrared
+emission in the unidentified infrared bands, supporting earlier findings that
+point toward far-ultraviolet (11 eV < E$_\mathrm{photon}$ < 13.6 eV) photons
+as the source of ERE excitation. The band-integrated absolute intensities of
+the ERE in IC 59 and IC 63 measured relative to the number density of photons
+available for ERE excitation are lower by about two orders of magnitude
+compared to ERE intensities observed in the high-latitude diffuse interstellar
+medium (ISM). This suggests that the lifetime of the ERE carriers is
+significantly reduced in the more intense radiation field prevailing in IC 59
+and IC 63, pointing toward potential carriers that are only marginally stable
+against photo-processing under interstellar conditions. A model involving
+isolated molecules or molecular ions, capable of inverse internal conversion
+and recurrent fluorescence, appears to provide the most likely explanation for
+our observational results.
+"
+28180,The Velocity of the Propagating Wave for General Coupled Scalar Systems,"  We consider spatially coupled systems governed by a set of scalar density
+evolution equations. Such equations track the behavior of message-passing
+algorithms used, for example, in coding, sparse sensing, or
+constraint-satisfaction problems. Assuming that the ""profile"" describing the
+average state of the algorithm exhibits a solitonic wave-like behavior after
+initial transient iterations, we derive a formula for the propagation velocity
+of the wave. We illustrate the formula with two applications, namely
+Generalized LDPC codes and compressive sensing.
+"
+28181,Adversarial Exploration Strategy for Self-Supervised Imitation Learning,"  We present an adversarial exploration strategy, a simple yet effective
+imitation learning scheme that incentivizes exploration of an environment
+without any extrinsic reward or human demonstration. Our framework consists of
+a deep reinforcement learning (DRL) agent and an inverse dynamics model
+contesting with each other. The former collects training samples for the
+latter, and its objective is to maximize the error of the latter. The latter is
+trained with samples collected by the former, and generates rewards for the
+former when it fails to predict the actual action taken by the former. In such
+a competitive setting, the DRL agent learns to generate samples that the
+inverse dynamics model fails to predict correctly, and the inverse dynamics
+model learns to adapt to the challenging samples. We further propose a reward
+structure that ensures the DRL agent collects only moderately hard samples and
+not overly hard ones that prevent the inverse model from imitating effectively.
+We evaluate the effectiveness of our method on several OpenAI gym robotic arm
+and hand manipulation tasks against a number of baseline models. Experimental
+results show that our method is comparable to that directly trained with expert
+demonstrations, and superior to the other baselines even without any human
+priors.
+"
+28182,Efficient Learning of Harmonic Priors for Pitch Detection in Polyphonic Music,"  Automatic music transcription (AMT) aims to infer a latent symbolic
+representation of a piece of music (piano-roll), given a corresponding observed
+audio recording. Transcribing polyphonic music (when multiple notes are played
+simultaneously) is a challenging problem, due to highly structured overlapping
+between harmonics. We study whether the introduction of physically inspired
+Gaussian process (GP) priors into audio content analysis models improves the
+extraction of patterns required for AMT. Audio signals are described as a
+linear combination of sources. Each source is decomposed into the product of an
+amplitude-envelope, and a quasi-periodic component process. We introduce the
+Matérn spectral mixture (MSM) kernel for describing frequency content of
+singles notes. We consider two different regression approaches. In the sigmoid
+model every pitch-activation is independently non-linear transformed. In the
+softmax model several activation GPs are jointly non-linearly transformed. This
+introduce cross-correlation between activations. We use variational Bayes for
+approximate inference. We empirically evaluate how these models work in
+practice transcribing polyphonic music. We demonstrate that rather than
+encourage dependency between activations, what is relevant for improving pitch
+detection is to learnt priors that fit the frequency content of the sound
+events to detect.
+"
+28183,BubbleTouch: A Quasi-Static Tactile Skin Simulator,"  We present BubbleTouch, an open source quasi-static simulator for robotic
+tactile skins. BubbleTouch can be used to simulate contact with a robot's
+tactile skin patches as it interacts with humans and objects. The simulator
+creates detailed traces of contact forces that can be used in experiments in
+tactile contact activities. We summarize the design of BubbleTouch and
+highlight our recent work that uses BubbleTouch for experiments with tactile
+object recognition.
+"
+28184,"ACIA, not ACID: Conditions, Properties and Challenges","  Although ACID is the previous golden rule for transaction support, durability
+is now not a basic requirement for data storage. Rather, high availability is
+becoming the first-class property required by online applications. We show that
+high availability of data is almost surely a stronger property than durability.
+We thus propose ACIA (Atomicity, Consistency, Isolation, Availability) as the
+new standard for transaction support. Essentially, the shift from ACID to ACIA
+is due to the change of assumed conditions for data management. Four major
+condition changes exist. With ACIA transactions, more diverse requirements can
+be flexibly supported for applications through the specification of consistency
+levels, isolation levels and fault tolerance levels. Clarifying the ACIA
+properties enables the exploitation of techniques used for ACID transactions,
+as well as bringing about new challenges for research.
+"
+28185,Automated Instruction Stream Throughput Prediction for Intel and AMD Microarchitectures,"  An accurate prediction of scheduling and execution of instruction streams is
+a necessary prerequisite for predicting the in-core performance behavior of
+throughput-bound loop kernels on out-of-order processor architectures. Such
+predictions are an indispensable component of analytical performance models,
+such as the Roofline and the Execution-Cache-Memory (ECM) model, and allow a
+deep understanding of the performance-relevant interactions between hardware
+architecture and loop code. We present the Open Source Architecture Code
+Analyzer (OSACA), a static analysis tool for predicting the execution time of
+sequential loops comprising x86 instructions under the assumption of an
+infinite first-level cache and perfect out-of-order scheduling. We show the
+process of building a machine model from available documentation and
+semi-automatic benchmarking, and carry it out for the latest Intel Skylake and
+AMD Zen micro-architectures. To validate the constructed models, we apply them
+to several assembly kernels and compare runtime predictions with actual
+measurements. Finally we give an outlook on how the method may be generalized
+to new architectures.
+"
+28186,DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks,"  3D scene understanding is important for robots to interact with the 3D world
+in a meaningful way. Most previous works on 3D scene understanding focus on
+recognizing geometrical or semantic properties of the scene independently. In
+this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a
+novel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a
+new recurrent neural network architecture for semantic labeling on RGB-D
+videos. The output of the network is integrated with mapping techniques such as
+KinectFusion in order to inject semantic information into the reconstructed 3D
+scene. Experiments conducted on a real world dataset and a synthetic dataset
+with RGB-D videos demonstrate the ability of our method in semantic 3D scene
+mapping.
+"
+28187,Imaging Polarimeter for a Sub-MeV Gamma-Ray All-Sky Survey using an Electron-Tracking Compton Camera,"  X-ray and gamma-ray polarimetry is a promising tool to study the geometry and
+the magnetic configuration of various celestial objects, such as binary black
+holes or gamma-ray bursts (GRBs). However, statistically significant
+polarizations have been detected in few of the brightest objects. Even though
+future polarimeters using X-ray telescopes are expected to observe weak
+persistent sources, there are no effective approaches to survey transient and
+serendipitous sources with a wide field of view (FoV). Here we present an
+electron-tracking Compton camera (ETCC) as a highly-sensitive gamma-ray imaging
+polarimeter. The ETCC provides powerful background rejection and a high
+modulation factor over a FoV of up to 2$\pi$ sr thanks to its excellent imaging
+based on a well-defined point spread function. Importantly, we demonstrated for
+the first time the stability of the modulation factor under realistic
+conditions of off-axis incidence and huge backgrounds using the SPring-8
+polarized X-ray beam. The measured modulation factor of the ETCC was 0.65 $\pm$
+0.01 at 150 keV for an off-axis incidence with an oblique angle of 30$^\circ$
+and was not degraded compared to the 0.58 $\pm$ 0.02 at 130 keV for on-axis
+incidence. These measured results are consistent with the simulation results.
+Consequently, we found that the satellite-ETCC proposed in Tanimori et al.
+(2015) would provide all-sky surveys of weak persistent sources of 13 mCrab
+with 10% polarization for a 10$^{7}$ s exposure and over 20 GRBs down to a
+$6\times10^{-6}$ erg cm$^{-2}$ fluence and 10% polarization during a one-year
+observation.
+"
+28188,Stability and Convergence Trade-off of Iterative Optimization Algorithms,"  The overall performance or expected excess risk of an iterative machine
+learning algorithm can be decomposed into training error and generalization
+error. While the former is controlled by its convergence analysis, the latter
+can be tightly handled by algorithmic stability. The machine learning community
+has a rich history investigating convergence and stability separately. However,
+the question about the trade-off between these two quantities remains open. In
+this paper, we show that for any iterative algorithm at any iteration, the
+overall performance is lower bounded by the minimax statistical error over an
+appropriately chosen loss function class. This implies an important trade-off
+between convergence and stability of the algorithm -- a faster converging
+algorithm has to be less stable, and vice versa. As a direct consequence of
+this fundamental tradeoff, new convergence lower bounds can be derived for
+classes of algorithms constrained with different stability bounds. In
+particular, when the loss function is convex (or strongly convex) and smooth,
+we discuss the stability upper bounds of gradient descent (GD) and stochastic
+gradient descent and their variants with decreasing step sizes. For Nesterov's
+accelerated gradient descent (NAG) and heavy ball method (HB), we provide
+stability upper bounds for the quadratic loss function. Applying existing
+stability upper bounds for the gradient methods in our trade-off framework, we
+obtain lower bounds matching the well-established convergence upper bounds up
+to constants for these algorithms and conjecture similar lower bounds for NAG
+and HB. Finally, we numerically demonstrate the tightness of our stability
+bounds in terms of exponents in the rate and also illustrate via a simulated
+logistic regression problem that our stability bounds reflect the
+generalization errors better than the simple uniform convergence bounds for GD
+and NAG.
+"
+28189,Use of difference tables at studying of properties of binomial coefficients,"  Some properties of diagonal binomial coefficients were studied in respect to
+frequency of their units digits. An approach was formulated that led to use of
+difference tables to predict if certain units digits can appear in the values
+of binomial coefficients at quadratic terms of the binomial theorem. Frequency
+distributions of units digits of binomial coefficients contain gaps (zero
+frequency) under most common numbering systems with supposed exclusion to
+systems with 2^n bases. Binomial coefficient arithmetics may be used to model
+cell population dynamics in a multicellular organism, which implies that the
+dynamics obeys power function laws.
+"
+28190,Classification of cryptocurrency coins and tokens by the dynamics of their market capitalisations,"  We empirically verify that the market capitalisations of coins and tokens in
+the cryptocurrency universe follow power-law distributions with significantly
+different values, with the tail exponent falling between 0.5 and 0.7 for coins,
+and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a
+simple proportional growth with birth & death model previously employed to
+describe the size distribution of firms, cities, webpages, etc. We empirically
+validate the model and its main predictions, in terms of proportional growth
+(Gibrat's law) of the coins and tokens. Estimating the main parameters of the
+model, the theoretical predictions for the power-law exponents of coin and
+token distributions are in remarkable agreement with the empirical estimations,
+given the simplicity of the model. Our results clearly characterize coins as
+being ""entrenched incumbents"" and tokens as an ""explosive immature ecosystem"",
+largely due to massive and exuberant Initial Coin Offering activity in the
+token space. The theory predicts that the exponent for tokens should converge
+to 1 in the future, reflecting a more reasonable rate of new entrants
+associated with genuine technological innovations.
+"
+28191,On weak $ε$-nets and the Radon number,"  We show that the Radon number characterizes the existence of weak nets in
+separable convexity spaces (an abstraction of the euclidean notion of
+convexity). The construction of weak nets when the Radon number is finite is
+based on Helly's property and on metric properties of VC classes. The lower
+bound on the size of weak nets when the Radon number is large relies on the
+chromatic number of the Kneser graph. As an application, we prove a
+boosting-type result for weak $\epsilon$-nets.
+"
+28192,Superconducting and density-wave instabilities of low dimensional conductors with a Zeeman coupling to a magnetic field,"  In the framework of the weak coupling renormalization group technique we
+examine the possible instabilities of the extended quasi-one-dimensional
+electron gas model with both intrachain and interchain electron-electron
+interactions, including the influence of umklapp scattering and the coupling of
+spins to a magnetic field. In the limit of purely repulsive intrachain
+interactions, we confirm the passage from singlet d-wave like superconductivity
+to an inhomogeneous FFLO state under magnetic field. The passage is accompanied
+by an anomalous increase of the upper critical field that scales with the
+antinesting distance from the quantum critical point joining superconductivity
+to antiferromagnetism in the phase diagram, as well as the strength of
+interactions. Adding weak repulsive interchain interactions promotes the
+passage from singlet to triplet $f$-wave superconductivity which is expanded
+under field by the development of a triplet FFLO state with zero angular
+momentum projection for the Cooper pairs. The connection between theory and
+experiments on the anomalous upper critical field in the Bechgaard salts is
+discussed.
+"
+28193,Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selection,"  There is a need for affordable, widely deployable maternal-fetal ECG monitors
+to improve maternal and fetal health during pregnancy and delivery. Based on
+the diffusion-based channel selection, here we present the mathematical
+formalism and clinical validation of an algorithm capable of accurate
+separation of maternal and fetal ECG from a two channel signal acquired over
+maternal abdomen.
+"
+28194,Targeted Undersmoothing,"  This paper proposes a post-model selection inference procedure, called
+targeted undersmoothing, designed to construct uniformly valid confidence sets
+for a broad class of functionals of sparse high-dimensional statistical models.
+These include dense functionals, which may potentially depend on all elements
+of an unknown high-dimensional parameter. The proposed confidence sets are
+based on an initially selected model and two additionally selected models, an
+upper model and a lower model, which enlarge the initially selected model. We
+illustrate application of the procedure in two empirical examples. The first
+example considers estimation of heterogeneous treatment effects using data from
+the Job Training Partnership Act of 1982, and the second example looks at
+estimating profitability from a mailing strategy based on estimated
+heterogeneous treatment effects in a direct mail marketing campaign. We also
+provide evidence on the finite sample performance of the proposed targeted
+undersmoothing procedure through a series of simulation experiments.
+"
+28195,A Geometric Framework for Stochastic Shape Analysis,"  We introduce a stochastic model of diffeomorphisms, whose action on a variety
+of data types descends to stochastic evolution of shapes, images and landmarks.
+The stochasticity is introduced in the vector field which transports the data
+in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework for
+shape analysis and image registration. The stochasticity thereby models errors
+or uncertainties of the flow in following the prescribed deformation velocity.
+The approach is illustrated in the example of finite dimensional landmark
+manifolds, whose stochastic evolution is studied both via the Fokker-Planck
+equation and by numerical simulations. We derive two approaches for inferring
+parameters of the stochastic model from landmark configurations observed at
+discrete time points. The first of the two approaches matches moments of the
+Fokker-Planck equation to sample moments of the data, while the second approach
+employs an Expectation-Maximisation based algorithm using a Monte Carlo bridge
+sampling scheme to optimise the data likelihood. We derive and numerically test
+the ability of the two approaches to infer the spatial correlation length of
+the underlying noise.
+"
+28196,Quantile Treatment Effects in the Regression Kink Design,"  This paper studies identification, estimation, and inference of quantile
+treatment effects in the fuzzy regression kink design with a binary treatment
+variable. We first show the identification of conditional quantile treatment
+effects given the event of local compliance. We then propose a bootstrap method
+of uniform inference for the local quantile process. This bootstrap method is
+fast and is robust against common optimal choices of bandwidth parameters. We
+provide practical guidelines as well as a formal theory. Simulation studies
+show accurate coverage probabilities for tests of uniform treatment
+significance and treatment heterogeneity.
+"
+28197,Periodic and quasi-periodic attractors for the spin-orbit evolution of Mercury with a realistic tidal torque,"  Mercury is entrapped in a 3:2 resonance: it rotates on its axis three times
+for every two revolutions it makes around the Sun. It is generally accepted
+that this is due to the large value of the eccentricity of its orbit. However,
+the mathematical model originally introduced to study its spin-orbit evolution
+proved not to be entirely convincing, because of the expression commonly used
+for the tidal torque. Only recently, in a series of papers mainly by Efroimsky
+and Makarov, a different model for the tidal torque has been proposed, which
+has the advantages of being more realistic, and of providing a higher
+probability of capture in the 3:2 resonance with respect to the previous
+models. On the other hand, a drawback of the model is that the function
+describing the tidal torque is not smooth and consists of a superposition of
+kinks, so that both analytical and numerical computations turn out to be rather
+delicate: indeed, standard perturbation theory based on power series expansion
+cannot be applied and the implementation of a fast algorithm to integrate the
+equations of motion numerically requires a high degree of care. In this paper,
+we make a detailed study of the spin-orbit dynamics of Mercury, as predicted by
+the realistic model: In particular, we present numerical and analytical results
+about the nature of the librations of Mercury's spin in the 3:2 resonance. The
+results provide evidence that the librations are quasi-periodic in time.
+"
+28198,Memetic Multilevel Hypergraph Partitioning,"  Hypergraph partitioning has a wide range of important applications such as
+VLSI design or scientific computing. With focus on solution quality, we develop
+the first multilevel memetic algorithm to tackle the problem. Key components of
+our contribution are new effective multilevel recombination and mutation
+operations that provide a large amount of diversity. We perform a wide range of
+experiments on a benchmark set containing instances from application areas such
+VLSI, SAT solving, social networks, and scientific computing. Compared to the
+state-of-the-art hypergraph partitioning tools hMetis, PaToH, and KaHyPar, our
+new algorithm computes the best result on almost all instances.
+"
+28199,Extrema Property of the $k$-Ranking of Directed Paths and Cycles,"  A $k$-ranking of a directed graph $G$ is a labeling of the vertex set of $G$
+with $k$ positive integers such that every directed path connecting two
+vertices with the same label includes a vertex with a larger label in between.
+The rank number of $G$ is defined to be the smallest $k$ such that $G$ has a
+$k$-ranking. We find the largest possible directed graph that can be obtained
+from a directed path or a directed cycle by attaching new edges to the vertices
+such that the new graphs have the same rank number as the original graphs. The
+adjacency matrix of the resulting graph is embedded in the Sierpiński
+triangle.
+We present a connection between the number of edges that can be added to
+paths and the Stirling numbers of the second kind. These results are
+generalized to create directed graphs which are unions of directed paths and
+directed cycles that maintain the rank number of a base graph of a directed
+path or a directed cycle.
+"
+28200,Status of the SoLid experiment: Search for sterile neutrinos at the SCK$\cdot$CEN BR2 reactor,"  The reactor antineutrino energy spectra and flux were reevaluated during the
+preparation of the recent experiments devoted to the measurement of
+$\theta_{13}$. Consequently some discrepancies between data and the theoretical
+predictions in reactor antineutrino experiments at short distances were
+observed when using the new predicted flux and spectra. This problem has been
+called the Reactor Antineutrino Anomaly (RAA), which together with the gallium
+anomaly, both show discrepancies with respect to the expectations at the $\sim$
+3 $\sigma$ level. Oscillations into a light sterile neutrino state ($\Delta
+m^{2} \sim 1eV^{2}$) could account for such deficits. The SoLid experiment has
+been conceived to give an unambiguous response to the hypothesis of a light
+sterile neutrino as the origin of the RAA. To this end, SoLid is searching for
+an oscillation pattern at short baselines (6-9 m) in the energy spectrum of the
+$\overline{\nu}_{e}$'s emitted by the
+SCK\raisebox{-0.9ex}{\scalebox{2.8}{$\cdot$}}CEN BR2 reactor in Belgium. The
+detector uses a novel technology, combining PVT (cubes of 5$\times$5$\times$5
+cm$^3$) and $^6$LiF:ZnS (sheets $\sim$ 250 $\mu$m thickness) scintillators. It
+is highly segmented (modules of 10 planes of 16$\times$16 cubes), and it's read
+out by a network of wavelength shifting fibers and SiPMs. The fine segmentation
+and the hybrid technology of the detector allows the clear identification of
+the neutrino signals, reducing significantly backgrounds. Thus, a high
+experimental sensitivity can be achieved. A 288 kg prototype was deployed in
+2015, showing the feasibility of the detection principle. A full scale detector
+(1.6 tons) is currently under construction, the data taking with the first
+detector modules is expected by the end of 2017.
+"
+28201,Reentrant Fulde-Ferrell-Larkin-Ovchinnikov superfluidity in the honeycomb lattice,"  We study superconducting properties of population-imbalanced ultracold Fermi
+mixtures in the honeycomb lattice that can be effectively described by the
+spin-imbalanced attractive Hubbard model in the presence of a Zeeman magnetic
+field. We use the mean-field theory approach to obtain ground state phase
+diagrams including some unconventional superconducting phases such as the
+Fulde--Ferrell--Larkin--Ovchinnikov (FFLO) phase. We show that this phase is
+characterized by atypical behaviour of the Cooper pairs total momentum in the
+external magnetic field. We show that the momentum changes its value as well as
+direction with change of the system parameters. We discuss the influence of van
+Hove singularities on the possibility of the reentrant FFLO phase occurrence,
+without a BCS precursor.
+"
+28202,General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv,"  The R package frailtySurv for simulating and fitting semi-parametric shared
+frailty models is introduced. Package frailtySurv implements semi-parametric
+consistent estimators for a variety of frailty distributions, including gamma,
+log-normal, inverse Gaussian and power variance function, and provides
+consistent estimators of the standard errors of the parameters' estimators. The
+parameters' estimators are asymptotically normally distributed, and therefore
+statistical inference based on the results of this package, such as hypothesis
+testing and confidence intervals, can be performed using the normal
+distribution. Extensive simulations demonstrate the flexibility and correct
+implementation of the estimator. Two case studies performed with publicly
+available datasets demonstrate applicability of the package. In the Diabetic
+Retinopathy Study, the onset of blindness is clustered by patient, and in a
+large hard drive failure dataset, failure times are thought to be clustered by
+the hard drive manufacturer and model.
+"
+28203,Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media,"  Domestic Violence (DV) is considered as big social issue and there exists a
+strong relationship between DV and health impacts of the public. Existing
+research studies have focused on social media to track and analyse real world
+events like emerging trends, natural disasters, user sentiment analysis,
+political opinions, and health care. However there is less attention given on
+social welfare issues like DV and its impact on public health. Recently, the
+victims of DV turned to social media platforms to express their feelings in the
+form of posts and seek the social and emotional support, for sympathetic
+encouragement, to show compassion and empathy among public. But, it is
+difficult to mine the actionable knowledge from large conversational datasets
+from social media due to the characteristics of high dimensions, short, noisy,
+huge volume, high velocity, and so on. Hence, this paper will propose a novel
+framework to model and discover the various themes related to DV from the
+public domain. The proposed framework would possibly provide unprecedentedly
+valuable information to the public health researchers, national family health
+organizations, government and public with data enrichment and consolidation to
+improve the social welfare of the community. Thus provides actionable knowledge
+by monitoring and analysing continuous and rich user generated content.
+"
+28204,Robust Registration of Gaussian Mixtures for Colour Transfer,"  We present a flexible approach to colour transfer inspired by techniques
+recently proposed for shape registration. Colour distributions of the palette
+and target images are modelled with Gaussian Mixture Models (GMMs) that are
+robustly registered to infer a non linear parametric transfer function. We show
+experimentally that our approach compares well to current techniques both
+quantitatively and qualitatively. Moreover, our technique is computationally
+the fastest and can take efficient advantage of parallel processing
+architectures for recolouring images and videos. Our transfer function is
+parametric and hence can be stored in memory for later usage and also combined
+with other computed transfer functions to create interesting visual effects.
+Overall this paper provides a fast user friendly approach to recolouring of
+image and video materials.
+"
+28205,Ranking in evolving complex networks,"  Complex networks have emerged as a simple yet powerful framework to represent
+and analyze a wide range of complex systems. The problem of ranking the nodes
+and the edges in complex networks is critical for a broad range of real-world
+problems because it affects how we access online information and products, how
+success and talent are evaluated in human activities, and how scarce resources
+are allocated by companies and policymakers, among others. This calls for a
+deep understanding of how existing ranking algorithms perform, and which are
+their possible biases that may impair their effectiveness. Well-established
+ranking algorithms (such as the popular Google's PageRank) are static in nature
+and, as a consequence, they exhibit important shortcomings when applied to real
+networks that rapidly evolve in time. The recent advances in the understanding
+and modeling of evolving networks have enabled the development of a wide and
+diverse range of ranking algorithms that take the temporal dimension into
+account. The aim of this review is to survey the existing ranking algorithms,
+both static and time-aware, and their applications to evolving networks. We
+emphasize both the impact of network evolution on well-established static
+algorithms and the benefits from including the temporal dimension for tasks
+such as prediction of real network traffic, prediction of future links, and
+identification of highly-significant nodes.
+"
+28206,Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression,"  In this paper, we introduce the use of a personalized Gaussian Process model
+(pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE,
+ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by
+learning a population-level model using multi-modal data from previously seen
+patients using the base Gaussian Process (GP) regression. Then, this model is
+adapted sequentially over time to a new patient using domain adaptive GPs to
+form the patient's pGP. We show that this new approach, together with an
+auto-regressive formulation, leads to significant improvements in forecasting
+future clinical status and cognitive scores for target patients when compared
+to modeling the population with traditional GPs.
+"
+28207,Updating quasar bolometric luminosity corrections. III. [O III] bolometric corrections,"  We present quasar bolometric corrections using the [O III] $\lambda5007$
+narrow emission line luminosity based on the detailed spectral energy
+distributions of 53 bright quasars at low to moderate redshift
+($0.0345<z<1.0002$). We adopted two functional forms to calculate
+$L_{\textrm{iso}}$, the bolometric luminosity determined under the assumption
+of isotropy: $L_{\textrm{iso}}=A\,L_{[O\,III]}$ for comparison with the
+literature and log$(L_{iso})=B+C\,$log$(L_{[O\,III]})$, which better
+characterizes the data. We also explored whether ""Eigenvector 1"", which
+describes the range of quasar spectral properties and quantifies their
+diversity, introduces scatter into the $L_{[O\,III]}-L_{iso}$ relationship. We
+found that the [O III] bolometric correction can be significantly improved by
+adding a term including the equivalent width ratio $R_{Fe\,II}\equiv
+EW_{Fe\,II}/EW_{H\beta}$, which is an Eigenvector 1 indicator. Inclusion of
+$R_{Fe\,II}$ in predicting $L_{iso}$ is significant at nearly the $3\sigma$
+level and reduces the scatter and systematic offset of the luminosity
+residuals. Typically, [O III] bolometric corrections are adopted for Type 2
+sources where the quasar continuum is not observed and in these cases,
+$R_{Fe\,II}$ cannot be measured. We searched for an alternative measure of
+Eigenvector 1 that could be measured in the optical spectra of Type 2 sources
+but were unable to identify one. Thus, the main contribution of this work is to
+present an improved [O III] bolometric correction based on measured bolometric
+luminosities and highlight the Eigenvector 1 dependence of the correction in
+Type 1 sources.
+"
+28208,Using text analysis to quantify the similarity and evolution of scientific disciplines,"  We use an information-theoretic measure of linguistic similarity to
+investigate the organization and evolution of scientific fields. An analysis of
+almost 20M papers from the past three decades reveals that the linguistic
+similarity is related but different from experts and citation-based
+classifications, leading to an improved view on the organization of science. A
+temporal analysis of the similarity of fields shows that some fields (e.g.,
+computer science) are becoming increasingly central, but that on average the
+similarity between pairs has not changed in the last decades. This suggests
+that tendencies of convergence (e.g., multi-disciplinarity) and divergence
+(e.g., specialization) of disciplines are in balance.
+"
+28209,A Newton-Based Method for Nonconvex Optimization with Fast Evasion of Saddle Points,"  Machine learning problems such as neural network training, tensor
+decomposition, and matrix factorization, require local minimization of a
+nonconvex function. This local minimization is challenged by the presence of
+saddle points, of which there can be many and from which descent methods may
+take inordinately large number of iterations to escape. This paper presents a
+second-order method that modifies the update of Newton's method by replacing
+the negative eigenvalues of the Hessian by their absolute values and uses a
+truncated version of the resulting matrix to account for the objective's
+curvature. The method is shown to escape saddles in at most $1 + \log_{3/2}
+(\delta/2\varepsilon)$ iterations where $\varepsilon$ is the target optimality
+and $\delta$ characterizes a point sufficiently far away from the saddle. This
+base of this exponential escape is $3/2$ independently of problem constants.
+Adding classical properties of Newton's method, the paper proves convergence to
+a local minimum with probability $1-p$ in $O\left(\log(1/p)) +
+O(\log(1/\varepsilon)\right)$ iterations.
+"
+28210,On the moments of the characteristic polynomial of a Ginibre random matrix,"  In this article we study the large $N$ asymptotics of complex moments of the
+absolute value of the characteristic polynomial of a $N\times N$ complex
+Ginibre random matrix with the characteristic polynomial evaluated at a point
+in the unit disk. More precisely, we calculate the large $N$ asymptotics of
+$\mathbb{E}|\det(G_N-z)|^{\gamma}$, where $G_N$ is a $N\times N$ matrix whose
+entries are i.i.d and distributed as $N^{-1/2}Z$, $Z$ being a standard complex
+Gaussian, $\mathrm{Re}(\gamma)>-2$, and $|z|<1$. This expectation is
+proportional to the determinant of a complex moment matrix with a symbol which
+is supported in the whole complex plane and has a Fisher-Hartwig type of
+singularity: $\det(\int_\mathbb{C} w^{i}\overline{w}^j |w-z|^\gamma
+e^{-N|w|^{2}}d^2 w)_{i,j=0}^{N-1}$. We study the asymptotics of this
+determinant using recent results due to Lee and Yang concerning the asymptotics
+of orthogonal polynomials with respect to the weight $|w-z|^\gamma
+e^{-N|w|^2}d^2 w$ along with differential identities familiar from the study of
+asymptotics of Toeplitz and Hankel determinants with Fisher-Hartwig
+singularities. To our knowledge, even in the case of one singularity, the
+asymptotics of the determinant of such a moment matrix whose symbol has support
+in a two-dimensional set and a Fisher-Hartwig singularity, have been previously
+unknown.
+"
+28211,Towards an Information Geometric characterization/classification of Complex Systems. I. Use of Generalized Entropies,"  Using the generalized entropies which depend on two parameters we propose a
+set of quantitative characteristics derived from the Information Geometry based
+on these entropies. Our aim, at this stage, is modest, as we are first
+constructing some fundamental geometric objects. We first establish the
+existence of a two-parameter family of probability distributions. Then using
+this family we derive the associated metric and we state a generalized
+Cramer-Rao inequality. This gives a first two-parameter classification of
+complex systems. Finally computing the scalar curvature of the information
+manifold we obtain a further discrimination of the corresponding classes. Our
+analysis is based on the two-parameter family of generalized entropies of Hanel
+and Thurner (2011)
+"
+28212,Large-scale asymmetry between spin patterns of spiral galaxies,"  Spin patterns of spiral galaxies can be broadly separated into galaxies with
+clockwise (Z-wise) patterns and galaxies with counterclockwise (S-wise) spin
+patterns. While the differences between these patterns are visually noticeable,
+they are a matter of the perspective of the observer, and therefore in a
+sufficiently large universe no other differences are expected between galaxies
+with Z-wise and S-wise patterns. Here large datasets of spiral galaxies
+separated by their spin patterns are used to show that spiral galaxies with
+Z-wise spin patterns are photometrically different from spiral galaxies with
+S-wise patterns. That asymmetry changes based on the direction of observation,
+such that the observed asymmetry in one hemisphere is aligned with the inverse
+observed asymmetry in the opposite hemisphere. The results are consistent
+across different sky surveys (SDSS and PanSTARRS) and analysis methods. The
+proximity of the most probable asymmetry axis to the galactic pole suggests
+that the asymmetry might be driven by relativistic beaming. Annotated data from
+SDSS and PanSTARRS are publicly available.
+"
+28213,A Study on Neural Network Language Modeling,"  An exhaustive study on neural network language modeling (NNLM) is performed
+in this paper. Different architectures of basic neural network language models
+are described and examined. A number of different improvements over basic
+neural network language models, including importance sampling, word classes,
+caching and bidirectional recurrent neural network (BiRNN), are studied
+separately, and the advantages and disadvantages of every technique are
+evaluated. Then, the limits of neural network language modeling are explored
+from the aspects of model architecture and knowledge representation. Part of
+the statistical information from a word sequence will loss when it is processed
+word by word in a certain order, and the mechanism of training neural network
+by updating weight matrixes and vectors imposes severe restrictions on any
+significant enhancement of NNLM. For knowledge representation, the knowledge
+represented by neural network language models is the approximate probabilistic
+distribution of word sequences from a certain training data set rather than the
+knowledge of a language itself or the information conveyed by word sequences in
+a natural language. Finally, some directions for improving neural network
+language modeling further is discussed.
+"
+28214,Lax Representations for Matrix Short Pulse Equations,"  The Lax representation for different matrix generalizations of Short Pulse
+Equations (SPE) is considered. The four-dimensional Lax representations of
+four-component Matsuno, Feng and Dimakis-Müller-Hoissen-Matsuno equations
+is obtained. The four-component Feng system is defined by generalization of the
+two-dimensional Lax representation to the four-component case. This system
+reduces to the original Feng equation or to the two-component Matsuno equation
+or to the Yao-Zang equation. The three component version of Feng equation is
+presented.
+The four-component version of Matsuno equation with its Lax representation is
+given .
+This equation reduces the new two-component Feng system.
+The two-component Dimakis-Müller-Hoissen-Matsuno equations are
+generalized to the four parameter family of the four-component SPE. The
+bi-Hamiltonian structure of this generalization, for special values of
+parameters, is defined. This four-component SPE in special case reduces to the
+new two-component SPE.
+"
+28215,A family of cyclic quartic fields with explicit fundamental units,"  We construct a family of quartic polynomials with cyclic Galois group and
+show that the roots of the polynomials are fundamental units or generate a
+subgroup of index 5.
+"
+28216,A Complex Fermionic Tensor Model in $d$ Dimensions,"  In this note, we study a melonic tensor model in $d$ dimensions based on
+three-index Dirac fermions with a four-fermion interaction. Summing the melonic
+diagrams at strong coupling allows one to define a formal large-$N$ saddle
+point in arbitrary $d$ and calculate the spectrum of scalar bilinear singlet
+operators. For $d=2-\epsilon$ the theory is an infrared fixed point, which we
+find has a purely real spectrum that we determine numerically for arbitrary
+$d<2$, and analytically as a power series in $\epsilon$. The theory appears to
+be weakly interacting when $\epsilon$ is small, suggesting that fermionic
+tensor models in 1-dimension can be studied in an $\epsilon$ expansion. For
+$d>2$, the spectrum can still be calculated using the saddle point equations,
+which may define a formal large-$N$ ultraviolet fixed point analogous to the
+Gross-Neveu model in $d>2$. For $2<d<6$, we find that the spectrum contains at
+least one complex scalar eigenvalue (similar to the complex eigenvalue present
+in the bosonic tensor model recently studied by Giombi, Klebanov and
+Tarnopolsky) which indicates that the theory is unstable. We also find that the
+fixed point is weakly-interacting when $d=6$ (or more generally $d=4n+2$) and
+has a real spectrum for $6<d<6.14$ which we present as a power series in
+$\epsilon$ in $6+\epsilon$ dimensions.
+"
+28217,Numerical framework for transcritical real-fluid reacting flow simulations using the flamelet progress variable approach,"  An extension to the classical FPV model is developed for transcritical
+real-fluid combustion simulations in the context of finite volume, fully
+compressible, explicit solvers. A double-flux model is developed for
+transcritical flows to eliminate the spurious pressure oscillations. A hybrid
+scheme with entropy-stable flux correction is formulated to robustly represent
+large density ratios. The thermodynamics for ideal-gas values is modeled by a
+linearized specific heat ratio model. Parameters needed for the cubic EoS are
+pre-tabulated for the evaluation of departure functions and a quadratic
+expression is used to recover the attraction parameter. The novelty of the
+proposed approach lies in the ability to account for pressure and temperature
+variations from the baseline table. Cryogenic LOX/GH2 mixing and reacting cases
+are performed to demonstrate the capability of the proposed approach in
+multidimensional simulations. The proposed combustion model and numerical
+schemes are directly applicable for LES simulations of real applications under
+transcritical conditions.
+"
+28218,Adaptive Neural Trees,"  Deep neural networks and decision trees operate on largely separate
+paradigms; typically, the former performs representation learning with
+pre-specified architectures, while the latter is characterised by learning
+hierarchies over pre-specified features with data-driven architectures. We
+unite the two via adaptive neural trees (ANTs), a model that incorporates
+representation learning into edges, routing functions and leaf nodes of a
+decision tree, along with a backpropagation-based training algorithm that
+adaptively grows the architecture from primitive modules (e.g., convolutional
+layers). ANTs allow increased interpretability via hierarchical clustering,
+e.g., learning meaningful class associations, such as separating natural vs.
+man-made objects. We demonstrate this on classification and regression tasks,
+achieving over 99% and 90% accuracy on the MNIST and CIFAR-10 datasets, and
+outperforming standard neural networks, random forests and gradient boosted
+trees on the SARCOS dataset. Furthermore, ANT optimisation naturally adapts the
+architecture to the size and complexity of the training data.
+"
+28219,Lattice Codes for Physical Layer Communications,"  Lattices are deceptively simple mathematical structures that have become
+indispensable for code design for physical layer communications. While
+lattice-related problems are interesting in their own right, the usefulness of
+these discrete structures in wireless communications provides additional
+motivation for their study and enables a multidisciplinary line of research.
+This thesis is devoted to the study of lattice code design for physical layer
+communications. Modern wireless communication networks are required to
+accommodate significantly varied types of mobile devices, differing in
+available computational power or number of equipped antennas. Additionally, the
+density of the networks increases rapidly, and many communication protocols
+diverge from the classical direct point-to-point transmission in favor of
+allowing for intermediate relays to process and forward data. An important
+consequence of this shift towards more sophisticated transmission protocols is
+that traditional well-performing codes become futile for modern communications,
+thus the study and development of novel codes is called for.
+Yet, however involved a transmission protocol may be, the characteristics of
+the physical medium, i.e., the wireless channel, stay unaffected. It is thus
+natural that an underlying lattice structure for code design remains crucial.
+This thesis consists of several articles considering lattice code design for
+four different communication settings relevant in modern wireless
+communications.
+"
+28220,A gap theorem for free boundary minimal surfaces in geodesic balls of hyperbolic space and hemisphere,"  In this paper we provide a pinching condition for the characterization of the
+totally geodesic disk and the rotational annulus among minimal surfaces with
+free boundary in geodesic balls of three-dimensional hyperbolic space and
+hemisphere. The pinching condition involves the length of the second
+fundamental form, the support function of the surface, and a natural potential
+function in hyperbolic space and hemisphere.
+"
+28221,On the Closest Vector Problem for Lattices Constructed from Polynomials and Their Cryptographic Applications,"  In this paper, we propose new classes of trapdoor functions to solve the
+closest vector problem in lattices. Specifically, we construct lattices based
+on properties of polynomials for which the closest vector problem is hard to
+solve unless some trapdoor information is revealed. We thoroughly analyze the
+security of our proposed functions using state-of-the-art attacks and results
+on lattice reductions. Finally, we describe how our functions can be used to
+design quantum-safe encryption schemes with reasonable public key sizes. In
+particular, our scheme can offer around $106$ bits of security with a public
+key size of around $6.4$ $\texttt{KB}$. Our encryption schemes are efficient
+with respect to key generation, encryption and decryption.
+"
+28222,Intrinsic group behaviour: dependence of pedestrian dyad dynamics on principal social and personal features,"  Being determined by human social behaviour, pedestrian group dynamics depends
+on ""intrinsic properties"" of the group such as the purpose of the pedestrians,
+their personal relation, their gender, age, and body size. In this work we
+quantitatively study the dynamical properties of pedestrian dyads by analysing
+a large data set of automatically tracked pedestrian trajectories in an
+unconstrained ""ecological"" setting (a shopping mall), whose relational group
+properties have been coded by three different human observers. We observed that
+females walk slower and closer than males, that workers walk faster, at a
+larger distance and more abreast than leisure oriented people, and that inter
+group relation has a strong effect on group structure, with couples walking
+very close and abreast, colleagues walking at a larger distance, and friends
+walking more abreast than family members. Pedestrian height (obtained
+automatically through our tracking system) influences velocity and abreast
+distance, both growing functions of the average group height. Results regarding
+pedestrian age show as expected that elderly people walk slowly, while active
+age adults walk at the maximum velocity. Groups with children have a strong
+tendency to walk in a non abreast formation, with a large distance (despite a
+low abreast distance). A cross-analysis of the interplay between these
+intrinsic features, taking in account also the effect of extrinsic crowd
+density, confirms these major effects but reveals also a richer structure. An
+interesting and unexpected result, for example, is that the velocity of groups
+with children {\it increases} with density, at least in the low-medium density
+range found under normal conditions in shopping malls. Children also appear to
+behave differently according to the gender of the parent.
+"
+28223,Policy Optimization by Genetic Distillation,"  Genetic algorithms have been widely used in many practical optimization
+problems. Inspired by natural selection, operators, including mutation,
+crossover and selection, provide effective heuristics for search and black-box
+optimization. However, they have not been shown useful for deep reinforcement
+learning, possibly due to the catastrophic consequence of parameter crossovers
+of neural networks. Here, we present Genetic Policy Optimization (GPO), a new
+genetic algorithm for sample-efficient deep policy optimization. GPO uses
+imitation learning for policy crossover in the state space and applies policy
+gradient methods for mutation. Our experiments on MuJoCo tasks show that GPO as
+a genetic algorithm is able to provide superior performance over the
+state-of-the-art policy gradient methods and achieves comparable or higher
+sample efficiency.
+"
+28224,Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data,"  Each year, thousands of software vulnerabilities are discovered and reported
+to the public. Unpatched known vulnerabilities are a significant security risk.
+It is imperative that software vendors quickly provide patches once
+vulnerabilities are known and users quickly install those patches as soon as
+they are available. However, most vulnerabilities are never actually exploited.
+Since writing, testing, and installing software patches can involve
+considerable resources, it would be desirable to prioritize the remediation of
+vulnerabilities that are likely to be exploited. Several published research
+studies have reported moderate success in applying machine learning techniques
+to the task of predicting whether a vulnerability will be exploited. These
+approaches typically use features derived from vulnerability databases (such as
+the summary text describing the vulnerability) or social media posts that
+mention the vulnerability by name. However, these prior studies share multiple
+methodological shortcomings that inflate predictive power of these approaches.
+We replicate key portions of the prior work, compare their approaches, and show
+how selection of training and test data critically affect the estimated
+performance of predictive models. The results of this study point to important
+methodological considerations that should be taken into account so that results
+reflect real-world utility.
+"
+28225,"Spectral analysis of Volterra integrodifferential equations with the kernels, depending on parameter","  Spectral analysis of operator-functions which are the symbols of the abstract
+integrodifferential equations of the Gurtin-Pipkin is provided. These equations
+represent abstract wave equations disturbed by terms involving Volterra
+operators. Correct solvability in the Sobolev space $W_{2}^{2}((0, T), A^2)$,
+for arbitrary $T>0$, of that abstract integrodifferential equations is also
+studied.
+"
+28226,Towards an unanimous international regulatory body for responsible use of Artificial Intelligence [UIRB-AI],"  Artificial Intelligence (AI), is once again in the phase of drastic
+advancements. Unarguably, the technology itself can revolutionize the way we
+live our everyday life. But the exponential growth of technology poses a
+daunting task for policy researchers and law makers in making amendments to the
+existing norms. In addition, not everyone in the society is studying the
+potential socio-economic intricacies and cultural drifts that AI can bring
+about. It is prudence to reflect from our historical past to propel the
+development of technology in the right direction. To benefit the society of the
+present and future, I scientifically explore the societal impact of AI. While
+there are many public and private partnerships working on similar aspects, here
+I describe the necessity for an Unanimous International Regulatory Body for all
+applications of AI (UIRB-AI). I also discuss the benefits and drawbacks of such
+an organization. To combat any drawbacks in the formation of an UIRB-AI, both
+idealistic and pragmatic perspectives are discussed alternatively. The paper
+further advances the discussion by proposing novel policies on how such
+organization should be structured and how it can bring about a win-win
+situation for everyone in the society.
+"
+28227,Log-Lindley generated family of distributions,"  A new generator of univariate continuous distributions, with two additional
+parameters, called the Log-Lindley generated family is introduced. Some special
+distributions in the new family are presented. Some mathematical properties of
+the new family are studied. The maximum likelihood method to estimate model
+parameters is employed. The potentiality of the new generator is illustrated
+using a real data set.
+"
+28228,Subordination approach to multi-term time-fractional diffusion-wave equations,"  This paper is concerned with the fractional evolution equation with a
+discrete distribution of Caputo time-derivatives such that the largest and the
+smallest orders, $\alpha$ and $\alpha_m$, satisfy the conditions $1<\alpha\le
+2$ and $\alpha-\alpha_m\le 1$. First, based on a study of the related
+propagation function, the nonnegativity of the fundamental solutions to the
+spatially one-dimensional Cauchy and signaling problems is proven and
+propagation speed of a disturbance is discussed. Next, we study the equation
+with a general linear spatial differential operator defined in a Banach space
+and suppose it generates a cosine family. A subordination principle is
+established, which implies the existence of a unique solution and gives an
+integral representation of the solution operator in terms of the corresponding
+cosine family and a probability density function. Explicit representation of
+the probability density function is derived. The subordination principle is
+applied for obtaining regularity results. The analytical findings are supported
+by numerical work.
+"
+28229,A systematic four-dimensional approach to strong field control of molecular torsions,"  We introduce a four-dimensional quantum model for describing the torsional
+control of $\rm G_{16}$-type molecules in the electronic ground state, based on
+the symmetry-adapted variational method. We define conditions for which
+lower-dimensional models, commonly used to simulate the strong-field control of
+molecular torsions, are reliable approximations to a four-dimensional
+treatment. In particular, we study the role of different types of
+rotational-torsional couplings---the field-free coupling and the field-induced
+coupling---and show that the conclusions recently drawn on the role of
+rotational-torsional couplings in the process of torsional alignment are not
+correct. Furthermore, we demonstrate how important an adequate description of
+the molecular polarizability is for reliably predicting the torsional
+alignment.
+"
+28230,The VHE anomaly in blazar spectra and related phenomena,"  Most of the recent research on extragalactic $\gamma$-ray propagation focused
+on the study of the $\gamma\gamma \rightarrow e^{+}e^{-}$ absorption process
+(""absorption-only model""). Starting from a possible anomaly at very high
+energies (VHE, E >100 GeV), we briefly review several existing deviations from
+this model. The exotic interpretation of the VHE anomaly is not supported by
+the recent works. On the other hand, the process of intergalactic
+electromagnetic cascade development naturally explains these effects. We
+discuss phenomenology of intergalactic cascades and the main spectral
+signatures of the electromagnetic cascade model. We also briefly consider the
+hadronic cascade model; it also may explain the data, but requires low strength
+of magnetic field around the source of primary protons or nuclei.
+"
+28231,Exploring the Potential of the Dark Matter Candidate from the Madala Hypothesis with Multi-frequency Indirect Detection,"  The Madala hypothesis was proposed by members of the Wits-ATLAS group to
+account for several anomalies in both ATLAS and CMS data at the LHC. This
+hypothesis extends the standard model through the addition of two scalar bosons
+and a hidden sector that can provide a dark matter candidate. This hidden
+sector interacts with the standard model only through the mediation of one of
+these scalars S. The couplings of S are not amenable to investigation in
+current collider data and so are assumed to be Higgs-like to reduce the
+parameter space of the model. Our previous work (Beck & Colafrancesco 2016) has
+shown that these couplings can be limited via indirect dark matter detection
+experiments in gamma-rays (for resonant annihilations into S). Here we will
+treat the dark matter and S masses independently, and we generalise our
+previous work and examine what fraction of the cosmological dark matter
+abundance can be accounted for by particles in the hidden sector of the Madala
+hypothesis dark matter when these annihilate to standard model products via a
+Higgs-like S. We will also extend our gamma-ray analysis of Madala hypothesis
+dark matter to include the constraints of diffuse radio data from the Coma
+galaxy cluster in addition to the Fermi-LAT gamma-ray data from both this
+target and the Reticulum II dwarf galaxy.
+Our analysis indicates that either the Madala hypothesis cannot provide the
+bulk of cosmological dark matter, or the S boson cannot be simply Higgs-like.
+These apply unless the candidate particle exceeds a mass of 200 GeV. Both these
+scenarios may reduce the attractiveness of the hypothesis as the second case
+will imply that many free parameters must be added to describe S, greatly
+weakening fits for the model. To investigate the full consequences of this
+further work will necessitate using larger astrophysical data sets to strongly
+constrain details about S.
+"
+28232,"Syllogistic Logic with Cardinality Comparisons, On Infinite Sets","  This paper enlarges classical syllogistic logic with assertions having to do
+with comparisons between the sizes of sets. So it concerns a logical system
+whose sentences are of the following forms: {\sf All $x$ are $y$} and {\sf Some
+$x$ are $y$}, {\sf There are at least as many $x$ as $y$}, and {\sf There are
+more $x$ than $y$}. Here $x$ and $y$ range over subsets (not elements) of a
+given \emph{infinite} set. Moreover, $x$ and $y$ may appear complemented (i.e.,
+as $\overset{-}{x}$ and $\overset{-}{y}$), with the natural meaning. We
+formulate a logic for our language that is based on the classical syllogistic.
+The main result is a soundness/completeness theorem. There are efficient
+algorithms for proof search and model construction.
+"
+28233,Counting Separable Polynomials in $\mathbb{Z}/n[x]$,"  For a commutative ring $R$, a polynomial $f\in R[x]$ is called separable if
+$R[x]/f$ is a separable $R$-algebra. We derive formulae for the number of
+separable polynomials when $R = \mathbb{Z}/n$, extending a result of L.
+Carlitz. For instance, we show that the number of separable polynomials in
+$\mathbb{Z}/n[x]$ that are separable is $\phi(n)n^d\prod_i(1-p_i^{-d})$ where
+$n = \prod p_i^{k_i}$ is the prime factorisation of $n$ and $\phi$ is Euler's
+totient function.
+"
+28234,A Large Deviation Inequality for $β$-mixing Time Series and its Applications to the Functional Kernel Regression Model,"  We give a new large deviation inequality for sums of random variables of the
+form $Z_k = f(X_k,X_t)$ for $k,t\in \mathbb{N}$, $t$ fixed, where the
+underlying process $X$ is $\beta$-mixing. The inequality can be used to derive
+concentration inequalities. We demonstrate its usefulness in the functional
+kernel regression model of Ferraty et al. (2007) where we study the consistency
+of dynamic forecasts.
+"
+28235,Deconvolutional Paragraph Representation Learning,"  Learning latent representations from long text sequences is an important
+first step in many natural language processing applications. Recurrent Neural
+Networks (RNNs) have become a cornerstone for this challenging task. However,
+the quality of sentences during RNN-based decoding (reconstruction) decreases
+with the length of the text. We propose a sequence-to-sequence, purely
+convolutional and deconvolutional autoencoding framework that is free of the
+above issue, while also being computationally efficient. The proposed method is
+simple, easy to implement and can be leveraged as a building block for many
+applications. We show empirically that compared to RNNs, our framework is
+better at reconstructing and correcting long paragraphs. Quantitative
+evaluation on semi-supervised text classification and summarization tasks
+demonstrate the potential for better utilization of long unlabeled text data.
+"
+28236,Double valley Dirac fermions for 3D and 2D Hg$_{1-x}$Cd$_x$Te with strong asymmetry,"  In this paper the possibility to bring about the double- valley Dirac
+fermions in some quantum structures is predicted. These quantum structures are:
+strained 3D Hg$_{1-x}$Cd$_x$Te topological insulator (TI) with strong interface
+inversion asymmetry and the asymmetric Hg$_{1-x}$Cd$_x$Te double quantum wells
+(DQW). The numerical analysis of the dispersion relation for 3D TI
+Hg$_{1-x}$Cd$_x$Te for the proper Cd ($x$)-content of in the
+Hg$_{1-x}$Cd$_x$Te-compound clearly show that the inversion symmetry breaking
+together with the unaxial tensile strain causes splitting each of Dirac nodes
+(two belonging to two interfaces) into two in the proximity of $\Gamma$-point.
+The similar effects can be obtained for asymmetric Hg$_{1-x}$Cd$_x$Te DQW with
+the proper content of Cd and proper width of the quantum wells. The aim of this
+work is to explore the inversion symmetry breaking in 3D TI and 2D DQWs mixed
+HgCdTe-systems. It is shown that this symmetry breaking leads to the dependence
+of carriers energy vs quasi-momentum similar to that of Weyl fermions.
+"
+28237,Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma,"  We present insights and empirical results from an extensive numerical study
+of the evolutionary dynamics of the iterated prisoner's dilemma. Fixation
+probabilities for Moran processes are obtained for all pairs of 164 different
+strategies including classics such as TitForTat, zero determinant strategies,
+and many more sophisticated strategies. Players with long memories and
+sophisticated behaviours outperform many strategies that perform well in a two
+player setting. Moreover we introduce several strategies trained with
+evolutionary algorithms to excel at the Moran process. These strategies are
+excellent invaders and resistors of invasion and in some cases naturally evolve
+handshaking mechanisms to resist invasion. The best invaders were those trained
+to maximize total payoff while the best resistors invoke handshake mechanisms.
+This suggests that while maximizing individual payoff can lead to the evolution
+of cooperation through invasion, the relatively weak invasion resistance of
+payoff maximizing strategies are not as evolutionarily stable as strategies
+employing handshake mechanisms.
+"
+28238,High-energy Neutrinos from Multi-body Decaying Dark Matter,"  Since the report of the PeV-TeV neutrinos by the IceCube Collaboration,
+various particle physics models have been proposed to explain the neutrino
+spectrum by dark matter particles decaying into neutrinos and other Standard
+Model particles.In such scenarios, simultaneous $\gamma$-ray emission is
+commonly expected. Therefore, multi-messenger connections are generally
+important for the indirect searches of dark matters. The recent development of
+$\gamma$-ray astronomy puts stringent constraints on the properties of dark
+matter, especially by observations with the Fermi $\gamma$-ray satellite in the
+last several years. Motivated by the lack of $\gamma$-ray as well as the shape
+of the neutrino spectrum observed by IceCube, we discuss a scenario in which
+the DM is a PeV scale particle which couples strongly to other invisible
+particles and its decay products do not contain a charged particle. As an
+example to realize such possibilities, we consider a model of fermionic dark
+matter that decays into a neutrino and many invisible fermions. The dark matter
+decay is secluded in the sense that the emitted products are mostly neutrinos
+and dark fermions. One remarkable feature of this model is the resulting
+broadband neutrino spectra around the energy scale of the dark matter. We apply
+this model to multi-PeV dark matter, and discuss possible observable
+consequences in light of the IceCube data. In particular, this model could
+account for the large flux at medium energies of $\sim10-100$ TeV, possibly as
+well as the second peak at PeV, without violating the stringent $\gamma$-ray
+constraints from Fermi and air-shower experiments such as CASA-MIA.
+"
+28239,Multi-sensor Transmission Management for Remote State Estimation under Coordination,"  This paper considers the remote state estimation in a cyber-physical system
+(CPS) using multiple sensors. The measurements of each sensor are transmitted
+to a remote estimator over a shared channel, where simultaneous transmissions
+from other sensors are regarded as interference signals. In such a competitive
+environment, each sensor needs to choose its transmission power for sending
+data packets taking into account of other sensors' behavior. To model this
+interactive decision-making process among the sensors, we introduce a
+multi-player non-cooperative game framework. To overcome the inefficiency
+arising from the Nash equilibrium (NE) solution, we propose a correlation
+policy, along with the notion of correlation equilibrium (CE). An analytical
+comparison of the game value between the NE and the CE is provided,
+with/without the power expenditure constraints for each sensor. Also, numerical
+simulations demonstrate the comparison results.
+"
+28240,Rogue Waves and Large Deviations in Deep Sea,"  The appearance of rogue waves in deep sea is investigated using the modified
+nonlinear Schrödinger (MNLS) equation in one spatial-dimension with random
+initial conditions that are assumed to be normally distributed, with a spectrum
+approximating realistic conditions of a uni-directional sea state. It is shown
+that one can use the incomplete information contained in this spectrum as prior
+and supplement this information with the MNLS dynamics to reliably estimate the
+probability distribution of the sea surface elevation far in the tail at later
+times. Our results indicate that rogue waves occur when the system hits
+unlikely pockets of wave configurations that trigger large disturbances of the
+surface height. The rogue wave precursors in these pockets are wave patterns of
+regular height but with a very specific shape that is identified explicitly,
+thereby allowing for early detection. The method proposed here combines Monte
+Carlo sampling with tools from large deviations theory that reduce the
+calculation of the most likely rogue wave precursors to an optimization problem
+that can be solved efficiently. This approach is transferable to other problems
+in which the system's governing equations contain random initial conditions
+and/or parameters.
+"
+28241,Bulk and surface electronic properties of SmB6: a hard x-ray photoelectron spectroscopy study,"  We have carried out bulk-sensitive hard x-ray photoelectron spectroscopy
+(HAXPES) measurements on in-situ cleaved and ex-situ polished SmB6 single
+crystals. Using the multiplet-structure in the Sm 3d core level spectra, we
+determined reliably that the valence of Sm in bulk SmB6 is close to 2.55 at ~5
+K. Temperature dependent measurements revealed that the Sm valence gradually
+increases to 2.64 at 300 K. From a detailed line shape analysis we can clearly
+observe that not only the J=0 but also the J=1 state of the Sm 4f 6
+configuration becomes occupied at elevated temperatures. Making use of the
+polarization dependence, we were able to identify and extract the Sm 4f
+spectral weight of the bulk material. Finally, we revealed that the oxidized or
+chemically damaged surface region of the ex-situ polished SmB6 single crystal
+is surprisingly thin, about 1 nm only.
+"
+28242,Per-decision Multi-step Temporal Difference Learning with Control Variates,"  Multi-step temporal difference (TD) learning is an important approach in
+reinforcement learning, as it unifies one-step TD learning with Monte Carlo
+methods in a way where intermediate algorithms can outperform either extreme.
+They address a bias-variance trade off between reliance on current estimates,
+which could be poor, and incorporating longer sampled reward sequences into the
+updates. Especially in the off-policy setting, where the agent aims to learn
+about a policy different from the one generating its behaviour, the variance in
+the updates can cause learning to diverge as the number of sampled rewards used
+in the estimates increases. In this paper, we introduce per-decision control
+variates for multi-step TD algorithms, and compare them to existing methods.
+Our results show that including the control variates can greatly improve
+performance on both on and off-policy multi-step temporal difference learning
+tasks.
+"
+28243,The True Destination of EGO is Multi-local Optimization,"  Efficient global optimization is a popular algorithm for the optimization of
+expensive multimodal black-box functions. One important reason for its
+popularity is its theoretical foundation of global convergence. However, as the
+budgets in expensive optimization are very small, the asymptotic properties
+only play a minor role and the algorithm sometimes comes off badly in
+experimental comparisons. Many alternative variants have therefore been
+proposed over the years. In this work, we show experimentally that the
+algorithm instead has its strength in a setting where multiple optima are to be
+identified.
+"
+28244,Mind Your POV: Convergence of Articles and Editors Towards Wikipedia's Neutrality Norm,"  Wikipedia has a strong norm of writing in a 'neutral point of view' (NPOV).
+Articles that violate this norm are tagged, and editors are encouraged to make
+corrections. But the impact of this tagging system has not been quantitatively
+measured. Does NPOV tagging help articles to converge to the desired style? Do
+NPOV corrections encourage editors to adopt this style? We study these
+questions using a corpus of NPOV-tagged articles and a set of lexicons
+associated with biased language. An interrupted time series analysis shows that
+after an article is tagged for NPOV, there is a significant decrease in biased
+language in the article, as measured by several lexicons. However, for
+individual editors, NPOV corrections and talk page discussions yield no
+significant change in the usage of words in most of these lexicons, including
+Wikipedia's own list of 'words to watch.' This suggests that NPOV tagging and
+discussion does improve content, but has less success enculturating editors to
+the site's linguistic norms.
+"
+28245,Measurement of the response of a liquid scintillation detector to monoenergetic electrons and neutrons,"  The response of the liquid scintillator (EJ-301 equivalent to NE-213) to the
+monoenergetic electrons produced in Compton scattered $\gamma$-ray tagging has
+been carried out for various radioactive $\gamma$-ray sources. The measured
+electron response is found to be linear up to $\sim$4~MeVee and the resolution
+of the liquid scintillator at 1~MeVee is observed to be $\sim$~11\%. The pulse
+shape discrimination and pulse height response of the liquid scintillator for
+neutrons has been measured using $^7$Li(p,n$_1$)$^7$Be*(0.429 MeV) reaction.
+Non linear response to mono-energetic neutrons for the liquid scintillator is
+observed at E$_n$=5.3, 9.0 and 12.7 MeV. The measured response of the liquid
+scintillator for electrons and neutrons have been compared with Geant4
+simulation.
+"
+28246,Localized Gaussian width of $M$-convex hulls with applications to Lasso and convex aggregation,"  Upper and lower bounds are derived for the Gaussian mean width of the
+intersection of a convex hull of $M$ points with an Euclidean ball of a given
+radius. The upper bound holds for any collection of extreme point bounded in
+Euclidean norm. The upper bound and the lower bound match up to a
+multiplicative constant whenever the extreme points satisfy a one sided
+Restricted Isometry Property.
+This bound is then applied to study the Lasso estimator in fixed-design
+regression, the Empirical Risk Minimizer in the anisotropic persistence
+problem, and the convex aggregation problem in density estimation.
+"
+28247,Semi-supervised learning for structured regression on partially observed attributed graphs,"  Conditional probabilistic graphical models provide a powerful framework for
+structured regression in spatio-temporal datasets with complex correlation
+patterns. However, in real-life applications a large fraction of observations
+is often missing, which can severely limit the representational power of these
+models. In this paper we propose a Marginalized Gaussian Conditional Random
+Fields (m-GCRF) structured regression model for dealing with missing labels in
+partially observed temporal attributed graphs. This method is aimed at learning
+with both labeled and unlabeled parts and effectively predicting future values
+in a graph. The method is even capable of learning from nodes for which the
+response variable is never observed in history, which poses problems for many
+state-of-the-art models that can handle missing data. The proposed model is
+characterized for various missingness mechanisms on 500 synthetic graphs. The
+benefits of the new method are also demonstrated on a challenging application
+for predicting precipitation based on partial observations of climate variables
+in a temporal graph that spans the entire continental US. We also show that the
+method can be useful for optimizing the costs of data collection in climate
+applications via active reduction of the number of weather stations to
+consider. In experiments on these real-world and synthetic datasets we show
+that the proposed model is consistently more accurate than alternative
+semi-supervised structured models, as well as models that either use imputation
+to deal with missing values or simply ignore them altogether.
+"
+28248,Supervised Learning with Indefinite Topological Kernels,"  Topological Data Analysis (TDA) is a recent and growing branch of statistics
+devoted to the study of the shape of the data. In this work we investigate the
+predictive power of TDA in the context of supervised learning. Since
+topological summaries, most noticeably the Persistence Diagram, are typically
+defined in complex spaces, we adopt a kernel approach to translate them into
+more familiar vector spaces. We define a topological exponential kernel, we
+characterize it, and we show that, despite not being positive semi-definite, it
+can be successfully used in regression and classification tasks.
+"
+28249,Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach,"  Short-term passenger demand forecasting is of great importance to the
+on-demand ride service platform, which can incentivize vacant cars moving from
+over-supply regions to over-demand regions. The spatial dependences, temporal
+dependences, and exogenous dependences need to be considered simultaneously,
+however, which makes short-term passenger demand forecasting challenging. We
+propose a novel deep learning (DL) approach, named the fusion convolutional
+long short-term memory network (FCL-Net), to address these three dependences
+within one end-to-end learning architecture. The model is stacked and fused by
+multiple convolutional long short-term memory (LSTM) layers, standard LSTM
+layers, and convolutional layers. The fusion of convolutional techniques and
+the LSTM network enables the proposed DL approach to better capture the
+spatio-temporal characteristics and correlations of explanatory variables. A
+tailored spatially aggregated random forest is employed to rank the importance
+of the explanatory variables. The ranking is then used for feature selection.
+The proposed DL approach is applied to the short-term forecasting of passenger
+demand under an on-demand ride service platform in Hangzhou, China.
+Experimental results, validated on real-world data provided by DiDi Chuxing,
+show that the FCL-Net achieves better predictive performance than traditional
+approaches including both classical time-series prediction models and neural
+network based algorithms (e.g., artificial neural network and LSTM). This paper
+is one of the first DL studies to forecast the short-term passenger demand of
+an on-demand ride service platform by examining the spatio-temporal
+correlations.
+"
+28250,Unmixing the mixed volume computation,"  Computing mixed volume of convex polytopes is an important problem in
+computational algebraic geometry. This paper establishes sufficient conditions
+under which the mixed volume of several convex polytopes exactly equals the
+normalized volume of the convex hull of their union. Under these conditions the
+problem of computing mixed volume of several polytopes can be transformed into
+a volume computation problem for a single polytope in the same dimension. We
+demonstrate through problems from real world applications that substantial
+reduction in computational costs can be achieved via this transformation in
+situations where the convex hull of the union of the polytopes has less complex
+geometry than the original polytopes. We also discuss the important
+implications of this result in the polyhedral homotopy method for solving
+polynomial systems.
+"
+28251,PT-symmetry in nonlinear twisted multi-core fibers,"  We address propagation of light in nonlinear twisted multi-core fibers with
+alternating amplifying and absorbing cores that are arranged into the PT -
+symmetric structure. In this structure, the coupling strength between
+neighboring cores and global energy transport can be controlled not only by the
+nonlinearity strength, but also by gain and losses and by the fiber twisting
+rate. The threshold level of gain/losses, at which PT -symmetry breaking
+occurs, is a non-monotonic function of the fiber twisting rate and it can be
+reduced nearly to zero or, instead, notably increased just by changing this
+rate. Nonlinearity usually leads to the monotonic reduction of the symmetry
+breaking threshold in such fibers.
+"
+28252,Prediction of employment and unemployment rates from Twitter daily rhythms in the US,"  By modeling macro-economical indicators using digital traces of human
+activities on mobile or social networks, we can provide important insights to
+processes previously assessed via paper-based surveys or polls only. We
+collected aggregated workday activity timelines of US counties from the
+normalized number of messages sent in each hour on the online social network
+Twitter. In this paper, we show how county employment and unemployment
+statistics are encoded in the daily rhythm of people by decomposing the
+activity timelines into a linear combination of two dominant patterns. The
+mixing ratio of these patterns defines a measure for each county, that
+correlates significantly with employment ($0.46\pm0.02$) and unemployment rates
+($-0.34\pm0.02$). Thus, the two dominant activity patterns can be linked to
+rhythms signaling presence or lack of regular working hours of individuals. The
+analysis could provide policy makers a better insight into the processes
+governing employment, where problems could not only be identified based on the
+number of officially registered unemployed, but also on the basis of the
+digital footprints people leave on different platforms.
+"
+28253,Bend-Induced Twist Waves and the Structure of Nucleosomal DNA,"  Recent work indicates that twist-bend coupling plays an important role in DNA
+micromechanics. Here we investigate its effect on bent DNA. We provide an
+analytical solution of the minimum-energy shape of circular DNA, showing that
+twist-bend coupling induces sinusoidal twist waves. This solution is in
+excellent agreement with both coarse-grained simulations of minicircles and
+nucleosomal DNA data, which is bent and wrapped around histone proteins in a
+superhelical conformation. Our analysis shows that the observed twist
+oscillation in nucleosomal DNA, so far attributed to the interaction with the
+histone proteins, is an intrinsic feature of free bent DNA, and should be
+observable in other protein-DNA complexes.
+"
+28254,Optimal Shrinkage of Singular Values Under Random Data Contamination,"  A low rank matrix X has been contaminated by uniformly distributed noise,
+missing values, outliers and corrupt entries. Reconstruction of X from the
+singular values and singular vectors of the contaminated matrix Y is a key
+problem in machine learning, computer vision and data science. In this paper we
+show that common contamination models (including arbitrary combinations of
+uniform noise,missing values, outliers and corrupt entries) can be described
+efficiently using a single framework. We develop an asymptotically optimal
+algorithm that estimates X by manipulation of the singular values of Y , which
+applies to any of the contamination models considered. Finally, we find an
+explicit signal-to-noise cutoff, below which estimation of X from the singular
+value decomposition of Y must fail, in a well-defined sense.
+"
+28255,Care about you: towards large-scale human-centric visual relationship detection,"  Visual relationship detection aims to capture interactions between pairs of
+objects in images. Relationships between objects and humans represent a
+particularly important subset of this problem, with implications for challenges
+such as understanding human behaviour, and identifying affordances, amongst
+others. In addressing this problem we first construct a large-scale
+human-centric visual relationship detection dataset (HCVRD), which provides
+many more types of relationship annotation (nearly 10K categories) than the
+previous released datasets.
+This large label space better reflects the reality of human-object
+interactions, but gives rise to a long-tail distribution problem, which in turn
+demands a zero-shot approach to labels appearing only in the test set. This is
+the first time this issue has been addressed. We propose a webly-supervised
+approach to these problems and demonstrate that the proposed model provides a
+strong baseline on our HCVRD dataset.
+"
+28256,Combinatorial Multi-Armed Bandits with Filtered Feedback,"  Motivated by problems in search and detection we present a solution to a
+Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward
+distributions and a new class of feedback, filtered semibandit feedback. In a
+CMAB problem an agent pulls a combination of arms from a set $\{1,...,k\}$ in
+each round, generating random outcomes from probability distributions
+associated with these arms and receiving an overall reward. Under semibandit
+feedback it is assumed that the random outcomes generated are all observed.
+Filtered semibandit feedback allows the outcomes that are observed to be
+sampled from a second distribution conditioned on the initial random outcomes.
+This feedback mechanism is valuable as it allows CMAB methods to be applied to
+sequential search and detection problems where combinatorial actions are made,
+but the true rewards (number of objects of interest appearing in the round) are
+not observed, rather a filtered reward (the number of objects the searcher
+successfully finds, which must by definition be less than the number that
+appear). We present an upper confidence bound type algorithm, Robust-F-CUCB,
+and associated regret bound of order $\mathcal{O}(\ln(n))$ to balance
+exploration and exploitation in the face of both filtering of reward and heavy
+tailed reward distributions.
+"
+28257,The Cosmic Quarks,"  There are at least three sources of cosmic quarks in the universe. One, the
+quark nuggets which may survive beyond a certain baryon number during the phase
+transition from quarks to hadrons microseconds after the big bang. These quark
+nuggets can very well be candidate of cold dark matter and these nuggets
+consist of strange quarks. Second, the interior of the neutron star may well be
+made of quarks due to very very high pressure. It is further shown that the
+interior of heavy neutron star, recently discovered $\approx$ 2M$_{\odot}$,
+with an appropriate equation of state, can also be made of quark core. Finally,
+using the property of colour entanglement among quarks it is entirely possible
+to have free orphan quarks roaming around in the cosmos. Some better
+understanding of dark energy and dark matter is possible with these entangled
+orphan quarks.
+"
+28258,Gutkin billiard tables in higher dimensions and rigidity,"  E. Gutkin found a remarkable class of convex billiard tables in the plane
+which have a constant angle invariant curve. In this paper we prove that in
+dimension 3 only round sphere has such a property. For dimension greater than 3
+it must be either a sphere or to have a very special geometric properties. In
+2-dimensional case we prove a rigidity result for Gutkin billiard tables. This
+is done with the help of a new generating function introduced recently for
+billiards in our joint paper with A.E. Mironov. A formula for this generating
+function in higher dimensions is found.
+"
+28259,Synchronization Problems in Automata without Non-trivial Cycles,"  We study the computational complexity of various problems related to
+synchronization of weakly acyclic automata, a subclass of widely studied
+aperiodic automata. We provide upper and lower bounds on the length of a
+shortest word synchronizing a weakly acyclic automaton or, more generally, a
+subset of its states, and show that the problem of approximating this length is
+hard. We investigate the complexity of finding a synchronizing set of states of
+maximum size. We also show inapproximability of the problem of computing the
+rank of a subset of states in a binary weakly acyclic automaton and prove that
+several problems related to recognizing a synchronizing subset of states in
+such automata are NP-complete.
+"
+28260,OptNet: Differentiable Optimization as a Layer in Neural Networks,"  This paper presents OptNet, a network architecture that integrates
+optimization problems (here, specifically in the form of quadratic programs) as
+individual layers in larger end-to-end trainable deep networks. These layers
+encode constraints and complex dependencies between the hidden states that
+traditional convolutional and fully-connected layers often cannot capture. In
+this paper, we explore the foundations for such an architecture: we show how
+techniques from sensitivity analysis, bilevel optimization, and implicit
+differentiation can be used to exactly differentiate through these layers and
+with respect to layer parameters; we develop a highly efficient solver for
+these layers that exploits fast GPU-based batch solves within a primal-dual
+interior point method, and which provides backpropagation gradients with
+virtually no additional cost on top of the solve; and we highlight the
+application of these approaches in several problems. In one notable example, we
+show that the method is capable of learning to play mini-Sudoku (4x4) given
+just input and output games, with no a priori information about the rules of
+the game; this highlights the ability of our architecture to learn hard
+constraints better than other neural architectures.
+"
+28261,On the strict convexity of the K-energy,"  Let (X,L) be a polarized projective complex manifold. We show, by a simple
+toric one-dimensional example, that Mabuchi's K-energy functional on the
+geodesically complete space of bounded positive (1,1)-forms in the first Chern
+class of L, endowed with the Mabuchi metric, is not strictly convex modulo
+automorphisms. However, under some further assumptions the strict convexity in
+question does hold in the toric case. This leads to a uniqueness result saying
+that a finite energy minimizer of the K-energy (which exists on any toric
+polarized manifold (X,L) which is uniformly K-stable) is uniquely determined
+modulo automorphisms under the assumption that there exists some minimizer with
+strictly positive curvature current.
+"
+28262,Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging,"  Nowadays, a lot of scientific efforts are concentrated on the diagnosis of
+Alzheimer's Disease (AD) applying deep learning methods to neuroimaging data.
+Even for 2017, there were published more than a hundred papers dedicated to AD
+diagnosis, whereas only a few works considered a problem of mild cognitive
+impairments (MCI) conversion to the AD. However, the conversion prediction is
+an important problem since approximately 15% of patients with MCI converges to
+the AD every year. In the current work, we are focusing on the conversion
+prediction using brain Magnetic Resonance Imaging and clinical data, such as
+demographics, cognitive assessments, genetic, and biochemical markers. First of
+all, we applied state-of-the-art deep learning algorithms on the neuroimaging
+data and compared these results with two machine learning algorithms that we
+fit using the clinical data. As a result, the models trained on the clinical
+data outperform the deep learning algorithms applied to the MR images. To
+explore the impact of neuroimaging further, we trained a deep feed-forward
+embedding using similarity learning with Histogram loss on all available MRIs
+and obtained 64-dimensional vector representation of neuroimaging data. The use
+of learned representation from the deep embedding allowed to increase the
+quality of prediction based on the neuroimaging. Finally, the current results
+on this dataset show that the neuroimaging does affect conversion prediction,
+however, cannot noticeably increase the quality of the prediction. The best
+results of predicting MCI-to-AD conversion are provided by XGBoost algorithm
+trained on the clinical and embedding data. The resulting accuracy is 0.76 +-
+0.01 and the area under the ROC curve - 0.86 +- 0.01.
+"
+28263,Quantum critical metrology,"  Quantum metrology fundamentally relies upon the efficient management of
+quantum uncertainties. We show that, under equilibrium conditions, the
+management of quantum noise becomes extremely flexible around the quantum
+critical point of a quantum many-body system: this is due to the critical
+divergence of quantum fluctuations of the order parameter, which, via
+Heisenberg's inequalities, may lead to the critical suppression of the
+fluctuations in conjugate observables. Taking the quantum Ising model as the
+paradigmatic incarnation of quantum phase transitions, we show that it exhibits
+quantum critical squeezing of one spin component, providing a scaling for the
+precision of interferometric parameter estimation which, in dimensions $d \geq
+2$, lies in between the standard quantum limit and the Heisenberg limit.
+Quantum critical squeezing saturates the maximum metrological gain allowed by
+the quantum Fisher information in $d=\infty$ (or with infinite-range
+interactions) at all temperatures, and it approaches closely the bound in a
+broad range of temperatures in $d=2$ and 3. This demonstrates the immediate
+metrological potential of equilibrium many-body states close to quantum
+criticality, which are accessible \emph{e.g.} to atomic quantum simulators via
+elementary adiabatic protocols.
+"
+28264,Admissible Abstractions for Near-optimal Task and Motion Planning,"  We define an admissibility condition for abstractions expressed using angelic
+semantics and show that these conditions allow us to accelerate planning while
+preserving the ability to find the optimal motion plan. We then derive
+admissible abstractions for two motion planning domains with continuous state.
+We extract upper and lower bounds on the cost of concrete motion plans using
+local metric and topological properties of the problem domain. These bounds
+guide the search for a plan while maintaining performance guarantees. We show
+that abstraction can dramatically reduce the complexity of search relative to a
+direct motion planner. Using our abstractions, we find near-optimal motion
+plans in planning problems involving $10^{13}$ states without using a separate
+task planner.
+"
+28265,$L_{\infty}$ Algebras and Field Theory,"  We review and develop the general properties of $L_\infty$ algebras focusing
+on the gauge structure of the associated field theories. Motivated by the
+$L_\infty$ homotopy Lie algebra of closed string field theory and the work of
+Roytenberg and Weinstein describing the Courant bracket in this language we
+investigate the $L_\infty$ structure of general gauge invariant perturbative
+field theories. We sketch such formulations for non-abelian gauge theories,
+Einstein gravity, and for double field theory. We find that there is an
+$L_\infty$ algebra for the gauge structure and a larger one for the full
+interacting field theory. Theories where the gauge structure is a strict Lie
+algebra often require the full $L_\infty$ algebra for the interacting theory.
+The analysis suggests that $L_\infty$ algebras provide a classification of
+perturbative gauge invariant classical field theories.
+"
+28266,A Multi-Index Markov Chain Monte Carlo Method,"  In this article we consider computing expectations w.r.t.~probability laws
+associated to a certain class of stochastic systems. In order to achieve such a
+task, one must not only resort to numerical approximation of the expectation,
+but also to a biased discretization of the associated probability. We are
+concerned with the situation for which the discretization is required in
+multiple dimensions, for instance in space and time. In such contexts, it is
+known that the multi-index Monte Carlo (MIMC) method can improve upon
+i.i.d.~sampling from the most accurate approximation of the probability law.
+Indeed by a non-trivial modification of the multilevel Monte Carlo (MLMC)
+method and it can reduce the work to obtain a given level of error, relative to
+the afore mentioned i.i.d.~sampling and relative even to MLMC. In this article
+we consider the case when such probability laws are too complex to sampled
+independently. We develop a modification of the MIMC method which allows one to
+use standard Markov chain Monte Carlo (MCMC) algorithms to replace independent
+and coupled sampling, in certain contexts. We prove a variance theorem which
+shows that using our MIMCMC method is preferable, in the sense above, to
+i.i.d.~sampling from the most accurate approximation, under assumptions. The
+method is numerically illustrated on a problem associated to a stochastic
+partial differential equation (SPDE).
+"
+28267,Cats and Captions vs. Creators and the Clock: Comparing Multimodal Content to Context in Predicting Relative Popularity,"  The content of today's social media is becoming more and more rich,
+increasingly mixing text, images, videos, and audio. It is an intriguing
+research question to model the interplay between these different modes in
+attracting user attention and engagement. But in order to pursue this study of
+multimodal content, we must also account for context: timing effects, community
+preferences, and social factors (e.g., which authors are already popular) also
+affect the amount of feedback and reaction that social-media posts receive. In
+this work, we separate out the influence of these non-content factors in
+several ways. First, we focus on ranking pairs of submissions posted to the
+same community in quick succession, e.g., within 30 seconds, this framing
+encourages models to focus on time-agnostic and community-specific content
+features. Within that setting, we determine the relative performance of author
+vs. content features. We find that victory usually belongs to ""cats and
+captions,"" as visual and textual features together tend to outperform
+identity-based features. Moreover, our experiments show that when considered in
+isolation, simple unigram text features and deep neural network visual features
+yield the highest accuracy individually, and that the combination of the two
+modalities generally leads to the best accuracies overall.
+"
+28268,Combined Reinforcement Learning via Abstract Representations,"  In the quest for efficient and robust reinforcement learning methods, both
+model-free and model-based approaches offer advantages. In this paper we
+propose a new way of explicitly bridging both approaches via a shared
+low-dimensional learned encoding of the environment, meant to capture
+summarizing abstractions. We show that the modularity brought by this approach
+leads to good generalization while being computationally efficient, with
+planning happening in a smaller latent state space. In addition, this approach
+recovers a sufficient low-dimensional representation of the environment, which
+opens up new strategies for interpretable AI, exploration and transfer
+learning.
+"
+28269,The Gysin homomorphism for homogeneous spaces via residues - PhD Thesis,"  The subject of this dissertation is the Gysin homomorphism in equivariant
+cohomology for spaces with torus action. We consider spaces which are quotients
+of classical semisimple complex linear algebraic groups by a parabolic subgroup
+with the natural action of a maximal torus, the so-called partial flag
+varieties. We derive formulas for the Gysin homomorphism for the projection to
+a point, of the form \[\int_X \alpha = Res_{\mathbf{z}=\infty}
+\mathcal{Z}(\mathbf{z}, \mathbf{t}) \cdot \alpha(\mathbf{t}),\] for a certain
+residue operation and a map $\mathcal{Z}(\mathbf{z}, \mathbf{t})$, associated
+to the root system. The mentioned description relies on two following
+generalizations of theorems in symplectic geometry. We adapt to the equivariant
+setting (for torus actions) the Jeffrey--Kirwan nonabelian localization and a
+theorem relating the cohomology of symplectic reductions by a compact Lie group
+and by its maximal torus, following the approach by Martin. Applying the
+generalized theorems to certain contractible spaces acted upon by a product of
+unitary groups we derive the push-forward formula for partial flag varieties of
+types $A$, $B$, $C$ and $D$.
+"
+28270,Quantum Thermalization and the Expansion of Atomic Clouds,"  The ultimate consequence of quantum many-body physics is that even the air we
+breathe is governed by strictly unitary time evolution. The reason that we
+perceive it nonetheless as a completely classical high temperature gas is due
+to the incapacity of our measurement machines to keep track of the dense
+many-body entanglement of the gas molecules. The question thus arises whether
+there are instances where the quantum time evolution of a macroscopic system is
+qualitatively different from the equivalent classical system? Here we study
+this question through the expansion of noninteracting atomic clouds. While in
+many cases the full quantum dynamics is indeed indistinguishable from classical
+ballistic motion, we do find a notable exception. The subtle quantum
+correlations in a Bose gas approaching the condensation temperature appear to
+affect the expansion of the cloud, as if the system has turned into a diffusive
+collision-full classical system.
+"
+28271,Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation,"  Visual Domain Adaptation is a problem of immense importance in computer
+vision. Previous approaches showcase the inability of even deep neural networks
+to learn informative representations across domain shift. This problem is more
+severe for tasks where acquiring hand labeled data is extremely hard and
+tedious. In this work, we focus on adapting the representations learned by
+segmentation networks across synthetic and real domains. Contrary to previous
+approaches that use a simple adversarial objective or superpixel information to
+aid the process, we propose an approach based on Generative Adversarial
+Networks (GANs) that brings the embeddings closer in the learned feature space.
+To showcase the generality and scalability of our approach, we show that we can
+achieve state of the art results on two challenging scenarios of synthetic to
+real domain adaptation. Additional exploratory experiments show that our
+approach: (1) generalizes to unseen domains and (2) results in improved
+alignment of source and target distributions.
+"
+28272,Efficient asymptotic variance reduction when estimating volatility in high frequency data,"  This paper shows how to carry out efficient asymptotic variance reduction
+when estimating volatility in the presence of stochastic volatility and
+microstructure noise with the realized kernels (RK) from [Barndorff-Nielsen et
+al., 2008] and the quasi-maximum likelihood estimator (QMLE) studied in [Xiu,
+2010]. To obtain such a reduction, we chop the data into B blocks, compute the
+RK (or QMLE) on each block, and aggregate the block estimates. The ratio of
+asymptotic variance over the bound of asymptotic efficiency converges as B
+increases to the ratio in the parametric version of the problem, i.e. 1.0025 in
+the case of the fastest RK Tukey-Hanning 16 and 1 for the QMLE. The impact of
+stochastic sampling times and jump in the price process is examined carefully.
+The finite sample performance of both estimators is investigated in
+simulations, while empirical work illustrates the gain in practice.
+"
+28273,On the strongest three-valued paraconsistent logic contained in classical logic,"  LP$^{\supset,\mathsf{F}}$ is a three-valued paraconsistent propositional
+logic which is essentially the same as J3. It has most properties that have
+been proposed as desirable properties of a reasonable paraconsistent
+propositional logic. However, it follows easily from already published results
+that there are exactly 8192 different three-valued paraconsistent propositional
+logics that have the properties concerned. In this note, properties concerning
+the logical equivalence relation of a logic are used to distinguish
+LP$^{\supset,\mathsf{F}}$ from the others. As one of the bonuses of focussing
+on the logical equivalence relation, it is found that only 32 of the 8192
+logics have a logical equivalence relation that satisfies the identity,
+annihilation, idempotent, and commutative laws for conjunction and disjunction.
+"
+28274,Sparse Quadratic Logistic Regression in Sub-quadratic Time,"  We consider support recovery in the quadratic logistic regression setting -
+where the target depends on both p linear terms $x_i$ and up to $p^2$ quadratic
+terms $x_i x_j$. Quadratic terms enable prediction/modeling of higher-order
+effects between features and the target, but when incorporated naively may
+involve solving a very large regression problem. We consider the sparse case,
+where at most $s$ terms (linear or quadratic) are non-zero, and provide a new
+faster algorithm. It involves (a) identifying the weak support (i.e. all
+relevant variables) and (b) standard logistic regression optimization only on
+these chosen variables. The first step relies on a novel insight about
+correlation tests in the presence of non-linearity, and takes $O(pn)$ time for
+$n$ samples - giving potentially huge computational gains over the naive
+approach. Motivated by insights from the boolean case, we propose a non-linear
+correlation test for non-binary finite support case that involves hashing a
+variable and then correlating with the output variable. We also provide
+experimental results to demonstrate the effectiveness of our methods.
+"
+28275,Distribution of Base Pair Alternations in a Periodic DNA Chain: Application of Polya Counting to a Physical System,"  In modeling DNA chains, the number of alternations between Adenine-Thymine
+(AT) and Guanine-Cytosine (GC) base pairs can be considered as a measure of the
+heterogeneity of the chain, which in turn could affect its dynamics. A
+probability distribution function of the number of these alternations is
+derived for circular or periodic DNA. Since there are several symmetries to
+account for in the periodic chain, necklace counting methods are used. In
+particular, Polya's Enumeration Theorem is extended for the case of a group
+action that preserves partitioned necklaces. This, along with the treatment of
+generating functions as formal power series, allows for the direct calculation
+of the number of possible necklaces with a given number of AT base pairs, GC
+base pairs and alternations. The theoretically obtained probability
+distribution functions of the number of alternations are accurately reproduced
+by Monte Carlo simulations and fitted by Gaussians. The effect of the number of
+base pairs on the characteristics of these distributions is also discussed, as
+well as the effect of the ratios of the numbers of AT and GC base pairs.
+"
+28276,Dialectical Multispectral Classification of Diffusion-Weighted Magnetic Resonance Images as an Alternative to Apparent Diffusion Coefficients Maps to Perform Anatomical Analysis,"  Multispectral image analysis is a relatively promising field of research with
+applications in several areas, such as medical imaging and satellite
+monitoring. A considerable number of current methods of analysis are based on
+parametric statistics. Alternatively, some methods in Computational
+Intelligence are inspired by biology and other sciences. Here we claim that
+Philosophy can be also considered as a source of inspiration. This work
+proposes the Objective Dialectical Method (ODM): a method for classification
+based on the Philosophy of Praxis. ODM is instrumental in assembling evolvable
+mathematical tools to analyze multispectral images. In the case study described
+in this paper, multispectral images are composed of diffusion-weighted (DW)
+magnetic resonance (MR) images. The results are compared to ground-truth images
+produced by polynomial networks using a morphological similarity index. The
+classification results are used to improve the usual analysis of the apparent
+diffusion coefficient map. Such results proved that gray and white matter can
+be distinguished in DW-MR multispectral analysis and, consequently, DW-MR
+images can also be used to furnish anatomical information.
+"
+28277,A Min.Max Algorithm for Spline Based Modeling of Violent Crime Rates in USA,"  This paper focuses on modeling violent crime rates against population over
+the years 1960-2014 for the United States via cubic spline based method. We
+propose a new min/max algorithm on knots detection and estimation for cubic
+spline regression. We employ least squares estimation to find potential
+regression coefficients based upon the cubic spline model and the knots chosen
+by the min/max algorithm. We then utilize the best subsets regression method to
+aid in model selection in which we find the minimum value of the Bayesian
+Information Criteria. Finally, we report the $R_{adj}^{2}$ as a measure of
+overall goodness-of-fit of our selected model. Among the fifty states and
+Washington D.C., we have found 42 out of 51 with $R_{adj}^{2}$ value that was
+greater than $90\%$. We also present an overall model for the United States as
+a whole. Our method can serve as a unified model for violent crime rate over
+future years.
+"
+28278,Camera Based Two Factor Authentication Through Mobile and Wearable Devices,"  We introduce Pixie, a novel, camera based two factor authentication solution
+for mobile and wearable devices. A quick and familiar user action of snapping a
+photo is sufficient for Pixie to simultaneously perform a graphical password
+authentication and a physical token based authentication, yet it does not
+require any expensive, uncommon hardware. Pixie establishes trust based on both
+the knowledge and possession of an arbitrary physical object readily accessible
+to the user, called trinket. Users choose their trinkets similar to setting a
+password, and authenticate by presenting the same trinket to the camera. The
+fact that the object is the trinket, is secret to the user. Pixie extracts
+robust, novel features from trinket images, and leverages a supervised learning
+classifier to effectively address inconsistencies between images of the same
+trinket captured in different circumstances.
+Pixie achieved a false accept rate below 0.09% in a brute force attack with
+14.3 million authentication attempts, generated with 40,000 trinket images that
+we captured and collected from public datasets. We identify master images, that
+match multiple trinkets, and study techniques to reduce their impact.
+In a user study with 42 participants over 8 days in 3 sessions we found that
+Pixie outperforms text based passwords on memorability, speed, and user
+preference. Furthermore, Pixie was easily discoverable by new users and
+accurate under field use. Users were able to remember their trinkets 2 and 7
+days after registering them, without any practice between the 3 test dates.
+"
+28279,Finite-Temperature Screening of U(1) Fractons,"  We investigate the finite-temperature screening behavior of three-dimensional
+U(1) spin liquid phases with fracton excitations. Several features are shared
+with the conventional U(1) spin liquid. The system can exhibit spin liquid
+physics over macroscopic length scales at low temperatures, but screening
+effects eventually lead to a smooth finite-temperature crossover to a trivial
+phase at sufficiently large distances. However, unlike more conventional U(1)
+spin liquids, we find that complete low-temperature screening of fractons
+requires not only very large distances, but also very long timescales. At the
+longest timescales, a charged disturbance (fracton) will acquire a screening
+cloud of other fractons, resulting in only short-range correlations in the
+system. At intermediate timescales, on the other hand, a fracton can only be
+partially screened by a cloud of mobile excitations, leaving weak power-law
+correlations in the system. Such residual power-law correlations may be a
+useful diagnostic in an experimental search for U(1) fracton phases.
+"
+28280,Identifying Unmaintained Projects in GitHub,"  Background: Open source software has an increasing importance in modern
+software development. However, there is also a growing concern on the
+sustainability of such projects, which are usually managed by a small number of
+developers, frequently working as volunteers. Aims: In this paper, we propose
+an approach to identify GitHub projects that are not actively maintained. Our
+goal is to alert users about the risks of using these projects and possibly
+motivate other developers to assume the maintenance of the projects. Method: We
+train machine learning models to identify unmaintained or sparsely maintained
+projects, based on a set of features about project activity (commits, forks,
+issues, etc). We empirically validate the model with the best performance with
+the principal developers of 129 GitHub projects. Results: The proposed machine
+learning approach has a precision of 80%, based on the feedback of real open
+source developers; and a recall of 96%. We also show that our approach can be
+used to assess the risks of projects becoming unmaintained. Conclusions: The
+model proposed in this paper can be used by open source users and developers to
+identify GitHub projects that are not actively maintained anymore.
+"
+28281,Location Optimization of ATM Networks,"  ATMs enable the public to perform financial transactions. Banks try to
+strategically position their ATMs in order to maximize transactions and
+revenue. In this paper, we introduce a model which provides a score to an ATM
+location, which serves as an indicator of its relative likelihood of
+transactions. In order to efficiently capture the spatially dynamic features,
+we utilize two concurrent prediction models: the local model which encodes the
+spatial variance by considering highly energetic features in a given location,
+and the global model which enforces the dominant trends in the entire data and
+serves as a feedback to the local model to prevent overfitting. The major
+challenge in learning the model parameters is the lack of an objective
+function. The model is trained using a synthetic objective function using the
+dominant features returned from the k-means clustering algorithm in the local
+model. The results obtained from the energetic features using the models are
+encouraging.
+"
+28282,Efficient Implementation of Evaluation Strategies via Token-Guided Graph Rewriting,"  In implementing evaluation strategies of the lambda-calculus, both
+correctness and efficiency of implementation are valid concerns. While the
+notion of correctness is determined by the evaluation strategy, regarding
+efficiency there is a larger design space that can be explored, in particular
+the trade-off between space versus time efficiency. We contributed to the study
+of this trade-off by the introduction of an abstract machine for call-by-need,
+inspired by Girard's Geometry of Interaction, a machine combining token passing
+and graph rewriting. This work presents an extension of the machine, to
+additionally accommodate left-to-right and right-to-left call-by-value
+strategies. We show soundness and completeness of the extended machine with
+respect to each of the call-by-need and two call-by-value strategies. Analysing
+time cost of its execution classifies the machine as ""efficient"" in Accattoli's
+taxonomy of abstract machines.
+"
+28283,A Universal Logic Operator for Interpretable Deep Convolution Networks,"  Explaining neural network computation in terms of probabilistic/fuzzy logical
+operations has attracted much attention due to its simplicity and high
+interpretability. Different choices of logical operators such as AND, OR and
+XOR give rise to another dimension for network optimization, and in this paper,
+we study the open problem of learning a universal logical operator without
+prescribing to any logical operations manually. Insightful observations along
+this exploration furnish deep convolution networks with a novel logical
+interpretation.
+"
+28284,Dark matter in the Sun: scattering off electrons vs nucleons,"  The annihilation of dark matter (DM) particles accumulated in the Sun could
+produce a flux of neutrinos, which is potentially detectable with neutrino
+detectors/telescopes and the DM elastic scattering cross section can be
+constrained. Although the process of DM capture in astrophysical objects like
+the Sun is commonly assumed to be due to interactions only with nucleons, there
+are scenarios in which tree-level DM couplings to quarks are absent, and even
+if loop-induced interactions with nucleons are allowed, scatterings off
+electrons could be the dominant capture mechanism. We consider this possibility
+and study in detail all the ingredients necessary to compute the neutrino
+production rates from DM annihilations in the Sun (capture, annihilation and
+evaporation rates) for velocity-independent and isotropic, velocity-dependent
+and isotropic and momentum-dependent scattering cross sections for DM
+interactions with electrons and compare them with the results obtained for the
+case of interactions with nucleons. Moreover, we improve the usual calculations
+in a number of ways and provide analytical expressions in three appendices.
+Interestingly, we find that the evaporation mass in the case of interactions
+with electrons could be below the GeV range, depending on the high-velocity
+tail of the DM distribution in the Sun, which would open a new mass window for
+searching for this type of scenarios.
+"
+28285,Shape optimization to decrease failure probability,"  Ceramic is a material frequently used in industry because of its favorable
+properties. Common approaches in shape optimization for ceramic structures aim
+to minimize the tensile stress acting on the component, as it is the main
+driver for failure. In contrast to this, we follow a more natural approach by
+minimizing the component's probability of failure under a given tensile load.
+Since the fundamental work of Weibull, the probabilistic description of the
+strength of ceramics is standard and has been widely applied. Here, for the
+first time, the resulting failure probabilities are used as objective functions
+in PDE constrained shape optimization.
+To minimize the probability of failure, we choose a gradient based method
+combined with a first discretize then optimize approach. For discretization
+finite elements are used. Using the Lagrangian formalism, the shape gradient
+via the adjoint equation is calculated at low computational cost. The
+implementation is verified by comparison of it with a finite difference method
+applied to a minimal 2d example. Furthermore, we construct shape flows towards
+an optimal / improved shape in the case of a simple beam and a bended joint.
+"
+28286,Exponential sums over finite fields and the large sieve,"  By using a variant of Kowalski's large sieve for Frobenius in compatible
+systems, we obtain zero-density estimates for arguments of $\ell$-adic trace
+functions over finite fields with values in some algebraic subsets of the
+cyclotomic integers, when the monodromy groups are known. This applies in
+particular to hyper-Kloosterman sums and general exponential sums considered by
+Katz.
+"
+28287,An Optimal Strategy for Accurate Bulge-to-disk Decomposition of Disk Galaxies,"  The development of two-dimensional (2D) bulge-to-disk decomposition
+techniques has shown their advantages over traditional one-dimensional (1D)
+techniques, especially for galaxies with non-axisymmetric features. However,
+the full potential of 2D techniques has yet to be fully exploited. Secondary
+morphological features in nearby disk galaxies, such as bars, lenses, rings,
+disk breaks, and spiral arms, are seldom accounted for in 2D image
+decompositions, even though some image-fitting codes, such as GALFIT, are
+capable of handling them. We present detailed, 2D multi-model and
+multi-component decomposition of high-quality $R$-band images of a
+representative sample of nearby disk galaxies selected from the Carnegie-Irvine
+Galaxy Survey, using the latest version of GALFIT. The sample consists of five
+barred and five unbarred galaxies, spanning Hubble types from S0 to Sc.
+Traditional 1D decomposition is also presented for comparison. In detailed case
+studies of the 10 galaxies, we successfully model the secondary morphological
+features. Through a comparison of best-fit parameters obtained from different
+input surface brightness models, we identify morphological features that
+significantly impact bulge measurements. We show that nuclear and inner
+lenses/rings and disk breaks must be properly taken into account to obtain
+accurate bulge parameters, whereas outer lenses/rings and spiral arms have a
+negligible effect. We provide an optimal strategy to measure bulge parameters
+of typical disk galaxies, as well as prescriptions to estimate realistic
+uncertainties of them, which will benefit subsequent decomposition of a larger
+galaxy sample.
+"
+28288,Invariant Theory of finite general linear groups modulo Frobenius powers,"  We prove some cases of a conjecture of Lewis, Reiner and Stanton regarding
+Hilbert series corresponding to the action of $Gl_n(\mathbb{F}_q)$ on a
+polynomial ring modulo Frobenius powers. We also give a few conjectures about
+the invariant ring for certain cases that we don't prove completely.
+"
+28289,Generation and spectroscopic signatures of a fractional quantum Hall liquid of photons in an incoherently pumped optical cavity,"  We theoretically investigate a driven-dissipative model of strongly
+interacting photons in a nonlinear optical cavity in the presence of a
+synthetic magnetic field. We show the possibility of using a
+frequency-dependent incoherent pump to create a strongly-correlated $\nu = 1/2$
+bosonic Laughlin state of light: thanks to the incompressibility of the
+Laughlin state, fluctuations in the total particle number and excitation of
+edge modes can be tamed by imposing a suitable external potential profile for
+photons. We further propose angular momentum-selective spectroscopy of the
+emitted light as a tool to obtain unambiguous signatures of the microscopic
+physics of the quantum Hall liquid of light.
+"
+28290,Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining,"  One of the main problems that emerges in the classic approach to semantics is
+the difficulty in acquisition and maintenance of ontologies and semantic
+annotations. On the other hand, the Internet explosion and the massive
+diffusion of mobile smart devices lead to the creation of a worldwide system,
+which information is daily checked and fueled by the contribution of millions
+of users who interacts in a collaborative way. Search engines, continually
+exploring the Web, are a natural source of information on which to base a
+modern approach to semantic annotation. A promising idea is that it is possible
+to generalize the semantic similarity, under the assumption that semantically
+similar terms behave similarly, and define collaborative proximity measures
+based on the indexing information returned by search engines. The PMING
+Distance is a proximity measure used in data mining and information retrieval,
+which collaborative information express the degree of relationship between two
+terms, using only the number of documents returned as result for a query on a
+search engine. In this work, the PMINIG Distance is updated, providing a novel
+formal algebraic definition, which corrects previous works. The novel point of
+view underlines the features of the PMING to be a locally normalized linear
+combination of the Pointwise Mutual Information and Normalized Google Distance.
+The analyzed measure dynamically reflects the collaborative change made on the
+web resources.
+"
+28291,Human Robot Interface for Assistive Grasping,"  This work describes a new human-in-the-loop (HitL) assistive grasping system
+for individuals with varying levels of physical capabilities. We investigated
+the feasibility of using four potential input devices with our assistive
+grasping system interface, using able-bodied individuals to define a set of
+quantitative metrics that could be used to assess an assistive grasping system.
+We then took these measurements and created a generalized benchmark for
+evaluating the effectiveness of any arbitrary input device into a HitL grasping
+system. The four input devices were a mouse, a speech recognition device, an
+assistive switch, and a novel sEMG device developed by our group that was
+connected either to the forearm or behind the ear of the subject. These
+preliminary results provide insight into how different interface devices
+perform for generalized assistive grasping tasks and also highlight the
+potential of sEMG based control for severely disabled individuals.
+"
+28292,Maximal $L^p$-$L^q$ regularity for the Stokes problem with Navier-type boundary conditions,"  Maximal $L^p$-$L^q$ regularity is proved for the strong, weak and very weak
+solutions of the inhomogeneous Stokes problem with Navier-type boundary
+conditions in a bounded domain $\Omega$, not necessarily simply connected. This
+extends previous results of the authors (2017).
+"
+28293,On nonlinear cross-diffusion systems: an optimal transport approach,"  We study a nonlinear, degenerate cross-diffusion model which involves two
+densities with two different drift velocities. A general framework is
+introduced based on its gradient flow structure in Wasserstein space to derive
+a notion of discrete-time solutions. Its continuum limit, due to the possible
+mixing of the densities, only solves a weaker version of the original system.
+In one space dimension, where the densities are guaranteed to be segregated, a
+stable interface appears between the two densities, and a stronger convergence
+result, in particular derivation of a standard weak solution to the system, is
+available. We also study the incompressible limit of the system, which
+addresses transport under a height constraint on the total density. In one
+space dimension we show that the problem leads to a two-phase Hele-Shaw type
+flow.
+"
+28294,Ground state solution for a class of indefinite variational problems with critical growth,"  In this paper we study the existence of ground state solution for an
+indefinite variational problem of the type $$ \left\{\begin{array}{l} -\Delta
+u+(V(x)-W(x))u=f(x,u) \quad \mbox{in} \quad \R^{N}, u\in H^{1}(\R^{N}),
+\end{array}\right. \eqno{(P)} $$ where $N \geq 2$, $V,W:\mathbb{R}^N \to
+\mathbb{R}$ and $f:\mathbb{R}^N \times \mathbb{R} \to \mathbb{R}$ are
+continuous functions verifying some technical conditions and $f$ possesses a
+critical growth. Here, we will consider the case where the problem is
+asymptotically periodic, that is, $V$ is $\mathbb{Z}^N$-periodic, $W$ goes to 0
+at infinity and $f$ is asymptotically periodic.
+"
+28295,Target contrastive pessimistic risk for robust domain adaptation,"  In domain adaptation, classifiers with information from a source domain adapt
+to generalize to a target domain. However, an adaptive classifier can perform
+worse than a non-adaptive classifier due to invalid assumptions, increased
+sensitivity to estimation errors or model misspecification. Our goal is to
+develop a domain-adaptive classifier that is robust in the sense that it does
+not rely on restrictive assumptions on how the source and target domains relate
+to each other and that it does not perform worse than the non-adaptive
+classifier. We formulate a conservative parameter estimator that only deviates
+from the source classifier when a lower risk is guaranteed for all possible
+labellings of the given target samples. We derive the classical least-squares
+and discriminant analysis cases and show that these perform on par with
+state-of-the-art domain adaptive classifiers in sample selection bias settings,
+while outperforming them in more general domain adaptation settings.
+"
+28296,Turbulent skin-friction reduction by wavy surfaces,"  Direct numerical simulations of fully-developed turbulent channel flows with
+wavy walls are undertaken. The wavy walls, skewed with respect to the mean flow
+direction, are introduced as a means of emulating a Spatial Stokes Layer (SSL)
+induced by in-plane wall motion. The transverse shear strain above the wavy
+wall is shown to be similar to that of a SSL, thereby affecting the turbulent
+flow, and leading to a reduction in the turbulent skin-friction drag. The
+pressure- and friction-drag levels are carefully quantified for various flow
+configurations, exhibiting a combined maximum overall-drag reduction of about
+0.5%. The friction-drag reduction is shown to behave approximately
+quadratically for small wave slopes and then linearly for higher slopes, whilst
+the pressure-drag penalty increases quadratically. Unlike in the SSL case,
+there is a region of increased turbulence production over a portion of the
+wall, above the leeward side of the wave, thus giving rise to a local increase
+in dissipation. The transverse shear-strain layer is shown to be approximately
+Reynolds-number independent when the wave geometry is scaled in wall units.
+"
+28297,Improved Algorithm for Reconstructing Singular Connection in Multi-Block CFD Applications,"  An improved algorithm is proposed for the reconstruction of singular
+connectivity from the available pairwise connections during preprocessing
+phase. To evaluate the performance of the algorithm, an in-house computational
+fluid dynamics (CFD) code is used in which high-order finite-difference method
+for spatial discretization running on the Tianhe-1A supercomputer is employed.
+Test cases with a varied amount of mesh points are chosen, and the test results
+indicate that the improved singular connection reconstruction algorithm can
+achieve a speedup factor of 1000X or more when compared with the naive search
+method adopted in the former version of our code. Moreover, the parallel
+efficiency can benefit from the strategy of local communication based on the
+algorithm.
+"
+28298,Reconstructing unrooted phylogenetic trees from symbolic ternary metrics,"  In 1998, Böcker and Dress gave a 1-to-1 correspondence between
+symbolically dated rooted trees and symbolic ultrametrics. We consider the
+corresponding problem for unrooted trees. More precisely, given a tree $T$ with
+leaf set $X$ and a proper vertex colouring of its interior vertices, we can map
+every triple of three different leaves to the colour of its median vertex. We
+characterise all ternary maps that can be obtained in this way in terms of 4-
+and 5-point conditions, and we show that the corresponding tree and its
+colouring can be reconstructed from a ternary map that satisfies those
+conditions. Further, we give an additional condition that characterises whether
+the tree is binary, and we describe an algorithm that reconstructs general
+trees in a bottom-up fashion.
+"
+28299,Structured Deformations of Continua: Theory and Applications,"  The scope of this contribution is to present an overview of the theory of
+structured deformations of continua, together with some applications.
+Structured deformations aim at being a unified theory in which elastic and
+plastic behaviours, as well as fractures and defects can be described in a
+single setting. Since its introduction in the scientific community of rational
+mechanicists (Del Piero-Owen, ARMA 1993), the theory has been put in the
+framework of variational calculus (Choksi-Fonseca, ARMA 1997), thus allowing
+for solution of problems via energy minimization. Some background, three
+problems and a discussion on future directions are presented.
+"
+28300,Improved resection margins in breast-conserving surgery using Terahertz Pulsed imaging data,"  New statistical methods were employed to improve the ability to distinguish
+benign from malignant breast tissue ex vivo in a recent study. The ultimately
+aim was to improve the intraoperative assessment of positive tumour margins in
+breast-conserving surgery (BCS), potentially reducing patient re-operation
+rates. A multivariate Bayesian classifier was applied to the waveform samples
+produced by a Terahertz Pulsed Imaging (TPI) handheld probe system in order to
+discriminate tumour from benign breast tissue, obtaining a sensitivity of 96%
+and specificity of 95%.
+We compare these results to traditional and to state-of-the-art methods for
+determining resection margins. Given the general nature of the classifier, it
+is expected that this method can be applied to other tumour types where
+resection margins are also critical.
+"
+28301,Disseminando a Aprendizagem Colaborativa atraves do Ambiente Canvas,"  Virtual learning environments are actual solutions that facilitate
+collaborative learning, both in classroom and distance education. However, such
+environments are not yet fully disseminated in Brazilian universities. This
+work reports a learning experience designed as a workshop that aims to
+popularize the use of the Canvas virtual learning environment in an university.
+The workshop emphasizes the support to collaborative learning through resources
+of the environment. Learned lessons include: the value of adequate planning of
+workshop activities; the potential of the environment regarding various uses by
+instructors; and the relevance of collaborative learning for instructors.
+"
+28302,Coherence-based Time Series Clustering for Brain Connectivity Visualization,"  We develop the hierarchical cluster coherence (HCC) method for brain signals,
+a procedure for characterizing connectivity in a network by clustering nodes or
+groups of channels that display high level of coordination as measured by
+""cluster-coherence"". While the most common approach to measuring dependence
+between clusters is through pairs of single time series, our method proposes
+cluster coherence which measures dependence between whole clusters rather than
+between single elements. Thus it takes into account both the dependence between
+clusters and within channels in a cluster. Using our method, the identified
+clusters contain time series that exhibit high cross-dependence in the spectral
+domain. That is, these clusters correspond to connected brain regions with
+synchronized oscillatory activity. In the simulation studies, we show that the
+proposed HCC outperforms commonly used clustering algorithms, such as average
+coherence and minimum coherence based methods. To study clustering in a network
+of multichannel electroencephalograms (EEG) during an epileptic seizure, we
+applied the HCC method and identified connectivity in alpha (8-12) Hertz and
+beta (16-30) Hertz bands at different phases of the recording: before an
+epileptic seizure, during the early and middle phases of the seizure episode.
+To increase the potential impact of this work in neuroscience, we also
+developed the HCC-Vis, an R-Shiny app (RStudio), which can be downloaded from
+this this https URL.
+"
+28303,On the algebra of symmetries of Laplace and Dirac operators,"  We consider a generalization of the classical Laplace operator, which
+includes the Laplace-Dunkl operator defined in terms of the
+differential-difference operators associated with finite reflection groups
+called Dunkl operators. For this Laplace-like operator, we determine a set of
+symmetries commuting with it, which are generalized angular momentum operators,
+and we present the algebraic relations for the symmetry algebra. In this
+context, the generalized Dirac operator is then defined as a square root of our
+Laplace-like operator. We explicitly determine a family of graded operators
+which commute or anti-commute with our Dirac-like operator depending on their
+degree. The algebra generated by these symmetry operators is shown to be a
+generalization of the standard angular momentum algebra and the recently
+defined higher rank Bannai-Ito algebra.
+"
+28304,Evidence for Weyl fermions in a canonical heavy-fermion semimetal YbPtBi,"  The manifestation of Weyl fermions in strongly correlated electron systems is
+of particular interest. We report evidence for Weyl fermions in the heavy
+fermion semimetal YbPtBi from electronic structure calculations, angle-resolved
+photoemission spectroscopy, magnetotransport and calorimetric measurements. At
+elevated temperatures where $4f$-electrons are localized, there are triply
+degenerate points, yielding Weyl nodes in applied magnetic fields. These are
+revealed by a contribution from the chiral anomaly in the magnetotransport,
+which at low temperatures becomes negligible due to the influence of electronic
+correlations. Instead, Weyl fermions are inferred from the topological Hall
+effect, which provides evidence for a Berry curvature, and a cubic temperature
+dependence of the specific heat, as expected from the linear dispersion near
+the Weyl nodes. The results suggest that YbPtBi is a Weyl heavy fermion
+semimetal, where the Kondo interaction renormalizes the bands hosting Weyl
+points. These findings open up an opportunity to explore the interplay between
+topology and strong electronic correlations.
+"
+28305,Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models,"  Volatility prediction--an essential concept in financial markets--has
+recently been addressed using sentiment analysis methods. We investigate the
+sentiment of annual disclosures of companies in stock markets to forecast
+volatility. We specifically explore the use of recent Information Retrieval
+(IR) term weighting models that are effectively extended by related terms using
+word embeddings. In parallel to textual information, factual market data have
+been widely used as the mainstream approach to forecast market risk. We
+therefore study different fusion methods to combine text and market data
+resources. Our word embedding-based approach significantly outperforms
+state-of-the-art methods. In addition, we investigate the characteristics of
+the reports of the companies in different financial sectors.
+"
+28306,On the rate of convergence in the central limit theorem for hierarchical Laplacian,"  Let $(X,d)$ be a proper ultrametric space. Given a measure $m$ on $X$ and a
+function $C(B)$ defined on the set of all non-singleton balls $B$ we consider
+the hierarchical Laplacian $L=L_{C}$. Choosing a sequence $\{\varepsilon (B)\}$
+of i.i.d. random variables we define the perturbed function $C(B,\omega )$ and
+the perturbed hierarchical Laplacian $L^{\omega }=L_{C(\omega )}.$ We study the
+arithmetic means $\overline{\lambda }(\omega )$ of the $L^{\omega
+}$-eigenvalues. Under some mild assumptions the normalized arithmetic means
+$\big( \overline{\lambda }-\mathbb{E}\overline{\lambda }\big) /\sigma \big(
+\overline{\lambda }\big) $ converge in law to the standard normal distribution.
+In this note we study convergence in the total variation distance and estimate
+the rate of convergence.
+"
+28307,Detailed abundances from integrated-light spectroscopy: Milky Way globular clusters,"  We test the performance of our analysis technique for integrated-light
+spectra by applying it to seven well-studied Galactic GCs that span a wide
+range of metallicities. Integrated-light spectra were obtained by scanning the
+slit of the UVES spectrograph on the ESO Very Large Telescope across the
+half-light diameters of the clusters. We modelled the spectra using resolved
+HST colour-magnitude diagrams (CMDs), as well as theoretical isochrones, in
+combination with standard stellar atmosphere and spectral synthesis codes. The
+abundances of Fe, Na, Mg, Ca, Ti, Cr, and Ba were compared with literature data
+for individual stars in the clusters. The typical differences between iron
+abundances derived from our integrated-light spectra and those compiled from
+the literature are less than 0.1 dex. A larger difference is found for one
+cluster (NGC 6752), and is most likely caused primarily by stochastic
+fluctuations in the numbers of bright red giants within the scanned area. As
+expected, the alpha-elements (Ca, Ti) are enhanced by about 0.3 dex compared to
+the Solar-scaled composition, while the [Cr/Fe] ratios are close to Solar. When
+using up-to-date line lists, our [Mg/Fe] ratios also agree well with literature
+data. Our [Na/Fe] ratios are, on average, 0.08-0.14 dex lower than average
+values quoted in the literature, and our [Ba/Fe] ratios may be overestimated by
+0.20-0.35 dex at the lowest metallicities. We find that analyses based on
+theoretical isochrones give very similar results to those based on resolved
+CMDs. Overall, the agreement between our integrated-light abundance
+measurements and the literature data is satisfactory. Refinements of the
+modelling procedure, such as corrections for stellar evolutionary and non-LTE
+effects, might further reduce some of the remaining offsets.
+"
+28308,Large-Scale Structure Topology in Non-Standard Cosmologies: Impact of Dark Sector Physics,"  Even as our measurements of cosmological parameters improve, the physical
+nature of the dark sector of the universe largely remains a mystery. Many
+effects of dark sector models are most prominent at very large scales and will
+rely on future galaxy surveys to elucidate. In this paper we compare the
+topological properties of the large scale dark matter distribution in a number
+of cosmological models using hydrodynamical simulations and the cosmological
+genus statistic. Genus curves are computed from z = 11 to z = 0 for
+{\Lambda}CDM, Quintessence and Warm Dark Matter models, over a scale range of 1
+to 20 Mpc/h. The curves are analysed in terms of their Hermite spectra to
+describe the power contained in non-Gaussian deformations to the cosmological
+density field. We find that the {\Lambda}CDM and {\Lambda}WDM models produce
+nearly identical genus curves indicating no topological differences in
+structure formation. The Quintessence model, which differs solely in its
+expansion history, produces significant differences in the strength and
+redshift evolution of non-Gaussian modes associated with higher cluster
+abundances and lower void abundances. These effects are robust to cosmic
+variance and are characteristically different from those produced by tweaking
+the parameters of a {\Lambda}CDM model. Given the simplicity and similarity of
+the models, detecting these discrepancies represents a promising avenue for
+understanding the effect of non-standard cosmologies on large-scale structure.
+"
+28309,Causal Inference in Disease Spread across a Heterogeneous Social System,"  Diffusion processes are governed by external triggers and internal dynamics
+in complex systems. Timely and cost-effective control of infectious disease
+spread critically relies on uncovering the underlying diffusion mechanisms,
+which is challenging due to invisible causality between events and their
+time-evolving intensity. We infer causal relationships between infections and
+quantify the reflexivity of a meta-population, the level of feedback on event
+occurrences by its internal dynamics (likelihood of a regional outbreak
+triggered by previous cases). These are enabled by our new proposed model, the
+Latent Influence Point Process (LIPP) which models disease spread by
+incorporating macro-level internal dynamics of meta-populations based on human
+mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
+causal inference, outbreaks are more likely driven by statewide global
+diffusion over time, leading to complex behavior of disease spread. In terms of
+reflexivity, precursory growth and symmetric decline in populous regions is
+attributed to slow but persistent feedback on preceding outbreaks via
+inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
+is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
+model reveals probabilistic causal relationships between discrete events based
+on intra- and inter-group dynamics and also covers direct and indirect
+diffusion processes (contact-based and vector-borne disease transmissions).
+"
+28310,Discrete Flux and Velocity Fields of Probability and Their Global Maps in Reaction Systems,"  Stochasticity plays important roles in reaction systems. Vector fields of
+probability flux and velocity characterize time-varying and steady-state
+properties of these systems, including high probability paths, barriers,
+checkpoints among different stable regions, as well as mechanisms of dynamic
+switching among them. However, conventional fluxes on continuous space are
+ill-defined and are problematic when at boundaries of the state space or when
+copy numbers are small. By re-defining the derivative and divergence operators
+based on the discrete nature of reactions, we introduce new formulations of
+discrete fluxes. Our flux model fully accounts for the discreetness of both the
+state space and the jump processes of reactions. The reactional discrete flux
+satisfies the continuity equation and describes the behavior of the system
+evolving along directions of reactions. The species discrete flux directly
+describes the dynamic behavior in the state space of the reactants such as the
+transfer of probability mass. With the relationship between these two fluxes
+specified, we show how to construct time-evolving and steady-state global
+flow-maps of probability flux and velocity in the directions of every species
+at every microstate, and how they are related to the outflow and inflow of
+probability fluxes when tracing out reaction trajectories. We also describe how
+to impose proper conditions enabling exact quantification of flux and velocity
+in the boundary regions, without the difficulty of enforcing artificial
+reflecting conditions. We illustrate the computation of probability flux and
+velocity using three model systems, namely, the birth-death process, the
+bistable Schlögl model, and the oscillating Schnakenberg model.
+"
+28311,Gated Recurrent Neural Tensor Network,"  Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling
+temporal and sequential data need to capture long-term dependencies on datasets
+and represent them in hidden layers with a powerful model to capture more
+information from inputs. For modeling long-term dependencies in a dataset, the
+gating mechanism concept can help RNNs remember and forget previous
+information. Representing the hidden layers of an RNN with more expressive
+operations (i.e., tensor products) helps it learn a more complex relationship
+between the current input and the previous hidden layer information. These
+ideas can generally improve RNN performances. In this paper, we proposed a
+novel RNN architecture that combine the concepts of gating mechanism and the
+tensor product into a single model. By combining these two concepts into a
+single RNN, our proposed models learn long-term dependencies by modeling with
+gating units and obtain more expressive and direct interaction between input
+and hidden layers using a tensor product on 3-dimensional array (tensor) weight
+parameters. We use Long Short Term Memory (LSTM) RNN and Gated Recurrent Unit
+(GRU) RNN and combine them with a tensor product inside their formulations. Our
+proposed RNNs, which are called a Long-Short Term Memory Recurrent Neural
+Tensor Network (LSTMRNTN) and Gated Recurrent Unit Recurrent Neural Tensor
+Network (GRURNTN), are made by combining the LSTM and GRU RNN models with the
+tensor product. We conducted experiments with our proposed models on word-level
+and character-level language modeling tasks and revealed that our proposed
+models significantly improved their performance compared to our baseline
+models.
+"
+28312,On resilient control of dynamical flow networks,"  Resilience has become a key aspect in the design of contemporary
+infrastructure networks. This comes as a result of ever-increasing loads,
+limited physical capacity, and fast-growing levels of interconnectedness and
+complexity due to the recent technological advancements. The problem has
+motivated a considerable amount of research within the last few years,
+particularly focused on the dynamical aspects of network flows, complementing
+more classical static network flow optimization approaches. In this tutorial
+paper, a class of single-commodity first-order models of dynamical flow
+networks is considered. A few results recently appeared in the literature and
+dealing with stability and robustness of dynamical flow networks are gathered
+and originally presented in a unified framework. In particular, (differential)
+stability properties of monotone dynamical flow networks are treated in some
+detail, and the notion of margin of resilience is introduced as a quantitative
+measure of their robustness. While emphasizing methodological aspects --
+including structural properties, such as monotonicity, that enable tractability
+and scalability -- over the specific applications, connections to
+well-established road traffic flow models are made.
+"
+28313,Comparing Graph Spectra of Adjacency and Laplacian Matrices,"  Typically, graph structures are represented by one of three different
+matrices: the adjacency matrix, the unnormalised and the normalised graph
+Laplacian matrices. The spectral (eigenvalue) properties of these different
+matrices are compared. For each pair, the comparison is made by applying an
+affine transformation to one of them, which enables comparison whilst
+preserving certain key properties such as normalised eigengaps. Bounds are
+given on the eigenvalue differences thus found, which depend on the minimum and
+maximum degree of the graph. The monotonicity of the bounds and the structure
+of the graphs are related. The bounds on a real social network graph, and on
+three model graphs, are illustrated and analysed. The methodology is extended
+to provide bounds on normalised eigengap differences which again turn out to be
+in terms of the graph's degree extremes. It is found that if the degree extreme
+difference is large, different choices of representation matrix may give rise
+to disparate inference drawn from graph signal processing algorithms; smaller
+degree extreme differences result in consistent inference, whatever the choice
+of representation matrix. The different inference drawn from signal processing
+algorithms is visualised using the spectral clustering algorithm on the three
+representation matrices corresponding to a model graph and a real social
+network graph.
+"
+28314,Massively Parallel Algorithms and Hardness for Single-Linkage Clustering Under $\ell_p$-Distances,"  We present massively parallel (MPC) algorithms and hardness of approximation
+results for computing Single-Linkage Clustering of $n$ input $d$-dimensional
+vectors under Hamming, $\ell_1, \ell_2$ and $\ell_\infty$ distances. All our
+algorithms run in $O(\log n)$ rounds of MPC for any fixed $d$ and achieve
+$(1+\epsilon)$-approximation for all distances (except Hamming for which we
+show an exact algorithm). We also show constant-factor inapproximability
+results for $o(\log n)$-round algorithms under standard MPC hardness
+assumptions (for sufficiently large dimension depending on the distance used).
+Efficiency of implementation of our algorithms in Apache Spark is demonstrated
+through experiments on a variety of datasets exhibiting speedups of several
+orders of magnitude.
+"
+28315,Correlated Multivariate Poisson Processes and Extreme Measures,"  Multivariate Poisson processes have many important applications in Insurance,
+Finance, and many other areas of Applied Probability. In this paper we study
+the backward simulation approach to modelling multivariate Poisson processes
+and analyze the connection to the extreme measures describing the joint
+distribution of the processes at the terminal simulation time.
+"
+28316,Linking Mathematical Software in Web Archives,"  The Web is our primary source of all kinds of information today. This
+includes information about software as well as associated materials, like
+source code, documentation, related publications and change logs. Such data is
+of particular importance in research in order to conduct, comprehend and
+reconstruct scientific experiments that involve software. swMATH, a
+mathematical software directory, attempts to identify software mentions in
+scientific articles and provides additional information as well as links to the
+Web. However, just like software itself, the Web is dynamic and most likely the
+information on the Web has changed since it was referenced in a scientific
+publication. Therefore, it is crucial to preserve the resources of a software
+on the Web to capture its states over time.
+We found that around 40% of the websites in swMATH are already included in an
+existing Web archive. Out of these, 60% of contain some kind of documentation
+and around 45% even provide downloads of software artifacts. Hence, already
+today links can be established based on the publication dates of corresponding
+articles. The contained data enable enriching existing information with a
+temporal dimension. In the future, specialized infrastructure will improve the
+coverage of software resources and allow explicit references in scientific
+publications.
+"
+28317,A Tale of Two Animats: What does it take to have goals?,"  What does it take for a system, biological or not, to have goals? Here, this
+question is approached in the context of in silico artificial evolution. By
+examining the informational and causal properties of artificial organisms
+('animats') controlled by small, adaptive neural networks (Markov Brains), this
+essay discusses necessary requirements for intrinsic information, autonomy, and
+meaning. The focus lies on comparing two types of Markov Brains that evolved in
+the same simple environment: one with purely feedforward connections between
+its elements, the other with an integrated set of elements that causally
+constrain each other. While both types of brains 'process' information about
+their environment and are equally fit, only the integrated one forms a causally
+autonomous entity above a background of external influences. This suggests that
+to assess whether goals are meaningful for a system itself, it is important to
+understand what the system is, rather than what it does.
+"
+28318,Building Function Approximators on top of Haar Scattering Networks,"  In this article we propose building general-purpose function approximators on
+top of Haar Scattering Networks. We advocate that this architecture enables a
+better comprehension of feature extraction, in addition to its implementation
+simplicity and low computational costs. We show its approximation and feature
+extraction capabilities in a wide range of different problems, which can be
+applied on several phenomena in signal processing, system identification,
+econometrics and other potential fields.
+"
+28319,Sparsity Analysis of a Sonomyographic Muscle-Computer Interface,"  Objective: The objectives of this paper are to determine the optimal location
+for ultrasound transducer placement on the anterior forearm for imaging maximum
+muscle deformations during different hand motions and to investigate the effect
+of using a sparse set of ultrasound scanlines for motion classification for
+ultrasound-based muscle computer interfaces (MCIs). Methods: The optimal
+placement of the ultrasound transducer along the forearm is identified using
+freehand 3D reconstructions of the muscle thickness during rest and motion
+completion. From the ultrasound images acquired from the optimally placed
+transducer, we determine classification accuracy with equally spaced scanlines
+across the cross-sectional field-of-view (FOV). Furthermore, we investigated
+the unique contribution of each scanline to class discrimination using Fisher
+criteria (FC) and mutual information (MI) with respect to motion
+discriminability. Results: Experiments with 5 able-bodied subjects show that
+the maximum muscle deformation occurred between 30-50% of the forearm length
+for multiple degrees-of-freedom. The average classification accuracy was 94.6%
+with the entire 128 scanline image and 94.5% with 4 equally-spaced scanlines.
+However, no significant improvement in classification accuracy was observed
+with optimal scanline selection using FC and MI. Conclusion: For an optimally
+placed transducer, a small subset of ultrasound scanlines can be used instead
+of a full imaging array without sacrificing performance in terms of
+classification accuracy for multiple degrees-of-freedom. Significance: The
+selection of a small subset of transducer elements can enable the reduction of
+computation, and simplification of the instrumentation and power consumption of
+wearable sonomyographic MCIs particularly for rehabilitation and gesture
+recognition applications.
+"
+28320,Additivity of Information in Multilayer Networks via Additive Gaussian Noise Transforms,"  Multilayer (or deep) networks are powerful probabilistic models based on
+multiple stages of a linear transform followed by a non-linear (possibly
+random) function. In general, the linear transforms are defined by matrices and
+the non-linear functions are defined by information channels. These models have
+gained great popularity due to their ability to characterize complex
+probabilistic relationships arising in a wide variety of inference problems.
+The contribution of this paper is a new method for analyzing the fundamental
+limits of statistical inference in settings where the model is known. The
+validity of our method can be established in a number of settings and is
+conjectured to hold more generally. A key assumption made throughout is that
+the matrices are drawn randomly from orthogonally invariant distributions.
+Our method yields explicit formulas for 1) the mutual information; 2) the
+minimum mean-squared error (MMSE); 3) the existence and locations of certain
+phase-transitions with respect to the problem parameters; and 4) the stationary
+points for the state evolution of approximate message passing algorithms. When
+applied to the special case of models with multivariate Gaussian channels our
+method is rigorous and has close connections to free probability theory for
+random matrices. When applied to the general case of non-Gaussian channels, our
+method provides a simple alternative to the replica method from statistical
+physics. A key observation is that the combined effects of the individual
+components in the model (namely the matrices and the channels) are additive
+when viewed in a certain transform domain.
+"
+28321,Superhuman Accuracy on the SNEMI3D Connectomics Challenge,"  For the past decade, convolutional networks have been used for 3D
+reconstruction of neurons from electron microscopic (EM) brain images. Recent
+years have seen great improvements in accuracy, as evidenced by submissions to
+the SNEMI3D benchmark challenge. Here we report the first submission to surpass
+the estimate of human accuracy provided by the SNEMI3D leaderboard. A variant
+of 3D U-Net is trained on a primary task of predicting affinities between
+nearest neighbor voxels, and an auxiliary task of predicting long-range
+affinities. The training data is augmented by simulated image defects. The
+nearest neighbor affinities are used to create an oversegmentation, and then
+supervoxels are greedily agglomerated based on mean affinity. The resulting
+SNEMI3D score exceeds the estimate of human accuracy by a large margin. While
+one should be cautious about extrapolating from the SNEMI3D benchmark to
+real-world accuracy of large-scale neural circuit reconstruction, our result
+inspires optimism that the goal of full automation may be realizable in the
+future.
+"
+28322,On the normality of the null-fiber of the moment map for $θ$- and tori representations,"  Let (G, V) be a representation with either G a torus or (G, V) a locally free
+stable $\theta$-representation. We study the fiber at 0 of the associated
+moment map, which is a commuting variety in the latter case. We characterize
+the cases where this fiber is normal. The quotient (i.e. the symplectic
+reduction) turns out to be a specific orbifold when the representation is
+polar. In the torus case, this confirms a conjecture stated by C. Lehn, M.
+Lehn, R. Terpereau and the author in a former article. In the $\theta$-case,
+the conjecture was already known but the present approach yield another proof.
+"
+28323,Iron Partitioning between Ferropericlase and Bridgmanite in the Earth's Lower Mantle,"  Earth's lower mantle is generally believed to be seismically and chemically
+homogeneous because most of the key seismic parameters can be explained using a
+simplified mineralogical model at expected press-temperature conditions.
+However, recent high-resolution tomographic images have revealed seismic and
+chemical stratification in the middle-to-lower parts of the lower mantle. Thus
+far, the mechanism for the compositional stratification and seismic
+inhomogeneity, especially their relationship with the speciation of iron in the
+lower mantle, remains poorly understood. We have built a complete and
+integrated thermodynamic model of iron and aluminum chemistry for lower mantle
+conditions, and from this model has emerged a stratified picture of the
+valence, spin and composition profile in the lower mantle. Within this picture
+the lower mantle has an upper region with Fe3+ enriched bridgmanite with
+high-spin ferropericlase and metallic Fe, and a lower region with low-spin,
+iron-enriched ferropericlase coexisting with iron-depleted bridgmanite and
+almost no metallic Fe. The transition between the regions occurs at a depth of
+around 1600km and is driven by the spin transition in ferropericlase, which
+significantly changes the iron partitioning and speciation to one that favors
+Fe2+ in ferropericlase and suppresses Fe3+ and metallic iron formation These
+changes lead to lowered bulk sound velocity by 3-4% around the mid-lower mantle
+and enhanced density by ~1% toward the lowermost mantle. The predicted
+chemically and seismically stratified lower mantle differs dramatically from
+the traditional homogeneous model.
+"
+28324,"Dynamical centers for the elliptic quantum algebra ${\mathcal{B}}_{q,λ}(\hat{gl}_2)_c$","  We identify generating functionals that satisfy dynamical exchange relations
+with the Lax matrices defining the face-type elliptic quantum algebra
+${\mathcal{B}}_{q,\lambda}(\hat{gl}_{2})_c$, when the central charge takes the
+two possible values $c =\pm 2$. These generating functionals are constructed as
+quadratic trace-like objects in terms of the Lax matrices. The obtained
+structures are characterized as ""dynamical centers"", i.e. the centrality
+property is deformed by dynamical shifts. For these values, the functionals
+define (genuine) abelian subalgebras of
+${\mathcal{B}}_{q,\lambda}(\hat{gl}_{2})_c$.
+"
+28325,On finitely generated submonoids of virtually free groups,"  We prove that it is decidable whether or not a finitely generated submonoid
+of a virtually free group is graded, introduce a new geometric characterization
+as quasi-geodesic monoids, and show that their word problem is rational (as a
+relation). We also solve the isomorphism problem for this class of monoids,
+generalizing earlier results for submonoids of free monoids. We also prove that
+the classes of graded monoids, regular monoids and Kleene monoids coincide for
+submonoids of free groups.
+"
+28326,Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding,"  Minimizing adverse reactions caused by drug-drug interactions has always been
+a momentous research topic in clinical pharmacology. Detecting all possible
+interactions through clinical studies before a drug is released to the market
+is a demanding task. The power of big data is opening up new approaches to
+discover various drug-drug interactions. However, these discoveries contain a
+huge amount of noise and provide knowledge bases far from complete and
+trustworthy ones to be utilized. Most existing studies focus on predicting
+binary drug-drug interactions between drug pairs but ignore other interactions.
+In this paper, we propose a novel framework, called PRD, to predict drug-drug
+interactions. The framework uses the graph embedding that can overcome data
+incompleteness and sparsity issues to achieve multiple DDI label prediction.
+First, a large-scale drug knowledge graph is generated from different sources.
+Then, the knowledge graph is embedded with comprehensive biomedical text into a
+common low dimensional space. Finally, the learned embeddings are used to
+efficiently compute rich DDI information through a link prediction process. To
+validate the effectiveness of the proposed framework, extensive experiments
+were conducted on real-world datasets. The results demonstrate that our model
+outperforms several state-of-the-art baseline methods in terms of capability
+and accuracy.
+"
+28327,On the set of principal congruences in a distributive congruence lattice of an algebra,"  Let $Q$ be a subset of a finite distributive lattice $D$. An algebra $A$
+represents the inclusion $Q\subseteq D$ by principal congruences if the
+congruence lattice of $A$ is isomorphic to $D$ and the ordered set of principal
+congruences of $A$ corresponds to $Q$ under this isomorphism. If there is such
+an algebra for every subset $Q$ containing $0$, $1$, and all join-irreducible
+elements of $D$, then $D$ is said to be fully (A1)-representable. We prove that
+every fully (A1)-representable finite distributive lattice is planar and it has
+at most one join-reducible coatom. Conversely, we prove that every finite
+planar distributive lattice with at most one join-reducible coatom is fully
+chain-representable in the sense of a recent paper of G. Grätzer. Combining
+the results of this paper with another paper by the present author, it follows
+that every fully (A1)-representable finite distributive lattice is ""fully
+representable"" even by principal congruences of finite lattices. Finally, we
+prove that every chain-representable inclusion $Q\subseteq D$ can be
+represented by the principal congruences of a finite (and quite small) algebra.
+"
+28328,Ultrafast field-driven monochromatic photoemission from carbon nanotubes,"  Ultrafast electron pulses, combined with laser-pump and electron-probe
+technologies, allow for various forms of ultrafast microscopy and spectroscopy
+to elucidate otherwise challenging to observe physical and chemical
+transitions. However, the pursuit of simultaneous ultimate spatial and temporal
+resolution has been largely subdued by the low monochromaticity of the electron
+pulses and their poor phase synchronization to the optical excitation pulses.
+State-of-the-art photon-driven sources have good monochromaticity but poor
+phase synchronization. In contrast, field-driven photoemission has much higher
+light phase synchronization, due to the intrinsic sub-cycle emission dynamics,
+but poor monochromaticity. Such sources suffer from larger electron energy
+spreads (3 - 100 eV) attributed to the relatively low field enhancement of the
+conventional metal tips which necessitates long pump wavelengths (> 800 nm) in
+order to gain sufficient ponderomotive potential to access the field-driven
+regime. In this work, field-driven photoemission from ~1 nm radius carbon
+nanotubes excited by a femtosecond laser at a short wavelength of 410 nm has
+been realized. The energy spread of field-driven electrons is effectively
+compressed to 0.25 eV outperforming all conventional ultrafast electron
+sources. Our new nanotube-based ultrafast electron source opens exciting
+prospects for attosecond imaging and emerging light-wave electronics.
+"
+28329,Classification of Book Representations of $K_6$,"  A book representation of a graph is a particular way of embedding a graph in
+three dimensional space so that the vertices lie on a circle and the edges are
+chords on disjoint topological disks. We describe a set of operations on book
+representations that preserves ambient isotopy, and apply these operations to
+$K_6$, the complete graph with six vertices. We prove there are exactly 59
+distinct book representations for $K_6$, and we identify the number and type of
+knotted and linked cycles in each representation. We show that book
+representations of $K_6$ contain between one and seven links, and up to nine
+knotted cycles. Furthermore, all links and cycles in a book representation of
+$K_6$ have crossing number at most four.
+"
+28330,Precision Luminosity of LHC Proton-Proton Collisions at 13 TeV Using Hit-Counting with TPX Pixel Devices,"  A network of Timepix (TPX) devices installed in the ATLAS cavern measures the
+LHC luminosity as a function of time as a stand-alone system. The data were
+recorded from 13 TeV proton-proton collisions in 2015. Using two TPX devices,
+the number of hits created by particles passing the pixel matrices was counted.
+A van der Meer scan of the LHC beams was analysed using bunch-integrated
+luminosity averages over the different bunch profiles for an approximate
+absolute luminosity normalization. It is demonstrated that the TPX network has
+the capability to measure the reduction of LHC luminosity with precision.
+Comparative studies were performed among four sensors (two sensors in each TPX
+device) and the relative short-term precision of the luminosity measurement was
+determined to be 0.1% for 10 s time intervals. The internal long-term time
+stability of the measurements was below 0.5% for the data-taking period.
+"
+28331,On the Optimal Reconstruction of Partially Observed Functional Data,"  We propose a new reconstruction operator that aims to recover the missing
+parts of a function given the observed parts. This new operator belongs to a
+new, very large class of functional operators which includes the classical
+regression operators as a special case. We show the optimality of our
+reconstruction operator and demonstrate that the usually considered regression
+operators generally cannot be optimal reconstruction operators. Our estimation
+theory allows for autocorrelated functional data and considers the practically
+relevant situation in which each of the $n$ functions is observed at $m_i$,
+$i=1,\dots,n$, discretization points. We derive rates of consistency for our
+nonparametric estimation procedures using a double asymptotic. For data
+situations, as in our real data application where $m_i$ is considerably smaller
+than $n$, we show that our functional principal components based estimator can
+provide better rates of convergence than conventional nonparametric smoothing
+method.
+"
+28332,Learning Speaker Representations with Mutual Information,"  Learning good representations is of crucial importance in deep learning.
+Mutual Information (MI) or similar measures of statistical dependence are
+promising tools for learning these representations in an unsupervised way. Even
+though the mutual information between two random variables is hard to measure
+directly in high dimensional spaces, some recent studies have shown that an
+implicit optimization of MI can be achieved with an encoder-discriminator
+architecture similar to that of Generative Adversarial Networks (GANs). In this
+work, we learn representations that capture speaker identities by maximizing
+the mutual information between the encoded representations of chunks of speech
+randomly sampled from the same sentence. The proposed encoder relies on the
+SincNet architecture and transforms raw speech waveform into a compact feature
+vector. The discriminator is fed by either positive samples (of the joint
+distribution of encoded chunks) or negative samples (from the product of the
+marginals) and is trained to separate them. We report experiments showing that
+this approach effectively learns useful speaker representations, leading to
+promising results on speaker identification and verification tasks. Our
+experiments consider both unsupervised and semi-supervised settings and compare
+the performance achieved with different objective functions.
+"
+28333,Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models,"  Dynamical systems comprising of multiple components that can be partitioned
+into distinct blocks originate in many scientific areas. A pertinent example is
+the interactions between financial assets and selected macroeconomic
+indicators, which has been studied at aggregate level---e.g. a stock index and
+an employment index---extensively in the macroeconomics literature. A key
+shortcoming of this approach is that it ignores potential influences from other
+related components (e.g. Gross Domestic Product) that may exert influence on
+the system's dynamics and structure and thus produces incorrect results. To
+mitigate this issue, we consider a multi-block linear dynamical system with
+Granger-causal ordering between blocks, wherein the blocks' temporal dynamics
+are described by vector autoregressive processes and are influenced by blocks
+higher in the system hierarchy. We derive the maximum likelihood estimator for
+the posited model for Gaussian data in the high-dimensional setting based on
+appropriate regularization schemes for the parameters of the block components.
+To optimize the underlying non-convex likelihood function, we develop an
+iterative algorithm with convergence guarantees. We establish theoretical
+properties of the maximum likelihood estimates, leveraging the decomposability
+of the regularizers and a careful analysis of the iterates. Finally, we develop
+testing procedures for the null hypothesis of whether a block ""Granger-causes""
+another block of variables. The performance of the model and the testing
+procedures are evaluated on synthetic data, and illustrated on a data set
+involving log-returns of the US S&P100 component stocks and key macroeconomic
+variables for the 2001--16 period.
+"
+28334,SCUBA-2 follow-up of Herschel-SPIRE observed Planck overdensities,"  We present SCUBA-2 follow-up of 61 candidate high-redshift Planck sources. Of
+these, 10 are confirmed strong gravitational lenses and comprise some of the
+brightest such submm sources on the observed sky, while 51 are candidate
+proto-cluster fields undergoing massive starburst events. With the accompanying
+Herschel-SPIRE observations and assuming an empirical dust temperature prior of
+$34^{+13}_{-9}$ K, we provide photometric redshift and far-IR luminosity
+estimates for 172 SCUBA-2-selected sources within these Planck overdensity
+fields. The redshift distribution of the sources peak between a redshift of 2
+and 4, with one third of the sources having $S_{500}$/$S_{350} > 1$. For the
+majority of the sources, we find far-IR luminosities of approximately
+$10^{13}\,\mathrm{L}_\odot$, corresponding to star-formation rates of around
+$1000$ M$_\odot \mathrm{yr}^{-1}$. For $S_{850}>8$ mJy sources, we show that
+there is up to an order of magnitude increase in star-formation rate density
+and an increase in uncorrected number counts of $6$ for $S_{850}>8$ mJy when
+compared to typical cosmological survey fields. The sources detected with
+SCUBA-2 account for only approximately $5$ per cent of the Planck flux at 353
+GHz, and thus many more fainter sources are expected in these fields.
+"
+28335,Using Taste Groups for Collaborative Filtering,"  Implicit feedback is the simplest form of user feedback that can be used for
+item recommendation. It is easy to collect and domain independent. However,
+there is a lack of negative examples. Existing works circumvent this problem by
+making various assumptions regarding the unconsumed items, which fail to hold
+when the user did not consume an item because she was unaware of it. In this
+paper, we propose as a novel method for addressing the lack of negative
+examples in implicit feedback. The motivation is that if there is a large group
+of users who share the same taste and none of them consumed an item, then it is
+highly likely that the item is irrelevant to this taste. We use Hierarchical
+Latent Tree Analysis(HLTA) to identify taste-based user groups and make
+recommendations for a user based on her memberships in the groups.
+"
+28336,On Best-Response Dynamics in Potential Games,"  The paper studies the convergence properties of (continuous) best-response
+dynamics from game theory. Despite their fundamental role in game theory,
+best-response dynamics are poorly understood in many games of interest due to
+the discontinuous, set-valued nature of the best-response map. The paper
+focuses on elucidating several important properties of best-response dynamics
+in the class of multi-agent games known as potential games---a class of games
+with fundamental importance in multi-agent systems and distributed control. It
+is shown that in almost every potential game and for almost every initial
+condition, the best-response dynamics (i) have a unique solution, (ii) converge
+to pure-strategy Nash equilibria, and (iii) converge at an exponential rate.
+"
+28337,On the characterization of $M$-metrizable spaces,"  We introduced the concept of a metric value set (MVS) in an earlier paper
+\cite{GM} and developed the idea further in \cite{AS}. In this paper we study
+locally $M$-metrizable spaces and the products of $M$-metrizable spaces.
+Finally we prove a characterization of commutatively metrizable topological
+spaces by means of the bases of a quasiuniformity.
+"
+28338,Close Yet Distinctive Domain Adaptation,"  Domain adaptation is transfer learning which aims to generalize a learning
+model across training and testing data with different distributions. Most
+previous research tackle this problem in seeking a shared feature
+representation between source and target domains while reducing the mismatch of
+their data distributions. In this paper, we propose a close yet discriminative
+domain adaptation method, namely CDDA, which generates a latent feature
+representation with two interesting properties. First, the discrepancy between
+the source and target domain, measured in terms of both marginal and
+conditional probability distribution via Maximum Mean Discrepancy is minimized
+so as to attract two domains close to each other. More importantly, we also
+design a repulsive force term, which maximizes the distances between each label
+dependent sub-domain to all others so as to drag different class dependent
+sub-domains far away from each other and thereby increase the discriminative
+power of the adapted domain. Moreover, given the fact that the underlying data
+manifold could have complex geometric structure, we further propose the
+constraints of label smoothness and geometric structure consistency for label
+propagation. Extensive experiments are conducted on 36 cross-domain image
+classification tasks over four public datasets. The comprehensive results show
+that the proposed method consistently outperforms the state-of-the-art methods
+with significant margins.
+"
+28339,A Minimal Solution for Two-view Focal-length Estimation using Two Affine Correspondences,"  A minimal solution using two affine correspondences is presented to estimate
+the common focal length and the fundamental matrix between two semi-calibrated
+cameras - known intrinsic parameters except a common focal length. To the best
+of our knowledge, this problem is unsolved. The proposed approach extends point
+correspondence-based techniques with linear constraints derived from local
+affine transformations. The obtained multivariate polynomial system is
+efficiently solved by the hidden-variable technique. Observing the geometry of
+local affinities, we introduce novel conditions eliminating invalid roots. To
+select the best one out of the remaining candidates, a root selection technique
+is proposed outperforming the recent ones especially in case of high-level
+noise. The proposed 2-point algorithm is validated on both synthetic data and
+104 publicly available real image pairs. A Matlab implementation of the
+proposed solution is included in the paper.
+"
+28340,Characterization of topological phases of modified dimerized Kitaev chain via edge correlation functions,"  We study analytically a modified dimerized Kitaev chain model under both
+periodic and open boundary conditions and determine its phase diagram by
+calculating the topological number and edge correlation functions of Majorana
+fermions. We explicitly give analytical solutions for eigenstates of the model
+under open boundary conditions, which permit us to calculate two introduced
+edge correlation functions of Majorana fermions analytically. Our results
+indicate that the two edge correlation functions can be used to characterize
+the trivial, Su-Schrieffer-Heeger-like topological and topological
+superconductor phases. Furthermore, we find the existence of two different
+coupling patterns of Majorana fermions in topological superconductor phase
+characterized by different edge correlation functions, which can also be
+distinguished by investigating the survival probability of an edge Majorana
+under sudden quenching of parameters.
+"
+28341,Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization,"  This paper considers stochastic optimization problems for a large class of
+objective functions, including convex and continuous submodular. Stochastic
+proximal gradient methods have been widely used to solve such problems;
+however, their applicability remains limited when the problem dimension is
+large and the projection onto a convex set is costly. Instead, stochastic
+conditional gradient methods are proposed as an alternative solution relying on
+(i) Approximating gradients via a simple averaging technique requiring a single
+stochastic gradient evaluation per iteration; (ii) Solving a linear program to
+compute the descent/ascent direction. The averaging technique reduces the noise
+of gradient approximations as time progresses, and replacing projection step in
+proximal methods by a linear program lowers the computational complexity of
+each iteration. We show that under convexity and smoothness assumptions, our
+proposed method converges to the optimal objective function value at a
+sublinear rate of $O(1/t^{1/3})$. Further, for a monotone and continuous
+DR-submodular function and subject to a general convex body constraint, we
+prove that our proposed method achieves a $((1-1/e)OPT-\eps)$ guarantee with
+$O(1/\eps^3)$ stochastic gradient computations. This guarantee matches the
+known hardness results and closes the gap between deterministic and stochastic
+continuous submodular maximization. Additionally, we obtain $((1/e)OPT -\eps)$
+guarantee after using $O(1/\eps^3)$ stochastic gradients for the case that the
+objective function is continuous DR-submodular but non-monotone and the
+constraint set is down-closed. By using stochastic continuous optimization as
+an interface, we provide the first $(1-1/e)$ tight approximation guarantee for
+maximizing a monotone but stochastic submodular set function subject to a
+matroid constraint and $(1/e)$ approximation guarantee for the non-monotone
+case.
+"
+28342,An in-depth characterisation of Bots and Humans on Twitter,"  Recent research has shown a substantial active presence of bots in online
+social networks (OSNs). In this paper we utilise our past work on studying bots
+(Stweeler) to comparatively analyse the usage and impact of bots and humans on
+Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter
+dataset and define various metrics based on tweet metadata. We divide and
+filter the dataset in four popularity groups in terms of number of followers.
+Using a human annotation task we assign 'bot' and 'human' ground-truth labels
+to the dataset, and compare the annotations against an online bot detection
+tool for evaluation. We then ask a series of questions to discern important
+behavioural bot and human characteristics using metrics within and among four
+popularity groups. From the comparative analysis we draw important differences
+as well as surprising similarities between the two entities, thus paving the
+way for reliable classification of automated political infiltration,
+advertisement campaigns, and general bot detection.
+"
+28343,Probing positron cooling in noble gases via annihilation $γ$ spectra,"  $\gamma$ spectra for positron annihilation in noble-gas atoms are calculated
+using many-body theory for positron momenta up to the positronium-formation
+threshold. This data is used, together with time-evolving positron-momentum
+distributions determined in [arXiv:1706.01434 (2017)], to calculate the
+time-varying $\gamma$ spectra produced during positron cooling in noble gases.
+The $\gamma$-spectra and their $\bar{S}$ and $\bar{W}$ shape parameters are
+shown to be sensitive probes of the time evolution of the positron momentum
+distribution, and thus provide a means of studying positron cooling that is
+complementary to positron lifetime spectroscopy.
+"
+28344,Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation,"  Arctic ocean modelling is a complex, computationally-intensive task that
+often requires the use of models of different scales. The ice-ocean model
+NEMO-LIM3 required several modifications to run multigrid simulations of Arctic
+seas properly. Firstly, we generate a custom stereographic curvilinear grid to
+decrease distortions and improve the effectiveness of spatial coverage. Then,
+we improve the ice and tracer boundary conditions, allowing us to implement the
+correct interaction between coarse and refined grids through a long open
+boundary. Finally, we integrate a heat flux-based ice restoration algorithm
+into the model, which enables the annual ice dynamics to reach
+quasi-equilibrium with a reduced spin-up time
+"
+28345,Adaptive posterior convergence rates in non-linear latent variable models,"  Non-linear latent variable models have become increasingly popular in a
+variety of applications. However, there has been little study on theoretical
+properties of these models. In this article, we study rates of posterior
+contraction in univariate density estimation for a class of non-linear latent
+variable models where unobserved U(0,1) latent variables are related to the
+response variables via a random non-linear regression with an additive error.
+Our approach relies on characterizing the space of densities induced by the
+above model as kernel convolutions with a general class of continuous mixing
+measures. The literature on posterior rates of contraction in density
+estimation almost entirely focuses on finite or countably infinite mixture
+models. We develop approximation results for our class of continuous mixing
+measures. Using an appropriate Gaussian process prior on the unknown regression
+function, we obtain the optimal frequentist rate up to a logarithmic factor
+under standard regularity conditions on the true density.
+"
+28346,High-Speed Trajectory Planning for Autonomous Vehicles Using a Simple Dynamic Model,"  To improve safety and energy efficiency, autonomous vehicles are expected to
+drive smoothly in most situations, while maintaining their velocity below a
+predetermined speed limit. However, some scenarios such as low road adherence
+or inadequate speed limit may require vehicles to automatically adapt their
+velocity without external input, while nearing the limits of their dynamic
+capacities. Many of the existing trajectory planning approaches are incapable
+of making such adjustments, since they assume a feasible velocity reference is
+given. Moreover, near-limits trajectory planning often implies high-complexity
+dynamic vehicle models, making computations difficult. In this article, we use
+a simple dynamic model derived from numerical simulations to design a
+trajectory planner for high-speed driving of an autonomous vehicle based on
+model predictive control. Unlike existing techniques, our formulation includes
+the selection of a feasible velocity to track a predetermined path while
+avoiding obstacles. Simulation results on a highly precise vehicle model show
+that our approach can be used in real-time to provide feasible trajectories
+that can be tracked using a simple control architecture. Moreover, the use of
+our simplified model makes the planner more robust and yields better
+trajectories compared to kinematic models commonly used in trajectory planning.
+"
+28347,Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering,"  Deep metric learning has been widely applied in many computer vision tasks,
+and recently, it is more attractive in \emph{zero-shot image retrieval and
+clustering}(ZSRC) where a good embedding is requested such that the unseen
+classes can be distinguished well. Most existing works deem this 'good'
+embedding just to be the discriminative one and thus race to devise powerful
+metric objectives or hard-sample mining strategies for leaning discriminative
+embedding. However, in this paper, we first emphasize that the generalization
+ability is a core ingredient of this 'good' embedding as well and largely
+affects the metric performance in zero-shot settings as a matter of fact. Then,
+we propose the Energy Confused Adversarial Metric Learning(ECAML) framework to
+explicitly optimize a robust metric. It is mainly achieved by introducing an
+interesting Energy Confusion regularization term, which daringly breaks away
+from the traditional metric learning idea of discriminative objective devising,
+and seeks to 'confuse' the learned model so as to encourage its generalization
+ability by reducing overfitting on the seen classes. We train this confusion
+term together with the conventional metric objective in an adversarial manner.
+Although it seems weird to 'confuse' the network, we show that our ECAML indeed
+serves as an efficient regularization technique for metric learning and is
+applicable to various conventional metric methods. This paper empirically and
+experimentally demonstrates the importance of learning embedding with good
+generalization, achieving state-of-the-art performances on the popular CUB,
+CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks.
+\textcolor[rgb]{1, 0, 0}{Code available at this http URL}.
+"
+28348,An infinite family of 3d Floquet topological paramagnets,"  We uncover an infinite family of time-reversal symmetric 3d interacting
+topological insulators of bosons or spins, in time-periodically driven systems,
+which we term Floquet topological paramagnets (FTPMs). These FTPM phases
+exhibit intrinsically dynamical properties that could not occur in thermal
+equilibrium, and are governed by an infinite set of $Z_2$-valued topological
+invariants, one for each prime number. The topological invariants are
+physically characterized by surface magnetic domain walls that act as
+unidirectional quantum channels, transferring quantized packets of information
+during each driving period. We construct exactly solvable models realizing each
+of these phases, and discuss the anomalous dynamics of their topologically
+protected surface states. Unlike previous encountered examples of Floquet SPT
+phases, these 3d FTPMs are not captured by group cohomology methods, and cannot
+be obtained from equilibrium classifications simply by treating the discrete
+time-translation as an ordinary symmetry. The simplest such FTPM phase can
+feature anomalous $Z_2$ (toric code) surface topological order, in which the
+gauge electric and magnetic excitations are exchanged in each Floquet period,
+which cannot occur in a pure 2d system without breaking time reversal symmetry.
+"
+28349,Non-Poisson Renewal Events and Memory,"  We study two different forms of fluctuation-dissipation processes generating
+anomalous relaxations to equilibrium of an initial out of equilibrium
+condition, the former being based on a stationary although very slow
+correlation function and the latter characterized by the occurrence of crucial
+events, namely, non-Poisson renewal events, incompatible with the stationary
+condition. Both forms of regression to equilibrium have the same
+non-exponential Mittag-Leffler structure. We analyze the single trajectories of
+the two processes by recording the time distances between two consecutive
+origin re-crossings and establishing the corresponding waiting time probability
+density function (PDF), $\psi(t)$. In the former case, with no crucial events,
+$\psi(t)$ is exponential and in the latter case, with crucial events, $\psi(t)$
+is an inverse power law PDF with a diverging first moment. We discuss the
+consequences that this result is expected to have for the correct
+interpretation of some anomalous relaxation processes.
+"
+28350,A Distributed Control Framework for a Team of Unmanned Aerial Vehicles for Dynamic Wildfire Tracking,"  Wildland fire fighting is a very dangerous job, and the lack of information
+of the fire front is one of main reasons that causes many accidents. Using
+unmanned aerial vehicle (UAV) to cover wildfire is promising because it can
+replace human in hazardous fire tracking and save operation costs
+significantly. In this paper we propose a distributed control framework
+designed for a team of UAVs that can closely monitor a wildfire in open space,
+and precisely track its development. The UAV team, designed for flexible
+deployment, can effectively avoid in-flight collision as well as cooperate well
+with other neighbors. Experimental results are conducted to demonstrate the
+capabilites of the UAV team in covering a spreading wildfire.
+"
+28351,A Multi-component Matched Filter Cluster Confirmation Tool for eROSITA: Initial Application to the RASS and DES-SV Datasets,"  We describe a multi-component matched filter cluster confirmation tool (MCMF)
+designed for the study of large X-ray source catalogs produced by the upcoming
+X-ray all-sky survey mission eROSITA. We apply the method to confirm a sample
+of 88 clusters with redshifts $0.05<z<0.8$ in the recently published 2RXS
+catalog from the ROSAT all-sky survey (RASS) over the 208 deg$^2$ region
+overlapped by the Dark Energy Survey (DES) science verification (DES-SV)
+dataset. In our pilot study, we examine all X-ray sources, regardless of their
+extent. Our method employs a multi-color red sequence (RS) algorithm that
+incorporates the X-ray count rate and peak position in determining the region
+of interest for followup and extracts the positionally and color weighted
+optical richness $\lambda_{\mathrm{MCMF}}$ as a function of redshift for each
+source. Peaks in the $\lambda_{\mathrm{MCMF}}$-redshift distribution are
+identified and used to extract photometric redshifts, richness and
+uncertainties. The significances of all optical counterparts are characterized
+using the distribution of richnesses defined along random lines of sight. These
+significances are used to extract cluster catalogs and to estimate the
+contamination by random superpositions of unassociated optical systems. The
+delivered photometric redshift accuracy is $\delta z / (1+z)=0.010$. We find a
+well defined X-ray luminosity-$\lambda_{\mathrm{MCMF}}$ relation with an
+intrinsic scatter of $\delta \ln(\lambda_\mathrm{MCMF}| L_\mathrm{x})=0.21$.
+Matching our catalog with the DES-SV redMaPPer catalog yields good agreement in
+redshift and richness estimates; comparing our catalog with the South Pole
+Telescope (SPT) selected clusters shows no inconsistencies. SPT clusters in our
+dataset are consistent with the high mass extension of the RASS based
+$\lambda_{\mathrm{MCMF}}$-mass relation
+"
+28352,Relations between permutation representations in positive characteristic,"  Given a finite group G and a field F, a G-set X gives rise to an
+F[G]-permutation module F[X]. This defines a map from the Burnside ring of G to
+its representation ring over F. It is an old problem in representation theory,
+with wide-ranging applications in algebra, number theory, and geometry, to give
+explicit generators of the kernel K_F(G) of this map, i.e. to classify pairs of
+G-sets X, Y such that F[X] is isomorphic to F[Y]. When F has characteristic 0,
+a complete description of K_F(G) is now known. In this paper, we give a similar
+description of K_F(G) when F is a field of characteristic p>0 in all but the
+most complicated case, which is when G has a subquotient that is a
+non-p-hypo-elementary (p,p)-Dress group.
+"
+28353,Self-Adaptation of Activity Recognition Systems to New Sensors,"  Traditional activity recognition systems work on the basis of training,
+taking a fixed set of sensors into account. In this article, we focus on the
+question how pattern recognition can leverage new information sources without
+any, or with minimal user input. Thus, we present an approach for opportunistic
+activity recognition, where ubiquitous sensors lead to dynamically changing
+input spaces. Our method is a variation of well-established principles of
+machine learning, relying on unsupervised clustering to discover structure in
+data and inferring cluster labels from a small number of labeled dates in a
+semi-supervised manner. Elaborating the challenges, evaluations of over 3000
+sensor combinations from three multi-user experiments are presented in detail
+and show the potential benefit of our approach.
+"
+28354,Tautological classes with twisted coefficients,"  Let $M_g$ be the moduli space of smooth genus $g$ curves. We define a notion
+of Chow groups of $M_g$ with coefficients in a representation of $Sp(2g)$, and
+we define a subgroup of tautological classes in these Chow groups with twisted
+coefficients. Studying the tautological groups of $M_g$ with twisted
+coefficients is equivalent to studying the tautological rings of all fibered
+powers $C_g^n$ of the universal curve $C_g \to M_g$ simultaneously. By taking
+the direct sum over all irreducible representations of the symplectic group in
+fixed genus, one obtains the structure of a twisted commutative algebra on the
+tautological classes. We obtain some structural results for this twisted
+commutative algebra, and we are able to calculate it explicitly when $g \leq
+4$. Thus we determine $R^\ast(C_g^n)$ completely, for all $n$, in these genera.
+We also give some applications to the Faber conjecture.
+"
+28355,Cellular switches orchestrate rhythmic circuits,"  Small inhibitory neuronal circuits have long been identified as key neuronal
+motifs to generate and modulate the coexisting rhythms of various motor
+functions. Our paper highlights the role of a cellular switching mechanism to
+orchestrate such circuits. The cellular switch makes the circuits
+reconfigurable, robust, adaptable, and externally controllable. Without this
+cellular mechanism, the circuits rhythms entirely rely on specific tunings of
+the synaptic connectivity, which makes them rigid, fragile, and difficult to
+control externally. We illustrate those properties on the much studied
+architecture of a small network controlling both the pyloric and gastric
+rhythms of crabs. The cellular switch is provided by a slow negative
+conductance often neglected in mathematical modeling of central pattern
+generators. We propose that this conductance is simple to model and key to
+computational studies of rhythmic circuit neuromodulation.
+"
+28356,A Constant Introduction of Additional Food Does Not Yield Pest Eradication in Finite Time,"  Biological control, the use of predators and pathogens to control target
+pests, is a promising alternative to chemical control. It is hypothesized that
+the introduced predators efficacy can be boosted by providing them with an
+additional food source. The current literature \cite{SP11} claims that if the
+additional food is of sufficient constant quantity and quality then pest
+eradication is possible in \emph{finite} time. The purpose of the current
+manuscript is to show that to the contrary, pest eradication is \emph{not
+possible} in finite time, for \emph{any} quantity and quality of constant
+additional food. However for a density dependent quantity of additional food,
+we show pest eradication in finite time is indeed possible. Our results have
+large scale implications for the effective design of biological control methods
+involving additional food.
+"
+28357,CVXR: An R Package for Disciplined Convex Optimization,"  CVXR is an R package that provides an object-oriented modeling language for
+convex optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows
+the user to formulate convex optimization problems in a natural mathematical
+syntax rather than the restrictive form required by most solvers. The user
+specifies an objective and set of constraints by combining constants,
+variables, and parameters using a library of functions with known mathematical
+properties. CVXR then applies signed disciplined convex programming (DCP) to
+verify the problem's convexity. Once verified, the problem is converted into
+standard conic form using graph implementations and passed to a cone solver
+such as ECOS or SCS. We demonstrate CVXR's modeling framework with several
+applications.
+"
+28358,Main and Interaction Effects Selection for Quadratic Discriminant Analysis via Penalized Linear Regression,"  Discriminant analysis is a useful classification method. Variable selection
+for discriminant analysis is becoming more and more im- portant in a
+high-dimensional setting. This paper is concerned with the binary-class
+problems of main and interaction effects selection for the quadratic
+discriminant analysis. We propose a new penalized quadratic discriminant
+analysis (QDA) for variable selection in binary classification. Under sparsity
+assumption on the relevant variables, we conduct a penalized liner regression
+to derive sparse QDA by plug- ging the main and interaction effects in the
+model. Then the QDA problem is converted to a penalized sparse ordinary least
+squares op- timization by using the composite absolute penalties (CAP). Coor-
+dinate descent algorithm is introduced to solve the convex penalized least
+squares. The penalized linear regression can simultaneously se- lect the main
+and interaction effects, and also conduct classification. Compared with the
+existing methods of variable selection in QDA, the extensive simulation studies
+and two real data analyses demon- strate that our proposed method works well
+and is robust in the performance of variable selection and classification.
+"
+28359,Goodness-of-fit tests for complete spatial randomness based on Minkowski functionals of binary images,"  We propose a class of goodness-of-fit tests for complete spatial randomness
+(CSR). In contrast to standard tests, our procedure utilizes a transformation
+of the data to a binary image, which is then characterized by geometric
+functionals. Under a suitable limiting regime, we derive the asymptotic
+distribution of the test statistics under the null hypothesis and almost sure
+limits under certain alternatives. The new tests are computationally efficient,
+and simulations show that they are strong competitors to other tests of CSR.
+The tests are applied to a real data set in gamma-ray astronomy, and immediate
+extensions are presented to encourage further work.
+"
+28360,Functoriality of colored link homologies,"  We prove that the bigraded colored Khovanov-Rozansky type A link and tangle
+invariants are functorial with respect to link and tangle cobordisms.
+"
+28361,On better training the infinite restricted Boltzmann machines,"  The infinite restricted Boltzmann machine (iRBM) is an extension of the
+classic RBM. It enjoys a good property of automatically deciding the size of
+the hidden layer according to specific training data. With sufficient training,
+the iRBM can achieve a competitive performance with that of the classic RBM.
+However, the convergence of learning the iRBM is slow, due to the fact that the
+iRBM is sensitive to the ordering of its hidden units, the learned filters
+change slowly from the left-most hidden unit to right. To break this dependency
+between neighboring hidden units and speed up the convergence of training, a
+novel training strategy is proposed. The key idea of the proposed training
+strategy is randomly regrouping the hidden units before each gradient descent
+step. Potentially, a mixing of infinite many iRBMs with different permutations
+of the hidden units can be achieved by this learning method, which has a
+similar effect of preventing the model from over-fitting as the dropout. The
+original iRBM is also modified to be capable of carrying out discriminative
+training. To evaluate the impact of our method on convergence speed of learning
+and the model's generalization ability, several experiments have been performed
+on the binarized MNIST and CalTech101 Silhouettes datasets. Experimental
+results indicate that the proposed training strategy can greatly accelerate
+learning and enhance generalization ability of iRBMs.
+"
+28362,On Structural Controllability of Symmetric (Brain) Networks,"  The question of controllability of natural and man-made network systems has
+recently received considerable attention. In the context of the human brain,
+the study of controllability may not only shed light into the organization and
+function of different neural circuits, but also inform the design and
+implementation of minimally invasive yet effective intervention protocols to
+treat neurological disorders. While the characterization of brain
+controllability is still in its infancy, some results have recently appeared
+and given rise to scientific debate. Among these, [1] has numerically shown
+that a class of brain networks constructed from DSI/DTI imaging data are
+controllable from one brain region. That is, a single brain region is
+theoretically capable of moving the whole brain network towards any desired
+target state. In this note we provide evidence supporting controllability of
+brain networks from a single region as discussed in [1], thus contradicting the
+main conclusion and methods developed in [2].
+"
+28363,Fast mixing for Latent Dirichlet allocation,"  Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in probability
+theory in general and in machine learning in particular. A Markov chain is
+devised so that its stationary distribution is some probability distribution of
+interest. Then one samples from the given distribution by running the Markov
+chain for a ""long time"" until it appears to be stationary and then collects the
+sample. However these chains are often very complex and there are no
+theoretical guarantees that stationarity is actually reached. In this paper we
+study the Gibbs sampler of the posterior distribution of a very simple case of
+Latent Dirichlet Allocation, the arguably most well known Bayesian unsupervised
+learning model for text generation and text classification. It is shown that
+when the corpus consists of two long documents of equal length $m$ and the
+vocabulary consists of only two different words, the mixing time is at most of
+order $m^2\log m$ (which corresponds to $m\log m$ rounds over the corpus). It
+will be apparent from our analysis that it seems very likely that the mixing
+time is not much worse in the more relevant case when the number of documents
+and the size of the vocabulary are also large as long as each word is
+represented a large number in each document, even though the computations
+involved may be intractable.
+"
+28364,A New Variational Model for Binary Classification in the Supervised Learning Context,"  We examine the supervised learning problem in its continuous setting and give
+a general optimality condition through techniques of functional analysis and
+the calculus of variations. This enables us to solve the optimality condition
+for the desired function u numerically and make several comparisons with other
+widely utilized supervised learning models. We employ the accuracy and area
+under the receiver operating characteristic curve as metrics of the
+performance. Finally, 3 analyses are conducted based on these two mentioned
+metrics where we compare the models and make conclusions to determine whether
+or not our method is competitive.
+"
+28365,Topological mixture estimation,"  Density functions that represent sample data are often multimodal, i.e. they
+exhibit more than one maximum. Typically this behavior is taken to indicate
+that the underlying data deserves a more detailed representation as a mixture
+of densities with individually simpler structure. The usual specification of a
+component density is quite restrictive, with log-concave the most general case
+considered in the literature, and Gaussian the overwhelmingly typical case. It
+is also necessary to determine the number of mixture components \emph{a
+priori}, and much art is devoted to this. Here, we introduce \emph{topological
+mixture estimation}, a completely nonparametric and computationally efficient
+solution to the one-dimensional problem where mixture components need only be
+unimodal. We repeatedly perturb the unimodal decomposition of Baryshnikov and
+Ghrist to produce a topologically and information-theoretically optimal
+unimodal mixture. We also detail a smoothing process that optimally exploits
+topological persistence of the unimodal category in a natural way when working
+directly with sample data. Finally, we illustrate these techniques through
+examples.
+"
+28366,Residual Policy Learning,"  We present Residual Policy Learning (RPL): a simple method for improving
+nondifferentiable policies using model-free deep reinforcement learning. RPL
+thrives in complex robotic manipulation tasks where good but imperfect
+controllers are available. In these tasks, reinforcement learning from scratch
+remains data-inefficient or intractable, but learning a residual on top of the
+initial controller can yield substantial improvements. We study RPL in six
+challenging MuJoCo tasks involving partial observability, sensor noise, model
+misspecification, and controller miscalibration. For initial controllers, we
+consider both hand-designed policies and model-predictive controllers with
+known or learned transition models. By combining learning with control
+algorithms, RPL can perform long-horizon, sparse-reward tasks for which
+reinforcement learning alone fails. Moreover, we find that RPL consistently and
+substantially improves on the initial controllers. We argue that RPL is a
+promising approach for combining the complementary strengths of deep
+reinforcement learning and robotic control, pushing the boundaries of what
+either can achieve independently. Video and code at
+this https URL.
+"
+28367,Psychiatric Illnesses as Disorders of Network Dynamics,"  This review provides a dynamical systems perspective on psychiatric symptoms
+and disease, and discusses its potential implications for diagnosis, prognosis,
+and treatment. After a brief introduction into the theory of dynamical systems,
+we will focus on the idea that cognitive and emotional functions are
+implemented in terms of dynamical systems phenomena in the brain, a common
+assumption in theoretical and computational neuroscience. Specific
+computational models, anchored in biophysics, for generating different types of
+network dynamics, and with a relation to psychiatric symptoms, will be briefly
+reviewed, as well as methodological approaches for reconstructing the system
+dynamics from observed time series (like fMRI or EEG recordings). We then
+attempt to outline how psychiatric phenomena, associated with schizophrenia,
+depression, PTSD, ADHD, phantom pain, and others, could be understood in
+dynamical systems terms. Most importantly, we will try to convey that the
+dynamical systems level may provide a central, hub-like level of convergence
+which unifies and links multiple biophysical and behavioral phenomena, in the
+sense that diverse biophysical changes can give rise to the same dynamical
+phenomena and, vice versa, similar changes in dynamics may yield different
+behavioral symptoms depending on the brain area where these changes manifest.
+If this assessment is correct, it may have profound implications for the
+diagnosis, prognosis, and treatment of psychiatric conditions, as it puts the
+focus on dynamics. We therefore argue that consideration of dynamics should
+play an important role in the choice and target of interventions.
+"
+28368,The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data,"  Goel et al. (2016) examined diffusion data from Twitter to conclude that
+online petitions are shared more virally than other types of content. Their
+definition of structural virality, which measures the extent to which diffusion
+follows a broadcast model or is spread person to person (virally), depends on
+knowing the topology of the diffusion cascade. But often the diffusion
+structure cannot be observed directly. We examined time-stamped signature data
+from the Obama White House's We the People petition platform. We developed
+measures based on temporal dynamics that, we argue, can be used to infer
+diffusion structure as well as the more intrinsic notion of virality sometimes
+known as infectiousness. These measures indicate that successful petitions are
+likely to be higher in both intrinsic and structural virality than unsuccessful
+petitions are. We also investigate threshold effects on petition signing that
+challenge simple contagion models, and report simulations for a theoretical
+model that are consistent with our data.
+"
+28369,Understanding Quality Factor Degradation in Superconducting Niobium Cavities at Low Microwave Field Amplitudes,"  In niobium superconducting radio frequency (SRF) accelerating cavities a
+decrease of the quality factor at lower fields - a so called \emph{low field Q
+slope or LFQS} - has been a long-standing unexplained effect. By extending the
+high $Q$ measurement techniques to ultralow fields we discover two previously
+unknown features of the effect: i) saturation at rf fields lower than
+$E_\mathrm{acc} \sim 0.1$~MV/m; ii) strong degradation enhancement by growing
+thicker niobium pentoxide. Our findings suggest that the LFQS may be caused by
+the two level systems in the natural niobium oxide on the inner cavity surface,
+thereby identifying a new source of residual resistance and providing guidance
+for potential non-accelerator low field applications of SRF cavities.
+"
+28370,Equations Of Kalman Varieties,"  The Kalman variety of a linear subspace is a vector space consisting of all
+endomorphisms that have an eigenvector in that subspace. We resolve a
+conjecture of Ottaviani and Sturmfels and give the minimal defining equations
+of the Kalman variety over a field of characteristic 0.
+"
+28371,ZM-Net: Real-time Zero-shot Image Manipulation Network,"  Many problems in image processing and computer vision (e.g. colorization,
+style transfer) can be posed as 'manipulating' an input image into a
+corresponding output image given a user-specified guiding signal. A holy-grail
+solution towards generic image manipulation should be able to efficiently alter
+an input image with any personalized signals (even signals unseen during
+training), such as diverse paintings and arbitrary descriptive attributes.
+However, existing methods are either inefficient to simultaneously process
+multiple signals (let alone generalize to unseen signals), or unable to handle
+signals from other modalities. In this paper, we make the first attempt to
+address the zero-shot image manipulation task. We cast this problem as
+manipulating an input image according to a parametric model whose key
+parameters can be conditionally generated from any guiding signal (even unseen
+ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a
+fully-differentiable architecture that jointly optimizes an
+image-transformation network (TNet) and a parameter network (PNet). The PNet
+learns to generate key transformation parameters for the TNet given any guiding
+signal while the TNet performs fast zero-shot image manipulation according to
+both signal-dependent parameters from the PNet and signal-invariant parameters
+from the TNet itself. Extensive experiments show that our ZM-Net can perform
+high-quality image manipulation conditioned on different forms of guiding
+signals (e.g. style images and attributes) in real-time (tens of milliseconds
+per image) even for unseen signals. Moreover, a large-scale style dataset with
+over 20,000 style images is also constructed to promote further research.
+"
+28372,On the Behavior of Convolutional Nets for Feature Extraction,"  Deep neural networks are representation learning techniques. During training,
+a deep net is capable of generating a descriptive language of unprecedented
+size and detail in machine learning. Extracting the descriptive language coded
+within a trained CNN model (in the case of image data), and reusing it for
+other purposes is a field of interest, as it provides access to the visual
+descriptors previously learnt by the CNN after processing millions of images,
+without requiring an expensive training phase. Contributions to this field
+(commonly known as feature representation transfer or transfer learning) have
+been purely empirical so far, extracting all CNN features from a single layer
+close to the output and testing their performance by feeding them to a
+classifier. This approach has provided consistent results, although its
+relevance is limited to classification tasks. In a completely different
+approach, in this paper we statistically measure the discriminative power of
+every single feature found within a deep CNN, when used for characterizing
+every class of 11 datasets. We seek to provide new insights into the behavior
+of CNN features, particularly the ones from convolutional layers, as this can
+be relevant for their application to knowledge representation and reasoning.
+Our results confirm that low and middle level features may behave differently
+to high level features, but only under certain conditions. We find that all CNN
+features can be used for knowledge representation purposes both by their
+presence or by their absence, doubling the information a single CNN feature may
+provide. We also study how much noise these features may include, and propose a
+thresholding approach to discard most of it. All these insights have a direct
+application to the generation of CNN embedding spaces.
+"
+28373,Consistent Kernel Density Estimation with Non-Vanishing Bandwidth,"  Consistency of the kernel density estimator requires that the kernel
+bandwidth tends to zero as the sample size grows. In this paper we investigate
+the question of whether consistency is possible when the bandwidth is fixed, if
+we consider a more general class of weighted KDEs. To answer this question in
+the affirmative, we introduce the fixed-bandwidth KDE (fbKDE), obtained by
+solving a quadratic program, and prove that it consistently estimates any
+continuous square-integrable density. We also establish rates of convergence
+for the fbKDE with radial kernels and the box kernel under appropriate
+smoothness assumptions. Furthermore, in an experimental study we demonstrate
+that the fbKDE compares favorably to the standard KDE and the previously
+proposed variable bandwidth KDE.
+"
+28374,Characterization of Carbon-Contaminated B4C-Coated Optics after Chemically Selective Cleaning with Low-Pressure RF Plasma,"  Boron carbide (B4C) is one of the few materials that is expected to be mostly
+resilient with respect to the extremely high brilliance of the photon beam
+generated by free electron lasers (FELs) and is thus of considerable interest
+for optical applications in this field. However, as in the case of many other
+optics operated at modern light source facilities, B4C-coated optics are
+subject to ubiquitous carbon contaminations. These contaminations represent a
+serious issue for the operation of high performance FEL beamlines due to severe
+reduction of photon flux, beam coherence, creation of destructive interference,
+and scattering losses. A variety of B4C cleaning technologies were developed at
+different laboratories with varying success. We present a study regarding the
+low-pressure RF plasma cleaning of carbon contaminated B4C test samples via
+inductively coupled O2/Ar, H2/Ar, and pure O2 RF plasma produced following
+previous studies using the same IBSS GV10x downstream plasma source. Results
+regarding the chemistry, morphology as well as other aspects of the B4C optical
+coating before and after the plasma cleaning are reported. We conclude from
+these comparative plasma processes that pure O2 feedstock plasma only exhibits
+the required chemical selectivity for maintaining the integrity of the B4C
+optical coating.
+"
+28375,Discontinuities in the Electromagnetic Fields of Vortex Beams in the Complex Source/Sink Model,"  An analytical discontinuity is reported in what was thought to be the
+discontinuity-free exact nonparaxial vortex beam phasor obtained within the
+complex source/sink model. This discontinuity appears for all odd values of the
+orbital angular momentum mode. Such discontinuities in the phasor lead to
+nonphysical discontinuities in the real electromagnetic field components. We
+identify the source of the discontinuities, and provide graphical evidence of
+the discontinuous real electric fields for the first and third orbital angular
+momentum modes. A simple means of avoiding these discontinuities is presented.
+"
+28376,Effects of diffusion in competitive contact processes on bipartite lattices,"  We investigate the influence of particle diffusion in the two-dimension
+contact process (CP) with a competitive dynamics in bipartite sublattices,
+proposed in [Phys. Rev. E 84, 011125 (2011)]. The particle creation depends on
+its first and second neighbors and the extinction increases according to the
+local density. In contrast to the standard CP model, mean-field theory and
+numerical simulations predict three stable phases: inactive (absorbing), active
+symmetric and active asymmetric, signed by distinct sublattice particle
+occupations. Our results from MFT and Monte Carlo simulations reveal that low
+diffusion rates do not destroy sublattice ordering, ensuring the maintenance of
+the asymmetric phase. On the other hand, for diffusion larger than a threshold
+value Dc, the sublattice ordering is suppressed and only the usual active
+(symmetric)-inactive transition is presented. We also show the critical
+behavior and universality classes are not affected by the diffusion.
+"
+28377,Ground-based photometry of the 21-day Neptune HD106315c,"  Space-based transit surveys such as K2 and TESS allow the detection of small
+transiting planets with orbital periods beyond 10 days. Few of these warm
+Neptunes are currently known around stars bright enough to allow for detailed
+follow-up observations dedicated to their atmospheric characterization. The
+21-day period and 3.95 $R_\oplus$ planet HD106315c has been discovered based on
+the observation of two of its transits by K2. We have observed HD106315 using
+the 1.2m Euler telescope equipped with the EulerCam camera on two instances to
+confirm the transit using broad band photometry and refine the planetary
+period. Based on two observed transits of HD106315c, we detect its $\sim$1 mmag
+transit and obtain a precise measurement of the planetary ephemerids, which are
+critical for planning further follow-up observations. We have used the attained
+precision together with the predicted yield from the TESS mission to evaluate
+the potential for ground-based confirmation of Neptune-sized planets found by
+TESS. We find that 1-meter-class telescopes on the ground equipped with precise
+photometers could substantially contribute to the follow-up of 162 TESS
+candidates orbiting stars with magnitudes of $V \leq 14$. Out of these, 74
+planets orbit stars with $V \leq 12$ and 12 planets orbit $V \leq 10$, which
+makes these candidates high-priority objects for atmospheric characterization
+with high-end instrumentation.
+"
+28378,A Flexible Approach for Finding Optimal Paths with Minimal Conflicts,"  Complex systems are usually modelled through a combination of structural and
+behavioural models, where separate behavioural models make it easier to design
+and understand partial behaviour. When partial models are combined, we need to
+guarantee that they are consistent, and several automated techniques have been
+developed to check this. We argue that in some cases it is impossible to
+guarantee total consistency, and instead we want to find execution paths across
+such models with minimal conflicts with respect to a certain metric of
+interest. We present an efficient and scalable solution to find optimal paths
+through a combination of the theorem prover Isabelle with the constraint solver
+Z3. Our approach has been inspired by a healthcare problem, namely how to
+detect conflicts between medications taken by patients with multiple chronic
+conditions, and how to find preferable alternatives automatically.
+"
+28379,Private Exploration Primitives for Data Cleaning,"  Data cleaning, or the process of detecting and repairing inaccurate or
+corrupt records in the data, is inherently human-driven. State of the art
+systems assume cleaning experts can access the data (or a sample of it) to tune
+the cleaning process. However, in many cases, privacy constraints disallow
+unfettered access to the data. To address this challenge, we observe and
+provide empirical evidence that data cleaning can be achieved without access to
+the sensitive data, but with access to a (noisy) query interface that supports
+a small set of linear counting query primitives. Motivated by this, we present
+DPClean, a first of a kind system that allows engineers tune data cleaning
+workflows while ensuring differential privacy. In DPClean, a cleaning engineer
+can pose sequences of aggregate counting queries with error tolerances. A
+privacy engine translates each query into a differentially private mechanism
+that returns an answer with error matching the specified tolerance, and allows
+the data owner track the overall privacy loss. With extensive experiments using
+human and simulated cleaning engineers on blocking and matching tasks, we
+demonstrate that our approach is able to achieve high cleaning quality while
+ensuring a reasonable privacy loss.
+"
+28380,A Radical Characterization of Abelian Varieties,"  Let $A$ be a square-free abelian variety defined over a number field $K$. Let
+$S$ be a density one set of prime ideals $\mathfrak{p}$ of $\mathcal{O}_K$. A
+famous theorem of Faltings says that the Frobenius polynomials
+$P_{A,\mathfrak{p}}(x)$ for $\mathfrak{p}\in S$ determine $A$ up to isogeny. We
+show that the prime factors of
+$|A(\mathbb{F}_{\mathfrak{p}})|=P_{A,\mathfrak{p}}(1)$ for $\mathfrak{p}\in S$
+also determine $A$ up to isogeny over an explicit finite extension of $K$. The
+proof relies on understanding the $\ell$-adic monodromy groups which come from
+the $\ell$-adic Galois representations of $A$, and the absolute Weyl group
+action on their weights.
+"
+28381,The Sequential Normal Scores Transformation,"  The sequential analysis of series often requires nonparametric procedures,
+where the most powerful ones frequently use rank transformations. Re-ranking
+the data sequence after each new observation can become too intensive
+computationally. This led to the idea of sequential ranks, where only the most
+recent observation is ranked. However, difficulties finding, or approximating,
+the null distribution of the statistics may have contributed to the lack of
+popularity of these methods. In this paper, we propose transforming the
+sequential ranks into sequential normal scores which are independent, and
+asymptotically standard normal random variables. Thus original methods based on
+the normality assumption may be used.
+A novel approach permits the inclusion of a priori information in the form of
+quantiles. It is developed as a strategy to increase the sensitivity of the
+scoring statistic. The result is a powerful convenient method to analyze
+non-normal data sequences. Also, four variations of sequential normal scores
+are presented using examples from the literature. Researchers and practitioners
+might find this approach useful to develop nonparametric procedures to address
+new problems extending the use of parametric procedures when distributional
+assumptions are not met. These methods are especially useful with large data
+streams where efficient computational methods are required.
+"
+28382,Sign Flip in the Casimir Force for Interacting Fermion Systems,"  In this work we consider a fermionic chain of finite length $\ell$. Fermions
+are allowed to interact and are forced to obey boundary conditions, thus
+altering the process of condensation. Our goal is to explore how this affects
+the quantum vacuum energy for this system. We approach this problem by using a
+self-consistent method and observe a nontrivial behavior in the Casimir force,
+displaying a switch from an attractive to a repulsive regime. This flip stems
+from the competition between the attractive contribution from the usual
+fermionic Casimir effect and a repulsive one coming from the condensate.
+"
+28383,Coloring Cantor sets and resolvability of pseudocompact spaces,"  Let us denote by $\Phi(\lambda,\mu)$ the statement that $\mathbb{B}(\lambda)
+= D(\lambda)^\omega$, i.e. the Baire space of weight $\lambda$, has a coloring
+with $\mu$ colors such that every homeomorphic copy of the Cantor set
+$\mathbb{C}$ in $\mathbb{B}(\lambda)$ picks up all the $\mu$ colors.
+We call a space $X\,$ {\em $\pi$-regular} if it is Hausdorff and for every
+non-empty open set $U$ in $X$ there is a non-empty open set $V$ such that
+$\overline{V} \subset U$. We recall that a space $X$ is called {\em feebly
+compact} if every locally finite collection of open sets in $X$ is finite. A
+Tychonov space is pseudocompact iff it is feebly compact.
+The main result of this paper is the following.
+Theorem. Let $X$ be a crowded feebly compact $\pi$-regular space and $\mu$ be
+a fixed (finite or infinite) cardinal. If $\Phi(\lambda,\mu)$ holds for all
+$\lambda < \widehat{c}(X)$ then $X$ is $\mu$-resolvable, i.e. contains $\mu$
+pairwise disjoint dense subsets. (Here $\widehat{c}(X)$ is the smallest
+cardinal $\kappa$ such that $X$ does not contain $\kappa$ many pairwise
+disjoint open sets.)
+This significantly improves earlier results of van Mill , resp.
+Ortiz-Castillo and Tomita.
+"
+28384,Stochastic geometric models with non-stationary spatial correlations in Lagrangian fluid flows,"  Inspired by spatiotemporal observations from satellites of the trajectories
+of objects drifting near the surface of the ocean in the National Oceanic and
+Atmospheric Administration's `Global Drifter Program', this paper develops
+data-driven stochastic models of geophysical fluid dynamics (GFD) with
+non-stationary spatial correlations representing the dynamical behaviour of
+oceanic currents. Three models are considered. Model 1 from Holm [2015] is
+reviewed, in which the spatial correlations are time independent. Two new
+models, called Model 2 and Model 3, introduce two different symmetry breaking
+mechanisms by which the spatial correlations may be advected by the flow. These
+models are derived using reduction by symmetry of stochastic variational
+principles, leading to stochastic Hamiltonian systems, whose momentum maps,
+conservation laws and Lie-Poisson bracket structures are used in developing the
+new stochastic Hamiltonian models of GFD.
+"
+28385,"The Hawking-Penrose singularity theorem for $C^{1,1}$-Lorentzian metrics","  We show that the Hawking--Penrose singularity theorem, and the generalisation
+of this theorem due to Galloway and Senovilla, continue to hold for Lorentzian
+metrics that are of $C^{1, 1}$-regularity. We formulate appropriate weak
+versions of the strong energy condition and genericity condition for
+$C^{1,1}$-metrics, and of $C^0$-trapped submanifolds. By regularisation, we
+show that, under these weak conditions, causal geodesics necessarily become
+non-maximising. This requires a detailed analysis of the matrix Riccati
+equation for the approximating metrics, which may be of independent interest.
+"
+28386,The converse of the passivity and small-gain theorems for input-output maps,"  We prove the following converse of the passivity theorem. Consider a causal
+system given by a sum of a linear time-invariant and a passive linear
+time-varying input-output map. Then, in order to guarantee stability (in the
+sense of finite L2-gain) of the feedback interconnection of the system with an
+arbitrary nonlinear output strictly passive system, the given system must
+itself be output strictly passive. The proof is based on the S-procedure
+lossless theorem. We discuss the importance of this result for the control of
+systems interacting with an output strictly passive, but otherwise completely
+unknown, environment. Similarly, we prove the necessity of the small-gain
+condition for closed-loop stability of certain time-varying systems, extending
+the well-known necessity result in linear robust control.
+"
+28387,Robust Fusion Methods for Structured Big Data,"  We address one of the important problems in Big Data, namely how to combine
+estimators from different subsamples by robust fusion procedures, when we are
+unable to deal with the whole sample. We propose a general framework based on
+the classic idea of `divide and conquer'. In particular we address in some
+detail the case of a multivariate location and scatter matrix, the covariance
+operator for functional data, and clustering problems.
+"
+28388,Network Inference from Consensus Dynamics,"  We consider the problem of identifying the topology of a weighted, undirected
+network $\mathcal G$ from observing snapshots of multiple independent consensus
+dynamics. Specifically, we observe the opinion profiles of a group of agents
+for a set of $M$ independent topics and our goal is to recover the precise
+relationships between the agents, as specified by the unknown network $\mathcal
+G$. In order to overcome the under-determinacy of the problem at hand, we
+leverage concepts from spectral graph theory and convex optimization to unveil
+the underlying network structure. More precisely, we formulate the network
+inference problem as a convex optimization that seeks to endow the network with
+certain desired properties -- such as sparsity -- while being consistent with
+the spectral information extracted from the observed opinions. This is
+complemented with theoretical results proving consistency as the number $M$ of
+topics grows large. We further illustrate our method by numerical experiments,
+which showcase the effectiveness of the technique in recovering synthetic and
+real-world networks.
+"
+28389,Bounding Entities within Dense Subtensors,"  Group-based fraud detection is a promising methodology to catch frauds on the
+Internet because 1) it does not require a long activity history for a single
+user; and 2) it is difficult for fraudsters to avoid due to their economic
+constraints. Unfortunately, existing work does not cover the entire picture of
+a fraud group: they either focus on the grouping feature based on graph
+features like edge density, or probability-based features, but not both. To our
+knowledge, we are the first to combine these features into a single set of
+metrics: the complicity score and fraud density score. Both scores allow
+customization to accommodate different data types and data distributions. Even
+better, algorithms built around these metrics only use localized graph
+features, and thus scale easily on modern big data frameworks. We have applied
+our algorithms to a real production dataset and achieve state-of-the-art
+results comparing to other existing approaches.
+"
+28390,Studies of braided non-Abelian anyons using anyonic tensor networks,"  The content of this thesis can be broadly summarised into two categories:
+first, I constructed modified numerical algorithms based on tensor networks to
+simulate systems of anyons in low dimensions, and second, I used those methods
+to study the topological phases the anyons form when they braid around one
+another. In the first phase of my thesis, I extended the anyonic tensor network
+algorithms, by incorporating U(1) symmetry to give a modified ansatz,
+Anyon-U(1) tensor networks, which are capable of simulating anyonic systems at
+any rational filling fraction. In the second phase, I used the numerical
+methods to study some models of non-Abelian anyons that naturally allows for
+exchange of anyons. I proposed a lattice model of anyons, which I dubbed
+anyonic Hubbard model, which is a pair of coupled chains of anyons (or simply
+called anyonic ladder). Each site of the ladder can either host a single
+anyonic charge, or it can be empty. The anyons are able to move around,
+interact with one another, and exchange positions with other anyons, when
+vacancies exist. Exchange of anyons is a non-trivial process which may
+influence the formation of different kinds of new phases of matter. I studied
+this model using the two prominent species of anyons: Fibonacci and Ising
+anyons, and made a number of interesting discoveries about their phase
+diagrams. I identified new phases of matter arising from both the interaction
+between these anyons and their exchange braid statistics.
+"
+28391,Model-Free Based Digital Control for Magnetic Measurements,"  This paper presents a novel digital control strategy successfully implemented
+for a soft magnetic material characterization bench (Epstein frame type). The
+main objective is to control the magnetic induction waveform whatever the
+applied excitation and the material under study. Given the nonlinear nature of
+the magnetization curves of magnetic materials, an original model-free based
+control technique is considered. Special mention should be made of the
+interesting dynamic properties in closed-loop against the changes of the
+operating point related basically to the hysteresis form. The operation and the
+performances of the digital control method are illustrated in different working
+conditions through both simulation and experimental measurements.
+"
+28392,What Do the Hitomi Observations Tell Us About the Turbulent Velocities in the Perseus Cluster? Probing the Velocity Field with Mock Observations,"  Hitomi made the first direct measurements of galaxy cluster gas motions in
+the Perseus cluster, which implied that its core is fairly ""quiescent"", with
+velocities less than $\sim$200 km s$^{-1}$, despite the presence of an active
+galactic nucleus and sloshing cold fronts. Building on previous work, we use
+synthetic Hitomi/SXS observations of the hot plasma of a simulated cluster with
+sloshing gas motions and varying viscosity to analyze its velocity structure in
+a similar fashion. We find that sloshing motions can produce line shifts and
+widths similar to those measured by Hitomi. We find these measurements are
+unaffected by the value of the gas viscosity, since its effects are only
+manifested clearly on angular scales smaller than the SXS $\sim$1' PSF. The PSF
+biases the line shift of regions near the core as much as $\sim 40-50$ km
+s$^{-1}$, so it is crucial to model this effect carefully. We also infer that
+if sloshing motions dominate the observed velocity gradient, Perseus must be
+observed from a line of sight which is somewhat inclined from the plane of
+these motions, but one that still allows the spiral pattern to be visible.
+Finally, we find that assuming isotropy of motions can underestimate the total
+velocity and kinetic energy of the core in our simulation by as much as
+$\sim$60%. However, the total kinetic energy in our simulated cluster core is
+still less than 10% of the thermal energy in the core, in agreement with the
+Hitomi observations.
+"
+28393,An unified approach to the Junta theorem for discrete and continuous models,"  In a recent paper, T. Austin has proved an analogous theorem for the
+continuous torus of the original Junta theorem proved by Friedgut in the case
+of the Boolean cube. Analogous statements have been established recently in
+discrete cases such as the discrete Tori by Ellis et.al., and in the case of
+slices of the Boolean cube by Wimmer and Filmus. In the continuous case,
+through the notion of geometric influences, a statement has also been
+established by Keller, Mossel and Sen for Boltzmann probability measures. In
+this article, we broaden the scope of the arguments of T. Austin to get an
+unified proof of these results, removing the restriction to Boolean functions.
+Indeed, the proof of T. Austin relies on semigroup arguments and can be
+performed in a general framework that covers both Cayley or Schreier graphs or
+product of log-concave probability measures.
+"
+28394,A note on time hierarchies for reasonable semantic classes without advice,"  We show time hierarchies for reasonable semantic classes without advice by
+eliminating the constant bits of advice in previous results.The elimination is
+done by a contrapositive argument that for any reasonable computational
+model,let $\text{CTIME}(f(n))/{g(n)}$ denote the set of all languages decide by
+machines running in time $O(f(n))$ with advice of $g(n)$ bits in that model, if
+$\text{CTIME}(t(n))\subseteq \text{CTIME}(T(n))/{A(n)}$ then
+$\text{CTIME}(t(n))/a \subseteq \text{CTIME}(T(n))/{a+2^aA(n)}$ where $a$ is a
+constant integer.
+"
+28395,"A Generally Applicable, Highly Scalable Measurement Computation and Optimization Approach to Sequential Model-Based Diagnosis","  Model-Based Diagnosis deals with the identification of the real cause of a
+system's malfunction based on a formal system model and observations of the
+system behavior. When a malfunction is detected, there is usually not enough
+information available to pinpoint the real cause and one needs to discriminate
+between multiple fault hypotheses (called diagnoses). To this end, Sequential
+Diagnosis approaches ask an oracle for additional system measurements.
+This work presents strategies for (optimal) measurement selection in
+model-based sequential diagnosis. In particular, assuming a set of leading
+diagnoses being given, we show how queries (sets of measurements) can be
+computed and optimized along two dimensions: expected number of queries and
+cost per query. By means of a suitable decoupling of two optimizations and a
+clever search space reduction the computations are done without any inference
+engine calls. For the full search space, we give a method requiring only a
+polynomial number of inferences and show how query properties can be guaranteed
+which existing methods do not provide. Evaluation results using real-world
+problems indicate that the new method computes (virtually) optimal queries
+instantly independently of the size and complexity of the considered diagnosis
+problems and outperforms equally general methods not exploiting the proposed
+theory by orders of magnitude.
+"
+28396,"Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization","  Neural networks with ReLU activations have achieved great empirical success
+in various domains. However, existing results for learning ReLU networks either
+pose assumptions on the underlying data distribution being e.g. Gaussian, or
+require the network size and/or training size to be sufficiently large. In this
+context, the problem of learning a two-layer ReLU network is approached in a
+binary classification setting, where the data are linearly separable and a
+hinge loss criterion is adopted. Leveraging the power of random noise, this
+contribution presents a novel stochastic gradient descent (SGD) algorithm,
+which can provably train any single-hidden-layer ReLU network to attain global
+optimality, despite the presence of infinitely many bad local minima and saddle
+points in general. This result is the first of its kind, requiring no
+assumptions on the data distribution, training/network size, or initialization.
+Convergence of the resultant iterative algorithm to a global minimum is
+analyzed by establishing both an upper bound and a lower bound on the number of
+effective (non-zero) updates to be performed. Furthermore, generalization
+guarantees are developed for ReLU networks trained with the novel SGD. These
+guarantees highlight a fundamental difference (at least in the worst case)
+between learning a ReLU network as well as a leaky ReLU network in terms of
+sample complexity. Numerical tests using synthetic data and real images
+validate the effectiveness of the algorithm and the practical merits of the
+theory.
+"
+28397,Self-exciting point processes with spatial covariates: modeling the dynamics of crime,"  Crime has both varying patterns in space, related to features of the
+environment, economy, and policing, and patterns in time arising from criminal
+behavior, such as retaliation. Serious crimes may also be presaged by minor
+crimes of disorder. We demonstrate that these spatial and temporal patterns are
+generally confounded, requiring analyses to take both into account, and propose
+a spatio-temporal self-exciting point process model which incorporates spatial
+features, near-repeat and retaliation effects, and triggering. We develop
+inference methods and diagnostic tools, such as residual maps, for this model,
+and through extensive simulation and crime data obtained from Pittsburgh,
+Pennsylvania, demonstrate its properties and usefulness.
+"
+28398,Simple and accurate exchange energy for density functional theory,"  Density functional theory, the most widely used theoretical model in
+chemistry and material science, is gaining more predictive power at the cost of
+being more complicated. We believe, however, that it is possible to reach
+higher accuracy while retaining simplicity. Here we present a novel, and
+coherent concept of viewing exchange energy of an inhomogeneous electron
+density as a gradual progression between the perfectly uniform electron gas,
+and the Coulombic decay when asymptotically far away from nuclei. The concept
+leads to a simple functional; and despite its unique simplicity, it is one of
+the most accurate exchange functional to date. The interpolating variable is
+further interpreted as an expansion of separating distance between neighboring
+electrons. Connections between the topology interpretation and the applications
+for identifying chemical bonds are briefly discussed.
+"
+28399,Integrable Combinatorics,"  We explore various combinatorial problems mostly borrowed from physics, that
+share the property of being continuously or discretely integrable, a feature
+that guarantees the existence of conservation laws that often make the problems
+exactly solvable. We illustrate this with: random surfaces, lattice models, and
+structure constants in representation theory.
+"
+28400,New examples of complete Calabi-Yau metrics on $\mathbb{C}^n$ for $n\ge 3$,"  For each $n\ge 3$, we construct on $\mathbb{C}^n$ examples of complete
+Calabi-Yau metrics of Euclidean volume growth having a tangent cone at infinity
+with singular cross-section.
+"
+28401,Assessing Test Case Prioritization on Real Faults and Mutants,"  Test Case Prioritization (TCP) is an important component of regression
+testing, allowing for earlier detection of faults or helping to reduce testing
+time and cost. While several TCP approaches exist in the research literature, a
+growing number of studies have evaluated them against synthetic software
+defects, called mutants. Hence, it is currently unclear to what extent TCP
+performance on mutants would be representative of the performance achieved on
+real faults. To answer this fundamental question, we conduct the first
+empirical study comparing the performance of TCP techniques applied to both
+real-world and mutation faults. The context of our study includes eight
+well-studied TCP approaches, 35k+ mutation faults, and 357 real-world faults
+from five Java systems in the Defects4J dataset. Our results indicate that the
+relative performance of the studied TCP techniques on mutants may not strongly
+correlate with performance on real faults, depending upon attributes of the
+subject programs. This suggests that, in certain contexts, the best performing
+technique on a set of mutants may not be the best technique in practice when
+applied to real faults. We also illustrate that these correlations vary for
+mutants generated by different operators depending on whether chosen operators
+reflect typical faults of a subject program. This highlights the importance,
+particularly for TCP, of developing mutation operators tailored for specific
+program domains.
+"
+28402,Efficiently Finding Simple Schedules in Gaussian Half-Duplex Relay Line Networks,"  The problem of operating a Gaussian Half-Duplex (HD) relay network optimally
+is challenging due to the exponential number of listen/transmit network states
+that need to be considered. Recent results have shown that, for the class of
+Gaussian HD networks with N relays, there always exists a simple schedule,
+i.e., with at most N +1 active states, that is sufficient for approximate
+(i.e., up to a constant gap) capacity characterization. This paper investigates
+how to efficiently find such a simple schedule over line networks. Towards this
+end, a polynomial-time algorithm is designed and proved to output a simple
+schedule that achieves the approximate capacity. The key ingredient of the
+algorithm is to leverage similarities between network states in HD and edge
+coloring in a graph. It is also shown that the algorithm allows to derive a
+closed-form expression for the approximate capacity of the Gaussian line
+network that can be evaluated distributively and in linear time. Additionally,
+it is shown using this closed-form that the problem of Half-Duplex routing is
+NP-Hard.
+"
+28403,Reinforcement Learning with Budget-Constrained Nonparametric Function Approximation for Opportunistic Spectrum Access,"  Opportunistic spectrum access is one of the emerging techniques for
+maximizing throughput in congested bands and is enabled by predicting idle
+slots in spectrum. We propose a kernel-based reinforcement learning approach
+coupled with a novel budget-constrained sparsification technique that
+efficiently captures the environment to find the best channel access actions.
+This approach allows learning and planning over the intrinsic state-action
+space and extends well to large state spaces. We apply our methods to evaluate
+coexistence of a reinforcement learning-based radio with a multi-channel
+adversarial radio and a single-channel CSMA-CA radio. Numerical experiments
+show the performance gains over carrier-sense systems.
+"
+28404,Jeffreys priors for mixture estimation: properties and alternatives,"  While Jeffreys priors usually are well-defined for the parameters of mixtures
+of distributions, they are not available in closed form. Furthermore, they
+often are improper priors. Hence, they have never been used to draw inference
+on the mixture parameters. The implementation and the properties of Jeffreys
+priors in several mixture settings are studied. It is shown that the associated
+posterior distributions most often are improper. Nevertheless, the Jeffreys
+prior for the mixture weights conditionally on the parameters of the mixture
+components will be shown to have the property of conservativeness with respect
+to the number of components, in case of overfitted mixture and it can be
+therefore used as a default priors in this context.
+"
+28405,Quasi-one-dimensional Quantum Anomalous Hall Systems as New Platforms for Scalable Topological Quantum Computation,"  Quantum anomalous Hall insulator/superconductor heterostructures emerged as a
+competitive platform to realize topological superconductors with chiral
+Majorana edge states as shown in recent experiments [He et al. Science {\bf
+357}, 294 (2017)]. However, chiral Majorana modes, being extended, cannot be
+used for topological quantum computation. In this work, we show that
+quasi-one-dimensional quantum anomalous Hall structures exhibit a large
+topological regime (much larger than the two-dimensional case) which supports
+localized Majorana zero energy modes. The non-Abelian properties of a
+cross-shaped quantum anomalous Hall junction is shown explicitly by
+time-dependent calculations. We believe that networks of such
+quasi-one-dimensional quantum anomalous Hall systems can be easily fabricated
+for scalable topological quantum computation.
+"
+28406,On overfitting and asymptotic bias in batch reinforcement learning with partial observability,"  This paper provides an analysis of the tradeoff between asymptotic bias
+(suboptimality with unlimited data) and overfitting (additional suboptimality
+due to limited data) in the context of reinforcement learning with partial
+observability. Our theoretical analysis formally characterizes that while
+potentially increasing the asymptotic bias, a smaller state representation
+decreases the risk of overfitting. This analysis relies on expressing the
+quality of a state representation by bounding L1 error terms of the associated
+belief states. Theoretical results are empirically illustrated when the state
+representation is a truncated history of observations, both on synthetic POMDPs
+and on a large-scale POMDP in the context of smartgrids, with real-world data.
+Finally, similarly to known results in the fully observable setting, we also
+briefly discuss and empirically illustrate how using function approximators and
+adapting the discount factor may enhance the tradeoff between asymptotic bias
+and overfitting in the partially observable context.
+"
+28407,Unimodular rows over monoid rings,"  For a commutative Noetherian ring R of dimension d and a commutative
+cancellative monoid M, the elementary action on unimodular n-rows over the
+monoid ring R[M] is transitive for n>=max(d+2,3). The starting point is the
+case of polynomial rings, considered by A. Suslin in the 1970s. The main result
+completes a project, initiated in the early 1990s, and suggests a new direction
+in the study of K-theory of monoid rings.
+"
+28408,A Convex Optimization Approach to Discrete Optimal Control,"  In this paper, we bring the celebrated max-weight features (myopic and
+discrete actions) to mainstream convex optimization. Myopic actions are
+important in control because decisions need to be made in an online manner and
+without knowledge of future events, and discrete actions because many systems
+have a finite (so non-convex) number of control decisions. For example, whether
+to transmit a packet or not in communication networks. Our results show that
+these two features can be encompassed in the subgradient method for the
+Lagrange dual problem by the use of stochastic and $\epsilon$-subgradients. One
+of the appealing features of our approach is that it decouples the choice of a
+control action from a specific choice of subgradient, which allows us to design
+control policies without changing the underlying convex updates. Two classes of
+discrete control policies are presented: one that can make discrete actions by
+looking only at the system's current state, and another that selects actions
+using blocks. The latter class is useful for handling systems that have
+constraints on the order in which actions are selected.
+"
+28409,Ultrafast Modification of the Polarity at LaAlO$_3$/SrTiO$_3$ Interfaces,"  Oxide growth with semiconductor-like accuracy has led to atomically precise
+thin films and interfaces that exhibit a plethora of phases and functionalities
+not found in the oxide bulk material. This yielded spectacular discoveries such
+as the conducting, magnetic or even superconducting LaAlO$_3$/SrTiO$_3$
+interfaces separating two prototypical insulating perovskite materials. All
+these investigations, however, consider the static state at the interface,
+although studies on fast oxide interface dynamics would introduce a powerful
+degree of freedom to understanding the nature of the LaAlO$_3$/SrTiO$_3$
+interface state. Here we show that the polarization state at the
+LaAlO$_3$/SrTiO$_3$ interface can be optically enhanced or attenuated within
+picoseconds. Our observations are explained by a model based on charge
+propagation effects in the interfacial vicinity and transient polarization
+buildup at the interface.
+"
+28410,An optimization technique on pseudorandom generators based on chaotic iterations,"  Internet communication systems involving cryptography and data hiding often
+require billions of random numbers. In addition to the speed of the algorithm,
+the quality of the pseudo-random number generator and the ease of its
+implementation are common practical aspects. In this work we will discuss how
+to improve the quality of random numbers independently from their generation
+algorithm. We propose an additional implementation technique in order to take
+advantage of some chaotic properties. The statistical quality of our solution
+stems from some well-defined discrete chaotic iterations that satisfy the
+reputed Devaney's definition of chaos, namely the chaotic iterations technique.
+Pursuing recent researches published in the previous International Conference
+on Evolving Internet (Internet 09, 10, and 11), three methods to build
+pseudorandom generators by using chaotic iterations are recalled. Using
+standard criteria named NIST and DieHARD (some famous batteries of tests), we
+will show that the proposed technique can improve the statistical properties of
+a large variety of defective pseudorandom generators, and that the issues
+raised by statistical tests decrease when the power of chaotic iterations
+increase.
+"
+28411,Copula Index for Detecting Dependence and Monotonicity between Stochastic Signals,"  This paper introduces a nonparametric copula-based index for detecting the
+strength and monotonicity structure of linear and nonlinear statistical
+dependence between pairs of random variables or stochastic signals. Our index,
+termed Copula Index for Detecting Dependence and Monotonicity (CIM), satisfies
+several desirable properties of measures of association, including Renyi's
+properties, the data processing inequality (DPI), and consequently
+self-equitability. Synthetic data simulations reveal that the statistical power
+of CIM compares favorably to other state-of-the-art measures of association
+that are proven to satisfy the DPI. Simulation results with real-world data
+reveal the CIM's unique ability to detect the monotonicity structure among
+stochastic signals to find interesting dependencies in large datasets.
+Additionally, simulations show that the CIM shows favorable performance to
+estimators of mutual information when discovering Markov network structure.
+"
+28412,Lax integrability and the peakon problem for the modified Camassa-Holm equation,"  Peakons are special weak solutions of a class of nonlinear partial
+differential equations modelling non-linear phenomena such as the breakdown of
+regularity and the onset of shocks. We show that the natural concept of weak
+solutions in the case of the modified Camassa-Holm equation studied in this
+paper is dictated by the distributional compatibility of its Lax pair and, as a
+result, it differs from the one proposed and used in the literature based on
+the concept of weak solutions used for equations of the Burgers type.
+Subsequently, we give a complete construction of peakon solutions satisfying
+the modified Camassa-Holm equation in the sense of distributions; our approach
+is based on solving certain inverse boundary value problem the solution of
+which hinges on a combination of classical techniques of analysis involving
+Stieltjes' continued fractions and multi-point Padé approximations. We
+propose sufficient conditions needed to ensure the global existence of peakon
+solutions and analyze the large time asymptotic behaviour whose special
+features include a formation of pairs of peakons which share asymptotic speeds,
+as well as Toda-like sorting property.
+"
+28413,Nonlinear Spectral Image Fusion,"  In this paper we demonstrate that the framework of nonlinear spectral
+decompositions based on total variation (TV) regularization is very well suited
+for image fusion as well as more general image manipulation tasks. The
+well-localized and edge-preserving spectral TV decomposition allows to select
+frequencies of a certain image to transfer particular features, such as
+wrinkles in a face, from one image to another. We illustrate the effectiveness
+of the proposed approach in several numerical experiments, including a
+comparison to the competing techniques of Poisson image editing, linear
+osmosis, wavelet fusion and Laplacian pyramid fusion. We conclude that the
+proposed spectral TV image decomposition framework is a valuable tool for semi-
+and fully-automatic image editing and fusion.
+"
+28414,The impact of numerical oversteepening on the fragmentation boundary in self-gravitating disks,"  Context. It is still an open issue whether a self-gravitating accretion disk
+fragments. There are many different physical and numerical explanations for
+fragmentation, but simulations often show a non-convergent behavior for ever
+better resolution.
+Aims. We investigate the influence of different numerical limiters in Godunov
+type schemes on the fragmentation boundary in self- gravitating disks.
+Methods. We compare the linear and non-linear outcome in two-dimensional
+shearingsheet simulations using the VANLEER and the SUPERBEE limiter.
+Results. We show that choosing inappropriate limiting functions to handle
+shock-capturing in Godunov type schemes can lead to an overestimation of the
+surface density in regions with shallow density gradients. The effect amplifies
+itself on timescales comparable to the dynamical timescale even at high
+resolutions. This is exactly the environment, where clumps are expected to
+form. The effect is present without, but scaled up by, self-gravity and also
+does not depend on cooling. Moreover it can be backtracked to a well known
+effect called oversteepening. If the effect is also observed in the linear
+case, the fragmentation limit is shifted to larger values of the critical
+cooling timescale.
+"
+28415,Mode-shape perturbation induced by analyte adsorption in nanomechanical sensors,"  Nanomechanical resonators offer important benefits for the sensing of
+physical stimuli such as the mass of an added molecule. To map out the local
+shape properties of the physical stimuli, such as the distribution of the mass
+density of a molecule, sensory information should be collected through multiple
+modes of a mechanical sensor. By utilizing the specific mode shapes, the
+spatial distribution of a physical stimulus can be reverse calculated. However,
+the mode shapes of a sensor may deviate from their ideal forms once analytes
+start to accumulate on the sensor. As a result, algorithms based on the ideal
+form of the mode shapes no longer work accurately. Here, we developed a
+theoretical framework to calculate the change in the mode shapes of a
+nanomechanical beam after analyte adsorption. We verified the theoretical model
+by performing finite element simulations and comparing the change in the mode
+shapes obtained from each approach. Monte Carlo simulations were performed to
+relate the maximum deviation in the mode shapes to the surface roughness of the
+sensor after analyte accumulation. By predicting the change in the mode shapes
+and using the corrected forms, the accuracy of the nanomechanical sensing can
+be improved significantly.
+"
+28416,Fast Change Point Detection on Dynamic Social Networks,"  A number of real world problems in many domains (e.g. sociology, biology,
+political science and communication networks) can be modeled as dynamic
+networks with nodes representing entities of interest and edges representing
+interactions among the entities at different points in time. A common
+representation for such models is the snapshot model - where a network is
+defined at logical time-stamps. An important problem under this model is change
+point detection. In this work we devise an effective and efficient
+three-step-approach for detecting change points in dynamic networks under the
+snapshot model. Our algorithm achieves up to 9X speedup over the
+state-of-the-art while improving quality on both synthetic and real world
+networks.
+"
+28417,Caenorhabditis elegans and the network control framework - FAQs,"  Control is essential to the functioning of any neural system. Indeed, under
+healthy conditions the brain must be able to continuously maintain a tight
+functional control between the system's inputs and outputs. One may therefore
+hypothesise that the brain's wiring is predetermined by the need to maintain
+control across multiple scales, maintaining the stability of key internal
+variables, and producing behaviour in response to environmental cues. Recent
+advances in network control have offered a powerful mathematical framework to
+explore the structure-function relationship in complex biological, social, and
+technological networks, and are beginning to yield important and precise
+insights for neuronal systems. The network control paradigm promises a
+predictive, quantitative framework to unite the distinct datasets necessary to
+fully describe a nervous system, and provide mechanistic explanations for the
+observed structure and function relationships. Here, we provide a thorough
+review of the network control framework as applied to C. elegans, in the style
+of a FAQ. We present the theoretical, computational, and experimental aspects
+of network control, and discuss its current capabilities and limitations,
+together with the next likely advances and improvements. We further present the
+Python code to enable exploration of control principles in a manner specific to
+this prototypical organism.
+"
+28418,Analytical Methods in Physics,"  This set of lecture notes constitutes the free textbook project I initiated
+towards the end of Summer 2015, while preparing for the Fall 2015 Analytical
+Methods in Physics course I taught to upper level undergraduates at the
+University of Minnesota Duluth. During Fall 2017, I taught Differential
+Geometry and Physics in Curved Spacetimes at National Central University,
+Taiwan; and this gave me an opportunity to expand on the text. Topics currently
+covered include: complex numbers, matrix algebra, finite and infinite
+dimensional vector spaces, calculus on the complex plane, special/approximation
+methods in calculus, differential geometry in curved space(time)s, and partial
+differential equations.
+"
+28419,Asset Price Distributions and Efficient Markets,"  We explore a decomposition in which returns on a large class of portfolios
+relative to the market depend on a smooth non-negative drift and changes in the
+asset price distribution. This decomposition is obtained using general
+continuous semimartingale price representations, and is thus consistent with
+virtually any asset pricing model. Fluctuations in portfolio relative returns
+depend on stochastic time-varying dispersion in asset prices. Thus, our
+framework uncovers an asset pricing factor whose existence emerges from an
+accounting identity universal across different economic and financial
+environments, a fact that has deep implications for market efficiency. In
+particular, in a closed, dividend-free market in which asset price dispersion
+is relatively constant, a large class of portfolios must necessarily outperform
+the market portfolio over time. We show that price dispersion in commodity
+futures markets has increased only slightly, and confirm the existence of
+substantial excess returns that co-vary with changes in price dispersion as
+predicted by our theory.
+"
+28420,Porous medium equation with nonlocal pressure in a bounded domain,"  We study a quite general family of nonlinear evolution equations of diffusive
+type with nonlocal effects. More precisely, we study porous medium equations
+with a fractional Laplacian pressure, and the problem is posed on a bounded
+space domain. We prove existence of weak solutions and suitable a priori bounds
+and regularity estimates.
+"
+28421,Graph-Sparse Logistic Regression,"  We introduce Graph-Sparse Logistic Regression, a new algorithm for
+classification for the case in which the support should be sparse but connected
+on a graph. We val- idate this algorithm against synthetic data and benchmark
+it against L1-regularized Logistic Regression. We then explore our technique in
+the bioinformatics context of proteomics data on the interactome graph. We make
+all our experimental code public and provide GSLR as an open source package.
+"
+28422,Discrete energy levels of Caroli-de Gennes-Martricon states in quantum limit due to small Fermi energy in FeTe$_{0.55}$Se$_{0.45}$,"  Caroli-de Gennes-Martricon (CdGM) states were predicted in 1964 as low energy
+excitations within vortex cores of type-II superconductors. In the quantum
+limit, namely $T/T_\mathrm{c} \ll \Delta/E_\mathrm{F}$, the energy levels of
+these states were predicted to be discrete with the basic levels at $E_\mu =
+\pm \mu \Delta^2/E_\mathrm{F}$ ($\mu = 1/2$, $3/2$, $5/2$, ...). However, due
+to the small ratio of $\Delta/E_\mathrm{F}$ in most type-II superconductors, it
+is very difficult to observe the discrete CdGM states, but rather a symmetric
+peak appears at zero-bias at the vortex center. Here we report the clear
+observation of these discrete energy levels of CdGM states in
+FeTe$_{0.55}$Se$_{0.45}$. The rather stable energies of these states versus
+space clearly validates our conclusion. Analysis based on the energies of these
+CdGM states indicates that the Fermi energy in the present system is very
+small.
+"
+28423,Telexistence and Teleoperation for Walking Humanoid Robots,"  This paper proposes an architecture for achieving telexistence and
+teleoperation of humanoid robots. The architecture combines several
+technological set-ups, methodologies, locomotion and manipulation algorithms in
+a novel manner, thus building upon and extending works available in literature.
+The approach allows a human operator to command and telexist with the robot.
+Therefore, in this work we treat aspects pertaining not only to the proposed
+architecture structure and implementation, but also the human operator
+experience in terms of ability to adapt to the robot and to the architecture.
+Also the proprioception aspects and embodiment of the robot are studied through
+specific experimental results, which are also treated in a somewhat formal,
+albeit high-level manner. Application of the proposed architecture and
+experiments incorporating user training and experience are addressed using an
+illustrative bipedal humanoid robot, namely the iCub robot.
+"
+28424,Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach,"  We propose an efficient method to estimate the accuracy of classifiers using
+only unlabeled data. We consider a setting with multiple classification
+problems where the target classes may be tied together through logical
+constraints. For example, a set of classes may be mutually exclusive, meaning
+that a data instance can belong to at most one of them. The proposed method is
+based on the intuition that: (i) when classifiers agree, they are more likely
+to be correct, and (ii) when the classifiers make a prediction that violates
+the constraints, at least one classifier must be making an error. Experiments
+on four real-world data sets produce accuracy estimates within a few percent of
+the true accuracy, using solely unlabeled data. Our models also outperform
+existing state-of-the-art solutions in both estimating accuracies, and
+combining multiple classifier outputs. The results emphasize the utility of
+logical constraints in estimating accuracy, thus validating our intuition.
+"
+28425,Traveling foreshocks and transient foreshock phenomena,"  We use the multi-spacecraft capabilities of the Cluster and THEMIS missions
+to show that two types of foreshock may be detected in spacecraft data. One is
+the global foreshock that appears upstream of the Earth's quasi-parallel
+bow-shock under steady or variable interplanetary magnetic field. Another type
+is a traveling foreshock that is bounded by two rotational discontinuities in
+the interplanetary magnetic field and propagates along the bow-shock. Foreshock
+compressional boundaries are found at the edges of both types of foreshock. We
+show that isolated foreshock cavities are a subset of the traveling foreshock
+that form when two bounding rotational discontinuities are so close that the
+ultra-low frequency waves do not develop in the region between them. We also
+report observations of a spontaneous hot flow anomaly inside a traveling
+foreshock. This means that other phenomena, such as foreshock cavitons, may
+also exist inside this type of foreshock. In the second part of this work we
+present statistical properties of phenomena related to the foreshock, namely
+foreshock cavities, cavitons, spontaneous hot flow anomalies and foreshock
+compressional boundaries. We show that spontaneous hot flow anomalies are the
+most depleted transient structures in terms of the B-field and plasma density
+inside them and that the foreshock compressional boundaries and foreshock
+cavities are closely related structures.
+"
+28426,Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization,"  We present VI-DSO, a novel approach for visual-inertial odometry, which
+jointly estimates camera poses and sparse scene geometry by minimizing
+photometric and IMU measurement errors in a combined energy functional. The
+visual part of the system performs a bundle-adjustment like optimization on a
+sparse set of points, but unlike key-point based systems it directly minimizes
+a photometric error. This makes it possible for the system to track not only
+corners, but any pixels with large enough intensity gradients. IMU information
+is accumulated between several frames using measurement preintegration, and is
+inserted into the optimization as an additional constraint between keyframes.
+We explicitly include scale and gravity direction into our model and jointly
+optimize them together with other variables such as poses. As the scale is
+often not immediately observable using IMU data this allows us to initialize
+our visual-inertial system with an arbitrary scale instead of having to delay
+the initialization until everything is observable. We perform partial
+marginalization of old variables so that updates can be computed in a
+reasonable time. In order to keep the system consistent we propose a novel
+strategy which we call ""dynamic marginalization"". This technique allows us to
+use partial marginalization even in cases where the initial scale estimate is
+far from the optimum. We evaluate our method on the challenging EuRoC dataset,
+showing that VI-DSO outperforms the state of the art.
+"
+28427,QMDP-Net: Deep Learning for Planning under Partial Observability,"  This paper introduces the QMDP-net, a neural network architecture for
+planning under partial observability. The QMDP-net combines the strengths of
+model-free learning and model-based planning. It is a recurrent policy network,
+but it represents a policy for a parameterized set of tasks by connecting a
+model with a planning algorithm that solves the model, thus embedding the
+solution structure of planning in a network learning architecture. The QMDP-net
+is fully differentiable and allows for end-to-end training. We train a QMDP-net
+on different tasks so that it can generalize to new ones in the parameterized
+task set and ""transfer"" to other similar tasks beyond the set. In preliminary
+experiments, QMDP-net showed strong performance on several robotic tasks in
+simulation. Interestingly, while QMDP-net encodes the QMDP algorithm, it
+sometimes outperforms the QMDP algorithm in the experiments, as a result of
+end-to-end learning.
+"
+28428,Temporal Pattern Mining from Evolving Networks,"  Recently, evolving networks are becoming a suitable form to model many
+real-world complex systems, due to their peculiarities to represent the systems
+and their constituting entities, the interactions between the entities and the
+time-variability of their structure and properties. Designing computational
+models able to analyze evolving networks becomes relevant in many applications.
+The goal of this research project is to evaluate the possible contribution of
+temporal pattern mining techniques in the analysis of evolving networks. In
+particular, we aim at exploiting available snapshots for the recognition of
+valuable and potentially useful knowledge about the temporal dynamics exhibited
+by the network over the time, without making any prior assumption about the
+underlying evolutionary schema. Pattern-based approaches of temporal pattern
+mining can be exploited to detect and characterize changes exhibited by a
+network over the time, starting from observed snapshots.
+"
+28429,Abelian-Square-Rich Words,"  An abelian square is the concatenation of two words that are anagrams of one
+another. A word of length $n$ can contain at most $\Theta(n^2)$ distinct
+factors, and there exist words of length $n$ containing $\Theta(n^2)$ distinct
+abelian-square factors, that is, distinct factors that are abelian squares.
+This motivates us to study infinite words such that the number of distinct
+abelian-square factors of length $n$ grows quadratically with $n$. More
+precisely, we say that an infinite word $w$ is {\it abelian-square-rich} if,
+for every $n$, every factor of $w$ of length $n$ contains, on average, a number
+of distinct abelian-square factors that is quadratic in $n$; and {\it uniformly
+abelian-square-rich} if every factor of $w$ contains a number of distinct
+abelian-square factors that is proportional to the square of its length. Of
+course, if a word is uniformly abelian-square-rich, then it is
+abelian-square-rich, but we show that the converse is not true in general. We
+prove that the Thue-Morse word is uniformly abelian-square-rich and that the
+function counting the number of distinct abelian-square factors of length $2n$
+of the Thue-Morse word is $2$-regular. As for Sturmian words, we prove that a
+Sturmian word $s_{\alpha}$ of angle $\alpha$ is uniformly abelian-square-rich
+if and only if the irrational $\alpha$ has bounded partial quotients, that is,
+if and only if $s_{\alpha}$ has bounded exponent.
+"
+28430,Conditions for Extinction Events in Chemical Reaction Networks with Discrete State Spaces,"  We study chemical reaction networks with discrete state spaces, such as the
+standard continuous time Markov chain model, and present sufficient conditions
+on the structure of the network that guarantee the system exhibits an
+extinction event. The conditions we derive involve creating a modified chemical
+reaction network called a domination-expanded reaction network and then
+checking properties of this network. We apply the results to several networks
+including an EnvZ-OmpR signaling pathway in Escherichia coli. This analysis
+produces a system of equalities and inequalities which, in contrast to previous
+results on extinction events, allows algorithmic implementation. Such an
+implementation will be investigated in a companion paper where the results are
+applied to 458 models from the European Bioinformatics Institute's BioModels
+database.
+"
+28431,Learning to Write Stylized Chinese Characters by Reading a Handful of Examples,"  Automatically writing stylized Chinese characters is an attractive yet
+challenging task due to its wide applicabilities. In this paper, we propose a
+novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly
+generate Chinese characters. Specifically, we propose to capture the different
+characteristics of a Chinese character by disentangling the latent features
+into content-related and style-related components. Considering of the complex
+shapes and structures, we incorporate the structure information as prior
+knowledge into our framework to guide the generation. Our framework shows a
+powerful one-shot/low-shot generalization ability by inferring the style
+component given a character with unseen style. To the best of our knowledge,
+this is the first attempt to learn to write new-style Chinese characters by
+observing only one or a few examples. Extensive experiments demonstrate its
+effectiveness in generating different stylized Chinese characters by fusing the
+feature vectors corresponding to different contents and styles, which is of
+significant importance in real-world applications.
+"
+28432,Region Detection in Markov Random Fields: Gaussian Case,"  We consider the problem of model selection in Gaussian Markov fields in the
+sample deficient scenario. The benchmark information-theoretic results in the
+case of d-regular graphs require the number of samples to be at least
+proportional to the logarithm of the number of vertices to allow consistent
+graph recovery. When the number of samples is less than this amount, reliable
+detection of all edges is impossible. In many applications, it is more
+important to learn the distribution of the edge (coupling) parameters over the
+network than the specific locations of the edges. Assuming that the entire
+graph can be partitioned into a number of spatial regions with similar edge
+parameters and reasonably regular boundaries, we develop new
+information-theoretic sample complexity bounds and show that a bounded number
+of samples can be sufficient to consistently recover these regions. Finally, we
+introduce and analyze an efficient region growing algorithm capable of
+recovering the regions with high accuracy. We show that it is consistent and
+demonstrate its performance benefits in synthetic simulations.
+"
+28433,G-Valued Galois Deformation Rings when $\ell \neq p$,"  For a smooth group scheme $G$ over an extension of $\mathbf{Z}_p$ such that
+the generic fiber of $G$ is reductive, we study the generic fiber of the Galois
+deformation ring for a $G$-valued mod $p$ representation of the absolute Galois
+group of a finite extension of $\mathbf{Q}_\ell$ with $\ell \neq p$. In
+particular, we show it admits a regular dense open locus, and that it is
+equidimensional of dimension $\dim G$.
+"
+28434,Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning,"  Learning a generative model is a key component of model-based reinforcement
+learning. Though learning a good model in the tabular setting is a simple task,
+learning a useful model in the approximate setting is challenging. In this
+context, an important question is the loss function used for model learning as
+varying the loss function can have a remarkable impact on effectiveness of
+planning. Recently Farahmand et al. (2017) proposed a value-aware model
+learning (VAML) objective that captures the structure of value function during
+model learning. Using tools from Asadi et al. (2018), we show that minimizing
+the VAML objective is in fact equivalent to minimizing the Wasserstein metric.
+This equivalence improves our understanding of value-aware models, and also
+creates a theoretical foundation for applications of Wasserstein in model-based
+reinforcement~learning.
+"
+28435,On the Motion of a Compressible Gravity Water Wave with Vorticity,"  We prove a priori estimates for the compressible Euler equations modeling the
+motion of a liquid with moving physical vacuum boundary in an unbounded initial
+domain. The liquid is under influence of gravity but without surface tension.
+Our fluid is not assumed to be irrotational. But the physical sign condition
+needs to be assumed on the free boundary. We generalize the method used in
+\cite{LL} to prove the energy estimates in an unbounded domain up to arbitrary
+order. To our knowledge, this result appears to be the first that concerns a
+priori energy bounds for the compressible water wave. In addition to that, the
+a priori energy estimates are in fact uniform in the sound speed $\kappa$. As a
+consequence, we obtain the convergence of solutions of compressible Euler
+equations with a free boundary to solutions of the incompressible equations,
+generalizing the result of \cite{LL} to when you have an unbounded domain. On
+the other hand, we prove that there are initial data satisfying the
+compatibility condition in some weighted Sobolev spaces, and this will
+propagate within a short time interval, which is essential for proving long
+time existence for slightly compressible irrotational water waves.
+"
+28436,Stratégies d'automédiation: de l'expression de soi au jeu des intersubjectivités : Etude de la représentation de l'usager dans Livejournal et Touchgraph,"  Actor of its presentation and actor of its online representation, the diarist
+draws his diegetic existence by setting up a strategy of automediation. The
+Self-representation is a personal creation determined by the interface and the
+functionalities of the software. A pragmatic approach of the
+Self-representation in the Livejournal Blog and the Touchgraph Livejournal
+browser provides a way to observe the play between intimacy and
+intersubjectivity. The software leads the user from the lonely space of writing
+to the community space of publication.
+"
+28437,Ehlers-Kundt Conjecture about Gravitational Waves and Dynamical Systems,"  Ehlers-Kundt conjecture is a physical assertion about the fundamental role of
+plane waves for the description of gravitational waves. Mathematically, it
+becomes equivalent to a problem on the Euclidean plane ${\mathbb R}^2$ with a
+very simple formulation in Classical Mechanics: given a non-necessarily
+autonomous potential $V(z,u)$, $(z,u)\in {\mathbb R}^2\times {\mathbb R}$,
+harmonic in $z$ (i.e. source-free), the trajectories of its associated
+dynamical system $\ddot{z}(s)=-\nabla_z V(z(s),s)$ are complete (they live
+eternally) if and only if $V(z,u)$ is a polynomial in $z$ of degree at most $2$
+(so that $V$ is a standard mathematical idealization of vacuum). Here, the
+conjecture is solved in the significative case that $V$ is bounded polynomially
+in $z$ for finite values of $u\in {\mathbb R}$. The mathematical and physical
+implications of this {\em polynomial EK conjecture}, as well as the
+non-polynomial one, are discussed beyond their original scope.
+"
+28438,"Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning","  Principal Component Analysis (PCA) and Kernel Principal Component Analysis
+(KPCA) are fundamental methods in machine learning for dimensionality
+reduction. The former is a technique for finding this approximation in finite
+dimensions and the latter is often in an infinite dimensional Reproducing
+Kernel Hilbert-space (RKHS). In this paper, we present a geometric framework
+for computing the principal linear subspaces in both situations as well as for
+the robust PCA case, that amounts to computing the intrinsic average on the
+space of all subspaces: the Grassmann manifold. Points on this manifold are
+defined as the subspaces spanned by $K$-tuples of observations. The intrinsic
+Grassmann average of these subspaces are shown to coincide with the principal
+components of the observations when they are drawn from a Gaussian
+distribution. We show similar results in the RKHS case and provide an efficient
+algorithm for computing the projection onto the this average subspace. The
+result is a method akin to KPCA which is substantially faster. Further, we
+present a novel online version of the KPCA using our geometric framework.
+Competitive performance of all our algorithms are demonstrated on a variety of
+real and synthetic data sets.
+"
+28439,Non-perturbative methodologies for low-dimensional strongly-correlated systems: From non-abelian bosonization to truncated spectrum methods,"  We review two important non-perturbative approaches for extracting the
+physics of low-dimensional strongly correlated quantum systems. Firstly, we
+start by providing a comprehensive review of non-Abelian bosonization. This
+includes an introduction to the basic elements of conformal field theory as
+applied to systems with a current algebra, and we orient the reader by
+presenting a number of applications of non-Abelian bosonization to models with
+large symmetries. We then tie this technique into recent advances in the
+ability of cold atomic systems to realize complex symmetries. Secondly, we
+discuss truncated spectrum methods for the numerical study of systems in one
+and two dimensions. For one-dimensional systems we provide the reader with
+considerable insight into the methodology by reviewing canonical applications
+of the technique to the Ising model (and its variants) and the sine-Gordon
+model. Following this we review recent work on the development of
+renormalization groups, both numerical and analytical, that alleviate the
+effects of truncating the spectrum. Using these technologies, we consider a
+number of applications to one-dimensional systems: properties of carbon
+nanotubes, quenches in the Lieb-Liniger model, 1+1D quantum chromodynamics, as
+well as Landau-Ginzburg theories. In the final part we move our attention to
+consider truncated spectrum methods applied to two-dimensional systems. This
+involves combining truncated spectrum methods with matrix product state
+algorithms. We describe applications of this method to two-dimensional systems
+of free fermions and the quantum Ising model, including their non-equilibrium
+dynamics.
+"
+28440,Comparison of variational balance models for the rotating shallow water equations,"  We present an extensive numerical comparison of a family of balance models
+appropriate to the semi-geostrophic limit of the rotating shallow water
+equations, and derived by variational asymptotics in Oliver (2006) for small
+Rossby numbers ${\mathrm{Ro}}$. This family of generalized large-scale
+semi-geostrophic (GLSG) models contains the $L_1$-model introduced by Salmon
+(1983) as a special case. We use these models to produce balanced initial
+states for the full shallow water equations. We then numerically investigate
+how well these models capture the dynamics of an initially balanced shallow
+water flow. It is shown that, whereas the $L_1$-member of the GLSG family is
+able to reproduce the balanced dynamics of the full shallow water equations on
+time scales of ${\mathcal{O}}(1/{\mathrm{Ro}})$ very well, all other members
+develop significant unphysical high wavenumber contributions in the
+ageostrophic vorticity which spoil the dynamics.
+"
+28441,Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification,"  Provable safety is one of the most critical challenges in automated driving.
+The behavior of numerous traffic participants in a scene cannot be predicted
+reliably due to complex interdependencies and the indiscriminate behavior of
+humans. Additionally, we face high uncertainties and only incomplete
+environment knowledge. Recent approaches minimize risk with probabilistic and
+machine learning methods - even under occlusions. These generate comfortable
+behavior with good traffic flow, but cannot guarantee safety of their
+maneuvers.
+Therefore, we contribute a safety verification method for trajectories under
+occlusions. The field-of-view of the ego vehicle and a map are used to identify
+critical sensing field edges, each representing a potentially hidden obstacle.
+The state of occluded obstacles is unknown, but can be over-approximated by
+intervals over all possible states.
+Then set-based methods are extended to provide occupancy predictions for
+obstacles with state intervals. The proposed method can verify the safety of
+given trajectories (e.g. if they ensure collision-free fail-safe maneuver
+options) w.r.t. arbitrary safe-state formulations. The potential for provably
+safe trajectory planning is shown in three evaluative scenarios.
+"
+28442,Computing modular Galois representations for small $\ell$,"  In this paper we describe an algorithm for computing mod $\ell$ Galois
+representations associated to modular forms of weight $k$ when $\ell <k-1$. As
+applications, we use this algorithm to explicitly compute the cases with
+$\Delta_{k}$ for $k=16,20, 22, 26$ and all the unexceptional primes $\ell$ with
+$\ell <k-1$.
+"
+28443,Merlinization of complexity classes above BQP,"  We study how complexity classes above BQP, such as postBQP, ${\rm
+postBQP}_{\rm FP}$, and SBQP, change if we ""Merlinize"" them, i.e., if we allow
+an extra input quantum state (or classical bit string) given by Merlin as
+witness. Main results are the following three: First, the Merlinized version of
+postBQP is equal to PSPACE. Second, if the Merlinized postBQP is restricted in
+such a way that the postselection probability is equal to all witness states,
+then the class is equal to PP. Finally, the Merlinization does not change the
+class SBQP.
+"
+28444,Searching with Consistent Prioritization for Multi-Agent Path Finding,"  We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing
+prioritized MAPF algorithms depend on rule-of-thumb heuristics and random
+assignment to determine a fixed total priority ordering of all agents a priori.
+We instead explore the space of all possible partial priority orderings as part
+of a novel systematic and conflict-driven combinatorial search framework. In a
+variety of empirical comparisons, we demonstrate state-of-the-art solution
+qualities and success rates, often with similar runtimes to existing
+algorithms. We also develop new theoretical results that explore the
+limitations of prioritized planning, in terms of completeness and optimality,
+for the first time.
+"
+28445,Band Structure of Two-dimensional Dirac Semimetal from Cyclotron Resonance,"  Knowing the band structure of materials is one of the prerequisites to
+understand their properties. Therefore, especially in the last decades,
+angle-resolved photoemission spectroscopy (ARPES) has become a highly demanded
+experimental tool to investigate the band structure. However, especially in
+thin film materials with a layered structure and several capping layers, access
+to the electronic structure by ARPES is limited. Therefore, several alternative
+methods to obtain the required information have been suggested. Here, we
+directly invert the results by cyclotron resonance experiments to obtain the
+band structure of a two-dimensional (2D) material. This procedure is applied to
+the mercury telluride quantum well with critical thickness which is
+characterized by a 2D electron gas with linear dispersion relations. The
+Dirac-like band structure in this material could be mapped both on the electron
+and on the hole side of the band diagram. In this material, purely linear
+dispersion of the hole-like carriers is in contrast to detectable quadratic
+corrections for the electrons.
+"
+28446,Support varieties - an axiomatic approach,"  We provide an axiomatic approach for studying support varieties of objects in
+a triangulated category via the action of a tensor triangulated category, where
+the tensor product is not necessarily symmetric. This is illustrated by
+examples, taken in particular from representation theory of finite dimensional
+algebras.
+"
+28447,Randomized Consensus based Distributed Kalman Filtering over Wireless Sensor Networks,"  This paper is concerned with developing a novel distributed Kalman filtering
+algorithm over wireless sensor networks based on randomized consensus strategy.
+Compared with the centralized algorithm, distributed filtering techniques
+require less computation per sensor and lead to more robust estimation since
+they simply use the information from the neighboring nodes in the network.
+However, poor local sensor estimation caused by limited observability and
+network topology changes which interfere the global consensus are challenging
+issues. Motivated by this observation, we propose a novel randomized
+gossip-based distributed Kalman filtering algorithm. Information exchange and
+computation in the proposed algorithm can be carried out in an arbitrarily
+connected network of nodes. In addition, the computational burden can be
+distributed for a sensor which communicates with a stochastically selected
+neighbor at each clock step under schemes of gossip algorithm. In this case,
+the error covariance matrix changes stochastically at every clock step, thus
+the convergence is considered in a probabilistic sense. We provide the mean
+square convergence analysis of the proposed algorithm. Under a sufficient
+condition, we show that the proposed algorithm is quite appealing as it
+achieves better mean square error performance theoretically than the
+noncooperative decentralized Kalman filtering algorithm. Besides, considering
+the limited computation, communication, and energy resources in the wireless
+sensor networks, we propose an optimization problem which minimizes the average
+expected state estimation error based on the proposed algorithm. To solve the
+proposed problem efficiently, we transform it into a convex optimization
+problem. And a sub-optimal solution is attained. Examples and simulations are
+provided to illustrate the theoretical results.
+"
+28448,Refined interlacing properties for zeros of paraorthogonal polynomials on the unit circle,"  The purpose of this note is to extend in a simple and unified way the known
+results on interlacing of zeros of paraorthogonal polynomials on the unit
+circle. These polynomials can be regarded as the characteristic polynomials of
+any matrix similar to an unitary upper Hessenberg matrix with positive
+subdiagonal elements.
+"
+28449,Symmetric Mahler's conjecture for the volume product in the three dimensional case,"  In this paper, we prove Mahler's conjecture concerning the volume product of
+centrally symmetric convex bodies in $\mathbb{R}^n$ in the case where $n=3$.
+Furthermore, we determine the equality condition.
+"
+28450,A New Method for Patterning Azopolymer Thin Film Surfaces,"  We present a simple bottom-up approach via an incoherent unpolarized
+illumination and the choice of a solvent-droplet-induced-dewetting method to
+photoinducenano doughnuts on the surface of azopolymer thin films. We
+demonstrate that doughnut-shaped nanostructures can be formed and tailored with
+a wide range of typical sizes, thus providing a rich field of applications
+using surface photo-patterning. Furthermore, due to the presence of highly
+photoactive azobenzene derivative in the material, illumination of these
+nanostructures by a polarized laser light shows the possibility of a further
+growth and reshaping opening the way for fundamental studies of size-dependent
+scaling laws of optical properties and possible fabrication of nano-reactor or
+nano-trap patterns
+"
+28451,Global solutions for the generalized SQG patch equation,"  We consider the inviscid generalized surface quasi-geostrophic equation
+(gSQG) in a patch setting, where the parameter $\alpha \in (1,2)$. The cases
+$\alpha = 0$ and $\alpha = 1$ correspond to 2d Euler and SQG respectively, and
+our choice of the parameter $\alpha$ results in a velocity more singular than
+in the SQG case.
+Our main result concerns the global stability of the half-plane patch
+stationary solution, under small and localized perturbations. Our theorem
+appears to be the first construction of stable global solutions for the
+gSQG-patch equations. The only other nontrivial global solutions known so far
+in the patch setting are the so-called V-states, which are uniformly rotating
+and periodic in time solutions.
+"
+28452,Characterising inflations of monotone grid classes of permutations,"  We characterise those permutation classes whose simple permutations are
+monotone griddable. This characterisation is obtained by identifying a set of
+nine substructures, at least one of which must occur in any simple permutation
+containing a long sum of 21s.
+"
+28453,Data-driven identification of a thermal network in multi-zone building,"  System identification of smart buildings is necessary for their optimal
+control and application in demand response. The thermal response of a building
+around an operating point can be modeled using a network of interconnected
+resistors with capacitors at each node/zone called RC network. The development
+of the RC network involves two phases: obtaining the network topology, and
+estimating thermal resistances and capacitance's. In this article, we present a
+provable method to reconstruct the interaction topology of thermal zones of a
+building solely from temperature measurements. We demonstrate that our learning
+algorithm accurately reconstructs the interaction topology for a $5$ zone
+office building in EnergyPlus with real-world conditions. We show that our
+learning algorithm is able to recover the network structure in scenarios where
+prior research prove insufficient.
+"
+28454,Fluctuations for stationary $q$-TASEP,"  We consider the $q$-totally asymmetric simple exclusion process ($q$-TASEP)
+in the stationary regime and study the fluctuation of the position of a
+particle. We first observe that the problem can be studied as a limiting case
+of an $N$-particle $q$-TASEP with a random initial condition and with particle
+dependent hopping rate. Then we explain how this $N$-particle $q$-TASEP can be
+encoded in a dynamics on a two-sided Gelfand-Tsetlin cone described by a
+two-sided $q$-Whittaker process and present a Fredholm determinant formula for
+the $q$-Laplace transform of the position of a particle. Two main ingredients
+in its derivation is the Ramanujan's bilateral summation formula and the Cauchy
+determinant identity for the theta function with an extra parameter. Based on
+this we establish that the position of a particle obeys the universal
+stationary KPZ distribution (the Baik-Rains distribution) in the long time
+limit.
+"
+28455,Low ionospheric reactions on tropical depressions prior hurricanes,"  We study the reactions of the low ionosphere during tropical depressions
+(TDs) which have been detected before the hurricane appearances in the Atlantic
+Ocean. We explore 41 TD events using very low frequency (VLF) radio signals
+emitted by NAA transmitter located in the USA and recorded by VLF receiver
+located in Belgrade (Serbia). We found VLF signal deviations (caused
+ionospheric turbulence) in the case of 36 out of 41 TD events (88%).
+Additionally, we explore 27 TDs which have not been developed in hurricanes and
+found similar low ionospheric reactions. However, in the sample of 41 TDs which
+are followed by hurricanes the typical low ionosphere perturbations seem to be
+more frequent than other TDs.
+"
+28456,Raman study of lattice vibrations in type II superlattice InAs/InAs1-xSbx,"  In this work, we report a polarized Raman study on the vibrational properties
+of the InAs/InAs1-xSbx SLs as well as selected InAs1-xSbx alloys, all grown on
+GaSb substrates by either MBE or MOCVD, from both growth surface and cleaved
+edge.
+"
+28457,Low-complexity computations for nilpotent subgroup problems,"  We solve the following algorithmic problems using TC0 circuits, or in
+logspace and quasilinear time, uniformly in the class of nilpotent groups with
+bounded nilpotency class and rank: subgroup conjugacy, computing the normalizer
+and isolator of a subgroup, coset intersection, and computing the torsion
+subgroup. Additionally, if any input words are provided in compressed form as
+straight-line programs or in Mal'cev coordinates the algorithms run in quartic
+time.
+"
+28458,Zeros of some special entire functions,"  The real and complex zeros of some special entire functions such as Wright,
+hyper-Bessel, and a special case of generalized hypergeometric functions are
+studied by using some classical results of Laguerre, Obreschkhoff, Pólya and
+Runckel. The obtained results extend the known theorem of Hurwitz on exact
+number of nonreal zeros of Bessel functions of the first kind. Moreover,
+results on zeros of derivatives of Bessel functions and cross-product of Bessel
+functions are also given, which are related to some recent open problems.
+"
+28459,Optical conductivity in multiferroic GaV$_4$S$_8$ and GeV$_4$S$_8$: Phonons and electronic transitions,"  We report on optical spectroscopy on the lacunar spinels GaV$_4$S$_8$ and
+GeV$_4$S$_8$ in the spectral range from 100 to 23000 cm$^{-1}$ and for
+temperatures from 5 to 300 K. These multiferroic spinel systems reveal
+Jahn-Teller driven ferroelectricity and complex magnetic order at low
+temperatures. We study the infrared-active phonon modes and the low-lying
+electronic excitations in the cubic high-temperature phase, as well as in the
+orbitally and in the magnetically ordered low-temperature phases. We compare
+the phonon modes in these two compounds, which undergo different
+symmetry-lowering Jahn-Teller transitions into ferroelectric and orbitally
+ordered phases, and exhibit different magnetic ground states. We follow the
+splitting of the phonon modes at the structural phase transition and detect
+additional splittings at the onset of antiferromagnetic order in GeV$_4$S$_8$.
+We observe electronic transitions within the $d$-derived bands of the V$_4$
+clusters and document a significant influence of the structural and magnetic
+phase transitions on the narrow electronic band gaps.
+"
+28460,Geometric Models of Helium,"  A previous paper (M.F.Atiyah and N.S.Manton, Complex geometry of nuclei and
+atoms, arXiv:1609.02816) modelled atoms and their isotopes by complex algebraic
+surfaces, with the projective plane modelling Hydrogen. In this paper, models
+of the stable isotopes Helium-4 and Helium-3 are constructed.
+"
+28461,Time-Varying Sensor and Actuator Selection for Uncertain Cyber-Physical Systems,"  We propose methods to solve time-varying, sensor and actuator (SaA) selection
+problems for uncertain cyber-physical systems. We show that many SaA selection
+problems for optimizing a variety of control and estimation metrics can be
+posed as semidefinite optimization problems with mixed-integer bilinear matrix
+inequalities (MIBMIs). Although this class of optimization problems are
+computationally challenging, we present tractable approaches that directly
+tackle MIBMIs, providing both upper and lower bounds, and that lead to
+effective heuristics for SaA selection. The upper and lower bounds are obtained
+via successive convex approximations and semidefinite programming relaxations,
+respectively, and selections are obtained with a novel slicing algorithm from
+the solutions of the bounding problems. Custom branch-and-bound and
+combinatorial greedy approaches are also developed for a broad class of systems
+for comparison. Finally, comprehensive numerical experiments are performed to
+compare the different methods and illustrate their effectiveness.
+"
+28462,Auto-Differentiating Linear Algebra,"  Development systems for deep learning (DL), such as Theano, Torch,
+TensorFlow, or MXNet, are easy-to-use tools for creating complex neural network
+models. Since gradient computations are automatically baked in, and execution
+is mapped to high performance hardware, these models can be trained end-to-end
+on large amounts of data. However, it is currently not easy to implement many
+basic machine learning primitives in these systems (such as Gaussian processes,
+least squares estimation, principal components analysis, Kalman smoothing),
+mainly because they lack efficient support of linear algebra primitives as
+differentiable operators. We detail how a number of matrix decompositions
+(Cholesky, LQ, symmetric eigen) can be implemented as differentiable operators.
+We have implemented these primitives in MXNet, running on CPU and GPU in single
+and double precision. We sketch use cases of these new operators, learning
+Gaussian process and Bayesian linear regression models, where we demonstrate
+very substantial reductions in implementation complexity and running time
+compared to previous codes. Our MXNet extension allows end-to-end learning of
+hybrid models, which combine deep neural networks (DNNs) with Bayesian
+concepts, with applications in advanced Gaussian process models, scalable
+Bayesian optimization, and Bayesian active learning.
+"
+28463,On orthogonal projections for dimension reduction and applications in variational loss functions for learning problems,"  The use of orthogonal projections on high-dimensional input and target data
+in learning frameworks is studied. First, we investigate the relations between
+two standard objectives in dimension reduction, maximizing variance and
+preservation of pairwise relative distances. The derivation of their asymptotic
+correlation and numerical experiments tell that a projection usually cannot
+satisfy both objectives. In a standard classification problem we determine
+projections on the input data that balance them and compare subsequent results.
+Next, we extend our application of orthogonal projections to deep learning
+frameworks. We introduce new variational loss functions that enable integration
+of additional information via transformations and projections of the target
+data. In two supervised learning problems, clinical image segmentation and
+music information classification, the application of the proposed loss
+functions increase the accuracy.
+"
+28464,Neural Ranking Models with Weak Supervision,"  Despite the impressive improvements achieved by unsupervised deep neural
+networks in computer vision and NLP tasks, such improvements have not yet been
+observed in ranking for information retrieval. The reason may be the complexity
+of the ranking problem, as it is not obvious how to learn from queries and
+documents when no supervised signal is available. Hence, in this paper, we
+propose to train a neural ranking model using weak supervision, where labels
+are obtained automatically without human annotators or any external resources
+(e.g., click data). To this aim, we use the output of an unsupervised ranking
+model, such as BM25, as a weak supervision signal. We further train a set of
+simple yet effective ranking models based on feed-forward neural networks. We
+study their effectiveness under various learning scenarios (point-wise and
+pair-wise models) and using different input representations (i.e., from
+encoding query-document pairs into dense/sparse vectors to using word embedding
+representation). We train our networks using tens of millions of training
+instances and evaluate it on two standard collections: a homogeneous news
+collection(Robust) and a heterogeneous large-scale web collection (ClueWeb).
+Our experiments indicate that employing proper objective functions and letting
+the networks to learn the input representation based on weakly supervised data
+leads to impressive performance, with over 13% and 35% MAP improvements over
+the BM25 model on the Robust and the ClueWeb collections. Our findings also
+suggest that supervised neural ranking models can greatly benefit from
+pre-training on large amounts of weakly labeled data that can be easily
+obtained from unsupervised IR models.
+"
+28465,Charge exchange in the ultraviolet: implication for interacting clouds in the core of NGC 1275,"  Charge exchange emission is known to provide a key diagnostic to the
+interface between hot and cold matter in many astrophysical environments. Most
+of the recent charge exchange studies focus on its emission in the X-ray band,
+but few on the UV part, although the latter can also provide a powerful probe
+of the charge exchange process. An atomic calculation, as well as an
+application to observed data, are presented to explore and describe the
+potential use of the UV data for the study of cosmic charge exchange. Using the
+newest charge exchange model in the SPEX code v3.03, we re-analyze an archival
+Hubble STIS data of the central region of NGC 1275. The NGC 1275 spectrum shows
+hints for three possible weak lines at about 1223.6~{\AA}, 1242.4~{\AA}, and
+1244.0~{\AA}, each with a significance of about $2-3\sigma$. The putative
+features are best explained by charge exchange between highly ionized hydrogen,
+neon, and sulfur with neutral matter. The wavelengths of the charge exchange
+lines are found robustly with uncertainties $\leq 0.3$~{\AA}. The possible
+charge exchange emission shows a line-of-sight velocity offset of about $-3400$
+km s$^{-1}$ with respect to the NGC 1275 nucleus, which resembles one of the
+Ly$\alpha$ absorbers reported in Baum et al. (2005). This indicates that the
+charge exchange lines might be emitted as the same position of the absorber,
+which could be ascribed to outflowing gas from the nucleus.
+"
+28466,"BaMF$_4$ (M = Mn, Co, Ni): New Electrode Materials for Hybrid Supercapacitor with Layered Polar Structure","  To pursuit high electrochemical performance of supercapacitors based on
+Faradaic charge-transfer with redox reaction or absorption/desorption effect,
+the intercalation efficiency of electrolyte ions into electrode materials is a
+crucial prerequisite to surpass the pure surface capacity with extra bulk
+contribution. Here we report layered barium transition metal fluorides, BaMF4
+(M = Mn, Co, Ni) to be a series of new electrode materials applied in standard
+three-electrode configuration. Benefiting from the efficient immersing of
+electrolyte ions, these materials own prominent specific capacitance.
+Electrochemical characterizations demonstrate that all the BaMF4 electrodes
+show both capacitive behavior and Faradaic redox reactions in the cyclic
+voltammograms, and ability of charge storage by charging-discharging cycling
+with high cycling stability. Particularly, BaCoF4 shows the the highest
+specific capacitance of 360 F g-1 at current density of 0.6 A g-1, even the
+particle size is far beyond nanometer scale. In addition, first principles
+calculations reveal the possible underlying mechanisms.
+"
+28467,Non-linear Pattern Matching with Backtracking for Non-free Data Types,"  Non-free data types are data types whose data have no canonical forms. For
+example, multisets are non-free data types because the multiset $\{a,b,b\}$ has
+two other equivalent but literally different forms $\{b,a,b\}$ and $\{b,b,a\}$.
+Pattern matching is known to provide a handy tool set to treat such data types.
+Although many studies on pattern matching and implementations for practical
+programming languages have been proposed so far, we observe that none of these
+studies satisfy all the criteria of practical pattern matching, which are as
+follows: i) efficiency of the backtracking algorithm for non-linear patterns,
+ii) extensibility of matching process, and iii) polymorphism in patterns.
+This paper aims to design a new pattern-matching-oriented programming
+language that satisfies all the above three criteria. The proposed language
+features clean Scheme-like syntax and efficient and extensible pattern matching
+semantics. This programming language is especially useful for the processing of
+complex non-free data types that not only include multisets and sets but also
+graphs and symbolic mathematical expressions. We discuss the importance of our
+criteria of practical pattern matching and how our language design naturally
+arises from the criteria. The proposed language has been already implemented
+and open-sourced as the Egison programming language.
+"
+28468,Reduced fluid models for self-propelled particles interacting through alignment,"  The asymptotic analysis of kinetic models describing the behavior of
+particles interacting through alignment is performed. We will analyze the
+asymptotic regime corresponding to large alignment frequency where the
+alignment effects are dominated by the self propulsion and friction forces. The
+former hypothesis leads to a macroscopic fluid model due to the fast averaging
+in velocity, while the second one imposes a fixed speed in the limit, and thus
+a reduction of the dynamics to a sphere in the velocity space. The analysis
+relies on averaging techniques successfully used in the magnetic confinement of
+charged particles. The limiting particle distribution is supported on a sphere,
+and therefore we are forced to work with measures in velocity. As for the
+Euler-type equations, the fluid model comes by integrating the kinetic equation
+against the collision invariants and its generalizations in the velocity space.
+The main difficulty is their identification for the averaged alignment kernel
+in our functional setting of measures in velocity.
+"
+28469,Automatic Compositor Attribution in the First Folio of Shakespeare,"  Compositor attribution, the clustering of pages in a historical printed
+document by the individual who set the type, is a bibliographic task that
+relies on analysis of orthographic variation and inspection of visual details
+of the printed page. In this paper, we introduce a novel unsupervised model
+that jointly describes the textual and visual features needed to distinguish
+compositors. Applied to images of Shakespeare's First Folio, our model predicts
+attributions that agree with the manual judgements of bibliographers with an
+accuracy of 87%, even on text that is the output of OCR.
+"
+28470,Isomorphisms between curve graphs of infinite-type surfaces are geometric,"  Let $\phi:\mathcal{C}(S)\to\mathcal{C}(S')$ be a simplicial isomorphism
+between the curve graphs of two infinite-type surfaces. In this paper we show
+that in this situation $S$ and $S'$ are homeomorphic and $\phi$ is induced by a
+homeomorphism $h:S\to S'$.
+"
+28471,A phenomenological thermodynamic potential for DyCo$_2$,"  A phenomenological thermodynamic potential was constructed based on the
+symmetry analysis and property characterization of bulk DyCo2. An eight-order
+polynomial of Landau expansion was employed to describe the thermodynamic
+behavior of DyCo2. Several properties were reproduced including ferromagnetic
+transition temperature, magnetization curve, temperature dependence of
+magnetization. The transition behavior was analyzed via the thermodynamic
+potential. The Landau phenomenological thermodynamic model predicts the correct
+metamagnetic transition near the Curie temperature.
+"
+28472,Entrenched time delays versus accelerating opinion dynamics: are advanced democracies inherently unstable?,"  Modern societies face the challenge that the time scale of opinion formation
+is continuously accelerating in contrast to the time scale of political
+decision making. With the latter remaining of the order of the election cycle
+we examine here the case that the political state of a society is determined by
+the continuously evolving values of the electorate. Given this assumption we
+show that the time lags inherent in the election cycle will inevitable lead to
+political instabilities for advanced democracies characterized both by an
+accelerating pace of opinion dynamics and by high sensibilities (political
+correctness) to deviations from mainstream values. Our result is based on the
+observation that dynamical systems become generically unstable whenever time
+delays become comparable to the time it takes to adapt to the steady state. The
+time needed to recover from external shocks grows in addition dramatically
+close to the transition. Our estimates for the order of magnitude of the
+involved time scales indicate that socio-political instabilities may develop
+once the aggregate time scale for the evolution of the political values of the
+electorate falls below 7-15 months.
+"
+28473,Maximizing the Smallest Eigenvalue of a Symmetric Matrix: A Submodular Optimization Approach,"  This paper studies the problem of selecting a submatrix of a positive
+definite matrix in order to achieve a desired bound on the smallest eigenvalue
+of the submatrix. Maximizing this smallest eigenvalue has applications to
+selecting input nodes in order to guarantee consensus of networks with negative
+edges as well as maximizing the convergence rate of distributed systems. We
+develop a submodular optimization approach to maximizing the smallest
+eigenvalue by first proving that positivity of the eigenvalues of a submatrix
+can be characterized using the probability distribution of the quadratic form
+induced by the submatrix. We then exploit that connection to prove that
+positive-definiteness of a submatrix can be expressed as a constraint on a
+submodular function. We prove that our approach results in polynomial-time
+algorithms with provable bounds on the size of the submatrix. We also present
+generalizations to non-symmetric matrices, alternative sufficient conditions
+for the smallest eigenvalue to exceed a desired bound that are valid for
+Laplacian matrices, and a numerical evaluation.
+"
+28474,Compiling quantum circuits to realistic hardware architectures using temporal planners,"  To run quantum algorithms on emerging gate-model quantum hardware, quantum
+circuits must be compiled to take into account constraints on the hardware. For
+near-term hardware, with only limited means to mitigate decoherence, it is
+critical to minimize the duration of the circuit. We investigate the
+application of temporal planners to the problem of compiling quantum circuits
+to newly emerging quantum hardware. While our approach is general, we focus on
+compiling to superconducting hardware architectures with nearest neighbor
+constraints. Our initial experiments focus on compiling Quantum Alternating
+Operator Ansatz (QAOA) circuits whose high number of commuting gates allow
+great flexibility in the order in which the gates can be applied. That freedom
+makes it more challenging to find optimal compilations but also means there is
+a greater potential win from more optimized compilation than for less flexible
+circuits. We map this quantum circuit compilation problem to a temporal
+planning problem, and generated a test suite of compilation problems for QAOA
+circuits of various sizes to a realistic hardware architecture. We report
+compilation results from several state-of-the-art temporal planners on this
+test set. This early empirical evaluation demonstrates that temporal planning
+is a viable approach to quantum circuit compilation.
+"
+28475,Predictive Systems Toxicology,"  In this review we address to what extent computational techniques can augment
+our ability to predict toxicity. The first section provides a brief history of
+empirical observations on toxicity dating back to the dawn of Sumerian
+civilization. Interestingly, the concept of dose emerged very early on, leading
+up to the modern emphasis on kinetic properties, which in turn encodes the
+insight that toxicity is not solely a property of a compound but instead
+depends on the interaction with the host organism. The next logical step is the
+current conception of evaluating drugs from a personalized medicine
+point-of-view. We review recent work on integrating what could be referred to
+as classical pharmacokinetic analysis with emerging systems biology approaches
+incorporating multiple omics data. These systems approaches employ advanced
+statistical analytical data processing complemented with machine learning
+techniques and use both pharmacokinetic and omics data. We find that such
+integrated approaches not only provide improved predictions of toxicity but
+also enable mechanistic interpretations of the molecular mechanisms
+underpinning toxicity and drug resistance. We conclude the chapter by
+discussing some of the main challenges, such as how to balance the inherent
+tension between the predictive capacity of models, which in practice amounts to
+constraining the number of features in the models versus allowing for rich
+mechanistic interpretability, i.e. equipping models with numerous molecular
+features. This challenge also requires patient-specific predictions on
+toxicity, which in turn requires proper stratification of patients as regards
+how they respond, with or without adverse toxic effects. In summary, the
+transformation of the ancient concept of dose is currently successfully
+operationalized using rich integrative data encoded in patient-specific models.
+"
+28476,Breakdown of Nonlinear Elasticity in Stress-Controlled Thermal Amorphous Solids,"  In recent work it was clarified that amorphous solids under strain control do
+not possess nonlinear elastic theory in the sense that the shear modulus exists
+but nonlinear moduli exhibit sample to sample fluctuations that grow without
+bound with the system size. More relevant however for experiments are the
+conditions of stress control. In the present Communication we show that also
+under stress control the shear modulus exists but higher order moduli show
+unbounded sample to sample fluctuation. The unavoidable consequence is that the
+characterization of stress-strain curves in experiments should be done with a
+stress-dependent shear modulus rather than with nonlinear expansions.
+"
+28477,Coupling and Convergence for Hamiltonian Monte Carlo,"  Based on a new coupling approach, we prove that the transition step of the
+Hamiltonian Monte Carlo algorithm is contractive w.r.t. a carefully designed
+Kantorovich (L1 Wasserstein) distance. The lower bound for the contraction rate
+is explicit. Global convexity of the potential is not required, and thus
+multimodal target distributions are included. Explicit quantitative bounds for
+the number of steps required to approximate the stationary distribution up to a
+given error are a direct consequence of contractivity. These bounds show that
+HMC can overcome diffusive behaviour if the duration of the Hamiltonian
+dynamics is adjusted appropriately.
+"
+28478,Clustering by latent dimensions,"  This paper introduces a new clustering technique, called {\em dimensional
+clustering}, which clusters each data point by its latent {\em pointwise
+dimension}, which is a measure of the dimensionality of the data set local to
+that point. Pointwise dimension is invariant under a broad class of
+transformations. As a result, dimensional clustering can be usefully applied to
+a wide range of datasets. Concretely, we present a statistical model which
+estimates the pointwise dimension of a dataset around the points in that
+dataset using the distance of each point from its $n^{\text{th}}$ nearest
+neighbor. We demonstrate the applicability of our technique to the analysis of
+dynamical systems, images, and complex human movements.
+"
+28479,"Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes","  To model the complex non-stationary dependence structure of precipitation
+extremes over the entire contiguous U.S., we propose a flexible local approach
+based on factor copula models. Our sub-asymptotic spatial modeling framework
+yields non-trivial tail dependence structures, with a weakening dependence
+strength as events become more extreme, a feature commonly observed with
+precipitation data but not accounted for in classical asymptotic extreme-value
+models. To estimate the local extremal behavior, we fit the proposed model in
+small regional neighborhoods to high threshold exceedances, under the
+assumption of local stationarity. This allows us to gain in flexibility while
+making inference for such a large and complex dataset feasible. Adopting a
+local censored likelihood approach, inference is made on a fine spatial grid,
+and local estimation is performed by taking advantage of distributed computing
+resources and the embarrassingly parallel nature of this estimation procedure.
+The local model is efficiently fitted at all grid points, and uncertainty is
+measured using a block bootstrap procedure. An extensive simulation study shows
+that our approach can adequately capture complex, non-stationary dependencies,
+while our study of U.S. winter precipitation data reveals interesting
+differences in local tail structures over space, which has important
+implications on regional risk assessment of extreme precipitation events.
+"
+28480,Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement,"  We present a hybrid framework that leverages the trade-off between temporal
+and frequency precision in audio representations to improve the performance of
+speech enhancement task. We first show that conventional approaches using
+specific representations such as raw-audio and spectrograms are each effective
+at targeting different types of noise. By integrating both approaches, our
+model can learn multi-scale and multi-domain features, effectively removing
+noise existing on different regions on the time-frequency space in a
+complementary way. Experimental results show that the proposed hybrid model
+yields better performance and robustness than using each model individually.
+"
+28481,Partially separable convexly-constrained optimization with non-Lipschitzian singularities and its complexity,"  An adaptive regularization algorithm using high-order models is proposed for
+partially separable convexly constrained nonlinear optimization problems whose
+objective function contains non-Lipschitzian $\ell_q$-norm regularization terms
+for $q\in (0,1)$. It is shown that the algorithm using an $p$-th order Taylor
+model for $p$ odd needs in general at most $O(\epsilon^{-(p+1)/p})$ evaluations
+of the objective function and its derivatives (at points where they are
+defined) to produce an $\epsilon$-approximate first-order critical point. This
+result is obtained either with Taylor models at the price of requiring the
+feasible set to be 'kernel-centered' (which includes bound constraints and many
+other cases of interest), or for non-Lipschitz models, at the price of passing
+the difficulty to the computation of the step. Since this complexity bound is
+identical in order to that already known for purely Lipschitzian minimization
+subject to convex constraints [CartGoulToin2016], the new result shows that
+introducing non-Lipschitzian singularities in the objective function may not
+affect the worst-case evaluation complexity order. The result also shows that
+using the problem's partially separable structure (if present) does not affect
+complexity order either. A final (worse) complexity bound is derived for the
+case where Taylor models are used with a general convex feasible set.
+"
+28482,Simultaneous Localization and Mapping (SLAM) using RTAB-MAP,"  This paper implements Simultaneous Localization and Mapping (SLAM) technique
+to construct a map of a given environment. A Real Time Appearance Based Mapping
+(RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d
+occupancy grid and 3d octomap was created from a provided simulated
+environment. Next, a personal simulated environment was created for mapping as
+well. In this appearance based method, a process called Loop Closure is used to
+determine whether a robot has seen a location before or not. In this paper, it
+is seen that RTAB-Map is optimized for large scale and long term SLAM by using
+multiple strategies to allow for loop closure to be done in real time and the
+results depict that it can be an excellent solution for SLAM to develop robots
+that can map an environment in both 2d and 3d.
+"
+28483,Empowering individual trait prediction using interactions,"  One component of precision medicine is to construct prediction models with
+their predictive ability as high as possible, e.g. to enable individual risk
+prediction. In genetic epidemiology, complex diseases have a polygenic basis
+and a common assumption is that biological and genetic features affect the
+outcome under consideration via interactions. In the case of omics data, the
+use of standard approaches such as generalized linear models may be suboptimal
+and machine learning methods are appealing to make individual predictions.
+However, most of these algorithms focus mostly on main or marginal effects of
+the single features in a dataset. On the other hand, the detection of
+interacting features is an active area of research in the realm of genetic
+epidemiology. One big class of algorithms to detect interacting features is
+based on the multifactor dimensionality reduction (MDR). Here, we extend the
+model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm,
+to enable interaction empowered individual prediction. Using a comprehensive
+simulation study we show that our new algorithm can use information hidden in
+interactions more efficiently than two other state-of-the-art algorithms,
+namely the Random Forest and Elastic Net, and clearly outperforms these if
+interactions are present. The performance of these algorithms is comparable if
+no interactions are present. Further, we show that our new algorithm is
+applicable to real data by comparing the performance of the three algorithms on
+a dataset of rheumatoid arthritis cases and healthy controls. As our new
+algorithm is not only applicable to biological/genetic data but to all datasets
+with discrete features, it may have practical implications in other
+applications as well, and we made our method available as an R package.
+"
+28484,Singular vector formulas for Verma modules of simple Lie superalgebras,"  For a simple Lie superalgebra of type BDFG, we give explicit formulas for
+singular vectors in a Verma module of highest weight $\lambda - \rho$, which
+have weight $s_{\gamma}\lambda - \rho$ for certain positive non-isotropic roots
+$\gamma.$ This implies the existence of a nonzero homomorphism between the
+corresponding Verma modules.
+"
+28485,Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning,"  Learning is an inherently continuous phenomenon. When humans learn a new task
+there is no explicit distinction between training and inference. After we learn
+a task, we keep learning about it while performing the task. What we learn and
+how we learn it varies during different stages of learning. Learning how to
+learn and adapt is a key property that enables us to generalize effortlessly to
+new settings. This is in contrast with conventional settings in machine
+learning where a trained model is frozen during inference. In this paper we
+study the problem of learning to learn at both training and inference time in
+the context of visual navigation. A fundamental challenge in navigation is
+generalization to unseen scenes. In this paper we propose a self-adaptive
+visual navigation method (SAVN) which learns to adapt to new environments
+without any explicit supervision. Our solution is a meta-reinforcement learning
+approach where an agent learns a self-supervised interaction loss that
+encourages effective navigation. Our experiments, performed in the AI2-THOR
+framework, show major improvements in both success rate and SPL for visual
+navigation in novel scenes.
+"
+28486,AST-Based Deep Learning for Detecting Malicious PowerShell,"  With the celebrated success of deep learning, some attempts to develop
+effective methods for detecting malicious PowerShell programs employ neural
+nets in a traditional natural language processing setup while others employ
+convolutional neural nets to detect obfuscated malicious commands at a
+character level. While these representations may express salient PowerShell
+properties, our hypothesis is that tools from static program analysis will be
+more effective. We propose a hybrid approach combining traditional program
+analysis (in the form of abstract syntax trees) and deep learning. This poster
+presents preliminary results of a fundamental step in our approach: learning
+embeddings for nodes of PowerShell ASTs. We classify malicious scripts by
+family type and explore embedded program vector representations.
+"
+28487,Exploring the Imposition of Synaptic Precision Restrictions For Evolutionary Synthesis of Deep Neural Networks,"  A key contributing factor to incredible success of deep neural networks has
+been the significant rise on massively parallel computing devices allowing
+researchers to greatly increase the size and depth of deep neural networks,
+leading to significant improvements in modeling accuracy. Although deeper,
+larger, or complex deep neural networks have shown considerable promise, the
+computational complexity of such networks is a major barrier to utilization in
+resource-starved scenarios. We explore the synaptogenesis of deep neural
+networks in the formation of efficient deep neural network architectures within
+an evolutionary deep intelligence framework, where a probabilistic generative
+modeling strategy is introduced to stochastically synthesize increasingly
+efficient yet effective offspring deep neural networks over generations,
+mimicking evolutionary processes such as heredity, random mutation, and natural
+selection in a probabilistic manner. In this study, we primarily explore the
+imposition of synaptic precision restrictions and its impact on the
+evolutionary synthesis of deep neural networks to synthesize more efficient
+network architectures tailored for resource-starved scenarios. Experimental
+results show significant improvements in synaptic efficiency (~10X decrease for
+GoogLeNet-based DetectNet) and inference speed (>5X increase for
+GoogLeNet-based DetectNet) while preserving modeling accuracy.
+"
+28488,Center-Excised X-ray Luminosity as an Efficient Mass Proxy for Future Galaxy Cluster Surveys,"  The cosmological constraining power of modern galaxy cluster catalogs can be
+improved by obtaining low-scatter mass proxy measurements for even a small
+fraction of sources. In the context of large upcoming surveys that will reveal
+the cluster population down to the group scale and out to high redshifts,
+efficient strategies for obtaining such mass proxies will be valuable. In this
+work, we use high-quality weak lensing and X-ray mass estimates for massive
+clusters in current X-ray selected catalogs to revisit the scaling relations of
+the projected, center-excised X-ray luminosity ($L_{ce}$), which previous work
+suggests correlates tightly with total mass. Our data confirm that this is the
+case, with $L_{ce}$ having an intrinsic scatter at fixed mass comparable to
+that of gas mass, temperature or $Y_X$. Compared to these other proxies,
+however, $L_{ce}$ is less susceptible to systematic uncertainties due to
+background modeling, and can be measured precisely with shorter exposures. This
+opens up the possibility of using $L_{ce}$ to estimate masses for large numbers
+of clusters discovered by new X-ray surveys (e.g. eROSITA) directly from the
+survey data, as well as for clusters discovered at other wavelengths, with
+relatively short follow-up observations. We describe a simple procedure for
+making such estimates from X-ray surface brightness data, and comment on the
+spatial resolution required to apply this method as a function of cluster mass
+and redshift. We also explore the potential impact of Chandra and XMM-Newton
+follow-up observations over the next decade on dark energy constraints from new
+cluster surveys.
+"
+28489,Effect of dust radial drift on viscous evolution of gaseous disk,"  The total amount of dust (or ""metallicity"") and the dust distribution in
+protoplanetary disks are crucial for planet formation. Dust grains radially
+drift due to gas--dust friction, and the gas is affected by the feedback from
+dust grains. We investigate the effects of the feedback from dust grains on the
+viscous evolution of the gas, taking into account the vertical dust settling.
+The feedback from the grains pushes the gas outward. When the grains are small
+and the dust-to-gas mass ratio is much smaller than unity, the radial drift
+velocity is reduced by the feedback effect but the gas still drifts inward.
+When the grains are sufficiently large or piled-up, the feedback is so
+effective that forces the gas flows outward. Although the dust feedback is
+affected by dust settling, we found that the 2D approximation reasonably
+reproduces the vertical averaged flux of gas and dust. We also performed the 2D
+two-fluid hydrodynamic simulations to examine the effect of the feedback from
+the grains on the evolution of the gas disk. We show that when the feedback is
+effective, the gas flows outward and the gas density at the region within $\sim
+10\ \mbox{AU}$ is significantly depleted. As a result, the dust-to-gas mass
+ratio at the inner radii may significantly excess unity, providing the
+environment where planetesimals are easily formed via, e.g., streaming
+instability. We also show that a simplified 1D model well reproduces the
+results of the 2D two-fluid simulations, which would be useful for future
+studies.
+"
+28490,Optical Navigation for Interplanetary CubeSats,"  CubeSats and small satellites are emerging as low-cost tools for performing
+science and exploration in deep space. These new classes of satellite exploit
+the latest advancement in miniaturization of electronics, power systems, and
+communication technologies to promise reduced launch cost and development
+cadence. JPL's MarCO CubeSats, part of the Mars Insight mission, will head on
+an Earth escape trajectory to Mars in 2018 and serve as communication relays
+for the Mars Insight Lander during Entry, Descent and Landing. Incremental
+advancements to the MarCO CubeSats, particularly in propulsion and GNC, could
+enable these spacecraft to get to another planet or to Near Earth Objects. This
+can have substantial science return with the right science instrument. We have
+developed an interplanetary CubeSat concept that includes onboard green
+monopropellant propulsion system and that can get into a capture orbit around a
+neighboring planet or chase a small-body. One such candidate is the Martian
+moon Phobos. Because of the limits of current CubeSat hardware and lack of an
+accurate ephemeris of Phobos, there will be a 2 to 5 km uncertainty in distance
+between the spacecraft and Phobos. This presents a major GNC challenge when the
+CubeSat first attempts to get into visual range of the moon. One solution to
+this challenge is to develop optical navigation technology that enables the
+CubeSat to take epicyclic orbits around the most probable location of the
+target, autonomously search and home-in on the target body. In worst-case
+scenarios, the technology would narrow down the uncertainty of the small-body
+location and then use optical flow, a computer vision algorithm to track
+movement of objects in the field of view. A dimly lit small-body can be
+detected by the occlusion of one or more surrounding stars. Our studies present
+preliminary simulations that support the concept.
+"
+28491,TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations,"  Summary: TreeGrafter is a new software tool for annotating protein sequences
+using annotated phylogenetic trees. Cur-rently, the tool provides annotations
+to Gene Ontology terms, and PANTHER protein class, family and subfamily. The
+ap-proach is generalizable to any annotations that have been made to internal
+nodes of a reference phylogenetic tree. Tree-Grafter takes each input query
+protein sequence, finds the best matching homologous family in a library of
+pre-calculated, pre-annotated gene trees, and then grafts it to the best
+location in the tree. It then annotates the sequence by propagating
+anno-tations from its ancestral nodes in the reference tree. We show that
+TreeGrafter outperforms subfamily HMM scoring for cor-rectly assigning
+subfamily membership, and that it produces highly specific annotations of GO
+terms based on annotated reference phylogenetic trees. This method will be
+further inte-grated into InterProScan, enabling an even broader user
+com-munity.
+Availability: TreeGrafter is freely available on the web at
+this https URL.
+"
+28492,Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity,"  The determinantal point process (DPP) is an elegant probabilistic model of
+repulsion with applications in various machine learning tasks including
+summarization and search. However, the maximum a posteriori (MAP) inference for
+DPP which plays an important role in many applications is NP-hard, and even the
+popular greedy algorithm can still be too computationally expensive to be used
+in large-scale real-time scenarios. To overcome the computational challenge, in
+this paper, we propose a novel algorithm to greatly accelerate the greedy MAP
+inference for DPP. In addition, our algorithm also adapts to scenarios where
+the repulsion is only required among nearby few items in the result sequence.
+We apply the proposed algorithm to generate relevant and diverse
+recommendations. Experimental results show that our proposed algorithm is
+significantly faster than state-of-the-art competitors, and provides a better
+relevance-diversity trade-off on several public datasets, which is also
+confirmed in an online A/B test.
+"
+28493,Hierarchical colloidal nanostructures - from fundamentals to applications,"  Editorial for the Special Issue of ""Zeitschrift fuer Physikalische Chemie"" on
+""Hierarchical Colloidal Nanostructures""
+"
+28494,Dimensionality reduction of SDPs through sketching,"  We show how to sketch semidefinite programs (SDPs) using positive maps in
+order to reduce their dimension. More precisely, we use
+Johnson\hyp{}Lindenstrauss transforms to produce a smaller SDP whose solution
+preserves feasibility or approximates the value of the original problem with
+high probability. These techniques allow to improve both complexity and storage
+space requirements. They apply to problems in which the Schatten 1-norm of the
+matrices specifying the SDP and also of a solution to the problem is constant
+in the problem size. Furthermore, we provide some results which clarify the
+limitations of positive, linear sketches in this setting.
+"
+28495,Modeling the complexity of acoustic emission spectra during intermittent plastic deformation: Power laws and multifractal spectra,"  Scale invariant power law distributions for acoustic emission signals are
+ubiquitous to several plastically deforming materials. However, power law
+distributions for the acoustic emission energies are reported in distinctly
+different plastically deforming situations such as in hcp and, fcc single and
+polycrystalline samples exhibiting smooth stress-strain curves, and in dilute
+metallic alloys exhibiting discontinuous flow. This is surprising since the
+underlying dislocation mechanisms in these two types of deformations are very
+different. So far, there has been no models that predict the power law
+statistics for the discontinuous flow. Furthermore, the statistics of the
+acoustic emission signals in jerky flow is even more complex requiring
+multifractal measures for a proper characterization. There has been no model
+that explains the complex statistics either. Here, we address the problem of
+statistical characterization of the acoustic emission signals associated with
+the three types of Portevin-Le Chatelier bands. We set-up a wave equation for
+elastic degrees of freedom with plastic strain rate as a source term. Using the
+plastic strain rate obtained from the Ananthakrishna model for the Portevin-Le
+Chatelier effect, we compute the acoustic emission spectrum corresponding to
+the three Portevin-Le Chatelier bands, which is used for further statistical
+characterization. Our results show that the model predicts power law statistics
+for all the three types of Portevin-Le Chatelier bands with the exponent values
+increasing with increasing strain rate. The calculated multifractal spectra
+corresponding to the acoustic emission signals associated with these three band
+types has a maximum spread for the type C decreasing with type B and A. We
+further show that the acoustic emission signals associated with Lüders like
+band also exhibits power law distribution and multifractality.
+"
+28496,On the Spin States of Habitable Zone Exoplanets Around M Dwarfs: The Effect of a Near-Resonant Companion,"  One longstanding problem for the potential habitability of planets within M
+dwarf systems is their likelihood to be tidally locked in a synchronously
+rotating spin state. This problem thus far has largely been addressed only by
+considering two objects: the star and the planet itself. However, many systems
+have been found to harbor multiple planets, with some in or very near to
+mean-motion resonances. The presence of a planetary companion near a
+mean-motion resonance can induce oscillatory variations in the mean-motion of
+the planet, which we demonstrate can have significant effects on the spin-state
+of an otherwise synchronously rotating planet. In particular, we find that a
+planetary companion near a mean-motion resonance can excite the spin states of
+planets in the habitable zone of small, cool stars, pushing otherwise
+synchronously rotating planets into higher amplitude librations of the spin
+state, or even complete circulation resulting in effective stellar days with
+full surface coverage on the order of years or decades. This increase in
+illuminated area can have potentially dramatic influences on climate, and thus
+on habitability. We also find that the resultant spin state can be very
+sensitive to initial conditions due to the chaotic nature of the spin state at
+early times within certain regimes. We apply our model to two hypothetical
+planetary systems inspired by the K00255 and TRAPPIST-1 systems, which both
+have Earth-sized planets in mean-motion resonances orbiting cool stars.
+"
+28497,A multi-layer agent-based model for the analysis of energy distribution networks in urban areas,"  Significant research contributions and Directives approach the issue of the
+insertion of renewable-based energy systems on urban territory in order to face
+with the growing energy needs of citizens. The introduction of such systems
+gives raise to installers to both satisfy their energy demands and distribute
+eventual energy excesses to close neighbours. This paper presents a multi-layer
+agent-based computational model that simulates multiple event of the network of
+the energy distribution occurring within urban areas. The model runs on the
+NetLogo platform and aims at elaborating the most suitable strategy when
+dealing with the design of a network of energy distribution. Experimental data
+are discussed on the basis of two main scenarios within an operating period of
+24 hours. Scenarios consider both the variation of the percentages of
+installers of renewable-based energy systems and the distance along which
+energy exchanges occur.
+"
+28498,Lieb-Schultz-Mattis theorems for symmetry protected topological phases,"  The Lieb-Schultz-Mattis (LSM) theorem and its descendants represent a class
+of powerful no-go theorems that rule out any short-range-entangled (SRE)
+symmetric ground state irrespective of the specific Hamiltonian, based only on
+certain microscopic inputs such as symmetries and particle filling numbers. In
+this work, we introduce and prove a new class of LSM-type theorems, where any
+symmetry-allowed SRE ground state must be a symmetry-protected topological
+(SPT) phase with robust gapless edge states. The key ingredient is to replace
+the lattice translation symmetry in usual LSM theorems by magnetic translation
+symmetry. These theorems provide new insights into numerical models and
+experimental realizations of SPT phases in interacting bosons and fermions.
+"
+28499,Kinetic formulation of a 2 $\times$ 2 hyperbolic system arising in gas chromatography,"  The PSA system commonly used in the context of gas-solid chromatography is
+reformulated as a single kinetic equation using an additional kinetic variable.
+A kinetic numerical scheme is built from this new formulation and its behavior
+is tested on solving the Riemann problem in different configurations leading to
+single or composite waves.
+"
+28500,"Weighted allocations, their concomitant-based estimators, and asymptotics","  Various members of the class of weighted insurance premiums and risk capital
+allocation rules have been researched from a number of perspectives.
+Corresponding formulas in the case of parametric families of distributions have
+been derived, and they have played a pivotal role when establishing parametric
+statistical inference in the area. Non-parametric inference results have also
+been derived in special cases such as the tail conditional expectation,
+distortion risk measure, and several members of the class of weighted premiums.
+For weighted allocation rules, however, non-parametric inference results have
+not yet been adequately developed. In the present paper, therefore, we put
+forward empirical estimators for the weighted allocation rules and establish
+their consistency and asymptotic normality under practically sound conditions.
+Intricate statistical considerations rely on the theory of induced order
+statistics, known as concomitants.
+"
+28501,How can we distinguish positive cooperativity from auto-catalysis in enzyme kinetics?,"  Different graphical plots involving the catalytic rate with the (initial)
+substrate concentration exist in the enzyme kinetics literature to estimate the
+reaction constants. But, none of these standard plots can unambiguously
+distinguish between the two important mechanisms of rate enhancement: positive
+cooperativity among the active sites of an oligomeric enzyme and auto-catalysis
+of the intermediate complex of an enzyme with a single active site. We achieve
+this distinction here by providing a nice linear plot for the latter.
+Importantly, to accomplish this task, no extra information other than the
+steady-state rate as a function of substrate concentration is required.
+"
+28502,Inference for high-dimensional instrumental variables regression,"  This paper concerns statistical inference for the components of a
+high-dimensional regression parameter despite possible endogeneity of each
+regressor. Given a first-stage linear model for the endogenous regressors and a
+second-stage linear model for the response variable, we develop a novel
+adaptation of the parametric one-step update to a generic second-stage
+estimator. We provide high-level conditions under which the scaled update is
+asymptotically normal. We introduce a two-stage Lasso procedure and show that,
+under a sub-Gaussian noise regime, the second-stage Lasso estimator satisfies
+the aforementioned conditions. Using these results, we construct asymptotically
+valid confidence intervals for the components of the second-stage regression
+vector. We complement our asymptotic theory with empirical studies, which
+demonstrate the relevance of our method in finite samples.
+"
+28503,A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning,"  This paper takes a step towards temporal reasoning in a dynamically changing
+video, not in the pixel space that constitutes its frames, but in a latent
+space that describes the non-linear dynamics of the objects in its world. We
+introduce the Kalman variational auto-encoder, a framework for unsupervised
+learning of sequential data that disentangles two latent representations: an
+object's representation, coming from a recognition model, and a latent state
+describing its dynamics. As a result, the evolution of the world can be
+imagined and missing data imputed, both without the need to generate high
+dimensional frames at each time step. The model is trained end-to-end on videos
+of a variety of simulated physical systems, and outperforms competing methods
+in generative and missing data imputation tasks.
+"
+28504,Unconventional magnetism of the Kondo lattice,"  A review is given of the experimental situation and theoretical predictions
+concerning unconventional magnetic ordering in dense Kondo 4f- and 5f-systems,
+including recently investigated on cerium, ytterbium, and actinide based
+ternary systems. This unconventional ordering is characterized by a small
+magnetic moment and exhibits features typical of both localized-spin and
+itinerant-electron magnets. Particular attention is paid to ferromagnetic
+systems and the competition among different types of magnetic ordering. The
+perturbation theory method, renormalization group approach, and auxiliary
+pseudofermion representation are considered as tools for describing the
+formation of the Kondo lattice ground state. The problem of non-Fermi-liquid
+behavior is also discussed.
+"
+28505,"Graph Bayesian Optimization: Algorithms, Evaluations and Applications","  Network structure optimization is a fundamental task in complex network
+analysis. However, almost all the research on Bayesian optimization is aimed at
+optimizing the objective functions with vectorial inputs. In this work, we
+first present a flexible framework, denoted graph Bayesian optimization, to
+handle arbitrary graphs in the Bayesian optimization community. By combining
+the proposed framework with graph kernels, it can take full advantage of
+implicit graph structural features to supplement explicit features guessed
+according to the experience, such as tags of nodes and any attributes of
+graphs. The proposed framework can identify which features are more important
+during the optimization process. We apply the framework to solve four problems
+including two evaluations and two applications to demonstrate its efficacy and
+potential applications.
+"
+28506,Two-photon photoassociation spectroscopy of an ultracold heteronuclear molecule,"  We report on two-photon photoassociation (PA) spectroscopy of ultracold
+heteronuclear LiRb molecules. This is used to determine the binding energies of
+the loosely bound levels of the electronic ground singlet and the lowest
+triplet states of LiRb. We observe strong two-photon PA lines with power
+broadened line widths greater than 20 GHz at relatively low laser intensity of
+30 W/cm$^{2}$. The implication of this observation on direct atom to molecule
+conversion using stimulated Raman adiabatic passage (STIRAP) is discussed and
+the prospect for electronic ground state molecule production is theoretically
+analyzed.
+"
+28507,Topological properties in one-dimensional periodic systems,"  Recently there is trend to study topological properties in
+one-dimensional(1D) periodic systems. Concepts such as Zak phase are considered
+as topological invariants that characterize the bulk bands. The bulk 1D systems
+are classified to topologically nontrivial and trivial phases according to the
+value of the so-called topological invariant. The existence of edge states or
+interface states is viewed as a hallmark of the topological nontriviality of
+the 1D systems. In this work we demonstrate the so-called topological
+properties in 1D systems are not topological by showing they are
+description-dependent: the same system can be both topological and trivial
+depending on how we describe the system. We demonstrate that Zak phase and
+other related concepts are not topological invariants by showing they depend on
+the choice of gauge, especially on the choice of unit cell. We show, for the
+same bulk system, edge states or interface states can be both present and
+absent depending the choices of boundaries. So the existence of localized
+states in 1D system is only a boundary property.
+"
+28508,Enhancing human color vision by breaking binocular redundancy,"  To see color, the human visual system combines the response of three types of
+cone cells in the retina--a compressive process that discards a significant
+amount of spectral information. Here, we present an approach to enhance human
+color vision by breaking its inherent binocular redundancy, providing different
+spectral content to each eye. We fabricated a set of optical filters that
+""splits"" the response of the short-wavelength cone between the two eyes in
+individuals with typical trichromatic vision, simulating the presence of
+approximately four distinct cone types (""tetrachromacy""). Such an increase in
+the number of effective cone types can reduce the prevalence of metamers--pairs
+of distinct spectra that resolve to the same tristimulus values. This technique
+may result in an enhancement of spectral perception, with applications ranging
+from camouflage detection and anti-counterfeiting to new types of artwork and
+data visualization.
+"
+28509,Distributed Colour Reduction Revisited,"  We give a new, simple distributed algorithm for graph colouring in paths and
+cycles. Our algorithm is fast and self-contained, it does not need any globally
+consistent orientation, and it reduces the number of colours from $10^{100}$ to
+$3$ in three iterations.
+"
+28510,Climbing the Kaggle Leaderboard by Exploiting the Log-Loss Oracle,"  In the context of data-mining competitions (e.g., Kaggle, KDDCup, ILSVRC
+Challenge), we show how access to an oracle that reports a contestant's
+log-loss score on the test set can be exploited to deduce the ground-truth of
+some of the test examples. By applying this technique iteratively to batches of
+$m$ examples (for small $m$), all of the test labels can eventually be
+inferred. In this paper, (1) We demonstrate this attack on the first stage of a
+recent Kaggle competition (Intel & MobileODT Cancer Screening) and use it to
+achieve a log-loss of $0.00000$ (and thus attain a rank of #4 out of 848
+contestants), without ever training a classifier to solve the actual task. (2)
+We prove an upper bound on the batch size $m$ as a function of the
+floating-point resolution of the probability estimates that the contestant
+submits for the labels. (3) We derive, and demonstrate in simulation, a more
+flexible attack that can be used even when the oracle reports the accuracy on
+an unknown (but fixed) subset of the test set's labels. These results underline
+the importance of evaluating contestants based only on test data that the
+oracle does not examine.
+"
+28511,On Linear and unconditionally energy stable Algorithms for Variable Mobility Cahn-Hilliard Type Equation with Logarithmic Flory-Huggins Potential,"  In this paper, we consider the numerical approximations for the fourth order
+Cahn-Hilliard equation with concentration dependent mobility, and the
+logarithmic Flory-Huggins potential. One challenge in solving such a diffusive
+system numerically is how to develop proper temporal discretization for
+nonlinear terms in order to preserve the energy stability at the time-discrete
+level. We resolve this issue by developing a set of the first and second order
+time marching schemes based on a novel, called ""Invariant Energy
+Quadratization"" approach. Its novelty is that the proposed scheme is linear and
+symmetric positive definite because all nonlinear terms are treated
+semi-explicitly. We further prove all proposed schemes are unconditionally
+energy stable rigorously. Various 2D and 3D numerical simulations are presented
+to demonstrate the stability, accuracy and efficiency of the proposed schemes
+thereafter.
+"
+28512,Proximal algorithms for large-scale statistical modeling and optimal sensor/actuator selection,"  Several problems in modeling and control of stochastically-driven dynamical
+systems can be cast as regularized semi-definite programs. We examine two such
+representative problems and show that they can be formulated in a similar
+manner. The first, in statistical modeling, seeks to reconcile observed
+statistics by suitably and minimally perturbing prior dynamics. The second
+seeks to optimally select a subset of available sensors and actuators for
+control purposes. To address modeling and control of large-scale systems we
+develop a unified algorithmic framework using proximal methods. Our customized
+algorithms exploit problem structure and allow handling statistical modeling,
+as well as sensor and actuator selection, for substantially larger scales than
+what is amenable to current general-purpose solvers. We establish linear
+convergence of the proximal gradient algorithm, draw contrast between the
+proposed proximal algorithms and alternating direction method of multipliers,
+and provide examples that illustrate the merits and effectiveness of our
+framework.
+"
+28513,Geometric correlations mitigate the extreme vulnerability of multiplex networks against targeted attacks,"  We show that real multiplex networks are unexpectedly robust against targeted
+attacks on high degree nodes, and that hidden interlayer geometric correlations
+predict this robustness. Without geometric correlations, multiplexes exhibit an
+abrupt breakdown of mutual connectivity, even with interlayer degree
+correlations. With geometric correlations, we instead observe a multistep
+cascading process leading into a continuous transition, which apparently
+becomes fully continuous in the thermodynamic limit. Our results are important
+for the design of efficient protection strategies and of robust interacting
+networks in many domains.
+"
+28514,Temporal Overdrive Recurrent Neural Network,"  In this work we present a novel recurrent neural network architecture
+designed to model systems characterized by multiple characteristic timescales
+in their dynamics. The proposed network is composed by several recurrent groups
+of neurons that are trained to separately adapt to each timescale, in order to
+improve the system identification process. We test our framework on time series
+prediction tasks and we show some promising, preliminary results achieved on
+synthetic data. To evaluate the capabilities of our network, we compare the
+performance with several state-of-the-art recurrent architectures.
+"
+28515,Hydrodynamic shocks in microroller suspensions,"  We combine experiments, large scale simulations and continuum models to study
+the emergence of coherent structures in a suspension of magnetically driven
+microrollers sedimented near a floor. Collective hydrodynamic effects are
+predominant in this system, leading to strong density-velocity coupling. We
+characterize a uniform suspension and show that density waves propagate freely
+in all directions in a dispersive fashion. When sharp density gradients are
+introduced in the suspension, we observe the formation of a shock. Unlike
+Burgers' shock-like structures observed in other active and driven confined
+hydrodynamic systems, the shock front in our system has a well-defined finite
+width and moves rapidly compared to the mean suspension velocity. We introduce
+a continuum model demonstrating that the finite width of the front is due to
+far-field nonlocal hydrodynamic interactions and governed by a geometric
+parameter: the average particle height above the floor.
+"
+28516,Speaker Fluency Level Classification Using Machine Learning Techniques,"  Level assessment for foreign language students is necessary for putting them
+in the right level group, furthermore, interviewing students is a very
+time-consuming task, so we propose to automate the evaluation of speaker
+fluency level by implementing machine learning techniques. This work presents
+an audio processing system capable of classifying the level of fluency of
+non-native English speakers using five different machine learning models. As a
+first step, we have built our own dataset, which consists of labeled audio
+conversations in English between people ranging in different fluency
+domains/classes (low, intermediate, high). We segment the audio conversations
+into 5s non-overlapped audio clips to perform feature extraction on them. We
+start by extracting Mel cepstral coefficients from the audios, selecting 20
+coefficients is an appropriate quantity for our data. We thereafter extracted
+zero-crossing rate, root mean square energy and spectral flux features, proving
+that this improves model performance. Out of a total of 1424 audio segments,
+with 70% training data and 30% test data, one of our trained models (support
+vector machine) achieved a classification accuracy of 94.39%, whereas the other
+four models passed an 89% classification accuracy threshold.
+"
+28517,Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis,"  The genetic regulatory network (GRN) plays a key role in controlling the
+response of the cell to changes in the environment. Although the structure of
+GRNs has been the subject of many studies, their large scale structure in the
+light of feedbacks from the metabolic network (MN) has received relatively
+little attention. Here we study the causal structure of the GRNs, namely the
+chain of influence of one component on the other, taking into account feedback
+from the MN. First we consider the GRNs of E. coli and B. subtilis without
+feedback from MN and illustrate their causal structure. Next we augment the
+GRNs with feedback from their respective MNs by including (a) links from genes
+coding for enzymes to metabolites produced or consumed in reactions catalyzed
+by those enzymes and (b) links from metabolites to genes coding for
+transcription factors whose transcriptional activity the metabolites alter by
+binding to them. We find that the inclusion of feedback from MN into GRN
+significantly affects its causal structure, in particular the number of levels
+and relative positions of nodes in the hierarchy, and the number and size of
+the strongly connected components (SCCs). We then study the functional
+significance of the SCCs. For this we identify condition specific feedbacks
+from the MN into the GRN by retaining only those enzymes that are essential for
+growth in specific environmental conditions simulated via the technique of flux
+balance analysis (FBA). We find that the SCCs of the GRN augmented by these
+feedbacks can be ascribed specific functional roles in the organism. Our
+algorithmic approach thus reveals relatively autonomous subsystems with
+specific functionality, or regulatory modules in the organism. This automated
+approach could be useful in identifying biologically relevant modules in other
+organisms for which network data is available, but whose biology is less well
+studied.
+"
+28518,Magnetism and charge ordering in high- and low-temperature phases of Nb2O2F3,"  To sum up, we show in the present paper that magnetic response in
+Nb$_2$O$_2$F$_3$ at the high-temperatures ($T>$90 K) is related to the orbital
+selective regime, when part of the electrons form molecular orbitals while
+other electrons have local magnetic moments. The charge disproportionation,
+which occurs at $T\sim$90 K is seen in the GGA calculations, but its degree
+($\delta n \sim 0.1$ electron) is far from what one would expect from naive
+expectations based on the formal ionic valences. The mechanism of the charge
+ordering is argued to be related with a sizable kinetic energy gain due to
+formation of two molecular orbitals in short Nb$^{3+}$-Nb$^{3+}$ dimers caused
+by a strong nonlinearity of the distance dependence on electron hopping. We
+think that this mechanism of charge ordering, stabilized not by decrease of
+interaction energy, but rather by the gain in kinetic energy, may be operative
+in many other systems, especially consisting of structural dimers.
+"
+28519,Threshold Response to Stochasticity in Morphogenesis,"  During development of biological organisms, multiple complex structures are
+formed. In many instances, these structures need to exhibit a high degree of
+order to be functional, although many of their constituents are intrinsically
+stochastic. Hence, it has been suggested that biological robustness ultimately
+must rely on complex gene regulatory networks and clean-up mechanisms. Here we
+explore developmental processes that have evolved inherent robustness against
+stochasticity. In the context of the Drosophila eye disc, multiple optical
+units, ommatidia, develop into crystal-like patterns. During the larva-to-pupa
+stage of metamorphosis, the centers of the ommatidia are specified initially
+through the diffusion of morphogens, followed by the specification of R8 cells.
+Establishing the R8 cell is crucial in setting up the geometric, and
+functional, relationships of cells within an ommatidium and among neighboring
+ommatidia. Here we study a mathematical model of these spatio-temporal
+processes in the presence of stochasticity, defining and applying measures that
+quantify order within the resulting spatial patterns. We observe a universal
+sigmoidal response to increasing transcriptional noise. Ordered patterns
+persist up to a threshold noise level in the model parameters. As the noise is
+further increased past a threshold point of no return, these ordered patterns
+rapidly become disordered. Such robustness in development allows for the
+accumulation of genetic variation without any observable changes in phenotype.
+We argue that the observed sigmoidal dependence introduces robustness allowing
+for sizable amounts of genetic variation and transcriptional noise to be
+tolerated in natural populations without resulting in phenotype variation.
+"
+28520,Weakly Supervised Deep Recurrent Neural Networks for Basic Dance Step Generation,"  A deep recurrent neural network with audio input is applied to model basic
+dance steps. The proposed model employs multilayered Long Short-Term Memory
+(LSTM) layers and convolutional layers to process the audio power spectrum.
+Then, another deep LSTM layer decodes the target dance sequence. This
+end-to-end approach has an auto-conditioned decode configuration that reduces
+accumulation of feedback error. Experimental results demonstrate that, after
+training using a small dataset, the model generates basic dance steps with low
+cross entropy and maintains a motion beat F-measure score similar to that of a
+baseline dancer. In addition, we investigate the use of a contrastive cost
+function for music-motion regulation. This cost function targets motion
+direction and maps similarities between music frames. Experimental result
+demonstrate that the cost function improves the motion beat f-score.
+"
+28521,Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks,"  Deep convolutional neural networks (CNNs) have demonstrated impressive
+performance on many visual tasks. Recently, they became useful models for the
+visual system in neuroscience. However, it is still not clear what are learned
+by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used
+for the visual system, it is not easy to compare the structure components of
+CNN with possible neuroscience underpinnings due to highly complex circuits
+from the retina to higher visual cortex. Here we address this issue by focusing
+on single retinal ganglion cells with biophysical models and recording data
+from animals. By training CNNs with white noise images to predict neuronal
+responses, we found that fine structures of the retinal receptive field can be
+revealed. Specifically, convolutional filters learned are resembling biological
+components of the retinal circuit. This suggests that a CNN learning from one
+single retinal cell reveals a minimal neural network carried out in this cell.
+Furthermore, when CNNs learned from different cells are transferred between
+cells, there is a diversity of transfer learning performance, which indicates
+that CNNs are cell-specific. Moreover, when CNNs are transferred between
+different types of input images, here white noise v.s. natural images, transfer
+learning shows a good performance, which implies that CNN indeed captures the
+full computational ability of a single retinal cell for different inputs. Taken
+together, these results suggest that CNN could be used to reveal structure
+components of neuronal circuits, and provide a powerful model for neural system
+identification.
+"
+28522,What is the best fractional derivative to fit data?,"  The aim of this work is to show, based on concrete data observation, that the
+choice of the fractional derivative when modelling a problem is relevant for
+the accuracy of a method. Using the least squares fitting technique, we
+determine the order of the fractional differential equation that better
+describes the experimental data, for different types of fractional derivatives.
+"
+28523,Efficiency versus Speed in Quantum Heat Engines: Rigorous Constraint from Lieb-Robinson Bound,"  A long standing open problem whether a heat engine with finite power achieves
+the Carnot efficiency is investigated. We rigorously prove a general trade-off
+inequality on thermodynamic efficiency and time interval of a cyclic process
+with quantum heat engines. In a first step, employing the Lieb-Robinson bound
+we establish an inequality on the change in a local observable caused by an
+operation far from support of the local observable. This inequality provides a
+rigorous characterization of the following intuitive picture that most of the
+energy released from the engine to the cold bath remains near the engine when
+the cyclic process is finished. Using the above description, we finally prove
+an upper bound on efficiency with the aid of quantum information geometry. In
+addition, since our inequality falls down into the conventional second law of
+thermodynamics in Markovian limit, we adopt a completely different treatment
+which is developed in the context of classical stochastic processes. Our result
+generally excludes the possibility of a process with finite speed at the Carnot
+efficiency in quantum heat engines. In particular, the obtained constraint
+covers engines evolving with non-Markovian dynamics, which almost all previous
+studies on this topics fail to address.
+"
+28524,Neural-Guided Symbolic Regression with Semantic Prior,"  Symbolic regression has been shown to be quite useful in many domains from
+discovering scientific laws to industrial empirical modeling. Existing methods
+focus on numerically fitting the given data. However, in many domains,
+symbolically derivable properties of the desired expressions are known. We
+illustrate these ""semantic priors"" with leading powers (the polynomial behavior
+as the input approaches 0 and $\infty$). We introduce an expression generating
+neural network that significantly favors the generation of expressions with
+desired leading powers, even generalizing to powers not in the training set. We
+then describe our Neural-Guided Monte Carlo Tree Search (NG-MCTS) algorithm for
+symbolic regression. We extensively evaluate our method on thousands of
+symbolic regression tasks and desired expressions to show that it significantly
+outperforms baseline algorithms and exhibits discovery of novel expressions
+outside of the training set.
+"
+28525,Nonparametric estimation of the conditional distribution at regression boundary points,"  Nonparametric regression is a standard statistical tool with increased
+importance in the Big Data era. Boundary points pose additional difficulties
+but local polynomial regression can be used to alleviate them. Local linear
+regression, for example, is easy to implement and performs quite well both at
+interior as well as boundary points. Estimating the conditional distribution
+function and/or the quantile function at a given regressor point is immediate
+via standard kernel methods but problems ensue if local linear methods are to
+be used. In particular, the distribution function estimator is not guaranteed
+to be monotone increasing, and the quantile curves can ""cross"". In the paper at
+hand, a simple method of correcting the local linear distribution estimator for
+monotonicity is proposed, and its good performance is demonstrated via
+simulations and real data examples.
+"
+28526,Specht modules labelled by hook bipartitions I,"  Brundan, Kleshchev and Wang equip the Specht modules $S_{\lambda}$ over the
+cyclotomic Khovanov--Lauda--Rouquier algebra $\mathscr{H}_n^{\Lambda}$ with a
+homogeneous $\mathbb{Z}$-graded basis. In this paper we begin the study of
+graded Specht modules labelled by hook bipartitions $((n-m),(1^m))$ in level
+$2$ of $\mathscr{H}_n^{\Lambda}$, which are precisely the Hecke algebras of
+type B, with quantum characteristic at least three. We give an explicit
+description of the action of the Khovanov--Lauda--Rouquier algebra generators
+$\psi_1,\dots,\psi_{n-1}$ on the basis elements of $S_{((n-m),(1^m))}$.
+Introducing certain Specht module homomorphisms, we construct irreducible
+submodules of these Specht modules, and thereby completely determining the
+composition series of Specht modules labelled by hook bipartitions for
+$e\geqslant{3}$.
+"
+28527,On the One-Dimensional Nonlinear Monodomain Equations with Moving Controls,"  In this paper local exact controllability to the trajectories for the
+one-dimensional monodomain equations with the FitzHugh-Nagumo and
+Rogers-McCulloch ionic models using distributed controls with a moving support
+is investigated. In a first step a new Carleman inequality for the adjoint of
+the linearized monodomain equations, under assumptions on the movement of the
+control region, is presented. It leads to null controllability at any positive
+time. Subsequently, a local result concerning the exact controllability to the
+trajectories for the nonlinear monodomain equations is deduced.
+"
+28528,Carbon monoxide in the distantly active Centaur (60558) 174P/Echeclus at 6 AU,"  (60558) 174P/Echeclus is an unusual object that belongs to a class of minor
+planets called Centaurs, which may be intermediate between Kuiper Belt Objects
+and Jupiter Family comets. It is sporadically active throughout its orbit at
+distances too far for water ice to sublimate, the source of activity for most
+comets. Thus, its coma must be triggered by another mechanism. In 2005,
+Echeclus had a strong outburst with peculiar behavior that raised questions
+about the nucleus homogeneity. In order to test nucleus models, we performed
+the most sensitive search to date for the highly volatile CO molecule via its
+J=2-1 emission toward Echeclus during 2016 May-June (at 6.1 astronomical units
+from the Sun) using the Arizona Radio Observatory 10-m Submillimeter Telescope.
+We obtained a 3.6-sigma detection with a slightly blue-shifted (delta v = -0.55
++- 0.1 km/s) and narrow (FWHM = 0.53 +- 0.23 km/s) line. The data are
+consistent with emission from a cold gas from the sunward side of the nucleus,
+as seen in two other comets at 6 AU. We derive a production rate of Q(CO) =
+(7.7 +- 3.3)x10^26 mol/s, which is capable of driving the estimated dust
+production rates. Echeclus CO outgassing rate is ~40 times lower than what is
+typically seen for another Centaur at this distance, 29P/Schwassmann-Wachmann
+1. We also used the IRAM 30-m telescope to search for the CO J=2-1 line, and
+derive an upper limit that is above the SMT detection. Compared to the
+relatively unprocessed comet C/1995 O1 (Hale-Bopp), Echeclus produces
+significantly less CO, as do Chiron and four other Centaurs.
+"
+28529,Nash Equilibrium Approximation under Communication and Computation Constraints in Large-Scale Non-cooperative Games,"  This paper studies the problem of Nash equilibrium approximation in
+large-scale heterogeneous mean-field games under communication and computation
+constraints. A deterministic mean-field game is considered in which the
+non-linear utility function of each agent depends on its action, the average of
+other agents' actions (called the mean variable of that agent) and a
+deterministic parameter. It is shown that the equilibrium mean variables of all
+agents converge uniformly to a constant, called asymptotic equilibrium mean
+(AEM), as the number of agents tends to infinity. The AEM, which depends on the
+limit of empirical distribution of agents' parameters, determines the
+asymptotic equilibrium behavior of agents. Next, the problem of approximating
+the AEM at a processing center under communication and computation constraints
+is studied. Three approximation methods are proposed to substantially reduce
+the communication and computation costs of approximating AEM at the processing
+center. The accuracy of the proposed approximation methods is analyzed and
+illustrated through numerical examples.
+"
+28530,On the $C^*$-algebra generated by the Koopman representation of a topological full group,"  Let $(X,T,\mu)$ be a Cantor minimal sytem and $[[T]]$ the associated
+topological full group. We analyze $C^*_\pi([[T]])$, where $\pi$ is the Koopman
+representation attached to the action of $[[T]]$ on $(X,\mu)$.
+Specifically, we show that $C^*_\pi([[T]])=C^*_\pi([[T]]')$ and that the
+kernel of the character $\tau$ on $C^*_\pi([[T]])$ coming from weak containment
+of the trivial representation is a hereditary $C^*$-subalgebra of
+$C(X)\rtimes\mathbb{Z}$. Consequently, $\ker\tau$ is stably isomorphic to
+$C(X)\rtimes\mathbb{Z}$, and $C^*_\pi([[T]]')$ is not AF.
+We also prove that if $G$ is a finitely generated, elementary amenable group
+and $C^ *(G)$ has real rank zero, then $G$ is finite.
+"
+28531,Quantifying gerrymandering using the vote distribution,"  To assess the presence of gerrymandering, one can consider the shapes of
+districts or the distribution of votes. The ""efficiency gap,"" which does the
+latter, plays a central role in a 2016 federal court case on the
+constitutionality of Wisconsin's state legislative district plan.
+Unfortunately, however, the efficiency gap reduces to proportional
+representation, an expectation that is not a constitutional right. We present a
+new measure of partisan asymmetry that does not rely on the shapes of
+districts, is simple to compute, is provably related to the ""packing and
+cracking"" integral to gerrymandering, and that avoids the constitutionality
+issue presented by the efficiency gap. In addition, we introduce a
+generalization of the efficiency gap that also avoids the equivalency to
+proportional representation. We apply the first function to US congressional
+and state legislative plans from recent decades to identify candidate
+gerrymanders.
+"
+28532,Misspecified Nonconvex Statistical Optimization for Phase Retrieval,"  Existing nonconvex statistical optimization theory and methods crucially rely
+on the correct specification of the underlying ""true"" statistical models. To
+address this issue, we take a first step towards taming model misspecification
+by studying the high-dimensional sparse phase retrieval problem with
+misspecified link functions. In particular, we propose a simple variant of the
+thresholded Wirtinger flow algorithm that, given a proper initialization,
+linearly converges to an estimator with optimal statistical accuracy for a
+broad family of unknown link functions. We further provide extensive numerical
+experiments to support our theoretical findings.
+"
+28533,Zero-temperature limit of quantum weighted Hurwitz numbers,"  The partition function for quantum weighted double Hurwitz numbers can be
+interpreted in terms of the energy distribution of a quantum Bose gas with
+vanishing fugacity. We compute the leading term of the partition function and
+the quantum weighted Hurwitz numbers in the zero temperature limit $T
+\rightarrow 0$, as well as the next order corrections. The leading term is
+shown to reproduce the case of uniformly weighted Hurwitz numbers of Belyi
+curves. In particular, the KP or Toda $\tau$-function serving as generating
+function for the quantum Hurwitz numbers is shown in the limit to give the one
+for Belyi curves and, with suitable scaling, the same holds true for the
+partition function, the weights and the expectations of Hurwitz numbers.
+"
+28534,Joint Elastic Side-Scattering Lidar and Raman Lidar Measurements of Aerosol Optical Properties in South East Colorado,"  We describe an experiment, located in south-east Colorado, USA, that measured
+aerosol optical depth profiles using two Lidar techniques. Two independent
+detectors measured scattered light from a vertical UV laser beam. One detector,
+located at the laser site, measured light via the inelastic Raman
+backscattering process. This is a common method used in atmospheric science for
+measuring aerosol optical depth profiles. The other detector, located
+approximately 40km distant, viewed the laser beam from the side. This detector
+featured a 3.5m2 mirror and measured elastically scattered light in a bistatic
+Lidar configuration following the method used at the Pierre Auger cosmic ray
+observatory. The goal of this experiment was to assess and improve methods to
+measure atmospheric clarity, specifically aerosol optical depth profiles, for
+cosmic ray UV fluorescence detectors that use the atmosphere as a giant
+calorimeter. The experiment collected data from September 2010 to July 2011
+under varying conditions of aerosol loading. We describe the instruments and
+techniques and compare the aerosol optical depth profiles measured by the Raman
+and bistatic Lidar detectors.
+"
+28535,A Complexity Trichotomy for the Six-Vertex Model,"  We prove a complexity classification theorem that divides the six-vertex
+model on graphs into exactly three types. For every setting of the parameters
+of the model, the computation of the partition function is precisely: Either
+(1) solvable in polynomial time for every graph, or (2) #P-hard for general
+graphs but solvable in polynomial time for planar graphs, or (3) #P-hard even
+for planar graphs. The classification has an explicit criterion. In addition to
+matchgates and matchgates-transformable signatures, we discover previously
+unknown families of planar-tractable partition functions by a non-local
+connection to #CSP, defined in terms of a ""loop space"". For the proof of
+#P-hardness, we introduce the use of Möbius transformations as a powerful new
+tool to prove that certain complexity reductions succeed in polynomial time.
+"
+28536,"X-ray photoelectron spectroscopy, Magnetotransport and Magnetization study of Nb2PdS5 superconductor","  In the present report, we investigate various properties of the Nb2PdS5
+superconductor. Scanning electron microscopy displayed slabs like laminar
+growth of Nb2PdS5while X-ray photoelectron spectroscopy exhibited the
+hybridization of Sulphur (2p) with both Palladium (3d)and Niobium (3d). High
+field (140kOe) magneto-transport measurements revealed that superconductivity
+(Tc onset =7K and Tc R = 0 = 6.2K) of the studied Nb2PdS5material is quite
+robust against magnetic field with the upper critical field (Hc2) outside the
+Pauli paramagnetic limit. Thermally activated flux flow (TAFF) of the compound
+showed that resistivity curves follow Arrhenius behaviour. The activation
+energy for Nb2PdS5 is found to decrease from 15.15meV at 10kOe to 2.35meV at
+140kOe. Seemingly, the single vortex pinning is dominant in low field regions,
+while collective pinning is dominant in high field region. The temperature
+dependence of AC susceptibility confirmed the Tc at 6K, further varying
+amplitude and frequency showed well coupled granular nature of
+superconductivity. The lower critical field (Hc1) is extracted from DC
+magnetisation measurements at various T below Tc. It is found that Hc1(T) of
+Nb2PdS5 material seemingly follows the multiband nature of superconductivity.
+"
+28537,Robust Routing in Interdependent Networks,"  We consider a model of two interdependent networks, where every node in one
+network depends on one or more supply nodes in the other network and a node
+fails if it loses all of its supply nodes. We develop algorithms to compute the
+failure probability of a path, and obtain the most reliable path between a pair
+of nodes in a network, under the condition that each supply node fails
+independently with a given probability. Our work generalizes the classical
+shared risk group model, by considering multiple risks associated with a node
+and letting a node fail if all the risks occur. Moreover, we study the diverse
+routing problem by considering two paths between a pair of nodes. We define two
+paths to be $d$-failure resilient if at least one path survives after removing
+$d$ or fewer supply nodes, which generalizes the concept of disjoint paths in a
+single network, and risk-disjoint paths in a classical shared risk group model.
+We compute the probability that both paths fail, and develop algorithms to
+compute the most reliable pair of paths.
+"
+28538,"On rational cuspidal plane curves, and the local cohomology of Jacobian rings","  This note gives the complete projective classification of rational, cuspidal
+plane curves of degree at least 6, and having only weighted homogeneous
+singularities. It also sheds new light on some previous characterizations of
+free and nearly free curves in terms of Tjurina numbers. Finally, we suggest a
+stronger form of Terao's conjecture on the freeness of a line arrangement being
+determined by its combinatorics.
+"
+28539,Human-Robot Mutual Adaptation in Shared Autonomy,"  Shared autonomy integrates user input with robot autonomy in order to control
+a robot and help the user to complete a task. Our work aims to improve the
+performance of such a human-robot team: the robot tries to guide the human
+towards an effective strategy, sometimes against the human's own preference,
+while still retaining his trust. We achieve this through a principled
+human-robot mutual adaptation formalism. We integrate a bounded-memory
+adaptation model of the human into a partially observable stochastic decision
+model, which enables the robot to adapt to an adaptable human. When the human
+is adaptable, the robot guides the human towards a good strategy, maybe unknown
+to the human in advance. When the human is stubborn and not adaptable, the
+robot complies with the human's preference in order to retain their trust. In
+the shared autonomy setting, unlike many other common human-robot collaboration
+settings, only the robot actions can change the physical state of the world,
+and the human and robot goals are not fully observable. We address these
+challenges and show in a human subject experiment that the proposed mutual
+adaptation formalism improves human-robot team performance, while retaining a
+high level of user trust in the robot, compared to the common approach of
+having the robot strictly following participants' preference.
+"
+28540,PrivacyProxy: Leveraging Crowdsourcing and In Situ Traffic Analysis to Detect and Mitigate Information Leakage,"  Many smartphone apps transmit personally identifiable information (PII),
+often without the users knowledge. To address this issue, we present
+PrivacyProxy, a system that monitors outbound network traffic and generates
+app-specific signatures to represent sensitive data being shared. PrivacyProxy
+uses a crowd-based approach to detect likely PII in an adaptive and scalable
+manner by anonymously combining signatures from different users of the same
+app. Furthermore, we do not observe users network traffic and instead rely on
+hashed signatures. We present the design and implementation of PrivacyProxy and
+evaluate it with a lab study, a field deployment, a user survey, and a
+comparison against prior work. Our field study shows PrivacyProxy can
+automatically detect PII with an F1 score of 0.885. PrivacyProxy also achieves
+an F1 score of 0.759 in our controlled experiment for the 500 most popular
+apps. The F1 score also improves to 0.866 with additional training data for 40
+apps that initially had the most false positives. We also show performance
+overhead of using PrivacyProxy is between 8.6% to 14.2%, slightly more than
+using a standard unmodified VPN, and most users report no perceptible impact on
+battery life or the network.
+"
+28541,Axiomatic S^1 Morse-Bott theory,"  In various situations in Floer theory, one extracts homological invariants
+from ""Morse-Bott"" data in which the ""critical set"" is a union of manifolds, and
+the moduli spaces of ""flow lines"" have evaluation maps taking values in the
+critical set. This requires a mix of analytic arguments (establishing
+properties of the moduli spaces and evaluation maps) and formal arguments
+(defining or computing invariants from the analytic data). The goal of this
+paper is to isolate the formal arguments, in the case when the critical set is
+a union of circles. Namely, we state axioms for moduli spaces and evaluation
+maps (encoding a minimal amount of analytical information that one needs to
+verify in any given Floer-theoretic situation), and using these axioms we
+define homological invariants. More precisely, we define a (almost) category of
+""Morse-Bott systems"". We construct a ""cascade homology"" functor on this
+category, based on ideas of Bourgeois and Frauenfelder, which is ""homotopy
+invariant"". This machinery is used in our work on cylindrical contact homology.
+"
+28542,Pressure-induced metallization and superconducting phase in ReS2,"  Among the family of TMDs, ReS2 takes a special position, which crystalizes in
+a unique distorted low-symmetry structure at ambient conditions. The interlayer
+interaction in ReS2 is rather weak, thus its bulk properties are similar to
+that of monolayer. However, how does compression change its structure and
+electronic properties is unknown so far. Here using ab initio crystal structure
+searching techniques, we explore the high-pressure phase transitions of ReS2
+extensively and predict two new high-pressure phases. The ambient pressure
+phase transforms to a ""distorted-1T"" structure at very low pressure and then to
+a tetragonal I41/amd structure at around 90 GPa. The ""distorted-1T"" structure
+undergoes a semiconductor-metal transition (SMT) at around 70 GPa with a band
+overlap mechanism. Electron-phonon calculations suggest that the I41/amd
+structure is superconducting and has a critical superconducting temperature of
+about 2 K at 100 GPa. We further perform high-pressure electrical resistance
+measurements up to 102 GPa. Our experiments confirm the SMT and the
+superconducting phase transition of ReS2 under high pressure. These
+experimental results are in good agreement with our theoretical predictions.
+"
+28543,Statistical Analysis of Hubble/WFC3 Transit Spectroscopy of Extrasolar Planets,"  Transmission spectroscopy provides a window to study exoplanetary
+atmospheres, but that window is fogged by clouds and hazes. Clouds and haze
+introduce a degeneracy between the strength of gaseous absorption features and
+planetary physical parameters such as abundances. One way to break that
+degeneracy is via statistical studies. We collect all published HST/WFC3
+transit spectra for 1.1-1.65 $\mu$m water vapor absorption, and perform a
+statistical study on potential correlations between the water absorption
+feature and planetary parameters. We fit the observed spectra with a template
+calculated for each planet using the Exo-Transmit code. We express the
+magnitude of the water absorption in scale heights, thereby removing the known
+dependence on temperature, surface gravity, and mean molecular weight. We find
+that the absorption in scale heights has a positive baseline correlation with
+planetary equilibrium temperature; our hypothesis is that decreasing cloud
+condensation with increasing temperature is responsible for this baseline
+slope. However, the observed sample is also intrinsically degenerate in the
+sense that equilibrium temperature correlates with planetary mass. We compile
+the distribution of absorption in scale heights, and we find that this
+distribution is closer to log-normal than Gaussian. However, we also find that
+the distribution of equilibrium temperatures for the observed planets is
+similarly log-normal. This indicates that the absorption values are affected by
+observational bias, whereby observers have not yet targeted a sufficient sample
+of the hottest planets.
+"
+28544,Gravitational Waves from Orphan Memory,"  Gravitational-wave memory manifests as a permanent distortion of an idealized
+gravitational-wave detector and arises generically from energetic astrophysical
+events. For example, binary black hole mergers are expected to emit memory
+bursts a little more than an order of magnitude smaller in strain than the
+oscillatory parent waves. We introduce the concept of ""orphan memory"":
+gravitational-wave memory for which there is no detectable parent signal. In
+particular, high-frequency gravitational-wave bursts ($\gtrsim$ kHz) produce
+orphan memory in the LIGO/Virgo band. We show that Advanced LIGO measurements
+can place stringent limits on the existence of high-frequency gravitational
+waves, effectively increasing the LIGO bandwidth by orders of magnitude. We
+investigate the prospects for and implications of future searches for orphan
+memory.
+"
+28545,Solving a Path Planning Problem in a Partially Known Environment using a Swarm Algorithm,"  This paper proposes a path planning strategy for an Autonomous Ground Vehicle
+(AGV) navigating in a partially known environment. Global path planning is
+performed by first using a spatial database of the region to be traversed
+containing selected attributes such as height data and soil information from a
+suitable spatial database. The database is processed using a biomimetic swarm
+algorithm that is inspired by the nest building strategies followed by
+termites. Local path planning is performed online utilizing information
+regarding contingencies that affect the safe navigation of the AGV from various
+sensors. The simulation discussed has been implemented on the open source
+Player-Stage-Gazebo platform.
+"
+28546,Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches,"  As an indispensable component, Batch Normalization (BN) has successfully
+improved the training of deep neural networks (DNNs) with mini-batches, by
+normalizing the distribution of the internal representation for each hidden
+layer. However, the effectiveness of BN would diminish with scenario of
+micro-batch (e.g., less than 10 samples in a mini-batch), since the estimated
+statistics in a mini-batch are not reliable with insufficient samples. In this
+paper, we present a novel normalization method, called Batch Kalman
+Normalization (BKN), for improving and accelerating the training of DNNs,
+particularly under the context of micro-batches. Specifically, unlike the
+existing solutions treating each hidden layer as an isolated system, BKN treats
+all the layers in a network as a whole system, and estimates the statistics of
+a certain layer by considering the distributions of all its preceding layers,
+mimicking the merits of Kalman Filtering. BKN has two appealing properties.
+First, it enables more stable training and faster convergence compared to
+previous works. Second, training DNNs using BKN performs substantially better
+than those using BN and its variants, especially when very small mini-batches
+are presented. On the image classification benchmark of ImageNet, using BKN
+powered networks we improve upon the best-published model-zoo results: reaching
+74.0% top-1 val accuracy for InceptionV2. More importantly, using BKN achieves
+the comparable accuracy with extremely smaller batch size, such as 64 times
+smaller on CIFAR-10/100 and 8 times smaller on ImageNet.
+"
+28547,Witten Genus on String Toric Complete Intersections,"  By using the equivariant localization formula of toric varieties. We prove
+the vanishing of the Witten genus of some string complete intersections in
+smooth toric varieties.
+"
+28548,Flexible Prior Distributions for Deep Generative Models,"  We consider the problem of training generative models with deep neural
+networks as generators, i.e. to map latent codes to data points. Whereas the
+dominant paradigm combines simple priors over codes with complex deterministic
+models, we argue that it might be advantageous to use more flexible code
+distributions. We demonstrate how these distributions can be induced directly
+from the data. The benefits include: more powerful generative models, better
+modeling of latent structure and explicit control of the degree of
+generalization.
+"
+28549,A Perfect Specialization Model for Gravity Equation in Bilateral Trade based on Production Structure,"  Although initially originated as a totally empirical relationship to explain
+the volume of trade between two partners, gravity equation has been the focus
+of several theoretic models that try to explain it. Specialization models are
+of great importance in providing a solid theoretic ground for gravity equation
+in bilateral trade. Some research papers try to improve specialization models
+by adding imperfect specialization to model, but we believe it is unnecessary
+complication. We provide a perfect specialization model based on the phenomenon
+we call tradability, which overcomes the problems with simpler initial. We
+provide empirical evidence using estimates on panel data of bilateral trade of
+40 countries over 10 years that support the theoretical model. The empirical
+results have implied that tradability is the only reason for deviations of data
+from basic perfect specialization models.
+"
+28550,A Homological Theory of Functions,"  In computational complexity, a complexity class is given by a set of problems
+or functions, and a basic challenge is to show separations of complexity
+classes $A \not= B$ especially when $A$ is known to be a subset of $B$. In this
+paper we introduce a homological theory of functions that can be used to
+establish complexity separations, while also providing other interesting
+consequences. We propose to associate a topological space $S_A$ to each class
+of functions $A$, such that, to separate complexity classes $A \subseteq B'$,
+it suffices to observe a change in ""the number of holes"", i.e. homology, in
+$S_A$ as a subclass $B$ of $B'$ is added to $A$. In other words, if the
+homologies of $S_A$ and $S_{A \cup B}$ are different, then $A \not= B'$. We
+develop the underlying theory of functions based on combinatorial and
+homological commutative algebra and Stanley-Reisner theory, and recover Minsky
+and Papert's 1969 result that parity cannot be computed by nonmaximal degree
+polynomial threshold functions. In the process, we derive a ""maximal principle""
+for polynomial threshold functions that is used to extend this result further
+to arbitrary symmetric functions. A surprising coincidence is demonstrated,
+where the maximal dimension of ""holes"" in $S_A$ upper bounds the VC dimension
+of $A$, with equality for common computational cases such as the class of
+polynomial threshold functions or the class of linear functionals in $\mathbb
+F_2$, or common algebraic cases such as when the Stanley-Reisner ring of $S_A$
+is Cohen-Macaulay. As another interesting application of our theory, we prove a
+result that a priori has nothing to do with complexity separation: it
+characterizes when a vector subspace intersects the positive cone, in terms of
+homological conditions. By analogy to Farkas' result doing the same with
+*linear conditions*, we call our theorem the Homological Farkas Lemma.
+"
+28551,DroidBugs: An Android Benchmark for Automated Program Repair,"  Automated Program Repair (APR) is an emerging research field. Many APR
+techniques, for different programming language and platforms, have been
+proposed and evaluated on several Benchmarks. However, for our best knowledge,
+there not exists a well-defined benchmark based on mobile projects,
+consequently, there is a gap to leverage APR methods for mobile development.
+Therefore, regarding the amount of Android Applications around the world, we
+present DroidBugs, an introductory benchmark based on the analyzes of 360 open
+projects for Android, each of them with more than 5,000 downloads. From five
+applications, DroidBugs contains 13 single-bugs classified by the type of test
+that exposed them. By using an APR tool, called Astor4Android, and two common
+Fault Localization strategy, it was observed how challenging is to find and fix
+mobile bugs.
+"
+28552,"Comment on ""Ultrathin Interface Regime of Core-Shell Magnetic Nanoparticles for Effective Magnetism Tailoring""","  There has been renewed interest in the use of magnetic nanoparticles to
+convert high frequency electromagnetic energy into heat. During the last
+decade, numerous examples in the field of catalysis, lightweight thermoplastic
+composites for aeronautical and automotive engineering, and biomedical
+applications as well, have flourished. In a recent Letter ""Ultrathin Interface
+Regime of Core-Shell Magnetic Nanoparticles for Effective Magnetism Tailoring"",
+Moon et al. DOI: 10.1021/acs.nanolett.6b04016 claim to have observed a new
+interface spin canting phenomenon in core-shell ferrrites that pushes heating
+efficiencies above 10,000 W/g. If this were true, it represents new
+opportunities for further tailoring nanoscale agents to be used in drug
+delivery and selective destruction of tumours by hyperthermia. But here I argue
+that these claims are inconsistent with the facts of physics.
+"
+28553,Effective Inference for Generative Neural Parsing,"  Generative neural models have recently achieved state-of-the-art results for
+constituency parsing. However, without a feasible search procedure, their use
+has so far been limited to reranking the output of external parsers in which
+decoding is more tractable. We describe an alternative to the conventional
+action-level beam search used for discriminative neural models that enables us
+to decode directly in these generative models. We then show that by improving
+our basic candidate selection strategy and using a coarse pruning function, we
+can improve accuracy while exploring significantly less of the search space.
+Applied to the model of Choe and Charniak (2016), our inference procedure
+obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior
+state-of-the-art results for single-model systems.
+"
+28554,Finite Mixtures of Multivariate Skew Laplace Distributions,"  In this paper, we propose finite mixtures of multivariate skew Laplace
+distributions to model both skewness and heavy-tailedness in the heterogeneous
+data sets. The maximum likelihood estimators for the parameters of interest are
+obtained by using the EM algorithm. We give a small simulation study and a real
+data example to illustrate the performance of the proposed mixture model.
+"
+28555,Blockchain Based Intelligent Vehicle Data sharing Framework,"  The Intelligent vehicle (IV) is experiencing revolutionary growth in research
+and industry, but it still suffers from many security vulnerabilities.
+Traditional security methods are incapable to provide secure IV data sharing.
+The major issues in IV data sharing are trust, data accuracy and reliability of
+data sharing data in the communication channel. Blockchain technology works for
+the crypto currency, Bit-coin, which is recently used to build trust and
+reliability in peer-to-peer networks having similar topologies as IV Data
+sharing. In this paper, we have proposed Intelligent Vehicle data sharing we
+are proposing a trust environment based Intelligent Vehicle framework. In
+proposed framework, we have use the blockchain technology as backbone of the IV
+data-sharing environment. The blockchain technology is provide the trust
+environment between the vehicles with the based on proof of driving.
+"
+28556,Duality between cooperation and defection in the presence of tit-for-tat in replicator dynamics,"  The prisoner's dilemma describes a conflict between a pair of players, in
+which defection is a dominant strategy whereas cooperation is collectively
+optimal. The iterated version of the dilemma has been extensively studied to
+understand the emergence of cooperation. In the evolutionary context, the
+iterated prisoner's dilemma is often combined with population dynamics, in
+which a more successful strategy replicates itself with a higher growth rate.
+Here, we investigate the replicator dynamics of three representative
+strategies, i.e., unconditional cooperation, unconditional defection, and
+tit-for-tat, which prescribes reciprocal cooperation by mimicking the
+opponent's previous move. Our finding is that the dynamics is self-dual in the
+sense that it remains invariant when we apply time reversal and exchange the
+fractions of unconditional cooperators and defectors in the population. The
+duality implies that the fractions can be equalized by tit-for-tat players,
+although unconditional cooperation is still dominated by defection.
+Furthermore, we find that mutation among the strategies breaks the exact
+duality in such a way that cooperation is more favored than defection, as long
+as the cost-to-benefit ratio of cooperation is small.
+"
+28557,Recent Developments in CMS Fast Simulation,"  CMS has developed a fast detector simulation package, which serves as a fast
+and reliable alternative to the detailed GEANT4-based (full) simulation, and
+enables efficient simulation of large numbers of standard model and new physics
+events. Fast simulation becomes particularly important with the current
+increase in the LHC luminosity. Here, I will discuss the basic principles
+behind the CMS fast simulation framework, and how they are implemented in the
+different detector components in order to simulate and reconstruct sufficiently
+accurate physics objects for analysis. I will focus on recent developments in
+tracking and geometry interface, which improve the flexibility and emulation
+performance of the framework, and allow a better synchronization with the full
+simulation. I will then show how these developments have led to an improved
+agreement of basic analysis objects and event variables between fast and full
+simulation.
+"
+28558,Transition-Based Generation from Abstract Meaning Representations,"  This work addresses the task of generating English sentences from Abstract
+Meaning Representation (AMR) graphs. To cope with this task, we transform each
+input AMR graph into a structure similar to a dependency tree and annotate it
+with syntactic information by applying various predefined actions to it.
+Subsequently, a sentence is obtained from this tree structure by visiting its
+nodes in a specific order. We train maximum entropy models to estimate the
+probability of each individual action and devise an algorithm that efficiently
+approximates the best sequence of actions to be applied. Using a substandard
+language model, our generator achieves a Bleu score of 27.4 on the LDC2014T12
+test set, the best result reported so far without using silver standard
+annotations from another corpus as additional training data.
+"
+28559,Some Gruss type Inequalities using Pompeiu's mean value theorem on Conformable Fractional Calculus,"  The main objective of this paper is to obtain some Gruss Like Inequalities
+using Pompeiu's mean value theorem on Conformable Fractional Calculus.
+"
+28560,Sonification in security operations centres: what do security practitioners think?,"  In Security Operations Centres (SOCs) security practitioners work using a
+range of tools to detect and mitigate malicious computer-network activity.
+Sonification, in which data is represented as sound, is said to have potential
+as an approach to addressing some of the unique challenges faced by SOCs. For
+example, sonification has been shown to enable peripheral monitoring of
+processes, which could aid practitioners multitasking in busy SOCs. The
+perspectives of security practitioners on incorporating sonification into their
+actual working environments have not yet been examined, however. The aim of
+this paper therefore is to address this gap by exploring attitudes to using
+sonification in SOCs. We report on the results of a study consisting of an
+online survey (N=20) and interviews (N=21) with security practitioners working
+in a range of different SOCs. Our contribution is a refined appreciation of the
+contexts in which sonification could aid in SOC working practice, and an
+understanding of the areas in which sonification may not be beneficial or may
+even be problematic.We also analyse the critical requirements for the design of
+sonification systems and their integration into the SOC setting. Our findings
+clarify insights into the potential benefits and challenges of introducing
+sonification to support work in this vital security-monitoring environment.
+"
+28561,AutoEncoder Inspired Unsupervised Feature Selection,"  High-dimensional data in many areas such as computer vision and machine
+learning tasks brings in computational and analytical difficulty. Feature
+selection which selects a subset from observed features is a widely used
+approach for improving performance and effectiveness of machine learning models
+with high-dimensional data. In this paper, we propose a novel AutoEncoder
+Feature Selector (AEFS) for unsupervised feature selection which combines
+autoencoder regression and group lasso tasks. Compared to traditional feature
+selection methods, AEFS can select the most important features by excavating
+both linear and nonlinear information among features, which is more flexible
+than the conventional self-representation method for unsupervised feature
+selection with only linear assumptions. Experimental results on benchmark
+dataset show that the proposed method is superior to the state-of-the-art
+method.
+"
+28562,Zeeman-limited Superconductivity in Crystalline Al Films,"  We report the evolution of the Zeeman-mediated superconducting phase diagram
+(PD) in ultra-thin crystalline Al films. Parallel critical field measurements,
+down to 50 mK, were made across the superconducting tricritical point of films
+ranging in thickness from 7 ML to 30 ML. The resulting phase boundaries were
+compared with the quasi-classical theory of a Zeeman-mediated transition
+between a homogeneous BCS condensate and a spin polarized Fermi liquid. Films
+thicker than $\sim$20 ML showed good agreement with theory, but thinner films
+exhibited an anomalous PD that cannot be reconciled within a homogeneous BCS
+framework.
+"
+28563,"Measuring Light Pollution with Fisheye Lens Imagery from A Moving Boat, A Proof of Concept","  Near all-sky imaging photometry was performed from a boat on the Gulf of
+Aqaba to measure the night sky brightness in a coastal environment. The boat
+was not anchored, and therefore drifted and rocked. The camera was mounted on a
+tripod without any inertia/motion stabilization. A commercial digital single
+lens reflex (DSLR) camera and fisheye lens were used with ISO setting of 6400,
+with the exposure time varied between 0.5 s and 5 s. We find that despite
+movement of the vessel the measurements produce quantitatively comparable
+results apart from saturation effects. We discuss the potential and limitations
+of this method for mapping light pollution in marine and freshwater systems.
+This work represents the proof of concept that all-sky photometry with a
+commercial DSLR camera is a viable tool to determine light pollution in an
+ecological context from a moving boat.
+"
+28564,Quantile contours and allometric modelling with an application to anthropometric charts in preterm infants,"  We develop an approach to risk classification based on quantile contours and
+allometric modelling of multivariate anthropometric measurements. We propose
+the definition of allometric direction tangent to the directional quantile
+envelope, which divides ratios of measurements into half-spaces. This in turn
+provides an operational definition of directional quantile that can be used as
+cutoff for risk assessment. Throughout the paper, we show the application of
+the proposed approach using a large dataset from the Vermont Oxford Network
+containing observations of birthweight and head circumference for more than
+150,000 preterm infants.
+"
+28565,Data-Driven Planning of Plug-in Hybrid Electric Taxi Charging Stations in Urban Environments: A Case in the Central Area of Beijing,"  Plug-in electric vehicles (PEVs) can contribute to energy and environmental
+challenges. Among different types of PEVs, plug-in hybrid electric taxis
+(PHETs) go in advance. In this study, we provide a spatial and temporal PHET
+charging demand forecasting method based on one-month global positioning system
+(GPS)-based taxi travel data in Beijing. Then, using the charging demand
+forecasting results, a mixed integer linear programming (MILP) model is
+formulated to plan PHET charging stations in the central area of Beijing. The
+model minimizes both investment and operation costs of all the PHET charging
+stations and takes into account the service radius of charging stations,
+charging demand satisfaction and rational occupation rates of chargers. At
+last, the test of the planning method is carried out numerically through
+simulations and the analysis is complemented according to the results.
+"
+28566,Reduced Basis Kriging for Big Spatial Fields,"  In spatial statistics, a common method for prediction over a Gaussian random
+field (GRF) is maximum likelihood estimation combined with kriging. For massive
+data sets, kriging is computationally intensive, both in terms of CPU time and
+memory, and so fixed rank kriging has been proposed as a solution. The method
+however still involves operations on large matrices, so we develop an
+alteration to this method by utilizing the approximations made in fixed rank
+kriging combined with restricted maximum likelihood estimation and sparse
+matrix methodology. Experiments show that our methodology can provide
+additional gains in computational efficiency over fixed-rank kriging without
+loss of accuracy in prediction. The methodology is applied to climate data
+archived by the United States National Climate Data Center, with very good
+results.
+"
+28567,Isotopies vis-à-vis level-preserving embeddings,"  Various standard texts on differential topology maintain that the
+level-preserving map defined by the track of an isotopy of embeddings is itself
+an embedding. This note describes a simple counterexample to this assertion.
+"
+28568,Convex Geometry of the Generalized Matrix-Fractional Function,"  Generalized matrix-fractional (GMF) functions are a class of matrix support
+functions introduced by Burke and Hoheisel as a tool for unifying a range of
+seemingly divergent matrix optimization problems associated with inverse
+problems, regularization and learning. In this paper we dramatically simplify
+the support function representation for GMF functions as well as the
+representation of their subdifferentials. These new representations allow the
+ready computation of a range of important related geometric objects whose
+formulations were previously unavailable.
+"
+28569,A unifying model for random matrix theory in arbitrary space dimensions,"  A sparse random block matrix model suggested by the Hessian matrix used in
+the study of elastic vibrational modes of amorphous solids is presented and
+analyzed. By evaluating some moments, benchmarked against numerics, differences
+in the eigenvalue spectrum of this model in different limits of space dimension
+$d$, and for arbitrary values of the lattice coordination number $Z$, are shown
+and discussed. As a function of these two parameters (and their ratio $Z/d$),
+the most studied models in random matrix theory (Erdos-Renyi graphs, effective
+medium, replicas) can be reproduced in the various limits of block
+dimensionality $d$. Remarkably, the Marchenko-Pastur spectral density (which is
+recovered by replica calculations for the Laplacian matrix) is reproduced
+exactly in the limit of infinite size of the blocks, or $d\rightarrow\infty$,
+which for the first time clarifies the physical meaning of space dimension in
+these models. The approximate results for $d=3$ provided by our method have
+many potential applications in the future, from the vibrational spectrum of
+glasses and elastic networks, to wave-localization, disordered conductors,
+random resistor networks and random walks.
+"
+28570,A Linear Algebra Formulation for Boolean Satisfiability Testing,"  In the article \The State of SAT"", the authors asked whether a procedure
+dramatically different from DPLL can be found for handling unsatisfiable
+instances. This study proposes a new linear programming approach to address
+this issue efficiently. Our experiments showed that the new method works for
+many unsatisfiable instances. However, we must concede that this method should
+be incomplete; otherwise, it will imply P=co-NP.
+"
+28571,Mechanical Rainbow Trapping and Bloch Oscillations in Structured Elastic Beams,"  We demonstrate, both experimentally and numerically, the mechanical rainbow
+trapping effect and the mechanical Bloch oscillations for torsional waves
+propagating in chirped mechanical beams. After extensive simulations, three
+quasi-one-dimensional chirped structures were designed, constructed and
+experimentally characterized by Doppler spectroscopy. When the chirp intensity
+vanishes a perfect periodic system, with bands and gaps, is obtained. The
+mechanical rainbow trapping effect occurs for small values of the chirp
+intensity. The wave packet traveling along the beam is progressively slowing
+down and is reflected back at a certain depth, which depends on its central
+frequency. In this case a new kind of oscillation, here named ""{\em rectified
+rainbow-Bloch oscillation}"", appears since the wave packet is reflected at one
+side by the interface between the structure and the uniform rod and by the
+minigap at the opposite side. For larger values of the chirp parameter the
+rainbow trapping yields the penetration length where the mechanical Bloch
+oscillations emerge. Numerical simulations based on the transfer matrix method
+are in agreement with experimental data.
+"
+28572,SNS: A Solution-based Nonlinear Subspace method for time-dependent nonlinear model order reduction,"  Several reduced order models have been successfully developed for nonlinear
+dynamical systems. To achieve a considerable speedup, a hyper-reduction step is
+needed to reduce the computational complexity due to nonlinear terms. Many
+hyper-reduction techniques require the construction of nonlinear term basis,
+which introduces a computationally expensive offline phase. A novel way of
+constructing nonlinear term basis within the hyper-reduction process is
+introduced. In contrast to the traditional hyper-reduction techniques where the
+collection of nonlinear term snapshots is required, the SNS method completely
+avoids the use of the nonlinear term snapshots. Instead, it uses the solution
+snapshots that are used for building a solution basis. Furthermore, it avoids
+an extra data compression of nonlinear term snapshots. As a result, the SNS
+method provides a more efficient offline strategy than the traditional model
+order reduction techniques, such as the DEIM, GNAT, and ST-GNAT methods. The
+SNS method is justified by the conforming subspace condition and the subspace
+inclusion relation. Numerical results support that the accuracy of the solution
+from the SNS method is comparable to the traditional methods.
+"
+28573,MDS-Coded Distributed Caching for Low Delay Wireless Content Delivery,"  We investigate the use of maximum distance separable (MDS) codes to cache
+popular content to reduce the download delay of wireless content delivery. In
+particular, we consider a cellular system where devices roam in an out of a
+cell according to a Poisson random process. Popular content is cached in a
+limited number of the mobile devices using an MDS code and can be downloaded
+from the mobile devices using device-to-device communication. We derive an
+analytical expression for the delay incurred in downloading content from the
+wireless network and show that distributed caching using MDS codes can
+dramatically reduce the download delay with respect to the scenario where
+content is always downloaded from the base station and to the case of uncoded
+distributed caching.
+"
+28574,A Line-Point Unified Solution to Relative Camera Pose Estimation,"  In this work we present a unified method of relative camera pose estimation
+from points and lines correspondences. Given a set of 2D points and lines
+correspondences in three views, of which two are known, a method has been
+developed for estimating the camera pose of the third view. Novelty of this
+algorithm is to combine both points and lines correspondences in the camera
+pose estimation which enables us to compute relative camera pose with a small
+number of feature correspondences. Our central idea is to exploit the
+tri-linear relationship between three views and generate a set of linear
+equations from the points and lines correspondences in the three views. The
+desired solution to the system of equations are expressed as a linear
+combination of the singular vectors and the coefficients are computed by
+solving a small set of quadratic equations generated by imposing orthonormality
+constraints for general camera motion. The advantages of the proposed method
+are demonstrated by experimenting on publicly available data set. Results show
+the robustness and efficiency of the method in relative camera pose estimation
+for both small and large camera motion with a small set of points and line
+features.
+"
+28575,Prepaid or Postpaid? That is the question. Novel Methods of Subscription Type Prediction in Mobile Phone Services,"  In this paper we investigate the behavioural differences between mobile phone
+customers with prepaid and postpaid subscriptions. Our study reveals that (a)
+postpaid customers are more active in terms of service usage and (b) there are
+strong structural correlations in the mobile phone call network as connections
+between customers of the same subscription type are much more frequent than
+those between customers of different subscription types. Based on these
+observations we provide methods to detect the subscription type of customers by
+using information about their personal call statistics, and also their
+egocentric networks simultaneously. The key of our first approach is to cast
+this classification problem as a problem of graph labelling, which can be
+solved by max-flow min-cut algorithms. Our experiments show that, by using both
+user attributes and relationships, the proposed graph labelling approach is
+able to achieve a classification accuracy of $\sim 87\%$, which outperforms by
+$\sim 7\%$ supervised learning methods using only user attributes. In our
+second problem we aim to infer the subscription type of customers of external
+operators. We propose via approximate methods to solve this problem by using
+node attributes, and a two-ways indirect inference method based on observed
+homophilic structural correlations. Our results have straightforward
+applications in behavioural prediction and personal marketing.
+"
+28576,Supra-galactic Colour Patterns in Globular Cluster Systems,"  An analysis of globular cluster systems associated with galaxies included in
+the Virgo and Fornax HST Advanced Camera Surveys reveals distinct (g-z) colour
+modulation patterns. These features appear on composite samples of globular
+clusters and, most evidently, in galaxies with absolute magnitudes Mg in the
+range from -20.2 to -19.2. These colour modulations are also detectable on some
+samples of globular clusters in the central galaxies NGC 1399 and NGC 4486 (and
+confirmed on data sets obtained with different instruments and photometric
+systems), as well as in other bright galaxies in these clusters. After
+discarding field contamination, photometric errors and statistical effects, we
+conclude that these supra-galactic colour patterns are real and reflect some
+previously unknown characteristic. These features suggest that the globular
+cluster formation process was not entirely stochastic but included a fraction
+of clusters that formed in a rather synchronized fashion over large spatial
+scales, and in a tentative time lapse of about 1.5 Gy at redshifts z between 2
+and 4. We speculate that the putative mechanism leading to that synchronism may
+be associated with large scale feedback effects connected with violent star
+forming events and/or with super massive black holes.
+"
+28577,Improved performance of semiconductor laser tracking frequency gauge,"  We describe new results from the semiconductor-laser tracking frequency
+gauge, an instrument that can perform sub-picometer distance measurements and
+has applications in gravity research and in space-based astronomical
+instruments proposed for the study of light from extrasolar planets. Compared
+with previous results, we have improved incremental distance accuracy by a
+factor of two, to 0.9 pm in 80 s averaging time, and absolute distance accuracy
+by a factor of 20, to 0.17 $\mu$m in 1000 s. After an interruption of operation
+of a tracking frequency gauge used to control a distance, it is now possible,
+using a nonresonant measurement interferometer, to restore the distance to
+picometer accuracy by combining absolute and incremental distance measurements.
+"
+28578,Fractional Sobolev metrics on spaces of immersed curves,"  Motivated by applications in the field of shape analysis, we study
+reparametrization invariant, fractional order Sobolev-type metrics on the space
+of smooth regular curves $\operatorname{Imm}(S^1,\mathbb{R}^d)$ and on its
+Sobolev completions $\mathcal{I}^{q}(S^1,\mathbb{R}^{d})$. We prove local
+well-posedness of the geodesic equations both on the Banach manifold
+$\mathcal{I}^{q}(S^1,\mathbb{R}^{d})$ and on the Fréchet-manifold
+$\operatorname{Imm}(S^1,\mathbb{R}^d)$ provided the order of the metric is
+greater or equal to one. In addition we show that the $H^s$-metric induces a
+strong Riemannian metric on the Banach manifold
+$\mathcal{I}^{s}(S^1,\mathbb{R}^{d})$ of the same order $s$, provided $s>\frac
+32$. These investigations can be also interpreted as a generalization of the
+analysis for right invariant metrics on the diffeomorphism group.
+"
+28579,Programmable Agents,"  We build deep RL agents that execute declarative programs expressed in formal
+language. The agents learn to ground the terms in this language in their
+environment, and can generalize their behavior at test time to execute new
+programs that refer to objects that were not referenced during training. The
+agents develop disentangled interpretable representations that allow them to
+generalize to a wide variety of zero-shot semantic tasks.
+"
+28580,DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning,"  As an emerging field, Automated Machine Learning (AutoML) aims to reduce or
+eliminate manual operations that require expertise in machine learning. In this
+paper, a graph-based architecture is employed to represent flexible
+combinations of ML models, which provides a large searching space compared to
+tree-based and stacking-based architectures. Based on this, an evolutionary
+algorithm is proposed to search for the best architecture, where the mutation
+and heredity operators are the key for architecture evolution. With Bayesian
+hyper-parameter optimization, the proposed approach can automate the workflow
+of machine learning. On the PMLB dataset, the proposed approach shows the
+state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn.
+Some of the optimized models are with complex structures which are difficult to
+obtain in manual design.
+"
+28581,TROVE Feature Detection for Online Pose Recovery by Binocular Cameras,"  This paper proposes a new and efficient method to estimate 6-DoF ego-states:
+attitudes and positions in real time. The proposed method extract information
+of ego-states by observing a feature called ""TROVE"" (Three Rays and One
+VErtex). TROVE features are projected from structures that are ubiquitous on
+man-made constructions and objects. The proposed method does not search for
+conventional corner-type features nor use Perspective-n-Point (PnP) methods,
+and it achieves a real-time estimation of attitudes and positions up to 60 Hz.
+The accuracy of attitude estimates can reach 0.3 degrees and that of position
+estimates can reach 2 cm in an indoor environment. The result shows a promising
+approach for unmanned robots to localize in an environment that is rich in
+man-made structures.
+"
+28582,CO Spectral Line Energy Distributions in Galactic Sources: Empirical Interpretation of Extragalactic Observations,"  The relative populations in rotational transitions of CO can be useful for
+inferring gas conditions and excitation mechanisms at work in the interstellar
+medium. We present CO emission lines from rotational transitions observed with
+Herschel/HIFI in the star-forming cores Orion S, Orion KL, Sgr B2(M), and W49N.
+Integrated line fluxes from these observations are combined with those from
+Herschel/PACS observations of the same sources to construct CO spectral line
+energy distributions (SLEDs) from $5\leq J_u\leq48$. These CO SLEDs are
+compared to those reported in other galaxies, with the intention of empirically
+determining which mechanisms dominate excitation in such systems. We find that
+CO SLEDs in Galactic star-forming cores cannot be used to reproduce those
+observed in other galaxies, although the discrepancies arise primarily as a
+result of beam filling factors. The much larger regions sampled by the Herschel
+beams at distances of several Mpc contain significant amounts of cooler gas
+which dominate the extragalactic CO SLEDs, in contrast to observations of
+Galactic star-forming regions which are focused specifically on cores
+containing primarily hot molecular gas.
+"
+28583,Wireless Interference Identification with Convolutional Neural Networks,"  The steadily growing use of license-free frequency bands requires reliable
+coexistence management for deterministic medium utilization. For interference
+mitigation, proper wireless interference identification (WII) is essential. In
+this work we propose the first WII approach based upon deep convolutional
+neural networks (CNNs). The CNN naively learns its features through
+self-optimization during an extensive data-driven GPU-based training process.
+We propose a CNN example which is based upon sensing snapshots with a limited
+duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs
+between 15 classes. They represent packet transmissions of IEEE 802.11 b/g,
+IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the
+2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII
+approaches and has a classification accuracy greater than 95% for
+signal-to-noise ratio of at least -5 dB.
+"
+28584,IVE-GAN: Invariant Encoding Generative Adversarial Networks,"  Generative adversarial networks (GANs) are a powerful framework for
+generative tasks. However, they are difficult to train and tend to miss modes
+of the true data generation process. Although GANs can learn a rich
+representation of the covered modes of the data in their latent space, the
+framework misses an inverse mapping from data to this latent space. We propose
+Invariant Encoding Generative Adversarial Networks (IVE-GANs), a novel GAN
+framework that introduces such a mapping for individual samples from the data
+by utilizing features in the data which are invariant to certain
+transformations. Since the model maps individual samples to the latent space,
+it naturally encourages the generator to cover all modes. We demonstrate the
+effectiveness of our approach in terms of generative performance and learning
+rich representations on several datasets including common benchmark image
+generation tasks.
+"
+28585,An Attention Mechanism for Answer Selection Using a Combined Global and Local View,"  We propose a new attention mechanism for neural based question answering,
+which depends on varying granularities of the input. Previous work focused on
+augmenting recurrent neural networks with simple attention mechanisms which are
+a function of the similarity between a question embedding and an answer
+embeddings across time. We extend this by making the attention mechanism
+dependent on a global embedding of the answer attained using a separate
+network.
+We evaluate our system on InsuranceQA, a large question answering dataset.
+Our model outperforms current state-of-the-art results on InsuranceQA. Further,
+we visualize which sections of text our attention mechanism focuses on, and
+explore its performance across different parameter settings.
+"
+28586,Two-stage electron acceleration by 3D collisionless guide field magnetic reconnection,"  We report a newly found two-stage mechanism of electron acceleration near
+X-lines of 3D collisionless guide-field magnetic reconnection in the
+non-relativistic regime typical, e.g., for stellar coronae. We found that after
+electrons are first pre-accelerated during the linear growth of reconnection,
+they become additionally accelerated in the course of the nonlinear stage of 3D
+guide-field magnetic reconnection. This additional acceleration is due to the
+filamentation of electric and magnetic fields caused by streaming
+instabilities. In addition to enhanced parallel electric fields, the
+filamentation leads to additional curvature-driven electron acceleration in the
+guide-field direction. As a result, part of the the accelerated electron
+spectra becomes a power law with a spectral index of $\sim-1.6$ near the
+X-line. This second stage of acceleration due to nonlinear reconnection is
+relevant for the production of energetic electrons in, e.g., thin current
+sheets of stellar coronae.
+"
+28587,License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks,"  This work details Sighthounds fully automated license plate detection and
+recognition system. The core technology of the system is built using a sequence
+of deep Convolutional Neural Networks (CNNs) interlaced with accurate and
+efficient algorithms. The CNNs are trained and fine-tuned so that they are
+robust under different conditions (e.g. variations in pose, lighting,
+occlusion, etc.) and can work across a variety of license plate templates (e.g.
+sizes, backgrounds, fonts, etc). For quantitative analysis, we show that our
+system outperforms the leading license plate detection and recognition
+technology i.e. ALPR on several benchmarks. Our system is available to
+developers through the Sighthound Cloud API at
+this https URL
+"
+28588,Interaction Between Singularity Theory and the Minimal Model Program,"  We survey some recent topics on singularities, with a focus on their
+connection to the minimal model program. This includes the construction and
+properties of dual complexes, the proof of the ACC conjecture for log canonical
+thresholds and the recent progress on the `local stability theory' of an
+arbitrary Kawamata log terminal singularity.
+"
+28589,"Robustly Learning a Gaussian: Getting Optimal Error, Efficiently","  We study the fundamental problem of learning the parameters of a
+high-dimensional Gaussian in the presence of noise -- where an
+$\varepsilon$-fraction of our samples were chosen by an adversary. We give
+robust estimators that achieve estimation error $O(\varepsilon)$ in the total
+variation distance, which is optimal up to a universal constant that is
+independent of the dimension.
+In the case where just the mean is unknown, our robustness guarantee is
+optimal up to a factor of $\sqrt{2}$ and the running time is polynomial in $d$
+and $1/\epsilon$. When both the mean and covariance are unknown, the running
+time is polynomial in $d$ and quasipolynomial in $1/\varepsilon$. Moreover all
+of our algorithms require only a polynomial number of samples. Our work shows
+that the same sorts of error guarantees that were established over fifty years
+ago in the one-dimensional setting can also be achieved by efficient algorithms
+in high-dimensional settings.
+"
+28590,Hard and Easy Instances of L-Tromino Tilings,"  In this work we study tilings of regions in the square lattice with L-shaped
+trominoes. Deciding the existence of a tiling with L-trominoes for an arbitrary
+region in general is NP-complete, nonetheless, we identify restrictions to the
+problem where it either remains NP-complete or has a polynomial time algorithm.
+First, we characterize the possibility of when an Aztec rectangle has an
+L-tromino tiling, and hence also an Aztec diamond; if an Aztec rectangle has an
+unknown number of defects or holes, however, the problem of deciding a tiling
+is NP-complete. Then, we study tilings of arbitrary regions where only
+$180^\circ$ rotations of L-trominoes are available. For this particular case we
+show that deciding the existence of a tiling remains NP-complete; yet, if a
+region contains certain so-called `forbidden polyominoes' as subregions, then
+there exists a polynomial time algorithm for deciding a tiling.
+"
+28591,FO and MSO approach to Some Graph Problems: Approximation and Poly time Results,"  The focus of this paper is two fold. Firstly, we present a logical approach
+to graph modification problems such as minimum node deletion, edge deletion,
+edge augmentation problems by expressing them as an expression in first order
+(FO) logic. As a consequence, it follows that these problems have constant
+factor polynomial-time approximation algorithms. In particular, node
+deletion/edge deletion on a graph $G$ whose resultant is cograph, split,
+threshold, comparable, interval and permutation are $O(1)$ approximable.
+Secondly, we present a monadic second order (MSO) logic to minimum graph
+modification problems, minimum dominating set problem and minimum coloring
+problem and their variants. As a consequence, it follows that these problems
+have linear-time algorithms on bounded tree-width graphs. In particular, we
+show the existance of linear-time algorithms on bounded tree-width graphs for
+star coloring, cd-coloring, rainbow coloring, equitable coloring, total
+dominating set, connected dominating set. In a nut shell, this paper presents a
+unified framework and an algorithmic scheme through logical expressions for
+some graph problems through FO and MSO.
+"
+28592,Asymptotic topology of random subcomplexes in a finite simplicial complex,"  We consider a finite simplicial complex $K$ together with its successive
+barycentric subdivisions $Sd^d(K), d\geq0,$ and study the expected topology of
+a random subcomplex in $Sd^d(K), d\gg0$. We get asymptotic upper and lower
+bounds for the expected Betti numbers of those subcomplexes, together with the
+average Morse inequalities and expected Euler characteristic.
+"
+28593,New results on the order of functions at infinity,"  Recently, new classes of positive and measurable functions,
+$\mathcal{M}(\rho)$ and $\mathcal{M}(\pm \infty)$, have been defined in terms
+of their asymptotic behaviour at infinity, when normalized by a logarithm
+(Cadena et al., 2015, 2016, 2017). Looking for other suitable normalizing
+functions than logarithm seems quite natural. It is what is developed in this
+paper, studying new classes of functions of the type $\displaystyle
+\lim_{x\rightarrow \infty}\log U(x)/H(x)=\rho <\infty$ for a large class of
+normalizing functions $H$. It provides subclasses of $\mathcal{M}(0)$ and
+$\mathcal{M}(\pm\infty)$.
+"
+28594,Direct evidence for Ly$α$ depletion in the protocluster core,"  We have carried out a panoramic Ly$\alpha$ narrowband imaging with
+Suprime-Cam on Subaru towards the known protocluster USS1558--003 at $z=2.53$.
+Our previous narrowband imaging at near-infrared has identified multiple dense
+groups of H$\alpha$ emitters (HAEs) within the protocluster. We have now
+identified the large-scale structures across a $\sim$50 comoving Mpc scale
+traced by Ly$\alpha$ emitters (LAEs) in which the protocluster traced by the
+HAEs is embedded. On a smaller scale, however, there are remarkably few LAEs in
+the regions of HAE overdensities. Moreover, the stacking analyses of the images
+show that HAEs in higher-density regions show systematically lower escape
+fractions of Ly$\alpha$ photons than those of HAEs in lower-density regions.
+These phenomena may be driven by the extra depletion of Ly$\alpha$ emission
+lines along our line of sight by more intervening cold
+circumgalactic/intergalactic medium and/or dust existing in the dense core. We
+also caution that all the past high-$z$ protocluster surveys using LAEs as the
+tracers would have largely missed galaxies in the very dense cores of the
+protoclusters where we would expect to see any early environmental effects.
+"
+28595,Finite generation of the numerical Grothendieck group,"  Let k be a finite base field. In this note, making use of topological
+periodic cyclic homology and of the theory of noncommutative motives, we prove
+that the numerical Grothendieck group of every smooth proper dg k-linear
+category is a finitely generated free abelian group. Along the way, we prove
+moreover that the category of noncommutative numerical motives over k is
+abelian semi-simple, as conjectured by Kontsevich. Furthermore, we show that
+the zeta functions of endomorphisms of noncommutative Chow motives are rational
+and satisfy a functional equation.
+"
+28596,Number-conserving cellular automata with a von Neumann neighborhood of range one,"  We present necessary and sufficient conditions for a cellular automaton with
+a von Neumann neighborhood of range one to be number-conserving. The conditions
+are formulated for any dimension and for any set of states containing zero. The
+use of the geometric structure of the von Neumann neighborhood allows for
+computationally tractable conditions even in higher dimensions.
+"
+28597,Lattice Boltzmann modeling and simulation of liquid jet breakup,"  A three-dimensional color-fluid lattice Boltzmann model for immiscible
+two-phase flows is developed in the framework of a three-dimensional
+27-velocity (D3Q27) lattice. The collision operator comprises the D3Q27
+versions of three sub-operators: a multiple-relaxation-time (MRT) collision
+operator, a generalized Liu--Valocchi--Kang perturbation operator, and a
+Latva-Kokko--Rothman recoloring operator. A D3Q27 version of an enhanced
+equilibrium distribution function is also incorporated into this model to
+improve the Galilean invariance. Three types of numerical tests, namely, a
+static droplet, an oscillating droplet, and the Rayleigh--Taylor instability,
+show a good agreement with analytical solutions and numerical simulations.
+Following these numerical tests, this model is applied to liquid-jet-breakup
+simulations. The simulation conditions are matched to the conditions of the
+previous experiments. In this case, numerical stability is maintained
+throughout the simulation, although the kinematic viscosity for the continuous
+phase is set as low as $1.8\times10^{-4}$, in which case the corresponding
+Reynolds number is $3.4\times10^{3}$; the developed lattice Boltzmann model
+based on the D3Q27 lattice enables us to perform the simulation with parameters
+directly matched to the experiments. The jet's liquid column transitions from
+an asymmetrical to an axisymmetrical shape, and entrainment occurs from the
+side of the jet. The measured time history of the jet's leading-edge position
+shows a good agreement with the experiments. Finally, the reproducibility of
+the regime map for liquid-liquid systems is assessed. The present lattice
+Boltzmann simulations well reproduce the characteristics of predicted regimes,
+including varicose breakup, sinuous breakup, and atomization.
+"
+28598,A Stability Index for Traveling Waves in Activator-Inhibitor Systems,"  We consider the stability of nonlinear traveling waves in a class of
+activator-inhibitor systems. The eigenvalue equation arising from linearizing
+about the wave is seen to preserve the manifold of Lagrangian planes for a
+nonstandard symplectic form. This allows us to define a Maslov index for the
+wave corresponding to the spatial evolution of the unstable bundle. We
+formulate the Evans function for the eigenvalue problem and show that the
+parity of the Maslov index determines the sign of the derivative of the Evans
+function at the origin. The connection between the Evans function and the
+Maslov index is established by a ""detection form,"" which identifies conjugate
+points for the curve of Lagrangian planes.
+"
+28599,A Brief Survey of Deep Reinforcement Learning,"  Deep reinforcement learning is poised to revolutionise the field of AI and
+represents a step towards building autonomous systems with a higher level
+understanding of the visual world. Currently, deep learning is enabling
+reinforcement learning to scale to problems that were previously intractable,
+such as learning to play video games directly from pixels. Deep reinforcement
+learning algorithms are also applied to robotics, allowing control policies for
+robots to be learned directly from camera inputs in the real world. In this
+survey, we begin with an introduction to the general field of reinforcement
+learning, then progress to the main streams of value-based and policy-based
+methods. Our survey will cover central algorithms in deep reinforcement
+learning, including the deep $Q$-network, trust region policy optimisation, and
+asynchronous advantage actor-critic. In parallel, we highlight the unique
+advantages of deep neural networks, focusing on visual understanding via
+reinforcement learning. To conclude, we describe several current areas of
+research within the field.
+"
+28600,Hierarchical Change Point Detection on Dynamic Networks,"  This paper studies change point detection on networks with community
+structures. It proposes a framework that can detect both local and global
+changes in networks efficiently. Importantly, it can clearly distinguish the
+two types of changes. The framework design is generic and as such several
+state-of-the-art change point detection algorithms can fit in this design.
+Experiments on both synthetic and real-world networks show that this framework
+can accurately detect changes while achieving up to 800X speedup.
+"
+28601,Which groups are amenable to proving exponent two for matrix multiplication?,"  The Cohn-Umans group-theoretic approach to matrix multiplication suggests
+embedding matrix multiplication into group algebra multiplication, and bounding
+$\omega$ in terms of the representation theory of the host group. This
+framework is general enough to capture the best known upper bounds on $\omega$
+and is conjectured to be powerful enough to prove $\omega = 2$, although
+finding a suitable group and constructing such an embedding has remained
+elusive. Recently it was shown, by a generalization of the proof of the Cap Set
+Conjecture, that abelian groups of bounded exponent cannot prove $\omega = 2$
+in this framework, which ruled out a family of potential constructions in the
+literature.
+In this paper we study nonabelian groups as potential hosts for an embedding.
+We prove two main results:
+(1) We show that a large class of nonabelian groups---nilpotent groups of
+bounded exponent satisfying a mild additional condition---cannot prove $\omega
+= 2$ in this framework. We do this by showing that the shrinkage rate of powers
+of the augmentation ideal is similar to the shrinkage rate of the number of
+functions over $(\mathbb{Z}/p\mathbb{Z})^n$ that are degree $d$ polynomials;
+our proof technique can be seen as a generalization of the polynomial method
+used to resolve the Cap Set Conjecture.
+(2) We show that symmetric groups $S_n$ cannot prove nontrivial bounds on
+$\omega$ when the embedding is via three Young subgroups---subgroups of the
+form $S_{k_1} \times S_{k_2} \times \dotsb \times S_{k_\ell}$---which is a
+natural strategy that includes all known constructions in $S_n$.
+By developing techniques for negative results in this paper, we hope to
+catalyze a fruitful interplay between the search for constructions proving
+bounds on $\omega$ and methods for ruling them out.
+"
+28602,Scalar extensions of categorical resolutions of singularities,"  Let $X$ be a quasi-compact, separated scheme over a field k and we can
+consider the categorical resolution of singularities of $X$. In this paper let
+$k^{\prime}/k$ be a field extension and we study the scalar extension of a
+categorical resolution of singularities of $X$ and we show how it gives a
+categorical resolution of the base change scheme $X_{k^{\prime}}$. Our
+construction involves the scalar extension of derived categories of DG-modules
+over a DG algebra. As an application we use the technique of scalar extension
+developed in this paper to prove the non-existence of full exceptional
+collections of categorical resolutions for a projective curve of genus $\geq 1$
+over a non-algebraically closed field.
+"
+28603,Unsupervised Domain Adaptation with Random Walks on Target Labelings,"  Unsupervised Domain Adaptation (DA) is used to automatize the task of
+labeling data: an unlabeled dataset (target) is annotated using a labeled
+dataset (source) from a related domain. We cast domain adaptation as the
+problem of finding stable labels for target examples. A new definition of label
+stability is proposed, motivated by a generalization error bound for large
+margin linear classifiers: a target labeling is stable when, with high
+probability, a classifier trained on a random subsample of the target with that
+labeling yields the same labeling. We find stable labelings using a random walk
+on a directed graph with transition probabilities based on labeling stability.
+The majority vote of those labelings visited by the walk yields a stable label
+for each target example. The resulting domain adaptation algorithm is
+strikingly easy to implement and apply: It does not rely on data
+transformations, which are in general computational prohibitive in the presence
+of many input features, and does not need to access the source data, which is
+advantageous when data sharing is restricted. By acting on the original feature
+space, our method is able to take full advantage of deep features from external
+pre-trained neural networks, as demonstrated by the results of our experiments.
+"
+28604,A hybrid quantum eraser scheme for characterization of free-space and fiber communication channels,"  We demonstrate a simple projective measurement based on the quantum eraser
+concept that can be used to characterize the disturbances of any communication
+channel. Quantum erasers are commonly implemented as spatially separated path
+interferometric schemes. Here we exploit the advantages of redefining the
+which-path information in terms of spatial modes, replacing physical paths with
+abstract paths of orbital angular momentum (OAM). Remarkably, vector modes
+(natural modes of free-space and fiber) have a non-separable feature of
+spin-orbit coupled states, equivalent to the description of two independently
+marked paths. We explore the effects of fiber perturbations by probing a
+step-index optical fiber channel with a vector mode, relevant to high-order
+spatial mode encoding of information for ultra-fast fiber communications.
+"
+28605,"Technical Design Report (TDR): Searching for a Sterile Neutrino at J-PARC MLF (E56, JSNS2)","  In this document, the technical details of the JSNS$^2$ (J-PARC Sterile
+Neutrino Search at J-PARC Spallation Neutron Source) experiment are described.
+The search for sterile neutrinos is currently one of the hottest topics in
+neutrino physics. The JSNS$^2$ experiment aims to search for the existence of
+neutrino oscillations with $\Delta m^2$ near 1 eV$^2$ at the J-PARC Materials
+and Life Science Experimental Facility (MLF). A 1 MW beam of 3 GeV protons
+incident on a spallation neutron target produces an intense neutrino beam from
+muon decay at rest. Neutrinos come predominantly from $\mu^+$ decay: $\mu^{+}
+\to e^{+} + \bar{\nu}_{\mu} + \nu_{e}$. The experiment will search for
+$\bar{\nu}_{\mu}$ to $\bar{\nu}_{e}$ oscillations which are detected by the
+inverse beta decay interaction $\bar{\nu}_{e} + p \to e^{+} + n$, followed by
+gammas from neutron capture on Gd. The detector has a fiducial volume of 17
+tons and is located 24 meters away from the mercury target. JSNS$^2$ offers the
+ultimate direct test of the LSND anomaly.
+In addition to the sterile neutrino search, the physics program includes
+cross section measurements with neutrinos with a few 10's of MeV from muon
+decay at rest and with monochromatic 236 MeV neutrinos from kaon decay at rest.
+These cross sections are relevant for our understanding of supernova explosions
+and nuclear physics.
+"
+28606,Cryogenic System for the Cryomodule Test Stand at Fermilab,"  This paper describes the cryogenic system for the Cryomodule Test Stand
+(CMTS) at the new Cryomodule Test Facility (CMTF) located at Fermilab. CMTS is
+designed for production testing of the 1.3 GHz and 3.9 GHz cryomodules to be
+used in the Linac Coherent Light Source II (LCLSII), which is an upgrade to an
+existing accelerator at Stanford Linear Accelerator Laboratory (SLAC). This
+paper will focus on the cryogenic system that extends from the helium
+refrigeration plant to the CMTS cave. Topics covered will include component
+design, installation and commissioning progress, and operational plans. The
+paper will conclude with a description of the heat load measurement plan.
+"
+28607,Magnetic and Electronic Properties of Spin-Orbit Coupled Dirac Electrons on a $(001)$ Thin Film of Double Perovskite Sr$_2$FeMoO$_6$,"  We present an interacting model for the electronic and magnetic behavior of a
+strained $(001)$ atomic layer of Sr$_2$FeMoO$_6,$ which shows room-temperature
+ferrimagnetism and magnetoresistance with potential spintronics application in
+the bulk. We find that the strong spin-orbit coupling in the molybdenum 4$d$
+shell gives rise to a robust ferrimagnetic state with an emergent
+spin-polarized electronic structure consisting of flat bands and four massive
+or massless Dirac dispersions. Based on the spin-wave theory, we demonstrate
+that the magnetic order remains intact for a wide range of doping, leading to
+the possibility of exploring flat band physics, such as Wigner crystallization
+in electron-doped Sr$_{2-x}$La$_{x}$FeMoO$_6.$
+"
+28608,Exact Methods for Recursive Circle Packing,"  Packing rings into a minimum number of rectangles is an optimization problem
+which appears naturally in the logistics operations of the tube industry. It
+encompasses two major difficulties, namely the positioning of rings in
+rectangles and the recursive packing of rings into other rings. This problem is
+known as the Recursive Circle Packing Problem (RCPP). We present the first
+dedicated method for solving RCPP that provides strong dual bounds based on an
+exact Dantzig--Wolfe reformulation of a nonconvex mixed-integer nonlinear
+programming formulation. The key idea of this reformulation is to break
+symmetry on each recursion level by enumerating one-level packings, i.e.,
+packings of circles into other circles, and by dynamically generating packings
+of circles into rectangles. We use column generation techniques to design a
+""price-and-verify"" algorithm that solves this reformulation to global
+optimality. Extensive computational experiments on a large test set show that
+our method not only computes tight dual bounds, but often produces primal
+solutions better than those computed by heuristics from the literature.
+"
+28609,CASCONet: A Conference dataset,"  Knowledge mobilization and translation describes the process of moving
+knowledge from research and development (R&D) labs into environments where it
+can be put to use. There is increasing interest in understanding mechanisms for
+knowledge mobilization, specifically with respect to academia and industry
+collaborations. These mechanisms include funding programs, research centers,
+and conferences, among others. In this paper, we focus on one specific
+knowledge mobilization mechanism, the CASCON conference, the annual conference
+of the IBM Centre for Advanced Studies (CAS). The mandate of CAS when it was
+established in 1990 was to foster collaborative work between the IBM Toronto
+Lab and university researchers from around the world. The first CAS Conference
+(CASCON) was held one year after CAS was formed in 1991. The focus of this
+annual conference was, and continues to be, bringing together academic
+researchers, industry practitioners, and technology users in a forum for
+sharing ideas and showcasing the results of the CAS collaborative work. We
+collected data about CASCON for the past 25 years including information about
+papers, technology showcase demos, workshops, and keynote presentations. The
+resulting dataset, called ""CASCONet"" is available for analysis and integration
+with related datasets. Using CASCONet, we analyzed interactions between R&D
+topics and changes in those topics over time. Results of our analysis show how
+the domain of knowledge being mobilized through CAS had evolved over time. By
+making CASCONet available to others, we hope that the data can be used in
+additional ways to understand knowledge mobilization and translation in this
+unique context.
+"
+28610,Large-Scale YouTube-8M Video Understanding with Deep Neural Networks,"  Video classification problem has been studied many years. The success of
+Convolutional Neural Networks (CNN) in image recognition tasks gives a powerful
+incentive for researchers to create more advanced video classification
+approaches. As video has a temporal content Long Short Term Memory (LSTM)
+networks become handy tool allowing to model long-term temporal clues. Both
+approaches need a large dataset of input data. In this paper three models
+provided to address video classification using recently announced YouTube-8M
+large-scale dataset. The first model is based on frame pooling approach. Two
+other models based on LSTM networks. Mixture of Experts intermediate layer is
+used in third model allowing to increase model capacity without dramatically
+increasing computations. The set of experiments for handling imbalanced
+training data has been conducted.
+"
+28611,Saraswati: An Extremely Massive ~ 200 Megaparsec Scale Supercluster,"  Here we report the discovery of an extremely massive and large supercluster
+(called Saraswati) found in the Stripe 82 region of SDSS. This supercluster is
+a major concentration of galaxies and galaxy clusters, forming a wall-like
+structure spanning at least 200 Mpc across at the redshift $z \approx 0.3$.
+This enormous structure is surrounded by a network of galaxy filaments,
+clusters, and large, $\sim40 - 170$ Mpc diameter, voids. The mean density
+contrast $\delta$ (relative to the background matter density of the universe)
+of Saraswati is $\gtrsim 1.62$ and the main body of the supercluster comprises
+at least 43 massive galaxy clusters (mean $z = 0.28$) with a total mass of
+$\sim 2 \times 10^{16} M_{\odot}$. The spherical collapse model suggests that
+the central region of radius $\sim20$ Mpc and mass at least $ 4 \times 10^{15}
+M_{\odot}$ may be collapsing. This places it among the few largest and most
+massive superclusters known, comparable to the most massive `Shapley
+Concentration' ($z \approx 0.046$) in the nearby universe. The Saraswati
+supercluster and its environs reveal that some extreme large-scale, prominent
+matter density enhancements had formed $\sim4$ Gy in the past when dark energy
+had just started to dominate structure formation. This galactic concentration
+sheds light on the role of dark energy and cosmological initial conditions in
+supercluster formation, and tests the competing cosmological models.
+"
+28612,Maximum Likelihood Estimation based on Random Subspace EDA: Application to Extrasolar Planet Detection,"  This paper addresses maximum likelihood (ML) estimation based model fitting
+in the context of extrasolar planet detection. This problem is featured by the
+following properties: 1) the candidate models under consideration are highly
+nonlinear; 2) the likelihood surface has a huge number of peaks; 3) the
+parameter space ranges in size from a few to dozens of dimensions. These
+properties make the ML search a very challenging problem, as it lacks any
+analytical or gradient based searching solution to explore the parameter space.
+A population based searching method, called estimation of distribution
+algorithm (EDA), is adopted to explore the model parameter space starting from
+a batch of random locations. EDA is featured by its ability to reveal and
+utilize problem structures. This property is desirable for characterizing the
+detections. However, it is well recognized that EDAs can not scale well to
+large scale problems, as it consists of iterative random sampling and model
+fitting procedures, which results in the well-known dilemma curse of
+dimensionality. A novel mechanism to perform EDAs in interactive random
+subspaces spanned by correlated variables is proposed and the hope is to
+alleviate the curse of dimensionality for EDAs by performing the operations of
+sampling and model fitting in lower dimensional subspaces. The effectiveness of
+the proposed algorithm is verified via both benchmark numerical studies and
+real data analysis.
+"
+28613,Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations,"  A long-standing problem at the interface of artificial intelligence and
+applied mathematics is to devise an algorithm capable of achieving human level
+or even superhuman proficiency in transforming observed data into predictive
+mathematical models of the physical world. In the current era of abundance of
+data and advanced machine learning capabilities, the natural question arises:
+How can we automatically uncover the underlying laws of physics from
+high-dimensional data generated from experiments? In this work, we put forth a
+deep learning approach for discovering nonlinear partial differential equations
+from scattered and potentially noisy observations in space and time.
+Specifically, we approximate the unknown solution as well as the nonlinear
+dynamics by two deep neural networks. The first network acts as a prior on the
+unknown solution and essentially enables us to avoid numerical differentiations
+which are inherently ill-conditioned and unstable. The second network
+represents the nonlinear dynamics and helps us distill the mechanisms that
+govern the evolution of a given spatiotemporal data-set. We test the
+effectiveness of our approach for several benchmark problems spanning a number
+of scientific domains and demonstrate how the proposed framework can help us
+accurately learn the underlying dynamics and forecast future states of the
+system. In particular, we study the Burgers', Korteweg-de Vries (KdV),
+Kuramoto-Sivashinsky, nonlinear Schrödinger, and Navier-Stokes equations.
+"
+28614,Validation of a fractional model for erythrocyte sedimentation rate,"  We present the validation of a recent fractional mathematical model for
+erythrocyte sedimentation proposed by Sharma et al. \cite{GMR}. The model uses
+a Caputo fractional derivative to build a time fractional diffusion equation
+suitable to predict blood sedimentation rates. This validation was carried out
+by means of erythrocyte sedimentation tests in laboratory. Data on
+sedimentation rates (percentages) were analyzed and compared with the
+analytical solution of the time fractional diffusion equation. The behavior of
+the analytical solution related to each blood sample sedimentation data was
+described and analyzed.
+"
+28615,Expectation-Maximization for Adaptive Mixture Models in Graph Optimization,"  Non-Gaussian and multimodal distributions are an important part of many
+recent robust sensor fusion algorithms. In difference to robust cost functions,
+they are probabilistically founded and have good convergence properties. Since
+their robustness depends on a close approximation of the real error
+distribution, their parametrization is crucial. We propose a novel approach
+that allows to adapt a multi-modal Gaussian mixture model to the error
+distribution of a sensor fusion problem. By applying expectation-maximization,
+we are able to provide a computationally efficient solution with well-behaved
+convergence properties. We demonstrate the performance of these algorithms on
+several real-world GNSS and indoor localization datasets. The proposed
+self-tuning mixture algorithm outperforms state of the art approaches with
+static parametrization.
+"
+28616,Constraining interacting dark energy with CMB and BAO future surveys,"  In this paper, we perform a forecast analysis to test the capacity of future
+baryon acoustic oscillation (BAO) and cosmic microwave background (CMB)
+experiments to constrain phenomenological interacting dark energy models using
+the Fisher matrix formalism. We consider a Euclid-like experiment, in which BAO
+measurements is one of the main goals, to constrain the cosmological parameters
+of alternative cosmological models. Moreover, additional experimental probes
+can more efficiently provide information on the parameters forecast, justifying
+also the inclusion in the analysis of a future ground-based CMB experiment
+mainly designed to measure the polarization signal with high precision. In the
+interacting dark energy scenario, a coupling between dark matter and dark
+energy modifies the conservation equations such that the fluid equations for
+both constituents are conserved as the total energy density of the dark sector.
+In this context, we consider three phenomenological models which have been
+deeply investigated in literature over the past years. We find that the
+combination of both CMB and BAO information can break degeneracies among the
+dark sector parameters for all three models, although to different extents. We
+found powerful constraints on, for example, the coupling constant when
+comparing it with present limits for two of the models, and their future
+statistical 3-$\sigma$ bounds could potentially exclude the null interaction
+for the combination of probes that is considered. However, for one of the
+models, the constraint on the coupling parameter does not improve the present
+result (achieved using a large combination of surveys), and a larger
+combination of probes appears to be necessary to eventually claim whether or
+not interaction is favored in that context.
+"
+28617,Clustering Stable Instances of Euclidean k-means,"  The Euclidean k-means problem is arguably the most widely-studied clustering
+problem in machine learning. While the k-means objective is NP-hard in the
+worst-case, practitioners have enjoyed remarkable success in applying
+heuristics like Lloyd's algorithm for this problem. To address this disconnect,
+we study the following question: what properties of real-world instances will
+enable us to design efficient algorithms and prove guarantees for finding the
+optimal clustering? We consider a natural notion called additive perturbation
+stability that we believe captures many practical instances. Stable instances
+have unique optimal k-means solutions that do not change even when each point
+is perturbed a little (in Euclidean distance). This captures the property that
+the k-means optimal solution should be tolerant to measurement errors and
+uncertainty in the points. We design efficient algorithms that provably recover
+the optimal clustering for instances that are additive perturbation stable.
+When the instance has some additional separation, we show an efficient
+algorithm with provable guarantees that is also robust to outliers. We
+complement these results by studying the amount of stability in real datasets
+and demonstrating that our algorithm performs well on these benchmark datasets.
+"
+28618,Unusual rainbows as auroral candidates: another point of view,"  Several auroral events that occurred in the past have not been catalogued as
+such due to fact that they were described in the historical sources with
+different terminology. Hayakawa et al. (2016) have reviewed historical oriental
+chronicles and have proposed the terms ""unusual rainbow"" and ""white rainbow"" as
+candidates to auroras. In this work, we present three events that took place in
+the 18th century in two different settings (the Iberian Peninsula and Brazil)
+that were originally described with similar definition/wording used by the
+oriental chronicles, despite the inherent differences in terms associated to
+oriental and Latin languages. We show that these terms are indeed applicable to
+the three case studies from Europe and South America. Thus, the auroral
+catalogues available can be extended for occidental sources with this new
+terminology.
+"
+28619,Deriving real delay time statistics from the complex delay time statistics in weakly disordered optical media,"  Considering the complex reflection amplitude R=|R|exp(i{\theta}) of a light
+wave, real delay time {\tau}_r (i.e., sojourn or Wigner delay time), which is
+the energy derivative of the real phase ({\tau}_r =d{\theta}/cdk), and complex
+delay time {\tau}_i , which is the energy derivative of the reflection
+coefficient ({\tau}_i=d{\theta}_i/cdk, |R|=r^1/2=exp(-{\theta}_i)), have the
+same statistical form and a mirror image with a shift in time in weak disorder
+and short length regime. Real delay time statistics obtained from the
+reflection coefficient can be attributed to the strong correlation between the
+reflection coefficient and its phase in this regime.
+"
+28620,A Bottleneck Principle for Techno-Metabolic Chains,"  A plausible mechanism of self-regulation in technological chains of the kind
+wherein input resources are converted into common final product through
+sequences of processing links having uniform kinetic properties is discussed. A
+bottleneck principle is derived according to which output of the overall chain
+is completely determined by its slowest link, if all the links are featured by
+satiation and weak outflow.
+"
+28621,Setting up a Reinforcement Learning Task with a Real-World Robot,"  Reinforcement learning is a promising approach to developing hard-to-engineer
+adaptive solutions for complex and diverse robotic tasks. However, learning
+with real-world robots is often unreliable and difficult, which resulted in
+their low adoption in reinforcement learning research. This difficulty is
+worsened by the lack of guidelines for setting up learning tasks with robots.
+In this work, we develop a learning task with a UR5 robotic arm to bring to
+light some key elements of a task setup and study their contributions to the
+challenges with robots. We find that learning performance can be highly
+sensitive to the setup, and thus oversights and omissions in setup details can
+make effective learning, reproducibility, and fair comparison hard. Our study
+suggests some mitigating steps to help future experimenters avoid difficulties
+and pitfalls. We show that highly reliable and repeatable experiments can be
+performed in our setup, indicating the possibility of reinforcement learning
+research extensively based on real-world robots.
+"
+28622,Learning Generative Models with Sinkhorn Divergences,"  The ability to compare two degenerate probability distributions (i.e. two
+probability distributions supported on two distinct low-dimensional manifolds
+living in a much higher-dimensional space) is a crucial problem arising in the
+estimation of generative models for high-dimensional observations such as those
+arising in computer vision or natural language. It is known that optimal
+transport metrics can represent a cure for this problem, since they were
+specifically designed as an alternative to information divergences to handle
+such problematic scenarios. Unfortunately, training generative machines using
+OT raises formidable computational and statistical challenges, because of (i)
+the computational burden of evaluating OT losses, (ii) the instability and lack
+of smoothness of these losses, (iii) the difficulty to estimate robustly these
+losses and their gradients in high dimension. This paper presents the first
+tractable computational method to train large scale generative models using an
+optimal transport loss, and tackles these three issues by relying on two key
+ideas: (a) entropic smoothing, which turns the original OT loss into one that
+can be computed using Sinkhorn fixed point iterations; (b) algorithmic
+(automatic) differentiation of these iterations. These two approximations
+result in a robust and differentiable approximation of the OT loss with
+streamlined GPU execution. Entropic smoothing generates a family of losses
+interpolating between Wasserstein (OT) and Maximum Mean Discrepancy (MMD), thus
+allowing to find a sweet spot leveraging the geometry of OT and the favorable
+high-dimensional sample complexity of MMD which comes with unbiased gradient
+estimates. The resulting computational architecture complements nicely standard
+deep network generative models by a stack of extra layers implementing the loss
+function.
+"
+28623,Strong spin-orbit coupling and magnetism in (111) (La$_{0.3}$Sr$_{0.7}$)(Al$_{0.65}$Ta$_{0.35})$/SrTiO$_3$,"  Strong correlations, multiple lattice degrees of freedom, and the ease of
+doping make complex oxides a source of great research interest. Complex oxide
+heterointerfaces break inversion symmetry and can host a two dimensional
+carrier gas, which can display a variety of coexisting and competing phenomena.
+In the case of heterointerfaces based on SrTiO$_3$, many of these phenomena can
+be effectively tuned by using an electric gate, due to the large dielectric
+constant of SrTiO$_3$. Most studies so far have focused on (001) oriented
+heterostructures; however, (111) oriented heterostructures have recently gained
+attention due to the possibility of finding exotic physics in these systems due
+their hexagonal surface crystal symmetry. In this work, we use
+magnetoresistance to study the evolution of spin-orbit interaction and
+magnetism in a new system, (111) oriented
+(La$_{0.3}$Sr$_{0.7}$)(Al$_{0.65}$Ta$_{0.35}$)/SrTiO$_3$. At more positive
+values of the gate voltage, which correspond to high carrier densities, we find
+that transport is multiband, and dominated by high mobility carriers with a
+tendency towards weak localization. At more negative gate voltages, the carrier
+density is reduced, the high mobility bands are depopulated, and weak
+antilocalization effects begin to dominate, indicating that spin-orbit
+interaction becomes stronger. At millikelvin temperatures, and gate voltages
+corresponding to the strong spin-orbit regime, we observe hysteresis in
+magnetoresistance, indicative of ferromagnetism in the system. Our results
+suggest that in the (111)
+(La$_{0.3}$Sr$_{0.7}$)(Al$_{0.65}$Ta$_{0.35}$)/SrTiO$_3$ system, low mobility
+carriers which experience strong spin-orbit interactions participate in
+creating magnetic order in the system.
+"
+28624,Etherington's Distance Duality with Birefringence,"  We consider light propagation in a spacetime whose kinematics allow weak
+birefringence, and whose dynamics have recently been derived by gravitational
+closure. Revisiting the definitions of luminosity and angular diameter
+distances in this setting, we present a modification of the Etherington
+distance duality relation in a weak gravitational field around a point mass.
+This provides the first concrete example of how the non-metricities implied by
+gravitational closure of birefringent electrodynamics affect observationally
+testable relations.
+"
+28625,On the Discrimination-Generalization Tradeoff in GANs,"  Generative adversarial training can be generally understood as minimizing
+certain moment matching loss defined by a set of discriminator functions,
+typically neural networks. The discriminator set should be large enough to be
+able to uniquely identify the true distribution (discriminative), and also be
+small enough to go beyond memorizing samples (generalizable). In this paper, we
+show that a discriminator set is guaranteed to be discriminative whenever its
+linear span is dense in the set of bounded continuous functions. This is a very
+mild condition satisfied even by neural networks with a single neuron. Further,
+we develop generalization bounds between the learned distribution and true
+distribution under different evaluation metrics. When evaluated with neural
+distance, our bounds show that generalization is guaranteed as long as the
+discriminator set is small enough, regardless of the size of the generator or
+hypothesis set. When evaluated with KL divergence, our bound provides an
+explanation on the counter-intuitive behaviors of testing likelihood in GAN
+training. Our analysis sheds lights on understanding the practical performance
+of GANs.
+"
+28626,"Angular dependence of vortex instability in a layered superconductor: the case study of Fe(Se,Te) material","  Anisotropy effects on flux pinning and flux flow are strongly effective in
+cuprate as well as iron-based superconductors due to their intrinsically
+layered crystallographic structure. However $\textrm{Fe(Se,Te)}$ thin films
+grown on $\textrm{CaF}_2$ substrate result less anisotropic with respect to all
+the other iron based superconductors. We present the first study on the angular
+dependence of the flux flow instability, which occurs in the flux flow regime
+as a current driven transition to the normal state at the instability point
+($I^*$,$V^*$) in the current-voltage characteristics. The voltage jumps are
+systematically investigated as a function of the temperature, the external
+magnetic field, and the angle between the field and the $\textrm{Fe(Se,Te)}$
+film. The scaling procedure based on the anisotropic Ginzburg-Landau approach
+is successfully applied to the observed angular dependence of the critical
+voltage $V^*$. Anyway, we find out that $\textrm{Fe(Se,Te)}$ represents the
+case study of a layered material characterized by a weak anisotropy of its
+static superconducting properties, but with an increased anisotropy in its
+vortex dynamics due to the predominant perpendicular component of the external
+applied magnetic field. Indeed, $I^*$ shows less sensitivity to angle
+variations, thus being promising for high field applications.
+"
+28627,"A relationship between the ideals of $\mathbb{F}_q\left[x, y, x^{-1}, y^{-1} \right]$ and the Fibonacci numbers","  Let $C_n(q)$ be the number of ideals of codimension $n$ of
+$\mathbb{F}_q\left[x, y, x^{-1}, y^{-1} \right]$, where $\mathbb{F}_q$ is the
+finite field with $q$ elements. Kassel and Reutenauer [KasselReutenauer2015A]
+proved that $C_n(q)$ is a polynomial in $q$ for any fixed value of $n \geq 1$.
+For $q = \frac{3+\sqrt{5}}{2}$, this combinatorial interpretation of $C_n(q)$
+is lost. Nevertheless, an unexpected connexion with Fibonacci numbers appears.
+Let $f_n$ be the $n$-th Fibonacci number (following the convention $f_0 = 0$,
+$f_1 = 1$). Define the series
+$$ F(t) = \sum_{n \geq 1} f_{2n}\,t^n. $$
+We will prove that for each $n \geq 1$,
+$$ C_n\left( \frac{3+\sqrt{5}}{2}\right) = \lambda_n \, \left(f_{2n}
+\frac{3+\sqrt{5}}{2} - f_{2n-2} \right) , $$ where the integers $\lambda_n \geq
+0$ are given by the following generating function
+$$ \prod_{m \geq 1} \left(1+F\left( t^m\right)\right) = 1 + \sum_{n \geq 1}
+\lambda_n\,t^n. $$
+"
+28628,Ambiguity and Incomplete Information in Categorical Models of Language,"  We investigate notions of ambiguity and partial information in categorical
+distributional models of natural language. Probabilistic ambiguity has
+previously been studied using Selinger's CPM construction. This construction
+works well for models built upon vector spaces, as has been shown in quantum
+computational applications. Unfortunately, it doesn't seem to provide a
+satisfactory method for introducing mixing in other compact closed categories
+such as the category of sets and binary relations. We therefore lack a uniform
+strategy for extending a category to model imprecise linguistic information.
+In this work we adopt a different approach. We analyze different forms of
+ambiguous and incomplete information, both with and without quantitative
+probabilistic data. Each scheme then corresponds to a suitable enrichment of
+the category in which we model language. We view different monads as
+encapsulating the informational behaviour of interest, by analogy with their
+use in modelling side effects in computation. Previous results of Jacobs then
+allow us to systematically construct suitable bases for enrichment.
+We show that we can freely enrich arbitrary dagger compact closed categories
+in order to capture all the phenomena of interest, whilst retaining the
+important dagger compact closed structure. This allows us to construct a model
+with real convex combination of binary relations that makes non-trivial use of
+the scalars. Finally we relate our various different enrichments, showing that
+finite subconvex algebra enrichment covers all the effects under consideration.
+"
+28629,Laguerre Functions and Their Applications to Tempered Fractional Differential Equaitons on Infinite Intervals,"  Tempered fractional derivatives originated from the tempered fractional
+diffusion equations (TFDEs) modeled on the whole space R (see [23]). For
+numerically solving TFDEs, two kinds of generalized Laguerre functions were
+defined and some important properties were proposed to establish the
+approximate theory. The related prototype tempered fractional differ- ential
+problems was proposed and solved as the guidance. TFDEs are numerically solved
+by two domains Laguerre spectral method and the numerical experiments show some
+properties of the TFDEs and verify the efficiency of the spectral scheme.
+"
+28630,The orbit method for the Baum-Connes Conjecture for algebraic groups over local function fields,"  The main purpose of this paper is to modify the orbit method for the
+Baum-Connes conjecture as developed by Chabert, Echterhoff and Nest in their
+proof of the Connes-Kasparov conjecture for almost connected groups
+\cite{MR2010742} in order to deal with linear algebraic groups over local
+function fields (i.e., non-archimedean local fields of positive
+characteristic). As a consequence, we verify the Baum-Connes conjecture for
+certain Levi-decomposable linear algebraic groups over local function fields.
+One of these is the Jacobi group, which is the semidirect product of the
+symplectic group and the Heisenberg group.
+"
+28631,Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time,"  User experience in modern content discovery applications critically depends
+on high-quality personalized recommendations. However, building systems that
+provide such recommendations presents a major challenge due to a massive pool
+of items, a large number of users, and requirements for recommendations to be
+responsive to user actions and generated on demand in real-time. Here we
+present Pixie, a scalable graph-based real-time recommender system that we
+developed and deployed at Pinterest. Given a set of user-specific pins as a
+query, Pixie selects in real-time from billions of possible pins those that are
+most related to the query. To generate recommendations, we develop Pixie Random
+Walk algorithm that utilizes the Pinterest object graph of 3 billion nodes and
+17 billion edges. Experiments show that recommendations provided by Pixie lead
+up to 50% higher user engagement when compared to the previous Hadoop-based
+production system. Furthermore, we develop a graph pruning strategy at that
+leads to an additional 58% improvement in recommendations. Last, we discuss
+system aspects of Pixie, where a single server executes 1,200 recommendation
+requests per second with 60 millisecond latency. Today, systems backed by Pixie
+contribute to more than 80% of all user engagement on Pinterest.
+"
+28632,Solutions to the overdetermined boundary problem for semilinear equations with position-dependent nonlinearities,"  We show that a wide range of overdetermined boundary problems for semilinear
+equations with position-dependent nonlinearities admits nontrivial solutions.
+The result holds true both on the Euclidean space and on compact Riemannian
+manifolds. As a byproduct of the proofs we also obtain some rigidity, or
+partial symmetry, results for solutions to overdetermined problems on
+Riemannian manifolds of nonconstant curvature.
+"
+28633,Embedding all contact 3-manifolds in a fixed contact 5-manifold,"  In this note we observe that one can contact embed all contact 3-manifolds
+into a Stein fillable contact structure on the twisted $S^3$-bundle over $S^2$
+and also into a unique overtwisted contact structure on $S^3\times S^2$. These
+results are proven using ""spun embeddings"" and Lefschetz fibrations.
+"
+28634,Jordan Isomorphisms of Finitary Incidence Algebras,"  Let $X$ be a partially ordered set, $R$ a commutative $2$-torsionfree unital
+ring and $FI(X,R)$ the finitary incidence algebra of $X$ over $R$. In this note
+we prove that each $R$-linear Jordan isomorphism of $FI(X,R)$ onto an
+$R$-algebra $A$ is the near-sum of a homomorphism and an anti-homomorphism.
+"
+28635,Large linear magnetoresistance in a transition-metal stannide $β$-RhSn$_4$,"  Materials exhibiting large magnetoresistance may not only be of fundamental
+research interest, but also can lead to wide-ranging applications in magnetic
+sensors and switches. Here we demonstrate a large linear-in-field
+magnetoresistance, $\Delta \rho/\rho$ reaching as high as $\sim$600$\%$ at 2 K
+under a 9 Tesla field, in the tetragonal phase of a transiton-metal stannide
+$\beta$-RhSn$_4$. Detailed analyses show that its magnetic responses are
+overall inconsistent with the classical model based on the multiple electron
+scattering by mobility fluctuations in an inhomogenous conductor, but rather in
+line with the quantum effects due to the presence of Dirac-like dispersions in
+the electronic structure. Our results may help guiding the future quest for
+quantum magnetoresistive materials into the family of stannides, similar to the
+role played by PtSn$_4$ with topological node arcs.
+"
+28636,Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy,"  In recent years, endomicroscopy has become increasingly used for diagnostic
+purposes and interventional guidance. It can provide intraoperative aids for
+real-time tissue characterization and can help to perform visual investigations
+aimed for example to discover epithelial cancers. Due to physical constraints
+on the acquisition process, endomicroscopy images, still today have a low
+number of informative pixels which hampers their quality. Post-processing
+techniques, such as Super-Resolution (SR), are a potential solution to increase
+the quality of these images. SR techniques are often supervised, requiring
+aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to
+train a model. However, in our domain, the lack of HR images hinders the
+collection of such pairs and makes supervised training unsuitable. For this
+reason, we propose an unsupervised SR framework based on an adversarial deep
+neural network with a physically-inspired cycle consistency, designed to impose
+some acquisition properties on the super-resolved images. Our framework can
+exploit HR images, regardless of the domain where they are coming from, to
+transfer the quality of the HR images to the initial LR images. This property
+can be particularly useful in all situations where pairs of LR/HR are not
+available during the training. Our quantitative analysis, validated using a
+database of 238 endomicroscopy video sequences from 143 patients, shows the
+ability of the pipeline to produce convincing super-resolved images. A Mean
+Opinion Score (MOS) study also confirms this quantitative image quality
+assessment.
+"
+28637,A Space-efficient Parameterized Algorithm for the Hamiltonian Cycle Problem by Dynamic Algebraziation,"  An NP-hard graph problem may be intractable for general graphs but it could
+be efficiently solvable using dynamic programming for graphs with bounded width
+(or depth or some other structural parameter). Dynamic programming is a
+well-known approach used for finding exact solutions for NP-hard graph problems
+based on tree decompositions. It has been shown that there exist algorithms
+using linear time in the number of vertices and single exponential time in the
+width (depth or other parameters) of a given tree decomposition for many
+connectivity problems. Employing dynamic programming on a tree decomposition
+usually uses exponential space. In 2010, Lokshtanov and Nederlof introduced an
+elegant framework to avoid exponential space by algebraization. Later, Fürer
+and Yu modified the framework in a way that even works when the underlying set
+is dynamic, thus applying it to tree decompositions. In this work, we design
+space-efficient algorithms to solve the Hamiltonian Cycle and the Traveling
+Salesman problems, using polynomial space while the time complexity is only
+slightly increased. This might be inevitable since we are reducing the space
+usage from an exponential amount (in dynamic programming solution) to
+polynomial. We give an algorithm to solve Hamiltonian cycle in time
+$\mathcal{O}((4w)^d\, nM(n\log{n}))$ using $\mathcal{O}(dn\log{n})$ space,
+where $M(r)$ is the time complexity to multiply two integers, each of which
+being represented by at most $r$ bits. Then, we solve the more general
+Traveling Salesman problem in time $\mathcal{O}((4w)^d poly(n))$ using space
+$\mathcal{O}(\mathcal{W}dn\log{n})$, where $w$ and $d$ are the width and the
+depth of the given tree decomposition and $\mathcal{W}$ is the sum of weights.
+Furthermore, this algorithm counts the number of Hamiltonian Cycles.
+"
+28638,Matrix of Polynomials Model based Polynomial Dictionary Learning Method for Acoustic Impulse Response Modeling,"  We study the problem of dictionary learning for signals that can be
+represented as polynomials or polynomial matrices, such as convolutive signals
+with time delays or acoustic impulse responses. Recently, we developed a method
+for polynomial dictionary learning based on the fact that a polynomial matrix
+can be expressed as a polynomial with matrix coefficients, where the
+coefficient of the polynomial at each time lag is a scalar matrix. However, a
+polynomial matrix can be also equally represented as a matrix with polynomial
+elements. In this paper, we develop an alternative method for learning a
+polynomial dictionary and a sparse representation method for polynomial signal
+reconstruction based on this model. The proposed methods can be used directly
+to operate on the polynomial matrix without having to access its coefficients
+matrices. We demonstrate the performance of the proposed method for acoustic
+impulse response modeling.
+"
+28639,Witten Deformation and Its Application toward Morse Inequalities,"  In this undergraduate thesis, we present an analytical proof of the Morse
+inequalities for closed smooth $n$-manifolds following Witten's approach. Using
+techniques from PDE theory, the proof is reduced to study the eigenspaces and
+eigenvalues of harmonic oscillators on $\mathbb{R}^n$.
+"
+28640,Parametric Summability and Its Applications,"  In this paper we study summability based on double sequences of complex
+constants as it is defined in ""Linear Operators, General Theory"" by N. Dunford
+and J. T. Schwartz. We define ""power double sequences"" or infinite ""power
+matrices"" as certain generalizations of double sequences and power series. We
+relate the summability and boundedness of the power double sequences to the
+summability and boundedness of the double sequence \textit{A}. While others do
+investigate ""power matrices"" their definitions, as far as we were able to find,
+differ from our definitions. Using these definitions we extend some summability
+results for double sequences of constants to our power double sequences. We
+investigate also other possible generalizations of double sequences and assess
+their usefulness in summability study.
+"
+28641,Computing Entropies With Nested Sampling,"  The Shannon entropy, and related quantities such as mutual information, can
+be used to quantify uncertainty and relevance. However, in practice, it can be
+difficult to compute these quantities for arbitrary probability distributions,
+particularly if the probability mass functions or densities cannot be
+evaluated. This paper introduces a computational approach, based on Nested
+Sampling, to evaluate entropies of probability distributions that can only be
+sampled. I demonstrate the method on three examples: a simple gaussian example
+where the key quantities are available analytically; (ii) an experimental
+design example about scheduling observations in order to measure the period of
+an oscillating signal; and (iii) predicting the future from the past in a
+heavy-tailed scenario.
+"
+28642,Odd Yao-Yao Graphs are Not Spanners,"  It is a long standing open problem whether Yao-Yao graphs $\mathsf{YY}_{k}$
+are all spanners [li2002sparse]. Bauer and Damian [bauer2013infinite] showed
+that all $\mathsf{YY}_{6k}$ for $k \geq 6$ are spanners. Li and Zhan
+[li2016almost] generalized their result and proved that all even Yao-Yao graphs
+$\mathsf{YY}_{2k}$ are spanners (for $k\geq 42$). However, their technique
+cannot be extended to odd Yao-Yao graphs, and whether they are spanners are
+still elusive. In this paper, we show that, surprisingly, for any integer $k
+\geq 1$, there exist odd Yao-Yao graph $\mathsf{YY}_{2k+1}$ instances, which
+are not spanners.
+"
+28643,How fragile are information cascades?,"  It is well known that sequential decision making may lead to information
+cascades. That is, when agents make decisions based on their private
+information, as well as observing the actions of those before them, then it
+might be rational to ignore their private signal and imitate the action of
+previous individuals. If the individuals are choosing between a right and a
+wrong state, and the initial actions are wrong, then the whole cascade will be
+wrong. This issue is due to the fact that cascades can be based on very little
+information.
+We show that if agents occasionally disregard the actions of others and base
+their action only on their private information, then wrong cascades can be
+avoided. Moreover, we study the optimal asymptotic rate at which the error
+probability at time $t$ can go to zero. The optimal policy is for the player at
+time $t$ to follow their private information with probability $p_{t} = c/t$,
+leading to a learning rate of $c'/t$, where the constants $c$ and $c'$ are
+explicit.
+"
+28644,Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains,"  The number of neurons that can be simultaneously recorded doubles every seven
+years. This ever increasing number of recorded neurons opens up the possibility
+to address new questions and extract higher dimensional stimuli from the
+recordings. Modeling neural spike trains as point processes, this task of
+extracting dynamical signals from spike trains is commonly set in the context
+of nonlinear filtering theory. Particle filter methods relying on importance
+weights are generic algorithms that solve the filtering task numerically, but
+exhibit a serious drawback when the problem dimensionality is high: they are
+known to suffer from the 'curse of dimensionality' (COD), i.e. the number of
+particles required for a certain performance scales exponentially with the
+observable dimensions. Here, we first briefly review the theory on filtering
+with point process observations in continuous time. Based on this theory, we
+investigate both analytically and numerically the reason for the COD of
+weighted particle filtering approaches: Similarly to particle filtering with
+continuous-time observations, the COD with point-process observations is due to
+the decay of effective number of particles, an effect that is stronger when the
+number of observable dimensions increases. Given the success of unweighted
+particle filtering approaches in overcoming the COD for continuous- time
+observations, we introduce an unweighted particle filter for point-process
+observations, the spike-based Neural Particle Filter (sNPF), and show that it
+exhibits a similar favorable scaling as the number of dimensions grows.
+Further, we derive rules for the parameters of the sNPF from a maximum
+likelihood approach learning. We finally employ a simple decoding task to
+illustrate the capabilities of the sNPF and to highlight one possible future
+application of our inference and learning algorithm.
+"
+28645,Finding the chiral gravitational wave background of an axion-SU(2) inflationary model using CMB observations and laser interferometers,"  A detection of B-mode polarization of the Cosmic Microwave Background (CMB)
+anisotropies would confirm the presence of a primordial gravitational wave
+background (GWB). In the inflation paradigm this would be an unprecedented
+probe of the energy scale of inflation as it is directly proportional to the
+power spectrum of the GWB. However, similar tensor perturbations can be
+produced by the matter fields present during inflation, breaking this simple
+relationship. It is therefore important to be able to distinguish between
+different generation mechanisms of the GWB. In this paper, we analyse the
+detectability of a new axion-SU(2) gauge field model using its chiral,
+scale-dependent tensor spectrum. We forecast the detectability of the resulting
+CMB TB and EB cross-correlations by the LiteBIRD satellite, considering the
+effects of residual foregrounds, gravitational lensing, and for the first time
+assess the ability of such an experiment to jointly detect primordial TB and EB
+spectra and self-calibrate its polarimeter. We find that LiteBIRD will be able
+to detect the chiral signal for $r_*>0.03$ with $r_*$ denoting the
+tensor-to-scalar ratio at the peak scale, and that the maximum signal-to-noise
+for $r_*<0.07$ is $\sim 2$. We go on to consider an advanced stage of a
+LISA-like mission, and find that such experiments would complement CMB
+observations by providing sensitivity to GWB chirality on scales inaccessible
+to the CMB. We conclude that in order to use the CMB to distinguish this model
+from a conventional vacuum fluctuation model two-point statistics provide some
+power, but to achieve high statistical significance we would require higher
+order statistics which take advantage of the model's non-Gaussianity. On the
+other hand, in the case of a spectrum peaked at very small scales, inaccessible
+to the CMB, a highly significant detection could be made using space-based
+laser interferometers.
+"
+28646,A note on parameter estimation for discretely sampled SPDEs,"  We consider a parameter estimation problem for one dimensional stochastic
+heat equations, when data is sampled discretely in time or spatial component.
+We establish some general results on derivation of consistent and
+asymptotically normal estimators based on computation of the $p$-variations of
+stochastic processes and their smooth perturbations. We apply these results to
+the considered SPDEs, by using some convenient representations of the
+solutions. For some equations such results were ready available, while for
+other classes of SPDEs we derived the needed representations along with their
+statistical asymptotical properties. We prove that the real valued parameter
+next to the Laplacian, and the constant parameter in front of the noise (the
+volatility) can be consistently estimated by observing the solution at a fixed
+time and on a discrete spatial grid, or at a fixed space point and at discrete
+time instances of a finite interval, assuming that the mesh-size goes to zero.
+"
+28647,Fast computation of quadrupole and hexadecapole approximations in microlensing with a single point-source evaluation,"  The exoplanet detection rate from gravitational microlensing has grown
+significantly in recent years thanks to a great enhancement of resources and
+improved observational strategy. Current observatories include ground-based
+wide-field and/or robotic world-wide networks of telescopes, as well as
+space-based observatories such as satellites Spitzer or Kepler/K2. This results
+in a large quantity of data to be processed and analyzed, which is a challenge
+for modeling codes because of the complexity of the parameter space to be
+explored, and the intensive computations required to evaluate the models. In
+this work, I present a method that allows to compute the quadrupole and
+hexadecapole approximation of the finite-source magnification with more
+efficiency that previously available codes, with routines about x6 and x4
+faster respectively. The quadrupole takes just about twice the time of a
+point-source evaluation, which advocates for generalizing its use to large
+portion of the light curves. The corresponding routines are available as
+open-source python codes.
+"
+28648,Asymptotic convergence in distribution of the area bounded by prevalence-weighted Kaplan-Meier curves using empirical process modeling,"  The Kaplan-Meier product-limit estimator is a simple and powerful tool in
+time to event analysis. An extension exists for populations stratified into
+cohorts where a population survival curve is generated by weighted averaging of
+cohort-level survival curves. For making population-level comparisons using
+this statistic, we analyze the statistics of the area between two such weighted
+survival curves. We derive the large sample behavior of this statistic based on
+an empirical process of product-limit estimators. This estimator was used by an
+interdisciplinary NIH-SSA team in the identification of medical conditions to
+prioritize for adjudication in disability benefits processing.
+"
+28649,R-Rec: A rule-based system for contextual suggestion using tag-description similarity,"  Contextual Suggestion deals with search techniques for complex information
+needs that are highly focused on context and user needs. In this paper, we
+propose \emph{R-Rec}, a novel rule-based technique to identify and recommend
+appropriate points-of-interest to a user given her past preferences. We try to
+embody the information that the user shares in the form of rating and tags of
+any previous point(s)-of-interest and use it to rank the unrated candidate
+suggestions. The ranking function is computed based on the similarity between a
+suggestion and the places that the user like and the dissimilarity between the
+suggestion and the places disliked by the user. Experiments carried out on
+TREC-Contextual Suggestion 2015 dataset reveal the efficacy of our method.
+"
+28650,f-VAEs: Improve VAEs with Conditional Flows,"  In this paper, we integrate VAEs and flow-based generative models
+successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid
+images, solved the blurred-image problem of VAEs. Compared with flow-based
+models such as Glow, f-VAE is more lightweight and converges faster, achieving
+the same performance under smaller-size architecture.
+"
+28651,Organization and hierarchy of the human functional brain network lead to a chain-like core,"  The brain is a paradigmatic example of a complex system as its functionality
+emerges as a global property of local mesoscopic and microscopic interactions.
+Complex network theory allows to elicit the functional architecture of the
+brain in terms of links (correlations) between nodes (grey matter regions) and
+to extract information out of the noise. Here we present the analysis of
+functional magnetic resonance imaging data from forty healthy humans during the
+resting condition for the investigation of the basal scaffold of the functional
+brain network organization. We show how brain regions tend to coordinate by
+forming a highly hierarchical chain-like structure of homogeneously clustered
+anatomical areas. A maximum spanning tree approach revealed the centrality of
+the occipital cortex and the peculiar aggregation of cerebellar regions to form
+a closed core. We also report the hierarchy of network segregation and the
+level of clusters integration as a function of the connectivity strength
+between brain regions.
+"
+28652,Maximum Induced Matching Algorithms via Vertex Ordering Characterizations,"  We study the maximum induced matching problem on a graph g. Induced matchings
+correspond to independent sets in L2(g), the square of the line graph of g. The
+problem is NP-complete on bipartite graphs. In this work, we show that for a
+number of graph families with forbidden vertex orderings, almost all forbidden
+patterns on three vertices are preserved when taking the square of the line
+graph. These orderings can be computed in linear time in the size of the input
+graph. In particular, given a graph class G characterized by a vertex ordering,
+and a graph g = (V, E) in G with a corresponding vertex ordering \sigma of V ,
+one can produce (in linear time in the size of g) an ordering on the vertices
+of L2(g), that shows that L2(g) in G - for a number of graph classes G -
+without computing the line graph or the square of the line graph of g. These
+results generalize and unify previous ones on showing closure under L2(.) for
+various graph families. Furthermore, these orderings on L2(g) can be exploited
+algorithmically to compute a maximum induced matching on G faster. We
+illustrate this latter fact in the second half of the paper where we focus on
+cocomparability graphs, a large graph class that includes interval,
+permutation, trapezoid graphs, and co-graphs, and we present the first O(mn)
+time algorithm to compute a maximum weighted induced matching on
+cocomparability graphs; an improvement from the best known O(n4) time algorithm
+for the unweighted case.
+"
+28653,Adaptive estimation of the sparsity in the Gaussian vector model,"  Consider the Gaussian vector model with mean value {\theta}. We study the
+twin problems of estimating the number |{\theta}|_0 of non-zero components of
+{\theta} and testing whether |{\theta}|_0 is smaller than some value. For
+testing, we establish the minimax separation distances for this model and
+introduce a minimax adaptive test. Extensions to the case of unknown variance
+are also discussed. Rewriting the estimation of |{\theta}|_0 as a multiple
+testing problem of all hypotheses {|{\theta}|_0 <= q}, we both derive a new way
+of assessing the optimality of a sparsity estimator and we exhibit such an
+optimal procedure. This general approach provides a roadmap for estimating the
+complexity of the signal in various statistical models.
+"
+28654,On the removable singularities of complex analytic sets,"  There is proved the sufficiency of several conditions for the removability of
+singularities of complex-analytic sets in domains of $\mathbb C^n$.
+"
+28655,Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks,"  The problem where a tropical cyclone intensifies dramatically within a short
+period of time is known as rapid intensification. This has been one of the
+major challenges for tropical weather forecasting. Recurrent neural networks
+have been promising for time series problems which makes them appropriate for
+rapid intensification. In this paper, recurrent neural networks are used to
+predict rapid intensification cases of tropical cyclones from the South Pacific
+and South Indian Ocean regions. A class imbalanced problem is encountered which
+makes it very challenging to achieve promising performance. A simple strategy
+was proposed to include more positive cases for detection where the false
+positive rate was slightly improved. The limitations of building an efficient
+system remains due to the challenges of addressing the class imbalance problem
+encountered for rapid intensification prediction. This motivates further
+research in using innovative machine learning methods.
+"
+28656,Khristianovich-Geertsma-de Klerk problem with stress contrast,"  The paper contains an extension of the Khristianovich-Geertsma-de Klerk (KGD)
+model to the case when the confining rock pressure, which closes a hydraulic
+fracture, varies in the direction of its propagation. The extension is impelled
+by the need to simulate fracture hampering (acceleration) when it penetrates
+into a layer with increased (decreased) rock pressure. The paper presents the
+problem formulation, an efficient numerical method for its solving, examples of
+fractures propagating through layers with various stresses and general
+conclusions. It is established that when the fracture enters a layer with
+increased rock stresses (positive stress contrast), it actually stops. In this
+case, the fluid particle velocity drops practically to zero and the velocity
+near the tip oscillates about a small value until the fluid pressure increases
+to the level of the increased rock pressure. In the opposite case, when the
+fracture enters a layer with decreased stresses (negative stress contrast), the
+fracture front accelerates until the fluid pressure drops to the level of the
+decreased rock pressure. Physical considerations and numerical results imply
+that the transition through a boundary of layers with different stresses may be
+characterized by a simple dimensionless parameter. The latter is defined as the
+ratio of the average difference between the fluid and rock pressures (at the
+moment of reaching the contact) to the stress jump at the contact. The
+parameter is applicable to contacts with positive as well as negative
+contrasts. The model and the method developed are to serve for improvement of
+the popular pseudo three-dimensional (P3D) model.
+"
+28657,Trapping and Escape in a Turbid Medium,"  We investigate the absorption of diffusing molecules in a fluid-filled
+spherical beaker that contains many small reactive traps. The molecules are
+absorbed either by hitting a trap or by escaping via the beaker walls. In the
+physical situation where the number $N$ of traps is large and their radii $a$
+are small compared to the beaker radius $R$, the fraction of molecules $E$ that
+escape to the beaker wall and the complementary fraction $T$ that eventually
+are absorbed by the traps depend only on the dimensionless parameter
+combination $\lambda = Na/R$. We compute $E$ and $T$ as a function of $\lambda$
+for a spherical beaker and for beakers of other three-dimensional shapes. The
+asymptotic behavior is found to be universal: $1- E\sim \lambda$ for
+$\lambda\to 0$ and $E\sim\lambda^{-1/2}$ for $\lambda\to\infty$.
+"
+28658,Behavioural equivalences for timed systems,"  Timed transition systems are behavioural models that include an explicit
+treatment of time flow and are used to formalise the semantics of several
+foundational process calculi and automata. Despite their relevance, a general
+mathematical characterisation of timed transition systems and their behavioural
+theory is still missing. We introduce the first uniform framework for timed
+behavioural models that encompasses known behavioural equivalences such as
+timed bisimulations, timed language equivalences as well as their weak and
+time-abstract counterparts. All these notions of equivalences are naturally
+organised by their discriminating power in a spectrum. We prove that this
+result does not depend on the type of the systems under scrutiny: it holds for
+any generalisation of timed transition system. We instantiate our framework to
+timed transition systems and their quantitative extensions such as timed
+probabilistic systems.
+"
+28659,Hybrid PS-V Technique: A Novel Sensor Fusion Approach for Fast Mobile Eye-Tracking with Sensor-Shift Aware Correction,"  This paper introduces and evaluates a hybrid technique that fuses efficiently
+the eye-tracking principles of photosensor oculography (PSOG) and video
+oculography (VOG). The main concept of this novel approach is to use a few fast
+and power-economic photosensors as the core mechanism for performing high speed
+eye-tracking, whereas in parallel, use a video sensor operating at low
+sampling-rate (snapshot mode) to perform dead-reckoning error correction when
+sensor movements occur. In order to evaluate the proposed method, we simulate
+the functional components of the technique and present our results in
+experimental scenarios involving various combinations of horizontal and
+vertical eye and sensor movements. Our evaluation shows that the developed
+technique can be used to provide robustness to sensor shifts that otherwise
+could induce error larger than 5 deg. Our analysis suggests that the technique
+can potentially enable high speed eye-tracking at low power profiles, making it
+suitable to be used in emerging head-mounted devices, e.g. AR/VR headsets.
+"
+28660,Strider: A Hybrid Adaptive Distributed RDF Stream Processing Engine,"  Real-time processing of data streams emanating from sensors is becoming a
+common task in Internet of Things scenarios. The key implementation goal
+consists in efficiently handling massive incoming data streams and supporting
+advanced data analytics services like anomaly detection. In an on-going,
+industrial project, we found out that a 24/7 available stream processing engine
+usually faces dynamically changing data and workload characteristics. These
+changes impact the engine's performance and reliability. We propose Strider, a
+hybrid adaptive distributed RDF Stream Processing engine that optimizes logical
+query plan according to the state of data streams. Strider has been designed to
+guarantee important industrial properties such as scalability, high
+availability, fault-tolerant, high throughput and acceptable latency. These
+guarantees are obtained by designing the engine's architecture with
+state-of-the-art Apache components such as Spark and Kafka. We highlight the
+efficiency (e.g., on a single machine machine, up to 60x gain on throughput
+compared to state-of-the-art systems, a throughput of 3.1 million
+triples/second on a 9 machines cluster, a major breakthrough in this system's
+category) of Strider on real-world and synthetic data sets.
+"
+28661,"Adsorption of Metallic, Metalloidic, and Nonmetallic Adatoms on Two-Dimensional C3N","  Two-dimensional polyaniline with a C3N stoichiometry, is a newly fabricated
+material that has expected to possess fascinating electronic, thermal,
+mechanical and chemical properties . The possibility of further tuning the C3N
+properties upon the adsorption of foreign adatoms is thus among the most
+attractive researches. We carried out extensive ab-initio density functional
+theory (DFT) simulations to investigate the adsorption of various elements
+including nonmetallic, metalloidic and metallic elements on the C3N monolayer.
+While pristine C3N acts as a semiconductor with an indirect electronic band
+gap; the functionalization with nonmetallic and semimetallic elements leads to
+a p-type doping and induces metallic behavior to the monolayer. On the other
+hand, metallic adsorption depending on the adatom size and the number of
+valence electrons may result in semiconducting, half-metallic or metallic
+properties. Whenever metallic foreign atoms conduct metallic characteristics,
+they mostly lead to the n-type doping by electron donation to the surface.
+Moreover, adsorption of transition metals could enhance the magnetic behavior
+of the monolayer due to the contribution of d electronic states. These results
+suggest that C3N illustrates viable electronic-magnetic properties which could
+be promising for semiconducting, nanosensores and catalytic applications.
+"
+28662,Prevalent Intrinsic Emission from Nonaromatic Amino Acids and Poly(Amino Acids),"  Nonaromatic amino acids are generally believed to be nonemissive, owing to
+their lack of apparently remarkable conjugation within individual molecules.
+Here we report the intrinsic visible emission of nonaromatic amino acids and
+poly(amino acids) in concentrated solutions and solid powders. This unique and
+widespread luminescent characteristic can be well rationalized by the
+clustering-triggered emission (CTE) mechanism, namely the clustering of
+nonconventional chromophores (i.e. amino, carbonyl, and hydroxyl) and
+subsequent electron cloud overlap with simultaneously conformation
+rigidification. Such CTE mechanism is further supported by the single crystal
+structure analysis. Besides prompt fluorescence, room temperature
+phosphorescence (RTP) are also detected from the solids. Moreover, persistent
+RTP is observed in the powders of exampled poly(amino acid) of
+{\epsilon}-poly-L-lysine ({\epsilon}-PLL) after ceasing UV irradiation. These
+results not only illustrate the feasibility of employing the building blocks of
+nonaromatic amino acids in the exploration of new luminescent biomolecules, but
+also provide significant implications for the RTP of peptides and proteins at
+aggregated or crystalline states.
+"
+28663,On orthogonal tensors and best rank-one approximation ratio,"  As is well known, the smallest possible ratio between the spectral norm and
+the Frobenius norm of an $m \times n$ matrix with $m \le n$ is $1/\sqrt{m}$ and
+is (up to scalar scaling) attained only by matrices having pairwise orthonormal
+rows. In the present paper, the smallest possible ratio between spectral and
+Frobenius norms of $n_1 \times \dots \times n_d$ tensors of order $d$, also
+called the best rank-one approximation ratio in the literature, is
+investigated. The exact value is not known for most configurations of $n_1 \le
+\dots \le n_d$. Using a natural definition of orthogonal tensors over the real
+field (resp., unitary tensors over the complex field), it is shown that the
+obvious lower bound $1/\sqrt{n_1 \cdots n_{d-1}}$ is attained if and only if a
+tensor is orthogonal (resp., unitary) up to scaling. Whether or not orthogonal
+or unitary tensors exist depends on the dimensions $n_1,\dots,n_d$ and the
+field. A connection between the (non)existence of real orthogonal tensors of
+order three and the classical Hurwitz problem on composition algebras can be
+established: existence of orthogonal tensors of size $\ell \times m \times n$
+is equivalent to the admissibility of the triple $[\ell,m,n]$ to the Hurwitz
+problem. Some implications for higher-order tensors are then given. For
+instance, real orthogonal $n \times \dots \times n$ tensors of order $d \ge 3$
+do exist, but only when $n = 1,2,4,8$. In the complex case, the situation is
+more drastic: unitary tensors of size $\ell \times m \times n$ with $\ell \le m
+\le n$ exist only when $\ell m \le n$. Finally, some numerical illustrations
+for spectral norm computation are presented.
+"
+28664,Infrared spectroscopy of molecular ions in selected rotational and spin-orbit states,"  First results are presented obtained with an experimental setup developed to
+record IR spectra of rotationally state-selected ions. The method we use is a
+state-selective version of a method developed by S. Schlemmer, D. Gerlich and
+coworkers (Int. J. Mass. Spec. 185, 589 (1999); J. Chem. Phys. 117, 2068
+(2002)) to record IR spectra of ions. Ions are produced in specific rotational
+levels using mass-analyzed-threshold-ionization spectroscopy. The
+state-selected ions generated by pulsed-field ionization of Rydberg states of
+high principal quantum number ($n\approx200$) are extracted toward an octupole
+ion guide containing a neutral target gas. Prior to entering the octupole, the
+ions are excited by an IR laser. The target gas is chosen so that only excited
+ions react to form product ions. These product ions are detected mass
+selectively as a function of the IR laser wavenumber. To illustrate this
+method, we present IR spectra of C$_2$H$_2^+$ in selected rotational levels of
+the $^2\Pi_{\textrm{u,}3/2}$ and $^2\Pi_{\textrm{u,}1/2}$ spin-orbit components
+of the vibronic ground state.
+"
+28665,Constructions of Batch Codes via Finite Geometry,"  A primitive $k$-batch code encodes a string $x$ of length $n$ into string $y$
+of length $N$, such that each multiset of $k$ symbols from $x$ has $k$ mutually
+disjoint recovering sets from $y$. We develop new explicit and random coding
+constructions of linear primitive batch codes based on finite geometry. In some
+parameter regimes, our proposed codes have lower redundancy than previously
+known batch codes.
+"
+28666,SO*(2N) coherent states for loop quantum gravity,"  A SU(2) intertwiner with N legs can be interpreted as the quantum state of a
+convex polyhedron with N faces (when working in 3d). We show that the
+intertwiner Hilbert space carries a representation of the non-compact group
+SO*(2N). This group can be viewed as the subgroup of the symplectic group
+Sp(4N,R) which preserves the SU(2) invariance. We construct the associated
+Perelomov coherent states and discuss the notion of semi-classical limit, which
+is more subtle that we could expect. Our work completes the work by Freidel and
+Livine which focused on the U(N) subgroup of SO*(2N).
+"
+28667,A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I,"  In recent years, we have observed a significant trend towards filling the gap
+between social network analysis and control. This trend was enabled by the
+introduction of new mathematical models describing dynamics of social groups,
+the advancement in complex networks theory and multi-agent systems, and the
+development of modern computational tools for big data analysis. The aim of
+this tutorial is to highlight a novel chapter of control theory, dealing with
+applications to social systems, to the attention of the broad research
+community. This paper is the first part of the tutorial, and it is focused on
+the most classical models of social dynamics and on their relations to the
+recent achievements in multi-agent systems.
+"
+28668,von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification,"  A number of pattern recognition tasks, \textit{e.g.}, face verification, can
+be boiled down to classification or clustering of unit length directional
+feature vectors whose distance can be simply computed by their angle. In this
+paper, we propose the von Mises-Fisher (vMF) mixture model as the theoretical
+foundation for an effective deep-learning of such directional features and
+derive a novel vMF Mixture Loss and its corresponding vMF deep features. The
+proposed vMF feature learning achieves the characteristics of discriminative
+learning, \textit{i.e.}, compacting the instances of the same class while
+increasing the distance of instances from different classes. Moreover, it
+subsumes a number of popular loss functions as well as an effective method in
+deep learning, namely normalization. We conduct extensive experiments on face
+verification using 4 different challenging face datasets, \textit{i.e.}, LFW,
+YouTube faces, CACD and IJB-A. Results show the effectiveness and excellent
+generalization ability of the proposed approach as it achieves state-of-the-art
+results on the LFW, YouTube faces and CACD datasets and competitive results on
+the IJB-A dataset.
+"
+28669,Combinatorial Optimization on Gate Model Quantum Computers: A Survey,"  The advent of quantum computing processors with possibility to scale beyond
+experimental capacities magnifies the importance of studying their
+applications. Combinatorial optimization problems can be one of the promising
+applications of these new devices. These problems are recurrent in industrial
+applications and they are in general difficult for classical computing
+hardware. In this work, we provide a survey of the approaches to solving
+different types of combinatorial optimization problems, in particular quadratic
+unconstrained binary optimization (QUBO) problems on a gate model quantum
+computer. We focus mainly on four different approaches including digitizing the
+adiabatic quantum computing, global quantum optimization algorithms, the
+quantum algorithms that approximate the ground state of a general QUBO problem,
+and quantum sampling. We also discuss the quantum algorithms that are custom
+designed to solve certain types of QUBO problems.
+"
+28670,An effective and secure user authenticated protocol for Location based services in road networks,"  Road network is employed for exchanging the information among the vehicles
+where accidents and traffic information can be delivered, or receive services
+by an infrastructure. Although wireless communication systems yield an
+efficient traffic system and provide ease to the drivers, there is a chance of
+a traffic disturbance and risk to drivers through malicious information. So,
+there should be examined a way to limit the chance of an intermediate attack.
+This paper presents an efficient user based authentication protocol for
+location based services to secure address configuration for IPv6-based
+mix-zones over the road network. This protocol authenticates to inspect
+vehicles actions confidentially and have the following characteristics (1)
+Anonymous authentication: a message issuer can be authenticated. (2) Privacy:
+Communication content is confidential. The cost must be reduced through the
+address configuration scheme to improve the scalability. (3) Efficiency: it
+attains quick message verification, low storage requirements, and in case of a
+dispute, provides cost efficient identity tracking. Vehicles movement, the
+variation of velocity and distance are considered to maintain as many common
+users as possible by reducing the cost. The performance evaluation and cost
+analysis show that our framework can reduce the cost and gain outperformed
+results. This model can achieve reliability and efficiency with packet rate
+information. This user authenticated key establishment protocol has
+comparatively shorter time response, diminishes cost, less packet loss
+information and enhanced privacy preservation against malicious attacks
+compared with existing methods.
+"
+28671,Performance Evaluation of the Zero-Multipole Summation Method in Modern Molecular Dynamics Software,"  We evaluate the practical performance of the zero-multiple summation method
+(ZMM), a method for approximately calculating electrostatic interactions in
+molecular dynamics simulations. The performance of the ZMM is compared with
+that of the smooth particle mesh Ewald method (SPME). Even though the ZMM uses
+a larger cutoff distance than the SPME does, the performance of the ZMM is
+found to be comparable to or better than that of the SPME. In particular, the
+ZMM with quadrupole or octupole cancellation and no damping factor is an
+excellent candidate for the fast calculation of electrostatic potentials.
+"
+28672,Exact Limits of Inference in Coalescent Models,"  Recovery of population size history from molecular sequence data is an
+important problem in population genetics. Inference commonly relies on a
+coalescent model linking the population size history to genealogies. The high
+computational cost of estimating parameters from these models usually compels
+researchers to select a subset of the available data or to rely on
+non-sufficient summary statistics for statistical inference. We consider the
+problem of recovering the true population size history from two possible
+alternatives on the basis of coalescent time data. We give exact expressions
+for the probability of selecting the correct alternative in a variety of
+biologically interesting cases as a function of the separation between the
+alternative size histories, the number of individuals, loci, and the sampling
+times. The results are applied to human population history. This work has
+significant implications for optimal design when the inferential goal is to
+test scientific hypotheses about population size trajectories in coalescent
+models with and without recombination.
+"
+28673,The phases of large networks with edge and triangle constraints,"  Based on numerical simulation and local stability analysis we describe the
+structure of the phase space of the edge/triangle model of random graphs. We
+support simulation evidence with mathematical proof of continuity and
+discontinuity for many of the phase transitions. All but one of themany phase
+transitions in this model break some form of symmetry, and we use this model to
+explore how changes in symmetry are related to discontinuities at these
+transitions.
+"
+28674,Information-theoretic Model Identification and Policy Search using Physics Engines with Application to Robotic Manipulation,"  We consider the problem of a robot learning the mechanical properties of
+objects through physical interaction with the object, and introduce a
+practical, data-efficient approach for identifying the motion models of these
+objects. The proposed method utilizes a physics engine, where the robot seeks
+to identify the inertial and friction parameters of the object by simulating
+its motion under different values of the parameters and identifying those that
+result in a simulation which matches the observed real motions. The problem is
+solved in a Bayesian optimization framework. The same framework is used for
+both identifying the model of an object online and searching for a policy that
+would minimize a given cost function according to the identified model.
+Experimental results both in simulation and using a real robot indicate that
+the proposed method outperforms state-of-the-art model-free reinforcement
+learning approaches.
+"
+28675,Yang-Baxter representations of the infinite symmetric group,"  Every unitary involutive solution of the quantum Yang-Baxter equation
+(""R-matrix"") defines an extremal character and a representation of the infinite
+symmetric group $S_\infty$. We give a complete classification of all such
+Yang-Baxter characters and determine which extremal characters of $S_\infty$
+are of Yang-Baxter form.
+Calling two involutive R-matrices equivalent if they have the same character
+and the same dimension, we show that equivalence classes are classified by
+pairs of Young diagrams, and construct an explicit normal form R-matrix for
+each class. Using operator-algebraic techniques (subfactors), we prove that two
+R-matrices are equivalent if and only if they have similar partial traces.
+Furthermore, we describe the algebraic structure of the equivalence classes
+of all involutive R-matrices, and discuss several classes of examples. These
+include unitary Yang-Baxter representations of the Temperley-Lieb algebra at
+loop parameter $\delta=2$, which can be completely classified in terms of their
+rank and dimension.
+"
+28676,Probability of radiation of twisted photons by classical currents,"  The general formula for the probability of radiation of a twisted photon by a
+classical current is derived. The general theory of generation of twisted
+photons by undulators is developed. It is proved that the probability to record
+a twisted photon produced by a classical current is equal to the average number
+of twisted photons in a given state. The general formula for the projection of
+the total angular momentum of twisted photons with given the energy, the
+longitudinal projection of momentum, and the helicity is obtained. The symmetry
+property of the average number of twisted photons produced by a charged
+particle moving along a planar trajectory is found. The explicit formulas for
+the average number of twisted photons generated by undulators both in the
+dipole and wiggler regimes are obtained. It is established that, for the
+forward radiation of an ideal right-handed helical undulator, the harmonic
+number $n$ of the twisted photon coincides with its projection of the total
+angular momentum $m$. As for the ideal left-handed helical undulator, we obtain
+that $m=-n$. It is found that the forward radiation of twisted photons by a
+planar undulator obeys the selection rule that $n+m$ is an even number. It
+turns out that the average number of twisted photons produced by the undulator
+and detected off the undulator axis is a periodic function of $m$ in a certain
+spectral band of the quantum numbers $m$.
+"
+28677,Interval observer for uncertain time-varying SIR-SI epidemiological model of vector-borne disease,"  The issue of state estimation is considered for an SIR-SI epidemiological
+model describing a vector-borne disease such as dengue fever, subject to
+seasonal variations. Assuming continuous measurement of the incidence rate
+(that is the number of new infectives in the host population per unit time), a
+class of interval observers with estimate-dependent gain is constructed, and
+asymptotic error bounds are provided. The synthesis method is based on the
+search for a common linear Lyapunov function for monotone systems that
+represent the evolution of the estimation errors.
+"
+28678,Customer Lifetime Value Prediction Using Embeddings,"  We describe the Customer LifeTime Value (CLTV) prediction system deployed at
+ASOS.com, a global online fashion retailer. CLTV prediction is an important
+problem in e-commerce where an accurate estimate of future value allows
+retailers to effectively allocate marketing spend, identify and nurture high
+value customers and mitigate exposure to losses. The system at ASOS provides
+daily estimates of the future value of every customer and is one of the
+cornerstones of the personalised shopping experience. The state of the art in
+this domain uses large numbers of handcrafted features and ensemble regressors
+to forecast value, predict churn and evaluate customer loyalty. Recently,
+domains including language, vision and speech have shown dramatic advances by
+replacing handcrafted features with features that are learned automatically
+from data. We detail the system deployed at ASOS and show that learning feature
+representations is a promising extension to the state of the art in CLTV
+modelling. We propose a novel way to generate embeddings of customers, which
+addresses the issue of the ever changing product catalogue and obtain a
+significant improvement over an exhaustive set of handcrafted features.
+"
+28679,Non-Adiabatic Quantum Molecular Dynamics with Detailed Balance,"  We present an approach for carrying out non-adiabatic molecular dynamics
+simulations of systems in which non-adiabatic transitions arise from the
+coupling between the classical atomic motions and a quasi-continuum of
+electronic quantum states. Such conditions occur in many research areas,
+including chemistry at metal surfaces, radiation damage of materials, and warm
+dense matter physics. The classical atomic motions are governed by stochastic
+Langevin-like equations, while the quantum electron dynamics is described by a
+master equation for the populations of the electronic states. These working
+equations are obtained from a first-principle derivation. Remarkably, unlike
+the widely used Ehrenfest and surface-hopping methods, the approach naturally
+satisfies the principle of detailed balance at equilibrium and, therefore, can
+describe the evolution to thermal equilibrium from an arbitrary initial state.
+In addition, unlike other schemes, there is no need to explicitly propagate
+wave functions in time.
+"
+28680,Inferring Personal Economic Status from Social Network Location,"  It is commonly believed that patterns of social ties affect individuals'
+economic status. Here, we translate this concept into an operational definition
+at the network level, which allows us to infer the economic wellbeing of
+individuals through a measure of their location and influence in the social
+network. We analyze two large-scale sources: telecommunications and financial
+data of a whole country's population. Our results show that an individual's
+location, measured as the optimal collective influence to the structural
+integrity of the social network, is highly correlated with personal economic
+status. The observed social network patterns of influence mimics the patterns
+of economic inequality. For pragmatic use and validation, we carry out a
+marketing campaign that shows a three-fold increase in response rate by
+targeting individuals identified by our social network metrics as compared to
+random targeting. Our strategy can also be useful in maximizing the effects of
+large-scale economic stimulus policies.
+"
+28681,Complexity Analysis of a Trust Funnel Algorithm for Equality Constrained Optimization,"  A method is proposed for solving equality constrained nonlinear optimization
+problems involving twice continuously differentiable functions. The method
+employs a trust funnel approach consisting of two phases: a first phase to
+locate an $\epsilon$-feasible point and a second phase to seek optimality while
+maintaining at least $\epsilon$-feasibility. A two-phase approach of this kind
+based on a cubic regularization methodology was recently proposed along with a
+supporting worst-case iteration complexity analysis. Unfortunately, however, in
+that approach, the objective function is completely ignored in the first phase
+when $\epsilon$-feasibility is sought. The main contribution of the method
+proposed in this paper is that the same worst-case iteration complexity is
+achieved, but with a first phase that also accounts for improvements in the
+objective function. As such, the method typically requires fewer iterations in
+the second phase, as the results of numerical experiments demonstrate.
+"
+28682,A Decomposition Algorithm to Solve the Multi-Hop Peer-to-Peer Ride-Matching Problem,"  In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P)
+ride-matching problem as a binary program. We formulate this problem as a
+many-to-many problem in which a rider can travel by transferring between
+multiple drivers, and a driver can carry multiple riders. We propose a
+pre-processing procedure to reduce the size of the problem, and devise a
+decomposition algorithm to solve the original ride-matching problem to
+optimality by means of solving multiple smaller problems. We conduct extensive
+numerical experiments to demonstrate the computational efficiency of the
+proposed algorithm and show its practical applicability to reasonably-sized
+dynamic ride-matching contexts. Finally, in the interest of even lower solution
+times, we propose heuristic solution methods, and investigate the trade-offs
+between solution time and accuracy.
+"
+28683,JOKARUS - Design of a compact optical iodine frequency reference for a sounding rocket mission,"  We present the design of a compact absolute optical frequency reference for
+space applications based on hyperfine transitions in molecular iodine with a
+targeted fractional frequency instability of better than $3\cdot 10^{-14}$. It
+is based on a micro-integrated extended cavity diode laser with integrated
+optical amplifier, fiber pigtailed second harmonic generation wave-guide
+modules, and a quasi-monolithic spectroscopy setup with operating electronics.
+The instrument described here is scheduled for launch end of 2017 aboard the
+TEXUS 54 sounding rocket as an important qualification step towards space
+application of iodine frequency references and related technologies. The
+payload will operate autonomously and its optical frequency will be compared to
+an optical frequency comb during its space flight.
+"
+28684,"Dark matter haloes in modified gravity and dark energy: interaction rate, small-, and large-scale alignment","  We study the properties of dark matter haloes in a wide range of modified
+gravity models, namely, $f(R)$, DGP, and interacting dark energy models. We
+study the effects of modified gravity and dark energy on the internal
+properties of haloes, such as the spin and the structural parameters. We find
+that $f(R)$ gravity enhance the median value of the Bullock spin parameter, but
+could not detect such effects for DGP and coupled dark energy. $f(R)$ also
+yields a lower median sphericity and oblateness, while coupled dark energy has
+the opposite effect. However, these effects are very small. We then study the
+interaction rate of haloes in different gravity, and find that only strongly
+coupled dark energy models enhance the interaction rate. We then quantify the
+enhancement of the alignment of the spins of interacting halo pairs by modified
+gravity. Finally, we study the alignment of the major axes of haloes with the
+large-scale structures. The alignment of the spins of interacting pairs of
+haloes in DGP and coupled dark energy models show no discrepancy with GR, while
+$f(R)$ shows a weaker alignment. Strongly coupled dark energy shows a stronger
+alignment of the halo shape with the large-scale structures.
+"
+28685,"Luminous, pc-scale CO 6-5 emission in the obscured nucleus of NGC1377","  High resolution submm observations are important in probing the morphology,
+column density and dynamics of obscured active galactic nuclei (AGNs). With
+high resolution (0.06 x 0.05) ALMA 690 GHz observations we have found bright
+(TB >80 K) and compact (FWHM 10x7 pc) CO 6-5 line emission in the nucleus of
+the extremely radio-quiet galaxy NGC1377. The integrated CO 6-5 intensity is
+aligned with the previously discovered jet/outflow of NGC1377 and is tracing
+the dense (n>1e4 cm-3), hot gas at the base of the outflow. The velocity
+structure is complex and shifts across the jet/outflow are discussed in terms
+of jet-rotation or separate, overlapping kinematical components. High velocity
+gas (deltaV +-145 km/s) is detected inside r<2-3 pc and we suggest that it is
+emerging from an inclined rotating disk or torus of position angle PA=140+-20
+deg with a dynamical mass of approx 3e6 Msun. This mass is consistent with that
+of a supermassive black hole (SMBH), as inferred from the M-sigma relation. The
+gas mass of the proposed disk/torus constitutes <3% of the nuclear dynamical
+mass. In contrast to the intense CO 6-5 line emission, we do not detect dust
+continuum with an upper limit of S(690GHz)<2mJy. The corresponding, 5 pc, H2
+column density is estimated to N(H2)<3e23 cm-2, which is inconsistent with a
+Compton Thick (CT) source. We discuss the possibility that CT obscuration may
+be occuring on small (subparsec) or larger scales. From SED fitting we suggest
+that half of the IR emission of NGC1377 is nuclear and the rest (mostly the
+far-infrared (FIR)) is more extended. The extreme radio quietness, and the lack
+of emission from other star formation tracers, raise questions on the origin of
+the FIR emission. We discuss the possibility that it is arising from the
+dissipation of shocks in the molecular jet/outflow or from irradiation by the
+nuclear source along the poles.
+"
+28686,Grounded Language Learning in a Simulated 3D World,"  We are increasingly surrounded by artificially intelligent technology that
+takes decisions and executes actions on our behalf. This creates a pressing
+need for general means to communicate with, instruct and guide artificial
+agents, with human language the most compelling means for such communication.
+To achieve this in a scalable fashion, agents must be able to relate language
+to the world and to actions; that is, their understanding of language must be
+grounded and embodied. However, learning grounded language is a notoriously
+challenging problem in artificial intelligence research. Here we present an
+agent that learns to interpret language in a simulated 3D environment where it
+is rewarded for the successful execution of written instructions. Trained via a
+combination of reinforcement and unsupervised learning, and beginning with
+minimal prior knowledge, the agent learns to relate linguistic symbols to
+emergent perceptual representations of its physical surroundings and to
+pertinent sequences of actions. The agent's comprehension of language extends
+beyond its prior experience, enabling it to apply familiar language to
+unfamiliar situations and to interpret entirely novel instructions. Moreover,
+the speed with which this agent learns new words increases as its semantic
+knowledge grows. This facility for generalising and bootstrapping semantic
+knowledge indicates the potential of the present approach for reconciling
+ambiguous natural language with the complexity of the physical world.
+"
+28687,Central limit theorem for exponentially quasi-local statistics of spin models on Cayley graphs,"  Central limit theorems for linear statistics of lattice random fields
+(including spin models) are usually proven under suitable mixing conditions or
+quasi-associativity. Many interesting examples of spin models do not satisfy
+mixing conditions, and on the other hand, it does not seem easy to show central
+limit theorem for local statistics via quasi-associativity. In this work, we
+prove general central limit theorems for local statistics and exponentially
+quasi-local statistics of spin models on discrete Cayley graphs with polynomial
+growth. Further, we supplement these results by proving similar central limit
+theorems for random fields on discrete Cayley graphs and taking values in a
+countable space but under the stronger assumptions of {\alpha}-mixing (for
+local statistics) and exponential {\alpha}-mixing (for exponentially
+quasi-local statistics). All our central limit theorems assume a suitable
+variance lower bound like many others in the literature. We illustrate our
+general central limit theorem with specific examples of lattice spin models and
+statistics arising in computational topology, statistical physics and random
+networks. Examples of clustering spin models include quasi-associated spin
+models with fast decaying covariances like the off-critical Ising model, level
+sets of Gaussian random fields with fast decaying covariances like the massive
+Gaussian free field and determinantal point processes with fast decaying
+kernels. Examples of local statistics include intrinsic volumes, face counts,
+component counts of random cubical complexes while exponentially quasi-local
+statistics include nearest neighbour distances in spin models and Betti numbers
+of sub-critical random cubical complexes.
+"
+28688,NA62 Charged Particle Hodoscope. Design and performance in 2016 run,"  The NA62 experiment at CERN SPS is aimed to measure the branching ratio of
+the ultra-rare decay $K^+\rightarrow\pi^+ \nu \bar{\nu}$ with 10\% accuracy.
+The experiment operates with a 75 GeV/c high intensity (750 MHz) secondary
+beam. A new detector, named Charged Particle Hodoscope (CHOD), designed to
+produce an input signal to the L0 trigger processor for events with charged
+particles produced in kaon decays, has been assembled, installed, integrated in
+NA62 Data Acquisition System (DAQ) and commissioned in 2016. During the whole
+2016 run the detector has been in continuous operation. Design and performance
+features of the detector are presented.
+"
+28689,New efficient algorithms for multiple change-point detection with kernels,"  Several statistical approaches based on reproducing kernels have been
+proposed to detect abrupt changes arising in the full distribution of the
+observations and not only in the mean or variance. Some of these approaches
+enjoy good statistical properties (oracle inequality, \ldots). Nonetheless,
+they have a high computational cost both in terms of time and memory. This
+makes their application difficult even for small and medium sample sizes ($n<
+10^4$). This computational issue is addressed by first describing a new
+efficient and exact algorithm for kernel multiple change-point detection with
+an improved worst-case complexity that is quadratic in time and linear in
+space. It allows dealing with medium size signals (up to $n \approx 10^5$).
+Second, a faster but approximation algorithm is described. It is based on a
+low-rank approximation to the Gram matrix. It is linear in time and space. This
+approximation algorithm can be applied to large-scale signals ($n \geq 10^6$).
+These exact and approximation algorithms have been implemented in \texttt{R}
+and \texttt{C} for various kernels. The computational and statistical
+performances of these new algorithms have been assessed through empirical
+experiments. The runtime of the new algorithms is observed to be faster than
+that of other considered procedures. Finally, simulations confirmed the higher
+statistical accuracy of kernel-based approaches to detect changes that are not
+only in the mean. These simulations also illustrate the flexibility of
+kernel-based approaches to analyze complex biological profiles made of DNA copy
+number and allele B frequencies. An R package implementing the approach will be
+made available on github.
+"
+28690,Approximating geodesics via random points,"  Given a `cost' functional $F$ on paths $\gamma$ in a domain
+$D\subset\mathbb{R}^d$, in the form $F(\gamma) = \int_0^1
+f(\gamma(t),\dot\gamma(t))dt$, it is of interest to approximate its minimum
+cost and geodesic paths. Let $X_1,\ldots, X_n$ be points drawn independently
+from $D$ according to a distribution with a density. Form a random geometric
+graph on the points where $X_i$ and $X_j$ are connected when $0<|X_i -
+X_j|<\epsilon$, and the length scale $\epsilon=\epsilon_n$ vanishes at a
+suitable rate.
+For a general class of functionals $F$, associated to Finsler and other
+distances on $D$, using a probabilistic form of Gamma convergence, we show that
+the minimum costs and geodesic paths, with respect to types of approximating
+discrete `cost' functionals, built from the random geometric graph, converge
+almost surely in various senses to those corresponding to the continuum cost
+$F$, as the number of sample points diverges. In particular, the geodesic path
+convergence shown appears to be among the first results of its kind.
+"
+28691,Topological Nodal-line Semimetals in Two Dimensions with time-reversal symmetry breaking,"  Topological nodal-line semimetals (TNLSs) exhibit exotic physical phenomena
+due to a one-dimensional (1D) band touching line, rather than discrete (Dirac
+or Weyl) points. While so far proposed two-dimensional (2D) TNLSs possess
+closed nodal lines (NLs) only when spin-orbit coupling (SOC) is neglected, here
+using Na$_3$Bi trilayers as an example, we show that 2D TNLSs can been obtained
+from topological (crystalline) insulators (TI/TCI) by time-reversal symmetry
+breaking even in the presence of SOC. We further reveal that these obtained NLs
+are protected by crystalline mirror symmetry, while a mirror symmetry breaking
+perturbation opens a full gap thus giving rise to a phase transition from 2D
+TNLS to a quantum anomalous Hall insulator (QAHI). We thereby uncover a close
+correlation between various topological phases. Remarkably, a strong spin Hall
+effect, important for transport applications, is predicted in 2D TNLS. Finally,
+a Na$_2$CrBi trilayer is proposed to realize the 2D TNLS without extrinsic
+magnetic field. Our work not only proposes a new strategy for realizing 2D
+TNLSs with truely closed NLs, but also reveals potential applications of TNLS
+in spintronics.
+"
+28692,Almost Sure Invariance Principle for non-autonomous holomorphic dynamics in $\Bbb{P}^k$,"  We prove almost sure invariance principle, a strong form of approximation by
+Brownian motion, for non-autonomous holomorphic dynamical systems on complex
+projective space $\Bbb{P}^k$ for Hölder continuous and DSH observables.
+"
+28693,Cluster validation by measurement of clustering characteristics relevant to the user,"  There are many cluster analysis methods that can produce quite different
+clusterings on the same dataset. Cluster validation is about the evaluation of
+the quality of a clustering; ""relative cluster validation"" is about using such
+criteria to compare clusterings. This can be used to select one of a set of
+clusterings from different methods, or from the same method ran with different
+parameters such as different numbers of clusters.
+There are many cluster validation indexes in the literature. Most of them
+attempt to measure the overall quality of a clustering by a single number, but
+this can be inappropriate. There are various different characteristics of a
+clustering that can be relevant in practice, depending on the aim of
+clustering, such as low within-cluster distances and high between-cluster
+separation.
+In this paper, a number of validation criteria will be introduced that refer
+to different desirable characteristics of a clustering, and that characterise a
+clustering in a multidimensional way. In specific applications the user may be
+interested in some of these criteria rather than others. A focus of the paper
+is on methodology to standardise the different characteristics so that users
+can aggregate them in a suitable way specifying weights for the various
+criteria that are relevant in the clustering application at hand.
+"
+28694,Galaxy And Mass Assembly (GAMA): The galaxy stellar mass function to $z=0.1$ from the r-band selected equatorial regions,"  We derive the low redshift galaxy stellar mass function (GSMF), inclusive of
+dust corrections, for the equatorial Galaxy And Mass Assembly (GAMA) dataset
+covering 180 deg$^2$. We construct the mass function using a density-corrected
+maximum volume method, using masses corrected for the impact of optically thick
+and thin dust. We explore the galactic bivariate brightness plane
+($M_\star-\mu$), demonstrating that surface brightness effects do not
+systematically bias our mass function measurement above 10$^{7.5}$ M$_{\odot}$.
+The galaxy distribution in the $M-\mu$-plane appears well bounded, indicating
+that no substantial population of massive but diffuse or highly compact
+galaxies are systematically missed due to the GAMA selection criteria. The GSMF
+is {fit with} a double Schechter function, with $\mathcal
+M^\star=10^{10.78\pm0.01\pm0.20}M_\odot$,
+$\phi^\star_1=(2.93\pm0.40)\times10^{-3}h_{70}^3$Mpc$^{-3}$,
+$\alpha_1=-0.62\pm0.03\pm0.15$,
+$\phi^\star_2=(0.63\pm0.10)\times10^{-3}h_{70}^3$Mpc$^{-3}$, and
+$\alpha_2=-1.50\pm0.01\pm0.15$. We find the equivalent faint end slope as
+previously estimated using the GAMA-I sample, although we find a higher value
+of $\mathcal M^\star$. Using the full GAMA-II sample, we are able to fit the
+mass function to masses as low as $10^{7.5}$ $M_\odot$, and assess limits to
+$10^{6.5}$ $M_\odot$. Combining GAMA-II with data from G10-COSMOS we are able
+to comment qualitatively on the shape of the GSMF down to masses as low as
+$10^{6}$ $M_\odot$. Beyond the well known upturn seen in the GSMF at $10^{9.5}$
+the distribution appears to maintain a single power-law slope from $10^9$ to
+$10^{6.5}$. We calculate the stellar mass density parameter given our
+best-estimate GSMF, finding $\Omega_\star= 1.66^{+0.24}_{-0.23}\pm0.97
+h^{-1}_{70} \times 10^{-3}$, inclusive of random and systematic uncertainties.
+"
+28695,DNA Steganalysis Using Deep Recurrent Neural Networks,"  Recent advances in next-generation sequencing technologies have facilitated
+the use of deoxyribonucleic acid (DNA) as a novel covert channels in
+steganography. There are various methods that exist in other domains to detect
+hidden messages in conventional covert channels. However, they have not been
+applied to DNA steganography. The current most common detection approaches,
+namely frequency analysis-based methods, often overlook important signals when
+directly applied to DNA steganography because those methods depend on the
+distribution of the number of sequence characters. To address this limitation,
+we propose a general sequence learning-based DNA steganalysis framework. The
+proposed approach learns the intrinsic distribution of coding and non-coding
+sequences and detects hidden messages by exploiting distribution variations
+after hiding these messages. Using deep recurrent neural networks (RNNs), our
+framework identifies the distribution variations by using the classification
+score to predict whether a sequence is to be a coding or non-coding sequence.
+We compare our proposed method to various existing methods and biological
+sequence analysis methods implemented on top of our framework. According to our
+experimental results, our approach delivers a robust detection performance
+compared to other tools.
+"
+28696,Deconstructing Generative Adversarial Networks,"  We deconstruct the performance of GANs into three components:
+1. Formulation: we propose a perturbation view of the population target of
+GANs. Building on this interpretation, we show that GANs can be viewed as a
+generalization of the robust statistics framework, and propose a novel GAN
+architecture, termed as Cascade GANs, to provably recover meaningful
+low-dimensional generator approximations when the real distribution is
+high-dimensional and corrupted by outliers.
+2. Generalization: given a population target of GANs, we design a systematic
+principle, projection under admissible distance, to design GANs to meet the
+population requirement using finite samples. We implement our principle in
+three cases to achieve polynomial and sometimes near-optimal sample
+complexities: (1) learning an arbitrary generator under an arbitrary
+pseudonorm; (2) learning a Gaussian location family under total variation
+distance, where we utilize our principle provide a new proof for the optimality
+of Tukey median viewed as GANs; (3) learning a low-dimensional Gaussian
+approximation of a high-dimensional arbitrary distribution under Wasserstein
+distance. We demonstrate a fundamental trade-off in the approximation error and
+statistical error in GANs, and show how to apply our principle with empirical
+samples to predict how many samples are sufficient for GANs in order not to
+suffer from the discriminator winning problem.
+3. Optimization: we demonstrate alternating gradient descent is provably not
+even locally stable in optimizating the GAN formulation of PCA. We diagnose the
+problem as the minimax duality gap being non-zero, and propose a new GAN
+architecture whose duality gap is zero, where the value of the game is equal to
+the previous minimax value (not the maximin value). We prove the new GAN
+architecture is globally stable in optimization under alternating gradient
+descent.
+"
+28697,A plug-in approach to maximising precision at the top and recall at the top,"  For information retrieval and binary classification, we show that precision
+at the top (or precision at k) and recall at the top (or recall at k) are
+maximised by thresholding the posterior probability of the positive class. This
+finding is a consequence of a result on constrained minimisation of the
+cost-sensitive expected classification error which generalises an earlier
+related result from the literature.
+"
+28698,Perturb and Combine to Identify Influential Spreaders in Real-World Networks,"  Recent research has shown that graph degeneracy algorithms, which decompose a
+network into a hierarchy of nested subgraphs of decreasing size and increasing
+density, are very effective at detecting the good spreaders in a network.
+However, it is also known that degeneracy-based decompositions of a graph are
+unstable to small perturbations of the network structure. In Machine Learning,
+the performance of unstable classification and regression methods, such as
+fully-grown decision trees, can be greatly improved by using Perturb and
+Combine (P&C) strategies such as bagging (bootstrap aggregating). Therefore, we
+propose a P&C procedure for networks that (1) creates many perturbed versions
+of a given graph, (2) applies a node scoring function separately to each graph
+(such as a degeneracy-based one), and (3) combines the results. We conduct
+real-world experiments on the tasks of identifying influential spreaders in
+large social networks, and influential words (keywords) in small word
+co-occurrence networks. We use the k-core, generalized k-core, and PageRank
+algorithms as our vertex scoring functions. In each case, using the aggregated
+scores brings significant improvements compared to using the scores computed on
+the original graphs. Finally, a bias-variance analysis suggests that our P&C
+procedure works mainly by reducing bias, and that therefore, it should be
+capable of improving the performance of all vertex scoring functions, not only
+unstable ones.
+"
+28699,A local characterization of $B_2$ regular crystals,"  Stembridge characterizes regular crystals associated with a simply-laced GCM
+in terms of local graph-theoretic quantities. We give a similar axiomatization
+for $B_2$ regular crystals (and thus for regular crystals of finite GCM except
+$G_2$ and affine GCM except $A^{(1)}_{1},G^{(1)}_{2},A^{(2)}_{2},D^{(3)}_4$).
+Our motivation comes from a generalization of Schur partition theorem by the
+author jointly with Masaki Watanabe proved indirectly via theory of perfect
+crystal.
+"
+28700,Trust architectures in payment systems: the great bifurcation,"  Payments architectures are on the verge of a great bifurcation that must be
+documented in order to be debated. Google is moving towards a quasi bank while
+Apple and Google disseminate payment systems over smartphones. At the same
+time, block chain might become a distributed ledger introducing a radical new
+model of trusted third-party. The detailed history of credit card systems helps
+understand why the game of security has always been trigged by a delegation
+process of the risk to third parties and by the cat-and-mouse game of security
+and fraud. Technologies were designed to solve these issues but have always
+been closely related to innovations in institutional assemblages. These
+payments systems shape our social life and the stakes of trust that we put in
+these architectures require a truly political examination.
+"
+28701,The UTMOST: A hybrid digital signal processor transforms the MOST,"  The Molonglo Observatory Synthesis Telescope (MOST) is an 18,000 square meter
+radio telescope situated some 40 km from the city of Canberra, Australia. Its
+operating band (820-850 MHz) is now partly allocated to mobile phone
+communications, making radio astronomy challenging. We describe how the
+deployment of new digital receivers (RX boxes), Field Programmable Gate Array
+(FPGA) based filterbanks and server-class computers equipped with 43 GPUs
+(Graphics Processing Units) has transformed MOST into a versatile new
+instrument (the UTMOST) for studying the dynamic radio sky on millisecond
+timescales, ideal for work on pulsars and Fast Radio Bursts (FRBs). The
+filterbanks, servers and their high-speed, low-latency network form part of a
+hybrid solution to the observatory's signal processing requirements. The
+emphasis on software and commodity off-the-shelf hardware has enabled rapid
+deployment through the re-use of proven 'software backends' for its signal
+processing. The new receivers have ten times the bandwidth of the original MOST
+and double the sampling of the line feed, which doubles the field of view. The
+UTMOST can simultaneously excise interference, make maps, coherently dedisperse
+pulsars, and perform real-time searches of coherent fan beams for dispersed
+single pulses. Although system performance is still sub-optimal, a pulsar
+timing and FRB search programme has commenced and the first UTMOST maps have
+been made. The telescope operates as a robotic facility, deciding how to
+efficiently target pulsars and how long to stay on source, via feedback from
+real-time pulsar folding. The regular timing of over 300 pulsars has resulted
+in the discovery of 7 pulsar glitches and 3 FRBs. The UTMOST demonstrates that
+if sufficient signal processing can be applied to the voltage streams it is
+possible to perform innovative radio science in hostile radio frequency
+environments.
+"
+28702,Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target,"  Multi-step methods such as Retrace($\lambda$) and $n$-step $Q$-learning have
+become a crucial component of modern deep reinforcement learning agents. These
+methods are often evaluated as a part of bigger architectures and their
+evaluations rarely include enough samples to draw statistically significant
+conclusions about their performance. This type of methodology makes it
+difficult to understand how particular algorithmic details of multi-step
+methods influence learning. In this paper we combine the $n$-step action-value
+algorithms Retrace, $Q$-learning, Tree Backup, Sarsa, and $Q(\sigma)$ with an
+architecture analogous to DQN. We test the performance of all these algorithms
+in the mountain car environment; this choice of environment allows for faster
+training times and larger sample sizes. We present statistical analyses on the
+effects of the off-policy correction, the backup length parameter $n$, and the
+update frequency of the target network on the performance of these algorithms.
+Our results show that (1) using off-policy correction can have an adverse
+effect on the performance of Sarsa and $Q(\sigma)$; (2) increasing the backup
+length $n$ consistently improved performance across all the different
+algorithms; and (3) the performance of Sarsa and $Q$-learning was more robust
+to the effect of the target network update frequency than the performance of
+Tree Backup, $Q(\sigma)$, and Retrace in this particular task.
+"
+28703,Self-calibrating d-scan: measuring ultrashort laser pulses on-target using an arbitrary pulse compressor,"  In most applications of ultrashort pulse lasers, temporal compressors are
+used to achieve a desired pulse duration in a target or sample, and precise
+temporal characterization is important. The dispersion-scan (d-scan) pulse
+characterization technique usually involves using glass wedges to impart
+variable, well-defined amounts of dispersion to the pulses, while measuring the
+spectrum of a nonlinear signal produced by those pulses. This works very well
+for broadband few-cycle pulses, but longer, narrower bandwidth pulses are much
+more difficult to measure this way. Here we demonstrate the concept of
+self-calibrating d-scan, which extends the applicability of the d-scan
+technique to pulses of arbitrary duration, enabling their complete measurement
+without prior knowledge of the introduced dispersion. In particular, we show
+that the pulse compressors already employed in chirped pulse amplification
+(CPA) systems can be used to simultaneously compress and measure the temporal
+profile of the output pulses on-target in a simple way, without the need of
+additional diagnostics or calibrations, while at the same time calibrating the
+often-unknown differential dispersion of the compressor itself. We demonstrate
+the technique through simulations and experiments under known conditions.
+Finally, we apply it to the measurement and compression of 27.5 fs pulses from
+a CPA laser.
+"
+28704,"Shaken Snow Globes: Kinematic Tracers of the Multiphase Condensation Cascade in Massive Galaxies, Groups, and Clusters","  We propose a novel method to constrain turbulence and bulk motions in massive
+galaxies, groups and clusters, exploring both simulations and observations. As
+emerged in the recent picture of the top-down multiphase condensation, the hot
+gaseous halos are tightly linked to all other phases in terms of cospatiality
+and thermodynamics. While hot halos (10^7 K) are perturbed by subsonic
+turbulence, warm (10^4 K) ionized and neutral filaments condense out of the
+turbulent eddies. The peaks condense into cold molecular clouds (< 100 K)
+raining in the core via chaotic cold accretion (CCA). We show all phases are
+tightly linked via the ensemble (wide-aperture) velocity dispersion along the
+line of sight. The correlation arises in complementary long-term AGN feedback
+simulations and high-resolution CCA runs, and is corroborated by the combined
+Hitomi and new IFU measurements in Perseus cluster. The ensemble multiphase gas
+distributions are characterized by substantial spectral line broadening
+(100-200 km/s) with mild line shift. On the other hand, pencil-beam detections
+sample the small-scale clouds displaying smaller broadening and significant
+line shift up to several 100 km/s, with increased scatter due to the turbulence
+intermittency. We present new ensemble sigma_v of the warm Halpha+[NII] gas in
+72 observed cluster/group cores: the constraints are consistent with the
+simulations and can be used as robust proxies for the turbulent velocities, in
+particular for the challenging hot plasma (otherwise requiring extremely long
+X-ray exposures). We show the physically motivated criterion C = t_cool/t_eddy
+~ 1 best traces the condensation extent region and presence of multiphase gas
+in observed clusters/groups. The ensemble method can be applied to many
+available datasets and can substantially advance our understanding of
+multiphase halos in light of the next-generation multiwavelength missions.
+"
+28705,Wave front sensor-less adaptive optics based on sharpness metrics for particle image velocimetry,"  Optical distortions can significantly deteriorate the measurement accuracy in
+imaging systems. Such distortions can occur at fluctuating phase boundaries as
+well as multiple-phase flows and result from the accompanied refractive index
+changes. Due to multiple reflexes arising from a fluid flow setup, the usage of
+a wave front sensor (WFS) can be hindered. In this work we outline a wave front
+sensor-less approach which includes iterative aberration correction with a fast
+deformable mirror (DM). A combination of sharpness metric (SM) image evaluation
+and iterative optimization is demonstrated. The SM was measured for each image
+while adjusting seven Zernike modes (after Noll index enumeration) in their
+amplitude. The SM is used as an indicator for wave front aberrations without
+using a wave front sensor to correct wave front distortions that are generated
+by the DM. The proposed method allows for the reduction of systematic
+measurement uncertainties in fluid flow measurement techniques as particle
+image velocimetry (PIV). Five different sharpness metrics are demonstrated for
+reliable sharpness maximization with a deformable mirror of 69 elements. A
+systematic linear search (LS) algorithm was applied in order to find the
+optimal mirror shape. The reduction of measurement uncertainty for PIV
+measurements is shown. The iterative approach offers a way to reduce static or
+slowly changing wave front distortions in a fluid flow setup where a WFS is not
+applicable.
+"
+28706,Enhancement of the effective mass at high magnetic fields in CeRhIn$_5$,"  The Kondo-lattice compound CeRhIn$_5$ displays a field-induced Fermi surface
+reconstruction at $B^*\approx30$ T, which occurs within the antiferromagnetic
+state, prior to the quantum critical point at $B_{c0}\approx50$ T. Here, in
+order to investigate the nature of the Fermi surface change, we measured the
+magnetostriction, specific heat, and magnetic torque of CeRhIn$_5$ across a
+wide range of magnetic fields. Our observations uncover the field-induced
+itineracy of the $4f$ electrons, where above $B_{\rm onset}\approx17$ T there
+is a significant enhancement of the Sommerfeld coefficient, and spin-dependent
+effective cyclotron masses determined from quantum oscillations. Upon crossing
+$B_{\rm onset}$, the temperature dependence of the specific heat also shows
+distinctly different behavior from that at low fields. Our results indicate
+that the Kondo coupling is remarkably robust upon increasing the magnetic
+field. This is ascribed to the delocalization of the $4f$ electrons at the
+Fermi surface reconstruction at $B^*$.
+"
+28707,Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis,"  Group factor analysis (GFA) methods have been widely used to infer the common
+structure and the group-specific signals from multiple related datasets in
+various fields including systems biology and neuroimaging. To date, most
+available GFA models require Gibbs sampling or slice sampling to perform
+inference, which prevents the practical application of GFA to large-scale data.
+In this paper we present an efficient collapsed variational inference (CVI)
+algorithm for the nonparametric Bayesian group factor analysis (NGFA) model
+built upon an hierarchical beta Bernoulli process. Our CVI algorithm proceeds
+by marginalizing out the group-specific beta process parameters, and then
+approximating the true posterior in the collapsed space using mean field
+methods. Experimental results on both synthetic and real-world data demonstrate
+the effectiveness of our CVI algorithm for the NGFA compared with
+state-of-the-art GFA methods.
+"
+28708,Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing,"  Scavenging the idling computation resources at the enormous number of mobile
+devices can provide a powerful platform for local mobile cloud computing. The
+vision can be realized by peer-to-peer cooperative computing between edge
+devices, referred to as co-computing. This paper considers a co-computing
+system where a user offloads computation of input-data to a helper. The helper
+controls the offloading process for the objective of minimizing the user's
+energy consumption based on a predicted helper's CPU-idling profile that
+specifies the amount of available computation resource for co-computing.
+Consider the scenario that the user has one-shot input-data arrival and the
+helper buffers offloaded bits. The problem for energy-efficient co-computing is
+formulated as two sub-problems: the slave problem corresponding to adaptive
+offloading and the master one to data partitioning. Given a fixed offloaded
+data size, the adaptive offloading aims at minimizing the energy consumption
+for offloading by controlling the offloading rate under the deadline and buffer
+constraints. By deriving the necessary and sufficient conditions for the
+optimal solution, we characterize the structure of the optimal policies and
+propose algorithms for computing the policies. Furthermore, we show that the
+problem of optimal data partitioning for offloading and local computing at the
+user is convex, admitting a simple solution using the sub-gradient method.
+Last, the developed design approach for co-computing is extended to the
+scenario of bursty data arrivals at the user accounting for data causality
+constraints. Simulation results verify the effectiveness of the proposed
+algorithms.
+"
+28709,Inplane anisotropy of longitudinal thermal conductivities and weak localization of magnons in a disordered spiral magnet,"  We demonstrate the inplane anisotropy of longitudinal thermal conductivities
+and the weak localization of magnons in a disordered screw-type spiral magnet
+on a square lattice. We consider a disordered spin system, described by a spin
+Hamiltonian for the antiferromagnetic Heisenberg interaction and the
+Dzyaloshinsky-Moriya interaction with the mean-field type potential of
+impurities. We derive longitudinal thermal conductivities for the disordered
+screw-type spiral magnet in the weak-localization regime by using the
+linear-response theory with the linear-spin-wave approximation and performing
+perturbation calculations. We show that the inplane longitudinal thermal
+conductivities are anisotropic due to the Dzyaloshinsky-Moriya interaction.
+This anisotropy may be useful for experimentally estimating the magnitude of a
+ratio of the Dzyaloshinsky-Moriya interaction to the Heisenberg interaction. We
+also show that the main correction term gives a logarithmic suppression with
+the length scale due to the critical back scattering. This suggests that the
+weak localization of magnons is ubiquitous for the disordered two-dimensional
+magnets having global time-reversal symmetry. We finally discuss several
+implications for further research.
+"
+28710,Towards an Embodied Semantic Fovea: Semantic 3D scene reconstruction from ego-centric eye-tracker videos,"  Incorporating the physical environment is essential for a complete
+understanding of human behavior in unconstrained every-day tasks. This is
+especially important in ego-centric tasks where obtaining 3 dimensional
+information is both limiting and challenging with the current 2D video analysis
+methods proving insufficient. Here we demonstrate a proof-of-concept system
+which provides real-time 3D mapping and semantic labeling of the local
+environment from an ego-centric RGB-D video-stream with 3D gaze point
+estimation from head mounted eye tracking glasses. We augment existing work in
+Semantic Simultaneous Localization And Mapping (Semantic SLAM) with collected
+gaze vectors. Our system can then find and track objects both inside and
+outside the user field-of-view in 3D from multiple perspectives with reasonable
+accuracy. We validate our concept by producing a semantic map from images of
+the NYUv2 dataset while simultaneously estimating gaze position and gaze
+classes from recorded gaze data of the dataset images.
+"
+28711,BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography,"  Computer vision systems are designed to work well within the context of
+everyday photography. However, artists often render the world around them in
+ways that do not resemble photographs. Artwork produced by people is not
+constrained to mimic the physical world, making it more challenging for
+machines to recognize.
+This work is a step toward teaching machines how to categorize images in ways
+that are valuable to humans. First, we collect a large-scale dataset of
+contemporary artwork from Behance, a website containing millions of portfolios
+from professional and commercial artists. We annotate Behance imagery with rich
+attribute labels for content, emotions, and artistic media. Furthermore, we
+carry out baseline experiments to show the value of this dataset for artistic
+style prediction, for improving the generality of existing object classifiers,
+and for the study of visual domain adaptation. We believe our Behance Artistic
+Media dataset will be a good starting point for researchers wishing to study
+artistic imagery and relevant problems.
+"
+28712,Abnormal Event Detection in Videos using Generative Adversarial Nets,"  In this paper we address the abnormality detection problem in crowded scenes.
+We propose to use Generative Adversarial Nets (GANs), which are trained using
+normal frames and corresponding optical-flow images in order to learn an
+internal representation of the scene normality. Since our GANs are trained with
+only normal data, they are not able to generate abnormal events. At testing
+time the real data are compared with both the appearance and the motion
+representations reconstructed by our GANs and abnormal areas are detected by
+computing local differences. Experimental results on challenging abnormality
+detection datasets show the superiority of the proposed method compared to the
+state of the art in both frame-level and pixel-level abnormality detection
+tasks.
+"
+28713,Mimicking Word Embeddings using Subword RNNs,"  Word embeddings improve generalization over lexical features by placing each
+word in a lower-dimensional space, using distributional information obtained
+from unlabeled data. However, the effectiveness of word embeddings for
+downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which
+embeddings do not exist. In this paper, we present MIMICK, an approach to
+generating OOV word embeddings compositionally, by learning a function from
+spellings to distributional embeddings. Unlike prior work, MIMICK does not
+require re-training on the original word embedding corpus; instead, learning is
+performed at the type level. Intrinsic and extrinsic evaluations demonstrate
+the power of this simple approach. On 23 languages, MIMICK improves performance
+over a word-based baseline for tagging part-of-speech and morphosyntactic
+attributes. It is competitive with (and complementary to) a supervised
+character-based model in low-resource settings.
+"
+28714,Hyperbolic Covariant Coherent Structures in two dimensional flows,"  A new method to describe hyperbolic patterns in two dimensional flows is
+proposed. The method is based on the Covariant Lyapunov Vectors (CLVs), which
+have the properties to be covariant with the dynamics, and thus being mapped by
+the tangent linear operator into another CLVs basis, they are norm independent,
+invariant under time reversal and can be not orthonormal. CLVs can thus give a
+more detailed information on the expansion and contraction directions of the
+flow than the Lyapunov Vector bases, that are instead always orthogonal. We
+suggest a definition of Hyperbolic Covariant Coherent Structures (HCCSs), that
+can be defined on the scalar field representing the angle between the CLVs.
+HCCSs can be defined for every time instant and could be useful to understand
+the long term behaviour of particle tracers.
+We consider three examples: a simple autonomous Hamiltonian system, as well
+as the non-autonomous ""double gyre"" and Bickley jet, to see how well the angle
+is able to describe particular patterns and barriers. We compare the results
+from the HCCSs with other coherent patterns defined on finite time by the
+Finite Time Lyapunov Exponents (FTLEs), to see how the behaviour of these
+structures change asymptotically.
+"
+28715,Mott physics beyond Brinkman-Rice scenario,"  The main flaw of the well-known Brinkman-Rice description, obtained through
+the Gutzwiller approximation, of the paramagnetic Mott transition in the
+Hubbard model is in neglecting high-energy virtual processes that generate for
+instance the antiferromagnetic exchange $J\sim t^2/U$. Here we propose a way to
+capture those processes by combining the Brinkman-Rice approach with a
+variational Schrieffer-Wolff transformation, and apply this method to study the
+single-band metal-to-insulator transition in a Bethe lattice with infinite
+coordination number, where the Gutzwiller approximation becomes exact. We
+indeed find for the Mott transition a description very close to the real one
+provided by dynamical mean-field theory; an encouraging result in view of
+possible applications to more involved models.
+"
+28716,Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples,"  Many deep learning algorithms can be easily fooled with simple adversarial
+examples. To address the limitations of existing defenses, we devised a
+probabilistic framework that can generate an exponentially large ensemble of
+models from a single model with just a linear cost. This framework takes
+advantage of neural network depth and stochastically decides whether or not to
+insert noise removal operators such as VAEs between layers. We show empirically
+the important role that model gradients have when it comes to determining
+transferability of adversarial examples, and take advantage of this result to
+demonstrate that it is possible to train models with limited adversarial attack
+transferability. Additionally, we propose a detection method based on metric
+learning in order to detect adversarial examples that have no hope of being
+cleaned of maliciously engineered noise.
+"
+28717,A Compressive Method for Centralized PSD Map Construction with Imperfect Reporting Channel,"  Spectrum resources management of growing demands is a challenging problem and
+Cognitive Radio (CR) known to be capable of improving the spectrum utilization.
+Recently, Power Spectral Density (PSD) map is defined to enable the CR to reuse
+the frequency resources regarding to the area. For this reason, the sensed PSDs
+are collected by the distributed sensors in the area and fused by a Fusion
+Center (FC). But, for a given zone, the sensed PSDs by neighbor CR sensors may
+contain a shared common component for a while. This component can be exploited
+in the theory of the Distributed Source Coding (DSC) to make the sensors
+transmission data more compressed. However, uncertain channel fading and random
+shadowing would lead to varying signal strength at different CRs, even placed
+close to each other. Hence, existence of some perturbations in the transmission
+procedure yields to some imperfection in the reporting channel and as a result
+it degrades the performance remarkably. The main focus of this paper is to be
+able to reconstruct the PSDs of sensors \textit{robustly} based on the
+Distributed Compressive Sensing (DCS) when the data transmission is slightly
+imperfect. Simulation results verify the robustness of the proposed scheme.
+"
+28718,HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks,"  On electronic game platforms, different payment transactions have different
+levels of risk. Risk is generally higher for digital goods in e-commerce.
+However, it differs based on product and its popularity, the offer type
+(packaged game, virtual currency to a game or subscription service), storefront
+and geography. Existing fraud policies and models make decisions independently
+for each transaction based on transaction attributes, payment velocities, user
+characteristics, and other relevant information. However, suspicious
+transactions may still evade detection and hence we propose a broad learning
+approach leveraging a graph based perspective to uncover relationships among
+suspicious transactions, i.e., inter-transaction dependency. Our focus is to
+detect suspicious transactions by capturing common fraudulent behaviors that
+would not be considered suspicious when being considered in isolation. In this
+paper, we present HitFraud that leverages heterogeneous information networks
+for collective fraud detection by exploring correlated and fast evolving
+fraudulent behaviors. First, a heterogeneous information network is designed to
+link entities of interest in the transaction database via different semantics.
+Then, graph based features are efficiently discovered from the network
+exploiting the concept of meta-paths, and decisions on frauds are made
+collectively on test instances. Experiments on real-world payment transaction
+data from Electronic Arts demonstrate that the prediction performance is
+effectively boosted by HitFraud with fast convergence where the computation of
+meta-path based features is largely optimized. Notably, recall can be improved
+up to 7.93% and F-score 4.62% compared to baselines.
+"
+28719,Preliminary steps toward a universal economic dynamics for monetary and fiscal policy,"  We consider the relationship between economic activity and intervention,
+including monetary and fiscal policy, using a universal dynamic framework.
+Central bank policies are designed for growth without excess inflation.
+However, unemployment, investment, consumption, and inflation are interlinked.
+Understanding dynamics is crucial to assessing the effects of policy,
+especially in the aftermath of the financial crisis. Here we lay out a program
+of research into monetary and economic dynamics and preliminary steps toward
+its execution. We use principles of response theory to derive implications for
+policy. We find that the current approach, which considers the overall money
+supply, is insufficient to regulate economic growth. While it can achieve some
+degree of control, optimizing growth also requires a fiscal policy balancing
+monetary injection between two dominant loop flows, the consumption and wages
+loop, and investment and returns loop. The balance arises from a composite of
+government tax, entitlement, subsidy policies, corporate policies, as well as
+monetary policy. We show empirically that a transition occurred in 1980 between
+two regimes--an oversupply to the consumption and wages loop, to an oversupply
+of the investment and returns loop. The imbalance is manifest in savings and
+borrowing by consumers and investors, and in inflation. The latter increased
+until 1980, and decreased subsequently, resulting in a zero rate largely
+unrelated to the financial crisis. Three recessions and the financial crisis
+are part of this dynamic. Optimizing growth now requires shifting the balance.
+Our analysis supports advocates of greater income and / or government support
+for the poor who use a larger fraction of income for consumption. This promotes
+investment due to growth in demand. Otherwise, investment opportunities are
+limited, capital remains uninvested, and does not contribute to growth.
+"
+28720,Density of algebraic points on Noetherian varieties,"  Let $\Omega\subset{\mathbb R}^n$ be a relatively compact domain. A finite
+collection of real-valued functions on $\Omega$ is called a \emph{Noetherian
+chain} if the partial derivatives of each function are expressible as
+polynomials in the functions. A \emph{Noetherian function} is a polynomial
+combination of elements of a Noetherian chain. We introduce \emph{Noetherian
+parameters} (degrees, size of the coefficients) which measure the complexity of
+a Noetherian chain. Our main result is an explicit form of the Pila-Wilkie
+theorem for sets defined using Noetherian equalities and inequalities: for any
+$\epsilon>0$, the number of points of height $H$ in the transcendental part of
+the set is at most $C\cdot H^{\epsilon}$ where $C$ can be \emph{explicitly}
+estimated from the Noetherian parameters and $\epsilon$.
+We show that many functions of interest in arithmetic geometry fall within
+the Noetherian class, including elliptic and abelian functions, modular
+functions and universal covers of compact Riemann surfaces, Jacobi theta
+functions, periods of algebraic integrals, and the uniformizing map of the
+Siegel modular variety $\mathcal{A}_g$. We thus effectivize the (geometric side
+of) Pila-Zannier strategy for unlikely intersections in those instances that
+involve only compact domains.
+"
+28721,Language-depedent I-Vectors for LRE15,"  A standard recipe for spoken language recognition is to apply a Gaussian
+back-end to i-vectors. This ignores the uncertainty in the i-vector extraction,
+which could be important especially for short utterances. A recent paper by
+Cumani, Plchot and Fer proposes a solution to propagate that uncertainty into
+the backend. We propose an alternative method of propagating the uncertainty.
+"
+28722,Optimal Compression for Two-Field Entries in Fixed-Width Memories,"  Data compression is a well-studied (and well-solved) problem in the setup of
+long coding blocks. But important emerging applications need to compress data
+to memory words of small fixed widths. This new setup is the subject of this
+paper. In the problem we consider we have two sources with known discrete
+distributions, and we wish to find codes that maximize the success probability
+that the two source outputs are represented in $L$ bits or less. A good
+practical use for this problem is a table with two-field entries that is stored
+in a memory of a fixed width $L$. Such tables of very large sizes drive the
+core functionalities of network switches and routers. After defining the
+problem formally, we solve it optimally with an efficient code-design
+algorithm. We also solve the problem in the more constrained case where a
+single code is used in both fields (to save space for storing code
+dictionaries). For both code-design problems we find decompositions that yield
+efficient dynamic-programming algorithms. With an empirical study we show the
+success probabilities of the optimal codes for different distributions and
+memory widths. In particular, the study demonstrates the superiority of the new
+codes over existing compression algorithms.
+"
+28723,Stable habitable zones of single Jovian planet systems,"  With continued improvement in telescope sensitivity and observational
+techniques, the search for rocky planets in stellar habitable zones is entering
+an exciting era. With so many exoplanetary systems available for follow-up
+observations to find potentially habitable planets, one needs to prioritise the
+ever-growing list of candidates. We aim to determine which of the known
+planetary systems are dynamically capable of hosting rocky planets in their
+habitable zones, with the goal of helping to focus future planet search
+programs. We perform an extensive suite of numerical simulations to identify
+regions in the habitable zones of single Jovian planet systems where Earth mass
+planets could maintain stable orbits, specifically focusing on the systems in
+the Catalog of Earth-like Exoplanet Survey Targets (CELESTA). We find that
+small, Earth-mass planets can maintain stable orbits in cases where the
+habitable zone is largely, or partially, unperturbed by a nearby Jovian, and
+that mutual gravitational interactions and resonant mechanisms are capable of
+producing stable orbits even in habitable zones that are significantly or
+completely disrupted by a Jovian. Our results yield a list of 13 single Jovian
+planet systems in CELESTA that are not only capable of supporting an Earth-mass
+planet on stable orbits in their habitable zone, but for which we are also able
+to constrain the orbits of the Earth-mass planet such that the induced radial
+velocity signals would be detectable with next generation instruments.
+"
+28724,Lock-in transition of charge density waves in quasi-one-dimensional conductors: reinterpretation of McMillan's theory,"  We investigated the lock-in transition of charge density waves (CDWs) in
+quasi-one-dimensional conductors, based on McMillan's free energy. The
+higher-order umklapp terms play an essential role in this study. McMillan's
+theory was extended by Nakanishi and Shiba in order to treat multiple CDW
+vectors. Although their theories were aimed at understanding CDWs in
+quasi-two-dimensional conductors, we applied them to the quasi-one-dimensional
+conductors, including K$_{0.3}$MoO$_3$, NbSe$_3$, and $m$-TaS$_3$, and
+confirmed its validity for these cases. Then we discussed our previous
+experimental result of $o$-TaS$_3$, which revealed the coexistence of
+commensurate and incommensurate states. We found that the coexistence of
+multiple CDW vectors is essential for the lock-in transition to occur in
+$o$-TaS$_3$. The even- and odd-order terms in the free energy play roles for
+amplitude development and phase modulation, respectively. Moreover,
+consideration of the condition of being commensurate CDWs allowed us to relate
+it with that of the weak localization in random media.
+"
+28725,Integrable many-body systems of Calogero-Ruijsenaars type,"  This thesis presents our results on Liouville integrable systems of
+Calogero-Ruijsenaars type: 1. We prove an explicit formula providing canonical
+spectral coordinates for the rational Calogero-Moser system. 2. We explore
+action-angle duality for the trigonometric BC(n) Sutherland system using
+Hamiltonian reduction. 3. We derive a Poisson-Lie deformation of the
+trigonometric BC(n) Sutherland system using Hamiltonian reduction. 4. We
+construct a Lax pair for the hyperbolic Ruijsenaars-Schneider system with two
+couplings. 5. We present an explicit construction of compactified trigonometric
+and elliptic Ruijsenaars-Schneider systems.
+"
+28726,Dynamic Reconfiguration of Mission Parameters in Underwater Human-Robot Collaboration,"  This paper presents a real-time programming and parameter reconfiguration
+method for autonomous underwater robots in human-robot collaborative tasks.
+Using a set of intuitive and meaningful hand gestures, we develop a
+syntactically simple framework that is computationally more efficient than a
+complex, grammar-based approach. In the proposed framework, a convolutional
+neural network is trained to provide accurate hand gesture recognition;
+subsequently, a finite-state machine-based deterministic model performs
+efficient gesture-to-instruction mapping, and further improves robustness of
+the interaction scheme. The key aspect of this framework is that it can be
+easily adopted by divers for communicating simple instructions to underwater
+robots without using artificial tags such as fiducial markers, or requiring
+them to memorize a potentially complex set of language rules. Extensive
+experiments are performed both on field-trial data and through simulation,
+which demonstrate the robustness, efficiency, and portability of this framework
+in a number of different scenarios. Finally, a user interaction study is
+presented that illustrates the gain in usability of our proposed interaction
+framework compared to the existing methods for underwater domains.
+"
+28727,Estimating Bayesian Optimal Treatment Regimes for Dichotomous Outcomes using Observational Data,"  Optimal treatment regimes (OTR) are individualised treatment assignment
+strategies that identify a medical treatment as optimal given all background
+information available on the individual. We discuss Bayes optimal treatment
+regimes estimated using a loss function defined on the bivariate distribution
+of dichotomous potential outcomes. The proposed approach allows considering
+more general objectives for the OTR than maximization of an expected outcome
+(e.g., survival probability) by taking into account, for example, unnecessary
+treatment burden. As a motivating example we consider the case of oropharynx
+cancer treatment where unnecessary burden due to chemotherapy is to be avoided
+while maximizing survival chances. Assuming ignorable treatment assignment we
+describe Bayesian inference about the OTR including a sensitivity analysis on
+the unobserved partial association of the potential outcomes. We evaluate the
+methodology by simulations that apply Bayesian parametric and more flexible
+non-parametric outcome models. The proposed OTR for oropharynx cancer reduces
+the frequency of the more burdensome chemotherapy assignment by approximately
+75% without reducing the average survival probability. This regime thus offers
+a strong increase in expected quality of life of patients.
+"
+28728,Excitonic insulators as a model of $d-d$ and Mott transitions in strongly correlated materials,"  We show how strongly correlated materials could be described within the
+framework of an excitonic insulator formalism, and delineate the relationship
+between inter- and intra-band ordering phenomena. Our microscopic model of
+excitons clarifies the fundamental role of Van-Hove-singularity-nesting in
+driving both inter- and intra-band ordering transitions, and uncovers an
+interesting connection with resonating-valence-bond physics.
+"
+28729,Frame-Recurrent Video Super-Resolution,"  Recent advances in video super-resolution have shown that convolutional
+neural networks combined with motion compensation are able to merge information
+from multiple low-resolution (LR) frames to generate high-quality images.
+Current state-of-the-art methods process a batch of LR frames to generate a
+single high-resolution (HR) frame and run this scheme in a sliding window
+fashion over the entire video, effectively treating the problem as a large
+number of separate multi-frame super-resolution tasks. This approach has two
+main weaknesses: 1) Each input frame is processed and warped multiple times,
+increasing the computational cost, and 2) each output frame is estimated
+independently conditioned on the input frames, limiting the system's ability to
+produce temporally consistent results.
+In this work, we propose an end-to-end trainable frame-recurrent video
+super-resolution framework that uses the previously inferred HR estimate to
+super-resolve the subsequent frame. This naturally encourages temporally
+consistent results and reduces the computational cost by warping only one image
+in each step. Furthermore, due to its recurrent nature, the proposed method has
+the ability to assimilate a large number of previous frames without increased
+computational demands. Extensive evaluations and comparisons with previous
+methods validate the strengths of our approach and demonstrate that the
+proposed framework is able to significantly outperform the current state of the
+art.
+"
+28730,Electronic Structure Evolution during the Growth of Graphene Nanoribbons on Au(110),"  Surface-assisted polymerization of molecular monomers into extended chains
+can be used as the seed of graphene nanoribbon (GNR) formation, resulting from
+a subsequent cyclo-dehydrogenation process. By means of valence-band
+photoemission and ab-initio density-functional theory (DFT) calculations, we
+investigate the evolution of molecular states from monomer
+10,10'-dibromo-9,9'bianthracene (DBBA) precursors to polyanthryl polymers, and
+eventually to GNRs, as driven by the Au(110) surface. The molecular orbitals
+and the energy level alignment at the metal-organic interface are studied in
+depth for the DBBA precursors deposited at room temperature. On this basis, we
+can identify a spectral fingerprint of C-Au interaction in both DBBA
+single-layer and polymerized chains obtained upon heating. Furthermore, DFT
+calculations help us evidencing that GNRs interact more strongly than DBBA and
+polyanthryl with the Au(110) substrate, as a result of their flatter
+conformation.
+"
+28731,Electron-polaron--electron-polaron bound states in mass-gap graphene-like planar quantum electrodynamics: $s$-wave bipolarons,"  A Lorentz invariant version of a mass-gap graphene-like planar quantum
+electrodynamics, the parity-preserving $U(1)\times U(1)$ massive QED$_3$,
+exhibits attractive interaction in low-energy
+electron-polaron--electron-polaron $s$-wave scattering, favoring quasiparticles
+bound states, the $s$-wave bipolarons.
+"
+28732,Geometrically distinct solutions given by symmetries of variational problems with the $O(N)$-symmetry,"  For variational problems with $O(N)$-symmetry the existence of several
+geometrically distinct solutions had been shown by use of group theoretic
+approach in previous articles. It was done by a crafty choice of a family $H_i
+\subset O(N)$ subgroups such that the fixed point subspaces $E^{H_i} \subset E$
+of the action in a corresponding functional space are linearly independent,
+next restricting the problem to each $E^{H_i}$ and using the Palais symmetry
+principle. In this work we give a thorough explanation of this approach showing
+a correspondence between the equivalence classes of such subgroups, partial
+orthogonal flags in $\mathbb{R}^N$, and unordered partitions of the number $N$.
+By showing that spaces of functions invariant with respect to different classes
+of groups are linearly independent we prove that the amount of series of
+geometrically distinct solutions obtained in this way grows exponentially in
+$N$, in contrast to logarithmic, and linear growths of earlier papers.
+"
+28733,Post-processing partitions to identify domains of modularity optimization,"  We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP)
+algorithm to prune and prioritize different network community structures
+identified across multiple runs of possibly various computational heuristics.
+Given a set of partitions, CHAMP identifies the domain of modularity
+optimization for each partition ---i.e., the parameter-space domain where it
+has the largest modularity relative to the input set---discarding partitions
+with empty domains to obtain the subset of partitions that are ""admissible""
+candidate community structures that remain potentially optimal over indicated
+parameter domains. Importantly, CHAMP can be used for multi-dimensional
+parameter spaces, such as those for multilayer networks where one includes a
+resolution parameter and interlayer coupling. Using the results from CHAMP, a
+user can more appropriately select robust community structures by observing the
+sizes of domains of optimization and the pairwise comparisons between
+partitions in the admissible subset. We demonstrate the utility of CHAMP with
+several example networks. In these examples, CHAMP focuses attention onto
+pruned subsets of admissible partitions that are 20-to-1785 times smaller than
+the sets of unique partitions obtained by community detection heuristics that
+were input into CHAMP.
+"
+28734,Distributed Robust Subspace Recovery,"  We propose distributed solutions to the problem of Robust Subspace Recovery
+(RSR). Our setting assumes a huge dataset in an ad hoc network without a
+central processor, where each node has access only to one chunk of the dataset.
+Furthermore, part of the whole dataset lies around a low-dimensional subspace
+and the other part is composed of outliers that lie away from that subspace.
+The goal is to recover the underlying subspace for the whole dataset, without
+transferring the data itself between the nodes. We first apply the
+Consensus-Based Gradient method to the Geometric Median Subspace algorithm for
+RSR. For this purpose, we propose an iterative solution for the local dual
+minimization problem and establish its r-linear convergence. We then explain
+how to distributedly implement the Reaper and Fast Median Subspace algorithms
+for RSR. The proposed algorithms display competitive performance on both
+synthetic and real data.
+"
+28735,Connecting Network Science and Information Theory,"  A framework integrating information theory and network science is proposed,
+giving rise to a potentially new area. By incorporating and integrating
+concepts such as complexity, coding, topological projections and network
+dynamics, the proposed network-based framework paves the way not only to
+extending traditional information science, but also to modeling, characterizing
+and analyzing a broad class of real-world problems, from language communication
+to DNA coding. Basically, an original network is supposed to be transmitted,
+with or without compaction, through a sequence of symbols or time-series
+obtained by sampling its topology by some network dynamics, such as random
+walks. We show that the degree of compression is ultimately related to the
+ability to predict the frequency of symbols based on the topology of the
+original network and the adopted dynamics. The potential of the proposed
+approach is illustrated with respect to the efficiency of transmitting several
+types of topologies by using a variety of random walks. Several interesting
+results are obtained, including the behavior of the Barabási-Albert model
+oscillating between high and low performance depending on the considered
+dynamics, and the distinct performances obtained for two geographical models.
+"
+28736,Pressure Predictions of Turbine Blades with Deep Learning,"  Deep learning has been used in many areas, such as feature detections in
+images and the game of go. This paper presents a study that attempts to use the
+deep learning method to predict turbomachinery performance. Three different
+deep neural networks are built and trained to predict the pressure
+distributions of turbine airfoils. The performance of a library of turbine
+airfoils were firstly predicted using methods based on Euler equations, which
+were then used to train and validate the deep learning neural networks. The
+results show that network with four layers of convolutional neural network and
+two layers of fully connected neural network provides the best predictions. For
+the best neural network architecture, the pressure prediction on more than 99%
+locations are better than 3% and 90% locations are better than 1%.
+"
+28737,PhaseMAC: A 14 TOPS/W 8bit GRO based Phase Domain MAC Circuit for In-Sensor-Computed Deep Learning Accelerators,"  PhaseMAC (PMAC), a phase domain Gated-Ring-Oscillator (GRO) based 8bit MAC
+circuit, is proposed to minimize both area and power consumption of deep
+learning accelerators. PMAC composes of only digital cells and consumes
+significantly smaller power than standard digital designs, owing to its
+efficient analog accumulation nature. It occupies 26.6 times smaller area than
+conventional analog designs, which is competitive to digital MAC circuits. PMAC
+achieves a peak efficiency of 14 TOPS/W, which is best reported and 48% higher
+than conventional arts. Results in anomaly detection tasks are demonstrated,
+which is the hottest application in the industrial IoT scene.
+"
+28738,A Social Network Analysis of Articles on Social Network Analysis,"  A collection of articles on the statistical modelling and inference of social
+networks is analysed in a network fashion. The references of these articles are
+used to construct a citation network data set, which is almost a directed
+acyclic graph because only existing articles can be cited. A mixed membership
+stochastic block model is then applied to this data set to soft cluster the
+articles. The results obtained from a Gibbs sampler give us insights into the
+influence and the categorisation of these articles.
+"
+28739,Optimised frequency modulation for continuous-wave optical magnetic resonance sensing using nitrogen-vacancy ensembles,"  Magnetometers based on ensembles of nitrogen-vacancy centres are a promising
+platform for continuously sensing static and low-frequency magnetic fields.
+Their combination with phase-sensitive (lock-in) detection creates a highly
+versatile sensor with a sensitivity that is proportional to the derivative of
+the optical magnetic resonance lock-in spectrum, which is in turn dependant on
+the lock-in modulation parameters. Here we study the dependence of the lock-in
+spectral slope on the modulation of the spin-driving microwave field. Given the
+presence of the intrinsic nitrogen hyperfine spin transitions, we
+experimentally show that when the ratio between the hyperfine linewidth and
+their separation is $\gtrsim 1/4$, square-wave based frequency modulation
+generates the steepest slope at modulation depths exceeding the separation of
+the hyperfine lines, compared to sine-wave based modulation. We formulate a
+model for calculating lock-in spectra which shows excellent agreement with our
+experiments, and which shows that an optimum slope is achieved when the
+linewidth/separation ratio is $\lesssim 1/4$ and the modulation depth is less
+then the resonance linewidth, irrespective of the modulation function used.
+"
+28740,A Bayesian model of acquisition and clearance of bacterial colonization,"  Bacterial populations that colonize a host play important roles in host
+health, including serving as a reservoir that transmits to other hosts and from
+which invasive strains emerge, thus emphasizing the importance of understanding
+rates of acquisition and clearance of colonizing populations. Studies of
+colonization dynamics have been based on assessment of whether serial samples
+represent a single population or distinct colonization events. A common
+solution to estimate acquisition and clearance rates is to use a fixed genetic
+distance threshold. However, this approach is often inadequate to account for
+the diversity of the underlying within-host evolving population, the time
+intervals between consecutive measurements, and the uncertainty in the
+estimated acquisition and clearance rates. Here, we summarize recently
+submitted work \cite{jarvenpaa2018named} and present a Bayesian model that
+provides probabilities of whether two strains should be considered the same,
+allowing to determine bacterial clearance and acquisition from genomes sampled
+over time. We explicitly model the within-host variation using population
+genetic simulation, and the inference is done by combining information from
+multiple data sources by using a combination of Approximate Bayesian
+Computation (ABC) and Markov Chain Monte Carlo (MCMC). We use the method to
+analyse a collection of methicillin resistant Staphylococcus aureus (MRSA)
+isolates.
+"
+28741,Towards a Quantum World Wide Web,"  We elaborate a quantum model for the meaning associated with corpora of
+written documents, like the pages forming the World Wide Web. To that end, we
+are guided by how physicists constructed quantum theory for microscopic
+entities, which unlike classical objects cannot be fully represented in our
+spatial theater. We suggest that a similar construction needs to be carried out
+by linguists and computational scientists, to capture the full meaning carried
+by collections of documental entities. More precisely, we show how to associate
+a quantum-like 'entity of meaning' to a 'language entity formed by printed
+documents', considering the latter as the collection of traces that are left by
+the former, in specific results of search actions that we describe as
+measurements. In other words, we offer a perspective where a collection of
+documents, like the Web, is described as the space of manifestation of a more
+complex entity - the QWeb - which is the object of our modeling, drawing its
+inspiration from previous studies on operational-realistic approaches to
+quantum physics and quantum modeling of human cognition and decision-making. We
+emphasize that a consistent QWeb model needs to account for the observed
+correlations between words appearing in printed documents, e.g.,
+co-occurrences, as the latter would depend on the 'meaning connections'
+existing between the concepts that are associated with these words. In that
+respect, we show that both 'context and interference (quantum) effects' are
+required to explain the probabilities calculated by counting the relative
+number of documents containing certain words and co-ocurrrences of words.
+"
+28742,Combining Slater-type orbitals and effective core potentials,"  We present a general methodology to evaluate matrix elements of the effective
+core potentials (ECPs) within one-electron basis set of Slater-type orbitals
+(STOs). The scheme is based on translation of individual STO distributions in
+the framework of Barnett-Coulson method. We discuss different types of
+integrals which naturally appear and reduce them to few basic quantities which
+can be calculated recursively or purely numerically. Additionally, we consider
+evaluation of the STOs matrix elements involving the core polarisation
+potentials (CPP) and effective spin-orbit potentials. Construction of the STOs
+basis sets designed specifically for use with ECPs is discussed and differences
+in comparison with all-electron basis sets are briefly summarised. We verify
+the validity of the present approach by calculating excitation energies, static
+dipole polarisabilities and valence orbital energies for the alkaline earth
+metals (Ca, Sr, Ba). Finally, we evaluate interaction energies, permanent
+dipole moments and ionisation energies for barium and strontium hydrides, and
+compare them with the best available experimental and theoretical data.
+"
+28743,Is Goldbach Conjecture true?,"  We answer the question positively. In fact, we believe to have proved that
+every even integer $2N\geq3\times10^{6}$ is the sum of two odd distinct primes.
+Numerical calculations extend this result for $2N$ in the range
+$8-3\times10^{6}$. So, a fortiori, it is shown that every even integer $2N>2$
+is the sum of two primes (Goldbach conjecture). Of course, we would be grateful
+for comments and objections.
+"
+28744,Decentralized Ergodic Control: Distribution-Driven Sensing and Exploration for Multi-Agent Systems,"  We present a decentralized ergodic control policy for time-varying area
+coverage problems for multiple agents with nonlinear dynamics. Ergodic control
+allows us to specify distributions as objectives for area coverage problems for
+nonlinear robotic systems as a closed-form controller. We derive a variation to
+the ergodic control policy that can be used with consensus to enable a fully
+decentralized multi-agent control policy. Examples are presented to illustrate
+the applicability of our method for multi-agent terrain mapping as well as
+target localization. An analysis on ergodic policies as a Nash equilibrium is
+provided for game theoretic applications.
+"
+28745,Detection of the linearly polarised spectrum of the red supergiant star alpha Ori,"  In the solar limb, linear polarisation is due to anisotropy of the radiation
+field induced by limb darkening. It is maximal when it is seen parallel to the
+limb and it vanishes when it is integrated over the spherically-symmetric solar
+disk. Therefore for distant stars, that present spherical symmetry, linear
+polarisation signatures are very difficult to observe. However strong linear
+polarisation features have been reported in the prototypical red supergiant
+star alpha Ori (Betelgeuse). With an analytical model we propose to explain
+them.
+"
+28746,Higher-dimensional attractors with absolutely continuous invariant probability,"  Consider a dynamical system $T:\mathbb{T}\times \mathbb{R}^{d} \rightarrow
+\mathbb{T}\times \mathbb{R}^{d} $ given by $ T(x,y) = (E(x), C(y) + f(x))$,
+where $E$ is a linear expanding map of $\mathbb{T}$, $C$ is a linear
+contracting map of $\mathbb{R}^d$ and $f$ is in $C^2(\mathbb{T},\mathbb{R}^d)$.
+We prove that if $T$ is volume expanding and $u\geq d$, then for every $E$
+there exists an open set $\mathcal{U}$ of pairs $(C,f)$ for which the
+corresponding dynamic $T$ admits an absolutely continuous invariant
+probability. A geometrical characteristic of transversality between
+self-intersections of images of $\mathbb{T}\times\{ 0 \}$ is present in the
+dynamic of the maps in $\mathcal{U}$. In addition, we give a condition between
+$E$ and $C$ under which it is possible to perturb $f$ to obtain a pair
+$(C,\tilde{f})$ in $\mathcal{U}$.
+"
+28747,Modeling Individual Cyclic Variation in Human Behavior,"  Cycles are fundamental to human health and behavior. However, modeling cycles
+in time series data is challenging because in most cases the cycles are not
+labeled or directly observed and need to be inferred from multidimensional
+measurements taken over time. Here, we present CyHMMs, a cyclic hidden Markov
+model method for detecting and modeling cycles in a collection of
+multidimensional heterogeneous time series data. In contrast to previous cycle
+modeling methods, CyHMMs deal with a number of challenges encountered in
+modeling real-world cycles: they can model multivariate data with discrete and
+continuous dimensions; they explicitly model and are robust to missing data;
+and they can share information across individuals to model variation both
+within and between individual time series. Experiments on synthetic and
+real-world health-tracking data demonstrate that CyHMMs infer cycle lengths
+more accurately than existing methods, with 58% lower error on simulated data
+and 63% lower error on real-world data compared to the best-performing
+baseline. CyHMMs can also perform functions which baselines cannot: they can
+model the progression of individual features/symptoms over the course of the
+cycle, identify the most variable features, and cluster individual time series
+into groups with distinct characteristics. Applying CyHMMs to two real-world
+health-tracking datasets -- of menstrual cycle symptoms and physical activity
+tracking data -- yields important insights including which symptoms to expect
+at each point during the cycle. We also find that people fall into several
+groups with distinct cycle patterns, and that these groups differ along
+dimensions not provided to the model. For example, by modeling missing data in
+the menstrual cycles dataset, we are able to discover a medically relevant
+group of birth control users even though information on birth control is not
+given to the model.
+"
+28748,A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations,"  We show that simple transformations, namely translations and rotations alone,
+are sufficient to fool neural network-based vision models on a significant
+fraction of inputs. This is in sharp contrast to previous work that relied on
+more complicated optimization approaches that are unlikely to appear outside of
+a truly adversarial setting. Moreover, fooling rotations and translations are
+easy to find and require only a few black-box queries to the target model.
+Overall, our findings emphasize the need for designing robust classifiers even
+in natural, benign contexts.
+"
+28749,Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training,"  Large-scale distributed training requires significant communication bandwidth
+for gradient exchange that limits the scalability of multi-node training, and
+requires expensive high-bandwidth network infrastructure. The situation gets
+even worse with distributed training on mobile devices (federated learning),
+which suffers from higher latency, lower throughput, and intermittent poor
+connections. In this paper, we find 99.9% of the gradient exchange in
+distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to
+greatly reduce the communication bandwidth. To preserve accuracy during
+compression, DGC employs four methods: momentum correction, local gradient
+clipping, momentum factor masking, and warm-up training. We have applied Deep
+Gradient Compression to image classification, speech recognition, and language
+modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and
+Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a
+gradient compression ratio from 270x to 600x without losing accuracy, cutting
+the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from
+488MB to 0.74MB. Deep gradient compression enables large-scale distributed
+training on inexpensive commodity 1Gbps Ethernet and facilitates distributed
+training on mobile.
+"
+28750,The direct image of a flat fibration with complex fibers,"  We consider a proper flat fibration with real base and complex fibers. First
+we construct odd characteristic classes for such fibrations by a method that
+generalizes constructions of Bismut-Lott. Then we consider the direct image of
+a fiberwise holomorphic vector bundle, which is a flat vector bundle on the
+base. We give a Riemann-Roch-Grothendieck theorem calculating the odd real
+characteristic classes of this flat vector bundle.
+"
+28751,Laguerre polynomials and transitional asymptotics of the modified Korteweg-de Vries equation for step-like initial data,"  We consider the compressive wave for the modified Korteweg--de Vries equation
+with background constants $c>0$ for $x\to-\infty$ and $0$ for $x\to+\infty.$ We
+study the asymptotics of solutions in the transition zone $4c^2t-\varepsilon
+t<x<4c^2t-\beta t^{\sigma}\ln t$ for $\varepsilon>0,$ $\sigma\in(0,1),$
+$\beta>0.$ In this region we have a bulk of nonvanishing oscillations, the
+number of which grows as $\frac{\varepsilon t}{\ln t}.$ Also we show how to
+obtain Khruslov--Kotlyarov's asymptotics in the domain $4c^2t-\rho\ln
+t<x<4c^2t$ with the help of parametrices constructed out of Laguerre
+polynomials in the corresponding Riemann-Hilbert problem.
+"
+28752,Bernstein - von Mises theorems for statistical inverse problems II: Compound Poisson processes,"  We study nonparametric Bayesian statistical inference for the parameters
+governing a pure jump process of the form $$Y_t = \sum_{k=1}^{N(t)} Z_k,~~~ t
+\ge 0,$$ where $N(t)$ is a standard Poisson process of intensity $\lambda$, and
+$Z_k$ are drawn i.i.d. from jump measure $\mu$. A high-dimensional wavelet
+series prior for the Lévy measure $\nu = \lambda \mu$ is devised and the
+posterior distribution arises from observing discrete samples $Y_\Delta,
+Y_{2\Delta}, \dots, Y_{n\Delta}$ at fixed observation distance $\Delta$, giving
+rise to a nonlinear inverse inference problem. We derive contraction rates in
+uniform norm for the posterior distribution around the true Lévy density that
+are optimal up to logarithmic factors over Hölder classes, as sample size $n$
+increases. We prove a functional Bernstein-von Mises theorem for the
+distribution functions of both $\mu$ and $\nu$, as well as for the intensity
+$\lambda$, establishing the fact that the posterior distribution is
+approximated by an infinite-dimensional Gaussian measure whose covariance
+structure is shown to attain the Cramér-Rao lower bound for this inverse
+problem. As a consequence posterior based inferences, such as nonparametric
+credible sets, are asymptotically valid and optimal from a frequentist point of
+view.
+"
+28753,Droplet localization in the random XXZ model and its manifestations,"  We examine many-body localization properties for the eigenstates that lie in
+the droplet sector of the random-field spin-$\frac 1 2$ XXZ chain. These states
+satisfy a basic single cluster localization property (SCLP), derived in
+\cite{EKS}. This leads to many consequences, including dynamical exponential
+clustering, non-spreading of information under the time evolution, and a zero
+velocity Lieb Robinson bound. Since SCLP is only applicable to the droplet
+sector, our definitions and proofs do not rely on knowledge of the spectral and
+dynamical characteristics of the model outside this regime. Rather, to allow
+for a possible mobility transition, we adapt the notion of restricting the
+Hamiltonian to an energy window from the single particle setting to the many
+body context.
+"
+28754,Structurally Observable Distributed Networks of Agents under Cost and Robustness Constraints,"  In many problems, agents cooperate locally so that a leader or fusion center
+can infer the state of every agent from probing the state of only a small
+number of agents. Versions of this problem arise when a fusion center
+reconstructs an extended physical field by accessing the state of just a few of
+the sensors measuring the field, or a leader monitors the formation of a team
+of robots. Given a link cost, the paper presents a polynomial time algorithm to
+design a minimum cost coordinated network dynamics followed by the agents,
+under an observability constraint. The problem is placed in the context of
+structural observability and solved even when up to k agents in the coordinated
+network dynamics fail.
+"
+28755,Extending Owen's integral table and a new multivariate Bernoulli distribution,"  We extend some equatilites in the Owen's table of normal integrals (A table
+of normal integrals in Communication in Statistics-Simulation and Computation,
+1980). Furthermore a new probabilistic model for a vector of binary random
+variables is proposed.
+"
+28756,Unconventional magnetic order in the conical state of MnSi,"  In the temperature-magnetic field phase diagram, the binary metallic compound
+MnSi exhibits three magnetic phases below Tc ~ 29 K. An unconventional
+helicoidal phase is observed in zero field. At moderate field intensity a
+conical phase sets in. Near Tc, in an intermediate field range, a skyrmion
+lattice phase appears. Here we show the magnetic structure in the conical phase
+to strongly depend on the field direction and to deviate substantially from a
+conventional conical structure.
+"
+28757,Characterization of Defect Structure in Electrodeposited Nanocrystalline Ni Films,"  The microstructure of electrodeposited Ni films produced without and with
+organic additives (saccharin and formic acid) was investigated by X-ray
+diffraction (XRD) line profile analysis and cross-sectional transmission
+electron microscopy (TEM). Whereas the general effect of these additives on the
+microstructure (elimination of columnar growth as well as grain refinement) was
+reproduced, the pronounced intention of this study was to compare the results
+of various seldom-used high-performance structural characterization methods on
+identical electrodeposited specimens in order to reveal fine details of
+structural changes qualitatively not very common in this field. In the film
+deposited without additives, a columnar structure was observed showing
+similarities to the T-zone of structure zone models. Both formic acid and
+saccharin additives resulted in equiaxed grains with reduced size, as well as
+increased dislocation and twin fault densities in the nanocrystalline films.
+Moreover, the structure became homogeneous and free of texture within the total
+film thickness due to the additives. Saccharin yielded smaller grain size and
+larger defect density than formic acid. A detailed analysis of the grain size
+and twin boundary spacing distributions was carried out with the complementary
+application of TEM and XRD, by carefully distinguishing between the TEM and XRD
+grain sizes.
+"
+28758,Visual binary stars: data to investigate formation of binaries,"  Statistics of orbital parameters of binary stars as well as statistics of
+their physical characteristics bear traces of star formation history. However,
+statistical investigations of binaries are complicated by lacking or incomplete
+observational data and by a number of observational selection effects.
+Visual binaries are the most numerous observed binaries, the number of pairs
+exceeds 130000. The most complete list of presently known visual binary stars
+was compiled by cross-matching objects and combining data of the three largest
+catalogues of visual binaries. It was supplemented by the data on parallaxes,
+multicolor photometry, spectral characteristics of the stars of this list taken
+from other catalogues. This allowed us to compensate partly for the lack of
+observational data for these objects. Combined data allowed us to check
+validity of observational values and to investigate statistics of the orbital
+and physical parameters of visual binaries. Corrections for incompleteness of
+observational data are discussed. Obtained datasets and modern distributions of
+binary parameters will be used to reconstruct the initial distributions and
+parameters of the function of star formation for binary systems.
+"
+28759,On a problem of Hopf for circle bundles over aspherical manifolds with hyperbolic fundamental groups,"  We prove that a circle bundle over a closed oriented aspherical manifold with
+hyperbolic fundamental group admits a self-map of absolute degree greater than
+one if and only if it is the trivial bundle. This generalizes in every
+dimension the case of circle bundles over hyperbolic surfaces, for which the
+result was known by the work of Brooks and Goldman on the Seifert volume. As a
+consequence, we verify the following strong version of a problem of Hopf for
+the above class of manifolds: Every self-map of non-zero degree of a circle
+bundle over a closed oriented aspherical manifold with hyperbolic fundamental
+group is either homotopic to a homeomorphism or homotopic to a non-trivial
+covering and the bundle is trivial.
+As another application, we derive the first examples of non-vanishing
+numerical invariants that are monotone with respect to the mapping degree on
+non-trivial circle bundles over aspherical manifolds with hyperbolic
+fundamental groups of any dimension. Moreover, we obtain the first examples of
+manifolds (given by the aforementioned bundles with torsion Euler class) which
+do not admit self-maps of absolute degree greater than one, but admit maps of
+infinitely many different degrees from other manifolds.
+"
+28760,Fast and Accurate Entity Recognition with Iterated Dilated Convolutions,"  Today when many practitioners run basic NLP on the entire web and
+large-volume traffic, faster methods are paramount to saving time and energy
+costs. Recent advances in GPU hardware have led to the emergence of
+bi-directional LSTMs as a standard method for obtaining per-token vector
+representations serving as input to labeling tasks such as NER (often followed
+by prediction in a linear-chain CRF). Though expressive and accurate, these
+models fail to fully exploit GPU parallelism, limiting their computational
+efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER:
+Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better
+capacity than traditional CNNs for large context and structured prediction.
+Unlike LSTMs whose sequential processing on sentences of length N requires O(N)
+time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions
+to run in parallel across entire documents. We describe a distinct combination
+of network structure, parameter sharing and training procedures that enable
+dramatic 14-20x test-time speedups while retaining accuracy comparable to the
+Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire
+document are even more accurate while maintaining 8x faster test time speeds.
+"
+28761,Orbital Stability of Domain Walls in Coupled Gross-Pitaevskii Systems,"  Domain walls are minimizers of energy for coupled one-dimensional
+Gross--Pitaevskii systems with nontrivial boundary conditions at infinity. It
+has been shown that these solutions are orbitally stable in the space of
+complex $\dot{H}^1$ functions with the same limits at infinity. In the present
+work we adopt a new weighted $H^1$ space to control perturbations of the domain
+walls and thus to obtain an improved orbital stability result. A major
+difficulty arises from the degeneracy of linearized operators at the domain
+walls and the lack of coercivity.
+"
+28762,Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion,"  Radiomics aims to extract and analyze large numbers of quantitative features
+from medical images and is highly promising in staging, diagnosing, and
+predicting outcomes of cancer treatments. Nevertheless, several challenges need
+to be addressed to construct an optimal radiomics predictive model. First, the
+predictive performance of the model may be reduced when features extracted from
+an individual imaging modality are blindly combined into a single predictive
+model. Second, because many different types of classifiers are available to
+construct a predictive model, selecting an optimal classifier for a particular
+application is still challenging. In this work, we developed multi-modality and
+multi-classifier radiomics predictive models that address the aforementioned
+issues in currently available models. Specifically, a new reliable classifier
+fusion strategy was proposed to optimally combine output from different
+modalities and classifiers. In this strategy, modality-specific classifiers
+were first trained, and an analytic evidential reasoning (ER) rule was
+developed to fuse the output score from each modality to construct an optimal
+predictive model. One public data set and two clinical case studies were
+performed to validate model performance. The experimental results indicated
+that the proposed ER rule based radiomics models outperformed the traditional
+models that rely on a single classifier or simply use combined features from
+different modalities.
+"
+28763,Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection,"  The classification of MRI images according to the anatomical field of view is
+a necessary task to solve when faced with the increasing quantity of medical
+images. In parallel, advances in deep learning makes it a suitable tool for
+computer vision problems. Using a common architecture (such as AlexNet)
+provides quite good results, but not sufficient for clinical use. Improving the
+model is not an easy task, due to the large number of hyper-parameters
+governing both the architecture and the training of the network, and to the
+limited understanding of their relevance. Since an exhaustive search is not
+tractable, we propose to optimize the network first by random search, and then
+by an adaptive search based on Gaussian Processes and Probability of
+Improvement. Applying this method on a large and varied MRI dataset, we show a
+substantial improvement between the baseline network and the final one (up to
+20\% for the most difficult classes).
+"
+28764,Nanoclustering phase competition induces the resistivity hump in colossal magnetoresistive manganites,"  Using a two-band double-exchange model with Jahn-Teller lattice distortions
+and super-exchange interactions, supplemented by quenched disorder, at electron
+density $n=0.65$, we explicitly demonstrate the coexistence of the $n$ =
+1/2-type ($\pi, \pi$) charge-ordered and the ferromagnetic nanoclusters above
+the ferromagnetic transition temperature $T_{\rm c}$ in colossal
+magnetoresistive (CMR) manganites. The resistivity increases due to the
+enhancement of the volume fraction of the charge-ordered and the ferromagnetic
+nanoclusters with decreasing the temperature down to $T_{\rm c}$. The
+ferromagnetic nanoclusters start to grow and merge, and the volume fraction of
+the charge-ordered nanoclusters decreases below $T_{\rm c}$, leading to the
+sharp drop in the resistivity. By applying a small external magnetic field $h$,
+we show that the resistivity above $T_{\rm c}$ increases, as compared with the
+case when $h=0$, a fact which further confirms the coexistence of the
+charge-ordered and the ferromagnetic nanoclusters. In addition, we show that
+the volume fraction of the charge-ordered nanoclusters decreases with
+increasing the bandwidth and consequently the resistivity hump diminishes for
+large bandwidth manganites, in good qualitative agreement with experiments. The
+obtained insights from our calculations provide a complete pathway to
+understand the phase competition in CMR manganites.
+"
+28765,Energy Harvesting Communications under Explicit and Implicit Temperature Constraints,"  We consider an energy harvesting communication system where the temperature
+dynamics is governed by the transmission power policy. Different from the
+previous work, we consider a discrete time system where transmission power is
+kept constant in each slot. We consider two models that capture different
+effects of temperature. In the first model, the temperature is constrained to
+be below a critical temperature at all time instants; we coin this model as
+explicit temperature constrained model. We investigate throughput optimal power
+allocation for multiple energy arrivals under general, as well as temperature
+and energy limited regimes. We show that the optimal power allocation for the
+temperature limited case is monotone decreasing. In the second model, we
+consider the effect of the temperature on the channel quality via its influence
+on additive noise power; we coin this model as implicit temperature constrained
+model. In this model, the change in the variance of the additive noise due to
+previous transmissions is non-negligible. In particular, transmitted signals
+contribute as interference for all subsequent slots and thus affect the signal
+to interference plus noise ratio (SINR). In this case, we investigate
+throughput optimal power allocation under general, as well as low and high SINR
+regimes. We show in the low SINR regime that the optimal allocation dictates
+the transmitter to save its harvested energy till the last slot. In the high
+SINR regime, we show that the optimal power sequence is monotone increasing.
+Finally, we consider the case in which implicit and explicit temperature
+constraints are simultaneously active and we show under certain conditions that
+the optimal power sequence is monotone decreasing.
+"
+28766,Markov chain sampling of the $O(n)$ loop models on the infinite plane,"  It was recently proposed in
+this https URL [Herdeiro &
+Doyon Phys.,Rev.,E (2016)] a numerical method showing a precise sampling of the
+infinite plane 2d critical Ising model for finite lattice subsections. The
+present note extends the method to a larger class of models, namely the $O(n)$
+loop gas models for $n \in (1,2]$. We argue that even though the Gibbs measure
+is non local, it is factorizable on finite subsections when sufficient
+information on the loops touching the boundaries is stored. Our results attempt
+to show that provided an efficient Markov chain mixing algorithm and an
+improved discrete lattice dilation procedure the planar limit of the $O(n)$
+models can be numerically studied with efficiency similar to the Ising case.
+This confirms that scale invariance is the only requirement for the present
+numerical method to work.
+"
+28767,Efficient Approximation Algorithms for String Kernel Based Sequence Classification,"  Sequence classification algorithms, such as SVM, require a definition of
+distance (similarity) measure between two sequences. A commonly used notion of
+similarity is the number of matches between $k$-mers ($k$-length subsequences)
+in the two sequences. Extending this definition, by considering two $k$-mers to
+match if their distance is at most $m$, yields better classification
+performance. This, however, makes the problem computationally much more
+complex. Known algorithms to compute this similarity have computational
+complexity that render them applicable only for small values of $k$ and $m$. In
+this work, we develop novel techniques to efficiently and accurately estimate
+the pairwise similarity score, which enables us to use much larger values of
+$k$ and $m$, and get higher predictive accuracy. This opens up a broad avenue
+of applying this classification approach to audio, images, and text sequences.
+Our algorithm achieves excellent approximation performance with theoretical
+guarantees. In the process we solve an open combinatorial problem, which was
+posed as a major hindrance to the scalability of existing solutions. We give
+analytical bounds on quality and runtime of our algorithm and report its
+empirical performance on real world biological and music sequences datasets.
+"
+28768,Feedback computability on Cantor space,"  We introduce the notion of feedback computable functions from $2^\omega$ to
+$2^\omega$, extending feedback Turing computation in analogy with the standard
+notion of computability for functions from $2^\omega$ to $2^\omega$. We then
+show that the feedback computable functions are precisely the effectively Borel
+functions. With this as motivation we define the notion of a feedback
+computable function on a structure, independent of any coding of the structure
+as a real. We show that this notion is absolute, and as an example characterize
+those functions that are computable from a Gandy ordinal with some finite
+subset distinguished.
+"
+28769,Skeleton-based Action Recognition Using LSTM and CNN,"  Recent methods based on 3D skeleton data have achieved outstanding
+performance due to its conciseness, robustness, and view-independent
+representation. With the development of deep learning, Convolutional Neural
+Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have
+achieved promising performance for action recognition. However, for CNN-based
+methods, it is inevitable to loss temporal information when a sequence is
+encoded into images. In order to capture as much spatial-temporal information
+as possible, LSTM and CNN are adopted to conduct effective recognition with
+later score fusion. In addition, experimental results show that the score
+fusion between CNN and LSTM performs better than that between LSTM and LSTM for
+the same feature. Our method achieved state-of-the-art results on NTU RGB+D
+datasets for 3D human action analysis. The proposed method achieved 87.40% in
+terms of accuracy and ranked $1^{st}$ place in Large Scale 3D Human Activity
+Analysis Challenge in Depth Videos.
+"
+28770,Improved Approximate Rips Filtrations with Shifted Integer Lattices,"  Rips complexes are important structures for analyzing topological features of
+metric spaces. Unfortunately, generating these complexes constitutes an
+expensive task because of a combinatorial explosion in the complex size. For
+$n$ points in $\mathbb{R}^d$, we present a scheme to construct a
+$3\sqrt{2}$-approximation of the multi-scale filtration of the $L_\infty$-Rips
+complex, which extends to a $O(d^{0.25})$-approximation of the Rips filtration
+for the Euclidean case. The $k$-skeleton of the resulting approximation has a
+total size of $n2^{O(d\log k)}$. The scheme is based on the integer lattice and
+on the barycentric subdivision of the $d$-cube.
+"
+28771,Area difference bounds for dissections of a square into an odd number of triangles,"  Monsky's theorem from 1970 states that a square cannot be dissected into an
+odd number of triangles of the same area, but it does not give a lower bound
+for the area differences that must occur.
+We extend Monsky's theorem to ""constrained framed maps""; based on this we can
+apply a gap theorem from semi-algebraic geometry to a polynomial area
+difference measure and thus get a lower bound for the area differences that
+decreases doubly-exponentially with the number of triangles. On the other hand,
+we obtain the first superpolynomial upper bounds for this problem, derived from
+an explicit construction that uses the Thue-Morse sequence.
+"
+28772,Topological Corner States on Kagome Lattice Based Chiral Higher-Order Topological Insulator,"  The higher-order topological insulator (HOTI) protected by spacial symmetry
+has been studied in-depth on models with square lattice. Our work, based on an
+alternative model on the breathing Kagome lattice, revealed that the different
+types of corners in the lattice could actually be conditionally gapless, or
+always gapped. Using the Wilson loop formalism, we argue that these corner
+states occur when the eigenvalues of the Wannier Hamiltonian cross through a
+certain reference point during the conceptual ""pumping"" procedure. The results
+demonstrate the corner of the Kagome lattice based HOTI is a zero-dimensional
+analogue of the 1D chiral edge states on the boundary of a Chern insulator, but
+with a sensitive dependence on the shape of the corner. Our method of the
+pumping cylinder, which reveals the symmetry/gapless-ability correspondence,
+can be generalized into a general scheme in determining the classification of
+corner(hinge) states in HOTI.
+"
+28773,Circumstellar disks and outflows in turbulent molecular cloud cores: possible formation mechanism for misaligned systems,"  We investigate the formation of circumstellar disks and outflows subsequent
+to the collapse of molecular cloud cores with the magnetic field and
+turbulence. Numerical simulations are performed by using an adaptive mesh
+refinement to follow the evolution up to $\sim 1000$~yr after the formation of
+a protostar. In the simulations, circumstellar disks are formed around the
+protostars; those in magnetized models are considerably smaller than those in
+nonmagnetized models, but their size increases with time. The models with
+stronger magnetic field tends to produce smaller disks. During evolution in the
+magnetized models, the mass ratios of a disk to a protostar is approximately
+constant at $\sim 1-10$\%. The circumstellar disks are aligned according to
+their angular momentum, and the outflows accelerate along the magnetic field on
+the $10-100$~au scale; this produces a disk that is misaligned with the
+outflow. The outflows are classified into two types: a magneto-centrifugal wind
+and a spiral flow. In the latter, because of the geometry, the axis of rotation
+is misaligned with the magnetic field. The magnetic field has an internal
+structure in the cloud cores, which also causes misalignment between the
+outflows and the magnetic field on the scale of the cloud core. The
+distribution of the angular momentum vectors in a core also has a non-monotonic
+internal structure. This should create a time-dependent accretion of angular
+momenta onto the circumstellar disk. Therefore, the circumstellar disks are
+expected to change their orientation as well as their sizes in the long-term
+evolutions.
+"
+28774,Effects of neutrino mass hierarchies on dynamical dark energy models,"  We investigate how three different possibilities of neutrino mass
+hierarchies, namely normal, inverted, and degenerate, can affect the
+observational constraints on three well known dynamical dark energy models,
+namely the Chevallier-Polarski-Linder, logarithmic, and the
+Jassal-Bagla-Padmanabhan parametrizations. In order to impose the observational
+constraints on the models, we performed a robust analysis using Planck 2015
+temperature and polarization data, Supernovae type Ia from Joint Light curve
+analysis, baryon acoustic oscillations distance measurements, redshift space
+distortion characterized by $f(z)\sigma_8(z)$ data, weak gravitational lensing
+data from Canada-France-Hawaii Telescope Lensing Survey, and cosmic
+chronometers data plus the local value of the Hubble parameter. We find that
+different neutrino mass hierarchies return similar fit on almost all model
+parameters and mildly change the dynamical dark energy properties.
+"
+28775,Automorphism groups of finite topological rank,"  We offer a criterion for showing that the automorphism group of an
+ultrahomogeneous structure is topologically 2-generated and even has a
+cyclically dense conjugacy class. We then show how finite topological rank of
+the automorphism group of an $\omega$-categorical structure can go down to
+reducts. Together, those results prove that a large number of
+$\omega$-categorical structures that appear in the literature have an
+automorphism group of finite topological rank. In fact, we are not aware of any
+$\omega$-categorical structure to which they do not apply (assuming the
+automorphism group has no compact quotients). We end with a few questions and
+conjectures.
+"
+28776,Asking Biological Questions of Physical Systems: the Device Approach to Emergent Properties,"  Life occurs in concentrated `Ringer Solutions' derived from seawater that
+Lesser Blum studied for most of his life. As we worked together, Lesser and I
+realized that the questions asked of those solutions were quite different in
+biology from those in the physical chemistry he knew. Biology is inherited.
+Information is passed by handfuls of atoms in the genetic code. A few atoms in
+the proteins built from the code change macroscopic function. Indeed, a few
+atoms often control biological function in the same sense that a gas pedal
+controls the speed of a car. Biological questions then are most productive when
+they are asked in the context of evolution. What function does a system
+perform? How is the system built to perform that function? What forces are used
+to perform that function? How are the modules that perform functions connected
+to make the machinery of life. Physiologists have shown that much of life is a
+nested hierarchy of devices, one on top of another, linking atomic ions in
+concentrated solutions to current flow through proteins, current flow to
+voltage signals, voltage signals to changes in current flow, all connected to
+make a regenerative system that allows electrical action potentials to move
+meters, under the control of a few atoms. The hierarchy of devices allows
+macroscopic properties to emerge from atomic scale interactions. The structures
+of biology create these devices. The concentration and electrical fields of
+biology power these devices, more than anything else. The resulting organisms
+reproduce. Evolution selects the organisms that reproduce more and thereby
+selects the devices that allow macroscopic control to emerge from the atomic
+structures of genes and proteins and their motions.
+"
+28777,An Optimal Extraction Problem with Price Impact,"  A price-maker company extracts an exhaustible commodity from a reservoir, and
+sells it instantaneously in the spot market. In absence of any actions of the
+company, the commodity's spot price evolves either as a drifted Brownian motion
+or as an Ornstein-Uhlenbeck process. While extracting, the company affects the
+market price of the commodity, and its actions have an impact on the dynamics
+of the commodity's spot price. The company aims at maximizing the total
+expected profits from selling the commodity, net of the total expected
+proportional costs of extraction. We model this problem as a two-dimensional
+degenerate singular stochastic control problem with finite fuel. To determine
+its solution, we construct an explicit solution to the associated
+Hamilton-Jacobi-Bellman equation, and then verify its actual optimality through
+a verification theorem. On the one hand, when the (uncontrolled) price is a
+drifted Brownian motion, it is optimal to extract whenever the current price
+level is larger or equal than an endogenously determined constant threshold. On
+the other hand, when the (uncontrolled) price evolves as an Ornstein-Uhlenbeck
+process, we show that the optimal extraction rule is triggered by a curve
+depending on the current level of the reservoir. Such a curve is a strictly
+decreasing $C^{\infty}$-function for which we are able to provide an explicit
+expression. Finally, our study is complemented by a theoretical and numerical
+analysis of the dependency of the optimal extraction strategy and value
+function on the model's parameters.
+"
+28778,Dark Energy Survey Year 1 Results: Galaxy-Galaxy Lensing,"  We present galaxy-galaxy lensing measurements from 1321 sq. deg. of the Dark
+Energy Survey (DES) Year 1 (Y1) data. The lens sample consists of a selection
+of 660,000 red galaxies with high-precision photometric redshifts, known as
+redMaGiC, split into five tomographic bins in the redshift range $0.15 < z <
+0.9$. We use two different source samples, obtained from the Metacalibration
+(26 million galaxies) and Im3shape (18 million galaxies) shear estimation
+codes, which are split into four photometric redshift bins in the range $0.2 <
+z < 1.3$. We perform extensive testing of potential systematic effects that can
+bias the galaxy-galaxy lensing signal, including those from shear estimation,
+photometric redshifts, and observational properties. Covariances are obtained
+from jackknife subsamples of the data and validated with a suite of log-normal
+simulations. We use the shear-ratio geometric test to obtain independent
+constraints on the mean of the source redshift distributions, providing
+validation of those obtained from other photo-$z$ studies with the same data.
+We find consistency between the galaxy bias estimates obtained from our
+galaxy-galaxy lensing measurements and from galaxy clustering, therefore
+showing the galaxy-matter cross-correlation coefficient $r$ to be consistent
+with one, measured over the scales used for the cosmological analysis. The
+results in this work present one of the three two-point correlation functions,
+along with galaxy clustering and cosmic shear, used in the DES cosmological
+analysis of Y1 data, and hence the methodology and the systematics tests
+presented here provide a critical input for that study as well as for future
+cosmological analyses in DES and other photometric galaxy surveys.
+"
+28779,Twitter Hashtag Recommendation using Matrix Factorization,"  Twitter, one of the biggest and most popular microblogging Websites, has
+evolved into a powerful communication platform which allows millions of active
+users to generate huge volume of microposts and queries on a daily basis. To
+accommodate effective categorization and easy search, users are allowed to make
+use of hashtags, keywords or phrases prefixed by hash character, to categorize
+and summarize their posts. However, valid hashtags are not restricted and thus
+are created in a free and heterogeneous style, increasing difficulty of the
+task of tweet categorization. In this paper, we propose a low-rank weighted
+matrix factorization based method to recommend hashtags to the users solely
+based on their hashtag usage history and independent from their tweets'
+contents. We confirm using two-sample t-test that users are more likely to
+adopt new hashtags similar to the ones they have previously adopted. In
+particular, we formulate the problem of hashtag recommendation into an
+optimization problem and incorporate hashtag correlation weight matrix into it
+to account for the similarity between different hashtags. We finally leverage
+widely used matrix factorization from recommender systems to solve the
+optimization problem by capturing the latent factors of users and hashtags.
+Empirical experiments demonstrate that our method is capable to properly
+recommend hashtags.
+"
+28780,Uniform spaces and the Newtonian structure of (big)data affinity kernels,"  Let $X$ be a (data) set. Let $K(x,y)>0$ be a measure of the affinity between
+the data points $x$ and $y$. We prove that $K$ has the structure of a Newtonian
+potential $K(x,y)=\varphi(d(x,y))$ with $\varphi$ decreasing and $d$ a
+quasi-metric on $X$ under two mild conditions on $K$. The first is that the
+affinity of each $x$ to itself is infinite and that for $x\neq y$ the affinity
+is positive and finite. The second is a quantitative transitivity; if the
+affinity between $x$ and $y$ is larger than $\lambda>0$ and the affinity of $y$
+and $z$ is also larger than $\lambda$, then the affinity between $x$ and $z$ is
+larger than $\nu(\lambda)$. The function $\nu$ is concave, increasing,
+continuous from $\mathbb{R}^+$ onto $\mathbb{R}^+$ with $\nu(\lambda)<\lambda$
+for every $\lambda>0$.
+"
+28781,Deep State Space Models for Unconditional Word Generation,"  Autoregressive feedback is considered a necessity for successful
+unconditional text generation using stochastic sequence models. However, such
+feedback is known to introduce systematic biases into the training process and
+it obscures a principle of generation: committing to global information and
+forgetting local nuances. We show that a non-autoregressive deep state space
+model with a clear separation of global and local uncertainty can be built from
+only two ingredients: An independent noise source and a deterministic
+transition function. Recent advances on flow-based variational inference can be
+used to train an evidence lower-bound without resorting to annealing, auxiliary
+losses or similar measures. The result is a highly interpretable generative
+model on par with comparable auto-regressive models on the task of word
+generation.
+"
+28782,The Multi-Armed Bandit Problem: An Efficient Non-Parametric Solution,"  Lai and Robbins (1985) and Lai (1987) provided efficient parametric solutions
+to the multi-armed bandit problem, showing that arm allocation via upper
+confidence bounds (UCB) achieves minimum regret. These bounds are constructed
+from the Kullback-Leibler information of the reward distributions, estimated
+from specified parametric families. In recent years there has been renewed
+interest in the multi-armed bandit problem due to new applications in machine
+learning algorithms and data analytics. Non-parametric arm allocation
+procedures like $\epsilon$-greedy, Boltzmann exploration and BESA were studied,
+and modified versions of the UCB procedure were also analyzed under
+non-parametric settings. However unlike UCB these non-parametric procedures are
+not efficient under general parametric settings. In this paper we propose
+efficient non-parametric procedures.
+"
+28783,Electronic properties of topological insulator candidate CaAgAs,"  The topological phases of matter provide the opportunity to observe many
+exotic properties, like the existence of two dimensional topological surface
+states in the form of Dirac cone in topological insulators, chiral transport
+through open Fermi arc in Weyl semimetals etc. However, these properties can
+only affect the transport characteristics and therefore can be useful for
+applications only if the topological phenomena occur near the Fermi level.
+CaAgAs is a promising candidate, wherein the ab-initio calculations predict
+line-node at the Fermi level which on including spin-orbit coupling transforms
+into a topological insulator. In this report, we study the electronic structure
+of CaAgAs with angle resolved photoemission spectroscopy (ARPES), ab-initio
+calculations and transport measurements. The ARPES results show that the bulk
+valence band crosses the Fermi energy at gamma-point and the band dispersion
+matches the ab-initio calculations closely on shifting the Fermi energy by -0.5
+eV. ARPES results are in good agreement with our transport measurements which
+show abundant p-type carriers.
+"
+28784,Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments,"  Mobile robot navigation in complex and dynamic environments is a challenging
+but important problem. Reinforcement learning approaches fail to solve these
+tasks efficiently due to reward sparsities, temporal complexities and
+high-dimensionality of sensorimotor spaces which are inherent in such problems.
+We present a novel approach to train action policies to acquire navigation
+skills for wheel-legged robots using deep reinforcement learning. The policy
+maps height-map image observations to motor commands to navigate to a target
+position while avoiding obstacles. We propose to acquire the multifaceted
+navigation skill by learning and exploiting a number of manageable navigation
+behaviors. We also introduce a domain randomization technique to improve the
+versatility of the training samples. We demonstrate experimentally a
+significant improvement in terms of data-efficiency, success rate, robustness
+against irrelevant sensory data, and also the quality of the maneuver skills.
+"
+28785,Roots of unity in $K(n)$-local $E_\infty$-rings,"  We prove that no $K(n)$-local $E_\infty$-ring can have a primitive $p^k$th
+root of unity in its homotopy, allowing us to prove that the Lubin-Tate tower
+does not lift to a tower of derived stacks over Morava $E$-theory.
+"
+28786,Optimal stability for a first order coefficient in a non-self-adjoint wave equation from dirichlet-to-neumann map,"  This paper is focused on the study of an inverse problem for a
+non-self-adjoint hyperbolic equation. More precisely, we attempt to stably
+recover a first order coefficient appearing in a wave equation from the
+knowledge of Neumann boundary data. We show in dimension n greater than two, a
+stability estimate of H{ö}lder type for the inverse problem under
+consideration. The proof involves the reduction to an auxiliary inverse problem
+for an electromagnetic wave equation and the use of an appropriate Carleman
+estimate.
+"
+28787,Learning with Feature Evolvable Streams,"  Learning with streaming data has attracted much attention during the past few
+years. Though most studies consider data stream with fixed features, in real
+practice the features may be evolvable. For example, features of data gathered
+by limited-lifespan sensors will change when these sensors are substituted by
+new ones. In this paper, we propose a novel learning paradigm: \emph{Feature
+Evolvable Streaming Learning} where old features would vanish and new features
+would occur. Rather than relying on only the current features, we attempt to
+recover the vanished features and exploit it to improve performance.
+Specifically, we learn two models from the recovered features and the current
+features, respectively. To benefit from the recovered features, we develop two
+ensemble methods. In the first method, we combine the predictions from two
+models and theoretically show that with the assistance of old features, the
+performance on new features can be improved. In the second approach, we
+dynamically select the best single prediction and establish a better
+performance guarantee when the best model switches. Experiments on both
+synthetic and real data validate the effectiveness of our proposal.
+"
+28788,Regularization of the Reissner-Nordström black hole,"  An inner de Sitter region is glued smoothly and consistently with an outer
+Reissner-Nordström (RN) spacetime on a spherical thin-shell. Mass and
+charge of the outer RN spacetime are defined by the de Sitter and shell
+parameters. Radius of the shell plays the role of a cut-off which by virtue of
+regular de Sitter inside removes the singularity at $r=0.$ The topology of
+inner de Sitter with the radius of the thin-shell becomes compact. For
+stability the perturbed shell is shown to satisfy a modified polytropic
+equation of state which has vanishing mass and pressure on the unperturbed
+shell as dictated by the junction conditions.
+"
+28789,Subaru/SCExAO First-Light Direct Imaging of a Young Debris Disk around HD 36546,"  We present $H$-band scattered light imaging of a bright debris disk around
+the A0 star HD 36546 obtained from the Subaru Coronagraphic Extreme Adaptive
+Optics (SCExAO) system with data recorded by the HiCIAO camera using the vector
+vortex coronagraph. SCExAO traces the disk from $r$ $\sim$ 0.3"" to $r$ $\sim$
+1"" (34--114 au). The disk is oriented in a near east-west direction (PA $\sim$
+75$^{o}$), is inclined by $i$ $\sim$ 70--75$^{o}$ and is strongly
+forward-scattering ($g$ $>$ 0.5). It is an extended disk rather than a sharp
+ring; a second, diffuse dust population extends from the disk's eastern side.
+While HD 36546 intrinsic properties are consistent with a wide age range ($t$
+$\sim$ 1--250 $Myr$), its kinematics and analysis of coeval stars suggest a
+young age (3--10 $Myr$) and a possible connection to Taurus-Auriga's star
+formation history. SCExAO's planet-to-star contrast ratios are comparable to
+the first-light Gemini Planet Imager contrasts; for an age of 10 $Myr$, we rule
+out planets with masses comparable to HR 8799 b beyond a projected separation
+of 23 au. A massive icy planetesimal disk or an unseen superjovian planet at
+$r$ $>$ 20 au may explain the disk's visibility. The HD 36546 debris disk may
+be the youngest debris disk yet imaged, is the first newly-identified object
+from the now-operational SCExAO extreme AO system, is ideally suited for
+spectroscopic follow up with SCExAO/CHARIS in 2017, and may be a key probe of
+icy planet formation and planet-disk interactions.
+"
+28790,Simulating realistically complex comparative effectiveness studies with time-varying covariates and right-censored outcomes,"  Simulation studies are useful for evaluating and developing statistical
+methods for the analyses of complex problems. Performance of methods may be
+affected by multiple complexities present in real scenarios. Generating
+sufficiently realistic data for this purpose, however, can be challenging. Our
+study of the comparative effectiveness of HIV protocols on the risk of
+cardiovascular disease -- involving the longitudinal assessment of HIV patients
+-- is such an example. The correlation structure across covariates and within
+subjects over time must be considered as well as right-censoring of the outcome
+of interest, time to myocardial infarction. A challenge in simulating the
+covariates is to incorporate a joint distribution for variables of mixed type
+-- continuous, binary or polytomous. An additional challenge is incorporating
+within-subject correlation where some variables may vary over time and others
+may remain static. To address these issues, we extend the work of Demirtas and
+Doganay (2012). Identifying a model from which to simulate the right-censored
+outcome as a function of these covariates builds on work developed by Sylvestre
+and Abrahamowicz (2007). In this paper, we describe a cohesive and
+user-friendly approach accompanied by R code to simulate comparative
+effectiveness studies with right-censored outcomes that are functions of
+time-varying covariates.
+"
+28791,Dissections and automorphisms of regular Courant algebroids,"  In this note, given a regular Courant algebroid, we compute its group of
+automorphisms relative to a dissection. We also propose an infinitesimal
+version and recover examples of the literature.
+"
+28792,Universally Optimal Designs for the Two-dimensional Interference Model,"  There have been some major advances in the theory of optimal designs for
+interference models. However, the majority of them focus on one-dimensional
+layout of the block and the study for two-dimensional interference model is
+quite limited partly due to technical difficulties. This paper tries to fill
+this gap. Specifically, it systematically characterizes all possible
+universally optimal designs simultaneously. Computational issues are also
+addressed with theoretical backup.
+"
+28793,On the Complexity of Semantic Integration of OWL Ontologies,"  We propose a new mechanism for integration of OWL ontologies using semantic
+import relations. In contrast to the standard OWL importing, we do not require
+all axioms of the imported ontologies to be taken into account for reasoning
+tasks, but only their logical implications over a chosen signature. This
+property comes natural in many ontology integration scenarios, especially when
+the number of ontologies is large. In this paper, we study the complexity of
+reasoning over ontologies with semantic import relations and establish a range
+of tight complexity bounds for various fragments of OWL.
+"
+28794,Impurities in weakly coupled quantum spin chains Sr$_{2}$CuO$_{3}$ and SrCuO$_{2}$,"  We study the effect of non-magnetic Zn$^{2+}$ (spin-$0$) and magnetic
+Ni$^{2+}$ (spin-$1$) impurities on the ground state and low-lying excitations
+of the quasi-one-dimensional spin-$1/2$ Heisenberg antiferromagnet
+Sr$_{2}$CuO$_{3}$ using inelastic neutron scattering, specific heat and bulk
+magnetization measurements. We show that 1 \% Ni$^{2+}$ doping in Sr$_2$CuO$_3$
+results in a sizable spin gap in the spinon excitations, analogous to the case
+of Ni-doped SrCuO$_2$ previously reported [ref. 1]. However, a similar level of
+Zn$^{2+}$ doping in SrCuO$_2$, investigated here for comparison, did not reveal
+any signs of a spin gap. Magnetic ordering temperature was found to be
+suppressed in the presence of both Zn$^{2+}$ and Ni$^{2+}$ impurities, however,
+the rate of suppression due to Ni$^{2+}$ was found to be much more pronounced
+than for Zn$^{2+}$. Effect of magnetic field on the ordering temperature is
+investigated. We found that with increasing magnetic field, not only the
+magnetic ordering temperature gradually increases but the size of specific heat
+anomaly associated with the magnetic ordering also progressively enhances,
+which can be qualitatively understood as due to the field induced suppression
+of quantum fluctuations.
+"
+28795,BELIEF: A distance-based redundancy-proof feature selection method for Big Data,"  With the advent of Big Data era, data reduction methods are highly demanded
+given its ability to simplify huge data, and ease complex learning processes.
+Concretely, algorithms that are able to filter relevant dimensions from a set
+of millions are of huge importance. Although effective, these techniques suffer
+from the ""scalability"" curse as well.
+In this work, we propose a distributed feature weighting algorithm, which is
+able to rank millions of features in parallel using large samples. This method,
+inspired by the well-known RELIEF algorithm, introduces a novel redundancy
+elimination measure that provides similar schemes to those based on entropy at
+a much lower cost. It also allows smooth scale up when more instances are
+demanded in feature estimations. Empirical tests performed on our method show
+its estimation ability in manifold huge sets --both in number of features and
+instances--, as well as its simplified runtime cost (specially, at the
+redundancy detection step).
+"
+28796,Modeling Asymmetric Relationships from Symmetric Networks,"  Many relationships requiring mutual agreement between pairs of actors produce
+observable networks that are symmetric and undirected. Nevertheless the
+unobserved, asymmetric network is often of primary scientific interest. We
+propose a method that probabilistically reconstructs the unobserved, asymmetric
+network from the observed, symmetric graph using a regression-based framework
+that allows for inference on predictors of actors' decisions. We apply this
+model to the bilateral investment treaty network. Our approach extracts
+politically relevant information about the network structure that is
+inaccessible to alternative approaches and has superior predictive performance.
+"
+28797,Social Complex Contagion in Music Listenership: A Natural Experiment with 1.3 Million Participants,"  Can live music events generate complex contagion in music streaming? This
+paper finds evidence in the affirmative, but only for the most popular artists.
+We generate a novel dataset from Last.fm, a music tracking website, to analyse
+the listenership history of 1.3 million users over a two-month time horizon. We
+use daily play counts along with event attendance data to run a regression
+discontinuity analysis in order to show the causal impact of concert attendance
+on music listenership among attendees and their friends network. First, we show
+that attending a music artist's live concert increases that artist's
+listenership among the attendees of the concert by approximately 1 song per day
+per attendee (p-value<0.001). Moreover, we show that this effect is contagious
+and can spread to users who did not attend the event. However, the extent of
+contagion depends on the type of artist. We only observe contagious increases
+in listenership for well-established, popular artists (.06 more daily plays per
+friend of an attendee [p<0.001]), while the effect is absent for emerging
+stars. We also show that the contagion effect size increases monotonically with
+the number of friends who have attended the live event.
+"
+28798,The saturation bifurcation in coupled oscillators,"  We examine examples of weakly nonlinear systems whose steady states undergo a
+bifurcation with increasing forcing, such that a forced subsystem abruptly
+ceases to absorb additional energy, instead diverting it into an initially
+quiescent, unforced subsystem. We derive and numerically verify analytical
+predictions for the existence and behavior of such saturated states for a class
+of oscillator pairs. We also examine related phenomena, including
+zero-frequency response to periodic forcing, Hopf bifurcations to
+quasiperiodicity, and bifurcations to periodic behavior with multiple
+frequencies.
+"
+28799,Boundary representation of Dirichlet forms on discrete spaces,"  We describe the set of all Dirichlet forms associated to a given infinite
+graph in terms of Dirichlet forms on its Royden boundary. Our approach is
+purely analytical and uses form methods.
+"
+28800,Computational Coverage of TLG: Nonlinearity,"  We study nonlinear connectives (exponentials) in the context of Type Logical
+Grammar (TLG). We devise four conservative extensions of the displacement
+calculus with brackets, DbC, DbCM, DbCb and DbCbMr which contain the universal
+and existential exponential modalities of linear logic (LL). These modalities
+do not exhibit the same structural properties as in LL, which in TLG are
+especially adapted for linguistic purposes. The universal modality ! for TLG
+allows only the commutative and contraction rules, but not weakening, whereas
+the existential modality ? allows the so-called (intuitionistic) Mingle rule,
+which derives a restricted version of weakening. We provide a Curry-Howard
+labelling for both exponential connectives. As it turns out, controlled
+contraction by ! gives a way to account for the so-called parasitic gaps, and
+controlled Mingle ? iteration, in particular iterated coordination. Finally,
+the four calculi are proved to be Cut-Free, and decidability is proved for a
+linguistically sufficient special case of DbCbMr (and hence DbCb).
+"
+28801,Cluster analysis of stocks using price movements of high frequency data from National Stock Exchange,"  This paper aims to develop new techniques to describe joint behavior of
+stocks, beyond regression and correlation. For example, we want to identify the
+clusters of the stocks that move together. Our work is based on applying Kernel
+Principal Component Analysis(KPCA) and Functional Principal Component
+Analysis(FPCA) to high frequency data from NSE. Since we dealt with high
+frequency data with a tick size of 30 seconds, FPCA seems to be an ideal
+choice. FPCA is a functional variant of PCA where each sample point is
+considered to be a function in Hilbert space L^2. On the other hand, KPCA is an
+extension of PCA using kernel methods. Results obtained from FPCA and Gaussian
+Kernel PCA seems to be in synergy but with a lag. There were two prominent
+clusters that showed up in our analysis, one corresponding to the banking
+sector and another corresponding to the IT sector. The other smaller clusters
+were seen from the automobile industry and the energy sector. IT sector was
+seen interacting with these small clusters. The learning gained from these
+interactions is substantial as one can use it significantly to develop trading
+strategies for intraday traders.
+"
+28802,Solving Equations of Random Convex Functions via Anchored Regression,"  We consider the question of estimating a solution to a system of equations
+that involve convex nonlinearities, a problem that is common in machine
+learning and signal processing. Because of these nonlinearities, conventional
+estimators based on empirical risk minimization generally involve solving a
+non-convex optimization program. We propose anchored regression, a new approach
+based on convex programming that amounts to maximizing a linear functional
+(perhaps augmented by a regularizer) over a convex set. The proposed convex
+program is formulated in the natural space of the problem, and avoids the
+introduction of auxiliary variables, making it computationally favorable.
+Working in the native space also provides great flexibility as structural
+priors (e.g., sparsity) can be seamlessly incorporated.
+For our analysis, we model the equations as being drawn from a fixed set
+according to a probability law. Our main results provide guarantees on the
+accuracy of the estimator in terms of the number of equations we are solving,
+the amount of noise present, a measure of statistical complexity of the random
+equations, and the geometry of the regularizer at the true solution. We also
+provide recipes for constructing the anchor vector (that determines the linear
+functional to maximize) directly from the observed data.
+"
+28803,Learning to Compute Word Embeddings On the Fly,"  Words in natural language follow a Zipfian distribution whereby some words
+are frequent but most are rare. Learning representations for words in the ""long
+tail"" of this distribution requires enormous amounts of data. Representations
+of rare words trained directly on end tasks are usually poor, requiring us to
+pre-train embeddings on external data, or treat all rare words as
+out-of-vocabulary words with a unique representation. We provide a method for
+predicting embeddings of rare words on the fly from small amounts of auxiliary
+data with a network trained end-to-end for the downstream task. We show that
+this improves results against baselines where embeddings are trained on the end
+task for reading comprehension, recognizing textual entailment and language
+modeling.
+"
+28804,Influence of Clustering on Cascading Failures in Interdependent Systems,"  We study the influence of clustering, more specifically triangles, on
+cascading failures in interdependent networks or systems, in which we model the
+dependence between comprising systems using a dependence graph. First, we
+propose a new model that captures how the presence of triangles in the
+dependence graph alters the manner in which failures transmit from affected
+systems to others. Unlike existing models, the new model allows us to
+approximate the failure propagation dynamics using a multi-type branching
+process, even with triangles. Second, making use of the model, we provide a
+simple condition that indicates how increasing clustering will affect the
+likelihood that a random failure triggers a cascade of failures, which we call
+the probability of cascading failures (PoCF). In particular, our condition
+reveals an intriguing observation that the influence of clustering on PoCF
+depends on the vulnerability of comprising systems to an increasing number of
+failed neighboring systems and the current PoCF, starting with different types
+of failed systems. Our numerical studies hint that increasing clustering
+impedes cascading failures under both (truncated) power law and Poisson degree
+distributions. Furthermore, our finding suggests that, as the degree
+distribution becomes more concentrated around the mean degree with smaller
+variance, increasing clustering will have greater impact on the PoCF. A
+numerical investigation of networks with Poisson and power law degree
+distributions reflects this finding and demonstrates that increasing clustering
+reduces the PoCF much faster under Poisson degree distributions in comparison
+to power law degree distributions.
+"
+28805,Deep learning for undersampled MRI reconstruction,"  This paper presents a deep learning method for faster magnetic resonance
+imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and
+provides a rationale for why the proposed approach works well. Uniform
+subsampling is used in the time-consuming phase-encoding direction to capture
+high-resolution image information, while permitting the image-folding problem
+dictated by the Poisson summation formula. To deal with the localization
+uncertainty due to image folding, very few low-frequency k-space data are
+added. Training the deep learning net involves input and output images that are
+pairs of Fourier transforms of the subsampled and fully sampled k-space data.
+Numerous experiments show the remarkable performance of the proposed method;
+only 29% of k-space data can generate images of high quality as effectively as
+standard MRI reconstruction with fully sampled data.
+"
+28806,Cost-Optimal Learning of Causal Graphs,"  We consider the problem of learning a causal graph over a set of variables
+with interventions. We study the cost-optimal causal graph learning problem:
+For a given skeleton (undirected version of the causal graph), design the set
+of interventions with minimum total cost, that can uniquely identify any causal
+graph with the given skeleton. We show that this problem is solvable in
+polynomial time. Later, we consider the case when the number of interventions
+is limited. For this case, we provide polynomial time algorithms when the
+skeleton is a tree or a clique tree. For a general chordal skeleton, we develop
+an efficient greedy algorithm, which can be improved when the causal graph
+skeleton is an interval graph.
+"
+28807,Geodesics around oscillatons made of exponential scalar field potential,"  Oscillatons are spherically symmetric solutions to the Einstein Klein Gordon
+(EKG) equations for soliton stars made of real time dependent scalar fields.
+These equations are non singular and satisfy flatness conditions asymptotically
+with periodic time dependency. In this paper, we investigate the geodesic
+motion of particles moving around an oscillaton related to a time dependent
+scalar field. Bound orbital is found for these particles under the condition of
+particular values of angular momentum L and initial radial position. We discuss
+this topic for an exponential scalar field potential which could be of the
+exponential form with a scalar field and investigate whether the radial
+coordinates of such particles oscillate in time or not and thereby we could
+predict the corresponding oscillating period as well as amplitude. It is
+necessary to recall, in general relativity, a geodesic generalizes the notion
+of a straight line to curved space time. Importantly, the world line of a
+particle free from all external, non gravitational forces, is a particular type
+of geodesic. In other words, a freely moving or falling particle always moves
+along a geodesic. In general relativity, gravity can be regarded as not a force
+but a consequence of a curved space time geometry where the source of curvature
+is the stress energy tensor (representing matter, for instance). Thus, for
+example, the path of a planet orbiting around a star is the projection of a
+geodesic of the curved 4D space time geometry around the star onto 3D space.
+"
+28808,"An Integrated Conceptual Model for Information System Security Risk Management and Enterprise Architecture Management based on TOGAF, ArchiMate, IAF and DoDAF","  Risk management is today a major steering tool for any organization wanting
+to deal with Information System (IS) security. However, IS Security Risk
+Management (ISSRM) remains difficult to establish and maintain, mainly in a
+context of multi-regulations with complex and inter-connected IS. We claim that
+a connection with Enterprise Architecture Management (EAM) contributes to deal
+with these issues. A first step towards a better integration of both domains is
+to define an integrated EAM-ISSRM conceptual model. Among the steps of the
+research method followed to define such an integrated EAM-ISSRM conceptual,
+this technical report presents the whole outputs (through alignment tables) of
+the conceptual alignment between concepts used to model EA (based on ArchiMate,
+TOGAF, IAF and DoDAF) and concepts of the ISSRM domain model.
+"
+28809,Automatic Measurement of Pre-aspiration,"  Pre-aspiration is defined as the period of glottal friction occurring in
+sequences of vocalic/consonantal sonorants and phonetically voiceless
+obstruents. We propose two machine learning methods for automatic measurement
+of pre-aspiration duration: a feedforward neural network, which works at the
+frame level; and a structured prediction model, which relies on manually
+designed feature functions, and works at the segment level. The input for both
+algorithms is a speech signal of an arbitrary length containing a single
+obstruent, and the output is a pair of times which constitutes the
+pre-aspiration boundaries. We train both models on a set of manually annotated
+examples. Results suggest that the structured model is superior to the
+frame-based model as it yields higher accuracy in predicting the boundaries and
+generalizes to new speakers and new languages. Finally, we demonstrate the
+applicability of our structured prediction algorithm by replicating linguistic
+analysis of pre-aspiration in Aberystwyth English with high correlation.
+"
+28810,A Bosonization of $U_q(\widehat{sl}(M|N))$,"  A bosonization of the quantum affine superalgebra $U_q(\widehat{sl}(M|N))$ is
+presented for an arbitrary level $k \in {\bf C}$. Screening operators that
+commute with $U_q(\widehat{sl}(M|N))$ are presented for the level $k \neq
+-M+N$.
+"
+28811,Persistent homology machine learning for fingerprint classification,"  The fingerprint classification problem is to sort fingerprints into
+pre-determined groups, such as arch, loop, and whorl. It was asserted in the
+literature that minutiae points, which are commonly used for fingerprint
+matching, are not useful for classification. We show that, to the contrary,
+near state-of-the-art classification accuracy rates can be achieved when
+applying topological data analysis (TDA) to 3-dimensional point clouds of
+oriented minutiae points. We also apply TDA to fingerprint ink-roll images,
+which yields a lower accuracy rate but still shows promise, particularly since
+the only preprocessing is cropping; moreover, combining the two approaches
+outperforms each one individually. These methods use supervised learning
+applied to persistent homology and allow us to explore feature selection on
+barcodes, an important topic at the interface between TDA and machine learning.
+We test our classification algorithms on the NIST fingerprint database SD-27.
+"
+28812,Some remarks on Fourier restriction estimates,"  We provide $L^p \to L^q$ refinements on some Fourier restriction estimates
+obtained using polynomial partitioning. Let $S\subset \mathbb{R}^3$ be a
+compact $C^\infty$ surface with strictly positive second fundamental form. We
+derive sharp $L^p(S) \to L^q(\mathbb{R}^3)$ estimates for the associated
+Fourier extension operator for $q> 3.25$ and $q\geq 2p'$ from an estimate of
+Guth that was used to obtain $L^\infty(S) \to L^q(\mathbb{R}^3)$ bounds for
+$q>3.25$. We present a slightly weaker result when $S$ is the hyperbolic
+paraboloid in $\mathbb{R}^3$ based on the work of Cho and Lee. Finally, we give
+some refinements for the truncated paraboloid in higher dimensions.
+"
+28813,Interaction effects on galaxy pairs with GeminiGMOS-III: Stellar population synthesis,"  We present an observational study of the impacts of the interactions on the
+stellar population in a sample of galaxy pairs. Long-slit spectra in the
+wavelength range 3440-7300 {\AA} obtained with the Gemini Multi-Object
+Spectrograph (GMOS) at Gemini South for fifteen galaxies in nine close pairs
+were used. The spatial distributions of the stellar population contributions
+were obtained using the stellar population synthesis code STARLIGHT. Taking
+into account the different contributions to the emitted light, we found that
+most of the galaxies in our sample are dominated by the young/intermediate
+stellar populations. This result differs from the one derived for isolated
+galaxies where the old stellar population dominates the disc surface
+brightness. We interpreted such different behavior as being due to the effect
+of gas inflows along the disk of interacting galaxies on the star formation in
+a time scale of the order of about 2Gyr. We also found that, in general, the
+secondary galaxy of the pairs has a higher contribution of the young stellar
+population than the primary one. We compared the estimated values of the
+stellar and nebular extinctions derived from the synthesis method and the
+H{\alpha}/H\b{eta} emission-line ratio finding that the nebular extinctions are
+systematically higher than stellar ones by about a factor of 2. We did not find
+any correlation between nebular and stellar metallicities. We neither found a
+correlation between stellar metallicities and ages while a positive correlation
+between nebular metallicities and stellar ages was obtained, with the older
+regions being the most metal-rich.
+"
+28814,Finite generation of the algebra of type A conformal blocks via birational geometry,"  We study birational geometry of the moduli space of parabolic bundles over a
+projective line, in the framework of Mori's program. We show that the moduli
+space is a Mori dream space. As a consequence, we obtain the finite generation
+of the algebra of type A conformal blocks. Furthermore, we compute the
+H-representation of the effective cone which was previously obtained by
+Belkale. For each big divisor, the associated birational model is described in
+terms of moduli space of parabolic bundles.
+"
+28815,Gated Orthogonal Recurrent Units: On Learning to Forget,"  We present a novel recurrent neural network (RNN) based model that combines
+the remembering ability of unitary RNNs with the ability of gated RNNs to
+effectively forget redundant/irrelevant information in its memory. We achieve
+this by extending unitary RNNs with a gating mechanism. Our model is able to
+outperform LSTMs, GRUs and Unitary RNNs on several long-term dependency
+benchmark tasks. We empirically both show the orthogonal/unitary RNNs lack the
+ability to forget and also the ability of GORU to simultaneously remember long
+term dependencies while forgetting irrelevant information. This plays an
+important role in recurrent neural networks. We provide competitive results
+along with an analysis of our model on many natural sequential tasks including
+the bAbI Question Answering, TIMIT speech spectrum prediction, Penn TreeBank,
+and synthetic tasks that involve long-term dependencies such as algorithmic,
+parenthesis, denoising and copying tasks.
+"
+28816,Exploring Dimensionality Reductions with Forward and Backward Projections,"  Dimensionality reduction is a common method for analyzing and visualizing
+high-dimensional data across domains. Dimensionality-reduction algorithms
+involve complex optimizations and the reduced dimensions computed by these
+algorithms generally lack clear relation to the initial data dimensions.
+Therefore, interpreting and reasoning about dimensionality reductions can be
+difficult. In this work, we introduce two interaction techniques,
+\textit{forward projection} and \textit{backward projection}, for reasoning
+dynamically about scatter plots of dimensionally reduced data. We also
+contribute two related visualization techniques, \textit{prolines} and
+\textit{feasibility map} to facilitate and enrich the effective use of the
+proposed interactions, which we integrate in a new tool called \textit{Praxis}.
+To evaluate our techniques, we first analyze their time and accuracy
+performance across varying sample and dimension sizes. We then conduct a user
+study in which twelve data scientists use \textit{Praxis} so as to assess the
+usefulness of the techniques in performing exploratory data analysis tasks.
+Results suggest that our visual interactions are intuitive and effective for
+exploring dimensionality reductions and generating hypotheses about the
+underlying data.
+"
+28817,A neural attention model for speech command recognition,"  This paper introduces a convolutional recurrent network with attention for
+speech command recognition. Attention models are powerful tools to improve
+performance on natural language, image captioning and speech tasks. The
+proposed model establishes a new state-of-the-art accuracy of 94.1% on Google
+Speech Commands dataset V1 and 94.5% on V2 (for the 20-commands recognition
+task), while still keeping a small footprint of only 202K trainable parameters.
+Results are compared with previous convolutional implementations on 5 different
+tasks (20 commands recognition (V1 and V2), 12 commands recognition (V1), 35
+word recognition (V1) and left-right (V1)). We show detailed performance
+results and demonstrate that the proposed attention mechanism not only improves
+performance but also allows inspecting what regions of the audio were taken
+into consideration by the network when outputting a given category.
+"
+28818,Quantum thermalization dynamics with Matrix-Product States,"  We study the dynamics of thermalization following a quantum quench using
+tensor-network methods. Contrary to the common belief that the rapid growth of
+entanglement and the resulting exponential growth of the bond dimension
+restricts simulations to short times, we demonstrate that the long time limit
+of local observables can be well captured using the time-dependent variational
+principle. This allows to extract transport coefficients such as the energy
+diffusion constant from simulations with rather small bond dimensions. We
+further study the characteristic of the chaotic wave that precedes the
+emergence of hydrodynamics, to find a ballistic diffusively-broadening
+wave-front.
+"
+28819,On Single-Channel Speech Enhancement and On Non-Linear Modulation-Domain Kalman Filtering,"  This report focuses on algorithms that perform single-channel speech
+enhancement. The author of this report uses modulation-domain Kalman filtering
+algorithms for speech enhancement, i.e. noise suppression and dereverberation,
+in [1], [2], [3], [4] and [5]. Modulation-domain Kalman filtering can be
+applied for both noise and late reverberation suppression and in [2], [1], [3]
+and [4], various model-based speech enhancement algorithms that perform
+modulation-domain Kalman filtering are designed, implemented and tested. The
+model-based enhancement algorithm in [2] estimates and tracks the speech phase.
+The short-time-Fourier-transform-based enhancement algorithm in [5] uses the
+active speech level estimator presented in [6]. This report describes how
+different algorithms perform speech enhancement and the algorithms discussed in
+this report are addressed to researchers interested in monaural speech
+enhancement. The algorithms are composed of different processing blocks and
+techniques [7]; understanding the implementation choices made during the system
+design is important because this provides insights that can assist the
+development of new algorithms. Index Terms - Speech enhancement,
+dereverberation, denoising, Kalman filter, minimum mean squared error
+estimation.
+"
+28820,Mechanisms for tuning clustering and degree-correlations in directed networks,"  With complex networks emerging as an effective tool to tackle
+multidisciplinary problems, models of network generation have gained an
+importance of their own. These models allow us to extensively analyze the data
+obtained from real-world networks, study their relevance and corroborate
+theoretical results. In this work, we introduce methods, based on degree
+preserving rewiring, that can be used to tune the clustering and
+degree-correlations in directed networks with random and scale-free topologies.
+They provide null-models to investigate the role of the mentioned properties
+along with their strengths and limitations. We find that in the case of
+clustering, structural relationships, that are independent of topology and
+rewiring schemes are revealed, while in the case of degree-correlations, the
+network topology is found to play an important role in the working of the
+mechanisms. We also study the effects of link-density on the efficiency of
+these rewiring mechanisms and find that in the case of clustering, the topology
+of the network plays an important role in determining how link-density affects
+the rewiring process, while in the case of degree-correlations, the
+link-density and topology, play no role for sufficiently large number of
+rewiring steps. Besides the intended purpose of tuning network properties, the
+proposed mechanisms can also be used as a tool to reveal structural
+relationships and topological constraints.
+"
+28821,Triangulated categories of motives over fs log schemes,"  We introduce the notion of log motivic triangulated categories, which is the
+theoretical framework for understanding the motivic aspect of cohomology
+theories for fs log schemes. Then we study the Grothendieck six operations
+formalism for log motivic triangulated categories.
+"
+28822,DropLasso: A robust variant of Lasso for single cell RNA-seq data,"  Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure
+the genome-wide transcriptome of many individual cells in parallel, but results
+in noisy data with many dropout events. Existing methods to learn molecular
+signatures from bulk transcriptomic data may therefore not be adapted to
+scRNA-seq data, in order to automatically classify individual cells into
+predefined classes. We propose a new method called DropLasso to learn a
+molecular signature from scRNA-seq data. DropLasso extends the dropout
+regularisation technique, popular in neural network training, to esti- mate
+sparse linear models. It is well adapted to data corrupted by dropout noise,
+such as scRNA-seq data, and we clarify how it relates to elastic net
+regularisation. We provide promising results on simulated and real scRNA-seq
+data, suggesting that DropLasso may be better adapted than standard regularisa-
+tions to infer molecular signatures from scRNA-seq data.
+"
+28823,Enhancing Physical Layer Security in Dual-Hop Multiuser Transmission,"  In this paper, we consider the Physical Layer Security(PLS) problem in
+orthogonal frequency division multiple access (OFDMA) based dual-hop system
+which consists of multiple users, multiple amplify and forward relays, and an
+eavesdropper. The aim is to enhance PLS of the entire system by maximizing sum
+secrecy rate of secret users through optimal resource allocation under various
+practical constraints. Specifically, the sub-carrier allocation to different
+users, the relay assignments, and the power loading over different sub-carriers
+at transmitting nodes are optimized. The joint optimization problem is modeled
+as a mixed binary integer programming problem subject to exclusive sub-carrier
+allocation and separate power budget constraints at each node. A joint
+optimization solution is obtained through Lagrangian dual decomposition where
+KKT conditions are exploited to find the optimal power allocation at base
+station. Further, to reduce the complexity, a sub-optimal scheme is presented
+where the optimal power allocation is derived under fixed sub-carrier-relay
+assignment. Simulation results are also provided to validate the performance of
+proposed schemes.
+"
+28824,Controlling the Error on Target Motion through Real-time Mesh Adaptation: Applications to Deep Brain Stimulation,"  We present an error-controlled mesh refinement procedure for needle insertion
+simulation and apply it to the simulation of electrode implantation for deep
+brain stimulation, including brain shift. Our approach enables to control the
+error in the computation of the displacement and stress fields around the
+needle tip and needle shaft by suitably refining the mesh, whilst maintaining a
+coarser mesh in other parts of the domain. We demonstrate through academic and
+practical examples that our approach increases the accuracy of the displacement
+and stress fields around the needle without increasing the computational
+expense. This enables real-time simulations. The proposed methodology has
+direct implications to increase the accuracy and control the computational
+expense of the simulation of percutaneous procedures such as biopsy,
+brachytherapy, regional anesthesia, or cryotherapy and can be essential to the
+development of robotic guidance.
+"
+28825,On the Difficulty of Inserting Trojans in Reversible Computing Architectures,"  Fabrication-less design houses outsource their designs to 3rd party foundries
+to lower fabrication cost. However, this creates opportunities for a rogue in
+the foundry to introduce hardware Trojans, which stay inactive most of the time
+and cause unintended consequences to the system when triggered. Hardware
+Trojans in traditional CMOS-based circuits have been studied and
+Design-for-Trust (DFT) techniques have been proposed to detect them.
+Different from traditional circuits in many ways, reversible circuits
+implement one-to-one, bijective input/output mappings. We will investigate the
+security implications of reversible circuits with a particular focus on
+susceptibility to hardware Trojans. We will consider inherently reversible
+circuits and non-reversible functions embedded in reversible circuits.
+"
+28826,Towards Accurate Binary Convolutional Neural Network,"  We introduce a novel scheme to train binary convolutional neural networks
+(CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time.
+It has been known that using binary weights and activations drastically reduce
+memory size and accesses, and can replace arithmetic operations with more
+efficient bitwise operations, leading to much faster test-time inference and
+lower power consumption. However, previous works on binarizing CNNs usually
+result in severe prediction accuracy degradation. In this paper, we address
+this issue with two major innovations: (1) approximating full-precision weights
+with the linear combination of multiple binary weight bases; (2) employing
+multiple binary activations to alleviate information loss. The implementation
+of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much
+closer performance to its full-precision counterpart, and even reach the
+comparable prediction accuracy on ImageNet and forest trail datasets, given
+adequate binary weight bases and activations.
+"
+28827,The generalized Kurepa hypothesis at singular cardinals,"  We discuss the generalized Kurepa hypothesis $KH_{\lambda}$ at singular
+cardinals $\lambda$. In particular, we answer questions of Erdös-Hajnal [1]
+and Todorcevic [6], [7] by showing that $GCH$ does not imply
+$KH_{\aleph_\omega}$ nor the existence of a family $ \mathcal{F} \subseteq
+[\aleph_\omega]^{\aleph_0}$ of size $\aleph_{\omega+1}$ such that $\mathcal{F}
+\restriction X$ has size $\aleph_0$ for every $X \subseteq S, |X|=\aleph_0$.
+"
+28828,Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo,"  Asymptotically-optimal motion planners such as RRT* have been shown to
+incrementally approximate the shortest path between start and goal states. Once
+an initial solution is found, their performance can be dramatically improved by
+restricting subsequent samples to regions of the state space that can
+potentially improve the current solution. When the motion planning problem lies
+in a Euclidean space, this region $X_{inf}$, called the informed set, can be
+sampled directly. However, when planning with differential constraints in
+non-Euclidean state spaces, no analytic solutions exists to sampling $X_{inf}$
+directly.
+State-of-the-art approaches to sampling $X_{inf}$ in such domains such as
+Hierarchical Rejection Sampling (HRS) may still be slow in high-dimensional
+state space. This may cause the planning algorithm to spend most of its time
+trying to produces samples in $X_{inf}$ rather than explore it. In this paper,
+we suggest an alternative approach to produce samples in the informed set
+$X_{inf}$ for a wide range of settings. Our main insight is to recast this
+problem as one of sampling uniformly within the sub-level-set of an implicit
+non-convex function. This recasting enables us to apply Monte Carlo sampling
+methods, used very effectively in the Machine Learning and Optimization
+communities, to solve our problem. We show for a wide range of scenarios that
+using our sampler can accelerate the convergence rate to high-quality solutions
+in high-dimensional problems.
+"
+28829,"""You are no Jack Kennedy"": On Media Selection of Highlights from Presidential Debates","  Political speeches and debates play an important role in shaping the images
+of politicians, and the public often relies on media outlets to select bits of
+political communication from a large pool of utterances. It is an important
+research question to understand what factors impact this selection process.
+To quantitatively explore the selection process, we build a three- decade
+dataset of presidential debate transcripts and post-debate coverage. We first
+examine the effect of wording and propose a binary classification framework
+that controls for both the speaker and the debate situation. We find that
+crowdworkers can only achieve an accuracy of 60% in this task, indicating that
+media choices are not entirely obvious. Our classifiers outperform crowdworkers
+on average, mainly in primary debates. We also compare important factors from
+crowdworkers' free-form explanations with those from data-driven methods and
+find interesting differences. Few crowdworkers mentioned that ""context
+matters"", whereas our data show that well-quoted sentences are more distinct
+from the previous utterance by the same speaker than less-quoted sentences.
+Finally, we examine the aggregate effect of media preferences towards different
+wordings to understand the extent of fragmentation among media outlets. By
+analyzing a bipartite graph built from quoting behavior in our data, we observe
+a decreasing trend in bipartisan coverage.
+"
+28830,Scaling laws for jets of single cavitation bubbles,"  Fast liquid jets, called micro-jets, are produced within cavitation bubbles
+experiencing an aspherical collapse. Here we review micro-jets of different
+origins, scales and appearances, and propose a unified framework to describe
+their dynamics by using an anisotropy parameter $\zeta$, representing a
+dimensionless measure of the liquid momentum at the collapse point (Kelvin
+impulse). This parameter is rigorously defined for various jet drivers,
+including gravity and nearby boundaries. Combining theoretical considerations
+with hundreds of high-speed visualisations of bubbles collapsing near a rigid
+surface, near a free surface or in variable gravity, we classify the jets into
+three distinct regimes: weak, intermediate and strong. Weak jets
+($\zeta<10^{-3}$) hardly pierce the bubble, but remain within it throughout the
+collapse and rebound. Intermediate jets ($10^{-3}<\zeta<0.1$) pierce the
+opposite bubble wall close to the last collapse phase and clearly emerge during
+the rebound. Strong jets ($\zeta>0.1$) pierce the bubble early during the
+collapse. The dynamics of the jets is analysed through key observables, such as
+the jet impact time, jet speed, bubble displacement, bubble volume at jet
+impact and vapour-jet volume. We find that, upon normalising these observables
+to dimensionless jet parameters, they all reduce to straightforward functions
+of $\zeta$, which we can reproduce numerically using potential flow theory. An
+interesting consequence of this result is that a measurement of a single
+observable, such as the bubble displacement, suffices to estimate any other
+parameter, such as the jet speed. Remarkably, the dimensionless parameters of
+intermediate and weak jets only depend on $\zeta$, not on the jet driver. In
+the same regime, the jet parameters are found to be well approximated by
+power-laws of $\zeta$, which we explain through analytical arguments.
+"
+28831,Reasoning about proof and knowledge,"  In previous work [Lewitzka, Log. J. IGPL 2017], we presented a hierarchy of
+classical modal systems, along with algebraic semantics, for the reasoning
+about intuitionistic truth, belief and knowledge. Deviating from Gödel's
+interpretation of IPC in S4, our modal systems contain IPC in the way
+established in [Lewitzka, J. Log. Comp. 2015]. The modal operator can be viewed
+as a predicate for intuitionistic truth, i.e. proof. Epistemic principles are
+partially adopted from Intuitionistic Epistemic Logic IEL [Artemov and
+Protopopescu, Rev. Symb. Log. 2016]. In the present paper, we show that the
+S5-style systems of our hierarchy correspond to an extended
+Brouwer-Heyting-Kolmogorov interpretation and are complete w.r.t. a relational
+semantics based on intuitionistic general frames. In this sense, our S5-style
+logics are adequate and complete systems for the reasoning about proof combined
+with belief or knowledge. The proposed relational semantics is a uniform
+framework in which also IEL can be modeled. Verification-based intuitionistic
+knowledge formalized in IEL turns out to be a special case of the kind of
+knowledge described by our S5-style systems.
+"
+28832,Improved Complexity Bounds for Counting Points on Hyperelliptic Curves,"  We present a probabilistic Las Vegas algorithm for computing the local zeta
+function of a hyperelliptic curve of genus $g$ defined over $\mathbb{F}_q$. It
+is based on the approaches by Schoof and Pila combined with a modeling of the
+$\ell$-torsion by structured polynomial systems. Our main result improves on
+previously known complexity bounds by showing that there exists a constant
+$c>0$ such that, for any fixed $g$, this algorithm has expected time and space
+complexity $O((\log q)^{cg})$ as $q$ grows and the characteristic is large
+enough.
+"
+28833,Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results,"  In this work we present a novel system for PET estimation using CT scans. We
+explore the use of fully convolutional networks (FCN) and conditional
+generative adversarial networks (GAN) to export PET data from CT data. Our
+dataset includes 25 pairs of PET and CT scans where 17 were used for training
+and 8 for testing. The system was tested for detection of malignant tumors in
+the liver region. Initial results look promising showing high detection
+performance with a TPR of 92.3% and FPR of 0.25 per case. Future work entails
+expansion of the current system to the entire body using a much larger dataset.
+Such a system can be used for tumor detection and drug treatment evaluation in
+a CT-only environment instead of the expansive and radioactive PET-CT scan.
+"
+28834,Formation of freely floating sub-stellar objects via close encounters,"  We numerically studied close encounters between a young stellar system
+hosting a massive, gravitationally fragmenting disk and an intruder diskless
+star with the purpose to determine the evolution of fragments that have formed
+in the disk prior to the encounter. Numerical hydrodynamics simulations in the
+non-inertial frame of reference of the host star were employed to simulate the
+prograde and retrograde co-planar encounters. The initial configuration of the
+target system (star plus disk) was obtained via a separate numerical simulation
+featuring the gravitational collapse of a solar-mass pre-stellar core. We found
+that close encounters can lead to the ejection of fragments that have formed in
+the disk of the target prior to collision. In particular, prograde encounters
+are more efficient in ejecting the fragments than the retrograde encounters.
+The masses of ejected fragments are in the brown-dwarf mass regime. They also
+carry away an appreciable amount of gas in their gravitational radius of
+influence, implying that these objects may possess extended disks or envelopes,
+as also suggested by Thies et al. (2015). Close encounters can also lead to the
+ejection of entire spiral arms, followed by fragmentation and formation of
+freely-floating objects straddling the planetary mass limit. However, numerical
+simulations with a higher resolution are needed to confirm this finding.
+"
+28835,Dipolar Dark Matter as an Effective Field Theory,"  Dipolar Dark Matter (DDM) is an alternative model motivated by the challenges
+faced by the standard cold dark matter model to describe the right
+phenomenology at galactic scales. A promising realisation of DDM was recently
+proposed in the context of massive bigravity theory. The model contains dark
+matter particles, as well as a vector field coupled to the effective composite
+metric of bigravity. This model is completely safe in the gravitational sector
+thanks to the underlying properties of massive bigravity. In this work we
+investigate the exact decoupling limit of the theory, including the
+contribution of the matter sector, and prove that it is free of ghosts in this
+limit. We conclude that the theory is acceptable as an Effective Field Theory
+below the strong coupling scale.
+"
+28836,Predicting Yelp Star Reviews Based on Network Structure with Deep Learning,"  In this paper, we tackle the real-world problem of predicting Yelp
+star-review rating based on business features (such as images, descriptions),
+user features (average previous ratings), and, of particular interest, network
+properties (which businesses has a user rated before). We compare multiple
+models on different sets of features -- from simple linear regression on
+network features only to deep learning models on network and item features.
+In recent years, breakthroughs in deep learning have led to increased
+accuracy in common supervised learning tasks, such as image classification,
+captioning, and language understanding. However, the idea of combining deep
+learning with network feature and structure appears to be novel. While the
+problem of predicting future interactions in a network has been studied at
+length, these approaches have often ignored either node-specific data or global
+structure.
+We demonstrate that taking a mixed approach combining both node-level
+features and network information can effectively be used to predict Yelp-review
+star ratings. We evaluate on the Yelp dataset by splitting our data along the
+time dimension (as would naturally occur in the real-world) and comparing our
+model against others which do no take advantage of the network structure and/or
+deep learning.
+"
+28837,Learning what to look in chest X-rays with a recurrent visual attention model,"  X-rays are commonly performed imaging tests that use small amounts of
+radiation to produce pictures of the organs, tissues, and bones of the body.
+X-rays of the chest are used to detect abnormalities or diseases of the
+airways, blood vessels, bones, heart, and lungs. In this work we present a
+stochastic attention-based model that is capable of learning what regions
+within a chest X-ray scan should be visually explored in order to conclude that
+the scan contains a specific radiological abnormality. The proposed model is a
+recurrent neural network (RNN) that learns to sequentially sample the entire
+X-ray and focus only on informative areas that are likely to contain the
+relevant information. We report on experiments carried out with more than
+$100,000$ X-rays containing enlarged hearts or medical devices. The model has
+been trained using reinforcement learning methods to learn task-specific
+policies.
+"
+28838,Continual Learning Through Synaptic Intelligence,"  While deep learning has led to remarkable advances across diverse
+applications, it struggles in domains where the data distribution changes over
+the course of learning. In stark contrast, biological neural networks
+continually adapt to changing domains, possibly by leveraging complex molecular
+machinery to solve many tasks simultaneously. In this study, we introduce
+intelligent synapses that bring some of this biological complexity into
+artificial neural networks. Each synapse accumulates task relevant information
+over time, and exploits this information to rapidly store new memories without
+forgetting old ones. We evaluate our approach on continual learning of
+classification tasks, and show that it dramatically reduces forgetting while
+maintaining computational efficiency.
+"
+28839,Data-Efficient Weakly Supervised Learning for Low-Resource Audio Event Detection Using Deep Learning,"  We propose a method to perform audio event detection under the common
+constraint that only limited training data are available. In training a deep
+learning system to perform audio event detection, two practical problems arise.
+Firstly, most datasets are ""weakly labelled"" having only a list of events
+present in each recording without any temporal information for training.
+Secondly, deep neural networks need a very large amount of labelled training
+data to achieve good quality performance, yet in practice it is difficult to
+collect enough samples for most classes of interest. In this paper, we propose
+a data-efficient training of a stacked convolutional and recurrent neural
+network. This neural network is trained in a multi instance learning setting
+for which we introduce a new loss function that leads to improved training
+compared to the usual approaches for weakly supervised learning. We
+successfully test our approach on two low-resource datasets that lack temporal
+labels.
+"
+28840,Singularity formation for the 1-D cubic NLS and the Schrödinger map on $\mathbb S^2$,"  In this note we consider the 1-D cubic Schrödinger equation with data given
+as small perturbations of a Dirac-$\delta$ function and some other related
+equations. We first recall that although the problem for this type of data is
+ill-posed one can use the geometric framework of the Schrödinger map to
+define the solution beyond the singularity time. Then, we find some natural and
+well defined geometric quantities that are not regular at time zero. Finally,
+we make a link between these results and some known phenomena in fluid
+mechanics that inspired this note.
+"
+28841,Recommendation via matrix completion using Kolmogorov complexity,"  A usual way to model a recommendation system is as a matrix completion
+problem. There are several matrix completion methods, typically using
+optimization approaches or collaborative filtering. Most approaches assume that
+the matrix is either low rank, or that there are a small number of latent
+variables that encode the full problem. Here, we propose a novel matrix
+completion algorithm for recommendation systems, without any assumptions on the
+rank and that is model free, i.e., the entries are not assumed to be a function
+of some latent variables. Instead, we use a technique akin to information
+theory. Our method performs hybrid neighborhood-based collaborative filtering
+using Kolmogorov complexity. It decouples the matrix completion into a vector
+completion problem for each user. The recommendation for one user is thus
+independent of the recommendation for other users. This makes the algorithm
+scalable because the computations are highly parallelizable. Our results are
+competitive with state-of-the-art approaches on both synthetic and real-world
+dataset benchmarks.
+"
+28842,Social Balance and the Bernoulli Equation,"  Since the 1940's there has been an interest in the question why social
+networks often give rise to two antagonistic factions. Recently a dynamical
+model of how and why such a balance might occur was developed. This note
+provides an introduction to the notion of social balance and a new (and
+simplified) analysis of that model. This new analysis allows us to choose
+general initial conditions, as opposed to the symmetric ones previously
+considered. We show that for general initial conditions, four factions will
+evolve instead of two. We characterize the four factions, and we give an idea
+of their relative sizes.
+"
+28843,Design principles for the energy level tuning in donor/acceptor conjugated polymers,"  To identify reliable molecular design principles for energy level tuning in
+donor/acceptor conjugated polymers (CPs), we studied the governing factors by
+means of ab initio calculations based on density-functional theory (DFT). We
+investigated a series of CPs in which we independently and systematically
+varied the electron withdrawing power of the acceptor unit and the electron
+donating power of the donor unit, while maintaining the same conjugated chain
+conformation. We observed that the introduction of a stronger acceptor unit,
+while keeping the same donor unit in the CP, lowers the LUMO level, but leaves
+the HOMO level almost unchanged. Conversely, enhancing the strength of the
+donor unit for the same acceptor unit raises the HOMO level, while maintaining
+the LUMO level. We identified strong correlations between the frontier orbital
+energy levels and the degree of orbital localization, which depends on the
+electron donating or withdrawing power of the molecular groups carrying the
+orbitals. Moreover, the HOMO/LUMO gap of the CP is directly proportional to the
+charge transfer between donating and accepting units, which provides a robust
+design criterion for CPs.
+"
+28844,Very Fast Kernel SVM under Budget Constraints,"  In this paper we propose a fast online Kernel SVM algorithm under tight
+budget constraints. We propose to split the input space using LVQ and train a
+Kernel SVM in each cluster. To allow for online training, we propose to limit
+the size of the support vector set of each cluster using different strategies.
+We show in the experiment that our algorithm is able to achieve high accuracy
+while having a very high number of samples processed per second both in
+training and in the evaluation.
+"
+28845,Reservoir Computing on the Hypersphere,"  Reservoir Computing (RC) refers to a Recurrent Neural Networks (RNNs)
+framework, frequently used for sequence learning and time series prediction.
+The RC system consists of a random fixed-weight RNN (the input-hidden reservoir
+layer) and a classifier (the hidden-output readout layer). Here we focus on the
+sequence learning problem, and we explore a different approach to RC. More
+specifically, we remove the non-linear neural activation function, and we
+consider an orthogonal reservoir acting on normalized states on the unit
+hypersphere. Surprisingly, our numerical results show that the system's memory
+capacity exceeds the dimensionality of the reservoir, which is the upper bound
+for the typical RC approach based on Echo State Networks (ESNs). We also show
+how the proposed system can be applied to symmetric cryptography problems, and
+we include a numerical implementation.
+"
+28846,Effect of dipolar interactions and DC magnetic field on the specific absorption rate of an array of magnetic nanoparticles,"  We address the issue of inter-particle dipolar interactions in the context of
+magnetic hyperthermia. More precisely, the main question dealt with here is
+concerned with the conditions under which the specific absorption rate is
+enhanced or reduced by dipolar interactions. For this purpose, we propose a
+theory for the calculation of the AC susceptibility, and thereby the specific
+absorption rate, for a monodisperse two-dimensional assembly of nanoparticles
+with oriented anisotropy, in the presence of a DC magnetic field, in addition
+to the AC magnetic field. We also study the competition between the dipolar
+interactions and the DC field, both in the transverse and longitudinal
+configurations. In both cases, we find that the specific absorption rate has a
+maximum at some critical DC field that depends on the inter-particle
+separation. In the longitudinal setup, this critical field falls well within
+the range of experiments.
+"
+28847,No Tits alternative for cellular automata,"  We show that the automorphism group of a one-dimensional full shift (the
+group of reversible cellular automata) does not satisfy the Tits alternative.
+That is, we construct a finitely-generated subgroup which is not virtually
+solvable yet does not contain a free group on two generators. We give
+constructions both in the two-sided case (spatially acting group $\Z$) and the
+one-sided case (spatially acting monoid $\N$, alphabet size at least eight).
+Lack of Tits alternative follows for several groups of symbolic (dynamical)
+origin: automorphism groups of two-sided one-dimensional uncountable sofic
+shifts, automorphism groups of multidimensional subshifts of finite type with
+positive entropy and dense minimal points, automorphism groups of full shifts
+over non-periodic groups, and the mapping class groups of two-sided
+one-dimensional transitive SFTs. We also show that the classical Tits
+alternative applies to one-dimensional (multi-track) reversible linear cellular
+automata over a finite field.
+"
+28848,Seeded Ising Model and Statistical Natures of Human Iris Templates,"  We propose a variant of Ising model, called the Seeded Ising Model, to model
+probabilistic nature of human iris templates. This model is an Ising model in
+which the values at certain lattice points are held fixed throughout Ising
+model evolution. Using this we show how to reconstruct the full iris template
+from partial information, and we show that about 1/6 of the given template is
+needed to recover almost all information content of the original one in the
+sense that the resulting Hamming distance is well within the range to assert
+correctly the identity of the subject. This leads us to propose the concept of
+effective statistical degree of freedom of iris templates and show it is about
+1/6 of the total number of bits. In particular, for a template of $2048$ bits,
+its effective statistical degree of freedom is about $342$ bits, which
+coincides very well with the degree of freedom computed by the completely
+different method proposed by Daugman.
+"
+28849,Distance multivariance: New dependence measures for random vectors,"  We introduce two new measures for the dependence of $n \ge 2$ random
+variables: distance multivariance and total distance multivariance. Both
+measures are based on the weighted $L^2$-distance of quantities related to the
+characteristic functions of the underlying random variables. These extend
+distance covariance (introduced by Székely, Rizzo and Bakirov) from pairs of
+random variables to $n$-tuplets of random variables. We show that total
+distance multivariance can be used to detect the independence of $n$ random
+variables and has a simple finite-sample representation in terms of distance
+matrices of the sample points, where distance is measured by a continuous
+negative definite function. Under some mild moment conditions, this leads to a
+test for independence of multiple random vectors which is consistent against
+all alternatives.
+"
+28850,Nonlinear Deconvolution by Sampling Biophysically Plausible Hemodynamic Models,"  Non-invasive methods to measure brain activity are important to understand
+cognitive processes in the human brain. A prominent example is functional
+magnetic resonance imaging (fMRI), which is a noisy measurement of a delayed
+signal that depends non-linearly on the neuronal activity through the
+neurovascular coupling. These characteristics make the inference of neuronal
+activity from fMRI a difficult but important step in fMRI studies that require
+information at the neuronal level. In this article, we address this inference
+problem using a Bayesian approach where we model the latent neural activity as
+a stochastic process and assume that the observed BOLD signal results from a
+realistic physiological (Balloon) model. We apply a recently developed
+smoothing method called APIS to efficiently sample the posterior given single
+event fMRI time series. To infer neuronal signals with high likelihood for
+multiple time series efficiently, a modification of the original algorithm is
+introduced. We demonstrate that our adaptive procedure is able to compensate
+the lacking of inputs in the model to infer the neuronal activity and that it
+outperforms dramatically the standard bootstrap particle filter-smoother in
+this setting. This makes the proposed procedure especially attractive to
+deconvolve resting state fMRI data. To validate the method, we evaluate the
+quality of the signals inferred using the timing information contained in them.
+APIS obtains reliable event timing estimates based on fMRI data gathered during
+a reaction time experiment with short stimuli. Hence, we show for the first
+time that one can obtain accurate absolute timing of neuronal activity by
+reconstructing the latent neural signal.
+"
+28851,Hyperparameter Importance Across Datasets,"  With the advent of automated machine learning, automated hyperparameter
+optimization methods are by now routinely used in data mining. However, this
+progress is not yet matched by equal progress on automatic analyses that yield
+information beyond performance-optimizing hyperparameter settings. In this
+work, we aim to answer the following two questions: Given an algorithm, what
+are generally its most important hyperparameters, and what are typically good
+values for these? We present methodology and a framework to answer these
+questions based on meta-learning across many datasets. We apply this
+methodology using the experimental meta-data available on OpenML to determine
+the most important hyperparameters of support vector machines, random forests
+and Adaboost, and to infer priors for all their hyperparameters. The results,
+obtained fully automatically, provide a quantitative basis to focus efforts in
+both manual algorithm design and in automated hyperparameter optimization. The
+conducted experiments confirm that the hyperparameters selected by the proposed
+method are indeed the most important ones and that the obtained priors also
+lead to statistically significant improvements in hyperparameter optimization.
+"
+28852,Investigating Cluster Astrophysics and Cosmology with Cross-Correlation of the Thermal Sunyaev-Zel'dovich Effect and Weak Lensing,"  Recent detections of the cross-correlation of the thermal Sunyaev-Zel'dovich
+(tSZ) effect and weak gravitational lensing (WL) enable unique studies of
+cluster astrophysics and cosmology. In this work, we present constraints on the
+amplitude of the non-thermal pressure fraction in galaxy clusters, $\alpha_0$,
+and the amplitude of the matter power spectrum, $\sigma_8$, using measurements
+of the tSZ power spectrum from Planck, and the tSZ-WL cross-correlation from
+Planck and the Red Cluster Sequence Lensing Survey. We fit the data to a
+semi-analytic model with the covariance matrix using $N$-body simulations. We
+find that the tSZ power spectrum alone prefers $\sigma_8 \sim 0.85$ and a large
+fraction of non-thermal pressure ($\alpha_0 \sim 0.2$-$0.3$). The tSZ-WL
+cross-correlation on the other hand prefers a significantly lower $\sigma_8
+\sim 0.6$, and low $\alpha_0 \sim 0.05$. We show that this tension can be
+mitigated by allowing for a steep slope in the stellar-mass-halo-mass relation,
+which would cause a reduction of the gas in low-mass halos. In such a model,
+the combined data prefer $\sigma_8 \sim 0.7$ and $\alpha_0 \sim 0.2$,
+consistent with predictions from hydrodynamical simulations.
+"
+28853,Maximum Lyapunov exponent revisited: Long-term attractor divergence of gait dynamics is highly sensitive to the noise structure of stride intervals,"  The local dynamic stability method (maximum Lyapunov exponent) can assess
+gait stability. Two variants of the method exist: the short-term divergence
+exponent (DE), and the long-term DE. Only the short-term DE can predict fall
+risk. The significance of long-term DE has been unclear so far. Some studies
+have suggested that the complex, fractal-like structure of fluctuations among
+consecutive strides correlates with long-term DE. The aim, therefore, was to
+assess whether the long-term DE is a gait complexity index. The study
+reanalyzed a dataset of trunk accelerations from 100 healthy adults walking at
+preferred speed on a treadmill for 10 minutes. By interpolation, the stride
+intervals were modified within the acceleration signals for the purpose of
+conserving the original shape of the signal, while imposing a known
+stride-to-stride fluctuation structure. 4 types of hybrid signals with
+different noise structures were built: constant, anti-correlated, random, and
+correlated (fractal). Short- and long-term DEs were then computed. The results
+show that long-term DEs, but not short-term DEs, are sensitive to the noise
+structure of stride intervals. It was that observed that random hybrid signals
+exhibited significantly lower long-term DEs than hybrid correlated signals did
+(0.100 vs 0.144, i.e. a 44% difference). Long-term DEs from constant hybrid
+signals were close to zero (0.006). Short-term DEs of anti-correlated, random,
+and correlated hybrid signals were closely grouped (2.49, 2.50, and 2.51). The
+short- and long-term DEs, although they are both computed from divergence
+curves, should not be interpreted in a similar way. The long-term DE is very
+likely an index of gait complexity, which may be associated with gait
+automaticity or cautiousness. To better differentiate between short- and
+long-term DEs, the use of the term attractor complexity index (ACI) is proposed
+for the latter.
+"
+28854,Characterization of Gradient Dominance and Regularity Conditions for Neural Networks,"  The past decade has witnessed a successful application of deep learning to
+solving many challenging problems in machine learning and artificial
+intelligence. However, the loss functions of deep neural networks (especially
+nonlinear networks) are still far from being well understood from a theoretical
+aspect. In this paper, we enrich the current understanding of the landscape of
+the square loss functions for three types of neural networks. Specifically,
+when the parameter matrices are square, we provide an explicit characterization
+of the global minimizers for linear networks, linear residual networks, and
+nonlinear networks with one hidden layer. Then, we establish two quadratic
+types of landscape properties for the square loss of these neural networks,
+i.e., the gradient dominance condition within the neighborhood of their full
+rank global minimizers, and the regularity condition along certain directions
+and within the neighborhood of their global minimizers. These two landscape
+properties are desirable for the optimization around the global minimizers of
+the loss function for these neural networks.
+"
+28855,The cosmic merger rate of stellar black hole binaries from the Illustris simulation,"  The cosmic merger rate density of black hole binaries (BHBs) can give us an
+essential clue to constraining the formation channels of BHBs, in light of
+current and forthcoming gravitational wave detections. Following a Monte Carlo
+approach, we couple new population-synthesis models of BHBs with the Illustris
+cosmological simulation, to study the cosmic history of BHB mergers. We explore
+six population-synthesis models, varying the prescriptions for supernovae,
+common envelope, and natal kicks. In most considered models, the cosmic BHB
+merger rate follows the same trend as the cosmic star formation rate. The
+normalization of the cosmic BHB merger rate strongly depends on the treatment
+of common envelope and on the distribution of natal kicks. We find that most
+BHBs merging within LIGO's instrumental horizon come from relatively metal-poor
+progenitors (<0.2 Zsun). The total masses of merging BHBs span a large range of
+values, from ~6 to ~82 Msun. In our fiducial model, merging BHBs consistent
+with GW150914, GW151226 and GW170104 represent ~6, 3, and 12 per cent of all
+BHBs merging within the LIGO horizon, respectively. The heavy systems, like
+GW150914, come from metal-poor progenitors (<0.15 Zsun). Most GW150914-like
+systems merging in the local Universe appear to have formed at high redshift,
+with a long delay time. In contrast, GW151226-like systems form and merge all
+the way through the cosmic history, from progenitors with a broad range of
+metallicities. Future detections will be crucial to put constraints on common
+envelope, on natal kicks, and on the BHB mass function.
+"
+28856,A time-variant channel prediction and feedback framework for interference alignment,"  In interference channels, channel state information (CSI) can be exploited to
+reduce the interference signal dimensions and thus achieve the optimal capacity
+scaling, i.e. degrees of freedom, promised by the interference alignment
+technique. However, imperfect CSI, due to channel estimation error, imperfect
+CSI feedback and time selectivity of the channel, lead to a performance loss.
+In this work, we propose a novel limited feedback algorithm for single-input
+single-output interference alignment in time-variant channels. The feedback
+algorithm encodes the channel evolution in a small number of subspace
+coefficients, which allow for reduced-rank channel prediction to compensate for
+the channel estimation error due to time selectivity of the fading process and
+feedback delay. An upper bound for the rate loss caused by feedback
+quantization and channel prediction is derived. Based on this bound, we develop
+a dimension switching algorithm for the reduced-rank predictor to find the best
+tradeoff between quantization- and prediction-error. Besides, we characterize
+the scaling of the required number of feedback bits in order to decouple the
+rate loss due to channel quantization from the transmit power. Simulation
+results show that a rate gain over the traditional non-predictive feedback
+strategy can be secured and a 60% higher rate is achieved at 20 dB
+signal-to-noise ratio with moderate mobility.
+"
+28857,"Fluids, Superfluids and Supersolids: Dynamics and Cosmology of Self Gravitating Media","  We compute cosmological perturbations for a generic self-gravitating media
+described by four derivatively- coupled scalar fields. Depending on the
+internal symmetries of the action for the scalar fields, one can describe
+perfect fluids, superfluids, solids and supersolids media. Symmetries dictate
+both dynamical and thermodynamical properties of the media. Generically, scalar
+perturbations include, besides the gravitational potential, an additional
+non-adiabatic mode associated with the entropy per particle {\sigma}. While
+perfect fluids and solids are adiabatic with {\sigma} constant in time,
+superfluids and supersolids feature a non-trivial dynamics for {\sigma}.
+Special classes of isentropic media with zero {\sigma} can also be found.
+Tensor modes become massive for solids and supersolids. Such an effective
+approach can be used to give a very general and symmetry driven modelling of
+the dark sector.
+"
+28858,ExoMol line lists -- XXII. The rotation-vibration spectrum of silane up to 1200 K,"  A variationally computed $^{28}$SiH$_4$ rotation-vibration line list
+applicable for temperatures up to $T=1200\,$K is presented. The line list,
+called OY2T, considers transitions with rotational excitation up to $J=42$ in
+the wavenumber range $0$--$5000\,$cm$^{-1}$ (wavelengths $\lambda> 2\,\mu$m).
+Just under 62.7 billion transitions have been calculated between 6.1 million
+energy levels. Rovibrational calculations have utilized a new `spectroscopic'
+potential energy surface determined by empirical refinement to 1452
+experimentally derived energy levels up to $J=6$, and a previously reported
+\textit{ab initio} dipole moment surface. The temperature-dependent partition
+function of silane, the OY2T line list format, and the temperature dependence
+of the OY2T line list are discussed. Comparisons with the PNNL spectral library
+and other experimental sources indicate that the OY2T line list is robust and
+able to accurately reproduce weaker intensity features. The full line list is
+available from the ExoMol database and the CDS database.
+"
+28859,The Bayes Lepski's Method and Credible Bands through Volume of Tubular Neighborhoods,"  For a general class of priors based on random series basis expansion, we
+develop the Bayes Lepski's method to estimate unknown regression function. In
+this approach, the series truncation point is determined based on a stopping
+rule that balances the posterior mean bias and the posterior standard
+deviation. Equipped with this mechanism, we present a method to construct
+adaptive Bayesian credible bands, where this statistical task is reformulated
+into a problem in geometry, and the band's radius is computed based on finding
+the volume of certain tubular neighborhood embedded on a unit sphere. We
+consider two special cases involving B-splines and wavelets, and discuss some
+interesting consequences such as the uncertainty principle and self-similarity.
+Lastly, we show how to program the Bayes Lepski stopping rule on a computer,
+and numerical simulations in conjunction with our theoretical investigations
+concur that this is a promising Bayesian uncertainty quantification procedure.
+"
+28860,Random Subsets of Structured Deterministic Frames have MANOVA Spectra,"  We draw a random subset of $k$ rows from a frame with $n$ rows (vectors) and
+$m$ columns (dimensions), where $k$ and $m$ are proportional to $n$. For a
+variety of important deterministic equiangular tight frames (ETFs) and tight
+non-ETF frames, we consider the distribution of singular values of the
+$k$-subset matrix. We observe that for large $n$ they can be precisely
+described by a known probability distribution -- Wachter's MANOVA spectral
+distribution, a phenomenon that was previously known only for two types of
+random frames. In terms of convergence to this limit, the $k$-subset matrix
+from all these frames is shown to be empirically indistinguishable from the
+classical MANOVA (Jacobi) random matrix ensemble. Thus empirically the MANOVA
+ensemble offers a universal description of the spectra of randomly selected
+$k$-subframes, even those taken from deterministic frames. The same
+universality phenomena is shown to hold for notable random frames as well. This
+description enables exact calculations of properties of solutions for systems
+of linear equations based on a random choice of $k$ frame vectors out of $n$
+possible vectors, and has a variety of implications for erasure coding,
+compressed sensing, and sparse recovery. When the aspect ratio $m/n$ is small,
+the MANOVA spectrum tends to the well known Marcenko-Pastur distribution of the
+singular values of a Gaussian matrix, in agreement with previous work on highly
+redundant frames. Our results are empirical, but they are exhaustive, precise
+and fully reproducible.
+"
+28861,Weighted $L_q$-estimates for stationary Stokes system with partially BMO coefficients,"  We prove the unique solvability of solutions in Sobolev spaces to the
+stationary Stokes system on a bounded Reifenberg flat domain when the
+coefficients are partially BMO functions, i.e., locally they are merely
+measurable in one direction and have small mean oscillations in the other
+directions. Using this result, we establish the unique solvability in
+Muckenhoupt type weighted Sobolev spaces for the system with partially BMO
+coefficients on a Reifenberg flat domain. We also present weighted a priori
+$L_q$-estimates for the system when the domain is the whole Euclidean space or
+a half space.
+"
+28862,"Superconductivity, charge-density waves, antiferromagnetism, and phase separation in the Hubbard-Holstein model","  By using variational wave functions and quantum Monte Carlo techniques, we
+investigate the interplay between electron-electron and electron-phonon
+interactions in the two-dimensional Hubbard-Holstein model. Here, the
+ground-state phase diagram is triggered by several energy scales, i.e., the
+electron hopping $t$, the on-site electron-electron interaction $U$, the phonon
+energy $\omega_0$, and the electron-phonon coupling $g$. At half filling, the
+ground state is an antiferromagnetic insulator for $U \gtrsim 2g^2/\omega_0$,
+while it is a charge-density-wave (or bi-polaronic) insulator for $U \lesssim
+2g^2/\omega_0$. In addition to these phases, we find a superconducting phase
+that intrudes between them. For $\omega_0/t=1$, superconductivity emerges when
+both $U/t$ and $2g^2/t\omega_0$ are small; then, by increasing the value of the
+phonon energy $\omega_0$, it extends along the transition line between
+antiferromagnetic and charge-density-wave insulators. Away from half filling,
+phase separation occurs when doping the charge-density-wave insulator, while a
+uniform (superconducting) ground state is found when doping the superconducting
+phase. In the analysis of finite-size effects, it is extremely important to
+average over twisted boundary conditions, especially in the weak-coupling limit
+and in the doped case.
+"
+28863,A Conceptual Model for Holistic Classification of Insider,"  The process through which an insider to an organization can be described or
+classified is lined within the orthodox paradigm of classification in which an
+organization considers only subject with requisite employee criterion as an
+insider to that organization. This is further clouded with the relative
+rigidity in operational security policies being implemented in organizations.
+Establishing investigation process in instances of misuse occurrence and or
+ascertaining the efficiency of staff member using such archaic paradigm is
+maligned with endless possibilities of uncertainties. This study, therefore,
+proposes a holistic model for which insider classification can be crystallized
+using the combination of qualitative research process and analysis of moment
+structure evaluation process. A full comprehension of this proposition could
+serve as a hinge through which insider misuse investigation can be thoroughly
+carried out. In addition, integrating this paradigm into existing operational
+security policies could serve as a metric upon which an organization can
+understand insider dynamics, in order to prevent misuses, and enhance staff
+management.
+"
+28864,Simulation Based Computation of Certificates for Safety of Hybrid Dynamical Systems,"  In this paper, we present an algorithm for synthesizing
+certificates---so-called barrier certificates---for safety of hybrid dynamical
+systems. Unlike the usual approach of using constraint solvers to compute the
+certificate from the system dynamics, we synthesize the certificate from system
+simulations. This makes the algorithm applicable even in cases where the
+dynamics is either not explicitly available, or too complicated to be analyzed
+by constraint solvers, for example, due to the presence of transcendental
+function symbols.
+The algorithm itself allows the usage of heuristic techniques in which case
+it does not formally guarantee correctness of the result. However, in cases
+that do allow rigorous constraint solving, the computed barrier certificate can
+be rigorously verified, if desired. Hence, in such cases, our algorithm reduces
+the problem of finding a barrier certificate to the problem of formally
+verifying a given barrier certificate.
+"
+28865,Latent Multi-task Architecture Learning,"  Multi-task learning (MTL) allows deep neural networks to learn from related
+tasks by sharing parameters with other networks. In practice, however, MTL
+involves searching an enormous space of possible parameter sharing
+architectures to find (a) the layers or subspaces that benefit from sharing,
+(b) the appropriate amount of sharing, and (c) the appropriate relative weights
+of the different task losses. Recent work has addressed each of the above
+problems in isolation. In this work we present an approach that learns a latent
+multi-task architecture that jointly addresses (a)--(c). We present experiments
+on synthetic data and data from OntoNotes 5.0, including four different tasks
+and seven different domains. Our extension consistently outperforms previous
+approaches to learning latent architectures for multi-task problems and
+achieves up to 15% average error reductions over common approaches to MTL.
+"
+28866,Dynamical aspects of the total QSSA in Enzyme Kinetics,"  In this paper we prove that the well-known quasi-steady state approximations,
+commonly used in enzyme kinetics, which can be interpreted as the reduced
+system of a differential system depending on a perturbative parameter,
+according to Tihonov theory, are asymptotically equivalent to the center
+manifold of the system. This allows to give a mathematical foundation for the
+application of a mechanistic method to determine the center manifold of (at
+this moment, still simple) enzyme reactions.
+"
+28867,Improved second-order evaluation complexity for unconstrained nonlinear optimization using high-order regularized models,"  The unconstrained minimization of a sufficiently smooth objective function
+$f(x)$ is considered, for which derivatives up to order $p$, $p\geq 2$, are
+assumed to be available. An adaptive regularization algorithm is proposed that
+uses Taylor models of the objective of order $p$ and that is guaranteed to find
+a first- and second-order critical point in at most $O \left(\max\left(
+\epsilon_1^{-\frac{p+1}{p}}, \epsilon_2^{-\frac{p+1}{p-1}} \right) \right)$
+function and derivatives evaluations, where $\epsilon_1$ and $\epsilon_2 >0$
+are prescribed first- and second-order optimality tolerances. Our approach
+extends the method in Birgin et al. (2016) to finding second-order critical
+points, and establishes the novel complexity bound for second-order criticality
+under identical problem assumptions as for first-order, namely, that the $p$-th
+derivative tensor is Lipschitz continuous and that $f(x)$ is bounded from
+below. The evaluation-complexity bound for second-order criticality improves on
+all such known existing results.
+"
+28868,Nonparametric intensity estimation from noisy observations of a Poisson process under unknown error distribution,"  We consider the nonparametric estimation of the intensity function of a
+Poisson point process in a circular model from indirect observations
+$N_1,\ldots,N_n$. These observations emerge from hidden point process
+realizations with the target intensity through contamination with additive
+error. In case that the error distribution can only be estimated from an
+additional sample $Y_1,\ldots,Y_m$ we derive minimax rates of convergence with
+respect to the sample sizes $n$ and $m$ under abstract smoothness conditions
+and propose an orthonormal series estimator which attains the optimal rate of
+convergence. The performance of the estimator depends on the correct
+specification of a dimension parameter whose optimal choice relies on
+smoothness characteristics of both the intensity and the error density. We
+propose a data-driven choice of the dimension parameter based on model
+selection and show that the adaptive estimator attains the minimax optimal
+rate.
+"
+28869,A Proof of Stavi's Theorem,"  Kamp's theorem established the expressive equivalence of the temporal logic
+with Until and Since and the First-Order Monadic Logic of Order (FOMLO) over
+the Dedekind-complete time flows. However, this temporal logic is not
+expressively complete for FOMLO over the rationals. Stavi introduced two
+additional modalities and proved that the temporal logic with Until, Since and
+Stavi's modalities is expressively equivalent to FOMLO over all linear orders.
+We present a simple proof of Stavi's theorem.
+"
+28870,Tensor product decompositions of II$_1$ factors arising from extensions of amalgamated free product groups,"  In this paper we introduce a new family of icc groups $\Gamma$ which satisfy
+the following product rigidity phenomenon, discovered in [DHI16] (see also
+[dSP17]): all tensor product decompositions of the II$_1$ factor $L(\Gamma)$
+arise only from the canonical direct product decompositions of the underlying
+group $\Gamma$. Our groups are assembled from certain HNN-extensions and
+amalgamated free products and include many remarkable groups studied throughout
+mathematics such as graph product groups, poly-amalgam groups, Burger-Mozes
+groups, Higman group, various integral two-dimensional Cremona groups, etc. As
+a consequence, we obtain several new examples of groups that give rise to prime
+factors.
+"
+28871,Towards Real-Time Accurate Object Detection in Both Images and Videos Based on Dual Refinement,"  Object detection has been vigorously studied for years but fast accurate
+detection for real-world applications remains a very challenging problem: i)
+Most existing methods have either high accuracy or fast speed; ii) Most
+prior-art approaches focus on static images, ignoring temporal information in
+real-world scenes. Overcoming drawbacks of single-stage detectors, we take aim
+at precisely detecting objects in both images and videos in real time. Firstly,
+as a dual refinement mechanism, a novel anchor-offset detection including an
+anchor refinement, a feature offset refinement, and a deformable detection head
+is designed for two-step regression and capturing accurate detection features.
+Based on the anchor-offset detection, a dual refinement network (DRN) is
+developed for high-performance static detection, where a multi-deformable head
+is further designed to leverage contextual information for describing objects.
+As for video detection, temporal refinement networks (TRN) and temporal dual
+refinement networks (TDRN) are developed by propagating the refinement
+information across time. Our proposed methods are evaluated on PASCAL VOC,
+COCO, and ImageNet VID datasets. Extensive comparison on static and temporal
+detection validate the superiority of the DRN, TRN and TDRN. Consequently, our
+developed approaches achieve a significantly enhanced detection accuracy and
+make prominent progress in accuracy vs. speed trade-off. Codes will be publicly
+available.
+"
+28872,A Grammar for Reproducible and Painless Extract-Transform-Load Operations on Medium Data,"  Many interesting data sets available on the Internet are of a medium
+size---too big to fit into a personal computer's memory, but not so large that
+they won't fit comfortably on its hard disk. In the coming years, data sets of
+this magnitude will inform vital research in a wide array of application
+domains. However, due to a variety of constraints they are cumbersome to
+ingest, wrangle, analyze, and share in a reproducible fashion. These
+obstructions hamper thorough peer-review and thus disrupt the forward progress
+of science. We propose a predictable and pipeable framework for R (the
+state-of-the-art statistical computing environment) that leverages SQL (the
+venerable database architecture and query language) to make reproducible
+research on medium data a painless reality.
+"
+28873,Density Estimation for Geolocation via Convolutional Mixture Density Network,"  Nowadays, geographic information related to Twitter is crucially important
+for fine-grained applications. However, the amount of geographic information
+avail- able on Twitter is low, which makes the pursuit of many applications
+challenging. Under such circumstances, estimating the location of a tweet is an
+important goal of the study. Unlike most previous studies that estimate the
+pre-defined district as the classification task, this study employs a
+probability distribution to represent richer information of the tweet, not only
+the location but also its ambiguity. To realize this modeling, we propose the
+convolutional mixture density network (CMDN), which uses text data to estimate
+the mixture model parameters. Experimentally obtained results reveal that CMDN
+achieved the highest prediction performance among the method for predicting the
+exact coordinates. It also provides a quantitative representation of the
+location ambiguity for each tweet that properly works for extracting the
+reliable location estimations.
+"
+28874,On recent advances in 2D Constrained Delaunay triangulation algorithms,"  In this article, recent works on 2D Constrained Delaunay triangulation(CDT)
+algorithms have been reported. Since the review of CDT algorithms presented by
+de Floriani(Issues on Machine Vision, Springer Vienna, pg. 95--104, 1989),
+different algorithms for construction and applications of CDT have appeared in
+literature each concerned with different aspects of computation and different
+suitabilities. Therefore, objective of this work is to report an update over
+that review article considering contemporary prominent algorithms and
+generalizations for the problem of two dimensional Constrained Delaunay
+triangulation.
+"
+28875,Where to Play: Retrieval of Video Segments using Natural-Language Queries,"  In this paper, we propose a new approach for retrieval of video segments
+using natural language queries. Unlike most previous approaches such as
+concept-based methods or rule-based structured models, the proposed method uses
+image captioning model to construct sentential queries for visual information.
+In detail, our approach exploits multiple captions generated by visual features
+in each image with `Densecap'. Then, the similarities between captions of
+adjacent images are calculated, which is used to track semantically similar
+captions over multiple frames. Besides introducing this novel idea of 'tracking
+by captioning', the proposed method is one of the first approaches that uses a
+language generation model learned by neural networks to construct semantic
+query describing the relations and properties of visual information. To
+evaluate the effectiveness of our approach, we have created a new evaluation
+dataset, which contains about 348 segments of scenes in 20 movie-trailers.
+Through quantitative and qualitative evaluation, we show that our method is
+effective for retrieval of video segments using natural language queries.
+"
+28876,Simultaneous Task Allocation and Planning Under Uncertainty,"  We propose novel techniques for task allocation and planning in multi-robot
+systems operating in uncertain environments. Task allocation is performed
+simultaneously with planning, which provides more detailed information about
+individual robot behaviour, but also exploits independence between tasks to do
+so efficiently. We use Markov decision processes to model robot behaviour and
+linear temporal logic to specify tasks and safety constraints. Building upon
+techniques and tools from formal verification, we show how to generate a
+sequence of multi-robot policies, iteratively refining them to reallocate tasks
+if individual robots fail, and providing probabilistic guarantees on the
+performance (and safe operation) of the team of robots under the resulting
+policy. We implement our approach and evaluate it on a benchmark multi-robot
+example.
+"
+28877,An Integrated Approach to Autonomous Environment Modeling,"  In this paper, we present an integrated solution to memory-efficient
+environment modeling by an autonomous mobile robot equipped with a laser
+range-finder.
+Majority of nowadays approaches to autonomous environment modeling, called
+exploration, employs occupancy grids as environment representation where the
+working space is divided into small cells each storing information about the
+corresponding piece of the environment in the form of a probabilistic estimate
+of its state. In contrast, the presented approach uses a polygonal
+representation of the explored environment which consumes much less memory,
+enables fast planning and decision-making algorithms and it is thus reliable
+for large-scale environments.
+Simultaneous localization and mapping (SLAM) has been integrated into the
+presented framework to correct odometry errors and to provide accurate position
+estimates. This involves also a refinement of the already generated environment
+model in case of loop closure, i.e. when the robot detects that it revisited an
+already explored place.
+The framework has been implemented in Robot Operating System (ROS) and tested
+with a real robot in various environments. The experiments show that the
+polygonal representation with SLAM integrated can be used in the real world as
+it is fast, memory efficient and accurate. Moreover, the refinement can be
+executed in real-time during the exploration process.
+"
+28878,More on one class of fractals,"  This article is devoted to Cantor-like sets. We describe topological, metric
+and fractal properties of certain sets whose elements have restrictions on
+using digits or combinations of digits in own representations. Also, such
+properties are considered for the cases of special type of sets, i.e., sets
+whose elements represented in terms of certain series related with classical
+representations of real numbers.
+"
+28879,Inhomogeneous Hypergraph Clustering with Applications,"  Hypergraph partitioning is an important problem in machine learning, computer
+vision and network analytics. A widely used method for hypergraph partitioning
+relies on minimizing a normalized sum of the costs of partitioning hyperedges
+across clusters. Algorithmic solutions based on this approach assume that
+different partitions of a hyperedge incur the same cost. However, this
+assumption fails to leverage the fact that different subsets of vertices within
+the same hyperedge may have different structural importance. We hence propose a
+new hypergraph clustering technique, termed inhomogeneous hypergraph
+partitioning, which assigns different costs to different hyperedge cuts. We
+prove that inhomogeneous partitioning produces a quadratic approximation to the
+optimal solution if the inhomogeneous costs satisfy submodularity constraints.
+Moreover, we demonstrate that inhomogenous partitioning offers significant
+performance improvements in applications such as structure learning of
+rankings, subspace segmentation and motif clustering.
+"
+28880,Energy Efficiency in Cache Enabled Small Cell Networks With Adaptive User Clustering,"  Using a network of cache enabled small cells, traffic during peak hours can
+be reduced considerably through proactively fetching the content that is most
+probable to be requested. In this paper, we aim at exploring the impact of
+proactive caching on an important metric for future generation networks,
+namely, energy efficiency (EE). We argue that, exploiting the correlation in
+user content popularity profiles in addition to the spatial repartitions of
+users with comparable request patterns, can result in considerably improving
+the achievable energy efficiency of the network. In this paper, the problem of
+optimizing EE is decoupled into two related subproblems. The first one
+addresses the issue of content popularity modeling. While most existing works
+assume similar popularity profiles for all users in the network, we consider an
+alternative caching framework in which, users are clustered according to their
+content popularity profiles. In order to showcase the utility of the proposed
+clustering scheme, we use a statistical model selection criterion, namely
+Akaike information criterion (AIC). Using stochastic geometry, we derive a
+closed-form expression of the achievable EE and we find the optimal active
+small cell density vector that maximizes it. The second subproblem investigates
+the impact of exploiting the spatial repartitions of users with comparable
+request patterns. After considering a snapshot of the network, we formulate a
+combinatorial optimization problem that enables to optimize content placement
+such that the used transmission power is minimized. Numerical results show that
+the clustering scheme enable to considerably improve the cache hit probability
+and consequently the EE compared with an unclustered approach. Simulations also
+show that the small base station allocation algorithm results in improving the
+energy efficiency and hit probability.
+"
+28881,MindDesktop: a general purpose brain computer interface,"  Recent advances in electroencephalography (EEG) and electromyography (EMG)
+enable communication for people with severe disabilities. In this paper we
+present a system that enables the use of regular computers using an
+off-the-shelf EEG/EMG headset, providing a pointing device and virtual keyboard
+that can be used to operate any Windows based system, minimizing the user
+effort required for interacting with a personal computer. Effectiveness of the
+proposed system is evaluated by a usability study, indicating decreasing
+learning curve for completing various tasks. The proposed system is available
+in the link provided.
+"
+28882,Concept Transfer Learning for Adaptive Language Understanding,"  Semantic transfer is an important problem of the language understanding (LU),
+which is about how the recognition pattern of a semantic concept benefits other
+associated concepts. In this paper, we propose a new semantic representation
+based on combinatory concepts. Semantic slot is represented as a composition of
+different atomic concepts in different semantic dimensions. Specifically, we
+propose the concept transfer learning methods for extending combinatory
+concepts in LU. The concept transfer learning makes use of the common ground of
+combinatory concepts shown in the literal description. Our methods are applied
+to two adaptive LU problems: semantic slot refinement and domain adaptation,
+and respectively evaluated on two benchmark LU datasets: ATIS and DSTC 2&3. The
+experiment results show that the concept transfer learning is very efficient
+for semantic slot refinement and domain adaptation in the LU.
+"
+28883,Finite Groups with a Trivial Chermak-Delgado Subgroup,"  The Chermak-Delgado lattice of a finite group is a modular, self-dual
+sublattice of the lattice of subgroups of $G$. The least element of the
+Chermak-Delgado lattice of $G$ is known as the Chermak-Delgado subgroup of $G$.
+This paper concerns groups with a trivial Chermak-Delgado subgroup. We prove
+that if the Chermak-Delgado lattice of such a group is lattice isomorphic to a
+Cartesian product of lattices, then the group splits as a direct product, with
+the Chermak-Delgado lattice of each direct factor being lattice isomorphic to
+one of the lattices in the Cartesian product. We establish many properties of
+such groups and properties of subgroups in the Chermak-Delgado lattice. We
+define a CD-minimal group to be an indecomposable group with a trivial
+Chermak-Delgado subgroup. We establish lattice theoretic properties of
+Chermak-Delgado lattices of CD-minimal groups. We prove an extension theorem
+for CD-minimal groups, and use the theorem to produce twelve examples of
+CD-minimal groups, each having different CD lattices. Curiously,
+quasi-antichain $p$-group lattices play a major role in the author's
+constructions.
+"
+28884,Automated Diagnosis of Epilepsy Employing Multifractal Detrended Fluctuation Analysis Based Features,"  This contribution reports an application of MultiFractal Detrended
+Fluctuation Analysis, MFDFA based novel feature extraction technique for
+automated detection of epilepsy. In fractal geometry, Multifractal Detrended
+Fluctuation Analysis MFDFA is a popular technique to examine the
+self-similarity of a nonlinear, chaotic and noisy time series. In the present
+research work, EEG signals representing healthy, interictal (seizure free) and
+ictal activities (seizure) are acquired from an existing available database.
+The acquired EEG signals of different states are at first analyzed using MFDFA.
+To requisite the time series singularity quantification at local and global
+scales, a novel set of fourteen different features. Suitable feature ranking
+employing students t-test has been done to select the most statistically
+significant features which are henceforth being used as inputs to a support
+vector machines (SVM) classifier for the classification of different EEG
+signals. Eight different classification problems have been presented in this
+paper and it has been observed that the overall classification accuracy using
+MFDFA based features are reasonably satisfactory for all classification
+problems. The performance of the proposed method are also found to be quite
+commensurable and in some cases even better when compared with the results
+published in existing literature studied on the similar data set.
+"
+28885,LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes,"  Deep neural network (DNN) architectures have been shown to outperform
+traditional pipelines for object segmentation and pose estimation using RGBD
+data, but the performance of these DNN pipelines is directly tied to how
+representative the training data is of the true data. Hence a key requirement
+for employing these methods in practice is to have a large set of labeled data
+for your specific robotic manipulation task, a requirement that is not
+generally satisfied by existing datasets. In this paper we develop a pipeline
+to rapidly generate high quality RGBD data with pixelwise labels and object
+poses. We use an RGBD camera to collect video of a scene from multiple
+viewpoints and leverage existing reconstruction techniques to produce a 3D
+dense reconstruction. We label the 3D reconstruction using a human assisted
+ICP-fitting of object meshes. By reprojecting the results of labeling the 3D
+scene we can produce labels for each RGBD image of the scene. This pipeline
+enabled us to collect over 1,000,000 labeled object instances in just a few
+days. We use this dataset to answer questions related to how much training data
+is required, and of what quality the data must be, to achieve high performance
+from a DNN architecture.
+"
+28886,Ultralow-temperature thermal conductivity of the Kitaev honeycomb magnet $α$-RuCl$_3$ across the field-induced phase transition,"  Recently, there have been increasingly hot debates on whether there exists a
+quantum spin liquid in the Kitaev honeycomb magnet $\alpha$-RuCl$_3$ in high
+magnetic field. To investigate this issue, we perform the ultralow-temperature
+thermal conductivity measurements on the single crystals of $\alpha$-RuCl$_3$
+down to 80 mK and up to 9 T. Our experiments clearly show a field-induced phase
+transition occurring at $H_c$ $\approx$ 7.5 T, above which the zigzag magnetic
+order is completely suppressed. The minimum of thermal conductivity at 7.5 T is
+attributed to the strong scattering of phonons by the magnetic fluctuations.
+Most importantly, above 7.5 T, we do not observe any significant contribution
+of thermal conductivity from gapless magnetic excitations, which puts a strong
+constraint on the nature of the high-field phase of $\alpha$-RuCl$_3$.
+"
+28887,ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding,"  We present ErasureHead, a new approach for distributed gradient descent (GD)
+that mitigates system delays by employing approximate gradient coding. Gradient
+coded distributed GD uses redundancy to exactly recover the gradient at each
+iteration from a subset of compute nodes. ErasureHead instead uses approximate
+gradient codes to recover an inexact gradient at each iteration, but with
+higher delay tolerance. Unlike prior work on gradient coding, we provide a
+performance analysis that combines both delay and convergence guarantees. We
+establish that down to a small noise floor, ErasureHead converges as quickly as
+distributed GD and has faster overall runtime under a probabilistic delay
+model. We conduct extensive experiments on real world datasets and distributed
+clusters and demonstrate that our method can lead to significant speedups over
+both standard and gradient coded GD.
+"
+28888,High-Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transition,"  We consider a sparse linear regression model Y=X\beta^{*}+W where X has a
+Gaussian entries, W is the noise vector with mean zero Gaussian entries, and
+\beta^{*} is a binary vector with support size (sparsity) k. Using a novel
+conditional second moment method we obtain a tight up to a multiplicative
+constant approximation of the optimal squared error
+\min_{\beta}\|Y-X\beta\|_{2}, where the minimization is over all k-sparse
+binary vectors \beta. The approximation reveals interesting structural
+properties of the underlying regression problem. In particular, a) We establish
+that n^*=2k\log p/\log (2k/\sigma^{2}+1) is a phase transition point with the
+following ""all-or-nothing"" property. When n exceeds n^{*},
+(2k)^{-1}\|\beta_{2}-\beta^*\|_0\approx 0, and when n is below n^{*},
+(2k)^{-1}\|\beta_{2}-\beta^*\|_0\approx 1, where \beta_2 is the optimal
+solution achieving the smallest squared error. With this we prove that n^{*} is
+the asymptotic threshold for recovering \beta^* information theoretically. b)
+We compute the squared error for an intermediate problem
+\min_{\beta}\|Y-X\beta\|_{2} where minimization is restricted to vectors \beta
+with \|\beta-\beta^{*}\|_0=2k \zeta, for \zeta\in [0,1]. We show that a lower
+bound part \Gamma(\zeta) of the estimate, which corresponds to the estimate
+based on the first moment method, undergoes a phase transition at three
+different thresholds, namely n_{\text{inf,1}}=\sigma^2\log p, which is
+information theoretic bound for recovering \beta^* when k=1 and \sigma is
+large, then at n^{*} and finally at n_{\text{LASSO/CS}}. c) We establish a
+certain Overlap Gap Property (OGP) on the space of all binary vectors \beta
+when n\le ck\log p for sufficiently small constant c. We conjecture that OGP is
+the source of algorithmic hardness of solving the minimization problem
+\min_{\beta}\|Y-X\beta\|_{2} in the regime n<n_{\text{LASSO/CS}}.
+"
+28889,Statistical Physics Models of Belief Dynamics: Theory and Empirical Tests,"  We build simple computational models of belief dynamics within the framework
+of discrete-spin statistical physics models, and explore how suitable they are
+for understanding and predicting real-world belief change on both the
+individual and group levels. We find that accurate modeling of real-world
+patterns requires attending to social interaction rules that people use,
+network structures in which they are embedded, distributions of initial beliefs
+and intrinsic preferences, and the relative importance of social information
+and intrinsic preferences. We demonstrate that these model parameters can be
+constrained by empirical measurement, and the resulting models can be used to
+investigate the mechanisms underlying belief dynamics in actual societies. We
+use data from two longitudinal studies of belief change, one on 80~individuals
+living in an MIT dorm during the 2008 presidential election season, and another
+on 94~participants recruited from Mechanical Turk during the 2016 presidential
+election primary season. We find that simple statistical physics-based models
+contain predictive value for real-world belief dynamics and enable empirical
+tests of different assumptions about the underlying network structure and the
+social interaction rules.
+"
+28890,Scene Text Synthesis for Efficient and Effective Deep Network Training,"  A large amount of annotated training images is critical for training accurate
+and robust deep network models but the collection of a large amount of
+annotated training images is often time-consuming and costly. Image synthesis
+alleviates this constraint by generating annotated training images
+automatically by machines which has attracted increasing interest in the recent
+deep learning research. We develop an innovative image synthesis technique that
+composes annotated training images by realistically embedding foreground
+objects of interest (OOI) into background images. The proposed technique
+consists of two key components that in principle boost the usefulness of the
+synthesized images in deep network training. The first is context-aware
+semantic coherence which ensures that the OOI are placed around semantically
+coherent regions within the background image. The second is harmonious
+appearance adaptation which ensures that the embedded OOI are agreeable to the
+surrounding background from both geometry alignment and appearance realism. The
+proposed technique has been evaluated over two related but very different
+computer vision challenges, namely, scene text detection and scene text
+recognition. Experiments over a number of public datasets demonstrate the
+effectiveness of our proposed image synthesis technique - the use of our
+synthesized images in deep network training is capable of achieving similar or
+even better scene text detection and scene text recognition performance as
+compared with using real images.
+"
+28891,"Equiangular surfaces, self-similar surfaces and the geometry of sea shells","  We investigate three-dimensional surfaces where the normal vector forms a
+constant angle with the radius vector. These surfaces naturally extend
+equiangular (logarithmic) spirals in the plane.
+"
+28892,Cycloid Experiment for freshmen physics labs,"  We establish an instructive experiment to investigate the minimum time curve
+traveled by a small billiard ball rolling in a grooved track under gravity. Our
+intention is to popularize the concept of \textit{minimum time curve} anew, and
+to propose it as a feasible physics experiment both for freshmen and sophomore
+classes. We observed that even the non-physics major students did enjoy such a
+cycloid experiment.
+"
+28893,Time-Dependent Perpendicular Transport of Energetic Particles for Different Turbulence Configurations and Parallel Transport Models,"  Recently a new theory for the transport of energetic particles across a mean
+magnetic field was presented. Compared to other non-linear theories the new
+approach has the advantage that it provides a full time-dependent description
+of the transport. Furthermore, a diffusion approximation is no longer part of
+that theory. It is the purpose of the current paper to combine this new
+approach with a time-dependent model for parallel transport and different
+turbulence configurations in order to explore the parameter regimes for which
+we get ballistic transport, compound sub-diffusion, and normal Markovian
+diffusion.
+"
+28894,Evolution models of helium white dwarf--main sequence star merger remnants,"  It is predicted that orbital decay by gravitational-wave radiation and tidal
+interaction will cause some close-binary stars to merge within a Hubble time.
+The merger of a helium-core white dwarf with a main-sequence star can produce a
+red giant branch star that has a low-mass hydrogen envelope when helium is
+ignited and thus become a hot subdwarf. Because detailed calculations have not
+been made, we compute post-merger models with a stellar evolution code. We find
+the evolutionary paths available to merger remnants and find the pre-merger
+conditions that lead to the formation of hot subdwarfs. We find that some such
+mergers result in the formation of stars with intermediate helium-rich
+surfaces. These stars later develop helium-poor surfaces owing to diffusion.
+Combining our results with a model population and comparing to observed stars,
+we find that some observed intermediate helium-rich hot subdwarfs can be
+explained as the remnants of the mergers of helium-core white dwarfs with
+low-mass main-sequence stars.
+"
+28895,Learning User Preferences to Incentivize Exploration in the Sharing Economy,"  We study platforms in the sharing economy and discuss the need for
+incentivizing users to explore options that otherwise would not be chosen. For
+instance, rental platforms such as Airbnb typically rely on customer reviews to
+provide users with relevant information about different options. Yet, often a
+large fraction of options does not have any reviews available. Such options are
+frequently neglected as viable choices, and in turn are unlikely to be
+evaluated, creating a vicious cycle. Platforms can engage users to deviate from
+their preferred choice by offering monetary incentives for choosing a different
+option instead. To efficiently learn the optimal incentives to offer, we
+consider structural information in user preferences and introduce a novel
+algorithm - Coordinated Online Learning (CoOL) - for learning with structural
+information modeled as convex constraints. We provide formal guarantees on the
+performance of our algorithm and test the viability of our approach in a user
+study with data of apartments on Airbnb. Our findings suggest that our approach
+is well-suited to learn appropriate incentives and increase exploration on the
+investigated platform.
+"
+28896,Unsupervised Disentangled Representation Learning with Analogical Relations,"  Learning the disentangled representation of interpretable generative factors
+of data is one of the foundations to allow artificial intelligence to think
+like people. In this paper, we propose the analogical training strategy for the
+unsupervised disentangled representation learning in generative models. The
+analogy is one of the typical cognitive processes, and our proposed strategy is
+based on the observation that sample pairs in which one is different from the
+other in one specific generative factor show the same analogical relation.
+Thus, the generator is trained to generate sample pairs from which a designed
+classifier can identify the underlying analogical relation. In addition, we
+propose a disentanglement metric called the subspace score, which is inspired
+by subspace learning methods and does not require supervised information.
+Experiments show that our proposed training strategy allows the generative
+models to find the disentangled factors, and that our methods can give
+competitive performances as compared with the state-of-the-art methods.
+"
+28897,Riesz transform via heat kernel and harmonic functions on non-compact manifolds,"  Let $M$ be a complete non-compact manifold satisfying the volume doubling
+condition, with doubling index $N$ and reverse doubling index $n$, $n\le N$,
+both for large balls. Assume a Gaussian upper bound for the heat kernel, and an
+$L^2$-Poincaré inequality outside a compact set.
+If $2<n$, then we show that for $p\in (2,n)$, $(R_p)$: $L^p$-boundedness of
+the Riesz transform, $(G_p)$: $L^p$-boundedness of the gradient of the heat
+semigroup, and $(RH_p)$: reverse $L^p$-Hölder inequality for the gradient of
+harmonic functions, are equivalent to each other. Our characterization implies
+that for $p\in (2,n)$, $(R_p)$ has an open ended property and is stable under
+gluing operations. This substantially extends the well known equivalence of
+$(R_p)$ and $(G_p)$ from [4] to more general settings, and is optimal in the
+sense that $(R_p)$ does not hold for any $p\ge n>2$ on manifolds having at
+least two Euclidean ends of dimension $n$.
+For $p\in (\max\{N,2\},\infty)$, the fact that $(R_p)$, $(G_p)$ and $(RH_p)$
+are equivalent essentially follows from [22]; moreover, if $M$ is
+non-parabolic, then any of these conditions implies that $M$ has only one end.
+For the proof, we develop a new criteria for boundedness of the Riesz
+transform, which was nontrivially adapted from [4], and make an essential
+application of results from [22]. Our result allows extensions to non-smooth
+settings.
+"
+28898,An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning,"  Distributed optimization algorithms are essential for training machine
+learning models on very large-scale datasets. However, they often suffer from
+communication bottlenecks. Confronting this issue, a communication-efficient
+primal-dual coordinate ascent framework (CoCoA) and its improved variant CoCoA+
+have been proposed, achieving a convergence rate of $\mathcal{O}(1/t)$ for
+solving empirical risk minimization problems with Lipschitz continuous losses.
+In this paper, an accelerated variant of CoCoA+ is proposed and shown to
+possess a convergence rate of $\mathcal{O}(1/t^2)$ in terms of reducing
+suboptimality. The analysis of this rate is also notable in that the
+convergence rate bounds involve constants that, except in extreme cases, are
+significantly reduced compared to those previously provided for CoCoA+. The
+results of numerical experiments are provided to show that acceleration can
+lead to significant performance gains.
+"
+28899,Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market,"  The original research question here is given by marketers in general, i.e.,
+how to explain the changes in the desired timescale of the market. Tangled
+String, a sequence visualization tool based on the metaphor where contexts in a
+sequence are compared to tangled pills in a string, is here extended and
+diverted to detecting stocks that trigger changes in the market and to
+explaining the scenario of contextual shifts in the market. Here, the
+sequential data on the stocks of top 10 weekly increase rates in the First
+Section of the Tokyo Stock Exchange for 12 years are visualized by Tangled
+String. The changing in the prices of stocks is a mixture of various timescales
+and can be explained in the time-scale set as desired by using TS. Also, it is
+found that the change points found by TS coincided by high precision with the
+real changes in each stock price. As TS has been created from the data-driven
+innovation platform called Innovators Marketplace on Data Jackets and is
+extended to satisfy data users, this paper is as evidence of the contribution
+of the market of data to data-driven innovations.
+"
+28900,Absence of Thermalization in Finite Isolated Interacting Floquet Systems,"  Conventional wisdom suggests that the long time behavior of isolated
+interacting periodically driven (Floquet) systems is a featureless maximal
+entropy state characterized by an infinite temperature. Efforts to thwart this
+uninteresting fixed point include adding sufficient disorder to realize a
+Floquet many-body localized phase or working in a narrow region of drive
+frequencies to achieve glassy non-thermal behavior at long time. Here we show
+that in clean systems the Floquet eigenstates can exhibit non-thermal behavior
+due to finite system size. We consider a one-dimensional system of spinless
+fermions with nearest-neighbor interactions where the interaction term is
+driven. Interestingly, even with no static component of the interaction, the
+quasienergy spectrum contains gaps and a significant fraction of the Floquet
+eigenstates, at all quasienergies, have non-thermal average doublon densities.
+We show that this non-thermal behavior arises due to emergent integrability at
+large interaction strength and discuss how the integrability breaks down with
+power-law dependence on system size.
+"
+28901,Effect of tendon structural control on the appearance of horseshoes chaos on a cantilever beam due to seismic action,"  This paper deals with the problem of improving the seismic strength of
+mechanical structures by using a tendon system as actuation device. It consists
+of a pair of tension cables transmitting a control torque to the structure at
+the moment arm position. The purpose of the paper is to, establish the
+analytical framework consisting of mathematical modelling of cantilever beam
+under active tendon structural control, analyze the stability and, determine
+the physical characteristics of tendon system leading to the suppression of
+horseshoes chaos. The control efficiency is found by analyzing the behavior of
+the controlled system through the Melnikov methods. We also provide the
+critical control gain parameters leading to the efficiency of the control
+process.
+"
+28902,Non-equilibrium superconductivity in driven alkali-doped fullerides,"  We investigate the formation of non-equilibrium superconducting states in
+driven alkali-doped fullerides A$_3$C$_{60}$. Within a minimal three-orbital
+model for the superconductivity of these materials, it was recently
+demonstrated theoretically that an orbital-dependent imbalance of the
+interactions leads to an enhancement of superconductivity at equilibrium [M.
+Kim et al. Phys. Rev. B 94, 155152 (2016)]. We investigate the dynamical
+response to a time periodic modulation of this interaction imbalance, and show
+that it leads to the formation of a transient superconducting state which
+survives much beyond the equilibrium critical temperature $T_c$. For a specific
+range of modulation frequencies, we find that the driving reduces
+superconductivity when applied to a superconducting state below $T_c$, while
+still inducing a superconducting state when the initial temperature is larger
+than $T_c$. These findings reinforce the relevance of the interaction-imbalance
+mechanism as a possible explanation of the recent experimental observation of
+light-induced superconductivity in alkali-doped fullerenes.
+"
+28903,SDSS J105754.25+275947.5: a period-bounce eclipsing cataclysmic variable with the lowest-mass donor yet measured,"  We present high-speed, multicolour photometry of the faint, eclipsing
+cataclysmic variable (CV) SDSS J105754.25+275947.5. The light from this system
+is dominated by the white dwarf. Nonetheless, averaging many eclipses reveals
+additional features from the eclipse of the bright spot. This enables the
+fitting of a parameterised eclipse model to these average light curves,
+allowing the precise measurement of system parameters. We find a mass ratio of
+q = 0.0546 $\pm$ 0.0020 and inclination i = 85.74 $\pm$ 0.21$^{\circ}$. The
+white dwarf and donor masses were found to be M$_{\mathrm{w}}$ = 0.800 $\pm$
+0.015 M$_{\odot}$ and M$_{\mathrm{d}}$ = 0.0436 $\pm$ 0.0020 M$_{\odot}$,
+respectively. A temperature T$_{\mathrm{w}}$ = 13300 $\pm$ 1100 K and distance
+d = 367 $\pm$ 26 pc of the white dwarf were estimated through fitting model
+atmosphere predictions to multicolour fluxes. The mass of the white dwarf in
+SDSS 105754.25+275947.5 is close to the average for CV white dwarfs, while the
+donor has the lowest mass yet measured in an eclipsing CV. A low-mass donor and
+an orbital period (90.44 min) significantly longer than the period minimum
+strongly suggest that this is a bona fide period-bounce system, although
+formation from a white dwarf/brown dwarf binary cannot be ruled out. Very few
+period-minimum/period-bounce systems with precise system parameters are
+currently known, and as a consequence the evolution of CVs in this regime is
+not yet fully understood.
+"
+28904,Dependent Session Types,"  Session types offer a type-based discipline for enforcing communication
+protocols in distributed programming. We have previously formalized simple
+session types in the setting of multi-threaded $\lambda$-calculus with linear
+types. In this work, we build upon our earlier work by presenting a form of
+dependent session types (of DML-style). The type system we formulate provides
+linearity and duality guarantees with no need for any runtime checks or special
+encodings. Our formulation of dependent session types is the first of its kind,
+and it is particularly suitable for practical implementation. As an example, we
+describe one implementation written in ATS that compiles to an Erlang/Elixir
+back-end.
+"
+28905,Neural Embeddings of Graphs in Hyperbolic Space,"  Neural embeddings have been used with great success in Natural Language
+Processing (NLP). They provide compact representations that encapsulate word
+similarity and attain state-of-the-art performance in a range of linguistic
+tasks. The success of neural embeddings has prompted significant amounts of
+research into applications in domains other than language. One such domain is
+graph-structured data, where embeddings of vertices can be learned that
+encapsulate vertex similarity and improve performance on tasks including edge
+prediction and vertex labelling. For both NLP and graph based tasks, embeddings
+have been learned in high-dimensional Euclidean spaces. However, recent work
+has shown that the appropriate isometric space for embedding complex networks
+is not the flat Euclidean space, but negatively curved, hyperbolic space. We
+present a new concept that exploits these recent insights and propose learning
+neural embeddings of graphs in hyperbolic space. We provide experimental
+evidence that embedding graphs in their natural geometry significantly improves
+performance on downstream tasks for several real-world public datasets.
+"
+28906,First NuSTAR Limits on Quiet Sun Hard X-Ray Transient Events,"  We present the first results of a search for transient hard X-ray (HXR)
+emission in the quiet solar corona with the \textit{Nuclear Spectroscopic
+Telescope Array} (\textit{NuSTAR}) satellite. While \textit{NuSTAR} was
+designed as an astrophysics mission, it can observe the Sun above 2~keV with
+unprecedented sensitivity due to its pioneering use of focusing optics.
+\textit{NuSTAR} first observed quiet Sun regions on 2014 November 1, although
+out-of-view active regions contributed a notable amount of background in the
+form of single-bounce (unfocused) X-rays. We conducted a search for quiet Sun
+transient brightenings on time scales of 100 s and set upper limits on emission
+in two energy bands. We set 2.5--4~keV limits on brightenings with time scales
+of 100 s, expressed as the temperature T and emission measure EM of a thermal
+plasma. We also set 10--20~keV limits on brightenings with time scales of 30,
+60, and 100 s, expressed as model-independent photon fluxes. The limits in both
+bands are well below previous HXR microflare detections, though not low enough
+to detect events of equivalent T and EM as quiet Sun brightenings seen in soft
+X-ray observations. We expect future observations during solar minimum to
+increase the \textit{NuSTAR} sensitivity by over two orders of magnitude due to
+higher instrument livetime and reduced solar background.
+"
+28907,Missing Value Imputation Based on Deep Generative Models,"  Missing values widely exist in many real-world datasets, which hinders the
+performing of advanced data analytics. Properly filling these missing values is
+crucial but challenging, especially when the missing rate is high. Many
+approaches have been proposed for missing value imputation (MVI), but they are
+mostly heuristics-based, lacking a principled foundation and do not perform
+satisfactorily in practice. In this paper, we propose a probabilistic framework
+based on deep generative models for MVI. Under this framework, imputing the
+missing entries amounts to seeking a fixed-point solution between two
+conditional distributions defined on the missing entries and latent variables
+respectively. These distributions are parameterized by deep neural networks
+(DNNs) which possess high approximation power and can capture the nonlinear
+relationships between missing entries and the observed values. The learning of
+weight parameters of DNNs is performed by maximizing an approximation of the
+log-likelihood of observed values. We conducted extensive evaluation on 13
+datasets and compared with 11 baselines methods, where our methods largely
+outperforms the baselines.
+"
+28908,Zariski locality of quasi-coherent sheaves associated with tilting,"  A classic result by Raynaud and Gruson says that the notion of an (infinite
+dimensional) vector bundle is Zariski local. This result may be viewed as a
+particular instance (for n = 0) of the locality of more general notions of
+quasi-coherent sheaves related to (infinite dimensional) n-tilting modules and
+classes. Here, we prove the latter locality for all n and all schemes. We also
+prove that the notion of a tilting module descends along arbitrary faithfully
+flat ring morphisms in several particular cases (including the case when the
+base ring is noetherian).
+"
+28909,Negative Cycle Separation in Wireless Network Design,"  The Wireless Network Design Problem (WND) consists in choosing values of
+radio-electrical parameters of transmitters of a wireless network, to maximize
+network coverage. We present a pure 0-1 Linear Programming formulation for the
+WND that may contain an exponential number of constraints. Violated
+inequalities of this formulation are hard to separate both theoretically and in
+practice. However, a relevant subset of such inequalities can be separated more
+efficiently in practice and can be used to strengthen classical MILP
+formulations for the WND. Preliminary computational experience confirms the
+effectiveness of our new technique both in terms of quality of solutions found
+and provided bounds.
+"
+28910,Defining Big Data Analytics Benchmarks for Next Generation Supercomputers,"  The design and construction of high performance computing (HPC) systems
+relies on exhaustive performance analysis and benchmarking. Traditionally this
+activity has been geared exclusively towards simulation scientists, who,
+unsurprisingly, have been the primary customers of HPC for decades. However,
+there is a large and growing volume of data science work that requires these
+large scale resources, and as such the calls for inclusion and investments in
+data for HPC have been increasing. So when designing a next generation HPC
+platform, it is necessary to have HPC-amenable big data analytics benchmarks.
+In this paper, we propose a set of big data analytics benchmarks and sample
+codes designed for testing the capabilities of current and next generation
+supercomputers.
+"
+28911,The Dynamical History Of 2060 Chiron And Its Proposed Ring System,"  The surprising discovery of a ring system around the Centaur 10199 Chariklo
+in 2013 led to a reanalysis of archival stellar occultation data for the
+Centaur 2060 Chiron by Ortiz et al. One possible interpretation of that data is
+that a system of rings exists around Chiron. In this work, we study the
+dynamical history of the proposed Chiron ring system by integrating nearly
+36,000 clones of the Centaur backward in time for 100 Myr under the influence
+of the Sun and the four giant planets. The severity of all close encounters
+between the clones and planets while the clones are in the Centaur region is
+recorded, along with the mean time between close encounters. We find that
+severe and extreme close encounters are very rare, making it possible that the
+Chiron ring system has remained intact since its injection into the Centaur
+region, which we find likely occurred within the past 8.5 Myr. Our simulations
+yield a backward dynamical half-life for Chiron of 0.7 Myr. The dynamical
+classes of a sample of clones are found. It is found that, on average, the
+Centaur lifetimes of resonance hopping clones are twice those of random-walk
+clones because of resonance sticking in mean motion resonances. In addition, we
+present MEGNO and chaotic lifetime maps of the region bound by 13 au $\le a
+\le$14 au and e $\le$0.5. We confirm that the current mean orbital parameters
+of Chiron are located in a highly chaotic region of a-e phase space.
+"
+28912,A Linear-Time Kernel Goodness-of-Fit Test,"  We propose a novel adaptive test of goodness-of-fit, with computational cost
+linear in the number of samples. We learn the test features that best indicate
+the differences between observed samples and a reference model, by minimizing
+the false negative rate. These features are constructed via Stein's method,
+meaning that it is not necessary to compute the normalising constant of the
+model. We analyse the asymptotic Bahadur efficiency of the new test, and prove
+that under a mean-shift alternative, our test always has greater relative
+efficiency than a previous linear-time kernel test, regardless of the choice of
+parameters for that test. In experiments, the performance of our method exceeds
+that of the earlier linear-time test, and matches or exceeds the power of a
+quadratic-time kernel test. In high dimensions and where model structure may be
+exploited, our goodness of fit test performs far better than a quadratic-time
+two-sample test based on the Maximum Mean Discrepancy, with samples drawn from
+the model.
+"
+28913,Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning,"  Reinforcement learning is widely used for dialogue policy optimization where
+the reward function often consists of more than one component, e.g., the
+dialogue success and the dialogue length. In this work, we propose a structured
+method for finding a good balance between these components by searching for the
+optimal reward component weighting. To render this search feasible, we use
+multi-objective reinforcement learning to significantly reduce the number of
+training dialogues required. We apply our proposed method to find optimized
+component weights for six domains and compare them to a default baseline.
+"
+28914,Distributed SAGA: Maintaining linear convergence rate with limited communication,"  In recent years, variance-reducing stochastic methods have shown great
+practical performance, exhibiting linear convergence rate when other stochastic
+methods offered a sub-linear rate. However, as datasets grow ever bigger and
+clusters become widespread, the need for fast distribution methods is pressing.
+We propose here a distribution scheme for SAGA which maintains a linear
+convergence rate, even when communication between nodes is limited.
+"
+28915,Derived equivalences and Kodaira fibers,"  We give necessary conditions for two (including non-reduced and multiple)
+Kodaira curves to be derived equivalent. We classify Fourier-Mukai partners of
+any reduced Kodaira curve. We prove that the derived category of singularities
+of any non-reduced and non-multiple Kodaira curve is idempotent complete.
+"
+28916,Frequency spectrum of an optical resonator in a curved spacetime,"  The effect of gravity and proper acceleration on the frequency spectrum of an
+optical resonator - both rigid or deformable - is considered in the framework
+of general relativity. The optical resonator is modeled either as a rod of
+matter connecting two mirrors or as a dielectric rod whose ends function as
+mirrors. Explicit expressions for the frequency spectrum are derived for the
+case that it is only perturbed slightly. For a deformable resonator, the
+perturbation of the frequency spectrum depends on the speed of sound in the rod
+supporting the mirrors. A connection is found to a relativistic concept of
+rigidity when the speed of sound approaches the speed of light. In contrast,
+the corresponding result for the assumption of Born rigidity is recovered when
+the speed of sound becomes infinite. The results presented in this article can
+be used as the basis for the description of optical and opto-mechanical systems
+in a curved spacetime. We apply our results to the examples of a uniformly
+accelerating resonator and an optical resonator in the gravitational field of a
+small moving sphere. Our approach is not limited to weak gravitational fields,
+which we exemplify by its application to the fictitious situation of an optical
+resonator falling into a black hole.
+"
+28917,Ultrahigh-order Maxwell solver with extreme scalability for electromagnetic PIC simulations of plasmas,"  The advent of massively parallel supercomputers, with their
+distributed-memory technology using many processing units, has favored the
+development of highly-scalable local low-order solvers at the expense of
+harder-to-scale global very high-order spectral methods. Indeed, FFT-based
+methods, which were very popular on shared memory computers, have been largely
+replaced by finite-difference (FD) methods for the solution of many problems,
+including plasmas simulations with electromagnetic Particle-In-Cell methods.
+For some problems, such as the modeling of so-called ""plasma mirrors"" for the
+generation of high-energy particles and ultra-short radiations, we have shown
+that the inaccuracies of standard FD-based PIC methods prevent the modeling on
+present supercomputers at sufficient accuracy. We demonstrate here that a new
+method, based on the use of local FFTs, enables ultrahigh-order accuracy with
+unprecedented scalability, and thus for the first time the accurate modeling of
+plasma mirrors in 3D.
+"
+28918,$GW$ vertex corrected calculations for molecular systems,"  Hedin's scheme is solved with the inclusion of the vertex function
+($GW\Gamma$) for a set of small molecules.
+The computational scheme allows for the consistent inclusion of the vertex
+both at the polarizability level and in the self-energy.
+A diagrammatic analysis shows that the self-energy formed with this
+four-point vertex does not lead to double counting of diagrams, that can be
+classified as direct ""bubbles"" and exchange diagrams.
+By removing the exchange diagrams from the self-energy, a simpler
+approximation is obtained, called $GW^{\rm{tc-tc}}$.
+Very good agreement with expensive wavefunction-based methods is obtained for
+both approximations.
+"
+28919,Log canonical degenerations of del Pezzo surfaces in Q-Gorenstein families,"  We classify del Pezzo surfaces of Picard number one with log canonical
+singularities admitting Q-Gorenstein smoothings.
+"
+28920,Shimura curves and the abc conjecture,"  We develop a general framework to study Szpiro's conjecture and the $abc$
+conjecture by means of Shimura curves and their maps to elliptic curves,
+introducing new techniques that allow us to obtain several unconditional
+results for these conjectures. We first prove various general results about
+modular and Shimura curves, including bounds for the Manin constant in the case
+of additive reduction, a detailed study of maps from Shimura curves to elliptic
+curves and comparisons between their degrees, and lower bounds for the
+Petersson norm of integral modular forms on Shimura curves. Our main
+applications for Szpiro's conjecture and the $abc$ conjecture include improved
+effective bounds for the Faltings height of elliptic curves over $\mathbb{Q}$
+in terms of the conductor, bounds for products of $p$-adic valuations of the
+discriminant of elliptic curves over $\mathbb{Q}$ which are polynomial on the
+conductor, and results that yield a modular approach to Szpiro's conjecture
+over totally real number fields with the expected dependence on the
+discriminant of the field. These applications lie beyond the scope of previous
+techniques in the subject. A main difficulty in the theory is the lack of
+$q$-expansions, which we overcome by making essential use of suitable integral
+models and CM points. Our proofs require a number of tools from Arakelov
+geometry, analytic number theory, Galois representations, complex-analytic
+estimates on Shimura curves, automorphic forms, known cases of the Colmez
+conjecture, and results on generalized Fermat equations.
+"
+28921,Trace reconstruction with varying deletion probabilities,"  In the trace reconstruction problem an unknown string ${\bf
+x}=(x_0,\dots,x_{n-1})\in\{0,1,...,m-1\}^n$ is observed through the deletion
+channel, which deletes each $x_k$ with a certain probability, yielding a
+contracted string $\widetilde{\bf X}$. Earlier works have proved that if each
+$x_k$ is deleted with the same probability $q\in[0,1)$, then $\exp(O(n^{1/3}))$
+independent copies of the contracted string $\widetilde{\bf X}$ suffice to
+reconstruct $\bf x$ with high probability. We extend this upper bound to the
+setting where the deletion probabilities vary, assuming certain regularity
+conditions. First we consider the case where $x_k$ is deleted with some known
+probability $q_k$. Then we consider the case where each letter $\zeta\in
+\{0,1,...,m-1\}$ is associated with some possibly unknown deletion probability
+$q_\zeta$.
+"
+28922,Deep Functional Maps: Structured Prediction for Dense Shape Correspondence,"  We introduce a new framework for learning dense correspondence between
+deformable 3D shapes. Existing learning based approaches model shape
+correspondence as a labelling problem, where each point of a query shape
+receives a label identifying a point on some reference domain; the
+correspondence is then constructed a posteriori by composing the label
+predictions of two input shapes. We propose a paradigm shift and design a
+structured prediction model in the space of functional maps, linear operators
+that provide a compact representation of the correspondence. We model the
+learning process via a deep residual network which takes dense descriptor
+fields defined on two shapes as input, and outputs a soft map between the two
+given objects. The resulting correspondence is shown to be accurate on several
+challenging benchmarks comprising multiple categories, synthetic models, real
+scans with acquisition artifacts, topological noise, and partiality.
+"
+28923,Applying High-Resolution Visible Imagery to Satellite Melt Pond Fraction Retrieval: A Neural Network Approach,"  During summer, melt ponds have a significant influence on Arctic sea-ice
+albedo. The melt pond fraction (MPF) also has the ability to forecast the
+Arctic sea-ice in a certain period. It is important to retrieve accurate melt
+pond fraction (MPF) from satellite data for Arctic research. This paper
+proposes a satellite MPF retrieval model based on the multi-layer neural
+network, named MPF-NN. Our model uses multi-spectral satellite data as model
+input and MPF information from multi-site and multi-period visible imagery as
+prior knowledge for modeling. It can effectively model melt ponds evolution of
+different regions and periods over the Arctic. Evaluation results show that the
+MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and
+a correlation coefficient of 0.73. The seasonal distribution of MPF is also
+consistent with previous results.
+"
+28924,Tangential deflection and formation of counterstreaming flows at the impact of a plasma jet on a tangential discontinuity,"  In this letter we report three-dimensional particle-in-cell simulations of
+the interaction between a nonpenetrable magnetosheath jet and the magnetopause,
+for northward interplanetary magnetic field. The magnetopause is modeled as a
+tangential discontinuity with no magnetic shear. We investigate the deflection
+of the plasma jet in the direction tangential to the magnetopause. We find that
+as the frontal edge of the jet interacts with the magnetopause, the electrons
+and ions are scattered in opposite directions, tangential to the magnetopause,
+by the energy-dependent gradient-B drift. This effect is more effective on the
+nonthermal particles that tend to accumulate at the two sides of the jet and
+sustain a polarization electric field in the direction normal to the
+discontinuity surface. The electric drift of the bulk of particles under the
+action of this polarization electric field explains the deflection and
+counterstreaming at the impact of the plasma jet on the tangential
+discontinuity.
+"
+28925,A characterization of the canonical extension of Boolean homomorphisms,"  This article aims to obtain a characterization of the canonical extension of
+Boolean homomorphisms through the Stone-Čech compactification. Then, we
+will show that one-to-one homomorphisms and onto homomorphisms extend to
+one-to-one homomorphisms and onto homomorphisms, respectively.
+"
+28926,Spatial structure of several diffuse interstellar band carriers,"  Diffuse interstellar bands (DIBs) hold a lot of information about the state
+and the structure of the ISM. Structure can most directly be observed by
+extensive spectroscopic surveys, including surveys of stars where DIBs are
+especially important, as they are conveniently found in all observed bands.
+Large surveys lack the quality of spectra to detect weak DIBs, so many spectra
+from small regions on the sky have to be combined before a sufficient
+signal-to-noise ratio (SNR) is achieved. However, the clumpiness of the DIB
+clouds is unknown, which poses a problem, as the measured properties can end up
+being averaged over a too large area. We use a technique called Gaussian
+processes to accurately measure profiles of interstellar absorption lines in
+145 high SNR and high resolution spectra of hot stars. Together with Bayesian
+MCMC approach we also get reliable estimates of the uncertainties. We derive
+scales at which column densities of 18 DIBs, CH, CH$^+$, Ca I, and Ca II show
+some spatial correlation. This correlation scale is associated with the size of
+the ISM clouds. Scales expressed as the angle on the sky vary significantly
+from DIB to DIB between $\sim0.23^\circ$ for the DIB at 5512 {\AA} and
+3.5$^\circ$ for the DIB at 6196 {\AA}, suggesting that different DIB carriers
+have different clumpiness but occupy the same general space. Our study includes
+lines-of-sight all over the northern Milky Way, as well as out of the Galactic
+plane, covering regions with different physical conditions. The derived
+correlation scales therefore represent a general image of the Galactic ISM on
+the scales of $\sim5$ pc to $100$ pc.
+"
+28927,Emergent magnetic degeneracy in iron pnictides due to the interplay between spin-orbit coupling and quantum fluctuations,"  Recent experiments in iron pnictide superconductors reveal that, as the
+putative magnetic quantum critical point is approached, different types of
+magnetic order coexist over a narrow region of the phase diagram. Although
+these magnetic configurations share the same wave-vectors, they break distinct
+symmetries of the lattice. Importantly, the highest superconducting transition
+temperature takes place close to this proliferation of near-degenerate magnetic
+states. In this paper, we employ a renormalization group calculation to show
+that such a behavior naturally arises due to the effects of spin-orbit coupling
+on the quantum magnetic fluctuations. Formally, the enhanced magnetic
+degeneracy near the quantum critical point is manifested as a stable Gaussian
+fixed point with a large basin of attraction. Implications of our findings to
+the superconductivity of the iron pnictides are also discussed.
+"
+28928,Software for full-color 3D reconstruction of the biological tissues internal structure,"  A software for processing sets of full-color images of biological tissue
+histological sections is developed. We used histological sections obtained by
+the method of high-precision layer-by-layer grinding of frozen biological
+tissues. The software allows restoring the image of the tissue for an arbitrary
+cross-section of the tissue sample. Thus, our method is designed to create a
+full-color 3D reconstruction of the biological tissue structure. The resolution
+of 3D reconstruction is determined by the quality of the initial histological
+sections. The newly developed technology available to us provides a resolution
+of up to 5 - 10 {\mu}m in three dimensions.
+"
+28929,The slight spin of the old stellar halo,"  We combine Gaia data release 1 astrometry with Sloan Digital Sky Survey
+(SDSS) images taken some ~10-15 years earlier, to measure proper motions of
+stars in the halo of our Galaxy. The SDSS-Gaia proper motions have typical
+statistical errors of 2 mas/yr down to r ~ 20 mag, and are robust to variations
+with magnitude and colour. Armed with this exquisite set of halo proper
+motions, we identify RR Lyrae, blue horizontal branch (BHB), and K giant stars
+in the halo, and measure their net rotation with respect to the Galactic disc.
+We find evidence for a gently rotating prograde signal (< $V_\phi$ > ~ 5-25
+km/s) in the halo stars, which shows little variation with Galactocentric
+radius out to 50 kpc. The average rotation signal for the three populations is
+< $V_\phi$ > = 14 +/- 2 +/- 10 (syst.) km/s. There is also tentative evidence
+for a kinematic correlation with metallicity, whereby the metal richer BHB and
+K giant stars have slightly stronger prograde rotation than the metal poorer
+stars. Using the Auriga simulation suite we find that the old (T >10 Gyr) stars
+in the simulated halos exhibit mild prograde rotation, with little dependence
+on radius or metallicity, in general agreement with the observations. The weak
+halo rotation suggests that the Milky Way has a minor in situ halo component,
+and has undergone a relatively quiet accretion history.
+"
+28930,Fair Algorithms for Learning in Allocation Problems,"  Settings such as lending and policing can be modeled by a centralized agent
+allocating a resource (loans or police officers) amongst several groups, in
+order to maximize some objective (loans given that are repaid or criminals that
+are apprehended). Often in such problems fairness is also a concern. A natural
+notion of fairness, based on general principles of equality of opportunity,
+asks that conditional on an individual being a candidate for the resource, the
+probability of actually receiving it is approximately independent of the
+individual's group. In lending this means that equally creditworthy individuals
+in different racial groups have roughly equal chances of receiving a loan. In
+policing it means that two individuals committing the same crime in different
+districts would have roughly equal chances of being arrested.
+We formalize this fairness notion for allocation problems and investigate its
+algorithmic consequences. Our main technical results include an efficient
+learning algorithm that converges to an optimal fair allocation even when the
+frequency of candidates (creditworthy individuals or criminals) in each group
+is unknown. The algorithm operates in a censored feedback model in which only
+the number of candidates who received the resource in a given allocation can be
+observed, rather than the true number of candidates. This models the fact that
+we do not learn the creditworthiness of individuals we do not give loans to nor
+learn about crimes committed if the police presence in a district is low.
+As an application of our framework, we consider the predictive policing
+problem. The learning algorithm is trained on arrest data gathered from its own
+deployments on previous days, resulting in a potential feedback loop that our
+algorithm provably overcomes. We empirically investigate the performance of our
+algorithm on the Philadelphia Crime Incidents dataset.
+"
+28931,Multichannel Molecular State and Rectified Short-range Boundary Condition for Spin-orbit Coupled Ultracold Fermions Near p-wave Resonances,"  We study the interplay of spin-orbit coupling (SOC) and strong p-wave
+interaction to the scattering property of spin-1/2 ultracold Fermi gases. Based
+on a two-channel square-well potential generating p-wave resonance, we show
+that the presence of an isotropic SOC, even for its length much longer than the
+potential range, can greatly modify the p-wave short-range boundary
+condition(BC). As a result, the conventional p-wave BC cannot predict the
+induced molecules near p-wave resonance, which can be fully destroyed to vanish
+due to strong interference between s- and p-wave channels. By analyzing the
+intrinsic reasons for the breakdown of conventional BC, we propose a new p-wave
+BC that can excellently reproduce the exact molecule solutions and also equally
+apply for a wide class of single-particle potentials besides SOC. This work
+reveals the significant effect of SOC to both the short- and long-range
+properties of fermions near p-wave resonance, paving the way for future
+exploring interesting few- and many-body physics in such system.
+"
+28932,Monoidal computer III: A coalgebraic view of computability and complexity,"  Monoidal computer is a categorical model of intensional computation, where
+many different programs correspond to the same input-output behavior. The
+upshot of yet another model of computation is that a categorical formalism
+should provide a much needed high level language for theory of computation,
+flexible enough to allow abstracting away the low level implementation details
+when they are irrelevant, or taking them into account when they are genuinely
+needed. A salient feature of the approach through monoidal categories is the
+formal graphical language of string diagrams, which supports visual reasoning
+about programs and computations.
+In the present paper, we provide a coalgebraic characterization of monoidal
+computer. It turns out that the availability of interpreters and specializers,
+that make a monoidal category into a monoidal computer, is equivalent with the
+existence of a *universal state space*, that carries a weakly final state
+machine for any pair of input and output types. Being able to program state
+machines in monoidal computers allows us to represent Turing machines, to
+capture their execution, count their steps, as well as, e.g., the memory cells
+that they use. The coalgebraic view of monoidal computer thus provides a
+convenient diagrammatic language for studying computability and complexity.
+"
+28933,Competitive Equilibrium with Indivisible Goods and Generic Budgets,"  Competitive equilibrium from equal incomes (CEEI) is a classic solution to
+the problem of fair and efficient allocation of goods [Foley'67, Varian'74].
+Every agent receives an equal budget of artificial currency with which to
+purchase goods, and prices match demand and supply. However, a CEEI is not
+guaranteed to exist when the goods are indivisible, even in the simple
+two-agent, single-item market. Yet, it is easy to see that once the two budgets
+are slightly perturbed (made generic), a competitive equilibrium does exist.
+In this paper we aim to extend this approach beyond the single-item case, and
+study the existence of equilibria in markets with two agents and additive
+preferences over multiple items. We show that for agents with equal budgets,
+making the budgets generic -- by adding vanishingly small random perturbations
+-- ensures the existence of an equilibrium. We further consider agents with
+arbitrary non-equal budgets, representing non-equal entitlements for goods. We
+show that competitive equilibrium guarantees a new notion of fairness among
+non-equal agents, and that it exists in cases of interest (like when the agents
+have identical preferences) if budgets are perturbed. Our results open
+opportunities for future research on generic equilibrium existence and fair
+treatment of non-equals.
+"
+28934,Transversals in generalized Latin squares,"  We are seeking a sufficient condition that forces a transversal in a
+generalized Latin square. A generalized Latin square of order $n$ is equivalent
+to a proper edge-coloring of $K_{n,n}$. A transversal corresponds to a
+multicolored perfect matching. Akbari and Alipour defined $l(n)$ as the least
+integer such that every properly edge-colored $K_{n,n}$, which contains at
+least $l(n)$ different colors, admits a multicolored perfect matching. They
+conjectured that $l(n)\leq n^2/2$ if $n$ is large enough. In this note we prove
+that $l(n)$ is bounded from above by $0.75n^2$ if $n>1$. We point out a
+connection to anti-Ramsey problems. We propose a conjecture related to a
+well-known result by Woolbright and Fu, that every proper edge-coloring of
+$K_{2n}$ admits a multicolored $1$-factor.
+"
+28935,Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks,"  Surface Electromyography (sEMG) is to record muscles' electrical activity
+from a restricted area of the skin by using electrodes. The sEMG-based gesture
+recognition is extremely sensitive of inter-session and inter-subject
+variances. We propose a model and a deep-learning-based domain adaptation
+method to approximate the domain shift for recognition accuracy enhancement.
+Experiments on sparse and High-Density (HD) sEMG datasets validate that our
+approach outperforms state-of-the-art methods.
+"
+28936,On isolated singularities of Kirchhoff--type Laplacian problems,"  In this paper, we study isolated singular positive solutions for the
+following Kirchhoff--type Laplacian problem: \begin{equation*}
+-\left(\theta+\int_{\Omega} |\nabla u| dx\right)\Delta u =u^p \quad{\rm
+in}\quad \Omega\setminus \{0\},\qquad u=0\quad {\rm on}\quad \partial \Omega,
+\end{equation*} where $p>1$, $\theta\in \R$, $\Omega$ is a bounded smooth
+domain containing the origin in $\R^N$ with $N\ge 2$. In the subcritical case:
+$1<p<N/(N-2)$ if $N\ge3$, $1<p<+\infty$ if $N=2$, we employ the Schauder
+fixed-point theorem to derive a sequence of positive isolated singular
+solutions for the above problem such that $M_\theta(u)>0$. To estimate
+$M_\theta(u)$, we make use of the rearrangement argument. Furthermore, we
+obtain a sequence of isolated singular solutions such that $M_\theta(u)<0$, by
+analyzing relationship between the parameter $\lambda$ and the unique solution
+$u_\lambda$ of $$-\Delta u+\lambda u^p=k\delta_0\quad{\rm in}\quad
+B_1(0),\qquad u=0\quad {\rm on}\quad \partial B_1(0).$$ In the supercritical
+case: $N/(N-2)\le p<(N+2)/(N-2)$ with $N\ge3$, we obtain two isolated singular
+solutions $u_i$ with $i=1,2$ such that $M_\theta(u_i)>0$ under some appropriate
+assumptions.
+"
+28937,Sublinear extension of Grothendieck Algebraic K--theory (This paper is dedicated to the 80-th anniversary of Yuri I. Manin),"  A commutative diagram that connects the basic objects of commutative algebra
+with the main objects of commutative analysis is constructed. Namely, with the
+help of five types of canonical embeddings we constructed a diagram between two
+sets of objects: Abelian semigroups -- Abelian regular (cancellative)
+semigroups -- Abelian groups, on the first hand, and convex cones -- regular
+convex cones -- linear spaces, on the other hand. Thus, some extension of the
+Grothendieck algebraic K--theory arises, that includes the basic objects not
+only of linear (smooth) analysis but of sublinear (nonsmooth) analysis also.
+"
+28938,A Clustering-based Consistency Adaptation Strategy for Distributed SDN Controllers,"  Distributed controllers are oftentimes used in large-scale SDN deployments
+where they run a myriad of network applications simultaneously. Such
+applications could have different consistency and availability preferences.
+These controllers need to communicate via east/west interfaces in order to
+synchronize their state information. The consistency and the availability of
+the distributed state information are governed by an underlying consistency
+model. Earlier, we suggested the use of adaptively-consistent controllers that
+can autonomously tune their consistency parameters in order to meet the
+performance requirements of a certain application. In this paper, we examine
+the feasibility of employing adaptive controllers that are built on-top of
+tunable consistency models similar to that of Apache Cassandra. We present an
+adaptation strategy that uses clustering techniques (sequential k-means and
+incremental k-means) in order to map a given application performance indicator
+into a feasible consistency level that can be used with the underlying tunable
+consistency model. In the cases that we modeled and tested, our results show
+that in the case of sequential k-means, with a reasonable number of clusters
+(>= 50), a plausible mapping (low RMSE) could be estimated between the
+application performance indicators and the consistency level indicator. In the
+case of incremental k-means, the results also showed that a plausible mapping
+(low RMSE) could be estimated using a similar number of clusters (>= 50) by
+using a small threshold (~$ 0.01).
+"
+28939,Non-isothermal physical and chemical processes in superfluid helium,"  Metal atoms and small clusters introduced into superfluid helium (He II)
+concentrate there in quantized vortices to form (by further coagulation) the
+thin nanowires. The nanowires' thickness and structure are well predicted by a
+double-staged mechanism. On the first stage the coagulation of cold particles
+in the vortex cores leads to melting of their fusion product, which acquires a
+spherical shape due to surface tension. Then (second stage) when these
+particles reach a certain size they do not possess sufficient energy to melt
+and eventually coalesce into the nanowires. Nevertheless the assumption of
+melting for such refractory metal as tungsten, especially in He II, which
+possesses an extremely high thermal conductivity, induces natural skepticism.
+That is why we decided to register directly the visible thermal emission
+accompanying metals coagulation in He II. The brightness temperatures of this
+radiation for the tungsten, molybdenum, and platinum coagulation were found to
+be noticeably higher than even the metals' melting temperatures. The region of
+He II that contained suspended metal particles expanded with the velocity of 50
+m/s, being close to the Landau velocity, but coagulation took place even more
+quickly, so that the whole process of nanowire growth is completed at distances
+about 1.5 mm from the place of metal injection into He II. High rate of
+coagulation of guest metal particles as well as huge local overheating are
+associated with them concentrating in quantized vortex cores. The same process
+should take place not only for metals but for any atoms, molecules and small
+clusters embedded into He II.
+"
+28940,General Scattering Characteristics of Resonant Core-Shell Spheres,"  This article presents and discusses the general features and aspects
+regarding the electromagnetic scattering by a small core-shell plasmonic
+sphere. First, the thickness effects on the plasmonic resonances are presented
+in the electrostatic (Rayleigh) limit, utilizing the MacLaurin expansion of the
+Mie coefficients of hollow scatterers. Several aspects regarding the core
+effects are given, illustrating the enabling mechanisms and peculiarities of
+its resonant scattering response on it electrostatic limit. The electrodynamic
+aspects of the scattering process are revealed through the newly introduced
+Padé expansion of the Mie coefficients. Additionally we expose how the core
+material affects the dynamic mechanisms, such as the dynamic depolarization and
+radiative damping. The described method can be expanded for other type of
+resonances and canonical shapes, while the general characteristics presented
+here are expected to stimulate further studies regarding the functionalities of
+the core-shell scatterers.
+"
+28941,Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization,"  This work studies how an AI-controlled dog-fighting agent with tunable
+decision-making parameters can learn to optimize performance against an
+intelligent adversary, as measured by a stochastic objective function evaluated
+on simulated combat engagements. Gaussian process Bayesian optimization (GPBO)
+techniques are developed to automatically learn global Gaussian Process (GP)
+surrogate models, which provide statistical performance predictions in both
+explored and unexplored areas of the parameter space. This allows a learning
+engine to sample full-combat simulations at parameter values that are most
+likely to optimize performance and also provide highly informative data points
+for improving future predictions. However, standard GPBO methods do not provide
+a reliable surrogate model for the highly volatile objective functions found in
+aerial combat, and thus do not reliably identify global maxima. These issues
+are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multi-point
+Sampling (HRMS) techniques. Simulation studies show that HRMS improves the
+accuracy of GP surrogate models, allowing AI decision-makers to more accurately
+predict performance and efficiently tune parameters.
+"
+28942,Monoidality of Kato's Reflection Functors,"  Kato has constructed reflection functors for KLR algebras which categorify
+the braid group action on a quantum group by algebra automorphisms. We prove
+that these reflection functors are monoidal.
+"
+28943,"Stable, high-performance operation of a fiber-coupled superconducting nanowire avalanche photon detector","  We present a stable and high-performance fiber-coupled NbTiN superconducting
+nanowire avalanche photon detector (SNAP). We demonstrate afterpulse-free
+operation in serially connected two SNAPs (SC-2SNAP), even in the absence of a
+choke inductor, achieving a 7.7 times faster response speed than standard
+SSPDs. The SC-2SNAP device showed a system detection efficiency (SDE) of 81.0%
+with wide bias current margin, a dark count rate of 6.8 counts/s, and full
+width at half maximum timing jitter of 68 ps, operating at 2.3 K.
+"
+28944,Thermal transport in a two-dimensional $\mathbb{Z}_2$ spin liquid,"  We study the dynamical thermal conductivity of the two-dimensional Kitaev
+spin-model on the honeycomb lattice. We find a strongly temperature dependent
+low-frequency spectral intensity as a direct consequence of fractionalization
+of spins into mobile Majorana matter and a static $\mathbb{Z}_{2}$ gauge field.
+The latter acts as an emergent thermally activated disorder, leading to the
+appearance of a pseudogap which partially closes in the thermodynamic limit,
+indicating a dissipative heat conductor. Our analysis is based on complementary
+calculations of the current correlation function, comprising exact
+diagonalization by means of a complete summation over all gauge sectors, as
+well as a phenomenological mean-field treatment of thermal gauge fluctuations,
+valid at intermediate and high temperatures. The results will also be
+contrasted against the conductivity discarding gauge fluctuations.
+"
+28945,Plus-Minus Player Ratings for Soccer,"  The paper presents a plus-minus rating for use in association football
+(soccer). We first describe the standard plus-minus methodology as used in
+basketball and ice-hockey and then adapt it for use in soccer. The usual
+goal-differential plus-minus is considered before two variations are proposed.
+For the first variation, we present a methodology to calculate an expected
+goals plus-minus rating. The second variation makes use of in-play
+probabilities of match outcome to evaluate an expected points plus-minus
+rating. We use the ratings to examine who are the best players in European
+football, and demonstrate how the players' ratings evolve over time. Finally,
+we shed light on the debate regarding which is the strongest league. The model
+suggests the English Premier League is the strongest, with the German
+Bundesliga a close runner-up.
+"
+28946,RIoTBench: A Real-time IoT Benchmark for Distributed Stream Processing Platforms,"  The Internet of Things (IoT) is an emerging technology paradigm where
+millions of sensors and actuators help monitor and manage, physical,
+environmental and human systems in real-time. The inherent closedloop
+responsiveness and decision making of IoT applications make them ideal
+candidates for using low latency and scalable stream processing platforms.
+Distributed Stream Processing Systems (DSPS) hosted on Cloud data-centers are
+becoming the vital engine for real-time data processing and analytics in any
+IoT software architecture. But the efficacy and performance of contemporary
+DSPS have not been rigorously studied for IoT applications and data streams.
+Here, we develop RIoTBench, a Realtime IoT Benchmark suite, along with
+performance metrics, to evaluate DSPS for streaming IoT applications. The
+benchmark includes 27 common IoT tasks classified across various functional
+categories and implemented as reusable micro-benchmarks. Further, we propose
+four IoT application benchmarks composed from these tasks, and that leverage
+various dataflow semantics of DSPS. The applications are based on common IoT
+patterns for data pre-processing, statistical summarization and predictive
+analytics. These are coupled with four stream workloads sourced from real IoT
+observations on smart cities and fitness, with peak streams rates that range
+from 500 to 10000 messages/sec and diverse frequency distributions. We validate
+the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure
+public Cloud, and present empirical observations. This suite can be used by
+DSPS researchers for performance analysis and resource scheduling, and by IoT
+practitioners to evaluate DSPS platforms.
+"
+28947,Active Perception and Modeling of Deformable Surfaces using Gaussian Processes and Position-based Dynamics,"  Exploring and modeling heterogeneous elastic surfaces requires multiple
+interactions with the environment and a complex selection of physical material
+parameters. The most common approaches model deformable properties from sets of
+offline observations using computationally expensive force-based simulators. In
+this work we present an online probabilistic framework for autonomous
+estimation of a deformability distribution map of heterogeneous elastic
+surfaces from few physical interactions. The method takes advantage of Gaussian
+Processes for constructing a model of the environment geometry surrounding a
+robot. A fast Position-based Dynamics simulator uses focused environmental
+observations in order to model the elastic behavior of portions of the
+environment. Gaussian Process Regression maps the local deformability on the
+whole environment in order to generate a deformability distribution map. We
+show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm
+and an Optoforce sensor on different deformable surfaces.
+"
+28948,Factorized Runge-Kutta-Chebyshev Methods,"  The second-order extended stability Factorized Runge-Kutta-Chebyshev (FRKC2)
+class of explicit schemes for the integration of large systems of PDEs with
+diffusive terms is presented. FRKC2 schemes are straightforward to implement
+through ordered sequences of forward Euler steps with complex stepsizes, and
+easily parallelised for large scale problems on distributed architectures.
+Preserving 7 digits for accuracy at 16 digit precision, the schemes are
+theoretically capable of maintaining internal stability at acceleration factors
+in excess of 6000 with respect to standard explicit Runge-Kutta methods. The
+stability domains have approximately the same extents as those of RKC schemes,
+and are a third longer than those of RKL2 schemes. Extension of FRKC methods to
+fourth-order, by both complex splitting and Butcher composition techniques, is
+discussed.
+A publicly available implementation of the FRKC2 class of schemes may be
+obtained from maths.dit.ie/frkc
+"
+28949,Distinguishing between convergent evolution and violation of the molecular clock,"  We give a non-technical introduction to convergence-divergence models, a new
+modeling approach for phylogenetic data that allows for the usual divergence of
+species post speciation but also allows for species to converge, i.e. become
+more similar over time. By examining the $3$-taxon case in some detail we
+illustrate that phylogeneticists have been ""spoiled"" in the sense of not having
+to think about the structural parameters in their models by virtue of the
+strong assumption that evolution is treelike. We show that there are not always
+good statistical reasons to prefer the usual class of treelike models over more
+general convergence-divergence models. Specifically we show many $3$-taxon
+datasets can be equally well explained by supposing violation of the molecular
+clock due to change in the rate of evolution along different edges, or by
+keeping the assumption of a constant rate of evolution but instead assuming
+that evolution is not a purely divergent process. Given the abundance of
+evidence that evolution is not strictly treelike, our discussion is an
+illustration that as phylogeneticists we often need to think clearly about the
+structural form of the models we use.
+"
+28950,SPH simulation of fuel drop impact on heated surfaces,"  The interaction of liquid drops and heated surfaces is of great importance in
+many applications. This paper describes a numerical method, based on smoothed
+particle hydrodynamics (SPH), for simulating n-heptane drop impact on a heated
+surface. The SPH method uses numerical Lagrangian particles, which obey the
+laws of fluid dynamics, to describe the fluid flows. By incorporating the
+Peng-Robinson equation of state, the present SPH method can directly simulate
+both the liquid and vapor phases and the phase change process between them. The
+numerical method was validated by two experiments on drop impact on heated
+surfaces at low impact velocities. The numerical method was then used to
+predict drop-wall interactions at various temperatures and velocities. The
+model was able to predict the different outcomes, such as rebound, spread,
+splash, breakup, and the Leidenfrost phenomenon, consistent with the physical
+understanding.
+"
+28951,An iBeacon based Proximity and Indoor Localization System,"  Indoor localization and Location Based Services (LBS) can greatly benefit
+from the widescale proliferation of communication devices. The basic
+requirements of a system that can provide the aforementioned services are
+energy efficiency, scalability, lower costs, wide reception range, high
+localization accuracy and availability. Different technologies such as WiFi,
+UWB, RFID have been leveraged to provide LBS and Proximity Based Services
+(PBS), however they do not meet the aforementioned requirements. Apple's
+Bluetooth Low Energy (BLE) based iBeacon solution primarily intends to provide
+Proximity Based Services (PBS). However, it suffers from poor proximity
+detection accuracy due to its reliance on Received Signal Strength Indicator
+(RSSI) that is prone to multipath fading and drastic fluctuations in the indoor
+environment. Therefore, in this paper, we present our iBeacon based accurate
+proximity and indoor localization system. Our two algorithms Server-Side
+Running Average (SRA) and Server-Side Kalman Filter (SKF) improve the proximity
+detection accuracy of iBeacons by 29% and 32% respectively, when compared with
+Apple's current moving average based approach. We also present our novel
+cascaded Kalman Filter-Particle Filter (KFPF) algorithm for indoor
+localization. Our cascaded filter approach uses a Kalman Filter (KF) to reduce
+the RSSI fluctuation and then inputs the filtered RSSI values into a Particle
+Filter (PF) to improve the accuracy of indoor localization. Our experimental
+results, obtained through experiments in a space replicating real-world
+scenario, show that our cascaded filter approach outperforms the use of only PF
+by 28.16% and 25.59% in 2-Dimensional (2D) and 3-Dimensional (3D) environments
+respectively, and achieves a localization error as low as 0.70 meters in 2D
+environment and 0.947 meters in 3D environment.
+"
+28952,Reverse Engineering Human Mobility in Large-scale Natural Disasters,"  Delay/Disruption-Tolerant Networks (DTNs) have been around for more than a
+decade and have especially been proposed to be used in scenarios where
+communication infrastructure is unavailable. In such scenarios, DTNs can offer
+a best-effort communication service by exploiting user mobility. Natural
+disasters are an important application scenario for DTNs when the cellular
+network is destroyed by natural forces. To assess the performance of such
+networks before deployment, we require appropriate knowledge of human mobility.
+In this paper, we address this problem by designing, implementing, and
+evaluating a novel mobility model for large-scale natural disasters. Due to the
+lack of GPS traces, we reverse-engineer human mobility of past natural
+disasters (focusing on 2010 Haiti earthquake and 2013 Typhoon Haiyan) by
+leveraging knowledge of 126 experts from 71 Disaster Response Organizations
+(DROs). By means of simulation-based experiments, we compare and contrast our
+mobility model to other well-known models, and evaluate their impact on DTN
+performance. Finally, we make our source code available to the public.
+"
+28953,Robust Localization of an Arbitrary Distribution of Radioactive Sources for Aerial Inspection,"  Radiation source detection has seen various applications in the past decade,
+ranging from the detection of dirty bombs in public places to scanning critical
+nuclear facilities for leakage or flaws, and in the autonomous inspection of
+nuclear sites. Despite the success in detecting single point sources or a small
+number of spatially separated point sources, most of the existing algorithms
+fail to localize sources in complex scenarios with a large number of point
+sources or non-trivial distributions & bulk sources. Even in simpler
+environments, most existing algorithms are not scalable to larger regions
+and/or higher dimensional spaces. For effective autonomous inspection, we not
+only need to estimate the positions of the sources, but also the number,
+distribution, and intensities of each of them. In this paper, we present a
+novel algorithm for the robust localization of an arbitrary distribution of
+radiation sources using multi-layer sequential Monte Carlo methods coupled with
+suitable clustering algorithms. We achieve near-perfect accuracy, in terms of
+F1-scores (> 0.95), while allowing the algorithm to scale, both to large
+regions in space and to higher dimensional spaces (5 tested).
+"
+28954,Network Cache Design under Stationary Requests: Exact Analysis and Poisson Approximation,"  The design of caching algorithms to maximize hit probability has been
+extensively studied. In this paper, we associate each content with a utility,
+which is a function of either corresponding content hit rate or hit
+probability. We formulate a cache optimization problem to maximize the sum of
+utilities over all contents under stationary and ergodic request process, which
+is non-convex in general. We find that the problem can be reformulated as a
+convex optimization problem when the inter-request distribution has a
+non-increasing hazard rate function. We provide explicit optimal solutions for
+some inter-request distributions, and compare the solutions of the hit-rate
+based (HRB) and hit-probability based (HPB) problems. We also propose
+decentralized algorithms that can be implemented using limited information and
+are guaranteed to provide optimal solutions. We find that decentralized
+algorithms that solve HRB are more robust than decentralized HPB algorithms.
+Informed by these results, we further propose lightweight Poisson approximate
+decentralized and online algorithms that are accurate and efficient in
+achieving optimal hit rates and hit probabilities.
+"
+28955,"Online Controlled Experiments for Personalised e-Commerce Strategies: Design, Challenges, and Pitfalls","  Online controlled experiments are the primary tool for measuring the causal
+impact of product changes in digital businesses. It is increasingly common for
+digital products and services to interact with customers in a personalised way.
+Using online controlled experiments to optimise personalised interaction
+strategies is challenging because the usual assumption of statistically
+equivalent user groups is violated. Additionally, challenges are introduced by
+users qualifying for strategies based on dynamic, stochastic attributes.
+Traditional A/B tests can salvage statistical equivalence by pre-allocating
+users to control and exposed groups, but this dilutes the experimental metrics
+and reduces the test power. We present a stacked incrementality test framework
+that addresses problems with running online experiments for personalised user
+strategies. We derive bounds that show that our framework is superior to the
+best simple A/B test given enough users and that this condition is easily met
+for large scale online experiments. In addition, we provide a test power
+calculator and describe a selection of pitfalls and lessons learnt from our
+experience using it.
+"
+28956,Emotion-Recognition Using Smart Watch Accelerometer Data: Preliminary Findings,"  This study investigates the use of accelerometer data from a smart watch to
+infer an individual's emotional state. We present our preliminary findings on a
+user study with 50 participants. Participants were primed either with
+audio-visual (movie clips) or audio (classical music) to elicit emotional
+responses. Participants then walked while wearing a smart watch on one wrist
+and a heart rate strap on their chest. Our hypothesis is that the accelerometer
+signal will exhibit different patterns for participants in response to
+different emotion priming. We divided the accelerometer data using sliding
+windows, extracted features from each window, and used the features to train
+supervised machine learning algorithms to infer an individual's emotion from
+their walking pattern. Our discussion includes a description of the
+methodology, data collected, and early results.
+"
+28957,Negative differential response in chemical reactions,"  Reaction currents in chemical networks usually increase when increasing their
+driving affinities. But far from equilibrium the opposite can also happen. We
+find that such negative differential response (NDR) occurs in reaction schemes
+of major biological relevance, namely, substrate inhibition and autocatalysis.
+We do so by deriving the full counting statistics of two minimal representative
+models using large deviation methods. We argue that NDR implies the existence
+of optimal affinities that maximize the robustness against environmental and
+intrinsic noise at intermediate values of dissipation. An analogous behavior is
+found in dissipative self-assembly, for which we identify the optimal working
+conditions set by NDR.
+"
+28958,Vehicle Routing with Drones,"  We introduce a package service model where trucks as well as drones can
+deliver packages. Drones can travel on trucks or fly; but while flying, drones
+can only carry one package at a time and have to return to a truck to charge
+after each delivery. We present a heuristic algorithm to solve the problem of
+finding a good schedule for all drones and trucks. The algorithm is based on
+two nested local searches, thus the definition of suitable neighbourhoods of
+solutions is crucial for the algorithm. Empirical tests show that our algorithm
+performs significantly better than a natural Greedy algorithm. Moreover, the
+savings compared to solutions without drones turn out to be substantial,
+suggesting that delivery systems might considerably benefit from using drones
+in addition to trucks.
+"
+28959,Symmetric Implication Zroupoids and Weak Associative Laws,"  An algebra $\mathbf A = \langle A, \to, 0 \rangle$, where $\to$ is binary and
+$0$ is a constant, is called an implication zroupoid ($\mathcal I$-zroupoid,
+for short) if $\mathbf A$ satisfies the identities: $(x \to y) \to z \approx
+((z' \to x) \to (y \to z)')'$ and $0'' \approx 0$, where $x' : = x \to 0$. An
+implication zroupoid is symmetric if it satisfies $x'' \approx x$ and $(x \to
+y')' \approx (y \to x')'$. The variety of symmetric $\mathcal I$-zroupoids is
+denoted by $\mathcal S$. We began a systematic analysis of weak associative
+laws of length $\leq 4$ in [CS16e], by examining the identities of Bol-Moufang
+type in the context of the variety $\mathcal S$. In this paper we complete the
+analysis by investigating the rest of the weak associative laws of length $\leq
+4$ relative to $\mathcal S$. We show that, of the 155 subvarieties of $\mathcal
+S$ defined by the weak associative laws of size $\leq 4$, there are exactly $6$
+distinct ones. We also give an explicit description of the poset of the
+(distinct) subvarieties of $\mathcal S$ defined by weak associative laws of
+length $\leq 4$.
+"
+28960,The Dawn of FIMP Dark Matter: A Review of Models and Constraints,"  We present an overview of scenarios where the observed Dark Matter (DM)
+abundance consists of Feebly Interacting Massive Particles (FIMPs), produced
+non-thermally by the so-called freeze-in mechanism. In contrast to the usual
+freeze-out scenario, frozen-in FIMP DM interacts very weakly with the particles
+in the visible sector and never attained thermal equilibrium with the
+baryon-photon fluid in the early Universe. Instead of being determined by its
+annihilation strength, the DM abundance depends on the decay and annihilation
+strengths of particles in equilibrium with the baryon-photon fluid, as well as
+couplings in the DM sector. This makes frozen-in DM very difficult but not
+impossible to test. In this review, we present the freeze-in mechanism and its
+variations considered in the literature (dark freeze-out and reannihilation),
+compare them to the standard DM freeze-out scenario, discuss several aspects of
+model building, and pay particular attention to observational properties and
+general testability of such feebly interacting DM.
+"
+28961,Jet formation in the interaction of localized waves on the free surface of dielectric liquid in a tangential electric field,"  Nonlinear dynamics of the free surface of finite depth non-conducting fluid
+with high dielectric constant subjected to a strong horizontal electric field
+is considered. Using the conformal transformation of the region occupied by the
+fluid into a strip, the process of counter-propagating waves interaction is
+numerically simulated. The nonlinear solitary waves on the surface can
+separately propagate along or against the direction of electric field without
+distortion. At the same time, the shape of the oppositely traveling waves can
+be distorted as the result of their interaction. In the problem under study,
+the nonlinearity leads to increasing the waves amplitudes and the duration of
+their interaction. This effect is inversely proportional to the fluid depth. In
+the shallow water limit, the tendency to the formation of a vertical liquid jet
+is observed.
+"
+28962,Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization,"  One of the most common approaches for multiobjective optimization is to
+generate a solution set that well approximates the whole Pareto-optimal
+frontier to facilitate the later decision-making process. However, how to
+evaluate and compare the quality of different solution sets remains
+challenging. Existing measures typically require additional problem knowledge
+and information, such as a reference point or a substituted set of the
+Pareto-optimal frontier. In this paper, we propose a quality measure, called
+dominance move (DoM), to compare solution sets generated by multiobjective
+optimizers. Given two solution sets, DoM measures the minimum sum of move
+distances for one set to weakly Pareto dominate the other set. DoM can be seen
+as a natural reflection of the difference between two solutions, capturing all
+aspects of solution sets' quality, being compliant with Pareto dominance, and
+does not need any additional problem knowledge and parameters. We present an
+exact method to calculate the DoM in the biobjective case. We show the
+necessary condition of constructing the optimal partition for a solution set's
+minimum move, and accordingly propose an efficient algorithm to recursively
+calculate the DoM. Finally, DoM is evaluated on several groups of artificial
+and real test cases as well as by a comparison with two well-established
+quality measures.
+"
+28963,Lifelong Learning in Costly Feature Spaces,"  An important long-term goal in machine learning systems is to build learning
+agents that, like humans, can learn many tasks over their lifetime, and
+moreover use information from these tasks to improve their ability to do so
+efficiently. In this work, our goal is to provide new theoretical insights into
+the potential of this paradigm. In particular, we propose a lifelong learning
+framework that adheres to a novel notion of resource efficiency that is
+critical in many real-world domains where feature evaluations are costly. That
+is, our learner aims to reuse information from previously learned related tasks
+to learn future tasks in a feature-efficient manner. Furthermore, we consider
+novel combinatorial ways in which learning tasks can relate. Specifically, we
+design lifelong learning algorithms for two structurally different and widely
+used families of target functions: decision trees/lists and
+monomials/polynomials. We also provide strong feature-efficiency guarantees for
+these algorithms; in fact, we show that in order to learn future targets, we
+need only slightly more feature evaluations per training example than what is
+needed to predict on an arbitrary example using those targets. We also provide
+algorithms with guarantees in an agnostic model where not all the targets are
+related to each other. Finally, we also provide lower bounds on the performance
+of a lifelong learner in these models, which are in fact tight under some
+conditions.
+"
+28964,Long range forces in a performance portable Molecular Dynamics framework,"  Molecular Dynamics (MD) codes predict the fundamental properties of matter by
+following the trajectories of a collection of interacting model particles. To
+exploit diverse modern manycore hardware, efficient codes must use all
+available parallelism. At the same time they need to be portable and easily
+extendible by the domain specialist (physicist/chemist) without detailed
+knowledge of this hardware. To address this challenge, we recently described a
+new Domain Specific Language (DSL) for the development of performance portable
+MD codes based on a ""Separation of Concerns"": a Python framework automatically
+generates efficient parallel code for a range of target architectures.
+Electrostatic interactions between charged particles are important in many
+physical systems and often dominate the runtime. Here we discuss the inclusion
+of long-range interaction algorithms in our code generation framework. These
+algorithms require global communications and careful consideration has to be
+given to any impact on parallel scalability. We implemented an Ewald summation
+algorithm for electrostatic forces, present scaling comparisons for different
+system sizes and compare to the performance of existing codes. We also report
+on further performance optimisations delivered with OpenMP shared memory
+parallelism.
+"
+28965,On the global well-posedness of 3-D axi-symmetric Navier-Stokes system with small swirl component,"  In this paper, we prove the local well-posedness of 3-D axi-symmetric
+Navier-Stokes system with initial data in the critical Lebesgue spaces. We also
+obtain the global well-posedness result with small initial data. Furthermore,
+with the initial swirl component of the velocity being sufficiently small in
+the almost critical spaces, we can still prove the global well-posedness of the
+system.
+"
+28966,Mining Object Parts from CNNs via Active Question-Answering,"  Given a convolutional neural network (CNN) that is pre-trained for object
+classification, this paper proposes to use active question-answering to
+semanticize neural patterns in conv-layers of the CNN and mine part concepts.
+For each part concept, we mine neural patterns in the pre-trained CNN, which
+are related to the target part, and use these patterns to construct an And-Or
+graph (AOG) to represent a four-layer semantic hierarchy of the part. As an
+interpretable model, the AOG associates different CNN units with different
+explicit object parts. We use an active human-computer communication to
+incrementally grow such an AOG on the pre-trained CNN as follows. We allow the
+computer to actively identify objects, whose neural patterns cannot be
+explained by the current AOG. Then, the computer asks human about the
+unexplained objects, and uses the answers to automatically discover certain CNN
+patterns corresponding to the missing knowledge. We incrementally grow the AOG
+to encode new knowledge discovered during the active-learning process. In
+experiments, our method exhibits high learning efficiency. Our method uses
+about 1/6-1/3 of the part annotations for training, but achieves similar or
+better part-localization performance than fast-RCNN methods.
+"
+28967,Electrical Transport of perpendicularly magnetized L10-MnGa and MnAl films,"  Ferromagnetic films of L10-ordered MnGa and MnAl that exhibit giant
+perpendicular magnetic anisotropy and great controllability in the magnetism
+and structural disorders show promise not only in the applications in magnetic
+recording, permanent magnets and spintronics, but also in controllable studies
+of disorder-relevant electrical transport phenomena. In this article, we review
+the intriguing experimental observations of the orbital two-channel Kondo
+effect and anomalous Hall effect in L10-ordered MnGa and MnAl thin films with
+perpendicular magnetic anisotropy. We also give a perspective with regards to
+the future technological and fundamental applications of these perpendicularly
+magnetized Mn-based binary alloy films.
+"
+28968,Unsupervised Learning by Competing Hidden Units,"  It is widely believed that the backpropagation algorithm is essential for
+learning good feature detectors in early layers of artificial neural networks,
+so that these detectors are useful for the task performed by the higher layers
+of that neural network. At the same time, the traditional form of
+backpropagation is biologically implausible. In the present paper we propose an
+unusual learning rule, which has a degree of biological plausibility, and which
+is motivated by Hebb's idea that change of the synapse strength should be local
+- i.e. should depend only on the activities of the pre and post synaptic
+neurons. We design a learning algorithm that utilizes global inhibition in the
+hidden layer, and is capable of learning early feature detectors in a
+completely unsupervised way. These learned lower layer feature detectors can be
+used to train higher layer weights in a usual supervised way so that the
+performance of the full network is comparable to the performance of standard
+feedforward networks trained end-to-end with a backpropagation algorithm.
+"
+28969,A topological interpretation of three Leibnizian principles within the functional extensions,"  Three philosophical principles are often quoted in connection with Leibniz:
+""objects sharing the same properties are the same object"" (Identity of
+indiscernibles), ""everything can possibly exist, unless it yields
+contradiction"" (Possibility as consistency), and ""the ideal elements correctly
+determine the real things"" (Transfer). Here we give a precise
+logico-mathematical formulation of these principles within the framework of the
+Functional Extensions, mathematical structures that generalize at once
+compactifications, completions, and elementary extensions of models. In this
+context, the above Leibnizian principles appear as topological or algebraic
+properties, namely: a property of separation, a property of compactness, and a
+property of directeness, respectively. Abiding by this interpretation, we
+obtain the somehow surprising conclusion that these Leibnizian principles may
+be fulfilled in pairs, but not all three together.
+"
+28970,Quantum Biomimetic Modeling of Diamond NV$^{-}$ Center Spin Dynamics,"  The spin dynamics of the diamond NV$^{-}$ center turns out to be similar to
+that of the chemical compass responsible for avian magnetoreception. We present
+a simulation framework based on quantum master equation for the former that is
+based upon the Radical Pair model of the latter. We show that this framework
+captures all the experimentally studied behavior of the NV$^{-}$ center spin
+system and can therefore be a predictive modeling tool.
+"
+28971,Large-scale Stability and Astronomical Constraints for Coupled Dark-Energy Models,"  We study large-scale inhomogeneous perturbations and instabilities of
+interacting dark energy (IDE) models. Past analysis of large-scale perturbative
+instabilities, has shown that we can only test IDE models with observational
+data when its parameter ranges are either $w_{x}\geq -1$ and $\xi \geq 0,$ or
+$w_{x}\leq -1~$ and $~\xi \leq 0$, where $w_{x}$ is the dark energy equation of
+state (EoS), and $\xi$ is a coupling parameter governing the strength and
+direction of the energy transfer. We show that by adding a factor $(1+w_{x})$
+to the background energy transfer, the whole parameter space can be tested
+against all the data and thus, the instabilities in such interaction models can
+be removed. We test three classes of interaction model using the latest
+astronomical data from different sources. Precise constraints are found. Our
+analysis shows that a very small but non-zero deviation from pure
+$\Lambda$-cosmology is suggested by the observational data while the
+no-interaction scenario can be recovered at the 68.3% confidence-level. In
+particular, for three IDE models, identified as IDE 1, IDE 2, and IDE 3, the
+68.3% CL constraints on the interaction coupling strengths are, $\xi=
+0.0360_{-0.0360}^{+0.0091}$ (IDE 1), $\xi= 0.0433_{-0.0433}^{+0.0062}$ (IDE 2),
+$\xi= 0.1064_{-0.1064}^{+0.0437}$ (IDE 3). In addition, we find that the dark
+energy EoS tends towards the phantom region taking the 68.3% CL constraints,
+$w_x= -1.0230_{-0.0257}^{+0.0329}$ (IDE 1), $w_x= -1.0247_{-0.0302}^{+0.0289}$
+(IDE 2), and $w_x= -1.0275_{-0.0318}^{+0.0228}$ (IDE 3). However, the
+possibility of $w_{x}>-1$ is also not rejected by the astronomical data used
+here. Moreover, we find in all IDE models that, as the value of Hubble constant
+decreases, the behavior of the dark energy EoS shifts from phantom to
+quintessence type with its EoS very close to that a simple cosmological
+constant at the present time.
+"
+28972,Numerical simulation of MHD boundary layer flow and heat transfer over a nonlinear stretching sheet in the porous medium with viscous dissipation using hybrid approach,"  In the present study, MHD boundary layer flow with heat and mass transfer of
+a nanofluid with viscous dissipation over a non-linear stretching sheet
+embedded in a porous medium is studied. The governing non-linear partial
+differential equations are converted into coupled non-linear ordinary
+differential equations which are solved by hybrid technique consisting of
+finite element method with symbolic computation. The effect of Viscous
+dissipation, magnetic influence parameter, permeability parameter, Brownian
+motion, Eckert number, Thermophoresis parameter, Prandtl number parameter,
+Dufour Lewis number and nanofluid Lewis number are studied for velocity
+$(f'(\eta))$, temperature $(\theta(\eta))$, concentration of salt ($S(\eta)$)
+and concentration of nanoparticles $(\gamma(\eta))$ and results are shown
+graphically. Applications of such type of problems are found in the
+electromagnetic control of complex magnetic nanofluid materials relevant to
+energy and biomedical systems.
+"
+28973,Inline self-diffraction dispersion-scan of over octave-spanning pulses in the single-cycle regime,"  We present an implementation of dispersion-scan based on self-diffraction (SD
+d-scan) and apply it to the measurement of over octave-spanning sub-4-fs
+pulses. The results are compared with second-harmonic generation (SHG) d-scan.
+The efficiency of the SD process is derived theoretically and compared with the
+spectral response retrieved by the d-scan algorithm. The new SD d-scan has a
+robust inline setup and enables measuring pulses with over-octave spectra,
+single-cycle durations and wavelength ranges beyond those of SHG crystals, such
+as the ultraviolet and the deep-ultraviolet.
+"
+28974,Configuration Space Metrics,"  When robot manipulators decide how to reach for an object, hand it over, or
+obey some task constraint, they implicitly assume a Euclidean distance metric
+in their configuration space. Their notion of what makes a configuration closer
+or further is dictated by this assumption. But different distance metrics will
+lead to different solutions. What is efficient under a Euclidean metric might
+not necessarily look the most efficient or natural to a person observing the
+robot. In this paper, we analyze the effect of the metric on robot behavior,
+examining both Euclidean, as well as non-Euclidean metrics -- metrics that make
+certain joints cheaper, or that correlate different joints. Our user data
+suggests that tasks on a 3DOF arm and the Jaco 7DOF arm can typically be
+grouped into ones where a Euclidean metric works well, and tasks where that is
+no longer the case: there, surprisingly, penalizing elbow motion (and sometimes
+correlating the shoulder and wrist) leads to solutions that are more aligned
+with what users prefer.
+"
+28975,Capacity Expansion Planning of Wind Power Generation in A Market Environment with Topology Control,"  Wind power integration is an essential problem for modern power industry. In
+this paper, we develop a novel bilevel mixed integer optimization model to
+investigate wind power generation planning problem in an electricity market
+environment with topology control operations. Different from existing
+formulations, the lower level market clearing problem introduces binary
+variables to model line switching decisions, which have been proven very
+effective to improve transmission capability under different load profiles. To
+solve this challenging bilevel mixed integer program, a recent decomposition
+method is customized and a couple of enhancement techniques based on grid
+structure are designed. Through computing instances from typical IEEE test
+beds, our solution methods demonstrate a strong solution capacity. Also, we
+observe that applying topology control on a small number of lines could be very
+helpful to reduce wind power curtailment and improve the wind penetration
+level.
+"
+28976,Characteristic cycles and the conductor of direct image,"  We prove the functoriality for proper push-forward of the characteristic
+cycles of constructible complexes by morphisms of smooth projective schemes
+over a perfect field, under the assumption that the direct image of the
+singular support has the dimension at most that of the target of the morphism.
+The functoriality is deduced from a conductor formula which is a special case
+for morphisms to curves. The conductor formula in the constant coefficient case
+gives the geometric case of a formula conjectured by Bloch.
+"
+28977,On Calculating the Dynamics of Very Large Quantum Systems,"  Due to the exponential growth of the state space of coupled quantum systems
+it is not possible, in general, to numerically store the state of a very large
+number of quantum systems within a classical computer. We demonstrate a method
+for modelling the dynamical behaviour of measurable quantities for very large
+numbers of interacting quantum systems. Our approach makes use of a symbolic
+non-commutative algebra engine that we have recently developed in conjunction
+with the well-known Ehrenfest theorem. Here we show the possibility of
+determining the dynamics of experimentally observable quantities, without
+approximation, for very large numbers of interacting harmonic oscillators. Our
+analysis removes a large number of significant constraints present in previous
+analysis of this example system (such as having no entanglement in the initial
+state). This method will be of value in simulating the operation of large
+quantum machines, emergent behaviour in quantum systems, open quantum systems
+and quantum chemistry to name but a few.
+"
+28978,Finite dimensional quantum Teichmüller space from the quantum torus at root of unity,"  Representation theory of the quantum torus Hopf algebra, when the parameter
+$q$ is a root of unity, is studied. We investigate a decomposition map of the
+tensor product of two irreducibles into the direct sum of irreducibles,
+realized as a `multiplicity module' tensored with an irreducible
+representation. The isomorphism between the two possible decompositions of the
+triple tensor product yields a map ${\bf T}$ between the multiplicity modules,
+called the 6j-symbols. We study the left and right dual representations, and
+correspondingly, the left and right representations on the ${\rm Hom}$ spaces
+of linear maps between representations. Using the isomorphisms of irreducibles
+to left and right duals, we construct a map ${\bf A}$ on a multiplicity module,
+encoding the permutation of the roles of the irreducible representations in the
+identification of the multiplicity module as the space of intertwiners between
+representations. We show that ${\bf T}$ and ${\bf A}$ satisfy certain
+consistency relations, forming a Kashaev-type quantization of the Teichmüller
+spaces of bordered Riemann surfaces. All constructions and proofs in the
+present work uses only plain representation theoretic language with the help of
+the notions of the left and the right dual and Hom representations, and
+therefore can be applied easily to other Hopf algebras for future works.
+"
+28979,EVE: Explainable Vector Based Embedding Technique Using Wikipedia,"  We present an unsupervised explainable word embedding technique, called EVE,
+which is built upon the structure of Wikipedia. The proposed model defines the
+dimensions of a semantic vector representing a word using human-readable
+labels, thereby it readily interpretable. Specifically, each vector is
+constructed using the Wikipedia category graph structure together with the
+Wikipedia article link structure. To test the effectiveness of the proposed
+word embedding model, we consider its usefulness in three fundamental tasks: 1)
+intruder detection - to evaluate its ability to identify a non-coherent vector
+from a list of coherent vectors, 2) ability to cluster - to evaluate its
+tendency to group related vectors together while keeping unrelated vectors in
+separate clusters, and 3) sorting relevant items first - to evaluate its
+ability to rank vectors (items) relevant to the query in the top order of the
+result. For each task, we also propose a strategy to generate a task-specific
+human-interpretable explanation from the model. These demonstrate the overall
+effectiveness of the explainable embeddings generated by EVE. Finally, we
+compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across
+the three tasks, and report improvements over the state-of-the-art.
+"
+28980,Distributions on homogeneous spaces and applications,"  Let $G$ be a complex semisimple algebraic group. In 2006, Belkale-Kumar
+defined a new product $odot\_0$ on thecohomology group $H^*(G/P,{\mathbb C})$
+of any projective $G$-homogeneousspace $G/P$.Their definition uses the notion
+of Levi-movability for triples ofSchubert varieties in $G/P$.In this article,
+we introduce a family of $G$-equivariant subbundlesof the tangent bundle of
+$G/P$ and the associated filtration of the DeRham complex of $G/P$ viewed as a
+manifold. As a consequence one gets a filtration of the ring $H^*(G/P,{\mathbb
+C})$and proves that $\odot\_0$ is the associated graded product.One of the aim
+of this more intrinsic construction of $\odot\_0$ isthat there is a natural
+notion of fundamental class$[Y]\_{\odot\_0}\in(H^*(G/P),\odot\_0)$ for any
+irreducible subvariety $Y$ of $G/P$.Given two Schubert classes $\sigma\_u$ and
+$\sigma\_v$ in$H^*(G/P)$, we define a subvariety $\Sigma\_u^v$ of $G/P$. This
+variety should play the role of the Richardson variety; moreprecisely, we
+conjecture that$[\Sigma\_u^v]\_{\odot\_0}=\sigma\_u\odot\_0\sigma\_v$.We give
+some evidence for this conjecture, and prove special cases.Finally, we use the
+subbundles of $TG/P$ to give a geometriccharacterization of the $G$-homogeneous
+locus of any Schubertsubvariety of $G/P$.
+"
+28981,Stability Analysis of Optimal Adaptive Control using Value Iteration with Approximation Errors,"  Adaptive optimal control using value iteration initiated from a stabilizing
+control policy is theoretically analyzed in terms of stability of the system
+during the learning stage without ignoring the effects of approximation errors.
+This analysis includes the system operated using any single/constant resulting
+control policy and also using an evolving/time-varying control policy. A
+feature of the presented results is providing estimations of the \textit{region
+of attraction} so that if the initial condition is within the region, the whole
+trajectory will remain inside it and hence, the function approximation results
+remain valid.
+"
+28982,When mmWave Communications Meet Network Densification: A Scalable Interference Coordination Perspective,"  The millimeter-wave (mmWave) communication is envisioned to provide orders of
+magnitude capacity improvement. However, it is challenging to realize a
+sufficient link margin due to high path loss and blockages. To address this
+difficulty, in this paper, we explore the potential gain of ultra-densification
+for enhancing mmWave communications from a network-level perspective. By
+deploying the mmWave base stations (BSs) in an extremely dense and amorphous
+fashion, the access distance is reduced and the choice of serving BSs is
+enriched for each user, which are intuitively effective for mitigating the
+propagation loss and blockages. Nevertheless, co-channel interference under
+this model will become a performance-limiting factor. To solve this problem, we
+propose a large-scale channel state information (CSI) based interference
+coordination approach. Note that the large-scale CSI is highly
+location-dependent, and can be obtained with a quite low cost. Thus, the
+scalability of the proposed coordination framework can be guaranteed.
+Particularly, using only the large-scale CSI of interference links, a
+coordinated frequency resource block allocation problem is formulated for
+maximizing the minimum achievable rate of the users, which is uncovered to be a
+NP-hard integer programming problem. To circumvent this difficulty, a greedy
+scheme with polynomial-time complexity is proposed by adopting the bisection
+method and linear integer programming tools. Simulation results demonstrate
+that the proposed coordination scheme based on large-scale CSI only can still
+offer substantial gains over the existing methods. Moreover, although the
+proposed scheme is only guaranteed to converge to a local optimum, it performs
+well in terms of both user fairness and system efficiency.
+"
+28983,A de Bruijn identity for discrete random variables,"  We discuss properties of the ""beamsplitter addition"" operation, which
+provides a non-standard scaled convolution of random variables supported on the
+non-negative integers. We give a simple expression for the action of
+beamsplitter addition using generating functions. We use this to give a
+self-contained and purely classical proof of a heat equation and de Bruijn
+identity, satisfied when one of the variables is geometric.
+"
+28984,Geometric phase effects in excited state dynamics through a conical intersection in large molecules: N-dimensional linear vibronic coupling model study,"  We investigate geometric phase (GP) effects in nonadiabatic transitions
+through a conical intersection (CI) in an N-dimensional linear vibronic
+coupling (ND-LVC) model. This model allows for the coordinate transformation
+encompassing all nonadiabatic effects within a two-dimensional (2D) subsystem
+while the other N-2 dimensions form a system of uncoupled harmonic oscillators
+identical for both electronic states and coupled bi-linearly with the subsystem
+coordinates. The 2D subsystem governs ultra-fast nonadiabatic dynamics through
+the CI and provides a convenient model for studying GP effects. Parameters of
+the original ND-LVC model define the Hamiltonian of the transformed 2D
+subsystem and thus influence GP effects directly. Our analysis reveals what
+values of ND-LVC parameters can introduce symmetry breaking in the 2D subsystem
+that diminishes GP effects.
+"
+28985,Deep Graph Attention Model,"  Graph classification is a problem with practical applications in many
+different domains. Most of the existing methods take the entire graph into
+account when calculating graph features. In a graphlet-based approach, for
+instance, the entire graph is processed to get the total count of different
+graphlets or sub-graphs. In the real-world, however, graphs can be both large
+and noisy with discriminative patterns confined to certain regions in the graph
+only. In this work, we study the problem of attentional processing for graph
+classification. The use of attention allows us to focus on small but
+informative parts of the graph, avoiding noise in the rest of the graph. We
+present a novel RNN model, called the Graph Attention Model (GAM), that
+processes only a portion of the graph by adaptively selecting a sequence of
+""interesting"" nodes. The model is equipped with an external memory component
+which allows it to integrate information gathered from different parts of the
+graph. We demonstrate the effectiveness of the model through various
+experiments.
+"
+28986,Boundary states in the chiral symmetric systems with a spatial symmetry,"  We study topological systems with both a chiral and a spatial symmetry which
+result in an additional spatial chiral symmetry. We distinguish the
+topologically nontrivial states according to the chiral symmetries protecting
+them and study several models in 1D and 3D systems. The perturbations breaking
+the spatial symmetry can break only one of the two chiral symmetries while the
+perturbations preserving the spatial symmetry always break or preserve both of
+them. In 3D systems, besides the 3D symmetries, the topologically nontrivial
+boundary modes may also be protected by the hidden lower dimensional
+symmetries. We then figure out the corresponding topological invariants and
+connect them with the 3D invariants.
+"
+28987,On skew braces (with an appendix by N. Byott and L. Vendramin),"  Braces are generalizations of radical rings, introduced by Rump to study
+involutive non-degenerate set-theoretical solutions of the Yang-Baxter equation
+(YBE). Skew braces were also recently introduced as a tool to study not
+necessarily involutive solutions. Roughly speaking, skew braces provide
+group-theoretical and ring-theoretical methods to understand solutions of the
+YBE. It turns out that skew braces appear in many different contexts, such as
+near-rings, matched pairs of groups, triply factorized groups, bijective
+1-cocycles and Hopf-Galois extensions. These connections and some of their
+consequences are explored in this paper. We produce several new families of
+solutions related in many different ways with rings, near-rings and groups. We
+also study the solutions of the YBE that skew braces naturally produce. We
+prove, for example, that the order of the canonical solution associated with a
+finite skew brace is even: it is two times the exponent of the additive group
+modulo its center.
+"
+28988,"The almost Einstein operator for $(2, 3, 5)$ distributions","  For the geometry of oriented $(2, 3, 5)$ distributions $(M, {\mathbf D})$,
+which correspond to regular, normal parabolic geometries of type
+$(\mathrm{G}_2, P)$ for a particular parabolic subgroup $P < \mathrm{G}_2$, we
+develop the corresponding tractor calculus and use it to analyze the first BGG
+operator $\Theta_0$ associated to the $7$-dimensional irreducible
+representation of $\mathrm{G}_2$. We give an explicit formula for the normal
+connection on the corresponding tractor bundle and use it to derive explicit
+expressions for this operator. We also show that solutions of this operator are
+automatically normal, yielding a geometric interpretation of $\ker \Theta_0$:
+For any $(M, {\mathbf D})$, this kernel consists precisely of the almost
+Einstein scales of the Nurowski conformal structure on $M$ that ${\mathbf D}$
+determines.
+We apply our formula for $\Theta_0$ (1) to recover efficiently some known
+solutions, (2) to construct a distribution with root type $[3, 1]$ with a
+nonzero solution, and (3) to show efficiently that the conformal holonomy of a
+particular $(2, 3, 5)$ conformal structure is equal to $\mathrm{G}_2$.
+"
+28989,"Remarks on minimizers for $(p,q)$-Laplace equations with two parameters","  We study in detail the existence, nonexistence and behavior of global
+minimizers, ground states and corresponding energy levels of the
+$(p,q)$-Laplace equation $-\Delta_p u -\Delta_q u = \alpha |u|^{p-2}u + \beta
+|u|^{q-2}u$ in a bounded domain $\Omega \subset \mathbb{R}^N$ under zero
+Dirichlet boundary condition, where $p > q > 1$ and $\alpha, \beta \in
+\mathbb{R}$. A curve on the $(\alpha,\beta)$-plane which allocates a set of the
+existence of ground states and the multiplicity of positive solutions is
+constructed. Additionally, we show that eigenfunctions of the $p$- and
+$q$-Laplacians under zero Dirichlet boundary condition are linearly
+independent.
+"
+28990,Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images: AutoCloud+,"  The proposed Earth observation (EO) based value adding system (EO VAS),
+hereafter identified as AutoCloud+, consists of an innovative EO image
+understanding system (EO IUS) design and implementation capable of automatic
+spatial context sensitive cloud/cloud shadow detection in multi source multi
+spectral (MS) EO imagery, whether or not radiometrically calibrated, acquired
+by multiple platforms, either spaceborne or airborne, including unmanned aerial
+vehicles (UAVs). It is worth mentioning that the same EO IUS architecture is
+suitable for a large variety of EO based value adding products and services,
+including: (i) low level image enhancement applications, such as automatic MS
+image topographic correction, co registration, mosaicking and compositing, (ii)
+high level MS image land cover (LC) and LC change (LCC) classification and
+(iii) content based image storage/retrieval in massive multi source EO image
+databases (big data mining).
+"
+28991,Topological aspects of group $C^*$-algebras,"  We discuss basic topological properties of unitary dual spaces of nilpotent
+Lie groups, using some ideas from operator algebras and their noncommutative
+dimension theory. The general results are illustrated by many examples.
+"
+28992,Comparison of Lasserre's measure--based bounds for polynomial optimization to bounds obtained by simulated annealing,"  Comparison of Lasserre's measure--based bounds for polynomial optimization to
+bounds obtained by simulated annealing. We consider the problem of minimizing a
+continuous function $f$ over a compact set $\mathbf{K}$. We compare the
+hierarchy of upper bounds proposed by Lasserre in [{\em SIAM J. Optim.} $21(3)$
+$(2011)$, pp. $864-885$] to bounds that may be obtained from simulated
+annealing.
+We show that, when $f$ is a polynomial and $\mathbf{K}$ a convex body, this
+comparison yields a faster rate of convergence of the Lasserre hierarchy than
+what was previously known in the literature.
+"
+28993,Spintronics based Stochastic Computing for Efficient Bayesian Inference System,"  Bayesian inference is an effective approach for solving statistical learning
+problems especially with uncertainty and incompleteness. However, inference
+efficiencies are physically limited by the bottlenecks of conventional
+computing platforms. In this paper, an emerging Bayesian inference system is
+proposed by exploiting spintronics based stochastic computing. A stochastic
+bitstream generator is realized as the kernel components by leveraging the
+inherent randomness of spintronics devices. The proposed system is evaluated by
+typical applications of data fusion and Bayesian belief networks. Simulation
+results indicate that the proposed approach could achieve significant
+improvement on inference efficiencies in terms of power consumption and
+inference speed.
+"
+28994,Self-Taught Convolutional Neural Networks for Short Text Clustering,"  Short text clustering is a challenging problem due to its sparseness of text
+representation. Here we propose a flexible Self-Taught Convolutional neural
+network framework for Short Text Clustering (dubbed STC^2), which can flexibly
+and successfully incorporate more useful semantic features and learn non-biased
+deep text representation in an unsupervised manner. In our framework, the
+original raw text features are firstly embedded into compact binary codes by
+using one existing unsupervised dimensionality reduction methods. Then, word
+embeddings are explored and fed into convolutional neural networks to learn
+deep feature representations, meanwhile the output units are used to fit the
+pre-trained binary codes in the training process. Finally, we get the optimal
+clusters by employing K-means to cluster the learned representations. Extensive
+experimental results demonstrate that the proposed framework is effective,
+flexible and outperform several popular clustering methods when tested on three
+public short text datasets.
+"
+28995,Topological Recurrent Neural Network for Diffusion Prediction,"  In this paper, we study the problem of using representation learning to
+assist information diffusion prediction on graphs. In particular, we aim at
+estimating the probability of an inactive node to be activated next in a
+cascade. Despite the success of recent deep learning methods for diffusion, we
+find that they often underexplore the cascade structure. We consider a cascade
+as not merely a sequence of nodes ordered by their activation time stamps;
+instead, it has a richer structure indicating the diffusion process over the
+data graph. As a result, we introduce a new data model, namely diffusion
+topologies, to fully describe the cascade structure. We find it challenging to
+model diffusion topologies, which are dynamic directed acyclic graphs (DAGs),
+with the existing neural networks. Therefore, we propose a novel topological
+recurrent neural network, namely Topo-LSTM, for modeling dynamic DAGs. We
+customize Topo-LSTM for the diffusion prediction task, and show it improves the
+state-of-the-art baselines, by 20.1%--56.6% (MAP) relatively, across multiple
+real-world data sets. Our code and data sets are available online at
+this https URL.
+"
+28996,Generalized Ideals and Co-Granular Rough Sets,"  Lattice-theoretic ideals have been used to define and generate non granular
+rough approximations over general approximation spaces over the last few years
+by few authors. The goal of these studies, in relation based rough sets, have
+been to obtain nice properties comparable to those of classical rough
+approximations. In this research paper, these ideas are generalized in a severe
+way by the present author and associated semantic features are investigated by
+her. Granules are used in the construction of approximations in implicit ways
+and so a concept of co-granularity is introduced. Knowledge interpretation
+associable with the approaches is also investigated. This research will be of
+relevance for a number of logico-algebraic approaches to rough sets that
+proceed from point-wise definitions of approximations and also for using
+alternative approximations in spatial mereological contexts involving actual
+contact relations. The antichain based semantics invented in earlier papers by
+the present author also applies to the contexts considered.
+"
+28997,Wireless Full-duplex Medium Access Control for Enhancing Energy Efficiency,"  Recent years have witnessed a proliferation of battery-powered mobile
+devices, e.g., smartphones, tablets, sensors, and laptops, which leads a
+significant demand for high capacity wireless communication with high energy
+efficiency. Among technologies to provide the efficiency is full-duplex
+wireless communication. Full-duplex wireless enhances capacity by
+simultaneously transmitting uplink and downlink data with limited frequency
+resources. Previous studies on full-duplex wireless mostly focuses on doubling
+the network capacity, whereas in this paper we discuss that full-duplex
+wireless can also provide higher energy efficiency. We propose low power
+communication by wireless full-duplexing (LPFD), focusing on the fact that the
+full-duplex communication duration becomes half of the halfduplex communication
+duration. In the LPFD, by using the sleep state in which the transceiver
+provided in the wireless communication terminal is turned off, power
+consumption of the wireless communication terminal is reduced and energy
+efficiency in wireless full duplex is improved. Simulation results show that
+the energy efficiency achieved by LPFD is up to approximately 17.3 times higher
+than the energy efficiency achieved by existing full-duplex medium access
+protocol. Further, it is up to approximately 27.5 times higher than the energy
+efficiency using power saving mode of half-duplex communication.
+"
+28998,Variational Deep Q Network,"  We propose a framework that directly tackles the probability distribution of
+the value function parameters in Deep Q Network (DQN), with powerful
+variational inference subroutines to approximate the posterior of the
+parameters. We will establish the equivalence between our proposed surrogate
+objective and variational inference loss. Our new algorithm achieves efficient
+exploration and performs well on large scale chain Markov Decision Process
+(MDP).
+"
+28999,Deep Network Flow for Multi-Object Tracking,"  Data association problems are an important component of many computer vision
+applications, with multi-object tracking being one of the most prominent
+examples. A typical approach to data association involves finding a graph
+matching or network flow that minimizes a sum of pairwise association costs,
+which are often either hand-crafted or learned as linear functions of fixed
+features. In this work, we demonstrate that it is possible to learn features
+for network-flow-based data association via backpropagation, by expressing the
+optimum of a smoothed network flow problem as a differentiable function of the
+pairwise association costs. We apply this approach to multi-object tracking
+with a network flow formulation. Our experiments demonstrate that we are able
+to successfully learn all cost functions for the association problem in an
+end-to-end fashion, which outperform hand-crafted costs in all settings. The
+integration and combination of various sources of inputs becomes easy and the
+cost functions can be learned entirely from data, alleviating tedious
+hand-designing of costs.
+"
+29000,DLOPT: Deep Learning Optimization Library,"  Deep learning hyper-parameter optimization is a tough task. Finding an
+appropriate network configuration is a key to success, however most of the
+times this labor is roughly done. In this work we introduce a novel library to
+tackle this problem, the Deep Learning Optimization Library: DLOPT. We briefly
+describe its architecture and present a set of use examples. This is an open
+source project developed under the GNU GPL v3 license and it is freely
+available at this https URL
+"
+29001,Large-Scale Study of Curiosity-Driven Learning,"  Reinforcement learning algorithms rely on carefully engineering environment
+rewards that are extrinsic to the agent. However, annotating each environment
+with hand-designed, dense rewards is not scalable, motivating the need for
+developing reward functions that are intrinsic to the agent. Curiosity is a
+type of intrinsic reward function which uses prediction error as reward signal.
+In this paper: (a) We perform the first large-scale study of purely
+curiosity-driven learning, i.e. without any extrinsic rewards, across 54
+standard benchmark environments, including the Atari game suite. Our results
+show surprisingly good performance, and a high degree of alignment between the
+intrinsic curiosity objective and the hand-designed extrinsic rewards of many
+game environments. (b) We investigate the effect of using different feature
+spaces for computing prediction error and show that random features are
+sufficient for many popular RL game benchmarks, but learned features appear to
+generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We
+demonstrate limitations of the prediction-based rewards in stochastic setups.
+Game-play videos and code are at
+this https URL
+"
+29002,Extended Phase Graph formalism for systems with Magnetization Transfer and Chemical Exchange,"  An Extended Phase Graph framework for modelling systems with exchange or
+magnetization transfer (MT) is proposed. The framework, referred to as EPG-X,
+models coupled two-compartment systems by describing each compartment with
+separate phase graphs that exchange during evolution periods. There are two
+variants: EPG-X(BM) for systems governed by the Bloch-McConnell equations; and
+EPG-X(MT) for the pulsed MT formalism. For the MT case the ""bound"" protons have
+no transverse components so their phase graph consists only longitudinal
+states. EPG-X was used to model steady-state gradient echo imaging, MT effects
+in multislice Turbo Spin Echo imaging, multiecho CPMG for multicomponent T2
+relaxometry and transient variable flip angle gradient echo imaging of the type
+used for MR Fingerprinting. Experimental data were also collected for the final
+case. Steady-state predictions from EPG-X closely match directly derived
+steady-state solutions which differ substantially from classic ""single pool""
+EPG predictions. EPG-X(MT) predicts similar MT related levels of signal
+attenuation in white matter as have been reported elsewhere in the literature.
+Modelling of CPMG echo trains with EPG-X(BM) suggests that exchange processes
+can lead to an underestimate of the fraction of short T2 species. Modelling of
+transient gradient echo sequences with EPG-X(MT) suggests that measurable MT
+effects result from variable saturation of bound protons, particularly after
+inversion pulses. In conclusion, EPG-X can be used for modelling of the
+transient signal response of systems exhibiting chemical exchange or MT. This
+may be particularly beneficial for relaxometry approaches that rely on
+characterising transient rather than steady-state sequences.
+"
+29003,Of puzzles and partitions: Introducing Partiti,"  We introduce Partiti, the puzzle that will run in Mathematics Magazine in
+2018, and use the opportunity to recall some basic properties of integer
+partitions.
+"
+29004,Byzantine-Tolerant Machine Learning,"  The growth of data, the need for scalability and the complexity of models
+used in modern machine learning calls for distributed implementations. Yet, as
+of today, distributed machine learning frameworks have largely ignored the
+possibility of arbitrary (i.e., Byzantine) failures. In this paper, we study
+the robustness to Byzantine failures at the fundamental level of stochastic
+gradient descent (SGD), the heart of most machine learning algorithms. Assuming
+a set of $n$ workers, up to $f$ of them being Byzantine, we ask how robust can
+SGD be, without limiting the dimension, nor the size of the parameter space.
+We first show that no gradient descent update rule based on a linear
+combination of the vectors proposed by the workers (i.e, current approaches)
+tolerates a single Byzantine failure. We then formulate a resilience property
+of the update rule capturing the basic requirements to guarantee convergence
+despite $f$ Byzantine workers. We finally propose Krum, an update rule that
+satisfies the resilience property aforementioned. For a $d$-dimensional
+learning problem, the time complexity of Krum is $O(n^2 \cdot (d + \log n))$.
+"
+29005,Bihamiltonian Cohomologies and Integrable Hierarchies II: the Tau Structures,"  Starting from a so-called flat exact semisimple bihamiltonian structures of
+hydrodynamic type, we arrive at a Frobenius manifold structure and a tau
+structure for the associated principal hierarchy. We then classify the
+deformations of the principal hierarchy which possess tau structures.
+"
+29006,How Does Knowledge of the AUC Constrain the Set of Possible Ground-truth Labelings?,"  Recent work on privacy-preserving machine learning has considered how
+data-mining competitions such as Kaggle could potentially be ""hacked"", either
+intentionally or inadvertently, by using information from an oracle that
+reports a classifier's accuracy on the test set. For binary classification
+tasks in particular, one of the most common accuracy metrics is the Area Under
+the ROC Curve (AUC), and in this paper we explore the mathematical structure of
+how the AUC is computed from an n-vector of real-valued ""guesses"" with respect
+to the ground-truth labels. We show how knowledge of a classifier's AUC on the
+test set can constrain the set of possible ground-truth labelings, and we
+derive an algorithm both to compute the exact number of such labelings and to
+enumerate efficiently over them. Finally, we provide empirical evidence that,
+surprisingly, the number of compatible labelings can actually decrease as n
+grows, until a test set-dependent threshold is reached.
+"
+29007,Random walks on products of trees and certain t.d.l.c. groups,"  The Poisson boundary of a finite direct product of affine automorphism groups
+of homogeneous trees is considered. The Poisson boundary is shown to be a
+product of ends of trees with a hitting measure for spread-out, aperiodic
+measures of finite first moment whose closed support generates a subgroup which
+is not fully exceptional. The Poisson boundary of a semi-direct product
+associated with every automorphism $\alpha$ and tidy compact open subgroup $V$
+in a locally compact, totally disconnected group $G$ is also shown to be the
+space of ends of the tree with the hitting measure under similar assumptions.
+Necessary and sufficient conditions for boundary triviality are given in both
+cases. The method of proof is largely an extension of the prior work of
+Cartwright, Kaimanovich and Woess on affine automorphism groups of homogeneous
+trees.
+"
+29008,"Effects of optical attenuation, heat diffusion and acoustic coherence in photoacoustic signals produced by nanoparticles","  Behavior of the photoacoustic signal produced by nanoparticles as a function
+of their concentration was studied in detail. As the concentration of
+nanoparticles is increased in a sample, the peak-to-peak photoacoustic
+amplitude increases linearly up to a certain value, after which an asymptotic
+saturated behavior is observed. To elucidate the mechanisms responsible for
+these observations, we evaluate the effects of nanoparticles concentration, the
+optical attenuation and the effects of heat propagation from nano-sources to
+their surroundings. We found that the saturation effect of the photoacoustic
+signal as a function the concentration of nanoparticles is explained by a
+combination of two different mechanisms. As has been suggested previously, but
+not modeled correctly, the most important mechanism is attributed to optical
+attenuation. The second mechanism is due to an interference destructive process
+attributed to the superimposition of the photoacoustic amplitudes generated for
+each nanoparticle, this explanation is reinforced through our experimental and
+simulations results; based on this, it is found that the linear behavior of the
+photoacoustic amplitude could be restricted to optical densities $\le0.5$.
+"
+29009,Critical threshold for ancestral reconstruction by maximum parsimony on general phylogenies,"  We consider the problem of inferring an ancestral state from observations at
+the leaves of a tree, assuming the state evolves along the tree according to a
+two-state symmetric Markov process. We establish a general branching rate
+condition under which maximum parsimony, a common reconstruction method
+requiring only the knowledge of the tree, succeeds better than random guessing
+uniformly in the depth of the tree. We thereby generalize previous results of
+(Zhang et al., 2010) and (Gascuel and Steel, 2010). Our results apply to both
+deterministic and i.i.d. edge weights.
+"
+29010,Mining Best Closed Itemsets for Projection-antimonotonic Constraints in Polynomial Time,"  The exponential explosion of the set of patterns is one of the main
+challenges in pattern mining. This challenge is approached by introducing a
+constraint for pattern selection. One of the first constraints proposed in
+pattern mining is support (frequency) of a pattern in a dataset. Frequency is
+an anti-monotonic function, i.e., given an infrequent pattern, all its
+superpatterns are not frequent. However, many other constraints for pattern
+selection are neither monotonic nor anti-monotonic, which makes it difficult to
+generate patterns satisfying these constraints.
+In order to deal with nonmonotonic constraints we introduce the notion of
+""projection antimonotonicity"" and SOFIA algorithm that allow generating best
+patterns for a class of nonmonotonic constraints. Cosine interest, robustness,
+stability of closed itemsets, and the associated delta-measure are among these
+constraints. SOFIA starts from light descriptions of transactions in dataset (a
+small set of items in the case of itemset description) and then iteratively
+adds more information to these descriptions (more items with indication of
+tidsets they describe).
+"
+29011,Distributed Learning with Compressed Gradient Differences,"  Training very large machine learning models requires a distributed computing
+approach, with communication of the model updates often being the bottleneck.
+For this reason, several methods based on the compression (e.g., sparsification
+and/or quantization) of the updates were recently proposed, including QSGD
+(Alistarh et al., 2017), TernGrad (Wen et al., 2017), SignSGD (Bernstein et
+al., 2018), and DQGD (Khirirat et al., 2018). However, none of these methods
+are able to learn the gradients, which means that they necessarily suffer from
+several issues, such as the inability to converge to the true optimum in the
+batch mode, inability to work with a nonsmooth regularizer, and slow
+convergence rates. In this work we propose a new distributed learning
+method---DIANA---which resolves these issues via compression of gradient
+differences. We perform a theoretical analysis in the strongly convex and
+nonconvex settings and show that our rates are vastly superior to existing
+rates. Our analysis of block-quantization and differences between $\ell_2$ and
+$\ell_\infty$ quantization closes the gaps in theory and practice. Finally, by
+applying our analysis technique to TernGrad, we establish the first convergence
+rate for this method.
+"
+29012,Ultrasensitivity and Fluctuations in the Barkai-Leibler Model of Chemotaxis Receptors in {\it Escherichia coli},"  A stochastic version of the Barkai-Leibler model of chemotaxis receptors in
+{\it E. coli} is studied here to elucidate the effects of intrinsic network
+noise in their conformational dynamics. It was originally proposed to explain
+the robust and near-perfect adaptation of {\it E. coli} observed across a wide
+range of spatially uniform attractant/repellent (ligand) concentrations. A
+receptor is either active or inactive and can stochastically switch between the
+two states. Enzyme CheR methylates inactive receptors while CheB demethylates
+active ones and the probability for it to be active depends on its level of
+methylation and ligandation. A simple version of the model with two methylation
+sites per receptor (M=2) shows zero-order ultrasensitivity (ZOU) akin to the
+classical 2-state model of covalent modification studied by Goldbeter and
+Koshland (GK). For extremely small and large ligand concentrations, the system
+reduces to two 2-state GK modules. A quantitative measure of the spontaneous
+fluctuations in activity (variance) estimated mathematically under linear noise
+approximation (LNA) is found to peak near the ZOU transition. The variance is a
+weak, non-monotonic and decreasing functions of ligand and receptor
+concentrations. Gillespie simulations for M=2 show excellent agreement with
+analytical results obtained under LNA. Numerical results for M=2, 3 and 4 show
+ZOU in mean activity; the variance is found to be smaller for larger M. The
+magnitude of receptor noise deduced from available experimental data is
+consistent with our predictions. A simple analysis of the downstream signaling
+pathway shows that this noise is large enough to have a beneficial effect on
+the motility of the organism. The response of mean receptor activity to small
+time-dependent changes in the external ligand concentration, computed within
+linear response theory, is found to have a bilobe form.
+"
+29013,Faster batched range minimum queries,"  Range Minimum Query (RMQ) is an important building brick of many compressed
+data structures and string matching algorithms. Although this problem is
+essentially solved in theory, with sophisticated data structures allowing for
+constant time queries, there are scenarios in which the number of queries, $q$,
+is rather small and given beforehand, which encourages to use a simpler
+approach. A recent work by Alzamel et al. starts with contracting the input
+array to a much shorter one, with its size proportional to $q$. In this work,
+we build upon their solution, speeding up handling small batches of queries by
+a factor of 3.8--7.8 (the gap grows with $q$). The key idea that helped us
+achieve this advantage is adapting the well-known Sparse Table technique to
+work on blocks, with speculative block minima comparisons. We also propose an
+even much faster (but possibly using more space) variant without the array
+contraction.
+"
+29014,Causal Reasoning from Meta-reinforcement Learning,"  Discovering and exploiting the causal structure in the environment is a
+crucial challenge for intelligent agents. Here we explore whether causal
+reasoning can emerge via meta-reinforcement learning. We train a recurrent
+network with model-free reinforcement learning to solve a range of problems
+that each contain causal structure. We find that the trained agent can perform
+causal reasoning in novel situations in order to obtain rewards. The agent can
+select informative interventions, draw causal inferences from observational
+data, and make counterfactual predictions. Although established formal causal
+reasoning algorithms also exist, in this paper we show that such reasoning can
+arise from model-free reinforcement learning, and suggest that causal reasoning
+in complex settings may benefit from the more end-to-end learning-based
+approaches presented here. This work also offers new strategies for structured
+exploration in reinforcement learning, by providing agents with the ability to
+perform -- and interpret -- experiments.
+"
+29015,Vanishing pressure limit for compressible Navier-Stokes equations with degenerate viscosities,"  In this paper we study a vanishing pressure process for highly compressible
+Navier-Stokes equations as the Mach number tends to infinity. We first prove
+the global existence of weak solutions for the pressureless system in the
+framework [Li-Xin, arXiv:1504.06826v2], where the weak solutions are
+established for compressible Navier-Stokes equations with degenerate viscous
+coefficients. Furthermore, a rate of convergence of the density in
+$L^{\infty}\left(0,T;L^{2}(\rr)\right)$ is obtained, in case when the velocity
+corresponds to the gradient of density at initial time.
+"
+29016,Electronic structure and nematic phase transition in superconducting multi-layer FeSe films grown by pulsed laser deposition method,"  We report comprehensive angle-resolved photoemission investigations on the
+electronic structure of single crystal multiple-layer FeSe films grown on CaF2
+substrate by pulsed laser deposition (PLD) method. Measurements on FeSe/CaF2
+samples with different superconducting transition temperatures Tc of 4 K, 9 K
+and 14 K reveal electronic difference in their Fermi surface and band
+structure. Indication of the nematic phase transition is observed from
+temperature-dependent measurements of these samples; the nematic transition
+temperature is 140-160 K, much higher than 90 K for the bulk FeSe. Potassium
+deposition is applied onto the surface of these samples; the nematic phase is
+suppressed by potassium deposition which introduces electrons to these FeSe
+films and causes a pronounced electronic structure change. We compared and
+discussed the electronic structure and superconductivity of the FeSe/CaF2 films
+by PLD method with the FeSe/SrTiO3 films by molecular beam epitaxy (MBE) method
+and bulk FeSe. The PLD-grown multilayer FeSe/CaF2 is more hole-doped than that
+in MBE-grown multiple-layer FeSe films. Our results on FeSe/CaF2 films by PLD
+method establish a link between bulk FeSe single crystal and FeSe/SrTiO3 films
+by MBE method, and provide important information to understand
+superconductivity in FeSe-related systems.
+"
+29017,The $L^p$ Dirichlet boundary problem for second order Elliptic Systems with rough coefficients,"  We establish solvability methods for strongly elliptic second order systems
+in divergence form with lower order (drift) terms on a domain above a Lipschitz
+graph, satisfying $L^p$-boundary data for $p$ near $2$. The main novel aspect
+of our result is that the coefficients of the operator do not have to be
+constant or have very high regularity, instead they will satisfy a natural
+Carleson condition that has appeared first in the scalar case. A particular
+example of a system where this result can be applied is the Lamé operator for
+isotropic inhomogeneous materials.
+The systems case poses substantial new challenges not present in the scalar
+case. In particular, there is no maximum principle for general elliptic systems
+and De Giorgi - Nash - Moser theory is also not available. Despite this we are
+able to establish estimates for the square and nontangential maximal functions
+for the solution of the elliptic system and use these estimates to establish
+$L^p$ solvability for $p$ near $2$.
+"
+29018,Surface quasi periodic and random structures based on nanomotor lithography for light trapping,"  We compare the characteristics of two types of patterns obtained with two
+azopolymer materials: a Gaussian random pattern and a quasi-random grating
+pattern. The surface structurations have been obtained with a simple bottom-up
+technique by illuminating azopolymer thin films with a single laser beam. We
+demonstrate the interesting generated properties of these two surfaces. In
+particular, the surface with quasi-random gratings can address beam splittings
+for light coupling in different directions in an ultra-thin film. We use these
+two surfaces as a mold and replicate them on a transparent elastomeric material
+and we demonstrate a very good light entrapment. We also show that the
+efficiency of light trapping is 20% better with the quasi-random gratings than
+with the Gaussian random surface, and is close to 40%.
+"
+29019,Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods,"  Precise financial series predicting has long been a difficult problem because
+of unstableness and many noises within the series. Although Traditional time
+series models like ARIMA and GARCH have been researched and proved to be
+effective in predicting, their performances are still far from satisfying.
+Machine Learning, as an emerging research field in recent years, has brought
+about many incredible improvements in tasks such as regressing and classifying,
+and it's also promising to exploit the methodology in financial time series
+predicting. In this paper, the predicting precision of financial time series
+between traditional time series models and mainstream machine learning models
+including some state-of-the-art ones of deep learning are compared through
+experiment using real stock index data from history. The result shows that
+machine learning as a modern method far surpasses traditional models in
+precision.
+"
+29020,Using KL-divergence to focus Deep Visual Explanation,"  We present a method for explaining the image classification predictions of
+deep convolution neural networks, by highlighting the pixels in the image which
+influence the final class prediction. Our method requires the identification of
+a heuristic method to select parameters hypothesized to be most relevant in
+this prediction, and here we use Kullback-Leibler divergence to provide this
+focus. Overall, our approach helps in understanding and interpreting deep
+network predictions and we hope contributes to a foundation for such
+understanding of deep learning networks. In this brief paper, our experiments
+evaluate the performance of two popular networks in this context of
+interpretability.
+"
+29021,"Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks","  While Deep Neural Networks (DNNs) push the state-of-the-art in many machine
+learning applications, they often require millions of expensive floating-point
+operations for each input classification. This computation overhead limits the
+applicability of DNNs to low-power, embedded platforms and incurs high cost in
+data centers. This motivates recent interests in designing low-power,
+low-latency DNNs based on fixed-point, ternary, or even binary data precision.
+While recent works in this area offer promising results, they often lead to
+large accuracy drops when compared to the floating-point networks. We propose a
+novel approach to map floating-point based DNNs to 8-bit dynamic fixed-point
+networks with integer power-of-two weights with no change in network
+architecture. Our dynamic fixed-point DNNs allow different radix points between
+layers. During inference, power-of-two weights allow multiplications to be
+replaced with arithmetic shifts, while the 8-bit fixed-point representation
+simplifies both the buffer and adder design. In addition, we propose a hardware
+accelerator design to achieve low-power, low-latency inference with
+insignificant degradation in accuracy. Using our custom accelerator design with
+the CIFAR-10 and ImageNet datasets, we show that our method achieves
+significant power and energy savings while increasing the classification
+accuracy.
+"
+29022,Edge sliding and ergodic hyperfinite decomposition,"  We use edge slidings and saturated disjoint Borel families to give a
+conceptually simple proof of Hjorth's theorem on cost attained: if a countable
+p.m.p. ergodic equivalence relation $E$ is treeable and has cost $n \in
+\mathbb{N} \cup \{\infty\}$ then it is induced by an a.e. free p.m.p. action of
+the free group $\mathbb{F}_n$ on $n$ generators. More importantly, our
+techniques give a significant strengthening of this theorem: the action of
+$\mathbb{F}_n$ can be arranged so that each of the $n$ generators alone acts
+ergodically.
+The existence of an ergodic action for the first generator immediately
+follows from a powerful theorem of Tucker-Drob, whose proof however uses a
+recent substantial result in probability theory as a black box. We give a
+constructive and purely descriptive set theoretic proof of a weaker version of
+Tucker-Drob's theorem, which is enough for many of its applications, including
+our strengthening of Hjorth's theorem. Our proof uses new tools, such as
+asymptotic means on graphs, packed disjoint Borel families, and a cost
+threshold for finitizing the connected components of nonhyperfinite graphs.
+"
+29023,Predicting readmission risk from doctors' notes,"  We develop a model using deep learning techniques and natural language
+processing on unstructured text from medical records to predict hospital-wide
+$30$-day unplanned readmission, with c-statistic $.70$. Our model is
+constructed to allow physicians to interpret the significant features for
+prediction.
+"
+29024,Proof of Cluckers-Veys's conjecture on exponential sums for polynomials with log-canonical threshold at most a half,"  In this paper, we will give two proofs of the Cluckers-Veys conjecture on
+exponential sums for the case of polynomials in
+$\mathbb{Z}[x_{1},\ldots,x_{n}]$ having log-canonical thresholds at most one
+half. In particular, these results imply Igusa's conjecture and Denef-Sperber's
+conjecture under the same restriction on the log-canonical threshold.
+"
+29025,Internal characterizations of productively Lindelöf spaces,"  We present an internal characterization for the productively Lindelöf
+property, thus answering a long-standing problem attributed to Tamano. We also
+present some results about the relation Alster spaces vs. productively
+Lindelöf spaces.
+"
+29026,A novel entropy recurrence quantification analysis,"  The growing study of time series, especially those related to nonlinear
+systems, has challenged the methodologies to characterize and classify
+dynamical structures of a signal. Here we conceive a new diagnostic tool for
+time series based on the concept of information entropy, in which the
+probabilities are associated to microstates defined from the recurrence phase
+space. Recurrence properties can properly be studied using recurrence plots, a
+methodology based on binary matrices where trajec- tories in phase space of
+dynamical systems are evaluated against other embedded trajectory. Our novel
+entropy methodology has several advantages compared to the traditional
+recurrence entropy defined in the literature, namely, the correct evaluation of
+the chaoticity level of the signal, the weak dependence on parameters, correct
+evaluation of periodic time series properties and more sensitivity to noise
+level of time series. Furthermore, the new entropy quantifier developed in this
+manuscript also fixes inconsistent results of the traditional recurrence
+entropy concept, reproducing classical results with novel insights.
+"
+29027,Robust Stability of Optimization-based State Estimation,"  Optimization-based state estimation is useful for nonlinear or constrained
+dynamic systems for which few general methods with established properties are
+available. The two fundamental forms are moving horizon estimation (MHE) which
+uses the nearest measurements within a moving time horizon, and its theoretical
+ideal, full information estimation (FIE) which uses all measurements up to the
+time of estimation. Despite extensive studies, the stability analyses of FIE
+and MHE for discrete-time nonlinear systems with bounded process and
+measurement disturbances, remain an open challenge. This work aims to provide a
+systematic solution for the challenge. First, we prove that FIE is robustly
+globally asymptotically stable (RGAS) if the cost function admits a property
+mimicking the incremental input/output-to-state stability (i-IOSS) of the
+system and has a sufficient sensitivity to the uncertainty in the initial
+state. Second, we establish an explicit link from the RGAS of FIE to that of
+MHE, and use it to show that MHE is RGAS under enhanced conditions if the
+moving horizon is long enough to suppress the propagation of uncertainties. The
+theoretical results imply flexible MHE designs with assured robust stability
+for a broad class of i-IOSS systems. Numerical experiments on linear and
+nonlinear systems are used to illustrate the designs and support the findings.
+"
+29028,On local integration of Lie brackets,"  We give a direct, explicit and self-contained construction of a local Lie
+groupoid integrating a given Lie algebroid which only depends on the choice of
+a spray vector field lifting the underlying anchor map. This construction leads
+to a complete account of local Lie theory and, in particular, to a
+finite-dimensional proof of the fact that the category of germs of local Lie
+groupoids is equivalent to that of Lie algebroids.
+"
+29029,Overcomplete Independent Component Analysis via SDP,"  We present a novel algorithm for overcomplete independent components analysis
+(ICA), where the number of latent sources k exceeds the dimension p of observed
+variables. Previous algorithms either suffer from high computational complexity
+or make strong assumptions about the form of the mixing matrix. Our algorithm
+does not make any sparsity assumption yet enjoys favorable computational and
+theoretical properties. Our algorithm consists of two main steps: (a)
+estimation of the Hessians of the cumulant generating function (as opposed to
+the fourth and higher order cumulants used by most algorithms) and (b) a novel
+semi-definite programming (SDP) relaxation for recovering a mixing component.
+We show that this relaxation can be efficiently solved with a projected
+accelerated gradient descent method, which makes the whole algorithm
+computationally practical. Moreover, we conjecture that the proposed program
+recovers a mixing component at the rate k < p^2/4 and prove that a mixing
+component can be recovered with high probability when k < (2 - epsilon) p log p
+when the original components are sampled uniformly at random on the hyper
+sphere. Experiments are provided on synthetic data and the CIFAR-10 dataset of
+real images.
+"
+29030,GPU System Calls,"  GPUs are becoming first-class compute citizens and are being tasked to
+perform increasingly complex work. Modern GPUs increasingly support
+programmability- enhancing features such as shared virtual memory and hardware
+cache coherence, enabling them to run a wider variety of programs. But a key
+aspect of general-purpose programming where GPUs are still found lacking is the
+ability to invoke system calls. We explore how to directly invoke generic
+system calls in GPU programs. We examine how system calls should be meshed with
+prevailing GPGPU programming models where thousands of threads are organized in
+a hierarchy of execution groups: Should a system call be invoked at the
+individual GPU task, or at different execution group levels? What are
+reasonable ordering semantics for GPU system calls across these hierarchy of
+execution groups? To study these questions, we implemented GENESYS -- a
+mechanism to allow GPU pro- grams to invoke system calls in the Linux operating
+system. Numerous subtle changes to Linux were necessary, as the existing kernel
+assumes that only CPUs invoke system calls. We analyze the performance of
+GENESYS using micro-benchmarks and three applications that exercise the
+filesystem, networking, and memory allocation subsystems of the kernel. We
+conclude by analyzing the suitability of all of Linux's system calls for the
+GPU.
+"
+29031,Collective behaviour of social bots is encoded in their temporal Twitter activity,"  Computational propaganda deploys social or political bots to try to shape,
+steer and manipulate online public discussions and influence decisions.
+Collective behaviour of populations of social bots has not been yet widely
+studied, though understanding of collective patterns arising from interactions
+between bots would aid social bot detection. Here we show that there are
+significant differences in collective behaviour between population of bots and
+population of humans as detected from their Twitter activity. Using a large
+dataset of tweets we have collected during the UK EU referendum campaign, we
+separated users into population of bots and population of humans based on the
+length of sequences of their high-frequency tweeting activity. We show that
+while pairwise correlations between users are weak they co-exist with
+collective correlated states, however the statistics of correlations and
+co-spiking probability differ in both populations. Our results demonstrate that
+populations of social bots and human users in social media exhibit collective
+properties similar to the ones found in social and biological systems placed
+near a critical point.
+"
+29032,Extended playing techniques: The next milestone in musical instrument recognition,"  The expressive variability in producing a musical note conveys information
+essential to the modeling of orchestration and style. As such, it plays a
+crucial role in computer-assisted browsing of massive digital music corpora.
+Yet, although the automatic recognition of a musical instrument from the
+recording of a single ""ordinary"" note is considered a solved problem, automatic
+identification of instrumental playing technique (IPT) remains largely
+underdeveloped. We benchmark machine listening systems for query-by-example
+browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets
+of instrument, mute, and technique. We identify and discuss three necessary
+conditions for significantly outperforming the traditional mel-frequency
+cepstral coefficient (MFCC) baseline: the addition of second-order scattering
+coefficients to account for amplitude modulation, the incorporation of
+long-range temporal dependencies, and metric learning using large-margin
+nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the
+Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for
+instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition
+(baseline at 44.5%). We interpret this gain through a qualitative assessment of
+practical usability and visualization using nonlinear dimensionality reduction.
+"
+29033,The Quantum Hall Effect with Wilczek's charged magnetic flux tubes instead of electrons,"  Composites formed from charged particles and magnetic flux tubes, proposed by
+Wilczek, are one model for anyons - particles obeying fractional statistics.
+Here we propose a scheme for realizing charged flux tubes, in which a charged
+object with an intrinsic magnetic dipole moment is placed between two
+semi-infinite blocks of a high permeability ($\mu_r$) material, and the images
+of the magnetic moment create an effective flux tube. We show that the scheme
+can lead to a realization of Wilczek's anyons, when a two-dimensional electron
+system, which exhibits the integer quantum Hall effect (IQHE), is sandwiched
+between two blocks of the high-$\mu_r$ material with a temporally fast response
+(in the cyclotron and Larmor frequency range). The signature of Wilczek's
+anyons is a slight shift of the resistivity at the plateau of the IQHE. Thus,
+the quest for high-$\mu_r$ materials at high frequencies, which is underway in
+the field of metamaterials, and the quest for anyons, are here found to be on
+the same avenue.
+"
+29034,Plasma Injection Schemes for Laser-Plasma Accelerators,"  Plasma injection schemes are crucial for producing high-quality electron
+beams in laser-plasma accelerators. This article introduces the general
+concepts of plasma injection. First, a Hamiltonian model for particle trapping
+and acceleration in plasma waves is introduced; ionization injection and
+colliding-pulse injection are described in the framework of this Hamiltonian
+model. We then proceed to consider injection in plasma density gradients.
+"
+29035,Parameter identification in Markov chain choice models,"  This work studies the parameter identification problem for the Markov chain
+choice model of Blanchet, Gallego, and Goyal used in assortment planning. In
+this model, the product selected by a customer is determined by a Markov chain
+over the products, where the products in the offered assortment are absorbing
+states. The underlying parameters of the model were previously shown to be
+identifiable from the choice probabilities for the all-products assortment,
+together with choice probabilities for assortments of all-but-one products.
+Obtaining and estimating choice probabilities for such large assortments is not
+desirable in many settings. The main result of this work is that the parameters
+may be identified from assortments of sizes two and three, regardless of the
+total number of products. The result is obtained via a simple and efficient
+parameter recovery algorithm.
+"
+29036,What constitutes elemental shape information for biological vision?,"  We do not yet understand how the vertebrate visual system provides for
+recognition of ob- jects. Countless experiments have been performed to examine
+the contribution of cues such as color, texture, and shadowing, but the most
+important cues are the con- tours of the outer boundary. Most objects that we
+can name can be identified as a silhouette, or equally well as a line drawing
+of the boundary. This has long been ap- preciated, so it is somewhat surprising
+that after more than a century of experimental research, we have not yet
+established how our visual system encodes this shape in- formation.
+"
+29037,A Unified Framework for Low-Rank plus Sparse Matrix Recovery,"  We propose a unified framework to solve general low-rank plus sparse matrix
+recovery problems based on matrix factorization, which covers a broad family of
+objective functions satisfying the restricted strong convexity and smoothness
+conditions. Based on projected gradient descent and the double thresholding
+operator, our proposed generic algorithm is guaranteed to converge to the
+unknown low-rank and sparse matrices at a locally linear rate, while matching
+the best-known robustness guarantee (i.e., tolerance for sparsity). At the core
+of our theory is a novel structural Lipschitz gradient condition for low-rank
+plus sparse matrices, which is essential for proving the linear convergence
+rate of our algorithm, and we believe is of independent interest to prove fast
+rates for general superposition-structured models. We illustrate the
+application of our framework through two concrete examples: robust matrix
+sensing and robust PCA. Experiments on both synthetic and real datasets
+corroborate our theory.
+"
+29038,A remark on Sarnak's conjecture,"  We investigate Sarnak's conjecture on the Möbius function in the special
+case when the test function is the indicator of the set of integers for which a
+real additive function assumes a given value.
+"
+29039,Tensor SVD: Statistical and Computational Limits,"  Tensors, or high-order arrays, attract much attention in recent research. In
+this paper, we propose a general framework for tensor principal component
+analysis (tensor PCA), which focuses on the methodology and theory for
+extracting the hidden low-rank structure from the high-dimensional tensor data.
+A unified solution is provided to tensor PCA in both statistical limits and
+computational costs. The problem exhibits three different phases according to
+the signal-noise-ratio (SNR). In particular, with strong SNR, we propose a fast
+spectral power iteration method that achieves the minimax optimal rate of
+convergence in estimation; with weak SNR, the information-theoretical lower
+bound shows that it is impossible to have consistent estimation in general;
+with moderate SNR, we show that the non-convex maximum likelihood estimation
+provides optimal solution, but with NP-hard computational cost; moreover, under
+the hardness hypothesis the hypergraphic planted clique detection, there are no
+polynomial-time algorithms performing consistently in general. Simulation
+studies show that the proposed spectral power iteration method has good
+performs under a variety of settings.
+"
+29040,"Fractional $S$-duality, Classification of Fractional Topological Insulators and Surface Topological Order","  In this paper, we propose a generalization of the $S$-duality of
+four-dimensional quantum electrodynamics ($\text{QED}_4$) to $\text{QED}_4$
+with fractionally charged excitations, the fractional $S$-duality. Such
+$\text{QED}_4$ can be obtained by gauging the $\text{U(1)}$ symmetry of a
+topologically ordered state with fractional charges. When time-reversal
+symmetry is imposed, the axion angle ($\theta$) can take a nontrivial but still
+time-reversal invariant value $\pi/t^2$ ($t\in\mathbb{Z}$). Here, $1/t$
+specifies the minimal electric charge carried by bulk excitations. Such states
+with time-reversal and $\text{U(1)}$ global symmetry (fermion number
+conservation) are fractional topological insulators (FTI). We propose a
+topological quantum field theory description, which microscopically justifies
+the fractional $S$-duality. Then, we consider stacking operations (i.e., a
+direct sum of Hamiltonians) among FTIs. We find that there are two
+topologically distinct classes of FTIs: type-I and type-II. Type-I
+($t\in\mathbb{Z}_{\rm odd}$) can be obtained by directly stacking a
+non-interacting topological insulator and a fractionalized gapped fermionic
+state with minimal charge $1/t$ and vanishing $\theta$. But type-II
+($t\in\mathbb{Z}_{\rm even}$) cannot be realized through any stacking. Finally,
+we study the Surface Topological Order of fractional topological insulators.
+"
+29041,Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs,"  Markov chain Monte Carlo (MCMC) methods require a large number of samples to
+approximate a posterior distribution, which can be costly when the likelihood
+or prior is expensive to evaluate. The number of samples can be reduced if we
+can avoid repeated samples and those that are close to each other. This is the
+idea behind deterministic sampling methods such as Quasi-Monte Carlo (QMC).
+However, the existing QMC methods aim at sampling from a uniform hypercube,
+which can miss the high probability regions of the posterior distribution and
+thus the approximation can be poor. Minimum energy design (MED) is a recently
+proposed deterministic sampling method, which makes use of the posterior
+evaluations to obtain a weighted space-filling design in the region of
+interest. However, the existing implementation of MED is inefficient because it
+requires several global optimizations and thus numerous evaluations of the
+posterior. In this article, we develop an efficient algorithm that can generate
+MED with few posterior evaluations. We also make several improvements to the
+MED criterion to make it perform better in high dimensions. The advantages of
+MED over MCMC and QMC are illustrated using an example of calibrating a
+friction drilling process.
+"
+29042,"Quantized Laplacian growth, I: Statistical theory of Laplacian growth","  We regularize the Laplacian growth problem with zero surface tension by
+introducing a short-distance cutoff $\hbar$, so that the change of the area of
+domains is quantized and equals an integer multiple of the area quanta $\hbar$.
+The domain can be then considered as an aggregate of tiny particles (area
+quanta) obeying the Pauli exclusion principle. The statistical theory of
+Laplacian growth is introduced by using Laughlin's description of the integer
+quantum Hall effect. The semiclassical evolution of the aggregate is similar to
+classical deterministic Laplacian growth. However, the quantization procedure
+generates inevitable fluctuations at the edge of the droplet. The statistical
+properties of the edge fluctuations are universal and common to that of quantum
+chaotic systems, which are generally described by Dyson's circular ensembles on
+symmetric unitary matrices.
+"
+29043,Cross-media Similarity Metric Learning with Unified Deep Networks,"  As a highlighting research topic in the multimedia area, cross-media
+retrieval aims to capture the complex correlations among multiple media types.
+Learning better shared representation and distance metric for multimedia data
+is important to boost the cross-media retrieval. Motivated by the strong
+ability of deep neural network in feature representation and comparison
+functions learning, we propose the Unified Network for Cross-media Similarity
+Metric (UNCSM) to associate cross-media shared representation learning with
+distance metric in a unified framework. First, we design a two-pathway deep
+network pretrained with contrastive loss, and employ double triplet similarity
+loss for fine-tuning to learn the shared representation for each media type by
+modeling the relative semantic similarity. Second, the metric network is
+designed for effectively calculating the cross-media similarity of the shared
+representation, by modeling the pairwise similar and dissimilar constraints.
+Compared to the existing methods which mostly ignore the dissimilar constraints
+and only use sample distance metric as Euclidean distance separately, our UNCSM
+approach unifies the representation learning and distance metric to preserve
+the relative similarity as well as embrace more complex similarity functions
+for further improving the cross-media retrieval accuracy. The experimental
+results show that our UNCSM approach outperforms 8 state-of-the-art methods on
+4 widely-used cross-media datasets.
+"
+29044,How to desynchronize quorum-sensing networks,"  In this paper we investigate how so-called quorum-sensing networks can be
+de-synchronized. Such networks, which arise in many important application
+fields such as systems biology, are characterized by the fact that direct
+communication between network nodes is superimposed to communication with a
+shared, environmental, variable. In particular, we provide a new sufficient
+condition ensuring that the trajectories of these quorum-sensing networks
+diverge from their synchronous evolution. Then, we apply our result to study
+two applications.
+"
+29045,Transverse fields to tune an Ising-nematic quantum critical transition,"  The paradigmatic example of a continuous quantum phase transition is the
+transverse field Ising ferromagnet. In contrast to classical critical systems,
+whose properties depend only on symmetry and the dimension of space, the nature
+of a quantum phase transition also depends on the dynamics. In the transverse
+field Ising model, the order parameter is not conserved and increasing the
+transverse field enhances quantum fluctuations until they become strong enough
+to restore the symmetry of the ground state. Ising pseudo-spins can represent
+the order parameter of any system with a two-fold degenerate broken-symmetry
+phase, including electronic nematic order associated with spontaneous
+point-group symmetry breaking. Here, we show for the representative example of
+orbital-nematic ordering of a non-Kramers doublet that an orthogonal strain or
+a perpendicular magnetic field plays the role of the transverse field, thereby
+providing a practical route for tuning appropriate materials to a quantum
+critical point. While the transverse fields are conjugate to seemingly
+unrelated order parameters, their non-trivial commutation relations with the
+nematic order parameter, which can be represented by a Berry-phase term in an
+effective field theory, intrinsically intertwines the different order
+parameters.
+"
+29046,Global optimality conditions for deep neural networks,"  We study the error landscape of deep linear and nonlinear neural networks
+with the squared error loss. Minimizing the loss of a deep linear neural
+network is a nonconvex problem, and despite recent progress, our understanding
+of this loss surface is still incomplete. For deep linear networks, we present
+necessary and sufficient conditions for a critical point of the risk function
+to be a global minimum. Surprisingly, our conditions provide an efficiently
+checkable test for global optimality, while such tests are typically
+intractable in nonconvex optimization. We further extend these results to deep
+nonlinear neural networks and prove similar sufficient conditions for global
+optimality, albeit in a more limited function space setting.
+"
+29047,Estimating the Probability that a Function Observed with Noise is Convex,"  Consider a real-valued function that can only be observed with stochastic
+noise at a finite set of design points within a Euclidean space. We wish to
+determine whether there exists a convex function that goes through the true
+function values at the design points. We develop an asymptotically consistent
+Bayesian sequential sampling procedure that estimates the posterior probability
+of this being true. In each iteration, the posterior probability is estimated
+using Monte Carlo simulation. We offer three variance reduction methods --
+change of measure, acceptance-rejection, and conditional Monte Carlo. Numerical
+experiments suggest that the conditional Monte Carlo method should be
+preferred.
+"
+29048,Chandra and JVLA observations of HST Frontier Fields cluster MACS J0717.5+3745,"  To investigate the relationship between thermal and non-thermal components in
+merger galaxy clusters, we present deep JVLA and Chandra observations of the
+HST Frontier Fields cluster MACS J0717.5+3745. The Chandra image shows a
+complex merger event, with at least four components belonging to different
+merging subclusters. NW of the cluster, $\sim 0.7$ Mpc from the center, there
+is a ram-pressure-stripped core that appears to have traversed the densest
+parts of the cluster after entering the ICM from the direction of a galaxy
+filament to the SE. We detect a density discontinuity NNE of this core which we
+speculate is associated with a cold front. Our radio images reveal new details
+for the complex radio relic and radio halo in this cluster. In addition, we
+discover several new filamentary radio sources with sizes of 100-300 kpc. A few
+of these seem to be connected to the main radio relic, while others are either
+embedded within the radio halo or projected onto it. A narrow-angled-tailed
+(NAT) radio galaxy, a cluster member, is located at the center of the radio
+relic. The steep spectrum tails of this AGN leads into the large radio relic
+where the radio spectrum flattens again. This morphological connection between
+the NAT radio galaxy and relic provides evidence for re-acceleration (revival)
+of fossil electrons. The presence of hot $\gtrsim 20$ keV ICM gas detected by
+Chandra near the relic location provides additional support for this
+re-acceleration scenario.
+"
+29049,Homotopy Classification of Line Bundles Over Rigid Analytic Varieties,"  We construct a motivic homotopy theory for rigid analytic varieties with the
+rigid analytic affine line $\mathbb{A} ^1_\mathrm{rig}$ as an interval object.
+This motivic homotopy theory is inspired from, but not equal to, Ayoub's
+motivic homotopy theory for rigid analytic varieties. Working in the so
+constructed homotopy theory, we prove that a homotopy classification of vector
+bundles of rank $n$ over rigid analytic quasi-Stein spaces follows from
+$\mathbb{A}^1_\mathrm{rig}$-homotopy invariance of vector bundles. This
+$\mathbb{A}^1_\mathrm{rig}$-homotopy invariance is equivalent to a rigid
+analytic version of Lindel's solution to the Bass--Quillen conjecture.
+Moreover, we establish a homotopy classification of line bundles over rigid
+analytic quasi-Stein spaces. In fact, line bundles are classified by infinite
+projective space.
+"
+29050,A dependency look at the reality of constituency,"  A comment on ""Neurophysiological dynamics of phrase-structure building during
+sentence processing"" by Nelson et al (2017), Proceedings of the National
+Academy of Sciences USA 114(18), E3669-E3678.
+"
+29051,An improved lion strategy for the lion and man problem,"  In this paper, a novel lion strategy for David Gale's lion and man problem is
+proposed. The devised approach enhances a popular strategy proposed by Sgall,
+which relies on the computation of a suitable ""center"". The key idea of the new
+strategy is to update the center at each move, instead of computing it once and
+for all at the beginning of the game. Convergence of the proposed lion strategy
+is proven and an upper bound on the game length is derived, which dominates the
+existing bounds.
+"
+29052,Solar energetic particles and radio burst emission,"  We present a statistical study on the observed solar radio burst emission
+associated with the origin of in situ detected solar energetic particles.
+Several proton event catalogs in the period 1996$-$2016 are used. At the time
+of appearance of the particle origin (flare and coronal mass ejection) we
+identified radio burst signatures of types II, III and IV by inspecting dynamic
+radio spectral plots. The information from observatory reports is also
+accounted for during the analysis. The occurrence of solar radio burst
+signatures is evaluated within selected wavelength ranges during the solar
+cycle 23 and the ongoing 24. Finally, we present the burst occurrence trends
+with respect to the intensity of the proton events and the location of their
+solar origin.
+"
+29053,Pseudophase-Change Effects in Turbulent Channel Flow under Transcritical Temperature Conditions,"  We have performed direct numerical simulations (DNS) of compressible
+turbulent channel flow at supercritical pressure with top and bottom isothermal
+walls kept respectively at a supercritical (Ttop > Tpb) and subcritical
+temperature (Tbot < Tpb), where Tpb is the pseudoboiling temperature. The DNS
+are conducted using a high-order discretization of the fully compressible
+Navier-Stokes equations in conservative form closed with the Peng-Robinsion
+(PR) state equation. Bulk density is adjusted to obtain a bulk pressure of
+approximately pb = 1.1pcr where pcr is the critical pressure of the working
+fluid. Top-to-bottom temperature differences investigated are DT = 5 K, 10 K,
+and 20 K, where Ttop/bot = Tpb +- DT / 2; buoyancy effects are neglected.
+Varying DT modifies the average location of pseudophase change from ypb/h =
+-0.23 (DT = 5 K) to 0.89 (DT = 20 K), where h is the channel half-height and y
+= 0 the centerline position. Real-fluid effects cause visible deviations from
+classical scaling laws in the mean velocity profile. Enstrophy generation due
+stretching and tilting decreases with DT. The proximity to the
+pseudotransitioning layer inhibits the intensity of the velocity fluctuations,
+while enhancing the density and temperature fluctuations. Conditional
+probability analysis reveals that the sheet of fluid undergoing pseudophase
+change is characterized by a dramatic reduction in the kurtosis of density
+fluctuations and becomes thinner as DT is increased. Instantaneous
+visualizations show dense fluid ejections from the pseudoliquid viscous
+sublayer, some reaching the channel core, causing positive values of density
+skewness in the respective buffer-layer region (vice versa for the top wall).
+"
+29054,On Codes over $\mathbb{F}_{q}+v\mathbb{F}_{q}+v^{2}\mathbb{F}_{q}$,"  In this paper we investigate linear codes with complementary dual (LCD) codes
+and formally self-dual codes over the ring $R=\F_{q}+v\F_{q}+v^{2}\F_{q}$,
+where $v^{3}=v$, for $q$ odd. We give conditions on the existence of LCD codes
+and present construction of formally self-dual codes over $R$. Further, we give
+bounds on the minimum distance of LCD codes over $\F_q$ and extend these to
+codes over $R$.
+"
+29055,A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems,"  The proven efficacy of learning-based control schemes strongly motivates
+their application to robotic systems operating in the physical world. However,
+guaranteeing correct operation during the learning process is currently an
+unresolved issue, which is of vital importance in safety-critical systems. We
+propose a general safety framework based on Hamilton-Jacobi reachability
+methods that can work in conjunction with an arbitrary learning algorithm. The
+method exploits approximate knowledge of the system dynamics to guarantee
+constraint satisfaction while minimally interfering with the learning process.
+We further introduce a Bayesian mechanism that refines the safety analysis as
+the system acquires new evidence, reducing initial conservativeness when
+appropriate while strengthening guarantees through real-time validation. The
+result is a least-restrictive, safety-preserving control law that intervenes
+only when (a) the computed safety guarantees require it, or (b) confidence in
+the computed guarantees decays in light of new observations. We prove
+theoretical safety guarantees combining probabilistic and worst-case analysis
+and demonstrate the proposed framework experimentally on a quadrotor vehicle.
+Even though safety analysis is based on a simple point-mass model, the
+quadrotor successfully arrives at a suitable controller by policy-gradient
+reinforcement learning without ever crashing, and safely retracts away from a
+strong external disturbance introduced during flight.
+"
+29056,Experimental confirmation of the negentropic character of the diffraction polarization of diffuse radiation,"  In the course of analyzing the axiomatic principles on which statistical
+physics is based, the assumption of the limited correctness of the postulate
+that all allowable microstates of a closed system are equally probable was
+checked. This article reports the results of a study of a quasi-equivalent
+system within which isotropic radiation interacts with a phase diffraction
+grating. A simulated computer model of such interaction showed that anisotropic
+polarization must arise in the diffracted radiation, which reduces the
+Boltzmann entropy of the entire system and allows an external observer to
+obtain information on the grating's surface topology. This prediction was
+confirmed when it was experimentally checked directly on actual apparatus.
+"
+29057,Darwinian Data Structure Selection,"  Data structure selection and tuning is laborious but can vastly improve an
+application's performance and memory footprint. Some data structures share a
+common interface and enjoy multiple implementations. We call them Darwinian
+Data Structures (DDS), since we can subject their implementations to survival
+of the fittest. We introduce ARTEMIS a multi-objective, cloud-based
+search-based optimisation framework that automatically finds optimal, tuned DDS
+modulo a test suite, then changes an application to use that DDS. ARTEMIS
+achieves substantial performance improvements for \emph{every} project in $5$
+Java projects from DaCapo benchmark, $8$ popular projects and $30$ uniformly
+sampled projects from GitHub. For execution time, CPU usage, and memory
+consumption, ARTEMIS finds at least one solution that improves \emph{all}
+measures for $86\%$ ($37/43$) of the projects. The median improvement across
+the best solutions is $4.8\%$, $10.1\%$, $5.1\%$ for runtime, memory and CPU
+usage.
+These aggregate results understate ARTEMIS's potential impact. Some of the
+benchmarks it improves are libraries or utility functions. Two examples are
+gson, a ubiquitous Java serialization framework, and xalan, Apache's XML
+transformation tool. ARTEMIS improves gson by $16.5$\%, $1\%$ and $2.2\%$ for
+memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by
+$23.5$\%. \emph{Every} client of these projects will benefit from these
+performance improvements.
+"
+29058,Phase transitions in the $q$-coloring of random hypergraphs,"  We study in this paper the structure of solutions in the random hypergraph
+coloring problem and the phase transitions they undergo when the density of
+constraints is varied. Hypergraph coloring is a constraint satisfaction problem
+where each constraint includes $K$ variables that must be assigned one out of
+$q$ colors in such a way that there are no monochromatic constraints, i.e.
+there are at least two distinct colors in the set of variables belonging to
+every constraint. This problem generalizes naturally coloring of random graphs
+($K=2$) and bicoloring of random hypergraphs ($q=2$), both of which were
+extensively studied in past works. The study of random hypergraph coloring
+gives us access to a case where both the size $q$ of the domain of the
+variables and the arity $K$ of the constraints can be varied at will. Our work
+provides explicit values and predictions for a number of phase transitions that
+were discovered in other constraint satisfaction problems but never evaluated
+before in hypergraph coloring. Among other cases we revisit the hypergraph
+bicoloring problem ($q=2$) where we find that for $K=3$ and $K=4$ the
+colorability threshold is not given by the one-step-replica-symmetry-breaking
+analysis as the latter is unstable towards more levels of replica symmetry
+breaking. We also unveil and discuss the coexistence of two different 1RSB
+solutions in the case of $q=2$, $K \ge 4$. Finally we present asymptotic
+expansions for the density of constraints at which various phase transitions
+occur, in the limit where $q$ and/or $K$ diverge.
+"
+29059,A simple analysis of flying capacitor converter,"  The paper aims to emphasise how switched systems can be analysed with
+elementary techniques which require only undergraduate-level linear algebra and
+differential equations. It is also emphasised how math software can become
+useful for simplifying analytic complications. The time domain voltage balance
+methodology is used for stability analysis. As for deriving formulas for the
+asymptotic average of both capacitor voltage and inductor current, a new simple
+analytic method is introduced. It was shown analytically that the time average
+of capacitor voltage converges to half of the source voltage. A formula for the
+time average of the current of the inductor is also computed. As a by-product
+it was discovered that the period of the current is half of the switching
+period. Numerical simulations are obtained to illustrate the accuracy of the
+results. Higher dimensional generalisations could become a bit complicated as
+stability analysis of higher dimensional exponential matrices are not so easy
+to handle. On the other hand, the new discovery on the period of the current is
+more likely to give new insights in handling higher dimensional systems.
+Analytical formulas are exact and it helps in accurately modelling flying
+capacitor converts in practice. FCC is well studied in engineering society.
+However, not much is done in obtaining exact formulas using analysis. Also,
+math software is much used in computation of numerical results and obtaining
+simulations. In this paper, one more important aspect of math software is
+emphasised, namely, use symbolic computations in analysis.
+"
+29060,On the relativistic theory of force-free particles with any spin,"  In the free case, it is possible to define quantum fields which describe
+particles with integer or half-integer spin larger than one. It is shown that
+particles with integer spin must have Bose statistic and particles with
+half-integer-spin must follow Fermi-Dirac statistic. In the free case, the
+fields with spin smaller or equal to one are already well defined, so that for
+them alone, the charge and energy density are clearly identified and gauge
+invariant; while for the higher spin this is the case only for the total charge
+and total energy.
+"
+29061,Detection-by-Localization: Maintenance-Free Change Object Detector,"  Recent researches demonstrate that self-localization performance is a very
+useful measure of likelihood-of-change (LoC) for change detection. In this
+paper, this ""detection-by-localization"" scheme is studied in a novel
+generalized task of object-level change detection. In our framework, a given
+query image is segmented into object-level subimages (termed ""scene parts""),
+which are then converted to subimage-level pixel-wise LoC maps via the
+detection-by-localization scheme. Our approach models a self-localization
+system as a ranking function, outputting a ranked list of reference images,
+without requiring relevance score. Thanks to this new setting, we can
+generalize our approach to a broad class of self-localization systems. Our
+ranking based self-localization model allows to fuse self-localization results
+from different modalities via an unsupervised rank fusion derived from a field
+of multi-modal information retrieval (MMR).
+"
+29062,Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities,"  We propose a probabilistic model for refining coarse-grained spatial data by
+utilizing auxiliary spatial data sets. Existing methods require that the
+spatial granularities of the auxiliary data sets are the same as the desired
+granularity of target data. The proposed model can effectively make use of
+auxiliary data sets with various granularities by hierarchically incorporating
+Gaussian processes. With the proposed model, a distribution for each auxiliary
+data set on the continuous space is modeled using a Gaussian process, where the
+representation of uncertainty considers the levels of granularity. The
+fine-grained target data are modeled by another Gaussian process that considers
+both the spatial correlation and the auxiliary data sets with their
+uncertainty. We integrate the Gaussian process with a spatial aggregation
+process that transforms the fine-grained target data into the coarse-grained
+target data, by which we can infer the fine-grained target Gaussian process
+from the coarse-grained data. Our model is designed such that the inference of
+model parameters based on the exact marginal likelihood is possible, in which
+the variables of fine-grained target and auxiliary data are analytically
+integrated out. Our experiments on real-world spatial data sets demonstrate the
+effectiveness of the proposed model.
+"
+29063,Warm-hot Gas in X-ray Bright Galaxy Clusters and the H I-deficient Circumgalactic Medium in Dense Environments,"  We analyze the intracluster medium (ICM) and circumgalactic medium (CGM) in 7
+X-ray detected galaxy clusters using spectra of background QSOs (HST-COS/STIS),
+optical spectroscopy of the cluster galaxies (MMT/Hectospec and SDSS), and
+X-ray imaging/spectroscopy (XMM-Newton and Chandra). First, we report a very
+low covering fraction of H I absorption in the CGM of these cluster galaxies,
+f_c = 25% +25%/-15%, to stringent detection limits (log N(H I) < 13 cm^-2). As
+field galaxies have an H I covering fraction of ~100% at similar radii, the
+dearth of CGM H I in our data indicates that the cluster environment has
+effectively stripped or overionized the gaseous halos of these cluster
+galaxies. Second, we assess the contribution of warm-hot (10^5 - 10^6 K) gas to
+the ICM as traced by O VI and broad Ly-alpha (BLA) absorption. Despite the high
+signal-to-noise of our data, we do not detect O VI in any cluster, and we only
+detect BLA features in the QSO spectrum probing one cluster. We estimate that
+the total column density of warm-hot gas along this line of sight totals to ~3%
+of that contained in the hot T > 10^7 K X-ray emitting phase. Residing at high
+relative velocities, these features may trace pre-shocked material outside the
+cluster. Comparing gaseous galaxy halos from the low-density 'field' to galaxy
+groups and high-density clusters, we find that the CGM is progressively
+depleted of H I with increasing environmental density, and the CGM is most
+severely transformed in galaxy clusters. This CGM transformation may play a key
+role in environmental galaxy quenching.
+"
+29064,Self-Interference in Full-Duplex Multi-User MIMO Channels,"  We consider a multi-user multiple-input multiple-output (MIMO) setup where
+full-duplex (FD) multi-antenna nodes apply linear beamformers to simultaneously
+transmit and receive multiple streams over Rician fading channels. The exact
+first and second positive moments of the residual self-interference (SI),
+involving the squared norm of a sum of non-identically distributed random
+variables, are derived in closed-form. The method of moments is hence invoked
+to provide a Gamma approximation for the residual SI distribution. The proposed
+theorem holds under arbitrary linear precoder/decoder design, number of
+antennas and streams, and SI cancellation capability.
+"
+29065,Monocular Object and Plane SLAM in Structured Environments,"  We present a monocular Simultaneous Localization and Mapping (SLAM) using
+high level object and plane landmarks, in addition to points. The resulting map
+is denser, more compact and meaningful compared to point only SLAM. We first
+propose a high order graphical model to jointly infer the 3D object and layout
+planes from single image considering occlusions and semantic constraints. The
+extracted cuboid object and layout planes are further optimized in a unified
+SLAM framework. Objects and planes can provide more semantic constraints such
+as Manhattan and object supporting relationships compared to points.
+Experiments on various public and collected datasets including ICL NUIM and TUM
+mono show that our algorithm can improve camera localization accuracy compared
+to state-of-the-art SLAM and also generate dense maps in many structured
+environments.
+"
+29066,On the use of supervised clustering in stochastic NMPC design,"  In this paper, a supervised clustering based-heuristic is proposed for the
+real-time implementation of approximate solutions to stochastic nonlinear model
+predictive control frameworks. The key idea is to update on-line a low
+cardinality set of uncertainty vectors to be used in the expression of the
+stochastic cost and constraints. These vectors are the centers of uncertainty
+clusters that are built using the optimal control sequences, cost and
+constraints indicators as supervision labels. The use of a moving clustering
+data buffer which accumulates recent past computations enables to reduce the
+computational burden per sampling period while making available at each period
+a relevant amount of samples for the clustering task. A relevant example is
+given to illustrate the contribution and the associated algorithms.
+"
+29067,Fundamental transitions and ionization energies of the hydrogen molecular ions at the few ppt level,"  We calculate ionization energies and fundamental vibrational transitions for
+H$_2^+$, D$_2^+$, and HD$^+$ molecular ions. The NRQED expansion for the energy
+in terms of the fine structure constant $\alpha$ is used. Previous calculations
+of orders $m\alpha^6$ and $m\alpha^7$ are improved by including second-order
+contributions due to the vibrational motion of nuclei. Furthermore, we evaluate
+the largest corrections at the order $m\alpha^8$. That allows to reduce the
+fractional uncertainty to the level of $7\cdot10^{-12}$ for fundamental
+transitions and to $4\cdot10^{-12}$ for the ionization energies.
+"
+29068,Multiscale Granger causality,"  In the study of complex physical and biological systems represented by
+multivariate stochastic processes, an issue of great relevance is the
+description of the system dynamics spanning multiple temporal scales. While
+methods to assess the dynamic complexity of individual processes at different
+time scales are well-established, multiscale analysis of directed interactions
+has never been formalized theoretically, and empirical evaluations are
+complicated by practical issues such as filtering and downsampling. Here we
+extend the very popular measure of Granger causality (GC), a prominent tool for
+assessing directed lagged interactions between joint processes, to quantify
+information transfer across multiple time scales. We show that the multiscale
+processing of a vector autoregressive (AR) process introduces a moving average
+(MA) component, and describe how to represent the resulting ARMA process using
+state space (SS) models and to combine the SS model parameters for computing
+exact GC values at arbitrarily large time scales. We exploit the theoretical
+formulation to identify peculiar features of multiscale GC in basic AR
+processes, and demonstrate with numerical simulations the much larger
+estimation accuracy of the SS approach compared with pure AR modeling of
+filtered and downsampled data. The improved computational reliability is
+exploited to disclose meaningful multiscale patterns of information transfer
+between global temperature and carbon dioxide concentration time series, both
+in paleoclimate and in recent years.
+"
+29069,"Ionization of small molecules induced by H$^+$, He$^+$ and N$^+$ projectiles: comparison of experiment with quantum and classical calculations","  We report the energy and angular distribution of ejected electrons from
+CH$_4$ and H$_{2}$O molecules impacted by 1 MeV H$^+$, He$^+$ and 650 keV N$^+$
+ions. Spectra were measured at different observation angles, from 2 eV to 2000
+eV. The obtained absolute double-differential-electron-emission cross sections
+(DDCS) were compared with the results of CTMC and CDW-EIS calculations. For the
+bare H+ projectile both theories show remarkable agreement with the experiment
+at all observed angles and energies. The CTMC results are in similarly good
+agreement with the DDCS spectra obtained for impact by dressed He$^+$ and N$^+$
+ions, where screening effects and electron loss from the projectile gain
+importance. The CDW-EIS calculations slightly overestimate the electron loss
+for 1 MeV He$^+$ impact, and overestimate both the target and projectile
+ionization at low emitted electron energies for 650 keV N$^+$ impact. The
+contribution of multiple electron scattering by the projectile and target
+centers (Fermi-shuttle) dominates the N$^+$-impact spectra at higher electron
+energies, and it is well reproduced by the non-perturbative CTMC calculations.
+The contributions of different processes in medium velocity collisions of
+dressed ions with molecules are determined.
+"
+29070,Acceleration of low-latency gravitational wave searches using Maxwell-microarchitecture GPUs,"  Low-latency detections of gravitational waves (GWs) are crucial to enable
+prompt follow-up observations to astrophysical transients by conventional
+telescopes. We have developed a low-latency pipeline using a technique called
+Summed Parallel Infinite Impulse Response (SPIIR) filtering, realized by a
+Graphic Processing Unit (GPU). In this paper, we exploit the new
+\textit{Maxwell} memory access architecture in NVIDIA GPUs, namely the
+read-only data cache, warp-shuffle, and cross-warp atomic techniques. We report
+a 3-fold speed-up over our previous implementation of this filtering technique.
+To tackle SPIIR with relatively few filters, we develop a new GPU thread
+configuration with a nearly 10-fold speedup. In addition, we implement a
+multi-rate scheme of SPIIR filtering using Maxwell GPUs. We achieve more than
+100-fold speed-up over a single core CPU for the multi-rate filtering scheme.
+This results in an overall of 21-fold CPU usage reduction for the entire SPIIR
+pipeline.
+"
+29071,Existence and multiplicity of solutions for a class of quasilinear elliptic field equation on $\mathbb{R}^{N}$,"  In this paper, we establish existence and multiplicity of solutions for the
+following class of quasilinear field equation $$ -\Delta
+u+V(x)u-\Delta_{p}u+W'(u)=0, \,\,\, \mbox{in} \,\,\, \mathbb{R}^{N}, \eqno{(P)}
+$$ where $u=(u_1,u_2,...,u_{N+1})$, $p>N \geq 2$, $W$ is a singular function
+and $V$ is a positive continuous function.
+"
+29072,Robust Regularized ZF in Cooperative Broadcast Channel under Distributed CSIT,"  In this work, we consider the sum rate performance of joint processing
+coordinated multi-point transmission network (JP-CoMP, a.k.a Network MIMO) in a
+so-called distributed channel state information (D-CSI) setting. In the D-CSI
+setting, the various transmitters (TXs) acquire a local, TX-dependent, estimate
+of the global multi-user channel state matrix obtained via terminal feedback
+and limited backhauling. The CSI noise across TXs can be independent or
+correlated, so as to reflect the degree to which TXs can exchange information
+over the backhaul, hence allowing to model a range of situations bridging fully
+distributed and fully centralized CSI settings. In this context we aim to study
+the price of CSI distributiveness in terms of sum rate at finite SNR when
+compared with conventional centralized scenarios. We consider the family of
+JP-CoMP precoders known as regularized zero-forcing (RZF). We conduct our study
+in the large scale antenna regime, as it is currently envisioned to be used in
+real 5G deployments. It is then possible to obtain accurate approximations on
+so-called deterministic equivalents of the signal to interference and noise
+ratios. Guided by the obtained deterministic equivalents, we propose an
+approach to derive a RZF scheme that is robust to the distributed aspect of the
+CSI, whereby the key idea lies in the optimization of a TX-dependent power
+level and regularization factor. Our analysis confirms the improved robustness
+of the proposed scheme with respect to CSI inconsistency at different TXs, even
+with moderate number of antennas and receivers (RXs).
+"
+29073,Approximating Constrained Minimum Input Selection for State Space Structural Controllability,"  This paper looks at two problems, minimum constrained input selection and
+minimum cost constrained input selection for state space structured systems.
+The input matrix is constrained in the sense that the set of states that each
+input can influence is pre-specified and each input has a cost associated with
+it. Our goal is to optimally select an input set from the set of inputs given
+that the system is controllable. These problems are known to be NP-hard.
+Firstly, we give a new necessary and sufficient graph theoretic condition for
+checking structural controllability using flow networks. Using this condition
+we give a polynomial reduction of both these problems to a known NP-hard
+problem, the minimum cost fixed flow problem (MCFF). Subsequently, we prove
+that an optimal solution to the MCFF problem corresponds to an optimal solution
+to the original controllability problem. We also show that approximation
+schemes of MCFF directly applies to minimum cost constrained input selection
+problems. Using the special structure of the flow network constructed for the
+structured system, we give a polynomial approximation algorithm based on
+minimum weight bipartite matching and a greedy selection scheme for solving
+MCFF on system flow network. The proposed algorithm gives a
+$\Delta$-approximate solution to MCFF, where $\Delta$ denotes the maximum
+in-degree of input vertices in the flow network of the structured system.
+"
+29074,The ACS Survey of Galactic Globular Clusters XIV: Bayesian Single-Population Analysis of 69 Globular Clusters,"  We use Hubble Space Telescope (HST) imaging from the ACS Treasury Survey to
+determine fits for single population isochrones of 69 Galactic globular
+clusters. Using robust Bayesian analysis techniques, we simultaneously
+determine ages, distances, absorptions, and helium values for each cluster
+under the scenario of a ""single"" stellar population on model grids with solar
+ratio heavy element abundances. The set of cluster parameters is determined in
+a consistent and reproducible manner for all clusters using the Bayesian
+analysis suite BASE-9. Our results are used to re-visit the age-metallicity
+relation. We find correlations with helium and several other parameters such as
+metallicity, binary fraction, and proxies for cluster mass. The helium
+abundances of the clusters are also considered in the context of CNO abundances
+and the multiple population scenario.
+"
+29075,Dark matter and Inflation in PeV scale SUSY,"  We construct an unified model of inflation and PeV dark matter which is
+consistent with the CMB observations and can explain the PeV neutrinos at
+IceCube. A no-scale Kähler potential and an appropriate choice of gauge
+kinetic function in MSSM superfields can result in Starobinsky like
+Higgs-sneutrino plateau inflation from $D$-term potential. The SUSY breaking at
+the PeV scale is achieved through the Polonyi field. This sets the scales $m_0,
+m_{\frac{1}{2}}, A_0$ at the GUT scale in terms of the parameters of the
+theory. The low energy (PeV scale) spectrum is obtained by running the RGEs. We
+show that the $\sim$125 GeV higgs and the gauge coupling unification can be
+achieved in this model. The $6$ PeV bino-type dark matter is a subdominant
+fraction ($\sim 11\%$) of the relic density and its decay gives the PeV scale
+neutrino flux observed at IceCube by choosing the couplings of the $R$-parity
+violating operators.
+"
+29076,Tracy-Widom limit for Kendall's tau,"  In this paper, we study a high-dimensional random matrix model from
+nonparametric statistics called Kendall rank correlation matrix, which is a
+natural multivariate extension of Kendall rank correlation coefficient. We
+establish the Tracy-Widom law for its largest eigenvalue. It is the first
+Tracy-Widom law obtained for a nonparametric random matrix model, and is also
+the first Tracy-Widom law for a high-dimensional U-statistics.
+"
+29077,A bright future for financial agent-based models,"  The history of research in finance and economics has been widely impacted by
+the field of Agent-based Computational Economics (ACE). While at the same time
+being popular among natural science researchers for its proximity to the
+successful methods of physics and chemistry for example, the field of ACE has
+also received critics by a part of the social science community for its lack of
+empiricism. Yet recent trends have shifted the weights of these general
+arguments and potentially given ACE a whole new range of realism. At the base
+of these trends are found two present-day major scientific breakthroughs: the
+steady shift of psychology towards a hard science due to the advances of
+neuropsychology, and the progress of artificial intelligence and more
+specifically machine learning due to increasing computational power and big
+data. These two have also found common fields of study in the form of
+computational neuroscience, and human-computer interaction, among others. We
+outline here the main lines of a computational research study of collective
+economic behavior via Agent-Based Models (ABM) or Multi-Agent System (MAS),
+where each agent would be endowed with specific cognitive and behavioral biases
+known to the field of neuroeconomics, and at the same time autonomously
+implement rational quantitative financial strategies updated by machine
+learning. We postulate that such ABMs would offer a whole new range of realism.
+"
+29078,Growth of groups of wind generated waves,"  In this paper we demonstrate numerical computations of turbulent wind blowing
+over group of waves that are growing in time. The numerical model adopted for
+the turbulence model is based on differential second-moment model that was
+adopted for growing idealized waves by Drullion and Sajjadi (2014). The results
+obtained here demonstrate the formation of cat's-eye which appear
+asymmetrically over the waves within a group.
+"
+29079,Ensemble Control of Cycling Energy Loads: Markov Decision Approach,"  A Markov decision process (MDP) framework is adopted to represent ensemble
+control of devices with cyclic energy consumption patterns, e.g.,
+thermostatically controlled loads. Specifically we utilize and develop the
+class of MDP models previously coined linearly solvable MDPs, that describe
+optimal dynamics of the probability distribution of an ensemble of many cycling
+devices. Two principally different settings are discussed. First, we consider
+optimal strategy of the ensemble aggregator balancing between minimization of
+the cost of operations and minimization of the ensemble welfare penalty, where
+the latter is represented as a KL-divergence between actual and normal
+probability distributions of the ensemble. Then, second, we shift to the demand
+response setting modeling the aggregator's task to minimize the welfare penalty
+under the condition that the aggregated consumption matches the targeted
+time-varying consumption requested by the system operator. We discuss a
+modification of both settings aimed at encouraging or constraining the
+transitions between different states. The dynamic programming feature of the
+resulting modified MDPs is always preserved; however, `linear solvability' is
+lost fully or partially, depending on the type of modification. We also
+conducted some (limited in scope) numerical experimentation using the
+formulations of the first setting. We conclude by discussing future
+generalizations and applications.
+"
+29080,Another Approach to Consensus of Multi-agents,"  In this short note, we recommend another approach to deal with the topic
+Consensus of Multi-agents, which was proposed in \cite{Chena}.
+"
+29081,Fast rates for online learning in Linearly Solvable Markov Decision Processes,"  We study the problem of online learning in a class of Markov decision
+processes known as linearly solvable MDPs. In the stationary version of this
+problem, a learner interacts with its environment by directly controlling the
+state transitions, attempting to balance a fixed state-dependent cost and a
+certain smooth cost penalizing extreme control inputs. In the current paper, we
+consider an online setting where the state costs may change arbitrarily between
+consecutive rounds, and the learner only observes the costs at the end of each
+respective round. We are interested in constructing algorithms for the learner
+that guarantee small regret against the best stationary control policy chosen
+in full knowledge of the cost sequence. Our main result is showing that the
+smoothness of the control cost enables the simple algorithm of following the
+leader to achieve a regret of order $\log^2 T$ after $T$ rounds, vastly
+improving on the best known regret bound of order $T^{3/4}$ for this setting.
+"
+29082,Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems,"  The process of transforming observed data into predictive mathematical models
+of the physical world has always been paramount in science and engineering.
+Although data is currently being collected at an ever-increasing pace, devising
+meaningful models out of such observations in an automated fashion still
+remains an open problem. In this work, we put forth a machine learning approach
+for identifying nonlinear dynamical systems from data. Specifically, we blend
+classical tools from numerical analysis, namely the multi-step time-stepping
+schemes, with powerful nonlinear function approximators, namely deep neural
+networks, to distill the mechanisms that govern the evolution of a given
+data-set. We test the effectiveness of our approach for several benchmark
+problems involving the identification of complex, nonlinear and chaotic
+dynamics, and we demonstrate how this allows us to accurately learn the
+dynamics, forecast future states, and identify basins of attraction. In
+particular, we study the Lorenz system, the fluid flow behind a cylinder, the
+Hopf bifurcation, and the Glycoltic oscillator model as an example of
+complicated nonlinear dynamics typical of biological systems.
+"
+29083,Fibre Homogenisation,"  In this article we present a novel method for studying the asymptotic
+behaviour, with order-sharp error estimates, of the resolvents of
+parameter-dependent operator families. The method is applied to the study of
+differential equations with rapidly oscillating coefficients in the context of
+second-order PDE systems and the Maxwell system. This produces a non-standard
+homogenisation result that is characterised by `fibre-wise' homogenisation of
+the related Floquet-Bloch PDEs. These fibre-homogenised resolvents are shown to
+be asymptotically equivalent to a whole class of operator families, including
+those obtained by standard homogenisation methods.
+"
+29084,Systematic evaluation of the population-level effects of alternative treatment strategies on the basic reproduction number,"  An approach to estimate the influence of the treatment-type controls on the
+basic reproduction number, R 0 , is proposed and elaborated. The presented
+approach allows one to estimate the effect of a given treatment strategy or to
+compare a number of different treatment strategies on the basic reproduction
+number. All our results are valid for sufficiently small values of the control.
+However, in many cases it is possible to extend this analysis to larger values
+of the control as was illustrated by examples.
+"
+29085,Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs,"  Binary code analysis allows analyzing binary code without having access to
+the corresponding source code. A binary, after disassembly, is expressed in an
+assembly language. This inspires us to approach binary analysis by leveraging
+ideas and techniques from Natural Language Processing (NLP), a rich area
+focused on processing text of various natural languages. We notice that binary
+code analysis and NLP share a lot of analogical topics, such as semantics
+extraction, summarization, and classification. This work utilizes these ideas
+to address two important code similarity comparison problems. (I) Given a pair
+of basic blocks for different instruction set architectures (ISAs), determining
+whether their semantics is similar or not; and (II) given a piece of code of
+interest, determining if it is contained in another piece of assembly code for
+a different ISA. The solutions to these two problems have many applications,
+such as cross-architecture vulnerability discovery and code plagiarism
+detection. We implement a prototype system INNEREYE and perform a comprehensive
+evaluation. A comparison between our approach and existing approaches to
+Problem I shows that our system outperforms them in terms of accuracy,
+efficiency and scalability. And the case studies utilizing the system
+demonstrate that our solution to Problem II is effective. Moreover, this
+research showcases how to apply ideas and techniques from NLP to large-scale
+binary code analysis.
+"
+29086,X-ray emission from the nuclear region of Arp 220,"  We present an imaging and spectral analysis of the nuclear region of the
+ULIRG merger Arp 220, using deep \textit{Chandra}-ACIS observations summing up
+to \(\sim 300\mbox{ ks}\). Narrow-band imaging with sub-pixel resolution of the
+innermost nuclear region reveals two distinct Fe-K emitting sources, coincident
+with the infrared and radio nuclear clusters. These sources are separated by 1'
+(\(\sim 380\) pc). The X-ray emission is extended and elongated in the eastern
+nucleus, like the disk emission observed in millimeter radio images, suggesting
+starburst dominance in this region. We estimate Fe-K equivalent width \(\gtrsim
+1\) keV for both sources, and observed 2-10 keV luminosities \(\sim
+2\times{10}^{40}\mbox{ erg}\mbox{ s}^{-1}\) (W) and \(\sim 3 \times
+{10}^{40}\mbox{ erg}\mbox{ s}^{-1}\) (E). In the 6-7 keV band the emission from
+these regions is dominated by the 6.7 keV Fe \textsc{xxv} line, suggesting
+contribution from collisionally ionized gas. The thermal energy content of this
+gas is consistent with kinetic energy injection in the interstellar medium by
+Type II SNe. However, nuclear winds from hidden AGN (\(\varv\sim 2000 \mbox{
+km}\mbox{ s}^{-1}\)) cannot be excluded. The \(3\sigma\) upper limits on the
+neutral Fe-K\(\alpha\) flux of the nuclear regions correspond to intrinsic AGN
+2-10 keV luminosities \(< 1\times {10}^{42}\mbox{ erg}\mbox{ s}^{-1}\) (W) and
+\(< 0.4\times {10}^{42}\mbox{ erg}\mbox{ s}^{-1}\) (E). For typical AGN SEDs
+the bolometric luminosities are \(< 3\times {10}^{43}\mbox{ erg}\mbox{
+s}^{-1}\) (W) and \(< 8\times {10}^{43}\mbox{ erg}\mbox{ s}^{-1}\) (E), and
+black hole masses \(<1\times{10}^5 M_{\astrosun}\) (W) and \(< 5\times{10}^5
+M_{\astrosun}\) (E) for Eddington limited AGNs with a standard 10\% efficiency.
+"
+29087,Mechanical response of all-MoS2 single-layer hetrostructures: A ReaxFF investigation,"  Molybdenum disulfide (MoS2) is a highly attractive 2D material due to its
+interesting electronic properties. Recent experimental advances confirm the
+possibility of further tuning the electronic properties of MoS2 through the
+fabrication of single-layer heterostructures consisting of semiconducting (2H)
+and metallic (1T) MoS2 phases. Nonetheless, despite significant technological
+and scientific interest, there is currently limited information concerning the
+mechanical properties of these heterostructure systems. This investigation aims
+to extend our understanding of the mechanical properties of all-MoS2
+single-layer structures at room temperature. This goal was achieved by
+performing extensive classical molecular dynamics simulations using a recently
+developed RexFF forcefield. We first studied the direction dependent mechanical
+properties of defect-free 2H and 1T phases. Our modelling results for pristine
+2H MoS2 were found to be in good agreement with the experimental tests and
+first-principles theoretical predictions. We also discuss the mechanical
+response of 2H/1T single layer heterostructures. Our reactive molecular
+dynamics results suggest all-MoS2 heterostructures as suitable candidates to
+provide a strong and flexible material with tuneable electronic properties.
+"
+29088,Construction of type II blowup solutions for the 1-corotational energy supercritical wave maps,"  We consider the energy supercritical wave maps from $\mathbb{R}^d$ into the
+$d$-sphere $\mathbb{S}^d$ with $d \geq 7$. Under an additional assumption of
+1-corotational symmetry, the problem reduces to the one dimensional semilinear
+wave equation $$\partial_t^2 u = \partial^2_r u + \frac{(d-1)}{r}\partial_r u -
+\frac{(d-1)}{2r^2}\sin(2u).$$ We construct for this equation a family of
+$\mathcal{C}^{\infty}$ solutions which blow up in finite time via concentration
+of the universal profile $$u(r,t) \sim Q\left(\frac{r}{\lambda(t)}\right),$$
+where $Q$ is the stationary solution of the equation and the speed is given by
+the quantized rates $$\lambda(t) \sim c_u(T-t)^\frac{\ell}{\gamma}, \quad \ell
+\in \mathbb{N}^*, \;\; \ell > \gamma = \gamma(d) \in (1,2].$$ The construction
+relies on two arguments: the reduction of the problem to a finite-dimensional
+one thanks to a robust universal energy method and modulation techniques
+developed by Merle, Raphaël and Rodnianski for the energy supercritical
+nonlinear Schrödinger equation, then we proceed by contradiction to solve the
+finite-dimensional problem and conclude using the Brouwer fixed point theorem.
+"
+29089,Optimally solving the joint order batching and picker routing problem,"  In this work we investigate the problem of order batching and picker routing
+in storage areas. These are labour and capital intensive problems, often
+responsible for a substantial share of warehouse operating costs. In
+particular, we consider the case of online grocery shopping in which orders may
+be composed of dozens of items.
+We present a formulation for the problem based on an exponential number of
+connectivity constraints and we introduce a significant number of valid
+inequalities based on the standard layout of warehouses, composed of parallel
+aisles and two or more cross-aisles. The proposed inequalities are highly
+effective and greatly improve computational results.
+Instances involving up to 20 orders are solved to proven optimality when we
+jointly consider order batching and picker routing. Instances involving up to
+5000 orders are considered where order batching is done heuristically, but
+picker routing is done optimally.
+"
+29090,Automated pick-up of suturing needles for robotic surgical assistance,"  Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate
+cancer that involves complete or nerve sparing removal prostate tissue that
+contains cancer. After removal the bladder neck is successively sutured
+directly with the urethra. The procedure is called urethrovesical anastomosis
+and is one of the most dexterity demanding tasks during RALP. Two suturing
+instruments and a pair of needles are used in combination to perform a running
+stitch during urethrovesical anastomosis. While robotic instruments provide
+enhanced dexterity to perform the anastomosis, it is still highly challenging
+and difficult to learn. In this paper, we presents a vision-guided needle
+grasping method for automatically grasping the needle that has been inserted
+into the patient prior to anastomosis. We aim to automatically grasp the
+suturing needle in a position that avoids hand-offs and immediately enables the
+start of suturing. The full grasping process can be broken down into: a needle
+detection algorithm; an approach phase where the surgical tool moves closer to
+the needle based on visual feedback; and a grasping phase through path planning
+based on observed surgical practice. Our experimental results show examples of
+successful autonomous grasping that has the potential to simplify and decrease
+the operational time in RALP by assisting a small component of urethrovesical
+anastomosis.
+"
+29091,Direct Testing against Experiment of a Fundamental Ultrashort Pulse Laser Damage Simulation Technique with Utility for the Modeling of Nanostructure Formation,"  We have developed the first laser damage simulation algorithm capable of
+determining crater and surface modification morphology from microscopic
+physics. Rapid progress in the field of high intensity ultrafast lasers has
+enabled its utility in a myriad of applications. Simulation plays an important
+role in this research by allowing for closer analysis of the physical
+mechanisms involved, but current techniques struggle to meet both the spatial
+scope or resolution requirements for modeling such dynamics, typically
+specializing in one or the other. Consequently, it is difficult to extract the
+physical form of the laser induced surface modification, hampering direct
+comparison of simulation to experimental results. Our algorithm offers a
+compromise to existing simulation techniques and enables the production of a
+complete density profile in addition to the simulation of intermediate
+dynamics. We use this capability to directly test our simulation against
+experimentally produced copper craters. Additionally, we show how our algorithm
+can be used to model the formation of surface roughness and nanoparticles.
+"
+29092,The Igusa-Todorov $ϕ$ function for truncated path algebras,"  Given a truncated path algebra $A=\frac{\Bbbk Q}{J^k}$ we prove that $\fidim
+A = \fidim A^{\op}$. We also compute the $\phi$-dimension of $A$ in function of
+the $\phi$-dimension of $\frac{\Bbbk Q}{J^2}$ when $Q$ has no sources nor
+sinks. This allows us to bound the $\phi$-dimension for truncated path
+algebras. Finally, we characterize $A$ when its $\phi$-dimension is equal to
+$1$.
+"
+29093,Threshold of transverse mode coupling instability with arbitrary space charge,"  Threshold of the transverse mode coupling instability is calculated in
+frameworks of the square well model at arbitrary value of space charge tune
+shift. A new method of calculation is developed beyond the traditional
+expansion technique. The square, resistive, and exponential wakes are
+investigated. It is shown that the instability threshold goes up without limit
+when the tune shift increases. A resemblance of the results to conventional
+case of the parabolic potential well is demonstrated and explained.
+"
+29094,Special Kähler geometry of the Hitchin system and topological recursion,"  We investigate the special Kähler geometry of the base of the Hitchin
+integrable system in terms of spectral curves and topological recursion. The
+Taylor expansion of the special Kähler metric about any point in the base may
+be computed by integrating the $g = 0$ Eynard-Orantin invariants of the
+corresponding spectral curve over cycles. In particular, we show that the
+Donagi-Markman cubic is computed by the invariant $W^{(0)}_3$. We use
+topological recursion to go one step beyond this and compute the symmetric
+quartic of second derivatives of the period matrix.
+"
+29095,An OpenGL and C++ based function library for curve and surface modeling in a large class of extended Chebyshev spaces,"  We propose a platform-independent multi-threaded function library that
+provides data structures to generate, differentiate and render both the
+ordinary basis and the normalized B-basis of a user-specified extended
+Chebyshev (EC) space that comprises the constants and can be identified with
+the solution space of a constant-coefficient homogeneous linear differential
+equation defined on a sufficiently small interval. Using the obtained
+normalized B-bases, our library can also generate, (partially) differentiate,
+modify and visualize a large family of so-called B-curves and tensor product
+B-surfaces. Moreover, the library also implements methods that can be used to
+perform dimension elevation, to subdivide B-curves and B-surfaces by means of
+de Casteljau-like B-algorithms, and to generate basis transformations for the
+B-representation of arbitrary integral curves and surfaces that are described
+in traditional parametric form by means of the ordinary bases of the underlying
+EC spaces. Independently of the algebraic, exponential, trigonometric or mixed
+type of the applied EC space, the proposed library is numerically stable and
+efficient up to a reasonable dimension number and may be useful for academics
+and engineers in the fields of Approximation Theory, Computer Aided Geometric
+Design, Computer Graphics, Isogeometric and Numerical Analysis.
+"
+29096,Sample-Efficient Algorithms for Recovering Structured Signals from Magnitude-Only Measurements,"  We consider the problem of recovering a signal $\mathbf{x}^* \in
+\mathbf{R}^n$, from magnitude-only measurements $y_i =
+|\left\langle\mathbf{a}_i,\mathbf{x}^*\right\rangle|$ for $i=[m]$. Also called
+the phase retrieval, this is a fundamental challenge in bio-,astronomical
+imaging and speech processing. The problem above is ill-posed; additional
+assumptions on the signal and/or the measurements are necessary. In this paper
+we first study the case where the signal $\mathbf{x}^*$ is $s$-sparse. We
+develop a novel algorithm that we call Compressive Phase Retrieval with
+Alternating Minimization, or CoPRAM. Our algorithm is simple; it combines the
+classical alternating minimization approach for phase retrieval with the CoSaMP
+algorithm for sparse recovery. Despite its simplicity, we prove that CoPRAM
+achieves a sample complexity of $O(s^2\log n)$ with Gaussian measurements
+$\mathbf{a}_i$, matching the best known existing results; moreover, it
+demonstrates linear convergence in theory and practice. Additionally, it
+requires no extra tuning parameters other than signal sparsity $s$ and is
+robust to noise. When the sorted coefficients of the sparse signal exhibit a
+power law decay, we show that CoPRAM achieves a sample complexity of $O(s\log
+n)$, which is close to the information-theoretic limit. We also consider the
+case where the signal $\mathbf{x}^*$ arises from structured sparsity models. We
+specifically examine the case of block-sparse signals with uniform block size
+of $b$ and block sparsity $k=s/b$. For this problem, we design a recovery
+algorithm Block CoPRAM that further reduces the sample complexity to $O(ks\log
+n)$. For sufficiently large block lengths of $b=\Theta(s)$, this bound equates
+to $O(s\log n)$. To our knowledge, this constitutes the first end-to-end
+algorithm for phase retrieval where the Gaussian sample complexity has a
+sub-quadratic dependence on the signal sparsity level.
+"
+29097,Performance Analysis of Inband FD-D2D Communications with Imperfect SI Cancellation for Wireless Video Distribution,"  Tremendous growing demand for high data rate services is the main driver for
+increasing traffic in wireless cellular networks. Device-to-Device (D2D)
+communications have recently been proposed to offload data via direct
+communications by bypassing cellular base stations (BSs). Such an offloading
+schemes increase capacity and reduce end-to-end delay in cellular networks and
+help to serve the dramatically increasing demand for high data rate. In this
+paper, we aim to analyze inband fullduplex (FD) D2D performance for the
+wireless video distribution by considering imperfect self-interference (SI)
+cancellation. Using tools from stochastic geometry, we analyze outage
+probability and spectral efficiency. Analytic and simulation results are used
+to demonstrate achievable gain against its half-duplex (HD) counterpart.
+"
+29098,Word sense induction using word embeddings and community detection in complex networks,"  Word Sense Induction (WSI) is the ability to automatically induce word senses
+from corpora. The WSI task was first proposed to overcome the limitations of
+manually annotated corpus that are required in word sense disambiguation
+systems. Even though several works have been proposed to induce word senses,
+existing systems are still very limited in the sense that they make use of
+structured, domain-specific knowledge sources. In this paper, we devise a
+method that leverages recent findings in word embeddings research to generate
+context embeddings, which are embeddings containing information about the
+semantical context of a word. In order to induce senses, we modeled the set of
+ambiguous words as a complex network. In the generated network, two instances
+(nodes) are connected if the respective context embeddings are similar. Upon
+using well-established community detection methods to cluster the obtained
+context embeddings, we found that the proposed method yields excellent
+performance for the WSI task. Our method outperformed competing algorithms and
+baselines, in a completely unsupervised manner and without the need of any
+additional structured knowledge source.
+"
+29099,Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments,"  We propose strategies to estimate and make inference on key features of
+heterogeneous effects in randomized experiments. These key features include
+best linear predictors of the effects using machine learning proxies, average
+effects sorted by impact groups, and average characteristics of most and least
+impacted units. The approach is valid in high dimensional settings, where the
+effects are proxied by machine learning methods. We post-process these proxies
+into the estimates of the key features. Our approach is generic, it can be used
+in conjunction with penalized methods, deep and shallow neural networks,
+canonical and new random forests, boosted trees, and ensemble methods. Our
+approach is agnostic and does not make unrealistic or hard-to-check
+assumptions; we don't require conditions for consistency of the ML methods.
+Estimation and inference relies on repeated data splitting to avoid overfitting
+and achieve validity. For inference, we take medians of p-values and medians of
+confidence intervals, resulting from many different data splits, and then
+adjust their nominal level to guarantee uniform validity. This variational
+inference method is shown to be uniformly valid and quantifies the uncertainty
+coming from both parameter estimation and data splitting. The inference method
+could be of substantial independent interest in many machine learning
+applications. An empirical application to the impact of micro-credit on
+economic development illustrates the use of the approach in randomized
+experiments. An additional application to the impact of the gender
+discrimination on wages illustrates the potential use of the approach in
+observational studies, where machine learning methods can be used to condition
+flexibly on very high-dimensional controls.
+"
+29100,Algebraic integrability of foliations with numerically trivial canonical bundle,"  Given a reflexive sheaf on a mildly singular projective variety, we prove a
+flatness criterion under certain stability conditions. This implies the
+algebraicity of leaves for sufficiently stable foliations with numerically
+trivial canonical bundle such that the second Chern class does not vanish.
+Combined with the recent work of Druel and Greb-Guenancia-Kebekus this
+establishes the Beauville-Bogomolov decomposition for minimal models with
+trivial canonical class.
+"
+29101,Weakly Inscribed Polyhedra,"  We study convex polyhedra in $\mathbb{R}\mathbb{P}^3$ with all their vertices
+on a sphere. We do not require, in particular, that the polyhedra lie in the
+interior of the sphere, hence the term ""weakly inscribed"". Such polyhedra can
+be interpreted as ideal polyhedra, if we regard $\mathbb{R}\mathbb{P}^3$ as a
+combination of the hyperbolic space and the de Sitter space, with the sphere as
+the common ideal boundary. We have three main results: (1) the $1$-skeleta of
+weakly inscribed polyhedra are characterized in a purely combinatorial way, (2)
+the exterior dihedral angles are characterized by linear programming, and (3)
+we also describe the hyperbolic-de Sitter structure induced on the boundary of
+weakly inscribed polyhedra.
+"
+29102,Giant magnetoresistance and structure of electrodeposited Co/Cu multilayers: the influence of layer thicknesses and Cu deposition potential,"  The giant magnetoresistance (GMR) and structure was investigated for
+electrodeposited Co/Cu multilayers prepared by a conventional
+galvanostatic/potentiostatic pulse combination from a pure sulfate electrolyte
+with various layer thicknesses, total multilayer thickness and Cu deposition
+potential. X-ray diffraction (XRD) measurements revealed superlattice satellite
+reflections for many of the multilayers having sufficiently large thickness (at
+least 2 nm) of both constituent layers. The bilayer repeats derived from the
+positions of the visible superlattice reflections were typically 10-20% higher
+than the nominal values.The observed GMR was found to be dominated by the
+multilayer-like ferromagnetic (FM) contribution even for multilayers without
+visible superlattice satellites. There was always also a modest
+superparamagnetic (SPM) contribution to the GMR and this term was the largest
+for multilayers with very thin (0.5 nm) magnetic layers containg apparently a
+small amount of magnetically decoupled SPM regions. No oscillatory GMR behavior
+with spacer thickness was observed at any magnetic layer thickness. The
+saturation of the coercivity as measured by the peak position of the MR(H)
+curves indicated a complete decoupling of magnetic layers for large spacer
+thicknesses. The GMR increased with total multilayer thickness which could be
+ascribed to an increasing SPM contribution to the GMR due to an increasing
+surface roughness, also indicated by the increasing coercivity. For multilayers
+with Cu layers deposited at more and more positive potentials, the GMRFM term
+increased and the GMRSPM term decreased. At the same time, a corresponding
+reduction of surface roughness measured with atomic force microscopy indicated
+an improvement of the multilayer structural quality which was, however, not
+accompanied by an increase of the superlattice reflection intensities.
+"
+29103,Deep Learning in Multiple Multistep Time Series Prediction,"  The project aims to research on combining deep learning specifically
+Long-Short Memory (LSTM) and basic statistics in multiple multistep time series
+prediction. LSTM can dive into all the pages and learn the general trends of
+variation in a large scope, while the well selected medians for each page can
+keep the special seasonality of different pages so that the future trend will
+not fluctuate too much from the reality. A recent Kaggle competition on 145K
+Web Traffic Time Series Forecasting [1] is used to thoroughly illustrate and
+test this idea.
+"
+29104,"Model compression as constrained optimization, with application to neural nets. Part I: general framework","  Compressing neural nets is an active research problem, given the large size
+of state-of-the-art nets for tasks such as object recognition, and the
+computational limits imposed by mobile devices. We give a general formulation
+of model compression as constrained optimization. This includes many types of
+compression: quantization, low-rank decomposition, pruning, lossless
+compression and others. Then, we give a general algorithm to optimize this
+nonconvex problem based on the augmented Lagrangian and alternating
+optimization. This results in a ""learning-compression"" algorithm, which
+alternates a learning step of the uncompressed model, independent of the
+compression type, with a compression step of the model parameters, independent
+of the learning task. This simple, efficient algorithm is guaranteed to find
+the best compressed model for the task in a local sense under standard
+assumptions.
+We present separately in several companion papers the development of this
+general framework into specific algorithms for model compression based on
+quantization, pruning and other variations, including experimental results on
+compressing neural nets and other models.
+"
+29105,Sustainable computational science: the ReScience initiative,"  Computer science offers a large set of tools for prototyping, writing,
+running, testing, validating, sharing and reproducing results, however
+computational science lags behind. In the best case, authors may provide their
+source code as a compressed archive and they may feel confident their research
+is reproducible. But this is not exactly true. James Buckheit and David Donoho
+proposed more than two decades ago that an article about computational results
+is advertising, not scholarship. The actual scholarship is the full software
+environment, code, and data that produced the result. This implies new
+workflows, in particular in peer-reviews. Existing journals have been slow to
+adapt: source codes are rarely requested, hardly ever actually executed to
+check that they produce the results advertised in the article. ReScience is a
+peer-reviewed journal that targets computational research and encourages the
+explicit replication of already published research, promoting new and
+open-source implementations in order to ensure that the original research can
+be replicated from its description. To achieve this goal, the whole publishing
+chain is radically different from other traditional scientific journals.
+ReScience resides on GitHub where each new implementation of a computational
+study is made available together with comments, explanations, and software
+tests.
+"
+29106,High-Order Localized Dissipation Weighted Compact Nonlinear Scheme for Shock- and Interface-Capturing in Compressible Flows,"  Simulations of single- and multi-species compressible flows with shock waves
+and discontinuities are conducted using a weighted compact nonlinear scheme
+(WCNS) with a newly developed sixth order localized dissipative interpolation.
+In smooth regions, the scheme applies the central nonlinear interpolation with
+minimum dissipation to well resolve fluctuating flow features while in regions
+containing discontinuities and high wavenumber features, the scheme suppresses
+spurious numerical oscillations by hybridizing the central interpolation with
+the more dissipative upwind-biased nonlinear interpolation. In capturing
+material interfaces between species of different densities, a
+quasi-conservative five equation model that can conserve mass of each species
+is used to prevent pressure oscillations across the interfaces. Compared to
+upwind-biased interpolations with classical nonlinear weights and improved
+weights, and the interpolation with adaptive central-upwind weights for
+scale-separation, it is shown that WCNS with the proposed localized dissipative
+interpolation has better performance to simultaneously capture discontinuities
+and resolve smooth features.
+"
+29107,Modeling Customer Engagement from Partial Observations,"  It is of high interest for a company to identify customers expected to bring
+the largest profit in the upcoming period. Knowing as much as possible about
+each customer is crucial for such predictions. However, their demographic data,
+preferences, and other information that might be useful for building loyalty
+programs is often missing. Additionally, modeling relations among different
+customers as a network can be beneficial for predictions at an individual
+level, as similar customers tend to have similar purchasing patterns. We
+address this problem by proposing a robust framework for structured regression
+on deficient data in evolving networks with a supervised representation
+learning based on neural features embedding. The new method is compared to
+several unstructured and structured alternatives for predicting customer
+behavior (e.g. purchasing frequency and customer ticket) on user networks
+generated from customer databases of two companies from different industries.
+The obtained results show $4\%$ to $130\%$ improvement in accuracy over
+alternatives when all customer information is known. Additionally, the
+robustness of our method is demonstrated when up to $80\%$ of demographic
+information was missing where it was up to several folds more accurate as
+compared to alternatives that are either ignoring cases with missing values or
+learn their feature representation in an unsupervised manner.
+"
+29108,Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach,"  We study the classical problem of maximizing a monotone submodular function
+subject to a cardinality constraint k, with two additional twists: (i) elements
+arrive in a streaming fashion, and (ii) m items from the algorithm's memory are
+removed after the stream is finished. We develop a robust submodular algorithm
+STAR-T. It is based on a novel partitioning structure and an exponentially
+decreasing thresholding rule. STAR-T makes one pass over the data and retains a
+short but robust summary. We show that after the removal of any m elements from
+the obtained summary, a simple greedy algorithm STAR-T-GREEDY that runs on the
+remaining elements achieves a constant-factor approximation guarantee. In two
+different data summarization tasks, we demonstrate that it matches or
+outperforms existing greedy and streaming methods, even if they are allowed the
+benefit of knowing the removed subset in advance.
+"
+29109,Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex,"  A fundamental challenge in neuroscience is to understand what structure in
+the world is represented in spatially distributed patterns of neural activity
+from multiple single-trial measurements. This is often accomplished by learning
+a simple, linear transformations between neural features and features of the
+sensory stimuli or motor task. While successful in some early sensory
+processing areas, linear mappings are unlikely to be ideal tools for
+elucidating nonlinear, hierarchical representations of higher-order brain areas
+during complex tasks, such as the production of speech by humans. Here, we
+apply deep networks to predict produced speech syllables from cortical surface
+electric potentials recorded from human sensorimotor cortex. We found that deep
+networks had higher decoding prediction accuracy compared to baseline models,
+and also exhibited greater improvements in accuracy with increasing dataset
+size. We further demonstrate that deep network's confusions revealed
+hierarchical latent structure in the neural data, which recapitulated the
+underlying articulatory nature of speech motor control. Finally, we used deep
+networks to compare task-relevant information in different neural frequency
+bands, and found that the high-gamma band contains the vast majority of
+information relevant for the speech prediction task, with little-to-no
+additional contribution from lower-frequencies. Together, these results
+demonstrate the utility of deep networks as a data analysis tool for
+neuroscience.
+"
+29110,Why So Many Published Sensitivity Analyses Are False. A Systematic Review of Sensitivity Analysis Practices,"  Sensitivity analysis (SA) has much to offer for a very large class of
+applications, such as model selection, calibration, optimization, quality
+assurance and many others. Sensitivity analysis offers crucial contextual
+information regarding a prediction by answering the question ""Which uncertain
+input factors are responsible for the uncertainty in the prediction?"" SA is
+distinct from uncertainty analysis (UA), which instead addresses the question
+""How uncertain is the prediction?"" As we discuss in the present paper much
+confusion exists in the use of these terms. A proper uncertainty analysis of
+the output of a mathematical model needs to map what the model does when the
+input factors are left free to vary over their range of existence. A fortiori,
+this is true of a sensitivity analysis. Despite this, most UA and SA still
+explore the input space; moving along mono-dimensional corridors which leave
+the space of variation of the input factors mostly unscathed. We use results
+from a bibliometric analysis to show that many published SA fail the elementary
+requirement to properly explore the space of the input factors. The results,
+while discipline-dependent, point to a worrying lack of standards and of
+recognized good practices. The misuse of sensitivity analysis in mathematical
+modelling is at least as serious as the misuse of the p-test in statistical
+modelling. Mature methods have existed for about two decades to produce a
+defensible sensitivity analysis. We end by offering a rough guide for proper
+use of the methods.
+"
+29111,Characterizations of pretameness and the Ord-cc,"  It is well known that pretameness implies the forcing theorem, and that
+pretameness is characterized by the preservation of the axioms of
+$\mathsf{ZF}^-$, that is $\mathsf{ZF}$ without the power set axiom, or
+equivalently, by the preservation of the axiom scheme of replacement, for class
+forcing over models of $\mathsf{ZF}$. We show that pretameness in fact has
+various other characterizations, for instance in terms of the forcing theorem,
+the preservation of the axiom scheme of separation, the forcing equivalence of
+partial orders and their dense suborders, and the existence of nice names for
+sets of ordinals. These results show that pretameness is a strong dividing line
+between well and badly behaved notions of class forcing, and that it is exactly
+the right notion to consider in applications of class forcing. Furthermore, for
+most properties under consideration, we also present a corresponding
+characterization of the $\mathrm{Ord}$-chain condition.
+"
+29112,Using smartphones for low-cost robotics,"  Smartphones and robots can have an adversarial or a symbiotic relationship
+because they strive to serve overlapping customer needs. While smartphones are
+prevalent, humanoid robots are not. Even though considerable public and private
+resources are being invested in developing and commercializing humanoid robots,
+progress seems stalled and no humanoid robot can be said to be successful with
+consumers. A part from the obvious engineering differences between humanoids
+and smartphones, other economic factors influence this situation. On one hand,
+the product cycle of robots is slower than smartphones. This makes robot
+computing hardware, (as it with automobile's infotainment systems), perennially
+outdated when side-by-side to a smartphone. On the other hand, the incentives
+to develop Apps are high for smartphones and they are comparatively low for
+robot platforms. Here, we point to how smartphones could be used to lower
+hardware cost and foster robot app development.
+"
+29113,"(1, k)-Swap Local Search for Maximum Clique Problem","  Given simple undirected graph G = (V, E), the Maximum Clique Problem(MCP) is
+that of finding a maximum-cardinality subset Q of V such that any two vertices
+in Q are adjacent. We present a modified local search algorithm for this
+problem. Our algorithm build some maximal solution and can determine in
+polynomial time if a maximal solution can be improved by replacing a single
+vertex with k, k > 1, others. We test our algorithms on DIMACS[5], Sloane[15],
+BHOSLIB[1], Iovanella[8] and our random instances.
+"
+29114,Can decaying particle explain cosmic infrared background excess?,"  Recently the CIBER experiment measured the diffuse cosmic infrared background
+(CIB) flux and claimed an excess compared with integrated emission from
+galaxies. We show that the CIB spectrum can be fitted by the additional photons
+produced by the decay of a new particle. However, it also contributes too much
+to the anisotropy of the CIB, which is in contradiction with the anisotropy
+measurements by the CIBER and Hubble Space Telescope.
+"
+29115,Lossy Asymptotic Equipartition Property For Geometric Networked Data Structures,"  This article extends the Generalized Asypmtotic Equipartition Property of
+Networked Data Structures to cover the Wireless Sensor Network modelled as
+coloured geometric random graph (CGRG). The main techniques used to prove this
+result remains large deviation principles for properly defined empirical
+measures on CGRGs. As a motivation for this article, we apply our results to
+some data from Wireless Sensor Network for Monitoring Water Quality from a
+Lake..
+"
+29116,Supercompiling String Programs Using Word Equations as Constraints,"  We describe a general parameterized scheme of program and constraint analyses
+allowing us to specify both the program specialization method known as
+Turchin's supercompilation and Hmelevskii's algorithm solving the quadratic
+word equations. The scheme is specified for both sorts of the analysis and
+works in a joint algorithm in which these two sorts of the analysis are used
+together. The word equations and the inequalities on regular patterns are used
+as the string constraint language in the algorithm.
+"
+29117,A Self-Training Method for Semi-Supervised GANs,"  Since the creation of Generative Adversarial Networks (GANs), much work has
+been done to improve their training stability, their generated image quality,
+their range of application but nearly none of them explored their self-training
+potential. Self-training has been used before the advent of deep learning in
+order to allow training on limited labelled training data and has shown
+impressive results in semi-supervised learning. In this work, we combine these
+two ideas and make GANs self-trainable for semi-supervised learning tasks by
+exploiting their infinite data generation potential. Results show that using
+even the simplest form of self-training yields an improvement. We also show
+results for a more complex self-training scheme that performs at least as well
+as the basic self-training scheme but with significantly less data
+augmentation.
+"
+29118,Vector Field Based Neural Networks,"  A novel Neural Network architecture is proposed using the mathematically and
+physically rich idea of vector fields as hidden layers to perform nonlinear
+transformations in the data. The data points are interpreted as particles
+moving along a flow defined by the vector field which intuitively represents
+the desired movement to enable classification. The architecture moves the data
+points from their original configuration to anew one following the streamlines
+of the vector field with the objective of achieving a final configuration where
+classes are separable. An optimization problem is solved through gradient
+descent to learn this vector field.
+"
+29119,Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study,"  Deep neural networks (DNNs) have achieved unprecedented performance on a wide
+range of complex tasks, rapidly outpacing our understanding of the nature of
+their solutions. This has caused a recent surge of interest in methods for
+rendering modern neural systems more interpretable. In this work, we propose to
+address the interpretability problem in modern DNNs using the rich history of
+problem descriptions, theories and experimental methods developed by cognitive
+psychologists to study the human mind. To explore the potential value of these
+tools, we chose a well-established analysis from developmental psychology that
+explains how children learn word labels for objects, and applied that analysis
+to DNNs. Using datasets of stimuli inspired by the original cognitive
+psychology experiments, we find that state-of-the-art one shot learning models
+trained on ImageNet exhibit a similar bias to that observed in humans: they
+prefer to categorize objects according to shape rather than color. The
+magnitude of this shape bias varies greatly among architecturally identical,
+but differently seeded models, and even fluctuates within seeds throughout
+training, despite nearly equivalent classification performance. These results
+demonstrate the capability of tools from cognitive psychology for exposing
+hidden computational properties of DNNs, while concurrently providing us with a
+computational model for human word learning.
+"
+29120,Orthogonal Recurrent Neural Networks with Scaled Cayley Transform,"  Recurrent Neural Networks (RNNs) are designed to handle sequential data but
+suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent
+Neural Networks (uRNNs) have been used to address this issue and in some cases,
+exceed the capabilities of Long Short-Term Memory networks (LSTMs). We propose
+a simpler and novel update scheme to maintain orthogonal recurrent weight
+matrices without using complex valued matrices. This is done by parametrizing
+with a skew-symmetric matrix using the Cayley transform. Such a parametrization
+is unable to represent matrices with negative one eigenvalues, but this
+limitation is overcome by scaling the recurrent weight matrix by a diagonal
+matrix consisting of ones and negative ones. The proposed training scheme
+involves a straightforward gradient calculation and update step. In several
+experiments, the proposed scaled Cayley orthogonal recurrent neural network
+(scoRNN) achieves superior results with fewer trainable parameters than other
+unitary RNNs.
+"
+29121,BPS jumping loci and special cycles,"  We study BPS jumping loci, or the subloci in moduli spaces of supersymmetric
+string vacua where BPS states come into existence discontinuously. This
+phenomenon is distinct from wall-crossing. We argue that these loci should be
+thought of as special cycles in the sense of Noether-Lefschetz loci or special
+Shimura subvarieties, which are indeed examples of BPS jumping loci for certain
+string compactifications. We use the Hodge-elliptic genus as an informative
+tool, suggesting that our work can be extended to understand the jumping
+behavior of motivic Donaldson-Thomas invariants.
+"
+29122,Rank and Nielsen equivalence in hyperbolic extensions,"  In this note, we generalize a theorem of Juan Souto on rank and Nielsen
+equivalence in the fundamental group of a hyperbolic fibered 3-manifold to a
+large class of hyperbolic group extensions. This includes all hyperbolic
+extensions of surfaces groups as well as hyperbolic extensions of free groups
+by convex cocompact subgroups of Out$(F_n)$.
+"
+29123,"Finite Ramanujan expansions and shifted convolution sums of arithmetical functions, II","  We continue our study of convolution sums of two arithmetical functions $f$
+and $g$, of the form $\sum_{n \le N} f(n) g(n+h)$, in the context of heuristic
+asymptotic formul\ae. Here, the integer $h\ge 0$ is called, as usual, the {\it
+shift} of the convolution sum. We deepen the study of finite Ramanujan
+expansions of general $f,g$ for the purpose of studying their convolution sum.
+Also, we introduce another kind of Ramanujan expansion for the convolution sum
+of $f$ and $g$, namely in terms of its shift $h$ and we compare this \lq \lq
+shifted Ramanujan expansion\rq \rq, with our previous finite expansions in
+terms of the $f$ and $g$ arguments. Last but not least, we give examples of
+such shift expansions, in classical literature, for the heuristic formul\ae.
+"
+29124,A compositional modeling framework for the optimal energy management of a district network,"  This paper proposes a compositional modeling framework for the optimal energy
+management of a district network. The focus is on cooling of buildings, which
+can possibly share resources to the purpose of reducing maintenance costs and
+using devices at their maximal efficiency. Components of the network are
+described in terms of energy fluxes and combined via energy balance equations.
+Disturbances are accounted for as well through their contribution in terms of
+energy. Different district configurations can be built, and the dimension and
+complexity of the resulting model will depend on the number and type of
+components and on the adopted disturbance description. Control inputs are
+available to efficiently operate and coordinate the district components, thus
+enabling energy management strategies to minimize the electrical energy costs
+or track some consumption profile agreed with the main grid operator.
+"
+29125,GOTaxon: Representing the evolution of biological functions in the Gene Ontology,"  The Gene Ontology aims to define the universe of functions known for gene
+products, at the molecular, cellular and organism levels. While the ontology is
+designed to cover all aspects of biology in a ""species independent manner"", the
+fact remains that many if not most biological functions are restricted in their
+taxonomic range. This is simply because functions evolve, i.e. like other
+biological characteristics they are gained and lost over evolutionary time.
+Here we introduce a general method of representing the evolutionary gain and
+loss of biological functions within the Gene Ontology. We then apply a variety
+of techniques, including manual curation, logical reasoning over the ontology
+structure, and previously published ""taxon constraints"" to assign evolutionary
+gain and loss events to the majority of terms in the GO. These gain and loss
+events now almost triple the number of terms with taxon constraints, and
+currently cover a total of 76% of GO terms, including 40% of molecular function
+terms, 78% of cellular component terms, and 89% of biological process terms.
+Database URL: GOTaxon is freely available at
+this https URL
+"
+29126,A Minimax Algorithm Better Than Alpha-beta?: No and Yes,"  This paper has three main contributions to our understanding of fixed-depth
+minimax search: (A) A new formulation for Stockman's SSS* algorithm, based on
+Alpha-Beta, is presented. It solves all the perceived drawbacks of SSS*,
+finally transforming it into a practical algorithm. In effect, we show that
+SSS* = alpha-beta + ransposition tables. The crucial step is the realization
+that transposition tables contain so-called solution trees, structures that are
+used in best-first search algorithms like SSS*. Having created a practical
+version, we present performance measurements with tournament game-playing
+programs for three different minimax games, yielding results that contradict a
+number of publications. (B) Based on the insights gained in our attempts at
+understanding SSS*, we present a framework that facilitates the construction of
+several best-first fixed- depth game-tree search algorithms, known and new. The
+framework is based on depth-first null-window Alpha-Beta search, enhanced with
+storage to allow for the refining of previous search results. It focuses
+attention on the essential differences between algorithms. (C) We present a new
+instance of the framework, MTD(f). It is well-suited for use with iterative
+deepening, and performs better than algorithms that are currently used in most
+state-of-the-art game-playing programs. We provide experimental evidence to
+explain why MTD(f) performs better than the other fixed-depth minimax
+algorithms.
+"
+29127,Metallization of solid molecular hydrogen in two dimensions: Mott-Hubbard-type transition,"  We analyze the pressure-induced metal-insulator transition in a
+two-dimensional vertical stack of $H_2$ molecules in x-y plane, and show that
+it represents a striking example of the Mott-Hubbard-type transition. Our
+combined exact diagonalization approach, formulated and solved in the second
+quantization formalism, includes also simultaneous ab initio readjustment of
+the single-particle wave functions, contained in the model microscopic
+parameters. The system is studied as a function of applied side force
+(generalized pressure), both in the $H_2$-molecular and $H$-quasiatomic states.
+Extended Hubbard model is taken at the start, together with longer-range
+electron-electron interactions incorporated into the scheme. The stacked
+molecular plane transforms discontinuously into a (quasi)atomic state under the
+applied force via a two-step transition: the first between molecular insulating
+phases and the second from the molecular to the quasiatomic metallic phase. No
+quasiatomic insulating phase occurs. All the transitions are accompanied by an
+abrupt changes of the bond length and the intermolecular distance (lattice
+parameter), as well as by discontinuous changes of the principal electronic
+properties, which are characteristic of the Mott-Hubbard transition here
+associated with the jumps of the predetermined equilibrium lattice parameter
+and the effective bond length. The phase transition can be interpreted in terms
+of the solid hydrogen metallization under pressure exerted by e.g., the
+substrate covered with a monomolecular $H_2$ film of the vertically stacked
+molecules. Both the Mott and Hubbard criteria at the insulator to metal
+transition are discussed.
+"
+29128,Role of the effective payoff function in evolutionary game dynamics,"  In most studies regarding evolutionary game dynamics, the effective payoff, a
+quantity that translates the payoff derived from game interactions into
+reproductive success, is usually assumed to be a specific function of the
+payoff. Meanwhile, the effect of different function forms of effective payoff
+on evolutionary dynamics is always left in the basket. With introducing a
+generalized mapping that the effective payoff of individuals is a non-negative
+function of two variables on selection intensity and payoff, we study how
+different effective payoff functions affect evolutionary dynamics in a
+symmetrical mutation-selection process. For standard two-strategy two-player
+games, we find that under weak selection the condition for one strategy to
+dominate the other depends not only on the classical {\sigma}-rule, but also on
+an extra constant that is determined by the form of the effective payoff
+function. By changing the sign of the constant, we can alter the direction of
+strategy selection. Taking the Moran process and pairwise comparison process as
+specific models in well-mixed populations, we find that different fitness or
+imitation mappings are equivalent under weak selection. Moreover, the sign of
+the extra constant determines the direction of one-third law and risk-dominance
+for sufficiently large populations. This work thus helps to elucidate how the
+effective payoff function as another fundamental ingredient of evolution affect
+evolutionary dynamics.
+"
+29129,Kuga-Satake construction and cohomology of hyperkahler manifolds,"  Let M be a simple hyperkahler manifold. Kuga-Satake construction gives an
+embedding of H^2(M,C) into the second cohomology of a torus, compatible with
+the Hodge structure. We construct a torus T and an embedding of the graded
+cohomology space H^*(M,C) \to H^{*+l}(T,C) for some l, which is compatible with
+the Hodge structures and the Poincare pairing. Moreover, this embedding is
+compatible with an action of the Lie algebra generated by all Lefschetz
+sl(2)-triples on M.
+"
+29130,Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation,"  This paper discusses the approach taken by the UWaterloo team to arrive at a
+solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of
+SemEval 2017. The paper describes the document vectorization and sentiment
+score prediction techniques used, as well as the design and implementation
+decisions taken while building the system for this task. The system uses text
+vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled
+with regression model variants to predict the sentiment scores. Amongst the
+methods examined, unigrams and bigrams coupled with simple linear regression
+obtained the best baseline accuracy. The paper also explores data augmentation
+methods to supplement the training dataset. This system was designed for
+Subtask 2 (News Statements and Headlines).
+"
+29131,"Been There, Done That: Meta-Learning with Episodic Recall","  Meta-learning agents excel at rapidly learning new tasks from open-ended task
+distributions; yet, they forget what they learn about each task as soon as the
+next begins. When tasks reoccur - as they do in natural environments -
+metalearning agents must explore again instead of immediately exploiting
+previously discovered solutions. We propose a formalism for generating
+open-ended yet repetitious environments, then develop a meta-learning
+architecture for solving these environments. This architecture melds the
+standard LSTM working memory with a differentiable neural episodic memory. We
+explore the capabilities of agents with this episodic LSTM in five
+meta-learning environments with reoccurring tasks, ranging from bandits to
+navigation and stochastic sequential decision problems.
+"
+29132,Effect of filter type on the statistics of energy transfer between resolved and subfilter scales from a-priori analysis of direct numerical simulations of isotropic turbulence,"  The effects of different filtering strategies on the statistical properties
+of the resolved-to-sub-filter scale (SFS) energy transfer are analyzed in
+forced homogeneous and isotropic turbulence. We carry out a priori analyses of
+statistical characteristics of SFS energy transfer by filtering data obtained
+from direct numerical simulations (DNS) with up to $2048^3$ grid points as a
+function of the filter cutoff scale. In order to quantify the dependence of
+extreme events and anomalous scaling on the filter, we compare a sharp Fourier
+Galerkin projector, a Gaussian filter and a novel class of Galerkin projectors
+with non-sharp spectral filter profiles. Of interest is the importance of
+Galilean invariance and we confirm that local SFS energy transfer displays
+intermittency scaling in both skewness and flatness as a function of the cutoff
+scale. Furthermore, we quantify the robustness of scaling as a function of the
+filtering type.
+"
+29133,Dynamics of Dirac solitons in networks,"  We study dynamics of Dirac solitons in prototypical networks modeling them by
+the nonlinear Dirac equation on metric graphs. Soliton solutions of the
+nonlinear Dirac equation on simple metric graphs are obtained. It is shown that
+these solutions provide reflectionless vertex transmission of the Dirac
+solitons under suitable conditions. The constraints for bond nonlinearity
+coefficients, allowing reflectionless transmission over a Y-junction are
+derived. The analytical results are confirmed by direct numerical simulations.
+"
+29134,Analogue of double-$Λ$-type atomic medium and vector dromions in a plasmonic metamaterial,"  We consider an array of the meta-atom consisting of two cut-wires and a
+split-ring resonator interacting with an electromagnetic field with two
+polarization components. We prove that such metamaterial system can be taken as
+a classical analogue of an atomic medium with a double- $\Lambda$-type
+four-level configuration coupled with four laser fields, exhibits an effect of
+plasmon induced transparency (PIT), and displays a similar behavior of atomic
+four-wave mixing (FWM). We demonstrate that when nonlinear varactors are
+mounted onto the gaps of the split-ring resonators the system can acquire giant
+second- and third-order Kerr nonlinearities via the PIT and a
+longwave-shortwave interaction. We also demonstrate that the system supports
+high-dimensional vector plasmonic dromions [i.e. (2+1)-dimensional plasmonic
+solitons with two polarization components, each of which has a coupling between
+a longwave and a shortwave], which have very low generation power and are
+robust during propagation. Our work gives not only a plasmonic analogue of the
+FWM in coherent atomic systems but also provides the possibility for obtaining
+new type of nonlinear polaritons in plasmonic metamaterials.
+"
+29135,"Multiplicative-Additive Proof Equivalence is Logspace-complete, via Binary Decision Trees","  Given a logic presented in a sequent calculus, a natural question is that of
+equivalence of proofs: to determine whether two given proofs are equated by any
+denotational semantics, ie any categorical interpretation of the logic
+compatible with its cut-elimination procedure. This notion can usually be
+captured syntactically by a set of rule permutations.
+Very generally, proofnets can be defined as combinatorial objects which
+provide canonical representatives of equivalence classes of proofs. In
+particular, the existence of proof nets for a logic provides a solution to the
+equivalence problem of this logic. In certain fragments of linear logic, it is
+possible to give a notion of proofnet with good computational properties,
+making it a suitable representation of proofs for studying the cut-elimination
+procedure, among other things.
+It has recently been proved that there cannot be such a notion of proofnets
+for the multiplicative (with units) fragment of linear logic, due to the
+equivalence problem for this logic being Pspace-complete.
+We investigate the multiplicative-additive (without unit) fragment of linear
+logic and show it is closely related to binary decision trees: we build a
+representation of proofs based on binary decision trees, reducing proof
+equivalence to decision tree equivalence, and give a converse encoding of
+binary decision trees as proofs. We get as our main result that the complexity
+of the proof equivalence problem of the studied fragment is Logspace-complete.
+"
+29136,When Does Diversity of User Preferences Improve Outcomes in Selfish Routing?,"  We seek to understand when heterogeneity in user preferences yields improved
+outcomes in terms of overall cost. That this might be hoped for is based on the
+common belief that diversity is advantageous in many settings. We investigate
+this in the context of routing. Our main result is a sharp characterization of
+the network settings in which diversity always helps, versus those in which it
+is sometimes harmful. Specifically, we consider routing games, where diversity
+arises in the way that users trade-off two criteria (such as time and money,
+or, in the case of stochastic delays, expectation and variance of delay). Our
+main contributions are the following: 1) A participant-oriented measure of cost
+in the presence of user diversity, together with the identification of the
+natural benchmark: the same cost measure for an appropriately defined average
+of the diversity. 2) A full characterization of those network topologies for
+which diversity always helps, for all latency functions and demands. For
+single-commodity routings, these are series-parallel graphs, while for
+multi-commodity routings, they are the newly-defined ""block-matching"" networks.
+The latter comprise a suitable interweaving of multiple series-parallel graphs
+each connecting a distinct source-sink pair. While the result for the
+single-commodity case may seem intuitive in light of the well-known Braess
+paradox, the two problems are different: there are instances where diversity
+helps although the Braess paradox occurs, and vice-versa. But the main
+technical challenge is to establish the ""only if"" direction of the result for
+multi-commodity networks. This follows by constructing an instance where
+diversity hurts, and showing how to embed it in any network which is not
+block-matching, by carefully exploiting the way the simple source-sink paths of
+the commodities intersect in the ""non-block-matching"" portion of the network.
+"
+29137,Visualizing test diversity to support test optimisation,"  Diversity has been used as an effective criteria to optimise test suites for
+cost-effective testing. Particularly, diversity-based (alternatively referred
+to as similarity-based) techniques have the benefit of being generic and
+applicable across different Systems Under Test (SUT), and have been used to
+automatically select or prioritise large sets of test cases. However, it is a
+challenge to feedback diversity information to developers and testers since
+results are typically many-dimensional. Furthermore, the generality of
+diversity-based approaches makes it harder to choose when and where to apply
+them. In this paper we address these challenges by investigating: i) what are
+the trade-off in using different sources of diversity (e.g., diversity of test
+requirements or test scripts) to optimise large test suites, and ii) how
+visualisation of test diversity data can assist testers for test optimisation
+and improvement. We perform a case study on three industrial projects and
+present quantitative results on the fault detection capabilities and redundancy
+levels of different sets of test cases. Our key result is that test similarity
+maps, based on pair-wise diversity calculations, helped industrial
+practitioners identify issues with their test repositories and decide on
+actions to improve. We conclude that the visualisation of diversity information
+can assist testers in their maintenance and optimisation activities.
+"
+29138,The Sobering Reality of Perovskite/Si Tandem Solar Cells under Realistic Operating Conditions,"  Perovskite/Si tandem solar cells have the potential to considerably
+out-perform conventional solar cells. Under standard test conditions,
+perovskite/Si tandem solar cells already outperform the Si single junction.
+Under realistic conditions, however, as we show, those tandem solar cells are
+hardly more efficient than the Si cell alone. We model the performance of
+realistic perovskite/Si tandem solar cells under real-world climate conditions,
+by incorporating parasitic cell resistances, non-radiative recombination, and
+optical losses into the detailed-balance limit. We show quantitatively that
+optimizing these parameters in the perovskite top cell, perovskite/Si tandem
+solar cells reach an efficiency advantage of up to 14% absolute, even while
+leaving the Si cell untouched. Despite the rapid efficiency increase of
+perovskite solar cells, our results emphasize the need for further material
+development, careful device design, and light management strategies, all
+necessary for highly efficient perovskite/Si tandem solar cells.
+"
+29139,Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms,"  Despite the recent successes of probabilistic programming languages (PPLs) in
+AI applications, PPLs offer only limited support for random variables whose
+distributions combine discrete and continuous elements. We develop the notion
+of measure-theoretic Bayesian networks (MTBNs) and use it to provide more
+general semantics for PPLs with arbitrarily many random variables defined over
+arbitrary measure spaces. We develop two new general sampling algorithms that
+are provably correct under the MTBN framework: the lexicographic likelihood
+weighting (LLW) for general MTBNs and the lexicographic particle filter (LPF),
+a specialized algorithm for state-space models. We further integrate MTBNs into
+a widely used PPL system, BLOG, and verify the effectiveness of the new
+inference algorithms through representative examples.
+"
+29140,An interpretable latent variable model for attribute applicability in the Amazon catalogue,"  Learning attribute applicability of products in the Amazon catalog (e.g.,
+predicting that a shoe should have a value for size, but not for battery-type
+at scale is a challenge. The need for an interpretable model is contingent on
+(1) the lack of ground truth training data, (2) the need to utilise prior
+information about the underlying latent space and (3) the ability to understand
+the quality of predictions on new, unseen data. To this end, we develop the
+MaxMachine, a probabilistic latent variable model that learns distributed
+binary representations, associated to sets of features that are likely to
+co-occur in the data. Layers of MaxMachines can be stacked such that higher
+layers encode more abstract information. Any set of variables can be clamped to
+encode prior information. We develop fast sampling based posterior inference.
+Preliminary results show that the model improves over the baseline in 17 out of
+19 product groups and provides qualitatively reasonable predictions.
+"
+29141,Complete diagrammatics of the single ring theorem,"  Using diagrammatic techniques, we provide explicit functional relations
+between the cumulant generating functions for the biunitarily invariant
+ensembles in the limit of large size of matrices. The formalism allows to map
+two distinct areas of free random variables: Hermitian positive definite
+operators and non-normal R-diagonal operators. We also rederive the
+Haagerup-Larsen theorem and show how its recent extension to the eigenvector
+correlation function appears naturally within this approach.
+"
+29142,Fastest Rates for Stochastic Mirror Descent Methods,"  Relative smoothness - a notion introduced by Birnbaum et al. (2011) and
+rediscovered by Bauschke et al. (2016) and Lu et al. (2016) - generalizes the
+standard notion of smoothness typically used in the analysis of gradient type
+methods. In this work we are taking ideas from well studied field of stochastic
+convex optimization and using them in order to obtain faster algorithms for
+minimizing relatively smooth functions. We propose and analyze two new
+algorithms: Relative Randomized Coordinate Descent (relRCD) and Relative
+Stochastic Gradient Descent (relSGD), both generalizing famous algorithms in
+the standard smooth setting. The methods we propose can be in fact seen as a
+particular instances of stochastic mirror descent algorithms. One of them,
+relRCD corresponds to the first stochastic variant of mirror descent algorithm
+with linear convergence rate.
+"
+29143,Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift,"  In this paper we revisit the method of off-policy corrections for
+reinforcement learning (COP-TD) pioneered by Hallak et al. (2017). Under this
+method, online updates to the value function are reweighted to avoid divergence
+issues typical of off-policy learning. While Hallak et al.'s solution is
+appealing, it cannot easily be transferred to nonlinear function approximation.
+First, it requires a projection step onto the probability simplex; second, even
+though the operator describing the expected behavior of the off-policy learning
+algorithm is convergent, it is not known to be a contraction mapping, and
+hence, may be more unstable in practice. We address these two issues by
+introducing a discount factor into COP-TD. We analyze the behavior of
+discounted COP-TD and find it better behaved from a theoretical perspective. We
+also propose an alternative soft normalization penalty that can be minimized
+online and obviates the need for an explicit projection step. We complement our
+analysis with an empirical evaluation of the two techniques in an off-policy
+setting on the game Pong from the Atari domain where we find discounted COP-TD
+to be better behaved in practice than the soft normalization penalty. Finally,
+we perform a more extensive evaluation of discounted COP-TD in 5 games of the
+Atari domain, where we find performance gains for our approach.
+"
+29144,Bootstrap-Based Inference for Cube Root Consistent Estimators,"  This note proposes a consistent bootstrap-based distributional approximation
+for cube root consistent estimators such as the maximum score estimator of
+Manski (1975) and the isotonic density estimator of Grenander (1956). In both
+cases, the standard nonparametric bootstrap is known to be inconsistent. Our
+method restores consistency of the nonparametric bootstrap by altering the
+shape of the criterion function defining the estimator whose distribution we
+seek to approximate. This modification leads to a generic and easy-to-implement
+resampling method for inference that is conceptually distinct from other
+available distributional approximations based on some form of modified
+bootstrap. We offer simulation evidence showcasing the performance of our
+inference method in finite samples. An extension of our methodology to general
+M-estimation problems is also discussed.
+"
+29145,Cell-cell and Cell-noise Interactions of Bacterial Cells in a Shallow Circular Pool and Transitions of Collective Motions,"  We have experimentally investigated the transitions of collective motions of
+bacterial cells in a shallow circular pool using the bacterial species
+$Bacillus$ $subtilis$. In our previous paper, we reported that the collective
+motions were classified into the six phases on a phase diagram with the two
+parameters, the reduced cell length \lambda\ and the cell density \rho. In the
+present study, we focused on the sharp transitions at $\lambda\cong0.1$
+($\equiv \lambda_{\rm C1}$) with low values of $\rho$ between the $random$
+$motion$ phase and the $one$-$way$ $rotational$ $motion$ phase. By introducing
+the order parameter $Q$ which measures the aligned cell motion along the
+circumferential direction of a pool, the transitions at $\lambda=\lambda_{\rm
+C1}$ were clearly characterized. Based on the detailed observations of
+single-cell trajectories in a pool, we verified that the effect of cell-noise
+interactions was widely distributed. We conclude that, even in such random
+environment, sharp transitions of collective motions were caused by the
+cell-cell interactions at $\lambda=\lambda_{\rm C1}$.
+"
+29146,PHOTOMETRYPIPELINE: An Automated Pipeline for Calibrated Photometry,"  PHOTOMETRYPIPELINE (PP) is an automated pipeline that produces calibrated
+photometry from imaging data through image registration, aperture photometry,
+photometric calibration, and target identification with only minimal human
+interaction. PP utilizes the widely used Source Extractor software for source
+identification and aperture photometry; SCAMP is used for image registration.
+Both image registration and photometric calibration are based on matching field
+stars with star catalogs, requiring catalog coverage of the respective field. A
+number of different astrometric and photometric catalogs can be queried online.
+Relying on a sufficient number of background stars for image registration and
+photometric calibration, PP is well-suited to analyze data from small to
+medium-sized telescopes. Calibrated magnitudes obtained by PP are typically
+accurate within 0.03 mag and astrometric accuracies are of the order of 0.3
+arcsec relative to the catalogs used in the registration. The pipeline consists
+of an open-source software suite written in Python 2.7, can be run on
+Unix-based systems on a simple desktop machine, and is capable of realtime data
+analysis. PP has been developed for observations of moving targets, but can be
+used for analyzing point source observations of any kind.
+"
+29147,Multifaceted Schwinger effect in de Sitter space,"  We investigate particle production à la Schwinger mechanism in an
+expanding, flat de Sitter patch as is relevant for the inflationary epoch of
+our universe. Defining states and particle content in curved spacetime is
+certainly not a unique process. There being different prescriptions on how that
+can be done, we have used the Schrödinger formalism to define instantaneous
+particle content of the state etc. This allows us to go past the adiabatic
+regime to which the effect has been restricted in the previous studies and
+bring out its multifaceted nature in different settings. Each of these settings
+gives rise to contrasting features and behaviour as per the effect of electric
+field and expansion rate on the instantaneous mean particle number. We also
+quantify the degree of classicality of the process during its evolution using a
+""classicality parameter"" constructed out of parameters of the Wigner function
+to obtain information about the quantum to classical transition in this case.
+"
+29148,Clonal analysis of newborn hippocampal dentate granule cell proliferation and development in temporal lobe epilepsy,"  Hippocampal dentate granule cells are among the few neuronal cell types
+generated throughout adult life in mammals. In the normal brain, new granule
+cells are generated from progenitors in the subgranular zone and integrate in a
+typical fashion. During the development of epilepsy, granule cell integration
+is profoundly altered. The new cells migrate to ectopic locations and develop
+misoriented basal dendrites. Although it has been established that these
+abnormal cells are newly generated, it is not known whether they arise
+ubiquitously throughout the progenitor cell pool or are derived from a smaller
+number of bad actor progenitors. To explore this question, we conducted a
+clonal analysis study in mice expressing the Brainbow fluorescent protein
+reporter construct in dentate granule cell progenitors. Mice were examined 2
+months after pilocarpine-induced status epilepticus, a treatment that leads to
+the development of epilepsy. Brain sections were rendered translucent so that
+entire hippocampi could be reconstructed and all fluorescently labeled cells
+identified. Our findings reveal that a small number of progenitors produce the
+majority of ectopic cells following status epilepticus, indicating that either
+the affected progenitors or their local microenvironments have become
+pathological. By contrast, granule cells with basal dendrites were equally
+distributed among clonal groups. This indicates that these progenitors can
+produce normal cells and suggests that global factors sporadically disrupt the
+dendritic development of some new cells. Together, these findings strongly
+predict that distinct mechanisms regulate different aspects
+"
+29149,Persistence Terrace for Topological Inference of Point Cloud Data,"  Topological data analysis (TDA) is a rapidly developing collection of methods
+for studying the shape of point cloud and other data types. One popular
+approach, designed to be robust to noise and outliers, is to first use a
+smoothing function to convert the point cloud into a manifold and then apply
+persistent homology to a Morse filtration. A significant challenge is that this
+smoothing process involves the choice of a parameter and persistent homology is
+highly sensitive to that choice; moreover, important scale information is lost.
+We propose a novel topological summary plot, called a persistence terrace, that
+incorporates a wide range of smoothing parameters and is robust, multi-scale,
+and parameter-free. This plot allows one to isolate distinct topological
+signals that may have merged for any fixed value of the smoothing parameter,
+and it also allows one to infer the size and point density of the topological
+features. We illustrate our method in some simple settings where noise is a
+serious issue for existing frameworks and then we apply it to a real data set
+by counting muscle fibers in a cross-sectional image.
+"
+29150,Irrational Guards are Sometimes Needed,"  In this paper we study the art gallery problem, which is one of the
+fundamental problems in computational geometry. The objective is to place a
+minimum number of guards inside a simple polygon such that the guards together
+can see the whole polygon. We say that a guard at position $x$ sees a point $y$
+if the line segment $xy$ is fully contained in the polygon.
+Despite an extensive study of the art gallery problem, it remained an open
+question whether there are polygons given by integer coordinates that require
+guard positions with irrational coordinates in any optimal solution. We give a
+positive answer to this question by constructing a monotone polygon with
+integer coordinates that can be guarded by three guards only when we allow to
+place the guards at points with irrational coordinates. Otherwise, four guards
+are needed. By extending this example, we show that for every $n$, there is
+polygon which can be guarded by $3n$ guards with irrational coordinates but
+need $4n$ guards if the coordinates have to be rational. Subsequently, we show
+that there are rectilinear polygons given by integer coordinates that require
+guards with irrational coordinates in any optimal solution.
+"
+29151,Machine learning search for variable stars,"  Photometric variability detection is often considered as a hypothesis testing
+problem: an object is variable if the null-hypothesis that its brightness is
+constant can be ruled out given the measurements and their uncertainties.
+Uncorrected systematic errors limit the practical applicability of this
+approach to high-amplitude variability and well-behaving data sets. Searching
+for a new variability detection technique that would be applicable to a wide
+range of variability types while being robust to outliers and underestimated
+measurement uncertainties, we propose to consider variability detection as a
+classification problem that can be approached with machine learning. We compare
+several classification algorithms: Logistic Regression (LR), Support Vector
+Machines (SVM), k-Nearest Neighbors (kNN) Neural Nets (NN), Random Forests (RF)
+and Stochastic Gradient Boosting classifier (SGB) applied to 18 features
+(variability indices) quantifying scatter and/or correlation between points in
+a light curve. We use a subset of OGLE-II Large Magellanic Cloud (LMC)
+photometry (30265 light curves) that was searched for variability using
+traditional methods (168 known variable objects identified) as the training set
+and then apply the NN to a new test set of 31798 OGLE-II LMC light curves.
+Among 205 candidates selected in the test set, 178 are real variables, 13
+low-amplitude variables are new discoveries. We find that the considered
+machine learning classifiers are more efficient (they find more variables and
+less false candidates) compared to traditional techniques that consider
+individual variability indices or their linear combination. The NN, SGB, SVM
+and RF show a higher efficiency compared to LR and kNN.
+"
+29152,Co-design of jump estimators and transmission policies for wireless multi-hop networks with fading channels [Extended],"  We study transmission power budget minimization of battery-powered nodes in a
+remote state estimation problem over multi-hop wireless networks. Communication
+links between nodes are subject to fading, thereby generating random dropouts.
+Relay nodes help to transmit measurements from distributed sensors to an
+estimator node. Hopping through each relay node introduces a unit delay.
+Motivated by the need for estimators with low computational and implementation
+cost, we propose a jump estimator whose modes depend on a Markovian parameter
+that describes measurement transmission outcomes over a finite interval. It is
+well known that transmission power helps to increase the reliability of
+measurement transmissions, at the expense of reducing the life-time of the
+nodes' battery. Motivated by this, we derive a tractable iterative procedure,
+based on semi-definite programming, to design a finite set of filter gains, and
+associated power control laws to minimize the energy budget while guaranteeing
+an estimation performance level. This procedure allows us to tradeoff the
+complexity of the filter implementation with performance and energy use.
+"
+29153,Cortical Microcircuits from a Generative Vision Model,"  Understanding the information processing roles of cortical circuits is an
+outstanding problem in neuroscience and artificial intelligence. The
+theoretical setting of Bayesian inference has been suggested as a framework for
+understanding cortical computation. Based on a recently published generative
+model for visual inference (George et al., 2017), we derive a family of
+anatomically instantiated and functional cortical circuit models. In contrast
+to simplistic models of Bayesian inference, the underlying generative model's
+representational choices are validated with real-world tasks that required
+efficient inference and strong generalization. The cortical circuit model is
+derived by systematically comparing the computational requirements of this
+model with known anatomical constraints. The derived model suggests precise
+functional roles for the feedforward, feedback and lateral connections observed
+in different laminae and columns, and assigns a computational role for the path
+through the thalamus.
+"
+29154,Excision in algebraic K-theory revisited,"  By a theorem of Suslin, a Tor-unital (not necessarily unital) ring satisfies
+excision in algebraic K-theory. We give a new and direct proof of Suslin's
+result based on an exact sequence of categories of perfect modules. In fact, we
+prove a more general descent result for a pullback square of ring spectra and
+any localizing invariant. Besides Suslin's result, this also contains Nisnevich
+descent of algebraic K-theory for affine schemes as a special case. Moreover,
+the role of the Tor-unitality condition becomes very transparent.
+"
+29155,Online Learning for Structured Loss Spaces,"  We consider prediction with expert advice when the loss vectors are assumed
+to lie in a set described by the sum of atomic norm balls. We derive a regret
+bound for a general version of the online mirror descent (OMD) algorithm that
+uses a combination of regularizers, each adapted to the constituent atomic
+norms. The general result recovers standard OMD regret bounds, and yields
+regret bounds for new structured settings where the loss vectors are (i) noisy
+versions of points from a low-rank subspace, (ii) sparse vectors corrupted with
+noise, and (iii) sparse perturbations of low-rank vectors. For the problem of
+online learning with structured losses, we also show lower bounds on regret in
+terms of rank and sparsity of the source set of the loss vectors, which implies
+lower bounds for the above additive loss settings as well.
+"
+29156,Dynamic Interface Rearrangement in LaFeO$_3$ / $n$-SrTiO$_3$ Heterojunctions,"  Thin film synthesis methods developed over the past decades have unlocked
+emergent interface properties ranging from conductivity to ferroelectricity.
+However, our attempts to exercise precise control over interfaces are
+constrained by a limited understanding of growth pathways and kinetics. Here we
+demonstrate that shuttered molecular beam epitaxy induces rearrangements of
+atomic planes at a polar / non-polar junction of LaFeO$_3$ (LFO) /
+$n$-SrTiO$_3$ (STO) depending on the substrate termination. Surface
+characterization confirms that substrates with two different (TiO$_2$ and SrO)
+terminations were prepared prior to LFO deposition; however, local electron
+energy loss spectroscopy measurements of the final heterojunctions show a
+predominantly LaO / TiO$_2$ interfacial junction in both cases. Ab initio
+simulations suggest that the interfaces can be stabilized by trapping extra
+oxygen (in LaO / TiO$_2$) and forming oxygen vacancies (in FeO$_2$ / SrO),
+which points to different growth kinetics in each case and may explain the
+apparent disappearance of the FeO$_2$ / SrO interface. We conclude that
+judicious control of deposition timescales can be used to modify growth
+pathways, opening new avenues to control the structure and properties of
+interfacial systems.
+"
+29157,Balanced and functionally balanced $P$-groups,"  In relation to Itzkowitz's problem, we show that a $\mathfrak c$-bounded
+$P$-group is balanced if and only if it is functionally balanced. We prove that
+for an arbitrary $P$-group, being functionally balanced is equivalent to being
+strongly functionally balanced. A special focus is given to the uniform free
+topological group defined over a uniform $P$-space. In particular, we show that
+this group is (functionally) balanced precisely when its subsets $B_n,$
+consisting of words of length at most $n,$ are all (resp., functionally)
+balanced.
+"
+29158,Weyl-type topological phase transitions in fractional quantum Hall like systems,"  We develop a method to characterize topological phase transitions for
+strongly correlated Hamiltonians defined on two-dimensional lattices based on
+the many-body Berry curvature. Our goal is to identify a class of quantum
+critical points between topologically nontrivial phases with fractionally
+quantized Hall (FQH) conductivity and topologically trivial gapped phases
+through the discontinuities of the many-body Berry curvature in the so-called
+flux Brillouin zone (fBZ), the latter being defined by imposing all possible
+twisted boundary conditions. For this purpose, we study the finite-size
+signatures of several quantum phase transitions between fractional Chern
+insulators and charge-ordered phases for two-dimensional lattices by evaluating
+the many-body Berry curvature numerically using exact diagonalization. We
+observe degeneracy points (nodes) of many-body energy levels at high-symmetry
+points in the fBZ, accompanied by diverging Berry curvature. We find a
+correspondence between the number and order of these nodal points, and the
+change of the topological invariants of the many-body ground states across the
+transition, in close analogy with Weyl nodes in non-interacting band
+structures. This motivates us to apply a scaling procedure, originally
+developed for non-interacting systems, for the Berry curvature at the nodal
+points. This procedure offers a useful tool for the classification of
+topological phase transitions in interacting systems harboring FQH-like
+topological order.
+"
+29159,Delayed acceptance ABC-SMC,"  Approximate Bayesian computation (ABC) is now an established technique for
+statistical inference used in cases where the likelihood function is
+computationally expensive or not available. It relies on the use of a model
+that is specified in the form of a simulator, and approximates the likelihood
+at a parameter $\theta$ by simulating auxiliary data sets $x$ and evaluating
+the distance of $x$ from the true data $y$. However, ABC is not computationally
+feasible in cases where using the simulator for each $\theta$ is very
+expensive. This paper investigates this situation in cases where a cheap, but
+approximate, simulator is available. The approach is to employ delayed
+acceptance Markov chain Monte Carlo (MCMC) within an ABC sequential Monte Carlo
+(SMC) sampler in order to, in a first stage of the kernel, use the cheap
+simulator to rule out parts of the parameter space that are not worth
+exploring, so that the ""true"" simulator is only run (in the second stage of the
+kernel) where there is a reasonable chance of accepting proposed values of
+$\theta$. We show that this approach can be used quite automatically, with the
+only tuning parameter choice additional to ABC-SMC being the number of
+particles we wish to carry through to the second stage of the kernel.
+Applications to stochastic differential equation models and latent doubly
+intractable distributions are presented.
+"
+29160,Using learning to control artificial avatars in human motor coordination tasks,"  Designing artificial cyber-agents able to interact with human safely, smartly
+and in a natural way is a current open problem in control. Solving such an
+issue will allow the design of cyber-agents capable of co-operatively
+interacting with people in order to fulfil common joint tasks in a multitude of
+different applications. This is particularly relevant in the context of
+healthcare applications. Indeed, the use has been proposed of artificial agents
+interacting and coordinating their movements with those of a patient suffering
+from social or motor disorders. Specifically, it has been shown that an
+artificial agent exhibiting certain kinematic properties could provide
+innovative and efficient rehabilitation strategies for these patients.
+Moreover, it has also been shown that the level of motor coordination is
+enhanced if these kinematic properties are similar to those of the individual
+it is interacting with. In this paper we discuss, first, a new method based on
+Markov Chains to confer ""human motor characteristics"" on a virtual agent, so as
+that it can coordinate its motion with that of a target individual while
+exhibiting specific kinematic properties. Then, we embed such synthetic model
+in a control architecture based on reinforcement learning to synthesize a
+cyber-agent able to mimic the behaviour of a specific human performing a joint
+motor task with one or more individuals.
+"
+29161,Expressions for the Entropy of Binomial-Type Distributions,"  We develop a general method for computing logarithmic and log-gamma
+expectations of distributions. As a result, we derive series expansions and
+integral representations of the entropy for several fundamental distributions,
+including the Poisson, binomial, beta-binomial, negative binomial, and
+hypergeometric distributions. Our results also establish connections between
+the entropy functions and to the Riemann zeta function and its generalizations.
+"
+29162,The switch Markov chain for sampling irregular graphs and digraphs,"  The problem of efficiently sampling from a set of (undirected, or directed)
+graphs with a given degree sequence has many applications. One approach to this
+problem uses a simple Markov chain, which we call the switch chain, to perform
+the sampling. The switch chain is known to be rapidly mixing for regular degree
+sequences, both in the undirected and directed setting.
+We prove that the switch chain for undirected graphs is rapidly mixing for
+any degree sequence with minimum degree at least 1 and with maximum degree
+$d_{\max}$ which satisfies $3\leq d_{\max}\leq \frac{1}{3}\, \sqrt{M}$, where
+$M$ is the sum of the degrees. The mixing time bound obtained is only a factor
+$n$ larger than that established in the regular case, where $n$ is the number
+of vertices. Our result covers a wide range of degree sequences, including
+power-law graphs with parameter $\gamma > 5/2$ and sufficiently many edges.
+For directed degree sequences such that the switch chain is irreducible, we
+prove that the switch chain is rapidly mixing when all in-degrees and
+out-degrees are positive and bounded above by $\frac{1}{4}\, \sqrt{m}$, where
+$m$ is the number of arcs, and not all in-degrees and out-degrees equal 1. The
+mixing time bound obtained in the directed case is an order of $m^2$ larger
+than that established in the regular case.
+"
+29163,Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments,"  An autonomous and resilient controller is proposed for leader-follower
+multi-agent systems under uncertainties and cyber-physical attacks. The leader
+is assumed non-autonomous with a nonzero control input, which allows changing
+the team behavior or mission in response to environmental changes. A resilient
+learning-based control protocol is presented to find optimal solutions to the
+synchronization problem in the presence of attacks and system dynamic
+uncertainties. An observer-based distributed H_infinity controller is first
+designed to prevent propagating the effects of attacks on sensors and actuators
+throughout the network, as well as to attenuate the effect of these attacks on
+the compromised agent itself. Non-homogeneous game algebraic Riccati equations
+are derived to solve the H_infinity optimal synchronization problem and
+off-policy reinforcement learning is utilized to learn their solution without
+requiring any knowledge of the agent's dynamics. A trust-confidence based
+distributed control protocol is then proposed to mitigate attacks that hijack
+the entire node and attacks on communication links. A confidence value is
+defined for each agent based solely on its local evidence. The proposed
+resilient reinforcement learning algorithm employs the confidence value of each
+agent to indicate the trustworthiness of its own information and broadcast it
+to its neighbors to put weights on the data they receive from it during and
+after learning. If the confidence value of an agent is low, it employs a trust
+mechanism to identify compromised agents and remove the data it receives from
+them from the learning process. Simulation results are provided to show the
+effectiveness of the proposed approach.
+"
+29164,"Reaching Optimized Parameter Set, Protein Secondary Structure Prediction Using Neural Network","  We propose an optimized parameter set for protein secondary structure
+prediction using three layer feed forward back propagation neural network. The
+methodology uses four parameters viz. encoding scheme, window size, number of
+neurons in the hidden layer and type of learning algorithm. The input layer of
+the network consists of neurons changing from 3 to 19, corresponding to
+different window sizes. The hidden layer chooses a natural number from 1 to 20
+as the number of neurons. The output layer consists of three neurons, each
+corresponding to known secondary structural classes viz. alpha helix, beta
+strands and coils respectively. It also uses eight different learning
+algorithms and nine encoding schemes. Exhaustive experiments were performed
+using non-homologues dataset. The experimental results were compared using
+performance measures like Q3, sensitivity, specificity, Mathew correlation
+coefficient and accuracy. The paper also discusses the process of obtaining a
+stabilized cluster of 2530 records from a collection of 11340 records. The
+graphs of these stabilized clusters of records with respect to accuracy are
+concave, convergence is monotonic increasing and rate of convergence is
+uniform. The paper gives BLOSUM62 as the encoding scheme, 19 as the window
+size, 19 as the number of neurons in the hidden layer and One- Step Secant as
+the learning algorithm with the highest accuracy of 78%. These parameter values
+are proposed as the optimized parameter set for the three layer feed forward
+back propagation neural network for the protein secondary structure predictionv
+"
+29165,Explicit error bounds for lattice Edgeworth expansions,"  Motivated, roughly, by comparing the mean and median of an IID sum of bounded
+lattice random variables, we develop explicit and effective bounds on the
+errors involved in the one-term Edgeworth expansion for such sums.
+"
+29166,The direct localization of a fast radio burst and its host,"  Fast radio bursts are astronomical radio flashes of unknown physical nature
+with durations of milliseconds. Their dispersive arrival times suggest an
+extragalactic origin and imply radio luminosities orders of magnitude larger
+than any other kind of known short-duration radio transient. Thus far, all FRBs
+have been detected with large single-dish telescopes with arcminute
+localizations, and attempts to identify their counterparts (source or host
+galaxy) have relied on contemporaneous variability of field sources or the
+presence of peculiar field stars or galaxies. These attempts have not resulted
+in an unambiguous association with a host or multi-wavelength counterpart. Here
+we report the sub-arcsecond localization of FRB 121102, the only known
+repeating burst source, using high-time-resolution radio interferometric
+observations that directly image the bursts themselves. Our precise
+localization reveals that FRB 121102 originates within 100 mas of a faint 180
+uJy persistent radio source with a continuum spectrum that is consistent with
+non-thermal emission, and a faint (25th magnitude) optical counterpart. The
+flux density of the persistent radio source varies by tens of percent on day
+timescales, and very long baseline radio interferometry yields an angular size
+less than 1.7 mas. Our observations are inconsistent with the fast radio burst
+having a Galactic origin or its source being located within a prominent
+star-forming galaxy. Instead, the source appears to be co-located with a
+low-luminosity active galactic nucleus or a previously unknown type of
+extragalactic source. [Truncated] If other fast radio bursts have similarly
+faint radio and optical counterparts, our findings imply that direct
+sub-arcsecond localizations of FRBs may be the only way to provide reliable
+associations.
+"
+29167,3D non-Fermi liquid behavior from 1D quantum critical local moments,"  We study the temperature dependence of the electrical resistivity in a system
+composed of critical spin chains interacting with three dimensional conduction
+electrons and driven to criticality via an external magnetic field. The
+relevant experimental system is Yb$_2$Pt$_2$Pb, a metal where itinerant
+electrons coexist with localized moments of Yb-ions which can be described in
+terms of effective S = 1/2 spins with dominantly one-dimensional exchange
+interaction. The spin subsystem becomes critical in a relatively weak magnetic
+field, where it behaves like a Luttinger liquid. We theoretically examine a
+Kondo lattice with different effective space dimensionalities of the two
+interacting subsystems. We characterize the corresponding non-Fermi liquid
+behavior due to the spin criticality by calculating the electronic relaxation
+rate and the dc resistivity and establish its quasi linear temperature
+dependence.
+"
+29168,Continuous time Gaussian process dynamical models in gene regulatory network inference,"  One of the focus areas of modern scientific research is to reveal mysteries
+related to genes and their interactions. The dynamic interactions between genes
+can be encoded into a gene regulatory network (GRN), which can be used to gain
+understanding on the genetic mechanisms behind observable phenotypes. GRN
+inference from time series data has recently been a focus area of systems
+biology. Due to low sampling frequency of the data, this is a notoriously
+difficult problem. We tackle the challenge by introducing the so-called
+continuous-time Gaussian process dynamical model (GPDM), based on Gaussian
+process framework that has gained popularity in nonlinear regression problems
+arising in machine learning. The model dynamics are governed by a stochastic
+differential equation, where the dynamics function is modelled as a Gaussian
+process. We prove the existence and uniqueness of solutions of the stochastic
+differential equation. We derive the probability distribution for the Euler
+discretised trajectories and establish the convergence of the discretisation.
+We develop a GRN inference method based on the developed framework. The method
+is based on MCMC sampling of trajectories of the GPDM and estimating the
+hyperparameters of the covariance function of the Gaussian process. Using
+benchmark data examples, we show that our method is computationally feasible
+and superior in dealing with poor time resolution.
+"
+29169,Implementation of the force term in half-range lattice Boltzmann models,"  In the frame of the Boltzmann equation, wall-bounded flows of rarefied gases
+require the implementation of boundary conditions at the kinetic level. Such
+boundary conditions induce a discontinuity in the distribution function with
+respect to the component of the momentum which is normal to the boundary.
+Expanding the distribution function with respect to half-range polynomials
+allows this discontinuity to be captured. The implementation of this concept
+has been reported in the literature only for force-free flows. In the case of
+general forces which can have non-zero components in the direction
+perpendicular to the walls, the implementation of the force term requires
+taking the momentum space gradient of a discontinuous function. Our proposed
+method deals with this difficulty by employing the theory of distributions. We
+validate our procedure by considering the simple one-dimensional flow between
+diffuse-reflective walls of equal or different temperatures driven by the
+constant gravitational force. For this flow, a comparison between the results
+obtained with the full-range and the half-range Gauss-Hermite LB models is also
+presented.
+"
+29170,The asymptotic dimension of box spaces of virtually nilpotent groups,"  We show that every box space of a virtually nilpotent group has asymptotic
+dimension equal to the Hirsch length of that group.
+"
+29171,Explainable Failure Predictions with RNN Classifiers based on Time Series Data,"  Given key performance indicators collected with fine granularity as time
+series, our aim is to predict and explain failures in storage environments.
+Although explainable predictive modeling based on spiky telemetry data is key
+in many domains, current approaches cannot tackle this problem. Deep learning
+methods suitable for sequence modeling and learning temporal dependencies, such
+as RNNs, are effective, but opaque from an explainability perspective. Our
+approach first extracts the anomalous spikes from time series as events and
+then builds an RNN classifier with attention mechanisms to embed the
+irregularity and frequency of these events. A preliminary evaluation on real
+world storage environments shows that our approach can predict failures within
+a 3-day prediction window with comparable accuracy as traditional RNN-based
+classifiers. At the same time it can explain the predictions by returning the
+key anomalous events which led to those failure predictions.
+"
+29172,Encouraging LSTMs to Anticipate Actions Very Early,"  In contrast to the widely studied problem of recognizing an action given a
+complete sequence, action anticipation aims to identify the action from only
+partially available videos. As such, it is therefore key to the success of
+computer vision applications requiring to react as early as possible, such as
+autonomous navigation. In this paper, we propose a new action anticipation
+method that achieves high prediction accuracy even in the presence of a very
+small percentage of a video sequence. To this end, we develop a multi-stage
+LSTM architecture that leverages context-aware and action-aware features, and
+introduce a novel loss function that encourages the model to predict the
+correct class as early as possible. Our experiments on standard benchmark
+datasets evidence the benefits of our approach; We outperform the
+state-of-the-art action anticipation methods for early prediction by a relative
+increase in accuracy of 22.0% on JHMDB-21, 14.0% on UT-Interaction and 49.9% on
+UCF-101.
+"
+29173,Additive cyclic codes over finite commutative chain rings,"  Additive cyclic codes over Galois rings were investigated in previous works.
+In this paper, we investigate the same problem but over a more general ring
+family, finite commutative chain rings. When we focus on non-Galois finite
+commutative chain rings, we observe two different kinds of additivity. One of
+them is a natural generalization of the previous studies, whereas the other one
+has some unusual properties especially while constructing dual codes. We
+interpret the reasons of such properties and illustrate our results giving
+concrete examples.
+"
+29174,Multiplex Network Structure Enhances the Role of Generalized Reciprocity in Promoting Cooperation,"  In multi-agent systems, cooperative behavior is largely determined by the
+network structure which dictates the interactions among neighboring agents.
+These interactions often exhibit multidimensional features, either as
+relationships of different types or temporal dynamics, both of which may be
+modeled as a ""multiplex"" network. Against this background, here we advance the
+research on cooperation models inspired by generalized reciprocity, a simple
+pay-it-forward behavioral mechanism, by considering a multidimensional
+networked society. Our results reveal that a multiplex network structure can
+act as an enhancer of the role of generalized reciprocity in promoting
+cooperation by acting as a latent support, even when the parameters in some of
+the separate network dimensions suggest otherwise (i.e. favor defection). As a
+result, generalized reciprocity forces the cooperative contributions of the
+individual agents to concentrate in the dimension which is most favorable for
+the existence of cooperation.
+"
+29175,Low is a Dividing Line in Keisler's Order,"  We show in $ZFC$ that the class of low theories forms a dividing line in
+Keisler's order. That is, if $T$ is low and $T' \trianglelefteq T$ then $T'$ is
+low. We also show there is a minimal nonlow theory $T_{cas}$.
+"
+29176,Fractionalized Fermi liquids and exotic superconductivity in the Kitaev-Kondo lattice,"  Fractionalized Fermi liquids (FL$^\ast$) have been introduced as
+non-Fermi-liquid metallic phases, characterized by coexisting electron-like
+charge carriers and local moments which itself form a fractionalized spin
+liquid. Here we investigate a Kondo lattice model on the honeycomb lattice with
+compass interactions among the local moments, a concrete model hosting
+FL$^\ast$ phases based on Kitaev's $\mathbb{Z}_2$ spin liquid. We characterize
+the FL$^\ast$ phases via perturbation theory, and we employ a Majorana-fermion
+mean-field theory to map out the full phase diagram. Most remarkably we find
+triplet superconducting phases which mask the quantum phase transition between
+fractionalized and conventional Fermi liquid phases. Their pairing structure is
+inherited from the Kitaev spin liquid, i.e., superconductivity is driven by
+Majorana glue.
+"
+29177,Deployment and calibration procedures for accurate timing and directional reconstruction of EAS particle-fronts with HELYCON stations,"  High energy cosmic rays, with energies thousands of times higher than those
+encountered in particle accelerators, offer scientists the means of
+investigating the elementary properties of matter. In order to detect high
+energy cosmic rays, new detection hardware and experimental methods are being
+developed. In this work, we describe the network of HELYCON (HEllenic LYceum
+Cosmic Observatories Network) autonomous stations for the detection and
+directional reconstruction of Extended Atmospheric Showers (EAS)
+particle-fronts. HELYCON stations are hybrid stations consisting of three large
+plastic scintillators plus a CODALEMA antenna for the RF detection of EAS
+particle-fronts. We present the installation, operation and calibration of
+three HELYCON stations and the electronic components for the remote control,
+monitor and Data Acquisition. We report on the software package developed for
+the detailed simulation of the detectors' response and for the stations'
+operation. The simulation parameters have been fine tuned in order to
+accurately describe each individual detector's characteristics and the
+operation of each HELYCON station. Finally, the evaluation of the stations'
+performance in reconstructing the direction of the EAS particle-front is being
+presented.
+"
+29178,Topological groups that realize homogeneity of topological spaces,"  We present results on simplifying an acting group preserving properties of
+actions: transitivity, being a coset space and preserving a fixed
+equiuniformity in case of a $G$-Tychonoff space.
+"
+29179,D-STREAMON: from middlebox to distributed NFV framework for network monitoring,"  Many reasons make NFV an attractive paradigm for IT security: lowers costs,
+agile operations and better isolation as well as fast security updates,
+improved incident responses and better level of automation. On the other side,
+the network threats tend to be increasingly complex and distributed, implying
+huge traffic scale to be monitored and increasingly strict mitigation delay
+requirements. Considering the current trend of the net- working and the
+requirements to counteract to the evolution of cyber-threats, it is expected
+that also network monitoring will move towards NFV based solutions. In this
+paper, we present D- StreaMon an NFV-capable distributed framework for network
+monitoring realized to face the above described challenges. It relies on the
+StreaMon platform, a solution for network monitoring originally designed for
+traditional middleboxes. An evolution path which migrates StreaMon from
+middleboxes to Virtual Network Functions (VNFs) has been realized.
+"
+29180,Fractional iterated Ornstein-Uhlenbeck Processes,"  In this work we present a Gaussian process that arise from the iteration of p
+fractional Ornstein-Uhlenbeck processes generated by the same fractional
+Brownian motion. This iteration results, when the values of lambdas are
+pairwise differents, in a particular linear combination of those processes.
+Although for $H>1/2$ each term of the linear combination is a long memory
+processes, we prove that it results in a short memory processes. We include
+applications to real data that show improvement in predictive performance
+compared with different ARMA models.
+"
+29181,A hot X-ray filament associated with A3017 galaxy cluster,"  Recent simulations and observations have shown large scale filaments in the
+cosmic web connecting nodes, with accreting materials (baryonic and dark
+matter) flowing through them. Current high sensitivity observations also show
+that the propagation of shocks through filaments can heat them up, and make
+filaments visible between two or more galaxy clusters or around massive
+clusters, based on optical and/or X-ray observations. We are reporting here the
+special case of the cluster A3017 associated with a hot filament. The
+temperature of the filament is 3.4$^{-0.77}_{+1.30}$ ~keV and its length is
+$\sim$ 1 Mpc. We have analysed its archival {\it Chandra} data and report
+various properties. We also analysed GMRT 235/610 MHz radio data. Radio
+observations have revealed symmetric two-sided lobes which fill cavities in the
+A3017 cluster core region, associated with central AGN. In the radio map, we
+also noticed a peculiar linear vertical radio structure in the X-ray filament
+region which might be associated with a cosmic filament shock. This radio
+structure could be a radio phoenix or old plasma where an old relativistic
+population is re-accelerated by shock propagation. Finally we put an upper
+limit on the radio luminosity of the filament region.
+"
+29182,On geodesic ray bundles in buildings,"  Let $X$ be a building, identified with its Davis realisation. In this paper,
+we provide for each $x\in X$ and each $\eta$ in the visual boundary $\partial
+X$ of $X$ a description of the geodesic ray bundle $Geo(x,\eta)$, namely, of
+the reunion of all combinatorial geodesic rays (corresponding to infinite
+minimal galleries in the chamber graph of $X$) starting from $x$ and pointing
+towards $\eta$. When $X$ is locally finite and hyperbolic, we show that the
+symmetric difference between $Geo(x,\eta)$ and $Geo(y,\eta)$ is always finite,
+for $x,y\in X$ and $\eta\in\partial X$. This gives a positive answer to a
+question of Huang, Sabok and Shinko in the setting of buildings. Combining
+their results with a construction of Bourdon, we obtain examples of hyperbolic
+groups $G$ with Kazhdan's property (T) such that the $G$-action on its Gromov
+boundary is hyperfinite.
+"
+29183,Tourists' digital footprint in cities: comparing big data sources,"  There is little knowledge available on the spatial behaviour of urban
+tourists, and yet tourists generate an enormous quantity of data (Big Data)
+when they visit cities. These data sources can be used to track their presence
+through their activities. The aim of this paper is to analyse the digital
+footprint of urban tourists through Big Data. Unlike other papers that use a
+single data source, this article examines three sources of data to reflect
+different tourism activities in cities: Panoramio (sightseeing), Foursquare
+(consumption), and Twitter (being connected). Tourist density in the three data
+sources is compared via maps, correlation analysis (OLS) and spatial
+self-correlation analysis (Global Moran's I statistic and LISA). Finally the
+data are integrated using cluster analysis and combining the spatial clusters
+identified in the LISA analysis in the different data sources. The results show
+that the data from the three activities are partly spatially redundant and
+partly complementary, and allow the characterisation of multifunction tourist
+spaces (with several activities) and spaces specialising in one or various
+activities (for example, sightseeing and consumption). The case study analysed
+(Madrid) reveals a significant presence of tourists in the city centre, and
+increasing specialisation from the centre outwards towards the periphery. The
+main conclusion of the paper is that it is not sufficient to use one data
+source to analyse the presence of tourists in cities; several must be used in a
+complementary manner.
+"
+29184,A canonical barycenter via Wasserstein regularization,"  We introduce a weak notion of barycenter of a probability measure $\mu$ on a
+metric measure space $(X, d, {\bf m})$, with the metric $d$ and reference
+measure ${\bf m}$. Under the assumption that optimal transport plans are given
+by mappings, we prove that our barycenter $B(\mu)$ is well defined; it is a
+probability measure on $X$ supported on the set of the usual metric barycenter
+points of the given measure $\mu$. The definition uses the canonical embedding
+of the metric space $X$ into its Wasserstein space $P(X)$, pushing a given
+measure $\mu$ forward to a measure on $P(X)$. We then regularize the measure by
+the Wasserstein distance to the reference measure ${\bf m}$, and obtain a
+uniquely defined measure on $X$ supported on the barycentric points of $\mu$.
+We investigate various properties of $B(\mu)$
+"
+29185,Scientific Article Summarization Using Citation-Context and Article's Discourse Structure,"  We propose a summarization approach for scientific articles which takes
+advantage of citation-context and the document discourse model. While citations
+have been previously used in generating scientific summaries, they lack the
+related context from the referenced article and therefore do not accurately
+reflect the article's content. Our method overcomes the problem of
+inconsistency between the citation summary and the article's content by
+providing context for each citation. We also leverage the inherent scientific
+article's discourse for producing better summaries. We show that our proposed
+method effectively improves over existing summarization approaches (greater
+than 30% improvement over the best performing baseline) in terms of
+\textsc{Rouge} scores on TAC2014 scientific summarization dataset. While the
+dataset we use for evaluation is in the biomedical domain, most of our
+approaches are general and therefore adaptable to other domains.
+"
+29186,A Moving-Horizon Hybrid Stochastic Game for Secure Control of Cyber-Physical Systems,"  In this paper, we establish a zero-sum, hybrid state stochastic game model
+for designing defense policies for cyber-physical systems against different
+types of attacks. With the increasingly integrated properties of cyber-physical
+systems (CPS) today, security is a challenge for critical infrastructures.
+Though resilient control and detecting techniques for a specific model of
+attack have been proposed, to analyze and design detection and defense
+mechanisms against multiple types of attacks for CPSs requires new system
+frameworks. Besides security, other requirements such as optimal control cost
+also need to be considered. The hybrid game model we propose in this work
+contains physical states that are described by the system dynamics, and a cyber
+state that represents the detection mode of the system composed by a set of
+subsystems. A strategy means selecting a subsystem by combining one controller,
+one estimator and one detector among a finite set of candidate components at
+each state. Based on the game model, we propose a suboptimal value iteration
+algorithm for a finite horizon game, and prove that the algorithm results an
+upper bound for the value of the finite horizon game. A moving-horizon approach
+is also developed in order to provide a scalable and real-time computation of
+the switching strategies. Both algorithms aims at obtaining a saddle-point
+equilibrium policy for balancing the system's security overhead and control
+cost. The paper illustrates these concepts using numerical examples, and we
+compare the results with previously system designs that only equipped with one
+type of controller.
+"
+29187,Non-negative Matrix Factorization: Robust Extraction of Extended Structures,"  We apply the vectorized Non-negative Matrix Factorization (NMF) method to
+post-processing of direct imaging data for exoplanetary systems such as
+circumstellar disks. NMF is an iterative approach, which first creates a
+non-orthogonal and non-negative basis of components using given reference
+images, then models a target with the components. The constructed model is then
+rescaled with a factor to compensate for the contribution from a disk. We
+compare NMF with existing methods (classical reference differential imaging
+method, and the Karhunen-Loève image projection algorithm) using synthetic
+circumstellar disks, and demonstrate the superiority of NMF: with no need for
+prior selection of references, NMF can detect fainter circumstellar disks,
+better preserve low order disk morphology, and does not require forward
+modeling. As an application to a well-known disk example, we process the
+archival Hubble Space Telescope (HST) STIS coronagraphic observations of
+HD~181327 with different methods and compare them. NMF is able to extract some
+circumstellar material inside the primary ring for the first time. In the
+appendix, we mathematically investigate the stability of NMF components during
+iteration, and the linearity of NMF modeling.
+"
+29188,Fast Point Spread Function Modeling with Deep Learning,"  Modeling the Point Spread Function (PSF) of wide-field surveys is vital for
+many astrophysical applications and cosmological probes including weak
+gravitational lensing. The PSF smears the image of any recorded object and
+therefore needs to be taken into account when inferring properties of galaxies
+from astronomical images. In the case of cosmic shear, the PSF is one of the
+dominant sources of systematic errors and must be treated carefully to avoid
+biases in cosmological parameters. Recently, forward modeling approaches to
+calibrate shear measurements within the Monte-Carlo Control Loops ($MCCL$)
+framework have been developed. These methods typically require simulating a
+large amount of wide-field images, thus, the simulations need to be very fast
+yet have realistic properties in key features such as the PSF pattern. Hence,
+such forward modeling approaches require a very flexible PSF model, which is
+quick to evaluate and whose parameters can be estimated reliably from survey
+data. We present a PSF model that meets these requirements based on a fast
+deep-learning method to estimate its free parameters. We demonstrate our
+approach on publicly available SDSS data. We extract the most important
+features of the SDSS sample via principal component analysis. Next, we
+construct our model based on perturbations of a fixed base profile, ensuring
+that it captures these features. We then train a Convolutional Neural Network
+to estimate the free parameters of the model from noisy images of the PSF. This
+allows us to render a model image of each star, which we compare to the SDSS
+stars to evaluate the performance of our method. We find that our approach is
+able to accurately reproduce the SDSS PSF at the pixel level, which, due to the
+speed of both the model evaluation and the parameter estimation, offers good
+prospects for incorporating our method into the $MCCL$ framework.
+"
+29189,Non-exhibition of Bragg phenomenon by chevronic sculptured thin films: experiment and theory,"  The unit cell of a chevronic sculptured thin film (ChevSTF) comprises two
+identical columnar thin films (CTFs) except that the nanocolumns of the first
+are oriented at an angle \chi and nanocolumns of the second are oriented at an
+angle \pi-\chi with respect to the interface of the two CTFs. A ChevSTF
+containing 10 unit cells was fabricated using resistive-heating physical vapor
+deposition of zinc selenide. Planewave reflectance and transmittance spectrums
+of this ChevSTF were measured for a wide variety of incidence conditions over
+the 500--900-nm range of the free-space wavelength. Despite its structural
+periodicity, the ChevSTF did not exhibit the Bragg phenomenon. Theoretical
+calculations with the CTFs modeled as biaxial dielectric materials indicated
+that the Bragg phenomenon would not be manifested for normal and near-normal
+incidence, but vestigial manifestation was possible for suffciently oblique
+incidence. Thus, structural periodicity does not always lead to electromagnetic
+periodicity that underlies the exhibition of the Bragg phenomenon.
+"
+29190,"Generating Black Metal and Math Rock: Beyond Bach, Beethoven, and Beatles","  We use a modified SampleRNN architecture to generate music in modern genres
+such as black metal and math rock. Unlike MIDI and symbolic models, SampleRNN
+generates raw audio in the time domain. This requirement becomes increasingly
+important in modern music styles where timbre and space are used
+compositionally. Long developmental compositions with rapid transitions between
+sections are possible by increasing the depth of the network beyond the number
+used for speech datasets. We are delighted by the unique characteristic
+artifacts of neural synthesis.
+"
+29191,The Fierz convergence criterion: a controlled approach to strongly-interacting systems with small embedded clusters,"  We present an embedded-cluster method, based on the TRILEX formalism. It
+turns the Fierz ambiguity, inherent to approaches based on a bosonic decoupling
+of local fermionic interactions, into a convergence criterion. It is based on
+the approximation of the three-leg vertex by a coarse-grained vertex computed
+by solving a self-consistently determined multi-site effective impurity model.
+The computed self-energies are, by construction, continuous functions of
+momentum. We show that, in three interaction and doping regimes of parameters
+of the two-dimensional Hubbard model, self-energies obtained with clusters of
+size four only are very close to numerically exact benchmark results. We show
+that the Fierz parameter, which parametrizes the freedom in the
+Hubbard-Stratonovich decoupling, can be used as a quality control parameter. By
+contrast, the $GW$+extended dynamical mean field theory approximation with four
+cluster sites is shown to yield good results only in the weak-coupling regime
+and for a particular decoupling. Finally, we show that the vertex has spatially
+nonlocal components only at low Matsubara frequencies.
+"
+29192,Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems,"  This article is concerned with learning and stochastic control in physical
+systems which contain unknown input signals. These unknown signals are modeled
+as Gaussian processes (GP) with certain parametrized covariance structures. The
+resulting latent force models (LFMs) can be seen as hybrid models that contain
+a first-principles physical model part and a non-parametric GP model part. We
+briefly review the statistical inference and learning methods for this kind of
+models, introduce stochastic control methodology for the models, and provide
+new theoretical observability and controllability results for them.
+"
+29193,On the modeling of neural cognition for social network applications,"  In this paper, we study neural cognition in social network. A stochastic
+model is introduced and shown to incorporate two well-known models in Pavlovian
+conditioning and social networks as special case, namely Rescorla-Wagner model
+and Friedkin-Johnsen model. The interpretation and comparison of these model
+are discussed. We consider two cases when the disturbance is independent
+identical distributed for all time and when the distribution of the random
+variable evolves according to a markov chain. We show that the systems for both
+cases are mean square stable and the expectation of the states converges to
+consensus.
+"
+29194,Understanding Differences among Executions with Variational Traces,"  One of the main challenges of debugging is to understand why the program
+fails for certain inputs but succeeds for others. This becomes especially
+difficult if the fault is caused by an interaction of multiple inputs. To debug
+such interaction faults, it is necessary to understand the individual effect of
+the input, how these inputs interact and how these interactions cause the
+fault. The differences between two execution traces can explain why one input
+behaves differently than the other. We propose to compare execution traces of
+all input options to derive explanations of the behavior of all options and
+interactions among them. To make the relevant information stand out, we
+represent them as variational traces that concisely represents control-flow and
+data-flow differences among multiple concrete traces. While variational traces
+can be obtained from brute-force execution of all relevant inputs, we use
+variational execution to scale the generation of variational traces to the
+exponential space of possible inputs. We further provide an Eclipse plugin
+Varviz that enables users to use variational traces for debugging and
+navigation. In a user study, we show that users of variational traces are more
+than twice as fast to finish debugging tasks than users of the standard Eclipse
+debugger. We further show that variational traces can be scaled to programs
+with many options.
+"
+29195,Unsupervised Learning of Semantic Audio Representations,"  Even in the absence of any explicit semantic annotation, vast collections of
+audio recordings provide valuable information for learning the categorical
+structure of sounds. We consider several class-agnostic semantic constraints
+that apply to unlabeled nonspeech audio: (i) noise and translations in time do
+not change the underlying sound category, (ii) a mixture of two sound events
+inherits the categories of the constituents, and (iii) the categories of events
+in close temporal proximity are likely to be the same or related. Without
+labels to ground them, these constraints are incompatible with classification
+loss functions. However, they may still be leveraged to identify geometric
+inequalities needed for triplet loss-based training of convolutional neural
+networks. The result is low-dimensional embeddings of the input spectrograms
+that recover 41% and 84% of the performance of their fully-supervised
+counterparts when applied to downstream query-by-example sound retrieval and
+sound event classification tasks, respectively. Moreover, in
+limited-supervision settings, our unsupervised embeddings double the
+state-of-the-art classification performance.
+"
+29196,Join-Idle-Queue with Service Elasticity: Large-Scale Asymptotics of a Non-monotone System,"  We consider the model of a token-based joint auto-scaling and load balancing
+strategy, proposed in a recent paper by Mukherjee, Dhara, Borst, and van
+Leeuwaarden (SIGMETRICS '17, arXiv:1703.08373), which offers an efficient
+scalable implementation and yet achieves asymptotically optimal steady-state
+delay performance and energy consumption as the number of servers $N\to\infty$.
+In the above work, the asymptotic results are obtained under the assumption
+that the queues have fixed-size finite buffers, and therefore the fundamental
+question of stability of the proposed scheme with infinite buffers was left
+open. In this paper, we address this fundamental stability question. The system
+stability under the usual subcritical load assumption is not automatic.
+Moreover, the stability may not even hold for all $N$. The key challenge stems
+from the fact that the process lacks monotonicity, which has been the powerful
+primary tool for establishing stability in load balancing models. We develop a
+novel method to prove that the subcritically loaded system is stable for large
+enough $N$, and establish convergence of steady-state distributions to the
+optimal one, as $N \to \infty$. The method goes beyond the state of the art
+techniques -- it uses an induction-based idea and a ""weak monotonicity""
+property of the model; this technique is of independent interest and may have
+broader applicability.
+"
+29197,A note on linear Sperner families,"  In an earlier work we described Gröbner bases of the ideal of polynomials
+over a field, which vanish on the set of characteristic vectors $\mathbf{v} \in
+\{0,1\}^n$ of the complete $d$ unifom set family over the ground set $[n]$. In
+particular, it turns out that the standard monomials of the above ideal are
+{\em ballot monomials}. We give here a partial extension of the latter fact. We
+prove that the lexicographic standard monomials for linear Sperner systems are
+also ballot monomials. A set family is a linear Sperner system if the
+characteristic vectors satisfy a linear equation $a_1v_1+\cdots +a_nv_n=k$,
+where $0<a_q\leq a_2\leq \cdots \leq a_n$ and $k$ are integers. As an
+application, we confirm a conjecture of Frankl for linear Sperner systems.
+"
+29198,A Highly-Sensitive Cryogenic Phased Array Feed for the Green Bank Telescope,"  In this paper, we describe the development of a new L-band (1.4 GHz)
+Cryogenic Phased Array Feed (PAF) system, referred to as the GBT2 array.
+Results from initial measurements made with the GBT2 array are also presented.
+The PAF was developed for the Green Bank Telescope (GBT) as part of the Focal
+L-band Array for the GBT (FLAG) project. During the first stage of the
+development work (Phase I), a prototype cryogenic 19 element dual-polarized
+array with ""Kite"" dipole elements was developed and tested on the GBT. The
+measured system temperature over efficiency ($T_{sys}/\eta$) ratio for the bore
+sight beam of the Kite array was 45.5 K at 1.55 GHz. The off-boresight
+$T_{sys}/\eta$ shows an increase by 13 K at an offset equal to the half power
+beam width (7$^{'}$.2 at 1.7 GHz). Our measurements indicate that the
+off-boresight degradation and field-of-view (FoV) limitation of the Kite array
+is simply due to the fixed array size. To increase the FoV, a new 19-element
+GBT2 array with larger array spacing was developed during FLAG Phase II. The
+frequency response of the array was optimized from 1.2 to 1.6 GHz. A system
+with larger cryostat, new low noise amplifiers (LNAs), down-conversion and
+digitization close to the front end, unformatted digital transmission over
+fiber, ROACH II based polyphase filter banks (PFBs) with bandwidth 150 MHz and
+a data acquisition system that records voltage samples from one of the PFB
+channels were all developed. The data presented here is processed off-line. The
+receiver temperature measured with the new system is 17 K at 1.4 GHz, an
+improvement $>$ 8 K over the previous Kite array. Measurements with the GBT2
+array on the telescope are in progress. A real time 150 MHz beamformer is also
+being developed as part of an NSF-funded collaboration between NRAO/GBO/BYU \&
+West Virginia University (Beamformer Project) to support science observations.
+"
+29199,On the binomial approximation of the American put,"  We consider the binomial approximation of the American put price in the
+Black-Scholes model (with continuous dividend yield). Our main result is that
+the error of approximation is $O((ln n) $\alpha$ /n)$ where n is the number of
+time periods and the exponent $\alpha$ is a positive number, the value of which
+may differ according to the respective levels of the interest rate and the
+dividend yield.
+"
+29200,Greedy Search for Descriptive Spatial Face Features,"  Facial expression recognition methods use a combination of geometric and
+appearance-based features. Spatial features are derived from displacements of
+facial landmarks, and carry geometric information. These features are either
+selected based on prior knowledge, or dimension-reduced from a large pool. In
+this study, we produce a large number of potential spatial features using two
+combinations of facial landmarks. Among these, we search for a descriptive
+subset of features using sequential forward selection. The chosen feature
+subset is used to classify facial expressions in the extended Cohn-Kanade
+dataset (CK+), and delivered 88.7% recognition accuracy without using any
+appearance-based features.
+"
+29201,Absence of the Pauli-Paramagnetic Limit in a Superconducting U$_6$Co,"  We performed $^{59}$Co nuclear magnetic resonance (NMR) measurements of
+single-crystalline U$_6$Co. There is a small decrease in the Knight shift in
+the superconducting (SC) state, but this change mainly arises from the SC
+diamagnetic effect. The negligible change of the spin part of the Knight shift,
+together with the absence of the Pauli-paramagnetic effect in the SC U$_6$Co,
+is understood as a consequence of the small spin susceptibility. The nuclear
+spin-lattice relaxation rate $1/T_1$ is also measured in the SC state under the
+magnetic field, and exhibits a tiny Hebel-Slichter peak just below the SC
+transition temperature and exponential behavior at lower temperatures. These
+behaviors are in agreement with the full-gap s-wave pairing in U$_6$Co.
+"
+29202,Anomalous diffusion in run-and-tumble motion,"  A random walk scheme, consisting of alternating phases of regular Brownian
+motion and Lévy walks, is proposed as a model for run-and-tumble bacterial
+motion. Within the continuous-time random walk approach we obtain the long-time
+and short-time behavior of the mean squared displacement of the walker as
+depending on the properties of dwelling time distribution in each phase.
+Depending on these distributions, normal diffusion, superdiffusion and
+ballistic spreading may arise.
+"
+29203,Simulated Galactic methanol maser distribution to constrain Milky Way parameters,"  Using trigonometric parallaxes and proper motions of masers associated with
+massive young stars, the Bar and Spiral Structure Legacy (BeSSeL) survey has
+reported the most accurate values of the Galactic parameters so far. The
+determination of these parameters with high accuracy has a widespread impact on
+Galactic and extragalactic measurements. This research is aimed at establishing
+the confidence with which such parameters can be determined. This is relevant
+for the data published in the context of the BeSSeL survey collaboration, but
+also for future observations, in particular from the Southern Hemisphere. In
+addition, some astrophysical properties of the masers can be constrained,
+notably the luminosity function. We have simulated the population of
+maser-bearing young stars associated with Galactic spiral structure, generating
+several samples and comparing them with the observed samples used in the BeSSeL
+survey. Consequently, we checked the determination of Galactic parameters for
+observational biases introduced by the sample selection. Galactic parameters
+obtained by the BeSSeL survey do not seem to be biased by the sample selection
+used. In fact, the published error estimates appear to be conservative for most
+of the parameters. We show that future BeSSeL data and future observations with
+Southern arrays will improve the Galactic parameters estimates and smoothly
+reduce their mutual correlation. Moreover, by modeling future parallax data
+with larger distance and, thus, greater relative uncertainties for a larger
+numbers of sources, we found that parallax-distance biasing is an important
+issue. Hence, using fractional parallax uncertainty in the weighting of the
+motion data is imperative. Finally, the luminosity function for 6.7 GHz
+methanol masers was determined, allowing us to estimate the number of Galactic
+methanol masers.
+"
+29204,Human-Centric Data Cleaning [Vision],"  Data Cleaning refers to the process of detecting and fixing errors in the
+data. Human involvement is instrumental at several stages of this process,
+e.g., to identify and repair errors, to validate computed repairs, etc. There
+is currently a plethora of data cleaning algorithms addressing a wide range of
+data errors (e.g., detecting duplicates, violations of integrity constraints,
+missing values, etc.). Many of these algorithms involve a human in the loop,
+however, this latter is usually coupled to the underlying cleaning algorithms.
+There is currently no end-to-end data cleaning framework that systematically
+involves humans in the cleaning pipeline regardless of the underlying cleaning
+algorithms. In this paper, we highlight key challenges that need to be
+addressed to realize such a framework. We present a design vision and discuss
+scenarios that motivate the need for such a framework to judiciously assist
+humans in the cleaning process. Finally, we present directions to implement
+such a framework.
+"
+29205,Exact tests to compare contingency tables under quasi-independence and quasi-symmetry,"  In this work we define log-linear models to compare several square
+contingency tables under the quasi-independence or the quasi-symmetry model,
+and the relevant Markov bases are theoretically characterized. Through Markov
+bases, an exact test to evaluate if two or more tables fit a common model is
+introduced. Two real-data examples illustrate the use of these models in
+different fields of applications.
+"
+29206,"Learn More, Pay Less! Lessons Learned from Applying the Wizard-of-Oz Technique for Exploring Mobile App Requirements","  Mobile apps have exploded in popularity, encouraging developers to provide
+content to the massive user base of the main app stores. Although there exist
+automated techniques that can classify user comments into various topics with
+high levels of precision, recent studies have shown that the top apps in the
+app stores do not have customer ratings that directly correlate with the app's
+success. This implies that no single requirements elicitation technique can
+cover the full depth required to produce a successful product and that applying
+alternative requirements gathering techniques can lead to success when these
+two are combined. Since user involvement has been found to be the most
+impactful contribution to project success, in this paper we will explore how
+the Wizard of Oz (WOz) technique and user reviews available in Google Play, can
+be integrated to produce a product that meets the demand of more stakeholders
+than either method alone. To compare the role of early interactive requirements
+specification and app reviews, we conducted two studies (i) a case study
+analysis on 13 mobile app development teams who used very early stages
+Requirements Engineering (RE) by applying WOz, and (ii) a study analyzing 40
+(70, 592 reviews) similar mobile apps on Google Play. The results of both
+studies show that while each of WOz and app review analysis techniques can be
+applied to capture specific types of requirements, an integrated process
+including both methods would eliminate the communication gap between users and
+developers at early stages of the development process and mitigates the risk of
+requirements change in later stages.
+"
+29207,Spin-projected matrix product states (SP-MPS): a versatile tool for strongly correlated systems,"  We present a new wavefunction ansatz that combines the strengths of spin
+projection with the language of matrix product states (MPS) and matrix product
+operators (MPO) as used in the density matrix renormalization group (DMRG).
+Specifically, spin-projected matrix product states (SP-MPS) are constructed as
+$|\Psi^{(N,S,M)}_{SP-MPS}\rangle=\mathcal{P}_S|\Psi_{MPS}^{(N,M)}\rangle$,
+where $\mathcal{P}_S$ is the spin projector for total spin $S$ and
+$|\Psi_{MPS}^{(N,M)}\rangle$ is an MPS wavefunction with a given particle
+number $N$ and spin projection $M$. This new ansatz possesses several
+attractive features: (1) It provides a much simpler route to achieve
+spin-adaptation (i.e. to create eigenfunctions of $\hat{S}^2$) compared to
+explicitly incorporating the non-Abelian SU(2) symmetry into the MPS. In
+particular, since the underlying state in the SP-MPS uses only Abelian
+symmetries, one does not need the singlet embedding scheme for non-singlet
+states, as normally employed in spin-adapted DMRG, to achieve a single
+consistent variationally optimized state. (2) Due to the use of
+$|\Psi_{MPS}^{(N,M)}\rangle$ as its underlying state, the SP-MPS can be closely
+connected to broken symmetry mean-field states. This allows to
+straightforwardly generate the large number of broken symmetry guesses needed
+to explore complex electronic landscapes in magnetic systems. Further, this
+connection can be exploited in the future development of quantum embedding
+theories for open-shell systems. (3) The sum of MPOs representation for the
+Hamiltonian and spin projector $\mathcal{P}_S$ naturally leads to an
+embarrassingly parallel algorithm for computing expectation values and
+optimizing SP-MPS. (4) Optimizing SP-MPS belongs to the
+variation-after-projection (VAP) class of spin projected theories.
+"
+29208,A note on the initial conditions within the effective field theory approach of cosmic acceleration,"  By using the effective field theory approach, we investigate the role of
+initial condition for the dark energy or modified gravity models. In details,
+we consider the constant and linear parametrization of the effective Newton
+constant models. Firstly, under the adiabatic assumption, the correction from
+the extra scalar degree of freedom in the beyond $\Lambda$CDM model is found to
+be negligible. The dominant ingredient in this setup is the primordial
+curvature perturbation originated from inflation mechanism, and the energy
+budget of the matter components is not very crucial. Secondly, the
+iso-curvature perturbation sourced by the extra scalar field is studied. For
+the constant and linear model of the effective Newton constant, there is no
+such kind of scalar mode exist. For the quadratic model, there is a non-trivial
+one. However, the amplitude of the scalar field is damped away very fast on all
+scales. Consequently, it could not support a reasonable structure formation.
+Finally, we study the importance of the setup of the scalar field starting
+time. By setting different turn-on time, namely $a=10^{-2} $ and $a=10^{-7} $,
+we compare the cosmic microwave background radiation temperature, lensing
+deflection angle auto-correlation function as well as the matter power spectrum
+in the constant and linear model. We find there is an order of
+$\mathcal{O}(1\%)$ difference in the observable spectra for constant model,
+while for the linear model, it is smaller than $\mathcal{O}(0.1\%)$.
+"
+29209,How to Maximize the Spread of Social Influence: A Survey,"  This survey presents the main results achieved for the influence maximization
+problem in social networks. This problem is well studied in the literature and,
+thanks to its recent applications, some of which currently deployed on the
+field, it is receiving more and more attention in the scientific community. The
+problem can be formulated as follows: given a graph, with each node having a
+certain probability of influencing its neighbors, select a subset of vertices
+so that the number of nodes in the network that are influenced is maximized.
+Starting from this model, we introduce the main theoretical developments and
+computational results that have been achieved, taking into account different
+diffusion models describing how the information spreads throughout the network,
+various ways in which the sources of information could be placed, and how to
+tackle the problem in the presence of uncertainties affecting the network.
+Finally, we present one of the main application that has been developed and
+deployed exploiting tools and techniques previously discussed.
+"
+29210,Topolectrical circuits,"  Invented by Alessandro Volta and Félix Savary in the early 19th century,
+circuits consisting of resistor, inductor and capacitor (RLC) components are
+omnipresent in modern technology. The behavior of an RLC circuit is governed by
+its circuit Laplacian, which is analogous to the Hamiltonian describing the
+energetics of a physical system. We show that topological semimetal band
+structures can be realized as admittance bands in a periodic RLC circuit, where
+we employ the grounding to adjust the spectral position of the bands similar to
+the chemical potential in a material. Topological boundary resonances (TBRs)
+appear in the impedance read-out of a topolectrical circuit, providing a robust
+signal for the presence of topological admittance bands. For experimental
+illustration, we build the Su-Schrieffer-Heeger circuit, where our impedance
+measurement detects a TBR related to the midgap state. Due to the versatility
+of electronic circuits, our topological semimetal construction can be
+generalized to band structures with arbitrary lattice symmetry. Topolectrical
+circuits establish a bridge between electrical engineering and topological
+states of matter, where the accessibility, scalability, and operability of
+electronics synergizes with the intricate boundary properties of topological
+phases.
+"
+29211,Normal stress effects in the gravity driven flow of granular materials,"  In this paper, we study the fully developed gravity-driven flow of granular
+materials between two inclined planes. We assume that the granular materials
+can be represented by a modified form of the second-grade fluid where the
+viscosity depends on the shear rate and volume fraction and the normal stress
+coefficients depend on the volume fraction. We also propose a new isotropic
+(spherical) part of the stress tensor which can be related to the compactness
+of the (rigid) particles. This new term ensures that the rigid solid particles
+cannot be compacted beyond a point, namely when the volume fraction has reached
+the critical/maximum packing value. The numerical results indicate that the
+newly proposed stress tensor has an obvious and physically meaningful effects
+on both the velocity and the volume fraction fields.
+"
+29212,On embeddings as an alternative paradigm for relational learning,"  Many real-world domains can be expressed as graphs and, more generally, as
+multi-relational knowledge graphs. Though reasoning and learning with knowledge
+graphs has traditionally been addressed by symbolic approaches, recent methods
+in (deep) representation learning has shown promising results for specialized
+tasks such as knowledge base completion. These approaches abandon the
+traditional symbolic paradigm by replacing symbols with vectors in Euclidean
+space. With few exceptions, symbolic and distributional approaches are explored
+in different communities and little is known about their respective strengths
+and weaknesses. In this work, we compare representation learning and relational
+learning on various relational classification and clustering tasks and analyse
+the complexity of the rules used implicitly by these approaches. Preliminary
+results reveal possible indicators that could help in choosing one approach
+over the other for particular knowledge graphs.
+"
+29213,"Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost","  Dexterous multi-fingered robotic hands can perform a wide range of
+manipulation skills, making them an appealing component for general-purpose
+robotic manipulators. However, such hands pose a major challenge for autonomous
+control, due to the high dimensionality of their configuration space and
+complex intermittent contact interactions. In this work, we propose deep
+reinforcement learning (deep RL) as a scalable solution for learning complex,
+contact rich behaviors with multi-fingered hands. Deep RL provides an
+end-to-end approach to directly map sensor readings to actions, without the
+need for task specific models or policy classes. We show that contact-rich
+manipulation behavior with multi-fingered hands can be learned by directly
+training with model-free deep RL algorithms in the real world, with minimal
+additional assumption and without the aid of simulation. We learn a variety of
+complex behaviors on two different low-cost hardware platforms. We show that
+each task can be learned entirely from scratch, and further study how the
+learning process can be further accelerated by using a small number of human
+demonstrations to bootstrap learning. Our experiments demonstrate that complex
+multi-fingered manipulation skills can be learned in the real world in about
+4-7 hours for most tasks, and that demonstrations can decrease this to 2-3
+hours, indicating that direct deep RL training in the real world is a viable
+and practical alternative to simulation and model-based control.
+\url{this https URL}
+"
+29214,On period polynomials of degree $2^m$ and weight distributions of certain irreducible cyclic codes,"  We explicitly determine the values of reduced cyclotomic periods of order
+$2^m$, $m\ge 4$, for finite fields of characteristic $p\equiv 3$ or
+$5\pmod{8}$. These evaluations are applied to obtain explicit factorizations of
+the corresponding reduced period polynomials. As another application, the
+weight distributions of certain irreducible cyclic codes are described.
+"
+29215,Auction Theory Adaptations for Real Life Applications,"  We develop extensions to auction theory results that are useful in real life
+scenarios.
+1. Since valuations are generally positive we first develop approximations
+using the log-normal distribution. This would be useful for many finance
+related auction settings since asset prices are usually non-negative.
+2. We formulate a positive symmetric discrete distribution, which is likely
+to be followed by the total number of auction participants, and incorporate
+this into auction theory results.
+3. We develop extensions when the valuations of the bidders are
+interdependent and incorporate all the results developed into a final combined
+realistic setting.
+4. Our methods can be a practical tool for bidders and auction sellers to
+maximize their profits. The models developed here could be potentially useful
+for inventory estimation and for wholesale procurement of financial instruments
+and also non-financial commodities.
+All the propositions are new results and they refer to existing results which
+are stated as Lemmas.
+"
+29216,Anatomy of high-order harmonic emission from p states in bicircular fields,"  High-order harmonic generation with bicircular fields - the combination of
+counter-rotating circularly polarized pulses at different frequencies - results
+in a series of short-wavelength XUV harmonics with alternating circular
+polarizations, and experiments show that there is an asymmetry in the emission
+between the two helicities: a slight one in helium, and a larger one in neon
+and argon, where the emission is carried out by p-shell electrons.
+Here we analyze this asymmetry by switching to a rotating frame in which the
+field is linearly polarized; this induces an effective magnetic field which
+lowers the ionization potential of the $p_+$ orbital that co-rotates with the
+lower-frequency driver, enhancing its harmonic emission and the overall
+helicity of the generated harmonics, while also introducing nontrivial effects
+from the transformation to a non-inertial frame in complex time. In addition,
+this analysis directly relates the small asymmetry produced by s-shell emission
+to the imaginary part of the recollision velocity in the standard
+strong-field-approximation formalism.
+"
+29217,Orthogonal series estimation of the pair correlation function of a spatial point process,"  The pair correlation function is a fundamental spatial point process
+characteristic that, given the intensity function, determines second order
+moments of the point process. Non-parametric estimation of the pair correlation
+function is a typical initial step of a statistical analysis of a spatial point
+pattern. Kernel estimators are popular but especially for clustered point
+patterns suffer from bias for small spatial lags. In this paper we introduce a
+new orthogonal series estimator. The new estimator is consistent and
+asymptotically normal according to our theoretical and simulation results. Our
+simulations further show that the new estimator can outperform the kernel
+estimators in particular for Poisson and clustered point processes.
+"
+29218,Binding energies: new values and impact on the efficiency of chemical desorption,"  Recent laboratory measurements have confirmed that chemical desorption
+(desorption of products due to exothermic surface reactions) can be an
+efficient process. The impact of including this process into gas-grain chemical
+models entirely depends on the formalism used and the associated parameters.
+Among these parameters, binding energies are probably the most uncertain ones
+for the moment. We propose a new model to compute binding energy of species to
+water ice surfaces. We have also compared the model results using either the
+new chemical desorption model proposed by Minissale et al. (2016) or the one of
+Garrod et al. (2007). The new binding energies have a strong impact on the
+formation of complex organic molecules. In addition, the new chemical
+desorption model from Minissale produces a much smaller desorption of these
+species and also of methanol. Combining the two effects, the abundances of
+CH3OH and COMs observed in cold cores cannot be reproduced by astrochemical
+models anymore.
+"
+29219,Optimizing sequential decisions in the drift-diffusion model,"  To make decisions organisms often accumulate information across multiple
+timescales. However, most experimental and modeling studies of decision-making
+focus on sequences of independent trials. On the other hand, natural
+environments are characterized by long temporal correlations, and evidence used
+to make a present choice is often relevant to future decisions. To understand
+decision-making under these conditions we analyze how a model ideal observer
+accumulates evidence to freely make choices across a sequence of correlated
+trials. We use principles of probabilistic inference to show that an ideal
+observer incorporates information obtained on one trial as an initial bias on
+the next. This bias decreases the time, but not the accuracy of the next
+decision. Furthermore, in finite sequences of trials the rate of reward is
+maximized when the observer deliberates longer for early decisions, but
+responds more quickly towards the end of the sequence. Our model also explains
+experimentally observed patterns in decision times and choices, thus providing
+a mathematically principled foundation for evidence-accumulation models of
+sequential decisions.
+"
+29220,Mechanisms of localization in isotope-substituted dynamical Jahn-Teller systems,"  The mechanisms of localization of Jahn-Teller deformations and vibronic
+wavefunctions in isotope substituted dynamical Jahn-Teller systems are
+elucidated. It is found that the localization in the trough is of potential
+type in the case of strong vibronic coupling, while it becomes of kinetic type
+in the case of intermediate and weak coupling. It is shown that the vibronic
+levels in the linear $E\otimes e$-problem remain double degenerate upon
+arbitrary isotope substitution on the reasons similar to time reversal symmetry
+in which the role of spin is played by orbital pseudospin.
+"
+29221,"Notwithstanding Bohr, the Reasons for QBism","  Without Niels Bohr, QBism would be nothing. But QBism is not Bohr. This paper
+attempts to show that, despite a popular misconception, QBism is no minor tweak
+to Bohr's interpretation of quantum mechanics. It is something quite distinct.
+Along the way, we lay out three tenets of QBism in some detail: 1) The Born
+Rule---the foundation of what quantum theory means for QBism---is a normative
+statement. It is about the decision-making behavior any individual agent should
+strive for; it is not a descriptive ""law of nature"" in the usual sense. 2) All
+probabilities, including all quantum probabilities, are so subjective they
+never tell nature what to do. This includes probability-1 assignments. Quantum
+states thus have no ""ontic hold"" on the world. 3) Quantum measurement outcomes
+just are personal experiences for the agent gambling upon them. Particularly,
+quantum measurement outcomes are not, to paraphrase Bohr, instances of
+""irreversible amplification in devices whose design is communicable in common
+language suitably refined by the terminology of classical physics."" Finally, an
+explicit comparison is given between QBism and Bohr with regard to three
+subjects: a) The issue of the ""detached observer"" as it arose in a debate
+between Pauli and Bohr, b) Bohr's reply to Einstein, Podolsky, and Rosen, and
+c) Bohr's mature notion of ""quantum phenomena."" At the end, we discuss how
+Bohr's notion of phenomena may have something to offer the philosophy of
+William James: A physics from which to further develop his vision of the
+world---call it an ontology if you will---in which ""new being comes in local
+spots and patches.""
+"
+29222,Global-in-time Strichartz estimates for Schrodinger on scattering manifolds,"  We study the global-in-time Strichartz estimates for the Schrödinger
+equation on a class of scattering manifolds $X^{\circ}$. Let
+$\mathcal{L}_V=\Delta_g+V$ where $\Delta_g$ is the Beltrami-Laplace operator on
+the scattering manifold and $V$ is a real potential function on this setting.
+We first extend the global-in-time Strichartz estimate in Hassell-Zhang
+\cite{HZ} on the requirement of $V(z)=O(\langle z\rangle^{-3})$ to $O(\langle
+z\rangle^{-2})$ and secondly generalize the result to the scattering manifold
+with a mild trapped set as well as Bouclet-Mizutani\cite{BM} but with a
+potential. We also obtain a global-in-time local smoothing estimate on this
+geometry setting.
+"
+29223,Improving the local scoring algorithm using gradient sampling,"  We adapt the gradient sampling algorithm to the local scoring algorithm to
+solve complex estimation problems based on an optimization of an objective
+function. This overcomes non-differentiability and non-smoothness of the
+objective function. The new algorithm estimates the Clarke generalized
+subgradient used in the local scoring, thus reducing numerical instabilities.
+The method is applied to quantile regression and to the peaks-over-threshold
+method, as two examples. Real applications are provided for a retail store and
+temperature data analysis.
+"
+29224,On a twisted conical Kähler-Ricci flow,"  In this paper, we discuss diameter bound and Gromov-Hausdorff convergence of
+a twisted conical Kähler-Ricci flow on the total spaces of some holomorphic
+submersions. We also observe that, starting from a model conical Kähler
+metric with possibly unbounded scalar curvature, the conical Kähler-Ricci
+flow will instantly have bounded scalar curvature for $t>0$, and the bound is
+of the form $\frac{C}{t}$. Several key results will be obtained by direct
+arguments on the conical equation without passing to a smooth approximation. In
+the last section, we present several remarks on a twisted Kähler-Ricci flow
+and its convergence.
+"
+29225,Resonant x-ray scattering reveals possible disappearance of magnetic order under hydrostatic pressure in the Kitaev candidate $γ$-Li$_2$IrO$_3$,"  Honeycomb iridates such as $\gamma$-Li$_2$IrO$_3$ are argued to realize
+Kitaev spin-anisotropic magnetic exchange, along with Heisenberg and possibly
+other couplings. While systems with pure Kitaev interactions are candidates to
+realize a quantum spin liquid ground state, in $\gamma$-Li$_2$IrO$_3$ it has
+been shown that the balance of magnetic interactions leads to the
+incommensurate spiral spin order at ambient pressure below 38 K. We study the
+fragility of this state in single crystals of $\gamma$-Li$_2$IrO$_3$ using
+resonant x-ray scattering (RXS) under applied hydrostatic pressures of up to
+3.0 GPa. RXS is a direct probe of the underlying electronic order, and we
+observe the abrupt disappearance of the $q$=(0.57, 0, 0) spiral order at a
+critical pressure $P_c = 1.5\ $GPa with no accompanying change in the symmetry
+of the lattice. This dramatic disappearance is in stark contrast with recent
+studies of $\beta$-Li$_2$IrO$_3$ that show continuous suppression of the spiral
+order in magnetic field; under pressure, a new and possibly nonmagnetic ground
+state emerges.
+"
+29226,A method to align time series segments based on envelope features as anchor points,"  In the time series analysis field, there is not a unique recipe for studying
+signal similarities. On the other hand, averaging signals of the same nature is
+an essential tool in the analysis of different kinds of data. Here we propose a
+method to align and average segments of time series with similar patterns. A
+simple implementation based on \textit{python} code is provided for the
+procedure. The analysis was inspired by the study of canary sound syllables,
+but it is possible to apply it in semi periodic signals of different nature and
+not necessarily related to sounds.
+"
+29227,Quickest Hub Discovery in Correlation Graphs,"  A sequential test is proposed for detection and isolation of hubs in a
+correlation graph. Hubs in a correlation graph of a random vector are variables
+(nodes) that have a strong correlation edge. It is assumed that the random
+vectors are high-dimensional and are multivariate Gaussian distributed. The
+test employs a family of novel local and global summary statistics generated
+from small samples of the random vectors. Delay and false alarm analysis of the
+test is obtained and numerical results are provided to show that the test is
+consistent in identifying hubs, as the false alarm rate goes to zero.
+"
+29228,ExGUtils: A python package for statistical analysis with the ex-gaussian probability density,"  The study of reaction times and their underlying cognitive processes is an
+important field in Psychology. Reaction times are usually modeled through the
+ex-Gaussian distribution, because it provides a good fit to multiple empirical
+data. The complexity of this distribution makes the use of computational tools
+an essential element in the field. Therefore, there is a strong need for
+efficient and versatile computational tools for the research in this area. In
+this manuscript we discuss some mathematical details of the ex-Gaussian
+distribution and apply the ExGUtils package, a set of functions and numerical
+tools, programmed for python, developed for numerical analysis of data
+involving the ex-Gaussian probability density. In order to validate the
+package, we present an extensive analysis of fits obtained with it, discuss
+advantages and differences between the least squares and maximum likelihood
+methods and quantitatively evaluate the goodness of the obtained fits (which is
+usually an overlooked point in most literature in the area). The analysis done
+allows one to identify outliers in the empirical datasets and criteriously
+determine if there is a need for data trimming and at which points it should be
+done.
+"
+29229,Speaker-adaptive neural vocoders for statistical parametric speech synthesis systems,"  This paper proposes speaker-adaptive neural vocoders for statistical
+parametric speech synthesis (SPSS) systems. Recently proposed WaveNet-based
+neural vocoding systems successfully generate a time sequence of speech signal
+with an autoregressive framework. However, building high-quality speech
+synthesis systems with limited training data for a target speaker remains a
+challenge. To generate more natural speech signals with the constraint of
+limited training data, we employ a speaker adaptation task with an effective
+variation of neural vocoding models. In the proposed method, a
+speaker-independent training method is applied to capture universal attributes
+embedded in multiple speakers, and the trained model is then fine-tuned to
+represent the specific characteristics of the target speaker. Experimental
+results verify that the proposed SPSS systems with speaker-adaptive neural
+vocoders outperform those with traditional source-filter model-based vocoders
+and those with WaveNet vocoders, trained either speaker-dependently or
+speaker-independently.
+"
+29230,Segmentation hiérarchique faiblement supervisée,"  Image segmentation is the process of partitioning an image into a set of
+meaningful regions according to some criteria. Hierarchical segmentation has
+emerged as a major trend in this regard as it favors the emergence of important
+regions at different scales. On the other hand, many methods allow us to have
+prior information on the position of structures of interest in the images. In
+this paper, we present a versatile hierarchical segmentation method that takes
+into account any prior spatial information and outputs a hierarchical
+segmentation that emphasizes the contours or regions of interest while
+preserving the important structures in the image. An application of this method
+to the weakly-supervised segmentation problem is presented.
+"
+29231,Graphical Nonconvex Optimization for Optimal Estimation in Gaussian Graphical Models,"  We consider the problem of learning high-dimensional Gaussian graphical
+models. The graphical lasso is one of the most popular methods for estimating
+Gaussian graphical models. However, it does not achieve the oracle rate of
+convergence. In this paper, we propose the graphical nonconvex optimization for
+optimal estimation in Gaussian graphical models, which is then approximated by
+a sequence of convex programs. Our proposal is computationally tractable and
+produces an estimator that achieves the oracle rate of convergence. The
+statistical error introduced by the sequential approximation using the convex
+programs are clearly demonstrated via a contraction property. The rate of
+convergence can be further improved using the notion of sparsity pattern. The
+proposed methodology is then extended to semiparametric graphical models. We
+show through numerical studies that the proposed estimator outperforms other
+popular methods for estimating Gaussian graphical models.
+"
+29232,The epsilon-delta game and the chess,"  The limit of a sequence by the definition with $\varepsilon$ is introduced by
+the notion of checkmate in two moves. The idea is also extended to define the
+limit of a function with $\varepsilon$ and $\delta$.
+"
+29233,How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior,"  Big data has shown its uniquely powerful ability to reveal, model, and
+understand driver behaviors. The amount of data affects the experiment cost and
+conclusions in the analysis. Insufficient data may lead to inaccurate models
+while excessive data waste resources. For projects that cost millions of
+dollars, it is critical to determine the right amount of data needed. However,
+how to decide the appropriate amount has not been fully studied in the realm of
+driver behaviors. This paper systematically investigates this issue to estimate
+how much naturalistic driving data (NDD) is needed for understanding driver
+behaviors from a statistical point of view. A general assessment method is
+proposed using a Gaussian kernel density estimation to catch the underlying
+characteristics of driver behaviors. We then apply the Kullback-Liebler
+divergence method to measure the similarity between density functions with
+differing amounts of NDD. A max-minimum approach is used to compute the
+appropriate amount of NDD. To validate our proposed method, we investigated the
+car-following case using NDD collected from the University of Michigan Safety
+Pilot Model Deployment (SPMD) program. We demonstrate that from a statistical
+perspective, the proposed approach can provide an appropriate amount of NDD
+capable of capturing most features of the normal car-following behavior, which
+is consistent with the experiment settings in many literatures.
+"
+29234,Reconstructing Speech Stimuli From Human Auditory Cortex Activity Using a WaveNet Approach,"  The superior temporal gyrus (STG) region of cortex critically contributes to
+speech recognition. In this work, we show that a proposed WaveNet, with limited
+available data, is able to reconstruct speech stimuli from STG intracranial
+recordings. We further investigate the impulse response of the fitted model for
+each recording electrode and observe phoneme level temporospectral tuning
+properties for the recorded area of cortex. This discovery is consistent with
+previous studies implicating the posterior STG (pSTG) in a phonetic
+representation of speech and provides detailed acoustic features that certain
+electrode sites possibly extract during speech recognition.
+"
+29235,Some Analysis of the Knockoff Filter and its Variants,"  In many applications, we need to study a linear regression model that
+consists of a response variable and a large number of potential explanatory
+variables and determine which variables are truly associated with the response.
+In 2015, Barber and Candes introduced a new variable selection procedure called
+the knockoff filter to control the false discovery rate (FDR) and proved that
+this method achieves exact FDR control. In this paper, we provide some analysis
+of the knockoff filter and its variants. Based on our analysis, we propose a
+PCA prototype group selection filter that has exact group FDR control and
+several advantages over existing group selection methods for strongly
+correlated features. Another contribution is that we propose a new noise
+estimator that can be incorporated into the knockoff statistic from a penalized
+method without violating the exchangeability property. Our analysis also
+reveals that some knockoff statistics, including the Lasso path and the
+marginal correlation statistics, suffer from the alternating sign effect. To
+overcome this deficiency, we introduce the notion of a good statistic and
+propose several alternative statistics that take advantage of the good
+statistic property. Finally, we present a number of numerical experiments to
+demonstrate the effectiveness of our methods and confirm our analysis.
+"
+29236,A characterization of Gromov hyperbolicity via quasigeodesic subspaces,"  By a geodesic subspace of a metric space $X$ we mean a subset $A$ of $X$ such
+that any two points in $A$ can be connected by a geodesic in $A$. It is easy to
+check that a geodesic metric space $X$ is an $\mathbb{R}$-tree (that is, a
+$0$-hyperbolic space in the sense of Gromov) if and only if the union of any
+two intersecting geodesic subspaces is again a geodesic subspace. In this
+paper, we prove an analogous characterization of general Gromov hyperbolic
+spaces, where we replace geodesic subspaces by quasigeodesic subspaces.
+"
+29237,Polynomial Norms,"  In this paper, we study polynomial norms, i.e. norms that are the
+$d^{\text{th}}$ root of a degree-$d$ homogeneous polynomial $f$. We first show
+that a necessary and sufficient condition for $f^{1/d}$ to be a norm is for $f$
+to be strictly convex, or equivalently, convex and positive definite. Though
+not all norms come from $d^{\text{th}}$ roots of polynomials, we prove that any
+norm can be approximated arbitrarily well by a polynomial norm. We then
+investigate the computational problem of testing whether a form gives a
+polynomial norm. We show that this problem is strongly NP-hard already when the
+degree of the form is 4, but can always be answered by testing feasibility of a
+semidefinite program (of possibly large size). We further study the problem of
+optimizing over the set of polynomial norms using semidefinite programming. To
+do this, we introduce the notion of r-sos-convexity and extend a result of
+Reznick on sum of squares representation of positive definite forms to positive
+definite biforms. We conclude with some applications of polynomial norms to
+statistics and dynamical systems.
+"
+29238,Analysis of Invariance and Robustness via Invertibility of ReLU-Networks,"  Studying the invertibility of deep neural networks (DNNs) provides a
+principled approach to better understand the behavior of these powerful models.
+Despite being a promising diagnostic tool, a consistent theory on their
+invertibility is still lacking. We derive a theoretically motivated approach to
+explore the preimages of ReLU-layers and mechanisms affecting the stability of
+the inverse. Using the developed theory, we numerically show how this approach
+uncovers characteristic properties of the network.
+"
+29239,Spin-Diffusions and Diffusive Molecular Dynamics,"  Metastable condensed matter typically fluctuates about local energy minima at
+the femtosecond time scale before transitioning between local minima after
+nanoseconds or microseconds. This vast scale separation limits the
+applicability of classical molecular dynamics methods and has spurned the
+development of a host of approximate algorithms. One recently proposed method
+is diffusive molecular dynamics which aims to integrate a system of ordinary
+differential equations describing the likelihood of occupancy by one of two
+species, in the case of a binary alloy, while quasistatically evolving the
+locations of the atoms. While diffusive molecular dynamics has shown to be
+efficient and provide agreement with observations, it is fundamentally a model,
+with unclear connections to classical molecular dynamics.
+In this work, we formulate a spin-diffusion stochastic process and show how
+it can be connected to diffusive molecular dynamics. The spin-diffusion model
+couples a classical overdamped Langevin equation to a kinetic Monte Carlo model
+for exchange amongst the species of a binary alloy. Under suitable assumptions
+and approximations, spin-diffusion can be shown to lead to diffusive molecular
+molecular dynamics type models. The key assumptions and approximations include
+a well defined time scale separation, a choice of spin exchange rates, a low
+temperature approximation, and a mean field type approximation. We derive
+several models from different assumptions and show their relationship to
+diffusive molecular dynamics. Differences and similarities amongst the models
+are explored in a simple test problem.
+"
+29240,World influence and interactions of universities from Wikipedia networks,"  We present Wikipedia Ranking of World Universities (WRWU) based on analysis
+of networks of 24 Wikipedia editions collected in May 2017. With PageRank and
+CheiRank algorithms we determine ranking of universities averaged over cultural
+views of these editions. The comparison with the Shanghai ranking gives overlap
+of 60% for top 100 universities showing that WRWU gives more significance to
+their historical development. We show that the new reduced Google matrix
+algorithm allows to determine interactions between leading universities on a
+scale of ten centuries. This approach also determines the influence of specific
+universities on world countries. We also compare different cultural views of
+Wikipedia editions on significance and influence of universities.
+"
+29241,Dynamical anchoring of distant Arrhythmia Sources by Fibrotic Regions via Restructuring of the Activation Pattern,"  Rotors are functional reentry sources identified in clinically relevant
+cardiac arrhythmias, such as ventricular and atrial fibrillation. Ablation
+targeting rotor sites has resulted in arrhythmia termination. Recent clinical,
+experimental and modelling studies demonstrate that rotors are often anchored
+around fibrotic scars or regions with increased fibrosis. However the
+mechanisms leading to abundance of rotors at these locations are not clear. The
+current study explores the hypothesis whether fibrotic scars just serve as
+anchoring sites for the rotors or whether there are other active processes
+which drive the rotors to these fibrotic regions. Rotors were induced at
+different distances from fibrotic scars of various sizes and degree of
+fibrosis. Simulations were performed in a 2D model of human ventricular tissue
+and in a patient-specific model of the left ventricle of a patient with remote
+myocardial infarction. In both the 2D and the patient-specific model we found
+that without fibrotic scars, the rotors were stable at the site of their
+initiation. However, in the presence of a scar, rotors were eventually
+dynamically anchored from large distances by the fibrotic scar via a process of
+dynamical reorganization of the excitation pattern. This process coalesces with
+a change from polymorphic to monomorphic ventricular tachycardia.
+"
+29242,Testing Gaussian Process with Applications to Super-Resolution,"  This article introduces exact testing procedures on the mean of a Gaussian
+process $X$ derived from the outcomes of $\ell_1$-minimization over the space
+of complex valued measures. The process $X$ can be thought as the sum of two
+terms: first, the convolution between some kernel and a target atomic measure
+(mean of the process); second, a random perturbation by an additive centered
+Gaussian process. The first testing procedure considered is based on a dense
+sequence of grids on the index set of~$X$ and we establish that it converges
+(as the grid step tends to zero) to a randomized testing procedure: the
+decision of the test depends on the observation $X$ and also on an independent
+random variable. The second testing procedure is based on the maxima and the
+Hessian of $X$ in a grid-less manner. We show that both testing procedures can
+be performed when the variance is unknown (and the correlation function of $X$
+is known). These testing procedures can be used for the problem of
+deconvolution over the space of complex valued measures, and applications in
+frame of the Super-Resolution theory are presented. As a byproduct, numerical
+investigations may demonstrate that our grid-less method is more powerful
+(it~detects sparse alternatives) than tests based on very thin grids.
+"
+29243,Metric Mahler measures over number fields,"  For an algebraic number $\alpha$, the metric Mahler measure $m_1(\alpha)$ was
+first studied by Dubickas and Smyth in 2001 and was later generalized to the
+$t$-metric Mahler measure $m_t(\alpha)$ by the author in 2010. The definition
+of $m_t(\alpha)$ involves taking an infimum over a certain collection
+$N$-tuples of points in $\overline{\mathbb Q}$, and from previous work of
+Jankauskas and the author, the infimum in the definition of $m_t(\alpha)$ is
+attained by rational points when $\alpha\in \mathbb Q$. As a consequence of our
+main theorem in this article, we obtain an analog of this result when $\mathbb
+Q$ is replaced with any imaginary quadratic number field of class number equal
+to $1$. Further, we study examples of other number fields to which our methods
+may be applied, and we establish various partial results in those cases.
+"
+29244,Análisis estadístico ex post del conteo rápido institucional de la elección de gobernador del Estado de México en el año 2017,"  A statistical analysis of an electoral quick count based on the total count
+of votes in the election of the State of Mexico's governor in 2017 is performed
+in order to verify precision, confidence level of interval estimations,
+possible bias and derived conclusions therein, with the main purpose of
+checking compliance with the objectives of such statistical procedure.
+-----
+Se realiza un análisis estadístico de las estimaciones del conteo
+rápido institucional desde la perspectiva ideal de los resultados de los
+cómputos distritales de la elección de gobernador del Estado de México
+del año 2017, particularmente aspectos como la precisión de las
+estimaciones, el nivel de confianza de los intervalos, el posible sesgo
+respecto al cómputo distrital y las conclusiones que se derivaron y
+reportaron, con el objetivo de determinar el grado de cumplimiento de los
+objetivos de este ejercicio estadístico de carácter informativo.
+"
+29245,"Virus genome sequence classification using features based on nucleotides, words and compression","  The ICTV develops, refines and maintains a universal virus taxonomy; Order is
+the highest taxon in the branching hierarchy of recognised viral taxa.
+Historically, ICTV (sub)committees have classified viruses on the basis of
+morphological characteristics and various other attributes. Today, virtually
+all new viral genomes are assembled from metagenomic datasets and are not
+linked directly to biological agents. Thus, placing a virus into a taxonomic
+scheme solely from primary genome structure is an increasingly important
+problem. Various simple descriptive statistics of a viral genome sequence have
+been used successfully for virus classification. Here, we use the NCBI's viral
+and viroid reference sequence collection (RefSeq) and a common experimental
+framework to compare the performance of different genome sequence-derived
+features and classifiers in the task of assigning a virus to one of seven ICTV
+Orders. The nucleotide-, word-, and compression-based features we consider
+include genome length, the k-mer Natural Vector (k = 1, ..., 6) and its
+derivatives, return time distribution, and general-purpose and DNA-specific
+compression ratios; the classifiers used are the k-NN and SVM. The combination
+of genome length and k-NN has the worst, yet still respectable, performance
+(mean error rate of 0.137); the best performance is achieved using 4-mer counts
+and SVM (mean error rate of 0.006). We investigate the main causes of
+misclassification, explore which viruses are more difficult to classify, and
+use the best performing combination to predict the Orders of 1,834 unclassified
+viruses. A subsequent version of RefSeq assigned Orders to 17 of these
+previously unlabelled viruses. Since 16 of our predictions match these
+assignments, our approach could aid virologists dealing with viruses that are
+known only from sequence data.
+"
+29246,fc: A Package for Generalized Function Composition Using Standard Evaluation,"  In this article, we present a new R package fc that provides a streamlined,
+standard evaluation-based approach to function composition. Using fc, a
+sequence of functions can be composed together such that returned objects from
+composed functions are used as intermediate values directly passed to the next
+function. Unlike with magrittr and purrr, no intermediate values need to be
+stored. When benchmarked, functions composed using fc achieve favorable
+runtimes in comparison to other implementations.
+"
+29247,On groups with $S^2$ Bowditch boundary,"  We prove that a relatively hyperbolic pair $(G,P)$ has Bowditch boundary a
+2-sphere if and only if it is a 3-dimensional Poincare duality pair. We prove
+this by studying the relationship between the Bowditch and Dahmani boundaries
+of relatively hyperbolic groups.
+"
+29248,Tracking yeast pheromone receptor Ste2 endocytosis using fluorogen-activating protein tagging,"  To observe internalization of the yeast pheromone receptor Ste2 by
+fluorescence microscopy in live cells in real time, we visualized only those
+molecules present at the cell surface at the time of agonist engagement (rather
+than the total cellular pool) by tagging this receptor at its N-terminus with
+an exocellular fluorogen-activating protein (FAP). A FAP is a single-chain
+antibody engineered to bind tightly a nonfluorescent, cell-impermeable dye
+(fluorogen), thereby generating a fluorescent complex. The utility of FAP
+tagging to study trafficking of integral membrane proteins in yeast, which
+possesses a cell wall, had not been examined previously. A diverse set of
+signal peptides and propeptide sequences were explored to maximize expression.
+Maintenance of the optimal FAP-Ste2 chimera intact required deletion of two,
+paralogous, glycosylphosphatidylinositol (GPI)-anchored extracellular aspartyl
+proteases (Yps1 and Mkc7). FAP-Ste2 exhibited a much brighter and distinct
+plasma membrane signal than Ste2-GFP or Ste2-mCherry yet behaved quite
+similarly. Using FAP-Ste2, new information was obtained about the mechanism of
+its internalization, including novel insights about the roles of the
+cargo-selective endocytic adaptors Ldb19/Art1, Rod1/Art4, and Rog3/Art7.
+"
+29249,Stochastic Gradient Trees,"  We present an online algorithm that induces decision trees using gradient
+information as the source of supervision. In contrast to previous approaches to
+gradient-based tree learning, we do not require soft splits or construction of
+a new tree for every update. In experiments, our method performs comparably to
+standard incremental classification trees and outperforms state of the art
+incremental regression trees. We also show how the method can be used to
+construct a novel type of neural network layer suited to learning
+representations from tabular data and find that it increases accuracy of
+multiclass and multi-label classification.
+"
+29250,Model-Powered Conditional Independence Test,"  We consider the problem of non-parametric Conditional Independence testing
+(CI testing) for continuous random variables. Given i.i.d samples from the
+joint distribution $f(x,y,z)$ of continuous random vectors $X,Y$ and $Z,$ we
+determine whether $X \perp Y | Z$. We approach this by converting the
+conditional independence test into a classification problem. This allows us to
+harness very powerful classifiers like gradient-boosted trees and deep neural
+networks. These models can handle complex probability distributions and allow
+us to perform significantly better compared to the prior state of the art, for
+high-dimensional CI testing. The main technical challenge in the classification
+problem is the need for samples from the conditional product distribution
+$f^{CI}(x,y,z) = f(x|z)f(y|z)f(z)$ -- the joint distribution if and only if $X
+\perp Y | Z.$ -- when given access only to i.i.d. samples from the true joint
+distribution $f(x,y,z)$. To tackle this problem we propose a novel nearest
+neighbor bootstrap procedure and theoretically show that our generated samples
+are indeed close to $f^{CI}$ in terms of total variational distance. We then
+develop theoretical results regarding the generalization bounds for
+classification for our problem, which translate into error bounds for CI
+testing. We provide a novel analysis of Rademacher type classification bounds
+in the presence of non-i.i.d near-independent samples. We empirically validate
+the performance of our algorithm on simulated and real datasets and show
+performance gains over previous methods.
+"
+29251,On the warping sum of knots,"  The warping sum $e(K)$ of a knot $K$ is the minimal value of the sum of the
+warping degrees of a minimal diagram of $K$ with both orientations. In this
+paper, knots $K$ with $e(K) \le 3$ are characterized, and some knots $K$ with
+$e(K)=4$ are given.
+"
+29252,Remote detection of rotating machinery with a portable atomic magnetometer,"  We demonstrate remote detection of rotating machinery, using an atomic
+magnetometer at room temperature and in an unshielded environment. The system
+relies on the coupling of the AC magnetic signature of the target with the
+spin-polarized, precessing atomic vapor of a radio-frequency optical atomic
+magnetometer. The AC magnetic signatures of rotating equipment or electric
+motors appear as sidebands in the power spectrum of the atomic sensor, which
+can be tuned to avoid noisy bands that would otherwise hamper detection. A
+portable apparatus is implemented and experimentally tested. Proof-of-concept
+investigations are performed with test targets mimicking possible applications,
+and the operational conditions for optimum detection are determined. Our
+instrument provides comparable or better performance than a commercial fluxgate
+and allows detection of rotating machinery behind a wall. These results
+demonstrate the potential for ultrasensitive devices for remote industrial and
+usage monitoring, security, and surveillance.
+"
+29253,Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks,"  Autonomous robots need to interact with unknown, unstructured and changing
+environments, constantly facing novel challenges. Therefore, continuous online
+adaptation for lifelong-learning and the need of sample-efficient mechanisms to
+adapt to changes in the environment, the constraints, the tasks, or the robot
+itself are crucial. In this work, we propose a novel framework for
+probabilistic online motion planning with online adaptation based on a
+bio-inspired stochastic recurrent neural network. By using learning signals
+which mimic the intrinsic motivation signalcognitive dissonance in addition
+with a mental replay strategy to intensify experiences, the stochastic
+recurrent network can learn from few physical interactions and adapts to novel
+environments in seconds. We evaluate our online planning and adaptation
+framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
+shown by learning unknown workspace constraints sample-efficiently from few
+physical interactions while following given way points.
+"
+29254,Energy Conditions in Modified $f(G)$ Gravity,"  In this paper, we have considered flat
+Friedmann-Lemaître-Robertson-Walker metric in the framework of perfect
+fluid models and modified $f(G)$ gravity (where $G$ is the Gauss Bonnet
+invariant). Particularly, we have considered particular realistic $f(G)$
+configurations that could be used to cure finite-time future singularities
+arising in the late-time cosmic accelerating epochs. We have then developed the
+viability bounds of these models induced by weak and null energy conditions, by
+using the recent estimated numerical figures of the deceleration, Hubble, snap
+and jerk parameters.
+"
+29255,Accretion disk coronae of Intermediate Polar Cataclysmic Variables - 3D MagnetoHydro-Dynamic modeling and thermal X-ray emission,"  IPCVs contain a magnetic, rotating white dwarf surrounded by a magnetically
+truncated accretion disk. To explain their strong flickering X-ray emission,
+accretion has been successfully taken into account. Nevertheless, observations
+suggest that accretion phenomena could not be the only process behind it. An
+intense flaring activity occurring on the surface of the disk may generate a
+corona, contribute to the thermal X-ray emission and influence the system
+stability. Our purposes are: investigating the formation of an extended corona
+above the accretion disk, due to an intense flaring activity occurring on the
+disk surface; studying its effects on the disk and stellar magnetosphere;
+assessing its contribution to the observed X-ray flux. We have developed a 3D
+MHD model of a IPCV. The model takes into account gravity, disk viscosity,
+thermal conduction, radiative losses and coronal flare heating. To perform a
+parameter space exploration, several system conditions have been considered,
+with different magnetic field intensity and disk density values. From the
+results of the evolution of the model, we have synthesized the thermal X-ray
+emission. The simulations show the formation of an extended corona, linking
+disk and star. The flaring activity is capable of strongly influencing the disk
+configuration and its stability, effectively deforming the magnetic field
+lines. Hot plasma evaporation phenomena occur in the layer immediately above
+the disk. The flaring activity gives rise to a thermal X-ray emission in both
+the [0.1-2.0] keV and the [2.0-10] keV bands. An intense coronal activity
+occurring on the disk surface of an IPCV can affect the structure of the disk
+depending noticeably on the density of the disk and the magnetic field of the
+central object. Moreover, the synthesis of the thermal X-ray fluxes shows that
+this flaring activity may contribute to the observed thermal X-ray emission.
+"
+29256,A theoretical and numerical study of gravity driven coating flow on cylinder and sphere: fingering instability,"  To find the regularities of formed fingers in gravity driven coating flows on
+upper cylinder and sphere, the mathematical formulation to model the fingering
+instability on cylindrical or spherical surface which consists of a capillary
+wave equation and a linear perturbation equation is constructed, based on the
+leading order governing equation in standard cylindrical or spherical
+coordinate system. A disjoining pressure model is introduced to simulate the
+partial wetting process near moving contact line. The fingering number in high
+$Bo$ coating flow is focused on from a linear perspective. Using an asymptotic
+theory, the high $Bo$ limits of the linear perturbation equations for both the
+cylindrical and spherical problems are proved to degenerate into a common
+eigenvalue problem. Two analytical formulae concerning the most unstable wave
+number which can be described as two power laws are derived via a method of
+modal analysis for the cylindrical and spherical problem, respectively. These
+formulae provide a succinct method to estimate the fingering number if the
+onset of the fingering instability is known. A method of transient growth
+analysis which makes no high $Bo$ assumptions is used to verify the accuracy of
+the asymptotic theory and modal analysis. In particular, an asymptotic behavior
+of the most unstable wave number as $Bo$ increases is highlighted. We show that
+the linear growth rate may be improved by the disjoining pressure, which can be
+attributed to the effect of partial wetting, but the most unstable wave number
+is insensitive to the wetting model.
+"
+29257,Trigger for the SoLid Reactor Antineutrino Experiment,"  SoLid, located at SCK-CEN in Mol, Belgium, is a reactor antineutrino
+experiment at a very short baseline of 5.5 -- 10m aiming at the search for
+sterile neutrinos and for high precision measurement of the neutrino energy
+spectrum of Uranium-235. It uses a novel approach using Lithium-6 sheets and
+PVT cubes as scintillators for tagging the Inverse Beta-Decay products (neutron
+and positron). Being located overground and close to the BR2 research reactor,
+the experiment faces a large amount of backgrounds. Efficient real-time
+background and noise rejection is essential in order to increase the
+signal-background ratio for precise oscillation measurement and decrease data
+production to a rate which can be handled by the online software. Therefore, a
+reliable distinction between the neutrons and background signals is crucial.
+This can be performed online with a dedicated firmware trigger. A peak counting
+algorithm and an algorithm measuring time over threshold have been identified
+as performing well both in terms of efficiency and fake rate, and have been
+implemented onto FPGA.
+"
+29258,Real-time 3D Face-Eye Performance Capture of a Person Wearing VR Headset,"  Teleconference or telepresence based on virtual reality (VR) headmount
+display (HMD) device is a very interesting and promising application since HMD
+can provide immersive feelings for users. However, in order to facilitate
+face-to-face communications for HMD users, real-time 3D facial performance
+capture of a person wearing HMD is needed, which is a very challenging task due
+to the large occlusion caused by HMD. The existing limited solutions are very
+complex either in setting or in approach as well as lacking the performance
+capture of 3D eye gaze movement. In this paper, we propose a convolutional
+neural network (CNN) based solution for real-time 3D face-eye performance
+capture of HMD users without complex modification to devices. To address the
+issue of lacking training data, we generate massive pairs of HMD face-label
+dataset by data synthesis as well as collecting VR-IR eye dataset from multiple
+subjects. Then, we train a dense-fitting network for facial region and an eye
+gaze network to regress 3D eye model parameters. Extensive experimental results
+demonstrate that our system can efficiently and effectively produce in real
+time a vivid personalized 3D avatar with the correct identity, pose, expression
+and eye motion corresponding to the HMD user.
+"
+29259,Ab initio explanation of disorder and off-stoichiometry in Fe-Mn-Al-C kappa carbides,"  Carbides play a central role for the strength and ductility in many
+materials. Simulating the impact of these precipitates on the mechanical
+performance requires the knowledge about their atomic configuration. In
+particular, the C content is often observed to substantially deviate from the
+ideal stoichiometric composition. In the present work, we focus on Fe-Mn-Al-C
+steels, for which we determined the composition of the nano-sized kappa
+carbides (Fe,Mn)3AlC by atom probe tomography (APT) in comparison to larger
+precipitates located in grain boundaries. Combining density functional theory
+with thermodynamic concepts, we first determine the critical temperatures for
+the presence of chemical and magentic disorder in these carbides. Secondly, the
+experimentally observed reduction of the C content is explained as a compromise
+between the gain in chemical energy during partitioning and the elastic strains
+emerging in coherent microstructures.
+"
+29260,Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks,"  A major drawback of backpropagation through time (BPTT) is the difficulty of
+learning long-term dependencies, coming from having to propagate credit
+information backwards through every single step of the forward computation.
+This makes BPTT both computationally impractical and biologically implausible.
+For this reason, full backpropagation through time is rarely used on long
+sequences, and truncated backpropagation through time is used as a heuristic.
+However, this usually leads to biased estimates of the gradient in which longer
+term dependencies are ignored. Addressing this issue, we propose an alternative
+algorithm, Sparse Attentive Backtracking, which might also be related to
+principles used by brains to learn long-term dependencies. Sparse Attentive
+Backtracking learns an attention mechanism over the hidden states of the past
+and selectively backpropagates through paths with high attention weights. This
+allows the model to learn long term dependencies while only backtracking for a
+small number of time steps, not just from the recent past but also from
+attended relevant past states.
+"
+29261,When Does Stochastic Gradient Algorithm Work Well?,"  In this paper, we consider a general stochastic optimization problem which is
+often at the core of supervised learning, such as deep learning and linear
+classification. We consider a standard stochastic gradient descent (SGD) method
+with a fixed, large step size and propose a novel assumption on the objective
+function, under which this method has the improved convergence rates (to a
+neighborhood of the optimal solutions). We then empirically demonstrate that
+these assumptions hold for logistic regression and standard deep neural
+networks on classical data sets. Thus our analysis helps to explain when
+efficient behavior can be expected from the SGD method in training
+classification models and deep neural networks.
+"
+29262,SME investment best strategies. Outliers for assessing how to optimize performance,"  Any research on strategies for reaching business excellence aims at revealing
+the appropriate course of actions any executive should consider. Thus,
+discussions take place on how effective a performance measurement system can be
+estimated, or/and validated. Can one find an adequate measure (i) on the
+performance result due to whatever level of investment, and (ii) on the timing
+of such investments? We argue that extreme value statistics provide the answer.
+We demonstrate that the level and timing of investments allow to be forecasting
+small and medium size enterprises (SME) performance, - at financial crisis
+times. The ""investment level"" is taken as the yearly total tangible asset
+(TTA). The financial/economic performance indicators defining growth are the
+sales or total assets variations; profitability is defined from returns on
+investments or returns on sales. Companies on the Italian Stock Exchange STAR
+Market serve as example. It is found from the distributions extreme values that
+outlier companies (with positive performance) are those with the lowest but
+growing TTA. In contrast, the SME with low TTA, but which did not increase its
+TTA, before the crisis, became a negative outlier. The outcome of these
+statistical findings should suggest strategies to SME board members.
+"
+29263,The Ideal Structure of Steinberg Algebras,"  Given an ample action of an inverse semigroup on a locally compact and
+Hausdorff topological space, we study the ideal structure of the crossed
+product algebra associated with it. By developing a theory of induced ideals,
+we manage to prove that every ideal in the crossed product algebra may be
+obtained as the intersection of ideals induced from isotropy group algebras.
+This can be interpreted as an algebraic version of the Effros-Hahn conjecture.
+Finally, as an application of our result, we study the ideal structure of a
+Steinberg algebra associated with an ample groupoid by interpreting it as an
+inverse semigroup crossed product algebra.
+"
+29264,Comparing two deep learning sequence-based models for protein-protein interaction prediction,"  Biological data are extremely diverse, complex but also quite sparse. The
+recent developments in deep learning methods are offering new possibilities for
+the analysis of complex data. However, it is easy to be get a deep learning
+model that seems to have good results but is in fact either overfitting the
+training data or the validation data. In particular, the fact to overfit the
+validation data, called ""information leak"", is almost never treated in papers
+proposing deep learning models to predict protein-protein interactions (PPI).
+In this work, we compare two carefully designed deep learning models and show
+pitfalls to avoid while predicting PPIs through machine learning methods. Our
+best model predicts accurately more than 78% of human PPI, in very strict
+conditions both for training and testing. The methodology we propose here allow
+us to have strong confidences about the ability of a model to scale up on
+larger datasets. This would allow sharper models when larger datasets would be
+available, rather than current models prone to information leaks. Our solid
+methodological foundations shall be applicable to more organisms and whole
+proteome networks predictions.
+"
+29265,Convergence of Cubic Regularization for Nonconvex Optimization under KL Property,"  Cubic-regularized Newton's method (CR) is a popular algorithm that guarantees
+to produce a second-order stationary solution for solving nonconvex
+optimization problems. However, existing understandings of the convergence rate
+of CR are conditioned on special types of geometrical properties of the
+objective function. In this paper, we explore the asymptotic convergence rate
+of CR by exploiting the ubiquitous Kurdyka-Lojasiewicz (KL) property of
+nonconvex objective functions. In specific, we characterize the asymptotic
+convergence rate of various types of optimality measures for CR including
+function value gap, variable distance gap, gradient norm and least eigenvalue
+of the Hessian matrix. Our results fully characterize the diverse convergence
+behaviors of these optimality measures in the full parameter regime of the KL
+property. Moreover, we show that the obtained asymptotic convergence rates of
+CR are order-wise faster than those of first-order gradient descent algorithms
+under the KL property.
+"
+29266,"See, Hear, and Read: Deep Aligned Representations","  We capitalize on large amounts of readily-available, synchronous data to
+learn a deep discriminative representations shared across three major natural
+modalities: vision, sound and language. By leveraging over a year of sound from
+video and millions of sentences paired with images, we jointly train a deep
+convolutional network for aligned representation learning. Our experiments
+suggest that this representation is useful for several tasks, such as
+cross-modal retrieval or transferring classifiers between modalities. Moreover,
+although our network is only trained with image+text and image+sound pairs, it
+can transfer between text and sound as well, a transfer the network never
+observed during training. Visualizations of our representation reveal many
+hidden units which automatically emerge to detect concepts, independent of the
+modality.
+"
+29267,On a dual and an overpartition generalization of a family of identities of Andrews,"  We present a dual of a family of partition identities of Andrews involving
+partitions with no repeated odd parts (among other conditions), along with an
+overpartition generalization that encapsulates both families. These were
+discovered during the course of research for an upcoming article by the authors
+along with Debajyoti Nandi. The proof uses Appell's comparison theorem.
+"
+29268,Derived Azumaya algebras and twisted $K$-theory,"  We construct a relative version of topological $K$-theory of dg categories
+over an arbitrary quasi-compact, quasi-separated $\mathbb{C}$-scheme $X$. This
+has as input a $\text{Perf}(X)$-linear stable $\infty$-category and output a
+sheaf of spectra on $X(\mathbb{C})$, the space of complex points of $X$. We
+then characterize the values of this functor on inputs of the form
+$Mod_{A}^{\omega}$, for $A$ a derived Azumaya algebra over $X$. In such cases
+we show that this coincides with the $\alpha$-twisted topological $K$-theory of
+$X(\mathbb{C})$ for some appropriately defined twist of $K$-theory. We use this
+to provide a topological analogue of a classical result of Quillen's on the
+algebraic $K$-theory of Severi-Brauer varieties.
+"
+29269,Accelerating quantum instanton calculations of the kinetic isotope effects,"  Path integral implementation of the quantum instanton approximation currently
+belongs among the most accurate methods for computing quantum rate constants
+and kinetic isotope effects, but its use has been limited due to the rather
+high computational cost. Here we demonstrate that the efficiency of quantum
+instanton calculations of the kinetic isotope effects can be increased by
+orders of magnitude by combining two approaches: The convergence to the quantum
+limit is accelerated by employing high-order path integral factorizations of
+the Boltzmann operator, while the statistical convergence is improved by
+implementing virial estimators for relevant quantities. After deriving several
+new virial estimators for the high-order factorization and evaluating the
+resulting increase in efficiency, using $\mathrm{\cdot
+H_{\alpha}+H_{\beta}H_{\gamma}\rightarrow H_{\alpha}H_{\beta}+\cdot H_{\gamma
+}}$ reaction as an example, we apply the proposed method to obtain several
+kinetic isotope effects on $\mathrm{CH_{4}+\cdot H\rightleftharpoons\cdot
+CH_{3}+H_{2}}$ forward and backward reactions.
+"
+29270,Middle dimensional symplectic rigidity and its effect on Hamiltonian PDEs,"  In the first part of the article we study Hamiltonian diffeomorphisms of
+$\mathbb{R}^{2n}$ which are generated by sub-quadratic Hamiltonians and prove a
+middle dimensional rigidity result for the image of coisotropic cylinders. The
+tools that we use are Viterbo's symplectic capacities and a series of
+inequalities coming from their relation with symplectic reduction. In the
+second part we consider the nonlinear string equation and treat it as an
+infinite-dimensional Hamiltonian system. In this context we are able to apply
+Kuksin's approximation by finite dimensional Hamiltonian flows and prove a PDE
+version of the rigidity result for coisotropic cylinders. As a particular
+example, this result can be applied to the Sine-Gordon equation.
+"
+29271,Role of the global water ocean on the evolution of Titan's primitive atmosphere,"  During the accretion of Titan, impact heating may have been sufficient to
+allow the global melting of water ice and the release of volatile compounds,
+mainly constituted of CO2, CH4 and NH3. The duration and efficiency of exchange
+between the primitive massive atmosphere and the global impact-induced water
+ocean likely play a key role in the chemical evolution of the early Titan's
+atmosphere. To investigate the atmospheric composition of early Titan for a
+wide range of global (atmosphere + ocean) composition in volatils, we first
+developed a gas-liquid equilibrium model of the NH3-CO2-H2O system, where the
+non-ideal behavior of both gas and liquid phases, and the speciation of
+volatiles dissolved in the aqueous phase are taken into account. We show that
+the relative abundance of CO2 and NH3 determine the composition of Titan's
+atmosphere. For CO2/NH3 < 1, CO2 is massively dissolved in the ocean. On the
+contrary, for CO2/NH3 > 1, CO2 is the main constituent of Titan's primitive
+atmosphere while the NH3 atmospheric content is dramatically decreased. We then
+investigate the conditions for the formation of CH4-rich clathrates hydrates at
+Titan's surface that could be the main reservoir of methane for the present-day
+atmosphere. In absence of reliable experimental data in the CH4-CO2-NH3-H2O
+system, the dissolution of methane in water is included using a simplified
+Henry's law approach. We find that if the concentration of CH4 in Titan's
+building block was higher than ~0.1 mol.kg-1 and CO2/NH3 < 3, a large fraction
+of methane may be stored in the primordial crust, which would form at
+temperature below ~10°C.
+"
+29272,Semantic Compression for Edge-Assisted Systems,"  A novel semantic approach to data selection and compression is presented for
+the dynamic adaptation of IoT data processing and transmission within ""wireless
+islands"", where a set of sensing devices (sensors) are interconnected through
+one-hop wireless links to a computational resource via a local access point.
+The core of the proposed technique is a cooperative framework where local
+classifiers at the mobile nodes are dynamically crafted and updated based on
+the current state of the observed system, the global processing objective and
+the characteristics of the sensors and data streams. The edge processor plays a
+key role by establishing a link between content and operations within the
+distributed system. The local classifiers are designed to filter the data
+streams and provide only the needed information to the global classifier at the
+edge processor, thus minimizing bandwidth usage. However, the better the
+accuracy of these local classifiers, the larger the energy necessary to run
+them at the individual sensors. A formulation of the optimization problem for
+the dynamic construction of the classifiers under bandwidth and energy
+constraints is proposed and demonstrated on a synthetic example.
+"
+29273,Summary statistics from training images as prior information in probabilistic inversion,"  A strategy is presented to incorporate prior information from conceptual
+geological models in probabilistic inversion of geophysical data. The
+conceptual geological models are represented by multiple-point statistics
+training images (TIs) featuring the expected lithological units and structural
+patterns. Information from an ensemble of TI realizations is used in two
+different ways. First, dominant modes are identified by analysis of the
+frequency content in the realizations, which drastically reduces the model
+parameter space in the frequency-amplitude domain. Second, the distributions of
+global, summary metrics (e.g. model roughness) are used to formulate a prior
+probability density function. The inverse problem is formulated in a Bayesian
+framework and the posterior pdf is sampled using Markov chain Monte Carlo
+simulation. The usefulness and applicability of this method is demonstrated on
+two case studies in which synthetic crosshole ground-penetrating radar
+traveltime data are inverted to recover 2-D porosity fields. The use of prior
+information from TIs significantly enhances the reliability of the posterior
+models by removing inversion artefacts and improving individual parameter
+estimates. The proposed methodology reduces the ambiguity inherent in the
+inversion of high-dimensional parameter spaces, accommodates a wide range of
+summary statistics and geophysical forward problems.
+"
+29274,"Beyond the law of large numbers: Introducing progressive sampling, weaving, the geometric triangle, and corresponding distributions","  In probability theory and statistics, the IID model represents a single
+population, and a large, potentially infinite sample from this population. Main
+theorems, in particular the central limit theorem and laws of large number
+(LLN) assure convergence, making asymptotic statistics possible.
+To avoid convergence, it is thus straightforward to consider two populations
+and a sample that ceaselessly fluctuates between them. It is the aim of this
+contribution to study the effects that thus occur. To this end, we introduce
+""progressive sampling,"" leading to a straightforward model that is analytically
+tractable. With a minimum of technical overhead, a number of interesting
+results thus ensue:
+In particular, one encounters a multiplicate structure (similar to Pascal's
+triangle) that is associated with a new class of distributions (related to the
+binomial). Although the argument is completely probabilistic, it entails a
+well-known fractal structure. It also turns out that the new (global) operation
+of ""weaving"" is equivalent to a certain (local) cascade process.
+"
+29275,Joint Maximum a Posteriori State Path and Parameter Estimation in Stochastic Differential Equations,"  In this article, we introduce the joint maximum a posteriori state path and
+parameter estimator (JME) for continuous-time systems described by stochastic
+differential equations (SDEs). This estimator can be applied to nonlinear
+systems with discrete-time (sampled) measurements with a wide range of
+measurement distributions. We also show that the minimum-energy state path and
+parameter estimator (MEE) obtains the joint maximum a posteriori noise path,
+initial conditions, and parameters. These estimators are demonstrated in
+simulated experiments, in which they are compared to the prediction error
+method (PEM) using the unscented Kalman filter and smoother. The experiments
+show that the MEE is biased for the damping parameters of the drift function.
+Furthermore, for robust estimation in the presence of outliers, the JME attains
+lower state estimation errors than the PEM.
+"
+29276,Tingley's problem through the facial structure of an atomic JBW*-triple,"  We prove that every surjective isometry between the unit spheres of two
+atomic JBW$^*$-triples $E$ and $B$ admits a unit extension to a surjective real
+linear isometry from $E$ into $B$. This result constitutes a new positive
+answer to Tignley's problem in the Jordan setting.
+"
+29277,Relation Extraction : A Survey,"  With the advent of the Internet, large amount of digital text is generated
+everyday in the form of news articles, research publications, blogs, question
+answering forums and social media. It is important to develop techniques for
+extracting information automatically from these documents, as lot of important
+information is hidden within them. This extracted information can be used to
+improve access and management of knowledge hidden in large text corpora.
+Several applications such as Question Answering, Information Retrieval would
+benefit from this information. Entities like persons and organizations, form
+the most basic unit of the information. Occurrences of entities in a sentence
+are often linked through well-defined relations; e.g., occurrences of person
+and organization in a sentence may be linked through relations such as employed
+at. The task of Relation Extraction (RE) is to identify such relations
+automatically. In this paper, we survey several important supervised,
+semi-supervised and unsupervised RE techniques. We also cover the paradigms of
+Open Information Extraction (OIE) and Distant Supervision. Finally, we describe
+some of the recent trends in the RE techniques and possible future research
+directions. This survey would be useful for three kinds of readers - i)
+Newcomers in the field who want to quickly learn about RE; ii) Researchers who
+want to know how the various RE techniques evolved over time and what are
+possible future research directions and iii) Practitioners who just need to
+know which RE technique works best in various settings.
+"
+29278,SmoothGrad: removing noise by adding noise,"  Explaining the output of a deep network remains a challenge. In the case of
+an image classifier, one type of explanation is to identify pixels that
+strongly influence the final decision. A starting point for this strategy is
+the gradient of the class score function with respect to the input image. This
+gradient can be interpreted as a sensitivity map, and there are several
+techniques that elaborate on this basic idea. This paper makes two
+contributions: it introduces SmoothGrad, a simple method that can help visually
+sharpen gradient-based sensitivity maps, and it discusses lessons in the
+visualization of these maps. We publish the code for our experiments and a
+website with our results.
+"
+29279,Multivalued Functions in Digital Topology,"  We study several types of multivalued functions in digital topology.
+"
+29280,A High-Performance Algorithm for Identifying Frequent Items in Data Streams,"  Estimating frequencies of items over data streams is a common building block
+in streaming data measurement and analysis. Misra and Gries introduced their
+seminal algorithm for the problem in 1982, and the problem has since been
+revisited many times due its practicality and applicability. We describe a
+highly optimized version of Misra and Gries' algorithm that is suitable for
+deployment in industrial settings. Our code is made public via an open source
+library called DataSketches that is already used by several companies and
+production systems.
+Our algorithm improves on two theoretical and practical aspects of prior
+work. First, it handles weighted updates in amortized constant time, a common
+requirement in practice. Second, it uses a simple and fast method for merging
+summaries that asymptotically improves on prior work even for unweighted
+streams. We describe experiments confirming that our algorithms are more
+efficient than prior proposals.
+"
+29281,Sliced Wasserstein Kernel for Persistence Diagrams,"  Persistence diagrams (PDs) play a key role in topological data analysis
+(TDA), in which they are routinely used to describe topological properties of
+complicated shapes. PDs enjoy strong stability properties and have proven their
+utility in various learning contexts. They do not, however, live in a space
+naturally endowed with a Hilbert structure and are usually compared with
+specific distances, such as the bottleneck distance. To incorporate PDs in a
+learning pipeline, several kernels have been proposed for PDs with a strong
+emphasis on the stability of the RKHS distance w.r.t. perturbations of the PDs.
+In this article, we use the Sliced Wasserstein approximation SW of the
+Wasserstein distance to define a new kernel for PDs, which is not only provably
+stable but also provably discriminative (depending on the number of points in
+the PDs) w.r.t. the Wasserstein distance $d_1$ between PDs. We also demonstrate
+its practicality, by developing an approximation technique to reduce kernel
+computation time, and show that our proposal compares favorably to existing
+kernels for PDs on several benchmarks.
+"
+29282,Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification,"  Music tag words that describe music audio by text have different levels of
+abstraction. Taking this issue into account, we propose a music classification
+approach that aggregates multi-level and multi-scale features using pre-trained
+feature extractors. In particular, the feature extractors are trained in
+sample-level deep convolutional neural networks using raw waveforms. We show
+that this approach achieves state-of-the-art results on several music
+classification datasets.
+"
+29283,Modeling and analysis of RNA-seq data: a review from a statistical perspective,"  Background: Since the invention of next-generation RNA sequencing (RNA-seq)
+technologies, they have become a powerful tool to study the presence and
+quantity of RNA molecules in biological samples and have revolutionized
+transcriptomic studies. The analysis of RNA-seq data at four different levels
+(samples, genes, transcripts, and exons) involve multiple statistical and
+computational questions, some of which remain challenging up to date.
+Results: We review RNA-seq analysis tools at the sample, gene, transcript,
+and exon levels from a statistical perspective. We also highlight the
+biological and statistical questions of most practical considerations.
+Conclusion: The development of statistical and computational methods for
+analyzing RNA- seq data has made significant advances in the past decade.
+However, methods developed to answer the same biological question often rely on
+diverse statical models and exhibit different performance under different
+scenarios. This review discusses and compares multiple commonly used
+statistical models regarding their assumptions, in the hope of helping users
+select appropriate methods as needed, as well as assisting developers for
+future method development.
+"
+29284,Are the different layers of a social network conveying the same information?,"  Comprehensive and quantitative investigations of social theories and
+phenomena increasingly benefit from the vast breadth of data describing human
+social relations, which is now available within the realm of computational
+social science. Such data are, however, typically proxies for one of the many
+interaction layers composing social networks, which can be defined in many ways
+and are typically composed of communication of various types (e.g., phone
+calls, face-to-face communication, etc.). As a result, many studies focus on
+one single layer, corresponding to the data at hand. Several studies have,
+however, shown that these layers are not interchangeable, despite the presence
+of a certain level of correlations between them. Here, we investigate whether
+different layers of interactions among individuals lead to similar conclusions
+with respect to the presence of homophily patterns in a population---homophily
+represents one of the widest studied phenomenon in social networks. To this
+aim, we consider a dataset describing interactions and links of various nature
+in a population of Asian students with diverse nationalities, first language
+and gender. We study homophily patterns, as well as their temporal evolutions
+in each layer of the social network. To facilitate our analysis, we put forward
+a general method to assess whether the homophily patterns observed in one layer
+inform us about patterns in another layer. For instance, our study reveals that
+three network layers---cell phone communications, questionnaires about
+friendship, and trust relations---lead to similar and consistent results
+despite some minor discrepancies. The homophily patterns of the co-presence
+network layer, however, does not yield any meaningful information about other
+network layers.
+"
+29285,PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training,"  Massive data exist among user local platforms that usually cannot support
+deep neural network (DNN) training due to computation and storage resource
+constraints. Cloud-based training schemes provide beneficial services but
+suffer from potential privacy risks due to excessive user data collection. To
+enable cloud-based DNN training while protecting the data privacy
+simultaneously, we propose to leverage the intermediate representations of the
+data, which is achieved by splitting the DNNs and deploying them separately
+onto local platforms and the cloud. The local neural network (NN) is used to
+generate the feature representations. To avoid local training and protect data
+privacy, the local NN is derived from pre-trained NNs. The cloud NN is then
+trained based on the extracted intermediate representations for the target
+learning task. We validate the idea of DNN splitting by characterizing the
+dependency of privacy loss and classification accuracy on the local NN topology
+for a convolutional NN (CNN) based image classification task. Based on the
+characterization, we further propose PrivyNet to determine the local NN
+topology, which optimizes the accuracy of the target learning task under the
+constraints on privacy loss, local computation, and storage. The efficiency and
+effectiveness of PrivyNet are demonstrated with the CIFAR-10 dataset.
+"
+29286,Ideals in the Goldman Algebra,"  The goal of this work is to study the ideals of the Goldman Lie algebra $S$.
+To do so, we construct an algebra homomorphism from $S$ to a simpler algebraic
+structure, and focus on finding ideals of this new structure instead. The
+structure $S$ can be regarded as either a $\mathbb{Q}$-module or a
+$\mathbb{Q}$-module generated by free homotopy classes. For $\mathbb{Z}$-module
+case, we proved that there is an infinite class of ideals of $S$ that contain a
+certain finite set of free homotopy classes. For $\mathbb{Q}$-module case, we
+can classify all the ideals of the new structure and consequently obtain a new
+class of ideals of the original structure. Finally, we show an interesting
+infinite chain of ideals that are not those ideals obtained by considering the
+new structure.
+"
+29287,How to Detect Inclined Water Maser Disks (and Possibly Measure Black Hole Masses),"  We describe a method to identify inclined water maser disks orbiting massive
+black holes and to potentially use them to measure black hole masses. Due to
+the geometry of maser amplification pathways, the minority of water maser disks
+are observable: only those viewed nearly edge-on have been identified,
+suggesting that an order of magnitude additional maser disks exist. We suggest
+that inward-propagating masers will be gravitationally deflected by the central
+black hole, thereby scattering water maser emission out of the disk plane and
+enabling detection. The signature of an inclined water maser disk would be
+narrow masers near the systemic velocity that appear to emit from the black
+hole position, as identified by the radio continuum core. To explore this
+possibility, we present high resolution (0.07""-0.17"") Very Large Array line and
+continuum observations of 13 galaxies with narrow water maser emission and show
+that three are good inclined disk candidates (five remain ambiguous). In the
+best case, for CGCG 120$-$039, we show that the maser and continuum emission
+are coincident to within $3.5\pm1.4$ pc ($6.7\pm2.7$ milliarcsec). Subsequent
+very long baseline interferometric maps can confirm candidate inclined disks
+and have the potential to show maser rings or arcs that provide a direct
+measurement of black hole mass, although the mass precision will rely on
+knowledge of the size of the maser disk.
+"
+29288,Minimization algorithm in the simulation of the Wall Touching Kink Modes,"  In this paper the discretized variational principle in the simulation of the
+Wall Touching Kink Modes (WTKM) is reformulated in terms of independent
+variables and a corresponding constrained minimization algorithm is elaborated.
+In a frame of a general formalism is proposed an algoritm for constrained
+linear minimization adapted to this class of problems. The FORTRAN programme
+that realize the algorithm is described.
+"
+29289,Supporting Requirements Engineering Research that Industry Needs: The Naming the Pain in Requirements Engineering Initiative,"  In light of the 40th jubilee of Requirements Engineering (RE), roughly 40
+experts met in Switzerland to discuss where our discipline stands today. As of
+today, the common view is, indisputably, that RE as a discipline is stable and
+respected, as pointed out by Sarah Gregory when covering the seminar in her
+column to which articles like this one are invited to present ongoing research.
+However, it is also evident that after 40 years of promising research,
+conducting research that industry needs is still an ongoing challenge. Research
+that industry needs means research that solves industrial problems
+practitioners face; but do we really understand those problems? Here, I want to
+recapitulate on this research challenge and outline an initiative, the Naming
+the Pain in Requirements Engineering Initiative, that aims at tackling this
+problem.
+"
+29290,On the Uniqueness Problem for Notations of Recursive Ordinals,"  In the article 'Ordinal Logics and the Characterizations of the Informal
+Concept of Proof', Georg Kreisel poses the problem of assigning unique
+notations to recursive ordinals, and additionally suggests that the methods
+which are developed for its solution will be non-constructive in character. In
+this paper we develop methods in which various uniqueness results for notations
+of recursive ordinals can be obtained, and thereafter apply these results to
+investigate the problems surrounding the hierarchical classification of the
+computable functions.
+"
+29291,A Model for Metastable Magnetism in the Hidden-Order Phase of URu2Si2,"  We propose an explanation for the experiment by Schemm et al., Phys. Rev. B
+91, 140506 (2015) where the polar Kerr effect (PKE), indicating time-reversal
+symmetry (TRS) breaking, was observed in the hidden-order (HO) phase of
+URu2Si2. The PKE signal on warmup was seen only if a training magnetic field
+was present on cool-down. Using a Ginzburg-Landau model for a complex order
+parameter, we show that the system can have a metastable ferromagnetic state
+producing the PKE, even if the HO ground state respects TRS. We predict that a
+strong reversed magnetic field should reset the PKE to zero.
+"
+29292,Flattening a non-degenerate CR singular point of real codimension two,"  This paper continues the previous studies in two papers of Huang-Yin [HY3-4]
+on the flattening problem of a CR singular point of real codimension two
+sitting in a submanifold in ${\mathbb C}^{n+1}$ with $n+1\ge 3$, whose CR
+points are non-minimal. Partially based on the geometric approach initiated in
+[HY3] and a formal theory approach used in [HY4], we are able to provide a very
+general flattening theorem for a non-degenerate CR singular point. As an
+application, we provide a solution to the local complex Plateau problem and
+obtain the analyticity of the local hull of holomorphy near a real analytic
+definite CR singular point in a general setting.
+"
+29293,Influence of telopeptides on the structural and physical properties of polymeric and monomeric acid-soluble type I collagen,"  Currently two factors hinder the use of collagen as building block of
+regenerative devices: the potential antigenicity, and limited mechanical
+strength in aqueous environment. Polymeric collagen is naturally found in the
+cross-linked state and is mechanically tougher than the monomeric,
+cross-link-free (acid-soluble) collagen ex vivo. The antigenicity of collagen,
+on the other hand, is mainly ascribed to inter-species variations in amino acid
+sequences, which are primarily located in the non-helical terminal
+telopeptides. Although these can be removed through enzymatic treatment to
+produce atelocollagen, the effect of telopeptide removal on triple helix
+organization, amino acidic composition and thermal properties is often
+disregarded. Here, we compare the structural, chemical and physical properties
+of polymeric and monomeric type I collagen with and without telopeptides, in an
+effort to elucidate the influence that either covalent crosslinks or
+telopeptides possess. Circular dichroism (CD) was used to examine the triple
+helical conformation and quantify the denaturation temperature (Td) of both
+monomeric collagen (36.5 °C) and monomeric atelocollagen (35.5 °C).
+CD measurements were combined with differential scanning calorimetry (DSC) in
+order to gain insight into the triple helix-to-coil thermal transition and
+shrinkage temperature (Ts) of polymeric atelo collagen (44.8 °C),
+polymeric collagen (62.7 °C), monomeric atelo collagen (51.4 °C) and
+monomeric collagen (66.5 °C). Structural and thermal analyses were
+combined with high pressure liquid chromatography (HPLC) to determine the
+content of specific collagen amino acidic residues used as markers for the
+presence of telopeptides and mature crosslinks.
+"
+29294,Persistence of Li-Yorke chaos in systems with relay,"  It is rigorously proved that the chaotic dynamics of the non-smooth system
+with relay function is persistent even if a chaotic perturbation is applied. We
+consider chaos in a modified Li-Yorke sense such that infinitely many almost
+periodic motions take place in its basis. It is demonstrated that the system
+under investigation possesses countable infinity of chaotic sets of solutions.
+Coupled Duffing oscillators are used to show the effectiveness of our
+technique, and simulations that support the theoretical results are
+represented. Moreover, a chaos control procedure based on the Ott-Grebogi-Yorke
+algorithm is proposed to stabilize the unstable almost periodic motions
+embedded in the chaotic attractor.
+"
+29295,Optically pumped GeSn micro-disks with 16 % Sn lasing at 3.1 um up to 180K,"  Recent demonstrations of optically pumped lasers based on GeSn alloys put
+forward the prospect of efficient laser sources monolithically integrated on a
+Si photonic platform. For instance, GeSn layers with 12.5% of Sn were reported
+to lase at 2.5 um wavelength up to 130 K. In this work, we report a longer
+emitted wavelength and a significant improvement in lasing temperature. The
+improvements resulted from the use of higher Sn content GeSn layers of
+optimized crystalline quality, grown on graded Sn content buffers using Reduced
+Pressure CVD. The fabricated GeSn micro-disks with 13% and 16% of Sn showed
+lasing operation at 2.6 um and 3.1 um wavelengths, respectively. For the
+longest wavelength (i.e 3.1 um), lasing was demonstrated up to 180 K, with a
+threshold of 377 kW/cm2 at 25 K.
+"
+29296,Quantum-chemical insights from interpretable atomistic neural networks,"  With the rise of deep neural networks for quantum chemistry applications,
+there is a pressing need for architectures that, beyond delivering accurate
+predictions of chemical properties, are readily interpretable by researchers.
+Here, we describe interpretation techniques for atomistic neural networks on
+the example of Behler-Parrinello networks as well as the end-to-end model
+SchNet. Both models obtain predictions of chemical properties by aggregating
+atom-wise contributions. These latent variables can serve as local explanations
+of a prediction and are obtained during training without additional cost. Due
+to their correspondence to well-known chemical concepts such as atomic energies
+and partial charges, these atom-wise explanations enable insights not only
+about the model but more importantly about the underlying quantum-chemical
+regularities. We generalize from atomistic explanations to 3d space, thus
+obtaining spatially resolved visualizations which further improve
+interpretability. Finally, we analyze learned embeddings of chemical elements
+that exhibit a partial ordering that resembles the order of the periodic table.
+As the examined neural networks show excellent agreement with chemical
+knowledge, the presented techniques open up new venues for data-driven research
+in chemistry, physics and materials science.
+"
+29297,Constant scalar curvature hypersurfaces in $(3+1)$-dimensional GHMC Minkowski spacetimes,"  We prove that every $(3+1)$-dimensional flat GHMC Minkowski spacetime which
+is not a translation spacetime or a Misner spacetime carries a unique foliation
+by spacelike hypersurfaces of constant scalar curvature. In otherwords, we
+prove that every such spacetime carries a unique time function with isochrones
+of constant scalar curvature. Furthermore, this time function is a smooth
+submersion.
+"
+29298,Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification,"  It has been demonstrated that deep neural networks are prone to noisy
+examples particular adversarial samples during inference process. The gap
+between robust deep learning systems in real world applications and vulnerable
+neural networks is still large. Current adversarial training strategies improve
+the robustness against adversarial samples. However, these methods lead to
+accuracy reduction when the input examples are clean thus hinders the
+practicability. In this paper, we investigate an approach that protects the
+neural network classification from the adversarial samples and improves its
+accuracy when the input examples are clean. We demonstrate the versatility and
+effectiveness of our proposed approach on a variety of different networks and
+datasets.
+"
+29299,Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals,"  Local climate conditions play a major role in the development of the mosquito
+population responsible for transmitting Dengue Fever. Since the {\em Aedes
+Aegypti} mosquito is also a primary vector for the recent Zika and Chikungunya
+epidemics across the Americas, a detailed monitoring of periods with favorable
+climate conditions for mosquito profusion may improve the timing of
+vector-control efforts and other urgent public health strategies. We apply
+dimensionality reduction techniques and machine-learning algorithms to climate
+time series data and analyze their connection to the occurrence of Dengue
+outbreaks for seven major cities in Brazil. Specifically, we have identified
+two key variables and a period during the annual cycle that are highly
+predictive of epidemic outbreaks. The key variables are the frequency of
+precipitation and temperature during an approximately two month window of the
+winter season preceding the outbreak. Thus simple climate signatures may be
+influencing Dengue outbreaks even months before their occurrence. Some of the
+more challenging datasets required usage of compressive-sensing procedures to
+estimate missing entries for temperature and precipitation records. Our results
+indicate that each Brazilian capital considered has a unique frequency of
+precipitation and temperature signature in the winter preceding a Dengue
+outbreak. Such climate contributions on vector populations are key factors in
+dengue dynamics which could lead to more accurate prediction models and early
+warning systems. Finally, we show that critical temperature and precipitation
+signatures may vary significantly from city to city, suggesting that the
+interplay between climate variables and dengue outbreaks is more complex than
+generally appreciated.
+"
+29300,Principal component analysis of geomagnetic activity: New information on solar wind,"  We use the principal component analysis (PCA) to study geomagnetic activity
+at annual resolution using a network of 26 magnetic stations in 1966-2015, and
+an extended network of 40 stations in 1980-2015. The first principal component
+(PC1) describes the long-term evolution of global geomagnetic activity, and has
+an excellent correlation with indices like the Kp/Ap index. The two networks
+give identical results for PC1. The second principal component (PC2) is highly
+correlated with the annual percentage of high-speed streams (HSS). The extended
+network has a slightly higher sensitivity to HSSs. We verify the non-trivial
+latitudinal distribution of the second empirical orthogonal function (EOF2). We
+find that the amplitude of the 22-year variation of geomagnetic activity has a
+closely similar latitudinal distribution as EOF2. This verifies that the
+22-year variation of geomagnetic activity is related to HSSs. The most likely
+cause is the Russell-McPherron mechanism.
+"
+29301,Rogue periodic waves of the mKdV equation,"  Traveling periodic waves of the modified Korteweg-de Vries (mKdV) equation
+are considered in the focusing case. By using one-fold and two-fold Darboux
+transformations, we construct explicitly the rogue periodic waves of the mKdV
+equation expressed by the Jacobian elliptic functions dn and cn respectively.
+The rogue dn-periodic wave describes propagation of an algebraically decaying
+soliton over the dn-periodic wave, the latter wave is modulationally stable
+with respect to long-wave perturbations. The rogue cn-periodic wave represents
+the outcome of the modulation instability of the cn-periodic wave with respect
+to long-wave perturbations and serves for the same purpose as the rogue wave of
+the nonlinear Schrodinger equation (NLS), where it is expressed by the rational
+function. We compute the magnification factor for the cn-periodic wave of the
+mKdV equation and show that it remains the same as in the small-amplitude NLS
+limit for all amplitudes. As a by-product of our work, we find explicit
+expressions for the periodic eigenfunctions of the AKNS spectral problem
+associated with the dn- and cn-periodic waves of the mKdV equation.
+"
+29302,Rosetta Alice Ultraviolet Spectrograph Flight Operations and Lessons Learned,"  This paper explores the uniqueness of ESA Rosetta mission operations from the
+Alice instrument point of view, documents lessons learned, and suggests
+operations ideas for future missions. The Alice instrument mounted on the
+Rosetta orbiter is an imaging spectrograph optimized for cometary
+far-ultraviolet (FUV) spectroscopy with the scientific objectives of measuring
+properties of the escaping gas and dust, and studying the surface properties,
+including searching for exposed ices. We describe the operations processes
+during the comet encounter period, the many interfaces to contend with, the
+constraints that impacted Alice, and how the Alice science goals of measuring
+the cometary gas characteristics and their evolution were achieved. We provide
+details that are relevant to the use and interpretation of Alice data and
+published results. All these flight experiences and lessons learned will be
+useful for future cometary missions that include ultraviolet spectrographs in
+particular, and multi-instrument international payloads in general.
+"
+29303,Penalizing Unfairness in Binary Classification,"  We present a new approach for mitigating unfairness in learned classifiers.
+In particular, we focus on binary classification tasks over individuals from
+two populations, where, as our criterion for fairness, we wish to achieve
+similar false positive rates in both populations, and similar false negative
+rates in both populations. As a proof of concept, we implement our approach and
+empirically evaluate its ability to achieve both fairness and accuracy, using
+datasets from the fields of criminal risk assessment, credit, lending, and
+college admissions.
+"
+29304,Are screening methods useful in feature selection? An empirical study,"  Filter or screening methods are often used as a preprocessing step for
+reducing the number of variables used by a learning algorithm in obtaining a
+classification or regression model. While there are many such filter methods,
+there is a need for an objective evaluation of these methods. Such an
+evaluation is needed to compare them with each other and also to answer whether
+they are at all useful, or a learning algorithm could do a better job without
+them. For this purpose, many popular screening methods are partnered in this
+paper with three regression learners and five classification learners and
+evaluated on ten real datasets to obtain accuracy criteria such as R-square and
+area under the ROC curve (AUC). The obtained results are compared through curve
+plots and comparison tables in order to find out whether screening methods help
+improve the performance of learning algorithms and how they fare with each
+other. Our findings revealed that the screening methods were only useful in one
+regression and three classification datasets out of the ten datasets evaluated.
+"
+29305,Generalizations of Edge Overlap to Weighted and Directed Networks,"  With the increasing availability of behavioral data from diverse digital
+sources, such as social media sites and cell phones, it is now possible to
+obtain detailed information on the strength and directionality of social
+interactions in various settings. While most metrics used to characterize
+network structure have traditionally been defined for unweighted and undirected
+networks only, the richness of current network data calls for extending these
+metrics to weighted and directed networks. One fundamental metric, especially
+in social networks, is edge overlap, the proportion of friends shared by two
+connected individuals. Here we extend definitions of edge overlap to weighted
+and directed networks, and we present closed-form expressions for the mean and
+variance of each version for the classic \er random graph and its weighted and
+directed counterparts. We apply these results to social network data collected
+in rural villages, and we use our analytical results to quantify the extent to
+which the average edge overlap in the empirical social networks deviates from
+that of corresponding random graphs. Finally, we carry out comparisons across
+attribute categories including sex, caste, and age, finding that women tend to
+form more tightly clustered friendship circles than men, where the extent of
+overlap depends on the nature of social interaction in question.
+"
+29306,A balance index for phylogenetic trees based on quartets,"  We define a new balance index for phylogenetic trees based on the symmetry of
+the evolutive history of every quartet of leaves. This index makes sense for
+polytomic trees and it can be computed in time linear in the number of leaves.
+We compute its maximum and minimum values for arbitrary and bifurcating trees,
+and we provide exact formulas for its expected value and variance for
+bifurcating trees under Ford's $\alpha$-model and Aldous' $\beta$-model and for
+arbitrary trees under the $\alpha$-$\gamma$-model.
+"
+29307,Resonance tongues for the Hill equation with Duffing coefficients and instabilities in a nonlinear beam equation,"  We consider a class of Hill equations where the periodic coefficient is the
+squared solution of some Duffing equation plus a constant. We study the
+stability of the trivial solution of this Hill equation and we show that a
+criterion due to Burdina (V.I. Burdina, Boundedness of solutions of a system of
+differential equations) is very helpful for this analysis. In some cases, we
+are also able to determine exact solutions in terms of Jacobi elliptic
+functions. Overall, we obtain a fairly complete picture of the stability and
+instability regions. These results are then used to study the stability of
+nonlinear modes in some beam equations.
+"
+29308,Trend and Variable-Phase Seasonality Estimation from Functional Data,"  The problem of estimating trend and seasonal variation in time-series data
+has been studied over several decades, although mostly using single time
+series. This paper studies the problem of estimating these components from
+functional data, i.e. multiple time series, in situations where seasonal
+effects exhibit arbitrary time warpings or phase variability across different
+observations. Rather than ignoring the phase variability, or using an
+off-the-shelf alignment method to remove phase, we take a model-based approach
+and seek MLEs of the trend and the seasonal effects, while performing
+alignments over the seasonal effects at the same time. The MLEs of trend,
+seasonality, and phase are computed using a coordinate-descent based
+optimization method. We use bootstrap replication for computing confidence
+bands and for testing hypothesis about the estimated components. We also
+utilize log-likelihood for selecting the trend subspace, and for comparisons
+with other candidate models. This framework is demonstrated using experiments
+involving synthetic data and three real data (Berkeley Growth Velocity, U.S.
+electricity price, and USD exchange fluctuation).
+"
+29309,Conditioning of implicit Runge-Kutta integration for finite element approximation of linear diffusion equations on anisotropic meshes,"  The conditioning of implicit Runge-Kutta (RK) integration for linear finite
+element approximation of diffusion equations on general anisotropic meshes is
+investigated. Bounds are established for the condition number of the resulting
+linear system with and without diagonal preconditioning for the implicit Euler
+(the simplest implicit RK method) and general implicit RK methods. It is shown
+that the conditioning of an implicit RK method behaves like that of the
+implicit Euler method. Two solution strategies are considered for the linear
+system resulting from general implicit RK integration: the simultaneous
+solution where the system is solved as a whole and a successive solution which
+follows the commonly used implementation of implicit RK methods to first
+transform the system into a number of smaller systems using the Jordan normal
+form of the RK matrix and then solve them successively. The obtained bounds for
+the condition number have explicit geometric interpretations and take the
+interplay between the diffusion matrix and the mesh geometry into full
+consideration. They show that there are three mesh-dependent factors that can
+affect the conditioning: the number of elements, the mesh nonuniformity
+measured in the Euclidean metric, and the mesh nonuniformity with respect to
+the inverse of the diffusion matrix. They also reveal that the preconditioning
+using the diagonal of the system matrix, the mass matrix, or the lumped mass
+matrix can effectively eliminate the effects of the mesh nonuniformity measured
+in the Euclidean metric. Illustrative numerical examples are given.
+"
+29310,Berry phase and model wavefunction in the half-filled Landau Level,"  We construct model wavefunctions for the half-filled Landau level
+parameterized by ""composite fermion occupation-number configurations"" in a
+two-dimensional momentum space which correspond to a Fermi sea with
+particle-hole excitations. When these correspond to a weakly-excited Fermi sea,
+they have large overlap with wavefunctions obtained by exact diagonalization of
+lowest-Landau-level electrons interacting with a Coulomb interaction, allowing
+exact states to be identified with quasiparticle configurations. We then
+formulate a many-body version of the single-particle Berry phase for adiabatic
+transport of a single quasiparticle around a path in momentum space, and
+evaluate it using a sequence of exact eigenstates in which a single
+quasiparticle moves incrementally. In this formulation the standard
+free-particle construction in terms of the overlap between ""periodic parts of
+successive Bloch wavefunctions"" is reinterpreted as the matrix element of a
+""momentum boost"" operator between the full Bloch states, which becomes the
+matrix elements of a Girvin-MacDonald-Platzman density operator in the
+many-body context. This allows computation of the Berry phase for transport of
+a single composite fermion around the Fermi surface. In addition to a phase
+contributed by the density operator, we find a phase of exactly $\pi$ for this
+process.
+"
+29311,CO2 condensation is a serious limit to the deglaciation of Earth-like planets,"  It is widely believed that the carbonate-silicate cycle is the main agent to
+trigger deglaciations by CO$_2$ greenhouse warming on Earth and on Earth-like
+planets when they get in frozen state. Here we use a 3D Global Climate Model to
+simulate the ability of frozen planets to escape from glaciation by
+accumulating enough gaseous CO$_2$. We find that Earth-like planets orbiting a
+Sun-like star may never be able to escape from glaciation if their orbital
+distance is greater than $\sim$ 1.27 AU (Flux $<$ 847 W m$^{-2}$ or 62$\%$ of
+the Solar constant), because CO$_2$ would condense at the poles forming
+permanent CO$_2$ ice caps. This limits the amount of CO$_2$ in the atmosphere
+and thus its greenhouse effect. The amount of CO$_2$ that can be trapped in the
+polar caps depends on the efficiency of CO$_2$ ice to flow laterally as well as
+its graviational stability relative to subsurface water ice. The flow of CO$_2$
+ice from poles to equator is mostly controlled by the bottom temperature, and
+hence by the internal heat flux. We find that a frozen Earth-like planet
+located at 1.30 AU of a Sun-like star could store as much as 1.5/4.5/15 bars of
+dry ice at the poles, for internal heat fluxes of 100/30/10 mW m$^{-2}$. But
+these amounts are lower limits. For planets with a significant water ice cover,
+we show that CO$_2$ ice deposits should be gravitationnally unstable. They get
+buried beneath the water ice cover in geologically short timescales of
+$\sim$~10$^4$ yrs, mainly controlled by the viscosity of water ice. CO$_2$
+would be permanently sequestered underneath the water ice cover, in the form of
+CO$_2$ liquids, CO$_2$ clathrate hydrates and/or dissolved in subglacial water
+reservoirs (if any). This would considerably increase the amount of CO$_2$
+trapped and further reduce the probability of deglaciation.
+"
+29312,Rethinking the Form of Latent States in Image Captioning,"  RNNs and their variants have been widely adopted for image captioning. In
+RNNs, the production of a caption is driven by a sequence of latent states.
+Existing captioning models usually represent latent states as vectors, taking
+this practice for granted. We rethink this choice and study an alternative
+formulation, namely using two-dimensional maps to encode latent states. This is
+motivated by the curiosity about a question: how the spatial structures in the
+latent states affect the resultant captions? Our study on MSCOCO and Flickr30k
+leads to two significant observations. First, the formulation with 2D states is
+generally more effective in captioning, consistently achieving higher
+performance with comparable parameter sizes. Second, 2D states preserve spatial
+locality. Taking advantage of this, we visually reveal the internal dynamics in
+the process of caption generation, as well as the connections between input
+visual domain and output linguistic domain.
+"
+29313,A Denotational Semantics for SPARC TSO,"  The SPARC TSO weak memory model is defined axiomatically, with a
+non-compositional formulation that makes modular reasoning about programs
+difficult. Our denotational approach uses pomsets to provide a compositional
+semantics capturing exactly the behaviours permitted by SPARC TSO. It uses
+buffered states and an inductive definition of execution to assign an
+input-output meaning to pomsets. We show that our denotational account is sound
+and complete relative to the axiomatic account, that is, that it captures
+exactly the behaviours permitted by the axiomatic account. Our compositional
+approach facilitates the study of SPARC TSO and supports modular analysis of
+program behaviour.
+"
+29314,Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation,"  Domain adaptation (DA) is transfer learning which aims to leverage labeled
+data in a related source domain to achieve informed knowledge transfer and help
+the classification of unlabeled data in a target domain. In this paper, we
+propose a novel DA method, namely Robust Data Geometric Structure Aligned,
+Close yet Discriminative Domain Adaptation (RSA-CDDA), which brings closer, in
+a latent joint subspace, both source and target data distributions, and aligns
+inherent hidden source and target data geometric structures while performing
+discriminative DA in repulsing both interclass source and target data. The
+proposed method performs domain adaptation between source and target in solving
+a unified model, which incorporates data distribution constraints, in
+particular via a nonparametric distance, i.e., Maximum Mean Discrepancy (MMD),
+as well as constraints on inherent hidden data geometric structure segmentation
+and alignment between source and target, through low rank and sparse
+representation. RSA-CDDA achieves the search of a joint subspace in solving the
+proposed unified model through iterative optimization, alternating Rayleigh
+quotient algorithm and inexact augmented Lagrange multiplier algorithm.
+Extensive experiments carried out on standard DA benchmarks, i.e., 16
+cross-domain image classification tasks, verify the effectiveness of the
+proposed method, which consistently outperforms the state-of-the-art methods.
+"
+29315,"Model Uncertainty, Recalibration, and the Emergence of Delta-Vega Hedging","  We study option pricing and hedging with uncertainty about a Black-Scholes
+reference model which is dynamically recalibrated to the market price of a
+liquidly traded vanilla option. For dynamic trading in the underlying asset and
+this vanilla option, delta-vega hedging is asymptotically optimal in the limit
+for small uncertainty aversion. The corresponding indifference price
+corrections are determined by the disparity between the vegas, gammas, vannas,
+and volgas of the non-traded and the liquidly traded options.
+"
+29316,Seminar Innovation Management - Winter Term 2017,"  This document contains the results obtained by the Innovation Management
+Seminar in winter term 2017. In total 11 ideas have been developed by the team.
+In the document all 11 ideas show improvements for future applications in
+ophthalmology. The 11 ideas are AR/VR Glasses with Medical Applications,
+Augmented Reality Eye Surgery, Game Diagnosis, Intelligent Adapting Glasses, MD
+Facebook, Medical Crowd Segmentation, Personalized 3D Model of the Human Eye,
+Photoacoustic Contact Lens, Power Supply Smart Contact Lens, VR-Cornea and Head
+Mount for Fundus Imaging
+"
+29317,Convergence of the CR Yamabe Flow,"  We consider the CR Yamabe flow on a compact strictly pseudoconvex CR manifold
+$M$ of real dimension $2n+1$. We prove convergence of the CR Yamabe flow when
+$n=1$ or $M$ is spherical.
+"
+29318,Optimization over Continuous and Multi-dimensional Decisions with Observational Data,"  We consider the optimization of an uncertain objective over continuous and
+multi-dimensional decision spaces in problems in which we are only provided
+with observational data. We propose a novel algorithmic framework that is
+tractable, asymptotically consistent, and superior to comparable methods on
+example problems. Our approach leverages predictive machine learning methods
+and incorporates information on the uncertainty of the predicted outcomes for
+the purpose of prescribing decisions. We demonstrate the efficacy of our method
+on examples involving both synthetic and real data sets.
+"
+29319,Is completeness necessary? Penalized estimation in non-identified models,"  This paper studies non-identified ill-posed inverse models with estimated
+operator. Leading examples are the nonparametric IV regression and the
+functional linear IV regression. We argue that identification of
+infinite-dimensional parameters is less crucial than identification of
+finite-dimensional parameters. We show that in the case of identification
+failures, a very general family of continuously-regularized estimators is
+consistent for the best approximation of the parameter of interest and obtain
+$L_2$ and $L_\infty$ finite-sample risk bounds. This class includes Tikhonov,
+iterated Tikhonov, spectral cut-off, and Landweber-Fridman as special cases. We
+show that in many cases the best approximation coincides with the structural
+parameter and can be a useful and tractable object to infer relation between
+structural variables otherwise. Unlike in the identified case, estimation of
+the operator may have a non-negligible impact on the estimation accuracy and
+inference. We develop inferential methods for linear functionals in
+non-identified models as well as honest uniform confidence sets for the best
+approximation. Lastly, we demonstrate the discontinuity in the asymptotic
+distribution for extreme cases of identification failures where we observe a
+degenerate $U$-statistics asymptotics.
+"
+29320,Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification,"  Deep neural networks (DNNs) have achieved great success in a wide variety of
+medical image analysis tasks. However, these achievements indispensably rely on
+the accurately-annotated datasets. If with the noisy-labeled images, the
+training procedure will immediately encounter difficulties, leading to a
+suboptimal classifier. This problem is even more crucial in the medical field,
+given that the annotation quality requires great expertise. In this paper, we
+propose an effective iterative learning framework for noisy-labeled medical
+image classification, to combat the lacking of high quality annotated medical
+data. Specifically, an online uncertainty sample mining method is proposed to
+eliminate the disturbance from noisy-labeled images. Next, we design a sample
+re-weighting strategy to preserve the usefulness of correctly-labeled hard
+samples. Our proposed method is validated on skin lesion classification task,
+and achieved very promising results.
+"
+29321,Transport and field emission properties of buckypapers obtained from aligned carbon nanotubes,"  We produce 120 um thick buckypapers from aligned carbon nanotubes. Transport
+characteristics evidence ohmic behavior in a wide temperature range, non
+linearity appearing in the current-voltage curves only close to 4.2 K. The
+temperature dependence of the conductance shows that transport is mostly due to
+thermal fluctuation induced tunneling, although to explain the whole
+temperature range from 4.2 K to 430 K a further linear contribution is
+necessary. The field emission properties are measured by means of a
+nanocontrolled metallic tip acting as collector electrode to access local
+information about buckypaper properties from areas as small as 1 um2. Emitted
+current up to 10-5A and turn-on field of about 140V/um are recorded. Long
+operation, stability and robustness of emitters have been probed by field
+emission intensity monitoring for more than 12 hours at pressure of 10-6 mbar.
+Finally, no tuning of the emitted current was observed for in plane applied
+currents in the buckypaper.
+"
+29322,An intermediate-mass black hole in the centre of the globular cluster 47 Tucanae,"  Intermediate mass black holes play a critical role in understanding the
+evolutionary connection between stellar mass and super-massive black holes.
+However, to date the existence of these species of black holes remains
+ambiguous and their formation process is therefore unknown. It has been long
+suspected that black holes with masses $10^{2}-10^{4}M_{\odot}$ should form and
+reside in dense stellar systems. Therefore, dedicated observational campaigns
+have targeted globular cluster for many decades searching for signatures of
+these elusive objects. All candidates found in these targeted searches appear
+radio dim and do not have the X-ray to radio flux ratio predicted by the
+fundamental plane for accreting black holes. Based on the lack of an
+electromagnetic counterpart upper limits of $2060 M_{\odot}$ and $470
+M_{\odot}$ have been placed on the mass of a putative black hole in 47 Tucanae
+(NGC 104) from radio and X-ray observations respectively. Here we show there is
+evidence for a central black hole in 47 Tuc with a mass of M$_{\bullet}\sim2300
+M_{\odot}$$_{-850}^{+1500}$ when the dynamical state of the globular cluster is
+probed with pulsars. The existence of an intermediate mass black hole in the
+centre of one of the densest clusters with no detectable electromagnetic
+counterpart suggests that the black hole is not accreting at a sufficient rate
+and therefore contrary to expectations is gas starved. This intermediate mass
+black hole might be a member of electromagnetically invisible population of
+black holes that are the elusive seeds leading to the formation of supermassive
+black holes in galaxies.
+"
+29323,Region-Based Classification of PolSAR Data Using Radial Basis Kernel Functions With Stochastic Distances,"  Region-based classification of PolSAR data can be effectively performed by
+seeking for the assignment that minimizes a distance between prototypes and
+segments. Silva et al (2013) used stochastic distances between complex
+multivariate Wishart models which, differently from other measures, are
+computationally tractable. In this work we assess the robustness of such
+approach with respect to errors in the training stage, and propose an extension
+that alleviates such problems. We introduce robustness in the process by
+incorporating a combination of radial basis kernel functions and stochastic
+distances with Support Vector Machines (SVM). We consider several stochastic
+distances between Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square,
+Rényi, and Hellinger. We perform two case studies with PolSAR images, both
+simulated and from actual sensors, and different classification scenarios to
+compare the performance of Minimum Distance and SVM classification frameworks.
+With this, we model the situation of imperfect training samples. We show that
+SVM with the proposed kernel functions achieves better performance with respect
+to Minimum Distance, at the expense of more computational resources and the
+need of parameter tuning. Code and data are provided for reproducibility.
+"
+29324,On denoising autoencoders trained to minimise binary cross-entropy,"  Denoising autoencoders (DAEs) are powerful deep learning models used for
+feature extraction, data generation and network pre-training. DAEs consist of
+an encoder and decoder which may be trained simultaneously to minimise a loss
+(function) between an input and the reconstruction of a corrupted version of
+the input. There are two common loss functions used for training autoencoders,
+these include the mean-squared error (MSE) and the binary cross-entropy (BCE).
+When training autoencoders on image data a natural choice of loss function is
+BCE, since pixel values may be normalised to take values in [0,1] and the
+decoder model may be designed to generate samples that take values in (0,1). We
+show theoretically that DAEs trained to minimise BCE may be used to take
+gradient steps in the data space towards regions of high probability under the
+data-generating distribution. Previously this had only been shown for DAEs
+trained using MSE. As a consequence of the theory, iterative application of a
+trained DAE moves a data sample from regions of low probability to regions of
+higher probability under the data-generating distribution. Firstly, we validate
+the theory by showing that novel data samples, consistent with the training
+data, may be synthesised when the initial data samples are random noise.
+Secondly, we motivate the theory by showing that initial data samples
+synthesised via other methods may be improved via iterative application of a
+trained DAE to those initial samples.
+"
+29325,Fair Pipelines,"  This work facilitates ensuring fairness of machine learning in the real world
+by decoupling fairness considerations in compound decisions. In particular,
+this work studies how fairness propagates through a compound decision-making
+processes, which we call a pipeline. Prior work in algorithmic fairness only
+focuses on fairness with respect to one decision. However, many decision-making
+processes require more than one decision. For instance, hiring is at least a
+two stage model: deciding who to interview from the applicant pool and then
+deciding who to hire from the interview pool. Perhaps surprisingly, we show
+that the composition of fair components may not guarantee a fair pipeline under
+a $(1+\varepsilon)$-equal opportunity definition of fair. However, we identify
+circumstances that do provide that guarantee. We also propose numerous
+directions for future work on more general compound machine learning decisions.
+"
+29326,"Considerations on Interdisciplinary Instruction and Design Influenced by Adaptive Learning. A Case Study Involving Biology, Computer Science, Mathematics, and Statistics","  ALICE (Adaptive Learning for Interdisciplinary Collaborative Environments) is
+an open-source web based adaptive learning system designed for
+interdisciplinary instruction. ALICE has the potential to transform education
+by empowering transdisciplinary knowledge acquisition. This is particularly
+important in fields that accept newcomers with diverse scholastic backgrounds,
+e.g. Systems Biology. With traditional interdisciplinary instruction, the
+instructor must cover pre-requisite information from multiple disciplines to
+ensure all students begin at a common baseline - slowing the learning process.
+With ALICE, students follow a personalized syllabus based on their previous
+knowledge and work towards individual goals. Implementing an adaptive learning
+system in an interdisciplinary course requires careful considerations of the
+instructional design. Structuring material, formulating assessments, and other
+instructional design aspects must be carefully considered. These considerations
+are detailed through the exploration of a case study implementing ALICE in a
+graduate level Systems Biology course.
+"
+29327,Systemic Risk: Conditional Distortion Risk Measures,"  In this paper, we introduce the rich classes of conditional distortion (CoD)
+risk measures and distortion risk contribution ($\Delta$CoD) measures as
+measures of systemic risk and analyze their properties and representations. The
+classes include the well-known conditional Value-at-Risk, conditional Expected
+Shortfall, and risk contribution measures in terms of the VaR and ES as special
+cases. Sufficient conditions are presented for two random vectors to be ordered
+by the proposed CoD-risk measures and distortion risk contribution measures.
+These conditions are expressed using the conventional stochastic dominance,
+increasing convex/concave, dispersive, and excess wealth orders of the
+marginals and canonical positive/negative stochastic dependence notions.
+Numerical examples are provided to illustrate our theoretical findings. This
+paper is the second in a triplet of papers on systemic risk by the same
+authors. In \cite{DLZorder2018a}, we introduce and analyze some new stochastic
+orders related to systemic risk. In a third (forthcoming) paper, we attribute
+systemic risk to the different participants in a given risky environment.
+"
+29328,"Multi-frequency search for Dark Matter: the role of HESS, CTA, and SKA","  Dark Matter (DM) remains a vital, but elusive, component in our current
+understanding of the universe. Accordingly, many experimental searches are
+devoted to uncovering its nature. However, both the existing direct detection
+methods, and the prominent $\gamma$-ray search with the Fermi Large Area
+Telescope (Fermi-LAT), are most sensitive to DM particles with masses below 1
+TeV, and are significantly less sensitive to the hard spectra produced in
+annihilation via heavy leptons. The High Energy Stereoscopic System (HESS) has
+had some success in improving on the Fermi-LAT search for higher mass DM
+particles, particularly annihilating via heavy lepton states. However, the
+recent discovery of high J-factor dwarf spheroidal galaxies by the Dark Energy
+Survey (DES) opens up the possibility of investing more HESS observation time
+in the search for DM $\gamma$-ray signatures in dwarf galaxies. This work
+explores the potential of HESS to extend its current limits using these new
+targets, as well as the future constraints derivable with the up-coming
+Cherenkov Telescope Array (CTA). These limits are further compared with those
+we derived at low radio frequencies for the Square Kilometre Array (SKA).
+Finally, we explore the impact of HESS, CTA, and Fermi-LAT on the phenomenology
+of the ""Madala"" boson hypothesized based on anomalies in the data from the
+Large Hadron Collider (LHC) run 1. The power of these limits from differing
+frequency bands is suggestive of a highly effective multi-frequency DM hunt
+strategy making use of both existing and up-coming Southern African telescopes.
+"
+29329,Introducing machine learning for power system operation support,"  We address the problem of assisting human dispatchers in operating power
+grids in today's changing context using machine learning, with theaim of
+increasing security and reducing costs. Power networks are highly regulated
+systems, which at all times must meet varying demands of electricity with a
+complex production system, including conventional power plants, less
+predictable renewable energies (such as wind or solar power), and the
+possibility of buying/selling electricity on the international market with more
+and more actors involved at a Europeanscale. This problem is becoming ever more
+challenging in an aging network infrastructure. One of the primary goals of
+dispatchers is to protect equipment (e.g. avoid that transmission lines
+overheat) with few degrees of freedom: we are considering in this paper solely
+modifications in network topology, i.e. re-configuring the way in which lines,
+transformers, productions and loads are connected in sub-stations. Using years
+of historical data collected by the French Transmission Service Operator (TSO)
+""Réseau de Transport d'Electricité"" (RTE), we develop novel machine
+learning techniques (drawing on ""deep learning"") to mimic human decisions to
+devise ""remedial actions"" to prevent any line to violate power flow limits
+(so-called ""thermal limits""). The proposed technique is hybrid. It does not
+rely purely on machine learning: every action will be tested with actual
+simulators before being proposed to the dispatchers or implemented on the grid.
+"
+29330,Secure and Privacy-Preserving Consensus,"  Consensus is fundamental for distributed systems since it underpins key
+functionalities of such systems ranging from distributed information fusion,
+decision-making, to decentralized control. In order to reach an agreement,
+existing consensus algorithms require each agent to exchange explicit state
+information with its neighbors. This leads to the disclosure of private state
+information, which is undesirable in cases where privacy is of concern. In this
+paper, we propose a novel approach that enables secure and privacy-preserving
+average consensus in a decentralized architecture in the absence of an
+aggregator or third-party. By leveraging partial homomorphic cryptography to
+embed secrecy in pairwise interaction dynamics, our approach can guarantee
+consensus to the exact value in a deterministic manner without disclosing a
+node's state to its neighbors. In addition to enabling resilience to passive
+attackers aiming to steal state information, the approach also allows easy
+incorporation of defending mechanisms against active attackers which try to
+alter the content of exchanged messages. Furthermore, in contrast to existing
+noise-injection based privacy-preserving mechanisms which have to reconfigure
+the entire network when the topology or number of nodes varies, our approach is
+applicable to dynamic environments with time-varying coupling topologies. This
+secure and privacy-preservation approach is also applicable to weighted average
+consensus as well as maximum/minimum consensus under a new update rule. The
+approach is light-weight in computation and communication. Implementation
+details and numerical examples are provided to demonstrate the capability of
+our approach.
+"
+29331,Free quantum fields in 4D and Calabi-Yau spaces,"  We develop general counting formulae for primary fields in free four
+dimensional (4D) scalar conformal field theory (CFT). Using a duality map
+between primary operators in scalar field theory and multi-variable polynomial
+functions subject to differential constraints, we identify a sector of
+holomorphic primary fields corresponding to polynomial functions on a class of
+permutation orbifolds. These orbifolds have palindromic Hilbert series, which
+indicates they are Calabi-Yau. We construct the top-dimensional holomorphic
+form expected from the Calabi-Yau property. This sector includes and extends
+previous constructions of infinite families of primary fields. We sketch the
+generalization of these results to free 4D vector and matrix CFTs.
+"
+29332,Irreducible polynomials over finite fields produced by composition of quadratics,"  For a set $S$ of quadratic polynomials over a finite field, let $C$ be the
+(infinite) set of arbitrary compositions of elements in $S$. In this paper we
+show that there are examples with arbitrarily large $S$ such that every
+polynomial in $C$ is irreducible. As a second result, we give an algorithm to
+determine whether all the elements in $C$ are irreducible, using only $O( \#S
+(\log q)^3 q^{1/2} )$ operations.
+"
+29333,Identifying beneficial task relations for multi-task learning in deep neural networks,"  Multi-task learning (MTL) in deep neural networks for NLP has recently
+received increasing interest due to some compelling benefits, including its
+potential to efficiently regularize models and to reduce the need for labeled
+data. While it has brought significant improvements in a number of NLP tasks,
+mixed results have been reported, and little is known about the conditions
+under which MTL leads to gains in NLP. This paper sheds light on the specific
+task relations that can lead to gains from MTL models over single-task setups.
+"
+29334,Minimax lower bounds for function estimation on graphs,"  We study minimax lower bounds for function estimation problems on large graph
+when the target function is smoothly varying over the graph. We derive minimax
+rates in the context of regression and classification problems on graphs that
+satisfy an asymptotic shape assumption and with a smoothness condition on the
+target function, both formulated in terms of the graph Laplacian.
+"
+29335,Deep Neural Network Compression with Single and Multiple Level Quantization,"  Network quantization is an effective solution to compress deep neural
+networks for practical usage. Existing network quantization methods cannot
+sufficiently exploit the depth information to generate low-bit compressed
+network. In this paper, we propose two novel network quantization approaches,
+single-level network quantization (SLQ) for high-bit quantization and
+multi-level network quantization (MLQ) for extremely low-bit quantization
+(ternary).We are the first to consider the network quantization from both width
+and depth level. In the width level, parameters are divided into two parts: one
+for quantization and the other for re-training to eliminate the quantization
+loss. SLQ leverages the distribution of the parameters to improve the width
+level. In the depth level, we introduce incremental layer compensation to
+quantize layers iteratively which decreases the quantization loss in each
+iteration. The proposed approaches are validated with extensive experiments
+based on the state-of-the-art neural networks including AlexNet, VGG-16,
+GoogleNet and ResNet-18. Both SLQ and MLQ achieve impressive results.
+"
+29336,On the Relation of Silicates and SiO Maser in Evolved Stars,"  The SiO molecule is one of the candidates for the seed of silicate dust in
+the circumstellar envelope of evolved stars, but this opinion is challenged. In
+this work we investigate the relation of the SiO maser emis- sion power and the
+silicate dust emission power. With both our own observation by using the
+PMO/Delingha 13.7-m telescope and archive data, a sample is assembled of 21 SiO
+v=1,J=2-1 sources and 28 SiO v=1,J=1- 0 sources that exhibit silicate emission
+features in the ISO/SWS spectrum as well. The analysis of their SiO maser and
+silicate emission power indicates a clear correlation, which is not against the
+hypothesis that the SiO molecules are the seed nuclei of silicate dust. On the
+other hand, no correlation is found between SiO maser and silicate
+crystallinity, which may imply that silicate crystallinity does not correlate
+with mass loss rate.
+"
+29337,Frontier Based Exploration for Autonomous Robot,"  Exploration is process of selecting target points that yield the biggest
+contribution to a specific gain function at an initially unknown environment.
+Frontier-based exploration is the most common approach to exploration, wherein
+frontiers are regions on the boundary between open space and unexplored space.
+By moving to a new frontier, we can keep building the map of the environment,
+until there are no new frontiers left to detect. In this paper, an autonomous
+frontier-based exploration strategy, namely Wavefront Frontier Detector (WFD)
+is described and implemented on Gazebo Simulation Environment as well as on
+hardware platform, i.e. Kobuki TurtleBot using Robot Operating System (ROS).
+The advantage of this algorithm is that the robot can explore large open spaces
+as well as small cluttered spaces. Further, the map generated from this
+technique is compared and validated with the map generated using
+turtlebot_teleop ROS Package.
+"
+29338,Intrinsically Knotted and 4-Linked Directed Graphs,"  We consider intrinsic linking and knotting in the context of directed graphs.
+We construct an example of a directed graph that contains a consistently
+oriented knotted cycle in every embedding. We also construct examples of
+intrinsically 3-linked and 4-linked directed graphs. We introduce two
+operations, consistent edge contraction and H-cyclic subcontraction, as special
+cases of minors for digraphs, and show that the property of having a linkless
+embedding is closed under these operations. We analyze the relationship between
+the number of distinct knots and links in an undirected graph $G$ and its
+corresponding symmetric digraph $\overline{DG}$. Finally, we note that the
+maximum number of edges for a graph that is not intrinsically linked is $O(n)$
+in the undirected case, but $O(n^2)$ for directed graphs.
+"
+29339,Private PAC learning implies finite Littlestone dimension,"  We show that every approximately differentially private learning algorithm
+(possibly improper) for a class $H$ with Littlestone dimension~$d$ requires
+$\Omega\bigl(\log^*(d)\bigr)$ examples. As a corollary it follows that the
+class of thresholds over $\mathbb{N}$ can not be learned in a private manner;
+this resolves an open question due to [Bun et al. FOCS '15]. We leave as an
+open question whether every class with a finite Littlestone dimension can be
+learned by an approximately differentially private algorithm.
+"
+29340,Curvature dimension inequalities on directed graphs,"  In this paper, we define the curvature dimension inequalities CD(m, K) on
+finite directed graphs modifying the case of undirected graphs. As a main
+result, we evaluate m and K on finite directed graphs.
+"
+29341,Observable tensor-to-scalar ratio and secondary gravitational wave background,"  In this paper we will highlight how a simple vacuum energy dominated
+inflection-point inflation can match the current data from cosmic microwave
+background radiation, and predict large primordial tensor to scalar ratio, $r
+\sim \mathcal{O}(10^{-3}-10^{-2})$, with observable second order gravitational
+wave background, which can be potentially detectable from future experiments,
+such as DECi-hertz Interferometer Gravitational wave Observatory (DECIGO),
+Laser Interferometer Space Antenna (eLISA), Cosmic Explorer (CE), and Big Bang
+Observatory (BBO).
+"
+29342,Detection of Molecular Scattering Field by Polarization Analysis of Sky Background During the Transitive Twilight and Temperature Measurements near the Stratopause,"  The simplest version of the method of detection of single molecular
+scattering basing on the polarization measurements of the twilight sky
+background by all-sky cameras is considered. The method can be used during the
+transitive twilight (solar zenith angles 94-98 degrees), when effective single
+scattering takes place in upper stratosphere and lower mesosphere. The
+application of the method to the multi-year measurements in the vicinity of
+Moscow and Apatity allows determination of temperature of these atmospheric
+layers and estimation of contribution and properties of multiple scattering
+during the transitive twilight period.
+"
+29343,Spectral analysis for nonstationary audio,"  A new approach for the analysis of nonstationary signals is proposed, with a
+focus on audio applications. Following earlier contributions, nonstationarity
+is modeled via stationarity-breaking operators acting on Gaussian stationary
+random signals. The focus is on time warping and amplitude modulation, and an
+approximate maximum-likelihood approach based on suitable approximations in the
+wavelet transform domain is developed. This paper provides theoretical analysis
+of the approximations, and introduces JEFAS, a corresponding estimation
+algorithm. The latter is tested and validated on synthetic as well as real
+audio signal.
+"
+29344,Loss of energy concentration in nonlinear evolution beam equations,"  Motivated by the oscillations that were seen at the Tacoma Narrows Bridge, we
+introduce the notion of solutions with a prevailing mode for the nonlinear
+evolution beam equation $$ u_{tt} + u_{xxxx} + f(u)= g(x, t) $$ in bounded
+space-time intervals. We give a new definition of instability for these
+particular solutions, based on the loss of energy concentration on their
+prevailing mode. We distinguish between two different forms of energy transfer,
+one physiological (unavoidable and depending on the nonlinearity) and one due
+to the insurgence of instability. We then prove a theoretical result allowing
+to reduce the study of this kind of infinite-dimensional stability to that of a
+finite-dimensional approximation. With this background, we study the occurrence
+of instability for three different kinds of nonlinearities $f$ and for some
+forcing terms $g$, highlighting some of their structural properties and
+performing some numerical simulations.
+"
+29345,Chiral-reversing vortex radiation from a single emitter by eigenstates phase locking,"  The radiation of an emitter does not depend only on its intrinsic properties
+but also on the surrounding photonic environment, the notion of which is
+essential in the developments of lasers, quantum optics and other light-matter
+interaction related fields. However, in conventional wisdom, an emitter
+radiates into photonic eigenstates in the weak coupling regime and does not
+alter the property of the latter. Here, we report a counterintuitive phenomenon
+where the radiation field of a dipole in a parity-time symmetric ring resonator
+displays the opposite handedness to the eigenstates of the system. This
+chiral-reversing radiation takes place at an exception point of the underlying
+non-Hermitian system, where the singularity at the exceptional point forces a
+phase locking of the coalesced eigenstates when interacting with the dipole
+emitter. Such an intriguing phenomenon has been employed to construct vortex
+radiation with controllable topological charge from a single quantum dot
+embedded plasminic nanocavity with Purcell enhancement factor up to 1000. Our
+scheme enriches the interesting physics of an exception point in the quantum
+region and may open a new paradigm for chiral quantum optics and vortex lasers
+at nanoscale.
+"
+29346,Giant Enhancement in Ferroelectric Polarization under Illumination,"  We report optical enhancement in polarization and dielectric constant near
+room temperature in Pb0.6Li0.2Bi0.2Zr0.2Ti0.8O3 (PLBZT) electro-ceramics; these
+are doubly substituted members of the most important commercial ferroelectric
+PbZr0.2Ti0.8O3 (PZT:20/80). Partial (40%) substitution of equal amounts of Li+1
+and Bi+3 in PZT: 20/80 retains the PZT tetragonal structure with space group
+P4mm. Under illumination of white light and weak 405-nm near-ultraviolet laser
+light (30 mW), an unexpectedly large (200-300%) change in polarization and
+displacement current was observed. Light also changes the dc conduction current
+density by one to two orders of magnitude with a large switchable open circuit
+voltage (Voc ~ 2 V) and short circuit current (Jsc ~ 5x10-8 A). The samples
+show a photo-current ON/OFF ratio of order 6:1 under illumination of weak
+light.
+"
+29347,One-Shot Learning in Discriminative Neural Networks,"  We consider the task of one-shot learning of visual categories. In this paper
+we explore a Bayesian procedure for updating a pretrained convnet to classify a
+novel image category for which data is limited. We decompose this convnet into
+a fixed feature extractor and softmax classifier. We assume that the target
+weights for the new task come from the same distribution as the pretrained
+softmax weights, which we model as a multivariate Gaussian. By using this as a
+prior for the new weights, we demonstrate competitive performance with
+state-of-the-art methods whilst also being consistent with 'normal' methods for
+training deep networks on large data.
+"
+29348,The Mirage of Action-Dependent Baselines in Reinforcement Learning,"  Policy gradient methods are a widely used class of model-free reinforcement
+learning algorithms where a state-dependent baseline is used to reduce gradient
+estimator variance. Several recent papers extend the baseline to depend on both
+the state and action and suggest that this significantly reduces variance and
+improves sample efficiency without introducing bias into the gradient
+estimates. To better understand this development, we decompose the variance of
+the policy gradient estimator and numerically show that learned
+state-action-dependent baselines do not in fact reduce variance over a
+state-dependent baseline in commonly tested benchmark domains. We confirm this
+unexpected result by reviewing the open-source code accompanying these prior
+papers, and show that subtle implementation decisions cause deviations from the
+methods presented in the papers and explain the source of the previously
+observed empirical gains. Furthermore, the variance decomposition highlights
+areas for improvement, which we demonstrate by illustrating a simple change to
+the typical value function parameterization that can significantly improve
+performance.
+"
+29349,Automated proton track identification in MicroBooNE using gradient boosted decision trees,"  MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrino
+experiment that is currently running in the Booster Neutrino Beam at Fermilab.
+LArTPC technology allows for high-resolution, three-dimensional representations
+of neutrino interactions. A wide variety of software tools for automated
+reconstruction and selection of particle tracks in LArTPCs are actively being
+developed. Short, isolated proton tracks, the signal for low- momentum-transfer
+neutral current (NC) elastic events, are easily hidden in a large cosmic
+background. Detecting these low-energy tracks will allow us to probe
+interesting regions of the proton's spin structure. An effective method for
+selecting NC elastic events is to combine a highly efficient track
+reconstruction algorithm to find all candidate tracks with highly accurate
+particle identification using a machine learning algorithm. We present our work
+on particle track classification using gradient tree boosting software
+(XGBoost) and the performance on simulated neutrino data.
+"
+29350,A bagging and importance sampling approach to Support Vector Machines,"  An importance sampling and bagging approach to solving the support vector
+machine (SVM) problem in the context of large databases is presented and
+evaluated. Our algorithm builds on the nearest neighbors ideas presented in
+Camelo at al. (2015). As in that reference, the goal of the present proposal is
+to achieve a faster solution of the SVM problem without a significance loss in
+the prediction error. The performance of the methodology is evaluated in
+benchmark examples and theoretical aspects of subsample methods are discussed.
+"
+29351,Properly ergodic structures,"  We consider ergodic $\mathrm{Sym}(\mathbb{N})$-invariant probability measures
+on the space of $L$-structures with domain $\mathbb{N}$ (for $L$ a countable
+relational language), and call such a measure a properly ergodic structure when
+no isomorphism class of structures is assigned measure $1$. We characterize
+those theories in countable fragments of $\mathcal{L}_{\omega_1, \omega}$ for
+which there is a properly ergodic structure concentrated on the models of the
+theory. We show that for a countable fragment $F$ of $\mathcal{L}_{\omega_1,
+\omega}$ the almost-sure $F$-theory of a properly ergodic structure has
+continuum-many models (an analogue of Vaught's Conjecture in this context), but
+its full almost-sure $\mathcal{L}_{\omega_1, \omega}$-theory has no models. We
+also show that, for an $F$-theory $T$, if there is some properly ergodic
+structure that concentrates on the class of models of $T$, then there are
+continuum-many such properly ergodic structures.
+"
+29352,On the Applicability of Low-Dimensional Models for Convective Flow Reversals at Extreme Prandtl Numbers,"  Constructing simpler models, either stochastic or deterministic, for
+exploring the phenomenon of flow reversals in fluid systems is in vogue across
+disciplines. Using direct numerical simulations and nonlinear time series
+analysis, we illustrate that the basic nature of flow reversals in convecting
+fluids can depend on the dimensionless parameters describing the system.
+Specifically, we find evidence of low-dimensional determinism in flow reversals
+occurring at zero Prandtl number, whereas we fail to find such signatures for
+reversals at infinite Prandtl number. Thus, even in a single system, as one
+varies the system parameters, one can encounter reversals that are
+fundamentally different in nature. Consequently, we conclude that a single
+general low-dimensional deterministic model cannot faithfully characterize flow
+reversals for every set of parameter values.
+"
+29353,On the local-global divisibility over ${\rm GL}_2$-type varieties,"  Let $k$ be a number field and let ${\mathcal{A}}$ be a ${\rm GL}_2$-type
+variety defined over $k$ of dimension $d$. We show that for every prime number
+$p$ satisfying certain conditions (see Theorem 2), if the local-global
+divisibility principle by a power of $p$ does not hold for ${\mathcal{A}}$ over
+$k$, then there exists a cyclic extension $\widetilde{k}$ of $k$ of degree
+bounded by a constant depending on $d$ such that ${\mathcal{A}}$ is
+$\widetilde{k}$-isogenous to a ${\rm GL}_2$-type variety defined over
+$\widetilde{k}$ that admits a $\widetilde{k}$-rational point of order $p$.
+Moreover, we explain how our result is related to a question of Cassels on the
+divisibility of the Tate-Shafarevich group, studied by Ciperiani and Stix and
+Creutz.
+"
+29354,Discovery and visualization of structural biomarkers from MRI using transport-based morphometry,"  Disease in the brain is often associated with subtle, spatially diffuse, or
+complex tissue changes that may lie beneath the level of gross visual
+inspection, even on magnetic resonance imaging (MRI). Unfortunately, current
+computer-assisted approaches that examine pre-specified features, whether
+anatomically-defined (i.e. thalamic volume, cortical thickness) or based on
+pixelwise comparison (i.e. deformation-based methods), are prone to missing a
+vast array of physical changes that are not well-encapsulated by these metrics.
+In this paper, we have developed a technique for automated pattern analysis
+that can fully determine the relationship between brain structure and
+observable phenotype without requiring any a priori features. Our technique,
+called transport-based morphometry (TBM), is an image transformation that maps
+brain images losslessly to a domain where they become much more separable. The
+new approach is validated on structural brain images of healthy older adult
+subjects where even linear models for discrimination, regression, and blind
+source separation enable TBM to independently discover the characteristic
+changes of aging and highlight potential mechanisms by which aerobic fitness
+may mediate brain health later in life. TBM is a generative approach that can
+provide visualization of physically meaningful shifts in tissue distribution
+through inverse transformation. The proposed framework is a powerful technique
+that can potentially elucidate genotype-structural-behavioral associations in
+myriad diseases.
+"
+29355,On a conjecture in second-order optimality conditions,"  In this paper we deal with optimality conditions that can be verified by a
+nonlinear optimization algorithm, where only a single Lagrange multiplier is
+avaliable. In particular, we deal with a conjecture formulated in [R. Andreani,
+J.M. Martinez, M.L. Schuverdt, ""On second-order optimality conditions for
+nonlinear programming"", Optimization, 56:529--542, 2007], which states that
+whenever a local minimizer of a nonlinear optimization problem fulfills the
+Mangasarian-Fromovitz Constraint Qualification and the rank of the set of
+gradients of active constraints increases at most by one in a neighborhood of
+the minimizer, a second-order optimality condition that depends on one single
+Lagrange multiplier is satisfied. This conjecture generalizes previous results
+under a constant rank assumption or under a rank deficiency of at most one. In
+this paper we prove the conjecture under the additional assumption that the
+Jacobian matrix has a smooth singular value decomposition, which is weaker than
+previously considered assumptions. We also review previous literature related
+to the conjecture.
+"
+29356,On Partial Covering For Geometric Set Systems,"  We study a generalization of the Set Cover problem called the \emph{Partial
+Set Cover} in the context of geometric set systems. The input to this problem
+is a set system $(X, \mathcal{S})$, where $X$ is a set of elements and
+$\mathcal{S}$ is a collection of subsets of $X$, and an integer $k \le |X|$.
+The goal is to cover at least $k$ elements of $X$ by using a minimum-weight
+collection of sets from $\mathcal{S}$. The main result of this article is an LP
+rounding scheme which shows that the integrality gap of the Partial Set Cover
+LP is at most a constant times that of the Set Cover LP for a certain
+projection of the set system $(X, \mathcal{S})$. As a corollary of this result,
+we get improved approximation guarantees for the Partial Set Cover problem for
+a large class of geometric set systems.
+"
+29357,On-sky performance of the QACITS pointing control technique with the Keck/NIRC2 vortex coronagraph,"  A vortex coronagraph is now available for high contrast observations with the
+Keck/NIRC2 instrument at L band. Reaching the optimal performance of the
+coronagraph requires fine control of the wavefront incident on the phase mask.
+In particular, centering errors can lead to significant stellar light leakage
+that degrades the contrast performance and prevents the observation of faint
+planetary companions around the observed stars. It is thus critical to correct
+for the possible slow drift of the star image from the phase mask center,
+generally due to mechanical flexures induced by temperature and/or gravity
+field variation, or to misalignment between the optics that rotate in pupil
+tracking mode. A control loop based on the QACITS algorithm for the vortex
+coronagraph has thus been developed and deployed for the Keck/NIRC2 instrument.
+This algorithm executes the entire observing sequence, including the
+calibration steps, initial centering of the star on the vortex center and
+stabilisation during the acquisition of science frames. On-sky data show that
+the QACITS control loop stabilizes the position of the star image down to 2.4
+mas rms at a frequency of about 0.02 Hz. However, the accuracy of the estimator
+is probably limited by a systematic error due to a misalignment of the Lyot
+stop with respect to the entrance pupil, estimated to be on the order of 4.5
+mas. A method to reduce the amplitude of this bias down to 1 mas is proposed.
+The QACITS control loop has been successfully implemented and provides a robust
+method to center and stabilize the star image on the vortex mask. In addition,
+QACITS ensures a repeatable pointing quality and significantly improves the
+observing efficiency compared to manual operations. It is now routinely used
+for vortex coronagraph observations at Keck/NIRC2, providing contrast and
+angular resolution capabilities suited for exoplanet and disk imaging.
+"
+29358,Process Monitoring on Sequences of System Call Count Vectors,"  We introduce a methodology for efficient monitoring of processes running on
+hosts in a corporate network. The methodology is based on collecting streams of
+system calls produced by all or selected processes on the hosts, and sending
+them over the network to a monitoring server, where machine learning algorithms
+are used to identify changes in process behavior due to malicious activity,
+hardware failures, or software errors. The methodology uses a sequence of
+system call count vectors as the data format which can handle large and varying
+volumes of data.
+Unlike previous approaches, the methodology introduced in this paper is
+suitable for distributed collection and processing of data in large corporate
+networks. We evaluate the methodology both in a laboratory setting on a
+real-life setup and provide statistics characterizing performance and accuracy
+of the methodology.
+"
+29359,Magnetic anisotropy of the alkali iridate Na$_{2}$IrO$_{3}$ at high magnetic fields: evidence for strong ferromagnetic Kitaev correlations,"  The magnetic field response of the Mott-insulating honeycomb iridate
+Na$_{2}$IrO$_{3}$ is investigated using torque magnetometry measurements in
+magnetic fields up to 60 tesla. A peak-dip structure is observed in the torque
+response at magnetic fields corresponding to an energy scale close to the
+zigzag ordering ($\approx 15~K$) temperature. Using exact diagonalization
+calculations, we show that such a distinctive signature in the torque response
+constrains the effective spin models for these classes of Kitaev materials to
+ones with dominant ferromagnetic Kitaev interactions, while alternative models
+with dominant antiferromagnetic Kitaev interactions are excluded. We further
+show that at high magnetic fields, long range spin correlation functions decay
+rapidly, signaling a transition to a long-sought-after field-induced quantum
+spin liquid beyond the peak-dip structure. Kitaev systems are thus revealed to
+be excellent candidates for field-induced quantum spin liquids, similar physics
+having been suggested in another Kitaev material $\alpha-$RuCl$_{3}$.
+"
+29360,Embeddability of right-angled Artin groups on the complements of linear forests,"  In this article, we prove that embeddings of right-angled Artin group $A_1$
+on the complement of a linear forest into another right-angled Artin group
+$A_2$ can be reduced to full embeddings of the defining graph of $A_1$ into the
+extension graph of the defining graph of $A_2$.
+"
+29361,Enhancing the Efimov correlation in Bose polarons with large mass imbalance,"  We study the effect of Efimov physics (in the few-body sector) to the
+spectral response of Bose polaron, a many-body system consisting of an impurity
+immersed in a bath of bosonic atoms. We find that the Efimov correlation can be
+greatly enhanced by increasing the mass ratio between the bosons and the
+impurity, which results in visible signatures in the rf spectrum of the
+polaron. Using a diagrammatic approach up to the third-order virial expansion,
+we show how the mass imbalance and the enhanced three-body effect modify the
+line shape and the width of the polaron spectrum. Moreover, we study the effect
+of a finite boson-boson interaction to the spectrum. Taking the realistic
+system of Li impurities immersed in Cs bosons with a positive Cs-Cs scattering
+length, we find a visible Efimov branch, which is associated with the second
+lowest Efimov trimer, in the polaron spectrum. In particular, by adjusting the
+boson density the Efimov branch can greatly hybridize with the attractive
+polaron branch leading to the spectrum broadening near their avoided level
+crossing. Our results can be directly probed in the cold atoms experiments of
+Li-Cs and Li-Rb Bose polarons.
+"
+29362,Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI,"  A novel deep learning architecture (XmasNet) based on convolutional neural
+networks was developed for the classification of prostate cancer lesions, using
+the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end
+training was performed for XmasNet, with data augmentation done through 3D
+rotation and slicing, in order to incorporate the 3D information of the lesion.
+XmasNet outperformed traditional machine learning models based on engineered
+features, for both train and test data. For the test data, XmasNet outperformed
+69 methods from 33 participating groups and achieved the second highest AUC
+(0.84) in the PROSTATEx challenge. This study shows the great potential of deep
+learning for cancer imaging.
+"
+29363,Activation Adaptation in Neural Networks,"  Many neural network architectures rely on the choice of the activation
+function for each hidden layer. Given the activation function, the neural
+network is trained over the bias and the weight parameters. The bias catches
+the center of the activation, and the weights capture the scale. Here we
+propose to train the network over a shape parameter as well. This view allows
+each neuron to tune its own activation function and adapt the neuron curvature
+towards a better prediction. This modification only adds one further equation
+to the back-propagation for each neuron. Re-formalizing activation functions as
+CDF generalizes the class of activation function extensively. We aimed at
+generalizing an extensive class of activation functions to study: i) skewness
+and ii) smoothness of activation functions. Here we introduce adaptive Gumbel
+activation function as a bridge between Gumbel and sigmoid. A similar approach
+is used to invent a smooth version of ReLU. Our comparison with common
+activation functions suggests different data representation especially in early
+neural network layers. This adaptation also provides prediction improvement.
+"
+29364,Independent Experimentation on the Activation of Deuterium-Loaded Materials by X-Ray Exposure,"  In an attempt to replicate work performed elsewhere [1], we have searched for
+x-ray induced nuclear activation of deuterated materials. Our first results,
+reported in September 2015, showed no evidence of nuclear activation, contrary
+to the results reported in [1]. The primary shortcoming of our first attempt,
+however, was that the x-ray tube we used was capable of a maximum accelerating
+potential of only 160kV, less than the 200kV used on the comparison test sample
+from [1]. Thus, our results could not exclude processes initiated by x-rays
+near 200 keV in energy. A second potential shortcoming was that our x-ray tube
+did not have a microfocus beam, like the one used in [1]. Recently, we have
+irradiated additional samples with an x-ray tube matching the specifications of
+the tube used in [1]. By matching the test conditions with essentially
+identical equipment, we have overcome both of the shortcomings of our previous
+measurements. As before, we find no evidence of activation. The bulk of this
+report, pages 2 through 35, contain the results and analysis from the first
+round of testing. The extension to 200kV is found in the final appendix, namely
+Appendix A5.
+"
+29365,The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation,"  Trajectory interpolation, the process of filling-in the gaps and removing
+noise from observed agent trajectories, is an essential task for the motion
+inference in multi-agent setting. A desired trajectory interpolation method
+should be robust to noise, changes in environments or agent densities, while
+also being yielding realistic group movement behaviors. Such realistic
+behaviors are, however, challenging to model as they require avoidance of
+agent-agent or agent-environment collisions and, at the same time, seek
+computational efficiency. In this paper, we propose a novel framework composed
+of data-driven priors (local, global or combined) and an efficient optimization
+strategy for multi-agent trajectory interpolation. The data-driven priors
+implicitly encode the dependencies of movements of multiple agents and the
+collision-avoiding desiderata, enabling elimination of costly pairwise
+collision constraints and resulting in reduced computational complexity and
+often improved estimation. Various combinations of priors and optimization
+algorithms are evaluated in comprehensive simulated experiments. Our
+experimental results reveal important insights, including the significance of
+the global flow prior and the lesser-than-expected influence of data-driven
+collision priors.
+"
+29366,$G$-Sasaki manifolds and K-energy,"  In this paper, we introduce a class of Sasaki manifolds with a reductive
+$G$-group action, called $G$-Sasaki manifolds. By reducing K-energy to a
+functional defined on a class of convex functions on a moment polytope, we give
+a criterion for the properness of K-energy. In particular, we deduce a
+sufficient and necessary condition related to the polytope for the existence of
+transverse $G$-Sasaki Einstein metrics. A similar result is also obtained for
+transverse $G$-Sasaki Ricci solitons. As an application, we construct several
+examples of $G$-Sasaki Ricci solitons by an established openness theorem for
+transverse $G$-Sasaki Ricci solitons.
+"
+29367,Dispersive shallow water wave modelling. Part II: Numerical simulation on a globally flat space,"  In this paper, we describe a numerical method to solve numerically the weakly
+dispersive fully nonlinear Serre-Green-Naghdi (SGN) celebrated model. Namely,
+our scheme is based on reliable finite volume methods, proven to be very
+effective for the hyperbolic part of equations. The particularity of our study
+is that we develop an adaptive numerical model using moving grids. Moreover, we
+use a special form of the SGN equations where non-hydrostatic part of pressure
+is found by solving a nonlinear elliptic equation. Moreover, this form of
+governing equations allows determining the natural form of boundary conditions
+to obtain a well-posed (numerical) problem.
+"
+29368,Conditional Equivalence Testing: an alternative remedy for publication bias,"  We introduce a publication policy that incorporates conditional equivalence
+testing (CET), a two-stage testing scheme in which standard NHST is followed
+conditionally by testing for equivalence. The idea of CET is carefully
+considered as it has the potential to address recent concerns about
+reproducibility and the limited publication of null results. In this paper we
+detail the implementation of CET, investigate similarities with a Bayesian
+testing scheme, and outline the basis for how a scientific journal could
+proceed to reduce publication bias while remaining relevant.
+"
+29369,Small-world networks and RNA secondary structures,"  Let Sn denote the network of all RNA secondary structures of length n, in
+which undirected edges exist between structures s, t such that t is obtained
+from s by the addition, removal or shift of a single base pair. Using
+context-free grammars, generating functions and complex analysis, we show that
+the asymptotic average degree is O(n) and that the asymptotic clustering
+coeffcient is O(1/n), from which it follows that the family Sn, n = 1,2,3,...
+of secondary structure networks is not small-world.
+"
+29370,Saikosaponins with similar structures but different mechanisms lead to combined hepatotoxicity,"  Radix Bupleuri is a hepatoprotective traditional Chinese medicine (TCM) used
+for thousands of years in clinical, which was reported to be linked with liver
+damage. Previous studies have revealed that saikosaponins are the major types
+of components that contribute to the hepatotoxicity of Radix Bupleuri. However
+the underlying molecular mechanism is far from being understood. In order to
+clarify whether these structural analogues exert toxicity effects through the
+same molecular targets, a systematic comparison study was done in this paper.
+The effects of SSa, b2, c, and d on isolated rat liver mitochondria and human
+hepatocyte L02 cells were explored, respectively. The collective results
+indicated that although saikosaponins share the similar structures but they
+have quite different mechanisms. SSb2 and SSd showed most serious damage on the
+function of mitochondria and survival rate of cell, respectively. SSb2 could
+cause mitochondrial permeability transition pore (mPTP) opening and collapse of
+mitochondrial membrane potential ({\Delta}{\Psi}m) by impairing the
+mitochondrial respiratory chain complex III. While SSd destroyed plasma
+membrane and led to the release of lactate dehydrogenase (LDH) mainly through
+activating caspase-1. Furthermore, the combine index (CI) demonstrated that the
+combined hepatotoxicity of SSb2 and SSd could be additive. This finding might
+yield more in depth understanding of hepatotoxicity of Radix Bupleuri possess
+many different saikosaponins.
+"
+29371,Neural Models for Information Retrieval,"  Neural ranking models for information retrieval (IR) use shallow or deep
+neural networks to rank search results in response to a query. Traditional
+learning to rank models employ machine learning techniques over hand-crafted IR
+features. By contrast, neural models learn representations of language from raw
+text that can bridge the gap between query and document vocabulary. Unlike
+classical IR models, these new machine learning based approaches are
+data-hungry, requiring large scale training data before they can be deployed.
+This tutorial introduces basic concepts and intuitions behind neural IR models,
+and places them in the context of traditional retrieval models. We begin by
+introducing fundamental concepts of IR and different neural and non-neural
+approaches to learning vector representations of text. We then review shallow
+neural IR methods that employ pre-trained neural term embeddings without
+learning the IR task end-to-end. We introduce deep neural networks next,
+discussing popular deep architectures. Finally, we review the current DNN
+models for information retrieval. We conclude with a discussion on potential
+future directions for neural IR.
+"
+29372,On the Complexity of Exact Counting of Dynamically Irreducible Polynomials,"  We give an efficient algorithm to enumerate all sets of $r\ge 1$ quadratic
+polynomials over a finite field, which remain irreducible under iterations and
+compositions.
+"
+29373,Sea surface temperature prediction and reconstruction using patch-level neural network representations,"  The forecasting and reconstruction of ocean and atmosphere dynamics from
+satellite observation time series are key challenges. While model-driven
+representations remain the classic approaches, data-driven representations
+become more and more appealing to benefit from available large-scale
+observation and simulation datasets. In this work we investigate the relevance
+of recently introduced bilinear residual neural network representations, which
+mimic numerical integration schemes such as Runge-Kutta, for the forecasting
+and assimilation of geophysical fields from satellite-derived remote sensing
+data. As a case-study, we consider satellite-derived Sea Surface Temperature
+time series off South Africa, which involves intense and complex upper ocean
+dynamics. Our numerical experiments demonstrate that the proposed patch-level
+neural-network-based representations outperform other data-driven models,
+including analog schemes, both in terms of forecasting and missing data
+interpolation performance with a relative gain up to 50\% for highly dynamic
+areas.
+"
+29374,Ultra-diffuse and Ultra-compact Galaxies in the Frontier Fields Cluster Abell 2744,"  We report the discovery of a large population of Ultra-diffuse Galaxies
+(UDGs) in the massive galaxy cluster Abell 2744 (z=0.308) as observed by the
+Hubble Frontier Fields program. Since this cluster is ~5 times more massive
+than Coma, our observations allow us to extend 0.7 dex beyond the high-mass end
+of the relationship between UDG abundance and cluster mass reported by van der
+Burg et al. 2016. Using the same selection criteria as van der Burg et al.
+2016, A2744 hosts an estimated 2133 +/- 613 UDGs, ten times the number in Coma.
+As noted by Lee & Jang 2016, A2744 contains numerous unresolved compact
+objects, which those authors identified predominantly as globular clusters.
+However, these objects have luminosities that are more consistent with
+ultra-compact dwarf (UCD) galaxies. The abundances of both UCDs and UDGs scale
+with cluster mass as a power law with a similar exponent, although UDGs and
+UCDs have very different radial distributions within the cluster. The radial
+surface density distribution of UCDs rises sharply toward the cluster centre,
+while the surface density distribution of the UDG population is essentially
+flat. Together, these observations hint at a picture where some UCDs in A2744
+may have once been associated with infalling UDGs. As UDGs fall in and
+dissolve, they leave behind a residue of unbound ultra-compact dwarfs.
+"
+29375,Mīmā\d{m}sā deontic logic: proof theory and applications,"  Starting with the deontic principles in M\={\i}mā\d{m}sā texts we
+introduce a new deontic logic. We use general proof-theoretic methods to obtain
+a cut-free sequent calculus for this logic, resulting in decidability,
+complexity results and neighbourhood semantics. The latter is used to analyse a
+well known example of conflicting obligations from the Vedas.
+"
+29376,Local Terahertz Field Enhancement for Time-Resolved X-ray Diffraction,"  We report local field strength enhancement of single-cycle terahertz (THz)
+pulses in an ultrafast time-resolved x-ray diffraction experiment. We show that
+patterning the sample with gold microstructures increases the THz field without
+changing the THz pulse shape or drastically affecting the quality of the x-ray
+diffraction pattern. We find a five-fold increase in THz-induced x-ray
+diffraction intensity change in the presence of microstructures on a SrTiO3
+thin-film sample.
+"
+29377,Extremal rays in the Hermitian eigenvalue problem,"  The Hermitian eigenvalue problem asks for the possible eigenvalues of a sum
+of $n\times n$ Hermitian matrices, given the eigenvalues of the summands. The
+regular faces of the cones $\Gamma_n(s)$ controlling this problem have been
+characterized in terms of classical Schubert calculus by the work of several
+authors.
+We determine extremal rays of $\Gamma_n(s)$ (which are never regular faces)
+by relating them to the geometry of flag varieties: The extremal rays either
+arise from ""modular intersection loci"", or by ""induction"" from extremal rays of
+smaller groups. Explicit formulas are given for both the extremal rays coming
+from such intersection loci, and for the induction maps.
+"
+29378,Joint Domain Based Massive Access for Small Packets Traffic of Uplink Wireless Channel,"  The fifth generation (5G) communication scenarios such as the cellular
+network and the emerging machine type communications will produce massive small
+packets. To support massive connectivity and avoid signaling overhead caused by
+the transmission of those small packets, this paper proposes a novel method to
+improve the transmission efficiency for massive connections of wireless uplink
+channel. The proposed method combines compressive sensing (CS) with power
+domain NOMA jointly, especially neither the scheduling nor the centralized
+power allocation is necessary in the method. Both the analysis and simulation
+show that the method can support up to two or three times overloading.
+"
+29379,The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications,"  The principle goal of computational mechanics is to define pattern and
+structure so that the organization of complex systems can be detected and
+quantified. Computational mechanics developed from efforts in the 1970s and
+early 1980s to identify strange attractors as the mechanism driving weak fluid
+turbulence via the method of reconstructing attractor geometry from measurement
+time series and in the mid-1980s to estimate equations of motion directly from
+complex time series. In providing a mathematical and operational definition of
+structure it addressed weaknesses of these early approaches to discovering
+patterns in natural systems.
+Since then, computational mechanics has led to a range of results from
+theoretical physics and nonlinear mathematics to diverse applications---from
+closed-form analysis of Markov and non-Markov stochastic processes that are
+ergodic or nonergodic and their measures of information and intrinsic
+computation to complex materials and deterministic chaos and intelligence in
+Maxwellian demons to quantum compression of classical processes and the
+evolution of computation and language.
+This brief review clarifies several misunderstandings and addresses concerns
+recently raised regarding early works in the field (1980s). We show that
+misguided evaluations of the contributions of computational mechanics are
+groundless and stem from a lack of familiarity with its basic goals and from a
+failure to consider its historical context. For all practical purposes, its
+modern methods and results largely supersede the early works. This not only
+renders recent criticism moot and shows the solid ground on which computational
+mechanics stands but, most importantly, shows the significant progress achieved
+over three decades and points to the many intriguing and outstanding challenges
+in understanding the computational nature of complex dynamic systems.
+"
+29380,Hierarchical Surface Prediction for 3D Object Reconstruction,"  Recently, Convolutional Neural Networks have shown promising results for 3D
+geometry prediction. They can make predictions from very little input data such
+as a single color image. A major limitation of such approaches is that they
+only predict a coarse resolution voxel grid, which does not capture the surface
+of the objects well. We propose a general framework, called hierarchical
+surface prediction (HSP), which facilitates prediction of high resolution voxel
+grids. The main insight is that it is sufficient to predict high resolution
+voxels around the predicted surfaces. The exterior and interior of the objects
+can be represented with coarse resolution voxels. Our approach is not dependent
+on a specific input type. We show results for geometry prediction from color
+images, depth images and shape completion from partial voxel grids. Our
+analysis shows that our high resolution predictions are more accurate than low
+resolution predictions.
+"
+29381,Existence result for impulsive coupled systems on the half-line,"  This work considers a second order impulsive coupled system of differential
+equations with generalized jump conditions in half-line, which can depend on
+the impulses of the unknown functions and their first derivatives. The
+arguments apply the fixed point theory, Green's functions technique,
+L^11-Carathéodory functions and sequences and Schauder's fixed point theorem.
+The method is based on Carathéodory concept of functions and sequences,
+together with the equiconvergence on infinity and on each impulsive moment, and
+it allows to consider coupled fully nonlinearities and very general impulsive
+functions.
+"
+29382,Berry Phases on Virasoro Orbits,"  We point out that unitary representations of the Virasoro algebra contain
+Berry phases obtained by acting on a primary state with conformal
+transformations that trace a closed path on a Virasoro coadjoint orbit. These
+phases can be computed exactly thanks to the Maurer-Cartan form on the Virasoro
+group, and they persist after combining left- and right-moving sectors.
+Thinking of Virasoro representations as particles in AdS_3 dressed with
+boundary gravitons, the Berry phases associated with Brown-Henneaux
+diffeomorphisms provide a gravitational extension of Thomas precession.
+"
+29383,On the third coefficient of TYZ expansion for radial scalar flat metrics,"  We classify radial scalar flat metrics with constant third coeffcient of its
+TYZ expansion. As a byproduct of our analysis we provide a characterization of
+Simanca's scalar flat metric.
+"
+29384,How to Cache in Mobile Hybrid IoT Networks?,"  Content-centric mobile hybrid Internet-of-Things (IoT) networks consisting of
+mobile devices and static femto access points (FAPs) are studied, where each
+device moves according to the random walk mobility model and requests a content
+object from the library independently at random according to a Zipf popularity
+distribution. Instead of allowing access to content objects at macro base
+stations via costly backhaul providing connection to the core network, we
+consider a more practical scenario where mobile devices and static FAPs, each
+having a finite-size cache space, are able to cache a subset of content objects
+so that each request is served by other mobile devices or static FAPs. Under a
+general multihop-based content delivery protocol, we analyze the order-optimal
+throughput--delay trade-off by presenting a new cache allocation strategy. In
+particular, under a given caching strategy, we first characterize a
+throughput--delay trade-off in terms of scaling laws along with the general
+content delivery multihop routing protocol. Then, the order-optimal
+throughput--delay trade-off is characterized by presenting the order-optimal
+cache allocation strategy, which jointly finds the replication sets at mobile
+devices and static FAPs via a novel variable decoupling approach. In our mobile
+IoT network, an interesting observation is that highly popular content objects
+are mainly served by mobile devices while the rest of content objects are
+served by static FAPs. We perform numerical evaluation to validate our
+analytical results. We also show that the order-optimal strategy strictly
+outperforms a baseline approach, where the replication sets at mobile devices
+and static FAPs are optimized separately.
+"
+29385,Exponentially Twisted Sampling: a Unified Approach for Centrality Analysis in Attributed Networks,"  In our recent works, we developed a probabilistic framework for structural
+analysis in undirected networks and directed networks. The key idea of that
+framework is to sample a network by a symmetric and asymmetric bivariate
+distribution and then use that bivariate distribution to formerly defining
+various notions, including centrality, relative centrality, community, and
+modularity. The main objective of this paper is to extend the probabilistic
+definition to attributed networks, where sampling bivariate distributions by
+exponentially twisted sampling. Our main finding is that we find a way to deal
+with the sampling of the attributed network including signed network. By using
+the sampling method, we define the various centralities in attributed networks.
+The influence centralities and trust centralities correctly show that how to
+identify centralities in signed network. The advertisement-specific influence
+centralities also perfectly define centralities when the attributed networks
+that have node attribute. Experimental results on real-world dataset
+demonstrate the different centralities with changing the temperature. Further
+experiments are conducted to gain a deeper understanding of the importance of
+the temperature.
+"
+29386,Monomial tropical cones for multicriteria optimization,"  We present an algorithm to compute all $n$ nondominated points of a
+multicriteria discrete optimization problem with $d$ objectives using at most
+$\mathcal{O}(n^{\lfloor d/2 \rfloor})$ scalarizations. The method is similar to
+an algorithm by Klamroth et al. (2015) with the same complexity. As a
+difference, our method employs a tropical convex hull computation, and it
+exploits a particular kind of duality which is special for the tropical cones
+arising. This duality can be seen as a generalization of the Alexander duality
+of monomial ideals.
+"
+29387,Axion minicluster power spectrum and mass function,"  When Peccei-Quinn (PQ) symmetry breaking happens after inflation, the axion
+field takes random values in causally disconnected regions. This leads to
+fluctuations of order one in the axion energy density around the QCD epoch.
+These over-densities eventually decouple from the Hubble expansion and form
+so-called miniclusters. We present a semi-analytical method to calculate the
+average axion energy density, as well as the power spectrum, from the
+re-alignment mechanism in this scenario. Furthermore, we develop a modified
+Press & Schechter approach, suitable to describe the collapse of non-linear
+density fluctuations during radiation domination, which is relevant for the
+formation of axion miniclusters. It allows us to calculate the double
+differential distribution of gravitationally collapsed miniclusters as a
+function of their mass and size. For instance, assuming a PQ scale of $10^{11}$
+GeV, minicluster masses range from about $5 \times 10^{-16}$ to $3 \times
+10^{-13}$ solar masses and have sizes from about $4\times 10^4$ to $7\times
+10^5$ km at the time they start to collapse.
+"
+29388,Likelihood Ratio as Weight of Forensic Evidence: A Closer Look,"  The forensic science community has increasingly sought quantitative methods
+for conveying the weight of evidence. Experts from many forensic laboratories
+summarize their findings in terms of a likelihood ratio. Several proponents of
+this approach have argued that Bayesian reasoning proves it to be normative. We
+find this likelihood ratio paradigm to be unsupported by arguments of Bayesian
+decision theory, which applies only to personal decision making and not to the
+transfer of information from an expert to a separate decision maker. We further
+argue that decision theory does not exempt the presentation of a likelihood
+ratio from uncertainty characterization, which is required to assess the
+fitness for purpose of any transferred quantity. We propose the concept of a
+lattice of assumptions leading to an uncertainty pyramid as a framework for
+assessing the uncertainty in an evaluation of a likelihood ratio. We
+demonstrate the use of these concepts with illustrative examples regarding the
+refractive index of glass and automated comparison scores for fingerprints.
+"
+29389,Primitive root biases for prime pairs I: existence and non-totality of biases,"  We study the difference between the number of primitive roots modulo $p$ and
+modulo $p+k$ for prime pairs $p,p+k$. Assuming the Bateman-Horn conjecture, we
+prove the existence of strong sign biases for such pairs. More importantly, we
+prove that for a small positive proportion of prime pairs $p,p+k$, the dominant
+inequality is reversed.
+"
+29390,Circulant q-Butson Hadamard matrices,"  If $q = p^n$ is a prime power, then a $d$-dimensional \emph{$q$-Butson
+Hadamard matrix} $H$ is a $d\times d$ matrix with all entries $q$th roots of
+unity such that $HH^* = dI_d$. We use algebraic number theory to prove a strong
+constraint on the dimension of a circulant $q$-Butson Hadamard matrix when $d =
+p^m$ and then explicitly construct a family of examples in all possible
+dimensions. These results relate to the long-standing circulant Hadamard matrix
+conjecture in combinatorics.
+"
+29391,Estimating the resolution of real images,"  Image resolvability is the primary concern in imaging. This paper reports an
+estimation of the full width at half maximum of the point spread function from
+a Fourier domain plot of real sample images by neither using test objects, nor
+defining a threshold criterion. We suggest that this method can be applied to
+any type of image, independently of the imaging modality.
+"
+29392,"Operator hydrodynamics, OTOCs, and entanglement growth in systems without conservation laws","  Thermalization and scrambling are the subject of much recent study from the
+perspective of many-body quantum systems with locally bounded Hilbert spaces
+(`spin chains'), quantum field theory and holography. We tackle this problem in
+1D spin-chains evolving under random local unitary circuits and prove a number
+of exact results on the behavior of out-of-time-ordered commutators (OTOCs),
+and entanglement growth in this setting. These results follow from the
+observation that the spreading of operators in random circuits is described by
+a `hydrodynamical' equation of motion, despite the fact that random unitary
+circuits do not have locally conserved quantities (e.g., no conserved energy).
+In this hydrodynamic picture quantum information travels in a front with a
+`butterfly velocity' $v_{\text{B}}$ that is smaller than the light cone
+velocity of the system, while the front itself broadens diffusively in time.
+The OTOC increases sharply after the arrival of the light cone, but we do
+\emph{not} observe a prolonged exponential regime of the form $\sim
+e^{\lambda_\text{L}(t-x/v)}$ for a fixed Lyapunov exponent $\lambda_\text{L}$.
+We find that the diffusive broadening of the front has important consequences
+for entanglement growth, leading to an entanglement velocity that can be
+significantly smaller than the butterfly velocity. We conjecture that the
+hydrodynamical description applies to more generic ergodic systems and support
+this by verifying numerically that the diffusive broadening of the operator
+wavefront also holds in a more traditional non-random Floquet spin-chain. We
+also compare our results to Clifford circuits, which have less rich
+hydrodynamics and consequently trivial OTOC behavior, but which can
+nevertheless exhibit linear entanglement growth and thermalization.
+"
+29393,The Mittag-Leffler Theorem for regular functions of a quaternionic variable,"  We prove a version of the classical Mittag-Leffler Theorem for regular
+functions over quaternions. Our result relies upon an appropriate notion of
+principal part, that is inspired by the recent definition of spherical
+analyticity.
+"
+29394,On the number of types in sparse graphs,"  We prove that for every class of graphs $\mathcal{C}$ which is nowhere dense,
+as defined by Nesetril and Ossona de Mendez, and for every first order formula
+$\phi(\bar x,\bar y)$, whenever one draws a graph $G\in \mathcal{C}$ and a
+subset of its nodes $A$, the number of subsets of $A^{|\bar y|}$ which are of
+the form $\{\bar v\in A^{|\bar y|}\, \colon\, G\models\phi(\bar u,\bar v)\}$
+for some valuation $\bar u$ of $\bar x$ in $G$ is bounded by
+$\mathcal{O}(|A|^{|\bar x|+\epsilon})$, for every $\epsilon>0$. This provides
+optimal bounds on the VC-density of first-order definable set systems in
+nowhere dense graph classes.
+We also give two new proofs of upper bounds on quantities in nowhere dense
+classes which are relevant for their logical treatment. Firstly, we provide a
+new proof of the fact that nowhere dense classes are uniformly quasi-wide,
+implying explicit, polynomial upper bounds on the functions relating the two
+notions. Secondly, we give a new combinatorial proof of the result of Adler and
+Adler stating that every nowhere dense class of graphs is stable. In contrast
+to the previous proofs of the above results, our proofs are completely
+finitistic and constructive, and yield explicit and computable upper bounds on
+quantities related to uniform quasi-wideness (margins) and stability (ladder
+indices).
+"
+29395,Phase curves of WASP-33b and HD 149026b and a New Correlation Between Phase Curve Offset and Irradiation Temperature,"  We present new 3.6 and 4.5 $\mu m$ Spitzer phase curves for the highly
+irradiated hot Jupiter WASP-33b and the unusually dense Saturn-mass planet HD
+149026b. As part of this analysis, we develop a new variant of pixel level
+decorrelation that is effective at removing intrapixel sensitivity variations
+for long observations (>10 hours) where the position of the star can vary by a
+significant fraction of a pixel. Using this algorithm, we measure eclipse
+depths, phase amplitudes, and phase offsets for both planets at 3.6 $\mu m$ and
+4.5 $\mu m$. We use a simple toy model to show that WASP-33b's phase offset,
+albedo, and heat recirculation efficiency are largely similar to those of other
+hot Jupiters despite its very high irradiation. On the other hand, our fits for
+HD 149026b prefer a very high albedo and an unusually high recirculation
+efficiency. We also compare our results to predictions from general circulation
+models, and find that while neither are a good match to the data, the
+discrepancies for HD 149026b are especially large. We speculate that this may
+be related to its high bulk metallicity, which could lead to enhanced
+atmospheric opacities and the formation of reflective cloud layers in localized
+regions of the atmosphere. We then place these two planets in a broader context
+by exploring relationships between the temperatures, albedos, heat transport
+efficiencies, and phase offsets of all planets with published thermal phase
+curves. We find a striking relationship between phase offset and irradiation
+temperature--the former drops with increasing temperature until around 3400 K,
+and rises thereafter. Although some aspects of this trend are mirrored in the
+circulation models, there are notable differences that provide important clues
+for future modeling efforts.
+"
+29396,Grain Boundary Structures and Collective Dynamics of Inversion Domains in Binary Two-Dimensional Materials,"  Understanding and controlling the properties and dynamics of topological
+defects is a lasting challenge in the study of two-dimensional materials, and
+is crucial to achieve high-quality films required for technological
+applications. Here grain boundary structures, energies, and dynamics of binary
+two-dimensional materials are investigated through the development of a phase
+field crystal model that is parameterized to match the ordering, symmetry,
+energy and length scales of hexagonal boron nitride. Our studies reveal some
+new dislocation core structures for various symmetrically and asymmetrically
+tilted grain boundaries, in addition to those obtained in previous experiments
+and first-principles calculations. We also identify a defect-mediated growth
+dynamics for inversion domains governed by the collective atomic migration and
+defect core transformation at grain boundaries and junctions, a process that is
+related to inversion symmetry breaking in binary lattice.
+"
+29397,Volume Integral Formulation for the Calculation of Material Independent Modes of Dielectric Scatterers,"  In the frame of volume integral equation methods, we introduce an alternative
+representation of the electromagnetic field scattered by a homogeneous object
+of arbitrary shape at a given frequency, in terms of a set of modes independent
+of its permittivity. This is accomplished by introducing an auxiliary
+eigenvalue problem, based on a volume integral operator. With this modal basis
+the expansion coefficients of the scattered field are simple rational functions
+of the permittivity of the scatterer. We show, by studying the electromagnetic
+scattering from a sphere and a cylinder of dimensions comparable to the
+incident wavelength, that only a moderate number of modes is needed to
+accurately describe the scattered far field. This method can be used to
+investigate resonant scattering phenomena, including plasmonic and photonic
+resonances, and to design the permittivity of the object to pursue a prescribed
+tailoring of the scattered field. Moreover, the presented modal expansion is
+computationally advantageous compared to direct solution of the volume integral
+equation when the scattered field has to be computed for many different values
+of the dielectric permittivity, given the size and shape of the dielectric
+body.
+"
+29398,Diagrammatic routes to nonlocal correlations beyond dynamical mean field theory,"  Strong electronic correlations pose one of the biggest challenges to solid
+state theory. We review recently developed methods that address this problem by
+starting with the local, eminently important correlations of dynamical mean
+field theory (DMFT). On top of this, non-local correlations on all length
+scales are generated through Feynman diagrams, with a local two-particle vertex
+instead of the bare Coulomb interaction as a building block. With these
+diagrammatic extensions of DMFT long-range charge-, magnetic-, and
+superconducting fluctuations as well as (quantum) criticality can be addressed
+in strongly correlated electron systems. We provide an overview of the
+successes and results achieved---hitherto mainly for model Hamiltonians---and
+outline future prospects for realistic material calculations.
+"
+29399,Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions,"  In this work, we perform an exploratory study on synthesizing deep neural
+networks using biological synaptic strength distributions, and the potential
+influence of different distributions on modelling performance particularly for
+the scenario associated with small data sets. Surprisingly, a CNN with
+convolutional layer synaptic strengths drawn from biologically-inspired
+distributions such as log-normal or correlated center-surround distributions
+performed relatively well suggesting a possibility for designing deep neural
+network architectures that do not require many data samples to learn, and can
+sidestep current training procedures while maintaining or boosting modelling
+performance.
+"
+29400,Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE,"  We propose a number of new algorithms for learning deep energy models and
+demonstrate their properties. We show that our SteinCD performs well in term of
+test likelihood, while SteinGAN performs well in terms of generating realistic
+looking images. Our results suggest promising directions for learning better
+models by combining GAN-style methods with traditional energy-based learning.
+"
+29401,Security enhanced memory for quantum state,"  Security enhancement is important in terms of both classical and quantum
+information. The recent development of a quantum storage device is noteworthy,
+and a coherence time of one second or longer has been demonstrated. On the
+other hand, although the encryption of a quantum bit or quantum memory has been
+proposed theoretically, no experiment has yet been carried out. Here we report
+the demonstration of a quantum memory with an encryption function that is
+realized by scrambling and retrieving the recorded quantum phase. We developed
+two independent Ramsey interferometers on an atomic ensemble trapped below a
+persistent supercurrent atom chip. By operating the two interferometers with
+random phases, the quantum phase recorded by a pulse of the first
+interferometer was modulated by the second interferometer pulse. The scrambled
+quantum phase was restored by employing another pulse of the second
+interferometer with a specific time delay. This technique paves way for
+improving the security of quantum information technology.
+"
+29402,Robust Pricing and Hedging around the Globe,"  We consider the martingale optimal transport duality for càdlàg processes
+with given initial and terminal laws. Strong duality and existence of dual
+optimizers (robust semi-static superhedging strategies) are proved for a class
+of payoffs that includes American, Asian, Bermudan, and European options with
+intermediate maturity. We exhibit an optimal superhedging strategy for which
+the static part solves an auxiliary problem and the dynamic part is given
+explicitly in terms of the static part. In the case of finitely supported
+marginal laws, solving for the static part reduces to a semi-infinite linear
+program.
+"
+29403,4D Generic Video Object Proposals,"  Many high-level video understanding methods require input in the form of
+object proposals. Currently, such proposals are predominantly generated with
+the help of networks that were trained for detecting and segmenting a set of
+known object classes, which limits their applicability to cases where all
+objects of interest are represented in the training set. This is a restriction
+for automotive scenarios, where unknown objects can frequently occur. We
+propose an approach that can reliably extract spatio-temporal object proposals
+for both known and unknown object categories from stereo video. Our 4D Generic
+Video Tubes (4D-GVT) method leverages motion cues, stereo data, and object
+instance segmentation to compute a compact set of video-object proposals that
+precisely localizes object candidates and their contours in 3D space and time.
+We show that given only a small amount of labeled data, our 4D-GVT proposal
+generator generalizes well to real-world scenarios, in which unknown categories
+appear. It outperforms other approaches that try to detect as many objects as
+possible by increasing the number of classes in the training set to several
+thousand.
+"
+29404,Integrated and Differentiated Spaces of Triangular Fuzzy Numbers,"  Fuzzy sets are the cornerstone of a non-additive uncertainty theory, namely
+possibility theory, and of a versatile tool for both linguistic and numerical
+modeling. Numerous works now combine fuzzy concepts with other scientific
+disciplines as well as modern technologies. In mathematics, fuzzy sets have
+triggered new research topics in connection with category theory, topology,
+algebra, analysis. In this paper, we use the triangular fuzzy numbers for
+matrix domains of sequence spaces with infinite matrices. We construct the new
+space with triangular fuzzy numbers and investigate to structural, topological
+and algebraic properties of these spaces.
+"
+29405,"""The Capacity of the Relay Channel"": Solution to Cover's Problem in the Gaussian Case","  Consider a memoryless relay channel, where the relay is connected to the
+destination with an isolated bit pipe of capacity $C_0$. Let $C(C_0)$ denote
+the capacity of this channel as a function of $C_0$. What is the critical value
+of $C_0$ such that $C(C_0)$ first equals $C(\infty)$? This is a long-standing
+open problem posed by Cover and named ""The Capacity of the Relay Channel,"" in
+$Open \ Problems \ in \ Communication \ and \ Computation$, Springer-Verlag,
+1987. In this paper, we answer this question in the Gaussian case and show that
+$C(C_0)$ can not equal to $C(\infty)$ unless $C_0=\infty$, regardless of the
+SNR of the Gaussian channels. This result follows as a corollary to a new upper
+bound we develop on the capacity of this channel. Instead of
+""single-letterizing"" expressions involving information measures in a
+high-dimensional space as is typically done in converse results in information
+theory, our proof directly quantifies the tension between the pertinent
+$n$-letter forms. This is done by translating the information tension problem
+to a problem in high-dimensional geometry. As an intermediate result, we
+develop an extension of the classical isoperimetric inequality on a
+high-dimensional sphere, which can be of interest in its own right.
+"
+29406,Minimal Forbidden Factors of Circular Words,"  Minimal forbidden factors are a useful tool for investigating properties of
+words and languages. Two factorial languages are distinct if and only if they
+have different (antifactorial) sets of minimal forbidden factors. There exist
+algorithms for computing the minimal forbidden factors of a word, as well as of
+a regular factorial language. Conversely, Crochemore et al. [IPL, 1998] gave an
+algorithm that, given the trie recognizing a finite antifactorial language $M$,
+computes a DFA recognizing the language whose set of minimal forbidden factors
+is $M$. In the same paper, they showed that the obtained DFA is minimal if the
+input trie recognizes the minimal forbidden factors of a single word. We
+generalize this result to the case of a circular word. We discuss several
+combinatorial properties of the minimal forbidden factors of a circular word.
+As a byproduct, we obtain a formal definition of the factor automaton of a
+circular word. Finally, we investigate the case of minimal forbidden factors of
+the circular Fibonacci words.
+"
+29407,Drop Trampoline,"  Rigid superhydrophobic materials have the ability to repel millimetric water
+drops, in typically 10 ms. Yet, most natural water-repellent materials can be
+deformed by impacting drops. To test the effect of deformability, we perform
+impacts of non-wetting drops onto thin ($\sim$ 10 \micro m), circular PDMS
+membranes. The bouncing mechanism is markedly modified compared to that on a
+rigid material: the liquid leaves the substrate as it is kicked upwards by the
+membrane. We show that the rebound is controlled by an interplay between the
+dynamics of the drop and that of the soft substrate, so that we can
+continuously vary the contact time by playing on the membrane's characteristics
+and reduce it up to 70\%.
+"
+29408,Modelos de Resposta para Experimentos Randomizados em Redes Sociais de Larga Escala,"  A/B tests are randomized experiments frequently used by companies that offer
+services on the Web for assessing the impact of new features. During an
+experiment, each user is randomly redirected to one of two versions of the
+website, called treatments. Several response models were proposed to describe
+the behavior of a user in a social network website, where the treatment
+assigned to her neighbors must be taken into account. However, there is no
+consensus as to which model should be applied to a given dataset. In this work,
+we propose a new response model, derive theoretical limits for the estimation
+error of several models, and obtain empirical results for cases where the
+response model was misspecified.
+"
+29409,Quantisation and nilpotent limits of Mishchenko-Fomenko subalgebras,"  For any simple Lie algebra $\mathfrak{g}$ and an element
+$\mu\in\mathfrak{g}^*$, the corresponding commutative subalgebra
+$\mathcal{A}_{\mu}$ of $\mathcal{U}(\mathfrak{g})$ is defined as a homomorphic
+image of the Feigin-Frenkel centre associated with $\mathfrak{g}$. It is known
+that when $\mu$ is regular this subalgebra solves Vinberg's quantisation
+problem, as the graded image of $\mathcal{A}_{\mu}$ coincides with the
+Mishchenko-Fomenko subalgebra $\overline{\mathcal{A}}_{\mu}$ of
+$\mathcal{S}(\mathfrak{g})$. By a conjecture of Feigin, Frenkel and Toledano
+Laredo, this property extends to an arbitrary element $\mu$. We give sufficient
+conditions which imply the property for certain choices of $\mu$. In
+particular, this proves the conjecture in type C and gives a new proof in type
+A. We show that the algebra $\mathcal{A}_{\mu}$ is free in both cases and
+produce its generators in an explicit form. Moreover, we prove that in all
+classical types generators of $\mathcal{A}_{\mu}$ can be obtained via the
+canonical symmetrisation map from certain generators of
+$\overline{\mathcal{A}}_{\mu}$. The symmetrisation map is also used to produce
+free generators of nilpotent limits of the algebras $\mathcal{A}_{\mu}$ and
+give a positive solution of Vinberg's problem for these limit subalgebras.
+"
+29410,Towards Faithful Graph Visualizations,"  Readability criteria have been addressed as a measurement of the quality of
+graph visualizations. In this paper, we argue that readability criteria are
+necessary but not sufficient. We propose a new kind of criteria, namely
+faithfulness, to evaluate the quality of graph layouts. We introduce a general
+model for quantify faithfulness, and contrast it with the well established
+readability criteria. We show examples of common visualization techniques, such
+as multidimensional scaling, edge bundling and several other visualization
+metaphors for the study of faithfulness.
+"
+29411,Identities of graded simple algebras,"  We study identities of finite dimensional algebras over a field of
+characteristic zero, graded by an arbitrary groupoid $\Gamma$. First we prove
+that its graded colength has a polynomially bounded growth. For any graded
+simple algebra $A$ we prove the existence of the graded PI-exponent, provided
+that $\Gamma$ is a commutative semigroup. If $A$ is simple in a non-graded
+sense the existence of the graded PI-exponent is proved without any
+restrictions on $\Gamma$.
+"
+29412,A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence,"  The study of causality or causal inference - how much a given treatment
+causally affects a given outcome in a population - goes way beyond correlation
+or association analysis of variables, and is critical in making sound data
+driven decisions and policies in a multitude of applications. The gold standard
+in causal inference is performing ""controlled experiments"", which often is not
+possible due to logistical or ethical reasons. As an alternative, inferring
+causality on ""observational data"" based on the ""Neyman-Rubin potential outcome
+model"" has been extensively used in statistics, economics, and social sciences
+over several decades. In this paper, we present a formal framework for sound
+causal analysis on observational datasets that are given as multiple relations
+and where the population under study is obtained by joining these base
+relations. We study a crucial condition for inferring causality from
+observational data, called the ""strong ignorability assumption"" (the treatment
+and outcome variables should be independent in the joined relation given the
+observed covariates), using known conditional independences that hold in the
+base relations. We also discuss how the structure of the conditional
+independences in base relations given as graphical models help infer new
+conditional independences in the joined relation. The proposed framework
+combines concepts from databases, statistics, and graphical models, and aims to
+initiate new research directions spanning these fields to facilitate powerful
+data-driven decisions in today's big data world.
+"
+29413,Graphical Models and Belief Propagation-hierarchy for Optimal Physics-Constrained Network Flows,"  In this manuscript we review new ideas and first results on application of
+the Graphical Models approach, originated from Statistical Physics, Information
+Theory, Computer Science and Machine Learning, to optimization problems of
+network flow type with additional constraints related to the physics of the
+flow. We illustrate the general concepts on a number of enabling examples from
+power system and natural gas transmission (continental scale) and distribution
+(district scale) systems.
+"
+29414,How real is the random censorship model in medical studies?,"  In survival analysis the random censorship model refers to censoring and
+survival times being independent of each other. It is one of the fundamental
+assumptions in the theory of survival analysis. We explain the reason for it
+being so ubiquitous, and we investigate its presence in medical studies. We
+differentiate two types of censoring in medical studies (dropout and
+administrative), and we explain their importance in examining the existence of
+the random censorship model. We show that in order to presume the random
+censorship model it is not enough to have a design study which conforms to it,
+but that one needs to provide evidence for its presence in the results. Blindly
+presuming the random censorship model might lead to the Kaplan-Meier estimator
+producing biased results, which might have serious consequences when estimating
+survival in medical studies.
+"
+29415,Testing Untestable Neural Machine Translation: An Industrial Case,"  Neural Machine Translation (NMT) has been widely adopted recently due to its
+advantages compared with the traditional Statistical Machine Translation (SMT).
+However, an NMT system still often produces translation failures due to the
+complexity of natural language and sophistication in designing neural networks.
+While in-house black-box system testing based on reference translations (i.e.,
+examples of valid translations) has been a common practice for NMT quality
+assurance, an increasingly critical industrial practice, named in-vivo testing,
+exposes unseen types or instances of translation failures when real users are
+using a deployed industrial NMT system. To fill the gap of lacking test oracle
+for in-vivo testing of an NMT system, in this paper, we propose a new approach
+for automatically identifying translation failures, without requiring reference
+translations for a translation task; our approach can directly serve as a test
+oracle for in-vivo testing. Our approach focuses on properties of natural
+language translation that can be checked systematically and uses information
+from both the test inputs (i.e., the texts to be translated) and the test
+outputs (i.e., the translations under inspection) of the NMT system. Our
+evaluation conducted on real-world datasets shows that our approach can
+effectively detect targeted property violations as translation failures. Our
+experiences on deploying our approach in both production and development
+environments of WeChat (a messenger app with over one billion monthly active
+users) demonstrate high effectiveness of our approach along with high industry
+impact.
+"
+29416,Characterizing Political Fake News in Twitter by its Meta-Data,"  This article presents a preliminary approach towards characterizing political
+fake news on Twitter through the analysis of their meta-data. In particular, we
+focus on more than 1.5M tweets collected on the day of the election of Donald
+Trump as 45th president of the United States of America. We use the meta-data
+embedded within those tweets in order to look for differences between tweets
+containing fake news and tweets not containing them. Specifically, we perform
+our analysis only on tweets that went viral, by studying proxies for users'
+exposure to the tweets, by characterizing accounts spreading fake news, and by
+looking at their polarization. We found significant differences on the
+distribution of followers, the number of URLs on tweets, and the verification
+of the users.
+"
+29417,Optimal Sparsity Testing in Linear regression Model,"  We consider the problem of sparsity testing in the high-dimensional linear
+regression model. The problem is to test whether the number of non-zero
+components (aka the sparsity) of the regression parameter $\theta^*$ is less
+than or equal to $k_0$. We pinpoint the minimax separation distances for this
+problem, which amounts to quantifying how far a $k_1$-sparse vector $\theta^*$
+has to be from the set of $k_0$-sparse vectors so that a test is able to reject
+the null hypothesis with high probability. Two scenarios are considered. In the
+independent scenario, the covariates are i.i.d. normally distributed and the
+noise level is known. In the general scenario, both the covariance matrix of
+the covariates and the noise level are unknown. Although the minimax separation
+distances differ in these two scenarios, both of them actually depend on $k_0$
+and $k_1$ illustrating that for this composite-composite testing problem both
+the size of the null and of the alternative hypotheses play a key role. Along
+the way, we introduce a new variable selection procedure, which can be of
+independent interest.
+"
+29418,Single-atom trapping and transport in DMD-controlled optical tweezers,"  We demonstrate the trapping and manipulation of single neutral atoms in
+reconfigurable arrays of optical tweezers. Our approach offers unparalleled
+speed by using a Texas Instruments Digital Micro-mirror Device (DMD) as a
+holographic amplitude modulator with a frame rate of 20,000 per second. We show
+the trapping of static arrays of up to 20 atoms, as well as transport of
+individually selected atoms over a distance of 25{\mu}m with laser cooling and
+4{\mu}m without. We discuss the limitations of the technique and the scope for
+technical improvements.
+"
+29419,A Convenient Category for Higher-Order Probability Theory,"  Higher-order probabilistic programming languages allow programmers to write
+sophisticated models in machine learning and statistics in a succinct and
+structured way, but step outside the standard measure-theoretic formalization
+of probability theory. Programs may use both higher-order functions and
+continuous distributions, or even define a probability distribution on
+functions. But standard probability theory does not handle higher-order
+functions well: the category of measurable spaces is not cartesian closed.
+Here we introduce quasi-Borel spaces. We show that these spaces: form a new
+formalization of probability theory replacing measurable spaces; form a
+cartesian closed category and so support higher-order functions; form a
+well-pointed category and so support good proof principles for equational
+reasoning; and support continuous probability distributions. We demonstrate the
+use of quasi-Borel spaces for higher-order functions and probability by:
+showing that a well-known construction of probability theory involving random
+functions gains a cleaner expression; and generalizing de Finetti's theorem,
+that is a crucial theorem in probability theory, to quasi-Borel spaces.
+"
+29420,Configuration spaces of spatial linkages: Taking Collisions Into Account,"  We construct a completed version C(Gamma) of the configuration space of a
+linkage Gamma in R^3 which takes into account the ways one link can touch
+another. We also describe a simplified version of C(Gamma) which is a blow-up
+of the space of immersions of Gamma in R^3 A number of simple detailed examples
+are given.
+"
+29421,Mapping knowledge with ontologies: the case of obesity,"  Scientometric techniques have been remarkably successful at mapping science
+but they face important difficulties when mapping research for societal
+problems possibly because they they are derived only from scientific documents
+and thus do not rely on non-academic expert knowledge. Here we aim to explore
+how ontologies can be used in science mapping, thus enriching current
+algorithmic techniques with systematic domain expert knowledge. This study
+introduces the methodology behind the construction of an ontology and tests
+potential uses in science mapping. We use obesity as a topic of case study.
+"
+29422,Sequential Maximum Margin Classifiers for Partially Labeled Data,"  In many real-world applications, data is not collected as one batch, but
+sequentially over time, and often it is not possible or desirable to wait until
+the data is completely gathered before analyzing it. Thus, we propose a
+framework to sequentially update a maximum margin classifier by taking
+advantage of the Maximum Entropy Discrimination principle. Our maximum margin
+classifier allows for a kernel representation to represent large numbers of
+features and can also be regularized with respect to a smooth sub-manifold,
+allowing it to incorporate unlabeled observations. We compare the performance
+of our classifier to its non-sequential equivalents in both simulated and real
+datasets.
+"
+29423,Brownian Thermal Noise in Functional Optical Surfaces,"  We present a formalism to compute Brownian thermal noise in functional
+optical surfaces such as grating reflectors, photonic crystal slabs or complex
+metamaterials. Such computations are based on a specific readout variable,
+typically a surface integral of a dielectric interface displacement weighed by
+a form factor. This paper shows how to relate this form factor to Maxwell's
+stress tensor computed on all interfaces of the moving surface. As an example,
+we examine Brownian thermal noise in monolithic T-shape grating reflectors. The
+previous computations by Heinert et al. [Heinert et al., PRD 88 (2013)]
+utilizing a simplified readout form factor produced estimates of thermal noise
+that are tens of percent higher than those of the exact analysis in the present
+paper. The relation between the form factor and Maxwell's stress tensor implies
+a close correlation between the optical properties of functional optical
+surfaces and thermal noise.
+"
+29424,Continuous $\ast$-g-Frame in Hilbert $C^{\ast}$-Modules,"  In this paper, we introduce the concept of Continuous $\ast$-g-Frame in
+Hilbert $C^{\ast}$-Modules and we establish some results. We also discuss the
+stability problem for Continuous $\ast$-g-Frame.
+"
+29425,Radiation Reaction in a Lorentz-Violating Electrodynamics,"  We study radiation reaction in a Lorentz violating electrodynamics [1]. We
+explore the possible modification whatsoever present in the radiation reaction
+force experienced by an accelerating charge in the modified Maxwell theory.
+However it turns out that radiation reaction receives no change due to Lorentz
+violation whereas electromagnetic mass manifests anisotropy.
+"
+29426,Embedding Words as Distributions with a Bayesian Skip-gram Model,"  We introduce a method for embedding words as probability densities in a
+low-dimensional space. Rather than assuming that a word embedding is fixed
+across the entire text collection, as in standard word embedding methods, in
+our Bayesian model we generate it from a word-specific prior density for each
+occurrence of a given word. Intuitively, for each word, the prior density
+encodes the distribution of its potential 'meanings'. These prior densities are
+conceptually similar to Gaussian embeddings. Interestingly, unlike the Gaussian
+embeddings, we can also obtain context-specific densities: they encode
+uncertainty about the sense of a word given its context and correspond to
+posterior distributions within our model. The context-dependent densities have
+many potential applications: for example, we show that they can be directly
+used in the lexical substitution task. We describe an effective estimation
+method based on the variational autoencoding framework. We also demonstrate
+that our embeddings achieve competitive results on standard benchmarks.
+"
+29427,Josephson parametric reflection amplifier with integrated directionality,"  A directional superconducting parametric amplifier in the GHz frequency range
+is designed and analyzed, suitable for low-power read-out of microwave kinetic
+inductance detectors employed in astrophysics and when combined with a
+nonreciprocal device at its input also for circuit quantum electrodynamics
+(cQED). It consists of an one wavelength long nondegenerate Josephson
+parametric reflection amplifier circuit. The device has two Josephson junction
+oscillators, connected via a tailored impedance to an on-chip passive circuit
+which directs the in- to the output port. The amplifier provides a gain of 20
+dB over a bandwidth of 220 MHz on the signal as well as on the idler portion of
+the amplified input and the total photon shot noise referred to the input
+corresponds to maximally 1.3 photons per second per Hertz of bandwidth. We
+predict a factor of four increase in dynamic range compared to conventional
+Josephson parametric amplifiers.
+"
+29428,Social media and mobility landscape: uncovering spatial patterns of urban human mobility with multi source data,"  In this paper, we present a three-step methodological framework, including
+location identification, bias modification, and out-of-sample validation, so as
+to promote human mobility analysis with social media data. More specifically,
+we propose ways of identifying personal activity-specific places and commuting
+patterns in Beijing, China, based on Weibo (China's Twitter) check-in records,
+as well as modifying sample bias of check-in data with population synthesis
+technique. An independent citywide travel logistic survey is used as the
+benchmark for validating the results. Obvious differences are discerned from
+Weibo users' and survey respondents' activity-mobility patterns, while there is
+a large variation of population representativeness between data from the two
+sources. After bias modification, the similarity coefficient between commuting
+distance distributions of Weibo data and survey observations increases
+substantially from 23% to 63%. Synthetic data proves to be a satisfactory
+cost-effective alternative source of mobility information. The proposed
+framework can inform many applications related to human mobility, ranging from
+transportation, through urban planning to transport emission modelling.
+"
+29429,Exploring the Lyapunov instability properties of high-dimensional atmospheric and climate models,"  The stability properties of intermediate-order climate models are
+investigated by computing their Lyapunov exponents (LEs). The two models
+considered are PUMA (Portable University Model of the Atmosphere), a
+primitive-equation simple general circulation model, and MAOOAM (Modular
+Arbitrary-Order Ocean-Atmosphere Model), a quasi-geostrophic coupled
+ocean-atmosphere model on a beta-plane. We wish to investigate the effect of
+the different levels of filtering on the instabilities and dynamics of the
+atmospheric flows. Moreover, we assess the impact of the oceanic coupling, the
+dissipation scheme and the resolution on the spectra of LEs.
+The PUMA Lyapunov spectrum is computed for two different values of the
+meridional temperature gradient defining the Newtonian forcing. The increase of
+the gradient gives rise to a higher baroclinicity and stronger instabilities,
+corresponding to a larger dimension of the unstable manifold and a larger first
+LE. The convergence rate of the rate functional for the large deviation law of
+the finite-time Lyapunov exponents (FTLEs) is fast for all exponents, which can
+be interpreted as resulting from the absence of a clear-cut atmospheric
+time-scale separation in such a model.
+The MAOOAM spectra show that the dominant atmospheric instability is
+correctly represented even at low resolutions. However, the dynamics of the
+central manifold, which is mostly associated to the ocean dynamics, is not
+fully resolved because of its associated long time scales, even at intermediate
+orders.
+This paper highlights the need to investigate the natural variability of the
+atmosphere-ocean coupled dynamics by associating rate of growth and decay of
+perturbations to the physical modes described using the formalism of the
+covariant Lyapunov vectors and to consider long integrations in order to
+disentangle the dynamical processes occurring at all time scales.
+"
+29430,Pattern-Driven Data Cleaning,"  Data is inherently dirty and there has been a sustained effort to come up
+with different approaches to clean it. A large class of data repair algorithms
+rely on data-quality rules and integrity constraints to detect and repair the
+data. A well-studied class of integrity constraints is Functional Dependencies
+(FDs, for short) that specify dependencies among attributes in a relation. In
+this paper, we address three major challenges in data repairing: (1) Accuracy:
+Most existing techniques strive to produce repairs that minimize changes to the
+data. However, this process may produce incorrect combinations of attribute
+values (or patterns). In this work, we formalize the interaction of FD-induced
+patterns and select repairs that result in preserving frequent patterns found
+in the original data. This has the potential to yield a better repair quality
+both in terms of precision and recall. (2) Interpretability of repairs: Current
+data repair algorithms produce repairs in the form of data updates that are not
+necessarily understandable. This makes it hard to debug repair decisions and
+trace the chain of steps that produced them. To this end, we define a new
+formalism to declaratively express repairs that are easy for users to reason
+about. (3) Scalability: We propose a linear-time algorithm to compute repairs
+that outperforms state-of-the-art FD repairing algorithms by orders of
+magnitude in repair time. Our experiments using both real-world and synthetic
+data demonstrate that our new repair approach consistently outperforms existing
+techniques both in terms of repair quality and scalability.
+"
+29431,A generalised framework for detailed classification of swimming paths inside the Morris Water Maze,"  The Morris Water Maze is commonly used in behavioural neuroscience for the
+study of spatial learning with rodents. Over the years, various methods of
+analysing rodent data collected in this task have been proposed. These methods
+span from classical performance measurements (e.g. escape latency, rodent
+speed, quadrant preference) to more sophisticated methods of categorisation
+which classify the animal swimming path into behavioural classes known as
+strategies. Classification techniques provide additional insight in relation to
+the actual animal behaviours but still only a limited amount of studies utilise
+them mainly because they highly depend on machine learning knowledge. We have
+previously demonstrated that the animals implement various strategies and by
+classifying whole trajectories can lead to the loss of important information.
+In this work, we developed a generalised and robust classification methodology
+which implements majority voting to boost the classification performance and
+successfully nullify the need of manual tuning. Based on this framework, we
+built a complete software, capable of performing the full analysis described in
+this paper. The software provides an easy to use graphical user interface (GUI)
+through which users can enter their trajectory data, segment and label them and
+finally generate reports and figures of the results.
+"
+29432,Causal Consistency of Structural Equation Models,"  Complex systems can be modelled at various levels of detail. Ideally, causal
+models of the same system should be consistent with one another in the sense
+that they agree in their predictions of the effects of interventions. We
+formalise this notion of consistency in the case of Structural Equation Models
+(SEMs) by introducing exact transformations between SEMs. This provides a
+general language to consider, for instance, the different levels of description
+in the following three scenarios: (a) models with large numbers of variables
+versus models in which the `irrelevant' or unobservable variables have been
+marginalised out; (b) micro-level models versus macro-level models in which the
+macro-variables are aggregate features of the micro-variables; (c) dynamical
+time series models versus models of their stationary behaviour. Our analysis
+stresses the importance of well specified interventions in the causal modelling
+process and sheds light on the interpretation of cyclic SEMs.
+"
+29433,Weierstrass Sigma Function Coefficients Divisibility Hypothesis,"  We consider the coefficients in the series expansion at zero of the
+Weierstrass sigma function \[ \sigma(z) = z \sum_{i, j \geqslant 0} {a_{i,j}
+\over (4 i + 6 j + 1)!} \left({g_2 z^4 \over 2}\right)^i \left(2 g_3
+z^6\right)^j. \] We have $a_{i,j} \in \mathbb{Z}$. We present the divisibility
+Hypothesis for the integers $a_{i,j}$ \begin{align*} \nu_2(a_{i,j}) &=
+\nu_2((4i + 6j + 1)!) - \nu_2(i!) - \nu_2(j!) - 3 i - 4 j, & \nu_3(a_{i,j}) &=
+\nu_3((4i + 6j + 1)!) - \nu_3(i!) - \nu_3(j!) - i - j. \end{align*} If this
+conjecture holds, then $\sigma(z)$ is a Hurwitz series over the ring
+$\mathbb{Z}[{g_2 \over 2}, 6 g_3]$.
+"
+29434,Online dynamic mode decomposition for time-varying systems,"  Dynamic mode decomposition (DMD) is a popular technique for modal
+decomposition, flow analysis, and reduced-order modeling. In situations where a
+system is time varying, one would like to update the system's description
+online as time evolves. This work provides an efficient method for computing
+DMD in real time, updating the approximation of a system's dynamics as new data
+becomes available. The algorithm does not require storage of past data, and
+computes the exact DMD matrix using rank-1 updates. A weighting factor that
+places less weight on older data can be incorporated in a straightforward
+manner, making the method particularly well suited to time-varying systems. A
+variant of the method may also be applied to online computation of ""windowed
+DMD"", in which only the most recent data are used. The efficiency of the method
+is compared against several existing DMD algorithms: for problems in which the
+state dimension is less than about~200, the proposed algorithm is the most
+efficient for real-time computation, and it can be orders of magnitude more
+efficient than the standard DMD algorithm. The method is demonstrated on
+several examples, including a time-varying linear system and a more complex
+example using data from a wind tunnel experiment. In particular, we show that
+the method is effective at capturing the dynamics of surface pressure
+measurements in the flow over a flat plate with an unsteady separation bubble.
+"
+29435,A Learning Framework for High Precision Industrial Assembly,"  Automatic assembly has broad applications in industries. Traditional assembly
+tasks utilize predefined trajectories or tuned force control parameters, which
+make the automatic assembly time-consuming, difficult to generalize, and not
+robust to uncertainties. In this paper, we propose a learning framework for
+high precision industrial assembly. The framework combines both the supervised
+learning and the reinforcement learning. The supervised learning utilizes
+trajectory optimization to provide the initial guidance to the policy, while
+the reinforcement learning utilizes actor-critic algorithm to establish the
+evaluation system when the supervisor is not accurate. The proposed learning
+framework is more efficient compared with the reinforcement learning and
+achieves better stability performance than the supervised learning. The
+effectiveness of the method is verified by both the simulation and experiment.
+Experimental videos are available at~\cite{website}.
+"
+29436,Most Rigid Representations and Cayley index,"  For any finite group $G$, a natural question to ask is the order of the
+smallest possible automorphism group for a Cayley graph on $G$. A particular
+Cayley graph whose automorphism group has this order is referred to as an MRR
+(Most Rigid Representation), and its Cayley index is a numerical indicator of
+this value. Study of GRRs showed that with the exception of two infinite
+families and seven individual groups, every group admits a Cayley graph whose
+MRR is a GRR, so that the Cayley index is 1. The full answer to the question of
+finding the smallest possible Cayley index for a Cayley graph on a fixed group
+was almost completed in previous work, but the precise answers for some finite
+groups and one infinite family of groups were left open. We fill in the
+remaining gaps to completely answer this question.
+"
+29437,Solving the L1 regularized least square problem via a box-constrained smooth minimization,"  In this paper, an equivalent smooth minimization for the L1 regularized least
+square problem is proposed. The proposed problem is a convex box-constrained
+smooth minimization which allows applying fast optimization methods to find its
+solution. Further, it is investigated that the property ""the dual of dual is
+primal"" holds for the L1 regularized least square problem. A solver for the
+smooth problem is proposed, and its affinity to the proximal gradient is shown.
+Finally, the experiments on L1 and total variation regularized problems are
+performed, and the corresponding results are reported.
+"
+29438,Synthesis-based Robust Low Resolution Face Recognition,"  Recognition of low resolution face images is a challenging problem in many
+practical face recognition systems. Methods have been proposed in the face
+recognition literature for the problem which assume that the probe is low
+resolution, but a high resolution gallery is available for recognition. These
+attempts have been aimed at modifying the probe image such that the resultant
+image provides better discrimination. We formulate the problem differently by
+leveraging the information available in the high resolution gallery image and
+propose a dictionary learning approach for classifying the low-resolution probe
+image. An important feature of our algorithm is that it can handle resolution
+change along with illumination variations. Furthermore, we also kernelize the
+algorithm to handle non-linearity in data and present a joint dictionary
+learning technique for robust recognition at low resolutions. The effectiveness
+of the proposed method is demonstrated using standard datasets and a
+challenging outdoor face dataset. It is shown that our method is efficient and
+can perform significantly better than many competitive low resolution face
+recognition algorithms.
+"
+29439,Regression with I-priors,"  We consider the problem of estimating the regression function $f$ in the
+regression model $y_i=f(x_i)+\varepsilon_i$, where $f$ is assumed to lie in a
+reproducing kernel Hilbert space (RKHS) and the errors are multivariate normal.
+This model has wide ranging applications, from regression with a functional
+covariate to (naive) classification. The main contribution of this paper is a
+proposal for an objective prior for the regression function $f$, defined as the
+distribution maximizing entropy subject to a suitable constraint based on the
+Fisher information on the regression function. The prior, which we call
+I-prior, is Gaussian with covariance kernel proportional to the Fisher
+information, and mean chosen a priori (e.g., 0). The I-prior has the
+intuitively appealing property that the more information is available about a
+linear functional of the regression function, the larger its prior variance,
+and, broadly speaking, the less influential the prior is on the posterior. The
+I-prior methodology can be used as a principled alternative to Tikhonov
+regularization, which suffers from well-known theoretical problems which we
+briefly review. We describe in some detail the case that the regression
+function lies in the multidimensional fractional Brownian motion RKHS, when the
+I-prior methodology has some particular appeal. Analysis of some real data sets
+and a small-scale simulation study show competitive performance of the I-prior
+methodology, which is implemented in the R-package \verb|iprior|
+\cite{jamil17}.
+"
+29440,Degrees of irreducible polynomials over binary field,"  An algorithm for factoring polynomials over finite fields is given by
+Berlekamp in 1967. The main tool was the matrix Q corresponding to each
+polynomial. This paper studies the degrees of polynomials over binary field
+that associated with their corresponding matrices Q and irreducibility.
+"
+29441,Attend and Diagnose: Clinical Time Series Analysis using Attention Models,"  With widespread adoption of electronic health records, there is an increased
+emphasis for predictive models that can effectively deal with clinical
+time-series data. Powered by Recurrent Neural Network (RNN) architectures with
+Long Short-Term Memory (LSTM) units, deep neural networks have achieved
+state-of-the-art results in several clinical prediction tasks. Despite the
+success of RNNs, its sequential nature prohibits parallelized computing, thus
+making it inefficient particularly when processing long sequences. Recently,
+architectures which are based solely on attention mechanisms have shown
+remarkable success in transduction tasks in NLP, while being computationally
+superior. In this paper, for the first time, we utilize attention models for
+clinical time-series modeling, thereby dispensing recurrence entirely. We
+develop the \textit{SAnD} (Simply Attend and Diagnose) architecture, which
+employs a masked, self-attention mechanism, and uses positional encoding and
+dense interpolation strategies for incorporating temporal order. Furthermore,
+we develop a multi-task variant of \textit{SAnD} to jointly infer models with
+multiple diagnosis tasks. Using the recent MIMIC-III benchmark datasets, we
+demonstrate that the proposed approach achieves state-of-the-art performance in
+all tasks, outperforming LSTM models and classical baselines with
+hand-engineered features.
+"
+29442,The arXiv of the future will not look like the arXiv,"  The arXiv is the most popular preprint repository in the world. Since its
+inception in 1991, the arXiv has allowed researchers to freely share
+publication-ready articles prior to formal peer review. The growth and the
+popularity of the arXiv emerged as a result of new technologies that made
+document creation and dissemination easy, and cultural practices where
+collaboration and data sharing were dominant. The arXiv represents a unique
+place in the history of research communication and the Web itself, however it
+has arguably changed very little since its creation. Here we look at the
+strengths and weaknesses of arXiv in an effort to identify what possible
+improvements can be made based on new technologies not previously available.
+Based on this, we argue that a modern arXiv might in fact not look at all like
+the arXiv of today.
+"
+29443,A Simple Scheme for Distributed Passive Load Balancing in Mobile Ad-hoc Networks,"  Efficient routing is one of the key challenges for next generation vehicular
+networks in order to provide fast and reliable communication in a smart city
+context. Various routing protocols have been proposed for determining optimal
+routing paths in highly dynamic topologies. However, it is the dilemma of those
+kinds of networks that good paths are used intensively, resulting in congestion
+and path quality degradation. In this paper, we adopt ideas from multipath
+routing and propose a simple decentral scheme for Mobile Ad-hoc Network (MANET)
+routing, which handles passive load balancing without requiring additional
+communication effort. It can easily be applied to existing routing protocols to
+achieve load balancing without changing the routing process itself. In
+comprehensive simulation studies, we apply the proposed load balancing
+technique to multiple example protocols and evaluate its effects on the network
+performance. The results show that all considered protocols can achieve
+significantly higher reliability and improved Packet Delivery Ratio (PDR)
+values by applying the proposed load balancing scheme.
+"
+29444,Critical points of master functions and mKdV hierarchy of type $A^{(2)}_{2n}$,"  We consider the population of critical points generated from the critical
+point of the master function with no variables, which is associated with the
+trivial representation of the twisted affine Lie algebra $A^{(2)}_{2n}$. The
+population is naturally partitioned into an infinite collection of complex
+cells $\mathbb{C}^m$, where $m$ are some positive integers. For each cell we
+define an injective rational map $\mathbb{C}^m \to M(A^{(2)}_{2n})$ of the cell
+to the space $M(A^{(2)}_{2n})$ of Miura opers of type $A^{(2)}_{2n}$. We show
+that the image of the map is invariant with respect to all mKdV flows on
+$M(A^{(2)}_{2n})$ and the image is point-wise fixed by all mKdV flows
+$\frac\partial{\partial t_r}$ with index $r$ greater than $4m$.
+"
+29445,Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback,"  Recommendation is the task of improving customer experience through
+personalized recommendation based on users' past feedback. In this paper, we
+investigate the most common scenario: the user-item (U-I) matrix of implicit
+feedback. Even though many recommendation approaches are designed based on
+implicit feedback, they attempt to project the U-I matrix into a low-rank
+latent space, which is a strict restriction that rarely holds in practice. In
+addition, although misclassification costs from imbalanced classes are
+significantly different, few methods take the cost of classification error into
+account. To address aforementioned issues, we propose a robust framework by
+decomposing the U-I matrix into two components: (1) a low-rank matrix that
+captures the common preference, and (2) a sparse matrix that detects the
+user-specific preference of individuals. A cost-sensitive learning model is
+embedded into the framework. Specifically, this model exploits different costs
+in the loss function for the observed and unobserved instances. We show that
+the resulting non-smooth convex objective can be optimized efficiently by an
+accelerated projected gradient method with closed-form solutions. Morever, the
+proposed algorithm can be scaled up to large-sized datasets after a relaxation.
+The theoretical result shows that even with a small fraction of 1's in the U-I
+matrix $M\in\mathbb{R}^{n\times m}$, the cost-sensitive error of the proposed
+model is upper bounded by $O(\frac{\alpha}{\sqrt{mn}})$, where $\alpha$ is a
+bias over imbalanced classes. Finally, empirical experiments are extensively
+carried out to evaluate the effectiveness of our proposed algorithm.
+Encouraging experimental results show that our algorithm outperforms several
+state-of-the-art algorithms on benchmark recommendation datasets.
+"
+29446,A Upper Bound for the Number of Solutions of Ternary Purely Exponential Diophantine Equations,"  Let $a,b,c$ be fixed coprime positive integers with $\min\{a,b,c\}>1$. In
+this paper, combining the Gel'fond-Baker method with an elementary approach, we
+prove that if $\max\{a,b,c\}>5\times 10^{27}$, then the equation $a^x+b^y=c^z$
+has at most three positive integer solutions $(x,y,z)$.
+"
+29447,An Empirical Study of Teaching Methodologies and Learning Outcomes for Online and in-class Networking Course Sections,"  To enhance student learning, we demonstrate an experimental study to analyze
+student learning outcomes in online and in-class sections of a core data
+communications course of the Undergraduate IT program in the Information
+Sciences and Technology (IST) Department at George Mason University (GMU). In
+this study, student performance is evaluated based on course assessments. This
+includes home and lab assignments, skill-based assessment, and traditional
+midterm exam across all 4 sections of the course. All sections have analogous
+content, assessment plan and teaching methodologies. Student demographics such
+as exam type and location preferences that may play an important role in their
+learning process are considered in our study. We had to collect vast amount of
+data from the learning management system (LMS), Blackboard (BB) Learn, in order
+to compare and study the results of several assessment outcomes for all
+students within their respective section and amongst students of other
+sections. We then tried to understand whether demographics have any influence
+on student performance by correlating individual student survey response to
+his/her performance in the class. The numerical results up to mid-semester
+reveal remarkable insights on student success in the online and face-to-face
+(F2F) sections.
+"
+29448,Measuring Inconsistency in Argument Graphs,"  There have been a number of developments in measuring inconsistency in
+logic-based representations of knowledge. In contrast, the development of
+inconsistency measures for computational models of argument has been limited.
+To address this shortcoming, this paper provides a general framework for
+measuring inconsistency in abstract argumentation, together with some proposals
+for specific measures, and a consideration of measuring inconsistency in
+logic-based instantiations of argument graphs, including a review of some
+existing proposals and a consideration of how existing logic-based measures of
+inconsistency can be applied.
+"
+29449,Completing graphs to metric spaces,"  We prove that certain classes of metrically homogeneous graphs omitting
+triangles of odd short perimeter as well as triangles of long perimeter have
+the extension property for partial automorphisms and we describe their Ramsey
+expansions.
+"
+29450,Eichler cohomology and zeros of polynomials associated to derivatives of $L$-functions,"  In recent years, a number of papers have been devoted to the study of roots
+of period polynomials of modular forms. Here, we study cohomological analogues
+of the Eichler-Shimura period polynomials corresponding to higher
+$L$-derivatives. We state general conjectures about the locations of the roots
+of the full and odd parts of the polynomials, in analogy with the existing
+literature on period polynomials, and we also give numerical evidence that
+similar results hold for our higher derivative ""period polynomials"" in the case
+of cusp forms. We prove a special case of this conjecture in the case of
+Eisenstein series.
+"
+29451,Neutron astronomy,"  Neutrons travel along straight lines in free space, but only survive for a
+distance which depends on their energy. Thus, detecting neutrons in space in
+principle provides directional and distance information. Apart from secondary
+neutrons produced by cosmic-ray interactions in the Earth atmosphere, which are
+the dominant background, direct neutron emission is caused by solar flares,
+with clear time correlation with X-rays, which can be measured from few tens
+MeV up to few GeV. There is no detectable astrophysical source up to the PeV
+scale, when neutrons coming from supernova remnants may reach the Earth before
+decaying. In addition, ultra high energy neutrons are the most plausible
+explanation for the measured anisotropy of cosmic-ray showers produced in the
+atmosphere above $10^{18}$ eV. From the GeV to the PeV scale, the expected
+neutron flux is very low and not too different from the antiproton flux, as the
+same cosmic-ray collisions with the interstellar medium which can produce
+antiprotons can also produce neutrons and antineutrons. This background flux of
+cosmic-ray neutrons is very low and has not yet been detected. Measuring the
+neutron energy spectrum in space is a very effective way of searching for
+decays of exotic particles. For example, dark matter could consist of Weakly
+Interacting Massive Particles (WIMPs), which may annihilate into final states
+with particle and antiparticle pairs. Consequently, a number of indirect WIMP
+searches are being carried on, focusing on positron and antiproton spectra.
+However, no experiment is presently foreseen to look for ""bumps"" in the neutron
+energy spectrum, which is virtually background free. Here we consider the
+implications of a measurement of the neutron energy spectrum in astronomy and
+astrophysics and list the interesting energy regions in the search for WIMPs.
+"
+29452,The Geometric Block Model,"  To capture the inherent geometric features of many community detection
+problems, we propose to use a new random graph model of communities that we
+call a Geometric Block Model. The geometric block model generalizes the random
+geometric graphs in the same way that the well-studied stochastic block model
+generalizes the Erdos-Renyi random graphs. It is also a natural extension of
+random community models inspired by the recent theoretical and practical
+advancement in community detection. While being a topic of fundamental
+theoretical interest, our main contribution is to show that many practical
+community structures are better explained by the geometric block model. We also
+show that a simple triangle-counting algorithm to detect communities in the
+geometric block model is near-optimal. Indeed, even in the regime where the
+average degree of the graph grows only logarithmically with the number of
+vertices (sparse-graph), we show that this algorithm performs extremely well,
+both theoretically and practically. In contrast, the triangle-counting
+algorithm is far from being optimum for the stochastic block model. We simulate
+our results on both real and synthetic datasets to show superior performance of
+both the new model as well as our algorithm.
+"
+29453,Robust Budget Allocation via Continuous Submodular Functions,"  The optimal allocation of resources for maximizing influence, spread of
+information or coverage, has gained attention in the past years, in particular
+in machine learning and data mining. But in applications, the parameters of the
+problem are rarely known exactly, and using wrong parameters can lead to
+undesirable outcomes. We hence revisit a continuous version of the Budget
+Allocation or Bipartite Influence Maximization problem introduced by Alon et
+al. (2012) from a robust optimization perspective, where an adversary may
+choose the least favorable parameters within a confidence set. The resulting
+problem is a nonconvex-concave saddle point problem (or game). We show that
+this nonconvex problem can be solved exactly by leveraging connections to
+continuous submodular functions, and by solving a constrained submodular
+minimization problem. Although constrained submodular minimization is hard in
+general, here, we establish conditions under which such a problem can be solved
+to arbitrary precision $\epsilon$.
+"
+29454,Using Discretization for Extending the Set of Predictive Features,"  To date, attribute discretization is typically performed by replacing the
+original set of continuous features with a transposed set of discrete ones.
+This paper provides support for a new idea that discretized features should
+often be used in addition to existing features and as such, datasets should be
+extended, and not replaced, by discretization. We also claim that
+discretization algorithms should be developed with the explicit purpose of
+enriching a non-discretized dataset with discretized values. We present such an
+algorithm, D-MIAT, a supervised algorithm that discretizes data based on
+Minority Interesting Attribute Thresholds. D-MIAT only generates new features
+when strong indications exist for one of the target values needing to be
+learned and thus is intended to be used in addition to the original data. We
+present extensive empirical results demonstrating the success of using D-MIAT
+on $ 28 $ benchmark datasets. We also demonstrate that $ 10 $ other
+discretization algorithms can also be used to generate features that yield
+improved performance when used in combination with the original non-discretized
+data. Our results show that the best predictive performance is attained using a
+combination of the original dataset with added features from a ""standard""
+supervised discretization algorithm and D-MIAT.
+"
+29455,Results on MeV-scale dark matter from a gram-scale cryogenic calorimeter operated above ground,"  Models for light dark matter particles with masses below 1 GeV/c$^2$ are a
+natural and well-motivated alternative to so-far unobserved weakly interacting
+massive particles. Gram-scale cryogenic calorimeters provide the required
+detector performance to detect these particles and extend the direct dark
+matter search program of CRESST. A prototype 0.5 g sapphire detector developed
+for the $\nu$-cleus experiment has achieved an energy threshold of
+$E_{th}=(19.7\pm 0.9)$ eV, which is one order of magnitude lower than previous
+results and independent of the type of particle interaction. The result
+presented here is obtained in a setup above ground without significant
+shielding against ambient and cosmogenic radiation. Although operated in a
+high-background environment, the detector probes a new range of light-mass dark
+matter particles previously not accessible by direct searches. We report the
+first limit on the spin-independent dark matter particle-nucleon cross section
+for masses between 140 MeV/c$^2$ and 500 MeV/c$^2$.
+"
+29456,Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking,"  This manuscript presents control of a high-DOF fully actuated lower-limb
+exoskeleton for paraplegic individuals. The key novelty is the ability for the
+user to walk without the use of crutches or other external means of
+stabilization. We harness the power of modern optimization techniques and
+supervised machine learning to develop a smooth feedback control policy that
+provides robust velocity regulation and perturbation rejection. Preliminary
+evaluation of the stability and robustness of the proposed approach is
+demonstrated through the Gazebo simulation environment. In addition,
+preliminary experimental results with (complete) paraplegic individuals are
+included for the previous version of the controller.
+"
+29457,Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process,"  The objective is to study an on-line Hidden Markov model (HMM)
+estimation-based Q-learning algorithm for partially observable Markov decision
+process (POMDP) on finite state and action sets. When the full state
+observation is available, Q-learning finds the optimal action-value function
+given the current action (Q function). However, Q-learning can perform poorly
+when the full state observation is not available. In this paper, we formulate
+the POMDP estimation into a HMM estimation problem and propose a recursive
+algorithm to estimate both the POMDP parameter and Q function concurrently.
+Also, we show that the POMDP estimation converges to a set of stationary points
+for the maximum likelihood estimate, and the Q function estimation converges to
+a fixed point that satisfies the Bellman optimality equation weighted on the
+invariant distribution of the state belief determined by the HMM estimation
+process.
+"
+29458,Importance of phonon contribution to the free migration energy of the O vacancy in BaZrO3,"  BaZrO3 exhibits excellent proton conductivity and good high-temperature
+stability. It is therefore a promising electrolyte material for solid oxide
+fuel cells. The stability of BaZrO3 at high temperatures is generally explained
+by the low diffusivity of the O vacancy. Present first-principle
+Density-Functional-Theory calculations show that the slow migration of the
+doubly positively charged O vacancy at high temperature cannot be solely caused
+by the ground-state migration energy but by the contribution of phonon
+excitations to the free migration energy. With increasing temperature, the
+effective barrier for oxygen vacancy migration increases. At about 1000K, which
+is the operating temperature of fuel cells, the calculated O vacancy
+diffusivity is more than one order of magnitude lower than that determined
+using solely the ground-state migration barrier. The calculated diffusivity
+data agree well with experimental results from literature. The present work
+reveals that the high-temperature stability of BaZrO3 is mainly due to the
+phonon contribution to the free migration energy of the O vacancy.
+"
+29459,Pore-geometry recognition: on the importance of quantifying similarity in nanoporous materials,"  In most applications of nanoporous materials the pore structure is as
+important as the chemical composition as a determinant of performance. For
+example, one can alter performance in applications like carbon capture or
+methane storage by orders of magnitude by only modifying the pore structure
+(1,2). For these applications it is therefore important to identify the optimal
+pore geometry and use this information to find similar materials. However, the
+mathematical language and tools to identify materials with similar pore
+structures, but different composition, has been lacking. Here we develop a pore
+recognition approach to quantify similarity of pore structures and classify
+them using topological data analysis (3,4). Our approach allows us to identify
+materials with similar pore geometries, and to screen for materials that are
+similar to given top-performing structures. Using methane storage as a case
+study, we also show that materials can be divided into topologically distinct
+classes -- and that each class requires different optimization strategies. In
+this work we have focused on pore space, but our topological approach can be
+generalised to quantify similarity of any geometric object, which, given the
+many different Materials Genomics initiatives (5,6), opens many interesting
+avenues for big-data science.
+"
+29460,Shot Noise Neuron Model,"  In this paper, we propose a shot noise-based leaky integrated and firing
+neuron model and provide a detailed analysis of the performance of this model
+compared to the traditional diffusion approximated model. In theoretical
+neuroscience, there are three general neuron models in the field: Compartmental
+neuron model is a conductance-based model, in which it views the biological
+neurons as a large circuit. The problem of this model comes from its structural
+complexity and the number of its free parameters; Leaky integrated and firing
+model is a more flexible model due to the special design called
+threshold-resetting, in which the voltage of the neuron is reset after reaching
+the threshold. This model is proposed as an alternative to the compartment
+model to provide a more biologically realistic model that can capture spike
+timing behaviors that are observed in experiments. In addition to that, it is
+more computationally efficient since it has much less free parameters; Firing
+rate-based neuron model uses the firing rate of the neuron as the coupling
+terms in the coupled neuronal network models. Models of this kind can be easily
+analyzed on the network level. Theoretical analysis for this model proves the
+existence of phase transition and shows that the computational capacity is
+boosted in the chaotic regime.
+"
+29461,On the displacement of generators of free Fuchsian groups,"  We prove an inequality that must be satisfied by displacement of generators
+of free Fuchsian groups, which is the two-dimensional version of the $\log
+(2k-1)$ Theorem for Kleinian groups due to Anderson-Canary-Culler-Shalen. As
+applications, we obtain quantitative results on the geometry of hyperbolic
+surfaces such as the two-dimensional Margulis constant and lengths of closed
+curves, which improves a result of Buser's.
+"
+29462,An Attempt at Analyzing the Information Nature of Money,"  Money was invented to address the difficulty in the double coincidence of
+wants between the supply and demand when people exchanged their goods and
+services. There are two information states in society: one is the initial state
+that people have goods and services due to division of labor; the other is the
+final state that people have different goods and services with the initial
+state due to exchange of goods and services between them. The process is that
+the initial state is changed to the final state with the help of money. Because
+the direct exchanges of goods and services are difficult to achieve the double
+coincidence of wants in time and space, it can be achieved with the help of
+money which is as a medium and bridge. In this paper the changing process of
+the state information is analyzed through the matrix representation of money,
+and then the nature of money with a kind of information of reliable ledger is
+more apparently shown. This paper also analyzes the common characteristics of
+physical money, electronic money and digital currency, that is, reliable ledger
+and explores the future trend of money development from the perspective of
+history and security technology of money.
+"
+29463,Identifying Coherent Anomalies in Multi-Scale Spatio-Temporal Data using Markov Random Fields,"  Many physical processes involve spatio-temporal observations, which can be
+studied at different spatial and temporal scales. For example, rainfall data
+measured daily by rain gauges can be considered at daily, monthly or annual
+temporal scales, and local, grid-wise, region-wise or country-wise spatial
+scales. In this work, we focus on detection of anomalies in such multi-scale
+spatio-temporal data. We consider an anomaly as an event where the measured
+values over a spatio-temporally extended region are significantly different
+from their long-term means. However we aim to avoid setting any thresholds on
+the measured values and spatio-temporal sizes, because not only are thresholds
+subjective but also the long-term mean values often vary spatially and
+temporally. For this purpose we use spatio-Temporal Markov Random Field, where
+latent states indicate anomaly type (positive anomaly, negative anomaly, no
+anomaly/normal). Spatio-temporal coherence is maintained through suitable edge
+potentials. The model is extended to multiple spatio-temporal scales to achieve
+our second goal: anomalies at any scale should be defined both on the data at
+that scale, and also on anomalies at other scales. This allows us to trace an
+anomaly at a coarse scale to finer scales. For example, whether rainfall in a
+particular year is anomalous over a region should depend not only on the total
+volume of rainfall over the entire region, but also on whether there were such
+anomalies at the grid-scale, and the monthly scale. We use this approach to
+study rainfall anomalies over India -extremely diverse with respect to
+rainfall- for the period 1901-2011, and show its benefits over existing
+approaches.
+"
+29464,A Deep Learning Framework for Short-term Power Load Forecasting,"  The scheduling and operation of power system becomes prominently complex and
+uncertain, especially with the penetration of distributed power. Load
+forecasting matters to the effective operation of power system. This paper
+proposes a novel deep learning framework to forecast the short-term grid load.
+First, the load data is processed by Box-Cox transformation, and two parameters
+(electricity price and temperature) are investigated. Then, to quantify the
+tail-dependence of power load on the two parameters, parametric Copula models
+are fitted and the threshold of peak load are computed. Next, a deep belief
+network is built to forecast the hourly load of the power grid. One year grid
+load data collected from an urbanized area in Texas, United States is utilized
+in the case studies. Short-term load forecasting are examined in four seasons
+independently. Day-ahead and week-ahead load forecasting experiments are
+conducted in each season using the proposed framework. The proposed framework
+is compared with classical neural networks, support vector regression machine,
+extreme learning machine, and classical deep belief networks. The load
+forecasting performances are assessed by mean absolute percentage error, root
+mean square error, and hit rate. Computational results confirm the
+effectiveness of the proposed data-driven deep learning framework. The
+prediction accuracies of both day-ahead forecasting and week-ahead forecasting
+demonstrate that the proposed framework outperforms the tested algorithms.
+"
+29465,Implicitly Incorporating Morphological Information into Word Embedding,"  In this paper, we propose three novel models to enhance word embedding by
+implicitly using morphological information. Experiments on word similarity and
+syntactic analogy show that the implicit models are superior to traditional
+explicit ones. Our models outperform all state-of-the-art baselines and
+significantly improve the performance on both tasks. Moreover, our performance
+on the smallest corpus is similar to the performance of CBOW on the corpus
+which is five times the size of ours. Parameter analysis indicates that the
+implicit models can supplement semantic information during the word embedding
+training process.
+"
+29466,Arc representations,"  This paper was inspired by four articles: surface cluster algebras studied by
+Fomin-Shapiro-Thurston \cite{fst}, the mutation theory of quivers with
+potentials initiated by Derksen-Weyman-Zelevinsky \cite{dwz}, string modules
+associated to arcs on unpunctured surfaces by
+Assem-Br$\ddot{u}$stle-Charbonneau-Plamondon \cite{acbp} and Quivers with
+potentials associated to triangulated surfaces, part II: Arc representations by
+Labardini-Fragoso. \cite{lf2}. For a surface with marked points ($\Sigma,M$)
+Labardini-Fragoso associated a quiver with potential $(Q(\tau),S(\tau))$ then
+for an ideal triangulation of ($\Sigma,M$) and an ideal arc Labardini-Fragoso
+defined an arc representation of $(Q(\tau),S(\tau))$. This paper focuses on
+extent the definition of arc representation to a more general context by
+considering a tagged triangulation and a tagged arc. We associate in an
+explicit way a representation of the quiver with potential constructed
+Labardini-Fragoso and prove that the Jacobian relations are met.
+"
+29467,Symmetrization and extension of planar bi-Lipschitz maps,"  We show that every centrally symmetric bi-Lipschitz embedding of the circle
+into the plane can be extended to a global bi-Lipschitz map of the plane with
+linear bounds on the distortion. This answers a question of Daneri and Pratelli
+in the special case of centrally symmetric maps. For general bi-Lipschitz
+embeddings our distortion bound has a combination of linear and cubic growth,
+which improves on the prior results. The proof involves a symmetrization result
+for bi-Lipschitz maps which may be of independent interest.
+"
+29468,"Effect of Fe and Co substitution on martensitic stability, elastic, electronic and magnetic properties of Mn$_2$NiGa: insights from \textit{ab initio} calculations","  We investigate the effects of Fe and Co substitutions on the phase stability
+of the martensitic phase, mechanical, electronic and magnetic properties of
+magnetic shape memory system Mn$_{2}$NiGa by first-principles Density
+functional theory(DFT) calculations. The evolution of these aspects upon
+substitution of Fe and Co at different crystallographic sites are investigated
+by computing the electronic structure, mechanical properties and magnetic
+exchange parameters. We find that the martensitic phase of Mn$_{2}$NiGa
+gradually de-stabilises with increase in concentration of Fe/Co due to the
+weakening of the minority spin hybridisation of Ni and Mn atoms occupying
+crystallographically equivalent sites. The interplay between relative
+structural stability and the compositional changes are understood from the
+variations in the elastic modulii and electronic structures. We find that the
+elastic shear modulus C$^{\prime}$ can be considered as a predictor of
+composition dependence of martensitic transformation temperature in substituted
+Mn$_{2}$NiGa. The magnetic properties of Mn$_{2}$NiGa are found to be greatly
+improved by the substitutions due to stronger ferromagnetic interactions in the
+compounds. The gradually weaker(stronger) Jahn-Teller distortion (covalent
+bonding) in the minority spin densities of states due to substitutions lead to
+a half-metallic like gap in these compounds resulting in materials with high
+spin-polarisation when the substitutions are complete. The substitutions at the
+Ga site result in two new compounds Mn$_{2}$NiFe and Mn$_{2}$NiCo with very
+high magnetic moments and Curie temperatures. Thus, our work indicates that
+although the substitutions de-stabilise the martensitic phase in Mn$_{2}$NiGa,
+new magnetic materials with very good magnetic parameters and potentially
+useful for novel magnetic applications can be obtained.
+"
+29469,Topology Reduction in Deep Convolutional Feature Extraction Networks,"  Deep convolutional neural networks (CNNs) used in practice employ potentially
+hundreds of layers and $10$,$000$s of nodes. Such network sizes entail
+significant computational complexity due to the large number of convolutions
+that need to be carried out; in addition, a large number of parameters needs to
+be learned and stored. Very deep and wide CNNs may therefore not be well suited
+to applications operating under severe resource constraints as is the case,
+e.g., in low-power embedded and mobile platforms. This paper aims at
+understanding the impact of CNN topology, specifically depth and width, on the
+network's feature extraction capabilities. We address this question for the
+class of scattering networks that employ either Weyl-Heisenberg filters or
+wavelets, the modulus non-linearity, and no pooling. The exponential feature
+map energy decay results in Wiatowski et al., 2017, are generalized to
+$\mathcal{O}(a^{-N})$, where an arbitrary decay factor $a>1$ can be realized
+through suitable choice of the Weyl-Heisenberg prototype function or the mother
+wavelet. We then show how networks of fixed (possibly small) depth $N$ can be
+designed to guarantee that $((1-\varepsilon)\cdot 100)\%$ of the input signal's
+energy are contained in the feature vector. Based on the notion of
+operationally significant nodes, we characterize, partly rigorously and partly
+heuristically, the topology-reducing effects of (effectively) band-limited
+input signals, band-limited filters, and feature map symmetries. Finally, for
+networks based on Weyl-Heisenberg filters, we determine the prototype function
+bandwidth that minimizes---for fixed network depth $N$---the average number of
+operationally significant nodes per layer.
+"
+29470,On the critical Choquard equation with potential well,"  In this paper we are interested in the following nonlinear Choquard equation
+$$ -\Delta u+(\lambda V(x)-\beta)u =\big(|x|^{-\mu}\ast
+|u|^{2_{\mu}^{\ast}}\big)|u|^{2_{\mu}^{\ast}-2}u\hspace{4.14mm}\mbox{in}\hspace{1.14mm}
+\mathbb{R}^N, $$ where $\lambda,\beta\in\mathbb{R}^+$, $0<\mu<N$, $N\geq4$,
+$2_{\mu}^{\ast}=(2N-\mu)/(N-2)$ is the upper critical exponent due to the
+Hardy-Littlewood-Sobolev inequality and the nonnegative potential function
+$V\in \mathcal{C}(\mathbb{R}^N,\mathbb{R})$ such that $\Omega :=\mbox{int}
+V^{-1}(0)$ is a nonempty bounded set with smooth boundary. If $\beta>0$ is a
+constant such that the operator $-\Delta +\lambda V(x)-\beta$ is
+non-degenerate, we prove the existence of ground state solutions which localize
+near the potential well int $V^{-1}(0)$ for $\lambda$ large enough and also
+characterize the asymptotic behavior of the solutions as the parameter
+$\lambda$ goes to infinity. Furthermore, for any $0<\beta<\beta_{1}$, we are
+able to find the existence of multiple solutions by the Lusternik-Schnirelmann
+category theory, where $\beta_{1}$ is the first eigenvalue of $-\Delta$ on
+$\Omega$ with Dirichlet boundary condition.
+"
+29471,Backpropagation Training for Fisher Vectors within Neural Networks,"  Fisher-Vectors (FV) encode higher-order statistics of a set of multiple local
+descriptors like SIFT features. They already show good performance in
+combination with shallow learning architectures on visual recognitions tasks.
+Current methods using FV as a feature descriptor in deep architectures assume
+that all original input features are static. We propose a framework to jointly
+learn the representation of original features, FV parameters and parameters of
+the classifier in the style of traditional neural networks. Our proof of
+concept implementation improves the performance of FV on the Pascal Voc 2007
+challenge in a multi-GPU setting in comparison to a default SVM setting. We
+demonstrate that FV can be embedded into neural networks at arbitrary
+positions, allowing end-to-end training with back-propagation.
+"
+29472,A Spectral Method for Activity Shaping in Continuous-Time Information Cascades,"  Information Cascades Model captures dynamical properties of user activity in
+a social network. In this work, we develop a novel framework for activity
+shaping under the Continuous-Time Information Cascades Model which allows the
+administrator for local control actions by allocating targeted resources that
+can alter the spread of the process. Our framework employs the optimization of
+the spectral radius of the Hazard matrix, a quantity that has been shown to
+drive the maximum influence in a network, while enjoying a simple convex
+relaxation when used to minimize the influence of the cascade. In addition,
+use-cases such as quarantine and node immunization are discussed to highlight
+the generality of the proposed activity shaping framework. Finally, we present
+the NetShape influence minimization method which is compared favorably to
+baseline and state-of-the-art approaches through simulations on real social
+networks.
+"
+29473,Gorenstein homological invariant properties under Frobenius extensions,"  We prove that for a Frobenius extension, a module over the extension ring is
+Gorenstein projective if and only if its underlying module over the base ring
+is Gorenstein projective. For a separable Frobenius extension between Artin
+algebras, we obtain that the extension algebra is CM-finite (resp. CM-free) if
+and only if so is the base algebra. Furthermore, we prove that the
+reprensentation dimension of Artin algebras is invariant under separable
+Forbenius extensions.
+"
+29474,On the Virtues of Automated QSAR The New Kid on the Block,"  Quantitative Structure-Activity Relationship (QSAR) has proved an invaluable
+tool in medicinal chemistry. Data availability at unprecedented levels through
+various databases have collaborated to a resurgence in the interest for QSAR.
+In this context, rapid generation of quality predictive models is highly
+desirable for hit identification and lead optimization. We showcase the
+application of an automated QSAR approach, which randomly selects multiple
+training/test sets and utilizes machine-learning algorithms to generate
+predictive models. Results demonstrate that AutoQSAR produces models of
+improved or similar quality to those generated by practitioners in the field
+but in just a fraction of the time. Despite the potential of the concept to the
+benefit of the community, the AutoQSAR opportunity has been largely
+undervalued.
+"
+29475,Deep Learning on Small Datasets without Pre-Training using Cosine Loss,"  Two things seem to be indisputable in the contemporary deep learning
+discourse: 1. The categorical cross-entropy loss after softmax activation is
+the method of choice for classification. 2. Training a CNN classifier from
+scratch on small datasets does not work well. In contrast to this, we show that
+the cosine loss function provides significantly better performance than
+cross-entropy on datasets with only a handful of samples per class. For
+example, the accuracy achieved on the CUB-200-2011 dataset without pre-training
+is by 30% higher than with the cross-entropy loss. Further experiments on four
+other popular datasets confirm our findings. Moreover, we show that the
+classification performance can be improved further by integrating prior
+knowledge in the form of class hierarchies, which is straightforward with the
+cosine loss.
+"
+29476,Slicing the Vacuum: New Accelerating Mirror Solutions of the Dynamical Casimir Effect,"  Radiation from accelerating mirrors in a Minkowski spacetime provides
+insights into the nature of horizons, black holes, and entanglement entropy. We
+introduce new, simple, symmetric and analytic moving mirror solutions and study
+their particle, energy, and entropy production. This includes an asymptotically
+static case with finite emission that is the black hole analog of complete
+evaporation. The total energy, total entropy, total particles, and spectrum are
+the same on both sides of the mirror. We also study its asymptotically
+inertial, drifting analog (which gives a black hole remnant) to explore
+differences in finite and infinite production.
+"
+29477,Reduction of manifolds with semi-negative holomorphic sectional curvature,"  In this note, we continue the investigation of a projective Kähler manifold
+$M$ of semi-negative holomorphic sectional curvature $H$. We introduce a new
+differential geometric numerical rank invariant which measures the number of
+linearly independent {\it truly flat} directions of $H$ in the tangent spaces.
+We prove that this invariant is bounded above by the nef dimension and bounded
+below by the numerical Kodaira dimension of $M$. We also prove a splitting
+theorem for $M$ in terms of the nef dimension and, under some additional
+hypotheses, in terms of the new rank invariant.
+"
+29478,Influence of small dispersion on self-focusing in spatially one-dimensional case,"  The effect of the small dispersion on the self-focusing of solutions of the
+equations of nonlinear geometric optics in one-dimensional case is
+investigated. In the main order this influence is described by means of the
+universal special solution of the nonlinear Schrödinger equation, which is
+isomonodromic. Analytic and asymptotic properties of this solution are
+described.
+"
+29479,Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization,"  Policy search reinforcement learning allows robots to acquire skills by
+themselves. However, the learning procedure is inherently unsafe as the robot
+has no a-priori way to predict the consequences of the exploratory actions it
+takes. Therefore, exploration can lead to collisions with the potential to harm
+the robot and/or the environment. In this work we address the safety aspect by
+constraining the exploration to happen in safe-to-explore state spaces. These
+are formed by decomposing target skills (e.g., grasping) into higher ranked
+sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked
+movement tasks (e.g., reaching). Sub-tasks are defined as concurrent
+controllers (policies) in different operational spaces together with associated
+Jacobians representing their joint-space mapping. Safety is ensured by only
+learning policies corresponding to lower ranked sub-tasks in the redundant null
+space of higher ranked ones. As a side benefit, learning in sub-manifolds of
+the state-space also facilitates sample efficiency. Reaching skills performed
+in simulation and grasping skills performed on a real robot validate the
+usefulness of the proposed approach.
+"
+29480,Nonlinear standing waves on planar branched systems: Shrinking into metric graph,"  We treat the stationary nonlinear Schroodinger equation on two-dimensional
+branched domains, so-called fat graphs. The shrinking limit when the domain
+becomes one-dimensional metric graph is studied by using analytical estimate of
+the convergence of fat graph boundary conditions into those for metric graph.
+Detailed analysis of such convergence on the basis of numerical solution of
+stationary nonlinear Schrodinger equation on a fat graph is provided.
+Possibility for reproducing different metric graph boundary conditions studied
+in earlier works is shown. Practical applications of the proposed model for
+such problems as Bose-Einstein condensation in networks, branched optical
+media, DNA, conducting polymers and wave dynamics in branched capillary
+networks are discussed.
+"
+29481,Choosing Between Methods of Combining p-values,"  Combining p-values from independent statistical tests is a popular approach
+to meta-analysis, particularly when the data underlying the tests are either no
+longer available or are difficult to combine. A diverse range of p-value
+combination methods appear in the literature, each with different statistical
+properties. Yet all too often the final choice used in a meta-analysis can
+appear arbitrary, as if all effort has been expended building the models that
+gave rise to the p-values. Birnbaum (1954) showed that any reasonable p-value
+combiner must be optimal against some alternative hypothesis. Starting from
+this perspective and recasting each method of combining p-values as a
+likelihood ratio test, we present theoretical results for some of the standard
+combiners which provide guidance about how a powerful combiner might be chosen
+in practice.
+"
+29482,Matrix supergroup Chern-Simons models for vortex-antivortex systems,"  We study a $U(N|M)$ supermatrix Chern-Simons model with an $SU(p|q)$ internal
+symmetry. We propose that the model describes a system consisting of $N$
+vortices and $M$ antivortices involving $SU(p|q)$ internal spin degrees of
+freedom. We present both classical and quantum ground state solutions, and
+demonstrate the relation to Calogero models. We present evidence that a large
+$N$ limit describes $SU(p|q)$ WZW models. In particular, we derive
+$\widehat{\mathfrak{su}}(p|q)$ Kac-Moody algebras. We also present some results
+on the calculation of the partition function involving a supersymmetric
+generalization of the Hall-Littlewood polynomials, indicating the mock modular
+properties.
+"
+29483,Efficient Electronic Structure Theory via Hierarchical Scale-Adaptive Coupled-Cluster Formalism: I. Theory and Computational Complexity Analysis,"  A novel reduced-scaling, general-order coupled-cluster approach is formulated
+by exploiting hierarchical representations of many-body tensors, combined with
+the recently suggested formalism of scale-adaptive tensor algebra. Inspired by
+the hierarchical techniques from the renormalization group approach,
+H/H2-matrix algebra and fast multipole method, the computational scaling
+reduction in our formalism is achieved via coarsening of quantum many-body
+interactions at larger interaction scales, thus imposing a hierarchical
+structure on many-body tensors of coupled-cluster theory. In our approach, the
+interaction scale can be defined on any appropriate Euclidean domain (spatial
+domain, momentum-space domain, energy domain, etc.). We show that the
+hierarchically resolved many-body tensors reduce the storage requirements to
+O(N), where N is the number of simulated quantum particles. Subsequently, we
+prove that any connected many-body diagram with arbitrary-order tensors, e.g.,
+an arbitrary coupled-cluster diagram, can be evaluated in O(NlogN)
+floating-point operations. On top of that, we elaborate an additional
+approximation to further reduce the computational complexity of higher-order
+coupled-cluster equations, i.e., equations involving higher than double
+excitations, which otherwise would introduce a large prefactor into formal
+O(NlogN) scaling.
+"
+29484,"Proceedings of the First Workshop on Coalgebra, Horn Clause Logic Programming and Types","  The Workshop on Coalgebra, Horn Clause Logic Programming and Types was held
+on the 28-29 November 2016 in Edinburgh. The workshop marked the end of the
+EPSRC Grant Coalgebraic Logic Programming for Type Inference, by E.
+Komendantskaya, Heriot-Watt University and J. Power, University of Bath, UK.
+"
+29485,Enumeration Problems for Regular Path Queries,"  Evaluation of regular path queries (RPQs) is a central problem in graph
+databases. We investigate the corresponding enumeration problem, that is, given
+a graph and an RPQ, enumerate all paths in the graph that match the RPQ. We
+consider several versions of this problem, corresponding to different semantics
+of RPQs that have recently been considered: arbitrary paths, shortest paths,
+simple paths, and trails. Whereas arbitrary and shortest paths can be
+enumerated in polynomial delay, the situation is much more intricate for simple
+paths and trails. For instance, already the question if a given graph contains
+a simple path or trail of a certain length has cases with highly non-trivial
+solutions and cases that are long-standing open problems. In this setting, we
+study RPQ evaluation from a parameterized complexity perspective. We define a
+class of simple transitive expressions that is prominent in practice and for
+which we can prove two dichotomy-like results: one for simple paths and one for
+trails paths. We observe that, even though simple path semantics and trail
+semantics are intractable for RPQs in general, they are feasible for the vast
+majority of the kinds of RPQs that users use in practice. At the heart of this
+study is a result of independent interest on the parameterized complexity of
+finding disjoint paths in graphs: the two disjoint paths problem is W[1]-hard
+if parameterized by the length of one of the two paths.
+"
+29486,Knowledge discovery through text-based similarity searches for astronomy literature,"  The increase in the number of researchers coupled with the ease of publishing
+and distribution of scientific papers (due to technological advancements) has
+resulted in a dramatic increase in astronomy literature. This has likely led to
+the predicament that the body of the literature is too large for traditional
+human consumption and that related and crucial knowledge is not discovered by
+researchers. In addition to the increased production of astronomical
+literature, recent decades have also brought several advancements in
+computational linguistics. Especially, the machine-aided processing of
+literature dissemination might make it possible to convert this stream of
+papers into a coherent knowledge set. In this paper, we present the application
+of computational linguistics techniques to astronomy literature. In particular,
+we developed a tool that will find similar articles purely based on text
+content from an input paper. We find that our technique performs robustly in
+comparison with other tools recommending articles given a reference paper
+(known as recommender system). Our novel tool shows the great power in
+combining computational linguistics with astronomy literature and suggests that
+additional research in this endeavor will likely produce even better tools that
+will help researchers cope with the vast amounts of knowledge being produced.
+"
+29487,Tuning a 3D Microwave Cavity via Superfluid Helium at MilliKelvin Temperatures,"  Frequency tunability of 3D microwave cavities opens up numerous possibilities
+for their use in hybrid quantum systems and related technologies. For many
+applications it is desirable to tune the resonance at cryogenic temperatures
+without mechanical actuation. We show that a superconducting 3D microwave
+cavity can be tuned at the percent level by taking advantage of the dielectric
+properties of superfluid $^4$He at milliKelvin temperatures, without affecting
+its intrinsic quality factor -- reaching $3\times10^5$ in the present
+experiment.
+"
+29488,Error bounds for the large-argument asymptotic expansions of the Lommel and allied functions,"  In this paper, we reconsider the large-$z$ asymptotic expansion of the Lommel
+function $S_{\mu,\nu}(z)$ and its derivative. New representations for the
+remainder terms of the asymptotic expansions are found and used to obtain sharp
+and realistic error bounds. We also give re-expansions for these remainder
+terms and provide their error estimates. Applications to the asymptotic
+expansions of the Anger--Weber-type functions, the Scorer functions, the Struve
+functions and their derivatives are provided. The sharpness of our error bounds
+is discussed in detail, and numerical examples are given.
+"
+29489,Limitations in Predicting the Space Radiation Health Risk for Exploration Astronauts,"  Despite years of research, understanding of the space radiation environment
+and the risk it poses to long-duration astronauts remains limited. There is a
+disparity between research results and observed empirical effects seen in human
+astronaut crews, likely due to the numerous factors that limit terrestrial
+simulation of the complex space environment and extrapolation of human clinical
+consequences from varied animal models. Given the intended future of human
+spaceflight, with efforts now to rapidly expand capabilities for human missions
+to the moon and Mars, there is a pressing need to improve upon the
+understanding of the space radiation risk, predict likely clinical outcomes of
+interplanetary radiation exposure, and develop appropriate and effective
+mitigation strategies for future missions. To achieve this goal, the space
+radiation and aerospace community must recognize the historical limitations of
+radiation research and how such limitations could be addressed in future
+research endeavors. We have sought to highlight the numerous factors that limit
+understanding of the risk of space radiation for human crews and to identify
+ways in which these limitations could be addressed for improved understanding
+and appropriate risk posture regarding future human spaceflight.
+"
+29490,A multiplier theorem for sub-Laplacians with drift on Lie groups,"  We prove a general multiplier theorem for symmetric left-invariant
+sub-Laplacians with drift on non-compact Lie groups. This considerably improves
+and extends a result by Hebisch, Mauceri, and Meda. Applications include groups
+of polynomial growth and solvable extensions of stratified groups.
+"
+29491,#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate,"  Serious concerns have been raised about the role of 'socialbots' in
+manipulating public opinion and influencing the outcome of elections by
+retweeting partisan content to increase its reach. Here we analyze the role and
+influence of socialbots on Twitter by determining how they contribute to
+retweet diffusions. We collect a large dataset of tweets during the 1st U.S.
+Presidential Debate in 2016 (#DebateNight) and we analyze its 1.5 million users
+from three perspectives: user influence, political behavior (partisanship and
+engagement) and botness. First, we define a measure of user influence based on
+the user's active contributions to information diffusions, i.e. their tweets
+and retweets. Given that Twitter does not expose the retweet structure - it
+associates all retweets with the original tweet - we model the latent diffusion
+structure using only tweet time and user features, and we implement a scalable
+novel approach to estimate influence over all possible unfoldings. Next, we use
+partisan hashtag analysis to quantify user political polarization and
+engagement. Finally, we use the BotOrNot API to measure user botness (the
+likelihood of being a bot). We build a two-dimensional ""polarization map"" that
+allows for a nuanced analysis of the interplay between botness, partisanship
+and influence. We find that not only social bots are more active on Twitter -
+starting more retweet cascades and retweeting more -- but they are 2.5 times
+more influential than humans, and more politically engaged. Moreover,
+pro-Republican bots are both more influential and more politically engaged than
+their pro-Democrat counterparts. However we caution against blanket statements
+that software designed to appear human dominates political debates. We find
+that many highly influential Twitter users are in fact pro-Democrat and that
+most pro-Republican users are mid-influential and likely to be human (low
+botness).
+"
+29492,Pod-Galerkin Reduced Order Methods for CFD Using Finite Volume Discretisation: Vortex Shedding Around a Circular Cylinder,"  Vortex shedding around circular cylinders is a well known and studied
+phenomenon that appears in many engineering fields. A Reduced Order Model (ROM)
+of the incompressible flow around a circular cylinder is presented in this
+work. The ROM is built performing a Galerkin projection of the governing
+equations onto a lower dimensional space. The reduced basis space is generated
+using a Proper Orthogonal Decomposition (POD) approach. In particular the focus
+is into (i) the correct reproduction of the pressure field, that in case of the
+vortex shedding phenomenon, is of primary importance for the calculation of the
+drag and lift coefficients; (ii) the projection of the Governing equations
+(momentum equation and Poisson equation for pressure) performed onto different
+reduced basis space for velocity and pressure, respectively; (iii) all the
+relevant modifications necessary to adapt standard finite element POD-Galerkin
+methods to a finite volume framework. The accuracy of the reduced order model
+is assessed against full order results.
+"
+29493,"One random jump and one permutation: sufficient conditions to chaotic, statistically faultless, and large throughput PRNG for FPGA","  Sub-categories of mathematical topology, like the mathematical theory of
+chaos, offer interesting applications devoted to information security. In this
+research work, we have introduced a new chaos-based pseudorandom number
+generator implemented in FPGA, which is mainly based on the deletion of a
+Hamilton cycle within the $n$-cube (or on the vectorial negation), plus one
+single permutation. By doing so, we produce a kind of post-treatment on
+hardware pseudorandom generators, but the obtained generator has usually a
+better statistical profile than its input, while running at a similar speed. We
+tested 6 combinations of Boolean functions and strategies that all achieve to
+pass the most stringent TestU01 battery of tests. This generation can reach a
+throughput/latency ratio equal to 6.7 Gbps, being thus the second fastest FPGA
+generator that can pass TestU01.
+"
+29494,Lagrangian Reachabililty,"  We introduce LRT, a new Lagrangian-based ReachTube computation algorithm that
+conservatively approximates the set of reachable states of a nonlinear
+dynamical system. LRT makes use of the Cauchy-Green stretching factor (SF),
+which is derived from an over-approximation of the gradient of the solution
+flows. The SF measures the discrepancy between two states propagated by the
+system solution from two initial states lying in a well-defined region, thereby
+allowing LRT to compute a reachtube with a ball-overestimate in a metric where
+the computed enclosure is as tight as possible. To evaluate its performance, we
+implemented a prototype of LRT in C++/Matlab, and ran it on a set of
+well-established benchmarks. Our results show that LRT compares very favorably
+with respect to the CAPD and Flow* tools.
+"
+29495,Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing,"  This paper will explore the use of autoencoders for semantic hashing in the
+context of Information Retrieval. This paper will summarize how to efficiently
+train an autoencoder in order to create meaningful and low-dimensional
+encodings of data. This paper will demonstrate how computing and storing the
+closest encodings to an input query can help speed up search time and improve
+the quality of our search results. The novel contributions of this paper
+involve using the representation of the data learned by an auto-encoder in
+order to augment our search query in various ways. I present and evaluate the
+new gradient search augmentation (GSA) approach, as well as the more well-known
+pseudo-relevance-feedback (PRF) adjustment. I find that GSA helps to improve
+the performance of the TF-IDF based information retrieval system, and PRF
+combined with GSA works best overall for the systems compared in this paper.
+"
+29496,A statistical analysis of particle trajectories in living cells,"  Recent advances in molecular biology and fluorescence microscopy imaging have
+made possible the inference of the dynamics of single molecules in living
+cells. Such inference allows to determine the organization and function of the
+cell. The trajectories of particles in the cells, computed with tracking
+algorithms, can be modelled with diffusion processes. Three types of diffusion
+are considered : (i) free diffusion; (ii) subdiffusion or (iii) superdiffusion.
+The Mean Square Displacement (MSD) is generally used to determine the different
+types of dynamics of the particles in living cells (Qian, Sheetz and Elson
+1991). We propose here a non-parametric three-decision test as an alternative
+to the MSD method. The rejection of the null hypothesis -- free diffusion -- is
+accompanied by claims of the direction of the alternative (subdiffusion or a
+superdiffusion). We study the asymptotic behaviour of the test statistic under
+the null hypothesis, and under parametric alternatives which are currently
+considered in the biophysics literature, (Monnier et al,2012) for example. In
+addition, we adapt the procedure of Benjamini and Hochberg (2000) to fit with
+the three-decision test setting, in order to apply the test procedure to a
+collection of independent trajectories. The performance of our procedure is
+much better than the MSD method as confirmed by Monte Carlo experiments. The
+method is demonstrated on real data sets corresponding to protein dynamics
+observed in fluorescence microscopy.
+"
+29497,The Vehicle Routing Problem with Service Level Constraints,"  We consider a vehicle routing problem which seeks to minimize cost subject to
+service level constraints on several groups of deliveries. This problem
+captures some essential challenges faced by a logistics provider which operates
+transportation services for a limited number of partners and should respect
+contractual obligations on service levels. The problem also generalizes several
+important classes of vehicle routing problems with profits. To solve it, we
+propose a compact mathematical formulation, a branch-and-price algorithm, and a
+hybrid genetic algorithm with population management, which relies on
+problem-tailored solution representation, crossover and local search operators,
+as well as an adaptive penalization mechanism establishing a good balance
+between service levels and costs. Our computational experiments show that the
+proposed heuristic returns very high-quality solutions for this difficult
+problem, matches all optimal solutions found for small and medium-scale
+benchmark instances, and improves upon existing algorithms for two important
+special cases: the vehicle routing problem with private fleet and common
+carrier, and the capacitated profitable tour problem. The branch-and-price
+algorithm also produces new optimal solutions for all three problems.
+"
+29498,The ALAMO approach to machine learning,"  ALAMO is a computational methodology for leaning algebraic functions from
+data. Given a data set, the approach begins by building a low-complexity,
+linear model composed of explicit non-linear transformations of the independent
+variables. Linear combinations of these non-linear transformations allow a
+linear model to better approximate complex behavior observed in real processes.
+The model is refined, as additional data are obtained in an adaptive fashion
+through error maximization sampling using derivative-free optimization. Models
+built using ALAMO can enforce constraints on the response variables to
+incorporate first-principles knowledge. The ability of ALAMO to generate simple
+and accurate models for a number of reaction problems is demonstrated. The
+error maximization sampling is compared with Latin hypercube designs to
+demonstrate its sampling efficiency. ALAMO's constrained regression methodology
+is used to further refine concentration models, resulting in models that
+perform better on validation data and satisfy upper and lower bounds placed on
+model outputs.
+"
+29499,GLASS: A General Likelihood Approximate Solution Scheme,"  We present a technique for constructing suitable posterior probability
+distributions in situations for which the sampling distribution of the data is
+not known. This is very useful for modern scientific data analysis in the era
+of ""big data"", for which exact likelihoods are commonly either unknown,
+computationally prohibitively expensive or inapplicable because of systematic
+effects in the data. The scheme involves implicitly computing the changes in an
+approximate sampling distribution as model parameters are changed via
+explicitly-computed moments of statistics constructed from the data.
+"
+29500,Loosely-Coupled Semi-Direct Monocular SLAM,"  We propose a novel semi-direct approach for monocular simultaneous
+localization and mapping (SLAM) that combines the complementary strengths of
+direct and feature-based methods. The proposed pipeline loosely couples direct
+odometry and feature-based SLAM to perform three levels of parallel
+optimizations: (1) photometric bundle adjustment (BA) that jointly optimizes
+the local structure and motion, (2) geometric BA that refines keyframe poses
+and associated feature map points, and (3) pose graph optimization to achieve
+global map consistency in the presence of loop closures. This is achieved in
+real-time by limiting the feature-based operations to marginalized keyframes
+from the direct odometry module. Exhaustive evaluation on two benchmark
+datasets demonstrates that our system outperforms the state-of-the-art
+monocular odometry and SLAM systems in terms of overall accuracy and
+robustness.
+"
+29501,Superion: Grammar-Aware Greybox Fuzzing,"  In recent years, coverage-based greybox fuzzing has proven itself to be one
+of the most effective techniques for finding security bugs in practice.
+Particularly, American Fuzzy Lop (AFL for short) is deemed to be a great
+success in fuzzing relatively simple test inputs. Unfortunately, when it meets
+structured test inputs such as XML and JavaScript, those grammar-blind trimming
+and mutation strategies in AFL hinder the effectiveness and efficiency.
+To this end, we propose a grammar-aware coverage-based greybox fuzzing
+approach to fuzz programs that process structured inputs. Given the grammar
+(which is often publicly available) of test inputs, we introduce a
+grammar-aware trimming strategy to trim test inputs at the tree level using the
+abstract syntax trees (ASTs) of parsed test inputs. Further, we introduce two
+grammar-aware mutation strategies (i.e., enhanced dictionary-based mutation and
+tree-based mutation). Specifically, tree-based mutation works via replacing
+subtrees using the ASTs of parsed test inputs. Equipped with grammar-awareness,
+our approach can carry the fuzzing exploration into width and depth.
+We implemented our approach as an extension to AFL, named Superion; and
+evaluated the effectiveness of Superion on real-life large-scale programs (a
+XML engine libplist and three JavaScript engines WebKit, Jerryscript and
+ChakraCore). Our results have demonstrated that Superion can improve the code
+coverage (i.e., 16.7% and 8.8% in line and function coverage) and bug-finding
+capability (i.e., 31 new bugs, among which we discovered 21 new vulnerabilities
+with 16 CVEs assigned and 3.2K USD bug bounty rewards received) over AFL and
+jsfunfuzz. We also demonstrated the effectiveness of our grammar-aware trimming
+and mutation.
+"
+29502,Electronic and magnetic properties of epitaxial SrRhO3 films,"  Strong interplay of fundamental order parameters in complex oxides are known
+to give rise to exotic physical phenomena. The 4d transition metal oxide SrRhO3
+has generated much interest, but advances have been hindered by difficulties in
+preparing single crystalline phases. Here, we have epitaxially stabilized high
+quality single crystalline SrRhO3 films and investigated their structural,
+electronic, and magnetic properties. We determine that their properties
+significantly differ from the paramagnetic metallic ground state that governs
+bulk samples and are strongly related to rotations of the RhO6 octahedra.
+"
+29503,Global continuation of monotone waves for a unimodal bistable reaction-diffusion equation with delay,"  We study the existence of monotone wavefronts for a general family of
+bistable reaction-diffusion equations with delayed reaction term $g$.
+Differently from previous works, we do not assume the monotonicity of $g(u,v)$
+with respect to the delayed variable $v$ that does not allow to apply the
+comparison techniques. Thus our proof is based on a variant of the Hale-Lin
+functional-analytic approach to heteroclinic solutions of functional
+differential equations where Lyapunov-Schmidt reduction is done in appropriate
+weighted spaces of $C^2$-smooth functions. This method requires a detailed
+analysis of associated linear differential Fredholm operators and their formal
+adjoints. For two different types of $v-$unimodal functions $g(u,v)$, we prove
+the existence of a maximal continuous family of bistable monotone wavefronts..
+Depending on the type of unimodality (equivalently, on the sign of the wave
+speed), two different scenarios can be observed for the bistable waves: 1)
+independently on the size of delay, each bistable wavefront is monotone; 2)
+wavefronts are monotone for moderate values of delays and can oscillate for
+large delays.
+"
+29504,A class of cubic hypersurfaces and quaternionic Kähler manifolds of co-homogeneity one,"  We classify all complete projective special real manifolds with reducible
+cubic potential, obtaining four series. For two of the series the manifolds are
+homogeneous, for the two others the respective automorphism group acts with
+co-homogeneity one. Complete projective special real manifolds give rise to
+complete quaternionic Kähler manifolds via the supergravity q-map, which is
+the composition of the supergravity c-map and r-map. We develop curvature
+formulas for manifolds in the image of the q-map. Applying the q-map to one of
+the above series of projective special real manifolds, we obtain a series of
+complete quaternionic Kähler manifolds, which are shown to be inhomogeneous
+(of co-homogeneity one) based on our curvature formulas.
+"
+29505,Positive solutions of an elliptic Neumann problem with a sublinear indefinite nonlinearity,"  Let $\Omega\subset\mathbb{R}^{N}$ ($N\geq1$) be a bounded and smooth domain
+and $a:\Omega\rightarrow\mathbb{R}$ be a sign-changing weight satisfying
+$\int_{\Omega}a<0$. We prove the existence of a positive solution $u_{q}$ for
+the problem $(P_{a,q})$:
+$-\Delta u=a(x)u^{q}$ in $\Omega$, $\frac{\partial u}{\partial\nu}=0$ on
+$\partial\Omega$,
+if $q_{0}<q<1$, for some $q_{0}=q_{0}(a)>0$. In doing so, we improve the
+existence result previously established in [16]. In addition, we provide the
+asymptotic behavior of $u_{q}$ as $q\rightarrow1^{-}$. When $\Omega$ is a ball
+and $a$ is radial, we give some explicit conditions on $q$ and $a$ ensuring the
+existence of a positive solution of $(P_{a,q})$. We also obtain some properties
+of the set of $q$'s such that $(P_{a,q})$ admits a solution which is positive
+on $\overline{\Omega}$. Finally, we present some results on nonnegative
+solutions having dead cores. Our approach combines bifurcation techniques, a
+priori bounds and the sub-supersolution method. Several methods and results
+apply as well to the Dirichlet counterpart of $(P_{a,q})$.
+"
+29506,Infrared excesses in stars with and without planets using revised ${\it WISE}$ photometry,"  We present an analysis on the potential prevalence of mid infrared excesses
+in stars with and without planetary companions. Based on an extended database
+of stars detected with the ${\it WISE}$ satellite, we studied two stellar
+samples: one with 236 planet hosts and another with 986 objects for which
+planets have been searched but not found. We determined the presence of an
+excess over the photosphere by comparing the observed flux ratio at 22 $\mu$m
+and 12 $\mu$m ($f_{22}/f_{12}$) with the corresponding synthetic value, derived
+from results of classical model photospheres. We found a detection rate of
+0.85$\%$ at 22 $\mu$m (2 excesses) in the sample of stars with planets and
+0.1$\%$ (1 detection) for the stars without planets. The difference of the
+detection rate between the two samples is not statistically significant, a
+result that is independent of the different approaches found in the literature
+to define an excess in the wavelength range covered by ${\it WISE}$
+observations. As an additional result, we found that the ${\it WISE}$ fluxes
+required a normalisation procedure to make them compatible with synthetic data,
+probably pointing out a revision of the ${\it WISE}$ data calibration.
+"
+29507,Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders,"  While recent neural encoder-decoder models have shown great promise in
+modeling open-domain conversations, they often generate dull and generic
+responses. Unlike past work that has focused on diversifying the output of the
+decoder at word-level to alleviate this problem, we present a novel framework
+based on conditional variational autoencoders that captures the discourse-level
+diversity in the encoder. Our model uses latent variables to learn a
+distribution over potential conversational intents and generates diverse
+responses using only greedy decoders. We have further developed a novel variant
+that is integrated with linguistic prior knowledge for better performance.
+Finally, the training procedure is improved by introducing a bag-of-word loss.
+Our proposed models have been validated to generate significantly more diverse
+responses than baseline approaches and exhibit competence in discourse-level
+decision-making.
+"
+29508,Massive Outflows Driven by Magnetic Effects in Star Forming Clouds with High Mass Accretion Rates,"  The relation between the mass accretion rate onto the circumstellar disc and
+the rate of mass ejection by magnetically driven winds is investigated using
+three-dimensional magnetohydrodynamics simulations. Using a spherical cloud
+core with a varying ratio of thermal to gravitational energy, which determines
+the mass accretion rate onto the disc, to define the initial conditions, the
+outflow propagation for approximately 10^4 yr after protostar formation is then
+calculated for several cloud cores. The mass ejection rate and accretion rate
+are comparable only when the magnetic energy of the initial cloud core is
+comparable to the gravitational energy. Consequently, in strongly magnetised
+clouds a higher mass accretion rate naturally produces both massive protostars
+and massive outflows. The simulated outflow mass, momentum, kinetic energy and
+momentum flux agree well with observations, indicating that massive stars form
+through the same mechanism as low-mass stars but require a significantly strong
+magnetic field to launch massive outflows.
+"
+29509,Action Representations in Robotics: A Taxonomy and Systematic Classification,"  Understanding and defining the meaning of ""action"" is substantial for
+robotics research. This becomes utterly evident when aiming at equipping
+autonomous robots with robust manipulation skills for action execution.
+Unfortunately, to this day we still lack both a clear understanding of the
+concept of an action and a set of established criteria that ultimately
+characterize an action. In this survey we thus first review existing ideas and
+theories on the notion and meaning of action. Subsequently we discuss the role
+of action in robotics and attempt to give a seminal definition of action in
+accordance with its use in robotics research. Given this definition we then
+introduce a taxonomy for categorizing action representations in robotics along
+various dimensions. Finally, we provide a systematic literature survey on
+action representations in robotics where we categorize relevant literature
+along our taxonomy. After discussing the current state of the art we conclude
+with an outlook towards promising research directions.
+"
+29510,Symplectic dynamics of contact isotropic torus complements,"  We determine the homotopy type of isotropic torus complements in closed
+contact manifolds in terms of Reeb dynamics of special contact forms. For that
+we utilize holomorphic curve techniques known from symplectic field theory as
+Gromov-Hofer compactness and localized transversality on non-compact contact
+manifolds.
+"
+29511,Convergence and Divergence of Mersenne Variations of the $3x+1$ Function,"  The Collatz problem is one of many names (the Collatz Problem, the Syracuse
+Problem, the Hailstone Problem, the 3x+1 problem). Most commonly, however, the
+problem goes by either the 3x+1 problem or the Collatz problem. In addition to
+having many names, the Collatz problem has many variations, such as those in
+the form introduced by Jeffrey Lagarias in 1985. This writing discusses several
+variations of the Collatz function which involve the Mersenne numbers.
+Following that, we observe the convergent cycles of these functions which we
+can then relate back to the original Collatz 3x+1 function. Lastly, we give a
+proof of the No Divergent Trajectories Theorem and show why the same cannot be
+shown for similar functions.
+"
+29512,Directed Graph Embeddings,"  Definitions of graph embeddings and graph minors for directed graphs are
+introduced. For the class of 2-terminal directed acyclic graphs (TDAGs) the
+definitions coincide, and the class is closed under both operations. The
+usefulness of the directed embedding operation is demonstrated be
+characterizing all TDAGs with parallel-width at most $k$, which generalizes
+earlier characterizations of series-parallel graphs.
+"
+29513,Brill-Noether theory for curves of a fixed gonality,"  We prove a generalization of the Brill-Noether theorem for the variety of
+special divisors $W^r_d(C)$ on a general curve $C$ of prescribed gonality. Our
+main theorem gives a closed formula for the dimension of $W^r_d(C)$. We build
+on previous work of Pflueger, who used an analysis of the tropical divisor
+theory of special chains of cycles to give upper bounds on the dimensions of
+Brill--Noether varieties on such curves. We prove his conjecture, that this
+upper bound is achieved for a general curve. Our methods introduce logarithmic
+stable maps as a systematic tool in Brill-Noether theory. A precise relation
+between the divisor theory on chains of cycles and the corresponding tropical
+maps theory is exploited to prove new regeneration theorems for linear series
+with negative Brill-Noether number. The strategy involves blending an analysis
+of obstruction theories for logarithmic stable maps with the geometry of
+Berkovich curves. To show the utility of these methods, we provide a short new
+derivation of lifting for special divisors on a chain of cycles with generic
+edge lengths, proved using different techniques by Cartwright, Jensen, and
+Payne. A crucial technical result is a new realizability theorem for tropical
+stable maps in obstructed geometries, generalizing a well-known theorem of
+Speyer on genus one curves to arbitrary genus.
+"
+29514,Kafka versus RabbitMQ,"  Publish/subscribe is a distributed interaction paradigm well adapted to the
+deployment of scalable and loosely coupled systems.
+Apache Kafka and RabbitMQ are two popular open-source and
+commercially-supported pub/sub systems that have been around for almost a
+decade and have seen wide adoption. Given the popularity of these two systems
+and the fact that both are branded as pub/sub systems, two frequently asked
+questions in the relevant online forums are: how do they compare against each
+other and which one to use?
+In this paper, we frame the arguments in a holistic approach by establishing
+a common comparison framework based on the core functionalities of pub/sub
+systems. Using this framework, we then venture into a qualitative and
+quantitative (i.e. empirical) comparison of the common features of the two
+systems. Additionally, we also highlight the distinct features that each of
+these systems has. After enumerating a set of use cases that are best suited
+for RabbitMQ or Kafka, we try to guide the reader through a determination table
+to choose the best architecture given his/her particular set of requirements.
+"
+29515,Carbon nanotubes as target for directional detection of light WIMP,"  In this paper I will briefly introduce the idea of using Carbon Nanotubes
+(CNT) as target for the detection of low mass WIMPs with the additional
+information of directionality. I will also present the experimental efforts of
+developing a Time Projection Chamber with a CNT target inside and the results
+of a test beam at the Beam Test Facility of INFN-LNF.
+"
+29516,Low significance of evidence for black hole echoes in gravitational wave data,"  Recent detections of merging black holes allow observational tests of the
+nature of these objects. In some proposed models, non-trivial structure at or
+near the black hole horizon could lead to echo signals in gravitational wave
+data. Recently, Abedi et al. claimed tentative evidence for repeating damped
+echo signals following the gravitational-wave signals of the binary black hole
+merger events recorded in the first observational period of the Advanced LIGO
+interferometers. We reanalyse the same data, addressing some of the
+shortcomings of their method using more background data and a modified
+procedure. We find a reduced statistical significance for the claims of
+evidence for echoes, calculating increased p-values for the null hypothesis of
+echo-free noise. The reduced significance is entirely consistent with noise,
+and so we conclude that the analysis of Abedi et al. does not provide any
+observational evidence for the existence of Planck-scale structure at black
+hole horizons.
+"
+29517,On the Brain Networks of Complex Problem Solving,"  Complex problem solving is a high level cognitive process which has been
+thoroughly studied over the last decade. The Tower of London (TOL) is a task
+that has been widely used to study problem-solving. In this study, we aim to
+explore the underlying cognitive network dynamics among anatomical regions of
+complex problem solving and its sub-phases, namely planning and execution. A
+new brain network construction model establishing dynamic functional brain
+networks using fMRI is proposed. The first step of the model is a preprocessing
+pipeline that manages to decrease the spatial redundancy while increasing the
+temporal resolution of the fMRI recordings. Then, dynamic brain networks are
+estimated using artificial neural networks. The network properties of the
+estimated brain networks are studied in order to identify regions of interest,
+such as hubs and subgroups of densely connected brain regions. The major
+similarities and dissimilarities of the network structure of planning and
+execution phases are highlighted. Our findings show the hubs and clusters of
+densely interconnected regions during both subtasks. It is observed that there
+are more hubs during the planning phase compared to the execution phase, and
+the clusters are more strongly connected during planning compared to execution.
+"
+29518,Automatic feature learning for vulnerability prediction,"  Code flaws or vulnerabilities are prevalent in software systems and can
+potentially cause a variety of problems including deadlock, information loss,
+or system failure. A variety of approaches have been developed to try and
+detect the most likely locations of such code vulnerabilities in large code
+bases. Most of them rely on manually designing features (e.g. complexity
+metrics or frequencies of code tokens) that represent the characteristics of
+the code. However, all suffer from challenges in sufficiently capturing both
+semantic and syntactic representation of source code, an important capability
+for building accurate prediction models. In this paper, we describe a new
+approach, built upon the powerful deep learning Long Short Term Memory model,
+to automatically learn both semantic and syntactic features in code. Our
+evaluation on 18 Android applications demonstrates that the prediction power
+obtained from our learned features is equal or even superior to what is
+achieved by state of the art vulnerability prediction models: 3%--58%
+improvement for within-project prediction and 85% for cross-project prediction.
+"
+29519,Different types of ion populations upstream of the 8 October 2013 interplanetary shock,"  We show for the first time that different types of suprathermal ion
+distributions may exist upstream of a single interplanetary shock. ACE and the
+two ARTEMIS satellites observed a shock on 8 October 2013. The ARTEMIS P1 and
+P2 spacecraft first observed field-aligned ions (P1) and gyrating ions (P2)
+arriving from the shock. These were followed by intermediate ions and later by
+a diffuse population. At the location of the P2 the shock exhibited an
+Alfvénic Mach number of M$_A$=5.7 and was marginally quasi-perpendicular,
+($\theta_{Bn}$=47$^\circ$). At P1 spacecraft the shock was weaker (M$_A$=4.9)
+and more perpendicular ($\theta_{Bn}$=61$^\circ$). Consequently the observed
+suprathermal ion and ultra low frequency wave properties were somewhat
+different. At P2 the ULF waves are more intense and extend farther upstream
+from the shock. The energies of field aligned and gyrating ions in the shock
+rest frame were $\sim$20~keV, which is much more than in the case of the
+stronger (M$_A$=6-7) Earth's bow-shock, where they are less than 10~keV
+"
+29520,"Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities","  New technologies have enabled the investigation of biology and human health
+at an unprecedented scale and in multiple dimensions. These dimensions include
+a myriad of properties describing genome, epigenome, transcriptome, microbiome,
+phenotype, and lifestyle. No single data type, however, can capture the
+complexity of all the factors relevant to understanding a phenomenon such as a
+disease. Integrative methods that combine data from multiple technologies have
+thus emerged as critical statistical and computational approaches. The key
+challenge in developing such approaches is the identification of effective
+models to provide a comprehensive and relevant systems view. An ideal method
+can answer a biological or medical question, identifying important features and
+predicting outcomes, by harnessing heterogeneous data across several dimensions
+of biological variation. In this Review, we describe the principles of data
+integration and discuss current methods and available implementations. We
+provide examples of successful data integration in biology and medicine.
+Finally, we discuss current challenges in biomedical integrative methods and
+our perspective on the future development of the field.
+"
+29521,Proton tunneling in hydrogen bonds and its implications in an induced-fit model of enzyme catalysis,"  The role of proton tunneling in biological catalysis is investigated here
+within the frameworks of quantum information theory and thermodynamics. We
+consider the quantum correlations generated through two hydrogen bonds between
+a substrate and a prototypical enzyme that first catalyzes the tautomerization
+of the substrate to move on to a subsequent catalysis, and discuss how the
+enzyme can derive its catalytic potency from these correlations. In particular,
+we show that classical changes induced in the binding site of the enzyme
+spreads the quantum correlations among all of the four hydrogen-bonded atoms
+thanks to the directionality of hydrogen bonds. If the enzyme rapidly returns
+to its initial state after the binding stage, the substrate ends in a new
+transition state corresponding to a quantum superposition. Open quantum system
+dynamics can then naturally drive the reaction in the forward direction from
+the major tautomeric form to the minor tautomeric form without needing any
+additional catalytic activity. We find that in this scenario the enzyme lowers
+the activation energy so much that there is no energy barrier left in the
+tautomerization, even if the quantum correlations quickly decay.
+"
+29522,Embedding Watermarks into Deep Neural Networks,"  Deep neural networks have recently achieved significant progress. Sharing
+trained models of these deep neural networks is very important in the rapid
+progress of researching or developing deep neural network systems. At the same
+time, it is necessary to protect the rights of shared trained models. To this
+end, we propose to use a digital watermarking technology to protect
+intellectual property or detect intellectual property infringement of trained
+models. Firstly, we formulate a new problem: embedding watermarks into deep
+neural networks. We also define requirements, embedding situations, and attack
+types for watermarking to deep neural networks. Secondly, we propose a general
+framework to embed a watermark into model parameters using a parameter
+regularizer. Our approach does not hurt the performance of networks into which
+a watermark is embedded. Finally, we perform comprehensive experiments to
+reveal the potential of watermarking to deep neural networks as a basis of this
+new problem. We show that our framework can embed a watermark in the situations
+of training a network from scratch, fine-tuning, and distilling without hurting
+the performance of a deep neural network. The embedded watermark does not
+disappear even after fine-tuning or parameter pruning; the watermark completely
+remains even after removing 65% of parameters were pruned. The implementation
+of this research is: this https URL
+"
+29523,Cuckoo++ Hash Tables: High-Performance Hash Tables for Networking Applications,"  Hash tables are an essential data-structure for numerous networking
+applications (e.g., connection tracking, firewalls, network address
+translators). Among these, cuckoo hash tables provide excellent performance by
+allowing lookups to be processed with very few memory accesses (2 to 3 per
+lookup). Yet, for large tables, cuckoo hash tables remain memory bound and each
+memory access impacts performance. In this paper, we propose algorithmic
+improvements to cuckoo hash tables allowing to eliminate some unnecessary
+memory accesses; these changes are conducted without altering the properties of
+the original cuckoo hash table so that all existing theoretical analysis remain
+applicable. On a single core, our hash table achieves 37M lookups per second
+for positive lookups (i.e., when the key looked up is present in the table),
+and 60M lookups per second for negative lookups, a 50% improvement over the
+implementation included into the DPDK. On a 18-core, with mostly positive
+lookups, our implementation achieves 496M lookups per second, a 45% improvement
+over DPDK.
+"
+29524,From First-Order Logic to Assertional Logic,"  First-Order Logic (FOL) is widely regarded as one of the most important
+foundations for knowledge representation. Nevertheless, in this paper, we argue
+that FOL has several critical issues for this purpose. Instead, we propose an
+alternative called assertional logic, in which all syntactic objects are
+categorized as set theoretic constructs including individuals, concepts and
+operators, and all kinds of knowledge are formalized by equality assertions. We
+first present a primitive form of assertional logic that uses minimal assumed
+knowledge and constructs. Then, we show how to extend it by definitions, which
+are special kinds of knowledge, i.e., assertions. We argue that assertional
+logic, although simpler, is more expressive and extensible than FOL. As a case
+study, we show how assertional logic can be used to unify logic and
+probability, and more building blocks in AI.
+"
+29525,Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models,"  The approaches taken to describe and develop spatial discretisations of the
+domains required for geophysical simulation models are commonly ad hoc, model
+or application specific and under-documented. This is particularly acute for
+simulation models that are flexible in their use of multi-scale, anisotropic,
+fully unstructured meshes where a relatively large number of heterogeneous
+parameters are required to constrain their full description. As a consequence,
+it can be difficult to reproduce simulations, ensure a provenance in model data
+handling and initialisation, and a challenge to conduct model intercomparisons
+rigorously. This paper takes a novel approach to spatial discretisation,
+considering it much like a numerical simulation model problem of its own. It
+introduces a generalised, extensible, self-documenting approach to carefully
+describe, and necessarily fully, the constraints over the heterogeneous
+parameter space that determine how a domain is spatially discretised. This
+additionally provides a method to accurately record these constraints, using
+high-level natural language based abstractions, that enables full accounts of
+provenance, sharing and distribution. Together with this description, a
+generalised consistent approach to unstructured mesh generation for geophysical
+models is developed, that is automated, robust and repeatable, quick-to-draft,
+rigorously verified and consistent to the source data throughout. This
+interprets the description above to execute a self-consistent spatial
+discretisation process, which is automatically validated to expected discrete
+characteristics and metrics.
+"
+29526,On the Bickel-Rosenblatt test of goodness-of-fit for the residuals of autoregressive processes,"  We investigate in this paper a Bickel-Rosenblatt test of goodness-of-fit for
+the density of the noise in an autoregressive model. Since the seminal work of
+Bickel and Rosenblatt, it is well-known that the integrated squared error of
+the Parzen-Rosenblatt density estimator, once correctly renormalized, is
+asymptotically Gaussian for independent and identically distributed (i.i.d.)
+sequences. We show that the result still holds when the statistic is built from
+the residuals of general stable and explosive autoregressive processes. In the
+univariate unstable case, we prove that the result holds when the unit root is
+located at $-1$ whereas we give further results when the unit root is located
+at $1$. In particular, we establish that except for some particular asymmetric
+kernels leading to a non-Gaussian limiting distribution and a slower
+convergence, the statistic has the same order of magnitude. We also study some
+common unstable cases, like the integrated seasonal process. Finally we build a
+goodness-of-fit Bickel-Rosenblatt test for the true density of the noise
+together with its empirical properties on the basis of a simulation study.
+"
+29527,Optimal Alarms for Vehicular Collision Detection,"  An important application of intelligent vehicles is advance detection of
+dangerous events such as collisions. This problem is framed as a problem of
+optimal alarm choice given predictive models for vehicle location and motion.
+Techniques for real-time collision detection are surveyed and grouped into
+three classes: random Monte Carlo sampling, faster deterministic
+approximations, and machine learning models trained by simulation. Theoretical
+guarantees on the performance of these collision detection techniques are
+provided where possible, and empirical analysis is provided for two example
+scenarios. Results validate Monte Carlo sampling as a robust solution despite
+its simplicity.
+"
+29528,Costas cubes,"  A Costas array is a permutation array for which the vectors joining pairs of
+$1$s are all distinct. We propose a new three-dimensional combinatorial object
+related to Costas arrays: an order $n$ Costas cube is an array $(d_{i,j,k})$ of
+size $n \times n \times n$ over $\mathbb{Z}_2$ for which each of the three
+projections of the array onto two dimensions, namely $(\sum_i d_{i,j,k})$ and
+$(\sum_j d_{i,j,k})$ and $(\sum_k d_{i,j,k})$, is an order $n$ Costas array. We
+determine all Costas cubes of order at most $29$, showing that Costas cubes
+exist for all these orders except $18$ and $19$ and that a significant
+proportion of the Costas arrays of certain orders occur as projections of
+Costas cubes. We then present constructions for four infinite families of
+Costas cubes.
+"
+29529,Vertical Thermospheric Density Profiles from EUV Solar Occultations made by PROBA2 LYRA for Solar Cycle 24,"  A new dataset of summed neutral N2 and O number density profiles, spanning
+altitudes between 150 and 400 km, and observed during Northern Winter from 2010
+through 2016 is presented. The neutral density profiles are derived from solar
+occultation measurements made by the 0.1-20 nm Zr channel on the LYRA
+instrument onboard PROBA2. The climatology derived from the vertical profiles
+is consistent with that predicted by the NRLMSISE-00 model, and the systematic
+error and random uncertainty of the measurements is less than 13% and 6%,
+respectively. The density profiles are used to characterize the response of
+thermospheric density to solar EUV irradiance variability. Peak correlation
+coefficients between neutral density and EUV irradiance occur near 300 km at
+the dusk terminator and 220 km at the dawn terminator. The sensitivity of
+thermospheric temperature to EUV irradiance decreases with altitude above 300
+km at both terminators.
+"
+29530,Facing the phase problem in Coherent Diffractive Imaging via Memetic Algorithms,"  Coherent Diffractive Imaging is a lensless technique that allows imaging of
+matter at a spatial resolution not limited by lens aberrations. This technique
+exploits the measured diffraction pattern of a coherent beam scattered by
+periodic and non-periodic objects to retrieve spatial information. The
+diffracted intensity, for weak-scattering objects, is proportional to the
+modulus of the Fourier Transform of the object scattering function. Any phase
+information, needed to retrieve its scattering function, has to be retrieved by
+means of suitable algorithms. Here we present a new approach, based on a
+memetic algorithm, i.e. a hybrid genetic algorithm, to face the phase problem,
+which exploits the synergy of deterministic and stochastic optimization
+methods. The new approach has been tested on simulated data and applied to the
+phasing of transmission electron microscopy coherent electron diffraction data
+of a $\text{SrTiO}_\text{3}$ sample. We have been able to quantitatively
+retrieve the projected atomic potential, and also image the oxygen columns,
+which are not directly visible in the relevant high-resolution transmission
+electron microscopy images. Our approach proves to be a new powerful tool for
+the study of matter at atomic resolution and opens new perspectives in those
+applications in which effective phase retrieval is necessary.
+"
+29531,Network-ensemble comparisons with stochastic rewiring and von Neumann entropy,"  Assessing whether a given network is typical or atypical for a random-network
+ensemble (i.e., network-ensemble comparison) has widespread applications
+ranging from null-model selection and hypothesis testing to clustering and
+classifying networks. We develop a framework for network-ensemble comparison by
+subjecting the network to stochastic rewiring. We study two rewiring processes,
+uniform and degree-preserved rewiring, which yield random-network ensembles
+that converge to the Erdos-Renyi and configuration-model ensembles,
+respectively. We study convergence through von Neumann entropy (VNE), a network
+summary statistic measuring information content based on the spectra of a
+Laplacian matrix, and develop a perturbation analysis for the expected effect
+of rewiring on VNE. Our analysis yields an estimate for how many rewires are
+required for a given network to resemble a typical network from an ensemble,
+offering a computationally efficient quantity for network-ensemble comparison
+that does not require simulation of the corresponding rewiring process.
+"
+29532,Basic mechanisms in the laser control of non-Markovian dynamics,"  Referring to a Fano-type model qualitative analogy we develop a comprehensive
+basic mechanism for the laser control of the non-Markovian bath response in
+strongly coupled Open Quantum Systems (OQS). A converged Hierarchy Equations Of
+Motion (HEOM) is worked out to numerically solve the master equation of a
+spin-boson Hamiltonian to reach the reduced electronic density matrix of a
+heterojunction in the presence of strong THz laser pulses. Robust and efficient
+control is achieved increasing by a factor ?2 non-Markovianity measured by the
+time evolution of the volume of accessible states. The consequences of such
+fields on the central system populations and coherence are examined, putting
+the emphasis on the relation between the increase of non- Markovianity and the
+slowing down of decoherence processes.
+"
+29533,Continuous Semantic Topic Embedding Model Using Variational Autoencoder,"  This paper proposes the continuous semantic topic embedding model (CSTEM)
+which finds latent topic variables in documents using continuous semantic
+distance function between the topics and the words by means of the variational
+autoencoder(VAE). The semantic distance could be represented by any symmetric
+bell-shaped geometric distance function on the Euclidean space, for which the
+Mahalanobis distance is used in this paper. In order for the semantic distance
+to perform more properly, we newly introduce an additional model parameter for
+each word to take out the global factor from this distance indicating how
+likely it occurs regardless of its topic. It certainly improves the problem
+that the Gaussian distribution which is used in previous topic model with
+continuous word embedding could not explain the semantic relation correctly and
+helps to obtain the higher topic coherence. Through the experiments with the
+dataset of 20 Newsgroup, NIPS papers and CNN/Dailymail corpus, the performance
+of the recent state-of-the-art models is accomplished by our model as well as
+generating topic embedding vectors which makes possible to observe where the
+topic vectors are embedded with the word vectors in the real Euclidean space
+and how the topics are related each other semantically.
+"
+29534,On-Chart Success Dynamics of Popular Songs,"  In the modern era where highly-commodified cultural products compete heavily
+for mass consumption, finding the principles behind the complex process of how
+successful, ""hit"" products emerge remains a vital scientific goal that requires
+an interdisciplinary approach. Here we present a framework for tracing the
+cycle of prosperity-and-decline of a product to find insights into influential
+and potent factors that determine its success. As a rapid, high-throughput
+indicator of the preference of the public, popularity charts have emerged as a
+useful information source for finding the market performance patterns of
+products over time, which we call the on-chart life trajectories that show how
+the products enter the chart, fare inside it, and eventually exit from it. We
+propose quantitative parameters to characterise a life trajectory, and analyse
+a large-scale data set of nearly $7\,000$ songs from Gaon Chart, a major weekly
+Korean Pop (K-Pop) chart that cover a span of six years. We find that a
+significant role is played by non-musical extrinsic factors such as the
+established fan base of the artist and the might of production companies in the
+on-chart success of songs, strongly indicative of the commodified nature of
+modern cultural products. We also review a possible mathematical model of this
+phenomenon, and discuss several nontrivial yet intriguing trajectories that we
+call the ""Late Bloomers"" and the ""Re-entrants"" that appears to be strongly
+driven by serendipitous exposure on mass media and the changes of seasons.
+"
+29535,"Precision Theoretical Analysis of Neutron Radiative Beta Decay to Order ""O(α^2/π^2)""","  In the Standard Model (SM) we calculate the decay rate of the neutron
+radiative beta decay to order ""O(\alpha^2/\pi^2 ~ 10^{-5})"", where ""\alpha$""is
+the fine--structure constant, and radiative corrections to order ""O(\alpha/\pi
+~ 10^{-3})"". The obtained results together with the recent analysis of the
+neutron radiative beta decay to next-to-leading order in the large proton-mass
+expansion, performed by Ivanov et al. Phys. Rev. D95, 033007 (2017), describe
+recent experimental data by the RDK II Collaboration (Bales et al., Phys. Rev.
+Lett. 116, 242501 (2016)) within 1.5 standard deviations. We argue a
+substantial influence of strong low-energy interactions of hadrons coupled to
+photons on the properties of the amplitude of the neutron radiative beta decay
+under gauge transformations of real and virtual photons.
+"
+29536,Two-Scalar Turbulent Rayleigh-Benard Convection: Numerical Simulations and Unifying Theory,"  We conduct direct numerical simulations for turbulent Rayleigh-Bénard
+(RB) convection, driven simultaneously by two scalar components (say,
+temperature and salt concentration) with different molecular diffusivities, and
+measure the respective fluxes and the Reynolds number. To account for the
+results, we generalize the Grossmann-Lohse theory for traditional RB
+convections~(Grossmann and Lohse, J. Fluid Mech., 407, 27-56; Phys. Rev. Lett.,
+86, 3316-3319; Stevens et al., J. Fluid Mech., 730, 295-308) to this two-scalar
+turbulent convection. Our numerical results suggest that the generalized theory
+can successfully predict the overall trends for the fluxes of two scalars and
+the Reynolds number. In fact, for most of the parameters explored here, the
+theory can even predict the absolute values of the fluxes and the Reynolds
+number with good accuracy. The current study extends the generality of the
+Grossmann-Lohse theory in the area of the buoyancy-driven convection flows.
+"
+29537,Subspace-Orbit Randomized Decomposition for Low-rank Matrix Approximation,"  An efficient, accurate and reliable approximation of a matrix by one of lower
+rank is a fundamental task in numerical linear algebra and signal processing
+applications. In this paper, we introduce a new matrix decomposition approach
+termed Subspace-Orbit Randomized singular value decomposition (SOR-SVD), which
+makes use of random sampling techniques to give an approximation to a low-rank
+matrix. Given a large and dense data matrix of size $m\times n$ with numerical
+rank $k$, where $k \ll \text{min} \{m,n\}$, the algorithm requires a few passes
+through data, and can be computed in $O(mnk)$ floating-point operations.
+Moreover, the SOR-SVD algorithm can utilize advanced computer architectures,
+and, as a result, it can be optimized for maximum efficiency. The SOR-SVD
+algorithm is simple, accurate, and provably correct, and outperforms previously
+reported techniques in terms of accuracy and efficiency. Our numerical
+experiments support these claims.
+"
+29538,Robust Wald-type tests for non-homogeneous observations based on minimum density power divergence estimator,"  This paper considers the problem of robust hypothesis testing under
+non-identically distributed data. We propose Wald-type tests for both simple
+and composite hypothesis for independent but non-homogeneous observations based
+on the robust minimum density power divergence estimator of the common
+underlying parameter. Asymptotic and theoretical robustness properties of the
+proposed tests have been discussed. Application to the problem of testing the
+general linear hypothesis in a generalized linear model with fixed-design has
+been considered in detail with specific illustrations for its special cases
+under normal and Poisson distributions.
+"
+29539,A Successive-Elimination Approach to Adaptive Robotic Sensing,"  We study the adaptive sensing problem for the multiple source seeking
+problem, where a mobile robot must identify the strongest emitters in an
+environment with background emissions. Background signals may be highly
+heterogeneous, and can mislead algorithms which are based on receding horizon
+control, greedy heuristics, or smooth background priors. We propose AdaSearch,
+a general algorithm for adaptive sensing. AdaSearch combines global trajectory
+planning with principled confidence intervals in order to concentrate
+measurements in promising regions while still guaranteeing sufficient coverage
+of the entire area. Theoretical analysis shows that AdaSearch significantly
+outperforms a uniform sampling strategy when the distribution of background
+signals is highly variable. Simulation studies demonstrate that when applied to
+the problem of radioactive source-seeking, AdaSearch outperforms both uniform
+sampling and a receding time horizon information-maximization approach based on
+the current literature. We corroborate these findings with a hardware
+demonstration, using a small quadrotor helicopter in a motion-capture arena.
+"
+29540,Manipulating magnetoelectric effect -- Essence learned from Co$_4$Nb$_2$O$_9$,"  Recent experiments for linear magnetoelectric (ME) response in honeycomb
+antiferromagnet Co$_4$Nb$_2$O$_9$ revealed that the electric polarization can
+be manipulated by the in-plane rotating magnetic field in a systematic way. We
+propose the minimal model by extracting essential ingredients of
+Co$_4$Nb$_2$O$_9$ to exhibit such ME response. It is the three-orbital model
+with $xy$-type atomic spin-orbit coupling (SOC) on the single-layer honeycomb
+structure, and it is shown to reproduce qualitatively the observed field-angle
+dependence of the electric polarization. The obtained results can be understood
+by the perturbative calculation with respect to the atomic SOC. These findings
+could be useful to explore further ME materials having similar manipulability
+of the electric polarization.
+"
+29541,Benchmark Graphs for Practical Graph Isomorphism,"  The state-of-the-art solvers for the graph isomorphism problem can readily
+solve generic instances with tens of thousands of vertices. Indeed, experiments
+show that on inputs without particular combinatorial structure the algorithms
+scale almost linearly. In fact, it is non-trivial to create challenging
+instances for such solvers and the number of difficult benchmark graphs
+available is quite limited. We describe a construction to efficiently generate
+small instances for the graph isomorphism problem that are difficult or even
+infeasible for said solvers. Up to this point the only other available
+instances posing challenges for isomorphism solvers were certain incidence
+structures of combinatorial objects (such as projective planes, Hadamard
+matrices, Latin squares, etc.). Experiments show that starting from 1500
+vertices our new instances are several orders of magnitude more difficult on
+comparable input sizes. More importantly, our method is generic and efficient
+in the sense that one can quickly create many isomorphism instances on a
+desired number of vertices. In contrast to this, said combinatorial objects are
+rare and difficult to generate and with the new construction it is possible to
+generate an abundance of instances of arbitrary size. Our construction hinges
+on the multipedes of Gurevich and Shelah and the Cai-Fürer-Immerman gadgets
+that realize a certain abelian automorphism group and have repeatedly played a
+role in the context of graph isomorphism. Exploring limits of such
+constructions, we also explain that there are group theoretic obstructions to
+generalizing the construction with non-abelian gadgets.
+"
+29542,Deep Haptic Model Predictive Control for Robot-Assisted Dressing,"  Robot-assisted dressing offers an opportunity to benefit the lives of many
+people with disabilities, such as some older adults. However, robots currently
+lack common sense about the physical implications of their actions on people.
+The physical implications of dressing are complicated by non-rigid garments,
+which can result in a robot indirectly applying high forces to a person's body.
+We present a deep recurrent model that, when given a proposed action by the
+robot, predicts the forces a garment will apply to a person's body. We also
+show that a robot can provide better dressing assistance by using this model
+with model predictive control. The predictions made by our model only use
+haptic and kinematic observations from the robot's end effector, which are
+readily attainable. Collecting training data from real world physical
+human-robot interaction can be time consuming, costly, and put people at risk.
+Instead, we train our predictive model using data collected in an entirely
+self-supervised fashion from a physics-based simulation. We evaluated our
+approach with a PR2 robot that attempted to pull a hospital gown onto the arms
+of 10 human participants. With a 0.2s prediction horizon, our controller
+succeeded at high rates and lowered applied force while navigating the garment
+around a persons fist and elbow without getting caught. Shorter prediction
+horizons resulted in significantly reduced performance with the sleeve catching
+on the participants' fists and elbows, demonstrating the value of our model's
+predictions. These behaviors of mitigating catches emerged from our deep
+predictive model and the controller objective function, which primarily
+penalizes high forces.
+"
+29543,Elliptic Quadratic Operator Equations,"  In the present paper is devoted to the study of elliptic quadratic operator
+equations over the finite dimensional Euclidean space. We provide necessary and
+sufficient conditions for the existence of solutions of elliptic quadratic
+operator equations. The iterative Newton-Kantorovich method is also presented
+for stable solutions.
+"
+29544,The geomagnetic/magneto-telluric induction problem with spatial conductivity fluctuations,"  The electromagnetic induction equation (Helmholtz equation) for the
+electrically conducting Earth is generalised to the inclusion of a spatially
+fluctuating internal conductivity spectrum that is superimposed on a
+one-dimensional large-scale conductivity reference-profile which depends solely
+on the vertical coordinate z>0. This large-scale profile is assumed to be
+known. The distribution of the fluctuations is allowed to be arbitrary. The
+conductivity-fluctuation generalised Helmholtz equation depends on the spectral
+density of the fluctuations as function of the vertical and horizontal
+coordinates. Application to the inverse geomagnetic/magneto-telluric problem is
+discussed. We show analytically that the inverse problem yields the
+conductivity fluctuation spectrum. The relation between the spectral density
+and the obtained conductivity profile is given.
+"
+29545,Fast and Accurate Inference with Adaptive Ensemble Prediction for Deep Neural Networks,"  Ensembling multiple predictions is a widely-used technique to improve the
+accuracy of various machine learning tasks. One obvious drawback of the
+ensembling is its higher execution cost during inference. In this paper, we
+first describe our insights on relationship between the probability of the
+prediction and the effect of ensembling with current deep neural networks;
+ensembling does not help mispredictions for inputs predicted with a high
+probability even when there is a non-negligible number of mispredicted inputs.
+This finding motivates us to develop a new technique called adaptive ensemble
+prediction, which achieves the benefits of ensembling with much smaller
+additional execution costs. If the prediction for an input reaches a high
+enough probability on the basis of the confidence level, we stop ensembling for
+this input to avoid wasting computation power. We evaluated the adaptive
+ensembling by using various datasets and showed that it reduces the computation
+cost significantly while achieving similar accuracy to the naive ensembling. We
+also showed that our statistically rigorous confidence-level-based termination
+condition reduces the burden of the task-dependent parameter tuning compared to
+the naive termination based on the pre-defined threshold in addition to
+yielding a better accuracy with the same cost.
+"
+29546,Incompressible solvable representations of surface groups,"  The fundamental group of every surface that is not the projective plane or
+Klein bottle has a representation to a torsion-free group of upper-triangular
+matrices in SL(2,R) with no simple loop (i.e. a nontrivial element representing
+a simple closed curve) in the kernel.
+"
+29547,Binary sampling ghost imaging: add random noise to fight quantization caused image quality decline,"  When the sampling data of ghost imaging is recorded with less bits, i.e.,
+experiencing quantization, decline of image quality is observed. The less bits
+used, the worse image one gets. Dithering, which adds suitable random noise to
+the raw data before quantization, is proved to be capable of compensating image
+quality decline effectively, even for the extreme binary sampling case. A brief
+explanation and parameter optimization of dithering are given.
+"
+29548,Lipschitz linearization of the maximal hyperbolic cross multiplier,"  We study the linearized maximal operator associated with dilates of the
+hyperbolic cross multiplier in dimension two. Assuming a Lipschitz condition
+and a lower bound on the linearizing function, we obtain $L^{p}(\mathbb{R}^{2})
+\to L^{p}(\mathbb{R}^{2})$ bounds for all $1<p <\infty$. We discuss various
+related results.
+"
+29549,Online Clustering of Contextual Cascading Bandits,"  We consider a new setting of online clustering of contextual cascading
+bandits, an online learning problem where the underlying cluster structure over
+users is unknown and needs to be learned from a random prefix feedback. More
+precisely, a learning agent recommends an ordered list of items to a user, who
+checks the list and stops at the first satisfactory item, if any. We propose an
+algorithm of CLUB-cascade for this setting and prove a $T$-step regret bound of
+order $\tilde{O}(\sqrt{T})$. Previous work corresponds to the degenerate case
+of only one cluster, and our general regret bound in this special case also
+significantly improves theirs. We conduct experiments on both synthetic and
+real data, and demonstrate the effectiveness of our algorithm and the advantage
+of incorporating online clustering method.
+"
+29550,Maximal rank of space curves in the range A,"  We prove the following statement, which has been conjectured since 1985:
+There exists a constant $K$ such that for all natural numbers $d,g$ with $g\le
+Kd^{3/2}$ there exists an irreducible component of the Hilbert scheme of
+$\mathbb{P}^3$ whose general element is a smooth, connected curve of degree $d$
+and genus $g$ of maximal rank.
+"
+29551,Surface energy of the one-dimensional supersymmetric $t-J$ model with unparallel boundary fields,"  We investigate the thermodynamic limit of the exact solution, which is given
+by an inhomogeneous $T-Q$ relation, of the one-dimensional supersymmetric $t-J$
+model with unparallel boundary magnetic fields. It is shown that the
+contribution of the inhomogeneous term at the ground state satisfies the
+$L^{-1}$ scaling law, where $L$ is the system-size. This fact enables us to
+calculate the surface (or boundary) energy of the system. The method used in
+this paper can be generalized to study the thermodynamic limit and surface
+energy of other models related to rational R-matrices.
+"
+29552,Dynamics of Bianchi $VI_h$ Universe with Bulk Viscous Fluid in Modified Gravity,"  In this paper, the dynamical behavior of the anisotropic universe has been
+investigated in $f(R,T)$ theory of gravity. The functional $f(R,T)$ has been
+rescaled in the form $f(R,T)=\mu R+\mu T$, where $R$ is the Ricci scalar and
+$T$ is the trace of energy momentum tensor. Three cosmological models are
+constructed using the power law expansion in Bianchi type $VI_h$ ($BVI_h$)
+universe for three different values of $h=-1,0,1$, where the matter field is
+considered to be of bulk viscous fluid. It is found from the model that the
+anisotropic $BVI_h$ model in the modified theory of gravity is in agreement
+with a quintessence phase for the value of $h=-1$ and $0$. We could not obtain
+a viable cosmological model in accordance with the present day observations for
+$h=1$. The bulk viscous coefficient in both the cases are found to be positive
+and remain constant at late time. The physical behaviors of the models along
+with the energy conditions are also studied.
+"
+29553,The resonant state at filling factor ν = 1/2 in chiral fermionic ladders,"  Helical liquids have been experimentally detected in both nanowires and
+ultracold atomic chains as the result of strong spin-orbit interactions. In
+both cases the inner degrees of freedom can be considered as an additional
+space dimension, providing an interpretation of these systems as synthetic
+ladders, with artificial magnetic fluxes determined by the spin-orbit terms. In
+this work, we characterize the helical state which appears at filling
+$\nu=1/2$: this state is generated by a gap arising in the spin sector of the
+corresponding Luttinger liquid and it can be interpreted as the one-dimensional
+(1D) limit of a fractional quantum Hall state of bosonic pairs of fermions. We
+study its main features, focusing on entanglement properties and correlation
+functions. The techniques developed here provide a key example for the study of
+similar quasi-1D systems beyond the semiclassical approximation commonly
+adopted in the description of the Laughlin-like states.
+"
+29554,Coastline Kriging: A Bayesian Approach,"  Statistical interpolation of chemical concentrations at new locations is an
+important step in assessing a worker's exposure level. When measurements are
+available from coastlines, as is the case in coastal clean-up operations in oil
+spills, one may need a mechanism to carry out spatial interpolation at new
+locations along the coast. In this paper we present a simple model for
+analyzing spatial data that is observed over a coastline. We demonstrate four
+different models using two different representations of the coast using curves.
+The four models were demonstrated on simulated data and one of them was also
+demonstrated on a dataset from the GuLF STUDY. Our contribution here is to
+offer practicing hygienists and exposure assessors with a simple and easy
+method to implement Bayesian hierarchical models for analyzing and
+interpolating coastal chemical concentrations.
+"
+29555,Label Languages of 8-directional Array P System,"  An 8-directional array P system is one where the rewriting of an array can
+happen in any 8-directions. The array rules of such a system are labelled thus
+resulting in a labelled 8-directional array P system. The labelling is not
+unique and the label language is obtained by recording the strings over the
+labels used in any terminating derivation of the P system. The system is shown
+to generate interesting pictures. The label language is compared with Chomsky
+hierarchy.
+"
+29556,Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task,"  In the present work we propose a Deep Feed Forward network architecture which
+can be trained according to a sequential learning paradigm, where tasks of
+increasing difficulty are learned sequentially, yet avoiding catastrophic
+forgetting. The proposed architecture can re-use the features learned on
+previous tasks in a new task when the old tasks and the new one are related.
+The architecture needs fewer computational resources (neurons and connections)
+and less data for learning the new task than a network trained from scratch
+"
+29557,Contributions to the Generalized Coupon Collector and LRU Problems,"  Based upon inequalities on Subset Probabilities, proofs of several
+conjectures on the Generalized Coupon Collector Problem (i.e. CCP with unequal
+popularity) are presented. Then we derive a very simple asymptotic relation
+between the expectation of the waiting time for a partial collection in the
+CCP, and the Miss rate of a LRU cache.
+"
+29558,Nonlinear descent on moduli of local systems,"  We study the Diophantine geometry of moduli spaces for special linear rank
+two local systems on surfaces. We generalize Bers's boundedness of systoles and
+Mumford's compactness criterion from hyperbolic surfaces to local systems. We
+prove a structure theorem for mapping class group dynamics on the integral
+points of the moduli spaces, extending classical work of Markoff.
+"
+29559,Fast Locality-Sensitive Hashing Frameworks for Approximate Near Neighbor Search,"  The Indyk-Motwani Locality-Sensitive Hashing (LSH) framework (STOC 1998) is a
+general technique for constructing a data structure to answer approximate near
+neighbor queries by using a distribution $\mathcal{H}$ over locality-sensitive
+hash functions that partition space. For a collection of $n$ points, after
+preprocessing, the query time is dominated by $O(n^{\rho} \log n)$ evaluations
+of hash functions from $\mathcal{H}$ and $O(n^{\rho})$ hash table lookups and
+distance computations where $\rho \in (0,1)$ is determined by the
+locality-sensitivity properties of $\mathcal{H}$. It follows from a recent
+result by Dahlgaard et al. (FOCS 2017) that the number of locality-sensitive
+hash functions can be reduced to $O(\log^2 n)$, leaving the query time to be
+dominated by $O(n^{\rho})$ distance computations and $O(n^{\rho} \log n)$
+additional word-RAM operations. We state this result as a general framework and
+provide a simpler analysis showing that the number of lookups and distance
+computations closely match the Indyk-Motwani framework, making it a viable
+replacement in practice. Using ideas from another locality-sensitive hashing
+framework by Andoni and Indyk (SODA 2006) we are able to reduce the number of
+additional word-RAM operations to $O(n^\rho)$.
+"
+29560,Haptic Assembly Using Skeletal Densities and Fourier Transforms,"  Haptic-assisted virtual assembly and prototyping has seen significant
+attention over the past two decades. However, in spite of the appealing
+prospects, its adoption has been slower than expected. We identify the main
+roadblocks as the inherent geometric complexities faced when assembling objects
+of arbitrary shape, and the computation time limitation imposed by the
+notorious 1 kHz haptic refresh rate. We addressed the first problem in a recent
+work by introducing a generic energy model for geometric guidance and
+constraints between features of arbitrary shape. In the present work, we
+address the second challenge by leveraging Fourier transforms to compute the
+constraint forces and torques. Our new concept of 'geometric energy' field is
+computed automatically from a cross-correlation of 'skeletal densities' in the
+frequency domain, and serves as a generalization of the manually specified
+virtual fixtures or heuristically identified mating constraints proposed in the
+literature. The formulation of the energy field as a convolution enables
+efficient computation using fast Fourier transforms (FFT) on the graphics
+processing unit (GPU). We show that our method is effective for low-clearance
+assembly of objects of arbitrary geometric and syntactic complexity.
+"
+29561,Suppression of 1/f noise in solid state quantum devices by surface spin desorption,"  Noise and decoherence due to spurious two-level systems (TLS) located at
+material interfaces is a long-standing issue in solid state quantum
+technologies. Efforts to mitigate the effects of TLS have been hampered by a
+lack of surface analysis tools sensitive enough to identify their chemical and
+physical nature. Here we measure the dielectric loss, frequency noise and
+electron spin resonance (ESR) spectrum in superconducting resonators and
+demonstrate that desorption of surface spins is accompanied by an almost
+tenfold reduction in the frequency noise. We provide experimental evidence that
+simultaneously reveals the chemical signatures of adsorbed magnetic moments and
+demonstrates their coupling via the electric-field degree of freedom to the
+resonator, causing dielectric (charge) noise in solid state quantum devices.
+"
+29562,Understanding Latent Factors Using a GWAP,"  Recommender systems relying on latent factor models often appear as black
+boxes to their users. Semantic descriptions for the factors might help to
+mitigate this problem. Achieving this automatically is, however, a
+non-straightforward task due to the models' statistical nature. We present an
+output-agreement game that represents factors by means of sample items and
+motivates players to create such descriptions. A user study shows that the
+collected output actually reflects real-world characteristics of the factors.
+"
+29563,Determination of the p-spray profile for n+p silicon sensors using a MOSFET,"  The standard technique to electrically isolate the $n^+$ implants of
+segmented silicon sensors fabricated on high-ohmic $p$-type silicon are
+$p^+$-implants. Although the knowledge of the $p^+$-implant dose and of the
+doping profile is highly relevant for the understanding and optimisation of
+sensors, this information is usually not available from the vendors, and
+methods to obtain it are highly welcome. The paper presents methods to obtain
+this information from circular MOSFETs fabricated as test structures on the
+same wafer as the sensors. Two circular MOSFETs, one with and one without a
+$p^+$-implant under the gate, are used for this study. They were produced on
+Magnetic Czochralski silicon doped with $\approx 3.5 \times 10^{12}$ cm$^{-2}$
+of boron and $\langle 1 0 0 \, \rangle$ crystal orientation. The drain-source
+current as function of gate voltage for different back-side voltages is
+measured at a drain-source voltage of 50 mV in the linear MOSFET region, and
+the values of threshold voltage and mobility extracted using the standard
+MOSFET formulae. To determine the bulk doping, the implantation dose and
+profile from the data, two methods are used, which give compatible results. The
+doping profile, which varies between $3.5 \times 10^{12}$ cm$^{-3}$ and $2
+\times 10^{15}$ cm$^{-3}$ for the MOSFET with $p^+$-implant, is determined down
+to a distance of a fraction of a $\mu $m from the Si-SiO$_2$ interface. The
+method of extracting the doping profiles is verified using data from a TCAD
+simulation of the two MOSFETs. The details of the methods and of the problems
+encountered are discussed.
+"
+29564,"Existence of weak solutions for a pseudo-parabolic system coupling chemical reactions, diffusion and momentum equations","  We study the weak solvability of a nonlinearly coupled system of parabolic
+and pseudo-parabolic equations describing the interplay between mechanics,
+chemical reactions, diffusion and flow in a mixture theory framework. Our
+approach relies on suitable discrete-in-time energy-like estimates and discrete
+Gronwall inequalities. In selected parameter regimes, these estimates ensure
+the convergence of the Rothe method for the discretized partial differential
+equations.
+"
+29565,Creating a Social Brain for Cooperative Connected Autonomous Vehicles: Issues and Challenges,"  The connected autonomous vehicle has been often touted as a technology that
+will become pervasive in society in the near future. Rather than being stand
+alone, we examine the need for autonomous vehicles to cooperate and interact
+within their socio-cyber-physical environments, including the problems
+cooperation will solve, but also the issues and challenges.
+"
+29566,Inhomogeneous preferential concentration of inertial particles in turbulent channel flow,"  Turbophoresis leading to preferential concentration of inertial particles in
+regions of low turbulent diffusivity is a unique feature of inhomogeneous
+turbulent flows, such as free shear flows or wall-bounded flows. In this work,
+the theory for clustering of weakly inertial particles in homogeneous
+turbulence is extended to the inhomogeneous case of a turbulent channel flow.
+The inhomogeneity contributes to the cluster formation in addition to
+clustering in homogeneous turbulence. A space-dependent rate for the creation
+of inhomogeneous particle concentration is derived in terms of local statistics
+of turbulence. We provide the formula for the pair-correlation function of
+concentration that factorizes in product of time and space-dependent average
+concentrations and time-independent factor of clustering that obeys a power-law
+in the distance between the points. This power-law characterizes inhomogeneous
+multifractality of the particle distribution. A unique demonstration and
+quantification of the combined effects of turbophoresis and fractal clustering
+in a direct numerical simulation of particle motion in a turbulent channel flow
+is performed according to the presented theory. The strongest contribution to
+clustering coming from the inhomogeneity of the flow occurs in the transitional
+region between viscous sublayer and the buffer layer. Further the ratio of
+homogeneous and inhomogeneous term depends on the wall distance. The
+inhomogeneous terms may significantly increase the preferential concentration
+of inertial particles, thus the overall degree of clustering in inhomogeneous
+turbulence is potentially stronger compared to particles with the same inertia
+in purely homogeneous turbulence.
+"
+29567,The ARIEL Mission Reference Sample,"  The ARIEL (Atmospheric Remote-sensing Exoplanet Large-survey) mission concept
+is one of the three M4 mission candidates selected by the European Space Agency
+(ESA) for a Phase A study, competing for a launch in 2026. ARIEL has been
+designed to study the physical and chemical properties of a large and diverse
+sample of exoplanets, and through those understand how planets form and evolve
+in our galaxy. Here we describe the assumptions made to estimate an optimal
+sample of exoplanets - including both the already known exoplanets and the
+""expected"" ones yet to be discovered - observable by ARIEL and define a
+realistic mission scenario. To achieve the mission objectives, the sample
+should include gaseous and rocky planets with a range of temperatures around
+stars of different spectral type and metallicity. The current ARIEL design
+enables the observation of ~1000 planets, covering a broad range of planetary
+and stellar parameters, during its four year mission lifetime. This nominal
+list of planets is expected to evolve over the years depending on the new
+exoplanet discoveries.
+"
+29568,Inducing and Manipulating Magnetization in Two-Dimensional ZnO by Strain and External Gating,"  Two-dimensional structures that exhibit intriguing magnetic phenomena such as
+perpendicular magnetic anisotropy and switchable magnetization are of great
+interests in spintronics research. Herein, the density-functional theory
+studies reveal the critical impacts of strain and external gating on
+vacancy-induced magnetism and its spin direction in a graphene-like single
+layer of zinc oxide (ZnO). In contrast to the pristine and defective ZnO with
+an O-vacancy, the presence of a Zn-vacancy induces significant magnetic moments
+to its first neighboring O and Zn atoms due to the charge deficit. We further
+predict that the direction of magnetization easy axis reverses from an in-plane
+to perpendicular orientation under a practically achieved biaxial compressive
+strain of $\sim$1--2\% or applying an electric-field by means of the charge
+density modulation. This magnetization reversal is driven by the strain- and
+electric-field-induced changes in the spin-orbit coupled \emph{d} states of the
+first-neighbor Zn atom to the Zn-vacancy. These findings open interesting
+prospects for exploiting strain and electric-field engineering to manipulate
+magnetism and magnetization orientation of two-dimensional materials.
+"
+29569,Schauder estimates for discrete fractional integrals,"  In this note we focus on the discrete fractional integrals as a natural
+continuation of our previous work about nonlocal fractional derivatives,
+discrete and continuous. We define the discrete fractional integrals by using
+the semigroup theory and we study the regularity of {discrete fractional
+integrals} on the discrete Hölder spaces, which it is known in the
+differential equations field as the discrete Schauder estimates.
+"
+29570,Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Convolutional Neural Network,"  Nowadays, deep learning can be employed to a wide ranges of fields including
+medicine, engineering, etc. In deep learning, Convolutional Neural Network
+(CNN) is extensively used in the pattern and sequence recognition, video
+analysis, natural language processing, spam detection, topic categorization,
+regression analysis, speech recognition, image classification, object
+detection, segmentation, face recognition, robotics, and control. The benefits
+associated with its near human level accuracies in large applications lead to
+the growing acceptance of CNN in recent years. The primary contribution of this
+paper is to analyze the impact of the pattern of the hidden layers of a CNN
+over the overall performance of the network. To demonstrate this influence, we
+applied neural network with different layers on the Modified National Institute
+of Standards and Technology (MNIST) dataset. Also, is to observe the variations
+of accuracies of the network for various numbers of hidden layers and epochs
+and to make comparison and contrast among them. The system is trained utilizing
+stochastic gradient and backpropagation algorithm and tested with feedforward
+algorithm.
+"
+29571,Adaptive Huber Regression,"  Big data can easily be contaminated by outliers or contain variables with
+heavy-tailed distributions, which makes many conventional methods inadequate.
+To address this challenge, we propose the adaptive Huber regression for robust
+estimation and inference. The key observation is that the robustification
+parameter should adapt to the sample size, dimension and moments for optimal
+tradeoff between bias and robustness. Our theoretical framework deals with
+heavy-tailed distributions with bounded $(1+\delta)$-th moment for any $\delta
+> 0$. We establish a sharp phase transition for robust estimation of regression
+parameters in both low and high dimensions: when $\delta \geq 1$, the estimator
+admits a sub-Gaussian-type deviation bound without sub-Gaussian assumptions on
+the data, while only a slower rate is available in the regime $0<\delta< 1$.
+Furthermore, this transition is smooth and optimal. In addition, we extend the
+methodology to allow both heavy-tailed predictors and observation noise.
+Simulation studies lend further support to the theory. In a genetic study of
+cancer cell lines that exhibit heavy-tailedness, the proposed methods are shown
+to be more robust and predictive.
+"
+29572,Approximate Bayes learning of stochastic differential equations,"  We introduce a nonparametric approach for estimating drift and diffusion
+functions in systems of stochastic differential equations from observations of
+the state vector. Gaussian processes are used as flexible models for these
+functions and estimates are calculated directly from dense data sets using
+Gaussian process regression. We also develop an approximate expectation
+maximization algorithm to deal with the unobserved, latent dynamics between
+sparse observations. The posterior over states is approximated by a piecewise
+linearized process of the Ornstein-Uhlenbeck type and the maximum a posteriori
+estimation of the drift is facilitated by a sparse Gaussian process
+approximation.
+"
+29573,Mind the Gap: Analyzing the Performance of WebAssembly vs. Native Code,"  All major web browsers now support WebAssembly, a low-level bytecode intended
+to serve as a compilation target for code written in languages like C and C++.
+A key goal of WebAssembly is performance parity with native code; previous work
+reports near parity, with many applications compiled to WebAssembly running on
+average 10% slower than native code. However, this evaluation was limited to a
+suite of scientific kernels, each consisting of roughly 100 lines of code.
+Running more substantial applications was not possible because compiling code
+to WebAssembly is only part of the puzzle: standard Unix APIs are not available
+in the web browser environment. To address this challenge, we build
+BROWSIX-WASM, a significant extension to BROWSIX that, for the first time,
+makes it possible to run unmodified WebAssembly-compiled Unix applications
+directly inside the browser. We then use BROWSIX-WASM to conduct the first
+large-scale evaluation of the performance of WebAssembly vs. native. Across the
+SPEC CPU suite of benchmarks, we find a substantial performance gap:
+applications compiled to WebAssembly run slower by an average of 50% (Firefox)
+to 89% (Chrome), with peak slowdowns of 2.6x (Firefox) and 3.14x (Chrome). We
+identify the causes of this performance degradation, some of which are due to
+missing optimizations and code generation issues, while others are inherent to
+the WebAssembly platform.
+"
+29574,Gear Training: A new way to implement high-performance model-parallel training,"  The training of Deep Neural Networks usually needs tremendous computing
+resources. Therefore many deep models are trained in large cluster instead of
+single machine or GPU. Though major researchs at present try to run whole model
+on all machines by using asynchronous asynchronous stochastic gradient descent
+(ASGD), we present a new approach to train deep model parallely -- split the
+model and then seperately train different parts of it in different speed.
+"
+29575,Do phonons carry spin?,"  Recently the question of phonon angular momentum has been raised : phonon
+angular momentum in a magnetic crystal could play an important role in the
+angular momentum balance in the Einstein-de Haas effect. This proposition is
+quite natural and understandable, however the idea of phonon spin seems
+ambiguous in the literature. In 1988 McLellan presented phonon angular momentum
+states for circular and elliptical polarizations of phonons that represent
+orbital angular momentum and also pointed out analogy to isotropic quantum
+harmonic oscillator. In view of its fundamental importance we investigate this
+issue and conclude that phonon spin hypothesis is physically untenable, and the
+phonon angular momentum has to be interpreted as orbital. The photon-phonon
+analogy is also misleading. It is argued that Cosserat elastic medium having
+internal torque may have lattice excitations different from phonons having spin
+one or spin two that we term as Cosseratons. In addition a new source of
+angular momentum balance originationg from anomalous electron magnetic moment
+is suggested.
+"
+29576,Thixotropic flow past a cylinder,"  We study the flow of a thixotropic fluid around a cylinder. The rheology of
+the fluid is described by means of a structural viscoplastic model based on the
+Bingham constitutive equation, regularised using the Papanastasiou
+regularisation. The yield stress is assumed to vary linearly with the
+structural parameter, which varies from zero (completely broken structure) to
+one (fully developed skeleton structure), following a first-order rate equation
+which accounts for material structure break-down and build-up. The results were
+obtained numerically using the Finite Element Method. Simulations were
+performed for a moderate Reynolds number of 45, so that flow recirculation is
+observed behind the cylinder, but vortex shedding does not occur. The effects
+of the Bingham number and of the thixotropy parameters are studied. The results
+show that the viscous character of the flow can be controlled within certain
+limits through these parameters, despite the fact that the Reynolds number is
+fixed.
+"
+29577,A class of C*-algebraic locally compact quantum groupoids Part II: Main theory,"  This is Part II in our multi-part series of papers developing the theory of a
+class of locally compact quantum groupoids, based on the purely algebraic
+notion of weak multiplier Hopf algebras. Definition was given in Part I. The
+existence of a certain canonical idempotent element $E$ plays a central role.
+In this Part II, we develop the main theory, discussing the structure of our
+quantum groupoids. We will construct from the defining axioms the right/left
+regular representations and the antipode map.
+"
+29578,"Core Decomposition in Multilayer Networks: Theory, Algorithms, and Applications","  Multilayer networks are a powerful paradigm to model complex systems, where
+various relations might occur among the same set of entities. Despite the keen
+interest in a variety of problems, algorithms, and analysis methods in this
+type of network, the problem of extracting dense subgraphs has remained largely
+unexplored.
+As a first step in this direction, we study the problem of core decomposition
+of a multilayer network. Unlike the single-layer counterpart in which cores are
+all nested into one another, in the multilayer context no total order exists
+among multilayer cores: they form a lattice whose size is exponential in the
+number of layers. In this setting we devise three algorithms which differ in
+the way they visit the core lattice and in their pruning techniques. We assess
+time and space efficiency of the three algorithms on a large variety of
+real-world multilayer networks.
+We then study the problem of extracting only the inner-most cores, i.e., the
+cores that are not dominated by any other core in terms of their index on all
+the layers. As inner-most cores are orders of magnitude less than all the
+cores, it is desirable to develop algorithms that effectively exploit the
+maximality property and extract inner-most cores directly, without first
+computing a complete decomposition.
+Moreover, we showcase an application of the multilayer core-decomposition
+tool to the problem of densest-subgraph extraction from multilayer networks. We
+introduce a definition of multilayer densest subgraph that trades-off between
+high density and number of layers in which the high density holds, and show how
+multilayer core decomposition can be exploited to approximate this problem with
+quality guarantees. We also exploit multilayer core decomposition to speed-up
+the extraction of frequent cross-graph quasi-cliques and to generalize the
+community-search problem to the multilayer setting.
+"
+29579,"Exciton-Exciton Annihilation Is Coherently Suppressed in H-Aggregates, but Not in J-Aggregates","  We theoretically demonstrate a strong dependence of the annihilation rate
+between (singlet) excitons on the sign of dipole-dipole couplings between
+molecules. For molecular H-aggregates, where this sign is positive, the phase
+relation of the delocalized two-exciton wavefunctions causes a destructive
+interference in the annihilation probability. For J-aggregates, where this sign
+is negative, the interference is constructive instead, as a result of which no
+such coherent suppression of the annihilation rate occurs. As a consequence,
+room temperature annihilation rates of typical H- and J-aggregates differ by a
+factor of ~3, while an order of magnitude difference is found for
+low-temperature aggregates with a low degree of disorder. These findings, which
+explain experimental observations, reveal a fundamental principle underlying
+exciton-exciton annihilation, with major implications for technological devices
+and experimental studies involving high excitation densities.
+"
+29580,Energy-dependent quenching adjusts the excitation diffusion length to regulate photosynthetic light harvesting,"  An important determinant of crop yields is the regulation of photosystem II
+(PSII) light harvesting by energy-dependent quenching (qE). However, the
+molecular details of excitation quenching have not been quantitatively
+connected to the PSII yield, which only emerges on the 100 nm scale of the
+grana membrane and determines flux to downstream metabolism. Here, we
+incorporate excitation dissipation by qE into a pigment-scale model of
+excitation transfer and trapping for a 200 nm x 200 nm patch of the grana
+membrane. We demonstrate that single molecule measurements of qE are consistent
+with a weak-quenching regime. Consequently, excitation transport can be
+rigorously coarse-grained to a 2D random walk with an excitation diffusion
+length determined by the extent of quenching. A diffusion-corrected lake model
+substantially improves the PSII yield determined from variable chlorophyll
+fluorescence measurements and offers an improved model of PSII for
+photosynthetic metabolism.
+"
+29581,A ROS-based Software Framework for the NimbRo-OP Humanoid Open Platform,"  Over the past few years, a number of successful humanoid platforms have been
+developed, including the Nao and the DARwIn-OP, both of which are used by many
+research groups for the investigation of bipedal walking, full-body motions,
+and human-robot interaction. The NimbRo-OP is an open humanoid platform under
+development by team NimbRo of the University of Bonn. Significantly larger than
+the two aforementioned humanoids, this platform has the potential to interact
+with a more human-scale environment. This paper describes a software framework
+for the NimbRo-OP that is based on the Robot Operating System (ROS) middleware.
+The software provides functionality for hardware abstraction, visual
+perception, and behavior generation, and has been used to implement basic
+soccer skills. These were demonstrated at RoboCup 2013, as part of the winning
+team of the Humanoid League competition.
+"
+29582,Secondary fans and secondary polyhedra of punctured Riemann surfaces,"  A famous construction of Gelfand, Kapranov and Zelevinsky associates to each
+finite point configuration $A \subset \mathbb{R}^d$ a polyhedral fan, which
+stratifies the space of weight vectors by the combinatorial types of regular
+subdivisions of $A$. That fan arises as the normal fan of a convex polytope. In
+a completely analogous way we associate to each hyperbolic Riemann surface $R$
+with punctures a polyhedral fan. Its cones correspond to the ideal cell
+decompositions of $R$ that occur as the horocyclic Delaunay decompositions
+which arise via the convex hull construction of Epstein and Penner. Similar to
+the classical case, this secondary fan of $R$ turns out to be the normal fan of
+a convex polyhedron, the secondary polyhedron of $R$.
+"
+29583,Tensor network language model,"  We propose a new statistical model suitable for machine learning of systems
+with long distance correlations such as natural languages. The model is based
+on directed acyclic graph decorated by multi-linear tensor maps in the vertices
+and vector spaces in the edges, called tensor network. Such tensor networks
+have been previously employed for effective numerical computation of the
+renormalization group flow on the space of effective quantum field theories and
+lattice models of statistical mechanics. We provide explicit algebro-geometric
+analysis of the parameter moduli space for tree graphs, discuss model
+properties and applications such as statistical translation.
+"
+29584,Techniques in Lattice Basis Reduction,"  The credit on {\it reduction theory} goes back to the work of Lagrange,
+Gauss, Hermite, Korkin, Zolotarev, and Minkowski. Modern reduction theory is
+voluminous and includes the work of A. Lenstra, H. Lenstra and L. Lovasz who
+created the well known LLL algorithm, and many other researchers such as L.
+Babai and C. P. Schnorr who created significant new variants of basis reduction
+algorithms. In this paper, we propose and investigate the efficacy of new
+optimization techniques to be used along with LLL algorithm. The techniques we
+have proposed are: i) {\it hill climbing (HC)}, ii) {\it lattice diffusion-sub
+lattice fusion (LDSF)}, and iii) {\it multistage hybrid LDSF-HC}. The first
+technique relies on the sensitivity of LLL to permutations of the input basis
+$B$, and optimization ideas over the symmetric group $S_m$ viewed as a metric
+space. The second technique relies on partitioning the lattice into
+sublattices, performing basis reduction in the partition sublattice blocks,
+fusing the sublattices, and repeating. We also point out places where parallel
+computation can reduce run-times achieving almost linear speedup. The
+multistage hybrid technique relies on the lattice diffusion and sublattice
+fusion and hill climbing algorithms.
+"
+29585,A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning,"  Multi-task learning leverages potential correlations among related tasks to
+extract common features and yield performance gains. However, most previous
+works only consider simple or weak interactions, thereby failing to model
+complex correlations among three or more tasks. In this paper, we propose a
+multi-task learning architecture with four types of recurrent neural layers to
+fuse information across multiple related tasks. The architecture is
+structurally flexible and considers various interactions among tasks, which can
+be regarded as a generalized case of many previous works. Extensive experiments
+on five benchmark datasets for text classification show that our model can
+significantly improve performances of related tasks with additional information
+from others.
+"
+29586,Electronic Structure and Magnetic Properties of Half-metallic Ferrimagnet Mn$_{2}$VAl Probed by Soft X-ray Spectroscopies,"  We have studied the electronic structure of ferrimagnetic Mn2VAl single
+crystal by means of soft X-ray absorption spectroscopy (XAS), X-ray absorption
+magnetic circular dichroism (XMCD) and resonant soft X-ray inelastic scattering
+(RIXS). We have successfully observed the XMCD signals for all constitute
+elements, supporting the spin polarized states at the Fermi level. The Mn
+$L_{2,3}$ XAS and XMCD spectra are reproduced by the spectral simulation based
+on density-functional theory (DFT), indicating itinerant character of the Mn 3d
+states. On the other hand, V $3d$ electrons are rather localized since the
+ionic model can qualitatively explain the V $L_{2,3}$ XAS and XMCD spectra as
+well as the local dd excitation revealed by V $L_3$ RIXS.
+"
+29587,Validating Intelligent Power and Energy Systems - A Discussion of Educational Needs,"  Traditional power systems education and training is flanked by the demand for
+coping with the rising complexity of energy systems, like the integration of
+renewable and distributed generation, communication, control and information
+technology. A broad understanding of these topics by the current/future
+researchers and engineers is becoming more and more necessary. This paper
+identifies educational and training needs addressing the higher complexity of
+intelligent energy systems. Education needs and requirements are discussed,
+such as the development of systems-oriented skills and cross-disciplinary
+learning. Education and training possibilities and necessary tools are
+described focusing on classroom but also on laboratory-based learning methods.
+In this context, experiences of using notebooks, co-simulation approaches,
+hardware-in-the-loop methods and remote labs experiments are discussed.
+"
+29588,Lagrangian fillings and complicated Legendrian unknots,"  An exact Lagrangian submanifold $L$ in the symplectization of standard
+contact $(2n-1)$-space with Legendrian boundary $\Sigma$ can be glued to itself
+along $\Sigma$. This gives a Legendrian embedding $\Lambda(L,L)$ of the double
+of $L$ into contact $(2n+1)$-space. We show that the Legendrian isotopy class
+of $\Lambda(L,L)$ is determined by formal data: the manifold $L$ together with
+a trivialization of its complexified tangent bundle. In particular, if $L$ is a
+disk then $\Lambda(L,L)$ is the Legendrian unknot.
+"
+29589,Memetic search for overlapping topics based on a local evaluation of link communities,"  In spite of recent advances in field delineation methods, bibliometricians
+still don't know the extent to which their topic detection algorithms
+reconstruct `ground truths', i.e. thematic structures in the scientific
+literature. In this paper, we demonstrate a new approach to the delineation of
+thematic structures that attempts to match the algorithm to theoretically
+derived and empirically observed properties all thematic structures have in
+common. We cluster citation links rather than publication nodes, use
+predominantly local information and search for communities of links starting
+from seed subgraphs in order to allow for pervasive overlaps of topics. We
+evaluate sets of links with a new cost function and assume that local minima in
+the cost landscape correspond to link communities. Because this cost landscape
+has many local minima we define a valid community as the community with the
+lowest minimum within a certain range. Since finding all valid communities is
+impossible for large networks, we designed a memetic algorithm that combines
+probabilistic evolutionary strategies with deterministic local searches. We
+apply our approach to a network of about 15,000 Astronomy & Astrophysics papers
+published 2010 and their cited sources, and to a network of about 100,000
+Astronomy & Astrophysics papers (published 2003--2010) which are linked through
+direct citations.
+"
+29590,Aggregation of BiTe Monolayer on Bi$_2$Te$_3$(111) Induced by Diffusion of Intercalated Atoms in van der Waals Gap,"  We report a post-growth aging mechanism of Bi$_2$Te$_3$(111) films with
+scanning tunneling microscopy in combination with density functional theory
+calculation. It is found that a monolayered structure with a squared lattice
+symmetry gradually aggregates from surface steps. Theoretical calculations
+indicate that the van der Waals (vdW) gap not only acts as a natural reservoir
+for self-intercalated Bi and Te atoms, but also provides them easy diffusion
+pathways. Once hopping out of the gap, these defective atoms prefer to develop
+into a two dimensional BiTe superstructure on the Bi$_2$Te$_3$(111) surface
+driven by positive energy gain. Considering the common nature of weakly bonding
+between vdW layers, we expect such unusual diffusion and aggregation of the
+intercalated atoms may be of general importance for most kinds of vdW layered
+materials.
+"
+29591,Mechanics of tissue competition: Interfaces stabilize coexistence,"  Mechanical forces influence the dynamics of growing tissues. Computer
+simulations are employed to study the importance of interfacial effects in
+tissue competition. It was speculated that mechanical pressure determines the
+competition, where the determining quantity is the homeostatic pressure - the
+pressure where division and apoptosis balance; the tissue with the higher
+homeostatic pressure overwhelms the other. Surprisingly, a weaker tissue can
+persist in stable coexistence with a stronger tissue, if adhesion between them
+is small enough. An analytic continuum description can quantitatively describe
+the underlying mechanism and reproduce the resulting pressures and cell-number
+fractions. Computer simulations furthermore display a variety of coexisting
+structures, ranging from spherical inclusions to a bicontinuous state.
+"
+29592,A Unified Approach on the Local Power of Panel Unit Root Tests,"  In this paper, a unified approach is proposed to derive the exact local
+asymptotic power for panel unit root tests, which is one of the most important
+issues in nonstationary panel data literature. Two most widely used panel unit
+root tests known as Levin-Lin-Chu (LLC, Levin, Lin and Chu (2002)) and
+Im-Pesaran-Shin (IPS, Im, Pesaran and Shin (2003)) tests are systematically
+studied for various situations to illustrate our method. Our approach is
+characteristic function based, and can be used directly in deriving the moments
+of the asymptotic distributions of these test statistics under the null and the
+local-to-unity alternatives. For the LLC test, the approach provides an
+alternative way to obtain the results that can be derived by the existing
+method. For the IPS test, the new results are obtained, which fills the gap in
+the literature where few results exist, since the IPS test is non-admissible.
+Moreover, our approach has the advantage in deriving Edgeworth expansions of
+these tests, which are also given in the paper. The simulations are presented
+to illustrate our theoretical findings.
+"
+29593,Mining Twitter Conversations around E-commerce Promotional Events,"  With Social Media platforms establishing themselves as the de facto
+destinations for their customers views and opinions, brands around the World
+are investing heavily on invigorating their customer connects by utilizing such
+platforms to their fullest. In this paper, we develop a novel technique for
+mining conversations in Twitter by weaving together all conversations around an
+event into one unified graph (Conversation Graph, henceforth). The structure of
+the Conversation Graph emerges as a variant of the BOWTIE structure (dubbed
+ASKEWBOWTIE henceforth) as a result of the complex communication patterns
+amongst these players. Finally, we investigate the structural properties of the
+ASKEWBOWTIE structure to understand the configuration of the components and
+their temporal evolution.
+"
+29594,Alpha-invariants and purely log terminal blow-ups,"  We prove that the sum of the $\alpha$-invariants of two different Kollár
+components of a Kawamata log terminal singularity is less than $1$.
+"
+29595,On the stabilized symplectic embedding problem for ellipsoids,"  This note constructs sharp obstructions for stabilized symplectic embeddings
+of an ellipsoid into a ball, in the case when the initial four-dimensional
+ellipsoid has `eccentricity' of the form 3n-1 for some integer n.
+"
+29596,Stochastic thermodynamic limit on E. coli adaptation by Information geometric approach,"  Biological systems process information under noisy environment. Sensory
+adaptation model of E. coli is suitable for investigation because of its
+simplicity. To understand the adaptation processing quantitatively, stochastic
+thermodynamic approach has been attempted. Information processing can be
+assumed as state transition of a system that consists of signal transduction
+molecules using thermodynamic approach, and efficiency can be measured as
+thermodynamic cost. Recently, using information geometry and stochastic
+thermodynamics, a relationship between speed of the transition and the
+thermodynamic cost has been investigated for a chemical reaction model. Here,
+we introduce this approach to sensory adaptation model of E. coli, and examined
+a relationship between adaptation speed and the thermodynamic cost, and
+efficiency of the adaptation speed. For increasing external noise level in
+stimulation, the efficiency decreased, but the efficiency was highly robust to
+external stimulation strength. Moreover, we demonstrated that there is the best
+noise to achieve the adaptation in the aspect of thermodynamic efficiency. Our
+quantification method provides a framework to understand the adaptation speed
+and the thermodynamic cost for various biological systems.
+"
+29597,Spectral Lanczos' tau method for systems of nonlinear integro-differential equations,"  In this paper an extension of the spectral Lanczos' tau method to systems of
+nonlinear integro-differential equations is proposed. This extension includes
+(i) linearization coefficients of orthogonal polynomials products issued from
+nonlinear terms and (ii) recursive relations to implement matrix inversion
+whenever a polynomial change of basis is required and (iii) orthogonal
+polynomial evaluations directly on the orthogonal basis. All these improvements
+ensure numerical stability and accuracy in the approximate solution. Exposed in
+detail, this novel approach is able to significantly outperform numerical
+approximations with other methods as well as different tau implementations.
+Numerical results on a set of problems illustrate the impact of the
+mathematical techniques introduced.
+"
+29598,"A hybrid exact-ACO algorithm for the joint scheduling, power and cluster assignment in cooperative wireless networks","  Base station cooperation (BSC) has recently arisen as a promising way to
+increase the capacity of a wireless network. Implementing BSC adds a new design
+dimension to the classical wireless network design problem: how to define the
+subset of base stations (clusters) that coordinate to serve a user. Though the
+problem of forming clusters has been extensively discussed from a technical
+point of view, there is still a lack of effective optimization models for its
+representation and algorithms for its solution. In this work, we make a further
+step towards filling such gap: 1) we generalize the classical network design
+problem by adding cooperation as an additional decision dimension; 2) we
+develop a strong formulation for the resulting problem; 3) we define a new
+hybrid solution algorithm that combines exact large neighborhood search and ant
+colony optimization. Finally, we assess the performance of our new model and
+algorithm on a set of realistic instances of a WiMAX network.
+"
+29599,PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments,"  Traditional approaches to stereo visual SLAM rely on point features to
+estimate the camera trajectory and build a map of the environment. In
+low-textured environments, though, it is often difficult to find a sufficient
+number of reliable point features and, as a consequence, the performance of
+such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM
+system that combines both points and line segments to work robustly in a wider
+variety of scenarios, particularly in those where point features are scarce or
+not well-distributed in the image. PL-SLAM leverages both points and segments
+at all the instances of the process: visual odometry, keyframe selection,
+bundle adjustment, etc. We contribute also with a loop closure procedure
+through a novel bag-of-words approach that exploits the combined descriptive
+power of the two kinds of features. Additionally, the resulting map is richer
+and more diverse in 3D elements, which can be exploited to infer valuable,
+high-level scene structures like planes, empty spaces, ground plane, etc. (not
+addressed in this work). Our proposal has been tested with several popular
+datasets (such as KITTI and EuRoC), and is compared to state of the art methods
+like ORB-SLAM, revealing a more robust performance in most of the experiments,
+while still running in real-time. An open source version of the PL-SLAM C++
+code will be released for the benefit of the community.
+"
+29600,Coactivated Clique Based Multisource Overlapping Brain Subnetwork Extraction,"  Subnetwork extraction using community detection methods is commonly used to
+study the brain's modular structure. Recent studies indicated that certain
+brain regions are known to interact with multiple subnetworks. However, most
+existing methods are mainly for non-overlapping subnetwork extraction. In this
+paper, we present an approach for overlapping brain subnetwork extraction using
+cliques, which we defined as co-activated node groups performing multiple
+tasks. We proposed a multisource subnetwork extraction approach based on the
+co-activated clique, which (1) uses task co-activation and task connectivity
+strength information for clique identification, (2) automatically detects
+cliques of different sizes having more neuroscientific justifications, and (3)
+shares the subnetwork membership, derived from a fusion of rest and task data,
+among the nodes within a clique for overlapping subnetwork extraction. On real
+data, compared to the commonly used overlapping community detection techniques,
+we showed that our approach improved subnetwork extraction in terms of
+group-level and subject-wise reproducibility. We also showed that our
+multisource approach identified subnetwork overlaps within brain regions that
+matched well with hubs defined using functional and anatomical information,
+which enables us to study the interactions between the subnetworks and how hubs
+play their role in information flow across different subnetworks. We further
+demonstrated that the assignments of interacting/individual nodes using our
+approach correspond with the posterior probability derived independently from
+our multimodal random walker based approach.
+"
+29601,Distributionally Robust Groupwise Regularization Estimator,"  Regularized estimators in the context of group variables have been applied
+successfully in model and feature selection in order to preserve
+interpretability. We formulate a Distributionally Robust Optimization (DRO)
+problem which recovers popular estimators, such as Group Square Root Lasso
+(GSRL). Our DRO formulation allows us to interpret GSRL as a game, in which we
+learn a regression parameter while an adversary chooses a perturbation of the
+data. We wish to pick the parameter to minimize the expected loss under any
+plausible model chosen by the adversary - who, on the other hand, wishes to
+increase the expected loss. The regularization parameter turns out to be
+precisely determined by the amount of perturbation on the training data allowed
+by the adversary. In this paper, we introduce a data-driven (statistical)
+criterion for the optimal choice of regularization, which we evaluate
+asymptotically, in closed form, as the size of the training set increases. Our
+easy-to-evaluate regularization formula is compared against cross-validation,
+showing good (sometimes superior) performance.
+"
+29602,Mixing time estimation in reversible Markov chains from a single sample path,"  The spectral gap $\gamma$ of a finite, ergodic, and reversible Markov chain
+is an important parameter measuring the asymptotic rate of convergence. In
+applications, the transition matrix $P$ may be unknown, yet one sample of the
+chain up to a fixed time $n$ may be observed. We consider here the problem of
+estimating $\gamma$ from this data. Let $\pi$ be the stationary distribution of
+$P$, and $\pi_\star = \min_x \pi(x)$. We show that if $n =
+\tilde{O}\bigl(\frac{1}{\gamma \pi_\star}\bigr)$, then $\gamma$ can be
+estimated to within multiplicative constants with high probability. When $\pi$
+is uniform on $d$ states, this matches (up to logarithmic correction) a lower
+bound of $\tilde{\Omega}\bigl(\frac{d}{\gamma}\bigr)$ steps required for
+precise estimation of $\gamma$. Moreover, we provide the first procedure for
+computing a fully data-dependent interval, from a single finite-length
+trajectory of the chain, that traps the mixing time $t_{\text{mix}}$ of the
+chain at a prescribed confidence level. The interval does not require the
+knowledge of any parameters of the chain. This stands in contrast to previous
+approaches, which either only provide point estimates, or require a reset
+mechanism, or additional prior knowledge. The interval is constructed around
+the relaxation time $t_{\text{relax}} = 1/\gamma$, which is strongly related to
+the mixing time, and the width of the interval converges to zero roughly at a
+$1/\sqrt{n}$ rate, where $n$ is the length of the sample path.
+"
+29603,Surprises from Complete Vector Portal Theories: New Insights into the Dark Sector and its Interplay with Higgs Physics,"  We study UV complete theories where the Standard Model (SM) gauge group is
+extended with a new abelian $U(1)$, and the field content is augmented by an
+arbitrary number of scalar and fermion SM singlets, potentially including dark
+matter (DM) candidates. Considerations such as classical and quantum gauge
+invariance of the full theory and S-matrix unitarity, not applicable within a
+simplified model approach, are shown to have significant phenomenological
+consequences. The lack of gauge anomalies leads to compact relations among the
+$U(1)$ fermion charges, and puts a lower bound on the number of dark fermions.
+Contrary to naive expectations, the DM annihilation to Zh is found to be p-wave
+suppressed, as hinted by perturbative unitarity of S-matrix, with dramatic
+implications for DM thermal relic density and indirect searches. Within this
+framework, the interplay between dark matter, new vector boson and Higgs
+physics is rather natural and generic.
+"
+29604,Asymptotically normal estimators for Zipf's law,"  Zipf's law states that sequential frequencies of words in a text correspond
+to a power function. Its probabilistic model is an infinite urn scheme with
+asymptotically power distribution. The exponent of this distribution must be
+estimated. We use the number of different words in a text and similar
+statistics to construct asymptotically normal estimators of the exponent.
+"
+29605,Dynamic behaviour of a ring coupled boost converter system with passivity-based control,"  This paper discusses a dispersed generation system of multiple DC/DC
+converters with DC power sources connected in a ring formulation. Here is
+presented the analysis of the system based on the stored energy and passivity
+characteristics of the system. Passivity Based Control (PBC), with its
+energy-modifying and damping-injection technique, is applied to a ring coupled
+converter system to stabilize itself at a desired DC voltage in the presence of
+external disturbances. The numerical results reveal the effective application
+of the control as a robust and flexible technique.
+"
+29606,Spectroscopy of bulk and few-layer superconducting NbSe$_2$ with van der Waals tunnel junctions,"  Tunnel junctions, a well-established platform for high-resolution
+spectroscopy of superconductors, require defect-free insulating barriers with
+clean engagement to metals on both sides. Extending the range of materials
+accessible to tunnel junction fabrication, beyond the limited selection which
+allows high-quality oxide formation, requires the development of alternative
+fabrication techniques. Here we show that van-der-Waals (vdW) tunnel barriers,
+fabricated by stacking layered semiconductors on top of the transition metal
+dichalcogenide (TMD) superconductor NbSe$_2$, sustain a stable, low noise
+tunneling current, and exhibit strong suppression of sub-gap tunneling. We
+utilize the technique to measure the spectra of bulk (20 nm) and ultrathin (3-
+and 4-layer) devices at 70 mK. The spectra exhibit two distinct energy gaps,
+the larger of which decreases monotonously with thickness and $T_C$, in
+agreement with BCS theory. The spectra are analyzed using a two-band model
+modified to account for depairing. We show that in the bulk, the smaller gap
+exhibits strong depairing in an in-plane magnetic field, consistent with a high
+Fermi velocity. In the few-layer devices, depairing of the large gap is
+negligible, consistent with out-of-plane spin-locking due to Ising spin-orbit
+coupling. Our results demonstrate the utility of vdW tunnel junctions in
+mapping the intricate spectral evolution of TMD superconductors over a range of
+magnetic fields.
+"
+29607,YellowFin and the Art of Momentum Tuning,"  Hyperparameter tuning is one of the most time-consuming workloads in deep
+learning. State-of-the-art optimizers, such as AdaGrad, RMSProp and Adam,
+reduce this labor by adaptively tuning an individual learning rate for each
+variable. Recently researchers have shown renewed interest in simpler methods
+like momentum SGD as they may yield better test metrics. Motivated by this
+trend, we ask: can simple adaptive methods based on SGD perform as well or
+better? We revisit the momentum SGD algorithm and show that hand-tuning a
+single learning rate and momentum makes it competitive with Adam. We then
+analyze its robustness to learning rate misspecification and objective
+curvature variation. Based on these insights, we design YellowFin, an automatic
+tuner for momentum and learning rate in SGD. YellowFin optionally uses a
+negative-feedback loop to compensate for the momentum dynamics in asynchronous
+settings on the fly. We empirically show that YellowFin can converge in fewer
+iterations than Adam on ResNets and LSTMs for image recognition, language
+modeling and constituency parsing, with a speedup of up to 3.28x in synchronous
+and up to 2.69x in asynchronous settings.
+"
+29608,Electronic Structure of Manganese Phthalocyanine Modified via Potassium Intercalation: a Comprehensive Experimental Study,"  Potassium (K) intercalated manganese phthalocyanine (MnPc) reveals vast
+changes of its electronic states close to the Fermi level. However, theoretical
+studies are controversial regarding the electronic configuration. Here, MnPc
+doped with K was studied by ultraviolet, X-ray, and inverse photoemission, as
+well as near edge X-ray absorption fine structure spectroscopy. Upon K
+intercalation the Fermi level shifts toward the lowest unoccupied molecular
+orbital filling it up with donated electrons with the appearance of an
+additional feature in the energy region of the occupied states. The electronic
+bands are pinned 0.5 eV above and 0.4 eV below the Fermi level. The branching
+ratio of the Mn L3 and L2 edges indicate an increase of the spin state.
+Moreover, the evolution of the Mn L and N K edges reveals strong hybridization
+between Mn 3d and N 2p states of MnPc and sheds light on the electron
+occupation in the ground and n-doped configurations.
+"
+29609,Electronic structure study of screw dislocation core energetics in Aluminum and core energetics informed forces in a dislocation aggregate,"  We use a real-space formulation of orbital-free DFT to study the core
+energetics and core structure of an isolated screw dislocation in Aluminum.
+Using a direct energetics based approach, we estimate the core size of a
+perfect screw dislocation to be $\approx$ 7 $|{\bf b}|$, which is considerably
+larger than previous estimates of $1-3~|{\bf b}|$ based on displacement fields.
+The perfect screw upon structural relaxation dissociates into two Shockley
+partials with partial separation distances of 8.2 \AA~and 6.6 \AA~ measured
+from the screw and edge component differential displacement plots,
+respectively. Similar to a previous electronic structure study on edge
+dislocation, we find that the core energy of the relaxed screw dislocation is
+strongly dependent on macroscopic deformations. Next, we use the edge and screw
+dislocation core energetics data with physically reasonable assumptions to
+develop a continuum energetics model for an aggregate of dislocations. Further,
+we use this model in a discrete dislocation network, and from the variations of
+the core energy with respect to the nodal positions of the network, we obtain
+the nodal core force which can directly be incorporated into discrete
+dislocation dynamics frameworks. We analyze and classify the nodal core force
+into three different contributions based on their decay behavior. Two of these
+contributions to the core force, both arising from the core energy dependence
+on macroscopic deformations, are not accounted for in currently used discrete
+dislocation dynamics models which assume the core energy to be a constant.
+Using case studies involving simple dislocation structures, we demonstrate that
+the contribution to the core force from the core energy dependence on
+macroscopic deformations can be significant in comparison to the elastic
+Peach-Koehler force even up to distances of $10-15$ nm between dislocation
+structures.
+"
+29610,TrustShadow: Secure Execution of Unmodified Applications with ARM TrustZone,"  The rapid evolution of Internet-of-Things (IoT) technologies has led to an
+emerging need to make it smarter. A variety of applications now run
+simultaneously on an ARM-based processor. For example, devices on the edge of
+the Internet are provided with higher horsepower to be entrusted with storing,
+processing and analyzing data collected from IoT devices. This significantly
+improves efficiency and reduces the amount of data that needs to be transported
+to the cloud for data processing, analysis and storage. However, commodity OSes
+are prone to compromise. Once they are exploited, attackers can access the data
+on these devices. Since the data stored and processed on the devices can be
+sensitive, left untackled, this is particularly disconcerting.
+In this paper, we propose a new system, TrustShadow that shields legacy
+applications from untrusted OSes. TrustShadow takes advantage of ARM TrustZone
+technology and partitions resources into the secure and normal worlds. In the
+secure world, TrustShadow constructs a trusted execution environment for
+security-critical applications. This trusted environment is maintained by a
+lightweight runtime system that coordinates the communication between
+applications and the ordinary OS running in the normal world. The runtime
+system does not provide system services itself. Rather, it forwards requests
+for system services to the ordinary OS, and verifies the correctness of the
+responses. To demonstrate the efficiency of this design, we prototyped
+TrustShadow on a real chip board with ARM TrustZone support, and evaluated its
+performance using both microbenchmarks and real-world applications. We showed
+TrustShadow introduces only negligible overhead to real-world applications.
+"
+29611,Gaussian Beam-Propagation Theory for Nonlinear Optics - Featuring an Exact Treatment of Orbital Angular Momentum Transfer,"  We present a general, Gaussian spatial mode propagation formalism for
+describing the generation of higher order multi-spatial-mode beams generated
+during nonlinear interactions. Furthermore, to implement the theory, we
+simulate optical angular momentum transfer interactions, and show how one can
+optimize the interaction to reduce the undesired modes. Past theoretical
+treatments of this problem have often been phenomenological, at best. Here we
+present an exact solution for the single-pass no-cavity regime, in which the
+the nonlinear interaction is not overly strong. We apply our theory to two
+experiments, with very good agreement, and give examples of several more
+configurations, easily tested in the laboratory.
+"
+29612,Machine Learning Spatial Geometry from Entanglement Features,"  Motivated by the close relations of the renormalization group with both the
+holography duality and the deep learning, we propose that the holographic
+geometry can emerge from deep learning the entanglement feature of a quantum
+many-body state. We develop a concrete algorithm, call the entanglement feature
+learning (EFL), based on the random tensor network (RTN) model for the tensor
+network holography. We show that each RTN can be mapped to a Boltzmann machine,
+trained by the entanglement entropies over all subregions of a given quantum
+many-body state. The goal is to construct the optimal RTN that best reproduce
+the entanglement feature. The RTN geometry can then be interpreted as the
+emergent holographic geometry. We demonstrate the EFL algorithm on 1D free
+fermion system and observe the emergence of the hyperbolic geometry (AdS$_3$
+spatial geometry) as we tune the fermion system towards the gapless critical
+point (CFT$_2$ point).
+"
+29613,One-dimensional von Kármán models for elastic ribbons,"  By means of a variational approach we rigorously deduce three one-dimensional
+models for elastic ribbons from the theory of von Kármán plates, passing to
+the limit as the width of the plate goes to zero. The one-dimensional model
+found starting from the ""linearized"" von Kármán energy corresponds to that
+of a linearly elastic beam that can twist but can deform in just one plane;
+while the model found from the von Kármán energy is a non-linear model that
+comprises stretching, bendings, and twisting. The ""constrained"" von Kármán
+energy, instead, leads to a new Sadowsky type of model.
+"
+29614,Generalized model for $k$-core percolation and interdependent networks,"  Cascading failures in complex systems have been studied extensively using two
+different models: $k$-core percolation and interdependent networks. We combine
+the two models into a general model, solve it analytically and validate our
+theoretical results through extensive simulations. We also study the complete
+phase diagram of the percolation transition as we tune the average local
+$k$-core threshold and the coupling between networks. We find that the phase
+diagram of the combined processes is very rich and includes novel features that
+do not appear in the models studying each of the processes separately. For
+example, the phase diagram consists of first and second-order transition
+regions separated by two tricritical lines that merge together and enclose a
+novel two-stage transition region. In the two-stage transition, the size of the
+giant component undergoes a first-order jump at a certain occupation
+probability followed by a continuous second-order transition at a lower
+occupation probability. Furthermore, at certain fixed interdependencies, the
+percolation transition changes from first-order $\rightarrow$ second-order
+$\rightarrow$ two-stage $\rightarrow$ first-order as the $k$-core threshold is
+increased. The analytic equations describing the phase boundaries of the
+two-stage transition region are set up and the critical exponents for each type
+of transition are derived analytically.
+"
+29615,ESASky v.2.0: all the skies in your browser,"  With the goal of simplifying the access to science data to scientists and
+citizens, ESA recently released ESASky (this http URL), a new open-science
+easy-to-use portal with the science-ready Astronomy data from ESA and other
+major data providers. In this presentation, we announced version 2.0 of the
+application, which includes access to all science-ready images, catalogues and
+spectra, a feature to help planning of future JWST observations, the
+possibility to search for data of all (targeted and serendipitously observed)
+Solar System Objects in Astronomy images, a first support to mobile devices and
+several other smaller usability features. We also discussed the future
+evolution of the portal and the lessons learnt from the 1+ year of operations
+from the point of view of access, visualization and manipulation of big
+datasets (all sky maps, also called HiPS) and large catalogues (like e.g. the
+Gaia DR1 catalogues or the Hubble Source Catalogue) and the design and
+validation principles for the development of friendly GUIs for thin layer web
+clients aimed at scientists.
+"
+29616,Semidefinite Programming and Nash Equilibria in Bimatrix Games,"  We explore the power of semidefinite programming (SDP) for finding additive
+epsilon-approximate Nash equilibria in bimatrix games. We introduce an SDP
+relaxation for a quadratic programming formulation of the Nash equilibrium (NE)
+problem and provide a number of valid inequalities to improve the quality of
+the relaxation. If a rank-1 solution to this SDP is found, then an exact NE can
+be recovered. We show that for a strictly competitive game, our SDP is
+guaranteed to return a rank-1 solution. Furthermore, we prove that if a rank-2
+solution to our SDP is found, then a 5/11-NE can be recovered for any game, or
+a 1/3-NE for a symmetric game. We propose two algorithms based on iterative
+linearization of smooth nonconvex objective functions that are designed so that
+their global minima coincide with rank-1 solutions. Empirically, we demonstrate
+that these algorithms often recover solutions of rank at most two and epsilon
+close to zero. We then show how our SDP approach can address two (NP-hard)
+problems of economic interest: finding the maximum welfare achievable under any
+NE, and testing whether there exists a NE where a particular set of strategies
+is not played. Finally, we show the connection between our SDP and the first
+level of the Lasserre/sum of squares hierarchy.
+"
+29617,Ground states in the Many Interacting Worlds approach,"  Recently the Many-Interacting-Worlds (MIW) approach to a quantum theory
+without wave functions was proposed. This approach leads quite naturally to
+numerical integrators of the Schrödinger equation. It has been suggested that
+such integrators may feature advantages over fixed-grid methods for higher
+numbers of degrees of freedom. However, as yet, little is known about concrete
+MIW models for more than one spatial dimension and/or more than one particle.
+In this work we develop the MIW approach further to treat arbitrary degrees of
+freedom, and provide a systematic study of a corresponding numerical
+implementation for computing one-particle ground and excited states in one
+dimension, and ground states in two spatial dimensions. With this step towards
+the treatment of higher degrees of freedom we hope to stimulate their further
+study.
+"
+29618,Long-lived quantum coherences in a V-type system strongly driven by a thermal environment,"  We explore the coherent dynamics of a three-level V-system interacting with a
+thermal bath in the regime where thermal excitation occurs much faster than
+spontaneous decay. We present analytic solutions of the Bloch-Redfield quantum
+master equations, which show that strong incoherent pumping can generate
+long-lived quantum coherences among the excited states of the V-system in the
+overdamped regime defined by the condition $\Delta/(\bar{n}\gamma)<f(p)$, where
+$\Delta$ is the excited-state level splitting, $\gamma$ is the spontaneous
+decay rate, $\bar{n}\gg 1$ is the effective photon occupation number
+proportional to the pumping intensity, and $f(p)$ is a universal function of
+the transition dipole alignment parameter $p$. In the limit of nearly parallel
+transition dipoles ($p\to 1$) the coherence lifetime $\tau_c = 1.34
+(\bar{n}/\gamma) (\Delta/\gamma)^{-2}$ scales linearly with $\bar{n}$ and is
+enhanced by the factor $0.67 \bar{n}$ with respect to the weak-pumping limit
+[Phys. Rev. Lett. 113, 113601 (2014); J. Chem. Phys. 144, 244108 (2016)]. We
+also establish the existence of long-lived quasistationary states, which occur
+in the overdamped regime and affect the process of thermalization of the
+V-system with the bath, slowing down the approach to thermal equilibrium. In
+the case of nonparallel transition dipole moments ($p<1$), no quasistationary
+states are formed and the coherence lifetime decreases sharply. Our results
+reveal new regimes of long-lived quantum coherent dynamics, which could be
+observed in thermally driven atomic and molecular systems.
+"
+29619,Clustering and Variable Selection in the Presence of Mixed Variable Types and Missing Data,"  We consider the problem of model-based clustering in the presence of many
+correlated, mixed continuous and discrete variables, some of which may have
+missing values. Discrete variables are treated with a latent continuous
+variable approach and the Dirichlet process is used to construct a mixture
+model with an unknown number of components. Variable selection is also
+performed to identify the variables that are most influential for determining
+cluster membership. The work is motivated by the need to cluster patients
+thought to potentially have autism spectrum disorder (ASD) on the basis of many
+cognitive and/or behavioral test scores. There are a modest number of patients
+(~480) in the data set along with many (~100) test score variables (many of
+which are discrete valued and/or missing). The goal of the work is to (i)
+cluster these patients into similar groups to help identify those with similar
+clinical presentation, and (ii) identify a sparse subset of tests that inform
+the clusters in order to eliminate unnecessary testing. The proposed approach
+compares very favorably to other methods via simulation of problems of this
+type. The results of the ASD analysis suggested three clusters to be most
+likely, while only four test scores had high (>0.5) posterior probability of
+being informative. This will result in much more efficient and informative
+testing. The need to cluster observations on the basis of many correlated,
+continuous/discrete variables with missing values, is a common problem in the
+health sciences as well as in many other disciplines.
+"
+29620,Total curvature of planar graphs with nonnegative combinatorial curvature,"  We prove that the total curvature of any planar graph with nonnegative
+combinatorial curvature is an integral multiple of $\frac{1}{12}.$ As a
+corollary, this answers a question proposed by T. Réti.
+"
+29621,Assessing Agile Transformation Success Factors,"  Research on success factors involved in the agile transformation process is
+not conclusive and there is still need for guidelines to help in the
+transformation process considering the organizational context (culture, values,
+needs, reality and goals). The usage of success factors as a tool to help agile
+adoption raises the following research question: What are the success factors
+for an organization and their teams in preparation for the agile transformation
+process? This research presents an assessment of the organizational environment
+including the company's goals and the perception of the team members to provide
+awareness of how the organization should prepare for the next steps in the
+agile transformation. The findings show that a company based in Chicago, USA,
+succeeded implementing customer involvement and self-organized teams but faces
+challenges with measurement models and training. The main contribution of the
+research is understand which success factors exist in their environment and how
+they can be used during agile adoption.
+"
+29622,Variational Inference based on Robust Divergences,"  Robustness to outliers is a central issue in real-world machine learning
+applications. While replacing a model to a heavy-tailed one (e.g., from
+Gaussian to Student-t) is a standard approach for robustification, it can only
+be applied to simple models. In this paper, based on Zellner's optimization and
+variational formulation of Bayesian inference, we propose an outlier-robust
+pseudo-Bayesian variational method by replacing the Kullback-Leibler divergence
+used for data fitting to a robust divergence such as the beta- and
+gamma-divergences. An advantage of our approach is that superior but complex
+models such as deep networks can also be handled. We theoretically prove that,
+for deep networks with ReLU activation functions, the \emph{influence function}
+in our proposed method is bounded, while it is unbounded in the ordinary
+variational inference. This implies that our proposed method is robust to both
+of input and output outliers, while the ordinary variational method is not. We
+experimentally demonstrate that our robust variational method outperforms
+ordinary variational inference in regression and classification with deep
+networks.
+"
+29623,On fractional powers of the Bessel operator on a semiaxis,"  In this paper we study fractional powers of the Bessel differential operator
+defined on a semiaxis. Some important properties of such fractional powers of
+the Bessel differential operator are proved. They include connections with
+Legendre functions for kernel representations, fractional integral operators of
+Liouville and Saigo, Mellin transform and index laws. Possible applications are
+indicated to differential equations with fractional powers of the Bessel
+differential operator.
+"
+29624,Pair Events in Superluminal Optics,"  When an object moves faster than emissions it creates, it may appear at two
+positions simultaneously. The appearance or disappearance of this bifurcation
+is referred to as a pair event. Inherently convolved with superluminal motion,
+pair events have no subluminal counterparts. Common examples of superluminal
+motions that exhibit pair events include Cherenkov radiation, sonic booms,
+illumination fronts from variable light sources, and rotating beams. The
+minimally simple case of pair events from a single massive object is explored
+here: uniform linear motion. A pair event is perceived when the radial
+component of the object's speed toward the observer drops from superluminal to
+subluminal. Emission from the pair creation event will reach the observer
+before emission from either of the two images created. Potentially observable
+image pair events are described for sonic booms and Cherenkov light. To date,
+no detection of discrete images following a projectile pair event have ever
+been reported, and so the pair event nature of sonic booms and Cherenkov
+radiation, for example, remains unconfirmed. Recent advances in modern
+technology have made such pair event tracking feasible. If measured, pair
+events could provide important information about object distance and history.
+"
+29625,Learning rates for classification with Gaussian kernels,"  This paper aims at refined error analysis for binary classification using
+support vector machine (SVM) with Gaussian kernel and convex loss. Our first
+result shows that for some loss functions such as the truncated quadratic loss
+and quadratic loss, SVM with Gaussian kernel can reach the almost optimal
+learning rate, provided the regression function is smooth. Our second result
+shows that, for a large number of loss functions, under some Tsybakov noise
+assumption, if the regression function is infinitely smooth, then SVM with
+Gaussian kernel can achieve the learning rate of order $m^{-1}$, where $m$ is
+the number of samples.
+"
+29626,Deep Neural Networks Learn Non-Smooth Functions Effectively,"  We theoretically discuss why deep neural networks (DNNs) performs better than
+other models in some cases by investigating statistical properties of DNNs for
+non-smooth functions. While DNNs have empirically shown higher performance than
+other standard methods, understanding its mechanism is still a challenging
+problem. From an aspect of the statistical theory, it is known many standard
+methods attain the optimal rate of generalization errors for smooth functions
+in large sample asymptotics, and thus it has not been straightforward to find
+theoretical advantages of DNNs. This paper fills this gap by considering
+learning of a certain class of non-smooth functions, which was not covered by
+the previous theory. We derive the generalization error of estimators by DNNs
+with a ReLU activation, and show that convergence rates of the generalization
+by DNNs are almost optimal to estimate the non-smooth functions, while some of
+the popular models do not attain the optimal rate. In addition, our theoretical
+result provides guidelines for selecting an appropriate number of layers and
+edges of DNNs. We provide numerical experiments to support the theoretical
+results.
+"
+29627,Blind Justice: Fairness with Encrypted Sensitive Attributes,"  Recent work has explored how to train machine learning models which do not
+discriminate against any subgroup of the population as determined by sensitive
+attributes such as gender or race. To avoid disparate treatment, sensitive
+attributes should not be considered. On the other hand, in order to avoid
+disparate impact, sensitive attributes must be examined, e.g., in order to
+learn a fair model, or to check if a given model is fair. We introduce methods
+from secure multi-party computation which allow us to avoid both. By encrypting
+sensitive attributes, we show how an outcome-based fair model may be learned,
+checked, or have its outputs verified and held to account, without users
+revealing their sensitive attributes.
+"
+29628,Torque-Controlled Stepping-Strategy Push Recovery: Design and Implementation on the iCub Humanoid Robot,"  One of the challenges for the robotics community is to deploy robots which
+can reliably operate in real world scenarios together with humans. A crucial
+requirement for legged robots is the capability to properly balance on their
+feet, rejecting external disturbances. iCub is a state-of-the-art humanoid
+robot which has only recently started to balance on its feet. While the current
+balancing controller has proved successful in various scenarios, it still
+misses the capability to properly react to strong pushes by taking steps. This
+paper goes in this direction. It proposes and implements a control strategy
+based on the Capture Point concept [1]. Instead of relying on position control,
+like most of Capture Point related approaches, the proposed strategy generates
+references for the momentum-based torque controller already implemented on the
+iCub, thus extending its capabilities to react to external disturbances, while
+retaining the advantages of torque control when interacting with the
+environment. Experiments in the Gazebo simulator and on the iCub humanoid robot
+validate the proposed strategy.
+"
+29629,Improving Malware Detection Accuracy by Extracting Icon Information,"  Detecting PE malware files is now commonly approached using statistical and
+machine learning models. While these models commonly use features extracted
+from the structure of PE files, we propose that icons from these files can also
+help better predict malware. We propose an innovative machine learning approach
+to extract information from icons. Our proposed approach consists of two steps:
+1) extracting icon features using summary statics, histogram of gradients
+(HOG), and a convolutional autoencoder, 2) clustering icons based on the
+extracted icon features. Using publicly available data and by using machine
+learning experiments, we show our proposed icon clusters significantly boost
+the efficacy of malware prediction models. In particular, our experiments show
+an average accuracy increase of 10% when icon clusters are used in the
+prediction model.
+"
+29630,On Drinfel'd associators,"  In 1986, in order to study the linear representations of the braid group
+$B\_n$coming from the monodromy of the Knizhnik-Zamolodchikov differential
+equations,Drinfel'd introduced a class of formal power series $\Phi$on
+noncommutative variables. These formal series can be considered as a class of
+associators. We here give an interpretation of them as well as some new tools
+over Noncommutative Evolution Equations. Asymptotic phenomena are also
+discussed.
+"
+29631,Representations of the super Jordan plane,"  It is shown that the finite-dimensional simple representations of the super
+Jordan plane $B$ are one-dimensional. The indecomposable representations of
+dimension $2$ and $3$ of $B$ are classified. Two families of indecomposable
+representations of $B$ of arbitrary dimension are presented.
+"
+29632,Erdős-Pósa property of chordless cycles and its applications,"  A chordless cycle, or equivalently a hole, in a graph $G$ is an induced
+subgraph of $G$ which is a cycle of length at least four. We prove that the
+Erdős-Pósa property holds for chordless cycles, which resolves the major
+open question concerning the Erdős-Pósa property. Our proof for chordless
+cycles is constructive: in polynomial time, one can find either $k+1$
+vertex-disjoint chordless cycles, or $ck^2 \log k$ vertices hitting every
+chordless cycle for some constant $c$. It immediately implies an approximation
+algorithm of factor $\mathcal{O}(\sf{opt}\log {\sf opt})$ for Chordal Vertex
+Deletion. We complement our main result by showing that chordless cycles of
+length at least $\ell$ for any fixed $\ell\ge 5$ do not have the
+Erdős-Pósa property.
+"
+29633,Calibration of Sobol indices estimates in case of noisy output,"  This paper presents a simple noise correction method for Sobol' indices
+estimation. Sobol' indices, especially total Sobol' indices are quite sensitive
+to the noise in the output and tend to be severly biased (overestimated) if no
+noise correction is done, which may make their computation meaningless in case
+of even quite moderate noise levels. Proposed method allows to get
+approximately unbiased noise free estimation of Sobol' indices at the cost of
+variance of estimate increase if noise can be represented as a combination of
+additive and multiplicative stationary noise. %Also we show that it is
+impossible to do precise noise correction for more complex noise settings.
+Proposed method is more straightforward than schemes found in the literature
+and does not introduce any assumptions on the function and noise distribution
+(except that it assumes noise to be stationary and be a combination of additive
+and multiplicative). One of the appealing features is that there is actual
+analytical noise correction expression derived.
+"
+29634,Displayed Categories,"  We introduce and develop the notion of *displayed categories*.
+A displayed category over a category C is equivalent to ""a category D and
+functor F : D --> C"", but instead of having a single collection of ""objects of
+D"" with a map to the objects of C, the objects are given as a family indexed by
+objects of C, and similarly for the morphisms. This encapsulates a common way
+of building categories in practice, by starting with an existing category and
+adding extra data/properties to the objects and morphisms.
+The interest of this seemingly trivial reformulation is that various
+properties of functors are more naturally defined as properties of the
+corresponding displayed categories. Grothendieck fibrations, for example, when
+defined as certain functors, use equality on objects in their definition. When
+defined instead as certain displayed categories, no reference to equality on
+objects is required. Moreover, almost all examples of fibrations in nature are,
+in fact, categories whose standard construction can be seen as going via
+displayed categories.
+We therefore propose displayed categories as a basis for the development of
+fibrations in the type-theoretic setting, and similarly for various other
+notions whose classical definitions involve equality on objects.
+Besides giving a conceptual clarification of such issues, displayed
+categories also provide a powerful tool in computer formalisation, unifying and
+abstracting common constructions and proof techniques of category theory, and
+enabling modular reasoning about categories of multi-component structures. As
+such, most of the material of this article has been formalised in Coq over the
+UniMath library, with the aim of providing a practical library for use in
+further developments.
+"
+29635,The Intrinsic Scale of Networks is Small,"  We define the intrinsic scale at which a network begins to reveal its
+identity as the scale at which subgraphs in the network (created by a random
+walk) are distinguishable from similar sized subgraphs in a perturbed copy of
+the network. We conduct an extensive study of intrinsic scale for several
+networks, ranging from structured (e.g. road networks) to ad-hoc and
+unstructured (e.g. crowd sourced information networks), to biological. We find:
+(a) The intrinsic scale is surprisingly small (7-20 vertices), even though the
+networks are many orders of magnitude larger. (b) The intrinsic scale
+quantifies ``structure'' in a network -- networks which are explicitly
+constructed for specific tasks have smaller intrinsic scale. (c) The structure
+at different scales can be fragile (easy to disrupt) or robust.
+"
+29636,On generalized $σ$-soluble groups,"  Let $\sigma =\{\sigma_{i} | i\in I\}$ be a partition of the set of all primes
+$\Bbb{P}$ and $G$ a finite group. Let $\sigma (G)=\{\sigma _{i} : \sigma
+_{i}\cap \pi (G)\ne \emptyset$. A set ${\cal H}$ of subgroups of $G$ is said to
+be a complete Hall $\sigma $-set of $G$ if every member $\ne 1$ of ${\cal H}$
+is a Hall $\sigma _{i}$-subgroup of $G$ for some $i\in I$ and $\cal H$ contains
+exactly one Hall $\sigma _{i}$-subgroup of $G$ for every $i$ such that $\sigma
+_{i}\in \sigma (G)$. We say that $G$ is $\sigma$-full if $G$ possesses a
+complete Hall $\sigma $-set. A complete Hall $\sigma $-set $\cal H$ of $G$ is
+said to be a $\sigma$-basis of $G$ if every two subgroups $A, B \in\cal H$ are
+permutable, that is, $AB=BA$. In this paper, we study properties of finite
+groups having a $\sigma$-basis. In particular, we prove that if $G$ has a a
+$\sigma$-basis, then $G$ is generalized $\sigma$-soluble, that is, $G$ has a
+complete Hall $\sigma $-set and for every chief factor $H/K$ of $G$ we have
+$|\sigma (H/K)|\leq 2$. Moreover, answering to Problem 8.28 in [A.N. Skiba, On
+some results in the theory of finite partially soluble groups, Commun. Math.
+Stat., 4(3) (2016), 281--309], we prove the following Theorem A. Suppose that
+$G$ is $\sigma$-full. Then every complete Hall $\sigma$-set of $G$ forms a
+$\sigma$-basis of $G$ if and only if $G$ is generalized $\sigma$-soluble and
+for the automorphism group $G/C_{G}(H/K)$, induced by $G$ on any its chief
+factor $H/K$, we have either $\sigma (H/K)=\sigma (G/C_{G}(H/K))$ or $\sigma
+(H/K) =\{\sigma _{i}\}$ and $G/C_{G}(H/K)$ is a $\sigma _{i} \cup \sigma
+_{j}$-group for some $i\ne j$.
+"
+29637,Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization,"  Model based predictions of future trajectories of a dynamical system often
+suffer from inaccuracies, forcing model based control algorithms to re-plan
+often, thus being computationally expensive, suboptimal and not reliable. In
+this work, we propose a model agnostic method for estimating the uncertainty of
+a model?s predictions based on reconstruction error, using it in control and
+exploration. As our experiments show, this uncertainty estimation can be used
+to improve control performance on a wide variety of environments by choosing
+predictions of which the model is confident. It can also be used for active
+learning to explore more efficiently the environment by planning for
+trajectories with high uncertainty, allowing faster model learning.
+"
+29638,Rare Disease Physician Targeting: A Factor Graph Approach,"  In rare disease physician targeting, a major challenge is how to identify
+physicians who are treating diagnosed or underdiagnosed rare diseases patients.
+Rare diseases have extremely low incidence rate. For a specified rare disease,
+only a small number of patients are affected and a fractional of physicians are
+involved. The existing targeting methodologies, such as segmentation and
+profiling, are developed under mass market assumption. They are not suitable
+for rare disease market where the target classes are extremely imbalanced. The
+authors propose a graphical model approach to predict targets by jointly
+modeling physician and patient features from different data spaces and
+utilizing the extra relational information. Through an empirical example with
+medical claim and prescription data, the proposed approach demonstrates better
+accuracy in finding target physicians. The graph representation also provides
+visual interpretability of relationship among physicians and patients. The
+model can be extended to incorporate more complex dependency structures. This
+article contributes to the literature of exploring the benefit of utilizing
+relational dependencies among entities in healthcare industry.
+"
+29639,On Zero Error Capacity of Nearest Neighbor Error Channels with Multilevel Alphabet,"  This paper studies the zero error capacity of the Nearest Neighbor Error
+(NNE) channels with a multilevel alphabet. In the NNE channels, a transmitted
+symbol is a $d$-tuple of elements in $\{0,1,2,\dots, n-1 \}$. It is assumed
+that only one element error to a nearest neighbor element in a transmitted
+symbol can occur. The NNE channels can be considered as a special type of
+limited magnitude error channels, and it is closely related to error models for
+flash memories. In this paper, we derive a lower bound of the zero error
+capacity of the NNE channels based on a result of the perfect Lee codes. An
+upper bound of the zero error capacity of the NNE channels is also derived from
+a feasible solution of a linear programming problem defined based on the
+confusion graphs of the NNE channels. As a result, a concise formula of the
+zero error capacity is obtained using the lower and upper bounds.
+"
+29640,On expansions for the Black-Scholes prices and hedge parameters,"  We derive new formulas for the price of the European call and put options in
+the Black-Scholes model, under the form of uniformly convergent series
+generalizing previously known approximations. We also provide precise
+boundaries for the convergence speed and apply the results to the calculation
+of hedge parameters (Greeks).
+"
+29641,"On the simultaneous Diophantine equations m.(x_1^k+....+x_{t_1}^k)=n.(y_1^k+....+y_{t_2}^k); k=1,3","  In this paper, we solve the simultaneous Diophantine equations
+m.(x_1^k+....+x_{t_1}^k)=n.(y_1^k+....+y_{t_2}^k); k=1,3, where t_1, t_2>3, and
+m, n are fixed arbitrary and relatively prime positive integers. This is done
+by choosing two appropriate trivial parametric solutions and obtaining
+infinitely many nontrivial parametric solutions. Also we work out some
+examples, in particular the Diophantine systems of A^k+B^k+C^k=D^k+E^4; k=1,3.
+"
+29642,Semi-extraspecial Groups,"  We survey the results regarding semi-extraspecial $p$-groups.
+Semi-extraspecial groups can be viewed as generalizations of extraspecial
+groups. We present the connections between semi-extraspecial groups and Camina
+groups and VZ-groups, and give upper bounds on the order of the center and the
+orders of abelian normal subgroups. We define ultraspecial groups to be
+semi-extraspecial groups where the center is as large as possible, and
+demonstrate a connection between ultraspecial groups that have at least two
+abelian subgroups whose order is the maximum and semifields.
+"
+29643,"Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication","  This paper proposes a computational approach for analysis of strokes in line
+drawings by artists. We aim at developing an AI methodology that facilitates
+attribution of drawings of unknown authors in a way that is not easy to be
+deceived by forged art. The methodology used is based on quantifying the
+characteristics of individual strokes in drawings. We propose a novel algorithm
+for segmenting individual strokes. We designed and compared different
+hand-crafted and learned features for the task of quantifying stroke
+characteristics. We also propose and compare different classification methods
+at the drawing level. We experimented with a dataset of 300 digitized drawings
+with over 80 thousands strokes. The collection mainly consisted of drawings of
+Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of
+representative works of other artists. The experiments shows that the proposed
+methodology can classify individual strokes with accuracy 70%-90%, and
+aggregate over drawings with accuracy above 80%, while being robust to be
+deceived by fakes (with accuracy 100% for detecting fakes in most settings).
+"
+29644,Strong solutions in $L^2$ framework for fluid-rigid body interaction problem - mixed case,"  The paper deals with the well-posedness of the strong solution of
+fluid-structure interaction problem when the mixed boundary conditions are
+considered.
+"
+29645,SSGAN: Secure Steganography Based on Generative Adversarial Networks,"  In this paper, a novel strategy of Secure Steganograpy based on Generative
+Adversarial Networks is proposed to generate suitable and secure covers for
+steganography. The proposed architecture has one generative network, and two
+discriminative networks. The generative network mainly evaluates the visual
+quality of the generated images for steganography, and the discriminative
+networks are utilized to assess their suitableness for information hiding.
+Different from the existing work which adopts Deep Convolutional Generative
+Adversarial Networks, we utilize another form of generative adversarial
+networks. By using this new form of generative adversarial networks,
+significant improvements are made on the convergence speed, the training
+stability and the image quality. Furthermore, a sophisticated steganalysis
+network is reconstructed for the discriminative network, and the network can
+better evaluate the performance of the generated images. Numerous experiments
+are conducted on the publicly available datasets to demonstrate the
+effectiveness and robustness of the proposed method.
+"
+29646,Deterministic Quantum Annealing Expectation-Maximization Algorithm,"  Maximum likelihood estimation (MLE) is one of the most important methods in
+machine learning, and the expectation-maximization (EM) algorithm is often used
+to obtain maximum likelihood estimates. However, EM heavily depends on initial
+configurations and fails to find the global optimum. On the other hand, in the
+field of physics, quantum annealing (QA) was proposed as a novel optimization
+approach. Motivated by QA, we propose a quantum annealing extension of EM,
+which we call the deterministic quantum annealing expectation-maximization
+(DQAEM) algorithm. We also discuss its advantage in terms of the path integral
+formulation. Furthermore, by employing numerical simulations, we illustrate how
+it works in MLE and show that DQAEM outperforms EM.
+"
+29647,Bivariate Rician shadowed fading model,"  In this paper we present a bivariate Rician shadowed fading model where the
+shadowing is assumed to follow a Nakagami-$m$ distribution. We derive exact
+expressions involving a single integral for both the joint probability density
+function (PDF) and the joint cumulative distribution function (CDF) and we also
+derive an exact closed-form expression for the moment generating function
+(MGF). As a direct consequence we obtain a closed-form expression for the power
+correlation coefficient between Rician shadowed variables as a function of the
+correlation coefficient between the underlying variables of the model.
+Additionally, in the particular case of integer $m$ we show that the PDF can be
+expressed in closed-form in terms of a sum of m Meijer G-functions of two
+variables. Results are applied to analyze the outage probability (OT) of a
+dual-branch selection combiner (SC) in correlated Rician shadowed fading and
+the evaluation of the level crossing rate (LCR) and average fade duration (AFD)
+of a sampled Rician shadowed fading envelope.
+"
+29648,The Non-Linear Growth of the Magnetic Rayleigh-Taylor Instability,"  This work examines the effect of the embedded magnetic field strength on the
+non-linear development of the magnetic Rayleigh-Taylor Instability (RTI) (with
+a field-aligned interface) in an ideal gas close to the incompressible limit in
+three dimensions. Numerical experiments are conducted in a domain sufficiently
+large so as to allow the predicted critical modes to develop in a physically
+realistic manner. The ratio between gravity, which drives the instability in
+this case (as well as in several of the corresponding observations), and
+magnetic field strength is taken up to a ratio which accurately reflects that
+of observed astrophysical plasma, in order to allow comparison between the
+results of the simulations and the observational data which served as
+inspiration for this work. This study finds reduced non-linear growth of the
+rising bubbles of the RTI for stronger magnetic fields, and that this is
+directly due to the change in magnetic field strength, rather than the indirect
+effect of altering characteristic length scales with respect to domain size. By
+examining the growth of the falling spikes, the growth rate appears to be
+enhanced for the strongest magnetic field strengths, suggesting that rather
+than affecting the development of the system as a whole, increased magnetic
+field strengths in fact introduce an asymmetry to the system. Further
+investigation of this effect also revealed that the greater this asymmetry, the
+less efficiently the gravitational energy is released. By better understanding
+the under-studied regime of such a major phenomenon in astrophysics, deeper
+explanations for observations may be sought, and this work illustrates that the
+strength of magnetic fields in astrophysical plasmas influences observed RTI in
+subtle and complex ways.
+"
+29649,Computation Diversity in Emerging Networking Paradigms,"  Nowadays, computation is playing an increasingly more important role in the
+future generation of computer and communication networks, as exemplified by the
+recent progress in software defined networking (SDN) for wired networks as well
+as cloud radio access networks (C-RAN) and mobile cloud computing (MCC) for
+wireless networks. This paper proposes a unified concept, i.e., computation
+diversity, to describe the impact and diverse forms of the computation
+resources on both wired and wireless communications. By linking the computation
+resources to the communication networks based on quality of service (QoS)
+requirements, we can show how computation resources influence the networks.
+Moreover, by analyzing the different functionalities of computation resources
+in SDN, C-RAN, and MCC, we can show diverse and flexible form that the
+computation resources present in different networks. The study of computation
+diversity can provide guidance to the future networks design, i.e., how to
+allocate the resources jointly between computation (e.g., CPU capacity) and
+communication (e.g., bandwidth), and thereby saving system energy and increase
+users' experiences.
+"
+29650,Hyper-track selector nuclear emulsion readout system aimed at scanning an area of one thousand square meters,"  Automatic nuclear emulsion readout systems have seen remarkable progress
+since the original idea was developed almost 40 years ago. After the success of
+its full application to a large-scale neutrino experiment, OPERA, a much faster
+readout system, the hyper-track selector (HTS), has been developed. HTS, which
+has an extremely wide-field objective lens, reached a scanning speed of 4700
+cm$^2$/h, which is nearly 100 times faster than the previous system and
+therefore strongly promotes many new experimental projects. We will describe
+the concept, specifications, system structure, and achieved performance in this
+paper.
+"
+29651,HI emission from the red giant Y CVn with the VLA and FAST,"  Imaging studies with the VLA have revealed HI emission associated with the
+extended circumstellar shells of red giants. We analyse the spectral map
+obtained on Y CVn, a J-type carbon star on the AGB. The HI line profiles can be
+interpreted with a model of a detached shell resulting from the interaction of
+a stellar outflow with the local interstellar medium. We reproduce the spectral
+map by introducing a distortion along a direction corresponding to the star's
+motion in space. We then use this fitting to simulate observations expected
+from the FAST radiotelescope, and discuss its potential for improving
+ourdescription of the outer regions of circumstellar shells.
+"
+29652,Macroscopic scalar curvature and areas of cycles,"  In this paper we prove the following. Let $\Sigma$ be an $n$--dimensional
+closed hyperbolic manifold and let $g$ be a Riemannian metric on $\Sigma \times
+\mathbb{S}^1$. Given an upper bound on the volumes of unit balls in the
+Riemannian universal cover $(\widetilde{\Sigma\times
+\mathbb{S}^1},\widetilde{g})$, we get a lower bound on the area of the
+$\mathbb{Z}_2$--homology class $[\Sigma \times \ast]$ on $\Sigma \times
+\mathbb{S}^1$, proportional to the hyperbolic area of $\Sigma$. The theorem is
+based on a theorem of Guth and is analogous to a theorem of Kronheimer and
+Mrowka involving scalar curvature.
+"
+29653,Irreducible Components of Exotic Springer fibres II: The Exotic Robinson-Schensted Algorithm,"  Kato's exotic nilpotent cone was introduced as a substitute for the ordinary
+nilpotent cone of type C with cleaner properties. The geometric
+Robinson-Schensted correspondence is obtained by parametrizing the irreducible
+components of the Steinberg variety (the conormal variety for the action of a
+semisimple group on two copies of its flag variety); in type A the bijection
+coincides with the classical Robinson-Schensted algorithm for the symmetric
+group. Here we give a combinatorial description of the bijection obtained by
+using the exotic nilpotent cone instead of ordinary type C nilpotent cone in
+the geometric Robinson-Schensted correspondence; we refer this as the ""exotic
+Robinson-Schensted bijection"". This is interesting from a combinatorial
+perspective, and not a naive extension of the type A Robinson-Schensted
+bijection.
+"
+29654,Cosmological Implications of Nonlocal Gravity,"  We present extensions of the treatment contained in our recent paper on
+nonlocal Newtonian cosmology [C. Chicone and B. Mashhoon, J. Math. Phys. 57,
+072501 (2016)]. That is, the implications of the recent nonlocal generalization
+of Einstein's theory of gravitation are further investigated within the regime
+of Newtonian cosmology. In particular, we treat the nonlocal problem of
+structure formation for a spherically symmetric expanding dust model and show
+numerically that as the central density contrast grows, it tends to decrease
+slowly with radial distance as the universe expands. The nonlocal violation of
+Newton's shell theorem provides a physical interpretation of our numerical
+results.
+"
+29655,A self-excited threshold autoregressive state-space model for menstrual cycles: forecasting menstruation and identifying ovarian phases based on basal body temperature,"  The menstrual cycle is composed of the follicular phase and subsequent luteal
+phase based on events occurring in the ovary. Basal body temperature (BBT)
+reflects this biphasic aspect of menstrual cycle and tends to be relatively low
+during the follicular phase. In the present study, we proposed a state-space
+model that explicitly incorporates the biphasic nature of the menstrual cycle,
+in which the probability density distributions for the advancement of the
+menstrual phase and that for BBT switch depend on a latent state variable. Our
+model derives the predictive distribution of the day of the next menstruation
+onset that is adaptively adjusted by accommodating new observations of BBT
+sequentially. It also enables us to obtain conditional probabilities of the
+woman being in the early or late stages of the cycle, which can be used to
+identify the duration of follicular and luteal phases, as well as to estimate
+the day of ovulation. By applying the model to real BBT and menstruation data,
+we show that the proposed model can properly capture the biphasic
+characteristics of menstrual cycles, providing a good prediction of the
+menstruation onset in a wide range of age groups. An application to a large
+data set containing 25,622 cycles provided by 3,533 woman subjects further
+highlighted the between-age differences in the population characteristics of
+menstrual cycles, suggesting wide applicability of the proposed model.
+"
+29656,Self-regulating Supply-Demand Systems,"  Supply-demand systems in Smart City sectors such as energy, transportation,
+telecommunication, are subject of unprecedented technological transformations
+by the Internet of Things. Usually, supply-demand systems involve actors that
+produce and consume resources, e.g. energy, and they are regulated such that
+supply meets demand, or demand meets available supply. Mismatches of supply and
+demand may increase operational costs, can cause catastrophic damage in
+infrastructure, for instance power blackouts, and may even lead to social
+unrest and security threats. Long-term, operationally offline and top-down
+regulatory decision-making by governmental officers, policy makers or system
+operators may turn out to be ineffective for matching supply-demand under new
+dynamics and opportunities that Internet of Things technologies bring to
+supply-demand systems, for instance, interactive cyber-physical systems and
+software agents running locally in physical assets to monitor and apply
+automated control actions in real-time. e.g. power flow redistributions by
+smart transformers to improve the Smart Grid reliability. Existing work on
+online regulatory mechanisms of matching supply-demand either focuses on
+game-theoretic solutions with assumptions that cannot be easily met in
+real-world systems or assume centralized management entities and local access
+to global information. This paper contributes a generic decentralized
+self-regulatory framework, which, in contrast to related work, is shaped around
+standardized control system concepts and Internet of Things technologies for an
+easier adoption and applicability. The framework involves a decentralized
+combinatorial optimization mechanism that matches supply-demand under different
+regulatory scenarios.
+"
+29657,High Throughput Synchronous Distributed Stochastic Gradient Descent,"  We introduce a new, high-throughput, synchronous, distributed, data-parallel,
+stochastic-gradient-descent learning algorithm. This algorithm uses amortized
+inference in a compute-cluster-specific, deep, generative, dynamical model to
+perform joint posterior predictive inference of the mini-batch gradient
+computation times of all worker-nodes in a parallel computing cluster. We show
+that a synchronous parameter server can, by utilizing such a model, choose an
+optimal cutoff time beyond which mini-batch gradient messages from slow workers
+are ignored that maximizes overall mini-batch gradient computations per second.
+In keeping with earlier findings we observe that, under realistic conditions,
+eagerly discarding the mini-batch gradient computations of stragglers not only
+increases throughput but actually increases the overall rate of convergence as
+a function of wall-clock time by virtue of eliminating idleness. The principal
+novel contribution and finding of this work goes beyond this by demonstrating
+that using the predicted run-times from a generative model of cluster worker
+performance to dynamically adjust the cutoff improves substantially over the
+static-cutoff prior art, leading to, among other things, significantly reduced
+deep neural net training times on large computer clusters.
+"
+29658,Investigating Human + Machine Complementarity for Recidivism Predictions,"  When might human input help (or not) when assessing risk in fairness domains?
+Dressel and Farid (2018) asked Mechanical Turk workers to evaluate a subset of
+defendants in the ProPublica COMPAS data for risk of recidivism, and concluded
+that COMPAS predictions were no more accurate or fair than predictions made by
+humans. We delve deeper into this claim to explore differences in human and
+algorithmic decision making. We construct a Human Risk Score based on the
+predictions made by multiple Turk workers, characterize the features that
+determine agreement and disagreement between COMPAS and Human Scores, and
+construct hybrid Human+Machine models to predict recidivism. Our key finding is
+that on this data set, Human and COMPAS decision making differed, but not in
+ways that could be leveraged to significantly improve ground-truth prediction.
+We present the results of our analyses and suggestions for data collection best
+practices to leverage complementary strengths of human and machines in the
+fairness domain.
+"
+29659,Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression,"  High-dimensional settings, where the data dimension ($d$) far exceeds the
+number of observations ($n$), are common in many statistical and machine
+learning applications. Methods based on $\ell_1$-relaxation, such as Lasso, are
+very popular for sparse recovery in these settings. Restricted Eigenvalue (RE)
+condition is among the weakest, and hence the most general, condition in
+literature imposed on the Gram matrix that guarantees nice statistical
+properties for the Lasso estimator. It is natural to ask: what families of
+matrices satisfy the RE condition? Following a line of work in this area, we
+construct a new broad ensemble of dependent random design matrices that have an
+explicit RE bound. Our construction starts with a fixed (deterministic) matrix
+$X \in \mathbb{R}^{n \times d}$ satisfying a simple stable rank condition, and
+we show that a matrix drawn from the distribution $X \Phi^\top \Phi$, where
+$\Phi \in \mathbb{R}^{m \times d}$ is a subgaussian random matrix, with high
+probability, satisfies the RE condition. This construction allows incorporating
+a fixed matrix that has an easily {\em verifiable} condition into the design
+process, and allows for generation of {\em compressed} design matrices that
+have a lower storage requirement than a standard design matrix. We give two
+applications of this construction to sparse linear regression problems,
+including one to a compressed sparse regression setting where the regression
+algorithm only has access to a compressed representation of a fixed design
+matrix $X$.
+"
+29660,The spin-1/2 Heisenberg antiferromagnet on the pyrochlore lattice: An exact diagonalisation study,"  We present exact diagonalisation calculations for the spin-1/2 nearest
+neighbour antiferromagnet on the pyrochlore lattice. We study a section of the
+lattice in the $[111]$ direction and analyse the Hamiltonian of the breathing
+pyrochlore system with two coupling constants $J_1$ and $J_2$ for tetrahedra of
+different orientations and investigate the evolution of the system from the
+limit of disconnected tetrahedra ($J_2 = 0$) to a correlated state at
+$J_1=J_2$. We evaluate the low energy spectrum, two and four spin correlations,
+and spin chirality correlations for a system size of up to 36 sites. The model
+shows a fast decay of spin correlations and we confirm the presence of several
+singlet excitations below the lowest magnetic excitation. We find chirality
+correlations near $J_1=J_2$ to be small at the length scales available at this
+system size. Evaluation of dimer-dimer correlations and analysis of the nature
+of the entanglement of the tetrahedral unit shows that the triplet sector of
+the tetrahedron contributes significantly to the ground state entanglement at
+$J_1 = J_2$.
+"
+29661,On Scalable Supervisory Control of Multi-Agent Discrete-Event Systems,"  In this paper we study multi-agent discrete-event systems where the agents
+can be divided into several groups, and within each group the agents have
+similar or identical state transition structures. We employ a relabeling map to
+generate a ""template structure"" for each group, and synthesize a scalable
+supervisor whose state size and computational process are independent of the
+number of agents. This scalability allows the supervisor to remain invariant
+(no recomputation or reconfiguration needed) if and when there are agents
+removed due to failure or added for increasing productivity. The constant
+computational effort for synthesizing the scalable supervisor also makes our
+method promising for handling large-scale multi-agent systems. Moreover, based
+on the scalable supervisor we design scalable local controllers, one for each
+component agent, to establish a purely distributed control architecture. Three
+examples are provided to illustrate our proposed scalable supervisory synthesis
+and the resulting scalable supervisors as well as local controllers.
+"
+29662,Coordinating Collaborative Chat in Massive Open Online Courses,"  An earlier study of a collaborative chat intervention in a Massive Open
+Online Course (MOOC) identified negative effects on attrition stemming from a
+requirement for students to be matched with exactly one partner prior to
+beginning the activity. That study raised questions about how to orchestrate a
+collaborative chat intervention in a MOOC context in order to provide the
+benefit of synchronous social engagement without the coordination difficulties.
+In this paper we present a careful analysis of an intervention designed to
+overcome coordination difficulties by welcoming students into the chat on a
+rolling basis as they arrive rather than requiring them to be matched with a
+partner before beginning. The results suggest the most positive impact when
+experiencing a chat with exactly one partner rather than more or less. A
+qualitative analysis of the chat data reveals differential experiences between
+these configurations that suggests a potential explanation for the effect and
+raises questions for future research.
+"
+29663,Weight Initialization of Deep Neural Networks(DNNs) using Data Statistics,"  Deep neural networks (DNNs) form the backbone of almost every
+state-of-the-art technique in the fields such as computer vision, speech
+processing, and text analysis. The recent advances in computational technology
+have made the use of DNNs more practical. Despite the overwhelming performances
+by DNN and the advances in computational technology, it is seen that very few
+researchers try to train their models from the scratch. Training of DNNs still
+remains a difficult and tedious job. The main challenges that researchers face
+during training of DNNs are the vanishing/exploding gradient problem and the
+highly non-convex nature of the objective function which has up to million
+variables. The approaches suggested in He and Xavier solve the vanishing
+gradient problem by providing a sophisticated initialization technique. These
+approaches have been quite effective and have achieved good results on standard
+datasets, but these same approaches do not work very well on more practical
+datasets. We think the reason for this is not making use of data statistics for
+initializing the network weights. Optimizing such a high dimensional loss
+function requires careful initialization of network weights. In this work, we
+propose a data dependent initialization and analyze its performance against the
+standard initialization techniques such as He and Xavier. We performed our
+experiments on some practical datasets and the results show our algorithm's
+superior classification accuracy.
+"
+29664,Quantum dynamics and spectroscopy of ab initio liquid water: the interplay of nuclear and electronic quantum effects,"  Understanding the reactivity and spectroscopy of aqueous solutions at the
+atomistic level is crucial for the elucidation and design of chemical
+processes. However, the simulation of these systems requires addressing the
+formidable challenges of treating the quantum nature of both the electrons and
+nuclei. Exploiting our recently developed methods that provide acceleration by
+up to two orders of magnitude, we combine path integral simulations with
+on-the-fly evaluation of the electronic structure at the hybrid density
+functional theory level to capture the interplay between nuclear quantum
+effects and the electronic surface. Here we show that this combination provides
+accurate structure and dynamics, including the full infra-red and Raman spectra
+of liquid water. This allows us to demonstrate and explain the failings of
+lower-level density functionals for dynamics and vibrational spectroscopy when
+the nuclei are treated quantum mechanically. These insights thus provide a
+foundation for the reliable investigation of spectroscopy and reactivity in
+aqueous environments.
+"
+29665,Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks,"  Empirical evidence suggests that heavy-tailed degree distributions occurring
+in many real networks are well-approximated by power laws with exponents $\eta$
+that may take values either less than and greater than two. Models based on
+various forms of exchangeability are able to capture power laws with $\eta <
+2$, and admit tractable inference algorithms; we draw on previous results to
+show that $\eta > 2$ cannot be generated by the forms of exchangeability used
+in existing random graph models. Preferential attachment models generate power
+law exponents greater than two, but have been of limited use as statistical
+models due to the inherent difficulty of performing inference in
+non-exchangeable models. Motivated by this gap, we design and implement
+inference algorithms for a recently proposed class of models that generates
+$\eta$ of all possible values. We show that although they are not exchangeable,
+these models have probabilistic structure amenable to inference. Our methods
+make a large class of previously intractable models useful for statistical
+inference.
+"
+29666,Importance of van der Waals interactions and cation-anion coupling in an organic quantum spin liquid,"  The Mott insulator $\beta'$-EtMe$_3$Sb[Pd(dmit)$_2$]$_2$ belongs to a class
+of charge transfer solids with highly-frustrated triangular lattice of $S=1/2$
+molecular dimers and a quantum-spin-liquid ground state. Our experimental and
+ab initio theoretical studies show the fingerprints of strong correlations and
+disorder, important role of cation-dimer bonding in charge redistribution, no
+sign of intra- and inter-dimer dipoles, and the decisive van der Waals
+contribution to inter-dimer interactions and the ground state structure. The
+latter consists of quasi-degenerate electronic states related to the different
+configurations of cation moieties which permit two different equally probable
+orientations. Upon reducing the temperature, the low-energy excitations slow
+down, indicating glassy signatures as the cation motion freezes out.
+"
+29667,Pearl-Necklace-Like Local Ordering Drives Polypeptide Collapse,"  Collapse of the polypeptide backbone is an integral part of protein folding.
+Using polyglycine as a probe, we explore the nonequilibrium pathways of protein
+collapse in water. We find that the collapse depends on the competition between
+hydration effects and intra-peptide interactions. Once intra-peptide van der
+Waal interactions dominate, the chain collapses along a nonequilibrium pathway
+characterized by formation of pearl-necklace-like local clusters as
+intermediates that eventually coagulate into a single globule. By describing
+this coarsening through the contact probability as a function of distance along
+the chain, we extract a time-dependent length scale that grows in linear
+fashion. The collapse dynamics is characterized by a dynamical critical
+exponent $z=0.5$ that is much smaller than the values of $z=1-2$ reported for
+non-biological polymers. This difference in the exponents is explained by the
+instantaneous formation of intra-chain hydrogen bonds and local ordering that
+may be correlated with the observed fast folding times in proteins.
+"
+29668,FluidDyn: a Python open-source framework for research and teaching in fluid dynamics,"  FluidDyn is a project to foster open-science and open-source in the fluid
+dynamics community. It is thought of as a research project to channel
+open-source dynamics, methods and tools to do science. We propose a set of
+Python packages forming a framework to study fluid dynamics with different
+methods, in particular laboratory experiments (package fluidlab), simulations
+(packages fluidfft, fluidsim and fluidfoam) and data processing (package
+fluidimage). In the present article, we give an overview of the specialized
+packages of the project and then focus on the base package called fluiddyn,
+which contains common code used in the specialized packages. Packages fluidfft
+and fluidsim are described with greater detail in two companion papers, Mohanan
+et al. (2018a,b). With the project FluidDyn, we demonstrate that specialized
+scientific code can be written with methods and good practices of the
+open-source community. The Mercurial repositories are available in Bitbucket
+(this https URL). All codes are documented using Sphinx and
+Read the Docs, and tested with continuous integration run on Bitbucket,
+Pipelines and Travis. To improve the reuse potential, the codes are as modular
+as possible, leveraging the simple object-oriented programming model of Python.
+All codes are also written to be highly efficient, using C++, Cython and
+Pythran to speedup the performance of critical functions.
+"
+29669,Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously,"  We develop the first general semi-bandit algorithm that simultaneously
+achieves $\mathcal{O}(\log T)$ regret for stochastic environments and
+$\mathcal{O}(\sqrt{T})$ regret for adversarial environments without knowledge
+of the regime or the number of rounds $T$. The leading problem-dependent
+constants of our bounds are not only optimal in some worst-case sense studied
+previously, but also optimal for two concrete instances of semi-bandit
+problems. Our algorithm and analysis extend the recent work of (Zimmert &
+Seldin, 2019) for the special case of multi-armed bandit, but importantly
+requires a novel hybrid regularizer designed specifically for semi-bandit.
+Experimental results on synthetic data show that our algorithm indeed performs
+well uniformly over different environments. We finally provide a preliminary
+extension of our results to the full bandit feedback.
+"
+29670,The role of environment on the star formation history of disc galaxies,"  NGC\,2403, NGC\,300 and M33 are three nearby pure-disc galaxies with similar
+stellar mass in different environments, they are benchmarks for understanding
+late-type spiral galaxies in different environments. The chemical evolution and
+growth of their discs are investigated by using the simple chemical evolution
+model, in which their discs are assumed to originate and grow through the
+accretion of the primordial gas, and the gas outflow process is also taken into
+account. Through comparative study of the best-fitting model predicted star
+formation histories for them, we hope to derive a picture of the local
+environment on the evolution and star formation histories of galaxies and
+whether or not the isolated galaxies follow similar evolution history. Our
+results show that these three galaxies accumulated more than 50 percent of
+their stellar mass at $z\,<\,1$. It can be also found that the metallicity
+gradients in isolated spiral galaxies NGC\,2403 and NGC\,300 are similar and
+obviously steeper than that in M33, when the metallicity gradients are
+expressed in ${\rm dex}\,R_{\rm 25}^{-1}$. The similar metallicity gradients in
+NGC\,2403 and NGC\,300 indicate that they may experience similar chemical
+evolutionary histories. The principal epoch of star formation on the discs of
+NGC\,2403 and NGC\,300 is earlier than that on the disc of M33, and the mean
+age of stellar populations along the discs of both NGC\,2403 and NGC\,300 is
+older than that of M33. Our results indicates that the evolution and star
+formation history of a galaxy indeed depends on its local environment, at least
+for galaxies with stellar mass of $10^{9.2}\,\rm M_{\odot}\sim10^{9.7}\,\rm
+M_{\odot}$.
+"
+29671,Regularity of twisted spectral triples and pseudodifferential calculi,"  We investigate the regularity condition for twisted spectral triples. This
+condition is equivalent to the existence of an appropriate pseudodifferential
+calculus compatible with the spectral triple. A natural approach to obtain such
+a calculus is to start with a twisted algebra of abstract differential
+operators, in the spirit of Higson. Under an appropriate algebraic condition on
+the twisting, we obtain a pseudodifferential calculus which admits an
+asymptotic expansion, similarly to the untwisted case. We present some examples
+coming from the theory of quantum groups. Finally we discuss zeta functions and
+the residue (twisted) traces on differential operators.
+"
+29672,On automorphism groups of toroidal circle planes,"  Schenkel proved that the automorphism group of a flat Minkowski plane is a
+Lie group of dimension at most 6 and described planes whose automorphism group
+has dimension at least 4 or one of whose kernels has dimension 3. We extend
+these results to the case of toroidal circle planes.
+"
+29673,Derivative Based Focal Plane Array Nonuniformity Correction,"  This paper presents a fast and robust method for fixed pattern noise
+nonuniformity correction of infrared focal plane arrays. The proposed method
+requires neither shutter nor elaborate calibrations and therefore enables a
+real time correction with no interruptions. Based on derivative estimation of
+the fixed pattern noise from pixel sized translations of the focal plane array,
+the proposed method has the advantages of being invariant to the noise
+magnitude and robust to unknown camera and inter-scene movements while being
+virtually transparent to the end-user.
+"
+29674,"Edge Preserving Maps of the Nonseparating Curve Graphs, Curve Graphs and Rectangle Preserving Maps of the Hatcher-Thurston Graphs","  Let $R$ be a compact, connected, orientable surface of genus $g$ with $n$
+boundary components with $g \geq 2$, $n \geq 0$. Let $\mathcal{N}(R)$ be the
+nonseparating curve graph, $\mathcal{C}(R)$ be the curve graph and
+$\mathcal{HT}(R)$ be the Hatcher-Thurston graph of $R$. We prove that if
+$\lambda : \mathcal{N}(R) \rightarrow\mathcal{N}(R)$ is an edge preserving map,
+then $\lambda$ is induced by a homeomorphism of $R$. We prove that if $\theta :
+\mathcal{C}(R) \rightarrow \mathcal{C}(R)$ is an edge preserving map, then
+$\theta$ is induced by a homeomorphism of $R$. We prove that if $\tau:
+\mathcal{HT}(R) \rightarrow\mathcal{HT}(R)$ is a rectangle preserving map, then
+$\tau$ is induced by a homeomorphism of $R$. We also prove that these
+homeomorphisms are unique up to isotopy when $(g, n) \neq (2, 0)$.
+"
+29675,Online Linear Quadratic Control,"  We study the problem of controlling linear time-invariant systems with known
+noisy dynamics and adversarially chosen quadratic losses. We present the first
+efficient online learning algorithms in this setting that guarantee
+$O(\sqrt{T})$ regret under mild assumptions, where $T$ is the time horizon. Our
+algorithms rely on a novel SDP relaxation for the steady-state distribution of
+the system. Crucially, and in contrast to previously proposed relaxations, the
+feasible solutions of our SDP all correspond to ""strongly stable"" policies that
+mix exponentially fast to a steady state.
+"
+29676,Auslander's Theorem for permutation actions on noncommutative algebras,"  When $A = \mathbb{k}[x_1, \ldots, x_n]$ and $G$ is a small subgroup of
+$\operatorname{GL}_n(\mathbb{k})$, Auslander's Theorem says that the skew group
+algebra $A \# G$ is isomorphic to $\operatorname{End}_{A^G}(A)$ as graded
+algebras. We prove a generalization of Auslander's Theorem for permutation
+actions on $(-1)$-skew polynomial rings, $(-1)$-quantum Weyl algebras,
+three-dimensional Sklyanin algebras, and a certain graded down-up algebra. We
+also show that certain fixed rings $A^G$ are graded isolated singularities in
+the sense of Ueyama.
+"
+29677,A Hybrid Approach for Secured Optimal Power Flow and Voltage Stability with TCSC Placement,"  This paper proposes a hybrid technique for secured optimal power flow coupled
+with enhancing voltage stability with FACTS device installation. The hybrid
+approach of Improved Gravitational Search algorithm (IGSA) and Firefly
+algorithm (FA) performance is analyzed by optimally placing TCSC controller.
+The algorithm is implemented in MATLAB working platform and the power flow
+security and voltage stability is evaluated with IEEE 30 bus transmission
+systems. The optimal results generated are compared with those available in
+literature and the superior performance of algorithm is depicted as minimum
+generation cost, reduced real power losses along with sustaining voltage
+stability.
+"
+29678,Pivotal decomposition schemes inducing clones of operations,"  We study pivotal decomposition schemes and investigate classes of pivotally
+decomposable operations. We provide sufficient conditions on pivotal operations
+that guarantee that the corresponding classes of pivotally decomposable
+operations are clones, and show that under certain assumptions these conditions
+are also necessary. In the latter case, the pivotal operation together with the
+constant operations generate the corresponding clone.
+"
+29679,Ephemeral Context to Support Robust and Diverse Music Recommendations,"  While prior work on context-based music recommendation focused on fixed set
+of contexts (e.g. walking, driving, jogging), we propose to use multiple
+sensors and external data sources to describe momentary (ephemeral) context in
+a rich way with a very large number of possible states (e.g. jogging fast along
+in downtown of Sydney under a heavy rain at night being tired and angry). With
+our approach, we address the problems which current approaches face: 1) a
+limited ability to infer context from missing or faulty sensor data; 2) an
+inability to use contextual information to support novel content discovery.
+"
+29680,"Sensitivity analysis of spatio-temporal models describing nitrogen transfers, transformations and losses at the landscape scale","  Modelling complex systems such as agroecosystems often requires the
+quantification of a large number of input factors. Sensitivity analyses are
+useful to determine the appropriate spatial and temporal resolution of models
+and to reduce the number of factors to be measured or estimated accurately.
+Comprehensive spatial and temporal sensitivity analyses were applied to the
+NitroScape model, a deterministic spatially distributed model describing
+nitrogen transfers and transformations in rural landscapes. Simulations were
+led on a theoretical landscape that represented five years of intensive farm
+management and covering an area of $3\, km^2$. Cluster analyses were applied to
+summarize the results of the sensitivity analysis on the ensemble of model
+outputs. The methodology we applied is useful to synthesize sensitivity
+analyses of models with multiple space-time input and output variables and
+could be ported to other models than NitroScape.
+"
+29681,A flexible sequential Monte Carlo algorithm for shape-constrained regression,"  We propose an algorithm that is capable of imposing shape constraints on
+regression curves, without requiring the constraints to be written as
+closed-form expressions, nor assuming the functional form of the loss function.
+Our algorithm, which is based on Sequential Monte Carlo-Simulated Annealing,
+only relies on an indicator function that assesses whether or not the
+constraints are fulfilled, thus allowing us to enforce various complex
+constraints by specifying an appropriate indicator function without altering
+other parts of the algorithm. We demonstrate our algorithm by fitting rational
+function models subject to monotonicity and continuity constraints. The
+algorithm was implemented using R (R Core Team, 2018) and the code is freely
+available on GitHub.
+"
+29682,Low-Profile Spoof Surface Plasmon Polaritons Traveling-Wave Antenna for Endfire Radiation,"  This paper proposes a low-profile and highly efficient endfire radiating
+travelling-wave antenna based on spoof surface plasmon polaritons (SSPPs)
+transmission line. The aperture is approximately
+$0.32\lambda_0\times0.01\lambda_0$ where $\lambda_0$ is the space wavelength at
+the operational frequency 8 GHz. This antenna provides an endfire radiation
+beam within 7.5-8.5 GHz. The maximum gain and total efficiency reaches 9.2 dBi
+and $96\%$, respectively. In addition to the endfire operation, it also
+provides a beam scanning functionality within 9-12 GHz. Measurement results are
+finally given to validate the proposed SSPPs antenna.
+"
+29683,Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation,"  Parallel sentence extraction is a task addressing the data sparsity problem
+found in multilingual natural language processing applications. We propose a
+bidirectional recurrent neural network based approach to extract parallel
+sentences from collections of multilingual texts. Our experiments with noisy
+parallel corpora show that we can achieve promising results against a
+competitive baseline by removing the need of specific feature engineering or
+additional external resources. To justify the utility of our approach, we
+extract sentence pairs from Wikipedia articles to train machine translation
+systems and show significant improvements in translation performance.
+"
+29684,"A Simple Deterministic Distributed MST Algorithm, with Near-Optimal Time and Message Complexities","  Distributed minimum spanning tree (MST) problem is one of the most central
+and fundamental problems in distributed graph algorithms. Garay et al.
+\cite{GKP98,KP98} devised an algorithm with running time $O(D + \sqrt{n} \cdot
+\log^* n)$, where $D$ is the hop-diameter of the input $n$-vertex $m$-edge
+graph, and with message complexity $O(m + n^{3/2})$. Peleg and Rubinovich
+\cite{PR99} showed that the running time of the algorithm of \cite{KP98} is
+essentially tight, and asked if one can achieve near-optimal running time
+**together with near-optimal message complexity**.
+In a recent breakthrough, Pandurangan et al. \cite{PRS16} answered this
+question in the affirmative, and devised a **randomized** algorithm with time
+$\tilde{O}(D+ \sqrt{n})$ and message complexity $\tilde{O}(m)$. They asked if
+such a simultaneous time- and message-optimality can be achieved by a
+**deterministic** algorithm.
+In this paper, building upon the work of \cite{PRS16}, we answer this
+question in the affirmative, and devise a **deterministic** algorithm that
+computes MST in time $O((D + \sqrt{n}) \cdot \log n)$, using $O(m \cdot \log n
++ n \log n \cdot \log^* n)$ messages. The polylogarithmic factors in the time
+and message complexities of our algorithm are significantly smaller than the
+respective factors in the result of \cite{PRS16}. Also, our algorithm and its
+analysis are very **simple** and self-contained, as opposed to rather
+complicated previous sublinear-time algorithms \cite{GKP98,KP98,E04b,PRS16}.
+"
+29685,The Essence Theory of Software Engineering - Large-Scale Classroom Experiences from 450+ Software Engineering BSc Students,"  Software Engineering as an industry is highly diverse in terms of development
+methods and practices. Practitioners employ a myriad of methods and tend to
+further tailor them by e.g. omitting some practices or rules. This diversity in
+development methods poses a challenge for software engineering education,
+creating a gap between education and industry. General theories such as the
+Essence Theory of Software Engineering can help bridge this gap by presenting
+software engineering students with higher-level frameworks upon which to build
+an understanding of software engineering methods and practical project work. In
+this paper, we study Essence in an educational setting to evaluate its
+usefulness for software engineering students while also investigating barriers
+to its adoption in this context. To this end, we observe 102 student teams
+utilize Essence in practical software engineering projects during a semester
+long, project-based course.
+"
+29686,Polynomial Counting in Anonymous Dynamic Networks with Applications to Anonymous Dynamic Algebraic Computations,"  Starting with Michail, Chatzigiannakis, and Spirakis work, the problem of
+Counting the number of nodes in Anonymous Dynamic Networks has attracted a lot
+of attention. The problem is challenging because nodes are indistinguishable
+(they lack identifiers and execute the same program) and the topology may
+change arbitrarily from round to round of communication, as long as the network
+is connected in each round. The problem is central in distributed computing as
+the number of participants is frequently needed to make important decisions,
+such as termination, agreement, synchronization, and many others. A variety of
+algorithms built on top of mass-distribution techniques have been presented,
+analyzed, and also experimentally evaluated; some of them assumed additional
+knowledge of network characteristics, such as bounded degree or given upper
+bound on the network size. However, the question of whether Counting can be
+solved deterministically in sub-exponential time remained open. In this work,
+we answer this question positively by presenting Methodical Counting, which
+runs in polynomial time and requires no knowledge of network characteristics.
+Moreover, we also show how to extend Methodical Counting to compute the sum of
+input values and more complex functions without extra cost. Our analysis
+leverages previous work on random walks in evolving graphs, combined with
+carefully chosen alarms in the algorithm that control the process and its
+parameters. To the best of our knowledge, our Counting algorithm and its
+extensions to other algebraic and Boolean functions are the first that can be
+implemented in practice with worst-case guarantees.
+"
+29687,An ADMM Approach to Masked Signal Decomposition Using Subspace Representation,"  Signal decomposition is a classical problem in signal processing, which aims
+to separate an observed signal into two or more components each with its own
+property. Usually each component is described by its own subspace or
+dictionary. Extensive research has been done for the case where the components
+are additive, but in real world applications, the components are often
+non-additive. For example, an image may consist of a foreground object overlaid
+on a background, where each pixel either belongs to the foreground or the
+background. In such a situation, to separate signal components, we need to find
+a binary mask which shows the location of each component. Therefore it requires
+to solve a binary optimization problem. Since most of the binary optimization
+problems are intractable, we relax this problem to the approximated continuous
+problem, and solve it by alternating optimization technique. We show the
+application of the proposed algorithm for three applications: separation of
+text from background in images, separation of moving objects from a background
+undergoing global camera motion in videos, separation of sinusoidal and spike
+components in one dimensional signals. We demonstrate in each case that
+considering the non-additive nature of the problem can lead to significant
+improvement.
+"
+29688,Jastrow-like ground states for quantum many-body potentials with near-neighbors interactions,"  We completely solve the problem of classifying all one-dimensional quantum
+potentials with nearest- and next-to-nearest-neighbors interactions whose
+ground state is Jastrow-like, i.e., of Jastrow type but depending only on
+differences of consecutive particles. In particular, we show that these models
+must necessarily contain a three-body interaction term, as was the case with
+all previously known examples. We discuss several particular instances of the
+general solution, including a new hyperbolic potential and a model with
+elliptic interactions which reduces to the known rational and trigonometric
+ones in appropriate limits.
+"
+29689,Massive Star Cluster Formation and Destruction in Luminous Infrared Galaxies in GOALS,"  We present the results of a {\it Hubble Space Telescope} ACS/HRC FUV, ACS/WFC
+optical study into the cluster populations of a sample of 22 Luminous Infrared
+Galaxies in the Great Observatories All-Sky LIRG Survey. Through integrated
+broadband photometry we have derived ages and masses for a total of 484 star
+clusters contained within these systems. This allows us to examine the
+properties of star clusters found in the extreme environments of LIRGs relative
+to lower luminosity star-forming galaxies in the local Universe. We find that
+by adopting a Bruzual \& Charlot simple stellar population (SSP) model and
+Salpeter initial mass function, the age distribution of clusters declines as
+$dN/d\tau = \tau^{-0.9 +/- 0.3}$, consistent with the age distribution derived
+for the Antennae Galaxies, and interpreted as evidence for rapid cluster
+disruption occuring in the strong tidal fields of merging galaxies. The large
+number of $10^{6} M_{\odot}$ young clusters identified in the sample also
+suggests that LIRGs are capable of producing more high-mass clusters than what
+is observed to date in any lower luminosity star-forming galaxy in the local
+Universe. The observed cluster mass distribution of $dN/dM = M^{-1.95 +/-
+0.11}$ is consistent with the canonical -2 power law used to describe the
+underlying initial cluster mass function (ICMF) for a wide range of galactic
+environments. We interpret this as evidence against mass-dependent cluster
+disruption, which would flatten the observed CMF relative to the underlying
+ICMF distribution.
+"
+29690,MatchZoo: A Toolkit for Deep Text Matching,"  In recent years, deep neural models have been widely adopted for text
+matching tasks, such as question answering and information retrieval, showing
+improved performance as compared with previous methods. In this paper, we
+introduce the MatchZoo toolkit that aims to facilitate the designing, comparing
+and sharing of deep text matching models. Specifically, the toolkit provides a
+unified data preparation module for different text matching problems, a
+flexible layer-based model construction process, and a variety of training
+objectives and evaluation metrics. In addition, the toolkit has implemented two
+schools of representative deep text matching models, namely
+representation-focused models and interaction-focused models. Finally, users
+can easily modify existing models, create and share their own models for text
+matching in MatchZoo.
+"
+29691,Interference Minimization in 5G Heterogeneous Networks,"  In this paper, we focus on one of the representative 5G network scenarios,
+namely multi-tier heterogeneous cellular networks. User association is
+investigated in order to reduce the down-link co-channel interference. Firstly,
+in order to analyze the multi-tier heterogeneous cellular networks where the
+base stations in different tiers usually adopt different transmission powers,
+we propose a Transmission Power Normalization Model (TPNM), which is able to
+convert a multi-tier cellular network into a single-tier network, such that all
+base stations have the same normalized transmission power. Then using TPNM, the
+signal and interference received at any point in the complex multi-tier
+environment can be analyzed by considering the same point in the equivalent
+single-tier cellular network model, thus significantly simplifying the
+analysis. On this basis, we propose a new user association scheme in
+heterogeneous cellular networks, where the base station that leads to the
+smallest interference to other co-channel mobile stations is chosen from a set
+of candidate base stations that satisfy the quality-of-service (QoS) constraint
+for an intended mobile station. Numerical results show that the proposed user
+association scheme is able to significantly reduce the down-link interference
+compared with existing schemes while maintaining a reasonably good QoS.
+"
+29692,Covariance Functions for Multivariate Gaussian Fields Evolving Temporally over Planet Earth,"  The construction of valid and flexible cross-covariance functions is a
+fundamental task for modeling multivariate space-time data arising from
+climatological and oceanographical phenomena. Indeed, a suitable specification
+of the covariance structure allows to capture both the space-time dependencies
+between the observations and the development of accurate predictions. For data
+observed over large portions of planet Earth it is necessary to take into
+account the curvature of the planet. Hence the need for random field models
+defined over spheres across time. In particular, the associated covariance
+function should depend on the geodesic distance, which is the most natural
+metric over the spherical surface. In this work, we propose a flexible
+parametric family of matrix-valued covariance functions, with both marginal and
+cross structure being of the Gneiting type. We additionally introduce a
+different multivariate Gneiting model based on the adaptation of the latent
+dimension approach to the spherical context. Finally, we assess the performance
+of our models through the study of a bivariate space-time data set of surface
+air temperatures and precipitations.
+"
+29693,Asymptotic Expansion of Risk for a Regression Model with respect to $α$-Divergence with an Application to the Sample Size Problem -- Complete Version,"  For a regression model, we consider the risk of the maximum likelihood
+estimator with respect to $\alpha$-divergence, which includes the special cases
+of Kullback-Leibler divergence, Hellinger distance and $\chi^2$ divergence. The
+asymptotic expansion of the risk with respect to the sample size $n$ is given
+up to the order $n^{-2}$. We are interested in how the risk convergence speed
+(to zero) is affected by the error term distributions of the regression model
+and the magnitude of the joint moments of the standardized explanatory
+variables. Besides the general result (which is given by Mathematica program),
+we consider three concrete error term distributions; a normal distribution, a
+t-distribution and a skew-normal distribution. We use the (approximated) risk
+of m.l.e. as a measure of the difficulty of estimation for the regression
+model. Especially comparing the value of the (approximated) risk with that of a
+binomial distribution, we can give a certain standard for the sample size
+required to estimate the regression model.
+"
+29694,A Stabilized Cut Streamline Diffusion Finite Element Method for Convection-Diffusion Problems on Surfaces,"  We develop a stabilized cut finite element method for the stationary
+convection diffusion problem on a surface embedded in ${\mathbb{R}}^d$. The cut
+finite element method is based on using an embedding of the surface into a
+three dimensional mesh consisting of tetrahedra and then using the restriction
+of the standard piecewise linear continuous elements to a piecewise linear
+approximation of the surface. The stabilization consists of a standard
+streamline diffusion stabilization term on the discrete surface and a so called
+normal gradient stabilization term on the full tetrahedral elements in the
+active mesh. We prove optimal order a priori error estimates in the standard
+norm associated with the streamline diffusion method and bounds for the
+condition number of the resulting stiffness matrix. The condition number is of
+optimal order $O(h^{-1})$ for a specific choice of method parameters. Numerical
+example supporting our theoretical results are also included.
+"
+29695,Impact of Indirect Contacts in Emerging Infectious Disease on Social Networks,"  Interaction patterns among individuals play vital roles in spreading
+infectious diseases. Understanding these patterns and integrating their impact
+in modeling diffusion dynamics of infectious diseases are important for
+epidemiological studies. Current network-based diffusion models assume that
+diseases transmit through interactions where both infected and susceptible
+individuals are co-located at the same time. However, there are several
+infectious diseases that can transmit when a susceptible individual visits a
+location after an infected individual has left. Recently, we introduced a
+diffusion model called same place different time (SPDT) transmission to capture
+the indirect transmissions that happen when an infected individual leaves
+before a susceptible individual's arrival along with direct transmissions. In
+this paper, we demonstrate how these indirect transmission links significantly
+enhance the emergence of infectious diseases simulating airborne disease
+spreading on a synthetic social contact network. We denote individuals having
+indirect links but no direct links during their infectious periods as hidden
+spreaders. Our simulation shows that indirect links play similar roles of
+direct links and a single hidden spreader can cause large outbreak in the SPDT
+model which causes no infection in the current model based on direct link. Our
+work opens new direction in modeling infectious diseases.
+"
+29696,Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations,"  We show that the gradient descent algorithm provides an implicit
+regularization effect in the learning of over-parameterized matrix
+factorization models and one-hidden-layer neural networks with quadratic
+activations. Concretely, we show that given $\tilde{O}(dr^{2})$ random linear
+measurements of a rank $r$ positive semidefinite matrix $X^{\star}$, we can
+recover $X^{\star}$ by parameterizing it by $UU^\top$ with $U\in \mathbb
+R^{d\times d}$ and minimizing the squared loss, even if $r \ll d$. We prove
+that starting from a small initialization, gradient descent recovers
+$X^{\star}$ in $\tilde{O}(\sqrt{r})$ iterations approximately. The results
+solve the conjecture of Gunasekar et al.'17 under the restricted isometry
+property. The technique can be applied to analyzing neural networks with
+one-hidden-layer quadratic activations with some technical modifications.
+"
+29697,Diamonds levitating in a Paul trap under vacuum: measurements of laser-induced heating via NV center thermometry,"  We present measurements of the Electronic Spin Resonance (ESR) of Nitrogen
+Vacancy (NV) centers in diamonds that are levitating in a ring Paul trap under
+vacuum. We observe ESR spectra of NV centers embedded in micron-sized diamonds
+at vacuum pressures down to $2\times 10^{-1}$ mbar, limited by the green laser
+heating of the diamond. We also observe the NV photoluminescence at vacuum
+pressures down to $10^{-2}$ mbar and discuss the steps required to control the
+NV spin under ultra-high vacuum.
+"
+29698,From model checking to a temporal proof for partial models: preliminary example,"  This paper describes in detail the example introduced in the preliminary
+evaluation of THRIVE. Specifically, it evaluates THRIVE over an abstraction of
+the ground model proposed for a critical component belonging to a medical
+device used by optometrists and ophtalmologits to dected visual problems.
+"
+29699,The reference frames of Mercury after MESSENGER,"  We report on recent refinements and the current status for the rotational
+state models and the reference frame of the planet Mercury. We summarize the
+performed measurements of Mercury rotation based on terrestrial radar
+observations as well as data from the Mariner 10 and the MESSENGER missions.
+Further, we describe the different available definitions of reference systems
+for Mercury, which are realized using data obtained by instruments on board
+MESSENGER. In particular, we discuss the dynamical frame, the principal-axes
+frame, the ellipsoid frame, as well as the cartographic frame. We also describe
+the reference frame adopted by the MESSENGER science team for the release of
+their cartographic products and we provide expressions for transformations from
+this frame to the other reference frames.
+"
+29700,Distributional Semantics Approach to Detect Intent in Twitter Conversations on Sexual Assaults,"  The recent surge in women reporting sexual assault and harassment (e.g.,
+#metoo campaign) has highlighted a longstanding societal crisis. This injustice
+is partly due to a culture of discrediting women who report such crimes and
+also, rape myths (e.g., 'women lie about rape'). Social web can facilitate the
+further proliferation of deceptive beliefs and culture of rape myths through
+intentional messaging by malicious actors. This multidisciplinary study
+investigates Twitter posts related to sexual assaults and rape myths for
+characterizing the types of malicious intent, which leads to the beliefs on
+discrediting women and rape myths. Specifically, we first propose a novel
+malicious intent typology for social media using the guidance of social
+construction theory from policy literature that includes Accusational,
+Validational, or Sensational intent categories. We then present and evaluate a
+malicious intent classification model for a Twitter post using semantic
+features of the intent senses learned with the help of convolutional neural
+networks. Lastly, we analyze a Twitter dataset of four months using the intent
+classification model to study narrative contexts in which malicious intents are
+expressed and discuss their implications for gender violence policy design.
+"
+29701,Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches,"  Developing state-of-the-art approaches for specific tasks is a major driving
+force in our research community. Depending on the prestige of the task,
+publishing it can come along with a lot of visibility. The question arises how
+reliable are our evaluation methodologies to compare approaches?
+One common methodology to identify the state-of-the-art is to partition data
+into a train, a development and a test set. Researchers can train and tune
+their approach on some part of the dataset and then select the model that
+worked best on the development set for a final evaluation on unseen test data.
+Test scores from different approaches are compared, and performance differences
+are tested for statistical significance.
+In this publication, we show that there is a high risk that a statistical
+significance in this type of evaluation is not due to a superior learning
+approach. Instead, there is a high risk that the difference is due to chance.
+For example for the CoNLL 2003 NER dataset we observed in up to 26% of the
+cases type I errors (false positives) with a threshold of p < 0.05, i.e.,
+falsely concluding a statistically significant difference between two identical
+approaches.
+We prove that this evaluation setup is unsuitable to compare learning
+approaches. We formalize alternative evaluation setups based on score
+distributions.
+"
+29702,Optical reconstruction of computer-generated holograms 3D scenes via spatial light modulators,"  This work presents the optical reconstruction of computer-generated holograms
+(CGHs) 3D scenes via spatial light modulators (SLMs). Holography is an optical
+technique that allows the recording and reconstruction of images of 3D objects,
+as a hologram carries the intensity and phase information about the object.
+With the evolution of data storage and processing in computers, the viability
+of generating holograms by computers has increased. Currently the experimental
+implementation of CGHs (Computer-Generated Holograms) is done through new
+optoelectronic devices such as SLMs (Spatial Light Modulators) and
+high-resolution CCDs cameras, in addition to new photosensitive materials,
+allowing the optical recording and reconstruction of 3D images and special
+optical beams. Therefore, presented here with good results are CGHs numerical
+construction (recording) of images both static and dynamic, 2D and 3D, and its
+reconstruction (reproduction) using optical SLMs devices to obtain 3D images
+and static and dynamic scenes.
+"
+29703,Gated Path Planning Networks,"  Value Iteration Networks (VINs) are effective differentiable path planning
+modules that can be used by agents to perform navigation while still
+maintaining end-to-end differentiability of the entire architecture. Despite
+their effectiveness, they suffer from several disadvantages including training
+instability, random seed sensitivity, and other optimization problems. In this
+work, we reframe VINs as recurrent-convolutional networks which demonstrates
+that VINs couple recurrent convolutions with an unconventional max-pooling
+activation. From this perspective, we argue that standard gated recurrent
+update equations could potentially alleviate the optimization issues plaguing
+VIN. The resulting architecture, which we call the Gated Path Planning Network,
+is shown to empirically outperform VIN on a variety of metrics such as learning
+speed, hyperparameter sensitivity, iteration count, and even generalization.
+Furthermore, we show that this performance gap is consistent across different
+maze transition types, maze sizes and even show success on a challenging 3D
+environment, where the planner is only provided with first-person RGB images.
+"
+29704,Representability theorem in derived analytic geometry,"  We prove the representability theorem in derived analytic geometry. The
+theorem asserts that an analytic moduli functor is a derived analytic stack if
+and only if it is compatible with Postnikov towers, has a global analytic
+cotangent complex, and its truncation is an analytic stack. Our result applies
+to both derived complex analytic geometry and derived non-archimedean analytic
+geometry (rigid analytic geometry). The representability theorem is of both
+philosophical and practical importance in derived geometry. The conditions of
+representability are natural expectations for a moduli functor. So the theorem
+confirms that the notion of derived analytic space is natural and sufficiently
+general. On the other hand, the conditions are easy to verify in practice. So
+the theorem enables us to enhance various classical moduli spaces with derived
+structures, thus provides plenty of down-to-earth examples of derived analytic
+spaces. For the purpose of proof, we study analytification, square-zero
+extensions, analytic modules and cotangent complexes in the context of derived
+analytic geometry. We will explore applications of the representability theorem
+in our subsequent works. In particular, we will establish the existence of
+derived mapping stacks via the representability theorem.
+"
+29705,Solvability of the inverse scattering problem for the selfadjoint matrix Schrodinger operator on the half line,"  In this work we study the inverse scattering problem for the selfadjoint
+matrix Schrodinger operator on the half line. We provide the necessary and
+sufficient conditions for the solvability of the inverse scattering problem.
+"
+29706,Control What You Include! Server-Side Protection against Third Party Web Tracking,"  Third party tracking is the practice by which third parties recognize users
+accross different websites as they browse the web. Recent studies show that 90%
+of websites contain third party content that is tracking its users across the
+web. Website developers often need to include third party content in order to
+provide basic functionality. However, when a developer includes a third party
+content, she cannot know whether the third party contains tracking mechanisms.
+If a website developer wants to protect her users from being tracked, the only
+solution is to exclude any third-party content, thus trading functionality for
+privacy. We describe and implement a privacy-preserving web architecture that
+gives website developers a control over third party tracking: developers are
+able to include functionally useful third party content, the same time ensuring
+that the end users are not tracked by the third parties.
+"
+29707,Electron scattering in tantalum monoarsenide,"  We report comprehensive studies of the single crystal growth and electrical
+transport properties for various samples of TaAs, the first experimentally
+confirmed inversion symmetry-breaking Weyl semimetal. The transport parameters
+for different samples are obtained through the fitting of the two band model
+and the analysis of Shubnikov de Haas oscillations. We find that the ratio
+factor of transport lifetime to quantum lifetime is intensively enhanced when
+the Fermi level approaches the Weyl node. This result is consistent with the
+side-jump interpretation derived from a chirality-protected shift in the
+scattering process for a Weyl semimetal.
+"
+29708,Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation,"  A semi-supervised Partial Membership Latent Dirichlet Allocation approach is
+developed for hyperspectral unmixing and endmember estimation while accounting
+for spectral variability and spatial information. Partial Membership Latent
+Dirichlet Allocation is an effective approach for spectral unmixing while
+representing spectral variability and leveraging spatial information. In this
+work, we extend Partial Membership Latent Dirichlet Allocation to incorporate
+any available (imprecise) label information to help guide unmixing.
+Experimental results on two hyperspectral datasets show that the proposed
+semi-supervised PM-LDA can yield improved hyperspectral unmixing and endmember
+estimation results.
+"
+29709,Uniform upper bounds of the distribution of relatively r-prime lattice points,"  We estimate the distribution of relatively $r$-prime lattice points in number
+fields $K$ with their components having a norm less than $x$. In the previous
+paper we obtained uniform upper bounds as $K$ runs through all number fields
+under assuming the Lindelöf hypothesis. And we also showed unconditional
+results for abelian extensions with a degree less than or equal to $6$. In this
+paper we remove all assumption about number fields and improve uniform upper
+bounds. Throughout this paper we consider estimates for distribution of ideals
+of the ring of integer $\mathcal{O}_K$ and obtain uniform upper bounds. And
+when $K$ runs through cubic extension fields we show better uniform upper
+bounds than that under the Lindel\"" of Hypothesis.
+"
+29710,SAGA with Arbitrary Sampling,"  We study the problem of minimizing the average of a very large number of
+smooth functions, which is of key importance in training supervised learning
+models. One of the most celebrated methods in this context is the SAGA
+algorithm. Despite years of research on the topic, a general-purpose version of
+SAGA---one that would include arbitrary importance sampling and minibatching
+schemes---does not exist. We remedy this situation and propose a general and
+flexible variant of SAGA following the {\em arbitrary sampling} paradigm. We
+perform an iteration complexity analysis of the method, largely possible due to
+the construction of new stochastic Lyapunov functions. We establish linear
+convergence rates in the smooth and strongly convex regime, and under a
+quadratic functional growth condition (i.e., in a regime not assuming strong
+convexity). Our rates match those of the primal-dual method Quartz for which an
+arbitrary sampling analysis is available, which makes a significant step
+towards closing the gap in our understanding of complexity of primal and dual
+methods for finite sum problems.
+"
+29711,Finding Competitive Network Architectures Within a Day Using UCT,"  The design of neural network architectures for a new data set is a laborious
+task which requires human deep learning expertise. In order to make deep
+learning available for a broader audience, automated methods for finding a
+neural network architecture are vital. Recently proposed methods can already
+achieve human expert level performances. However, these methods have run times
+of months or even years of GPU computing time, ignoring hardware constraints as
+faced by many researchers and companies. We propose the use of Monte Carlo
+planning in combination with two different UCT (upper confidence bound applied
+to trees) derivations to search for network architectures. We adapt the UCT
+algorithm to the needs of network architecture search by proposing two ways of
+sharing information between different branches of the search tree. In an
+empirical study we are able to demonstrate that this method is able to find
+competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day.
+Extending the search time to five GPU days, we are able to outperform human
+architectures and our competitors which consider the same types of layers.
+"
+29712,Electrostatic limit of the $T$-matrix for electromagnetic scattering: Exact results for spheroidal particles,"  The $T$-matrix, often obtained with Waterman's extended boundary condition
+method (EBCM), is a widely-used tool for fast calculations of electromagnetic
+scattering by particles. Here we investigate the quasistatic or long-wavelength
+limit of this approach, where it reduces to an electrostatics problem. We first
+present a fully electrostatic version of the EBCM/T-matrix method (dubbed
+ES-EBCM). Explicit expressions are then given to quantitatively express the
+long-wavelength limit of the EBCM matrix elements in terms of those of the
+ES-EBCM formalism. From this connection we deduce a number of symmetry
+properties of the ES-EBCM matrices. We then investigate the matrix elements of
+the ES-EBCM formalism in the special case of prolate spheroids. Using the
+general electrostatic solution in spheroidal coordinates, we derive fully
+analytic expressions (in the form of finite sums) for all matrix elements.
+Those can be used for example for studies of the convergence of the $T$-matrix
+formalism. We illustrate this by discussing the validity of the Rayleigh
+hypothesis, where analytical expressions highlight clearly the link with
+analytical continuation of series.
+"
+29713,Multiplicity formulas for fundamental strings of representations of classical Lie algebras,"  We call the \emph{$p$-fundamental string} of a complex simple Lie algebra to
+the sequence of irreducible representations having highest weights of the form
+$k\omega_1+\omega_p$ for $k\geq0$, where $\omega_j$ denotes the $j$-th
+fundamental weight of the associated root system. For a classical complex Lie
+algebra, we establish a closed explicit formula for the weight multiplicities
+of any representation in any $p$-fundamental string.
+"
+29714,Toward safe separation distance monitoring from RGB-D sensors in human-robot interaction,"  The interaction of humans and robots in less constrained environments gains a
+lot of attention lately and the safety of such interaction is of utmost
+importance. Two ways of risk assessment are prescribed by recent safety
+standards: (i) power and force limiting and (ii) speed and separation
+monitoring. Unlike typical solutions in the industry that are restricted to
+mere safety zone monitoring, we present a framework that realizes separation
+distance monitoring between a robot and a human operator in a detailed, yet
+versatile, transparent, and tunable fashion. The separation distance is
+assessed pair-wise for all keypoints on the robot and the human body and as
+such can be selectively modified to account for specific conditions. The
+operation of this framework is illustrated on a Nao humanoid robot interacting
+with a human partner perceived by a RealSense RGB-D sensor and employing the
+OpenPose human skeleton estimation algorithm.
+"
+29715,Discriminative Optimization: Theory and Applications to Computer Vision Problems,"  Many computer vision problems are formulated as the optimization of a cost
+function. This approach faces two main challenges: (i) designing a cost
+function with a local optimum at an acceptable solution, and (ii) developing an
+efficient numerical method to search for one (or multiple) of these local
+optima. While designing such functions is feasible in the noiseless case, the
+stability and location of local optima are mostly unknown under noise,
+occlusion, or missing data. In practice, this can result in undesirable local
+optima or not having a local optimum in the expected place. On the other hand,
+numerical optimization algorithms in high-dimensional spaces are typically
+local and often rely on expensive first or second order information to guide
+the search. To overcome these limitations, this paper proposes Discriminative
+Optimization (DO), a method that learns search directions from data without the
+need of a cost function. Specifically, DO explicitly learns a sequence of
+updates in the search space that leads to stationary points that correspond to
+desired solutions. We provide a formal analysis of DO and illustrate its
+benefits in the problem of 3D point cloud registration, camera pose estimation,
+and image denoising. We show that DO performed comparably or outperformed
+state-of-the-art algorithms in terms of accuracy, robustness to perturbations,
+and computational efficiency.
+"
+29716,Geometric aspects of Newton-Okounkov bodies,"  This is a survey article on Newton-Okounkov bodies in projective geometry
+focusing on the relationship between positivity of divisors and Newton-Okounkov
+bodies.
+"
+29717,Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning,"  Learning-based pattern classifiers, including deep networks, have shown
+impressive performance in several application domains, ranging from computer
+vision to cybersecurity. However, it has also been shown that adversarial input
+perturbations carefully crafted either at training or at test time can easily
+subvert their predictions. The vulnerability of machine learning to such wild
+patterns (also referred to as adversarial examples), along with the design of
+suitable countermeasures, have been investigated in the research field of
+adversarial machine learning. In this work, we provide a thorough overview of
+the evolution of this research area over the last ten years and beyond,
+starting from pioneering, earlier work on the security of non-deep learning
+algorithms up to more recent work aimed to understand the security properties
+of deep learning algorithms, in the context of computer vision and
+cybersecurity tasks. We report interesting connections between these
+apparently-different lines of work, highlighting common misconceptions related
+to the security evaluation of machine-learning algorithms. We review the main
+threat models and attacks defined to this end, and discuss the main limitations
+of current work, along with the corresponding future challenges towards the
+design of more secure learning algorithms.
+"
+29718,Effect of processing conditions on the thermal and electrical conductivity of poly (butylene terephthalate) nanocomposites prepared via ring-opening polymerization,"  Successful preparation of polymer nanocomposites, exploiting graphene-related
+materials, via melt mixing technology requires precise design, optimization and
+control of processing. In the present work, the effect of different processing
+parameters during the preparation of poly (butylene terephthalate)
+nanocomposites, through ring-opening polymerization of cyclic butylene
+terephthalate in presence of graphite nanoplatelets (GNP), was thoroughly
+addressed. Processing temperature (240°C or 260°C), extrusion time (5
+or 10 minutes) and shear rate (50 or 100 rpm) were varied by means of a full
+factorial design of experiment approach, leading to the preparation of
+polybutylene terephthalate/GNP nanocomposite in 8 different processing
+conditions. Morphology and quality of GNP were investigated by means of
+electron microscopy, X-ray photoelectron spectroscopy, thermogravimetry and
+Raman spectroscopy. Molecular weight of the polymer matrix in nanocomposites
+and nanoflake dispersion were experimentally determined as a function of the
+different processing conditions. The effect of transformation parameters on
+electrical and thermal properties was studied by means of electrical and
+thermal conductivity measurement. Heat and charge transport performance
+evidenced a clear correlation with the dispersion and fragmentation of the GNP
+nanoflakes; in particular, gentle processing conditions (low shear rate, short
+mixing time) turned out to be the most favourable condition to obtain high
+conductivity values.
+"
+29719,"Time-dependent spatially varying graphical models, with application to brain fMRI data analysis","  In this work, we present an additive model for space-time data that splits
+the data into a temporally correlated component and a spatially correlated
+component. We model the spatially correlated portion using a time-varying
+Gaussian graphical model. Under assumptions on the smoothness of changes in
+covariance matrices, we derive strong single sample convergence results,
+confirming our ability to estimate meaningful graphical structures as they
+evolve over time. We apply our methodology to the discovery of time-varying
+spatial structures in human brain fMRI signals.
+"
+29720,Independence clustering (without a matrix),"  The independence clustering problem is considered in the following
+formulation: given a set $S$ of random variables, it is required to find the
+finest partitioning $\{U_1,\dots,U_k\}$ of $S$ into clusters such that the
+clusters $U_1,\dots,U_k$ are mutually independent. Since mutual independence is
+the target, pairwise similarity measurements are of no use, and thus
+traditional clustering algorithms are inapplicable. The distribution of the
+random variables in $S$ is, in general, unknown, but a sample is available.
+Thus, the problem is cast in terms of time series. Two forms of sampling are
+considered: i.i.d.\ and stationary time series, with the main emphasis being on
+the latter, more general, case. A consistent, computationally tractable
+algorithm for each of the settings is proposed, and a number of open directions
+for further research are outlined.
+"
+29721,LIUBoost : Locality Informed Underboosting for Imbalanced Data Classification,"  The problem of class imbalance along with class-overlapping has become a
+major issue in the domain of supervised learning. Most supervised learning
+algorithms assume equal cardinality of the classes under consideration while
+optimizing the cost function and this assumption does not hold true for
+imbalanced datasets which results in sub-optimal classification. Therefore,
+various approaches, such as undersampling, oversampling, cost-sensitive
+learning and ensemble based methods have been proposed for dealing with
+imbalanced datasets. However, undersampling suffers from information loss,
+oversampling suffers from increased runtime and potential overfitting while
+cost-sensitive methods suffer due to inadequately defined cost assignment
+schemes. In this paper, we propose a novel boosting based method called
+LIUBoost. LIUBoost uses under sampling for balancing the datasets in every
+boosting iteration like RUSBoost while incorporating a cost term for every
+instance based on their hardness into the weight update formula minimizing the
+information loss introduced by undersampling. LIUBoost has been extensively
+evaluated on 18 imbalanced datasets and the results indicate significant
+improvement over existing best performing method RUSBoost.
+"
+29722,Optimal Throughput Fairness Trade-offs for Downlink Non-Orthogonal Multiple Access over Fading Channels,"  Recently, non-orthogonal multiple access (NOMA) has attracted considerable
+interest as one of the 5G-enabling techniques. However, users with better
+channel conditions in downlink communications intrinsically benefits from NOMA
+thanks to successive decoding, judicious designs are required to guarantee user
+fairness. In this paper, a two-user downlink NOMA system over fading channels
+is considered. For delay-tolerant transmission, the average sum-rate is
+maximized subject to both average and peak power constraints as well as a
+minimum average user rate constraint. The optimal resource allocation is
+obtained using Lagrangian dual decomposition under full channel state
+information at the transmitter (CSIT), while an effective power allocation
+policy under partial CSIT is also developed based on analytical results. In
+parallel, for delay-limited transmission, the sum of delay-limited throughput
+(DLT) is maximized subject to a maximum allowable user outage constraint under
+full CSIT, and the analysis for the sum of DLT is also performed under partial
+CSIT. Furthermore, an optimal orthogonal multiple access (OMA) scheme is also
+studied as a benchmark to prove the superiority of NOMA over OMA under full
+CSIT. Finally, the theoretical analysis is verified by simulations via
+different trade-offs for the average sum-rate (sum-DLT) versus the minimum
+(maximum) average user rate (outage) requirement.
+"
+29723,Microvariability in AGNs: study of different statistical methods - I. Observational analysis,"  We present the results of a study of different statistical methods currently
+used in the literature to analyse the (micro)variability of active galactic
+nuclei (AGNs) from ground-based optical observations. In particular, we focus
+on the comparison between the results obtained by applying the so-called C and
+F statistics, which are based on the ratio of standard deviations and
+variances, respectively. The motivation for this is that the implementation of
+these methods leads to different and contradictory results, making the
+variability classification of the light curves of a certain source dependent on
+the statistics implemented.
+For this purpose, we re-analyse the results on an AGN sample observed along
+several sessions with the 2.15m ""Jorge Sahade"" telescope (CASLEO), San Juan,
+Argentina. For each AGN we constructed the nightly differential light curves.
+We thus obtained a total of 78 light curves for 39 AGNs, and we then applied
+the statistical tests mentioned above, in order to re-classify the variability
+state of these light curves and in an attempt to find the suitable statistical
+methodology to study photometric (micro)variations. We conclude that, although
+the $C$ criterion is not proper a statistical test, it could still be a
+suitable parameter to detect variability and that its application allows us to
+get more reliable variability results, in contrast with the F test.
+"
+29724,Target Patterns in a 2-D Array of Oscillators with Nonlocal Coupling,"  We analyze the effect of adding a weak, localized, inhomogeneity to a two
+dimensional array of oscillators with nonlocal coupling. We propose and also
+justify a model for the phase dynamics in this system. Our model is a
+generalization of a viscous eikonal equation that is known to describe the
+phase modulation of traveling waves in reaction-diffusion systems. We show the
+existence of a branch of target pattern solutions that bifurcates from the
+spatially homogeneous state when $\varepsilon$, the strength of the
+inhomogeneity, is nonzero and we also show that these target patterns have an
+asymptotic wavenumber that is small beyond all orders in $\varepsilon$.
+The strategy of our proof is to pose a good ansatz for an approximate form of
+the solution and use the implicit function theorem to prove the existence of a
+solution in its vicinity. The analysis presents two challenges. First, the
+linearization about the homogeneous state is a convolution operator of
+diffusive type and hence not invertible on the usual Sobolev spaces. Second, a
+regular perturbation expansion in $\varepsilon$ does not provide a good ansatz
+for applying the implicit function theorem since the nonlinearities play a
+major role in determining the relevant approximation, which also needs to be
+""correct"" to all orders in $\varepsilon$. We overcome these two points by
+proving Fredholm properties for the linearization in appropriate Kondratiev
+spaces and using a refined ansatz for the approximate solution, which obtained
+using matched asymptotics.
+"
+29725,MOA-2012-BLG-505Lb: A super-Earth mass planet probably in the Galactic bulge,"  We report the discovery of a super-Earth mass planet in the microlensing
+event MOA-2012-BLG-505. This event has the second shortest event timescale of
+$t_{\rm E}=10 \pm 1$ days where the observed data show evidence of planetary
+companion. Our 15 minute high cadence survey observation schedule revealed the
+short subtle planetary signature. The system shows the well known close/wide
+degeneracy. The planet/host-star mass ratio is $q =2.1 \times 10^{-4}$ and the
+projected separation normalized by the Einstein radius is s = 1.1 or 0.9 for
+the wide and close solutions, respectively. We estimate the physical parameters
+of the system by using a Bayesian analysis and find that the lens consists of a
+super-Earth with a mass of $6.7^{+10.7}_{-3.6}M_{\oplus}$ orbiting around a
+brown-dwarf or late M-dwarf host with a mass of $0.10^{+0.16}_{-0.05}M_{\odot}$
+with a projected star-planet separation of $0.9^{+0.3}_{-0.2}$AU. The system is
+at a distance of $7.2 \pm 1.1$ kpc, i.e., it is likely to be in the Galactic
+bulge. The small angular Einstein radius ($\theta_{\rm E}=0.12 \pm 0.02$ mas)
+and short event timescale are typical for a low-mass lens in the Galactic
+bulge. Such low-mass planetary systems in the Bulge are rare because the
+detection efficiency of planets in short microlensing events is relatively low.
+This discovery may suggest that such low mass planetary systems are abundant in
+the Bulge and currently on-going high cadence survey programs will detect more
+such events and may reveal an abundance of such planetary systems.
+"
+29726,A streaming algorithm for bivariate empirical copulas,"  Empirical copula functions can be used to model the dependence structure of
+multivariate data. The Greenwald and Khanna algorithm is adapted in order to
+provide a space-memory efficient approximation to the empirical copula function
+of a bivariate stream of data. A succinct space-memory efficient summary of
+values seen in the stream up to a certain time is maintained and can be queried
+at any point to return an approximation to the empirical bivariate copula
+function with guaranteed error bounds. An example then illustrates how these
+summaries can be used as a tool to compute approximations to higher dimensional
+copula decompositions containing bivariate copulas. The computational benefits
+and approximation error of the algorithm is theoretically and numerically
+assessed.
+"
+29727,Apparatus to control and visualize the impact of a high-energy laser pulse on a liquid target,"  We present an experimental apparatus to control and visualize the response of
+a liquid target to a laser-induced vaporization. We use a millimeter-sized drop
+as target and present two liquid-dye solutions that allow a variation of the
+absorption coefficient of the laser light in the drop by seven orders of
+magnitude. The excitation source is a Q-switched Nd:YAG laser at its
+frequency-doubled wavelength emitting nanosecond pulses with energy densities
+above the local vaporization threshold. The absorption of the laser energy
+leads to a large-scale liquid motion at timescales that are separated by
+several orders of magnitude, which we spatiotemporally resolve by a combination
+of ultra-high-speed and stroboscopic high-resolution imaging in two orthogonal
+views. Surprisingly, the large-scale liquid motion at upon laser impact is
+completely controlled by the spatial energy distribution obtained by a precise
+beam-shaping technique. The apparatus demonstrates the potential for accurate
+and quantitative studies of laser-matter interactions.
+"
+29728,Image Segmentation by Iterative Inference from Conditional Score Estimation,"  Inspired by the combination of feedforward and iterative computations in the
+virtual cortex, and taking advantage of the ability of denoising autoencoders
+to estimate the score of a joint distribution, we propose a novel approach to
+iterative inference for capturing and exploiting the complex joint distribution
+of output variables conditioned on some input variables. This approach is
+applied to image pixel-wise segmentation, with the estimated conditional score
+used to perform gradient ascent towards a mode of the estimated conditional
+distribution. This extends previous work on score estimation by denoising
+autoencoders to the case of a conditional distribution, with a novel use of a
+corrupted feedforward predictor replacing Gaussian corruption. An advantage of
+this approach over more classical ways to perform iterative inference for
+structured outputs, like conditional random fields (CRFs), is that it is not
+any more necessary to define an explicit energy function linking the output
+variables. To keep computations tractable, such energy function
+parametrizations are typically fairly constrained, involving only a few
+neighbors of each of the output variables in each clique. We experimentally
+find that the proposed iterative inference from conditional score estimation by
+conditional denoising autoencoders performs better than comparable models based
+on CRFs or those not using any explicit modeling of the conditional joint
+distribution of outputs.
+"
+29729,Seminar Users in the Arabic Twitter Sphere,"  We introduce the notion of ""seminar users"", who are social media users
+engaged in propaganda in support of a political entity. We develop a framework
+that can identify such users with 84.4% precision and 76.1% recall. While our
+dataset is from the Arab region, omitting language-specific features has only a
+minor impact on classification performance, and thus, our approach could work
+for detecting seminar users in other parts of the world and in other languages.
+We further explored a controversial political topic to observe the prevalence
+and potential potency of such users. In our case study, we found that 25% of
+the users engaged in the topic are in fact seminar users and their tweets make
+nearly a third of the on-topic tweets. Moreover, they are often successful in
+affecting mainstream discourse with coordinated hashtag campaigns.
+"
+29730,Examples of foliations with infinite dimensional special cohomology,"  We present examples of foliations with infinite dimensional basic symplectic
+and com- plex cohomologies, along with a general sufficient condition for such
+phenomena. This puts re- strictions on possible generalizations of several
+finiteness results from Riemannian foliations to any broader class. The
+examples are also noteworthy for the unusual behaviour of their basic de Rham
+cohomology.
+"
+29731,An efficient quantum algorithm for generative machine learning,"  A central task in the field of quantum computing is to find applications
+where quantum computer could provide exponential speedup over any classical
+computer. Machine learning represents an important field with broad
+applications where quantum computer may offer significant speedup. Several
+quantum algorithms for discriminative machine learning have been found based on
+efficient solving of linear algebraic problems, with potential exponential
+speedup in runtime under the assumption of effective input from a quantum
+random access memory. In machine learning, generative models represent another
+large class which is widely used for both supervised and unsupervised learning.
+Here, we propose an efficient quantum algorithm for machine learning based on a
+quantum generative model. We prove that our proposed model is exponentially
+more powerful to represent probability distributions compared with classical
+generative models and has exponential speedup in training and inference at
+least for some instances under a reasonable assumption in computational
+complexity theory. Our result opens a new direction for quantum machine
+learning and offers a remarkable example in which a quantum algorithm shows
+exponential improvement over any classical algorithm in an important
+application field.
+"
+29732,Limitations on Transversal Computation through Quantum Homomorphic Encryption,"  Transversality is a simple and effective method for implementing quantum
+computation fault-tolerantly. However, no quantum error-correcting code (QECC)
+can transversally implement a quantum universal gate set (Eastin and Knill,
+Phys. Rev. Lett., 102, 110502). Since reversible classical computation is often
+a dominating part of useful quantum computation, whether or not it can be
+implemented transversally is an important open problem. We show that, other
+than a small set of non-additive codes that we cannot rule out, no binary QECC
+can transversally implement a classical reversible universal gate set. In
+particular, no such QECC can implement the Toffoli gate transversally.
+We prove our result by constructing an information theoretically secure (but
+inefficient) quantum homomorphic encryption (ITS-QHE) scheme inspired by Ouyang
+et al. (arXiv:1508.00938). Homomorphic encryption allows the implementation of
+certain functions directly on encrypted data, i.e. homomorphically. Our scheme
+builds on almost any QECC, and implements that code's transversal gate set
+homomorphically. We observe a restriction imposed by Nayak's bound (FOCS 1999)
+on ITS-QHE, implying that any ITS quantum fully homomorphic scheme (ITS-QFHE)
+implementing the full set of classical reversible functions must be highly
+inefficient. While our scheme incurs exponential overhead, any such QECC
+implementing Toffoli transversally would still violate this lower bound through
+our scheme.
+"
+29733,Characteristics and stability of soliton crystals in optical fibres for the purpose of optical frequency comb generation,"  We study the properties of a soliton crystal, an bound state of several
+optical pulses that propagate with a fixed temporal separation through the
+optical fibres of the proposed approach for generation of optical frequency
+combs (OFC) for astronomical spectrograph calibration. This approach - also
+being suitable for subpicosecond pulse generation for other applications -
+consists of a conventional single-mode fibre and a suitably pumped Erbium-doped
+fibre. Two continuous-wave lasers are used as light source. The soliton crystal
+arises out of the initial deeply modulated laser field at low input powers; for
+higher input powers, it dissolves into free solitons. We study the soliton
+crystal build-up in the first fibre stage with respect to different fibre
+parameters (group-velocity dispersion, nonlinearity, and optical losses) and to
+the light source characteristics (laser frequency separation and intensity
+difference). We show that the soliton crystal can be described by two
+quantities, its fundamental frequency and the laser power-threshold at which
+the crystal dissolves into free solitons. The soliton crystal exhibits features
+of a linear and nonlinear optical pattern at the same time and is insensitive
+to the initial laser power fluctuations. We perform our studies using the
+numerical technique called Soliton Radiation Beat Analysis.
+"
+29734,Diophantine approximation in prescribed degree,"  We investigate approximation to a given real number by algebraic numbers and
+algebraic integers of prescribed degree. We deal with both best and uniform
+approximation, and highlight the similarities and differences compared with the
+intensely studied problem of approximation by algebraic numbers (and integers)
+of bounded degree. We establish the answer to a question of Bugeaud concerning
+approximation to transcendental real numbers by quadratic irrational numbers,
+and thereby we refine a result of Davenport and Schmidt from 1967. We also
+obtain several new characterizations of Liouville numbers, and certain new
+insights on inhomogeneous Diophantine approximation. As an auxiliary side
+result, we provide an upper bound for the number of certain linear combinations
+of two given relatively prime integer polynomials with a linear factor. We
+conclude with several open problems.
+"
+29735,Muckenhoupt $A_p$-properties of distance functions and applications to Hardy-Sobolev -type inequalities,"  Let $X$ be a metric space equipped with a doubling measure. We consider
+weights $w(x)=\operatorname{dist}(x,E)^{-\alpha}$, where $E$ is a closed set in
+$X$ and $\alpha\in\mathbb R$. We establish sharp conditions, based on the
+Assouad (co)dimension of $E$, for the inclusion of $w$ in Muckenhoupt's $A_p$
+classes of weights, $1\le p<\infty$. With the help of general $A_p$-weighted
+embedding results, we then prove (global) Hardy-Sobolev inequalities and also
+fractional versions of such inequalities in the setting of metric spaces.
+"
+29736,Encoding of phonology in a recurrent neural model of grounded speech,"  We study the representation and encoding of phonemes in a recurrent neural
+network model of grounded speech. We use a model which processes images and
+their spoken descriptions, and projects the visual and auditory representations
+into the same semantic space. We perform a number of analyses on how
+information about individual phonemes is encoded in the MFCC features extracted
+from the speech signal, and the activations of the layers of the model. Via
+experiments with phoneme decoding and phoneme discrimination we show that
+phoneme representations are most salient in the lower layers of the model,
+where low-level signals are processed at a fine-grained level, although a large
+amount of phonological information is retain at the top recurrent layer. We
+further find out that the attention mechanism following the top recurrent layer
+significantly attenuates encoding of phonology and makes the utterance
+embeddings much more invariant to synonymy. Moreover, a hierarchical clustering
+of phoneme representations learned by the network shows an organizational
+structure of phonemes similar to those proposed in linguistics.
+"
+29737,About the Schouten-Weyl tensor on 3-dimensional Lorenzian Lie groups,"  The main purpose of this paper is to investigate the Schouten-Weyl tensor on
+the three-dimensional Lie groups with left-invariant Lorenzian metrics. The
+left-invariant Lorentzian metrics on the three-dimensional Lie groups with
+squared length zero Schouten-Weyl tensor are studied. Moreover, the
+three-dimensional metric Lie groups with almost harmonic (i.e. with zero curl
+and divergence) Schouten-Weyl tensor are investigated. In addition, the
+question about the harmonicity of contraction of the Schouten-Weyl tensor is
+considered.
+"
+29738,Exploration of Finite 2D Square Grid by a Metamorphic Robotic System,"  We consider exploration of finite 2D square grid by a metamorphic robotic
+system consisting of anonymous oblivious modules. The number of possible shapes
+of a metamorphic robotic system grows as the number of modules increases. The
+shape of the system serves as its memory and shows its functionality. We
+consider the effect of global compass on the minimum number of modules
+necessary to explore a finite 2D square grid. We show that if the modules agree
+on the directions (north, south, east, and west), three modules are necessary
+and sufficient for exploration from an arbitrary initial configuration,
+otherwise five modules are necessary and sufficient for restricted initial
+configurations.
+"
+29739,Up and down grover walks on simplicial complexes,"  A notion of up and down Grover walks on simplicial complexes are proposed and
+their properties are investigated. These are abstract Szegedy walks, which is a
+special kind of unitary operators on a Hilbert space. The operators introduced
+in the present paper are usual Grover walks on graphs defined by using
+combinatorial structures of simplicial complexes. But the shift operators are
+modified so that it can contain information of orientations of each simplex in
+the simplicial complex. It is well-known that the spectral structures of this
+kind of unitary operators are completely determined by its discriminant
+operators. It has strong relationship with combinatorial Laplacian on
+simplicial complexes and geometry, even topology, of simplicial complexes. In
+particular, theorems on a relation between spectrum of up and down
+discriminants and orientability, on a relation between symmetry of spectrum of
+discriminants and combinatorial structure of simplicial complex are given. Some
+examples, both of finite and infinite simplicial complexes, are also given.
+Finally, some aspects of finding probability and stationary measures are
+discussed.
+"
+29740,On enhanced hydrogen adsorption on alkali (Cesium) doped $C_{60}$ and effects of the quantum nature of the H2 molecule on physisorption energies,"  Hydrogen storage by physisorption in carbon based materials is hindered by
+low adsorption energies. In the last decade doping of carbon materials with
+alkali, earth alkali or other metal atoms was proposed as a means to enhance
+adsorption energies, and some experiments have shown promising results. We
+investigate the upper bounds of hydrogen storage capacities of $C_{60}Cs$
+clusters grown in ultracold helium nanodroplets by analyzing anomalies in the
+ion abundance that indicate shell closure of hydrogen adsorption shells. On
+bare $C_{60}^{+}$, a commensurate phase with 32 $H_2$ molecules was identified
+in previous experiments. Doping $C_{60}$ with a single cesium atom leads to an
+increase in relative ion abundance for the first 10 $H_2$ molecules, and the
+closure of the commensurate phase is shifted from 32 to 42 $H_2$ molecules.
+Density functional theory calculations indicate that thirteen energetically
+enhanced adsorption sites exist, where six of them fill the groove between Cs
+and $C_{60}$ and 7 are located at the cesium atom. We emphasize the large
+effect of the quantum nature of the hydrogen molecule on the adsorption
+energies, i.e. the adsorption energies are decreased by around 50% for
+$(H_2)C_{60}Cs$ and up to 80% for $(H_2)C_{60}$ by harmonic zero-point
+corrections, which represent an upper bound to corrections for dissociation
+energies ($D_e$ to $D_0$) by the vibrational ground states. Five normal modes
+of libration and vibration of $H_2$ physisorbed on the substrate contribute
+primarily to this large decrease in adsorption energies. A similar effect can
+be found for H2 physisorbed on benzene and is expected to be found for any
+other weakly $H_2$-binding substrate.
+"
+29741,Exceeding the asymptotic limit of polymer drag reduction,"  The drag of turbulent flows can be drastically decreased by addition of small
+amounts of high molecular weight polymers. While drag reduction initially
+increases with polymer concentration, it eventually saturates to what is known
+as the maximum drag reduction (MDR) asymptote; this asymptote is generally
+attributed to the dynamics being reduced to a marginal yet persistent state of
+subdued turbulent motion. Contrary to this accepted view we will show in the
+following that for an appropriate choice of parameters polymers can reduce the
+drag beyond the suggested asymptotic limit, eliminating turbulence and giving
+way to laminar flow. However at higher polymer concentrations the laminar state
+becomes unstable, resulting in a fluctuating flow with the characteristic drag
+of the MDR asymptote. The asymptotic state is hence dynamically disconnected
+from ordinary turbulence.
+"
+29742,Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations,"  We present novel minibatch stochastic optimization methods for empirical risk
+minimization problems, the methods efficiently leverage variance reduced
+first-order and sub-sampled higher-order information to accelerate the
+convergence speed. For quadratic objectives, we prove improved iteration
+complexity over state-of-the-art under reasonable assumptions. We also provide
+empirical evidence of the advantages of our method compared to existing
+approaches in the literature.
+"
+29743,Fair Clustering Through Fairlets,"  We study the question of fair clustering under the {\em disparate impact}
+doctrine, where each protected class must have approximately equal
+representation in every cluster. We formulate the fair clustering problem under
+both the $k$-center and the $k$-median objectives, and show that even with two
+protected classes the problem is challenging, as the optimum solution can
+violate common conventions---for instance a point may no longer be assigned to
+its nearest cluster center! En route we introduce the concept of fairlets,
+which are minimal sets that satisfy fair representation while approximately
+preserving the clustering objective. We show that any fair clustering problem
+can be decomposed into first finding good fairlets, and then using existing
+machinery for traditional clustering algorithms. While finding good fairlets
+can be NP-hard, we proceed to obtain efficient approximation algorithms based
+on minimum cost flow. We empirically quantify the value of fair clustering on
+real-world datasets with sensitive attributes.
+"
+29744,Hydrodynamics of flagellated microswimmers near free-slip interfaces,"  The hydrodynamics of a flagellated microorganism is investigated when
+swimming close to a planar free-slip surface by means of numerical solu- tions
+of the Stokes equations obtained via a Boundary Element Method. Depending on
+the initial condition, the swimmer can either escape from the free-slip surface
+or collide with the boundary. Interestingly, the mi- croorganism does not
+exhibit a stable orbit. Independently of escape or attraction to the interface,
+close to a free-slip surface, the swimmer fol- lows a counter-clockwise
+trajectory, in agreement with experimental find- ings, [15]. The hydrodynamics
+is indeed modified by the free-surface. In fact, when the same swimmer moves
+close to a no-slip wall, a set of initial conditions exists which result in
+stable orbits. Moreover when moving close to a free-slip or a no-slip boundary
+the swimmer assumes a different orientation with respect to its trajectory.
+Taken together, these results contribute to shed light on the hydrodynamical
+behaviour of microorgan- isms close to liquid-air interfaces which are relevant
+for the formation of interfacial biofilms of aerobic bacteria.
+"
+29745,Anisotropy in finite continuum percolation: Threshold estimation by Minkowski functionals,"  We examine the interplay between anisotropy and percolation, i.e., the
+spontaneous formation of a system spanning cluster in an anisotropic model. We
+simulate an extension of a benchmark model of continuum percolation, the
+Boolean model, which is formed by overlapping grains. Here we introduce an
+orientation bias of the grains that controls the degree of anisotropy of the
+generated patterns. We analyze in the Euclidean plane the percolation
+thresholds above which percolating clusters in $x$- and in $y$-direction
+emerge. Only in finite systems, distinct differences between effective
+percolation thresholds for different directions appear. If extrapolated to
+infinite system sizes, these differences vanish independent of the details of
+the model. In the infinite system, the uniqueness of the percolating cluster
+guarantees a unique percolation threshold. While percolation is isotropic even
+for anisotropic processes, the value of the percolation threshold depends on
+the model parameters, which we explore by simulating a score of models with
+varying degree of anisotropy. To which precision can we predict the percolation
+threshold without simulations? We discuss analytic formulas for approximations
+(based on the excluded area or the Euler characteristic) and compare them to
+our simulation results. Empirical parameters from similar systems allow for
+accurate predictions of the percolation thresholds (with deviations of $<5\%$
+in our examples), but even without any empirical parameters, the explicit
+approximations from integral geometry provide, at least for the systems studied
+here, lower bounds that capture well the qualitative dependence of the
+percolation threshold on the system parameters (with deviations of
+$5\%$--$30\%$). As an outlook, we suggest further candidates for explicit and
+geometric approximations based on second moments of the so-called Minkowski
+functionals.
+"
+29746,Poincaré square series of small weight,"  We extend the author's earlier computation and give coefficient formulas for
+the (quasimodular) Poincaré square series of weight 3/2 and weight 2 for the
+dual Weil representation for an even lattice.
+"
+29747,Normal State Gap in Parent Cuprate Pr2CuO4+/-δ,"  We present a tunneling study on single crystalline parent cuprate thin films,
+i.e. a series of Pr2CuO4+/-{\delta}(PCO) with tunable superconducting
+transition temperature. The zero-bias anomaly of differential conductance, well
+reported in the normal state of R2-xCexCuO4 (R = Pr, Nd, La) and named as
+normal state gap (NSG), is observed in the Ce-free samples. This NSG behaves
+quite robust against the magnetic field up to 16 T, but fades away with
+increasing the temperature. Most importantly, we find that the magnitude of the
+NSG becomes larger with increasing point-contact junction resistance on the
+superconducting films, which is further enhanced in the non-superconducting
+samples of more oxygen disorders. The origination of NSG can be understood in
+the framework of Altshuler-Aronov-Lee (AAL) theory, where the disorder-induced
+electron-electron interactions suppress the density of states and thereby
+result in a soft Coulomb gap.
+"
+29748,"Mass, Density, and Formation Constraints in the Compact, Sub-Earth Kepler-444 System including Two Mars-Mass Planets","  Kepler-444 is a five planet system around a host-star approximately 11
+billion years old. The five transiting planets all have sub-Earth radii and are
+in a compact configuration with orbital periods between 3 and 10 days. Here we
+present a transit-timing analysis of the system using the full Kepler data set
+in order to determine the masses of the planets. Two planets, Kepler-444 d
+($M_\mathrm{d}=0.036^{+0.065}_{-0.020}M_\oplus$) and Kepler-444 e
+($M_\mathrm{e}=0.034^{+0.059}_{-0.019}M_\oplus $), have confidently detected
+masses due to their proximity to resonance which creates transit timing
+variations. The mass ratio of these planets combined with the magnitude of
+possible star-planet tidal effects suggests that smooth disk migration over a
+significant distance is unlikely to have brought the system to its currently
+observed orbital architecture without significant post-formation perturbations.
+"
+29749,Practical Aspects of Autonomous Exploration with a Kinect2 sensor,"  Exploration of an unknown environment by a mobile robot is a complex task
+involving solution of many fundamental problems from data processing,
+localization to high-level planning and decision making. The exploration
+framework we developed is based on processing of RGBD data provided by a MS
+Kinect2 sensor, which allows to take advantage of state-of-the-art SLAM
+(Simultaneous Localization and Mapping) algorithms and to autonomously build a
+realistic 3D map of the environment with projected visual information about the
+scene. In this paper, we describe practical issues that appeared during
+deployment of the framework in real indoor and outdoor environments and discuss
+especially properties of SLAM algorithms processing MS Kinect2 data on an
+embedded computer.
+"
+29750,"Information Scientific and Educational Resource ""Electrophysics""","  The paper discusses an advanced level information system to support
+educational, research and scientific activities of the Department
+""Electrophysical Facilities"" (DEF) of the National Research Nuclear University
+""MEPhI"" (NRNU MEPhI), which is used for training of specialists of the course
+""Physics of Charged Particle Beams and Accelerator Technology"".
+"
+29751,From Least Squares to Signal Processing and Particle Filtering,"  De Facto, signal processing is the interpolation and extrapolation of a
+sequence of observations viewed as a realization of a stochastic process. Its
+role in applied statistics ranges from scenarios in forecasting and time series
+analysis, to image reconstruction, machine learning, and the degradation
+modeling for reliability assessment. A general solution to the problem of
+filtering and prediction entails some formidable mathematics. Efforts to
+circumvent the mathematics has resulted in the need for introducing more
+explicit descriptions of the underlying process. One such example, and a
+noteworthy one, is the Kalman Filter Model, which is a special case of state
+space models or what statisticians refer to as Dynamic Linear Models.
+Implementing the Kalman Filter Model in the era of ""big and high velocity
+non-Gaussian data"" can pose computational challenges with respect to efficiency
+and timeliness. Particle filtering is a way to ease such computational burdens.
+The purpose of this paper is to trace the historical evolution of this
+development from its inception to its current state, with an expository focus
+on two versions of the particle filter, namely, the propagate first-update next
+and the update first-propagate next version. By way of going beyond a pure
+review, this paper also makes transparent the importance and the role of a less
+recognized principle, namely the principle of conditionalization, in filtering
+and prediction based on Bayesian methods. Furthermore, the paper also
+articulates the philosophical underpinnings of the filtering and prediction
+set-up, a matter that needs to ne made explicit, and Yule's decomposition of a
+random variable in terms of a sequence of innovations.
+"
+29752,Characterizing the homogeneous variety F_4(4),"  In this paper we consider the $15$-dimensional homogeneous variety of Picard
+number one ${\rm F}_4(4)$, and provide a characterization of it in terms of its
+varieties of minimal rational tangents.
+"
+29753,Global Optimality in Low-rank Matrix Optimization,"  This paper considers the minimization of a general objective function $f(X)$
+over the set of rectangular $n\times m$ matrices that have rank at most $r$. To
+reduce the computational burden, we factorize the variable $X$ into a product
+of two smaller matrices and optimize over these two matrices instead of $X$.
+Despite the resulting nonconvexity, recent studies in matrix completion and
+sensing have shown that the factored problem has no spurious local minima and
+obeys the so-called strict saddle property (the function has a directional
+negative curvature at all critical points but local minima). We analyze the
+global geometry for a general and yet well-conditioned objective function
+$f(X)$ whose restricted strong convexity and restricted strong smoothness
+constants are comparable. In particular, we show that the reformulated
+objective function has no spurious local minima and obeys the strict saddle
+property. These geometric properties imply that a number of iterative
+optimization algorithms (such as gradient descent) can provably solve the
+factored problem with global convergence.
+"
+29754,OBJ2TEXT: Generating Visually Descriptive Language from Object Layouts,"  Generating captions for images is a task that has recently received
+considerable attention. In this work we focus on caption generation for
+abstract scenes, or object layouts where the only information provided is a set
+of objects and their locations. We propose OBJ2TEXT, a sequence-to-sequence
+model that encodes a set of objects and their locations as an input sequence
+using an LSTM network, and decodes this representation using an LSTM language
+model. We show that our model, despite encoding object layouts as a sequence,
+can represent spatial relationships between objects, and generate descriptions
+that are globally coherent and semantically relevant. We test our approach in a
+task of object-layout captioning by using only object annotations as inputs. We
+additionally show that our model, combined with a state-of-the-art object
+detector, improves an image captioning model from 0.863 to 0.950 (CIDEr score)
+in the test benchmark of the standard MS-COCO Captioning task.
+"
+29755,Revisiting stochastic off-policy action-value gradients,"  Off-policy stochastic actor-critic methods rely on approximating the
+stochastic policy gradient in order to derive an optimal policy. One may also
+derive the optimal policy by approximating the action-value gradient. The use
+of action-value gradients is desirable as policy improvement occurs along the
+direction of steepest ascent. This has been studied extensively within the
+context of natural gradient actor-critic algorithms and more recently within
+the context of deterministic policy gradients. In this paper we briefly discuss
+the off-policy stochastic counterpart to deterministic action-value gradients,
+as well as an incremental approach for following the policy gradient in lieu of
+the natural gradient.
+"
+29756,Policy Optimization via Importance Sampling,"  Policy optimization is an effective reinforcement learning approach to solve
+continuous control tasks. Recent achievements have shown that alternating
+online and offline optimization is a successful choice for efficient trajectory
+reuse. However, deciding when to stop optimizing and collect new trajectories
+is non-trivial, as it requires to account for the variance of the objective
+function estimate. In this paper, we propose a novel, model-free, policy search
+algorithm, POIS, applicable in both action-based and parameter-based settings.
+We first derive a high-confidence bound for importance sampling estimation;
+then we define a surrogate objective function, which is optimized offline
+whenever a new batch of trajectories is collected. Finally, the algorithm is
+tested on a selection of continuous control tasks, with both linear and deep
+policies, and compared with state-of-the-art policy optimization methods.
+"
+29757,"From Near to Eternity: Spin-glass planting, tiling puzzles, and constraint satisfaction problems","  We present a methodology for generating Ising Hamiltonians of tunable
+complexity and with a priori known ground states based on a decomposition of
+the model graph into edge-disjoint subgraphs. The idea is illustrated with a
+spin-glass model defined on a cubic lattice, where subproblems, whose couplers
+are restricted to the two values {-1,+1}, are specified on unit cubes and are
+parametrized by their local degeneracy. The construction is shown to be
+equivalent to a type of three-dimensional constraint satisfaction problem known
+as the tiling puzzle. By varying the proportions of subproblem types, the
+Hamiltonian can span a dramatic range of typical computational complexity, from
+fairly easy to many orders of magnitude more difficult than prototypical
+bimodal and Gaussian spin glasses in three space dimensions. We corroborate
+this behavior via experiments with different algorithms and discuss
+generalizations and extensions to different types of graphs.
+"
+29758,Consistent Alignment of Word Embedding Models,"  Word embedding models offer continuous vector representations that can
+capture rich contextual semantics based on their word co-occurrence patterns.
+While these word vectors can provide very effective features used in many NLP
+tasks such as clustering similar words and inferring learning relationships,
+many challenges and open research questions remain. In this paper, we propose a
+solution that aligns variations of the same model (or different models) in a
+joint low-dimensional latent space leveraging carefully generated synthetic
+data points. This generative process is inspired by the observation that a
+variety of linguistic relationships is captured by simple linear operations in
+embedded space. We demonstrate that our approach can lead to substantial
+improvements in recovering embeddings of local neighborhoods.
+"
+29759,Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection,"  The problem of universal outlying sequence detection is studied, where the
+goal is to detect outlying sequences among $M$ sequences of samples. A sequence
+is considered as outlying if the observations therein are generated by a
+distribution different from those generating the observations in the majority
+of the sequences. In the universal setting, we are interested in identifying
+all the outlying sequences without knowing the underlying generating
+distributions. In this paper, a class of tests based on distribution clustering
+is proposed. These tests are shown to be exponentially consistent with linear
+time complexity in $M$. Numerical results demonstrate that our clustering-based
+tests achieve similar performance to existing tests, while being considerably
+more computationally efficient.
+"
+29760,Monotonicity-based Electrical Impedance Tomography for Lung Imaging,"  This paper presents a monotonicity-based spatiotemporal conductivity imaging
+method for continuous regional lung monitoring using electrical impedance
+tomography (EIT). The EIT data (i.e., the boundary current-voltage data) can be
+decomposed into pulmonary, cardiac and other parts using their different
+periodic natures. The time-differential current-voltage operator corresponding
+to the lung ventilation can be viewed as either semi-positive or semi-negative
+definite owing to monotonic conductivity changes within the lung regions. We
+used this monotonicity constraints to improve the quality of lung EIT imaging.
+We tested the proposed methods in numerical simulations, phantom experiments
+and human experiments.
+"
+29761,The Sprague-Grundy function for some nearly disjunctive sums of Nim and Silver Dollar games,"  We introduce and analyse an extension of the disjunctive sum operation on
+some classical impartial games. Whereas the disjunctive sum describes positions
+formed from independent subpositions, our operation combines positions that are
+not completely independent but interact only in a very restricted way. We
+extend the games Nim and Silver Dollar, played by moving counters along
+one-dimensional strips of cells, by joining several strips at their initial
+cell. We prove that, in certain cases, computing the Sprague-Grundy function
+can be simplified to that of a simpler game with at most two tokens in each
+strip. We give an algorithm that, for each Sprague-Grundy value g, computes the
+positions of two-token Star Nim whose Sprague-Grundy values are g. We establish
+that the sequence of differences of entries of these positions is ultimately
+additively periodic.
+"
+29762,On two new means of two arguments III,"  In this paper authors establish the two sided inequalities for the following
+two new means $$X=X(a,b)=Ae^{G/P-1},\quad Y=Y(a,b)=Ge^{L/A-1}.$$ As well as
+many other well known inequalities involving the identric mean $I$ and the
+logarithmic mean are refined from the literature as an application.
+"
+29763,"Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability","  Algorithmic predictions are increasingly used to aid, or in some cases
+supplant, human decision-making, and this development has placed new demands on
+the outputs of machine learning procedures. To facilitate human interaction, we
+desire that they output prediction functions that are in some fashion simple or
+interpretable. And because they influence consequential decisions, we also
+desire equitable prediction functions, ones whose allocations benefit (or at
+the least do not harm) disadvantaged groups.
+We develop a formal model to explore the relationship between simplicity and
+equity. Although the two concepts appear to be motivated by qualitatively
+distinct goals, our main result shows a fundamental inconsistency between them.
+Specifically, we formalize a general framework for producing simple prediction
+functions, and in this framework we show that every simple prediction function
+is strictly improvable: there exists a more complex prediction function that is
+both strictly more efficient and also strictly more equitable. Put another way,
+using a simple prediction function both reduces utility for disadvantaged
+groups and reduces overall welfare. Our result is not only about algorithms but
+about any process that produces simple models, and as such connects to the
+psychology of stereotypes and to an earlier economics literature on statistical
+discrimination.
+"
+29764,Stabilizing GAN Training with Multiple Random Projections,"  Training generative adversarial networks is unstable in high-dimensions as
+the true data distribution tends to be concentrated in a small fraction of the
+ambient space. The discriminator is then quickly able to classify nearly all
+generated samples as fake, leaving the generator without meaningful gradients
+and causing it to deteriorate after a point in training. In this work, we
+propose training a single generator simultaneously against an array of
+discriminators, each of which looks at a different random low-dimensional
+projection of the data. Individual discriminators, now provided with restricted
+views of the input, are unable to reject generated samples perfectly and
+continue to provide meaningful gradients to the generator throughout training.
+Meanwhile, the generator learns to produce samples consistent with the full
+data distribution to satisfy all discriminators simultaneously. We demonstrate
+the practical utility of this approach experimentally, and show that it is able
+to produce image samples with higher quality than traditional training with a
+single discriminator.
+"
+29765,Simultaneous Stereo Video Deblurring and Scene Flow Estimation,"  Videos for outdoor scene often show unpleasant blur effects due to the large
+relative motion between the camera and the dynamic objects and large depth
+variations. Existing works typically focus monocular video deblurring. In this
+paper, we propose a novel approach to deblurring from stereo videos. In
+particular, we exploit the piece-wise planar assumption about the scene and
+leverage the scene flow information to deblur the image. Unlike the existing
+approach [31] which used a pre-computed scene flow, we propose a single
+framework to jointly estimate the scene flow and deblur the image, where the
+motion cues from scene flow estimation and blur information could reinforce
+each other, and produce superior results than the conventional scene flow
+estimation or stereo deblurring methods. We evaluate our method extensively on
+two available datasets and achieve significant improvement in flow estimation
+and removing the blur effect over the state-of-the-art methods.
+"
+29766,Material supply and magnetic configuration of an active region filament,"  It is important to study the fine structures of solar filaments with
+high-resolution observations since it can help us understand the magnetic and
+thermal structures of the filaments and their dynamics. In this paper, we study
+a newly-formed filament located inside the active region NOAA 11762, which was
+observed by the 1.6 m New Solar Telescope (NST) at Big Bear Solar Observatory
+(BBSO) from 16:40:19 UT to 17:07:58 UT on 2013 June 5. As revealed by the
+H$\alpha$ filtergrams, cool material is seen to be injected into the filament
+spine with a speed of 5--10 km s$^{-1}$. At the source of the injection,
+brightenings are identified in the chromosphere, which is accompanied by
+magnetic cancellation in the photosphere, implying the importance of magnetic
+reconnection in replenishing the filament with plasmas from the lower
+atmosphere. Counter-streamings are detected near one endpoint of the filament,
+with the plane-of-the-sky speed being 7--9 km s$^{-1}$ in the H$\alpha$
+red-wing filtergrams and 9--25 km s$^{-1}$ in the blue-wing filtergrams. The
+observations are indicative of that this active region filament is supported by
+a sheared arcade without magnetic dips, and the counter-streamings are due to
+unidirectional flows with alternative directions, rather than due to the
+longitudinal oscillations of filament threads as in many other filaments.
+"
+29767,Dynamics and control of gold-encapped gallium arsenide nanowires imaged by 4D electron microscopy,"  Eutectic related reaction is a special chemical/physical reaction involving
+multiple phases, solid and liquid. Visualization of phase reaction of composite
+nanomaterials with high spatial and temporal resolution provides a key
+understanding of alloy growth with important industrial applications. However,
+it has been a rather challenging task. Here we report the direct imaging and
+control of the phase reaction dynamics of a single, as-grown free-standing
+gallium arsenide nanowire encapped with a gold nanoparticle, free from
+environmental confinement or disturbance, using four-dimensional electron
+microscopy. The non-destructive preparation of as-grown free-standing nanowires
+without supporting films allows us to study their anisotropic properties in
+their native environment with better statistical character. A laser heating
+pulse initiates the eutectic related reaction at a temperature much lower than
+the melting points of the composite materials, followed by a precisely
+time-delayed electron pulse to visualize the irreversible transient states of
+nucleation, growth and solidification of the complex. Combined with theoretical
+modeling, useful thermodynamic parameters of the newly formed alloy phases and
+their crystal structures could be determined. This technique of dynamical
+control and 4D imaging of phase reaction processes on the nanometer-ultrafast
+time scale open new venues for engineering various reactions in a wide variety
+of other systems.
+"
+29768,Nonunitary Lagrangians and unitary non-Lagrangian conformal field theories,"  In various dimensions, we can sometimes compute observables of interacting
+conformal field theories (CFTs) that are connected to free theories via the
+renormalization group (RG) flow by computing protected quantities in the free
+theories. On the other hand, in two dimensions, it is often possible to
+algebraically construct observables of interacting CFTs using free fields
+without the need to explicitly construct an underlying RG flow. In this note,
+we begin to extend this idea to higher dimensions by showing that one can
+compute certain observables of an infinite set of unitary strongly interacting
+four-dimensional $\mathcal{N}=2$ superconformal field theories (SCFTs) by
+performing simple calculations involving sets of non-unitary free
+four-dimensional hypermultiplets. These free fields are distant cousins of the
+Majorana fermion underlying the two-dimensional Ising model and are not
+obviously connected to our interacting theories via an RG flow. Rather
+surprisingly, this construction gives us Lagrangians for particular observables
+in certain subsectors of many ""non-Lagrangian"" SCFTs by sacrificing unitarity
+while preserving the full $\mathcal{N}=2$ superconformal algebra. As a
+byproduct, we find relations between characters in unitary and non-unitary
+affine Kac-Moody algebras. We conclude by commenting on possible
+generalizations of our construction.
+"
+29769,On approximate least squares estimators of parameters on one-dimensional chirp signal,"  Chirp signals are quite common in many natural and man-made systems like
+audio signals, sonar, radar etc. Estimation of the unknown parameters of a
+signal is a fundamental problem in statistical signal processing. Recently,
+Kundu and Nandi \cite{2008} studied the asymptotic properties of least squares
+estimators of the unknown parameters of a simple chirp signal model under the
+assumption of stationary noise. In this paper, we propose periodogram-type
+estimators called the approximate least squares estimators to estimate the
+unknown parameters and study the asymptotic properties of these estimators
+under the same error assumptions. It is observed that the approximate least
+squares estimators are strongly consistent and asymptotically equivalent to the
+least squares estimators. Similar to the periodogram estimators, these
+estimators can also be used as initial guesses to find the least squares
+estimators of the unknown parameters. We perform some numerical simulations to
+see the performance of the proposed estimators and compare them with the least
+squares estimators and the estimators proposed by Lahiri et al., \cite{2013}.
+We have analysed two real data sets for illustrative purposes.
+"
+29770,Automatic Keyword Extraction for Text Summarization: A Survey,"  In recent times, data is growing rapidly in every domain such as news, social
+media, banking, education, etc. Due to the excessiveness of data, there is a
+need of automatic summarizer which will be capable to summarize the data
+especially textual data in original document without losing any critical
+purposes. Text summarization is emerged as an important research area in recent
+past. In this regard, review of existing work on text summarization process is
+useful for carrying out further research. In this paper, recent literature on
+automatic keyword extraction and text summarization are presented since text
+summarization process is highly depend on keyword extraction. This literature
+includes the discussion about different methodology used for keyword extraction
+and text summarization. It also discusses about different databases used for
+text summarization in several domains along with evaluation matrices. Finally,
+it discusses briefly about issues and research challenges faced by researchers
+along with future direction.
+"
+29771,Optimal Design of Experiments for Nonlinear Response Surface Models,"  Many chemical and biological experiments involve multiple treatment factors
+and often it is convenient to fit a nonlinear model in these factors. This
+nonlinear model can be mechanistic, empirical or a hybrid of the two. Motivated
+by experiments in chemical engineering, we focus on D-optimal design for
+multifactor nonlinear response surfaces in general. In order to find and study
+optimal designs, we first implement conventional point and coordinate exchange
+algorithms. Next, we develop a novel multiphase optimisation method to
+construct D-optimal designs with improved properties. The benefits of this
+method are demonstrated by application to two experiments involving nonlinear
+regression models. The designs obtained are shown to be considerably more
+informative than designs obtained using traditional design optimality
+algorithms.
+"
+29772,Branched covers of quasipositive links and L-spaces,"  Let $L$ be a oriented link such that $\Sigma_n(L)$, the $n$-fold cyclic cover
+of $S^3$ branched over $L$, is an L-space for some $n \geq 2$. We show that if
+either $L$ is a strongly quasipositive link other than one with Alexander
+polynomial a multiple of $(t-1)^{2g(L) + (|L|-1)}$, or $L$ is a quasipositive
+link other than one with Alexander polynomial divisible by $(t-1)^{2g_4(L) +
+(|L|-1)}$, then there is an integer $n(L)$, determined by the Alexander
+polynomial of $L$ in the first case and the Alexander polynomial of $L$ and the
+smooth $4$-genus of $L$, $g_4(L)$, in the second, such that $n \leq n(L)$. If
+$K$ is a strongly quasipositive knot with monic Alexander polynomial such as an
+L-space knot, we show that $\Sigma_n(K)$ is not an L-space for $n \geq 6$, and
+that the Alexander polynomial of $K$ is a non-trivial product of cyclotomic
+polynomials if $\Sigma_n(K)$ is an L-space for some $n = 2, 3, 4, 5$. Our
+results allow us to calculate the smooth and topological 4-ball genera of, for
+instance, quasi-alternating quasipositive links. They also allow us to classify
+strongly quasipositive alternating links and $3$-strand pretzel links.
+"
+29773,Volume preserving flow by powers of $k$-th mean curvature,"  We consider the flow of closed convex hypersurfaces in Euclidean space
+$\mathbb{R}^{n+1}$ with speed given by a power of the $k$-th mean curvature
+$E_k$ plus a global term chosen to impose a constraint involving the enclosed
+volume $V_{n+1}$ and the mixed volume $V_{n+1-k}$ of the evolving hypersurface.
+We prove that if the initial hypersurface is strictly convex, then the solution
+of the flow exists for all time and converges to a round sphere smoothly. No
+curvature pinching assumption is required on the initial hypersurface.
+"
+29774,Limitations of rotating-wave approximation in magnetic resonance: Characterization and elimination of the Bloch-Siegert shift in magneto-optics,"  We present investigations of radio-frequency resonances observed in a
+rubidium vapor. With measurements of a systematic shift of radio-frequency
+resonances, induced by an off-resonance component of an oscillating field (the
+Bloch-Siegert shift), we demonstrate limitations of the rotating-wave
+approximation. Experimental results are supported with numerical calculations,
+reproducing all features of the observed signals. Based on the studies, we
+develop experimental scheme free from the problem. By liberating the
+measurements from a systematic error (the resonance shift), the new approach
+will increase accuracy of spectroscopic measurements and magnetic-field
+detection.
+"
+29775,Effect of Local Population Uncertainty on Cooperation in Bacteria,"  Bacteria populations rely on mechanisms such as quorum sensing to coordinate
+complex tasks that cannot be achieved by a single bacterium. Quorum sensing is
+used to measure the local bacteria population density, and it controls
+cooperation by ensuring that a bacterium only commits the resources for
+cooperation when it expects its neighbors to reciprocate. This paper proposes a
+simple model for sharing a resource in a bacterial environment, where knowledge
+of the population influences each bacterium's behavior. Game theory is used to
+model the behavioral dynamics, where the net payoff (i.e., utility) for each
+bacterium is a function of its current behavior and that of the other bacteria.
+The game is first evaluated with perfect knowledge of the population. Then, the
+unreliability of diffusion introduces uncertainty in the local population
+estimate and changes the perceived payoffs. The results demonstrate the
+sensitivity to the system parameters and how population uncertainty can
+overcome a lack of explicit coordination.
+"
+29776,Deep Automated Multi-task Learning,"  Multi-task learning (MTL) has recently contributed to learning better
+representations in service of various NLP tasks. MTL aims at improving the
+performance of a primary task, by jointly training on a secondary task. This
+paper introduces automated tasks, which exploit the sequential nature of the
+input data, as secondary tasks in an MTL model. We explore next word
+prediction, next character prediction, and missing word completion as potential
+automated tasks. Our results show that training on a primary task in parallel
+with a secondary automated task improves both the convergence speed and
+accuracy for the primary task. We suggest two methods for augmenting an
+existing network with automated tasks and establish better performance in topic
+prediction, sentiment analysis, and hashtag recommendation. Finally, we show
+that the MTL models can perform well on datasets that are small and colloquial
+by nature.
+"
+29777,Strongly Interacting Bose Gases near a $d$-wave Shape Resonance,"  Many unconventional quantum matters, such as fractional quantum Hall effect
+and $d$-wave high-Tc superconductor, are discovered in strongly interacting
+systems. Understanding quantum many-body systems with strong interaction and
+the unconventional phases therein is one of the most challenging problems in
+physics nowadays. Cold atom systems possess a natural way to create strong
+interaction by bringing the system to the vicinity of a scattering resonance.
+Although this has been a focused topic in cold atom physics for more than a
+decade, these studies have so far mostly been limited for $s$-wave resonance.
+Here we report the experimental observation of a broad $d$-wave shape resonance
+in degenerate ${}^{41}$K gas. We further measure the molecular binding energy
+that splits into three branches as a hallmark of $d$-wave molecules, and find
+that the lifetime of this many-body system is reasonably long at strongly
+interacting regime. From analyzing the breathing mode excited by ramping
+through this resonance, it suggests that a quite stable low-temperature atom
+and molecule mixture is produced. Putting all the evidence together, our system
+offers great promise to reach a $d$-wave molecular superfluid.
+"
+29778,Optimal non-asymptotic bound of the Ruppert-Polyak averaging without strong convexity,"  This paper is devoted to the non-asymptotic control of the mean-squared error
+for the Ruppert-Polyak stochastic averaged gradient descent introduced in the
+seminal contributions of [Rup88] and [PJ92]. In our main results, we establish
+non-asymptotic tight bounds (optimal with respect to the Cramer-Rao lower
+bound) in a very general framework that includes the uniformly strongly convex
+case as well as the one where the function f to be minimized satisfies a weaker
+Kurdyka-Lojiasewicz-type condition [Loj63, Kur98]. In particular, it makes it
+possible to recover some pathological examples such as on-line learning for
+logistic regression (see [Bac14]) and recursive quan- tile estimation (an even
+non-convex situation).
+"
+29779,Signals of HE atmospheric muon decay in flight around the Sun's albedo versus astrophysical muon and tau neutrino traces in the Moon's shadow,"  The Sun albedo of cosmic rays at GeVs energy has been discovered recently by
+FERMI satellite. They are traces of atmospheric CR hitting solar atmosphere and
+reflecting skimming gamma photons. Even if relevant for astrophysics, as being
+trace of atmospheric solar Cosmic Ray noises they cannot offer any signal of
+neutrino astronomy. On the contrary the Moon, with no atmosphere, may become
+soon a novel filtering calorimeter and an amplifier of energetic muon
+astronomical neutrinos (at TeV up to hundred TeV energy); these lepton tracks
+leave an imprint in their beta decay while in flight to Earth. Their TeV
+electron air-shower are among the main signals. Also a more energetic, but more
+rare, PeV up to EeV tau lunar neutrino events may be escaping as a tau lepton
+from the Moon: PeVs secondaries may be shining on Earth atmosphere in lunar
+shadows in a surprising rich way. One or a few gamma air-shower event inside
+the Moon shadows may occur each year in near future CTA or LHAASO TeVs gamma
+array detector, assuming a non negligible astrophysical TeV up to hundred TeV
+neutrino component (respect to our terrestrial ruling atmospheric ones); these
+signals will open a new wonderful passepartout keyhole for neutrino been seen
+along the Moon. The lunar solid angle is small and the muon or tau expected
+rate is rare, but future largest tau radio array as GRAND one might well
+discover such neutrino imprint.
+"
+29780,Greedy Sampling of Graph Signals,"  Sampling is a fundamental topic in graph signal processing, having found
+applications in estimation, clustering, and video compression. In contrast to
+traditional signal processing, the irregularity of the signal domain makes
+selecting a sampling set non-trivial and hard to analyze. Indeed, though
+conditions for graph signal interpolation from noiseless samples exist, they do
+not lead to a unique sampling set. The presence of noise makes choosing among
+these sampling sets a hard combinatorial problem. Although greedy sampling
+schemes are commonly used in practice, they have no performance guarantee. This
+work takes a twofold approach to address this issue. First, universal
+performance bounds are derived for the Bayesian estimation of graph signals
+from noisy samples. In contrast to currently available bounds, they are not
+restricted to specific sampling schemes and hold for any sampling sets. Second,
+this paper provides near-optimal guarantees for greedy sampling by introducing
+the concept of approximate submodularity and updating the classical greedy
+bound. It then provides explicit bounds on the approximate supermodularity of
+the interpolation mean-square error showing that it can be optimized with
+worst-case guarantees using greedy search even though it is not supermodular.
+Simulations illustrate the derived bound for different graph models and show an
+application of graph signal sampling to reduce the complexity of kernel
+principal component analysis.
+"
+29781,Model misspecification and bias for inverse probability weighting and doubly robust estimators,"  In the causal inference literature an estimator belonging to a class of
+semi-parametric estimators is called robust if it has desirable properties
+under the assumption that at least one of the working models is correctly
+specified. In this paper we propose a crude analytical approach to study the
+large sample bias of semi-parameteric estimators of the average causal effect
+when all working models are misspecified. We apply our approach to three
+prototypical estimators, two inverse probability weighting (IPW) estimators,
+using a misspecified propensity score model, and a doubly robust (DR)
+estimator, using misspecified models for the outcome regression and the
+propensity score. To analyze the question of when the use of two misspecified
+models are better than one we derive necessary and sufficient conditions for
+when the DR estimator has a smaller bias than a simple IPW estimator and when
+it has a smaller bias than an IPW estimator with normalized weights. If the
+misspecificiation of the outcome model is moderate the comparisons of the
+biases of the IPW and DR estimators suggest that the DR estimator has a smaller
+bias than the IPW estimators. However, all biases include the PS-model error
+and we suggest that a researcher is careful when modeling the PS whenever such
+a model is involved.
+"
+29782,Attribute Based Administration of Role Based Access Control : A Detailed Description,"  Administrative Role Based Access Control (ARBAC) models deal with how to
+manage user-role assignments (URA), permission-role assignments (PRA), and
+role-role assignments (RRA). A wide variety of approaches has been proposed in
+the literature for URA, PRA, and RRA. In this paper, we propose attribute-based
+administrative models that unify many prior approaches for URA and PRA. The
+motivating factor is that attributes of various RBAC entities such as admin
+users, regular users and permissions can be used to administer URA and PRA in a
+highly flexible manner. We develop an attribute-based URA model called AURA and
+an attribute-based PRA model called ARPA. We demonstrate that AURA and ARPA can
+express and unify many prior URA and PRA models.
+"
+29783,Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case,"  In an effort to overcome the data deluge in computational biology and
+bioinformatics and to facilitate bioinformatics research in the era of big
+data, we identify some of the most influential algorithms that have been widely
+used in the bioinformatics community. These top data mining and machine
+learning algorithms cover classification, clustering, regression, graphical
+model-based learning, and dimensionality reduction. The goal of this study is
+to guide the focus of scalable computing experts in the endeavor of applying
+new storage and scalable computation designs to bioinformatics algorithms that
+merit their attention most, following the engineering maxim of ""optimize the
+common case"".
+"
+29784,Singular Hochschild cohomology and algebraic string operations,"  Given a differential graded (dg) symmetric Frobenius algebra $A$ we construct
+an unbounded complex $\mathcal{D}^{*}(A,A)$, called the Tate-Hochschild
+complex, which arises as a totalization of a double complex having Hochschild
+chains as negative columns and Hochschild cochains as non-negative columns. We
+prove that the complex $\mathcal{D}^*(A,A)$ computes the singular Hochschild
+cohomology of $A$. We construct a cyclic (or Calabi-Yau) $A$-infinity algebra
+structure, which extends the classical Hochschild cup and cap products, and an
+$L$-infinity algebra structure extending the classical Gerstenhaber bracket, on
+$\mathcal{D}^*(A,A)$. Moreover, we prove that the cohomology algebra
+$H^*(\mathcal{D}^*(A,A))$ is a Batalin-Vilkovisky (BV) algebra with BV operator
+extending Connes' boundary operator. Finally, we show that if two Frobenius
+algebras are quasi-isomorphic as dg algebras then their Tate-Hochschild
+cohomologies are isomorphic and we use this invariance result to relate the
+Tate-Hochschild complex to string topology.
+"
+29785,Crossover from impurity-controlled to granular superconductivity in (TMTSF)2ClO4,"  Using a proper cooling procedure, a controllable amount of non-magnetic
+structural disorder can be introduced at low temperature in (TMTSF)2ClO4. Here
+we performed simultaneous measurements of transport and magnetic properties of
+(TMTSF)2ClO4 in its normal and superconducting states, while finely covering
+three orders of magnitude of the cooling rate around the anion ordering
+temperature. Our result reveals, with increasing density of disorder, the
+existence of a crossover between homogeneous defect-controlled d-wave
+superconductivity and granular superconductivity. At slow cooling rates, with
+small amount of disorder, the evolution of superconducting properties is well
+described with the Abrikosov-Gorkov theory, providing further confirmation of
+non-s-wave pairing in this compound. In contrast, at fast cooling rates, zero
+resistance and diamagnetic shielding are achieved through a randomly
+distributed network of superconducting puddles embedded in an normal conducting
+background and interconnected by proximity effect coupling. The temperature
+dependence of the AC complex susceptibility reveals features typical for a
+network of granular superconductors. This makes (TMTSF)2ClO4 a model system for
+granular superconductivity where the grain size and their concentration are
+tunable within the same sample.
+"
+29786,Asymptotics of quantum $6j$ symbols,"  Asymptotics of quantum $6j$ symbols corresponding to a hyperbolic tetrahedra
+is investigated and the first two leading terms are determined for the case
+that the tetrahedron has a ideal or ultra-ideal vertex. These terms are given
+by the volume and the determinant of the Gram matrix of the tetrahedron. A
+relation to the volume conjecture of the Turaev-Viro invariant is also
+discussed.
+"
+29787,Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds,"  Conditional kernel mean embeddings are nonparametric models that encode
+conditional expectations in a reproducing kernel Hilbert space. While they
+provide a flexible and powerful framework for probabilistic inference, their
+performance is highly dependent on the choice of kernel and regularization
+hyperparameters. Nevertheless, current hyperparameter tuning methods
+predominantly rely on expensive cross validation or heuristics that is not
+optimized for the inference task. For conditional kernel mean embeddings with
+categorical targets and arbitrary inputs, we propose a hyperparameter learning
+framework based on Rademacher complexity bounds to prevent overfitting by
+balancing data fit against model complexity. Our approach only requires batch
+updates, allowing scalable kernel hyperparameter tuning without invoking kernel
+approximations. Experiments demonstrate that our learning framework outperforms
+competing methods, and can be further extended to incorporate and learn deep
+neural network weights to improve generalization.
+"
+29788,Quadric Complexes,"  Quadric complexes are square complexes satisfying a certain combinatorial
+nonpositive curvature condition. These complexes generalize 2-dimensional
+CAT(0) cube complexes and are a square analog of systolic complexes. We
+introduce and study the basic properties of these complexes. Using a form of
+dismantlability for the 1-skeleta of finite quadric complexes we show that
+every finite group acting on a quadric complex stabilizes a complete bipartite
+subgraph of its 1-skeleton. Finally, we prove that C(4)-T(4) small cancellation
+groups act on quadric complexes.
+"
+29789,On J-Colorability of Certain Derived Graph Classes,"  A vertex $v$ of a given graph $G$ is said to be in a rainbow neighbourhood of
+$G$, with respect to a proper coloring $C$ of $G$, if the closed neighbourhood
+$N[v]$ of the vertex $v$ consists of at least one vertex from every colour
+class of $G$ with respect to $C$. A maximal proper colouring of a graph $G$ is
+a $J$-colouring of $G$ if and only if every vertex of G belongs to a rainbow
+neighbourhood of $G$. In this paper, we study certain parameters related to
+$J$-colouring of certain Mycielski type graphs.
+"
+29790,Spectral equations for the modular oscillator,"  Motivated by applications for non-perturbative topological strings in toric
+Calabi--Yau manifolds, we discuss the spectral problem for a pair of commuting
+modular conjugate (in the sense of Faddeev) Harper type operators,
+corresponding to a special case of the quantized mirror curve of local
+$\mathbb{P}^1\times\mathbb{P}^1$ and complex values of Planck's constant. We
+illustrate our analytical results by numerical calculations.
+"
+29791,Improved Online Wilson Score Interval Method for Community Answer Quality Ranking,"  In this paper, a fast and easy-to-deploy method with a strong
+interpretability for community answer quality ranking is proposed. This method
+is improved based on the Wilson score interval method [Wilson, 1927], which
+retains its advantages and simultaneously improve the degree of satisfaction
+with the ranking of the high-quality answers. The improved answer quality score
+considers both Wilson score interval and the spotlight index, the latter of
+which will be introduced in the article. This method could significantly
+improve the ranking of the best answers with high attention in diverse
+scenarios.
+"
+29792,Crystal Growth in Fluid Flow: Nonlinear Response Effects,"  We investigate crystal-growth kinetics in the presence of strong shear flow
+in the liquid, using molecular-dynamics simulations of a binary-alloy model.
+Close to the equilibrium melting point, shear flow always suppresses the growth
+of the crystal-liquid interface. For lower temperatures, we find that the
+growth velocity of the crystal depends non-monotonically on the shear rate.
+Slow enough flow enhances the crystal growth, due to an increased particle
+mobility in the liquid. Stronger flow causes a growth regime that is nearly
+temperature-independent, in striking contrast to what one expects from the
+thermodynamic and equilibrium kinetic properties of the system, which both
+depend strongly on temperature. We rationalize these effects of flow on crystal
+growth as resulting from the nonlinear response of the fluid to strong shearing
+forces.
+"
+29793,Generalized and hybrid Metropolis-Hastings overdamped Langevin algorithms,"  It has been shown that the nonreversible overdamped Langevin dynamics enjoy
+better convergence properties in terms of spectral gap and asymptotic variance
+than the reversible one. In this article we propose a variance reduction method
+for the Metropolis-Hastings Adjusted Langevin Algorithm (MALA) that makes use
+of the good behaviour of the these nonreversible dynamics. It consists in
+constructing a nonreversible Markov chain (with respect to the target invariant
+measure) by using a Generalized Metropolis-Hastings adjustment on a lifted
+state space. We present two variations of this method and we discuss the
+importance of a well-chosen proposal distribution in terms of average rejection
+probability. We conclude with numerical experimentations to compare our
+algorithms with the MALA, and show variance reduction of several order of
+magnitude in some favourable toy cases.
+"
+29794,Safe Element Screening for Submodular Function Minimization,"  Submodular functions are discrete analogs of convex functions, which have
+applications in various fields, including machine learning and computer vision.
+However, in large-scale applications, solving Submodular Function Minimization
+(SFM) problems remains challenging. In this paper, we make the first attempt to
+extend the emerging technique named screening in large-scale sparse learning to
+SFM for accelerating its optimization process. We first conduct a careful
+studying of the relationships between SFM and the corresponding convex proximal
+problems, as well as the accurate primal optimum estimation of the proximal
+problems. Relying on this study, we subsequently propose a novel safe screening
+method to quickly identify the elements guaranteed to be included (we refer to
+them as active) or excluded (inactive) in the final optimal solution of SFM
+during the optimization process. By removing the inactive elements and fixing
+the active ones, the problem size can be dramatically reduced, leading to great
+savings in the computational cost without sacrificing any accuracy. To the best
+of our knowledge, the proposed method is the first screening method in the
+fields of SFM and even combinatorial optimization, thus pointing out a new
+direction for accelerating SFM algorithms. Experiment results on both synthetic
+and real datasets demonstrate the significant speedups gained by our approach.
+"
+29795,Thermodynamics of Evolutionary Games,"  How cooperation can evolve between players is an unsolved problem of biology.
+Here we use Hamiltonian dynamics of models of the Ising type to describe
+populations of cooperating and defecting players to show that the equilibrium
+fraction of cooperators is given by the expectation value of a thermal
+observable akin to a magnetization. We apply the formalism to the Public Goods
+game with three players, and show that a phase transition between cooperation
+and defection occurs that is equivalent to a transition in one-dimensional
+Ising crystals with long-range interactions. We then investigate the effect of
+punishment on cooperation and find that punishment plays the role of a magnetic
+field that leads to an ""alignment"" between players, thus encouraging
+cooperation. We suggest that a thermal Hamiltonian picture of the evolution of
+cooperation can generate other insights about the dynamics of evolving groups
+by mining the rich literature of critical dynamics in low-dimensional spin
+systems.
+"
+29796,Thermal vacancy formation energies of random solid solutions,"  Vacancy mechanism plays a dominant role in the atomic migration when a
+close-packed disordered alloy undergoes ordering transition. However, the
+calculation of thermal vacancy formation energies (VFEs) of random solid
+solutions is usually cumbersome due to the difficulty in considering various
+local atomic environments. Here, we propose a transparent way that combines
+coherent potential approximation and supercell-local cluster expansion to
+investigate VFEs of random solid solutions. This method is used to study the
+effects of temperature, strain and magnetism on the VFEs of random A1-FePt
+alloy. The results show that the mean VFE increases with increasing
+temperature, decreases under (001) in-plane tensile and compressive strains,
+and can be further reduced by the magnetic excitation. These effects are
+explained by discussing the dependence of VFE on local atomic environments and
+the overall bond strength within.
+"
+29797,Analytical modeling of light transport in scattering materials with strong absorption,"  We have investigated the transport of light through slabs that both scatter
+and strongly absorb, a situation that occurs in diverse application fields
+ranging from biomedical optics, powder technology, to solid-state lighting. In
+particular, we study the transport of light in the visible wavelength range
+between $420$ and $700$ nm through silicone plates filled with YAG:Ce$^{3+}$
+phosphor particles, that even re-emit absorbed light at different wavelengths.
+We measure the total transmission, the total reflection, and the ballistic
+transmission of light through these plates. We obtain average single particle
+properties namely the scattering cross-section $\sigma_s$, the absorption
+cross-section $\sigma_a$, and the anisotropy factor $\mu$ using an analytical
+approach, namely the P3 approximation to the radiative transfer equation. We
+verify the extracted transport parameters using Monte-Carlo simulations of the
+light transport. Our approach fully describes the light propagation in phosphor
+diffuser plates that are used in white LEDs and that reveal a strong absorption
+($L/\ell_{\mathrm{a}} > 1$) up to $L/\ell_{\mathrm{a}} = 4$, where $L$ is the
+slab thickness, $\ell_{\mathrm{a}}$ is the absorption mean free path. In
+contrast, the widely used diffusion theory fails to describe this parameter
+range. Our approach is a suitable analytical tool for industry, since it
+provides a fast yet accurate determination of key transport parameters, and
+since it introduces predictive power into the design process of white light
+emitting diodes.
+"
+29798,"Quillen equivalences between the model categories of smooth spaces, simplicial sets, and arc-gengerated spaces","  In the preceding paper, we have constructed a compactly generated model
+structure on the category $\dcal$ of diffeological spaces together with the
+adjoint pairs $|\ |_\dcal : \scal \rightleftarrows \dcal : S^\dcal$ and
+$\tilde{\cdot} : \dcal \rightleftarrows \ccal^0 : R$, where $\scal$ and
+$\ccal^0$ denote the category of simplicial sets and that of arc-generated
+spaces, respectively. In this paper, we show that $(|\ |_\dcal, S^\dcal)$ and
+$(\tilde{\cdot}, R)$ are pairs of Quillen equivalences. Since our approach
+developed in the preceding paper applies to the category $\ccal h$ of Chen
+spaces as well, $\ccal h$ is also a compactly generated model category. We also
+show that the adjoint pair $\mathfrak{S}_\mathfrak{o} : \ccal h
+\rightleftarrows \dcal : \mathfrak{Ch}^\sharp$ introduced by Stacey is a pair
+of Quillen equivalences.
+"
+29799,Optimizing Passenger Comfort in Cost Functions for Trajectory Planning,"  Current advances in the development of autonomous cars suggest that
+driverless cars may see wide-scale deployment in the near future. Research by
+both industry and academia is driven by potential benefits of this new
+technology, including reductions in fatalities and improvements in traffic and
+fuel efficiency as well as greater mobility for people who will or cannot drive
+cars themselves. A deciding factor for the adoption of self-driving cars
+besides safety will be the comfort of the passengers. This report looks at cost
+functions currently used in motion planning methods for autonomous on-road
+driving. Specifically, how the human perception of how comfortable a trajectory
+is can be formulated within cost functions.
+"
+29800,Krein's trace theorem revisited,"  We supply the first proof of Krein's Trace Theorem which does not use complex
+analysis. Our proof holds for~$\sigma$-finite von Neumann algebras
+$\mathcal{M}$ of type II and unbounded perturbations from the predual
+of~$\mathcal{M}$.
+"
+29801,Autoencoder Node Saliency: Selecting Relevant Latent Representations,"  The autoencoder is an artificial neural network model that learns hidden
+representations of unlabeled data. With a linear transfer function it is
+similar to the principal component analysis (PCA). While both methods use
+weight vectors for linear transformations, the autoencoder does not come with
+any indication similar to the eigenvalues in PCA that are paired with the
+eigenvectors. We propose a novel supervised node saliency (SNS) method that
+ranks the hidden nodes by comparing class distributions of latent
+representations against a fixed reference distribution. The latent
+representations of a hidden node can be described using a one-dimensional
+histogram. We apply normalized entropy difference (NED) to measure the
+""interestingness"" of the histograms, and conclude a property for NED values to
+identify a good classifying node. By applying our methods to real data sets, we
+demonstrate the ability of SNS to explain what the trained autoencoders have
+learned.
+"
+29802,Learning to Gather without Communication,"  A standard belief on emerging collective behavior is that it emerges from
+simple individual rules. Most of the mathematical research on such collective
+behavior starts from imperative individual rules, like always go to the center.
+But how could an (optimal) individual rule emerge during a short period within
+the group lifetime, especially if communication is not available. We argue that
+such rules can actually emerge in a group in a short span of time via
+collective (multi-agent) reinforcement learning, i.e learning via rewards and
+punishments. We consider the gathering problem: several agents (social animals,
+swarming robots...) must gather around a same position, which is not determined
+in advance. They must do so without communication on their planned decision,
+just by looking at the position of other agents. We present the first
+experimental evidence that a gathering behavior can be learned without
+communication in a partially observable environment. The learned behavior has
+the same properties as a self-stabilizing distributed algorithm, as processes
+can gather from any initial state (and thus tolerate any transient failure).
+Besides, we show that it is possible to tolerate the brutal loss of up to 90\%
+of agents without significant impact on the behavior.
+"
+29803,FADO: A Deterministic Detection/Learning Algorithm,"  This paper proposes and studies a detection technique for adversarial
+scenarios (dubbed deterministic detection). This technique provides an
+alternative detection methodology in case the usual stochastic methods are not
+applicable: this can be because the studied phenomenon does not follow a
+stochastic sampling scheme, samples are high-dimensional and subsequent
+multiple-testing corrections render results overly conservative, sample sizes
+are too low for asymptotic results (as e.g. the central limit theorem) to kick
+in, or one cannot allow for the small probability of failure inherent to
+stochastic approaches. This paper instead designs a method based on insights
+from machine learning and online learning theory: this detection algorithm -
+named Online FAult Detection (FADO) - comes with theoretical guarantees of its
+detection capabilities. A version of the margin is found to regulate the
+detection performance of FADO. A precise expression is derived for bounding the
+performance, and experimental results are presented assessing the influence of
+involved quantities. A case study of scene detection is used to illustrate the
+approach. The technology is closely related to the linear perceptron rule,
+inherits its computational attractiveness and flexibility towards various
+extensions.
+"
+29804,Scattering of kinks of the sinh-deformed $φ^4$ model,"  We consider the scattering of kinks of the sinh-deformed $\varphi^4$ model,
+which is obtained from the well-known $\varphi^4$ model by means of the
+deformation procedure. Depending on the initial velocity $v_{in}$ of the
+colliding kinks, different collision scenarios are realized. There is a
+critical value $v_{cr}$ of the initial velocity, which separates the regime of
+reflection (at $v_{in}>v_{cr}$) and that of a complicated interaction (at
+$v_{in}<v_{cr}$) with kinks' capture and escape windows. Besides that, at
+$v_{in}$ below $v_{cr}$ we observe the formation of a bound state of two
+oscillons, as well as their escape at some values of $v_{in}$.
+"
+29805,"Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps","  Autonomously driving vehicles require a complete and robust perception of the
+local environment. A main challenge is to perceive any other road users, where
+multi-object tracking or occupancy grid maps are commonly used. The presented
+approach combines both methods to compensate false positives and receive a
+complementary environment perception. Therefore, an environment perception
+framework is introduced that defines a common representation, extracts objects
+from a dynamic occupancy grid map and fuses them with tracks of a Labeled
+Multi-Bernoulli filter. Finally, a confidence value is developed, that
+validates object estimates using different constraints regarding physical
+possibilities, method specific characteristics and contextual information from
+a digital map. Experimental results with real world data highlight the
+robustness and significance of the presented fusing approach, utilizing the
+confidence value in rural and urban scenarios.
+"
+29806,Ising Superconductivity and Quantum Phase Transition in Macro-Size Monolayer NbSe2,"  Two-dimensional (2D) transition metal dichalcogenides (TMDs) have a range of
+unique physics properties and could be used in the development of electronics,
+photonics, spintronics and quantum computing devices. The mechanical
+exfoliation technique of micro-size TMD flakes has attracted particular
+interest due to its simplicity and cost effectiveness. However, for most
+applications, large area and high quality films are preferred. Furthermore,
+when the thickness of crystalline films is down to the 2D limit (monolayer),
+exotic properties can be expected due to the quantum confinement and symmetry
+breaking. In this paper, we have successfully prepared macro-size atomically
+flat monolayer NbSe2 films on bilayer graphene terminated surface of
+6H-SiC(0001) substrates by molecular beam epitaxy (MBE) method. The films
+exhibit an onset superconducting critical transition temperature above 6 K, 2
+times higher than that of mechanical exfoliated NbSe2 flakes. Simultaneously,
+the transport measurements at high magnetic fields reveal that the parallel
+characteristic field Bc// is at least 4.5 times higher than the paramagnetic
+limiting field, consistent with Zeeman-protected Ising superconductivity
+mechanism. Besides, by ultralow temperature electrical transport measurements,
+the monolayer NbSe2 film shows the signature of quantum Griffiths singularity
+when approaching the zero-temperature quantum critical point.
+"
+29807,Unreasonable effectiveness of Monte Carlo,"  This is a comment on the article ""Probabilistic Integration: A Role in
+Statistical Computation?"" by F.-X. Briol, C. J. Oates, M. Girolami, M. A.
+Osborne and D. Sejdinovic to appear in Statistical Science.
+There is a role for statistical computation in numerical integration.
+However, the competition from incumbent methods looks to be stiffer for this
+problem than for some of the newer problems being handled by probabilistic
+numerics. One of the challenges is the unreasonable effectiveness of the
+central limit theorem. Another is the unreasonable effectiveness of
+pseudorandom number generators. A third is the common $O(n^3)$ cost of methods
+based on Gaussian processes. Despite these advantages, the classical methods
+are weak in places where probabilistic methods could bring an improvement.
+"
+29808,Study of the composition dependence of the ionic conductivity of LiH_{x}D_{1-x} alloys,"  Recent observations have shown that at a certain composition of solid
+solutions AgCl$_x$Br$_{1-x}$, AgCl-CdCl$_2$, AgBr-CdBr$_2$, of ionic crystals,
+the electrical conductivity exhibits a value appreciably larger than that of
+the end constituents. Here, we investigate the electrical conductivity $\sigma$
+of LiH$_x$D$_{1-x}$ solid solutions -which are of prominent importance in fuel
+hydrogen storage applications- and find that in the whole composition range no
+maximum is likely to occur in the $\sigma$ versus x dependence.
+"
+29809,Induced Waveform Transitions of Dissipative Solitons,"  The effect of an externally applied force upon dynamics of dissipative
+solitons is analyzed in the framework of the one-dimensional cubic-quintic
+complex Ginzburg-Landau equation supplemented by a linear potential term. The
+potential accounts for the external force manipulations and consists of three
+symmetrically arranged potential wells whose depth is considered to be variable
+along the longitudinal coordinate. It is found out that under an influence of
+such potential a transition between different soliton waveforms coexisted under
+the same physical conditions can be achieved. A low-dimensional phase-space
+analysis is applied in order to demonstrate that by only changing the potential
+profile, transitions between different soliton waveforms can be performed in a
+controllable way. In particular, it is shown that by means of a selected
+potential, propagating stationary dissipative soliton can be transformed into
+another stationary solitons as well as into periodic, quasi-periodic, and
+chaotic spatiotemporal dissipative structures.
+"
+29810,Amorphized graphene: A stiff material with low thermal conductivity,"  All-carbon heterostructures have been produced recently via focused ion beam
+patterning of single layer graphene. Amorphized graphene is similar to a
+graphene sheet in which some hexagons are replaced by a combination of
+pentagonal, heptagonal and octagonal rings. The present investigation provides
+a general view regarding phonon and load transfer along amorphous graphene. The
+developed models for the evaluation of mechanical and thermal conductivity
+properties yield accurate results for pristine graphene and acquired findings
+for amorphized graphene films are size independent. Our atomistic results show
+that amorphous graphene sheets could exhibit a remarkably high elastic modulus
+of ~500 GPa and tensile strengths of ~50 GPa at room temperature. However, our
+results show that mechanical properties of amorphous graphene decline at higher
+temperatures. Furthermore, we show that amorphized graphene present a low
+thermal conductivity ~15 W/mK which is two orders of magnitude smaller than
+pristine graphene, and we verify that its thermal conductivity is almost
+insensitive to temperature since it is dominated by phonon-defect scattering
+rather than phonon-phonon scattering. Finally, our results show that amorphized
+graphene structures present a remarkably high elastic modulus and mechanical
+strength, along with a low thermal conductivity, which is an unusual
+combination for carbon-based materials.
+"
+29811,A measure of the torsional performances of partially hinged rectangular plates,"  We introduce a new functional, named gap function, which measures the
+torsional instability of a partially hinged rectangular plate, aiming to model
+the deck of a bridge. Then we test the performances of the gap function on a
+plate subject to two kinds of loads: one modeled by a force concentrating on
+one of the free edges and one in resonance with the eigenmodes.
+"
+29812,Measurement of weak static magnetic fields with nitrogen-vacancy color center,"  We propose a strategy to measure weak static magnetic fields with
+nitrogen-vacancy color center in diamond. Inspired by avian magnetoreception
+models, we consider the feasibility of utilizing quantum coherence phenomena to
+measure weak static magnetic fields. Nitrogen-vacancy (NV) color centers are
+regarded as the ideal platform to study quantum sciences as a result of its
+long coherence time up to a millisecond timescale. In high-purity diamond,
+hyperfine interaction with 13C nuclear spins dominates the decoherence process.
+In this paper, we numerically simulate the decoherence process between 0 and +1
+of the individual NV color center spin in 13C nuclear baths with various of
+magnitudes of external magnetic fields. By applying Hahn echo into the system,
+we obtain the coherence of NV color center spin as a function of total
+evolution time and magnetic field. Furthermore we obtain the high-accuracy
+relationship between the three decoherence-characteristic timescales, i.e. T_W,
+T_R, T_2, and magnetic field B. And we draw a conclusion that T_R has the
+highest sensitivity about magnetic field among the three time-scales. Thus, for
+a certain NV color center, T_R can be the scale for the magnitude of magnetic
+field, or rather, the component along the NV electronic spin axis. When
+measuring an unknown magnetic field, we adjust the NV axis to three mutually
+orthogonal directions respectively. By this means, we obtain the three
+components of the magnetic field and thus the magnitude and direction of the
+actual magnetic field. The accuracy could reach 60 nT/Hz^{1/2},and could be
+greatly improved by using an ensemble of NV color centers or diamond crystals
+purified with 12C atoms.
+"
+29813,Existence of positive solutions to boundary value problems of Caputo fractional difference equations,"  In this paper, we studied the sufficient conditions for the existence of
+positive solutions to the boundary value problems of Caputo fractional
+difference equations depending on parameters with non local boundary
+conditions. We construct and analyse the Green's function to the corresponding
+boundary value problem and then established the existence results using
+Krasnosel'skii fixed point theorem.
+"
+29814,Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs,"  Even though end-to-end supervised learning has shown promising results for
+sensorimotor control of self-driving cars, its performance is greatly affected
+by the weather conditions under which it was trained, showing poor
+generalization to unseen conditions. In this paper, we show how knowledge can
+be transferred using semantic maps to new weather conditions without the need
+to obtain new ground truth data. To this end, we propose to divide the task of
+vehicle control into two independent modules: a control module which is only
+trained on one weather condition for which labeled steering data is available,
+and a perception module which is used as an interface between new weather
+conditions and the fixed control module. To generate the semantic data needed
+to train the perception module, we propose to use a generative adversarial
+network (GAN)-based model to retrieve the semantic information for the new
+conditions in an unsupervised manner. We introduce a master-servant
+architecture, where the master model (semantic labels available) trains the
+servant model (semantic labels not available). We show that our proposed method
+trained with ground truth data for a single weather condition is capable of
+achieving similar results on the task of steering angle prediction as an
+end-to-end model trained with ground truth data of 15 different weather
+conditions.
+"
+29815,Birational geometry of moduli spaces of configurations of points on the line,"  In this paper we study the geometry of GIT configurations of $n$ ordered
+points on $\mathbb{P}^1$ both from the the birational and the biregular
+viewpoint. In particular, we prove that any extremal ray of the Mori cone of
+effective curves of the quotient $(\mathbb{P}^1)^n//PGL(2)$, taken with the
+symmetric polarization, is generated by a one dimensional boundary stratum of
+the moduli space. Furthermore, we develop some technical machinery that we use
+to compute the canonical divisor and the Hilbert polynomial of
+$(\mathbb{P}^1)^n//PGL(2)$ in its natural embedding, and its group of
+automorphisms.
+"
+29816,The Effect of non-Hermiticity on Adiabatic Elimination,"  We investigate the influence of non-Hermiticity on the adiabatic elimination
+in coupled waveguides. We show that adiabatic elimination is not affected when
+the system is in parity-time symmetric phase. However, in the broken phase the
+eliminated waveguide loses its darkness namely its amplitude starts increasing,
+which means adiabatic elimination does not hold in the broken phase. Our
+results can advance the control of the dynamics in coupled laser cavities, and
+help the design of controllable absorbers.
+"
+29817,Computational polarimetric microwave imaging,"  We propose a polarimetric microwave imaging technique that exploits recent
+advances in computational imaging. We utilize a frequency-diverse cavity-backed
+metasurface, allowing us to demonstrate high-resolution polarimetric imaging
+using a single transceiver and frequency sweep over the operational microwave
+bandwidth. The frequency-diverse metasurface imager greatly simplifies the
+system architecture compared with active arrays and other conventional
+microwave imaging approaches. We further develop the theoretical framework for
+computational polarimetric imaging and validate the approach experimentally
+using a multi-modal leaky cavity. The scalar approximation for the interaction
+between the radiated waves and the target---often applied in microwave
+computational imaging schemes---is thus extended to retrieve the susceptibility
+tensors, and hence providing additional information about the targets.
+Computational polarimetry has relevance for existing systems in the field that
+extract polarimetric imagery, and particular for ground observation. A growing
+number of short-range microwave imaging applications can also notably benefit
+from computational polarimetry, particularly for imaging objects that are
+difficult to reconstruct when assuming scalar estimations.
+"
+29818,Higgs Scalaron Mixed Inflation,"  We discuss the inflationary dynamics of a system with a non-minimal coupling
+between the Higgs and the Ricci scalar as well as a Ricci scalar squared term.
+There are two scalar modes in this system, i.e. the Higgs and the spin-zero
+mode of the graviton, or the scalaron. We study the two-field dynamics of the
+Higgs and the scalaron during inflation, and clarify the condition where
+inflation is dominated by the Higgs/scalaron. We also find that the cut-off
+scale at around the vacuum is as large as the Planck scale, and hence there is
+no unitarity issue, although there is a constraint on the couplings from the
+perturbativity of the theory at around the vacuum.
+"
+29819,Automatic Traceability Maintenance via Machine Learning Classification,"  Previous studies have shown that software traceability, the ability to link
+together related artifacts from different sources within a project (e.g.,
+source code, use cases, documentation, etc.), improves project outcomes by
+assisting developers and other stakeholders with common tasks such as impact
+analysis, concept location, etc. Establishing traceability links in a software
+system is an important and costly task, but only half the struggle. As the
+project undergoes maintenance and evolution, new artifacts are added and
+existing ones are changed, resulting in outdated traceability information.
+Therefore, specific steps need to be taken to make sure that traceability links
+are maintained in tandem with the rest of the project. In this paper we address
+this problem and propose a novel approach called TRAIL for maintaining
+traceability information in a system. The novelty of TRAIL stands in the fact
+that it leverages previously captured knowledge about project traceability to
+train a machine learning classifier which can then be used to derive new
+traceability links and update existing ones. We evaluated TRAIL on 11 commonly
+used traceability datasets from six software systems and compared it to seven
+popular information Retrieval (IR) techniques including the most common
+approaches used in previous work. The results indicate that TRAIL outperforms
+all IR approaches in terms of precision, recall, and F-score.
+"
+29820,Improving Text Proposals for Scene Images with Fully Convolutional Networks,"  Text Proposals have emerged as a class-dependent version of object proposals
+- efficient approaches to reduce the search space of possible text object
+locations in an image. Combined with strong word classifiers, text proposals
+currently yield top state of the art results in end-to-end scene text
+recognition. In this paper we propose an improvement over the original Text
+Proposals algorithm of Gomez and Karatzas (2016), combining it with Fully
+Convolutional Networks to improve the ranking of proposals. Results on the
+ICDAR RRC and the COCO-text datasets show superior performance over current
+state-of-the-art.
+"
+29821,Interpretable & Explorable Approximations of Black Box Models,"  We propose Black Box Explanations through Transparent Approximations (BETA),
+a novel model agnostic framework for explaining the behavior of any black-box
+classifier by simultaneously optimizing for fidelity to the original model and
+interpretability of the explanation. To this end, we develop a novel objective
+function which allows us to learn (with optimality guarantees), a small number
+of compact decision sets each of which explains the behavior of the black box
+model in unambiguous, well-defined regions of feature space. Furthermore, our
+framework also is capable of accepting user input when generating these
+approximations, thus allowing users to interactively explore how the black-box
+model behaves in different subspaces that are of interest to the user. To the
+best of our knowledge, this is the first approach which can produce global
+explanations of the behavior of any given black box model through joint
+optimization of unambiguity, fidelity, and interpretability, while also
+allowing users to explore model behavior based on their preferences.
+Experimental evaluation with real-world datasets and user studies demonstrates
+that our approach can generate highly compact, easy-to-understand, yet accurate
+approximations of various kinds of predictive models compared to
+state-of-the-art baselines.
+"
+29822,High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis,"  Independent Component Analysis (ICA) is a dimensionality reduction technique
+that can boost efficiency of machine learning models that deal with probability
+density functions, e.g. Bayesian neural networks. Algorithms that implement
+adaptive ICA converge slower than their nonadaptive counterparts, however, they
+are capable of tracking changes in underlying distributions of input features.
+This intrinsically slow convergence of adaptive methods combined with existing
+hardware implementations that operate at very low clock frequencies necessitate
+fundamental improvements in both algorithm and hardware design. This paper
+presents an algorithm that allows efficient hardware implementation of ICA.
+Compared to previous work, our FPGA implementation of adaptive ICA improves
+clock frequency by at least one order of magnitude and throughput by at least
+two orders of magnitude. Our proposed algorithm is not limited to ICA and can
+be used in various machine learning problems that use stochastic gradient
+descent optimization.
+"
+29823,Bayesian Nonlinear Support Vector Machines for Big Data,"  We propose a fast inference method for Bayesian nonlinear support vector
+machines that leverages stochastic variational inference and inducing points.
+Our experiments show that the proposed method is faster than competing Bayesian
+approaches and scales easily to millions of data points. It provides additional
+features over frequentist competitors such as accurate predictive uncertainty
+estimates and automatic hyperparameter search.
+"
+29824,Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes,"  Prediction and modelling of competitive sports outcomes has received much
+recent attention, especially from the Bayesian statistics and machine learning
+communities. In the real world setting of outcome prediction, the seminal
+Élő update still remains, after more than 50 years, a valuable baseline
+which is difficult to improve upon, though in its original form it is a
+heuristic and not a proper statistical ""model"". Mathematically, the Élő
+rating system is very closely related to the Bradley-Terry models, which are
+usually used in an explanatory fashion rather than in a predictive supervised
+or on-line learning setting.
+Exploiting this close link between these two model classes and some newly
+observed similarities, we propose a new supervised learning framework with
+close similarities to logistic regression, low-rank matrix completion and
+neural networks. Building on it, we formulate a class of structured log-odds
+models, unifying the desirable properties found in the above: supervised
+probabilistic prediction of scores and wins/draws/losses, batch/epoch and
+on-line learning, as well as the possibility to incorporate features in the
+prediction, without having to sacrifice simplicity, parsimony of the
+Bradley-Terry models, or computational efficiency of Élő's original
+approach.
+We validate the structured log-odds modelling approach in synthetic
+experiments and English Premier League outcomes, where the added expressivity
+yields the best predictions reported in the state-of-art, close to the quality
+of contemporary betting odds.
+"
+29825,Boundedness of parameterized Marcinkiewicz integrals with variable kernels on Hardy spaces and weak Hardy spaces,"  In this paper, by using the atomic decomposition theory of Hardy space and
+weak Hardy space, the author establishes the boundedness of parameterized
+Marcinkiewicz integral with variable kernel on these spaces, under the Dini
+condition or Hörmander condition imposed on kernel.
+"
+29826,Modeling Personalized Dynamics of Social Network and Opinion at Individual Level,"  Network dynamics has always been a meaningful topic deserving exploration in
+the realm of academy. previous network models contain two parts: (1) generating
+structure as per user property; (2) changing property as per network structure.
+Properties in these models, however, cannot be interpreted to concept in
+prevalent social theories or empirical truth. Also, they usually treat everyone
+in an uniform fashion. While such assumption is quite misguiding, and thus
+saliently limits their performance. To overcome these flaws, we devise a
+personalized evolving model for social network and opinion (PENO), where
+citizens' ideology is revealed by variable opinions and four dimensions of
+personality are considered for each entity - leadership, openness,
+agreeableness, and neuroticism. Opinion propagates via social tie, tie
+generates from opinion affinity, and personalities integrally work with opinion
+and tie across evolution. To our best knowledge, PENO is the first attempt to
+introduce personality impact in network dynamics and verify social science with
+reasonable visualization during simulation. We also present its probabilistic
+graph and conceive iterative learning algorithm. Experiments show PENO
+outperforms several state-of-the-art baselines over two typical prediction
+tasks - congress voting prediction for legislative bills and friendship
+prediction on a book-commenting website. Finally, we discuss its scalability to
+do multi-task learning and transfer learning in daily scenarios.
+"
+29827,"Expected volumes of Gaussian polytopes, external angles, and multiple order statistics","  Let $X_1,\ldots,X_n$ be a standard normal sample in $\mathbb R^d$. We compute
+exactly the expected volume of the Gaussian polytope
+$\mathrm{conv}[X_1,\ldots,X_n]$, the symmetric Gaussian polytope
+$\mathrm{conv}[\pm X_1,\ldots,\pm X_n]$, and the Gaussian zonotope
+$[0,X_1]+\ldots+[0,X_n]$ by exploiting their connection to the regular simplex,
+the regular crosspolytope, and the cube with the aid of Tsirelson's formula.
+The expected volumes of these random polytopes are given by essentially the
+same expressions as the intrinsic volumes and external angles of the regular
+polytopes. For all these quantities, we obtain asymptotic formulae which are
+more precise than the results which were known before. More generally, we
+determine the expected volumes of some heteroscedastic random polytopes
+including $ \mathrm{conv}[l_1X_1,\ldots,l_nX_n] $ and $ \mathrm{conv}[\pm l_1
+X_1,\ldots, \pm l_n X_n], $ where $l_1,\ldots,l_n\geq 0$ are parameters, and
+the intrinsic volumes of the corresponding deterministic polytopes. Finally, we
+relate the $k$-th intrinsic volume of the regular simplex $S^{n-1}$ to the
+expected maximum of independent standard Gaussian random variables
+$\xi_1,\ldots,\xi_n$ given that the maximum has multiplicity $k$. Namely, we
+show that $$ V_k(S^{n-1}) = \frac {(2\pi)^{\frac k2}} {k!} \cdot
+\lim_{\varepsilon\downarrow 0} \varepsilon^{1-k} \mathbb E
+[\max\{\xi_1,\ldots,\xi_n\} 1_{\{\xi_{(n)} - \xi_{(n-k+1)}\leq \varepsilon\}}],
+$$ where $\xi_{(1)} \leq \ldots \leq \xi_{(n)}$ denote the order statistics. A
+similar result holds for the crosspolytope if we replace $\xi_1,\ldots,\xi_n$
+by their absolute values.
+"
+29828,Analytic model of thermalization: Quantum emulation of classical cellular automata,"  We introduce a novel method of quantum emulation of a classical reversible
+cellular automaton. By applying this method to a chaotic cellular automaton,
+the obtained quantum many-body system thermalizes while all the energy
+eigenstates and eigenvalues are solvable. These explicit solutions allow us to
+verify the validity of some scenarios of thermalization to this system. We find
+that two leading scenarios, the eigenstate thermalization hypothesis scenario
+and the large effective dimension scenario, do not explain thermalization in
+this model.
+"
+29829,$β$-mixing and moments properties of a non-stationary copula-based Markov process,"  This paper provides conditions under which a non-stationary copula-based
+Markov process is $\beta$-mixing. We introduce, as a particular case, a
+convolution-based gaussian Markov process which generalizes the standard random
+walk allowing the increments to be dependent.
+"
+29830,Rings and Fields from Semigroups,"  We introduce a ring and a field, generated by a semigroup, and we investigate
+some of their properties.
+"
+29831,A proof of Kurdyka's conjecture on arc-analytic functions,"  We prove a conjecture of Kurdyka stating that every arc-symmetric
+semialgebraic set is precisely the zero locus of an arc-analytic semialgebraic
+function. This implies, in particular, that arc-symmetric semialgebraic sets
+are in one-to-one correspondence with radical ideals of the ring of
+arc-analytic semialgebraic functions.
+"
+29832,Autoignition of n-Butanol at Elevated Pressure and Low to Intermediate Temperature,"  Autoignition experiments for n-butanol have been performed using a heated
+rapid compression machine at compressed pressures of 15 and 30 bar, in the
+compressed temperature range of 675-925 K, and for equivalence ratios of 0.5,
+1.0, and 2.0. Over the conditions studied, the ignition delay decreases
+monotonically as temperature increases, and the autoignition response exhibits
+single-stage characteristics. A non-linear fit to the experimental data is
+performed and the reactivity, in terms of the inverse of ignition delay, shows
+nearly second order dependence on the initial oxygen mole fraction and slightly
+greater than first order dependence on initial fuel mole fraction and
+compressed pressure. Experimentally measured ignition delays are also compared
+to simulations using several reaction mechanisms available in the literature.
+Agreement between simulated and experimental ignition delay is found to be
+unsatisfactory. Sensitivity analysis is performed on one recent mechanism and
+indicates that uncertainties in the rate coefficients of parent fuel
+decomposition reactions play a major role in causing the poor agreement. Path
+analysis of the fuel decomposition reactions supports this conclusion and also
+highlights the particular importance of certain pathways. Further experimental
+investigations of the fuel decomposition, including speciation measurements,
+are required.
+"
+29833,Theme Enrichment Analysis: A Statistical Test for Identifying Significantly Enriched Themes in a List of Stories with an Application to the Star Trek Television Franchise,"  In this paper, we describe how the hypergeometric test can be used to
+determine whether a given theme of interest occurs in a storyset at a frequency
+more than would be expected by chance. By a storyset we mean simply a list of
+stories defined according to a common attribute (e.g., author, movement,
+period). The test works roughly as follows: Given a background storyset and a
+sub-storyset of interest, the test determines whether a given theme is
+over-represented in the sub-storyset, based on comparing the proportions of
+stories in the sub-storyset and background storyset featuring the theme. A
+storyset is said to be ""enriched"" for a theme with respect to a particular
+background storyset, when the theme is identified as being significantly
+over-represented by the test. Furthermore, we introduce here a toy dataset
+consisting of 280 manually themed Star Trek television franchise episodes. As a
+proof of concept, we use the hypergeometric test to analyze the Star Trek
+stories for enriched themes. The hypergeometric testing approach to theme
+enrichment analysis is implemented for the Star Trek thematic dataset in the R
+package stoRy. A related R Shiny web application can be found at
+this https URL.
+"
+29834,Spectra Originated from Fredholm Theory and Browder's Theorem,"  We give a new characterization of Browders theorem through equality between
+the pseudo B-Weyl spectrum and the generalized Drazin spectrum. Also, we will
+give conditions under which pseudo B-Fredholm and pseudo B-Weyl spectrum
+introduced in [11] and [30] become stable under commuting Riesz perturbations.
+"
+29835,Infection Analysis on Irregular Networks through Graph Signal Processing,"  In a networked system, functionality can be seriously endangered when nodes
+are infected, due to internal random failures or a contagious virus that
+develops into an epidemic. Given a snapshot of the network representing the
+nodes' states (infected or healthy), infection analysis refers to
+distinguishing an epidemic from random failures and gathering information for
+effective countermeasure design. This analysis is challenging due to irregular
+network structure, heterogeneous epidemic spreading, and noisy observations.
+This paper treats a network snapshot as a graph signal, and develops effective
+approaches for infection analysis based on graph signal processing. For the
+macro (network-level) analysis aiming to distinguish an epidemic from random
+failures, 1) multiple detection metrics are defined based on the graph Fourier
+transform (GFT) and neighborhood characteristics of the graph signal; 2) a new
+class of graph wavelets, distance-based graph wavelets (DBGWs), are developed;
+and 3) a machine learning-based framework is designed employing either the GFT
+spectrum or the graph wavelet coefficients as features for infection analysis.
+DBGWs also enable the micro (node-level) infection analysis, through which the
+performance of epidemic countermeasures can be improved. Extensive simulations
+are conducted to demonstrate the effectiveness of all the proposed algorithms
+in various network settings.
+"
+29836,A Parametric Interpolation Framework for 1D Scalar Conservation Laws using the Equal Area Principle,"  In this paper we develop a novel framework for numerically solving scalar
+conservation laws in one space dimension. Utilizing the method of
+characteristics in conjunction with the equal area principle we develop an
+approach where the weak solution is obtained purely as the solution of a
+parametric interpolation problem. As this framework hinges on the validity of
+the equal area principle, we provide a rigorous discussion of the equal area
+principle and show that, indeed, the equal area principle is equivalent to the
+Rankine-Hugoniot condition, within the specific context studied in this paper.
+Combining these results with properties of the characteristic equations yields
+the desired setting to define the equivalent parametric interpolation problem.
+We conclude by applying this framework to Burgers' equation and show how one
+obtains machine precision in the shock position when the initial condition can
+be represented exactly in the chosen space of parametric polynomials.
+"
+29837,Hybrid Subspace Learning for High-Dimensional Data,"  The high-dimensional data setting, in which p >> n, is a challenging
+statistical paradigm that appears in many real-world problems. In this setting,
+learning a compact, low-dimensional representation of the data can
+substantially help distinguish signal from noise. One way to achieve this goal
+is to perform subspace learning to estimate a small set of latent features that
+capture the majority of the variance in the original data. Most existing
+subspace learning models, such as PCA, assume that the data can be fully
+represented by its embedding in one or more latent subspaces. However, in this
+work, we argue that this assumption is not suitable for many high-dimensional
+datasets; often only some variables can easily be projected to a
+low-dimensional space. We propose a hybrid dimensionality reduction technique
+in which some features are mapped to a low-dimensional subspace while others
+remain in the original space. Our model leads to more accurate estimation of
+the latent space and lower reconstruction error. We present a simple
+optimization procedure for the resulting biconvex problem and show synthetic
+data results that demonstrate the advantages of our approach over existing
+methods. Finally, we demonstrate the effectiveness of this method for
+extracting meaningful features from both gene expression and video background
+subtraction datasets.
+"
+29838,Selfless Sequential Learning,"  Sequential learning, also called lifelong learning, studies the problem of
+learning tasks in a sequence with access restricted to only the data of the
+current task. In this paper we look at a scenario with fixed model capacity,
+and postulate that the learning process should not be selfish, i.e. it should
+account for future tasks to be added and thus leave enough capacity for them.
+To achieve Selfless Sequential Learning we study different regularization
+strategies and activation functions. We find that imposing sparsity at the
+level of the representation (i.e. neuron activations) is more beneficial for
+sequential learning than encouraging parameter sparsity. In particular, we
+propose a novel regularizer, that encourages representation sparsity by means
+of neural inhibition. It results in few active neurons which in turn leaves
+more free neurons to be utilized by upcoming tasks. As neural inhibition over
+an entire layer can be too drastic, especially for complex tasks requiring
+strong representations, our regularizer only inhibits other neurons in a local
+neighbourhood, inspired by lateral inhibition processes in the brain. We
+combine our novel regularizer with state-of-the-art lifelong learning methods
+that penalize changes to important previously learned parts of the network. We
+show that our new regularizer leads to increased sparsity which translates in
+consistent performance improvement on diverse datasets.
+"
+29839,On a covariance structure of some subset of self-similar Gaussian processes,"  We introduce a class of self-similar Gaussian processes and provide
+sufficient and necessary conditions for a member of the class to admit a unique
+small scale limit in the Skorokhod space. The class includes several well known
+processes. An example of application to the problem of estimation is given.
+"
+29840,ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning,"  Distributed machine learning algorithms enable learning of models from
+datasets that are distributed over a network without gathering the data at a
+centralized location. While efficient distributed algorithms have been
+developed under the assumption of faultless networks, failures that can render
+these algorithms nonfunctional occur frequently in the real world. This paper
+focuses on the problem of Byzantine failures, which are the hardest to
+safeguard against in distributed algorithms. While Byzantine fault tolerance
+has a rich history, existing work does not translate into efficient and
+practical algorithms for high-dimensional distributed learning. In this paper,
+two variants of an algorithm termed \emph{Byzantine-resilient distributed
+coordinate descent} (ByRDiE) are developed and analyzed that enable distributed
+learning in the presence of Byzantine failures. Theoretical analysis and
+numerical experiments presented in the paper highlight the usefulness of ByRDiE
+for high-dimensional distributed learning in the presence of Byzantine
+failures.
+"
+29841,Interactive Music Generation with Positional Constraints using Anticipation-RNNs,"  Recurrent Neural Networks (RNNS) are now widely used on sequence generation
+tasks due to their ability to learn long-range dependencies and to generate
+sequences of arbitrary length. However, their left-to-right generation
+procedure only allows a limited control from a potential user which makes them
+unsuitable for interactive and creative usages such as interactive music
+generation. This paper introduces a novel architecture called Anticipation-RNN
+which possesses the assets of the RNN-based generative models while allowing to
+enforce user-defined positional constraints. We demonstrate its efficiency on
+the task of generating melodies satisfying positional constraints in the style
+of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using
+the Anticipation-RNN is of the same order of complexity than sampling from the
+traditional RNN model. This fast and interactive generation of musical
+sequences opens ways to devise real-time systems that could be used for
+creative purposes.
+"
+29842,Triangulated categories with cluster-tilting subcategories,"  Let $\C$ be a triangulated category with a cluster tilting subcategory $\T$.
+We introduce the notion of $\T[1]$-cluster tilting subcategories (also called
+ghost cluster tilting subcategories) of $\C$, which are a generalization of
+cluster tilting subcategories. We first develop a basic theory on ghost cluster
+tilting subcategories. Secondly, we study links between ghost cluster tilting
+theory and $\tau$-tilting theory: Inspired by the work of Iyama, J{\o}rgensen
+and Yang \cite{ijy}, we introduce the notion of $\tau$-tilting subcategories
+and tilting subcategories of $\mod\T$. We show that there exists a bijection
+between weak $\T[1]$-cluster tilting subcategories of $\C$ and support
+$\tau$-tilting subcategories of $\mod\T$. Moreover, we figure out the
+subcategories of $\mod\T$ which correspond to cluster tilting subcategories of
+$\C$. This generalizes and improves several results by Adachi-Iyama-Reiten
+\cite{AIR}, Beligiannis \cite{Be2}, and Yang-Zhu \cite{YZ}. Finally, we prove
+that the definition of ghost cluster tilting objects is equivalent to the
+definition of relative cluster tilting objects introduced by the first and the
+third author in \cite{YZ}.
+"
+29843,Software Defined Media: Virtualization of Audio-Visual Services,"  Internet-native audio-visual services are witnessing rapid development. Among
+these services, object-based audio-visual services are gaining importance. In
+2014, we established the Software Defined Media (SDM) consortium to target new
+research areas and markets involving object-based digital media and
+Internet-by-design audio-visual environments. In this paper, we introduce the
+SDM architecture that virtualizes networked audio-visual services along with
+the development of smart buildings and smart cities using Internet of Things
+(IoT) devices and smart building facilities. Moreover, we design the SDM
+architecture as a layered architecture to promote the development of innovative
+applications on the basis of rapid advancements in software-defined networking
+(SDN). Then, we implement a prototype system based on the architecture, present
+the system at an exhibition, and provide it as an SDM API to application
+developers at hackathons. Various types of applications are developed using the
+API at these events. An evaluation of SDM API access shows that the prototype
+SDM platform effectively provides 3D audio reproducibility and interactiveness
+for SDM applications.
+"
+29844,Statistical Detection of the HeII Transverse Proximity Effect: Evidence for Sustained Quasar Activity for $>$25 Million Years,"  The HeII transverse proximity effect -- enhanced HeII Ly$\alpha$~transmission
+in a background sightline caused by the ionizing radiation of a foreground
+quasar -- offers a unique opportunity to probe the morphology of quasar-driven
+HeII reionization. We conduct a comprehensive spectroscopic survey to find
+$z\sim3$ quasars in the foreground of 22 background quasar sightlines with
+HST/COS HeII Ly$\alpha$~transmission spectra. With our two-tiered survey
+strategy, consisting of a deep pencil-beam survey and a shallow wide-field
+survey, we discover 131 new quasars, which we complement with known SDSS/BOSS
+quasars in our fields. Using a restricted sample of 66 foreground quasars with
+inferred HeII photoionization rates greater than the expected UV background at
+these redshifts ($\Gamma_\mathrm{QSO}^\mathrm{HeII} > 5 \times
+10^{-16}\,\mathrm{s}^{-1}$) we perform the first statistical analysis of the
+HeII transverse proximity effect. Our results show qualitative evidence for a
+large object-to-object variance: among the four foreground quasars with the
+highest $\Gamma_\mathrm{QSO}^\mathrm{HeII}$ only one (previously known) quasar
+is associated with a significant HeII transmission spike. We perform a stacking
+analysis to average down these fluctuations, and detect an excess in the
+average HeII transmission near the foreground quasars at $3\sigma$
+significance. This statistical evidence for the transverse proximity effect is
+corroborated by a clear dependence of the signal strength on
+$\Gamma_\mathrm{QSO}^\mathrm{HeII}$. Our detection places a purely geometrical
+lower limit on the quasar lifetime of $t_\mathrm{Q} > 25\,\mathrm{Myr}$.
+Improved modeling would additionally constrain quasar obscuration and the mean
+free path of HeII-ionizing photons.
+"
+29845,Blurred Complex Exponentiation,"  It is shown that the complex field equipped with the ""approximate exponential
+map"", defined up to ambiguity from a small group, is quasiminimal: every
+automorphism-invariant subset of the field is countable or co-countable. If the
+ambiguity is taken to be from a subfield analogous to a field of constants then
+the resulting ""blurred exponential field"" is isomorphic to the result of an
+equivalent blurring of Zilber's exponential field, and to a suitable reduct of
+a differentially closed field. These results are progress towards Zilber's
+conjecture that the complex exponential field itself is quasiminimal. A key
+ingredient in the proofs is to prove the analogue of the exponential-algebraic
+closedness property using the density of the group governing the ambiguity with
+respect to the complex topology.
+"
+29846,Staircase Network: structural language identification via hierarchical attentive units,"  Language recognition system is typically trained directly to optimize
+classification error on the target language labels, without using the external,
+or meta-information in the estimation of the model parameters. However labels
+are not independent of each other, there is a dependency enforced by, for
+example, the language family, which affects negatively on classification. The
+other external information sources (e.g. audio encoding, telephony or video
+speech) can also decrease classification accuracy. In this paper, we attempt to
+solve these issues by constructing a deep hierarchical neural network, where
+different levels of meta-information are encapsulated by attentive prediction
+units and also embedded into the training progress. The proposed method learns
+auxiliary tasks to obtain robust internal representation and to construct a
+variant of attentive units within the hierarchical model. The final result is
+the structural prediction of the target language and a closely related language
+family. The algorithm reflects a ""staircase"" way of learning in both its
+architecture and training, advancing from the fundamental audio encoding to the
+language family level and finally to the target language level. This process
+not only improves generalization but also tackles the issues of imbalanced
+class priors and channel variability in the deep neural network model. Our
+experimental findings show that the proposed architecture outperforms the
+state-of-the-art i-vector approaches on both small and big language corpora by
+a significant margin.
+"
+29847,EXIT Analysis for Community Detection,"  This paper employs the extrinsic information transfer (EXIT) method, a
+technique imported from the analysis of the iterative decoding of error control
+codes, to study the performance of belief propagation in community detection in
+the presence of side information. We consider both the detection of a single
+(hidden) community, as well as the problem of identifying two symmetric
+communities. For single community detection, this paper demonstrates the
+suitability of EXIT to predict the asymptotic phase transition for weak
+recovery. More importantly, EXIT analysis is leveraged to produce useful
+insights such as the performance of belief propagation near the threshold. For
+two symmetric communities, the asymptotic residual error for belief propagation
+is calculated under finite-alphabet side information, generalizing a previous
+result with noisy labels. EXIT analysis is used to illuminate the effect of
+side information on community detection, its relative importance depending on
+the correlation of the graphical information with node labels, as well as the
+effect of side information on residual errors.
+"
+29848,ZICS: an application for calculating the stationary probability distribution of stochastic reaction networks,"  Stochastic formalisms are necessary to describe the behavior of many
+biological systems. However, there remains a lack of numerical methods
+available to calculate the stationary probability distributions of stochastic
+reaction networks. We have previously development a numerical approach to
+calculate stationary probability distributions of stochastic networks, named ZI
+closure scheme. In this work, we present a free applications based on
+ZI-closure scheme, called ZICS.
+"
+29849,Localization and compactness of Operators on Fock Spaces,"  For $0<p\leq\infty$, let $F^{p}_\varphi$ be the Fock space induced by a
+weight function $\varphi$ satisfying $ dd^c \varphi \simeq \omega_0$. In this
+paper, given $p\in (0, 1]$ we introduce the concept of weakly localized
+operators on $ F^{p}_\varphi$, we characterize the compact operators in the
+algebra generated by weakly localized operators. As an application, for
+$0<p<\infty$ we prove that an operator $T$ in the algebra generated by bounded
+Toeplitz operators with $\textrm{BMO}$ symbols is compact on $F^p_\varphi$ if
+and only if its Berezin transform satisfies certain vanishing property at
+$\infty$. In the classical Fock space, we extend the Axler-Zheng condition on
+linear operators $T$, which ensures $T$ is compact on $F^p_{\alpha}$ for all
+possible $0<p<\infty$.
+"
+29850,Earthquake precursors in the light of peroxy defects theory: critical review of systematic observations,"  The starting point of the present review is to acknowledge that there are
+innumerable reports of non-seismic types of earthquake precursory phenomena
+that are intermittent and seem not to occur systematically, while associated
+reports are not widely accepted by the geoscience community at large because no
+one could explain their origins. We review a unifying theory for a solid-state
+mechanism, based on decades of research bridging semi-conductor physics,
+chemistry and rock physics. A synthesis has emerged that all pre-earthquake
+phenomena could trace back to one fundamental physical process: the activation
+of electronic charges (electrons and positive holes) in rocks subjected to
+ever-increasing tectonic stresses prior to any major seismic activity, via the
+rupture of peroxy bonds. In the second part of the review, we critically
+examine satellite and ground station data, recorded before past large
+earthquakes, as they have been claimed to provide evidence that precursory
+signals tend to become measurable days, sometimes weeks before the disasters.
+We review some of the various phenomena that can be directly predicted by the
+peroxy defect theory , namely, radon gas emanations, corona discharges, thermal
+infrared emissions, air ionization, ion and electron content in the ionosphere,
+and electro-magnetic anomalies. Our analysis demonstrates the need for further
+systematic investigations, in particular with strong continuous statistical
+testing of the relevance and confidence of the precursors. Only then, the
+scientific community will be able to assess and improve the performance of
+earthquake forecasts.
+"
+29851,Composition of Jupiter irregular satellites sheds light on their origin,"  Irregular satellites of Jupiter with their highly eccentric, inclined and
+distant orbits suggest that their capture took place just before the giant
+planet migration. We aim to improve our understanding of the surface
+composition of irregular satellites of Jupiter to gain insight into a narrow
+time window when our Solar System was forming. We observed three Jovian
+irregular satellites, Himalia, Elara, and Carme, using a medium-resolution
+0.8-5.5 micro m spectrograph on the National Aeronautics and Space
+Administration (NASA) Infrared Telescope Facility (IRTF). Using a linear
+spectral unmixing model we have constrained the major mineral phases on the
+surface of these three bodies. Our results confirm that the surface of Himalia,
+Elara, and Carme are dominated by opaque materials such as those seen in
+carbonaceous chondrite meteorites. Our spectral modeling of NIR spectra of
+Himalia and Elara confirm that their surface composition is the same and
+magnetite is the dominant mineral. A comparison of the spectral shape of
+Himalia with the two large main C-type asteroids, Themis (D 176 km) and Europa
+(D 352 km), suggests surface composition similar to Europa. The NIR spectrum of
+Carme exhibits blue slope up to 1.5 microm and is spectrally distinct from
+those of Himalia and Elara. Our model suggests that it is compositionally
+similar to amorphous carbon. Himalia and Elara are compositionally similar but
+differ significantly from Carme. These results support the hypotheses that the
+Jupiter irregular satellites are captured bodies that were subject to further
+breakup events and clustered as families based on their similar physical and
+surface compositions.
+"
+29852,Stay-point Identification as Curve Extrema,"  In a nutshell, stay-points are locations that a person has stopped for some
+amount of time. Previous work depends mainly on stay-point identification
+methods using experimentally fine tuned threshold values. These behave well on
+their experimental datasets but may exhibit reduced performance on other
+datasets.
+In this work, we demonstrate the potential of a geometry-based method for
+stay-point extraction. This is accomplished by transforming the user's
+trajectory path to a two-dimensional discrete time series curve that in turn
+transforms the stay-points to the local minima of the first derivative of this
+curve.
+To demonstrate the soundness of the proposed method, we evaluated it on raw,
+noisy trajectory data acquired over the period of 28 different days using four
+different techniques. The results demonstrate, among others, that given a good
+trajectory tracking technique, we can identify correctly 86% to 98% of the
+stay-points.
+"
+29853,The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games,"  Multiplayer Online Battle Arena (MOBA) games have received increasing
+popularity recently. In a match of such games, players compete in two teams of
+five, each controlling an in-game avatars, known as heroes, selected from a
+roster of more than 100. The selection of heroes, also known as pick or draft,
+takes place before the match starts and alternates between the two teams until
+each player has selected one hero. Heroes are designed with different strengths
+and weaknesses to promote team cooperation in a game. Intuitively, heroes in a
+strong team should complement each other's strengths and suppressing those of
+opponents. Hero drafting is therefore a challenging problem due to the complex
+hero-to-hero relationships to consider. In this paper, we propose a novel hero
+recommendation system that suggests heroes to add to an existing team while
+maximizing the team's prospect for victory. To that end, we model the drafting
+between two teams as a combinatorial game and use Monte Carlo Tree Search
+(MCTS) for estimating the values of hero combinations. Our empirical evaluation
+shows that hero teams drafted by our recommendation algorithm have
+significantly higher win rate against teams constructed by other baseline and
+state-of-the-art strategies.
+"
+29854,Are tumor cell lineages solely shaped by mechanical forces?,"  This paper investigates cells proliferation dynamics in small tumor cell
+aggregates using an individual based model (IBM). The simulation model is
+designed to study the morphology of the cell population and of the cell
+lineages as well as the impact of the orientation of the division plane on this
+morphology. Our IBM model is based on the hypothesis that cells are
+incompressible objects that grow in size and divide once a threshold size is
+reached, and that newly born cell adhere to the existing cell cluster. We
+performed comparisons between the simulation model and experimental data by
+using several statistical indicators. The results suggest that the emergence of
+particular morphologies can be explained by simple mechanical interactions.
+"
+29855,B-T phase diagram of Pd/Fe/Ir(111) computed with parallel tempering Monte Carlo,"  We use an atomistic spin model derived from density functional theory
+calculations for the ultra-thin film Pd/Fe/Ir(111) to show that temperature
+induces coexisting non-zero skyrmion and antiskyrmion densities. We apply the
+parallel tempering Monte Carlo method in order to reliably compute
+thermodynamical quantities and the B-T phase diagram in the presence of
+frustrated exchange interactions. We evaluate the critical temperatures using
+the topological susceptibility. We show that the critical temperatures depend
+on the magnetic field in contrast to previous work. In total, we identify five
+phases: spin spiral, skyrmion lattice, ferromagnetic phase, intermediate region
+with finite topological charge and paramagnetic phase. To explore the effect of
+frustrated exchange interactions, we calculate the B-T phase diagram, when only
+effective exchange parameters are taken into account.
+"
+29856,User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living,"  The detection of the environment where user is located, is of extreme use for
+the identification of Activities of Daily Living (ADL). ADL can be identified
+by use of the sensors available in many off-the-shelf mobile devices, including
+magnetic and motion, and the environment can be also identified using acoustic
+sensors. The study presented in this paper is divided in two parts: firstly, we
+discuss the recognition of the environment using acoustic sensors (i.e.,
+microphone), and secondly, we fuse this information with motion and magnetic
+sensors (i.e., motion and magnetic sensors) for the recognition of standing
+activities of daily living. The recognition of the environments and the ADL are
+performed using pattern recognition techniques, in order to develop a system
+that includes data acquisition, data processing, data fusion, and artificial
+intelligence methods. The artificial intelligence methods explored in this
+study are composed by different types of Artificial Neural Networks (ANN),
+comparing the different types of ANN and selecting the best methods to
+implement in the different stages of the system developed. Conclusions point to
+the use of Deep Neural Networks (DNN) with normalized data for the
+identification of ADL with 85.89% of accuracy, the use of Feedforward neural
+networks with non-normalized data for the identification of the environments
+with 86.50% of accuracy, and the use of DNN with normalized data for the
+identification of standing activities with 100% of accuracy.
+"
+29857,A bicategorical interpretation for relative Cuntz--Pimsner algebras,"  We interpret the construction of relative Cuntz-Pimsner algebras of
+correspondences in terms of the correspondence bicategory, as a reflector into
+a certain sub-bicategory. This generalises a previous characterisation of
+absolute Cuntz-Pimsner algebras of proper correspondences as colimits in the
+correspondence bicategory.
+"
+29858,The Archive and Package (arcp) URI scheme,"  The arcp URI scheme is introduced for location-independent identifiers to
+consume or reference hypermedia and linked data resources bundled inside a file
+archive, as well as to resolve archived resources within programmatic
+frameworks for Research Objects. The Research Object for this article is
+available at this http URL
+"
+29859,Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups,"  Anomaly detection is one of the frequent and important subroutines deployed
+in large-scale data processing systems. Even being a well-studied topic,
+existing techniques for unsupervised anomaly detection require storing
+significant amounts of data, which is prohibitive from memory and latency
+perspective. In the big-data world existing methods fail to address the new set
+of memory and latency constraints. In this paper, we propose ACE (Arrays of
+(locality-sensitive) Count Estimators) algorithm that can be 60x faster than
+the ELKI package~\cite{DBLP:conf/ssd/AchtertBKSZ09}, which has the fastest
+implementation of the unsupervised anomaly detection algorithms. ACE algorithm
+requires less than $4MB$ memory, to dynamically compress the full data
+information into a set of count arrays. These tiny $4MB$ arrays of counts are
+sufficient for unsupervised anomaly detection. At the core of the ACE
+algorithm, there is a novel statistical estimator which is derived from the
+sampling view of Locality Sensitive Hashing(LSH). This view is significantly
+different and efficient than the widely popular view of LSH for near-neighbor
+search. We show the superiority of ACE algorithm over 11 popular baselines on 3
+benchmark datasets, including the KDD-Cup99 data which is the largest available
+benchmark comprising of more than half a million entries with ground truth
+anomaly labels.
+"
+29860,Numerical solution of Lyapunov equations related to Markov jump linear systems,"  We suggest and compare different methods for the numerical solution of
+Lyapunov like equations with application to control of Markovian jump linear
+systems. First, we consider fixed point iterations and associated Krylov
+subspace formulations. Second, we reformulate the equation as an optimization
+problem and consider steepest descent, conjugate gradient, and a trust-region
+method.
+Numerical experiments illustrate that for large-scale problems the
+trust-region method is more effective than the steepest descent and the
+conjugate gradient methods. The fixed-point approach, however, is superior to
+the optimization methods. As an application we consider a networked control
+system, where the Markov jumps are induced by the wireless communication
+protocol.
+"
+29861,Context-Bounded Model Checking for POWER,"  We propose an under-approximate reachability analysis algorithm for programs
+running under the POWER memory model, in the spirit of the work on
+context-bounded analysis intitiated by Qadeer et al. in 2005 for detecting bugs
+in concurrent programs (supposed to be running under the classical SC model).
+To that end, we first introduce a new notion of context-bounding that is
+suitable for reasoning about computations under POWER, which generalizes the
+one defined by Atig et al. in 2011 for the TSO memory model. Then, we provide a
+polynomial size reduction of the context-bounded state reachability problem
+under POWER to the same problem under SC: Given an input concurrent program P,
+our method produces a concurrent program P' such that, for a fixed number of
+context switches, running P' under SC yields the same set of reachable states
+as running P under POWER. The generated program P' contains the same number of
+processes as P, and operates on the same data domain. By leveraging the
+standard model checker CBMC, we have implemented a prototype tool and applied
+it on a set of benchmarks, showing the feasibility of our approach.
+"
+29862,Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision,"  Training deep neural networks requires massive amounts of training data, but
+for many tasks only limited labeled data is available. This makes weak
+supervision attractive, using weak or noisy signals like the output of
+heuristic methods or user click-through data for training. In a semi-supervised
+setting, we can use a large set of data with weak labels to pretrain a neural
+network and then fine-tune the parameters with a small amount of data with true
+labels. This feels intuitively sub-optimal as these two independent stages
+leave the model unaware about the varying label quality. What if we could
+somehow inform the model about the label quality? In this paper, we propose a
+semi-supervised learning method where we train two neural networks in a
+multi-task fashion: a ""target network"" and a ""confidence network"". The target
+network is optimized to perform a given task and is trained using a large set
+of unlabeled data that are weakly annotated. We propose to weight the gradient
+updates to the target network using the scores provided by the second
+confidence network, which is trained on a small amount of supervised data. Thus
+we avoid that the weight updates computed from noisy labels harm the quality of
+the target network model. We evaluate our learning strategy on two different
+tasks: document ranking and sentiment classification. The results demonstrate
+that our approach not only enhances the performance compared to the baselines
+but also speeds up the learning process from weak labels.
+"
+29863,Molecular Hydrodynamics: Vortex Formation and Sound Wave Propagation,"  In the present study, quantitative feasibility tests of the hydrodynamic
+description of a two-dimensional fluid at the molecular level are performed,
+both with respect to length and time scales. Using high-resolution fluid
+velocity data obtained from extensive molecular dynamics simulations, we
+computed the transverse and longitudinal components of the velocity field by
+the Helmholtz decomposition and compared them with those obtained from the
+linearized Navier-Stokes (LNS) equations with time-dependent transport
+coefficients. By investigating the vortex dynamics and the sound wave
+propagation in terms of these field components, we confirm the validity of the
+LNS description for times comparable to or larger than several mean collision
+times. The LNS description still reproduces the transverse velocity field
+accurately at smaller times, but it fails to predict characteristic patterns of
+molecular origin visible in the longitudinal velocity field. Based on these
+observations, we validate the main assumptions of the mode-coupling approach.
+The assumption that the velocity autocorrelation function can be expressed in
+terms of the fluid velocity field and the tagged particle distribution is found
+to be remarkably accurate even for times comparable to or smaller than the mean
+collision time. This suggests that the hydrodynamic-mode description remains
+valid down to the molecular scale.
+"
+29864,Balanced Distribution Adaptation for Transfer Learning,"  Transfer learning has achieved promising results by leveraging knowledge from
+the source domain to annotate the target domain which has few or none labels.
+Existing methods often seek to minimize the distribution divergence between
+domains, such as the marginal distribution, the conditional distribution or
+both. However, these two distances are often treated equally in existing
+algorithms, which will result in poor performance in real applications.
+Moreover, existing methods usually assume that the dataset is balanced, which
+also limits their performances on imbalanced tasks that are quite common in
+real problems. To tackle the distribution adaptation problem, in this paper, we
+propose a novel transfer learning approach, named as Balanced Distribution
+\underline{A}daptation~(BDA), which can adaptively leverage the importance of
+the marginal and conditional distribution discrepancies, and several existing
+methods can be treated as special cases of BDA. Based on BDA, we also propose a
+novel Weighted Balanced Distribution Adaptation~(W-BDA) algorithm to tackle the
+class imbalance issue in transfer learning. W-BDA not only considers the
+distribution adaptation between domains but also adaptively changes the weight
+of each class. To evaluate the proposed methods, we conduct extensive
+experiments on several transfer learning tasks, which demonstrate the
+effectiveness of our proposed algorithms over several state-of-the-art methods.
+"
+29865,On a partially overdetermined problem in a cone,"  We prove a rigidity result for Serrin's overdetermined problem in a cone that
+is contained in a half-space in arbitrary dimensions. In the special case where
+the cone is an epigraph, this result was shown previously in low dimensions
+with a different approach.
+"
+29866,Dictionary Learning from Incomplete Data,"  This paper extends the recently proposed and theoretically justified
+iterative thresholding and $K$ residual means algorithm ITKrM to learning
+dicionaries from incomplete/masked training data (ITKrMM). It further adapts
+the algorithm to the presence of a low rank component in the data and provides
+a strategy for recovering this low rank component again from incomplete data.
+Several synthetic experiments show the advantages of incorporating information
+about the corruption into the algorithm. Finally, image inpainting is
+considered as application example, which demonstrates the superior performance
+of ITKrMM in terms of speed at similar or better reconstruction quality
+compared to its closest dictionary learning counterpart.
+"
+29867,Learning to Race through Coordinate Descent Bayesian Optimisation,"  In the automation of many kinds of processes, the observable outcome can
+often be described as the combined effect of an entire sequence of actions, or
+controls, applied throughout its execution. In these cases, strategies to
+optimise control policies for individual stages of the process might not be
+applicable, and instead the whole policy might have to be optimised at once. On
+the other hand, the cost to evaluate the policy's performance might also be
+high, being desirable that a solution can be found with as few interactions as
+possible with the real system. We consider the problem of optimising control
+policies to allow a robot to complete a given race track within a minimum
+amount of time. We assume that the robot has no prior information about the
+track or its own dynamical model, just an initial valid driving example.
+Localisation is only applied to monitor the robot and to provide an indication
+of its position along the track's centre axis. We propose a method for finding
+a policy that minimises the time per lap while keeping the vehicle on the track
+using a Bayesian optimisation (BO) approach over a reproducing kernel Hilbert
+space. We apply an algorithm to search more efficiently over high-dimensional
+policy-parameter spaces with BO, by iterating over each dimension individually,
+in a sequential coordinate descent-like scheme. Experiments demonstrate the
+performance of the algorithm against other methods in a simulated car racing
+environment.
+"
+29868,"Information, Privacy and Stability in Adaptive Data Analysis","  Traditional statistical theory assumes that the analysis to be performed on a
+given data set is selected independently of the data themselves. This
+assumption breaks downs when data are re-used across analyses and the analysis
+to be performed at a given stage depends on the results of earlier stages. Such
+dependency can arise when the same data are used by several scientific studies,
+or when a single analysis consists of multiple stages.
+How can we draw statistically valid conclusions when data are re-used? This
+is the focus of a recent and active line of work. At a high level, these
+results show that limiting the information revealed by earlier stages of
+analysis controls the bias introduced in later stages by adaptivity.
+Here we review some known results in this area and highlight the role of
+information-theoretic concepts, notably several one-shot notions of mutual
+information.
+"
+29869,To bee or not to bee: Investigating machine learning approaches for beehive sound recognition,"  In this work, we aim to explore the potential of machine learning methods to
+the problem of beehive sound recognition. A major contribution of this work is
+the creation and release of annotations for a selection of beehive recordings.
+By experimenting with both support vector machines and convolutional neural
+networks, we explore important aspects to be considered in the development of
+beehive sound recognition systems using machine learning approaches.
+"
+29870,Speeding Up BigClam Implementation on SNAP,"  We perform a detailed analysis of the C++ implementation of the Cluster
+Affiliation Model for Big Networks (BigClam) on the Stanford Network Analysis
+Project (SNAP). BigClam is a popular graph mining algorithm that is capable of
+finding overlapping communities in networks containing millions of nodes. Our
+analysis shows a key stage of the algorithm - determining if a node belongs to
+a community - dominates the runtime of the implementation, yet the computation
+is not parallelized. We show that by parallelizing computations across multiple
+threads using OpenMP we can speed up the algorithm by 5.3 times when solving
+large networks for communities, while preserving the integrity of the program
+and the result.
+"
+29871,Ground vehicle odometry using a non-intrusive inertial speed sensor,"  This paper describes the design and development of a non-intrusive inertial
+speed sensor that can be reliably used to replace a conventional optical or
+hall effect-based speedometer on any kind of ground vehicle. The design allows
+for simple assembly-disassembly from tyre rims. The sensor design and data flow
+are explained. Algorithms and filters for pre-processing and processing the
+data are detailed. Comparison with a real optical encoder proves the accuracy
+of the proposed sensor. Finally, it is shown that factor graph-based
+localization is possible with the developed sensor.
+"
+29872,Singularities of the area preserving curve shortening flow with a Neumann free boundary condition,"  We consider the area preserving curve shortening flow with Neumann free
+boundary conditions outside of a convex domain or at a straight line. We give a
+criterion on initial curves that guarantees the appearance of a singularity in
+finite time. We prove that the singularity is of type II. Furthermore, if these
+initial curves are convex, then an appropriate rescaling at the finite maximal
+time of existence yields a grim reaper or half a grim reaper as limit flow. We
+construct examples of initial curves satisfying the mentioned criterion.
+"
+29873,U-Phylogeny: Undirected Provenance Graph Construction in the Wild,"  Deriving relationships between images and tracing back their history of
+modifications are at the core of Multimedia Phylogeny solutions, which aim to
+combat misinformation through doctored visual media. Nonetheless, most recent
+image phylogeny solutions cannot properly address cases of forged composite
+images with multiple donors, an area known as multiple parenting phylogeny
+(MPP). This paper presents a preliminary undirected graph construction solution
+for MPP, without any strict assumptions. The algorithm is underpinned by robust
+image representative keypoints and different geometric consistency checks among
+matching regions in both images to provide regions of interest for direct
+comparison. The paper introduces a novel technique to geometrically filter the
+most promising matches as well as to aid in the shared region localization
+task. The strength of the approach is corroborated by experiments with
+real-world cases, with and without image distractors (unrelated cases).
+"
+29874,Generalized coinvariant algebras for wreath products,"  Let $r$ be a positive integer and let $G_n$ be the reflection group of $n
+\times n$ monomial matrices whose entries are $r^{th}$ complex roots of unity
+and let $k \leq n$. We define and study two new graded quotients $R_{n,k}$ and
+$S_{n,k}$ of the polynomial ring $\mathbb{C}[x_1, \dots, x_n]$ in $n$
+variables. When $k = n$, both of these quotients coincide with the classical
+coinvariant algebra attached to $G_n$. The algebraic properties of our
+quotients are governed by the combinatorial properties of $k$-dimensional faces
+in the Coxeter complex attached to $G_n$ (in the case of $R_{n,k}$) and
+$r$-colored ordered set partitions of $\{1, 2, \dots, n\}$ with $k$ blocks (in
+the case of $S_{n,k}$). Our work generalizes a construction of Haglund,
+Rhoades, and Shimozono from the symmetric group $\mathfrak{S}_n$ to the more
+general wreath products $G_n$.
+"
+29875,Hyperbolic rank rigidity for manifolds of $\frac14$-pinched negative curvature,"  A Riemannian manifold $M$ has higher hyperbolic rank if every geodesic has a
+perpendicular Jacobi field making sectional curvature -1 with the geodesic. If
+in addition, the sectional curvatures of $M$ lie in the interval
+$[-1,-\frac14]$, and $M$ is closed, we show that $M$ is a locally symmetric
+space of rank one. This partially extends work by Constantine using completely
+different methods. It is also a partial converse to Hamenstädt's hyperbolic
+rank rigidity result for sectional curvatures $\leq -1$, and complements
+well-known results on Euclidean and spherical rank rigidity.
+"
+29876,Non-torsion non-algebraic classes in the Brown-Peterson tower,"  Generalizing the classical work of Atiyah and Hirzebruch on non-algebraic
+classes, recently Quick proved the existence of torsion non-algebraic elements
+in the Brown-Peterson tower. We construct non-torsion non-algebraic elements in
+the Brown-Peterson tower for the prime number 2.
+"
+29877,SIM-CE: An Advanced Simulink Platform for Studying the Brain of Caenorhabditis elegans,"  We introduce SIM-CE, an advanced, user-friendly modeling and simulation
+environment in Simulink for performing multi-scale behavioral analysis of the
+nervous system of Caenorhabditis elegans (C. elegans). SIM-CE contains an
+implementation of the mathematical models of C. elegans's neurons and synapses,
+in Simulink, which can be easily extended and particularized by the user. The
+Simulink model is able to capture both complex dynamics of ion channels and
+additional biophysical detail such as intracellular calcium concentration. We
+demonstrate the performance of SIM-CE by carrying out neuronal, synaptic and
+neural-circuit-level behavioral simulations. Such environment enables the user
+to capture unknown properties of the neural circuits, test hypotheses and
+determine the origin of many behavioral plasticities exhibited by the worm.
+"
+29878,An efficient counting method for the colored triad census,"  The triad census is an important approach to understand local structure in
+network science, providing comprehensive assessments of the observed relational
+configurations between triples of actors in a network. However, researchers are
+often interested in combinations of relational and categorical nodal
+attributes. In this case, it is desirable to account for the label, or color,
+of the nodes in the triad census. In this paper, we describe an efficient
+algorithm for constructing the colored triad census, based, in part, on
+existing methods for the classic triad census. We evaluate the performance of
+the algorithm using empirical and simulated data for both undirected and
+directed graphs. The results of the simulation demonstrate that the proposed
+algorithm reduces computational time many-fold over the naive approach. We also
+apply the colored triad census to the Zachary karate club network dataset. We
+simultaneously show the efficiency of the algorithm, and a way to conduct a
+statistical test on the census by forming a null distribution from 1,000
+realizations of a mixing-matrix conditioned graph and comparing the observed
+colored triad counts to the expected. From this, we demonstrate the method's
+utility in our discussion of results about homophily, heterophily, and
+bridging, simultaneously gained via the colored triad census. In sum, the
+proposed algorithm for the colored triad census brings novel utility to social
+network analysis in an efficient package.
+"
+29879,Lensless computational imaging through deep learning,"  Deep learning has been proven to yield reliably generalizable answers to
+numerous classification and decision tasks. Here, we demonstrate for the first
+time, to our knowledge, that deep neural networks (DNNs) can be trained to
+solve inverse problems in computational imaging. We experimentally demonstrate
+a lens-less imaging system where a DNN was trained to recover a phase object
+given a raw intensity image recorded some distance away.
+"
+29880,Frobenius structures and characters of affine Lie algebras,"  The explicit description of the Frobenius structure for the elliptic root
+system of type $D_4^{(1,1)}$ in terms of the characters of an affine Lie
+algebra of type $D_4^{(1)}$ is given.
+"
+29881,Joint Gaussian Processes for Biophysical Parameter Retrieval,"  Solving inverse problems is central to geosciences and remote sensing.
+Radiative transfer models (RTMs) represent mathematically the physical laws
+which govern the phenomena in remote sensing applications (forward models). The
+numerical inversion of the RTM equations is a challenging and computationally
+demanding problem, and for this reason, often the application of a nonlinear
+statistical regression is preferred. In general, regression models predict the
+biophysical parameter of interest from the corresponding received radiance.
+However, this approach does not employ the physical information encoded in the
+RTMs. An alternative strategy, which attempts to include the physical
+knowledge, consists in learning a regression model trained using data simulated
+by an RTM code. In this work, we introduce a nonlinear nonparametric regression
+model which combines the benefits of the two aforementioned approaches. The
+inversion is performed taking into account jointly both real observations and
+RTM-simulated data. The proposed Joint Gaussian Process (JGP) provides a solid
+framework for exploiting the regularities between the two types of data. The
+JGP automatically detects the relative quality of the simulated and real data,
+and combines them accordingly. This occurs by learning an additional
+hyper-parameter w.r.t. a standard GP model, and fitting parameters through
+maximizing the pseudo-likelihood of the real observations. The resulting scheme
+is both simple and robust, i.e., capable of adapting to different scenarios.
+The advantages of the JGP method compared to benchmark strategies are shown
+considering RTM-simulated and real observations in different experiments.
+Specifically, we consider leaf area index (LAI) retrieval from Landsat data
+combined with simulated data generated by the PROSAIL model.
+"
+29882,Energy-Sustainable Traffic Steering for 5G Mobile Networks,"  Renewable energy harvesting (EH) technology is expected to be pervasively
+utilized in the next generation (5G) mobile networks to support sustainable
+network developments and operations. However, the renewable energy supply is
+inherently random and intermittent, which could lead to energy outage, energy
+overflow, quality of service (QoS) degradation, etc. Accordingly, how to
+enhance renewable energy sustainability is a critical issue for green
+networking. To this end, an energy-sustainable traffic steering framework is
+proposed in this article, where the traffic load is dynamically adjusted to
+match with energy distributions in both spatial and temporal domains by means
+of inter- and intra-tier steering, caching and pushing. Case studies are
+carried out, which demonstrate the proposed framework can reduce on-grid energy
+demand while satisfying QoS requirements. Research topics and challenges of
+energy-sustainable traffic steering are also discussed.
+"
+29883,Combining electronic structure and many-body theory with large data-bases: a method for predicting the nature of 4f states in Ce compounds,"  Here we present the first large scale investigation of electronic properties
+and correlated magnetism in Ce-based compounds accompanied by a systematic
+study of the electronic structure and 4f-hybridization function of a large body
+of Ce compounds. We systematically study the electronic structure and
+4f-hybridization function of a large body of Ce compounds with the goal of
+elucidating the nature of the 4f states and their interrelation with the
+measured Kondo energy in these compounds. The hybridization function has been
+analyzed for more than 350 data sets of cubic Ce compounds using electronic
+structure theory that relies on a full-potential approach. We demonstrate that
+the strength of the hybridization function, evaluated in this way, allows us to
+draw precise conclusions about the degree of localization of the 4f states in
+these compounds. The theoretical results are entirely consistent with all
+experimental information, relevant to the degree of 4f localization for all
+investigated materials. Furthermore, a more detailed analysis of the electronic
+structure and the hybridization function allows us to make precise statements
+about Kondo correlations in these systems. The calculated hybridization
+functions, together with the corresponding density of states, reproduce the
+expected exponential behavior of the observed Kondo temperatures and prove a
+consistent trend in real materials. This trend allows us to predict which
+systems may be correctly identified as Kondo systems. A strong anti-correlation
+between the size of the hybridization function and the volume of the systems
+has been observed. Our approach demonstrates the predictive power of materials
+informatics when a large number of materials is used to establish significant
+trends which can be used to design new materials with desired properties.
+"
+29884,LSSGalPy: Interactive Visualization of the Large-scale Environment Around Galaxies,"  New tools are needed to handle the growth of data in astrophysics delivered
+by recent and upcoming surveys. We aim to build open-source, light, flexible,
+and interactive software designed to visualize extensive three-dimensional (3D)
+tabular data. Entirely written in the Python language, we have developed
+interactive tools to browse and visualize the positions of galaxies in the
+universe and their positions with respect to its large-scale structures (LSS).
+Motivated by a previous study, we created two codes using Mollweide projection
+and wedge diagram visualizations, where survey galaxies can be overplotted on
+the LSS of the universe. These are interactive representations where the
+visualizations can be controlled by widgets. We have released these open-source
+codes that have been designed to be easily re-used and customized by the
+scientific community to fulfill their needs. The codes are adaptable to other
+kinds of 3D tabular data and are robust enough to handle several millions of
+objects.
+"
+29885,A generalized approach to model the spectra and radiation dose rate of solar particle events on the surface of Mars,"  For future human missions to Mars, it is important to study the surface
+radiation environment during extreme and elevated conditions. In the long term,
+it is mainly Galactic Cosmic Rays (GCRs) modulated by solar activity that
+contributes to the radiation on the surface of Mars, but intense solar
+energetic particle (SEP) events may induce acute health effects. Such events
+may enhance the radiation level significantly and should be detected as
+immediately as possible to prevent severe damage to humans and equipment.
+However, the energetic particle environment on the Martian surface is
+significantly different from that in deep space due to the influence of the
+Martian atmosphere. Depending on the intensity and shape of the original solar
+particle spectra as well as particle types, the surface spectra may induce
+entirely different radiation effects. In order to give immediate and accurate
+alerts while avoiding unnecessary ones, it is important to model and well
+understand the atmospheric effect on the incoming SEPs including both protons
+and helium ions. In this paper, we have developed a generalized approach to
+quickly model the surface response of any given incoming proton/helium ion
+spectra and have applied it to a set of historical large solar events thus
+providing insights into the possible variety of surface radiation environments
+that may be induced during SEP events. Based on the statistical study of more
+than 30 significant solar events, we have obtained an empirical model for
+estimating the surface dose rate directly from the intensities of a power-law
+SEP spectra.
+"
+29886,On Riesz type inequalities for harmonic mappings on the unit disk,"  We prove some sharp inequalities for complex harmonic functions on the unit
+disk. The results extend a M. Riesz conjugate function theorem and some
+well-known estimates for holomorphic functions. We apply some of results to the
+isoperimetric inequality for harmonic mappings.
+"
+29887,Decorated Corelations,"  Let $\mathcal C$ be a category with finite colimits, and let $(\mathcal
+E,\mathcal M)$ be a factorisation system on $\mathcal C$ with $\mathcal M$
+stable under pushouts. Writing $\mathcal C;\mathcal M^{\mathrm{op}}$ for the
+symmetric monoidal category with morphisms cospans of the form $\stackrel{c}\to
+\stackrel{m}\leftarrow$, where $c \in \mathcal C$ and $m \in \mathcal M$, we
+give method for constructing a category from a symmetric lax monoidal functor
+$F\colon (\mathcal C; \mathcal M^{\mathrm{op}},+) \to (\mathrm{Set},\times)$. A
+morphism in this category, termed a \emph{decorated corelation}, comprises (i)
+a cospan $X \to N \leftarrow Y$ in $\mathcal C$ such that the canonical
+copairing $X+Y \to N$ lies in $\mathcal E$, together with (ii) an element of
+$FN$. Functors between decorated corelation categories can be constructed from
+natural transformations between the decorating functors $F$. This provides a
+general method for constructing hypergraph categories---symmetric monoidal
+categories in which each object is a special commutative Frobenius monoid in a
+coherent way---and their functors. Such categories are useful for modelling
+network languages, for example circuit diagrams, and such functors their
+semantics.
+"
+29888,From $r$-dual sets to uniform contractions,"  Let $M^d$ denote the $d$-dimensional Euclidean, hyperbolic, or spherical
+space. The $r$-dual set of given set in $M^d$ is the intersection of balls of
+radii $r$ centered at the points of the given set. In this paper we prove that
+for any set of given volume in $M^d$ the volume of the $r$-dual set becomes
+maximal if the set is a ball. As an application we prove the following. The
+Kneser-Poulsen Conjecture states that if the centers of a family of $N$
+congruent balls in Euclidean $d$-space is contracted, then the volume of the
+intersection does not decrease. A uniform contraction is a contraction where
+all the pairwise distances in the first set of centers are larger than all the
+pairwise distances in the second set of centers. We prove the Kneser-Poulsen
+conjecture for uniform contractions (with $N$ sufficiently large) in $M^d$.
+"
+29889,Optical Imaging of Surface Chemistry and Dynamics in Confinement,"  The interfacial structure and dynamics of water in a microscopically confined
+geometry is imaged in three dimensions and on millisecond time scales. We
+developed a 3D wide-field second harmonic microscope that employs structured
+illumination. We image pH induced chemical changes on the curved and confined
+inner and outer surfaces of a cylindrical glass micro-capillary immersed in
+aqueous solution. The image contrast reports on the orientational order of
+interfacial water, induced by charge-dipole interactions between water
+molecules and surface charges. The images constitute surface potential maps.
+Spatially resolved surface pKa,s values are determined for the silica
+deprotonation reaction. Values range from 2.3<pKa,s<10.7, highlighting the
+importance of surface heterogeneities. Water molecules that rotate along an
+oscillating external electric field are also imaged. With this approach, real
+time movies of surface processes that involve flow, heterogeneities and
+potentials can be made, which will further developments in electrochemistry,
+geology, catalysis, biology, and microtechnology.
+"
+29890,Effective Cosmological Constant and Dark Energy,"  Two very different methods are used to estimate the magnitude of the
+effective cosmological constant / dark energy (for the present cosmic epoch).
+Their results agree with each other and are in agreement with observations. One
+method makes use of unimodular gravity and causal set theory, while the other
+one employs arguments involving spacetime foam and holography. I also motivate
+and discuss the possibility that quanta of (both) dark energy (and dark matter
+in the Modified Dark Matter model) are extended/non-local, obeying infinite
+statistics, also known as quantum Boltzmann statistics. Such quanta out-number
+ordinary particles obeying Bose-Einstein or Fermi-Dirac statistics by a factor
+of $\sim 10^{30}$.
+"
+29891,Identifying Patch Correctness in Test-Based Program Repair,"  Test-based automatic program repair has attracted a lot of attention in
+recent years. However, the test suites in practice are often too weak to
+guarantee correctness and existing approaches often generate a large number of
+incorrect patches.
+To reduce the number of incorrect patches generated, we propose a novel
+approach that heuristically determines the correctness of the generated
+patches. The core idea is to exploit the behavior similarity of test case
+executions. The passing tests on original and patched programs are likely to
+behave similarly while the failing tests on original and patched programs are
+likely to behave differently. Also, if two tests exhibit similar runtime
+behavior, the two tests are likely to have the same test results. Based on
+these observations, we generate new test inputs to enhance the test suites and
+use their behavior similarity to determine patch correctness.
+Our approach is evaluated on a dataset consisting of 139 patches generated
+from existing program repair systems including jGenProg, Nopol, jKali, ACS and
+HDRepair. Our approach successfully prevented 56.3\% of the incorrect patches
+to be generated, without blocking any correct patches.
+"
+29892,A Multiversion Programming Inspired Approach to Detecting Audio Adversarial Examples,"  Adversarial examples (AEs) are crafted by adding human-imperceptible
+perturbations to inputs such that a machine-learning based classifier
+incorrectly labels them. They have become a severe threat to the
+trustworthiness of machine learning. While AEs in the image domain have been
+well studied, audio AEs are less investigated. Recently, multiple techniques
+are proposed to generate audio AEs, which makes countermeasures against them an
+urgent task. Our experiments show that, given an AE, the transcription results
+by different Automatic Speech Recognition (ASR) systems differ significantly,
+as they use different architectures, parameters, and training datasets.
+Inspired by Multiversion Programming, we propose a novel audio AE detection
+approach, which utilizes multiple off-the-shelf ASR systems to determine
+whether an audio input is an AE. The evaluation shows that the detection
+achieves accuracies over 98.6%.
+"
+29893,Local Weak Limits of Laplace Eigenfunctions,"  In this paper, we introduce a new notion of convergence for the Laplace
+eigenfunctions in the semiclassical limit, the local weak convergence. This
+allows us to give a rigorous statement of Berry's random wave conjecture. Using
+recent results of Bourgain, Buckley and Wigman, we will prove that some
+deterministic families of eigenfunctions on $\mathbb{T}^2$ satisfy the
+conclusions of the random wave conjecture. We also show that on an arbitrary
+domain, a sequence of Laplace eigenfunctions always admits local weak limits.
+We explain why these local weak limits can be a powerful tool to study the
+asymptotic number of nodal domains.
+"
+29894,Strictly local one-dimensional topological quantum error correction with symmetry-constrained cellular automata,"  Active quantum error correction on topological codes is one of the most
+promising routes to long-term qubit storage. In view of future applications,
+the scalability of the used decoding algorithms in physical implementations is
+crucial. In this work, we focus on the one-dimensional Majorana chain and
+construct a strictly local decoder based on a self-dual cellular automaton. We
+study numerically and analytically its performance and exploit these results to
+contrive a scalable decoder with exponentially growing decoherence times in the
+presence of noise. Our results pave the way for scalable and modular designs of
+actively corrected one-dimensional topological quantum memories.
+"
+29895,Lossy Kernels for Connected Dominating Set on Sparse Graphs,"  For $\alpha > 1$, an $\alpha$-approximate (bi-)kernel is a polynomial-time
+algorithm that takes as input an instance $(I, k)$ of a problem $\mathcal{Q}$
+and outputs an instance $(I',k')$ (of a problem $\mathcal{Q}'$) of size bounded
+by a function of $k$ such that, for every $c\geq 1$, a $c$-approximate solution
+for the new instance can be turned into a $(c\cdot\alpha)$-approximate solution
+of the original instance in polynomial time. This framework of lossy
+kernelization was recently introduced by Lokshtanov et al. We study Connected
+Dominating Set (and its distance-$r$ variant) parameterized by solution size on
+sparse graph classes like biclique-free graphs, classes of bounded expansion,
+and nowhere dense classes. We prove that for every $\alpha>1$, Connected
+Dominating Set admits a polynomial-size $\alpha$-approximate (bi-)kernel on all
+the aforementioned classes. Our results are in sharp contrast to the
+kernelization complexity of Connected Dominating Set, which is known to not
+admit a polynomial kernel even on $2$-degenerate graphs and graphs of bounded
+expansion, unless $\textsf{NP} \subseteq \textsf{coNP/poly}$. We complement our
+results by the following conditional lower bound. We show that if a class
+$\mathcal{C}$ is somewhere dense and closed under taking subgraphs, then for
+some value of $r\in\mathbb{N}$ there cannot exist an $\alpha$-approximate
+bi-kernel for the (Connected) Distance-$r$ Dominating Set problem on
+$\mathcal{C}$ for any $\alpha>1$ (assuming the Gap Exponential Time
+Hypothesis).
+"
+29896,How AGN and SNe feedback affect mass transport and black hole growth in high redshift galaxies,"  By using cosmological hydrodynamical simulations we study the effect of
+supernova (SN) and active galactic nuclei (AGN) feedback on the mass transport
+of gas on to galactic nuclei and the black hole (BH) growth down to redshift
+z~6. We study the BH growth in relation with the mass transport processes
+associated with gravity and pressure torques, and how they are modified by
+feedback. Cosmological gas funelled through cold flows reaches the galactic
+outer region close to free-fall. Then torques associated to pressure triggered
+by gas turbulent motions produced in the circum-galactic medium by shocks and
+explosions from SNe are the main source of mass transport beyond the central ~
+100 pc. Due to high concentrations of mass in the central galactic region,
+gravitational torques tend to be more important at high redshift. The combined
+effect of almost free-falling material and both gravity and pressure torques
+produces a mass accretion rate of order ~ 1 M_sun/yr at ~ pc scales. In the
+absence of SN feedback, AGN feedback alone does not affect significantly either
+star formation or BH growth until the BH reaches a sufficiently high mass of
+$\sim 10^6$ M_sun to self-regulate. SN feedback alone, instead, decreases both
+stellar and BH growth. Finally, SN and AGN feedback in tandem efficiently
+quench the BH growth, while star formation remains at the levels set by SN
+feedback alone due to the small final BH mass, ~ few 10^5 M_sun. SNe create a
+more rarefied and hot environment where energy injection from the central AGN
+can accelerate the gas further.
+"
+29897,Frequency-dependent Study of Solid Helium-4 Contained in a Rigid Double-torus Torsional Oscillator,"  The rigid double-torus torsional oscillator (TO) is constructed to reduce any
+elastic effects in-herent to complicate TO structures, allowing explicit
+probing for a genuine supersolid signature. We investigated the frequency- and
+temperature-dependent response of the rigid double-torus TO containing solid
+4He with 0.6 ppb 3He and 300 ppb 3He. We did not find evidence to support the
+frequency-independent contribution proposed to be a property of supersolid
+helium. The frequency-dependent contribution which comes from the simple
+elastic effect of solid helium coupled to TO is essentially responsible for the
+entire response. The magnitude of the period drop is linearly proportional to
+${f}^{2}$, indicating that the responses observed in this TO are mostly caused
+by the overshoot of `soft' solid helium against the wall of the torus.
+Dissipation of the rigid TO is vastly suppressed compared to those of non-rigid
+TOs.
+"
+29898,Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters,"  With ever growing data volume and model size, an error-tolerant,
+communication efficient, yet versatile distributed algorithm has become vital
+for the success of many large-scale machine learning applications. In this work
+we propose m-PAPG, an implementation of the flexible proximal gradient
+algorithm in model parallel systems equipped with the partially asynchronous
+communication protocol. The worker machines communicate asynchronously with a
+controlled staleness bound $s$ and operate at different frequencies. We
+characterize various convergence properties of m-PAPG: 1) Under a general
+non-smooth and non-convex setting, we prove that every limit point of the
+sequence generated by m-PAPG is a critical point of the objective function; 2)
+Under an error bound condition, we prove that the function value decays
+linearly for every $s$ steps; 3) Under the Kurdyka-${\L}$ojasiewicz inequality,
+we prove that the sequences generated by m-PAPG converge to the same critical
+point, provided that a proximal Lipschitz condition is satisfied.
+"
+29899,UCAC5: New Proper Motions using Gaia DR1,"  New astrometric reductions of the US Naval Observatory CCD Astrograph Catalog
+(UCAC) all-sky observations were performed from first principles using the TGAS
+stars in the 8 to 11 magnitude range as reference star catalog. Significant
+improvements in the astrometric solutions were obtained and the UCAC5 catalog
+of mean positions at a mean epoch near 2001 was generated. By combining UCAC5
+with Gaia DR1 data new proper motions on the Gaia coordinate system for over
+107 million stars were obtained with typical accuracies of 1 to 2 mas/yr (R =
+11 to 15 mag), and about 5 mas/yr at 16th mag. Proper motions of most TGAS
+stars are improved over their Gaia data and the precision level of TGAS proper
+motions is extended to many millions more, fainter stars. External comparisons
+were made using stellar cluster fields and extragalactic sources. The TGAS data
+allow us to derive the limiting precision of the UCAC x,y data, which is
+significantly better than 1/100 pixel.
+"
+29900,Parallel Statistical and Machine Learning Methods for Estimation of Physical Load,"  Several statistical and machine learning methods are proposed to estimate the
+type and intensity of physical load and accumulated fatigue . They are based on
+the statistical analysis of accumulated and moving window data subsets with
+construction of a kurtosis-skewness diagram. This approach was applied to the
+data gathered by the wearable heart monitor for various types and levels of
+physical activities, and for people with various physical conditions. The
+different levels of physical activities, loads, and fitness can be
+distinguished from the kurtosis-skewness diagram, and their evolution can be
+monitored. Several metrics for estimation of the instant effect and accumulated
+effect (physical fatigue) of physical loads were proposed. The data and results
+presented allow to extend application of these methods for modeling and
+characterization of complex human activity patterns, for example, to estimate
+the actual and accumulated physical load and fatigue, model the potential
+dangerous development, and give cautions and advice in real time.
+"
+29901,Symbolic Reasoning for Automatic Signal Placement (Extended Version),"  Explicit signaling between threads is a perennial cause of bugs in concurrent
+programs. While there are several run-time techniques to automatically notify
+threads upon the availability of some shared resource, such techniques are not
+widely-adopted due to their run-time overhead. This paper proposes a new
+solution based on static analysis for automatically generating a performant
+explicit-signal program from its corresponding implicit-signal implementation.
+The key idea is to generate verification conditions that allow us to minimize
+the number of required signals and unnecessary context switches, while
+guaranteeing semantic equivalence between the source and target programs. We
+have implemented our method in a tool called Expresso and evaluate it on
+challenging benchmarks from prior papers and open-source software.
+Expresso-generated code significantly outperforms past automatic signaling
+mechanisms (avg. 1.56x speedup) and closely matches the performance of
+hand-optimized explicit-signal code.
+"
+29902,Stochastic gene expression with a multistate promoter: breaking down exact distributions,"  We consider a stochastic model of gene expression in which transcription
+depends on a multistate promoter, including the famous two-state model and
+refractory promoters as special cases, and focus on deriving the exact
+stationary distribution. Building upon several successful approaches, we
+present a more unified viewpoint that enables us to simplify and generalize
+existing results. In particular, the original jump process is deeply related to
+a multivariate piecewise-deterministic Markov process that may also be of
+interest beyond the biological field. In a very particular case of promoter
+configuration, this underlying process is shown to have a simple Dirichlet
+stationary distribution. In the general case, the corresponding marginal
+distributions extend the well-known class of Beta products, involving complex
+parameters that directly relate to spectral properties of the promoter
+transition matrix. Finally, we illustrate these results with biologically
+plausible examples.
+"
+29903,Observation of individual spin quantum transitions of a single antiproton,"  We report on the detection of individual spin quantum transitions of a single
+trapped antiproton in a Penning trap. The spin-state determination, which is
+based on the unambiguous detection of axial frequency shifts in presence of a
+strong magnetic bottle, reaches a fidelity of 92.1$\%$. Spin-state
+initialization with $>99.9\%$ fidelity and an average initialization time of 24
+min are demonstrated. This is a major step towards an antiproton magnetic
+moment measurement with a relative uncertainty on the part-per-billion level.
+"
+29904,Geometric Hydrodynamics via Madelung Transform,"  We introduce a geometric framework to study Newton's equations on
+infinite-dimensional configuration spaces of diffeomorphisms and smooth
+probability densities. It turns out that several important PDEs of
+hydrodynamical origin can be described in this framework in a natural way. In
+particular, the Madelung transform between the Schrödinger equation and
+Newton's equations is a symplectomorphism of the corresponding phase spaces.
+Furthermore, the Madelung transform turns out to be a Kähler map when the
+space of densities is equipped with the Fisher-Rao information metric. We
+describe several dynamical applications of these results.
+"
+29905,Detectability of Galactic Faraday Rotation in Multi-wavelength CMB Observations: A Cross-Correlation Analysis of CMB and Radio Maps,"  We introduce a new cross-correlation method to detect and verify the
+astrophysical origin of Faraday Rotation (FR) in multiwavelength surveys. FR is
+well studied in radio astronomy from radio point sources but the $\lambda^{2}$
+suppression of FR makes detecting and accounting for this effect difficult at
+millimeter and sub-millimeter wavelengths. Therefore statistical methods are
+used to attempt to detect FR in the cosmic microwave background (CMB). Most
+estimators of the FR power spectrum rely on single frequency data. In contrast,
+we investigate the correlation of polarized CMB maps with FR measure maps from
+radio point sources. We show a factor of $\sim30$ increase in sensitivity over
+single frequency estimators and predict detections exceeding $10\sigma$
+significance for a CMB-S4 like experiment. Improvements in observations of FR
+from current and future radio polarization surveys will greatly increase the
+usefulness of this method.
+"
+29906,Prethermal time crystals in a one-dimensional periodically driven Floquet system,"  Motivated by experimental observations of time-symmetry breaking behavior in
+a periodically driven (Floquet) system, we study a one-dimensional spin model
+to explore the stability of such Floquet discrete time crystals (DTCs) under
+the interplay between interaction and the microwave driving. For intermediate
+interactions and high drivings, from the time evolution of both stroboscopic
+spin polarization and mutual information between two ends, we show that Floquet
+DTCs can exist in a prethermal time regime without the tuning of strong
+disorder. For much weak interactions the system is a symmetry-unbroken phase,
+while for strong interactions it gives its way to a thermal phase. Through
+analyzing the entanglement dynamics, we show that large driving fields protect
+the prethermal DTCs from many-body localization and thermalization. Our results
+suggest that by increasing the spin interaction, one can drive the experimental
+system into optimal regime for observing a robust prethermal DTC phase.
+"
+29907,Encounters with Alex,"  In the 80th anniversary book for Alex Müller I wrote a story of our
+scientific collaboration, Shared Fascinations. This time I will be more
+personal, about the human side of our collaboration and encounters, while also
+referring to episodes mentioned in Shared Fascinations.
+"
+29908,Skew Carleson measures in strongly pseudoconvex domains,"  Given a bounded strongly pseudoconvex domain $D$ in $\mathbb{C}^n$ with
+smooth boundary, we give a characterization through products of functions in
+weighted Bergman spaces of $(\lambda,\gamma)$-skew Carleson measures on $D$,
+with $\lambda>0$ and $\gamma>1-\frac{1}{n+1}$.
+"
+29909,Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests,"  Indoor localization based on SIngle Of Fingerprint (SIOF) is rather
+susceptible to the changing environment, multipath, and non-line-of-sight
+(NLOS) propagation. Building SIOF is also a very time-consuming process.
+Recently, we first proposed a GrOup Of Fingerprints (GOOF) to improve the
+localization accuracy and reduce the burden of building fingerprints. However,
+the main drawback is the timeliness. In this paper, we propose a novel
+localization framework by Fusing A Group Of fingerprinTs (FAGOT) based on
+random forests. In the offline phase, we first build a GOOF from different
+transformations of the received signals of multiple antennas. Then, we design
+multiple GOOF strong classifiers based on Random Forests (GOOF-RF) by training
+each fingerprint in the GOOF. In the online phase, we input the corresponding
+transformations of the real measurements into these strong classifiers to
+obtain multiple independent decisions. Finally, we propose a Sliding Window
+aIded Mode-based (SWIM) fusion algorithm to balance the localization accuracy
+and time. Our proposed approaches can work better in an unknown indoor
+scenario. The burden of building fingerprints can also be reduced drastically.
+We demonstrate the performance of our algorithms through simulations and real
+experimental data using two Universal Software Radio Peripheral (USRP)
+platforms.
+"
+29910,Inverse Edelstein effect of the surface states of a topological insulator,"  The surface states of three-dimensional topological insulators posses the
+unique property of spin-momentum interlocking. This property gives rise to the
+interesting inverse Edelstein effect (IEE), in which an applied spin bias $\mu$
+is converted to a measurable charge voltage difference $V$. We develop a
+semiclassical theory for the IEE of the surface states of
+$\text{Bi}_2\text{Se}_3$ thin films, which is applicable from the ballistic
+regime to diffusive regime. We find that the IEE efficiency ratio
+$\gamma=V/\mu$ exhibits universal dependence on sample size, and approaches
+$\pi/4$ in the ballistic limit and $1$ in the diffusive limit.
+"
+29911,Phaseless Sampling and Reconstruction of Real-Valued Signals in Shift-Invariant Spaces,"  Sampling in shift-invariant spaces is a realistic model for signals with
+smooth spectrum. In this paper, we consider phaseless sampling and
+reconstruction of real-valued signals in a shift-invariant space from their
+magnitude measurements on the whole Euclidean space and from their phaseless
+samples taken on a discrete set with finite sampling density. We introduce an
+undirected graph to a signal and use connectivity of the graph to characterize
+whether the signal can be determined, up to a sign, from its magnitude
+measurements on the whole Euclidean space. Under the local complement property
+assumption on a shift-invariant space, we find a discrete set with finite
+sampling density such that signals in the shift-invariant space, that are
+determined from their magnitude measurements on the whole Euclidean space, can
+be reconstructed in a stable way from their phaseless samples taken on that
+discrete set. In this paper, we also propose a reconstruction algorithm which
+provides a suboptimal approximation to the original signal when its noisy
+phaseless samples are available only. Finally, numerical simulations are
+performed to demonstrate the robust reconstruction of box spline signals from
+their noisy phaseless samples.
+"
+29912,Wavepacket Dynamics in One-Dimensional System with Long-Range Correlated Disorder,"  We numerically investigate dynamical property in the one-dimensional
+tight-binding model with long-range correlated disorder having power spectrum
+$1/f^\alpha$ ($\alpha:$spectrum exponent) generated by Fourier filtering
+method. For relatively small $\alpha<\alpha_c(=2)$ time-dependence of mean
+square displacement (MSD) of the initially localized wavepacket shows ballistic
+spread and localizes as time elapses. It is shown that $\alpha-$dependence of
+the dynamical localization length (DLL) determined by the MSD exhibits a simple
+scaling law in the localization regime for the relatively weak disorder
+strength $W$. Furthermore, scaled MSD by the DLL almost obeys an universal
+function from the ballistic to the localization regime in the various
+combinations of the parameters $\alpha$ and $W$.
+"
+29913,A Six-Planet System Around the Star HD 34445,"  We present a new precision radial velocity dataset that reveals a
+multi-planet system orbiting the G0V star HD 34445. Our 18-year span consists
+of 333 precision radial velocity observations, 56 of which were previously
+published, and 277 which are new data from Keck Observatory, Magellan at Las
+Campanas Observatory, and the Automated Planet Finder at Lick Observatory.
+These data indicate the presence of six planet candidates in Keplerian motion
+about the host star with periods of 1057, 215, 118, 49, 677, and 5700 days, and
+minimum masses of 0.63, 0.17, 0.1, 0.05, 0.12 and 0.38 Jupiter masses
+respectively. The HD 34445 planetary system, with its high degree of
+multiplicity, its long orbital periods, and its induced stellar radial velocity
+half-amplitudes in the range $2 \,{\rm m\, s^{-1}} \lesssim K \lesssim 5\,{\rm
+m\, s^{-1}}$ is fundamentally unlike either our own solar system (in which only
+Jupiter and Saturn induce significant reflex velocities for the Sun), or the
+Kepler multiple-transiting systems (which tend to have much more compact
+orbital configurations)
+"
+29914,An Explicit Determination of the Springer Morphism,"  Let $G$ be a simply connected semisimple algebraic group over $\mathbb{C}$
+and let $\rho :G\rightarrow GL(V_\lambda)$ be an irreducible representation of
+highest weight $\lambda$. Suppose that $\rho$ has finite kernel. Springer
+defined adjoint-invariant regular map with Zariski dense image from the group
+to its Lie algebra, $\theta_\lambda:G\rightarrow\mathfrak{g}$, which depends on
+$\lambda$ [Kumar]. By a lemma in Kumar's recent paper, $\theta_\lambda$ takes
+the maximal torus to its Lie algebra $\mathfrak{t}$. Thus, for a given simple
+group $G$ and an irreducible representation $V_\lambda$, one may write
+$\theta_\lambda (t)=\sum\limits_{i=1}^n c_i(t)\check{\alpha_i}$, where the
+simple co-roots $\{\check{\alpha_i}\}$ are a basis for $\mathfrak{t}$. We give
+a complete determination of these coefficients $c_i(t)$ for any simple group
+$G$ as a sum over the weights of the torus action on $V_\lambda$.
+"
+29915,Prediction of next career moves from scientific profiles,"  Changing institution is a scientist's key career decision, which plays an
+important role in education, scientific productivity, and the generation of
+scientific knowledge. Yet, our understanding of the factors influencing a
+relocation decision is very limited. In this paper we investigate how the
+scientific profile of a scientist determines their decision to move (i.e.,
+change institution). To this aim, we describe a scientist's profile by three
+main aspects: the scientist's recent scientific career, the quality of their
+scientific environment and the structure of their scientific collaboration
+network. We then design and implement a two-stage predictive model: first, we
+use data mining to predict which researcher will move in the next year on the
+basis of their scientific profile; second we predict which institution they
+will choose by using a novel social-gravity model, an adaptation of the
+traditional gravity model of human mobility. Experiments on a massive dataset
+of scientific publications show that our approach performs well in both the
+stages, resulting in a 85% reduction of the prediction error with respect to
+the state-of-the-art approaches.
+"
+29916,Faster Subgradient Methods for Functions with Hölderian Growth,"  The purpose of this manuscript is to derive new convergence results for
+several subgradient methods applied to minimizing nonsmooth convex functions
+with Hölderian growth. The growth condition is satisfied in many applications
+and includes functions with quadratic growth and weakly sharp minima as special
+cases. To this end there are three main contributions. First, for a constant
+and sufficiently small stepsize, we show that the subgradient method achieves
+linear convergence up to a certain region including the optimal set, with error
+of the order of the stepsize. Second, if appropriate problem parameters are
+known, we derive a decaying stepsize which obtains a much faster convergence
+rate than is suggested by the classical $O(1/\sqrt{k})$ result for the
+subgradient method. Thirdly we develop a novel ""descending stairs"" stepsize
+which obtains this faster convergence rate and also obtains linear convergence
+for the special case of weakly sharp functions. We also develop an adaptive
+variant of the ""descending stairs"" stepsize which achieves the same convergence
+rate without requiring an error bound constant which is difficult to estimate
+in practice.
+"
+29917,Deep Probabilistic Programming,"  We propose Edward, a Turing-complete probabilistic programming language.
+Edward defines two compositional representations---random variables and
+inference. By treating inference as a first class citizen, on a par with
+modeling, we show that probabilistic programming can be as flexible and
+computationally efficient as traditional deep learning. For flexibility, Edward
+makes it easy to fit the same model using a variety of composable inference
+methods, ranging from point estimation to variational inference to MCMC. In
+addition, Edward can reuse the modeling representation as part of inference,
+facilitating the design of rich variational models and generative adversarial
+networks. For efficiency, Edward is integrated into TensorFlow, providing
+significant speedups over existing probabilistic systems. For example, we show
+on a benchmark logistic regression task that Edward is at least 35x faster than
+Stan and 6x faster than PyMC3. Further, Edward incurs no runtime overhead: it
+is as fast as handwritten TensorFlow.
+"
+29918,Meromorphic continuation of Selberg zeta functions with twists having non-expanding cusp monodromy,"  We initiate the study of Selberg zeta functions $Z_{\Gamma,\chi}$ for
+geometrically finite Fuchsian groups $\Gamma$ and finite-dimensional
+representations $\chi$ with non-expanding cusp monodromy. We show that for all
+choices of $(\Gamma,\chi)$, the Selberg zeta function $Z_{\Gamma,\chi}$
+converges on some half-plane in $\mathbb{C}$. In addition, under the assumption
+that $\Gamma$ admits a strict transfer operator approach, we show that
+$Z_{\Gamma,\chi}$ extends meromorphically to all of $\mathbb{C}$.
+"
+29919,Amanuensis: The Programmer's Apprentice,"  This document provides an overview of the material covered in a course taught
+at Stanford in the spring quarter of 2018. The course draws upon insight from
+cognitive and systems neuroscience to implement hybrid connectionist and
+symbolic reasoning systems that leverage and extend the state of the art in
+machine learning by integrating human and machine intelligence. As a concrete
+example we focus on digital assistants that learn from continuous dialog with
+an expert software engineer while providing initial value as powerful
+analytical, computational and mathematical savants. Over time these savants
+learn cognitive strategies (domain-relevant problem solving skills) and develop
+intuitions (heuristics and the experience necessary for applying them) by
+learning from their expert associates. By doing so these savants elevate their
+innate analytical skills allowing them to partner on an equal footing as
+versatile collaborators - effectively serving as cognitive extensions and
+digital prostheses, thereby amplifying and emulating their human partner's
+conceptually-flexible thinking patterns and enabling improved access to and
+control over powerful computing resources.
+"
+29920,Convolutional Phase Retrieval via Gradient Descent,"  We study the convolutional phase retrieval problem, which considers
+recovering an unknown signal $\mathbf x \in \mathbb C^n $ from $m$ measurements
+consisting of the magnitude of its cyclic convolution with a known kernel
+$\mathbf a \in \mathbb C^m $. This model is motivated by applications such as
+channel estimation, optics, and underwater acoustic communication, where the
+signal of interest is acted on by a given channel/filter, and phase information
+is difficult or impossible to acquire. We show that when $\mathbf a$ is random
+and the sample number $m$ is sufficiently large, with high probability $\mathbf
+x$ can be efficiently recovered up to a global phase using a combination of
+spectral initialization and generalized gradient descent. The main challenge is
+coping with dependencies in the measurement operator. We overcome this
+challenge by using ideas from decoupling theory, suprema of chaos processes and
+the restricted isometry property of random circulant matrices, and recent
+analysis for alternating minimization methods.
+"
+29921,RRA: Recurrent Residual Attention for Sequence Learning,"  In this paper, we propose a recurrent neural network (RNN) with residual
+attention (RRA) to learn long-range dependencies from sequential data. We
+propose to add residual connections across timesteps to RNN, which explicitly
+enhances the interaction between current state and hidden states that are
+several timesteps apart. This also allows training errors to be directly
+back-propagated through residual connections and effectively alleviates
+gradient vanishing problem. We further reformulate an attention mechanism over
+residual connections. An attention gate is defined to summarize the individual
+contribution from multiple previous hidden states in computing the current
+state. We evaluate RRA on three tasks: the adding problem, pixel-by-pixel MNIST
+classification and sentiment analysis on the IMDB dataset. Our experiments
+demonstrate that RRA yields better performance, faster convergence and more
+stable training compared to a standard LSTM network. Furthermore, RRA shows
+highly competitive performance to the state-of-the-art methods.
+"
+29922,The KPZ fixed point,"  An explicit Fredholm determinant formula is derived for the multipoint
+distribution of the height function of the totally asymmetric simple exclusion
+process (TASEP) with arbitrary right finite initial condition. The method is by
+solving the biorthogonal ensemble/non-intersecting path representation found by
+[Sas05; BFPS07]. The resulting kernel involves transition probabilities of a
+random walk forced to hit a curve defined by the initial data.
+In the KPZ 1:2:3 scaling limit the formula leads in a transparent way to a
+Fredholm determinant formula, in terms of analogous kernels based on Brownian
+motion, for the transition probabilities of the scaling invariant Markov
+process at the centre of the KPZ universality class. The formula readily
+reproduces known special self-similar solutions such as the Airy$_1$ and
+Airy$_2$ processes. The process takes values in real valued functions which
+look locally like Brownian motion, and is Hölder $1/3-$ in time.
+Both the KPZ fixed point and TASEP are shown to be stochastic integrable
+systems in the sense that the time evolution of their transition probabilities
+can be linearized through a new Brownian scattering transform and its discrete
+analogue.
+"
+29923,"BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth","  We introduce a novel method for 3D object detection and pose estimation from
+color images only. We first use segmentation to detect the objects of interest
+in 2D even in presence of partial occlusions and cluttered background. By
+contrast with recent patch-based methods, we rely on a ""holistic"" approach: We
+apply to the detected objects a Convolutional Neural Network (CNN) trained to
+predict their 3D poses in the form of 2D projections of the corners of their 3D
+bounding boxes. This, however, is not sufficient for handling objects from the
+recent T-LESS dataset: These objects exhibit an axis of rotational symmetry,
+and the similarity of two images of such an object under two different poses
+makes training the CNN challenging. We solve this problem by restricting the
+range of poses used for training, and by introducing a classifier to identify
+the range of a pose at run-time before estimating it. We also use an optional
+additional step that refines the predicted poses. We improve the
+state-of-the-art on the LINEMOD dataset from 73.7% to 89.3% of correctly
+registered RGB frames. We are also the first to report results on the Occlusion
+dataset using color images only. We obtain 54% of frames passing the Pose 6D
+criterion on average on several sequences of the T-LESS dataset, compared to
+the 67% of the state-of-the-art on the same sequences which uses both color and
+depth. The full approach is also scalable, as a single network can be trained
+for multiple objects simultaneously.
+"
+29924,Minimum Covariance Determinant and Extensions,"  The Minimum Covariance Determinant (MCD) method is a highly robust estimator
+of multivariate location and scatter, for which a fast algorithm is available.
+Since estimating the covariance matrix is the cornerstone of many multivariate
+statistical methods, the MCD is an important building block when developing
+robust multivariate techniques. It also serves as a convenient and efficient
+tool for outlier detection. The MCD estimator is reviewed, along with its main
+properties such as affine equivariance, breakdown value, and influence
+function. We discuss its computation, and list applications and extensions of
+the MCD in applied and methodological multivariate statistics. Two recent
+extensions of the MCD are described. The first one is a fast deterministic
+algorithm which inherits the robustness of the MCD while being almost affine
+equivariant. The second is tailored to high-dimensional data, possibly with
+more dimensions than cases, and incorporates regularization to prevent singular
+matrices.
+"
+29925,Typed Table Transformations,"  Spreadsheet tables are often labeled, and these labels effectively constitute
+types for the data in the table. In such cases tables can be considered to be
+built from typed data where the placement of values within the table is
+controlled by the types used for rows and columns. We present a new approach to
+the transformations of spreadsheet tables that is based on transformations of
+row and column types. We illustrate the basic idea of type-based table
+construction and transformation and lay out a series of research questions that
+should be addressed in future work.
+"
+29926,On Reject and Refine Options in Multicategory Classification,"  In many real applications of statistical learning, a decision made from
+misclassification can be too costly to afford; in this case, a reject option,
+which defers the decision until further investigation is conducted, is often
+preferred. In recent years, there has been much development for binary
+classification with a reject option. Yet, little progress has been made for the
+multicategory case. In this article, we propose margin-based multicategory
+classification methods with a reject option. In addition, and more importantly,
+we introduce a new and unique refine option for the multicategory problem,
+where the class of an observation is predicted to be from a set of class
+labels, whose cardinality is not necessarily one. The main advantage of both
+options lies in their capacity of identifying error-prone observations.
+Moreover, the refine option can provide more constructive information for
+classification by effectively ruling out implausible classes. Efficient
+implementations have been developed for the proposed methods. On the
+theoretical side, we offer a novel statistical learning theory and show a fast
+convergence rate of the excess $\ell$-risk of our methods with emphasis on
+diverging dimensionality and number of classes. The results can be further
+improved under a low noise assumption. A set of comprehensive simulation and
+real data studies has shown the usefulness of the new learning tools compared
+to regular multicategory classifiers. Detailed proofs of theorems and extended
+numerical results are included in the supplemental materials available online.
+"
+29927,Rational deformations of conformal mechanics,"  We study deformations of the quantum conformal mechanics of De
+Alfaro-Fubini-Furlan with rational additional potential term generated by
+applying the generalized Darboux-Crum-Krein-Adler transformations to the
+quantum harmonic oscillator and by using the method of dual schemes and mirror
+diagrams. In this way we obtain infinite families of isospectral and
+non-isospectral deformations of the conformal mechanics model with special
+values of the coupling constant $g=m(m+1)$, $m\in {\mathbb N}$, in the inverse
+square potential term, and for each completely isospectral or gapped
+deformation given by a mirror diagram, we identify the sets of the
+spectrum-generating ladder operators which encode and coherently reflect its
+fine spectral structure. Each pair of these operators generates a nonlinear
+deformation of the conformal symmetry, and their complete sets pave the way for
+investigation of the associated superconformal symmetry deformations.
+"
+29928,Coflow Scheduling in Input-Queued Switches: Optimal Delay Scaling and Algorithms,"  A coflow is a collection of parallel flows belonging to the same job. It has
+the all-or-nothing property: a coflow is not complete until the completion of
+all its constituent flows. In this paper, we focus on optimizing
+\emph{coflow-level delay}, i.e., the time to complete all the flows in a
+coflow, in the context of an $N\times N$ input-queued switch. In particular, we
+develop a throughput-optimal scheduling policy that achieves the best scaling
+of coflow-level delay as $N\rightarrow\infty$. We first derive lower bounds on
+the coflow-level delay that can be achieved by any scheduling policy. It is
+observed that these lower bounds critically depend on the variability of flow
+sizes. Then we analyze the coflow-level performance of some existing
+coflow-agnostic scheduling policies and show that none of them achieves
+provably optimal performance with respect to coflow-level delay. Finally, we
+propose the Coflow-Aware Batching (CAB) policy which achieves the optimal
+scaling of coflow-level delay under some mild assumptions.
+"
+29929,Development of the Phase-up Technology of the Radio Telescopes: 6.7 GHz Methanol Maser Observations with Phased Hitachi 32 m and Takahagi 32 m Radio Telescopes,"  For the sake of high-sensitivity 6.7 GHz methanol maser observations, we
+developed a new technology for coherently combining the two signals from the
+Hitachi 32 m radio telescope and the Takahagi 32 m radio telescope of the
+Japanese Very long baseline interferometer Network (JVN), where the two
+telescopes were separated by about 260 m. After the two telescopes were phased
+as a twofold larger single telescope, the mean signal-to-noise ratio (SNR) of
+the 6.7 GHz methanol masers observed by the phased telescopes was improved to
+1.254-fold higher than that of the single dish, through a Very Long Baseline
+Interferometry (VLBI) experiment on the 50 km baseline of the Kashima 34 m
+telescope and the 1000 km baseline of the Yamaguchi 32 m telescope.
+Furthermore, we compared the SNRs of the 6.7 GHz maser spectra for two methods.
+One is a VLBI method and the other is the newly developed digital position
+switching, which is a similar technology to that used in noise-cancelling
+headphones. Finally, we confirmed that the mean SNR of method of the digital
+position switching (ON-OFF) was 1.597-fold higher than that of the VLBI method.
+"
+29930,Decomposable polynomials in second order linear recurrence sequences,"  We study elements of second order linear recurrence sequences $(G_n)_{n=
+0}^{\infty}$ of polynomials in $\mathbb{C}[x]$ which are decomposable, i.e.
+representable as $G_n=g\circ h$ for some $g, h\in \mathbb{C}[x]$ satisfying
+$\operatorname{deg}g,\operatorname{deg}h>1$. Under certain assumptions, and
+provided that $h$ is not of particular type, we show that $\operatorname{deg}g$
+may be bounded by a constant independent of $n$, depending only on the
+sequence.
+"
+29931,Transfer Learning with Neural AutoML,"  We reduce the computational cost of Neural AutoML with transfer learning.
+AutoML relieves human effort by automating the design of ML algorithms. Neural
+AutoML has become popular for the design of deep learning architectures,
+however, this method has a high computation cost. To address this we propose
+Transfer Neural AutoML that uses knowledge from prior tasks to speed up network
+design. We extend RL-based architecture search methods to support parallel
+training on multiple tasks and then transfer the search strategy to new tasks.
+On language and image classification tasks, Transfer Neural AutoML reduces
+convergence time over single-task training by over an order of magnitude on
+many tasks.
+"
+29932,How Unique is Your .onion? An Analysis of the Fingerprintability of Tor Onion Services,"  Recent studies have shown that Tor onion (hidden) service websites are
+particularly vulnerable to website fingerprinting attacks due to their limited
+number and sensitive nature. In this work we present a multi-level feature
+analysis of onion site fingerprintability, considering three state-of-the-art
+website fingerprinting methods and 482 Tor onion services, making this the
+largest analysis of this kind completed on onion services to date.
+Prior studies typically report average performance results for a given
+website fingerprinting method or countermeasure. We investigate which sites are
+more or less vulnerable to fingerprinting and which features make them so. We
+find that there is a high variability in the rate at which sites are classified
+(and misclassified) by these attacks, implying that average performance figures
+may not be informative of the risks that website fingerprinting attacks pose to
+particular sites.
+We analyze the features exploited by the different website fingerprinting
+methods and discuss what makes onion service sites more or less easily
+identifiable, both in terms of their traffic traces as well as their webpage
+design. We study misclassifications to understand how onion service sites can
+be redesigned to be less vulnerable to website fingerprinting attacks. Our
+results also inform the design of website fingerprinting countermeasures and
+their evaluation considering disparate impact across sites.
+"
+29933,Intersection Poincare polynomial for Nagaraj-Seshadri moduli space,"  We compute Betti numbers of both the components of the moduli space of rank 2
+semi-stable torsion-free sheaves with fixed determinant over a reducible nodal
+curve with two smooth components intersecting at a node. We also compute the
+intersection Betti numbers of the moduli space.
+"
+29934,Anonymity Properties of the Bitcoin P2P Network,"  Bitcoin is a popular alternative to fiat money, widely used for its perceived
+anonymity properties. However, recent attacks on Bitcoin's peer-to-peer (P2P)
+network demonstrated that its gossip-based flooding protocols, which are used
+to ensure global network consistency, may enable user deanonymization---the
+linkage of a user's IP address with her pseudonym in the Bitcoin network. In
+2015, the Bitcoin community responded to these attacks by changing the
+network's flooding mechanism to a different protocol, known as diffusion.
+However, no systematic justification was provided for the change, and it is
+unclear if diffusion actually improves the system's anonymity. In this paper,
+we model the Bitcoin networking stack and analyze its anonymity properties,
+both pre- and post-2015. In doing so, we consider new adversarial models and
+spreading mechanisms that have not been previously studied in the
+source-finding literature. We theoretically prove that Bitcoin's networking
+protocols (both pre- and post-2015) offer poor anonymity properties on networks
+with a regular-tree topology. We validate this claim in simulation on a 2015
+snapshot of the real Bitcoin P2P network topology.
+"
+29935,Propagation of transition front in bi-stable nondegenerate chains: model dependence and universality,"  We consider a propagation of transition fronts in one-dimensional chains with
+bi-stable nondegenerate on-site potential. If one adopts linear coupling in the
+chain and piecewise linear on-site force, then it is possible to develop
+well-known exact solutions for the front and accompanying oscillatory tail. We
+demonstrate that these solutions are essentially non-robust. Various
+approximations for the on-site potential with the same basic parameters (height
+and coordinate of the potential barrier, energy effect and distance between the
+potential wells) lead to substantially different front velocities. Besides,
+inclusion of even weak nearest neighbor nonlinearity drastically modifies the
+front structure and parameters. The energy concentration in the front zone
+leads to a dominance of the nonlinear term. It turns out that the dynamics can
+be efficiently studied in terms of an equivalent model with a single degree of
+freedom. This estimation leads to accurate prediction of the front velocity and
+parameters of the oscillatory tail. Moreover, it turns out that the solution is
+robust - exact shape of the on-site potential weakly effects the front
+parameters. This finding also conforms to the simplified model, since the
+latter invokes only the general shape characteristics of the on-site potential.
+"
+29936,Prototypical Networks for Few-shot Learning,"  We propose prototypical networks for the problem of few-shot classification,
+where a classifier must generalize to new classes not seen in the training set,
+given only a small number of examples of each new class. Prototypical networks
+learn a metric space in which classification can be performed by computing
+distances to prototype representations of each class. Compared to recent
+approaches for few-shot learning, they reflect a simpler inductive bias that is
+beneficial in this limited-data regime, and achieve excellent results. We
+provide an analysis showing that some simple design decisions can yield
+substantial improvements over recent approaches involving complicated
+architectural choices and meta-learning. We further extend prototypical
+networks to zero-shot learning and achieve state-of-the-art results on the
+CU-Birds dataset.
+"
+29937,Cavity Magnon Polaritons with Lithium Ferrite and 3D Microwave Resonators at milli-Kelvin Temperatures,"  Single crystal Lithium Ferrite (LiFe) spheres of sub-mm dimension are
+examined at mK temperatures, microwave frequencies and variable DC magnetic
+field, for use in hybrid quantum systems and condensed matter and fundamental
+physics experiments. Strong coupling regimes of the photon-magnon interaction
+(cavity magnon polariton quasi-particles) were observed with coupling strength
+of up to 250 MHz at 9.5 GHz (2.6\%) with magnon linewidths of order 4 MHz (with
+potential improvement to sub-MHz values). We show that the photon-magnon
+coupling can be significantly improved and exceed that of the widely used
+Yttrium Iron Garnet crystal, due to the small unit cell of LiFe, allowing twice
+more spins per unit volume. Magnon mode softening was observed at low DC fields
+and combined with the normal Zeeman effect creates magnon spin wave modes that
+are insensitive to first order order magnetic field fluctuations. This effect
+is observed in the Kittel mode at 5.5 GHz (and another higher order mode at 6.5
+GHz) with a DC magnetic field close to 0.19 Tesla. We show that if the cavity
+is tuned close to this frequency, the magnon polariton particles exhibit an
+enhanced range of strong coupling and insensitivity to magnetic field
+fluctuations with both first order and second order insensitivity to magnetic
+field as a function of frequency (double magic point clock transition), which
+could potentially be exploited in cavity QED experiments.
+"
+29938,Studying Continuous Symmetry Breaking using Energy Level Spectroscopy,"  Tower of States analysis is a powerful tool for investigating phase
+transitions in condensed matter systems. Spontaneous symmetry breaking implies
+a specific structure of the energy eigenvalues and their corresponding quantum
+numbers on finite systems. In these lecture notes we explain the group
+representation theory used to derive the spectral structure for several
+scenarios of symmetry breaking. We give numerous examples to compute quantum
+numbers of the degenerate groundstates, including translational symmetry
+breaking or spin rotational symmetry breaking in Heisenberg antiferromagnets.
+These results are then compared to actual numerical data from Exact
+Diagonalization.
+"
+29939,What Stays in Mind? - Retention Rates in Programming MOOCs,"  This work presents insights about the long-term effects and retention rates
+of knowledge acquired within MOOCs. In 2015 and 2017, we conducted two
+introductory MOOCs on object-oriented programming in Java with each over 10,000
+registered participants. In this paper, we analyze course scores, quiz results
+and self-stated skill levels of our participants. The aim of our analysis is to
+uncover factors influencing the retention of acquired knowledge, such as time
+passed or knowledge application, in order to improve long-term success. While
+we know that some participants learned the programming basics within our
+course, we lack information on whether this knowledge was applied and fortified
+after the course's end. To fill this knowledge gap, we conducted a survey in
+2018 among all participants of our 2015 and 2017 programming MOOCs. The first
+part of the survey elicits responses on whether and how MOOC knowledge was
+applied and gives participants opportunity to voice individual feedback. The
+second part of the survey contains several questions of increasing difficulty
+and complexity regarding course content in order to learn about the
+consolidation of the acquired knowledge. We distinguish three programming
+knowledge areas in the survey: First, understanding of concepts, such as loops
+and boolean algebra. Second, syntax knowledge, such as specific keywords.
+Third, practical skills including debugging and coding. We further analyzed the
+long-term effects separately per participant skill group. While answer rates
+were low, the collected data shows a decrease of knowledge over time,
+relatively unaffected by skill level. Application of the acquired knowledge
+improves the memory retention rates of MOOC participants across all skill
+levels.
+"
+29940,Scavenger 0.1: A Theorem Prover Based on Conflict Resolution,"  This paper introduces Scavenger, the first theorem prover for pure
+first-order logic without equality based on the new conflict resolution
+calculus. Conflict resolution has a restricted resolution inference rule that
+resembles (a first-order generalization of) unit propagation as well as a rule
+for assuming decision literals and a rule for deriving new clauses by (a
+first-order generalization of) conflict-driven clause learning.
+"
+29941,Scaling up Data Augmentation MCMC via Calibration,"  There has been considerable interest in making Bayesian inference more
+scalable. In big data settings, most literature focuses on reducing the
+computing time per iteration, with less focused on reducing the number of
+iterations needed in Markov chain Monte Carlo (MCMC). This article focuses on
+data augmentation MCMC (DA-MCMC), a widely used technique. DA-MCMC samples tend
+to become highly autocorrelated in large data samples, due to a miscalibration
+problem in which conditional posterior distributions given augmented data are
+too concentrated. This makes it necessary to collect very long MCMC paths to
+obtain acceptably low MC error. To combat this inefficiency, we propose a
+family of calibrated data augmentation algorithms, which appropriately adjust
+the variance of conditional posterior distributions. A Metropolis-Hastings step
+is used to eliminate bias in the stationary distribution of the resulting
+sampler. Compared to existing alternatives, this approach can dramatically
+reduce MC error by reducing autocorrelation and increasing the effective number
+of DA-MCMC samples per computing time. The approach is simple and applicable to
+a broad variety of existing data augmentation algorithms, and we focus on three
+popular models: probit, logistic and Poisson log-linear. Dramatic gains in
+computational efficiency are shown in applications.
+"
+29942,A concentration inequality for the excess risk in least-squares regression with random design and heteroscedastic noise,"  We prove a new and general concentration inequality for the excess risk in
+least-squares regression with random design and heteroscedastic noise. No
+specific structure is required on the model, except the existence of a suitable
+function that controls the local suprema of the empirical process. So far, only
+the case of linear contrast estimation was tackled in the literature with this
+level of generality on the model. We solve here the case of a quadratic
+contrast, by separating the behavior of a linearized empirical process and the
+empirical process driven by the squares of functions of models.
+"
+29943,Ergodic robust maximization of asymptotic growth,"  We consider the problem of robustly maximizing the growth rate of investor
+wealth in the presence of model uncertainty. Possible models are all those
+under which the assets' region $E$ and instantaneous covariation $c$ are known,
+and where additionally the assets are stable in that their occupancy time
+measures converge to a law with density $p$. This latter assumption is
+motivated by the observed stability of ranked relative market capitalizations
+for equity markets. We seek to identify the robust optimal growth rate, as well
+as a trading strategy which achieves this rate in all models. Under minimal
+assumptions upon $(E,c,p)$, we identify the robust growth rate with the
+Donsker-Varadhan rate function from occupancy time Large Deviations theory. We
+also prove existence of, and explicitly identify, the optimal trading strategy.
+We then apply our results in the case of drift uncertainty for ranked relative
+market capitalizations. Assuming regularity under symmetrization for the
+covariance and limiting density of the ranked capitalizations, we explicitly
+identify the robust optimal trading strategy in this setting.
+"
+29944,Large Fluctuations of Radiation in Stochastically Activated Two-Level Systems,"  We study the large fluctuations of emitted radiations in the system of $N$
+non-interacting two-level atoms. Two methods are used to calculate the
+probability of the large fluctuations and the time dependence of the excitation
+and emission. The first method is based on the large deviation principle for
+Markov processes. The second one uses an analogue of the quantum formalism for
+classical probability problems. Particularly we prove that in a large
+fluctuation limit approximately a half of the atoms are excited. This fact is
+independent on the fraction of the excited atoms in the stationary state.
+"
+29945,Refractive index of dense materials,"  We show that applying the Lorentz-Lorenz transformation to the refractive
+index of metals, semiconductors and insulators allows for a less empirical
+modeling of this refractive index.
+"
+29946,Robust Regulation of MIMO systems: A Reformulation of the Internal Model Principle,"  The internal model principle is a fundamental result stating a necessary and
+sufficient condition for a stabilizing controller to be robustly regulating.
+Its classical formulation is given in terms of coprime factorizations and the
+largest invariant factor of the signal generator which sets unnecessary
+restrictions for the theory and its applicability. In this article, the
+internal model principle is formulated using a general factorization approach
+and the generators of the fractional ideals generated by the elements of the
+signal generator. The proposed results are related to the classical ones.
+"
+29947,Collaborative vehicle routing: a survey,"  In horizontal collaborations, carriers form coalitions in order to perform
+parts of their logistics operations jointly. By exchanging transportation
+requests among each other, they can operate more efficiently and in a more
+sustainable way. Collaborative vehicle routing has been extensively discussed
+in the literature. We identify three major streams of research: (i) centralized
+collaborative planning, (ii) decentralized planning without auctions, and (ii)
+auction-based decentralized planning. For each of them we give a structured
+overview on the state of knowledge and discuss future research directions.
+"
+29948,Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations,"  This paper develops bootstrap methods to construct uniform confidence bands
+for nonparametric spectral estimation of Lévy densities under
+high-frequency observations. We assume that we observe $n$ discrete
+observations at frequency $1/\Delta > 0$, and work with the high-frequency
+setup where $\Delta = \Delta_{n} \to 0$ and $n\Delta \to \infty$ as $n \to
+\infty$. We employ a spectral (or Fourier-based) estimator of the Lévy
+density, and develop novel implementations of Gaussian multiplier (or wild) and
+empirical (or Efron's) bootstraps to construct confidence bands for the
+spectral estimator on a compact set that does not intersect the origin. We
+provide conditions under which the proposed confidence bands are asymptotically
+valid. Our confidence bands are shown to be asymptotically valid for a wide
+class of Lévy processes. We also develop a practical method for bandwidth
+selection, and conduct simulation studies to investigate the finite sample
+performance of the proposed confidence bands.
+"
+29949,SERoCS: Safe and Efficient Robot Collaborative Systems for Next Generation Intelligent Industrial Co-Robots,"  Human-robot collaborations have been recognized as an essential component for
+future factories. It remains challenging to properly design the behavior of
+those co-robots. Those robots operate in dynamic uncertain environment with
+limited computation capacity. The design objective is to maximize their task
+efficiency while guaranteeing safety. This paper discusses a set of design
+principles of a safe and efficient robot collaboration system (SERoCS) for the
+next generation co-robots, which consists of robust cognition algorithms for
+environment monitoring, efficient task planning algorithms for reference
+generations, and safe motion planning and control algorithms for safe
+human-robot interactions. The proposed SERoCS will address the design
+challenges and significantly expand the skill sets of the co-robots to allow
+them to work safely and efficiently with their human counterparts. The
+development of SERoCS will create a significant advancement toward adoption of
+co-robots in various industries. The experiments validate the effectiveness of
+SERoCS.
+"
+29950,Attitude Quaternion Estimation Using a Spectral Perturbation Approach,"  All quaternion methods for static attitude determination currently rely on
+either the spectral decomposition of a 4 x 4 matrix (q-Method) or finding the
+maximum eigenvalue of a 4th-order characteristic equation (QUEST). Using a
+spectral perturbation approach, we show it is possible to analytically estimate
+the attitude quaternion to high accuracy and recursively calculate the
+maximum-likelihood attitude quaternion to arbitrary numerical precision.
+Analytic or recursive estimation removes several numerical difficulties which
+are inherent to state-of-the-art algorithms, suggesting our results may
+substantially benefit high-frequency and limited memory embedded software
+implementations, such as those commonly used on spacecraft computers.
+"
+29951,Pearson's correlation coefficient in the theory of networks: A comment,"  In statistics, the Pearson correlation coefficient $r_{x,y}$ determines the
+degree of linear correlation between two variables and it is known that $-1 \le
+r_{x,y} \le 1$. In the theory of networks, a curious expression proposed in
+[PRL {\bf 89} 208701 (2002)] for degree-degree correlation coefficient
+$r_{j_i,k_i}, i\in [1,M]$ has been in use. We realize that the suggested form
+is the conventional Pearson's coefficient for $\{(j_i,k_i), (k_i,j_i)\}$ for
+$2M$ data points and hence it is rightly dedicated to undirected networks.
+"
+29952,The Common Limit of the Linear Statistics of Zeros of Random Polynomials and Their Derivatives,"  Let $ p_n(x) $ be a random polynomial of degree $n$ and
+$\{Z^{(n)}_j\}_{j=1}^n$ and $\{X^{n, k}_j\}_{j=1}^{n-k}, k<n$, be the zeros of
+$p_n$ and $p_n^{(k)}$, the $k$th derivative of $p_n$, respectively. We show
+that if the linear statistics $\frac{1}{a_n} \left[ f\left( \frac
+{Z^{(n)}_1}{b_n} \right)+ \cdots + f \left(\frac {Z^{(n)}_n}{b_n} \right)
+\right]$ associated with $\{Z^{(n)}_j\}$ has a limit as $n\to\infty$ at some
+mode of convergence, the linear statistics associated with $\{X^{n, k}_j\}$
+converges to the same limit at the same mode. Similar statement also holds for
+the centered linear statistics associated with the zeros of $p_n$ and
+$p_n^{(k)}$, provided the zeros $\{Z^{(n)}_j\}$ and the sequences $\{a_n\}$ and
+$\{b_n\}$ of positive numbers satisfy some mild conditions.
+"
+29953,Hierarchical modeling of molecular energies using a deep neural network,"  We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN)
+to model molecular properties from datasets of quantum calculations. Inspired
+by a many-body expansion, HIP-NN decomposes properties, such as energy, as a
+sum over hierarchical terms. These terms are generated from a neural network--a
+composition of many nonlinear transformations--acting on a representation of
+the molecule. HIP-NN achieves state-of-the-art performance on a dataset of 131k
+ground state organic molecules, and predicts energies with 0.26 kcal/mol mean
+absolute error. With minimal tuning, our model is also competitive on a dataset
+of molecular dynamics trajectories. In addition to enabling accurate energy
+predictions, the hierarchical structure of HIP-NN helps to identify regions of
+model uncertainty.
+"
+29954,Active Convolution: Learning the Shape of Convolution for Image Classification,"  In recent years, deep learning has achieved great success in many computer
+vision applications. Convolutional neural networks (CNNs) have lately emerged
+as a major approach to image classification. Most research on CNNs thus far has
+focused on developing architectures such as the Inception and residual
+networks. The convolution layer is the core of the CNN, but few studies have
+addressed the convolution unit itself. In this paper, we introduce a
+convolution unit called the active convolution unit (ACU). A new convolution
+has no fixed shape, because of which we can define any form of convolution. Its
+shape can be learned through backpropagation during training. Our proposed unit
+has a few advantages. First, the ACU is a generalization of convolution; it can
+define not only all conventional convolutions, but also convolutions with
+fractional pixel coordinates. We can freely change the shape of the
+convolution, which provides greater freedom to form CNN structures. Second, the
+shape of the convolution is learned while training and there is no need to tune
+it by hand. Third, the ACU can learn better than a conventional unit, where we
+obtained the improvement simply by changing the conventional convolution to an
+ACU. We tested our proposed method on plain and residual networks, and the
+results showed significant improvement using our method on various datasets and
+architectures in comparison with the baseline.
+"
+29955,Construction of Kuranishi structures on the moduli spaces of pseudo holomorphic disks: I,"  This is the first of two articles in which we provide detailed and
+self-contained account of the construction of a system of Kuranishi structures
+on the moduli spaces of pseudo holomorphic disks, using the exponential decay
+estimate given in [FOOO7]. This article completes the construction of a
+Kuranishi structure of a single moduli space. This article is an improved
+version of [FOOO4, Part 4] and its mathematical content is taken from our
+earlier writing [FOn,FOOO2,FOOO4,FOOO7].
+"
+29956,Connectedness properties and splittings of groups with isolated flats,"  In this paper we study CAT(0) groups and their splittings as graphs of
+groups. For one-ended CAT(0) groups with isolated flats we prove a theorem
+characterizing exactly when the visual boundary is locally connected. This
+characterization depends on whether the group has a certain type of splitting
+over a virtually abelian subgroup. In the locally connected case, we describe
+the boundary as a tree of metric spaces in the sense of Świątkowski.
+A significant tool used in the proofs of the above results is a general
+convex splitting theorem for arbitrary CAT(0) groups. If a CAT(0) group splits
+as a graph of groups with convex edge groups, then the vertex groups are also
+CAT(0) groups.
+"
+29957,Supporting mixed-datatype matrix multiplication within the BLIS framework,"  We approach the problem of implementing mixed-datatype support within the
+general matrix multiplication (GEMM) operation of the BLIS framework, whereby
+each matrix operand A, B, and C may be stored as single- or double-precision
+real or complex values. Another factor of complexity, whereby the computation
+is allowed to take place in a precision different from the storage precisions
+of either A or B, is also included in the discussion. We first break the
+problem into mostly orthogonal dimensions, considering the mixing of domains
+separately from mixing precisions. Support for all combinations of matrix
+operands stored in either the real or complex domain is mapped out by
+enumerating the cases and describing an implementation approach for each.
+Supporting all combinations of storage and computation precisions is handled by
+typecasting the matrices at key stages of the computation---during packing
+and/or accumulation, as needed. Several optional optimizations are also
+documented. Performance results gathered on a 56-core Marvell ThunderX2 and a
+52-core Intel Xeon Platinum demonstrate that high performance is mostly
+preserved, with modest slowdowns incurred from unavoidable typecast
+instructions. The mixed-datatype implementation confirms that combinatoric
+intractability is avoided, with the framework relying on only two assembly
+microkernels to implement 128 datatype combinations.
+"
+29958,An axiomatic basis for Blackwell optimality,"  In the theory of Markov decision processes (MDPs), a Blackwell optimal policy
+is a policy that is optimal for every discount factor sufficiently close to
+one. This paper provides an axiomatic basis for Blackwell optimality in
+discrete-time MDPs with finitely many states and finitely many actions.
+"
+29959,GeneVis - An interactive visualization tool for combining cross-discipline datasets within genetics,"  GeneVis is a web-based tool to visualize complementary data sets of different
+disciplines within the field of genetics. It overlays gene-cluster information,
+gene-interaction data and gene-disease association data by means of web-based
+interactive graph visualizations. This allows an intuitive and quick assessment
+of possible relations between the different datasets. By starting from a
+high-level graph abstraction based on gene clusters, which can be selected for
+detailed inspection at the gene-interaction level in a separate window, GeneVis
+circumvents the common visual clutter problem when using gene datasets with a
+high number of gene entries.
+"
+29960,Quantifying the causal effect of speed cameras on road traffic accidents via an approximate Bayesian doubly robust estimator,"  This paper quantifies the effect of speed cameras on road traffic collisions
+using an approximate Bayesian doubly-robust (DR) causal inference estimation
+method. Previous empirical work on this topic, which shows a diverse range of
+estimated effects, is based largely on outcome regression (OR) models using the
+Empirical Bayes approach or on simple before and after comparisons. Issues of
+causality and confounding have received little formal attention. A causal DR
+approach combines propensity score (PS) and OR models to give an average
+treatment effect (ATE) estimator that is consistent and asymptotically normal
+under correct specification of either of the two component models. We develop
+this approach within a novel approximate Bayesian framework to derive posterior
+predictive distributions for the ATE of speed cameras on road traffic
+collisions. Our results for England indicate significant reductions in the
+number of collisions at speed cameras sites (mean ATE = -30%). Our proposed
+method offers a promising approach for evaluation of transport safety
+interventions.
+"
+29961,Cube-magic labelings of grids,"  We show that the vertices and edges of a $d$-dimensional grid graph $G=(V,E)$
+($d\geqslant 2$) can be labeled with the integers from $\{1,\ldots,\lvert
+V\rvert\}$ and $\{1,\ldots,\lvert E\rvert\}$, respectively, in such a way that
+for every subgraph $H$ isomorphic to a $d$-cube the sum of all the labels of
+$H$ is the same. As a consequence, for every $d\geqslant 2$, every
+$d$-dimensional grid graph is $Q_d$-supermagic where $Q_d$ is the $d$-cube.
+"

From 283b61af56089a05fdae778234a3af823781c6b6 Mon Sep 17 00:00:00 2001
From: Simran Shaikh <141727239+SimranShaikh20@users.noreply.github.com>
Date: Sun, 10 Nov 2024 18:49:10 +0530
Subject: [PATCH 3/7] Create README.md

---
 .../Model/README.md                           | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 Research Topic Prediction using Deep Learning/Model/README.md

diff --git a/Research Topic Prediction using Deep Learning/Model/README.md b/Research Topic Prediction using Deep Learning/Model/README.md
new file mode 100644
index 000000000..b88f5ee50
--- /dev/null
+++ b/Research Topic Prediction using Deep Learning/Model/README.md	
@@ -0,0 +1,20 @@
+# Research-topic-Prediction
+
+### Problem Statement
+Researchers have access to large online archives of scientific articles. As a consequence, finding relevant articles has become more difficult. Tagging or topic modelling provides a way to give token of identification to research articles which facilitates recommendation and search process.
+
+Given the abstract and title for a set of research articles, predict the topics for each article included in the test set. 
+
+Note that a research article can possibly have more than 1 topic. The research article abstracts and titles are sourced from the following 6 topics: 
+
+1. Computer Science
+
+2. Physics
+
+3. Mathematics
+
+4. Statistics
+
+5. Quantitative Biology
+
+6. Quantitative Finance

From 30eb609418113a0a5d040c8e7a3c154ed9d6b975 Mon Sep 17 00:00:00 2001
From: Simran Shaikh <141727239+SimranShaikh20@users.noreply.github.com>
Date: Sun, 10 Nov 2024 18:49:43 +0530
Subject: [PATCH 4/7] Add files via upload

---
 .../Model/ResearchtopicPrediction.ipynb       | 711 ++++++++++++++++++
 1 file changed, 711 insertions(+)
 create mode 100644 Research Topic Prediction using Deep Learning/Model/ResearchtopicPrediction.ipynb

diff --git a/Research Topic Prediction using Deep Learning/Model/ResearchtopicPrediction.ipynb b/Research Topic Prediction using Deep Learning/Model/ResearchtopicPrediction.ipynb
new file mode 100644
index 000000000..36b9a207c
--- /dev/null
+++ b/Research Topic Prediction using Deep Learning/Model/ResearchtopicPrediction.ipynb	
@@ -0,0 +1,711 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "OdBntg3UTJg3"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "train = pd.read_csv('/content/drive/My Drive/ml/train.csv')\n",
+    "test = pd.read_csv('/content/drive/My Drive/ml/test.csv')\n",
+    "subission_pd = pd.read_csv('/content/drive/My Drive/ml/sample_submission_UVKGLZE.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 68
+    },
+    "colab_type": "code",
+    "id": "c8wedvYeT4nm",
+    "outputId": "1a91f831-6556-49e3-dcba-e87c7567241e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train shape: (20972, 9)\n",
+      "Test shape: (8989, 3)\n",
+      "Sample shape: (8989, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Train shape:',train.shape)\n",
+    "print('Test shape:',test.shape)\n",
+    "print('Sample shape:',subission_pd.shape)\n",
+    "\n",
+    "l = ['Computer Science', 'Physics', 'Mathematics', 'Statistics', 'Quantitative Biology', 'Quantitative Finance']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 51
+    },
+    "colab_type": "code",
+    "id": "_8-A4zRtT8Ss",
+    "outputId": "91bb890b-601a-42ef-cb0c-1e2aaafcba28"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(18874, 2) (2098, 2)\n",
+      "(18874, 6) (2098, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "test = test.drop(['ID'],axis=1)\n",
+    "\n",
+    "X = train.loc[:,['TITLE','ABSTRACT']]\n",
+    "y = train.loc[:,l]\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42, shuffle=True)\n",
+    "\n",
+    "print(X_train.shape, X_test.shape)\n",
+    "print(y_train.shape, y_test.shape)\n",
+    "\n",
+    "y_test.reset_index(drop=True,inplace=True)\n",
+    "X_test.reset_index(drop=True,inplace=True)\n",
+    "\n",
+    "y1 = np.array(y_train)\n",
+    "y2 = np.array(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ENRy0yNfUaCK"
+   },
+   "outputs": [],
+   "source": [
+    "#Removing Punctuations\n",
+    "\n",
+    "X_train.replace('[^a-zA-Z]',' ', regex=True, inplace=True)\n",
+    "X_test.replace('[^a-zA-Z]',' ', regex=True, inplace=True)\n",
+    "\n",
+    "test.replace('[^a-zA-Z]',' ', regex=True, inplace=True)\n",
+    "\n",
+    "#Converting to lower case characters\n",
+    "\n",
+    "for index in X_train.columns:\n",
+    "  X_train[index] = X_train[index].str.lower()\n",
+    "\n",
+    "for index in X_test.columns:\n",
+    "  X_test[index] = X_test[index].str.lower()\n",
+    "\n",
+    "for index in test.columns:\n",
+    "  test[index] = test[index].str.lower()\n",
+    "\n",
+    "#Removing one letter words\n",
+    "\n",
+    "X_train['ABSTRACT'] = X_train['ABSTRACT'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')\n",
+    "X_test['ABSTRACT'] = X_test['ABSTRACT'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')\n",
+    "\n",
+    "test['ABSTRACT'] = test['ABSTRACT'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')\n",
+    "\n",
+    "#Removing multiple blank spaces\n",
+    "\n",
+    "X_train = X_train.replace('\\s+', ' ', regex=True)\n",
+    "X_test = X_test.replace('\\s+', ' ', regex=True)\n",
+    "\n",
+    "test = test.replace('\\s+', ' ', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 374
+    },
+    "colab_type": "code",
+    "id": "VJD5hEUkUfNY",
+    "outputId": "1b4eff7d-4a42-44a3-8e4e-ae4f2ef4a533"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     /root/nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>combined</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13275</th>\n",
+       "      <td>clustering in hilbert space of a quantum optim...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19273</th>\n",
+       "      <td>graph heat mixture model learning  graph infer...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6427</th>\n",
+       "      <td>fast and unsupervised methods for multilingual...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19168</th>\n",
+       "      <td>natasha faster non convex stochastic optimizat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14148</th>\n",
+       "      <td>kustaanheimo stiefel transformation with an ar...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                combined\n",
+       "13275  clustering in hilbert space of a quantum optim...\n",
+       "19273  graph heat mixture model learning  graph infer...\n",
+       "6427   fast and unsupervised methods for multilingual...\n",
+       "19168  natasha faster non convex stochastic optimizat...\n",
+       "14148  kustaanheimo stiefel transformation with an ar..."
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('wordnet')\n",
+    "nltk.download('stopwords')\n",
+    "nltk.download('averaged_perceptron_tagger')\n",
+    "from nltk import sent_tokenize, word_tokenize\n",
+    "from nltk.stem.snowball import SnowballStemmer\n",
+    "from nltk.stem.wordnet import WordNetLemmatizer\n",
+    "from nltk.corpus import stopwords\n",
+    "\n",
+    "\n",
+    "stop_words = set(stopwords.words('english')) \n",
+    "# len(stop_words)\n",
+    "# X_train['ABSTRACT'] = X_train['ABSTRACT'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))\n",
+    "# X_test['ABSTRACT'] = X_test['ABSTRACT'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))\n",
+    "\n",
+    "# test['ABSTRACT'] = test['ABSTRACT'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))\n",
+    "\n",
+    "X_train['combined'] = X_train['TITLE']+' '+X_train['ABSTRACT']\n",
+    "X_test['combined'] = X_test['TITLE']+' '+X_test['ABSTRACT']\n",
+    "\n",
+    "test['combined'] = test['TITLE']+' '+test['ABSTRACT']\n",
+    "\n",
+    "X_train = X_train.drop(['TITLE','ABSTRACT'],axis=1)\n",
+    "X_test = X_test.drop(['TITLE','ABSTRACT'],axis=1)\n",
+    "\n",
+    "test = test.drop(['TITLE','ABSTRACT'],axis=1)\n",
+    "\n",
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ZZpJCI5cYCap"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "X_lines = []\n",
+    "for row in range(0,X.shape[0]):\n",
+    "  X_lines.append(' '.join(str(x) for x in X.iloc[row,:]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "aKsTUdeRUrMd"
+   },
+   "outputs": [],
+   "source": [
+    "train_lines = []\n",
+    "for row in range(0,X_train.shape[0]):\n",
+    "  train_lines.append(' '.join(str(x) for x in X_train.iloc[row,:]))\n",
+    "\n",
+    "test_lines = []\n",
+    "for row in range(0,X_test.shape[0]):\n",
+    "  test_lines.append(' '.join(str(x) for x in X_test.iloc[row,:]))\n",
+    "\n",
+    "predtest_lines = []\n",
+    "for row in range(0,test.shape[0]):\n",
+    "  predtest_lines.append(' '.join(str(x) for x in test.iloc[row,:]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "jGDABzRWZlOi",
+    "outputId": "a9321f83-b977-43e7-9d7b-243bfeeaa754"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18874"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(train_lines)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "O05zD3CTUu_x"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "\n",
+    "countvector = CountVectorizer(ngram_range=(1,2))\n",
+    "X_train_cv = countvector.fit_transform(train_lines)\n",
+    "X_test_cv = countvector.transform(test_lines)\n",
+    "\n",
+    "test_cv = countvector.transform(predtest_lines)\n",
+    "\n",
+    "#Using TfidfVectorizer\n",
+    "\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\n",
+    "\n",
+    "tfidfvector = TfidfTransformer()\n",
+    "X_train_tf = tfidfvector.fit_transform(X_train_cv)\n",
+    "X_test_tf = tfidfvector.fit_transform(X_test_cv)\n",
+    "\n",
+    "test_tf = tfidfvector.fit_transform(test_cv)\n",
+    "\n",
+    "X_cv = countvector.transform(X_lines)\n",
+    "\n",
+    "X_tf = tfidfvector.fit_transform(X_cv) #x_tf,y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 153
+    },
+    "colab_type": "code",
+    "id": "CcnLYLSQV3wk",
+    "outputId": "eec93633-b54c-4efb-9403-19467293f4c0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiOutputClassifier(estimator=LinearSVC(C=0.5, class_weight='balanced',\n",
+       "                                          dual=True, fit_intercept=True,\n",
+       "                                          intercept_scaling=1,\n",
+       "                                          loss='squared_hinge', max_iter=1000,\n",
+       "                                          multi_class='ovr', penalty='l2',\n",
+       "                                          random_state=42, tol=0.0001,\n",
+       "                                          verbose=0),\n",
+       "                      n_jobs=None)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import LinearSVC\n",
+    "from sklearn.multioutput import MultiOutputClassifier\n",
+    "\n",
+    "model = LinearSVC(C=0.5, class_weight='balanced', random_state=42)\n",
+    "models = MultiOutputClassifier(model)\n",
+    "\n",
+    "models.fit(X_train_tf, y1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 136
+    },
+    "colab_type": "code",
+    "id": "MITfjxaeWGNf",
+    "outputId": "b5b6e7e6-ac08-43ba-ced2-d19bd292fe39"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, 1, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0],\n",
+       "       [1, 0, 0, 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 1, 0, 0, 1, 0],\n",
+       "       [1, 0, 0, 1, 0, 0],\n",
+       "       [0, 1, 0, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds = models.predict(X_test_tf)\n",
+    "preds\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 272
+    },
+    "colab_type": "code",
+    "id": "f09g5ZevWI6e",
+    "outputId": "a1410f03-caed-44f3-dd73-b19e1592f054"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.81      0.91      0.85       853\n",
+      "           1       0.88      0.89      0.88       623\n",
+      "           2       0.84      0.84      0.84       580\n",
+      "           3       0.72      0.86      0.78       516\n",
+      "           4       0.53      0.40      0.46        58\n",
+      "           5       0.86      0.69      0.77        26\n",
+      "\n",
+      "   micro avg       0.81      0.86      0.83      2656\n",
+      "   macro avg       0.77      0.76      0.76      2656\n",
+      "weighted avg       0.81      0.86      0.83      2656\n",
+      " samples avg       0.84      0.89      0.84      2656\n",
+      "\n",
+      "0.6611058150619638\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+    "\n",
+    "#print(confusion_matrix(y2,preds))\n",
+    "print(classification_report(y2,preds))\n",
+    "print(accuracy_score(y2,preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "hlszaq_ujFje"
+   },
+   "outputs": [],
+   "source": [
+    "# for i in range(100):\n",
+    "#     print(str(y2[i])+str(preds[i]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 136
+    },
+    "colab_type": "code",
+    "id": "mi9nVKeqWK-p",
+    "outputId": "c27fb35b-0f4a-410e-ecdf-1b1b0aae4dae"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 1, 0, 0],\n",
+       "       [0, 1, 0, 0, 0, 0],\n",
+       "       [1, 0, 0, 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, 0, 1, 0],\n",
+       "       [0, 0, 0, 1, 0, 0],\n",
+       "       [1, 0, 0, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predssv = models.predict(test_tf)\n",
+    "predssv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "EBUnVCwlWPOZ",
+    "outputId": "e99d8905-3d96-4471-8c2e-a43e2d4eef76"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Computer Science</th>\n",
+       "      <th>Physics</th>\n",
+       "      <th>Mathematics</th>\n",
+       "      <th>Statistics</th>\n",
+       "      <th>Quantitative Biology</th>\n",
+       "      <th>Quantitative Finance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20973</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20974</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20975</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20976</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20977</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID  Computer Science  ...  Quantitative Biology  Quantitative Finance\n",
+       "0  20973                 0  ...                     0                     0\n",
+       "1  20974                 0  ...                     0                     0\n",
+       "2  20975                 1  ...                     0                     0\n",
+       "3  20976                 0  ...                     0                     0\n",
+       "4  20977                 1  ...                     0                     0\n",
+       "\n",
+       "[5 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test = pd.read_csv('/content/drive/My Drive/ml/test.csv')\n",
+    "\n",
+    "submit = pd.DataFrame({'ID': test.ID, 'Computer Science': predssv[:,0],'Physics':predssv[:,1],\n",
+    "                       'Mathematics':predssv[:,2],'Statistics':predssv[:,3],'Quantitative Biology':predssv[:,4],\n",
+    "                       'Quantitative Finance':predssv[:,5]})\n",
+    "submit.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ybS28j2AWbio"
+   },
+   "outputs": [],
+   "source": [
+    "submit.to_csv('OutPutFile_Submission.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyMhmjaEvacjvO1SJCWFndxi",
+   "collapsed_sections": [],
+   "mount_file_id": "1-RAfjiGOy2brJkcnEiA6loSQDSn5oPQ1",
+   "name": "independenceday_challange",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From bd53dd9da75420c2e3a2b29d7f110d4bd8b4b46a Mon Sep 17 00:00:00 2001
From: Simran Shaikh <141727239+SimranShaikh20@users.noreply.github.com>
Date: Sun, 10 Nov 2024 18:51:13 +0530
Subject: [PATCH 5/7] Add files via upload

---
 .../Dataset/OutPutFile_Submission.csv         | 8990 +++++++++++++++++
 1 file changed, 8990 insertions(+)
 create mode 100644 Research Topic Prediction using Deep Learning/Dataset/OutPutFile_Submission.csv

diff --git a/Research Topic Prediction using Deep Learning/Dataset/OutPutFile_Submission.csv b/Research Topic Prediction using Deep Learning/Dataset/OutPutFile_Submission.csv
new file mode 100644
index 000000000..e3d7db90d
--- /dev/null
+++ b/Research Topic Prediction using Deep Learning/Dataset/OutPutFile_Submission.csv	
@@ -0,0 +1,8990 @@
+ID,Computer Science,Physics,Mathematics,Statistics,Quantitative Biology,Quantitative Finance
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From be79e9776ead6bfee15abd61aa1bde64abe337f4 Mon Sep 17 00:00:00 2001
From: Simran Shaikh <141727239+SimranShaikh20@users.noreply.github.com>
Date: Sun, 10 Nov 2024 18:55:56 +0530
Subject: [PATCH 6/7] Add files via upload

---
 .../requirements.txt                               | 14 ++++++++++++++
 1 file changed, 14 insertions(+)
 create mode 100644 Research Topic Prediction using Deep Learning/requirements.txt

diff --git a/Research Topic Prediction using Deep Learning/requirements.txt b/Research Topic Prediction using Deep Learning/requirements.txt
new file mode 100644
index 000000000..a41afdfb3
--- /dev/null
+++ b/Research Topic Prediction using Deep Learning/requirements.txt	
@@ -0,0 +1,14 @@
+warnings
+nltk
+pandas
+numpy
+matplotlib
+scikit-learn
+CountVectorizer
+TfidfVectorizer
+TfidfTransformer
+MultiOutputClassifier
+LinearSVC
+accuracy_score
+confusion_matrix
+classification_report
\ No newline at end of file

From 85e9aec7efb5abff89beb8ae5661b1e660c50ec0 Mon Sep 17 00:00:00 2001
From: Simran Shaikh <141727239+SimranShaikh20@users.noreply.github.com>
Date: Sun, 10 Nov 2024 19:15:20 +0530
Subject: [PATCH 7/7] Update README.md

---
 .../Model/README.md                           | 45 +++++++++++++++----
 1 file changed, 36 insertions(+), 9 deletions(-)

diff --git a/Research Topic Prediction using Deep Learning/Model/README.md b/Research Topic Prediction using Deep Learning/Model/README.md
index b88f5ee50..cb93ee4bb 100644
--- a/Research Topic Prediction using Deep Learning/Model/README.md	
+++ b/Research Topic Prediction using Deep Learning/Model/README.md	
@@ -1,20 +1,47 @@
 # Research-topic-Prediction
 
-### Problem Statement
+## Overview
+
 Researchers have access to large online archives of scientific articles. As a consequence, finding relevant articles has become more difficult. Tagging or topic modelling provides a way to give token of identification to research articles which facilitates recommendation and search process.
 
-Given the abstract and title for a set of research articles, predict the topics for each article included in the test set. 
+## Dataset
+
+The dataset used in this challenge consists of research papers with their titles, abstracts, and corresponding categories. The categories include:
+
+- Computer Science
+- Physics
+- Mathematics
+- Statistics
+- Quantitative Biology
+- Quantitative Finance
+
+## Approach
+
+The approach used to solve this challenge is as follows:
+
+1. **Data Preprocessing**: The title and abstract of each research paper are combined and preprocessed by removing punctuation, converting to lowercase, and removing stop words.
+2. **Feature Extraction**: The preprocessed text data is then converted into numerical features using the CountVectorizer and TfidfTransformer from scikit-learn.
+3. **Model Training**: A MultiOutputClassifier with a LinearSVC estimator is trained on the feature data to predict the categories of the research papers.
+4. **Model Evaluation**: The performance of the model is evaluated using accuracy score, precision, recall, and F1-score.
+5. **Submission**: The predicted categories for the test data are submitted in a CSV file.
 
-Note that a research article can possibly have more than 1 topic. The research article abstracts and titles are sourced from the following 6 topics: 
+## Code Structure
 
-1. Computer Science
+The code is organized into the following sections:
 
-2. Physics
+1. **Importing Libraries**: The necessary libraries, including scikit-learn, pandas, and numpy, are imported.
+2. **Loading Data**: The training and test data are loaded from CSV files.
+3. **Data Preprocessing**: The title and abstract of each research paper are combined and preprocessed.
+4. **Feature Extraction**: The preprocessed text data is converted into numerical features.
+5. **Model Training**: The MultiOutputClassifier with a LinearSVC estimator is trained on the feature data.
+6. **Model Evaluation**: The performance of the model is evaluated using accuracy score, precision, recall, and F1-score.
+7. **Submission**: The predicted categories for the test data are submitted in a CSV file.
 
-3. Mathematics
+## Dependencies
 
-4. Statistics
+The following dependencies are required to run the code:
 
-5. Quantitative Biology
+- scikit-learn
+- pandas
+- numpy
 
-6. Quantitative Finance